From 3cee920e05436afd4f3eea067d56b8de7da7709f Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com>
Date: Mon, 18 Mar 2024 10:57:49 +0100
Subject: [PATCH 001/140] Update python-pypi-test.yml

try fix the issue with test-pypi
---
 .github/workflows/python-pypi-test.yml | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/python-pypi-test.yml b/.github/workflows/python-pypi-test.yml
index 65df7566..066e006e 100644
--- a/.github/workflows/python-pypi-test.yml
+++ b/.github/workflows/python-pypi-test.yml
@@ -14,6 +14,9 @@ jobs:
   build_and_deploy:
     name: Build the release and deploy to test pypi
     runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+      contents: read
     steps:
       - name: Checkout code
         uses: actions/checkout@master
@@ -39,4 +42,4 @@ jobs:
           skip-existing: true
           verbose: true
           password: ${{ secrets.RAMP_TEST}}
-          repository-url: https://test.pypi.org/legacy/
\ No newline at end of file
+          repository-url: https://test.pypi.org/legacy/

From f142bfc4961799521c60b509da83c6b55d3068d6 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com>
Date: Mon, 18 Mar 2024 11:51:19 +0100
Subject: [PATCH 002/140] Update python-publish-pypi.yml

solve the similar issue of test-pypi with the workflow in the publish-pypi workflow
---
 .github/workflows/python-publish-pypi.yml | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/python-publish-pypi.yml b/.github/workflows/python-publish-pypi.yml
index c29d1e7a..85145c9a 100644
--- a/.github/workflows/python-publish-pypi.yml
+++ b/.github/workflows/python-publish-pypi.yml
@@ -8,6 +8,9 @@ jobs:
   build_and_deploy:
     name: Build the release and deploy to test pypi
     runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+      contents: read
     steps:
       - name: Checkout code
         uses: actions/checkout@master
@@ -32,4 +35,4 @@ jobs:
         with:
           verbose: true
           password: ${{ secrets.RAMP_PYPI_PW }}
-          repository_url: https://upload.pypi.org/legacy/
\ No newline at end of file
+          repository_url: https://upload.pypi.org/legacy/

From 76e5342d4743bcd4a0c90e35f09f6817dc5d6de2 Mon Sep 17 00:00:00 2001
From: Johann Kraft <johann.kraft@student.kit.edu>
Date: Thu, 29 Feb 2024 13:22:30 +0100
Subject: [PATCH 003/140] Fix issue 114. Skip app if func_time or rand_time==0

---
 ramp/core/core.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index e21047b2..3f08ec14 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1799,6 +1799,8 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
             or (self.pref_index != 0 and self.user.rand_daily_pref != self.pref_index)
             # checks if the app is allowed in the given yearly behaviour pattern
             or self.wd_we_type not in [day_type, 2]
+            # check if the app has a func_time of 0 (fix issue 114)
+            or self.func_time == 0
         ):
             return
 
@@ -1845,7 +1847,7 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
         ]
 
         tot_time = 0
-        while tot_time <= rand_time:
+        while tot_time <= rand_time and rand_time!=0:  # Check if rand_time is 0 (-> fix issue 114)
             # one option could be to generate a lot of them at once
             indexes = self.rand_switch_on_window(
                 rand_time=rand_time,  # TODO maybe only consider rand_time-tot_time ...

From 1f87cc062229856dea5ad6f3e3c982550bc3fd04 Mon Sep 17 00:00:00 2001
From: Johann Kraft <johann.kraft@student.kit.edu>
Date: Thu, 29 Feb 2024 13:52:39 +0100
Subject: [PATCH 004/140] Add warning if appliance with func_time=0 is defined

---
 ramp/core/core.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 3f08ec14..8b79bcf4 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1029,10 +1029,17 @@ def __init__(
             2. power array size is not (366,1)
         """
 
+
         self.user = user
         self.name = name
         self.number = number
         self.num_windows = num_windows
+
+        if func_cycle == 0:
+            warnings.warn(
+                UserWarning(f"Func_time of appliance '{self.name}' is defined as 0. Ignore if this is intended")
+            )
+
         self.func_time = func_time
         self.time_fraction_random_variability = time_fraction_random_variability
         self.func_cycle = func_cycle
@@ -1847,7 +1854,7 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
         ]
 
         tot_time = 0
-        while tot_time <= rand_time and rand_time!=0:  # Check if rand_time is 0 (-> fix issue 114)
+        while tot_time <= rand_time and rand_time != 0:  # Check if rand_time is 0 (-> fix issue 114)
             # one option could be to generate a lot of them at once
             indexes = self.rand_switch_on_window(
                 rand_time=rand_time,  # TODO maybe only consider rand_time-tot_time ...

From d9fcb0a245a4738dd02d68cabe30556e2fcc2f31 Mon Sep 17 00:00:00 2001
From: Johann Kraft <johann.kraft@student.kit.edu>
Date: Thu, 29 Feb 2024 14:22:02 +0100
Subject: [PATCH 005/140] Add tests and fix faulty func_time warning

---
 ramp/core/core.py            |  2 +-
 tests/test_func_time_zero.py | 44 ++++++++++++++++++++++++++++++++++++
 2 files changed, 45 insertions(+), 1 deletion(-)
 create mode 100644 tests/test_func_time_zero.py

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 8b79bcf4..6d225138 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1035,7 +1035,7 @@ def __init__(
         self.number = number
         self.num_windows = num_windows
 
-        if func_cycle == 0:
+        if func_time == 0:
             warnings.warn(
                 UserWarning(f"Func_time of appliance '{self.name}' is defined as 0. Ignore if this is intended")
             )
diff --git a/tests/test_func_time_zero.py b/tests/test_func_time_zero.py
new file mode 100644
index 00000000..ae516ac6
--- /dev/null
+++ b/tests/test_func_time_zero.py
@@ -0,0 +1,44 @@
+import numpy as np
+import pytest
+from ramp import UseCase, User
+
+def test_skip_when_func_time_zero():
+
+    # Generate test user and appliance
+    user = User(user_name="Test User", num_users=1)
+    appliance = user.add_appliance(
+        name="Test Appliance",
+        func_time=0,  # Set func_time to be 0
+        func_cycle=20,
+        time_fraction_random_variability=0.1,
+    )
+
+    # Add to use_case
+    use_case = UseCase(
+        name='test_use_case',
+        users=[user]
+    )
+
+    # Calculate peak time range for this use_case
+    peak_time_range = use_case.calc_peak_time_range()
+
+    # Generated one load profile of this appliance
+    power = 1000
+    appliance.generate_load_profile(prof_i=0, peak_time_range=peak_time_range, day_type=0, power=power)
+
+    # Check that no use of this appliance is simulated -> the appliances load profile is always smaller than it's power
+    # (Checking for the load_profile to always be 0 might be unreliable, since the RAMP core "marks" potential usage
+    # windows with small power values larger 0)
+    assert np.max(appliance.daily_use) < power
+
+def test_warning_when_func_time_zero():
+    user = User(user_name="Test User", num_users=1)
+    appliance = user.add_appliance(
+        name="Test Appliance",
+        func_time=0,  # Set func_time to be 0
+        func_cycle=20,
+        time_fraction_random_variability=0.1,
+    )
+
+with pytest.warns():
+    test_warning_when_func_time_zero()
\ No newline at end of file

From 8d5d5045f1b30aff7ec212a214aeb937d461842a Mon Sep 17 00:00:00 2001
From: Johann Kraft <johann.kraft@student.kit.edu>
Date: Mon, 11 Mar 2024 11:39:30 +0100
Subject: [PATCH 006/140] Formatted code with black.

---
 ramp/__init__.py             |  1 -
 ramp/core/core.py            |  9 ++++++---
 tests/test_func_time_zero.py | 14 ++++++++------
 3 files changed, 14 insertions(+), 10 deletions(-)

diff --git a/ramp/__init__.py b/ramp/__init__.py
index 3f23af7e..d3753eb8 100644
--- a/ramp/__init__.py
+++ b/ramp/__init__.py
@@ -13,7 +13,6 @@
     - random
 """
 
-
 from ramp._version import __version__
 from ramp.core.core import UseCase, User, Appliance
 from ramp.core.utils import yearly_pattern, get_day_type
diff --git a/ramp/core/core.py b/ramp/core/core.py
index 6d225138..d6109944 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1029,7 +1029,6 @@ def __init__(
             2. power array size is not (366,1)
         """
 
-
         self.user = user
         self.name = name
         self.number = number
@@ -1037,7 +1036,9 @@ def __init__(
 
         if func_time == 0:
             warnings.warn(
-                UserWarning(f"Func_time of appliance '{self.name}' is defined as 0. Ignore if this is intended")
+                UserWarning(
+                    f"Func_time of appliance '{self.name}' is defined as 0. Ignore if this is intended"
+                )
             )
 
         self.func_time = func_time
@@ -1854,7 +1855,9 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
         ]
 
         tot_time = 0
-        while tot_time <= rand_time and rand_time != 0:  # Check if rand_time is 0 (-> fix issue 114)
+        while (
+            tot_time <= rand_time and rand_time != 0
+        ):  # Check if rand_time is 0 (-> fix issue 114)
             # one option could be to generate a lot of them at once
             indexes = self.rand_switch_on_window(
                 rand_time=rand_time,  # TODO maybe only consider rand_time-tot_time ...
diff --git a/tests/test_func_time_zero.py b/tests/test_func_time_zero.py
index ae516ac6..774ac897 100644
--- a/tests/test_func_time_zero.py
+++ b/tests/test_func_time_zero.py
@@ -2,6 +2,7 @@
 import pytest
 from ramp import UseCase, User
 
+
 def test_skip_when_func_time_zero():
 
     # Generate test user and appliance
@@ -14,23 +15,23 @@ def test_skip_when_func_time_zero():
     )
 
     # Add to use_case
-    use_case = UseCase(
-        name='test_use_case',
-        users=[user]
-    )
+    use_case = UseCase(name="test_use_case", users=[user])
 
     # Calculate peak time range for this use_case
     peak_time_range = use_case.calc_peak_time_range()
 
     # Generated one load profile of this appliance
     power = 1000
-    appliance.generate_load_profile(prof_i=0, peak_time_range=peak_time_range, day_type=0, power=power)
+    appliance.generate_load_profile(
+        prof_i=0, peak_time_range=peak_time_range, day_type=0, power=power
+    )
 
     # Check that no use of this appliance is simulated -> the appliances load profile is always smaller than it's power
     # (Checking for the load_profile to always be 0 might be unreliable, since the RAMP core "marks" potential usage
     # windows with small power values larger 0)
     assert np.max(appliance.daily_use) < power
 
+
 def test_warning_when_func_time_zero():
     user = User(user_name="Test User", num_users=1)
     appliance = user.add_appliance(
@@ -40,5 +41,6 @@ def test_warning_when_func_time_zero():
         time_fraction_random_variability=0.1,
     )
 
+
 with pytest.warns():
-    test_warning_when_func_time_zero()
\ No newline at end of file
+    test_warning_when_func_time_zero()

From 3d864d252409b1a978edb4efca5f6aab5353960b Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 14 Mar 2024 19:28:25 +0100
Subject: [PATCH 007/140] Remove comments about the issue number

---
 ramp/core/core.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index d6109944..2e69110b 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1807,7 +1807,7 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
             or (self.pref_index != 0 and self.user.rand_daily_pref != self.pref_index)
             # checks if the app is allowed in the given yearly behaviour pattern
             or self.wd_we_type not in [day_type, 2]
-            # check if the app has a func_time of 0 (fix issue 114)
+            # skip if the app has a func_time of 0
             or self.func_time == 0
         ):
             return
@@ -1855,9 +1855,7 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
         ]
 
         tot_time = 0
-        while (
-            tot_time <= rand_time and rand_time != 0
-        ):  # Check if rand_time is 0 (-> fix issue 114)
+        while tot_time <= rand_time and rand_time != 0:
             # one option could be to generate a lot of them at once
             indexes = self.rand_switch_on_window(
                 rand_time=rand_time,  # TODO maybe only consider rand_time-tot_time ...

From 0c65148152775523c44b5f663a9ac07c07a53724 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 14 Mar 2024 19:29:17 +0100
Subject: [PATCH 008/140] Check for raising of warning within the test function

---
 tests/test_func_time_zero.py | 18 +++++++-----------
 1 file changed, 7 insertions(+), 11 deletions(-)

diff --git a/tests/test_func_time_zero.py b/tests/test_func_time_zero.py
index 774ac897..0cf1d0af 100644
--- a/tests/test_func_time_zero.py
+++ b/tests/test_func_time_zero.py
@@ -4,7 +4,6 @@
 
 
 def test_skip_when_func_time_zero():
-
     # Generate test user and appliance
     user = User(user_name="Test User", num_users=1)
     appliance = user.add_appliance(
@@ -34,13 +33,10 @@ def test_skip_when_func_time_zero():
 
 def test_warning_when_func_time_zero():
     user = User(user_name="Test User", num_users=1)
-    appliance = user.add_appliance(
-        name="Test Appliance",
-        func_time=0,  # Set func_time to be 0
-        func_cycle=20,
-        time_fraction_random_variability=0.1,
-    )
-
-
-with pytest.warns():
-    test_warning_when_func_time_zero()
+    with pytest.warns():
+        appliance = user.add_appliance(
+            name="Test Appliance",
+            func_time=0,  # Set func_time to be 0
+            func_cycle=20,
+            time_fraction_random_variability=0.1,
+        )

From 898be3c01e7a5a0ef8b6a5cb8bd7a0e5de408651 Mon Sep 17 00:00:00 2001
From: Johann Kraft <johann.kraft@student.kit.edu>
Date: Fri, 2 Feb 2024 10:40:57 +0100
Subject: [PATCH 009/140] Implemented fix for duty cylce repetition issue 78
 (https://github.com/RAMP-project/RAMP/issues/78) - in Appliance ->
 .update_daily_use:   - duration of switch-on event is corrected to be only as
 long as duty cylce   - corrected duration of switch-on event is returned - in
 Appliance -> .generate_load_profile   - calculation of total time of
 operation is performed using corrected duration of switch-on event

---
 ramp/core/core.py | 29 ++++++++++++++++++++++++-----
 1 file changed, 24 insertions(+), 5 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 2e69110b..14b171c1 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1425,12 +1425,21 @@ def update_daily_use(self, coincidence, power, indexes):
             if evaluate in range(self.cw11[0], self.cw11[1]) or evaluate in range(
                 self.cw12[0], self.cw12[1]
             ):
+                # Correct selected switch_on windows for duty cycle duration (fix issue 78):
+                # Limit switch_on_window to duration of duty_cycle
+                indexes = indexes[0:len(self.random_cycle1)]
                 np.put(self.daily_use, indexes, (self.random_cycle1 * coincidence))
             elif evaluate in range(self.cw21[0], self.cw21[1]) or evaluate in range(
                 self.cw22[0], self.cw22[1]
             ):
+                # Correct selected switch_on windows for duty cycle duration (fix issue 78):
+                # Limit switch_on_window to duration of duty_cycle
+                indexes = indexes[0:len(self.random_cycle2)]
                 np.put(self.daily_use, indexes, (self.random_cycle2 * coincidence))
             else:
+                # Correct selected switch_on windows for duty cycle duration (fix issue 78):
+                # Limit switch_on_window to duration of duty_cycle
+                indexes = indexes[0:len(self.random_cycle3)]
                 np.put(self.daily_use, indexes, (self.random_cycle3 * coincidence))
         else:  # if no duty cycles are specified, a regular switch_on event is modelled
             # randomises also the App Power if thermal_p_var is on
@@ -1442,6 +1451,10 @@ def update_daily_use(self, coincidence, power, indexes):
         # updates the time ranges remaining for switch on events, excluding the current switch_on event
         self.update_available_time_for_switch_on_events(indexes)
 
+        # return corrected indexes (where operation was actually planned after correction for duty cycles)
+        # this is required to correctly track the functioning time of an appliance (fix issue 78)
+        return indexes
+
     def calc_rand_window(self, window_idx=1, window_range_limits=[0, 1440]):
         _window = self.__getattribute__(f"window_{window_idx}")
         _random_var = self.__getattribute__(f"random_var_{window_idx}")
@@ -1863,9 +1876,6 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
             if indexes is None:
                 break  # exit cycle and go to next Appliance as there are no available windows anymore
 
-            # the count of total time is updated with the size of the indexes array
-            tot_time = tot_time + indexes.size
-
             if tot_time > rand_time:
                 # the total functioning time is reached, a correction is applied to avoid overflow of indexes
                 indexes_adj = indexes[: -(tot_time - rand_time)]
@@ -1880,7 +1890,9 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
                     # Computes how many of the 'n' of the Appliance instance are switched on simultaneously
                     coincidence = self.calc_coincident_switch_on(inside_peak_window)
                     # Update the daily use depending on existence of duty cycles of the Appliance instance
-                    self.update_daily_use(coincidence, power=power, indexes=indexes_adj)
+                    # Fix issue 78: update_daily_use now returns the indexes at which a switch_on_event
+                    # actually occurred
+                    indexes = self.update_daily_use(coincidence, power=power, indexes=indexes_adj)
                 break  # exit cycle and go to next Appliance
 
             else:
@@ -1890,4 +1902,11 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
 
                 coincidence = self.calc_coincident_switch_on(inside_peak_window)
                 # Update the daily use depending on existence of duty cycles of the Appliance instance
-                self.update_daily_use(coincidence, power=power, indexes=indexes)
+                # Fix issue 78: update_daily_use now returns the indexes at which a switch_on_event
+                # actually occurred
+                indexes = self.update_daily_use(coincidence, power=power, indexes=indexes)
+
+            # the count of total time is updated with the size of the indexes array
+            # Fix issue 78: tot_time is now updated with the size of the indexes array after correction
+            # performed in update_daily_use
+            tot_time = tot_time + indexes.size
\ No newline at end of file

From df3c22fb9162366d0351893e40e3053a942347b4 Mon Sep 17 00:00:00 2001
From: Johann Kraft <johann.kraft@student.kit.edu>
Date: Fri, 2 Feb 2024 12:20:55 +0100
Subject: [PATCH 010/140] Implemented unit test for fix of issue 78
 (https://github.com/RAMP-project/RAMP/issues/78)

---
 tests/test_duty_cycle_repetition.py | 75 +++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)
 create mode 100644 tests/test_duty_cycle_repetition.py

diff --git a/tests/test_duty_cycle_repetition.py b/tests/test_duty_cycle_repetition.py
new file mode 100644
index 00000000..9b2a16e0
--- /dev/null
+++ b/tests/test_duty_cycle_repetition.py
@@ -0,0 +1,75 @@
+import os
+import pytest
+import numpy as np
+
+from ramp import User, UseCase
+import pandas as pd
+pd.options.plotting.backend = "plotly"
+import plotly.io as pio
+pio.renderers.default = "browser"
+
+@pytest.fixture
+def test_use_case():
+    # Create an instance of UseCase to test fix for the duty cycle repetition issue 78
+
+    #%% Create test user
+    test_user = User(
+        user_name='test_user',
+        num_users=1
+    )
+
+    # Create test appliance
+    test_appliance = test_user.add_appliance(
+        name="test_appliance_with_duty_cycles",
+        func_time=4*60,  # runs for 2 hours per day
+
+        window_1=[6 * 60, 20 * 60],  # usage timeframe from 10:00 to 17:00
+
+        num_windows =1,
+
+        fixed_cycle=1,  # appliance uses duty cycles
+
+        # Duty cycle 1
+        p_11=8000,  # power of the first cycle
+        t_11=2,  # time needed for the first cycle
+        p_12=2000,  # power of the second cycle
+        t_12=18,  # time needed for the second cycle
+    )
+
+    # Create and initialize UseCase
+    duty_cycle_test_uc = UseCase(name='duty_cycle_test', users=[test_user])
+    duty_cycle_test_uc.initialize(num_days=3)
+
+    return duty_cycle_test_uc
+
+class TestUseCase:
+    @pytest.mark.usefixtures("test_use_case")
+
+    def test_daily_load_profile(self, test_use_case):
+
+        # Generate load profiles
+        daily_load_profile = pd.DataFrame(test_use_case.generate_daily_load_profiles(),
+                                          index=test_use_case.datetimeindex)
+
+        # Count separate switch-on events -> whenever there is a jump in the load profile from smaller or equal 0.001
+        # to a value larger 0.001 (0.001 is used in the RAMP algorithm to "mark" available switch-on events, therefore
+        # load profiles are often not actually 0 even though there is no appliance use scheduled
+
+        # create a boolean mask for the condition
+        mask = (daily_load_profile[0] <= 0.001) & (daily_load_profile[0].shift(-1) > 0.001)
+        # Sum the number of switch on events
+        switch_on_count = mask.sum()
+
+        # Get the duration of the test duty cycle
+        test_duty_cycle_duration = test_use_case.appliances[0].t_11 + test_use_case.appliances[0].t_12
+
+        # Calculate how many switch-on events are expected
+        expected_switch_on_count = test_use_case.appliances[0].func_time/test_duty_cycle_duration
+
+        # In rare cases, it might happen that switch-on events are scheduled in direct succession
+        # Therefore, the test should not fail if the number of expected switch-on events is not fully reached.
+        # For the defined test, I allow 2 switch-on events less than expected
+
+        # The test fails, if there are 2 switch-on events less than expected
+
+        assert switch_on_count >= expected_switch_on_count-2
\ No newline at end of file

From f92054d8e35815d48320a75b3de76ab02c81d6d0 Mon Sep 17 00:00:00 2001
From: Johann Kraft <johann.kraft@student.kit.edu>
Date: Mon, 11 Mar 2024 11:58:50 +0100
Subject: [PATCH 011/140] Formatted code with black.

---
 ramp/core/core.py                   | 16 ++++++-----
 tests/test_duty_cycle_repetition.py | 42 ++++++++++++++++-------------
 2 files changed, 33 insertions(+), 25 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 14b171c1..2cbd39eb 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1427,19 +1427,19 @@ def update_daily_use(self, coincidence, power, indexes):
             ):
                 # Correct selected switch_on windows for duty cycle duration (fix issue 78):
                 # Limit switch_on_window to duration of duty_cycle
-                indexes = indexes[0:len(self.random_cycle1)]
+                indexes = indexes[0 : len(self.random_cycle1)]
                 np.put(self.daily_use, indexes, (self.random_cycle1 * coincidence))
             elif evaluate in range(self.cw21[0], self.cw21[1]) or evaluate in range(
                 self.cw22[0], self.cw22[1]
             ):
                 # Correct selected switch_on windows for duty cycle duration (fix issue 78):
                 # Limit switch_on_window to duration of duty_cycle
-                indexes = indexes[0:len(self.random_cycle2)]
+                indexes = indexes[0 : len(self.random_cycle2)]
                 np.put(self.daily_use, indexes, (self.random_cycle2 * coincidence))
             else:
                 # Correct selected switch_on windows for duty cycle duration (fix issue 78):
                 # Limit switch_on_window to duration of duty_cycle
-                indexes = indexes[0:len(self.random_cycle3)]
+                indexes = indexes[0 : len(self.random_cycle3)]
                 np.put(self.daily_use, indexes, (self.random_cycle3 * coincidence))
         else:  # if no duty cycles are specified, a regular switch_on event is modelled
             # randomises also the App Power if thermal_p_var is on
@@ -1892,7 +1892,9 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
                     # Update the daily use depending on existence of duty cycles of the Appliance instance
                     # Fix issue 78: update_daily_use now returns the indexes at which a switch_on_event
                     # actually occurred
-                    indexes = self.update_daily_use(coincidence, power=power, indexes=indexes_adj)
+                    indexes = self.update_daily_use(
+                        coincidence, power=power, indexes=indexes_adj
+                    )
                 break  # exit cycle and go to next Appliance
 
             else:
@@ -1904,9 +1906,11 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
                 # Update the daily use depending on existence of duty cycles of the Appliance instance
                 # Fix issue 78: update_daily_use now returns the indexes at which a switch_on_event
                 # actually occurred
-                indexes = self.update_daily_use(coincidence, power=power, indexes=indexes)
+                indexes = self.update_daily_use(
+                    coincidence, power=power, indexes=indexes
+                )
 
             # the count of total time is updated with the size of the indexes array
             # Fix issue 78: tot_time is now updated with the size of the indexes array after correction
             # performed in update_daily_use
-            tot_time = tot_time + indexes.size
\ No newline at end of file
+            tot_time = tot_time + indexes.size
diff --git a/tests/test_duty_cycle_repetition.py b/tests/test_duty_cycle_repetition.py
index 9b2a16e0..43c4fb86 100644
--- a/tests/test_duty_cycle_repetition.py
+++ b/tests/test_duty_cycle_repetition.py
@@ -4,31 +4,27 @@
 
 from ramp import User, UseCase
 import pandas as pd
+
 pd.options.plotting.backend = "plotly"
 import plotly.io as pio
+
 pio.renderers.default = "browser"
 
+
 @pytest.fixture
 def test_use_case():
     # Create an instance of UseCase to test fix for the duty cycle repetition issue 78
 
-    #%% Create test user
-    test_user = User(
-        user_name='test_user',
-        num_users=1
-    )
+    # %% Create test user
+    test_user = User(user_name="test_user", num_users=1)
 
     # Create test appliance
     test_appliance = test_user.add_appliance(
         name="test_appliance_with_duty_cycles",
-        func_time=4*60,  # runs for 2 hours per day
-
+        func_time=4 * 60,  # runs for 2 hours per day
         window_1=[6 * 60, 20 * 60],  # usage timeframe from 10:00 to 17:00
-
-        num_windows =1,
-
+        num_windows=1,
         fixed_cycle=1,  # appliance uses duty cycles
-
         # Duty cycle 1
         p_11=8000,  # power of the first cycle
         t_11=2,  # time needed for the first cycle
@@ -37,34 +33,42 @@ def test_use_case():
     )
 
     # Create and initialize UseCase
-    duty_cycle_test_uc = UseCase(name='duty_cycle_test', users=[test_user])
+    duty_cycle_test_uc = UseCase(name="duty_cycle_test", users=[test_user])
     duty_cycle_test_uc.initialize(num_days=3)
 
     return duty_cycle_test_uc
 
+
 class TestUseCase:
     @pytest.mark.usefixtures("test_use_case")
-
     def test_daily_load_profile(self, test_use_case):
 
         # Generate load profiles
-        daily_load_profile = pd.DataFrame(test_use_case.generate_daily_load_profiles(),
-                                          index=test_use_case.datetimeindex)
+        daily_load_profile = pd.DataFrame(
+            test_use_case.generate_daily_load_profiles(),
+            index=test_use_case.datetimeindex,
+        )
 
         # Count separate switch-on events -> whenever there is a jump in the load profile from smaller or equal 0.001
         # to a value larger 0.001 (0.001 is used in the RAMP algorithm to "mark" available switch-on events, therefore
         # load profiles are often not actually 0 even though there is no appliance use scheduled
 
         # create a boolean mask for the condition
-        mask = (daily_load_profile[0] <= 0.001) & (daily_load_profile[0].shift(-1) > 0.001)
+        mask = (daily_load_profile[0] <= 0.001) & (
+            daily_load_profile[0].shift(-1) > 0.001
+        )
         # Sum the number of switch on events
         switch_on_count = mask.sum()
 
         # Get the duration of the test duty cycle
-        test_duty_cycle_duration = test_use_case.appliances[0].t_11 + test_use_case.appliances[0].t_12
+        test_duty_cycle_duration = (
+            test_use_case.appliances[0].t_11 + test_use_case.appliances[0].t_12
+        )
 
         # Calculate how many switch-on events are expected
-        expected_switch_on_count = test_use_case.appliances[0].func_time/test_duty_cycle_duration
+        expected_switch_on_count = (
+            test_use_case.appliances[0].func_time / test_duty_cycle_duration
+        )
 
         # In rare cases, it might happen that switch-on events are scheduled in direct succession
         # Therefore, the test should not fail if the number of expected switch-on events is not fully reached.
@@ -72,4 +76,4 @@ def test_daily_load_profile(self, test_use_case):
 
         # The test fails, if there are 2 switch-on events less than expected
 
-        assert switch_on_count >= expected_switch_on_count-2
\ No newline at end of file
+        assert switch_on_count >= expected_switch_on_count - 2

From afe1d022f0dceb6cf8355c2424a5f074f837f65b Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 14 Mar 2024 23:55:34 +0100
Subject: [PATCH 012/140] Move duty_cycle selection from update_daily_use to
 rand_switch_on_window

- Introduce current_duty_cycle_id as attribute of Appliance to
keep track of the current duty cycle and avoid re-evaluating
which duty-cycle is concerned by a switch on event
- Change x in range(a,b) to a <= x < b as the latter is about 5 times
faster 243 ns vs 51.4 ns
- Remove direct mention of specific issue in the comments
---
 ramp/core/core.py                   | 80 +++++++++++++++++------------
 tests/test_duty_cycle_repetition.py |  1 -
 2 files changed, 48 insertions(+), 33 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 2cbd39eb..a9b0ffc6 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1111,6 +1111,10 @@ def __init__(
         self.random_cycle2 = np.array([])
         self.random_cycle3 = np.array([])
 
+        # attribute used to know if a switch on event falls within a given duty cycle window
+        # if it is 0, then no switch on events happen within any duty cycle windows
+        self.current_duty_cycle_id = 0
+
     def save(self) -> pd.DataFrame:
         """returns a pd.DataFrame containing the appliance data
 
@@ -1420,27 +1424,19 @@ def update_daily_use(self, coincidence, power, indexes):
         if (
             self.fixed_cycle > 0
         ):  # evaluates if the app has some duty cycles to be considered
-            evaluate = np.round(np.mean(indexes)) if indexes.size > 0 else 0
-            # selects the proper duty cycle and puts the corresponding power values in the indexes range
-            if evaluate in range(self.cw11[0], self.cw11[1]) or evaluate in range(
-                self.cw12[0], self.cw12[1]
-            ):
-                # Correct selected switch_on windows for duty cycle duration (fix issue 78):
-                # Limit switch_on_window to duration of duty_cycle
-                indexes = indexes[0 : len(self.random_cycle1)]
+            # the proper duty cycle was selected in self.rand_switch_on_window()
+            # now setting the corresponding power values in the indexes range
+            if self.current_duty_cycle_id == 1:
                 np.put(self.daily_use, indexes, (self.random_cycle1 * coincidence))
-            elif evaluate in range(self.cw21[0], self.cw21[1]) or evaluate in range(
-                self.cw22[0], self.cw22[1]
-            ):
-                # Correct selected switch_on windows for duty cycle duration (fix issue 78):
-                # Limit switch_on_window to duration of duty_cycle
-                indexes = indexes[0 : len(self.random_cycle2)]
+            elif self.current_duty_cycle_id == 2:
                 np.put(self.daily_use, indexes, (self.random_cycle2 * coincidence))
-            else:
-                # Correct selected switch_on windows for duty cycle duration (fix issue 78):
-                # Limit switch_on_window to duration of duty_cycle
-                indexes = indexes[0 : len(self.random_cycle3)]
+            elif self.current_duty_cycle_id == 3:
                 np.put(self.daily_use, indexes, (self.random_cycle3 * coincidence))
+            else:
+                print(
+                    f"The app {self.name} has duty cycle option on, however the switch on event fell outside the provided duty cycle windows"
+                )
+
         else:  # if no duty cycles are specified, a regular switch_on event is modelled
             # randomises also the App Power if thermal_p_var is on
             np.put(
@@ -1451,10 +1447,6 @@ def update_daily_use(self, coincidence, power, indexes):
         # updates the time ranges remaining for switch on events, excluding the current switch_on event
         self.update_available_time_for_switch_on_events(indexes)
 
-        # return corrected indexes (where operation was actually planned after correction for duty cycles)
-        # this is required to correctly track the functioning time of an appliance (fix issue 78)
-        return indexes
-
     def calc_rand_window(self, window_idx=1, window_range_limits=[0, 1440]):
         _window = self.__getattribute__(f"window_{window_idx}")
         _random_var = self.__getattribute__(f"random_var_{window_idx}")
@@ -1748,6 +1740,38 @@ def rand_switch_on_window(self, rand_time: int):
                 raise ValueError(
                     "There is something fishy with upper limit in switch on..."
                 )
+
+            if (
+                self.fixed_cycle > 0
+            ):  # evaluates if the app has some duty cycles to be considered
+                evaluate = np.round(np.mean(indexes)) if indexes.size > 0 else 0
+                # selects the proper duty cycle
+                if (
+                    self.cw11[0] <= evaluate < self.cw11[1]
+                    or self.cw12[0] <= evaluate < self.cw12[1]
+                ):
+                    self.current_duty_cycle_id = 1
+                    duty_cycle_duration = len(self.random_cycle1)
+                elif (
+                    self.cw21[0] <= evaluate < self.cw21[1]
+                    or self.cw22[0] <= evaluate < self.cw22[1]
+                ):
+                    self.current_duty_cycle_id = 2
+                    duty_cycle_duration = len(self.random_cycle2)
+                elif (
+                    self.cw31[0] <= evaluate < self.cw31[1]
+                    or self.cw32[0] <= evaluate < self.cw32[1]
+                ):
+                    self.current_duty_cycle_id = 3
+                    duty_cycle_duration = len(self.random_cycle3)
+                else:
+                    print(
+                        f"The app {self.name} has duty cycle option on, however the switch on event fell outside the provided duty cycle windows"
+                    )
+
+                if indexes.size > duty_cycle_duration:
+                    # Limit switch_on_window to duration of duty_cycle
+                    indexes = indexes[0:duty_cycle_duration]
         else:
             indexes = None
             # there are no available windows anymore
@@ -1890,11 +1914,7 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
                     # Computes how many of the 'n' of the Appliance instance are switched on simultaneously
                     coincidence = self.calc_coincident_switch_on(inside_peak_window)
                     # Update the daily use depending on existence of duty cycles of the Appliance instance
-                    # Fix issue 78: update_daily_use now returns the indexes at which a switch_on_event
-                    # actually occurred
-                    indexes = self.update_daily_use(
-                        coincidence, power=power, indexes=indexes_adj
-                    )
+                    self.update_daily_use(coincidence, power=power, indexes=indexes_adj)
                 break  # exit cycle and go to next Appliance
 
             else:
@@ -1904,11 +1924,7 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
 
                 coincidence = self.calc_coincident_switch_on(inside_peak_window)
                 # Update the daily use depending on existence of duty cycles of the Appliance instance
-                # Fix issue 78: update_daily_use now returns the indexes at which a switch_on_event
-                # actually occurred
-                indexes = self.update_daily_use(
-                    coincidence, power=power, indexes=indexes
-                )
+                self.update_daily_use(coincidence, power=power, indexes=indexes)
 
             # the count of total time is updated with the size of the indexes array
             # Fix issue 78: tot_time is now updated with the size of the indexes array after correction
diff --git a/tests/test_duty_cycle_repetition.py b/tests/test_duty_cycle_repetition.py
index 43c4fb86..6f432198 100644
--- a/tests/test_duty_cycle_repetition.py
+++ b/tests/test_duty_cycle_repetition.py
@@ -42,7 +42,6 @@ def test_use_case():
 class TestUseCase:
     @pytest.mark.usefixtures("test_use_case")
     def test_daily_load_profile(self, test_use_case):
-
         # Generate load profiles
         daily_load_profile = pd.DataFrame(
             test_use_case.generate_daily_load_profiles(),

From 293242aa814dfed7b3e0cd8708bc2d573f408532 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 14 Mar 2024 23:58:00 +0100
Subject: [PATCH 013/140] Compute total time before while loop

As now indexes are not updated by update daily_use but by
rand_switch_on_window, it is less risky for backward compatibility
to update the total time before entering the loop
---
 ramp/core/core.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index a9b0ffc6..21e786b4 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1900,6 +1900,9 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
             if indexes is None:
                 break  # exit cycle and go to next Appliance as there are no available windows anymore
 
+            # the count of total time is updated with the size of the indexes array
+            tot_time = tot_time + indexes.size
+
             if tot_time > rand_time:
                 # the total functioning time is reached, a correction is applied to avoid overflow of indexes
                 indexes_adj = indexes[: -(tot_time - rand_time)]
@@ -1925,8 +1928,3 @@ def generate_load_profile(self, prof_i, peak_time_range, day_type, power):
                 coincidence = self.calc_coincident_switch_on(inside_peak_window)
                 # Update the daily use depending on existence of duty cycles of the Appliance instance
                 self.update_daily_use(coincidence, power=power, indexes=indexes)
-
-            # the count of total time is updated with the size of the indexes array
-            # Fix issue 78: tot_time is now updated with the size of the indexes array after correction
-            # performed in update_daily_use
-            tot_time = tot_time + indexes.size

From 1355a164e6dfd5dc0764b1b8e1b22bff290ba4cf Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Fri, 15 Mar 2024 00:23:24 +0100
Subject: [PATCH 014/140] Keep assignement to duty cycle 3 similar to past code

---
 ramp/core/core.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 21e786b4..3a86481e 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1768,6 +1768,11 @@ def rand_switch_on_window(self, rand_time: int):
                     print(
                         f"The app {self.name} has duty cycle option on, however the switch on event fell outside the provided duty cycle windows"
                     )
+                    # TODO previously duty_cycle3 was always considered as default if the evaluate proxy did neither
+                    #  get selected by duty_cycle1 nor duty_cycle2, for default is kept but not silently anymore in
+                    #  order to see wheather this is an issue or not
+                    self.current_duty_cycle_id = 3
+                    duty_cycle_duration = len(self.random_cycle3)
 
                 if indexes.size > duty_cycle_duration:
                     # Limit switch_on_window to duration of duty_cycle

From 8f52080c6b81b958989cdcf9b00c1fb9ba2b2ca7 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Fri, 15 Mar 2024 00:37:21 +0100
Subject: [PATCH 015/140] Parameter to switch on/off continuous operation
 within duty_cycles

When this parameter is set to False (default) te behavior should
be the same as previously, i.e. continuous duty cyles during the
entire switch on event (can contain several duty cycles).

Only when set to True would the switch on event be limited to the
duration of the duty cycle in order to better model productive use
of appliances (mill, welder)
---
 ramp/core/core.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 3a86481e..873505f0 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1115,6 +1115,8 @@ def __init__(
         # if it is 0, then no switch on events happen within any duty cycle windows
         self.current_duty_cycle_id = 0
 
+        self.continuous_duty_cycle = True
+
     def save(self) -> pd.DataFrame:
         """returns a pd.DataFrame containing the appliance data
 
@@ -1774,7 +1776,10 @@ def rand_switch_on_window(self, rand_time: int):
                     self.current_duty_cycle_id = 3
                     duty_cycle_duration = len(self.random_cycle3)
 
-                if indexes.size > duty_cycle_duration:
+                if (
+                    indexes.size > duty_cycle_duration
+                    and self.continuous_duty_cycle is False
+                ):
                     # Limit switch_on_window to duration of duty_cycle
                     indexes = indexes[0:duty_cycle_duration]
         else:

From 2e162de1a431daf9c71f0185d78f233a6d9c332d Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 18 Mar 2024 12:35:47 +0100
Subject: [PATCH 016/140] Fix the duty cycle test

---
 tests/test_duty_cycle_repetition.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_duty_cycle_repetition.py b/tests/test_duty_cycle_repetition.py
index 6f432198..7ecf6e03 100644
--- a/tests/test_duty_cycle_repetition.py
+++ b/tests/test_duty_cycle_repetition.py
@@ -31,7 +31,7 @@ def test_use_case():
         p_12=2000,  # power of the second cycle
         t_12=18,  # time needed for the second cycle
     )
-
+    test_appliance.continuous_duty_cycle = False
     # Create and initialize UseCase
     duty_cycle_test_uc = UseCase(name="duty_cycle_test", users=[test_user])
     duty_cycle_test_uc.initialize(num_days=3)

From d5029ea527ca09300dd28573076c3d1edbba81df Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 18 Mar 2024 16:33:21 +0100
Subject: [PATCH 017/140] Make continuous_duty_cycle an Appliance attribute

---
 ramp/core/core.py                   | 13 ++++++++++---
 tests/test_duty_cycle_repetition.py |  2 +-
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 873505f0..8ea65739 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -966,6 +966,7 @@ def __init__(
         func_cycle: int = 1,
         fixed: str = "no",
         fixed_cycle: int = 0,
+        continuous_duty_cycle: int = 1,
         occasional_use: float = 1,
         flat: str = "no",
         thermal_p_var: int = 0,
@@ -998,6 +999,13 @@ def __init__(
         func_cycle : int[0,1440], optional
             minimum time(minutes) the appliance is kept on after switch-on event, by default 1
 
+        continuous_duty_cycle : int[0,1], optional
+            if value is 0 : the duty cycle is executed once per switch on event (like a
+            welder, or other productive use appliances)
+            if value is 1 : whether the duty cycle are filling the whole switch on event of
+            the appliance (like a fridge or other continuous use appliance)
+            by default 1
+
         fixed : str, optional
             if 'yes', all the 'n' appliances of this kind are always switched-on together, by default "no"
 
@@ -1046,6 +1054,7 @@ def __init__(
         self.func_cycle = func_cycle
         self.fixed = fixed
         self.fixed_cycle = fixed_cycle
+        self.continuous_duty_cycle = continuous_duty_cycle
         self.occasional_use = occasional_use
         self.flat = flat
         self.thermal_p_var = thermal_p_var
@@ -1115,8 +1124,6 @@ def __init__(
         # if it is 0, then no switch on events happen within any duty cycle windows
         self.current_duty_cycle_id = 0
 
-        self.continuous_duty_cycle = True
-
     def save(self) -> pd.DataFrame:
         """returns a pd.DataFrame containing the appliance data
 
@@ -1778,7 +1785,7 @@ def rand_switch_on_window(self, rand_time: int):
 
                 if (
                     indexes.size > duty_cycle_duration
-                    and self.continuous_duty_cycle is False
+                    and self.continuous_duty_cycle == 0
                 ):
                     # Limit switch_on_window to duration of duty_cycle
                     indexes = indexes[0:duty_cycle_duration]
diff --git a/tests/test_duty_cycle_repetition.py b/tests/test_duty_cycle_repetition.py
index 7ecf6e03..b9153d15 100644
--- a/tests/test_duty_cycle_repetition.py
+++ b/tests/test_duty_cycle_repetition.py
@@ -30,8 +30,8 @@ def test_use_case():
         t_11=2,  # time needed for the first cycle
         p_12=2000,  # power of the second cycle
         t_12=18,  # time needed for the second cycle
+        continuous_duty_cycle=0,
     )
-    test_appliance.continuous_duty_cycle = False
     # Create and initialize UseCase
     duty_cycle_test_uc = UseCase(name="duty_cycle_test", users=[test_user])
     duty_cycle_test_uc.initialize(num_days=3)

From 21f6d4010815ac609eb8cb70559141399503ee55 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 19 Mar 2024 14:42:20 +0100
Subject: [PATCH 018/140] Update example notebook for use of non continuous
 duty cycle

---
 docs/notebooks/multi_cycle.ipynb | 186 ++++++++++++++++++++++++++-----
 1 file changed, 158 insertions(+), 28 deletions(-)

diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb
index 330ff16b..9770fd99 100644
--- a/docs/notebooks/multi_cycle.ipynb
+++ b/docs/notebooks/multi_cycle.ipynb
@@ -161,18 +161,18 @@
    "id": "bdeb861f",
    "metadata": {},
    "source": [
-    "### Buidling the profiles"
+    "### Building the profiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "9b9c1541",
    "metadata": {},
    "outputs": [],
    "source": [
     "use_case = UseCase(users=[household])\n",
-    "peak_time_range = use_case.calc_peak_time_range()"
+    "use_case.peak_time_range = use_case.calc_peak_time_range()"
    ]
   },
   {
@@ -180,8 +180,32 @@
    "execution_count": 8,
    "id": "49589857",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The app Fridge has duty cycle option on, however the switch on event fell outside the provided duty cycle windows\n",
+      "The app Fridge has duty cycle option on, however the switch on event fell outside the provided duty cycle windows\n",
+      "      2020-05-16  2020-08-16  2020-12-16\n",
+      "0          0.001       0.001       0.001\n",
+      "1          0.001       0.001       0.001\n",
+      "2          0.001       0.001       0.001\n",
+      "3          0.001       0.001       0.001\n",
+      "4          0.001       0.001       0.001\n",
+      "...          ...         ...         ...\n",
+      "1435       0.001     200.000       0.001\n",
+      "1436       0.001     200.000       0.001\n",
+      "1437       0.001       0.001       0.001\n",
+      "1438       0.001       0.001       0.001\n",
+      "1439       0.001       0.001       0.001\n",
+      "\n",
+      "[1440 rows x 3 columns]\n"
+     ]
+    }
+   ],
    "source": [
+    "fridge.contunous_duty_cycle = 0\n",
     "# days to build the profiles\n",
     "days = [\n",
     "    \"2020-05-16\",\n",
@@ -194,48 +218,140 @@
     "for day_idx, day in enumerate(days):\n",
     "    profile = household.generate_single_load_profile(\n",
     "        prof_i=day_idx,  # the day to generate the profile\n",
-    "        peak_time_range=peak_time_range,\n",
     "        day_type=get_day_type(day),\n",
     "    )\n",
     "\n",
-    "    profiles[day] = profile"
+    "    profiles[day] = profile\n",
+    "\n",
+    "print(profiles)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "fa5688ff",
+   "id": "2143c46d-b324-4844-ae06-f55d81d48bce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting a part of the days\n",
+    "profiles.iloc[400:500].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dacea141-4969-4d31-8991-45f8ad252b53",
+   "metadata": {},
+   "source": [
+    "## Appliances with continuous duty cycle or productive use\n",
+    "\n",
+    "By default, RAMP models duty cycle in contunuous mode. Given time of use window some switch-on events of random duration are generated and filled with duty cycles. If the duration of the switch-on events is longer than one duty cycle, the load profile is filled with repetitions of the duty cycle.\n",
+    "\n",
+    "In case of productive uses such as welding, milling or the use of machinery in a carpentry, where appliances are switched on many times within the working hours, switch-on events duration should be limited to the duration of the duty cycle, resulting in one duty cycle per switch-on event.\n",
+    "\n",
+    "In order to model an appliance having a productive use duty cycle, set the attribute `continuous_duty_cycle` to `0` as in this example below\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5f2985fb-dec1-456a-b198-4c5a3a2acd1c",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "      2020-05-16  2020-08-16  2020-12-16\n",
-      "0          0.001       5.000       0.001\n",
-      "1          0.001       5.000       0.001\n",
-      "2          0.001       5.000       0.001\n",
-      "3          5.000       5.000       0.001\n",
-      "4          5.000       5.000       0.001\n",
-      "...          ...         ...         ...\n",
-      "1435       0.001       0.001       0.001\n",
-      "1436       0.001       0.001       0.001\n",
-      "1437       0.001       0.001       0.001\n",
-      "1438       0.001       0.001       0.001\n",
-      "1439       0.001       0.001       0.001\n",
-      "\n",
-      "[1440 rows x 3 columns]\n"
+      "You will simulate 1 day(s) from 2024-03-19 00:00:00 until 2024-03-20 00:00:00\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "print(profiles)"
+    "test_user = User(user_name=\"test_user\", num_users=1)\n",
+    "\n",
+    "# Create test appliance\n",
+    "test_appliance = test_user.add_appliance(\n",
+    "    name=\"test_appliance_with_duty_cycles\",\n",
+    "    func_time=4 * 60,  # runs for 2 hours per day\n",
+    "    num_windows=1,\n",
+    "    window_1=[6 * 60, 20 * 60],  # usage timeframe from 06:00 to 20:00\n",
+    "    fixed_cycle=1,  # appliance uses duty cycles\n",
+    "    # Duty cycle 1\n",
+    "    p_11=8000,  # power of the first cycle\n",
+    "    t_11=2,  # time needed for the first cycle\n",
+    "    p_12=2000,  # power of the second cycle\n",
+    "    t_12=18,  # time needed for the second cycle\n",
+    "    continuous_duty_cycle=0,  # appliance run the duty cycle once per switch on event\n",
+    ")\n",
+    "# Create and initialize UseCase\n",
+    "uc = UseCase(name=\"duty_cycle_test\", users=[test_user])\n",
+    "uc.initialize(num_days=1)\n",
+    "\n",
+    "daily_load_profile = pd.DataFrame(\n",
+    "    uc.generate_daily_load_profiles(),\n",
+    "    index=uc.datetimeindex,\n",
+    ")\n",
+    "\n",
+    "daily_load_profile.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "751c06be-deb2-4029-a31f-3ba3b56d5d8b",
+   "metadata": {},
+   "source": [
+    "In order to illustrate how a continuous cycle woud look like, let's set the `continuous_duty_cycle` back to `1`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "2143c46d-b324-4844-ae06-f55d81d48bce",
+   "execution_count": 11,
+   "id": "04aec0b2-8a81-4456-b9d4-3cc16387927a",
    "metadata": {},
    "outputs": [
     {
@@ -244,13 +360,13 @@
        "<AxesSubplot:>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -262,8 +378,22 @@
     }
    ],
    "source": [
-    "# plotting a part of the days\n",
-    "profiles.iloc[400:500].plot()"
+    "test_appliance.continuous_duty_cycle = 1\n",
+    "\n",
+    "daily_load_profile = pd.DataFrame(\n",
+    "    uc.generate_daily_load_profiles(),\n",
+    "    index=uc.datetimeindex,\n",
+    ")\n",
+    "\n",
+    "daily_load_profile.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67bbb185-fd3b-4a40-b4e4-5968a70990ea",
+   "metadata": {},
+   "source": [
+    "One can see that the switch on events are longer and more than one duty cycle run within the switch on event"
    ]
   }
  ],

From 35428a4877622ce511200308b8f1af30d9695444 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 19 Mar 2024 15:07:08 +0100
Subject: [PATCH 019/140] Add continuous_use_duty_cycle to appliance attributes

---
 docs/source/input_parameters.rst | 7 +++++++
 ramp/core/constants.py           | 2 ++
 2 files changed, 9 insertions(+)

diff --git a/docs/source/input_parameters.rst b/docs/source/input_parameters.rst
index ea8938b5..a0d489bd 100644
--- a/docs/source/input_parameters.rst
+++ b/docs/source/input_parameters.rst
@@ -108,6 +108,13 @@ The **"allowed values"** column provide information about the format one should
      - integer
      - no
      - 0
+  * - continuous_use_duty_cycle
+     - NA
+     - {0,1}
+     - Duty cycle mode, 0 triggers once per switch-on event, 1 let the duty cycle repeat during the entire switch-on event
+     - integer
+     - no
+     - 1
    * - occasional_use
      - %
      - in [0,1]
diff --git a/ramp/core/constants.py b/ramp/core/constants.py
index d3433aff..846a8e96 100644
--- a/ramp/core/constants.py
+++ b/ramp/core/constants.py
@@ -73,6 +73,7 @@ def switch_on_parameters():
     "func_cycle",
     "fixed",
     "fixed_cycle",
+    "continuous_duty_cycle",
     "occasional_use",
     "flat",
     "thermal_p_var",
@@ -115,6 +116,7 @@ def switch_on_parameters():
     "func_cycle",
     "fixed",
     "fixed_cycle",
+    "continuous_duty_cycle",
     "occasional_use",
     "flat",
     "thermal_p_var",

From 3d34976d1a7a44ddf9e703891b1dcbdff72dedee Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 19 Mar 2024 15:23:30 +0100
Subject: [PATCH 020/140] Update changelog

---
 CHANGELOG.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 61e8906e..f19e403c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,8 +4,11 @@ Release History
 0.5.2 (dev)
 -----------
 
+**|new|**       Introduction of an Appliance parameter to model productive use duty cycles: `continuous_use_duty_cycle`
+
 0.5.1 (2024-02-08)
------------
+------------------
+
 **|fixed|**     Plotting a cloud of profiles from the command line is fixed
 
 0.5.0 (2023-12-06)

From 3819467726ef2d4d7e465885f25a1407372818a4 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 19 Mar 2024 16:14:05 +0100
Subject: [PATCH 021/140] Fix old comparative tests script

It needs to be run from the root of the repository
---
 ramp/test/results/.gitkeep |  0
 ramp/test/test_run.py      | 25 ++++++++++++++++++++++++-
 2 files changed, 24 insertions(+), 1 deletion(-)
 create mode 100644 ramp/test/results/.gitkeep

diff --git a/ramp/test/results/.gitkeep b/ramp/test/results/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/ramp/test/test_run.py b/ramp/test/test_run.py
index 9717fbb1..7a4f0918 100644
--- a/ramp/test/test_run.py
+++ b/ramp/test/test_run.py
@@ -8,6 +8,8 @@
 # %% Import required modules
 import pandas as pd
 import matplotlib.pyplot as plt
+from ramp.core.core import UseCase
+from ramp.post_process import post_process as pp
 import os
 
 # %% Function to test the output against reference results
@@ -74,6 +76,7 @@ def series_to_average(profile_series, num_days):
     axes[n - 1].legend()
     axes[n - 1].set_xticklabels([])
     axes[n - 2].set_xticklabels([])
+    plt.show()
 
 
 # %% Testing the output and providing visual result
@@ -84,5 +87,25 @@ def series_to_average(profile_series, num_days):
 by the tested code changes. If any differences are there, the developers should 
 evaluate whether these are as expected/designed or not
 """
+from ramp.example.input_file_1 import User_list as User_list1
+from ramp.example.input_file_2 import User_list as User_list2
+from ramp.example.input_file_3 import User_list as User_list3
 
-test_output("../results", "../test", num_input_files=3)
+for i, ul in enumerate([User_list1, User_list2, User_list3]):
+    of_path = os.path.join(pp.BASE_PATH, "test", "results", f"output_file_{i + 1}.csv")
+    if os.path.exists(of_path) is False:
+        uc = UseCase(
+            users=ul,
+            parallel_processing=False,
+        )
+        uc.initialize(peak_enlarge=0.15, num_days=30)
+
+        Profiles_list = uc.generate_daily_load_profiles(flat=True)
+
+        pp.export_series(Profiles_list, ofname=of_path)
+
+test_output(
+    os.path.join(pp.BASE_PATH, "test", "results"),
+    os.path.join(pp.BASE_PATH, "test"),
+    num_input_files=3,
+)

From 5b42bdca90986be35a90c555ff60543fd47504aa Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 19 Mar 2024 16:23:24 +0100
Subject: [PATCH 022/140] Prevent load from excel to throw error if new
 attribute not present

---
 ramp/core/core.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 8ea65739..3cf3d824 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -584,7 +584,10 @@ def load(self, filename: str) -> None:
                 :, ~user_df.columns.isin(["user_name", "num_users", "user_preference"])
             ].to_dict(orient="records"):
                 # assign Appliance arguments
-                appliance_parameters = {k: row[k] for k in APPLIANCE_ARGS}
+                appliance_parameters = {}
+                for k in APPLIANCE_ARGS:
+                    if k in row:
+                        appliance_parameters[k] = row[k]
 
                 # assign windows arguments
                 for k in WINDOWS_PARAMETERS:

From 0564791c3591159ef249570e48c0e6426d15af5b Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 26 Mar 2024 16:47:07 +0100
Subject: [PATCH 023/140] Remove unused line

---
 docs/notebooks/multi_cycle.ipynb | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb
index 9770fd99..69aa3eb3 100644
--- a/docs/notebooks/multi_cycle.ipynb
+++ b/docs/notebooks/multi_cycle.ipynb
@@ -205,7 +205,6 @@
     }
    ],
    "source": [
-    "fridge.contunous_duty_cycle = 0\n",
     "# days to build the profiles\n",
     "days = [\n",
     "    \"2020-05-16\",\n",

From c5937c920f81088995beeee530e33bb909b7ce7f Mon Sep 17 00:00:00 2001
From: Johann <jok.kraft@gmail.com>
Date: Tue, 16 Apr 2024 15:15:13 +0200
Subject: [PATCH 024/140] Round rand_peak_enlarge to be at least 1 to avoid
 empty peak_time_range

---
 ramp/core/core.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 3cf3d824..f811c921 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -356,11 +356,12 @@ def calc_peak_time_range(self, peak_enlarge=None):
                 sigma=1 / 3 * (peak_window[-1] - peak_window[0]),
             )
         )
-        rand_peak_enlarge = round(
+        # Rand_peak_enlarge is rounded to be at least 1 -> if rounded to 0 peak_time_range would be empty
+        rand_peak_enlarge = max(round(
             math.fabs(
                 peak_time - random.gauss(mu=peak_time, sigma=peak_enlarge * peak_time)
             )
-        )
+        ), 1)
         # The peak_time is randomly enlarged based on the calibration parameter peak_enlarge
         return np.arange(peak_time - rand_peak_enlarge, peak_time + rand_peak_enlarge)
 

From 57b13ee6bc7a38d2383da5b3524204163bf6e654 Mon Sep 17 00:00:00 2001
From: "F.L" <f.lombardi@outlook.com>
Date: Thu, 7 Dec 2023 11:39:26 +0100
Subject: [PATCH 025/140] adding the paper files for JOSS

---
 joss_paper/example_output.png | Bin 0 -> 818499 bytes
 joss_paper/paper.bib          | 222 ++++++++++++++++++++++++++++++++++
 joss_paper/paper.md           |  75 ++++++++++++
 joss_paper/paper_compiled.pdf | Bin 0 -> 1080912 bytes
 4 files changed, 297 insertions(+)
 create mode 100644 joss_paper/example_output.png
 create mode 100644 joss_paper/paper.bib
 create mode 100644 joss_paper/paper.md
 create mode 100644 joss_paper/paper_compiled.pdf

diff --git a/joss_paper/example_output.png b/joss_paper/example_output.png
new file mode 100644
index 0000000000000000000000000000000000000000..d4fb8d6045278ef114a53df0769f59d0b1a8cdb7
GIT binary patch
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diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib
new file mode 100644
index 00000000..6069919e
--- /dev/null
+++ b/joss_paper/paper.bib
@@ -0,0 +1,222 @@
+
+@article{dimovski_holistic:2023,
+	title = {Holistic {MILP}-based approach for rural electrification planning},
+	volume = {49},
+	issn = {2211-467X},
+	url = {https://www.sciencedirect.com/science/article/pii/S2211467X23001219},
+	doi = {10.1016/j.esr.2023.101171},
+	urldate = {2023-10-31},
+	journal = {Energy Strategy Reviews},
+	author = {Dimovski, Aleksandar and Corigliano, Silvia and Edeme, Darlain and Merlo, Marco},
+	month = sep,
+	year = {2023},
+	keywords = {Geospatial planning, Least-cost electrification, MILP, Rural electrification, Sustainable development},
+	pages = {101171},
+}
+
+@article{baruah_modelling:2023,
+	title = {Modelling of an off-grid roof-top residential photovoltaic nano grid system for an urban locality in {India}},
+	volume = {74},
+	issn = {0973-0826},
+	url = {https://www.sciencedirect.com/science/article/pii/S0973082623000935},
+	doi = {10.1016/j.esd.2023.05.004},
+	urldate = {2023-10-31},
+	journal = {Energy for Sustainable Development},
+	author = {Baruah, Abhinandan and Basu, Mousumi},
+	month = jun,
+	year = {2023},
+	keywords = {Correlation analysis, HOMER Pro, Off-grid rooftop PV, RAMP, Rooftop area feasibility, Sensitivity analysis},
+	pages = {471--498},
+}
+
+@article{villarroel-schneider_open-source:2023,
+	title = {Open-source model applied for techno-economic optimization of a hybrid solar {PV} biogas-based polygeneration plant: {The} case of a dairy farmers’ association in central {Bolivia}},
+	volume = {291},
+	issn = {0196-8904},
+	shorttitle = {Open-source model applied for techno-economic optimization of a hybrid solar {PV} biogas-based polygeneration plant},
+	url = {https://www.sciencedirect.com/science/article/pii/S0196890423005691},
+	doi = {10.1016/j.enconman.2023.117223},
+	urldate = {2023-10-31},
+	journal = {Energy Conversion and Management},
+	author = {Villarroel-Schneider, J. and Balderrama, Sergio and Sánchez, Claudia and Cardozo, Evelyn and Malmquist, Anders and Martin, Andrew},
+	month = sep,
+	year = {2023},
+	keywords = {Biogas, Cogeneration, Dairy, Hybrid solar-biogas, Optimization, Techno-economic},
+	pages = {117223},
+}
+
+@article{pasqui_new:2023,
+	title = {A new smart batteries management for {Renewable} {Energy} {Communities}},
+	volume = {34},
+	issn = {2352-4677},
+	url = {https://www.sciencedirect.com/science/article/pii/S2352467723000516},
+	doi = {10.1016/j.segan.2023.101043},
+	urldate = {2023-10-31},
+	journal = {Sustainable Energy, Grids and Networks},
+	author = {Pasqui, Mattia and Felice, Alex and Messagie, Maarten and Coosemans, Thierry and Bastianello, Tommaso Tiozzo and Baldi, Duccio and Lubello, Pietro and Carcasci, Carlo},
+	month = jun,
+	year = {2023},
+	keywords = {Battery management, Energy system simulation, Load forecasting, Photovoltaic systems, Renewable Energy Communities},
+	pages = {101043},
+}
+
+@article{lubello_assessment:2022,
+	title = {Assessment of hydrogen-based long term electrical energy storage in residential energy systems},
+	volume = {8},
+	issn = {2666-9552},
+	url = {https://www.sciencedirect.com/science/article/pii/S2666955222000260},
+	doi = {10.1016/j.segy.2022.100088},
+	urldate = {2023-10-31},
+	journal = {Smart Energy},
+	author = {Lubello, Pietro and Pasqui, Mattia and Mati, Alessandro and Carcasci, Carlo},
+	month = nov,
+	year = {2022},
+	keywords = {Energy forecasting, Energy systems, Hydrogen storage, Residential building, Self-consumption},
+	pages = {100088},
+}
+
+@article{mangipinto_impact:2022,
+	title = {Impact of mass-scale deployment of electric vehicles and benefits of smart charging across all {European} countries},
+	volume = {312},
+	issn = {0306-2619},
+	url = {https://www.sciencedirect.com/science/article/pii/S0306261922001416},
+	doi = {10.1016/j.apenergy.2022.118676},
+	urldate = {2023-10-31},
+	journal = {Applied Energy},
+	author = {Mangipinto, Andrea and Lombardi, Francesco and Sanvito, Francesco Davide and Pavičević, Matija and Quoilin, Sylvain and Colombo, Emanuela},
+	month = apr,
+	year = {2022},
+	keywords = {Electric vehicles, Sector coupling, Smart charging, stochastic demand simulation, Time series},
+	pages = {118676},
+}
+
+@article{stevanato_rural:2021,
+	title = {Rural areas electrification strategies through shadow costs analysis - {Bolivian} {Highlands} case study},
+	volume = {65},
+	issn = {0973-0826},
+	url = {https://www.sciencedirect.com/science/article/pii/S0973082621001253},
+	doi = {10.1016/j.esd.2021.10.009},
+	urldate = {2023-10-31},
+	journal = {Energy for Sustainable Development},
+	author = {Stevanato, Nicolò and Corigliano, Silvia and Petrelli, Marina and Tonini, Francesco and Merlo, Marco and Colombo, Emanuela},
+	month = dec,
+	year = {2021},
+	keywords = {Electricity demand, Electrification costs, Grid extension, Grid routing, Rural electrification, Solar home systems},
+	pages = {162--174},
+}
+
+@article{stevanato_long-term:2020,
+	title = {Long-term sizing of rural microgrids: {Accounting} for load evolution through multi-step investment plan and stochastic optimization},
+	volume = {58},
+	issn = {0973-0826},
+	shorttitle = {Long-term sizing of rural microgrids},
+	url = {https://www.sciencedirect.com/science/article/pii/S0973082620302635},
+	doi = {10.1016/j.esd.2020.07.002},
+	urldate = {2023-10-31},
+	journal = {Energy for Sustainable Development},
+	author = {Stevanato, Nicolò and Lombardi, Francesco and Guidicini, Giulia and Rinaldi, Lorenzo and Balderrama, Sergio L. and Pavičević, Matija and Quoilin, Sylvain and Colombo, Emanuela},
+	month = oct,
+	year = {2020},
+	keywords = {Load evolution, Microgrid, Multi-step investment, Off-grid, Rural electrification},
+	pages = {16--29},
+}
+
+@article{lombardi_generating:2019,
+	title = {Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model},
+	volume = {177},
+	issn = {0360-5442},
+	url = {https://www.sciencedirect.com/science/article/pii/S0360544219307303},
+	doi = {10.1016/j.energy.2019.04.097},
+	urldate = {2023-10-31},
+	journal = {Energy},
+	author = {Lombardi, Francesco and Balderrama, Sergio and Quoilin, Sylvain and Colombo, Emanuela},
+	month = jun,
+	year = {2019},
+	keywords = {Energy demand, Load profile, Multi-energy system, Off-grid, Rural areas},
+	pages = {433--444},
+}
+
+
+@article{secchi_smart:2023,
+	title = {Smart electric vehicles charging with centralised vehicle-to-grid capability for net-load variance minimisation under increasing {EV} and {PV} penetration levels},
+	volume = {35},
+	issn = {2352-4677},
+	url = {https://www.sciencedirect.com/science/article/pii/S2352467723001285},
+	doi = {10.1016/j.segan.2023.101120},
+	urldate = {2023-10-31},
+	journal = {Sustainable Energy, Grids and Networks},
+	author = {Secchi, M. and Barchi, G. and Macii, D. and Petri, D.},
+	month = sep,
+	year = {2023},
+	keywords = {Electric vehicles (EV), Photovoltaic Generator, Smart EV charging, Vehicle-to-grid},
+	pages = {101120},
+}
+
+
+@article{pickering_diversity:2022,
+	title = {Diversity of options to eliminate fossil fuels and reach carbon neutrality across the entire {European} energy system},
+	volume = {6},
+	issn = {2542-4785, 2542-4351},
+	url = {https://www.cell.com/joule/abstract/S2542-4351(22)00236-7},
+	doi = {10.1016/j.joule.2022.05.009},
+	language = {English},
+	number = {6},
+	urldate = {2022-06-16},
+	journal = {Joule},
+	author = {Pickering, Bryn and Lombardi, Francesco and Pfenninger, Stefan},
+	month = jun,
+	year = {2022},
+	note = {Publisher: Elsevier},
+	keywords = {near-optimal solutions, SPORES, flexibility, modeling to generate alternatives, Calliope, Carbon neutrality, energy self-sufficiency, energy system optimisation, renewable energy, sector coupling},
+	pages = {1253--1276},
+}
+
+
+@article{stevanato_modeling:2020,
+	title = {Modeling of a {Village}-{Scale} {Multi}-{Energy} {System} for the {Integrated} {Supply} of {Electric} and {Thermal} {Energy}},
+	volume = {10},
+	copyright = {http://creativecommons.org/licenses/by/3.0/},
+	issn = {2076-3417},
+	url = {https://www.mdpi.com/2076-3417/10/21/7445},
+	doi = {10.3390/app10217445},
+	language = {en},
+	number = {21},
+	urldate = {2023-11-06},
+	journal = {Applied Sciences},
+	author = {Stevanato, Nicolo and Rinaldi, Lorenzo and Pistolese, Stefano and Balderrama Subieta, Sergio Luis and Quoilin, Sylvain and Colombo, Emanuela},
+	month = jan,
+	year = {2020},
+	note = {Number: 21
+Publisher: Multidisciplinary Digital Publishing Institute},
+	keywords = {electric, energy system, modeling, off-grid, rural electrification, thermal},
+	pages = {7445},
+}
+
+
+@misc{ramp_github,
+	title = {{RAMP: stochastic multi-energy demand profiles}},
+	url = {https://github.com/RAMP-project},
+	year = {2019},
+    publisher = {GitHub},
+    journal = {GitHub repository},
+}
+
+
+@article{hart_gui:2023,
+	title = {Sustainable {Energy} {System} {Planning} in {Developing} {Countries}: {Facilitating} {Load} {Profile} {Generation} in {Energy} {System} {Simulations}},
+	isbn = {978-0-9981331-6-4},
+	url = {https://hdl.handle.net/10125/102726},
+	author = {Hart, Maria C. G. and Eckhoff, Sarah and Breitner, Michael H.},
+	year = {2023},
+    journal = {Hawaii International Conference on System Sciences}
+}
+
+
+@misc{ramp_zenodo:2023,
+	title = {{RAMP}-project/{RAMP}: v0.5.0},
+	url = {https://doi.org/10.5281/zenodo.10275752},
+	publisher = {Zenodo},
+	author = {Lombardi, Francesco and Duc, Pierre-Francois and Tahavori, Mohammad Amin and Sanchez-Solis, Claudia and Eckhoff, Sarah and Hart, Maria C.G. and Sanvito, Francesco Davide and Ireland, Gregory and Balderrama, Sergio and Quoilin, Sylvain},
+	year = {2023},
+	doi = {10.5281/zenodo.10275752},
+}
diff --git a/joss_paper/paper.md b/joss_paper/paper.md
new file mode 100644
index 00000000..238ef8ed
--- /dev/null
+++ b/joss_paper/paper.md
@@ -0,0 +1,75 @@
+---
+title: 'RAMP: stochastic simulation of user-driven energy demand time series'
+tags:
+  - Python
+  - energy demand
+  - stochastic
+  - time series
+  - synthetic data
+authors:
+  - name: Francesco Lombardi
+    orcid: 0000-0002-7624-5886
+    corresponding: true # (This is how to denote the corresponding author)
+    affiliation: 1
+  - name: Pierre-François Duc
+    orcid: 
+    affiliation: 2
+  - name: Mohammad Amin Tahavori
+    orcid: 0000-0002-7753-0523
+    affiliation: 3
+  - name: Claudia Sanchez-Solis
+    orcid: 0000-0003-2385-7392
+    affiliation: "4,7"
+  - name: Sarah Eckhoff
+    orcid: 0000-0002-6168-4835
+    affiliation: 5  
+  - name: Maria C.G. Hart
+    orcid: 0000-0002-1031-9782
+    affiliation: 5  
+  - name: Francesco Sanvito
+    orcid: 0000-0002-9152-9684
+    affiliation: 1
+  - name: Gregory Ireland
+    orcid: 
+    affiliation: "2,6"
+  - name: Sergio Balderrama
+    orcid: 
+    affiliation: 7
+  - name: Sylvain Quoilin
+    orcid: 
+    affiliation: 4
+affiliations:
+ - name: TU Delft, Faculty of Technology, Policy and Management, Delft, The Netherlands
+   index: 1
+ - name: Reiner Lemoine Institut, Berlin, Germany
+   index: 2
+ - name: VITO, Mol, Belgium
+   index: 3
+ - name: University of Liège, Integrated and Sustainable Energy Systems, Thermodynamics Laboratory, Liège, Belgium
+   index: 4
+ - name: Leibniz Universität Hannover, Information Systems Institute, Hannover, Germany
+   index: 5
+ - name: University of Cape Town, Cape Town, South Africa
+   index: 6
+ - name: Universidad Mayor de San Simon, Centro Universitario de Investigacion en Energias, Cochabamba, Bolivia
+   index: 7
+date: 6 December 2023
+bibliography: paper.bib
+
+---
+
+# Summary
+
+The urgency of the energy transition is leading to a rapid evolution of energy system design worldwide. In areas with widespread energy infrastructure, existing electricity, heat and mobility networks are being re-designed for carbon neutrality and are increasingly interconnected. In areas where energy infrastructure is limited, instead, networks and systems are being rapidly expanded to ensure access to energy for all. And yet, re-designing and expanding energy systems in these directions requires information on future user behaviour and associated energy demand, which is often unavailable. In fact, historical data are either entirely missing or poorly representative of future behaviour within transitioning systems. This results in the reliance on inadequate demand data, which affect system design and its resilience to rapid behaviour evolution.
+
+# Statement of need
+
+RAMP is an open-source, Python-based software suite that enables the stochastic simulation of any user-driven energy demand time series based on few simple inputs. In fact, the software is designed to require only a basic understanding of the expected user activity patterns and owned appliances as inputs, to be provided in tabular (`.xlsx`) format. Then, it leverages stochasticity (using the `random` package) to make up for the lack of more detailed information and to account for the unpredictability of human behaviour (see Figure \ref{fig:example}). This way, RAMP allows generating and visualising synthetic data wherever metered data does not exist, such as when designing systems in remote areas [@lombardi_generating:2019] or when looking at future electric-vehicle fleets [@mangipinto_impact:2022]. Moreover, it features several degrees of customisations, allowing users to explicitly simulate radically different but equally plausible behaviour scenarios as a key ingredient to robust system design.
+
+RAMP has already been used in many scientific publications, for instance, for the simulation of electricity [@dimovski_holistic:2023], heating [@stevanato_modeling:2020], cooking [@stevanato_long-term:2020] and electric mobility [@secchi_smart:2023] demand time series at scales ranging from districts [@pasqui_new:2023] or villages [@villarroel-schneider_open-source:2023] to continents [@pickering_diversity:2022]. It has dozens of users globally and has recently become a multi-institution software development effort, actively contributed by TU Delft, VITO, Sympheny, the Reiner Lemoine Institut, the University of Liège and the Leibniz University Hannover. The joint development process has brought major improvements to the code structure, syntax and efficiency, more extensive documentation, and a web-based graphical user interface for users with no Python experience [@hart_gui:2023].
+
+RAMP is developed openly on GitHub [@ramp_github] and each new release is archived on Zenodo [@ramp_zenodo:2023].
+
+![Example output (normalised by peak demand) for the simulation of the electricity load of three households in a small village over five days. The thick blue line represents the five-day average, while individual days are plotted in a lighter colour. \label{fig:example}](example_output.png)
+
+# References
\ No newline at end of file
diff --git a/joss_paper/paper_compiled.pdf b/joss_paper/paper_compiled.pdf
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HcmV?d00001


From cd3ac194907f3de93f9f51a9e06ef537e7444a07 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <mohammadamin.tahavori@vito.be>
Date: Fri, 29 Mar 2024 18:13:27 +0100
Subject: [PATCH 026/140] Adding depreciation warning to Appliance method in
 User class

to ensure the Back-compatibility with legacy code, the Appliance method
of User class is present. However the method is not further maintained
since the nex-gen version of the sofware. To warn the users to move
towards the new method, a DeprecationWarning is added.
---
 ramp/core/core.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index f811c921..41777073 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -42,6 +42,7 @@ def single_appliance_daily_load_profile(args):
 
     return args[0], app.daily_use
 
+warnings.simplefilter('always', DeprecationWarning)
 
 class UseCase:
     def __init__(
@@ -849,6 +850,9 @@ def Appliance(
         ------
         refer to Appliance class docs
         """
+
+        warnings.warn("This function is deprecated and not supported since version v0.4.0. Instead use the add_appliance method.", DeprecationWarning)
+
         return self.add_appliance(
             number=number,
             power=power,

From 3388542d64bb0ad684912597b51e775f316a33b0 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 10 Apr 2024 11:19:29 +0200
Subject: [PATCH 027/140] Enhance qualitative test user experience

---
 ramp/test/test_run.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/ramp/test/test_run.py b/ramp/test/test_run.py
index 7a4f0918..e9d892af 100644
--- a/ramp/test/test_run.py
+++ b/ramp/test/test_run.py
@@ -91,6 +91,22 @@ def series_to_average(profile_series, num_days):
 from ramp.example.input_file_2 import User_list as User_list2
 from ramp.example.input_file_3 import User_list as User_list3
 
+TEST_OUTPUT_PATH = os.path.join(pp.BASE_PATH, "test", "results")
+
+remove_old_tests = False
+for file in os.listdir(TEST_OUTPUT_PATH):
+    if file.endswith(".csv"):
+        if remove_old_tests is False:
+            answer = input(
+                "Some result file for the qualitative testing exists already, do you want to overwrite them? (y/n)"
+            )
+            if answer == "y" or answer == "yes":
+                remove_old_tests = True
+            else:
+                break
+        if remove_old_tests is True:
+            os.remove(os.path.join(TEST_OUTPUT_PATH, file))
+
 for i, ul in enumerate([User_list1, User_list2, User_list3]):
     of_path = os.path.join(pp.BASE_PATH, "test", "results", f"output_file_{i + 1}.csv")
     if os.path.exists(of_path) is False:

From 724dda56a0211f8a2efaca59510477a4bbec3a22 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 10 Apr 2024 11:39:27 +0200
Subject: [PATCH 028/140] Adapt Testing guidelines

Issue: it was not easy to find that there were extra dependencies for
testing

Solution: mention it explicitely in the CONTRIBUTING.md
---
 CONTRIBUTING.md | 24 +++++++++++++++++++-----
 1 file changed, 19 insertions(+), 5 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 4bf3c62b..f2c1cb6d 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -44,17 +44,31 @@ Please try to write clear commit messages. One-line messages are fine for small
 
 ## Testing
 
-We have a qualitative testing functionality that allows to compare the results arising from a modified version of the code against default ones, for the 3 reference input files provided within the code itself.
+Testing is used by RAMP developers to make sure their new feature/bug fix is not breaking existing code. As RAMP is stochastic some tests are only qualitative, other unit tests are ran by GitHub Actions. 
 
-This functionality is accessible via `test/test_run.py`. To run the qualitative test, you'll have to go through the following steps:
-   1. run your modified code for the 3 reference input files for 30 days each. This will create 3 corresponding output files in the `results` folder
-   2. run `test/test_run.py` and visualise the comparison between the results of your code and those obtainable with the latest stable version
+Before running the tests locally, you need to install the testing dependencies
+
+```
+pip install -r tests/requirements.txt
+```
+
+### Qualitative testing
+
+The qualitative testing functionality allows to compare the results arising from a modified version of the code against default ones, for the 3 reference input files provided within the code itself.
+
+To run the qualitative test, you'll have to run
+ ```
+ python ramp/test/test_run.py
+ ```
+from the root level of this repository.
+
+If you already ran this script, you will be asked if you want to overwrite the results files (if you decide not to, the results are not going to be regenerated from your latest code version). You should compare the results of your code and those saved from the latest stable version thanks to the image which is displayed after the script ran.
    
 Ideally, the difference between reference and new results should be minimal and just due to the stochastic nature of the code. If more pronounced, it should be fully explainable based on the changes made to the code and aligned to the expectations of the developers (i.e. it should reflect a change in the output *wanted* and precisely *sought* with the commit in question).
 
 ### Unit tests
 
-Install `pytest` (`pip install pytest`) and run `pytest tests/` form the root of the repository to run the unit tests
+Run `pytest tests/` form the root of the repository to run the unit tests.
 
 ## Attribution
 

From 7f33a16a077fce8d415dfa4335dff34fb8943b33 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 10 Apr 2024 14:47:58 +0200
Subject: [PATCH 029/140] Modify switch_on test

issue: the test was failing randomly when accessing whether the sampled
coincidence were following a normal distribution, due to the fact we
apply math.ceil to the random.gauss to get integer number of appliances
to be switched on simultaneously

solution: get an experimental probability density function and fit a
normal distribution to it, look how large is the error of mean and std
as a proxy of visual inspection of the graph
---
 tests/test_switch_on.py | 61 ++++++++++++++++++++++++++++++++++++-----
 1 file changed, 54 insertions(+), 7 deletions(-)

diff --git a/tests/test_switch_on.py b/tests/test_switch_on.py
index be7be8ee..1b51f71f 100644
--- a/tests/test_switch_on.py
+++ b/tests/test_switch_on.py
@@ -7,7 +7,10 @@
 """
 
 from ramp import User
-from scipy import stats
+import numpy as np
+import scipy.optimize as opt
+
+from ramp.core.constants import switch_on_parameters
 
 
 class TestRandSwitchOnWindow:
@@ -26,20 +29,64 @@ def test_all_appliances_switched_on_together(self):
     def test_coincidence_normality_on_peak(self):
         # Create an instance of the Appliance class with the desired parameters
 
-        appliance = self.user.add_appliance(number=10, fixed="no")
+        N = 100
+        appliance = self.user.add_appliance(number=N, fixed="no")
 
         # Generate a sample of 'coincidence' values
-        sample_size = 30
+        sample_size = N * 10
         coincidence_sample = []
         for _ in range(sample_size):
             coincidence = appliance.calc_coincident_switch_on(inside_peak_window=True)
             coincidence_sample.append(coincidence)
 
-        # Perform the Shapiro-Wilk test for normality
-        _, p_value = stats.shapiro(coincidence_sample)
+        def normed_dist(bins, mu, sigma):
+            return (
+                1
+                / (sigma * np.sqrt(2 * np.pi))
+                * np.exp(-((bins - mu) ** 2) / (2 * sigma**2))
+            )
+
+        # exclude the tail values i.e. only one appliance is switched on or all of them are, see https://github.com/RAMP-project/RAMP/issues/99 for illustrations
+        coincidence_sample = np.array(coincidence_sample)
+        max_val = np.max(coincidence_sample)
+        coincidence_sample_reduced = coincidence_sample[
+            np.where(coincidence_sample != 1)
+        ]
+        coincidence_sample_reduced = coincidence_sample_reduced[
+            np.where(coincidence_sample_reduced != max_val)
+        ]
+
+        # compute the experimental probability density function for appliance numbers from 2 to N-1
+        exp_pdf, bins = np.histogram(
+            coincidence_sample_reduced,
+            bins=[i for i in range(2, N + 1, 1)],
+            density=True,
+        )
 
-        # Assert that the p-value is greater than a chosen significance level
-        assert p_value > 0.05, "The 'coincidence' values are not normally distributed."
+        s_peak, mu_peak, op_factor = switch_on_parameters()
+        mu = mu_peak * N
+        sigma = s_peak * N * mu_peak
+
+        p0 = [mu, sigma]  # Inital guess of mean and std
+        errfunc = (
+            lambda p, x, y: normed_dist(x, *p) - y
+        )  # Distance to the target function
+        p1, success = opt.leastsq(errfunc, p0[:], args=(bins[:-1], exp_pdf))
+
+        # if not then the fit did not succeed
+        assert success in [1, 2, 3, 4]
+
+        fit_mu, fit_stdev = p1
+        tolerance_mu = 0.05  # arbitrary
+        tolerance_sigma = 0.1  # arbitrary
+        err_mu = np.abs(mu - fit_mu) / mu
+        err_sigma = np.abs(sigma - fit_stdev) / sigma
+        assert (
+            err_mu < tolerance_mu
+        ), f"The mean value of a normal fit onto the sampled coincidence histogram ({fit_mu}) divert more than {tolerance_mu*100} % of the provided gaussian mean ({mu})"
+        assert (
+            err_sigma < tolerance_sigma
+        ), f"The std value of a normal fit onto the sampled coincidence histogram ({fit_stdev}) divert more than {tolerance_sigma*100} % of the provided gaussian std ({sigma})"
 
     # Tests that the method returns a list of indexes within the available functioning windows when there are multiple available functioning windows and the random time is larger than the duration of the appliance's function cycle.
 

From 9d28c328f938bf5879e25953168dcdd4dce162c8 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 10 Apr 2024 14:59:53 +0200
Subject: [PATCH 030/140] Correct the sampling of coincident switch-on on peak

Related to https://github.com/RAMP-project/RAMP/issues/99
---
 ramp/core/core.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 41777073..f617a2d4 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1824,7 +1824,7 @@ def calc_coincident_switch_on(self, inside_peak_window: bool = True):
                     1,
                     math.ceil(
                         random.gauss(
-                            mu=(self.number * mu_peak + 0.5),
+                            mu=(self.number * mu_peak),
                             sigma=(s_peak * self.number * mu_peak),
                         )
                     ),

From 033c393e61a2c3570bb47342a0e1c26f7193c4c3 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 2 Apr 2024 16:41:10 +0200
Subject: [PATCH 031/140] Fix running excel from command line

issue: using the excel file generated by the "Using tabular inputs to
build a model" example one could not run successfully `ramp -i
example_excel_usecase.xlsx -n 10`

solution: pass the argument flat=False to the Usecase method
generate_daily_load_profiles. The postprocessing should actually
make use of the newest capabilities
---
 ramp/ramp_run.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ramp/ramp_run.py b/ramp/ramp_run.py
index 5484ef21..861f7e42 100644
--- a/ramp/ramp_run.py
+++ b/ramp/ramp_run.py
@@ -51,7 +51,7 @@ def run_usecase(
         )
         usecase.initialize(num_days=num_days)
         usecase.load(fname)
-        Profiles_list = usecase.generate_daily_load_profiles()
+        Profiles_list = usecase.generate_daily_load_profiles(flat=False)
 
         if plot is True:
             # TODO use new plotting

From f592cff640402876d040890adc9c6b132c2bd4ea Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 8 Apr 2024 13:04:34 +0200
Subject: [PATCH 032/140] Fix multi-year in cli

issue: the user could not simulate more than one year because of a
requirement that the number of power values for an appliance should not
exceed 366

solution: the checks were moved to a function of Appliance class which
is called when the usecase is initialized. If the Appliance instance has
constant power, its power timeseries is adjusted and a warning is
issued. However if the power timeseries was provided by user and does
not contain enough values to cover the simulation range, an error is
raised.
---
 .../year_simulation/year_simulation.rst       |  2 +-
 ramp/core/core.py                             | 35 ++++++++++++-------
 2 files changed, 23 insertions(+), 14 deletions(-)

diff --git a/docs/source/examples/year_simulation/year_simulation.rst b/docs/source/examples/year_simulation/year_simulation.rst
index 43a1e373..1cb0903b 100644
--- a/docs/source/examples/year_simulation/year_simulation.rst
+++ b/docs/source/examples/year_simulation/year_simulation.rst
@@ -34,7 +34,7 @@ will generate a daily profile for 10 days of starting on 3rd January 2022
 
 .. code-block:: bash
 
-   ramp -i path-to-input-file --end-date 2022-02-09 -n
+   ramp -i path-to-input-file --end-date 2022-02-09 -n 10
 
 will generate a daily profile for 10 days ending on 9th Febuary 2022
 
diff --git a/ramp/core/core.py b/ramp/core/core.py
index f617a2d4..27fba20b 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -290,6 +290,11 @@ def initialize(
                 print(
                     f"You will simulate {self.__num_days} day(s) from {self.days[0]} until {self.days[-1]+datetime.timedelta(days=1)}"
                 )
+
+            # Verify that each appliance has long enough power data for the simulated time
+            for app in self.appliances:
+                app.check_power_values(self.__num_days)
+
             if peak_enlarge is not None:
                 self.peak_enlarge = peak_enlarge
             else:
@@ -728,7 +733,7 @@ def num_days(self):
         return answer
 
     def save(self, filename: str = None) -> Union[pd.DataFrame, None]:
-        """Saves/returns the model databas including allappliances as a single pd.DataFrame or excel file.
+        """Saves/returns the model database including all appliances as a single pd.DataFrame or excel file.
 
         Parameters
         ----------
@@ -892,9 +897,6 @@ def generate_single_load_profile(
             load profile for the requested day
         """
 
-        if prof_i not in range(366):
-            raise ValueError(f"prof_i should be an integer in range of 0 to 364")
-
         if peak_time_range is None:
             if self.usecase is None:
                 # logging warning
@@ -949,9 +951,6 @@ def generate_aggregated_load_profile(
         Each single load profile has its own separate randomisation
         """
 
-        if prof_i not in range(366):
-            raise ValueError(f"prof_i should be an integer in range of 0 to 364")
-
         self.load = np.zeros(1440)  # initialise empty load for User instance
         for _ in range(self.num_users):
             # iterates for every single user within a User class.
@@ -1069,19 +1068,17 @@ def __init__(
         self.pref_index = pref_index
         self.wd_we_type = wd_we_type
 
+        self.__constant_power = False
+
         if isinstance(power, pd.DataFrame):
-            if len(power) >= self.user.num_days:
-                power = power.values[:, 0]
-            else:
-                raise ValueError(
-                    f"Wrong number of power values for appliance '{self.name}'. Number should be at least {self.user.num_days} values or a constant value."
-                )
+            power = power.values[:, 0]
 
         elif isinstance(power, str):
             power = pd.read_json(power).values[:, 0]
 
         elif isinstance(power, (float, int)):
             power = power * np.ones(self.user.num_days + 1)
+            self.__constant_power = True
 
         else:
             raise ValueError(f"Wrong data type for power of appliance '{self.name}'.")
@@ -1132,6 +1129,18 @@ def __init__(
         # if it is 0, then no switch on events happen within any duty cycle windows
         self.current_duty_cycle_id = 0
 
+    def check_power_values(self, num_days):
+        if len(self.power) < num_days:
+            if self.__constant_power is True:
+                self.power = self.power[0] * np.ones(num_days + 1)
+                warnings.warn(
+                    f"Appliance {self.name} of user {self.user.user_name} of constant power had its power timeseries updated to match total number of days of the usecase {self.user.usecase.name}"
+                )
+            else:
+                raise ValueError(
+                    f"Wrong number of values for appliance '{self.name}''s power of user {self.user.user_name}: {len(self.power)}. Number of values should be at least match the total number of days: {num_days}. Alternatively the power of the appliance can be set to a constant value."
+                )
+
     def save(self) -> pd.DataFrame:
         """returns a pd.DataFrame containing the appliance data
 

From 7703d0d87de4283fee924887b0748c74dabff8ae Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 8 Apr 2024 13:08:35 +0200
Subject: [PATCH 033/140] Add test for multi-year input in cli

---
 tests/__test_year_behavior.py                | 104 +++++++++++++++++++
 tests/test_cli.py                            |  58 +++++++++++
 tests/test_inputs/example_excel_usecase.xlsx | Bin 0 -> 5944 bytes
 tests/utils.py                               |   4 +
 4 files changed, 166 insertions(+)
 create mode 100644 tests/__test_year_behavior.py
 create mode 100644 tests/test_cli.py
 create mode 100644 tests/test_inputs/example_excel_usecase.xlsx
 create mode 100644 tests/utils.py

diff --git a/tests/__test_year_behavior.py b/tests/__test_year_behavior.py
new file mode 100644
index 00000000..52d00c4c
--- /dev/null
+++ b/tests/__test_year_behavior.py
@@ -0,0 +1,104 @@
+import os
+import pytest
+import numpy as np
+
+from ramp.core.core import User
+
+
+class TestApplianceClass:
+    def setup_method(self):
+        HI = User("high income", 1)
+        self.HI_Freezer = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+        self.HI_Freezer.windows([0, 1440], [0, 0])
+        self.HI_Freezer.specific_cycle_1(200, 20, 5, 10)
+        self.HI_Freezer.specific_cycle_2(200, 15, 5, 15)
+        self.HI_Freezer.specific_cycle_3(200, 10, 5, 20)
+        self.HI_Freezer.cycle_behaviour(
+            [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
+        )
+        self.HI_Freezer.assign_random_cycles()
+
+        self.HI_Freezer_fixed = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+        self.HI_Freezer_fixed.windows([0, 1440], [0, 0])
+
+        self.HI_Freezer_var = HI.add_appliance(
+            1, 200, 1, 1440, 0, 30, "yes", 3, thermal_p_var=0.1
+        )
+        self.HI_Freezer_var.windows([0, 1440], [0, 0])
+
+    def teardown_method(self):
+        pass
+
+    def test_first_cycle_correctly_updated(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+
+        with pytest.raises(ValueError):
+            print(self.HI_Freezer.daily_use)
+            self.HI_Freezer.update_daily_use(
+                coincidence=1, power=1, indexes=np.array([1])
+            )
+            print(self.HI_Freezer.daily_use)
+        # print(self.HI_Freezer.random_cycle1)
+
+        # pytest.fail()
+
+        # test that the daily use is updated by the value (random_cycle_1 * coincidence)
+
+    def test_first_cycle_correctly_updated(self):
+        pass
+
+    def test_second_cycle_correctly_updated(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+
+        self.HI_Freezer.update_daily_use(coincidence=4, power=1, indexes=np.array([1]))
+
+        # test that the daily use is updated by the value (random_cycle_1 * coincidence)
+
+    def test_coincidence_number(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+
+        self.HI_Freezer.update_daily_use(coincidence=2, power=1, indexes=np.array([1]))
+        print(self.HI_Freezer.random_cycle1)
+        print(self.HI_Freezer.daily_use)
+        assert self.HI_Freezer.daily_use[9] == 400.0
+        # test that the daily use is updated by the value (random_cycle_1 * coincidence)
+
+    def test_third_cycle_correctly_updated(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+
+        self.HI_Freezer.update_daily_use(coincidence=1, power=1, indexes=np.array([1]))
+
+        # test that the daily use is updated by the value (random_cycle_1 * coincidence)
+
+    def test_daily_use_without_duty_cycle_correctly_updated(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+
+        self.HI_Freezer_fixed.update_daily_use(
+            coincidence=1, power=1, indexes=np.array([1])
+        )
+
+    def test_daily_use_without_duty_cycle_but_thermal_var_correctly_updated(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+
+        self.HI_Freezer_var.update_daily_use(
+            coincidence=1, power=1, indexes=np.array([1])
+        )
+
+        # test that the daily use is updated by the value (random_cycle_1 * coincidence)
+
+
+def test_something():
+    """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+    HI = User("high income", 1)
+    HI_Freezer = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+    HI_Freezer.windows([0, 1440], [0, 0])
+    HI_Freezer.specific_cycle_1(200, 20, 5, 10)
+    HI_Freezer.specific_cycle_2(200, 15, 5, 15)
+    HI_Freezer.specific_cycle_3(200, 10, 5, 20)
+    HI_Freezer.cycle_behaviour(
+        [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
+    )
+    HI_Freezer.assign_random_cycles()
+    # do somthing here for this particular usecase
+
+    # test that the daily use is updated by the value (random_cycle_1 * coincidence)
diff --git a/tests/test_cli.py b/tests/test_cli.py
new file mode 100644
index 00000000..797161cc
--- /dev/null
+++ b/tests/test_cli.py
@@ -0,0 +1,58 @@
+import os
+import shutil
+
+import mock
+import matplotlib.pyplot as plt
+import pytest
+from ramp.cli import parser as ramp_parser, main as ramp_main
+from .utils import TEST_PATH, TEST_OUTPUT_PATH
+
+
+class TestProcessUserArguments:
+    def setup_method(self):
+        if os.path.exists(TEST_OUTPUT_PATH):
+            shutil.rmtree(TEST_OUTPUT_PATH, ignore_errors=True)
+        if os.path.exists(TEST_OUTPUT_PATH) is False:
+            os.mkdir(TEST_OUTPUT_PATH)
+
+    @mock.patch(
+        "argparse.ArgumentParser.parse_args",
+        return_value=ramp_parser.parse_args(
+            ["--start-date", "2022-01-01", "-y", "2022"]
+        ),
+    )
+    def test_impossible_option_combinaison_start_date_year(self, m_args):
+        with pytest.raises(ValueError):
+            ramp_main()
+
+    @mock.patch(
+        "argparse.ArgumentParser.parse_args",
+        return_value=ramp_parser.parse_args(["--end-date", "2022-01-01", "-y", "2022"]),
+    )
+    def test_impossible_option_combinaison_end_date_year(self, m_args):
+        with pytest.raises(ValueError):
+            ramp_main()
+
+    @mock.patch(
+        "argparse.ArgumentParser.parse_args",
+        return_value=ramp_parser.parse_args(
+            [
+                "-i",
+                os.path.join(TEST_PATH, "test_inputs", "example_excel_usecase.xlsx"),
+                "-y",
+                "2022",
+                "2023",
+                "-o",
+                os.path.join(TEST_OUTPUT_PATH, "example_excel.csv"),
+            ]
+        ),
+    )
+    def test_multiple_year_is_possible(self, m_args, monkeypatch):
+        monkeypatch.setattr(
+            plt, "show", lambda: None
+        )  # prevents the test to output figure
+        ramp_main()
+
+    def teardown_method(self):
+        if os.path.exists(TEST_OUTPUT_PATH):
+            shutil.rmtree(TEST_OUTPUT_PATH, ignore_errors=True)
diff --git a/tests/test_inputs/example_excel_usecase.xlsx b/tests/test_inputs/example_excel_usecase.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..011cabc955feed5ca8819f0d5b30b96386874e74
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HcmV?d00001

diff --git a/tests/utils.py b/tests/utils.py
new file mode 100644
index 00000000..0b65a076
--- /dev/null
+++ b/tests/utils.py
@@ -0,0 +1,4 @@
+import os
+
+TEST_PATH = os.path.dirname(os.path.abspath(__file__))
+TEST_OUTPUT_PATH = os.path.join(TEST_PATH, "test_outputs")

From 0b90f45ef5563205c4f05f907da406a0126f1885 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 8 Apr 2024 13:41:55 +0200
Subject: [PATCH 034/140] Fix seasonality input of RAMP cli

Also provide a test of the CLI with example file
---
 ramp/cli.py       | 28 ++++++++++++++++++++++------
 tests/test_cli.py | 38 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 60 insertions(+), 6 deletions(-)

diff --git a/ramp/cli.py b/ramp/cli.py
index d4f9e94f..e53ab22e 100644
--- a/ramp/cli.py
+++ b/ramp/cli.py
@@ -182,15 +182,19 @@ def main():
                 month_end = datetime.date(
                     year, i + 1, pd.Period(month_start, freq="D").days_in_month
                 )
-                run_usecase(
+                month_profiles = run_usecase(
                     fname=fname,
-                    ofname=ofnames[i],
+                    ofname=None,
                     num_days=num_days[i],
                     date_start=month_start,
                     date_end=month_end,
                     plot=False,
                     parallel=parallel_processing,
                 )
+                month_profiles = month_profiles.reshape(
+                    1, 1440 * month_profiles.shape[0]
+                ).squeeze()
+                year_profile.append(month_profiles)
 
             # Create a dataFrame to save the year profile with timestamps every minutes
             series_frame = pd.DataFrame(
@@ -200,10 +204,19 @@ def main():
                 ),
             )
             # Save to minute and hour resolution
-            # TODO let the user choose where to save the files/file_name, make sure the user wants to overwrite the file
-            # if it already exists
+            if len(ofnames) == 1:
+                ofname = ofnames[0]
+            else:
+                ofname = None
+
+            if ofname is None:
+                ofname = os.path.abspath(os.path.curdir)
+
+            if not os.path.exists(ofname):
+                os.mkdir(ofname)
+
             series_frame.to_csv(
-                os.path.join(BASE_PATH, "yearly_profile_min_resolution.csv")
+                os.path.join(ofname, "yearly_profile_min_resolution.csv")
             )
             resampled = pd.DataFrame()
 
@@ -212,7 +225,10 @@ def main():
             resampled["min"] = series_frame.resample("H").min()
             # TODO add more columns with other resampled functions (do this in Jupyter)
             resampled.to_csv(
-                os.path.join(BASE_PATH, "yearly_profile_hourly_resolution.csv")
+                os.path.join(ofname, "yearly_profile_hourly_resolution.csv")
+            )
+            print(
+                f"Results of the yearly RAMP simulation with seasonality are located in the folder {ofname}"
             )
 
 
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 797161cc..4448355d 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -53,6 +53,44 @@ def test_multiple_year_is_possible(self, m_args, monkeypatch):
         )  # prevents the test to output figure
         ramp_main()
 
+    @mock.patch(
+        "argparse.ArgumentParser.parse_args",
+        return_value=ramp_parser.parse_args(
+            [
+                "-i",
+                os.path.join(TEST_OUTPUT_PATH),
+                "-y",
+                "2022",
+                "-n",
+                "1",
+            ]
+        ),
+    )
+    def test_month_variation_without_month_files(self, m_args):
+        with pytest.raises(ValueError):
+            ramp_main()
+
+    @mock.patch(
+        "argparse.ArgumentParser.parse_args",
+        return_value=ramp_parser.parse_args(
+            [
+                "-i",
+                os.path.join(TEST_OUTPUT_PATH),
+                "-y",
+                "2022",
+                "-n",
+                "1",
+            ]
+        ),
+    )
+    def test_month_variation_with_month_files(self, m_args):
+        for i in range(12):
+            shutil.copy(
+                os.path.join(TEST_PATH, "test_inputs", "example_excel_usecase.xlsx"),
+                os.path.join(TEST_OUTPUT_PATH, f"example_excel_usecase_{i}.xlsx"),
+            )
+        ramp_main()
+
     def teardown_method(self):
         if os.path.exists(TEST_OUTPUT_PATH):
             shutil.rmtree(TEST_OUTPUT_PATH, ignore_errors=True)

From 0d72c6f48e86b9cc429aa8bd80120084f82cf671 Mon Sep 17 00:00:00 2001
From: Francesco Lombardi <f.lombardi@outlook.com>
Date: Fri, 12 Apr 2024 10:21:46 +0200
Subject: [PATCH 035/140] Fix small typo in core.py

Remove typo in a string
---
 ramp/core/core.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 27fba20b..a6390c66 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1138,7 +1138,7 @@ def check_power_values(self, num_days):
                 )
             else:
                 raise ValueError(
-                    f"Wrong number of values for appliance '{self.name}''s power of user {self.user.user_name}: {len(self.power)}. Number of values should be at least match the total number of days: {num_days}. Alternatively the power of the appliance can be set to a constant value."
+                    f"Wrong number of values for appliance '{self.name}''s power of user {self.user.user_name}: {len(self.power)}. Number of values should at least match the total number of days: {num_days}. Alternatively the power of the appliance can be set to a constant value."
                 )
 
     def save(self) -> pd.DataFrame:

From 79bccf0e5e7ff58516c479498dc2ae13cfb4b560 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 29 Apr 2024 14:30:14 +0200
Subject: [PATCH 036/140] Add issue templates

---
 .github/ISSUE_TEMPLATE/bug_report.md | 17 +++++++++++++++++
 .github/ISSUE_TEMPLATE/custom.md     | 10 ++++++++++
 2 files changed, 27 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md
 create mode 100644 .github/ISSUE_TEMPLATE/custom.md

diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 00000000..d4015b76
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,17 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: "[Bug] Bug_short_description"
+labels: bug
+assignees: ''
+
+---
+
+Describe_the_bug
+
+Checklist to make sure that the bug report ist complete:
+- [ ] OS: *your_operating_system*, *your_distribution*
+- [ ] RAMP version or Branch: *RAMP version* or *branch_name*, updated on *update_date*
+- [ ] If applicable: Attach full error message
+- [ ] If applicable: Share screenshots/images of your problem
+- [ ] If applicable: Share used input data
diff --git a/.github/ISSUE_TEMPLATE/custom.md b/.github/ISSUE_TEMPLATE/custom.md
new file mode 100644
index 00000000..48d5f81f
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/custom.md
@@ -0,0 +1,10 @@
+---
+name: Custom issue template
+about: Describe this issue template's purpose here.
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+

From bc768cc79e1208b4e8b6e654c2a528d5c8b29571 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 2 May 2024 12:39:58 +0200
Subject: [PATCH 037/140] Link contributing guidelines within issue template

---
 .github/ISSUE_TEMPLATE/bug_report.md | 2 ++
 .github/ISSUE_TEMPLATE/custom.md     | 8 +++++++-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
index d4015b76..19759639 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -15,3 +15,5 @@ Checklist to make sure that the bug report ist complete:
 - [ ] If applicable: Attach full error message
 - [ ] If applicable: Share screenshots/images of your problem
 - [ ] If applicable: Share used input data
+
+<sub>*For more information on how to contribute, please check the [Contributing Guidelines](https://github.com/RAMP-project/RAMP/blob/main/CONTRIBUTING.md).*<sub>
\ No newline at end of file
diff --git a/.github/ISSUE_TEMPLATE/custom.md b/.github/ISSUE_TEMPLATE/custom.md
index 48d5f81f..c23c2cd0 100644
--- a/.github/ISSUE_TEMPLATE/custom.md
+++ b/.github/ISSUE_TEMPLATE/custom.md
@@ -1,6 +1,6 @@
 ---
 name: Custom issue template
-about: Describe this issue template's purpose here.
+about: If your issue is not a bug
 title: ''
 labels: ''
 assignees: ''
@@ -8,3 +8,9 @@ assignees: ''
 ---
 
 
+Providing the following information will help RAMP developers assisting you:
+- [ ] OS: *your_operating_system*, *your_distribution*
+- [ ] RAMP version or Branch: *RAMP version* or *branch_name*, updated on *update_date*
+- [ ] If applicable: Share used input data
+- 
+<sub>*For more information on how to contribute, please check the [Contributing Guidelines](https://github.com/RAMP-project/RAMP/blob/main/CONTRIBUTING.md).*<sub>
\ No newline at end of file

From 1dff6d9a8887542ede425971c29bf6edbd873eea Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 2 May 2024 12:40:10 +0200
Subject: [PATCH 038/140] Pull request template

---
 .github/pull_request_template.md | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100644 .github/pull_request_template.md

diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
new file mode 100644
index 00000000..99e2dd37
--- /dev/null
+++ b/.github/pull_request_template.md
@@ -0,0 +1,6 @@
+Fix #Issue
+
+**Changes proposed in this pull request**:
+- Your_changes
+
+<sub>*For more information on how to contribute, please check the [Contributing Guidelines](https://github.com/RAMP-project/RAMP/blob/main/CONTRIBUTING.md).*<sub>

From 2b1bddbfaec7e58cc6829e2de70ed3a248065254 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 2 May 2024 12:42:31 +0200
Subject: [PATCH 039/140] Update CONTRIBUTE.md

Add a section about issues
---
 CONTRIBUTING.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index f2c1cb6d..0de3761e 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -13,6 +13,16 @@ Some of the resources to look at if you're interested in contributing:
 
 By contributing to RAMP, e.g. through opening a pull request or submitting a patch, you represent that your contributions are your own original work and that you have the right to license them, and you agree that your contributions are licensed under the EUROPEAN UNION PUBLIC LICENCE v. 1.2.
 
+## Open an issue
+
+[Open an issue](https://github.com/RAMP-project/RAMP/issues) to report a bug or for a feature request, please try to provide the information summarized below
+
+- OS: *your_operating_system*, *your_distribution*
+- RAMP version or Branch: *RAMP version* or *branch_name*, updated on *update_date*
+- If applicable: Attach full error message
+- If applicable: Share screenshots/images of your problem
+- If applicable: Share used input data
+
 ## Submitting changes
 
 To contribute changes:

From b9942cc08ed6d1c57dfcf964f20396f9fbf805b3 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <mohammadamin.tahavori@vito.be>
Date: Wed, 1 May 2024 12:10:06 +0200
Subject: [PATCH 040/140] improving the readthedocs template

within the current template, 'basicstrap', the navigation on the left sidebar (with the partial TOC) is a bit
counter-intuitive. The new template provides a more intituitive
sidebars.
---
 docs/docs-requirements.txt | 4 +---
 docs/source/conf.py        | 6 +++++-
 docs/source/index.rst      | 4 +++-
 3 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/docs/docs-requirements.txt b/docs/docs-requirements.txt
index 4259dc0b..c338880b 100644
--- a/docs/docs-requirements.txt
+++ b/docs/docs-requirements.txt
@@ -11,9 +11,7 @@ sphinx >= 1.6.4
 nbsphinx
 ipykernel
 sphinx-autobuild
-sphinx-pdj-theme # TODO to comment
 sphinx-copybutton
-sphinxjp.themes.sphinxjp
-sphinxjp.themes.basicstrap
+sphinx_wagtail_theme
 nbformat
 nbconvert
diff --git a/docs/source/conf.py b/docs/source/conf.py
index b65416c9..8f621652 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -43,8 +43,11 @@
     "sphinx.ext.doctest",
     "sphinx_copybutton",
     "sphinx.ext.autosectionlabel",
+    "sphinx_wagtail_theme"
 ]
 
+
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 
@@ -60,7 +63,8 @@
 # a list of builtin themes.
 #
 extensions.append("sphinxjp.themes.basicstrap")
-html_theme = "basicstrap"
+
+html_theme = 'sphinx_wagtail_theme'
 # html_theme_path = [sphinx_pdj_theme.get_html_theme_path()]
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 4b9244eb..ca073e87 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,4 +1,6 @@
-
+******************
+RAMP Documentation
+******************
 .. image:: https://github.com/RAMP-project/RAMP/blob/main/docs/figures/RAMP_logo_basic.png?raw=true
    :width: 300
 

From d8e34c857c1783215b2c4bba13b752793c95f059 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <mohammadamin.tahavori@vito.be>
Date: Wed, 1 May 2024 12:18:37 +0200
Subject: [PATCH 041/140] fixing extra imports in conf.py of the previous
 themes

---
 docs/source/conf.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 8f621652..a1146748 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -12,7 +12,7 @@
 #
 import os
 import sys
-import sphinx_pdj_theme
+
 
 sys.path.insert(0, os.path.abspath("."))
 sys.path.insert(0, os.path.abspath(".."))
@@ -62,7 +62,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-extensions.append("sphinxjp.themes.basicstrap")
+
 
 html_theme = 'sphinx_wagtail_theme'
 # html_theme_path = [sphinx_pdj_theme.get_html_theme_path()]

From 8397966067becc176fae869b202479a1c95941ea Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 2 May 2024 10:27:26 +0200
Subject: [PATCH 042/140] Fix rst title length

---
 docs/source/intro.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/intro.rst b/docs/source/intro.rst
index ebbded96..0ba0a510 100644
--- a/docs/source/intro.rst
+++ b/docs/source/intro.rst
@@ -1,7 +1,7 @@
 
-*******************
+*****************
 RAMP Introduction
-*******************
+*****************
 
 .. include:: ../../README.rst
 

From a411a62682d9db88d70d45e1fbee2bf5f7ebef38 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 2 May 2024 10:44:17 +0200
Subject: [PATCH 043/140] Update conversion of notebook code

Add a line at end of section with link to download the notebook
---
 docs/notebooks_convert.py | 34 +++++++++++++++++++---------------
 1 file changed, 19 insertions(+), 15 deletions(-)

diff --git a/docs/notebooks_convert.py b/docs/notebooks_convert.py
index 60a491c1..879ec4c4 100644
--- a/docs/notebooks_convert.py
+++ b/docs/notebooks_convert.py
@@ -1,13 +1,8 @@
 from nbconvert import RSTExporter
-from nbconvert.writers import FilesWriter
 import nbformat
 import os
 
-path = os.path.abspath(
-    os.path.join(
-        os.path.dirname(__file__),
-    )
-)
+DOC_PATH = os.path.abspath(os.path.dirname(__file__))
 
 
 def convert_notebook_to_rst(input_file, output_dir, file_name):
@@ -26,6 +21,9 @@ def convert_notebook_to_rst(input_file, output_dir, file_name):
 
     output_file = os.path.join(output_dir, f"{file_name}.rst")
 
+    # Add a link to the jupyter notebook file to the documentation
+    rst_content += f"\n:download:`Link to the jupyter notebook file </../notebooks/{file_name}.ipynb>`.\n"
+
     # Write RST content to the output file
     with open(output_file, "w", encoding="utf-8") as f:
         f.write(rst_content)
@@ -33,26 +31,32 @@ def convert_notebook_to_rst(input_file, output_dir, file_name):
     print(f"Conversion successful. RST file saved to: {output_file}")
 
     # Copy images to the output folder
-    image_dir = output_dir
     for image_filename, image_data in resources["outputs"].items():
         image_path = os.path.join(output_dir, image_filename)
         with open(image_path, "wb") as img_file:
             img_file.write(image_data)
 
 
-def get_all_ipynb_files(examples_path="notebooks"):
-    all_files = os.listdir(examples_path)
-    return [
-        (os.path.join(path, examples_path, file), file.split(".ipynb")[0])
-        for file in all_files
+def get_all_ipynb_files(doc_path=DOC_PATH, examples_path="notebooks"):
+    notebooks_path = os.path.join(doc_path, examples_path)
+    notebooks = [
+        (os.path.join(notebooks_path, file), file.split(".ipynb")[0])
+        for file in os.listdir(notebooks_path)
         if file.endswith("ipynb")
     ]
+    return notebooks
 
 
-if __name__ == "__main__":
-    files = get_all_ipynb_files()
+def update_notebooks_rst_files(doc_path=DOC_PATH):
+    files = get_all_ipynb_files(doc_path)
 
     for file, folder in files:
         convert_notebook_to_rst(
-            file, os.path.join(path, "source", "examples", folder), folder
+            file,
+            output_dir=os.path.join(doc_path, "source", "examples", folder),
+            file_name=folder,
         )
+
+
+if __name__ == "__main__":
+    update_notebooks_rst_files()

From dc43cd6aa03e85d538b90e782b6e6c1e89ba5270 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 2 May 2024 10:45:23 +0200
Subject: [PATCH 044/140] Update the notebooks examples automatically upon RTD
 build

---
 docs/source/conf.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index a1146748..e12d3334 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -19,6 +19,9 @@
 sys.path.insert(0, os.path.abspath("../.."))
 sys.path.insert(0, os.path.abspath("../../.."))
 
+from notebooks_convert import update_notebooks_rst_files
+
+update_notebooks_rst_files()
 # -- Project information -----------------------------------------------------
 
 project = "RAMP"

From 18b74204a91cc64cfca68ea123736e038d4cc9c3 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 2 May 2024 10:45:30 +0200
Subject: [PATCH 045/140] Lint with black

---
 docs/source/conf.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index e12d3334..e83d58c8 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -46,11 +46,10 @@
     "sphinx.ext.doctest",
     "sphinx_copybutton",
     "sphinx.ext.autosectionlabel",
-    "sphinx_wagtail_theme"
+    "sphinx_wagtail_theme",
 ]
 
 
-
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 
@@ -67,7 +66,7 @@
 #
 
 
-html_theme = 'sphinx_wagtail_theme'
+html_theme = "sphinx_wagtail_theme"
 # html_theme_path = [sphinx_pdj_theme.get_html_theme_path()]
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,

From ef006b8d1714dbd800dd05c93c544f4d565d1d17 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 2 May 2024 12:18:19 +0200
Subject: [PATCH 046/140] Change documentation page title

---
 docs/source/conf.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index e83d58c8..31762d3d 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -67,6 +67,9 @@
 
 
 html_theme = "sphinx_wagtail_theme"
+html_theme_options = dict(
+    project_name="RAMP Documentation",
+)
 # html_theme_path = [sphinx_pdj_theme.get_html_theme_path()]
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,

From 8f064fc3822b55c4a140e3a8ec5a69db33cf3f41 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <mohammadamin.tahavori@vito.be>
Date: Fri, 29 Mar 2024 10:58:25 +0100
Subject: [PATCH 047/140] Python=3.8 to be outdated as the prefered python
 version

issue:

Python 3.8 is mentioned in the README as the prefered python version for
the installation. However, this python vesion is going to be outdated by
October 2024.

Current RAMP dev can be safely used by python 3.10.

Changes:

- README.rst --> updateing the installation guide
- environment.yml --> updating the python dependency for installing
  environment through yml file
 - setup.py --> updating the python_requires accordingly
---
 README.rst      | 2 +-
 environment.yml | 2 +-
 setup.py        | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.rst b/README.rst
index 60f88eae..9b9be5fb 100644
--- a/README.rst
+++ b/README.rst
@@ -44,7 +44,7 @@ Installing through pip
 
 .. code-block:: python
 
-   conda create -n ramp python=3.8
+   conda create -n ramp python=3.10
 
 
 2. If you create a new environment for RAMP, you'll need to activate it each time before using it, by writing
diff --git a/environment.yml b/environment.yml
index 715b4764..7f00928d 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,6 +1,6 @@
 name: ramp
 dependencies:
-- python=3.8
+- python=3.10
 - pip=21.0.1
 - pip:
    - -r requirements.txt
diff --git a/setup.py b/setup.py
index 81211ae9..055399da 100644
--- a/setup.py
+++ b/setup.py
@@ -13,7 +13,7 @@
     version=__version__,
     packages=find_packages(),
     license="European Union Public License 1.2",
-    python_requires=">=3.6.0",
+    python_requires="<=3.10",
     package_data={"": ["*.txt", "*.dat", "*.doc", "*.rst", "*.xlsx", "*.csv"]},
     install_requires=[
         "pandas >= 1.3.3",

From 5fb1872c6e7cbee430eab1cecc7cdd967c709349 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <mohammadamin.tahavori@vito.be>
Date: Fri, 29 Mar 2024 10:19:28 +0100
Subject: [PATCH 048/140] User empty object print ValueError

issue:
When a user object is empty cannot be printed due as the __repr__ method
uses the save() method to print the properties of the users, which raise
Exception in case no appliances are added to the user.

solution:
in case save() methods returns an exception, the function prints the
user_name, and user_num with a message to mention no appliances are
assigned to the user object yet.
---
 ramp/core/core.py | 19 ++++++++++++++++---
 1 file changed, 16 insertions(+), 3 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index a6390c66..ba29143d 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -660,10 +660,23 @@ def __init__(
             []
         )  # each instance of User (i.e. each user class) has its own list of Appliances
 
+    def __str__(self):
+
+        try:
+            return self.save()[
+                ["user_name", "num_users", "name", "number", "power"]
+            ].to_string()
+        
+        except Exception:
+            return (f"""
+user_name: {self.user_name} \n
+num_users: {self.num_users} \n
+appliances: no appliances assigned to the user.
+                    """)
+        
     def __repr__(self):
-        return self.save()[
-            ["user_name", "num_users", "name", "number", "power"]
-        ].to_string()
+
+        return self.__str__()
 
     def add_appliance(self, *args, **kwargs):
         """adds an appliance to the user category with all the appliance characteristics in a single function

From 1227ad5bf1dcd4054cde89200ce2c26299c20a85 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 4 Apr 2024 15:01:22 +0200
Subject: [PATCH 049/140] Add _add_appliance_instance method to add an
 Appliance to a User

---
 ramp/core/core.py               | 25 ++++++++++++++++---
 tests/test_object_comparison.py | 33 +++++++++++++++++++++++++
 tests/test_object_creation.py   | 44 +++++++++++++++++++++++++++++++++
 3 files changed, 99 insertions(+), 3 deletions(-)
 create mode 100644 tests/test_object_comparison.py
 create mode 100644 tests/test_object_creation.py

diff --git a/ramp/core/core.py b/ramp/core/core.py
index ba29143d..223e5bdf 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -678,6 +678,17 @@ def __repr__(self):
 
         return self.__str__()
 
+    def _add_appliance_instance(self, appliances):
+        if isinstance(appliances, Appliance):
+            appliances = [appliances]
+        for app in appliances:
+            if not isinstance(app, Appliance):
+                raise TypeError(
+                    f"You are trying to add an object of type {type(app)} as an appliance to the user {self.user_name}"
+                )
+            if app not in self.App_list:
+                self.App_list.append(app)
+
     def add_appliance(self, *args, **kwargs):
         """adds an appliance to the user category with all the appliance characteristics in a single function
 
@@ -690,7 +701,13 @@ def add_appliance(self, *args, **kwargs):
 
         # parse the args into the kwargs
         if len(args) > 0:
+            if isinstance(args[0], Appliance):
+                # if the first argument is an Appliance instance, it is assumed all arguments are
+                # if this is not the case, error will be thrown by _add_appliance_instance method
+                self._add_appliance_instance(args)
+                return
             for a_name, a_val in zip(APPLIANCE_ARGS, args):
+                # TODO here we could do validation of the arguments
                 kwargs[a_name] = a_val
 
         # collects windows arguments
@@ -716,6 +733,8 @@ def add_appliance(self, *args, **kwargs):
         for i in duty_cycle_parameters:
             app.specific_cycle(i, **duty_cycle_parameters[i])
 
+        self._add_appliance_instance(app)
+
         return app
 
     @property
@@ -1354,9 +1373,9 @@ def windows(
         )  # calculate the random variability of window1, i.e. the maximum range of time they can be enlarged or shortened
         self.random_var_2 = int(random_var_w * np.diff(self.window_2))  # same as above
         self.random_var_3 = int(random_var_w * np.diff(self.window_3))  # same as above
-        self.user.App_list.append(
-            self
-        )  # automatically appends the appliance to the user's appliance list
+
+        # automatically appends the appliance to the user's appliance list
+        self.user._add_appliance_instance(self)
 
         if self.fixed_cycle == 1:
             self.cw11 = self.window_1
diff --git a/tests/test_object_comparison.py b/tests/test_object_comparison.py
new file mode 100644
index 00000000..f726b18c
--- /dev/null
+++ b/tests/test_object_comparison.py
@@ -0,0 +1,33 @@
+import pytest
+from ramp import User, Appliance
+
+
+@pytest.fixture
+def test_user():
+    # Create a User instance (you may need to provide the required arguments for User)
+    user = User(user_name="Test User", num_users=1)
+    return user
+
+
+@pytest.mark.usefixtures("test_user")
+def test_compare_two_appliances():
+    appliance1 = Appliance(
+        test_user,
+        name="test_appliance1",
+        func_time=4 * 60,  # runs for 4 hours per day
+    )
+
+    appliance2 = Appliance(
+        test_user,
+        name="test_appliance2",
+        func_time=4 * 60,  # runs for 4 hours per day
+    )
+
+    appliance3 = Appliance(
+        test_user,
+        name="test_appliance1",
+        func_time=4 * 60,  # runs for 4 hours per day
+    )
+
+    assert appliance1 != appliance2
+    assert appliance1 == appliance3
diff --git a/tests/test_object_creation.py b/tests/test_object_creation.py
new file mode 100644
index 00000000..309ef569
--- /dev/null
+++ b/tests/test_object_creation.py
@@ -0,0 +1,44 @@
+import pytest
+
+from ramp import User, Appliance
+
+
+@pytest.fixture
+def test_user():
+    # Create a User instance (you may need to provide the required arguments for User)
+    user = User(user_name="Test User", num_users=1)
+    return user
+
+
+@pytest.mark.usefixtures("test_user")
+def test_add_several_appliances_to_user(test_user):
+    assert len(test_user.App_list) == 0
+    appliance1 = Appliance(
+        test_user,
+        name="test_appliance1",
+        func_time=4 * 60,  # runs for 4 hours per day
+    )
+    appliance2 = Appliance(
+        test_user,
+        name="test_appliance2",
+        func_time=4 * 60,  # runs for 4 hours per day
+    )
+    test_user.add_appliance(appliance1, appliance2)
+    assert len(test_user.App_list) == 2
+
+
+@pytest.mark.usefixtures("test_user")
+def test_skip_add_existing_appliances_to_user(test_user):
+    assert len(test_user.App_list) == 0
+    appliance1 = Appliance(
+        test_user,
+        name="test_appliance1",
+        func_time=4 * 60,  # runs for 4 hours per day
+    )
+    test_user.add_appliance(appliance1)
+
+    assert len(test_user.App_list) == 1
+
+    test_user.add_appliance(appliance1)
+
+    assert len(test_user.App_list) == 1

From b3b495a21793333bcda0ce5b788abb4e49da26ee Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 4 Apr 2024 15:02:18 +0200
Subject: [PATCH 050/140] Fix comparison fonction of Appliance class

The comparisons were not saved so the return value was always "True"
---
 ramp/core/core.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 223e5bdf..92f98ea8 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1255,7 +1255,7 @@ def __eq__(self, other_appliance) -> bool:
         answer = np.array([])
         for attribute in APPLIANCE_ATTRIBUTES:
             if hasattr(self, attribute) and hasattr(other_appliance, attribute):
-                np.append(
+                answer = np.append(
                     answer,
                     [getattr(self, attribute) == getattr(other_appliance, attribute)],
                 )
@@ -1263,7 +1263,7 @@ def __eq__(self, other_appliance) -> bool:
                 hasattr(self, attribute) is False
                 and hasattr(other_appliance, attribute) is False
             ):
-                np.append(answer, True)
+                answer = np.append(answer, [True])
             else:
                 if hasattr(self, attribute) is False:
                     print(f"{attribute} of appliance {self.name} is not assigned")
@@ -1271,7 +1271,7 @@ def __eq__(self, other_appliance) -> bool:
                     print(
                         f"{attribute} of appliance {other_appliance.name} is not assigned"
                     )
-                np.append(answer, False)
+                answer = np.append(answer, [False])
         return answer.all()
 
     def windows(

From 296ef55eba7128043b9f3a505400e698d3529310 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 4 Apr 2024 15:03:23 +0200
Subject: [PATCH 051/140] Lint with black

---
 ramp/core/core.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 92f98ea8..12c72076 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -661,21 +661,19 @@ def __init__(
         )  # each instance of User (i.e. each user class) has its own list of Appliances
 
     def __str__(self):
-
         try:
             return self.save()[
                 ["user_name", "num_users", "name", "number", "power"]
             ].to_string()
-        
+
         except Exception:
-            return (f"""
+            return f"""
 user_name: {self.user_name} \n
 num_users: {self.num_users} \n
 appliances: no appliances assigned to the user.
-                    """)
-        
-    def __repr__(self):
+                    """
 
+    def __repr__(self):
         return self.__str__()
 
     def _add_appliance_instance(self, appliances):

From 4515baae03c7a0145c41bbc101e202bd6a820f65 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 7 May 2024 18:18:31 +0200
Subject: [PATCH 052/140] Fix test fixture

---
 tests/test_object_comparison.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_object_comparison.py b/tests/test_object_comparison.py
index f726b18c..115cefef 100644
--- a/tests/test_object_comparison.py
+++ b/tests/test_object_comparison.py
@@ -10,7 +10,7 @@ def test_user():
 
 
 @pytest.mark.usefixtures("test_user")
-def test_compare_two_appliances():
+def test_compare_two_appliances(test_user):
     appliance1 = Appliance(
         test_user,
         name="test_appliance1",

From 02776499dd64621723783dec486a27ab63dff233 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 2 May 2024 12:10:40 +0200
Subject: [PATCH 053/140] Link contribute section of README to CONTRIBUTE.md
 file

---
 README.rst | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/README.rst b/README.rst
index 9b9be5fb..fa29acac 100644
--- a/README.rst
+++ b/README.rst
@@ -279,16 +279,7 @@ Contributing
 ============
 This project is open-source. Interested users are therefore invited to test, comment or contribute to the tool. Submitting issues is the best way to get in touch with the development team, which will address your comment, question, or development request in the best possible way. We are also looking for contributors to the main code, willing to contribute to its capabilities, computational-efficiency, formulation, etc.
 
-To contribute changes:
-
-#. Fork the project on GitHub
-#. Create a feature branch (e.g. named "add-this-new-feature") to work on in your fork
-#. Add your name to the `AUTHORS <https://github.com/RAMP-project/RAMP/blob/development/AUTHORS>`_ file
-#. Commit your changes to the feature branch
-#. Push the branch to GitHub
-#. On GitHub, create a new pull request from the feature branch
-
-When committing new changes, please also take care of checking code stability by means of the `qualitative testing <https://github.com/RAMP-project/RAMP/blob/development/CONTRIBUTING.md>`_ functionality.
+To contribute changes please consult our `Contribution guidelines <https://github.com/RAMP-project/RAMP/blob/main/CONTRIBUTING.md>`_
 
 
 How to cite

From 926deec9a0b7f6741742e5f4e9dd07ca2524a884 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 2 May 2024 12:15:50 +0200
Subject: [PATCH 054/140] Copy README.rst from repo source and adapt link to
 contributing

As the contribution guidelines have a dedicated in the documentation we
replace the link within the readme which was pointing towards the file
CONTRIBUTING.md on github with a link which points towards the dedicated
contribution guidelines within the documentation
---
 docs/source/conf.py   | 18 ++++++++++++++++++
 docs/source/intro.rst |  2 +-
 2 files changed, 19 insertions(+), 1 deletion(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 31762d3d..106d9ec4 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -22,6 +22,24 @@
 from notebooks_convert import update_notebooks_rst_files
 
 update_notebooks_rst_files()
+
+
+def copy_readme():
+    with open("../../README.rst", "r", encoding="utf8") as fp:
+        data = fp.readlines()
+
+    # Replace the reference to contributing guidelines with an internal link
+    idx = data.index(
+        "To contribute changes please consult our `Contribution guidelines <https://github.com/RAMP-project/RAMP/blob/main/CONTRIBUTING.md>`_\n"
+    )
+    data[
+        idx
+    ] = "To contribute changes please consult our `Contribution guidelines <contributing.html>`_\n"
+    with open("readme.rst", "w") as fp:
+        fp.writelines(data)
+
+
+copy_readme()
 # -- Project information -----------------------------------------------------
 
 project = "RAMP"
diff --git a/docs/source/intro.rst b/docs/source/intro.rst
index 0ba0a510..90ae0dd2 100644
--- a/docs/source/intro.rst
+++ b/docs/source/intro.rst
@@ -3,5 +3,5 @@
 RAMP Introduction
 *****************
 
-.. include:: ../../README.rst
+.. include:: readme.rst
 

From e6b9731301273c58c048f2178e0bc3a420db8e3c Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 2 May 2024 12:16:32 +0200
Subject: [PATCH 055/140] Add CONTRIBUTING.md content to RTD in a dedicated
 section

---
 docs/source/conf.py   | 11 +++++++++++
 docs/source/index.rst |  1 +
 2 files changed, 12 insertions(+)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 106d9ec4..c30c43c8 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -39,7 +39,18 @@ def copy_readme():
         fp.writelines(data)
 
 
+def copy_contributing():
+    with open("../../CONTRIBUTING.md", "r", encoding="utf8") as fp:
+        data = fp.readlines()
+
+    # Change the title of the file
+    data[0] = "# Contribute\n"
+    with open("contributing.md", "w") as fp:
+        fp.writelines(data)
+
+
 copy_readme()
+copy_contributing()
 # -- Project information -----------------------------------------------------
 
 project = "RAMP"
diff --git a/docs/source/index.rst b/docs/source/index.rst
index ca073e87..3041cea6 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -13,6 +13,7 @@ User Guide:
    algorithm
    examples
    input_parameters
+   contributing
    api_references
 
 

From d8285cd66026757fea3402151af51301454c64a5 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <mohammadamin.tahavori@vito.be>
Date: Fri, 29 Mar 2024 12:32:07 +0100
Subject: [PATCH 056/140] exmples imrpvement - Simple Appliances

issue:
The lack of clear explanation of the example notebooks makes the core
context of each example very difficult to be understood and followed.

changes:
improving the explanation of the example
---
 docs/notebooks/simple_bulb.ipynb | 174 +++++++++++++++++++++++--------
 1 file changed, 128 insertions(+), 46 deletions(-)

diff --git a/docs/notebooks/simple_bulb.ipynb b/docs/notebooks/simple_bulb.ipynb
index 23d7da02..eed6c7ab 100644
--- a/docs/notebooks/simple_bulb.ipynb
+++ b/docs/notebooks/simple_bulb.ipynb
@@ -5,7 +5,9 @@
    "id": "208fe9e9",
    "metadata": {},
    "source": [
-    "# Simple Appliances with multiple functioning windows"
+    "# Simple Appliances with multiple functioning windows\n",
+    "\n",
+    "In this example we are going to build a load simulation of a households category with only access to some indoor light bulbs."
    ]
   },
   {
@@ -16,7 +18,7 @@
    "outputs": [],
    "source": [
     "# importing functions\n",
-    "from ramp import User, UseCase, get_day_type\n",
+    "from ramp import User, UseCase\n",
     "import pandas as pd"
    ]
   },
@@ -25,12 +27,14 @@
    "id": "a07be2a6",
    "metadata": {},
    "source": [
-    "### Creating a user category "
+    "### Creating a user category \n",
+    "\n",
+    "To represent a user category, which is a collection of users with similar consumption characteristics such as appliance ownership and consumption behavior, the class **User** should be utilized. Each User class is initially characterized by the number of users assigned to the user category. For example, let's consider a household category comprising 10 households:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "id": "77e6919a",
    "metadata": {},
    "outputs": [],
@@ -46,33 +50,68 @@
    "id": "8628f23a",
    "metadata": {},
    "source": [
-    "### Creating a simple appliance with two functioning time"
+    "### Creating a simple appliance with two functioning time\n",
+    "In the most basic use cases of RAMP, you can model appliances with random usage variability across different time windows throughout the day, as will be created in this example.\n",
+    "\n",
+    "To model appliances within a household category, parameters such as the number of appliances owned, power usage, number of usage windows, and total usage time need to be specified. Additionally, parameters like the minimum usage time after a switch-on event can enhance the realism of simulations. The randomness of appliance usage can be adjusted through parameters such as variability in the usage window or total usage time.\n",
+    "\n",
+    "For example, let's consider a household category where each household owns 6 \"Indoor Light Bulbs,\" each consuming 7 Watts. These light bulbs are used for 120 minutes in two time windows: from 00:00 to 00:30 and from 19:30 to 24:00. In RAMP simulations, time resolution is measured in minutes, with each simulation representing a single day from 00:00 (corresponding to minute 0) to 24:00 (corresponding to minute 1440).\n",
+    "\n",
+    "Assuming a minimum usage time of 10 minutes each time a light bulb is switched on, we can simulate this appliance by creating a new appliance using the **add_appliance** method of the User object:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "id": "76ff56d6",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# add_appliance is meth\n",
     "indoor_bulb = household.add_appliance(\n",
-    "    name=\"Indoor Light Bulb\",\n",
-    "    number=6,\n",
-    "    power=7,\n",
-    "    num_windows=2,\n",
-    "    func_time=120,\n",
-    "    time_fraction_random_variability=0.2,\n",
-    "    func_cycle=10,\n",
-    "    window_1=[1170, 1440],  # from 19:30 to 24:00\n",
-    "    window_2=[0, 30],  # from 24 to 00:30\n",
-    "    random_var_w=0.35,\n",
+    "    name=\"Indoor Light Bulb\", # the name of the appliance\n",
+    "    number=6, # how many of this appliance each user has in this user category\n",
+    "    power=7, # the power (in Watt) of each single appliance. RAMP does not deal with units of measures, you should check the consistency of the unit of measures throughout your model\n",
+    "    num_windows=2, # how many usage time windows throughout the day?\n",
+    "    func_time=120, # the total usage time of appliances\n",
+    "    func_cycle=10, # the minimum usage time after a switch on event\n",
+    "    window_1=[0, 30],  # from 24 to 00:30\n",
+    "    window_2=[1170, 1440],  # from 19:30 to 24:00\n",
+    "    random_var_w=0.35, # Variability of the windows in percentage\n",
+    "    time_fraction_random_variability=0.2, # randomizes the total time the appliance is on (between 0 and 1)\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
+   "id": "7d02bfd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   user_name  num_users               name  number  power\n",
+      "0  Household         10  Indoor Light Bulb       6    7.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(household)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1363283c",
+   "metadata": {},
+   "source": [
+    "You can check the  **maximum theoretical profile** of the appliance and user category by calling the **maximum_profile** property of each user or appliance object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
    "id": "f07cd11c",
    "metadata": {
     "scrolled": true
@@ -84,13 +123,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -100,7 +139,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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hw3XBBRdozpw5GjVqlDZt2nTOne8MrBkBgNDjYnB2lKDCSEtLi6qqqpSdnf31C4SFKTs7W+Xl5X6fb4xRWVmZdu3apcsvv/y07Zqbm9XY2OjzAAAAzhRUGDl06JBaW1vldrt9jrvdbtXW1p72eQ0NDerdu7ciIyM1efJkPfTQQ/re97532vYlJSWKi4vzPpKTk4PpZodgyQgA2MdY7EydcjdNnz59VF1drbfeekv33nuvCgsLtXHjxtO2X7BggRoaGryPffv2dUY3AQCABRHBNE5ISFB4eLjq6up8jtfV1SkxMfG0zwsLC9OFF14oSRozZox27typkpISXXHFFW22j4qKUlRUVDBd60DMSwJAqGFkdpagroxERkZq3LhxKisr8x7zeDwqKytTVlZWwK/j8XjU3NwczFsDAACHCurKiCQVFhYqLy9P6enpysjIUGlpqY4cOaL8/HxJ0vTp0zVo0CCVlJRI+mr9R3p6ui644AI1NzfrhRde0J///GctX768fSvpYExTAoB9htHYkYIOI7m5uTp48KCKiopUW1urMWPGaN26dd5FrTU1NQoL+/qCy5EjRzRr1ix9/PHH6tmzp1JTU/Xkk08qNze3/aoAAABdVtBhRJIKCgpUUFDQ5rlTF6b+6le/0q9+9auzeRsAANAN8Nk0frCvDgCEHsZmZyGMAAAAqwgjAWKjHQAIAYzFjkQYAQAAVhFG/GBaEgBCj4vR2VEIIwAAwCrCSIDYaAcA7GMkdibCCAAAsIow4gf3sgNA6GFsdhbCCAAAsIowEiD2GQEA+xiLnYkwAgAArCKM+MG97AAAdCzCCAAAsIowEiCmKQHAPvZ8cibCCAAAsIow4gf3sgNA6HExODsKYQQAAFhFGAkUN7cDgHUMxc5EGAEAAFYRRvxgVhIAQg9js7MQRgAAgFWEEQAAYBVhJECsmQIA+xiLnYkwAgAArCKM+MHGOgAQehianYUwAgAArCKMBIiNdgDAPsNg7EiEEQAAYBVhBADQ5bBmxFkIIwAAwCrCSIAMd7cDgHWMxM5EGAEAAFYRRvxgXhIAQo+Lj8pzFMIIAACwijASIG5tB4AQwFjsSIQRAABgFWHED+YlASD0sJ7PWQgjAADAKsJIgJimBAD72PPJmQgjAADAKsKIH8xLAkDoYWh2FsIIAACwijACAACsIowEiE3PAMA+xmJnIowAAACrCCN+sEgKAEIQdxc4CmEEAABYRRgJEBvtAIB9rBlxJsIIAACwijDiB9OSABB6GJqdhTACAACsIowEinlKALCOodiZCCMAAMAqwogfLhaNAEDIYWh2FsIIAACwijASIOYpAcA+w0YjjkQYAQAAVhFG/GBaEgBCD2OzsxBGAACAVYSRADFPCQD2MRI7E2EEAABYRRjxh4lJAAg57AHlLIQRAABgFWEkQCwZAQD7GIudiTACAACsIoz44WLRCAAAHYowAgDocvhvorMQRgAAgFVnFUaWLVumlJQURUdHKzMzU5WVladt+8gjj+iyyy5T37591bdvX2VnZ5+xfahizRQAhAJGYycKOoysXr1ahYWFKi4u1pYtWzR69Gjl5OSovr6+zfYbN27UlClT9PLLL6u8vFzJycm68sor9cknn5xz5wEAQNcXdBhZunSpZs6cqfz8fI0YMUIrVqxQTEyMVq5c2Wb7p556SrNmzdKYMWOUmpqqRx99VB6PR2VlZefc+c7AvjoAEHoYm50lqDDS0tKiqqoqZWdnf/0CYWHKzs5WeXl5QK9x9OhRHTt2TP369Tttm+bmZjU2Nvo8AACAMwUVRg4dOqTW1la53W6f4263W7W1tQG9xvz585WUlOQTaE5VUlKiuLg47yM5OTmYbnYINtoBAPsYi52pU++mWbx4sVatWqU1a9YoOjr6tO0WLFighoYG72Pfvn2d2EsAANCZIoJpnJCQoPDwcNXV1fkcr6urU2Ji4hmfe//992vx4sV68cUXNWrUqDO2jYqKUlRUVDBd6zBMSwJA6GFDSmcJ6spIZGSkxo0b57P49MRi1KysrNM+77777tM999yjdevWKT09/ex7CwAAHCeoKyOSVFhYqLy8PKWnpysjI0OlpaU6cuSI8vPzJUnTp0/XoEGDVFJSIkn6zW9+o6KiIj399NNKSUnxri3p3bu3evfu3Y6ldCzDve0AYB0jsTMFHUZyc3N18OBBFRUVqba2VmPGjNG6deu8i1pramoUFvb1BZfly5erpaVFP/jBD3xep7i4WHfddde59R4AAHR5QYcRSSooKFBBQUGb5zZu3Ojz9717957NW4QM7mUHgBDE2OwofDYNAACwijASIO5tBwD7GIudiTACAACsIoz4wb3sABB6GJmdhTACAACsIowAALoM9nxyJsIIAACwijDiB/uMAEDoYWx2FsIIAACwijACAACsIowEyLDTDgBYx1DsTIQRAABgFWHED9ZIAUDoYUNKZyGMAAAAqwgjAWKaEgDsYyx2JsIIAACwijDiDzvrAEDIYWh2FsIIAACwijASIO5tBwD72PPJmQgjAADAKsKIH0xLAkDoYc2IsxBGAACAVYSRABnubgcAoEMQRgAAgFWEET+YlwSA0MNn0zgLYQQAAFhFGAkQt7YDgH2Mxc5EGAEAAFYRRvxgXhIAQg/r+ZyFMAIAAKwijAAAAKsIIwFizRQA2McGlM5EGAEAAFYRRvxgkRQAAB2LMAIAAKwijASIjXYAwD7GYmcijAAAAKsII36wZAQAQo+LBX2OQhgBAABWEUYCxkQlANjGmhFnIowAAACrCCN+MC0JAKGHodlZCCMAAMAqwkiAmKcEAPsYip2JMAIAAKwijPjBvewAEHoYmp2FMAIAAKwijASINSMAYJ9hMHYkwggAALCKMAIA6HJYMuIshBEAAGAVYSRAhrvbAcA6RmJnIowAAACrCCN+cC87AIQe9oByFsIIAACwijACAACsIowEiH12ACAEMBY7EmEEAABYRRjxw8XWOgAQchiZnYUwAgAArCKMBIhpSgCwjw0onYkwAgAArCKM+MG+OgAQehibnYUwAgAArCKMBIh9RgDAPsZiZyKMAAAAqwgjfjAtCQChiNHZSQgjAADAKsJIgLi3HQDsYyR2prMKI8uWLVNKSoqio6OVmZmpysrK07Z99913ddNNNyklJUUul0ulpaVn21cAAOBAQYeR1atXq7CwUMXFxdqyZYtGjx6tnJwc1dfXt9n+6NGjGjp0qBYvXqzExMRz7nBn4152AAg9jM3OEnQYWbp0qWbOnKn8/HyNGDFCK1asUExMjFauXNlm+/Hjx2vJkiX60Y9+pKioqHPuMAAAcJagwkhLS4uqqqqUnZ399QuEhSk7O1vl5eXt1qnm5mY1Njb6PKxjohIArGOfEWcKKowcOnRIra2tcrvdPsfdbrdqa2vbrVMlJSWKi4vzPpKTk9vttQEAQGgJybtpFixYoIaGBu9j37591vri4l52AAg5jMzOEhFM44SEBIWHh6uurs7neF1dXbsuTo2KimJ9CQAA3URQV0YiIyM1btw4lZWVeY95PB6VlZUpKyur3TsHAACcL6grI5JUWFiovLw8paenKyMjQ6WlpTpy5Ijy8/MlSdOnT9egQYNUUlIi6atFrzt27PD++ZNPPlF1dbV69+6tCy+8sB1L6VismQIA+9iA0pmCDiO5ubk6ePCgioqKVFtbqzFjxmjdunXeRa01NTUKC/v6gsv+/fs1duxY79/vv/9+3X///Zo4caI2btx47hUAAIAuLegwIkkFBQUqKCho89ypASMlJUWmC9+LxcY6ABB6GJudJSTvpgEAAN0HYSRAXfnqDgA4BUOxMxFGAACAVYQRAECXw4aUzkIYAQAAVhFGAsQ0JQDYx1jsTIQRAABgFWHEDxc3swNAyGFodhbCCAAAsIowEiDubQeAEMBg7EiEEQAAYBVhxA+mJQEg9LBmxFkIIwAAwCrCSICYpQQA+xiLnYkwAgAArCKM+MG8JACEHj6bxlkIIwAAwCrCCAAAsIowEiDDRjsAYB1DsTMRRgAAgFWEET9YIgUAIYjB2VEIIwAAwCrCSICYpgQA+1i/50yEEQAAYBVhxA8Xu54BQMhhZHYWwggAALCKMBIopikBwDqGYmcijAAAAKsII36wZAQAQg/r+ZyFMAIAAKwijATIMFMJANaxzYgzEUYAAIBVhBE/mJUEgNDD2OwshBEAAGAVYSRAzFMCgH0Mxc5EGAEAAFYRRvzhXnYACDkMzc5CGAEAAFYRRgLEmhEAsM8wGDsSYQQAAFhFGPGDaUkACD2Mzc5CGAEAAFYRRgAAgFWEkQDxQXkAAHQMwggAALCKMOIHG+sAQOhxMTg7CmEEAABYRRgJEPvsAIB9jMXORBgBAABWEUb8cLG1DgCEHEZmZyGMAAAAqwgjAWKaEgDsY88nZyKMAAAAqwgjfnArOwCEIMZmRyGMAAAAqwgjAeLedgCwj7HYmQgjAADAKsKIH0xLAkDoYQ8oZyGMAAAAqwgjAWOiEgBsYyR2JsIIAACwijDiB/uMAEDoYWx2FsIIAACwijACAACsIowEiI12AMA+xmJnIowAAACrCCN+sLEOAIQeRmZnIYwAAACrCCMBYpoSAOwzjMaOdFZhZNmyZUpJSVF0dLQyMzNVWVl5xvbPPvusUlNTFR0drbS0NL3wwgtn1VkAAOA8QYeR1atXq7CwUMXFxdqyZYtGjx6tnJwc1dfXt9n+jTfe0JQpUzRjxgxt3bpV119/va6//npt3779nDvfKZiYBICQw6ZnzhJ0GFm6dKlmzpyp/Px8jRgxQitWrFBMTIxWrlzZZvsHH3xQV111lebNm6fhw4frnnvu0SWXXKKHH374nDsPAAC6vohgGre0tKiqqkoLFizwHgsLC1N2drbKy8vbfE55ebkKCwt9juXk5Gjt2rWnfZ/m5mY1Nzd7/97Y2BhMNzvE+3VNWvS3d213AwC6tW0fN9juAjpAUGHk0KFDam1tldvt9jnudrv13nvvtfmc2traNtvX1tae9n1KSkq0aNGiYLrWYWKjv/oSffzZF3rs9b12OwMAkCT1juphuwtoR0GFkc6yYMECn6spjY2NSk5OttKX76YOUPE1I3To82b/jQEAHS46Ily54+38TkDHCCqMJCQkKDw8XHV1dT7H6+rqlJiY2OZzEhMTg2ovSVFRUYqKigqmax0mKiJc+ROG2O4GAACOFdQC1sjISI0bN05lZWXeYx6PR2VlZcrKymrzOVlZWT7tJWnDhg2nbQ8AALqXoKdpCgsLlZeXp/T0dGVkZKi0tFRHjhxRfn6+JGn69OkaNGiQSkpKJElz5szRxIkT9cADD2jy5MlatWqVNm/erD/84Q/tWwkAAOiSgg4jubm5OnjwoIqKilRbW6sxY8Zo3bp13kWqNTU1Cgv7+oLLpZdeqqefflq//OUv9Z//+Z8aNmyY1q5dq5EjR7ZfFQAAoMtyGRP6H8jc2NiouLg4NTQ0KDY21nZ3AABAAAL9/c1n0wAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrgt4O3oYTm8Q2NjZa7gkAAAjUid/b/jZ77xJhpKmpSZKUnJxsuScAACBYTU1NiouLO+35LvHZNB6PR/v371efPn3kcrna7XUbGxuVnJysffv2dbvPvKH27ld7d61bovbuWHt3rVsKrdqNMWpqalJSUpLPh+ieqktcGQkLC9N5553XYa8fGxtr/RtmC7V3v9q7a90StXfH2rtr3VLo1H6mKyInsIAVAABYRRgBAABWdeswEhUVpeLiYkVFRdnuSqej9u5Xe3etW6L27lh7d61b6pq1d4kFrAAAwLm69ZURAABgH2EEAABYRRgBAABWEUYAAIBV3TqMLFu2TCkpKYqOjlZmZqYqKyttd+mclJSUaPz48erTp48GDBig66+/Xrt27fJp8+WXX2r27Nnq37+/evfurZtuukl1dXU+bWpqajR58mTFxMRowIABmjdvno4fP96ZpZyTxYsXy+Vyae7cud5jTq77k08+0b//+7+rf//+6tmzp9LS0rR582bveWOMioqKNHDgQPXs2VPZ2dn64IMPfF7j008/1dSpUxUbG6v4+HjNmDFDn3/+eWeXEpTW1lbdeeedGjJkiHr27KkLLrhA99xzj89nYDil9ldffVXXXHONkpKS5HK5tHbtWp/z7VXntm3bdNlllyk6OlrJycm67777Orq0MzpT3ceOHdP8+fOVlpamXr16KSkpSdOnT9f+/ft9XqMr1i35/56f7NZbb5XL5VJpaanP8S5Vu+mmVq1aZSIjI83KlSvNu+++a2bOnGni4+NNXV2d7a6dtZycHPPYY4+Z7du3m+rqavP973/fDB482Hz++efeNrfeeqtJTk42ZWVlZvPmzeZf/uVfzKWXXuo9f/z4cTNy5EiTnZ1ttm7dal544QWTkJBgFixYYKOkoFVWVpqUlBQzatQoM2fOHO9xp9b96aefmvPPP9/cfPPNpqKiwnz44Ydm/fr1Zvfu3d42ixcvNnFxcWbt2rXm7bffNtdee60ZMmSI+eKLL7xtrrrqKjN69Gjz5ptvmtdee81ceOGFZsqUKTZKCti9995r+vfvb/7+97+bPXv2mGeffdb07t3bPPjgg942Tqn9hRdeMAsXLjTPPfeckWTWrFnjc7496mxoaDBut9tMnTrVbN++3fzlL38xPXv2NL///e87q8xvOFPdhw8fNtnZ2Wb16tXmvffeM+Xl5SYjI8OMGzfO5zW6Yt3G+P+en/Dcc8+Z0aNHm6SkJPPb3/7W51xXqr3bhpGMjAwze/Zs799bW1tNUlKSKSkpsdir9lVfX28kmVdeecUY89UPb48ePcyzzz7rbbNz504jyZSXlxtjvvoBCAsLM7W1td42y5cvN7Gxsaa5ublzCwhSU1OTGTZsmNmwYYOZOHGiN4w4ue758+eb73znO6c97/F4TGJiolmyZIn32OHDh01UVJT5y1/+YowxZseOHUaSeeutt7xt/vd//9e4XC7zySefdFznz9HkyZPNj3/8Y59jN954o5k6daoxxrm1n/qLqb3q/N3vfmf69u3r8+99/vz55tvf/nYHVxSYM/1CPqGystJIMh999JExxhl1G3P62j/++GMzaNAgs337dnP++ef7hJGuVnu3nKZpaWlRVVWVsrOzvcfCwsKUnZ2t8vJyiz1rXw0NDZKkfv36SZKqqqp07Ngxn7pTU1M1ePBgb93l5eVKS0uT2+32tsnJyVFjY6PefffdTux98GbPnq3Jkyf71Cc5u+7/+Z//UXp6uv7t3/5NAwYM0NixY/XII494z+/Zs0e1tbU+tcfFxSkzM9On9vj4eKWnp3vbZGdnKywsTBUVFZ1XTJAuvfRSlZWV6f3335ckvf3229q0aZOuvvpqSc6u/WTtVWd5ebkuv/xyRUZGetvk5ORo165d+uyzzzqpmnPT0NAgl8ul+Ph4Sc6u2+PxaNq0aZo3b54uvvjib5zvarV3yzBy6NAhtba2+vzikSS3263a2lpLvWpfHo9Hc+fO1YQJEzRy5EhJUm1trSIjI70/qCecXHdtbW2bX5cT50LVqlWrtGXLFpWUlHzjnJPr/vDDD7V8+XINGzZM69ev109/+lPddttt+tOf/iTp676f6d96bW2tBgwY4HM+IiJC/fr1C+na/+M//kM/+tGPlJqaqh49emjs2LGaO3eupk6dKsnZtZ+sversqj8DJ3z55ZeaP3++pkyZ4v1wOCfX/Zvf/EYRERG67bbb2jzf1WrvEp/ai+DNnj1b27dv16ZNm2x3pcPt27dPc+bM0YYNGxQdHW27O53K4/EoPT1dv/71ryVJY8eO1fbt27VixQrl5eVZ7l3HeuaZZ/TUU0/p6aef1sUXX6zq6mrNnTtXSUlJjq8dvo4dO6Yf/vCHMsZo+fLltrvT4aqqqvTggw9qy5YtcrlctrvTLrrllZGEhASFh4d/426Kuro6JSYmWupV+ykoKNDf//53vfzyyzrvvPO8xxMTE9XS0qLDhw/7tD+57sTExDa/LifOhaKqqirV19frkksuUUREhCIiIvTKK6/ov/7rvxQRESG32+3IuiVp4MCBGjFihM+x4cOHq6amRtLXfT/Tv/XExETV19f7nD9+/Lg+/fTTkK593rx53qsjaWlpmjZtmn7+8597r445ufaTtVedXfVn4EQQ+eijj7RhwwbvVRHJuXW/9tprqq+v1+DBg71j3kcffaTbb79dKSkpkrpe7d0yjERGRmrcuHEqKyvzHvN4PCorK1NWVpbFnp0bY4wKCgq0Zs0avfTSSxoyZIjP+XHjxqlHjx4+de/atUs1NTXeurOysvTOO+/4/CM+8QN+6i+9UDFp0iS98847qq6u9j7S09M1depU75+dWLckTZgw4Ru3b7///vs6//zzJUlDhgxRYmKiT+2NjY2qqKjwqf3w4cOqqqrytnnppZfk8XiUmZnZCVWcnaNHjyoszHcICw8Pl8fjkeTs2k/WXnVmZWXp1Vdf1bFjx7xtNmzYoG9/+9vq27dvJ1UTnBNB5IMPPtCLL76o/v37+5x3at3Tpk3Ttm3bfMa8pKQkzZs3T+vXr5fUBWvv9CWzIWLVqlUmKirKPP7442bHjh3mJz/5iYmPj/e5m6Kr+elPf2ri4uLMxo0bzYEDB7yPo0ePetvceuutZvDgweall14ymzdvNllZWSYrK8t7/sQtrldeeaWprq4269atM9/61rdC/hbXU518N40xzq27srLSREREmHvvvdd88MEH5qmnnjIxMTHmySef9LZZvHixiY+PN88//7zZtm2bue6669q87XPs2LGmoqLCbNq0yQwbNizkbm89VV5enhk0aJD31t7nnnvOJCQkmDvuuMPbxim1NzU1ma1bt5qtW7caSWbp0qVm69at3rtG2qPOw4cPG7fbbaZNm2a2b99uVq1aZWJiYqze4nqmultaWsy1115rzjvvPFNdXe0z5p18d0hXrNsY/9/zU516N40xXav2bhtGjDHmoYceMoMHDzaRkZEmIyPDvPnmm7a7dE4ktfl47LHHvG2++OILM2vWLNO3b18TExNjbrjhBnPgwAGf19m7d6+5+uqrTc+ePU1CQoK5/fbbzbFjxzq5mnNzahhxct1/+9vfzMiRI01UVJRJTU01f/jDH3zOezwec+eddxq3222ioqLMpEmTzK5du3za/N///Z+ZMmWK6d27t4mNjTX5+fmmqampM8sIWmNjo5kzZ44ZPHiwiY6ONkOHDjULFy70+UXklNpffvnlNn+28/LyjDHtV+fbb79tvvOd75ioqCgzaNAgs3jx4s4qsU1nqnvPnj2nHfNefvll72t0xbqN8f89P1VbYaQr1e4y5qTtCgEAADpZt1wzAgAAQgdhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFX/P50KofcMeeraAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -124,7 +163,11 @@
    "id": "91442f65",
    "metadata": {},
    "source": [
-    "### Whole year profile functionality"
+    "### Whole year profile functionality\n",
+    "\n",
+    "To generate profiles spanning more than a single day, the UseCase class in RAMP allows for specifying calendar days as the start and end dates of the simulation. The UseCase class serves as a collector of different user categories and provides user-friendly methods for generating profiles with various settings.\n",
+    "\n",
+    "For example, suppose you want to simulate the behavior of 10 households for the entire year 2020. Using the UseCase class, you can specify the calendar days for the start and end dates of the simulation, creating a comprehensive profile spanning the entire year:"
    ]
   },
   {
@@ -142,14 +185,46 @@
     }
    ],
    "source": [
-    "whole_year_profile = []\n",
-    "use_case = UseCase(users=[household], date_start=\"2020-01-01\", date_end=\"2020-12-31\")\n",
+    "use_case = UseCase(\n",
+    "    users=[household], # A list of all the user categories to be included in the simulation. In this case, we only have household user category\n",
+    "    date_start=\"2020-01-01\", # starting date of the simulation\n",
+    "    date_end=\"2020-12-31\", # end date of the simulation\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2678cc9d",
+   "metadata": {},
+   "source": [
+    "To generate the profiles, you can use, **generate_daily_load_profiles** methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ef095488",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'use_case' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m whole_year_profile \u001b[38;5;241m=\u001b[39m \u001b[43muse_case\u001b[49m\u001b[38;5;241m.\u001b[39mgenerate_daily_load_profiles()\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'use_case' is not defined"
+     ]
+    }
+   ],
+   "source": [
     "whole_year_profile = use_case.generate_daily_load_profiles()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "10d23827-9ea6-4a34-b00c-66f811b024bb",
    "metadata": {},
    "outputs": [
@@ -187,22 +262,35 @@
    "metadata": {},
    "source": [
     "### Generating a profile for a single day\n",
-    "provide day_type=0 for weekday and day_type=1 for weekends"
+    "\n",
+    "To generate daily profiles for specific user categories, the User object in RAMP offers various methods. One such method is **generate_single_load_profile**, which constructs a load profile for a single user, accounting for all their appliances, based on a specific day of the year or a designated day_type. In RAMP, day_types represent weekdays and weekends, with day_type=0 indicating weekdays and day_type=1 signifying weekends. When defining appliances using the add_appliance method, users can specify whether the appliance is utilized throughout the designated wd_we_type argument.\n",
+    "\n",
+    "For instance, let's consider the scenario where we aim to generate a single-day load profile for a weekday of the year for a single user within the household category:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "id": "e4ea14ed",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you \n"
+     ]
+    }
+   ],
    "source": [
-    "single_profile = household.generate_single_load_profile(day_type=0)"
+    "single_profile = household.generate_single_load_profile(\n",
+    "    day_type=0\n",
+    "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "04545584",
    "metadata": {},
    "outputs": [
@@ -212,13 +300,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -239,22 +327,24 @@
    "source": [
     "### Generating aggregated_load_profile for the user category\n",
     "\n",
-    "Single daily profiles are aggregated for all the users defined within the User class"
+    "If instead of a single user from the categoy, you want to generate load profiles of the aggregated users of the category, for a single day, you can use the similar method in User object, named **generate_aggregated_load_profile**:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "id": "3ed3f527",
    "metadata": {},
    "outputs": [],
    "source": [
-    "aggregated_profile = household.generate_aggregated_load_profile()"
+    "aggregated_profile = household.generate_aggregated_load_profile(\n",
+    "    day_type=0\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "id": "9bb8f29c",
    "metadata": {},
    "outputs": [
@@ -264,13 +354,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -283,14 +373,6 @@
     "aggregated_profile = pd.DataFrame(aggregated_profile, columns=[\"household\"])\n",
     "aggregated_profile.plot()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "130f4d0e-15cd-44b0-9cde-5a95a6431405",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -309,7 +391,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,

From 73909a6d9aa063093a412abae6bcc9f17b19a021 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <mohammadamin.tahavori@vito.be>
Date: Fri, 29 Mar 2024 14:02:55 +0100
Subject: [PATCH 057/140] exmples imrpvement - Fixed-Flatt appliances

Improving the explanation and analysis of the example
---
 docs/notebooks/fixed_flat_app.ipynb | 194 +++++++++++++++++++++-------
 1 file changed, 146 insertions(+), 48 deletions(-)

diff --git a/docs/notebooks/fixed_flat_app.ipynb b/docs/notebooks/fixed_flat_app.ipynb
index d7d0ce38..5c42b472 100644
--- a/docs/notebooks/fixed_flat_app.ipynb
+++ b/docs/notebooks/fixed_flat_app.ipynb
@@ -5,19 +5,26 @@
    "id": "0b4c1189",
    "metadata": {},
    "source": [
-    "# Fixed-Flat Appliance"
+    "# Fixed-Flat Appliance\n",
+    "\n",
+    "Some of the appliances have a very deterministic consumption patterns. For example, light bulbs that are often used for securty purposes, are usually switched on with a specific schdule. Appliances as such are identified as flat appliances in RAMP, meaning that there are not subjected to random variability in terms of total usage time. \n",
+    "\n",
+    "In case that there are multiple appliances of the same type that are always switched on togehter, you can attribute the appliance defintion with the fixed flag, which makes the swtich on and off events synced for the appliances of the same type. \n",
+    "\n",
+    "In this example, we will model the security lights of a school to clarify on this characteristics:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 64,
    "id": "45adebb9",
    "metadata": {},
    "outputs": [],
    "source": [
     "# importing functions\n",
-    "from ramp import User, UseCase, get_day_type\n",
-    "import pandas as pd"
+    "from ramp import User, UseCase\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -30,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 57,
    "id": "240f3bd8",
    "metadata": {},
    "outputs": [],
@@ -48,27 +55,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 58,
    "id": "63d2c6f2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "indoor_bulb = school.add_appliance(\n",
-    "    name=\"Indoor Light Bulb\",\n",
+    "security_lights = school.add_appliance(\n",
+    "    name=\"Security Light Bulb\",\n",
     "    number=10,\n",
     "    power=25,\n",
     "    num_windows=1,\n",
     "    func_time=210,\n",
-    "    time_fraction_random_variability=0.2,\n",
     "    func_cycle=60,\n",
     "    fixed=\"yes\",  # This means all the 'n' appliances of this kind are always switched-on together\n",
     "    flat=\"yes\",  # This means the appliance is not subject to random variability in terms of total usage time\n",
-    ")\n",
-    "indoor_bulb.windows(\n",
-    "    window_1=[1200, 1440],  # from 20:00 to 24:00\n",
-    "    window_2=[0, 0],\n",
-    "    random_var_w=0.35,\n",
-    ")"
+    "    window_1 = [1200, 1440],  # from 20:00 to 24:00\n",
+    ")\n"
    ]
   },
   {
@@ -76,12 +78,14 @@
    "id": "2928b830",
    "metadata": {},
    "source": [
-    "### Initialize the usecase (it defines the peak time range and simulation time)"
+    "### Generating the profiles using UseCase class\n",
+    "\n",
+    "Similar to previous example, you can generate the load profiles using the UseCase class. In this example, we use another functionality of the UseCase class by identifying the starting date of the simulation and the number of days for generating the profiles:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 59,
    "id": "0ce9c747",
    "metadata": {},
    "outputs": [
@@ -94,61 +98,155 @@
     }
    ],
    "source": [
-    "school_case = UseCase(users=[school], date_start=\"2023-01-01\")\n",
-    "school_case.initialize(num_days=7)"
+    "school_case = UseCase(\n",
+    "    users=[school], # users of the usecase\n",
+    "    date_start=\"2023-01-01\", # start date\n",
+    "    )\n",
+    "\n",
+    "# when both date_start and date_end are not given, you need to initialize the usecase by this method and by passing the number of days as num_days\n",
+    "school_case.initialize(\n",
+    "    num_days=7\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "18a0760d-4864-47e3-b2c1-30952bc2410a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fixed_flat = school_case.generate_daily_load_profiles()\n",
+    "fixed_flat = pd.DataFrame(fixed_flat, columns=[\"fixed_flat\"])\n",
+    "fixed_flat.plot()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e6f59618",
+   "id": "03124f17",
    "metadata": {},
    "source": [
-    "### Generating a profile for the 1st week of the year\n",
-    "\n",
-    "From the usecase directly"
+    "As it can be seen, in each day, all the 10 lights with 25 Watt power (aggregated power of 250) are always switched on together on the specified schedule."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a5316d0",
+   "metadata": {},
+   "source": [
+    "To make the effect of parameters, lets look how the profiles would look like without using fixed and flat properties:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "18a0760d-4864-47e3-b2c1-30952bc2410a",
+   "execution_count": 61,
+   "id": "773ca6cd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "first_week = school_case.generate_daily_load_profiles(flat=True)"
+    "security_lights.fixed = \"no\"\n",
+    "security_lights.flat = \"no\"\n",
+    "no_fix_no_flat = school_case.generate_daily_load_profiles()\n",
+    "no_fix_no_flat = pd.DataFrame(no_fix_no_flat, columns=[\"no_fix_no_flat\"])\n",
+    "\n",
+    "security_lights.fixed = \"yes\"\n",
+    "fix_no_flat = school_case.generate_daily_load_profiles()\n",
+    "fix_no_flat = pd.DataFrame(fix_no_flat, columns=[\"fix_no_flat\"])"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "779b95d7-2fc4-442e-858a-02dd228fcfad",
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "498c54f7",
    "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "Line2D.set() got an unexpected keyword argument 'subplot'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[63], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfixed_flat\u001b[49m\u001b[43m,\u001b[49m\u001b[43mno_fix_no_flat\u001b[49m\u001b[43m,\u001b[49m\u001b[43mfix_no_flat\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubplot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_core.py:1031\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1028\u001b[0m             label_name \u001b[38;5;241m=\u001b[39m label_kw \u001b[38;5;129;01mor\u001b[39;00m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m   1029\u001b[0m             data\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m label_name\n\u001b[1;32m-> 1031\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_backend\u001b[38;5;241m.\u001b[39mplot(data, kind\u001b[38;5;241m=\u001b[39mkind, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     69\u001b[0m         kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft_ax\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax)\n\u001b[0;32m     70\u001b[0m plot_obj \u001b[38;5;241m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m---> 71\u001b[0m \u001b[43mplot_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     72\u001b[0m plot_obj\u001b[38;5;241m.\u001b[39mdraw()\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_obj\u001b[38;5;241m.\u001b[39mresult\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:453\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    451\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compute_plot_data()\n\u001b[0;32m    452\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup_subplots()\n\u001b[1;32m--> 453\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    454\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_table()\n\u001b[0;32m    455\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_legend()\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1392\u001b[0m, in \u001b[0;36mLinePlot._make_plot\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1389\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mark_right_label(label, index\u001b[38;5;241m=\u001b[39mi)\n\u001b[0;32m   1390\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m label\n\u001b[1;32m-> 1392\u001b[0m newlines \u001b[38;5;241m=\u001b[39m plotf(\n\u001b[0;32m   1393\u001b[0m     ax,\n\u001b[0;32m   1394\u001b[0m     x,\n\u001b[0;32m   1395\u001b[0m     y,\n\u001b[0;32m   1396\u001b[0m     style\u001b[38;5;241m=\u001b[39mstyle,\n\u001b[0;32m   1397\u001b[0m     column_num\u001b[38;5;241m=\u001b[39mi,\n\u001b[0;32m   1398\u001b[0m     stacking_id\u001b[38;5;241m=\u001b[39mstacking_id,\n\u001b[0;32m   1399\u001b[0m     is_errorbar\u001b[38;5;241m=\u001b[39mis_errorbar,\n\u001b[0;32m   1400\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds,\n\u001b[0;32m   1401\u001b[0m )\n\u001b[0;32m   1402\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_append_legend_handles_labels(newlines[\u001b[38;5;241m0\u001b[39m], label)\n\u001b[0;32m   1404\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_ts_plot():\n\u001b[0;32m   1405\u001b[0m     \u001b[38;5;66;03m# reset of xlim should be used for ts data\u001b[39;00m\n\u001b[0;32m   1406\u001b[0m     \u001b[38;5;66;03m# TODO: GH28021, should find a way to change view limit on xaxis\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1421\u001b[0m, in \u001b[0;36mLinePlot._plot\u001b[1;34m(cls, ax, x, y, style, column_num, stacking_id, **kwds)\u001b[0m\n\u001b[0;32m   1419\u001b[0m     \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_initialize_stacker(ax, stacking_id, \u001b[38;5;28mlen\u001b[39m(y))\n\u001b[0;32m   1420\u001b[0m y_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_get_stacked_values(ax, stacking_id, y, kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m-> 1421\u001b[0m lines \u001b[38;5;241m=\u001b[39m MPLPlot\u001b[38;5;241m.\u001b[39m_plot(ax, x, y_values, style\u001b[38;5;241m=\u001b[39mstyle, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m   1422\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_update_stacker(ax, stacking_id, y)\n\u001b[0;32m   1423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lines\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\converter.py:97\u001b[0m, in \u001b[0;36mregister_pandas_matplotlib_converters.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     94\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m     95\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m pandas_converters():\n\u001b[1;32m---> 97\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:891\u001b[0m, in \u001b[0;36mMPLPlot._plot\u001b[1;34m(cls, ax, x, y, style, is_errorbar, **kwds)\u001b[0m\n\u001b[0;32m    888\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    889\u001b[0m     \u001b[38;5;66;03m# prevent style kwarg from going to errorbar, where it is unsupported\u001b[39;00m\n\u001b[0;32m    890\u001b[0m     args \u001b[38;5;241m=\u001b[39m (x, y, style) \u001b[38;5;28;01mif\u001b[39;00m style \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m (x, y)\n\u001b[1;32m--> 891\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mplot(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\axes\\_axes.py:1721\u001b[0m, in \u001b[0;36mAxes.plot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1478\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1479\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[0;32m   1480\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1718\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[0;32m   1719\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1720\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[1;32m-> 1721\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[0;32m   1722\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[0;32m   1723\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\axes\\_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[1;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    301\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m    302\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m--> 303\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\axes\\_base.py:539\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[1;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[0;32m    537\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n\u001b[0;32m    538\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 539\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [l[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m result]\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\axes\\_base.py:539\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    537\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n\u001b[0;32m    538\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 539\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [l[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m result]\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\axes\\_base.py:532\u001b[0m, in \u001b[0;36m<genexpr>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    529\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    530\u001b[0m     labels \u001b[38;5;241m=\u001b[39m [label] \u001b[38;5;241m*\u001b[39m n_datasets\n\u001b[1;32m--> 532\u001b[0m result \u001b[38;5;241m=\u001b[39m (\u001b[43mmake_artist\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mncx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mncy\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    533\u001b[0m \u001b[43m                      \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    534\u001b[0m           \u001b[38;5;28;01mfor\u001b[39;00m j, label \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(labels))\n\u001b[0;32m    536\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_kwargs:\n\u001b[0;32m    537\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\axes\\_base.py:346\u001b[0m, in \u001b[0;36m_process_plot_var_args._makeline\u001b[1;34m(self, axes, x, y, kw, kwargs)\u001b[0m\n\u001b[0;32m    344\u001b[0m default_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getdefaults(\u001b[38;5;28mset\u001b[39m(), kw)\n\u001b[0;32m    345\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setdefaults(default_dict, kw)\n\u001b[1;32m--> 346\u001b[0m seg \u001b[38;5;241m=\u001b[39m mlines\u001b[38;5;241m.\u001b[39mLine2D(x, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n\u001b[0;32m    347\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m seg, kw\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\lines.py:407\u001b[0m, in \u001b[0;36mLine2D.__init__\u001b[1;34m(self, xdata, ydata, linewidth, linestyle, color, gapcolor, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[0;32m    403\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_markeredgewidth(markeredgewidth)\n\u001b[0;32m    405\u001b[0m \u001b[38;5;66;03m# update kwargs before updating data to give the caller a\u001b[39;00m\n\u001b[0;32m    406\u001b[0m \u001b[38;5;66;03m# chance to init axes (and hence unit support)\u001b[39;00m\n\u001b[1;32m--> 407\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_internal_update\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    408\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpickradius \u001b[38;5;241m=\u001b[39m pickradius\n\u001b[0;32m    409\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mind_offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\artist.py:1219\u001b[0m, in \u001b[0;36mArtist._internal_update\u001b[1;34m(self, kwargs)\u001b[0m\n\u001b[0;32m   1212\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_internal_update\u001b[39m(\u001b[38;5;28mself\u001b[39m, kwargs):\n\u001b[0;32m   1213\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1214\u001b[0m \u001b[38;5;124;03m    Update artist properties without prenormalizing them, but generating\u001b[39;00m\n\u001b[0;32m   1215\u001b[0m \u001b[38;5;124;03m    errors as if calling `set`.\u001b[39;00m\n\u001b[0;32m   1216\u001b[0m \n\u001b[0;32m   1217\u001b[0m \u001b[38;5;124;03m    The lack of prenormalization is to maintain backcompatibility.\u001b[39;00m\n\u001b[0;32m   1218\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1219\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_props\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1220\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{cls.__name__}\u001b[39;49;00m\u001b[38;5;124;43m.set() got an unexpected keyword argument \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[0;32m   1221\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{prop_name!r}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\artist.py:1193\u001b[0m, in \u001b[0;36mArtist._update_props\u001b[1;34m(self, props, errfmt)\u001b[0m\n\u001b[0;32m   1191\u001b[0m             func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   1192\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(func):\n\u001b[1;32m-> 1193\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[0;32m   1194\u001b[0m                     errfmt\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m), prop_name\u001b[38;5;241m=\u001b[39mk))\n\u001b[0;32m   1195\u001b[0m             ret\u001b[38;5;241m.\u001b[39mappend(func(v))\n\u001b[0;32m   1196\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret:\n",
+      "\u001b[1;31mAttributeError\u001b[0m: Line2D.set() got an unexpected keyword argument 'subplot'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "or from the user"
+    "\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))  # Adjust figsize as needed\n",
+    "\n",
+    "# Plot each column on a separate subplot\n",
+    "for i, df in enumerate([fixed_flat,no_fix_no_flat,fix_no_flat]):\n",
+    "    df.plot(ax=axes[i], color='blue', marker='o', linestyle='-')\n",
+    "    axes[i].set_title(df.columns[0])\n",
+    "\n",
+    "# Adjust layout and display the plot\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "bb051491",
+   "execution_count": 24,
+   "id": "4721c37b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "first_week = []\n",
-    "\n",
-    "for day_idx, day in enumerate(school_case.days):\n",
-    "    first_week.extend(\n",
-    "        school.generate_single_load_profile(\n",
-    "            prof_i=day_idx,  # the day to generate the profile\n",
-    "            peak_time_range=school_case.peak_time_range,\n",
-    "            day_type=get_day_type(day),\n",
-    "        )\n",
-    "    )"
+    "first_week = school_case.generate_daily_load_profiles()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "b28eb307-d5db-47fd-9e45-e3e746f628d6",
+   "execution_count": 25,
+   "id": "784764bb",
    "metadata": {},
    "outputs": [
     {
@@ -157,13 +255,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -173,14 +271,14 @@
     }
    ],
    "source": [
-    "first_week = pd.DataFrame(first_week, columns=[\"household\"])\n",
+    "first_week = pd.DataFrame(first_week, columns=[\"school\"])\n",
     "first_week.plot()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f811d970-650d-4342-9dee-8d80ca20ddae",
+   "id": "0d2ae7ea",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -202,7 +300,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

From 332bbecd7e1f291623174650b673cedcf8789a54 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <mohammadamin.tahavori@vito.be>
Date: Fri, 29 Mar 2024 14:34:09 +0100
Subject: [PATCH 058/140] exmples imrpvement - occasional use

- Imroving the example with occasional uses
- fixing some typos
---
 docs/notebooks/fixed_flat_app.ipynb | 121 +++++---------------
 docs/notebooks/occasional_use.ipynb | 170 ++++++++--------------------
 2 files changed, 76 insertions(+), 215 deletions(-)

diff --git a/docs/notebooks/fixed_flat_app.ipynb b/docs/notebooks/fixed_flat_app.ipynb
index 5c42b472..44bed895 100644
--- a/docs/notebooks/fixed_flat_app.ipynb
+++ b/docs/notebooks/fixed_flat_app.ipynb
@@ -7,16 +7,16 @@
    "source": [
     "# Fixed-Flat Appliance\n",
     "\n",
-    "Some of the appliances have a very deterministic consumption patterns. For example, light bulbs that are often used for securty purposes, are usually switched on with a specific schdule. Appliances as such are identified as flat appliances in RAMP, meaning that there are not subjected to random variability in terms of total usage time. \n",
+    "Some appliances exhibit deterministic consumption patterns, such as security lights used for safety purposes, which often adhere to specific schedules. In RAMP, such appliances are categorized as **flat appliances** indicating they lack random variability in total usage time.\n",
     "\n",
-    "In case that there are multiple appliances of the same type that are always switched on togehter, you can attribute the appliance defintion with the fixed flag, which makes the swtich on and off events synced for the appliances of the same type. \n",
+    "When multiple appliances of the same type are consistently switched on together, they can be attributed with the 'fixed' flag in their definition. This synchronizes the switch-on and switch-off events for appliances of the same type.\n",
     "\n",
-    "In this example, we will model the security lights of a school to clarify on this characteristics:"
+    "For instance, let's model the security lights of a school to illustrate these characteristics:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 1,
    "id": "45adebb9",
    "metadata": {},
    "outputs": [],
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 2,
    "id": "240f3bd8",
    "metadata": {},
    "outputs": [],
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 3,
    "id": "63d2c6f2",
    "metadata": {},
    "outputs": [],
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 4,
    "id": "0ce9c747",
    "metadata": {},
    "outputs": [
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 5,
    "id": "18a0760d-4864-47e3-b2c1-30952bc2410a",
    "metadata": {},
    "outputs": [
@@ -121,7 +121,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -155,21 +155,31 @@
    "id": "9a5316d0",
    "metadata": {},
    "source": [
-    "To make the effect of parameters, lets look how the profiles would look like without using fixed and flat properties:"
+    "To clarify the impact of parameters, let's examine how the profiles would vary under different combinations:\n",
+    "\n",
+    "1.  no_fixed_no_flat consumption: when appliances are not synchronized and they can randomly switch on during the window time\n",
+    "\n",
+    "2. fixed_no_flat consumption: when appliances are synchronized and they can randomly switch on during the window time\n",
+    "\n",
+    "3. fixed_no_flat consumption (original example): when appliances are synchronized and they are always switched on during the window time\n",
+    "\n",
+    "By observing the load profiles generated under these different combinations, we can gain insights into how each parameter influences overall energy consumption and demand patterns within simulation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 6,
    "id": "773ca6cd",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# no fixed and no flat property\n",
     "security_lights.fixed = \"no\"\n",
     "security_lights.flat = \"no\"\n",
     "no_fix_no_flat = school_case.generate_daily_load_profiles()\n",
     "no_fix_no_flat = pd.DataFrame(no_fix_no_flat, columns=[\"no_fix_no_flat\"])\n",
     "\n",
+    "# not flat but fixed\n",
     "security_lights.fixed = \"yes\"\n",
     "fix_no_flat = school_case.generate_daily_load_profiles()\n",
     "fix_no_flat = pd.DataFrame(fix_no_flat, columns=[\"fix_no_flat\"])"
@@ -177,42 +187,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 7,
    "id": "498c54f7",
    "metadata": {},
    "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "Line2D.set() got an unexpected keyword argument 'subplot'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[63], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfixed_flat\u001b[49m\u001b[43m,\u001b[49m\u001b[43mno_fix_no_flat\u001b[49m\u001b[43m,\u001b[49m\u001b[43mfix_no_flat\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubplot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_core.py:1031\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1028\u001b[0m             label_name \u001b[38;5;241m=\u001b[39m label_kw \u001b[38;5;129;01mor\u001b[39;00m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m   1029\u001b[0m             data\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m label_name\n\u001b[1;32m-> 1031\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_backend\u001b[38;5;241m.\u001b[39mplot(data, kind\u001b[38;5;241m=\u001b[39mkind, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     69\u001b[0m         kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft_ax\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax)\n\u001b[0;32m     70\u001b[0m plot_obj \u001b[38;5;241m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m---> 71\u001b[0m \u001b[43mplot_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     72\u001b[0m plot_obj\u001b[38;5;241m.\u001b[39mdraw()\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_obj\u001b[38;5;241m.\u001b[39mresult\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:453\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    451\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compute_plot_data()\n\u001b[0;32m    452\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup_subplots()\n\u001b[1;32m--> 453\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    454\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_table()\n\u001b[0;32m    455\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_legend()\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1392\u001b[0m, in \u001b[0;36mLinePlot._make_plot\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1389\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mark_right_label(label, index\u001b[38;5;241m=\u001b[39mi)\n\u001b[0;32m   1390\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m label\n\u001b[1;32m-> 1392\u001b[0m newlines \u001b[38;5;241m=\u001b[39m plotf(\n\u001b[0;32m   1393\u001b[0m     ax,\n\u001b[0;32m   1394\u001b[0m     x,\n\u001b[0;32m   1395\u001b[0m     y,\n\u001b[0;32m   1396\u001b[0m     style\u001b[38;5;241m=\u001b[39mstyle,\n\u001b[0;32m   1397\u001b[0m     column_num\u001b[38;5;241m=\u001b[39mi,\n\u001b[0;32m   1398\u001b[0m     stacking_id\u001b[38;5;241m=\u001b[39mstacking_id,\n\u001b[0;32m   1399\u001b[0m     is_errorbar\u001b[38;5;241m=\u001b[39mis_errorbar,\n\u001b[0;32m   1400\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds,\n\u001b[0;32m   1401\u001b[0m )\n\u001b[0;32m   1402\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_append_legend_handles_labels(newlines[\u001b[38;5;241m0\u001b[39m], label)\n\u001b[0;32m   1404\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_ts_plot():\n\u001b[0;32m   1405\u001b[0m     \u001b[38;5;66;03m# reset of xlim should be used for ts data\u001b[39;00m\n\u001b[0;32m   1406\u001b[0m     \u001b[38;5;66;03m# TODO: GH28021, should find a way to change view limit on xaxis\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1421\u001b[0m, in \u001b[0;36mLinePlot._plot\u001b[1;34m(cls, ax, x, y, style, column_num, stacking_id, **kwds)\u001b[0m\n\u001b[0;32m   1419\u001b[0m     \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_initialize_stacker(ax, stacking_id, \u001b[38;5;28mlen\u001b[39m(y))\n\u001b[0;32m   1420\u001b[0m y_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_get_stacked_values(ax, stacking_id, y, kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m-> 1421\u001b[0m lines \u001b[38;5;241m=\u001b[39m MPLPlot\u001b[38;5;241m.\u001b[39m_plot(ax, x, y_values, style\u001b[38;5;241m=\u001b[39mstyle, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m   1422\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_update_stacker(ax, stacking_id, y)\n\u001b[0;32m   1423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lines\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\converter.py:97\u001b[0m, in \u001b[0;36mregister_pandas_matplotlib_converters.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     94\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m     95\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m pandas_converters():\n\u001b[1;32m---> 97\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:891\u001b[0m, in \u001b[0;36mMPLPlot._plot\u001b[1;34m(cls, ax, x, y, style, is_errorbar, **kwds)\u001b[0m\n\u001b[0;32m    888\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    889\u001b[0m     \u001b[38;5;66;03m# prevent style kwarg from going to errorbar, where it is unsupported\u001b[39;00m\n\u001b[0;32m    890\u001b[0m     args \u001b[38;5;241m=\u001b[39m (x, y, style) \u001b[38;5;28;01mif\u001b[39;00m style \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m (x, y)\n\u001b[1;32m--> 891\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mplot(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\axes\\_axes.py:1721\u001b[0m, in \u001b[0;36mAxes.plot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1478\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1479\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[0;32m   1480\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1718\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[0;32m   1719\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1720\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[1;32m-> 1721\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[0;32m   1722\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[0;32m   1723\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\axes\\_base.py:303\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[1;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    301\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m    302\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m--> 303\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\axes\\_base.py:539\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[1;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[0;32m    537\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n\u001b[0;32m    538\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 539\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [l[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m result]\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\axes\\_base.py:539\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    537\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n\u001b[0;32m    538\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 539\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [l[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m result]\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\axes\\_base.py:532\u001b[0m, in \u001b[0;36m<genexpr>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    529\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    530\u001b[0m     labels \u001b[38;5;241m=\u001b[39m [label] \u001b[38;5;241m*\u001b[39m n_datasets\n\u001b[1;32m--> 532\u001b[0m result \u001b[38;5;241m=\u001b[39m (\u001b[43mmake_artist\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mncx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mncy\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    533\u001b[0m \u001b[43m                      \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    534\u001b[0m           \u001b[38;5;28;01mfor\u001b[39;00m j, label \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(labels))\n\u001b[0;32m    536\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_kwargs:\n\u001b[0;32m    537\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\axes\\_base.py:346\u001b[0m, in \u001b[0;36m_process_plot_var_args._makeline\u001b[1;34m(self, axes, x, y, kw, kwargs)\u001b[0m\n\u001b[0;32m    344\u001b[0m default_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getdefaults(\u001b[38;5;28mset\u001b[39m(), kw)\n\u001b[0;32m    345\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setdefaults(default_dict, kw)\n\u001b[1;32m--> 346\u001b[0m seg \u001b[38;5;241m=\u001b[39m mlines\u001b[38;5;241m.\u001b[39mLine2D(x, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n\u001b[0;32m    347\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m seg, kw\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\lines.py:407\u001b[0m, in \u001b[0;36mLine2D.__init__\u001b[1;34m(self, xdata, ydata, linewidth, linestyle, color, gapcolor, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[0;32m    403\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_markeredgewidth(markeredgewidth)\n\u001b[0;32m    405\u001b[0m \u001b[38;5;66;03m# update kwargs before updating data to give the caller a\u001b[39;00m\n\u001b[0;32m    406\u001b[0m \u001b[38;5;66;03m# chance to init axes (and hence unit support)\u001b[39;00m\n\u001b[1;32m--> 407\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_internal_update\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    408\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpickradius \u001b[38;5;241m=\u001b[39m pickradius\n\u001b[0;32m    409\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mind_offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\artist.py:1219\u001b[0m, in \u001b[0;36mArtist._internal_update\u001b[1;34m(self, kwargs)\u001b[0m\n\u001b[0;32m   1212\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_internal_update\u001b[39m(\u001b[38;5;28mself\u001b[39m, kwargs):\n\u001b[0;32m   1213\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1214\u001b[0m \u001b[38;5;124;03m    Update artist properties without prenormalizing them, but generating\u001b[39;00m\n\u001b[0;32m   1215\u001b[0m \u001b[38;5;124;03m    errors as if calling `set`.\u001b[39;00m\n\u001b[0;32m   1216\u001b[0m \n\u001b[0;32m   1217\u001b[0m \u001b[38;5;124;03m    The lack of prenormalization is to maintain backcompatibility.\u001b[39;00m\n\u001b[0;32m   1218\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1219\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_props\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1220\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{cls.__name__}\u001b[39;49;00m\u001b[38;5;124;43m.set() got an unexpected keyword argument \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[0;32m   1221\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{prop_name!r}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\TAHAVORM\\AppData\\Local\\miniconda3\\envs\\ramp\\lib\\site-packages\\matplotlib\\artist.py:1193\u001b[0m, in \u001b[0;36mArtist._update_props\u001b[1;34m(self, props, errfmt)\u001b[0m\n\u001b[0;32m   1191\u001b[0m             func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   1192\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(func):\n\u001b[1;32m-> 1193\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[0;32m   1194\u001b[0m                     errfmt\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m), prop_name\u001b[38;5;241m=\u001b[39mk))\n\u001b[0;32m   1195\u001b[0m             ret\u001b[38;5;241m.\u001b[39mappend(func(v))\n\u001b[0;32m   1196\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret:\n",
-      "\u001b[1;31mAttributeError\u001b[0m: Line2D.set() got an unexpected keyword argument 'subplot'"
-     ]
-    },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1200x400 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -221,67 +204,25 @@
    ],
    "source": [
     "\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))  # Adjust figsize as needed\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))  \n",
+    "\n",
     "\n",
-    "# Plot each column on a separate subplot\n",
-    "for i, df in enumerate([fixed_flat,no_fix_no_flat,fix_no_flat]):\n",
-    "    df.plot(ax=axes[i], color='blue', marker='o', linestyle='-')\n",
+    "for i, df in enumerate([no_fix_no_flat,fix_no_flat,fixed_flat,]):\n",
+    "    df.plot(ax=axes[i],legend=False)\n",
     "    axes[i].set_title(df.columns[0])\n",
     "\n",
-    "# Adjust layout and display the plot\n",
+    "\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "4721c37b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "first_week = school_case.generate_daily_load_profiles()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
+   "cell_type": "markdown",
    "id": "784764bb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "first_week = pd.DataFrame(first_week, columns=[\"school\"])\n",
-    "first_week.plot()"
+    "As it can be seen, in case of no flat and no fix, both scheduling of appliances and the switched on events of appliances are randomized. While when appliances are fixed, not flat, their scheduling is randomized keeping their switch on events synced as it can be observed that power usage is always kept at 250 Watt with more intermittent swithced on/off events. "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0d2ae7ea",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/notebooks/occasional_use.ipynb b/docs/notebooks/occasional_use.ipynb
index 6cfef8c9..733bf7f1 100644
--- a/docs/notebooks/occasional_use.ipynb
+++ b/docs/notebooks/occasional_use.ipynb
@@ -7,17 +7,11 @@
    "source": [
     "# Appliances with occasional use\n",
     "\n",
-    "There are some appliances that are occasionally included in the mix of\n",
-    "appliances that the user switches on during the day. For example, an iron,\n",
-    "a stereo, printers, etc.\n",
+    "There are some appliances that are occasionally included in the mix of appliances that the user switches on during the day. For example, an iron, a stereo, printers, etc.\n",
     "\n",
-    "Within RAMP, the user may specify the probability of using an appliance\n",
-    "on the daily mix with a parameter called **occasional_use**.\n",
+    "Within RAMP, the user may specify the probability of using an appliance on the daily mix with a parameter called **occasional_use**.\n",
     "\n",
-    "When `occasional_use = 0`, the appliance is always present in the mix, and\n",
-    "when `occasional_use = 1`,  the appliance is never present. Any in-between\n",
-    "values will lead to a probabilistic calculation to decide whether the appliance\n",
-    "is used or not on a given day.\n",
+    "When `occasional_use = 1`, the appliance is always present in the mix, and when `occasional_use = 0`,  the appliance is never present. Any in-between values will lead to a probabilistic calculation to decide whether the appliance is used or not on a given day.\n",
     "\n",
     "The following example investigates the effect of this parameter by modelling two user categories:\n",
     "* A household that uses a computer occasionally\n",
@@ -32,7 +26,8 @@
    "outputs": [],
    "source": [
     "# importing functions\n",
-    "from ramp import User, UseCase, get_day_type\n",
+    "from ramp import User, UseCase\n",
+    "import matplotlib.pyplot as plt\n",
     "import pandas as pd"
    ]
   },
@@ -67,9 +62,10 @@
     "    number=1,\n",
     "    power=50,\n",
     "    num_windows=1,\n",
-    "    func_time=210,\n",
+    "    func_time=210, # 3.5 hours\n",
+    "    func_cycle=210,  \n",
     "    occasional_use=0.5,  # 50% chance of occasional use,\n",
-    "    window_1=[510, 750],\n",
+    "    window_1=[480, 750], # start from 8AM\n",
     ")"
    ]
   },
@@ -85,72 +81,25 @@
     "    number=1,\n",
     "    power=50,\n",
     "    num_windows=1,\n",
-    "    func_time=210,\n",
-    "    time_fraction_random_variability=0.2,\n",
-    "    func_cycle=10,\n",
+    "    func_time=210, # 3.5 hours\n",
+    "    func_cycle=210,  \n",
     "    occasional_use=1,  # always present in the mix of appliances,\n",
-    "    window_1=[510, 750],\n",
+    "    window_1=[480, 750], # start from 8AM\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c241c086",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Checking the maximum profile of the two appliances\n",
-    "\n",
-    "max_profile_c1 = pd.DataFrame(computer_0.maximum_profile, columns=[computer_0.name])\n",
-    "max_profile_c2 = pd.DataFrame(computer_1.maximum_profile, columns=[computer_1.name])\n",
-    "\n",
-    "max_profile_c1.plot()\n",
-    "max_profile_c2.plot()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "f60846e8",
    "metadata": {},
    "source": [
-    "### Generating profiles"
+    "### Generating profiles\n",
+    "As the profiles of each specific User category is important, we will use the User object profile genertor methods for 5 consecutive days:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "320b7296",
    "metadata": {},
    "outputs": [
@@ -158,66 +107,37 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "You will simulate 5 day(s) from 2023-12-01 00:00:00 until 2023-12-06 00:00:00\n"
+      "You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you \n",
+      "You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you \n"
      ]
     }
    ],
    "source": [
-    "use_case = UseCase(users=[household, school])\n",
-    "use_case.initialize(5)"
+    "number_of_days = 5\n",
+    "household_profiles = []\n",
+    "school_profiles = []\n",
+    "\n",
+    "for day in range(1,number_of_days+1):\n",
+    "    household_profiles.extend(\n",
+    "        household.generate_single_load_profile(prof_i=day)\n",
+    "    )\n",
+    "\n",
+    "    school_profiles.extend(\n",
+    "        school.generate_single_load_profile(prof_i=day)\n",
+    "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "f9a65f08",
+   "execution_count": 6,
+   "id": "fadd6c63",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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MGKH58+c7bDpdvny5+vfvrx49eujWW2/Vzp079dJLL2nkyJF65ZVX7O0KCgrUrVs37dy5U+PHj1edOnU0a9YsHThwQJs3b1azZs3sbYcPH67XX3+9yM+0s3N1Zf1AuVRx+AHKZe3ataZDhw4mICCgxCJjH374oWnTpo0JCgoyDRs2NE8//bR57bXXin3mwxhj3nnnHSPJjBo1qlLm3aBBAyOp2EeDBg2KtF+3bp255pprTFBQkKlbt65JSUlxeCbEGGO6d+9+yT4v/H7k5uaaMWPGmDZt2piwsDDj7+9vGjRoYEaOHOnSswUljXdhpdGS5tS9e3enx3vllVdMs2bNTEBAgLn88svN9OnTTWFhoUObihhr586d5oYbbjAhISEmIiLCDBkyxKSnpxdpV1BQYP75z3+aBg0amICAANOqVSvz5ptvFmk3f/58I8nMnz+/yLmlS5eaq666ygQGBpq4uDjz6KOPFlvk7vfffzcjR440kZGRJiQkxHTv3t1s3ry5SLthw4YV+zPt7FxdWT9QHjzzARTjgw8+0MCBA/Xll1/q2muvrerpAIBHIXwAxbjxxhu1e/du/fTTT0WKMwEAyod3uwAXWLx4sb7//nt9/PHHmjFjBsEDACoBz3wAF7DZbAoNDdXgwYM1Z84cp+pDAABcw29W4AJkcQCofFQ4BQAAliJ8AAAAS7ndyy6FhYU6cuSIatasyWY/AACqCWOMjh8/rtjYWIePAiiO24WPI0eOqH79+lU9DQAAUAYHDx5UXFxciW3cLnyc/1TPgwcPKiwsrIpnAwAAnJGTk6P69esX+XTu4rhd+Dj/UktYWBjhAwCAasaZLRNsOAUAAJYifAAAAEsRPgAAgKXcbs8HAKB6Msbo7NmzKigoqOqpoJL4+/vL19e33P0QPgAA5ZaXl6e0tDSdOnWqqqeCSmSz2RQXF6fQ0NBy9UP4AACUS2Fhofbv3y9fX1/FxsYqICCAIpEeyBijo0eP6tChQ2rSpEm5ngEhfAAAyiUvL0+FhYWqX7++QkJCqno6qER169bVL7/8ovz8/HKFDzacAgAqRGkltVH9VdQzWvykAAAASxE+AACApVwKH48//rhsNpvDo3nz5vbzZ86cUUpKiiIjIxUaGqrk5GRlZGRU+KQBAPAmjz/+uK666qpy9fHFF1/IZrMpKyvrkm0WLFigiIiIco3jDJef+WjVqpXS0tLsj6+++sp+7v7779dHH32kJUuWaO3atTpy5IhuvvnmCp0wAAAVZfjw4Ro4cGCR487cqFF2Lr/bxc/PT9HR0UWOZ2dna968eVq0aJF69uwpSZo/f75atGihDRs26Oqrry62v9zcXOXm5tq/zsnJcXVKQPVwdK/03RtSwVnH4/FdpFY3SVteO9dGks6ekfJOSiGR/2uXd0L6ba900xypduPix/hpjbRvdeXM/2L+QZIx0tncS7dpcaPUsJuUe0LaMFs6dcy5vsNipKvvkXz9K2auANyKy+Fj3759io2NVVBQkBISEpSamqr4+Hht3bpV+fn5SkxMtLdt3ry54uPjtX79+kuGj9TUVD3xxBNlXwFQXXz2lLT7w6LHN8+VajWSlt/vXD//6ihN/r34c+/f6fwN3gp7P5HGbJN2fSB9/n+uXRvVWrqiV6VMC5XPGKPT+dZXOg32962UGiPvvfeeJk2apJ9++kkxMTG699579cADD9jP22w2LV261OFZlIiICL3wwgsaPny48vLyNG7cOL333nv6448/FBUVpbvuuksTJ06UJGVlZenBBx/UBx98oNzcXHXs2FHTp09X27ZtHebx73//W4899pj++OMP9enTR3PnzrV/hH1ubq7Gjx+vxYsXKycnx95Hp06dLrmuBQsWaNKkSfrtt9+UlJSkbt26VeB37dJcCh9dunTRggUL1KxZM6WlpemJJ57Qtddeq507dyo9PV0BAQFFXiuKiopSenr6JfucOHGixo0bZ/86JydH9evXd20VQHWQd/Lcf5v2kaJaSqZQ+mq6VHhWOv3fMBFQU2rY9dxNW5LqNpea95UK8qRv/nXumCnhF/r5MTrdKQWFVc46JOnHFdLR3ef+XLvxuWduLnTyqPTtG/+bz/n/1mkqtehXct/bF0s5h/93Daql0/kFajlpleXj7noySSEBFVvCauvWrRo0aJAef/xxDR48WN98843uueceRUZGavjw4U718eKLL+rDDz/UO++8o/j4eB08eFAHDx60n7/lllsUHByslStXKjw8XC+//LJ69eqlvXv3qnbt2pKkn3/+WcuWLdPy5cv1xx9/aNCgQZo6daqmTJkiSXrooYf03nvv6fXXX1eDBg00bdo0JSUl6aeffrL3caGNGzdq5MiRSk1N1cCBA/XJJ59o8uTJ5f+GOcGl/0N9+vSx/7lNmzbq0qWLGjRooHfeeUfBwcFlmkBgYKACAwPLdC1QLbUaKLW9VSosOBc+LhQULrUc8L/wEX2l1GvSuZctzocPZ3QdI0VUYojPPvS/8FGn2bk5Xijjh3Ph42JRrYq2vdiv35wLH4BFli9fXqRc+IWfT/P888+rV69eeuyxxyRJTZs21a5du/TMM884HT4OHDigJk2aqFu3brLZbGrQoIH93FdffaVNmzYpMzPTfj989tlntWzZMr377rsaNWqUpHOVZBcsWGB/pmPo0KFas2aNpkyZopMnT2r27NlasGCB/V49d+5crV69WvPmzdP48eOLzGnGjBnq3bu3HnroIfu6vvnmG33yySdOrak8yhUPIyIi1LRpU/3000+6/vrrlZeXp6ysLIdnPzIyMordIwIA8FzB/r7a9WRSlYzrqh49emj27NkOxzZu3Kjbb79dkrR7924NGDDA4XzXrl31wgsvqKCgwKlKn8OHD9f111+vZs2aqXfv3rrxxht1ww03SJK2b9+uEydOKDIy0uGa06dP6+eff7Z/3bBhQ3vwkKSYmBhlZmZKOvesSH5+vrp27Wo/7+/vr86dO2v37t3Fzmn37t266SbHZy0TEhLcP3ycOHFCP//8s4YOHaoOHTrI399fa9asUXJysiRpz549OnDggBISEipkskD1Zko4VcI5l4aooH4qjLnov2W5FtWRzWar8Jc/KkuNGjV0xRVXOBw7dOiQS33YbDaZi/7+5efn2//cvn177d+/XytXrtSnn36qQYMGKTExUe+++65OnDihmJgYffHFF0X6vfAf8/7+jhuwbTabCgsLXZqnu3DpJ+PBBx9Uv3791KBBAx05ckSTJ0+Wr6+vbrvtNoWHh2vkyJEaN26cateurbCwMN17771KSEi45GZTAOdxowXcVYsWLfT11187HPv666/VtGlT+7MedevWVVpamv38vn37inzCb1hYmAYPHqzBgwfrL3/5i3r37q3ff/9d7du3V3p6uvz8/NSwYcMyzfHyyy9XQECAvv76a/tLOvn5+dq8ebPGjh17yXVt3LjR4diGDRvKNL6rXAofhw4d0m233aZjx46pbt266tatmzZs2KC6detKkqZPny4fHx8lJycrNzdXSUlJmjVrVqVMHKi+bBf998JTtouO2y447soQlf2Johf0X+xYlxrfmXnxaahwLw888IA6deqkp556SoMHD9b69ev10ksvOdzfevbsqZdeekkJCQkqKCjQhAkTHJ6peP755xUTE6N27drJx8dHS5YsUXR0tCIiIpSYmKiEhAQNHDhQ06ZNU9OmTXXkyBF9/PHHuummm9SxY8dS51ijRg3dfffdGj9+vGrXrq34+HhNmzZNp06d0siRI4u95r777lPXrl317LPPasCAAVq1apUlL7lILoaPxYsXl3g+KChIM2fO1MyZM8s1KQAA3EX79u31zjvvaNKkSXrqqacUExOjJ5980mGz6XPPPacRI0bo2muvVWxsrGbMmKGtW7faz9esWVPTpk3Tvn375Ovrq06dOmnFihX2D+NbsWKFHnnkEY0YMUJHjx5VdHS0rrvuOkVFRTk9z6lTp6qwsFBDhw7V8ePH1bFjR61atUq1atUqtv3VV1+tuXPnavLkyZo0aZISExP16KOP6qmnnirbN8oFNnPxi1RVLCcnR+Hh4crOzlZYWCW+VRCw2hsDpf98Lt30itR2sFRYKD35318KQ96TFiZL4fWlHo9Iy+46d/zKQVLy3HNvO/1n7P/6ejy7+DGeqnvubbn3/yCFx1XeWt7/u/T9f/8x0uzP0m1vOZ7P2CXNTpBC6kgP/SxtmCN9MkFqdbN0y/yS+36tj3TgG+mW18+9Mwhu78yZM9q/f78aNWqkoKCgqp4OKlFJ/69duX/zwXKAW3CrfwMAQKUifABWs5W0j8PmeNxWwv6Qkgcpw8Rc6b6YfSmXPO/EcVfbAKjWCB8AAMBShA8AAGApwgdgGYqMle1aAJ6G8AG4BW60ALwH4QOwXAkbTm0XnC+tbYlDUGQMgPsifAAAAEsRPgCrlLQfo8L2arjZyzfn11WW9bnd/hUAFYXwAbgFbrRAdbNgwQKHT52tLMOHD9fAgQMrfRwrET4Aq5W4H6M6Fhlz4TxFxuBmjh49qrvvvlvx8fEKDAxUdHS0kpKSinyKLSqWSx8sBwCAJ0lOTlZeXp5ef/11NW7cWBkZGVqzZo2OHTtW1VPzaDzzAViGOh9luxbVkjHnPhDR6ocLfweysrK0bt06Pf300+rRo4caNGigzp07a+LEierfv7+9zd///ndFRUUpKChIrVu31vLlyx36WbVqlVq0aKHQ0FD17t1baWlp9nOFhYV68sknFRcXp8DAQF111VVFPrZ+x44d6tmzp4KDgxUZGalRo0bpxIkT5fjmuz+e+QDcAjdaeJj8U46fxGyVfxyRAmo41TQ0NFShoaFatmyZrr76agUGBjqcLywsVJ8+fXT8+HG9+eabuvzyy7Vr1y75+vra25w6dUrPPvus/v3vf8vHx0e33367HnzwQS1cuFCSNGPGDD333HN6+eWX1a5dO7322mvq37+/fvjhBzVp0kQnT55UUlKSEhIStHnzZmVmZupvf/ubRo8erQULFlTYt8XdED6AKmWTQ/Cw2USdD8Aafn5+WrBgge68807NmTNH7du3V/fu3XXrrbeqTZs2+vTTT7Vp0ybt3r1bTZs2lSQ1btzYoY/8/HzNmTNHl19+uSRp9OjRevLJJ+3nn332WU2YMEG33nqrJOnpp5/W559/rhdeeEEzZ87UokWLdObMGb3xxhuqUeNcaHrppZfUr18/Pf3004qKirLiW2E5wgcAoOL5h5x7FqIqxnVBcnKy+vbtq3Xr1mnDhg1auXKlpk2bpldffVWZmZmKi4uzB4/ihISE2IOHJMXExCgzM1OSlJOToyNHjqhr164O13Tt2lXbt2+XJO3evVtt27a1B4/z5wsLC7Vnzx7CB4Byos5H2a5F9WSzOf3yR1ULCgrS9ddfr+uvv16PPfaY/va3v2ny5Ml68MEHS73W39/f4WubzSbDz26p2HAKuAV+WQHuomXLljp58qTatGmjQ4cOae/evWXqJywsTLGxsUXetvv111+rZcuWkqQWLVpo+/btOnnypMN5Hx8fNWvWrOyLcHOED8BqxdbxsB+oJnU+ShmLOh+oBo4dO6aePXvqzTff1Pfff6/9+/dryZIlmjZtmgYMGKDu3bvruuuuU3JyslavXq39+/dr5cqVRd6tUpLx48fr6aef1ttvv609e/bo4Ycf1rZt2zRmzBhJ0pAhQxQUFKRhw4Zp586d+vzzz3Xvvfdq6NChHvuSi8TLLgAALxUaGqouXbpo+vTp+vnnn5Wfn6/69evrzjvv1D/+8Q9J0nvvvacHH3xQt912m06ePKkrrrhCU6dOdXqM++67T9nZ2XrggQeUmZmpli1b6sMPP1STJk0kndszsmrVKo0ZM0adOnVSSEiIkpOT9fzzz1fKmt0F4QNwB9T5ACwXGBio1NRUpaamXrJN7dq19dprrxV7bvjw4Ro+fLjDsYEDBzrs+fDx8dHkyZM1efLkS45x5ZVX6rPPPrvkeU98yy0vuwBugZszAO9B+AAsV0wdD/uXxewBKfZ4aUNU5zof7PkAPB3hAwAAWIrwAVjFK+t8nP8vdT4A/A/hA3AL3GhR/VFcy/NV1P9jwgcAoFzOV/k8depUFc8ElS0vL0+SHD5cryx4qy1gtYuLiDn8Q+LiImNF/uDsIGWamvPdl7Bptsh5J4672gZuxdfXVxEREfbPNAkJCZGN/48ep7CwUEePHlVISIj8/MoXHwgfAIByi46OliR7AIFn8vHxUXx8fLnDJeEDsIwVG07dTXmKjHnq98Qz2Ww2xcTEqF69esrPz6/q6aCSBAQEyMen/Ds2CB+AW+BGC8/g6+tb7v0A8HxsOAUsV1qRsWLOV7siY05cV642AKozwgcAALAU4QOwSmUXGXPHfSPn50SRMQAXIHwAAABLET4Aq11c58Px5CXOU+cDgOcgfAAAAEsRPgDLeOGeD+p8ACgG4QNwC9xoAXgPwgdgOW+o83Gp8anzAYDwAQAALEb4AKxS2XU+3PGlG+p8ACgG4QNwC9xoAXgPwgdgtbLU+XC32hdlrvPhat8APBHhAwAAWIrwAViGOh9luxaApyF8AG6BGy0A70H4ACxXhjofLg9BnQ8A7ovwAQAALEX4AAAAliJ8AFahyFjZrgXgcQgfgFvgRgvAexA+AKuVpciY64OU8Tpnuy9rkTEn5kWRMcDjET4AAIClCB+AZSgyVrZrAXiacoWPqVOnymazaezYsfZjZ86cUUpKiiIjIxUaGqrk5GRlZGSUd56Ah+NGC8B7lDl8bN68WS+//LLatGnjcPz+++/XRx99pCVLlmjt2rU6cuSIbr755nJPFPAcFBlzum8AHqlM4ePEiRMaMmSI5s6dq1q1atmPZ2dna968eXr++efVs2dPdejQQfPnz9c333yjDRs2VNikAQBA9VWm8JGSkqK+ffsqMTHR4fjWrVuVn5/vcLx58+aKj4/X+vXri+0rNzdXOTk5Dg/AI3l1nY9yXAvA4/i5esHixYv17bffavPmzUXOpaenKyAgQBEREQ7Ho6KilJ6eXmx/qampeuKJJ1ydBuBhuNEC8B4uPfNx8OBBjRkzRgsXLlRQUFCFTGDixInKzs62Pw4ePFgh/QJuizofLvQNwBO5FD62bt2qzMxMtW/fXn5+fvLz89PatWv14osvys/PT1FRUcrLy1NWVpbDdRkZGYqOji62z8DAQIWFhTk8AACA53LpZZdevXppx44dDsdGjBih5s2ba8KECapfv778/f21Zs0aJScnS5L27NmjAwcOKCEhoeJmDVRL1Pko27UAPI1L4aNmzZpq3bq1w7EaNWooMjLSfnzkyJEaN26cateurbCwMN17771KSEjQ1VdfXXGzBjwON1oA3sPlDaelmT59unx8fJScnKzc3FwlJSVp1qxZFT0MUI1R58PpvgF4pHKHjy+++MLh66CgIM2cOVMzZ84sb9cAAMAD8dkugFW8us5HGebmlntYAFQEwgfgFrjRAvAehA/AatT5cKFvAJ6I8AEAACxF+AAAAJYifACWochY2a4F4GkIHwAAwFKED8ByJRUZK+W800NQZAyA+yJ8AAAASxE+AKtQZKxs1wLwOIQPwC1wowXgPQgfgNUciogVOUmRMYqMAR6P8AEAACxF+AAsQ52Psl0LwNMQPgC3wI0WgPcgfACWK6nOh63k804PYWWdj1LOO3UcgDchfAAAAEsRPgCrUOejbNcC8DiED8AtcKMF4D0IH4DVHLZ0XLwHgjof1PkAPB/hAwAAWIrwAViGOh9luxaApyF8AG6BGy0A70H4ACznCXU+ShurPHU+2PMBeDrCBwAAsBThA7BKSa+seGqdj/Oo8wHgAoQPwC1wowXgPQgfAADAUoQPwGolFhGzlVKEzOlBynids91TZAxA2RE+AACApQgfgGW8sciY/jsvN50bgCpB+ADcAjdnAN6D8AFYzpUiY2UdorL3TZT24XcUGQNwaYQPAABgKcIHYJWS9mR4cpExYygyBsAB4QNwC9xoAXgPwgdgtVLrfJS2n8KpQcp4nbPdU+cDQNkRPgAAgKUIH4BlvLTOh8pa58Nd1wOgvAgfgFvgRgvAexA+AMu5UuejjPsfqrzOhxPXlasNgOqM8AEAACxF+ACsUmKdD+umYTnqfAC4COEDcAvcaAF4D8IHYDXqfLjQNwBPRPgAAACWInwAlqHOh+vXAfBEhA/ALXCjBeA9CB+A5byhzkd5xmfPB+DpCB8AAMBShA8AAGApwgdglRKLjFXEng833TdCkTEAFyF8AG6BGy0A70H4AKxmRZGxyt5wSpExAOVA+AAAAJYifACWociY69cB8EQuhY/Zs2erTZs2CgsLU1hYmBISErRy5Ur7+TNnziglJUWRkZEKDQ1VcnKyMjIyKnzSgOfhRgvAe7gUPuLi4jR16lRt3bpVW7ZsUc+ePTVgwAD98MMPkqT7779fH330kZYsWaK1a9fqyJEjuvnmmytl4kD1ZUGRsUpX1iJjzqzHXdcMoKL4udK4X79+Dl9PmTJFs2fP1oYNGxQXF6d58+Zp0aJF6tmzpyRp/vz5atGihTZs2KCrr7664mYNAACqrTLv+SgoKNDixYt18uRJJSQkaOvWrcrPz1diYqK9TfPmzRUfH6/169dfsp/c3Fzl5OQ4PACPRJ0P168D4JFcDh87duxQaGioAgMDddddd2np0qVq2bKl0tPTFRAQoIiICIf2UVFRSk9Pv2R/qampCg8Ptz/q16/v8iKA6o8bLQDv4XL4aNasmbZt26aNGzfq7rvv1rBhw7Rr164yT2DixInKzs62Pw4ePFjmvoBqodLrfFiwZ4I6HwDKwaU9H5IUEBCgK664QpLUoUMHbd68WTNmzNDgwYOVl5enrKwsh2c/MjIyFB0dfcn+AgMDFRgY6PrMAQBAtVTuOh+FhYXKzc1Vhw4d5O/vrzVr1tjP7dmzRwcOHFBCQkJ5hwE8gDfX+bDyOgDuzqVnPiZOnKg+ffooPj5ex48f16JFi/TFF19o1apVCg8P18iRIzVu3DjVrl1bYWFhuvfee5WQkMA7XYBScaMF4D1cCh+ZmZm64447lJaWpvDwcLVp00arVq3S9ddfL0maPn26fHx8lJycrNzcXCUlJWnWrFmVMnGg+qrkOh+W7JmgzgeAsnMpfMybN6/E80FBQZo5c6ZmzpxZrkkBAADPxWe7AFahzofr1wHwSIQPwC1wowXgPQgfgNWo8+FC3wA8EeEDAABYivABAAAsRfgALOPNRcbKMjd3XQ+A8iJ8AG6BGy0A70H4ACxHkTGn+wbgkQgfAADAUoQPwCoUGXP9OgAeifABAAAsRfgArOZQRKy08xQZA+B5CB8AAMBShA/AMtT5cP06AJ6I8AG4BW60ALwH4QOwHHU+nO4bgEcifAAAAEsRPgCrUOfD9esAeCTCB+AWuNEC8B6ED8BqJdbxsFXDOh8unHeqzofr0wFQvRA+AACApQgfgGWo8+H6dQA8EeEDcAvcaAF4D8IHYDnqfDjdNwCPRPgAAACWInwAVinplRXqfBR/HQCPRPgA3AI3WgDeg/ABWM1hS0d1rfNRynjlqvPBng/A0xE+AACApQgfAADAUoQPwDIUGXP9OgCeiPABuAVutAC8B+EDsBxFxpzuG4BHInwAAABLET4Aq5S0J4MiY8VcV/FTAeAeCB+AW+BOC8B7ED4Aq5VYRMxW7i0f1hQZK2WSFBkDUALCBwAAsBThA7CMt9b5kKjzAeBChA/ALXCjBeA9CB+A5ajz4XTfADwS4QMAAFiK8AFYhTofrl8HwCMRPgC3wI0WgPcgfABWK7XOR2n7KUodoCyzcnEI6nwAKDvCBwAAsBThA7BMSXs+KqJ7d33pxog6HwAuRPgA3AI3WgDeg/ABWM4L6nxccg7U+QBA+AAAABYjfAAAAEsRPgCrVHqRMTdFkTEAFyF8AG6BGy0A70H4AKzmDUXGLnmdq30D8ESEDwAAYCnCB2CZSt7z4bZ7JCgyBsCRS+EjNTVVnTp1Us2aNVWvXj0NHDhQe/bscWhz5swZpaSkKDIyUqGhoUpOTlZGRkaFThrwPNxoAXgPl8LH2rVrlZKSog0bNmj16tXKz8/XDTfcoJMnT9rb3H///froo4+0ZMkSrV27VkeOHNHNN99c4RMHqq/KLjJWhimVZxCXCopRZAyA5OdK408++cTh6wULFqhevXraunWrrrvuOmVnZ2vevHlatGiRevbsKUmaP3++WrRooQ0bNujqq6+uuJkDAIBqqVx7PrKzsyVJtWvXliRt3bpV+fn5SkxMtLdp3ry54uPjtX79+mL7yM3NVU5OjsMD8EiVXufDTV+6oc4HgIuUOXwUFhZq7Nix6tq1q1q3bi1JSk9PV0BAgCIiIhzaRkVFKT09vdh+UlNTFR4ebn/Ur1+/rFMCqjFutAC8R5nDR0pKinbu3KnFixeXawITJ05Udna2/XHw4MFy9Qe4PW+p8+HSB8652AZAtebSno/zRo8ereXLl+vLL79UXFyc/Xh0dLTy8vKUlZXl8OxHRkaGoqOji+0rMDBQgYGBZZkGAACohlx65sMYo9GjR2vp0qX67LPP1KhRI4fzHTp0kL+/v9asWWM/tmfPHh04cEAJCQkVM2Og2qLOh+vXAfBELj3zkZKSokWLFumDDz5QzZo17fs4wsPDFRwcrPDwcI0cOVLjxo1T7dq1FRYWpnvvvVcJCQm80wUoETdaAN7DpfAxe/ZsSdKf/vQnh+Pz58/X8OHDJUnTp0+Xj4+PkpOTlZubq6SkJM2aNatCJgt4hsqu82HFngnqfAAoO5fCh3Hiad2goCDNnDlTM2fOLPOkAACA5+KzXQCreHWdjzJeB8AjET4At8CNFoD3IHwAVqPOhwt9A/BEhA8AAGApwgcAALAU4QOwDEXGXL8OgCcifABugRstAO9B+AAsR5Exp/sG4JEIHwAAwFKED8AqXl1krAxzc9s9LADKi/ABuAVutAC8B+EDsJpDEbHSzlNkDIDnIXwAAABLET4Ay1Dnw/XrAHgiwgcAALAU4QOwHHU+nO4bgEcifAAAAEsRPgCrUOfD9esAeCTCB+AWuNEC8B6ED8BqJdbxsFHngzofgMcjfAAAAEsRPgDLUOfD9esAeCLCB+AWuNEC8B6ED8Bynlbnw4k2pTcG4EUIHwAAwFKED8AqJb2yQp2P4q8D4JEIH4Bb4EYLwHsQPgCrUefDhb4BeCLCBwAAsBThAwAAWIrwAViGImOuXwfAExE+ALfAjRaA9yB8AFWquhYZc2a8shYZY8Mp4OkIHwAAwFKED8AqJe3JoMhY8dcB8EiED8AtcKMF4D0IH4DVKDLmQt8APBHhAwAAWIrwAViGOh8AIBE+ADfBzRmA9yB8AJYrYb9ERex3sGTPhDP7UqjzAaB4hA8AAGApwgdgFep8uH4dAI9E+ADcAjdaAN6D8AFYjTofLvQNwBMRPgAAgKUIH4BlvLXOh1S2l5XceT0AyoPwAQAALEX4ACxXWp0PJ/ZTlNg9dT4AuDfCBwAAsBThAwAAWIrwAViFImOuXwfAIxE+ALfAjRaA9yB8AFbzmiJjpV3nTN8APBHhAwAAWIrwAVimsouMlb+LymFEkTEAF3I5fHz55Zfq16+fYmNjZbPZtGzZMofzxhhNmjRJMTExCg4OVmJiovbt21dR8wU8FDdaAN7D5fBx8uRJtW3bVjNnziz2/LRp0/Tiiy9qzpw52rhxo2rUqKGkpCSdOXOm3JMFPENlFxkrw5TKM4hLRcZc7BuAR/Jz9YI+ffqoT58+xZ4zxuiFF17Qo48+qgEDBkiS3njjDUVFRWnZsmW69dZbyzdbAABQ7VXono/9+/crPT1diYmJ9mPh4eHq0qWL1q9fX+w1ubm5ysnJcXgAHok6H65fB8AjVWj4SE9PlyRFRUU5HI+KirKfu1hqaqrCw8Ptj/r161fklIBqghstAO9R5e92mThxorKzs+2PgwcPVvWUgMrlNXU+ijlOnQ8AquDwER0dLUnKyMhwOJ6RkWE/d7HAwECFhYU5PAAAgOeq0PDRqFEjRUdHa82aNfZjOTk52rhxoxISEipyKKAaquw6H+760g11PgA4cvndLidOnNBPP/1k/3r//v3atm2bateurfj4eI0dO1b/93//pyZNmqhRo0Z67LHHFBsbq4EDB1bkvAEPw40WgPdwOXxs2bJFPXr0sH89btw4SdKwYcO0YMECPfTQQzp58qRGjRqlrKwsdevWTZ988omCgoIqbtZAtVbZdT6s2DNR1jofzsyNPR+Ap3M5fPzpT3+SKeHpXZvNpieffFJPPvlkuSYGAAA8U5W/2wXwGtT5cP06AB6J8AG4BW60ALwH4QOwGnU+XOgbgCcifAAAAEsRPgDLeHOdDyuvA+DuCB+AW+BGC8B7ED4AAIClCB+A5Sgy5nTfADwS4QMAAFiK8AFYhSJjrl8HwCMRPgC3wI0WgPcgfABWo8iYC30D8ESEDwAAYCnCB2AZby4yVpa5uet6AJQX4QNwC9xoAXgPwgdguRL2NNguPk+dDwCeh/ABAAAsRfgArFJinY8KGaAiOql41PkAcBHCB+AWuNEC8B6ED8Bq1PlwoW8AnojwAQAALEX4ACxDnQ/XrwPgiQgfgFvgRgvAexA+AMuVsF/CZiv5vFPdU+cDgHsjfAAAAEsRPgB3UCH7Ndz0pRvqfAC4COEDcAvcaAF4D8IHYDWPq/PhQhunrnN9OgCqF8IHAACwFOEDAABYivABWKG0zZMUGbvEdQA8EeEDcAvcaAF4D8IHYDmKjDndNwCPRPgAAACWInwAVrBiz4e7vnRDkTEAFyF8AG6BGy0A70H4AKzmcUXGLjFemYuMsecD8HSEDwAAYCnCB2AJ6nyU7ToAnojwAbgFbrQAvAfhA6hSpdX5KEuXFu+ZoM4HABcRPgAAgKUIH4AVqPNRhusqfioA3APhA3AL3GkBeA/CB2A1V+p8lG2Acl7vzBDU+QBQdoQPAABgKcIHYAnqfJTtOgCeiPABuAVutAC8B+EDsFwJ+yUqYr+DJXsmnPn8Gep8ACge4QMAAFiK8AEAACxF+ACsUGqRsQoZpCI6qXhGZSwy5qbrAVBuhA/ALXCjBeA9CB+A1bymyFhp1znTNwBPRPgAAACWInwAlqDIWNmuA+CJCB+AW+BGC8B7VFr4mDlzpho2bKigoCB16dJFmzZtqqyhgGqmtCJj5dzzYMmWCYqMASi7Sgkfb7/9tsaNG6fJkyfr22+/Vdu2bZWUlKTMzMzKGA4AAFQjfpXR6fPPP68777xTI0aMkCTNmTNHH3/8sV577TU9/PDDDm1zc3OVm5tr/zonJ6cypqRjGYf003uPV0rfXsFt9xNUDz4qUKf//jn1kx+V51tDknRb5nE1lZR34ncFSNq0/3d9lvujzv8tef/bQ9rx6w+SpMkX9Ldx5sgiY4Tnpau5pLTsM3rlox8qaSXntPjjoAb9988fbj+i7w4XHe++U/mqJemH96Yo7sQuhUt677tD2vlryXNLSM/QDZJ++/Fr/Xx+nf99dsXwrAhQMULq6urh/6yy4Ss8fOTl5Wnr1q2aOHGi/ZiPj48SExO1fv36Iu1TU1P1xBNPVPQ0ijiVc0xdMpdU+jhASXKNvxZsOKxcBUiS2vpLTX2lgMLTkqSNaWc1/2CaHg461/7Dvaf0ReEvkqTJQf/rp8vRdy85xq8n/TT/618qY/p23XxOatC5JWjlvlNauafoeH8J8FctH6nVH5/Zjy3fc1KfF5Y8t9O+Z3SDv1Qn94DqHD1QgbMGcN4Bn8skeVD4+O2331RQUKCoqCiH41FRUfrxxx+LtJ84caLGjRtn/zonJ0f169ev6GkpNKKe1l82osL79TY8/1E+h2u21d9qtbB/nXFmvL76baV8CvOV7xssW73++pt/Lb37x3SF56apddQAtbKd+2s698x76vHLdB0LbqwCm2/xA9h89GNkolJCGlfqOmymgf6/jEL5mLNqEjVQjX0CirTZcvIpZR/7Qud/ak4G1FHregPV0qfkXzv+BTH6LCNUQWezz41lP8NPH1BRbCGRiq/C8SvlZRdXBAYGKjAwsNLHqVU3Rgl3vlDp4wCuaS6pp/2r7g7HpeuLtE3UFaX0eE0Fzax0rSVJiZc831zSAIcj1xffsBjtyzQjANVDhW84rVOnjnx9fZWRkeFwPCMjQ9HR0RU9HAAAqGYqPHwEBASoQ4cOWrNmjf1YYWGh1qxZo4SEhIoeDgAAVDOV8rLLuHHjNGzYMHXs2FGdO3fWCy+8oJMnT9rf/QIAALxXpYSPwYMH6+jRo5o0aZLS09N11VVX6ZNPPimyCRUAAHgfmzHuVcAhJydH4eHhys7OVlhYWFVPBwAAOMGV+zef7QIAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWKrKP9X2YudrnuXk5FTxTAAAgLPO37edqV3qduHj+PHjkqT69etX8UwAAICrjh8/rvDw8BLbuF159cLCQh05ckQ1a9aUzWar0L5zcnJUv359HTx40OtKt3vr2r113RJr98a1e+u6JdbuDms3xuj48eOKjY2Vj0/Juzrc7pkPHx8fxcXFVeoYYWFhXvfDeZ63rt1b1y2xdm9cu7euW2LtVb320p7xOI8NpwAAwFKEDwAAYCmvCh+BgYGaPHmyAgMDq3oqlvPWtXvruiXW7o1r99Z1S6y9uq3d7TacAgAAz+ZVz3wAAICqR/gAAACWInwAAABLET4AAIClCB8AAMBSXhM+Zs6cqYYNGyooKEhdunTRpk2bqnpK5ZKamqpOnTqpZs2aqlevngYOHKg9e/Y4tDlz5oxSUlIUGRmp0NBQJScnKyMjw6HNgQMH1LdvX4WEhKhevXoaP368zp49a+VSym3q1Kmy2WwaO3as/Zgnr/3w4cO6/fbbFRkZqeDgYF155ZXasmWL/bwxRpMmTVJMTIyCg4OVmJioffv2OfTx+++/a8iQIQoLC1NERIRGjhypEydOWL0UpxUUFOixxx5To0aNFBwcrMsvv1xPPfWUwwdYecq6v/zyS/Xr10+xsbGy2WxatmyZw/mKWuf333+va6+9VkFBQapfv76mTZtW2UsrVUlrz8/P14QJE3TllVeqRo0aio2N1R133KEjR4449OGJa7/YXXfdJZvNphdeeMHheLVau/ECixcvNgEBAea1114zP/zwg7nzzjtNRESEycjIqOqplVlSUpKZP3++2blzp9m2bZv585//bOLj482JEyfsbe666y5Tv359s2bNGrNlyxZz9dVXm2uuucZ+/uzZs6Z169YmMTHRfPfdd2bFihWmTp06ZuLEiVWxpDLZtGmTadiwoWnTpo0ZM2aM/binrv333383DRo0MMOHDzcbN240//nPf8yqVavMTz/9ZG8zdepUEx4ebpYtW2a2b99u+vfvbxo1amROnz5tb9O7d2/Ttm1bs2HDBrNu3TpzxRVXmNtuu60qluSUKVOmmMjISLN8+XKzf/9+s2TJEhMaGmpmzJhhb+Mp616xYoV55JFHzPvvv28kmaVLlzqcr4h1Zmdnm6ioKDNkyBCzc+dO89Zbb5ng4GDz8ssvW7XMYpW09qysLJOYmGjefvtt8+OPP5r169ebzp07mw4dOjj04Ylrv9D7779v2rZta2JjY8306dMdzlWntXtF+OjcubNJSUmxf11QUGBiY2NNampqFc6qYmVmZhpJZu3atcaYc39R/f39zZIlS+xtdu/ebSSZ9evXG2PO/bD7+PiY9PR0e5vZs2ebsLAwk5uba+0CyuD48eOmSZMmZvXq1aZ79+728OHJa58wYYLp1q3bJc8XFhaa6Oho88wzz9iPZWVlmcDAQPPWW28ZY4zZtWuXkWQ2b95sb7Ny5Upjs9nM4cOHK2/y5dC3b1/z17/+1eHYzTffbIYMGWKM8dx1X3wTqqh1zpo1y9SqVcvhZ33ChAmmWbNmlbwi55V0Az5v06ZNRpL59ddfjTGev/ZDhw6Zyy67zOzcudM0aNDAIXxUt7V7/MsueXl52rp1qxITE+3HfHx8lJiYqPXr11fhzCpWdna2JKl27dqSpK1btyo/P99h3c2bN1d8fLx93evXr9eVV16pqKgoe5ukpCTl5OTohx9+sHD2ZZOSkqK+ffs6rFHy7LV/+OGH6tixo2655RbVq1dP7dq109y5c+3n9+/fr/T0dIe1h4eHq0uXLg5rj4iIUMeOHe1tEhMT5ePjo40bN1q3GBdcc801WrNmjfbu3StJ2r59u7766iv16dNHkueu+2IVtc7169fruuuuU0BAgL1NUlKS9uzZoz/++MOi1ZRfdna2bDabIiIiJHn22gsLCzV06FCNHz9erVq1KnK+uq3d48PHb7/9poKCAoebjCRFRUUpPT29imZVsQoLCzV27Fh17dpVrVu3liSlp6crICDA/pfyvAvXnZ6eXuz35fw5d7Z48WJ9++23Sk1NLXLOk9f+n//8R7Nnz1aTJk20atUq3X333brvvvv0+uuvS/rf3Ev6eU9PT1e9evUczvv5+al27dpuu/aHH35Yt956q5o3by5/f3+1a9dOY8eO1ZAhQyR57rovVlHrrK4//xc6c+aMJkyYoNtuu83+Sa6evPann35afn5+uu+++4o9X93W7mfpaKgUKSkp2rlzp7766quqnoolDh48qDFjxmj16tUKCgqq6ulYqrCwUB07dtQ///lPSVK7du20c+dOzZkzR8OGDavi2VWed955RwsXLtSiRYvUqlUrbdu2TWPHjlVsbKxHrxvFy8/P16BBg2SM0ezZs6t6OpVu69atmjFjhr799lvZbLaqnk6F8PhnPurUqSNfX98i73TIyMhQdHR0Fc2q4owePVrLly/X559/rri4OPvx6Oho5eXlKSsry6H9heuOjo4u9vty/py72rp1qzIzM9W+fXv5+fnJz89Pa9eu1Ysvvig/Pz9FRUV57NpjYmLUsmVLh2MtWrTQgQMHJP1v7iX9vEdHRyszM9Ph/NmzZ/X777+77drHjx9vf/bjyiuv1NChQ3X//ffbn/ny1HVfrKLWWV1//qX/BY9ff/1Vq1evtj/rIXnu2tetW6fMzEzFx8fbf+f9+uuveuCBB9SwYUNJ1W/tHh8+AgIC1KFDB61Zs8Z+rLCwUGvWrFFCQkIVzqx8jDEaPXq0li5dqs8++0yNGjVyON+hQwf5+/s7rHvPnj06cOCAfd0JCQnasWOHww/s+b/MF9/g3EmvXr20Y8cObdu2zf7o2LGjhgwZYv+zp669a9euRd5SvXfvXjVo0ECS1KhRI0VHRzusPScnRxs3bnRYe1ZWlrZu3Wpv89lnn6mwsFBdunSxYBWuO3XqlHx8HH9d+fr6qrCwUJLnrvtiFbXOhIQEffnll8rPz7e3Wb16tZo1a6ZatWpZtBrXnQ8e+/bt06effqrIyEiH85669qFDh+r77793+J0XGxur8ePHa9WqVZKq4dot3+JaBRYvXmwCAwPNggULzK5du8yoUaNMRESEwzsdqpu7777bhIeHmy+++MKkpaXZH6dOnbK3ueuuu0x8fLz57LPPzJYtW0xCQoJJSEiwnz//dtMbbrjBbNu2zXzyySembt26bv920+Jc+G4XYzx37Zs2bTJ+fn5mypQpZt++fWbhwoUmJCTEvPnmm/Y2U6dONREREeaDDz4w33//vRkwYECxb8Vs166d2bhxo/nqq69MkyZN3O4tpxcaNmyYueyyy+xvtX3//fdNnTp1zEMPPWRv4ynrPn78uPnuu+/Md999ZySZ559/3nz33Xf2d3RUxDqzsrJMVFSUGTp0qNm5c6dZvHixCQkJqfK3m5a09ry8PNO/f38TFxdntm3b5vB778J3b3ji2otz8btdjKlea/eK8GGMMf/6179MfHy8CQgIMJ07dzYbNmyo6imVi6RiH/Pnz7e3OX36tLnnnntMrVq1TEhIiLnppptMWlqaQz+//PKL6dOnjwkODjZ16tQxDzzwgMnPz7d4NeV3cfjw5LV/9NFHpnXr1iYwMNA0b97cvPLKKw7nCwsLzWOPPWaioqJMYGCg6dWrl9mzZ49Dm2PHjpnbbrvNhIaGmrCwMDNixAhz/PhxK5fhkpycHDNmzBgTHx9vgoKCTOPGjc0jjzzicNPxlHV//vnnxf7dHjZsmDGm4ta5fft2061bNxMYGGguu+wyM3XqVKuWeEklrX3//v2X/L33+eef2/vwxLUXp7jwUZ3WbjPmghKBAAAAlczj93wAAAD3QvgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEv9/5hyOFNpVESTAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 800x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -225,20 +145,20 @@
     }
    ],
    "source": [
-    "for day_idx, day in enumerate(use_case.days):\n",
-    "    household_profile = household.generate_single_load_profile(\n",
-    "        prof_i=day_idx, day_type=get_day_type(day)\n",
-    "    )\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) \n",
     "\n",
-    "    school_profile = school.generate_single_load_profile(\n",
-    "        prof_i=day_idx, day_type=get_day_type(day)\n",
-    "    )\n",
     "\n",
-    "    pd.DataFrame(\n",
-    "        data=[household_profile, school_profile],\n",
-    "        columns=range(1440),\n",
-    "        index=[household.user_name, school.user_name],\n",
-    "    ).T.plot(title=f\"day - {day}\")"
+    "i = 0\n",
+    "for name, df in dict(\n",
+    "    household_profiles=pd.DataFrame(household_profiles),\n",
+    "    school_profiles=pd.DataFrame(school_profiles)\n",
+    "    ).items():\n",
+    "    df.plot(ax=axes[i],legend=False)\n",
+    "    axes[i].set_title(name)\n",
+    "    i+=1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -268,7 +188,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,

From d6d081d411a0d349355c634c9d456c3df3bb143a Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <mohammadamin.tahavori@vito.be>
Date: Fri, 29 Mar 2024 16:06:24 +0100
Subject: [PATCH 059/140] exmples imrpvement - cooking appliances

Improving the example explanatio for cooking appliances
---
 docs/notebooks/cooking_app.ipynb | 126 ++++++++++++++++++++++++-------
 1 file changed, 99 insertions(+), 27 deletions(-)

diff --git a/docs/notebooks/cooking_app.ipynb b/docs/notebooks/cooking_app.ipynb
index 9214a21e..66428105 100644
--- a/docs/notebooks/cooking_app.ipynb
+++ b/docs/notebooks/cooking_app.ipynb
@@ -7,8 +7,9 @@
    "source": [
     "# Cooking Appliances\n",
     "\n",
-    "In this example, appliances with multiple preferences index and\n",
-    "attributes are modelled.\n",
+    "Some appliances load profiles, highly depend on the user choices and preferences. For example, electric stoves power usage, highly depends on the type of food and that a user wants to cook. This kind of appliances in RAMP are flagged by the user category consumption preferences.\n",
+    "\n",
+    "In this example, we will see how the electric cookstoves with multiple user preferences can be modelled in RAMP.\n",
     "\n",
     "To have a better understanding of RAMP features for modelling this category of appliances, two households are considered:\n",
     "\n",
@@ -20,14 +21,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 39,
    "id": "4761038e",
    "metadata": {},
    "outputs": [],
    "source": [
     "# importing functions\n",
-    "from ramp import User, UseCase, get_day_type\n",
-    "import pandas as pd"
+    "from ramp import User, UseCase\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -40,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 31,
    "id": "77e6919a",
    "metadata": {},
    "outputs": [],
@@ -48,13 +50,13 @@
     "user_1 = User(\n",
     "    user_name=\"Household with single lunch habit\",\n",
     "    num_users=1,\n",
-    "    user_preference=1,  # user_1 has only one lunch preference\n",
+    "    user_preference=1,  # user_1 has only consumption preference\n",
     ")\n",
     "\n",
     "user_2 = User(\n",
     "    user_name=\"Household with different lunch habits\",\n",
     "    num_users=1,\n",
-    "    user_preference=2,  # user_2 has two lunch preferences\n",
+    "    user_preference=2,  # user_2 has two consumption preferences\n",
     ")"
    ]
   },
@@ -65,6 +67,8 @@
    "source": [
     "### Defining the cycles for cooking soup and rice \n",
     "\n",
+    "For cookstoves, it will be more realistic to have different operation cycles as cooking a food usually requires different levels of heat for the different parts of food processing:\n",
+    "\n",
     "For cooking soup it is assumed that the user needs 25 minutes divided into two parts:\n",
     "\n",
     "| cycle         | power | time |\n",
@@ -83,25 +87,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 32,
    "id": "768250cd",
    "metadata": {},
    "outputs": [],
    "source": [
     "# soup for lunch\n",
+    "lunch_window = [12 * 60, 12 * 60+26]\n",
+    "\n",
     "soup_1 = user_1.add_appliance(\n",
     "    name=\"soup for lunch\",\n",
-    "    power=1200,\n",
-    "    func_time=25,\n",
-    "    func_cycle=25,\n",
-    "    thermal_p_var=0.2,\n",
-    "    fixed_cycle=1,\n",
-    "    window_1=[12 * 60, 15 * 60],\n",
+    "    power=1200, # nominal power of appliance\n",
+    "    func_time=25, # the cooking time\n",
+    "    func_cycle=25, # we always need  25 minute for cooking\n",
+    "    fixed_cycle=1, # the cookstove is not a continus power usage appliance, it has cycles as mentioned earlier \n",
+    "    window_1=lunch_window, # lunch is always prepared from 12\n",
     "    p_11=1200,  # power of the first cycle\n",
     "    t_11=5,  # time needed for the first cycle\n",
     "    p_12=750,  # power of the second cycle\n",
     "    t_12=20,  # time needed for the second cycle\n",
-    "    cw11=[12 * 60, 15 * 60],\n",
+    "    cw11=lunch_window, # the time window of the working cycle \n",
     ")"
    ]
   },
@@ -124,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 35,
    "id": "0d0efb25",
    "metadata": {},
    "outputs": [],
@@ -135,21 +140,20 @@
     "    power=1200,\n",
     "    func_time=25,\n",
     "    func_cycle=25,\n",
-    "    thermal_p_var=0.2,\n",
     "    fixed_cycle=1,\n",
-    "    pref_index=1,  # the first preference\n",
-    "    window_1=[12 * 60, 15 * 60],\n",
+    "    window_1=lunch_window,\n",
     "    p_11=1200,  # power of the first cycle\n",
     "    t_11=5,  # time needed for the first cycle\n",
     "    p_12=750,  # power of the second cycle\n",
     "    t_12=20,  # time needed for the second cycle\n",
-    "    cw11=[12 * 60, 15 * 60],\n",
+    "    cw11=lunch_window,\n",
+    "    pref_index=1,  # the first preference\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 36,
    "id": "71f30884",
    "metadata": {},
    "outputs": [],
@@ -160,18 +164,86 @@
     "    power=1200,\n",
     "    func_time=15,\n",
     "    func_cycle=15,\n",
-    "    thermal_p_var=0.2,\n",
-    "    pref_index=2,  # the second preference\n",
     "    fixed_cycle=1,\n",
-    "    window_1=[12 * 60, 15 * 60],\n",
+    "    window_1=lunch_window,\n",
     "    p_11=1200,  # power of the first cycle\n",
     "    t_11=5,  # time needed for the first cycle\n",
     "    p_12=600,  # power of the second cycle\n",
     "    t_12=10,  # time needed for the second cycle\n",
-    "    cw11=[12 * 60, 15 * 60],\n",
+    "    cw11=lunch_window,\n",
+    "    pref_index=2,  # the second preference\n",
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "25b5eea1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you \n"
+     ]
+    }
+   ],
+   "source": [
+    "number_of_days = 5\n",
+    "user_1_profiles = {}\n",
+    "user_2_profiles = {}\n",
+    "for day in range(1,number_of_days+1):\n",
+    "    user_1_profiles[f\"day {day}\"] = pd.Series(user_1.generate_single_load_profile())\n",
+    "    user_2_profiles[f\"day {day}\"] = pd.Series(user_2.generate_single_load_profile())\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "ee119e57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) \n",
+    "\n",
+    "\n",
+    "i = 0\n",
+    "for name, df in dict(\n",
+    "    user_1_profiles=pd.concat(user_1_profiles),\n",
+    "    user_2_profiles=pd.concat(user_2_profiles)\n",
+    "    ).items():\n",
+    "    df.plot(ax=axes[i],legend=False)\n",
+    "    axes[i].set_title(name)\n",
+    "    i+=1\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "pd.concat(user_2_profiles,axis=1).iloc[lunch_window[0]-5:lunch_window[1]+5].plot()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -802,7 +874,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

From 7b918dc086c906fab64dd7e0dbba877005ff2572 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <mohammadamin.tahavori@vito.be>
Date: Fri, 29 Mar 2024 17:59:42 +0100
Subject: [PATCH 060/140] exmples improvement - thermal load

---
 docs/notebooks/thermal_app.ipynb | 344 +++++++------------------------
 1 file changed, 69 insertions(+), 275 deletions(-)

diff --git a/docs/notebooks/thermal_app.ipynb b/docs/notebooks/thermal_app.ipynb
index 6c59948e..99ac670d 100644
--- a/docs/notebooks/thermal_app.ipynb
+++ b/docs/notebooks/thermal_app.ipynb
@@ -7,12 +7,7 @@
    "source": [
     "# Thermal loads\n",
     "\n",
-    "This example input file represents a single household user whose only load\n",
-    "is the \"shower\". The example showcases how to model thermal loads\n",
-    "by: \n",
-    "1) using a time-varying average `power` attribute, pre-calculated as a function of the average daily groundwater temperature; \n",
-    "and\n",
-    "2) using the `thermal_p_var` attribute to add further variability to the actual power absorbed by the appliance in each usage event, which reflects the randomness of user behaviour in preferring a slightly warmer or colder shower temperature."
+    "Some of the energy requirements of users are drived my technologies that have time variant power consumption. Hot water boilers are an example of technologies that can have variant powers throughout time. To show case how loads as such can be modelled in RAMP, we take the example of a household thermal load for generating shower hot water.\n"
    ]
   },
   {
@@ -23,8 +18,9 @@
    "outputs": [],
    "source": [
     "# importing functions\n",
-    "from ramp import User, UseCase, load_data, get_day_type\n",
-    "import pandas as pd"
+    "from ramp import User, UseCase, load_data\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -37,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 2,
    "id": "70b0f3cb",
    "metadata": {},
    "outputs": [],
@@ -50,26 +46,14 @@
    "id": "808da9d5",
    "metadata": {},
    "source": [
-    "When the power varies across days of the year, for instance, as a function of the average daily groundwater temperature,\n",
-    "the “power” parameter can be passed as a `pd.DataFrame` or `np.array` with a daily profile (365 rows\n",
-    "of data). For this exercise, data can be loaded from the default examples in ramp:"
+    "RAMP allows the user to give variant power profile for an appliance with a daily resolution. In this case,the power property in an appliance should be given as a timeseries pd.DataFrame or np.array with daily profile (365 rows of data). For this exercise, we will use a built-in power profile for thermal load example that can be loaded using load_data util function:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 3,
    "id": "41280681",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "shower_power = load_data(example=\"shower\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "1d5b6d41",
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -77,13 +61,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -93,13 +77,14 @@
     }
    ],
    "source": [
+    "shower_power = load_data(example=\"shower\")\n",
     "# shower power distribution\n",
     "shower_power.plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 4,
    "id": "e5e87e38",
    "metadata": {},
    "outputs": [],
@@ -107,14 +92,12 @@
     "shower = household.add_appliance(\n",
     "    name=\"Shower\",\n",
     "    number=1,\n",
-    "    power=shower_power,\n",
-    "    num_windows=2,\n",
-    "    func_time=15,\n",
-    "    time_fraction_random_variability=0.1,\n",
-    "    func_cycle=3,\n",
-    "    window_1=[390, 540],\n",
-    "    window_2=[1080, 1200],\n",
-    "    random_var_w=0.2,\n",
+    "    power=shower_power, # pass the pd.DataFrame or np.array instead of a number\n",
+    "    num_windows=2, # two possibe time window for shower\n",
+    "    func_time=15, # each shower takes 15 minute\n",
+    "    func_cycle=3, # every\n",
+    "    window_1=[390, 540], #  morning shower from 6:30 to 9:00 AM\n",
+    "    window_2=[1080, 1200], # evening shower from 18:00 to 20:00\n",
     ")"
    ]
   },
@@ -128,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 5,
    "id": "320b7296",
    "metadata": {},
    "outputs": [
@@ -138,6 +121,16 @@
      "text": [
       "You will simulate 365 day(s) from 2020-01-01 00:00:00 until 2020-12-31 00:00:00\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tahavorm\\downloads\\gitrepos\\ramp\\ramp\\core\\core.py:299: FutureWarning: 'T' is deprecated and will be removed in a future version. Please use 'min' instead of 'T'.\n",
+      "  end=self.days[-1] + pd.Timedelta(1, \"d\") - pd.Timedelta(1, \"T\"),\n",
+      "c:\\users\\tahavorm\\downloads\\gitrepos\\ramp\\ramp\\core\\core.py:297: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  self.__datetimeindex = pd.date_range(\n"
+     ]
     }
    ],
    "source": [
@@ -145,25 +138,34 @@
     "usecase.initialize(num_days=365)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cf52f91d",
+   "metadata": {},
+   "source": [
+    "As everyone has a different habit in the water temperature for taking shower, we can also consider a variability in the thermal power through the thermal_p_var property. Using the `thermal_p_var` attribute to add further variability to the actual power absorbed by the appliance in each usage event, which reflects the randomness of user behaviour in preferring a slightly warmer or colder shower temperature.To better understand the effect of this parameters, let's perform a sensitivity analysis on themal_p_var function. To do so, we use the following function **thermal_p_var_sensitivty**:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 6,
    "id": "285d7b2f-7d20-475d-847c-769810ae34db",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def thermal_p_var_sensitivity(values):\n",
+    "def thermal_p_var_sensitivity(sensitivity_values):\n",
     "    # buidling a pd.DataFrame for saving sensitivity results\n",
     "    results = pd.DataFrame(\n",
-    "        index=pd.date_range(start=\"2020-01-01\", periods=1440 * 365, freq=\"T\"),\n",
-    "        columns=[f\"p_var = {value}\" for value in values],\n",
+    "        index=range(0,1440*365),\n",
+    "        columns=[f\"p_var = {value}\" for value in sensitivity_values],\n",
     "    )\n",
     "\n",
-    "    for value in values:\n",
+    "    for value in sensitivity_values:\n",
+    "\n",
     "        # changing the thermal_P_var\n",
     "        shower.thermal_p_var = value\n",
     "\n",
-    "        profiles = usecase.generate_daily_load_profiles(flat=True)\n",
+    "        profiles = usecase.generate_daily_load_profiles()\n",
     "\n",
     "        # assigning the yearly profile for a given sensitivity case\n",
     "        results[f\"p_var = {value}\"] = profiles\n",
@@ -173,213 +175,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "id": "03cff922",
+   "execution_count": 7,
+   "id": "3b5450e6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "sensitivity_results = thermal_p_var_sensitivity([0, 0.25, 0.5, 0.75, 1])"
+    "# generating 5 senstivities on thermal_p_var\n",
+    "sensitivity_results = thermal_p_var_sensitivity([0, 0.25, 0.5])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "id": "2b544ef0",
+   "execution_count": 8,
+   "id": "8c8ca6d2",
    "metadata": {},
    "outputs": [
     {
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",
       "text/plain": [
-       "                     p_var = 0  p_var = 0.25  p_var = 0.5  p_var = 0.75  \\\n",
-       "2020-01-01 00:00:00        0.0           0.0          0.0           0.0   \n",
-       "2020-01-01 00:01:00        0.0           0.0          0.0           0.0   \n",
-       "2020-01-01 00:02:00        0.0           0.0          0.0           0.0   \n",
-       "2020-01-01 00:03:00        0.0           0.0          0.0           0.0   \n",
-       "2020-01-01 00:04:00        0.0           0.0          0.0           0.0   \n",
-       "...                        ...           ...          ...           ...   \n",
-       "2020-12-30 23:55:00        0.0           0.0          0.0           0.0   \n",
-       "2020-12-30 23:56:00        0.0           0.0          0.0           0.0   \n",
-       "2020-12-30 23:57:00        0.0           0.0          0.0           0.0   \n",
-       "2020-12-30 23:58:00        0.0           0.0          0.0           0.0   \n",
-       "2020-12-30 23:59:00        0.0           0.0          0.0           0.0   \n",
-       "\n",
-       "                     p_var = 1  \n",
-       "2020-01-01 00:00:00        0.0  \n",
-       "2020-01-01 00:01:00        0.0  \n",
-       "2020-01-01 00:02:00        0.0  \n",
-       "2020-01-01 00:03:00        0.0  \n",
-       "2020-01-01 00:04:00        0.0  \n",
-       "...                        ...  \n",
-       "2020-12-30 23:55:00        0.0  \n",
-       "2020-12-30 23:56:00        0.0  \n",
-       "2020-12-30 23:57:00        0.0  \n",
-       "2020-12-30 23:58:00        0.0  \n",
-       "2020-12-30 23:59:00        0.0  \n",
-       "\n",
-       "[525600 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sensitivity_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "78743706",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# showing the daily average of the load profiles\n",
-    "average_daily_profiles = sensitivity_results.resample(\"1d\").mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "29e7de24",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1000x1000 with 9 Axes>"
       ]
      },
      "metadata": {},
@@ -387,50 +202,29 @@
     }
    ],
    "source": [
-    "average_daily_profiles.plot()"
+    "days_to_plot = [1,100,200] # which days of the year to plot\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=len(days_to_plot), nrows=sensitivity_results.shape[1], figsize=(10, 10)) \n",
+    "\n",
+    "for j,day in enumerate(days_to_plot):\n",
+    "    for i, col in enumerate(sensitivity_results):\n",
+    "        sensitivity_results[col].iloc[1440*(day-1):1440*(day)].plot(ax=axes[i,j]) # just plot for the first day\n",
+    "        axes[i,j].set_title(f\"Day {day} - thermal_p_var = {col}\",fontsize=8)\n",
+    "        axes[i,j].set_ylim(0,24000)\n",
+    "\n",
+    "    \n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "ad0238d2-f90f-4df3-a0de-986f1de75d70",
+   "cell_type": "markdown",
+   "id": "03cff922",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "first_day = pd.date_range(\n",
-    "    start=\"2020-01-01 00:00:00\", freq=\"1min\", periods=24 * 60  # a full day\n",
-    ")\n",
-    "sensitivity_results.loc[first_day].plot()"
+    "As it can be onserved, the power consumption of hot water supply technology varies across different days of the year, primarily due to fluctuations in the nominal power. When adjusting the parameter **thermal_p_var** from 0, indicating no variability in power consumption due to user preferences, to higher values, which signify the probability of changes in hot water temperature, the power consumption also varies accordingly."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "da74d4b3-1dfb-460e-836a-a383e40265a6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -449,7 +243,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,

From 7222eb041df4c953f303a95e3b3ced9325e95b08 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com>
Date: Tue, 9 Apr 2024 11:24:38 +0200
Subject: [PATCH 061/140] Update docs/notebooks/cooking_app.ipynb

Co-authored-by: Pierre Francois <pierre-francois.duc@rl-institut.de>
---
 docs/notebooks/cooking_app.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/notebooks/cooking_app.ipynb b/docs/notebooks/cooking_app.ipynb
index 66428105..fdcce37a 100644
--- a/docs/notebooks/cooking_app.ipynb
+++ b/docs/notebooks/cooking_app.ipynb
@@ -50,7 +50,7 @@
     "user_1 = User(\n",
     "    user_name=\"Household with single lunch habit\",\n",
     "    num_users=1,\n",
-    "    user_preference=1,  # user_1 has only consumption preference\n",
+    "    user_preference=1,  # user_1 has only one consumption preference\n",
     ")\n",
     "\n",
     "user_2 = User(\n",

From 6d78d907e294fa11272aed920534396dd9e9b8cd Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com>
Date: Tue, 9 Apr 2024 11:26:08 +0200
Subject: [PATCH 062/140] Update docs/notebooks/fixed_flat_app.ipynb

Co-authored-by: Pierre Francois <pierre-francois.duc@rl-institut.de>
---
 docs/notebooks/fixed_flat_app.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/notebooks/fixed_flat_app.ipynb b/docs/notebooks/fixed_flat_app.ipynb
index 44bed895..49c531a9 100644
--- a/docs/notebooks/fixed_flat_app.ipynb
+++ b/docs/notebooks/fixed_flat_app.ipynb
@@ -161,7 +161,7 @@
     "\n",
     "2. fixed_no_flat consumption: when appliances are synchronized and they can randomly switch on during the window time\n",
     "\n",
-    "3. fixed_no_flat consumption (original example): when appliances are synchronized and they are always switched on during the window time\n",
+    "3. fixed_flat consumption (original example): when appliances are synchronized and they are always switched on during the window time\n",
     "\n",
     "By observing the load profiles generated under these different combinations, we can gain insights into how each parameter influences overall energy consumption and demand patterns within simulation."
    ]

From c349a7fd6b30937cec9cc43b4f83b64bc3e1c758 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com>
Date: Tue, 9 Apr 2024 11:26:25 +0200
Subject: [PATCH 063/140] Update docs/notebooks/simple_bulb.ipynb

Co-authored-by: Pierre Francois <pierre-francois.duc@rl-institut.de>
---
 docs/notebooks/simple_bulb.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/notebooks/simple_bulb.ipynb b/docs/notebooks/simple_bulb.ipynb
index eed6c7ab..d89edd58 100644
--- a/docs/notebooks/simple_bulb.ipynb
+++ b/docs/notebooks/simple_bulb.ipynb
@@ -29,7 +29,7 @@
    "source": [
     "### Creating a user category \n",
     "\n",
-    "To represent a user category, which is a collection of users with similar consumption characteristics such as appliance ownership and consumption behavior, the class **User** should be utilized. Each User class is initially characterized by the number of users assigned to the user category. For example, let's consider a household category comprising 10 households:"
+    "To represent a user category, which is a collection of users with similar consumption characteristics such as appliance ownership and consumption behavior, the class **User** should be used. Each User instance is initially characterized by the number of users assigned to the user category. For example, let's consider a household category comprising 10 households:"
    ]
   },
   {

From 8fa6a08ea155b483ca01a94ea5df02a08d56910f Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com>
Date: Tue, 9 Apr 2024 11:26:38 +0200
Subject: [PATCH 064/140] Update docs/notebooks/thermal_app.ipynb

Co-authored-by: Pierre Francois <pierre-francois.duc@rl-institut.de>
---
 docs/notebooks/thermal_app.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/notebooks/thermal_app.ipynb b/docs/notebooks/thermal_app.ipynb
index 99ac670d..9b65f20e 100644
--- a/docs/notebooks/thermal_app.ipynb
+++ b/docs/notebooks/thermal_app.ipynb
@@ -223,7 +223,7 @@
    "id": "03cff922",
    "metadata": {},
    "source": [
-    "As it can be onserved, the power consumption of hot water supply technology varies across different days of the year, primarily due to fluctuations in the nominal power. When adjusting the parameter **thermal_p_var** from 0, indicating no variability in power consumption due to user preferences, to higher values, which signify the probability of changes in hot water temperature, the power consumption also varies accordingly."
+    "As it can be observed, the power consumption of hot water supply technology varies across different days of the year, primarily due to fluctuations in the nominal power. When adjusting the parameter **thermal_p_var** from 0, indicating no variability in power consumption due to user preferences, to higher values, which signify the probability of changes in hot water temperature, the power consumption also varies accordingly."
    ]
   }
  ],

From 9612562e54e0c402248bea29ef8351860ea923b4 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com>
Date: Tue, 9 Apr 2024 11:26:49 +0200
Subject: [PATCH 065/140] Update docs/notebooks/simple_bulb.ipynb

Co-authored-by: Pierre Francois <pierre-francois.duc@rl-institut.de>
---
 docs/notebooks/simple_bulb.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/notebooks/simple_bulb.ipynb b/docs/notebooks/simple_bulb.ipynb
index d89edd58..1254cf8a 100644
--- a/docs/notebooks/simple_bulb.ipynb
+++ b/docs/notebooks/simple_bulb.ipynb
@@ -327,7 +327,7 @@
    "source": [
     "### Generating aggregated_load_profile for the user category\n",
     "\n",
-    "If instead of a single user from the categoy, you want to generate load profiles of the aggregated users of the category, for a single day, you can use the similar method in User object, named **generate_aggregated_load_profile**:"
+    "If instead of a single user from the categoy, you want to generate load profiles of the aggregated users of the category, for a single day, you can use the similar method of User class, named **generate_aggregated_load_profile**:"
    ]
   },
   {

From 321a5aea51d31c8889098193c820189616e294a1 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <50955527+mohammadamint@users.noreply.github.com>
Date: Tue, 9 Apr 2024 11:26:57 +0200
Subject: [PATCH 066/140] Update docs/notebooks/simple_bulb.ipynb

Co-authored-by: Pierre Francois <pierre-francois.duc@rl-institut.de>
---
 docs/notebooks/simple_bulb.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/notebooks/simple_bulb.ipynb b/docs/notebooks/simple_bulb.ipynb
index 1254cf8a..3d3cf48d 100644
--- a/docs/notebooks/simple_bulb.ipynb
+++ b/docs/notebooks/simple_bulb.ipynb
@@ -263,7 +263,7 @@
    "source": [
     "### Generating a profile for a single day\n",
     "\n",
-    "To generate daily profiles for specific user categories, the User object in RAMP offers various methods. One such method is **generate_single_load_profile**, which constructs a load profile for a single user, accounting for all their appliances, based on a specific day of the year or a designated day_type. In RAMP, day_types represent weekdays and weekends, with day_type=0 indicating weekdays and day_type=1 signifying weekends. When defining appliances using the add_appliance method, users can specify whether the appliance is utilized throughout the designated wd_we_type argument.\n",
+    "To generate daily profiles for specific user categories, the User class in RAMP offers various methods. One such method is **generate_single_load_profile**, which constructs a load profile for a single user, accounting for all their appliances, based on a specific day of the year or a designated day_type. In RAMP, day_types represent weekdays and weekends, with day_type=0 indicating weekdays and day_type=1 signifying weekends. When defining appliances using the add_appliance method, users can specify whether the appliance is utilized throughout the designated wd_we_type argument.\n",
     "\n",
     "For instance, let's consider the scenario where we aim to generate a single-day load profile for a weekday of the year for a single user within the household category:"
    ]

From 9e8ba9619c7d99cc8a587280b3a1327022be5ce8 Mon Sep 17 00:00:00 2001
From: Mohammad Amin Tahavori <mohammadamin.tahavori@vito.be>
Date: Tue, 9 Apr 2024 12:36:14 +0200
Subject: [PATCH 067/140] re-run example files

---
 docs/notebooks/cooking_app.ipynb    | 647 +---------------------------
 docs/notebooks/fixed_flat_app.ipynb |  32 +-
 docs/notebooks/multi_cycle.ipynb    |   2 +-
 docs/notebooks/occasional_use.ipynb |  10 +-
 docs/notebooks/thermal_app.ipynb    |   2 +-
 5 files changed, 51 insertions(+), 642 deletions(-)

diff --git a/docs/notebooks/cooking_app.ipynb b/docs/notebooks/cooking_app.ipynb
index fdcce37a..c61a2b1c 100644
--- a/docs/notebooks/cooking_app.ipynb
+++ b/docs/notebooks/cooking_app.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 1,
    "id": "4761038e",
    "metadata": {},
    "outputs": [],
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 2,
    "id": "77e6919a",
    "metadata": {},
    "outputs": [],
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 3,
    "id": "768250cd",
    "metadata": {},
    "outputs": [],
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 4,
    "id": "0d0efb25",
    "metadata": {},
    "outputs": [],
@@ -153,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 5,
    "id": "71f30884",
    "metadata": {},
    "outputs": [],
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 6,
    "id": "25b5eea1",
    "metadata": {},
    "outputs": [
@@ -185,6 +185,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you \n",
       "You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you \n"
      ]
     }
@@ -201,25 +202,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 7,
    "id": "ee119e57",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 800x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -232,630 +223,24 @@
     "\n",
     "i = 0\n",
     "for name, df in dict(\n",
-    "    user_1_profiles=pd.concat(user_1_profiles),\n",
-    "    user_2_profiles=pd.concat(user_2_profiles)\n",
+    "    user_1_profiles=pd.concat(user_1_profiles,axis=1).iloc[lunch_window[0]-5:lunch_window[1]+5], # take only the lunch window\n",
+    "    user_2_profiles=pd.concat(user_2_profiles,axis=1).iloc[lunch_window[0]-5:lunch_window[1]+5]# take only the lunch window\n",
     "    ).items():\n",
-    "    df.plot(ax=axes[i],legend=False)\n",
+    "    df.plot(ax=axes[i],legend=True)\n",
     "    axes[i].set_title(name)\n",
     "    i+=1\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.show()\n",
-    "pd.concat(user_2_profiles,axis=1).iloc[lunch_window[0]-5:lunch_window[1]+5].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "5b789866",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>user_name</th>\n",
-       "      <td>Household with single lunch habit</td>\n",
-       "      <td>Household with different lunch habit</td>\n",
-       "      <td>Household with different lunch habit</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>num_users</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>user_preference</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>name</th>\n",
-       "      <td>soup for lunch</td>\n",
-       "      <td>soup for lunch</td>\n",
-       "      <td>rice for lunch</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>number</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>power</th>\n",
-       "      <td>1200.0</td>\n",
-       "      <td>1200.0</td>\n",
-       "      <td>1200.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>num_windows</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>func_time</th>\n",
-       "      <td>25</td>\n",
-       "      <td>25</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time_fraction_random_variability</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>func_cycle</th>\n",
-       "      <td>25</td>\n",
-       "      <td>25</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fixed</th>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>fixed_cycle</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>occasional_use</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>flat</th>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>thermal_p_var</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>pref_index</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>wd_we_type</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_11</th>\n",
-       "      <td>1200</td>\n",
-       "      <td>1200</td>\n",
-       "      <td>1200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_11</th>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cw11_start</th>\n",
-       "      <td>720</td>\n",
-       "      <td>720</td>\n",
-       "      <td>720</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cw11_end</th>\n",
-       "      <td>900</td>\n",
-       "      <td>900</td>\n",
-       "      <td>900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_12</th>\n",
-       "      <td>750</td>\n",
-       "      <td>750</td>\n",
-       "      <td>600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_12</th>\n",
-       "      <td>20</td>\n",
-       "      <td>20</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cw12_start</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cw12_end</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>r_c1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_21</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_21</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cw21_start</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cw21_end</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_22</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_22</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cw22_start</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cw22_end</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>r_c2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_31</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_31</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cw31_start</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cw31_end</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_32</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_32</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cw32_start</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cw32_end</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>r_c3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>window_1_start</th>\n",
-       "      <td>720</td>\n",
-       "      <td>720</td>\n",
-       "      <td>720</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>window_1_end</th>\n",
-       "      <td>900</td>\n",
-       "      <td>900</td>\n",
-       "      <td>900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>window_2_start</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>window_2_end</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>window_3_start</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>window_3_end</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>random_var_w</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                  0  \\\n",
-       "user_name                         Household with single lunch habit   \n",
-       "num_users                                                         1   \n",
-       "user_preference                                                   1   \n",
-       "name                                                 soup for lunch   \n",
-       "number                                                            1   \n",
-       "power                                                        1200.0   \n",
-       "num_windows                                                       1   \n",
-       "func_time                                                        25   \n",
-       "time_fraction_random_variability                                  0   \n",
-       "func_cycle                                                       25   \n",
-       "fixed                                                            no   \n",
-       "fixed_cycle                                                       1   \n",
-       "occasional_use                                                    1   \n",
-       "flat                                                             no   \n",
-       "thermal_p_var                                                   0.2   \n",
-       "pref_index                                                        0   \n",
-       "wd_we_type                                                        2   \n",
-       "p_11                                                           1200   \n",
-       "t_11                                                              5   \n",
-       "cw11_start                                                      720   \n",
-       "cw11_end                                                        900   \n",
-       "p_12                                                            750   \n",
-       "t_12                                                             20   \n",
-       "cw12_start                                                        0   \n",
-       "cw12_end                                                          0   \n",
-       "r_c1                                                              0   \n",
-       "p_21                                                              0   \n",
-       "t_21                                                              0   \n",
-       "cw21_start                                                        0   \n",
-       "cw21_end                                                          0   \n",
-       "p_22                                                              0   \n",
-       "t_22                                                              0   \n",
-       "cw22_start                                                        0   \n",
-       "cw22_end                                                          0   \n",
-       "r_c2                                                              0   \n",
-       "p_31                                                              0   \n",
-       "t_31                                                              0   \n",
-       "cw31_start                                                        0   \n",
-       "cw31_end                                                          0   \n",
-       "p_32                                                              0   \n",
-       "t_32                                                              0   \n",
-       "cw32_start                                                        0   \n",
-       "cw32_end                                                          0   \n",
-       "r_c3                                                              0   \n",
-       "window_1_start                                                  720   \n",
-       "window_1_end                                                    900   \n",
-       "window_2_start                                                    0   \n",
-       "window_2_end                                                      0   \n",
-       "window_3_start                                                    0   \n",
-       "window_3_end                                                      0   \n",
-       "random_var_w                                                      0   \n",
-       "\n",
-       "                                                                     1  \\\n",
-       "user_name                         Household with different lunch habit   \n",
-       "num_users                                                            1   \n",
-       "user_preference                                                      2   \n",
-       "name                                                    soup for lunch   \n",
-       "number                                                               1   \n",
-       "power                                                           1200.0   \n",
-       "num_windows                                                          1   \n",
-       "func_time                                                           25   \n",
-       "time_fraction_random_variability                                     0   \n",
-       "func_cycle                                                          25   \n",
-       "fixed                                                               no   \n",
-       "fixed_cycle                                                          1   \n",
-       "occasional_use                                                       1   \n",
-       "flat                                                                no   \n",
-       "thermal_p_var                                                      0.2   \n",
-       "pref_index                                                           1   \n",
-       "wd_we_type                                                           2   \n",
-       "p_11                                                              1200   \n",
-       "t_11                                                                 5   \n",
-       "cw11_start                                                         720   \n",
-       "cw11_end                                                           900   \n",
-       "p_12                                                               750   \n",
-       "t_12                                                                20   \n",
-       "cw12_start                                                           0   \n",
-       "cw12_end                                                             0   \n",
-       "r_c1                                                                 0   \n",
-       "p_21                                                                 0   \n",
-       "t_21                                                                 0   \n",
-       "cw21_start                                                           0   \n",
-       "cw21_end                                                             0   \n",
-       "p_22                                                                 0   \n",
-       "t_22                                                                 0   \n",
-       "cw22_start                                                           0   \n",
-       "cw22_end                                                             0   \n",
-       "r_c2                                                                 0   \n",
-       "p_31                                                                 0   \n",
-       "t_31                                                                 0   \n",
-       "cw31_start                                                           0   \n",
-       "cw31_end                                                             0   \n",
-       "p_32                                                                 0   \n",
-       "t_32                                                                 0   \n",
-       "cw32_start                                                           0   \n",
-       "cw32_end                                                             0   \n",
-       "r_c3                                                                 0   \n",
-       "window_1_start                                                     720   \n",
-       "window_1_end                                                       900   \n",
-       "window_2_start                                                       0   \n",
-       "window_2_end                                                         0   \n",
-       "window_3_start                                                       0   \n",
-       "window_3_end                                                         0   \n",
-       "random_var_w                                                         0   \n",
-       "\n",
-       "                                                                     2  \n",
-       "user_name                         Household with different lunch habit  \n",
-       "num_users                                                            1  \n",
-       "user_preference                                                      2  \n",
-       "name                                                    rice for lunch  \n",
-       "number                                                               1  \n",
-       "power                                                           1200.0  \n",
-       "num_windows                                                          1  \n",
-       "func_time                                                           15  \n",
-       "time_fraction_random_variability                                     0  \n",
-       "func_cycle                                                          15  \n",
-       "fixed                                                               no  \n",
-       "fixed_cycle                                                          1  \n",
-       "occasional_use                                                       1  \n",
-       "flat                                                                no  \n",
-       "thermal_p_var                                                      0.2  \n",
-       "pref_index                                                           2  \n",
-       "wd_we_type                                                           2  \n",
-       "p_11                                                              1200  \n",
-       "t_11                                                                 5  \n",
-       "cw11_start                                                         720  \n",
-       "cw11_end                                                           900  \n",
-       "p_12                                                               600  \n",
-       "t_12                                                                10  \n",
-       "cw12_start                                                           0  \n",
-       "cw12_end                                                             0  \n",
-       "r_c1                                                                 0  \n",
-       "p_21                                                                 0  \n",
-       "t_21                                                                 0  \n",
-       "cw21_start                                                           0  \n",
-       "cw21_end                                                             0  \n",
-       "p_22                                                                 0  \n",
-       "t_22                                                                 0  \n",
-       "cw22_start                                                           0  \n",
-       "cw22_end                                                             0  \n",
-       "r_c2                                                                 0  \n",
-       "p_31                                                                 0  \n",
-       "t_31                                                                 0  \n",
-       "cw31_start                                                           0  \n",
-       "cw31_end                                                             0  \n",
-       "p_32                                                                 0  \n",
-       "t_32                                                                 0  \n",
-       "cw32_start                                                           0  \n",
-       "cw32_end                                                             0  \n",
-       "r_c3                                                                 0  \n",
-       "window_1_start                                                     720  \n",
-       "window_1_end                                                       900  \n",
-       "window_2_start                                                       0  \n",
-       "window_2_end                                                         0  \n",
-       "window_3_start                                                       0  \n",
-       "window_3_end                                                         0  \n",
-       "random_var_w                                                         0  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# you can have an overview of data inputs by usering User.export_to_dataframe method\n",
-    "user_lunch = UseCase(users=[user_1, user_2], date_start=\"2020-01-01\")\n",
-    "user_lunch.export_to_dataframe().T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2c5c1d61",
-   "metadata": {},
-   "source": [
-    "### Generating a profile for some months"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "e4ea14ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "You will simulate 90 day(s) from 2020-01-01 00:00:00 until 2020-03-31 00:00:00\n"
-     ]
-    }
-   ],
-   "source": [
-    "# number of days\n",
-    "n_days = 90\n",
-    "\n",
-    "user_lunch.initialize(num_days=n_days)\n",
-    "# storing all the profiles for all the users\n",
-    "profiles = pd.DataFrame(\n",
-    "    index=pd.date_range(start=\"2020-01-01\", periods=1440 * n_days, freq=\"T\")\n",
-    ")\n",
-    "\n",
-    "# here we need to use the\n",
-    "\n",
-    "for user in user_lunch.users:\n",
-    "    # storing daily profiles for a user\n",
-    "    user_profiles = []\n",
-    "    for day_idx, day in enumerate(user_lunch.days):\n",
-    "        single_profile = user.generate_single_load_profile(\n",
-    "            prof_i=day_idx,  # the day to generate the profile\n",
-    "            day_type=get_day_type(day),\n",
-    "        )\n",
-    "\n",
-    "        user_profiles.extend(single_profile)\n",
-    "\n",
-    "    profiles[user.user_name] = user_profiles"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "aa34afee",
+   "id": "a5af9eb2",
    "metadata": {},
    "source": [
-    "Considering that the second user has the possibility of cooking rice for lunch, which has a less energy-intensive cooking cycle, we expect to see a higher energy consumption for the user that only eats soup, in most of the cases."
+    "As it can be observed, user_1 always have the same demand profile for lunch prepration while user_2 can have two different profiles (for example on day 3 and 4, the user cooks rice for lunch)!"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "04545584",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'daily energy consumption'}>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# daily energy consumption\n",
-    "profiles.resample(\"1d\").sum().plot(title=\"daily energy consumption\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fe622733-3577-43f2-85f3-f53efbe32b7f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/notebooks/fixed_flat_app.ipynb b/docs/notebooks/fixed_flat_app.ipynb
index 49c531a9..f6325095 100644
--- a/docs/notebooks/fixed_flat_app.ipynb
+++ b/docs/notebooks/fixed_flat_app.ipynb
@@ -95,6 +95,16 @@
      "text": [
       "You will simulate 7 day(s) from 2023-01-01 00:00:00 until 2023-01-08 00:00:00\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tahavorm\\downloads\\gitrepos\\ramp\\ramp\\core\\core.py:299: FutureWarning: 'T' is deprecated and will be removed in a future version. Please use 'min' instead of 'T'.\n",
+      "  end=self.days[-1] + pd.Timedelta(1, \"d\") - pd.Timedelta(1, \"T\"),\n",
+      "c:\\users\\tahavorm\\downloads\\gitrepos\\ramp\\ramp\\core\\core.py:297: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
+      "  self.__datetimeindex = pd.date_range(\n"
+     ]
     }
    ],
    "source": [
@@ -127,7 +137,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -163,6 +173,8 @@
     "\n",
     "3. fixed_flat consumption (original example): when appliances are synchronized and they are always switched on during the window time\n",
     "\n",
+    "4. no_fixed_flat consumption: when appliances are not synchronized but they are always switched on during the window time\n",
+    "\n",
     "By observing the load profiles generated under these different combinations, we can gain insights into how each parameter influences overall energy consumption and demand patterns within simulation."
    ]
   },
@@ -182,7 +194,13 @@
     "# not flat but fixed\n",
     "security_lights.fixed = \"yes\"\n",
     "fix_no_flat = school_case.generate_daily_load_profiles()\n",
-    "fix_no_flat = pd.DataFrame(fix_no_flat, columns=[\"fix_no_flat\"])"
+    "fix_no_flat = pd.DataFrame(fix_no_flat, columns=[\"fix_no_flat\"])\n",
+    "\n",
+    "# not fixed but flat\n",
+    "security_lights.fixed = \"no\"\n",
+    "security_lights.flat = \"yes\"\n",
+    "no_fixed_flat = school_case.generate_daily_load_profiles()\n",
+    "no_fixed_flat = pd.DataFrame(no_fixed_flat, columns=[\"no_fixed_flat\"])"
    ]
   },
   {
@@ -193,9 +211,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1200x400 with 3 Axes>"
+       "<Figure size 1200x400 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -204,10 +222,10 @@
    ],
    "source": [
     "\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))  \n",
+    "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 4))  \n",
     "\n",
     "\n",
-    "for i, df in enumerate([no_fix_no_flat,fix_no_flat,fixed_flat,]):\n",
+    "for i, df in enumerate([no_fix_no_flat,fix_no_flat,fixed_flat,no_fixed_flat]):\n",
     "    df.plot(ax=axes[i],legend=False)\n",
     "    axes[i].set_title(df.columns[0])\n",
     "\n",
@@ -241,7 +259,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb
index 69aa3eb3..af16a093 100644
--- a/docs/notebooks/multi_cycle.ipynb
+++ b/docs/notebooks/multi_cycle.ipynb
@@ -412,7 +412,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/occasional_use.ipynb b/docs/notebooks/occasional_use.ipynb
index 733bf7f1..12aaff88 100644
--- a/docs/notebooks/occasional_use.ipynb
+++ b/docs/notebooks/occasional_use.ipynb
@@ -135,7 +135,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x400 with 2 Axes>"
       ]
@@ -170,6 +170,12 @@
     "the school's appliance mix while, for the household, it is only occasionally \n",
     "present."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb28fd04",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
@@ -188,7 +194,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/thermal_app.ipynb b/docs/notebooks/thermal_app.ipynb
index 9b65f20e..6a86f80b 100644
--- a/docs/notebooks/thermal_app.ipynb
+++ b/docs/notebooks/thermal_app.ipynb
@@ -192,7 +192,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x1000 with 9 Axes>"
       ]

From b1114a9030d73e408137a2725d9a2c07678dac99 Mon Sep 17 00:00:00 2001
From: "F.L" <f.lombardi@outlook.com>
Date: Thu, 11 Apr 2024 12:27:18 +0200
Subject: [PATCH 068/140] Improved fixed_cycle docs

---
 docs/notebooks/multi_cycle.ipynb | 36 ++++++++++++++++++++------------
 ramp/core/core.py                | 26 +++++++++++------------
 2 files changed, 36 insertions(+), 26 deletions(-)

diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb
index af16a093..b0b8be1a 100644
--- a/docs/notebooks/multi_cycle.ipynb
+++ b/docs/notebooks/multi_cycle.ipynb
@@ -5,7 +5,7 @@
    "id": "74d8e1ab",
    "metadata": {},
    "source": [
-    "# Appliances with multiple cycles"
+    "# Appliances with duty cycles"
    ]
   },
   {
@@ -14,12 +14,20 @@
    "metadata": {},
    "source": [
     "\n",
-    "An example of an appliance with multiple cycle is a fridge. Fridges\n",
-    "usually have different duty cycles, which can be estimated based on seasonal\n",
-    "temperature trends and/or frequency of user interaction (e.g., how often the \n",
-    "door gets opened).\n",
+    "Some appliances may operate based on fixed duty cycle. For instance, you may think of a\n",
+    "dishwasher that follows a pre-determined program. This can be modelled by means of the \n",
+    "`fixed_cycle` parameter, which allows to define a duty cycle consisting of up to two temporal\n",
+    "operation segments with distinct power consumption levels. \n",
     "\n",
-    "In this example a fridge with 3 different duty cycles is modelled. The\n",
+    "Furthermore, we acknowledge that some of the appliances that can operate based on duty cucles\n",
+    "may still follow multiple, different duty cycles throughout a day or across seasons. An example\n",
+    "of an appliance characterised by variable duty cycles is a fridge. Fridges tend to follow \n",
+    "different duty cycles throughout the day, depending on the ambient temperature (which also varies\n",
+    "across seasons) and/or frequency of user interaction (e.g., how often the door gets opened).\n",
+    "Accordingly, RAMP allows modelling up to 3 different duty cycles per appliance, giving room for\n",
+    "modulating the appliance's behaviour depending on any given factor of interest.\n",
+    "\n",
+    "In this example, we model a fridge with 3 different duty cycles. The\n",
     "time windows are defined for 3 different cycles across 3 different season\n",
     "types:\n",
     "\n",
@@ -91,7 +99,7 @@
    "outputs": [],
    "source": [
     "# setting the functioning windows\n",
-    "fridge.windows([0, 1440])  # always on during the whole year"
+    "fridge.windows([0, 1440])  # always on during the whole day, for all days of the year"
    ]
   },
   {
@@ -99,7 +107,9 @@
    "id": "9ca3e204",
    "metadata": {},
    "source": [
-    "### Assigining the specific cycles"
+    "### Characterising the specific cycles\n",
+    "\n",
+    "As anticipated above, each cycle is characterised by two operation segments at different power levels. They can be defined as per the code below. In addition, it is possile to apply a random variability to the exact duration of the two segments (see the API documentation)."
    ]
   },
   {
@@ -112,10 +122,10 @@
     "# assiging the specific cycles\n",
     "# first cycle: standard cycle\n",
     "fridge.specific_cycle_1(\n",
-    "    p_11=200,\n",
-    "    t_11=20,\n",
-    "    p_12=5,\n",
-    "    t_12=10,\n",
+    "    p_11=200, # power level for the first operation segment\n",
+    "    t_11=20,  # duration of the first operation segment\n",
+    "    p_12=5,   # power level for the second operation segment\n",
+    "    t_12=10,  # duration of the second operation segment\n",
     ")\n",
     "\n",
     "# second cycle: intermediate cycle\n",
@@ -140,7 +150,7 @@
    "id": "d503eb1f",
    "metadata": {},
    "source": [
-    "After defining the cycle power and duration parameters, the time windows of year at which the cycles happens should be specifid by:"
+    "After defining the cycle power and duration parameters, the time windows within a day at which the cycles occur should be specified by means of the 'cycle window' (`cw`). In fact, up to two time windows within a day can be specified for each of the 3 cycles. For instance, for `specific_cycle_3`, you may define an occurrence in the early morning (`cw31` in the code below) as well as one in the late evening (`cw32`).The windows defined across all cycles should not overlap."
    ]
   },
   {
diff --git a/ramp/core/core.py b/ramp/core/core.py
index 12c72076..4f5e2725 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1565,13 +1565,13 @@ def specific_cycle_1(
             Power rating for first part of first duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         t_11 : int[0,1440], optional
-            Duration (minutes) of first part of first duty cycle. Only necessary if fixed_cycle is set to I or greater, by default 0
+            Duration (minutes) of first part of first duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         p_12 : int, float, optional
             Power rating for second part of first duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         t_12 : int[0,1440], optional
-            Duration (minutes) of second part of first duty cycle. Only necessary if fixed_cycle is set to I or greater, by default 0
+            Duration (minutes) of second part of first duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         r_c1 : Percentage [0,1], optional
             randomization of the duty cycle parts duration. There will be a uniform random variation around t_i1 and t_i2. If this parameter is set to 0.1, then t_i1 and t_i2 will be randomly reassigned between 90% and 110% of their initial value; 0 means no randomisation, by default 0
@@ -1607,13 +1607,13 @@ def specific_cycle_2(
             Power rating for first part of second duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         t_21 : int[0,1440], optional
-            Duration (minutes) of first part of second duty cycle. Only necessary if fixed_cycle is set to I or greater, by default 0
+            Duration (minutes) of first part of second duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         p_22 : int, float, optional
             Power rating for second part of second duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         t_22 : int[0,1440], optional
-            Duration (minutes) of second part of second duty cycle. Only necessary if fixed_cycle is set to I or greater, by default 0
+            Duration (minutes) of second part of second duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
         r_c2 : Percentage [0,1], optional
             randomization of the duty cycle parts duration. There will be a uniform random variation around t_i1 and t_i2. If this parameter is set to 0.1, then t_i1 and t_i2 will be randomly reassigned between 90% and 110% of their initial value; 0 means no randomisation, by default 0
@@ -1645,25 +1645,25 @@ def specific_cycle_3(
 
         Parameters
         ----------
-        p_21 : float, optional
+        p_31 : float, optional
             Power rating for first part of third duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
-        t_21 : int[0,1440], optional
-            Duration (minutes) of first part of third duty cycle. Only necessary if fixed_cycle is set to I or greater, by default 0
+        t_31 : int[0,1440], optional
+            Duration (minutes) of first part of third duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
-        p_22 : int, float, optional
+        p_32 : int, float, optional
             Power rating for second part of third duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
-        t_22 : int[0,1440], optional
-            Duration (minutes) of second part of third duty cycle. Only necessary if fixed_cycle is set to I or greater, by default 0
+        t_32 : int[0,1440], optional
+            Duration (minutes) of second part of third duty cycle. Only necessary if fixed_cycle is set to 1 or greater, by default 0
 
-        r_c2 : Percentage [0,1], optional
+        r_c3 : Percentage [0,1], optional
             randomization of the duty cycle parts duration. There will be a uniform random variation around t_i1 and t_i2. If this parameter is set to 0.1, then t_i1 and t_i2 will be randomly reassigned between 90% and 110% of their initial value; 0 means no randomisation, by default 0
 
-        cw21 : Iterable, optional
+        cw31 : Iterable, optional
             Window time range for the first part of third duty cycle number (not neccessarily linked to the overall time window), by default None
 
-        cw22 : Iterable, optional
+        cw32 : Iterable, optional
             Window time range for the first part of third duty cycle number (not neccessarily linked to the overall time window), by default None, by default None
         """
         self.p_31 = p_31

From d1cb3279466374c67aa75414233dee8c2f3aac08 Mon Sep 17 00:00:00 2001
From: "F.L" <f.lombardi@outlook.com>
Date: Wed, 8 May 2024 09:22:06 +0200
Subject: [PATCH 069/140] Additional refinements

---
 docs/notebooks/multi_cycle.ipynb | 38 ++++++++++++++++++--------------
 1 file changed, 22 insertions(+), 16 deletions(-)

diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb
index b0b8be1a..27a5cc80 100644
--- a/docs/notebooks/multi_cycle.ipynb
+++ b/docs/notebooks/multi_cycle.ipynb
@@ -120,12 +120,12 @@
    "outputs": [],
    "source": [
     "# assiging the specific cycles\n",
-    "# first cycle: standard cycle\n",
+    "# first cycle: intensive cycle\n",
     "fridge.specific_cycle_1(\n",
     "    p_11=200, # power level for the first operation segment\n",
-    "    t_11=20,  # duration of the first operation segment\n",
+    "    t_11=10,  # duration of the first operation segment\n",
     "    p_12=5,   # power level for the second operation segment\n",
-    "    t_12=10,  # duration of the second operation segment\n",
+    "    t_12=20,  # duration of the second operation segment\n",
     ")\n",
     "\n",
     "# second cycle: intermediate cycle\n",
@@ -136,12 +136,12 @@
     "    t_22=15,\n",
     ")\n",
     "\n",
-    "# third cycle: intensive cycle\n",
+    "# third cycle: standard cycle\n",
     "fridge.specific_cycle_3(\n",
     "    p_31=200,\n",
-    "    t_31=10,\n",
+    "    t_31=20,\n",
     "    p_32=5,\n",
-    "    t_32=20,\n",
+    "    t_32=10,\n",
     ")"
    ]
   },
@@ -162,7 +162,7 @@
    "source": [
     "# defining cycle behaviour\n",
     "fridge.cycle_behaviour(\n",
-    "    cw11=[480, 1200], cw21=[300, 479], cw31=[0, 229], cw32=[1201, 1440]\n",
+    "     cw11=[0, 229], cw12=[1201, 1440], cw21=[300, 479], cw31=[480, 1200]\n",
     ")"
    ]
   },
@@ -217,9 +217,9 @@
    "source": [
     "# days to build the profiles\n",
     "days = [\n",
-    "    \"2020-05-16\",\n",
-    "    \"2020-08-16\",\n",
-    "    \"2020-12-16\",\n",
+    "    \"2020-05-16\", # a day in the 'Cold' season, based on RAMP's validation case study in Bolivia\n",
+    "    \"2020-08-16\", # a day in the 'Warm' season\n",
+    "    \"2020-12-16\", # a day in the 'Hot' season \n",
     "]\n",
     "\n",
     "profiles = pd.DataFrame(index=range(0, 1440), columns=days)\n",
@@ -366,7 +366,7 @@
     {
      "data": {
       "text/plain": [
-       "<AxesSubplot:>"
+       "<Axes: >"
       ]
      },
      "execution_count": 11,
@@ -377,12 +377,10 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -404,6 +402,14 @@
    "source": [
     "One can see that the switch on events are longer and more than one duty cycle run within the switch on event"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b27e1a44-193f-4ca9-808f-a83597f026d4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -422,7 +428,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.8.18"
   }
  },
  "nbformat": 4,

From 2c04462a2f0d30d56ebb9cdf527c8ba0e7a0e288 Mon Sep 17 00:00:00 2001
From: "F.L" <f.lombardi@outlook.com>
Date: Wed, 8 May 2024 09:23:54 +0200
Subject: [PATCH 070/140] Clean-up unwanted conda warning

---
 docs/notebooks/multi_cycle.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb
index 27a5cc80..00b36fa0 100644
--- a/docs/notebooks/multi_cycle.ipynb
+++ b/docs/notebooks/multi_cycle.ipynb
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "488c1fe3",
    "metadata": {},
    "outputs": [],

From 88558a63725c38dddb7c9d5a939e1301343c076d Mon Sep 17 00:00:00 2001
From: "F.L" <f.lombardi@outlook.com>
Date: Wed, 8 May 2024 11:42:18 +0200
Subject: [PATCH 071/140] Simplified multi_cycle documentation

---
 docs/notebooks/multi_cycle.ipynb | 30 ++++++++++++++++--------------
 1 file changed, 16 insertions(+), 14 deletions(-)

diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb
index 00b36fa0..c9ad8676 100644
--- a/docs/notebooks/multi_cycle.ipynb
+++ b/docs/notebooks/multi_cycle.ipynb
@@ -27,16 +27,17 @@
     "Accordingly, RAMP allows modelling up to 3 different duty cycles per appliance, giving room for\n",
     "modulating the appliance's behaviour depending on any given factor of interest.\n",
     "\n",
-    "In this example, we model a fridge with 3 different duty cycles. The\n",
-    "time windows are defined for 3 different cycles across 3 different season\n",
-    "types:\n",
+    "In this example, we model a fridge with 3 different duty cycles, which are representative of its\n",
+    "operation during a 'hot' season. The time windows are defined for 3 different cycles throughout\n",
+    "the day, corresponding to a different temperature and frequency of user interaction. For instance,\n",
+    "between 08:00 and 20:00, we assume that the fridge operates more intensely due to the combined effects\n",
+    "of temperature and frequent use. In the early morning, it operates at an intermediate intensity, \n",
+    "and during the night, it operates based on its standard cycle.\n",
     "\n",
     "\n",
     "| season type |               Standard cycle              |  Intermediate cycle |   Intensive cycle   |\n",
     "|-------------|:-----------------------------------------:|:-------------------:|:-------------------:|\n",
-    "| Hot         | 00:00:00 - 04:59:00 & 20:01:00 - 23:59:00 | 05:00:00 - 07:59:00 | 08:00:00 - 20:00:00 |\n",
-    "| Warm        | 00:00:00 - 04:59:00 & 18:01:00 - 23:59:00 | 05:00:00 - 09:39:00 | 09:40:00 - 18:00:00 |\n",
-    "| Cold        | 00:00:00 - 04:59:00 & 20:01:00 - 23:59:00 | 05:00:00 - 20:00:00 |          -          |"
+    "| Hot         | 00:00:00 - 04:59:00 & 20:01:00 - 23:59:00 | 05:00:00 - 07:59:00 | 08:00:00 - 20:00:00 |"
    ]
   },
   {
@@ -109,7 +110,7 @@
    "source": [
     "### Characterising the specific cycles\n",
     "\n",
-    "As anticipated above, each cycle is characterised by two operation segments at different power levels. They can be defined as per the code below. In addition, it is possile to apply a random variability to the exact duration of the two segments (see the API documentation)."
+    "As anticipated above, each cycle is characterised by two operation segments at different power levels. They can be defined as per the code below. In addition, it is possible to apply a random variability (`r_c1`, `r_c2`, `r_c3`, one for each specific cycle) to the exact duration of the two segments (see the API documentation). In this example, we do not apply such variability."
    ]
   },
   {
@@ -120,12 +121,13 @@
    "outputs": [],
    "source": [
     "# assiging the specific cycles\n",
-    "# first cycle: intensive cycle\n",
+    "# first cycle: standard cycle\n",
     "fridge.specific_cycle_1(\n",
     "    p_11=200, # power level for the first operation segment\n",
     "    t_11=10,  # duration of the first operation segment\n",
     "    p_12=5,   # power level for the second operation segment\n",
     "    t_12=20,  # duration of the second operation segment\n",
+    "    r_c1=0    # random variability assigned to the duration of each segment\n",
     ")\n",
     "\n",
     "# second cycle: intermediate cycle\n",
@@ -134,14 +136,16 @@
     "    t_21=15,\n",
     "    p_22=5,\n",
     "    t_22=15,\n",
+    "    r_c2=0\n",
     ")\n",
     "\n",
-    "# third cycle: standard cycle\n",
+    "# third cycle: intensive cycle\n",
     "fridge.specific_cycle_3(\n",
     "    p_31=200,\n",
     "    t_31=20,\n",
     "    p_32=5,\n",
     "    t_32=10,\n",
+    "    r_c3=0\n",
     ")"
    ]
   },
@@ -150,7 +154,7 @@
    "id": "d503eb1f",
    "metadata": {},
    "source": [
-    "After defining the cycle power and duration parameters, the time windows within a day at which the cycles occur should be specified by means of the 'cycle window' (`cw`). In fact, up to two time windows within a day can be specified for each of the 3 cycles. For instance, for `specific_cycle_3`, you may define an occurrence in the early morning (`cw31` in the code below) as well as one in the late evening (`cw32`).The windows defined across all cycles should not overlap."
+    "After defining the cycle power and duration parameters, the time windows within a day at which the cycles occur should be specified by means of the 'cycle window' (`cw`). In fact, up to two time windows within a day can be specified for each of the 3 cycles. For instance, according to the table discussed earlier, for the standard `specific_cycle_1` we may define an occurrence in the early morning (`cw11` in the code below) as well as one in the late evening (`cw12`).The windows defined across all cycles should not overlap."
    ]
   },
   {
@@ -162,7 +166,7 @@
    "source": [
     "# defining cycle behaviour\n",
     "fridge.cycle_behaviour(\n",
-    "     cw11=[0, 229], cw12=[1201, 1440], cw21=[300, 479], cw31=[480, 1200]\n",
+    "     cw11=[0, 299], cw12=[1201, 1440], cw21=[300, 479], cw31=[480, 1200]\n",
     ")"
    ]
   },
@@ -217,9 +221,7 @@
    "source": [
     "# days to build the profiles\n",
     "days = [\n",
-    "    \"2020-05-16\", # a day in the 'Cold' season, based on RAMP's validation case study in Bolivia\n",
-    "    \"2020-08-16\", # a day in the 'Warm' season\n",
-    "    \"2020-12-16\", # a day in the 'Hot' season \n",
+    "    \"2020-12-16\", # a day in the 'Hot' season, assuming a location in the Southern hemisphere\n",
     "]\n",
     "\n",
     "profiles = pd.DataFrame(index=range(0, 1440), columns=days)\n",

From 684af315dced203583e6c912d96325fbeba4c1f5 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 8 May 2024 16:31:15 +0200
Subject: [PATCH 072/140] Update docs

---
 .../examples/cooking_app/cooking_app.rst      | 462 +++---------------
 .../fixed_flat_app/fixed_flat_app.rst         | 151 ++++--
 .../examples/multi_cycle/multi_cycle.rst      | 125 +++--
 .../examples/multi_cycle/output_15_1.png      | Bin 18920 -> 42274 bytes
 .../occasional_use/occasional_use.rst         | 111 ++---
 .../parallel_processing.rst                   |   2 +
 .../source/examples/plot_class/plot_class.rst | 147 ++++--
 .../examples/simple_bulb/output_13_1.png      | Bin 6361 -> 23764 bytes
 .../examples/simple_bulb/simple_bulb.rst      | 157 +++++-
 .../examples/thermal_app/thermal_app.rst      | 261 +++-------
 .../examples/using_excel/using_excel.rst      |   2 +
 11 files changed, 610 insertions(+), 808 deletions(-)

diff --git a/docs/source/examples/cooking_app/cooking_app.rst b/docs/source/examples/cooking_app/cooking_app.rst
index 670a09c1..e6afec8c 100644
--- a/docs/source/examples/cooking_app/cooking_app.rst
+++ b/docs/source/examples/cooking_app/cooking_app.rst
@@ -1,8 +1,13 @@
 Cooking Appliances
 ==================
 
-In this example, appliances with multiple preferences index and
-attributes are modelled.
+Some appliances load profiles, highly depend on the user choices and
+preferences. For example, electric stoves power usage, highly depends on
+the type of food and that a user wants to cook. This kind of appliances
+in RAMP are flagged by the user category consumption preferences.
+
+In this example, we will see how the electric cookstoves with multiple
+user preferences can be modelled in RAMP.
 
 To have a better understanding of RAMP features for modelling this
 category of appliances, two households are considered:
@@ -16,8 +21,9 @@ The number of user preferences can be specified through
 .. code:: ipython3
 
     # importing functions
-    from ramp import User, UseCase, get_day_type
+    from ramp import User, UseCase
     import pandas as pd
+    import matplotlib.pyplot as plt
 
 Creating a user category
 ~~~~~~~~~~~~~~~~~~~~~~~~
@@ -27,18 +33,22 @@ Creating a user category
     user_1 = User(
         user_name="Household with single lunch habit",
         num_users=1,
-        user_preference=1,  # user_1 has only one lunch preference
+        user_preference=1,  # user_1 has only one consumption preference
     )
     
     user_2 = User(
         user_name="Household with different lunch habits",
         num_users=1,
-        user_preference=2,  # user_2 has two lunch preferences
+        user_preference=2,  # user_2 has two consumption preferences
     )
 
 Defining the cycles for cooking soup and rice
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+For cookstoves, it will be more realistic to have different operation
+cycles as cooking a food usually requires different levels of heat for
+the different parts of food processing:
+
 For cooking soup it is assumed that the user needs 25 minutes divided
 into two parts:
 
@@ -62,19 +72,20 @@ Cooking rice  600   10
 .. code:: ipython3
 
     # soup for lunch
+    lunch_window = [12 * 60, 12 * 60+26]
+    
     soup_1 = user_1.add_appliance(
         name="soup for lunch",
-        power=1200,
-        func_time=25,
-        func_cycle=25,
-        thermal_p_var=0.2,
-        fixed_cycle=1,
-        window_1=[12 * 60, 15 * 60],
+        power=1200, # nominal power of appliance
+        func_time=25, # the cooking time
+        func_cycle=25, # we always need  25 minute for cooking
+        fixed_cycle=1, # the cookstove is not a continus power usage appliance, it has cycles as mentioned earlier 
+        window_1=lunch_window, # lunch is always prepared from 12
         p_11=1200,  # power of the first cycle
         t_11=5,  # time needed for the first cycle
         p_12=750,  # power of the second cycle
         t_12=20,  # time needed for the second cycle
-        cw11=[12 * 60, 15 * 60],
+        cw11=lunch_window, # the time window of the working cycle 
     )
 
 The second user has two different preferences for lunch. Accordingly, we
@@ -98,15 +109,14 @@ rice is the second one (pref_index = 2).
         power=1200,
         func_time=25,
         func_cycle=25,
-        thermal_p_var=0.2,
         fixed_cycle=1,
-        pref_index=1,  # the first preference
-        window_1=[12 * 60, 15 * 60],
+        window_1=lunch_window,
         p_11=1200,  # power of the first cycle
         t_11=5,  # time needed for the first cycle
         p_12=750,  # power of the second cycle
         t_12=20,  # time needed for the second cycle
-        cw11=[12 * 60, 15 * 60],
+        cw11=lunch_window,
+        pref_index=1,  # the first preference
     )
 
 .. code:: ipython3
@@ -117,419 +127,57 @@ rice is the second one (pref_index = 2).
         power=1200,
         func_time=15,
         func_cycle=15,
-        thermal_p_var=0.2,
-        pref_index=2,  # the second preference
         fixed_cycle=1,
-        window_1=[12 * 60, 15 * 60],
+        window_1=lunch_window,
         p_11=1200,  # power of the first cycle
         t_11=5,  # time needed for the first cycle
         p_12=600,  # power of the second cycle
         t_12=10,  # time needed for the second cycle
-        cw11=[12 * 60, 15 * 60],
+        cw11=lunch_window,
+        pref_index=2,  # the second preference
     )
 
 .. code:: ipython3
 
-    # you can have an overview of data inputs by usering User.export_to_dataframe method
-    user_lunch = UseCase(users=[user_1, user_2], date_start="2020-01-01")
-    user_lunch.export_to_dataframe().T
-
+    number_of_days = 5
+    user_1_profiles = {}
+    user_2_profiles = {}
+    for day in range(1,number_of_days+1):
+        user_1_profiles[f"day {day}"] = pd.Series(user_1.generate_single_load_profile())
+        user_2_profiles[f"day {day}"] = pd.Series(user_2.generate_single_load_profile())
+        
 
 
+.. parsed-literal::
 
-.. raw:: html
+    You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you 
+    You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you 
 
-    <div>
-    <style scoped>
-        .dataframe tbody tr th:only-of-type {
-            vertical-align: middle;
-        }
-    
-        .dataframe tbody tr th {
-            vertical-align: top;
-        }
-    
-        .dataframe thead th {
-            text-align: right;
-        }
-    </style>
-    <table border="1" class="dataframe">
-      <thead>
-        <tr style="text-align: right;">
-          <th></th>
-          <th>0</th>
-          <th>1</th>
-          <th>2</th>
-        </tr>
-      </thead>
-      <tbody>
-        <tr>
-          <th>user_name</th>
-          <td>Household with single lunch habit</td>
-          <td>Household with different lunch habit</td>
-          <td>Household with different lunch habit</td>
-        </tr>
-        <tr>
-          <th>num_users</th>
-          <td>1</td>
-          <td>1</td>
-          <td>1</td>
-        </tr>
-        <tr>
-          <th>user_preference</th>
-          <td>1</td>
-          <td>2</td>
-          <td>2</td>
-        </tr>
-        <tr>
-          <th>name</th>
-          <td>soup for lunch</td>
-          <td>soup for lunch</td>
-          <td>rice for lunch</td>
-        </tr>
-        <tr>
-          <th>number</th>
-          <td>1</td>
-          <td>1</td>
-          <td>1</td>
-        </tr>
-        <tr>
-          <th>power</th>
-          <td>1200.0</td>
-          <td>1200.0</td>
-          <td>1200.0</td>
-        </tr>
-        <tr>
-          <th>num_windows</th>
-          <td>1</td>
-          <td>1</td>
-          <td>1</td>
-        </tr>
-        <tr>
-          <th>func_time</th>
-          <td>25</td>
-          <td>25</td>
-          <td>15</td>
-        </tr>
-        <tr>
-          <th>time_fraction_random_variability</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>func_cycle</th>
-          <td>25</td>
-          <td>25</td>
-          <td>15</td>
-        </tr>
-        <tr>
-          <th>fixed</th>
-          <td>no</td>
-          <td>no</td>
-          <td>no</td>
-        </tr>
-        <tr>
-          <th>fixed_cycle</th>
-          <td>1</td>
-          <td>1</td>
-          <td>1</td>
-        </tr>
-        <tr>
-          <th>occasional_use</th>
-          <td>1</td>
-          <td>1</td>
-          <td>1</td>
-        </tr>
-        <tr>
-          <th>flat</th>
-          <td>no</td>
-          <td>no</td>
-          <td>no</td>
-        </tr>
-        <tr>
-          <th>thermal_p_var</th>
-          <td>0.2</td>
-          <td>0.2</td>
-          <td>0.2</td>
-        </tr>
-        <tr>
-          <th>pref_index</th>
-          <td>0</td>
-          <td>1</td>
-          <td>2</td>
-        </tr>
-        <tr>
-          <th>wd_we_type</th>
-          <td>2</td>
-          <td>2</td>
-          <td>2</td>
-        </tr>
-        <tr>
-          <th>p_11</th>
-          <td>1200</td>
-          <td>1200</td>
-          <td>1200</td>
-        </tr>
-        <tr>
-          <th>t_11</th>
-          <td>5</td>
-          <td>5</td>
-          <td>5</td>
-        </tr>
-        <tr>
-          <th>cw11_start</th>
-          <td>720</td>
-          <td>720</td>
-          <td>720</td>
-        </tr>
-        <tr>
-          <th>cw11_end</th>
-          <td>900</td>
-          <td>900</td>
-          <td>900</td>
-        </tr>
-        <tr>
-          <th>p_12</th>
-          <td>750</td>
-          <td>750</td>
-          <td>600</td>
-        </tr>
-        <tr>
-          <th>t_12</th>
-          <td>20</td>
-          <td>20</td>
-          <td>10</td>
-        </tr>
-        <tr>
-          <th>cw12_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw12_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>r_c1</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>p_21</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>t_21</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw21_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw21_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>p_22</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>t_22</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw22_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw22_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>r_c2</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>p_31</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>t_31</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw31_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw31_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>p_32</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>t_32</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw32_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>cw32_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>r_c3</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>window_1_start</th>
-          <td>720</td>
-          <td>720</td>
-          <td>720</td>
-        </tr>
-        <tr>
-          <th>window_1_end</th>
-          <td>900</td>
-          <td>900</td>
-          <td>900</td>
-        </tr>
-        <tr>
-          <th>window_2_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>window_2_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>window_3_start</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>window_3_end</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-        <tr>
-          <th>random_var_w</th>
-          <td>0</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-      </tbody>
-    </table>
-    </div>
-
-
-
-Generating a profile for some months
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: ipython3
 
-    # number of days
-    n_days = 90
-    
-    user_lunch.initialize(num_days=n_days)
-    # storing all the profiles for all the users
-    profiles = pd.DataFrame(
-        index=pd.date_range(start="2020-01-01", periods=1440 * n_days, freq="T")
-    )
+    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) 
     
-    # here we need to use the
     
-    for user in user_lunch.users:
-        # storing daily profiles for a user
-        user_profiles = []
-        for day_idx, day in enumerate(user_lunch.days):
-            single_profile = user.generate_single_load_profile(
-                prof_i=day_idx,  # the day to generate the profile
-                day_type=get_day_type(day),
-            )
+    i = 0
+    for name, df in dict(
+        user_1_profiles=pd.concat(user_1_profiles,axis=1).iloc[lunch_window[0]-5:lunch_window[1]+5], # take only the lunch window
+        user_2_profiles=pd.concat(user_2_profiles,axis=1).iloc[lunch_window[0]-5:lunch_window[1]+5]# take only the lunch window
+        ).items():
+        df.plot(ax=axes[i],legend=True)
+        axes[i].set_title(name)
+        i+=1
     
-            user_profiles.extend(single_profile)
-    
-        profiles[user.user_name] = user_profiles
-
-
-.. parsed-literal::
-
-    You will simulate 90 day(s) from 2020-01-01 00:00:00 until 2020-03-31 00:00:00
-
-
-Considering that the second user has the possibility of cooking rice for
-lunch, which has a less energy-intensive cooking cycle, we expect to see
-a higher energy consumption for the user that only eats soup, in most of
-the cases.
-
-.. code:: ipython3
-
-    # daily energy consumption
-    profiles.resample("1d").sum().plot(title="daily energy consumption")
-
-
-
-
-.. parsed-literal::
-
-    <Axes: title={'center': 'daily energy consumption'}>
+    plt.tight_layout()
+    plt.show()
 
 
 
+.. image:: output_10_0.png
 
-.. image:: output_13_1.png
 
+As it can be observed, user_1 always have the same demand profile for
+lunch prepration while user_2 can have two different profiles (for
+example on day 3 and 4, the user cooks rice for lunch)!
 
+:download:`Link to the jupyter notebook file </../notebooks/cooking_app.ipynb>`.
diff --git a/docs/source/examples/fixed_flat_app/fixed_flat_app.rst b/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
index 12f01860..18bca4aa 100644
--- a/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
+++ b/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
@@ -1,11 +1,26 @@
 Fixed-Flat Appliance
 ====================
 
+Some appliances exhibit deterministic consumption patterns, such as
+security lights used for safety purposes, which often adhere to specific
+schedules. In RAMP, such appliances are categorized as **flat
+appliances** indicating they lack random variability in total usage
+time.
+
+When multiple appliances of the same type are consistently switched on
+together, they can be attributed with the ‘fixed’ flag in their
+definition. This synchronizes the switch-on and switch-off events for
+appliances of the same type.
+
+For instance, let’s model the security lights of a school to illustrate
+these characteristics:
+
 .. code:: ipython3
 
     # importing functions
-    from ramp import User, UseCase, get_day_type
+    from ramp import User, UseCase
     import pandas as pd
+    import matplotlib.pyplot as plt
 
 Creating a user
 ~~~~~~~~~~~~~~~
@@ -19,30 +34,38 @@ Adding an appliance with flat and fixed consumption
 
 .. code:: ipython3
 
-    indoor_bulb = school.add_appliance(
-        name="Indoor Light Bulb",
+    security_lights = school.add_appliance(
+        name="Security Light Bulb",
         number=10,
         power=25,
         num_windows=1,
         func_time=210,
-        time_fraction_random_variability=0.2,
         func_cycle=60,
-        fixed="yes", # This means all the 'n' appliances of this kind are always switched-on together
-        flat="yes", # This means the appliance is not subject to random variability in terms of total usage time
-    )
-    indoor_bulb.windows(
-        window_1=[1200, 1440],  # from 20:00 to 24:00
-        window_2=[0, 0],
-        random_var_w=0.35,
+        fixed="yes",  # This means all the 'n' appliances of this kind are always switched-on together
+        flat="yes",  # This means the appliance is not subject to random variability in terms of total usage time
+        window_1 = [1200, 1440],  # from 20:00 to 24:00
     )
 
-Initialize the usecase (it defines the peak time range and simulation time)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Generating the profiles using UseCase class
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Similar to previous example, you can generate the load profiles using
+the UseCase class. In this example, we use another functionality of the
+UseCase class by identifying the starting date of the simulation and the
+number of days for generating the profiles:
 
 .. code:: ipython3
 
-    school_case = UseCase(users=[school], date_start="2023-01-01")
-    school_case.initialize(num_days=7)
+    school_case = UseCase(
+        users=[school], # users of the usecase
+        date_start="2023-01-01", # start date
+        )
+    
+    # when both date_start and date_end are not given, you need to initialize the usecase by this method and by passing the number of days as num_days
+    school_case.initialize(
+        num_days=7
+        )
 
 
 .. parsed-literal::
@@ -50,45 +73,99 @@ Initialize the usecase (it defines the peak time range and simulation time)
     You will simulate 7 day(s) from 2023-01-01 00:00:00 until 2023-01-08 00:00:00
 
 
-Generating a profile for the 1st week of the year
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. parsed-literal::
+
+    c:\users\tahavorm\downloads\gitrepos\ramp\ramp\core\core.py:299: FutureWarning: 'T' is deprecated and will be removed in a future version. Please use 'min' instead of 'T'.
+      end=self.days[-1] + pd.Timedelta(1, "d") - pd.Timedelta(1, "T"),
+    c:\users\tahavorm\downloads\gitrepos\ramp\ramp\core\core.py:297: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.
+      self.__datetimeindex = pd.date_range(
 
-From the usecase directly
 
 .. code:: ipython3
 
-    first_week = school_case.generate_daily_load_profiles(flat=True)
+    fixed_flat = school_case.generate_daily_load_profiles()
+    fixed_flat = pd.DataFrame(fixed_flat, columns=["fixed_flat"])
+    fixed_flat.plot()
 
-or from the user
 
-.. code:: ipython3
 
-    first_week = []
-    
-    for day_idx, day in enumerate(school_case.days):
-        first_week.extend(
-            school.generate_single_load_profile(
-                prof_i=day_idx,  # the day to generate the profile
-                peak_time_range=school_case.peak_time_range,
-                day_type=get_day_type(day),
-            )
-        )
 
-.. code:: ipython3
+.. parsed-literal::
 
-    first_week = pd.DataFrame(first_week, columns=["household"])
-    first_week.plot()
+    <Axes: >
 
 
 
 
-.. parsed-literal::
+.. image:: output_8_1.png
 
-    <Axes: >
+
+As it can be seen, in each day, all the 10 lights with 25 Watt power
+(aggregated power of 250) are always switched on together on the
+specified schedule.
+
+To clarify the impact of parameters, let’s examine how the profiles
+would vary under different combinations:
+
+1. no_fixed_no_flat consumption: when appliances are not synchronized
+   and they can randomly switch on during the window time
+
+2. fixed_no_flat consumption: when appliances are synchronized and they
+   can randomly switch on during the window time
+
+3. fixed_flat consumption (original example): when appliances are
+   synchronized and they are always switched on during the window time
+
+4. no_fixed_flat consumption: when appliances are not synchronized but
+   they are always switched on during the window time
+
+By observing the load profiles generated under these different
+combinations, we can gain insights into how each parameter influences
+overall energy consumption and demand patterns within simulation.
+
+.. code:: ipython3
+
+    # no fixed and no flat property
+    security_lights.fixed = "no"
+    security_lights.flat = "no"
+    no_fix_no_flat = school_case.generate_daily_load_profiles()
+    no_fix_no_flat = pd.DataFrame(no_fix_no_flat, columns=["no_fix_no_flat"])
+    
+    # not flat but fixed
+    security_lights.fixed = "yes"
+    fix_no_flat = school_case.generate_daily_load_profiles()
+    fix_no_flat = pd.DataFrame(fix_no_flat, columns=["fix_no_flat"])
+    
+    # not fixed but flat
+    security_lights.fixed = "no"
+    security_lights.flat = "yes"
+    no_fixed_flat = school_case.generate_daily_load_profiles()
+    no_fixed_flat = pd.DataFrame(no_fixed_flat, columns=["no_fixed_flat"])
+
+.. code:: ipython3
+
+    
+    fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 4))  
+    
+    
+    for i, df in enumerate([no_fix_no_flat,fix_no_flat,fixed_flat,no_fixed_flat]):
+        df.plot(ax=axes[i],legend=False)
+        axes[i].set_title(df.columns[0])
+    
+    
+    plt.tight_layout()
+    plt.show()
 
 
 
+.. image:: output_12_0.png
 
-.. image:: output_12_1.png
 
+As it can be seen, in case of no flat and no fix, both scheduling of
+appliances and the switched on events of appliances are randomized.
+While when appliances are fixed, not flat, their scheduling is
+randomized keeping their switch on events synced as it can be observed
+that power usage is always kept at 250 Watt with more intermittent
+swithced on/off events.
 
+:download:`Link to the jupyter notebook file </../notebooks/fixed_flat_app.ipynb>`.
diff --git a/docs/source/examples/multi_cycle/multi_cycle.rst b/docs/source/examples/multi_cycle/multi_cycle.rst
index f03e52e0..248f9abc 100644
--- a/docs/source/examples/multi_cycle/multi_cycle.rst
+++ b/docs/source/examples/multi_cycle/multi_cycle.rst
@@ -1,14 +1,32 @@
-Appliances with multiple cycles
-===============================
-
-An example of an appliance with multiple cycle is a fridge. Fridges
-usually have different duty cycles, which can be estimated based on
-seasonal temperature trends and/or frequency of user interaction (e.g.,
-how often the door gets opened).
-
-In this example a fridge with 3 different duty cycles is modelled. The
-time windows are defined for 3 different cycles across 3 different
-season types:
+Appliances with duty cycles
+===========================
+
+Some appliances may operate based on fixed duty cycle. For instance, you
+may think of a dishwasher that follows a pre-determined program. This
+can be modelled by means of the ``fixed_cycle`` parameter, which allows
+to define a duty cycle consisting of up to two temporal operation
+segments with distinct power consumption levels.
+
+Furthermore, we acknowledge that some of the appliances that can operate
+based on duty cucles may still follow multiple, different duty cycles
+throughout a day or across seasons. An example of an appliance
+characterised by variable duty cycles is a fridge. Fridges tend to
+follow different duty cycles throughout the day, depending on the
+ambient temperature (which also varies across seasons) and/or frequency
+of user interaction (e.g., how often the door gets opened). Accordingly,
+RAMP allows modelling up to 3 different duty cycles per appliance,
+giving room for modulating the appliance’s behaviour depending on any
+given factor of interest.
+
+In this example, we model a fridge with 3 different duty cycles, which
+are representative of its operation during a ‘hot’ season. The time
+windows are defined for 3 different cycles throughout the day,
+corresponding to a different temperature and frequency of user
+interaction. For instance, between 08:00 and 20:00, we assume that the
+fridge operates more intensely due to the combined effects of
+temperature and frequent use. In the early morning, it operates at an
+intermediate intensity, and during the night, it operates based on its
+standard cycle.
 
 +--------+------------------------------+--------------+--------------+
 | season | Standard cycle               | Intermediate | Intensive    |
@@ -17,12 +35,6 @@ season types:
 | Hot    | 00:00:00 - 04:59:00 &        | 05:00:00 -   | 08:00:00 -   |
 |        | 20:01:00 - 23:59:00          | 07:59:00     | 20:00:00     |
 +--------+------------------------------+--------------+--------------+
-| Warm   | 00:00:00 - 04:59:00 &        | 05:00:00 -   | 09:40:00 -   |
-|        | 18:01:00 - 23:59:00          | 09:39:00     | 18:00:00     |
-+--------+------------------------------+--------------+--------------+
-| Cold   | 00:00:00 - 04:59:00 &        | 05:00:00 -   | -            |
-|        | 20:01:00 - 23:59:00          | 20:00:00     |              |
-+--------+------------------------------+--------------+--------------+
 
 Creating the user and appliance
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -56,20 +68,28 @@ Creating the user and appliance
 .. code:: ipython3
 
     # setting the functioning windows
-    fridge.windows([0, 1440])  # always on during the whole year
+    fridge.windows([0, 1440])  # always on during the whole day, for all days of the year
 
-Assigining the specific cycles
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Characterising the specific cycles
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+As anticipated above, each cycle is characterised by two operation
+segments at different power levels. They can be defined as per the code
+below. In addition, it is possible to apply a random variability
+(``r_c1``, ``r_c2``, ``r_c3``, one for each specific cycle) to the exact
+duration of the two segments (see the API documentation). In this
+example, we do not apply such variability.
 
 .. code:: ipython3
 
     # assiging the specific cycles
     # first cycle: standard cycle
     fridge.specific_cycle_1(
-        p_11=200,
-        t_11=20,
-        p_12=5,
-        t_12=10,
+        p_11=200, # power level for the first operation segment
+        t_11=10,  # duration of the first operation segment
+        p_12=5,   # power level for the second operation segment
+        t_12=20,  # duration of the second operation segment
+        r_c1=0    # random variability assigned to the duration of each segment
     )
     
     # second cycle: intermediate cycle
@@ -78,27 +98,35 @@ Assigining the specific cycles
         t_21=15,
         p_22=5,
         t_22=15,
+        r_c2=0
     )
     
     # third cycle: intensive cycle
     fridge.specific_cycle_3(
         p_31=200,
-        t_31=10,
+        t_31=20,
         p_32=5,
-        t_32=20,
+        t_32=10,
+        r_c3=0
     )
 
 After defining the cycle power and duration parameters, the time windows
-of year at which the cycles happens should be specifid by:
+within a day at which the cycles occur should be specified by means of
+the ‘cycle window’ (``cw``). In fact, up to two time windows within a
+day can be specified for each of the 3 cycles. For instance, according
+to the table discussed earlier, for the standard ``specific_cycle_1`` we
+may define an occurrence in the early morning (``cw11`` in the code
+below) as well as one in the late evening (``cw12``).The windows defined
+across all cycles should not overlap.
 
 .. code:: ipython3
 
     # defining cycle behaviour
     fridge.cycle_behaviour(
-        cw11=[480, 1200], cw21=[300, 479], cw31=[0, 229], cw32=[1201, 1440]
+         cw11=[0, 299], cw12=[1201, 1440], cw21=[300, 479], cw31=[480, 1200]
     )
 
-Buidling the profiles
+Building the profiles
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: ipython3
@@ -110,9 +138,7 @@ Buidling the profiles
 
     # days to build the profiles
     days = [
-        "2020-05-16",
-        "2020-08-16",
-        "2020-12-16",
+        "2020-12-16", # a day in the 'Hot' season, assuming a location in the Southern hemisphere
     ]
     
     profiles = pd.DataFrame(index=range(0, 1440), columns=days)
@@ -133,36 +159,39 @@ Buidling the profiles
 
 .. parsed-literal::
 
-          2020-05-16  2020-08-16  2020-12-16
-    0          0.001       5.000       0.001
-    1          0.001       5.000       0.001
-    2          0.001       5.000       0.001
-    3          5.000       5.000       0.001
-    4          5.000       5.000       0.001
-    ...          ...         ...         ...
-    1435       0.001       0.001       0.001
-    1436       0.001       0.001       0.001
-    1437       0.001       0.001       0.001
-    1438       0.001       0.001       0.001
-    1439       0.001       0.001       0.001
+          2020-12-16
+    0          0.001
+    1          0.001
+    2          0.001
+    3          0.001
+    4          5.000
+    ...          ...
+    1435       0.001
+    1436       0.001
+    1437       0.001
+    1438       0.001
+    1439       0.001
     
-    [1440 rows x 3 columns]
+    [1440 rows x 1 columns]
 
 
 .. code:: ipython3
 
-    # plotting a part of the days
-    profiles.iloc[400:500].plot()
+    # plotting the whole day
+    profiles.plot()
 
 
 
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
 
 .. image:: output_15_1.png
 
+
+
+:download:`Link to the jupyter notebook file </../notebooks/multi_cycle.ipynb>`.
diff --git a/docs/source/examples/multi_cycle/output_15_1.png b/docs/source/examples/multi_cycle/output_15_1.png
index e74e5e89bb03bf8bd6f504e0344af0b0ac311b87..0437a4a3e65ef557501c8bf223c082d80fcc223e 100644
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diff --git a/docs/source/examples/occasional_use/occasional_use.rst b/docs/source/examples/occasional_use/occasional_use.rst
index ad9be5a6..db1bd0f0 100644
--- a/docs/source/examples/occasional_use/occasional_use.rst
+++ b/docs/source/examples/occasional_use/occasional_use.rst
@@ -8,8 +8,8 @@ iron, a stereo, printers, etc.
 Within RAMP, the user may specify the probability of using an appliance
 on the daily mix with a parameter called **occasional_use**.
 
-When ``occasional_use = 0``, the appliance is always present in the mix,
-and when ``occasional_use = 1``, the appliance is never present. Any
+When ``occasional_use = 1``, the appliance is always present in the mix,
+and when ``occasional_use = 0``, the appliance is never present. Any
 in-between values will lead to a probabilistic calculation to decide
 whether the appliance is used or not on a given day.
 
@@ -20,7 +20,8 @@ occasionally \* A school that uses the computer every day
 .. code:: ipython3
 
     # importing functions
-    from ramp import User, UseCase, get_day_type
+    from ramp import User, UseCase
+    import matplotlib.pyplot as plt
     import pandas as pd
 
 Creating user categories and appliances
@@ -38,9 +39,10 @@ Creating user categories and appliances
         number=1,
         power=50,
         num_windows=1,
-        func_time=210,
+        func_time=210, # 3.5 hours
+        func_cycle=210,  
         occasional_use=0.5,  # 50% chance of occasional use,
-        window_1=[510, 750],
+        window_1=[480, 750], # start from 8AM
     )
 
 .. code:: ipython3
@@ -50,92 +52,67 @@ Creating user categories and appliances
         number=1,
         power=50,
         num_windows=1,
-        func_time=210,
-        time_fraction_random_variability=0.2,
-        func_cycle=10,
+        func_time=210, # 3.5 hours
+        func_cycle=210,  
         occasional_use=1,  # always present in the mix of appliances,
-        window_1=[510, 750],
+        window_1=[480, 750], # start from 8AM
     )
 
-.. code:: ipython3
-
-    # Checking the maximum profile of the two appliances
-    
-    max_profile_c1 = pd.DataFrame(computer_0.maximum_profile, columns=[computer_0.name])
-    max_profile_c2 = pd.DataFrame(computer_1.maximum_profile, columns=[computer_1.name])
-    
-    max_profile_c1.plot()
-    max_profile_c2.plot()
-
-
-
-
-.. parsed-literal::
-
-    <Axes: >
-
-
-
-
-.. image:: output_6_1.png
-
-
-
-.. image:: output_6_2.png
-
-
 Generating profiles
 ~~~~~~~~~~~~~~~~~~~
 
-.. code:: ipython3
-
-    use_case = UseCase(users=[household, school])
-    use_case.initialize(5)
-
-
-.. parsed-literal::
-
-    You will simulate 5 day(s) from 2023-12-01 00:00:00 until 2023-12-06 00:00:00
-
+As the profiles of each specific User category is important, we will use
+the User object profile genertor methods for 5 consecutive days:
 
 .. code:: ipython3
 
-    for day_idx, day in enumerate(use_case.days):
-        household_profile = household.generate_single_load_profile(
-            prof_i=day_idx, day_type=get_day_type(day)
-        )
+    number_of_days = 5
+    household_profiles = []
+    school_profiles = []
     
-        school_profile = school.generate_single_load_profile(
-            prof_i=day_idx, day_type=get_day_type(day)
+    for day in range(1,number_of_days+1):
+        household_profiles.extend(
+            household.generate_single_load_profile(prof_i=day)
         )
     
-        pd.DataFrame(
-            data=[household_profile, school_profile],
-            columns=range(1440),
-            index=[household.user_name, school.user_name],
-        ).T.plot(title=f"day - {day}")
-
-
-
-.. image:: output_9_0.png
-
-
+        school_profiles.extend(
+            school.generate_single_load_profile(prof_i=day)
+        )
 
-.. image:: output_9_1.png
 
+.. parsed-literal::
 
+    You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you 
+    You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you 
 
-.. image:: output_9_2.png
 
+.. code:: ipython3
 
+    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) 
+    
+    
+    i = 0
+    for name, df in dict(
+        household_profiles=pd.DataFrame(household_profiles),
+        school_profiles=pd.DataFrame(school_profiles)
+        ).items():
+        df.plot(ax=axes[i],legend=False)
+        axes[i].set_title(name)
+        i+=1
+    
+    plt.tight_layout()
+    plt.show()
 
-.. image:: output_9_3.png
 
 
 
-.. image:: output_9_4.png
+.. image:: output_8_0.png
 
 
 As it can be seen from the figures, the computer is always present in
 the school’s appliance mix while, for the household, it is only
 occasionally present.
+
+
+
+:download:`Link to the jupyter notebook file </../notebooks/occasional_use.ipynb>`.
diff --git a/docs/source/examples/parallel_processing/parallel_processing.rst b/docs/source/examples/parallel_processing/parallel_processing.rst
index 43571111..5c9d1ad9 100644
--- a/docs/source/examples/parallel_processing/parallel_processing.rst
+++ b/docs/source/examples/parallel_processing/parallel_processing.rst
@@ -154,3 +154,5 @@ You can also set the ``parallel_processing`` attribute of the
 .. image:: output_10_2.png
 
 
+
+:download:`Link to the jupyter notebook file </../notebooks/parallel_processing.ipynb>`.
diff --git a/docs/source/examples/plot_class/plot_class.rst b/docs/source/examples/plot_class/plot_class.rst
index 371b1688..f9676060 100644
--- a/docs/source/examples/plot_class/plot_class.rst
+++ b/docs/source/examples/plot_class/plot_class.rst
@@ -366,12 +366,12 @@ functions:
     2022-01-01 01:00:00  36.000000
     2022-01-01 02:00:00  36.000000
     2022-01-01 03:00:00  36.000000
-    2022-01-01 04:00:00  36.000008
-    2022-01-01 05:00:00  36.053523
-    2022-01-01 06:00:00  37.023682
-    2022-01-01 07:00:00   2.744088
-    2022-01-01 08:00:00   9.889124
-    2022-01-01 09:00:00  14.973023
+    2022-01-01 04:00:00  36.000009
+    2022-01-01 05:00:00  37.515430
+    2022-01-01 06:00:00  44.341832
+    2022-01-01 07:00:00   3.040070
+    2022-01-01 08:00:00   1.646964
+    2022-01-01 09:00:00   6.791291
      ......
 
 
@@ -391,12 +391,12 @@ functions:
     2022-01-01 01:00:00  396.000000
     2022-01-01 02:00:00  396.000000
     2022-01-01 03:00:00  396.000000
-    2022-01-01 04:00:00  396.000083
-    2022-01-01 05:00:00  396.588750
-    2022-01-01 06:00:00  407.260500
-    2022-01-01 07:00:00   30.184967
-    2022-01-01 08:00:00  108.780367
-    2022-01-01 09:00:00  164.703250
+    2022-01-01 04:00:00  396.000100
+    2022-01-01 05:00:00  412.669733
+    2022-01-01 06:00:00  487.760150
+    2022-01-01 07:00:00   33.440767
+    2022-01-01 08:00:00   18.116600
+    2022-01-01 09:00:00   74.704200
      ......
 
 
@@ -417,27 +417,27 @@ peak value for each column of your data using the ``peak`` property:
 
 .. parsed-literal::
 
-    {'case 1': 2022-10-28 06:00:00    146.00145
+    {'case 1': 2022-08-16 21:00:00    184.502267
      Freq: H, Name: case 1, dtype: float64,
-     'case 2': 2022-01-28 10:00:00    149.585283
+     'case 2': 2022-04-23 12:00:00    170.418517
      Freq: H, Name: case 2, dtype: float64,
-     'case 3': 2022-08-26 21:00:00    164.669633
+     'case 3': 2022-09-07 21:00:00    166.00135
      Freq: H, Name: case 3, dtype: float64,
-     'case 4': 2022-07-29 21:00:00    172.667983
+     'case 4': 2022-03-26 22:00:00    153.8342
      Freq: H, Name: case 4, dtype: float64,
-     'case 5': 2022-07-04 06:00:00    166.000917
+     'case 5': 2022-05-09 06:00:00    172.667167
      Freq: H, Name: case 5, dtype: float64,
-     'case 6': 2022-05-14 06:00:00    162.667917
+     'case 6': 2022-06-29 06:00:00    166.000667
      Freq: H, Name: case 6, dtype: float64,
-     'case 7': 2022-03-13 06:00:00    151.002083
+     'case 7': 2022-09-04 18:00:00    160.169433
      Freq: H, Name: case 7, dtype: float64,
-     'case 8': 2022-12-16 05:00:00    169.334517
+     'case 8': 2022-03-17 06:00:00    162.666883
      Freq: H, Name: case 8, dtype: float64,
-     'case 9': 2022-06-15 18:00:00    149.836033
+     'case 9': 2022-10-20 06:00:00    152.667083
      Freq: H, Name: case 9, dtype: float64,
-     'case 10': 2022-02-15 06:00:00    187.00175
+     'case 10': 2022-10-02 21:00:00    156.0024
      Freq: H, Name: case 10, dtype: float64,
-     'case 11': 2022-10-21 06:00:00    166.00235
+     'case 11': 2022-12-27 06:00:00    158.917483
      Freq: H, Name: case 11, dtype: float64}
 
 
@@ -469,7 +469,7 @@ your data to plot a simple line plot:
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -483,7 +483,34 @@ your data to plot a simple line plot:
     hourly_data.line(engine="plotly", plot_bgcolor="gray", title="My Beautiful Plot")
 
 
-.. image:: output_36_1.png
+
+.. raw:: html
+
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+    
+    var gd = document.getElementById('c3b793ca-e47b-4142-950f-ffefed07ef16');
+    var x = new MutationObserver(function (mutations, observer) {{
+            var display = window.getComputedStyle(gd).display;
+            if (!display || display === 'none') {{
+                console.log([gd, 'removed!']);
+                Plotly.purge(gd);
+                observer.disconnect();
+            }}
+    }});
+    
+    // Listen for the removal of the full notebook cells
+    var notebookContainer = gd.closest('#notebook-container');
+    if (notebookContainer) {{
+        x.observe(notebookContainer, {childList: true});
+    }}
+    
+    // Listen for the clearing of the current output cell
+    var outputEl = gd.closest('.output');
+    if (outputEl) {{
+        x.observe(outputEl, {childList: true});
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+                            })                };                });            </script>        </div>
 
 
 Load duration curve
@@ -506,7 +533,7 @@ To this end, go through the steps below.
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -524,7 +551,7 @@ To this end, go through the steps below.
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -542,7 +569,7 @@ To this end, go through the steps below.
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -561,7 +588,34 @@ the columns in your data.
     hourly_data.area(engine="plotly")
 
 
-.. image:: output_41_1.png
+
+.. raw:: html
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+    
+    var gd = document.getElementById('b1d1e3d6-e981-47cb-a9a5-08001e455fef');
+    var x = new MutationObserver(function (mutations, observer) {{
+            var display = window.getComputedStyle(gd).display;
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+                console.log([gd, 'removed!']);
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+    // Listen for the removal of the full notebook cells
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 Shadow Plot
@@ -580,7 +634,35 @@ shadow plot.
         engine="plotly",
     )
 
-.. image:: output_42_1.png
+
+
+.. raw:: html
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+    var gd = document.getElementById('f13fde2c-d783-49e8-abc1-6728a046762a');
+    var x = new MutationObserver(function (mutations, observer) {{
+            var display = window.getComputedStyle(gd).display;
+            if (!display || display === 'none') {{
+                console.log([gd, 'removed!']);
+                Plotly.purge(gd);
+                observer.disconnect();
+            }}
+    }});
+    
+    // Listen for the removal of the full notebook cells
+    var notebookContainer = gd.closest('#notebook-container');
+    if (notebookContainer) {{
+        x.observe(notebookContainer, {childList: true});
+    }}
+    
+    // Listen for the clearing of the current output cell
+    var outputEl = gd.closest('.output');
+    if (outputEl) {{
+        x.observe(outputEl, {childList: true});
+    }}
+    
+                            })                };                });            </script>        </div>
 
 
 General plot function
@@ -599,7 +681,7 @@ pd.DataFrame.plot function offering some more plotting routines:
 
 .. parsed-literal::
 
-    <AxesSubplot:>
+    <Axes: >
 
 
 
@@ -620,3 +702,6 @@ use the two save functions:
     
     # saving as xslx
     hourly_data.to_excel("path and name of excel file.xlsx")
+
+
+:download:`Link to the jupyter notebook file </../notebooks/plot_class.ipynb>`.
diff --git a/docs/source/examples/simple_bulb/output_13_1.png b/docs/source/examples/simple_bulb/output_13_1.png
index 3ffe73d83ab8f897b0640646a711289bb84c0bae..f125a91120b4bbfdce18bee3aa390f6f6dc742e7 100644
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diff --git a/docs/source/examples/simple_bulb/simple_bulb.rst b/docs/source/examples/simple_bulb/simple_bulb.rst
index fd4ed059..eb623922 100644
--- a/docs/source/examples/simple_bulb/simple_bulb.rst
+++ b/docs/source/examples/simple_bulb/simple_bulb.rst
@@ -1,15 +1,25 @@
 Simple Appliances with multiple functioning windows
 ===================================================
 
+In this example we are going to build a load simulation of a households
+category with only access to some indoor light bulbs.
+
 .. code:: ipython3
 
     # importing functions
-    from ramp import User, UseCase, get_day_type
+    from ramp import User, UseCase
     import pandas as pd
 
 Creating a user category
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
+To represent a user category, which is a collection of users with
+similar consumption characteristics such as appliance ownership and
+consumption behavior, the class **User** should be used. Each User
+instance is initially characterized by the number of users assigned to
+the user category. For example, let’s consider a household category
+comprising 10 households:
+
 .. code:: ipython3
 
     household = User(
@@ -20,21 +30,59 @@ Creating a user category
 Creating a simple appliance with two functioning time
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+In the most basic use cases of RAMP, you can model appliances with
+random usage variability across different time windows throughout the
+day, as will be created in this example.
+
+To model appliances within a household category, parameters such as the
+number of appliances owned, power usage, number of usage windows, and
+total usage time need to be specified. Additionally, parameters like the
+minimum usage time after a switch-on event can enhance the realism of
+simulations. The randomness of appliance usage can be adjusted through
+parameters such as variability in the usage window or total usage time.
+
+For example, let’s consider a household category where each household
+owns 6 “Indoor Light Bulbs,” each consuming 7 Watts. These light bulbs
+are used for 120 minutes in two time windows: from 00:00 to 00:30 and
+from 19:30 to 24:00. In RAMP simulations, time resolution is measured in
+minutes, with each simulation representing a single day from 00:00
+(corresponding to minute 0) to 24:00 (corresponding to minute 1440).
+
+Assuming a minimum usage time of 10 minutes each time a light bulb is
+switched on, we can simulate this appliance by creating a new appliance
+using the **add_appliance** method of the User object:
+
 .. code:: ipython3
 
+    # add_appliance is meth
     indoor_bulb = household.add_appliance(
-        name="Indoor Light Bulb",
-        number=6,
-        power=7,
-        num_windows=2,
-        func_time=120,
-        time_fraction_random_variability=0.2,
-        func_cycle=10,
-        window_1=[1170, 1440],  # from 19:30 to 24:00
-        window_2=[0, 30],  # from 24 to 00:30
-        random_var_w=0.35,
+        name="Indoor Light Bulb", # the name of the appliance
+        number=6, # how many of this appliance each user has in this user category
+        power=7, # the power (in Watt) of each single appliance. RAMP does not deal with units of measures, you should check the consistency of the unit of measures throughout your model
+        num_windows=2, # how many usage time windows throughout the day?
+        func_time=120, # the total usage time of appliances
+        func_cycle=10, # the minimum usage time after a switch on event
+        window_1=[0, 30],  # from 24 to 00:30
+        window_2=[1170, 1440],  # from 19:30 to 24:00
+        random_var_w=0.35, # Variability of the windows in percentage
+        time_fraction_random_variability=0.2, # randomizes the total time the appliance is on (between 0 and 1)
     )
 
+.. code:: ipython3
+
+    print(household)
+
+
+.. parsed-literal::
+
+       user_name  num_users               name  number  power
+    0  Household         10  Indoor Light Bulb       6    7.0
+
+
+You can check the **maximum theoretical profile** of the appliance and
+user category by calling the **maximum_profile** property of each user
+or appliance object:
+
 .. code:: ipython3
 
     # Checking the maximum profile of the appliance and user
@@ -55,21 +103,35 @@ Creating a simple appliance with two functioning time
 
 
 
-.. image:: output_6_1.png
+.. image:: output_8_1.png
 
 
 
-.. image:: output_6_2.png
+.. image:: output_8_2.png
 
 
 Whole year profile functionality
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+To generate profiles spanning more than a single day, the UseCase class
+in RAMP allows for specifying calendar days as the start and end dates
+of the simulation. The UseCase class serves as a collector of different
+user categories and provides user-friendly methods for generating
+profiles with various settings.
+
+For example, suppose you want to simulate the behavior of 10 households
+for the entire year 2020. Using the UseCase class, you can specify the
+calendar days for the start and end dates of the simulation, creating a
+comprehensive profile spanning the entire year:
+
 .. code:: ipython3
 
-    whole_year_profile = []
-    use_case = UseCase(users=[household], date_start="2020-01-01", date_end="2020-12-31")
-    whole_year_profile = use_case.generate_daily_load_profiles()
+    use_case = UseCase(
+        users=[household], # A list of all the user categories to be included in the simulation. In this case, we only have household user category
+        date_start="2020-01-01", # starting date of the simulation
+        date_end="2020-12-31", # end date of the simulation
+        )
+
 
 
 .. parsed-literal::
@@ -77,6 +139,28 @@ Whole year profile functionality
     You will simulate 366 day(s) from 2020-01-01 00:00:00 until 2021-01-01 00:00:00
 
 
+To generate the profiles, you can use, **generate_daily_load_profiles**
+methods.
+
+.. code:: ipython3
+
+    whole_year_profile = use_case.generate_daily_load_profiles()
+
+
+::
+
+
+    ---------------------------------------------------------------------------
+
+    NameError                                 Traceback (most recent call last)
+
+    Cell In[16], line 1
+    ----> 1 whole_year_profile = use_case.generate_daily_load_profiles()
+
+
+    NameError: name 'use_case' is not defined
+
+
 .. code:: ipython3
 
     whole_year_profile = pd.DataFrame(
@@ -94,17 +178,37 @@ Whole year profile functionality
 
 
 
-.. image:: output_9_1.png
+.. image:: output_13_1.png
 
 
 Generating a profile for a single day
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-provide day_type=0 for weekday and day_type=1 for weekends
+To generate daily profiles for specific user categories, the User class
+in RAMP offers various methods. One such method is
+**generate_single_load_profile**, which constructs a load profile for a
+single user, accounting for all their appliances, based on a specific
+day of the year or a designated day_type. In RAMP, day_types represent
+weekdays and weekends, with day_type=0 indicating weekdays and
+day_type=1 signifying weekends. When defining appliances using the
+add_appliance method, users can specify whether the appliance is
+utilized throughout the designated wd_we_type argument.
+
+For instance, let’s consider the scenario where we aim to generate a
+single-day load profile for a weekday of the year for a single user
+within the household category:
 
 .. code:: ipython3
 
-    single_profile = household.generate_single_load_profile(day_type=0)
+    single_profile = household.generate_single_load_profile(
+        day_type=0
+        )
+
+
+.. parsed-literal::
+
+    You are generating ramp demand from a User not bounded to a UseCase instance, a default one has been created for you 
+
 
 .. code:: ipython3
 
@@ -121,18 +225,22 @@ provide day_type=0 for weekday and day_type=1 for weekends
 
 
 
-.. image:: output_12_1.png
+.. image:: output_16_1.png
 
 
 Generating aggregated_load_profile for the user category
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Single daily profiles are aggregated for all the users defined within
-the User class
+If instead of a single user from the categoy, you want to generate load
+profiles of the aggregated users of the category, for a single day, you
+can use the similar method of User class, named
+**generate_aggregated_load_profile**:
 
 .. code:: ipython3
 
-    aggregated_profile = household.generate_aggregated_load_profile()
+    aggregated_profile = household.generate_aggregated_load_profile(
+        day_type=0
+    )
 
 .. code:: ipython3
 
@@ -149,6 +257,7 @@ the User class
 
 
 
-.. image:: output_15_1.png
+.. image:: output_19_1.png
 
 
+:download:`Link to the jupyter notebook file </../notebooks/simple_bulb.ipynb>`.
diff --git a/docs/source/examples/thermal_app/thermal_app.rst b/docs/source/examples/thermal_app/thermal_app.rst
index de36db0d..d7cb9dc0 100644
--- a/docs/source/examples/thermal_app/thermal_app.rst
+++ b/docs/source/examples/thermal_app/thermal_app.rst
@@ -1,20 +1,18 @@
 Thermal loads
 =============
 
-This example input file represents a single household user whose only
-load is the “shower”. The example showcases how to model thermal loads
-by: 1) using a time-varying average ``power`` attribute, pre-calculated
-as a function of the average daily groundwater temperature; and 2) using
-the ``thermal_p_var`` attribute to add further variability to the actual
-power absorbed by the appliance in each usage event, which reflects the
-randomness of user behaviour in preferring a slightly warmer or colder
-shower temperature.
+Some of the energy requirements of users are drived my technologies that
+have time variant power consumption. Hot water boilers are an example of
+technologies that can have variant powers throughout time. To show case
+how loads as such can be modelled in RAMP, we take the example of a
+household thermal load for generating shower hot water.
 
 .. code:: ipython3
 
     # importing functions
-    from ramp import User, UseCase, load_data, get_day_type
+    from ramp import User, UseCase, load_data
     import pandas as pd
+    import matplotlib.pyplot as plt
 
 Creating a user category and appliances
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -23,18 +21,16 @@ Creating a user category and appliances
 
     household = User()
 
-When the power varies across days of the year, for instance, as a
-function of the average daily groundwater temperature, the “power”
-parameter can be passed as a ``pd.DataFrame`` or ``np.array`` with a
-daily profile (365 rows of data). For this exercise, data can be loaded
-from the default examples in ramp:
+RAMP allows the user to give variant power profile for an appliance with
+a daily resolution. In this case,the power property in an appliance
+should be given as a timeseries pd.DataFrame or np.array with daily
+profile (365 rows of data). For this exercise, we will use a built-in
+power profile for thermal load example that can be loaded using
+load_data util function:
 
 .. code:: ipython3
 
     shower_power = load_data(example="shower")
-
-.. code:: ipython3
-
     # shower power distribution
     shower_power.plot()
 
@@ -48,7 +44,7 @@ from the default examples in ramp:
 
 
 
-.. image:: output_6_1.png
+.. image:: output_5_1.png
 
 
 .. code:: ipython3
@@ -56,14 +52,12 @@ from the default examples in ramp:
     shower = household.add_appliance(
         name="Shower",
         number=1,
-        power=shower_power,
-        num_windows=2,
-        func_time=15,
-        time_fraction_random_variability=0.1,
-        func_cycle=3,
-        window_1=[390, 540],
-        window_2=[1080, 1200],
-        random_var_w=0.2,
+        power=shower_power, # pass the pd.DataFrame or np.array instead of a number
+        num_windows=2, # two possibe time window for shower
+        func_time=15, # each shower takes 15 minute
+        func_cycle=3, # every
+        window_1=[390, 540], #  morning shower from 6:30 to 9:00 AM
+        window_2=[1080, 1200], # evening shower from 18:00 to 20:00
     )
 
 Generating profiles for increasing degrees of ``thermal_p_var``
@@ -80,20 +74,39 @@ Generating profiles for increasing degrees of ``thermal_p_var``
     You will simulate 365 day(s) from 2020-01-01 00:00:00 until 2020-12-31 00:00:00
 
 
+.. parsed-literal::
+
+    c:\users\tahavorm\downloads\gitrepos\ramp\ramp\core\core.py:299: FutureWarning: 'T' is deprecated and will be removed in a future version. Please use 'min' instead of 'T'.
+      end=self.days[-1] + pd.Timedelta(1, "d") - pd.Timedelta(1, "T"),
+    c:\users\tahavorm\downloads\gitrepos\ramp\ramp\core\core.py:297: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.
+      self.__datetimeindex = pd.date_range(
+
+
+As everyone has a different habit in the water temperature for taking
+shower, we can also consider a variability in the thermal power through
+the thermal_p_var property. Using the ``thermal_p_var`` attribute to add
+further variability to the actual power absorbed by the appliance in
+each usage event, which reflects the randomness of user behaviour in
+preferring a slightly warmer or colder shower temperature.To better
+understand the effect of this parameters, let’s perform a sensitivity
+analysis on themal_p_var function. To do so, we use the following
+function **thermal_p_var_sensitivty**:
+
 .. code:: ipython3
 
-    def thermal_p_var_sensitivity(values):
+    def thermal_p_var_sensitivity(sensitivity_values):
         # buidling a pd.DataFrame for saving sensitivity results
         results = pd.DataFrame(
-            index=pd.date_range(start="2020-01-01", periods=1440 * 365, freq="T"),
-            columns=[f"p_var = {value}" for value in values],
+            index=range(0,1440*365),
+            columns=[f"p_var = {value}" for value in sensitivity_values],
         )
     
-        for value in values:
+        for value in sensitivity_values:
+    
             # changing the thermal_P_var
             shower.thermal_p_var = value
     
-            profiles = usecase.generate_daily_load_profiles(flat=True)
+            profiles = usecase.generate_daily_load_profiles()
     
             # assigning the yearly profile for a given sensitivity case
             results[f"p_var = {value}"] = profiles
@@ -102,177 +115,37 @@ Generating profiles for increasing degrees of ``thermal_p_var``
 
 .. code:: ipython3
 
-    sensitivity_results = thermal_p_var_sensitivity([0, 0.25, 0.5, 0.75, 1])
+    # generating 5 senstivities on thermal_p_var
+    sensitivity_results = thermal_p_var_sensitivity([0, 0.25, 0.5])
 
 .. code:: ipython3
 
-    sensitivity_results
-
-
-
-
-.. raw:: html
-
-    <div>
-    <style scoped>
-        .dataframe tbody tr th:only-of-type {
-            vertical-align: middle;
-        }
+    days_to_plot = [1,100,200] # which days of the year to plot
     
-        .dataframe tbody tr th {
-            vertical-align: top;
-        }
+    fig, axes = plt.subplots(ncols=len(days_to_plot), nrows=sensitivity_results.shape[1], figsize=(10, 10)) 
     
-        .dataframe thead th {
-            text-align: right;
-        }
-    </style>
-    <table border="1" class="dataframe">
-      <thead>
-        <tr style="text-align: right;">
-          <th></th>
-          <th>p_var = 0</th>
-          <th>p_var = 0.25</th>
-          <th>p_var = 0.5</th>
-          <th>p_var = 0.75</th>
-          <th>p_var = 1</th>
-        </tr>
-      </thead>
-      <tbody>
-        <tr>
-          <th>2020-01-01 00:00:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-01-01 00:01:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-01-01 00:02:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-01-01 00:03:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-01-01 00:04:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>...</th>
-          <td>...</td>
-          <td>...</td>
-          <td>...</td>
-          <td>...</td>
-          <td>...</td>
-        </tr>
-        <tr>
-          <th>2020-12-30 23:55:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-12-30 23:56:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-12-30 23:57:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-12-30 23:58:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-        <tr>
-          <th>2020-12-30 23:59:00</th>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-          <td>0.0</td>
-        </tr>
-      </tbody>
-    </table>
-    <p>525600 rows × 5 columns</p>
-    </div>
-
-
-
-.. code:: ipython3
-
-    # showing the daily average of the load profiles
-    average_daily_profiles = sensitivity_results.resample("1d").mean()
-
-.. code:: ipython3
-
-    average_daily_profiles.plot()
-
-
-
-
-.. parsed-literal::
-
-    <Axes: >
-
-
-
-
-.. image:: output_14_1.png
-
-
-.. code:: ipython3
-
-    first_day = pd.date_range(
-        start="2020-01-01 00:00:00", freq="1min", periods=24 * 60  # a full day
-    )
-    sensitivity_results.loc[first_day].plot()
-
-
-
-
-.. parsed-literal::
-
-    <Axes: >
+    for j,day in enumerate(days_to_plot):
+        for i, col in enumerate(sensitivity_results):
+            sensitivity_results[col].iloc[1440*(day-1):1440*(day)].plot(ax=axes[i,j]) # just plot for the first day
+            axes[i,j].set_title(f"Day {day} - thermal_p_var = {col}",fontsize=8)
+            axes[i,j].set_ylim(0,24000)
+    
+        
+    
+    plt.tight_layout()
+    plt.show()
 
 
 
+.. image:: output_12_0.png
 
-.. image:: output_15_1.png
 
+As it can be observed, the power consumption of hot water supply
+technology varies across different days of the year, primarily due to
+fluctuations in the nominal power. When adjusting the parameter
+**thermal_p_var** from 0, indicating no variability in power consumption
+due to user preferences, to higher values, which signify the probability
+of changes in hot water temperature, the power consumption also varies
+accordingly.
 
+:download:`Link to the jupyter notebook file </../notebooks/thermal_app.ipynb>`.
diff --git a/docs/source/examples/using_excel/using_excel.rst b/docs/source/examples/using_excel/using_excel.rst
index a97dba6a..79efbef3 100644
--- a/docs/source/examples/using_excel/using_excel.rst
+++ b/docs/source/examples/using_excel/using_excel.rst
@@ -315,3 +315,5 @@ Generating load profiles for the single users of the usecase
 .. image:: output_19_1.png
 
 
+
+:download:`Link to the jupyter notebook file </../notebooks/using_excel.ipynb>`.

From 6e3ba80c37e242df357acd1d6447eaa56bab221a Mon Sep 17 00:00:00 2001
From: Johann Kraft <johann.kraft@student.kit.edu>
Date: Thu, 4 Apr 2024 12:28:59 +0200
Subject: [PATCH 073/140] Add seed parameter to UseCase

---
 ramp/core/core.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 4f5e2725..a716fc5a 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -53,6 +53,7 @@ def __init__(
         date_end: str = None,
         parallel_processing: bool = False,
         peak_enlarge: float = 0.15,
+        random_seed: int = None,
     ):
         """Creates a UseCase instance for gathering a list of User instances which own Appliance instances
 
@@ -70,6 +71,8 @@ def __init__(
             if set True, the profiles will be generated in parallel rather than sequencially
         peak_enlarge: float, optional
             percentage random enlargement or reduction of peak time range length, used in UseCase.calc_peak_time_range
+        random_seed: int, optional
+            specify seed for the random number generator to exactly reproduce results
 
         """
         self.name = name
@@ -90,6 +93,8 @@ def __init__(
         self.__num_days = None
         self.__datetimeindex = None
         self.daily_profiles = None
+        self.random_seed = random_seed
+
 
         self.appliances = []
         self.users = []
@@ -101,6 +106,10 @@ def __init__(
         if self.date_start is not None and self.date_end is not None:
             self.initialize()
 
+        # Set global random seed if it is specified
+        if self.random_seed:
+            random.seed(self.random_seed)
+
     @property
     def date_start(self):
         """Start date of the daily profiles generated by the UseCase instance"""

From 884a797d709bffd216014461537022166e756ad8 Mon Sep 17 00:00:00 2001
From: Johann Kraft <johann.kraft@student.kit.edu>
Date: Thu, 11 Apr 2024 12:27:52 +0200
Subject: [PATCH 074/140] Example file to demonstrate fixed random seed

---
 ramp/example/random_seed_example.py | 48 +++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)
 create mode 100644 ramp/example/random_seed_example.py

diff --git a/ramp/example/random_seed_example.py b/ramp/example/random_seed_example.py
new file mode 100644
index 00000000..836aa7f1
--- /dev/null
+++ b/ramp/example/random_seed_example.py
@@ -0,0 +1,48 @@
+import pandas as pd
+from copy import deepcopy
+
+from ramp.core.core import UseCase
+
+# Import users list of example file 1
+from ramp.example.input_file_1 import User_list
+
+# Build use case 1 with non-fixed seed
+uc_1 = UseCase(
+    users=deepcopy(User_list),
+    random_seed=None
+)
+# Initialize and generate load profile
+uc_1.initialize(peak_enlarge=0.15, num_days=3)
+uc_1_lp = uc_1.generate_daily_load_profiles()
+
+# Build use case 2 and fixed random seed
+uc_2 = UseCase(
+    users=deepcopy(User_list),
+    random_seed=1
+)
+# Initialize and generate load profile
+uc_2.initialize(peak_enlarge=0.15, num_days=3)
+uc_2_lp = uc_2.generate_daily_load_profiles()
+
+# Build use case 3 and same fixed random seed as uc_2
+uc_3 = UseCase(
+    users=deepcopy(User_list),
+    random_seed=1
+)
+
+# Initialize and generate load profile
+uc_3.initialize(peak_enlarge=0.15, num_days=3)
+uc_3_lp = uc_3.generate_daily_load_profiles()
+
+
+#%% Plot results
+import matplotlib.pyplot as plt
+
+lp_df = pd.DataFrame({'uc_1_non_fixed_seed': uc_1_lp,
+                      'uc_2_fixed_seed': uc_2_lp,
+                      'uc_3_fixed_seed': uc_3_lp,
+                      'diff uc_2 - uc-1': uc_2_lp - uc_1_lp,  # difference between uc_1 and uc_2 is not zero
+                      'diff uc_2 - uc_3': uc_2_lp - uc_3_lp  # difference between uc_2 and uc_3 is zero
+                      })
+lp_df.plot()
+plt.show()
\ No newline at end of file

From f0bacef8dc5396e43705c6f2df95eebf5058de4b Mon Sep 17 00:00:00 2001
From: Johann Kraft <johann.kraft@student.kit.edu>
Date: Fri, 12 Apr 2024 14:54:03 +0200
Subject: [PATCH 075/140] Added myself to AUTHORS

---
 AUTHORS | 1 +
 1 file changed, 1 insertion(+)

diff --git a/AUTHORS b/AUTHORS
index 5cab0e24..68650fcf 100644
--- a/AUTHORS
+++ b/AUTHORS
@@ -23,3 +23,4 @@ Maria C.G. Hart, Leibniz Universität Hannover <hart@iwi.uni-hannover.de>
 Francesco Sanvito, TU Delft <F.Sanvito@tudelft.nl>
 Gregory Ireland, Reiner Lemoine Institut <Gregory.Ireland@rl-institut.de>
 Sergio Balderrama, Universidad Mayor de San Simon <slbalderrama@fcyt.umss.edu.bo>
+Johann Kraft, Reiner Lemoine Institut <johann.kraft@rl-institut.de>

From 04f22a5a402192b83f0375a3ae2b52474ea06963 Mon Sep 17 00:00:00 2001
From: Johann <jok.kraft@gmail.com>
Date: Wed, 8 May 2024 17:09:28 +0200
Subject: [PATCH 076/140] Added Notebook demonstrating the random_seed
 functionality.

---
 docs/notebooks/random_seed.ipynb    | 196 ++++++++++++++++++++++++++++
 ramp/example/random_seed_example.py |  48 -------
 2 files changed, 196 insertions(+), 48 deletions(-)
 create mode 100644 docs/notebooks/random_seed.ipynb
 delete mode 100644 ramp/example/random_seed_example.py

diff --git a/docs/notebooks/random_seed.ipynb b/docs/notebooks/random_seed.ipynb
new file mode 100644
index 00000000..c8213b31
--- /dev/null
+++ b/docs/notebooks/random_seed.ipynb
@@ -0,0 +1,196 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# Specifing the Random Seed\n",
+    "\n",
+    "When building a UseCase, you can provide a random_seed value. It is used to initialize the pseudo-random number generator.\n",
+    "This makes it possible to exactly reproduce the results of running a RAMP UseCase, for example to provide a code sample which exactly produces the presented data and plots in a paper."
+   ],
+   "id": "ce224d1e9b768f7a"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Generate a UseCase without specified seed\n",
+    "- By default, the random_seed is None. In this case, the pseudo-random number generator uses the current system time as seed. (See https://docs.python.org/3/library/random.html#bookkeeping-functions for details). \n",
+    "- All UseCases in the following example are generated with the User_list from input_file_1 of the examples.\n",
+    "- The UseCase is initialized and a load profile is generated.\n"
+   ],
+   "id": "4f72c158e8d9fda0"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-08T14:52:38.256239Z",
+     "start_time": "2024-05-08T14:52:37.058279Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from ramp import UseCase\n",
+    "from copy import deepcopy"
+   ],
+   "id": "2a602befafcfae1f",
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-08T14:54:08.983642Z",
+     "start_time": "2024-05-08T14:54:08.329458Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Import users list of example file 1\n",
+    "from ramp.example.input_file_1 import User_list\n",
+    "\n",
+    "# Build use case 1 with non-fixed seed\n",
+    "uc_1 = UseCase(\n",
+    "    users=deepcopy(User_list),\n",
+    "    random_seed=None\n",
+    ")\n",
+    "# Initialize and generate load profile\n",
+    "uc_1.initialize(peak_enlarge=0.15, num_days=3)\n",
+    "uc_1_lp = uc_1.generate_daily_load_profiles()"
+   ],
+   "id": "441c5f7b2bf33409",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You will simulate 3 day(s) from 2024-05-08 00:00:00 until 2024-05-11 00:00:00\n"
+     ]
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Generate two UseCases with fixed random seed\n",
+    "- The same, fixed random seed is specified for UseCases 2 and 3. \n",
+    "- The users and parameters of initialization are identical with UseCase 1\n",
+    "- A load profile is generated for each UseCase"
+   ],
+   "id": "1c07481527fd933c"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-08T14:52:47.272310Z",
+     "start_time": "2024-05-08T14:52:45.855080Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Build use case 2 and fixed random seed\n",
+    "uc_2 = UseCase(\n",
+    "    users=deepcopy(User_list),\n",
+    "    random_seed=1\n",
+    ")\n",
+    "# Initialize and generate load profile\n",
+    "uc_2.initialize(peak_enlarge=0.15, num_days=3)\n",
+    "uc_2_lp = uc_2.generate_daily_load_profiles()\n",
+    "\n",
+    "# Build use case 3 and same fixed random seed as uc_2\n",
+    "uc_3 = UseCase(\n",
+    "    users=deepcopy(User_list),\n",
+    "    random_seed=1\n",
+    ")\n",
+    "\n",
+    "# Initialize and generate load profile\n",
+    "uc_3.initialize(peak_enlarge=0.15, num_days=3)\n",
+    "uc_3_lp = uc_3.generate_daily_load_profiles()"
+   ],
+   "id": "902bbd475ea04ac4",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You will simulate 3 day(s) from 2024-05-08 00:00:00 until 2024-05-11 00:00:00\n",
+      "You will simulate 3 day(s) from 2024-05-08 00:00:00 until 2024-05-11 00:00:00\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Comparing the results\n",
+    "The plot visualizes the effect of specifying the random seed:\n",
+    "- The load profiles of UseCase 2 and 3 are identical.\n",
+    "- The load profile of UseCase 1 is different, since the pseudo-random number generators seed was not specified.\n",
+    "If you rerun the above code and plot the new load profiles, you will notice that the load profile of UseCase 2 and 3 remain unchanged, while the result of UseCase 1 differs. "
+   ],
+   "id": "f72471c560cacc3c"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-08T14:59:11.000826Z",
+     "start_time": "2024-05-08T14:59:10.766744Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "lp_df = pd.DataFrame({'UseCase_1: non_fixed_seed': uc_1_lp,\n",
+    "                      'UseCase_2: fixed_seed': uc_2_lp,\n",
+    "                      'UseCase_3: fixed_seed': uc_3_lp,\n",
+    "                      'diff UseCase_2 - UseCase_1': uc_2_lp - uc_1_lp,  # difference between uc_1 and uc_2 is not zero\n",
+    "                      'diff UseCase_2 - UseCase_3': uc_2_lp - uc_3_lp  # difference between uc_2 and uc_3 is zero\n",
+    "                      })\n",
+    "lp_df.plot()\n",
+    "plt.show()"
+   ],
+   "id": "d65dc51db815e8b4",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 9
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ramp/example/random_seed_example.py b/ramp/example/random_seed_example.py
deleted file mode 100644
index 836aa7f1..00000000
--- a/ramp/example/random_seed_example.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import pandas as pd
-from copy import deepcopy
-
-from ramp.core.core import UseCase
-
-# Import users list of example file 1
-from ramp.example.input_file_1 import User_list
-
-# Build use case 1 with non-fixed seed
-uc_1 = UseCase(
-    users=deepcopy(User_list),
-    random_seed=None
-)
-# Initialize and generate load profile
-uc_1.initialize(peak_enlarge=0.15, num_days=3)
-uc_1_lp = uc_1.generate_daily_load_profiles()
-
-# Build use case 2 and fixed random seed
-uc_2 = UseCase(
-    users=deepcopy(User_list),
-    random_seed=1
-)
-# Initialize and generate load profile
-uc_2.initialize(peak_enlarge=0.15, num_days=3)
-uc_2_lp = uc_2.generate_daily_load_profiles()
-
-# Build use case 3 and same fixed random seed as uc_2
-uc_3 = UseCase(
-    users=deepcopy(User_list),
-    random_seed=1
-)
-
-# Initialize and generate load profile
-uc_3.initialize(peak_enlarge=0.15, num_days=3)
-uc_3_lp = uc_3.generate_daily_load_profiles()
-
-
-#%% Plot results
-import matplotlib.pyplot as plt
-
-lp_df = pd.DataFrame({'uc_1_non_fixed_seed': uc_1_lp,
-                      'uc_2_fixed_seed': uc_2_lp,
-                      'uc_3_fixed_seed': uc_3_lp,
-                      'diff uc_2 - uc-1': uc_2_lp - uc_1_lp,  # difference between uc_1 and uc_2 is not zero
-                      'diff uc_2 - uc_3': uc_2_lp - uc_3_lp  # difference between uc_2 and uc_3 is zero
-                      })
-lp_df.plot()
-plt.show()
\ No newline at end of file

From 20564177b6060f6ffdef99ead20e7780359cd52a Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 29 Apr 2024 14:54:52 +0200
Subject: [PATCH 077/140] Add coverage and coveralls to test requirements

---
 tests/requirements.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/requirements.txt b/tests/requirements.txt
index d3c960f9..3a879277 100644
--- a/tests/requirements.txt
+++ b/tests/requirements.txt
@@ -2,4 +2,6 @@ pytest
 scipy
 nbconvert
 ipykernel
+coverage
+coveralls
 -e .
\ No newline at end of file

From 8556519bd0524922afa0534a9201a98d5d607f22 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 29 Apr 2024 14:58:31 +0200
Subject: [PATCH 078/140] Update workflow upon PR opening

---
 .github/workflows/pytest.yml | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index 7e2e0c3f..51354081 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -37,4 +37,15 @@ jobs:
         pip install -r tests/requirements.txt
     - name: Test with pytest
       run: |
-        pytest tests/
+        coverage run -m pytest tests/
+
+    - name: Check test coverage
+      run: |
+        coverage report -m
+
+    - name: Report to coveralls
+      run: |
+        coveralls
+      env:
+        COVERALLS_REPO_TOKEN: ${{ secrets.COVERALL_TOKEN }}
+        COVERALLS_SERVICE_NAME: github

From 2e26f033149cf11025ddcc6e878313a349fc440b Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 8 May 2024 23:31:13 +0200
Subject: [PATCH 079/140] Lint with black

---
 docs/source/conf.py |  6 +++---
 ramp/core/core.py   | 10 +++++++---
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index c30c43c8..cb331cbf 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -32,9 +32,9 @@ def copy_readme():
     idx = data.index(
         "To contribute changes please consult our `Contribution guidelines <https://github.com/RAMP-project/RAMP/blob/main/CONTRIBUTING.md>`_\n"
     )
-    data[
-        idx
-    ] = "To contribute changes please consult our `Contribution guidelines <contributing.html>`_\n"
+    data[idx] = (
+        "To contribute changes please consult our `Contribution guidelines <contributing.html>`_\n"
+    )
     with open("readme.rst", "w") as fp:
         fp.writelines(data)
 
diff --git a/ramp/core/core.py b/ramp/core/core.py
index a716fc5a..99d304e9 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -42,7 +42,9 @@ def single_appliance_daily_load_profile(args):
 
     return args[0], app.daily_use
 
-warnings.simplefilter('always', DeprecationWarning)
+
+warnings.simplefilter("always", DeprecationWarning)
+
 
 class UseCase:
     def __init__(
@@ -95,7 +97,6 @@ def __init__(
         self.daily_profiles = None
         self.random_seed = random_seed
 
-
         self.appliances = []
         self.users = []
         if users is None:
@@ -895,7 +896,10 @@ def Appliance(
         refer to Appliance class docs
         """
 
-        warnings.warn("This function is deprecated and not supported since version v0.4.0. Instead use the add_appliance method.", DeprecationWarning)
+        warnings.warn(
+            "This function is deprecated and not supported since version v0.4.0. Instead use the add_appliance method.",
+            DeprecationWarning,
+        )
 
         return self.add_appliance(
             number=number,

From 6d8a1963406f63c2ec644382d34ba394a061e1d1 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 8 May 2024 23:31:32 +0200
Subject: [PATCH 080/140] Trigger jobs on joss-paper branch

---
 .github/workflows/pytest.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index 51354081..edfa4dc0 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -5,9 +5,9 @@ name: RAMP tests
 
 on:
   push:
-    branches: [ "main", "development" ]
+    branches: [ "main", "development", "joss-paper"]
   pull_request:
-    branches: [ "main", "development" ]
+    branches: [ "main", "development", "joss-paper"]
 
 permissions:
   contents: read

From c9f039bcd777768fe2fd62f6c33dc7b1ab5b83a1 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 8 May 2024 23:31:54 +0200
Subject: [PATCH 081/140] Fix test setup

---
 setup.py               | 2 +-
 tests/requirements.txt | 7 ++++---
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/setup.py b/setup.py
index 055399da..1dffb354 100644
--- a/setup.py
+++ b/setup.py
@@ -13,7 +13,7 @@
     version=__version__,
     packages=find_packages(),
     license="European Union Public License 1.2",
-    python_requires="<=3.10",
+    python_requires="<=3.11",
     package_data={"": ["*.txt", "*.dat", "*.doc", "*.rst", "*.xlsx", "*.csv"]},
     install_requires=[
         "pandas >= 1.3.3",
diff --git a/tests/requirements.txt b/tests/requirements.txt
index 3a879277..8ba4d4e7 100644
--- a/tests/requirements.txt
+++ b/tests/requirements.txt
@@ -1,7 +1,8 @@
 pytest
-scipy
+mock
+scipy==1.12.0
 nbconvert
 ipykernel
-coverage
-coveralls
+coverage==7.4.4
+coveralls==4.0.0
 -e .
\ No newline at end of file

From bef31c144978e43eae532723abc854c77d47fbe3 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 8 May 2024 23:32:07 +0200
Subject: [PATCH 082/140] Add coveralls badge to readme

---
 README.rst | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/README.rst b/README.rst
index fa29acac..50be80fd 100644
--- a/README.rst
+++ b/README.rst
@@ -11,6 +11,10 @@
     :target: https://rampdemand.readthedocs.io/en/latest/?badge=latest
     :alt: Documentation Status
 
+.. image:: https://coveralls.io/repos/github/RAMP-project/RAMP/badge.svg?branch=main
+:target: https://coveralls.io/github/RAMP-project/RAMP?branch=main
+
+
 .. image:: https://github.com/RAMP-project/RAMP/blob/main/docs/source/_static/RAMP_logo_basic.png?raw=true
    :width: 300
 

From cb2f955eba7aaf7e9e9733311124d9d08f157248 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 6 May 2024 13:58:19 +0200
Subject: [PATCH 083/140] Remove deprecated code usage

---
 docs/notebooks/multi_cycle.ipynb |   2 +-
 ramp/example/input_file_1.py     | 106 +++++++++++++++----------------
 ramp/example/input_file_2.py     |   2 +-
 ramp/example/input_file_3.py     |  24 +++----
 4 files changed, 67 insertions(+), 67 deletions(-)

diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb
index c9ad8676..552256ea 100644
--- a/docs/notebooks/multi_cycle.ipynb
+++ b/docs/notebooks/multi_cycle.ipynb
@@ -79,7 +79,7 @@
    "outputs": [],
    "source": [
     "# creating the appliance\n",
-    "fridge = household.Appliance(\n",
+    "fridge = household.add_appliance(\n",
     "    name=\"Fridge\",\n",
     "    number=1,\n",
     "    power=200,\n",
diff --git a/ramp/example/input_file_1.py b/ramp/example/input_file_1.py
index 8ff7cd6b..ae1179f4 100644
--- a/ramp/example/input_file_1.py
+++ b/ramp/example/input_file_1.py
@@ -46,43 +46,43 @@
 # Create new appliances
 
 # Church
-Ch_indoor_bulb = Church.Appliance(10, 26, 1, 210, 0.2, 60, "yes", flat="yes")
+Ch_indoor_bulb = Church.add_appliance(10, 26, 1, 210, 0.2, 60, "yes", flat="yes")
 Ch_indoor_bulb.windows([1200, 1440], [0, 0], 0.1)
 
-Ch_outdoor_bulb = Church.Appliance(7, 26, 1, 150, 0.2, 60, "yes", flat="yes")
+Ch_outdoor_bulb = Church.add_appliance(7, 26, 1, 150, 0.2, 60, "yes", flat="yes")
 Ch_outdoor_bulb.windows([1200, 1440], [0, 0], 0.1)
 
-Ch_speaker = Church.Appliance(1, 100, 1, 150, 0.2, 60)
+Ch_speaker = Church.add_appliance(1, 100, 1, 150, 0.2, 60)
 Ch_speaker.windows([1200, 1350], [0, 0], 0.1)
 
 # Public lighting
-Pub_lights = Public_lighting.Appliance(12, 40, 2, 310, 0.1, 300, "yes", flat="yes")
+Pub_lights = Public_lighting.add_appliance(12, 40, 2, 310, 0.1, 300, "yes", flat="yes")
 Pub_lights.windows([0, 336], [1110, 1440], 0.2)
 
-Pub_lights_2 = Public_lighting.Appliance(25, 150, 2, 310, 0.1, 300, "yes", flat="yes")
+Pub_lights_2 = Public_lighting.add_appliance(25, 150, 2, 310, 0.1, 300, "yes", flat="yes")
 Pub_lights_2.windows([0, 336], [1110, 1440], 0.2)
 
 
 # High-Income
-HI_indoor_bulb = HI.Appliance(6, 7, 2, 120, 0.2, 10)
+HI_indoor_bulb = HI.add_appliance(6, 7, 2, 120, 0.2, 10)
 HI_indoor_bulb.windows([1170, 1440], [0, 30], 0.35)
 
-HI_outdoor_bulb = HI.Appliance(2, 13, 2, 600, 0.2, 10)
+HI_outdoor_bulb = HI.add_appliance(2, 13, 2, 600, 0.2, 10)
 HI_outdoor_bulb.windows([0, 330], [1170, 1440], 0.35)
 
-HI_TV = HI.Appliance(2, 60, 3, 180, 0.1, 5)
+HI_TV = HI.add_appliance(2, 60, 3, 180, 0.1, 5)
 HI_TV.windows([720, 900], [1170, 1440], 0.35, [0, 60])
 
-HI_DVD = HI.Appliance(1, 8, 3, 60, 0.1, 5)
+HI_DVD = HI.add_appliance(1, 8, 3, 60, 0.1, 5)
 HI_DVD.windows([720, 900], [1170, 1440], 0.35, [0, 60])
 
-HI_Antenna = HI.Appliance(1, 8, 3, 120, 0.1, 5)
+HI_Antenna = HI.add_appliance(1, 8, 3, 120, 0.1, 5)
 HI_Antenna.windows([720, 900], [1170, 1440], 0.35, [0, 60])
 
-HI_Phone_charger = HI.Appliance(5, 2, 2, 300, 0.2, 5)
+HI_Phone_charger = HI.add_appliance(5, 2, 2, 300, 0.2, 5)
 HI_Phone_charger.windows([1110, 1440], [0, 30], 0.35)
 
-HI_Freezer = HI.Appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+HI_Freezer = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
 HI_Freezer.windows([0, 1440], [0, 0])
 HI_Freezer.specific_cycle_1(200, 20, 5, 10)
 HI_Freezer.specific_cycle_2(200, 15, 5, 15)
@@ -91,7 +91,7 @@
     [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
 )
 
-HI_Freezer2 = HI.Appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+HI_Freezer2 = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
 HI_Freezer2.windows([0, 1440], [0, 0])
 HI_Freezer2.specific_cycle_1(200, 20, 5, 10)
 HI_Freezer2.specific_cycle_2(200, 15, 5, 15)
@@ -100,32 +100,32 @@
     [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
 )
 
-HI_Mixer = HI.Appliance(1, 50, 3, 30, 0.1, 1, occasional_use=0.33)
+HI_Mixer = HI.add_appliance(1, 50, 3, 30, 0.1, 1, occasional_use=0.33)
 HI_Mixer.windows([420, 480], [660, 750], 0.35, [1140, 1200])
 
 # Higher-Middle Income
-HMI_indoor_bulb = HMI.Appliance(5, 7, 2, 120, 0.2, 10)
+HMI_indoor_bulb = HMI.add_appliance(5, 7, 2, 120, 0.2, 10)
 HMI_indoor_bulb.windows([1170, 1440], [0, 30], 0.35)
 
-HMI_outdoor_bulb = HMI.Appliance(2, 13, 2, 600, 0.2, 10)
+HMI_outdoor_bulb = HMI.add_appliance(2, 13, 2, 600, 0.2, 10)
 HMI_outdoor_bulb.windows([0, 330], [1170, 1440], 0.35)
 
-HMI_TV = HMI.Appliance(1, 60, 2, 120, 0.1, 5)
+HMI_TV = HMI.add_appliance(1, 60, 2, 120, 0.1, 5)
 HMI_TV.windows([1170, 1440], [0, 60], 0.35)
 
-HMI_DVD = HMI.Appliance(1, 8, 2, 40, 0.1, 5)
+HMI_DVD = HMI.add_appliance(1, 8, 2, 40, 0.1, 5)
 HMI_DVD.windows([1170, 1440], [0, 60], 0.35)
 
-HMI_Antenna = HMI.Appliance(1, 8, 2, 80, 0.1, 5)
+HMI_Antenna = HMI.add_appliance(1, 8, 2, 80, 0.1, 5)
 HMI_Antenna.windows([1170, 1440], [0, 60], 0.35)
 
-HMI_Radio = HMI.Appliance(1, 36, 2, 60, 0.1, 5)
+HMI_Radio = HMI.add_appliance(1, 36, 2, 60, 0.1, 5)
 HMI_Radio.windows([390, 450], [1140, 1260], 0.35)
 
-HMI_Phone_charger = HMI.Appliance(4, 2, 2, 300, 0.2, 5)
+HMI_Phone_charger = HMI.add_appliance(4, 2, 2, 300, 0.2, 5)
 HMI_Phone_charger.windows([1110, 1440], [0, 30], 0.35)
 
-HMI_Freezer = HMI.Appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+HMI_Freezer = HMI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
 HMI_Freezer.windows([0, 1440], [0, 0])
 HMI_Freezer.specific_cycle_1(200, 20, 5, 10)
 HMI_Freezer.specific_cycle_2(200, 15, 5, 15)
@@ -134,61 +134,61 @@
     [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
 )
 
-HMI_Mixer = HMI.Appliance(1, 50, 3, 30, 0.1, 1, occasional_use=0.33)
+HMI_Mixer = HMI.add_appliance(1, 50, 3, 30, 0.1, 1, occasional_use=0.33)
 HMI_Mixer.windows([420, 450], [660, 750], 0.35, [1020, 1170])
 
 # Lower-Midlle Income
-LMI_indoor_bulb = LMI.Appliance(3, 7, 2, 120, 0.2, 10)
+LMI_indoor_bulb = LMI.add_appliance(3, 7, 2, 120, 0.2, 10)
 LMI_indoor_bulb.windows([1170, 1440], [0, 30], 0.35)
 
-LMI_outdoor_bulb = LMI.Appliance(2, 13, 2, 600, 0.2, 10)
+LMI_outdoor_bulb = LMI.add_appliance(2, 13, 2, 600, 0.2, 10)
 LMI_outdoor_bulb.windows([0, 330], [1170, 1440], 0.35)
 
-LMI_TV = LMI.Appliance(1, 60, 3, 90, 0.1, 5)
+LMI_TV = LMI.add_appliance(1, 60, 3, 90, 0.1, 5)
 LMI_TV.windows([450, 660], [720, 840], 0.35, [1170, 1440])
 
-LMI_DVD = LMI.Appliance(1, 8, 3, 30, 0.1, 5)
+LMI_DVD = LMI.add_appliance(1, 8, 3, 30, 0.1, 5)
 LMI_DVD.windows([450, 660], [720, 840], 0.35, [1170, 1440])
 
-LMI_Antenna = LMI.Appliance(1, 8, 3, 60, 0.1, 5)
+LMI_Antenna = LMI.add_appliance(1, 8, 3, 60, 0.1, 5)
 LMI_Antenna.windows([450, 660], [720, 840], 0.35, [1170, 1440])
 
-LMI_Phone_charger = LMI.Appliance(4, 2, 1, 300, 0.2, 5)
+LMI_Phone_charger = LMI.add_appliance(4, 2, 1, 300, 0.2, 5)
 LMI_Phone_charger.windows([1020, 1440], [0, 0], 0.35)
 
-LMI_Mixer = LMI.Appliance(1, 50, 2, 30, 0.1, 1, occasional_use=0.33)
+LMI_Mixer = LMI.add_appliance(1, 50, 2, 30, 0.1, 1, occasional_use=0.33)
 LMI_Mixer.windows([660, 750], [1110, 1200], 0.35)
 
 # Low Income
-LI_indoor_bulb = LI.Appliance(2, 7, 2, 120, 0.2, 10)
+LI_indoor_bulb = LI.add_appliance(2, 7, 2, 120, 0.2, 10)
 LI_indoor_bulb.windows([1170, 1440], [0, 30], 0.35)
 
-LI_outdoor_bulb = LI.Appliance(1, 13, 2, 600, 0.2, 10)
+LI_outdoor_bulb = LI.add_appliance(1, 13, 2, 600, 0.2, 10)
 LI_outdoor_bulb.windows([0, 330], [1170, 1440], 0.35)
 
-LI_TV = LI.Appliance(1, 60, 3, 90, 0.1, 5)
+LI_TV = LI.add_appliance(1, 60, 3, 90, 0.1, 5)
 LI_TV.windows([750, 840], [1170, 1440], 0.35, [0, 30])
 
-LI_DVD = LI.Appliance(1, 8, 3, 30, 0.1, 5)
+LI_DVD = LI.add_appliance(1, 8, 3, 30, 0.1, 5)
 LI_DVD.windows([750, 840], [1170, 1440], 0.35, [0, 30])
 
-LI_Antenna = LI.Appliance(1, 8, 3, 60, 0.1, 5)
+LI_Antenna = LI.add_appliance(1, 8, 3, 60, 0.1, 5)
 LI_Antenna.windows([750, 840], [1170, 1440], 0.35, [0, 30])
 
-LI_Phone_charger = LI.Appliance(2, 2, 1, 300, 0.2, 5)
+LI_Phone_charger = LI.add_appliance(2, 2, 1, 300, 0.2, 5)
 LI_Phone_charger.windows([1080, 1440], [0, 0], 0.35)
 
 # Hospital
-Ho_indoor_bulb = Hospital.Appliance(12, 7, 2, 690, 0.2, 10)
+Ho_indoor_bulb = Hospital.add_appliance(12, 7, 2, 690, 0.2, 10)
 Ho_indoor_bulb.windows([480, 720], [870, 1440], 0.35)
 
-Ho_outdoor_bulb = Hospital.Appliance(1, 13, 2, 690, 0.2, 10)
+Ho_outdoor_bulb = Hospital.add_appliance(1, 13, 2, 690, 0.2, 10)
 Ho_outdoor_bulb.windows([0, 330], [1050, 1440], 0.35)
 
-Ho_Phone_charger = Hospital.Appliance(8, 2, 2, 300, 0.2, 5)
+Ho_Phone_charger = Hospital.add_appliance(8, 2, 2, 300, 0.2, 5)
 Ho_Phone_charger.windows([480, 720], [900, 1440], 0.35)
 
-Ho_Fridge = Hospital.Appliance(1, 150, 1, 1440, 0, 30, "yes", 3)
+Ho_Fridge = Hospital.add_appliance(1, 150, 1, 1440, 0, 30, "yes", 3)
 Ho_Fridge.windows([0, 1440], [0, 0])
 Ho_Fridge.specific_cycle_1(150, 20, 5, 10)
 Ho_Fridge.specific_cycle_2(150, 15, 5, 15)
@@ -197,7 +197,7 @@
     [580, 1200], [0, 0], [420, 579], [0, 0], [0, 419], [1201, 1440]
 )
 
-Ho_Fridge2 = Hospital.Appliance(1, 150, 1, 1440, 0, 30, "yes", 3)
+Ho_Fridge2 = Hospital.add_appliance(1, 150, 1, 1440, 0, 30, "yes", 3)
 Ho_Fridge2.windows([0, 1440], [0, 0])
 Ho_Fridge2.specific_cycle_1(150, 20, 5, 10)
 Ho_Fridge2.specific_cycle_2(150, 15, 5, 15)
@@ -206,7 +206,7 @@
     [580, 1200], [0, 0], [420, 579], [0, 0], [0, 299], [1201, 1440]
 )
 
-Ho_Fridge3 = Hospital.Appliance(1, 150, 1, 1440, 0.1, 30, "yes", 3)
+Ho_Fridge3 = Hospital.add_appliance(1, 150, 1, 1440, 0.1, 30, "yes", 3)
 Ho_Fridge3.windows([0, 1440], [0, 0])
 Ho_Fridge3.specific_cycle_1(150, 20, 5, 10)
 Ho_Fridge3.specific_cycle_2(150, 15, 5, 15)
@@ -215,29 +215,29 @@
     [580, 1200], [0, 0], [420, 479], [0, 0], [0, 419], [1201, 1440]
 )
 
-Ho_PC = Hospital.Appliance(2, 50, 2, 300, 0.1, 10)
+Ho_PC = Hospital.add_appliance(2, 50, 2, 300, 0.1, 10)
 Ho_PC.windows([480, 720], [1050, 1440], 0.35)
 
-Ho_Mixer = Hospital.Appliance(1, 50, 2, 60, 0.1, 1, occasional_use=0.33)
+Ho_Mixer = Hospital.add_appliance(1, 50, 2, 60, 0.1, 1, occasional_use=0.33)
 Ho_Mixer.windows([480, 720], [1050, 1440], 0.35)
 
 # School
-S_indoor_bulb = School.Appliance(8, 7, 1, 60, 0.2, 10)
+S_indoor_bulb = School.add_appliance(8, 7, 1, 60, 0.2, 10)
 S_indoor_bulb.windows([1020, 1080], [0, 0], 0.35)
 
-S_outdoor_bulb = School.Appliance(6, 13, 1, 60, 0.2, 10)
+S_outdoor_bulb = School.add_appliance(6, 13, 1, 60, 0.2, 10)
 S_outdoor_bulb.windows([1020, 1080], [0, 0], 0.35)
 
-S_Phone_charger = School.Appliance(5, 2, 2, 180, 0.2, 5)
+S_Phone_charger = School.add_appliance(5, 2, 2, 180, 0.2, 5)
 S_Phone_charger.windows([510, 750], [810, 1080], 0.35)
 
-S_PC = School.Appliance(18, 50, 2, 210, 0.1, 10)
+S_PC = School.add_appliance(18, 50, 2, 210, 0.1, 10)
 S_PC.windows([510, 750], [810, 1080], 0.35)
 
-S_Printer = School.Appliance(1, 20, 2, 30, 0.1, 5)
+S_Printer = School.add_appliance(1, 20, 2, 30, 0.1, 5)
 S_Printer.windows([510, 750], [810, 1080], 0.35)
 
-S_Freezer = School.Appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+S_Freezer = School.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
 S_Freezer.windows([0, 1440])
 S_Freezer.specific_cycle_1(200, 20, 5, 10)
 S_Freezer.specific_cycle_2(200, 15, 5, 15)
@@ -246,13 +246,13 @@
     [580, 1200], [0, 0], [510, 579], [0, 0], [0, 509], [1201, 1440]
 )
 
-S_TV = School.Appliance(1, 60, 2, 120, 0.1, 5, occasional_use=0.5)
+S_TV = School.add_appliance(1, 60, 2, 120, 0.1, 5, occasional_use=0.5)
 S_TV.windows([510, 750], [810, 1080], 0.35)
 
-S_DVD = School.Appliance(1, 8, 2, 120, 0.1, 5, occasional_use=0.5)
+S_DVD = School.add_appliance(1, 8, 2, 120, 0.1, 5, occasional_use=0.5)
 S_DVD.windows([510, 750], [810, 1080], 0.35)
 
-S_Stereo = School.Appliance(1, 150, 2, 90, 0.1, 5, occasional_use=0.33)
+S_Stereo = School.add_appliance(1, 150, 2, 90, 0.1, 5, occasional_use=0.33)
 S_Stereo.windows([510, 750], [810, 1080], 0.35)
 
 if __name__ == "__main__":
diff --git a/ramp/example/input_file_2.py b/ramp/example/input_file_2.py
index 4e738882..ef01a01c 100644
--- a/ramp/example/input_file_2.py
+++ b/ramp/example/input_file_2.py
@@ -24,7 +24,7 @@
 HH_shower_P = pd.read_csv("ramp/example/shower_P.csv")
 
 # High-Income
-HH_shower = HH.Appliance(1, HH_shower_P, 2, 15, 0.1, 3, thermal_P_var=0.2)
+HH_shower = HH.add_appliance(1, HH_shower_P, 2, 15, 0.1, 3, thermal_P_var=0.2)
 HH_shower.windows([390, 540], [1080, 1200], 0.2)
 
 
diff --git a/ramp/example/input_file_3.py b/ramp/example/input_file_3.py
index 1572e526..be145aee 100644
--- a/ramp/example/input_file_3.py
+++ b/ramp/example/input_file_3.py
@@ -24,70 +24,70 @@
 
 # Create Cooking appliances
 
-HH_lunch1_soup = HH.Appliance(
+HH_lunch1_soup = HH.add_appliance(
     1, 1800, 2, 70, 0.15, 60, thermal_P_var=0.2, pref_index=1, fixed_cycle=1
 )
 HH_lunch1_soup.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch1_soup.specific_cycle_1(1800, 10, 750, 60, 0.15)
 HH_lunch1_soup.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_lunch2_rice = HH.Appliance(
+HH_lunch2_rice = HH.add_appliance(
     1, 1800, 2, 25, 0.15, 20, thermal_P_var=0.2, pref_index=2, fixed_cycle=1
 )
 HH_lunch2_rice.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch2_rice.specific_cycle_1(1800, 10, 750, 15, 0.15)
 HH_lunch2_rice.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_lunch2_egg = HH.Appliance(1, 1200, 2, 3, 0.2, 3, thermal_P_var=0.2, pref_index=2)
+HH_lunch2_egg = HH.add_appliance(1, 1200, 2, 3, 0.2, 3, thermal_P_var=0.2, pref_index=2)
 HH_lunch2_egg.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 
-HH_lunch2_platano = HH.Appliance(
+HH_lunch2_platano = HH.add_appliance(
     1, 1800, 2, 10, 0.15, 5, thermal_P_var=0.2, pref_index=2, fixed_cycle=1
 )
 HH_lunch2_platano.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch2_platano.specific_cycle_1(1800, 5, 1200, 5, 0.15)
 HH_lunch2_platano.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_lunch2_meat = HH.Appliance(1, 1200, 2, 7, 0.15, 3, thermal_P_var=0.2, pref_index=2)
+HH_lunch2_meat = HH.add_appliance(1, 1200, 2, 7, 0.15, 3, thermal_P_var=0.2, pref_index=2)
 HH_lunch2_meat.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 
-HH_lunch3_beansnrice = HH.Appliance(
+HH_lunch3_beansnrice = HH.add_appliance(
     1, 1800, 2, 45, 0.2, 30, thermal_P_var=0.2, pref_index=3, fixed_cycle=1
 )
 HH_lunch3_beansnrice.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch3_beansnrice.specific_cycle_1(1800, 10, 750, 35, 0.2)
 HH_lunch3_beansnrice.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_lunch3_meat = HH.Appliance(1, 1200, 2, 10, 0.2, 5, thermal_P_var=0.2, pref_index=3)
+HH_lunch3_meat = HH.add_appliance(1, 1200, 2, 10, 0.2, 5, thermal_P_var=0.2, pref_index=3)
 HH_lunch3_meat.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 
-HH_lunch_yuca = HH.Appliance(
+HH_lunch_yuca = HH.add_appliance(
     1, 1800, 1, 25, 0.15, 10, thermal_P_var=0.2, pref_index=0, fixed_cycle=1
 )
 HH_lunch_yuca.windows([13 * 60, 14 * 60], [0, 0], 0.15)
 HH_lunch_yuca.specific_cycle_1(1800, 10, 750, 15, 0.15)
 HH_lunch_yuca.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_breakfast_huminta = HH.Appliance(
+HH_breakfast_huminta = HH.add_appliance(
     1, 1800, 1, 65, 0.15, 50, thermal_P_var=0.2, pref_index=0, fixed_cycle=1
 )
 HH_breakfast_huminta.windows([6 * 60, 9 * 60], [0, 0], 0.15)
 HH_breakfast_huminta.specific_cycle_1(1800, 5, 750, 60, 0.15)
 HH_breakfast_huminta.cycle_behaviour([6 * 60, 9 * 60], [0, 0])
 
-HH_breakfast_bread = HH.Appliance(
+HH_breakfast_bread = HH.add_appliance(
     1, 1800, 1, 15, 0.15, 10, thermal_P_var=0.2, pref_index=0, fixed_cycle=1
 )
 HH_breakfast_bread.windows([6 * 60, 9 * 60], [0, 0], 0.15)
 HH_breakfast_bread.specific_cycle_1(1800, 10, 1200, 5, 0.15)
 HH_breakfast_bread.cycle_behaviour([6 * 60, 9 * 60], [0, 0])
 
-HH_breakfast_coffee = HH.Appliance(
+HH_breakfast_coffee = HH.add_appliance(
     1, 1800, 1, 5, 0.15, 2, thermal_P_var=0.2, pref_index=0
 )
 HH_breakfast_coffee.windows([6 * 60, 9 * 60], [0, 0], 0.15)
 
-HH_mate = HH.Appliance(1, 1800, 1, 30, 0.3, 2, thermal_P_var=0.2, pref_index=0)
+HH_mate = HH.add_appliance(1, 1800, 1, 30, 0.3, 2, thermal_P_var=0.2, pref_index=0)
 HH_mate.windows([7 * 60, 20 * 60], [0, 0], 0.15)
 
 

From 307af07a16444a11e9122a7d6a917771094896b9 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 6 May 2024 13:58:47 +0200
Subject: [PATCH 084/140] Copy the appliance parameter docstrings to
 add_appliance docstring

---
 ramp/core/core.py | 46 +++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 45 insertions(+), 1 deletion(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 99d304e9..06d41a44 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -698,7 +698,51 @@ def _add_appliance_instance(self, appliances):
                 self.App_list.append(app)
 
     def add_appliance(self, *args, **kwargs):
-        """adds an appliance to the user category with all the appliance characteristics in a single function
+        """"adds an appliance to the user category with all the appliance characteristics in a single function
+
+        Parameters
+        ----------
+        number : int, optional
+            number of appliances of the specified kind, by default 1
+
+        power : Union[float.pd.DataFrame], optional
+            Power rating of appliance (average). If the appliance has variant daily power, a series (with the size of 366) can be passed., by default 0
+
+        num_windows : int [1,2,3], optional
+            Number of distinct time windows, by default 1
+
+        func_time : int[0,1440], optional
+            total time (minutes) the appliance is on during the day (not dependant on windows). Acceptable values are in range 0 to 1440, by default 0
+
+        time_fraction_random_variability : Percentage, optional
+            percentage of total time of use that is subject to random variability. For time (not for windows), randomizes the total time the appliance is on, by default 0
+
+        func_cycle : int[0,1440], optional
+            minimum time(minutes) the appliance is kept on after switch-on event, by default 1
+
+        fixed : str, optional
+            if 'yes', all the 'n' appliances of this kind are always switched-on together, by default "no"
+
+        fixed_cycle : int{0,1,2,3,4}, optional
+            Number of duty cycle, 0 means continuous power, if not 0 you have to fill the cw (cycle window) parameter (you may define up to 3 cws), by default 0
+
+        occasional_use : Percentage, optional
+            Defines how often the appliance is used, e.g. every second day will be 0.5, by default 1
+
+        flat : str{'yes','no'}, optional
+            allows to model appliances that are not subject to any kind of random variability, such as public lighting, by default "no"
+
+        thermal_p_var : Percentage, optional
+            Range of change of the power of the appliance (e.g. shower not taken at same temparature) or for the power of duty cycles (e.g. for a cooker, AC, heater if external temperature is different…), by default 0
+
+        pref_index : int{0,1,2,3}, optional
+            defines preference index for association with random User daily preference behaviour.This number must be smaller or equal to the value input in user_preference, by default 0
+
+        wd_we_type : int{0,1,2}, optional
+            Specify whether the appliance is used only on weekdays (0), weekend (1) or the whole week (2), by default 2
+
+        name : str, optional
+            the name of the appliance, by default ""
 
 
         Returns

From 1fd889172ff06c1416187bc3235c7157bab363df Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 14 May 2024 00:10:58 +0200
Subject: [PATCH 085/140] Correct argument name typo

---
 ramp/example/input_file_2.py |  2 +-
 ramp/example/input_file_3.py | 28 ++++++++++++++++------------
 2 files changed, 17 insertions(+), 13 deletions(-)

diff --git a/ramp/example/input_file_2.py b/ramp/example/input_file_2.py
index ef01a01c..36142f8b 100644
--- a/ramp/example/input_file_2.py
+++ b/ramp/example/input_file_2.py
@@ -24,7 +24,7 @@
 HH_shower_P = pd.read_csv("ramp/example/shower_P.csv")
 
 # High-Income
-HH_shower = HH.add_appliance(1, HH_shower_P, 2, 15, 0.1, 3, thermal_P_var=0.2)
+HH_shower = HH.add_appliance(1, HH_shower_P, 2, 15, 0.1, 3, thermal_p_var=0.2)
 HH_shower.windows([390, 540], [1080, 1200], 0.2)
 
 
diff --git a/ramp/example/input_file_3.py b/ramp/example/input_file_3.py
index be145aee..51dc482f 100644
--- a/ramp/example/input_file_3.py
+++ b/ramp/example/input_file_3.py
@@ -25,69 +25,73 @@
 # Create Cooking appliances
 
 HH_lunch1_soup = HH.add_appliance(
-    1, 1800, 2, 70, 0.15, 60, thermal_P_var=0.2, pref_index=1, fixed_cycle=1
+    1, 1800, 2, 70, 0.15, 60, thermal_p_var=0.2, pref_index=1, fixed_cycle=1
 )
 HH_lunch1_soup.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch1_soup.specific_cycle_1(1800, 10, 750, 60, 0.15)
 HH_lunch1_soup.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
 HH_lunch2_rice = HH.add_appliance(
-    1, 1800, 2, 25, 0.15, 20, thermal_P_var=0.2, pref_index=2, fixed_cycle=1
+    1, 1800, 2, 25, 0.15, 20, thermal_p_var=0.2, pref_index=2, fixed_cycle=1
 )
 HH_lunch2_rice.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch2_rice.specific_cycle_1(1800, 10, 750, 15, 0.15)
 HH_lunch2_rice.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_lunch2_egg = HH.add_appliance(1, 1200, 2, 3, 0.2, 3, thermal_P_var=0.2, pref_index=2)
+HH_lunch2_egg = HH.add_appliance(1, 1200, 2, 3, 0.2, 3, thermal_p_var=0.2, pref_index=2)
 HH_lunch2_egg.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 
 HH_lunch2_platano = HH.add_appliance(
-    1, 1800, 2, 10, 0.15, 5, thermal_P_var=0.2, pref_index=2, fixed_cycle=1
+    1, 1800, 2, 10, 0.15, 5, thermal_p_var=0.2, pref_index=2, fixed_cycle=1
 )
 HH_lunch2_platano.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch2_platano.specific_cycle_1(1800, 5, 1200, 5, 0.15)
 HH_lunch2_platano.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_lunch2_meat = HH.add_appliance(1, 1200, 2, 7, 0.15, 3, thermal_P_var=0.2, pref_index=2)
+HH_lunch2_meat = HH.add_appliance(
+    1, 1200, 2, 7, 0.15, 3, thermal_p_var=0.2, pref_index=2
+)
 HH_lunch2_meat.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 
 HH_lunch3_beansnrice = HH.add_appliance(
-    1, 1800, 2, 45, 0.2, 30, thermal_P_var=0.2, pref_index=3, fixed_cycle=1
+    1, 1800, 2, 45, 0.2, 30, thermal_p_var=0.2, pref_index=3, fixed_cycle=1
 )
 HH_lunch3_beansnrice.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch3_beansnrice.specific_cycle_1(1800, 10, 750, 35, 0.2)
 HH_lunch3_beansnrice.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_lunch3_meat = HH.add_appliance(1, 1200, 2, 10, 0.2, 5, thermal_P_var=0.2, pref_index=3)
+HH_lunch3_meat = HH.add_appliance(
+    1, 1200, 2, 10, 0.2, 5, thermal_p_var=0.2, pref_index=3
+)
 HH_lunch3_meat.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 
 HH_lunch_yuca = HH.add_appliance(
-    1, 1800, 1, 25, 0.15, 10, thermal_P_var=0.2, pref_index=0, fixed_cycle=1
+    1, 1800, 1, 25, 0.15, 10, thermal_p_var=0.2, pref_index=0, fixed_cycle=1
 )
 HH_lunch_yuca.windows([13 * 60, 14 * 60], [0, 0], 0.15)
 HH_lunch_yuca.specific_cycle_1(1800, 10, 750, 15, 0.15)
 HH_lunch_yuca.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
 HH_breakfast_huminta = HH.add_appliance(
-    1, 1800, 1, 65, 0.15, 50, thermal_P_var=0.2, pref_index=0, fixed_cycle=1
+    1, 1800, 1, 65, 0.15, 50, thermal_p_var=0.2, pref_index=0, fixed_cycle=1
 )
 HH_breakfast_huminta.windows([6 * 60, 9 * 60], [0, 0], 0.15)
 HH_breakfast_huminta.specific_cycle_1(1800, 5, 750, 60, 0.15)
 HH_breakfast_huminta.cycle_behaviour([6 * 60, 9 * 60], [0, 0])
 
 HH_breakfast_bread = HH.add_appliance(
-    1, 1800, 1, 15, 0.15, 10, thermal_P_var=0.2, pref_index=0, fixed_cycle=1
+    1, 1800, 1, 15, 0.15, 10, thermal_p_var=0.2, pref_index=0, fixed_cycle=1
 )
 HH_breakfast_bread.windows([6 * 60, 9 * 60], [0, 0], 0.15)
 HH_breakfast_bread.specific_cycle_1(1800, 10, 1200, 5, 0.15)
 HH_breakfast_bread.cycle_behaviour([6 * 60, 9 * 60], [0, 0])
 
 HH_breakfast_coffee = HH.add_appliance(
-    1, 1800, 1, 5, 0.15, 2, thermal_P_var=0.2, pref_index=0
+    1, 1800, 1, 5, 0.15, 2, thermal_p_var=0.2, pref_index=0
 )
 HH_breakfast_coffee.windows([6 * 60, 9 * 60], [0, 0], 0.15)
 
-HH_mate = HH.add_appliance(1, 1800, 1, 30, 0.3, 2, thermal_P_var=0.2, pref_index=0)
+HH_mate = HH.add_appliance(1, 1800, 1, 30, 0.3, 2, thermal_p_var=0.2, pref_index=0)
 HH_mate.windows([7 * 60, 20 * 60], [0, 0], 0.15)
 
 

From 160b72f290340133a0ea2d855b122a1d19f5053f Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 14 May 2024 00:19:58 +0200
Subject: [PATCH 086/140] Update the documentation

---
 docs/source/examples/multi_cycle/multi_cycle.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/examples/multi_cycle/multi_cycle.rst b/docs/source/examples/multi_cycle/multi_cycle.rst
index 248f9abc..867e5339 100644
--- a/docs/source/examples/multi_cycle/multi_cycle.rst
+++ b/docs/source/examples/multi_cycle/multi_cycle.rst
@@ -53,7 +53,7 @@ Creating the user and appliance
 .. code:: ipython3
 
     # creating the appliance
-    fridge = household.Appliance(
+    fridge = household.add_appliance(
         name="Fridge",
         number=1,
         power=200,

From 0fefa91959607a9fa1544d706f7e51b06d7d7518 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 14 May 2024 00:39:05 +0200
Subject: [PATCH 087/140] Explicit the arguments in methods the example files

Do this only for the first occurence as there are many appliances and
users in examples 1 and 3
---
 ramp/example/input_file_1.py |  6 +++---
 ramp/example/input_file_2.py |  8 +++-----
 ramp/example/input_file_3.py | 10 +++++-----
 3 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/ramp/example/input_file_1.py b/ramp/example/input_file_1.py
index ae1179f4..9d83c176 100644
--- a/ramp/example/input_file_1.py
+++ b/ramp/example/input_file_1.py
@@ -19,7 +19,7 @@
 """
 
 # Create new user classes
-HI = User("high income", 11, 3)
+HI = User(user_name="high income", num_users=11, user_preference=3)
 User_list.append(HI)
 
 HMI = User("higher middle income", 38, 3)
@@ -46,8 +46,8 @@
 # Create new appliances
 
 # Church
-Ch_indoor_bulb = Church.add_appliance(10, 26, 1, 210, 0.2, 60, "yes", flat="yes")
-Ch_indoor_bulb.windows([1200, 1440], [0, 0], 0.1)
+Ch_indoor_bulb = Church.add_appliance(number=10, power=26, num_windows=1, func_time=210, time_fraction_random_variability=0.2, func_cycle=60, fixed="yes", flat="yes")
+Ch_indoor_bulb.windows(window_1=[1200, 1440], window_2=[0, 0], random_var_w=0.1)
 
 Ch_outdoor_bulb = Church.add_appliance(7, 26, 1, 150, 0.2, 60, "yes", flat="yes")
 Ch_outdoor_bulb.windows([1200, 1440], [0, 0], 0.1)
diff --git a/ramp/example/input_file_2.py b/ramp/example/input_file_2.py
index 36142f8b..83878573 100644
--- a/ramp/example/input_file_2.py
+++ b/ramp/example/input_file_2.py
@@ -18,14 +18,12 @@
 """
 
 # Create new user classes
-HH = User("generic households", 1)
+HH = User(user_name="generic households", num_users=1)
 User_list.append(HH)
 
 HH_shower_P = pd.read_csv("ramp/example/shower_P.csv")
-
-# High-Income
-HH_shower = HH.add_appliance(1, HH_shower_P, 2, 15, 0.1, 3, thermal_p_var=0.2)
-HH_shower.windows([390, 540], [1080, 1200], 0.2)
+HH_shower = HH.add_appliance(number=1, power=HH_shower_P, num_windows=2, func_time=15, time_fraction_random_variability=0.1, func_cycle=3, thermal_p_var=0.2)
+HH_shower.windows(window_1=[390, 540], window_2=[1080, 1200], random_var_w=0.2)
 
 
 if __name__ == "__main__":
diff --git a/ramp/example/input_file_3.py b/ramp/example/input_file_3.py
index 51dc482f..74441eaf 100644
--- a/ramp/example/input_file_3.py
+++ b/ramp/example/input_file_3.py
@@ -17,7 +17,7 @@
 """
 
 # Create new user classes
-HH = User("generic household", 1, 3)
+HH = User(user_name="generic household", num_users=1, user_preference=3)
 User_list.append(HH)
 
 # Create new appliances
@@ -25,11 +25,11 @@
 # Create Cooking appliances
 
 HH_lunch1_soup = HH.add_appliance(
-    1, 1800, 2, 70, 0.15, 60, thermal_p_var=0.2, pref_index=1, fixed_cycle=1
+    number=1, power=1800, num_windows=2, func_time=70, time_fraction_random_variability=0.15, func_cycle=60, thermal_p_var=0.2, pref_index=1, fixed_cycle=1
 )
-HH_lunch1_soup.windows([12 * 60, 15 * 60], [0, 0], 0.15)
-HH_lunch1_soup.specific_cycle_1(1800, 10, 750, 60, 0.15)
-HH_lunch1_soup.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
+HH_lunch1_soup.windows(window_1=[12 * 60, 15 * 60], window_2=[0, 0], random_var_w=0.15)
+HH_lunch1_soup.specific_cycle_1(p_11=1800, t_11=10, p_12=750, t_12=60, r_c1=0.15)
+HH_lunch1_soup.cycle_behaviour(cw11=[12 * 60, 15 * 60], cw12=[0, 0])
 
 HH_lunch2_rice = HH.add_appliance(
     1, 1800, 2, 25, 0.15, 20, thermal_p_var=0.2, pref_index=2, fixed_cycle=1

From 652f6dfabb2caf7bd4864ac505bbaae73bf55f09 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 14 May 2024 14:46:09 +0200
Subject: [PATCH 088/140] Freeze black version for CI and add jupyter linting

---
 .github/workflows/pytest.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index edfa4dc0..b3a0c2f1 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -26,7 +26,7 @@ jobs:
     - name: Install black
       run: |
         python -m pip install --upgrade pip
-        pip install black
+        pip install black[jupyter]==24.4.2
     - name: Lint with black
       run: |
         black . --check

From dcf5d740d25172dad6c54ac4002a76824e65aa00 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 14 May 2024 14:47:43 +0200
Subject: [PATCH 089/140] Lint with black

---
 docs/notebooks/cooking_app.ipynb    | 35 ++++++++++++++++-------------
 docs/notebooks/fixed_flat_app.ipynb | 21 ++++++++---------
 docs/notebooks/multi_cycle.ipynb    | 26 ++++++---------------
 docs/notebooks/occasional_use.ipynb | 34 +++++++++++++---------------
 docs/notebooks/random_seed.ipynb    | 32 ++++++++++++--------------
 docs/notebooks/simple_bulb.ipynb    | 34 +++++++++++++---------------
 docs/notebooks/thermal_app.ipynb    | 31 +++++++++++++------------
 ramp/core/core.py                   |  2 +-
 ramp/example/input_file_1.py        | 15 +++++++++++--
 ramp/example/input_file_2.py        | 10 ++++++++-
 ramp/example/input_file_3.py        | 10 ++++++++-
 11 files changed, 129 insertions(+), 121 deletions(-)

diff --git a/docs/notebooks/cooking_app.ipynb b/docs/notebooks/cooking_app.ipynb
index c61a2b1c..6eb6aa3d 100644
--- a/docs/notebooks/cooking_app.ipynb
+++ b/docs/notebooks/cooking_app.ipynb
@@ -93,20 +93,20 @@
    "outputs": [],
    "source": [
     "# soup for lunch\n",
-    "lunch_window = [12 * 60, 12 * 60+26]\n",
+    "lunch_window = [12 * 60, 12 * 60 + 26]\n",
     "\n",
     "soup_1 = user_1.add_appliance(\n",
     "    name=\"soup for lunch\",\n",
-    "    power=1200, # nominal power of appliance\n",
-    "    func_time=25, # the cooking time\n",
-    "    func_cycle=25, # we always need  25 minute for cooking\n",
-    "    fixed_cycle=1, # the cookstove is not a continus power usage appliance, it has cycles as mentioned earlier \n",
-    "    window_1=lunch_window, # lunch is always prepared from 12\n",
+    "    power=1200,  # nominal power of appliance\n",
+    "    func_time=25,  # the cooking time\n",
+    "    func_cycle=25,  # we always need  25 minute for cooking\n",
+    "    fixed_cycle=1,  # the cookstove is not a continus power usage appliance, it has cycles as mentioned earlier\n",
+    "    window_1=lunch_window,  # lunch is always prepared from 12\n",
     "    p_11=1200,  # power of the first cycle\n",
     "    t_11=5,  # time needed for the first cycle\n",
     "    p_12=750,  # power of the second cycle\n",
     "    t_12=20,  # time needed for the second cycle\n",
-    "    cw11=lunch_window, # the time window of the working cycle \n",
+    "    cw11=lunch_window,  # the time window of the working cycle\n",
     ")"
    ]
   },
@@ -194,10 +194,9 @@
     "number_of_days = 5\n",
     "user_1_profiles = {}\n",
     "user_2_profiles = {}\n",
-    "for day in range(1,number_of_days+1):\n",
+    "for day in range(1, number_of_days + 1):\n",
     "    user_1_profiles[f\"day {day}\"] = pd.Series(user_1.generate_single_load_profile())\n",
-    "    user_2_profiles[f\"day {day}\"] = pd.Series(user_2.generate_single_load_profile())\n",
-    "    "
+    "    user_2_profiles[f\"day {day}\"] = pd.Series(user_2.generate_single_load_profile())"
    ]
   },
   {
@@ -218,17 +217,21 @@
     }
    ],
    "source": [
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) \n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
     "\n",
     "\n",
     "i = 0\n",
     "for name, df in dict(\n",
-    "    user_1_profiles=pd.concat(user_1_profiles,axis=1).iloc[lunch_window[0]-5:lunch_window[1]+5], # take only the lunch window\n",
-    "    user_2_profiles=pd.concat(user_2_profiles,axis=1).iloc[lunch_window[0]-5:lunch_window[1]+5]# take only the lunch window\n",
-    "    ).items():\n",
-    "    df.plot(ax=axes[i],legend=True)\n",
+    "    user_1_profiles=pd.concat(user_1_profiles, axis=1).iloc[\n",
+    "        lunch_window[0] - 5 : lunch_window[1] + 5\n",
+    "    ],  # take only the lunch window\n",
+    "    user_2_profiles=pd.concat(user_2_profiles, axis=1).iloc[\n",
+    "        lunch_window[0] - 5 : lunch_window[1] + 5\n",
+    "    ],  # take only the lunch window\n",
+    ").items():\n",
+    "    df.plot(ax=axes[i], legend=True)\n",
     "    axes[i].set_title(name)\n",
-    "    i+=1\n",
+    "    i += 1\n",
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
diff --git a/docs/notebooks/fixed_flat_app.ipynb b/docs/notebooks/fixed_flat_app.ipynb
index f6325095..3a2791ed 100644
--- a/docs/notebooks/fixed_flat_app.ipynb
+++ b/docs/notebooks/fixed_flat_app.ipynb
@@ -69,8 +69,8 @@
     "    func_cycle=60,\n",
     "    fixed=\"yes\",  # This means all the 'n' appliances of this kind are always switched-on together\n",
     "    flat=\"yes\",  # This means the appliance is not subject to random variability in terms of total usage time\n",
-    "    window_1 = [1200, 1440],  # from 20:00 to 24:00\n",
-    ")\n"
+    "    window_1=[1200, 1440],  # from 20:00 to 24:00\n",
+    ")"
    ]
   },
   {
@@ -109,14 +109,12 @@
    ],
    "source": [
     "school_case = UseCase(\n",
-    "    users=[school], # users of the usecase\n",
-    "    date_start=\"2023-01-01\", # start date\n",
-    "    )\n",
+    "    users=[school],  # users of the usecase\n",
+    "    date_start=\"2023-01-01\",  # start date\n",
+    ")\n",
     "\n",
     "# when both date_start and date_end are not given, you need to initialize the usecase by this method and by passing the number of days as num_days\n",
-    "school_case.initialize(\n",
-    "    num_days=7\n",
-    "    )"
+    "school_case.initialize(num_days=7)"
    ]
   },
   {
@@ -221,12 +219,11 @@
     }
    ],
    "source": [
+    "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 4))\n",
     "\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 4))  \n",
     "\n",
-    "\n",
-    "for i, df in enumerate([no_fix_no_flat,fix_no_flat,fixed_flat,no_fixed_flat]):\n",
-    "    df.plot(ax=axes[i],legend=False)\n",
+    "for i, df in enumerate([no_fix_no_flat, fix_no_flat, fixed_flat, no_fixed_flat]):\n",
+    "    df.plot(ax=axes[i], legend=False)\n",
     "    axes[i].set_title(df.columns[0])\n",
     "\n",
     "\n",
diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb
index 552256ea..51198d3c 100644
--- a/docs/notebooks/multi_cycle.ipynb
+++ b/docs/notebooks/multi_cycle.ipynb
@@ -123,30 +123,18 @@
     "# assiging the specific cycles\n",
     "# first cycle: standard cycle\n",
     "fridge.specific_cycle_1(\n",
-    "    p_11=200, # power level for the first operation segment\n",
+    "    p_11=200,  # power level for the first operation segment\n",
     "    t_11=10,  # duration of the first operation segment\n",
-    "    p_12=5,   # power level for the second operation segment\n",
+    "    p_12=5,  # power level for the second operation segment\n",
     "    t_12=20,  # duration of the second operation segment\n",
-    "    r_c1=0    # random variability assigned to the duration of each segment\n",
+    "    r_c1=0,  # random variability assigned to the duration of each segment\n",
     ")\n",
     "\n",
     "# second cycle: intermediate cycle\n",
-    "fridge.specific_cycle_2(\n",
-    "    p_21=200,\n",
-    "    t_21=15,\n",
-    "    p_22=5,\n",
-    "    t_22=15,\n",
-    "    r_c2=0\n",
-    ")\n",
+    "fridge.specific_cycle_2(p_21=200, t_21=15, p_22=5, t_22=15, r_c2=0)\n",
     "\n",
     "# third cycle: intensive cycle\n",
-    "fridge.specific_cycle_3(\n",
-    "    p_31=200,\n",
-    "    t_31=20,\n",
-    "    p_32=5,\n",
-    "    t_32=10,\n",
-    "    r_c3=0\n",
-    ")"
+    "fridge.specific_cycle_3(p_31=200, t_31=20, p_32=5, t_32=10, r_c3=0)"
    ]
   },
   {
@@ -166,7 +154,7 @@
    "source": [
     "# defining cycle behaviour\n",
     "fridge.cycle_behaviour(\n",
-    "     cw11=[0, 299], cw12=[1201, 1440], cw21=[300, 479], cw31=[480, 1200]\n",
+    "    cw11=[0, 299], cw12=[1201, 1440], cw21=[300, 479], cw31=[480, 1200]\n",
     ")"
    ]
   },
@@ -221,7 +209,7 @@
    "source": [
     "# days to build the profiles\n",
     "days = [\n",
-    "    \"2020-12-16\", # a day in the 'Hot' season, assuming a location in the Southern hemisphere\n",
+    "    \"2020-12-16\",  # a day in the 'Hot' season, assuming a location in the Southern hemisphere\n",
     "]\n",
     "\n",
     "profiles = pd.DataFrame(index=range(0, 1440), columns=days)\n",
diff --git a/docs/notebooks/occasional_use.ipynb b/docs/notebooks/occasional_use.ipynb
index 12aaff88..c2f03638 100644
--- a/docs/notebooks/occasional_use.ipynb
+++ b/docs/notebooks/occasional_use.ipynb
@@ -62,10 +62,10 @@
     "    number=1,\n",
     "    power=50,\n",
     "    num_windows=1,\n",
-    "    func_time=210, # 3.5 hours\n",
-    "    func_cycle=210,  \n",
+    "    func_time=210,  # 3.5 hours\n",
+    "    func_cycle=210,\n",
     "    occasional_use=0.5,  # 50% chance of occasional use,\n",
-    "    window_1=[480, 750], # start from 8AM\n",
+    "    window_1=[480, 750],  # start from 8AM\n",
     ")"
    ]
   },
@@ -81,10 +81,10 @@
     "    number=1,\n",
     "    power=50,\n",
     "    num_windows=1,\n",
-    "    func_time=210, # 3.5 hours\n",
-    "    func_cycle=210,  \n",
+    "    func_time=210,  # 3.5 hours\n",
+    "    func_cycle=210,\n",
     "    occasional_use=1,  # always present in the mix of appliances,\n",
-    "    window_1=[480, 750], # start from 8AM\n",
+    "    window_1=[480, 750],  # start from 8AM\n",
     ")"
    ]
   },
@@ -117,14 +117,10 @@
     "household_profiles = []\n",
     "school_profiles = []\n",
     "\n",
-    "for day in range(1,number_of_days+1):\n",
-    "    household_profiles.extend(\n",
-    "        household.generate_single_load_profile(prof_i=day)\n",
-    "    )\n",
+    "for day in range(1, number_of_days + 1):\n",
+    "    household_profiles.extend(household.generate_single_load_profile(prof_i=day))\n",
     "\n",
-    "    school_profiles.extend(\n",
-    "        school.generate_single_load_profile(prof_i=day)\n",
-    "    )"
+    "    school_profiles.extend(school.generate_single_load_profile(prof_i=day))"
    ]
   },
   {
@@ -145,20 +141,20 @@
     }
    ],
    "source": [
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) \n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
     "\n",
     "\n",
     "i = 0\n",
     "for name, df in dict(\n",
     "    household_profiles=pd.DataFrame(household_profiles),\n",
-    "    school_profiles=pd.DataFrame(school_profiles)\n",
-    "    ).items():\n",
-    "    df.plot(ax=axes[i],legend=False)\n",
+    "    school_profiles=pd.DataFrame(school_profiles),\n",
+    ").items():\n",
+    "    df.plot(ax=axes[i], legend=False)\n",
     "    axes[i].set_title(name)\n",
-    "    i+=1\n",
+    "    i += 1\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/docs/notebooks/random_seed.ipynb b/docs/notebooks/random_seed.ipynb
index c8213b31..556af272 100644
--- a/docs/notebooks/random_seed.ipynb
+++ b/docs/notebooks/random_seed.ipynb
@@ -51,10 +51,7 @@
     "from ramp.example.input_file_1 import User_list\n",
     "\n",
     "# Build use case 1 with non-fixed seed\n",
-    "uc_1 = UseCase(\n",
-    "    users=deepcopy(User_list),\n",
-    "    random_seed=None\n",
-    ")\n",
+    "uc_1 = UseCase(users=deepcopy(User_list), random_seed=None)\n",
     "# Initialize and generate load profile\n",
     "uc_1.initialize(peak_enlarge=0.15, num_days=3)\n",
     "uc_1_lp = uc_1.generate_daily_load_profiles()"
@@ -92,19 +89,13 @@
    "cell_type": "code",
    "source": [
     "# Build use case 2 and fixed random seed\n",
-    "uc_2 = UseCase(\n",
-    "    users=deepcopy(User_list),\n",
-    "    random_seed=1\n",
-    ")\n",
+    "uc_2 = UseCase(users=deepcopy(User_list), random_seed=1)\n",
     "# Initialize and generate load profile\n",
     "uc_2.initialize(peak_enlarge=0.15, num_days=3)\n",
     "uc_2_lp = uc_2.generate_daily_load_profiles()\n",
     "\n",
     "# Build use case 3 and same fixed random seed as uc_2\n",
-    "uc_3 = UseCase(\n",
-    "    users=deepcopy(User_list),\n",
-    "    random_seed=1\n",
-    ")\n",
+    "uc_3 = UseCase(users=deepcopy(User_list), random_seed=1)\n",
     "\n",
     "# Initialize and generate load profile\n",
     "uc_3.initialize(peak_enlarge=0.15, num_days=3)\n",
@@ -147,12 +138,17 @@
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "\n",
-    "lp_df = pd.DataFrame({'UseCase_1: non_fixed_seed': uc_1_lp,\n",
-    "                      'UseCase_2: fixed_seed': uc_2_lp,\n",
-    "                      'UseCase_3: fixed_seed': uc_3_lp,\n",
-    "                      'diff UseCase_2 - UseCase_1': uc_2_lp - uc_1_lp,  # difference between uc_1 and uc_2 is not zero\n",
-    "                      'diff UseCase_2 - UseCase_3': uc_2_lp - uc_3_lp  # difference between uc_2 and uc_3 is zero\n",
-    "                      })\n",
+    "lp_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"UseCase_1: non_fixed_seed\": uc_1_lp,\n",
+    "        \"UseCase_2: fixed_seed\": uc_2_lp,\n",
+    "        \"UseCase_3: fixed_seed\": uc_3_lp,\n",
+    "        \"diff UseCase_2 - UseCase_1\": uc_2_lp\n",
+    "        - uc_1_lp,  # difference between uc_1 and uc_2 is not zero\n",
+    "        \"diff UseCase_2 - UseCase_3\": uc_2_lp\n",
+    "        - uc_3_lp,  # difference between uc_2 and uc_3 is zero\n",
+    "    }\n",
+    ")\n",
     "lp_df.plot()\n",
     "plt.show()"
    ],
diff --git a/docs/notebooks/simple_bulb.ipynb b/docs/notebooks/simple_bulb.ipynb
index 3d3cf48d..455f302e 100644
--- a/docs/notebooks/simple_bulb.ipynb
+++ b/docs/notebooks/simple_bulb.ipynb
@@ -69,16 +69,16 @@
    "source": [
     "# add_appliance is meth\n",
     "indoor_bulb = household.add_appliance(\n",
-    "    name=\"Indoor Light Bulb\", # the name of the appliance\n",
-    "    number=6, # how many of this appliance each user has in this user category\n",
-    "    power=7, # the power (in Watt) of each single appliance. RAMP does not deal with units of measures, you should check the consistency of the unit of measures throughout your model\n",
-    "    num_windows=2, # how many usage time windows throughout the day?\n",
-    "    func_time=120, # the total usage time of appliances\n",
-    "    func_cycle=10, # the minimum usage time after a switch on event\n",
+    "    name=\"Indoor Light Bulb\",  # the name of the appliance\n",
+    "    number=6,  # how many of this appliance each user has in this user category\n",
+    "    power=7,  # the power (in Watt) of each single appliance. RAMP does not deal with units of measures, you should check the consistency of the unit of measures throughout your model\n",
+    "    num_windows=2,  # how many usage time windows throughout the day?\n",
+    "    func_time=120,  # the total usage time of appliances\n",
+    "    func_cycle=10,  # the minimum usage time after a switch on event\n",
     "    window_1=[0, 30],  # from 24 to 00:30\n",
     "    window_2=[1170, 1440],  # from 19:30 to 24:00\n",
-    "    random_var_w=0.35, # Variability of the windows in percentage\n",
-    "    time_fraction_random_variability=0.2, # randomizes the total time the appliance is on (between 0 and 1)\n",
+    "    random_var_w=0.35,  # Variability of the windows in percentage\n",
+    "    time_fraction_random_variability=0.2,  # randomizes the total time the appliance is on (between 0 and 1)\n",
     ")"
    ]
   },
@@ -186,10 +186,12 @@
    ],
    "source": [
     "use_case = UseCase(\n",
-    "    users=[household], # A list of all the user categories to be included in the simulation. In this case, we only have household user category\n",
-    "    date_start=\"2020-01-01\", # starting date of the simulation\n",
-    "    date_end=\"2020-12-31\", # end date of the simulation\n",
-    "    )\n"
+    "    users=[\n",
+    "        household\n",
+    "    ],  # A list of all the user categories to be included in the simulation. In this case, we only have household user category\n",
+    "    date_start=\"2020-01-01\",  # starting date of the simulation\n",
+    "    date_end=\"2020-12-31\",  # end date of the simulation\n",
+    ")"
    ]
   },
   {
@@ -283,9 +285,7 @@
     }
    ],
    "source": [
-    "single_profile = household.generate_single_load_profile(\n",
-    "    day_type=0\n",
-    "    )"
+    "single_profile = household.generate_single_load_profile(day_type=0)"
    ]
   },
   {
@@ -337,9 +337,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "aggregated_profile = household.generate_aggregated_load_profile(\n",
-    "    day_type=0\n",
-    ")"
+    "aggregated_profile = household.generate_aggregated_load_profile(day_type=0)"
    ]
   },
   {
diff --git a/docs/notebooks/thermal_app.ipynb b/docs/notebooks/thermal_app.ipynb
index 6a86f80b..39975b90 100644
--- a/docs/notebooks/thermal_app.ipynb
+++ b/docs/notebooks/thermal_app.ipynb
@@ -92,12 +92,12 @@
     "shower = household.add_appliance(\n",
     "    name=\"Shower\",\n",
     "    number=1,\n",
-    "    power=shower_power, # pass the pd.DataFrame or np.array instead of a number\n",
-    "    num_windows=2, # two possibe time window for shower\n",
-    "    func_time=15, # each shower takes 15 minute\n",
-    "    func_cycle=3, # every\n",
-    "    window_1=[390, 540], #  morning shower from 6:30 to 9:00 AM\n",
-    "    window_2=[1080, 1200], # evening shower from 18:00 to 20:00\n",
+    "    power=shower_power,  # pass the pd.DataFrame or np.array instead of a number\n",
+    "    num_windows=2,  # two possibe time window for shower\n",
+    "    func_time=15,  # each shower takes 15 minute\n",
+    "    func_cycle=3,  # every\n",
+    "    window_1=[390, 540],  #  morning shower from 6:30 to 9:00 AM\n",
+    "    window_2=[1080, 1200],  # evening shower from 18:00 to 20:00\n",
     ")"
    ]
   },
@@ -156,7 +156,7 @@
     "def thermal_p_var_sensitivity(sensitivity_values):\n",
     "    # buidling a pd.DataFrame for saving sensitivity results\n",
     "    results = pd.DataFrame(\n",
-    "        index=range(0,1440*365),\n",
+    "        index=range(0, 1440 * 365),\n",
     "        columns=[f\"p_var = {value}\" for value in sensitivity_values],\n",
     "    )\n",
     "\n",
@@ -202,17 +202,20 @@
     }
    ],
    "source": [
-    "days_to_plot = [1,100,200] # which days of the year to plot\n",
+    "days_to_plot = [1, 100, 200]  # which days of the year to plot\n",
     "\n",
-    "fig, axes = plt.subplots(ncols=len(days_to_plot), nrows=sensitivity_results.shape[1], figsize=(10, 10)) \n",
+    "fig, axes = plt.subplots(\n",
+    "    ncols=len(days_to_plot), nrows=sensitivity_results.shape[1], figsize=(10, 10)\n",
+    ")\n",
     "\n",
-    "for j,day in enumerate(days_to_plot):\n",
+    "for j, day in enumerate(days_to_plot):\n",
     "    for i, col in enumerate(sensitivity_results):\n",
-    "        sensitivity_results[col].iloc[1440*(day-1):1440*(day)].plot(ax=axes[i,j]) # just plot for the first day\n",
-    "        axes[i,j].set_title(f\"Day {day} - thermal_p_var = {col}\",fontsize=8)\n",
-    "        axes[i,j].set_ylim(0,24000)\n",
+    "        sensitivity_results[col].iloc[1440 * (day - 1) : 1440 * (day)].plot(\n",
+    "            ax=axes[i, j]\n",
+    "        )  # just plot for the first day\n",
+    "        axes[i, j].set_title(f\"Day {day} - thermal_p_var = {col}\", fontsize=8)\n",
+    "        axes[i, j].set_ylim(0, 24000)\n",
     "\n",
-    "    \n",
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
diff --git a/ramp/core/core.py b/ramp/core/core.py
index 06d41a44..3aa8b747 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -698,7 +698,7 @@ def _add_appliance_instance(self, appliances):
                 self.App_list.append(app)
 
     def add_appliance(self, *args, **kwargs):
-        """"adds an appliance to the user category with all the appliance characteristics in a single function
+        """Adds an appliance to the user category with all the appliance characteristics in a single function
 
         Parameters
         ----------
diff --git a/ramp/example/input_file_1.py b/ramp/example/input_file_1.py
index 9d83c176..f50ba444 100644
--- a/ramp/example/input_file_1.py
+++ b/ramp/example/input_file_1.py
@@ -46,7 +46,16 @@
 # Create new appliances
 
 # Church
-Ch_indoor_bulb = Church.add_appliance(number=10, power=26, num_windows=1, func_time=210, time_fraction_random_variability=0.2, func_cycle=60, fixed="yes", flat="yes")
+Ch_indoor_bulb = Church.add_appliance(
+    number=10,
+    power=26,
+    num_windows=1,
+    func_time=210,
+    time_fraction_random_variability=0.2,
+    func_cycle=60,
+    fixed="yes",
+    flat="yes",
+)
 Ch_indoor_bulb.windows(window_1=[1200, 1440], window_2=[0, 0], random_var_w=0.1)
 
 Ch_outdoor_bulb = Church.add_appliance(7, 26, 1, 150, 0.2, 60, "yes", flat="yes")
@@ -59,7 +68,9 @@
 Pub_lights = Public_lighting.add_appliance(12, 40, 2, 310, 0.1, 300, "yes", flat="yes")
 Pub_lights.windows([0, 336], [1110, 1440], 0.2)
 
-Pub_lights_2 = Public_lighting.add_appliance(25, 150, 2, 310, 0.1, 300, "yes", flat="yes")
+Pub_lights_2 = Public_lighting.add_appliance(
+    25, 150, 2, 310, 0.1, 300, "yes", flat="yes"
+)
 Pub_lights_2.windows([0, 336], [1110, 1440], 0.2)
 
 
diff --git a/ramp/example/input_file_2.py b/ramp/example/input_file_2.py
index 83878573..b8479ba8 100644
--- a/ramp/example/input_file_2.py
+++ b/ramp/example/input_file_2.py
@@ -22,7 +22,15 @@
 User_list.append(HH)
 
 HH_shower_P = pd.read_csv("ramp/example/shower_P.csv")
-HH_shower = HH.add_appliance(number=1, power=HH_shower_P, num_windows=2, func_time=15, time_fraction_random_variability=0.1, func_cycle=3, thermal_p_var=0.2)
+HH_shower = HH.add_appliance(
+    number=1,
+    power=HH_shower_P,
+    num_windows=2,
+    func_time=15,
+    time_fraction_random_variability=0.1,
+    func_cycle=3,
+    thermal_p_var=0.2,
+)
 HH_shower.windows(window_1=[390, 540], window_2=[1080, 1200], random_var_w=0.2)
 
 
diff --git a/ramp/example/input_file_3.py b/ramp/example/input_file_3.py
index 74441eaf..b7f165f3 100644
--- a/ramp/example/input_file_3.py
+++ b/ramp/example/input_file_3.py
@@ -25,7 +25,15 @@
 # Create Cooking appliances
 
 HH_lunch1_soup = HH.add_appliance(
-    number=1, power=1800, num_windows=2, func_time=70, time_fraction_random_variability=0.15, func_cycle=60, thermal_p_var=0.2, pref_index=1, fixed_cycle=1
+    number=1,
+    power=1800,
+    num_windows=2,
+    func_time=70,
+    time_fraction_random_variability=0.15,
+    func_cycle=60,
+    thermal_p_var=0.2,
+    pref_index=1,
+    fixed_cycle=1,
 )
 HH_lunch1_soup.windows(window_1=[12 * 60, 15 * 60], window_2=[0, 0], random_var_w=0.15)
 HH_lunch1_soup.specific_cycle_1(p_11=1800, t_11=10, p_12=750, t_12=60, r_c1=0.15)

From 3648b1839b64acc19450d4856935a2666805a63c Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 14 May 2024 14:48:31 +0200
Subject: [PATCH 090/140] Update documentation notebooks

---
 .../examples/cooking_app/cooking_app.rst      | 33 +++++++++--------
 .../fixed_flat_app/fixed_flat_app.rst         | 20 +++++------
 .../examples/multi_cycle/multi_cycle.rst      | 26 ++++----------
 .../occasional_use/occasional_use.rst         | 33 ++++++++---------
 .../examples/simple_bulb/simple_bulb.rst      | 35 +++++++++----------
 .../examples/thermal_app/thermal_app.rst      | 31 ++++++++--------
 6 files changed, 80 insertions(+), 98 deletions(-)

diff --git a/docs/source/examples/cooking_app/cooking_app.rst b/docs/source/examples/cooking_app/cooking_app.rst
index e6afec8c..530dba18 100644
--- a/docs/source/examples/cooking_app/cooking_app.rst
+++ b/docs/source/examples/cooking_app/cooking_app.rst
@@ -72,20 +72,20 @@ Cooking rice  600   10
 .. code:: ipython3
 
     # soup for lunch
-    lunch_window = [12 * 60, 12 * 60+26]
+    lunch_window = [12 * 60, 12 * 60 + 26]
     
     soup_1 = user_1.add_appliance(
         name="soup for lunch",
-        power=1200, # nominal power of appliance
-        func_time=25, # the cooking time
-        func_cycle=25, # we always need  25 minute for cooking
-        fixed_cycle=1, # the cookstove is not a continus power usage appliance, it has cycles as mentioned earlier 
-        window_1=lunch_window, # lunch is always prepared from 12
+        power=1200,  # nominal power of appliance
+        func_time=25,  # the cooking time
+        func_cycle=25,  # we always need  25 minute for cooking
+        fixed_cycle=1,  # the cookstove is not a continus power usage appliance, it has cycles as mentioned earlier
+        window_1=lunch_window,  # lunch is always prepared from 12
         p_11=1200,  # power of the first cycle
         t_11=5,  # time needed for the first cycle
         p_12=750,  # power of the second cycle
         t_12=20,  # time needed for the second cycle
-        cw11=lunch_window, # the time window of the working cycle 
+        cw11=lunch_window,  # the time window of the working cycle
     )
 
 The second user has two different preferences for lunch. Accordingly, we
@@ -142,10 +142,9 @@ rice is the second one (pref_index = 2).
     number_of_days = 5
     user_1_profiles = {}
     user_2_profiles = {}
-    for day in range(1,number_of_days+1):
+    for day in range(1, number_of_days + 1):
         user_1_profiles[f"day {day}"] = pd.Series(user_1.generate_single_load_profile())
         user_2_profiles[f"day {day}"] = pd.Series(user_2.generate_single_load_profile())
-        
 
 
 .. parsed-literal::
@@ -156,17 +155,21 @@ rice is the second one (pref_index = 2).
 
 .. code:: ipython3
 
-    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) 
+    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))
     
     
     i = 0
     for name, df in dict(
-        user_1_profiles=pd.concat(user_1_profiles,axis=1).iloc[lunch_window[0]-5:lunch_window[1]+5], # take only the lunch window
-        user_2_profiles=pd.concat(user_2_profiles,axis=1).iloc[lunch_window[0]-5:lunch_window[1]+5]# take only the lunch window
-        ).items():
-        df.plot(ax=axes[i],legend=True)
+        user_1_profiles=pd.concat(user_1_profiles, axis=1).iloc[
+            lunch_window[0] - 5 : lunch_window[1] + 5
+        ],  # take only the lunch window
+        user_2_profiles=pd.concat(user_2_profiles, axis=1).iloc[
+            lunch_window[0] - 5 : lunch_window[1] + 5
+        ],  # take only the lunch window
+    ).items():
+        df.plot(ax=axes[i], legend=True)
         axes[i].set_title(name)
-        i+=1
+        i += 1
     
     plt.tight_layout()
     plt.show()
diff --git a/docs/source/examples/fixed_flat_app/fixed_flat_app.rst b/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
index 18bca4aa..4af34757 100644
--- a/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
+++ b/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
@@ -43,10 +43,9 @@ Adding an appliance with flat and fixed consumption
         func_cycle=60,
         fixed="yes",  # This means all the 'n' appliances of this kind are always switched-on together
         flat="yes",  # This means the appliance is not subject to random variability in terms of total usage time
-        window_1 = [1200, 1440],  # from 20:00 to 24:00
+        window_1=[1200, 1440],  # from 20:00 to 24:00
     )
 
-
 Generating the profiles using UseCase class
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -58,14 +57,12 @@ number of days for generating the profiles:
 .. code:: ipython3
 
     school_case = UseCase(
-        users=[school], # users of the usecase
-        date_start="2023-01-01", # start date
-        )
+        users=[school],  # users of the usecase
+        date_start="2023-01-01",  # start date
+    )
     
     # when both date_start and date_end are not given, you need to initialize the usecase by this method and by passing the number of days as num_days
-    school_case.initialize(
-        num_days=7
-        )
+    school_case.initialize(num_days=7)
 
 
 .. parsed-literal::
@@ -144,12 +141,11 @@ overall energy consumption and demand patterns within simulation.
 
 .. code:: ipython3
 
-    
-    fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 4))  
+    fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 4))
     
     
-    for i, df in enumerate([no_fix_no_flat,fix_no_flat,fixed_flat,no_fixed_flat]):
-        df.plot(ax=axes[i],legend=False)
+    for i, df in enumerate([no_fix_no_flat, fix_no_flat, fixed_flat, no_fixed_flat]):
+        df.plot(ax=axes[i], legend=False)
         axes[i].set_title(df.columns[0])
     
     
diff --git a/docs/source/examples/multi_cycle/multi_cycle.rst b/docs/source/examples/multi_cycle/multi_cycle.rst
index 867e5339..d281b3f6 100644
--- a/docs/source/examples/multi_cycle/multi_cycle.rst
+++ b/docs/source/examples/multi_cycle/multi_cycle.rst
@@ -85,30 +85,18 @@ example, we do not apply such variability.
     # assiging the specific cycles
     # first cycle: standard cycle
     fridge.specific_cycle_1(
-        p_11=200, # power level for the first operation segment
+        p_11=200,  # power level for the first operation segment
         t_11=10,  # duration of the first operation segment
-        p_12=5,   # power level for the second operation segment
+        p_12=5,  # power level for the second operation segment
         t_12=20,  # duration of the second operation segment
-        r_c1=0    # random variability assigned to the duration of each segment
+        r_c1=0,  # random variability assigned to the duration of each segment
     )
     
     # second cycle: intermediate cycle
-    fridge.specific_cycle_2(
-        p_21=200,
-        t_21=15,
-        p_22=5,
-        t_22=15,
-        r_c2=0
-    )
+    fridge.specific_cycle_2(p_21=200, t_21=15, p_22=5, t_22=15, r_c2=0)
     
     # third cycle: intensive cycle
-    fridge.specific_cycle_3(
-        p_31=200,
-        t_31=20,
-        p_32=5,
-        t_32=10,
-        r_c3=0
-    )
+    fridge.specific_cycle_3(p_31=200, t_31=20, p_32=5, t_32=10, r_c3=0)
 
 After defining the cycle power and duration parameters, the time windows
 within a day at which the cycles occur should be specified by means of
@@ -123,7 +111,7 @@ across all cycles should not overlap.
 
     # defining cycle behaviour
     fridge.cycle_behaviour(
-         cw11=[0, 299], cw12=[1201, 1440], cw21=[300, 479], cw31=[480, 1200]
+        cw11=[0, 299], cw12=[1201, 1440], cw21=[300, 479], cw31=[480, 1200]
     )
 
 Building the profiles
@@ -138,7 +126,7 @@ Building the profiles
 
     # days to build the profiles
     days = [
-        "2020-12-16", # a day in the 'Hot' season, assuming a location in the Southern hemisphere
+        "2020-12-16",  # a day in the 'Hot' season, assuming a location in the Southern hemisphere
     ]
     
     profiles = pd.DataFrame(index=range(0, 1440), columns=days)
diff --git a/docs/source/examples/occasional_use/occasional_use.rst b/docs/source/examples/occasional_use/occasional_use.rst
index db1bd0f0..9b17d129 100644
--- a/docs/source/examples/occasional_use/occasional_use.rst
+++ b/docs/source/examples/occasional_use/occasional_use.rst
@@ -39,10 +39,10 @@ Creating user categories and appliances
         number=1,
         power=50,
         num_windows=1,
-        func_time=210, # 3.5 hours
-        func_cycle=210,  
+        func_time=210,  # 3.5 hours
+        func_cycle=210,
         occasional_use=0.5,  # 50% chance of occasional use,
-        window_1=[480, 750], # start from 8AM
+        window_1=[480, 750],  # start from 8AM
     )
 
 .. code:: ipython3
@@ -52,10 +52,10 @@ Creating user categories and appliances
         number=1,
         power=50,
         num_windows=1,
-        func_time=210, # 3.5 hours
-        func_cycle=210,  
+        func_time=210,  # 3.5 hours
+        func_cycle=210,
         occasional_use=1,  # always present in the mix of appliances,
-        window_1=[480, 750], # start from 8AM
+        window_1=[480, 750],  # start from 8AM
     )
 
 Generating profiles
@@ -70,14 +70,10 @@ the User object profile genertor methods for 5 consecutive days:
     household_profiles = []
     school_profiles = []
     
-    for day in range(1,number_of_days+1):
-        household_profiles.extend(
-            household.generate_single_load_profile(prof_i=day)
-        )
+    for day in range(1, number_of_days + 1):
+        household_profiles.extend(household.generate_single_load_profile(prof_i=day))
     
-        school_profiles.extend(
-            school.generate_single_load_profile(prof_i=day)
-        )
+        school_profiles.extend(school.generate_single_load_profile(prof_i=day))
 
 
 .. parsed-literal::
@@ -88,24 +84,23 @@ the User object profile genertor methods for 5 consecutive days:
 
 .. code:: ipython3
 
-    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) 
+    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))
     
     
     i = 0
     for name, df in dict(
         household_profiles=pd.DataFrame(household_profiles),
-        school_profiles=pd.DataFrame(school_profiles)
-        ).items():
-        df.plot(ax=axes[i],legend=False)
+        school_profiles=pd.DataFrame(school_profiles),
+    ).items():
+        df.plot(ax=axes[i], legend=False)
         axes[i].set_title(name)
-        i+=1
+        i += 1
     
     plt.tight_layout()
     plt.show()
 
 
 
-
 .. image:: output_8_0.png
 
 
diff --git a/docs/source/examples/simple_bulb/simple_bulb.rst b/docs/source/examples/simple_bulb/simple_bulb.rst
index eb623922..ff57a1bc 100644
--- a/docs/source/examples/simple_bulb/simple_bulb.rst
+++ b/docs/source/examples/simple_bulb/simple_bulb.rst
@@ -56,16 +56,16 @@ using the **add_appliance** method of the User object:
 
     # add_appliance is meth
     indoor_bulb = household.add_appliance(
-        name="Indoor Light Bulb", # the name of the appliance
-        number=6, # how many of this appliance each user has in this user category
-        power=7, # the power (in Watt) of each single appliance. RAMP does not deal with units of measures, you should check the consistency of the unit of measures throughout your model
-        num_windows=2, # how many usage time windows throughout the day?
-        func_time=120, # the total usage time of appliances
-        func_cycle=10, # the minimum usage time after a switch on event
+        name="Indoor Light Bulb",  # the name of the appliance
+        number=6,  # how many of this appliance each user has in this user category
+        power=7,  # the power (in Watt) of each single appliance. RAMP does not deal with units of measures, you should check the consistency of the unit of measures throughout your model
+        num_windows=2,  # how many usage time windows throughout the day?
+        func_time=120,  # the total usage time of appliances
+        func_cycle=10,  # the minimum usage time after a switch on event
         window_1=[0, 30],  # from 24 to 00:30
         window_2=[1170, 1440],  # from 19:30 to 24:00
-        random_var_w=0.35, # Variability of the windows in percentage
-        time_fraction_random_variability=0.2, # randomizes the total time the appliance is on (between 0 and 1)
+        random_var_w=0.35,  # Variability of the windows in percentage
+        time_fraction_random_variability=0.2,  # randomizes the total time the appliance is on (between 0 and 1)
     )
 
 .. code:: ipython3
@@ -127,11 +127,12 @@ comprehensive profile spanning the entire year:
 .. code:: ipython3
 
     use_case = UseCase(
-        users=[household], # A list of all the user categories to be included in the simulation. In this case, we only have household user category
-        date_start="2020-01-01", # starting date of the simulation
-        date_end="2020-12-31", # end date of the simulation
-        )
-
+        users=[
+            household
+        ],  # A list of all the user categories to be included in the simulation. In this case, we only have household user category
+        date_start="2020-01-01",  # starting date of the simulation
+        date_end="2020-12-31",  # end date of the simulation
+    )
 
 
 .. parsed-literal::
@@ -200,9 +201,7 @@ within the household category:
 
 .. code:: ipython3
 
-    single_profile = household.generate_single_load_profile(
-        day_type=0
-        )
+    single_profile = household.generate_single_load_profile(day_type=0)
 
 
 .. parsed-literal::
@@ -238,9 +237,7 @@ can use the similar method of User class, named
 
 .. code:: ipython3
 
-    aggregated_profile = household.generate_aggregated_load_profile(
-        day_type=0
-    )
+    aggregated_profile = household.generate_aggregated_load_profile(day_type=0)
 
 .. code:: ipython3
 
diff --git a/docs/source/examples/thermal_app/thermal_app.rst b/docs/source/examples/thermal_app/thermal_app.rst
index d7cb9dc0..26903dc3 100644
--- a/docs/source/examples/thermal_app/thermal_app.rst
+++ b/docs/source/examples/thermal_app/thermal_app.rst
@@ -52,12 +52,12 @@ load_data util function:
     shower = household.add_appliance(
         name="Shower",
         number=1,
-        power=shower_power, # pass the pd.DataFrame or np.array instead of a number
-        num_windows=2, # two possibe time window for shower
-        func_time=15, # each shower takes 15 minute
-        func_cycle=3, # every
-        window_1=[390, 540], #  morning shower from 6:30 to 9:00 AM
-        window_2=[1080, 1200], # evening shower from 18:00 to 20:00
+        power=shower_power,  # pass the pd.DataFrame or np.array instead of a number
+        num_windows=2,  # two possibe time window for shower
+        func_time=15,  # each shower takes 15 minute
+        func_cycle=3,  # every
+        window_1=[390, 540],  #  morning shower from 6:30 to 9:00 AM
+        window_2=[1080, 1200],  # evening shower from 18:00 to 20:00
     )
 
 Generating profiles for increasing degrees of ``thermal_p_var``
@@ -97,7 +97,7 @@ function **thermal_p_var_sensitivty**:
     def thermal_p_var_sensitivity(sensitivity_values):
         # buidling a pd.DataFrame for saving sensitivity results
         results = pd.DataFrame(
-            index=range(0,1440*365),
+            index=range(0, 1440 * 365),
             columns=[f"p_var = {value}" for value in sensitivity_values],
         )
     
@@ -120,17 +120,20 @@ function **thermal_p_var_sensitivty**:
 
 .. code:: ipython3
 
-    days_to_plot = [1,100,200] # which days of the year to plot
+    days_to_plot = [1, 100, 200]  # which days of the year to plot
     
-    fig, axes = plt.subplots(ncols=len(days_to_plot), nrows=sensitivity_results.shape[1], figsize=(10, 10)) 
+    fig, axes = plt.subplots(
+        ncols=len(days_to_plot), nrows=sensitivity_results.shape[1], figsize=(10, 10)
+    )
     
-    for j,day in enumerate(days_to_plot):
+    for j, day in enumerate(days_to_plot):
         for i, col in enumerate(sensitivity_results):
-            sensitivity_results[col].iloc[1440*(day-1):1440*(day)].plot(ax=axes[i,j]) # just plot for the first day
-            axes[i,j].set_title(f"Day {day} - thermal_p_var = {col}",fontsize=8)
-            axes[i,j].set_ylim(0,24000)
+            sensitivity_results[col].iloc[1440 * (day - 1) : 1440 * (day)].plot(
+                ax=axes[i, j]
+            )  # just plot for the first day
+            axes[i, j].set_title(f"Day {day} - thermal_p_var = {col}", fontsize=8)
+            axes[i, j].set_ylim(0, 24000)
     
-        
     
     plt.tight_layout()
     plt.show()

From 5c0e76ad7468fc2e942889240ce9359d7f87b080 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 15 Apr 2024 12:07:00 +0200
Subject: [PATCH 091/140] Add ramp_convert .py to .xlsx files to command line

---
 ramp/__init__.py                     | 1 +
 ramp/ramp_convert_old_input_files.py | 7 +++++--
 setup.py                             | 1 +
 3 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/ramp/__init__.py b/ramp/__init__.py
index d3753eb8..df02155e 100644
--- a/ramp/__init__.py
+++ b/ramp/__init__.py
@@ -18,6 +18,7 @@
 from ramp.core.utils import yearly_pattern, get_day_type
 from ramp.example.examples import load_data, download_example
 from ramp.post_process.post_process import Plot
+from ramp.ramp_convert_old_input_files import convert_old_user_input_file as ramp_py2xlsx
 
 __authors__ = "Listed in AUTHORS"
 __copyright__ = (
diff --git a/ramp/ramp_convert_old_input_files.py b/ramp/ramp_convert_old_input_files.py
index 747e30e8..9fc1355a 100644
--- a/ramp/ramp_convert_old_input_files.py
+++ b/ramp/ramp_convert_old_input_files.py
@@ -8,7 +8,7 @@
 
 parser = argparse.ArgumentParser(
     prog="python ramp_convert_old_input_files.py",
-    description="Convert old python input files to xlsx ones",
+    description="Convert RAMP python input files to xlsx ones",
 )
 parser.add_argument(
     "-i",
@@ -88,7 +88,7 @@ def convert_old_user_input_file(
     UseCase(users=user_list).save(output_fname)
 
 
-if __name__ == "__main__":
+def cli():
     args = vars(parser.parse_args())
     fname = args["fname_path"]
     output_path = args.get("output_path")
@@ -105,3 +105,6 @@ def convert_old_user_input_file(
                 )
         else:
             convert_old_user_input_file(fname, output_path=output_path, suffix=suffix)
+
+if __name__ == "__main__":
+    cli()
diff --git a/setup.py b/setup.py
index 1dffb354..f833a97a 100644
--- a/setup.py
+++ b/setup.py
@@ -39,6 +39,7 @@
     entry_points={
         "console_scripts": [
             "ramp=ramp.cli:main",
+            "ramp_convert=ramp.ramp_convert_old_input_files:cli",
         ],
     },
 )

From 59159f33bd3440010d08430e080c50adc2cce53f Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 13 May 2024 13:56:33 +0200
Subject: [PATCH 092/140] Fix Windows bug for py to xlsx converter

---
 ramp/ramp_convert_old_input_files.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/ramp/ramp_convert_old_input_files.py b/ramp/ramp_convert_old_input_files.py
index 9fc1355a..e89a1e50 100644
--- a/ramp/ramp_convert_old_input_files.py
+++ b/ramp/ramp_convert_old_input_files.py
@@ -81,6 +81,8 @@ def convert_old_user_input_file(
     output_fname = fname_path.split(os.path.sep)[-1].replace(".py", suffix)
     output_fname = os.path.join(output_path, output_fname)
 
+    if os.name != "posix":
+        sys.path.insert(0, os.path.abspath(fname_path))
     file_module = importlib.import_module(fname)
 
     user_list = file_module.User_list

From 5bbf9f7abdb87951346ade45771702eea6cde8ab Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 13 May 2024 14:18:26 +0200
Subject: [PATCH 093/140] Update documentation about ramp and ramp_convert
 commands

---
 README.rst                           | 5 +++--
 ramp/cli.py                          | 4 +++-
 ramp/ramp_convert_old_input_files.py | 2 +-
 3 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/README.rst b/README.rst
index 50be80fd..f7084d38 100644
--- a/README.rst
+++ b/README.rst
@@ -194,11 +194,12 @@ Other options are documented in the help of `ramp`, which you access with the ``
 
 
 If you have existing python input files, you can convert them to
-spreadsheet. To do so, go to the ``\ramp`` folder and run
+spreadsheet. Simply run
 
 .. code-block:: bash
 
-   python ramp_convert_old_input_files.py -i <path to the input file you wish to convert>
+   ramp_convert -i <path to the input file you wish to convert>
+
 
 For other examples of command lines options, such as setting date ranges, please visit `the dedicated section  <https://rampdemand.readthedocs.io/en/latest/examples/year_simulation/year_simulation.html#setting-date-range>`_ of the documentation.
 
diff --git a/ramp/cli.py b/ramp/cli.py
index e53ab22e..dc6a19fe 100644
--- a/ramp/cli.py
+++ b/ramp/cli.py
@@ -9,7 +9,9 @@
 BASE_PATH = os.path.dirname(os.path.abspath(__file__))
 
 parser = argparse.ArgumentParser(
-    prog="python ramp_run.py", description="Execute RAMP code"
+    prog="ramp",
+    description="Execute RAMP code",
+    epilog="To convert '.py' input files into '.xlsx' input files use the command 'ramp_convert'"
 )
 parser.add_argument(
     "-i",
diff --git a/ramp/ramp_convert_old_input_files.py b/ramp/ramp_convert_old_input_files.py
index e89a1e50..7c753261 100644
--- a/ramp/ramp_convert_old_input_files.py
+++ b/ramp/ramp_convert_old_input_files.py
@@ -7,7 +7,7 @@
 from ramp.core.core import UseCase
 
 parser = argparse.ArgumentParser(
-    prog="python ramp_convert_old_input_files.py",
+    prog="ramp_convert",
     description="Convert RAMP python input files to xlsx ones",
 )
 parser.add_argument(

From fee99663be00573fa59833adcfd31956be5b67f7 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 13 May 2024 14:43:37 +0200
Subject: [PATCH 094/140] Fix Windows path of .py to convert to .xlsx

---
 ramp/ramp_convert_old_input_files.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ramp/ramp_convert_old_input_files.py b/ramp/ramp_convert_old_input_files.py
index 7c753261..6ed48416 100644
--- a/ramp/ramp_convert_old_input_files.py
+++ b/ramp/ramp_convert_old_input_files.py
@@ -82,7 +82,7 @@ def convert_old_user_input_file(
     output_fname = os.path.join(output_path, output_fname)
 
     if os.name != "posix":
-        sys.path.insert(0, os.path.abspath(fname_path))
+        sys.path.insert(0, os.path.dirname(os.path.abspath(fname_path)))
     file_module = importlib.import_module(fname)
 
     user_list = file_module.User_list

From b18b6758bea234d0b19ccb4d96f452574f4fb9ac Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 14 May 2024 15:56:34 +0200
Subject: [PATCH 095/140] Lint with black

---
 ramp/__init__.py                     | 4 +++-
 ramp/cli.py                          | 2 +-
 ramp/ramp_convert_old_input_files.py | 1 +
 3 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/ramp/__init__.py b/ramp/__init__.py
index df02155e..235e2796 100644
--- a/ramp/__init__.py
+++ b/ramp/__init__.py
@@ -18,7 +18,9 @@
 from ramp.core.utils import yearly_pattern, get_day_type
 from ramp.example.examples import load_data, download_example
 from ramp.post_process.post_process import Plot
-from ramp.ramp_convert_old_input_files import convert_old_user_input_file as ramp_py2xlsx
+from ramp.ramp_convert_old_input_files import (
+    convert_old_user_input_file as ramp_py2xlsx,
+)
 
 __authors__ = "Listed in AUTHORS"
 __copyright__ = (
diff --git a/ramp/cli.py b/ramp/cli.py
index dc6a19fe..619f9c9e 100644
--- a/ramp/cli.py
+++ b/ramp/cli.py
@@ -11,7 +11,7 @@
 parser = argparse.ArgumentParser(
     prog="ramp",
     description="Execute RAMP code",
-    epilog="To convert '.py' input files into '.xlsx' input files use the command 'ramp_convert'"
+    epilog="To convert '.py' input files into '.xlsx' input files use the command 'ramp_convert'",
 )
 parser.add_argument(
     "-i",
diff --git a/ramp/ramp_convert_old_input_files.py b/ramp/ramp_convert_old_input_files.py
index 6ed48416..5622ded2 100644
--- a/ramp/ramp_convert_old_input_files.py
+++ b/ramp/ramp_convert_old_input_files.py
@@ -108,5 +108,6 @@ def cli():
         else:
             convert_old_user_input_file(fname, output_path=output_path, suffix=suffix)
 
+
 if __name__ == "__main__":
     cli()

From 23e793fd15cddfd9568f158f0799cf23d3aa94b5 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 11:56:04 +0200
Subject: [PATCH 096/140] Fix pandas deprecation warnings and update doc

---
 docs/notebooks/fixed_flat_app.ipynb                    | 10 ----------
 docs/source/examples/fixed_flat_app/fixed_flat_app.rst |  8 --------
 ramp/cli.py                                            |  8 ++++----
 3 files changed, 4 insertions(+), 22 deletions(-)

diff --git a/docs/notebooks/fixed_flat_app.ipynb b/docs/notebooks/fixed_flat_app.ipynb
index 3a2791ed..c85348d5 100644
--- a/docs/notebooks/fixed_flat_app.ipynb
+++ b/docs/notebooks/fixed_flat_app.ipynb
@@ -95,16 +95,6 @@
      "text": [
       "You will simulate 7 day(s) from 2023-01-01 00:00:00 until 2023-01-08 00:00:00\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\tahavorm\\downloads\\gitrepos\\ramp\\ramp\\core\\core.py:299: FutureWarning: 'T' is deprecated and will be removed in a future version. Please use 'min' instead of 'T'.\n",
-      "  end=self.days[-1] + pd.Timedelta(1, \"d\") - pd.Timedelta(1, \"T\"),\n",
-      "c:\\users\\tahavorm\\downloads\\gitrepos\\ramp\\ramp\\core\\core.py:297: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
-      "  self.__datetimeindex = pd.date_range(\n"
-     ]
     }
    ],
    "source": [
diff --git a/docs/source/examples/fixed_flat_app/fixed_flat_app.rst b/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
index 4af34757..2a9d1cfd 100644
--- a/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
+++ b/docs/source/examples/fixed_flat_app/fixed_flat_app.rst
@@ -70,14 +70,6 @@ number of days for generating the profiles:
     You will simulate 7 day(s) from 2023-01-01 00:00:00 until 2023-01-08 00:00:00
 
 
-.. parsed-literal::
-
-    c:\users\tahavorm\downloads\gitrepos\ramp\ramp\core\core.py:299: FutureWarning: 'T' is deprecated and will be removed in a future version. Please use 'min' instead of 'T'.
-      end=self.days[-1] + pd.Timedelta(1, "d") - pd.Timedelta(1, "T"),
-    c:\users\tahavorm\downloads\gitrepos\ramp\ramp\core\core.py:297: FutureWarning: 'T' is deprecated and will be removed in a future version, please use 'min' instead.
-      self.__datetimeindex = pd.date_range(
-
-
 .. code:: ipython3
 
     fixed_flat = school_case.generate_daily_load_profiles()
diff --git a/ramp/cli.py b/ramp/cli.py
index 619f9c9e..394f2387 100644
--- a/ramp/cli.py
+++ b/ramp/cli.py
@@ -202,7 +202,7 @@ def main():
             series_frame = pd.DataFrame(
                 np.hstack(year_profile),
                 index=pd.date_range(
-                    start=f"{year}-1-1", end=f"{year}-12-31 23:59", freq="T"
+                    start=f"{year}-1-1", end=f"{year}-12-31 23:59", freq="min"
                 ),
             )
             # Save to minute and hour resolution
@@ -222,9 +222,9 @@ def main():
             )
             resampled = pd.DataFrame()
 
-            resampled["mean"] = series_frame.resample("H").mean()
-            resampled["max"] = series_frame.resample("H").max()
-            resampled["min"] = series_frame.resample("H").min()
+            resampled["mean"] = series_frame.resample("h").mean()
+            resampled["max"] = series_frame.resample("h").max()
+            resampled["min"] = series_frame.resample("h").min()
             # TODO add more columns with other resampled functions (do this in Jupyter)
             resampled.to_csv(
                 os.path.join(ofname, "yearly_profile_hourly_resolution.csv")

From 15fcd9da7bf37e50a8b8170dde25aa93108f78dd Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 11:56:27 +0200
Subject: [PATCH 097/140] Add py to xlsx conversion fonction to the API doc

---
 docs/source/api_references.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/api_references.rst b/docs/source/api_references.rst
index c19abb51..ffc68c09 100644
--- a/docs/source/api_references.rst
+++ b/docs/source/api_references.rst
@@ -76,6 +76,7 @@ Utilities
 
     load_data
     download_example
+    ramp_py2xlsx
 
 
 *************

From b27d0ff06e9d9a669d3181b538240284774cb0c9 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 12:22:55 +0200
Subject: [PATCH 098/140] Improve docstring

---
 ramp/ramp_convert_old_input_files.py | 29 ++++++++++++++++++++--------
 1 file changed, 21 insertions(+), 8 deletions(-)

diff --git a/ramp/ramp_convert_old_input_files.py b/ramp/ramp_convert_old_input_files.py
index 5622ded2..7b740c3d 100644
--- a/ramp/ramp_convert_old_input_files.py
+++ b/ramp/ramp_convert_old_input_files.py
@@ -7,8 +7,7 @@
 from ramp.core.core import UseCase
 
 parser = argparse.ArgumentParser(
-    prog="ramp_convert",
-    description="Convert RAMP python input files to xlsx ones",
+    prog="ramp_convert", description="Convert RAMP python input files to xlsx ones"
 )
 parser.add_argument(
     "-i",
@@ -18,10 +17,7 @@
     help="path to the input file (including filename)",
 )
 parser.add_argument(
-    "-o",
-    dest="output_path",
-    type=str,
-    help="path where to save the converted filename",
+    "-o", dest="output_path", type=str, help="path where to save the converted filename"
 )
 parser.add_argument(
     "--suffix",
@@ -44,8 +40,25 @@
 def convert_old_user_input_file(
     fname_path, output_path=None, suffix="", keep_names=True
 ):
-    """
-    Imports an input file from a path and returns a processed User_list
+    """Convert old RAMP python input files to xlsx ones
+
+    The old (RAMP version < 0.5) .py input files defined all users and gathered them in a variable named User_list,
+    this variable must be defined in the .py file to be converted to .xlsx.
+
+    To convert a .py input file to an .xlsx using the UseCase objects, please refer to
+    https://rampdemand.readthedocs.io/en/latest/examples/using_excel/using_excel.html#exporting-the-database
+
+    Parameters
+    ----------
+    fname_path: path
+        path to a .py ramp input file containing a variable named User_list
+    output_path: path, optional
+        path to the converted .xlsx ramp input file, by default the same folder as the .py file
+    suffix: str, optional
+        suffix to be added to the converted .xlsx ramp input file name, default ''
+    keep_names: bool, optional
+        keep the variable names of the Appliance instances as their 'name' attribute, default True
+
     """
 
     line_to_change = -1

From 5bf3c9192c366471232d8a823efff1b3de2ff7ba Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 12:24:35 +0200
Subject: [PATCH 099/140] Improve README section about conversion from py to
 xlsx

---
 README.rst | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/README.rst b/README.rst
index f7084d38..2ef6da3f 100644
--- a/README.rst
+++ b/README.rst
@@ -193,13 +193,15 @@ Other options are documented in the help of `ramp`, which you access with the ``
    ramp -h
 
 
-If you have existing python input files, you can convert them to
-spreadsheet. Simply run
+If you have existing python input files from RAMP version prior to 0.5, you can convert them to
+spreadsheets input files. Simply run
 
 .. code-block:: bash
 
-   ramp_convert -i <path to the input file you wish to convert>
+   ramp_convert -i <path to the .py input file you wish to convert>
 
+If you want to save a RAMP model you created with a .py file into a spreadsheet refer to
+this `example <https://rampdemand.readthedocs.io/en/latest/examples/using_excel/using_excel.html#exporting-the-database>`_
 
 For other examples of command lines options, such as setting date ranges, please visit `the dedicated section  <https://rampdemand.readthedocs.io/en/latest/examples/year_simulation/year_simulation.html#setting-date-range>`_ of the documentation.
 

From 80c3ef2f9ca109befdebcb0f7dfed94e8d4aeac4 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 12:38:45 +0200
Subject: [PATCH 100/140] Add test for command line conversion

---
 tests/test_input_file_conversion.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/tests/test_input_file_conversion.py b/tests/test_input_file_conversion.py
index a2f259e3..68f30e78 100644
--- a/tests/test_input_file_conversion.py
+++ b/tests/test_input_file_conversion.py
@@ -69,6 +69,19 @@ def test_convert_py_to_xlsx(self):
                 if old_user != new_user:
                     pytest.fail()
 
+    def test_convert_py_to_xlsx_command_line(self):
+        """Convert the 3 example .py input files to xlsx and compare each appliance of each user"""
+        for i, j in enumerate(self.input_files_to_run):
+            old_user_list = load_py_usecase(j=j)
+            output_path = os.path.join("ramp", "test")
+            os.system(
+                f"ramp_convert -i {self.py_fnames[i]} -o {output_path} --suffix {self.file_suffix}"
+            )
+            new_user_list = load_xlsx_usecase(fname=self.xlsx_fnames[i])
+            for old_user, new_user in zip(old_user_list, new_user_list):
+                if old_user != new_user:
+                    pytest.fail()
+
 
 def test_define_appliance_window_directly_equivalent_to_use_windows_method():
     user = User("test user", 1)

From 5787a21e78cbe4315f9d3fe91c2b4136bf19c1c2 Mon Sep 17 00:00:00 2001
From: "F.L" <f.lombardi@outlook.com>
Date: Wed, 8 May 2024 11:01:52 +0200
Subject: [PATCH 101/140] Code to reproduce JOSS figure

---
 docs/notebooks/example_figure_joss.ipynb | 150 +++++++++++++++++++++++
 docs/notebooks/plot_class.ipynb          |   2 +-
 2 files changed, 151 insertions(+), 1 deletion(-)
 create mode 100644 docs/notebooks/example_figure_joss.ipynb

diff --git a/docs/notebooks/example_figure_joss.ipynb b/docs/notebooks/example_figure_joss.ipynb
new file mode 100644
index 00000000..ea8cd0e0
--- /dev/null
+++ b/docs/notebooks/example_figure_joss.ipynb
@@ -0,0 +1,150 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5e40c9fc-0a96-4198-8607-adb68a0a7a8e",
+   "metadata": {},
+   "source": [
+    "# Example output featured in RAMP's publication in JOSS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2e08c5a-fd06-4d11-a3b3-8f598c40964d",
+   "metadata": {},
+   "source": [
+    "The code in this notebook showcases how to produce a typical RAMP visual output. The same output is used as an illustrative figure in RAMP's publication in JOSS."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "280523b3-4580-4616-a260-fed9366e701c",
+   "metadata": {},
+   "source": [
+    "### Step 1. Importing some of the user types available from the Example Input File 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0465f589-cc6c-40de-9df7-5731e699b4d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ramp import UseCase\n",
+    "from ramp.example.input_file_1 import LMI, LI\n",
+    "\n",
+    "LMI.num_users = 2\n",
+    "LI.num_users = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5219a716-7b85-489b-9905-224c9533bc81",
+   "metadata": {},
+   "source": [
+    "### Step 2. Creating a UseCase and generating a list of load profiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8d506451-7951-4d54-b9b6-df76f1fe6762",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You will simulate 5 day(s) from 2024-05-08 00:00:00 until 2024-05-13 00:00:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "uc = UseCase(\"JOSS-paper-figure\", users=[LMI, LI])\n",
+    "uc.initialize(num_days=5)\n",
+    "\n",
+    "my_profiles_list = uc.generate_daily_load_profiles(flat='False')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0befd74-7c37-4ce5-9831-1fb68d2df72b",
+   "metadata": {},
+   "source": [
+    "### Step 3. Post-processing and plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "13d1fe35-2c1c-4641-9448-ea9a54e4e095",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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F2URE9Y7V6vkJsZu37xARERERVQu5ctZivCCYxSJo2hoEXjyrc+sGciWuHP5KlbM1NJzVziqcAt9QgmdVOGvlgmASpmgUdWxpQERkjH9GEhERERFVL3lBMMVzeguCWQK2NfBtC4pjlPtaa3g4qyWKwPYDOVi59ThcrgDFJGEuCCb1l7VaBFi9PXqLyxxY+NNhnMwpCWvOdUGNDGdPnjxZ3VMgIiIiIiIiIqI6RhR9N+8rw1dN4SwsFgG2AAuCuXXSWW2lrPS4xlaHagNniDicXYSiUgdOnSs1PkyxHU7lrNUiIDk+BrExvurZ03nG56vrohLO/t///V80hgEAHD9+HAMGDIjaeERERERERERERIA6j1QGsto73GwWdeWsf1sDxbHyeOrKWflxTQ1nNVTvyeQNf2fyS00vdiz1l7VaBMTYrBjasxU6tUrznLuWXKOqYGpBsGCeeeYZNGrUCBMmTIhoHCmY5eJfRERERERERET1w46DOTh1rhQtGyehacMEbDuQA6f3tvq0JDuu7JwRtfZgyopX5Zi6PWetFsVx6nHUgaTgPca7r7cjgNTWwBVOM9fzwL/nrP61CXTg2fxynMkvQ5MGCUHP56so9lwoq8WCWJtnux5ns9EJZwFg0qRJaNCgAf7whz+EdfzRo0cxaNAgHDp0iP34iIiIiIiIiIjqAVEUkXW6CABw4EQB3G4RxWUO+fXT50pRWuFEYlxMlM7n21aFs5osyhpkQTB1yKseQ66c9R6/ee8ZNEqNQ1GpQzVGgt2Gqy5qihhbzeg6qlcNrLufJtYtLXeaGl/uOWv1jS5orll9FLVP3+Vy4Y477sAPP/wQ8rGHDx9WVcxKK1kTUc0yYMAACIJQK3+AMn78eHnuhw8fru7phKw2X3vJ4MGDIQhC2D/ECyQ7OxvJyckQBAEzZsyI+vhERERERHR+SCFdmybJclWl0xW94E4ZAiqLZbU9Z60WiyacVb+ulyVqe86mJsbKr+UUlKPC4VL9yiuuQF5xRZjvJHJ+70mxrQ2rAzHbtkHZc1Y+j3fbbGuEuigq4eyIESMAABUVFRg9ejQ2bdpk+tisrCwMHDgQhw8fhiiKsNls+PTTT6MxLSJS2LZtGx577DH07t0bjRs3RmxsLOLj49GsWTNcddVVmDBhAj788EMcPHiwuqdKddDcuXOxYsUK2Gy2qPYplzRp0gRPPPEEAOBvf/sbCgoKon4OIiIiIiKqelLQl2C3yRWlLm+Lg2gwunXfr62BVR0iais7VeNIx2iqQC9q21B1jM0qYGD3FhjYvQUS46J2M3vUKEPWUOqCzLZt0AtnpfPU57YGUQlnv/zyS/Tr1w8AUFJSguHDh2P37t1Bjzt06BAGDhyII0eOAABiYmLw2WefYezYsdGYFhHB8//JO++8E5dddhnefPNN/Pzzz8jJyYHD4UB5eTlOnz6NjRs3Yvr06bj//vvRoUMHLFmypLqnTXWIw+HA008/DQC49dZb0bFjxyo5z8MPP4yUlBScOXMGr7/+epWcg4iIiIiIokubybncvpYANqtUORvNcNbzu7ZSVnuXolXQLgim3l8VJkptDTSVs4IgICXBVz1rsQhISYxFSmIsrFKD2hpUMaoOZ43TWe2UXSYrm3UrZwVWzkYlprfb7Vi4cCEyMzOxfft2nDt3DsOGDcO6devQpk0b3WMOHjyIgQMH4vjx4wA8weznn39eJbe7EtVXTqcT1113HX788UcAgM1mw4gRI9CvXz80b94cgiAgJycHO3fuxNq1a7Fnzx4AnjYlelavXn2+pk51yMcff4xDhw4B8FS1VpW0tDT86U9/wuuvv44pU6bgkUceQXp6epWdj4iIiIiIosAg6BMEX2/SqmhrIAgC9h7Nw4ET+nfdWa0W2CwWv+MkeguCWQV1OOs5j3IvQXtItdIGoi7FvENqaxBy5azvulp0rll9E7Ua6uTkZHz//ffo06cPDh48iBMnTmDIkCFYt24dMjIyVPv+/vvvGDRoEE6cOAEAiI2NxezZszF69OhoTYeIALz//vtyMNu2bVssXrwYXbp0Mdz/999/x7Rp05CWlnaeZkh1ndvtlqtY+/Xrh4suuqhKzzdx4kRMnjwZxcXF+OCDD/D3v/+9Ss9HRERERESR0UZyTu+iURahaitnBUHAsbPFqkBSKS3JrlroShtk6i2eJVjUbQ0AdcipyCTlY2pSJBl2WwOzPWe9nyPbGqhFdTm4xo0bY/ny5WjWrBkEQcCBAwcwbNgwVe+//fv3Y+DAgapgds6cOQxmiarArFmz5O33338/YDALAB07dsSrr76Kvn37VvXUqJ5Yvnw5fv/9dwDAXXfdVeXn69ChA66++moAnu+82x29/4gjIiIiIqKqoE7lpCpZi0WQK1edUUzupODUIviqdHt3bYIhPVqq9ktPiZMrdwG9tgb+6ayvclbxkrJYtoYu8CzNyqWXOOvQfhpm/93Ftgb6ohrOAkCbNm2wbNkyNGjQAIIgYMeOHRg5ciTKy8uxd+9eDBw4ECdPngTgCWbnzp2LG2+8MdrTICIAe/fulbczMzMjHm/AgAEQBMHwL5QZM2bIr8+YMQOA5wcyDz30EC688EIkJCQgLS0NV111Fd566y1UVlaaOu/OnTsxYcIEtGvXDnFxcWjatCkGDBiAjz76SP5LQDrvgAEDIn6f5eXl+PDDDzFixAi0atUKcXFxSE1NxcUXX4y//OUv2L9/f8TnUMrLy8Nzzz2HSy65BElJSUhLS0P37t3x0ksvITc31/Q4OTk5+Pjjj3HXXXehe/fuSEtLQ0xMDBo2bIju3bvj4YcflltX6Hnvvffk6/jPf/7T1DlffPFF+ZgPPvjA73Xpe2C1Wk39Wb99+3b8+c9/Rrdu3ZCamoqYmBg0atQInTt3xjXXXINnnnkGv/76a8AxpPY4J06cwA8//GDqfRARERERUTXRtjWQK2c9C2gB0a6c9bU1kMZNiotBQlyM376BFwTzbcsLgnlTNpdB5aygl3hWYyYpnVrQay0QaF6iOmQ1XTkrhbOK0Fuv2ri+qZKl4bp27YpFixZh8ODBKC0txfr163H99ddjz549OH36NABPn9q5c+di5MiRVTEFIoKn56wkOzsbbdu2Pa/nnzVrFiZOnIiysjL5ubKyMmzcuBEbN27EF198gaVLlyIlJcVwjLfffhuPP/6433vJzs7GmjVr8Nlnn+Hrr7+O2pzXrFmD22+/Xa7ul1RUVGDXrl3YtWsX/vvf/+Lll1+WF7mKxObNmzFy5EhkZ2ernt++fTu2b9+OadOmYeHChUHHOXToEDp16qS6TpK8vDzk5eVh+/bteOedd/Diiy/iueee89vvzjvvxN/+9jeUlJRg2rRpeOaZZwL+ZNflcmHatGkAgKSkJNx+++2q1ysrK7Fo0SIAwEUXXRS0/+vLL7+MF154we+nrrm5ucjNzcW+ffuwcuVKLFiwAL/99pvhOMqAfv78+Rg6dGjA8xIRERERUfXxWxDM5QtPq6KtgfTPDYugHxYqWQMsCKZX6SlXgRq0B7DolEjWhEhSEBDSRKRdrRYBLrcYcjhrUVXOesesCReimlRJOAsAvXr1wrx58zBq1Cg4HA6sXr1a/uLa7XZ89dVXuP7666vq9EQEzy3eO3fuBABMmTIFU6ZMOW/nXrp0Kb766iskJCTgwQcfxBVXXAG73Y5t27bhgw8+QEFBAX766Sc88cQTmDp1qu4Yn376KR5++GH58ZAhQzB69Gikp6fj8OHDmDlzJlauXIn77rsvKnNesmQJbrjhBjgcDlgsFlx77bUYPHgwWrRogfLycmzZsgUzZ85EQUEBnnnmGQCIKKDNysrC0KFDkZ+fDwDo1KkTxo8fj3bt2iEnJwfz58/HypUrceONNyI1NTXgWJWVlXA6nWjdujWuueYaXHLJJWjSpAliY2Nx9uxZbNy4EV9++SXKysrw/PPPIz09HQ888IBqjJSUFNx+++2YOnUqDh8+jGXLlmHYsGGG51y8eLG8qOOtt96K5ORk1esbNmxAUVERAKB3794B579gwQI8//zzAIC4uDiMGjUKffv2RePGjeF2u3Hq1Cls3boVy5cvDzgOAFx66aVISEhAaWkpli5dGnR/IiIiIiKqOaQg1mIR5NDUFcUFwZSVsxKrXmoKddWrNoxV5pHSWFLoqKycVZ5HvR3qzKuAov+uNp01c8Wl8DrYYl5ut4is04U4V1iuOg5QLAhWj9PZKgtnAWDo0KGYNWsWbr31VlUwO2/ePAwfPrwqT01EAG677TY5PHzrrbewf/9+3HvvvRg4cCAaNmxYpef+4osvcNFFF+H7779HixYt5OdvueUWjB8/HldccQWKi4sxY8YMvPzyy2jSpInq+HPnzuEvf/mL/Pi9997zCxMfe+wxTJgwAZ988knE8z116hTuuOMOOBwOZGRk4Ntvv/ULFMeNG4e//e1vuPbaa/Hbb7/hueeew+jRo9G5c+ewznn//ffLwezYsWMxa9YsxMbGyq8/+OCDePPNN/HYY48FHSsjIwM//vijYb/gSZMm4cUXX8SwYcOwf/9+PPPMMxg3bhySkpL89pPC8qlTpwYMZ5Wh+sSJE/1e//nnn+XtXr16BZy/NJbNZsP69etx+eWX6+7ncrmwcePGgGNZrVZcccUVWLNmDY4cOYLTp0+jadOmAY8hIiIiIqKaQeovW/ULgvmesxlUziq7EGjzR93KWZ2wUt3WoGay6E3MRFZqsVgAuIJWzmbnleK3rHPy41ibVd4WdKqN65uo95zVGjNmDN5//32Iooi4uDh88803DGYJgOcPMqfLXW9/nY9m14888oi8OBLgqQy9+eabkZ6ejgsuuAA333wzXnvtNWzatCnq87HZbJg/f74qmJV07twZDz74IADA4XDo9gWdMWMG8vLyAHiqMrXBrHSODz/8EO3bt494vq+99hrOnfP8ZfHVV18ZVnq2aNECX375JaxWK1wuF956662wzrdjxw4sW7YMANC6dWvMmDFDFcxKHn30Udx8881Bx2vYsGHQhdzatm2L//73vwCAgoICfPvtt377dO/eHVdddRUATzWr1IpG69ixY1iyZAkAoEePHujRo4ffPlLVNoCgAfaBAwcAAJdddplhMAt4gtc+ffoEHAuAavG7HTt2BN2fiIiIiIiqh99CW3I4C0U4G/0FwSQWi/G6KkrafzMrH8s9Z3WqQFVtDVTnqf6FsET4VxFrXzM4EIAv1A5WOevwhuvxdis6t0pDy8a+IiEpGK7H2ay5ylmr1Rp8JxMqKipMBbOCIOj2TaS6xeUWsWjjkeqeRrW5vncb45/ORUlcXBxWrFiB5557Dv/9739RWloqv5aVlYWsrCzMmzcPgGcxv0cffRQPPPAAYmL8G6GHasSIEbjwwgsNXx8yZAheffVVANDtH/rNN9/I248++qjhOHa7HQ888AAef/zxsOcqiiJmzpwJALjqqqvQr1+/gPt37twZV155JX766Sd8//33YZ1z/vz58vakSZMQHx9vuO9f//pXfPXVV2GdR0sZ4G7cuNGvT6w0n59++glOpxMfffSR3MJBafr06XC5XAD0q2YB4PDhw/J2sErtxMREAMDBgweRn5+PtLS0YG8lIOX5lPMgIiIiIqLaQRAE2LzJndNdNQuCAZDPoTsHRa2rX4gsqnYEoF85q2plYPE7pHrpVBGHcJivjYPJZDU5PhadWjdQPSctCFadIXV1MxXOiqIIQRAiulBymXI9vthE1SEuLg6vvfYannnmGXzzzTdYvnw5Nm7ciKysLNV+R44cwSOPPILZs2dj0aJFQRdvCkaqvjTSsmVLeVuqkJW43W788ssvAIDk5GT07Nkz4FgDBw4Mc5Yeu3fvRm5uLgCgQYMGqmDYiPRDq6ysLJSXlyMuLi6kc27atEnevuaaawLue8UVVyA5OVnu3xrIgQMHMHPmTKxduxb79u1DQUGBakE2JalfrNaYMWPw6KOPIjc3F9OmTcPTTz+t+g8Kl8uF6dOnA/B8PrfeeqvuOFIlMoCg36ehQ4fi119/xblz59C/f388+eSTGDFiRNghrfJ8ynkQEREREVHtYLFU0YJgUiDpfWy1mrupXJlnGWVbvspZ5XOKbWUkWwPSWTlkDbMBrtVsOCu9rHMa9pwNoedspKEqQ1nSsloEXN+7TXVPo9pYA/x0rio0aNAAd999N+6++24AnkB0y5YtWLVqFT777DMcPXoUgKdP6G233RZ2RaikUaNGAV+32+3ydnl5ueq1goICucq3Xbt2QW8xueCCC8KcpYeysnLx4sVYvHhxSMefO3cOzZs3D+mYkydPytsdOnQIuK8gCGjfvj22bdsWcL8XXngB//znP03feVBYWKj7fFxcHO655x689tpryMrKwvLlyzF06FD5deVCYLfffrtf31pJRUWFvK1dLEzrqaeewqJFi7Bz507s3LkTd955JywWCy699FJcddVVyMzMxHXXXYeUlBRT7025n1E4TURERERENYFx0GmrwgXBJAErZ1U9Z5XhrGY/7+/SumKGlbM1IJDVozevwDGe50WzC4IFyGblc9fn2NBUOLtq1aqqngfVQ4LiD1o6/xo0aIAhQ4ZgyJAhePHFF/HII4/I/UiXLVuG9evXm+rtacRisNqlGSUlJfJ2QkJC0P2lW+LDJS3KFa7KysqQjykuLpa3o/EeX3vtNbz44osAPNd+4MCB6NOnD1q3bo3k5GRVP9vRo0cDgNyWQM/EiRPx+uuvQxRFTJ06VRXOfvjhh6r9jCgD+MLCwoDVxampqfjpp5/w2muv4X//+x9OnjwJt9uNbdu2Ydu2bXj//fcRFxeHe++9F//85z+Rmpoa4Gp4An5JoJYRRERERERUvYwyOXXP2ehXzkpJodVkLqEMD/2qPL0JY7Ces4JOEFwTMkkzPXeVpLdn9f6732xbAz2snDUZzmZmZlb1PIioGsXExOCtt97C6tWrsXv3bgDA8uXLIwpnI6EMIpV9co0ow9xwKCs/H3vsMUyePDmi8UI9Z2lpqSrI1BPoPZaXl+Oll16Sx12xYgWuvPLKkMdRat++PYYOHYrvv/8eCxYsQHZ2Npo0aYJjx45h6dKlAIArr7wS3bt3NxxD21ogIyMj4DkTExPxwgsv4B//+Ad27tyJ9evXY8OGDVixYgVOnTqF8vJyvPfee1izZg02btwYMLCW2lQAwfvdEhERERFRNTLI5ASLAKs3uHM43SircCLebvoGcOPT+VXOhtPWQP2ar3I2cM9ZZVuDmlCqJr2PcG/stZpcECxQAu1rg+q7xtsP5qCwxCHvY4+1onuHRrDHRGdNrJom/NI2IqpTbDYbBgwYID8+depUtc0lNTVVribNysoK2hbl0KFDEZ1P2f/22LFjEY1lVosWLeTtAwcOBNxXFMWA7/Gnn36SK3EnTpxoGMwC8Os1HMgDDzwAAHA4HPjoo48AANOmTQu6EJikXbt28nYofV8FQcCll16KSZMmYdasWThx4gSWLVuGVq1aAfAsIPfBBx8EHEN5vrZt25o+NxERERER1QzKtgaVTjeWbTmGvUfzghwVnLZCM1DlrLqtgW87eM9ZUfGc/ni+wYzner7oVc4Gmpb0mtmesyLUi7ApKa+PKAJFZQ4cyS5GXnGF/Ov0uVKcyau77eoYzhKRLCYmRt426iN6PlgsFvTo0QMAUFRUhC1btgTcP9LWK927d5dvk1+1apWqV2pVUQaoK1euDLjv5s2bDfvDAsDp06fl7WD9a5csWWJyhsD1118vB6LTpk1ThbSpqam45ZZbAh5/ySWXyNt79uwxfV4tQRAwZMgQvP322/JzP/74Y8BjpApwAOjWrVvY5yYiIiIioqpl2NbAIiDBbkOjlDg51MwtLDfYO4TzSSf0/m4LuCCYLzlUBrLaLFKaX7DKWVU4WRNKZ+XQNLyj5Z6zohj2WlPKa+IWRbm/cGyMBb26NEFqoqdFX11ey4rhLFEdlp2dbXpfh8OhCu6UwVp1uOGGG+TtN99803C/iooKuVduuKxWK26//XYAQE5ODt54442IxjND6vsKAO+//77fomhKwdosKG/vD1SFm5eXhylTppieo9VqxZ/+9CcAnurkxx57TF4I7I477gjaK7d3797y9s8//2z6vEaUlbiBFj1zOp345ZdfAHiqZps0aRLxuYmIiIiI6PyyCJ7grs8lzdC7i+e/6Ssd4feedblFFJZWoqxC/W+JQIt1K18RQ6mcVYWzytf9x67OyFE6tyXUFcE0PWeB8HvGKrtKiCLgcns+41ibFU0bJsAeWzdbGSgxnCWqw3r27Inx48fjp59+CrhfcXEx7r77buzfvx+Apypy1KhR52OKhu6++240aNAAADB79my8//77fvs4nU5MnDgRBw8ejPh8zzzzDNLS0gAAzz77LKZMmQK32/gv/pKSEkybNg2zZ88O63yXXnqpvMjWkSNHcM8998DhcPjt984772Du3LkBx+rZs6f808Zp06bpXo9z587hxhtvxMmTJ0Oa54QJE+SK6nfffVd+PlhLAwC46qqr5IrkYOHsfffdhx07dgTcR/kdCNTrdseOHXKv4mHDhgWdJxERERERVSODTE8ZGMZ6e41WOo0XNQ54ClHEmm0nsGrrCew7lq86rTVg5ayPW9VzVj1pwRu1ypWzipctRpWzNSKe9Qi7rYGiJUSg1gbS5dKLwS3aylnvONrQvPqvUtWJvJMyEdVYDocDn3zyCT755BO0adMGmZmZ6N69OzIyMmC325Gbm4utW7di/vz5OHv2LADPH8pvv/12tS+i1LBhQ7z99tu48847AXj6n3799dcYPXo00tPTcfjwYcycORO7du3C2LFj5QDTYrKZu1aLFi0wd+5cjBw5EhUVFXj00Ufx3//+F6NHj0bXrl2RlJSEoqIiZGVlYcuWLVi5ciXKy8vx8ssvh/0eP/jgA1x++eXIz8/H7NmzsXXrVowfPx7t2rVDbm4u5s2bhxUrVqBdu3ZITU3Ftm3bdMdp3rw5xowZg7lz56KgoADdu3fHhAkT0K1bN9hsNmzduhWffPIJcnNzMX78eMyYMcP0HJs2bYrRo0erAuKrrrrKVGW1zWbDyJEj8emnn2LXrl04c+aM4aJg06ZNw7Rp09C5c2cMGjQIF198MdLT01FeXo6jR4/iyy+/lMPbBg0aYNKkSYbnXbt2rbytrFAmIiIiIqKaxyh0UwaGMTbPv/MczvAqZ93eXqbSWFZvy4TiMgdsgVbD0vRDVY4nvSwq9pOGMm5roHOOGlA6G3pbA8+BqmA12KJggG46q7w+ok44WyO6P1SxqIezbrcb3377Lb7//nv88ssvOHHiBAoKCgLesqslCELAW1aJyJxu3bph+fLlEEURR44cwcyZMzFz5kzD/Zs2bYp33nkHN99883mcpbE77rgD586dw+OPPw6n04nly5dj+fLlqn0GDRqE999/Xw4Pk5OTwz7fkCFDsG7dOtxxxx3Yt28ffv/9d/znP/8x3N9qtaJp06Zhn69du3b4/vvvMXLkSJw5cwZ79+7FU089pdqnVatW+Oabb/CXv/wl4FgffPABDhw4gF9//RXFxcW67QtuvvlmvP/++yGFswAwadIkVThrpmpWMm7cOHz66adwuVz4/PPP8cgjjwTcf+/evdi7d6/h661bt8a8efNUC6ppzZs3D4AntB48eLDpuRIRERERUXUwaBGgCE1jbZ7KWZdbhMvlNl3tKlEGh8N6toLVasG2A54CpUBjqdsa6FTOyumses7KKlt1WwNFUCuNZe4tVAnp3HqVs2YnZrUIns/FTDhrwCJ4Am+3CLnnrCVQaF7HRDWcXbhwIR544AGcOHFCfq4uN+wlqum+//57nDx5EsuWLcP69evx22+/ISsrC/n5+XC5XEhKSkKLFi1w6aWXYvjw4Rg9erSqf2lN8Je//AUDBgzAlClTsHLlSpw+fRqpqano0qULxo0bh7vuugs5OTny/pFW/Pbs2RO7d+/G/Pnz8e233+Lnn39GdnY2SkpKkJSUhFatWuGSSy7BgAEDMGrUqIjCWcCzMNjevXsxefJkfPPNN8jKyoLNZkPbtm1x00034c9//jPS09ODjtOgQQOsX78e77//PubMmYM9e/agsrISTZo0Qc+ePXHnnXfixhtvDGuOffv2RXx8PMrKypCWloaxY8eaPnbw4MHo1KkT9u3bh5kzZxqGsydOnMD333+PdevWYceOHcjKykJBQQGsVisaN26MSy+9FDfccAPuvPNOxMfHG57v4MGDWL9+PQBPqGy11v3+REREREREdZEym7NZBQiCp3q10ulGfIjhrDKbKne4UFFaibIKlzy2fE6LYFgBWuF04VyRp/CwtNxTUChls9IIej1nLUaVszUoe9TLQQO2NVBU3JoJZ+UQ2OB1wfvhim5R7jlrC/Ou2NooauHsW2+9hcceewyATu8N77dPL6jVli8TUXQ1b94c48ePx/jx4yMea/Xq1QFfD+U8bdu2Nf3/+UsvvRQfffSR4eubNm2St7t166a7z4wZM0xXjFosFtx8883nrYK4QYMGeOWVV/DKK68Y7hPs2gNAXFwcHn30UTz66KMB9wv1z9qFCxeirKwMgKcSNlA4qiUIAp544gncd9992Lp1K3bs2IFLL73Ub7/mzZvj7rvvxt133x3S3LQ+/PBDiKKIxMTEgK0PiIiIiIioZtD754kAbTsAAbE2Cyocbk84aw/tHMpK1h9+Oa56TRkC9rm4KX47dA4Xt/Mv+jlXWIEfd5zSP4F3qtYglbO6FarVSS4ADn9eUoVrabkTSfEx+ouLyfRfswgCXBBVPWfrU+VsVGLoDRs24PHHH4coihBFEbGxsbj77rvx6aefYtmyZXIQ0KJFC6xcuRLz58/H5MmTMWLECFgsFoiiCIvFghdeeAGrVq3CypUrozEtIqonlAtVDRw4sBpnUjcpF+K6//77Qz7+rrvuQvv27QEgoh69wRQUFODDDz8EADzyyCOmKo6JiIiIiKjm0QvmYrytDSodLjhdbuQWliO3wPMrv7giYBGKW1HpCXgqRRPsNjRIsiOjga/4pGFyHPp3a46GKXHe/X19TxPsNvWvOBvi7Tb5deX+Rj1n9YLL6qxTFH3pbNjxrBRI/7wnG+uMwusgb1Kw+HaTrp1ysTHvZOusqFTOPv/883C73RAEAa1bt8aSJUvQuXNnv/3i4+MxYMAA+fGjjz6KQ4cO4Z577sHatWvxyiuvoEWLFrj33nujMS0iquXKysqwY8cO9OrVS/d1t9uNZ555BsuWLQPgWajKqHKWwrN27Vr5+g4bNgxdunQJeYyYmBj861//wh//+EfMmzcP27dvr5LP6a233kJhYSEyMjLw17/+NerjExERERHR+aFXYRobYwHKPIuCrdt5CgUllarXu7ZpgI4t03THkwI/QRAgiiLSkuzod2lz0/OxWgUM6dnK7/kNu06hpNwJKdoMXjmr2DZ99vNDujaSQGG3suVuy8ZJOHCiAC63iLziCjicbnkBN3l/+Rz648ntIHQXBKtpVyr6Ig5nc3NzsWrVKvn/OHPnztUNZo1ccMEF+OGHHzBq1CgsXboUDz74ILp164aePXtGOjUiquVKSkrQu3dvdO3aFUOHDsVFF12EtLQ0lJWVYc+ePfjyyy9x4MABAIDdbscHH3xQzTOu/crKyrBmzRo4nU7s2rULr732GgDPX9QvvfRS2OOOHTsWU6dOxYoVK/Dcc89hwYIF0ZoyAODMmTPyXF999VWkpqZGdXwiIiIiIjp/9O5olxYFq3S6UFzmAOCpZnW6PK0OSsqNF5aXwlJthWsw8qJdBjmlqK3I9U7cZdhztuams4JmcTOzB3Vu3QCdWzfAkp+PoNLpRmmFA6m20PpOBApnpesk1uHS2YjD2fXr10MURQiCgIEDB+KKK64IfRI2G2bMmIGOHTuiuLgYjz/+ONasWRPp1Iiojti9ezd2795t+HqjRo3w5Zdf6vYypdBkZ2fjuuuu83v+ySefxJVXXhnR2D/88ENExweSkZGBoqKiKhufiIiIiIiqhl7kptfWINZbjVnpdMtha99LmuHomSLsPZof+Byir3IWEHXDX10hBqjS4mJOlyhnZUaVs4roN7STRJGyAja0drj+c06Is6GyuBKl5U6kJtp1dzdeEMw3H184W38WBIv4nZ44cULeVrYs0FNeXm74WkZGBkaPHg1RFLFu3TocPXo00qkRUS3XsGFDLFiwABMnTkTPnj3RunVrJCYmwm63o1mzZhgyZAjeeOMNHDp0KOifPxS6lJQUXHHFFZg1axb+/e9/V/d0iIiIiIioLtIpS9XrzSrdKl/pcCkqVgWYCTndbml/5XHRI42mDBTdqkDYQ1VF6/29RtSDCv7tAwLNS25ToHguMS4GAHQrmOWxgrU1cItwuTwflrUeLQgWceVsXl6evN28uX6/jri4OJSXl6O4uDjgWD169MDMmTMBeFZfb926daTTI6JazGKxYOTIkRg5cmR1T6XeaNu2bcDeQkRERERERFVNLzu16rUMsARvPQAog1Lj8XXnIY1t8LqoSSmVi1i5XCKsFm1bA3PnrQ6Rzi3Buzhaabkj9HN7P1tRr61BPRBx5WxcXJy87ZZ+FKGRkpICwLOSdqDqWWk/ADh1ymCFNyIiIiIiIiIiqhPMtjWQKlD9+rmayPDktgbenfUqc3XJ99sH2U0xrjR1p1sdCHu2dc5bjbUxymzZb2oB5qXttQt42hoAQEmFce9fow9LumZuZVsDK8NZ0zIyMuTtnJwc3X3atGkjb+/YscNwLGWLhNLS0kinRkRERERERERENZlOCKgXnkrPuDXhrJn2AG63tnI2tAXBjEbXW6TKavVEbdLt+RZVWwP/wWvGfYvK9hAeoc5Lqpwt1wlnRc2CbH5nF/wrZ6WA3kxldG0XcTjbuXNneXv79u26+3Tr1k3eXrhwoeFYytW7laEvERERERERERHVPbqVs3rhqU7lrNkCWOkQafdQb+E3zAV1mq9q2y8YVc7WhLpQUdHuwf+ahJaGSv12Xfo31XsE6zkr+u7K54JgIbj00kvl1gbr1q3T3WfUqFEAPB/6O++8gz179vjt88Ybb2DTpk3y4969e0c6NSIiIiIiIiIiqmUEnbRKCg+VVbCqClhTPWcF1e+mGYyttzCWzVs56/SmlEKt6TmrmZyJFcGUi4hJn1k4a5jI3SP0es7W4GsWLREvCBYbG4s+ffpgxYoVOHnyJNauXYv+/fur9rn22mvRrl07HD58GAUFBbjyyisxbtw4dOvWDeXl5Vi0aBF++OEH+Ytw5ZVXokuXLpFOjYiIiIiIiIiIaplAbQ3k2941QateiwGJ6Fa/ZnatKZMtZ1W0lbMWg8pZ3+RCGLwKaa9J4GxWU4oMZfWr/5F6Ibb63L5jXa76tyBYxOEsAIwZMwYrVqyAKIr4z3/+4xfO2mw2vPfeexgxYgQAoKSkBB988IHfOKIoIi4uDu+99140pkVERERERERERDWYXqWlbjiraWsQSvWrW1S3GAi5ctaAb2Es33jSQlZSyKh8zaJqa+DZdrrdWLvjJErLHQHPFWOzomenDKQmxkZl7gCgzlgjuybSewunclY+1g3/ytl6ICrh7B//+Ec4HJ4vkSAIqKysRGys+sty7bXXYtasWbj//vtRVFSk+2E1bNgQc+bMweWXXx6NaRERERERERERUS1j0QnmtG0NpJak8p4B2xrojxVcsB39T2rzTszpltoaGJzXu11W4UReUUXQmVQ43MjOK41qOCvPXrfnbGjkz0fvcwiS10otEdzKtgbWyBYoq02iEs6mpqbiwQcfDLrfrbfeikGDBuHjjz/GypUrcfLkSYiiiFatWmHIkCGYMGECUlNTozElIiIiIiIiIiKqhfSqJn1tDTyhp1yFaqL1gNuvrYG5JFLV0lYUDStulc9qK2ctBpWzynEBIDEuBld2ztAdf//xfJzIKfF7H9Hk13I2wKn02hTIrQl05ii1QTC6fqq2BpoFwepD/WxUwtlQNGnSBE899RSeeuqp831qIiIiIiIiIqJ6La+oAvnFnkpNi0VA8/RExNgiXi8+bFKUZ7MKaJWRjIpKFzq08C/cEzThn9mAFdBbECyCCSuIOimlFCqaXRDMm0UixiYgxaAq1h5j9e5bVeGsEFqrB533LVjCb2sgnbqk3KnT1qDux7PnPZwlIiIiIiIiIqLzz+V2Y/1vp+QADACKSx24qF3DapyVh0UQcOkF6YavSwGeS9s/1vt6wEpPv7YGJitnTe2l3s8mVc661fPUntc3b6lNg/HZpNf0FtuKhHRuAXrvNcACa97f1ZWznt8DtTUItiBY1qlC+Tn2nCUiIiIiIiIiojrF6fL19ExNjEVBSSVKK5zVOym9pE+HtICWr+esvLpX0FP4tzUIaYYAPNPUHqaXQ0qhonSdT+WW6p9XCjPl1wKEswFaBkSFTs/ZwGfy/9CUwbO2BYSyt62elo2TkF9cIV+zRqlxiPVWCysGDTij2ozhLBERERERERFRPeBWVHNe0DwFW3/PkW+/r25B81KpctawrYFxeOdWVIgCIVTOKncLlM4qdtS2Ndh/PF8xnnHP2eqonFUKq62BgvLzcIsirCGM1zgtHgMvaxnqKeuMKgtnt2/fjhUrVmDz5s04fvw48vPzUVpairZt22LFihWqfV0uF06cOAEAsFgsaNky8AdCREREREREREShUfZrtVnVIWJ18YVugcM8KeuT8km5f6zfOP60oab5IFKn+lNBXuhK8Zy2rYH6vP4jS7sFqpz1VeMGnXBIlPWvfrmzmQXBFAcps2W3G7BGqY1xtPoD12RRD2eXLVuG5557Dlu2bFE9L/0kwGq1+h0jCAIyMzNx9OhRCIKA3bt348ILL4z21IiIiIiIiIiI6i2pX6vVIiDGm545qr1y1lxNpKCJD/0KTQOFidJbFAyONUWvdNaf1XtdXS633+JY6gBWvYBWoB6rvmA6tPrR4jIHCksq0Sw9QT+QjmI5qqCYv3aecqAevdPVKVFbjk8URUyaNAnXXXcdtmzZAlEUVb8C/VTCYrHgz3/+s7zvJ598Eq1pEVEt0LZtWwiCgLZt21b3VIiIiIiIiOosZb/WmlI5azYg1MZK0q3+cnAZ4NjwK2cVdE7gq+L1PSeFrE636Fc9q9vWQG41YaZyNrQ0dcWvx7F53xlk55Xpvq6sgA11bC3l7A3bL9SHMtgwRC2cHT9+PKZOnSoHrDExMRg+fDheeOEFvP/++0ED2ltuuUXeXrhwYbSmRVTvDRgwAIIgmP51+PDh6p4yERERERERVQEpNPO0NfCGiM7q7eapd4u8Hu3roQSsclioE6aaPafZqyRd1zN5ZTibrw5F9c4rReOB2gDIC4KJIn4/nq/qY2tGfnGFwSu+xgZ+IX2gN6xTCevJFLwv+x1blzvGRi4qbQ3mzp2LWbNmyf/HuO222/DGG28gIyND3mfSpEkBx2jRogW6deuG7du347fffkNubi7S09OjMT0iIiIiIiIionpPWTkbY/O1NQhWUFcTRNLWwO0O1F4gcsq5Jdhj5O19x/LV+wn+26IiMDciVQk7HG7sPpIHAGjbJBmxMf6tQ8OlbW8hBrig8iuC9jMR4BJFv8pZZW9b8heVcPbZZ5+Vtx999FFMnjw5rHF69OiB7du3AwB27tyJAQMGRGN6ROT19ddfB91H+UMVIiIiIiIiqjv02hoAnlvapYrPmsqvrYG0IJj390BhovaOfdOVs4ptvTv1RZ1mqmlJsWjbNBmHTxehuMwR9BzSEJYAPWel9+p0+wLUSNsQeE7u+U0A4HSFMp7/QmiAN5yF6BeGR1I4K5+jDhffRhzO7ty5EwcOHIAgCLjgggvw6quvhj1Wp06d5O0DBw4wnCWKshtvvLG6p0BERERERETVRMrMLIKgWoDK6XKrwtrzKdzFogKFmf7nCLPnbBi7CYKA5umJOHy6yFSAGkrlbLT7A/sqYP2ri00dpyFYALj8g2yzrSvCOWddEPH/87Zu3SpvjxkzBjZb+HlvgwYN5O38/PxIpkVERERERERERApSAGe1eELEGG8g63BW86JgQNAgVBuohhL0SbfZSwGf+Vw38I5GgaHdRLsBaWS3qcpZz+/K6taoVM4GYLSml+dF7+8G1cyGC4KFo2YXdEdFxOFsdna2vN2hQ4eIxrJafV9ep9MZ0VhEVDMcP34cjz76KDp16oSEhAQ0atQIvXr1wuTJk1FWpr9ipNE4//3vf3HLLbega9euSE5ORkxMjDze008/jWPHjhke/9e//lVe9Oyzzz4zdc67775bPmbp0qWm56rkdDqxfPlyPPnkk8jMzESzZs0QGxuLxMREtG3bFmPGjMFXX30Ft1v/P4by8/MRFxcHQRDQvn17U+fMzs5GTEwMBEHAxRdfbLhfQUEBJk+ejMGDB6N58+aw2+1o2LAhevTogaeffhonTpwIeJ7x48f7LST3zTff4KabbkKbNm1gt9v9FpkTRRHr16/H888/jyFDhqBly5aIi4tDfHw8WrZsiVGjRuGjjz5CZWWlqfcKAN999x1GjRqFZs2aIS4uDq1bt8bNN9+MFStWAABWr14tz/OFF14wNd64cePQoUMHJCcnIyEhAe3atcMdd9yBH374wfS8iIiIiIhqGinQk4JOm02qyKwJdYmBUzjDtgbex4HyQF9VqPr9hzIj3bYJBiGlYS9Y5RBh9JxVVs5GNQCNEqEqwtl6IOK2BrGxsfJ2RYXR6m/mnDt3Tt7mYmBEtd+iRYtw6623oqioSH6urKwMubm52LRpE2bMmIHFixcHHWf16tUYNGiQ360oAJCbmyuP98Ybb+C///0v7r33Xr/9Jk6ciMmTJ0MURUydOhW33357wHPm5+fjiy++AAC0bdsWQ4cODTpPPUOHDsWqVav8nnc4HDhy5AiOHDmCr776CldffTXmz5+PJk2aqPZLS0vDyJEj8dVXX+HQoUNYt24d+vbtG/Ccs2fPln/ANW7cON19vvzyS9x///2qP3cBoLKyEnl5efj1118xZcoUfPDBB7jrrruCvs/KykrcfPPNmDdvXsD97r33Xnz88ce6r504cQInTpzAd999h9deew0LFixAx44dDcdyOp0YP368X9h+7NgxHDt2DPPmzcPjjz+OESNGBJ2/dNwf//hH/PTTT36vHT58GIcPH8Znn32GP/zhD5g5cyYSEhJMjUtEREREVFNIoZnU0sDTysAV9dvlQyEa9C/V0gaqcphpImcV1dms+QXB1Oms/7jyburxYmP0ayHtsf6hrames97XlP8kDqUNgZFwW0oYLfBlkQNn/QO014k8Ig5nlUHC3r17Ixrr119/lbebN28e0VhE5G/EiBHYunUrzp49i8TERDRv3hxXX301brvtNgwcODCq59q4cSNuuukmuQKyZ8+euO2229C8eXOcPHkSn3/+ObZs2YIxY8bA4QjcJL28vByiKKJTp04YOHAgunbtikaNGsFms+H06dNYu3YtvvnmG1RWVuK+++5DkyZN/AK5Dh06YMiQIVi2bBnWrl2Lffv2qfpca82aNUuu7L3vvvtgsYR3o0FpaSkSExMxYMAA9OjRA+3atUNycjJKSkqwZ88efPnllzh48CA2bNiA0aNHY+3atX7tYe666y589dVX8ryChbMzZ84EAFgsFtxxxx1+r//vf//DxIkTIYoiYmNjccMNN6B///5o0qQJiouLsW7dOnz++ecoLy/H+PHjERsbi1tvvTXgOR955BEsWbIEbdq0wbhx49C5c2eUl5dj06ZNsNvtqusRGxuLvn37olevXujQoQNSUlJQUVGBAwcOYP78+dixYwf27t2L6667Dr/++itSUlJ0z3n//ffLwazNZsMdd9yBzMxM2O127NixA9OnT8fkyZMDVlRLjh07hl69euHUqVMAgMsuuww33ngjOnToAIvFgn379mHmzJk4dOgQ5s2bh5KSEixevLjGr2hLRERERKQkLwjm/e9Yua1BNYazZmn/y1uwaCpnAxyrbWsQvZaz+me1CAJibRZUKtpFXNAsBXGxyn/rSYGrb5E2I3phcjTCWd9U/McPZ3S5clYzNxFhpsDhH1SrRBzO9ujRQ97+5ptvMHny5LD6zpaWlsq3DVsslqDhAxGFbtGiRfJ2fn4+8vPzsXv3bkybNg2DBg3Cp59+imbNmkV8HpfLhXvuuUcOZh999FG8/vrrqoDz4YcfxhNPPIE333wz6HhdunTBtm3b0K1bN93XH3roIWzbtg3Dhg3DmTNn8Nhjj+H666/3C84mTZqEZcuWAQCmTp2KyZMnG55z6tSpADyh3z333BN0jkZeeeUVXH311YZVli+//DIeeeQRvPfee/jpp58wZ84cv0D12muvRUZGBs6cOYO5c+fi7bffVgWeSrt27ZJ7gV9zzTV+P+jasWMH/vznP0MURXTs2BELFixA586dVfvcfffdeOKJJzB48GCcPHkSEydOxLBhw9CwYUPD97lkyRLceOONmD17NuLi4uTntdfugQcewPvvv6/qMa70/PPP49VXX8XTTz+NgwcP4u2338azzz7rt9+qVaswffp0AEBKSgqWLVuGXr16ya/feuutePzxxzF48GDMnTvXcN6A5z+G/vjHP+LUqVOwWq14//33cd999/nt99RTT2H8+PGYM2cOli5diunTp2PChAkBxyYiIiIiqkncmiBQWgSsOitnDcswNfzbGsivaAbyJ4WFvnA29MBPd3Qpc9QZzh5jVYWzKQmxqtf9es4GmJJecBtKz1mjoZVVyy0aJeJETonixQDjG7wkzXPbgRw0TLYjIc6GTq31/+0XKr07aeuKiHvOduzYER07doQoijh+/DheffXVsMZ5/fXXkZeXB0EQ0KdPH6SmpkY6NSLyatCgAcaOHYv//Oc/+OyzzzBnzhxMnjwZw4cPl/9SWrlyJa666iqcPn064vMtWrQIe/bsAQBceeWVmDx5sl/lqcViweTJk3HllVcGHa9NmzaGwayke/fu+Ne//gUA+P3337Fhwwa/fUaOHImWLVsCAD755BPDViwbNmzAb7/9BgC44YYb0LRp06BzNDJ48OCAt7/bbDZMmTIFbdu2leelt49UuZqfn48FCxYYjjdr1ix5W6+lwQsvvIDKykrExcVh8eLFfsGspEuXLpgxYwYAoKioCP/73/8MzwkALVq0wKxZs1TBrJ7+/fsbBrOA5z+SnnrqKfkHdHrXA4Aq1H/ttddUwaykUaNGmDNnTtAfGH733XdyK4MXXnhBN5gFALvdjk8++UT+rAKF+0RERERENZFcOasJZ3ceyoWrmqtnw25r4BWw56z0ookg1FCAtgZ6tH1n/XrWanvOhlo5G+Ww8vKOjdH/0uZonp7omVeAfY1CbuktlFY4cTynBPuPF+BcYXlE86r7dbNRCGcB4Mknn5S3//GPf+Ctt94K6fgvvvgCL7/8svz46aefjsa0qIYTRRFlFe56++t8/dTn3//+N06fPo0vvvgCf/3rX3Hbbbfhj3/8Ix577DEsWrQImzZtQuvWrQEAR44ciahKVDJ//nx5+9FHHzX8qaQgCHj88ccjPp9EWXG/ceNGv9etViv+9Kc/AfD0qjXqjypVzQKeXrVVzWazoXfv3gCATZs26X43lH1flQGsktvtlm/zT0pKwk033aR6PT8/H99++y0AYPTo0UEXcRwyZIhcSf39998H3Peee+5BUlJSwH1CIX2WBw4cQG5uruq18vJy+U6LtLQ0jB8/3nCczp0747rrrgt4LikAttvt+Mtf/hJwX2WLh7179+Lo0aMB9yciIiIiqkm0bQ3i7Z4A0ekScbYgshAtXGb/ZezX31RqaxBCz1m5UtTsgmCK/QLNU280uyacNSp0lXvOmlgQTDVeNNoaKIawWAQ0SLabbPmgf2696+p2i2H3tg3/oNol4rYGgOcf5dOmTcPPP/8MURTx2GOPYfbs2Xj44Ydx7bXX6lZJuVwubNy4Ee+++y7mzp0LURQhCAJGjRqFYcOGRWNaVMOVV4q4/tHj1T2NarPozZaIt1f9nzJXXXVVwNd79uyJpUuX4rLLLkNFRQWWLFmCzZs344orrgj7nJs2bZK3r7nmmoD7Bntdadu2bfj000/x008/4ffff0dhYaFh9evx4/rfrQkTJuDll1+Gw+HA1KlTcdttt6lez8/Pl2+Db9++PQYPHmx6fkZKS0vxxRdf4LvvvsPOnTuRnZ2N4uJi3RC2sLAQhYWFfncPXHbZZbj44ovx22+/YenSpTh79iwaN26s2mfVqlXy+/7DH/7gV7G7fv16uN2en4bb7XZ88803QeeenJyMU6dOYffu3QH369evX9CxJE6nE/Pnz8c333yDbdu24eTJkygqKpLnpnX8+HHVIpHbt2+X+xT36dNHtTClnoEDB+K7774zfH3t2rUAPD3UV65cGXT+eXl58vbu3bvlH24QEREREdV0Lk2V5oUt03DwZKHntZred1bzz+dQOhPIbQ0CtCEIzv/fb/I/6XTGS7DbNPuqj/c1YzgPPWcNhg61B698nEHYqjdP1SzrQdAajqiEs4IgYOHChejTpw/2798PANi8ebPcN1EZIBw9ehRt2rTB2bNn5VBF+oJ269bNb9VtIqp6Xbp0wZ133olp06YBABYuXKgKZ0tLS+VerXoSEhIwdOhQ+fHJkycBeHqBagNErfT0dKSlpSE/P99wH6fTiQcffBD/+9//TFccFxYW6j7frFkz3Hjjjfjyyy+xZs0a7N+/HxdeeKH8unYhsEgXfNqwYQNuueUWU4tSKeeu19pl3LhxePLJJ+FwODB79my/Ks9gLQ0OHz4sb8+YMUNuW2DGuXPnAr4utYsIZt++fbjpppuChr1K2s9S+n4BngA9mAsuuMDwtZKSEuTk5ADw/P00evRo0/MCgl8XIiIiIqKaRAr0rN5/58TGWNEoNQ45BeVhLQAVFfKJA//bS9C8LgWB8rMGb+BcYTmKytSLUEd7YV/t3ACgfYtU2GwC9h7NDzg/M5WzVp373l1VdDdusOupv7P3od5bqLutYqMmKuEs4AlYNmzYgPvuuw9ff/01AM+XXRRFnDlzRv7iOxwOOaSQXgc8VV4fffRRwN6MVLfExQpY9Ka5QKcuioutWT8yGjhwoBzOSv1iJWfOnAkYWrVp00YV/BUXFwOA6f8/JyYmBgxnH374YbnVQExMDK699lpceeWVaNmyJRITExETEyPPU2pD4HK5DMebNGkSvvzySwCeFgavv/66/JryPHfffbep+RvJysrCsGHD5OvRoUMHXHvttbjwwgvRqFEjxMXFyX82vv3221i1alXAud9xxx14+umn4XK5MGvWLFU4W1paKrdpaN26NQYOHOh3fKBrHIxUqWokPj4+6BgFBQUYNGiQHK42b94c119/Pbp06YImTZogLi5O7k08Z84cfPHFFwD8r0dJia9JvZnvWGJiouFrkVwTAPKid0REREREtYG8+FRUmlxGh6/VQOD9/BcE8z4h9W41OG7fsXzFuTTHmiAIngBVLwv16yOrEBdrRadWDVBS5sSxs8WGPWLlcDZA5axRu4AqEeR6BqL3HkTFWHohtll1OeONWjgLAA0bNsS8efOwdOlSTJkyBcuXL5df06t2E0URffv2xVNPPYXhw4dHcypUCwiCcF5u6ydzlBWukQZWSUlJKCgoQGlpqan9lWGb1rFjx/DBBx8A8Cw6tWrVKnTs2FF33127dpk638CBA9G5c2fs3bsXn3zyCf71r38hNjZWtRDY6NGjkZGRYWo8I//617/kYPZvf/sb/v3vfxv+hNbMXQPNmjXD4MGD8f3332PLli3Ys2cPunTpAgD4+uuv5XPdcccduudR9oR9++238dBDD4X8niLx7rvvysHs7bffjo8++siwJcH69esNx1GGrWa+Y4G+X8prcvnll+OXX34JOh4RERERUW2lXRAMUNxeX8PTL+2/cQSp56yiQYAeh9PTrqFd02ScOlfqHStKkwrQ1kAiBAk7pbzMGrBytmp7zoYbmpppa+A5T/hzrQ+pUVTDWcm1116La6+9Fvn5+diwYQN27NiB3NxcFBQUICEhAenp6ejUqRMyMzPRpEmTqpgCEYVIurUb8CyypNS2bduQFjBr0aIFCgoKUFhYqNsbVSk3NzdgGPzDDz/IvUifeuopw2AW8FSqmjVp0iQ8/PDDyMnJwbx583Drrbfiww8/lF+PxkJgUiuIjIwM/POf/wx464zZuY8bN05enGvmzJn497//LW8r99GjbD0QSpuFaJGuh81mwzvvvBOwV2yg69G8eXN5++DBg0HPe+jQIcPXUlNTkZSUhOLiYsM+xUREREREdYV2QTAA1Z5+mf2npn/lrGYcg+Nc3vfcND0RJ3OlcDaEytkAY5vIZuVzad+nHNpKAWmAamZBEPzmEZ1sVq95bPBrY3RqvcsqimLYvW3riyotZE9LS8Pw4cPx1FNP4bXXXsPUqVMxZcoUPPfccxg7diyDWaIaRLqlHgA6deoU0VhXXnmlvB1sgaUVK1YEfP306dPydocOHQLuu2TJEhOz8xg3bpx8S/zUqVORn58vtzro2LGjbluAUElzb9euHaxWq+F+p06dwvbt202NOXr0aKSkpADwVNuKoohTp07J1/HKK680/Pz69esn/4fB0qVLTb+PaJGuR3p6uu5CkZLy8nKsXr3a8PVu3brJrSw2bNgQtLWA8rutJzMzE4CnLQYrZ4mIiIioLnOL/pWzCFJ5er4Ey+0MqzSDTN/lLfaxWnytNQN0EDA8c7hXxxfCakeQxg1eOQv4twxwVVFbA18ldYDxDdLWUNpFkE/E4WxeXh5Wr16NL7/8EvPnz8e6desC3kJKRDXP/v37VYtJjRgxIqLxlP1pp0yZYviHuiiKePPNNwOOpbyF/cCBA4b7HTp0CJ988onpOaalpeHWW28FAKxevRrPPfecvBDYn/70p6g0iJfmfvDgwYB/sb300ktwOp2mxoyPj8fNN98MwFP9umrVKnz++edyX1ajqlnAU8F73XXXAQB27tyJ2bNnmzpntEjX48yZM4YLtgHAW2+9hdzcXMPX4+LiMGzYMACev4MCLWy2d+/eoKH9XXfdJW8/++yzIVWJExERERHVJi6dytmaEc0iaDrr19ZA8zhY5azVIsjhdEiVs4H6PkiFpwHGC9Y2wkzPWcA/+KzqnrOB6Z9beR2kVgxRm2W1f0GrTtjh7MqVK9G/f39kZGTgmmuuwS233IIxY8YgMzMTDRs2xMiRI7F169ZozpWIQvT2229jw4YNAffZunUrhg0bhvLycgDA0KFD0atXr4jOKy3yBAAbN27Ek08+6Rd4iaKIJ598Ehs3bgw41hVXXCFvv/7667qh3dGjRzFy5MiQfzD0wAMPyNvvvvsuACA2Nhbjx48PaRwj0txzcnIwefJk3X0mT54s99Q1SxnAzpw5U25pEBsbi1tuuSXgsf/85z/ldgITJkwIGtCeO3cOb7zxBn744YeQ5qhHuh6iKOLvf/+77j6zZ8/Gc889F3SsRx99VN7+61//ip9//tlvn5ycHNxyyy1Bg++bb75Z/s4vXboU48aNk/v36nG5XFi6dCleeeWVoPMkIiIiIqpJRL3K2WpOZ82e1q9yVu45G5hL0WdXbiEQRi2O3jwDLQgmkdsaaPaVpiBXM4dYOWu0wJjBLHSf1bseZi6NUTsH5RRtVotvZ73uCSQLq+fsCy+8gJdffhmAfpmzw+HAokWL8P333+ODDz7APffcE9ksiSgsK1euxMMPP4z27dtj8ODBuPjii5Geng6r1YqTJ09ixYoVWLx4sdzTtU2bNvj4448jPq/VasVHH32EzMxMVFZW4vXXX8eaNWtw2223oXnz5jh58iQ+//xzbN68Gb169cKxY8fkhaK0rrrqKvTq1Qs///wzjhw5gs6dO+NPf/oTunTpApfLhY0bN2LWrFkoKSnB+PHjA1ZRal1++eW48sorsWnTJvm5P/zhD2jUqFGklwAA8Mgjj8h9Vv/6179i1apVuPbaa9GkSRMcPXoUc+fOxebNm9GsWTNccskl8r7B9O/fH23btsXhw4cxe/Zs+bb+4cOHIz09PeCx3bt3x4cffoh7770XpaWluO222/Cf//wHI0eORMeOHREfH4+CggIcOHAAmzZtwtq1a+F0OlWV1eH685//jI8++ghOpxPvvvsufv31V9x8881o0aIFsrOz8e2332LFihVISkrCqFGjMG/ePMOxBg0ahHvvvRfTp09HYWEh+vXrhzvuuAP9+/eH3W7Hjh07MH36dJw9exZjx47F3LlzAQAWnWVpBUHAvHnzcNVVV+HYsWP49NNPsWjRIowZMwY9evRAw4YNUV5ejpMnT2L79u1Yvnw5zp49i2uuuQbPPvtsxNeFiIiIiOh8UVaRSmpK5WzQtgZBes4avQGpwtSmamsQ3ZgwcM9Zz+8GXQ1CqJxVP66yylmvgKMbvKh8DzargAqHKpsNK50NtqBaXRByOPvJJ5/gpZdeAuBtSCwIfgGt9FMBp9OJP/3pT2jfvr3c04+Izr+DBw8GXThp2LBh+Oijj1SLLUWid+/emDdvHm677TYUFRVh8+bN2Lx5s2qfSy65BF9++SX69esXcKw5c+Zg0KBByMrKQk5ODv71r3/57fPQQw/h0UcfDSmcBTwLgynD2WgsBCa57rrr8MILL+CFF14AACxevBiLFy9W7dOmTRvMnz8fb7/9tulxBUHAnXfeiZdfflnVbzVQSwOl8ePHo1mzZrjnnntw8uRJbNu2Ddu2bTPc3263RyWwvuSSS/Dhhx9i4sSJcDqd2LBhg19ld3p6Oj7//HNs2LAhYDgLAB988AHKysrw+eefw+Fw4OOPP/b74cLjjz+Oa6+9Vg5nk5OTdcdq0aIFtmzZgvHjx2PJkiXIy8vD1KlTA55fucAaEREREVFtYLZK87wSjeow1bStA6T34KtM1RtaVFTOWuRFtMJ6+3pdDUxM3bcgWOB4MWg469dz1o3cwnKIooj0lLiwWvMFnH7oLWdVrFLlbNRi1bobz4YUzpaVleGxxx5TfbE6dOiA0aNH44ILLoDT6cS+ffswb948nDp1CoIgwO1248EHH8Rvv/1WJW+AiIxNnjwZI0eOxM8//4zt27fjzJkzyMnJQUVFBVJTU9G2bVtcddVVuP322yNuZaBnxIgR2L17N15//XUsXrwYx44dQ0JCAtq3b48//vGPmDRpkrwoVyBt27bF1q1bMWXKFMyfP1/uPdu0aVNcffXVuPfeezFgwAAcPnw45DkOHTpU3u7cuXPUf5D0j3/8A/3798fbb7+Nn376CefOnUNaWhouuOACjB49GhMnTkRaWlrI444bN06+gwHwhJrXX3+96eOHDRuGQ4cO4fPPP8fixYvxyy+/4OzZsygvL0dycjLatm2Lbt26YdCgQRg5cmRYc9Rzzz33oHv37njjjTewZs0aZGdnIzk5Ga1bt8bIkSNx//33o3nz5kHbcQCAzWbDZ599hltuuQVTp07Fpk2bkJ+fj4yMDPTq1QuTJk3CNddcgy+++EI+pmHDhobjZWRkYPHixdi4cSM+++wzrFu3DseOHUN+fj7i4uLQtGlTdOnSBX379sWIESNw0UUXReWaEBERERGdL27FLf4SoabcbB7iNAQTpbPK6lJVJ4cwes4GigbDqpzVCLWtQXGZA+t2ngIAdG/fCG2a6heimKP4PkRQqaq83jarlB0qRwvnu1ZDvp9VKKRw9osvvkBeXp78JX722Wfxwgsv+N0m+tprr+H++++XF+fZs2cPfvzxx6DVcUQUXe3bt0f79u1x7733VtscWrZsiSlTpmDKlCmG+5gJVVNTU/GPf/wD//jHPwz3adu2bciLOUkVlYBnIbCqMHDgQAwcODDgPjNmzAip6rdDhw4RL1xlt9tx99134+677w7r+FDnLLn88svx6aefBtxHWXEczMiRIzFy5EjD15WV0d26dQs6Xu/evdG7d29T5yYiIiIiqk28He10e85W18K4ZmM7/8pZ9XF603epwllBsR3KDAUYRZVmrpgUfht0NfDNKciqUNrw1uF0y9slFY4gczCg+5mH3+hC2QdXOV+5t23II9YPIS0IplwQZsyYMXjppZd0+/fZ7XZMnz5d9Y/baCwmQ0QUTaIoyotxxcfH46677qrmGVG0FRQUyP1yGzdujIsvvriaZ0REREREVH18bQ2qeSJh8O85q0lndUjhrCCo9wupctb7u254LbdJMB5PesnpcmPj7tNYve0EsvNK/d5Q6AuCKbbD7D9rpj2B7nFy2KqZky8v1v9YauH37nwIKZzdunWrvP3UU08FHthiwWOPPaZ7LBFRTfDpp59i3759ADxtAgLd8k41T1ZWFo4cOWL4en5+PsaMGYOzZ88CACZMmACbLax1MImIiIiI6gS9tgbVzmSuqJ2xmYBVuQCaMlsNqT9rgF1FE5OXTpVbUI7svDIUlFQi61Shah+LRQg6J6s2nFUEsq5g4Ww4b9fMimDaRcrUF9mzpxilpgZ1t+VsaG0Nzpw5AwBITExE9+7dg+7ft29fv2OJiKrLuXPnsGnTJlRWVmLz5s144403AHiqZv/+979X8+woVL/88gtuueUW9OnTB5mZmejQoQMSExORl5eHX375BXPmzEF+fj4ATxsIfsZEREREVBe5XG5s3ncGpRVO2CwWXHJBOhok2+Fyi9iy7wxKyn23vJdVOgEAVkUQGKgtwPlgtnrTr62Bt9ww0GHqcFa//6xZ4V4ead5ORVlphcOFxLgY+bE9JnjtpD3GqnqsDELLK5xhzk5HJD1nFXNSNDWIcELRHKVmCimcLSgogCAIplftbty4sbxdWFgYYE8ioqq3Y8cOXHfddX7Pv/HGG2jVqlU1zIgi5XK5sHbtWqxdu9Zwn8suuwzffvstEhMTz+PMiIiIiIjOj3NFFcjOK5MfHz9bjAbJduQXVeD0uVK//S2CgHi7Ig6qQUW0gRi2NfDS7znrCUStFouqDUA4bQ10yW0Ngh/vcvkmUOFwq/aJtamDVz2xmnBW+X5P55Uhp6AMjVLjg46jHkQ9xxAP8ztOVF1kxb6RJKu15PsZiZDCWafTCUEQTN8WarX6vjhOZxRTfCKiCKWnp+Oiiy7C3/72NwwfPry6p0NhGDZsGKZPn47ly5dj165dyMnJwblz52C1WpGRkYErrrgCf/jDHzBmzBjd/uhERERERHWBNvdyujzBn8P7e1J8DC69IF1+PSk+RhX0hb/8U7QFTuEM2xoIxu9ACkQtisrZUPurBrpAphYE855Q2Xqg0uFS7WOPDR7Oaqtr3Zo0urTcCaSamJCCPEKU0lmdbBZQtjUI+eLXD2y+R0T1xoABA6ptBVKKvuTkZNxzzz245557qnsqREREREQ1hhTKSiFtXKwVjdMCVVRWb2Amh6ZB9vNvayCojtP7l55bp61BsIW3/M5rap/gC4IpudyiqmestmWBHu0+2n/basPacEXS5kJvDqLif0kfS4mIiIiIiIiIiGorTSAmV846Pb/HWE1GP9VdyBK056z6sZm+sVK1alFpJdbuOKU7jtmJaRf/UoWjgdoaGJxQGWTaI2xr4Bkv6BB+fMG4Yo4mLpCvcFa97yXtPBXanVulBQysSY3hLBERERERERFRLeXX1sDpeUYKaW22wNGPlMXVttpGcz1nPU+6Rc8iXACQnBAb3gkDXKBAMaTRay7FAmGxYSwI5nIHCIt1Hgck+G9qw2g1/dcaJNsx4qq26NS6ga/nrOjra8C4Vl9YbQ1OnDiBQYMGVdkxgiBgxYoV4UyNiIiIiIiIiKje0rY1sJmtnK0mRotLaWkrUKXHgQo9lQFolzYNkJEWj+SEmJDmZxRem40+jeanDFfN9ZwNvI87QOls1EPRAG/eqlPSHHZvW0TWZqG2CCucLS8vx5o1a0ztK/2fxewxoiiyQTARERERERERkRma0Mq3IJjnhRiryYW2akH4JQi+kM7Mmr/KADTRbkNakj16k1Fer3DaGvhyY1NtDbQLgvmNp/n8zHycAYPxAAP4Fvgy3kcOtWvB96q61ewfnxARERERERERkSEp+5J6yzpDrZyt7rYGZktnoe5xKrU1kJ4L1NbA7PiBBAoZQ10QDAAKSirkbW0/WT3Kz7FTqzSd+YWRzuqocW0u6kEBZ0iVs61bt2ZVKwUVUl8TIqJ6hn9GEhEREVFVsNkscLjccLpEZJ8rhdNZO9oaSMykTYIAX/9SKZ8KUPrrcpksbw12Th2Be7Iqz6o/QIXDVzqbYA8ezynzuIYpcX6vu7U9Z81MLsx/mkinMtNrV1TuH1GmWHf/HRVSOHv48OEqmgbVBRbvPQVuZW0+ERGpSH9GWszch0VEREREZFKMVUCZd3vjnmykeHurxgRbEKyal2nyRW7B5yFlexZFX9NARyn7sIb/Lg3TWb956R6tea1dsxSkJMTgRE4JcgrK0axhgqmeswAw6LIWKK1wolGqfzgr+kUxwcNMX3uCMK9OwMOUL9bdYDUawuo5S6THZrNBEARUVFQgMTGxuqdDRFQjVVRUQBAE2Gz8K5iIiIiIIifdmWWxWFQ9WcsrXQBCaGtQbXd4mT+vJ0gWobPmFErKnfjup8Oq9xGNt+SrAA2jMhW+9guSWJsFbZumoLTciZyCcsSbqJqVJCfEIjkhVvc1v8rZMEtnhZD6XARv5yCKYiidKwzPUJfjXZbtUNRYLBbEx8ejpKSkuqdCRFRjlZSUID4+npWzRERERBRVguDrOwv4wjpbkAXBqp2JW+QlcuWsMvBUbLrdIkQR8q+oCthzNgDNi3LVb5Q/Frd2RbBwydlsoPFE5a4UIZbtUFQlJSXh7NmzqKysRGys/k9ziIjqq8rKSpSUlKBx48bVPRUiIiIiqiOUEZrNakGlt9esFNbFBKmcre7KRPm8ptJZ728BbsPv3qERMtLi5ceb9mYjv7gy7CTRsOesyXa22pe0lbTh0KtydrnDqJwNIRjXOSwg1Zh1uew1Cli2Q1GVmpoKm82G48ePw+VyVfd0iIhqDJfLhePHj8NmsyE1NbW6p0NEREREdYwAz6JgEimrswXpOVubSLfcK29C0/bMtcdYEW+3yb+iEYYCgfPFQH17teeXKmcjCcX1gldtWwNT40gb4aazJlYEE0Vlb9sQz6N3zjrovFXO3nnnnThx4gQEQcCKFSvO12nPiwULFmDWrFnYvHkzTp8+jZSUFHTo0AGjR4/GxIkTkZKSEtXzHT58GNOnT8eqVauwd+9eFBQUwG63IyMjA927d8dNN92EP/7xj4iJiYnqec2w2Wxo1aoVDh8+jAMHDiA1NRVJSUmwWq0RrspHRFT7iKIIl8uFoqIiFBYWAgDatm3LfrNEREREFD2K0CpGp4VBsJ6zgiJEq+l02xpo9wnx+XAFvu1fcV5tWwP5cfgz0lbJ6j+nWAwthDxGDo0j/j4wAzLrvP3rcOPGjTh48GCdCuiKi4tx++23Y8GCBarnz549i7Nnz+Knn37CO++8g7lz56J3795ROecbb7yBZ555BhUVFarnnU4nsrKykJWVha+//hqvvPIKvvrqK1x88cVROW8o7HY72rVrh/z8fBQUFCAvL++8z4GIqCax2Wxo0KAB0tLS2PKFiIiIiKqGoB/EBl0QrJobG4gh3FovRUrKbMkvZtI8jvxdGSyQZbKtgXaCFu1qZmGkoHpVstqes6a6GuhdexMfhKnC2SgtNCcYXP66hKU7YXK5XBgzZgyWLl0KAGjSpAnuu+8+dO3aFefOncPs2bOxfv16HDt2DMOHD8f69evRpUuXiM757rvv4vHHH5cfX3311Rg1ahRatWqFwsJC7Nq1CzNmzEBxcTH27duHgQMHYufOnWjatGlE5w1HbGwsMjIy0LhxYzidTrY4IKJ6y2q1wmaz1akfThIRERFRzaEMyvyCPwBWnedqFvPprNzWIIzK2XD5wkHjeDDQObWX3+odMJLQUVklK3jH8AtsTQwsHXPsbDEuaJ4qjxf08FAnLX/ENf27WD0YzoZp2rRpcjDbtWtXrFy5Ek2aNJFff/DBB/HEE09g8uTJyMvLw8SJE7F27dqwz1dWVoZnnnlGfvy///0PEyZM8Nvv+eefxzXXXIOdO3ciJycH//nPf/DGG2+Efd5ICYKAmJiYammxQERERERERFT3+eLZcKIv+ZhqKk1Ux4yByW0NVD1nDXbyP0FUmR1W+6kIUQjLpSpZq0WAIABOl+i/IJiJccoqnACA/OJKiKLoLSgJPj9fD9ngIbkIRbDNbFZX3ekKfR65XC68+OKL8uNZs2apglnJq6++iu7duwMAfvzxRyxbtizsc65fvx5FRUUAgCuuuEI3mAWAxo0b49///rf8OJJAmIiIiIiIiIhqOFUKpwkCzRxfQ24bN9fWQFD9rnegdhwzQaKZefndna94HDCkNKicjYQUxNqsFrkyWtTpQxuMcirllZo7ngO2I6jub0vdwnA2DGvXrsWpU6cAAJmZmbj88st197NarfjLX/4iP549e3bY5zxz5oy83bFjx4D7Kl8vLi4O+5xEREREREREVLP5wked/p61oVIxjJxP3dYg2JuMMEg0GN78gmBBKmfDmJ5UOWuxCPL42rYGyodGfV+VT5eUO7zzDXtaKtK8RNFcj9r6jOFsGJYsWSJvDx8+POC+1113ne5xocrIyJC39+/fH3Bf5esXXXRR2OckIiIiIiIiotrD745+EwmbYWXoeSKf1kzPWamtgXHhrP8CYVFidHmCnc6ocjaSZdhcbrdnLIsgB9XKwtmcgjKs3X5Csb/+WZSLiJWUOU2fP5RF3IDI0lmhppR2VyGGs2HYuXOnvH3FFVcE3Ldp06Zo1aoVACA7Oxtnz54N65x9+/ZFo0aNAABbtmzBtGnTdPc7e/as3JvWYrHgscceC+t8RERERERERFTzBQpVzbU1qBn1jKG0NbAESmejzKiTr2gycNS+LM89hHk7XW4cP1uMM3mlKCypxOlzpQC84aw32at0uHAkuwhHsouw71g+yhRtCqTeslrKatvcwnLzE5IEeA/V3cu4NuGCYGHYt2+fvN2uXbug+7dr1w7Hjh2Tj23cuHHI54yLi8MHH3yAW265BU6nE/fddx9mzJiBUaNGoVWrVigsLMRvv/2GTz75BEVFRUhKSsK0adPQp0+fkM9FRERERERERLWLgEirX2t+iiYFfoF7vKpfC63KM8BJDS5u8MpZ9R7+64EFv+77juXjwIkCv+etVgGC4ElnK51ubDuQE3QsJWXl7LGzxWiYYo9eqKooeI2orYE8Ts3/fobrvIWzLVq0gNNpvkS6JsvPz5e3pWrWQNLT03WPDdUf/vAH/PDDD3jwwQexa9curF+/HuvXr1ftExMTg7///e+YOHGiXLFLRERERERERHVVgPTRTKsA9SjnXwgn9rU18L0xv7YGhgeHMqngh4lB9/C+qnnZV/Xr68kaTIXD5fdckwbxuKB5KopLK5FffA5Wi4BGqXEAgPziClQ43EHH1XY7OFdUgcQ4T1Ro5mMxf0nZdTaQ89bWYPXq1cjKykJWVtb5OmWVUS6yFRcXF3T/+Ph4ebuoqCiic/fv3x/vvvsuLrvsMt3XHQ4H3nvvPbzxxhsoKysLOl5FRQUKCwtVv4iIiIiIiIiodlCFhEIEEWu19Zz1nNiofYCS3NYg0K7nq+esyZJcv8pZi7rnbHgnB67s0gQZafFIS7IDAOyxVvTu2hS9uzZFcnys7lS1pMrZpHhPIOt0BQ90DabjR7kgWB0ueo2KkMLZ559/HitXrkR5eRh9KChiOTk5uOaaazBw4EAcPnwYb775Jg4ePIjKykrk5+djxYoVGD58OPLz8zFlyhQMGDAAubm5Acf897//jdTUVPkXq22JiIiIiIiIah9PWwPtimAmjqsp6y2FUOUrBEhnjULeiDNbgwsU6oJgctVvCBPSO7W2xYOoKIN1+IWsBguCeVPbGJsVAOByiYqRA3wjvMcFai+hPLdcN8vCWV0hhbOvvPIKhgwZgrS0NPTr1w/PPfccfvjhB1MVmnVJUlKSvG0mqFZen+Tk5LDOWVpain79+mHVqlVo0KABfv75ZzzyyCO44IILEBMTg9TUVAwaNAiLFi3Cgw8+CADYtGkTHnrooYDjPv300ygoKJB/Sb1xiYiIiIiIiKgWUGRodT370m1rUMWJn1F4bTbMNlwQLCTqswmCcnE0z3PKFgXaClijuYrekNVus8jHye83UDZrYsbV3i6jFgmrrUFlZSU2bNiAf/3rXxg2bBjS0tLQp08f/P3vf8eyZctQWloa7XnWKGlpafJ2Tk7wZsvK6lXlsaH473//i7179wIAnnjiCXTs2NFw31dffVU+zxdffIHTp08b7mu325GSkqL6RURERERERES1gxx+CeEFiFKlaWm5E2fyy3R/lVVU3RpCoSzYJc01UL6pzWqrLByU5h1qWwNB3dYgnPkpw2lp261IUx1OTeWszklcLrcc6MbEWOXjyqvwsw5HkPXY6oSQFwQTNVdDFEU4HA5s3LgRGzduxP/93//BZrOhR48eyMzMxIABA9C3b18kJiZGbdLVrVOnTnLv3KysLLRt2zbg/so+u506dQrrnAsXLpS3hw4dGnDfxMREXH311Vi8eDHcbjc2b96MkSNHhnVeIiIiIiIiIqr5vDe3+z2/bucpWK0CurZpgNREu9GBOJlbgpO5Jbpj26wChl3RGjZrFS5dZC6dBaCuPg16WMT31Eu9UzV5mNmj/RYEC2MKmpMJOpXDyvmZqZxdu+OUvG33hrNFZQ4UlTkAAC7tamHBBtRQVeCGEMDXRyF9Jfbu3YsPP/wQt912G1q0aOH3xQR8Ye3PP/+M//znPxg+fDgaNGiA3r17429/+xuWLFkS8aJY1e2SSy6Rtzdv3hxw3+zsbLlVQEZGBho3bhzWOU+ePClvp6amBt1fWaGrXMCMiIiIiIiIiOoQ0X9bGQjmFpbjTF4ZjmQHzwaS42OQkhir+gUATpeISocripMOjxREqqpRBe0+2qMiK7kMWhkbJHI0qpyVmZiedhdl5bC07fbmsS63G4FyVUlJuUPejtEJ3QMtDhZq3u2r7g4jnq0HiW5I4eyFF16I++67D59++imOHj2KgwcP4qOPPsJdd92FNm3aGIa1TqcTmzdvxuuvv44RI0YgPT0dV155JZ588kksWrQIhYWFUXtD58O1114rby9ZsiTgvosXL5a3hw8fHvY5lb1qzfSFPXLkiLydnp4e9nmJiIiIiIiIqOaSllsSIMghmBQAWi0Cmqd77mQWDRI7t+L5fpc2x8DuLVS/YqV+pGYSv7DmD+/8g9PtOavdR/NMKOOHQjTZj8FvfpqAOZyrqgx8pSpiqa2B06mfzfk9p9jWq+aN9NMWVM16pe8o6YmoHr1du3YYP348Pv74Yxw6dAhHjx7FrFmzMGHCBHTo0EH14UvbUlj7yy+/YPLkyRg1ahTS09PRs2dPPPHEE/juu+9QUFAQ2buqYpmZmWjatCkAYPXq1fj1119193O5XHj77bflx7fcckvY51RW63722WcB9z1w4AB+/vlnAIDFYkHPnj3DPi8RERERERER1QI6yZfFIiAlMQaAL8TVksJZAZ72BVpW73MuV1U3/Qwe3Ul7BFxTK8oJYKQLWymDVKvuxIOPrM1WlWGqcnxRFOEIUPGqHtM3qDXsdDb4xRYNv3kkiWqzkJYtW+L222/H1KlTsW/fPpw6dQpz5szBpEmTcNFFF6n2VYa1LpcLv/76K958803ceOONYd/6f75YrVY8//zz8uNx48bhzJkzfvs99dRT2LZtGwCgT58+GDZsmO54M2bMgCAIEAQBAwYM0N3ntttuk7c//vhjTJ8+XXe/06dPY+zYsXA6PQ2cR4wYgYYNG5p5W0RERERERERUB6ju+pd7kurv6xI9YZ7FIvjdgg/4grtAt7lHJISVnuSqU4vqDUZ7RvpE/YfBzq6cXlJ8TFSmorcgGOCpnpU+J2WnAr0rrLzsFp3QWK/aVvu86Yw8gvLlSMPx2iDkBcFC0aRJE4wdOxZjx44FAOTm5mLt2rVYs2YN1q5dix07duhW17pc1d/HJJj77rsPX3/9NZYvX45du3ahW7duuO+++9C1a1ecO3cOs2fPxrp16wB4+r9++OGHEZ1v6NChuPnmm/HVV19BFEVMmDABs2bNwg033ICWLVuirKwMW7ZswaxZs5Cfnw/A085g8uTJkb5VIiIiIiIiIqqhlBmaNk8TEDzckipi9as6fdW0AReIikAo/Ut9lbOB2hoYnCBMhvMyGTgGDWdNzU+9k6qtgWJ8txtwON2KfUTdc2iDV72PPuLCWeWCYBRQlYazWunp6Rg9ejRGjx4NACgoKMDChQvxf//3f9i1axcEQTBM5msam82GefPm4bbbbsPChQtx+vRpvPzyy377tWzZEl988YVf5XA4Pv30U6SkpOCjjz4CAKxZswZr1qzR3bdTp06YM2cOOnToEPF5iYiIiIiIiKhmE1Tb3mBOMFE56w1drTotDQBf5azLXUWVsyGQKjz1Kj0l2urfUBev0hlRNY7/q+YXBFOGs6FUhAZaEExZRSwqK2cFAU6D0f1CfL2LY2JiZi9pZH1/636n2vMazgJAfn4+fvzxRzlY3LZtG9xut/4XoYZLTk7Gd999h2+//RYzZ87E5s2bcebMGSQnJ6N9+/a46aabMHHiRKSmpkblfHa7HdOnT8dDDz2EGTNmYP369Th06BAKCwsRGxuLjIwM9OjRAzfeeCPGjh2L2NjYqJyXiIiIiIiIiGo4wX9bWTlrlM7K4axBLiOFts4q7zkbXJsmyXC5RTRrmFANZxc1j8xdD+VVTVZWzkYQgwkGlcP7j+cjr6gCgDrA1s5UO/dQOuGa/RYIQULtkNWSYs5wVHk4q2xlsGbNGuzcuVO3lYEkMTERV111VVVPK6puuOEG3HDDDWEfP378eIwfP970/t27d8eUKVPCPh8RERERERER1Q3KqkT/wFDw3V5ucLwUzhpVo9osVdvWQJqYmayycVo8GqfFq57TZsrRrv2TK1z9E05T51MGqQ1T4sKbhHZBMGU4KwiwCIBbBA6eLJSft1ktALxtQzWT93sveoWzRmGo8mkz11oU63bD2CiIejibnZ0t95Rds2YNdu/erXpd++E2bNgQffv2Rf/+/dGvXz9cfvnlsFqt0Z4WEREREREREVHdIyriWZ0QTK+tgdstQvC2PHA4PAGeUc9Zq3dlKVcVLQgmT6uKb6gO1n4gwIG6QskbM7s1h8stIt7uH8OZKQj1y1ItmseCIA+UGGdD++apKCqrRFGZQ/d4/3NGv+GAoPihgK+1ROjnEYL8cKEuiDicPXHihFwVu2bNGvz++++q17VhbMuWLdGvXz/5VzR6sRIRERERERER1Ud6oZVOhwO5qtbhdGP5lmNIT41D0wYJKC53AgAsFk3i52Wt6srZCGlDV238V9V3w5uJG9OS7GEdZ8SiCTktFkH+fBqlxqFdsxTsOnzO8HhtVqfb1sBE4WzoXWdJT8jh7JEjR1RhbFZWlup17QfcsWNHuSq2f//+aNu2bUQTJiIiIiIiIiIiNUGncNZTHevZluKak7klcLjcOH2uFDE2XyBrj9W/i9nmrZx1VlHlrE90Klv9qzND6JsQYHhtWKksWA5v4PDjWe17VBY9JyfE+j1nOPcAjHvqmuy1G8qKZ/VcSOFs27ZtcezYMfmxNoi1WCzo1q2bXBXbv39/ZGRkRGemRERERERERERkSK9FgKBpOqusunQrqmFjrNVTOStlSzV2mXjfimrBdghT6H0NtB0olGGtFM4GmpZbm87q9pw1N5dgRMVY0e4HXFeEFM4ePXpU9Tg2NhY9e/aUg9g+ffogJSUlqhMkIiIiIiIiIiIDosG2RM5mpYW/fC+pQzr91E0KZ52uGtAfwMRhRgFgBHWqAHT6tiKyUDmUwlJtFau2rUGszYrySk/v4BSpclYxM+3xeufM7NYcWacKcbagDGUVrgBz8QkUtgohvcP6LeS2BlIan5iYiHvuuQfDhg1D3759kZycHPXJERERERERERGRMfXd9erAUK+tgWBQOWtUKSm1NXC5q3hBsHBV+UJiBiJtaxABbVuDyzo2wsncEqQl2RHnbU/hUn6gfm0N/DvHpiXZcVnHxli17TjKKlyR9+rVfO8UT4UzTJ3OeMNeEKykpATvvPMO3nnnHVgsFlx66aXo37+//Cs9PT2a8yQiIiIiIiIiIiO6yZeyftFbOasI9sy0KrBavW0NqrhyNtIKVN9j9TNRC38NBoo4mw29qwG0a7elJdn9Fh07k1cmb2v7BQcKXi1yzK+/k9mwVflaHc5VoyKkcPbdd9/FmjVrsHbtWmRnZ8vPu1wubNu2Ddu2bcPbb78NAOjSpYsqrG3evHl0Z05ERERERERERF6CIjgT5P+Vqiyl15T9SpWtCowCNJulaitno146a7AeWMQLghkMGy65otnMztoWsSaatyoDWe1Hp+zz63f+ULoRmLimonKscJrOhnKdaqmQwtkHHngADzzwAABg3759WLt2LdasWYM1a9bgxIkTqn337NmDPXv24MMPPwQAtGvXTg5qMzMz0a5duyi9BSIiIiIiIiKi+ilg0Kba0fObMtjTVlTqqeqes9Ee9bx1GdC5nueLtuesHmU/Yb+eswHCXjnMNzUT9TzcbhEnc0vgcou+86vSWdITdluDTp06oVOnTrjvvvsAAIcOHVKFtYcPH1btn5WVhaysLHzyyScAgBYtWsgLifXv3x9dunQJ/10QEREREREREdVnqnRWlJ/Thm2CqnLWF84a3eoutzUw0QIhPKLfvEKhPU772Fc4G94JBIPSTaPb/quC9kymrpVxy1nf3HUS/eBD+w7Q7nvqXCl+2X8WANA4LU7euxrb89YKYYezWhdccAEuuOACjB8/HgBw/PhxOahds2YNfv/9d9X+J06cwJw5czBnzhwAQKNGjdC3b185rL3sssuiNTUiIiIiIiIiojpJPyJUtjXw7qeTvqorZ/VHsnrbGpipso1MtKI7o74GkQ1nFMaej56z2p3MVM4qj9B+9H6Vs8rtAN8X/5molZY75G1fpXW0Quy6W30btXBWq2XLlrj99ttx++23AwCys7NVYe2ePXtUH/TZs2fxzTff4JtvvoEgCHA6nVU1NSIiIiIiIiKiOsVTBOkfYAVqIWqmVYHNWzlbXunC95uPIi3JjvziCsTFWnH1Rc0QY7MEGSGIWpq5iRGWg0oVuaYqcDW7mGproKx01gStfrmrYrigbRqUx2p2rXQqKrGl8ytLZ8NS9+ttqyyc1WrSpAnGjh2LsWPHAgByc3PlNghLly7F/v37ARgn80REREREREREpCHqbStuPdcsCGYYuxg8H2+3IdZmQaXTjfJKF06fKwXgCWvziyvQOC0+7KkrTxtuBKc9zi9bjFLMFPW4KoQ37JelmsjDdb8W0uMAfYp9lbPBx9W+hUqHS96Wes6q2hqEvx5Y9K9/DRLhjzfCU1JSgi1btmDz5s3YvHkzsrKyqqWBMhERERERERFRbRYohFPGZ8GK4YxetVktGNyjFS69IN30MWGJUjprlM1Gq6et5FxRhfd8NXNBMFFRORtSW4NQKno1lJWzVdaiuA46L5WzhYWF+PHHH+WWBlu3boXL5Qp+IBERERERERERBeBbUEvU1KEKgvECWaGIsVmQlmTXOXX1J3B+4eh5KP4rKq3E3qN50gQiEs4VNPMW3QFGdgf43IKOrThUW2jpUIWz3spZ0fc1CSvIrge1nFUSzubl5eHHH3/E6tWrsWbNGuzYsQNut3IFQOkPDsHvJzedO3dGZmZmVUyLiIiIiIiIiKiOEnSTPm1bAyPBXrfH+N98HY1oNtr5brSzPL2evYWlDr/Xwx3X1EUMo+esqFi/TZu9+Z1SMV6w70ug6VY6FW0N5NJZRWODehC0hiMq4WxOTo7cP3bNmjX47bffVB+8URgriiK6du2KzMxMDBgwAJmZmcjIyIjGlIiIiIiIiIiI6j5F7iVqnhSgDBeDNZ0NLDbGGt78TAo7t/NrOqt+KEacC/o3YY1VLIJmZlG1QMOa4d8X1kQ4G+B4Xzbn33U2+NDG77fS4V+YWf211TVfWOFsdna2HMSuWbMGe/bskV9Thq96lbEXX3yxHMRmZmaiUaNGYU6diIiIiIiIiKh+k1IXl+hGkaKiEwCgaGsgLwgWdCR9NqtO5WxUkjcpPQ0vPg2SzUZMmtaBkwVo3igRcbE2WCy+s1Q4nFE+ox71hbYEeZN+/YWNes56s1l1z1n9Y4LOUBThcPovCKZqgxDakKpj6nLIG1I4O3HiRKxZswa///67/Jw2jNXq1q2bXBnbv39/NGzYMILpEhERERERERGRljqY9c9n5PgmjNvVq9J5O2+EqW1ZhQsbdp3GoMtaqrKwcCtnQwkdQ62cdWlW4zKunNWZl9TWwGBmRpXILreoWgRM6m6qaGpABkIKZ//3v/+pqmG1XwZBENC9e3e5MrZfv35o0KBB9GZLREREREREREQyKSxTBnJWb2mlAGV2E+Q282pO0MLu3aqTTUX3DD5SAB6diuEQ4lltz9kgpbNubTir7TmrGU95yUwXMGv2q1QsBgZoFh1Tr1NHGhH1nLVYLLjsssvkFgX9+vVDampqtOZGRERERERERERmKG8fl0Mwwa+tgYnDzZ8yGilllYfCvv674dA7LqpTroL37w7yuYgBronZBeS0x1Y6XN7jPccGq9Qmn5DCWavVih49eshtCvr27Yvk5OSqmhsREREREREREQXkXxGr7CkqQH2bujZQlcK06iJPtYqqKoO9NYdThEUArFaDCeg8HY1QWg7NTewb6tlCrZzVm1fwuah3dLo8lbNxsVaUVbgU+4uKwtkIPuQ6HPKGFM7m5+cjMTGxquZCREREREREREQhkDIrZbWksjIyWOWs3mLuRto1S0HWqUK/c9cKOrlgWYUbD72ejZIyNz75R3PExujWkvo9U51hthmh9pxVhqYWueesAYOms9LTnoXjXNC8GnC+ZtTwSx6RkMLZ8xXMnjx5Es2bNz8v5yIiIiIiIiIiqvWULT51kizpOe1LFgFww1w16EVtG6J5egJ2Hc5DfnFFtSZmoigGXRjLs6PxSzMXFeDQCU8f2ZwCF5o38o/JdNsanO901sTpjp8txpm8MgBAUVllwOPd2vEEnc0Q2xooq2OtFkEOiMUIVwQz9RnXcpZoDPJ///d/0RgGAHD8+HEMGDAgauMREREREREREdVZUuhqEBgGC7dCCb+sFgGNUuPlBceiElHKhZjm5/Hr/rNYsOEwNu4+jZyCsvBOK4pYurFEflxW7tbfUbetQVin1B829PXA/F8XRWz9PQfHzhbj2Nli5Berw1m/4wO2NVC3wTB9qKKi1qpZsCw6rSvqbu1sVMLZZ555BtOmTYt4HCmYPXjwYBRmRURERERERERUt+mtuyTnZIKyrYF+6Ww4eZnvmGjcrq5/m3wgx84WAwCy88pwJj9wOKtov6tyMseJgmJfIFtqFM7qjhm9oNDcSIH3cou+thbN04Pf9a5dMEx5bSItVBUAWK2+uDGa16quCqmtQSCTJk1CgwYN8Ic//CGs448ePYpBgwbh0KFD9aJkmYiIiIiIiIgoWtSVs/7LNumFuICJHqN6gvSx1XMqtwQnckrQINmOsgqnvGjUydySIEfqnF6xiJnLFV74tydLXV1aWmFQeazzXFS6GkQx+lIuANaycaLfNfXrORtoWtL3wWgng+eVT9uUlbOiYZta8opaOOtyuXDHHXcgNTUVgwcPDunYw4cPY9CgQThy5AgAwGq1RmtaRERERERERER1lzf4UvYR9QVrgn/Ypq2alIocq7jAccehXJRXunAiJ/QwVssiCHB534fTFaTi1eB97c6qUD02bGugN2RU09ngYwU7nXI+cbE6UZ9mAL/5K3vOBklQzbQosFoNaqsjSWfrcAFuVNoajBgxAgBQUVGB0aNHY9OmTaaPzcrKwsCBA3H48GGIogibzYZPP/00GtMiIiIiIiIiIqrTpNvGlXmb3q38UiDnvxZU6JWz4WRsLs0qVAlxNiTFx0Q0pt64Zh3LdqoeHzpVotu/Vi+EjGbP2Whkjso2BfaY4FFfoPmHezO7spWG1WI0h9AHrw/VtlEJZ7/88kv069cPAFBSUoLhw4dj9+7dQY87dOgQBg4cKFfMxsTE4LPPPsPYsWOjMS0iIiIiIiIionpBWQ0pKtLZYGGbRQgjJgyjrYF2GvGxNiTG2Yx3MClY5ayv0lN9guJSz3EJcZ7nj2aXYv1vp3VG8J9YVIs4wxjsaHYRNu4+jU17s5FfXCFXTVsEATE2/7vR/doa+PWc9b1HS5CwXjo20AJuNmXlrCjKo4X1EUvftXCOrSWiEs7a7XYsXLgQ3bt3hyAIOHfuHIYNGyaHrnoOHjyIAQMG4OjRowA8weznn3+OMWPGRGNKRERERERERER1UnmlEydzSlQVk6rKWcUDQfO63x3tYWWz4Qe6yvOGu+aQ8qxOTc/ZAycK5F9ZpwohivrhbaE3nM1o4AmIKx2mpg23W4xOW4MQ3rp0tq5tGgAAzhVVIDuvDKdyS3HoVKHcc9ZiUQejfgNID81UzgZ7i4bzFxBv94XuDqe7Tger0RCVcBYAkpOT8f3336N9+/YQBAEnTpzAkCFDcObMGb99f//9dwwYMADHjx8HAMTGxmLOnDlhLyZGRERERERERFRfrNl+Epv3ncGhk4WK0NU/AhOgWODJYEkwwRLGgmC6I4W2syCo872QYlrFWNrK2V2Hz8m/dhzKhVHXA6lyNqOhp9LUYRDOaifmcovRWRAsFN7zKdtANE9P8MzH5QuLLYKgCrxdbqC0XG9BMM13QNVzNryGC3JFrQB0bdNQ8bxiqPrQoyAMUQtnAaBx48ZYvnw5mjVrBkEQcODAAQwbNgwFBQXyPvv378fAgQNx4sQJAL5gdvTo0dGcChERERERERFRnVRe6QIAnMr1La6ljNIU64HJwZsc4mrGsoQRmIWZ36nHgKBpuRDKRHwn1us526JRIlITY1W7Kkd3u0UUl3nC2Sbeytlf9liwYVvwObjc7qhUzoZ2CX3Bp6RBcpznFVGUK6i1lchzv7fgf/OsyCtUh9iBK2eDtDXQzF9PjM0it6xg1WxwUQ1nAaBNmzZYtmwZGjRoAEEQsGPHDowcORLl5eXYu3cvBg4ciJMnTwLwBLNz587FjTfeGO1pEBERERERERHVaYYhm9wX1Pe/nqf9D5B7zoaRooVyiN9CZFFra6CunLVaBPTslIHWGUmaE/o2S8t91a9S5SwAbN4VPCarjspZueZZcV4pVHeLItzeS6Bdh+vMOc9Ovx8RUOFw+cYL8AbCXhBMOl7zu95rpBb1cBYAunbtikWLFiEhwVNivX79elx//fUYNGgQTp06BcDTp/bLL7/EqFGjqmIKRERERERERER1muh3g7r0vERbnWrcczaUvDHQYlCGtOfVjBFSKKhqa6AeOMZm8Y5nXAFa5G1pYI8RkJqkXkDLb7EszcRcbv1rHrYwB7NI7ShEyJWzFoOLaLWqP3dtsbHyqKBhvcmkVb7+UUqyz3srifPIFnyX8PTq1Qvz5s3DqFGj4HA4sHr1avkDsdvt+Oqrr3D99ddX1emJiIiIiIiIiOo0Tz9P/9RK+ZRg8LwkKT4WuYUVoZ1YbpVgPjHz21OIvEoTAFyaytlYbzhrCdCvQQpnkxMtiLer9xPFwPMKtCCYKIrYfjAXhSWV+gcLwAXNUtCycVLQ9gHqgaXDfROTQlS3qO45q8dmhWrxOP8yZsWmybDe70zSHHVKZ0X5tdA/8PpQbVsllbOSoUOHYtasWaqLb7fbMW/ePAazREREREREREQRUK61BPiHisoFwTz7i/IBjdPiMLxXG2SkxStGq0r+i1CF29bAeFQgxmaVx1edT7Eth7MJFiTEqaMxbQ9b7QxdLlEddMIXUheXOXAkuwh5xRX6v4oq8PuJAs2Iwa+7r4ewb19f5awIt3fO0uJu7ZqlyK0OAG/lrOJ9aeevfJfBPhPRoHRWWa3tGcf7vKh+NWRR+I7UdFVWOSsZM2YM8vPzMXHiRMTFxeHrr7/GsGHDqvq0RERERERERER1mraC0yIIcGnKZgVNBaMUrlktFk8LAM2CYWZEIy4TNI0NohXBSW0NjKpIAV84m5RgQYKmctats8CYksvtVgWfgCd6FOC7hjarBT0ubKw5ZyV2Hc4LOr5ZvspZX5sCqVj44nYN4XACQIl3PurQOUDhbPAF4gynH7gdBBkzFc5ardbgO5lQUVGB4cOHB91PEAQ4nc6onJOIiIiIiIiIqC7SBqoWQYArQJVi1Pp2hhHo6vW6VQV4UcryOrVK846vLZ31PS5WVM7GaytnFRMVRRFFpeoWBS63CLeoTmdFby8E6UirRUDThgmqfUrLgY+/seDiDm5cc7nv7Zq5hnr7SG/H7Rb9es5aBAEWwara1615X0aCV86qz699QdvVQIRvAbXIPuK623TWVDgriiIEQYioiW+0GwETEREREREREdV7ykJZnfRLG7b5+n96f/cfJqioVM6G2XM2WK6UlmQHELgCtLDEBcATzqrnJKoqW4tKHThbUK7ax+UW4XIpg05FeOrd0HtfC9aWoqhUwE87pJMFfBsa/u9ZbmsAX8sCZZ/d8gr1HFWVswF7ziraYHjzwJBmKH+vdJrXRvDFqctpoum2BpGGqgxliYiIiIiIiIgiI2oqIJVpi/ZWfkHQLggW3dLZSEYTBEGT1UX3NnhtqKh8pKycvaBFDFISBRSWiLAInhCzvNKN3HwX7HaX37ieylnP9vFs4LvVFthQghF9kwNej4Jid4BXzfJfEExUVM4q33NFpbLHLDSBsnFf3aBtDczO1OTCYkHHiXgmNZ+pcHbVqlVVPQ8iIiIiIiIiIgpCfXu6+jXBb9l3Qd1zFvBbzsl3p7P5OchjhnCQbrGmYnLRDuEChYwlZZ7ZJMZbYLMKePruBDz9bglcbk9P2Yn/Po1j2U68MinN79jdh8/JVajLfrKg0ingjc/zPOGspipZqbRcEZaG23dWcZhUJesWRb+eswBQrujG4HYD2w/mYM/RPKSn2HH0TLF2OJkyMv/hl+PyBxMXY8UVnTMMWxRon9eruK0PQWs4TIWzmZmZVT0PIiIiIiIiIiIKQr2wkzJu1amchfY2de2rSqEHhiEdoa3W1FT1mk3uzJ4z0O34pRWeKtaEOKlHq28STqcbx7I96yD9tLMCHdqoj610+ipgYwxSNW1NMKAOZ0srxJDaSWhbBgCKylTRF/aq2hpU+uYpip55VzrdKC5zBDyXctmp0grfelCl5U6cyStDckKs31xUtO0yxLrdkiAa/H6mQkRERERERERENZOy6tLtVmeeQRdzEkX5lvZw+r3K5wnjGP/KWSG8OZhM+rRBtXy4KCK/yBM6VjgqcfxsMRwuX/uCSodxheulF6Sj3yXN0Dzds9hXvF07NeOVr84V+s4x6vHj2P57pXRQcKLfhvz+PJWz6gXBAKC8Uv09aaZZoExJ+b2RtuPtVvS7pBn6XdIMGWnxANTBtFG4LwXTviEV6WwkX7o6nPCGFM5u3769quZBRERERERERERBKCtnXW43HIrAzKq9l1+qYgyQiYXRoUA+KKIWtoImFDR5mOnKWU3iJZ3qZG4JTp+rAACcyi3GL/vP4tS5Enk/h9O4bURakh0NU+IQG+MpL/ULZw2yWZdbxJlz6v61b3xeaPKd6JN7zoqiop2Csuesb1+3CNhjrfAT4GJaLRY0TIlDw5Q4JMbHAAAqnS45gA5Wd63uOat/TCjqcDYbWjh72WWXoU2bNnjwwQexdOlSVFZWBj+IiIiIiIiIiIiiQhnOOl0iHC5fONvYW+Eokft/whfk+RV3nq9GoNr+uEKYlbMmYzqjytniMgcc3rv1G6XFeOfie10Zdrs16axFE0rHx/leL6906/cfAJCb74JLsx6Yb37B34+2cFZQzNmtbGug7DlbYRwyB6LXgzjW5okPHQ7FezRKZ+UfCPjS2cgWjovg4Foi5LYGx48fxwcffIDrr78e6enpGD16ND766CNkZ2dXxfyIiIiIiIiIiMhLe6u9FCDG261I8lY4aimrGLX5YTjZl15P1WD0FgQLZxyzSZ9RiweH041Kb9vVnp3SYbEIqlCzMkDlrCg/79myKYpR8wrdvqpSzalLyjXJLIC0ZItqzMC0q20p2hq4fW0N1vzixD8/zsGrM3Nx8LivUtft1nkzACodwCcLLPhwfoHxOeELZyudvjGDZLPqytm6XPYaBaYWBJN06NABBw4ckB+XlJRgwYIFWLBgAQRBQI8ePTBy5EiMHDkS3bp1i/pkiYiIiIiIiIjqM5c2nJWrJo2jTkFT8el9VrVPSPmZPF74qZsgqEPMYP1yJeYXBPN7BoA6nE2Is8hVqBZBhFsUVD1ntW9Puta+kNZ3knOFLmQ01D+3sopV0iA5hHpJnYpcweKrcHWLIopKgAVrHQD8F/xyi4Co8+04fFJEfpEFi9aX4tFbRVgs+t8hqY1DpdMd1oJs0Wg5W5eFFM7u378f+/fvx3fffYfvvvsO69evh8vbNFkURWzZsgVbtmzBP/7xD7Rs2RIjRozAiBEjcM011yA2NrZK3gAREREREdU82XmlKC13Bt9RQRCApg0TEBcb0j9TiIjqFW3lrBTWCoLgV84oKEstIeovCKZuDmpKNDI2IUCYHA3atgbSI2U4Gx9nkd++xQK4XYDDpQxntVXK6ueVL3vCWUF1LgA4etqBHzaXQCs1yRvOmrjuflWp8LUwcIsiRDfkVg163G7FYmUKcYqeuWfyXGia7vv7V7l3jFw5618BrD1AXhDMeE/SCPm/ei688EI8/vjjePzxx5Gfn48lS5bgu+++w9KlS5Gfny/vJ7U/+OCDD5CQkIDBgwdj5MiRuP7669GkSZNovgciIiIiIqpBCksqsXF3eG3PsvPK0KsL/71ARGTEuHJWZ2cTC4L5hF4Fa/YIvQpbAajSUkq/6+F9XFHpgtPleZAYJ3jDRBEWCwAXUOFQ9pxVD+EWPa9Jb8etyCrzCl0QpZhN8b7Gv3RKd35W89msTFTEtMrKVJcoqubid5yo33dW+dzhkw5POKvTCjfW27+h0uEy7KsralovmKnWJo+IfiSdlpaGW2+9FbfeeitcLhd+/PFHLFy4EN999x1+//13eT+2PyAiIiIiqj8qHJ6762xWARmaxWmMlFW6kFdU4fmHHxFRPeZwurH197Mor/T8eWiPteKyDo3kW8uNes4GqkSV8zZRvwoTCC0kDGEtq4BjqNoamDzObCcFbZuEHQdz0OPCDBQr+r/G29WVs0DghbR8lbPqxwBwNs8FiJ7PSDrz2XzjctZARahaojr39LZhUISzLrdfkKyatxu6n5XymMOnHOh9if7f2bEx3gXBnMZ9dbUEnS9JRP2N63Df2qjdL2S1WjFgwAAMGDAAr7/+uqr9wYYNG+B0er6Qeu0Prr/+eowcOZLtD4iIiIiI6pB4uw1XdDZXBXsytwSb956p4hkREdV8ZwvKcOpcqfq5/DK0aJwEIHDPWW385QvzvP1JA504jKazerfKmx3aL0yOclGltq3BydxSZJwpRkmZJxW1WYHYGF8rCKnStkwVzmqutUvqOevf1uBUrhMi1JnWzgMVhvNzuUJPG5VHKN+ey+2rnE1JtKCwRJ38enrO+p/Prfh56NRv8nFxeztaZPh/V6QFwTznMTdv3QA/gs/Y7HetNgqh+3BopPYHq1evxpkzZ/DZZ5/hlltuQVpamtzjRBRFHD9+HB9++CFGjBiB9PR0jB49GtOnT0d2dni3QRERERERUfUK559PUSjCIiKqE1wuT7CWmhiL1ERP2OdSpIAuzf3r0kNBL+HRhGGeLMb7kvb28xDmGHLGpjO4ZyGucNK6EMNBhaKySpR6K2fj7VJvVM/vUuVsRaXv+ro01a3aXrOqcDbH6XdtteHshW2AZo09O3mXcDJdCaylDJ+disrZpASLJ3RWMGp5oH1/SzcWKx75JmazWlQ9e/Vo37vFzA8EzKgHnRCqLJxVktoffP755zh79ixWrVqFxx57DB07dlQFtVL7gz/96U9o0aIFevXqhVdeeQXbtm07H9MkIiIiIqIoCuvfU0xniaiekypj4+02xHsXSFRWKxq1NbAIguGt5ur+n9rGBmHQ7Sca6hhCeG0NTA/vP2JZhVOujE2Is0jTAODrAausnHVqqlulz0YKad1u3zlO5fhaGEiB75HTDtXxCXHAJR284ayccZp5R6JqXOW8pXl52+HCZgFiNPfJuw16zirnDwAlZYqdFJuCICDG29pgy/6zABC0DZHed7Ee5KxhOS/hrJLVakVmZiZef/117Nu3D3v37sVrr72GzMxM2Gw2Oah1u91y64OePXue72kSEREREVG4xND/4W/qllsionpACgCtFkGuhlUGa0ZtDQKFYaqgUtO/9HzQuyXd03NWUD9hbjBTLDqJV1GZAxXevFQOZzWnL6/0ncChaRnrDlA5m1/slqtypUELi9VVpvYY37y0wW8gouZ36dpJc3a5REjD2awCysq13xGDtgaap8rK3YaV1NKiYBLt/P17GTOKNeu8h7NaUvuDVatWBWx/QEREREREtQxLZ4mIQib3kLUI8q3hbkUuoq2clR5ZdBcEExT/621roH7J98OxELKXqLU1CHWcEGh7zgJAUakDDjmcVfd1kCpns8/5WhE4nAaVs943pA03z+R5qkmlMxdoer/GxAAWi7Zy1gSDj0Z6j063W66ctVj95yWK/mP07trEbw4l5caTkvrOBuf9zumEvGF1sagHorYgWDRI7Q9uvfVWuFwurFu3DgsWLMDChQure2pERERERGRSJDfMsi6DiOqzSocL5ZWegM9qEVQVmuWVTpSUO1Fc7tQ91iIIhn/whtNXNiA50DW3u+GCYOe5rQEAVDo9zydq2hpIFa3KalmntnJWbmsA1e+SM+fcgMUzpiiKKCxR3/pvjxXkENglLy4WAs2dKcrKWSmQtVr837deWwOLIMi9aONiBZRXiigrFw0/hxhtOKvTz1g5J/n6q04cfjpbl//zoNorZ41I7Q8mT56Mffv2Vfd0iIiIiIjIrDD+BcVqGiKq79xuEUs2HcWhU4UAPOGZlLNVOFz44ZfjWLfzFI6fLdY93mLxr0X1/dnqq471LdykrqoNhe+Y8CMzT5ZctX/46/3dIlXOxtnV718OZxV56u4sF/Yd9g3i0oSz2grVvCKp8tQTdmrbIthjIH+mctWqiUvoV+0sPfS+QZdblINWmxV4eWIjJMYLaNlE6o3r39ZAEHwLhSUnet58SbnbsJ9wbIy6rUEw0fp7vT7850FI4ey4cePw0Ucf4dChQ1U1HyIiIiIiquXqcnULEVFVcWjuMbcq2hqUljvlYDAxzobUxFhkpMWr9g9QOGuwIJhaOH92mz1Gr8JWkP+nik4K/9YGSfExsFo8IaM9Rqo+Fbz7evbRVssuXW+ByxvYurxppm9BMM/zifGegwuKvG0NBKCwxL9FQFysEFbPWT9Sta/U1sDlVlXO9umWgFcfSkD7lr4wWfsZCBDkY5ITPJPyLIam/6H4tTUIUjZtVLkcMrknR3SGq4lCamvw6aef4rPPPgMAtGjRApmZmcjMzMSAAQPQoUOHKpkgERERERHVTqH8u6w+VMYQEYXCahHgtEiVkZ6gzx5jxeAerQAA+47l4Ux+mby/Xo9V7WJXgE7rmTDCLyEKgZlfWwPT64GF0BtXMabNKuCay1vi9JkCAAWIsRlUzup0jSgpA1KSfNWu2rYGDVMsKClzIb9YRGvvc3rhrD1GQKUU9Lr8XjbmF6x6f5ercEW43Z4H8rpdgm9BObdb0MlSfdW2KVLlbJlbEbqqD4ixBamc1Sw0p/cV4V0y+sJqayCKIo4fP47PP/8cEydORKdOndCyZUvcfvvtmDp1Kvbv3x/teRIRERERUV0WYv9CIqK6Rrsgl2dBMM+2S14kzPe6zWLx29/vJ13ybfCKRb90KiiBqq2c1RONtgbBwj5lYC31YpUW+bJZ1T0C5HDW4T9Ocannd+kzkhcE84abDVM8B58558InCyyY871TN5yNjfWdRw56A78F1fm0H7Dy/UlzsUrvS/RVA7v9P3ZA8M1Bqpx1uYGHXs9BSZn//sEWBDNaaI6CC6ly1mq1wqUT7YuiiJMnT2LOnDmYM2cOAKBJkyZyVW1mZiY6d+4cnRkTEREREVGNJor6/4g0eXQ0p0JEVGto+5daLYKqpyigCRut2qDOeGzlgmCi5jmfqvzz139sAUJYlZTKDNsiCHAF+KmeMiCUrp1UGRujbWsg95z1n1RxqQDle9D2nJXC2d1ZTgACCopFfLOmCADQIMWCvEJPChoXK6DcG/762hqE06jd//352hr4RrXIlbP+4b8A34JgSQm+cY6fceGX3QIG91Kf0qb5vlktRmGterEypdIKh99xFkFAXKy1Xoe5IYWz+fn52LBhA9asWYM1a9Zg8+bNqKys9NtPFEWcPn0ac+fOxdy5cwEAGRkZ6N+/vxzWdu3aNTrvgIiIiIiIaqRQ/plVD1rKEREFJGrSWYui56zLW+IoqCpB1SGXIOjVoaqfCXh3QkhtDRSVuCbo7iaoA7xQq2gFeAJsl+K6+RUOK56wWKT+rJ79pfWtpF2sgSpnpe4RcjsD9cJgDVL8575+u+eg5o1syCv0ZGf2GAFSxpmT78bcZRbcMtTwLcrkRdyg/l35FfCrnIUvsBdFIKegXDXmup2n5FYIMTYB8XbB23NWv7WDzar+vsXGqB9L10Sem/InAl6rtp70HxhA++YpuLhduu5r9eG/D0IKZxMTEzFkyBAMGTIEAFBeXo6NGzfKYe3PP/+MsrIyv+NEUUR2dja++uorfPXVVwCAxo0bo3///nJ17UUXXRSFt0NERERERDVG/S2CISIKmVuTYFotFlgEz93LTrly1ve6tpIxcM9Z/7YGgqbC8XyHX35tDUL9O0PwBa5GY+i1Naj0tjWQe87Ki2t5ftcLJouktgbex3LlrDcQbZBsfMu/8nOyWgRVoHrqrIBf9gIj+xgerqKNoS0BKmch+t6btipbGkt6vqC4Am7FTjar5/XySif2H8tH26YpsGmutWGla5C2BlbFOKIowi0CecUV+mP5zbhuCimc1YqLi8OAAQMwYMAAAIDD4cCmTZvksPann35CcXGx33GiKOLMmTOYN28e5s2bBwBIT09XhbWXXHJJJFMjIiIiIqJqEs4/n9QriRMR1T/aP/+sFgGCtCCYt9pTVTlr9e856xeIyUGZ4jzalZvCEOqh+oWz4bU1ULIG6uUA9fuWKo2dUs9ZeUEwqa2BCEDQDWf92hp4f5fyzNQk43C2SUMbAE/4mJosILdI/bozpIXBNK0JFO+vbZNkACVy5awIUdXWQMseY5ED2eLySlQ4fO9BugZbf8/BmfwyHM4uQp+LmwWaim9Omrkpd2vSIB69uzaVH5/KLcGmvWeC/N1f93/SG1E4qxUTE4M+ffqgT58+eOaZZ+ByufDLL79gzZo1WLt2LdatW/f/7L15vN1Gfff/Geksd7++3uM4q7PaSRyH7JsNNAkEaCgQaIECZSl9oAUKpYU+PFAe6BKglKUPJQQKLfwaoG2AkCYkAbInTmI7TkjIvjiOd/vuy9mk+f2hM9LMaKQjnaNz1+/79UruOUfSaLRYGn30mc9gZGREWUbYng8ePIif/OQn+MlPfgLGGGo1w78GgiAIgiAIgiDmDPP/cYogCCI7dOesEmtQV9dkp6juZDQ5ZwV+13CDCBY3rVGBrbxQY6y5QaOCVHMWds6G1iF18RexBSJzNhcxIJhRnFXXLrQsIXrm80Bft6UMAtbVwXDxhi688zX9eOtlfShVOCbKI9CjWhtsgrZBYrvEsvLCXsHyuF0Wq9fTcJzytgXX9ZRhTefHVJnBdTkOjnpRCJwDz+5S9TyXu3j0+UN+rMSw5n61DK2AyG1NNiravCVTcVbHtm2cffbZOPvss/Hxj38cnHNs377dF2vvuusuHDp0CID3DyZpVglBEARBEARBELMYatYTBEGkxuicrYtZjiHWIOycDZcZijWQ1uPrek28SUvtnDWJwkwtJ3GZUgBrI+essr/qO6iqZ84mEGenSuqq9QHBLEsVZ3u7ge//zSr0ddtKOQ8/y5oSZ6NuqyZx23fO8mCbuME5m8tZcOrirF6nqRKULF8A2DM4qXyv1Vw8u3s0ss4m3V1/gRCclwu74RDtu24DjDFs2LABH/nIR3Dddddh//79+I//+A+sW7cOnPMFPTIbQRAEQRAEQcw7UjTvRbdS8msQBLFQiXPOBoJqjHPWih5SK3DHRozMlZpA7E1GeM5QrEHKajAYxD59HsU5Wx8QLBRrUJ8ekznraOKm7pxljKOrI5DYjj3cCgmzqNdXF2NNonqIiB3tKHkFXkGyZh+XOZuzrUBc1uo0WQrPf9SKHmOVlvZ14ITV/dLv9QgOg+qs/5Yk0mimMpGnk7Y6Z0088sgjfibtnXfeqThnCYIgCIIgCIKY+/gPZunUWX9pgiCIhYgunNqSOCtQBrjSnLNMt6Ii7I6VxgMLBgQLapC4rixQexMvYyyjhVgDwDAgGPT9FXwWLttqXZwt5NSdE+ecFRqonjXLfXGToasjWJkfmaDXjoUjBJLFGog4BfWY1Zxgb4j82JyfORtsU6Q4W98uywIW93EMjnrLThnE2SOW92LHvmBcKbHtSxd14oTV/XjqpZF6Pbzfzc5Z9TvTXj4sVNoqznLO8dBDD/li7F133YXh4WFlus6JJ56Iiy66qJ3VIgiCIAiCIAiinTTxkEVWDYIgFjq6gGZZDEwT8mTBS4hwAtt4Ia2LeYGaGlbCWsiPTbpIo8Gj9M9JV9p4QDBJzPbFWe+7vv8CB2u4zLC4qWa5WoyjW3LOFvPm+liWIdbAarwXA0Fd/VCtBc5Z4e71xV/OfTHUFGuQz6ni7BUvd/Hb5yzc/xuGSo2h5gA5yfzrmhReeIIrY17+r+tyX+szmTD134KzcmGrs5mKs47jYMuWLb4r9p577sHoaJA/oYuxlmVh/fr1uOiii3DxxRfjoosuwrJly7KsEkEQBEEQBEEQ00zoIbKJZQmCIBYaiZyzVlhsFDBmGoKpPs1fh3SNnuG3YoxpsQYJ7xrxztnwOvR5hXM2n1ddqGZx28NPD9AzZ4VL1AI6JedsIUJts1hYnE11HDR1tiblLTh1F60tO2cbxRq4gWu4rwc451QXDz7m/T5V8rJzBdHirFeGzRhc8ECwNsxrW7rb2/tLztkWqFareOCBB3xn7L333ovJySAgWL+wFAoFnHXWWb4Qe8EFF6C3t7eVKhAEQRAEQRAEMVtp5oFzgT+gEQSxcNHFL29AsOhYA8YYVi/rwUsHgq7mutLHtJ/jEmfTXH6zENUYay3ikpkGBIuKdUAwb00bEEwsY8p+tSwO12WRsQa+85RBcc7mC+btMmXOJtmHwTwiNshDHrTLd85KKrNwXrsm56zN/O0QblvGgI6ClzlbqqjirKNV1HfIWkLgZYATZCfrLxZMvwWxBgv75p9KnC2Xy9i8ebMvxm7evBmlUhBEoe/Mnp4enHfeeb4Ye84556BYLGZTc4IgCIIgCIIg5g3+gGAzXA+CIIiZQtenvAHB1N90vetlJyxDqVLDwZGS2YHJxB9JBOPKpGnBKAozNZm8GZ02zYBgQpytCOdsPRd2fLLqlaWJs+ec6uK044FrrmOS81Rkv6qxBoxBGRAsKtaAsfB6IgypZmLm9Z2zVjBvnHM2n7N9QVfN5q2Xpwm6cbEG3nL1c8wVsQbheUMxHcYSI+aZxw2EVOLsokWLUKlU/O+6GLt06VJceOGFuOiii3DRRRfhjDPOgJVo2DmCIAiCIAiCIOYLrRlg5vHTF0EQRAxuygHBTERONQwI5gu3cbbayOJS+m0NNwaG5pyzvmMTrGHmrDxZxBrUROZsjsFxXJSqDoDwIF62JQ2o5TLjvS1wznJ0RwwI9uzuETy/x4v8lDNiBdUaxy+37gRjDCcfNYBVS7pD8+iYdlvNqW+XYUAwY+aszZTMWYEV4bZ1NHE2iMeQnLOQBGvDsQmdvxRrAKAJ5yxjzP+HsHr1at8Ve/HFF+Pkk09uSyUJgiAIgiAIgph7NOXKWuAPaARBLFxCzlnGQgKXUcxM4IRVFzMng6calCmzWIPmlwdLlzlr65mzNjBZrvnTVwx0Agh6h9s2wFiwgZzD33VCSPcFTAZ0dZqds8/tGcVkKViPjusCE/XpL+4bixVnuba5YhCurmLOF0995yx4A+esZRRn7QhxNuSc9QdDU8sQ9TAdmvCAYAliDdK/O5hzNGVrZYyhp6cHF1xwAS644AJceOGFJMwSBEEQBEEQBFEnfZ/ZJoxbBEEQ8wrdOcsM+aQmMVJ3wupleJMCEUysphVhNO2iiWINmqhHGuesGIyqKjJnc0wRTXO2sihsS91HruQ65tz7zx/8ijEl1qCQDxYUcQMbjluKs05cHqojB3DC6n6/3NB06Ud9ay885TCsGOjEuWtXhDJnOZdcsBGxBoE4G8xg1fdDKNYgQkD1nbNafqzpRUJUTMdCv/enHhBM7OSJiQn8+Mc/xo9//GMAwOLFi3HhhRfi4osvxsaNG7Fhw4aWgp0JgiAIgiAIgpibRD9GpmdssoLxKS8PMGdbWNrfQc8ZBEHMS0zuwfCAYHElsEjBNdGAYE0oZIkXMczIAFX9THhpl32/jZyzcvlBrIEkzkrOWVuzL+bzgC2Ls2JQMC5yZ+XVqLEGsjgrRM2B3iJ6uwo4brwfQDCIG3eDgd9KFQe7D06go2hjcW9HeHt86yzzyzx37Upvu+oisCwyi803DQjWWbR90baQswB4M/mZs446f62u1jJ4x0BsvtivQigXDltmOKD68QoGBAvXT5Q1MRXtOp4vpBJnb7jhBn8wsG3btqFWC3bQoUOHcP311+P6668HAPT29uL888/HxRdfjIsvvhhnn302crnUWjBBEARBEARBEHOUNBKqSRyoVB3ctn2X8ttpxy7BMYf1ZVE9giCIWYXJ3Rg1ur2KEMPCMO2vnDnL9JlSkDan1jhbKNYgpTqLwA0bhSc6ehTznmpZrUtZ+RzDmCT86UV1dXBlACtfnBXfFXFWGxCsEEwL4gbM21dzOJ7cOQwAGJ2s4MEn9wMANq5fhUU9RWVeET1hOg1856wYmEvaJs6Bc05egfsf3+fPn7MD52xHwcKinjyGx8uRUQh+Xq5QZ+tYmnNWiNGmQxOKNfDjMcwn0vZnD2LnfiFkz19/bSq19PLLL8fll18OwHPO3nvvvb5Y++CDDyqDhY2OjuLmm2/GzTffDADo6OjAOeec4ztrzzvvPHR0GN4CEARBEARBEAQxtzHHGcZjmLdcdcC5N6lYsFGqOLG5fQRBEHMZWaC6+LRVAMJOWZO+l8jxKi/HoyelJVVOrQZDi7EGCTJnj17Zixf2jgEAli/qBBDEGuRyDAdHg4zZni5VTezuUEVQxw3iDADVjcoYR5fknC3mgy7+wklq1dVKvcayCJq3LXBw1ByOqXINi3qKifewiE/wHcA8OF9cF1i5uEtbr+tvw0lHLsJZJ3diz6FJ/DQ/7E13VBVWiLOaNuvvI0t3ziaJNQiqaiQQZuc3TVtZu7u7cckll+CSSy4BAJRKJWzevNkXazdv3oxSKTjJp6am/Gmf+9znkM/n8bKXvcx31l544YXo7e1tfYsIgiAIgiAIgphRmtFmTXOLcvI5C0cu78VTLw2jZuqbSRAEMQ8Qot/qZT0Y6PUck4kGBPMnNpgu1hNRVjt9iebIhmT1DZUllkd0hqlAuGWBQDysVr0SXtgzguHxsj99oFdduKvTK89iHC5ndRGVw63fh+RNmixVkc8FhkURayDntPrOWa2O8m2tv6cAl3MMjpaD8hMeGBFD4GfOIj5zlruB27a/J4+OQg7HHNaHXG4EAA9lzladBs7Z+vY5fuZseJ1RTvDYAcFEfRvOMXfJLGego6MDmzZtwqZNmwAA1WoVDzzwgC/I3nfffRgfDxTvSqWCzZs3Y/PmzfjCF74Ay7Jw2mmn+WLt7/3e72VVNYIgCIIgCIIgZjmBe8b8+CUeNkOjRRMEQcwT/O7gkn6li1mxA4IZCBaXRTDdOpt+VKagO3ryZcJlaBm5iXXaQJ7N6UGx+pza+JSuG4iOk+WKMu9An1qBznqigGUBruOJmRzAPY/urZcVzDs0Xsaug2UAnhhcyAXrE0TFGui3NQuNBUtRUrXGkc/VRVERn+Dr0Vw6TixUHpfWnZPCdXMRgm7gnFXVWd8569e7/t2gzoZiDURdkpxH8/j2H38Wt0A+n8cFF1yAv/7rv8bNN9+MoaEhbN68GVdddRUuv/xy9Pf310cJ9P5zHAfbt2/H1772NVx55ZXtqhZBEARBEARBEO2mmQcoU99GaURx8VDrkDhLEMQ8hYvu75KApetbJsHLnxfR+qbyuyZYMn3CNMFYk5EKUjWXL+rEqiXdCVbm/alJg1wx5hW0pM+L3Ozp0oVw9a8Qdafqg4j1dxf8oot5C4V8sKwYckm+Z4lDpw+UpXcI8Qfxqi8alde79YkSLv/ITlx325hSP18E5qrQr6/Hi1yo11cSZ4W4qw8I5ouzUeckUytsOrZ6Di2N7+nRNnFWx7ZtnH322fj4xz+OG264AYODg9i6dSu+/OUvY926df58QqwlCIIgCIIgCGJu4g9Y0lKSoeLLIXGWIIh5j7i8yVEG4W7ghgVTZs5yw29Ji9EXTSrfmGZjUJ2USe8YQSwDUMjbOOuk5QmX9JymAstSncpRQqESDSANqHbyUYsBeGLmQG8R+Rxw7mkcG05y0d/jLSQPBhYV4aCIplzq6i/WZIqEAPDxr+2H4wL//J9DALyBxQAgZ/tFKWKomO6vlwfrlg3IQU8VdZ0i1iA8qJcq84t6W4YRwVqJNZjPZBZrkIYnnnjCjzu44447sHfv3qZyRgiCIAiCIAiCmMWkaOKbBwUJdwd1KHOWIIh5iinWQNdKTM5ZJUM2NOBSilzZVOpsSg3HUHZ7dCBzmXIMgP+bpdYjSh+0rUCsrMfOApAyXiXh9YLTvcG8xPdgMLBoN7T8zpFL00V9TNUy1VWuj0A+l27Z8pIy/9BYyeiczWlOYUGkc1bTYP0oCcOhiDrmC1ybnR5x9tFHH/WF2DvvvBMHDhzwp3HOSZglCIIgCIIgCMLDEGsAALalupAIgiDmG9wfSEkS1xJ1A4+5LhrGoArEM6aU2YxztpUoBN05m5Q4IU8vTZ+16ggnazAxLj935eIudBarmJhyfSFTzCZuR7YdiOb6PUp2zkahvnPkYHXVOM5NOjQWfF61zJP2avK21beHSefPVNlBsRB8PzAsi7PB7yLWQH8XWqvFO2f1LTS9SAgP4Cbcto2Zz3f/toizDz/8MO644w7cfvvtuOuuuzA4OOhPM11sxG+9vb244IILsHHjxnZUiyAIgiAIgiCIaaAZB4zpAU3uuuo7Z535/HhGEMRCxuQ4TDIgmEzDOBnZ+pkBSUsyxhqw1jJH0y3qzV3z4mKRz7GQSA1wXLC+E/c8PIVjV3sTezryyNveQoFY6U2TM14tSRD1ykR9mXTiLEcgYMa9i9x3KPhcrar1CVywPDZztua4cHyBWXLO2kJoVucX1dE3Jcicrc8ndD9DkKp+/srHfyGbN1sWZznn2LZtm++MvfvuuzE8PKxMB8LKOuccfX19uPDCC7Fx40Zs2rQJZ5xxBmzbBkEQBEEQBEEQc5/mHrHMT6MWZc4SBDHPCWIN5C7wCWINYgZgMjlJeWha+qu1pGcmxJCbylhrA0IlWVbbOSLWIGcH+1t2zv7lHy7B7WsnMFk96BfhZ7CKzNl6kYHwashSrf91EsUaMPj7R86cra/I9MJzshT8ODrhKuuypcxZTwDn4JyFYwqcwA0sS3G5COdsUH/9nPSn+OtVf49eVnd0L1BtNr046zgOtmzZ4oux99xzD8bGAj91nBjb39+Piy66CJs2bcLGjRuxYcMGY0AwQRAEQRAEQRDzgBYfsuRn6mCAEhJnCYKYnxhjDSK6gRuJUWeDxXhIJ23FvZjYORsxI2siIIEb5GWpwIj1eAhxNp9nof3NAfR2WXjdRb342T2BOJuTBsjiUi0DYZMZRHP1hWLyWIP4mAWx+slS8FO5ylGuuEHmrHDB1ue1GOBw4Ds/sXHa8S5efrY3oVp1wDlTtlHfXhNR5ySDKiabXNxRA4J51eWIbTjM49t/KnH2sssuw7333ovJyUn/tzgxdmBgABdffLHvjF2/fv2CtSgTBEEQBEEQxEKhmeen2IdRRpmzBEHMf1xJTBNEuxTNREsuYRF0puWZkHM25eU9kXFW++6LszYLDcAWlfEa5ST1xVkLYBHd9U0DggEMywY4DgxJjlypvrpz1rQ9UyX197FJ14/98TNn69N6uoCRce/zI09bePnZnopbrgblq+Ks91d32gbblsTNzY0RHGFhV17GvL6FQCpx9tZbbwVjzPgmhXOOJUuW4OKLL/adsaeeeiqJsQRBEARBEASxQGmYfZiiJD9zNsrKQxAEMcfhQsgziF9uhDEOMMUUBIR+MwhgzVyp4wTE5GVo4lzSBdMbZ31qdXeplzmrOWcjKhDcf9RYCD+v1TKIjmIe4ZyVoyoAvPF3XDzwmzy2PeGGRN9w5qxUsfq0SZM4Wy9n96FxdHaWMD5VBQAM9AXirEy54gLwlNyc1Kk9KnM2qJ8mRItlFResWbQNidjSZ33/6+fWfNZum86c5Zxj2bJl2Lhxo//fKaeckmXdCIIgCIIgCIKYgzTzsG56oBbdRxnkh+P5/HhGEMRCRlzedE3LsgDXEZ8NV8uwdgf9FyUiNqbbeYOO5U1jui0wvQ4p7x3N1LMiMmdzYeesCQ7u57GKzFlRT9cJXLEhIVJzztpypCcDigXgpGNsbHvCVTfblDlrqJcuzo5OuL4r+MX9Y6g4wbSBPo4Xdgf1q9aAfA6oSgNsygOC5fMNYg20HSa2nckOYJcDdnjHxsUa6Fu6kO73qcTZ5cuX+67YTZs24eSTT25XvQiCIAiCIAiCmOOk6kRnci5J7ighznLuPew2GrGcIAhirhEVG+l9jxMSJVU36tKoOFS1EcR0fSzB5VURe5tEjzVInjmbAm3m8UlPcczbAHeDegBxsQb16a5apCPHGoQGuqq/UOSBgBtME8txv5yaA9RqAO8xOHmV+6L3RR4QDPDEWUcaoOzI5T0AgIlSFQN9FWXeUtkTZ2uSgJuTBgTLa5mz/d0FLO7rQKnswLZyqNTK6raKbZd2gcsBG2HSxBqExdn5K9amEmf37t3brnoQBEEQBEEQRIipcg3Vmvd00FGwUcibmvrEfCCchqhOkwdTcUicJQhiHuIahDxAdRs2jjUwZ5/G0cygXGnVWW66tmt1a0fmqJyve+v9E/j7fzsEAFh7TBEuLyv1iFp9kMHKFOexIw8Ipo91Xy/Tqc9kGhBM/DQxBXznOgulCsPLz3Lw+o31+sTsEOGc7e+xMDLuYmwiiDXI2RY2HL8MAPDSgXH09x5Qlp0qA73dQMVLPUBHQT3nbC1ztrcrj9OOXYL3/u0e7NxXwsffUTBuh0xU3eOiTxuLs/OXpmMN2sljjz2GdevWzXQ1CIIgCIIgiBnk4MgU7nk0MAdYDHjlGavR1ZGfwVoRiWjhecpknAXUB0fH5aCzgCCI+YY/yr0ea8Dkz/FqayMtlvNGA4Ils85m8XqMIb5bexSB8Jc8dLbmAF/9oSfM5nPAOy7vxz2PjQAI9mmUFiqcs25dmRWzuZJTNdRdX8wTIbjLv42OB9Ne3MND9YmLNVi9PIeR8YrnnNUGBAO8Y7x0kbrsVN34Wq4bajuK6vR8TsQIeY5t22I4NOLguV2emrvnIEdfr7yO8LYFGcnqfo2LNdAFfCcq9HYeomv7TfHBD34wi2IAAL/5zW/wile8IrPyCIIgCIIgiLnJ6KT3EGDVe2m6HP7gFsTsJm5wmqYKAgNjzH+QdWlQMIIg5iFBBqp69ZR7jRgF1RhN05RcEKuBprTOmhyxScsNDTSfdNX+8snnLZWZ343/qx9dgcX9tp/xG+xvU88N5uexuq46i8u9322LhcRXsW1B1EDY/Rxy28Jzq4rquCZ1ljE8tYOhWvO+nniUp6w+/VLFd7qq8bYMXR3AWy930NvF/X0BAKW6ONtVVOue02INLMvC1ieCkFsh3nrTzAchyE/WXOCGbfady9rudzXnbDuc1bOFTMTZf/mXf8GnP/3plst55JFH8IpXvAIHDx7MoFYEQRAEQRDEfGDl4m4s6vUePhZSF7d5QYoHZ9PDmT8gWH0aDQpGEMR8hkeIs90dQadnkximOGH1yUye6M0dvEBj6iQkF0hj9EwjptmyjDXoLHoC9oqBLq1Mr1DhFl3UY+Gko4tKt/socVAgnKiuq26H75y1TZmz6jxm52x4Xa48IJjYBml6rcpx093Bghs3dAIAtvy2hEpVrCtc7rIBYMVS73PgnPXW09mh1k3PnLUthq2PT/nTHSmrNiphiNe3O3Q6GhR1BnV7/fUsoHt9ZrEGf/u3f4ulS5fiQx/6UFPLb9++HZdccgkOHToUm0FBEARBEARBLBCkpyQS5QjAOw+qoPOAIIj5SeA2VH+X43xajjUAMrUgzsTV2FT9i05dhT2DkzhiWY9xmZKU0Qqorsw0sQayqOtI4mVIpBSZsybnLMRy4XW5riwWh+XZSi34fNgSG2uPKaK3y8LYZNCjxJbqIp8unUUvskKPNejsUOsQxBoE27d3MFBk5YHE1FgCaTsiduatW3Yin7Nx1knL0d9dCOqo7Vtv/QvnXp+Jc1bw0Y9+FN///vdTL7dt2zb8zu/8DgYHBwEA3d3dWVaLIAiCIAiCmIPITiASZ+cWsXmAkTSel84DgiDmM9zP6VSvh13FwFdn0mbjtdbwMGF65qwyrV2X1wSxBmmVXlV4zOHYw/qQz5llrqm6ENnX4zls5dtI4FQ1V0AMCKYn6lTrTtVcLuyMFd9M4qyYaHKdeuKsEIvD9alJdfj7P10O22Y44yRVXbWksVPlbeqsZ8sKobocFWuQE3WHX/dKRRKlkzhnuVin6gmt1FxMlKrYc2jC/y3KubyQ7vWZiLPvf//7wTmH67p4z3vegxtuuCHxslu3bsUll1yCwcFBcM7R09OD//mf/8miWgRBEARBEMQ8IcgaXTgN9flAs9KseCDVB8ex6301F9IDG0EQC4cgA1X9XYk1iAmdZYarbugXY75A4ioGizSIAgivtvGMifNrm2BKOGe7vfuILHyKGIDIWIOIzNnJumDZVbTCx4Wp7ZbEsQZucPz9yFlxL4TaDjpiuXderD8+GNErn+PoLEjrkFYrBv7SnbNdeqxBTs+cZShXg/XKArHinFXyeL0v569bGd5IABOlWlAGzGL0QrrXZyLOfuMb38CVV14JAKjVanjzm9+MO++8s+FyDz74IC655BIMDQ0BAHp7e3HjjTfioosuyqJaBEEQBEEQxFxGehixSZydm2RrnA2cswtoBGeCIBYOfk6n7pyVYg1iUw2YwY0q3LFC8PP/J192m4mWzD6OMrnQm7wGYl4hSPqxBkrmbHRJHFxxzspVLJW9e1FnBwuJs3HOWfGpUeZsqMnDAKeuadpWMN9pxwXi7FGrAjHZq3/wuVg/jSpV77dyfYzVkDhryJxVxFnFOWvyZEuD20VYaydLwQCvyrkp4SygwT8zEWcZY/jBD36ASy65BABQKpXwu7/7u9i+fXvkMvfffz8uvfRSDA8PAwiE2QsvvDCLKk0r119/Pa688kocffTR6OjowPLly3H++efji1/8IkZHR9u23oceeggf//jHsWHDBixbtgzFYhGHH344zjzzTPzpn/4p/uu//guO7DcnCIIgCIKYQ/iNdBY0/heSi2Iuk1WogV6OeLh9dvcont/TvnY2QRDETBAlaMldw22Tolcn6TU3uL+GIw+SZguEhxdrsM623L6T32UCcdZW6sMkQdsUIwBILwZlZRvAVMn73Fm0QqK5+O47Z+UZ6p9NmbOOMXM2oOY7cYPfjj4sEO+POkxdhvFgvbYdrAMASvUBwXRxNhfKnLX8wcYAwKlJQnODWAOdQj12QnbOisP48LMHse3pA/5/L+wdMxcyD8lsQLB8Po+f/OQneOUrX4n7778fo6OjuOyyy3D33Xfj+OOPV+a977778OpXv9oXLvv6+nDjjTfi/PPPz6o608L4+Dje9ra34frrr1d+P3DgAA4cOID77rsPX//61/HjH/8Y5557bmbrHR0dxYc//GH827/9W+gf6+7du7F7925s3boV/+///T8MDQ1h0aJFma2bIAiCIAhi+mGwberOvpDwhiyR8b4VC96T5aHREg6NlrBqaTeKeVtfnCAIYk6iR7kIbIvh4tNWwXW5MVM17s5o1s64Ok2aKbGIGqizmZG4KLmff0Km9AHBeCCa+pvixwioNYlyzk75sQYs0iEqxGuTOzcq1iAQi9VpDIFrVRZ2LYvhb963FA8/PYnVh2mCJpMHV/U+Cw+fH2vQqdatoMUa2BZTxNmaEx5MzauwvB3aOVanuyOPyngZ5aoDx3Fh25YfazA4WsbgaBlRzOcWYGbiLAB0dXX5sQSPP/44Dhw4gEsvvRR33303Dj/8cADAvffei8svv9wXZvv7+3HTTTdlKl5OB47j4Morr8QvfvELAMCKFSvwvve9D2vXrsXg4CCuvfZa3HPPPdi5cycuv/xy3HPPPTj55JNbXu/g4CAuu+wybNmyBQBw+OGH4w1veAPWr1+P/v5+jI2N4emnn8att96KrVu3trw+giAIgiCImcIfGAWArWW3EbObphxSes9IFhQknv3WHb0YAz1F/HbHEFzOUa25JM4SBDFn4ZzjwEgJ5YrnIqzWrYqmXNmB3mLot6Cg+l8WrVf6EiRXe6ZIf1KRfahBtHM1NF+aOtRn9p2z9cxZ2dEa1a1eIGICuJY5O1WuO2c7wpmzusCqGGdFuYbjrGbOcq1ezBdGdWH34g1dOO14G3c8PBbpZtWds0Kc7e5QC8vndeesGmtQlTpoR2izkceykLeQs73teH7vGFYMdCplrBjoxJK+YICz5/eMYqoy/3uEZyrOAsDAwABuueUWXHDBBXjxxRexY8cOXHrppbjzzjvx+OOP4zWveQ3Gxjwlv7+/H7/4xS9wzjnnZF2NtvPtb3/bF2bXrl2LX//611ixYoU//YMf/CD+4i/+Av/4j/+IoaEhvP/970+Uw9uIt771rb4w+7GPfQyf//zn0dHREZrv7/7u77B792709PS0vE6CIAiCIIgZhQVdPJ22DSNNZIsqqiZBHsiGa38FncUc1hzej6d3DaNc5eSkJghiTnNwpIT7Htsb+j3KhZmIUPd6VYHlijobJu1VNen8M321FuJsnx5rYCE21oCBIWfLsQZymfXM2WI4c9YnxuTLrPD6jM5ZySksXK8x6RbaSqRYg/oyYxPAE88zTNbdxN2d6iJ65ixjUGMNFHHWvN1ujILe3ZHHyEQFj70wiKdeshQX8LJFnVizql8p/7EXBr0vM30StZFMMmd1Vq1ahVtuuQXLli0DYwxPPPEENm3ahMsvv9wXZhctWoSbb755TgqzjuPgs5/9rP/9+9//viLMCq666iqcfvrpAIC77roLt9xyS0vr/d73voebb74ZAPC//tf/wpe+9CWjMCtYtWoVcrnM9XeCIAiCIIhpQW7X00BQc5VmBYb4JzDLEjEXdD4QBDF3KdUdgfmchWWLOuvCVB96O/MNllTxu84rr7ni5kd9/uZhjeymoZU2fmk30BPjDlbKSrhOadaJKW/FiwyxBo2Kj4w1kDNnNXVN3z2yiCk+Rg8IVv+s1YQhcPzaFvDIswex5cn9GJnwlGdj2oNUhHDOTpUZbr7XwvikN6funNUzZx2u7qOaFBcr7z9Z25Z7P6nbwHDyUQNYtshThKs1VzlrI0VufWPmGW0RZwHg+OOPxy9+8Qv09fUBAB577DGMj48D8ITZW265BWeffXa7Vt9W7rzzTuzZswcAsHHjRpxxxhnG+Wzbxoc+9CH/+7XXXtvSeq+66ioAQE9PD/7hH/6hpbIIgiAIgiDmEsJF5JJzdm7Q6mGKss7WEWI9xVwQBDG38a5hi3qKOH/dSpy/biVOOWZJpBuxQTHJZuXh+VlU3/REq063QF9XAeevW4lXnrHa/+13XrYa561bicV90eYzdZ3paliuAuOT3rfVyz3hO4gbCGINHJd7oqK2AltzkgpKInO2I+ycDXJso62ztuE31w16koRjDYBavQ4WA57fO4ZdByfw7G5tgEwlQkHOnA2vDwD6utUJcuas4wLbHq8o06NiDZTt0LMzpK8rBrpw9knLjWXorvF2xGfMRtomzgLA6aefjp/97GcoFov+P/ZFixbh1ltvxZlnntnOVbeVm266yf98+eWXx8776le/2rhcWu655x488cQTAIArrrjCF70JgiAIgiDmK3JOm++cJTFuTtCMK8ukDXDDNIDOB4Ig5gdZOFjlcrzCzOKWHuvtzWpyjbbnuiqXumxRJ3okd3B3Rx7LF3WGF2pAIg2bA4Mj3scl/TZ6unTnbCCGHhwpYfszB9XsVHCIMdhcV3WHTpZErIFliKJgYvXSN/HZ+2YZItPVzNlgGwSOIXM23KvIvGNsw/oY4+jVxNm85Jx98FGGf/z/RpTptYhYA/ncaZQfrDqJZeesPmPwcT7f8RP1eX/xxRebXsHRRx+NL3zhC/jQhz6Ejo4OfOc738GyZcsalnnkkUc2vc5285vf/Mb/fNZZZ8XOu3LlShxxxBHYuXMn9u3bhwMHDmDZsmWp13nHHXf4n0UUxHXXXYdvf/vb2LZtG4aGhrBkyRJs2LABb3rTm/CHf/iHFGlAEARBEMQcJ3ikscgpOTfJzPKiFhTEXND5QBAEIWCmAcFM6mwGMhfTBcRGtOVynewmMzjizXf0YYEg7He7l5yzAPDi/nGcduwSZXnfOauZakv1AcG6DJmz+v5RhPCYgdi8WAOzc5ZFZM4KR28QbyFXJPiYM4izHUX4mboCMSCY6wJbfhuuZa0W7IWoeORGPZ2ijlx8rMH8JZF6d/TRR6e31Rsol8t405ve1HA+xhhqcojFLOPJJ5/0Px9zzDEN5z/mmGOwc+dOf9lmxFkxCBgArFixAm984xtx3XXXKfPs2bMHe/bswY033oh/+qd/ws9+9rNE9SMIgiAIgpiVyDlp5JSc9yiuLi7+mjPr6HwgCGJeEGOdnSrX8PiOIfR25XFwpISujhxOO7aJyAPDKk097Zk2bXSigqd3DeOkIwbQ1ZHDb18YwnA92xRI/7I0qidEM0TdG6LWK5yzR60MJDBRfUsTZ8UyMnLmrDx1si7OdnZYIZFSjzUw1dUUM+C4UuasYRf7sQayczZOCJXyYk3r6yqGHdSFulWYc6YM/iWoSHJdVCQGlwYTk/HfFegnn/jYymB4c5hU1spGtuQ44ka/m2sMDw/7n5cuXdpw/iVLgrcu8rJpEBm3APDpT38aTz75JAqFAt7xjnfgwgsvRD6fx8MPP4xvf/vbGBwcxG9+8xu8/OUvx7Zt27B48eKm1kkQBEEQBDGTyA9yJMbNLZpq8sc9j+mxBjYNCEYQxNwnLtbgoWcO4sDwlPLbUSt6scgwaFZMrKmPcZpJna1z96N7UK25GJmo4JyTV+CZ3SP60t66Y9bZdhLqeMI5e+RKyTnrBgOUNRpGLednzqrzTZVFrAELCZzh7+FyjQOCuXH6GfPbQbbinBVvNP3ZjOs1xRp0dYbdr4UG49HJztmouOJI56xhP6ixBjH7cR43ARNnzrYqqnLO54UwC8Af2AwAOjoaB1Z3dgbZKWNjY02tc2hoyP/85JNPYmBgAJs3b8Y111yDd77znXjrW9+Kq666Co899hjWrl0LANixYwf++q//OrbccrmM0dFR5T+CIAiCIIjZBg0ANTdJ5/AyZR+aIbGeIIj5zkSpGvqt4aCYscJXOIcgwrwIAKjWLZpjk1X/cyFn4cwTlmFRTyGYMfVluHVnZNpVVutOT3ngKz9z1gpnQei7WYk14ME8U6W6c7bIwpmz+u5WMlbDv9m2bDvlyrKy+9YYa6BV2NQTBTA7ZzuLPCSIdnfYWHts9MtPRxKpuzrMSm7DWAN5f0izLlDjbDLn7PPPP9/uehANcDVXwJe+9CVs2LAhNN/KlSvxH//xHzj99NMBAN/73vfwhS98IXIAsb//+7/HZz/72czrSxAEQRAE0Spyu94iMW6Okf44KQ+TiH7QBOh8IAhi/pMzKWkNL3nhLvqmIvxiQn3LzcOBCXG2mLdx+LIe7Do4gWFUklbKmytFFEFSEsUaSIKqLKD6v5lyejWE47Qqded3nCB2oCsu1sCUA6tXAsCGE7mf7+ryCOcsA2qGAcHEi2vTkWjknC0WwlECFmO45DyOF3ZzTJbCNZfHHzthdb+0PdLHBKcFY958ch11kVttG8xfEomzRx11VLvrMafo6enxnaylUgk9PT2x809NBV0Rent7m1qnvFx3dzfe/va3R867fv16nHvuudi8eTPK5TLuuecevPrVrzbO+8lPfhIf/ehH/e+jo6M44ogjmqojQRAEQRCzB9flhpGD5yaM0YBgc42mRiA3PYFFFEROaoIg5gV+HEH4aqkP0iTNHlmQ6ZprzPg0KWd66KxEra7G2TbDM7tGMDpZ8ac1dPNGVagVUq5S3CpkcVLU2xsQTK+U7C5mOKoeh/DSPmDPAY6ebjV3tVhgkWpkMCBY8Ju8tped7GJojOFlazm2/Fatr+kWJ4RRuYkXijWAeWU5g97PYIgSqM/X1QlMlsLLVKtyxSTBW9pvboQYr74OYODgsbEGswHOOSo1F5wDxbyVyZhcOoljDYiARYsW+Z8PHjzYcP5Dhw4Zl03DwMCA//nUU09FoVCImRs488wz/c/PPvts5HzFYhF9fX3KfwRBEARBzG2Gx8u48f4dePql4ZmuSosEjXzbEhmjJMbNCZo4TGmyEv1YA4fOB4Ig5i48UGdDmJyzUVGR8s+N8lO99YZXG7eUEGcdl+OxFwYxUQqUSTfhdbipl3aNSFiY6IhsS4qmH2uQYECwtccUcfa6Ajhn+M0z3tRaPXWio8BgW+FYgyA3VlQ1vLc5gAvP4HjdRhd5yTrpa60i3kBaStz3bEOsge/SjbCbWjbAmLZ1LJyHK/ZTMSJ7tqKIs1HnZNS5bYh3kKcmyOqdbh5+9iB+8cCLuPnBF3HPo3vbsg4SZ5vgxBNP9D8niXyQ55GXTcNJJ53kf+7v74+ZMzwP5cgSBEEQxMLikWcPwXE5frtjqPHMsxj5YYQyRucWrR4l3TirP90FYj0NCEYQxPwkZxgtqqFJlaGxYKnkGpgn61Qd1QUpu3pn4ioctA+SKXdiv8mCpuxoDZVi2AlHH+app5W6KFupZ792drB6OVHd8cOCaaPBwcStzXS8feesJf8Wc0A1E61+Wi3pD7tVbcvC2SctxzGrzOpspWY+zeRaBLfnxsdIjTVoOPu0c3AksA8fGi21ZTytWbjZs59TTz3V//zggw/Gzrtv3z7s3LkTALB8+XIsW7asqXWuX7/e/zwyMtJwfnmeJGIuQRAEQRDErEMZxIK6sc9Fmna8+OqsuVskifUEQcx3TLEGUcRdCcV1OMhAlcTNRgpbHeGcFQJkIRfkAyTWqTK1zia/9nNws3PWDZyz+o5QfaF1p6oYFKxeVqWe7NDdEchqliG7gCfcbq8aXFnGFwGlMsSAYCbnrAllElPl7NNPdHHKcdx4Hhy2pBsf/YMVOO24olJHAChXXNP4clrmbESsgUGkVgcEmwVWWY2a5g5vR9uDxNkmeNWrXuV/vummm2LnvfHGG/3Pl19+edPrfPWrX+1fCH/zm9+gUqnEzr9lyxb/c7NuXYIgCIIgiJlEfnj0M2c5b4tjgZh5ZNdRyDmrd7m0SZwlCGLuE9nzGxGxBg3KizXOJhAHo9ZRc1zjvEDyzNl2aLNJy/IzZxXnrJQ5G1G+jFhW7Ipy1Vuqt0sSZ6XyAzFciJSG2mrrEdqxEID1KFkGZnTO+gOCGfaLfBYxqGLqhRs4LCs8CJdgUa+Nr3x0RbB8fTY5bzeKJOeFH/0gO2dDDmSpbTBDt3y9l047IpUSibONBMh2c+jQoYYO1elk48aNWLlyJQDg9ttvx7Zt24zzOY6Dr33ta/733//93296natXr8bGjRsBABMTE/jBD34QOe/DDz+MzZs3A/AGErvggguaXi9BEARBEMRsQG6sk3t2DtDyIYovgJyzBEHMKxJmzkaqU6kuhTwiBzWaWk04Z73vioA2A9bZqBd3UTMHA4LJmbPeX8sKO4hNWyTMwkKnK9f9cj1dUc5ZLXPWMEaXvh6mibNcelW55wBw3a+AvYec+rqC5UIJP/LhMf/slWEZ6h2DWGelyo07STHRJjjcYpL8cpbpQvE0GWl5/eW/bgDgnIfE2FobIpUSibOvec1rsGnTJtxxxx2ZVyCOoaEhfPazn8WaNWtmXCCWsW0bn/70p/3v73jHO7B///7QfJ/4xCewfft2AMAFF1yAyy67zFje9773PX+EwE2bNkWu9+/+7u/8z3/xF3+Bhx56KDTPvn378La3vc3//qEPfQidnZ2NNokgCIIgiHnEvJGrpIdH+YGKBLnZT6xTKIakc1skzhIEMc8x6WVRV7ygp0k4QDW4Dstiocl6KjsU1TVVa75aGKpb4qtwSrdrlpgzZyXnbGhnc2k+sawaa1Aqe39lcVYRGbXl1V1t3gtCLA1lznLgx7fYeGYncNuWijIv0KBXEVM/aykH9fUlO4pytZ263VVZUqqDGxVrYChPyZyNPUHac8/ffWgC/7N5B66/9wX8attL/vm+d3ASNz3wYigGpB3O2VzjWTzuuusuvOIVr8CZZ56JP//zP8cb3vAGFAqFzCsEAL/97W/xzW9+E9/97ncxOTnZlnW0yvve9z785Cc/wa233orHHnsM69evx/ve9z6sXbsWg4ODuPbaa3H33XcDABYtWoSrr7665XWed955+Ku/+itcddVVGBoawrnnnot3vvOduPDCC5HP57F9+3Z8+9vfxuDgIADgzDPPxKc+9amW10sQBEEQBDETyKNYW/VuhxzTJ8gdHJnCg0/uh8UYzlu3En1d7Wn7zmvSPoXXD3KQt1f/WStHDJTj0oBgBEHMYdJ6SaNNqsGEUFks4neEdLv6Ojj02+xUffQrRQTW6nRgeApbntwPxhjOW7cC/d1FpYxUbtcGpO3e7mfOKs7ZIHM2JM0ays/V1bNQrEGnHGsgibO6SG4QbnWxMSzOcsNc6rxynU3nE+NynZixsFoT7SrHAawclJ2lOmfjxeI7H5rE9XcwHL6c4aINweRwrEH72T805bctJ0o1jE5WsKSvA/c/vk+Zr5i3Uao4bXHOJhJn3//+9+Pb3/42HMfBli1b8La3vQ0DAwN4y1vegje84Q3YtGkTbNtuXFAMO3bswE9/+lP86Ec/wv333w8gOJjHHXeckvM6G8jlcvjv//5vvPWtb8UNN9yAvXv34nOf+1xovtWrV+NHP/oR1q1bl8l6/+Ef/gG2beOqq65CpVLBNddcg2uuuSY032WXXYZrr70WHR0dmayXIAiCIIi5w+wbSiEbLIvBcacvc3bf0BQqVa8BfnCkROLsjGCWLsQDsD5IB0EQxNwk2Z072f0voiyT29WkzgJwtIzZUj1klPuCplwn7+/uQxOo1B2HB4ZLIXG2HSRMNYhwztbLiA3qDfaV7pwVsQZy5qxtsH1yo3VWLVvgZ85qLyhN6KkXjsuNbzT1WAP5+9L+DoxPVbG4N/5YLeqxMDzu4vQTOvDI02WUqxylMpDPhQXZx59jWL6Y44jlsUXiiz84hIkp4InnLZx7ivRyIaRqx5eTBaYoAx3GgLxtoQRn5pyz//Iv/4I/+ZM/wcc+9jH8+te/BgAMDg7im9/8Jr75zW+it7cX559/Pi688EKcdtppWLt2LVavXh3prD148CCefvppPProo9iyZQtuv/12PPPMM/50sSMGBgbwiU98Ah/5yEeQz+db3dbM6e3txc9//nP87Gc/w7//+7/jwQcfxP79+9Hb24s1a9bgDW94A97//vejv78/0/X+7d/+Ld785jfjO9/5Dm699Vbs2rUL1WoVy5cvx/nnn493vOMdePWrX53pOgmCIAiCmJu4nM/KkW+TEPU8MyNyHA1Cloq4QW7i0B8co1xlwUMyHReCIOYwGd1b4q65xnuoYbVyDqreQ2Wq7KjrUcQ/78fJcjBKlEncava+YCZdYa5pEC3FOdu4oJw/MKn3vVQXZ7sjxFl/sCvxXS4sInRWd87GDaqlO2fleVXNXRY+AS45ac9ftxKcRw8IJvjaX6zADXeP48pX9uHPvrQXew85mCwBvd3qfE+8wHHLfV7F1h8XxEaoMFSqHBNTQb3K1eCzXpfpaMHqu9nonLastg5GmjjWYP369fjlL3+JO+64A5/73Ofw61//2v8HNzo6iptvvhk333yzsszAwAB6enrQ0dGBWq2GUqmEQ4cOoVKphMqX//EuW7YMf/Znf4YPf/jD6O3tbXbbpo0rrrgCV1xxRdPLv+td78K73vWuVMusX79eGWyMIAiCIAjChONwWLm5Kc76zFT1ufEjkYbU6iwDOG+4v9v5gEQQBDFd6FmWyjTD5a3hFY8ZyjLEGgSCofkiXdOcs0L483NEDYVNlmqh+aPq2CqN6q/P7A8I5gusHHsOeRGaFmPxA4LVt8XWYw0aOGdDmbMRL8rll5JicceV84G9v/kcR7UWlKHrqa5rvndKWqwxaiHJ+/vVy/P4kzcMAAAW9drYe8jBVCmom2DX/uDLeKmKF/ePobOgyo4MwNCYo/xWrcIfESusE7e/ERhyztb/2vUeW4DX7hCRSrqzPAsSi7OCjRs3YuPGjXj00Ufx9a9/Hf/5n/+J4eFhJUxZfB4cHPTzT03I8wLA+eefj/e85z1461vfimKx/RZ4giAIgiCI+Yj8UOS4LvLJxoCddehuE/8BYpr0uMYSIZE1/iOYyNmLyJy1aUAwgiAWIllc8uR+/jLSPdaJEFfFJdfSus1zzjFZqgbzGbWrtAm72cDBwUXmbL05tOfgBManvPratkGgNOSopo41YKIoLn9VPnOoh5RZ6jLy52IBqAb6d6xzVoZJP6cdpFNmx74xPL5jCG5d7Z0sheeRqzA8XsFDTx80ljU4qomzDke+vj1RIjbQvuaf3pQQ+1wRZ61gcNpmMnobkVqcFZxyyim4+uqr8fWvfx0333wzbrzxRtx666147rnnEpfR1dWFCy64AK961avw+te/HkcffXSz1SEIgiAIgiDqyILV/MjkVBvq82GL5jutPoI3dM7Wn0rLVQc3bt7h/97ZkcOFp6xEPtfaeBgEQRAzjyEaIOLqGHfNNcUamBy7QrjjCDtnBSJKRhfQShVHEbhMQmGm0mzKwnznbF1cK1UDcfCI5T2hDFHTXs5pkQOlsve3RxoQzJaCYHUh1Kg5aisS45XJu1/syo4CMD4Z/B4SZ11urniMczYNO/ePo1x1kM8xABYmS3oQEZTvyxd1YmisjKp+LjGDOFsFopJMpyOZS/93JfZ5zrb8HOWcbfltj1nhnNUpFAp43eteh9e97nUAPLfsQw89hGeffRY7d+7E8PAwpqamkM/n0d3djZUrV+Koo47CKaecgpNOOilWFScIgiAIgiDSIzcanbk8mn3o4c70IDBNqydFOB0808fwEJ0FG8W8jXLVUR78qhMVDI1XsHxRZ1vWSxAEkSkx0anGLupR9yJJbQ2XxeqTknY/4cYBjzqLNmq1IKdV5pldI1o9YzIZMo01SEYQa6D+vnppN7o78hid1KI35VgjX9itlyWcs3WjcE9k5qy6vKwy6nm0/u+aAOzVnYODo6CJlyZx1iS6K85c1nw7Sgj2nfUx5yf9WIOwyxgAzlu3Evc8ugcHR8IW26FRtW1adUKzmGlTWyycORtEGQhmrXM2isWLF+OVr3wlXvnKV2ZdNEEQBEEQBJEA2TnbjhFlpwv9ISN40Jn+bZq7e3FmaHZ/hbQDX7hQH8Ft28LvvGw1SpXgie7+x/d53VRp8DaCIOYI/tUqRmU8cnkPJss1o8ill8QalOXPHSMKgwfi05K+Is44YTk45+go2Pifek8FpgmDz+0ZVb6btKuZCTWor1vLnNXfHyYZeNTOiTgd77twzsqxBqaBtbh8bAxTlXUIcdYgDut1srUCo6Io1FiD5hFty64YcTbkBDbsDwZgSHPOVqrR9+3pGRAsKnNWdUWLQeHa0baemwFkBEEQBEEQRCTyG/12vN1fKNCea51mO8kF2my0gpCzLfR05v3//Ifu5lZJEAQxu5C6VvtOywgBTv21QXd6bp7ovwBF0APHtix0FXPo7siDgflCoeyctRiwakk3Vi/rxooBr9eC2+62R4riHYeD13NS7VDijSbWIjwgldjncqxBuQrUHG/GpM5ZNULCjJhHjTXw4gq41hFKd86q081ZBq30XBfnhBBnRyeAm+5mePNf78O9j0yG5uecG8VqIBxrUKsZZ/OYBnU2nDnr/c2FnLP1WIM29ErL3DlLEARBEARBzBycc+WhqB25WNOGpstN83hgxIwwvdEVBEEQswOT47I+haW7/0UKf0oZ0W7OOx7e7X+Wu3XXJEFK1txytoWzTloOAHjqpWHsG5qKjzXIgMCN2li5k4U3y3fOqtsv9ziyLMtYf39AMA7c/4j3+bClOfR0stA8om5yOUpd9cgDse76767kzKxrsyEBUXcvO5wH26XEGkh1aEHoFC/7Ozu8v3sOMOypb8iDvy3h/NO6lENcc1TnqeMAjz3L8Ntnqrj1/iml7GqcOCvtt3a1DkLOWWlAMPm3djpnSZwlCIIgCIKYR+gDgM3l0exD3T2nWZ01jZZMJKTJ3eUf4vr+ju16m/G6CYIgphtdJIwilF8aKkieWVvW8LspTmFJXwd2HZxQll3S1+F/FoIUY9EOTOGoNQ8IJpbP0AqZoCjZ5KhnzpqW16sntkS4KF2X44kXvM8feOMiZXtkMTJ0HCLqumxRJw4MT2FRTwGW5amUDmdgzDveYl/qZk091iDKrSzP1pJzVos1kNk3WFdXpSpUa1wRN3fsAW570AIQVmIrMeLsjMQa+G5npvwmnLM1cs4SBEEQBEEQcehdraJGXJ4bqKFwNIzs3KHpbMFGlq+4RaUuuQRBEHOKFm9wJpdtbNmGC+XLTliGU45Z4k+0GEMhH+QAiPZETutPLxclnKmzaSxS+SW1Ls6KXbOop4CuYg6TZU8lNGbO2qI8oFIfP2zN6oI2j+yc1cT0iFiD89auQKXqouo4+Fdrj7cOh4Mxz3nL6xXS96nunHUVl26AXAfGgLPWuXjwMQu/t6nHsJVm5F5Zi3oB2+Jw3GAt+4fCI3rp4myp4n0e6GU4e10XLAY8/dIEntmZJtagPXf4UKyB8JdrL+ltcs4SBEEQBEEQSdAbjHPaORtyTc5cpujc3YszTErBIWI8sPasjCAIYjbj3wNZw8zZeNRro+gmr09hjKGjEApl9RHtCdtmqgvUkNdqcs5mSZpeFTXZOWub2xGMMZy3biV+te2l8EQtf9Rx4QuTnUW1BvoAWEqkgCEHltfXXSzYcEquv/8chys9STh4SEDUM2dd15zxqkYMM5y3nuPV5/fgovUDoXmjkHtl5Wxg2WJg78Fg+v66c1b2A1RqXBGrxTE7bCnDX71jCQDgQ1+eBMBjnbPTQZRzVh/rTGxPO8ZzoAHBCIIgCIIg5hG6GKvHHMxJQoOZTM82UZJBKzTtnTWSJFdQj0QgCIKY7cRdKeMGRIzCdK3Uk4GUFaegJg0SpuaAypmm0SJyUzE1jUgSayCZOn3tMmb7ObgyXWxfrq5blyvBtGIhWpz1nK9SVY0HWa2IEFwdN4iIELOExFlTrIHhhFIFeO+/1cvtkJAch94rq7dLnT4+xTFZclGT9nWlyrXM1nq9pd/ydbtoXFt1GoyzoboF0Uqqc1a4xtsxngM5ZwmCIAiCIOY4h0ZLeOalEbico6o1GJ94cQhHr+hFMcYNM1vRnzFmtNs66X2paPohPGSdTbHjyThLEMQ8JS5zVhFCGTA0Vo4vRJJT02SQupJzNoq4zFm5jtOJnJXri28N8m+NsQZ14ZTzYJliPkachRYpgPBnZT2M+YJrzeG+GByVORtyzkbFGkifC/WNSPsSU++Vtf4EC0+/yNHdyQFYmJji2D/kwJHE2WpNzeANclyDeXxxNqFztl1NMbE/LMbgggfOWWWe4NxvR680EmcJgiAIgiDmOM/uGsHeocnI6XsGJ3D0yr5prNF8gRs+EalIHWugdjmVcxSTroqOFUEQc4WkGpl+bYzjuT2j+sLqOhOXpC0nvXSLyjQVWpwpczbL/hRpynJ94S1+vqD3BZSNEh9zObWAfC4s7sYPCBZfAYYgR9ZxgbzknOWGzFnbCkRnzj3B0CjPSsfHj3VIefj1bvwbTsyjXJ3C0kXArx/IY2Kqhv2DNcU5W61xZZ+ZxVkGgKMaNyBYlgPIRSA2z7aAmiO/Hw62+4jlPUHmLImzBEEQBEEQhI548DhqRU99ZGWGJf0d+NXWnXB5uCvcXEEfLZdpv0/X+uvfpmel84TW9xbtb4IgFgKBwBZH4JyNvzYyBA5X0zR/rU30blDcthELJnLOZiHP+uU3Lkt0KDLl5Cq/mfVMHz0GIG9Q06yQc9ZsnZVMzOryjANgqDk8dMz1wyqm5ywLVcdFpeqgkPPUXdflKFcdFPN2KHNWLjMpeqxBR8HGsau9z4v7GHbsAfYeqobE2YIU+2B62VrIiXk9UTTX5k5eE6UqAKC7I6/87jtnQ7EG3vRjD+vDUSt6MThaVn7PEhJnCYIgCIIg5jiijbi4rwNHLO/1fz9sSTd2HZyYwxmc+pPIzNSCaJ6mD5lmnU1UDllnCYKYsxgGckp4/dMvefr30PI8bmI0LudwXeCpHS7WHWvuWRKfOZvdxTlNSUKclU2taZb3nbNanEMhH543lDkrTTPnCqv4mbOOIXNWjzWoF2jbDFUHeOqlEV/4HBov4xcPvIh1Ry9Gd0cg+zFfPDdUJgY91qCQD1RUxy0DsLD5sSHUnGArq1rmrNhY+adcvWrP7AR+84yFi8/guOKCdHVLyu5DE3jwif0AgLNPWo7DlnQHVfPrpu5z8Xdpf4f2UiL7hgYNCEYQBEEQBDHXma9iVEibnV51Vs8aI1LQ5P7Sc4X9YlLEGhAEQcwZklwrWXzmbFqx1TT7TfeN44Nf2IvBEce0iL/glscYrv2Fi//vprLyu0A4D02xBv46M7pY7z0IPPZcTH3riC7oekarTlRUg8DWlje5PPU8XnVAsAaxBkweEEx1znJEZ84etrjLF471eg+OldQf/CzVdDfpmjaeQV9X3t9fPXWNc3hcjSeo1FRxVgjCinO2LnBPTDG4LsPtW8IHKat7+9hkMJLb6GRVmeZGOGeDSAy1B1c7IHGWIAiCIAhijhN07lObjW18wT8tROWNpn2oaLkCRGpmZtepA70QBEHMdpJ3zk8mC4W60kOKBjJ126//+MXvD+LxFyr4zvXDkWW7nOO+RzwJ6fHnZbEuWJ/Q4qYqNUUMawc/utnGv99QxY491dj5XEetGxCOTZIJO5G9X/TM2cbO2eBY6GvxM4RDsQbeX8eRXa5e6Kw+r6j6YUu68Zpzj0ZPZ7hCnJvvx2lfOOsZq/mcjc6iZ3s966RFAICxCaYMCFapcdh2owHBTI7x6F5TWb0o19chvtqW+biE26HZQ+IsQRAEQRDEnCeimchip849prnb+rzZbzOCyFFM5zOJzhVOIV3QgSMIYq5huMTJwm1c5iw3emEbECEaTpSiLa9Rwpj8s3AYVmsufv3QLkyVa6EZM3EfSivdua+KmsPx8FMlVKrhStYMsQbGG4XUdcP0ks/WnLKmzFl5QDD5hXnoVmi8YbFAnFWcs+I3tYhaTS3bNOCZq6mzwgmqn0fVmoOxyQomS4HQ7TguxiYrcDkPxRrI6126yNvm8UlombPhnF5vueC3gmEfHhxW3dBZuVXlTdbP5VDmLLT9JOrcxnZoy5mzDz30EL7//e8DAPr7+/GZz3ym5UoRBEEQBEEQyTG5Eeq/THdVMkXP25v+rZEy9Ujwmx50d4oQEJIc/Pn2MoIgCAKALPMZr2+60NSouAg3JRB/rxufCkSzJf3m0bP0F3LD42XfYZkmpqYRcjU//a2D6OpgmCxxvOaCbnzsbUuUeV1DrIHJsRzsY3UH+ZmzmrWxkXMWDOCmvvww7wIm1bHmcCn/lNedq+pSlZrW5d7kAuZcE5pFd/3gl1Klhl9ufcl3x55yzGIce1gffr19FyZLNSxb1IEVA12GGnssWeSp1mOTQLEQ/K5nzga5rsE8JufsMy9VsWygvcNj6QPWRWXOulqdfcdzG+rUsnP2jjvuwFe+8hV89atfxb59+7KoE0EQBEEQBJEBSUd3nvVoDxzTtjVzfLfNKE07pOb2CwWCIIg0xN6epYlxmbOK95IhdO8KXnBKrsAoF2xMfXbsCcRZWZhUnLOawpS290RS9HbNZMn7/j/3TITm9QcEU0Jl06+TMQbGggVNzllLy45oFFuhVIPJ4qx6zHXXLAAs6g3q5f01lM/NjlF5/42MV5TYgsHREmoOx2TJs+YOjZVDsQby8ot7LTAGuC7D6HgwTzVB5mw+H67087u1OIyMziF1P6jbI8RaW8ucDUwCeh2ybyC2LM7WaoFNfd26da0WRxAEQRAEQaQl0jk719Ebv9NrjeQx34h4mnVIKc4lNH6wNa6bDhVBEHMM0zXOv/4xeY7GF7jIVBiD2TVNu2F0IlAI5YGfZCzdIarkhYoohdYbK2mu865BnI3d/gjtmjGmiM9GcVYRI3l0z6aIwynm+9WDE/jxLS4qVdRjBYJ5Pvr2Trz2YgfLF6vL6Pte1EHbNO+v9PNURT2YU5XwIGuOSR2ub4RtMfTWjbUlSVfVxVmTc9YUazA4qq6rHU3bUKxB/a8VypxVe+/EDszXIi17hQ877DD/c7FYbLU4giAIgiAIIiWSt0b5fZojWjNHF+aCOLjp36K5ug/nHC2ctPPv5QRBEPOfZG9X04lCaS6gyXumTEwFUyvaGFyc87qzNMGFOINrdc2QgQoA/T1h/6HIS9VdvTpS5KwWaxB8sRkgpMuCwfWZz1k4cnkPHJejo2BjfMrbUeEBWw1ZrABy9VzbQyMuDo0Ai/oYLjhV3d7Vyy3Imx84Z02xBo2ds1Nlb4v6uwsYmahgqlxT2lmuy/HUSyNquVDbZL3dwKhmWq7UuJLBazrVi4ZoiKExrz6Ow2Hb2d3YlW0KxRqoERHcn8/72y4HuEzL4uzatWv9zy+88EKrxREEQRAEQRCpEW6UecYMK6LkwJx+Qh0HU7nC5/rrCIIgFhpZXK3kexUzFKobZ0PzJ6zQ+KTsnDXPaGkDQLlSd/gsr8xGIyeAJf126DfHNCBYZHd1MTlq+4LPJucsAGw4flnot6h7mLIeBvRo0a6TJZE5G/wmBibTM9nNsQZa5qyWpQp4mbMAMNBbxMhEBeWKA+6GFtErrtDbFc7TqFa5cj4E7tRgnsOWWjh2NcdUycYRywt44LclDI85ODhcw59+aR+OX13Ah36/J3rFaTCI1N7nwOEcjjXQ9rFh+axoOdZgw4YNOOGEEwAAP/3pT1stjiAIgiAIgkhJVBfydna/mg70boftbBQ3rMsc3YczhT6YW+rlQ58al0TSLEEQcxXjFU66jgrnXsN7ETPFGmg30YYrNjNRkpyzWqyBL7xp5elZpSlXGUkt3PMeANDVEZa4/AHBlFgD000qSqgNpjKpeNOAYKFlU4TOMgA9Xer+sq1w5qylFSbcnuZYA3UdLlTREQhiDRb1FCEk1pKcW2E45zjUNllvd3ieSk11vpoyZ3M5htdtdPF//6QXV/5OHwBgaNTFVf8+iP2DDu55ZEqNxgivJjHysvL2y7/rsQahtkwbXRAti7MA8KlPfQqcczz++OP42te+lkWRBEEQBEEQRFIiW6vzTK6admvwPNlvM0Iqy6vE9OYKEwRBzCgx1zr5xauexx1N42uuXEaaYY7kWAPHCbJc5QV1gVARZzO8rptEXwCoVMO/10zOWQNyzRURXBJY5V72Uc5ZmWBRcxavWlsWEjlrNZE5622EbUmZ7NptNto5K39X/wJBrEFXMYeOgmfLFYOByXVctqhD3TJpfb3d4ZVXa+EB0gD1N9lEMNDrHaB9gzVsfaIU3pgMUU5LaWf4sQbCOetnztajI9rYEM1EnH3729+O9773veCc46Mf/Sj+7u/+Do4T8SqDIAiCIAiCyJQoYwbTZ5hraN0Op1tqnqu7bTbQ7L4L5QqncODOdac4QRALD72HSBQs9gaoiq36NVCPNTBOFCXFxRpMqRNN7lU9m9MUa5BFfKcbITeVDeKsaxBnjb07ZB0x0lkZfE7mnFW7xTeiNxRrwBTnrCHCtWHmrOKcdVXREQhiDToKNjqKnuK8f3gqVJYsTHKutsn6usLrvvaWURwcrqGnMx/UBeq+EGVyzrGo1xOGp8qyWKoJohlZZ5XjK/0e6ZzV/520oYXYcuYsALz44ov41Kc+hc7OTnz961/H//k//wff/OY38Za3vAXnnXce1qxZg97eXuRyyVd35JFHZlE1giAIgiCIhcscNyGGux1Os3XW5JwhUtHqEYuK7CAIgpj/hF+9mm5FyV9IhaMRPDFXFqqiC5ssqdNqTiBQ8npHd92dqjpck8fUNKKmZc6uWZ3Hsy9VUamEw2gdQ6yBDzN+1PYz96ermbPeEjXHxY59YwAHjlzRi3wumCkq4icqpkkXfP3M2boYbVnR4rtp+1wtc9Z3hNZ3U7Xm+oONdRZz6CzkMIQyXtg7Fi4sVH7wgy4qC77ywyF84p0rcecju8xCdf1zzeE4NDoGxtTtczngZPTGVd0P0u/ygG9CnA25k/V8rUyqpJCJOHv00Ucraj3nHC+99BK+/OUvN1UeYwy1Wq3xjARBEARBEES48ThPYVFPM21CfaQkdTYVTe6u+X0GEwRBpEfNnI2/uDKm3q9cF/jhLRMYGZ9AzXFwzBHA0n619KjBtXQmdOesLNlExBq47Yo1cNTCjlzhibOJnbP1v5H3nEhnZfBZCKk794/j0ecHvfIshmMP6wsVF2qfGVZsasJNlUXmrFcJ29D33TI4Z0V2rDzYFeCJtUBwjtTqB58ByNkWjjmsF7sPTYRXolWZcygKc2+PeU/e+8gUrvxECX/0euafu7KILD6+uH8MpYqDjqKFqZIWjdGGTvmutFNc5fjqzllVUG5nD65MxFkd/cRrdAEhCIIgCIIgWsHc1ko8gMgsJerhaY5uzoKi6WOkRROkGVjM7/pIJwhBEHMFLb5HmZTwWhaaTfphxx7g+tsn/e+jkwzHHa7OXq0lW9FkTKyB3sVe4LQr1kDbOf09nmoZK84aV2y2zhr3CGOKsNhZ9P5WJRtvpaoqiTzyJpZsJ0yWvG2tSc5ZQ7WUv+Iz5+FzSL+3iv0oRMml/Z1Y0teBQ6ONM1/lLeiLcM4C3jF56Anz8RfnS7m+33o6Gaa0VctCfEupBpGxBmosCBAIttl5vRuTSeYsIBR5838EQRAEQRBE+4jKxJLmmK6qZMt0torj1k80TWZm7iQF6Xm1BEEQs5xwfI8Bli5TW57lxT3egkLUmyqr0xkzZ8eamCyrK68m6OzsRgzc1SqOtu6Bel7pxBTHv984gpIUb2DMazV0sY+ONQimy2X0dLLQvLr+FdWMiQuquOQ8F8V8XbSsMFRrXBKYw0uYMmdlp7U8v585W69n4MiVlw1VyYAqmHZ3McQ1mgq5iMxZ7bxetcwOLatHWGSBKtR6fy0mCfhc3U/6Pm6HzJmJc/b555/PohiCIAiCIAgiQ9rZ/Wo60B9q4gdEacf6zd0aifahH2J/pOQZqQ1BEMTsIPb2J/2oO1d31MXZl53UgQd/W0K1FhbRZOesGyGEff5fD6JS9T73dAHjk5pzNuIe6fD23Ef1KAbhnAWA790wgu/dMILXb+zBBeu7gszZhtZEad9J5UfVu9t3i5q7yHvLquKeviol/7f+29pjOT76llV4zUd3wXWB0QkXNceK3AaxnMkZ7HJVMA5iDerf/X2T7i7LtS+WxfD6V7jYtc/CyDjHUzvUiubzAK8buOOOw5svKeLV53agrwf43/9yCJwzPxO3VRSR2uCcZYyFxmqINj9k3yjMRJw96qijsiiGIAiCIAiCaIH5J2CZPSekk84BYrrqxtKC1XaaI4kJgiDainwHZDHW2ahYg1IZGBr1ljv5GAsP/haoVNWoAcaAmiTOVg1C2Mi4g19vCaIR+rotjE+6iRy3jiHQNou2il7sot6w4/Knd4zj1gcm8MZXdAIwD5gVVZeo20i5Gnzu6gjPrDuF00TzyHNZFkNXERifAiYmg0G9bAuhcyDInJVK8k8Xdd7AOat+T+KcVTy4XFpHveZHHQasWe3FGBy9shO33D/lz56PdM6qK+vrtnDeuh6MT1VhW94LAKeWTeuWR7wocKV6ifq4WgqA75w1LJ8VmcUaEARBEARBEDOD30bUGrlpukHOSkL1nl75mUd8JhqTqKuuAX92PxivmYUJgiDmBunEu6hLot6V3vsu4k9ti2NofAyAJ84Oj1f8eRmYIsiaogpe3Bsokket4ijWB8KSBwSLipNRs2EbZjAlRndTys5ZABjo875PTHE88rRXfzXWoP4hQVX8HhwMKJeD3y1m1afL5Zr3Q9R4YNzwm5hSENEGVe4PimVZplgD8VcSd6Xu93EDgjkG52zkS1WufzQtw2FbwO+co4bQ5m01PiBYTsXytyU4XsqxzqgxZsqcZVKmsL7f/F3bxnYGibMEQRAEQRBznXmqHPrPTtpgF9M2psE83a9zAa79TRE5S4eNIIi5h+kaZxCH0tz+XCHO2gwrBrzRq2Tx9eiVvbAspoispsHBduz1ZjjxKAtXbHKRr4uGNafRCFoRA4Il34RI9PiFnk4LOck8+y9/uRK/e1EPAGD7U2Fx1oR8n1FEZeljqSLNb6pXRKxBWkGaAyjkxTq5MTfXr0e9bMUZzER9dOesqJf4Xhdnlbxac530bQtcpDy0M7o71B9cyPfz6HXJ02whzmaVOWtwywKqaCwPLCq/cBC/t/MdMImzBEEQBEEQc5yoXE4avb411Ew12omzH7LOEgQxf1DFrOjXT7r7VnwXgl4hx3DmSUsBwM+NBYDjD+8HoAqyJnH2hT3eQocttcAYUMyJeRtvQ9sGBNPK7eywFMGtt8vCped2K/PI4qXvlkxw35AlOuU+Y1g08YBgpoGl5Jk4fOfsRInjZ3d4EQFerIFcTrhMryjZOSvHVngnxch4BXf9Zjcmy7V6uUksxJrb1LArxCxdRU2cdaOcs+p8wsHLIImz0jnZytkU5XB2JeesL2qDm52zGdQjikwyZ6MYHBzEgw8+iJdeegnDw8OYnJzEwMAA/vRP/7SdqyUIgiAIgiCAuT96vfbAOf3OyGweCBYiabvqCkKDvvnlpCiJhHSCIOYI8W5SOfOy/kvc5U0rRIizOZuhsy6WVSVxVghhaqyBwTlbF2dXLPHmLxSEc9ZUU70O5pzPVtHzbjuLTHHTdhQZ1h5TxHt/tx/fvn4EQIRzNkJrNfXQicynNcQG6NOijbOq9Cv/WqyLs7fcV8HOfd7GWRZT9nWU41V1AQe/+Y5ZzjE4WkZXXWlXYg0iKhuKU4j8AnR1qjubS+Iskydpy1lSVymr7oRWIyyykWcVY7S/f+JiDZjytx2NwszF2VKphO9+97v41re+hUceeSQ0fc2aNSFxtlwu4/d+7/dQKpWQy+Vw3XXXoaenJ+uqEQRBEARBzEua7DU36/Hbvn5jXZ/Q5vWTxtc6TZ6TgTabXOVt4zMTQRBEm4m/yCW5/enzuL44C3R1eIpYpebd27xMz7rIGuGcffyFMj7zrYM4OOwpoYcttbzu9jmxnLTyBLEGfj0zaKvoxXZqTk0hoh1/ZMH/reGAYLo6Kj4aFHTGuHE79LiFNA5dHRFrIIRZILwNch2ihFrhXl61pAt93UU8vmMIOZuh5nBf+FQGBDPUxZseTNFTH/TtE7nEAifKORuxPbJz1jCmXFOogmw4c9ZisqNZHxBMK6sNLY1Mxdm77roL7373u/Hcc88BSJ4HViwWcfjhh+M73/kOGGP40Y9+hPe85z1ZVo0gCIIgCGLBoXczm3uocQ3zTHue1zR/ytFRJghiARF3g5beTfkvrDiwb3DS744OAFU/lFN9Q+WLszkmdTNnqNY84U+IeVXJhSpHFdxy/4QvzJ53aidWLePYdTDobi/nr3LN5atnmjbLZLmG/UOT4BxY0t+Bvq4CKjUHYxNBRY9amfNdpjr9PUEQrewONdcqanp47v4eef6w0BcqOUKEjDz8UqyBuhzH2KQ0oFukczb4Ity8OdvGoh4ve9g79hw1J3DkBguHq2PbLCxISpsflx3r1SEic1aPNZAHNfNjDcL1aRVXEWeDesnt5qjEiXaRWebsDTfcgEsuuQTPPfecojIfffTROOeccxou/+53v9v//F//9V9ZVYsgCIIgCGLeIzcsFea4zhXVqXBGtOY5K3DPFKqwnpTgwYjX/6Zflo4VQRBzhUCwip4GydE3NFbG5sf34ZHnDvn/Pf7iEIBAYBMimiM5ZzuKgfAkBNhGztlyJfj8sbcu9j/3dnnLbX+CYWIqXO9LXnYEjlvVX6+DZrFEOhfptqcO4OFnve2899G9AIDHdwxhaNwTKA9byvH//nJ5ZFf8RT2B5KXEGhjuExHGWWX6ay92sHSA4zUXu0Y3c1SsQbLeH6o4XCyE53FcjsGxsv/djogjUMryM1WlatQ/OMI5GyOYAkDOskI7RZ6vkbObp3XOMilzVouwaBYlHMEQRcFk5yx05yzz59GXz4pMxNlnnnkGV155JSqVCjjnWLx4Mf7xH/8R+/fvx3PPPYf77rsPQHR2BQCce+65WLFiBTjnuOuuu1CVw1AIgiAIgiCIGOI9IHNWq9IeagLxbXq2KGKwZiIJsqqQhojZE3WDpVgDgiDmKeIaWKp4ymo+Z+GwxV3Kf6ceu1hZRoizts3AGEOhLvaJQcEC56xZnBWf/9cbF2Fxv+3fEzedWUTOBhyX4bmXRDfwYL2dxRxWLfUG45Kds2liagRTlcA2Wa5bfMtVR3IFA46eJSDRL4mz1Vq4LpEalcFZCQBrjgDedrmLpYtgbGTpzZOgGaPFLkTWOFjS5JytVIG87f3e25XHqccs8acprlNpGfkY6Jtbq++7AyMl7Nw/DgAoGUZ6s7WsW/UzD+0Lfb/KsQaKw1dbj+zgDcRZ87FITVSsgagLY4r4Ot2RYZnEGnzsYx9DuVwGYwzr1q3DrbfeihUrVqQqgzGG8847Dz/96U8xNTWFxx57DKeffnoW1SMIgiAIglgQGDrAzUAtsiNkOJlR8Y0kvzQ0u7fm/AsFgiCINiJcfr1deZx9coTmosca1Hv2F/NAueIJfLIIJncbrxmEWpExKwTNgV4L64/PYesTtcg8UFG+KXM2DY6jLu/10g62zWDoVCgWAplyqtRgZVKTyeicjeicFO+cDVyZJuLq3lEIL1SuAN2deQyPV7B6WY8vguvrUAcEC4ToQCT2/lYkwfuJnUM4YnkPxibCRsmOoq0OFse50luFNbh7u1Fip7aJQmBmYEGsgZNNi0A9TtLvbtDTx5Q5GxfDkCUtO2cPHTqEG2+8EYwxdHZ24uc//3lqYVZw2mmn+Z+feOKJVqtGEARBEASxIIgyKQYOgDkqdc14tTNyayxgWnWcpOgR6j80zdnznSCIhUeMmZRL0wLRyPvNirkqiitgIM568wqxr1pTXZaqIBtcQyvVujibV9fNGGDb6jr0q65tEGd5zLZGIZYfGQPGJ4WjkfvimsXCKzc5TgGgVJa+JHBxarPGzCHNGxFrEFqyvmLX5fjl1p2+K1jOPDXl6Ho5v0yZNyhSz29FfR1BHYIBt+rHuBao647D4XKOqkFxP/GIAShtIqkC8me/LtrybmSsgeYolkYE852ztazu6fK5GHwW4rUV5ZxVKiiVlnFbo2Vx9o477oDjeCfSG9/4Rhx11FFNl7V8+XL/8759+1qtGkEQBEEQxMJgnmpRclczYPoHOCONbwaINN8kCezLuC4EQRBtJlECDAv79ZK8+HJcb6a8cM4WhFsyEL4Atbu/9139mxfOWclJKFyNXOh42g1TiLOu62KqXMNUuRYItSmu1a7rYt8h4HvXW/jxLRZc14XbwDlbiOgfPiWJs41u78rmRMxsclGGExbMfePlbxOlGp7cORyaUDQ6kVnBUQABAABJREFUZ6O9m7roKdpOiptXW1jPXp2YMseL5m0rtk0UunUz4LJzA1ev6wLCa8vknNyIbWCQBgTLKnM2In4iaGuq26G3QUW92kXLsQa7du3yP5999tktldXdHRy8iYmJlsoiCIIgCIJYKDTMNJuzIqNWcRLf5g5NOKS8+UX32XoxDbqEEgRBLERkgUtHXDddKXMWADqK3vdqjSmCky5+VWte3mmsc1aIrxHtC0uafsuWnck2yrAdLgd+udkCwDA2AQyOut7vYtuswOJ47ikd2PxoCW94ea+xvKly8saQmkkaqc6KGXyiBgRrdA+bLIVFUZM4q6xX/1kTEb3v3M+c9cTH6Hwo1+UYmzSLs5yre4HLMbPcnN37F29fjEqV47atk0qsQSLnLAJ3dtXhsI21ah75OHHFOWuKNTCXwZFts7RlcXZyctL/3NXV1VJZ4+Pj/ufeXvM/KIIgCIIgCEIn/sFhrmqzYZfG9CaSJjDOEBEkcoMlKyEx03t2EARBZEFceIvUoV7P5kxwcXX8QbPUWAPPORssL0RYgYg5qNQdtbpz1mLMd94KcTbsXrWwbFEnDo1MKfMByW8LjuuJsAeHgyV2H6x6mbOS6CmK/j/vXorfvlDB6ccXjeUVC8HnQDSVBE3ps+k+Ei3SmbvLy+U02ubJcs2fT7g2i/n4ZcLCpvxFijUQIiOkbvuGCrmcY3SyYlwXB8KNsphICAbvHDt8uSc5uhEDgun4zllWF97B4LTbOSvVyxRroIrJcoHIVJ1tWZyVowhkF20zPPvss/7nZcuWtVQWQRAEQRDEQiG6wTs/5CopggzANG7N3N5ts4R0Ty6ZuL3puBEEMUeIy2GVhb1YIS5iOSFq+bEGdQdsXOYsEIiy1SjnLOTYAm2lfv0Yzl+30v/+6POH8OzuUWmLGuO4HGXNyLn7oIPeHq7GGtTX3dlh4WUndYTK+Ys/7MH3bxzDlb8jq50NbhQRQp6M6RjoTmLToFKmZafKYlS2ujzLOToK4RRS2+KRe89SnLOGWANJfDRtPufAhMHBK6apzllZmeThBlp9RSLv2InMnNW2wZ8oDQhWQ+aYMmdZhHNWPV/jBfxWaDlz9thjj/U///KXv2yprFtvvdX/vGHDhpbKIgiCIAiCWOjIXc7mBdOszvLIL0QjhJMotalEd3uniEdg+rIEQRBzhZQXS1M3cp/6RVAIhUIgy9Wtea4ri2DhAZdE1mzgnBXFBiKW6HLuRAwIpmNbgfSUNKbGcVx1EC8Auw9UvcxZg9AXxbpj8/j9V7lYtSyoQyNHa5r7iOrCjIg1CC2l/iIEcnnf6LEGR6zkeNOlQahtyK2qiZ7iuzIgmB8dZN7Cai08GJiYP8o4azKQiu854bCWxVkrPF9QbynWoD5fVXp50Np7W7UccawU56yYzkM6s7HELGlZnL3gggvQ19cHzjnuuusu3HvvvU2V88tf/hKPPfYYGGM44ogjcOKJJ7ZaNYIgCIIgiAVBpHM2yzCsGUB/eJp+H3CCzDnCTEa7K108wvxwihMEQQBQLmUhh2GCa2KQOVv/K+XAyi7LqtZtXIi1Qpwt5ILlRF1EmUlf/gqBOA2Oy1HWetnvOegombOec7aJa358GpRxhiRNLDcqhJfFfgUAHBie8venF2ugzvX6l7tYuQThRpH4qqizwTE2OmfBlHNAoLuoBcZdrKizzP8oY9nBuWNqq+ovGUSdGJMHBJPVb2P1msLf1ybnLLi/3xRHMgsvnxUti7O5XA5/8Ad/AMDbqLe85S144YUXUpWxf/9+vPe97/W//8mf/Emr1SIIgiAIgliAMOO3OStVNbYttHf1c3bHzSLSusHqf4N9nzSxjyAIYu4Rd4WTb4FxDsPwct6SItZAZM4KgVS4GP/P1Qfww1tHDc5ZrvwNMmeDdft5psI52+CGadvppSfH5Shp4uxtW6fwwKOuNCBYgjaOr02Gpdeol9pGN2xkdFRAZKxBAnX23sf2KgNVdRaDmQp57ouVyWMN6nVyufRbcCzFeSFTdSKcs0o3f9Uta3LOCnK2qEOyJp08zTaJsy2gn6K6c9aSxGvOEZw309T8aFmcBYD/+3//L3p7e8EYw65du/Cyl70MV199NSoVc5iwzM0334xzzjkHO3fuBGMMhx12GD784Q9nUS2CIAiCIIgFQpNdyGc9aks6bpThaa4K0YCmd5d2EsflMYYWNTxUEwRBzA/iM0tlhkaAbY8zP6817ztnvb+uCzz3Esc9D0/hWz8Z9h2ygvFJT6CrhDJng673tiT0JkEegIwxYMe+MTz6/CH8dscgJiNyTh2Xo1T2llvcH9Txkae54uJtHB+b4kaiL6NgPgbhLFa5HHXeODoKtrJghxRr0FFQJhkJxxqozlm12z5H3iCY16JiDfSV6hEHIRev91d/IQBEO1HlaQySOKtkzmZ3g/fiMTiefmm4XhemtCOCbZZFb6kmGbc1Wh4QDPAG7/rhD3+IK664Ao7jYGhoCB/4wAfwl3/5l9i0aRNOPvlkf96hoSF85jOfwa5du3DnnXf6g4BxztHZ2YnrrrsOnZ2dWVSLIAiCIAhiQRDV9Vt2SMxFQo4i/0Fo+jdoju7CGSf9C4MIv/f8e/NAEASR+OYSJWKZ+N7PgXIlEN6EQGZLXcxl1+TQmJprcGjU+y6yZ33nrF8XFgi9Ujf8OORYg0rVwRMvDvvfqzUX69csDS0jxxosXQS86nwH/3GTjQNDwPLF3u+W1WSbwOCm9b6Ht6Vh/w3D4FL6pJB4qRWx6fTDMTZZwdanDvjrlDNnj1rFpWUDAVMpk6kiYuBulvMxgo8dBRsTJXW0rUjnLNS2JAdXXpiHnMF17EaZs1EDpcmxBglfADTE4Jw9OFzCVMU73/M5SxkQLIg1kCuYUV0MZCLOAsCrX/1q/PjHP8a73/1uDA8PAwDGxsZwww034IYbbvA3cnBwEJ///Of95cSbhcWLF+Paa6/F2WefnVWVCIIgCIIgFgZJ+/TNNWa42nN0r80Omtx5ujRLoQYEQcxnZMEzaqLcRR3+/NFl6jmt/oBgUhdzWxJLd+5TBbpDI16uq3DO7j44ht+8MI6psjffQ08fQM3J+WUpGxJBTlLk9G7qUW5NV4o16CgyLFnkDU5WrQHDY3XB2WruBXSkm5YxgGuDX0XciEyvEqPq0ijWwBNT1R87isE+O+kYQ4VCea1SeVJ+quwyll/an7ZmKe5+ZA+qjouczVBzOGoR4iw4D4vg8gvziMiHnDFz1uxEBYKB6hiCQefk2I1W2mV6/TkHSlLg8gmrF/nbL6UaqI7kNrZGMok1ELz+9a/HI488gje/+c2w63tSz6aQf+Ocw7IsvOlNb8K2bdtwySWXZFkdgiAIgiCIBUGUgMX0GeYYesM46I43zRUgUhMrOMQRYZxNtSgdN4Ig5gzRFyxZQAw5PFNcW3N1S56IFuA8yKMFgCd3qGruoREHNWn6i/vHMFmq+dfWUsVBpT6D6yarhywG64NmRe0Bx3F9obmz4DkpVy/3NubAkPd7kt2Q5p5gajdFviQ0rDu0bRHZPCZNWB1siqO/m+HkY12cfKyLw5bGLS3KMLtQgzox6RbL0ddV8B3UVoOeVmo3//B8Ue1PcdwdJdYgekFZoA4GBDPXqVVczv0XA6uWdKOzmFOcs6LCUf/Wsu7FlZlzVrB69Wr88Ic/xK5du/Bf//VfuOeee/DII4/g0KFDGBkZQVdXF5YsWYITTzwRGzduxBvf+EYcd9xxWVeDIAiCIAiCMOShzWmm3T4puTXmzU6cLprbYSFtNk0xLLQ0QRDE/CBFrIGOyTnr8vDyF2/oxJ0PTeHQiOMPBgYADneQg9cNvlTvAu53mfdjDeKvu7bknHUiuv7ryAOCdXZ4f49cmcPzu2uYmKqLikkGBKuj5IUafoNhuumbucRgTs55KFYqPGf4F8vSxFXLwqXnRW+dSeCVPxszZ6VM1e3PHES56hrXrdMwgTdCnTUOCKbMZn7pwFgQiVCtZXNPNw0IVq27uEV0h7x/lFxjhD9n3dTIXJwVHH744fjwhz9Mg3sRBEEQBEG0GVNXsflA8FCj5quRcXbhkMaBS9IsQRBzjkjxTiVOiGtEMe+pXHIXc0eLFugoMpx/al2cHXb8SAPAE8lyNjO6GhPHGsQ6Z8MLc86x91DNHxCss+j9vnSR2vnbYuFBuCJJpc42fjkb9T6Qc3mwsHj3ZVAWCwnuUXppVFH6QFviu9jfTKvHjn1jwbKNxFkt6oFzBAYAQ+as75y1RB3g7ydLGvcsnKUcfPYHBJPP1Qxv8JzDjzHI1Vcmi+r+AHgRJ0nWbY22ibMEQRAEQRDEdGFuIs79bt76U+s0i8/K88Cc3YkzQrRbKB55gBHtA0EQxLzDv8IZLpZx+mGal7HFQl2czQVCWVWNmcUpxxaxYrEnDx2UnLO27QloXR15VKV8TiHOiojSRldqIX55y+hqZnj+H/xiFN/9+RjE1ncUvb/5nCZgWglWbqCRNmtKXo3u3q4tyzksaM5ZPdbA8F2JNYhZX1wZ/mcwMMbrdeDS7yqHLenCUSt68dsXBmPX5RUR4+KNqKpwzjpuAicq1G32z7GMYg30dpwca5AX4qyYl3Pp5XA2629EppmzBEEQBEEQxPQTKYTNk27eYiuCzZme7Znbe22WkF6dBSA9RDUj8tKBIwhiPiApiLpQ18DoqJCvC2S+c9ZVBa9jVuVx5St7sWSRN+NL+2t4bndVWbarmIOsqYr1J70d21KFhWu3UHf0mor47s9HlO9ddedsXrMX2gliDczTzfKscVy2KOdsxPpu3bIzNLBWEme07pxNHdmuqrOGAcHCA8utGOjCioGuBLEG2iBp4P7LVMlEq6wfCDJn5cxaZeAybUkhyDLGJOesvNYWMDicq8I5m9Ocs4gYwEz6nHVTNBPn7I033oiLLroIvb29WRRHEARBEATRFINjJbywZwyccyzp78DRK/tmukrTi+5AqP/lAJ7fM4rB0ZJxsaWLOnHUitnXjotq986I9kaC34wQ5yrTCbqREgRBzA0S98iPcRg2Qjhm/fxPHgheL39ZF/7Pe7zRpqbKgaD4yf93AIDnnAU8cXZoLGhDWJZXcRFr0Gg7bMk5K4TLQs5GpeomiiXo7DA7Z1mKWAMl1SDC0RrMkKjIelnqzJWaiz2HJnHE8h5pQLDGsQZMs85GdaePQomdAIPF1HqpAVFqtRplGHOu7hKuTwy1P70fbM1h7a0rVClpWtg5W3OyuavrpXDODbEG6nT9t/gSWyMTcfa1r30tcrkcTj/9dGzatAmbNm0isZYgCIIgiGnnyReHsX94CgDw0sEJHL60B/ncwukoFNk9z+V45LlDkcvtOjSBI5b1NHROTDuN+h22e/XtiTlbEDR76GbZGUgQBDEtJHFWKt9TOWfrQpnvnGW+4CWLnZ1FC++4vA//fuOo/5sQdLs6NOesyJxNeHOUmxfCrVjIW8BUsuW7OtT66PWIhae5IzEkDTKKOwbCKRzVs8m0rL4tjZpk4cG0lIlhAZ+F1yvKaJw5qwnwSq5utJAsu7WDekpOVG1+eVrO9lb48NMVnL8BKORjq5galwNVPdZAqpAjZfUqdUR72oSZZc46joOtW7di69at+Md//EfYtk1iLUEQBEEQ00pV60bmuC7yCyDFKXAXmhvq8sPBuqMX+9MdzvH4jqH6qLRBRtpsIXicqvs9pj2lgSTZpml212nnbJpug+G8WoIgiNlOtLNSkRRD9/cUztm6oOkPziQ5Z/WYgD+8vB8/+MWoL6YJ52NHwVaux0LL852zDS68oks9lwSxQr1iSa7zXR0MU5XmYg2MRImmxlnVzNboBNcAIUpG55aG22tKl3nwxsc4xk2tZ9iK2UPxGPXj29A563X0l77H10Xgu7XdYB5ZhFYyZrU65KRjfcdWhkvObe3mrp9ninNWxBpIGxI4Zw07UherMyATcbZQKKBSqSi/1Wo1EmsJgiAIgphW9BGA9e/zFn9EWTNufbplMaw5vN//vea4eHzHULtr1wLxSXHEbCZZV86kJBIifG2WzhCCIOYG8Vcr/RVlQJTR0dTFX8QaCKHVdQNxtpBXC7IthoFeG4dGvBlErEE+Zytl6wOCJbnsMsbAOfcdiYWcyJyNX9hiQEcBmKqEnbMsgY3RJJBGRub4Lwgjus5I64t7X+xnl0YIfCbHqKVVkKX0FuhZrqFzhIXPI1Gv1M5ZqNsQ5QwWbm3HDc4/tZ7hZQRHrABWLOHYd4jh6R0Mm87kIXG+FeTM2VjnrFmbzZxMNm1kZASbN2/G7bffjttvvx2bN29GuVxWTmgSawmCIAiCaDeu1nIMjQg8T4naSn+whvoM9myLLWhEUmvGdKx+YZxKmdGicTbOn0MQBDHvMN7dYi5/US+stA5EAAIXpy1FEdRq3mc9wxUABvosX5wVImzetpTqiLLS9W5QKeSTOWe7OhmsekV0cda2uK87DY+X8eTO4dCLeRF3laRW/rcksUYxTRJ9mSSxFZaqzaZyR0Ofn5l1Z5PQCKiuVeFwltFFfy7lzHIeXVc51kBsn+7wFegCcT7H8JbLXPzwpjz2D7l4fhfDCUc13ybQXwK4nKNW40o95bq5vjhrDjbIunWSiThbLBaxceNGbNy4EZ/5zGdQLpdbFmsvv/zyLKpGEARBEMQCIuScXei6juYk1Bu+8resu2dlge52CWIapqmyysPZLNxBc4D0crq6RNqkQGB2nssEQRBGYq5X8j0wPCCYeZlKNVygEGCFg9ZzzoYzZwWL+2wAVQCe+OnNZyn3XqY5Z5NltKqeQ+GcNS0sC4S9nSwYuEp3zkqxBs/tGcXewckENQnu6VH3lriX3qH2gGFmzyHs4oW9Y2JBtRzD91DkQKNUg5gymWEG4zos4ZyV18vghMTY+HtrhAFZeSFgjHhQYg20Murn/eknFnHL5ikcHAJOOCq6Dg0xCM6+c7Z+Lsp1eHb3SLi+QGCdzbixkaEpOKBVsfbLX/4yauJVDkEQBEEQREIWaqxBo8GAxfRQptgcM9JONwvj7GkTTe68lgR4Op8JgphjJL3S6R3SozJCTeKscJtapliDSHFWXcYTZ+X1B2UBSLQhlgXACb77ztmIeZ36vF0dti8q6mO8WpKI69SFtiOW9WDpIm8EsQNDU3jp4ER4BVFv/iQ3qD9rVKyBiC4wbAHnwM79434PrlzDkcvUGALOozNno251yjnCEDnigiyR+7EGEW5WGXk7PeNsRD6B9F3EGoxPBj/aVmg2pS46HfXoDdGkj9s3cehHSR6nIidVqquYw2S5hnLVm95dVEciC/fwyYa2iLM6ScRakVk7bU4IgiAIgiDmHXqsgf59/hPVPc/snJ3tapY+ynG7GsQxNTB8IpJgdMg0U0620bUEQRCzkoZjP4VchWnEWab85XKsQT5cjizOiszZnNZ+sCRHJJCsd0lvZx6HqmUs6ini6JW9fs6nvOyvHpzA4j4bFgt0XGYFYrCucXr185YXQuiS/g4cudyLzKzVXF+cVXoLibK1OprbGTw0vRGcc4xNVv3vR67oiZ2fMTVjdvehSf94hcqOqIuliZ6hnFsmuu6H21dy+1B3OAPe9oSa1NLL1PDLAnHOheuvOHzlWIOIc9qWBxVrBa3+h0bLXh0QxH4xxrBx/SqMTHj6pGUxDPQWW1xxMqZFnNUpl8sYGxvD2NgYRkdHUa1WGy9EEARBEATRAPESXDQ8F4pzVhBq1gpni3DOxsQazGr5UW+wT1eqgbyeWbx7CA8/Y3mG60EQBJEJsoCWNNagFh1rIAQolwMu9z43cs7mLG+5qC73aQQz4ZQ9cnkPjlrRiz2HPNFU3Gt37qvib797KLScLDTmbHX7Cvngmi/afKoLNMbdaSQsz0a95w9egIencQ5MlDyda/2aJegoqNKbaYAwud7P7BpJUdeIMqNOEkmdtQzOWZNIqm9iaJMjBjwzCczqIYk4VtJ8cm5tK+h1fn7PqFd+zlLWXcjbWLaoM7KcoIdPa/XRmRZxdmxsDHfeeSduv/123HbbbXj44YfhantWdsyuW7duOqpFEARBEMQ8Qzhl87aFSs1dEOKs0usoIrVAzBHlSpDnmV2omXB+g3hmKkNMAyz8XJyeBeeYJwhizpIgc9Yb3ClZrEG5Eh1rYEuxBp5YWEPeoAgt7gssmLbNQ3mzgBqRACS87EbNU/99ZNysvjEmZc5qztlCToo1qH+QBz+NbPc0qm+K24hpVpdzTJY8e3JXR94wh4pJSO3vLmB0opIi+iIgZ1sGQTg8n3Drqs7ZcNmm4yvfrqPMAbYhW0HNnA0+hpMfPAevreUbN4+3EUet6MHoZNU/p49YFu9qni7aIs6OjY3hrrvuwm233Ybbb78d27dvV8RY/R/22rVr/YHANm3ahKVLl7ajWgRBEARBzGM4574Ym6uLs/qABvMdgz/B++M/sDRcYFYRPnrTH2wgWFhnUusEXSabO8m49iFJOSTeEwQx1wgGpoq7xsW7D2VMztkg1qC+Ts78zFnTgGBnntyJ9ccXMThaw9pjHRRyVmiAVd8524SIGQzyKXo7qLEEOgzB/rE152xe0j1952wDoVFeZ5S7NElMQ1wUBecck+W6OFtMILuxcEbssYf1YWl/B0Ynq7j/8X0N188YwxHLe7Bz/zhsy8Kxq/rw7O4RlCqOWEVoObFfGzlnQzFh2veoIQ1sg3PWjsi31deru29b9VuIKh+2pBunH9fVdDlMSe3NjkzEWSHGigzZ7du3w3GCpGcSYwmCIAiCaDdycyOXs4Dywog1ULYwottj5IBgkQXNErRgtbguhG1ZvbKe2biDZjNaoF1i9LCNZsshCIKYQxivcUHvkVYyZ23fORuIXNW6iFswZM72dFn4pz9fgV0HJ7Dlyf31kew1MU53zhprE4/eRpkqR+cHCL1Vf8nsOWdVcdcO5adKBSWskwxvMN3EVMXx65NEnJWjGwS2xdDVkUdXRx6WxRK1aXOaa/iwJd1+932x/XKmbJDlKy0XGp8A4Nq6OdTjF/VywZg5q2Tjxgjpom5a5qzRqTud+NudbbswE3F28eLFvjPWVMGTTz5ZEWOXLVuWxWoJgiAIgiB85Lf6ef8t+wIQ1KJTDUKzxGfOzj5Cg17MpDNyAZxKswHfG93Ev93Zfj4TBEGkQREFtWkG/QxAIM4u6uUYHpPFuEAoc91gPpNzVlCt22vzth1yLdqaOJso10CbRV/zVMncb/2PX78InI0BACxmyJzloi5h56y8n+T1NRpsMknmvJ9zbpg+MeXlzXYWc0axM1SWoSKRyyWIwtDzWuXf1FgD4ZxFaD4Zsy4s9WaKqKpt2AZ5W1M5Z/1zDS3d8FttK7SrrZGJOOs4Dhhj4JyDMYaTTz4ZGzduJDGWIAiCIIhpQ3YU5OpPDQvOOasPDiHm4eEHlvhyZgkzXilu+EQkISSsJ0VbIFU5mguLIAhithN7uZIugEkHexKia0cB+OPf68e+QQdHrfRkH0sSuapej/sG4qxbn8cKXVf1AcGSRc5qUQIi1qC+8KTmnO3v5fjMe/tw+vEdeOhpT5xlWqyBnFPa0Dmb4EYSzCLf/3WZXO/REd56ESXQkTdYR7WSoqplEjbltSVx8eYNoa+mXWI1yOnVzQ76+RAyvWovBGTkzeoq5mBbDI7L0dddMBYavAhoLU6Ap9lxScrLpJSATDNnGWOwLAudnZ3o6upCd3c3Ojo6slwFQRAEQRCEETmrzK631heANovY5mGaWINZSNCOrrt/xO8zEmtApKLJfSf5cNQPc+vUJQiCSEbS5JaI2CIdIc7aNvB7m3pQlMRBObvTjzWIE2cdWZwNIhbkLu3NtrMcl+OW+6bw2x0MZ6/z1jNVVp2zhZwXsQBIIqthfeKnNJmz/vSIH6I2y1ScmPfUY5dg76EJHBgp+W3S8CBXCQtF/Av1yAW18yknibPBvggLsY0yZ029WZRYioiq5gyZs/I+KeRtXHrWEShVHPR2qgOnBdvg/W29TR+cwy0RaqhkQ2birDhYruti27Zt2LZtG7785S/Dtm2cfvrpePnLX45NmzbhoosuQk/P7BgNjSAIgiCI+YN4q29bTBpFeP6ra3J7OTwgQ92VUv8eijWQF5iVu0q3ZsygQjcr988coMVDlk6bbdMTE0EQRLuJucgxw+Sol61iQLCcHV7GltpGcZmzAtk5u+vghFcX5rU7RDuLc+a1Q1KmGmx/qox//fk4AAvlCsfrLwKmSmoheUmtEptrGjQslDnbQGj0ltEKDtYUqqyiS8Z0/2eAL4aLukQ5nP0FtEQA2RsaJc7yGJHRr6ofayCJs37mrLQ+EWugZM6Gy9V3u+g1r5etYypL36xCzkbBZLH1B4Hz/jrm1IvEZNUy0NvWWZGJOLtnzx5/MLDbb78dTz75pD+tVqth69at2Lp1K770pS/Btm2cccYZfuTBRRddhO7u7iyqQRAEQRDEAsZ3TDDmN8YXgjibCB5+YAnNMl11SUG0c3V6apsgco6IIBBVm1Rnm7DOTrezmiAIolXiLldx06JEv3IlcM7ql005u1OIuPkYRUiIszmb4bEXBgF4Ip7r8Hr2a1Beosuu5OocHgsGkJ8seX915+zgSCAkmtp1TMufdQ0RTtGpBuYaN3rFZ9zryktyISS6Sr2jyuKIPpZ2lO02xT3OG8xNWqG2PvGpo2CblxGrbNCeVvaz8jm8bWnfs+v5xs3e4uWc4adeGsaL+8bAARy5vAcnHjHQTIlN1sRMJuLsihUr8Ja3vAVvectbAAD79u2LFWsffPBBPPjgg/jiF7+IXC7ni7Uvf/nLceGFF6KrqyuLahEEQRAEsYCQG+WiYb4QBgRTnLP6RM0AEt9FbvbuK30Qi2mr6ezdJXOA5naeeJCruS72DU6iXM/tGx4vo5i3sKS/MzKHj6IPCIKYeyTrah3OnDXPV/Wdszz0csyWuodXEw0IJsTZQKw7+cgB/Ob5QTBJv0v9HpwBpUqwkMi/ndScs0ev4v4WmJyz4lbAudfeE+2hyMxZiSwSc3QHJWNS3IPvnE1dqF9g5L1OnjeEumXKgGCGxYR4vHxRJ848cTlqdXvq3sEppdRQ5iyCfct5uv2YYHw0r56++zd4CZAVz+8Z9XOBn9s9mkqcVeIcMiTTzFlBGrG2Wq3igQcewAMPPIAvfOELyOVyOPPMM3HPPfe0o2rENFCpOpgo1ZDPWRidqPi/F/IWlvR1xFv7CYIgCKJJRKPNsoIGran727wmYqRbP3M2aYt4tjNdmbMzsdL5Qou76+mXRvA0Rvzvz+0ZxXN7RnHcqn6sO2axcRkKNSAIYs6h90M3Tksea1CWMmd1LMmBGDhnG4uzskgoBm2So0Rd15xJqiPLhmWTOFt3zr7zNf0YHBvHkgHHb9cwo3O2Xi7nyu+JMmcbWGP1zfG78TfSS5na/kzS7oqqY3SsQTS+O7T+PWcYEMy0bsYYDl/q9WjfXY+wkIlvTnNlI0LnqqUKq2nbov5LBb+MmJDbGIJ9w5TjO1ueFdoizuqkFWs3b948HdUi2sQvt77kh4frnHnCMhy+jDKHCYIgiOxZuLEG0oNKg3lMD3PCpDEbTca6syVmPJA214AEv7RExvk1QJ9djOJcyFmo1FxMVWpZVI8gCGJWkCTWwCQKNhwQzArPk5cciEIQjRVnnbA46w8gJTtnkzoag6BX37UIBHWZKnvTF/dZOHlNDnsOVcLOWS6LsHIdzOKs3O4xxeybcmPVOaKmS8tKop/u8I2NNfBdsqqwKdYcF0Wl1yVqYl4ZEEx1+sq/NSo4JL5LbtmwVKp+u+6qw/HmT+6CuH0nbReI2Wy/N5y0wqbQlOs6jstDGbpJ6pU1ScaOy5zly5djzZo1WLNmDY499lh0dnaCMUaOynmCLswu6SuimPdOtckyNagJgiCI9uAs1FgD+Uvo4S3ocgZEjBo8m5tfzSp8Wa2eQmenH+1Qdxc9L8mKAS/2LNE/aTpWBEG0kXLVwe3bd+HWLTuxd3AykzLNtznZJarHGpjvi0KcNY2v5Gd3cpZqQDCr3niwpfYVWJD56vJkl115Htk5WxPibMlbX2fRUvJBAXPmrNgeDnkALl2Qba79oN9rguMcLo9Lop9Yt5Mi1iCtczbNPS5nyI+VCzCtwrTWcKyBVglpoWJBXWdft43F/ZJgblhptebgmV0jmJL1ovqO0TNnWyUsUac0c8ylWAMdzjm2bduG22+/HbfddhvuvvtujI2NTceqiRnGthguPHUVtj9zADv2jafPoyEIgiCIhCjOWSvciJ+3xGXOarPEOThmJ2oWn5+ftwAOK1HH73KZYFbfFUQnCEEQ7WNwtISRenzfroPjWLm4/WPmhGMNzPNN1AXOQt7QE0HKIhDCUiHCOcs5R7XmuVtFm0oR1bhXB4fXRbM02hYzZ84K52xnh6GXj+ZI9eoTbIsb4VRVv4a3PzxopUm6Ax54Yj9+9/yjDXOa6xlVH3V5zycrzyGvNypzVnFUR02r/5VjDUSerDJWgXHALkO5huMrO4flJS46dVVoXjlmw7RZjzx3CC8dmMBzu0dw6VlHKtugxxo0e4eXE0T0MhyXG6NATDQ90GkD2iLOcs7x0EMP4bbbbsPtt9+Ou+++G6Ojo8p0EyeeeCI2bdqETZs2taNaxAwgZ5gAybJoCIIgCKIZxFt925JiDRbAfSduC5k2k8mtIHehm22E6uVrs9Nf49m6j2Yreu5dUsxnqFJyU/UhCILIikyvQjGFxV1HoxyhoxOegtVRDC8oDw4lyEcoQq7LfWOV5TsYg277HJ446rj1zNmIbZgq13DvY3tRrbkoV4MoA5NzdrIuznYVLZQd4Txlyl/XMCAYwP3eU7qgGS2OmneuHlWgLKH/ZnKdJnQ4K8tHzBK9aNxJo36V94fjhJdLmGoQDHCGcBSWXqqprSlH35rE2QPDJQDAlBR3ISoilo1IzkyOLMhrlW4mdzbrtmgm4qwQY0WG7N13342RkRFluglZjN20aRNWrFiRRXWIWYSfSbOAHpIJgiCImcGVBl9YUM5ZidBDgCZmml0YDDMjdyZghivFI78QiUmtzurnsOqeTvMygiAIoi3IwlSL15tE6T3McGlsJM4W6gtK2Jo429PJQl3QBZVaoIQxqQ3BpKuxpWSBmnfEodESxqeq+ub4A5cBgNBsg1gDhtJkMK/81xPRhJO3XhOutgGVdUlfo1pAJkxb43JudOLKblX9sCQaECyiFlHHOO6ci3PV1tykztlwua78psAkXDdYXo7VMk03tU8D9698njVP3OKpxNkkDZImyEScXbJkCYmxhJHgTZf3nWeUE0IQBEEQOnIXMjGIQlb5VLOZuEZ6uBtkhEVCt0HMEvSHVr89PF1VjXGGEI3QO1c2B0tTjOToIgiCmFvEX+R0AS9K8xvznbM8VKKt6bCrluUj1yfGkcnnrMBBazGlmqIOcbEGxvs102MNvAGZRKxBV4eFoYlgXm9dwUv3k47heOJ5C+ecGpQh2oC6yMci1NmG9wlDxfUX/iGXLQu3sxJos03H6kdIt5Hz15JaT7VB1DgP7w7OpX2rxRqYxGbLcN4o02N2lJVV5iwXbmxzrEFS2qTNZiPODg8PgzEWEmVJjCXEP6SFNDALQRAEMTOIe4znnPV+cxbEfafxNnL5wWpOoqmz0waP+Ew0ollpNjS//5BIyitBELODbPuaqL0DTDDDDJHO2cmYWANdnF0aLQfV6s7ZQs5ShE/5JaksmkXtEfO+YihXAqWNc4ZqDZiUnLPc3y+q2cvlHJecC1xwOkdPF68vz31xTW/nRDZ7Im5SccJblIAX55xNPSBZmlMrYdGrl3Vjz6FJHLWiN9FK5GItxuBw7mt9JuOsfowbOmcNZu3onl1Azu8NF1vthsQ7Z2fezZFZ5iznnMRYIgTFGhAEQRDThbNABwSLzfbUHRwx3cZmM3q3xhkwzhJpycY465Mm1oCamwRBtJU2xBo0mqZfSqM0v3Ep1iC0jMXAGAfn3pRVy6LlIBFrkLMtRfiUxUbRpDBEmQbbYJimDwgGeMKsMHYWCyxo24h3s/J6LWCgLxhIjCOhc1aul5gepc4at0WNNQg+yqUld87qbZtMMLQJzzh+GfhxQRuw0SmrbCPz5NhAx/G+x72gMMqssnPWCquzeuSGvIxle+vyBwRr8d8cA8KZs3Encahe9f2YcWMjE3H22muvJTGWMBKKNaDGMkEQBNEmlMzZhfhSMCKxAJCcszEOjtm2p5RGLwt9mKZKSB9n2w6ap0SeooliDebCqwaCIGYDrstRqbnoKCQcol0i09tB3IssKdtHFxlN9/NqjfuDanUUzauzWCCmHiY5ZznnGJmo+Pe6kYkyAC/WQIizNlPlSD/z1Y2+R5oELIsxZUAwIHD8AkAxHy3U+d+VdQQ9pcKZsxH3hUY3dcNkOe82at6kzlnOJXkz5b0rySkjT2SMqatIcQJbuo4jWWejXoiattliwYImwdrsnPXIzDkrbYP4aFsMjstnhZkjE3H2LW95SxbFEPMQ8W9sQT4kEwRBENOKuMfYFhaUc1YQ37Q3P7TMFXx3yTRbIxfO2ZM9GRtnUw72RUeOIIh47n9iH/YPTeHlpx+Ovu5CCyW183oT4040XFxF3izAUcyHRTIGT1AVDlU51uDhZw9hx76xUJlqrIGl5KyqWaDJ94PFEBJng7oD+VzYOdsI1yAgi3WZkHRvBeF8NW2Nq2erMnVexsL7PKrd9dTOYdSccKRF5F7MMGWpsXOWhT67XKtrhE7tLRP+TTbLmsXZsJtWzBZElUXXOQ2yuzlne+JsM5mzWWMemo8gMiJwztYvXAvoIZkgCIKYXsQbdYsxZeCI+U6c80IaqwFA+KHFXNDsIGlXT2J+ER5IRH1CT9KVks4PgiAasX9oCgDw/J7R1MvK7wdbvd4keZHFkMyR6efNFlQxTEb+fcWSQJwdn6oCAAp5C13FHLqKOfR05nHk8l7FlSp7Z+VYg8jMWcMEy2IoVTXn7IQYgEwImmrmbKlSi1hDfOasvJ8SdUOPeROYpE2pH5aoHktP7Bw2rDMp0cp1bNSVtngU8rLBoG9qwbI2y9E4h1k+LI0yZ7m2EbYt6hBf70Zwg8ItROFUmbNt6hWeWeYsQZgIMme97wvgGZkgCIKYIeQBwUQjb0H02NDdDDEYM2c1AXfWEBe2NwPMuv0z22l2h8m5dIauvG1dN0EQC45S1ZnpKkSS9jY4OuFtS7EYnUNfkzTOxX2ySuatbf2xS7Fqabey2DO7RgDUBTTJLeqLZg4ir7u6IOq6wJ4DDkbG1f0+Vq97IS/yPEWdvT9OA+0sOnNWrkv4s76f4jrouFy1zpr2sS7GJrmFJbrLSXECiXqmtBJjJd+HLfV4hL3Y4lPYbRtVHZNzVm6f1hyOfC4o0fbrwFoTRA07TpQ9G5yzbRNn77//ftx444144IEH8PTTT2NoaAjj4+Po6enBwMAAjj/+eJxzzjl4zWteg7POOqtd1SBmGPFmzlpID8kEQRBEU3DO/S5ezVCrt9zlASscl6NaC7foc3Zzoo/jug3f3Ns2i812bYWa44YapvL26dvqD3AgMmdb6DPlDYQxfSqp+lAqeuIYJrZr/fqOpiZMKoJungw1x03sYpfdK5YlDwCS3LFEh4ogiKTEOTKjUBx4WVlnG5Hg/rv9KS8ntrOAyBx6xw0mdBSCRoEuhsqI67LinOVejJQ3PcY5q32//nYLO/YM+t9ti8Nxme/69cVZrTpxtwDH5ajUHL+OMnJ7zHgbStGscV2uzq45KBkLH6Yk7cFmjbNNTEw0j0lo1XUcZXC0BPfnRrEG8n5yXBd5WP7OtK2g/LQRGjLyOSWqLAYiSyPOiiOmL+G63H8WsS0G207X6M5cnP3Vr36Fj3/843j44Yf93+TG1NDQEIaGhvD888/jlltuwec+9zls2LABX/ziF/Hyl7886+oQM0wo1oDEWYIgCCKCzb/dh/3DUy2XY7HAOVuqOLjx/h2heVYMdOLctStTlXtgeAqbH9/XUGQq5m28YsPhKOTTDzISx87943jo6QORTVLH5cZtBYIGpPkhQbJjRLDr4AS2P3MAZ56wHCsWdyWtcotE12lmWhPUhkmHt78OjkzhkecONeV2qTkco5MVAMALe8ekUgmCILKhXGnNOZtZrIHp/iwV3kjAe3FvFd+7wXO4Rg0G1iyOwZXKIWWButG106/9+wfV7x1FYGIqyJwt5DSnZkSkjby7xP0BaOBcVSoT7z81HdfGRrPwi/9E77QTzGQzBrdeqySxT3ElNtoMk8s1TsfhDeoEqOKs8VSXytezeIWACmTfEzuINUhRsEGUrtQc/HrbLpSrwUuC89auQCGF8p5p5uxHP/pRXHrppXj44Yf9nRt1EOXp27Ztw+/8zu/gYx/7WJbVIWYB4VgDalITBEEQYTjnmQiztsWwtL8D3Z1eVloUB0dKqcseGisncv+Vq44vKGXJwZGplh4Cezvz6OoI75OgjRld+pYn96PmcGx+fF8LNUiJsTrTZ53V10AtmOYYHi9nm8sWU5bv9qGDRRBEQpqKNcj0GtP4RaRwZK4Y6AQAHLG8JzTvvsHAAbzhZNeQ362S094fx4l6rpTnKgtrItbASRNroM3XWReSReascM6GCkywz22LYcWA+gKZRThnG4qYplgDrfJM687DYHDOJhiIlXOOUqUW6eJ2XBcbjl+KYt7CWScu9+vRrs5McrGBc1Z8r9c5bZlyHIQpK1f6XNMyLGQDqhvj0m6ELvgDknM2Rc890xk6Nln1hVmvnhxDY+VU9cvMOfvnf/7n+OpXvwog2FjLsnDGGWfgtNNOw7Jly9Dd3Y2JiQkcOHAAjzzyCLZt2wbHccAYA+ccX/nKV+C6Lv7pn/4pq2oRM4w474MBwWawMgRBEMSsRW67X3bWESjoTw0JkUfKfcWGw0Oi0GS5hl9te6mphp1Y5sjlPVi/Zqlxntsf3oWxyWpbMtbFC861Rw1gzap+//fxqSpu274LOZvh1WcfpSwzOFbCPY/uRVcxh5dvONzszJm+pIJUKLEGTP07Le96SeBrCf0YHXtYH045ZnHD5R7fMYSn6/mGPZ15WAwYnawmW6n/0EgHjyCIaGTBkHPv/pomjmgmrjCMMZy7dmVkxFC15tXq+CPyOHKl0/De3t8Toc4akJ2zirClxBpEmPL0sjQtXLh8x+qxBkUtc1asTgxYJtDbWT2debwiqp3j16WxOhsnPnrtsHhhUT+PkpxXE6Uabn5wp3FapergV9teQqUeW7Xt6QPxLs8sTk6pzvoAu0z0tuLq2GmNtlIZEMw4kJnsnI0WZxtlD8cT3jn+GBUtNtzF8j2deQz0FLHzwHjqMjIRZ3/1q1/hq1/9qi+ydnV14a/+6q/wx3/8x1i+fHnkcvv378c111yDq666ChMTE+Cc42tf+xpe+9rX4pWvfGUWVSNmmMA5S5mzBEEQRDTy/SFnW4mcBo1gjIUdDC0ZL4VTgUXWL6tGngmRvWlb6vp94RLheqnzzVIVdpYSOoLUhGkSEXGV7BxUXFlWePAPOgwEQbSKLm6VKw46i01KIy1elKIGpooi6jpaj1xFPhddnrxoX7faiTouXkEebEueKouzUZgGBJMpFry/IyHnbL0+AB58Mhx75WgrTXKP4Y21WYTnCHBdruxDps/KDAOMNdn26ih44vnoZMUXZoHG3e9lt3XkPCliDZh/jNUTlWufG5XZqFkvL+/HGohlGANjHJwz7/xp8t+cLPiLInL1WINaowElJExGAfEc4w2a11z9Mok1+OQnP+l/XrNmDR566CF86lOfihVmAWD58uX43//7f+Ohhx7CmjVrfHH3r//6r7OoFjELEA+FwYBgM1kbgiAIYrYii5lZCLORtHlEKd1hkCWi4Re5f4ymWJHVFs30BQWkQ270Mu3v9NSVx3wj2ofk2LGYwY1FR4IgiNbQ79GpDUSKyJfRNcmYwykmNW4XCUHLTtiG0sXZuGurI8UaKCPd297vjhMjzsn7igMuV+uXz3kzbH3ci5vSM2fBGHYfnAwVqzezkjhUEw0sGROPk6Rpp4uxSZu0F556GM5bF4yFcMLqRQCgCLOhdTXd9Sl+Q+RSQxm+ShlSq4w1KLPBjpAPTSC8h13aKTTUeOrrE7EGWTln5UHz0pbYsji7c+dObNmyBYwxFItF/OxnP8Nxxx2Xqow1a9bgpz/9KQoF77XJli1bsHOn2dZNzC3C3RCpQU0QBEGEkZ0AaboWpiUD42xsUzh4GdkO56xZnG3s/pDnmqPo58Q0bA41WVqDax+SPkQqztkGA+RELUvHjiCIONKNzB5m+qJTkttqhXNWpEKZm1LBj/09mnM2ZlVKrEFKwUzeU6b5Ogrqd+Gc9fdxwgt6kqajcTywUEHRk12Xa3ms6rwM4f2X1DlrMYZCLjgmhbz3uVLPMe3rMoyjYCo6wT03zdm7pK/DvFIpyoBzADx+Oxs6Z6Va6c5Zzrk/oFgW3gfZtiBeZqS5Jpj2rd9Gb+EZpmVx9t577/U/v/71r8fatWubKmft2rX4vd/7PWO5xNyFYg0IgiCIJCjdgWYp/h0spopWE428pDRs+Jmcs0nEqtlqnZUqFPRsa+wEblttZt3+meWI5+oU4oKOZbPQwabDQBBEq+jPpK1c37O6JrXa+qnVM2dzueiSGICBPm++y84NDyoWVZGol8OWkjlrRjZnyUkE/+uNi/APH1yK9SeqS+a1zNmkOyaJCCoLgOJzmtgB1+VKfUoVRxTmE8qcTdiutSwo4qzYfuGc7esuhpYxa7ON77mNYw2ChTsKtjEvXo41UNYbgSUpj8Z6y7EGBhXfkl4ENPtvTnZji492veBU7XaD8dAx9W5LWdGWM2f37QtG7T3vvPNaKuu8887DD3/4QwDA3r17WyqLmB2If9ji33dmNnSCIAhiXpHFG+dUtEnhaWesgbHhB6Qx1swp4h8eZkSenYF1zmVU50sz2NIDVBJm7XsGgiBmFS07Z5M4MJOWlWCeJJfRqog18Mf5MmYd4S2XuRgcBc49RXdERq8rcM5ayjVduHQdB5E3bSWXU9IC3rCpF2Ace4aBk4918fhznkhW1DJnkwrnSTTQJGUpblANXdQ/MDyFk44cUPJ6I8c6aIDFGAr5YJA21+VwXI7JUg0AUMxnkkiaCCVzljFtMC/pc/1vkkOk7AdjhIfBOSuVX08fyGRAMHn1ItagXHUwOlFJVIrp+uHnMkvnQNpLQ8vibKlU8j93dXW1VFZnZ6f/uVwut1QWMTsQ/wjFwyrFGhAEQRAmGuapZky77kb2NMQa6F29w/5ShH6Jq06SPW4xNqO9X4JBz+pQrMGsp9ndJ5/exutBbMEkzxIE0Zh2vEBtGsnNFzUpCY4fa6Cao3SKBeCwpdHVMJYdMSBYIuesUg78ulkWwOtd4YtSj30/1iDlTTiRc5aHP6fp/B+ONQjfc/R6JHXmMsaU3mNVx8U9j+7B0Jini+VztmGhREWnRi9WNk5EtSsbRRdZDbRlNf5COzhcdc42Czd8Eft8aKyM27bvaro8UWWvns21RVoWZ+VBv5566qmWynr66af9z8uWLWupLGJ2EDhnKdaAIAiCiEY0tho13lqlFRdfkhGVZ2RAsFaV1wSDhlkW4DpJysoOY32adCMQM0fakchlbIuFTu/YhI4kMR4EQSx4dOdbWiEwQ+NsfAkpuvZXRaxBTDtKLobrxcpD2depOS72HJpAqeK5N70BwYLptiTORiHvW9HWy9mePiC6whdkcVaPZUi4g5OJs4bC9MVi2hleD6b49YSds0ljDZjvvOUcqNZcX5gFvMiDc05egfsf3xdTSqpTJhJ5XzKmFRaRBd8w1sDguFWKkB3WXDhcg/apkjnbdK5Bff1SYtLS/k70dxeCiIoGuJyjKgZpU1zhrZtMWhZnTzzxRP/zj370I3zuc5/zB/ZKQ6VSwY9+9CP/+0knndRq1YhZgDg5xV9qLBMEQRAmpj3WoBVi6jijA4IZezCmeXsfPY93XKb5Jj7jbQYe841oiL7DEv/TDmb0znXa8wRBZEu2L1BbjEio/2219eOIWIO6uNmwPE1nNNXjmV0jeHLnsP89Z6vd9m0l1iBiNdLvji/OqnUsGJ2zol5qwRYzDwrVbKxBhDbrk89ZvhjHXcMMUrkM4XZs0mat3OPY4RwTpaoyvZC3sXJxFy4+bRXufGR3fX0xvUsyak4zaGKtvxqeahyARvtBFfH1EnnQMy0T52xQmWLewqbTD09cxsh4Gbc/vFsrT32OabaXV8v+lHPPPRdLl3q++J07d+IjH/lIU+V85CMfwYsvvggAWLp0Kc4999xWq0bMAoKLjPeXnLPEfKRaczE8Xp5dXaQIYo4RmaeaOa2U3/jfuJVB4zGKVgTsRFenOAPuDIjmcnX8njhi2kzEGtAlPhVNxxpIn80DBNKBIAiiNULO2ZTLKw7MNl6SAp2t8T24Wjf++dGkpkXiijFsR7leaE9nHscd3o+BnqJ2jfb+Jo01EG2jvBCQ6/d2ozgrltcKztlmCStJO0XWIuTBobSSlLqecsxinLC6H4DXVmWhOdUf9HokHhDMH0jd+z4xpYmzdUt0Ps4anRHqJjBzu1PPCGjw70DpGdegvMA5W5+kxRq0+k+ulRatejwlQVl+jmmyl1fLR5Yxhj/90z/1L1BXX301rrzySuzalSyvYdeuXXjTm96Eq6++WilvJh4CiOwJxRqQeEXMMxyX45dbd+KOh3fjwSf2z3R1CGLOMl3OWaVJ1WQ3xvhYA+9vW5yzEQJ2bLfxBMbZJAMXTFcWsIrJ4kLtw7lGahOPNKNlhQcEi401SDITQRALntAzaQvXjJYvNxldr2r1WAPbd6WGr7pmWUn9brrNrl7ajXVHLw5pNIpzNgJlRHsp1kBGFmeLDTJncxHiZPrmgSoAhqbyoF0avHiPP1jMUI+k7VpdNxmvDwQmEOJsQRoYzNTWzCTWQFqaMW2bpDZjmntuo/3gykKnnjmL4EVAs+YH5XzSxOc02NLLAdUV3vpzTCay+1/+5V/i+OOP979fd911OP744/HGN74R11xzDTZv3oxnn30We/bswbPPPovNmzfjW9/6Ft74xjfi+OOPx09+8hN/Z51wwgn4y7/8yyyqNW1cf/31uPLKK3H00Uejo6MDy5cvx/nnn48vfvGLGB0dnZY6vOtd76pnlHj//c3f/M20rLcR+hsgMs4S841K1UGl3tVlbCrZCI8EQYQRDUyzUy5D2lx80gZ8M4iHmshdZGgQpgk1iGMmtNn4rNL2NyjIOJsN4c6Q8cgD3uVsK92BSNHFkiCIhYtptPWZIk4UTXM1EyPcm8aNCoiTZw1rNyh9skAbOGdZ9AtvJZfT+5vLqeJfMR/MlM/rL6DVcvMRztkkophcVtTWi2KEWGixwDkaEkMNL7fDA4I1rJa3Hi0O0s81rVPIewdW3n59Hq8yGcizTP1oGhBM/jI0Xmmo88jOWWOfGINz1rS81xZu7d9vK01a+TnFFMUwo5mzANDR0YFbb70VF198MV588UUwxlAqlfDTn/4UP/3pT2OX5TzIqjjqqKNw6623olgsZlGttjM+Po63ve1tuP7665XfDxw4gAMHDuC+++7D17/+dfz4xz9ua0zDTTfdhH/7t39rW/mt4I+uTAOCEQsAOrsJonmyaNS0nQT/yIWw1M4BwXQBmzdwf2TBjB4XFv44LdfbkChIV/mkqF1+0+23VUu7MTxRAcBxxLIe7Ng7lnhZ6ZEp1ToJglhY6Pfo9LEG2dUlfkXJZxWxBkKcbSgK6uZh/4VoWMDVXbgM2iBNcQOCSSvSM2dF2QVJnBUDggWZsyrRsQbRdRAoh73BvuX+QLUJnLOSwt70gGAsfn4RZ9Coh3m82J8M/QxgUhuQSQ0xUZeJUhUvHRyPLbPRgL+KOOuq6+Jccs42+W9PPfTBt7S7SW6Dy3WR2+jCSZ72OpFZYMWRRx6JrVu34s1vfnPo7QbnPPSfPv0tb3kLtmzZgtWrV2dVpbbiOA6uvPJKX5hdsWIFPvWpT+E//uM/8M///M+44IILAHg5vJdffjkef/zxttRjdHQU73//+wEA3d3dbVlHK+hvgID03UgJYjajx+0QBNEcMxFrkJYkDV5xv3MyvtdxzhsL2Iaf/cEa4mIN/HVEzzPrBmqbhustOWezgfsv6pPN31nM4WUnLMPLTliOzmKuqeNAx4ogiDjC9+iZyzWIu/emEdr8AcHsZBfbyA7x0uKRJsz695wUaxC1GSbRLS+5exkD5PHkC3lJ/TNUVM5clfdLklhMU1Rw1GJCwLMYFOdsI4dzeECwtLEG6u/dHTmsWdWniNKnHLMYq5Z0Y/lAZ6Ky0xLKzTVsAgfHSmn9E1oMQ7jM+HUqLtTQicelFwFNtkezSTVQxVlJnVWeY5psM2eaJrxkyRL88Ic/xNatW/He974Xhx12WKQQxznHqlWr8Md//MfYunUrrr32WixZsiTL6rSVb3/72/jFL34BAFi7di0efvhhfO5zn8Mf/MEf4IMf/CDuvvtufOxjHwMADA0N+QJq1nz84x/Hzp07ccQRR7RtHa0QxBqY3zAQxJxH6R5DJzdBNEtUnmo7ace/2HbFGsjNqZBQGtv9X5vJRIJdPhPO2aBpHnZsTM/Vlq7p2dDiuaM/S8TGGiSYhyCIBU/IOTtz2qxfQpJBv+KoJog1SKIZmYIPwtqsMGB53x0XkTtCzeX0/sqxBkDEgGD15XRTrizOykJZkmaK+Vkt7AoGAiE57JwNd/GX91PYOdu4Xur61QXWr1mKU45RdbI1q/px1knLm35x3lnwTpKkUWKyWCvXr6sjj+MP7weg/psyaYCL+wzu2whEWfL+VQYEa+IfnbpI8//W5H3hGATlGY810NmwYQO+9a1vAQB2796Np59+GkNDQ5iYmEBPTw8GBgZw3HHHYdWqVe1YfdtxHAef/exn/e/f//73sWLFitB8V111FX71q19h+/btuOuuu3DLLbfg0ksvzawev/71r3HNNdcAAL7xjW9gy5YtmZWdFUGsQfCb6/L2ZwoSxDTRQs9NgiAksgjST43axs6EyFyyFpHLCw0IFrNcEldskp0wI87Z+NDZaVs9kZ4sd10afxu1LgmCSILT7KhCddrSE7TFC1itblwUz9lpb9vGLRK3YVOOKgdsy5vBcaNNKvLvrinWgAFFSZz1x6rxC1DLLWjirMjaTeac5cpfwLDb/R+CdqninDXOK/2ku07Tq7OtLZ+g6XTuupV4fMcQTjpikbkKMdEMervSF64b/JtYf4KNJ3aUcdhS83yuyTkrqbNWi7EGynnEjB8TUz/94TrBdcTcuy1dZdsizsqsWrVqzoqwUdx5553Ys2cPAGDjxo0444wzjPPZto0PfehDePe73w0AuPbaazMTZycnJ/G+973Pj4R47WtfOyvFWZNzlmINiPmEkl9DpzZBNM20Zc62ufh2OWfl8kL7yJ9kekpoXHbQ9o2us7xKebyA6YApn6fVOqtA1/gUtHVfNS6cDhVBEHFkOyBY+644ad5RhgcEa9CfJurNl3y/b7BtthRrkGQ9QeZs8BsDQz4XzCQcwNGZs2qrwP+UMtbAUITyg+/eZLJzVq2PLlQyZnAZp2wvNZtZK4hrEgr6ugo45+SwuTCqDsy8y+vTvB8cxTlr3o4LTo8+n9T4C4NzloXnSwOP/JIexrx6KJmz0qC9zXbiabs4Ox+56aab/M+XX3557LyvfvWrjcu1yic/+Uk899xzWLx4Mb761a9mVm7WmLJTKNaAmE/QywaCyIYgSL+962n+fXawQFzXQ99dkfHNTm706g11f0Cw5rTZRKjZ8a0NNJEU4x70tVm69s4ZZsIATacHQRAxtBprkKU5Iyo6QCHBBVSIs3YuepG4YkxRQoFzViun/t2WBwRLEGvgOt6CeVlgZepgUbWaWEAVaQV2xMhSSe4xpmMVElOVtddjDeo/uqHxk8Jr1dto+vv0p18axshEJbKOeonNexZa6LoPfRvCkQT+/hH7RhZnm1incp4YDpQ8OFimNGWd9dRZeZtFxEErJpM2PwLNT37zm9/4n88666zYeVeuXIkjjjgCALBv3z4cOHCg5fXfe++9+Od//mcAwJe+9CVjpMJsQVw7mTRyIYlZxHyFxAKCaJ7gjXO7JZzm5VmeQGWyE3bvSos8CmwzJEo1iJlJdn5kvW2RzPAlla7ozRMnrKcui8d/V1YhBsBrblUEQSwQWnbOTrvxtvEFtFZ3r/rthNTqbLgiUVUT4p0QZ2tu9Lzy85Epc1Zv9ol5guqoJUeJX0lEMT/WoOGcAJeckHKvKGVZY6yB/j34Yapcw293DGHXwYnoFesREqnbfarrtClinLN6DyZ/IFz531SMuKqUoSDFGkiuZa843rpzNkJTbzbWANCiGOp1tuUNTVnXtjpnd+3ahS1btuCpp57C0NAQxsfH/czZE044AWedddacjDx48skn/c/HHHNMw/mPOeYY7Ny501922bJlTa+7VCrh3e9+N1zXxStf+Ur80R/9UdNlTQfyxchiDA7n0/dQRxDTQJZdJAhiITMTA4K1499s0EjNtlw3JpM3rtsjy8hqoAzs6XIgZsCRrPBdPIZG9LRcbrMczXvBMdP7aqbXTxDEbCbknE39stb8uSWMzZ/kQptwnObtmOJiXlCb7rmRVRPO2ZSxBmK352xPcHvpwDicuuP3/PU5PP6cg6MPr+Gpl4b9dqF+K44wzibq0WPSe6NcwS6CdqloA5UqNePxDtphLDbGoJagcRh2zqaMNUhkxU5ZByvcEBP/Zozt0kalGupmir9Qlm41c9ZcE3NlGi1vcAvLzzHNDl6buThbqVRwzTXX4F//9V+xffv2hvOvX78e733ve/He974XhUIh6+q0heHhYf/z0qVLG86/ZEkwup68bDN8+tOfxpNPPonOzk5cffXVLZUFAOVyGeVy2f8+OjracpkyqgWeAeCU2UbML7jxI0EQKZm2zFmJdvyblbu+ZUki8TpmUlxtkuxxue2dbVZfEmZKnVWh9ksKjG8um/23nXzH+6cHHSuCIGIIRQ+lvmZkd5GJKynNWkRWq2VHX2vT9h0KhD6znGVJsQaR111ZdKuLuDmb4dBoCduePuhPO+uUMs5cB+zYN2yuQx07QqxkYHBdjtHJCvq7C0aR1OycNZfnD3jFGPL1QcjKVVU1DLSO4C15nJaapPkUzmptvEzWhAY1MwwIFswbXr6Ze7DcbhbHSb6nWy23/7J7aBe6lsk5O2tiDe68806ceuqp+NCHPoTt27eDa5kc+mfOObZv344/+7M/w2mnnYa77rory+q0jfHxcf9zR0dHw/k7Ozv9z2NjY02v98EHH8SXv/xlAMBnP/tZrFmzpumyBH//93+P/v5+/z8RwZAV8rlpyiMhiLmO/KadHgYJonninKFZ0krxSQbmaN+AYKJ8Q73QuF5JSFrj6eoBozfO5c/TUQO6pDeP4ipL8O8maVkNmYGHWIIg5h5Oq/exTK2z4l5nUrnqfxNc20SsQU6KFUyw2uCr4VrtZ9pri4qixcBentPRvCPkZyXRlsnnGMpV1W7b15XHioFOHLm8BysGAv1Ej0S0pIZQZ9GWfge2PnUAdzy8G8/tNhvO/KJSHH/LYujvLuDkIwdw5PIe2JL4XcyHG2VyO1aPokrSNgzlvaYU+7JqE8qYhW7vr7l+hliDiM96eYBkApDfzYsxHXjrmbat7hyTESOL55jMxNkf//jHuPTSS/HMM8+ERFnLsjAwMIDVq1djYGBA+QcleOqpp3DJJZfgP//zP7Oq0ryiUqng3e9+NxzHwRlnnIGPfvSjmZT7yU9+EiMjI/5/In4hM0LO2WnMqiOIaUA+nSlPmSCaJ4sg/WkjportE2cTNPoM07IaQME0iu60wUxfpqEOrRqrCA+m/U0LHQeCIDKGa92m015X2qDNtoyINcjFxA4l0o0M9trwct4PInPWcWIyZw3d1XN2INSKokcnq9g/PIXjVy/C+jVB72RZO1jcW/Q/F/IWjl7RG9SIMew+5GW5Pr1rRKmDaJv1dxfkzQIA7Do4jl888CJuun8Hbrp/B0Yn1cG6LOZFFZxwxCJsOH4ZCrmwliULopbFcPKRA1jcV8S6oxdr+yKBOBuKWZj5dnFc0zwubkuh4WaEIwLkKe0aEKyZ3SsEdFe6jiixBloub1IyEWe3b9+OP/zDP0SlEpzIa9euxRe/+EU8/PDDmJycxMGDB/Hiiy/i4MGDmJycxMMPP4wvfvGLWLduHTjnYIyhUqng7W9/Ox555JEsqtU2enp6/M+lUqnh/FNTU/7n3t7emDmj+fznP49HH30Utm3jmmuugW1nE/ZWLBbR19en/NcuhCZP+hUxn6DzmSCywRik32ba8e/XatOLyFjx2uAwTUe6RuS0OWfrfxW3xcxps0QKsjxFQkXFlD3zj7AEQcwFQhmzLV2zWrvgJYk1iLq23XTfON7z+T3YdaCKWj3WIKlMEF5v2MEbVTdxLxbP944bPbMsSDqSczaqLVGuOugoBBtRqgQO28W9Hf66+zoLKBaClE55H+lNpY3rV+Holb0488TlofXtOjiBctVBpeaiUnMxVVYdvXpZpgxd/UCdcMQiXHTqKhxzmKqtJGk/hWMNUt7Z2tB4kQVivfjkmbNygeGfTCYA+VxUBgRropGR5W4xZc46solCy+VNSibi7Ac+8AFUq1UwxmBZFr7yla/gkUcewcc+9jGceuqpyOfzyvz5fB6nnnoqPvaxj+GRRx7BV7/6Vdi2DcYYqtUqPvCBD2RRrbaxaNEi//PBgwejZ6xz6NAh47JJefjhh/EP//APAICPfvSjOOOMM1KXMWPIDmpyzhLzHDqzCaJ5pmtAsFZKT9JVbCacs3FrMo+Iq82TqBEZ7pY4k8zI9ZYu8i2R3b/sOHXWWws1NQmCiCMcOZvuotGOa0wz76a/+P1BPL+7in+7YcSPNRBd6TPR9HznrLkwIQRzziLz6OVfRfvBtllkO0n/XRZnmRW0a/Rj9uTO4WA+rb59XQWsX7MUncUctj51ADfev8OfJup98pEDxvro7VI9qnOqXMNUpQYAGBwtI45kbUM91iDBInL9RCkZmh2MkVrCGJAs1aAhqugdztxo1TkrL9fqvjH1CBef9SiLNLQ8INiTTz6JzZs3+xX8zne+g3e84x2Jl2eM4c/+7M+waNEivPOd7wQA3HfffXjqqadwwgkntFq9tnDiiSfi+eefBwA8//zzOProo2PnF/OKZdPyve99D9VqFZZlIZ/P4/Of/7xxvjvvvFP5LOY78cQTceWVV6Zeb9Ywv8FMLWZi/qA0DOjcJoimmbYBwTIpProQ4fzNetCs2EZfvDobzFbvqdQM6sPVNF3rkqjh7Vy9oVsdMQM0ZXCjo0UQRDRZPo/OVPNf3oaqA1QTxRrIjQK9QDGT/JN540Q5tiTa1RzjrJoj0vubtyVhi6l10ddYrgufgOZK5NGDlUY1dRyX46UD4+pvdTtvX3d4cHqGsJDHtUe/Q6Ml/7cDI1M47vB+88rR7IBgaRtB2Z+Qch30893Ubh+eKGNFoUv5Tc2cDS+jDggm2qz175BzXlNVXUIt31SvpPjO2YwHBGtZnL3//vv9z+eee24qYVbmD//wD/HNb34T9913HwBg8+bNs1acPfXUU/GLX/wCgDdI18tf/vLIefft2+fnuC5fvhzLli1LvT5x4XVdF3/3d3+XaJnbbrsNt912GwDgiiuumBXirO+cpQHBiPlETGOCIIjkTNeAYOnHKpZnbzy/75zN+GktrtEXuCTCy7Vjb7Y8kEpCgudEOb++Po2ss7Oc7G6OzcQa0JEiCCKWli8S0yTuxkw7OByood0dDI4fa1B3zjZoASTQZiPbF+KrIs7WYEQWeP3M2Vy0c9Ybvyj4Ls8mabNeDql5lZFTHEPXHyHq5m0LjKnHw9TmcpW6cfVlfIPGSaJYA63usyFz1iTqiy0xtdsfeHw/XnveUepyKbdDPz2YONd4c//6Wh2cVKmLQdfyB+6Vz9GUFW051mDv3r3+59e97nUtlSUvL5c723jVq17lf77pppti573xxhv9z5dffnnb6jRrYeEHKtJmifmEfDqTcZYgmmdOxRokGBhBvPXPimTidboHMX2ppNWdvpespvVMYybxtK1p/sENn5t/wCQHM0EQ2aI7QtPerttxHYrrHW66fr6wp+p/Hp9yUa1rtf54VQ0vuQm2IkLQ0jNnAaDqRImtwefAORudOeu66tGR5xODc/l1i7ivRDpnDXWURW1b679v2u9cc3jK4myj5lGS9pO8ymbaxL4ImVFzKZQwoKmzZgGbo1pLl4Glt5ldV40yyExLCp3MTRSh1YVzLj3HWE03VVsWZ/v7A9v2ihUrWipLXl4ud7axceNGrFy5EgBw++23Y9u2bcb5HMfB1772Nf/77//+7ze1vq985Sv1N0jx/33mM5/xl/nMZz7j//7Tn/60qfVmQW9nkDccPLBSs5qYP9D5TBDZ4EhvnKeLdvzrlRupWbpn48RrP/fLsJzSOG+kzsahOUWmBcNqWNzENkOX+xTwyC/pi0qx+Mw6qwmCmCu0fI2YLnU2YkXDYw7+6p8P+N+HxlxJZIwpLoZA1JPvtCY/bQBjgG1589QSiLOOnzkbnV/P9YUU56xat6htjIs1iPrNthhyduO8VzXWgPuxCHpdTaQdEGw628RpiTIsiDpPVSJyLqLK03aNvq+sjO7vTM/RaEJJ1XUtuU52Cwpry7EGRx11lP953759LZUlLy+XO9uwbRuf/vSn/YHL3vGOd+DXv/41li9XR//7xCc+ge3btwMALrjgAlx22WXG8r73ve/hj/7ojwB4wu/tt9/etrq3G1moOn/dSnQWpVNsFl9cCKJZwnnlzWc6EgsTzjleOjCBUiWiP1gGLOnrwOK+jraV3wz7BicxMlnxv0+Vve1vJUg/ES2kGiRBbsg//dJIZk7gofogE7HOWeOkbORMedndBycwPlXFsv5ODPQWWyg12TqVTZ5O8Y0EPoIgiHlJ2A2YcvkWlo0uLXl74Rf3TSjfh8ccP3PWjsmcFWvhMN1Hw1sS5cKUn3Vytie6VqowIusDvnM2x+ByszrLOdf2r+ychdFNqRPVVjI5V2v1SuVsK9QGNQ7CqgwCpXVtb9A4idhkBTnWoJWor3a1poNNNPfosm0Lbs1FqVxDv5TjqzTltMrpxxyoO2cRiKCtirP+YqxVaTZYRpzPsujfyjFrWZzdtGkT+vv7MTo6ip///Of4xCc+0XRZP//5zwF4rtm4HNfZwPve9z785Cc/wa233orHHnsM69evx/ve9z6sXbsWg4ODuPbaa3H33XcDABYtWoSrr756hms8/eih2pQDRiwEOOg9BJGOgyMlbHv6QOMZWyBnM1x+zlGz5sVBqeJg8+PmF7q5XMudemYUizFYlpelJo8cnBV5w/5J/lhnvkKlvT/vOjiBXQcn8HxhFJeddWTCpeYe1F5pnnbuO3LFEgTRKiEpqJULS4vXpLh7eFROpog0EBmpw2MuRC/yXKOXwrpxsMG6on4T5HJAuQpUqo13hJ85a8dlzuruVKkerFGSbqP1RwvQts2Q02yPcTn/3rIcNanMRqdRWudsM+32rG+RXR2qZCj+7YhN0feRbTFUAUy1aDpxOVdOPKVnTDMbKfcya9V9G3LOSuKsxZrWvVoWZzs6OvDHf/zH+OIXv4jNmzfjBz/4Ad7+9renLucHP/gB7rvvPjDG8P73vx/FYvucGFmQy+Xw3//933jrW9+KG264AXv37sXnPve50HyrV6/Gj370I6xbt24GajmzRF5KqFFNzCOML51nh/5FzBEq9dZ0MW9j+UBnpmVzl+OlgxOoORwuB+xZcm5W60P6MgYcsazH/72rI4eBnvbe/1syzsY8uPjlM4Yzjl+G/UOTaUtviG0xHLsqJvbJUDFlDIfIDU53Yqxa0o3dhyb8c7ddmB4Up/dFL4/5RiQmwb+bBIvH/iIIHpiaXBlBEAuCLGMNwp6/5suK4tdbJvHLB0r4q3cuwaqlOby4zxNnP/L7A/ina4cwNhncjy0Ra9DsRdfUbtAKk7/m64pSuYFzdv2aJbh72ziAWt05G4hl8i5wNfUtNECXdJ2PEi+jfjcNCCawrWTOWRnOuSIyNzoXosTZKFdpU72vovIGUnL+upUYn6piid7zTtsEfR95AreDUopYA9Nu8ZyzwSpFzzTOm9Rmoya0YJ31hepMCs1AnAWAz3/+87jrrruwefNmvOc978Hw8DA++MEPJlb6v/GNb+DP//zPAQDnnXcePv/5z2dRrbbT29uLn//85/jZz36Gf//3f8eDDz6I/fv3o7e3F2vWrMEb3vAGvP/975/V+blZE/sPZZaIAgSRKVx/eCd1lkiLdw71dOZxxvHLMi25WnPx0kHR9W32nZs528KGjLe5nchdouI4fGk3Dl/a3e7qBLQofiUaEaw+af2aJVg+0IXdhyamT600tSenYd2h3UGCX3KUPoP145edOtuuhQiCWCC0HmvQhmtMzDXyn64dAQB85Mv78KO/XYUX93pK6ClrirAsNb81yE2NEC6RZnslt2FEVYU4W6lEOGHrf7s78ugs5AHU6pmzCZ2z8nqZ/JI2m8xZwCvTYl6XfJlG4qzL1TIbjUUSOSBYhFu2OX01m3Nz2aJOLFsUNo3opev6sXAfi7gygeII1suU9pttMTguDwnZ8jhwTSHayhnGGuhle+UnckUYyUSczefzuOWWW/CBD3wAP/jBD/DhD38YV199Nd797nfj0ksvxUknnQRbCj9xHAdPPPEEbrnlFnz3u9/FY489BsDLbv3GN76hzDsXuOKKK3DFFVc0vfy73vUuvOtd72q5Hn/zN3+Dv/mbv2m5nJaIOdMp1oCYj4Se3ekEJ9LSqrCWcj2ExDzZJ8FmREcWAK322hQPaHKXwvbuQG54KPSdkW1dcxTz5ISZBrIVLpKXNbtePxEEMVsJiWitXLLab5z1OTjs4JGny5gscVgWsHp5Hot6LAyOBuqsGKArWtjz5Nmo949e76a8WreYzFlfnK1Fi62iPmLQsLzNIl2kocxZucs4Y8F2xXgOIsXZiEHLbJuBMRZ2zjZI2uKcK27cLGINWsU3zra5fNMAckDwciB2QDA9c1Zb3nE5XFeNMhCHJkrfTlrvLBH7QC+7WSE5E3H22GOP9T9blgXXdfHYY4/hL/7iLwB4A2gtWrQInZ2dmJqawvDwMBzHO1jiH5tt27jzzjtxyimnNFwfYwzPPvtsFlUnMib+LQTJs8T8g8RYolWSujGbQXl5m33xTdPuhmMc6ki/aZnJmscg52jpJKhqqrvzdJ5TsSuY/jN6Nv0bWkg0Y2CmY0UQRBytdoyIcna2gjFzFkBN07geeqoEAFi1NId8jmH54hwGR4MBVu1GGVbCOhshUN/x8B686qwjUSzYiQwESWMNGGP+tjy9awjc9r640jq4+C7vX+mzpbXhohJoo36Pcs7adRU2p+27xrEGapkNBwRLcLK0MqDUdKBvgx69IJyz5djMWXUZuUzvWLh1l3HQQvXa77zlZ2+mjwjWTBlx1tkWyEScfeGFFzT7tRqQW6vVcPDgQeOyYl7XdbFjx46GI53TSOiznQQmcWoxE/MYEmsJYi6R7h9s3GAZM0mcwK8+oMzNC5SpK9x0XGu59sBIJEfZd63+u+HAxBRQyNdFgLiDQT4AgiAS0Kj7+bQSU5VDw8B/3araN3cf8ESv5QNeb+OTjy7giRdkcdb7G9nlP+J3WVg8OFrC4Uu7I6tmypxtFGvAANTq7tpK1fFdtH6ZFsBd4ZzloeXFemWtKUoWio41MGfOClHWthoPCCbjcq64cRs6Z2NiFfzPrTYyJadyWxCDYNVXpIvJUQ5XuT0aF2sg9rmrvQER5Tb7T5dLjRH1nJo9rfrMhkTmnIf+a2a5qLLSlEnMIDGHiNrLxHxEvy61JYOKIOYbs9SAmpg5Wu8Z7LWZ3TpnYN+zFh8IFjQZPHFOlYFvX2fjX3/qPbbEa7MzGXtBEMRcIWQaTXmBz/IaE1fW5keAyZJ67dxzyBNn+3s9FXbtMepAqg0iZxOtt5ivX28juq7LheeEOBvpnA3KEHXv7ODo6y4o81m+6Ko5k/VYg5h6BwuZf/7/2fvTaEuSgzoY3ZF5xjtPdW/N1VXdVT3P6la3hpaEhCQECIMM2PAALQSW1hPmmQVY8odtbIwB+wmW3wfoE2YQCBlZxkZICDWSkNSaW93qeaiuqq7umqvuPJ75ZMb7kRmZEZEROZ08d+jKvVbVPSdPxpCZkTHs2LGj38rZ7WRr0Pf43Q8yf60lO0NupaicZeJNKtgDECN4bhpk0Y3U9TUCr17CvGainP3oRz+aRTQ5XgEILX87dDCbI0cYAmU+HxHmSImrsYrc6mvebmQbpRQXF2reDreVkol9U4ORs/phykQhaNT1hv2uSmPTRgBbA8/zlpDtV1i2MTYaHVyY3+CO9HbvFledv80WQasNVMvJjA1aHQsXF2ooFYxY71OOHDle+eA9zXut3XtvHri2RoJqK54ri04fYXzIYatu5MhZg1OWxqQx1TmSGLgANatQzrY6GuWsG9nSmoXZJQuEUMxMAkVp8y0WZxiBSQziZYZS/ZyfLg4tOatTzkb2v6h2Q7ArS3VUSyZGh/zno/dL5Vehhya59aDCn4C62J/QjqHa86L0z2Xk7KmLK8I5vHI2jWhTllP1BDn/GpY2aSqZkLM/8zM/k0U0OV5h2PYVS44cWUCeed+aXOTYwegn5yPwcnnh9JB2MNZvwe/8SgOPnZwXjpWLpnK3XCWiBhERwWLdE5LBjrkxQRUD1k1dhZO/M6nwxKl5LK23ADiDrF7fmyI3WllaA8aGYgTint2J8yt4+fIaAKBaKmBytJIyJzly5HilgFdzplqdK4TpX2MxMhg8trjqkLNMObtn0sTRA0WcOt/BzdeW46/iCznNX9HsHpDVkdznYkzl7ImzzglTY0C5FCTYDGIAsAIrpkXlrKha1BHQOhJUZytQcJk/2a+XRKwzt6UNwRgpvFZr4zvHZwEAP/Taw5Hpi+n21sv0J+z701uVy5c8qZAmVX7TuHLJKdeLay3h955XMfXD7kHOTI9xZ0LO5sgRB5vpE5cjx2ZBbqDy8p0jNfrRh9qmk2T+a7JFGdymJqKtjtPBLxdN97uFVidkt1sXvAKoX9g2t2uzmGFFkjniod11yvGusSoOzQzj4sJGRAg9HFWS/31pleDwXv35quLR5t6hOO9Tjhw5XvngySDQ5E1KprYGIZFpLFIBAOMuOUsIwYf/9W4srFqYGjNxZbHmHNeEi9OMBv1C5Uj8jz45q4vROX7SJWf37KIomkYwDV4ZGQjNziF+m0ypfosbnXLW0pGjDgtrSipQM8bKJVvYEMz5u95QM9U6Re+haX/WsfcuR8hyqgzArkDeqM0WXqpw3lLnOUsIwa2HJzE1WsVzLy8KZUTnZZs03wDZhD0k0kkJMvOczZEDEF9CfSW+bYZ4OXL0jGAbm5fvHEnRTz3mdmWVduh7sknZHhkoYmzI8WLTLcFTQvG4eUWDfgAYp5zwBPDmlKvwznP/H4b/ZvL3cIeW3S3Asf2j2Dc12POjsjg+dWm1x7jCmI4cOXJcNQhsZrQNqnZVW8eqrGv2FPADrxOXDYwO+VSOaRLMTBSE1QpJmurAHhoB5aysjuQ9Z52TWhHK2dllpzKfGAWKBSNAVLJnYUvsrEwCescRb3Mz75hN9RuCucxfQbZaiLEhmLCxGUtX01egij7dnUencMOhCS7R0CS3HLLlBQDwbhCed7D8UsXwnCUAquUCjuwZQbHge3pQUN8+IfW7qgiY8l7rqg3ZczZplzFXzuboHzTs7DZo+3Lk6BvycXuOpNg8anYbFc4tzkrPtgb92wLXTUDcECEyVGwFgDouvxMZ5670a6FcaJKBj5tqa7DNB0rbDfKSSn5sf3mxhstL9dgPkAKCcnZxJfxhRA2IEk125MiR4xULr55yVtKniED5sTcoqjfG+91zUyXgf8qUs+kSCs81Izd1K3MEWwM3G7WGhcdPOdZMpYKBYwfGUCqYXkor605lPlilKBYMkdSEXjnLfzGIn3hYl0X+7ezsOp46vRDwuWXwPWfFK42zIRjfd2JNjC5rKtL44PRwaBpp0beui+IaDOKXqTT2A6r+dcEkAuHvexInyCufRqa2BroRFlH9HBs5OZsjYwgLD4Rf8rFNjlcicjI2x07BdiqqGiHGJsLpRCa/J/29i3znjikEsiCTWJdZW1/FF84GzqeUbs0GS5tQoKk00MgRF/rZgidfXPBsD+KCFzo1WhHtriJN/vw4kx05cuR45cNbRp1SPKQjD7MGdeu/ggmUiiKxyCtnhTARJJRqQla+BK/e1cXFfS+4jFKjRXF+zrexGawUcXjPiJfOMiNnK0CxYKLdFaW2nuJS9pzle0cG4TKr73/IJOiTLy4AgLb9YRuBycpZebMrGZRSoY0KKI4h9pNYE3RkzwiuLNVx7MBYaPxp0O9WTiUOVu0NkCzOYH+rWDC43311boC83wIErjEjn4ScnM2RKfTUrOakHDl2OIKes3kBz5EQmyWdzYumj0i2cmtBkEw563NhISOxHi81a0K907Uwt9IMrTPXam0nTcUuxpv55ARbA+STzWlBKUXHHRgf2z+GghnvTraaHQANAKKKVo3wOHPlbI4cOQC/+feWYyfuD2RXl4R1A1mdZxoElVJc5WxEx1J1WLocj2TUpCCQaCYLQ3DjwTE8caqGerODjpt5R10KrKw7EuWBKhzPWak+lglMVdYMQmDDJ0B1fRKVfUAYmHKWef4zlArhLqC2TYX8+spZjlzm8smueXSohFuPTCrj7LmP0edVPyppA29rQDySXTxH8JyVHpxqQiFAlGvilbG41kS92YFpGpgZq3p+wnwamd2ikGtMg5yczZEtYqgZ8m5xjlcS5AYiL985kmKzuNkcPnq9L/1bKuZ/NNyebhwyifrsrBJR3GxSq4Asrv+p04u4uFCLda6xRTskKNVHOTsbG8Hb5HshXrtvBKVCvCW5F67UwMhZGkHO+mWZL838ktO8lc6RI0dwGXViapZXRmaVGQVYF6BgElTK/JJvYLCqbozS9CsDyllJASqTaSrPWcsmOHm2iD/63zYAE4PlDo7td+Jud4G2K5QdqDikZ9Bz1vm7uNbEynrLz5ug0AQIZx2gu0a561QsGN7koAps46+xoRImRspYWmtharSCw3tGtGFU6Tj5FaWdNqUwQLzPQIRdAv9bisLlP//+dFYod/8Z+OvxP8qZ1+fHt8/gy7jBnwDTU87q41mvt/GNZy5734/sGVGQ4IQrVOnukSwU0F1p0seXk7M5+gcS+jVHjlck8nFfju2K7VQ0+79Lajwkvid9vole9MQfKGSzDDutjUM00nKVzXYXgLP5maxUEUAIDu/2/di8JaibWKBzAXoyBJ4NG8hxZVn29QtDh/ODdCwKQ56CYkTEn50rZ3PkyAEEbQ16jKzXCJw/ATUh9SakzAJQ5ZSzY8Om3lIopnBWWvsnpS0eD0QlLD93/nY6wHePN7zj52e73nXU3cOVknN+oWAgsDeXez1s1YyXA67eNggBjcF8yatyqiUzlJxlG38RQjBSLTnk7EglvH+iS1vKmmCtE4ec7Rn9bedo4INMzibvp6kmwwVbA1DB1kB3ifMrTfH7ql8elf7JWz0QkZCTszkyBf+ebLOyniNHX5DbGOTI0QN2WEPBk6f9BIHvc2bFqGOiyG5vnwZdXHGm+HUNfEp2lkV3/YFx7J0ajB9Qq8joI3LpbDp4yhLnWfHD4iQD006XU77ava0+zj1nc+TIAQRtDdj3TteGTSkKJvF8SJXhNZ9T5cX9G2lrUPbzMzWmJw1TaQKli/A3BFODj5uRs+0OxclzPrFaa/jqW0bODg8SN4wRGEPxc3aGQbz6mj+rWDBgtS33uP7Oy6rcSqmAtXpHc7bUJvW42pfaomcunxdGSId52fZMHvapmds9PoAry3UcnB7C2dkN4f7z16PrUipusQeVQlu2NWCTumGX17HE3f2ECVnuxehNN6uY1NFlKuGzyMnZHJsG0mtNlyPHDkBO1uZIDI9Yy57wIT0ujeo/tpjk2m73hMuP7zmbYPOkiNvZy+XyHdlMnlrKnvFmlhhlFrdbmdmG0C2pZANU0yCJNpHrcuRsfFuDYH6AXDmbI0cOsa/O10VXlup45IVZUOrYCLzpzv0YKG8tXWK7MtGCCcFzdnI0uaLTQ8QKA4CbyNK01aKtgfN3ZQOg1CfHak2fpKw1nfNLRacSLxWMQH3MNwu8tywPp/1ws0ZDOLH4K+qd9BKs5oiCTUW7A2FzM085m1lyAXiPLOM07r5+F5bWWxgsF3B2dkP4TSBeGYka1tzK3KZqQzBT3hCM9Yv10cq/qcblmdwWDTfL8p/WLmWLXLxyvGLBlcBAx1tSUOTI8UpA7jmbo1dE+YVml872gXJp0ZbkY3uB99zylLOxPGfhhkuHRN5YRExpq+7hpqSrGCxstzKzPaG+S2ywn8TSAAA6lh+fFTIYj5GFXDmbI0cOAXxrtrjW9Pr1XYsGltfzyFSLoYmLwp+QKpgEVc5zdiqMnI2Y9I9TA8u+ooG4FLYG8j2pNyj32fk7UHGOFc0gDUWEpfHBPBVNZ2IvzoSprJyN2iAsS7KUUiq0NcKGYTbrV4QoZ7epl1LBNDA9VvX6p/wtFmwNvE/xM6/qx8qbhpqcyl0XsyWxs/xXbkoms/saXQ8kSygnZ3Nkim1Uf+TIsSkIlPn8JcixbZEXToa0aoJN88olvHI2yXPTDMQ4lUl4uLC0dBRwunKVmlBOxCRnha2eRthh0E1aMsVQwlFwt8vFFaWcjVCr5MrZHDlyCKQSt1Rabm/DV8Pxasge8+P+DfRNKG9rAMHWYCKEnI2e9A/Wk3Ld6JGbiklKOepqGTCIH56RaPUm9e7Net35Ozjg/J0arWhypc+6aZpuXnxykM/10X2jmBmv+r8J1gL+eUf2jGCgIiqilcvyNc/1DbfvxYHpIfWPcO4dX5aoQNSmawe3E1R9aP56DE2fM+z5+spZ/xfRc5azIAnJW+g7zF6LDLhZ/Tiit+eak7M5MkYMhU/eL87xCoLcccuV4TlyxECvUs+tRl83cnCQSDmrGTxlCe0j2+QqjygGlf1Cv5YGvtIRuG/uAcarhvk4qsArZ20bMR9+cEAG5ORsjhw5xL67wdVTcv2wHWoLlqWCSQRbg1DlbGzwdaua1NLdA75dLBWBfTP+99fdUQbgkrNuDCvrToCxIeDmayaU5KSgJlW0uyVJSUlBhQ27BqtF3HVsF3cNweu554Zp3HpkMuB7LnrOhjf6Y0Nl3HndlPe9WhKfBaWSz6zic2zf9RT9j/4LCYIxqzYEixEMz51ZwoPfOYtHX5hzw/q/CZ6zlPqesxTQlczAOyx0BYJhsu7fEelv0kokJ2dzZIow4jUf2+S4GpBPPuRIi6uxjty6a2YdvKQvbH9fcD47ZgJyNgok4npjdSIzvnTV5g8JY8gsL1FJCLYGeR2fGp6tgZnsmfMbgsW2NeBAQwiIHDlyXH3gqwFebR9ob0Oqi/60BTL56C/LLpgEFc7WIFQ5q2i71Cf6HwPLwUXhrCIu8cDB3X5kUxOOTHajQdFoOR60q+vOb6PDFIT4imAhxnBuFgVXSclP/BULBsaHHTLYNIhAEoaRovL1qMlS/UPm+y4VyZfYplQoS3xZ8TcE00YtWEgc2T2iPzEKfZpZFlwXFEpgXtmsj8Q55+zsOtpdG+2uLYQFJM9ZQCJn1ZDbeNneQs5/WsjWDYH+NeF/jY98Q7AcfYGy0F+NzEOOVzzygXqOXuGVoT7VkQSub1leVj30Sgf2uzkj4Dc+iPHg4q1gDE8QMTuRJCsP1ozWgfYRO13gvV3A7iMbvCT1nA1uCBY2YGZpqX/PydkcOXII7pNcdZTM1kAdXy8I1ozUI/NMA6iWfLJqdDC9xo4o2nzLkkgtebMuKXcy73frUYqzlykOzFAMDzrHWm1gZb0FSoFVd/+osWG3X8qt+FETwMG74S9zF1fQsCaFEHGzSVE5K6YhTwwrbQ0COVCjWipgGS3vu23LnrM8URtDOev+tG9qEEf2piFnKR9N9lCpmjkLAr98yXcweI+D5cxHoSCWcYPznI3tNcx/Vnbq+iSdTYmcnM0RibOz6zg7u678baBcwF1Hd3kV2tJ6E4DzIjx1egG3HZkMVH55tzhHP9HuWjh7ZR1dt5MxOljC3qnBTUs/J8BybDswdnYbYcuzs93ZNpJMOeudEbGSrJf7vuXPzIVqUNn/NAWdyCamvDMRXFLpHGBFOfmGYP5nm5IUincfVt5I58hx1UO5kRENqkc3g5oV6rOAESdXb0obgk2P62kcn4OKX9fqbQ3Us79yzOUS8K63OPdvsFIG0EXXImh2LNQaQNciMAgc4pYQYWMsltZ6vePF11VIa4uyctbNJ8u6QcSNvUKVs1LcymaJuyWXFmowTYKZ8YHAaZWSSCJmZWtQKZmpVhf1u5WTe0QEQKngK7l1k6SqK5HJVP5yhU3jKLcXQwLlrKq/IEwO6KOKh4xvdk7O5ojEyQsrqDe7yt+W11s4vHsEk66p9/nZDe+3M1fWcd3eUQxWiwC2/1g4xysDZy6v4/i5ZeHY20YOoFLqT3WXe87myLFzkfht7fPrzUfPOu6qJVl69L+lzSqFTdtcrQeoOvV5DZ8A0qyAZ2vQg3IWUC+HlZPkIajDcuVsjhxXPUQ+lKkwqYLYiRdHvxoGCn8ZfLHgELT/6b1TaHdoqK1BGqWIPHHl3QpdWx1SjY8MmiCkA0oJ1jZsz9JgbNhRABMuPYP4fuQ1jm9Q1dWMrJOT5olenswU1KtuIrGUs9IkcLPdxaMnHE/Ud77mmkDYailoayBuCMbnNZieDqnnEvu97EfBzhaLnHI2ZsKU0iCBy91b/h5RAAXO1kB3awITsFTxMco/IwbkzUflW+51fxI+w1hsxde+9rVksWaABx54YNPTzKEGq1xuOTyBAc5T5dmXl1BvdYVZRnkQKVasEWvNcuTIAB3Xs2Z0sIT1ehs2haei7QcCMefFO0dCbEYfatsVyyipZ5/Rs61Bn7NNkFA5G6EAkDdmUqXnxBM/f37a8cLEiizB6ZvZlciFs2khDV5SK2cl0iCEnGWgmi+5rUGOHDkEIQVXHcnETrjgIvu6RK0sdP4youq1tweVmzKi+iqqdlS2NYjcECyk8S4XTZRLQLMFrNdtrNedc8dHiJcvyhGqLJXdEwO4slTXxqtWzvrXwZoWgxDYlAoTrLKdQDzPWQfMDxVwnodsm85Eav45alsDR+XLrlub3LaetAaC3Cwg2hoIpCqlPuEqXZjSD5Y7RySwKYhLzod7zorfxb5ARmrZOHGkHCjEImff+MY39rBhQ3IQQtDtqpWaOTYf7AXYNVrFyGDJO37ywqpLzqqmJBzwv23FUsQcVx9YR2rXWBX1Vhd21+5pCWRkevoJuhw5EqK/9Ox2KpssN5vYtdgZ4AyxEnnOMmjvp0iSxQ/H5y3h+f3CJhYa/2nkBTUJvPdbOs4GYmbYTigKyEOCMHKWKDqbuXI2R44cPHj/UZ6oDBKUIXFoPqfNiw7+hmA9JCJD0aQFNwRjhKIbRG57Q5rFUsEnZzfqNhquHetAhRGjxCPC+WgPzQyHkrOloug5C7jqYi8ujnil4vJ3+Rx5jjCM6+J/sW0bpuE8jDfduQ/NVhejQ2XhfEpFol9FdMfxnN2uwlmVdFawNYgZi6otH6wUvc/8PaIUKPCes7o4FT/aNoVhkMzeWQBKCxLl8YTIdJ1vFAHCe4rk2DmgmhkeZgMicrOyuiF/3jk2F8oOV39TlNLPy3yOhOh3mWGihLxs+ticyiExeLVLGs/ZGIvkNMejb4ifNxIrpbhIGpM4JNscZLMB2lUETYH0ydmEylnZ1iDGQ6CaYVjeL82RIwfliTquOuLrKMum4RV+P6oSRdXobwiWoN5kY6EECcmklk+cqSfbwnJTKhoYqgKr68Azp7totp3jA5zAlC315wm4qEtkBKDsOeupYt0IDPf5icpZNw2XvwjfEEzPjlo2BaMPRwZKGBkoBdqV4IZgbnSCaC3J09m+YPe1KChnxd9VwllCxPb4LXfvR7NtYZwjuk0pHpOzNdCBxWkQ/75bLjmrep5p77Wu1xyML1lFEXvqmhX8sH+A7/WhKnBKQ96Q83NsD7CCHajEmB+eHaz4GJQbL+T94hx9BK908vxg+ljmcr4rR6/gCbl+IG9dg+j1nmyGktJMo5zVIGoH+3hcNTeo0BxPgtRXFXEtmaL/8pOrCmxgGsdrj4dMztpJrYq402V1WI4cOa4+8FU7XxuxydACWz4dY8Iy+CVdXlh+hN849WcS5awXZ5StAZe61tZA0w6G9VnLBRN33uDUtV96tIOVNed4teyHtXiC3IsznKgtFdU3wd8QTFTFqpSzsTYE87hZ6R4gvtWUytaADxq2gETFsSRCvzUfEc+G7yOHZYW3eBisFDE5UpG8f/l4qPdbHOUsHw9r94XxVq/3SLvhvVi+kiYTi5y1bTvyX6fTwa/92q956tjJyUm8//3vx6c//WmcPn0aa2tr6Ha7WFtbw+nTp/HpT38a73//+zE1NQVKKQzDwL/7d/8OnU4HlmVFZyrHpkGnnPWWXPKzUiHK2W0qVMrxCoOqvG6mmjUna3NsV2ynorn1m0FFLPPXod9kndBx95WzUXUYv2Jgs5G6XG1lpmOCn+wLHMyhReA1cQ/wu44ngewbH4tfpcqPuXI2R44cUvPj9wfkTQvj9ul7M44Ko2c5W4NCmrYyfhh5zktW0gaVs/q4y0UDR/YDY8MUlgWcu+KcW634YSmncPTiFD4H458cLgd+czxnxbGfL87hBWQiGRymnJXB3wtVGyIfsanEj3Ces156IX2fVLZWivxshviRXRLvOUvEQbgmJOFUrup88mpxavt9B95nWIYqTplQF8puZrcom75FZrYG73rXu/B3f/d3oJTiX/2rf4Xf+I3fwNDQUOC8oaEhDA0N4fDhw/jBH/xB/M7v/A5+/dd/Hb/3e7+H3/zN38QzzzyDv/mbv8kqWzkygO4FV1UcAeUs/zJs3/FXjlcSONJnM8b8uedsjhw9YMvbhWRvbL+de3lSS+iU0qj6LJw19iZH9X1kMRpVCrrxY4/CjqRFYHMnelWDsLyWTwp2x9LbGojfw4SzUfsb2O5APl+xlyPH1QuZqHMPcspZ9QTu3EoDL11ahU2BRiuj/XEimpQ0tgY6YZUHRUNqS7NerM33lbOydFaffqloghBgagxYWQeaLZecLTNiNOgBK382CMDL9QgBCszWQMxpQBXrKU95UtVT16ovJ4wsjVTOyhvJcWUJ8J8hn5+wNiiJrZUa/e2nqHLOk7P8ChWh68g/a/jqaR0xzp9vg8bynPXtK4LkrEeOk961FoFwujwlfBSZkLP/9b/+V3zmM58BIQS//du/jQ984AOxww4ODuJDH/oQZmZm8IEPfACf/vSn8aEPfQi/8iu/kkXWcmQA3S7QSsm9POuWYvnYRqODx07Ood2JDjs2VMarrt+F1VobT7y4AIMAdx+bxlC1KJx3fm4DJ84vx5oBNQyCm6+ZwO6J4G6YV5bqeO7MknYmq1I28eobZrTLLvqB+ZUGnnl5UViOcmB6CDccHMelhRqeP7uMgYqJV984k3gTjp0Ib+BMktsanLqwgjOz66EV6b5dg7jp0EQwPe9AMPBarY3HT82j07VRKZm498YZlDexjOTY5tgsFWnOKQWxXe8JCXYsQ5eCR5WhmIUrzu0g8aPrLzbh2W29wnuHIkLRknhDsETK2einZds0sXo3R44crxzwdTs/oePVUd6u8GLdc+r8ChbWmtr4UuUl4lc25CxmWGepYpKJQJ9IVPMAYblh5OzEKAXO+2cOMFsD+Pea5yiNAFGrVprKwkzmXxtQznJtBfXSi1bOylZQqZSzfNqSRUQUz87ayNTK2X73XRTEsskZxHYtjpwNEQXQCOUsS4opZVm7HXZbZKIeCFp2iFMv2dwleQ4jaqJYh57J2W63i9/7vd8DIQS33nprImKWx6/+6q/ir/7qr/DUU0/hQx/6EH7pl34JppmTF9sBul0avSWXIbYGYkUfXGKgwuxyHSsb7Vh5q7e6qLfGcXmxjrVa2ws/VB0Vzjs7u45aM/4M5/m5dSU5e35uAxuNTmh+Ftaa2Ds5GDutXnFxYQPrdTFPL11eww0Hx93r7qDW7GBlo43JkYomllcO1BuCxasaX76yhkYr3FblpUtrAjkb4GYVYS4v1bHqls96q4vF1Sb2Tm1eGcmxveGVmT71otJ2EPqJbLtE6bGd7okMvmMZ1UGPKkPE64qq40n0HKSTU9/DlAG9QVfadFMlinxfvQTwBymiPIvdu143BAsjZ5UxS8/MohT5CCNHjqsXrC0Ul8f7akddHaXcSyVDyByVQxg7n5PQInH7WPzVyP0Mj1DUBQ4h1IoFA4QQTIjDcVSYrQEhnrqSaEjXuB63FEGfUcPbtNxXTLLrYOckU876dyG256yg2qXC36iVG76tVY8e6f0aV3CfVf1KcUJVf7/Y+xTWJyAgoKCuDWq06Ioq4rQsG989MYdWhxvjx3iVO10L33puVlDJD1QKuP+m3f4zzLhK6Jmc/epXv4q5uTkQQvCud72rp7je9a534amnnsL8/DweeughvPnNb+41ezl6hOiNIv5mqjYEk8ILnrMxKwgWZma8imMHxrTnfevZK64Xn+zrEjyXXceNh8YxNaonKK8s1XHqwqp2RobFc92+UeyZFMnbp08vOgTcJg/c2PVes3sYu0arePTEnJdPld/NKx18hyRqE5xAWPe8O49OBdTX7Y6N7xyfDXgwyVGHlT/d9xw5rl5sDT2bdkVzv0llv25wlP+G4Xhy9XtAuBWQVQbpY9gkiCKeHKFQt5PpbQ3kif+QkwVFlWNfID82y6IZGrvlyJFjp0Ep5OAqCm9DMFmAsdltMeXJ2QT1ZopsBpSzbro6e/iw3JgGgcGUsxwqJT8wi59fRyEue5dEYRr7A95zVrY1CGxqBs7WIBA/lw8Wt/uX5zPikLO2tCGYp8CNoRQFOFuD7dr3E9lZD5MjZSyutbBnahDPn112ftZcgqieDlfOwlXONtsdL0ndu+jZV3APtN7q4uJCTYgzzp1dWmthZaMlHGt1LCwLx7J9Rj13TV5++WXv8/79+3uKa9++fcp4c2wd+HIfa0MwWZ2QQo7P4igXTUwM64lU0yCwbGdmSmX4LcTp/h2uFkPj3HAVqPoX3jk+pIinqFkC02+w5AbKBYwNOa0eu+18VrZr/Z41RB+peGptP6zzd3SwhNHBsvBbs2155/B+dXHijiJ0c+Rw0C/KLztW6fJiLdBRSQO2kqHZ6eL42SXveL/fjXLRxDW7R9JHwLGzy+stXFmqJc7z2GA5UjnPSoJJCGxQdC0bL19eQ6OtXgGyuOoss1xeb+F57n4ahODA9FB8o9YwNQKXN9GnLyLOjKEaSPcLukFpjoRgA9OU5GxXWtCSWDkr4eSFFa/PliQSgxAcnBnGQDlndnPk2MkQ6nb3fef76v6GYLK4on950cG3NUgQp/tX33YFVX+ycja4IZgYWVi7SAiBQQjGRygMw6+zqxWg3XWJOYWKNKxp0BN4vvWDvKTcltoePh45LdE6SvzRDrlPgILEt0UOxFfwurFHOPv0uiEYQz9HFQx8Dl97yx50Ldub3JB/l+FtEhpyP5j3MKXU6+tSG1iptTChWBGsUs52AzO6GrW2BPYMRwdLuPPoFB4/OY+1egeWZWu71cT7m05Z23PvYnFx0fu8sLDQU1x8eD7eHFuHMONq5YZgYeE158iQZ7904MmxqIZNZ82gi1OvnGXnqQK754RnJXPwS3NkwvCqVGiyZ4Q0A2r3Xiqas7hjSeXkQGDmPWm+crySofP17lc6adHpWnj0hblM67h2x8bJC6sZxhiNKkes9HJLHj81H2pzE4a3jx4M9512C4Ph9krnlxt49syS/nwXq7W2Z6HCUGt2ItvfsF2XPejaxeiQ2WJTiVKfks6Fs9GYX2ng8VPz3pLGh568CMMgKEj9xVD/ZAUCtgYxH8TfP3wWhJDAwOzMlfVE6fOot7q46+iu1OFz5Mix9aBCf5/VT/7vHjkrh9PUPZZN8djJeQxVCzi2fwxdy8aJ8yviMmoN+HH00y8t4vr9Yxh0V+85CkwnL8l8sv226+nTC7jAqQYBoNN1Lvbbx6944225nlyttfH3D5/xvv/j4+eFcbQVuoTBqecLJjA5SjG/7ISrlFxylhCl5yyPgFJX8d1ZPculKW8I5v7I8xGGzOBCMenMoBhPx1bOKsRrKj9UFXrdEKzffRXZDoQ/XixIfVvq9A3Oza5jjbNhfPLFBay4/dWwPgHzHqbw31tKobUh9Alfn/FdkkQlcd8kZitRKpoYHSy719ZxypUUSaBu8HihZE+jZ3J2ZmbG+/zggw/iV3/1V1PH9eCDD3qfp6ene8pXjmwQqpxV2RrEUc7GnCGMItb4JetCBag8O96L4cepiSWkUt1MNY8AgYxk5Cxcv5vAaa94eNdJ0tsaqGptYcdICpiauFVJBWber5qnkSMR+kQ6ZRVt1/JL7pE9PahPAdRbHVxZaqBUMLB/11DvmYuBK0t11FtddLp26nvCVxFscLNvajD2Bn8vX14DhTuoUYTR1QwddxBULprYp1DdLq01sVJrY2Sg5Fn3rNfbmF9tevkMRaxOZLYyUm+yc8udh/UQmwR/gJBDjctLdW+VCeD0AS2bwpJ2zjbDZDIKqDYE41ewCOAOOX3Q5E9MVS+t1dtYWG2iG+d9ypEjx5bDtikuLdbQ5t7ZscESJkYqSlsD2909yjT0GwqHtQAX5jcAADPjA1jdaOP0pbXEeT4/t4GCaeC2I5NunvzfElWb1P9wZnZDTypbFFbINfF1r/M5vD4dKBdw9/XO5NVQtYhm28L0hE/OerwoNP6rId0BeezNvEhFC0XnnGLBuVms78RvDMai4flAmRyU7USjlLPyfZFtDQLK2ZjkbGrlrNd56V//it+oKzwrFM+fXQ6suru8VPc+R20IBjAxnnsfAbQ0K8nYOQVuMuOSNDlRKsZ7mWQPajZB0rVpzxvw6tAzOXv33XcDcG7EV7/6VXzqU5/CD//wDyeO51Of+hQeeugh7/urXvWqXrOWIwPwjVBQOev8FZc9+Aowing7GgZ+V1XWChhcZz9q+X7cpYmyR40MO6RSlTe+2CzwZCTftlBI13GVyDX5hs/fBCdmWPevUhjN31tKvbMCcSsSC7wGV8ejyLFdkHHfzCDAre7AIS1ml+u4stRAtVzoOa64qDU7qDNT/9T3JFhLHN03itGhsvp0CWdn1x2v9IjzvGVR0iTQYFV9v54/u4SVWhtToxXv97Oz65h3l4AlsWHRQR8ybZzczGICbGpLyz9u1rG5StrSNGBKKjZou+3IJJ5+adHrS3reiYmVs+L3uLYGBgHedOd+fPfEHFZrbdx6ZBLTY1Vt2OX1Jh4/tYBy0Qy8Z2eurGFhtZk33zly7BBcXKjh8VPzwjGDELz93oPiWFNqZw2DnzKUxBUhFUCpYKDdtWHZFF23khodLEVOQFuWjRfOr3jf25za1uLqPkY4xgHLJk+evfGOvd449zvHZ7UbZbOmrloy8eqbZvDQk5cAAK+/dY+QhwvzG4GVT+WSb0l4ZM8IFlabeM3tJp47TXHtviIAv43wyVk+bU7NGkM5C+pMnDGwpoXZ1rDJaS8t+P0hcSOy8DZJVM4GGyC5WDRblihekxS8UU2g0aNyludj+gVCiECYBn/3yx+7Z+PDZSyvOyTt2HAJK+txlbNuXKwvQYFGO6ic5VdT8/0MPos3HBzDwelhbyI57B4FyNlYpHlvd71ncvbWW2/F7bffjqeffhqUUvzkT/4kPvzhD+Pd73537Dg+9rGP4X3ve5938++44w7ceuutvWYtRwYQlLPSb6yC51c1CMpDKlYqfr0XXtF4jWOkBYF/vlgx6OOPel1kj5pg3oINiRz5ZnfcBTJSWmYgePFucr62CpRrkKKU0IrATljFAzake8t9UUUhHss9Z3O8AsDV5luYi6zAJlcSvo2q0zO8HbqqikZ0tFUTkP0iMbN++onjkyUtfYTMzW5SsjsW8kBysOoOM9zDvW4IZhjOEt/w8aoft2kYGKoWvYFftWQGNvvk0XbNbdMPiHPkyLFd0HSVddVyAeNDZVxarMGmjoe7v3KDV85y9ZNE2DJ4KygNEiBoSkUT7a4tEETDA0Vct280NJ/triWQsx1uYN3hlKvyavE4sLn88ntp8GTY7okBmAZBp2tjbqUBw93TBQCGqiXvvOGBkkDO8r954G4JWyGxe5LgT//tDCZGTDx+6orzI2drII73Q8bwGl7g0Rfm/HMMSTnrkrPe+J27bt533IjgvXkhWpxVwS9eEklrfy+YeAK07W5rAPDvjf53fpIAcCYrGDk7PlTxydmQ+2Fw5CwDpRBW6fDHGXQrdPbvGkK1XPDDE2B+pYuBsoHBqgFKKX73r5bw/Ett/OI/dyYaZHLWsmlAPCH309P2GTNxtP/whz+M17/+9QCAZrOJ97znPfjwhz+M97znPfje7/1eHDlyJBDmpZdewhe/+EV89KMfxaOPPuqb95om/vAP/zCLbOXIALKPCI9YnrPCGxuvM66aSVOBVwLZ0gsrwyeNowjfcHVRGHFMpHM2D0EykuUj6r68EqFUScfmZsM6Bf7nULuIGJ6z+eg+Bw+eAOoHMiOV+NGMi0arG9g0Ig6aroLVtilqKX1beVTLhUgfS96cP+29FpuSHp6c9pZp2j/v/ARpcQ8+qn1K0371KiRNWx79y6KpPX/jIo5f4HZHq2Pha09dQqtj4dYjkzg0MxwZptbs4OtPX9Zev2EQ3HndVEARJg8kZd9GW1KhxAVbWlssAK22a2sA9dsg9lsTJRN7QEwpdVT4CQtxuWQKG6XkyJGjf2BVwa7RCu48uguf/fYZfyNp9xxV3z7U1sD9XjQNtGyxflSJhqJIOCeA+JW3TuHroiSes57HqRtVSVLdWhzpe/exXSiYBtbqbcw9cVEUKoXUpyolLz+O4uvTw3tLwu8E/GpUP7ygZpXiDlPSFk0De6cGvPG5bGvA0uKbnqP7RzG7XMd6oxO0TJCIN8GiIKJ9qJRM7/m3OqJy189H+LPseUOwTRhnGoTAgl45y6SzOnVtgXsY4cpZ5y/10iJKcpZSinVORV0oqOOU7+nleeB3P3YJ1+4v4cP/ejceeqyOz33TsUE4/pKJgUGf6GU+to4SOIqoCv9Zh0zI2fvvvx8f+chH8L73vQ+Ac3Mee+wxPPbYYwCASqWCyclJVKtVNBoNLC0todFoeOH9WSgDH/nIR3Dfffdlka0cGYAqKjMGT9rNqzO5sTuFplKJqDCS2xpEKyOjVEd+nCwP6t/DiOOt8s3jsyqrOwXl7FXDzjp/CLjOVdygKmIX7JhaORvouCnjpdI5V8mzyPGKBHsTTl1YwfNnl3uKa73RwT8+fqHnPA1Xi3jTnfvC2w2ug5cF0lCz8UWfRIg78nyu3guJLiq5WGkIYXq5lT3OSlAKfCmDshMHwn3dYdX36kbLs/OYXarHImeX11uhxLRtU8wu1wPkrNznkwegvSpniybQgks4aNjZp1/yNxT2FGgxn5k48BLBT+48cWoB511/ySQoFgx87937gxum5MiRI3PwqlHAJ5NsmyrHmmzpOW9rIPcX2PeCSdCS5gZVG1Wnad545SxPoiab12FkoLuhkUSk8ml49XGAiA7vLUXZLKiWgPPjLO84T86GxKfynGWZvvfGaUyN+pY1rI7tuKshVP62paKJO66bwtefuRw5uc8P4yyVCIf7/NZXHfDSee7MEl68uOp7pUZsgsbAE9taf/Ww/Lp/+2g5G9mf5fuvqu4er2yN4znbtShs2wZgglJnA7tO1/bK4eOn5nFh3veWNTVx8krXbz9N8MgzzvcXzrTRtSg+9y0/jv/xD0286R7gun1B5WygsOr6tAn7jJmQswDwcz/3c9i7dy/e97734cIFv7NMKUWj0RCOMfAFbf/+/fjIRz6Cd7zjHVllKUcGCCNK1RuCcbUupUIFFndQGkYI81AZRAt5UMQZV42rU4LxFgLBwPr0+wn+2uQZ4KuFj+VBfZYiua0BvKBKGER1X6WOmyIpuTxdjc8lRwj6XR4yks7KwVc22HKkmOoQKS5eRZe2A0mp00lab3Rg2VTYACAUWdyTfjy3AAHqD/aAEFsDrt7zgnJRZnm5/ezsx0GlZGJ6rIql9eampGcQgr1TA1hYc0QFO636pprPoWHcEydHKrjnenGD3jNX1oQluDw8UtMNb0prRanmeHheKNzxNQruqCVMTMT7D/qDMAc9LSXlJnfYxiZJ6q6uRdHp2qg1uxgbysnZHDn6DXkMSwwAFlO2wv2NH/s5xwxCuPGDHKfzV6WAF8moeEIjdj6PLkecsrrPIMkIOhanr5wV6xx+o1A5Xl85K+7pEks5y50f5ptKQHzyPCaFHbh87rtMrrK8XZivYb1+EaODjnJXN2EYJAfFXhM/jkuiZjW8eymlF0FyyH6pW93vUkGnLvd/dz8I75t/IXx/PaxLwMLwK6VYfN945hIMg6BgGqi7HsrM69YwCAxCAmNw/vtzL4o3dn7Zwvyy78VcbwJ//3UT3/9a57yvPNrBo8cN/MsftzFYUWe610eVGTkLAO94xztw/Phx/P7v/z7+/M//HCdPngw9n1KKo0eP4md/9mfxC7/wCxgcDO5AnGNroVpywGAolLMM7PQ0cnzl7o0K8JVC6DLzBNA1xl7cXiWuCOud00MG0oBTTMnqTkHVvMnZ2ioIHS52LGFYXStI3BpfpRb3vitSi6OuzXH1ot+kV2bRBgquc+Dmw5M4smckUVRXlur4zvFZjA2V8Ybb96bOUtey8fcPn411Ll9Hp74nHAfq3Y4EkTGlh9Y6R4oy0K4kyjjfoEW0p1L6cWPPhOxN+DQIIbj/5t09pJwOvCLzaoFhOEvxeXikhOLh62wNZCRZnmvbfvkvMnLWJlHOIP5XSoMHNfBsGKjTF9WpeVhsr75xBrtCNhjj8flHzyk98nLkyNEfyEvZxY2k2YQnP25yCEvTJNp2ya+LFOQsv3FRTFGQCmylAOArZ0lSNxQ3ClYnF2PsTi+2yTQwdpHvSTFCysuUi5ZSxOVzBOI94p6HlJ6cPv/NIGxzKjdtru1ZrbWxWmsr0tLbDMh9L0E5G7EqWLXRmKeclcqkDjx5a9k0kszVo3+sbrQAyi1HmnMKMZWzhnA/WZrO37W6T9iy8jg9WsXsSsPdiweB5t8bw9Pg5qKzS10srgXb6dPnLfy3v7qCk+faAAi+9VQH33uf7+GsUpmnFUVkSs4CwODgID74wQ/igx/8IE6dOoVHH30UJ0+exMrKCmq1GgYHBzE2NoZjx47hnnvuwdGjR7POQo4MEaqcVfmhSCVQVYFFkZdxGzR/NkpWzqrijEf4GlIlKiOUON6iWS1ZMcVmifjOgXtiABYzbusz4njnEEJ6aHx8+BYWes+oqLCg1Nt1WoWu5f8uL2+xrWBYW679c+lsDgGpmLcEIEIqaSGTyL3kOkoJ2hcoEkt9TxQK1bR56Ok8F6ollMJzcj9/+7kr3u+mQXDXsV3YOzmYPEEvtV5MInZaPdgrHb1FSJXlkLdbev95sD4f+40tD5bPTWJr0OaICk85a7NUsq1B+HxZFoXBedapBlrbUc2UI0cOBwHlLE+euuc45I3z7cqSszrC5NQdAVsy97tSOcuFkdOmlOK7J+ax7KruAcdq4FXXTwdITmfDMkcpy5SzSa2qWa7ZuEtWzqoDSeMZm4a28FG2BioRF3/f/eM8+eaf22j5CkYWRvfdphT/+NgFz8JHmydZOetZWUgnBkhcjjBXcRua+ySvMo4rQOPzadkU+m0sw9HPJipK0Ca+D8H8CORsDM9ZVZq3HZnE0loTFxZq3r3lSxW/uR0Dn1/2eWrMxMKKhbOXO6g1ghf0h39dR9NflIPVmlgeHvzOOdx8eELOOUtFe20qZE7O8jh69GhOvu5whCm6WMWhmhHzlkRwv8WtIHx1aoTSR+HtE4WoPMhLW4J5Y+ep4s6GAEkK+RmxWSKZtO5Y4kzQcy8vBXaT7BeYOTozRleBEOCuo7sCHnZJoVLOJmBnAQDffO6KtzxChYeevKj97amXFvFUhMJqBw7vc7wisA1LXo89Rz74Zsx5UO2XPiEmER61VG5ypIK1Wls4ZtkUc8t1l5x1EXITAz8x1i3tjd+GxTEMvpJmZ2U8TZlNO/HC+yMCTjtsmsTb0IsdS7I8t8t1XYq8rYGun6Y6phgYqsC/P4HJ5LQyGDn4zio+OXLsWASVs+y4z84SAnTdE9l4dniglM7WgAsj1zmNtoVLizXh/EYLmF2uY99UcNzTtSiKBeLlLal2hbVTrG8ge84qw8jXwR9EcOxrGsSze/PTFX9nx9hKBP++EE+1yBOyAMFwtYh1xWafwWbDP1BrdiOJWScOiZyNyTlQ7iKTrApm0Z6b28DIQAmzK41Y6THBkm37PqtxwRPJ/fWcDRe0iU1msBEWbA1SKmcnRyuODchCjSuqPgGuem9sbhKZPcq9UwUsrFg4fsbpJ5eLBLcfLeOR5x37rKbYfcbsoo3hqk+Zdywbq97Ei5RowjY/3zI0RyhUyz4YwmbEGITZChKvZxtmpcCDf0HFZeaKGa2YI40oiX4YcZzW37RnSPWdzu5hfkX055tdqW9C5hw021YoMQs4+Z1faYSeEwv8/QhR+IQEDSVmM0E+OMvBIyZxkFU6vSPhi9VPJLhpyhYo9TWELKtOmgcOwWWE7Hh4IWFtrU6ReNuRSbz93oN4+z3OvxsOjgGAQJqF5Yv/lfUHei2vYZO/2xI7JZ8h6PdmlCpFkVwmk/jNAuISXyb+SrLoRzgvsg9IOJ9EfZ8lVTfvFVB+cuTYSdApZ4UNwUC8zbGKBQNvuH0vbjk8oRXcsDpUaWvghaGB9o2tqCuYBA/cthcz41UvL3wq7HzmO2szz9mEjI3scVoqqsm9KGsC5RJ+F4QQFAL3wT9fNdnFi7hUyllKKQ7OOGQ1u0d8euJ3PlknrpGBIt7x6oNauyz5PvrKWSlu6WoilbOaLhof77Nnlrxxbpx+T6gHegh48jiLFak6RE44cjdRdU7BiKecVZGz7BKpTblJAOdgzR3DL6w2NM+K54ycv3umnJnf5192CNaJURO/8wvTmBxV5+nygo0hl5xlNk1sIkUQyyH5MKOvytkcOx9hSlGTa+QC57uSGtXsUlQhTWpBIBuWK+MEn6/oOPW2Bm48SuXs1oDrAgDQ2z3I+WM/3XfjDCZGKn3L3+cfPefPRleLeP1twQbzpcureOHcSiaKEi8Kojf0V4aTTioVDbzlrgPCsS89fh6tjo3X3rIbo4OO18yTp+Zxacknum85PIGD0+Ju2I+8MIuFVZ8c3w6cVo7tA7/M9if+tB0EGbIVQS/kWsz5shhIEgMJfE5OVvH3IF5blQb6GNW/sIEfv/utXP+VucEZ+2xJnck0mbta6rMehZM7CyEvaNiALDgQIjAMA4BiZ/CY6HYZGeIPrEN40yB4jUCM001XrSRfi0oFlMwvOWdnc+TYTHjEm9vAGRyJw/e7WPtZKhgYG3L69jrBja+cDRPpBMey/gaoBsaHy6iWHQpGrmcKpoFO1/bI2Y7FyMP4182nx/7qLAj44yqC8Qo3vlH1dYqmgTYnvuGvRrCJsSkKpp9Gq2N5m5LxXIHzURzPeukrr8AN52ahYBooFkwMaIa18TcEk+LnLiyULJXzrIk3Kj3+nKT79/D5S9reJoEnBtP0ivgJDrWtQTzlrGAjRvyDTITGJnxZGoycnV1WC76828ORxntdcvbCnBN2atTpI1fKcmgHqxvARsMWshc2oZsEfSdnT5w4gQsXLmBlZQX1eh3Dw8P4J//kn/Q72RwZgRVa1UujVs6Kb58VQg6mSZOHqhHkw6sijavG1dsahAzGExCBWUIm0EW7B31m+KU5Ub5BvUDwijGIMi22PEi1uVxS8LOyJDUB44SV82q6g0yDuw4iNXwGUV+jKo85cuxIbCOOQcxKzPeK0tRqzZ7f3EiGTz0DGLUsu+sN/IIEtAqsM9sN8dbuN+IuNc/RG9K0N6GTJyEvjzw4IUSlnNWH//YzDTz3UgumAbz5nkEc3F30yIligcAgNgCi7aOJuQ87oodpEHSgJpoDsSUovH7ovP3PkaNfcPxanc/y5KPBjfH48Vy7y5Sz/gSm7tVm4VSKU6XHpjQeZio7XhXJV9FFl5xlal5mFZOUY2PjKd9zNpqc9S/E/xi1EWYgPF89uiIZSoF2x8LKRgutjiMFfvj5We+8yxwBLPj1BthZdb+IhQP8CWqdIlgev7t7wOk3f2QKTS6xJGSp7rnFUbT2qpw1SH/EAwz8+6SCv2Ca05Jz+RE9Z/XpmFwY/rYx/kfeYLRUIGh3KSaGy1jeaAV4Gd+b1n/3rt0vuvpOaMjZX/2pYfzBX6+h0SRYWHHKcpggMQ36Qs4++OCD+O///b/joYcewtramvDbtddeGyBnW60W/uW//JfodrsoFAr4gz/4A5RKpX5kLUdCyB6yPMIUDOx0ZUGNKLs29GkKaXAkZMieZHGSVMapQhhx7BGBW25r4B6mUoUZyHIKxVQKEGaCq8qCC14F3Sv4QWUy5az4Xcm/K+IL+FHFiDsfmuW46hHK/vQWpQ6ZKIi5fKeprqIuN3hb4uk12QCOVyL4n4Jh2XmBTn9IMtpH1muFtlPY2VeAdDbbrIux6fz/A+SsQnEGAGs1C//+j+bB5gtOnGvjd94/ja7rMlQwidDv1L1/yvbWK7wZDIh38PPPkeOVjBPnlvHC+ZXAcTbGUI0bCQG6HjkblOgF6hNO2CLDHwfyK43c+sRtoxnRpNoomQCOTUDLz1M3ra2BtAGVqSEreRKTEWgGMXDHdRN48sUFP2+aqlMmQeXq0TQcz/EvP3ERIwM+AUYg9ik4MaP32ZA6B3Ie+Iku7zoN8VnLWN1o4XPfOeuHYwS+dHv88Ow++r+FWj1I33m+oFIy0WxbymtRIS05y843khaahIjiTBgogkIyQP0OKdPhTuPDM1tLmegeKBfR7rZxcHoY642Op9D2wgWU2sB1+0sYqhJsuJuBTY44iVYkOvLafUUMVoFGE3jmxQ6qA0DBdd9g77jHx3gJxrpMD5k+tVOnTuF1r3sdfuAHfgCf+cxnsLq66s2AhKn4yuUyVlZW8Od//uf40z/9U3zqU5/KMls5ekDYroIquX1gppBvdGIOjOPbGrA8yspZhWpCUSmExamrZ/z7ofhxiwaYXlZZg69ZBqHrYPQ734KOSzeDyCr4DERc/rMm/pLDOOSs9F1V/lQNUUAFoyx/OTubQ4/NUhD2OvchVxlx6+p+QvQcSx4+i7m0ZFfv1iG6nzWTRFH1NVMsqvw8VWmxgRpbzhmLd+wbV7Uz2NmdkcsgqPZL3EAidGVFNYAkiK+cPXG2DV7IvVZzl/X2bGvAD/ejwd6hgK0B151IU2fH3HYhR44cKTG/2lQeZwpMXwgC7z00QLwVJEWlLZA4vmTfIjcEk47Jm3YqiTcCFN3JK7ZiwCfalJemhSx20dVVQc9YB7tGRV8AXfiZiYHQfOzmfq9x+3m86c59GBtymK97b5xBuejmg+rH7HIe+DEju0/8BPWBXYMKQheOMtn9x8KNDWrWr3tp+fdzZaMVJPw0meb7x8McOR3L1kBB4MdB1B4EWSFKACWucA7+XuAmQ8LadJXnLIvXsTUQr5N/91S32fOrpRSUEi8vh/f6TOyRfc7nsiioxeiQiUH31fiLv9/AJz9veBvaBfpAKQUhmZGzjz76KO699158+9vfFojYYrGImZmZyPD/4l/8C+/zJz/5yayylaNHhJGaKlsDX8bpNzobjQ42uJmLTtfyKppWx/KWOHgxeEsMwvMWd/k+n68ofzDPGyiVcpZPafNAvWtz8yF1BCAd98LRePekVxChUlWnxWbFMrU18P5TEKjqgGKeVGVe1RDF4F2D4rR8dJZDgT6RnNuRVOrH3FCStyptusp8p4pM08YkSZdDV6GcDWuUCvLAMBHL5P7p+eGJbdf2RwSxvgMQn5uNMfEiRWZZ6tjjkrNsl+TRIddywyVlO1zZFpdRxrsanpqNU9Y80iSO5UeKwruTy0+OHNsbztt197FdeM3Nu72jhtRm2Ta/zNq39+EJ16gxnXJDMM6DU568lgkznZCG5YHlqVflLLsSXVXO7zjPD+EDdb8mgsN7RoTv8jDu7mPT3uZc8r1khJzDJ/DiF3X7I2eBHzPynr4Mdx2bxjvuPYjpMX9jsfHhEr7nzn3Cv7fcvR/HDoyJaXkXFEwLAJ55WW33IN8l3jpqqOqTf3FEDV5blHBsrNvkLGtEKWeFDfJosL/H34Ow8T/RkbOAsCEYl7B3rorj8GwNuCRNA3jDXc5Ewv7pAt523yAarS4KRf/5DQ8YKJcMDFb9gMtrxCsjwf50uvufia3B7Ows3v72t2N1dRUAUCqV8N73vhc/9VM/hTvuuAOFQgGGYYQWxDe96U0YHx/H8vIyvvKVr8C27b7LsXNEw599CD47Q2pwgKCawLYpvvT4BSHc2dkNLK23cPM1E57nzKtvnPFm1+w4gwLwlYJIfqne77gqUT5OFcJsHqLC9g0SgS53BCKCbYKtAfdZc06WtgZ8YolsDYLBg1Eq8hmIWln+YjC4OXL0C1m94/1gVDeRmhM621vNCEbUAcHJtPDzVcrZsMGlt7ushlBTIetqa+dWgzss51T7JRV0q6BYGWQeg86X4HLa2UXgf/3jGmR8/UnHd/DWa8v4xlMNTzHrbwgm2Rpo8tfrFZox1Eo7rATkyHFVgL2X8t4PrO/Ob/rMi4/Yq86TWaoxHf9Z5eFKuPNkcZNMmAmes1x4pt5ltgapPWf5XY8QHFPfe8M0Li3WBFKSr9dkEZIu+eDxYO1oKJQthBBuMy7NyoQA5yYe4FPqKpSzAFAomBgo+3RXqWBieCCGdabUzsnj00arK3zXtQnNtn8eX2biPE/DmyhM1uL4/cE+k7PuX23uuBNUfBKfuzCRnfhe+sdt6jyXkmEK5xNuYkRFJbK0LE4baBgEP/j6IQwNGLjvlgpMk2B5uSXYGowOGTAMgsGqGB+zb9RtIpq0x5AJOfuBD3wAy8vLIIRg3759ePDBB3HzzTcnisM0Tdx///343Oc+h7W1NRw/fjxxHDmyh2qmg4F/6W1KYRAiNIwz41UsrvlLTHjT8/V6R/htdaPlkbNJLQjsgHI2+BLE5RREo2kqVCL8chblkveQ9PsJOTV2DZHkbMz7nCWibA2yMNNW2RrEiTXoOat4xioCPgbvGtjtNUZ+clxN6HeJyOYll+vR3rjabK5ZeE+jolSQSolzQYMfk1x/Ut9bfyl0eAjPz07j5ynDX7rNVAFxwrH+ADuXoKfn2BeyfxOwkyvwPuadV4Z1Oe81vq9IKfAXn+1io76ijee2oy456w6c2F+ZnE3Ezia4bm/iQjPQcl5F+V2Ig4Qvf44cOZKBnxRSEDr8ik+vX655H1VjOr4vPzxQQqlgeJuJ8enYPPnr/iYrZ1kbbNsckwviWTAwMpApZ2Pac3qwpcuTa6o9k4PYMzkoHvQuj2Q6NlSReITwG1cRYXzFr1IVehmBSWs/RkuhnGVQEfVJwZKaGCljaa0Ve3+ZydEqcGEVBdOQymR0Ptj5na7lcRK27ZCAhqG+VoBXEfeZnI1Uzrr5iXGvwob//FUIXX7Xez5oa0C8c1UrnVlafJoGcfoYb321/050LFsgZ8eGTZgKcpZF421ACPXvcdEzObuxseHZEJimib/7u79LTarefvvt+NznPgcAOTm7TcATXTL4mQzbpjBMIowf77tpt3D+c2eW8OLFVSEMAy/Z5yvrMIi2BsE8ixeivw5VnIDrYyKQtfx5qsAh6fcR8jMiMYlOmmpwkRziLJk6Lb/D1Ht6vM2DyjMqLCQPVU4NRXyyRYEqrYCtQT44y8Gh3xyV3zHuteBJOe0l49LAJQtEX516xj5NGoSXB2Y5itFcRNjEIMCpRhSdcVV9wzqzXcsZpPrEU/wykibMTkZSYn27IM1kRBpPVRU5CxChr7heAzbqDtHwPa8KehUe2l3ELUcc77+upJwtmNG7Q+uuxX9/os/X+vwlmQhSwC8/O60E5cixM8B3SYSd4L3xkXuepk4QxiiKMZ1M5lTKBbS7bS6M30iwdlFeZerZGnjKWc4+hciTQL7ndmpbg5jjX+dU6l1GlKWANg5F9eYrlrn4IPrw8uM13laHGMTzew2QXlx8zIZGNUFdTKhYVcHfcMwl1eXr1FTru0YreO0tuzFcLeHCwoafjxgZYWXlmZeXcPrSGu69cRrfeOYKupYNwyC4/6YZTI1WA+F8n+J+j++dv/ouoOK5a7IUxlkIylnuOIXzXALPnFPOqso9K0/8q6d6vzpdC9WKn6/xYQOmYWCgKubVn4CRxA4pb3/P5OxDDz2EVqsFQgje+c534vbbb08d1549e7zPly9f7jVrOTIAv+RABk+ermy0MDHim4fHqcR5ZaewqVjMOPhKIXSZOeJ3hoW+N6UQ5k65NMI8ZzcfYqOltzWQZpb6wCuoIMSvSUtFeqYFf11ecjGiDSpng+f4tgb6cMryF7iufHCWYwciw2KrU0JkFKsSIpnYKz2bUjnr5UEXuzhpFnW+8xv1lRKmSiESDMwPXG1KkwhnffT47Po9KZGDoYcXV9UOahSguk1I+O8LK87fa/YW8W/ePaVM8vQFh+xgXrOdLbI1CFuimLfgOXJsb/D1zmqtDcOoodV2ZKjLGy002OZUQnXlv9lqqzrpd3mvCu4suY+jtTWgoq0B71sLAIurTp6HwvfdCsDz1fTymyy8TFbpxDVxSF/deEpQzrrHqXDznPGhzYXRgU0IqtSiaZSzct+L7YfD4tetiAxYMRDiEagF3nYqRjamxwdweakOSoF6q4uLCzXPw9a2KRbXmqHk7JYrZ5Vje45oJQRjQ2WsbLRwYHpIGUena6PN7U0k8jQuORvwnPX7zyrSlb0bvDBQda/aHRv7Z4BbrrMxXB3Aj3/viFI5yy7Js58IzCIoL02LnsnZc+fOeZ8feOCBnuIaHh72Pm9sbIScmWPTEELg8ce++ewV7JlM1nII5KxAdvkzHmHgfUqjlLPxrRJ4Mlb8TdhQUxVPRCXVL8h+unpbA6khSTCb2gsEblaTVly1bxz4ih/iP5M44TR5Eo+xNIIEjcH8k0LKn+57jqsc/S4Peo4uEeTO544j11QTRZv9Msa9WUT+oM8n7xsbtzPOKw3i+pmlHeRFRrhDsFVFpmdknV/v/RcjjkPOzi87n4/slbZA5lAsOud0Oi45647LCqZka6BFbxcctPzQx76ZtlA5cuSIAPdy8hOV/KpNADg/53MMRCCLwB1nUXL9fWkcGBw3OH952wRfMCP6wusU+rIacX7FCTciORBEIaCcjRGGXzERECHFrOtUU1e6sZ+onPVJab6vwfw8o8Dub0Hh/1Aq+J6ksQlLKc8sr2x1kn64qo+fV4BGrQ4GgEMzwzgwPYQvPHoerY4V6Kvp7As3a0OwqJUs/PvgHZPOed2te9BsdzFYCfYJKKV46MmLqHP+vk6ZoKDUWa1NbZW9g//umUpbA+f3KOVsq2OhYAJvfjXFO1+zy5tQGPC1iCiY/rjf1ii8k6LnHbfW1nxT/5GRkZAzo1Gv173PAwMJp4hy9AV2CFFKCMGRPSOewfV6vRMalxyDrVHOers3RloQOH8dfxq+ZtBX4lEvDJ+k7JHCpxHmObvZ4zaZINERnUGCMJtKJAq6XRZ58D5QvcPvXSSyNYhxCr+ZgB+OvSMsmmBEgbIUnVSOqwiZk14S+veObw96Ng1pljbHwnLvVC9ywpRjEOF8B91ULP/SLTPkJ/LStF+9tnl+ud8hDNcOyaYMjfArVpgkl8yWlRqSOogfOC245Oy1+/UbshS9zeqc7/yGYGxOwZbUVcrMcweESdsI6JSzvRb4HUvu58ixQ8Avh5d5qYFyAaWiUxexv4HwChGOztZABW+fC8ptMsbqrJANwSCkK44z5pYcYmo4KTnrZTymOokHCbbLsUMr7pFO4MXfI9WY3nmOwT6NClaIcnZ6vIr9uwYxM17FNbvjcVUyOe/ZGriNEA2MsaPH1PykeNx+j0H8SUm5TdJNrG+aclazisb/3YGQbSlLpkGUxCzgXEdd2niNj0KnnOUFeSqCmv3O8yTyaZRSnJ8PCkUNg2BsGLjj+uDkbWCyRnM8Cj0rZ3ft2uV9np2d7Smus2fPep+nptTLnXJsLqIUp7cemcTeqUF845nLQiFXVjrE/0MRZmsgkl068A1Y2DJz8TqiCF//98CSBb7xVIbVp99XSAQ6y4es+tDlazNtDXRJ+Sro3tNT2RrEiVYmVcOUs0J5c/8ahMACVSaWuxrk2A7Iutj1Utel8bTUIp6wQlkfpL4EhWomZjA3XXXogL2KfFxxw/ideUmcCteFaRqwu7a3TG7zsdMqwh3KznKIfcfDCpwGuk1IVLYGYeRswR2ZMDsDZm9QLCCVrYFzKfErHI80CVGUp6q/dn7xyZFjx0Duwx/dP4rVWhtnrqxjz8Qgzs6uh4ZRTeTwBBwhJNhe8w22NCEU3BDMF9JQAPUm8IVvEWzU67Cpgfe9y8KNh4D5FWeWalS96lsLll6Syf+g2Cd5U6CqNbXKWY6wVu0jQyCRZiF56IYoZwumgbuPTUdlPRTs2fuesxGMpAKmoJyNn7bvWyz21aKUsyrVaJZQKWOVJ4QoZ8Oga4OZmNp2yVn5Mnlyf2Z8AItrLeF3dn9E5WyQ4NVd1nX7RrFeX8WTJ7LhLWT0rJw9ePCg9/lrX/taT3E99NBD3udbbrmlp7hyZAN+FlKFx0/N41vPXgEAYXaj3uris98+g8dOznnHiPRBtDXgyNnYtgYsbLQyMolDmBGsS4Q8+sssRPR7Yy0d5CuTzee14WLe514hLhlSpyXPLPcCvx8h7v4ZGU7XyRKOiTPafILyUm8xbono33GkRI4cekKipyokg+pnqzmPflShSeJkHdiCtClCVBRKhWBI1RR4/mllATsUUcT6doXOMzE0TMhvgQkDAI1WF6s1xyvW4NVB8NVCnS6w4vIhR/bpbQ1KBVYunT6BvyEY8XYsdwZVyZ9DnNeK5bfe6mJhteH9W6+7Xrhd2+sPtrqWNh5d2jur9OTIsXMgt1H8+14qmF4fnm8rVXue8IFV40td+8wLOKg0IaTfEMw5fu4ywdnLBIurFMtrBM+ccuqWhWWHQUqqnJXznayb4uYxgqzWYaPRwXeOz+Kbz1zGN5+5jEeOB8V7j77g8wOC56yUDaLZDEpG39SiTGXp/vU9Z5NHJZKz8fPpWWMEbA3UE+vMS3XTNgTT/e7+TTu0Z9dnGARH94/68br9AEagEiKWTuqVXyg9eT3PWWZDoLhNwdXT/uebr5nAG25zNr23bb1Cl8tQIvSsnH3d616HSqWCZrOJL3zhCzh+/DhuvPHGxPF897vfxSOPPAJCCHbt2tXTxmI5skOUOoD37WGwbaDVpqg3geW1Gu46St2KhXhxUYgNoqiidf5G1SniLBtP7qouhIUJj9OLl1KtrYGWzAxpyPsJWd3sdQ40Sy7kcP1GHCEXMfxn2Sv858SllyBeVj5VeVUR9/6stL7BlhumfFljDgGSwiJ7RPSgUkbXG5Ir8/Rw3tr4qkDN581ASoYmbMlcV/KyC4TVxOmcbwnK2STZ6vXJZaqezhELSYu72l9f/Nrp2vjS4xf8wbHU6LMBqaOaJRgdIhgfNqEDT5x0LYfUBRxbAypMyqvDByyckOy62Xt0ZamOK0v1wO/rDd/C65Hjs3j7vYeUaq0g8pKeI8emgGdn3Ze/UDC8Pjw/jmu2g8umnXOcv3zdEWb1xweioMlsDeBMRvGoN2202jZWa84PownJWRZfIiGOZNMW1++VP4tS4PzcurLu5LG07qgZi6aBgmmobQ1AYpOYnq2BmU09y5Jdb3Rw+tKq109i8evs6sJS5/toJAFxyk7tyrYGGtYzzOIhS0RtCMYQZQupA0+4D3HWB/wEsW834Vsh8TZGqvLg2RpI76jqHPEa/BN9T2sCy6YomARtW/X+JO939UzOVqtV/NAP/RA++clPwrIs/NiP/Ri+/vWvY2xsLHYc9Xod73nPe7zv7373u3vNVo6MEFapyy+jQZyC/sVvE7xwxim0BZPiJ7+XIrDUkoozPoJyNkKt60XDkWGi8Cf4GnDVQmicfrxU8WI6f3V13VZ3u73dvTXKWfmu+KRinzPGQes5S9SNXRoIA/4EnBTlA1KNOlqRz6ANh6L85WxsjhDICousEeaHnDJGFiH/LRGypGa9kUHEe6YabKW9IyTkW/xwQUTfl+Av2kFJhKyBkWCWTUPrL30W0nU89fFtc6Qk1rceitnEHiDfhlbH8vobo4MlHJwewuxywzt7ZnwABln0/GbDVLOAQ8IydLrUszcomIDl/mTZCQtNggpnz8QALi/W0O6KbEnXstFsWzAIcUQBcAiQdseKR87yI8ocOXL0DarXvGgaSjKp2ebU78Ly66DgQhbD6NSpsm8qEGFrQGlgk8N6C5hdcvJWLFCU9U4wSrBxSpQ9IQ+5ZuLHu6HhJQ6X1Z17Jwewd3IQjXYXz51ZFoLced0UTJNgdLActDXg0uTTbXdtLK+3MFQtolgQ61zP1kAzSZ0UrE7faHTw7MtL3nFGsPaunI0fTt5UjkFra7DJytlGq4u1WhvDA0XlPjNpJ+L5d4aPlxdKUUrRbHfd99Ut89y4XEVQexuCWWI+VecwyHea22MOtg2UCgba0FmEJSssPZOzAPBbv/Vb+NSnPoVOp4PnnnsOd999Nz784Q/jbW97W2TY48eP46d/+qfxzDPPAHA2FfvX//pfZ5GtHBkg0ayC+5cRswDQtQhabYpqhZtJcF8gredszIbEn0kSX4bVjTaefmlRmdHjZ5eEHTyLJsGRvaMoF/23zNA0utEzpoHkNgVUqvX4TV6U50mf+25rIFTU6rSidnxMmXAyW4NgcFWUgfh80l6fVtDWQMRavY1zs+s9XT+Bs7PnyGDCHlyOHDHhdXi87y62mFxLTo0GPariwleHJJny08cT/EFkkeJ4mXuKDmlQ4l+tOjAjcx1yN85V6Biuq4Ns2ikcsoxUTyd0MCXWAOxdKJoG3njHPnS4pf4EQLVcwOyiiS8/4hw7dqAcmjQ/6OlaFF3Pc5aAuPmy4rsJJL4Bg9UiXn/b3sDxK0t1fOf4LEYGS6g3Ox4BETf6nVp+cuTYKQh7F0sFwxeCcJ1tnpzlw6sIWF7RqQLhz5PqUDYmY3mQbQ0C5GyT4oUzjrp0cgzeUu44cMheRlSJ+UgC0YM3dupen2R8uIJ9u4aU5OzeqUFhUotvVXgegFfOzi43MLvcwGClgDfftV+IL2vl7O6JAdxwcAwXF2rChuceqa4jQUKSFzZsTdAJ9cqK1xYa6HTtGBuCZUNU68Cu4fSlNZy+tIZj+8dw46HxwO+8LWQSiOQsly5na7BW6+Dzj54XbQ288kOU94C9G+z2qW5T1KbqpmCNInsd91YGMyFnDx8+jD/8wz/Ez//8z4MQgpdffhnveMc7cN111+Htb3+7YHOwsbGBj370o7h48SK++tWv4itf+Ypvsmya+PjHP46JiYksspUjA4QpZ8P8OHjUWjaqleASNltDztpSA6YDy1O9IS5Jqbe6ePnymjLMOYUNQ8E0cHT/WCBe3VJ0PTebkYooIeT2gCg6H/x58ud+Dxji2BoYhjrPaaCyNYilGOQ6Ujqqwidgg0qkMJFasCyJB46fXY5cAhQH9VYXr75xpud4cuRIhhS1SC8jhogo+wpe0sGwCdcQtlEm68DKnrNRYAoTYYIz5CZqH1mPN36nkVY7mYqOm/dYEy+BGU19Gi9d8D/fd0vQA46HYTjespYNtDnlbLFAQF0eJXwPu2Dfxy+7WZS23krATi4/OXJsa4QM1IqcrYFNganRChZWm5gaq2B+pRk4X1VTyM2/TnjRaHUDftTeJk2mqJx18kO9Oo2NQepNimdOO+Tsvl00Ud0ljPc4oioynHT7BNIqJDwvnKXUsboBnAk7/q8cRvguCFx8EpzPd6lgoGPZqDW7WFgVn1nWhGTBNHD9gXF0urZIzpqKcSDitTE8oZdsQzDnL7vGoumSs9oNwXyv1n5Cjn3N9Z0P/K4pg5cXa9q4iwVD2GTUUEwUUAosrjX4JJyyyPEAKuWsZ2vA2RAsrYnlKWh3It5rfoWPRcV7TaS/SUV7mZCzAPCe97wHjUYDv/zLv4xutwtKKU6dOoUXX3zRO4dSitnZWfzcz/2ccAwAyuUyPvKRj+D7v//7s8pSjgwQRkjqlmoXTIqu5QeoNygw6v9OpEoGkJeJ69Pk4cUj5WOwUsC+KX9bS0opTl1cBQBcu28EpjvlMrdSx8pG21NlyPEGPVojZkw14foOacSsur+APPvrH++/clb3RZ0HSmlPeeJvRxzVmRyOdTPCbQ34cNJkQgQRqzqHzTLvmRzAcDW58nW90cblxfoW7rqeoxdslvdmz1VTgJ2j4tcUUW0mxI6SuoMdjeD5ya5frCfW6m2cvrjqtYHLrg/blaUa6q0O6k2ng8g6vUvrTWGjTQCouedo/cU0l8gGCi9fXvfUjqF3Q/Nj2mdJ4zb22wYh7ON2Rsb5jXDLUM7+sqbprfdVcfuxSmQaxQKB1abodn0fuaW1BoaHnMhtG3j6pUUUpDJvmoZiBQpPz/YOThTnR58jR44th6aLAsDxnOX3t7jnhmlcWqhhoFzwyVmh7uLJQgifCSFodyzUWyIBe+aKs+PhpUVfbHFhoYZ9u4Z8oslTzvokomX7tgbDgwRrNYrZJRsvnnfIq73TFPwe7ufm1jHvWcc4KBVN3HBwXCC1eKRpZuMrZ3l6Ft64uuASWCrbF3mMxY+h+QVEPNd6/827cebKGs7ObuCZlxehQtJJ6igUC6K4TL8hWHRDwPfRkjQb/oZgrnVDwQBaIRuC9WtzNAmyiK4jj0G9yRC1uOCRF8S+rIw9kwMAHMJd0ENwkywqTqbZBj7xoIE/+Zs5VMsGXncXcGiP/zu7b7b3PlN8/ZnLoXmRnzfvqmHbgNLZKOXtz4ycBYBf+IVfwP3334/3v//9eOQRZ/0SI1rkl5AfEL3qVa/CRz7yEdx1111ZZidHBghXzqrDFAt+ZxoA6i3nJRCr+DBbA32aPPzlKWJlMFQtCrJ6y/bJ2ev3j3s+NV3LxspGO6Cq1C1P9yweNJNyWzW89Jcauw2+xnNWsJ0TbA36mz++4ozynAWcStzshZzlOk8kwWCan2kDVedVvczJT89JSibFFWlJ31nDtX9qCHunErr+A7i4UMPlxXpuZbfT0ad3Mato5YFPFsUt00uOyhBRfsw0iSjI/r+nLqziwnxwRcdavYM1Tq3R6jjtXKNl4cK8Wm1QKYlduigSjZ2/stHyjsVavSBNBKZHenJ/K5C9d/PmoDciMYbaKuRMdowVq+GBeKomNh7uWL5ytt7uYMw9blnQrjZRqoUyeGS6O5E46p1VfHLk2LHgXzWDEGF/i1LBxDW7R7Cw2lCer6rv+XFCQ7ORGOCQYrZLMm64Gwh6tgYGG6v551sW5epIh5xdr/n52DPFnWtTPPnigrK/PzpUwsHpYU07HqMul0RIgloxLDjHzVL4JJ1KMavLDj95zrcpfB6KBQP7poZwdnZDULPyv2dNSMretkyZq7U1CAFPyCcZr7F70OWUs4C+v7ZZ5Kz8DGWBkLyiWCZSy0UDA5WgB3292UWrY3miBJ3nrHKZKwGuzFHMLTmFstaw8OI5gkN7goJARh8RAlRKpqhmtykavOWJdKsdn2QKSglsS+KserztmZKzAHD33Xfj4Ycfxne+8x38z//5P/HNb34TzzzzDFotv/NvmiaOHj2KN7zhDfjRH/1RfM/3fE/W2ciREcIUXTrFkTxx0mhyazUAELCXggbC8DNm0bYGYh7lPKsOKKOUztcvV4kgjROoNLOErG72bQ1s5Xny534PjOPZGviftTNQscEN+HsYTKtm45S2Bux8zfNXbXIWKFtcA5EGfjuVj/pyKNCvlzyMldlERCr5lIF6TFShDkwDpljdNzWIsaEyLi3UsLzRwq6xCqbHBvDS5VU0WhYGygXUW12MDpawf9dQIB7DINg3mWxi54YDYxiuFmHZFM+dcTa8CNuUMevaJa+tNh9x77k/SE8Qach7yLojhZhte7FIgAZFp0PR7rCBpk/aWjZwbP8oStx+AXMrDcwtNwKNsDDY7+W958OmKLyp6qkcOXLERtT4K2zfCPebf67id368FZbW7okBrGy0UHNJJsBf5ckIIMcP09npXVbOYt6P6z3vHEa1suJ9b3csUFdAcvM1jg3khfkNrGy00XLJJFU7nqjuSzgBK5xGga5raxC2UWKAU+MELoJIi7uUYsHArrEq7r1hGo1WF8+4G3URAtxyzQQmRiqZrwYtBchZX8DFr/SM08bwXGmSVVuy6Iqpg+WVvwy2NBHQLwSUs12ZnHVAOfsAHgemh70yzOP42WWcvLDi+UEbJlFuTqfipgmAlsTbN9vc7AEXzveBBu6/abewZ8tqrYWHnrzkfZfH14Q4ebKoQ5qr7nXau585Ocvw6le/Gq9+9au977VaDaurqxgYGMDY2Fi/ks2RMcIISX6zMP4FYRs1VMoUzRZBoyX13F1posrWQDVrqYNsNO3nWb4GXXj179p4PY5ZnTHCUWRbAZa61tZAdCHyPnVtim8+ewm1ZnAWksE0CG6/bgoz4wPC8bmVBp58cV5rSg6IyxwuzNc8tUu1XMBrbt6NUtEUKnhKKR47MYe5lUYgLoYD00OYGR/Aky8ucMbzZbz6xhlhOUwiW4NAQQieo2oQ/AZZXS7ipN3rzppJrjPH9sW2VxB65UueDd9qiB2vsLMAabCVNClNZzAu5DywunPPxAD27RrCRqOD5Y0WJocruG7fKC4v1tBoWaiUTdRbXQwPFHHdvtGYafmDCBVKRROH94wAAJ4/s+Qs/o5RiWz9886RCBryIRZU7aAmqrBywfqFcTdrKXKDTzYANQzA5MjZgzPDGORUN5QCc8uNvvXARG7WTyX+IDt/c3Lk2AxErdLjx3eCrR4vXFHZGnArFcNee0LgWSg4Gzf5mzfx/XzDJWdtnpwdMAA4FWapSPB9r6nia0+veNfEyN5SwcS1e52+QL3ZxcpG2xtv8Xu3JNmMSb4kUTkbr/6i8DcEK4TMxultDfyMECJ64jPF6B53IpqRs6bhbO7dDwSUs1wbxkjyuOCvOckWK2wDLPZcWZ50nrObpZyVrz1grSfxLPL51VJwPyLAJ5/FDcH4sujGq7mHzoSuf35LtMKF7b6LgjBKypvsGCGnReD0SSzbOVflieuFVWdTi76RszIGBwcxOJh8uW6OrUUcz1nCTSFSCli2c3K1DDRbjqk5wJGHinS8XSUFlWt4peIZu+t27lJ8FT1L1CpIQ/PS+wSaJkNbRJDJvn2q3UiF86TPa/U2lrllrTpcWqgFyFlGHMSFTSna7hLFdreNpfUWdk8MCM+63bVwYUFvEg4AZ2fXQamzARbD7HIDzbaltDWI80hkoklta6AoM6xcaMh+1cBNPmLz71IvyMnZHYl+P7aspo3CNqVKEZkTVwaEhU94RlxhbP80PdSdzCS9c/ErG3iYssJE27tLk1YMwpUQUEoDK1/iRJ+6zdth9VXP17tFEInEuGEcKEubpBTiem3ygYBPu6keiwXAvAo7XXi2BqbhDzZV5TTOQDTDqisxYtdTOXLk6Bmq90w1PtK9jhwFFzg3Vh+Ii7je6sJ2mSB+wyrTIOjA6QcIylkXI4NGYFzAyNkyR2wxsq7TEb00DQOwLXY98Ws/fxyUvMakFOjYMWwN5DS58RX/SHh1qC4//dw7RSZnS5wHrU0pjJStShrlLEPBszVQd9g2Szkr3/eOZQtqYvarrem/VspqGjJoJUGUnrMiBeun0Xa1ZgXTsdkMkLPM1oCbuAh0uSOeDyE+H2TbVL3aO2W53DRyNsfOhEdIqpSznkzdVy3xHeZK2flbl2wNwtLh34WoOkU1qwmoOs780CE4OA/MhmhI2yiFZFYESFL4M7luPtwPuhk1J4wP4n4ZKBfw6ptmAueem13H6UtrynhYGTiyZwSHdg8rz3n69AIW19wdR6cGcOzAOB47MSf4KQJOI2LbVFDhvumOfUK5aba6+Pbzs95yEgC4Zvcwzs9tOLPPlAr3Q2dRoYR0jtrWIHhqwHNWikf1GIKbzYnxJ4XO7zbHTkO/OlJ9ireH4kZ9drZ3JI6j9/ckqzeN1Xc+saSYnueO9q2EuGKdMFuD4DKT3tLUqSm2LXZKPvuMAGmhsYDikdjWwCVnuxZFh70jpm9rYFsIlEdP0aToE9IsXiBuRJhKfZ+Xnxw5+grfik+tbGUKRP431YbUbiSBc3lRUvS4wn/ha82uUs3IyDPb9idGKyX/d5VHd5uRs0X/N0ZmtbtB5Sy7qniEsph11VLyKPBjT97WwM+JGvyYnB9XWTFmjPvJQfIEs2kQQe2ps7yIgyRzdHKc7Hnb1CWIpRO2SjlLqVP25BUylxY2lOdXS2oaUrbDMA1DuEb2s6XShhF45OyucROXFyw0A+SsG94tWgYJcjuB1dOK5bWeSJBKRLhWXBEPW0rOrqysoNvtYmpqKvrkHFuCcOWs81cwNefq0KpLzjZaapk7D9aQ8C9D1EyY1n5A7pgLTGR4Ppx43TzJ8TAyOiI/my6KkBpTb1fHgHKWKj+zcKZBMDJQggy2aYzqulgS1XJBGRYAipxUplx0zmMqMT4fBgFs+IbnADA8UBTKAb8zMzur7NoiWHCWBvFlNoFwLCBMC1POqjpz3vmB8qdiZ8WvYZMgcZDbGuxw9PvB9UnyFzVhtd0gvKJbNJsmJ8vqO9aZ5QeXwvkpyKVLc138+acNvPZOirfcHX4uq0NpjEk9/3nvjOeeIwUStJlxSHbWN4xva+D87XQpuu4CGdnWQM5iQbesSdsJTIu8oc2RYzuh2bZw4vwymu4mXWfn1jE65I9JDk47Pu0qWwNdmyd7iQLimDisFuAFJADwwrllz4+TJ3G8lQCWb2vACTMxNCCSm4C/OWiZO5H5onYs0XOWTyuJrYHX/4i52kgXd0FT34eJDClE0VE3pE/ih+1fX4RXylbLBeGeqgn68LyMDJSwVm9jz+RA6Hk85LEhTxhbFoVR2CJyVnGtHYt67TRTeTMxljypUS3rbA1Uylk/rWoFwBrQaCJwuwl8z1mPnHUXB7OF3uz9sDhbA7kIheyp7oUxOJI4jJtN2mfoadsdhm63i7W1NaytraFWC1+OvLa2hl/+5V/G3r17MTk5iZmZGQwPD+MnfuIn8OKLL2aRnRwZQrAu0PzGV1T8LEal7Pxeb4mkpqqq8G0N/GPRnrNu2ECZl1UcXBjhs1rpqFdAxiXQNrfjLo/ZvfsS8JzlPvMdjJiDfpUaht+5VAvFzKuKK/INz23v3EC545e98CSsYjaccKazcZ5IUEGtuBTlTLqYfzkt1T0KnGPr37M42CKeKUdGiENu9IKsovWJQ+9IRjH3hjTlP+09Cd6DpM9N6kQzbzaZWCLyh+T3+v/78VWsbhB87uvR57Jm3Aol8KW20jvaaznYGSRvEpuc7YQ0czKhdVJEkVS9I4x4iLvK1bc1oJKtgfO70tbADHs+4ZPrcaC97Jj3N2+nc+ToDy7Mb+DMlXWPlDo/t4EVzq7t1iOTANSb+goCn6BuRRS2eL+FK2cpFd/z1Vrb8yctcYpX1u63uzZn/eLXUkNVIzBG8zxni7ytgfO5o1TO+nmOhnhNIreXrPYsmEQ7plGNo4lmfBe2p4kcth/gl9jLm4OJQp14bcwbbt+Lt997UPBLj4LO1gBQWDtyx/pvaxA8xvvOqsbV69yq2XJRTc7KdhiyrcFg1flbaxCo7njHndCdHnPEZe0Owa2HJwK2JjZPzkpxyJM2qlXWWuWsLK4I5DAcmZCzv/RLv4Tx8XGMj4/jl3/5l7Xnrays4P7778d/+2//DVeuXHFfQIparYZPfvKTuP322/HlL385iyzlyAhhyllWbvmfGDlrGBQlt95pNKOXJFDAKw8M0Z6zahI10F4KhG9wFlE+3Y9XHQ3RvDVbpV6Ul9XLuzrK5wHB2VHnsxph1xWHsBZ8YqTZWIE4Z5WmpY+Tr+j83aSJcjac42YTefv4irXwzoMPkXzV2RoI5KyUHX5zvVTQvAs5cvDIrHRk0N9TETibBuqnnPye9HgXpZkpS1bO6lJNcb8uzMX3A9dNSvYLYlu/KUn2DtWs4g5D/JzHnLUNAxfUU4UV4sXnbwgme846v1tWsKzqlLMUOSGaI8crGUzUwfeh2U7vgK8gVIl6xA1+g+2S2tYgvBmg8Mezx/aP4bYjk7jtyCRee8uegBITAOqtjjfhVDAIhgecsG++J6iubId4znq2BgpRSCzlrDReIQmVtzwCfrMRefEOcRU2AQm3WnLRTw6SJ92KBccDuJfxvmEQLSmpA5Eu0CCE818Pciy+cjYTmk+fL8V9ZxMEgILQBHB+foMLr35wcj/BNIjACXjkbFORCERbA4ap0aEAb2THsDVQTdI4eec9Z9OvfFUhE1uDv/3bv/UMgH/xF39Re94v/MIv4Pjx4wCC0mYAaDQa+JEf+RE8//zz2Lt3bxZZy9EjeAJM95vhzmhQyi1dMwBmJdJoyQVaXYAdz1Dnc5xCrqscg9ysuvbUhfcbb4ncZDNRmsHK1o0vxQqE5T/Uc5bvYLCDmnseNh6lXsUWQs4KLbIcpx+p4TU0+jj5Z+bnW01I8zNhcdpP2QNTdUk+CcyHY+er74H4DrGzZeLcPSdlD0Po1OTIsUnIhGDNouKM+aKrJ+TSvTRpXzX+cp0NuJyYZGKJSH+TpsfXrcUYPT1vcixkLjXwvNNmboeCXe7cSgONdnzie6haxORIpae0W20Lsyt1oR02DYLdEwOBJYChyOBZyY89bBKfwVfOxiRn3TLb6VJvQxjD4DxnQ5SzSmTANfNxidOzMW/qVfa+5MixaeDGjmzsxhSmQFC8Im4IphkjKse9LL6I7FD/3D2TAxgbKivPG6g4FV2t2RVsDX787TZarTLedPcAVjaYYSYRrktpayArZ3tkLeMTTvz4xkEhxGBcqeLlRDZx9yQomARdi2JqtBozn72BKZQNQmBRqlZd94EMkB8jMZxna9lUafvACNv+e84G4xeUsykbO7lPYwSUsxQAQa2hbk6ZcnZk0EClTNBsUazX7cDkjGoSw8s7/zxpMB0CjpylkoVIIDJFJkPQMzl79uxZXLx4EYQQHDt2DDfddJPyvBMnTuATn/iE9yCnp6fxb/7Nv8GxY8fw9NNP47d/+7extraG9fV1/Pqv/zr++I//uNes5cgAPvEU/I3fYZ5VVEw5a5p+59qzNYio5G1OORunPdDGJ5O12sZUrRSK2thJmzeN4rbfkJe8RJGEQhjui/6y9CMKrwyEjA2VM7dK4tP5a7mMrypOvkHniQKVlQMB4Z6lPn9yfGFQqmO9TqHwNZhP7prlczLznE0VOseWIwuSM0E6vQbXVKVbhjTJ9zqhQTSf44JCnEzSbWYEVZ2jwUOP1/Enn15Bt0uFCaTJ0ej8+Muz4lSEwp+escXFJzZY/azbIDMM33v3fgwkWMYo48kXF3BluR44fnT/KG46NBEaNtVkRKo6iWi/+Ut248Uk2hqwsBSGyQbwJLCcUzsQzahhFNpZkZ2NFz6bbOTIkUOCSvbASEzBC9IIju9s3busaHvjbggmL83XYdAlZ+vNjj+BZRIMVoFDu50xjO2OibpdG6curHj+nSXFhmCdri1M+gq2BjHGF2H2bmHBVT8FlLMRAfgxFH/vhqpFbDQ6GKoG28833r4PlxZruGb3iD6tDDHm+hizjdBVRaAf9bz87JhytgO1rcFmec6qxqzHzy2j1bGwb2owdbwBz1nTEO4BU87WG+r3sN1xzq2WCYarBpotCxt124uj0eqi0epyG9vrlbMeAu8GZ2sgK2clkVfSLkjP5Oxzzz3nfX7ta1+rPe8v/uIvvBs4MDCAhx9+GIcOHQIAvP3tb8drX/taPPDAA6CU4pOf/CR+//d/H5VKb0qDHL0j3HPW+evIwZ3PgnI2ga0BAGkzp+gKRd8P182CSt814XkPEVW8OnUjkc7bbLD0dQSfyMdyHYyoeL0OSvC3nm0NVMrZMFsDnuCUJgcAvXI2Hjsrp6VKP0j2euVCQwSL7xDVnOP8TduObvaS5BzZwntqfepHZUbeyzNBym8xo+ohbBBu+U9IPEkfe0g+3VXwXmqsE63NT4y28fPf3sCl+a4if9F50dn5xEGae7gTa6rrD47hzOX1RG38/GoTtk3R6tgY6KFL23QZyrGhEspFE7VmFxuNDlpxFLyCL148xKqTKPsjTvDyU6QMTttMhE09w8DbGjDlrGmIm+V0pEvXKYgpsq5vxLomfmlIWE/lyJEjFlRdE7b8n28zVeM73ltS4GYV/SZesBT2FjvKWf34mYFN2LU6Nmzq1F+Fgh8HAE8527FsPH922QvLLBEAX9HJzpPH0km7KOx8cRyWjJ0tSUv3mYhLlx92rN2x0HHJBAKC+26cwelLq7huX3CWebBaxNH9Y/p8ZYT7b5rB3EoDh10SWDWZ3c9aXW42HXLWAGAFPHl5LiXuBpxpoYp9eb2Fx9bnMVwtKn1a40C1IRh/D1hfqtZU91nZhO5AxcDwgIH5FQvrdRv8ArWnTi/AsopuvoJlkrc1cPoQwYQEW4MMHSR6JmfPnTvnfdapZgHg7//+7wE4D+Ynf/InPWKW4bWvfS3e8pa34Itf/CJqtRoee+yxULI3x+YgzAuTV84y4okpZwuccrbZFjvuulfTtilH9kXnTa8Qlb+rpy+J5nwd0eUt4dfZGoSQmP2E3AHQ1n2qGT6fL4xephMjbRUEX1tJccXfK3m5kTJOJTnrE+aictY/P84jkSteta2B81dokOX7F0dxLWXIjnEfwxAcFOfI0UdkRfbykWWBiAwRnhxJmSyV/jrxpoiHAl1u6Zm8QsGrzxXp6dB2vTnf885R3HNTFd94agMff3Aj1KqAgSjqtr6i1xu4BZgZH8DMePwdlgHgi989j3qrmxkhd/2BceyeGMDJCys4zg3Uw5D1E1V5xosJskbPP+SrwuKlUeSUs11vLwNxQzHmRcvQb5UQQ9pXxJ8szionOXLkALj+O/dutTrMh5YnZ4PjO12b528azaXD2ZSFes5Sv8YPq5UGOYLVtzUQJ3Gara4Xz/7pISdcpYjRwZIX1jQcJaVlU3S6NqecdfMQe2wh5jqucpbhhgNjqLe6MAwSULOahi/gCtvTg1+ZQohDwN527VTM/PcH0+MDmObafq8NVHnc9aFDIz8/QnzitdUVZymFFVlbsCEYQ7Nt6RaCRYJ5zLJ3U+wj88pZdXPKPGcHKgaGBpxOw3rNdu3DnPu1WmvDpg45a5Bg3qjXb3aIEtX7rlXO9oieed61Nf8lGh8fV56zsLCAZ5991vv+Yz/2Y8rzHnjgAe8z86bNsbWIo5zlpd2sjnCUs84JjWawo66CHXOmkSHue6BrIP0BhpqUk1/EMG+SrUSg8tMqZ9WEokrloopPpcqMs5GVqnFXzUh7nochBD3foPMb0imX5JJ0tgbeJEKocjZ4L71OXyBecXLCOYcKv8txJEbOzu5wZK3rkpFNvIFc8jKSxJFlV1hjK4OzuA09Zpu/VZanCIxWpMTx92VqgQMzRRw7WMKxg0U3neh8GarBhoRgKc2Gpd9mTWq2yKhulicB00Yb97WL5yctEiJyNcB/TUrOFjjP2U6HvSeioqYricRNnXKW0kzaRmECRSBs4kbghc6RI0eGUL2DvnLWPyaPM+TP0hAicFAYO4XZGiDeeLaqImc96wXnO/ORNU2Cu47uwl1Hd+H6A2OBeJm1wVefvISnTi8A8FckpqNmJVuEkHDstz1Tg7jz6C7cfu2UQB4DAOG86lS3ZPfEQGCjrO3aN1AqsL2J9T6kF7Ih2KMvzKHNEbT8BmFZEoYqhJXtrm0r1G/x4+Y3BZPJ2WrFibfehFI57CtnCYYHnXK3VreFlc/NtuWtyglVzmo4IQAw3OJq2WrPWdUETxz0TM52ud6R7iF9/etf9yqpSqUikLA8+E3AlpfjKQJy9Bdhnjk8icaevcV1wIuurcGJcx18/UnOK01TTkRbg+i8aZfvB9gxdXjdC2coGm8+Gl0536ql5fI9047zVY0IiJI85BE2CLQVvkaB8HzHiM3GKvx+2ZIAlVeSKi6bu/DoDcGSPxPVFYVV0npbA/F3Jz/B3wGEeveGQXU/c+wcxCNCskgn6/KRPr7tUlIzuSWJHpx/sucLxpNKgfxINXBIWozEKhWdk9jqFSuGcpZ1LEOVs5qf0tSv0pRo4vA7BX2/sn6/SKp2mCUtcrMelH0F92Ah5jJLZmvw6a9t4PycM84wDSc7huFEJitnVeoXOU9pV6fo4suRI8c2gOKFbIXZGnBtotjkBeUqqv66rh5hG3M5tgbsXH22DYPg0MwQTIMElbNuBG1viX84xoedTcc6lq+cZT6tKYWz4nX2uTGbGR/A2+89iBGe1N1uaigXKu/ifkIeDxODCJ6utYbPxfF+s1m1dzrw0cubnnatYM8wSW54z2KVrYFBKACCjYYYjgLoeMpZE9PjDoN6ab7rxcGIbaaud65DzF2QAwo+a56kV/IgASVLPPRMzo6M+LL1+fl55Tlf+cpXADgv+X333YdiUb0pAl/Iu/KUeI4tQVhDxM8KeuQsp5wdG/bP/T9fXvc+615OS7A1iH6F4yhEne/h5wfjdcPJJJu0TCQQLlbs/YPs5ypD3cEQIlDHG8LOevGEKL9UtgaqtAK2Boo4+SPseRBws+GSrUESqwnvFK9TEpJHwWdIni2VZvFUimsa/J2PPyliKwdzbG/0qRLJLFq5Tswg/s3se/OkUtpkVdecJC4/D9Tb1Zb34ZTjD5sck8HIqpKrOGAqw3i2BqxuCztLbDh6e3ZXV22V1dV65SLKWqDnxEMCRT13RVDLU4XFS53ZGlxe8McCo26fko3ZOpJihhAS4rHX+xPY6j5ejhw51FARJ96GYFydo94QjO/Pc1AILng7Nf54reF8v/7guHdeFJHLcMd1u/AD91+DsSGH3JI9Z5lyNiqee66fxvfcuQ9vcv+9+a79OLx7OFZY7/rcv+xsYTO1OJ6zMavZsPyYvFp3m1a6OhFXvyDfB4MA1+4dxYCrvObL82ZtBubky0/j0MwQvv++Qx5p3LVsBXkdP08FgZw1AmVm2HH4wLJif9au5ZxbLRNct98h+09faHvPrVxyCNtW27U+MYL3OI7Qj2XRtomobu7x1vfsOXvw4EHv86OPPqo85zOf+Yz3WaeaBRz7A4bh4WHteTk2D+zFqjU6nr+ZYQAHp4e9AZ9B/Arcsp0PpgmMDAJvuc/GPz5sYGnNiiyrfCUXp0LWnhNQLirIMeiVrrrjkb6gCYjArCDkMYRUdM4NfuZtDXS3M9zWIJywliOWfXFVnrNWSJz8vee9XP3yR4Vzvc5EHHJWkgHF3RCMnR9payAoZ7nOIP8IeyVnry6+I0dcZNRH8ye6sokvK/Cka6wThc9JXxqaKpQqD75yNmzGXUg2FMxzlilmC96Sq+jA3k7JEUs1VVlLg6unrspm5oxqX77oiFUTs3HDxHrW0symKqvMvy3ugJFf0ggAP/42C4NVJ07TADoIKmed+A10JR8PimxXJwTHbPFuaux6KkeOHImgeqe6is2F2ed218aXH78AAFhvdJRx+qKHIHlLuM/PnCL48iMGXnO7jduv9fMjb5QYBZbfQoGgw60Ij0vOEkIwPCBaCazV24nyoIrT+xx2Xqy44p2vWh6+3aAac/VzBZws3DG8sXSQJPbssvq8GZiTPpcng6BgGp7yu2sFfVqTjB14i4ti0QiEHRsGVtcdcnbftPgb85ytlgmudcnZx0+00OwQvOkeoFI0UW92BeXsC+eXYRID+3cNYnigFLQ1UORR7zlLuP+To2dy9p577gEhztLoz372szh//jwOHDjg/f63f/u3wqZhb3vb27RxPffcc97n/fv395q1HBmAvVhnrqwJipp6s4vJUWeWjxgK5az7Tu3d5QRaXLX8Ei6VVmZibtuU8ySNLtJaWwPN+fLZuok+lZcMf56em02gZOkD/JnO6HvH2xro/OKCYRTHYngqKUlOxb1KvyEYUW4Ixp8fa/AU4xS/QVZ01jQEqarB5s8RlbPReVDmKzed3dHIkvSKk862iDDLzKS6cSnr6wzzbVlsQ7AQOaFUh4Vdqk/OOmexTnIy5WzyC+yVbNpuZH+WUA3w00GaRFYfThxPGgTpYfUEOA8roecsbz1YLADTE/53FoeKnC2YBC011+Kgl7Kma2bj3tJXcDnPkWMrEeyR++DHEuWi6Y05laRsUO+iUc4Sd/UL8OVHnPb78eME73kniya+cpbBI2cNOOSsmxmfnI0VjfJ64oblNzwDpPFkaBzJ+lNh+eEn8Pq9LD8tQpWzfciyTlzGuo780NeyY/Qrs8oXd7HsnrB0u5bdU2/j5msmMDy3joFKEcPVYqCfOT5McRYES6vAc6cJDu2hqJSAi/P+/RioGBgZ9DsTz79EMTZC8M7XO8fYu2UQ4PRFR4K7vN7Ea27Z43MR7lSMSrjAPGdtGj6pkPQ+9EzO7tmzB294wxvw0EMPodVq4a1vfSs+9KEP4brrrsPDDz+MX/qlX/IK0XXXXYf77rtPG9e3vvUt7/P111/fa9ZyZADWOLCCPjFSxtJaC+2u7SkhDBCv4vA64G6dwHbUa7QoWm314NIjZylVNwgaxFGIOtfAAqjDB2d23EZG9huJ8Fclqpa8z1CZ1/dvQ7Dgb3FsKIS5JCL+5e8x8ZYH6OPkj3izWgg2lMF0tNnzIJeTcFsDLpxMUEtpefdIw7wK9yBtRyTBdea4+pB9X1HsiPfSf84mb/Fi4bmVtOmqiPQk7y2fh65bkfA+nPJkTqBZCUnLszVwPWcLKTxnaYxzGbbnsOmViUC5C1FzZJpgT6f4JcTzUyzEKzX8eft2FWAY/uorNuZUuZ8pB6QZ36T0PoP5JGqOHP0AeyfV+0H4n4sFA2++az82XGL29KVVzC77ppVUCBesZGVLuAuz/m+7p/zj/BghbveAiZsKBQJ0kytnVfDrqoRh2TjICByKSjFZAgroxkrbCUrlLPutD+kFNwRjf4N8xWbaGvBJsDwyOwJLaWsQHyODJdxyeJI7IsbFrDOfeMFJb9c4xdAA8PJFJx+EAOWSs3Havl0FXJx3OgyPPU/wg69z4rI8MRgwOVLG4loLTdcOhUqdcdWliMpZxUWkfAQ9k7MA8Fu/9Vt43eteB0opTp48iXe+853ebzxx8eu//uvaOJ566imcPn0aADA6Ooobb7wxi6zl6AGNVldotABg/9QQltZaoNT3hyUGtyRdUs6Wis7L0WpTrK6rR32maQBdG2eurAdItTAEDLKhXnDqv1/y+erRDWsA51YasGxntmSt3sZa3WnMlzdaeO7MUiA/6+7ykY1GB8+dWQIhwK7RKhbXmqHLSuvNLuoRUo/Du4dxcCZo9cETri+cW4FhEK/TIaPRsrx8N1pOJdXu2jg/t+7mw8l3qWDg8J4Rr4L1hxPBa2ADrjNX1rBaK+PQzDDOXFlHveWPmpbWm97nK0t1NNsWVmvOvbq86HwHgHX3/l5eqgEAVmstfPWpi9q7Ums6aZw4v+x1Xs5ccWa+KAW++tRFb7fWpfUmHjs5B9MwsFZvCw3GULWISqmAmnvf2myZg6JWZUWu0ep495Kv3AFgrdEWygd7HsIST0E56/ztZVfNfMiXYzMQtXlgorjYhwwiC6uj1GduD6iVswmlLhzYhmBMOcs2VZJWeSvhrxgJuYfyTz3cToUjzysSmc3Z9hA+zQApdC5AIi3kCQVVPcHaubgDxhJHzh6YKQBoAcRpldnkf9cKXpdqKadAuMRKXQ3d6qikdETeTufIkTF4lapU38l962q5gKrr1Tm30giMcxlU/QpeWUopRbvDqeWIH0bY/yJmpeMpZ7kNwSil6FjplbO9TqCLytkYkcSs3OIrZ+PFt9kI95zNPtOqDcEAbsUTV962ynOW5dGzNbB7szUISwsAxobFyOeXCdpuH3j3pIk33zPo3YP/692T+M5zDXzsc2totf3VNYzDIMSx61xca3m2ELbcr1HkyfRW7SLcczZho58JOXvffffhj/7oj/C+970PlmYU8PM///P4iZ/4CW0cH/3oRwE4N//1r399FtnK0SNOX1oNHPOXSfoSb4OolLN+SRwfNnBl0cLKhrp0VoomGq0urizVvWPFGLtGyLv+mgZRVgbaUZE3vhB/ZzsELq+3sLzeCgRbrbU9clGFRtvCixede3fqQvAepsFTp9tqcpbL+kuXFa7YEli+GDpd2+uY8PkuFUwc8ozkWWLB+FgZODe3gXNzGwCAp19a1Ka/sNrEwqpP1i6uNbG41hTOYeqvrkWxsqG/z4yQrbf8Oof5xwAQwnYtigvzNWU8ujRUjQgrl62OLdxLSoHHjltodoDpiW7gPgNAgSNghM6eRO6mQZgvcI4dgH4/tqxZAWkmOXSjiM1A0uujtOd7Ehas07WxvN5U/sZ2Xl7daHkTUzyhpIs3jp9Z250T820NnO8xLGe9jqVlUyyuNjE+Ug4MCLJQSiuxTQdgmSCja5PvfVrSN9uqRh55Qfvd34k8XsyDVb+9vGZPwY+O+ORsW+U5S5x+aLPlr9zKwlRCRMrYcnY2R46+gL3h7NUyiN/uhY0nA78pZnKU6kiXA+YpD9uGV0GLmxPHawS6Xh3pnN/u2rjMjYvTLFJPaq0gK4PjuhqE+XKGna/CzvCcVYy5+mg6Kz8/1jczFGV0Kz1nAV8562wIlnF68MvY+Ejwd9YH/vWfm8T1hyre8RsPl3HDNSX8j39Yg2UDrTYj1928E9/jlm3Ua8tjc5WtAWcrwfeXe73zmZCzAPCe97wH99xzDz70oQ/hq1/9Kq5cuYKBgQHccccdeO9734t/9s/+mTbswsIC/uRP/gSAU9B55W2OrQMjv0oFA+2uqPCxqbghE6s4umxZBtcBZ+TsWs1GuQLIxfaWwxO4slwXyv2BXUOR+SsWTLzq+mmcPL+CtXobu8aquLxU16oagp4t7u9SgCN7RkCIX8HVW11cWqzBMAhGB0sYHSgpK72NRgezyw1USibGhsoC2TwxUsb4UFl5HacvOaTqyEAxUAF3uhbqLSsW6TYxXMbEcAUUFKu1NrpdG4ZBsN7oCL4qh/eMoN7q4vJiHaWCgbGhMuZWGqiWTBRMwzmfWwcbbmsgfmdq1nLRxP5dzo6Ni2tNjwDdNVrByGAJc8sNrDc6mBguY3zYuS9dy8ZarY1m20KjbaFgEgxWilJ61FPYss5XwXQ8jztdWyiro4MltLsWGi55WzAJykUTtWZXuFbTAK7ZPYKNZgezS/4MuqrzMD5Uxq1HJtFoiuspX7wAfPQzGwBM/P6vBssuIcD4cBmPvDDnHBCUs+4kRw+znJmps3JsCfw6qj+dqaxiDRavLDqjvecubgz8ACItT6KzHeDxyAuzwiSUCie5iTt+V1oGP/54Ey+UUt/WwCVnTU+B41gHhSkpvNUvNsU3nr2M6w+M4QZ352kdsuOatusQbBsjQV1BtV/iBpKSlk6h0nEVbIl4iMJbXz2ItZoFmwLf95oqvvMCe18ITNOdwFXYGhRMA3/9BQOziwT/rx+wMDnqZjDDmYWgr3y8m5pzszly9AfyK3jL4UnUW10QAPtDxpNFqe3lp3G8sY8iHUIIKETLIMvmlLOCXVm8a2DKWbbipdO18SgbM8BXSyaBbkPsuIjvOZsMYYT1zvCcdf6q/Pz7kWP50cu2BsKGYJvpORumnLWCPq293hteGD88CAwPUKzX/Vg7riK2XAymRAhBuQTUm0Cz7dwjNrlCiLPpGMs3ELQEUbXbHjlrq9+xtHsRZUbOAsBtt92Gj33sY4nDDQ4OCpuB7dmzJ8ts5UgJVi4P7R7GqQurMAxfIcsrZwkhXkUhe84CwNiw82Vl3cZMBSBSMR0eKGFipII02Dc1iPmVBtbqbZTYDhKyV6w0E8jgvzTi+eWSKQxK51YauLRYw1CliAdu26vNy6XFGmaXGxgoF3BoZlggZ3ePD+Do/rFAGEqpR86+9pY9/jW4uDC/gcdOzkeqqQBgZnwAxw4E03js5JynGjVNA7ccnsTcch2XF+uolAvYv2sIcysNDA0UUS0VsN7oKJdqqLQncsXLZpoGKgXPK+b42SWPnN23awiHZobxRHce640OZiYGcEy6Ly9dXsMzLy1i11gV994wI/zWaHXxhe+eB+AvX5oZG4BpEpyb28DMxADOz22gYBp44x37cHGhhu+ecDo3pmFgeKCEWrOLYwfGMDlSwZefuAjDcO7JxYWaRM6qK/cje4LTdV96xLcxuPnwROB3wN81FVB39npZgbJN+y85YmNzpLMZC2d7QpYWCV6cGcYVG4oLYLYug5VigIzaaHRg2RQD5QKKBQOmSXBwmhs8yhchKVJ077rF7e5cdDumRW5peLdLYZb0d5upBpgHPG9NE0QYNR0XvLLolYusNgqVVeo+0Zcs5vjcrN+/ix+ZeK18SM/WIKZydmjAwLt/YAwAPMshFifrX3YUtgbX7BnB7OICAOCFlwlee4eoqeulrOXtbI4c2xNU+rBrrIqhalF3uodSiKrWFz349QxPdlJKBXKWJ2hEW4N4FYfvOSser5ZNT2SSFHEmztTnu+1MhmpAHqHKWT7NbVrnMqJctTl0X9KTbQ3c76p8bK6tgf+ZCYxMXjkbDNF7ehynMzUOrLtUS6no24yUS+qnUS4R1JsUC6sdVMr8PjUUJVdVaFNng3pegAioxU+mRjkbRLLSkSk5mxbVahWHDh3a6mzkkCATdAbxXz6bUsErk1UUsucs4JOzqxsUM7sQqG17rT/kmUHdKxDwnA154cLi14FXRQRVuurA4uxq8JzoyjXZ7KxnHs7CcN8cDyWngyFuUuWmpFTOOgct1wybNQqC9YrUuFsW9YgEleKEt8sI5F9xPwyD+OVSWobAR2FTcUIhsCylhxm+xdXojhMfn+g5G2MQHBk5u5b0UeS4GtBjAdEE76kK34rOdwbvSWgU7o93HZvCxLA48fiNZy5jca2Jm64Zx76poKJHG29EnpnfLACU3J6dQM5agHrthgNW/4wMlhw7nxjKyV4k+1dNVdXDPRLRg0o9lXRWj0BfK8ZyTtubuE+h/pLSZf3LrsLWYNeY52Ug9EOumvKWI8dVCH8s4fydXa7hxYv6CUZCnBWasq0Blc6RD8pVHW9r4BC1QSVj3CpP9pxluPHgOB4/tRAvEgmBDYujAwhfReHs5nTWNmM5fq8wFM+5r+kFNgQj7l+4+fB/2zrPWecvWwnG9nwRA/SWnmkYnjIYAI4epN4GYLz/c0mhnAWAYsG5N48+a+OFMybKJTdbRLQ46dq2sOk4oOYreOWsaMchpZ+wmGwLcjbH9oRcDg1CBAk9T1p6nrOMnBWUs86PqxrP2V6XLbBYVd4rzu/qgQPxT4iVQCQ5y41Y5FN1QYVNURQnRS1158PrZm0EclS6R4T4l3/qnI3/8WAbBAb+Pz9h4foDYt7l2+SY1Ts7H37zSYKhKvArPx2+q+jTL3bw3t+6gFaHYqBi4N++J7gmxPYIXgURyw+2uPLHL8nlr1NQ7thUiFsuLwHBWoJyGYucFeLzU7NDyOi48J9RPgTdkeiBd4mD2HVd3PgiJsI2H/EyJNoapL0IscOmemay+kSZh5h5jftu8/6bnucs1w6rNk8Kg5ye0DFV1K+JIYyCe4loe6PflxZnbJgxN6tFoJ7hLp4pzIoxlbPaFIg/IOqqml2hT0WQ5QXrePa44/P0dU6OHDlCIb1Tz728HPmarW60ceuRSfGg0MwR+ZAgHqEUCuUs9T4nBZts4idVnYwQNz/JkdQGVZ4Ii2trEGsOMmK8y2AKY9bt2TkgbhuktJztQ5ZlKoClr9wQjHnObgY5y332PWedvy0VOdtretLNveEwhWUBX3qEywehMAx1QXTIWYIXzrj7x7RZGKBgEM8u0eItGRT2Jl5anHJWyJokEEva5OfkbA4t5GWnhIgKRZ5U8skx51xeOTs6yJSzthAfQ68VWaThua5xCnnhFMFVMWjOl99SfVB+wKsi56IqVyHvmlOF+kJSizK1LAAcP2Oj03WOHn+5i++7Twyjs4s4fZ6AUoL1OnD+ioXBQWm2lfv8xAsdtFyFV71JcOayhftulq4ppIHjnzHljslLiVRLciiv9ja4Rs1NUO5UJCmW8chZ/7PQoNvB35MiLuGTY3vCe2x9Zmcz0u4FI0yR7x6CBuB3gBJcYZowYREJyPBFlCfUNHeMkbOm4XeSDZOAEApKiZrI4pPp4UGkudqrhJv1kNW75/fH0qUeNx9JBvW6OFlYp112y2SPvokEnK2BQjnLgw3QKA3JZBL0WlAzq3Ny5MjBg2r+Hts/Btl6s97s4tzcBrq2HaqchdBnp57PLPuNUiqQsLZGORuXYFTt2QLwY8MUdaeXh8RBpbQjUk8Yf9g92Qkbgqm8XvsJ/YZg4pge4DxnFXsZZJ+vYJ78DcGC96bnJlQxb3HrURtfesS/1mIReP7Msuchy0O2DPHicfvNpmnA7troWLb3boeJW0xeOZshK5+Tszm0CNgaGHyFJJJocWwNVjaCU4kEWcyMiaMIWXpO+daUT1tzfiD2uLYG3CA6SECrA/NLEVSn8OQs6xyIefM/65Wz/DluOMVvy6t+ZBv14D2RbxMrH42Wf2ytZmNwUPYM8j/Pr4gMQbMVTCdsgyxDuB/+Ma9chilnqahSDW50lp6d5clZ1XOSo+NTykY5G15OcuTIBjudVPD0Halj8AZ/IexyTwqKqKxp4uy4KziL3HIuAqfdtqxoIiuQjZ3+qLcbsmZnNwuqSVJIbaesJJUyy08MyMRDGpieclbRT+E+q2wNemsa1YHzVyVHjq2Frr06un80sOHmwmoD5+Y2AIoAOStMQUv1BwE/HnSVs5KtgVxDJJmL8jYEk/JEpDFuIiS2NRCTInxWMmRnw84WyNltOpYJF8Rkn2d5fMjuy5bbGnDXytIrhKTbsxSCuw9Fk6BjUVBQFEyKruX8ViwAC2vqDXmdjcIU43OXxyqYBjpdG5ZFOctBlvdg7lnVQreCnP2N3/gN4fu///f/PvT3LCCnkWPzIS/3Ngjh/DVk/07nuGpDsNEh58fVdcUsSgaF2SPpFEtQnO9qcjXuMD3uQJv/OaZwVvJ2DZ7F77ZIFYJcXUdCSFtBlArX5H5eXvPDrNdtLoyckp8fwNn5kIGRujoT+fllJ97BKlBrAM02AgjzSFJdosFPDkTIXy2FrQFLU76+uGWz3aHC4LPTBUqKfQiE+FQbDPS0I1j6oDlyxIbUcfcpmBQFcAsYDS/fvaQtkWRJrzwqD/JGTHHbKeY5W5KWRJouORvX1kDbVoWd06N0drsOwLJAvyxAAp7pYcg8cTHiAPEppWdxZc+Ul+zGgMxLm6ZDlXRUlpIqv/yMdNrcFGjqOHLkyJE90qjRKYJEqOjeI4lBCLi9Vpw0eVsDy0ageonbtvGbi8nWL4bUF0iCpH0dWcvE34Owfp5f9esTpDHGq8DmkIq9QqWc7ccmtwzy/WK3aKs3BBOfqUvOhil2e2w6+Uu64+gu1JtdvHBuGeUS0HX38x4ZNHHbkXFl+G88vqrMhO+XyyaU7QAXoXqXnIkECotKY/iAX22sy/MQi5z9D//hPwgVjEycyr9ngZyc3XoobQ24ConfEIwdZzMXBYVydqNBhVlGJ84s8gkv/mYr2LDplufVGhSNFjAW3JNFDO95DEaxs/4LHNxZMTzvujqUn0G0bBuGIV6criMhxCGQs2JAAgIKxzt2ac2PbL0erHDlQaBNnefJm3Cve+QsAuFtG1hYdXofe3cZOHXORrOtUs6yfAevRVXPyJMG/Hny+Zbb+yEGEX7jleBeWsHklVhZFwt1u0uVZuTyLDyftnMdMRNUQOZ9X8FcxysSSX3Bek0ndXj2IQOWM0t6I7ath+IG90MhGvo8Ez/keBfXUfnVEcKpDGOlElQGQ/pBgR652Vc4snmrY/dDlGG5zzFvvEyIpklPnrQHwlU1cTPjec4q1OD8EXk5MxdFTwikGvOm5vZDOXL0BzoVf5g9GqXhajfhJ8bOem27znM2XQ0j1JGymjckSkopltdbaHfFlaljQ2VUSqZg/5YMzvl8Hdr7uIJoPosQlbO9ptkfhE6O9iHPstWDr5wNksT2ZpKz3OV7ytkQcrZXSx++HA9Xi9g7OYiT51dQKjqCLwAYHSzg8J4RZfiRwQ0AwVldFq1vyWB7Y/OwdltUzqoyHHVFaiSyNQhbLhtr9j4mXskqip0ElXL2yoKF//V5A/feQrFvkqkQ/cLb6Th/eeXgUJWgYDqDw1oTGBv2f8tCBk5Bceoc8H//jxpADPzT71WXRT6p7x5v4AN/sARKDfz4W2284faQ+KUXVAd+5jDoi6IO7Mvm1b/zA5mwd6zdATaaNroW9Zay6ohMJ49+pil1KjW+c8DbGmjEMLBtKlgaAH7lqCKEaw1HwWUawMyES86qbA1CNgRTgRi857FDNM8vUzx7uoWVjQ7qTWDA3TCdqcf4Mgu49zZGp06FZkcM2O5QoBo8jx9U8+btmdsapI4lx5ajT21fv1rUgGIuReitau7TJpvo/VJenK5GjZeuLt/Mc7bE9epYWwCoiSxltuKAqTZ6enZXR03lz2NkNTOySQjx7ZCP8B72KvCq7V5sDdwpV29A1FaUaYtrWw13aZBqM7seMhB4FldHSc6RYxtD8xIqN+WMChQ4j5u05JYPybYGth2MUtevf/JkE7/zF4soFAj+03unsGfKb7j5OpItt9bh8lIdj74wFzg+WCngLXcfiG3LxyCfH5uPidWtiaucNWKdt5Vg+Uqz8VsaCGNqBXn9wrkVLKw4y1jX6s6SVFM1O5kxxNW/zl/DICgXDbQ6wZvTazdItQDVpqIgaqCiv+5qWf2bR86697ZrUVBvDxs3vRBbA0uyNQgyKMkQi5x94IEHQl/QqN9z7EyoPGc/8jdruLxA8OmHCB640y24nAqx7ZKzzo54LggwMWJibtlCvSGmkUmxocClOdeVhxJcWaDyz4HEnjrZcl9sgvOz4bVFbHKWa5zkDoEuqB+3+gx+tqxrU5QC4SmeP03wj98hoHQVlfIaCIA7r6/gN9+3C4BDFq5tACND/AyQSAo/86KY/oZCORsYkFCKhmTrstGgeOkCcP5yF5dm1wEAC6sttCzgzEUnnpnJAqoV53NLoZwNszVQgfePtW2K504TfOk7ADALwFm29J4fsVEuOrNhLIxQkVIqVLzrdeDE2S5WFL411+0vYWjAr+Blglnn7ahXTye73qi4KZvhz7GD0OfhvVcctiON0HtZjUt3CudtwiuShpqVVbeBWXtNvpmtgbzTM+ufd2LbGqglSFQ4J0dsZHSz5ImQJCpM8Zx45SB04kWXtuZaGTlLSDo/dN7DlhB/U4+GYnK3y4livLHpNqn20k3L5MiRIwqsjgi8WzHqL8MgnmBCqNOEMYLzN8rWICjMUef3b7+6jrllh9n96uN1/NM3+0o/3mrBICS0wqg3nQqvVDAwUCmCUorVWhu1ZtcZ18Qcv+pQ5WZ7KyX9zFqc6OP2IUxBJbo9exuqjbiiJtB7Af/8DOGz/0X2WR2o9H9bKfF18fNy/817sLjWxOxSHXMrPvHT6yS1PG633M3pyxw5Wy3rn8BARcO1uIdNQTkrjs3DbA10K1bTyqZiPbmHHnqop99z7EwElnkTggVu4yOv4MIv2G23Y8wrZykFJkYdcvbliwRHD4px9pxPiL6nna4Up8IHht+YihHK+vjDVSGq/MRtoH3VpPp3wdZAs/nFhTmAUuc8RhR+6+mGp3T/7Y82sLRm4qd/0ML4sFjJEACXFiw88oxTIe2ZNHB50cZGI6iUD9oaAHVJOXvyLHDyrAln2cAy94vfqO/bVUCl5PRoVJ6zNgVW14EnT3YwM2ZhYtQJu7xu4ZkXWzh1DtgzBVTLwPlZoNnowCAEtTZQnqRYWnXiGaoS1JoUnS7B+gZQHvdVNbxPMkuTXV6jBfzFZwxY1gaAjUD+Du0p4qP/bo/3vdkWZwfbHU0lHCBQ3bTd4FmtQMmXTO489LNTx8fca9HI1H4h03KaMEdU+TFZ2JBBD6+uSY2EYZmKsChZqngz+xG2Bl4ZobqyohpaBQcocbFZVh5bje1GyG1GPuRna/XaxkmZHndXX12YDRZqvv1VzStn8UoG7mHsm/pKL+05cmwNdE2QmpsV27i33n0AZ2bX8cK5ZfE8vs/OyF+OsJGVs86GYJIwR5EBy6Z44oQ/eDp7pSuM73hrPoP406XquJzKdc/kIO64bgrtroUHv3POzWu68atzvoOxoRJed8setLoWpscUSwI14aN+DBv7C5tBb9Mq01ARdn3syOs22eaPl4oGbjsyCQAoF01MjlSyz0hMjA6WMDpYQrtjSeSs83d6rIK7jk4njpcvNl2L4ovfPQ9KRc4pnJyNUM6G2BqoYLrvatDSRC9uiIP+0+o5diw85aw3U0hQ5EqMzZaIq5SzEjk7OeKU4EeeNXDbUYrBARZn7/mklKLR8iOSN4lQvRRzy/5JLR2Z5sXv/I1WzvqVdfBUdeBI5Sx3nF8K70egX1ZRb1IMVonnJXv+CsF1+71gbrrAypofwU98XxW/+/EabNtRwQ4PEOUAx8k7RaPp/GgYFLbt53VqjODGa5yG4dvPNAS/w3d//ygeemIFAJSes52ujb/6nIF2t4kvPTKHP/k1hwj9dx+Zx/MvtwGYmBihuPEIxTefNAA4FX/BNPD//nF/c653fc8IHvz2OuaW7ECZMCTPWZ5cWNsALMtZOrl/xi/IlkVxYa6Lc1c6AnEdWzmrLQNu56mHlyFfuZAjDNmVDpF16YVgy7Qf67Em8VWBvfJE/iunZGf1iGDroq5AV490PFsDvhPPKWcjbA0ClxPjVmb67HKEIqCoZsfjhVZ+TAuZ3NC+y+4Bz07ISKfgkt+5qXHnyNkrQe843kKhb8Q4Df0aI/x2oepz5HhlIJWXpfselksmhqusrx+sP7hThS6QTSksbtxj2wCImA9V3/zE2baw6fLZyx1v3GIQwDTFNjwMng2cO34wuQDM5i1OPB4UoqzJ0WxIPiHqMMKLJyIzSTl7ELdftdHo4NSFFVAA6zVHbdSPPOsIWcJxjaWCiX1TEZvoZIxdo1WMD5UxMiiv61WDlYFyqYByiBJbB/4+LKw10eo4L06p5Pfqw20N1MflDcEsadN7QC1CYOF4IpdSX+SwKZ6zOa4uBDxnDdELp9Z0fllaozhz2QaIT86WeXIWFN/zqgF882mHRFtZh0fOZqWcbQjKWWnGQtE4zS/7bGEnQjnrN8bheeWovsB1RS9p18TJzxIpyFkKeJ0D0xRncVfWLSF8tUIDlQwhxFvuum+a4IZrCigVKdodguV1C8MDhlbSb1PqKZYnR4F5btL5e+4p4n0/7Ngq/Nj/dQELK05H5OYjRdx4uIyHn3PiVNkarKzbaLvq57OXO7BtCsMgeOmS/6BWa8DqBkubYGWdomsRrG3YXienVCSuv4ztKboZWIVqEF81y3LCyvDuSVNQyDZaNr7/ly6AUoeQZdYMMsGsU86qvHKArDxn+bjzgV+O/kA9bAl+TYQMerJxo/Dr5d7p2TByOey3aMKICufFzSWr40RbA35DsIh6QepLxlIHbuKz27HImiGUWfQY9X2qpGNNvEidRA07y/olWVjgEQBTY87npTUbqxsWRof8jinvQ6vcJCgLQUDKcLqJ7hw5cvQGrXJW0a8Oew+FqoIL+8LZZRQLhufnSQgBtcVNriklATtAOXXLovjDv3YGSzcdLuH5l9u4MNfxVuAVCqJwRMx+8FosafMnXnVq8+Ss4lpVCFPphiNZ5RYWvbgh2PbsIbDx2pXlOq4s14Xf+uH1WuAIe/4zP27cjA3AZBgGwQO379X+Lj8+eaP5xOlxAXnBGs85VXvxnFXZGrjnqOqYAqecdeIh6jF4wkY/J2dzaEGlnq1BCDa42b6lNRsWBX7tD9fQtYBDewxPocgrbEGBN71qEP/7K6s4/nJX8OjJpOKlEDamkslWebMKSqlga9CKaWsQWZvwYyWZu9BcZxQxx4ezFRJZSqlXKeyZNHFhzr+ulQ0bHa7jYHKbhPENNiMzC6aTj2rFISh/808XUK0YsCwbG00Db7hbrF1s27/vE6MU88t+Xo/s9wdLvMcL+1xxJ9lUtgYrnJOAZQNrNRulIhEUqpZFPLLhzfdU8KVHW1hctdFoUViWk0a5SLzlDbJ1BWv8iTvNZVN/loydK8+wVUrEI3PrLYpqhV2DRM7G8JzlZ/l79YQKxp1jx6HfDy1jgkgx3Mkm4h6RjoRKeVMylf4q4pXZUnZUk57Kc5aA2xAs0tYgZrZC8pAuvu1RdvoFWWWaHmI/Ji3RF/d8uRjGicyvZsRBjeVt8hkVoSYZvo0kzhLG0SGK1Q2Cly52cOf1fn+D3/gu6/kEcXKHz1/e6ubIsaVI8Aqq/Lo/+40GnnuZ4F3fEyRXKYAz7h4afBwUNLBy0ZJs9eSx37MvtXD8TBuDFYJ/+7NT+NnfvIxmi+KcuwqgYIp1FE/2qOoumZwl7n4aNqWwuL004o+103Vs/PYo5EFwP4XlR/Cc3abdA96LFwD2TA6gaDpipkMzw5pQ6VEwDdx53RQW15rYt8tXxwqK2i0gZ6OgE7WlFSPx12txL9/eaYonXnA+V0r6uAeqUeSs8+HCfM3bo8bjTRThWP/apo7NiUGALPaIy8nZHFrYit750po/wltapTAL/qBvftmXcgues+5f5nHON2ZZ1CW2bYvKWUsTqXt4rWYL6sa2giDkEdvWgBuEyafqlbPx4gbU9gUUviplz5REzq5bqDf9QLbt329+dpQpjU13yeHkqOP5+uIFntEkePIEBd7B591Xzo6PAAMVinqTYGiAYs+kf0G8/4tHzrqzVyrl7NqGeKGLq1bAR9EJ65e1ge9LcY4AAQAASURBVArB4qpDrHYtdtwnZzsd1sVywBoGoZPm/tzuOAfl2TdCnPhqTYp608ak64XbbIn5lS0UvPC8tjpj5SyPfJy485BerRAPmXGzgX57b7Pgmw0+26nJrRgBwuxqvHpAE0/wcLy7y+pxYWKU8LYG4V1GuYzEIZx6KldXi+msh97evp5CZ9wmyO9OFNHe5ZSzvT1uP/T4iLN65uJ8F3de75/BT44yqyWxKF81BS5HjqsGKlJQuyJRqgMsi+J/fqEOwMDr7gycrKw/DcP1nJWaVXmFo5yH2SWnMrz+UAm7Jwu4bn8Jz55u4a+/tAbAIYcE5aycFwmyrQH7bFtUVM4m5Gb7UUvSyJbCgaCc7UM+soC82dad102hWEi+TD8JDs4M46BE/PIcylYoZyMhZalXMRL/brS5Pu01nHh3aVWvRBgZ1Ajh3H7yYMUhr1qcso0Rtqo+MbMg8ZSzboUhT6gk7YLl5GwOLWQD9E6HCCrBf/i2BUp9Aqve9IshP0Bk4eVCDGSjnG20nFkLhk4Xgicoy/HiCsWnHlrHovTiykveZcjK22D6Nr70aB0Lq23MLhPccUxhaxAVd4z7YGnYWdY52LtLbBhWNmwhXcv2G30+Xba7MesUvPV+Gxu1Mq7bNwYA+NbTNXzhOw20JBLbphS1uhPf0ADw42+zMbsE7J4EDG5Zh0jOGu4x57scJwCsbojV2OKqhZJLzu6ZKuDygpNhprotl3wSttWBZGvgkrPSM2b3wSFEnYqUpdrylLPBZ1KtGKg1LWGXaNmzOI6tAY8o3+E4CDMiz5EjK8jcLJUPJIkrw0GASgmjPtE9L4M0NcJWRKYQR2EC/5ri3h/Pc1azIVivytl+YRsOJzJF7LIZBeldI/LxGEHjng/wA5E4T4iqv7pBu+4gyjBiRheI3R/osODVijMNvrYhFuyuwtYgK2VroN5LGj6rspAjRw4BqndKW9VIbTC/kjIgrIFGMUeCG4IBvKjJDS/16xdWnMEIE3e8+/tH8Sv/9xyePOksQzRNcawQNS5g5Czv02oaBF3LVdV6/ax4FW+sFRMpITyjkPjNHWBrMMiRswXT6Dsxq8NW2xpEQc5RciW3CP4S2wKBCvyLfzKKj31uDT/4er3v7tSo+jmxePdNDaJcND0xw0ClgBcvrmrjMyXRYVbWRbHI2Y997GM9JpMcP/3TP73paeYQQblWxraBK4ticVtaBVQ1bKkgNi6sQma72om2Bsny1O5QPPxsAw1XrXh4bwkbot2LRzb6GXD+/M/PW5hfXlbEGZ5mlKrts9/YwP/zf1bcbwbGR4DX3Saeo7c1cEPFuA/yDC3Lm0fOTknk7LolDMgtm7sGjhhh9gCFgpOPcgnYP23gNTc7xsDL6x184TuNwH2iFKi5ytnBKsXIEDDi1on85aptDRiZqlDO1iRyds3yNrmZGTdxZbEDSglarqVCuWh48bY7xOss+Z6zKlsDlk93woD6Azh2Lp9vL/8uYcsrkmNvCCZsQOYf95WzymCx4RuR9xZPjhybim3a+e4FmYpCSehXD23VhmCIvyGYDPlsvl4h8ocUdU5eTSVDgCpNOQhIen4salZSw8h5ZX2U1G2cEKETCZvgXa1JK1d4D0jvL6fY6uWl1E2w9hBljhw5eofyHdTZxbEwbqDZJX/QGBhnsY61BJ1y1pLGGnIOmDhoasyhX+66oYLrD5Vw4qyjNikYYZ6zQVhs/CApZwHXc7afUtgeEEYWb8fl+TIqZZ8+M7O3mI0Nonju2wky95GlcpZXtwLAP3vrKH78e0dCid9KSf2w2K0jhGDXWFVK0/mrGlt75CwFAF+Yt15v48yVNUyOuP6HCTsJscjZd7/73Zs+e5GTs1sPRhzato1Pft7A3JLTeIwOUbzxHhtff9x0CVoRxWLwGODP6oi2BsnK1f/5yjr++G9X/LQKwI++RXzZOl3RWoDCWdqx6AZ77W1VlEsE1+438cd/ux5NzkZUJhfmRDZ4vaaefVXHHT2LxGZu1cpZ3/NoaMDAB396An/4v1ewXrexsmGjEbA1cJWzXOQdTznr54NXmvjEp5i0o5x1Pg+KdZnQ8CqVsxVmaxC8pIBydsVXzk6MmiiYTp6ZcrZSIt7mXG1OOVsuisd5BG0NnDQvzQGPHyfCdfNg+ebJ2UbMDcFEcOoez4+vtzqWgIAi1Z61ObYaCdUNSRG1lD4tehDOItvMJM9BEuVh6qTT3JiosZTmB5XnLAinnE1IzobeF1m9mWMLkODup5HOhiSTVEHKJoB7tTXguFnPt57f9RwQJyFUXaa+IPYtzd+YHDn6AZU6PvJto0Cz3cWzL/l+svWm5SnligVDG4dBnP62TM52ImwNFlYYOeuLaW68xidnK9KKPUJIaD/Lljxn+c+WbScmw9JOLMdpE4QJ3pjKWXmDte2CrGzoesW2tzXQIC2nyAdrd4INfFS8hAAFk3r2h6p4A2GYZWWErQHl4ml1bDx1ehFvvmu/EzY0V0EksjXYLNP77Spjv9rAaJ5mm2JuyXkmBZPipiMU1+wFnjpBsbQafFYliZxl5Yap/vllIEkf9dnLDst2aHcBlxa66HSBK4viOd0uBHaWUqDedMhmQoD/8PNTME2CU+frMcnZcAJVtknodIPnZuI5q3j9eOVsoUDwvfcMYWXDxkf+ZgVLaxZOn/fZT8sKkpGEEM/0mvc64l/1AY36tN220Wg55w9J5Cy/WaXKc7bqDqy6ljNw43efXK85f6fGDCys2IJydnLURKEgkrPlEsFgxc8jU06Xi8RTusq2Bj45618vBfDXX/Q7TKpdHVn+603/Bsmes2HkrErdyp4r6bFhdR0a8s1JdiD6rm7wuNkey0aGJHJvxK4If2AQfn38AIJ5TKVWHsbYdVb1W9QgRl5WGPf+tD3PWU51g/gbguk2O9KcLfxNVaqukmoqq6Xssv0NP/kcGZafDIyZj9D3M6rDIhVi3nM2Dfgss5QrrnJ2TVbOKmwNskKvdVXceipHjhzJkOSV8i3vKI6fXcbzZ2oAnMqp0bLw3Jkl7lx1HIbhvMeyrYFlwduQCwBMTlZ54vwyzs06yw2rFYqvP30JXcsGJX7m77ulioXVhve93uzg2ZekQS6fnsJz1uSVsxG2fEGklDa6pz95ekGwWODBE61h0QsK0B1RVW4dZyVsCLYNuTM5S6wIpB3u8tcoK2djhTcIKiVgoyEeDyVnQ7Qtgq0BDSsJyQpyLHL24MGDOWF6FYK6fd66u3ycEIrf+oUKriw1YNl+51gGI2dHB0tYrbW9IunNMHDnyqbaUWBE6D9/6wj+7LOrmFuyPKXl6CDBao16nrPewJECNfdFHB82vHwwos2ynQ69oDjiEEUiLLozoZWS48nb6dDgix7RWIVWqi7pZitkIJQCtls/FV1ecWzY+fCNJ+sCKenYGojkK4GonGUVJt+IDlTU5OzyOvORo4GywNcXA9zGWoxELXOEbb1pY2TQxFOnmvjjv13BuVkn7cN7C1hYaWOJ85yddJWzgL/ZB29r0GoDzCNcZWtwYRb45pMG/v5rVwAAu8Yp7r3VuV65c6e2NQgqZ+VNzcKWD7uPUqims7I18LAjOjM5diICJHIfytp//ugCvv5kI/pEDrZNcXCPgXuuj8hQlv0YL65gnDRwDh8udgKJjrN6XNw8kcA0nLawY8V9WP7AVQSVzlD/Fhdeu/qK71pm9bJI4fv4DkbBf2RO4rLayveVc76/4KrCDIIE5Z+DPGMBoFJ2DgbJWf+zt/qLV2ylSF6bHc13LV7xZT1Hju0D7YZg3IRZs2N5ghAAIDBwYNcAltZbqDU7MAjxrAN4GISA3/ODwbaBO49OYW65ARDg0LS/gdML51awuOoYb1t2C0vrzuB6dBgAnIHN2+4bxNkrK358FGi0nUGearxsKZSzhqecpYnb2bST5oz8rTcjNnHhQuhgEIIje0bQ6dqJOYLNxA0Hx/HCuWXccd3UluXhavOc5YNZKsVaZHiCsoKcjXPrVBNAHh8hTZ77YdJ10GKV+jNnzqSK/GrBZz7zGfzlX/4lHn30UVy5cgUjIyO47rrr8MM//MN473vfi5GRkUzSWV9fxxe+8AV85StfweOPP45Tp05hZWUF1WoVe/fuxb333ouf+ImfwNve9rZMyHRGGjFPzVIRuOvoNL7y5EVsNDoYVJBXgL9btME1gAAEUu3Nd+1HvdXF5LCG4dWAkbMTo6ZLvFnYqLvk7LCB1ZqFjiV3lqlHzk6OqpWRjZatNfSOUrcurjl52jNl4uVLXXSs4Cyl7mnE2QyKEXqqiohfVsPUpYf3Ouy4rBZ1bA38cCxd33M2uGEY4Ctf5Y3TltachAerwXvDf60KnrPOPS8WDJgGhWUT/MgHLuKfPDCE5Q0bz7/sDuIMiluuLeLR59uYX7FQ5sjZovSYKmVDUM5abj5LReJbMnSdu/jUCQNXFggAh6198Txw543sesX7qyrf7FrqnM9sU7Y1CCNnXeksX18n2RQuDIRtbtZTLDlyxIff8U9RdhUFtd50NldMg5cuEKVNij7xlO9bnBesJyF0uCJXd7zZdurjkjTJaCS0NfBEPNLpqtAk7MccAPrPofbt1seImMqfFPXA6Qtt/NlnVgH0opwVyV7A95yVNwTrcDs4M4GBUnqbBjoJTa6EzZFjS6F+BdUvO3/UtinWaxzBZZq469guPPvyIk5f6sA0CCybolQwhN3hDYPARpCc7XSB/buGsH9XcFMiSoG6OxYdrFKs1oED00O4/+YhnDo7C9ME9k2bmFv1Ix2sFHD7tVMgBBgfrgTitFXKWbaXhk0jx6/BTMY8TwIfv0GA+2/e7UdJgW89d0V7vgq3HplMl5FNxLH9ozi8exgleVC6iRBtDbbQ/FaHgHRWfTh+dL2Nk9m+OoF4Q25d2Koy3q6Twr+uetPhzPxNSZPlc/tOSewAbGxs4Cd/8ifxmc98Rjg+Pz+P+fl5fPvb38bv//7v43/9r/+F++67r6e0fu/3fg+/9mu/hmazGfhtfX0dJ06cwIkTJ/CXf/mXeP3rX4+Pf/zjOHjwYE9psrLElnCXikC91UGj5bBfpmmDLQXhwZSzMpfoy78phqpFDFU15rQAltdbeOblRVic2ocQYG7Z6YifvrTodcLX3bH86KBD1jrKWfE6ag2f3GMoFIhHENabFCODQL3VxROn5gUvk5bLcl5arGN5/aKQT9umWHQb0krZOa/Zpvjms5eF8548vYCCouLsuOv9VjZa+MoTFwO/A/59fOnyGi4tiMSFZdte5+DFyytoWY530nvfZWC95hDlT5y08fRJpxPBrsG/phoWVpnqtINn3OUza/WOlx+mkG22gL/56gWUS8DCMvDyZee4bGnA8nph3pmOZuQ1AJydXcZXnlhDu2Nh/wxw9rJTqT347Q1MTzjn3HsLxY1HbIA44WcXu54a7NLSsrexHMOLF5ewsOZ8bnd85eyTL87h0gL1jgN+WXnD3QRfe9zpuLQ7wFOnFzEuTRTMrmzgK0+I02srG07kx8+s4itPOPf6wrw4ODxxfg3L62WMD5fxzEuLWFj131lGsFs2xVeeuIjJkTJevuLEk8WGYEC+ZHInIq1aIXE6PRaNYPj0EapCXl5w6qXhAQN/9G92K84IwrIofuo/OPVtN2ImXexiBSei4kE8X9VXDJsiiSLr5OwEV2Gow52+4FRy+2f8bh0hfrsbpZz18qXhn8IedZpSkHy55Q5FRuysPMhOctfSvPehEy/SNYXVX8fPiDMmWT1tNsAKU84mIWziQPcok2nS87mMHDmyhqrN1fI43N4bNqXCptL+SkRX8OEOKMpFUyRnmXJWWlndDWlnHXs9AkKAatkhZ0cHS5geq+L+251xQdeyPas5wBGyyBsU8QhVztLktgZpV7QIy+sNgqlRMc8Fkwj35pXQ6hNCtpSYBbD9NwSTvvck6EDv42SDEBw7RHFpXhIxhMQbuiFYQDkLbNSBP/tbA9MTwPfclc74PidnU8KyLPzoj/4o/uEf/gEAMDMzg5//+Z/HTTfdhKWlJXziE5/AN7/5TZw/fx7veMc78M1vfhM33nhj6vROnjzpEbP79u3DW97yFtx9992Ynp5Gs9nEww8/jI9//OPY2NjA17/+dbzxjW/Eww8/jOnp6dRpso2KGq5KsFwEZpcbXmMge8sylIruUjcWj+c56zYYMWxCzs9vYNld8sHQtYB6030TSAeG4SwPWa87KqixYcONn3iziU76vq0B2yETcCqNYhGwWo5yFgBml+oCmcaj07XR6ToDjW89SXD6PMGuCQpKDRBCMVClAAx0u8B6Q/QAiFrqYdkUa/Vw2Ve7Y6PdCZ5j2851dzoW1uquxULF+QewStCAbYvXwK6p3XF+B6Gou8S7zeXHGeyYAAj+4JPBikbeDAwAmm0LTXcpjlOxOc/Npn4ef+hNwPIa8JefNdFoActrznPcP2NjbBhotS0ApkcOA4BpdlCQtsZsdy3vGlsd4nnONjsdt8I00XEfB+uE7dnVRbFgoN0haHeAjUZH0Yj4efWPOemsNyys1Z2EGi2nHJZLFK02wUaji/PzGxiqFvDS5bXgzXGxVm8Lz3ywop+siIN84LeD0Wd2Nutos4yP76NdcsnZvbsK2D0Zr3vCk6tRvqp+oPTXQAMfkiJmyiTwQRvasihOnnPqkhsOibIAXzmbTbaEc7MoCNtvPJEp+l4vJ4w4/uk9TLxwynF+Qnx1gx1NGZ9KOVu3Qam/SzKvELcVL+srvLjlyHFVQkWcRK4+oRS2Tb39KwB/ErNQEMcZpaIJcOM6w3B8ZZnbnGE4QhNdH4RSihV337GJEcMjk9l4pmAasGwL3a4tTKSqSKwXL67izJV1AP6Y7bGT8xgoF/Cq66c9ks5OYWuQFjy5pcqzaRjocoP/wRBxVo742Pa2Bhp2Nq0/bq/K2VLRxK1HKcolG7UG8I0nDDfekDTdv2py1lfOOuMKgrOXCSglmF2EsCl7EuTkbEr8yZ/8iUfM3nTTTfjyl7+MmZkZ7/f3v//9+JVf+RX87u/+LpaXl/He974XX/va11KnRwjBW9/6VvzKr/wK3vzmN7vEpI+f+ZmfwQc/+EG87W1vw4kTJ/Dyyy/jgx/8IP7sz/4sVXqU+pU6b2vAk56HdlfxDbQCYUtuqWLkLgvBCrG8DEQF5q96YNpZHrJWa+Pbzzom7aZBce3eAZSKDonKiNfRYf+eNDs2yiV/UMBIuYkR/xxCCEoFRxHKCGhGPE+NVnB0/xgA4NzsOi4u1LB7YgCH94zg3JUuHn1uGQCwtOZc09iQiUO7S3js+Sa6XeDeG6bxyAtzXlq3HJ7A8EBQSz+3XMfpS2sYGyrhxkMTynvxneNXYNvw0uexXmvDslcAADceGsN1B4LLXp45uQKgA8sC9kwO4JrdI8I1VctdABZGBou4+ZpBPHdmGZWSiTuP7nLuZcvCH/3vJSFOQoBd4wQ2tXHL0eADPbx7GLsnBwEABmoAnAdw59Fx7J8pY3a5jpcureHIvhKKBUftvOYuLRofNgFYKJfgEZ6AY4sxUPFtMxjuPDqJk+U2gDrabb987d81gJmxEj77tXW0u86kACsrb7prGl/41jKWOranqpWX/d54aAS3Hx0Qjl2Zq+Gx5+sYHxrA/Tc7XlJ//7VlAF2MDZmYXbJhWc57wttQsCU+jxyfhWVTz6qCYWSgiAPTwWVQSZBoP58cVxf6xM6m3dlXh0vzPjkbOyuEwDSc9z7u0n0K9JWlibWUUCNnjLoCy6Z45sUmWtzGg/MrFpptioEKwcHd4qCHzWVFqYqjspfVyvAcvcHjxblNbaJAtV/iBlLnIcB9KlTe/OqnZqv3ksPeKeZzb9tArUkxVHV+4IkNRpykGx71D/nilhw5soXynYqobih16gienGX8oSwCKUlkLSOX2HijXAQarXDl7LI7Xtw9ZXjqWKbQLRYMtDoWOpaNLqfQVV3CS5dX0WiJLDATxMwu1z2SzurB1iDpihaeNFOlVTAJWu5Y67W37MbkSHCsmiM5eD52Oypn5RXSBZM41o8pszrcI6k/VC1iarQM43ALF2b94+Ebgun7Wt6KcOrbGvCP4czliB3nNcjJ2RSwLAv/8T/+R+/7X/7lXwrELMN/+S//BV/60pfw5JNP4utf/zq+8IUv4K1vfWuqNP/zf/7PmJhQk3cMhw4dwic/+UnccccdAIBPfvKT+IM/+AMMDAyEhlOBL4KecrYkNoATI0VAQc6yTrPvtcGWXTjfFftaBcDGkcPVIoarRaysNz1ibaDq5I+RdGxjqELBb6zOXanj2n0DaLYtLK83PVuDQsHCpQVnuXyz0/XiuDBXx8HdhpfXarmAaXcpycKKk/BAuYDnXrTxn/5sOZDf8RHDI6U7FoRlKc61E+warQRmfdZq/v3jG2QexKXybEoxNVKBYRDUmh2sbrTRsWyvM0EIDcRRKBgYrJgAOrBsR505PVbFPHdNjjGbhWrZ8DyNTIN419/p2jDNRViWn/fxYQMf+Jkyzs1tKPM8OlT2wk8Md8DI2X27qpgeK3vWGOWiialR4PKi/+yqFcrFA8wtsc/ORmsFqdaaGS+7ipw6Gm2AuutyRweLKJplAOtodxgx61hZVEsUlZI7mHMVZR07GO+0tJxo11gXQB3UNrzfLGvFuScjBcwutR1yloqdInYu86+Sq/jx4WDZSA63AclHfjsOm2Zr0K/waTKuiMxTzk4l65oYjJyNUM6qLQgSwlvKzZsjaFPU5kFva0BxcQ6wOy2MDwNLGxYM0w/3D99q4zNfW1eGPXagJKgnCPGVs5/4/Dre8Zoh7NsV0blNswS+l2XzyYPuKHiD3IxsDXqXQCWYwEC85xN2bhhZEReq+AumQ4i0OsDymoWhqruCqBsM1++ZhbjlP8y7LkeOHL1AYWug85zlquR2xxbGNqwPUZTIWPm7aRB0Ldsbb5Q8clafu2V3Id30uIGOO3BjK0rZ365FhfGjalzAxtB3Hp3C06cXYdkU48NlLK+30LWoQM6yyim+rUG6GXfRc1alnPWPjQ2V803mMwJ/H7ejcnZqtIprZoZxZnYdQ9UCBislNNr11M//8J4RFAoGzs1ugFLqbaqXBIOVIpbWW6hy8wNpNwTjPWcBZwVPg8vSy5c6GBpK3uLn5GwKfO1rX8Ply47H3Rve8AbcddddyvNM08Qv/uIv4md/9mcBAJ/4xCdSk7NRxCzD7bffjuuvvx4nTpxAvV7Hiy++iNtuuy1xejzB03QLWrnobxIGANWSujQPO4JJ71x5Q7A4ylmmuiWE4GtPX0KzbXnk7GDV8U51GjM/D2u1putrQ/DEqSW8dMVXey6vOQ3r0sYaHj3hLzUvFZ3jz7y8gra9imv3OspU/kXlx0RffITb1pMH6WB+tQ3AQKdL8PipBeHnp04vujM2Itl39so6Ol3g1Lk2Tp2fw8Qop3SygOVV5xInRoG55QZOXVzB0X1j+OpTlzzPXWZr8NLlVcytBrPW6jhsuWUH1ZWE+IOnokm03iomAfg+R7FoaYlZFi8Dfy/ZBl18OhOjpkfOGgaFYfgFZEQgZ52/rBPDUCkRDA84havBWcSWi0C15NybjTrBmvvohgaAx07Nw6KOHQFTznYk1R2/YZx3zNsQzM9j0/08MuCc37Wc94eVfyNiRtk5R308CaJInxzbGf2lqrIXzhLpe3JQUDz2PME3H29hdMipL58+5TQ2SZSzgFNndpBs99a09ySQQozlUElw5hLF//6iCWDD/Qf807cA+9z535cvOXXl9LiJoQG/jiqaBP/srfLmowQTo/637zzbxI+8SU3OBvMafS+331BgG8Krl7OZGvGUs+xonGi5kxLnIlT9HX6YQCQrJkZpOm5ZkH/5EQwNGGit2viZ/3gZ//D/O4BSkQjtuHfZXD57KbP6vOetbo4cW4kkE4R8/4XtqcKgV86K3qKEiFZBRbdZ1YlsQH3l7PQ48c5j9gksvU7XRkcgZ4NRsbHF+FDZq3kGywUsr7dgWbZga+BHpM5WVoga5/CrfXNiNjvw9307KmcBYGjAeTlGB8te2U2bU0IIDk4P4+C0s3L1uyfmcHFBw8towO7TQMz96MOKK+Mj2OajhPib/gHAmUsWbjmWKHtOvMmDxMfGxgYuXbqE1dVVNJvNRIquBx54oI856w0PPvig9/kd73hH6Lnf933fpwzXT4yM+AO0RqMRcqYevLqV7UbvKGf9Z1jhZh0GqwS1hvPb0IDz15aetzjDEJG+N3lHPe9Stux9qOooPktFcYpyZNBEsWCjaznqVYZWG1hZZ42iY8BeMAksGygWHGau03EUjRZHCjN4huqEeBtCvfnVNk6cIbgw65w3MiQqMSeGy4EZHeYNxKPVsfGJBw2v0b7xMME/f7vTCfh//rqLyy7He81eih96k41Gy0LXsj1idqha8O7z2FARo0N+A1hrdtFsWyDu9tu27Vfk/u7HvkdroeD/zj87QoB2V6ydhqrONVIg4A0MiJ2fw3uLGBumGBrwOyDsdwqKyVG/GhqoOLuTToyUcWG+hpFBClaNj7jkrGxrUC4ZmBxxKn8/nxTFIsHokH/y//lH574ODzp5Z8/e8dylAU/GseEgOTvgEraXF7r4wsMOcbJWt914Xc9j27nXtlSWzl3pYGmVYFAhZM+iUQ0zLc9xtSOjTptUuHopakurtuv31HX/+bh2n2I71RAwU/4oW4OsVIxyrDyEfo7itnuHNHlYdee8BqsElZKBxVULlxcI9s04AeaXnfrmV39qEnffEL40kAC4+VqKZ05RzC4StMPuD5GvQ/qdv6w0u1LJuFqksxkhyaRAaNgMyn5gmZ+8FNaX+QrK2R96U28GAyI1C7z6lhI+903H3ur0hTZuPFwWPWdtlr2sXnj1TY8be9S7nyNHjnRIsiEYf7wuDV885awZRznrfy+75KxOfERBPeXs5Bjx1LFsTORvQGYJba+SnLVp4DOzDezaonKWjeniEqJprar46FVpFfiNq/I2PzMQrlhuR+UsAG8z9K5le2UjreesjL1Tg8nJWTfpCkfONkO2/PH6O4rBNZtzsFyROgER6pRnXuzg5qNI3OZnTs4eP34cH/nIR/D5z38eL774Yqoltg5hFLV7xdbhmWee8T7fc889oefu3r0bBw4cwPnz5zE7O4v5+Xns2rWrb3lrt9s4efKk9/3QoUOp4uEbugbvOcs9zgo3fp4aNVFrOM9syCWfZFsDthw9lueswihnftlNa5ziyJ4RVMuiTPTYgRF8ubDiLC3hig8LNzxAUa0Adx/bheGBEhqtLj7+9xeFPDFy1hDIWTcrAFbXndZ4YpS6G2E5540OArvGygDa6HSB1926B5/51hkhf6pXYXHFn00FgItzBK+7dS8aTRuXFy54xxeW/fvCP4Pr948DcKSldx2bwp5Jf7D+3JklvHhx1VPi8spZ/vZ6BvgmXwn5aajq0MEqxWtv3YO1WhtffepS4HfeErlUJPipH7BBiB8Xn48pbsOQgQpwZM8I9k8P4cJ8DQdmKJ54wflt/wzrzPhxm4bTERkbFme1C6ZjQD85auKNdw3gocf97VjHhglec/Nu/MVnzwPwl0HyHa1/8U8tlIrBCx92lWrnZ7v/f/b+O+6So74Sxk913/jkOPNMntFolDVKoIBAiCQwS7IxXpsFjJy9u16v+e0S3vWyLCxee5ddY+zXcV9jw5pgg21MDkaDEBIoozTSjDQ5PTnefLvr90d1VVdXV6d7+0lSn89n5rm3b3dVdapw6tT54nc/5fXh5WWwHOUsv4YGAe56sIKP/OUsAOD1LwMO7Pamm8ZMskx4Z9hk8Fd3q5RPd8+Gb2W1b0N8cP+xQh74pTcPie0TIzlcsjshOSutNgiFTsXY8SUhcpIeWBY7P+1VieCHuUfnJbtzuOZAD/7qK4uYcwZ1lAIzC6weTGL9MD7MyFl1dYAe+n30W9UHITk25nAiPSRSuIZBOT415XfQfgkG6UFLYdnKHPb5op0UA70xM/elLyfqfnz7a3sxNQc8eLiO5862cPm+oucZF30l3cRCB1CPJGrZYiaQtdAZMqSLsPq11rDxoydqwqd95xZ3vFCruwIQwB0Lqiv01O+GIbenFPmcI84JDAgGLDsc0nA/RaXp9ZzlCtq64iWrsyNQhR8AkHcGRm3LFkSybVMQHkNbX6xgJDzA4zmrOdg0n+8t/fpgowcEA+R4Q1T01dMa6wz3xZS/SuBiKLkMPLaNDmF9OFU5CwJUa25aU/M2Tp4HLtqRrNVPjZxtt9t4//vfj49//OOCjNMRs4SQTe+J+Mwzz4jP+/bti9x/3759OH36tDh2NcnZz3zmM1hcZKTl9ddfj4mJiY7SkW9Rw5lRKBW8906edSgWXDau3yFnha2Bsz2JclZWq3JMz7HP48MUxCAoFrxd454SEQTwA08auHQvxa4Jii9/j5Vtyyj7zZ1JBHI51jDzpSxuoyeVxflLCDDvKGfLRTjkLIOsnG239QMAW7Pk9swUy3/HOMHZaYqlio1KzcbUvHdyotl2r4t7bQB5BU0h582TF0GOJqjOABEQaaZYtjWQlLOahra3xxmYBLzKnvMnLllLVEKDAqNDbjXUW3b2d3bYuwP4u/++DQQEdz/O3iFZOWs6Xox9ZYKcSdF2vKP4dkIIPvhLY7jmnil88p9qqDeAKy4ynWBw7NoLz1nnb0+JolzUn/fVFxfxxpf2YXLOe38O7C5gfIj1go6cNLBSs93lG4SISOoAMDNPcGC398KlaWuQjfw2H1b7lq2e5UXnS5Q4CVkqEPz0K9Xl+MkglLNR5KyE1bomFMCXDhk4fYHgliva2LvNjDxGhjw45MG95hdZW7dSZedoGszWIAqiDXDq3zByligf/Htq+nOBv0RDbmGe10jp9ILmQVa9utf0ZaIIZ3kzV866fYAOEHKS+3fkHXKWtbEyOSIEAp3kqUNA4Tf5kCZDhk0P3TvIq66/+soi/u6fXZ/2nAm84w3MKo2Jj9wXW9i8qUpZ0/BMxhjEHTuZhjRBHNDGti0Ky4mPQgxXvMGVs/xvrekdW6jV71Mn5kQZ5HEaL2/bslHKs76BbVMtERWGThe0eANT+X/3euE/z9v8NcRmsDXg975tUXHv03oG5OeKUhorXZ1qtxZiXRs2VpBjKd316Fm02jZqDbZxoJdiqUJw7AzBvvUiZ9/1rnfh85//fCAhC3iJJfU3/vtmwMLCgvg8NjYWuf/o6Kj22LQxPT2N973vfeL7b//2b0ce02g00Gi4T+XSEpPoCJISrty7WAxWzq7UXJaw6GznaXTiOevmQ2HZwOIyhHpofIQ1BAXl6S0XDfSWgblF4NgZghPngBuv4svWgW1jzjnxxgpElImRelSU2dA8l602UHdUxD0lLzk72AePrQGlVDTkc4vAqfMEl+32nzi3Rbh0n4GVGsXiio0Ls21B/o0OmphdtJw0neXykpeprBDOKeQsPwfTZPtbNvENpmXPWdMMtjVQ0VNi765qXSGO0W6VlbNOnQCKrSMuydDXwypXuV7o7zE9FbBMzrL7R2AY7N7zJcE503sP9+8wceeb2fUf7s8DxPWI4p6z7DqQ0Jm9fI7gt96u93/+7oPu0oq7H7Zw3QH22TAg7DDk/GSk2VnZHLVohucDunnWeDtgajrySWEI5WySEiWnFrV9FPXVpcDpC2zj1++t4Nffqs7sh7/rrucdsGeCVXZzS6z+5/XbxGgukRKFk9etVoLrs9oVySbp721YJPGx6ehShxykEsQhKls+IWCQBFLcgJIQ4j2cArjIsUB57GgD//xABT/4sWvllb6tQYYMGTYmdJOHBLZNcddDbOXclRcVMLNgYXLOwt99y8BbX22jVvceE+Q5axrEI5U3JFsD04xevdOU2l7bdgduXHmXd/7WfeSst8I8enbRPWPplPNSQDFDsjUwNUKneEi2v0c5qw0IlkJHL4MP8qXeqMpZ/i5Zto0cTTZZEAWZkLZs6lO4Rx0zOkQxu0Bw6Z6wPkJwX4tzSDaFsJvknrMTY4yc1Y35I8uY/BA/Pv3pT+Nzn/uc+H7ZZZfhr//6r3HmzBk0m00xoLn44oth2zbm5ubw0EMP4WMf+xguvvhiUEphGAY+8pGPwLZtWFYC+cs6YGXFDYRUKoV7vgFAueyyeMvL+ijL3aLZbOKtb30rpqamAABvectb8JM/+ZORx/33//7fMTg4KP7t2rULgNQZNojwnC0VIAJ1AawCvvmqEi7emcfEiF/FYyuyBVnaHgUqHfv5bxj49FdMUEpQLlL0lVneRWXla7lI8OqbKG65xoZpUNg2EWpb06A4eAkfJPDyu0Qfb1BtXUPmFKVSZR9yJluKKy+vH+hzPYe4ApMn8emvmPjeQwYOPeQ3NZlkK92xb5uBiVFWmPMzbZx3opZftCPvXA+m7rUp9Sxp4SoRQvyVEj8Hvtmy/OM52dQ+lwsOCKZCp7CVETSDJy6rlM+tB8t4+2sHcMtBAzdczkhtlRyXSWCZhM7l3DRlslwlZ+X7aRBGUnNy3w0Ixv5yIiNpZ+aWq8si4NnMgjcg2MKyW6c1NBV1Gv47Yb44GTKkgs75FR84OZtGnz0XMTDiWA1xuYabFZhd9E/IuapDfSlk5eyO8Tzz/G4RTM+7vtmdBEwDEO45y0HdiTPPZt297+KCBilBn29w7WbSTS/Jpe9G2Zzo9mgKZUkERgcpBmTAQbF/J+sjHT/Xwkc/OYuz024lkHZT6Ct5wlOJUhxnyJChM2jfKQI8c7KJmQULPSWC//WbW/Ef3sHEUpUawXd+aKCmDMtcNSzxtE050/DUa0wc4whbDHn1jv7lrjfdvkDb5hZtrhBFKGdVW4OQOobnZBrEY2sge84m9ZCVV2YmgdfWwI/M1mB1sCmUs5z7sWS7v/SVs1ZMcYac98/cYePtr7ewd0dweUKVs9LKZIDVQ9xzlsfJabdJ4k5YKuTs7/zO74jPr3jFK/Dggw/ine98J7Zv345czj+IGBoawnXXXYf3vOc9eOqpp/CBD3wAlmXhgx/8IH7xF38xjSK9oGDbNn7hF34B3//+9wEA+/fvx1/+5V/GOvYDH/gAFhcXxT9uv+Aue3dtDYoFr1Ly6VPzeNXNTfzRf9yCLSP+++zyq64yk5U3xjlRppg9M2lhep49/IU8I1gJYS9XseB9mcpFguEB4MarqPC9nXUmGW+8mgoiVryYxCVY222uGHX2kd4MXv5lZzZkqM8EIV5bh3wOKDnBotpt5z1UKp+T5/3MwYqT5uiQgW2cnJ1t4/ws23fvNjeydrPN1eduGVstdjFNw98g8jpLGFbbbplcYkCyNZACgnlsDQjBAUX1u3ML3893SuKYgF+k/9l1KhYM/NKbh/Cml+cx2M/tCNwjbOrNp6AoZ/m+fT3KdukeehowwsqXV8h00dFKtgpZoFw08EtvZqZ6y1XZfoJgYVlWzvqvTRrtVDbw28RIkfTUQaTb5bMhDk+B5fSTNp0jamAkIJW7+2sSdKC7fWZB450fcZOFothkft39PeyAZ0+5B0YFAhNZidUT7HtLUxwVkZeD+D9mVU4MdFExRwWZ6yq9DhBVBJl45572YlK8g/w8hIGcAGUBR3/m1f247tIiLtqeV47z/l21oWvcy/l8n4nIkGGdEMDN4sfPMqbkhstKKOQJrr+0iJddy5Qclbo7vuXg7S8hxKOeVVWJhAANZ8yQz0kBuYKUs01O5FI0HFWNnH6grUFYrcUFIAZxCVlJOWtTumaToF5bA39mG1XVudlBpOu6Ua+xCAhm2x2T/0EghEhK8XgBR+Xns5AHxoej8mB/9Z6z7K9NCShl/I/t2JcMOuRsq528j9y1rcGxY8eEB2upVMJnPvMZ9PRowpEHwDRNfPSjHwWlFL/7u7+Lv/qrv8IrXvEKvOMd7+i2aKuGvr4+zM/PAwDq9Tr6+vpC96/V3GVW/f39qZaFUopf+7Vfw9/8zd8AAHbv3o3vfOc7GB6OeNocFItFFIt+Q2V5doM3XuUCgSp6Xam1sFhp4pfeNIRq3cY1l1AA3sh59aaFxUoDltPqxbE1oDbwnR8SPH2cqZSLeYpf+xn3QIO4SlWOnpLhIeoWV4CFZbZBtmAQnidwPWpV5awuIBi3bhjsZxXNxbsorrjIxoHdOQCWa2tgMYUxUc61T3ktqnVbEHXD/QQTToCXb99fEfYJ28dyyOfYy91qMaLSY2vglNsw/IQor4DkmR3eFZAbbOGxJBGd6n2+4yUUV11sYbAfqNWB8RHDuTb6KieQmuUDNJ3KUwygiGNtwK49VYKgqbYGvPMiX1/TVGcUIX3mZD/77toauGnKZU2CIefZWK5IRD8BFlbcHhvPzyDyZED3LdXq+YpmWG34SM+0sUrPRjcdf86jptGhZGnQ2DPnbPJM+hwTcWo7uUqb0Shno6AGJOH1EV8lMTFq4GdencyjN5bnrEKeZZM86WC16uVErgYd3MwkbglhKlv+TqZhX6LmQcH6Er/2U6y/+9yZJn75dy6I32U1S2ABO83cU45419edzMhergwZ0oS+jiOYceJ37NjCOvyEELz9tQP4/qM1tNv+KO3ywt28aYilyqZJPLYGRBob5/OyrYH+3ebByHKe8YtbKXLPWPU0wvpWNhc+GS6R7FfOuiKRWOhwIstra+D/PbM1WB3I3eeNeo1l5awtvT+ppW8w+5I4q7IBfYyXsNKI1U+aOsbreQtQiWUpFdj+QUECQ8uY/BAv7r//fgDsQr/xjW/E1q1bA/e1Q1jtD33oQ9i1axcopfjIRz7SbbFWFUNDQ+LzzMxM5P6zs7PaY7sFpRT/+l//a/zFX/wFAGDnzp347ne/i71793adtljeb7hkUqnoPpyX7x5Cf5kvuacYHjDwoV8ex6V7/Hz/2ZkKDj16DmdnGNEax7XCphRPH3cfz16F2HQDgrkoFYiWqAPgsUDQ2hrwgFuaKJiCnK2wD4O9rFyGAbzmFoqDl3ACmDeuBM0WU/iuSDx1Me8tL1dVFXIUpaKBvY6/4LOnWzgzxX7bPZF3SURFOUuIGy3UNPwNolBNGWwfr3KW7XPXAw2cn3E6DSZRztutiAo5gt3b2EzQxJjsTQstgpYshBEAasRn2f/Wa3wvzRRKhDJXmLFz8VsZuGVgn4uCnOWkuncQ2UnjMTrAmJSVKmA7LItfOetPP5WGKsloPcMGxcac+eaggSxH8nJbSqCgbhDl98Yh1W6dZaQ5TH115bpKp5yNUpvayiSRUAU7SY0OJb9gPI1YtgYRSOsJXXU14/MI8l1b6+ulbZpUxjmkXuBqbfGed3AC3tSDE5BXGgHB/ZNOkT2rGTJsTAQFBJtZZA3q2KC7RKdcdPr8bVc5O9jnV77mpKBgOcPwNdoNRw1byEUHBOP2gLIdXl6jnFWhBiaTIWKkyOSsbcNwKlvbponJ1k6FAnI7YWgOzmwNVgfqqtCNCK6c9U4WpJe+PBkRBzoxVCg5GzLBLttJ2lSK9WP4+aUk6Fo5yz1OAeDGG2/U7sOJh3q9rv0dAAqFAn7qp34Kf/AHf4Bnn30WjzzyCK677rpui7cquPTSS3H8+HEAwPHjxyPJUL4vPzYNUErxb/7Nv8Gf/umfAgB27NiBu+66C/v3708tfYDNGPBVFiXJ1iCfM0Vle99TkwCAgd4Clit+X1UOQyYJY+bPoZKtBiEesjNnOp6rRL9/seCm5wYE86uSZLUjh2XbuOdhgslZdm6Dfd7GkiuCi0X3oFqTBbZaXHHz5Q05x8wCy7S3h1UWt9/Qi+WqjcUKS2/LcA7XXlJEIQ9UaqwjYVPZyxReclapXlRbA9sGCK9eHJL38992g8Hlc8Rz3pTKSld4aqYof1MS0J8QfnmamlD2wZX/UirfF6IJCMbgV8663w0NUeu3NSDiWFbW5Bge5L5PrtK6bbmz5oCsnCWw4N7LbhFF+mTYyOD17Woh3ZS7p2ZdEjKVgGDC1iBqz9WXl8vtW7MF/Oxvn/X83mrZGB0ycOk79IXgHUzevrqqV/Y3ZKymBZHSaCYJCKZAakGlTylczw06oEgL4vS6uUYps7O0y2TUSQ5VQS/bYrXVSc9OMoypfFUJAJ8KrZO8Y6SQeD40a6QzZFg1yMMVPs4aG3IHCyVnrNZuA80m+zzYZ2Jxpe0RD+3Z2o+jZxYw0FNAX4+yVBOu6raQB3qcmBdnpvSdEN72ymMWmdhRY4aM9BdBAVy2ezjwPM/NsEBnTDnLyWUqyCrZImGh0sSpyeiYNys1HhQjWW3pEcBo+igbdcn9ZkfqIp9VgNwutyTRUmrpJyVndXnHKY4meXn8YtuciLZhGK5KPo6dmIquyVk5ONbo6Kh2n97eXqysrGBxcTE0rcsuu0x8Pnz48IYlZ6+++mp84xvfAAA88MADeMUrXhG47+TkpPBx3bJlC8bHx7vOnxOzf/InfwIA2L59O+666y5cfPHFXaft5sH+GoRHeGZKVZlAUx/wpRBiFgCKzqgyjnJWXRoiqyJ5/rJylqtLScD+HlsDKZGcMrNha5SzJ85RPHSYvXAAsG3Ua5LIy1rIExhOILJawwYBsFRx06mr5Kwzo9tbBkxCUMgTvPWV/uWqItBYi738chk9PqlK5UKkGRyAX3eHVAVQa3j3V9WmNqXaGVDAJRMDPWcDjhPJa8hdKggq4uTBulg2pSC2e5jP1sBJa0hyDCnmlUZL6hhwsrqo2Bqo0eM7aTtKBYJigaLRJJhfYgmq0WBd5ay7LY2GKhPObl6s1S1LLVgc6d4HU5CQqQYEi3d+8l5pvy8qATo152/w5pcJ5hYosMt/vGtrwP+yetCdPEoqaZE9Z4NP1g1c5RBu6q4hF6q7Zdobc0CRGlLgr3XcbJIAkL494rCzQnEVvGNkzsR9J9NQyKsl0Z36f/mlYfzX/zMPwJ3U1U0spAFlzjp6/9WfG8qQ4QUJqlaSjriEk7PjMjnrrHK0qRvwuq/MyU03mYu2DeCibdKYTHnheV++kAcu22vgnkcsPHKkhWaLoqCslGxoyNnxITeK8UBvAaWCiXrTAiHA3okB7NritUy0FfLp2PkllqZheGwNOFFbrbdRrbPB7YW5Ki7MVREXSbnUqPHM7i39eO7cIsYHy77fMnQO+T4Fqa/XGzIx33ZsQtLk6tNQzoZZQoi+lqblNqWVvLYNmDkTnJwVVpd8ZXaCwUbX5Gxvb6/4XK3qX/zBwUGsrKygWq1ifn4+0A81n3dnps6fP99t0VYNr3vd6/A//+f/BAB8/etfx3vf+97Afb/2ta+Jz69//eu7zlslZrdt24a77roLBw4c6DptGVydSQFYjrmxrJw1CAkllPZu7Uc+Z+Do2UUM9hawWGkKdZMd4wFdrqrkrPd3SqmHcC3kvemq+3NbAx4MiqeRN9kxLcVzloIK755KjW0bGyL4xTcN47pLc7jvKXeigVcIXNXZaAK//ruTsCnQttxr1GjBA77ktbdMA5Wm/NwAZmtg26xcAJwo3u7AR70dnDyXA4Lx87JtikrNu39eITQ93IsineX7CfsL4t0/qOJ1/X6dPKTf/MpZ5z7ZFLZzDoZB0N/rJt5TclPYPUHw4istrNSAay+linJW/sy+yNcV8HvOdkLaEELQW2LPwJxjZVANJGfla+3e16RgUWVdS4/UCLgMa4eYyrBOkVayaRbTnQzpPjVOPkZN/HkGEB3ko62vlH1kcvYP/8MWnyLm3//vSTRawUSyIGcNb/0tlLMJA6gR6ZgwctZ/QQJn3kKOiQ93Mu75jjTOMF3pbJwWQrUZ6jQh2Re/U0hhbSLP/rrLinjtS2x8814DlDepKS2l3KDCpAwZXvDQThBSilnH911WzpaLbmXERSp9PXzpdWgmHnDCNZ8DdmwB+nooVqrAw0/XcfPVXhKyIdkavP6mPSDES6YVciZe86JdLKAXAUwN0SYHPCIAxofLIAD2bRsQ/QxKgaHeIvZO9KPaaGOp0kS9aaGvlENP2a/+1aFgGtg5Hh5LR4XHRk5TS+dzBl5zw64Nq+7crDBNAzddvpXxGUmXVa0RCGEB6yybrpJyVrLxiAGdcjasfxImfPIoZ6m7j2kQvOr6bfi/X72wPsrZnTt3is8XLlzQ7nPJJZfg7Fm2tO/BBx/Ea17zGu1+R44c6bY4a4KXv/zlmJiYwIULF3Do0CE8/PDDuP766337WZaFT3ziE+L7z/7sz3ad97/9t/9WELMTExO46667cMkll3Sdrg/OA9Zqu6RcsUA8nqxhD7NpEuHXwx9sd3YjOvulFe/3fN570I8OMzuNG68iuDBLcNV+G4cePSftbwFwG2NOzvIKYW6pjh88cV4oZ3kArkVH/fv4sTk8fmwOxbyJlRpLZ+uIidfe3OdTCPMG0zAItowApy8AKzW/NKXRZH4r/PynF1zlbJBHKwBnBpai1SZYqjZx/9Ps3JerLZw6vwjA1NsaGN6/tu2eFwAfOWsahmJrIJOx3n35zBPfxzSIh2wgEYSLtrJTxoI8CZsCRFJy795m4NU3t1CtA5fvcxPImQZecq1bC0Z5zvJZraZzO93o8Vw5lpzkNAhBbw8wtwR85htVLFUNVKos4a0jJibnLLTaBLYNEbEVAH783Cx+/NxsULKh6Cvncfu1O9ZixXaGzYpV7g93krwgZxOSjTq4E1AJnn5RJyR5YzT7KifPfV0NQnH5vqKvbs/l2ERdO6Cswu5BKGfZX97BS369iPAdj9NJDLoqqc8frPKExEaBuJ7dTJpFP3YJj48jnQ1GkOWsup3A9WDsSjkr9w2I5gcFos/jU86mDdY3y9rcDBnWF7rqtVJnhJBBgJEBt+FkFm4UNiViZRtXzsZZ2cnRkGwNDINg73aKJ54leOxZDTnbYh2eXC7YR9YgBEYuuF6Wx1j5HMEtV0yI77I4yaYU1+wfAwA8dGQKZ6Yr2DMxgIt3DMY/uYSQh3xBw9mMmF0dTIz0RO+0zsiZxPGcZd9XxXM2psBJn3dwgdw+nP83g7h1iUHc1eWGwYLUA515znZNs19xxRXi84MPPqjd54YbbhCfP/vZz2r3aTQa+NznPie+y6TvRoNpmvjgBz8ovr/rXe/yeO9yvP/978ejjz4KALj11lvx2te+VpveX/3VXznR6Qluv/32wHx/4zd+A3/8x38MgBGzhw4dSs3DVgUfSFjOQ0UIW6ZhSw9eGKFINAoHPqgMiQsnsFSJ3gcAbrmG4idfaePAHu/2QWXSz1XOslLNLtVhU3fgy9UdjSZw76MEswvO95aFhRUmdeQm8qry17WAIHjLK2y88w0W/sM7/OrwlSrFL3zkPO74jdO44zdO40vfYyfZWw6X1JdLjjS+5f+N10U65aw2IJiEWt17QF6ZqgnjOWTilOXvTUv+NtRXxEBPHluHy9Lv7iwvh1BScXWtRAC7Cl2CcsHElfspXnwlRb8r3A8kkAFguL+IvGmAEGB8sATA9YiqNdgzaUnK2aG+AsrF5HNXhAAjg6yspyctLC4T8WzJnbWm5l52ipVaC9V6qzOuKcOGwFrxVF0/GlT7sSPYio1IN+Aq06hOkLi+nZ5HjJ1bjprGNPX3k5fVDiirUM7meP3t1P+ifkr4lBApIFgMz1nq+xCNbnjH5/1wLYUT9OhmeXohao6w4/UbUoZUofk8ZzsYlYnikoDtykbizOamHRAsEDFfgFQ8mjNkyOCBOvFFAJyZBO55mG0fHjB9dkBcmFNrsO295RjKWXUiVrI1IAC2MT4UTxzzW/zpbA2SQp58NpQxo0GIGPPIq/BWgwzTYTN4n2ZYP6gcR/QamPgwTC7861w5G/bIhtkaEELEymdCDLTFuIYFegeYfYplJWv2u1bOXn755RgZGcHc3Bzuuece1Ot1lEolzz5ve9vb8LGPfQwA8KlPfQq333473vWud4nfq9Uq7rzzTpw6dQoAq3Re/vKXd1u0VcUv//Iv4x/+4R/w7W9/G08++SSuueYa/PIv/zKuuOIKzM3N4bOf/SzuueceAMDQ0BD+7M/+rKv8fvu3fxt/9Ed/BIA9DL/5m7+Jw4cP4/Dhw6HHXX/99di9e3fi/Pgz3rKYMiCfYy8XpZYoQ2gFTPwVtCD0YpCzyxWvsmPvdhN50xaS+FuvmsDJyWWcmXZZ3FdetwP3PnEe9ZaNbWM5yK+CGBgoigpOSNqOdcNjR0w88CTwwJPAB3/FxGKlKUzfOUkaZMtgEEaSjgwCe7f5X63TkzZabe/J53MUO7fS0Iqhp8j8bpttoKeYw2W7h/Dw0Rn0l/Ow7JY4vzi2Bi++dAu2OCTp0uIygEWpLPy+sgbd4werdn4Uvzt1abIagOv2a3cox4uUpTz4j940bErFbgYhnginnnxUglj62t9TwOtu3A0qlbVHVFME9SZEpZrPEdx2cHtHHQyDELzkGordExRbBnsxvbSMS3eXcf0lY9gybOLr91bQbFE0W8DoYA4Vxw/qRZeOY+tw8tnPbz94Gs227eigghuQDC9spNUNEq8ogcIYJU8rjeXOHG5AsIhnX+JGOrkmutTVdJpSkEYdeFlbAe2g6jlrqsrZLgKChXvOehGnFunmuXqh1FIbY86MhnwLP0J/j4PuvH+Sti1NIKeOgBNRPfFTtNr2b6AJ7q2of14oT3+GDGsPywb+6ZAh2rutI35GNJ/ziiR4nJK2BRw51cT//L+zMA2C/3TnKHZtdewAlNeWWxUw8Q/BtnH2/ZmTDZ/vLFfZ5rpgXWTSVUcw5UyCpk1RbbQFecsJq9WmS70rFVc5swybDqqoYD09Z6NW9gZCt4KJACYBLDA7D2q7vEtJiouU1Nqga3IWAF7zmtfg85//PFZWVvCP//iPvuX7L37xi3Hbbbfh7rvvBqUUd955J37v934P11xzDer1Ou6++27Mz88DYC/4T//0T2NiYkKX1YZBLpfDF7/4Rbz97W/HV77yFVy4cAEf+chHfPvt3LkTn//853HllVd2lR8negFGiH3gAx+IddwnP/lJvPvd706cHyfd+APFyFl4PGfjPt+cNDIlkjAKy4598a4JiqsP2Ni3vYB602ZvANiyEHUmJmcaIE6kPNMwkDPbHs9XXm75/NRoenOL8IE3qlw5SwPKL89k8n1l8Dz2bc/jD96zFSv1Fn7wxDlfIC4VwlS6xcpNBOlKYDvnx1Ra+spPqEgsVkFyn6O5Je/6nbxTeRqEwKJUIWeVcxXXkX1n98JNT638fOenUf2og0F+iGxrQIj0g/ej73lUOy8qeWsaBKUCRb3JljbJytlOZ34JYbPol+4BLtudx+PHgbEhA1tH2IPWWyKCnJXLlzONjszcZbVsFhBsE2PNFF5pJUAUbjb5+8L9oRIHuNJABASLbFtWY9TgTbMpBWnU1SM54Y+rvxmq5ywnZ9vCczb5OcQJCBY3QFSiYzKkAl3svUSTcZ289yHsrI/Ip14CgEp7pmFrEOTvrDstCneymwsBRPm6fV6z5z1Dhg0Htc/bahPHkg9448v68Lpben3HqCsFuXLWtoFv/XAFz51hzO0/P1DBu98wpM2Xk7s8OPVQP9DXQ7BSpThxvoVLdhdQq9t48Ok6HniK+SfkU1LOavsWhoEmbPzgCb/F5GqrWb22BllFmcEL1UM5Xc/ZpMpZ/7aw0qh2TepxImYQIbClAKj5HLP/tG1HjJKgH5YKOfuzP/uz+PznPw9KKX7nd35H663653/+53jpS1+K2Vnmq/j000/j6aefBuAlnHbt2oU//MM/TKNYq47+/n58+ctfxpe+9CV86lOfwgMPPICpqSn09/dj//79+Kmf+in86q/+KgYHB9e7qInBGzseITqfY+Sju4Q/3NYAkB9ob+c8lnJWImcP7GbEmrpsQreMXyb2+nuB+SXvPkKN6bzEBT5odRpZ2Q5hep6gUHAb4CBbAzdtIaTQkrMcIwMm+noM2CBioB5GdHNLhlabEZW27Q404tgaGAGk+Myil5zlRLVMinKoZ+zuw++tXzETBt3A0h1AuSQx386fGYN4aaCwpTRxns9yCag3mbWB1emyYTlPw31GZAsQjt6ygfllG42WSix3mqd7jcTAOCNnNy02V6e2uwfNStHWwCUww8vkXt2Qyi0plFvG25JA5WzEJCX3nOW2Bt17zkq2BhHXR4a6WsKjmk4DQWvVn2dIfdJMvQGrzM3qyyDtp2OOpaO5Qr6791yenY04G+pXzqaHgH5OzHyyNjpDhvShTlDJq3J+6+dGtMeo5CwPCNa2KM7NuDK305OS5E2pfniQ51IBsG0bhAADPcBKlYlfTk+28J6PT2FWGmsFKWcppXjm9AIqNa/nWX9PAQd2DoIQ4vHU1E12bR/rxbNn/QqjYt7A6EDJf0CK8I7FVjWrDJsQ6uradD1nuUo8ruesLvPgAqkrhdXf+LtIQFxOxkmukAfqjXVSzr7hDW/A448/Lr63223klBrokksuwaFDh3DnnXfigQce8KVBKcUrX/lKfOpTn8LY2FgaxVozvPnNb8ab3/zmjo9/97vfHaluPXToUMfpdwIbXDnL/jLlLPF4f4ZJwwn8Co8k5OyKQ872OTadTKnr5qd+Z2VyX3hCGLE7v6SvEPh55JzVKq026zDLL9CffbGNwT4DA86kKzd3DgrsIVsC8H11GOr3pxNHOdtsc9LPVS+HERycKOTKWcsmeNeHZvCzrxnAu98whDmVnDX5/qwH4jlP5ZzdgGA8/+TEqJqsqo7h18S2qevpYnhJeTkX3fMQWgYA5SIwD6BaJ6kEKJKtGFSyGXCfi2Yrnc6MZ0YvM53dtFjtOxY285sEnne0y8TsFJc7m8JzKt7+lHZ2TXRVv/rqcuuaoHpEqHyjPGe5rQH3nO1QOUuIZGsQ4jnbSRXUTY3jeox3cPCmQhr1sqxFdf528U7HIwfjL4f1iWyleqItKUk6vdVy+jQiFQr47KtcdPewBR0d+x4875/1DBnWHmp91pZWfAZBJUn7HXLWsoDzEjl78oJElir58GDC5YKBWpONp8ycDYBgqWLj5Pmah5gFgj1nFytNPHN6QfNLBdtGe9DfU/AEEdWNsa7cO4Ir9+rJ6NVGOmKTDM9XqCtD11c56887wqUTQIBylsirlAls6uW7inmCeoMyjilW6RhSIWdN04y1bP+KK67Aj370I/zwhz/Ed7/7XZw7dw6UUuzatQuvec1rPIHDMqwvVFuDHLc1kBSMobYGxP/ymTHJWUqpS872uAM4dQm7j4yT/ycEt15L0WrbuGq/l9QFJM9Zp6GkYINiFjCFk4LA/BIR6tueovdYFYbhFJJS5HIEOZP6bBUAYLDP9F2HsIqh5OR7fprAdqId1hvAmRrF+Wm2D/Oc1ZOTcp3YbAHfvr+Kd79hSHQatowQ7NpquR61OuJUPVdlJsk3KxZBuIQpSHi55XIIFaryXHmIWuUaRhPEBD0llnCt7s62h3XookA8ZXZJdA7+DDVbpKPOzN2PVD0dxyNngIE+4LaDq6DQyrCGWHV6Nt18iGI520E/i5M2udCGJB54HdeOirS8BmOGlmRroAMjkmkgkeySs6yw/Nyo0+kLCPQcAtYWAVHK2bgXhwR8zrBaWJcqPUTZHHXX5UM9AcFSYGcJ1WxX4PecjU80Z8iQYXPBb2vA/hZC+vLqb31lVju0LeD8rNuRODPVhmVT3xgHYIIZgI3ReH+/XGRjyKWK5SNmgeDxBZ/ULeZNHNjJVts+c3oBrbYt6lBZObvRVlkZAeOyDBkAYMdYL+aX67BsFnC7t5QK/QhAImejYk44SDzkCBlbe5WzhliBy7flPdaZ8Xty6V2dBLj55ptx8803r0fWGWKCP4RyI8dsDZxOroYMVOEjy2J6zlIAFUHOOscqSlk5Qp6cn1DOgsnJ77iFmbJzawKu9qUO2yfPnrbbQMM53595dS8ePVrDkZNuYaMCghEiK0dYEDUdWTCsUc6GkXOcnJ1fInjgSYoDO218+isGqnVmQR0Enqaa9OyiBdumwnP2l95SQKVRFYUXitUQz1nV+sBvaxBNjDopS3nISxe9KlTXc1ZN2U+8i1+iamAClEusI1VteD1nO4VcBtl+goPfy7aldmaiW4sjp5r40F/M6HLFv7ilvUECz2TIEA+87uhGqc4hfFxjzpyn8Y4EpdGIsDUw43rOKgHB1OPjgkhlibW0ShBayuYwgiubEQpEKpNmsnOA1MeJm65/n+iDBB+qu+HSNkrDk+MqNsPo/AJ4lbPy9uC+GCB5zoofOi5CKsdnbXSGDKsB7xvViqGclX8jcFe1VetuLWMYTLAzOWdh+1jOVwFxMU+56NaxJceGbqliC3J2dNAUn4NWvvDxVqlgYv92Rs6euLCMVtsWy7XlgKcbLeiWVyizwQqXYd2xc7wPO8f7onfsAJyctbsICBaunA3uxI0OlMS72N9TwMJy0ykT21bMs0qj3Uaihn814qdmeB6ANxTcPy+XA0xClIBgwU+z7hf+AEe9P5Wqhabjddsr2Rp4o0Hq8vd6zvq3ymVwyFmTiEFDy3KXqWwdMTE+5E2/t+Qldv3n5+4fdopCOeuxNQje/6qL8uLz5Cwwv0RRrXtJyqZm0C3IWWVQ1GxRnJ1uC+KYE+BqWcIGfVHK2ciOQ4g6V9xDoRiT/Fs9DHh4hyAGN4uyY8NUqzPbB8D1euwEchk4USQ/F6UCO6lWW1GCx6iJz0yxl3F00MQdN/Xijpt6nQ4mwdySHeqLk2FjIyjgTVpwJ426SyfNR0udYe4GfHIoSjnr1XzySihJTsHLyzm4cjZokkeofAPaEVtRzqqDucSe2MQbECyqfoi8HJr6t5PHYrWf+ecTtNe3iwvX7WusZu1aVPgLJStnU7nXEYlQGr+vmVbW8dvc7GnPkCFtBNoa5P37csjkbD7vJ3K3jeWwZ4IlcNbpe6sVJ5+I7Sm5wqVSkW1bWrFFXI/9O9yCBBHGfLwgj6VU0smKsDVYT6jj8wwZ1gq8/9+NrUEYwvq5feW86F/3lgrSuB9YqjRRkK0zE+SZkbMZtOCNXVMEBKMwTeIOqCLIWYD4yTKHJLRtEtqZnVpgDVohT8WDTZQlcUQbAMvdJ2jpu6zG5MhJ0bA5yVkuEQwNeDNwlbP6chuExCI2uXJWTidspnF4wMBPvJSN2FeqBHNLbWc78JZXWhgZpLjmEr8cWShsNGU5coqx0EN9hugA6AJxBUElAv22BhHKWeevRwXjFc56LCjkaMsyLR1GcEYruwl6nI7U40cNPHeakyGhh4VCPm2ujPOQs45yttVWJw2iGws+837wQBHv//lRvP/nRzHUz36r1mOabWbY2FhldjYtroLAW8d1Uuw0A4Lxpf5Wmy//ozh6uomnTzSwUtW8G0zw4nyMf1Xi7Blpa8DLGkAkq97X6vXpKCCYmOgKXrmiVkFJrktXyMZxHYFoW9EgqDLo7nMPS0om3luWO1jp+GaL9KLW5Dj7Sc+7XMYN86hlE6gZMqQG9W1qOW1rIURokZd+K+T8cUJ2bslhoJdt432I4f6iNx8POcs+c3J2ueoqZy/aWRDHBAUE4wSsvApRJZ08tgYbTDorFyfjZjOsJUwzaUCwZOlH8Tr82W9b1OM5+8AzUygW+Jg/WabrYmuQYePD9Zxlf/M51lC4ylmE+op6BY7uEhEOyw4mwWbmGfnIVbMsP1UVGRAQTHjOere7n/lMpPtbPse8WB8+TNzGtmhguF9VznJSNXgpHXHWvajEpuw/y5WzcW0NCCHoLbN9KzVgbol9Hugj2LMNeOcbbG1lI0hWULzkWopGA5iay+P0pCXI2ZFBE2rgD52tgT9tOPvoyx9VDelUnlQagKn7yPkEqaL9ytlogph7GsvoxnNW7lhxZZz8nvCKut32bo+zDGjGmbQYG3RfnKIzeVGtU/Q4Cuhs3Jdh9aDMoHB00Bm3BQnZfU+eE5aObRt+/3Nz+NoPKgDY5NZnP7IDhbw7YdjxK6JR+qsnz4NuBXnDcuVrkMrXtVfRTxYlDggGL6HbbNF4aQTweWmNuzYcYbZKcNuxztPwTIQo714H1GzMTJ384uzo29fdIBTyZqzEAnKQMpDfwYATU20NVuthS0KPA90pzTNkyKBH98pZgsE+Ey9/kY3JWYI9W/vwlpf34a+/uggAqDZYBlftG8Xdj51jwZgtd0zXUzRQbTrKWdnWwOmzX7wzHeWsNyBY8LmtB7yCqA1WuAzPayRdKaMNCBbSOeC/BQkW5GD3vC4yDGCl1kIhxyqEyHgYCjJyNoMW/CHnXq35HHug5Qj00QGXxCcA3sakbQUPEPlSEHm5veo1qiVn4RJ3HtIuxNYAYCRwpQY88awhAqf0lIhQuHL0cFuDgBEBCwim/Qn5vPtyCuWsVJOEXUqDAH0OUV2pAfNLbMQx2BsuOeNEIaUUL3bi9R263/SQs2ODpq/CiWVrYHivhZkwEqMY1HjycNWxgFc5S6RAdEHEu5/ADy0CCAH2bgduOWhjYQWYXWDeUft3di7l89oa2L5ylcUsmv65DAP3CB6VyVmnI1ipU4yLBiTDZsVG79J6OQ75SUte8jSVs66PK/v7+LMN8dv8ko2z0y3s216QjkjvLVHPvNmlcpY3CyIgmNJOJr5exHvM335nCWem2jAN4C0v78fl+4rq7gDiXSFRsq4u50Z/6tNCNxdJc2ySG9WBcFbsE3V7qH9ft54gItCNGdw9ShUU1B/UNDbRHA5fv+aF8uhmyLCBoY7JYgUEy3s/EwDXXkphGsAbbhkFIPvQ2qg12nj4CIvAbFOK7z48CQAghGJmaUXUeaUi+3RuuoWGM1G7f4fb97Cpja/ff9JXnmbLGS9olLMPHpnGY8dnPZ6zG40A9doarGNBMrzgkNTST/fuRCwEd9LX/8zf05ZFRb+e97lzOS50TDZBnzo5+9hjj+Hzn/887r//fjzzzDNYWFhApVJJlAYhBO12nMgVGVYL1Rq7/rKxukGIpGCM0zh4ZxuY92m4aggA5hxytrcUPEtIiH9Zh1yctrT8Q35hiUIqEkLwulttfOrLpnOcMxNaNjA8oCdn49gaLFaant+G+pivac4ETNPC/LKN5VpLKnvIrA0hQkXctgguzGjIa+1x8JV3xDmnZ2TlLG179udk4nK1iVwAE9Bs2ZhfbqDeZGXRRTINhWZ3Sr2/iQGWTWHzaIhGwMHwV67RAcEITBO48WrK9c4AmIdMNzAIu+Z8JlwuV6noes7Kz+hStQUQgp4iq5LrzTZqDQs5k6C/p4C2ZePCbMtJg117wO1gzi+3sMMZBFfrLfF7FEyDoL8nv+E6ei80rBWhnqaqujtqVlaKdF8WYWtgUVg2xYVZVqcN9BpYqtg4N9NWyNmgCaIECDBNFbYGUQHBAhoSNTChmk4+oSc2AZvFNww2s/+pry2J36bmLfz+b20V+wEhz2LIdepMmZkSY7bBkVRduRrpdpZ3sNxUbi6i0hb2Pl3YBXkfFZ8+V9nZ3/cJUrmnhbh1SDqTGRkyZNCBEPYu8vFl3IBghZx+5oiTs7UGxeR8FSt1d8x27FwNgInesvd1LjtznednWSH6ewxsGXErP0pdIlYHj3JWqmjVYzaar6vX1mBjlS3D8xu+ydiY+8dFVLttCuUsleoep7fC+yIJnQdTI2fPnDmDX//1X8fXvvY1sS0LTLN5cWJyCT29/V7lrEFEMCymnA0+niBCOdu2EWR5PL/CnuKeEFuDoPx5ozAnkVO1pssEq56zBMy7dddWgtOTkpq25Lc14MRaUEAw2VbhoSPTnvN7w8ttTM+ZGB608aOnz/uODbuWBmE+RcUCRaNJ8NzZNis5cTsKuhLpGm+uuqw7y3RGB01QeCdC+GGPH58LLNO52QrOzbqTLknJWd1ST0Hi8yGYId2rIFuDEBP6eFMHPG85zcjih6fr9BC1nrOSrYH8jP7oMJuFf9nBbSjmTPzzw2dEma69eAzHzi/h7DS776en53H3Y/MsbYMAMHDyQhVbx9gRz51bwnPnXAImCpfsHMLle4Y7PNsMqUDwIKvTqU0tVRrwuYMMVsXWwGL2H602IzevOVDE9x+t4dx021PMTrsmcQ7jvuVB3nK8rgxUzvKAYE7nTl1hktxzlh2fNwkaStt14nxL3U0g8BqRgM8d4gUzjEvB1iC16iFGWZI4Afh4dulg7v+YipqKIFbZ1WWOq++fnIydzUZGGTKkByG2AQEFFf6Ohby+0llYacA0XbFQsaDdDWUnRkS1bqNa9761y87wp7/Xe0zJuxAFo4MmykV3LNhssX5+TvL5u/fJC2g4FaU8XpBVtD3FHG66fCvOzqzgyJnFVAKppomgOC8ZMqw24lgx6vb3boze37JtVLiozsNDsLqkbbkrojk5y+umpMFJUyFnjx8/jttvvx1nzpzJCNnnEZiizgRgI5/z9osNg3gaDh+IXEG7a8sIoaCUoN4Mfk4Wl1kjVZYaOZ0KUn3BCGFeQc22hWbLEktAcgYRXj2i085nMZzvA30EkMjZnqKBSt3AzQdtPHua4OJdVBtMTIZBCPZt68epSb7ExZ0q6SkB+3fZokzlYg5ty0arbSOfMzDUV9SmCQBjg2UM9xXRW26h0QSm5lg55E6BXqbvbtu3rR/Nlg1iFQC4pOrWkRxAG57992ztR7O96Bmc1xpeAjdnEuQdaVfeNLBtpAetto3zc1XsHFd6Kxq4qh83E39AMPbXphBBzbxext5OgKF0DqIDgrG/feUcVmru+ZXC1kLFgEEILFBhayCXg3vOUmrANGyPupZSYKXaQitvewZvK9UWlqtNVGqsN8Y6ezw9lke7TWAaTI3H7k10z61tUbTaNpZrzch9M6w2VrndJO4b1w2COOTOlLPsbzq2Bs6khw1BxE6M5rBzSx5ADedm2v6CdjmACCKvopSznLRtBylnOTnLlbNdes5y5PNudOmr9hfxxHMNLK7YWFyxHB/08GckCVkXBy+UnuKq+4x2kHCsQzopsOLpTOAqZ7t5z+X0qGdW1V9ICtcDjqqesykgJj+cIUOGNYJ4H52Xsy2t+FTRbFu4+7FzTl/cIWfzesdJoZytU1SVMdBy1RmHKXErSgrR+7Jry57vtg0M9RU945V8zhDkrM5zlu8z0FvA5Dwr00ZTp4ZZzGXIsJpI6uuvm9gI9Zx1flqqtvCdh89ofncCkllU9N8FOevUTqxs8XsOqZCzv/iLv4jTp0+LCzQxMYG3vvWtuP7667F161b09kaTNRk2Fop5E6+8bie+8r0LAFgj5618Y5BfysNu2+w4i7IGEtAvH190lLPlkjc/FZ5lFM7fvRMD2DsxgKdOzuHomUUAwP4dg3jm9ALbz6ecZd9ZVE6XTO0tG5hdJrjpaoqbrvYuTw+aASGE4NJdw7h0F1Mhfvl7J/Hkc0B/r/xysmt7x4t24eTkMh59dgYj/cVA+wCAEbm3XbMdf/mlk5hbdLdftL0MoBp4nHx9Lt89gnzOwMpuG2em2mJAfvsNPXji+IrnOH4NZXzpB8c93y/aNuhTW17dV8TVF40GlkeGztdWVd8EBwSTSFg5TekSxukc8Ht/5d5RPHZsVhDQSZcN+9I1AFjusmX5PnDP2YHeEnaMEZyaYtf+xNkC7nm0jT0/b6O3DPzfrxpYrjDPrDvf1EabutEe33TrTtFp/OFjZwDY6O8pYfuYgdNTK7hk5xAO7ByKLOeJC0v48XOzWQCxDYC0ia+ofDZCWi5pk4JyVrI14ETs9rEcto+zLs756WCbpETnIe0c1JkTAcE69ZwV5CxXzuqPjwteFeZNl1batz2P6fk2JucsnLzQwsGLTQ01q14Z3VPqVQdkWF14Jya9nf9Q+HZJcL8iRCZRgw6+zK8rpZfH8yj4CeUIsjVIhTPwFMH5EvtypjNJliFDBhfq2EF4zmqGmI2mBUqB8WH3HRzozbsTS9Krya3sqg0blbpCzgYoZ4sFZjm3UgWG+gz8y9ewsdS/uLWEe35cw5X7qW984iVk3YpSFkDxCWg3KPfGIkBVcUyGDGsFl0+I165qA4KFPLPDfUX09+RRa7R942VKXY/7ti1Zqjj9dp6sbSdr9bsmZw8fPoxDhw6JjuKv/Mqv4OMf/zhKpVLEkRk2MoR36gprkAp5qsi4w20NmHSWfZIfZsNgyqAnji9g99YJ7ZGcnO2RPGcJIZ4AWnc9ehYtybiWOts46k23IT09tSw+T87XcNejZ7HiSNObThoDUgNrGhT5nOFXh0WYTqvX41U3AkP9Ni7Zw/bnZF2zbeGuR8+i6cyULqw0Ydm2p1HWobfsLsMBWGCxFWdlvG1T3PXoWfSV8iAEWK61POW8+/FzGBso4eBFo/jXPz0MHerNNu55/BxaVnQVcnJyGRfm9cTwjrFeXBJADi5Vmnjs2AwabZcI/+4jZ0AIEdfjgSNTMA0DdYcsXaw0ROfl/FwVkws1cexKvS3uu27gytFoWXj4yDTqLfeZ4csTHjs2i4b0vJybrWJ+5SyCsGO0F5fs8p6fZVM8dGQKlXpbBEDhz9jRs4s46ZCwz55j1/bCXB3n59jndhv4x7ssAASHHq7joh0FzC6w8jdbwCPPtHHRLpZPT5EIYhZwZ+lrdZp42JfURH2zgFKKR5+dQW85H/gcvtCQWl85xUfFtTXoPi1OZD5z0sKZyXkAwLaxHLaNsS7OQ0/X8TP/z1nYNkUuZ+BnX0exVV8NhkJ3+mo7ERkQzCmrFeBBJZSzzmuukte5DiePZBXR9rEc9kzkMTln4dSFNg5eLO3ICa0Y97qr5+r5Ve0EQpD4XZwvVZeVJD1eyTzOvQ2dMFLKEbYv708YRueDdg+5GlV2qp/8XQ0kbnP5/i+QZz9DhrWAG0OEfefd/IKmreTDyOF+d9viiq11suZ2BNU6Ra0hWQABWK44ylmFnCUEuPNNBOMDo7hkd0H013/61WVcvFcff0deDWMEKGe5DQLvN220oFtBdnMZMqw2DM3EShiSPp8FR6yow+mpFfzjXbMA2LtpKbZkcCwPkrb5XZOzP/jBD8Tn66+/Hn/6p3/abZIZNgD4gGKpwp60YgEoFdzRpkFI6Myduvwc8C43+6t/auCOF1vIaUawi06esq2BQYg3gFLFvxRbtw0Aqg2XkGtbtmc//sKUJSK4kGcNX5CHaaCtgdJaFgvAi67ULLuj3rI2WhbmlxsYGyz79pXBZ3E5BvpcchZgaQZdg5VqCyvVFi7dNeRbts9PZ2G5idmleIGkGi1LLMNRUa23AkmxMzMrvjyWq95OT6XmnaFeWGmKe9FqexkN26bac1Y7LlPzVUxJpK4M1bKh1bbRagcv9a/UWj5ydmGlgfOzXrKaX9d60xKB09iMvol6g1kKLK4An/maS7YSQrBY8V7XlSrFipP0yKD3feEettU6TTwgTWqivlkwv9wQiuRNQ86u1T3oMh+PL7SsIu2gM56urYH7ueJ4wx28uIiLd+bRUyKo1ilmFvh7RXD8DMWluzoZQOhUe9502u14ytmgoJhCOev45flsDTq8XvKKgO3jOcwu5nH/U3XXd5Z4/vihZaY7K4uc3PN+HCe42e5fcr9meX3qbx81GyCyJiCp2BoE5+0HBYXqOZsmfMLZpAdnyJAhVXjbYipsDXTKWS7ykQMUnplyxwDy+IwQ1kgvVdpoKAG5lp0+uWprAAC9PcBLr+3xbNMFCOYIUsvqlLNyMOuNhMxzNsN6wV0pE7/B54G7RRod5m0Y7li63XZjzYjV0JSRszZFovFX1+Ts9PS0+Py2t72t2+QybBQ4D1vd4agGekwUpQgn8Tw9/b+PDQFnp4CZBYJag6Lf236BUiopZ93thpcLwC1XTmBuqS7sCgwC3HSFq8Q9fn4JF+b8ys7RgRIu2TWER45Oo960xAsql4MHP1PJVqE0DFA8xW0sS3kD112yBedmKjg5yVS9doxRRI+knCWEoq/HAGaldAumIAELOQM3XLoFP3zqAih1BxS6fPgW2zmx7aO92DPR79nnvicveL7v2dqP7WPeKeNmy8JDR6YDo5DL57ltpAfnnftz0xVbYRCC+5+ehGVRXHvxGMrFHI6cnsfsUoMd49RzQ30FDPcVcfwCu2695TwOXjSK+w9PevJV7wX/bbi/iMt2M8nck8dnsVRlRPKZ6RXhKbVtpAd7t3ltHQCg1bbw4DP68+Pbeoo52LaNessWz9b+7QPYMswesKGeJr50aBF5M4fxIQMPPNFAs+WWdaVqi1l006SwLIKVKkWlxraNKuRs0ZnAqDWopNCK1wIkNVHfLAh7/jYq1oqoSu3KkO7TSlM5KxM/r725F+/4iQHsGGcjs7/58HZMzbN68ROfn8WTx1pe1WrHz7/+OB5/Mui8OGlrBaxQYM8vQc5pf1SSN6nnLH+muN81AOwYz6FWZxfh6Cn9RFQA36Z9Rrt7FrKRXBS013e1L1vMmyqPOVx/XSr+8iXGjH/oTjpL1CQCyqhGSHYDBqUBv3z3edaEZsiwuaAsLHBtDXTKWcnOoI9iaYXgwC7XKFZehXn8LACYmF5gbaRBCGxKQSmw6LjB9feyfr/sSWtplsXwbbo6SA6KGuQ5ywlcYWuwwaSznkBmGTubYQ3hWjwlPEZuuDt8ZgkhMAzWZ29Lylm+Uo3IHG0CdE3O9vX1ic9btmzpNrkMGwQErENbd0SOPWXZp5U/kCHKWcgddRc/9Sobf/hZNtqsa8hZpihkn8sltxusBgTbMlT2LMc2DIItQ67ydHper5IsFUxsGSqLsvMXdKDXTb9QYNv9AcfYX9fzB5IXqjY7LXI5VoZFSfYah1CSyepyEWLwzpHPGYKczZkGO09CYFEK0wmKps2H8oEUS6+vnPNcSx36e/K+fbiSllL27OjIakFiltwp7fHBEkzDEJ2W0YES+sp5nJ5cBtBggz+njOViDv09bkeqkGPnaRrEc26qQwT/qezcfwA4mjcBtNDfk2cBtJzb0Vf2nxsAYbvA0vP6RnHSuZA30LYI0LJFnkN9RZHewqgJYBGtNlAu5FCpeVXESxVbPJujg8DUHLBSo6g4j/PYkJepKTuXolqnkkIrHpKaqG8WyI9d0HP4QsPqXILuHhxXOdt94WQC8+DFRUHMAsBgn+kEvAL6e7hxv759ioLH207+LKEd13NWM8ln2SxgJuAqXVXFoZm418bSeesre/HtH9Vx0fY89m3PC5L3yKkm2pZkiyI+xE25M6RLmG1cpKJw1Ryb5Lp1krVLuGpyikGQAl5liml0fq89SlWq2S7vS0NsDdJ42PzcbKJD0fnhGTJk0EBdlcDHkPkQ5SwB8NZX2Zia6cO/et0wSgUTEyM9mF+ui317S256xbyJ3lIOc8sNVOtAo8kqguF+4OBFo3jwyJQI+Bwm4NB1xnJByllp35zwnOW/+c9tPZHZGmRYL7h9rCTKWRa4W00jKVgcHPbZtqiIJZEzCXIm8UwUJ2n3uyZnr7jiCvF5cnKy2+QybBAQQtBouZHneorSYMppFaIaB/Gzh0Rlnq6WTVBv+ken845qNmdSNvPgdIT1Bs7SMgrl1YoKPqEqSLcMAzu3MhLsxqtc8lV3PpycNQ0DNp8NTdAYuUvu3DLEEfvJtgblop/U8MxcinvEKiDiMMlabpb/5ct9YrT6uvvhISsphanZh193uez8Mqim/rwclFJwRyi5ItTtG1Q+W3Nu8h6e7QH3Ut7HtikM03u+PF/18sll4TYE9Sbbf8VRxE6MUlyYJViuUhgGe6ZGBymm5rhyFs42va1BrUETk61JTdQ3C+T7Z9vUo0rI0B3CAvglRaq2BlIae7bpA00CAF/8EeT3GhskeGmz8JwNqEf5IEtnayAHCePnpHrMJlbOOn9vuqqIn7hlUGzfuSWH3jJBpUbxgx/XUMi30ZQcZtJYhp+ogM9XpHB+oarlOLeJhn6Nd5AEXzFEXeD9RX6eDUN3YFIQkBill/t/bLLYPb77Evg/J35Xnl9NboYM64rAgGAahsMddxEM9FG89qY+bBliO950+VbPvkdPN/HZb1zA/BLBtqEtyBermFtuYH6J/T7Yx/oU/T15jA+WxWpANsnqFQZYEimsIp5y1h0PARuPAM1sDTKsF5J6zgIQgbvF9w6fWUIkqzLbfc9Ng03oGMQVrSVB1+TsbbfdhpGREczPz+Nb3/oW3ve+93WbZIYNguUqJx4pigXDN2MX2TgE/J7LAVYTaDS9T+sDT9Vw10OscSs7M5ZcpKAq0OVyiB09WYeXTU3LNAne+mp2vrwR9JGUSgVgmkQYzydZxiGWk0sEQRxbg15ZOVvSEJIaryJeAQlCOCQfPsCIcy5actZDXupJF5fY9pOzAsSbnhwN2jCIthPg8wcOIme907tO+tTzLAUR+yo5C4knlclfolxjOcuiIGfZza9yReywQ85WKAhhx484PEq1DuE5qypnS0WXnG023aWkccCvxfPN1kC+3jalSGHVfAYFJKTujQvX1qD7nrxsf717IpicDVOtdoJjZ4Dv3t/Gd++fEdueOckYzuCAYMFlaEtWB8JzVg0I1imZrbafBsFle4p46Ok6/uv/YeUf6DXw734u4PAwMv75VYVsCrhtXPTF9+0Ro86P2yxQSG2OmPDzpxE1YR5eFpnYiNM/cT+zemZjqLQ7WX6ZIUOGcMjxEh8+THB2ir1nWlsD2fuVIrSi6ym6x//Wx6fw+/8/ZuU2t8i2D3P3M02QFcumnolUT74KZOWszsoAcD0stWOZDQC5OButbBme3+hEaJTWM2oQIqwLLFtWzrJAYty3OqnnbNealXw+jw984AOglOLQoUP49re/3W2SGTYACGH+lwALbGUYhmtrQLiKMex4V0Ohvi98uWddImebLYr//Gcz+MZ9LJqlancQpdRUf4567dxz8f9miPNTCT/vsXIjShK8SbqKJA5B1iOttO8pUV/5vJ4/3m1h/qLqQCqWl5H2urmfg86HEzLe60U9+fOyipliuWy+G6Yn0tVT0Pk0ycuavYSv/vzlrer5eZSzISpernRttVlFLuwKhtnf5SrFshMQb2SQOmkDs05ncGxQtTVg26t1ivd+ooofPR4/KmTg0s9NDlm9ZadEwq02XOJrdTq1aUSMTxu2NMPcLYb63ET6ysEJcqsAy5ZXdiTPj4Dds7sfMvD4UYq7HqyKf3NL7KFT2zAOXq/pPGdlcjbPlbM+z9mEZQ15pN5yex+2jeWEIn+pQtB0VEeB9YJ/fqu7ZfMdHLuZwN+9bl49l5z0X61uVbBR6Ob+tBUleNf3WklANxjjk/nuPtKzvFoPW+LLu4Eq4gwZNjmo1Jp8/2G3/Y/ynGXHBqNc8vYl+Jh4zlHO8j46gcbeSGnfLTt4laU8QR0YEMzgYzj/bxsBYePxDBlWEy6/kfwY8b3jvF2+4Z5H66LPY5rEUc6y75Susa0BALznPe/BoUOH8NWvfhU/8zM/gy984Qt41atelUbSGdYJBAo5S4joCAvyMqJxkAdusj8rX1rakGwNZhctNFsUORN4zU0lDA1WnDQIQP1EpPhNlDcZO+v6DvHpU+lQpz32kbPOXypIBcknqBPlrEzOxlHOlqWG2tTYGmgadZdojs7HVu5vUhDHcoDS4Hx4HrJfriCHFXWLu1SBiofJNLwNfzfKWXnyII6ZPfdZtm3qOz9ZOWsohKDsl1wuus9MqwVBzo4PseNXam66fT1AsUDRaBIxU6/aGvT1AltHKSZn2e+nzpPYLQCRr+/zCLJyePOogsNkiSlA1MXdXQ8dx9FpkdO0Ndg6auAtr7Bw8OL+0P2Ectai6ISvdvdlB3MbgJ+7YwAjAyzxWtPCudlF7NuhPzGhnNXYGrRFTBEqlIaqArdTpbHuPG892INbD/ag1aZ47b87DSBOW6S7+5vlPVtHpFEX6W59nGRp6NfEeXraV01iFMD9TxA8+rQTSEdpt5NCrnfipOOdKHY/p1G9aq0lUkg3Q4YMHULqPpWLFLUGe0l7iv5dVQVrWLUsW8kBwHKF7Tzv9Mf56jaWFhGfKeVkrNt4h9oayCSsVMF4tjudF9fWILjc6wHvuGyDFS7D8xqdKWfVNDp7Zpkgi30+dcHCKSd2OrM1MASfJK/giYNUyFlCCL74xS/iV3/1V/HXf/3XuOOOO/CmN70J73jHO3DjjTdi69atKBQK0Qll2DggRNgalAqMsBSqR8L/RpCzTjO0Umt5XgSu/Gm03Ad1dpGNVMeHTPzca3vw0JGKNy3DTywYnsZAn7cK/vLa1EuGyCm7alMlTWV2xjPbmeDF1vmjxCGRispqXZUcNzUEo3qvtMpZRbnazYwsD0AWSM5qSFK+p7w0CZA6JlS2XDA891aQswoP4vOc1SpnXXJSR/gGnZ8Nv3evfF7qeyFfznzO7bwtrDDvZQAYHebpAEtOB7BcYt7CDSmY+tiQt8o2TYJ/+VobsAbwic9X0GwnsTVw8nyejSzlRzxOoL0MnYD4iMqkSNPWgADYsx3YNhouKxVBNTpVVCsRwfjj9bpberFrK6ugF1Ya+N6PFwJJ5zi2BmwSinjKrJ5DmpDLGvTKqJNnXeMFIp3tRl2cVrq+fVIsDAX1td0A8PRxAh7vcv+OPACr83sd+EzqNnrb86SKlbiwbaDaSKZkF8XKmqUMGVKDeJ0I0NsD1BrAji0U28b9FY4Yx0rCnHqzjamFmn8SSxkvLVUoiiVg2bEZG+zzr2jgY6AT55dRLDDl3PaxvnBbA6kBDlLO5oRytjsRzWpBt3IzQ4a1QEeesym9P0xA6N9eb7ZRqRse5WwSpELOAkChUMAnP/lJDA8P4+Mf/zj+6Z/+Cf/0T//UUVqEELRdCUmGdQCBq+Ir5gOUsyEPNyHeQbf8XPLObK3hJ2dHh3JaAlGrnA1pAQKL5pyHzz9PVlcEKINVW4Och+gLLEpg2ZIqZ1m53P18alFpgK0qZ12f2wCZC+C7v53AMAgsmwbbGnBLCKkzwpYdylSP9/pTAIQTx6YaEEz/LKo2E2HKWXV72PkbjoevTzlL3d/DVLyEEJQKBLUGxf1Psme+WKAo5oFCnqLZcvftLRGUS8DCMvu+ZYRiQiGfCNj1KDuq6lYrfiPwvFXOJrQL2UjY8H3aFCOCpamcjauC5cQmy5sXPPkzwmtirn7NS0G73CXU+guTD7E1aFl8Esl9P9XrowYIiyxrjPeceXk7qx4sACRePdIN2eRysxv+qV93pM1jJyF0O8qTUkGCfPAXR3HwQA7ff/xc19xs3K6JvF/683Ps7f/q9w0cO0PwjjcAF29PcCgybjZDhjQhq0l5m3zzQRuGxm/OJ8yhFA8dmcbMYj0gdbfPvbhiY/YcwfwSO7jPsS4iRBKVmGwM9Oy5RXHcQqXpKmcjbA3MAHJWKGedenWjkbOZcjbDeqEz5azyjHb4yBoEuDDrP7jSaGFm0Q0KaNNkBG1q5OyxY8fwrne9C/fdd9/zdtD/QoNra8BsBVT1YdTs2GBvAX3lPFZqLc/2PLc1aHltDQC2bJtqFEW6yt5QSC/vAQGFkohZdpyzmbgbVb9WNUmqIRg7sTVISiLJWYwN+clprXKWBwbTEMIcqnK1W+UsEGJrICtMnbx99YTUyVHLGGhrEOLzCsjKWX8+zDUhJjkboED2Kmf1x3D0lAzUGha+9xCbgOp1vISH+oGpOfa5r4eimCcY6qc4P82Ov/VavyqXf+WqaqacjYc4y7o2IzyK9E2inF2rUnZ7r3UkSae1RarkbExwSx3Zc7aba+KuovD/FnRdwpSzfGAp11M+z9lVul6mwTxCLRswTCBwLbzmxDq7hJvj3ewW3RDYApprv+qkdgQj6rbf/uJRuAq1bWM5Se3dNT3r0ewHWSqoylmPtK5L8BSOnWGfHn2a4MbLXxjPcoYMGxFUer89Xvaa191W7AUogHqTNbzDfUXk894G9qdfbeEL32G/f/abLcihevp4HBApn20jvQ4RS1FvWphZrGO52hTqWDYJSnFhroqGE8l0YaUhjr8wX8VSlS2Xq0hj59nFGlptC5UG25YkzslaQBeoOUOGtUAnnrPqattOH1liEIwOUUzNeVPoLZkoFSgMwjpC66Kcfe655/CSl7wEMzMzzhJhkhGzmxyEECyucGUfa0yabW5o7uwT6TlLcPnuYTzwzJRnOx+cNqSAYNPzjKga7idoaUatuqzCCIIQblZPhOqUswEKSH1AsPivtuv/6m6Ls8yWEIKffk0bJ84SHLyE+q6Jzjyeb4qjnHWXy0SXJQgi2E2EctYwiDu6A7SEueqpy7YZ2kGpX+Xs/W5pZpvlQC1yRR3W6RGm/D7lrHteQYHkON7xugF8474KlmstzMzbOLCbHfv6l9pYqZRgmgbyhSoMw8TLrrOxc4uNUpHikj1+BkiQszzQWAuwLBstEYmFIGf6SV0Zlm2j1dY/gIZBtPchLmybdmwt0Gnecm6bRjmbtjROwWok2+2V5XXeaizTD4L7/qKjiyKfM7UpbMeWJOeZSApXFPP2r63xnG21/ZNIPluD1KbUlXI55KxN2fAz1v3t5tZ1J7zePOCTYF0koa0ePAQkDa3jO8s84iCi28Utg5i4MEj3NLz8rBDfZn/RCEAIBaUEti1ZcqTxsClpLFee909whgwbGnL9aImAPPq2hY9NRF1AXYHIlftGMDpQ8ux/46U2jp46hR8f8Q4MykUq2mJ5TNLfk8f+7YMAgMWVBg79+Byq9Tb6ynknX4Lzs1XfuJjj6JlF7fbjF5Y933Mqu7TOyKwMMqwXOlHO+gV9nT3ABiG49VqK/h4bz5wwsLjCto8PlTA6QEEM5oFi02Tpp9LN/8Vf/EVMT0+Lk52YmMBb3/pWXHfdddi6dSt6e3vTyCbDGqJt2Xji+CIAgmIBmF6sY9pZ9hHL1oD/Je53/trkncHpiQsVAEN45tQ8fvzcIgAD04vLePKE/wUjxE/JhUeH1JeNwkvYuC+oXzkZRNJRofiSj9Hvq4M2IFiMSsUgwI4tzEtJTsf9Xb4exPtX5ONPN03PWaEKDiCbbUGSQkziUEo9Pqn8TpuaG2ASeNVDAc+iegpUJoXFse5vcW0Ngrx7ZeVslIr3zS/vx5tf3o9Hn53ByUm3wzXYD1y9P4diwcTRM+z5KpeAK/ZTbTqAe62KebZP2yI4cWEFp6ZWxD47xnrxoku3eI6rNdq470nmXN5o2fjaj05qz9c0CG69ahuG+zWRFSJQa7Rx16NnA4nfKJgGwUuunMCI0lmOgkeRvkmUs6uOtDrONOBzB+CkvU51mhTuaoTw/fiqDV0wrqSQH+u8TKBGkI6m4nv77R9V8P9+YR5ty10KLke2Vz15k5LZYgwacW1YvUXduju+cLYjpChm3OCQWICO0d3VV3OOM4hJkqMuSI3rKa0rQTIEPyualUC8H0MAi67+ypDlSidWQqtYoAwZXmiQrAqEECNw/Oat2YI8szkIYf1wFb097mfTkMan0rvdU2KEbLNtC9GRQQgWKkwp21vKob+ngGbLwtwy2zY2WBIq26n5qmuZRoAtwyzTUsHEluEyNhIyK4MM64VOPGfV8XS50NlAhBC2+vWWaygqNQOLK+w9Nw3WVxeeswmHwV2Ts48//jjuvvtu8WL+2q/9Gn7/938fxWLywXyGjQPLsh1PWIKSEsttYoQ1EKEcnkIOyvRszmTpLlWZWnZ6sYaVKttPePjA2+02DIKxwTLOzlSwZYg1SqWCiaG+AhZWmtg6LLWU0DeyAJitgfSSuMMmmbB18oxSznZsa8CLkoxECiNjAVU5691H2Bpo8uHFSMNzVkRDj6GclYesOuXs8IC/DhkeKKNad5f68DR4mXMmgWVR0YnhsGz/uckz5+NDZZyZXkEuZ4QSkXGUs1uGyjg3WwGlQF85j96yvpq9dNcQJuerGBss4cx0hRclkCTXdxzZ30LB/bFlAUWpYzq9UPMdt1hpiuBDYbBsirnlekfk7GKl2TExy/OeX250QM66nzcLN+sOF1a3g9vt5ZCHNeJzh0W2IwZRnSH8DIWyX3osE10TaWeZ4JUJ5qj0uC0Bf/++fM8Klire92TrCETlptoa5JP2IWPeH55P2wbySHZdMrIpGHHJ8VBojiXKz+G3uYPMOxHOSsfyujdnknV5QHi91Gqz7OeXgO8/bOPEmQX8/L8Y1E5y/PhoHU8+14BhELzs2jJ2bMn79lGP4sGBkiF7YTJkSAse5aw8wanpnKjKWXk1pVYAQVhg3qBMTYO4KwHhfbPzOQP5nIFW2xbWCYS4dgX7Jgawf8egE0T0HADghkvGUSqwMcM/P3QaK3U2Ti6X8rjp8q0RV2L9kJGzGdYLnSln2d+R/gLmlpsoJO5YM8h1Rn+PxMGYjCNyLSWTtfpdk7P33nuv+Hz99dfjj//4j7tNMsMGQMuyUauzp6qnBBTzBvrKjKWdWazjnsfPxyIUXX7J3ZcvBeFLOL/7gI0zk2zH7aN5jA0YqLfaWKm1JVINKDij2qE+1lISQnDbwe2gNB6RxUthS42zaFCkU6k22vjBE+d9AVsefGYShBDhB+QNCBa/YZpbquMHT5zHUqUptsVTzoarQz1RPoWilH3nvr/PnVtEsWBi+yhTsx8/v4RFpxw6u4akMALISw6hMDVcb9aHj0xj15Y+sQ/PvbeUh0G8BNtATx61hhssUPWcvWTnEPbvGAzxnJXumWRr0F/OY3yoDMum+PGzM4HnV3GI4SdPzOHZs4ti+7JzfSv1Fq7cO4KdW/oAqjxjCsrFHO540S7MLTVcctZREgP6+/DoszMecprne3ZmGYbBCK/+UhEDfUSQm622jXufOO9Jh3tdcewY68VVe0c8DdTDR6dxdqbS8bian8dwfxEvvWpbomMfOTqNMzOVjoawm1M5u7rlTL3bnEKCwtYghfVwcbWJZs7Nu5NcZUJatuvIa4N06XMwcy5B3LYojp5i9e/H/t0WbB0xcdejZ9HX46bt9eSliVc2xL02nCSPY7Gjpp0hBClcJO1ESAJ2tqN6NHIPR2kdcKyrnCX68icpi0eNLiUSUsgtI8C5aeCRpwmuPQA8+BTBU88BDx9ewiW7C3jZtd4J3HrTxvv/aBqNFkv0vidq+IP3RJMhrXaCifnoYmfIkCEh5D6qpVl9IkP1nJWP1/XVCSHoLfnf2IYzfMtHmMD3FHNYbLtCBYMQQbj2OlYHnpV7Uvuekxr/tbR/6gSZrUGG9YJYkYIYFk8O3HeuuwdXzkomZ3MmQc5wlbNJh6Jdk7Nzc3Pi89ve9rZuk8uwQWDZFDXHo7xcomi0KBqtoGiWfnCJuF45y7a020CjaePeH3MvW4pCsYmZJX96pUJOH4yG+AMwAcBIv15tt2Wo7Jkl1c24tNq2NnLn7FLD811WE5aL8WddGi0bDSX9OCRSQTGqLyoyfLlx5A18ucheca7SqtTbOHJ6QZCzjx2bFceEdVAmhntwYd6ViIwEKCmDAmZxyNe+VMhhpdbC3HLDGzROtWdQyPSSdN58hlksHaD62W9bo5yVo7WeuLCMyXm/wjQIS9UWgJZv+6xs/RGjzieEeOwzbNvr1SeDUuqxQZBRa1jI5ww0mgQX5hpoStwrBYQliYz5JaDeALaOginSh8vYvaVf/C6I9g7ZWfl9TUwoOft34l3uVc5ukmHwqhdTekG6gvf4p44RfPdHBP/rU6dw0fY8/ui9W1EqxJPCiuWHazjo4AMcXTCupGi7c0QeAlW3xNtTBokEPX6uhUaLordMcO0lRRgGwWC/lzzOeSI5hyQciHj7m46tgfzOeDq6YrOnAYbyY2woQbOft1gtQi7RdaOhX8PzCcqId+mkiGDyru7kS4LMAiE6J56AYFpi2HmwbrnGxhe/Y+LwMRbMttlyS3fiXAsvu9Z73KkLbTRaVEwIHznJIqz7Jkk116Pe3CTtTIYMz0PI/UTZc1YbEEwZ51Aa3WaXy/4fto2zY1xyVn9wTyknBDBOaVGpsc4D96GVD5UDO8urM/MbzGNWRaaczbBekB+96FVEDMIKgafRYd4yp9DX4+2r501DiB6ovCw2BromZ8fHx8XnLVu2hOyZYbOh6vA55RIwPljCnq39nif4ieNzYqmGjJdcOYGxQUaO6ogyTs4uV4Hf+vgUmi2gWKD4pZ+ykTOB6w6MYWaxjtNTKxjqK+Dqi0bRV85LDWj0azTY6/Vi4B3u0YGSh6gjygvKMdJfxPaxXjxx3J18uGb/mBgol4s5jA6UcNvB7ag22sJqIQ5GBorYNzEAgKmQT04uxyKRdoz1CQKxVDAFMSnOUaOcvWrfKLYM9+DE+SXMLLEbGhSgKUyx+aJLxzG/0kBvKY9Gy0J/T8G3j1yGOMrZl1w5gdPTKzh8ch5taX/59poG8VkSDPUV8dKrt6HRtITvUhSRqA3ixs8bEPnvHOvF1pEeBOHI6QUs11rYu7Ufo4PuBMDRM4tYqjY74r7kzhjzv/KrfFmB+bNn4oo9IwCAk5PLmFmsY9tID3rLLTSaNvZvH8ZFO3Jotiw87jy/N1wy7knqieeq+NSX2fNww+U2Xno99SnFXR+fDslZMWBP3ux1Q2jIWq7NopztVlmWNJ+Oj5cIOkopjp8lsJygWMfOtfD0iSauvSSeDQUnSNVl+90VMPxnrtL1kLMdXRQiJryClk8GlsE53wuzFO/7QxYU5LI9Rfd9Vx4GOQBYR+Mz8TKFnyi3ZghSzoYFVerqucrGdJHQE9nedmOtL6Qq3JU3Ukql97v7cnkGUDGTGxtify2bBQWTn+uTF/wTqyfPs21XXFTE0VNNNFoUZ6fb2L3Va23gTppQtC32baWacLZnczRLGTJsCvDXiXX32DsZqZwVzS2VfF31lcv2MWDfDoqdWwn2bLfwxFGCF13JDuIrOt1m1vtyCwLWgeGMaQiYqhYAytJYTu73yytyohS6GTK8UCGPMZcqTUzOVwPb2IHeAraN9rqiqC7bYrnv39+rKGdlW4O19pzdv3+/+Dw1pY8+mGHzgVKg5pCzPUWgv6eAHeN9nn2OnFnUkrPjElHpqgLdN4APNg8fMwCwGcWrL6Zi0LpzrA+tlo3TAPpKeaGCDTNtV+HzZzUIbIsqnp7Bwp/ech7bR73k7M7xXs8yE4CpZ5P6cfaX89jpXMums9QlDokkD3LUcgD6pTH5nIEdY72YW6oLcpYTlWonwqLeY2WYpoGxQXZfuRpXhyjlrEy0los5TAz34PDJec/5y7l7CGfpsxpR1fWSjCaFRT4S+civxVB/UdwbHc5Mr2C51vLtd8pRtOoXeYZDLpM8i6+S5PxbIWeKvOeWG5hZrKO/p4D+Hhtzizb6yyXsHC+h3nTJ2R1jvZ534vuPukra6QUmgVKfh04bFc/JoDPCUfYD6zBbAJuHnF1tpE36Bj0bx862YpOzcvCrtMoTdbd522NbCJyYC4OYIIS7GiGIMA265HKArwUneMCNV8rXzLvsUiWvO9XNRp2n66edPO1OQL2U2/MWqQaBCvNrCoHv5xhliTu5Rj0Jyu2/Q5KYklotOtuwTALLqIP8XrYt74TMKR0562zbtz2PVpvimZNNHDvbEuRsrWHjo5+cxfFzBDcd9C5TbMcMMBi3nsqQIUMCOC+UrfrA61bRqf3SGMrZfI7gTbfb6C3lUKkDL38RlX5zGuigyk152Xl9Wi7mPOO0V1y7w7F7kwlZU/q8ecjZYof+nRkydAL5vX3k2RmPZaQOd7xoV9fiIw55mN4vafRME8jlCAzC+ZY19py97bbbsG3bNly4cAHf/OY38d73vrfbJDNsALTaEIqockk/AI2zUjlMOSvjRVd5lZOiAVWIKyB+B19aEe+Uw7tkkxB32KGORw0S38c2KeTGV/iRxBgQ646ToVPO6r5zwsrHWwnLgeiyBCFKOauqQvVBrvTnERaojERUtHLEU/cYXqjwgAAyBPnsCwjmJNVBPS/nSSmVbA28Lx3fy9CqfynKRfat1uDeVu6xqq5qYcl94PgkjHrLulbOKmVMAvFmdmRrIClnN4utwSYFX0K4ZcTE1JyF586Ed4pkpEnOxkVea2vQwTNCXELGVNozGvHgy5NsN11Zwq/85BD2bnPVNWppCtKgrLSKcVaF56xUAEpXX839/KZmZXReF1GFsAf8S/nSL0nMDhf19828wfIIbG4B0uHNlsXkchIzizVQOuTpM/B95XrFsqinj3V6so3Hn617jnvqGLOt2jORh2Uzcvb/+9ICvvL9ZWwby+GGy8u49zG2cunxowZs2z02LjmbIUOG9MH7eXK7bkQoZ/mvTLAT3v/nm3Xij7yqnFV+V+M7cKgCm4Fe/2pE2Y/fXMuOUoe4+YqtqDeDV1ZmyLAakN/bRpN1NiZGejwWiABwemoFlk3RbFu+iVJCCBZWGijmzVABmgqvctZ9R1mgQG9AsCTompw1TRPvfe978Vu/9Vs4dOgQvvWtb+GOO+7oNtkM6wxO2ORMinxOr6aM4yOpa+tUcvYl1wDFvLs/IURLlkU1oP68iUvUcKLJBmzipsNfLKI0qYQQXz5pRVL3kI8JfD29xKLmfgSYyrPv7meelUq6CQKzm4BgQjnr/00mHkXAsgB1qEgvQDmroiPlLC8X2HMBRBMRQferE8WsmiZPN8rWQEcwUwr0CHKWqwCkd8emgEQKza+45XXJWe85RKmgo+CSVB08T10ojDLl7OpBHtZQuO/5pbsLmJqr4akTTTz8dDxvchG4Y009Z715dwPhbZdUOSvtf8VFRezbHj6QyecJ3vwKC2enCC7a2cXzHHEoL1egrUEYV9fRi4qQBDOkik6ksxHQqkCdbW3pGcqZbLLfs0NKmFtuYGqhhq3Dfisiw0POep/rRoviN/+3fqXfnm159JQIvvaDCs5Ot3F2ug0808CFWZdkkf2mgXTqkwwZMnQGHiTYMEwAzueAqsZWRCjyuCSoq8r70lpyNoI0bVv6ujZOt3gzBQQDoK2HM2RYbcjvEn/fDuwc9MUempyvotawYNuy5yzbv9Gy8L0fnwMAvPnWfR3l3Sc9/pRSGIQonrOxk+2enAWA3/zN38S9996Lv/u7v8Pb3vY2/N3f/V1G0G5y1BwBVI/zbJskfBm97A0qQ7d0NKc8dQXJksdVJvLv7m8ijZhtlLybGzGPehSIQcpVgxBf456WgkhO1xTnG/3WymSbrixhRKZKAMp//eXrgpwVA3x/2vIm3ufw5aV8jaucDVK0iry1AcFcZagdRIiq+QQogzmh2onK1HNvbCpIEJ33Lys28X2mlKJcZBe1WneUs3KgMQrIcyKLy+4DX2147RRE2nKj0gHCPCqj0M1SYPmQzcLNrnZwJJFut9eDej9zgvLS3QV8/9EaTp5v4T98Ipm9UT6FXgiJyeabknK2k2si84my56xun6AHX56c3DOR9++gKc/e7cDe7Z29T3GXUpsaVbHWy1Q3OZSsSJ5jNv6Qszt0Y9EioCEP5OsWlbZv8jBGWZLcH3VfWTmbM4lW+ZsIon70p8CJGd++xI010JY8Zy/elUe9ob8AO7fkcPX+IkyziKF+E5WajX84tIynjjfxkDTp1FKybLXj3Vxe/m6XUmbIkMFFpc5eSD5OzZlc6OPf11aYWPlVDPKOd22cgpWzAsou7YCZmzgiGNNDzm585WyGDOsB+b3lPJSpeZdlnkANdOsJSt5h3j0l93OzzfofsgVcklY/FXIWAD73uc/h0ksvxUc/+lH8xE/8BN74xjfiHe94B2688UZMTEygUMhk7psJdSkYGBBABkobSwUTe7b2Y9tob+A+HKpyNpdzH1n+oOvIsiQBwdQyu9JyKjrI7MXxzp7I5e5GQRpeLolc44rPGJ11+XwMWRUsbXM/I/A3QSQGqD3SUc5qyFnbf5/Ve+n3CtZ/9uUrFK3633XPE/9E5d/j2hqoylnq/ZsEflsDTvqo18a/vzgHCpSdhoErZ3X3nGNBImdtm6DZ0k9QAN0rZzt5mtx7kzxvj63BZmFnOVaZnU2TmwXc9233RB5vfGkfnnSWBsdBpdHG1lEL/T1pmM7y8oWfYV4OetXlNWkL5a/yg+RLq4P8Xu/WkLPdCM71iJeQVjm7yV6f5yu0t6GL5yPWbY3ciQTuJNe7aXAK1GVnxXvR35PHcrXlEwXI3wyD+VBathug7BfeOISbr4oO4Mr3OXmhhaeOe+1aVBsDK66twfN9JiJDhnVAtc6IFW4FxscKuskc1XNW7t8GDX3CxB/C1iCgP9EKImdjNPBRcUYyZMjAINtYAuGrvS1KJeU8+xs0iZKoDCC45aCJk+fb2L/TZNaZHU7Op0LOmsrohFKKL3/5y/jyl7/cUXqEELTVdUMZ1hTVBnuiyo7HnW7pqdy2mAbBgZ1DwftID2bO9D6lOUMiUyjFE8dnMbNYd46XFZ/sb1zukB3rJapOXlgWHWQi2RqoLw7RROBOEpE7vFzu5yTKWQ8pR4ivzPGVs/zvaihng89Hzo8TFD51ckhZwpWzCMxX3u69huwvU43ybRHkbEDQHJ4+Bav/kjwrqsLVDVinT0N7DgB6FOWsXIbDp+Y9nbu5Je9oslr3E7i8sZpbquPJE3NIikXHlH2l1kp8/KwTvG56oY4nqf7YvnIee7b2AwDqzTbOTlewa2uf19Zg0ymUNvro3Z0go3Dfg3ye4LfePpIopXufPI/phTbW8pzNnP79jQ1JIujaGhDtLkHIS3zsjvHoLph6dZLa67gTOOEl05GzilBaW561wLmZNj78f2awuMIu+taRHD766+PoLb9wB6ze5yD83nZVDQbccM9z5VOLsb9iAjzVh4f3HYL6GlI/w3SCgbXd51oX8yAMOnW7qpy1ApYuZ8iQYfVRddTzxHCVs2yDf1+xKhP++iNKOat7y11PeM1gF8GkT2Jbg1USC2XI8HyAQYhH5KYlZzXKWX6E/J4mHcO7oPjJV+RxdqaJnEk8K7DX3HMWcE+E/40KzpNh46PhCKDKRXYPC5oerUoW6mAoLwDgtzWQlbO2TfHcuSXxXQ6GEra0TQe5SLyROz9X9aQtl0vWgQhPVML8bzt5T7cM9+DM9Ipvu84bNg6JpNoayIqRnqLpITrDAoJRTiQG5NkNBx02w8y3EYQpZ5X0ZEP8kM5J1HXUKmelHpfqQxWYj2a2HfAOfimSjUE9wdokz1mfcpb/lV4J9xxcW4MfPVFHrTEPAHjuHPepXPaktVQxPKWsNfznxCdIlqotLFUXE5yRF5V6G8+e7ez4hZUGFlaC1ZijAyX0lfP44VOTWKw0MbNUx/ig6zMU5EG88RCutuwe+oFDd6CCoOzIDy3ForhEUfh+ObF0v7vMCVxCJqmgZajfwKtusrFjvIh8Luy6Ec8fdXNsxNzf9e2On2BcOwktErTn9z9Zw5FTrnpxcs7Cg4frePn1G9/jrhuLFo6oVUOrUcvFEs4q+6rejOpql07rN90qDH4pfO+y9JW/m7KtQT5hXSUH6+NoKSsg4wYEW41aOEOGFzIopS45C1U564cYh2hIk6CxTxhRk4+Y7Wm3vQ2qOsYMQ6aczZAhHnzcgeb9MiWewOUC2Bspe0PbNu0oFgaVDiHgIj/nNwo8cnQ6dlqp2Rp047mYYeOh6XQ+uR/sliH/MjCPUjNwxtG/XW3LZM/ZnEmwd2JAfObKOMC7tC0O5EHf7q19qDcsTyTtXeN9QtFHebpCQcn+3nzFVkzN1zA+HL0MTsXBi0ZBQHF6uuItl4ZEjec5K3/22hrcetU2LEueKWHKWa7u1GUpB0nrBGEkqY4g9Q/egknlWJ6zQeSsxsPYrZqp1GELP/cgZbCcr21TGAkqdkKIePSo7XYWg8ho3XWgAEYG2Yv1zKkmnhFEhoFHn6Z48ZXAyEDBKSvQarNnZXSQYnaRoFb3z+zxcyzmTewa74t9PhyLlQamF+voLeWwbaQ3+gAJM0s1LKw0MdRbwNig/907MbmEtkXRcjq+XKV7Ya6KsQGXnN0s7dFalbLry6FIKfkzk1SNpibVNWK+brKtQVxCV4aksUGLk7PKuSsxKLW46mKKHWP+iyY/r2utkzFlywdRHvi/aArWkf1Igva82WL73nhFCfkcwQ8eq+HUhc48wtYPKb/l8nVLmHS8Z55PGMV5Er0JClW5qfu1CxBpklKjfFPz4v0Ly6IdByDctVVDzipkbFxyNmNnM2ToHudnK3j8+KyI0UCpo9J3XrBQ5axiayC3uUFjjLCxh7A1cL5fmKsiZxi4aPsACHH7CUnS5NhsAcEyZFgvyCulgXBbA6acZdt0tgYWpehgSANQ4qtPDGlF2lK1GXCgH6mQs3fddVcayWTYQOCdT74EU0cUeUjGgEk9Hb+kBoCRl3kW8yau3KtfHpvc1sD93FvM46Jtg5p93DdUfrV5wzk+VMa4hpiOg3zOwNhQWUPO6pSz0empnrKWpETtKeWFKb66r+67TfVEZpivaxzEUc6GEq4hytkwL9wwO4XAgF9S5UxjPltBJLCcbycEGDEIqE09yln/+bLvXpLezfMnXtKHVptipeYW4AePLeL0BYIfPQEAXjKjVKQY6ANmF4Faw+9hzL+Viyau3JdsyToAHDu/hOnFOgZ7i4mPf+rEHBZWmhgZKGmPPT9XRdtqaZ9hecumUc5Kk0argbSSlYtJ4aoswxWg4UjPWzUaQjlrdZ+xIHsC68yACUvnb2Q9kbJwNio7M2ZbRAK/rB54wKXRQRO7JvL4wWM1nNgk5Oxq8XFdcLPxEPM5oNK+fBtvD9PiEzzVI38vgpSzErTK2YSjnkKe4EO/PIaHDtfRWyb43LeXfbYGQRHZM2TIkD7OTFdQa3hnREYGSqjVWKUQTznr9AUSCmNk5EyC/jIbwHKSdmGliYWVOYwOljDYW/DbGvC6MsZYKycNyMxMOZshQyDiKGdlfoJ/bjhkV0Oace00VgmRO02O0G1dPWdf/vKXp5FMhg0E3vksOE+ITlEYx9ZAt71c8n4vSE9hmHIxeUAwuXz6fTym0ISIN6gb31UZupfca2sQvJ8KVTmrEo5mCJGpfqeUCnuDsP2SIqlyNtLWQENkh+arJWf9+wHuLDsNKFuSfDzK2Q7YWYMANjhR7AxsfYa8/mdTtpDpKxt4+2u9ExBbxhbw0FMUrVYO20bdJcCzS3WMDTdw5BQBQFCtaxRIIspZ4tPxHN/JqxTm8QWEvzebOiDYaiFtEs1Jz+7G1iBFxCYguTrUs2OSZ8Td1/WcDS+T/4eY7Vd0SvEQ8+Lwd8obKJJqPqWDJAEDOTmbzxHsdTxAT57fHOSsQBcXMI4aO0nWcZTOghANylN6rqiyTXjO+uQnnZ6Af/aKkOA2n0O0E5Y7mdJJXXXbdT247boeHD7ewOe+vQzb9qaR2RpkyLB2sJyZlst2D7FVWQToK+XxrfsXAACuG57/XbeVcaRscxB3HHvDJeMY7C2gVMgJUnb/jkHkcwaeO7eERstCs2XpiV9NPz4Ima1BhgzxoK7w0QkKZX6i6Cyj4xOr8rC9U1EPk/i5HWiDyFaIydJKzdYgw/ML3FOLq1p17YhHARq4HMS/rUclZyXlbGiDlWAwx8oX9MWfH6XedElK5uvatllKOmo5vvc4L9kslKg8uJbsz+pTzqrlCrY16AZCfaXxLRTWAkofw5CIZp+tQQzrDPm3MFJYTUNe1hSX+DcD8vF4znZEzhIA1HNf/I0LkfaVtwQP9vI54EVXUowNmrj1KleB+tixWRw/38CZKfa9Ugue2et2INnZExU+3Rh+v/WfNzJWWTjryyctcMFYJ7YG6+Fr4FHOdgKpzG1ha+Cf+IpXpHDVN4evSkr4kMTdnd9DK8jWIASrEXBKBp8szuUI9jgeoKcnW7CszrzBkmJ6oY3PfHMJeZPgXa8fRF9P/IFy1ERTp5Dbqsjrvwr1YNhVd1Xl6Zy8jigWytmQtlgoZy3ZgqXz5yUXsEIgfn2S0bMZMnQL3vb2lfIY6C2I7XwSj0/C6rrz7go+53uMvr/aFS8Xc+jvKXi2FfMmDuwcwuR8FY2WhZZkuyWDOsaUcURGZmZrkCFDLPi0TJrXhY8bLZtiz9Z+EAIcP7+EpWoLEyM9uODEJOp8xSX1jOW6Uc52PRXz2c9+FiMjIxgZGcHLXvaybpPLsEHQbLMnKlw5635OYqRuGkBBCgIm2xqEtVc2ohtRT97S8CGIa+UkLAVVSNNYWURCp+rQ2hrEqAzUgF+uzYOGtFMJUFU5a+tJxG6Vs0Tp8Mhwg25589ANuER55HMOqa2MUFKY+vbz5EXhu5ZBILGUs6FJ6NOVJglc5az3hHnJiOcc+HH6TEXgNWU7V033O2LaSpUEEs4dN1OR0qtgRCpnpesVnHGmnOVIq1sv846UyhHQk+ewKoR0RA+Ie85atDOaRH6k2x0qZ0PzpZoduwZv48IRNrEWnvLqgw+6Czlg64iJnMkI25nFTln2ZPjqPSv40vdW8IXvLuN7j1SjD0gZ0e9KQnY2lWrRfa7U105VlVPRd0sjX4ZgCyX3uylNOLgq/87zDDq2nbSdyZqlDBk6BlfOqkv91TZZV92onrMqWatD1Ao/GVzh2rZsv6UBAP7yxxlqyQINdTyQIUMGF6pYUMtZSeP3nGlg78QA+hxbkvGhMkoFU/zeURl8MXPkVZ7J0upaOXvy5EksLCyAEILXvOY13SaXYYOg7ShV8nka2BDFCwimPzafB5qKdYKapoqkS/s8pF/AsMZja+A5Np1RhNbXVSFZg/ZTEaycddIKVc56v9uSD2vYfknhRvzWkLOK0lfkaRCPf676W5yyqbPgMuSyeFWnLjEfdwm+UM4qFa3Hc7aDil0sKabU9b9VCXW+r4bMDnp8BKGruf8A0N/D/q7UNGpgnmOHsrhUbA0Csg61NZA+d2IxsS5Yq2KmlY/yHgQpyuKllVpxYtgaOOVOgdNTgx5xiDJEnVdEYYPJ3WQXTOwd8S6Id0peBSDvoJX1JiqKB0nIednWwDAIigWCdo2KQGGrjUrdzadaT9bLFverm6J26WvQSdbxbSfc1FUfx9T4BKksannUvob8jbeVbZu6Kv8u6qpCwLGxbQ1SeBQyZHihQ6xaUfrIqnJWV3lxvtQdezm7htStYXZrKgQ527ZDvajjCGHyrj8D8plyNkOGQHjEdQH9DlP0caXxOj8eskChwzGvx+OpO+Vs1+Rsf3+/+Lxz585uk8uwQSB7zgYNBr1kYfA+kpWrQN7jM+t+DmuvaIxG1Ju3/rNaPoC/T0R8WmvlLFdMhncQvNdbVaJ6VabedNROTJCtgc6nJQlcRWN85awRcqPCfHQ9aUQEBFPTYnnx3/XByvT5sL+eyl25lp15zrrXLcr/1vMciDJE5aAqfdnf3l72d6XqT6Nr5axSxiQQxHmEIjjKxmKzkLOuInN1O+DdXw25U+NGQM93FtrU+bt2gw4euMyWo6p2eFHcAaLyQwSpFUbQeLeR0HRiIzZxzf56xD4aolb3iHb3XEWfIY94ze9fIUdQARWD8dVGSyKB4xJxAuL6d19W9UrxvlVSW4NEJYk1yeBNkROmadkkeiY8lJVCoQHB+DPd7k7lzxFoa9DWbs6QIcMqgCtnVR9WPm51lbOsnV+sNNF0Av7YzrHqqrOwrlfYaj8VeSfzlmWjpVHOJhnH5kwDL716GyOOMs/ZDBkCoeNVVLirbfRpGCHislig3n6yYZBAAWAUuiZnt2/fLj5XKpWQPTNsJjQlz9lA5WyMmQqAvTQqySL7zKr7BiGpEi9OwDJxDpR6SOjVVM56yWji2VdVvMrwKCYR7jnr70woKkyqV3h2e95hlVuQcjaMRI/jayz/Fqac9S1Ncv5SyLPngVl485GVskqWndTr8iy+UM76yuInkN1zCiIx1f2cvblytsy2V+vwdSTFeXXaTnUj9ooglMLut7xp89garG45o56TuJDpVEqpCIzTie9nmtSsSCPi9GRihj8aSa6IrCa0AoKhSTrCBCn7jw5KZrX4+5w0UeiWJvzqdFWUBL1VTo5ycoxf8zUjZ6V8wtRQq4Vuc+xUB8IQNEno7qXuKZYXm95nqvPnxV8WdVmyb1dIylmLCq/HbmwNgtRrGmtJLeLWUxkyZAhGkN+7pU6YEuD8bBUPPDPlS8NvaxAmjFG+h9RkuVyUrQGc/AJ/8mB0oBS9U4YML3DoViT79tEFLJc+dq+c9ZfJtXpMllbX5Oytt94K0zRh2zYeffTRbpPLsEHALQfyuWjVKRC95FxtovLKk8cVIGHpdKXECySYiUhb3qVb71UO3Usun6Ocj22HK02ClqZrPWeV4uuVs/6ydTs5GxqoKaATpAtw5ZYnejZM/k13vd0AW97t8sw5Jyuj7rsgn6Xz8y2r7EQ5K3kfU+m+ysHSQPl99h8fnCWR/nfB0+wtAYSwgevKil4627niyyHFO3hjowax4npp+r7y9d885OwmgXQ55SA4+U6WCqd5ayLIfA6ZmOk4KJiTX/DSbf7cdwDtCaTTFkVVS/E9Z4n/cwd1XpgSV4W7koftnM8Tz/bVRksiZNsJCeFUpkUCDiadphvjoET9LXWCUgQEU/br8FGWyV2RhPMhzNaAK2eb0nPSnXJWv92KS9jHrKcyZMgQDP6+5ZTOsC8gGIDlWhMAUMgZKBfZCzzSXxTvoOpBq0Myz1n2Y6Nl4+Ej077feb7dWshlyJDBhfyOBq0A1vEEqtIV8AcZ7aZMrmVhsmO7Jme3bNmCO+64A1//+tfx93//9/i93/s9jI2NdZtshnVGuw3AZArXYNWpTsHnB/vN+2SqnVz+4C6sNHDvk+fF9mLexNUXjaKQMyXlbLxGTbf827ePpBaSg2g9e3YBJy4sB6ZdLpg4uH8Utg08fmwW9ZZ+pL9SbWrK5X6W65D7n57Evm0D2D7K1pqfuLCEc7Nu8BGZdJpaqGGxwtLmFUpotFGlsnr06ExoNMNOwfNZqjRx75MXPL/VnRGSXzkb/Bx5iOyQsnHFcatt+/Lls9eBS5NosOWCCl725ap7fioZ25GtgUSM1BrsWXri+JwnrUqdydlPT69gdrkBAKg12DWdXar7zhsAmo6EaW654fl9cYUdT0HRWyZYqQJnp1u498kL2DbSg33bBsSESrPlv6YytgyVcfGOQd92GjG6X6m18NTJOa0Sreqc69RCDfc+ecGXB3+Wnj236At+J1/+par/OQTYe3dgxxBGBzeWKmGjd9flOyUv7851M6mzhictEzPdKSCJOH9ViRf13Id5kMqbhOq90yIq6UTBcM7Dtl3SzyMw4O1vl+XpBK6tAZy/vL5fK+Ws+7lTW4NuGLlAItuZ1U46IRhrwi1Bkmr5+MRH6jFspPMPVM5K4OQwXwkGrJJyNvYzsdFr+AwZNjZk66/AgGCmvI3tu2tLH67aNyq2P3ZsFkDwyjoZ/kjwIcpZp0znZyv65dEJx7EZMmSIRtCKZBnBQUQZOI8Qe7JVARX/sX6+QYjUT0mWVtfkLAD8wR/8Ae655x4sLS3hne98J770pS+hUCikkXSGdULbIsiZ3HNWj7hesbrfLttLcfIcwUCf9yVotm1ML9Q927YM9WDXlr7Ey6S9gZP0BwXNsMwv+0lVFTvGetFq2zg9vRKvQJqyEEJQKpioNy3MLNbRbNmCnH3yxHzgsph600K9yXoiZSeimrzEp6CYQBbyBlMwO9dw3iHnVJSK3VUJ5SLLt21RTC/U9PsUvGWTB3Dq3eDpqZ9VFPKmUJkG5auemzxmjusDxa+17vwEoZGwEgYgokQCLrmr3iPe0avU26jUvZIx9t7ozzvsdwqgr4d5zi6ssH3mlurYt21ADPij0p5eqGHftn5fNNmoa/rMqSX88wM1jA5SnJkkvpnFUpHgsn0WGq0aphdquGjbgESOs9H2Sq2Fp07M+86JI+w5JIRsGHJ2ta1xo5TIidMjXnKzq4BgqSAeAyZPCnJLhmQklEtQWkHqwCiIbDu7GZ3r0COujXMelo1wSaaGIOtGvRnnfDgJy58z3ry9UGwNghD3WeiqfgmcZAhOW303uln1FHS84XyzQkY9ho6c7aKuCvScjRsQTHzaOM9QhgybCZ5+R5BylnvOEoKW4zmSVxpqt/6KXuHlF40El4/nIxOzl+8ZxuGT3n5q6hNXGTK8gBHLczZCGau1PUgIKnVsCQkOOh+FVMjZiy++GH/7t3+Ln/u5n8O3vvUtvPjFL8b/+B//A3fccUc2O7TJkQ9TzkovQNxAVhyX7qUoFS1sGXHScojD4b4i9m0bAAAcO7+IhZWm6HwnV87qP+v20b0320Z6sG20x7f9mdMLqNTbaNsUbacBHuwtYP92v3oQYC/6o8/OiO/qrM5Lr96G01MreOb0gmegwT9fvW8E+ZwJCopHjrJ0RgeK2LN1AIQw5SJP91XX74RNqc8ov5AzcdvB7bjvqQtotGz09+QFwSXjamlmuRMM9hZx61UTQv2pwjAY2e7Z5rlR3v13jPcx1TTc89QhnzNw2zXbsFTxnxPH+JBCxGlm0aLcLAZ6C3jp1dtQrfvX0z5zeh6Verujiv3y3SM476ikcyZB26K4fPcwjpxZEJ08TuLvHOvFlmF2DWeXajg5uYKBnjwu3jHkS/fxYzNoWdT3fB49s4DlWgs5k6DPuayNlgnAgmVTx+rBm9ZV+0ZQkCSSlAKPOM+1bVMfSSUTWTr836/V8eOj4T3U3hLBvp3sWls2lQLoyWSJ6pXLfhsbLGH3ln6omFuq48Tk8oayPKAUOHkOOIQarruU4Iln64LgOHigiB3jASbdcdENiRYAj3K2AzVat4SNjDCiyLMfITAIhU2JuL6dkqSWMxA0As69k/PSlt+nlkyWZlzhJp/cs2kXy+UTQKQf43z4oFvYGqy5cpZqP8eBCG7YxRWNIhCiU/buEaeJinw/Q/phIiCY793o8G2XyiuyDVLOSl9d5azbFnUT9DSonksaQGTjtDwZMmwuyGOklXoLwrqLyF607HcCt3+YU5b3CJW/k1xU3JSw7zLUfPZtG/D2m52/ma1BhgzpISwgOoepGfPLSlfeN0jSnntWLSmHybYGNk32vqdCzn74wx8GALzxjW/Epz/9aTzxxBN4/etfj/Hxcdx4443Yv38/+vv7kQsybNLggx/8YBpFy9AFDMIIlyi/ViDCD1STACHA3u3SPoYB27LRW85h15Y+AMDkfNUhZ9kTn3QwHyfAlygbpb6Uh/qL2KUhd05NrTASzqbiJe8pueVW0Wh6iUq1KL2lPCZGehxy1jlXiRzbMd6HoiMV4uRsbymvza+vHEziDPYVkc+ZaLRslAqmj5zt78kj39X6ZIaxwWASVYcoW4OtI36CXIfB3iIGe4vx83X+yrNocYj/0YESRgf82587twigs1m3Yt697sShRraN9uDY+SVYNnt+8jkD9aaFkYGS596fnFxBsWBqn4enTs6hZVm+5/Pk5DJQa8E0DPT1MKlcVRKYssBk3vPYKT2HAPudk7OWDfievAil+4+Pet+LW68po7+HXYdHj9RwYdZGreFeF/m6yiVTy8m/jg2WAt/JE5PLHXkDrxbOTgH/eJcJYMH552LHeA6f/q/bNUetA6RLxr03men9Og80YmZPwAZhtpVMAXlychlnpldc9Q1xgwD5AoIFrkFPhpDpzs4SjDhdfmrtti1WWFTrLeHTlyaZnhRt4YGvkLPd+AYngEzIduxVnEZ1E3Tx16Eqc0l/6hngAOz9ArwBVz0HJYT7SrkJnJlmAYj9nrPSZKvzTIso7l1YGvD8DcO/RDG2rUHcmZIMGTYIZpfqODW5jCv3jvhW5K0HeLtNCHDo0XOe3+aWWS9UDgjGA92qghWOOKIfv61BcPnUfMoFU5v2uveZMmR4HiFODCSdMlZWuobFrgmC2vtQIa9oTjLkTIWc/dCHPiQujBxkZ2pqCl/96lc7SjMjZ9cfTDUbrToFwhWHcdogvo/Ox1bEQ1oF5Wy4HUM4oSsH1UoU6VOzryG9NzztqHJ0An6+OgXOes3keuwn1jBfnWddN0uNPDx/4mPdM5c9sDy3xElXF/gtUjGoXFn3XQJ6HS59qeL+btvUF13S79lLhOJdR3QmvQz/7meGMT7MmqT//teTuDDbQLPpqvg8Ju5S4j7hVITKrKul2KuElap/2+V7Czh8oomz02202jRW0K3DJ+cxt+zawhRyhsdnrVu4amjiU6l0kFhqSFJvGAYAy1XMxMHjx2Y9JBCBJjK0QLhiPDY/w/tUAcfHRdznvZDnyzHdbefnqxgNmWwTZenqXkafUdMhR33kbGsdlLMJbQ1Ws76Jm3YneSdpx+QBDgCxokiNpt55+x5cmHbIQEpVzqaxlNjUkLNJbQ02UtuTIUMY7nmcxQAhBLj24vF1Lg1gKQ133jQAAiws27jnEfabRzkbZGugqOjC6qYkylk1n1LBS7PwejWtoNMZMmRQ+KgEAcFkcGu+RMpZTXAxwK1P5Oogie9saq4nPOK5HPlctz3OvwwbAzz4RjoBwcLB95DT5A+1qybl6UUm58s36BDDeRmp+E/+LfoF5y95WEPra9hD0uT9DvklTtObyJ1ECS7DWkNHNq4F+LWQK+JuCGqZtE9eFvezHD1WLo9QDxma4yKz9O4gTyr0OcLkZYmc1fn46Z5DI2QZSNLJlME+2V+Y/W20qG+2M6qNiBIuqhMhGwG6vsD7f35U1IHzS9Gj/0bTwpEzC5hZrIt/52arOD/r3tjUTplISwg7rJ9W4+rHITxFu2LFL4fu+ebH++08eF5BZVB31B695grVUoGdyOhA2V1VIBORIdLZzsi/6EExh/CcdaoI3jdZn4Bga19vdK1aViewEhwUWH+HPMdBnrMdQ+J+fRONqnJW+qoGBOu0rpKhm4xKHCQuQ4ZNBh6AeL2hTsa8+LIteMmVE3j8qFsviO4rIa5yNmBVoOgLh47hlO8h5VNX0pSLpud4Qc5m3GyGDKlB7qeYEZxVEDnrcjvxWVSPQwL193VkG6U1V87edtttmUT/eYiCs045cIwZQ0Ye9ZsvTcOfpt0xOSt/DlLRBUtPghpPl4RDLOWsmo6uE8A38fTslAhDXz6GXo2Vdj5J4CEb15CS4DnJ17qbekyQfh14mequvRzpEdB7VXXqZ2hL71JfD/uyVAEP/q0lIILKCFAtIR22knWl6m38eksEhby7JyeKGk32zFrSREgU+R1JCvNXfuNws9oZ1S3DJkYGTEwvWJhdsrBlJLy5bls2puaA+UWC3Vv7MbNYR8tq4tJdtsfzLC1wwqrzyaNk5H2CJANB4JZXEK4dPgdBtgZRga7CuFndNt/1WaUqknciy8W8kKvr6jIS+KVDxEhjvT1n23JAML/deCjE6XVT1AB2llvgrMdEk1wUtXi8PuvWRkBN32Fnfb9RSvUrklaDnNWkkdjqYgO1PRkybCZw5azTRMEwCIp5E1UpjrTOc9avnGV/hXI2pB3SrRoLgt/WIIdmS+7grUK/J0OGFzgMqWMQNCaJCvjVvecs9XWGZHI2CTWQCjl76NChNJLJsMEQqZz1BAQLTieZrYE/fVUxF5fA83rO6vfxKju8b06wcpb9ZcrZ8H1Z3mrDrkvTS0TL6sk0G3FRzJCBzFojLCDYqkLpnHVLTnOSuZM4U7qsffdeRzp2SDRS202LK2dXquydoaC+pWNBz2Ecjx7duZ2f9TIcQ/3eUXypyA5qtKhPkRw1qRk1iUOU/TYC1CX2+RxQLBgYHWLk7Nxi9Oh/YcXC337LgGURAFwta2K0v4WXXseD4XV30nK/o2tbgxQhLHBi7MvruX9+oIGtY8CWofD99cQXcW0NlPOPLkMIPatbF9UlZKupMAjPWYuKga/sxx2u6V3dl4mTo/m8Qs6ukYq12YWtgaiju8g/UDkb8xnx5R2j8otbP1I5A6c8XEkapGBJG5ZN/ZMkcN9NQc6mUFfpyNl2TKFNknoqQ4b1Rivug72GUJWzBiHCkofj2kuk/cWqC2Uc5vwVApswl3d1UiykWlPjdpSKJpYk2ypXZBGcRoYMGZIhlq2Bs1keb8ldlyhlrQ7yrrraUq4O1lw5m+H5CaGcDVSQSp8TBgTzw1F0atS4QjnL94zbqMkcVgyCWX1vknnOhhRDq3YJzsumNDXC0F8W5zprfls35ex62Ro4V8GSiPBu0J2tgT9z7unKoVsOFUU0BpWEl9EkBH2OpWSrDbQsAtOgsGyKY2eAR5428OqbbYwMRL8LKqp1G1/8joFmq4JCru75rd707l8seNMvcVuDpjwZ4i17EFxyNnmZ1wtBhPPoAGMTZmOQs8+eacKyCIp54JoDJTx3toHZRYozU+6xaS0vZrYG7GOng4zVufzRiRYLzMLj7oeb6CkZuPZA+P5Bs+htZem2ishJxBgq3yTbuwX3B5WtDNTn8tgZ4C8faKH8S3XccFlJlKaTexlIOGrQDAoIllDF2im8AcGSnSwJI+NjI/xqxaBaO845zv2JVM52+a5HxROzbQqY/qyEcpYHBFslW4Okz0RGz2bYDKg33Qp2oxC1XDggSE6DeUW226x2eNn1Ngb6DDRabC9ua+APdszHl863kLpBF6g4CKaj5G20LPSWcjANQxFZRKeRIUOGZIizktvUKWelzkv3yll/X8iUWNYknrMZOZshEEVBzuofdC+pFjLrGKNDTOAnOQOVsx00akFHhNoRpOg5y5VIbN/wvGybCsIwbR9Ycb4h6t21hm6Z/lpAXdbU7fnHVagFgQfXcr8Tz7NOpe2+PIMGe0Gb+WDXAHI5oFigaDQJ/u5bBAd2E9xypY0vf4+NQg89YOCnXxNQ5hDl7NHTFs5M8ic/GYtSdmwNmi3/JI3HhkJzim7QKj02Yp84qC8wMhifnD1+lsnD9u4AfvffbsEn/vYC/vFQE/PLNLW3Si5mWsrZtbwdhACveJGNJ54jOHzMQL0ZTZPoSHxC3KXMOVU5G8EkhTjp6G0NIsqXFvh9tGyA+5t4z52KOuE/fmIK3/3j3d2VLUE16fecJZ7tqw1PQLAOCeGuShrwSMW+/jT0azgCM5EfZO+QhA9uVM/Zzuve8OUQQYMp3qTzwHFpK2fzOfY8JAkumCHDZkGt6fY76k0r0D5kLaFabvH+YdviE3fub5S6ZVXtBkT/P8a4Uh0ahK8UJbj92u1YrDQx1Ff0HS/6p5l0NkOG1BBLORux0lPlnE5eWMbJyWUPd3PZrmGMD7lBcj0xtqT/eV+IBRmznXRjn05GzmYIRiEfTnJ4iNSQdiZsuYiA82bJxFO3nrPe5GMoZ5UXJ5lyNrxQhJBQcllVztJVU846fzW/rVdfwaMEXUvlrJOXJa51d+m5vsGdlod4HsJAWwONnUgwH6z/QXRInSdhfBg4MwlMzgJTcwS//BZ3tHniHMEXvwPc/cCUL53ZJRvbxgluudKfR73B/u7dZuC3fm7M9/vjx2fxf/5BTzqWJVuD+x4jaFsEL7mSirI/fZxgag647Qb/+UXWE12S6KsBO4B7HeXkbIyAYCfOM9ZoYpSd31A/+zu/pFnD0zHcNqEdEBArMVJ450USMc5v+xZgsJ/i8DHAtgkoDWdWgjpynJw1NMupU4VvYJgsP6HbjLg2rmpAVkB6VQEGobCpXGEnKooHSQg7PiB3PWfZ9jXznLXkzwnzFDeg8/zFoUHsbETavp8TlCVqkovKchEH1ip5zpKA8njIWelBF7YGQjnb/bsqnxMnZ+MGBIv7LmbIsBFQb7gzUZbNVKgFdTZyjbGw4nQsuQLV4BN1bKqer/gEAIu6/rTqu+8TZ4TaGiSrN0qFHEoFl17xiCwy5WyGDKkjjnJWZ1sg9y1U5ezhU/NotLyN+/HzSx5yVu56NJqWSHG52sT0Qg22bYt+c2ZrkCEVFArsbxzlbFhDE0c56+4rpelTzoaXJzTdgEPWQjkr8g8RVMnFWFXlLE8vxD90rbFeM8icmHQDAnRXjm4CgrH81e+KrYHIx7sPEG1r4FOXclsD5918/ctsnJ8Gvn6PibZFMDWnNEhngeNn69DhuTMGfv71ls+7s+FYFwwPGLj64pLvuNkVfZkBl5ydWQDueoACMPBLb3RmH22Kb97LCr57wsbeHd6zo5TikacJ2s0W9k740+6WRF8NqHzPT79yAAAw4tgaPHi4jt/969nQNB49wqIpbxtjJ8gtEeaX/R2RjiGxRNzLLY2lwl1DFvHF2FFevRC11CiQnHW2B8Va66g2kbJKTaEUMxnZc5bDVlQB/b3Aoua97ey58ioMwsBJWL+twdq8xLLnbFJyNgVuNiRt0lHacfZPkqZKtIuJC1/b3uEzLRdGk4RusMXyZ39bqxQQrJAHqvUEAcEyPuZ5hfnlBh4/Puv16CcEF20bwJ6t/etXsJhotS08+My0x75ARqPlbRzv/vH5yHfIMAxctW8EowNun2+l1sKjz057rBFGBko4eNEoCCE4P1vBM6cXIifM25aNaoO9bHzP+568gImRHrGigYuKALcvnssZmvbUW3eGq2G9nxNPkMq7C3I2URIZMmQIQRybTX1AMLcfKscTsixbELMvunQciytNHD27iKZi7yKnNc8njgAcPbuIo2cXWcrEYJYHG4GcPXfuHL75zW/i/vvvx9GjRzE/P4+VlRX09fVheHgYBw4cwE033YTXvva12LZt22oVI0MXiPSc9Sj4QkjOBA2ZRzkrSFD2PWq5chiCimB6lLPeNycomIU8+8IHBVGBLwxCYHHrBk3FQQiBYRCR5mp5zvL0iKaSMJKw6ClCt0x/TaDOnHeZN9FW/PFhyAw+WGOjm3H32Inw3xJKp/hYljitUbkIXLQTGOpnhOipCy2lbMB/fOeI7937g8/PodYALsxa2L/D+xv3lS0V9NdVVjuNDeoDgsnE2UqdfVmuuCPhWgM+nJ2ycfdDBu5+qIK3vmLU97sgNDYQOcvP8+XXl/HqG3vx4svZzOyuLayJnpqz8K0fVYIOFyCEYucWdk9Hhth5Lq7Q6OcjJuRUeGCOTtVx8am59MDzksnZqCXJKjdLKXBhhmLJuR2mopylEScWpvLV3adur09ccjAnec66inzvUTI5a1np2WWEgVIqBt4iIJi5tuQsXxYPAO018rmVEeW5GnV35dUEseu9iCUIYSpQn62BWKnRGaIIlNPTKygvsbpydsmdRDQNCoAI5WwaQU/l+o73kWOTsxmeVzg9vYL5ZX8n5Llzi5uCnJ1eqGNqoRZ7/0q9Fb0T2HJgmZw9M72C2SXvdVqqtnDJziGUizkcO7+ExUozdjm8ZWrjuXNLaDTZ++9VzjqTehpGWSeGCEK3YxT5eLcuy9jZDBnSgvw+Ba2Q0doaSB/5cZPzNVEf5UyC7aO9yJsGjp5dFB7W4nDpeNWasJg30GjZMAw2zohYpOdB6uTs4cOH8Z/+03/CV77yFVhKj0X2q7nrrrvw53/+5zBNE29605vwkY98BJdffnnaxcnQBYqRAcFkIjU4nSSNkMc3RCjcVOVs7OTcdAOGBTIfqQ4ygrhKMbtCpeXhcZSz4nMQ6cucSVi63rzSghh0a9jZdVPOEv3nVc/X+ZtWZykVWwPlu8fVQPgyyx1F57cg5WwAUeROKni3c3L26RNeBmKgD3jtzX2+9P/mm/M4PUkxNe8fnTacfnyxEFQ2ite/zMKzJ4v4jX857PmNk7Myag45e0FS9TY0/fmlinsxLIv6yDP38m0cdpaTs/09Bm492CO2HzxQxH+6cxQzC9Gj//nlOppWBaNDjD0Y7jdACIVtEyytpHuusudqx3VUikUKI4p0O8pVXVLl7JGTBN/4AQU/gaBVnoG1SWyVb8J0uzyCk07y9VC9PMtFKtKblN/5Tk4mknBkkD1eOSnLSdq1CAhm29RD4KvRwqMQtbohHiI6PzHTJiCxJ2oiJ080ajC+idcNvombFNp3XRLPOgoVFUI56zwnaazIzhnuJCpfuTw1T/Hl7y979ts9kcc1B7wrRuJOlGTYHLCdynL3lj7sHO/Dcq2Fx4/NdhAgbn3AiYbh/iIu3z2s3SdnGugr5107gRDMLNZw5MwiaooSt+bYI/Dr9NCRaTRaFmrNNsrFnPj9qn0jGOgJ6DACOHFhCedmqzAMf7vddCbQCnkIxduR06xeyGmWt6j1SHhQZ78gIgm8IovVEd5kyPBChpc7ChfWWZqVNoR4fakfPjoDAOgp5kEIQd7pPLTafl6To7+nIJG6BvZvH8RTJ+ddC5UE55MqOfuJT3wC73//+9FoNDxErMcwV/HdbLfb+Id/+Ad87Wtfw+/93u/hN37jN9IsUoYuwGcgg7x44hCOvv0Q3jHVK2ep5xlK09bAlJgFtVzxlLPh+7r5RzfuhkEAa42Us5pSrFdnweiy49MpfBFYuyTCuw0IpiOpPTPumvF5pApUmdhQ01JnGIcH2Bt65JS3cz0cIAIZHiCMnJ3TkLNcOVvUX1jLpjiwG/ilN45iqC/v+a2sUdtWODk76+a1XPXXKDKZVm1Q9Pf4SW9gY9ka8LKo5AEhBK96cW+sNJ47R/HE8Yq4p3nTQG8JWKkB/9+Xqtg6RjBxY3plbncdAb2L2TYVCdNIopxVCcoFh4PpKQFjwxSX7PbeNKmlCkgxRP+oIyx9Ep+QwoYhol7iz03bouAhLD22BhSeACvnplsYdOqFLrjZyPORbQS41+xa2hqofqLtjvPsvKxBRKnr+xoPfHFG2n7b6r20A9qXbjMgGtfZcsHEcL9Lgk4tVN1ghUr2aStn2SQBy+v3PzvvzYsAn/nIdmwZkYZZSW9Yhg0NS0yqFjA+VEYxbzrbN8cNbjsn0FPMeXwUdYj6HWCP95Ezi6g1vJVm3QksNjJQwvhQGT2lHBotC/WGBdpHxe8Twz3oLed96XLMLtWB2ap2QrXuTNQXNKzG+KDfVstf9phj2C7Hn6Iu3wh2UBkyPE9ANNyRCjNidev4UFmsLuKrBHpKrELJOxM8rRBbA7m/kzOJKAffvC62Bh/72Mfwvve9z0fKjo+P4+DBgxgfH0dvby8qlQqmp6fx2GOPYXp6Whxfr9fx7//9v0er1cJ73vOetIqVoQsU87qRogtd8K6o/aKgm/2wKfU81B0pZyOIVu1vMXxLXG/YqPyj89QFGkttcCPKkfx8VxvrZWugZtUtOS3fv87K43+fPJ06jQ9xlAo0qCRuhFrv9nLRAmDg5HlvA1Qo2PjWg6d96ZSKNgCCp09Vfb/PL7PfVmp1fOvB075qhHfInz41j+Vay1PYZosdK+Po6WXUWit48Bn3t+WK/wznltsA2Av59R+dEYGxxLk7A6d6o609p7UcOZcKOdx8+VZ3kqeL4FKq/zUxCEYGGTn75LE2Dh8neKUmgFoiSIdzAmSd44MoCD8/fnU9ytkIUbL6PvN7dcPleVx/RR19CvmPgHdLLUNoWVOsBuPyQfw+WtKrp6qG5a+nJ9sYGlj9+lomYPPrEBCsqeTR2pRL2Du4ThGH8DtPNVuFctbw7tPxvEKIzHqwr4gXX7ZFfP/ej89iYYUxNYZSN3VTv7ppuJ9lImjLiIlLdjHV32PPNrBUsXH0TNNLzgpsDvIuQzhcEQX7ztteK2o5xgZB2yEadMv+OwEPglVvtj1cAFfSlovs93Ihh3k0UGu20WrbgswuFcM7E0HjIdsGms5KrUKeBY5stikOXjSKHWO9KOT96fpXqgXn6x3rhhYx8vgsIFiGDOkjluestIrpqZNzuGLPiOd30yC4et8oHjvmxvfoKXrJ2bZFFZ5TOl7qX5gGEfsI5exak7MPPfQQ3v/+9zuFYAW688478eu//uu44YYbAo97+OGH8Sd/8if45Cc/CYCRD+9973tx++234/rrr0+jaBm6QHRAMPdz+JIQ+QtC+6XySxWonE1x5BrWJ4kT8Y8qhEgQBnoKqDdrKOYN8ZL7y+KmGxRobGywhJnFOvZODITmF4Swcq520PEgrNcMsl+FlA45m8RXRsZAT14s7+pz1AP9PQVMzns9wTzPZcSMHN+uW70AAAO9RWCm6n7v0ydkmu7SNBn9vUzJNLsAzCy4vxcLQKPJfjMNqj2WQz0/N08DluWe69yShVIJmJ5z1VOycrZtMYKpLq2+W6pYwbYK0J/TWqLWsDC7VBeEX1BwqThwfR7d2dpX32JjeraIL9/dhk1J4iXZKmSSpd2lrUG3hI0MPVEUsj+BWBoZ6TmrEpScSA8477hliF3WiO9pwSUWgmwiqEexdPxcC1cfMMVvq4WmFHiOl3EtPWfVPBIHBOtANaEiyNJJf580xytliZVn5B7yNAP1bBF10SpM3KjnoE6eeLznlH1XIyAYx5te2oe3v24QAPDf/nIG332wilMX2rj1oFQ2529GzT4/YCsT5mq0740ObmugW/bfCcoFVzncsmwUnBk/rqTlv5cdErbesFBzJukLOcOzkjEJPNY3eV4HUBRyhpaYBZKJM7q1XvMeszqrIjNkeCFDtkMZ6NGr7/N5AznTQNuy8dzZJVy2a9hnyaS+lkI5KzX8rbYt6hV5fCDXXznTkCbt2N81V86+973vhW3bIIRgdHQU//AP/4Bbb7018rjrr78ef/EXf4Ff+IVfwFve8hbMzMzAtm28733vw7e//e00ipahCwhbgyDl7GoHBJOVs9I+abZpRkhnIJFyNqJQN12+FUvVJnpL+cB0iXS+dkC6L7lyAo2WjVKhs1FPGIe8MZSza5ixamvQZd6cZO5UOXvjZVuFXw1vXK7YM4yd470o5k1856EzLB8PN8tn7+IrZ+Vxw56t/Xj61IIo85X78vjh4xYqVUaqrtRY+oO9Bm47uNWX1vLKLIA2zkwS/J+/d5/J/h7iLHmm2Dlexm0Hh3zHfv+xc57yXbN/FIO9RQDA3HIdhdwCapJK7Wv3+J95HpTpyecIvvsjgte/zAalpjjzy3eP4Yp9BTZYcjKr1Ft4+OgMinkDt1w5oblCa4MfPzeDxUrTed/Ztm6UXX5ylqC/B9gzQfDlu/k+XRXZBSG+/DYD5HfHjEvOqspZsWQ7Iq+oH9ZGOBsagEwGJ9Jk8lGNmyCTs8+daQKIXuoahOggV055eDCwnLvnmtoaqOTsGgUhi4W4DabHdyBZeMDALGRmWJlpsdQJjC5nYsKeFXXyxCMaUJWzqxQQDAAu21sUn/dMsB/UwJoZnl9QRRS8/aaU/bZefeq4cFe/pFNO02Tik1bbRr1hoZAz0Wrbwj6BK2v531qzjXrDq6oNQ1Dfmqtmc6aykqfDVZv+3+KNdeMc303slAwZMuixe2s/RgdLMAgJrEsMQnDHi3bhaz86CZtSLFaaPksm9f3mVjWGQWAaxJ14crbLY2/V1sCnnF3LgGDT09P43ve+50SbN2ITszJuueUW/P3f/z1uv/12WJaFQ4cOYWZmBmNjY90WL0MXEAHBAnrUOn9YHeI0ZrpI9F7lbLL04iIspSjfEitE4apLa6ivGLpPHOUsIaRjYpYfD+jH6etFsnj9eNeuDGpO621rYBgEw/3eZ4QQIghLHWEfuWRZ4zkrNyaGwXxxbKeTPtBn4l+9voU9W/txcnIZ9z1G8Mxxgtuuy/vKBgAHduUwMtjC/JJ752wKLFcplh1Bbn+v6TuWUj9BMNBTEPuxxg+oaWJQME8gCoCgWiP4xg8InjnBRt1f/b6Bm6+Wzo/kcOihJv7fL8x7GkZCDNz+IuD1N4e/k6sJrqC3bUmN2YXSzHIGQHz2VtgbSFe6+9WWrkKOE2Mdl3k1OK7INCVVnQG0EP2+Biln+byeWmcFBeFTS6DLlfr20gziktZTUXWEA17/P32iiWNngVffBFx1cbCtwfFzLVC7FC9xLeKNUvlzpidnO8k3GVRbA9WDNgqr2aK5z1K8G5AmIRCWlKsuV/ovq5Cpn5z1vuMy0lDyyiLDQo6R3QBwyR5XubPbIWdPnG/Bsqm4Dvc93sC9jxHccfMGIvgzdAy1TyY/79amIGcd5WxKtgYAsyxotZuoNdsY6C2g7lga5Ewi+jxcOVtrtoXlQZxxja6pXlgG5hbZ52Ihfn2jC8AbBPk2dlKHysfr7MkyZMjQPXpLwX7VHPmcgS3DZUzN1zDvCXLoilpkyPVCPmfAaloe39lgWwNDpLUunrPf//73hWr2da97XWJiluPWW2/FT/zET+ArX/kKbNvG9773Pbz1rW/ttngZuoBQBQQNMmMqHtVV2GHPp9xemYLs8hJKqQ4wOlD8ur4lwQrXTmBI58vHG+kHBHM+aG4CWTflrFSGNSyCb4lo1wHB2N+UY604aVLtBEZUJHBhayBtk8kogxBGMokl6l6C+ZaDFLccpI59gR/looF3vsHGpbuGcJkT7fc//MEkHn6mIe3jP1ZXXDVoXl7TOt1wuYHf+JeDePjIDP7qnwwsrRAcPSWrEojH1qBat/H9R/wBJCgleO70+g6QPR7Twtag8xdAp5xlIMLkXvGyh21T3PvkBSxVm9o0d4734eBFo+K7/Jz51HEJkebVj+urKsN0oq53bWsQeMs6uJcx5KQd6HZi7SUrjpotgmNnCK64yCudld+jepPi3//vJVy8m+Cdr09cqNhiSpecdbexQExrZWvg/Z7U1gARdXQcqIF0Ex/Pi6JuiIGoCVOqST/Ic7ZTelZOX01BnVzx9CFVcjYV5azbix0aAG643MbYcA59ZTfx3RPsYX3mZBN3/MZp3PmGQfyr1w3g459dAmDgiou6L0eG9YcqopD765ZNEU0VrC+4rUEanrNty8ZTJ+fRcCrMHz41KQIQAqz+u//pSQA8pgAwt9TA3BLrsC1XW+J3FT3FHK7YM+J715st4NNfNmA7gSrLRTcvAIna0XBbA3+fOwwPH53GhbkqCIADO4ewY8wN6pp5zmbIsL4Y7itiar6GJ47P+vpl6hBM/p7PGagr5Kw3IJhsa0BcAQfh+8YvY9c18vnz58XnV77ylV2l9YpXvEKbbob1QaStgYd0TU5y6uBpBPkycVU5Gzu1OPkF/xbkeCB788VVzsaBIZ2vm27XyXrgKmf9tUQ3xFA3WC9bA7WTtd7K2TDIScrPhEsI+/P0qFNlz1mPglS5/s7bxZ8/4v6ghfsuuOmPDHoVEOWi/yFWCS9eFvnz9nH/Pn09BLZNQQjw82+0nbS8hZPVtis1G7NLjCn4b782hi99bCfe+64hAG503/WCfO/45ch1MV3q+jwST/o2pYKUUINfLddamF2qo9W2tf9OTi4HFN5d3t3tuG69xihcRWdFKCEt5d1yLSjCj4s8rZBqItU2jmcXUS+pKkfLBmypkBSSatjZtdYAnnpudW9gU6ecda79etgadJ5n+mUV707MpONOYtAkHS6ZnXWOPT3pqPjNhAUMLpAojJqSTzkrFVjtP6USEMzwfn7p9RS3XuPNaNfWPPZtZx1oSoGv31fB5Jxb0azGBG6GtQdvc90ArkRaAbfxg4LxgGBpeM5OzlVx/PwSGi2vqow/6pZNcX62ivOzVcwu1X3HVxtt8bv677lzS5hdqnv6rsfOAJ/5mkvMApJy1vtHC/+qxOB9VeFAGGyb4vTUClptG822jdNTy97Avpo0M2TIsHaYGOkRghWA1d/cSjBMUc+V/4HKWXnVjmmIY9fFc3Z52R28DQ4OdpWWfHylUukqrQzdgRAqlCrBAcHiNS5e0kuaSpXAaSSdctayqYdMTNXWIIycDVTOsr9h3rCdwJDO17bTS1eXh26stF4zuV7F7tqVQc2p2+dKEN+rEAxCVbuKPJ2/4UukvY2CLVRYrMw6clwMeJ3XNU5wPI7RAS9rpQvIpSNn1TxufzHFbTcQ3P2QhceOstatr0xcVbkBDPVTLCx7j5NJ10rNxuwiGxTv2ppHf4+B8SHTt996wPWYDl4GnAQ+5axkk5Iz2XJsdbzIo0qXCiZeIvnvtto2vv/4eRGMUbZDefBJgh88vIxz05aTX8dFTg1x7VDkR4yTq1Fj6CDlLE9LzVkQWwFFCrOW0SrKu60TYx6uks2WTUCVi8MvxUd+bRz9PQb+3f+aTKQG8MA5bqliY3o+2J9gZoE9Z1pbg6Qq1g7AydhCnqDZopFkvoqU6MlQRJOt/FN6pZG7EjLh8MW7ljG7yLYINXa0IDwUbvr+yU9V+e55x5W6KY24RyWpPeNkkHo1cybBX/w/E5iat/D2/3wO52faeOQZl5DKyNnnB9SAYACEL2G3wTfXAmnaGjQdwmKgt4DtIz2e95QQgkLO8PSxLZui1eZ9iOBAyc+dW0Sl3karbYs0J0bK+IO/8XfgxCotXt+E9OnVcw7b1yscCK/F1GBwlk21/efM1SBDhvXBUF8RP3HjblFnFXKmqH/UV9WjnHXqjJYVoJyV+tA5k0iTds6+a+k5Oz4+Lj6fOHGiq7ROnjwpPmd+s+uLQi548MkRVy0ag5sV2+Q0+Wfvku5YWcZG0MCXE1c6yKSH6z+YAjkrLSnXdfrSgLimIb+tNdbL1kC99V0rZ8X96yoZLYI8l8NsDWSSuNGycH6WTXjxJWWy2kNNTx0AN1q2OF5Gpc4iMazUWuL3fN7LXuhsDXTqYrUDTAgw0AuUS+72YsHGouQTNNDHPMdkrNQo+M09NdlAtc7yatsNXJhroWWxMjeawIW5qq8cYciZBKMDJc81synF7GJdDHSiMDpQQiFviud+caUh1L/Vest3nUf6SyjG8GMTKh5liSWl3PuO+mwNLIurEg30S9FOPct2bCoUZys1ih88agBwB0Z9AZYXUUiVoOBjsgSJcluDSHJWSdJHpAeRsJEliCt3VL6uUh2pBoWxrOBzz+eAHVtY95FSop1siQIFcM/DBA8drgGoRe6vI2dXqjb+4h8XsFix0F828K9eN4i+nnRnC7hyt1xk5KxN4fERjY0unvfu+z/uhFycssg/x8vSPeLhp10S8sqLlCFGCs+ub7JEeeflunnrKLBnG8VShQW4vPbS7k1nb7zawFK1jZwJXHFRDktV6Ce7DYKJ0Rx2bsnhzFQbX7t3RfymEjgZNif4sygrpuTxwUYHn9xKw9aAP9MDPXlc6lhcpYHzc1VU6m20bZecDap7r78sB7lvEoa80t6FBkuW729EHaYjZ3VjyUw5myHD+iGfM5HP+fsDujg/7jGsnpxdqiNvGujvyWNBGo96bA0MQ9QVnVgedk3O7t+/X3z+whe+gA9/+MMdpUMpxRe+8AXx/aKLMlOm9YQ8AxBIUsZV5Eg7Bh2iDQgmK2dTVKjKCEouLB+PV6QoV/dlkVWIq6Wc5cnpiKSNERBsDfNF/M5ZHPDjW5aFlRojAEsFM1SV0GpbnmVgQZAfA6+dCIOOlJL7iMu1Fu5/espbXqGwdLfxjmWzZTnpsu0LKw3f8TKmFmqYWmAEy+wSgeyYQwgV14ODR+iVUWtaMEjL8zsPDOaWr4VTU25aA70uEcsxu+B+PnyiBoCgkKN47Ng0AKBSAwATjabki5YAl+8ZxkXbBsR9PXZuCU+emIt9/OhACS+9ept47k9NrcCmLK2zsyu4/2kv2zzQk8crrtsZmS5fRpnjAcEkZa6wNdAMHAD/uy8v/7VsKtqDljPuyZnAO1/fi3Ozy3jxFd1FBFvLIIAy+DVRCWsVfjLIOT7gtLsZkq+Goi6uVrJS814I3kTIymmZmM55npHOynZuxq2DwvgBQghuv75HfOfk7IVZC5/91pLYPjJo4m2vGuisMAHgAcDKRYJFh19rtynMQrznthM/5Lgg0gRMrP0RsyyxktOf/7GzrH7+6ddYGB4w4icXozwEugkDlZx1P+dMire8kmKgt4ClShM7xronoQZ6CW67geVZLBhAxPzeZXsLODPVxpPHXNJoE6x4zxADQcpZYHMQ8K5ytvs2WA1Imha43ZpluePAqt8VAZfvNfDy6/I4ctbdFnZWq6ecVdtRqu1jpm1ZlyFDhu7hszuU6vaCQ+aenlrB6akVqDCVwbmwNVgPcvalL30pBgYGsLy8jGeeeQb/7b/9N/z2b/924nQ++tGP4vDhwwCA/v5+vOxlL+u2aBm6gM7XUkXcgGA7xvswv9IAIQSzS3Vtp4XLxD0zlJKSdLWWgcnnUMwbgigLU5F6lbPpKVzXRDnrnO/Mor93s37KWb9ycy2gZtVtQDRe9umFOv754TMAgELOwKtv2KVdsrVSa+GuR84m9qiN+97J6Q73FaB2VXeMsyAF8vXnStL5lc7X/PeWvedz4sICzswsRB5335MXfNuaLRuFvFs+WUULAIN9/nSo5EHGLQ96XV5HslkgaLbc76ZBUCqYqNTDQ8AfPjmPo2cW8OobdqGYN1Gtu0R8uRjcpLYtG8vVFqoNtr9Hfev053tLOQz3G842isVKE1UNka1P31tnyJ7dQUv47QBy1iBuEDG5vuZEZiFP8MoXl/DgM0so5Ls1ne3u8E6zCiKsVfhtDbwdLl/xI1SO7oRKSDk1EzBhW2IhoprZucUbvoaTkkI5TQHuIpAzvUvEO1WJ8efxw78yhpcc7AnfWYIaLLCYJ2i0KI6eSt+rpNVi59ZTdCMnti2gmHpOwRDL+js9XvYFSIrI59jtn1VqNqbm2TUaG0qeVRBcVwa/StsXEEwqsPhllTqQccjuWw/24Dv3exncTiczMmwsuCvn3G2m07BYG/wmU0pdz9k0AoLZ3Mok3QadTxRbtuvNz21TAODVNxZx+f4qJkby/j58SFFUn93YnrMpKWezgGAZMmw8+KoQ6fuerf1YqbdgWTZmlxpQoYoLxVhsPcjZXC6Hd7/73fjEJz4BAPgv/+W/YGFhAR/+8IfR0xPd2a5Wq/jP//k/4+Mf/zgAdnJ33nknct1ERsnQNeKQswCwd2s/as02Bns1xpIO+sp53HwF8zN84vgsTlxY9jRgvGPQV84LU2ZWBq4KkX0y02/QOAnxkisncP/TU6jU2+HKWR2JmobnrI70Tfl8xwZL6C3l0GhZgszh579enYXRwRL6ynm02ja2jcQfoHeL/p4ChvqKWKk1YRqk67zlawswsqzZtlFrtJHP+d+PpWpTPD9hHVo5Orh6j8KUU3JQr9uu2RGYPk/TNIgnOJV3Hz15b9vsPSDEJfh6JHKWEIp8nvhUcSrpx/OXwX+XiZiesvu8AsBgf3hrt+w4BPSWXcLRNJiiqm0RtFoEPSWWl2VTbB/rxdEzi75AaYB3kqhtUVRqLRTzprheeyf6cemu4cCyLK40cOjH53xBlQB3kHfZnkHcdrAfALM4+PZDZ2I36CrRKiv8mZqF+iLNc5WHTu1iGgRti3ruky0GYMDqumgmQ2xFoHTN3fo2/BCV/PErZ73PCY2i0mJVsyEqnjiHy/vHVG5uG8vhT98/gWdPN/Gxv5kT3qo2pTDBzosHZMmZRFFXJyyUUyDXJiHZWan7v+zaMr7zQFWoNtMEtzUoSfYs6nsUBq4M74ofjBN+PFZZYmYX5xjlh2NngCeOMBJyeICgWEizv+a2Zb73UW2wQkmW7kuiS8KybEzO6yW0V1xE8Pnf2YZmE/jAH0/hzJSlfV8aTQuLFXfAlzMNDPcXs+XPGxi6fvpmUc6yWB4MQX6vidJLUTm7UmuJSemWI5ixLFtYdc055OzuCYKff0MfHnymqn2nkihnw1dLSmlGvI/yM2FTNpbzhzHMbA0yZNiICAsUPtBbwC0Ol/W1H54UosKcSUSfsJA30WxZGOwtiP6GG+w9fjlSYUA/9KEP4XOf+xymp6dBKcXv//7v42/+5m/w9re/Ha973etw9dVXY3x8HKZpwrIsTE9P47HHHsM3v/lNfOYzn8HU1JQgF7Zu3YoPfehDaRQrQxcwYzZG11yczBv4qn2jGBss40eHJ1k+BsG/uHmPdl/5pXAbvETZxQIhjJTKmQZuuGQL7n7sXLhyltstWC5Zk4pyVmeXkPLSl/6eAl59wy5YNsVX7jsBwCVh1ks521PM4VXXRy/bThumQfDya7anll5vKY9X37BLfP/mA6dQb1qBylje0RwbKOHWq7cFpnvo0bNYrDBFWNDzoPWcjam+5mlef2AclXoLT52cx2BvAYuVJnpLOVTqbVyya0hLPJ64sIQfPzeLrcM9uOnyrQCAb9x/Gnwwnc8BL7pkHNvHej3HLaw08L0fn/Nse/UNO1Eq5Hy/y+d22e5+lAoEz51jS5n37QAOHrBRbwLbx4EzUwSUMlJ2as49794yxbUXj2LXln602jb+7Aun0a4BL7p0Avt3FvDVH54E4Hqw7psYwNUXjXrK9+SJOTx7dhH5nOEJTsGvc1Rnm4jJJv/+fByXk+5VkP9vEIRFgeklZykNVs6qPrUyTMNA27I8y/T4AMg0SHpBfjo83oMO2FluNxVFLKqDbB25Hp5TfIhnIyyhxAnHvji4ZHdB2Bvw62LZAJ8yFbYGJvHYOnRCzlJQ93lKqLZSydnX3NSL7zxQxckLLbTaNDHZGwYeEKxYIDAMdg3aSYKCiaJ0TtYECV+TKDJs232Wo/dnO1g2UK1T5HP+G8yvCwCsVCm+/D0DAFuRs2NcmbDotq6QyqsG3PTbGki5UM+fdODpG7O/S9UWfvjUZOAhu7b04foD4xjsMxg5qzw/lFIc+vFZ1JveHw5eNIp929K16ciQHjarrcGTJ+Zw4oJrn/StB0779hnsK6DWaIsYBQAw1M/IibmlBh46MuWZpOJERdK6XMVKrSVWnsmoNtqinzS3xD6MDBLhHdtq2/76LaRPpnrOpqacFSSNId5n9VFYLwu5DBkyhEN9v4MmbYi7kMqpH/gKK0bOysG2+VijbcV/71MhZ4eGhvDNb34Tr3zlK7GwsAAAmJycxMc//nGhiAWYyrbd9i7RlP3MhoeH8Y1vfAODg4NpFCtDF/AoZ1NO2+ufGaZQdT/zJTOrMdkoD13jKFYNTecrXeWsO9hdLTWr3DdQl0NnSAdhwboASQ0eQcAbGsLOlwf8PsK8sxxJGjq/yxEo1YFF0HOoC35RKhi49doWTp4nuHQv1VYgumXQRkAHWCZCSgXvstacCbziRvf7NZdSmAZw8jzwxe+47NFAL7sPbcuGZdsoFpj37Pyy5UmPXwPd9VTVyfwwQZ5SvZczwK4Tgbt/27I96XHSS/aElgcavLEPg6uCJSJPnrZYwk/VY7xqWxm6QSa/FzlTXmocWqxgrMPYVS5qEGGtQlYkW54gkE6ayvlHiRy7FM4mRlJukBObsnKWQ6iGDXY9eJfUTkJWShDK2YS2xd7gYMB1l5bQVyZYqVG887+cC/WvTYpqw5loMhkR0LCph5jcDKjUgE9/xUCtAbz8BoLrD0SUnwLzS8Dnv2ngjz57TruLQYBX3Ejw4suASh0ACPI54A0v7cPFu5toWi3/Y9ztc038k1UU3nFEeDWZxoulT8M0CPrKXmuQVttGtdFGxfFcz4kl2t5jLZsKImegp4BGy0KjZfm82jNsLFia8cJmIGdPTi57+iotTb9FZ302vVBHpd7G+bkKak19pZ/rchzBn3nTIOgt57FcbYJSoNZoC8uI2QWHnB0gQgG7VG1iqdr0nE8S5Wxsz9mIOoT3w/I5iZxVnoU0bCQyZMiQPvyrU6P3C5I/8H24dV49QXOemnfANddcg/vuuw933nkn7rvvPrFdHoC2Wt6SyUtyX/KSl+CTn/wkDhw4kFaRMnQBr61BuqSdx5cjhkIVkDs66ROIsvrEjQYavL+OqEpjJpQnwZZPry5hymZ1vGRN5oGULuT7qQO/9lHXXX4EwhoOrv4MO17/O9vh0WdnxDZ1UBj0HMpqb7lML7qS4kVXalSADnTXxBsYzv28e4Lt21tmA/AoJallMxXtS661sVwBCjlG2j7y7Awecc6x6Pik3vP4FCYX3WO5yfvx80s4fn7JlzbABtzsL+t4Vx2P2sOn5nH41Lz2mHzOdUJsW9R3rzhJ9dixaVBj3ud7+/X7T4Weswy+rJBIz58YMCrjKa7y0NVfYhJKUse4ysnYxYlGCtVObG2o/C6JQXT4Ia71iAHLtjwEZUeQJlRi7a5coMSXi7dvMXfnHs////bOO85qKv3/n+SW6b3SO8iAUkRUkGJDREXFgutvLavL2suu+rWsCuziurrrKuLaViyrLquuvYCigCJSVEB6b8Mwvbdbc35/5CY3yU1ucstwZ+B5v14wyc3JyUlyzknyOc95Hr/C5ywQeD4q3BoA4nuCXzAPqqaH6NokkF+Elq45GTwy03g0tQoYOTgZdhuH0Sck47sN7bLP03jTp5sDW/e54fYauzUQBIaXPmxAaWWw//T4BDBwOG9cDGKNgeWp1XpfWQe0ucTUhyr0JtmGHq6yloPbY3xfBAYcrhQTS31ERiqPO67MxU87q1BWoyxwbO9vyr31NC+/wIJ1Uu8QMVruKuEMlrPSnJhwknomTlV9G1Zvqwz647RL5VXnqRTyJo/sjl2HG7DjUEOnFviOd5TvIcrnZ2cXZxlj8jsMIL6bTFTUW59fUM1q6lmQhiG9cvD95nK4vX54fYK8/4DumehTlIFlG4JRuGwxCo+SuJmdnoQzTuyGZRsOo7nNC69fkJ/ZZdVimuI8HhmpTqQ4bbpisZk1rDTgCoQXTDNSnEhJsqPd7UNhTopJ+YP56fntF7fRtxZBdEa039hG375GuphylpCUJCWJAeDgDnVTa0hcHbsOHjwY33//PT788EO89NJLWL58eYilrBKbzYazzjoLN910Ey655BLywdKJsOpzNhqU2YUTjjhlUBo5YFh8yyIdBxAFUdlPp0WL3mAesZdDaYWoNyIfbzil8050nBB8vBKMcK7/km55OnyYKVVOO4/sdCcawgTwKswJ70u3MCcFVQ3tIb/zHAen3YZWGPtg1rOcNfKLq0TvmqiSKZYz04HfXOJHslNMI+2bmmRXBctKT3HIojLPA6cMM/44kkYyRfEhuo8oSZR1ec3FIK+JehUU6mEakCwcGSkOpAaCkql8zhpM4Q+6QtD3OatMAwQFO7uNi32qcpT76WNNplKWVXKz5zfxHyooPrbcXn9QoDb5BjW9LjqDDB03BGkdh0ZAUrZVrThvs4npoo0+rxV7rZLk5PHvOd1QVu3DgB5iY/7jb/LxqymeqMsSDrudw4AeDny1thWi72b9dDsOevDeN806W3iMHBL98Q2t1C36NRB0BljMkO7/mKFJ+PNNBaptn6xswQvvN8h5ySK7waBNzPVacXp6zw71gF3wKFJsyJit/JUYPKd0Zx9ogkNJVoVaS3P5/ZbnAqJR1wgqdTyjeudR3Hue50O2dya0A0sOG6+y+NbOEEpNsiM9xQGnQ3z+ef1BcTYt2YGMVKfs6gmI3VDF5RHwzVoObo8Pn31bhbpmhuQkDhdMEOD1cfhoGY+yKrGMfbvzcNh5nDtGdNW241C97PLKCtKAKwCkJBmPODvsPM49uScEZn5+ytlIen77peMSBNH5sOrWQO2HOrCg7Du54HMhoZazcnk4DjNmzMCMGTPgcrmwfv167N69G/X19WhtbUV6ejpycnIwcOBAjB49GsnJyeaZEkedSBygR4rKrYGZP0yOg58FH24dFRAMCIjALPhgDVcm9Xp8yqXvc7bjPtOVo8bK4xPxIRjQLlbLWU53GRDr3cSTuoe10jB7ERzQPQt9izPAGFBW04KNe2qRn5WM00uKsW5HpepcjMqmtpw1t/TTK6/aclZNZlrweNKx+gd88W05UIfiXNHnrV8Q8MWag/K1nTKmF9Zsq0RTmwennFCIwuwUMMbw5apSABx6F2bhgtOysHjdIQgCQ35WMmoaXRjcMwuDemaryrD3SCN2HGoIOQfJB2JRTgrGDCkMOa8ftlSgvkU9ZDrt1N7YdqAeBypFIUd2a6C4VaMH5WP9btHS97wxvSwF7ZA+7qVlQOzXZLcGIeKs8QeV5DtO+ZGpK8LE2G2YTRO0lIc1jUqF7NbAZB+pvklT6bX3ytitgcGIu5TOYjmN/IxGitVr47Tg1kASU6V6Fa2VmGAi6oUjM82GzLTgjg47hyF9kqIqh1Wk8/YZuDU4cER8+x7Q04EZk8XAfv/6qAENLQK8HTBD3WpVUGp8fgGmlU9pJe10cEhyqvueZKc0cCNa4SoHbTqCYJPSnzmh7KNU7aMD9DGjp1u4AS5JEJMsZ7VjdbJlbSC9XM86qcBHGA9IS/ewswrrQXdXgUDAmmc/x3EqsdUR6JydgfcPpeWs9E6iTB+r8Lhhpwdb9ogBTCUf1gCPIX0YWtoEHCwPPHtsDLmZnFxmu40LfT826SGlAVcASEkKL4dwHAe97k0Q1JbIHoVIree3Xyw7fWsRRGck1HWglXThv4+TJWOgRFnOaklOTsa4ceMwbty4jjwM0QGofezEO2/lKLOJMBUQEIPibJwLg+ADnDEmCy3hyqUVMuIloCqtELWR1zsCbblpMDe+BN0a6G+X65rJLeZN2ov0YhoLkqWOI6BYSS/tZvVQL+J9SFKdXfWEIisDQkrLWZ7nQgRwG8+rvsd5noMUk9hh4+UPB2kk85PvWrF2ixv1zRx6dwNOHS7goxU8bHw7Upxqa2SXxw+fwOPMMQKyM4OilXQ8G8/pfpjoBtuy8aoXdD1rTKUYy/FcxNMF9eqNoeVsWJ+zCl/EfuW2WO3hEis86NVdPZSWs4DS76rReQeeVdEUytIljSznSAcOZZ+zGstZBh3L2UA9iMZalSmOEc8AXh1JMLCDft09WCEqsCMGJuH8cekAgHe+bkJDixBZEDENwUBx+tfJrCX5BXVfY6XlBf0Bhx7TrhqoCFoSS+JjiFV9HE1nleKsMiK6RLhDxP1dVrGs52tT6jOkPlT2OWvkXkYe9CDL2c6O1pWThF5Mis6E5I7JzvOiqwCd54NanFW/G3p9flngDW4LvpvE+s2y97A4c2hwbx4zJmfjv1834MARAbWNDC538JrmZIYOiIRccZOiMMUeyc7IRwi9PgHfrD8sC7xKJMtZQM+tAX1sEURnRNt9GVvOKoyJJOMQxXZO8XtywGbA5bbeN3aoOEt0XZTPjg61nLVoNXhULGcRtJwNVy5tGeJlcaprOduB1qxWpp8T0aP0qa1HMCBY+OvOqdpifMpmeCxJUA60N7N6qOdXV3s+evXK1HI2zGil0qpcz3JXiSCwoFCgyDM3S/xb2yigttEDgEN5DQdAwKFyDmIYTj01hcP2/RxOH8HkD2oz9xR6tzck4jgLTWtT+E8xqkPhUBbHLlvOqvMJL87yqjTKZbs9vlOVjxbKexS0+gy/T9C6k1et23j9K2D5tMIk7Fj7w/A4JJ+zAqfyl8cYg8Ak8UiyXhL30YpNVglaYneNZ48kIhsJrYcC4mzv4uA0YXuM10hF+PEAQ5TieaRuDfT8ATuUga2YQmTX3keNf+Woh3EUYq9qIJAXXQQouzVVP9zRfYyyvw7nGsYvus2yG1jrB2cw8IG8OrfAR6gHiVXPlU5+72T3AzYOXr++mzal2Kq0jpX217OclYjVKnR/mdhRjjrBjimnpWPD7mYcOCKgrklteTbpZH1hWYlZSXwKi1eb3oUwob7FrSvMSvkp278SexTHIgii4zEKuh2azjwv2XI2SWz/nkS6NSCODTrW52xklrMAcCgw/bcjPuGkxrf9UL380mFWLqVLgHhbztY2ueSHeUe6NQi1nO0aH8hdhaBwqL89KHxay0e73BFIdaDF5cXPu6rR3Cb5cDUQHgO/t7t9+HlXNQCgqVVtcbrncIMcaEuizaUNDqnO1+gsj9S0yhFweS5oOdvU6pWPr9QxmcFAx0mDgdwsP0p658PptOFPr9TA6wOOBHyZnX6iA9PGZ6mOvfiHRvywyYuWgHte2XLWxJJS79pp64QkOGj7XdkyLIrvPLUfPPGvVogUwvRhuj5nBfW2WIjnp2uIhZ4FZKHERKySBuwcGnFWqk4hV0JnMECJ3ii7ZldVrrFeaWl/q5dGacXqF4L1XCns22XLWSldFHeTMcTi1iARSMLDhp0u1DeHfpTvLRP7tT4KcVYWdGMwgmQA3F5g614/WlvbgscrZcjOsmI5q78c7ojhhHNl21G5NZC/KDpImOLUA1U8zwF+jeWsSptVD6DFpwj6LVKvT5SuHYP4nDCynJXdHgS2S1a45Nag82LklqqzBwTT+obVe/YrxVbpuSeLs35Bnrqv3QbEJjwuWd2CXYfExtG/u9jJdMu3A/CioQloD1jO3nCxAxnp/tCyay+5ycPTF6NlemsgxkFxTipOLSnCht3VOBR417XxnKFQ31UGIwnieEPPbaVuOsUGo/FgSVuS3Bq0dxa3BkTXRfl8dTri++UkRUoXl8Pnneywod3tQ2NA8ImkLIXZKaioazMVtJIcPNrdQFV9u+K38MdJctjkYERmaa2SHMin3R18c49X3nokOWxyACWnnSfL2TijtULVErToNBn955TLHXuPkpzS1DUBh6uDgqpRPZTqrF9gqvRKqhtdur8rsWrF3agQfpOcNtgC6dxev+7xBRa8/lqryZ5FwNjhyUhLdiA9FahvAspFF68Y2s+O8SepA6ntO9KGHzZ50drOQZzKrXZrEElUT8aY6r7qibOiRY54gGiCiyiPauhzVppOG6E4a7cjbtGr4lKlwwieRkj3y+eH3A/qIfvSC3yASkYwsnGRyfdhZOjsrW+MGJes9XAqxVm/GPiupd2LNlew8gR9zkptIfLi+BkDC1jidpWP1aSAVfFrnzWGTde7m0KclV0hxHbspat57C31AKhR/T52OIfxw8Pv69dazprUBaULC72u3y77oxbXfRqfs1L2bo8PTW0euDzRBzlUIlrOqt0aAGL7ldqF0v9jiFYTYzXz+PzwKSq7UvPVsxhUWuP5BMFwQCjUcpbcGnR2gi6f1L8HZ5wc/Xvn9Qnw6HQ0KU67/LwLEWcN3Bpol2Vx1ivIoqZT448W0G8Hbo+AJWta0e5myE7nMeXUtJD3pdpGP558sw4AkORk6F4o5t2zUJQp6puAtoC4kZcJeITQ9xamafGmwT5j1M9bA0YGaSliGZXXjVe4NdC+v5FbA4LonHC8dt3A6Eb5Xa78AJG/izj5e05ya+A2jtsdAomzhC48J9aw4f1y0bswPa55pyY7cFpJEVraveielxY27ejBBaisawOD+GJenBc+8rySvsUZSHLYkJMRPkjI6EEFqFQIszzPoYdJucYOLUJ1IMJ9UU6K5TKFozg3FaMH5cPtFV98UpPsyE53xiVvPUYNzEdFfRsYA/KzKDBfvDEPCGbNdYXacjZOhTMgM9WJU04oRJsr+DGdEqYepiY7cOrQIpW4VVrVgqa24FOob3EG0pIdIfu2e3zYF4isa/WjuTg3FXmZyUh22lAQqLNjBheg3aP+INlZWg+fX7SalS6/2ipVFJWkbRkBcVb6rsnJDFUkcjLEDFpDLGfNXD/oWc5KPVpAcNBxayBZBvsFFqVbA062vOWlqdUmooASvSl5soUcz8VLm40TkZdC+o5qd/vxzfrD5ukl4Un2u2oyqBJFmfRccMR6hcNZ6urhULwV+gVg075aAKLlJiBWJOnb0mZQr6ygjFivN3W+M3LVlEx8uKI5bHC1UYOTkJOhDlQGxOjWgAFNgbGnPt0cyEjlUdPgQ0WtH82t5pahSp3IL4SKGHrHE8LUc7vSrQGCfYQssgey33W4EbsOB4XsaIP/GYmgUh/1484q0/2kEkRLm8uLb9aXqVzoVNQFrZj1LAalATbGxGukvW4SvhCfs5zqd6LzIc+c6ySWsy6PD1//fFj3uBmpDpw5sgc4jpP9xUrvp3oDs+HcGkhGKUBwwFISGxkDDpX7MaSPXSW+vvJJI95f1iyv8zyHKacGv7FcHgFPL6qT12ecLciCb+8i8d2zuU0SkxnS0xjqms2NFbwmDyZtUORIkd57pfdbpfGQzcaBNxDqKSAYQXROtO8oRi1V1feoXntCB4/lgGAd5dagpKQkkuRRw3Ectm7delSORegjvWf2KkjvkFG+opxUFOWYp0tPcSC9R5Z5Qh04jkP3/PAiKwBkpDqRkRqZCJqV5kRWWnyFU57n0KswI655hiMtxYEBKdFdW8IcM3+oksgTmTjbsS91HMeZDphoKc5VD5g0tnpU4mzPgnTkZYaK/w0tboU4q7Wc1T9WUU4K+hZnqn7rURA6eLSnrBE+v9/QrYEsWAW2ZWhOOTcjtM/LzQx8nEjirOyLEyH5K9Gb5SfuGnSkqGs5y3GmQeXMkCxvpXwMA4KFsfpSBQTTBISKhTjONFa8QJlnGrgkSE6yBdY53aBHSpKTbOhXnImaRhcEJr5hcRzkQUMlslBmkGUwAKVpUcNl02FwHAcbL/kTDV4bn8L4MWg5K65HoyEpDbw6cIJIXJk8OhWTR1sfIAaCU/1jMYJkCjcDd16Zg1FDkvHB8mY89169ab6MsZh8zjp0vhKkcxKEgPCoSausDnYbD54T61WPgsieLUGCoxajBhXg513VGNIzGy3tXuwtb1Qd0OcXDPvLWNpSc5vX8FkOGAsuYsR20drQLgv1Ggs/yZc3L1mSJ876krCG0ve9Et7AWrKjaW7z6vqQ9wtM3ma3cUHXbZyx5axNT5y1qcVZZcAr6Zw37eLw7H+qMGNyOm6/MhftbgEPPV+NX3aLJq8Dezmwp9SLr9e1qsTZea/W4odN4ovV9Ek8CnP98PkFNLd5kJ7CkJrM0OYSj5GbDdQ1u0POEwg+U3PSkwDO3HDm1KFF2LSvFiMG5IVNZ0RLu3gt0lMC4qzSglgTEGxonxzsLWuExyeQ5SxBdFK032vGsUQU35OK35VGK7Jbg4B9oDRTzAoRibM7duwAx3Fx9d+kB02vTjxSBaV7QRDRERQA9bcHA4KZ5KP0bdMF/AJri2jme1N/H/2drPZHsqgp6Afs0t6bDI3ekpMZelMka7g2Fxfwxak5psHHud7HDxOYaoTWyOesWVA5MyTLW5tmGrJEuIBgelGnBeX05Ti9BsSzRlsqUkCdlQTGgT2yMe00a4NUE0/qjvm2wwAE2GxR+hGVTzi6C3g0egCbTRTcTuidi1GDxcGQuiY//vlOGYBgPZWFiCisQpXibFdxaxANDgNBLlIky1spP0kI9fs505qk9Tlr1p2o3BqYBQQz8U87blix6ewlM5QfPJmpTpw5soe8bXCvbFXaLftrsTcw6CfvHwczf9n3tCKSvRIjX5t2GwefX+xHJe0mZJDMLwVpCrg16OR+Swljf+3yoPxRvndSXclOd2LSCLF9MMbwyQ8HguWxISSuhp47JuUvWr+y0qwq1RR+Tgwe+f0Gcc8PVrRg+sQMbNvvloXZ04Yn47bLc3DNnHKs3+lCWZUXH3/XgkOVXqzbKrq+6tcD6FEkDn4qre4vORMorRSt0Pt2D7ZDoxl/+VnJKOmba3rNCrJTcPbonqbp9GCMoc0tuTWQxFmF5awyIJjAMLhnNhpb3DhS20biLEF0UqwaQFl1N8hzoo9+u40hEudOEbs16ChhNtaPUCK+0LODIGLDzHLWulsD/eXOSoj/WIMvYrVYanUqibUySCI2UwTT0vMRFLSclewpARvPkJEa2gFmpnHgOTFifZsr1HLWSF/SjrAyqOsEBwO3BjxnWofMkPa3GYkCYcTZoKCrcGugCOAkTY2OeQAvHnVayiOCy2SXp+RHdm2la2B22tGclq7RrTajCDOO5t3KYRMjy3p8SpcWQR+LUp72GNwayNcR+tblxwqy5XGMbg201qmy6Gvh2of4nI3geLpW9YrBHga9gGBB4hLUtBN8Fsi+yw22G1vOBsV5yXLWbJBMyosx8bgdGRiWiA6j9zfZz+hR/pYN1qFgZ8oFZt8ILLg9xHLWpG5Jfb0kxkrnpXR9wHEcauoBnz+Y158W1sjPs4mjUvDQ9flwOjgM6e3EzkMe3PmPStQ3BRvC9Ilp6NcrOKhi40W3TowxFOSK/yQmj+wRfubiUWguPn/QLZYUk8ERxnJW2gc4tgcjCeJ4QM9yljEW8q7CBfwaJTkBVzssE5E4e91110WS3DJffvklKioqyEqzEyFHoqZbQhBRwZlYUOhZdIbLx0razoDWutfQclaVxuo+Vi1ngx9IegHBtH44MxXibFqqelqfhN3OIzUZaGkHWtsUUewZQ3kNwDMBLa2hwc/2lQtobgecDqChGSjMDbVcM3JrIJczytmtsoWjwpJYid4HnUTYgGBx+biIgzmbJgcrn8McODAErYkjFc1k/5A8g9cfWnfNrPSkn1tdPuwsrVdtc0nBILnQ9OF+sUJ9s1t1PI7j0DM/Dak6/qAltxVHatpQVm1Dj4J03XtvJPpbIWgt2DX6NascqWlFc3vQrYvLK1pXeX1Mvv4OG49ehRmqj/lwMCjFWU71128S4EtpBQtYE2dNLWc1rhq8gWrr9oh1WumDPJ7abLTVRM/SNVLkmS4GZaioa5MDBAGihW+3vDSViwKjwQwpwJJkfWvXBBJz8l3E78dxhKHlLK9ol0eRoP948fit7V4cqW2FNCS890gjnA4bGlpES1ap2FYtxRwKq9A2F7BxB8OhIw24/OxM/LzNi+/Wi3V2YC8HKmv92FcWdP9z84wcOAPBFM8ck4qdhzyobxLgsAOzLslGdroNp5Q4seKXoDh7ypBCFOWmqqx/JQ5WNCHJaUOy044Upw08zyE7PQkHKkW/tkfjaaJ0OSJdS5XPWZ5TBPaTxFl1OycIonNh9V1Uz+csEPquIs1etEf4CI9InH3ttdciy92Er7/+Gn/84x9RWVl5TL2cHwsE3RokthwE0VWRXtiM3Rqo05nlIy53/gZp9WNc+VETuo9+JlbfaaXLpIrsrXQPIVsTiutZ6cFtaSn6/R7HcUhLFcXZd760of8N4ov2hh0cvvuZB9Aa+KdH8Mnco5Dh3DHBkDyMAQKTrFiCe0gBwYA4WM4aWIlqP+hUJdYVZwOWH3EIJRo/aTZIfbMbKzcdCZtGupZVDWIwnwMVzVi5qS3cLkh22jFqUD7sNl4Wc/WumRLDrYoNOw416CbRE8tN8zVAElPrmt2yrz6JhhY3xp5QJK97fQI27qkGx4nedCtqXfhplwuZaU7ZKkr5khmNf0Wvz4+Ne2pRURfwG3gM6U6tLm9IcKo2NwdArDfK++0XGAb1zLacd6hbA07+PezVZ3oBwcwI76rArrScFRg8HgEAj3aPN6ROm7WTSPhlby36FmdgQHdjNyR6XaUUGMhsOvHO0npUNYSat6QlO5ATCIpp9K1SVhPa908Z0ysY3EswDgim9f2tDSRGoZs7H1KfZ9PUh6Cf+KNsOSsPGooF2Li3BjWNwcFirasPaRBdz3I2IzU4YFdR68Pare04a0zQhcCGHRx+2soANOHfX0j5ivmcdXIaThqUhGU/tkJgwPABSSjOC1bgM09OxUsfNoAx4PYrcnDRBDHORlOrOpS51BY4jgtxJbK/ohlaehemK55DHf+erLR2l/oEpc9ZXmU5K5adLGcJ4thA5YIuXLrAxkjfcxPyyP/xxx/x4IMPYvny5fJvkhXZaaedhscffzwRxSIUSBUq2ui6BHG8YyasMYNovyH5qAKCxalwHUioFax+ocO6NTCyOrTsczZUWNS7jtJzp1cxhwG9GJpagFEnCLr3hOeAfj0YKmvFbeXV4u91jeLfzDQO+Vmhj9QjNV64FN8d1fVinQhOhVEfQ17mg2WO1t2PdL0MLWf9+tY/AEI+LJT723hOfwp+JHTQt6tWgDTC5/MD4NHm9qOu2cwblBu9CtNRnJsavGbyM1KN2mFFeJx2Ht00Afg4IHwgywgveJ+iDPh8TBW5us3tRXWDCx6v2my4uqEdR2rbghY/gV08XgF+v/h2qZy+LVsCRmB9XNXQjiO1rYE2YTumPlTdgetpt3HokS8GKsxMcwPwQxDEe1Hf4kZTq0dOawV10C0dy1mTxhRNQDBBFmdDtynFWUFRtvQUO/oWJYOB4WBlC4D4uDWQRJeWdi+2HawPK87qkey0oUd+Gvp3yzRM4xcEw8GSuiY3HLbwwVqLc1KR5BQv1uHqFvgFBo/Pr7CeE4LirObWa4U1cTkYSIzofEg+iDtLQDBBUNchl8cvl0cQGIpyUuTp98lOO3yBSqgVlwGxn3J7/cjNSMaD/6zGvjIvauozcfapeahvduOrNjcA/Xp5Ql8nSvoloaSfvp/pghw7Hro+D63tAi48IxjM1RMY9ZQGJZQDlKMHFaCirlXuU4pyUtDY6pHPEQAOVbXIy0fDhkHPJZRSnBXPQf0e6hOsDRIRBNG50ft+1evxtS7ArHJUxdnt27fjj3/8Iz7++GMA6g/O4cOH47HHHsNFF110NItEGCB/eB47300EcVQx8/UovbubBfnq8gHBDNIpf9f2M1b2CV+GUHFWfQy15azTzuPCiYJB2mCep57IsOsgQ10jB7dX3NkTmMl60YQU3Dg9P2S/h144gjWbg+Kfx8vB5Q6WSymWqAOCBd0aRPudFxwgEA/i9THUNAYtw7QfdEqkjyO3xy/v4wuchp/55anLHp+4PS3ZgZSkKF4p4lCllS9KWWnOkCBBSn7eWQWBAd3yUgC4UZCVgrEnpBum336oXox27RfAWNCiUL5XIeps+OnPygHPtGQHRg4MrTOq9Bb9MRuR7LRjWD91cJSKujZUN7hCLKmDH4/iuiQi1be40NhsC5SHobnNg4xUp+Lj03p5JOuhlCQHAAFOnWnzkdDcJgqdTocNmalh/BAeBaT2lOK0y/f1+w21AFohCBxGDszH9oP1aGr1RCjesBA3A6op8iZuDbQBwcwigjGFta2eWwO7wq2BoGgTBdlJGDEwH4KgEGfj8hKp7C8j7wzTkx0Y3i98RHZlWxgzpFBuv1v216Hd7VP4mtY/nxP65Mh+MKsb2tHm9onBGCXLWT8Dzwdc4QhQ9cMur9ixKkUbZSAxouNhjKGpzQufRRcYzW3i88+KOOv2+tHS5kWkcByQnZ5kaYDDp7G+lgZVkx3iAOSA7lkoyE6R02/aVxsobzAPxhgee60W67a2o0ehA3sPN8szRd7+sgmb9ybhb3cWoqFJnJ0yfUI6Kut8WLs1aKE7pLe6D253+9DmDr7/JDlsOPuU0MFHj3TdA5dNOWhXnJuK4txUuU/p1y0T+8ub4PLoO3Fsc/tQ2xTqYkoPO88hM82patdiXfCA4zjDZ4rugIqi/foFQd7W7vahprFdtv7Vc5tFEETXwei9Jmi0wqnSRdrkj4o4e/DgQcyePRtvv/02BEH8wJE6wv79+2Pu3Lm4+uqrybVBJ4Jc4hBEbPAmwlowoIRZPpzucmfFqhWs2q2BNWtbq1ZYnOwLU99yNuhzVl9I07vOUpnSkkVrWZdHEmcDv6foly0nI7QzrW30g9fxP6hyaxDHgGCiL0Qebq+AVVsqQtLpWS9KH3lNbV55H4+PA8CjrsmFfeXih1FtkxurtlTAxnOYckovVcTicHSU5JCSZEf3PGPL0/UB0xxx6qYbSQ5HiPWqkgMVzaI4KzDDe6UkkvNKVKAfI+uuoFgvrkvfy1sP1KOyFgBs8AsClm0ow/jhxXIdtmqNqTyGWE8EXfHPKjWN7ar6fOrQIhTnpkadX6xI/Y3yvmoDd+m5CzHDJzAwJlnMir85rbo1UAi7IpylYwuCJALriLNKtwYsVDhW9lfxqOPa0iq/IczSGv2mRRJbOAA9FJbruw83ot3tky1Yjc7GrnyeKdpX0JUBw5FaUVzy+aHbD6stZyOvJ0T0HK5uxfrd1RHvp31X0D6z/YKAb9YfjtrvcY/8NIwZUmiaTgg0Qp5X+zk1qkd6PnNrGvxY9pPo4mfnQbWbAQDYtMfEgXkkAABnMklEQVSNHza1o6JOVGyvvzAL2Rk2lFV7MesvFRjePwkpycEHo9vjx9c/Hw55f5lwUjfkZiSrfpNmcUgptQPGSiMHn5+FtT4trWpBqcKS1owT++eprOoPVjbjl72ieD32hELd9wPZJZRBOWw8ByGwrbK+HZX1QSHZcQzNFiGI4xGVy1lpmcn/yS8K0rZO5dagqqoK8+bNw8svvwyv16t6oSoqKsIjjzyCWbNmwR4PB3ZEXAn6nKWHCEFEg5nlrPWAYPrLnRWrAnLYZEZWhxG6NVBOCVXuqhXOrYjD0j6OgDs2V2D2vGQ5m5qs/5KemxX6e32TH/k56jIojwGIl8CsDpnRr1sG9pQJcNrF68AEID1FHQAqPytZFexDIi8zGQXZKWhXWL0Igric5ODhdDB4vKJ1iCAw+AWGdrffsjgrEe8qbdXHpWxh5A9/bZUft8q0ZgOYVkoRT3+ckWD0wR70fRlY93MAGJKdNiQ7xWWpyC3tXth1BkHMkNJKYqM9BnG2WWON1tzmSag4q2eJrrVCjsaPtE/hdUN2a+CwaLXMQtNYCYIX9Dkbus2h8J3q9zP53IKibZzFWc2lEgSmcq8RDwSDaeo2zUCG0SPIpgqUF9zHxgfdGngDrlQEFuyH2wMWtmIeimjvCncIRMcjBfFz2HhVYKdw2HigT5F61oV24MvtFWRhNk0n+KIRfkGAy+NXBdcLn15fjJXqkdFAnPLdZ/uBUEH2vl/nYsqpafjjC9VYt82Ft79sBGNAZhqP7AzxOvUocODtP3VHSpK6cbS0e0UXThyQmmSHy+OHX2BoavWEirMa8VoreiqL7/P5wz477TYOSQ5zXcHn98PtFVDX5FKJs0rXSE1tHl1x1mjW0fB+uahpdKF7fjrcHh/KMpPg8QbPLTs9KboZRgRBdBr0jX100vHSIHdk+XdID9HU1IQnn3wS8+fPR1tbm0qUzcrKwv33348777wTKSkpJjkRiYIsZwkiNmQLCgPhImg5G/4js+tZzmrX9csczp+10RarZy8d06/4oNb1cWviN06VZ+C3JAcDoHBrEBBOjMTZPB1xtq5JQF5AnFV+e8sRPgPlNfIVa5XehRnoXZiBLburAbTDbrPh7NE9Le1rt/EYN6xY9dvnKw8BAHoVpmFILwc276tFUU4q6lvcAZEhMUKC8m6ZBuoKbJaERZ+JOKv82FaKWoY+Z01ikqgGCSwIV9oU8RgwNeqbgh/40rr4t6RPDrrnOPA6quB08AAEccq2JMZFMHYgHVP2aRrDu4aR4JAo9CxnJQtXXwyWs149cVZpOWvi1kDbLH1mLpahFGdD61vQCoSDTxCCaWV3B/ozFqJGc35i3TNKG53trJ4PSeW6nwWfJXoofWTyinssXT+fwAKB9sT7IfXD63ZUorxWtFZUWt9Ky74E1+njBWngrU9xBob1zTVJbYxN8+4hiesOG49zTrb27AWCswKs9hPK+iu63xHX7YF66dN0AnqDETsOhPprP/fUNNhsHK48NxPrtrmwp1QUi/sUq4XmnIzQBin5kc1OS8LEEd2xfnc1SqtaQoRYACGWxXZNO9RazoZzDZCdnoTxw7sZbpcor23Fuh1VIQK4cl0prCrR6+sBYED3LNkndmqyAxNO7G5aDoIguhYqcVbnZZ/TpEuo5azL5cL8+fPx5JNPoqGhQSXKpqSk4K677sL//d//ISsrK56HJToAG3804l0SxLGLmVsDJqjTGaG2+Oz8rVL7smrs1sA4j1jdGkjJgsGb9PeT/QNpNusdRspDMshwe8QPBm/gPT491UCczQx9Ktc3ixZUQFA44Xkml0M6f0kQjtZyVsIuiwUxZaMSbJhCrNBal1kinppDhIInAPCyaBI+nVKcUQq58rehYZs0L4cly9kOaPI2xVRrJbI1kMba068Qpm0Ka1lpWYggIJh8zDhYzhpZ/iYKPYHPrhBRgWD9bG71YMm6QzBqCFlpSTitpAgcx6mm3EvX3K5yKRH+vP2C+hqbCX5KVwV690fpisLrU/inlaz0mL7QaQZjDD/trEZ1o9qXpFa4CXefdaVZC9VCb5q3cl3abnRsu47lrF9hObuvvEl+5vkFYPG6gwAAn0/RpyjzCCz/uKMK007tA0csoxiEKYIsZsbW4Wot44N9amT5yhbXFgc8lX2PanBEx5pdSt/mAp54owVtrlYAQF2T2EmdeQpQWgEM6AV8/XNpYH9gaD+gslZ8dxvQ2x3ov8KVSSx7Y6uYVprJtKu0AfuONKnSejUvJ9p2qCy/1+8Pe5+UPm7DkRawXm91qWf2tirFWYMXBKPBHIIgjn043cdx6LuBbAxii+zdNC7irN/vx8svv4x58+ahoqJC1cnZ7XbMmjULjzzyCIqKiuJxOOIowHEgdZYgYsBMWJNe3s2CfClfUvUfCJ0LqwGMTN05IPRRZ1WbDgYEE3SPpfULZ8WtgfSTM2Aw4vaI+0tuDdINLGfzs4O/Z2cADc1AfZMA6coIYUT6WH3OSmgtIaNFkKcva/OXBCjr5ewgbdbCx5JYs2y8ZNlk3XJWbeVstF9QSDMjUT5ntdZdEsEPTtE6XCnOhro8CIqzkbhTlC1nJXE2hunpWuEi0eKsnsDnVATPAoL1s83jDzuYUdXQjlaXD+kpDlkYt9uDfZNVn7PK4F4SfgtielBwDd2mvGdeX7BdSANXelOmreD1CzhS22pethj7Qz3MLGdlcVanv9DOzFDuk5XulJeVMyG0Fnkcp3Y5k5WWhOoGMahRfYsbhdk007Aj8RmI85HCy+99UFmwRiriWXW7I6H0MascRLQbPJsFxrDzAIc9perOgOcZ+nYXcNJgcd2tMCqdMk59TLfFGGcCE4OiyWUVGPxhRvSyNAG6pDwkPF4BSU5jU7S+RRmWypWWLHZYPj+D2ysg2WmDx+dXCbJeAx8wJM4SxPGL2q1BcFnbWyfMcvbtt9/G7NmzsX//fpUoy3Ec/t//+3+YO3cu+vXrF+thiKMMuTUgiNgwE9aOVbcG2ndVY7cGJuios+FcIegdU556FlIm8S/TpJcIJ5TK4qxX/OCWxdlU/advqiJQWM8iUZzdtMcLr98Pl5dD72Lpo1DvPALljFGLsEURuEkPn8JyVpV/VMFrJMvb+NZps49raWtQUA6fn57PWbsN8s3RHs3UrYFO3lbKG0+M/J6GWM4Kwd+DlrPBgQ+bIjCUVSRBVTp0pL641Hl1LstZXZ+zdsl3qLjOawbtTjmhEOkaX5Q/bC2H2yvIlmZen6LeBVAFGjM57RBx1uR+MUUaPYs/ZTm8vqBlruxz1sBdjBmyhTAHnDmyh/x7XbMbG/fUKMpv4sfB/KfQYxuILVrLWb28nBqrVqVbg955aZgyphfaXF4cqqgI/A6cOUo8v92lDThc04p+xRlIdgY/yUr65OBgZTO8PoH8zh4FBJMAT1ZRvqMJCnE20rYQ6TNVGaBKXuY5OXCW3iyJg+XiMa44OwMTR6XC5/dj84FKZKQCk0Z0j/h9kzGGhlbRNUJ2WhL2ljehtKoFPfPTMKhnNspqWrDrcCPyMpNxUv88NLV6ZNF2X0UT2lw+lPTNVfl/VeYt0dzmDbH+H9onB70L09Hq8iE3I8lSeW08j5QkO9rdPpRWNSMtxYE2l9rq1txylj6WCeJ4QxULRlpgUHxUSumkd6PI8o9anP3000/x8MMPY8uWLSpRljGG6dOn47HHHsOwYcOizZ5IMDy5NSCImDAT1oym1Ifmoz9C11mxWkbzQGhciNWx1fdgefqoX18AVEfXtCYocxpx1uNhcLkFWZgwcmtg43lcfq4fre0cnHYeW/YAhyr8OFQhujYo6S9FWdY5j3hZzhpMbYyU4FRnqIKSJjyyuI7VmnFa8Y/0smTmc1Z5bj5N4CMLhwm7IdKP33i1fpvCuktgTGFpLl6LJNHYD1V14l9BK0xD49YgInFWbTnriINbA2kqb6J8Hsvl0RlwM7KclZp0ZqozJEif3cbD7RXk85OFcUW9CwYE40z7B622ZyVyvGw5q3N/eF70hy0wMeiiX2HZq9w3Ut1CEqPtPI+MVKf8u1YwCdePRdsDBa2e1YW2aQcydA+gGazS9NtSACBle8kMnF9Ksl03D47jkJXmRE2jK+GDDscD8bKEVFYfQYg+X+VzR/mNbYSqL5SsaG1cMLCcpg55vALKKsXl805LQ/8eTrS7fThQJdZEPetVMw5WNGPj3loAwOhB+XI/mJ7qQGaaEy3tomjKIIrWP++uDskjO82pe62UfVxNkwtQe0VA36IMOB021QCHFdJTRHF228F63e0er4HlrD8ogBMEcXyhspwN9Pl6T2npedDh4uy3336Lhx56CGvWrBELE+gwGWOYPHky/vKXv+C0006LNFuikyGKs/TQIYhoiZ/lrP5yZyXURUB0+ejtFqnwKwXB0IoEnObeWAkIJv2ktJxtaQ+KHEZuDXiOQ49CAGBIsgOnjxCQlpSCmkYfNu70Y9s+cT89g53goKf+eVol+KEXWz4qn7PSj5wisngE4lgHzEwGYG7JIt1Z3mbtmgQt5wTFB3CYQReT8imfq1b8EKrqfJzav7J+CwJTXAux9CX9GX7ZCew6yONINcPs3wkh4qIyIFgk4lFIQLCY3BqIeTkdNlXU+0ShnFos4dD6nNX0YfoBtyRrW/Ei+TTCOAA4FPuFr8MsZLupWwOmaOsGzYnnRTcnXi9TuEBQDwJFOvgQdJ3B6f5utG6KheSmAcEiOCavs49fYCqfs1byl4M5keVshxM3cZZT963RWlgq0wsMUDYJxhiqG10q4bC+WbRY5XlOfu+x87xqpoOS8hoGn59DZhqHft0dgTTB9heNIUBzuydYnhaPHBDMGei4JL/JHq+AhhZ3YBsvD06lJtuRl5msm7fSmCEnPQlurx+tgUGbrDQnnHr+VywwqEc2GGtQDfjwHIfi3FRsOVBnOJBl1FcRBHHsYxjHJPBXes+X3RpEOFAdkTh73nnn4euvvxYLoPgyOfnkk/GXv/wF5557bmRHJzotYrTwRJeCILouRhHRJYK+Rs0tSLV5dmasujUwQ283qznJloAGAcFkw1n5hV8jzoaxnHU4xJ08XqClXfz4sNuY4ceBMiuHg8fY4X6c1D8FTW0eVNW14ki1mEAUcLXnIf6N1XJWDh4Uo3YVFNTUv8diORuPGq3Mw+r0UembytxyNmh5pGfBGFJVDOqUfhkSazkLBPonm2IZQGEucOIgAZt382hp47Cn1IeCLLXLAyFay1l5QF9cj8mtQeDeOe082t2dwK2BzmCPXTMIECr+hb61S34ipam7Pp+UVzCNQ/H27g3j+5EhOrcG4QKCAWI98PkBrz+YnyxEx+jWQHuNIhNnQ7dZqRWCoG8Jp3VDwSzkpnWFABhbmocL/BTN4AcRHfESZzmOA8cFZyX4DeqVGeo+WoCNDzb+8ro2/LijSnc/u9Zy1uDZXNsornfLtwUHs2Vr0Oim6itdALS0e+X6L4myTof41+vzoyUQdKtXYTqG98szzVvqWx02HhNO6o4jta3yNTASdK1QkJ2CAh1/zi6PD1sO1MHjE3Qtl8nnLEEcvyhjxagNGdX97FFxa7B06VJ5qinHcSgqKsLDDz+Myy67DBzHoapK/2ERDYWFOl+qxFHDxjHya0AQMSBPdTB0a8BU6YxQvvuZBQ/rDGjLGH2JOWgfdFY/9qX3aOkFOtStQeCDG5J4q7+/+tjiXz3LWYfDeDBLFdBNIbZyHIfLzxXQLhqQID2FCxFPJZGYxfhxLgtqFgIBhcOva+3IKSzCoihnnKu0dbcGaotGI1QBwXQsGLWYijdKS/iI23N8LpZSQFDeM6WYdNZYhoZmhtIKDuXVAnLTxd/tCsHIZtH6WEkw6r300hqL5ax4YMnnpxBBQLqOQM9KTuov/BqfvRL6lrPqwSWfTiA+pbsBbaRzFSxUnDWYqavCbzAQI5dRCgbnF0IGbaK3nA1Y/GnMTLQDnGHdGuhuMq8XRr5Bg24NjPfVtnk9QcwvCKqAYOHSareZDSARsRNPsU16c/H5BfgCgiXPGQeH1SeY1ucXVG2ipU20UE1y2JCR6kB9s1tVfyVrT5vKclZ97LpG8W9xXrCBS2mi7ZOVlryt7V45H2ngWrKg9fgEtLpEcVbr0sUIQfMuZ1fcp46wXnUqOj6vTwgZfI/WlzBBEF0f1Xd5YJkxhLxqSOmOSkAwSaCtrKzEHXfcgTvuuCOabMLm7/P5zBMSHQa5NSCI2IibWwPFU6ArvAfGza2BnuWsxcyCPjQF1bo270gsZ7UBwTxeoDUgzjodxmXTC+gmPsQZOA5IDRh9SNOEVeU0CNwUKfIHWsyWswwAJ7o1YOK5c4r8Y/VpGw9MLdGldBYFZdGHn+gn2NFdYQUl1R2D56SVmhrxh10c278U1VsrICnpXiCKsxW1Aob0UX+4+wUBNt4eWLZ+XOl4suVsHHzOSh/OibYwFHQ+2FWBu6Dtz/WnD2utKb3+UNGE58VBEUEIWtYal0u9biUCvJnbCZvssznUclbqryIVunwGAplf0/91xH02DAimiKch/tXZWfObXn/o97OQQHuAvpWthD0KdzFEdASttmML8NTU6pGF/GUbyuTfK+rb8ckPB6LK86ufDuv+np5ix/jh3bBy0xHUBdwa2HgeLsGPphZgzyEBGSkeMB7onqcRZ5vE9eK8oAwg+1E1mYPb7vZhZ2lDiLsNqQwA0Ob2ye+re8sacaiyWdWGahpdAICy6lZ5WYm2nXkDI1TS7VGW0R7jPdOD5znZl7lHR5wNWtpTQDCCON4wigUjuzXg1NuOWkAwTvPCQhxb8Dy5NSCIWNB+1GmRLWcjcGvQFQKChX6PR1dmvb2s5iT7/FNE/1blo7k3VgRlOSBY4Knp8QKtLkH1W7iyKPMVmJXJsUq3BhYShyFo0RZbPrrWdJxC/I1A/Q36Zood5f0yt6LhAunENZeboaLWWN2qqBXw9hc86pt8AMTZQXabwlpOczhmcmLKn62IVx3gchZAoF76mVpA0vRVuYGg2ZV1iqBPksDkZ1HVKzkgmOynNOKiy2inzSZaxNIT+OSy6VjOGtVV2a1BoD35ffoW27aAOOv1hbEkRah47jOxnGVM4UfWQDyXfNH6fExRN9RCY+RuDfTFoVDLWeP7rKudWuiWjMoslUUqgqV+W8daUVD4nNW1nNXpO7UW1ETHIQ/kxjgCXt3YHo/iWKLdIzY8MQiWJM6Kbg2+/IHHkWoBgAsOO49hfdVtpj4QUKtbfrBTkQZH7CbX4EBFMw5WNpuWT6r+VQ2h10RqGzVNocJsOJIC/lw62nIWEN0wtLv9KK1qVgT141Ccm0ZuDQjiOEbPclbv7SDZKfavHW45S2Ls8YFoOUsQRLQEhTj97dLvZu92epaXnZlQFwLxyQeI3q2Bdj9pTXqcaQXUcFawToXP2daAz1lnmJl5yqyUbgpCH6UcjPwVxfrc1RMFokEpwig1yHB+Ew3poFcJq35cJXFp3xEvrn7kiElqrWATn3aYyA876Tr5NdZ9SnICfmaPVDMsWd0CIGjp6jcQm8wIujUIlCMeAcHsncVyNlTgkXzD+gWxHav80Rrcf22APWm2sNbK2G4DvD4TsZWFirNWxL7g/dHfLtZdBq9Px3I2SrcGPkOfs4Jm/ehZztq0fbBOX6z9RemnWpl/pAHBjII5EfFHqrOR9EdVdT68t6wZbk/w3jW0uFGUD/QsAk4bWoTqhjbsLW9G76J0DOuTi20H63CwssU0b54T25Pby3B6STGy053ytrXbK1HX7JbrZEpSsJHabBzaPX5U1IrrYh8hzn6QKKv24nCluNw9X2k5a01wdAc6pKIctb/W7Qfr4RdYyJtMSZ8cud/bur9OtW14v1xV3qFj++pfinJSAufZsZazgOg2ot3tx67DjarfB3b3Wr5WBEEce6iMpgJ/9d5KhvTKxt4jTR1rOfvaa69FljvRZQknUBAEYY55QDBrH7BqcS8+ZetIjIJvxQOrXZJUBp/s1kA/H+0UFOW+RseXhFjGOPxniWgREk6cVYnrCv+FWttZvaPGz3JW+sgP3cYYMxR3tFZzwSBB6nTRBa6RfUpEsI8+StcCVgX8AT0d6F1kR0VdeDNCxhgy0hjqGiOz1LFSCt7SR2XHNPpgXVRY9+lYztptDF4fh237RT+HmWlB4UkSF2OynI2LW4NQMSwR6FvOqtuecptRXdX6GtXzOatcD+fWgCF4rW28WA4zP8vKfRxmbg0EpZWt+FcvMJoVjHxeRhQQLMoqIBiIs8GAYFD9DYd+QDBBERAsGLsjKMTruDUI9A/kc7bjicYS8p2vm/DhilChNSWJxw2XCuJzQp5xY4PTYUNjiw97DokWoE6DL/bGVg/yshl6FdkA+MDzUE+rl94LAu0uWTF1x8ZzOHDEB0HgkJ4C9Cq2Y/t+X8CKVqyT984Pxofpnh98eZGDl5m4NZBcDBTmpKJ/N3F6BWMMW/bXAQBGDcpHeW0bGID8rGQM6J4l73uwshnNbd7AdbKrtkWClRkIsTK0Ty4OlDfJXYrL40NDiwcNre5gFPYOOjZBEJ0X5Xedyq2BZkKdw25Dt7xU7D7UFlH+EYmz1113XUSZHy988sknePPNN/Hjjz+ioqICmZmZGDhwIC699FLcdNNNyMzM7HLH5HmQ6SxBxAAnf9QZuTVQpzNC9RDoAuqsNsBZtIM8uq4FLHZK0jGDbg201rzqe2P0oNXC8xwcdob0VIaWNg51TeLHTE6m8cezns9ggbFQEUHvfOMUEMxIRGt3Cfjd4xUoq9ZXd8aPSMGfbyqQ11XWjooihbP+MiKebg2UmZh9XEu3Nz2Vx+uzu5tmXd3Qjh+2VuCzb23YG3D9J7o1CDk0AKVbA/1yKOuXJbcGpimiw8i6T4nTDlxyloDGJgf6FGfA6eBw8gl2bNrfKgYE0wiPVtBazkZqUaBEGxAs0eKs3oCbU7pGfrHOKPsDI3+Fds1ghyS+GomznrDiXVA8dTqAdrc1sU8/+F8QXnZrEGo567c48Bh6TH0/jhEFBLP4W+ixDdwaaJ7jVtwm2HQGZv0Cg1LzWrK6FTYbh5Z2Dw5XA0P7hSrm0Q16EZHCGItKnD1UITbMSaNT0b+HKHK+v6wRTa0c9hziMOHE0Hw//taHjTttCIQMM8iZh93GcMdVUl+pX/+lv0mBwalDFcBXa9rwwxZxIK1PN5sszpbXiGm3H/CgMjAgOW6kgJxMhVsDybe1yTXwBAKOORQVWul/tnt+GnoVZujum57ikMXZ9OSoPSuqAqR11IyywuwUFCosg+uaXFi5uRyt7V6kJov3myxnCeL4Q9fdmLKbVmx32HiYjHeFEH3PSKClpQX/7//9P3zyySeq36urq1FdXY3Vq1djwYIFePfdd3Haaad1qWPayK0BQcSEmdVjMCBY+HysWnV2FuIXECx0R6vvwVI6I5EgNCCYtWPwAUOYX50voLoe6FeUgUPVzeheGG6fUOGXMWbJVYFZUDmr8Aa+QbfscxsKswCw6pd2tLQLSE/hVfs7lOIkF50421HEO3qydG7Zim9NuyIgWOiDkun/rIOlsnZQk9ez7tOKXhzHoUchw/D+PCaOEC2cmlo9gbRKS0Drx5UtZwOHijYyOBAcfHF0ErcGks9epTWVI2CY5hcAMHV/YPRhL1tTBgQP2RekxspYPA6TfdIalksWZzm0uxl8JvdLvK5c4Jj6aewKy1mt0B6tz1mfTuAzsfwRWM7qyacWqoVsOasNDimJs2H3Vm+Vfc6y4IUOirPitf3bW3WKPWzYtAv49qdq+ZdkJ4ep48TKow28RMQX5fM1kgBPR2rEZ+clE9MxYrAY2bO0qgVfr/Vj9yExmJRSnG13Cdi6V1w/oa8DKUn6x9p50I02F4ft+zkM6acnzop/ffLglA3V9cCH39gANMjp+nW3oVeRA4ALW/YKuPvpI6hpEPcZ3EfAKcPUblasW86qB8UAwOMNBssMdw2Vgq4jhpG5RFispqWI7bHd44c9cO4UEIwgjj9UrvACXYDRXEinnTd8jzKCxNko8fv9uOKKK7BkyRIAQFFREWbNmoWSkhLU1dVh0aJFWLVqFUpLSzFt2jSsWrUKQ4cO7TLH5PmuYaVHEJ2VcAHBlL+ZWZbqWV52ZuLlDkUvF6t9klYUCHW1EJ3lrLQtNRno0w0Y2tcOgTcXdLXlEqwZzqrSx4KRW4O9ZaIFy/gRKbj/2jzVthv/XI7qBj/2l3lw4sBk1f72gCgklTzR4qzy2lm1ZLGqd0v5ZaUrfgvzTRnJFUioz1mdexZy/6RBDsXFUgY7ksXZCAYPZHHWL4l/MYizss9ZafCAydPFE4Hsh1BxfIccJIsL+JzlwXFi/TP6rtfeG9mtgSa9pG14w7gpYAwqy1mxnOHPQ+nmxNitgSQMA4LsE1cKniUJUuGPoyUoZEVvOauPBUthA1cMchuN4JBGbctuB84YxXCoArJF3qEKL6rq/dhXBuwrUwdOstmSUDIw8YMOxzrK62u1T/b7GSrrRHG2e0HwU7p7YKJJQ7P43PZ4Bbz3FY/K2iYwNEEQgOwMhsdvy0VWWpJu3k++dRhLfhCw5AeGhhYOowdpByfEBs2YuJyabEd1vfSc4tCtAODgx4TRDqQ4RV+1re0cNu0ODsQO7hNqKWzVj6o38CLgUIqzOoKtHsp9HCZpw5EIQwWnnYfDxsPrF2TrX7KcJYjjD7XPWWUfEGqc4XDYQt7dzCBxNkpeeeUVWSQtKSnBsmXLUFRUJG+/7bbbcO+99+Kpp55CfX09brrpJnz33Xdd5pg8R5azBBELXBirR+VvZi+ZVoXDzkKof9foyqzv1sDqvhoxNsTVgvhXugsqATzMy7b2XslipwVBV7m/aDlruEtoOWO0nLXLIpr6931lohXk4N5O2TpWYkBPB6ob/Nhb5pXFWUFjISfRqQKCmbk1iDA/XhZng9aENhsn+ww2MJw1rBLKn618YHZUiw9azgbvmZ7lrDgtPlS88PtZUPQ3EfuUSMeTrUGjNJ5STkVW+mP0Cywma9xY0PO1qhYwGFJs4ss8AzPsa4K+RgPXymdkOYtAuvCNKVJx1quwxDWyUJOEV68/aDkr+5wNrEfs1sAvWe7pW87aeLU1oi7RGc4aClOyW4Mw+2q7Z12fs4H8Ty5hOLmE4aJxBeA5Du9+3YAXP2gCAORn23DdBVnYe9iDj75twd7DflGcJZ+zHYrs/gjWZ/pU1vshCGKdz8sK9j+Z6WJejS2AzyfgSLUfR6rVmY4cwpDkMO74xpTYseQH8dm8dQ8X0i97fQIOHAG65QN7DjeC5zl4PHYAAnoV+3H2qZI7BC+cDhtuuMSGqrpgP5+WAgzqbUd6ihP7y5vk32ubXADE2RG7DzcYls/lETuQqoY2ZKUnobbJhbJq0feuM8x5AWpr2VjEWSVHq7/nOA5pKXY0tHjk30icJYhjk7QwbleUzd7smZFk58MadOhB4mwU+P1+zJ07V15/8803VSKpxBNPPIFvvvkGGzduxMqVK/HVV19hypQpXeKYPB+XOC0EcdwiuzXQ0auUv5m7NQgV9zozelEso8wpbN7h0F7TULcGastZ9TUOUyLNNkloClcqXZ+zwtF2ayDm4/YAa7cGrbN2HBA/Mgb0CI1oNqCHE2u2uLBuazuK88RXBa/SQk4SIaGwpoxAROgoucE0yJZ07y2WQPr4ys8VRT+fH+hT7ACYW8pQld40V0VySx92HdTktdZ9SrFTe2iVZZktKFZxXEAEsHgzGWNocwFf/sCjrlEUAowsM81QFlX5kS8IDIjBj20s6AWVUgY8u+LBI3Kw1anjGYpywlmlhlrOanUP2XLWG75MAhPzS3KIYruZmKu830biufSx4feH+pyNNiCYz0AglcrjsPPwe/wR+5y1QtDaV91/ROMmRd9yVvMiEBjrGdDTKf80YmASLhifjopaHz76tgWHKnzw+SMc9CIixi+Ig6WVtRx+3OaytM++wKyT7vl2VR1JTRbAcwx+P4e6ZgFHqsU6MLCXHbN/m4+Vm8qQkgzDYGAAkJtpw29n+PHKBzY0t3FoalOPpvyyi+Gr1Tb068GQ5GwAAJTXcAB4lfudyvp2VNa3IyMNyEhTH6O5DWhu86G8NjRQTU2TCzVN5tdhZ2kj7DYbth2skwcowonOgNqyNlZxdni/XDS1elCg8Avb0WSkOFXibLwEZoIgOgcTT+qOXYcbUNInxzCN0be43vuHw2GL2AiBxNko+O6771BeXg4AmDRpEkaPHq2bzmaz4c4778QNN9wAAFi0aFHU4uzRPibPdQ0rPYLorIQLCKZya2AaEEyZaVyK1qFEMqIYjtj21Yqx+nlLt0FdZuuWs5JIEK6s+j5ndVwV6FkKa8oZLVIw59Z24MF/VodsVwoEwd9EwXbNFhfWbJE+1LhAfhzcvuBPNh1LMavE4zGj9hkYb8tZ8eMrNZlh0bzuqGsS0L+HA2u2VYj5GVrI6m9QWc4mMCCYLPxrfMCqjq0YTJBQXl9Jx7KqHfkFhj2lHA4c4QCIO0nCf6QoLX7ttqCrgEROAdcLKpXkBPKyGGobObS5gq5Alq7hsf+wD9/+VBuST5vbB47ncNYYtTirtb6Q3Iv4wpyz0r+s08EF8gt/jbzeYL9mVEcly9mmVsminCE1KeheAohc2JRESLvGH4KgEGddHn/E99hK/ykd29CtQQToWs5qyiytDegR7Hu75Ys3uCjXhux0Hg0tAj7/jsfl55DlbEciCAxb9nBYto7Hf78MfT6Go1u+uv/yCwIy0jg0tgCVtX6U14j1qn8PO7LSOaQki3UqXNtw2G1ISwEy04GmFjHw2In9xW2MMazZLO67v4zDy//j4XSIxwOAolwevQujEytrGl1oc/uQne5EZmroOwEgXqvDNa3yellNCxgT+6Ie+enoW6wfCCx4bsG2beYCwYwB3bNi2j8aBvfKht0mWvBnpDqRnhI6sE0QRNclJyMJpw4NNX5Uoei+5W88Bt1YFE6ynD06LF68WF6eNm1a2LTnn3++7n6d/Zg8BQQjiJgIZ/Wo/M2snXW1QRJVeS2WXe/TM5bT1oqoxpaz6nW9tKr9NB9UPlkgCifoKo8r/hV0AoLp5RAvy9k+3ewY0ldAYzOPzDT1R9eIQUkozAl9czh1eArGj0hBdX3QaqexxY3CPIaCbB6Ha4JpEx1VXHlYy4KQxaIqxZnsDB55WeFfm+T7aqEY1oSfjmn/2numL6xLaYIKn7J9RCrOCgJDXaO4PHFUCi4/KxMl/fRFADOUdY0PDBD4/CZT3jsYPctZjuPwq/MFNLUCZ47sjnXb3Hjh/QY0NnP4ebsAoNUgNx5D+4ltT7acNXJrYBzTDz6FOpukDE4WBmmXcD5jJUuQyoC2nJkOJCepRcmI3RroXD/l75KYE+4eR9tV6t07wFp/oj2knuVsaPsSRe3M9GDfO3Kw6IOU4zicODAJKze248ARDvvKyHK2I/ELDIcrxeXCHBuy0q2Jhg47h8vPypTXGROt0rMyRLF0y14vDlWI971vNzs8gYZsNvVfqudFuaI4u3GXF70KxQFSn4/B5Q6mbXdzaFesD+2bilGD8i2VX8uabRVoc/vQtygTfQxE1laXF4drWuXBMMmKNDs9CSMHmh83Xj5nE0V6igMnDYju+hIEcewhf08afFQ47WQ5e1TYvHmzvHzKKaeETVtcXIxevXqhtLQUlZWVqK6uRkFBQac/Js/HP+o1QRxPhLN6DIqCVgKCxblgHYwqimUM+RhZHloqQ4ilrEacDfyVHqZWrX21+Uof32H30Qg1gJHP2dBM5PQxCk42mziNOiWJw5QxxZb2SUni8eeb1M+Nz1YfgF9g4HgOSg0ytoBgsT9nBI1QF8/DKcUaQREEKx4kNCCYxnJW797pWc5yHCf7/pSKbzWYvF9gqGsUdzqlJAXDB+gHxLGUl2IKvFgmHj6/P6FTwPUsZzmIImpOJtC9wIGTBgbTcxzw24uzQ6rk56uaUVbtR2lA2JHOVfuCL2k8Sh+xWpTCbdByNvx5SD5uw1l7SOJ+db24nhvUqAyDa5nhk8/TwHI2UCArg1WiLa919O4dYLGNaspjxXJWKhzPcbj2Ij+aWoGhioGK26/MwcqNoguasiokNNDdsY5fEFDbIF7bu3+Vi9OGR2d56g2Makj+yb9YFVRN+3azw+u1FjRLEm8LcoDdh4Bv1nnxzboqRYpgPRjYi2HiyGy8+qk46tW7KLrBLkAxOBLG1Yx0jg4bLwcBA2DZglQtzibI/wxBEES8UHSXOoazcJDl7NFh586d8nK/fv1M0/fr1w+lpaXyvtGIs0f7mOK7Jb0IEkS0WLGctWJZ1BX8zCpRfstatWLSO8OY3BqYWD+FuDWwGBBM+3EsSIFEwpRVz5+twKwJDLJlYozGgJxCjo47AWEMsO4bkUVgOW4FrXgYjkivhDI7v8Agf4KaGMga/a6y+EygOCv7CWaS5WzAfzIHaI1/BaYWh7TibCSWs7UBy9m+3WKbDqq1soxtgCB2lD57bWFGe3oXBc87Mw341ZRMaKlu8OLDFa0oq9L4nA2xnDUXW73y9WDBgF1m4mxge7jqKemnNQ3i39wsJvczshVqxJazgYBgYXzOKtPpo+dGyMqx9S1nrYizVixnjdwaAEBBDoecTLXVd0G2Hbdeno3n/9eAiloOAmMRX0/CGi6PgLpAXCw9/+tW8QZGqYYNYKisZQB4+HwMedkCBvZyot0t+qk1FWcD24cNAPYdZhAYL/tyFQSGdrcPwwcyZGcyDOrFY+qpmdiyvxFJTgHZGdGX38jns+ocJXHWrhZn05ItirOKgRdHPEc6CYIgEoDZU9lh58ly9mjQ0NAgL+fnm09vyMvL0923Mx+TAoIRRGzwspUksGW/2q+g9IJrKVp7F2uIyvJaDboUb0LdGqi3S2WsbmjHlv21aHcHzcva3b6Q+6XcpqQsEEzD6xWwYmOZabkOV4tTmCvr20K+6D3eoGLiFxhWbCyT60ldkwuL1x40zd8ISQxtd/vx6Q/7o85H0g6+/eWILMCUVbegpkG08PL5WcTlXLW1POoBCOkSehXq1HKT+9AWuIdrtlVEfNylP5XKy9K12LC3Bpv2BeuL9IG+41A99h5pghalsLRy0xGYvdop25BfYFi+wbyeWcHlEa/D3rJGHKxolgUspZDlUtTJJesOycvSR/nByiYANtQ3Mdzz7AHTYzIALW3iB/m2Q0ewryKmU5DLsnjtQblM328uT/iw8rL1ZYoBoOAFXbJOahvimzrPM932KBrY2bC3FLjn2QM4VM4B4FDb1K5K7/ZyAHis3NiOnYcO6JbF4wUAHjYeaHV7APD4aYcbf5ivnx5AYJo0b1g+AGj3iMeWyMkEvllfCo7j5Daw50gjDlW1GB5HS0u7KF4dqGhCeV0wUJHU70ribHObV9XmlDS2BoL1KExnW11e035Jqj8b91SrLHetBG5kTN3vKK3RpeN6Neblyr5EEsWWbzisSlPfAgA2lFcD9//zEIiOwe0BGOOR7GRYs6006m8fqaYU5QG/Ol8AB0H+bd2OSnkGTEOrO+z7gi9QV1JT/Jg5FQAE8Fzw3UOp8/sEAV/9eAinjxD32byvNup3xuY2se0crm5BrUFAsFaXWI4kh03l/9mq5awyEJo9ymCQBEEQnQWpF6tucMGvM43MxnOw8cApwwSstJgnibNR0NISfNlMTk42TZ+SEpwi09zc3KmO6Xa74XYHp940NopmLW5XCzztKWhqCv24JAjCHJ9fQHubKHps3q3fBlOcdkttrK1V3N/jykQTPCapE4vAmFxep90W9vzc7S3wCwxFGZkh6TyuVrS1qj8QrPZHrjaXXAbxODyamhyK9Ra0tTajrRUor1Lv29YK1NU3WDqOklYj15HKvCPIr81Cfp0B7TlFco5A/M+zs1w3K+WIpqyJOj+9+8r8gM9jQ7MH+HGT9bzSUxl8HgEtcerKIq1ziSbZzqOljUO/YgEtLaHiX0Yy4PPwaPBw6usqqNPzjIPPw+PgYeDg4ZBsVDiSGDg/g8/Do6wcKCs3L6ctmaGlRd9KlfeLx5ZIT/KjUdM9R1tX97QavLP6HXK/bdZH222cKvCZ1ToSSZmVVuZG+xkd10p5kh0A/DzaPBzW/WK9XER0ZGcztLQeHbcojXHMS1mX4vF8MGx/CjKTBPCCF82BwRDmy0RTUxjn1wEEIfh+6GpvBfPpi8AEQRCdFY/bp/gut8nvJRLtbS1gvqDE6m5vRklfUcezMuBL4uxxzuOPP465c+eG/H7zFeMTUBqCIAiCII51Fi9IdAkSj1UrCjn9wtiO91UU+3z8tLV0sZaNIDoDix5PdAkIgiCIY5Xm5mZkZWWFTUPibBSkp6ejvl6MguByuZCenh42fXt7u7yckaEfATNRx3zwwQfxhz/8QV4XBAF1dXXIy8vrctOpCSKRNDU1yYH4MjND/QgSBKEPtR2CiBxqNwQRHdR2CCI6qO0QROQwxtDc3Izu3bubpiVxNgqys7NlobSmpsZUKK2tDfrGys7O7lTHTEpKQlKSOmJytGUkCALIzMykFxaCiAJqOwQROdRuCCI6qO0QRHRQ2yGIyDCzmJWgUIlRMGTIEHl5/37z4CrKNMp9O/sxCYIgCIIgCIIgCIIgCILoOEicjYITTzxRXv7xxx/Dpq2srERpqRiVtbCwEAUFBV3mmARBEARBEARBEARBEARBdBwkzkbB1KlT5eXFixeHTfvFF1/Iy9OmTetSxyQIwjpJSUmYPXt2iJsQgiDCQ22HICKH2g1BRAe1HYKIDmo7BNGxcIwxluhCdDX8fj969uyJiooKAMDPP/+M0aNH66YbM2YMNm7cCABYsmQJzjvvvC5zTIIgCIIgCIIgCIIgCIIgOg6ynI0Cm82GRx99VF6/9tprUVVVFZLugQcekEXS8ePHG4qkr7/+OjiOA8dxmDx58lE5JkEQBEEQBEEQBEEQBEEQiYUsZ6PE5/Nh2rRpWLp0KQCguLgYs2bNQklJCerq6rBo0SJ8//33AIDs7Gx8//33GDZsmG5er7/+On7zm98AACZNmoQVK1Z0+DEJgiAIgiAIgiAIgiAIgkgsJM7GQHNzM66++mp89tlnhml69uyJd955B+PGjTNMY1WcjecxCYIgCIIgCIIgCIIgCIJILOTWIAYyMjLw6aef4qOPPsKMGTPQq1cvJCUlIT8/H6eeeiqeeOIJbNmyJa4iaSKOSRCdjU8++QRXXHEF+vbti+TkZBQWFmLcuHH429/+hqamppjyXr58OXiel12N9O3bNz6F1hDPc9izZw/uu+8+DB8+HFlZWUhPT8eQIUNw2223yW5OCAKIX71zuVx44403cPHFF6NPnz5ITU2F0+lEYWEhJk6ciLlz56K0tLRDzuG7777DddddhwEDBiA1NRV5eXk4+eSTMXfuXNkvu1XKy8sxZ84cnHzyycjLy0NqaioGDBiA66+/Ht99912HlJ/oOvj9fmzZsgWvv/467rjjDpx++ulITU2Vnw/XX3+9pXwYY1izZg3mzZuHCy64AH379kVKSgqSk5PRvXt3TJ06FfPnz0dDQ0OHnQs9c4ijSbzajpb6+nosWLAA5557Lnr37o3k5GTk5uZi6NChuOyyy/Dcc8+hrKwsrudCbYc4WjQ3N+P999/H7bffjnHjxqGgoAAOhwOZmZk44YQTcO2112LJkiWIxLYuUXWO2g1BRAEjCILoIjQ3N7Pp06czAIb/evXqxVavXh1V/q2trax///6q/Pr06dOpz+Gll15iKSkphnnZbDY2d+7cuJ4D0fWIZ73bsGEDGzRoUNi8ALDk5GQ2f/78uJ2D1+tls2bNCnvM3Nxc9sknn1jK76OPPmI5OTlh87vpppuYz+eL2zkQXYsZM2aErR/XXXedaR47d+5kPXv2NG0vAFheXh773//+F9dzoGcOkQji0Xa0vPbaaywvL8+0HT399NNxOQdqO8TR5KmnnmLJycmWnhUTJkxgBw8eNM0zEXWO2g1BRI8dBEEQXQC/348rrrgCS5YsAQAUFRWF+FxetWoVSktLMW3aNKxatQpDhw6N6BgPPvgg9u3bh7S0NLS2tnb6c3jrrbdw0003AQB4nsdVV12Fs88+G3a7HatWrcIbb7wBt9uN2bNnIykpCffff3/cz4no/MSz3pWWluKss85CfX09AKCwsBDXX389Bg8ejKSkJBw4cACLFi3Ctm3b4HK5cNdddyE1NRW//e1vYz6PW265Ba+88goAICsrCzfeeCNGjx6N1tZWfPLJJ/j8889RV1eHK664Al999RUmTpxomNfy5ctx5ZVXwuPxAAAuuOACTJ8+HWlpaVi/fj0WLlyIxsZGvPTSS+A4Di+88ELM5Se6Hn6/X7Wem5uLvLw87N6923IedXV1OHz4MAAgKSkJZ555JsaPH4/evXsjKSkJe/bswdtvv43t27ejtrYWV155JRYtWoQrr7wyLuWnZw6RCOLRdpT86U9/wuzZswEADocDF110ESZOnIji4mIIgoDS0lKsXbsWX331VcxlB6jtEEefXbt2weVyAQB69OiBc845ByeffDIKCwvhcrmwZs0avPXWW2hpacHKlSsxefJkrFmzBoWFhbr5JaLOUbshiBhJtDpMEARhhRdffFEeJS0pKWEVFRUhae655x7VqHIkrFq1ivE8L1tdSPnE03I2nudQVVXFMjMzGQDG8zz7+OOPQ9KsXr2apaamMgDMbrezHTt2xO1ciK5DPOvdjTfeKKebMmUKa21tDUkjCAJ76KGH5HQFBQXM6/XGdA5LliyR8+vWrRvbtWtXSJpnn31WTjNgwADmdrt183K5XKxv375y2gULFoSk2blzJysuLpbTfPPNNzGVn+iaPPbYY+yBBx5g7733Htu3bx9jTLTek+qFFeu/1atXs169erFnn32W1dXV6abxer3stttuU1mA19fXx1x+euYQiSIebUfiP//5j7zfiBEj2O7duw3TulwuVllZGWvxqe0QR52bb76ZTZkyhX311VfM7/frpjlw4AAbMmSIXO9+85vf6KZLVJ2jdkMQsUHiLEEQnR6fz8e6desmP8x//vlnw3QjR46U03355ZeW8m9vb5dfdi677DK2f//+uIuz8T6H//u//5PT3HHHHYbHfeqpp+R0v/rVr+JyLkTXId71rkePHnKarVu3hj1uUVGRnHbz5s0xncfYsWPlvN5//33DdBdddJGc7qWXXtJN8/zzz8tpLrroIsO83n//fTnd6aefHlP5iWOHSAWmlpYWw4ECJYIgsNGjR8t5v/baazGVk545RGcjGnG2pqaG5ebmMgCsR48erLa2tmMLyajtEInBat3euHGjXE9SU1N1B8kTUeeo3RBE7JA4SxBEp2fZsmXyg3fSpElh07766qty2uuvv95S/tILQHZ2NisvL+8QcTbe59CnTx85jWSVokdTUxNLS0tjAFhaWhpra2uL5TSILka8653D4ZDTeDyesPmdfvrpctpo/UAzxti+ffvkfPr168cEQTBMqzzfyZMn66aZOHGinGb58uWGefn9fpWF7YEDB6I+B+LYIVrrPys8/vjjct6///3vY8qLnjlEZyOatqNsE6+++mrHFjAAtR2is6O0nv3ll19CtieizlG7IYjY4UEQBNHJWbx4sbw8bdq0sGnPP/983f2M+Omnn/DUU08BAJ588kkUFxdHXL7rr79ejkA8Z84c3TTxPIdt27bh4MGDAIChQ4eiX79+hnllZGRgwoQJAIDW1lZ8++23YY9NHFvEu+0ofZvt2rXLMC+/34+9e/cCAOx2O4YMGaKbbs6cOabRu5VlmTp1KjiOMzzuhAkTkJ6eDgBYuXJliO/o5uZmfP/99wDUbUMPnucxdepU3XIQREeQmZkpL7e3txumo2cOcbywcOFCAIDT6cTMmTNjzo/aDnEsEO5Z0RF1jtoNQRwdSJwlCKLTs3nzZnn5lFNOCZu2uLgYvXr1AgBUVlaiurraMK3X68UNN9wAv9+PyZMnxyVokRHxPIdI8tKmUe5LHPvEu+1ccskl8vLvf/97tLW1haRhjOGRRx5BVVUVAOCGG25ATk5ONMUHENk52O12jBo1CoAoEG/btk21fdu2bRAEAQAwatQo2Gy2sPlR2yGOJso61qdPn7jlRc8coitSXl6OPXv2AACGDx+O1NRU7N69G7fffjsGDhyIlJQU5Obm4uSTT8aDDz6II0eOxOW41HaIzozH41ENjmufFYmqc9RuCCJ2SJwlCKLTs3PnTnk53OipXhrlvlrmzZuHzZs3Izk5GS+//HJYi7xYiec5dNT1II494l1X5syZg0GDBgEAli5din79+uGBBx7Aq6++irfeeguPPfYYTjzxRDz++OMAgGuvvRbz58/vNOdAbYforNTX1+Odd96R1y+44IKY8qN2Q3R1fvzxR3m5d+/eePPNNzFixAj885//xN69e+FyuVBfX4/169fjr3/9KwYOHIg333wz5uNS2yE6M//5z3/Q2NgIABg9enTIjL9E1TlqNwQRO/ZEF4AgCMKMhoYGeTk/P980fV5enu6+Sn755RdZQHr00UdlwamjiOc5dMT1II5N4l1X8vPzsXbtWtx666343//+h6qqKjzxxBMh6c455xw89NBDOPPMM6MqtxJqO8TxwD333IP6+noAwPTp03HiiSfGlB+1G6KrU15eLi9v3rwZn376Kfx+P8aPH48rr7wSxcXFKCsrw6JFi/Djjz+ivb0d1157LdLS0jBjxoyoj0tth+isVFdX4/7775fXH3744ZA0iapz1G4IInZInCUIotPT0tIiLycnJ5umT0lJkZebm5tDtvt8Ptxwww3wer0YMWIE7rvvvpjK9/rrr+P1118Pmyae5xDv60Ecu3REXcnJycETTzyBgoICLFiwQDfNsmXLwHEccnJyMHLkSMPjzZkzx9B/mQS1HeJY58UXX8Rrr70GAMjOzja1NqdnDnE8IA1WAJB9mM+ePTvkmXH33Xfj//7v//D3v/8dAPC73/0O5513HtLS0kLypLZDdFU8Hg8uu+wy2WXUJZdcgksvvTQkXUfUOWo3BHF0ILcGBEEcdzz55JNYv349bDYbXnnlFdjtNE5FEFb5+9//jv79+2PBggW45ppr8MMPP6C5uRkulwvbtm3DnDlz4HQ6sXTpUkyYMAFLlixJdJEJotPy+eef44477gAgBqF77bXX0Ldv38QWiiA6AZJ/cImJEyfqDuZxHIcnnngCJ598MgCgtrYWb7311tEoIkEcFQRBwA033ICVK1cCAAYMGIBXX301waUiCCLekDhLEESnR4q+DgAul8s0vTJyaUZGhmrb9u3b8ac//QkAcOedd2LMmDFxKmV44nkO8cyLOLaJd1159NFHcd9998Hv9+Nvf/sb/v3vf+P0009Heno6kpKSMHToUMyePRtff/01nE4nWlpacNVVV4UNzHc0z4HaDtGZ+Prrr3H55ZfD5/OB4zi8/PLLqqB7sUDthujqaOvOTTfdZJiW53nMmjVLXl+2bFnUx6W2Q3QmGGO4+eab8fbbbwMQ/S9//fXXhoFWE1XnqN0QROyQOEsQRKcnOztbXq6pqTFNX1tbq7uvNPLsdrvRt29f/PnPf45nMcMSr3OId17EsU0868qRI0fw17/+FQAwZMgQ3HPPPYb5jB8/Htdeey0AoLGxUZ6yHQ3UdohjkWXLlmH69OlwuVzgOA4vvPACbrzxxrjlT+2G6OpoxSfJMtYI5WC75AYhGqjtEJ0FxhhuvfVW/Otf/wIA9OzZE8uWLQs7uyJRdY7aDUHEDs3lJQii0zNkyBDs378fALB//37TKZ9SWmlfic2bN2PNmjUAgGHDhuHpp5/W3V/pTL6xsRHz5s2T1++77z4kJSVFegpxOwftujJdNHkRxzbxrHdfffUVvF4vADHgF8dxYfOaMmUKXnnlFQDA2rVrIy26qhzLly+Xyzd58uSw6antEJ2dZcuW4aKLLpKtff75z3+GtQqMBnrmEF2dE044QbWelZUVNr1ye1NTU9THpbZDdAYYY7jtttvw4osvAgB69OiB5cuXY8CAAWH3S1Sdo3ZDELFDlrMEQXR6lFGrf/zxx7BpKysrUVpaCgAoLCxEQUGBvI0xJi9//vnneOSRR3T/PfXUU3K6hoYG1Tbl1JlEnEOkeWnTDB8+3FJ5iWODeNa7I0eOyMtmH8mA2npBGdwhUiI5B5/Phw0bNgAQp7mWlJSotpeUlIDnxVefDRs2wO/3h82P2g4RbyRhtq2tDQCwYMEC3HLLLXE/Dj1ziK7OsGHDVDEBGhsbw6ZXbrfyjDKC2g6RaCRh9oUXXgAAdO/eHcuXL8fAgQNN901UnaN2QxCxQ+IsQRCdnqlTp8rLixcvDpv2iy++kJenTZvWYWWKlHieQ0lJCXr37g1A9KF74MABw7xaWlrkAAKpqamYNGlSJMUmujjxrHdKP17SS3U4Dh48KC/n5eWZpjdCeQ5LlixRDbJoWblypSwET5w4MSRad0ZGBsaPHw9AjOj7/fffG+YlCAK+/PJLef3888+PqvwEIaEVZufPn4/bb7+9Q45Fzxyiq5OSkqKaKfHzzz+HTf/TTz/Jy7FYz1HbIRKJVpjt1q0bli9fjkGDBlnaP1F1jtoNQcQOibMEQXR6Jk2ahOLiYgDAihUrsH79et10fr8fzz77rLx+1VVXqbaPHDkSjDHTf8rpMX369FFti9aXUbzOQWLmzJny8j/+8Q/D47788stobW0FAEyfPh2pqakRl53ousSz3iktGT777DPTaaNS8AoAGDt2bETlVtK/f3+ccsopAMSpax9++KFhWqWrEqO2o/xdaSWv5aOPPpL7gtNOO810ih5BhGPFihUqYfaZZ57BnXfe2WHHo2cOcSzw61//Wl5+6aWXDNMJgiD75QRiG0yjtkMkkttvv10WZouLi7F8+XIMHjw4ojwSUeeo3RBEHGAEQRBdgOeff54BYADYsGHDWGVlZUiae++9V04zfvz4qI+1f/9+OZ8+ffqYpr/uuuvk9LNnzz4q51BZWckyMjIYAMbzPPv4449D0qxZs4alpqYyAMxut7Pt27ebngtx7BGveufxeFjv3r3ldNOmTWMtLS0h6QRBYA899JCcLiUlhZWVlenmOXv2bDndddddZ3gOX3zxhZyuW7dubPfu3SFpFixYIKfp168fc7vdunm1t7erzuO5554LSbNr1y5WXFwsp1m6dKlh2Yjji9dee81SnVWyYsUKuS8GwJ555pmYykDPHKIrEk3b8fl8rKSkRN5vzpw5IWkEQVDV4759+zKPx6ObH7UdojNz++23y3WquLiY7dixI6p84l3nqN0QxNGBAoIRBNElmDVrFj788EMsXboUW7duxYgRIzBr1iyUlJSgrq4OixYtkqcoZ2dnh7WwSBTxPIfCwkIsWLAA119/PQRBwKWXXoqrrroK5557Lmw2G1atWoU33ngDLpcLADB37tyQ4BrE8UG86p3D4cCCBQtw6aWXQhAEfPHFFxg8eDCuueYanHjiiXA4HNi3bx/eeecdbNy4Ud7vL3/5C7p37x7TOZx//vn4zW9+g9deew3l5eUYM2YMfvvb32L06NFobW3FJ598gs8++wwA4HQ6sXDhQjidTt28kpOTsXDhQkybNg1erxe33347lixZgunTpyMtLQ3r16/HK6+8IvsvnDVrFs4555yYyk90Tfbv34+FCxeqftu0aZO8vGHDBjz88MOq7WeddRbOOusseX3jxo244IILZIvZ8847D3369MFHH30U9tj5+fk444wzYio/PXOIRBGPtgMANpsNb7zxBs4880y0tLRgzpw5WLp0KWbOnIni4mKUlZXhP//5j+xz0ul04u2334bD4Yip/NR2iKPNww8/jOeeew4AwHEc7rrrLmzfvh3bt28Pu9/o0aNlFwASiapz1G4IIkYSrQ4TBEFYpampiV144YXyiKvev549e7JVq1bFdJyOspztiHN4/vnnWXJysmFeNpuNPfroo5byIo5d4lnv3n33XZaTkxM2LwAsKSmJPf3002Hzsmo5yxhjXq+X3XDDDWGPmZOTwz766CNL1+SDDz5g2dnZYfObNWsW8/l8lvIjjj2WL19uWs+1/7TPAKW1YCT/Jk2aZFgueuYQnZ14tB0l3377LevZs2fY/QsLC9ny5cvDlovaDtFZmTRpUlTPitdee80wz3jVOWo3BHF0IMtZgiC6DBkZGfj000/x8ccf49///jd+/PFHVFVVISMjAwMGDMCMGTNw0003xRSlt6OJ9znccsstOOecc/Diiy9iyZIlKC0thSAI6N69O84++2z87ne/w6hRozr4rIjOTjzr3RVXXIGzzz4bb775JpYsWYJNmzahrq4Ofr8f2dnZGDp0KM4880zceOON6NWrV9zOwW63Y+HChbjmmmuwcOFCrFq1CuXl5UhOTkbfvn0xffp03HzzzejWrZul/C699FKcdtppeOGFF/Dpp5/iwIEDcLlc6NatG8444wzceOONFFiC6PLQM4c4Fpg4cSK2bt2KV155BR9++CF2796Nuro6ZGVloaSkBNOnT8dNN92E9PT0uB2T2g7R1UlEnaN2QxDRwzEWJuwxQRAEQRAEQRAEQRAEQRAE0SHwiS4AQRAEQRAEQRAEQRAEQRDE8QiJswRBEARBEARBEARBEARBEAmAxFmCIAiCIAiCIAiCIAiCIIgEQOIsQRAEQRAEQRAEQRAEQRBEAiBxliAIgiAIgiAIgiAIgiAIIgGQOEsQBEEQBEEQBEEQBEEQBJEASJwlCIIgCIIgCIIgCIIgCIJIACTOEgRBEARBEARBEARBEARBJAASZwmCIAiCIAiCIAiCIAiCIBIAibMEQRAEQRAEQRAEQRAEQRAJgMRZgiAIgiAIgiAIgiAIgiCIBEDiLEEQBEEQBEEQBEEQBEEQRAIgcZYgCIIgCIIgCIIgCIIgCCIBkDhLEARBEARBEARBEARBEASRAEicJQiCIAiCIAiCIAiCIAiCSAAkzhIEQRAEQRAEQRAEQRAEQSQAEmcJgiAIgiAIgiAIgiAIgiASAImzBEEQBEEQBEEQBEEQBEEQCYDEWYIgCIIgCIIgCIIgCIIgiARA4ixBEARBEARBEARBEARBEEQCIHGWIAiCIAiCIAiCIAiCIAgiAZA4SxAEQRAEQRAEQRAEQRAEkQBInCUIgiAIgiAIgiAIgiAIgkgAJM4SBEEQBEEQBEEQBEEQBEEkABJnCYIgCIIgCIIgCIIgCIIgEgCJswRBEARBEARBEARBEARBEAmAxFmCIAiCIAiCIAiCIAiCIIgEQOIsQRAEQRAEQRAEQRAEQRBEAiBxliAIgiAIgiAIgiAIgiAIIgGQOEsQBEEQBEEQBEEQBEEQBJEASJwlCIIgCIIgCIIgCIIgCIJIACTOEgRBEARBEARBEARBEARBJAASZwmCIAiCIAiCIAiCIAiCIBIAibMEQRAEQRAEQRAEQRAEQRAJgMRZgiAIgiCOe1asWAGO4+R/ROzccsst8vX8/PPPddMcOHBAdd0PHDhwdAtJHNMo69aKFSsSXRwiAgRBwEknnQSO45CRkYEjR44kukgEQRAE0WGQOEsQBEEQBEHElV9++QX/+te/AABnnHEGLrjgggSXiCCIrgTP85g3bx4AoKWlBQ888ECCS0QQBEEQHQeJswRBEARBdHq0Fpbx/DdnzpxEn94xxz333AO/3w8AmDt3boJLQxDHJhs3bsScOXMwZ84cPPPMM4kuTtyZPn06Ro8eDQB46623sGHDhgSXiCAIgiA6BhJnCYIgCIIgiLjx7bff4ptvvgEAjBkzBmeddVaCS0QQxyYbN27E3LlzMXfu3GNSnAWA++67DwDAGMOjjz6a4NIQBEEQRMdgT3QBCIIgCIIgzHA4HBgwYIBpuqqqKjQ3N8vrVvbJzc3F5MmTwRiLqYyEyJ///Gd5+e67705cQYjjHmrTXZ8rrrgC9957L8rKyvDZZ59hw4YNGDVqVKKLRRAEQRBxhcRZgiAIgiA6PT169MCePXtM011//fV444035HUr+xDxY/PmzbLVbE5ODi677LIEl4ggiK6MzWbD9ddfj8ceewwAMH/+fLz++uuJLRRBEARBxBlya0AQBEEQBEHEheeee05enjlzJpKTkxNYGoIgjgWuu+46efm///0vamtrE1gagiAIgog/ZDlLEARBEAQRB3w+H7777jvs3bsX1dXVyM3Nxbhx43DSSScZ7uPxePDdd99h27ZtaGlpQUFBAcaPH4+SkpKoyyEIAtatW4edO3eisrISDocD3bp1w4QJE9CjR4+o8zXD7Xbj3XfflddnzJgRc54///wztmzZgvLycqSnp2Pw4MGYNGkSkpKSos5zw4YN2Lx5MyorK2Gz2VBUVIQxY8ZgyJAhMZe3oxEEAatXr8aePXtQUVGBlJQUTJ06FYMHD9ZNu3nzZmzcuBE1NTVob29HSkoK8vPz0b9/f4wcORIZGRlHrew7d+7Ezz//jLKyMvA8j5KSEpx11llh7+W2bduwZs0aVFZWIi0tDUOGDMGZZ54Jp9N51Mq9fft2rF+/HocPH0ZKSgr69euHM888E+np6UetDEeL0tJSrF69GpWVlWhtbUVhYSGGDRuGsWPHguO4iPOrra3FunXrsHfvXjQ1NYHneaSnp6NXr1444YQTMHjwYEv5Dho0CMOHD8eWLVvkfuaWW26J5hQJgiAIonPCCIIgCIIgjhGuu+46BkD+Z5Xly5db2s8o3TPPPMO6deum2ib9mzhxItu9e7cqH7/fz/7+97+z3Nxc3X0mT57M9u7dG9G5NzU1sQceeIDl5+fr5slxHJs4cSL76aefIsrXKh9//LF8rIyMDObxeEz32b9/v6qM+/fvZ4wx9vnnn7Phw4frnkd2djZ75plnIiqbz+djCxYsYD179tTNEwA74YQT2H//+19L+U2aNEneb/bs2Zb2iaWOCYLA/vGPf+iW/+mnnw4516effpr17t3b8FwBMJ7n2dixY9l//vMfS+W3Qp8+feT8X3vtNcYYY+vXr2fjxo3TLUNBQQF78803Q/L54Ycf2NixY3X3KSwstHyflPstX748onRr165lp59+um4ZUlJS2MMPP2ypjlstg5LZs2fL+0yaNClku/I6W/l33XXXhT3eBx98wEaOHGm4f7du3dhzzz3H/H6/pfJv376dXXzxxcxut4ctV15eHrv++utZdXW1aZ5//OMf5f0mTJhgqRwEQRAE0VUgtwYEQRAEQRBRIggCZs6cibvvvhvl5eW6ab777jucfvrpsv9bt9uNiy++GPfeey/q6up091mxYgUmTZqEQ4cOWSrHTz/9hMGDB+Ovf/0rampqdNMwxvDdd99h7NixePHFFy3lGwlffPGFvDxu3Dg4HI6o8nnsscdw4YUXYsuWLbrbGxoacPfdd+P222+3lF99fT0mTJiAO+64A4cPHzZMt2PHDlx11VW4/PLL4fF4oip7R+D1enHhhRfiD3/4Q9jyA4DL5cK0adPw+9//3rTuSBbW77zzjmGaFStWgOM4+d+cOXMiKvv//vc/jBs3Dj/88IPu9urqalxzzTV45pln5N/eeOMNTJw4EevWrdPdp6qqCldddRUWLlwYUVki4fXXX8eECROwevVq3e3t7e2YN28eLr/8cvj9/g4rR0fT1taGiy++GDNmzMDGjRsN05WXl+P222/Heeedh7a2trB5Ll68GCNHjsTHH38Mn88XNm1tbS1ef/1103oNAJMnT5aXV69ejcbGRtN9CIIgCKKrQG4NCIIgCIIgomT27NnyVP7JkyfjvPPOQ2FhIaqqqvDee+9h/fr1AICamhpce+21+OGHH3DzzTfjs88+k/eZMmUKioqKQvY5fPgwbrnlFnz++edhy7BmzRqce+65aGlpkX8bNWoUpk2bht69e8Pn8+GXX37Bu+++i4aGBgiCgFtvvRWZmZm4+uqr43Ytli5dKi+fccYZUeXx1ltv4ZFHHgEADB06FBdffDH69+8Pr9eLdevWYdGiRbJw+s9//hNTpkzB9OnTDfNzu90455xz5GsKAPn5+Zg5cyaGDx8On8+H9evX47333pOv3/vvv48rr7wSH330UVTnEG/uueceWfgeM2YMLrjgAvTs2RMtLS1Yv349UlJS5LQPP/wwvvrqK3m9d+/euOiii3DCCScgIyMDbW1tqKiowC+//ILly5ejqampw8q9ZcsWvPDCC3C5XOjduzeuuOIKDB48GD6fD6tWrcI777wjC5v33Xcfzj33XFRUVODGG2+E3+9H7969cfnll2Pw4MEQBAGrVq3Cf//7X3mfO+64A+eeey569+4d13KvWLECjz32GHw+H/r06YMZM2bI0+83bdqEN998E83NzQCATz75BM899xzuuuuuuJbBjL59+8Jut6O5uRlVVVUAALvdjj59+uimLyoqCvnN7XZjypQpWLVqlfxbfn4+Lr74YowYMQJpaWk4dOgQPvjgA2zevBkA8PXXX2PGjBlYvHixrjuC8vJyzJw5E263G4AYzGvKlCkYN24cunXrBp7n0dDQgJ07d2LNmjX45ZdfLJ/zuHHjwPM8BEGAz+fDsmXLcOmll1renyAIgiA6NYk23SUIgiAIgogXR9utAcdxLD09nX3xxRchaf1+P7v55ptV6e+++24GgGVmZrLFixfr7vO73/1Otc+GDRsMy9PY2Mj69u0rp83MzGQffPCBbtrKyko2efJkOW1OTg47cuSI+cWxQE1NjarMH3/8saX9tG4NeJ5nNpuNPfvss7pTqDdu3KhyBTFq1Kiw+d9///2q/KdPn87q6+tD0h06dIiNGTNGlfZf//qXYb5H060BAJaUlGTqfqClpYUlJyfL+9x6663M6/Uapne73ez9999n8+bNs1wWs3NVTrfnOI4BYLfddhtrb2/XzTspKUlOP2PGDNarVy8GgN1xxx3M5XKF7PPNN98wp9Mp73PXXXeFLY+y7FbdGvA8zwCwBx98kLnd7pC0Bw8eZP369VO5WQh3na2WQYmZWwOJ1157TU7Xp08fS3lL/P73v1eV7ZZbbmHNzc0h6QRBYE8++aQq7QsvvKCb5yOPPCKnKSgoCNt3McbYvn372D333MN27NhhqcyDBw+W87///vst7UMQBEEQXQESZwmCIAiCOGY42uIsAF1hVqK9vV3XF+2XX35puE9bWxsrLi6W0z7wwAOGaZXio9PpZD/88EPY82xpaWEDBw6U97n33nvDprfK0qVLVeen9bFrhFacBUJ9qGpZuHChKr2RsFNeXs4cDoecbuzYsbqCn0RVVRXr0aOHSnTTE+cYO/ri7L///W/T/L/55huVMBZOMLRKLOIsADZz5syw6aXBCuW/q6++Ouw+d911l5y2uLg4bNpoxFkroq/yWgNgS5YsibkMSjpanN26dassnktiuBkPPfSQnL5bt2669WvChAlymvnz51suj1UuvfRSOf9zzz037vkTBEEQRKIgn7MEQRAEQRBRcuGFF+L888833J6cnIzLLrtM9dtFF12EKVOmGO6TkpKi2sfI92ZbWxtefvllef2uu+7C6aefHra8aWlpeOKJJ+T1119/HV6vN+w+Vti7d69qvWfPnlHlU1JSYjpF/Fe/+hXS0tLkdaPr8+qrr6rObcGCBUhKSjLMt6CgAI8//ri8XlVVhffff99q0TuMU045Bddcc41puoqKCnm5X79+sNsT673M4XDg6aefDpvm17/+dcg+Tz31lOV9KioqUFpaGn0hdcjNzVXVAz3OOuss9O/fX143qoOdlWeffRaMMQBiW/3b3/5mus+jjz6KgoICAKL7gk8//TQkjbIODho0KE6lDdKrVy95WdvnEARBEERXhsRZgiAIgiCIKLEimo0aNSrifUaPHi0v79y5UzfNV199hfr6enn9zjvvNM0XAC6++GJkZmYCEH3hRuL30QilQJaTk4Pk5OSo8rn22mt1fVkqSUlJwYgRI+R1o+uj9NU7cuRIjB071vT4M2fORE5OjryuDHKWKKzUFwBITU2Vl7dv3x6XgEmTJ08GE2fagTEWUUCwc845B926dQub5sQTT4TNZpPXzz33XBQXF4fd56STTlLtY3T/o+XKK69U+fE14rTTTuuwMnQkjDHZTzYA3HzzzWEHLSSSkpJwxRVXyOvffPNNSBplHVyzZk2MJQ2le/fu8nJpaaksMBMEQRBEV4fEWYIgCIIgiCixIvhpg/FEuk9DQ4NuGmUgn+HDh1u2VrXZbCrx98cff7S0XziUZUxPT486n1NPPdVSOqVIo3d9fD4fNmzYIK9PnTrVUr5OpxPnnHOOvN4ZLCLNrKElRo4cKS83NzfjkksuwZ49ezqoVOZYqedOp1Mlhkezj1H7iJZ41cHOyrZt21SDOlbbBqC+P3r9hrIOPv7443jllVfiYpkvoexbvF4v2tvb45Y3QRAEQSQSEmcJgiAIgiCixMzKD4BqCj6gHzk93D6tra26aTZt2iQvDx061DRPozIcPnw4on31UIok0VrNAtauJ2B+fQ4fPqwq04knnmi5DCeddJK8vGfPnoRb5w0YMMBSur59+6rcZaxYsQJDhgzBxIkT8Ze//AWrVq2Cx+PpqGKGEM29tNI2tPsYtY9oiVcd7Kwo+w0gsr7DrN/43e9+Jy97vV7MmjULPXv2xG9/+1v85z//ibmv0Vo0t7W1xZQfQRAEQXQWEuuMiiAIgiAIogsTjRAZi3ippLa2Vl5+7733TN0BGBEPqz+HwyEv+3y+qPOJ5troiadKy0AAsq9MKyjTCoKApqYmZGVlRVyueJGRkWE57SuvvILJkydj3759AMTyr1y5EitXrgQgTjufMGECLr/8csycOTOivCPlaLWNeIvnnaEMHYmy3wBCB4+sotdvjBs3DvPmzcPDDz8s/1ZVVYWFCxdi4cKFAERftOeffz6uvfZanHzyyREdU2uFq+x3CIIgCKIrQ5azBEEQBEEQXZB4+BQF4mN9phR4XC5XzPnFitaSUekL0wytWNXS0hKXMkVLJIG9evXqhZ9++gl33nmnrujW1taGL7/8ErNmzUK/fv3w0ksvxbOoRBego/uNP/7xj1i8eHGIr22J3bt349lnn8WYMWNw/vnnRxTQTevGIFphmSAIgiA6G2Q5SxAEQRAE0QVRCo6ZmZkRWYcqKSwsjLksyqngtbW1YIxFbckbD7R+byMRoLXCbiw+dBNBTk4O5s+fj3nz5mHx4sX4+uuvsXLlSuzYsUOVrra2FjfffDMOHDiAxx9/PEGlJY422oEKq24zImHq1KmYOnUqNm7ciMWLF2PFihVYvXo1mpubVemWLFmCU045BWvXrkWfPn1M862urpaX8/LyIhq4IAiCIIjODD3RCIIgCIIguiB5eXny8vTp0/Hmm28mrCxKYcXj8aC6ujouom+0KANGAWpRxwxlWp7nkZmZGZImGuH5aAcvysjIwJVXXokrr7wSAFBRUYHPP/8cb7zxhuzmAACeeOIJzJw5UxXMiUgsHVlXlP0GAOzYsaPDRM6RI0di5MiRePDBB+Hz+bB27Vr873//w+uvvy67RaisrMTdd9+NDz/80DS/I0eOyMtWxFyCIAiC6CqQWwOCIAiCIIguyJAhQ+TlsrKyBJYEGD58uGp99+7dCSqJSI8ePVTBg7RBkMKhTDtw4EBdIVZpfWjVKreystJyGTqC4uJi3Hjjjfjuu+/wz3/+U/6dMYZFixYlsGTHNp2trij7DUAteHYkdrsd48ePx9NPP43du3erApF99tlnIVa1eij7FW2fQxAEQRBdGRJnCYIgCIIguiCTJk2Sl9euXQu3252wspxwwgkq/49btmxJWFkAUQgaPXq0vP7ll19a2s/r9eKbb76R10899VTddNnZ2fKyVXFrzZo1ltIdDW699VaVpez27dsTV5hjnI6sK8qAWIIgWNrn5JNPVrnq+Pbbby3tF0/y8/NVrjR8Pp+lAZ2tW7fKy5EGEyMIgiCIzgyJswRBEARBEF2QqVOnIiMjA4Bokffqq68mrCw8z2P8+PHy+k8//ZSwskhccMEF8vLGjRuxbt06033effdd1NXV6eahZPDgwfKylXzb2trw3nvvmaY7mijPwefzJbAkxzbK67x27VrT9GvXrsXOnTst5a0UWZuamiztY7fbcckll8jrSivqo4nyugDmdXD37t2yKwRAPThFEARBEF0dEmcJgiAIgiC6INnZ2bjpppvk9YceeihiC0iXyxW38kydOlVeXrFiRdzyjZYbbrhBZVl45513wuPxGKavqanBgw8+KK8XFhZixowZummVFrW7d+9W+XDV46GHHlKJvh3B4cOHLVtPMsZU1s1G/jtXrFgBjuPkf3PmzIlHUY8rlHXlgw8+QGNjo2Far9eLO+64w3LeyvvW2NiIw4cPW9rv/vvvl911rF27NuKAcIwxXUv9gwcPWs5j8+bNqvXevXuHTa+08C0uLsZJJ51k+VgEQRAE0dkhcZYgCIIgCKKL8vDDD8vR1hsaGnDGGWfgnXfeMRXpfvnlF/zhD3/A2LFj41YWpTXenj17sH///rjlHQ1FRUX4wx/+IK+vXbsWM2fO1BXHDh8+jAsuuAClpaXyb3/5y19U4q6Ss88+G/n5+fL6jTfeqOv31+Px4KGHHsL8+fOjCiIWCa+88gqGDh2K559/HjU1NYbp/H4/HnzwQWzbtk3+bfr06R1atuOZq666Sl6uq6vDb37zG11hs7q6GtOnT8ePP/5oua6UlJSorGcfeOABSxa0w4cPV7WNhx56CLfddpvpAEJNTQ1eeuklDB8+HKtXrw7ZPnDgQFx//fX4/vvvwRgzzGf79u2499575fWxY8eiuLg47LGXLl0qL19yySUd3p4IgiAI4mjSMaE5CYIgCIIgiA4nKysL77//Ps4880zU19ejrq4OV111FR566CFMmTIFQ4cORWZmJtrb21FTU4MtW7Zg7dq1soVbPCOe9+vXD+PGjcMPP/wAAPjoo4/w+9//Pm75R8PcuXOxdOlSrF+/Xi7ToEGDcNVVV2HYsGHw+XzYsGED3n33XVVAoosvvhg33nijYb4OhwMPPvgg7rnnHgCi9eywYcPw61//GieddBIYY9i1axfef/99HDx4EHa7HQ8++CD+/Oc/d+j57tq1C7fddhvuuusunHrqqRg7diz69OmD7OxstLe3Y9euXfjkk0+wd+9eeZ+zzjoL559/foeW63hm5MiRuOiii/Dpp58CAD788EOUlJTg6quvRr9+/dDa2or169fjgw8+QFNTE7p3744LL7wQL7/8smneTqcTV199tZz27bffxrvvvou+ffuqApFNnz4df/rTn1T7/vWvf8XmzZvx1VdfAQCef/55vP7665g6dSpOOeUUFBQUABAHffbs2YMNGzbgp59+gt/vNyyPz+fDG2+8gTfeeAM9evTA+PHjMWLECOTn58PhcKCqqgqrV6/G559/Lrsx4DgOTz75ZNjzdLvd+OKLL+T1X//616bXhiAIgiC6EiTOEgRBEARBdGFGjBiBNWvWYPr06bKvyn379uHFF1803ddms8W1LDfeeKMszr7zzjsJF2eTkpLw9ddfY9q0aXKQperqaixYsMBwnxkzZmDRokWmed91111Yvnw5PvvsMwDitHI9/50OhwMvvvgi+vfv3+HirITP58OqVauwatWqsOlOP/10/O9//zsqZTqeefHFF7F161bs27cPgNg+582bF5KuqKgIH3/8sVynrPD4449j9erVspsAr9cbElxLGfxNwm6347PPPsPtt98ui7ttbW344IMP8MEHH5ge16zvKCsrw7vvvot3333XMI3T6cQrr7xi6j/2888/R0tLCwAx+OC4ceNMy0cQBEEQXQlya0AQBEEQBNHFGTx4MDZt2oTnn38+JNCOFofDgYkTJ+If//iH5ajwVrn66qtli7u1a9eq/JomipycHKxcuRLPPvssevbsaZhuyJAhWLRoEd5//304nU7TfG02G95//3388Y9/REpKim6asWPHYuXKlbjhhhuiLr9Vfv3rX+Phhx/G6NGjTYWzAQMGYP78+Vi5ciVycnI6vGzHO927d8f333+PK6+8Unc6vt1ux2WXXYYNGzZgzJgxEeWdm5uLdevW4cUXX8T555+Pnj17GtZHLQ6HAy+99BJWr16NadOmmdb7gQMH4o477sC6deswYcKEkO1vvfUWrrzySpXLDz2cTicuv/xybNy4Eddcc41pORcuXCgv33nnneTSgCAIgjjm4Fg4h0AEQRAEQRBEl+PAgQNYt24dqqqq0NDQgNTUVOTl5WHIkCE48cQTkZaW1mHH/vOf/4xHH30UAHDbbbfhueee67BjRcP69euxadMmVFVVwWazoaioCCeffDKGDh0adZ4tLS1Yvnw59u3bB5fLhe7du2P06NEYNmxYHEseWXl++eUX7NmzB9XV1XC5XEhLS0NRURFGjRoV07kSsVFeXo7ly5ejrKwMNpsNvXr1woQJE0x9rh4NWlpasGrVKhw6dAi1tbUAxMCD/fr1w/Dhw9GjRw/Lee3evRvbt2/HoUOH0NTUBI7jkJ2djcGDB2PMmDHIysqylM/BgwcxYMAA+P1+FBQUYP/+/R3afxEEQRBEIiBxliAIgiAIgogbTU1N6Nu3L+rr65GamopDhw4hLy8v0cUiCKILcuedd8puSP7+97/Lfp4JgiAI4liC3BoQBEEQBEEQcSMzMxMPPPAAANGH5bPPPpvgEhEE0RWprq7Gq6++CgDo2bMnbr311gSXiCAIgiA6BhJnCYIgCIIgiLhy9913Y+DAgQCAp59+GtXV1QkuEUEQXY158+ahtbUVAPC3v/3Nsi9dgiAIguhq2BNdAIIgCIIgCOLYwul04s0338SSJUsAiNHppUBhBEEQZgiCgOLiYsyePRsZGRm46qqrEl0kgiAIgugwyOcsQRAEQRAEQRAEQRAEQRBEAiC3BgRBEARBEARBEARBEARBEAmAxFmCIAiCIAiCIAiCIAiCIIgEQOIsQRAEQRAEQRAEQRAEQRBEAiBxliAIgiAIgiAIgiAIgiAIIgGQOEsQBEEQBEEQBEEQBEEQBJEASJwlCIIgCIIgCIIgCIIgCIJIACTOEgRBEARBEARBEARBEARBJAASZwmCIAiCIAiCIAiCIAiCIBIAibMEQRAEQRAEQRAEQRAEQRAJgMRZgiAIgiAIgiAIgiAIgiCIBEDiLEEQBEEQBEEQBEEQBEEQRAL4/wD7RATfziQhAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creating a custom plot function based on the default 'plot cloud' functionality\n",
+    "\n",
+    "import matplotlib.pyplot as plt \n",
+    "from matplotlib.ticker import FormatStrFormatter\n",
+    "import pandas as pd\n",
+    "\n",
+    "def plot_cloud(df_profiles_list):\n",
+    "    plt.rcParams.update({'font.size': 22})\n",
+    "    fig = plt.figure(figsize=(16, 8))\n",
+    "    ax1=fig.add_subplot(111)\n",
+    "    \n",
+    "    normalised_load = df_profiles_list/df_profiles_list.max()\n",
+    "    ax1.plot(normalised_load[0], color='#b0c4de', label='Single day(s)')\n",
+    "    ax1.plot(normalised_load[[1,2,3,4]], color='#b0c4de')\n",
+    "    ax1.plot(normalised_load.mean(axis=1), color='#4169e1', label='5-day average')\n",
+    "     \n",
+    "    ax1.margins(0)\n",
+    "    ax1.set_xticks([60*4,60*8,60*12,60*16,60*20])\n",
+    "    ax1.set_xticklabels(['04:00','08:00','12:00','16:00','20:00'])\n",
+    "    ax1.set_yticks([0.0,0.2,0.4,0.6,0.8,1.0])\n",
+    "    ax1.set_ylabel('Normalised power (kW/kW-Peak)', fontsize='large', labelpad=20)\n",
+    "    ax1.set_xlabel('Time (hours:minutes)', fontsize='large', labelpad=20)\n",
+    "    ax1.legend()\n",
+    "    ax1.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
+    "\n",
+    "    return\n",
+    "\n",
+    "# Calling the plot function\n",
+    "my_df_profiles = pd.DataFrame(my_profiles_list).T\n",
+    "plot_cloud(my_df_profiles)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/plot_class.ipynb b/docs/notebooks/plot_class.ipynb
index 6571fb6d..9a3ffc33 100644
--- a/docs/notebooks/plot_class.ipynb
+++ b/docs/notebooks/plot_class.ipynb
@@ -388532,7 +388532,7 @@
         }
        }
       },
-      "image/png": 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",
+      "image/png": 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pNfYfOYW3Js3RltXcurTnbjNSCoSH6rrGxcvXtJODat5TDlt2HsTr78zEvE/e1E4JuvPUng1b92HA6OkYM6Q7mjesgdj4BGzavh+d2zeD9b8Nc7MENZczU6S4GACKFBcBsrjhBChSDEfukxVSpPhk2A3tNEWKobi9orLlf8jYtDlFpNStreKRhx0utZsixSV8LKyDAEWKDkh5JIu7RIo4NafNk4PweIdW2gavqUlIjHEff6ed1HNPxdJ4a/AL6D18Ctb+OE3LImayOFvao+caQ3p30WbD7N5/TDsyWcyKefTBFlodGR1//MsfGzBr3jKcjrqIoCB/1KtRCVPG9NUEkLclihQXI0aR4iJAFjecAEWK4ch9skKKFJ8Mu6GdpkgxFLdXVPbzLybs2JWyiWH5cipeeI4ixSsC58ONpEjxneC7S6T4DkHP6ylFiosxoUhxESCLG06AIsVw5D5ZIUWKT4bd0E5TpBiK2ysqW7hYxv6DKTNSIgqo6N+XIsUrAufDjaRI8Z3gU6TkvVhTpLgYU4oUFwGyuOEEKFIMR+6TFVKk+GTYDe00RYqhuL2ism/mmXD8RMqMFLMZeGuk3aV2c2mPS/hYWAcBihQdkPJIFoqUPBLIW7pBkeJiTClSXATI4oYToEgxHLlPVkiR4pNhN7TTFCmG4vaKyr74yoTIqBSRItIbQ+0IDMh+0ylSss+OJfURoEjRxykv5KJIyQtRvL0PPiVSduw9ghcHjof1ls1s1vwwDSHBgbh2IwYjxn6BPQdPIH++ULw7tId29rZId/uMIiXv/VHk9R5RpOT1CHtG/yhSPCMOebkVFCl5ObrZ69vHn5pw6XKaSHm1lx1FCmfvWqIURUr22bGkPgIUKfo45YVcFCl5IYo+LFJWrtuGzdsPYPSg59NFcvgHn2vnbPfr/hj2HTqBwe98imXfjoO/nxV3+4wiJe/9UeT1HlGk5PUIe0b/KFI8Iw55uRUUKXk5utnr24fTTIiOlhAUqCIuXkK3ZxRUrJD9I5ApUrIXB5bST4AiRT8rb89JkeLtEUzffp+akbJw6WpcvRGDPs93vo2Eoqho+nBfiNkpAf5W7bPXRk3H4x1aomXjWpl+dm/T2qBIyXt/FHm9RxQpeT3CntE/ihTPiENebgVFSl6Obvb6NnaCCYlJEqqUOIZDkRXw8EMK6tejSMkeTZYyggBFihGUPaMOihTPiENOtsKnRMqcBcvxzeIV8LNaEODvhy4Pt8Yzj7bFhUvX8NxrH2Dlgg9vsp3y+SLkCwvGQ22bZPpZj6c7UKTk5GjktQwhQJFiCGafr4QixeeHgNsBUKS4HbHXVfDWu2atzZ1q/oRf9zyKls0duK+Nmu1+cEZKttGxoE4CFCk6QeWBbBQpeSCId3TBp0SKw6EgyZaMwAA/nI66oM066df9UVQqVxL9Rk7Dr9+Ou4nn069/hpip0ql900w/69fjUSQlZ/9NR0bDySQDkiTB7sj+D3/eG6bsUU4SkCTAYpJhs+fs2M3JNvJa3k/AzyLDlqyA32TeH8us9sChqDDJaftUZLW83vwWswSHQ4XCQaYXWZ7Ol5gEDHnTgQBLPLrU/xbfbOqNRvUkPN815Tjk7CTx4kFVATGmmUjAHQRkGTBJEpJ53+8OvB51TXFfxJS3CPiUSLkzdIt+XavthyL2RXmq9xhtaU9qmjBjPiLyh6FTu6aZfvZS1w64Em3L0REh/sgsZhmxCa4d2ZejjeLF8hQBIetCAq24HpuzYzdPQWJnXCZQINSKq9E2ihSXSXrfBVSokOB+kRIWZEFckh12Ox9yvW+U5HyLb8RIGDsRCA+8jBebfo6pq95EubJArx7ZHx+B/iZNpCQkOXK+wbwiCQCwWmSIe/+YeN735/UBIe6LmPIWAZ8WKfN//hMnT5/HiNeeQbOH++F/8ychNDhQi3Dv4VPwZMd70aZ5nUw/a9uiLpf25K2/B5/oDZf2+ESYc72TXNqT6yHI8w3g0p48H+IsdfDSZRkffyqjWL4z6N1yGt76ZTLyh6sY+Fr2JQiX9mQpBMycDQJc2pMNaF5ahEt73Bu4fyMvoO/IaejS6V48/+T97q3sv6v7lEgRJ/bUqFoOQYH+OHv+Ml4ZNhlvD34BDWpXwVuTZqNAeBhe65Fyak+/Nz/C8u8mannv9hk3mzVknLKSHCRAkZKDMHmpTAlQpHBwuJsARYq7CXvX9c9ESpg124RyEUcwpP376Pv9txDLJsaMyv6bfooU7xoD3thaihRvjFr22uxOkXLqtIqkpOy1y5VSZUpL8POAiTZbdh7EB9PmokLZEqhdrTxFiitBzazsrO+W4bslq7SPQ0OC8PIzHfBw+2bav0fHxmPk2FnYse8IQoODtCOSmzWo7vQzihR3RIrXdCcBihR30uW1UwlQpHAsuJsARYq7CXvX9Y8dl/DtdyZUK7YbA9uOQ98F38KWbMWwwXYEB2evLxQp2ePGUvoJUKToZ+XtOd0pUkaPs+Pc+ewvY8wu21FDTChTKv3eL7FxCRg7fR42bd+v7Tk6vO8z6NC2EX5avh6z5/+ORFsyCoSH4sO3+qBE0YLYuusQxLYacfEJACSM7P8sWjSqifiERLw79Vvs3n8M/n5WjOz/nDYB4s507GQUgoMDsPjXtQgLCaJIyW5AjS5HkWI0cdbnKgGKFFcJsrweAhQpeigxjysEKFJcoZf3yu47IGPRDzLql96EXi0/wsilM3ApugB6v2xHsWLZ6y9FSva4sZR+AhQp+ll5e053ipRxU5NxPdp4Qr26m1GuVPo90d7+cI52Su6wvl2hOBTYku0IDgrA4eNnUKRQfk12TP5sERISkzBqYDc80fNtjBvZExXLlsCNmDjt4BWx3YaQKMGB/hjcqwuO/3sWLw+ZiN/mjkdggH+Gnf3oyx8RHhZMkWL8UMhejRQp2ePGUrlHgCIl99j7Us0UKb4U7dzpK0VK7nD31Fp37JTw868mNK+wBi80+RyTVr2LI+cqoWsXBVWrZO+UOooUT4123mkXRUreiaWznrhTpKz524HoGGctyPnP720uIywkvUhp2KE3Vi6crAmTzNLGbfu0lSIzxg7E0PdmIl9oMF598RGEh4XcLNK446tYMX/Szeu8MGAc+rzQGY3r3kORkvPhNP6KFCnGM2eNrhGgSHGNH0vrI0CRoo8Tc2WfAEVK9tnlxZIbN8n430oZ7ar+hi7152LOxtew8XgzdHhAQeOGFCl5MeZ5oU8UKXkhivr64CsiJT4hCc0798OOP2alA7N42VqsWrcdqqriRnQc8oeHYub4QRBlvl64HEt+X4dWTWpjSO8u2hKfBg/2QpmSRW5eJy4+UVve075VfYoUfcPOs3NRpHh2fNi69AQoUjgqjCBAkWIEZd+ugyLFt+N/Z+/X/CVD/K9TrR/wcM0fsGzPY1i6uwuaNVFwfzuKFI4WzyRAkeKZcXFHq3xFpAh2QoD8uXjqzdNwxX/7a9NufDLnJ8yeMgwhwYFYu3EXFv6yRhMpqclmS8Z70+YiNCQQQ/s8DTEjZfXiqQgM8NMVEi7t0YXJczJRpHhOLNgSfQQoUvRxYi7XCFCkuMaPpZ0ToEhxzsiXcixfKWPTJhld6n2Ldvf8jo3HW2DOxr6oXl1Fl8eydwQyl/b40gjKnb5SpOQO99yo1ZdEyoixs7S9Sob0fgqKqiI6Jg7rNu/GirVbNXGSZEvG8Pc/1/ZOEf9+6NhpVKlQSgvLvB9X4nTUBW3midgjRey1IvZIsZhNOB11EUUL5YfFYs4whBQpuTGyXaiTIsUFeCyaKwQoUnIFu89VSpHicyE3vMMUKYYj9+gKly4zYfsOCc83/hzNKm3GkXPlMHnlaJQqqeLl7hQpHh08H24cRYrvBN+dImXnHgVxCcaf2lOnhglBgeljKJbtvDv1G2zbfVj78I1+z6BVk1oYMPoTnIo8j4j8Yej2eHv8unKjJlJGjpuFzTsOwM9qRcliBTF2RE8tjzi1Z9KnC7Bu8x7YkpO1ZT6fT3w90xkqFCle9vdEkeJlAWNzQZHCQWAEAYoUIyj7dh0UKb4d/zt7v/BHE/bvl9CrxVTUqvAvrl0F3lw6DWFhKoYMoEjhaPFMAhQpnhkXd7TKnSLFHe3lNZ0TkFSx2wtTtglQpGQbHQvmEgGKlFwC72PVUqT4WMBzobsUKbkA3YOrnPs9cPSYGQNaT8Y9YRaowVvQe953kGXg7TftkNIfLOG0N1za4xQRM7hIgCLFRYBeVJwixYuCpbOpFCk6QWWWjSLFRYAsbjgBihTDkftkhRQpPhl2QztNkWIobo+v7MvZKk5HWvBGmykoj/JA8fnov/BrJNj8MWSQA2EhWX9vSJHi8WH3+gZSpHh9CHV3gCJFNyqvyUiR4mKoKFJcBMjihhOgSDEcuU9WSJHik2E3tNMUKYbi9vjKZsxUceGSBe+0m4zi8U2hlvsIo3+djvPXC6FnDwdKlqBI8fgg+mADKVJ8J+gUKXkv1hQpLsaUIsVFgCxuOAGKFMOR+2SFFCk+GXZDO02RYihuj69s8lQVN2IsmHj/NOS/+hDUKqMx+c8xOHS2Cro87kD1ahQpHh9EH2wgRYrvBJ0iJe/FmiLFxZhSpLgIkMUNJ0CRYjhyn6yQIsUnw25opylSDMXt8ZWNnSAhMcmE6Q98jMBzz0Kt+Sq+2dwHfx9thQfaKWjaRMlyH7i0J8vIWCCLBChSsgjMi7NTpHhx8DJpOkWKizGlSHERIIsbToAixXDkPlkhRYpPht3QTlOkGIrb4yt7+z0TVFXCrA4zIZ3qC7VeFyzf1xlLdnZFk0YKHryfIsXjg+iDDaRI8Z2gU6TkvVhTpLgYU4oUFwGyuOEEKFIMR+6TFVKk+GTYDe00RYqhuD26MlsS8P4EM/wsCfik9WJIZ16CWucFbDldC1/+/Rruqarg6ScpUjw6iD7aOIoU3wk8RUreizVFiosxzUyk2JKBOd+YYLWqeOB+FUUL61ubG+hngtViwvVYm4stY3ESyJgARQpHhhEEKFKMoOzbdVCk+Hb8b+19bKyEiVNMCA+8jIlN/oJ09mkoNQbi2I0gTFwxBsWLqej1siPLwLi0J8vIWCCLBChSsgjMi7NTpHhx8DJpOkWKizHNTKR8+52MY8dl7eqNGih46EF9b0IoUlwMCIs7JUCR4hQRM+QAAYqUHIDIS9yVAEUKB0gqgctXZEyfIaNYWCTerbMPuNAJSpX3cBVReOOnjxESomLoIIoUjhjPI0CR4nkxcVeLKFLcRTb3rkuR4iL7jETKkqUydu1OkSgiRRRQ0b+vvh9wihQXA8LiTglQpDhFxAw5QIAiJQcg8hIUKRwDughERkn44isTykUcxcgq54ErbaCU+QxKgdXoPe877Rpvj7LDlHZrpuu6nJGiCxMzuUCAIsUFeF5WlCLFfQH7/c8t+HzeL7h+Ixb584VgZP/n0KB2FfdV+N+VKVJcRHynSFmzzoQ1ayWYTEC92gq275ThUKC9CRFvRJwlihRnhPi5qwQoUlwlyPJ6CFCk6KHEPK4Q8OQZKQcPyahaRd9MVFcYsGwKgeMnZHwzT0bVorsxpKwDuNYIKPwz1JJzMXDRbMQlBWJwfwfy5XN+H3YrU4oUjjB3E6BIcTdhz7m+O0WK4/ghqInxhnfWXL4q4B9geL13VvjZt7/g4fZNUaxIBLbuOoQh73yKv5Z8BEmS3No2ihQX8d4qUsQsFDEbRcSsdk0FYWEq9u2XcfmKhEcedqBubec/4BQpLgaExZ0SoEhxiogZcoAARUoOQOQl7krA00SK2Bttxy4Zm7dIuHpVwqgRdlgtDKIRBPYflLFwsYx6pTajT5FwIKY6EL4BavkpGLNsGqKuFUGPFxwoU9r5fRhFihERYx2pBChSfGcsuFOkxAx6Do6oU4bDDB73Jczl08/8iI1LwNjp87Bp+34oiorhfZ9Bh7aN8NPy9Zg9/3ck2pJRIDwUH77VByWKFtTkx4QZ8xEXnwBAwsj+z6JFo5oBMqMDAAAgAElEQVSIT0jEu1O/xe79x+DvZ9U906TRQ33wx4IPERYS5FYmFCku4k0VKWI/FLEvikjVqiooWDDlxzrqrIyjxyTUrK7gicecv52iSHExICzulABFilNEzHALgZ27ZSxfIaF8WRVPZeHUC4oUDiN3E/AUkXLthoRNmyTs2ClDyJTUpHcmqrs5+cL1U19kNa/wJ14MrQyHWgYm02GoVd/ER6tHYV9UdTz5uAM1qlGk+MJ48KY+UqR4U7Rca6tbRcqbvaBeu+JaA7NROmjQezBVrJqu5NsfzoGf1YJhfbtCcSiwJdsRHBSAw8fPoEih/JrgmPzZIiQkJmHUwG54oufbGDeyJyqWLYEbMXHaTJLQ4EBNogQH+mNwry44/u9ZvDxkIn6bOx6BAf6Ztlbk6zdyGpZ/NzEbPcpaEYqUrPFKl1uIlAsXU9bmJicD5cqqKFUyTZjEJ0j4Z6sMP38Vbw5zvk8KRYqLAWFxpwQoUpwiYob/CIjvtMnTTBDfYyIN6OdAgfz6HkQoUjiM3E0gt0XKyVMyNm0BDh+RoWbwZ9Gvtx2FCrmbAq8vCGzaIoSvjPuq/I6nghrDIReCOekK1Fq98N0/PbH2cFu0a6ugRTPnL7RuJcqlPRxf7iZAkeJuwp5zfXeKlKQVS6DeuGZ4Z/3aPwopX/509Tbs0BsrF06+64yQjdv24bslqzBj7EAMfW8m8oUG49UXH0F4WMjN6zXu+CpWzJ908zovDBiHPi90RuO692TYV7vDgZ6vT8Jzj7VH2xZ13c6DIsVFxEKkTPjQhLh4CcWKKqhUMf3d1MbNMmw2Ca/2cqCIk2OQKVJcDAiLOyVAkeIUETP8R+CPVRL+3mi6yaNdGxUtmjsXwqKAJ4oUMXNw2XIJNaopaNtanxDiYPBcArkpUoQ8+W5B2s6l4re9dCkVAQGqtrwnOlrCyz0cKFWC48yIEfTXehP+XCPh4Vo/oK1/C8SFJqFQZDmo9Z/AyoMPYdG2blk6QTG1zRQpRkTPt+ugSPGd+PuKSIlPSELzzv2w449Z6YK7eNlarFq3Haqq4kZ0HPKHh2Lm+EEQZb5euBxLfl+HVk1qY0jvLtoSnwYP9kKZkkVuXicuPlFb3tO+Vf101xZLiN744HOUKl4Y/Xo8asjAokhxEfOJqESMnWjSdoLP7AHj0BEJ58/LuL+dgmZN7v42hCLFxYCwuFMCFClOETEDgGvXJUydniJRKpRXcey4pIlgIYT1JE8UKWIzSrEppdi/asgAff3Q01fmyR0CuSlSZn9jwql/JRQupKJcORV+1jRhsnefhCtXZXR7RkHFClmbAZE7JL2/1hUrJWzYZEKX+nNRP6A1Lhc6hUoHWkOt0wPbI6vis3UDUaWygmeeylo8KFK8f2x4eg8oUjw9QjnXPl8RKYKYECB/Lp6qLc9JTX9t2o1P5vyE2VOGISQ4EGs37sLCX9ZoIiU12WzJeG/aXISGBGJon6chZqSsXjwVgQF+dw2EEDNiOZFY8vNGv2dyLmhOrkSR4iLqQ6cS8eFUE6xWFU0bZ/wDffGShAMHZVQop+D55yhSXETO4i4SoEhxEaCPFBdv28Vb9yJFVFSppCB1Zp3e5T2eJlLE9/AnM1PEkJg1MGIoRYq3D+XcEilRZyV8/qUJsgztd99svn3WiTixRyz5feJRBTVrZO3B3dtjklvt//VXBVt3WvFCozmoGNQCF4ofRK1tj0GpMRgnY6wYt/w9FC2ios8rWfu7p0jJrYh6fr1XrkgoUMD1GWcUKZ4f65xqoS+JlBFjZyE8LBhDej8FRVURHROHdZt3Y8XarZo4SbIlY/j7n2t7p4h/P3TsNKpUKKWhnvfjSpyOuqDNPBF7pIi9VsQeKRazCaejLqJoofywWMy3hWXsdHHMvaqVMTJRpLhIe9/RREz7xKTdmDdqkPENk90upsfLMJuBt0ba71ojZ6S4GBAWd0qAIsUpIp/PkHqUqJhp17iRAotF1TbNFptnt22jopWO5T2eJlJ+/lXWNgMVSfz+jnbyXezzg8ALAOSWSFn8owl790soXkzMOEn/IJX6t/LQAwoaNaRIMWIo/fCDHXsO+KN3k9konK8BokruQeP1L0KpPBY3TCcx9MdPERSoYvjrFClGxCOv1/HxpzIuXZbR5QkHqt/jmkyhSMnroyWtf+4UKcn/rIMaF2M4TEuDFpCCQ9PVK5btvDv1G2zbfVj7TMwSadWkFgaM/gSnIs8jIn8Yuj3eHr+u3KiJlJHjZmHzjgPws1pRslhBjB3RU8sjTu2Z9OkCrNu8B7bkZG2Zz+cTX79thsqpM+fxULc3IMu3H3U8sOeTeKlrB7cyoUhxEe/2fQmYOcuMoCCgQb3Mf6C37zAhJhZ4sZtD25A2s0SR4mJAWNwpAYoUp4h8OoOiANNnmHD1mnTb5tnXb0gQJ2MULgT07X13ISwAepJISUiQMHGyCY5bnmnffct5H3x6IHhB53NDpETHSNoGzGJz2cYNFfj7p/89P3lKwr+nZbRppeDeVhQpRgylefNsOHIiEAOazUZQgeo4XW4b7l0xAGqZL6EW+B9emTdfa8bbo+zaUmy9iTNS9JLyrXxvvZvyNjxfPhUD+zogp20llmUQFClZRua1BdwpUrwWipc3nCLFxQBu3pmIL782ITRURd3amd8wnTgp4fQZGc2bKmh/X+b5KFJcDAiLOyVAkeIUkU9nSD39IsBfRcMGCqT/BL94cNwkNs5OlnSd3uNJIiV1I0rxPR0bJ0FxAKPesMNq9elQe33nc0OkiJNhxN9IRISK6vdk/Ft+JlLS9uJp0kjBg/dTpBgx0GZ/ZcOpqEAMbzEbSuHyOFlhEx5YOhoo9CvUUl9j8A9fIiYhGP37OhCRheUYFClGRM+76kiyAR+MT1tWcP99Cpo1zf7fOUWKd8XfldZSpLhCzzPLUqS4GJf1W5MgNjAMz6eiVs3Mv0jFxo2798goVlRF756Zz1yhSHExICzulABFilNEPpshPl7C1E9kJCVKqFldQf47jjoWG85GRslo01rFvS3uPkXek0RK6slqYgr24aNAcrKEN4Y6EBjg2pRsnx0obuq4GHd+GczwyKw6o0WKaN+kqSbYkoF6dRSEhGQ8fi5ckHDwsIzatRQ81jn7D1huwpwnLztzpg3nLgXinXu/xtViRRBZZgfaLRsBc/B2qOU/xLu/TcaZq8XR/XkFZcvojwlFSp4cLi51KjZWwsQpaVNQxHfWkP4Zz07TUxFFih5KeSMPRUreiOOtvaBIcTGmf25IwvxFsrbhlDhSMzWZ5UTcV+g9+Jtise3qCzgdXxfr/06ZDjxymCPD6cCiLEWKiwFhcacEKFKcIvLZDL/8JmPbdlkTKEKk3Jlu3JCwc7eMQgWBfn3uvjTGU0TKrt0Sliw1aaeqNGmsYPMWGYlJEgb3d2jTsplyn4D4XVy8xAS7Xc3SqSpGixSx19kfq2SEhaqoc5cZqJcuS9h/QM7WKTG5Hw3vbMHUaXZci/bHhHu/w7+lA7XNZluu6o8g5QrUqsPx8dqR2HOmJh7r7EDtWvr/7ilSvHM8uLPVV69KN/dG9LMCYtmr+G15sL1+QXdr+yhS3Bktz7o2RYpnxSMnWkOR4iLF//2ViB+WmFCooIp7qqZ9ibYuOB7FAvdqVz8R2xybrvTB7r0Srl2T8dQTDlTLZHMqTxUpJ0/JCA4GCkZk74fCRcwsnoMEKFJyEGYeupQ4ZWTGZylv2cSSnsxma4jlPUk2Cf37KogokPn3gaeIlE8/N+H8BUk7wrlEcQX/bJURn+C8/XkotB7flfkLZW0Gh0hiBoeYyaEnGS1SxGyUmBgJ1aupdx37qcKxTBkFPZ7X1xc9/WWezAmMn6ggPtGK6W0WYm8FB65GnELDDS+iwLVQqLVexvyt3bH60P26N8tOrYkihaPuTgLnLkiY+bkJwcEqKldWsX17ynfXoP4ObXZ6VhNFSlaJeW9+ihTvjV1mLfdZkTJt1g9YuW4bfps7XmNz7UYMRoz9AnsOnkD+fKF4d2gP1K1R0elny1YlQZwGUbSogsoVU75AGxf4DOWD199knugIwY+Rn+FMpIzjJyTUraPgkU4Z31x5okgRS5J+/Dnlh4LHOebOl8CevTJWrxXTySW00HFiyt1aSZGSOzH09FpTZ6OULKGgfLnMbwaPnZAQGSmj9b0qWrfMfHmPJ4iUf09L+Oprk7a5ZNMmDphMwLYdMsTUbHEMqjgOlSn3CNhswLz5Jpz6N22nfatV1SRdaCbLZm5trZEiJfV38G4n9KW2LS5OwtbtYuaWin59snZKTO5Fw7trTt38c1bbJdhQ9SpiQi+ixq7OKHGyFtR6T2L1kfsx/58XUb+ugoc76pdbuS1SoqMl+AeosFq8Oz55qfX/npHw1RwT8oWpmvQ9dFjWZL14QSpelGY1UaRklZj35qdI8d7YUaTcQmDvoZP4ZPYSRJ67dFOkDP/gcxQvEoF+3R/DvkMnMPidT7Hs23Hw97Pibp8tWZ6E3/8n3zwGsWa+JagR9iNUmBAf2hoBsX9DVhLx29kJiLxeUps2HxamYsiAjL9sPU2k/L3BhD/+TLvJbddGdflBPu/9Gbm/Rx99YsKVqylxKFVSxROPKdqPeHYSRUp2qOX9Mot+NGHffkmbWSceADNL4sZ+xy5ZeyMvHngzS54gUlJnOpQooaDCf3JILE0SMwZe6u5A6ZLZ+xvK+6PB/T0UJymJ/cXOnpO047Vr11RxOlKC2F+kXFkFL3Zz/rBrpEgRs7XErK1KFVUUK+qkbY44rN0Qqm1C//rArD9YuZ9+3qohORl4b5wZfuYEzGi+DqvrHEFSQAzKHW2Gyvvug1L7Zew+VwEz1g5BxQoKuj3jfGylEspNkSLuv/5cK8HhAMqXU1C5MlClosolibk8fI8dl/HtdzLyhyuoWUPVZmhu+UeGOPHulZccKFE8a78rFCm5HFADq6dIMRC2QVX53IwUmy0Z3V4bi9GDn8fw9z/XRIqiqGj6cF+s+WEaAvxTjnF4bdR0PN6hJVo2rpXpZ/c2rY2FS21YuVpCqZIK2tVYi8YFvtDKJwS3gN0cAb/47bDaTmPHtedwMPpBbNgkaxsdDnzNgfzh6b9sPUmk/LZcxpatKTNRxBRG8Ra3VXOHNjWWyVgCYmMzwV+8rbXZJPj5qej0UMb7WDhrmSeLlMRECYmJKUcKMhlL4LsFMg4fkVGjuooC+e/+oCFOLUlKkrS37ZlJl9wWKanr2AXFxo0U+PuljKk9eyVcvSbjhefEzBv9D1TGRiNv1yaOEP76WwmXr8haXGrVVCFmeogHRrH0SjyYdHxQ0ZaY3S0ZJVJOnJTx9VwZZnPKPjuZHZ8bYjmPaqFLUdJ/C/rO/0b7vh71BkWKu0ezOIlLHG8eHngZk+odxoomGwATUDiqMmpvfQJK9aE4HQ+8/9tYFC6kom9v/THJDZESGSnhp2UyLl1Me4l1K0PRh0qVFFSuBJQqwd9Kd4+vO6+//6CMhYtlFCyootp/S/pTjzwXEkXIlKwkTxUpQnbvOyBpy/qrVuZvZVZimlleipScoOhZ1/A5kTL5s0UoUig/OrRthOf6faCJlAuXruG51z7AygUf3ozOlM8XIV9YMB5q2yTTz3o83QFzf0jCX+tl1KpwHv2aDNTKJwU1RLK1hPbPFlsk/OL+wdmEWlh7aTgOHJS0t1qdOqho1DD9F1Og1QSLRcaNuORcHSkLFsva22mRqleD9nB77LiYHq+gw/384dYTHPGmNTwftAcEV9N7Y00QR+41a6Li6DHg4qXU2Kh4pJOa6ebFGdUrREq+ICsuRye52qxslxfC5PwF4NIlCecvqtr/X7wI7WjaenUVPPqw68yy3TgfLTj7axknTkmoU8v5G0/xXSCWKrZupaBt64xjVTifPy5eT0RuRfLX31PeEop9ncR3WGratw+4dEXGs0+rqFqFN4fuGe4i6hk/BArB9eUcCUKmiO/GOrWgieHUdO2ahF17JJjNwGuvKihwx8lRt7a3QIgVMQl22OzujaOQKOLEqjKlFJQtm55YAb8TuCf4Z5QM2nbzw55zF2j//P6YrD1UuSceefuqV65ImPqxjKJhkXi32kX8ce9qmGBB0JX8aLKuB5RKExBjPYIhiz/TToUa/Yb+8RISYIaiAnGJd99cOycIi9/FP1YB/2z77wVWELTlIlY/FVevSLh4GdrMVHF8e2oSpxDdbSlmTrSL17idwI5dEpb8LKNIERVVK6d8d4mYbNgswW6X0PWpNMGih52/xYQAPxnXYnP3vv/mb+QBGTt3qdqLFZHEy9PWLfX/zejps6/mKRLu76tdz7P99imRsufAcXz01Y/48sOhuB4de1Ok/Bt5Af1GTsOv3467GehPv/5Zm6nSqX3TTD/r1+NRLPjJjlVrVTxZ/3u0r/oLEFIDanCVtAGjJkM6/zMU1Yxll77FmbMm7NqronYN4NUeaefQ3ywgidtPCao4xiAXUkIiMONLB44cV7Ub2Yb1JOTPB5yOBPbsV9GiiYRuXdKOfcuFJnpFlbFxwOBRKTdeH08wQ+zs7kp6ZVDKtTren/JwciYK2Hcw5Q1uvjCgT3cTypbO+MElo3plWdLGt7vT5avAhYvAuQsqzl9QcP4iNHESHZN5zfXrSHjleY4xd8fmzuuPnerAqdMqmjeWtDF1t3Q9Gvh7k4oihYB3R2TwPQbAqDGWUTsTEoChY+wQe3A0a5QiNFPTzj0qos4BL3czoWFd/X8zRsfDm+tLdqiwmNKzPXcBmPSxHeL7MSwUaFRfynDvB/Hdduo0ULaUhBGDMv8ukCQJqlB1bvwqO3texZgJKU+u7VuLWYFpkSnktxsVApaioHV/unANWvQFYpNCMfUDM4ICvTmant/205Eq3p/sQLmII3i9SjzWt/oLZtkfSkwyWq8YBLX0HKDgMqTKrY/Hm+Hnp69fYoyJAebuW7Ltu1TMX+K4+dtYuYKEiuUzbuPFS8CxEyquXhe//WbUqamvL8yVMwTWrE+JVdlSQLWqad9zqffJEQWAsaMy/l3MsAW5fN8v2nToKLBlmwPbdomlSre38v42Eh7vxHuynBg94r6IKW8R8BmRkmRLxvOvjcWUd/pqe6GIzWVTZ6RcvHwdT/Ueoy3tSU0TZsxHRP4wdGrXNNPPXuraAZ99k6RtXvh841loVO0EkgJrpxshgTGrYXLcwKoLo3Am+h5s3Cxrb+DeHJ72WkEs29i1R1hgQBydmNtJTEmuVUNFUFDKHaqYAXHgoJj2r+DJx2imncUndTMykW/YEAeC/+PorFxGnyfbgffGmrXp5LduNCtOHtl/QILY2FAksXFmyxaZvwkR68j3iTcNOyWcOp37Y0wwEZvoiYeMgAAg2Sbh+ElxuoqC55/lGMvOWHGlzMczTdrMoIb1FQQGOn8yTT1GuF9vBwoVSst/9JiM7TuAA4dS3mblZgoJUVGvTtpYKhawByeOJWDz8abo3Em57bPcbKev1C1+K39ZJk6AEzNRFG3z34ySQwG2bpMh3tDf10ZFyzs22I6KkrB9l4R9+8RR1sbQK1JEQZVKaeO8RMAOtCo0WatckQNg9ysPm7UMVMmCgJi/8faPI3ExpigG9nNox4kzuY9A6tKrqkV3o09FE/aU/h8Cr5kRVTMBD/70FlBoOdRSX+L1H7/AjfhQbZZH44YKSpfKPC7XrkvYsRMQmwyL422NSuK7t2pVFSFO7hkOHZFx/ryERzsr2t8Sk3EE1v9tSlnSX0pBuTK3j6F/tsuIj5O0sdWogZgNmfkYE99v4jTPnbvEXlHGtd9ZTX5WVftNF9/DZ8/KWj8eepBjzBk3PZ9zaY8eSlnP43AoWPTrGsz94Q/ExSciLDQYQ/s8hRaN3G+ZfUakbN9zBK8M/RBmc8qdm5jxEZ+QhKBAf/z01Xt4oufb+N/8SQgNTnl11Hv4FDzZ8V60aV4HzR7ul+FnbVvUxcez4rB7nxUvNf8U1eoUyDD61oR98Es6ir03OmPP9S5I/aJ9ubsDcbHihhA4cjT3HzpSGy9+yIVEuXW69ZUrMvbul7R1kl2f4heqsz/zHTtl7TQnkTLbD8fZNVI/F8Jk/CSx5EtFsybp2Z85I+NMJGBLTrnZEzftLZurqFs7Ja+QOjt3SNh7QOzPo7fWnMkn9hQQokSMqcBA6T9pkrIfwp3pRnTKDYXYAFRsBMpkLIEp0024fl1Ck0bKbX/7mbVCnEAmlvfc28KBWjWBbf89dAgp7Cnpzo1z2xQaj7/21cGfhzqgw4MKGjvZg8NT+pFX2iH2CFuxUsatm/9m1rfUTY3F56/2cmjr9HfvSXm4vXTZ+N/LOwXjfYXfQ2H/Q7AFVEeSX8oJf6nJP+4fTPqlD05eroBeLzlQPIubT+aVeBvVD3F0tthYul6pTehaPhQnQ5Yj9JwZR9rF477fh8MSsBdqhfGY8ucYHDybNmNY7FNXrw5Qp46ivewQLy0OHJC1e7JTp4wfY2KvvXJl9Uk3sdQsMkpGhwcUTQoxGUdg1WoJ6/42aRJFyJRbk/jeOnRYgrhvE8nfP2Uj7Qb1oS0zFUm8bBDfY2KvFU9JZlOKPClcWMwWTBmD4iQicSJR3VoKHunMMZYTsXKnSNkWfwmxDoNv8gE0CCqEIDkLM7ByAuQd1xAz7L//aRUebNMIBcJDsf/wKbw8ZCLW/fwxLP8997uhWu2SPiNS7gR464wU8dlbk2ajQHgYXuuRcmpPvzc/wvLvJmqi5W6fTZ4Rg4NH/NG79QxUqh6RYZzM9osIiN2Ay0kVsOL8Ozh6TELU2fRfoOL8+aJFJRQvCiTbPetBUrydEW9m9J6m4K4B6y3XXb5ChtiQUyTxEFCksL6bo4z6J9hPnW5CgH/G++qklomKkvHvGWib0ab+gFssQExM2oOtmGFUrKiEMiUlJDvcv+Y7K/ES+6OIU614ZGhWqOVcXiHrxM2fkHVC2jlLYo8LIQzvTFaLqq0dL1/GAlW6Y46ws4u68fMwayQ6Fh2OJTufxvJ9j4AnkLkRdiaXXr1Gwtr1JpQpo2r7jThLqbLuznxitYXYcLN0SRnBIQ44HM7Hq7O6svK52BPlgSKjoUh+iAvrkK6oX/wuzPj9aRw4V0ubXSdm2TG5j8Cu3TKWLJXRrPwaPFS2IC6pKxB+xozdT8Shxep+CE6OhnrP67iWVAoLj47HufPAxYsybr3NKllC1U6Mst3yHCI2Ey1eVPwmKbAle1YMUzc3FXtUtWrhWfeL7ou0Z1w59SCG2lUuI1+h8AwbJe7bzp69XfoKkRIfLyEuPu2eTEgLcU9Worjn3fenzkTP7rHOnhEtz2qFO0VKtf0LcCDxmuEd3lblcdQLKpSu3ti4BIydPg+btu/XthIY3vcZbX/Sn5avx+z5vyPRlqxJjw/f6oMSRQti665DEKtB4uITtD3WRvZ/VptREp+QiHenfovd+49pJ+mO7P8cGtS+ZQuNTHrcvPNr+HnO+9rqEncmipS54zW+0bHxGDl2FnbsO4LQ4CCMHvQ8mjWo7vSz8VOjcexUAPq3m4YyVYpmEisHQq7/on226MyXOH85CHv3pT3sFi0C7UFbzACxmGWYZAmJNs/6YUx9aCpRQsUrPTyrbe78A8nutcXReOKIPJHEzCNxZHF2k9icWBy9KSRIg3rOb+ZE/n9PS9oPtkhiVohY9lOkMLRriDWaAVaTIZvnZaXPYprr5n9k7cSewf05xrLCLifyvjvWDLsduLelfvab/pGRlJgyzsSDbeHC6s3TyIIDLIhNMP7tSGYsGuX/AhVC/sJvex/Dz7u6aDNp2mSyUW5O8OQ10hNY/oeMTZtlVCivokRx599l4gpi483U7zKxVEv8XhYulLIsKNDPjCS78SKlWcTHKBO0OcPZKKLN1sQD+Hrl/dh2qim6PO646/R+jhPXCWzeKuP35TLaVvkdzcuWROKNlYg4bsXml6LRYFM3RFwuCLX2i0hyBOGHyJSTFUUSD4rnz0M7xSs1iWVnYoyJt/MWswo/i0nbh8fTRIqYDShEY7MmDtzfLvv3F67T970r/LxUxo7dMro3nQkltCb+jW+cKQRxul1kFHDunAS7I+W3Utzri3v+IkWgvSAzm2RYzBISkvT/9hpB/cpVWXtWqVRRwXNd9X1fG9Eub67DnSKl6cElOGePNxzPwnLt0DCocLp63/5wDvysFgzr2xWKQ8hoO4KDAnD4+Bnt0JewkCCIA2ASEpMwamA3bWXIuJE9UbFsCdyIiYPYn0qsEhESJTjQH4N7dcHxf89qM03EQTGBARlv3CtO5523ZCU2bN2HryYPczsPnxUpOUX2/UnROBUZgCEPTkbxCikn9WSUAmL/htl+CesuD8KpmPo4flzSvkRDQ27/AfRUkSL24di6Xc7y0YE5xdnbrjPlI9PNddXdnlVQ0YU3kuIoxC9mm7TplnX+W66jh4eY/i4eNvKH3/4D6KkiJdkuYcNGWZsKO3KYZ91Q6OHt7XneejdlamZWRMrZcykPIKkPtrcy8CSR4ifH4omSvbTmrTrwEBZu74amjRU80J43h0aO26XLxP45MipXFLMv9bEXS8XECV9Fi6YsE7w15YZICTJfwSPF+0OFGXFhD2h7otyZ/JKOYeGahlh3tB0efkhBfR0C3Mg45LW6/lqv4M81VnSs9gtqli8C+fRGFD5kwdrBN1Btd0eUOlEPav3HtW4vOD0HDvX23d/FLE6xN13+fCoC7tgfylNFivjuPXJU0saWGGNMxhFY9INJOxa4d8tpqFzsOJaeTdtf8W6tEDOe/P2BsLDb7/s9VaSIpb5i78YyZRT0eJ5jLCdGmDtFysyL+3DBLmZzGJv6FKyGwpb0O6o37NAbKxdO1oRJZmnjtn34bskqzBg7EEPfm4l8ocF49cVHEB4WcrNI446vYsX8STev88KAcejzQmc0rntPuss+/vJbOHoyUsazoKMAACAASURBVJvhMml0H1SrXMbtMChSXET81gcxOHvRHyM6TUTBMqUzvZo16Qj8EvbjSExbbL3aI9N8nipSEhJTjhIND1cx6DU+5N5t2IiTdN75IG294FOPx6Natewf2yPWQn/7nQli6Vetmq7/mHmqSBFM164zQUzbf2e0Zy07cvFrwuOLiw07x04wa+KtRbOc+fv2JJFSI9/PqBm2GMmW4thwoDq+29wdDera0Kmj56xR9/hBkgMNXLTEhH37JO3YaTGDydWUGyKlbvg8VA1dDptfeSQFZLyRncV2Br//XQHL9z+C+9ooaNnc9e9tV1nl5fJ/rEjC31uC8ETNn1C2cn6E7N6Novus+N+YqyhzvBGq7nkASp1XIJmu4JeoDxFjz2z2cHpKnipSxMzTg4dk1Kyu4onHcuY7Oy+PkZzs27zvVRw5ZkH/NuNRo/gu7Lj6LA7GpF/ip7dOTxUp0dHAjl0mFC+motfLHGN643m3fL4iUsQepM0798OOP2alw7F42VqsWrdd26v0RnQc8oeHYub4Qdq+pV8vXI4lv69Dqya1MaR3F22JT4MHe6FMySI3ryM2kxXLe9q3qp8harHx7O4DxzH03Zn4asqw28rmRAzvvAZFiotUR4yJwaVr/njrsfHIV7xsplczKTcQGL0aMcmF8cvZKV4nUsRxaJs2i+UlwPAhfMi927AR62I/+zJNpDzeMRq16mb//Etx+smCRTIiCqioXs31G3JPFiliAzdFAUa/YYcl++7Jxb9q3yseEwNMmmrWjnZt2jhnbpg8SaQ8XqI3/E0xiA+5Fzt3y/h6wyuoUyMJjz7KIx2NHO3z5ou36LL2PSa+z+5MFjkeyYr+70qjRYpFTsBjxfvBLCciLvR+KHLGbRX7ov21pQB+3PGMtufQ/e1c/942Mk7eVtdvvyRgy64QdKuzBPmrBqLouqOIOGbBqjdvIP/lSqj7Txco1YZDCjiGPy+OxPmEarq76Kki5fIVCfv2y1x2oTuSOZdxztcqTp62YGj7MahU+BBsSgCWRk2HLQvfXbe2xlNFSlwcsHW7CQUjVLz2as7cF+RcFLzzSr4iUkR0hAD5c/HUm4e4iP/216bd+GTOT5g9ZRhCggOxduMuLPxljSZSUpNYmvPetLkIDQnE0D5PQ8xIWb14KgIDdJ5Z/9+Fhr33GVo2roWO7Zq4dbBQpLiId8ibsbgR64f3nnofQYVu37n/zksH3/gVkmrHz1HTEWfP+IQfT52RIjZl+3uDSXu4FQ+5TJkTEMfZ/fhT2gNap3ZX0aBJaLaR7dojYcnPJu0NrniT62ryZJGycbMJNhswdJAdIWkz+1ztMss7IXDlioSPZjjf0DgrID1FpJQLXocmBT6Hw5wf8cGtcGjPVXzxVx9UrxyPLk/R1mUlpq7mnf2NCaf+lbSZdWKGXWqSJTuaFpgBs5SEtZf0r2k2WqTcE7oMdcLnI9laAomBDTLFYXJcxbbtJszd8grq1VXRuSMfQlwdO3cr/+OiOOw+FIZX6v8IczUZ5X+NQthZM9YPjINJikDTtS9DqTgJUthmbLrcCyfiWupujqeKlNQDAMqUVtHjBY4v3QHNgYyffaHi7HkL3uz4DooXioElORIHox/AjmvdsnV1TxUpqTPR84WpGDyAYyxbwb2jkC+JlBFjZyE8LBhDej8FRVURHROHdZt3Y8XarZo4SbIlY/j7n2t7p4h/P3TsNKpUKKURm/fjSpyOuqDNPBF7pIi9VsQeKeIEntNRF1G0UH5YLGkvrMUBMhcvX0fl8iW18sdPReHl1yfhy8nDUL50sZwIXabXoEhxEe9rw+KRkGTBhGffgSX/3XcR9o/bqn3hbrnaE8di7s2wZk8VKUX892HBH7W0Nr/7FkXK3YbNqjUy1q1PWzJwf6vLaNYqX7ZHmthscdnvMooXVVGxYt4WKVu2ykhIkDDgVQcKRLg+9T/b0H2soDjFYuYXZhQIjcYjzbcjSQnW3q4lK0Ha/6f8c0CWqHiKSOlQZATC/U4jMagRki3FcOLARXzy52uoXD4ezz5LkZKloLqY+bNZJpw9J6FeHcdNUSpmebQqOBmF/Q9qV996tTuOxNynqyajRcqjxfsh0HwN8SFt4DBlfhKArMTj4J7r+Hz9IFSrquCpJ13/3tYFxEczfT8vFodO5EP/Bj8hoU4Can57DQHXTdj6YjxiipnRZvkQKCXnQir8M/bceBJ7rz+im5SnipSYWGD7DpO2MW6fV3hPpjugOZBx+icKLl+14p3HRiOsaDkERa/Qrro0aipi7elPL3FWpaeKlNSZ6IGBwBuvc4w5i6Oez90pUpZeO4lrjiQ9zcjRPJ3zlUW4Of1sEbFs592p32Db7sNafW/0ewatmtTCgNGf4FTkee00nW6Pt8evKzdqImXkuFnYvOMA/KxWlCxWEGNH9NTyiFN7Jn26AOs274EtOVlbqvP5xNdvm6Fy4dI1DHv/M5w6c16TLeH5QtGrWyfc16JejvY1o4tRpLiI+OUBKadSTH3xbagh6Te+ufXyVtu/8IvfgX/jGuHvy/0zrNkTRUqlkD/RIP9s9J3/DWx2P4weacctItBFgnmv+PzFMg4elFE09AzORZdE6yYX0LpdxjOQ9PR+wyYTVqyU0LrKSlSrEIt90Q/rKZZpHk+ekbJthwyxuWTvng4UK0qR4lKgs1D4zBkJs+aYUKHgYQx/4O0MS56Jr4d1lwbrvqoniJTCfodwX5H3oEgB2sagIkUePY8p/xuA8qXi8MKLWZsqqrvzzJghgY9myLhyRUbDBgoCA1QEmG6gdaFxCLee0TZtldRkOBQLll94HzdsmW/ennpxI0VK2aC/0TRiJuzmCCQEt7hrhCXYEXnoFCavfBPlyip4sRtFijv/JOZ8GYuTZ/NheJOluFDvEppMT4bJJmF/50ScrpeAB34aDangSqilP8fRmDb45+pLupvjqSJFvHAQLx7yh6sYyH3rdMczJzJ+OEVFdKwFE54aCUuhWrDG74Wf7dhd7+3vVq+nihSHAqz/2wSLBRg9Iu+KlFOnZEBWUaaU++853SlScmJs8xpZJ0CRknVmt5VIFSmfvPwWbAF3X3crKwkIiv4fbI5ALI5MvwGPuLCniZS64XNRNfR/Wp8HL/ocMUlhGP66A0F37GzvIsY8VfzjT2WIE3Mal1uPzSdaoHnd82jfMSLbfVy9RsLa9SZ0rr0YHWv8iIuJlbDxSl/E2bN3TU8WKTv3yLhxXUL35xWULcOHj2wPmiwWPH5CxjfzZFQtuhsD2n8CVbYCqri3sEFSkyCpNu2Kh6Pvw7Zr3XVd3RNESsuCU1AycDtsATWR5Fdea/eFk5GYsGwIShaNR8+enJGiK5g5lGniFJMmSsWJSRGBZ9G28AfaDA9FCkRCSDNYEo/CajuFG7bimkxxKHePj5EipUPRNzThkxDcBHZz2sZ3GaGRE4NwOWoD3v99HIoVVdC7J7/LcmgIZXiZmZ/G49zlUIxpugxHG5xC6wkpU75PNk3AoQcS0Hb5UFj9DkOt+D6iEmph7UX9y8dyU6SImcAN8s+BXfXD2YSauJBYHecTq2t9S06WsGGTrN2LiXsyJuMIfDBOQlKyCVOfGwY1vB4kJCPoxgpNBK84/w4uJ1XIUmM8VaSITogDAETKqzPRr16VMO0T4/pIkZKlPw2vyEyR4mKYhEixmhMxtfskJPlXcnq1oOiVkJVYLD/3Aa7a0h/L5CkiRaxVbx7xEYoH7tb6pMhBePOHsbgcV1g7tUec3sOUMYHUY2Sfa/wV5m1+CQ1rnEfHR7MnPUQNy1fK2LRJxpP15uK+aqshqwnaMgvxVu1UXNY3UfJkkbL/gIpLl83o2kXJkf1gOEb1EThwUMaCxTLqlPoH3Ttsht1S+LaCZvt5BMRu0v7brutPY/+NTk4vnNsiJcRyHg8XG5JyTG2+B7X/F+nqmVN4/+fhKByRgL6vpj+61mnHmCHbBN4fb9b2QHr43uNoX2ws/ExxcMhhmkRRJT9IqgOBMX9CVuJwOKYdtl198a51GSVSivrvQ5vC46DIwYgLbZdpm8zXC8NyujIsF0vhYrk3MPK39xCez4FB/fl7me1Bo6PgtGlJuBodhAnNVmB3vZ1oMzFlKe35ajbsfCoWzdb0QWhSItRqg3DdVgK/nZug46opWXJDpIgjtsVLrFKBWzNs57mE6oiMr4Uvfu8Esxl4a2TenS2gO1AGZnz7PRNUVcKM7q8jKThlryS/pKOwJuzTJIqQKVlJnipSrKY4rP4rCHbFhFFv2LXN6PNaOnxEwncLUkTKsMF2BAe7t4cUKe7lmxtXp0hxkboQKSH+NzDuhU9hs5ZzejW/+N2w2k5k+jDiCSLF33wDrQtOQH7rv1AlMxKDm0C2R2Psoudw9npJ9O1tR+GsLwN1yiYvZLh8Rcb0GTIKBF3C43W/xxfrB6B2xQt4rGv2l/b8skyGWPLyXONZqFc/CJbEQ/BLOqLhOhXXGP9cfTlL+1d4gkgR+yLks5xBPmskQs2RKf9sicT3W7ph04kWeLSzA3Vq8eHDqL+J1A2Nm5Rbj64P7IXdlH68WpNPwy9uu9YkMSPqZGzTuzYvt0VKvfBvUCX0D20mipiRkppiLpzE24veQP6wBAwcQJFi1BgT9aRK5lndntaqtZsikBjcFKqUtjm3yXENgTFrtc/FzAExgyCzZJRIaV1wPIoF7kVSYB3YrBm8ADlfFtZT98AUG36zqbEVR2LgL+8gwF/BiGGckeLOcTZhogNxiX74qOUq7Ki0HS0+CYMS4IfoAnHY9Eo06m1+BoUuFYNa+3nthJXFZ77U3RyjRUqNsJ9RM99irX3iVKhkq9g8UQJUB8z2KxAbGaemV+bN1x7o3xlthyTp7hIzukDAlgy8P84Mi8mGaS+OQlJg6h4MDgTdWKm96Fp/aQBOxzfUXYsniRSTbEPJgB0oHbgeJQJ3YdCiLxCbFGqIZNANLAczbtwk438rU/Y07NfbjkJufrahSMnB4HnIpShSXAyEECkRwRcw5tlvYLOWdno1S/I5+MdtxrnE6lh9YUS6/KF+0QiznoMtOXemalrkJNTPPxtBpqsp062Dm0ExBUM8RE3+4VGcvFxBO09enCvPlJ7AwUMy5i+SUa3YLtxXZQU+Wj0cVUtdQNcXsy9SFi+RsXefjJ7Np6NqnZSZAib7VYjNi2U1Hgn2MByLbY3DMQ9qm4Q6S1ZzEooFnkKCzb1jzKFakaiEIcR8DvksZxFijkKo5RzCzFHwN0dn2Mzv/+mONYfvx0MPKmjUgA8fzmKZU59v3S7j199k3FvpDzza7pQ2SyCjlPrWTXzm7BjR0qEnkGhLzKkmZuk6MhS0LDRFOwUmLux+7bssNSVdPo4R80ciJCgRQ4ek7fqepQqYOcsEkpOB98aZ4WdOxCddX0SypQQSgzI++cYv8RCsiQeR5AjCb+cmIsGR8WbdxYIjASUGDsV9v0fi2OzmEdOhSlbEhj2U1m/FBL+T1WCNqgTJ5n/zvzsCVMhJElDhbfRcOkr773l1WnyWB4GbCqQKuplt/sTOQjvQ8OsQJEeEwWG7hjWvX0fVvQ+gzLFGUOt1BSQbFp75CnYlLWZ3a1Z+/ysINF9Cst29v0cBpuuoFbYIwZZLWnOS/CrDFlBZ/Nrf1jyxfMRsvwRz4jG8/v14xNlCMGKoAwEB7vsbyErYYmIkiFPgQkJUFMjgiPOsXMsT88YnSBg/yYQQvxsY/9wEJAWmiV6L7Qz847dpG86KjWf1pkBrPCL8ziApl+77JUmFCXaUCNyCMkGbYJZSlvKKNOKnj3A5tjAGDXAgPMwzxphernry/fKbjG3bU0RKjxcdbt8nhSJFT1S8Kw9FiovxEiKlWFgkRjy9UDsS0VkSm9AFX/9Vy7bgzJyba8DFFM5ywatRPGCPs0sY8rk4KjQhqIl28yiSJfksZvzcDofOV8cL3RwoXzbvfaHmBNi/1sv4c42MdlV/Q62ih/Dh6iGoUPQSnu+Z9qYyq/V8twA4fMSMV++digo10o7xEmPJGr8HYhNjkcQmjUdjW+NQTMcMj9cu5H8Y5YLWokzgZoi3DrmdxMO6YgqCIodANQVr+3H8trEaft/3CO5ro6Blc/feuOZ2/z2p/g0bZaxYJaN91WXo0OaitpQvs2RN2AO/pONIdgRg5cW3cc2WctycSGGWsygbvBblAv9GgPlGrndRnNIjTutJTeYrxaBeiMSAP7vC32rHyDdyvYk+04CYGGDSVDNCA65jfNd3kBjU+K59D4z5S3v7fj6hmibtUpOYMVk+aD3KBv2ljTejks2/KpL8007ms0ZWgv+hlLfOikVFcn4VyeEOqDIQcMoEU5Ep6P9HTyQkB2LEMDsC9D23G9WdPFNP6gwBscR6cqvNOBS0DXV+CIGtWASsUZew/J2rKH2yAe7Z1QFKrd6QLJdwJq4+TsS3QmR83Qw5CAFbOmiL9nspfjeNTHZzISQF1r7rd7Boj1/cDry18HVciSuIQf0dtx0nbmR776wr9bekWjUVTz3u3pc1udHPa9ckTP3YhIigCxjT9RPYAlL2rElNgdF/wqREa7+PJ+Ka4Vjcfbh+y2/krXmLB+xG2aC1KB30T250JcM6VUmG3VwMdr+S2hLLsQuex9kbJQyZrZEbEOZ8Y8LJf1Omc4nT1cQpa+5MFCnupJs716ZIcZG7ECllChzH4C7LnG5Al1pVQMw6mB1XtDPnA0yXUS5onbZWPDUp5vxQxFTOXEqKHKr9kN+azPYL+OqXptgdVR/PPq2gciX3ftnkUtddrvbHn0zYvVdCt0ZfoLQlGO///QxKRlxBz1czPyrTWaVff6PgxL9WDGo3ESWrpJ/1ZFKuwRJ/GBb7uZuXOh7bEgejO8GmBqB84HqUC1mDEPPFm5+r5lA44P5lDeIkDlXMBjAFaieniKnKqilA++c7k3hoWrsxH37a1RUtmilo15ZjzNnYyKnP1/wlQ/yvU80f0KZlvLZfxd1S6lHuiY4QrLo4CoWsR1EueC0i/I6lFTMFwH7LTJCcamtWrmMLqAGHKU1iBu5o+3/2zgM8quJr4797t2bTC5BAGr33FjpSVBBBRVQQe0PBBoKK7bNjBVGx/m2gKNjFAkiH0HvvJYUkkL7J9r3fMzcGCATYALtZY87z8KDs3DszZ86d8s4570EyLOG+hUMRt3DPP1P1NvoV0Y8vy4pb6nfe11Az+CjP3viBB+TsZkyFC1XelM15N5LvqKMebGNNG042W9Li0oaieB3Xl7AGdirzXQStGIJsCcZWy409sqwdGVM0aMM/4YnlV/vdQdeXY+6LusxmeP1tLaEBOTzXfTsp7rU0mx+CPb4Whn1pLHiygLD8+rRfeRPu+l9AxF8qKagQwTV2qDiJI8VdVMBOzF/1gxaRaFqJVj6ZRtSti8SLTk//qEnCaaivpmj3RPSWrbz6/b2k5cXzwH1OosvSWnnyCq+UWbhEZvESmfg4hbvvqHrza0YmTPtIS52wI0y84QvVc+hUEfsYQ/EmNK6TFwnZtkT2m/tysLgrwvOofuAi6gcuLeOZq2jDcJ3mfeSVATrLSxUMuPS1/wklKykkPNHfnH0dh7Lrc+9dLmLreH2i9WWX1breeFtDobnkvHX1VW46tvfuvrMaSPH5EHu9wmog5SJVLICUxrW2M/q6xWpaRE+k1G351LKCxM5piEcJSETWmrB6OezCk3aWWRycOXz9ZyvWHOrG9de6aNWy6k2oFdVJeeU//ERD+lGJCVc8R0heX55e3ZOaoXmMefj8ITdnq//jT12kpht4YuAr1KxfknmkPJHdheituxHupeWJOBzbdXG4AxIwBERQZPUvgjrZZWbNWolv1t5Jpw5uBg307oJ2Kca7qrxj7nxZzQAxtN3XdO8qleGsOFsfA8wr0DpPgnOinOBUEiEbLn08xuBozJaSA4s/iFwUStDKqyH6R+5bPACXouW5p5xoynrO+0NTq2Qbjh6FDz7REh9+iMeHzcBmaHjefooMPobijWeUExw+LmMi2sAEbC5wuXy7HumOxRGwuReKVsHc6Mx51JiuQWv6jhc2dCAlN5H773ESE3Pe7lYXuAANHM+WmPq+huiQNB7pcojcgjXUXx2GrV5tArYdZMWDRaCPoNvCe3EF5VKU9Ds6Rxpa26Ez5q9Tq3dpQnHqE5EDE3BLOuwO/1qPDLbdvP3Tjew/1pi7b3cR74PUrZ4Mz5/zZFaukgkLUxj7UNUDUlJSJD75XEO9qD1M6LuY4qgIkM/sp8adj9Z6UN2PCe/h8kRcKDn08Simumj1QVjEZOZHonOm896Pl7M7szm33+qiXqJv51lvq8LlgudfPhne2+cyN717ePc7rwZSvD2qvn9/NZBykToXQErLOhu5+5rVuGTPwjcEv4XJvETNIuEwxOHUxeHSlnBo+APZbHkqEYvC9/MasGxvH4YMctG+XdWaUC/SDE48/vxLGlxuiXdveBDrwTsZv7YtYSYzYx+7cL/u96e5yTyu59lrXyAstuztR3ntFrwpOuse9LaD6s/iYCtAOqe25MrKH8hmy2u3pFjZutHMZytG07qlwtBr/WtTcalsxB/fM+cPmTXrZEZ0/IwOSWcP6zm17cJTIMC8FI0rD4euDi59HA7dydNiZZPNnq5n484k9GkNoNYfPLiimxpyMXGCE+OFf5r+OJR+26ZDhyU++1JDw5o7efjaX7Dr63rU1gBzMsIjUoSZCh4ypyFRzZ4jxFdks6c3NHDtFWjya5TrjSLKGjJk9Lo/eGNXHLszmleHw3o00hdWKD0dPvxUS93IfdzZJQNb6jridkRia1gH0/o9rL/VQm68RL/fx6NobRT2LiFyFSLWHL09Ba3toBrGcNLGEtSQU3UsdRoUFL8DUvS2A3w4ZwDb0ttwywgXDRv4x57slzka1m+QkGVUoLqqkeDuPyDx5QwNzWK2MLZhLrawSGwJ289pvAJM0doPqmTBgo1E0AC4xFz2z77fn8hmT+2ImHc/+bU7W9PacfNNLho38g8bu7CZ4synSueO0l+6dHYz4IpqIOVS6fe/8p5qIOUiR1oAKR0SVjJy0I4TmztPXqmzp5bLqeKvQIo4nM/5uxZ/7xqoTjRiwqmWshrIz5d46x2NmsXpzcFvYD94Ew+ubUGAzsaTT174tffkKW5yC/S8NOw5TNHNPFa75LaBLDaBZQk1/RdIcbF3WzrTFj+mho6JELJq8Y0GfvwZNm3Rcke3j2jZrnxiz/JaIil2kOQzbEyU9ScgRXIaCF4yFJXAImoB49a1JN8SzvhHnQSXnJeqxcsa2LNXYsZMjXrxcO+gBWVcyM9VtTjsap25ZUC60vKVAaRoCiIJXDNATaRibuxQTep00R+TMbiX8/4RLRuPdPJJ7L2Xh89vX3/gkMQXX2loGr2VoZ3z0O/cQK3UKCxN4wlasZ0dV1k43NnClT89i4RE4WUzUTRngvSyuwAR1ny6+CuQIg7nX/7VjXWHu3DDUBctmvvHIfe7HzRs314SKuGLdLK+NszShAJt49cwOk5Bye+MucfPKkh3PhFgnVsWyH3Z/aC/AikaZzbT/2hbYmPXuWjRwj9s7Hx69vT3LVslvv/p5Fi0aqFw/XXevcCr9kjxdHT+PeWqgZSLHCsBpHStv4RhV+5HOQdBo6fV+CuQIik25i8O5o9t19L3Mje9vOz+5qm+/Kncvv0SX32toVHNHYzrshT56GXcs6bxRXMxTHodiq1a3hjxDJrIssRmF9J/fwVSRF/Sdu3hrfnPUDfBzR23VQMpFzK+F/LMt7Nc7Nhl4L7e79G4ZY0LecUZz/gTkGI42BLD/tY4Qtxo9clM3JjAMXM0D49xERlRtTaHl2TwvPCSbdslZv2gUS8ebh2wCqeHXBDnakplACmmLT3RZsVjj3Bjiy5/063LkTFaNvHFsWyW77+s2ovTC/ZU+sqduyRmztLQLm41fZOsRK7cQlhBDSzN6xK8aCOHO1vZcVUxff56DIMlEHPXX3CbCj1ukb8CKVrHUWb93Zrl+/zLvqZ/o2HvvhIgpSqGtG3aIvHjzxq61FvGXTWDIb8j9vidWBut99imTi/or0CK7Mpn9txGrNjfm2uudtGubdVaKxct1bBosURQkILZLNGgvptbb/buvrMaSLngz8TjB91uhdsefoUGiXV4btztHj93oQWrgZQL1dw/zwkg5bJG8xgWF4c7vAhneAZu40ni2Iq+3l+BFBQXy1bI/LTxJjWbisiqUi1lNbBytcyfc2V6NZrPzYl25JwWjNoYg9Ol55knnegukNv1hZdknG6Zqbc/iTO4LAnwhYyBPwMp2Qe28/LvL1A72sWoe6vWon0hY+WrZ2bMcLLngJGH+k8hscmlIXPwJyAleOn1aora4npOjPkbeH5bpN+RNPpqrCurHuHuL9z+uzdYxLD+23FpywJ2klOPZAvAHeh5tidfAymyJYigFdeoKixq6FSz9ZQnugIZY+5+ZhVtZ97OQVzR3023LtVrpjdsb9MmOz/+aqJr3RV06lJI/F/7CKAW1ibxBCVvI6tuERuGm+m6ZBShObUobv+3uk/zVPwVSNE4jjNnSSLzd17Flf3ddPUT+/r0cw1HUkqAlKoYDrJmvcyc32V6N5rHyPB4KGip9tXc7SfcARe29/dbIMVt5te/67Bw95VcdaWbzp2q1hw2+wcNW7dLxNZxk5omUztGYdQ9/16PlNxDCs7zO0Z5OvV5XC4iUUJz7vwEHr/rUhT8YtZfLFu9hfjaNauBlEuhUG+/QwApVzT7jWFcDUrJSdkdUIgzPBOX+BORgdtg8bgZfgukAKtXWvhu3e0kdXIx8MrqQ+7pg/rbHJm1G2Ru6vgFffQdkYtq8MhOI2ZbCE885sRk8tgMThQU2Siee1GrerW8d+dT2EytK/6S057wZyDFnLqZZ396hchwJw8/eNFdrX6Bhxr432cOKH7bNgAAIABJREFUDqcG8NiAN6nd4GQ6Yw8fL7eYvwAp+ox6GLd1xRWgUFzXSWDGDl7drePg8Ybcc5eLuCqYieBixs1bz5YCzf2a/MGgPim4NSdDyATIZdrQD0mRMHf+o1zyxvLa5WsgxbirI/rUxqpnkzX27BtubZFMQEYWvykL+WXzDdWXD94yKmDtqmJ+mxdCv4aLadQ9l6Yzj6IJicbWoIQjxRyYT/KoAtqtuYlaaY2xNl+JPWa/xy3yVyBFeAssWBbBb1uv57JebvWPP4jIaCMy2wjxRRYUX/dZkLILcvYrmv3KdaEtcbsbIIBTR8wBLM2TL6g5/gqkiLDKuYvC+Gv7EDWLosimWJWkNDmESHm8fadvCJK96ZEy7xknBUd9fzbr+7SG8MQzY1zNRRZemTqDleu3I7xEHh89goF9O/PTn8v4bOYfWO0OIsNDePPZ+4mNqcHaTbt47f2ZFBWLM7PExIdupkfnVhRbrLww+Ss2b9+H0aBn4kMj6dimSbmmeCglgyde/ojbbxzA6g07qoGUf8MHK4AUkTJ0iHM4LoOCxnpm2mJL6yU4apSfSeX0PvorkGI40IrVx5P5avVdtGvj4JrBlZee2V/tQhApCkLFR/u9TLPM8Uiylcf3W8k21+TRh1yEh1V8grNY4dXXtQToi3hzxGRswefPdHE+/fgzkOLMXM+EWa8TZHIx4bGK6+t8fa/+vXwNfPihk/QsIxMHTyIq4UwSULk4GEVv9ygOvLQGfwFSAlddhcYcrh58xQE4MPMAb+2zqCSgVTETgb/a+JKlMgsWywxq+SP9eubh/icUVrYGYVrXD/G3EHvsHqxN1njUDV8CKcJjJmjZdUgurerZ5DKefX4S+wDTYTsLTNOZuaY6C5lHg3mBhZYvLmTe0nAGNVlATK8MOkwrwBlXB3tiLTVrj7sohwWP59F4W3/q7e2Krf5mbHW3elyb3wIp7mKWr9Tx/YaRdE1yc+Xl/nHIfXuqhry8kv1hr55u+vb2j3Z5PODnKVia3nlw69kMCuyOJao2gXtLeOhERiiRGaqi4r9AiotFy7T8uvmGKjmWz75QMm5dktxqpim9Dp5+0rvZLL0JpCx81YE1r6LWd/Hlk+7TElHvzDPhc29+jkGvY8Lo4bhdbuwOJ0GBAezen0J0zQhCgwN568NZWKw2nn7kFq6/5zlenXgPDevGkl9YhCRJhASZVBAlyGRk7H03sP9wOnePe53fp0/CFFA2U4AAa+54dBJPjBnBkbQsVq3fXg2kXPzwev8NAkgZ2mY2VzCcovpOJDdoimU0ZtCaZWS7hCI7KUr6E7fp/C7L/gikGHckoU9vwOqwD/lk5V20bG5j2NALJ0/1/qhUTg2vvSFRZNHw2rCxRG57F+Rink3LID0vjtGjnNSqWfF25RdIvDVFQ3hANm8krcRpT1JvPZQKeDmdXqs/AynavFU8NH0yWo3Cs09518Wy4qNRdZ+Y+q6T47lGnh/yOhGGnohUwSV/QlQQQogrOJviDvNRNJ5tNPwBSNHm1cK0rr8agiFCMYQEZKTy/qGsKpuJwF+tdN7fMsuTRYrtb+je1YlIx64xh2Ha0BfJHqCOkewo2YxZWi/GUSP1vF3xJZBiONgCw/42uEwKxYnn/gbEuh+4T8uqyLf5dPmDtGzuZtjQqnWgPO/g+KjAgr9yWLKmJtc3/5vAXofo8bYTa6NYHLE1MOxNQ5+axV//l0vs4Xa02DgIe529WJuu9rh1lQ2kyDYTitaBojktlbxiZ8M6KzNW3aNmURTZFP1BXn1Di+UfJ+y2bRSuHewf7bpUuvlrnkzyKplh7afTV38VltoRJVm6cjQ4I9MpbruwwlX5K5AiOpK8wqGCdV26uBnQv+rMYQWFEm9O1qDXQ9ckF0uWaVA9wJ9yovHi8cabQMr+RS5sBRU2v4t+oF5vGWPomUBKp4GjmP/dWypgcjZJXreNr3/8m/dfeYTxL35AWEgQD9x+DeGhJ7MAJA16gLkz3zjxntsefpX7bxtCUruyyTc+//ZP1Xtl9B3XMnfx2mog5aJH1kcvEEDK8Hbf0lsZqd5SnS7GVI3q9ifIzYo6/aEuiOcSfwJSxM1bwJYeaLPrqE3eFPER7624kyYNLIwYcYGEHz4aF19XY7fDS5O0GLRW3rl5Ito17yJQtZezdpWEENzpIi624h4Wx45LvDtNQ3RIGi+13QVZV6peAdbmqzz2cjpdF/4MpAQUreKBz99GUSSef6bqpU70tV16Wt+bb7kpKNLzRt/PCU+/96yPOSPTKG67yKPX+gOQErCpF7rjcdii3dgjSjb0ARnH+TR1r99lu/BIqf/iQmVTbBvRFNQsCedx6lRwwhLnRJctYziuQdHaKery23nDYn0JpAQvHaoCPpZ4F86gcx8oHBozEVvD2VLjLaYufYiG9e3ccnM56X3+xePpL03/85dsVm6uxciWf6PttofuU2R+6dya9Lha3Ll5B4a9qSx8vJCQogQ6rLiZisxhoo+VCaQYDjVHeAPj1uAKOY4z4iiuyAw1dBwUdm7K5JNlD6vZVERWFX8QEYosDqRC6tdTuG2kf7TrUummNIR7ZNIndONmrHVMSC4I3KtTL1KL2/2NM8JzDh7RLn8GUtavLebrVXfQsZ2DqwdVHU/00mxfoSEKbdu4SV6lQezjxz3sIjS04nt1T+3rvwKkFFtsdB8yhg3zPjlDNbPnLObvpetRFIX8giIiwkP4YNKjiGe++O5PfvxjKb26tGHcqBvUEJ+OA+4jMS76xHuKiq1qeM/lvTqc+LcDR47y9KRP+XLqRHRaTTWQ4qlB+kM5AaTc1nEmXdwjsSSWs2AoYDqgRWOTcESlYmmz+F8BpIh0oab1fdAURqLICs4ghX36j3lr2V3USyjm9tv0/qB+v2nD4SMS//tCQ92ovYwfOBv92ufUtr2Vu4mdGc25bYSV+g3KpiH2pPGlee4TIvfzTJNM3IU9T9za2mvvw9ZknerxVBHxZyDFaN7I+G+epsgWxBPjnZgCKtKz6rIXqoFXJoHVrmVq36/Q59+h8omgA7fAS8X5TwFjqozkkrBH78faYuV5q6psIEWEIwUlD0GRoOiUNLXGzEKmH91A8v5eXDfERZvW3ts0nVdJ/6ECP/4ss2mLzJ3d3qdN3eYEbLwMya1VQQmL4Bv5B2cwHdSisUgqIaggBj2X+ApI0R+tj3F7F9x6haIGZ59v801HSA9bQ3bwHgb89Bz7ar3FpMUPEVvbxr13e/Ga8z9kR6d39adZx9m4K5p72izAlrSBnlOCuXVQX5YlxLJjyVI1vGfF6CKUgFC6L7gfV2AeRV3meKyxygBSRIpt444uqsdWeaLILlzhGWzXzuDdxWNp1MDJyBEed8lrBZ1OeOGVk/ucmjVgzP0V2594rXGX6MXf/6hhyzaJu7u/S3v3aKwxJROXPluDIVPGFZxDkeB5qoD4K5AiWwPZsi2Dz1beTesWNoZeV3XmsFLS4JhohcaN3Kxdr6GoyPuZpv4rQIowfwGALJg9WQ3PKZUlKzfz3uc/8dnbEwgOMrE4eRPf/bpIBVJKxW538OKU6YQEmxh//00Ij5SFsydjCjg7o+1H03/j02/mIMsl36PT6cLlcpEYF8PPn79Uga+x4kWrs/ZUXGdlnhBAyr2dv6GdcguWuPKRd+GuLMAUgVrb6m1R/5xN/MEjRUyepvV9kS0hqru1JcGFtkgizfkpryy9k7joYu65txpIOXUMSzNSdK23hJGd96HfNAq3VmFazjo2pbVhxPWFNGlWcVTg4CGJz78qSak8ob6Nwtpt0WeV3NoKcRvNWFotwxWS7bEl+wOQIm6hRciIbA5DUsNHwtAUhUGtmTyRfPlF8cp4rIjqgic08NyLwq1V4uP+32CTR5Z74y54HwIOapEUsCVuw9Zg0zk1WNlAinFXB/SpTVRPFOGRUiqGTDuzshazeM/lDBroplOHquOu7M8mPfM7Nzt367m/17u0zxqrNtUR7MZ62rqprpf7tertrrAxYWtnE18BKUErr1ZD3Wy1XdjDzrSXrNCtpIavothw7ERT+/32OMdqfsizC0YTFWHloTEVB9L9eTz9pW3fTs9mx8FaPNhuIcUtN9Llo0CuGTqQNbVrsXbtauqs2sGGERZyGiiIMRHeToW9Z3ncfF8CKWJdNOxriz61Ucn6rlVwCHsTjgAK6oWcCB0Xe0khu2Ne442FY0mMs3PnHZXv8SQOoq+9ddLOjUaYOKFqASnffCeza7fM6F5v0tw5oczaIrhSxPxlabkcR61DHtuYPwEpIjOZLjMBbWa8epG6PuxjPlh5B80bFXPjTVVn3//nPFnlRRFeU3GxbhXkF9w+woNK/Ju35L8EpDz5yieEhwYxbtSNuBWFgsIilq7arHqLCODEZnfw+Esfqdwp4v937TtCkwbxqupn/DCfI2mZqueJ4EgRXCuCI0V4mwj+k5iaEeh0Z19Tq2xoz96DqcxbvJbM47m8MP5OVVmCOEZI/YTa3rJbr75XACljkr6hObdgrXN2F0ZNsYTpUMmgF7dZjDOq/PhvvTMQrTUUu6Ny3CHFQm7c2Vl1YRbkuQJEUbQKunyZbMf/eG7JHdSKtDB6dHVoz6mG9ed8mZUrS+L/+9QPxbDtGtVd/bNjq1h9pAPXD8qhVbuQCtvi7j0SX3+roWWdDTwUZ8Jcp4SpWtzYirCxUk4BW/1N2OvsR9GfP0OURtERUFwDq937NibsSABzksWEZA1U/1v9f+dZFuTas/i/De1JzY3ngfucRNeqsMqqH6igBkpvETWykw8v+wWL/nrVdssTNRvJ4RIQz9pkLfbY3WetLdgSg8VaSSCFIiPCeoTHw6lpat2yE32GxG/H/yxJS9vPTbeuldTGCo7Tv734l1+52H/IwKM9p9H82MMqICGAiXLtrFAmIKXEzoo6/XlWoDjQVgOHTYPb5b2Nr1wUhsjWo2jA3PhkaK6wpZTwZDLCN+DQFKttlRUDQXIdit0ZdJt/N47wGYybP4ogk4MJj1Udt3h/ssUvP8ll/9EajO+4AHPiFtpPD6DP8GvYFRXOrF3b6T5/DbuutHKwazFX/PI0sltD4WXfesz1ZHSEgtWE0+ndeUKymjDubafuvYTYolzYa/wDopymcMHBI3g50kPf4IW/xxJd084DoyofSMnNk5g8VYPRoGC1ldj7M0860VWh7eKX02H/QS1j+7xBffvj2GudtAuxTzamadQLLnP3nz3+THRuI3pLBDYf7MnKbZQiocmvgTYrDk1hRJkiW8M/4p3kO2lYt5hbbqk6QMqMmRr27JVo0cxNVJSiZu05dkzi+mtdtGrpvfXEm0BK+kY39iLvtf1sBl27rQZ9OTQoImznhclfsm5zyT5RkMD26tKah595j0OpGURFhHLL0Mv5bX6yCqRMfPUTVm3YgUGvJ652DV558h61jOA9eWPatyxdtQW7w6GG+Xz0+mPn9FCpkkDKwuUbePzlj+neqQULl29k84L/qYoV/y5yPn81daLHk44/FRRAyriu31Cfkdhizr3Q6nM1GI7K6gIuXP/cphJWIBFGo8uMR3u0Ltq8C2Ak9YJCnIElN4XKP2uztlDG7PiCJxfdSniIlUcfqb5dO1Xt07+R2btPZkzvN2gafCWGfe3Um6RvM1aw5GASQ644RvvOJaSdFZFt2yRm/aihY8JK7o2OwRybeOJxcWNryNCgyzu5gRK3IPb4XbhCj5etxq1BdywW7dFElTOiMkV4OQkXefFHMUol/21QkG0SAZY/mbQzgX1ZTbjrdhcJ8b5fFCpTN5VRt8g0N+kNLYEGM+90WUpR0FXqeJxNBOeTAPGEWFotxVHzyImi2txotEcT0GUlnB0s82EnT/d4yAzdjJJnZvueLOZsu5Y+vRz07lV9wPXFkHz6iZMjR4082XsaCcUPntWDs7QtYq0Ua6Y4lIhQjFKSY+HJps1IQJeReCLTjy/aLw61thongR8BoOyr+ZdatVYxESjXwUgN9f9zlR10WDSM4JCfuXfuPeq/vfBs1bqZ94XOPanjo/cLSMuO4Mluf1Mcuo2WPxrpcPsNpAcHMin1CLf+vIAjHWxsv7qIy+aOxVgcjFtvwRF9EGfMYZVE+3QRYL8uMxFtRsmtvC9FhLqJvaRYJ88lhqMa8oLeZuK8h4kItfPIw5UPpGRkSkz7SENgoILLiQqmPPyAi8ioqrOOf/ypQmq6jon9XqOOcyL2yLJgsBrKby1JMuGsdaTEziKPnjGU4tJSd0zs+xPR5sT40sTOWZfY8zuD3ThDFETU+GHHp7y+7E4SYou5686qA6RMeVdDTq6keqSaTAp798qkHZUYeKWbpE7eA029CaT4jRH9xxris9Cewbc/xcQHbyapfTPa9L+bTfM/VVWddTyPwbdPZNWcaf9K1Qsg5cnu3xLLiDLI9Nk6I9BqgVq7DcXYE7ejPV4HbXZZbxzFpOCuxHVHpHW0xZRdHIRHjdM2g7ELbyYwwM7j4yt/0fYng5n8jkJuvo6Xrn2CCPO96FMbY6/p5ue0Zczb15WBvTNJ6lnxDVlpyFD3Bou4NaIZRXFnumgIAEJ/XFbtqlRECj5H/C4UnV110RQ3DeJ2vlSUAHALX2Evi3BNdhsVFD3q4dylV05wIZxetZoyNH8Z7xwMZGtaW0YOd9Gooffb6GUV+P3r8/Ml3npHQ7jpOG902EJRWN/zbuL1uTJiIy9EDfGxGdAfraeCwidEDy5N5Y6fLdpVwvfyj6xP/IjolEbs35XDT1uG0rOrlX79qkFhXxjp+9NcZB438HyfD4hyjFbnx/NJ4H6tCrA6ah3GHZhfAp4Un+LZJ1OShtjbZiahkuEKr5RSWVf3A6z6XIKlupgoexDKV/bScsWVROpW8MCi63C49Dz9hFPNEFEtl1YD70wuIrswlGd7zsem7KDhfAON7r0Zs0HP/ccyefrnvzkeX8S6kYU02z6I+P3tVK6nUnEHFOKIEYfddBU00R1NVG/nTxW3STlBoHppW3/ybYqk4IxU1HA3T0SfKWMPfIdH5z5IYICTx8d78pR3yxxOkfjf5xpCQko+yIICqcqlmH9/mkLmcR3PXzGJKMdE7OFlx0t4C4sLLvF3qYgEAaqN1UhDchj+udCKLTMYSoCCZyPvnTEUnucCPHGEnuJhUyCTaf8fLy65gzo1i7lvVNWYwNxueP7lElLkXj1cSBKIMPrDR2R693TTx4spu6uBFO/Yb2W+1WdAigBPVv42jQCjvgyQknEshwE3P87Gcph9K1MxntYtgJTnus2ilna46op5XjmFfPbUsuIWwhmqIIWBrJN8EnZx3raeUkAccjWWmYxeeBM6rYtnJnp751qR1lVuWZerZFIWoRHv3fYYHJig3siLUK+5KUv5bXd3+nVNp2e/insbrVwt8+dcmX5N/mBYSHeK484eHiQ2h7psSb3FLY2hPlUz4sDhClFwhSsYAmWKrP51QyrClAKPreejo1bWHurKsOtctGxRbWfetu4ymaFaHsFcs8sJT7Rz1a0/JmM4VpZ8ToBl4iAg7CwgXIvZcu4sZd7u26nvzws8yLbYmcQdas/RnYV8t/F6unQoYsDAsxOY+bJ9Vb2uyVNc5BYYmNT3I4JdD5xxk1te/0UIg8ovdsoJQ9zUl66XhnANNpVUzrfzhCCT3Vn7e2T01JBOZg4o7UOBcojGa7oS49zHuNVJ5FsiGPeoi9Bg37azqtuU6N/rr9kx20w83+9PnMf3EL/aQJ0xd6hdH5Sfywe/zqfYWMDyB/Ix2aJod+heRIiiJl9SMyqealvl7cnkCOFdoGB3VOYx98yRFOSmsuF97p87CklSeP4ZD/afXjYI4ZUrvHMjItxoNJIaKnHdNS7atKo6dj95ikJugY5JA17DpRuB0RCDfMolVamKxX5GXG5p8yTEPFaeiBBaAV4o4Qpao4TFVvljeGo7tWaZPNsXPLP4VmqEW3jwwaoRo1W65wkIUOjcseS7Tk2T2LdfVj1UBHeat6QaSPGWZivvvT4DUobe/aya27lPt7ZlgJTJH89m0/Z9fPnOk5WnhYuoWQApr/T4gRDtMBwRnn18klPCdFCj3pSKSdQV5D5xcPEHstny1CHZINDyI/csuE79udpN+aSWjh6FDz7REht+hCeGfop2+yNo82qpWZyWHFrK9zu707NdGv0GVZzwY/EymYWLZK5q+SODA66mOM6DhUwkXMmT0efI6kWtM0zY2UlXYX8gmy1/VwHB6bv5KjeFpXv7cfVVLjq2rzobsIuYZrz6aFq6xEefalAzQzUqoDCmbQm5oQdiTNcgWyVcoQqOU2xMPFrZZLOnN39bnW/JCzpA7ZSW5O+w8NX6G+jQxszgwUYPelpd5GI1MOl1hWKrjin9P0XruhfHaTe5Z3u/Opcdl1X7UoFg4YHyj/iKbPb0tm2J+4oCUypBUiKBnMnvVkQaCRtbEl+QzzNbEzmaH8foUU5qVRxLv1i1V/nnS4myXx78B+6d+wjZHUjD+0aq/W5pKWbOb/OhIJu/n8xFduvpuvexMjoRoIo47ApCfWFb4kJLhDaUhjX7kmy2IoOlE16Buv9x77zbVaLwZ59yoq3kpCrbtkvM+kFDjRoKBr1CappM/75uenTzbG9ckf5XVtlJr0OxVcuUgW+wp2Z9AvTRxGZ3PWdzxEWkamMFMooO1cbEfCa8QIT4E9nsqR0RnuhWy3QmLB5JaJCNcWMr2cAu0aDv3C0z8zuZyAiFli1KbDMzS2LnLpnmzdzceL337LUaSLlEg+hHr/EZkCLIZh5+9l36dGvHL3OXc+uwK1i/ZQ8HDqfz6VsTaNmkrh+pxfOmCCDlrV4/YdANLeMS5/kbypb0WyDFCUH5fzB6+WXYnAE8/aQTvQdn+gvVw7/pOZEKT6TE65iYzK1XrMCwdoya8aiovpN1h5cyfWsPujRPZcDQk3nQPe3f/IUSy5ZrGNpuJv21I8/LK+DJe/0WSAGCD6Uwy7KJeTuu5srL3XRN8t6CdrquNmySWbREUhnbr7nad/V6MmbeLFOaurtxrR2MrytT+A+h8cXW6U9AisWQjQjrEVLzaGOc25x8svZGWjcrYOj1J1PzXWyfq58/uwaefaEkhOqTKz7D6r5LvUS4WKkMIMVsPMqmhM8RJ+2aciek0rzNp3TGQiYx2xKpl2lg0gGjyvl09x0u4uMuvs8Xq7Oq9rywK4PWypODFmBcewBXVhhtb79B7Waoy8nm3+ehP5zJX8/lomgUkvaOQ+v23AvNX4EUbYGEUfmOR5ddidkWwpPjXYgb9sqU9RtkfpkjEx3tJihQ3PBL6o3/VQOqznr6wisyTqfMJ4NfY23NBHKiUuh8YAxaV8WzMpaOlb8CKbIIT7J9w8MLhmMy2nliQtUI6V+erGHe35Karac0Q48gSt68RaZeXTe33+I9e60GUipzhvJO3T4DUkTzc/IKmf3bYvYfSsPldlM/sQ43DelDRFiwd3p32luT123j/c9/VlMqGY0Gte67hg9US+XmF/LkKx+zZecBIsJC1KxC7Vo2PO9vAkh5r9dvKPrBl2Rj6K9AigjeDM5eyNi17SiwhDFhrJOgIJ8Mm99XsmCRzJJlMoNbz6ZP1xyClt8LTj3mRg62HFzGZ1t60K5hGtcMr7hHyu9/KKxep2N4+xn0NNyG9TTumgtRjj8DKUEHc/jN9Te/br6BXj3d9PVirOrpuvvhZw2bt0jE1lG49y7/crG9kHH29JmTmaE28lCdCMxxCZ4+es5y/gSkCFJQQQ5qIIrgrAg0W9xMW3MDzRvkc+OIcujmL4kGql9SqgGnC174J/zxw77fY9EMxxV48Ye+ygBSdsb8SHbILgKoTYh0kvz71NG2kUPEnkgaH4xlakYeW1Lbc/NNbho38t4G/b9obaXpdkMDcnlg0Apq/H2YDHsUvUdcc0IdO+cvJHTXYRaNL8Qa7FBDe0SIj6fir0CKmgnS+htPrOnI8aJaPPKgi4jwi/+mPNVLeeVWrJSZO18mto6bkBDYsVOmaRM3w2+oGnYvODWee1GrhlJ9PHAqK+PDyA9PIzq3PQ2yrrhg1fkrkCLZwVA0m/sXDkOjcfPcU1VjHH/+VcOGTRKNGrqpHVPyzRSaYf0Gjeo1KLwHvSXVQIq3NFt57/UpkFJ53Syp+bd5yTRtmECDunXIyzcz/IEXmPTUfbRuVp/HX/6IOtFRjLnjOrbtOsDY56cx56tXMRr05/xNACkf95rLtkQXGA1EFDcgtPjCDyJ+C6QIb4HMVTy5KZFj5mgeHuNS3eKqBb6dLasbhvt6TKZx61hCFowomZibOdizfwUfbOpGy4SjDLutLIGdJ7r76WcXG7cYuK3TdDrrb8dW6+IP+P4MpAQesrFA+pbv1t1Gl85uBlzhu4X74081pKZLqqeV8Lj6r0ipO3YHkRmqVn2K4i5N/IG/AClOrYVV9Serwxkht4ZsM4EbJKasvoGGCQXcclu1R4q3bf1EZih9Ie90X0SRcQju027PXRorNm0RJpvnpNy+BlKsujzW1Sshxq8ht1fTHZcnDgoJPKijxc4O/C9/JysP9ODaIS7atq5eMy+lrWVnS7zzvoZaIWmMGLSR+j8cYUtgba4ZOoAQSaFAkVi0chWN1+0keVQR+bVtNE+7iXBzPY+b4a9AigipDCxcyP9trUdqbgL33+skpuJOrx7rwZOCCxfLLF4qk5DgJiIMNm6Wia2tcO/dF79v8aR+b5exWOHV17UE6IuY2vdrlteXMIccU6ttf2gUAbayqYM9bY/fAinCE73wN+5ZNEgNH3v+GadKzPpvl0+/0HDkiETrVm4SotK5MuYZUvIb8+qcJwgOUhg/1nv2Wg2k/Nut58z2+wxI+X7OknNq7/pBvXyu3Uefe4/Le3Xkit6d6Dp4NIu+n6KS4Qp58OmpDB3Yk55Jrc/6W++ubRj1aDEfdl9OcvMjKjItRHYWXJjUAAAgAElEQVTrCCtOJKKoAeFFDTE4PHfd8GcgJShjI8/viCQ1N54H7nMSXXEHC5+PsS8qfO99F1nZBp4d/CzhtToSvPw6NbuDubGDw4dWMmV9FxrXzuDmuz2/BStt9+xZNrbuCuS+pC9ppb8DkYLzYsWfgRTTQZlk4/t8mXw/7dooXDPYewva6Xp8aZIG+z+kcP8lYkhxMyNuaLrVX8xtEW0pig+9WBNTn/cXICU1MplDUYvREkyk1BJrfjpRG3S8njyMxDoF3HlXNZBySQb8HC/Jy5N4e6qGyMBjvNZhE+aQ/momr1Kxa4vYFvs1Cgptj9xVLnljea/3NZCyv+ZcjoavVz2bwqRGZ+2xS7KiSbHQbv0AvnEuZuGuK1VQWIDD1XLpNJCeDh9+qiUxcj8Dr95Mm/9lMT82jjsG9aOuBg664PNt27hi0Vo23WThaDMLDTIHEp3XxuNG+CuQIjkgKHs1k/aGqqFjd97mIjGhcoE6QYwvCPJFuERUlMLqNTIhwQqPPeq7ddzjgb2AgvkFEm9N0RAekM3rPeewvIkbZ6ATK8eIMDekWdqwC3ir/3KkqJ7oOXN5MLkHFoeJiROcGKsApdikN7UUF0PXJBdtoubQNnwmTreW+7+eoY6fNzkgq4GUC/pE/PohnwEptz38ahlF2OwO0jOOk51bwKD+XXjtqft8pii3W2Hl+u28NOUrvpn2DHa7k5EPvsz8b9880Ya3P5pFWGgQV/Xtctbf7rxpIA+Nz2Vqxy0sbbsDe7AVkcDMKZnL9KVp+vVEFp5903VqYb8GUo7u4tU9MgeON+KeO1zEVcd7U5JGTYPIo/beHRNwuK8gaM1ANeVvUT0nmSmrmbSmMwk1jnHX/eEVtvEZM2zsORDIQ12+or5pOO6wiyem8WcgJeCwlk0BU/hw2RiaN3Vw4zDfXH+YzfD62yfT4N420nUidrbCg/Yve2DVGpk//pLp0/gvbgy9jOI4z/kDztVVfwFS1tR/B3FQD5OaYCACS1EqddYYeSn5eurULOS+URce2/4vG+pKa25WFrz3oZbaYSm80OIw5sjuKP98bjZdPlviZiD+FlIrvzUNM67yqK2+BFKEx8yqeu+iyA4ipdZoOXtImCI5cWUeo9PyW/hF+y2/bb2e3r1c9OlVuQddj5T6Lyp06ICVz2YE0bTWDrpfvYnO7xUwu0kDnri8B410EpvtCk8cOcxDvyxkb38b+3oUEXe8GwnZnl/c+SuQoh5y07fyTqqTrWltGTncRaOGlWtfP/+mYcPGkpCJ6GiFpctKyEmriifDqRnuXuy0nCVtCgkyxnHMvV7tZ8vUkYQWxVf4C/JXjxTRkeCspYxb10LNPCYAMQGM/ZvFZpd4eZIGWQM9u7noX+t5ahr3qF0a9c1MXC6JiY87RYCBV6QaSPGKWiv1pT4DUsrrpQA0vpo9l6zjuUwYPdwninhpynR++nMZWq2Gpx++hasv78rh1EzGTJzCb1+dBHumffEzon3i97P9NubOaxn/RAZvtDnMsi4bCAsuAUvcuLAr+RS7syl2Z6oeKh3S7iTQfv7QDo0sIQ66Dqf/3VwZUg7w9oFCdma05L47oVEDnwyZX1dy7BhMmgy1gtP5v+Ef4y7uj2F9b9xBCvZ6bo6lrOX55PbEhOXw2ISKu31O+6CY/Skmxnf7EltiFJYahTQ9Nhi903Mvp9MVKEug1cp+l85RtFN3RGanfjJTljxIo/oO7rvr4oEjTwxo/0GY9snJkkMGQc9zE/F78tp/RZkFi+GPeTCg+c9cG3wd9guPTCzTX3EAsTtcauaoypLM4G3sqPkzGslIjLYkTW1B0UHqrgzh2RXXUivCzITHLvxb8g3MV1nau/B6XYqC5hQf8MNH4J0PoV7UXiY2KsQW20HNDFWkP86GmBk4tGY0kgGXYlMrbZF5LTXNzc/bAHHx4HQrKG7vW9nh8GT2RyzEIIVSU9vyvG3LP7aPbovuY55xOrM23kTPLjauudpLu/Pztsa3Bbw/GiX92bElj/99G0bbOptpf9VGkqZa+V+rpky+LIlWRonFxQpD83N4d/ovpLa1sfWaIqILW9M062qPFaLVlHzlTh+n2Pakgcb9+/joWCZrD3dh5E3QtpUnT3mvzFffKGzeJtGyuaJ6LC9ZJmF3wLNPQGiI9+r11ZtTUmHKNEoy3LXexuKkTCJ1TchzHsSspBFkjaZj2t0Vbo4/7/uNaauYuCWOrIIYnhgLNSruWF1hfXjzgcMpMPUDCAmGnp0LGRJzz4nqHv3+S8wWAxMfg8iKb9c9anZooG/2tB41prrQJdFApQIppT24csQE/vrm9UvSIU9fciQti6cmfcLQq3rRtUMLbhz1f2poT6m89v5MoiJCubp/17P+Johqn37mEC81zWdJr+VEmZqcUX22cy9F7kyMzjA6pd2NznVuvzgBokiShMvlf0CKLiWd948cYVNKJ+66VaJlM19tlzwdVd+XS14Ns36CNrFruW/IMqTjvdBuScItUg4nuMlP28STy1oRGZTPM09XPGTizSnFpGaYeKbndLLrBZJZe5fKDt/s+NXUKPLMy+kMrUgSOo1/gnWaNJnDurd5ZeFDJNax88iDp/j/e3F4V66B7348WUHXTnBDSabvKi9/zHMxb6GGa1p/z8DAm7DULcSsP06x7jhm3THM+mPqgTfCkkiLrGs91odOq8EhWEYrUdbU+ZRCQwbh2noEyyVpanNsB2i2PILHlw0mIriYZ5+68NCe6hmw/MEVlxBiLSuVvfslpn2i0DRmM+Pi9djqNqfQkM6G6G8Qnh56KZQaumaY3UfJdx5Co+jolHovAY5ze/HpNDIut4JbsEB6WZbHT8GuNROlbUaAfP5ddk7eTnrOHUNyyJd8tnoEnVoXMXz4f4PY2FcA44Y1x/jqxxp0TVhP0yvW02Wqm8kdW/NXuzqMLNzE45FX0Km4iJ9m/kJ27SLW3lpAuCWRdkdL0iN7IhpZVkPOhE37m+gPpPNFznaW7+vLTUMhqWPltvDDzxR27ZFo3waiIhWS10gUFsLYMRAfW7ltuxS17zsA730MjaK3M77pEZZ1SydS21C9PE2zr0HBRfOsa4g2t6hQdZIsqcCz0w/3/frUTTy/PYSU3ETGPwx1YirUNb8rvG4jzPgOFegb3GkxHcM+PNHGJ3/9mOP5IYwdDfFx3mm68NStFu9o4MOvfuXD6b+q6cSFNKoXp0adeFsqHUgRgMHlNz3Ggtlve7uvZ7x/9pzFbN15gOcfu4Nug8fw18w3CAkq2VSPevxthg3qTZ/ubc/6W98e7Xjh//bybH0XCy+fR5jUtNw+5Li34ZAKCCmOp1XKuRdwfw7tCUjL5fOM7aw+1J2h17po3dL/Nha+NqJf58is2yAzpM13XNY1H11qVwx722KPdGGr5caZvYPxi5sRqLfw+BMVn0CnvmvneK6Jl3p9w5HGYI4qQGSEEFIrrw31j/VXPZ4qIv4a2lNsyMaQJVOkzOC5BQ9SM9LKmNEnw20q0seKlv1rnkzyKpmwUIW8fImEOIW77qhcEKCifbjQ8n/95SR5jZEb2syiSe0mbG8z56yvCimOo1n69R6leqzs0J580xG2xs1AQkMNqeOJNLWFykHaLa/Bo0sHERhg4/HxJe7n1eI9DezYJfPtLJm28Wu4P6Ym6Y0ktteZhVt2oFfCCNc0UdMJC8lVdmAnj0BrLdoevuucjfJVaE9m6Bb2Rs9BowQQJbf1SFEFlh30/vNhNoR+ybRVI2hav4DhN184aOdRpf+xQuuTM/jl71j61l1LXP8VdJ9q5PnunUhNtPJc2vd0avEqNZwO1n/7MzZtPksfzFcJQQUxqKfib6E9gvBYAHrCiytmZzg/F65l/o5BXNHPTbeulXsB98nnGlJSJNq0dqtr6dZtMtk5Ejfd4KZZk8ptm6fjfa5ypRnuWsWu54FG2SxPSiNEqqs+Ukw6hcoh9I5gOh14sELV+WtoT07QXmodLGTyzkD2H2tcJVK4/71IZukyWeUTuqX928QHrj0xVi/NnczhrBhuvslF40beOd94NbQn+wA4rBWyvUtSOKo+aCvf2/KVqTNo36qRynvqS/EZkLJy3fYz+mW12/ljwSoKzRY+fG2s1/u9bvNu2rZoiEYjq1l7Hv2/91QOFEF0++wbnxEZHsqDd5Zk7Rnz1Dv8+fXrBJqM5/xt0kvbGJ8YwNL+fxF6FvI5t+Qgx70FFzb18NswsyTlcnniz0CKPqOA77LWsHRvP66+ykXH9t6ZaLxuCJewgjenyBQUyEwc8BRRDVpi3NMB/ZGmanYde6QbOXcvDy9siFbj5NmnKl7xm2+5KCgy8Gbv79je1EJARCIWsihUDqh8PEZ7GE2OXkuQ1fNrgsoGUiyGbIp1ORQbsyjSZ1Gsy1b/W0jC/s5E5+xmwvwHCA2yM25syeHK2zJjpsyevTIN6yuI2/P/UuaeX3+1s26TiVvazyQ2ri77myWjJQANJnRSADLCi85FgXJAncOEzbVIG47Rfm5vgcoGUnbW/p7s4D0EEHNisyvsyKwcoVNyBGMWD0KvdfL0RG9bV/X7N26W+OkXDV3qLeXWWvX5+7IvVaUYiCRMalxGQWK9zHZvxo2dmNz21D9HWlFfASkbEj+m2HCcEKk+AZzJsq6xuglMtRCYYsF0xKL+97oReSRtfIrd4TN4I/nGamJjL3wGK/9O58/keAY1XEXtLmvo9LGecX26o62VzmtHviGu3Ye4kDjw/S/osrOZ93SuSmTcde8Ej1tTWUBKgSmFAmMaBQFHEOCJTWtWvbdOlaTlI1iav47fNl9P755u+vSuXLDi/Q+1ZGZBh3YugoJgzx6Z9AyJq65007lT5bbN4wE/R8Gt2yRm/6ihY2Iyt9e1srpLKkHSSU4UwZXilmwkHutDbE6Sx1X6E5Ai7C4raDvHQnao9tZtVT8+3Wlkx9FWVAXuuO9my2z/Jy33Qx1uRyvZsRkaYLDtY8riF9ie0ojrhrho46UMa14FUn4ZD/npHtvdJSs48EWIOjMTmrnIggA3BCep8Oh7fPQIBvbtrNJrfDbzD6x2B5HhIbz57P3ExtRg7aZdiGiQIpHmD4mJD91Mj86tKLZYeWHyV2zevk/NpDvxoZF0bHNmBMi456cx4tp+KpjiS/EZkNLvxnFn9EuAFM0bJfLIPcOoGRXm9X6LFMer1u9QgRQxGEOu6M69IwepoTQF5mImvvIJG7btISQokGcevZVuHUvc887129uT1jEmPpzkvvPVTdbZxEkxOcoW9fArNoZig1ieuIyFOPT52CuRI8WhKVYXbnHYtepzSxZxXT4tNl1F8sFU5u+8iiv7u+na5d+/MF6M0R3Plpj6vkZNhTf5pkcoDu1HwLZu6DLqYq3twhHmxlCYwuj5sRecOu7lSWCza5na+wfWtc4hKKih2mQXFvKU3Qi7EhJsiSU6vw01CpoiK+f2UFF0NhxBx7HavOtxIWA21Y70x7Hos7HosrEYSrxpzia1j7SiYVYaD827D4PexVNP+Aasm/KehpwciU4d3axZWwLePP6Yi0CTb+q/GDu82Gd/+N7K5h1B3N1pJuHxtcltnIOeM0ESNw7VW8BJERqXkebpwxAeKuWJW3biCjmOxW6/2OZd0PMO2cbOOrPVZ6Ok9mgouS2RnQpWSwrtt0dy/4ISQlNvMvRfUOOr4EPim5rzp8xljedyVXQiK7v9oAISZ1szHRSQo2xTNXEusnZnSBZWt1Ul/faWFOuPsa/WXGRFSw355E2XAE9i/8ggMNWG8VgJt8upsuEmMy0PPkJG8E88v3wY0VFmHnigCqS88JaiL+C9i39PZeH6RK5rspLolmtpN13P3QP70NC0jceP/kqbtlPJkgys+2MetfenMfeZPNw6N0n7H0Xr9Ixk2hmYi11jxun07lrglO3kG1MoDEhFHGbLFUVGgx4ZPS7JQtuVQ9mUv4JZG26hS5KLAZd7t43nG6K339GoHp2dO7oJCFA4dETm0CGJHt3c9O/rxY/0fA27RL+v3yDxyxwNPRou5IZ4mfVJ6ZgoCRkVYuU4+coeZLeeFmk3nnV9PL05bkMxdmNOpfHWidC1vIBDZIVuO0H6XdrGpDWX881uNxtTOjH8BjdN/+WeRaVg35VddjG0wf/hlkzYTG0IKErms5VjWbmvk1e9u7wKpPz5HBTnXSJrr8Brej0EwivlNHnuzc8x6HUqB6rb5cbucBIUGMDu/SlE14wgNDiQtz6chcVq4+lHbuH6e57j1Yn30LBuLPmFRerZXESJCBAlyGRk7H03sP9wOnePe53fp0/CFFB2Pb3/ickcSjmqhpRH14jgwbuuo3Pb8iNFKtC78xb1GZBy3pb8Swu8/+YK7oiNYe1lCwmWzs3SaCOXPGWn2tOWKTcTWpyAS2Mjz3SY3ICD5JsOnvegWZlqarL1cjYfTGHOtmvp29tJr56V2ZrKr3v1Wpnf/5TpkLCSO/vOxhrYGdOGvmhzYiiOd+IKUtAXHOOxxYEXnDru2RdKQls+6fMri9ukExpQFoUVXgIWMk4oQ4T51ChoQUx+a4KsJxf4fNNhckwHyDcdwhxwtFKVJzaBwuNBiwmtJDwfAtS/BdGk5qiV9mmF3DP3TrWNvjjklmRe0orES/Tq4WL9Rg0ii88dt7qom1i5G1NfDNS3My3s2BvMA0kzMSRGYq5vQ0dwuVWLGHAB4InQCyGNMwZTI78EcDYb08kLPEyuameHfdH089ZhUCIJk096PERtyIe0Q9Sq2ZhHFvXB4dLz9JNO1QOpWryngWUrZOYvkLmy+S/0rNWEbUmL1CxK55Ji0ihUDqugXfvDd6N3hCC82fICDpNnOkBu4CHcsu+AuiApjkBOAofRy3KoPa/Ek06IotfiDAvCbTKiS89mx5V51Mu5F4txIY8vH0JokIVxY6sN7VJa2bwfUlm+PZHhLZYTnbCZ5j9ouOHaK+nPUu7JWsiVrV5mizaK35cl03bTbhaPM2MJtSO5dYQVxxFe3ICwokRM9pMMmuIiKT/wkLpe5gbuw6EtuazwtWgUozoP6+Vg9W9Z0pe5JCl0H6L+ho4cyF3HV2tuo30bO0MG+8aD82y6ePUNLRYLdO3iUufUoxkSu/fItGmlcN013r248cX4iPBfEQbcr8kfXBUbzJbO6Wd4qGUrm9XLBiFi7oo0NyTS3ITwosQT4ydCGvMDjpSslYGHKDIc80XzPatDkTFKEQRINVUP1IZr6/PLHmuVCel//iUtgopm7ODPaBo6D7uhPg59AoGFC/lh0238tXWAV4E/rwIpu+eDtcCzcb6UpRr1g4AzOSA7DRzF/O/eUgGTs0nyum18/ePfvP/KI4x/8QPCQoJ44PZrCA89uQdNGvQAc2e+ceI9Igvw/bcNIaldszKvtdrsaDQalSNl7abdjP2/95n9yfPE1Dw/p9nFqKMaSLkY7YkD7uRF3FCnLlt7LymDTJ/ttaWbQ/G7OOiKw8fpopdCUCoRvJckzT+HXNOJA6+I/4zeUZc9+zP5act19OxqoV+///am8JvvZHbtlrk16SM6t8zAZmxC0KpByOYwNfWxSIGsNRfyzDI7ecWRjHvURWgFUsc57PDiJC06jZ0PeixmQZfdhMtnZotwYaVYycSqHMMtnTxYGO0RGJzBFBrTEB4Cp4pBCsbt9j4loIxOBUm0kgmNFICOgBNcCKfbvVi0ndmZJO3TM2bJEGyOAJ56wonBy3yzpSkNxQ2auEnbuUsmM0ti0AC36qFS1eWrLy3sOxzMo12+xdkgEFt8yfd/LslX9mGl5BAZbKlDsT77DLdznWQCt284bs7W1hA5ES0ns/I0e+cAObXNhCU05cklSRTZg3niMSemauoKr5r5gkUSS5ZpGNJ6Fu1rtWR/x/UEcn72yVz3TuxSLjqnCUmRsevMZdopDpdaxaiCoF4VSSJMblTmINvy9b3oCl3YGsbirBGK23hyogrYvJ8DbTKopYzEqFnP6GUDqsPIvDBAv36dxrr9CdzWdgnRIdtpOFdmwI2DubvoF4bmruaeJuP43dSIaZu2cM2y9ewepHCkk+2EJ2dpk/SOIEItCRQbss441AofEK0U6PU9mbh9FSm19VKwSr4sleYHP4vexJ6szpZGHDu+lY9X3kGLphZuGFa5e7LnXiy5kOjdswQ0EfwogidFXEiIi4l/uyxeJrNwkcyglj/SK7omOztlYiSyTLecmCl0H8FOAUgn9w8ipCzEEodbcpbrcWSQg3G7vL8nO9sYyJIWoxSFkZOgoo1s4jcksGBfXpUI6c/Lk3h7qga9Ht4cOooAbR7FQd1xyyEEFfzBwt0DmClAyXYKQwZ5x17/K0BKscVG9yFj2DDvlHSY/xif4Cf9e+l6FEUhv6CIiPAQPpj0KOKZL777kx//WEqvLm0YN+oGNcSn44D7SIyLPmG6RcVWNbzn8l4lmRjPJgKY6ZXUhkH9u3h16vEqkHLVLU943HjhpvNvlC/f/YtBMU3Y1XMZAZwc6HP1RdzoigmqVITbuXClN0rhBOrC0UgarHbvfMQXqmPh9RCxpxbp+44zc+MwktoXMfCqyicXutD+XOxzAtF+5TUtDge8dt0YAmrWw6mrQ/DS65HsRswNHYgIG02Rg5eTs8gsqMNDo10qk72nUlQEr72lJchQwOTOG1jYfSfhZyE0Ln2nnXwsyjFsHFfDyEpFHIx1hGKUwjDIoZj0eoqsZcEVT9vlrXLC26GoYB89t9XmsdVdLwh8upC2lRJhRkYotGzh5kiKzIGDJWE+Akyp6vLpJxaOHA3myW7fkddYgxITorqOn08E10gRqSeLKeKpEIxyBAbCCQkIwmxxnO81Pvs9ZF8RDb5MIauJg4BGzXhheWtyi6MY97CL0FDPv0ufNbgKVfTnXy5WrjFwY7uZNIppQkqb3Zg8WC/dkots18YyALFOCcEgh2MgjBBDKDanC5ePU9NGbson4YejuIIDKO54pmeNYecR0uumExRyLVGuQ9y9rIe6IXz+GSenZIWuQiNcOV2Z9Xk621LiubvTImo7dhK/UqbrrdfzyrHP6V2wnUmJNzM1oiePHjjI+N8XY43Ss+PheogwRRt52JUcbEo+ilR2LdQTil4KQ/wdpAtVs/bYHf61FgggO2pnbawZ+5iafDuN6hUxcmTl7cnEXujFV7WIZF09e5TsX8UeZu16DWJtfXiMf+1pL8RihVed8K67ru03dKqZwJFGRyHk7PmAheemTclFeKOLC69TRacEoZNCMUhhBGhD0Ws0WLwcbl3RPov9ZPSWOqzel1klQvr3H5D4coaGGuHFvDLoThS0mMMGqXNzcO7PrE/pxIdLHqVJYzcjbvTO9/5fAVKErQkAZMHsySeSuIh/W7JyM+99/hOfvT2B4CATi5M38d2vi1Qg5cQ5xu7gxSnTCQk2Mf7+mxAeKQtnT8YUULH5TXikDOybRL8e5VNpVPR7OFt5rwIpQkGeSu+ubTwt6lflvv3wV/pEN2Zf1w1lkNxzNlJyk+8+gF4KQk8YGpXMsUT8lWxWxH4GHwyiYE8+n6+7kfYtCxlyrWcxxn41YJeoMUdSJD79XEPN4AxevuYRioL74dYEE/J3SVamwqYOMTejKZZ4a/UBDufUZdQ9DmrHeH7jkJsvMfkdDVFBmbzYbi/Lu+8kVCrhSDmvSG6sZCMpkrohPPV2q7LJZs/V9lzrZvqua8EzGxtxtCCOh0a7iYr0zoJW2o7lKzTMWyARG+umQT2F7BwZQSpXN9HNHbd6t+7zjqMPCnwwzcrR40H8X4/ZpDWxo6tZx+NaLWTiUIpUEPh0XpXKJps9vRP1v0whdF8R2XWdaFo1442VjVWAU2zwxUa/WryngV9+sbN+s4nbO31NdO16ZDVP83i9dFCIRcnCoNpYqJqFqVR8RTZ7umaavH8QU4YNa4u6OGqeye+mP3CU7NAjSPFXEGMu4pENDTDbQpgwzkVQYNW3tUOHZGStQnysd/s6/eMM9mbE8kCP+cQe3UvNbTKt7h7OjENv08pymJ9r9uCB2JH0zcnh/9k7D7gqqzeOf+++XDYIoiIKAgoC4l65NffKkS1taNkw25Zl/dulWdlOK1tmpmY5cuXe4MCBgIAoKHvD5Q7u+H/el8jMwUXWpXo+Hz+l95zznvOc8573nN/zPL/nu2W/YlFKiZl3JRFhuaSUcmuJ6IknrLG/SkORzVb1NhrJxznZGeXFi7y9525atdBy333Vu2hU9Yzq/K4tk/D2OzLkcis3/ZE9yGSSsHe/FIUC5j1nX4ab6oytsqwQyi2EdN/R7SvaNQlGfTiGwp6tKAi7eijsX59hwSACKoK54e/7mD2Rzf61z4J3jcdpT06eyWJD7DiGDDTR56Yb0Zx91KkMxw9vlcijfedRrvBF71iRM9yxeDNJmX4s2Pw//FpaxQxFdSH/JiDluTeW4O7qxJMzb8VitVJcomX3weNs3hktAicGYzlzXvtc5E4R/h6flEq7wAry5u9XbyX1YpboeSJwpAhcKwJHikIuI/Vithiuo1Bc7vEsYA43dQ9HLpOJxLUCL+qaL1/D1eXaoUW1Mcd1CqTURgftvY2fP/+VHr4BnO9yGpWk5nFY9gqkCKi6+qKU8tMGFkdPJrxdCZMm/3uBlG07pezaLWVgu03c1vVrStzGIzGqcN49CaRQ0q7CEi81SPj4UDxnctpy311aWvnbftDJzIJPPpfTwi2V5yLTOdQ7DmcqUu3VROwZSMmxRjN0dx9ej/ckJTeI++8z49uibg/jP/8qI+a4hIh2xYT4nSe7uCk7DnqLWQeeeaLxH/6qWivvv28kv1jDG33WcCa8AI37lezrVbVxtd/tCUhR5xoRwnoEKWphorx7ez454E9qQWsenGGime1Jr25EFf/6OitXGjkZp+H+7stwEQiNgwtFr6WaSkMAKc5ntQQtTcOiUqDt3V60Zv5dFBdzKW2RQOkAACAASURBVLGcRRfeB79cJ+bGO5Jd3KzaXok11U9D1K8kYZdK4X8v1O3+ueTDPNIKmvLI4E34xyTjekGG7yN3czD2eXyNeSSpmtO3/Uv4Gg1ELVkOFivH5wZhdrA95bn9AinFqNPA81wRL+++C58mOh56qOFCe4QwnkUfyVCrrfT4S4ae3XtlIhn03GdMqBs513LlWeGeXp/Qskkk7juPojI4EPt4AFaZ7Uayv7+X9guk6HBJcCD5TAFrToynX289gwY1bLhuTfa0SiBsdOQ6xoQvQ+/YjXJFheHIoXQP2bkqXlq3EE9PK7MfboRASuphMFbw89Sr+HUB5ZVghRC288p73yBkzBXk2Udup1/PDsye9xHnLmTSxMOVuybczLqt+0UgZe6bSzh49DQqpZKWzb1447kZYhkha8+CT35k98ETGMvLxTCfz+c/dYWHyqPzPuDYyUQRYGnh04RnHrqN8JDaOc9eT5/1CqSYzGaycgpEFOrvEuDXOE+y65esIaJVSzIiz6PApcZr116BFCHeU56hR3FKwodRk2gXUMTtd9YtyldjZdZhA4u/lHHhooRHBswnzC+JMpdBSEtdcTo4GosStIEVa1ygLPny8ElOZoQzdXIRge1s11lamoQlS2UENDnD7PAijvSMuyzV3o0Oz56BFIGobeCuviw6K6u3dHuLv5Jx4YKECb23MizgS9LKOvHqz8+Isd4vPGsS42n/yTJ/gZlSnYqFfdZyvFPWn5mhajpmewJSWq7NxCu6EGNLLwzWfMp6BLP0kA9JOW2ZfrcZP7+6BetqqsvGXn/ZMh0Jyc482mMZkgAPylqbUUgucdfc6PgaAkhp810arme0IjeKsJ6uJvKcIox5Z8jt24U25wN4PbWYlLxAZtxrpmUde2ncqC5rq9658xK++qYCqJg7x4TadttBtbvw0btFZJd6MmvUetpuSQWtnICHppEU8ygaiwGzRIpfx08R3u60r5Yj0+qJe8QfXVPbO2WvQIoJHZLMYlonm3lu5+24u+h5/LGGu+RmZMCnS+Q4OkLXzpcuoYIXgE4n4ZEHTXhf/XWp9rw3VIXK1Lkz+72Hm3sPfDceQ10kJf1mbzL73Lgh1V6BFDNGNMmQHl/MipiJ9OyiZfgI29+dhpqnaz1XCOsRwnseHfgW4S1iKHUbLYb3CKLWRqMrLubJVYtxcIDnnq4bELhOPVLsTeH/kv7UG5CyY/8xnn19MQaDUUxNJBBrCUQzAnIkuPL8+OmLjVLlW778mcDWTXE5EYPZ05XiYGdKWzncMDptr0CKyVqGJS8PtxMOvHNgIgG+xdx977+ToVGvl/DGfBkSiZUPp9yN1MFLdA+U5fvgeHQwZo2VstYVm7AQer3sSAzRFyKZMjaX0A62p/mujOcMbXaCe0ONnOpxxiZC46peJHsGUgqssdy0pw9fni/jaGoPpkyyEBpSt+E1r70lw2iU8OjwLwlvshWD2Ylnf/2CUi3cd7eZVv/wS/Zrb4KxXM7HfX9jf9dUXNXXz6ZS1fqq/N1egBS5zkz420lIzFa0vcOQnE0kv48/P0V71BtYZ6vO/qnlln6lJ+WCE0/3/gFtoAMmXyFrV83N0/UNpFR6NlllUnEtWeVX92yQFWmRJMdxfnQo7RJ68G7OGU5ndOCu280EBf6zQbtTsRJ+Wl2hl+ryglV3/S94W0eJwZlHJ64m7McccpUOdLl3EqnHHvqzqfadPqQAJWeWr8Ept5CkO30pbms7iGevQIoFE8a8C0QkaHhsx2Q0aiPPPtNwWXsqATRXFys3dclGgoVSkzcxx6ViSuSpd5gJbNO41/53P8hITJIwe+CbKD170WblaeR6CRaFhFNPBmJytN3T6a9r3V6BFIFnT56qpShWz7dHJ9ElopQx42q+b1f3Pa+t8pXpuV8fNxsPdws6p0txSkrdSZT6ZO7/frn4uLris/oPSKmt2bSfduoNSBGIZ++/czRjbu7FhOkvsvqLV8Q4pzc//J7bxg0W3X0ao+xauopmAZ60/O0EDoUVHzGzUkpJG40IqhQFayh3sd3d0l6BFAtG9EWptDjizusHJuDbtIT7H/h3hvbExkkRLBO+nlm8NGI2RodQDKq2KDJb43DqJkzOFnQt/7DIWGH14aPsTe3EhGFZdOh2OcP79dZ8JQlqx5ZRTA6VcaZzspiSrqZiz0BKEWfouq8XK9Ny2Zfcn/FjzHSMrLvDV1kZvPWOHKkMXh7/Kj4OsaJ6/7dlKRezHBg90kzXznX3/JrOZW3Ur0yx/UW/rWzrHY+75MrMUDfyHHsBUnx25dL891xM3m7owvxRxSaSPsiX9dGOxKR15bZbLYS0rVuw7kb090+q89lnBtKzHZnXZzlZ7UDq7YX0D0tgTcZZ30CK3y+ZNDkieDZ5Ywi6NpeQRGfE4cgpTt/VmvCjY/i8bDfR53sycbyZiPB/9n5yMErKb5sqzkJ1DUS/8hqYLHJmT11O+OIiktxcmDhlCDEnn8Yi1yA1lTG4w9uclrlx6NeNtEzNJHWMD7ldbTdo2CuQIui3pDSOvqeaMWPbONGw8/K8uglHsOUdTTgjYdmPMvy8s5g3dDaZulC2ZT/P6Tgp2TkSxo0x06kOv+W29LGmZb5cCufT5MwZ/CbGJl1o93M6VpUSwQMtt7MbqeNsSzjx937YK5Ai9NOakYvppIUl0ZOJCClh4qTGee43m+Hl1+UiwPfZnXdSrgnHqLoU9qE0JqMqO8GsH79BX66qs2x+/wEpNX0L7a9+vQEpHW+eQdRvn4lEMePvfYE1X70mauNCRg4znnqHjcvetj/t2NCjQ9+twqWNE4E/J2Hx8EJeUIqsuOyymilTWlDQvmoyKqGSvQIpAjJdUhZHcHQLXtw7jqYepTz8SONFpm2Y2msWWbtByuEjUga138mUTp+hd+xBuaIZqrR2qBK6YHQ3Y2h26WK2/lgU2852Y8ygDLr0tt239fgJKat/kdIjYA/DQ1RkBJxD6vLPBlJKOEv4wW5svpjKtvgRjBhuoUcdpiCuJA12drbyv5FP4KLIEOf986g3OZzgT8/uFoYP/edesiszLajkej7sFc2Om07iLgmtyevxZ117AVIq09SWdWmL2UWD+lQKZ29uzq7DMqLO9WbSBDPh7Wv3cpt8PIXMCwaC27riFdg4w1ZrZRH80cgHHxjJLdTwRr+fSAzT4uB2JdeTsqgcmc6Czsd21/H6BFLkZWYi3kwUR1Tau714gbqeOG8/xtEHvOgUNYVvy7ewO2kAo4fp6NrNdsNKbc5BfbW1bbuEXXsrLPO3TbYQ0q7u9k8BBFbKDEy/92c6fVjEkaZePD2mM9tP/49yp2YoSjOYFvIsWx38+XX7brrGJpPRz5OMwbZ/h+0ZSCksP8nA6EAe3D2IcrOSV16sm3AEW9bOyVgJK1fLaNv0PE/dPAezRcGPaV+TnCIhLU3K4IFW+t7UcECPLWOoqsynn0NGlpyXhr1BoXsEIb8UYQjxw3F/hQGmumFjlc+zZyDFlJuFKkbGh4cmEhpYxJTbbQ9Pr0qf9fl7draEjz6T0dz1Ai+PeQqty1As0kte9Yryi6i1UcxZ8zH5pZ51lujgPyClPme9fp5Vb0DK0Nue5ouFT9OyuTe3PfQqi16ZhXcTN/QGIz1HP8yxq+Sarh8V1OwpR79bhaKdgtAVuZR1bSs2JjGZkRWUIM8rQpGeL/7bmemtxJCfqsRegRSh3/nGY0QeCOTZvaNxdy7j8cf/4eQR15isd96XUVws4Ymb3yak6TG0LkOwSJ1QJUWiOheG0duCocmlA8PWEwf5LbEHQ3tdoPdg2y0WUYelrP9NSr/grdzU1hW36ChkclfO39KccucbcyEVhmTPHilCOt2gQx05mBXP+pMTGNTfQr++dXcQP3JUwq/rZfg0tfLSkGnIBVIb4LeEe1gTNZQ2AVam3dm4D3/X23NKS2H+u3Kc1UUs6H6S3b1P4ya5MqtFVfvW1X63ByDF40QxrVemiwCKAKQIIqSmPTvQnYPHZexNGsDYUSY6d7qREV67zqeL8sgoakrvNicZekdI7TbeCFtb8I6ZkjIVC/v+zInIXBydL19jQjph3/VZWCWQOL2VzRwW9QmkNNuRS7PtuZQ3dUffvnWVs+C05yQxUx3peHQqq9nAxribGdKviD79GudFpMoB/1Hgl3Uyjh6rIN4cNcJCty51s3+XlVl56x0FLupCbp26ju4faNnu14JPBvqzKvEdjE1CUObG8bL/PXzu3oMPo48y4eBx8iJdOD+hua3DwZ6BlFxrDDfv6cRjUZEVWaGeMIkk6Q0h0UekrNsgJdI3nocH/E/swtbMFzh6NpSkZCldOlsYM7Ju1kJ9jXfRh1byChS8PvwNsl1CaLexInOXMukiqtRsSvw1JN5bkXWkOmLPQIqx8CIuR5S8e3ASga2KmDqtce5fp05L+WmVFMHDe+agL9A6D/pzihSZ/iiKzMiaLuLV3+aTmufHvdPMtG5VuwYW4YH/ASnVeTMaR9l6A1LeW7ySsHb+DOnbhSXL1rPn0AnGD+/D/sOnSM/MY9nHLzQOjf2tl6e+X4052EL7NWXoIttcMQZlUjqq1CzMaikJD7RG3+T64IM9AylCRpXeu8N4bO9wNGoDzz5z45f5RjnZQGVGApncyqdTbhe5fkrcxorDUZ/ugTI9EEMzM0b3SweG3bEHWRPfg4GdUuk/yvYD3N59VrZsUzA0ZAORIW74/H4M52w5JgcZqbc0o7DdjZ2Y7BlIKSOTVkdCic0+yaqjd9C7p5mhQ2r/Y1a5/jZvlbLvgJRAfwNzbpr257JMzO3I/I1zcHG28tTj/1wgJb9AwvsfymjimMXLXVOI6nSSZuktUOUbURQYUeWXoywoF/9eHOxI6vjmWOS2ZSewByCl3ScpaDIM6MP9KfeqcOdXJV0kuY+aYycVbD8zhJFDDXTvXnt7mUCs+OaCivZauGbwwGzbrd+NdV+sqt+VPDyf9FvLvm4XcVVWgEsynRm/XzNxjy35swlhf0u8z88mMKU+gZSItxKRa82UdWuH2alqo4jmUBwnJlrpeOJ+NsrXsvrkcPp2y2XwMNvDSqrSqz3+/v1yKWcSK0J7+vezMLBf3VyeC/PKePdjF5o6ZzLqjg30fL+cX4L82djdmc9TFqNr0R2Hi4dY3nQIT7aYyCMJiczdspeSAA2J99h+2bVnIKXAeop+uzsxLyaAnFIfMdOIkHGkIUT4jgrf057+R7j3pgViF07lTuD3cxMQwqHbBlm447a6WQv1Nd75C6FUK2fhiDdIdQyk7e8S0SNFYrbgeCAWidFE8p2+FFWDg0fouz0DKfqSVJoeduTN/RPw8ylm+v2Nkxtx1x4p23ZIGdb+V0b1PCqG5CtyWqJKjERa5opVlQXhD7Fo+3OcutiBKZMthNaBN91/QEp9va3195x6A1L+OqTychPvLl5J1LE4WjRrwlMzb8WvRdP6G3UtPinxh58pa6MjdL0VfdjVrVSCK7kiuxCDm4KEma2vS0hlz0BKrvUY/feG8tDuocilZl58oWE+2LU4fdVuKipayvqNUny9i3lp6P1YZK5onQeK7WiODUCe10LkRxF4UirlUPxBfoztQZ+wVIbcYjuQsmN7OTv2OjA6bB3B7R3xXxOP1KUpygs5YtO5nV25MKIpFmX1CObsFUhR5xgoN+TgVBhEWno830dPo2tHA6NH194l9+8TvuxHKQlnpHQNz+H+yFl//myxSnng+x/Ev/+TM/f8NcX2M5EZXDDvo+3Wax+UypqrxYNiuXPV2SEaGkhxOl9G8BepWNQKtL3C/pxb1bksknrA6dNKNsUP4+YBZdzUp/a86+Jjdfyw+lIo5wtztChVtoerVHtTagQVhBAMgcNhSd8t/N77DB6ScJzPltF6dTqKYpOYStgY1AJ5VoHIN2ArmFJfQEqTo0X4rcnA5O6MrmOgbRo/fpbkoUVExs1il3It3x0fLu4zo8fXPO2zbR1omFKfLZGRnlEBttalF0LWhXw+/sqbVu6pDJqyjl7vyfi+fTDx4QZeS1tBqf9gnFJ+J9axFUPazmVAVjbLftqA3lPJ6cdsT4lpT0CKsrAc1R/AtjK/nNS2F4k434u3T3qT1sCp3LftlLJrt5RBwbuY0v1Tcf6zS0NZde55jh2T0cwHHry/4UKPauNteO1NKcZyKR+NeotEpS9t92owBFZwJSky8lDHpWLwVBD72JVG1es9316BFLf4UnKdLtIs0ZuX94/Hp0kpDz3UOEP6hTB5IVx+Ws/P6NrGA0XiUGTFTf4yLVasXSbyzf4H2Js8gDEjzXSpA368/4CU2ngT7auNegNSNu2IYkDvjqiU/6z44PPLf6GgVQGhv6sxtG15zdnVHDmDwORf1kzNmel+17z82iuQItNbyJWdpF90KA/uHIzVKqkzVmv7ekUu780PK6TEJ0jpE36aqZGvUK5siV7TRSzkGDUcWbEnZf4mzA6XQKaYxIN8c6IH3YLSGHWb7XwJWzbp2RvlxMSIX2gRriRkWRq6XpHIC0pQnz6PxFCOwUPBucnN0bao2kJaOZKGBFIEC7SyyCQeBh2y9KizDAiZMBwyDWL38lubyOkTTmFaCksO3k1EqI6JE+tuz1j0sYy8PAkjescxPuBlTDIvJBIzMlM+T61ZSlGpAzPuM9OyxT8TNEy7IGHJV0KK7UQe7lBEXvEeWh92xeLogMVBhcVJjVWtRMgfKrgvy0p1lDvKOHtnS7S+1z9QNTSQEvDDBdziSsWDrtHvEreQAEQmd9STlOTI2tjhDOxdTP9BtWdl27I2l70xl0L4ZgxPpmXXVva8rdVp3wxGeP0tOWp5GR/0PsTO3vG03+RB0/0F4nPLvd0wtPP7MwOO+sRZFLm2gSn1BaSEfpCCAPTqOgRi8ryS76xUJuO4g4bjDo4c0ziI/31pxwFCQ2OJOPsU0er1fH5sKOGB2Uy6/cbTpNbpRNVS45Whr0JzgkVXsOzWhVw8k8HnP7aknVcyfUdtodunEj7pGIauTSZPZG2htM1QnBM3oJfKCIj8CCdjOWc+/170qIt5qSLMzxZpKCBFAIKd0nQ4ppSJ30jBO/DvEjtKSytzPz6JdeRMdgj3TDXj37phvlUbN0s5cEjKmLD1jO74vdhVi0XON0lL2X9IIaZFnvNk4wZSKonZl4yZzylrE4KPNcHod8kIrImOR1ai48LIpmT3sB0wtScgRVh37seLEcJiZQYLx27V0yI/gLl7xuDhquWx2Y3TKPDFEiOpGRqRKDjoYkWWWIvSiqGpBUWBBHmpFEuXO/n56Hg2xY5h8EALfW+q/b3rPyDFll23cZWpNyBlyJSnKCouFUN7Rg3uSbeOIchk1bOk26NqM37cQE6zi7Td54axzbW9DQTeFM3hBKRlBoqCHEmeeiXooigx455ShqrAiMlc+y+wrfoTCP8UJSYUpSYURSbx/6VmK9F3lRCR34Und3VHb9Iwd44JdePcU21VxWXlhCl54205AkHnjJtX0a3pKowOYRhUQWI5p73jkeod0QaZMKp1HPL/EJXBFccdHVh8vDuRrS9wy1TbOVI2rNVxKMaZOzuuwbUDdPimEG3PCiJQYT2p4tNQZFdcRrTN1RREulAQ7kK507W9BaRGC64pOlyyjRjL6/5QIy8xI1jRBCJJkUzSeP1DXqm3hXOjArCcz+SDfXfTto2WO+6om0VmsVSwuFutcO/wHfRs8jlGhR9WqQqVIZH52xaQmN7yH5Ft4FoLvjLFdojPSaaFG9Fl7KWJKRSTh8sVVQT3ZVXcedG7ThCBqyev45XlhN+cU8rwyDBg1Nf9Grvq2MxWmu3O5+9pavVSKaXZxeiDczh/3plVJ0fSp3sRQ4bWXtz35x+XcjHvUvjGsPan6TWhdnhnbmjjauBKxSUS3nlPhqtDPm91O80Jzyg6/6AR50YwPpT7XAks/AmmaGSi4UHvdeUeoEnX45mqx6wzYRVe4joSeakZr+hCLBo12h6X+G6KZHLe8mlGjIMjZ1VXgorP7T/MoKaHCM2YQ6zDZt47OpAg3xzuutf2C1YdDalOm/3fa3KEvVUQv5ZWpt9TN6GRKTEXWLq2NZHN4rmp/y4il1p5q2dnmvrEcndBFKWtB+F4fidSYwkhHT+mSCIn7dNvkZnMnHguCJOmak9HAcBwP6dHVmrEbKm7NSboyqyRIy8uxylVh9N53dXnSCrBolZiVSiQ6g0k9SrGUzOA7xPMnLjQmTummGkbXLf9vNbi+WWtjKMxEqZ0/p5BoevB5AzyErZkvsjKrRXnlpdeMNFYj/1GI7z2lhylXM/HQ5cQa1USdNoXY/NLXg2yQi2ao2fEUP6zt7dE21JdZSisQGLtdk6HQ46h4c79VgmScgvup0tEI1elCN6cp4YX0Lo0lCf2jMBZo+fpp6r2Rq3TDeYGG3/tDTCa5Lw78Aecs6di8LZQ/kcIvipDirJAhrnjI2yJ78Lqo3fQs6eF4UNq/x72H5BygxNox9XqDUgRdHDidDIbd0SxZWc0JrOZEYN6iKBK+7ZVE7fZqw4LftxImncybY80v8zqebX+SvVGNNEJSMpNf4ZlOJ8rwylJi8vZMhyyKqzy9ioxk0sJ1nZl3v4IinTuPP24CWfbkhHZ65Cq1a/zqRK+/FqGg4OV50a9TjP1KXSOPTApKrxMXLbfBhYZpSHlZKsTSWi9Uvx3n82D+PhId0JbXGTKfbaHsP28UktMnCv3df4ZdYiRiJ+MfxJmVnZcAFKUiReQGi5dWIvbaCiIdKUg1Fn0fHI6p8M5WYtzcilOafpqjbkuClsclOJh0OLkgNVBjcVRjUWjQmIwIj+dwKlpPjgmFrNg7zT8W2q55566AVIq+W7Uaiszhqwmwm0VBlUwFrk7DtpDLD86k+2x/end08LQOvig1oVuq9tmXIKU5SukRLaMZkKoBJL34+oQgdn52h5OqnOZKM9WZDfK6u3BxWHeokeRS+UaO6sTgVd7EKOvF4Zg3z+78pO7J3sM8KziAOkX3fnhxCh6dixg+Oja2cgqD9vCA6d3WcMXh8fTzjuF22de21vRHvRUl33Iy5Ww6BMZTV0u8nznc5znIMF7mqCPCBD3gWvJ38EUYS9zSdTiLHwvk7UIXpL1KfoQP8qbXUpf/7lXU971rtj7FVhpIpPQRApN5BKOGayMj4njHskOWuofIlN+kFcP98O3ST73P3R18LE+x1JXzyrTwVsLLl20PD2szH6kboCUhH0pLNsWRPeWp+jVcT/tf7Iwt39PejjtYVTZGbR+/XBIj0KuzaJfhwUkylw48/UKnEoqsiqW+jmICQC0rR3F/xcuv4LHpAACC99LlzNaVIVXeoHUle7+2q4AMlqcHTA7a7AIfwQPQbUCq+KSbpVn08nwOY+6aR/WJujFDGQTbzETEdYwe++KVVJiT0uZ3vtjugfsgdwh0GQrp3In8um2iQh74xOPmnFza5j+1XTeKonZXRwKWdj/J+JMRgJSAin3vhwYrQzlr3xecYCG0iBHigKdxIxkUpMVp8pzf4oWTbp9nftN7k6Ymnpg8nZHnlvIWf84Wpe349HdI1DJy3l+rm0caTXVd23W12rh7YVyVHIdiwbvR68ZJBKbV4qyQIoqQ4Y57AX2X/Tmm/0P0iHCyoRxtb93/Qek1ObM2kdb9QqkVA5ZsB7FxCaxZddhduw7JnqmbPjuLfvQSDV7oV2xlbNupwiKC7jskHWtZmQlZTgcTRTJqf4uVsHa4O4Erk6Y6tj6cd1hymWiO78Qty5+vFVKlKnZJIacwc/ajbcOBZJd0qxBic2qOU21Unz7Tik7d0tp0dzKnP4P4iArpNTlZqxSRzApcNl5K0igJKScRM3vZLWMEp/bcutA3o/uQRvvTKbN/GtM5vW79eMPJZxOcuehrmsgsISItXJ0Ha4eeyvPL0GekYc8uxDJX6yzghuz8OH+q5hdHJF6OlOFc0it6AylHItKiVUtrCXBknYdbxmdEceDsRx6xIVmCSZe2T2V5t5lzJxZe/wVfx2UEKIlhGp5eFiZ3m8JQU7bMTh0oFzpi1PRBqJSerNk7yyCg6zceVvtf1BrR8E1a+XEKQmrfpbR3X8fQ9ur0MQcwME78roXXOGJ8txi1LEpV93HhN+FkCCJpwtGScN6HZqaNxH3sEoZGtiOFjkFvGLaRl6uN18fG03nsHzG3lI7l9vEJCnf/SCljdcZpvQ4wevrJiKXljPveQmSxnf+rNni+qN2ejp89oWcVp7JzI7MIVt7gJbn/DEGVB3m+PdLyV87JHyfJJ7OmIRvVR1jKhKpBEPry70Je7UNI08uZ7iDBN+/OTds0lmIOHOeeTkb8VTfi9Z6mrmHe+DhXMxjj9deGFmtTFAtNpKTK+HDTy4pQ6ms4JiqCznxezKr9relX8AxugccJni9mYeH9uM2ya/0NGVT5tsbVU4sysKz3Bb6ArvULVm99wDdks6L4Rd/F4HD7u/AiQBoWD1cKkCNOj6TCWtMMCqYHTVYNFV/85QXc8lTJWEK7suuhDx2JQ5m9EgLXTvX8ctwjcn8dpmMpGQJswe+SVizOMyZs5A1X0h2SShvbXuRkhJE7yTBS6kxSl6+hEUfyfByyuSNXluIN+TROisck8flILxAOKvIzEOWV4y8oPTys5cA1l0FABbP/e5OlDfM1FX0USmn3Msdq+rSGU34zqd5ncRfHcKD24aLxRoyxfaNrpvUVAlffF0Rwvxk93KM6goPqUqRlUrQpMoxBy/iZJGWj3Y+XWfnvv+AlBudRdvqHTgcy6IvV5OTW0hznyZ89+Fc2yrWoFSDACkWi5UjJxLYuvsIuw7EiLwpa795owbDaLiqphU7iXc8RJtzYZi8XG3qiDyvGIfjyQjAidnNCYu7MwIKLFxw7ZUjRZGRz3nfOLwcuvNhdIsKYrP7TSKB2L9FFn8p48JFCR3CDDzScRpWH9tqhwAAIABJREFUZJS6jRGHLy1zxmn/WKwKKA0qJ8bra0o90sXfAnYMYMGBnvi653D/LNvdur/7upTEVDce7/YLxlY5hG11RR96fa4FAaATwBR5Zt6fH3HBJV342JuFP25OSJRyHJQytA0VdnGNBSOEKzntPsHBh5S0SVbz3K7b8XDV8djsuuFI2btfypbfpfi2sDC953xaOMSgc+yOSdEcx+ItZOa589L6d0QLmmBJ+ydK9BFYt0FOn8Dt9Al1wmN/FPKATn/yVVxvzEKYorCPSXUGkU9FWGPiXubmhFUpp6E5Uv7e953Orjzg5094Tj4fFK6lqKQZi4+MIaJtPhNvrR0gRVhPwroSMgME3JTHyu9Hk6P14tEpqTQJtp1o+p+01lLOSVj6rYy2TWO5L6KUkqwD+BQGYfS1DVSuBFOsKoW4tszuzpjdncQ1V18cKX+fj5XunrzQvKXogTJecyVCtktvwSUth8/jfsbBZwqK8gxmR0XioDTw3LNVh5Q01vmv9Np0dbFSVFyhl7q6eB1en8Tao+0Y0i6aHm7HabXTxNTRQ3hWt5RgaTm65t1QFKWgzj7F823uZ6lrZ2bkZvFUVgYI38kiLdLCEuQFWmTFWsQYTyHExtUJswDQebiIadMbiiOlqjUgzy1Cq02gqGtXjibksTluJDcPKuem3g2D2C7+SsaFCxLmDH2JQLdcjAULUDa9V+RJeX7Tt+TmSZk8wUxY+8YJpGRkwqeL5fi6n+fFrgc4W5IB+q40dbj2+UQ4j8kKSpDlFonGB6mxwsNJ8Pg0uzlXrDNXJ+RKOQq5BJ3Bvs4ZQqhSpsMJmruF8PjOAZSblcx7zoSibo5kVS35G/5dSMcupGXvGbCb29pHYFZenjlNUg5OiQqsvj+SLI3hrc2vigbTB6bX/nzUJZByXGdBW8eA79UmIdJBhqZhbWZit4Solxfmf8U7Lz5IcMAlT+QbXjg2Vqw3IKXSC2Xj9ig274xCILwcMbAHo4b0JCSo8RLxWVbuIVG6H7+cDiIYYqtIy4xXtTrYK5AifLQzXE7j6NWVpUc8Scpu16itC7bOU2U5g17C6/MrDsDjB8YzosX/MMvcKXPuL/6brMALxyNDRZLZMv9y9gXOxyqr2IQDdw3g7X098XIqYNYTtocQfLG4lNRMN57ruZYS7zRC9/tgCLJ9c5Dqy0WwTrjU/lUakmy2Kr0774jh0AwL7dM8eGzHrTiqjcx5pm526F/WSjkaIxUtD9M7zcFdmYrWuT8WmTvqssMojGnM+O5Hscvz5pq4jjNNVcOy298PHDCzcauKwW230DFMje/mk1giKsiTbREB/MJsvcyKVVnP3oCUu1oHEuXoROuiEr5JX4HO0IoPokcREpDHbXfaBoJXpZPFX1q5cFHB7P7vktfZmYSVPYlODWNitzgihlVwKf3bREiFK6TEjfA9zOQwC5bkg3hY24oks7aKANoJ4X9/l4YCUoYHthN5UQaqJbS5ipNdtMFKQW4J6/csx9J2NF5aI9OjK3hy6gpYsFWXdVnu1GkJP62S4dXEQnGxBINRUmfhHHt/TmDLqfaMCj9ID3MsPkfLGTthJJ/nL8RT5YjepxOysmw0Fw/xTYsRPNd0LEOLCvngwrmrqkDwFjZr1PydxMNegRQheYEpPY6Lg0NJii/l15Oj6d9by8BBdRMKW9W6+fAzGTnZEl4a9Qy+agna8vk4Kp4Gh1Te3vkxSWmeDL/ZQs8eDel2UdUorv17apqEL5bKCPSO5+kOp0jLvcBdbW5nlraA4UUVvGFViVSrRwCErfLLwVR7Ipv96xiE/uabj+HRLIznd3ej1ODCs0+Z0WgaFxi2dbOBPYccGd9hNX0DJ1x1mpxPK8BzJ1lNVvL8L+/XmQGtLoGUfollJBrq//3aGOBAh6twTpVqdbzxwfccOBKL4EAx5+HbGTGoO2s27uGr5b+hN5bj6e4iAh++zbyIjonn7Y+XoxViRJEw99E76NM9gjKdnlfe+5bjsUmoVUrmPnonXSPbXTGPj877gEmj+ot16lPqDUgZNOkJSst0FWSzQ3rSLTJEBFMau5hWHeC8YQ8+ukgxlrWmYq9AioBM50pPImvdlRVHHDmd0YFpd1poE1D/L21NdXwj9ePipCxfKUWwtE2+6Xe6eX6JUemHQdNZbE6e5YfmZF9MTlZygi8S0+rLPx8Tsr8fr+3sjau6hCefsT27zicfl5GZ58LLPTeQ53aGtsfbYGxtO8fKtcZpz0CK075Yom8romtOK2Zsu6VOLx5fLpVxPk1CZAcL09vfj0pWSqnrCKwSFUpjCqqyGJ775WNySzx58H4zzXwa1+HBlnW+Z7eRrTs1jAz9jeAIBYFrUjB27mBL1SrL2BOQkqB2YEybikwdHmV6fk38ChPBLIgaSWDLfKbeU3OPFIGE+tU3K27VH45+najwFpRs9eeX6H509jvD2LttT7lapXIbUYGTsRJWrpbRrfVehrdX4XD8IBqnENFDrqbSEEDKdmdXHvTzR3BEud1RIkRzXiEny+GIzsypVV9R0n0QzXPdmHXKGZ3RkeefMaNS//P2EkEJB6Ol/LZRCH+1iB4ppaUS0aorWHdrW37/8Qy7z4RyS+c99MpMwC3ZxMDbxrEp7QUkLr7ovcORGLU4nd9OlHM7xgU9Tohexy/JCdXqir0CKRK9AXnsKRIntiYrtpwfY8bSq0sJw0bYfsaoliKqKPzuIhmFRRLeGj8LD6s3Wtk8HLXfIvH6jaWH5rH/THt69zQzdEjtr4XaHMe12kpKlvLtMintm8fwSNh5stLOMbLjNOQKGZuT4lFZb/wsbK9AikRfTknxURwDwnh9Xzh5Wi+enG3G1bVxzeFP32k5leLKzB4raNvi1qtOsWOyHKnqFGWt5/Poiq9Qqaw8P6dxeaSMTi4j62+h/PXxbnzu50BHhysNni+9s1SMOHnm4duwmC1iggsnRwcSktPw8fbA1dmRhZ/9hE5v4IXH7mLijJd4c+4Mgvx9KSrRIpFIcHHSiCCKk0bNEw9MJvl8OtOfnC/SgWgcLid57zXmYWbfN4FVG3ZjsVi4dcwAJo8ZUOcqqDcgReBD6d+zA8p/WPpj/apDZBXtxlPWGatD1XGtVc2ovQIpAjJdrD2Ovn0nfjum5FhqNzGtoZDe8N8ga9dLOXxUSuvWViZGLqWt8xYM6jCM6gors/JCMOr4biIL+NmQKM423fynWkKie/Da1oGo5Aaen2u7W/f77xnIL3HkzV6byXGIJSCpLeUtbHOHv96c2DOQ4hiVQMyobDoVtmfmnkGYzEpenGtCXgdE8W+9I6esDHp1L+ee4LsQ2MdK3MeKSLjUXIRjyXY+2vEMxy904pZxZiIjGtfhwZb38vctZew+6ML4sA34RloJX56Dtvvl8cO2tHO1MvYEpDzVwo91bh6EKxAPGr8e+oxydRBvHRpJq6YF3PdAzS/1lRmQ/DxSmNNnPUfCfNDHm/lh9RRc1YU8+YztHos3qnN7rHf4iIS1G2T0Dfqd3u2daLLnMPIWYSLZdE2lIYCUO1oHctjRie5KCRHX+OQnmWCH3srpZd9SNKALrVKDmXO2TLyIPDbLjIf7P28vEeZy6zYJe/bJxBS8RUWQXyDhjikW2gbX/jnh5y/PEXMxkNtu+p1eMSk45JrpPW08+5LmYHQPxNBEyLBkxTlxPXqpQkyBrLJYOBF3olrLzl6BFCEUyXFPDMdmeFF2XMI3R2+hc3gRY8fXXgay6ijqzflydHp4f/J0NIZwtPLZOBRHI/NZyKbYKaw+Oo6wMCuTb6n9y2l1+nmjZWPjpKxYKaWT30HuaZdDUVIKPftNxyCX8URWOg/kZt9o09grkCKEwOnTDyNrH8Z7B4LIKPJl1kNmvJo0rv3rk0V6MouceLHfCtybXB1IcbggQ27IwBr+KDO+Wy6eA1963oTM9iO7TfNflx4pX+eVk9sARP/TPBR4ya80KXQbMZOtKxaKgMm1ZP/hUyz7+Xc+fuMxnn71U9xcnHjo7nG4u146k/UY9RCbly/4s51ps9/kwWlj6dHp0lnVYCyn89D7mX77SPE3wYtl6qw3eP3Z6USEXp1b0qYJs6FQvQEpNvSlURbRrommKGMXsc0H0NpixqfcWKNx2CuQIhBo6TOPkt89kt3HrBw825eJ4y1EhNf+AalGCqyjyp8tkZGeIaFjpIVbAl/HxyEWnVMvTPIKDxHV2Qjxj8HTzKmwVWR7xOOcKaPEx0z74115ZcMQsVx13Lrfnm9Cq1ezsP9Gso1x+GaHikzqNRV7BlI0McnE9c6gnTmCZ/Z3pVjvVieupHq9hDfmy5DLrAzrl8nYFo9jkWrQugz9U71OhetYGzOO9Scn0vcmM4MH1vzwILi6L3xPipMz3DvVgpNTzdusyXrYtKGU/UfcuD1yPe7hBiJWllHWqXZS9doLkJKpUNAvuL2optscJezWW1m5ZzESN39e3TeK5p5FzHy45peP7Tsl7NwtY0jIeoa1yycu1JUCayy/LXgMXbmGOTPzcPSunRCimsx5fdc9cNDCxi1Kbm63kYgIB/zWxWIJjRBJzGsq9Q2kxDpouCUgWMzSc4eTTPzv1STdAhvKrER9twr9CH8C4/rxSmYiqQWtmTnDTPNmDfve11Tv16pfGS45JOQwFwqbEpchpI+30Cmy9s8JX32Uybl8X+4bvoGum9KR68z0vG88B+LnYPAMwegRKHbTKWUbElMZgR0/pUwi5UDCKTxMthPg2i2QIqSZ332c6Ps1yE468kn0eMKDC5g0peag8I2sjxdfqbB2LLlrCpaCIZSp7kNZqkXpfR+n0zvw3rbnaOVn5b67GyeQcvyElNW/SOnVZieTA8soOZ1C52HTK85/FgvbE0/TpBrr6q86tlsgBTAlRmHsFs4XB31JyQtslPtX5dr8fMw6yhSjr7q8lTlSVPnlWDvewVOrPqNI58YzT5hr/Yz2bwFSynQGbhr7CEe3LLlC3yvX7+T33UcQKD+KirV4uLvw6VuPI9T5esVGfv5tN/16RvLkzMkioNV1+AO0bnmJkFNbphfDe27udykMvbzcJJaL+u2zPx02Pv32V+QyGTPuGHUjW5rNdf4DUmxW1dULFv4ShfHsXiJvvl8s4GAxE6jXE2A0EGTQE2Qw0L+kyOan2CuQIgzAkhDNxcEhHD4iZXfSAMaMMNClSy3DtTZrqn4LvvKGHOEb2buXhSmtZ6KWFVPqMgyrtMKqKnijCF4p+qZm9ndahEFdQuBONUn99QTGduC99YMxmlW88JwJW52yXnldgsks45PBG0jPS8TbEC6SxdZU7BlIUceeI6V9On7qDrwSHSJmh3r8UTPutZwyUSDFE8jxnJ2tDO9xmiE+r17GeSO+y6X7OJYUyOI9j9KurYXbb635ZeDESSmr1lS4QN4z1YJ/65q3WZP1sPbnYg6f8uDujutwDC0l/FfQRdROCIq9ACkLvJvxhVdTAuQwSC1hm97KVzuX4tSkJS/uGUkTlxIefazm3hFCanSBbPPh/gtp6t2ai0FO5FqPEPflJE5nBXFX/3iC+lZc7v4JIuyHF9IllGklhIZcex3v2mVk2y4NY8J+wz9CSsgPKeh6daoVFdQ3kDLbtzWbXN2IUFjprro2d1OBFVZpraxf9RuOA1xpGzeU+fknOJMVwj13avEPaBgei1pR+nUaETJWCZmrZg1cwJnMYDafHisC0AIQXdvy7oJSCnVuPHDrL3T9JlNsvu/MMew+ORe9Vzjlbq3Ff9NcPIisLIdekQs5J3VixdlEInVam7tjz0CK46E4Dt9hxjHek/eixhHcqoA7p9U/kFJuglffkCOTmvnsjjuwFIynTHUrEpNE5EnJLDcz79eFdcY7YfNk1qDgoWgpGzZKGdB2M6MCrOSfTGPSyKmoJJBihkn5ebyWkXZDT7BnIIX4YxT1bsfyKG8SskJFIEwAxBqLFBRKeO8DGZ6O2bw6IBWD6uoccPIiKQ4XZVg73cFLG14lvbAlD88009S7dsf6bwFShPUhABvbVr4nhudUyq4Dx/lo6Rq+evcZnJ007Nwfw4q1O0QgpVKMxnJeff87XJw1PP3gFASPlO0r30PjcP3v5sBJj7Pis5fw8qzgX1v0xWrcXJ2YNumSgbQu1u1/QEoNtZq37jAlZw7Qa+g912zpwexMHsup+NBXJfYMpEhjj5I8uhVx0Qq2JgxhxJAyevSsuVWxKp009O+FhRLe/UCGXG5lYJ8SJvnef1nGHvGwdqIv8mw/tH46dneej7RcguuGlhSMS8UvIYQvNwwQvSvmPGXG0UairkoU/YObN1OQFs9xj750kFlwuUGrR6Ue7RlIUZ25QLpvGk28OvHeEV/O5wfw4ANmmjWt3Y9ZzHEpP/8qFT+SwzvvpXeTjylXtEDv2O3P5aYyJJCdXsrL6+fj6WFl9iM1vwws/0lKXHzFBWzMKAtdOjUskLJqRTEnEjyY2Xkd0qB8wjdp0If61corZw9ASplURp/gEEplcsZqpHhLrewxWHl/2zKaNWvKcztH4qrR8uRTNb/YVr6v702eQbrjLWibO5Bl3Y927UA2nepBv6A4Bt3WeAlns7IlXEyXkHYBMjIkooeeID6eJTz08LWBqK2by9hzyIVJEevx7mii4xfZlPS7kofnolJFqVRKW/2VqWmvtSDrE0hJVyoZEFThSixwozheh+JND3xXamXxph0EdrbQLmEsH5dEcexCR26fkE+79jXn5KmVl7SWG6n03Hx+xFySskJYceQuenSzMGJY7e9zle/bQzNW0OnDAopUSu66qy9r415D59MRk3MFMbs6+wSKovNMDHuJ/crmLLiYypjCfJtHbs9AiuZoEkfHF+GR4subB8fg16yQ6TNqbmyxWTl/FCzVSpi/UIZGqWPRrfdgyZtKmcMI8VeBJ8Xovo1Hln8j/r06XrnV7Uddlt+7X8aW3yUMDf2Vga01ZB1P5/4xU+iukrBcW3E+WZccT7BeePurJ/YMpMhiT5I9KJBfDrpwKr0TU++wENim9t/n6mnM9tICsCsAvAK3zcwuTTEpml21slQvwfGsHEv4LBbumi6CRnVh7Po3ASnPvbEEd1cnnpx5KxarleISLbsPHmfzzmgROBHCcea89rnInSL8PT4plXaBFefP71dvJfViluh5InCkCFwrAkeKQi4j9WI2zbw9UPwtA8QHX64mIyuf1+bcR0FRCXc+8jofvPZonWfw+Q9Isf19vGrJjA1RFJ2OYcKI25noKCHPAkUWKLRAvsVKyh8epK+lpzGpIK/Kp9kzkKI4foq4SR6cj3Jg/elhDO5bQt/+NbfkVqmUBi5QSTImEGwN7RbLEJ/XrvBe0By+GXmhN5kh5zjW7hs8UuQcOdcF/wEH8U5rw6o1/cgp9bHZu0JvkPDG2zIcFGW8PnAv+oQEhvS+C7VcyuLUFAIM1f9YV6rRroGU89nkaVJQBkTy5XFPzmS2rxMLyNbtEvbsrYjlHx62lo7uyzGq2mBwuMT2LTNloynd92fmnpoeAIXMh0IcufkPPMYeiPd++K6I+BRPZnddj7lVBqG7PDEEtbjijUtWqWlqKsepsvM2vJP2AKR86+nF6z4t8JZZGfsHGZqQUeXlrT8R0MqNOb+PRqPS8+ycmpHwnEuV8NXXMpq7pfHyiHnEeN2G1UlFjuUI8mOBLN80lpZu6cx41NsGzdlXkaPHpWz4TYpApns1cVKW8Myz1/4ObFhbyqEYN+7quBaX9mV0WKZD2/NyHp417p684tMcucXK16lnaa8rs0kJ9QmkvObTgu88vQiUwwB11UT5S0qtzNsbxYCgXNolTuZrwz72nu3GhGEZdOjmZdP4Gluhd96Xidl65k94mMTsdizZM0tMdyukva1NKS02M/99Fe6aPCbevZXu7xaS6uzEvInhfHPmPXTNumFyqgi7VRacRZUby9NBD7PMOYJZOZk8km2bYUuob89AiuDBeXxwKs0yQnlp/0iaehbz8MM1T3pQ3bkSuHDe/1CGu6aQ+RNmYs57GJ1DH7GZSp6UWT9+jb5cXSehutXt742U375Dws49MsZF/kTXlq6kHink2bHjuEktQfimxJRD99ISvj2fXO3m7RlIUZ1M4NxQX7ZFazhyvge3TrLQ/joeiNUefB1XOHQINmyWi55EY8KHgPXqnoQSCzjFK7AG/49Pjw3iaGoPbp1opn1o7Rrx6hJI2VRsoqgBOFKGushxk135TRTCdl557xsOH68g+H72kdvp17MDs+d9xLkLmTTxcOWuCTezbut+EUiZ++YSDh49jUqppGVzL954boZYRuA7WfDJj+w+eAJjebkY5vP5/Keu8FDRG4z8b+HXHDgci4NaxQN3jWb88Ip9qC7lPyClhtpN2xRF9ol47h99C6McrlxIR4xWjv5Bm7L4fAr9Sq8f5mPPQIrj4QQOT1VSEOXKyhPD6du9gMFD69+NtIZTVu3qBw5K2bhFSnMfC6M6b6arxzeUK1uh11xyT3faPxZpmTMJXfZwtuV2AnarWeQxgtFhP+Oa68OWnwZzodCPR+4vw9unai+ekhIJC96T4eaQz9z+R5EcjSds9AyBDxVHs5lFF8/Tp6S42mMRKtgzkKJIz6W0/CzmDpH8dFLN8bSu3HmbheCg2rWA/PCTlPh4qfiRHBb0Ne1cNl9GHlyhWDPOhWt5/pf3xBCjmrp5VqYGrZy0du0s3D65dsdV3QXxzVfFJF/w4KmuG9E3T6FJjD/7QwNIVKpJVKk5p1JxRl1xSfYz6vk09RyBNoJ49gCkDAgKIV2pYrCDBP8/ohCPl8Njm36hc5CSxzeOE8dWU5Bs1x4Z23ZI6B+8hTva7yc6pAtyiRP51lNYtVZWLJolPmfeMzoUakV1p6lBy/+yTsrRY1Ix5aWTIzg7C3+s9HH7mg933Sv27dV5eqySq4NRv6wq5uhpD2Z0/hVFcCGRqyRou1ZkUCqSyXmhmS9bXC+lQhb2t6WpZ+lQVnX4RX0BKUI/+waHopdKuUUjwdOGjOzfa61MPXqKW73O0O7cVH4y7WJLYi9G9U+jW9+rW0UbdKJr4eF/5cmIzwxn4dbnad3aIvJBXU8q+SeELIBCNsCqJCutgI+XeuHvcZ5BU3bT410tsU08+HJ4C949u4SyFr0wazzFZuTaTBzSo/ncdxwvew8XvVEErxRbxZ6BFFXSReK6nMW3OJw5e4fi5qTliSdq7l1nq24qy2VkSvh0sYxmLlm8MnY25XnPYHCoOB8pSspQNb2Xeb++Q2axLw89YManlj1Mq9vfGym/cbOUA4ekTOn6DSHNmpFwRMfCscPoopJgskr4scyCzgqfpqYwsBrh/OIalUlRyCXoDLULON7IOP9ex+F4MgmjvDh0WMH+5P7cMtZEZO0k9quN7lXZxm+rtRyMdeX2Tj/T1b8iE+S1xClRAS0+4vukAHYlDmHUcAvdula9H1XZib8UqEsgpTr9+K9s7WngPyClhrpM2XaAc8fSeH7kCIZcBUgRmhfIDRNMFYRUy88lXdfaZs9AiiYmiejbzeiim7Ds+Ah6dMpjxKh/Pnli5UUisI2VcWFf0tbldwyaCIzKS0zQLjtuBbOCgwO/o8D1LGHLXZg56B4e8VqESu/Moe9GkJzbhvvvLsbXr2qLUW6elA8+ltLU5SKP9k3AcjCRzuPvxktqJcdSAdg9nZXB9Nysaq9gewVSBK8HXV4J7VNjKOrbgc2n4ODZPky8xUJEWO1+zD78REpOrpSunS0MbfU+rRyj0Gu6Uq6scAevFE3Jdj77fToxF7oyeaKZsBpYJwTGf4H5XwgnEsIkvLytzJrZsAenxZ+VciHbjRe6b6LYM57vCgaxLOzaZLNqi4W3L6YyrLiwynXX0EDKJhc3ZrdsjZOkgmS2UuJNMHXzRnoHSXlqQwXxXE2BlG++l5J8VsoDfd+ng6uSE+FBSCUKiqyJ6Mkh+pNppBa2YOboFJp3bFml7uypwFffyDh3XkKHCMtlXEXD3J/n7S1PU6Dz5NkHs9F4eVy12yuWFRGb7MmsruuwtM4k/DdHdB3acMDJmSdbtCJPLkeg+g1WQp7ZSqpZgrDOvkw9Sxdt6XVVUV9AyudeTXnXuxnNpTBSyHtsg6wus9IvLplHFdG0TZ/OOnbza1xPBnY7R/9hl+8zNjRn90V0OglvLpChVhj4cMo0Lha25H/rFuDlZWXWg9ff5wRLv2Dx92piYdZDVe/1Saey+PbnFnRsdpqOEw7S6x0jB5r7sKOfmrmpP6L164tFVXE2kRpLcDy/kz3ukdzq/yCRZaWsSEmyWZ/2DKQoU7M5G5hIC8J4YufNqJUG5j5b/7x1wv4g7BP+HueYO/JZjHmvYHSo+I5IzOAom8PCfXcigGt33W4hKLDqObZ5guqpYOU5cFqPz2nZLIBjR6x8N6a/mAlOkDMm2KW30sqgZ0tSfLV6Zc9Aijr2PAnDHDl+VM2OxEGNjhvxu8WlJGa68Vjv1bTymXD978l5OVLXH/klS8qGU7cwsL+V/n1r54yWly8hKUnC+OH1D3RWazH+V7jaGvgPSKm2yi6vkLhzJ6ePlLJoZH/6XMfdd4vOynkzuJnKWZWSREuj4apPtkcgRYgNT1Mo8Y9JJGdULpaY5nx1bASd2+cydsIlS2INVWm31b/4SkbqhYqLxOS2r+CtjqfMsTdmxR9u+lYpLttuF/u/adyrWCUWvJYEs2DKGGZI38WiknPqm5GcFogGp+TiH1y1zgT+ASHevJXHWe65KQXdvnSGTpjMXY4SduktnDFVHObHFuYzvxrWNfFgKZXgoJSh1dueuaC2JqdYLidDrhDXk+DpcEalIknlQKK6Ih9858xsVm5eR9rocA6dKmNnwlDGjLTQpXPtHrwqLafCR3Koz0s0USVR5tQHs/zy9NIq3XHWH+zGxlPj6N/HzMABN+bmKZDxvTVfjvDfbl0sRB2Wimn1hPRicv8XAAAgAElEQVR6DSkff6glq8CVV3r8ToHjCd61juJ0cCuaySW4ScFVIsFFaqXMClv1FrLNFevu7rxsnstMv27XGxpImegfxEmNIz1Ukj8Pu0KHBWLAEZt3MDjYyMubBokZdZ5/1oxKeWNzK7T56ptyMfTlnYkzkRl7kxheAZaUWlPRcoG8H0ex82wEIyLi6DGucfGkvPOejOISCT26W1CrKnQklZi4teW9vLnxFc7lteHhcbE0jajwMvm7fLe0mMQ0D57uth5ds3O03+XNywN68FWTitALwVOoj4MU1R8ZcCr3N6XFwuK0FHqWllxzndUXkNIzOIx8hZzhDhJ8bbynbtRZ8TufwVvFWwjMf5jtkt38cLonvcLOMeyWfx6QIgDTAkDt5ZjLG7c8QonehSdWLkbjYOXZp69/GfnlVylCCJkQ8j5vbtV74rF9aazZ5s+A1scIGnqMbouMbPZvSWqnUmZkbqS09SCsij8MFlYLzkkbKJA70T5iIe4mEwcTTtm87TYEkHLY0ZFjGkeOqR05qnFEYbUSZNCJ/BuBxookBgKXkFN6Lume8bRwC+GRrcPEMdUUFLZZMX8pmJAoZdlyKaE+sTw+5FX0+e9iUjf/s4Rj2bd8fcZP9GgYN9pCp461+z2/kT5Xt87K1TJOxkp4oM8i3Ju0Y98pFVuG9RJD/SpljdZKrhWez7zI1Lwcmx9h10BKwgXiB0qJP+nI5vibGTZIS6/ejQcMeHeBkUKdhjcHr0PtevWMPZUTpc6QoZBv5XddGj9GT6N7Vwsjh9fOWv1tk5SDUVK+WNS4PFJtXsT/4oL/ASk1nPyE3Zs5eELKiiE96SrQd19DhKPBb2UWsiwSEbH+KSUJN/PlB4YklZoTzk5kyhWYGiDOrbLr+XK5eNFNUyo5r6q44Ary6q6DhPWIRR7rz6dHhhMelMOk22qejreGU1Dn1V+fL8Ogl9Czu4U72sxAJdNS6jIcq7RCNxK9Bue9t2BRmNk86jU0uTLStnXl4KibuEX/PgZPE2e/Hs2x9HDuGJ9F2/AKl+PrSaWFJ9g7jkk3XSRvdwHTbhkj8vAIctoE+/QVl5qIMi1P5mQSrNdVmdYx5//snQd4Tuf7xz/nvG/23okMCUKQWLH33rNas1qlqoMqVVrVoRSlFC2KTqqoovYeRcXexEhkyd7Jm7zJm3f8r3MiCJlm+v/1vi5XIud5znnGfc7zPN/7vr+30ojzlhbcMjcnN+/JLBAl9SPRSAJOjIk1MibG2AiJ/LMosRIMGEmH9pQMjq7ZQPDLVQm+mMOuq73o0iGPFi3KZgUubVyl6ykpAgu+U8iHQulw2M99LObKFFRWnTAoChP1GWmiOH/ZjB//eYdafnoGPWIoztVgkbXrRTmVnkQweyxIRJMn8P44HRL3zvOS+fNzSFNZMqvlPlK5xIfWL+BYzQ23Yg6Lx3INXLnDlSF5Cyy8Hf5QykeVQsF5MwtuWFuiegY6VtTYqRQivzo4o7yTptb4ziHdMi8DbWY0jicy6F09na/2tJRJoCe9r8PS4tHm4Xa0wPIfpawAScx+YQzx2QOI8cv3zlATT4YhFJMDbfn9eHNqutxi8OiiyXwlAmQ3N8MTzxLwOLpVkI1Dusf9ljlHoxC6VPqMbw98wMXoQIa3PkWVtvWLfNQPy1RExtsypelOVHbBBIc0YHzbZnLq4OamItWLiAi6X8+KC4mVDptXrKzINID+KX7KpHVwi609tgK8dJ9nk5UmnReDlxc7vF84d+Nmnjsrw//AJ3sCJxRBrLjckHq+Ebww+GEeoseZp4pQNzxc5KeVIlUcQ/mo28dyk0atWiv/LO1wL9WT6kvy0Qc6zMxKfhcPb49g35mq9K1xEvemV6i/LJf1ftUwqh5C3+TjqKpKGfXuHVgsbu1F1OXg3WApGkRWhYfQuBRvJ6kt10zNOGdlTYpCRPeU92RZCpHzZuacNy87WWyfsEimxO7A1r0mk/e3kTMDfvKRFqNnfFa7eFnkz40iDb1OMrrNfLLTVqA3vhf2LfGkbImLYselFx7LIPE89VwCiiTA6N32czCxrMPuUCvOd2iE531rZbxOYItaj5VOy7e3I6ilVmPzwD7/wT5EGptw2dKSCBNj8rRF6730edMLgryK6REwCAb5p/R3gyAgOSnrDXd+CmAwSGWkOtLf8+tKIteR/na37p36BoNcpuA+ct07ZQadu0It/zjCg23ZfKULHVpl0KZd6V7Vz3OuCp4tGTckI4dSzGNRj+PkGpfMl2GcImKiPstx5VF+ODrmifI7/fGnAim8+z8gpSJoxpNtw39AymOO57W/t7LnhjWH2jYsZPUs6rYaBLZk6ZFSI9ZUZzM5IZbLZmacMbOQ/0nW+oos7584R+taJzALqcGC013x80liyLDSvSsqcp9Ka1sBG710/u/cJoX+7m9jEIxQ2dzLS67IdMDiRDdyLLI42PlrPM4Zsza5I8oWtWidshiNu5qY37oTFFmPgd2jqd0w3xJbkhQwjftXOku31sncPpjL5L6dCrmVS4v2nhw9d/AU+XY+uTkyt0CgOltO8WhAQDpwnLlj2Yo1Lp2fpbS2Pep1ab9hLxpkLwcbhYC9CNYKsL+DkUhEzXtTc7m+fDWXXqtE1BU9Gy/2pW3LbNq3f3LtLhhbOzs9dQMMDK08VO5Spm2/h7om6tUkh19j+vbZZXJRL25sCqxZVX0MdPbezqazLYlLt30qrPDlmZ/ZX2nJzjXl6zb7SFVdZoTXIOp42cveKMXJLcmFWa1HKwg4avOYHB8rbe3yv2PmFnc5VcrTjqdV1l8Jze7zFGwbsQ2nxHPsTu/BIJ8UvtvfIJ8EepwOu0cEtP4JUrB7r0CzKkcY0WIxIbxKplc+r4yGdFINV7APr8OK33tioszh4ykPf+fPXRDZtDl/0CU+CYlXoiJIYoLAt98r5IOtZJ0rED+T7QS6/s6q469z+GZHBgSexL9H0SmNC7yepjffTZrxZTZltGFNowA6mwly2FVxcjLXgMRnI4lE1m6v03LGzFzWsfIcNp/UOLY2FamhvHfQ6XhrI61u7yr29l+49+c327YcOPUzHrzPZeVpFlyqi59XJEOG37PWP6n2Pe/7FHBABXqe4s228+TmjP9jBapcKyZN0MkgcnEipSeV0pRKUhYuqu1rb3Pihjev1j2GfY0Qaq/M5sc6NfGvdJQmmTfJ9C1seTa7fQylOplG9RcQLdwjRpbCfAKzs2iYnU3tnGzCTEzvfMfMZU8QdTHA/9MeazsBXJTgIopYifnjJvFvJOoMJOkgUW8gD4FqKWmsP7sO4xq1mfF3A9LVdo8FCj9qv06fFdmyTaSN715ebvojWRnrMNz3rhhnZRGUtpbfjo+UvVEkr5R/m/y8UiQsXGRi5y8QTANZf9uZ6Fb15Exw98uBHAOh99lIfXNyCMxW0VCdRYBaTYZCcXetPG1uQWoF3/ePOneFlzyuEh3iyPrLXWndJIWOXf4dWcdi4wWWLlPgYRvB5DY6tMoqJaqdUiVilhTJVbuVzN83hbJyNpVFl3/8RUFE5H9ASlnG6t9W5j8g5TFn7NrBjfxx252rzQIKbbKKu63kIr85W49KgnwfECn02kUp4KAQ0D9NE1spfZY8AyTDm6UgYCka5N8v54Hfqav089iLVWRd5p7qTFX3ZF4d+f+bIyUsXODnlQqZYLFH84t0cJ6JVuGA2qr13VFUJrtjfq4d6Q6xHGu9HP/NFnzo05dqvm60iF+GzjuT5N+7cCg8kH5to6hfBqLBy5cF/tiooGHlINo0SufSaQWLurWmwwPhY5I+nc01kKBHzhhVmigw4KIQqGQsoteVoUJpNyzlupkoIG0KrUVKTBdasFGUCBpjvv2ZS684kHBTZM25/jRrmEm37k8uO9Q/x0R27xPxcDcQUD2FFzzewSAYo7LpUWRvTFP38PZvPyIZdaRQHLEMRJP330hKdPPlV0qkrNUDWx2jo/cifg0azdGQdvTqoZd5Wp6XTJshoNMr+KbDXtLjgulVdxidnS0oLSmJBAbvyjagKuZsJG0u3U0UCM9Ax0oauxpGhQ/rk4+NJ0VhynLty4xyi2HFobrEpHsw5k0tzo+YUOe3NQpu3BR4rflimnuf4rzNQAz2+fqqI4ckw1lMNXbsWjKA5GwHxg2NxqHqPTA1ITGfqLEgIVLb1nrat31+OnH/eF69JrL2DxF7e0MhnqIWFgvwdjzFlnND2Xq5F11qnqLFS0V7pMyfl0talgWzWu0mQxPMEkVHQhpUp14ZrOYSUbtE2F6USN8VD2MRydvIYHg0b6Kyv3cCgfdhuUZ6DRODPsBUpybHpe69MJI7NzRJuMz3ds2Z4f4CZ/b9gJ3tSKK1Ycy6VJPKTrGMfOv/X9YeiYhTIuTs6LeDgY1WyiPx6ZZ5xKa789YbWtxcix5taeqmfam861U0dJCeGtVL1v/ff4jnWow77zQ+jIVLJNXXq/imUV162GzBNycBVbX81LsFYhp/HqOMKBbVeJOddvWJ1UprZulejhJ3j5txfpijXjLtP0UREHBUCLiIYCKU/qyTGgNhqjyCdv6CplFtFh7xIyHDjXff0eHoUHr9J9mVo8dE9uwT6VJrKy/W3YRK81Oh20s8KTfjf2Fx0HBqVMlk6MtPbj1/kv0o6V7LfpDSvyv4uPN0NMb+LE/xRde4BjYPzJWUT/FmHsTqDMTpDOQWsde//znS98tVKeBs9Gz2ZOUZrySdgaZXQnnd9gQJka78fqE7zeon063Xv2Pff+mywPqNCgK9jjOscT0Mhnse9kWNg5gnYBGeTbjHF8zYMQtXV3j7jdJDDcsypgsWK0hJ/g9IKctY/dvK/E8BKVeuhzN/2R/cuBUlp0YaPrAbQ/p1kOdMyjn90czlXAy+hb2tNV98MIIGAfmx7CVdC96/lhXJNUgO9MW7iPRPRSmElBp5u9qAtQDOCnCWPqJ3DpoVkSNF6oO0MNhdCuUNy+3YJNVhxonOeDil8sZb/7+z9pw6I7J1u4iLi4G+DXfR0P5XNMY+5JrXuzu1RjFVMbvajFiPK5xv9Cctv7OmZ//htLU1pkn0LwjVkshc15E9oY3p1TySRh1Lt0aeO6dl01ZTWlQ5QpO6qRwJtuXP9k1oXkr4WKIOGVRJ0BmI14HkBSJtzFyM8jdoUrYJxXPkSCntA/lDpoHTP/9BUm9QRZrz45n+BAak06eftKV9MiJZziQLWnVfAwGVQ+jq9gk6hQ3ZVu2LfIBp1kk+/WMSSSoXmQhRIkQsj1y7LvL7OhErKx0ze4zG1EjF7iu9+fPsEJ53CuQCrpgFnQ+gCr1M/fajeaMkN4H7Oi5ZRA+odaTokcE55zv/HEUDEtb0vDlSHpyjBrFH6XNzJRkKMz43fodxdhH8FlSDsKRqjH5dh3ulRzt8fDlbQa5GkMN6HAwOnPCti7FRAfGqgXhDEIJBJGpVP07crsGAJsH4d8lfW6SU2JLFTCKic7WOJi7DHW/PHEa8VjG8Ewu8bTzc9Uhk2wXS2/k9rMwSOXBhNGsutqOFz3m6DPMv8rWYNVuHWmPC1+13k5EYzKcuPVH7eeJnVPpBVrqhBOKf1xjkNTJfx/LXTWmEnhVHyoMdaxq9n26h69CZWJHt1fahfktZYjaa+vBe5eEc3bMKZ5cXycpJ4+OL3jhbJzHmvf9/npx79wsc+UfBC/XX0q3GITBKk626wbF15Ew8knW3KJHSJUtpkwukZ3e9zCNVkixdlEZsmiOT2x1CSRQ+O7OY1rIxbyt+xhENKp/Ohaobp97EJOkaxyu1Z2e1QfI16XiUoJMOvBCnlUjcDdiIAi4KcJUADUU++P88OFLKsr5I70VQroGrf/xIZjs/fjnsTURKFd4cpaOS26N9y8ry3KLKSBnLpMxlUmrgHtX/IahSY5wza+OQ6YdSl394TY7dxoxjPalkn86bY57cev6obS5vvW8XG0hMNmJ69+lk6GvwtaYudnW8MbsTNlPc/SQDl6RjMdr8PZmJCK7SnkyZvyeTQLqKypESpjUgBkfxgXIfGXFe/Hj+38WNeHRPDnuOW9Kj9nY6+hVtKHtw3qyuGpFcczSTN36HtbWeie89/C26FSZy8rRAtSqGMvP3FewT/gvtKe+bV/HL/08BKZt2HpHzT9f39yUxOY0Boz9nxdwPqObjzuQvl+Hu6siY117g8rVbTJi2hG0rZ2FqYlziteDdK/k6JxCFf2XcygiklKQWFRVIua0D9bUoPtb/hX1OHT452glnm3TGjPv3LYjleS237xQ5cUpECsnoX3cFvlYHyDWvi8b4nougSVhtTELrE+Z7jJu+e/Ff4MnLbwyklSnUi1iLqd9t8jZ0YMv1JnSuH0bLXqVn7Th5XM22PVZ08D1E3VqJbI70Jqh5Heobl+3wUVIfnyfZbGlj/3u2gU2rN0PbDLTxdnx3qj/+vqkMGPzkALsCF8t6dfQEVDpNa6f55CldybFsVmTzjDWhLNvWhUvRgQweoKemX/mAlA1/iUjpPVvXOs2wwK/lZ5yLbMKSv8fzPFMg5+QKzPxKgZlxFtPbnSA7+Bqtuo7k5ft4IEqbr5KuVzQgZeypT3BU52e5et36Q6ZYhLLhlA/X42sxYrgOb6/yHz4KXIctTNUseOk1dCmtOVPPEyPu6WuC4RSSI77R1n6svVSTxpWv0/PV/IxfEq+AxC8ggSgTO09n4p/fy3//dIqWiuDxfRd0rGagUqV8vZc4ol70eAP0RgRdHsVPF1pTxy2YF0cVTaJbANYt7LKTjNCbvFmjLw5VXPF8AljR8wBSBIOBCScnY52bhtq1Plqrh4ljTRIv84/enKHV3mXPng24e7ZDkS4y4ZILFiYqJk8u2TL6OO/d86r71xaRs+dFXmu+lGZutxEUmaw4OYCT4S3p31cvk7UXJRFRAj/+fA9IadlCR+cOJb+Ls2flkZ1nxqfd92GUEIfbURXvt2/J17nzZO+gLO/CoLhSFYNZ7Blu2Aew2j8/FXlZpaICKeE6A3vVcHbdGlSdPdl41JUbcbUZ8aoO78rl+5YVrFGtWujoVMrYFzVu96cGbu91g9017u0LbbN8cFTVxCEUJh2qh7mRmg8/KoM7WikTpNVoSEtMxdG99FDpss51SeXmLYD0DCVzek4nKacKnyia4VfryZBGV1QgJUYKJQuJY2bOFtTp1Vhypht1aiTx4sB/BxD816oMzobZM6LhXwRU7lsmNbAIVZJXZRzvbJyHQmHgs48fJsouWLelDIxSKGJpUsDVIpX7D0gpbbT+fdf/p4CUB6dn7McL6dO1Je1bNKB573c4+OcCzEzz/XfHTl1E/+6tad20brHX2javx7XtPzBV0Qb36m7Yio9/yK2oQIrk6RAWnsDCpPXYGvsz5VAn7CxVjJ/w/29DeL+e/LJKREKfA/wNDKr5Gc6mN1BbtkarvEcYa3q9IcZRflzz30smp8g4XJNFL3WinpGAX8QWbP1uIPzVjj+vNqNtQBjt+5UOpPxzWMXuQ7Z099tPzSqx/Jxah/CGNaipfHwdq8hAyl/ZBhZt3I1T/SgUGZWYe7wfvpVTGfbqkwNSvvpaQVa2QPOmegIc9tDQ/peHvIzu1wGFLo0dh1zZfbUPHdoZaNOq9IXz/voFlohZ/d7F0TIB4farRFkcZNq2Oc81BbJKJTBnvgI7s2QmtbtE+qVb9O0+lP5lTO9a2nJXkYCUKmnBvHrxGwyCEoNCySjrd/lIGca2C25cjmnAK0Mlj4vyAWRS/yUWfomNv5Z7COPbT0Wd0pfgeg4I3DsoJBsuokWF98kXmbevOo7mSbw70Zaz50SklJpGCg1Te0zB2T6b2Vsmylbl117R41MBeFIKeAHqBOixt8s/nFUyuUA71zkYVDW5EtKYhZe64W0fyYgxRXvaSUCKsTKXWZ0Oo714jT7NBlLH3RancobIFaVvzwNIqZ10hgFXl6FXmpHl07HI18A47RY3sjLo4jeVNft2U9M7AOcUR16/kP8dK418tbR3qyJeX/W7iMQ/9V6H2dS0ViKI2fxxNZB9wT3o0klPi2ZFv18SyCwd5CU6Er0OAvz1vPRC8e9iAQGyxDc0udcRLC4n4HApk3e6tmFx5lfoTOzI9mpZaIgUmnTMIw6TZObCt42ml2v4KiqQkqSHTdkG9vyxFaGbNQf+seNCVCPKEhr14AB8t1SBFGLo7alhxGvlfzELsi4Nb/49TV2S2VtTQDSYoBfuZad0j6nK7+vbkZTlzEv9NAQElP85Be3WafOYPUcgV2vKS52uEdCsWrnm9FEKz5ojoM5RsKjXDKIz3fnQsi0rk75htf87RFv5PMot79apqECK5G16JTKFpUm/k6eu9a/jRvzhuywiU2z4sPU2XJzu8RqWNFlmt5Uo3D7h7a0fkaczfoi8WQJFZs0V0WpFLEw1TJ5Uuh5L/E8SD5SU4OC7OU+O7++xlO6/yk9sBP5ngZS8PC3dXp7MqkVTEEWRl8d+yd61+dZiSaQQIFsbS3p0aFbstRGDunN981LGWHenvrdDqS5+ZZm1igqkqPTwT0w6a26txtK6Fh8d7Iy5cS4ffljGXJBl6XwFLDN3voJMlUCThnpeqTESY1Etc2lInBoFYna5BUZxPlxsuAnzkJvsSm/MyQ4N8VEKeEXso5LfeZRbW7PuUkuaVw+n66DSrRgH9qZzKMiBvjX3U80rgrl5LdH6Vy5z+FhJQ1mRgZS9OTBxxyFqe13GWFuFL4P64umazqg3noznU8GCJqW6bdZUT13btfjbbEVjVotck6LTt0pjefF0Cr8EvYV/bS0D+pddUa/fEFi9VkEl21im9RoP8d1J1tbCxnUhb/2+CoUIn019MjG4ZW9VfsnkJIGFSxQ4W8cyps114s/F8EbP/nQ3e3ywTrp/RQJShl5aRPXUy2jsqqLIyWCU5StMMYSyJ9iVs5FNGfiSnto1yw+krPlDJPiaSJ962+gZ8BuJmQO5XbtwJrM0w3VySaZm9AvMXe0tb/5fG5jJqj/N0eoUsgW/oX8Kecbu7NjvyJ7gnrRupadju/K3p7w6UFp5KeRCCr1o3Egvp7GVJMB8HXWctpCb0o7425WZfqErdmapjP/gYbAzKwu+mqfEyjSdKR1OYBJ0g6Y9htHDwYwngdc9DyDl7TPTcMmKJtexlqxPRYnkAZGeFEKDgDl8fSiI1lWc8Iisx1s3suTN+dTJWoz/PRlES1MT+frSFUpiY2Fq94/wUPoiCNnsinBg0/nBMogigSlFiRQSIoWG2NvpSUkVqexpYORrxYPVSckCixYrcLGK4ZXuwTgdjcE+NItRfTqwIulLdOaOZLs/4F14JwWylJlkWqtlZepPQaGKCqRIEMVKlYGfth/Ao1UeQcedOBHRjP691dQtCwHRnQ5K4NW0mQo504so6PnkY728LpVH1q0XuRIsMrr1Aura69hfKxcboRomOEgsUagNiUj54Y33VmXl+d442Kp5d6yRzDv2KBJyIYyVm/M94KxNM5nwvpkMxD1N+Wy6EonPZ3nfGYQlOvORQ2v+iJ5NgpkbSxt9il4OpH40qahASrYeDsSr+OPWSjT4Me9k138VN+KXMw3kao1Y0OMABtOiQ7cfnDHjeBFjm4VMOjiY1GxHxr+rw872nofXxcsCf268N9fS/q209yUySuCHnxVYWxmYP+M/IOXR3pKKW+t/FkhZ9OMGsrJz+GjsUCJuxzNmygK2rpx1d6aW/PKXTC7Wq3PzYq+NGdGPm+sXMcR9ABNtY0i2qESmyeOlA5bWFWlxecq8ZuXWSCl5wqroLPaf/gkbT1/G784nc1sy79EXj3I34hlXUOfA+3fc+l7olkI3x7dBNEHn3LtQSxTH2yEkuXCq+W9U2ZXAp1U7YNa0Bk4KAYuQo1T1PYrFzhasPNeGxlUiGP5Oyczh0s3/WBfPoZOODA7Yi6d9KJMtulG5liduj+8RK7ddcp6qaDomteugSkf/vcfpaHEGUwtvPjncFxe7TD6b+mRcSc9eMPDDSr2cYjawnkigzXd4mR5Bb90Qg3nxVqXIa9eZuXUK7i65fDzpXuq/0vaBv67Rc/y0IZ83wG8fwqXviTbZQKXqu5n011xS1Q58+YkC+yfTvXK9IVExMHOeDi/7MF5tFUbE2TQ+79OTLlZPIOZC1jEB/VMnAS29y3bZ8bxx9CO5oM6vK2LsZUaZ9ecj9U0O3vLgeHhzhg/S0KRR+QkQP/hUhyoLPu3xCZ72NwnTvIrGvzCgkKy5RYbuNtVTO/HXn44EJ97Ts6ZSpp/uJ9Cb5f/t6rloFu17l6peuUwc93xTTEqWt3c/zD/Qdu98T9Obmn6Bm80VMmP6YJTrytij7eQyS+dJp6/8cnlaPZJRIDkVps7Q4WQZx9h2F7HdF4Lv0JG86/RkPmTSd0xSsfIFMpSuM8WV8Ey9wZBTs0FUoqvZFe5LsXt/HSE7FTH0EJUaLGPMmUu87CTgFdmUieHxpKntmTVVg61d+fXt0Vv+9Gt+NE1HWgbM7f82drouGAxqjiWm8vOxt2gSKDB8SNGn81Xr9Bw7aaBGNYHrIQbs7eDLqcXvK26E5PLNUiXVXYLp2SEaj70xWEdlM/KlDvwY8yV6K3du6/qizTWicq2Iux1XBO8AbS5LW88lw/SeR2lpI1Nw2K8An7KHmro4OY8vDgYRWCeR8+dd+Du0NYP7ZtO6Vdk9OMMiYc7Ce8DVxLEKqnqXNiqFry9ariP4OozvMIsq1tYcqpmOq7E/ZooCrijQGTT4/pPG1KOtSclyZOQrIoF1S1tBi27HL99f5eTNe4aPAZ0jaNul9H1V+XpVuPTb7+eP0YpecwiLs2CqR0t+j/xG/tth3/4E+ZSNg6OoNlTUfb/c39hsDpz9EZ1Fdb4M6oa3SyqTJjk+zlA+k7oZGfDhNB1WJunM632bPFM/chUZmOnsEPXF73GEZBB0vzL9THMiU3yY/MOGMyAAACAASURBVJ6Cyvc5kS/9Qcul4Ht6O2OqQv5mlSTnLsGKX3S4OBn4cup/QMrTUIDgmxEMG/tloVvn5ekY/Upv3n61z9N45N17/k8CKes2H2DvkTMsnTUeIyMlCUlpDHzzczm0p0C+WrwGR3sbenVqXuy1kYO7E7r6awb6DuL0tclyVY1oQpKFG4nmriRZuBNv6c5N+4AyT6JSISB5DGjynr818sFG/5ih59T+ZRj7+fHBjnwyt1nTyhfmUOaBqAAFo6IFliwXsbCAPq0u0s5pJjqlEznWhXPRmx/rgaiy5Z92y2iyWEOnF/vR0tteJkM0iblMXZ+d2O5rxo8n2xHgEcmQUe6l9m7T+mROXnZmeN29uJoFM9J9II0q22P1aPuOQs+TDh+SlU2tqXhzd05joMnRC7yScwwzFx8mHeqNjUU2H056Mubb7bsEjgaJVKsCXl562jvNwMX0KjlWLdEZFZ+2xZB6gfErv0Sp0DP903vHttIOcF/MEsnJEZjZbxzOiS+RkxVIlmIPNpVP8s3RoTI/x+vD9VTxKe1OpapMuQtIqfiW/ShS3TmYF1rFceWkhiW929LctJzmyGKeLHkLZOc+H2+b+5vU4/pvNIz9G62VO7mVAjFOuMQYi+5MTr3J0WhPDoe24oWeKhqWE0jJzhaY8ZWIkdLAksGDQWvGRfMBGCoVBkCyDbGk62/hlhZI5D4vttyoKTdP4kWZNHATovk9vTOkXWX8r5/L16dN1WH0ZPCGcuuGVCExSeCbb0U59XGzJvf0s7/rKIyNski5NgQzHJlyIQCVxopPJ6ZjamWZ/yzp1CkIxCcILFws4mkXzvB2NzDZHk3LV4cwzOrJ6JipkQKNTv/UM6oUDOCgS4upkXyePLuqaJxrFzuuglaNeeheateZT8eQOCaYpVE5qg2fxoQTk+7OhNGJOFa6d8h8lAm6dvomx06ZUt0nm5Zdi/eme5R7P0qdKZ/lgx8rhg1Cl/YWAnkEZ59mwf4PqV5Nz/BhRX/jfvxVJPSWQP26Bs5dyF/gZpawr7h0Pp01m+xpWvk0jdolUOXPOKySNLw2tCM/h88gy8iXnfs/lu/Tcdh2jEzzw0tMI46gyEnl13oTCbct+3gZK0U5K1Se7tl/o0ubhz9Vel4+eYEeXte5ds2DXdfb0aNDGi1alx1IOXlKy1/b7q2v3TuqadmqfIe9pStEom4LfNTtE1zMPDnil4Sjoh5GFPYkdT9lydW0s6w6PgpnZwPj33m0fe606RpytGYM9jvDmmuBWJll8sFEc4yejA3goWHPVgtMny1iaZLBNx1WEnlbz1S/ZqwMX4Gg08jlv20ykxSzR8vGJSUAkPb+uRVw3/9Lho5//l6O3qUGnx/pgqtdOuPeu/OdL01Bn+N1KePm8p9FfJ2uMbatC/HWMVxz2Sy3yEhngUmeDWYaa0xzbRDV1mSrnKhp5IOYLWCWtYP51924GluX14bpqV4t/90v0APpd0fLeDkBwZuv62UvupIk6KTIlu0C7m4Gpn1YvnerXEMYkQA5ktn7GYu3s3SoeMYPLf1xQ96eLjtLBNR8uiDr/xyQsnn3P/y57W+WzZmAuVk+v4e0SLboPYZda+ZibZm/CX5z8nxe6tmW9i3rF3utQ6sGRPz4NYPrvcCxq5+hN7JA1GQ+NLuHPbux36df6bMuveBKUc6qklMBD7m/ZxnYvecXLP19+HJPc7I0lnw8SYeJacXbYJQ02DdDRS5cAO/KlMi4fe6CyKbNIs5OBvo32UEDu1UyyaxENnu/WB5+AVFjzsmGC2i8QkHVd1+7S9iZl3SbVp5rcTzciGVHO1Hd5TYvjy4mD+R9N/1zbQoXbzjzZr29OApX6VV7OL0dzVGWIS1iaYpWkUN7bmjB/ex1JkcdwsTPiwl7+mBslMfUj54AggQyqaFEblivrh5bGwO9Kr2PtVEcWdad0ItFbw4EnRLzpESmbO9PcpYT743V3eWLKE3PVq0WZY+PjzstRnFxIRGVj+KQqkd0+oc/rtXnaEgHevfU07DBo20oS5vrsrSvdqVzdGuWwvELCv7q2py6T2g9rAihPWbabN4//gFG+jyyPVuiM7XDJOUG46xa8UFcKKcSK7P3Rlt6dEyjSfPybQ6joyUgSoG9TTZf9R4Bquqc9K6LkXlha3cuqaQZgrHLqortyRZ8d8oBnUHBBy/+gaNz4cE2yrvN/D/7yJmESsp08jjzXta6Bdmm7O311PHP/8ZbGcXRu9L7oHEg7UInlPZuzL3sRUyGG+8NC8PepzD/U1SUwIqfFfJmtl/7CAxbUnnplX70KyKuR+IeMdJpOO9SNOlzUe1+lqE99jmJjDuZf0BX+XTCoCyZH8zq5lba1Pwct0QDsw238I7qyuzEa4QkVeWNobfxqFr6OlDSXG1cHcb5UF+crBIZO/7xPGHLqhPFlVOrBWbNVWBulMXCQSPJS5fGKY8Y7Xo5jaibi4G3RhcN3EvcAVLIZdMmes6dF8jNFZg0QYelZdH7iuNHUthx0JmuvkfxaZdMrZ8SMFPpGPVKO1aEziJNqMO+vyfITfVvdRr36vleKaZx5zDKvM2W6q9wxrUwh0pJ/a+ooT1Sm3er9TS/FMJImyDCw6qyKbg97Zsn0rZj2fVh618ZnLpoT2X7WzI/U62qSQwaWj4XyQKOlWm9J2Ji7Mep6gk4CY0Q7+OKktprHWJBVe1GJu2YKHtjPgp5e/jlcH7aWI2q9pF85ObBF9G3iUzzokebaJq0eTrEs+npAvMWKnCwSGBm641EROpYUKM28xO3ozOxxiQ1lAgbX36q+8EjvUoVNbRH6sy6LD3bD/yKhUdlph7qir2Fivfer/jciOf/0bFxvwktq/5N/3ptCLXdSqzLpWLnR8xT0PzWZKRU3ZaJp1genSsTZb/YT4/EESaJlO1RImCv4XJZJg2XwoIHv5BBTf+SvUf3HRA4fFSBT2UDH094ikDKZ79BXOoj6eBjVfpoAHg//O6pstTMXPQbQWeuyMaOye8MoXuHJkiJX35as4McTR4OdtZ8/elbeLg5cer8NSQnhqxstezdOuXdobRqUodsdQ5ffLOSC1dC5AQwU959mUb1/Eps8qXgW8z8djVrlnzyWF0rS+X/KSBl96GT/LZhH99/NQEL88Ifgk/n/oSDnQ1jR+Rn7Rnz8UJ2rp4jlyvp2vUfFjEhoC1bwr4nu3JrBL0WMTcTRW4aYk46Uqy0YNCxo9pgTlTKd4EuSSoykLIpy8Dve9ZiX8eNb/YGygvhB+N1WFn9u4CUlb+JhNwSqVTJwJuvF++VsWefwNFj+R+/AfW/p5rV33LaYyn98f1ive9l+b9X3GfDcU9efrk3vc3yLa4pmSq6O3+Py4m6LNnfA2/7OEaMKd0tcs2vKQRHODMmcC9O2Vdp2vJNRpQxLW1pOlaRgRQpO5T+SgQLzu9GbOLB+B35TOtPipzxi5lKtFpo1VInx7UO8nwNhahBZdsLg+xD9LAY366BaRzMDxdkC0VZyfy2bM7m9AXr/LAeo0YImX6EeKzCJcsH0eICh5Ot2HBW4g/Q0aXTs3+HgoP1rFlvTAPPU7RrnMiuG3acbNOQ6kZPpi0VAUhpGbWbTmEb0Jnaku2Z70lmnB7BpyYBvBEXxcVUb7Zfb0/n1qm0bFt2K650n4JY6SqV4viow3vok9pxooEdphT2bNKSTbLhPOa5zgTeeh3La0YkKuMxabv3IWUTDBp2HbSR02M/agaN0t7/sl4/dlxk1x6R+1Mf+5gdobnz9+jTGqM9Xx2NpxPLQhy4llCN17tdxKtRrUK3DwkVWblapHal83RtHkfqwRzGv9SVrne+j1JhE10O3UPWUC8+SK67vdpgTpZhrZTKPksgpeeN1TSK+5s8Kw9yXOuXOowWYfsY4PMWyXku/JJylspxvVmUdpGLsTUZ3u8WVQK8Sr1HSQW+npNFRo6NXOTD8amYW5VPfx/r4Q9UTkwS+XaJiItVHDP6vkdO5lwEg55s5RwmbVgsgyISOFKUFGR1attaJ2f9kTh53hihw8Oj6O/Qnq0JHD1XiUG1/8aqfRINFiZjpNEz6vXWrAieQ6K+KX8ffVN+lEvl29TreEL+XQJQjZOvc9SzC3t9yk50VZGBlGO5BlxCopmi20FsfE3WXO5A8wbxdO1Z9tCl5d+ruJ1gy+stv+OHo2MwN8nhw8nlc+2QQAYJbPjqhTHkmtbhgncSLkLzh6ZbmaKjzo0MDmamsvrkCNlINeat8nnGblsXwsnrfgyodo2mjs5E5Nxg4fmmmJtkM2GCMcZPyBBwf+ML9LuSzW0+bb6bkDAda6q7MzXjODkudbCIOIiYl83m6q9y1rVFuV+tigykbM7Ws/LAGtx8nPloTw8sTNRMnvwUBrnco1ZyhQMbMjh0xZ7+ATtpWb0b592+R2WdQvNlNpiniqgcdaicdGQ56oirrUFtq8f3Wi9chAAso0NYk3qTA9e70a2LnmZN8oGUn35VEB4h8Gqz5USmenPwWmd6dUyiUfOSgccCMubqvgYmjXmKQMrs9ZCR9YRHsgy3G9UVfB42DHz29c+YGBsx6Z3B6HV6NHlaLC3MuB4ahauzPTZWFsz7/g/UOblMfW8YL476jFlTRuHr40F6ZhaCIMjODRKIYmluyoTRAwiNiOH19+ewfdXsu84QRbVw4hdLkRLC9OxYdsNMGXpaZJH/KSClzQvjSE7NKERw1aJRgAysZKiymTJzBWcv38Da0oJPxr9Ci0b+8qCVdO3isqV85V+fX2PXoXZv8vDCkZ2AWXT+Qv6n3+tccm5c4lxVZCBlp9rAoh2bcGvowPK9tYnPrMS4t3U4OD6Zg9ejKnF56kke519+pUCjyfdwmDJJh2kxHjWr1ii4eVOglp+OsQ1fQyloyLZqg+6+uF9BY4rV4RfJM1aTkLuY4+paLOzemnam+fe/rYWXzL7G6VJtlu7sg5t1Em+9V7q155fladyKc+S9xnuwSAmhS/uRDH5CaWkrMpAiscRHhsSxdv82cjp58tnudmRrLJkySYvpYxpBJCJEiRDRwhwaNdRhLGbzkucoGUCRgJTixPJoX0RDFutyg9gb3IOO7fW0blmyB4leD3PmasjONWda76W433yXbItU0oy3Y4U3BsMtbhrFsvTwBGpU18vgzLOWC+dz2LDFkmbex2lWL5510Z5ENvXHq3x76OLHzcwIlfo5uJne16L3T0yS09TmuDUkz9JNvmKkiuUbwYN+cUmEqKrwV3B72jZLoH2n8oVa/H1YZP8hkSZVL/B681lokvpzroEJxlgXGhMDWhIMJ+W47OY3J2Fx0wgxD1TNN6M3f9iD8dbVOL7bPw6vSjm8/voTmoxHUK6t20ROnRXlbEYe7vnf+EDLH/FzOEBuYk/04a7o7K1YE2XBySh/BjU5Ra0uhQEGiYBSIqIM9DxJm4ZxhJ004us+HWh9J5LAM+MWLwavwDY3uVALD1buzaHKpWdZeFZAinleFu8fn4TSkEe2VxvZCl2amEcd5R23/hwxDWBHxAEqJw1gheo8J6JqM6jLNWo1efRMI6nJKr5ZfG8deaX3DarVe7ruyyX1NzRM4NdVCjlM8IMu08hW/QAGPSYWb/Hm6tVy1WmfaB8iF03PEJi3QIGJiRQ+pudqsChnjxnQX4d/7aL3FRvWJHLhphuj6/6NtkMczWamkqNQMPWV+nx94zuiNe0JOv6K/ExRqaPjK5sRBANGmdGYxp3limMD/qiVD7SURSoykHJRA9m3k/g2eR0JGXX55WJ7GtaOp3f/sgMpBUDW/OHTmLbuXdLVdox9W4+TY9nXpJlzlOTkwIIBI0k0acoNzzTZI+VBydNl0nhPQ7Q1P2DyhsUyEDhkoB6/GmV7lrSuzpqtlQm7v/JTEd3ob/yutGROeDLhKVXo2CKB1h3K9x0viw7cjhZY/qMCH4cQPmh8jCu3dPzta8Hb6uvkOtVCoU7G/PYxchRmfNvoC1TG+QBnWaUiAym7cgx8u2cDVX2tmbyrJ0qFlk/zHfMqtKxbkc6VWAfearKDql4dCao2Dyl1vcuulowLqEvIfRvK6albqFT9Jg5RPtTMHox5TCrbs/ey5cIAea8n7flUWQJz5ilQCDrmDJzI4Rvt2Xy2F20axdKhW8khXQUZzQJqGxj3xlMEUg5dhEzJm+MZS5sAsH7YK6dx9zfZu26eDJgUJ8dOX2b1xn0snvkeH0xfiq21JW8P74udzT3DQNOeb7N7zdy793l13CzeerUPTRsUNtwUPCMuMYWhb8+Qo0yMlE+fx/N/Ckh5Gqp14vuf+K2mF/NT9pDj2qDIR0jupJJbqSSrAsYRYld8bHVFBlIO5hj4eOduqjYy4/f91WQiJsldV3Lb/bdIbJzA0uX3XqySsnUUuBy3bpzKsBpvYRCMUNkU3tyLmbZYnuiJyioJ8cpvzK/RmBPN6tLgzrcyQS/wom4e9lG+LN7cH3vzNN6bWHoIwYolGUQl2fNhi91o4qIZ3HEIfc2fDK9ARQNSTLXZchYM5+wYTNQpfG7cjsPrNpDUz5Nv9jWW0yVOfE+HtfXj6dnpM1Kcqoiri0HeuNkaRdOj0iQ5pEcK7SlKlInumF/I9yQ7Yv09v54YSb0ADS/0K3kuom+Es2xtNTzsIvi4fg5Gsf6EVD+EczwIohJRlUKK150UyE4GxpbTKvck3rczp7LYvNOGtlWPElgzhqVptelgdwGdtQfBDg3QKB5vwX/eHil1Ek/QP/jHh9LUKtVJ/JpnRbP4bBJzfVh7uQMtGybQuXv5NuAFVqYX6m+lm/9qUlOGEVrPCAUPj1u8/jgIepqETMA6wgJlhoja/yh5ruEPT2VGMBPu8KRM/VCL8eNNwyOrSkHqdymsRwrvkaSr48c4WISTHjUIZbYzBqWCbfEK9oc2pHfAKRr2KwykXDify4YtFrTwOUZT/2hOX3VkTZfmNDIRaB/+F20id8j3NSiMZbBLyMvCNP6C/DfJg1Py5CxJnhWQ0jZiG+0itqA1c0TtUTYLl2nsGabbtuQH5w4EXfqLyukvs1p9hoPhdejb+ioN2lZ/5Lm5ciqSdTvvASdt6l6iQ5987p3nIZeuCKzfoKCx9z+MavUtquy1cjMsTYcx7o+lZOdZ8uFEHebmhb/hUpilFG5pY22gfj09IbcEbt8W6dpZL6enL0p+Xp5KWJwTUxoeIbZtOC1mZJFgbsbP/Ssz+dYvhKl6cubsi3erNux6BAf3BJkfRQK3Yi09+b5B2d29KzKQEqaDi0lZ/BX8K0lCAN+f7UBA1TheGlq616s0QHHxAkuWKXC2imPKsO38vrMmJ8Oa07d7Fg0alp2XrACMkfhxgpUdiXZT4SDUe2j6dGiosT8QK68vOBBenzUnh1PJzcCbo8rmlRIRHMGP66viZXubT918+Kf9cipF+aOJUTH3dDuMFXm8PyGf1+lJSli4iJQK3s/1EmMbXOZ0qIEQn1wGGRLvZu4yib+IcUYElx0DWV9rdLkeX5GBlEM5Bj7bs426tUyYvDV///ukPITLNUjlLPzdvGwSsqz5vMN+9O4+XPFYi12EklHCaJLNTLEToKWZgKsI6ekJdHFdiagVaXprIubRJvxjWM5vx0fKYddS+HWBh2Ydj9OM7rZd9or69fBA6teIpd/AkoEU6cwhnT0a1Nfz9vCyv1fl7DJUICAlW51Lyz5jOLtnxUPdWL/tEPsOn5FpNdIzsrC3s2bp7PFIdX5Zt5ONOw7Tplk93n9zgBzi06jbaLw973m8SIlipPCezm0aFjlEkpeLmamxDMg8C/kPSHnMUT7w/WoZmf446wy5TsUDJCapIRgnBZMnGvFz3Q+ItiqaFr0iAimStVCKD9+rs6LX37fwb6pn2wEPbibUZMSrOrwrP9lF6zGnpMTqQSdEdu6+dwgObGCgT8+HF3GdlBLwy3xr8MjuB2jqsJw8o0rkWBT2OhKS7bA614NU+wjs9m9hSO9OCH6eVLuD1aQbBHpnLcQuw4PF6wZhbpTNhx+VfjL6dkEWiRk2TGmzi4TIVN7r0o8u97nDP84YPS8gxVKTjlN2HI7qOByy43BR3ZbBE8u8wlZ5v1qLuLbid6IGe/DDwTpEp3kx5k0tzsVzwZZpODZtUcgx+L6+epn0q5LZRdo5f4VW6YTasuiYeYuzHVGk5H/Abzot4avDo3B3VTP6jZJdW4P2XmNnkD9d6/1Nf01bBIOSyzWWUzkhH0EXcjUYV/mTt9cvlK2l0z4p20ayTB0tY6GgIxnsPGhPF9/D+PtGMjOvKbNVS3DXpMh3CHaoS7BT4CODKs8bSHnz7AzcVJHyd1lje+/QqdCk81eWjioJCnK0Xvx2qSNNAuLp0a/sVlxpfArcfMe2nU8dz5NEZL1GSs2iN0nJhgtoyaJe+EjsY9wwiVeg8Qomp/qZh2bLKC+G+X/2JCzJl2FDdPjeIbor47Q+sWLzFypISy+c+nho5aHy/ZMvDMXEMv+F/DtFx1/XmtOm2jk6DClMrH7yeBbb9tjQodoR6lWPYHdUVc61qMGX1xbhkZkPIkmeQrkudTHcyYBzv+HhgnNTNvqNKLZPzwpImXRsAhZalex1qr2PHLikwTZJusr3JtWZXakvR85voKrqVbbnnWFTSB3a1b9Cu15lJzx98Dnb19/iRHB16rne4HxcdbwdIhjxTukk5k9MOR64UcEho1PN7QwI2I1Kt0guYakYz9SdH8jeq2Pe0uL8wHnj/EWBjX8pcHExULOGHongPTRUpGljPd27Fg2kLJivJkVlxewmxwhpdoums1WE2FpzsJsloyI3ci15EJevdL3bQq/aIdRsegFBl4flrV3kicbMaPldmYfieQApXhkheGaE4pUegmd6KHpRQby5lLzAg0SLSiSYu5Fg4U4MxmzOMnDyyPck2dTi25OdqO6ewMsjywYKnzurYdM2cwIrn2Bo73BOnFawLuhFAmsl0OfFst0jTwPTZysxUuSxZMgwzpm1JdXRgJ1QNLDndNYRD+0lNE47ef/PFeRoTHh5sA4p7KE02f7HTU5cq8kLVYLpblKHI31+wqDOpvWBV5ifGMLVuABaN0mhY5fSPcZKe9b91wv4oup6nGZk3VscvalE7RVDFxOdHOonr+kGLRZhBxB0uawOGMsNu/IkmZBIywXUuc9+H1DaOBzPNfDm3v00r2Xgix3tyc0zq/Dp2yXPpWkzpE24gUV9z3DLXkWkaxBOJ614qcooAo1Fat+3hUvSQ1/Vd2hdcqh1vQuuyY24qFnKsmOjqV0jl4EDFXfTu49qtZDadcwJuWXMdztfpZp7Aq+U8r7Nma9ApRJkr7uRQ/43gBRJryQAZP/6b+5yj8r7haALfPfzJn6aPwkrS3MOHTvPui0HZSClQDSaPKYvWIW1lTkfvDUIySPlwPpvMDcrfewkMKbrkA/Y+ON0OWHMs5D/gJTHHOXN32/kVpUc3taFobGtWuLdTBIvYZwWjlppzg/1PyLJ7B45j01uCg45Cbiq4zDXqtDpyubq+JjNL7K6uSYT+5wk7NQJOOQk3i2zyLUbxrcq0aJeGvv/9uRyTD2GDdHi++geyk+j+SXec+0fIlevSfwoemJiRNnLQfJ2eFBi42DpcqVs2ZjY/Ru8zE+SY1aPPJPC/CiK0GpYhDUlxToY+7/3UX/EQJo6WeIs5m8KcgwCHZMX46CwYckvr5X50Dxvbi7pags+6bCdG2EalrVuyJiM/BCxB8WASJqpPclmrnKWKClzVFFiqlPL4IVTTgL22nTyJLKQpykGA7aSXqvjcMqKxVSXU+zT9EpzDAolitwMGgTM4ez3Gwkf5M7af6oTkliDUa/p8CyFGb20rny7RCFnIgmsL/H6QDXLgzRx+IE8Yy9yzAMfqq7IssUiqCcGBagraxE1PzFmr8SpouezqSW/n+t+CedKZDXeaX2Q+omdSbeNIdVsNw45vnefY+Kykym7x5KW7cCEd3XY2pa+kSytj+W5/ve+FPYfc6aX3yFqeN1iomlntkVPzSfNziscZ3vdPoDjnp3QCkpSTRzJNCk6PM1Ir8E5KwYHdTwuuhS0mucT2mOiVdMsej8GQUFWlc4YxHxQVKM2JSnMllCn01ikOKE0OPLz+c4E1oinz8DyASlff6MgI1Ngdr+xOJhlclk5mDzPot+9VMM1NKRQK3oATgm+mIcr0drGk92waJ6UPQet2HmlLy2ba+ncsTyz+mTKShvRz2fkj1mbVjo5JMPR5CZdXD+HbB8yT9dDcM83BpxLV7PySlsaeATTd8Q9/Zau/XMojd2HHele4xC1vUJYld6Qxjb/0DvllDw3uS4B5FkVJqiV6knhV6axp+X7BzvWY22ttwt1TAJg7dWJuOfGYdBqZMvW0xI7dSJ1E06gN7Iky/sez5mkSxcOFh+q62l3lFN+SUz0eoV9pzdQK+dVjurP8ct1fxpVv06vQSXvF0rqz5KFKcSlO/Nh7TBmX8lfkz6bokahfD7cBXv2iRw9JvJC/d/pVuU6KqbJbbIwzGDu0f6y0WX4KzqqeBeep0NHRA4cFKnspcfH20BSksDlqyI1/fQyEemDIk3ztBkieoPAt62OczUggsD5aZxxcSKyVS79YvdwLmYUoSEtsKqsJzNCxNQqizYDdsm3sgzdKfPY/e4/llRTexLMiwef7HKS5O+Ye04sSq36qWeGMtbm4JkZimdGWJlVOdSyMq2qT+HIgV/QOPswL6gzlZ2SGPlW6eHD0kN2bU3h2Dln+jbYQusWapJi05j552gcrDMY917Z0q9nZsLcb5RYm6Yz76XRHLNtRZ6VMdbCvW+BIlePIkf6p0PINMIvtDKGmh+z9dJgtpzvUyIZccFg6PV65s7JI0tjwVe1MrEwUXKu+07UhmTqHe5BmvY8M493l0NPJo4XHvJ+KvOgFlGwgA+ric8/DKwdzd4blrhUukigtU0hYFWZFY9ZzEkyjO1kADhXHWZVcwAAIABJREFUaUKSuVuxezIrTTqO2XE458ZjnZeBtph9v6jXIaJH1OsR0d39KfEQiQYDokGHKP8u/Sz4Xfq/HoVBJ/MV5deTdov36he+b375++tL6/l3Ll2wuOlGR/9MvtnZiowcWyZN0GJZumP14wz5Y9VNShZYtFjytIrl4y5aLjkdItUuCsOBKqxr/ALeRUR7uEbtwLv6VZxue1I7/lUi1cuYe3gUPh7Z9OlnwoJvFSjFPBYMGUuedWeS4tL4cuMInG3SGDOu+MGQvlmfTb+3lg578TFj1EsamQrkkSI186OZK7CzseT9NweiNxjIyMzi8PEL7D50SgZOcjV5TJ6xTOZOkf5/LSQSv2r53GG/bdhLZHS87HkicaRIXCsSR4oUqhMZnYCbs72cdfdB+X3TfpmU9qup5fMKexyF+w9IeZzRk0J1VuxE5xHFIEV6kRvCB28vbQ6lTWKGia3M8m2fnSiHNUix1xVVdCY2CPo8VtsEcjuhDj19ozgS5MXZqIYMejGbWrVK97CoKH2TMgtIGQYkt+HjJ0WkA8O4MToc7Atv8C5eEvhzkwJHBz3Tu76GUswly7oretHsblckfhTLo70R9MakabdhuBlD7VFDGGYBptLJ4440jf0BJ2sTlq0YgcEg8OnHWkoL25s5U0eO1oTPu27ln5smXKqrYEbEqjINo8rIiiQp/baZq7woOqrjccqORcpg8lxFEGVuAb3SAoOJZf7vRubojfOtR2KeCovwg3Sr+TG//B6EqoctW89IgF39x7bM52lh+kwl0qy0aZ0PnNWx2UiA7QZyTaujMX3Ym8z0SjOMY6uS66BD46LHKnkX759oIseQj39Xh10JwMeXM/PjuL9pdQOrpNpcC9iDS7SA0nBPf0TbIBaf7syNhFoMH6Z75imQ9+5I4shpV16sdZAqztd526EX+8I/JqtyW9k7wEgVgzIzGkVO2kNqk3snzbukYxJwJx04HHPiZD6SiiQamyrk3pemNuRsTULP1cK1xywy06vhINqw/Exn6lSJ48WXy+YOL/XvHtBgYMWwwaCqxulK9VHYFg3GZBhuoSaOKgmdcU9qiOV1Iwyilsz2+SEQD0p4cByL9o3Dw03NG6Oe/eFYAhwl4FHij5K8AyTxM99BoNNqdMnt0J9yJNc33xJ7MyuTJRc6Us0xjFfeLgyKHNiTwqHjzvSreUDO3LNAaM9rhvU00CaSU6mxHHZVnBQcSqTr0ZbeZBtb4pAVh31u0nNRsRyXeuRZ3+vfjZP+hF0q3qvE3eEsmc0PMbzqO/x5ZgPN1a9y2XCTBde8qeF+i6EjH41sVkq7Pfvr/E397AZJLL6lIyzFg9GDw3D3fRiUehaDtXGzgvMXBIY3W0pzFw1ZinHyY821S1h+rgFnIpvy0gs6Au5kfypo0+ZtCs6cFajuq5dDPKRD+ZlzimLDPbKy4Kt5SqxM05nRJITQKqHUWZrCQa9KKOtF0Cr5DEEh7xMdE4BrYz3xp0WJqoWWL+7FwiYD88jDKHLTCw1JiokjiRZupJo5YZWbJnvfuqminsWwFfsMvbEVWjM79Gb2GAreEekQnadC0GSi0GTJGSKl1LuV6y9l08ENWLk78NXhbrjapPL2uLIRD/+0Ip3wWAfe6forVX1NEXVpTF7+CrlasyJDsYpq8N1Dq3UsX/YZz37XJrheFvE9ZIlCrUOZUxgQu97bnBpRIzDUexWNkMf49T+jyVPwylAd1aoWD4hGXYtgxR9VcbeNZpqbN8lOYdxqdQk9Oswioc6FtnyXeo3zUQ1p3khF125P7sB66ozA1u0KWvvuo1eNdDbdcKCRy36qOlZ5iC+pYI9//1hJ+/1EMzeSzVww0anzDQ1ZMUhARUWX1Y4tSIz2p1/1JL7f04hElWuZMxc+r77dPC+yaotIgPs5RjTzJ6jKN+iUWi4cakd840Csi0gCeT4thTfdfpLDe5pfn0h6+io+P/QaLg5qatY24dBhEQlIG9ZxN2LYCLTpabx7qp4cTjb14+KzShYAjUoltGyu4+X+T04vHxrf87cgu3iD5VObj7pVwOLhfklhO1988yunL1yXH/3hmCG0aVaXcZ98R/jtONljZFj/zmzde0wGUqbMWsHxs1cxMTbGs5ITMz8aJZeRsvbMXbKWw8cvosnLk8N8ls2Z+JCHimRQ6f7yh8yZOvqppzy+fyz/A1IeU7MWrDhMFdczdDJXlNnl1+z2MZTqwuR6UjN00oHS1AqMLdHqn56FrdQui0r0JlayZVr6J4lxRjgHcpUcVLdjmNtVTp7xISi8Cf17plO3QfFEQqU+6xkWSE4SWHjf4eDSFZHkZIEe3fQ0aVR4sZdIJCUyST+fJN5vOaZIHg3l9dqYR9UnwzoO67/X84+HK0P6dWW4ZWH+jPq3V+HkomPd8iEyceqHH2gxL/78II/IZ9MVMugyvd9fbLrmiFDlNm/F7SLXvoi4eskiocmUWeMVmowSRlQCMqwwGFugMLXkmWTYVpqgN7ZEZ2Rxb0NYTAslsM4ydBevV3mL8TsjMG5lxIGrrpyKaMaA/nn41370FMgFZIhShqnA+vlz3cR+BdWsDpFrVheNSWGixgISYamcyleLwciARfxVvgnRERwXUKIrcnJsCgtXOONqHcMM98rys04HfkeNWw/EjJtcYuPtKhy52Z6ePfQ0Dny2Xmg7Nidx/IIrQ/0P4ml5mTGVu7MrZDqqqt3uenBIbZf0SgJVhNx0RM0dHZNOJ8WI/N0wtkRhbvNsdKwEjdfaeBU6rB/4rSd5uSbU6PgF13LqU01pxOJTXanpmcDg18rmyi49ruDwYG2exbz+IyGpLUfrWGGhLNrCnU0MmYZw3FMa45PYEYubSsQ8AVWzregtCh/spPsrVFcZ/8un6A0KngdPyvUbIqvXitj9H3vnARXV0b7x391dWFh671VpoqjYe8NekhhNUWNMNe1LMT1+pn6maXpMNMU0TTMxMRq7YgdEFESUDkrvdWH7/s/dlSAKipLC/5y853CA3Zlb3pl7Z+aZ930eJyN9z0s/jnB4j0DHozSVzUGapUDrZQaNSpvqeCN5Eu525Tz0WFsfbt9SyZHjntwauZdAmxSWOl3PioZVuNt7tEm36qgJZU2VWBcnmIhLLzSjRGoCYAWFE3pxvPqLHx1RPeDCd69eJ2Xfd9PQaSxxizIiXKR0VZslYGPMx3XEV0wNf5ZPjm9nqnoWxfpyXjrjgZdjKfc/3Hng7sJ7zzxVwbqNXoS65nCvcxY71AHsOhvB9GGnGDLx8pKQf9Vw+823UrKyBR4Z/xoRNt40W95uOpW15kd+yHQgNmNyG/WLluv48hspuXmCqY+JfU2tgbh4KTY28PTjl0ZMtkSK+jnl83C/eko9Moj4sopfQ4LoHZxAz8Zc9p56ierqALxHGqjJFGguFwgbkkpg70wTT4pUVY1EVY9E02hSV+zIRN4e8T2GOGZaWKHX/7VzMrGPiZtWehE8OZ/mdrn2alEhGhS1gtcOHqJHoCXLd0/HUdHIkic6t1j733IjGr0FK677FHljJEa3aj4/5M3JwmhuuaGBXn2uMFEBioth9Wcy/J3zWDbpNbYHB9LrdwUBCe1fQ+4IFQHqR5H4rgW3nXxxbBlHzkSaVJpEtaaObPvPmRxJ68XM3ke5Tj+Cs0GJVEVXg1FCpTGZ8VseptzhF54/eCNSwcDjS4zY2vw5bXYkTsL2XRIm9trC+BAt63I9ucl2Aw6+AzFK20YhivMYmbL0fB+rN0XZioBXeyZG5YnvMaPcBoncBm0HfUzsGyJXhFEizi0FzKzNEtOfYoyJ+X/xcwmmOxbOf9by+TU++BJlBbEGGxLqx3CbbzFf74+isMafBxbr8PxrlKav8UrbVju6tZEtxxyZGBrL2IGRHA/8DEWVlDWFtxASaiadv9iOaYw8rFyNwVtJRMYkHKuzeSL2FuwUamSWFtTUSnhk/BuEeAVjdXwe2GRwb1KAaYxe+owOeQf7yS3vrBaRg78USPlTvPfvQa7WA/8CKVfrsYvKv/xNAuPsdjLA0Q19B+HuF59CDC2Vl6WYFpYGcVfeyg79eZbv7siRIl6/rLGUk421fCaZwyP2iSSnBrEvZwTXTalmwOA/Nx+1i03SYfXjKRJEgsgWslExtSczWzDlZt96c9uZuKg0ISpOTI6OY07ke2jkPVBbR/1xbInKFlHNRbT8oC0Ebszjs6gI3h83lBsUbRf8EWd/wtO/lq2f3kS10pXHH9Hi4NAxKCBm3IgyvRZSDcumbeWLIl96uRzl+prjNPacekX3iGkZEnHH6nx6hri7ZbC0+WNB+U9xpFzxwgG7rM284DOXCXE6vKOaOXzWi4PZY7hhRjP9o699Z/7QYSk79wj4eBv+4JwY5/4m3tYpNNsMRWfRdnCV50Yh/mjtDah8zZM7eXkDP1ccYk/61MuSIaYdzeWH7aEMD07iTvlQql3OUeGwDe+a1qiXZokUa3Ueh/Vafjo+7x+RQN60oYKkM17cFRWLl/Qkj4TH8GvW2zT2nHbFphJ0KqRiPxP5bQyG86Crramftdg/zZFy8U0UZgSSdsicwtV35CscFIYy2ErLu0em0cOzgtvvdbrifbcUaJH1DXQrZOmUJ9CVzuXoYB3WtD+7VAtV1BoycKkPJ6JkNlaFUixEwtnIw2i9Lg3ll2lLeP/nqaa0toXzDSblnL/TWrikxBTI0PMcLdd5PoqtvIKavIVY1VqjP8/Sr1LV8uzxycilKpYubRtqu/nnChLTvLij7278DGnc3WMuvxW9AB5RaG29O3VL0uZqLGtyMFg5mKPYxPfZeYD/7+JIufhCz6b1JD2+L9ZuRlyjLm2bmgwJuvIG+o9+kUG9X+OllDhu0w5Bozay5LQCW3kDTz195UVqew6K/f0csUnBTA8+zvSK05zw78unaX3o7ZvBTXdee7pQpxqjg0KrP5VSXCKwbMYz+AgjabY0k1Jaa3azvaCaX5NF9Qs9MePbLmzfWyVuZkgYOsSAldz83b4D5rj79ggts7IliAoYUT5J3BRhQ5MijeAfK1jfK5SZHpux19Twe/y7NGsc8RuvR1kqUH1agpNnBYOnH2j36kWgWKKuN0V7GGVWphQug9wWo2Duy/8ER0pn2qIl/W1WxFLmHT3L8FAlL2+5DmtLFc8+c2W1L3ET6b1VUpwUlSyPqsKyrjdqu2z2GE+yMXEmI/sXMmnmpZKmlzwL5wQ+/1JKqGcaT476nO0hrvT/wY6frKNZJDdgvGCFaZ2SQ7VrFRa+c3DSV2MMeYVSZTgv/fYCOp3AzXP0RPZqD/ww8uYbKhrVdvx35HECq4aQHrWbpvPvpmbK8T0RSkBxIJ/UpnE0fwQTxygZNebKnAqd8fX+g1L2xArMiPqJoQFS1pQE8KSwBn2PSZ2pbgJSRG4uiboBJFL0FuI7TGHqb6J1V7JZmbKck/VV/KC7nvtcc9l4JMw0Jt17l/4PJbdOOeBvLrRtXTVxue4siN6BT393sj224XPCksd97me4XfvzyEI99C7eRXhoCq7FPgwscuCeXa1kpTaWjbx9y8MIqR8jzv+Rl/NcjoryBi8eeVCPi0v7oF3LXEGMYO4bZfhrI1L+Zj//ezqzB/4FUrrYEx7/4QSLpD8Q5BGKUda5nNLLnbK7AiniZLaoMofn7O7jBdlezmSEsCNrFNPGVzJ0ZOfyca/kap1WQ/yeXKRSCcMmXruiQUfn+WWThBMpEsJCDHh5GU0pPgmJEiwtjfz3mbY7IR98LKWiQuChiavo63mQZtsR6GStbKeWpwZgVRpBpXsukvJDOJ+o44nxI4iLCiXGui1IEpD/O/7BZ9n/6VxK6n145P4mXNw6TodqahZ4fYUUW3k9j0+K5b2qYGZZbmWkughlwJgrufGK33dnIMU2bzerXEbjc8qFvv6lHCv3YWfmBKZPbGDIsGtbeIgO+e5HCWfSJfSKMODuZh7wpnk9g5NlAU1249FLLyClMkixOzAbQSenqYcO/fkJvkWNhISGr/kmcQED+jZz3XXtD8i//5RLwulQFvY5ymjdCNL6bsWl1ICNxvycHLOx4TkvP/YkHSfd7QQf7VtCeKieebf8ObtnV+wA5wtsWF9Bao4X9/WPxU53hjfDo/m08BuUAeM7e4jLlutuQMqhnyairDODvoMHvsavihFMVihZcXAq/q5V3P1A54nJxPfG79skDA4+xj0jVtJQcSeno5uxFNo/htbYQDWp2DV70ffcHVhWScyEs34ZqMISL/GjYNSya7+CramzGTFMy+SJ1x6NdS2NuWWbhKOJovSxEV8fA3JJI3P8FoNOQdWJ67BycMcoNUfeCUolT50aiUZvybJnlFhYti5eflpXwclcL+6L3oNP3SluiZ7L/tznUPqNxGDVeeCqo3v4R4AUo8C+76eibrLGfYABeTspfg0FAmJUyqxR9xAYvYqFp0+zVBeAXb0Hd58xp/Feq/LF5x+Xc7bCm4f6JRJRWkSFjz8vJkUjTvSffqZzkQjX0icuV2fFO1IaGgRWzrkPG/VCVJbDTcXlmmTiKhL5Jv4ek2LF9TPbgk4tai9jz6dbinUSjkpoVgntpk8eP67j1y1WjOqxn/GhTuibswncXs4n0X243fYb5HoVvxxejUFihe9oPQatQNEBsZ8amXDbZmSWV59C3V2BFBH8sTm3n4eD7qFnusCNoWW8sPGGTvetU6kqfvzFln6+STxkN/R88xpJ7/EVK7fMx9etgnvvv/IzmpElYf13EkQ1kwcHb2JnuDVDP7fjicjJzNVVMULXGqFrmVeCvq6Y8glDCMweijH6VpBoeDfhM9IyzTwTdy40EBjYtp8UZxew+tsg3O1LeKmPgEW1HyeG/4LR01zHiIHm2nxG7l3Mftkuvkkdy5CIfKbPNacfdtV27BY4fETKnOhv6etty4qaIN7WvE9Dj1ZS466co7sCKRJ1HcXlmbwuv5OnFGfYkhTEmZIoFt1mIDjo7wX3r8a/36yqI6vKhSUjd9Pcu55y53Tc9rjy+qDbibRofyzVIrC9po7lnp8gIDAxaQSP7h9As8a8OTQ6ZBfz+mQgO9NKirqiOoWMsl7t9tmW6xXXHOLaw8PdaOJ++jci5Wpa8v9H2X+BlC62012b0ni5eTX2/kNB6LpedXcFUsRdG3VhAjd5L+X9pl/JOhvJ5vTRTBhRypgJ1xaifLHrc46d5aut5h21J+4rx96986H2nWlGkSyqukZg8EDDH0RkotqARiNw9yI9/v7mhayJ8Xu5DJEk6uP5C5BJDDQ4zjSHUprC7h2xiTfvuJ2YsIXwdcVYl6qZNncG+LmZpD0vNJf8WEIikkhaPZ/86gDuX1SFl3/Hi7aaGoF3PpDiYlPB/WMO8Yoqkic1X9FToqPZu2Nyw874QCzTnYEUUZ7yV0UoNeciibFP56QykN9OTyJmZA2jx3cu77s9P4jAlAhQDRlowPq8BOdc38VYShtpdJiKUWhdgFgW98Dq9DD0CiNNga3h5VKVQEHdl6w4dDv+Xkruvqf93a6PP6impMadl/vk4q0L4+DYt+mXYm63j909edfNvMuXcDoFg2I/r+x6EnenZh76z7VH3HS27S8st35tFRmFHvxn4F4EVS7rQgJ4tWI7zb7mRVBXrTsBKdVFHiRuH4lUbkTEuwf5vMUnrmO51aaEN/bNvCpeAdEv23ZKiIuXcH2/n5neZwNFdfdS0keLlPb7hAEtFcZELHQKhuQ8irRJMBHO6h0qUA7a0a6rz2aU8N7ORzulEtXVtrq4/lfrpOTkCvSJNJh22nysTzDWfSXU9UUZ540xpDWFRFSgejUjnPJGN5YsOoejf2ukybdrq0gv9OChQTvxK8pgztiZxGa8SKNIAHxRSPy13MM/AaSU5vqQEjsUCzujiYejPWuuFKhMkTBj2GP0HfA8gwureK/ZGueqQB7PLzfxLD3xUBX2V6kqIEYr/u81MXxfYPmQI1TUa/CQGlmZG0Wl0pVH7inCxevvjblvJVM08smCeWjrl6GxMKuTyXTnOFO7gY/2P27iQVlwa6u/RKJmkbBZlPcePrR1IyP5pITaWqF9ctq9SvYecmBm5A5693RCUnaOoH2VvDssmkclH5nO+dOBL7FQGPEcZj5X+TEJ6jqBqHEJeAUXXnU3665AingjYgTnB55TKK0IZ7FPLq9vnoZGZ9UpVZU92yrYn+jFjMgdXG8wz2dEaw7fxiO/jjOljCx7Vo/I7XA5S0kV+PkXKUMCD7Eo+gC7w2HMu47MjrmOcEM1r1S3crNJq+pRpORw8m4fopJmo49YjsTmOInVt7M1eTKFReaNrbsWGfDybN1Y2PVLJgdTezG5/35mG4cgVduRMPlbLGxaZaDqyaX/3pmcq6vgvdO9CPMrYP4d7adxXG0n2Py7hMQkCfMGf06YqwtvNQawvG4du04sZ8DEI9i71VztIduU765Aihh5qimI417nJ1guHGPXySCSCwcw/yYVYeFXjnrqklO6UHnF6xoaNApenXqYtIjjqKwaaNrah31jJ+N8mT2JjU1GVjR9gtGvgdGJw/lfXLiJE0a0Jye9RFj1baAM49CE1UQdu57vSnJIPDuYG2c10rdf+yD2gUMSdu+V4OdroEew8V8gpQvt2l2r/gukdLFl5mzP5pua19EF/jnSCt0VSBFRBbvsLQwIfZtvi9eRX9GXDafHMHpgCTHTLq+h3lkXb16XT2KuWQLojonZBA1rXyK6s8e7sJwoPSZKkMlkRkYOb53MZWYKFJdKGDfWyLjzu2ItIa821irenbPIFIkiRqS0mDx5BPLKIIr8T1I2sIzoZemmr3rcv5CBtjIuHl/kBUn0D43l1KdzyagI4e555fj37BgkKi8X+HC1FB/Hc9wx5DiPyaL5pGoljtbOqDw6L6nXkZ+6M5BiXXKMeMGBw3XjmaeJI0MazvcnJzN6UAUxUy/dHctJyaWkRENIhD0eAe2nCrRM2C9se6lEwy1+d5hc1OBo3sVrMVGpR1TsafbVo7M3YJBoKXCKQ2q0wC/7HI/G3oyVpZbnnrl0RFZrBJa/LsVK1sSH4ZZUOpdQ7LoVu8qBLPEJIMG2FQz6Pi+b/vLfeez3p0ynvtYd6mt5HsQ6X6ypIa/MjceH7aWurpiDwdY82ngClWf/az1km3rdCUhJ2jmcygIvHEMNaOoFejuu4TOfQdxpU8z/9szExaaeRx7vfEThtz9IECUxHxj7Nv19kjltWIA6+PKk22XGIyb/DM96CqlOhm26SDirp2H8d+37uyGDJV8+b/ruuaf1f6Q+/CmNc4WDvP2+1LSYbQEe+9j9SJTzJrQVM5AclNDct20KyUeZ7mRVBnHf9WfwjmpV62jpY0uG78QtM4cHYoazIetdGkJaF25duZ9/Akg5/EsMjdUOuPTWo+gAs9A1CZTESRgf/QozB9+OTYOM9VXNuFWG83JZDueq/Xnw9gI8Aq5uoXcuX81nX9vg53SOh72yWdAjjOeyznC63p+E4lBumnSG3kPbKid1xb+dqdtCfiumK71z0z2oGt5BJzXfl8SopLT5dV7b9go+3kYW390KmBQUCHz6hRQHeyP9+7WOyWcyJJSVCdx4vcEUCn+hbf61nsSTztzefzMeIY5YZBQTlFDDG2OH8rT2XQxYsvHAJ6YoITFaSLT6fAl1OQJePc4RNfbS6K8r3WN3BlJs8veyyS6S3/UxLLVJ5cPd400g3VNL9NjaXj7C8Zu1lWQVevLAwO1EK2dS5HcSn4Io9HZVrMwvMikt3TGvgaCel48EPZYk4bffJYwJ2cWc3insi9Aw6RUn+t0+D73USEKWOD8yj5WCTo/tgZMk3iVh0PEHMbrth4D3KWnuzd7yZ01Ro2XlgkktcfHdBpydzPewckUj9c2OPDX3F0JP3YRBoiPu+m+xpvX50dGEIk+KU2oIL6QG4+lYwQMPXzmi5krtL36/cZOE5BQJd41YhY+9L++qfXnu7A72nXwOa/sGRs7ejUR67REa3RVIEWN97LK2MNHvVVY1x7IvPYSjZwdz8w0NRHaCP6czvv2zy2g08L/XZVjK1LwxJ4NDwVuQquHw4WkwzAzwdmSHVUYmVO6nX8gx+qT14etD4eRWheBkXcWb1z2LkPIFOaGHyI7YR3TczcSW5LMzcyxTx9UybFT7yj1bt0tMwhY9g434+v4bkfJnt3d3ON6/QEoXW2FKbB6/5r5FWtO92Dk1YutQj41jAxLZtenBd1sgRZQOzN7GgF6v8WPWOooaovnm1BiG9y1mynWtKS9dcedrr2tp1pgH7Um9TzJy9uVfeldzLpHvROQ9cXU10PuCHNyKKglpaQJ+vkbuOU90Jk7kvvtBQrDHWZ6d9DQa6z6o5WaAR1rrhs2xyaa/E6duxKbRkl7v51Fkb8ug2+cy3VrA+6LAJHVJFmOCN5H1xfWcLOnFouuLCY7q2GeFRQKffC4l2DWTW/qksshlFPvznkbrGo7aqeta090ZSJGXp3JWrWIN83myeB+Z9j35KmUqg3uXM2P2peDTJ++XUVjrQ0+3XBbe374Khjg5E1N7nJwM9O1jnpjZycqY5bMEg2CN0qE1PFdW44kiKQaDhRFliDkapcougzPeP5v+nhw/hCeODDBJAD75mCij3Haymp9RztofvIn0PM1jTn1Naj0Vag33OY+k5qKtvTcLzzHbuJMXDtxMTZMrSx7R4+jw96X3rPmwnqJqZ54euZv86loK/RuZpytE4xpxNY9Wh2W7C5DSVG/HwQ2TECkPfEbpqcsVCJZ9yxehYdxvVchLO2/AzkrJk091Pp9+1WqpacIvckL4W0pJco1G4nZ5+eRKw3H0gooBefdhrXHGJluGRCOgHLoFve2lpJcybRkfbowxqTrNv9VgSkn8u+zilItxzq/ibZdGY/FtWJ7SoOnRFrT85qw9x4siWDjmOD3HtHJJ/dHHxm5HdqqUlWOC+aBnJ+UpAAAgAElEQVToB5SBf0762N8NpNSUuXJ0yxikVka8R1y+PQr2SBkcvpr/jBxFntSXXWcL8ajsx3t1KaQW9+KO2dkE9b66zYIjseVsP+jNuKBjzK7Lov/Mmdx8OpPR9XK+y4hkcEgaM27tWEnor+g/5RUCH34sxduhgJdmPUlT05cYaN2dVeme5NlNK3B0MLDkkVafpaYJbPhZakq1FFMuWywvX+DsOYmJT0XkVbnQvv2mnvQ8Zx4ZtBlpqA02SaUEJNfzypQRLGt4Cy22bDrwIdbuBlzPv+t1jQIlCRIs5GrGL9hy1S7ozkCKddFRjgu2vOJwB+/o4vny8DBKxfThy3A2tDjg9Te0NKmtWTkgDXtVbw6Mf5uR+x9BppXzk2w721MnMGn4OUbGXJ7L6NARKTt3C0yJ/JVJYedICFQy6g1Xgh9YaDrVF5nHGa5tTe+xiUvj5MxqIvMfxcIgYOw/31RuQ8GnaAwKTqUJVFZJcHQ0cu9dBhrLCvhoXSAutuU8PycZm4Tp1DuUcXrCASxpm1Zer85k4O7reDTZA7lMzdLnuh4lLl7bdxsknDkj4cGxb+Fi0ZN3BE8eT08k/sxDpmsP7neGkAGnr7pvtVTovkAK2ObuICZgGasr93LkbAgHc4dz44xa+kZ3T/3jshwJq9ZLCHDO5fZZDaR5/45rloxV+gUEBFw+ej5bB8nKJlZ6foRLlSdxW/qSWjSASb1+Y65fNarKuRyYuIqQ/QoUlmNIrSrjh1MxjIguZ/KM9jdHf/xJyqnTwh9p5f+m9lzzY9JtK/4LpHSxaaYeyGTdyXXEJi9rcyQrWyW2Dg3YOdfRM/pMp4GV7gykiLsfY0Ke4ovTv1KmjeKLk+MYEFbMdTd3HUjJz25k7betg2IvzyxuuTeoi63TWl3kMxB5DVpQ4ZZvdHoQiUhFa9n1PXhIyq69AuPDd3LroLUo7WIwSM2RBNaJ47Go8yY39Ai1vRvx21yG29FadgT7c8f0CdyiELBrK9pDeU0Zs7y/oeDrmRwt7MO8yWcJH9K+uod4jtxcLV+usybcI40bQ08y328CRzKeQuUZjdau43qddVa3BlJqslDVnOVRuyW8lbqDbP9g1hybRr+Qcmbf2nagUqv0LH+zdfH74KJiPPwv7Yst+c0B/gaCAs1AhYfVaWI8lqOXOdNk28o7Y508FotKX9SeBjTO5kl8ptcmyu3TTH+PSpzIR6kKMksjuX2BGKrZdjF1eNdZdsT14LqQRGbKhnNw3EfM185HhxRfQc/qvE/ZKffnfa9pPFBYwCONh1idOdiUZ/t35x1/8G4jFfWOLB2zk2OVGqw885hkqUHjcHWLu476XXcBUk4f7k9BejC2fgacQo00nJXgrd3M+khXHlSU8Py2G5FLNSxdetGDe5kHSiSDFtMsVt16O5Z1QzkcboPC+vJytjXGNDTUEVl4C07K4D8IZ1WRcWi8ci45m4COPbFWbDl1I8OGapg6qfPX19l3QXvlWiLyLpQ+vsnnLixkKioz78WuSovWve0C5tcSC/bnRXNDdCL9Z7RGNL33tpKqRgeejfmd+hONbBppw3+r9tPs28LJ0JUrhb8bSDm+axgV57xxCjFgez4VtKM7KImXEO62gRVj/dnm2J+k9FN4VQ7lC3UCh3OjmRNzhqjhVxc98u2XZaSf8+GevkcIT0kn4p75DC0u5bXiSlacGIC7XRkPPXZ5QK9rHr+0dm6ehC+/kRDumcrjE1bQqPq6TSELnuWhn19DKjXywtJWYKRlnPX3NxB8/r0sViwpkZCRJTAw2sCsGW3fr6s/bqK4wp4Xhm+mJswCp72V+GU08sKs0bxU9SYqowtbDr6Fra8Rp7DWukUHJRg0AkNm7MPR41LFxMv5pDsDKZaVZ6hqrOKGwP+yvmIX350YzNnqYBbfrcXHu+McBjHaTIw6E3nY3g0TqLGuJD10A74V0/A7258U5218cDiGUJ9CFtx1ecJZkYRVJGO9vt+PjOhZwUmXBgK/9GFt3wF82rcHtxRn8lJNa3qPVVo++b2KcbScjVt5T3TRjyOV5HOk8n7ylCNNqdUpJyXU1QsmXokg1yziT4cSE3WQ68ItsE4dRYlPGgVDspHRNoqw2VhJ4IkeLN/ZC41e/qdF8n21TkJOroQlMcux1vfiY2sXFp/I52TRbeibzX4eccNubJ0vVWDrzPPWnYEUxbl9TPN7lFXFe0ksDmdPzihmTqxk0LA/hxuxM/65mjKn96v4fr8tgwMSGDK1kWLnBHrGWvPkgAfpe4Xs6UYDfNdk5LPmz7H0UjOh+ASljW4mcnC7s0tI6HsclaSQMe/ZUzypL0V19axJnkxUaDlzbmkfSBGJmM+eE+gXZTCBg/8CKVfTmv8/yv4LpHSxnWYeOcWa+G0cOf1oh0e6HGP8xZW6M5CiKDzCHN87eCX9EM3SMNYkjSMquIg5C7qek73jt0IOJwcyomcsh7PHYW9VxxNP/Xmyyh+vkVJSZp6cXRzyejxZQn29YFLuERV8fv5VYhrIFw1bw7CQ+D8iFmSVviiSx6K1UHFi7EYi1yuxKVKZSBdvmjWJw75e3GN76eQlp0nFfJcPKft2Kofy+zN3bB59Rvt12F8yzjSxfoM9/XySmembwrywGA6nLaXJbwR6q67zxnRnIMWioQCr0mTmerzAl/u3kt0ngA/iZxDhX86ti9ree15aEV/8bJYXFm1A4GmuW3gpSfHaryXk50uI6m3E2dk8uQ5SHGa420doLXxQ2Zj5S6R1rtgkTsEoiGmwWlFVEaNgJL7n2+glalOZXumTOJhWxb6MyUybamDoRbLZ69cWk1Hoz5I+JwiyCOP3ft/wsHQRI4z1fH76VezVNfzoMpxHA25nZkkxbxUl83OjPQezJjBruoGBf6ME8soVKuqbbXl+4nZ2lljSxzWOKDtHdDZXVmrozGuzOwApWrUl+76fhkEnNcmhihwpTSUCjjX72DhQxkNWpSzdepPpdjqbWqVsEnhjpdSkjvH+zYswlNzCocG12AltASgxnunCLPI6Yw4qyuhZOg3Pun5YVkuRl0rQ+GagCm8/5aAwq5i3tz+Gj4eSxYs7HzHTmfbpqEyLMkqLyoC9rJiZPk+CypPq5NFYKVww2LTNB99XZWBTxjBiwpMYfVPfPw795hsaGtUKlk3fRE6ihMyBDdzZnIHaozVqpaVw2Vlf9Fop3j3Pdvry/04gpbnBhgM/TjEpi/qM1l+REk3kSPG23MuP43R85TaWY+kn8K4cxc/GA2xLH8bUoacZNunqSNWXv6pHrZPzyoh9FJ+qYuZNM3BQaziccoJlaYNQ66x59rF6rO06n6Z2JWe/8ZYUa2uYf7OZL+dia4ksGRp8kLsGbKTR8E6bIjbGV3lo0+Omhe2FUt6/bZFw7LiEsFAjXp6toEd1jYSTqQIhPQ3cNq8tkPLGmzqUKiveHbWdrF46PDdV4ZXfzAs3juGl0jdo1Pmy/cj/sA8y4nAByC2qKDUWCgRFpRM6yAyKd9a6M5DSMl4GRX3EnuwNbMwcTHpZBHfMbyCoR8cpORmnG1n/kyORXid5zHEAVfYHKXA/gsbTi8GHFtJkn8HDCcFYyjT897nLA7gtqQu3Dv6SPkEqcqX1WG0N5JR6Ik8tUOCgaeZoVsYf7rYsqKBanouy90B6ZoxBF/Y1UrtNFDQN4kCFeR6t12MSBhBTsltsyY0/0UPdC3lOX7LDDlAfaR6T25oReZ6Wbzf3oazBi4fvV+Hq1nUuj8/WCpwrlPLslBeRNkXyub0jt8Y3k6+ZiczSSP1ZCTYOdYycs7uz3apNue4MpFgXxnOj3yLeyd1Pcm1vtmaMZuqYcoaN6fpc9JqcdYVKh3+qZsdpd2ZF7sNlQh71tiU4/+bN+nE34S4xoJVcPgV3vdLIvJrDprn/lOwKkJeAzpbC2kc51f83hqy1xzlfRtGEHjRqBFYkxhDoXcWdd7fPe/juh1Kqq1vTZP8FUv6KVv9nj/kvkNJF/8+JS2LFwWOcqrgD92jzoC/ufOiaQKsUqM4SQA+uviUMmGzOkb+cdWcgxaokifvcZ/FAxhlkVgF8cHQ8EX6l3HpH18lm31rZTF2THS8NSuL15AiatQqefrgaG8euSyur1AKvviFFIuGSUGGxLfLPiQttgUEDDMycbqBFyvHpKc/j76NFrehnajJF3FRkShcyInfTY2MmsiY9BlsrTg6KZFrvKBwFmGtzKZCSoYO7LN6i/LcYYrMHcf2wPKIndgykpJ6oZ8NmZ4b4JzHWLZWnQgfzS+abKINi/pAwvlI/utz33RlIkTWVY12UwI3+/+WHX3eROcKHtw/NItijikWL2w5Usb9kEJsayQi/kxwuMC/MnnqoElvntjsl/3tNhkYLI4YZsLAwLwQi7bfQz+k7U8qWmLplat8T45BV+ZgiUcSIFNFqbfI45fsdEqwwoMKlogfKJIH1x29icL9GZsxqu6Bc/poOtdaKVf0LKfWq5HPnamQ48/yZd03Ha1D7cEjWm7sGzaZvTRU/ZWZxSFrGhhM3MWxQE1OnXn6Q70q7X1z31Vf1qHRyXpq2lW9K3Jhj/RPe7iHo/wQ1FfFc3QFIyU0JJ+tYJAp3Ay7nQ/1VVQLyoiS2j6jnPotaXtoxE63estNASgu3g69TMS/MWIKy7G5SBlW1kT5WNxtw+KmYhjneWFibFyJKCmk0nsO3ajiBlWNbCWftK1EO3t5u0woNGTx2nidl6VN65FZ/feqXmM8tLo5apI+DbA4x3PVjqBpD834Z+r6XgiDH6lSsTxvDYP9UZixqTQ17ZbkRrd6CV2b9xKEUB6ShWcwQ6lA7twIIep2M04f7UZxtBkVDB6YR1NfMO3Ul+zuBlJbIJrsAA47nZVcvd32iao+d8iTxMdms9JpJ/Omj+FePZ7dsP9+nDmd45Bmm3Nj5iJTSMoGP1khxtSnn6Ygz7K7Q8Mik0aZLSNqxjW+b+3KmIojbr8+iR9SfE83ZAqqJ55g62cCwIZemM7VIZU/qtZk54ckohbbRuQrdJyzdcx2VjR48+pAeZ2dzH/56nYTsXImJB0UE7VpM2QSJx6S4uRv5z31tU3taUs4+G3WQxH7VBH1djWuZmhdvHc2L596kRh3CnoSlJi4kO7/WYzZXCVQmS7B1rGfEjbuu1K3afN+dgRSpqhZFwUEG9XmN9Rlb2Xl2EMnFUSyYU0Nor47J2ffvLGVPvC9TQ+K5UTaKOps9lNumkj28jpgtT2OhteKl0mwKagJ48K46PHw63tRqUUO8c8TH+AfIqGyooyExmJryG/hoTh4nHXz4OvM4Q86n90hrlegLz5A5x4MBcbeic8tBGmDmCUusvhMrSTW2FhUYdWo+3buIykZXnBRVLLtrN/LUYcjLepA6cDNa//bJPR2ON/JLUhjp5SHccUslQaFdj5z4aLVAabmUF6e9iKE6hPVutkw5YEeFfCz2gUZK4yXomgV6RqfRo3/n3l0XdrLuDKRYlR7nXrfreDI/kfzGPmw8M5rxw0oZO7Hr8/6rehA7WXjT51UkFXlw15A91E5IMNWq/GUIskH2zD7zGcfcRpIYMIEaq/avf6/KyDm9jk/t32dsfjMW1pkYqsYR21uJS7qefhsdaYoOoc6qHguJE8/Gj8LZroFHH2sfuPzf61KToIXIzSjy9P0LpHSyIf8fFfsXSOliY917YD+PHSgmT3UjLpGXTjJEYsPy4xKMenAPKKK/+GALHU+GuzOQIi9P47/2w5meW46LwoN34ycQ7FHOosVdQ6ZbOEFElZqnB1fxaaoVWZWB3Dk5i8AhXZ8QZmYJrPtOagqrE8PrLjYxGkWMShGJzR79j54Xl8sw6OHDWxZhdOqLVuaNZWEYVumDaFbUUmb4lMA4K1N4uzoigK3OzjzqG4i/1Mjk84umC8+Rr4eFunep3jOaHRlDmRqdy7AZHacBJCXUsGmHG2MCjxLlfIrVQaF8nP8ZDSGiclDXrTsDKRJ1Azbn9nFL0FN8v+4A2VM9eT32erydarnvP21zcteuKiW/ypf7BsVxotCVhJIQxvRLY8KsVo6AsnJYtVrGhWkKogcHOH1FuP3OP/hvpA3O2CRMMzm3MVSHUWZ+RrPdt1PqdBw7IYBmozi50+F7wI+3428m0KeBO+9qHTzLStSs+tQGb8dCXvYKImnYd2xTufDfjK9Mx9LY+LJ5x8u49YjllukjcVKriM9O54whm0/i5xPuX828RV0HDjvbQ1oWJS/P2sz75f48a1yD1H/kn6KmIl7DPw2kGA0C+76bhkZlhftAA/Lz/DPaRjBkZHFgQh53GZt4LXYyTRpbnnlSh6ITCtvJJwU2/iol2v8Y949ZSVHtYgqi6pCfz9c3VmgI/PocLrU64me4YznE/H5spoJ6YxZu9b0JK5llGgZsz1iARE99B4SzMl05H/48wcSTcutNBpN84l9tLdLHosKAqDQQbf8VEU470ZbdhHRPPU1DLuXQSa9vYM2pGCLcs7n1PnNkTouaiyAYeXHGL2zJciXS8wgDbazR2pllSesrnDixdwiqxrYLNd/wHCKHJ7dwVHZ4y38XkKJTW7L32+kYjRJ8RhqQWF4Z0GosEjAWFFI8ZTfP+M1n35nDhFZN5qj8IJ8kD6VPYDZzF3Y+je5YXB2/7XIxAew3U8hqZy8OR9iQaNuTr7fsRmkVxOa8Xozrl8q4WX8Oz9GmLVKSjps3B0J7qFgw/9Ld/Z27JRw6IuHG6PVM8qujSfZAm/ZSaDfy+pE+5FWGtFHHe+9DKVXVbVX0xIpiypzIuyFSSj3/XKtqWk2twDvvS01cGW/0LiNhZCZhq2pxrNHy6oJhPJf3DuXKKA4kLcGltwGFR2sbGQ1QtF+K+HvsrVuRK5o7/Rh1ZyBFMOiwzdnGnJAlPJMXT0rJIOLO9Wfu9DL6DOg4xeu7r8s4k+/Dfb0TCbX2Q7A4S60mlVOTyul/dC6eRb1Yr4kjNmcgM8fmMmh0x/OV7zdIOH1Gwn1j3sExwA5tQR1FOWHom6ZxpEc6n0b14NbiTF5sSe8xGLE5lEzs02ombH0Co0yDbuB/kOkqL2mTaqUrr257hegeWcwcl4d1wiQsGtxJHPc9UqdL70+iNeDxezFb6/oSV9iHGycX0HfI1RE6t9cx3nlPoKZOymvTXqSusge7XKX0iw2jyaW/KY1MUydQdkyCIDEw8sbdKOwbOt2/xILdGUgR08eesxvOgrNZVKrD+CFtLKMGlDBx+p8jMnFVjupE4U/ebaCw3oknJu4gb1ASdmVS9mTOYLg0jYkNW/84QrZTJEd9xpHh3HZj4LQORNLZrxs+x93BQFCxC4WuzeR7lTL2XSeMPcLR2ylobDqLuyKQxUcGIxEMvLisnfWfVlRZk5molseM1iNuSoyY8ucIk3TCFf8W+Zs88C+Q0kVHPxW7jfn7DZRKYnAMaX9ypakVKDsu5gmAV89zRI3pmDm+OwMpljXZvGMZRp88Iz2dbFlxcCK+zpXc+1DXEP/YnVXExnswNWwfA8IcOXRGz76sAUzrm8rQ67o+Idy1R+DgYSmBgUYC/dtfiBw6LEGnF7jrDj2fi0oC1jWsnHM/jY4zkShdsU6YjMRgQVqfX4j4rhBtoC8aP/NAssbNg7fdvehtAcMukj4Wvy/Rw83KD1EmDOW3U8OZ0DubMbM7nkAnHKzk91hPJvWMw9c2k20B7rxU+pspIuXPsO4MpAgGDbY5O7g3aDGfrEvh3Ew3Xt514yWqKlotvPKaeVL/0PBdWKn8WHk8HLlUxZNP6LGUm9MgjqdI+HWTBDdXI5G9Wtt+tOs7+NkcQ6UYhNbSF+uU0VhU+KNxNKD2bt0Fje/xDjpZMy5CP5ooo9lYwpC4UJ6KnYNCruaZp1vJ7JITStm4w5fRgYkstB7O7uHv4JtRQnhzEWrXCM7WjOVk7BC8XU5w27weNEvlJKWnopKksnz/HXjY1fLgY38fgZsIpFhK1SyduouXlSGsqX+NA2eX4xlUhHtACVJZ6yLmWvrdPw2kFOf4kbpvMJb2RjwuSMHSa0B5vISTU49yi87Au4fHmsh+2yMPbu++RT4AkRdgWp+N3BD1C2nGBTQHglSQY32mkZ4birDQmseCPH85NfeYwWCRH0XkSbFv8iOq4DbTZ38Qzg7Zit6uul03790vZcvJOQwfomHK5L+eJ+Xr9RKycyT06W3ExdnAOKdX8bZPo6F4AdbHlKgize+uOIUVi4LCcdOq+fFEHCtSJuPjUMziR8w8RS2RgDaWjTwRs4N1Jf7MUWzExy3YlKKYnRRBTrKZUFzuYsC1F2gaBCpSBNNY6e5fTN8JCUgkHYNHfxeQkn28FzknIrDxMuJ8wXvkcs+FukagNrUJi6mfc1fwffx++jD9qyeTqUjgzaRoAtwKuev+zqfRbfyhkuQMT27rs59RJ7N4cOwI3B1ySbANYXJqFWMlClal9CXI7Sx33N91Li3x3l5fYaSp2UwsYCHTs+y5S+c4FyqaDHJxpNnCTB7aYlaa/axOVpBSMKgNGHgxofGFdUT+FL0Blj6jR34etDpXKPDZWik93dN5xl/BoclxRL9Wh6LJwNsLB7Ik50MKa4YRn7rYFB0sP6/40nLcypMSmisEIoYl49/rUk6ijtqyOwMp4jXb5u3icZ+5zCoooqgynNi8wdwwoZj+Izrmrlu5spn6Jjte65+JxLIOS6kBdW0Widefw7MkjP7xt3BMcozVaX3p1yOH2fNbU2gv9tOF/CGGADcUKU1kVvVCYRnDufw6nlqowFndTFx2a3qP4mg6h+4uYUjCI1ip7FEPfRdByMMgUYDUFoNUgUFQYJDYUF5rj0Yjw9e9GrvYmxH0Fhya9QUKmRmMvdDskutwPV7MPsVgfs8ZwOTh+YyIubTc5Z7b9r578y2BRqWUd2a9QEFxD1Kc1XjtGQtBPbB2NT8TNekSRPDU3q2aYTNjrwgCX3iebg2k1OaywiKUmIJq1PpAvkkdx7DexUyd3XVuxKtth86Uf2U5aPUylt62kXS/dPwS5XzksoD/5H1FhO40pdV98HA5jSDubotRx3IXjnmPIclzFE0WNlQZQJRBfqA6jgFhrVkEPfdb41sdhc7JPEdrrsvC3WYwD5/wpklry7NP6k1qUxdaC/hrJTcydIiBmd5P4DDsw87cxr9lrsED+QWlvLDyC8ora5BIJNw9bzo3TB11DUe6uir/AilX569LSr++ewPj93rQ4DjURGbYkamrBcqTzWCKX69seg1LabdodwNS1EoFKqUVqiZrFKo8tnjpsS1yZZCLwBuxk3Gzr+E/j3YcQtoZ937wvpKKWgeeG7gHdaQFZ7PgpyOj6eOdydy7gztziMuW+exLKefOCUT1aZXTu7iCyKpdWSkx7fiKKi9hHqd4dOrHqKxiTJEKEpUtpV5naFBuw6exF3qH1gXvsz5+bHR0YbiVQGQ76bjii3lm1Rr0qf3YmDKKkaHZTLqlYyDl4M4ydsX7MCP0MNbWRaT7wUM1cTT5jeyyL8QDdGcgRbw+u6zNvOxzI4u31KCPkfP8zptRWKp45plW5+bnKFm73gF/p7P4hJYRppByOMWdrGo/Zo5KY9A4c1RKSx5+cJAR/wuez8mez+Mqz6HJbjRGVTC2ceZoH1GpR1TsEa3RqpjkgC+RGq1wlUSjoZYa42lGJkfyzM7JqHVWPLVEh+35rvDbj8UcS/dnUeRhwl382Op5iNvy9qLyHIDOxp3kvYMpy/PDq0chL05QkabwY2NuJhHE8/ieexAw8tLz16b2dbUdo6kJXl8pw8aygScmxfI/VTDvV3zC5iPvmw4lkehx9S3DM1gEVYqvCVT5p4GUwxtjaKwRZWrb7k6L7+DKQw0UXLeVmU2WrDo+gvJ6Lx5+UI9rOxwQF/t24yYpySkCC4euYZR3PvHuEchd/HHdXYn/fvOu6iF/D0aeKzP9HfdkT+T2MgyCmgpDEpYaewbnmdUerIqkWNRJUEXEo/HJbrcZ8zIq+GDnQ/h5NXLPPe2Hs19t+1+u/LsfSKmuERg00ICNwsj1nv/BRl5NTfZCbM/pUQeYJ9BvONqx1scsgxwXt59XTk0ykfI99bQ5rEcki3zrXSlOikr+M3I/7ytDWab7GJ1zDEn7xlFf6QQScAoxYuvbOnaaojiTBYxaAQe3KgZNPYzUQtvuJf8dQIpBLyH22+noNJZ4DTMgU1w5GkW8WIMGig5K8ZvyGjeEL2F9RjzjKmIoUZxiWVIYTjY1PPZ458fOFk6jpcN2ExSbzeRbrmOBdje5UkcKGsN4pbaa508MQcDAsqVGpF3E3M4WCH9sKjha15hITNsjxP56vZTsHIFHJ7xKuCKaZstWFTTRD3JtKt+dLudg9gRmTDMweKCBhgZY8Y4MCwsx5fLSd56Y2iOm+Dx4nw6P8+s1Uc3lx5+lDAyI4x7nUI5O28ewZRWmfrH6jkjuy/ycnLIYTmQswHOoAQubtu0kLnLFxa6rbykDJh/u9CPzdwIpRqOAss6OhkpH6qscqKtypKHa0bTjbW2nROGgxMZeicK+8fyPEvuKfbznOJSeZRI0Nd7syB7KtGEFDJ3YfiRGo1LgzbekJhD9o0gDtdJ4pNYKJOWlHLgpDZnWggm7nqVBX8rjqa7YWCp5+pmO+ZlEhUExqvjZKcuoDvbGO1ZDqnoATq7DKNwvZc2iPE7Ze7Mu8ziDzqf3WKWf5eS4s/jU3IBnSQTNvQ+j9cy7bJtI1ApsD85GZdVA0rRfseZSENL7s3M41DUSFzSIb88MYkhEPtPndh1IeeVVKVqdwKpZL3GqMJgKx0rYPR/bPi5YnH+ExXV5cZwEg1ogfMhJAnpndbqPdWcgxaKhkLVGd8KLQY4bXyTHEB1SwvW3dr+IlPoKCSs/luBgXc3COzZT7FhE2CYHXo25h/cPPYqCJjbHvY93eCG9Q7dhUZuDRGsmQm6wdODDQS+jklrzRYAnzSIAACAASURBVKMBHQJf2b2NzsqIVZ2EwfuiMTi0ppfrynNwcBjHC9k6Sur8eGCxDs+L6CLzzwqs/UpqUnecPTKOka7vIww0K0D+FVanK0YvDjx/szlY+CAVrsDk+zdc0z1PrOS6KSOYETOMyuo6brz7eX7+7GVcndvnr/mzLulfIKWLnly94ysid/dD7xthkty7nKmqJFQkm8Nkew44Q49+p9HrpCjr7FHW2tFYa0tzvR1alRUGMS76HzKN2gJNk5UpJP5C83A6RdnYQ5SV92Kau5LlO6fiYN3I409e++S+olLgg4/MESBv9a4mtU86DQ1S1m6YgoNVLY8/1bUdenFX65VXZaYw81Ej9CaelPasuESCmALUYmPDdjB39FGkqQ8hq3On0a6C1PC1vNI4l2AMvFZa+kfZ+YE9OWZjy1RrAd921PYajTCu5AvkuWH8kDiWwUG5zLit41DZPb+Vsj/ZlxsiDtEkr0XtUcrUohpSzi1q99qNGFHYNWHj2IiNQwO2jg2XhJY21tqb+llTrS2NdXZolNYY/rkudtmeParny3zuOYzRB2S4jjLw3LZbENMDXlrWOuE+sK2A3YlBTAg+yn5/e/K87VmepGF1RiAOihqWPG6HIMCqNTLKyqBfX0MbaeEbfP6DQlaN0n4y8tMTsSgNQutkQOXVeo481z0UuSSgwMeU2mNET7kxgYjsCL7fF0F2eTh3LNT/oQT0wXv1VNQ5szw6heqeVextruWeplTUbr1Nz/nedTMx6KV4jzLweY+jJiWPdwrPMr0uiZdOTKKm2YXHHta34Qv4q14BLaoNzjYVPDAqjtcFf97I/4Vdx17CwtaItkmAC15nLr6lyCx0Jnl3WydzP1M4NCC9QOa9ucHWtAhQ1tnSUCP2MVsTaeA/YQa9QG25q4lcViSZbbFqmYq9tnkM3BKBYdpaBjW5sC5tAIU1Adx/rw6vTgQItLDwL4l5hQgLV2L7GOj3vT3OmUrTadYMDucm65/IzxlO/8J6EiY6YTHaPMMqM5p3uEZmPGf6bVktQV4qReOThSrCnM99senrc3nyq2eRCgaWPWdA8ucoerZ7LlEx46Xl5vflmFF6LKQqbva7CwyWVCfNQWGwQudqnpRM9w8k284cjbgr/iDvnxqH3ijhxaV60zW2vNu9HAq4s99RXpFFsbZ2ORuPfIZBJ8PCzohrb2O7wIS+SYziFNCrBROvxcCpB5ErVKibrWkyjZV2NNbZoK63R6vrImJwhQ6q1VjQUOVoippx79f60syW17Ag4NcOa8sNUtZ/uoiwkSuYFP0Ib+QkcVvJGNSKYh5MckMq6HlhWedewi3Pq1zWzGtDD2O/O5vQxQtYW/E9zZpmnne+le1H43m7eiBF9Z4snp+PT4+uLSC37ZQQFy9hfNg2rCzUbD11vYlnavLEtvOcjz6RIQ6Hz09/Ck9hDmq5mby7xWS6YnbkxrMldTbjxhhMPwWFAp+ulWJvZyS6/6XzppOnJCaCxgW36gk9H+l7JE7C9l0SYsK3cr1iHCcn7WTwiyXUyS3ZMdeTm/J+5EzBDaTlXYf3aD3Si+b1InedqN4jmqikaGmlNo+Vjg3IrVtTffRaC3P/En9q7FA12CH2gb96SqbTymistsdguLoHfErM6/xu50xjbQBu9Xb8lj6cCf3yGTOr/fbPSa/jqx9dCHPL4kF/d/Sy04iom0V5LQdnHkdnbWRM/NMoSqx45lwxlUo3ltxfi6Nb+3Mxcf4mPusvzXycrB496LNBT5LVGBzcIk28NAn90vmsTzDzijJ5oda8aLUsriLfLx08BhKWNgGNXzqqsGOXfRKl1Z7YHI+h2jWfzNGJl0gfy2u0RL5tjjQ6Nrw/q48PIdynkHlXUB26wuNv+roleurT6/7HgXOBWDmcpXTHEtyHWyK5oJ+1zPEFqZ7gPhnI5HpTHxPHS2u7xj9OJc4DxDm/eby0M73TxPm2OF6a+5mACKqJJv5u/Uz84Pw81fS5WLhtWXGjwNjy2fljtXxm+n3BcduUNZ1MMM1RLjR3xzMUjzuEotwVD5k9nx6bQFRgCXMWdj8g5dxxPZ9tkRPmcYb+t21Ha9mM3Xc9iZ8ykieOPoey2YVtiW+ZpNDHzdtqSsWSKcuwrMxAqqkjxX0oG8PvZGuzkSI9/J7+A4JTOa51vjho29IMSEtLEFyH89G5Ms6U9uG2eXpCLuLOagF/XV2N3D7iC1M6+V8JpBys+ohGnRlc/jttuPM9OFhcKpPeqGzm1ffXEZeUhsFg5OkH5zFtwhB+2XaQtd9tRaXR4uJkz8rn78fXy43E5HTeWPUdyibxfSzw3MPzGTUkiqZmFS+/8zUpadlYyS157uEFDOoXfskt3rz4JV5+6k7Cepg5KGfd/hwfvvoI/j5dF0S5nD//BVK62Nt+2LIK511TkEf4/pGDf7lDNpULVKWaB3NLaxWa5msHIbp46VeuLoDMyohEDno12EoKkI77kYMN41noXszyrTOwtlDx7LPXzop+8KCWXbHWjAvZwy1WY2nOX43aQcKykjtNLP/PPFaLwu7awZQWRNjeFqKjO17VqdUCImlei906eC2jnUOxODsOjWUTx/t9xoBvIOCuO01FIpsa+awgH2edjpFhkVTILJirEHBsZ04vRvn3L/wWlyJfvo2Loa9fHjfe0THZ7PYNJRw548fNffaTa6EjwPEkIZnunMhoGzJ9pQa0cajHYBRM4Nz/J5s89L/s9vTC9YQfof0beTl2tin6Y9kzOizOc7F+/UkZ2aU+3B29k2P2zbwaMoWE9GzeT/ShRuXIvGkiUBliyk81gWgj9Rfs0BqZH7DA5BKlfD62h2eb/74gGkX8/2jQh2gs63Fr6kX4d/UYZHB0YR2OFa6c3u/OgawJzJiiZfBggaYmgddXShEXOqt6N3J48K/U5TQx3kKL1jGIsjwfkvcORe5oxH2Aga3Oe/nUZzSPnM3nP8V5rKr0I8MkqaxH5Kb4q628HD5cLUNc5N4xKIm3rTxZmpbA4VOP/KFuo6qU0FQBzRVg0LYvpSnKvMsstTRWdy2976+6XzHd0u6CdL41rsf5wjmFFRtmEz70O3povfglvTe5laHcc4cevwsIKju6phVvS2loFHjthv/g2DSKFPtCBn6joNpKzpNTR7Oy+h2cdI0k6cYwIEFNoaec8gfNk7AKYxIG1AzMfRArrUMr4axdFcoh29o9pVWpBS/vGEJpvQ/33qXH1+ev6x+VVQLvr5LSEorsLM9hqufz0BRE44FwJH7BGKwsKbWwZEyoOS1HtM+OJRKbHklNsxNP3leMnbs7hcVGPvnMgiCXbOaHJ7Dcti8fla9h06EPsHY14Nr38vchgigVJwQTabvMQoPBKDEBMP+UufU3YHWeKFW8hhe89rPDLveyl/P9t7czuMeHjBj9APcWnubF/MHoZY08mq43Eao/s6QBhe2ViXlOpmj5aZM1fbxPsNCxHP2ZYqLunkdi4RpsmyuICFlK5pff84vHUPYXhjN9xCmGTLh0onk1vnvnHT01DXKemPiyqdrKXc/j6WHkgcVtx9GW5+GtOYuRqx9Ha9lWiUgwaogv+JbvExf9Qeieekpgw0Yp7m5GekVcCqRkZEkoKRFM5O8iCbxoO3fqOBRvxY39fmKE1TiyhhxmwBvFnLOzJXuahPGFOzmRvYCc4hj8JrQ/1pclShAjni42MeJJYa9Erbx0A+lqfPZnlBXBXxFkFCMd5HYCkvMRkka9WcRA02g0EZtqlWZhg+HDviHPuZD9mrEMqNey4dRoRvbKZ9Kc9oGUw3sK2XE4kInBSUxwFccss/qNtE5J8oBE6nz1ROfcicdJPz6vTyWuKPz/2DsP6Kiq9uv/ZiaT3kkhlYT0ACEQCL0X6dJBxIag2LCggoq9IirYCxYEAQEF6b0TSgIhhPTee0+mZOq37owBQkikqG/+3/JZK4tFcuu55557zn72szez7kqhSx//G97exysl1NaLWD75WS4EedDvGwvinCZi6uxBfb6Ikqpag3uPvUrB2fRUg16EpEFOlTqeooEuBpcgrV05st7722w+04JAzFMiyfM9T0mPYkxoPne2OFROyPFKtgZ2JtjTkk9OD8TNrpzHnna4o8fSqIJ3PzDBTKrg87Gfsz/fF1/zOBIOv4vX8Jb9rCJBjKL0xt9LwR5Z3ShFYHr/Xwlby0L0IzZRXh1KqJmYr86OJsijhHsfbn9is3H7atga7cRgv7M4zzqCVC4i/8AgrEL1zMz+gcKqXkRnPmGwrO4x8qxBt1IIkVaFVc5hBM2hjV2eYL1NGLEqeC05lYcK8lG5t7xXaWk1Mrdu/FGYxZnsQUyeoKRnz+bfpybhdg83PY8OfBc384R/FEg5U/UDjbqrgN2/1cfC7aZjL21ZTiqU2ZiZSnnxiXvQaXWo1BqsrSxIzcyno4sjdjZWfPzNZhTKRpY9cx/TF7zO+y8vIMDXk9p6GSKRCFtrSwOIYm1pznOPziQzt4j5iz9k97oPsLRoPgYcOnmBb9bu4LlHZ3D4ZCwSidgAuvzT8R+QcoctfGD7clT7HsIx0g7JTToqyEpEVCUaV9wiEz1SCzARSkMt9VjYiDA1F6P6X6VyhWsSCwAKzQT1ZMUiZBn1eA1fyc/6GSzukM4b2ye3YArcanOu/k5Ofoktz/bZhrtJPxyO/IHGTM8Kp7FkVvgwf2wG3r1vXpDv+vMfPynm8FExnh46/P3anrifixajUBo/gM+OWE6XomXoxVrODVhLjzV1FIttkfaIZa7fk6RaeOCsVvNpQQ5zfI3OCzeyPm66Hp/cbfhV2LH2xBhCOuZxzyMt0dumbbevL+FCpif39TjKKRNL7rLYjySuP5nFIw2L8OtDENDTyIWJlZFJIEy2tH/eR9O2Egs9Uksh+wtm1iKsbMUoNf8jukArnUTdIKI6VcTgHh+R6dZISXYv+vpW8lH0GGoVjlfKaASW0bvvgUZnwruRWzjtrGG1Qy9mlNXglenAr5n+eDgUM2ayi4GabmWlvzIZF05tKalkiuci9CJTtEVvIy30R22vQ3mNNorctIJY3+8QI6XLWT8895QZrjrmCRNqO9ShP9CZTXGT6RtRx7jxlqQnN7Buiz3d3C/xqGsQ+wN+YXRONPqOPdBauhB/pA/F2Z44BOkM4nSx5vt5O2AEkwsLWFFcxG+1Ik5mDWTC0DIiB9+ZePPNvIMFhXq++0GKT4dM7u12iS/sHHnsXAFxWXNvODlsrBWhkTX1MWN/E36uDWEBIPQvgVIvtRRhYy9Bqb0znZWbuZe2tjG10V+xqW0UaZjQeRP1EhUv77qL4cE7ccKVQ1nBJJd04cF7FXT2a5ueKghhvvWeIB6n47v75lBbOZeK6nRkiZ48O3EEy+p3M7zwMGobDxTWvphsB0u1hqinhVp6KVX6BNTU0S1/LnZybwPrxyZFCiIddcN/Nfx7bYg0ZlhHjeenhjOczhrMuLtU9O3zzzEwmoS5m6yPO1uepJ/zN+irBqLbI0XRz+hg9qu9I697XGXVvZCcgCzRhZxqLx6fnkLHUH+yMxT8tMGGENdkZnie4z3XbnyYt50D597CIViH9U0AQgJdXiiJFXTGhBALi0xLMBHGMysR1nZiNCKdIdv1j4aeZpob5SZyJvtuRivS87BtCHai5m5b+xR5JKmq+WrXdMZZbGbQmLn0rSpic4qg+aVnWWEeJXXuPP1wMR08/jqzu3t7NecuOTMl9BDDC0u5LLZgztQRJJSuQ6SqZ5LzNN7bfRmZewBrk8Lo6pnGzHm3XxZbXAJff2cs/Vv+wCrEqiKeWb8KlcaMFxdrsf6zbKZJUFi4p+/mzkFRvwqdScvsX3LJ13wX9RihgWpmzxZx4pSYQ0fEBjHjGwHHublisnNFDB6kY+Qw4zvx25ZG4pOtmB/5CwFmwyjqcpFuqwpIdHJEPaiK8LKzxKQsoKBmAO6DbvxtE0C5xhoRGoXe8J0UAAlhHBO+oU1hmP9YCv1LGMswzMkkZoLuwj8s9KwHqTVXgJO/6s/CnKwqSYy/fxQOPn/wkXQeUxpK2BA7jN5+uUy898Y6Ob+tLyA+04f5IbEEmtUgtTQKPYuVKtLdz1EcpiKweBI+8eFE1SWwNj2YfqHJjJ1+Y4cpwRFR0EP6bMrzHA9xpMcqVzI85yB1NDWAoIKjzbfzc0i0dmNd6gUiNUbGgzY9hpgHGxm1a+mf49/GNo0YzFN7YZofTHK3/SgCWo6BfisysKvT8NqssTxQm837x4ZiIVXw0kt3VnLQVIYmsKY/HLmGrQV+RHKe2Atv4t6/ZZ/QaUQoK/+ckwl9zNDPWiYjTAzfST0mFmBuK8bcSkRjO5uTCeOuPFeG26iVxNQPZJBlI59GjcHXpYyHFv7z85S/egeu//uRDWUcy3BnWthhmHAOl2QpexsmMZRoBlYdJrlkOoW68dRkiHD2LqLnqDNXDiGty8e8NA6FiSUvRLzHVrUFXRVyfs9Ku+FlCO5TDU4enCrPY0/yKEYOrmPw0OYAmTDGCWOdoM/4TL+nsDKp+EeBlFx5DCqdkRn7b4a3ZS/MxC0T3pHjFnJw08cGwKS1OH0+gfVbD/Hle8/wwttfY29rzeMPTsbB7moCuO+Ex9m/ccWV4zzw9Ps89sDd9O15NZkjHL++Qc4zr31BaUU1SmUjX7z3DMH+rbP//642+g9IucOWPP/7a+TsW4bX8Fv7yAofGIF6er36f3vTSGlqHmW1MTvYd8TzLLN6jLesEnlv5zg0OulN24Ze39R19SI+WinBwlTG530uUCy1x22Pkfq+tkskJzJ7MiH8MpF34ECwboMYwcKxaxc9Th3afkbpGSIKi4wf6I9HrMGuaAHxETtQVJfT53c55zs5EDxEhL4wmkXuM9lv3/3KLdmI9My2an2B45y7n5718OPBifh1KOCBJ1qvIfhtTQnxeZ7MjzzCZqkLz+jXkB97L+WKcEON/s2Gul5k0CC4vla8vWqkaJVQFCWhV9CP6L1TOFI5nnEORXyVPIjiWs8r1pkFBSK++1GCu10BC5zOUhlUyQHrIH4yjyAqSclrcdYoNRaEBtSSlG6Hm5uOoGuEoAVtFEEjRStywCRmtYFCKPPXoLvGiSOvwynynE4Y6rB7fyvGqlBpaPbCvhLix5XjeSSIT89OI8Czmvvm2XB0XwlHoz2ZGnKCLr627Bdl80jJdho6DUcrseHQuknohbKegTpDuUmx9iCPhw8joqKCDaUFnKyrZWvC6DYziTf73G9mu9xsFT+ssyTIJZkZAZdY28GKKSfFZFaPx63vzQNshkWIBoRJoeg6Rvr/WiPl+nb43S6FFa7GidPjRwdzn9MhMHclJs+PS4XhzJ0tIzCwdS0AYb/yCjGffyXGybqU96c8TZ78QepTcpkx4AGmm6l5/cyzmOg1Bj0jrbk9aUcbicit5/wQR8QjXajVp6Ok3LBQcanrargWq0wTxI0iZL33orWrbHbZ5smRmBYGcsx8C79cnEx4SA1TZ/w1Q2/3DhkJqeYM7qeg38C/3r7ppAKYvFuwPnbTGUoqultvoGuH3agrJiI5rEPey8g2eMrdgwMOzoyojeewXRiji/LoGaslvjiYB0dfonPfLlfAxXCPeCbbn2WFXxeWJl4kKv7JZi5KN9NfhbFMWNxe38f+DY2UG13fZ87RbHBIxF9qxyTLlkD/JVUFhxWFLI0awf21sTwwri86tQVnE+wx0ZjxYXUCaSVBLJiVg1fQX5fgfPVFAyVV9rzQZx9Bx3PYFOzPhn5ubFCeQ6RpZIXIE59kGwbZ2PBebEQzrZqbad/rtzl2QsKRYyIG+h9m+l05SJXpfLtvBvGFEUyfojPojQkhV4j4YIUEW/MaPp6xkAaZAAa2PGNR1TesOLoQHzcl8xaYsGuPmOjzYkMfE/ra9VFaJjJolQklmVPvNv59zU9KsvKteWHgWuzM+1HulkG31bmc8+iIbUQOIZVxnE5cRHljTzr2ufkxTDi2oGuhUWIATK5PiP2bGim38qyErHrRaTEWFpWM772YmS5v8ExVBj+eG0mYVw7TH7pxv/p0ZR2V9Y68GZ6KnaQcvdlVoKGEKDKGKvGuGIRf5hCqist4LcmJjnYlPP70jRkITWUv305fwoEAO/w+70SZ7z1X5h1Fp8TEDUhidZg/cwtSebXWWEZlFp/C4UWFDD74BFYNTjRE7kZnW91qE1jEDkVa5Ulsv9/ArbnmgThTRviafApsrPjyydk8nHiaTw73NbCal72kwfQOsBShxGzVFxJcbQt5Y8gfrC0IYJosipPZb9wwsdXaDQjfSWHeL5QCXa+z1F41UlR1UBojoeeoJWxUT2OadQUfHR33t5hM3Epfv9ltf/2mnKQyNx4esouaAfEE7bfgy64P8lT65/g0ZhFd/CLajsHGEj+RnmFzdhtK/JrCovAcJvIyEjqEM7rTY4ZfX0qOx1yoeb0uBOCxztqClJpS1sePp2+PKsZNbO66+McOscERNDhQx+I+cwxH+CdLe9oTkCJXNDLw7ieJPSDMsZvHll3HOHTigqE0rbZOhqODLV9/8CzCPms27WXrnhMM6RfO4oUzDfPz3mMfxcfr6ppJJlcamCajh/RqduD7nnqXWXcPN2ikXE7J5vk3v+Kb5c/h633nzl1t9cH/gJSbfUNb2S51wysknnwdj8E3v8Bt65TtFUhp+miPGPACD3os4jPxBT7ZPwKZyuaGatU306znYsTs3iumb+cTzLfuSZHVGdyPGmtcDw4IYdOFIXT3SGXaw0ZBw9sJobRDpRYE7XRI/6TIWusr8ZGeR65zQCb84EgDHaioFCPUNBqAnd7xZDtKKQ8rw3RnGeHRSqK6OREWIUKkbcQq7wQrnEezym284bI8JDDOwjiDNNGr6VydQmDlJbxrM/mq1+uY5Z1mmKqKH3ZOxcOulEefbt2a8JfvSkkr8eCxfgf5wtKHL+s+IirmFRotvHDufuf9rL0CKULb5R2WEOq9nc5+u/lcNZ/7tZn8UNyb7Ap/Fi7Q4u6m58yxavaecGaw72m8ZBWEelaT5OLOvV6DOZQWS3xWBw7lX6WXBwXqcet4td28LWMY5LwKXWMIksvvoLbTofRoPvGO8/6JBotiXBqCiPiwHL2JESUQabTse7OGsFgfXts/GxtzBS+8KGXtDxVkFHbk+Z7HkXcr41KJiocqDlIfMIGyXA8uHup3paxHOI664SQz+/XDRSHnZFYaiY1lfH9hIl065jGrDbbS7bwDN9onPUXGus12hLldZnTnBI7a6Yk45ks5fXG6gUX47Zy3vQEpMzttJc+sFhORmJlne/KiOJpKeyfSCnw4n9+T2VOqCe3WdhlcWrqYXzaKCXW7xLPDPyJOOpW60xp2jbubEYWHGJO1Ga2ZHXLvwYYmy0urpMtpHeWOJuQ/60+9Phc5hfhUDMWzsr9hm6uCs+cMWilNIZbbYn16kqEkPs/xN94+dTdOtg0seuavy0HfekdvALnNTZUsfk56xfnkr56jMB4L43KT9fFg+xV42cXRUDILs/MaGkOM2Z0eQV2Qm0jZlLGSWf7PElBbxYPnSonKDWdGnwt0u6s7SZeq+XW7M329Y5kgjeKzroHcf6aehOzpeA7VtgBF/urabvT3/wWQIpeomeCzCeHfWVZ+eJi0BKrKtQrWNaQxPjWEt5IbeXWCGxetOpN5QYOFwp5vldHEZPfgnnGphPRq+/umVIp470Pj+LNq9C6sdxTwQb8IKnzqeN20yuA+caaiiIPKkbxcVcpbOX2oU9ry3KMl2LveHv3+669VFJdb8uTILwhUjMDE7iynymFDzEN0D9MxbbJxPC0rE/HFNxI87XN5few7NKiMk+ZqfSwqcS2u+mGG/9fV/sDrhx7G2VbJU8+Y0JTgCOumx9Gh5TdN0ISJixcyuTrm3W/8+6efq6istuTdob/QaNGdastcuq8v4LCfN52DL+Fbk8rxS0uokwTjcgPdldvpX8I+7RVIEa5NWBAK5T3TBs9npseLvFCezXdn7iKoYz73PtJy8dCoFPHuhxJMxGre6xOPVNScMVhfG8Olu+twrg4huHgK1qlS5icbhZ5ff1mBxKQ5ItHknmdmomTVpPc55WaB9abOaDtPMzCbhahKFlOurmHpvZZ0UMo5nWnM8JulFXBqdhpBaXfjkd+9TcFtYXurU5OQKG05M/pnzKybM3qtfy0iMLGOn/uGkzWsJxMSo9l2xp+y+o4sekL3l0m0tvpGcamIr7+V4O2YzYsD9vN9cQjTK88TW/4csp7VeKvuXMiyvQIpQkl/0SkJgwa+zIcWc3jMKocPDk7AxaaaJ59tfyXjn66QUamw4/EZaygNKKLLOie+mvog7556DAlajlZ8g6WblPKLYpRVIgJ7J+AbdtVNyljicwSRTk2f8I/JF1vzc24mfRtubGddJ1ZQqpDx1YXxdPGvZNac5n2hyQGvb3g5D3d7yuBCJem59naHor/crz0BKcLFCgDI4S0rDeU5TXH8zCW++GkbP37yIjbWlhw7HcemHUcNQEpTqFRq3l61DlsbS154bDYCI+XIlpVYWrSe6CqvrOGex9/m0KaPrxxn1erfcLS34f4Zd/1l293JBv8BKXfSekD+968Sm/AqHfvc+QJXuJT2CqQIFPT8oxJG9HiDGV0fYbXsHF+fGkSVzInnn9Fia3vr1Oqff24kM9eKxwf8RM+qRyjXr8UsSY6tSk3c4I58ET2ZDlaVPL349j5UTR9ASws9kdfYn/YxW4N/x4Mtnny93InaRkucbUqRlSwlo38GUmyw/SkL/ywV5wY7Edr5T4q5qh7L/FPstAtnk9toXG1dGFoVS3B5LJ1rUg1gSlO8P+BTakvSmazP5vstM/7ynn78qpycCjcWDdjH2/ZBbCl5k+2nv8TcwwL768Ssbqf7tmcgpfi0GC+7k0QE/sgy06d5uvwSPzdEkFLSzTChQ+AiUQAAIABJREFUFibWG34sJ6XAjYd67qGwTox7vgdykSfP3KNndGURr6b5syTl6mKzV4TuChVdaC9B8CvC4Wf05aMQ5y5swUZRSeuI7vwFIkwIO+mH+8FyVO4dBCsbTAvKObWoEf8aR97dNs2oc/C8lk9W6lBppXwVGc9Z3xM4FZYQrKlG5jOc+GORFGd6YR+ow+ZPDQ7L2gTuGmjUMBAyHnWqPN6PnkZHmwoef/af1xtJvlTDxu1O9PKKo49nIhk2DbgcGYrSLqBVG/db7WvtCUg5aZXPCx6HsBZJ6WRiTacL7nxck0yqmyNVxe5E5UQybXwl3SPaHmvOxojZs1fM4ICD3Bd2hJOePuTvDyFtbF8WxSyjg6IMZcdw1DZGDSQTWQnq7eaGMe3UQh9EHrXU6TNxrelBQOlYwzamVRLMSsSo3DNQhp690swWcUORVnjS6KTFxPIETx6KRK01NfQ3yzacY/KzG1i97mofGtG3iCGjb86q8noG30TX57A1L6U65z6scnSovFyItrLmPh9/usrz+OjYZcaMM4LJnx+/xN60SMZ0jaX/1DAuRpezbZ8bQ31jGKC7yPZu9gw67kpe1ZBWSy9utY/9L4CUDQ4JfOYcQ0ehvMamuR5I0/ULAuCf117Gq9yRtUf8+X6SnAP24aTFNWDd0JENHONI8gAmDU6h19Aba080HasJvAtwTuHh0CKc9qfx6Nhh9LS8yBwb48JWlxfFvU7P8dvR43xvFUlcSQAzRyXTtd+NyzHaaucmy05B72nllG8xTXgBLHIp9vqYV7d/jPA9XfqCEXjOyhazZp2YEAFYHLABmX45Ml0OF0M2GP4emfwIpmIndPJ1LN57H+ZSDS+/hIHVJbC7BAefG/VlhUJkAPSEEjNBgFuIt94TodFI+HLkRgqtAqjVFdNjSyG7gv3o0SkKj4YcDsW+icLai6MDE8mSVvFS2Z073bVnIEUoERdKxccOeJlH/GbxYkkBn0eNx9uxmPlPtiwZy8+sYvV6F/w6ZPOoVw5Sy+alCOqCeM7NLcda1pHwgnmY50tYnp5LTrUnT9xfgKtPcyZtQ4OIDz8xmgW8N+ZrLthIUB/yx9x78pUuJi8VUZkglPdkk2jtzobUWCI0YqSlVcT3vow1PQm9NBaVezrK0BsLbqOTYHvkHsMxj01Zja3oKgtMr9QR9kE6Uq2eNxbORO9gTf/0aOKjXUgtC+She6rwDWjOFLiVcSYnT8SPayQEuiTxZJ/TfFMYzNTSFJabDeSbgad4o2QwY+puP9ln+E4IYr8mIhSNt8akupX7uN1t849IGN79TZZ4zWapNI3390/E1rye51/8a22n2z3n7e7XxI56+Lk3qTGXYvVzVxLHdeGx2Lepk7kTxzuGhJa8FCoTJFja1jFoRvO1gLShEPPiWJZ5zuZHl2EsKivhifKr5hLXXludrpJGPbx3bhzerjXMf7Q5qC4AcMI6ZHy/BCb7v4PGxBVp+Fe3e3t/uV97A1Jeem81DnbWLF44y2CgUlcv48TZS+w/FmMAThpVapa8861BO0X4f0pG3pVSnF9+P0heYamBeSJopAhaK4JGitREQl5hGW4ujkilVzVpNFotw6c/y7cfLiYkoJNBoHb+4hU8NW8q/Xp1+cu2u5MN/gNS7qT1BKr3529xrmAJzteo+d/JIdstkIKATIvp67+S+yOn8k3VRdZE96K4zounHtfh7HRrQFJTlk3IjHw2dBumxfciK1jFJrcuzElMR+an55WChwxZ1Veer8Psug/+zbRxE0392tIOc72MyV5PIpGo0Cj8DfQ+MXLEJnXCqsdwWK3Cm4t+Q5CYGOv6On2cQYcaDcnjHfF0vlq/YKKoxKLgqs/8tdckZKXFKhkivYZver5CnEzHQ+IYVv8yFxuzel740yL0Rvfx9ac1FNc68eygXbzuEMim/BVsO7UaxxAdVu63Dlhdf472DKSUxYpxFF1mQNdPWGq9iCXp59kgjuBiQQRzZzYQGGzOO+8KoIUpbw7cxOF6a9wudadW5snBBy6yxz6ECyk5/JYuJbbUD4lYw4xRSZQorg6k4fYb6WK3C1HhHNSyKSg9m09eihzOk+VyAHOc6b9KjFmVGnkPPwSvTsvoFLKGqVAE27F9Zz+DSOm4MTr27BMbMlZPhCs4aHaZ2WV70Vg6I+vYnyO/TECrkV4p6zFMnBTlPNdNSbqFGzsyU/GrT+OFmOnGBcNr/7yuSPz5Cn7b05EBnS4Q6JaGyrKQxoP3I+3kZNBw+TuiPQEpj3nu4aJlKUMt3FHqtIgTrViTWcw5P1tEJc4czerL5FFl9OzXdt238JwFAbkZEesZ1bGKU+5yopPH4xKg5f74lejFUho6jzYKTQmh15F2UklElozYPvaoJlhQrU/EVu5NWL5RBE2iEGGZbYLWuhpZ392G35lUu2J5YRR6E70B6DNRVvDp+QrSy4O5b46OAP/Wx9uT+ws4eM6HAJcU0suCkUrULH5ObFgA/1UIFHaByt67lxYrS7i3k1HguvLi/ViqpWg72LLCxY3vnV15qmQfQy9055mRUvLNnFgTdY7NSYMZ4HuJu+7rQvTRUnad9GC0/1m6qlKJC1HhdbQ/NYTi0vPWvhetXfe/DaRo0TG58xYEjZQJlp0IlLYOem6RZVDSqGT3+lHsn5LKBqeBJF2qwL7ehz3m+9l6cThDwpMYMenGYEzTPR86IOfEWVvGhuxjGGocLxYyetYknlfuop+DkdloXnye6a4PsH3tMfZ1G8Rv6V2IDExiwuy2j32jdo06I2b/QTG9fU6zwMkLscyotaKPmMWSbasMyZMnFmpxddEjuOv8ttXIKp3X/TQy8VIqOUNy0FHDPsEpg3ESDcRS/QeP7jAurl97WcP7H5qg1sBgwUmvFZMaobyoaTxsYlIY2KIDjpHm7IC8spTw3aVsCQ9heIdddFCWszdmBWUdzXlwzCbDvgsqe/BwpVHX53ajPQMpsiKRgfExoMcXvNs1jMcqa1h5fBKuNuU88WxLkdWYoznsPOnPsE6XmGJVida+uXaBJC2d44/kIVGb0S9rMSYNYjYlJnCqIJTpo9II69dcd6eySsSnf5a9vDxiEykiLdUXgrD5k6lr6DcaEQXHxVwemcTqrn7cV5DKsloFYnkjuR3OUxPYgf7H5qO1qULWZ88NH5NEZo/VmQnU25YRP3J/M+tj9blq+uwq5bxnR3beN86wv39uLI0xlpzND2PGuEK69bp9146MTDECs6Crx0UWRFziq+LOTMwtYbG7F3vCErHTmLI5dzp22rbLQtvqf+0ZSBGSW5GdV/FawGhek6bz1p7Jd2wycbvvYlv7VeaL+fQnMW52BQx6/GdsCqSkJQzGIVjGpLR15JQMpMT9ISMTUg+Fx8UI7n59Jx7FzqWq2aEtimPYK/XgUd9H6N9Qz0+5Rqb89dGgLsTMxJYXz4zEzkrG4sXN+4AAMgpg44MjDzDA7Ucazfwx77b8n7h9wzFLlSmo9cYy9H8zXM2CkIpbAmtC2c5bK3/m/CUj62fpk3MY0q87T7/6BTkFJQZb4vumjWbnwdMGIOXl91dzNjYJM1NTvNydee+lBYZtBFBkxVe/cuJsPCq12lDm8+2Hz7dgqAj7rvx2CzKF0lA2NG38YObNNo4J/2T8B6TcYesWfPwJa1RTyYwoxEdtj49K+LHDXLD4uI1oz0BK2XkxXV1+YuHQQXxdFMv6+Ehyq3x57BEtbh3/epJ+bXPEXRKzdbuYcK9onnS3pQ5nbI9vYd74EcyPS6R3dTEf2o8nu8qbhZOycQ9v3eWmtWbe/JuEhCQRwUE6g9uAEOHSrXRx/51GRQhHA6/SVEV6E8w19jjKzJCYmtNoZfzwijV6wt80DgKHXWYQ0DMRh45X7cWk9YWYl8Sil5iisXRFa+WCxsrZsKASfi/8fXPoo/wu7cyzkp18v3o+piaNLHu5dYvDTz+up1LmwJKh23nN0ZfVqevZF738lnUFWmuX9gykCJNCaW0+IyNeY6n94yyLiWGzY29O50Qyc1IlHdzsDTRbJ6sylvkeYIeVGZbHJ6LRmlM8/CLvh4WyNeUELuUR7JOnEeCSxsiQPcg1DuTI+pGn6E+Q1R58bU4jynkKmd1AdGbN+2681y/UWebhUudHxEfV6KUmNAzqZmhOy5gU5BYNnH8YcnZ0JSpzCG4uSorLzBnmf5yeERp21JuzpHQ1KjtfCpSjiD3YH1NbPa7XsKKED/k33hcMOjtf5GQytqCY1/O6GxYpzy3SYm9/a+/TrQ41F06Xsf2QOyM6x2DvkomnNImMA6/iGCbBvMPfc+72AqSkmVVxf6ftmIkkPGoTSoq6loIMNZvjtEQHSbAus2d/xgAmDCkhckjbpRAbNolJSRXz+NBP6GLpSqJpMb9L7mFu/WZCKi/R6OCPykkQFL0a5emFdI4ypdZaQsaSzpTpYgziipGZizDVWF8VnBXE0oZtRC/RYnV2PJIGBxo76lA5ahFpYU/yfg6k3sXwgbUMHd66eNtP35WRXeLOY2M2cSbRn7j8CAaE53LXpBuLT157rU0ZvaGDtdhLCxnv/iIo3ak90wupizc6cyl3dw4kxcKS31JWoa16gS+CkzjpGMDmM1GsSRhOmHsK0+f7E7WvmP3RXkwMPo2LrBC5fx6aI3PQO3Y0iC7/HfFvAykHbLJ4ze04tiJT5ts2f87X30+UophzqjJ+XTud7AlH+cxtHPGXi3CqDeSU7Q7WnBtLhH8ad89pWxR2zQ/1ZBU68HTkDkLPliJRafF77H4O5H+Fi5PRDcq0OoMPzEK592ADyqAgVsX1xNW2hCeeufXSnh9/UJBTaMMjA38isvIRFBa1WCjs0AUvZe3loZzKGMFdo/QM6Kc12CMLNsl3ddnBVP8S5CaPkGeynTy/RMN1ueZ3IkB+L+bqk7x8MNRg8S7MF77+ToKpVE//NjS/BLtjlVpkELeVy+GLryW42+fzVtdckjtpaMytIvxQGev6dGeKxUas1XXsOPMFOYEKnh68/crj+CV3Mv6Nt+/c0p6BFEGjqviMmCDvPezu18jEBlh5bAp2FrUsfqHlGLFjXQbns4O5t2sUvWxbzlHNUvI4Ni8drSn0y3gOE7U5MQlJrE8PpX+XVMZMa868KCoW8c1qiUG0/Kmhu8mvV1OU0Q07lxHNXgdBZ6PWvIolc4zXFJcUh4DrVimiSRpbz9htrxt+3zT+Xf8uScs6YRE/iBL3ZHL6JmLKVeag09c5eBcp+XbcYIq6G9ldlhXpBJxWsCezF2MG5NJ/xF+Pfa2NR4nJYjZtEdOr0xnmdEtndZUno5LVPBpqwlm/bMNuY2v9eL3UWM55O9GegRShDCa0wxre7x7BWyapvLpzhuEW/42Ez620ZeY5GT/vtyPcMw6/+/fgc9qMXdZTGKY5Qq/yU8Rl309j4JArh6xOFdNQIMIzMJsug2KbnUoo35cVXaBH6HtYajVcTEm44aXIFNl0tPJk/ikj8+36Nmn6ni6Z8g3+1sdotAzHPPTVW7mt/7b9P9AC/wEpd/iQMj/4npccwtjS60KzI3VUW+HbaI+fyoGHq8Kx0N2c2lV7BlIEeqavxe+8NdyPN4uS2J7ai9SyIObfX4+3T0s0sqpahJUVN6zP37hJR3KqKfMGfEP/qqco63ARl0OnCF0wh0XnL7HwYiJrQvtwKqsHd/e6TMS4tietN3qMTWhw30gd5uZ6JKiY6vYUpqYNJJn3gzwJDb18MMECETcGNrR51fReXUqerRXR1ffdcNAVq2XoBHn/68K0MgWzqnQO+U7hQ5cxvG7yPT999sRffoQ+/KCRBpUVy4Zu5TUXL1bEHOdkwvN/m65AewZSarPFqArqmdB3EW84PcjLh5P53SeMwxkDmDy8DK3Ulp37zenne4r789PY20eP6sDDhjZV2Tbw/DwpPauL2ZTjiJVWiabz74jN4xGJW4rY6fJfQ96hueq3RqLgrP9Kw/G6H/XH/WgVKg8nGoOMQJ5pQQVmafkcerkG3aFubI43ljUIsaDHfkyC8zil8eXprF9odO7KhfgZFKV3MpRk2XRqvnj8o8NxfnYfwPM52TxSU8U3uU6kCFTku7Px7X7rwOGtDGVnjhSz95QXY/zPonUqpZ/2HNHH3sOtv87giPJ3RHsBUl7teIyDttlEmDkzxNydXHU9Zwqr2HXCkXNd1LhVWbEzZSB39S1mwOi2HVQ+/1pCebmIVycsxcqkF1Vl+fwQMoflcUsMTSbzGYFOsJW5JqT1+TTstcNJ3sipB71Rdy5CSRmdKofgVTHAsOUVwdnIfYhltlgk9kcn1RssuZsiLW8nX8dMJMirinsfujFVXdCDEnShhFixYI3BlvSdXx8w/P+5p2TYO7SeNb1S0mGmp18fHd6W0Qxy/hSq+9B4yBJ1z3BKpVIGB3bBUqfk9NFvyHZ+ns32F9no3Y3fzh3jx/i78HHMY96T7hzZVsCxyz5M63YSfV0dnp3OkXlgGbYBJjfl2HMzffDfBlLmdvqDDLNqhpl70MOsbZAiS13HH/JsVmybjF3/A7zuPY3ziTm4V3flksNOPj89hgC3HO5b0LbY7IoVjdQrrPhg+A6c9hRRamVBj3mzyc75GFWHIEMzmSgqOFUvQ5EWykAHa5Yl9TYIbb66RIbU7OYz5U2lGsIxvxi6G/PSySQH/kqngruw6PAHsY3lfH38Wfw663hgro79h0REnZYws9c6hnU0Q2k2k2TbH6l0M1LhTRWmROY9j5kmieUnzcmt6mywNN65W4yNjZ6INrRMzseKDdncR+drERisP/9iLCFa3EnC5ZASLE6V4X+5ju+HRPKg9mtDOe1vJ9ZwqVc27/Q+jBQRavSGxNavOUaL+9uJ9gykCPcj6KS4WCeQM/Qc3Rvt+PTYDATNkldebgmUfLWygpL6jrzYYx9uFi3BJbOcUmJGp1DnriE87yGsFW6UpGezPN4XH+cC5j3WvLQnJ0fMj2vFBLkmMG/wSaoKFOSURmLr0q9ZU9dmiqjLEbN2Xjrnbb15LyuOaQpQVCZyYVoJgw49jnW9M7Le+9DaVbR4TGbZ3TDL7E5G4ElqutUi1hv7dH2ZiiGfZyE3kfDJM/ei/pPqr1HUMPxUFhsT+tI/NIcxrVhB30x/uHhJzLbtYgb4H2VqUBHf1zsx+IINcwdW0OAuo0hrZDJ/XnAXveWtuzG2da72DKRUJorxNd/Kqj6deFWXzTv7Jt+RycTNtPntbBOzs5ydF90YGXQCu2mn6LHJhq9HPsDChOW4KQs5mfEapiGdrhy6SUhXbKJhxNxdiCXNmcnShiKGOE41sC23pccTqmoJ/jfWZuBkE85zcR7UKe158Tkt1tbG+VODTMSHHxsB49cnv4GLeSoK60FYBj9zO7f33z7tuAX+A1Lu8OEkvb2Zp32dORSS0uqRuiid+LJg7E2xVNozkFKTLsJVc5TVI015rCSXQ5k9SBBsQ2dX0zmwpfDUjz80klNoZZgwuTrrcXXV4+wiooODnh/WGIGLTya/i23qG5TYHKQmqYTh90zh/uQEPjgUw/5+oWy5OJgeXilMeajtOvLrG18AcVZ93jzrFSo5SA/PNWiUvhwKsGHwh46kLjHqVLQW2uhieu+s5byHEzl505GaNTL83l03dCa4/hjCAsq8JI5Y1wG87vsAL2pXs+n7h1BqLFm2VINpc8fMK7u//a4etVbKq0O38KmjI0+ezScu50E8Bv899bPtGUhpsnScPvhBPnaewuI/ivk9PIS9KUMY37+E3BIJCVnO3NdzBz3Pl3O5t5JjqTMosa9lcGrAlTrs6NTzeJYPvfpIrLLQO0SB8GNmZBQp61aiMWkuyFdmd5m0jjsxw4GBn0gxrdEg7xl4hQItUmuwPnmZS9Ma0Nb48lXUtCvneGfwPi5b5OOil9G9Mg65e1/2//awsaxngK6FG0SM+RHeCxjMzPx83q6r5PciKWfzunHvwIv4De15hyNT27uf2FPIofOdmBx8hkL7BmbXHOfAhXfwHvH39DHh7O0BSBFKMO723WQQbF1gG4K1yJQqrZLNVdkc3hFCbFg1vvVm/J44hGE9Chg2sXU3LeGe3n7fBEFg8fN7HqJIPxFFUjn5AS6MzdmOxsoFhXufFg0vCNilnVXSM72R+B62KKfaUqmPx1RtS2TWk4btmwRnGwNjkeYGI260ROmlRW1zdfKmqj7MkiMjsDBt5KWlNwZ+s9JqWfNrBzo7p/PkbGOW7fd9nkSlD6BXcDaTZrYO0DVR2O3t9Aa3lG7W2wjr8BuqyjGYHBQh7x3MFocOLHP3Ylx1LC+eKaTaawpRJrF8FBLG2nPH+TV+NI6WlTzzvB37NxUSldqJe3oco7hOx1DHQ5w99p6hrMfMoSVYd8I6D7lYfUt6A/8mkHLBopgnvPZhipiFtl0MosVthUKv5eu6BBYeH8SQTud5Lmg6x5PSCKjqSbbjHt6NGkVHu1Ieb0N4vFEl4t0PJJibyHnzrhN0+D2Hs+6uvDy2OztK9lNY2wupqRoXzzz0Ocf5Uj2PJ4tyWaXqY7CifuKBPFw73fziTgAvduwSE+Z5nkVOflRbKihPP4RZ7zA61TYi9/yWRZt+NNz2qy9rDICIwC5dMOhzIuy7oDAdznnPlSitFIjVIgMYGJF0PzZI+TamiITCHvTorkNYnDo76ekS2jozSRB+FwTg75mpo7ERA4O1v98x5tn7E9crAdODlYQm1fPZ+CEsqlmOTm/C1pPfs3/oJb4PiWGEhQdnlaXI9BrmVYbzSGWP2xpT2zuQIiS4RLWVWI78EUuRH98eMX6Trs+OF6WX881GNwTtmxXBx9DatixjlBZXktItieKuKoKLJ+NUF4q4oJ6nz9lgKlGx7JXmfV5g5wksPYFZPH1AHKpEBVmyoVi4GB3JmkKwnC67IKZg8GU+7BlIaF052/ILoTiPU/ekExw/Gt/MfqhdclCEnWrxnMwT+2Fa7Ed8rz/QeF9NWql3l9LnbDUHugYQNXHQlf0UephxKppvzw8k1COP2Q/f/Dtw/cmbSsRHBO/lLr8afpFb0SOqM1PHJxDsbINKpyVaVYaL2pItOdMw0986G709Aym1WSJcGo/y/TALXpQX8MnxMTQ02vLKi1rMzP+epEtbL6ZQPtjJS4+dXdvn2rO2mLM5XsyI3INuZBzB33qw/t6pLIt6ynD4fVk/tWBCCq5XgpFGtyExuPvntbiMJTov/rDrzruZcUy/QcWMvqoYc4eufJBiQl61zxVDBOFATRqNVlZ63r97IRaSWmR2Y7AOWHBb49B/O7XfFvgPSLnDZ3Pxtb3M76GjvnMtE62MAlgKvYZKnZIqbSMnlcU06rX0VHRkVeFoTHWtl3QI+7ZnIKU+X4R19Xl2j6pkQlklsflhXCgIY86UcoK7tcxuNAECrTVxcMfLPBOWikn+PVRoN/KbxJW3BkbyYuY+ntlTTNwAD764MBFn63Keeu7WqLlNWQQXZz2hIcbJ2hTXRViaV5JpMoBS5Az42o6ExX6o7FtnC4l35xJ+VsEhX09qMiYYjtNnwjHsXZvblN7oHiWKSiwLTpNjF8ji0Od5SvE1O9ffT63SgSXPKrD6Uyjw+n2b6IAvD97Er3bm3H0asuom3ZLVXlvduj0DKU0Tron9F7HWvTcLtsnY0yuIrQnDGN4jn+gkJxoaLXh18C9UXjLHwamYVYqxbOl9kU83zCB6TALrgwNYm3CAYfUTaTSTIRabIBFbopfoQSoCyxSwikLFLPT65kyqJI8tVFmn41zdiV4r69GZSpEN7MonLm5Mr6nCW9WIeUI2pR6lqDp34L3dRp0Lwf7zxbHH2VZvyz3yXdg3VpJvPoNzB8ZjagOukS0BijzdUZ7uPog+5WWsLSviTK2SzYlD6OebwNj72gb4mp6v8LFuVIgMpXW3Mqk5tK2QE5c7MaNrFIm2WuYUx3A267lbstf+q6GzPQApnzpHs9Eh0aBlIWhaCKHR6/is7jKHf+1HYkQBQTJTNscPY2DXAkZPbR1IacrWW5nWs2rWAmIsJtEQJaWvzTEs1TLk7n0MpX03Cll6Gh2jHJCbi0l4JZBafRwa5HQpmIWDzO+K4KxerEGkM0FroUfu21wrx0yeztKjzobMV2tOFId3FXA81oex3Q8yarCx7ltWr+TNtXMMWcSn5lfi7H5jQd3oGDG79orp2FFHcKCeAXaf4WN/DlnZdMzOaVGG+vCkpw8H7exZkbeOkMQI1J5hZKgSeCEimNdjY4i5cJXivH1dAReyfXig12FiFRY8oN7PoYtvGhzuxH86qAnXpxZp+dj5LH/YG908FlT14OGKm9O1+DeBlOc8DnLaqoDeZi4MMr85O8Uf65MZeDGIx0yymN9zKhszUhhS0otK+7MsOROBpamMpUtbZ4w0lU50cszi4T6ZuG/LZENoAMd6WLA4XsbluFEGcF+w8bTOO8zr0rm8ffAS3/sP5mxBKLNHJxHa9+Z1Utavk5Gabce8iN/pL59NvvUvRDXYEtJDSfekwei7PcE7e94nt9KX++7VceYMZGSJWTzqXfzMx6KQhnA6eCUSlQiXLGuKg+sJSOuPq74/my+e4UzWEIOemiA06+WpM7hDCdHVdgdBtnvJlUdyvuohw+/SM0QUFokNGlQCoHT4iIjxXbcyRXwXF4Yew21dOe7FSpbPHs2S/LdQaazZcfoLvh9/gv3eacy29kenh82yDCR6EWvy7yZAeWvzCOE62juQ0lAkojpZTPdRL5NiPpD9J4YakjWvLNU2YwNv+LaAlFIfxgedYpSdBL1JSyDQpKqe/A7xZA5RXmHMmZVLeO1CCRUyZ559rA4H56uMu/jLYn7bJmjknGRk/xQsopTkcDe4XldKo4OC4xLQa3ntiRqqTaz5PSWW8JJ6Tk28gEhizoi9LyDSipBHHETjUNpsGLU8NxqTehfODf0FqaOx7Fquh7Dl6diP3REnAAAgAElEQVTKtHx53wTKPJuPvTNjolh5ahju9uUsXHTrz73pAk6eknDwiIixXbcx1LORzWoJ/kciuWvuQcZZeeMvtefn+hRqdCruqe7C0+WRf/VpbPH39gykNBSKsKo8z2931fNUTSVfRg83lCBfy7645Ru+iR0EFtr2XWISk0TMnKala5e2gZSfvqgku8qVeWM3Ig/KQby1J9kjPHg4/iOq63yJlb+K9Z9i/02nr88TUZMuxtGtnN7jTrS4qjVae963H8jcwlRerTHadl8bJhW1qF39+SmrmsuFEcyZpTNICQjRlJjo4KDhgwlzETjxDfaTsPU3zhn/i/9/WuA/IOUOn+W5l6KYPbQQZ3cxgyxaot5lWgVbZJkGMKWX3I1Pika1Caa0ZyBFUSGCnAzixsTRtaqR/IKuROX1YMa4Yrr1ak6Jb2KECIuOx4esolZpR0GtP1lVXShtcEerlDExbCtD1RMRKd2RFXzBY31HkuJmzhtluxmw1wSNt44l+fMNT2jZ8w2YWv617WfT4/xju5jYS2IC/fW4u+vwF52mj/eX6JXu7A9wwjvajC67rEif6UZ9t9adOqx/ziIwQ8Vu31CUWYMF7Uh8uqYS1OfGNZPXdqfk85ZE2m2iztSBhb2WM6/uS45snUNZvSvPPl6Lg1PLciBlo4j3lhszkI/228tZGwXhJzpTIelnEJv9O6I9AylNdnsjI17npKcTfY7YcjnMnY1xowh0LyetyNkAWrzVYxvHC5wIUlezzL4rmyJjeW7/cILEUt6Y4MLwwjzeKt9N6ii5ock8qiLxLR/ZZvPpxGpOB6wwbBN+2Be347WoPJ2pCfJgYGBXHNSNbM7LwaWsClFWBoWzxXy0/iGDk4qQkRsyKJPVuuF8lm4s8zhT8gaFaT7Y++mx8Wn57GSyc8ztG4GbvIFj2Rnkyyr45NJ4vOyLWLDo5lxWVnwE9XITpo5rJLxX2yDttTe/Z1MRZ1O9mRN2khhrmJmbTmLFQzj/jbah/2sg5VqbWmFR5S65+r59VZfAz1v6UxqeRVeFKesvjqBPUD7jZ7W+QM4rEPH9j0Y9gFeGfsdhXzcq93Zhlvl6dCaWyHyb6wJc296mNdlUHXbCrV7JyTleiELqqddn0aE+kJCi6VcEZ5v2kfto0P7pzFOkyUAhLsFV7cfWS3lczO/DtLsb6d695fP+7ssqCipdeHriRjr5XF0o7T3qxMGEEXTplMWsB4wWxtdHk5Cun68eLy8d45yW4mCVT3X+HKwyQOXtSs/gbsgkEmITlpBR+hZSFyvqG/K5v58bU9IykJ7xpkFlw7JFtfyxtYaEgk4s6HOQ4xpr5pSfJzrjqWbMukJpPS+4HyTLrLbZ5Yyq8zVoDpjo22Z9/FtASp5pLbN8tgra5Dxi1wVL0c1lnffK87BMt2FFST33DpnA0oIMFuWEo7Iq4PHzxgWhIL5q0srhEuLlbP7Dlt6dznG3RxkdjxTyzoBemLnl0v9MOMV5Rs2KsKHn8LXawZfS3jyzs4SdYZFsT+vJsLBLDJt8c44FKhW884HxQj4deACZiT8OJ/azNdIeV/8qhl54Bn34ffyRMJ7dl6cZyr+yc0SUlIp4Y+ILOIkfp0as5WLIZuyKTHEvcCM5MhenQneCGx5kX9Je9icbnaqECPDT4+Ghw1QiY7Lb00glxoXK+tz1hn/zC8RkZono31dnYIHFXBBzb+SPDKt/lJhxW+n6cTEWSh0fLBzL0suvI1M6szd6BS/N3k6GQzmP2IRgLTblkLyAeHWlocRnfc5kJLTdp65/L9o7kNKkkzIw8i12ufUnNaon1QpHXnhGgY2tMUlUnF7D1xudsJDKed1/J2YOV11vrr1fsUxJtSqOy1NkuNR2I7BkokFw9of4JOKKg5k7KZfA8KsgSfR5Mbv2iBkatJ/e/fIQHxVz1G44K0Ji6SnvSE+lGxFyN2y0plTEi1GUi7g46yw/ufVgZnEa75TUcjnkNDVeGvrFPoF9rhNaqxpk/XY1eww2R2ci0ppyYsK3WJsaNYXKkhoYs7GAfHtbvn/MKNR+bcy9dJzlR0ZhZSpnydJW6L8t9mr5i0NHRJw4JWFqj1+JdIJdIhXmR0Zzz5xd3GsdgKvEkhKtnA0N6Yj1In7Kn0iQ0igCfbPRnoEUwSZYn5XB6bHJzKhRsfZSX4PJxNNPaung+M8wUrJzBIBORH290SVTKM8XANW2YvkHamQqCx554EtECjkp+SNw9iphTOZmMgqH851nH1aEnmREvQ/vFhtt2QXrcKE0ToghM/dhbmMs02qKM1opD9rfRc+aYjYWNgf3hG0k9XJk9p3YXZxt0I6aNF6H4BIphLD+ENYh3m51vDryEbRiO+S2w/8DUm72pfg/tN1/QModPqzjiy8zacoF+nRwopv0xo4P14MpKwtHI21lgtiegRS1TETDpQrKx+7Crtaa+hI/juZEcPeIfCIGNF+AJMfXsPEPJ4I7JvHk+J1INamIdcZFbVPosEFyfg0yqwqsTm0kYOF99FBksKr+FBkxnvSrLOID24nk1niycEo27t1uXjdCUJIXFOV7RWixtoKJHZZga11AhWwM54OLCNxhh995CZl9ragd3/pxvVZm4Fyl4Q+XIVhZBCPQHC2sGxg8a3+bPSc/056kYyMQSlSEmDfgO2aUryZ6793k1whWguW4+rTMkgiaBh+vNNoJTu0RRZlVHo7HhqN2CcLG6/9/IEVoq/zDEvp3WUlhJxlW5wOoCLJjbeyYK+0d4X2Geco0dnZwJKhEwpIgOyq6VVBRqOPDLVNY9mQ1dSaWFH3+E6WhKmJnyQ3invYNnQkpnoJE1zIDrJLWk+9wmmKHC0ixZchyM6QyLfJegWx3sWaxX3fD+UPlMtbnZuFy4hKXHlCyc88Yciv9mNZtD05eafzhPZt3Y5YadDJ2nlqJutGsVd0Ri9pkxgw02pOmJsahqyvjxcRx6BDz+sutu1k0NURtnYiPVxkX0/0Cihl7T9v6Htd22G3ririY7c39PY4RZSnm7tRq8rQTcQj6+yZG/2sgZYNDIp85R9NRYsEc6+ZZ+V/q03jlj75IuybSVW7Oz7Gj6dm5gMlzW2ekXLwkYtt2iUF0cH7YWaI8GimN7sJM0e80OndBZd+6aKhYoyAzWkFYmpakUBsU97hSpjv/p+js05iqrLBJMS56NNY6FN5GBpNOrOGC2+c0WiuwLXCn9oI7WxNH0zesgnGTmzvGCJax76+QIDihffDIesTSq2wrlVLFGz9NM2SqH52bj0fnloDRuo0S0tNFhnILoexittcDBteriqT7sK6XcraTO/f6BBCiKGB77DfES5cjsdCjlyt5IEKFX6Wc3sfFFNa6sWhOJnsPm5Fe6sljffdwCBumZOeTWnXPFWadUMojCLcqRRpcJRZMsvSlRtfIDnmOIfHQXeHCR0WjDIuw1uLfAlLecz3FDrt0Qk0dGGNxYyDqRtd4SVVJYkk922IsmTp+JF0bajkQ54FO3MjSnCpDZvf5J2qw7dDcNrPpWCcOV3IoypUJwUcZJivALl3GgnHDGC85DHvmo1EZ20YQQB84YA2nVSLCj3QgJSyIH+L708UzjVnz2hazbTpXfIKI37ZKCOl4mcWu3mRb/YHtaT1p3dXUdtYRWT4JB8c1ZDQqWb7/TZxd9CjkIhoa4JMZjyDRfEiZ+DIpQSdxT7bBs6wL0UPOYqIyoW/2i0Sl7eG3y1cdFLp20eHUQU9Ph18Isd17pfmOli2hSBFGWbmIpGQxXUL1aDVqUtJMeXzgV/SsfJpzE3+lz1vl1JtKWf/oEBbGvkN1QycOx77J3AVrUJloeNbOOGYLOilr6pOp16l5sDKMhZURbX67r//j/xpIyTSrxkwvwVPVuoVv0UkxEZ2/ZVNIALVnQimpd+PpR6vp4Gosud7wTQEpZT6MCz7J8vBQPisvIlTRfD4mbCfSaFHnxnL24TpsFJ50z7sfkVrE4cRL7MwMY0TvNIaMvdqfTkaJOXhYzJgu2wnoX4J+nyUbO0XyYeeTV5pRWAr7NtoxM74nEef9MO2exMJhfphr1USlJlJuF0VhuBL/gpF0ju+PpFFEY+AFGr2TDccQNVpgc3KagV16dtxGbERGZqHJunzC0mRsHxxB7ADjs7427kk+zif7h6PTS3j1JY1gundb0QQwz+q1hu52FpwSV5IeNZOl0//gSduumBpsYOCwogDhfe/caMcveVMMoMrNRnsGUgSgrj6ugtzxBxnQIGFbYh+jycQCDW43R8y72WZAq4UDh8UGEetrw9NdzyPzWy87FsSoP/jIxFC2NuPZlbidMWe/y3SGy3fQtfw8MamP8NIQJYc7ZOGmtmJb9swrh6+IF6H4f+ydB3RU1fbGf1Myk0nvPSEJgRBCINQQEmrovVdFQUVFxYr67A2s8ATFAgqiAoIivfdOaOmNJKT3nkzK9P+aO0KICUWfz8db/7dZrJnMbeeec+65+3xn7+8rExPQI4X23ZNbXLdKr6ev7XgUWjWxaS23CX1To6XOwpIL5QXsShrD4Mh6Bg8x+ZcnTok5ckxMF798no58AY2ZB02WYf8DUu66N/z37Pg/IOVfbKuDi3IY8cBeplq2x0fatjNkvMTNYErfeg+WFQxrc2XkXgZSDDooPqVBOnY11fV+yEpdOZjZh7GRufQZ0jIa58SBQo5E+zDc/zJjfOXogpOQaiuQqrMwU+cJta7WdkEe+zYVdkloYy/S/b6pzK48zzv6TH5O8eGB2AzWBYVzJqsbE3om0nPM3aU7XCd5koihf6QOb1ECA3w+ALUDx1wDUVnVU76vO/efy6HI24yiBS2Z6K93CWP0Sfe3UhEbYKvkATzC5BRHi9CpRURMPoSVfe0te8/pPd2oLw5geM9XsLEs5KWeHxBevZuUI1FkVvjx+PQi3Du1nvhWVIhYsUqCi3UxESGXsZIl0nDkIRSdHFD8RWoq93JEirFCjXJ7XT3XI/FNIi89ErmflK8vNpO6zuyxlaFny/gl3A7vND/+0V9J147WfFebwlM7h5IdWcsRN39Sv92ITYOKuGddKLbPRI8GhcqB4MIZmKtNIFaF9VWKba5QZXXtRlu6lPnQ8zMlerkZ9RFduM/TjYt2zRPsMGUdG4+dIis4Cxt5NfUaMxxkjpyrdqaugz9zEj6jSeLB7mNLMbM24NanbQBM0ljFom6NZMtd2JuRQoeySpYVBJBb5cMTMwpxDbx9VEp6TCM/7DI5yu62lTz+9K0d7d931C1rC0nM9+HhPsc4bq5jWIwZuXZh1Levw0Ynx1ovw0L3Jz3P3y72nwRS9CIDE/y2CDK1Yyx8CDRrCVruaMhiyo4wAjpfEAgaV0cPJ9grjxnzb+0ZHjsp4dhxU4j3KP9qks3Kqcm1IkofjbL9SEGp63amy4jB7rQXGomIK68GUi9NR0UZvuWD8Kroh7xcgrgRVC76G0pSBZL9ZAWYuE4kGglep7qz/PxwvByqWPBkS16q1PhKNm53oZNbEgumtY6YOx1tw68XRuHnmsu8R03jdUGBiNw8Edk5kJklQq0W0bunHlfbEiZ4PgtqR6ouDkBm484nfn6sdnLh8ZIDPBBbRJGHKVrQqED1pn8WFXp7ZhyrIrU0gIdHJ3LwvCO5le48E7GbPVgwIklPEVFYB2lapPL0kDkxQOGBGNPEo0qv4pf6TGHi66m2ZmXBCDw1rTm4jPv+HUBKlbSJ8X6b0Yj0zLPuhL347slbje/9jXXpnNvRgYkzjCH/IrIvgExtxZLSNLIq/Fk4twA337blWbdtKiYm3Yt5PfbRK7VQkGKPmjWBT6q+J3PPS4jlBkFe1vhuHjx+I+a1J7mSPQoPLxvejxmMs1UpTz13e0nv6332l59qiL/qyJzQXXRw9cfl8En2e7QjSF1FWhcxgXY+BNQUgPuvLNz4g8Djdd3W3D8TZcNPZMu2ku+XRsdLHrgojRxEx2lw0NEjeSaZ+SmsvdQ8jgv9zK6UCR4m8kWNmTtmmiJSa4dzueoBamtFXIkV4+VpwKBTU1As55WolfiUPsXlvlsJ+6ycJGdHoqd1Zn7cx5RWd+JEwktMf+xbHCRyHrRq9heMhKA/KTOEd/n6vIl/KMXn7wRSVCItSYpy4s1LiFeUkGBeRp1ELdSPQi/FX21He5U9AWpHQYmoo8oBK50MI09KO/kudnS3QH+lA9mV7XhiTjGu7Z0oSatk1WYXLGVKRvU4yvyQEfRX1vJNTvM77+ZxS3bpCkdfqMJMqyAs81lhU0ZaMqsSOxPsk82MB5vJkW+O1nCIqISfnVgWZMsvPnl4NWnRWdpQpDMBNq61Nny+YTpN9vnsnlrNScsOvH4tnmHay6RHVOJRGEyH0omCFLwxxVEZuR2DrOmGFHylYw6pA09jjgvVSjUDP85CrDfw89zeJHmaVPVutqGpF9h3OoDSOrd/KXrienTzg/2+wk/uSKK2hCNpY/l81HEet2mO9tKgZ11tKkqDhifKenF/Vesy3er9cC8DKdf9fvX49bRvsOVIWneulgWy4IF6vNrd/Vh425ejUbq3VMSWrWKBzN1o7Xz0eHvB6bMmUOV2kXuF6Wq+2mSBv2Mm3R/dTOgWa74Z+RBPXHkDB1U5B668z5TZB6mXaIRz7c+cjd1vctVGEMUIpsgtGomccgipzLTPdYtQDKBcasPBlBja6X8HjhmgzkxHurKUH65MpHfXGsZNNEW+7tknJvqimAFdYrm/+weo5B1RK4L/B6TcqSP8F27/fwekGPWoX3rva6GpPlvy9I0mq6qp4x9LVxOfcg0HOxveWTyfHiGm1eLbbfv++TwemL2bh2yCsBXdPnzQ6FRtVqYLKyT9ld5C6HKjSItSokYpVlMv1tBopsEYkl5tUFEnUt343biPkYjvD4Dc/5bu+PKPo3Ef9hFx2t54VVqxKzWM4b2ziBzVMqpj07pCUvJ8eCT4CmH6MGr6bkVkZQrdNXLpy1TZ6Iu6osiYQpn1MZJK65g9bAgvlx1gnlzDxiwFj57MZX/fYH6J7U8vnxTGP2hqjzuZcRXrp5/FONjr6RpiYLjtEpztktHW3s/h4DjMGkQszZ3H0Y3b0Uoh/s22ARpNXhVhq0sotLIguul+PCL1XJdMa98jmYDuphWT35uqUcGxjaMQISIi+FPcHWP5Z/CzuNbHU3I6jKSSjjw8Pg+f0NaTtuKCBr741gYfu1x8Q1LppT9ByqF3ODIqkWgXEwD1r5rxVWAEU3TGBPJ70J7YO4i+ikN09N3NnpLp+Ho08Vn0hBslfWXQWvz3q9k/SIT84hDemHaNPs4O5OvqSU6rZ05pe1b29WPv1l2EFpaTNd2Diq7mVOqT0NEoRKQ4KAOotshCIzU5eSLEmOOMpcgTv+1VOF2uQe3jQmqQLyM7BqPQqTiYtpTRnV6lTixjTmEhLyTvxqFnEVLFNYp1EXxXMphQxzxGZW4mX9uZ82df5GxQBjM8TBKlbdkKnziO2wbzxbUMoupr+aVExpmsnkwOSyZ0xO25DY7/WsLRxOYw69/nxN+uaX9cXcTVYm8Whh/hmKKRsDPe/NxFxsrgMy0Os9eaY6OXY6uV46Azx01rJfx31VjhprMUPi30UgrNlJRI6ykxq6dYUid8r5Q3ojGikf8Ba0RDiqICK5EZC2yalZmkGi0OlWX8bKWl55HO9PO9Qojani/OjSLQrYA5C9qe0BpvYet2MXHxYh4IX01HD2uqy4rQaSsJFDfS4NXvjncpr0il+LQXPlWNnJnojrSnmEpDAjK1DX2yTKSzN5vGUM3FgK/QS/VIVGD0+8IuRfLiQZPMppHs8zeRCuHvfdsLOBffjgm99jEwvDXIq9VoeX/9SEGC1tWpgcpqCzQtaViE8wyI0OFtFccgl4+gphsNx53Qd+nKxPaBpJgr+Dl9OXZXe6H0GXKjuN+6xHPAuROvHcwgOi+EmRExHIv3oqTOmRf67WSPzIzw8y6UunTh+fCdQiqPORLGWLajnbQ1SGIkav21PpMSXaMQkfJx4VCcdAqKhT5m6mtFUiUVskZUaI1Yzr/NqsWNQnl9pVZMtmwG3V2Li1iw0qTw1ZbpxSKWvrOUFXVJ/Lw7mLfH+VEisyM1rgqbOm8+q71MXEFXHpiYRfuubUdFfrOqktwKF54fsIdOh/KE1CLfhXO5krCco8dfQ+GsR6IAZa4Yn6BU+jh/wNaaqUSJxLyQbor+uFuZ0iVLDai0ZiyLOEa2fRbdflHzQ4QbndKcyPNvwj2igPC4EAztP2LZkbdILTS9N20VlXwy/jWU2i9Isl9DlUsZXc8EYqPxIsvlIgWda2mf3gtVvoEV50bcqKr+ETr6u6zCz+osGjMvNOYBWNQdp07rws6Cf6JSiTgXLRZUMEQGLXX1Znw04jMUZQ+R4LeHPhsqONzBj7LB9sxK+oLC8h6cyHqc2Q+ux1dqzWTLlpE4JxoLuKwup53aluFKfyR6Ec46C9NYprUUIj7qxUZfTCP4Ytf9MpVMS51YTY1BhVKkRikx+mUawTdrlLTxAP3Jntgg0pBqfmfutd+ffl3ueLwznbAov8TxiCpk8f6klHbg0Un5eIa4senrfFKM3CidT/FxcBDZdqbnbUN2Or3qW6YyGH+3PJfMoeeL0csMhKc/L7wvG3PzeeWiF3aKap5b3LxgeH2yOLv3OqS9lOh3efBstyZiHRv58ngS6RNmC9cq0tVToGtg8rp+eFbZoZ5yiBe8B+BbX8XPmUeIj8rBvtyN4Ir5mOdLMKsVo3bPpCn4HLKCDpinhJHne4XCnlmYGWxQHS8k/EgtGW7mZEfZccqnOdLpev2EZlwk47wzaWUdmT+nBt/2t5aMv12TbflFQmKyiMcGfIojPhQ0FfJd2RD2DoplllVLnzRbW8ev9SaAysjzJBGJcNBZYFTxdNVa4qG2wjgXN/UzY18y9jU1DVINTWYaKvXX+9j1OYHR79diXBhoc4wxGDCIDOgxCPsInxiEfzrR9U8j1v3b34ab9rvpOMON37lxHpW4OQJk47fzcBy2EguNO5euhZBYFMy8WdX4dbj14vEfeQyu83MZjzGXG3kNDdjYGIhw+oy1h6cIqUQPz9fh49V2PaSeLGfjcTfC2l3Ga84B/L7z4dep43nlzCJ0OjO2JX3JtFnf3SjSx4VR9Fc2RxYaF++0jSIcPYvpNbKlD/SIOFAA/VZlxDJU1fquasW1VKjrWHlhMoG+1cyZa6oTo2S2UTp7Up99jA5cj8qyJ2ozn/8BKX+kY/yX7Pv/CkgpKq3kyVc+JTQ4gNLyqhZAyktLvsbTzYkn500mMfUaz739Bbu/fx9zuYzbbVvyaj6vTd7Fc7+Fkd6p3Y25lL8oM1D/W12/O5Xiz29ft3EOXUM/Z4+iP70r9fyS1I9BXbMYMrGlI/jJx43UNlrzVmgaXqouNNlloe7VcoCSXeqHebU/lfqf+crJh887B/NFya9EWcjYWa5nzu4KYsK9WBUzFjebYhY+c3u5yet3dT0U09/PQA/vLEa0e8UYL08Bz5Pgtw27DDlnciKYfiUDnzolSc/4o3JsDYJpLxXTZ0c1lz0cKW6cilM3PapKEaUxYmwcqwmfeKTNisy40pnMGJNcczf/DXTwOsSWgPuo1tahPd+RKwXBzB2eTUDf1rKXeVk1rPnBkY5OGYiDC5led5ALZ97joYe/p/a3lak/33r/HUe+eXgk45Xp9Oiwnu/r5tLXvoqPok1KBMYc7w8H/IDFHgOxw4rJPvEkKx+7eEOK9EBDHnN39eDTCe68dTKaBXHJFEU5UzTIUXAxqgwpaGjmYzDKKFqJPYRVrusS2N2WXEXSpKehTyAferuyxqM995ef4MPcjez0GMqTbtMwus8nft1BxqMZQrmc08xYZ7OAueXbCSs8RmLNYFLjHuDrAad5XtEdIyDRlv3ieJYNHn34R2YGDzYpuVCnZFNCFH3apTL2gdsrVf3wVTnppc2RMvOmNeAXdHe54N+uKiOnwp1FEQc4ZVFH4OE+fByVxUGP9P+OTnKXpRxg7k4veXNkT8/oc4zesY1lkd24KpvLOKdUuultWXFmDP5ORTy48NbpUUZ+FCNPyuJh74KbP41JSjqLL2Kw80XtdGd5domqmmtxaroki8jzUlD2aDvK9VfQiZrokj8Lu/qWgFuGxQaKvXPQZ9sJRLEy/zI6JkTwwylf8qvb8dA8naBmcN0+X1FLaY0Diyf/iI80AAxitE6FGKTN3l9MggU/HB934xijLLyVpdFpFWFlhfBdJjMQbLmHUKeNaCqGIjloRn6/LoQHdsFcpyY9/mkul7yKwbOZZ2G/xWW+bt+N5UfiOHytD+NDr3AyzZ/qRjteDd/KXgsIPNqLk921vNvlGO4SC8Zb+mF5G64RIzyyrz6bdO2tI//ushv8JbtNsfCnnVkz6DNx8yZC4mJue+4vnn2BLy3qePZAAPsG2ZFk4U1cQg7ONcGsV53m1LUwpgxNo1u/tqMil76vpUljzjtjt+PxSzGFVpZMmBPF7kvbOHXhCYF/ycLNQOEZMWKpjgkDn2FXYyTD8nR8rBtEfo0HTz9UgKPnrQFC4w0YOQnWfS/GzzGDuRF5uOxPQV4rZv8ILZITE6l20GH12AaGHViApPOTHEkbw08X7hfu3cc+m9eGrKGeJUS3+xiNuYawE30xM1hSJc8iKfwaDsWuuGe2550TJiU1qdTA+EFZjHJ/Rfi73mY4erElVjV7EBnU7CxcRp3GjeMnW/IArRm9irrKmWTKD9P9QBU/9g7BoquOyalryS6J4FjpfcyfuYFQmRNDFC0JT3UY+KEuTZis3qtmXORwFJvjKbXEU2qFh8QCG7FpTG8waCnRNVCqa6JEW0+pvpFavYauDS6sShtLQ0IxicMvIEvsSGxhZx4cmYWVjQWfb3HFSlbLiL6HeKfzWBzEImI0BoIbG/j1mong+WZTxKRzflYBdW46ugX3QnMAACAASURBVOfMx7LJDXmJhheiVTRorPjHYh0KhWnc2bbDQEycGfP7foOuSwVNRzsxo2clVQodp9ce5NdnF7c4d+0hBSMud8ExJIlnBrhQZmbD7viDlEQlIGtQ0CfvWURaEVbpUiHSzSiHLC1phzw3iNSQgyg7aBAjw+2zTDxKNaSENkA7MT91XtjqPlwLE5GdlXA+rxszxhYR3OPuU19vPtmPm8RcTRfz9JAPkag70lRTzLuGXqSF5TLKonWan5EXKUVTda92sT9VrtU/zSKo+zfUSF3JyQ7iSkE37ptSTsfglumlf+bk9Q0iPvzE9JwblT07BugxN2tkgPNy3MyTWX9uAaczhjByuF7gS2rLTm3N51CSL2O6nMBqxFl02/tQHmnPfYkrhUi17dWP8+iYn28c+vsUP4Fn6IIIdCLah6YQ0LM5jWe5zpGv7cJ5MjeFp+pajx21lKJFy7tnJ+HhXMdjj5tSar9dJyEnT8Sjg1fTy+soDVYD0Ukd/gek/JlOco8f8/8KSKlvaCItMw+1RsOGrYduACl6vYF+45/g2C+fojA3vbSeem0lU0YPYEDfbrfcNqhfKM8vTWPtqNPMt76zI329LxjBlK3114RAZpnI+FqQIDd+iqSYS8QoRFJhtUQukQpSi3Ikpv1EEiHK4T9h1zQ1XFKXsWznREa7/8AXbsMYV1HN5vhIIjplM2J6MyhwfWA0k6h5v8dZ5LreKBrtqOu6H4NL+Y3iWx2bjFhnQWP+V4wbNZpkWwd2lWyio4U1KfW1+O8WIfbS8WLeQ8Ixr79cj5nszqGEX62RYFQ76B6qZ7zHp3g5XkBbNZmrDnbkel1CdsUF1alBiOzSGJWZR9pkZ+q7tyYHE+3Po/uZeo76eqDTj8PGXy+83I3s88Zwx0Gz9yJXtGbyPr5xNMaolG3dY1lcUkFowA+c9xjMSbkb9hfcic7tyqxBmQQNaNa0v14pWUmVrNvqQrBrKmVd6ng+dz/HMl9h2n3rBILDsRZtk8T9kT4hEouQS8U0qf86qds/cv1b7VtrULO/IZcHL4bxSJaWfsErWK2ex3B5Me9dmiEc1tXzMg9bxVGSLcel6ylWx73P6YdihBVIoxm5FWLi6pF7BRCaW8fyI6ep7GpD9rTm1LNawzU01GMpMgIoLdvd9mo97X/IQ6+QUd2vCwMCAqmUmbMrby09y6KpkTvwbK8P2N9oYEZKBtP8d1PjpcPxmDPb+j7I/MQVdKhM5HTe/RRnRfHaxF3MIpCRdW1PlE4rTrIsoB+zc7J5U1lNvqqQZZcn4W5TyuPP3D4kf8kSPSqdjDDfM0RnRzCyRxH9xt6ds/jliiqKapx5LmIfF6yLcdgxjYcf3EGZWSOPWHfG+rc0FaMCmem/DqVeQ61eTZ1OTZ3ht+96DVqRQZA3tZGYYS0yw1oix1okw0WhQK36z/YxV6kFZr+NmSKDgSc/+QC7qip+DfJhs/8CHpbn0kViybLT4/G0L+PRp26t7vDRcjFKpZj3Jy8izb0b2uMWDDI/QKNHb7SWt5dNvt7npWln0F4OxlqtobSrNenTxNQasnCsCySosFlKu16XSUznzYKE7IdF9zFNnYxvwGVsCjzJOBXAqWsRjB5YTd+BptUvYxrEJ5+aSKo/nL8Ty1OmSa6RZ8DIN3DdRAYNF6M1eDrkcqnhBQyitgG+MJuvCLA/RWPZJMzOwYaInrzi6c2I6jjWXfuCE3VrMb+JaDCOWN4K6cLqU5fYnhrBwIBYorM70aQ1550+mzhkq8Z+91TWTLjMbperjFB4Eyy7u5ST041FxKjLsBbLhAgj46e1WCr0MyeZHINehF7374xJEULW8LqJrNimtoanPnwfsUGPdvBEDIqWK7PS6MOIKorZfP8DrPVzoNtxT5Q9RBy3CSY6OQXvyt5sN5xkd2o4w3qn0n9Ua9D0+nvUSl7Ly6NP4bYpjzNebqyK8uGts7nEp07EKVQvpHyWxYppqhAxNPIjUiVm9D2t4Cv/wVwqDGTO2FQCe9welL14qIJd51wZ5H+eDkPTCFvZSLS7Cx3bH2fvmeUU2ufRff4WglMm4+a0jFKVOa9u/1ToVsEesSzqc4Ra8SOc7/Q50iYRfc+bopW0NHJ+0FnEWjG944by4oFewu/GKJMXRy3B3TwRlbw9akVX4Xd5/SVkmjwuVT5AWt1wISLFGJliNCHyJXI/laoBlBefIjC+hpUjImnvXsiY9I1kFESxr2EsiyZuZaC5Bz3lrcdCY7pYob5eGMuuj2PG78LfBo3gbxn9LKM/JhdJkSNGYfTDRGKB187kk13fLhG+/1UmEoGzRCGU4W7MCDR+W5ssyDsvzx+Gz34fqsd/jz65u+BjzB6QypVUOamlfowNOcmywE6Eu0jo2FjIErMOqAwivsrNYnBdS6Jn86RsEgbnCfxiQYWTcazrJESIfBZ7lbSyAB6aXUa7ANM4uXmThqR0BQv7rUXrXUH1xRBGROYj1+ooXvYTH735Lip5s88Wd62Ox7YMQWJZx9Xpl1lhG87MvKsM67FLOF94xvNIdHIhzVFWKkZnXYHerAmzSk8u9/0JkYcjkqImun2RTY1chlXv0zSYWfNJXxNJ/M0mri4g+FQp+zPCGNkvn35D726M/v15vv1OQk6uiMUj3qK2phcWpUUsdO6AppuScHlrgFJt0JGjUwp9Svnbu1L4bjD1M0QioV/JRSa/3vhpLpaiEEtMfr9YKvx2Yzv/Ob+/XN/I0cYClu4eywjPrWTYeFCX58u53O7MGFdMcPe7W9y8XX828qHsOyjGyUlPl84GzCV1DHZ5HwdZDgbMOJMeyfrzj9K5k56Z09sGUn75tpD4Ah/m9N+DhXcySXkj8HPOYHDOTtLyRvGDXR+W9D2Ci8RcACLD6j1YUdAcHWcsX1OFmLJY01jTY/hZnL2LhO97tXKetR/G0LIcVpW2BsiUmnwszBU8f2o0luYqXnrRNCZ8+pkEo+jGW+P/gadtFkq7scL9/E+1525Gt/+uff5fASnXm+bcpSQ2bjt8A0gpKavivqeWcOinT2603vKvt2Bna8WYqPBbbps/czTzVsRwfFAiU61v76jcbbeQSkRIxGJUmv/sBOT35c3V1LFFmcniY1E8LdrD4k5jWFCWw48xg+jdPodJc5sjUtJTa1i3yR4/h2yeskkQJGTtJOPQmlfSNOA3UrlGc6xOTaFJXotZ6i94328if0ooXY+5pROS+jLOnvegf1k+79tMILfagyem5eDZ5faEsyo1vLPU5IiMiSxlgu8iMEhpavySGKdfqHEsovZ0IAcq3Biia+C+2EzSeymon9QaoFCszyTwqpq9/p2wshqIwsnkrJcniakvhKDweHy7mCISrltZnhuX9vejzLqONybsYdveICK7/JMMh2C+sQun82ULTl7rwZS+6fQc1XpynXKhkB/2eNHDI5G0YD2L006xr24uC8ZtpqOZLeOtbp0mcrd9TCwCuUxC4394kvv78hpXDD+tiiMi3Z+3zvsS1eMdvuIBJugLeD3GJBk3JXQzI2KrOePiQk/nM7xbsoiKWXnY3sRbkKaqwi/NmSxLR3b+socGT3OuLry7emu3pQD7uFrUfm7s7daRhd7t6dKQy3bNJSwyTQTDH0au4IpBwZVaDQlrNmCu0/N9j2CyRvXlqehXcWgsZf+Vt1Aqfbn/4fUMaWrHWyWmdIzfW7ruNC90DSOypIh1laU01V/jldhp6AwS3n7dmLrRtlNdUgorVolxsCxnSlACay4NJsi1iPsX3n7l+fr1l31YQ0WDPS/0302mLIumg08ydd63mCHmaXvTpOZfNSN/RYPqrwt9/1fLE5iUwNQfvhdOc8HTkTf6Pcab9WX4WVrwyekJuFhX8MwLbQMpWi288a6RxUPP6qkvsb+jPYZfOzLacjcNASMxSO4uEkhWksDnmjAWHslDrtNRNMCW2KFZQpnCrz2Lmc4kLRrj+Dl1DjWUp/nzusNEFkrKCXH4XuBJURzqxfrYKEJ9S5g+zzRZjL1QxpY9roT6xPBoJzvM8kxpYQaJhoZBv2K4KQXBvPYEEm0FZyueJKeh7ZSkYQ6v42SZSVXBTKzTxDweGcZeGzs+yv2RUdcKybR/lZuDSUoa0ljQ25/1Z8+wOWkIPbxSuJJvWlx4r8cPXLSrRbX3CRbN/0lIyZlnE4Sj5M6g+J3a3FwmQa3VY1wM+Tstas8u+p46icHVG31vU5TFzSZOvIAoO42jo0bzY78e1EXL6eRtYKtzHw6kJRBSFs5R8Uk2JoUTHnyVcdNb+w/ZOQZWr5Xg73SVub2y8NqexY/BHUkPbmTEYQ8Ky7rjPViPWApGRb3SGBFB/nvw9dmF5alQtoX0ZU96KMN7xDNoQpfbVs8va4u4kuPJzJ77aScpp8NJJT/29GWMOpr1ufNQmWfRc/wV3HRBBNclgd1Fnv75OxqazOnX/gQPdMmgVBJBbKdfsc2Xc7x2KnIMPFZeypVOJ1C6aIiIi+S5Pf0FVMrLtZo3hz8mSII22o/C8FtatFSdi1x5iaLGrhwvf5nLMWJqfpvnt3PM5PWuqRSLA9HGn8Err5Els8cQLr7CkKxtpOSM42dpX94cvo/xVr6C5PlfYTKpWIgf1mj/M2mKt7uHeFU5BxvyCVDb8+nuyehDv6OyoCunsnrS2+cqF3M7CtwoIwfs4t2AyWxO+whLTS2Pdl/CmSYDnZoa2ZXVMirF7Go+WYHZXOvfhF/FILwrIxCrRGyLv8zR7O6MG5hN+BBTJMa6b5pIz7Pgmf7forOpoiQ1lBER2QSVVXN27R7WP/YE+b7NvtXZpmIe/GoI9g0WdJp+kOEeA4XzbDasQOmipcfV2ViJTO9qRZpEAJKv25lh32Bp64vFtlw6XqrnWEdHBjvvETYvD/+IOnnLsbtaD6MORbMlMZKIzrmMmdE6Avhu+sfnX4mEhbnXRr5OVk0YPtmVTAxywidQSue7BIPvdB2JWISRJ+Ve8/vr9Rq+rEli4cn+PC05zgn3dpjnO3EiuxdThhXQM/JfZ5tdtlKEkRswpIsBX7cyopzfwVJajl5sRZNtf6qyUnhr1wdYKPS89nLbNfnZJ5UU1Tnx0PgN2NUWs899DuPKN9ChMoFzyQv5sLuWHQHJQirpnvocLHVmHMsyLTbcbNWZIkFQQizVMmDKURQ2Sq5p9IywG4d3Qw1Hc7JbHVNffw1na2ceOWkEj0W8+6YeIz/j62+L0elh5Yx5mMvUNDhMQNxoiUXwxDt1h/9t/5M1UFhczusfr+VaTiFODna8+fwDdAm8O9//T15SOOx/QAqQk18ipPzs+v79G3X5xXfbBeds3PB+t9z25PxJTPz2LPnhBYy2aR1d8KcaRiQsfmH4e/3COxa1Tqfh0/J4plwJ5YPyFF7t2ZenS/NZe3Ew3XyyefyZZlDg0N4cth72YkC7WGYVXMFMqUYZOQ2rejcaA08h61CApsAO85iRVNukIy5KpNOIYTho6ohX7Qe5Nei1/Bxtzoy4PL7tFMG57BCmhScQNS30tmVNToOVX+uwt4OFYWvwdTkCtSMxWD3MIc+l6KVa8o71YI+5DUMr1TwVnUqRp5TaZ1rzpNh9kIprhZYdbpH4BoVw3e+vK4aCywacPMoZMKllutK5PX0oynbnh74XKPI4yRv75zC21z8oN3fhde/7GRjTyOG0Pkzqlc6I2a2vee5gNuv3exPeLpYrgfB0bAobrPvxTsQhhlt7EWZxdxPlOzWoSCTCmBd7r9mXFUk45NuwfM8AxvR9htXSGcxSFvJskikq6aXBX9Nhn4G9nV3oLUrjRcbhPbklOZhxv5LYWk65dSd5zUb0MjHpS5p5Mm51zyKNngAjubBaj35AF+b6tuOorQsflmxnjoM5oozjiBqr2NxrMTkOQZxW6vCOz8C/qoaCkPa4+Tjy4sH5wum3nV5DqULNY3M34aBTcKLABAT93krrLnBfj660U9ZwpiQPQ1klHxQGkFXhx/Ozy+nQq+32PneonPX77OnpFcPMEAsW7wtELlWx4iPTRPxO9sLLNSjVViwesJMidR7F8Y8wa+r3eEgteMjx7qPrbncdIw/P3z3BvV15Zq5cgUd2DgaplCKFGZMnPc6qgiZ8XKR8eHwSdooaPljSdpREUTG8/ZFOIIF+O+oHTnvoUJ32ZKTlOfSBLVe2blcGUX0ZJ8qrWC+eyLo9ptTA6EVaKp1q6VAdhX9NBIXqUyR0OCakVjzeuBB3hTmjraX0rPkEtZ2WTmf6suTEEBws61j6rmmy+N232ZxP8mZmv70MrR4npPVcN33wFQx+zZMlUX0qoroE8pr6c7mmNTeL8bixTnORSlSUX52Nk9KCoLBwaiVSLiW8TGNeHxo7mLgPblyjupyRva347sJptsRF4WufR3aVt6Ci8HynbeRa1FITt4CJM9YKq7Evuna/Uxe9q+3COGac5v6NQ5m8ScXDb7+FXKVCOmAUYqfWz6guMwVdXDSJffqwffpU9seVMlmq4BvvCNZlJTGiIIwY6SVWJXQjxPcaTyxqzf917mwl63+xJdz3PBNd8nE8Wc47kX3wd4zBad84mgwuBET9NtE0QMZRPTZmWQzp/g7pqcMp9W/PuoQwQtul8tjTt5dAfv2NMsqUDjw1ajvtD5djVadl41AbwjK17KjzQ04aAVE6ZP5aBsd5YfDcxBdn3iaj0ItxIVsY7GFPjtyelIAz2Kc6MNlxntB+R/PS0ctPkxNcSZfUYJbvGUSdypYBnU5wf+8vwToEveVN70GDBnHJdvQGKbvL1nMpToLx2TNaqNdFnvRVkW9tj+2hs1grdbz19BxGlu+jb/Ze4q/NZI29H59HnuIRxyDcpHc3Ft6poxkjRYx2D74uhW7/eVkC1Xo1q85PoIvsAAWqjhxJNxIbm2xct5OsDAzgIXEis9M3Cr8d7Hw/TyoiURrgi5I8xiubo1JE2SXkK9JJnFCPpzKULhXjhefrbGw03yX3JCwoh3mPmPhnPvyojqxiC14a/A06XS1X83syITKdcWm5fL/9FLumzCA9IuxGWRKbKnHa6cHw5CCCw1L4OETFfqsgfqr7mnp/JXbVzvSsewipXgbVIL5mqnwjD8jpSV/iYh6Ez+vJguz14dEuRGnPYRxTt3V/inTnlmOKxgBRh0/wzaXBhHjl8sRzf25C9cb7akrLJLw35lViyvvSLtPAsH4qRvi742X253hXWvW5e9TvF9q49AojY0JYVprKjsB2eBXIOZTRl+lDChgy9u4VzNp6zvIKYMkyHTIzmDosh372S5CLazFIbTE4DAKxDFH1eRate1tQnFv6ugSHNtY6HnvOtPD8yCOf431Cz49TnuTJ409joa5jb/RyHpp6mBKbWl526c6K8gQa9Fp2FE3FX9P6ZHkXDNSXgbVdHUNmnEAiVtHebAQqsRnJ2SnYGKWFbrKGmnwUCndevtxe4B8zltHSAp7+hw6JRM9Xs2eDmT16x6EYStyQ9mwNvN9pDLrr7WWCTNnfb8bsaenff9nfX/GBp99nypgBjB8ewdlLibz24bfs/fFDgaLj32n/A1KMijrl1cx47C0htee6fbhqE04Otowb1u+W2x6aNZoBmw4h7tZAT9ndhdTfqTHvZdWez2ri6Z3hx+rUet7pH8ALJUV8fX4IgW65zFnQnDrx64Y8YjP9mN05miSRlgfTMhE5W2BlN0uQfVT234EoxxPr7AjKLU6Tq9cxtldPeiivsUmfCCLTJGD3NTGzTpawp3cI2xIi6OObzNi5tyfgvM4i7+ut5uX+DyORqGlSfk6NwkCM7xosyyQkJXTnkIeCwHwJ75+MRyuB+Ldagxo9Xk8VyrHNcg4evZudMiN/pjG9Bz0MmbMbM3NT3qQxnef4plEC2dcjD25kWm4OXS8/zKKuLwnbp3d+i+lXitmf1JfhXdKInNw6IuXK4Wy2nw1goN9lLgTpePhMLa93tGBXUDJzrDrgKvnXncN7WbVnd0M2pdVq1nw/R5CO3mA2mklFNVz1q6GszpUwj0psfpazu7cdIVW1PO/SnXYRrZ+qhiYVO+hN8uoN2KnUJCwOQGNz+5HePqkOv58K0FvIyezfjYEdgzHXq7lQ/D1ya2OeeDyy2hwO+U3htLdp8nyuyYBCIiJQCh6qcp658ApqLNh58guSvQv5cNxBIbd9Q85EQW3h92Zem8moCBMIa5RAFjeo+LVOwYnM3ozvlU6v0W07fwc2lXAm3ZOpwWfoH9iTdw7UUlHvwqJHm3ByvfMb7b0letQ6GYsH7aS2sohLRdNZOGorwWb2jGgj7/tO41Zb2/+Tqj2/L493TjYPfv0FmMnQ+XREfC2J4AWP8nO8Ga5+8MGxyVjKGnjp5bZfuGlXxWz4SUxn9zge6XueVLNaNNkyeluU0uTW4w9Vj1XGPkK6fMCoq0V8fPQMFX5aLsyrFUhne2Y9xGWfVagVagoSQ3nDNYpB5iI6SCGseD0ivzK6xPfkg/0DUWnNefE5nZAm8cknjdQ2WPPmoGN4lwynxD2NPL/L9Do7G72iDmXEjhtlFOtrsaw9gkpnyS/5q1uVXSGpYbLXQoFXqjx2BNk2vkzo2pV2TRWcS36F2OKFqLxbSsgaUx2fDSrk5YQMdsUMFriMGjUWQjrG/e0OoxHXklgyg/ljNuMjtWbq74hA/1AF3rTz36Ha8/uy9T11gmH79qC3dUAX0Zrk0ri/qLwI6YUj5LbzZf2jC9lanM2TpY4s7xjBgqJrvJXZlWzRNd5L9sbbqZBHFrZW6Dq2t5Bjl3yYFHyM/tV52GQ2MG9MFI/ptpC351UUzgacujZHSdTmiqlJFwnj5vni4di5evDJlUG42RSx8Jlb+ydNTSKWfmSSzX7hvq0ErK6m2NKC8n7pqOOiiFNl4KRJQTSwF5LwLKJODcPMdxllDR2pV7rj63ICbfVCkq2LKfTNQJvQnkfdTKuuz5UWMbP6AgkRGdiVO7BvyxgKq72Z1ec7BnU6Tr3tcKMeVYsqtlAaI6YqMcogn0rqRl6+yR8Y1PEg9zl4kuWqwm/zBeG3l/4xn6lXN9C7+ARX0h/kHV8LdoTGs/1UFo71jWybMevPdq0bx/2dqj1/prBXNdXsbsghpMyN72K1ZNu5sTelr3AqG/Nahg/ewTKPURyKfeHG6eulljzW62OOqCX4qJs4lG7ydYxmVlZNvfIqF+bVYdPgTdc808p9RXYK710Owt2unMcXmQDcVStUlNRY8tqwb2msbeB4eVcWRqTzzPlE3jwRx5E+gzg7sfkZMXK8JCU38uqekdg6VWEYe4oH7UYwuSyRST4HUVsZsGpwI6RgjkByq8iVIFWKUVqVc3n4djySHeiwqYBUBzusRulxrslCXpXOSe9RHPGb1Kr6pl04wadnhuFpV8qji+4ulfD3J/l4uYg6pUQgVD5S2gv/FBvCx+Qy3z4QhTEc7C+we1m1Z6MyHf80V9ZcrWdnZzsCy8zYkxLOqLBcwke0VOv8o1Wxe58YI9FsoE85T/d/ATNxk8Aj0mjZD4PIpAomU2fzxc6RJBd1Y/oUHV2CW6LmVdUi/rlSgqNlKYOe/ga3Te04NiqK56JfRqWxZuf5lYKSl5PYnLnWgexsyCZDU8PrxZGMqW0NYBvfZUXRYnSNIlzb5RM6NJppkq7EW/iwMT2OnuqW1xdVVSNxdmV5shVZ5QE8Mk+HpSV8+rkEa0UDy6fOR23mjcqyF9LsDlgMbQYW/2h93XH/nxAAyL/dJgNtiEwq6xtZuvJHzl1OEhbVXnpiNqOjwti27xRrN+2lSa3B0d6GT954HC93Zy7GpmKce9c3GCkTRLyyaA79w7piFIl555/fE5eUIYAiryy6j96hLedsjU1qRs5ezIlfV9y4/SdfWcGUsQMY3O+vWbi5Vb3+D0gRVhoMRIx/kv2bPsbGyjRRfeyl5UwbO4ghkd1vuS2qfw9Cdu7Av6OMAKntX9J372UgxTig2hRasfGsHcuG27K4sIRV54bj71TAgzelFKxaUUWJkX+h1xmWOrgzqFHJw4fOUhU5FPv6IOo9YxA3KVBUdKJKt50d/n684t2eiVWX+FD62/ITcLZSxYidtVzp68MXsaPxsC3isadvD1hdz2cd2S2JKV3fRV8XRoPkWUqsY0j32IdbnDnxhV3Z2UuC4qoZ753PwbemjpSn/Gl0aZ5EaYtq6fNFISWWCi7K5rJm+Eny5bUsKOtJaKMr5fFiGstEdIm8hGdgjtD2GTFBZF7pTIZ7Lv+YeJDP4ktRXRvP497rsDCvYETXj3gi5io7YyMZ1DGNITNbAynRezPZcymQYR2iudBJxdTDlswelEOhczVP2Hb5Szhy7mUg5XxTCWdVxWz+8iHG913EXtsejE3UYDbuqFDHVTlDcTio5UikGI8cBxaHuBEY3HaKwMlqL1btPkefolLS5/lQ5397EMprbwku56pQ+7vzae8QPnXxZEbFGd6VlmAQy5DV5CAvjSfBuTe/BD3S6nkPqEri/oQVVGo9OHp2KWdC0zk0IJ4kTRWLynozu6p1eL1YXcsTXRrIlztxKD0FH7WKiw01bIwdTg/vDCbOa5sTZ/XKavKrnXih3wU83fuw6UIsF/JCmTqgiK6D7gzqvvGOyQF8eshOxNkV/CQZwnvhh+hv7k7vm8hZ/5VB7V4CUqb/uJ7A5CT0ASEYrGyRxJ6m6xMz2HbaB7uQBt4/MhUziYbXX22bg+r8BTFGEusBHQ4xtEc+NaVVWKoKcbVxR233x3iLzItjeMs2knUug9l0/AwDE65y7Nlqmuz1WNbZUW9djV2enCnmpmiR+61EGJlMAgqP4tT+Cva57Th9OJTU4mDmTKvHwVnBZ1+IMXJpfBpoBhobzgz5mlrbYgYeXIRFvT2N3U6gcW5W/bKs3Y9Y38iB4rcpV7VMKzES/EW5LgFlJ+pOebIqcjifObvxUOlR3s3fzOnSTzDzbA0K/tMzqPfe+AAAIABJREFUhYG5tVy+2Jwu5GpVzFCvk7gotWxVRPJm+EH6yF2INP/XQ8KNdfN3AylivZ6nPlqKTW0tuu6R6N1v0faN9Zgd20aDhSXLXnsTIwHlkwkOvNetH5NKcvgkz50mjYbFiQ7YWlTz/Aut1S9+/r6QhGwf5vc+QM+EfOTVGgbPmcTG3BWcOb4UW38DNn7NQIpBCwWnJESFvkGyyJcgqTuLE0cJfeh2yj3ZGU2s3WiFv2MG4/pdJOTnGn4O8meaw0aOxLyJWeM/8Wm4ypUBL6IYd55ul4fh7rIMnd6MipoOuNgno6l5lctuZ6h2quR88gDWOPcWrtutvo7NWSmcGXRK+Lt6axh51e5M6L6Fdr4K1LLW9SdXpSFrTBZkkHcmziM90/RMTgzdwlhJBBntcgnYGMdVezs2PDaZKSnf0LXsAtEpj/NiSCOXOmRQ8PEG4ZjTg4dwbNjIf2UY414HUow3t6HuKiX6Rs7u6ESpD+xKiBTueWyPE6wO8OWdkh/opcwgNW80dpZ5uDkkCODDoy4TqDGIeLcoj+mVJsUgSU094oxUjj1fjUxrRZ/MRcLv5hWlPH7cNFu63p+Wf6KhukHB26O+obRYzzq1P5/1zObD/YksiIsjxr8Lux+ee6P+1ej5ojqR79c8gFwnFXjmxtiGkid35OyhX8gcnyuAKRZNrnTNn4OsUUGFJJEC98vo7c1p/30DbhlNfBMWwIygGqT1xSgKL3LNthPruz3Xqp1nxR/mkyOjhfSml15umw/qTp1j6QdGPjkxK8e/zo7SnrgluTFyUjJP2t69vPGdrnEvAyn7G3PR5pmx6ZwNe7tJCK0Usz0pgqhuWQyccPtU+zvdtxHANQK5b4x5BW+Ha2glzjRZhWMQNYOrYr2So6dt2Rk3lb599Iwe2TLFLie+km+3uxDkmkbwg1vR7OhHY28505O/prCiGwcKHxMIqLvIHBiu8OaCqoTTTcVMrunEiyXhbRbRqOBTfEEkSMsH9olnbXA9m2178EZ2EnPqW4Z8iJVNqB3t2ZjRSExeGLOm6bGwMghks+4OpbwzZhFqRWdU8kDMEnuimPjXRPy2WfBtRlbqO9X6v2H7UCNTcOvzvvnJOuQyM158YhZ6nR61RouVpULgKnVzccDW2pJlX22hsUnFa8/cz9RH3uT9Vx6hg58XNXX1GCNOjXNyI4hiZWHOc49OJzOnkIef/4g9PxjTvZqf6ZraeqY88gaHNy+7UZClKzfg5e7E3Gl3HzX8Z2rnf0DKb7X2xsdrcbS35an5JtWeJ19dwb4NH2FpYc7ttnke3cRQL1cB7fwr7F4GUowDanGVih27OvPNBBWLi6r45+mReNmVsGCRibTTKKf57lLTJO39nkeIDA4jVN3ID0dOoVM1oPA0hfzqpY2ItQrqazaxrEcIX7n78nTZERaaN5O31jVUItorR+Im5pV8U2rEG/9oRGpmQqrbsreXSDFG3r028hPaOV9CW/UCTfJeZNhvp9glGesTbpTl+3CpWymZIgceuqhh2LUCksbaowprHgm0l4vos72GWHdHcswnMnHYeuFysyo783R5GPVFIiqTxTj7FNJj2Dkh9PX4JhPJ7Kqo/RzvmM/RmBoSi3sx0+Y8znYpDAv9hOfiE9h6cTDh/lcZdV9LeUbj+U9uS+dwQhBjO53jUscaBu3qxOjZx/CUW7SSc/yz/e1eBlKMKwXGFYOvNsxkZsCHXHB2IuqEDNGDJ4TbVe0ZinmhlvhBZWiTI/nHUANdPFvLpxr3zS2wZHJMEbNS0skb60pZ2K2JRI37d/wmB6ucRhq6tWdgn14UmFuwNWc1XRxMqy7ipios805ToXBlZe93W1V/n4JjjMncRLayK5euPMfhYfHkdi0WJlF9GjxYmd/2YL6sXSKnbTqx5moqAzRN5Orz+Of5qThaVvD0860B2pufseVjryGS+xN9NZqfEsLo65fL6Ptvv0qkVsN7H0iFtIv7Io7hmFrH2z4BbAyKYZKFH35mNn+2a7U47l4BUuyqKnny4w+Mmt9oh0xF1KREenovwx4YzecXQ7HqruSfR02rmbeacF5XApvaYyNeoU1oErR015+l3jsSvfnt+9XNlVKY4UNlIsh672Ncp5dwVau5sHMfee0LSRve7AGZXezHXN9wnMUw0cI0kVQ0lNHNcb3Ak6Lc2Y+9af0Z3K0IGxfYccidPu0us8CiLxWOmVwY8CMe8TJklt0Jih+J1q6Ihl7NKmPyxlhkqiziayaRUD21RbsFWhyml/M6dJUD0R8xZ86kcZyxsmbNta8YWpbOBe0KZG10kZ/tYhDXylBGdxKinYzmbZdHd9cY/MrEvBfowpYO8Uyw8KW92V+z8PB3AynBcVeYvPknDOYKtIMnC8SRtzLpvo2IDHqBcDNaVMekaBlLe0fSv7yI1QUSbBvceDjR9C5rq999tbKaQiNYOnQ3nXbnC/t1fmwGZ6I/58il1wUVueu8XdfLYHwndbJeR71jGT7lvnxUO5BipRvPLijG3q1tYsgLh0rZfc6DQf7RDNYn45mq4psBnjyk/ZWfz65maPUoVCjY3u19fB/aiUtxR3ooT4G8BJ1OZor6rP2E0x02opVpeaPwYQpuat/zaYnktT9GrbuaEWlKRBbX0OucqHc08qW0NomuGou6Y4IM8tr4FSQmmep4fsSX9KubS2r7i3TalMpxPx+OzRzKnMTP6FiZwOnEZ3myXyH1Dtlc/HyLcIxBJOLbhU9R5Pnn+DGM5/hvAFIKdPVsVmaw8kgn2jnr2Rk7AFvzakaM3cJaaTg/pS+jstaPLVmP46/RMKzna+jEUp7o9SG7dJY4arUcTU/GXK9H1KjG6lwS+96pFOqwX/pixHozzKrFLL2US2GNFwsfasDNU8b77xto1JixdMIasq9JecHGiVPtS/j4lwYeztxGvp0L615sjoQxnu/r2iQeOTiAiAx/gsJjOBxYwYd2/ZmVk8a7Z85wfl4NKhs9lipnQvLuI8lzC3WKApxVwfRcWogBEV9P8eU+q3pEOhVW1w6iEZvxXuSqVp1pauphVuwzRcS88YoW6Z8IILm+6LB68mtsKOiN7KoXiyamcp/V7aOj/8hL9F4GUozAQ3J5Hbv2BnKoj5K+NQZ+jhtI/87ZDJv6558rozSwUSLY3baMd8Y/BcqO6PNfpDH0OHrzlrLc2Sn5rDz8PF4eahY83JI3LuZQAdvOtWNA+4t06HeMuPyxhNjEEpF/kKTsieyU9OWVkbsZpvAmROZAnraOn+uvEahyZH3O+Fs203WFTuMOhTMP8YHbAKYXZ/JuRV3LYzRa6i3sOVKcx7G0EQLQY21lYPMvEoI80ngu6k2aLHqjkXmhOD8Ms/v+mvT8NgueBLTWv/gjXfHP7WvMnG9jnbLP6Mc4tHmZAJjcyowpOBt+Pcyqpc+w+N0vsbOxYuGDE7G3bfbr+45dyIFNH984jzGF5/EHJtC3R8uU/QnzXuX5R6cLIjHxyZmCaMzDs8dw/1Rj5OO/z/4HpPxWt7XKBl5ZuoYriVexsbLk9WfnEtHbtIJ8u22K89+wwLET0t/SUf7VprqXgZSLqlKMCgoHNkSxf0IWD5WrWH5qFM5W5Tz1nCnUszCvnq/W2eJuU8jDnkmERw7AXqvlfEoCltHJVHXvgWOjabVKY9aIoXwvT4R1YY+TJ8tLdjLGonmQFOnUXDylILK0iPesJ1JQ486TM3Nx6dj2RNEoTWqUKLW20rJ80n2gtaC+cS0GCVz2/JJGqypqDwTTUOTApMtvsL1/P8zFA5gZn8XVbuYopzavjokO5NL9dAPHfd056d6NN0MOC2Xu0GTPD7kTMa7+5Z2QIBbribp/J1XFzlw+EIHYXM+UeWsF1vq8JDEHyx0JN9Tg736C6V3e5pG0dDafH0pPr3QmzG+dtnH4p3ROXg1icvAZrvoV4L5/CDNnbydS4U4fWRuxc3+iw93LQIox13ttnZFcbAyP2m2k0LueHgdN952wuD0hH2cKbP2SsAvExj7Bew8WEyxvm1SwPlOPV6mU105fpLSvPfljbv8CC333qsCPcmxUP+YEBBLQVMzB8l/R2DbnAVunm9QF3ov8HM1vspTXm2BkxhbCCw8TXzyKq1dnsH/OJWReWr6sTRR2OZkxF5m+tcrDJqeLbHHvzhsZV5mjaqChKYW34maj0claSE1ev05eloY1Pyjwssvh+ShTWlBpaQLvnwrB1aqCJ567/SRVqRTx0XIJNubVjOpzDpd4KQ+GqYl1LeJh66Abcpt/omu1OOReAVJGb99KzwvR6L0D0IX0RfR/7J0HeFTV+vV/UzIpk957D0lISAgl9N67gDQLIigoggWwoNeu2AEFvaLXggIiVXpvAVIJqaT33nuZPt8zM2KIYOfey/1/vs+TJzNzzpyzzz579tl77fWupVEjPv4dS6cO4cHicTj2auTLixP1qTIvrlVxK5x223eQkyvm0REbaOlrhOiMNcPFZ2kNmPKHqin24CjaG6TcNWQZA0LepNTYnl2FufRJTeLCQwbGiPs1Kz4zmsleGzv6GQuIuAE3HibfiNJahf2pQWxJGEWAYzWm5kpSC9xZFBrDUPVwLo/+jHbTSgbEjCTDP4HIxMcRqSW0DzyC2tzgOiBWVWPaFk2Dwotjleu6XUN/iy/pYXsGWf10xFECgufNoEMoIintGVSV/pS53Gw1qjtAtOQqqUbWuMRZUdNm+M0G2Ofjb5uFe5k9c8eXUmLZxCOWIXoHstsR/2kgZemH63GqrkId3A+Nz83poDdek/jSYQQtTXz1yGNcdXOk92UlH0QOJaSpnu9KG7BvjuDJ/HraFJY8t7odM2l3Zt1rb4BKI+aVmQdw/66SMgspj93dh41RcSTmLMZtmAahpDvNXNkOZsUXCeixB+PkcD63H05SVSAPzMjFL/zWaYL7t1aQVOzJgj5nGBNtsD8/MknF0MoqthYsZ2HxvUSZzSbFawiD7j2JRipi3DVjsO3SB2tqf5/YoG8waheysOMpxjWlYKxRcdi2r57tMFBzmpLeDQyNG4a5uABBcz9UQhdkQfFopN1dY3TnN28+ikAr59usj4hKMLSlp8a8RUjFS2QEHKbnrkJ2RvQkc+JAFie/i1dLHudSXmDxuDT8Ffns3nYUjakUYWc7jba2bHliNcpfWYD5tbb4vwCk6Mq/tz2fiCRX7vUowspegUAl4p/Kwbxe+i3+rY1sLbyPsObdlHasJyLwM/ycokl2GsRCj0XUaeC5qgoerK/RV4XF2SQurmimzVFNn6KHMJM7IpIJ2J6aRJyO+TipgrD+jlwHGd6a+ylZ6ebMDNRSbyHnnb2hLM1bqz/W6+ve7Va9u9ry8Mpy4snTo7F3rcZv0iUGWEzV75Ny6DDSlmpiHm5FIVVjJndAbtSCWiinZ0IAXofqOeHjgcOgDgIkhrGieeEpBCoZm/q9Rp1Zd2eeoTnxnDnfg7p2R55cqcbW5o+JKelExl9bJ0YikrNp+tt8Vx5GbbEvW6bk6oVLb1fcyUBKvm5xq72IkztGETO8mCHNAnZcHcVA/0Im3/PnGSnf7hCSmyfkvoFfMCLgFIKst/RgilYsp7N3FCrr6p+qV9N4jTXbXtO//znofHpnCVE5vsyMOIePbRKHXO5nXsVneDfnciltNV/4wteRcSw0D8ReZIISDZua0/TH+qUx2fUTf1WVw9hrwbSPOM3aiGH0aqlhT2nFTbe9xQiSGyvYnzqTYYM7sbAy5ugxIYMDLvHgwM10WIxCLbLG8vR98MjtajW3OM4dBKR0dMoZOmMFV09+flNBdx8+z+moRH02iI5JYmtjyT/ffgrdd77+/hj7jkYxYlBvVj+iMyER0H/SMrw9un7bOgdeXXrP+BEGF7jrkV9Uzlubd+jlOvqEBujn7hNHRd603+2+A/9fAim3sxKlVz/XDwxvV9zJQMp1tsD2nVMonJDArCYhH1yciqVJM2ueMSCOKbHl7D3pRX+3DNyklSwdYaCYns/JwL22EaOcIgTBDyBWmdBsXohRRw4zewWRZmHHnuqd9DLrTnE+k2rEXVcr+CZoEFFF4Qz0z2TyPTfnNerOcemyiJNnBPTyKOTxkWvRNI6jw3gJGqGC6ID3ESmg8Fw/ymstWRW3lloHe05ELGdFQjaVTmIqV3RR3M22FxKUJdc79rzaV0a8jWFFUBfHChZgozKh5qoQeaNAn0NZkeNBTYkrrX5VLB5/mIiaJk5U25DVKsGmwY5Qn108GrSKuYWVbI+eQIhzPvOW3vwgPvJNnt42dG6vC1S7FFIaP4kXph9irtQPd/HN9O8/0+7uZCBFdz0fNqey+MJg1ipjkHilI4kNw6FTRvoCN0K/KyfFyZZw/6N8n/Ei+xaV/GK9qPNlNCpc2Hr4NC3+UvIe+OWHvkmdgp4fFqAxlrBs7kSOWNvxctke7rUQof3RClhXNrOS84jkrfyr97OUWnZPzVqQvpmghlSiMx+jorY/Z56KxcxYxLet2dRqZHxYNp4BHW433bILZjFs9OvPwsICXuhoQdNQzqd1YeTW+bN0RjXu4d0tmhNP1XEgxplhPgnM6mMAJYWqZp4+bIJCbXxL8OXGkzY2KtiwyQwHaTWD+ybikODIiFlpaEUaVlrdHsce3fnuBCBF2t7Oqjdf1V++asR0tFIDlUJ0bj9v9A8gonkOvj3r2Bs7nBaZNc+uUSM1u3mwvXmTmppGY14ct56M/jLEB/wYbR1Hh8ctBHp+4UfZWmdN9IEx+q2DQzawLdSLjS5TuK+hlhfLy1DUXqPVsg0r0zBGBPanTmzETDMBD+RvJ6g+hVj3MahEVQh8awm60ps3T05GIlIiEGqQK415p08mxmJHLo7dglOuOQHlA2g1KkNu4Y1P/kAUznnIQmN/Kp1500EEqNlX9gmd6i7wbazt6zhZZNFcNRdZhhF9Jo/HXtVOauoqMirm0ep169WdQlUG211sGBqjJLfOMGnv5ZyFo20hTrk9GLPgnN5WW2evfbviPwmkeBfkc/+/tqDV6eyMno1W9OvWt8Kki4gqizl49zyS+/TB5HIDX/cdhVNnG6cKCrBvHMxLFUVUNLvx+EM12Lt2aThct7O2Ma3n8UmxeOwo5aK7Kz8MM2fRBTl5NRNxHXprFxn5tTKG93iZkrQxxHv04nh+byZHpjNw4q2Bn03rW/RCs0+MPUSvw+X68/TyPUFTeQT7mkIZX/wVUdI5tLo2029yGQqvdsYkBGHkbGB96KKi7R+kBh/EtMyUe4yX82rZLqwVnTzh+wAjWxr4oDKG5KFZmDdaEpn1BJIaAQKNgWki98hE4ZeKVtxFmb9ug3yp6iG2ntLxxuGVyS/iXvg2GR576Xmwgk1jBlEXGcyjia/h3F7GycR1LJh1gbm5Kbx1Mg7V0CmIUi4jaG0iYeBgjk//c24Z/ytAis6q9lxJLc8rhbha19OuknBa0IMvCj7lQMFihomPozW3Zn/BozirtEwe+CRCNDzb5w2+xQFLlYrzuZlINWrML6VxdXYjNcFKgivuxq7VwL6ITo9jd/YAhoYXM3KyG2+8ZWA2Pj/nW3ISrRk9sAVzuZLXYu5lZsISrDV1bF79LI12Xc+wk52lFLS18dUX9+uNnsfef4gXJC7stwhjTlkOGw/HIpMqubSyHZWRoU0IEDH0I3vM65Q8OXEgrzvn/9T2TCoSMGqvYl/QYlIcDdow1yOgKJmKS9bk1PZgyf1tePn8MeZ4R6eAt98T6dMm35v0MbvL/blSHcTlCYW3LT1RV9Y7GUhpUMv5ui2L7d9PoGR4GkMaTdh6ZTQR3oXd3Dr/SJ9+fTFH950P5y1BIgsiyl9C2JW7cKgxjMPlPRKRe2bqX0sUJazbuUDPhnposRpP965n9PYtlWRXe/DA6CO41RaydexSXri0AolGwcHozbw5NparPsWstOr1U2r8N63Z1GlkbCmZQrjs1guU8WYVfKXO4PmjExB4pLNytmHeodOy+3m0iDsp7qzmy4R5RIS2YW5lysXLImaE72Fq2B7arKYj6LTBPHr6/zdAiq6OdADImd0bfpLM0H12ISaFzV/t58v1z2Bhbsb56GS+P3hOD6RcD4VCyesbv8XSwoynH52PjpFydvcGzEx/v9OfWq1h2gNr+eaj5/V6p//O+BtI+Yu165b8DfPNb4/1sa4odzKQ0qCW8XVbNm/+MA2boWcY3i5lw4UZmBp1snatYdn02J5CYjICmBWYyGUbEYfCg/WrHZtLCxnX0oxxTintDrYgdUfQlI/I0Zb+PYJpMTImrvpbrM260+SjirVMOVdH+gRfNl4wDOBXLW/B2v5mHtl1hPv+yO0MDzyEsu4V5GZBNJsVk+axHdtCMSklPcgqNuGlhHf0x1p31xu8e8GATl99vWug6fpRPs61Sg54DuauiQZhu+vxauUIJrT60lYmpDFbgKN3OTXFOpaMgJhhF1gfkssDudW8J3NGpdVSmBPG4OBPeMlnEWNqOvg2ajIB9kXcv/xmWuQPXxZwtawHCyLO0GxeyqHyfnwzJJYV1mGI9X5Ofz3udCBle1sOg670YF1VMcGeh8nOmEhgYxN5kTb4xzdy3t+ekU6HWZf/KiX3liO9AejQ1Y5JRyc9sjPoqNOwLWwOl7/Zi8JaTPrqX/6d2qa24L27gk4Ha/zmG9I8UnPfwti5ex60Tt/CqLWMo/7ziXPV2d11xYorL+PQUcnpq6+RK7Ah7zGDgF9UZwVXFLXc0xjC47VdbgrXv5mpjeH5sP6MrCxnS0Mt4oYW9qktOJc7kKkR+URO6w647f+6mqQSNxb2vkyEX9dE/tOoXLJrA1g8rRbviF9ON6mpbGXz5za4W5UT0jsB27gQxs69gLPIlHtuI135TgBSRp48wbDzZ9A6uqPq16WWL447zVceFmiMFtPbp4ZTVwfqxXpXPaHG2upmIOX6yuv709eT6K1AcM6JoU5lyO1/PyiQfrEv5TneSKy0uJlGYdXrKMNDXjMw9rLTEajUiOtaSPFxY6ZvD3QZPYtN5DwdsxojjZJyc2/OOg/C3i8R5wJPvj80Xr/Sqgt782re9rYnKWIvVR55hEb7Ya0wMOxyvdLok/yo/nXrsL1ojQ3cX9O2GMSqKmLrHya/ratuZrssx0TSTF3BPWS22DF/YD/GNGXwbcGHxFc+h9bz1kB2e0cpzwdbsPBiNVcrDPXSzz0dK9sCJPn9uXv2EQKMrJhm9sc0ZX6tx/tPAinzt35BQHY2ar8QNIG/LV4nyklGmJfO5eEjOTtxMp2J9fwQMkp/OenpV7BtGskH9dfIrOnBkrnFeAV1gayFBSq+2mZCD6cMFvQqxONwKVt7BdERUEHEuR7U01ef2nOraK8SMMJ6GVcrR9Pp4Mc36ZH09clgxv03pyLIZQLefFekX3FfM24/vj808cngniwXfElK3n0kyMvxrqokw3ce5vLvcBvtgKZXLf2u9MXe6SvD6RUO5GnuIi8gDnmOM8tt7mV33DasWkWMH2sQe03KSebqEENq2cCslRhpLJBUi5A0GVgFWokMeUASChfDBNlIWY5JezwVnWFsOvMktkZFPDZkO9Ksd8m220bg6UbemDMBpb8bT8WtxVpez9G4D5ix6ADvnIplcXIuysn3IWhpRHz5qN5y59uHllHke7Mu2a+1L922/xUgRVdWnbXrkgIrNgQM11/W6czXURQEY0Y5Nk52+nr4Ji8Ur+qBhEduJsDkCoXWgcwNWEW5LiW6spz7G2oxS8gmd3AdhUNkeNeOwb3BIJBZUnCFDUn98HOqYva9Dnpmo05QesXMPRTE2TF2SCN9KhpZ2rSKvieexl+RxHcLHyQvqEsXQsdsviirZPO+u3GqtiZsZBwCn3LGW4zXO6NEnTqJf1Y5Tb4i4he1oEaOsdyM0W+aUG9iwjtz/HlR1MVWMG7MQ1KXSbzLSI78zE3MrC4Lm3Ma4st6M3dqNaF9ui9K/Na9b2wWsOFDEfbSGl4bt5UDVS7sbepJ48haQo3+nHjtrc55JwMpuqfhxuYUXvlhKlYDLzOgyYJ/xY8n1LWIuQ/9udSeS9EiTp4WEOl9mYeHfkym0TTaXW1o1RbjndYH3zyDzpbMORtFaAJCTQd7TnhxKW80E8drGDywq+/b8F4bjZ3WPDxvO9pkMYkjB/DYlVfokNtzNO59npi/B5GDkjnSrvHfyc4S0hU67bpI/bjsVvGW42WOWuTz7ecPINIKePfRSsqM7Tiam46fQtXtK83CBlo0zayPno+/ZwsWNuYkpQhZNPhTBgfE0GY5GaMaT0xTh/9/BaSsXfc5NlbmrH5kHhqtlpbWdqJiUzhxPkEPnMgVSp59Y4teO0X3PiuvhCB/AwN8295TlJRX65knOo0UndaKTiPFSCyipLwGF0dbjIxuzWyVyRWs37JLf/xX1xjkJP6d8TeQ8hdrt2fqTibdJpcL/QBCLETnKS9TdLfY+ovFvC1fv96hLj87gqGB0QSpbdh8fjoCgZZXXzSU94tPaiiuc2V5RDSvenqDhz0Jci3Laqv1Cv46Y3VpbAZCuRKVrSXV/QLpHxiKqVpOWsMu1D/TG8hultNvfwtVPbWc0gzgQlEfInxLmHlf9/QenTXhm++IUCgEvH/3o1iJBLTLPkErhjLbyxQ5XMA3yoREjSeZhY2svmqwAVw762nWJDXh19RC6nJPVC4GgOa6Y8/n7jNYOtkwAXATSTkvq2BacwAvVA9FrRBQcbErFcnMUcPGyD2c8mjhg9Rq7hcYqGjnc/syJ3Ajm52n0LPVhB1R0/C0ruChx29Gwnd+VkxGlZ+eZq3VVvCu0IdrYaW3Fay704GUEx2lWGVaszldQ0TANuKKpjOgvIY6J2Psq+WcCrdihPElVjc+ieQug0S5VXMTPTKuEXgtHa/CQoRaDZ1GEoY9s4u8T77T75P8ciAa8a3BKLfjNThdbiA21J9Zo4YxoyGBDZ0ABNJvAAAgAElEQVRXUFp2Z7FImgowrr1GktNgfghc1O139fLFZQi1WvZd+pxUjxpaFxgooCWqVva0F+hde3TuPT+PenkyD/ULxa+liaOlRQjkShIErWy/Mokwt0LuXtK9DBvf76Chw5J/jEjEzr7LPeVkWiLHcvoyPrSQobN+mX1TXlDPlm1O+NoV4R0SjzhpEPNmnritjj26a/xvAylGSiVPvv0GJp2dqAaMQ2vXldolSo/ntLiVOJcnGO1aTWxaHyqb3Vn5qAIHnTDJDdHcKuCDDSKkxm28Mu0rcsWdSHK1BDqbobL4fa4FSpkxZ7cb6OuugzXUJ3Uwrd8KJgQ/T5qZF18UFzC0rUW//VMHJzY4uhAkhlUN55maZ2i/akRs6PMSIQ7bESvEFHw/joRSw4R+mPcVFjj05sy4dxEpBAyIG4lQbbiONlE5QqNwnCqDkHkno/A3pJpJFAUYd6RQ2tGfqNon9Z+JhTLmeSzRiVhRmzqbrY5BbPD11bOzllafJqrpn5g4/oJGlULGspBO1lws5nyRoV0O9k7GzDaP0rJhPDPxEMNNXOh3m8SMdcf/TwEp9nW1PLL+Pb34nUrHRjH+7ZVtQUUB4uRosnuGsOu+ByguaiXbLox6IwsSMpJwaxjGv1qTiC0LZd7EXEIiu1JvEmNq9bo3w31jmWRWhMOVJl4dGslQi3Noz9+N1sETS99bAym6evGvf4sMvLC28ObDq8Nwtyln6cqbUxuL8lr4coctvvZ5POBwAbckJZ9M82B5w27OJ69FqfgXDaq+VPaeQ++0B2kfMxXhkDzcc3oSavI9CLTQEUSKSSCVXoVk5YTzlXkk2w5cpbXDiQ0zhURbBOoXUlwdj9LoLiMkawI2AkP70KWLGFeKEHV2sVPkgYkItErMmw/r97lQs5oRjh+gbeuJMOt1ioWf4ZUo57mlczC2s+C56KcwVbXzffyHzH9wN3t2nWV0fSeqoYbfmyg7GWF+Oq3mFny66mlkJr997278/f8vASnNWgWjLpeR6GOPrbKNZVmpFHU64u3QNeE4nQ9GlUvQOuRwd+j76FKoV4a/xF6RG6Nam/m0pBDT1HwqfGu5NrUdp6YIAqoNosXamixWXQzSM+EefUTIhx+LcLYsZ9HkIxTGOzJ+aB1TMxqYa7oapxMfEdl8kMNjppI0xgDs6OI6s/mRxEGMiQ/B2beM8FFxfKa24QPrIQS21HHm+xMIZQry5zpQ3UuD7zElztEtfBoRgldwPUMlXeNjUUcdZuUxlFt481nE8zfeOpTtDUScLeRE3iAmDypj4LjuqT/ddr7Fm6pq+GSLGFfrUp4fvoej1U78U+WJbaQKd/Ftsj6+wxkpumr5qjWL+04NIiQkkT4tlmyJnUSAUyn3L/tzouEffiykvl7Ik2PW4WduRUqoOxIMrAGloAVpsQmhVwxpswqLSjojLpCc2sq3MYsICWxj3jzDb1inh6jTRdQ5jt2/4nMqzoYjDlMyM/trSmsjicl8lAWPfElfEwe9kP71SFXUc7qzjDGt3rxZaQC3bwyNQMtEn+20iJW8fGAyoRWuHFyUzGnrYD4sSGdiZ3cgpZUqtKJOXr44H0fbNqxspeTmCXh89Dv09Cqhw3w4onIvpJnD/r1ASqGuwn6rVf8btuvWRm5BFtGl7by2QWdjn60/6XMr7mHEoHCeeHEzRWVVeqbI/bPHc+hUtB5Ief6tz4m9moGxRIKHqwPr1j6s30fn2vPeJzuJik1FoVTq03y2vLvmJobK17uOs23PSb122eTRA/Sap78EttzOWvgbSPmLtTk4fR+DTP5Y5/xrp7yTgRRdub9uzWJsXAj3WqdhbezIjotjUKklem0BnZDXa28IUGtFvB55lmkRQwg3FnCsU8uQtha+LC7QX7qopQNRWycqKylp9rbM8u1BYGc5h+TxtLXa0VRji1uPIv2+QmUHdYcEeLW0kTPDhw9OjUGtEbNimQzHG2xeK6vgn5+JsTJt4/27H0JbO5t26Rz9MTKcvqPBuhD7w660d1qSWp/IwmvR+m2fT55Cn6oAxhRWkjrJGtVgZ1RVrUR+XE6tTmjYfyCfDrzEVDMvbITGfNuWg5NSyoFCXe4eVMeLUPyoPeUQoWFh8FZKrNScutpI+I8rFjsL+rPSfz37bSORqrzYdW4KTuY1PLbq5hWNbz4uJ6/ei/n9zmDS1MAyLzOkvupuD4G/2GS504GURHktFcVKtka5M6jnJs5UzWJMflde6oUBIsJlpTxuNJuBbmmMOnUcl/Ly7g9Ca3v9auSCxz9l08EEAhqbyXzMh07nW1MDA74sxqKwkzWTRrDD35fv8zYQ4eCD9mf2hiJZA2all6mSevDPvi/+dE4reYPBbk9jxqFLn3C5bw7CcYbcfzVaNjenohv6XU8L61bYjiJmDjCs7FynjRaTz8boe7A2bWbV010DtvYOAe+8L8JIpOD9qXVoxF0T+dzSFD6JD6eHYxn3PfLLfVJRRjVf7nEj2DEX16BEyjPH8PSkA7fVsUd3Lf9tIKV/9GUmHj6A1sIa1TDDpOp6CAuzyKrLZUP4i9xrV0x6eh+KG7x5dEkbLm7dJ1pFxQK+3CrCyy6fRRNO0lrdiWtrPmLPwWjFv29SVpgaSE5CKCZ2Whx6a2jMFtLPbh17A7x51f1uZjQ18G55ib54c30CSDGTMs5EwHtJL+PYWflTub/o/SxeJodQWqkQHBjGnmsGwc6loZdx8hWSEXoO5xxz/Cu6WyyWORYTnH0vGnEHbSP3GfpWbSfS5uOotBJ2lX6BVivEXpLLBJdXoN2bprgezBw9ixwTUw7kvId/pZIM05cRm/6yzsAr3gUsu1LL4R9XFEf5XUFiXcipzp583i/mtqYo6q7hPwWkTN23m4grCWjcfFGHd7kS/VpfLGiuQ3z5OLUOjnz61BpK2tupkfuQa+bMsZw0Quv6s78jleOFvZg8OJuBY7vYEqf2l3IxzYdZoeeI1FRhf7WZhdPG8Wr7FlLOvopNL2N9W/qlsC/fRZNtLe7CYF5IGodIqOLlf9y8d/ypCg7HeDLK5wqzquKpVVnSODCP8I4i9kV/ytCmGfzg8RqC3v3pc+wJckfehdW0OExazBhZVQ6mpWhaBxLjbESrbTP78icjVrUx77Q3dRXu1MzazRue05lZX83jslMU9K7GtSgAX7nh2Xw9dEKmJhUivV5K2yCDDpVZaxQidT3F7QPwksahbRiGsOBJGhs2YFMs5snnlmClW8SJWqrf/+OUj1hxzy6ufHYQHzOHrvuk1SK6eBhhWzPpYb3ZP/+eX7ttN237XwFSbOvriIiPQ62xY5pNInKllEvNAwiz7e4y0lFUSGz5a/q0mkkzP8K8LplEqxCm+T2OmUZNUmYaxtmltJhWkbCwBcsOT8JKDWL/xk2drI2W0dxpw7y71XpBTR+7PGaPOENesivThlbzYGwno52XY3pxP2MqPuZSrwGcWzD7p3qtV8vY2pZNz2ZHXt0xHaFIrdeZE4o0jJUM0jv4fB0Txfgr+SjNRWQ84UfI+3mI5RomLJjKXqNL3dJtBVoVOkt5Xbw8vLude5sWxp+JYXfaCIYGlzB+zu8Dvq8XtrRUwOdfifC1z+WpQcc5UWXP62Z29AuVYv4zjbQ/1Kh+tvOdzEjRFVVnAND/UgCTnbMIUUr5OHoa3nYVLH7sj+v2lZYL+PwLA5PpvRlrSLGfgNbqZpBX3KgkMHoExnIprZ6xNJhd4PX9q5CaKHj2GcMiQVVFC5/8yxY3q3Im3LWdK9UzGSi+RGTlBVILFhDdNoRHF+xkupk3/jcIYNeqO/VjeRellP0/juVvvCVXpBWscDuBe0s7D14OJqxoPMkzL/GlV3+WleWwqrm7NU67qhwLqZYnzs3GRKLEysaI6mp4ccpzuDhrkZn1weTKSCRN7v9eIOWvNMK/v/una+BvIOVPV53hi5MzjvDJh5/SamFBraMztc7O1Do56V+3WdzaTeTXTnmnAym6DtUz1YkXFEW02bhyNiaSNrklz61R09HWwUefWmBj2sCSHqk8MWQQvSVCvm3XYKVWEZ9lWAm9MY5ZWPCkpx8TmlN4u7WRi3sMzibOPqWEj4rXZcuQckHL0MI6Eh+wJveqB6dzhxDiXcW8hV0uBDovep0nfaR3LA8P24iyaiNyS8NkMs5nPUqJjPZ9vfGrElPW/iVDGmT6gVWeXwAFrrOYm15EVqgJHfO8USVVE7mvkVRnGx4MtyHVq5TllqF6QeFPW67RoVWxq2gWngorWgqFNBcIEJlosR0sY2Tgdj0boixVi/jHSfiOskAeddlJvK07jcbhHDgxCWvTJlY9fbPmyb82VVPS6Mbdg85gXyhj3NAmRtk73zYnFV193OlASpGqlTO1lezbP5DRvV/naP0sJmd1ASnpQxqwrZfyhMcAVl3bTvC1dLQiIVo7V7TOnmid3NAaGSOOPcWamWuYk1zDxIISCue50Rh6699k+Bs5iOQaQh66BwtBC5dKNtPpYtAfuTF+adDm05TNotQPqJN5cD7+dU5PSEEa0bU0sK+9AN11vVg1jCkt3VOMROoOlvZsp0pio9cSclEqaNYk887V++hUSHlmtRpzqWHSlJ/SztYDVvRwyObR4YHdyqborOTZoy5IRAr+8UJ3VsWNO+YnVbL1kAe9XTNx9E4hurYPHw6Kuq2OPbrz/TeBFIFGw4oP3sG6sRF176FoXLunkwhrKmhLu8iSCS/ysHElxdkR5NQGsHRhM+7e3Vcac3KF7Nqlopf7VQZPTESVKiZCk0y7j0G74TdD5+i1cxLyDjOEfds57p+Fb6U9kwuycAw6Rp/QdzDVqInNvoZcICQyyCBy/pK2kEeS3taDeSqpsz6l7ITv3ahFlXqdFNeofmy6ZEh3fH9oNqlhJ2i3biM02h9rRfd0sHZhFVLlMCxanOgMvoTSzQBUm7WcQaRp4Wz1WiplofhLoxhgvwVN42DkF6T4zzdMoAqSV1BePow6z3t/utwidRnPO5+iVSLgULmBnfWVQwqz8lrYlWlIFZoQEItIWsFbVhYkehWz0rLXbRNm15ffWIxcpUat/mMikr95z27YQdrWxhPvvIlIre6ms/Nbx7guaqwRCnjrtbdoF2iprLImzsaHbzJSGCT3J6algp05oQwLz2LcjK5+4bsvK8gs8+ShQcfpH1OCSKZhwKI5RF17mUOxm28pNHtjeUwqEnF03I1J/UDerhlJfbsda5bXYGnfHbzft7WM5GJv7o24wKiYTLaFBnKf1VZa253ZmreK+0rn8WXE9zj2sMPm8Mek9hhB78VH9Kcal26LyOo86ubJnAmqQCPW8EzVo6wqPo5X7nzaKsHGax/zRk3FUinnfP55koalY9YipU/lE92rTwsWmQamU+vIXWjFCq7bIP+0Y9V01FULkBd9Qq3Cmq+Wz8FILecfl1eiUpnwVsErvDTzMHXv7YDACDQ+XWl3N6b46NhBOpbQ7407GUjRse5CUpMJv5KAZ7HhN10rNWXl9IUsK1Mz1EJ202UKWhrYVjYP9wZPXEafZpDwAEJFKyERH9EoMNaLX0dmFqCuL+P8qiYkSksiCwxW7KIOAVuS0rhWFcrAvu3EJkoJck5jSt/LxGc4s2hYHWvOGhPuew/q5CvMyH6ObBdfdq3sUtjU/VI3NKcg1ArYt2MxyhYBfSdcwt69motqEx6yHotUJSd9+36MWzqR2UvQaZglO9rxweQAPqFLH+X6xUmLziFUtrGlz/NUmHfv6yddiuJfCWMJcSth3pI/BqTkFwjZuk1IsEsqy/pe5lyFLU85S5jh9+dSWn6pzf2ngBSJQqEXW9cxZ4VqDTo795/+1LrPf3yv26ZV6/cRaLXsthMiTXPhEVEpfsbmfHRxKq6W1Tzy5M2pUsmHclGqof9dt04BPXRESEKikCm99jKqRz15wX4IdY4QtwhJsxGhZ0bqXT4Vgz5izZePIFOZ/ZSCmxtfzrfHvejjls7wkBPsd1/MrPJthNQlcj7leY7aSXhvwmketQzF9AZLZS1aveCsCi3H8+/BWt19ge1tx2h+sM5mdXQanvWOuJa9QM2geN4YEM6IujI+q67rVlq5ogx7c3MeiRqNSmOEsYkWXdrkB3MewcTGA4UoGIvz81CLlIge/mXX0d/bJ/29351VA38DKX/xfjwSv4e33994y6N0mpqSGxjcJbQlEOhppR1SKR1Sc4wUcszaOzDraMe0rQ2zjg6kne0YK5T6fLI7MVbPGEVtjR2fljWR6+nKtdie1Lc7sObxDsoL6/jukCdhTtnYu9Sitc/l3sYL9AzfRJNIwtncTNwU8m6X9ZmNFR+4+vBw3QXmFdqTcbnPT9t1YErYqARi0rRMuFpLRn8TrJzdeOPsCL2jybKH1Li5Gupp116R3ibxgUGfMdS5hHbNm2jFWmSSJq74fIJpk5CcmL44FJsTWPI6zl69EKbHozQS8/2otaxIyKHKQUTF4wEITpYScbGd896u3DW0FRcrYyabGSYmRzuKyVI28XTNQGY3GfJ+1XKBjnZAqkkWjwZF06u2iTNVXRoVR2ocmG6WTKWTgiTr4Zw/PL6brsyNFbJ5QwM1rY5MGXoe5zQYMiOPx6x6MeHECTxKim/ZJHQWjzITUzrMzOjUty3DX6eZVJ8TrWtfZu3tmLa361/rBDhNFXI0mjuzjUX178vjPhJ++Ho6M/o/w5G2GUxJ6cqJbh6RSk3xYJ7p78WGXeuxbmpCNWIGWml3kESYncTGiKk4NVmwIjGNyjH2VI682QLUuEFJyIZ8WkxNCHpoAU9WHuFxbTnKX0jbkBad0TOltvR5gQpzQ7voW3WR6TnfUljfj8RrKzhxXwKWN4y1dCybC7IKvbaOTmPn5/GWVzbxln58nZ3JIJUcWXMO39T1J7M6iAenVuPzY273pR+qOJnqzoSAGCaGDbrpOO+crKSq1YWVD7Tg4HULPzod6yW2jO0nvYl0T8feKYPPRW4cD8rkvRI142K76wHdeAK5sTEdZj+2LV0b+/G1WizG7Mf+y6y9Td/OTDs7MJd3olX9cvrBv7N/EyuVuJWVojUxQzV6VrdTFba6YCqvxyP+Sybf/SxPqlspLwrhWlVPFs+twzuouwvUgUNaEpOMmBWxE/H4UsxOOjLAMhuZS1da1a9dS22JC1dPDUZkrGXPXbHssL2mz4g4vH0akyOeZXbAGmIsAviwtAiFQMjT7p64i+BfRV8QVhuPwspbb7FsUp1Ehl0EiTZBep0U73wPhA1FCEVKHATLiInYqk/rGRTdXbvnetnqLVrxLpmEyrSajiGn9B9LOq9hLM8hs2USVxvvI8J8Oz3tjiJvmELrJWPC584gsLOac5kvkVS+DIV3l8bPLs051gcZJm/vXgtjuFFfTpolMqy6hW3XDCDTXUHRaIR1PNyjE42NitUdVkw6sP+23XodKKzvxv6Nz0tde3asqUbj4IK6v0Es+PeG+OxeBLJONq9+hkY7e2pLJJx2DOKt1ESm40xei5wtmcFE+OUx896uCeDmDU3UtNrz7PjDBBwso8Jcyn3zhrAnfhvnM1/Cbeivp/5q6poYZLWaipIx7BIMIr3Gn8Wz8/AO6T7J3LS+mdo2O54acYyQE8W8NaEfa9s2U1IzgN0NkdyT/yrfjfoBRwctmqhT5DsYMWx2IkpbFb1TJ2MtcqNDqCE++AuMWkQslD/J7nOHEFjMoq0cjBtyeHm+MTmmrmwvykPR+yAaIy2RWY8gEXQHdcwKxPpUn46Is6jsKhCpmzFrPftTVQvKHqCzeQSilK3E2blx+t6JWMibWBP3DJ1yG16qfZxtQ4+QuXkvqv6j0Tp0nzQLs5MR5acjMzUlbrBBBF8rEKIbp3Wa6/ozM0RqDUYKBUZK3Z8SI4USE40KsUKuTzURKxVIfvxcrNLt053i/3vbxe3az7m8HOMfx1S6xQSNqy9arx5kONoQLPtlTbXvc51xrp6C0D+ZSf1jMS2PZaXPEvbaROpTsFekZWKSUXyDBfIzCDViBBo4kR7NsdzBuDq0U1ErpbdHPIN7JrG3zJl1fWt5/ZQP3gEjaS+uZl7svTSaWrD5xS72pu7av2jJRJeGtOv0vQhyTfEIKqDnkCR9tTwqCOCseSDPpl3hifMG/TpdrB05iB7uFdwt7s4G0G0zqU7GqKWUo/4LiHPtnqox68ppNl2cjIdNNQ+v/P0aKXKFgLPnBMTECYnwiGNRWAox+XY83FvDLAdvHvvgHWzr6/VlUxgZ6duSVtcfCYX6P917HYiq/6+7N9e3//RfZNguFIJQACLRL47JdAsDesDjOsDx02vDZzqbdcM2DSKdhfWP+92u9vniPTOJ1gayrq4VF1sxH0XdhZ20nidW3yziue2fZeTUemMvrWPW0BbcB3S5HupckN59T4tMZ5k99SWKgyMRSX7dRMH3fE+sG9xo7XmQz6NdyagMZ+7dakJ7aok7XMyRq36MD4yjl/kVTkbexRPxL6DSmPDDpU/Z3e8qxwak3VLgXOceVaZu572KMQxr6yqjPq3HdwctIgWXvjyC3NyK0tYtmDmV8sRsDxzl7VzMM7ibXQ9tRxMm1sa8khhETWsXG/jz++cjkw6EhkGYpg2lxjkHx7tun2327epH/j7OX6uBv4GUv1Z/bN63ift2fq8fZGndAxA010NLAzrUX/Az0OAvnuqO+Pqrc6ayy9aTQ1fEnAl1ojXWh4oWF558pInk2GrOJwcy2S+JE55mrFJuJbCzghn+L5Fg6aafKExsMWhaXI9/ONix29GDN2qO0COmP1WFHpg5a+moEYAGnHxKEQTEMvRMPZVORlTPdSP/oohjWePw92xg4SKDC4dO9EynBP76jNU4d0ygzdKwUltreY1slwO4pEqIawygPs+b5clPoho5A1HcaQSd7bwx62XeP2cQBtUJzppuLyQ4S85hfz+mjS7uRgtMVzSgU50f2ebF2xXdJyw7tefZGFjIfbnVrJc5c7YqAi0C7EyvEahsQ+iRy07nmWTtMwzifm7jpvts/XttNHVaM2r4BQRZYp6cUsj95oGsef1lTDv/Gwbx//lml9sjkOGzBvDWd3fxZK+nONE5nglXDfoRBdYW+IacIq1gPm9MgC0b3kErFqMaP/+mggqqS9ln5kyGyzA2nrpIQ5glRbeg9tqkt+LzfTkxni7MnjGR3bkbCXfUpfXcepXEpDIRo7YKDgQ+wFUng9jruMK9DC09QXrRbLJKpnF2dSymRl3fv04ltVAbcSrfsMp/Y3zjcJX9zmG8lp3FPJUMUXUtB8U2nM4ewsReBQyeaXjQ79hSQ1a1K8v6xRDkdTOQsishjZiSXswZWESv8bdeNUs9X8ieqACGeqVgY53PGmcRxU51RO9PwD/HkMv6fyXUwX3R+HQJHcbVBfFD6RC9W8U7VeOYN/1BHtJIqa3wJ6miFwtn1OAf3n2Ct3GjhoYWCWvHricnUobxQU+GuFehsL61nezP605ni15X5oxFgJr5o3bQJDKAyS+eHc8ai39y1MOXVV4LDWLcGg2HrW0YKVawI36lfr9279GgBWnxWVolVmwOe5redt/odVIG5T2DUCmg2OwIRQHJOGdb4F8ZSa3YiEQzabf+tkNYi03rBCQKM9oijqGxq9enTuhSKFqUzhyq+ICRNu/gZplKa80csrLNuWvcSBbXnuWN0u+5ULUJE48ucO55092c9WjTl3FMqZQ3O+eSTgpe7XXsTDP0v3N6RqGSK5g6pIheElveP32VyJgu29z/pXamihyD1v6PaQKI404hqK/+SXCztQBOOQXzXFoKs0TmNLZZ8G66L/7OJSxc2jXxvy5uvG7Kbhz31rMzOIBzfU14ITad1PqlOPyC0Oz1+tQoBfSWryKrYiDZliGcLurFjMEZ9B3bNYiXyQSse1ek1xd4edABXM7UsX5BMKtKviKtcB6XZc30L43lyoT1eier5mtFdGpP03uCHKVPGx7XhuIlHk6TNon0oGMIS815QTiHj3+ohOBwZA0CapOEZM49zj9dR7GkuoTp0sPU+XTeMr3HuEqIpEGE3DsNuX+K/lKu2yDrXgsKH6dFFYjl+d3sCgvi2pTB2HVW83jCi7S0u7Km836u+e7nyI5TKMfOBYmkW/PSr8RfOqpnov5fCo25lR480epAlN9p8ZycU01zzfOopA1MmX8O07IYDpr6ssJ7CQPbWtmenIxpUh7nV7fQaaWiT+FSzBSGRYjM3Et8lmoYw+hikHcMIb7JvKJw5YRvLZvOjcHax5PWVphzdBJilLz78uvogPjrcZ2h+WHmJFzPu2Fs2snIe47qN1doBIyyMuhjpO/ci22t4dkftPRejshP4Ci4GSCSNBdjXJOqt3PeH9hdXHJe2nHWn56OuXEbzzz726mY5eUCYhMEpF8T6jU4dDEu+DATvatJyXBgyVg58wSOrHn9lf9LzehXr2X/wL68EjqIbzPEWHpq2XzhLixNWlnzjGm376llcl59tzujM8w1l8mTbTBztSY9XcWufSb0cMzggTFxlPv+9jPUokxKQPxgFOYVHFPGcCh5OoP7NTJxsgWHvikmociPef0vYCSvQeyjZGjZScqahxCb8jAbxp2lLrieqbcQOL9uArCoPoxH6rsWRRLMKljpfgK/hlaufH4QZWh/3hYvZUSJM6uWq+kQmujZ9TqW/fUQdijQ2on4ONWW3FqDaYWFSQvr5yyl3XIskmsTkFT7ktHrCD2HGNr23/F/pwb+BlL+4r089+5T+txpdc9+aLy72wvqJumC9hY9oKJVykEuM4ArShkolSAUoZUYg8QYgcRE/1pkYoJIIkGh48bdQSFobkCYdZUj/cJ4YFQ4Vw+78/VgO+xiHChs9OSxB2s4dbyDnEpvloTF8XjPABJyV+mvYJ3zXDa7juGh2hqerunuwf6Amyux1o58U7Wbtn1zUSkkuAzSoFFAdbJQz/SwDUlkdD00t8kAACAASURBVFaC/ljxr7vTI1rM8xdHoVAZs3iRGksL2LhJhFTSxsZ5D6Gs/BdyKwPKne94gkqbRIKPSYmztiAuK4QXk19ANXEBouRLCCuK+GD6EhalC/FrbCH5IRccD9biWqPi8+BgVgzLZ7lVKMIfHXNaNUo+b81ANyE+mX9fNx+dV032ccyzmXfSqhjS0p+v8g1pSgv9j+HQ3IGHzyme91qMYm+Env73j7UqJD9j+a1bp0SmMmXAqHNUFJjyw7hK7u4w4bEP3kUrMUET0TWA6dY8lPIf25YcrVz3WtfGdBM2ARgZo9UNYiSmCIwkYGKCsakpsp8pj//Xm5tSiejqBWTGxvRfvYiFB4bwkvs7xGl7MzzB8NCK9bRnoMdhLuQs48SQDJ75ahsaexfUkTevFOvqoDAtjtdmrOPQ7sN0uBiTtfzmB7friRqcLzXw3sAINvTvTWLcO5j/CvX7uktAgvMIDvcwpDrMu/ZPetYnEZe5nCvyIAqW3wxIXE8L+7p4GkHy7syYU+bxfOLTh4fy83ha1oaouZ14CxnbYifT07mE+T9OstatUyNTGfPG2EykVl0AwfV7l5ibwrbUcPp7FjBtUdcqy433NuF4Lofigxnll4iNuIL5YU1IzYWkvve1foVV3X+0Lv/r5uagUiJQKvT9mFYhQ6Dv0+Sg1ej7LoGRro3p+jGJ/r+phZRO+X93xVZrbY9W1CW0+H7G3dTLDatob7bMYfnIISzQ+NDY4EVcSW/mjy+n58CufO2mZgHrPxRhLJLx3l3vc9lThNkZOyJ8hXph7MzYMJy9K7Bx7k71vV55nS3mRO2egEAIhZOKeNrzNNZCCTKNGu9Ke/Yny3D3OUOP8A/1XzFRq5GJRHzQEsWCvO2ozOzpdDMAZub5xxFolKwf8BZhfIvSWkVAxn04iTxJcv6Idqs2el0OQKvxZa6bJ8UWlpiplExpaWFWcyN9OtpolQhxrR6MzCYNRd/rk9XDenHPA+UbGGO/DnPjWurKFrCv1ZVXI8LYXPQFIwobyJCsRWLArfUx3vNrWkwMrDaxBk7lzqddWa7/O5lmAJkX9DxLR6uQ6aPyGGfqwTfvfKgX/1X3HQHiv05xNpaIUKo1aP6NqT2669Cx/rS2N+fx/1afKUyPQ1SSy6nJU4kdOhxJdgdbvQYysryQj5qaUTd78sI1Fxysaln5hIHFWF8v0It46pyYng6Iwi6mnsfHDcfDKpMpsQLKRDOx8vltppd79T/Jwxa1WSA7rvUl0j+Dqfd0ASnFuY188Z0D/nYFLLaNorzGHE3/Uvo2phCV+gxt8h1o2xxpnPqYoVy14FiwBtuxPdGE12KXE0iwdjYV4uMU+F2lOcedBLU7ky4OwMLfRL8QUnpehLjPCZYPG4lXZwsHixK4MtxgHxqRMQ+pqEsXxqhFiEmZCJVNJR19DQ4/xu2JSJQG7SBBzst6oV6704f5ZMQAqgeH4NZaxNKkddS3+LFcPAWRyR4+PpOEaszdt7w1Alk7urEMCrm+L9OPy/SvdeMyJfofqlgEIrFBH0skQqx7SIvEerFn3TY9wK7bphOFu1U/+VuN4jZu14olaK3+uHuMsDibgzWvYiE3pd+8gzhri2mpz6N3r3f1pctNiNObAsQ92E6Dj5zg8jnYtRlSNVoqr/JydBdzeKTfJbzcMlkotUNlXMpz+cuxshGi0aV3/LAUF1UBXzy6ggqPrufRuc5ykhR1eteUkYd7oZYJGDD9LNYOjfpzfKyy5SObwYwvLeS5tCy2+ARwzcuEveou69n4I8Noa7Sm95hYHGxzMSu5SJ2pE5v6v96thqdnn2DLsclotCJe/ocK0S0ebbpbn5ImJCFBQGW1AagRi7Q4OmqZ7PsdA3wP0VA3iZRUO56dpOaR/Dpm79iGxsYBbY/wv3xHRSIhIpEAxS+YTOhYLQIdawUD60UnpinQMVt0oJK+DRo+088pdCa/+n2Ev2nR/lsFF8g6EJ/dR4O5lOBld3PptCvanu18dn4mErGcfzzffbGpPK6YLSf88LcrZqyvjF3X7GnosNODtRNC8smusiSv1pVFkTsxGW+LUNsd7Pyl8oQcGazXSrnq/AWfnFuIp1MjDy2z4F+bailpdGHx+CMUNtkxU/AtZqp2ojJfpKbWjzVz9+HtZkJfY4ebDp2raOZQZxH9213ZVG4Yq+viHcdo9ltn8/TlNNYm5uvHls9pApiYMpWtS7JItPBha0EGAztvUHVVa1CYC9idp+VKseF57WFTxEtTn6PVeiYW5+cgUBmT2mMTYaMNCyR/x/+dGvgbSPmL9zL3sVk41NagHjwJjfXvpw3+0mnvVI0U3UBDfGo3zWYmeK+czbmDPdg3SIr7FWtyav1YOr+CHfstaZOb82y/CywJ9CMqy6Bud8kiiLkBTzGorZWvi7vnt47y86fCxJwThbvJPnAfQolWn/+tC13ubNVVA5jSz3Yb3s1tJD7sgZnKnLz0Fg6kTcPDuZ3+A0zZd0Cop5g+1vssHcK1aMSGAX6K2xe0mlfj/p0rrdYyfqhxYm3eDlSDJyIsykaUkUBMZH9MVYMYWVhN0hgLQi+0YqSCR/v1ImpgFRNNu09Iv2zNokkj54viqYTIuzroBY7fUGit5tjVBnLK7ia1yVdfhqGOaXhrqhnptY25PZ7Fda87bQoLnl/dhom0+wrJ9ZXIsPEnuVxlQfvgVual5DJt3x40Th6GCchfjDtZI0V8Zi8CeSeTVz9ESEwE70t2kGPmQP8Yw2rWmZ72jLE5zLbCxzFxOsLYs2fR+IWiDux9y1oxOv8Dcx/6iJPfnkBlJCD1pe66Irov+X9dgmV+B/dPG4fMro2njzbjPa4BU6Nbg5nijhpMy+Mos/Dh84i1+vMuT3wVp/Zyzlx9hSgHMW3zugRCrxfseGcJGYpGltf1ZWFDWLfypgsTeTEknHHlpWxuqkeg0lBslcOGUw9gLmnnmeeMqasX8tHHQqxMG3ltoo4WfMOs9sej1dcX8cZ5bxyk9ay8Be1Wt1vMwWyOJYcwMSAOe3kzo0aWECkXc2Lj1lsKs/7Z5vbf1Ei5VZnTm7zZXtgFuD3TuZJ3+5szWTiIllY3LhX2Ze6oYkKHddnQ6iwM9x8QEuZ+hQXDLpEvVGCTLcPVP4ROpZAL26aBVohXZDLBoTrJ/O6RFRdGcXoAUhctb4w7TrS0nFGmbsg1amJkVRzaPYwpIW+x0G8lp60M2ihWQohNW4tOwFin01Pf0ROlQoKH8CDi9mp2Bz+MVJ2BwKcW5+yBuKl7kNjzG8QyAb3ix3G/lx/XzMxwUTRSKelKMfSRy1hdmcWUut4Yy8zpCIhB5ZWPSXsCRsoykpsW0Nva4BBUnX4vT7iGc9HViZhr/6AtdzhtfpN/urgSTRVzg44R0NCKjUJNvLM1L2cGMVUZyAVtAZkpBrHbhSGnyW4Xs3R4Hq+XqVmxfSdaW0dUAw2Mlb8a/wmNlL9SRmFRJqKMRK72j+TIzLsRNcnZZqJ73iiJy0jGvCOcFWkWGItlvPC8AfDLz2ln604rQpzTWFEXi1Gbmj6L5/JS7Xe4x4TT5jwYU/tfT8usFrdjU3EKsbQOqaQXHyUOxsuuhCWPdbFeEk6Xcijah9E+V5lRG8M27wiWmHyFWKvkh5hN9G+5h7PWSxGMNtz3TpkAj6hVqMYNQTQsF8tyJ8LalpAj3UaNewnpuX1wKu/Eq24GJraG8tUkCpGqC3lyoQV1RpYcy8tCbBZLSXg90iZzIqof/6l6BSoB5jk6AENF6+id+s+v2yDrXguurafavAOnk6d5c+Y4FEEe+DVlsjB1A1UNoTxoPZjezTt5OrvulsD6n72Pd7JGyp+9JkFrE1srxuJVG45F5CWGBBdjXniK4T1fJc/EmR15WYw8FkPGlA6KB8jwqR2LW4Mhrc+kpYSVZ1z0i0G6mBh4HnubAmZ4SgitqmSxfBXXySdeB1+nV+cFdt01n+zILvAlWVHH2c5yZjYFsuzyEFpLhPiGZxHQ75r+mDIETDYeRLnEFidZG9Um5qxsimaFqEG/va3Rgsv7DH2IzpggdFgiFrkGkeK3hnyITNTFlBiQF82Fs7oUdEeeelyNjfXNv50d3wvJyjYgLFaWWr1AqIOjVg+6DDN/H0+7JGqqpxJzzY6Ppwp4+sRFImOiUQeEoQno/iz/M/fkP6WR8mfKZnR2H8g68Hz6Po4d8UfVp5lvz0zXH+rnrOpz3+Xw/9g7Cyiryrb9/05Nd3d3MQwM3d2CgIAgiIqKhYXFa7wGWICNhQISKhIvXUPPwAwwwXR3MF1n5vT5r3MOcEjj1e//4be812IBZ8ez97OfHc91X/d1HSuMYEpYLsMjw1ErZRzKKeRQUfTVpk3F3Tw9dy8dXjeXWd/u+ByznfDN70mr1zaeP2xs+0ri8ampW7jY7sO8tm9QmTqw8/Aq/a7mPPwd020C8LyFw1KHRsE3HblYqiUkXGYJX1vWk7h2D2FiG9RxQ3mvvZJ+F9/g5Ixz/OIVwysV+SzouJ4hLjWF43WNHMk1ME4iPdJ4avSnyDX3YXF+DO22dbR1bsR70fP/zWX4Z5s7uAf+AVL+5MVpvWeQXuhSNfaPKcHfrtk7FUjRHa/o5G49LdZv6Ty+OBjJyd5mhKeZk1kXyswRlWw96o2ZuIsHY9NY56Xm48rvkMqdUFt2ERazGhuVinP51wvOhkYaJsBJ5w5xPnGyvqzHMdKYbdOBKZdShbj4bWdwWT2VAaZUz7cjJkXEy6dHIFVYYWWl1Zf1zOr1AyNtgpE69NPvUyNUkRRsyLDIf+hFD00Vm1RSFjdnoYnqqy/DEifup9HJmbSw+5mZXUGFlxifKhUtZiaMGuaFb5joJqHXhO4qMhRNPNrYi/svT4hlQhUjAjfocpcUp0pYkb0AtV4XH1xMW+hhncEs33WMC3uNHjtt9eJ/LzzcjJWbcTKsK7d+a7nhYzpgzGF2dkN8rDUzt20j9sJ5NOG9Uftfz3r6b4bvnQykCNNOIaot59n776GpphdfdZ6k3b6bkCQDYHUqzozB5sd5s/Y5prR8jF9JMarew9G6GCe/1/aJMP00T055hk92peLULSNzaSBKm+sz4THvFCCWaYh9YDb3tx8iaMc4xNFZDIy9GQzR7VtnF2lVchCl0IS3B32mb25Z4hOYqBX8J+kLjsVWIhploCNfG7nKFvZ3VdCry43Pqwx2kleiTpXD4p4hhLU18Z+qSv3PDSYpfHJmgV7M+bkn1VTnNPNjgjOxHhdZ0P82H29aeHlXl16Q7aUlXVjY3pzxOb41l6O50UwOP4tNczcjxhZzf2UHqzfv0o8v3Tj7K+JOA1I+y59CdZcR+LxP8SEJIWX0MB+PXOHE0eK+3DWwhF4jjcDpLzuEXMwUMjt+PY7xrUgKVfjL6tB49uNMlgvtyQbXHF24+lcRM+wcQqHh+aXRCDm6cRJqpQTTgV3cFbNZlytksU0kQoGAr9tzmHM6ni/Mv+G4awAPBhgEGYeqG9iS8S80YjOaXKZwcvsItAozesRsINjuKGc8R1Jk7qLXSbGp9sS+25HyoIu45VnzmtlcLlha6l3QdhZ+SJXAmZd6LSNPqUV6ef6wpyCBuPq79G1J4w8itEjTgylSlQOW4maQedB0oQcDBk9HIlCQnfksiVVvIfY3TsJ3kcTykHzuK6gjWiHkhSgX+taa8nHHvZxUnqE+J4KWbgfmRx/iR0R807OMlK2J+JeUoo4diMbjtyndv2cM3vFASkMNonNHKfcPYMMiw/XNv2RLqq0v36QnM7FzJI8VdKJQm+qd73SVGSnHq9lz0pfhgWeYm51Bqa0NA+dP53juG9ScXYhpTJA+4XC7aBPJecB7N/PzzXlAmIqFdiivpQ7Xs6qWLTOys3asryCtPID7YhMZejaTf909jLcvvYtU5sS6vJeZWzWDTUGfY9vLCD477PmMxoEROE8+h0mnOX2qnyHd9VM67Tr4sXQyD55owNRrqC5xrg+dELtOkD3p3lP86NKHF6qLWNjaSlrsabrt1AQU9cJDbcwGWxaIEaoESPvtRW3Vcp0NsiD9e2odinA/mMjLD96NiYudngWoYwNW1scz2zuM+XmbmNFtjibMOGn/PePo19b5vwik6M53b54Aq8YHULvrSi8uYll2lFddJ/O9y3Aea6hj2daDVPTu0oMpbi1xBNWP03eTuEPIyguFlDUZ2ERTI49jZV7B8AgF0zI6uNvWCI7ZJqxncOMPHOo7kuS7jNe5XNnBtq4S/btwVdYEA+Bm186g6QbtJl0c1+ng2RkB1+2t+4kUGZIbFVlB5CYbmCBCsYqR8/ZgVXMCkayF9THPUGJnZGt6VGXSetyKwoZQHlzQja/v9e//tjYBKz8W6chH9OqpwcLi+ntruPU7eDjkUF03hd1FzhwdJ+LNL9fiXlODqt/o/4qpduN4u5OBFFFGIsLqUgY8OZu3j0VjGtPCvlMjkKvMefUVFRLjI4U1HzVS2+7G84Or8HQxlhY3tVTxc6Zcn3Tt75tGz3l/rLxO1C2kx/6RIG7ntYomatq8mDtTxqatZliZtPPo0J/oVsro2XGBFsuBJOxfRK1tG0vu3coS25irjPIb+31NexbdWjVbyqbhr7DjvEUtT3gdwLNLSdanP6MJ7Yk6MJL9l9JpbnwTdY98Vsb2ZOqlMt5rvF6moN1ESVZ7JVtT5+ibGRh4jHlDdqCpfQLT8kiKQk4iP1NC5DKDMPs/8X+nB/4BUv7ktdQBKTp6n7q/8SXxZ3Z5RwMpmckIKwsZ8thM5iXHUxanJTrNigvVEfQOLuF8YQChTiXYBjdhZpvJrJpUKronEmzzE3GRH1BnasPhwlx8LmvHlJqaMS4oDF95PR8faqWiOBqHcA2WlwVkr/SjskNAeXcqY0uz9JPhmtHOqOzdyams5Je0WVe7e9nYN/BQvorC3kA3bDev4qLPBmyrRGSn96ZvpZIU9QGmiU3R+IboBQrFB3/Uq5j/MO5VnkwuuLqvLBd7Ro7VMs/NSD2+srBQ2crurvLrJsSZFLMo5CRhTe18eLEvOyoH6jMbUimo1AJ6Ox/jUZ/1DIx4k5H/keh1ZZ6ZX4u9n3FiJ2+X8c5HVliaSHEblMx66w4m+nhcFTVTDRiP9i9gPd3RQEpZHqKc82waPpBNjsPZXFaKpVsOzokGSnp23zYixbks7niBN5OW6EtRVKNm6MuebhXCsjze7TmR6ekt9Ku5ROFCHzoCjDoPJq1KolYW02xuStRD97Lh4jpajz6C0qqZSbOO3fZWtiw9jFAl47Pe/6ZbbMHSs0uRq83ZnbiGwxPSsYq53nJStyOd25OuvEekFXC86D4k1yjVq7urmdHHFQuVgrT8HH27HV0X+bluAFl1kcwfc4nSMgWnCryZFn6GIRE366NcOdhvTxeSfSmYB8ZW4Nf3ZpeCg5vzSCyKYnpkEk3tau4bns8Xx7OYk5yBuvdwNLcBpf7oc+1OAlLKpa58WTAJsVhLXKyWlPNC+sv2oPbYhrXLLEQaGw4VDGBC7yL6TTAKcn7woe46iHl93JtkxYlxOulID9M65M5R7Nk+AEmLO8dCC4ip8sBRaoWNYwtxY8/oa/6r8v3IPt0LU1ste8ek8ZVTKiESO72Vui6S5fU0lmrYm9VCoMdRAmM/o1so4eO6HcysOYDcIZxDpx5C3mRgOjo5ZDMs6gOqrP3YGPIQPZwMOikSuQnd1l2UZfXhLdfBOCg6OZr/Ji5Kw8fqO95PovCPpkoNeQotnuU1fKCuxrdyOGpJJ939d2HZte3q5dW29qLlrB3RU+czujWDL3M3kSJffZ3d7pUyxi9SaxklciI8WoxGIGBf/jRytZk4NFmj0ooItS7hWVtrGn3rSP3gO7RiCWrd/Xob/aE/OsbudCCFrg4kx/9Dp5UVq195TX966gIlP/v0Yk5RNiuaonmjpIX6Theeebwde0cLDmwtJyk3kFkxRxmdUsDGyBA+GRhGSvYr7E/9GIe+txdn7BIpecRzL4VmLYQ02pF/aQ+13dP5sGQwbTI7XnyqGUs7A3j/yco2GqWOPDcwgfCEQt5+ZAD/ylpFdWMcWxqH8lDR03zdZy8e/tc4Wpw8SJmbFb0eOK3fR3z+Es6FGsrSPiubzgv7LDALMz5zruikdA87youxA+nZXs+PlTV0iqtJH2TQJeuT+xAmQoONqq60R1fiIwtLQeFlfB+LygOwLBxArVMi7odTWfLcAuxMRMRdSuSu/PWU1A5lSoQLHxxbT7RdFPsFs5Cpxcz2O/5Hh9RN6/9fBVIac3LIbl6JRqRgzPzdWDRmkCBw4MGAxfoywF1bdtHk1cn5eR3YSv2IrjIkC3XMoV8unuF0qSFhdU+PBCSaS4zo2cXDSVoGezxwtQ9FyUcZX7acC4HR7Hvwvqu/t2sUfNuRi7PKkt3F91B9UohGJWDoPQcws5ZeXe9hQQgnrEJwUnWQ2H3i6u+ph/vTUGEcZzHDk/GzOohJWwlH/KdxytuYqBC1VOCS0MH5yp7MntxERM/rBVITjgk4cUpEn8BzBAaa6N3Lro0xdq/hbFtMac00vq9yonmIgLffelsvGK4aO/tPl8/o2rqTgRRhRQGirBQWzhnPiPxheIY3cDppIO0yO15aqsbC3AA8yRo6WL7GHjNxNyvuul47pdQpgWrHZEQJ8Vh0q7CYfP3y33OTep31x6UmiO8VJ0gsHkBUwCWySlwJcixjXMh+YhXJaAViCgQPk3m8D+d8y1k/OZG5Vrd2D9K1qXMhLVK28WrdICa2B/O+6xm22+axOLOc5ftOc0UXq7XkIsudFjFKpWbpmDBCO5rZVWEoObwSbeIOquW1fJNsGOfjo3Ywse95JBkvIZI6kBH1Hak57ix8+Pok2u8593/WubN74B8g5U9eHx2QovYPRxP++xwcfqu5OxlIEVSXIs5I5OEZo3G8NBqTSBkxabacqYjByaqJxk5HRvhmsj3ChmXSdbjWOlNnNoE4kzd40O8x9jv0YHVVORPaDHWwp6xteMgngCHtOczeGKnXR/EYoEF0+cF8pa9+scslua2BeHNrVu47r/85bbEjkRUKlp6aglRhra/B/Gzsd8glj+pdAXRR7ZBCqfMR/M6YckIeRki6BwLpO8T49ULjYPhwE589hKC5npVTX+StEwYHCl0c93PjqYkaRlt433TJ5Fo1n7dnIdEKOVo0Tz8h3sopVoYUMav4EuFpi6jsciE8TENjk4CGBgGRzsd4ynMzUyOeod8BK0qbfXh8SiGuscbMbFt1HSvXeuFg0Yxz74tsCGrmLqGjXtRMN/HQvbT1dbB/Mu5kIEXQ2oQ4aT/VLi5MnTCH3alKwnz2UpA/Dvd2KZpeGYjkpjxtOoMP970EFlYoh029bY8IWhv53tYPj3Zn5uYUUDnZjYY+RlcWu5wOArZUc9jPm0cnDObbc1vIy74X5w5rPEYmEO13fdbhSkPm1SmIuy6xLfxBWk0deTD9fZo6fTmW+m8OLUjB2v3W1+mHznwa1DJWVY9igNQ4toQaOQ9EdOk1ABLzs3BSqVA1lHFE7M7B3CGMiiiloNaKihZnnu6fgq+H0T3lxpM/mpXJ7vxoRoXlM+Sem4HAPesLSCmPYEbMKU4qYEWffDLW7MCno9vwYfgXTXLvJCDlh5KR5LT5Eeivxdtbw+kkIR5dufS2eIPmgAewElqzJ28go6LzGDLNYEPb0Cjg0y9EWEg6eW/y5xwPU2N1yJ14t06K5QHk7pyI1ETBikW7qenq5r1dU3BuskFiKqfX2CQyT8QhbbPVM+zm9v+ZWkknd1sG4Cc2uEsp0ehZKTu3RTMu9DPKLQLYEfUQT6e8ol+e1Pg0NTmxtJl160vgHeRCpg16RL/s9SFfM1ixGqWtIUOrczu5v+NZdHDij9mr6SPPo6rLFy+Lcn4xGUZmPyNj8kephs2HtuHjOwjn+iCUtnUIwl7TC8/qQtE8mrIkW4bPmMxLNTuZlN5Ng9d9+mO4EpO81tNooeHC+Ra8TR2Ya9/AYS8nns71JRgxj50yaASdCHRiTi8tgzor+HrTHj2ArY68/dj9o4+2Ox5I0b1j9m/Wu2pcEdwUlkrZ5N4fn84WDhcJ+byki6ImPx6eV41XgCsbv6ql4JI3i+P30utUJYvHDsXDtpBXKnazP//r2wrNKgUaHvPaT6Z5vb4bdfayDcmVVBHF4ZY4chsCeGhmET7hfshlWt55X4JEZ5Xe9zCFRWq0A7oZXrmf7NLpnJApGFf2C7tHbMbRwcgQbc8uoU6cytCpFSgclfjnDqQ0PBFJm5ij5b0YmTocK59rMvqXdVJMbWp4aoENcqGEpPwsHFUqityTqQvtxL7emciWRfpjNmkWYlonQulWSneUUZRYVBiGZXlvah2Ook0u45MnZ2OKln7VCYwv/on8ynGM6iPm0JbvqPZdxI/N0/T7G+6axhiP1D86rK5b//8qkCKsLGJz29O4t7riO/YA0baFKBpz9exhXZRt3IZW1cbRpYbvtdDaqTi3GyylU/KOsyXbYHF+b++DqJtbGTOogxePORLlbyi90IU8v5Dp6YupsHdj/VKDZt6V+LgtQ1e1zanC+XRkS5DWCgiJz8Q/xgigVWuEnNJaEk3XVTaKbvvD6+9CoxJj7auho1yIs3ctffv/glndBXIde/BjpEHXRxedCjl9DmVyuHAAE/rX0G+04dtPF2oNrFwtoFMq4t+Tn8fcQsC+2nevO86Jji9iZ1VFbuU0VrQ50d+lhnvXfYdOf0tXIv5XxJ0MpOjKwMSn9vDxgBg6mUdP3xounu9PY6cLzz3Ria2DIYlVkFDIxsRweroXMX+A0cq90SaHPPed+nX6rLPBsUTMuXneiEKvF6X9rX40qzcl4vQQToqOsSHLqBU4wCeLAKeLDBMc0jvcZVXMoiQjnJ2xGWQNLWGk+e2tqs/J6zklq2V6ayhL6wcwJnCTnqPbqwAAIABJREFU3q3nzPf7CatvRjnmHhCbIKwpZ45vL5akxrFooWH85Gcb9Xp0/28TNtKpbeTDxAX65bPj1zE4uhnTMy+iMOmizPIzUiQuzLv7H0bKb13rv9vyf4CUP3nF9EBK3FA0bjdPuP+bXd/RQMpl4ak18eGcdrqPHr5tRFx04XipkUZ7X1QKC+Mjyc5+hvKWWbQ5DiNWuoTvvAfwlud0FjbV81KdQXB2o60Nb3kFcG/TGfr9MAyRmRaPgdeL6H3hdJ4NDpmINEI+yIqiXSzi9dPn6HQXIO8dT1prJnsvTiXW5zz3ubkhdYm82u257ttpsskj9mcrEnxt6Dw/gEmlT2A2bDpNagdEAjX2xacQleSwe8RkBpS5EtjSod9+W6g3P04wx+fypOfGa7m5s4A6dTcfV42hb5cnb5vtYI9PK6+lN1J98SW9/tegAWrqLgkoKBQSbJ/EYtc9LI6ZTuwxO/J1ujKjs/DqbyzVKU3M4vuEWNxt6rCLyOFQDyUzC6qYsXkjWkc3VH0NlqJ/Nu5kIOVallDkU4+w75ATPYI2kuQ5miLHKH09fH1rGG/bRfLmnlVo3H1R9zSWV9zUNxoNiRVVZARO0Y+bhn52VE402tN5HG7A7WQTq+JjORdhwojqIrLkUQw+HoHKpZyJkw3A3Y2hs/U0acrXH1e9lRdT87+nvL4/yfkPc+r5FExupWgHnJTVcl5ezz0tETzbYNCRuBJv+heRZuXHDzlZ9NGqkNQ1k+KlZsPJSQQ41VLSaHAMWTmxBKGZQX/nVlFWW8THSUEEOVYy//GbXUa2ry0kvTqcOXEnWSlScTSshLr3N/2l2hW647pTgJQWhQ3vZ8/U6fAxsJ9aT+HOzBIibezmYdk9nI15DE8zK3ZkDWFYWB4j7jF8BJ67IGT3XiG9fc8wcXAaWT6tWOzxoHeIhINnItEUxJISW0zTmDq+78jVT1x/3jcPTYUxgy8Ua6kZX6V3AbAUiHnExvh80rchrycgwYdvrT9DLJKT4dKPHvVnaSWYIyeXoRJq+Pae47iX23PXmZ6M6f8iNpJLfBv7Ip7S0xDQoD9WZYkTT1jPZ46qjfcuvohcZcFJaTijbS9QqXXj26FvXh0qqQotrjll/KvjKG7q+zGT2aAO/xKhpYFW39F4N0cKnXhi+EB+KlyNed5Y1H7GUrJaQSvTgnfg2tlNZom5Htzdrr3EozEuhDdKWFoXyMIkQwnnkVAXxg5p4eu9Z5ieXYxq8ES01kbdltsO4t+54G8BpJzag24yckVwU6XVktAZSLPEiosXy9haoeFCbTjz7iojpIcXH63soFlqz+u9f8H7dCPhD89lY+lqghrkJDe/jW3ArYVmn/M4TKJVFaYCIQPN3PUaFOcSNXRprahU9eBYRSR3D80hdmgIFYUNfLvFnRCnYhY4J/GTVTiTbQ7j35pPYtYzNMp34dwsI3vsW5hfk9hobNAiqllB+Hg7lAEd2NY50+bWABU21GeHEKQehfiG0ohL54Uo2gTsuj+NI3YRvFOWwwypApVAxvk+SajMtYTnjcBR0A9RtwCLUjEa8w46B/7n6igQ50RjUdODWpt9lBdK2b/AoNsyrHwPw8t3kVM2jYHD2yn5fB1fB66nsNMweRKg5fHQXXha3FoM+vcMs/+rQArSDjZWR+FdPwpJ5HlG9s7X66SMDVtGpoUPKTv24lVVT/oiM2q9a/T2x3FlD2OmtKOu+iTvnR2i776F/fZT19DBjIHtLD8ai3dAz6vd2tHYzZyEySiFIt59e8V13b2hI59GjYwN5VPwrnKi8aIQO5cm+k7+dRZRW4MDZ3cNR2ypxTVOS9UpnRWAllGzf8auZj+dEhs+6P/h1ba6tVpGHE3kl4sjGBxWxuh7jBPr7FwhP20VEuBUwMvjDYyx041PUS41vpunOD+DtUU9F6um87zKlSX1xxl0/BjqgIi/rITsTgZSdH0iPvwzR70c2RT0LFOcKihL60tVmydPP9qGg4sBENm7vpjk8lDmROfRJ8TwXSs1u0Sa71r9v8MOWuCfaABdpOYiCp8NRGN2C+XfX7kpQw/F0aas4bV043fzXZHnsNLmM9z8CFKfYVw4NYr6ci8+HXkCix5yIq7RCbtx11UqKT9LiwiTOfJUYx89EO2qEZP3wXowt0Q53ADI6u6VJ6w6uPv0Mj55pIpSM1d2FOcTITPqpHRQh8ikjWXH5+s3eXTIR8S4+GGSPZMqn3Tqiy7g5pyGxxxDOfg/8X+nB/4BUv7ktdQBKaoRd6M1M5YL/Jld3slAiv6BemwHJ1ysWdH3Qe62lONX5MrhIqOmwpNxiTwe6cPpnFdJVb6D0sIN76avKfdoYXrIc/SRdvJDWZG+i951tOd7N19eqDyG17ZxekFGh4jL+gICLW+7nmafTRFWChU7fkrgkPdjnB4axLKELCaWlJM304VQTTU4JILS1uDW46Smw7yaSockmq0MXu8RX3lQF17LjvzpvFj0HFv8V5HaHEyQVTWLbL9FdP44JUHBdFqPY0iZIZv3UVwAFSPtr3Plufa6npbVkiKvZ15zFE80xnOf80YK7ZV8cMySvMo5uLtrCQ3WoFQKSDwjxMKkhZc81rGiVxwhSa5k1IYxf2Aa5t725Ka3kV3hRVOXgcLvZ1+BJKiI/D4mzNp3gL5JiWiCe6AONgp2/ZkxdkcDKbrsekoCwsZaBj81jzX7ohgUtoaLTn0ocYxgav46yuoGscWmm8VHt6OJ6I36BresG/tGnXKEN8a+ypf7TtIcYE7ZQkNphS6CNlRgU9jFgsmj6CVOY9pPP/DdQ0/gv3EYFkoTou7ei6e97KbuFkvrMK85R6ldKJXWAQyp3E9O+TSOtA2j/FFjRu3GDa/Uhvt1WfBjlbEsTbfeWpeL7HGNYHluDtM1CkSd3RT5lvDJbiNV2t2mkhdG/zpoq1JIWbrbUp9tfnXZzR8qW74qJfdSMHN7HeNxeykaqzLOrtmGJqQH6qC/ZozpzudOAVJ2VAwkpSkMby8NgQGGbHl5hZDSMgGvN01lS/+H6WFmztaLwxkQmM+4uQYWz5atQnJzhczv9w0MaEMuUOJwxBb/UDcOb5yASGnGyUWpSBzV7OsqJ0/Zyt1tYTyeMYDmHKHestjaT8OqQcc4ZF1Kf1NX+psZQTxdGyq0JBTUsz+nEm+X5KvD5ejFl2luDeWXIedxH6Ahv0HKA2uHExu8jiD34xwMmEG9UKLXSdHF0cKhtHjEMzpjD7M6dpHWGYmnuQM2mlTMJFLeCX0FhauhZKlTC1ukWk79sI2uCRqicxeCeQXayGf0y5tq5vCuLIDNESEUZCzhQv1qzNyMWjsHOM8bIZnMKKrji24DUKfQqgmM0CIXi9iUGcW9ZwzZuv0RbkwY1EjWF9txF1qgHvjX0pr/DkCKKPUUwrpyds6cTWZPQ9KhqVzEIddITmVlc7ZGwrHyaKaNKCK6nx9vXtbJ+jh0A1UVJky7dyI56U9S09STAvGTtxSafc39uH6MiQVC7rEKxFVowaftmaxMNaeHphultic/5vRkQFg24+4J5dyRMnYnBTHKL4NJzad5bdzdrCj7N6ZqGXvOfkxE50LSJRPomnC9laxMDpLUZbiNDkETW3d1vDYX+OJ5LALzWwhvthYL6CgT0jDpJG8F9WVkQwVf1BtEQxvNisjrV45Jtwm9yhcjElhinSvR3zsdQ35Ba2J49koyemPeEMYlsx0ktpmTcZcBPB9X9DP9a46QXjyXWQMySfo5kRetDGVq/r5aSssFOJi080z4dsTC/84J8e8KpCg0EkyEN5eYXvtOSsqqR9n6Miq7OiZOT9TrpKxwHsvnbmPZcuQEQ3NLqB3lTO6QBuQ0Y9ntRs+KBxC0pvN0gkHfbtGAfaS0dvFMXAsfnZ2FnYeRaaCz2B61exZ2mkY+WfoybfZGEHVXVylFynberh3GyDZ/qk6I9E5PI+buQWJmsIi/VRSnh1N0IQIrLy32oRq9xbauhCysXwaRZp/pnc0+7Ps+HaZG5unoU0dZd34c0R4VzHzIWBL0/QYBpWUiHhi4hriIOkxl+bQpPdhT88HVpqe7PYaZaRsppffwkrUtHx3bhG9pKerew9BcowNy2wP+HQvudCBFdOEE1d3NPD7yOR40baUqsyelzX48Pr8BVz/DNX3/3W69icJro5qwt3VEKe4izfs7FCbtuGea0JocTETAUbLKhzGi9BIVIZY03vfHEtB2pXYEXIzk8TyVXqNFF/PjT2GtPE+MZTk1plM5t3eIXp/s5bt3MjjIEXvh7S2vVVoNn7Rn6vczqS2YPbaFTG/R8O3XW9C4+aCOM4CFuvimKQ1l2zLKhtdx0DmC98vzuavTCKR0a2qxtezi8QQD+/PFsa/jL5uDuD6azB5bSc80Y4S0EM9nrmdm/Y7h8c8qf6AHyqsu8fgrH3HP5GHMn2mU3MjMK+Vf731LQ1MrYYE+vPevR3B2ND4j/kATN636D5DyZ3oPaJk/Rg+k/FVxpwMpOuGpuvY6Js9cxPPdYtxqXdmbb6RqPzwgiXXualYV/EyG5Uf6brFpPo2v7WaCYj/BXKMmPdfw4HrMzYUERw8+yEzANGECjhEaLNy1qAQaXnRP0GfXbJUa9m7cT0S7nE3hPZB5T+OryEiObd6OmUsbVj7jMJNUgDSYmiAtpW4naLcwiHWKlGKCjpuQXx1CD9t0VrUN4pWqb3ld8jXdasPD9bnAjbifXotSIuZkv2eZnlWu//3J0aGYxN6+Fr1S1clWaTGhMgfWV9xFv5Dv9ds9vWUSHUo34mLV2FzWkU25IKJLCk97bODHPs54nffmfNX1dbi6bS0kUqJdi/A1qeGUk5Cuvg4s+vQj3GprUPUZhdbp+knYfzvm7nggpSAdYVEWi+ZOYkbacGYFfEKxVSgVDsEMr9hDVvldlCn2MSi/CFX/sWjtb7a2u7ZvRLkXeGnYw3y98yxSGyH5S40WoDErChF3qen5wCzeLtjOwN0baXJ05JuIFfTOCEYQmMWYYTdbGev0UXQ6KTp9lGL7CKIazpOS9wi7rb3ommmcYNx4jcQKGR915KDQWWwC1ioJcTJ3enV74Cxv45PA3vRsbuTH2ir98mqrJNaemk9LtwFkG+CXysxevy2kuCqhhspWD5bMqMYx4nrb1g1fVFLU6M+8+ASm+1xiWE0ha7cf/119+UfG3J0ApHQqzVmRPRstQvr11WB6WaRTZ2ucniHk6ebH2DxwPKNMLdmcPoJ433wmLzAAKe+9B1K5mLfHfUBanFKvteRRLKG0O5amUyMoc22idmGJft1WjULPShFrhewovQebZgsaMgRY91MwKXwzCqFaz0bRsVJujPOKBv611YrR/t/pF7V0eZJw/h3OB5TRMbMOM4GIenU3sWujGCzOp0/YV2Q79WK33zR6y7fp6emb7BbjL4LZJ98nnCJ+6uzNCAtz6jpaiLbNYqP5WArjp19ten+3loFZRbyTdpyK8eGEZY8F921oBSoudYp42DmOTlslm5O2U+b4DNce9rumu9jp28TK1Cqs6mYxzuMC5iI5j1vWszXAmbey3XkuuQZzlZptUR4s6lVNyUc/o47qh8bHSPn+I2Ppduv+LYCUy8+z08NHcGz05VKAwm62ePdlXUEmqmoTthfFMCo+n7DeQXy2RoQOMH1VlsAmX3+O93Nnfc5yCqrG0OgxC9E1kiW6fvnM6RwbHbIQIWC6ZSBelx0q9nSVEVQoZEVnIWrBcD6/0Jcg51LmL/Zmx4Zy0soCuT82hfD0HFYtnsA7KS8gU1izLuttZtdMZZvbq5gOvdklzurQpwhGe2Iy2JCo0EVFdiiBp8dhHXzzpEWmK29NF2IeWMqiyYZJ7LG8NDzUhlqx7KBEWrxkuJf7Eyibg0WZGFGXgK6YE6hcDO9y0/ODMW31pZ4tbLf0o3awgSE1OW8DvetPc6JsAe+EnWLN8SZWSD7WO7PERGvQuW61twvo55TLXd5J/9WQ+7sBKZ0qczaWjCDGvpQBzga9rduFJiOFw92fYaaS0G/Of3DpvMAZrRVzgpbw8oVMnkw6T0O8HRVTnGjUpKMRKPBs6ktIeSzLz3Sj0QqY3TuZtS1C9rmX8nTdU1jf8NkUtnMpQfI0Ns5/kNIwo3CxrqRCx8h7pDGOhc099IyUbl0Z9KALeIUay6xvPPaUvUNoqXPGKUaDubNWXxKkA65tnFoY0Xs54q4Gfox4jFwno5Pf1PMH+fzUZHzsL/HQk4Z36RWbcX3p5uxlKO0GYNGegEjTztmmRyjuNEyiZ3k+gFgs51TRLF73NmfbJx8g1GpQjZml13z6K+JOB1KEJTmI8lIZPPcZnu2GpuJw8uqDeWRmLZ7hzrQW1rNqi4e+LP3V8QY77oveG2m3qMC6ToTDDj9aosrorygjQ+yN5xlPXLq6Kbjbg86eN7sP3q5PBWqI2TOMj+sLyKk3fMc93e8QnurdSKzCOXLoIb1EQKpPBasnJPCY3c3f2Tfu+woz6srv35+rYurRE6hDYtEEGbdPL01hjdciejopWBPci4U1JbzUYjQVUKiasbeRseTkGLoVliyftgSX/M/RoiXHZxX5Umu8ivrT56U/7/J02zHXVQTqmxN/f8UY/dV9WAbBrwBW/+PtX24gOS2Xdz76gSB/L2IjA68CKWq1hgnzXuRfT89ncN9oNm47zJkL2Xy+/Om/5ND+AVL+ZDc2L7kXdU8javknd8edDqQIKwoRZSUT/MT9fFjpiKPUkZ3ZBuFLH9tqrOKaMLXIZlpBC5WOhnp+sayFXqLnGRTxJiVmrhwoykNnxTnJz49CSzvWJKSgzByMx2ANCjMlz3sc5oJFHXZaIce+2YFfSyfqvqPo6mjma4v72DXaA9tOOLZlJyV3BxBcNJ4Om3pOj1yjb0+IBEuBF16HhfidbOH7XsGMaC3mK60VDzZms0LxztXL1Nsxn1nFzyPo7mTTuJd4PLmUdhMJTy4Mw8nm1z3uV7Vl6PfzSfFQngo8obcCHbLnGT0Vum+8kX5dXCKgskrIXI9fSIsD28xgzpQbFOfdreuIdK4m0rSB0MYuloaHcMDPjwmlSZiHW7D0zdf+UlEzff8IBZibiJDKVH92uP6PbC+87HSxZnBvxJ3zWOq5mnoTVypsgujdmEhC5Rwicz7ATKVBOXaO7oR+9TiEteUs6zmRL3ek6ddLfz0UjViASbuKqA+KaDYzpe8Dd/PdkW+ITt6rXycpZCBddW/r/z1w7k6szG7OZloVH9Bnv6RiKyxVnRxNf5X/hCkQj+i87fEMTDhCflsFu0K9SXd1QHaN5P2zxSYcDxqjt6xdWVzEJFknTarz7KkeTHq14eU7N+YMvYONQrOFDjtosivGVGGht7Q1VVpiIrfiXKIHp8qimdErj5iJ109ev/nkEpWtnsyLP8Tw8Ar+fTyNJ8/noxrz12jwXDn5OwFIOVwTx9FLPXFz1RIWarwnNRo4lShiRssHHOjrz1QrO364MIoYj0JmPORP7SUBa74SYW/RyOtjt3EqvAm3NDMitGq2n56J5JIfJ8ZkYxLXdfVaX2GlzGyN4Ln6vmjV8ItDDitdk/EVW+knubcKHStFcUDEGptP9YvPFzzEaXkU2QtzsTERY9XRgVNDPcmlYczN8mR8/FI6TGz5sJ8ha9quFWAj0GIil7Es2eCY8Ut3NENN7chrkzHY/hz5Al82D152tfkSFSTItGSs/QlpTDNmDpNwqzU4XTRo1jGk7xQmdaSwIFmD1GfEdYc9y/UHym1VHE1sZVPx87iZN/NI8F4SqWFOrCORTSIOHBLj1SFlbZw7O2xS+WFXEuqRM9CKbwaS/sxD5O8ApAiqSxBnJJEbGcUvcw207+7Wbnaa9eWexlpm5nbyfV4UfcMKCQ61YeN/XPXOXE+UJPHghBEMscxnQc020svuQx5s0KW4Ej/Z57DaOVnPnLzLMoCAa0pRc5XNnGi9RFlBFt2SibyZPBRLk05efMmMT1a20ih14rmBJymp6Ma0r5xRpdv1QrObm0axqPBx1oV+h1Os0cHqSpvCU/vpihbhOcnwPNVFdXIkweVTMHO6uexIq4GqYwbgeN3DBaReFlueVlvEo63teGg6SRl2Tr88Nmc6Do3hmDaJkPtmIw82tGF2ZjQmUleaFOv52r8XsmiDttiMi18R3XqBXZUL2eWyh9kZAfwoWkxgoAZvTy0yuYDkFKGuYpSHg/bhb31rJ7ZfG4N/JyClsMOLH0uH0qU2w9fyEo+G7PnV20tQVcJ66Tx8G8JxHHSc/h6ZaBsyCezxKeNKKvhubwKtoVaUzPNCSQfNWkMSLLZ4AW6iV0Akp7x5Ao+329KlLuIB4dMIbngduxz4hD5tu/jPqClcHGHUtshSNnOoq5LxbYG8fmkIXbUCmnIMeidxY24NeqlVIo6sN2iieQ7VoCud1LFYqk6K9M/bceNXYiXN5JTXOI4EGBOc07P38cmhqVibdrD0RQOTYd8BIWdThIwM28e0YdkoJD5IVDWYdSYjVTmxs9ogojzXd67+74TcOazxa2ftp9+htbJFNWTyr/btH1l4pwMpOq05cdIBJsyeyyOdHrTV+nOxJpwHp1TiG+tOxp5CtqWGM9AnjxnxYRS7HKTW/gKSLgGhG1zZHmfHk51H6fIZgnnVab5TjWbRyWqUEgH5TwWgsPv9gJRrujsX8lTsKjaUyS7tt5sQ1X84lPEuHa1OyOzkPDhzCx5m5npNst8K3RjUjUVdOInMSdh0BJ/yMtTxI9A4G9lLmsJ0hgwazkuVniwbEMvA5lq+q71k3L1SjsS+E1NXnXsnaLQOiC58Q6NLCYUdCXgqqqgUvMLAh25Awn/rAP/Acm3WEpAZknD/XyP8fQS3+L7plHaz/JONetBCo9Hy4uP3MmFkX3bsP8V3W/YhUyhxtLfhw9cW4+XuzLn0PN77fAvSLh3TR8ArT81lcN8YurplvLl6AxnZRZiZmvDKU/OIj73ZwbSotBorK3O27j6OrbXlVSAlM7eEFZ9uYvMXrxqujUbL0LufYt/G97C2+vPVJP8AKX9ytDW/8fRvlhb8kSbudCBF0NmG+ORuht0/gZdrorDBlm2ZA/Sn2N8rlx/6OvBG83rcq3rR6mj86IvseJmXwyayzaEfH1aXM7m1hZiwKOQiMRu35NPeEY17fw0Pee8hy7wBB4EJezcdIKyyFrVPsMGuWNbNWwo1Th79WRPWg6VpqUxTJZM7wTCZMZFJcC23x7HGoMpumdmBTaOCL4f6MKgQDggyiOnw5mfFPFyctdQ3CPQ6KW9rH0FSW8yGiQ8hKxlFt4mS9lm//cG1TVpMuaqT+BZ7ztm3MKGgDfezz+nLB3RlBFeipUVARqaQUW676ezZgkdVGEK5OcF2UlxkUjC3gMuaGlfsoKfUnCJGVce969aitXVCNfCvETXTHdOdDqQIVErEh34i3dWRn4JeZ439SmQCM8rNfQntyueH2oVMvvAGWhtHVIN+u1RA0C3lc/sgZqe3E9rSSu7j/nS7mWKX10nApioSfD1ZP9SNZ7auxa+1Wu/mpNvm69C1BNQGYBZ7lqG9qm+6jc2rziDuNtbe7zrzGfvGFmEZdWs6talCzhMr3sFCLmON42o8Vfm4WOwlx82c8x6OJPl58FKTO2+GTMK1W8rBsmJoyiZRFMDeLANY+8rQDJydDCBcgesu6u2utxO/cpCa4/HsSBqt/2+oSzHjJpvh6Glgpny+qolLna7MjT/MiPByftp6lNGdIlS9r5+k/ZHn1q3W/d8GUpQaMcszZyPTmNK3twbzG7QbUlNFxNX9yJkYOXMc3Vl3bgzhrsXMecSXM2eF7D8kZEDgcQYMyaPUvRn3na64eIpI3LkAuVjFhSUXEUqM9/m1rJTdJbOxU5syz3cnRaYtTLTwJVRyexppZbmcR/d4IBIqqOgK5NiCVOydDDOS+7/8DO+KCp4dN4cJSQ8zuf+TmEo6WNnvPdqvqf8Oyj/LfZe+o6QrELGJK+ZCEQqdHorknF5/5Y0ey/XPkiuxvlPDfRl5vHXyLGceUeFbM4pWxypsT5YQP3EBK8s34JM/BbG7cZt2oZwxQZux75bz/ckYdl8ylFjoNCgeDtpDVFgXrWYmnD4j0bt77HE2w+TSCZ6qkqOOub3T1H871v4WQMrlSUi9qytfLXnu6qnmXbIjzdaHQ4llfJQTSrh3Gf6eavadDWR0cCKzMjPpuehetpV8QoCsiJTyZ9EGGSYPVSbtfOmQyhGbUv3/x5v7EH6DFoCu3Eonil54phWtXQyfZgxAqrDi2cXNrFrjgKlYxit9TrJJ5MGjqrXYKFpIzHqWNHE5c4tW89WAw3jdgnnfmVNErVkBA+ZkXRV2b9oxnCCrPggMeMlNUX9eiLxNgN1dx1jv5ECCtfEjeEJdCY+rE6mLrce2wZGeFYsxrxChsqunq/ch/b4sTk1GLLelreUrlg8dj5mHgVUwN/VjQjqzWV/7IBWiLVgWzyfHpB/9+xnZZ9W1AgoLhVhLung2fBtmIsUfGm5/ByBFrRWyv6Y3ifXXl2Yui96MldhYfnDjiQvk3WwrtcexcS5av1zGD03V66RMDVmKQmrHvp930+VhRt7iK2WBFUipQqyyYNTZOARqMy5aVDJNLCK+sopJDotv6lvzkzsZWfsZJ6P7c2LOZc0JHfimkvKTtIhImTNrKyahVUHlCZHePn7kfbsQiW9OXjRWuXLh4CAkNuAWb1zenC1EWiegZ699BFr+TIltGOt7GEsoJhTv55tdBsv3N19ToVTBByuFyORC3pr6ApbeOvaKYfBadBxFpG7jfPNCijqHMNtnIWhMOJB9N0lWJTy3dTca72DU0ddrnP2hQXXDync6kHJFu+7R8f2ZqB1Ed6Mn5yqjmD+mhKB+Pvz0dTnZdYE81KsQ5x5yCtx26c+w5wY73omK59OGz5H6DkNjYo1I1kpXbTqn6iYyxzBRAAAgAElEQVQwLb+cJl9zyh8yllv/Vj9KOsUID3mzOtcXF8sGpoSdwLu1gTMXF+t1Fg+OS+cr9wu3LKW91b4vKpo40m0AHwaauLL93U8xUSqvCs1e2UZQV8GkcDOWZk3gybvCcFDIOFNocB67ElqrNqxdDcxSdXck4uw3yY05QFpBF9G1lnT5DaXvnNuXGv3Wuf/Wck3uywiUBnHo/58hCHwOLG92R3r9w+8xNZHwwuNz0Kg1KJQqrCzNyS+uxM3FQQ92rPzyZ7plcv719H3MWPQ6K15ZRLC/F20dUgQCATZWFnoQxcrCjGcfuYfi8hoeeu599v7wLhbmt+7Lj7/dhr2t1VUgZc/hM3ow552XHrraLbMXv8myJfcRHWY0/Phv++wfIOW/7bnL2zV99DYaO+OH5p/c3R3PSNGdn/jIVhaPjmNa92BsTGzYljFQf9ozwi7wwOAoMjOfp7zrBZQWRjTXvfEHDoaIed1zJgsa63mwuYEhIZE4KdtZuU6O2s6T8/HFvOp+HDuhCToL15GnTqO1sEI9ePJVi7nMkhQyTJ/hi0nuSEVmnDuwh7JRRfidMccz/dYMknUTbAhJiqBS+DmFshcp0wQR11NDZaXOmUPIA5LPiKjYRnqv3uyefs/vvoQ6Sv7JboNwri7mJ4qRFN/PwP4aJJedg3S/67Jhuux3pEMCnnGFrPaaQ492Ke/VGGjLV6JVKKRveAz2qk76iPO4+9QxBh9L+EtFzXRt3elAin6Mnd6DUtrBQ6PfYZt2PRJJF60iG+zU7XzY+AAPnX1d7wDytemrFHV4Mcw1nbEeBr2IW8WeDhUuXT56bZ3SWZ60RFnjfrQB92NNrI7vgcStlnkbPkUdEQ+ObgjP7OeY6wg0HS+jMu1kwtxDCHR+h9eESWMupi0GvR+lyoz/JH3J4YUpWLne2rFnSMJhhiYcJtekD6f6vUtrK7TWyxkk3c4o6UYQKJg39x4kzoNJswxgcWkxzxXnkeYv5PujU/XOVKsmN6EWu1HotpdLthkIFQKi9toiVJnRJRHQYatGaqWm21aFoiiWQzkx+rplXfQKLGDkBCe+/lZAa7c9M+MPMi68kotrduDhHYnG10i7/t03wa+s+L8NpCQ1RLC7qj8ODlpiom7OlBcWCbEtOUtDcCKTPSL5Lnk8wU7l3PeYJ5t+0JBfasKDA76ibWgj5s0izPZ402AagCyzP2kRZcimGIRer439XRXkKlu4tyWSsR2BLPDZhalAxOM2v04x1rFS7H/wIrDeidPjc7GKMEz24s4lM3GHQfNhd4g3ndLPmeL9HZ5OaWwNf5gsZ6M+1fjENfRTp7GtO54hpsYPjBZpPSHW+XxrfReVPSdePdxEmZZSmZqLa39EaCHj9BMdOBWIUCYHMHXKRPakfY5M+fR1LmrHBJm8HHyeSSV19LywmJrLJWe6nXpb1FMZ8y1rQ1x456It3RYivvFq5ZsdhxjgFYfmL7Buv7G//xZAilqJ+OBPqMRiVry5/OopyApV7PCO4/T5YpanReBlX4Ono5TkomDujT6MS2krL80ZwZ7U51CrJZysW400SMvXjqnstjWW1Yww9yTW5NbfILry00+SZHhY2HK8PI6CJl/G9M3jUHIYIY6lLHA7y7cRUSwrWUmn3IEDyavQ2rxHZF0hR4Z/rb93bgydE11H0xcMnCRD7qJA0iRB8uM9mIXf3h2jrUhAe7mQgB55BPfOplwj4nvc2G4dpXfy0cUGi1UoLbXEZ96PU5EvWqGajhFb9MtsjszT/y2t/JiX7luAg5lhmwdS3sVXVsKqhgdwbvmWc22fIbCyIb739ZNwXRmfrpyvh10Js/1vb2t/q8fZnQ6ktCis2VA8ijqZA0KRlogwqKtD7xg4wTOZwS63BtuvnGvNhQsUdn+BWveem3dQr5PykdNwfrAZSdp3P6G0FpH5gnGS1KzNQkk7PqXxmMgsaegoYaKdgDmpMMBr9k1dqMlIZUreC2R7BrP9cYM7ky66tEq+bM/BQi3haLHh+tanCpG3COgxIhk3/5sz6/kpUZRlhmLjp8E20Dg2r5SPWds0MDZ2KXKhKcsHGRh+uuhRnsjZg9E0S5149ik1JeUCdv5HSIhLDksmb0JuYSy3EKvqMO88g0xtw4G6t5nq+RQobdieOR5aTjDyQgbq2IFoPP78BOzK8d3xQMpl7boPQx0Jsp6CttVFz6qePSSPiKH+vPmOEJVGwvKJXVwM/xqlSRfh+yxY7jiWV7vX4mLjisLOyA4xrc/gF0EAI44LcZN2UTPWmbpBBnD02hAqNMgblEg8r2dx+J0KxaHBB41QxXHJOrQZQ2mV+SId3MzSwIPUS7quc8i71X195bcGdTc/dBp07Z5TuvCvVavRmluiuiI0e3lFgayL17VluErnsWWUL21iS04WZOOqNCbO5BadOLp/o99CWz8RYcUDZIR9TlW9AJu6eZg4m/yPAina+gOgavu10/0fWSZwHgu3SBT1mfAoh39aqQdMbhdJ57PYtP2Ivsxm6VtrsLOx4rH7p2Jva/hu1UW/SY9xcMsHV/ezYMkKFi+4i35xBhexG+NGIGXrnuPkFlbw2jMGRqgu7n/6Xf0++vY0MHH/TPwDpPyZ3tPRoNd9yaocXe25FgfTDhxN23Ey68DRpF3/b92fPxJ3OiNFdy464amPAmwINp2Ak5Ut2y4YgJRFPc/waEwAyelvkmb+yXWnbdWahtR1P1NDXqC3tJNnGmqZ6xdMfGcR938XhF2EhIf6bKPEtI15HWZ8+oVB6VvdfyyaazQwhNWlPObSF383Nzb4x3DXpVI+vlCIVn1z9mKrvQM1pmb0tz9KbeK9+EmX8LVkB2KxlkEDNLS1C0hLFxKoyWTxpadodHZmzTNLf/fluqTuYlOn8YN28W4frIWjiY68edKmcwnRyDMZ1+cw9wU9wfCONr6sMGQTr0S6UsWs2N4EdtXTx6GOBV+vwaesFFXcULR/kSuUrq2/A5AizEpGVFHIvVNf4ce2Y9hYGgGrf9fPZUnKcjqjhvNGk0Fp30Ik418xm/Xq/beK+vyLHPGfxZILF6ke6cSlYU4EbqzCNr+T+yeNYkrDaYYf3IBq9EyD3d3l8qLNXltxaXfCechR4oKvR/olndWY1RqsNVs6/Dic/jqJL5xDfAuLalNZN0vefQdThYKPXL4hYKi/viKpsVFIbp4AE5WUCV3fciEsD0vvEXwQZbCQPHc6kea4fEzqG5EIhDhbL6PQdR+X7NIRKgX0+NGWef2mk2N3a7bDt1WJyAocOVQaof/QuTbG9N3LwoBKqlf9pKcp6+jKf2X8bwIpWgR8kDWDFqUNsTEa7OxuHhc6RlpDZhWOHp8xMGAoa89OxNe+mgefdOXt5QIUKhErJnzE+dguwvdbIBUKyUpZiEhmzbF7UzHzufmZo2OlfNeRi6lGxOiOAL14XU8TJ4abe/5m116QN9CmVTDCzLCuZWcnj616HzOZDKWVEyp5Ky/2W8kLzVVE+f/MGY9RHAgyAL86sPblk09hLpCxWhnDPJHxWua1djPY4TxpglB2DjYyIho1sKNLywvJaTydko5GJECo1vJ9dBjLB/dk/6G9NHldL4i82nQfP/le4vXzVVTlvIFIqKVnrJb0DAEqtQBX18MsH1uOjUyAXCJALtJQ/fV+JP0NLit/dfwdgBTdOYsTfkEgl/Hp8y/S6mCYMMjLpWx37U9KZiFvnI3C2qwNZ6sOShq9eCpuN0nddtgGVjOn9gA55VNY6xvJZ+FJer0dXUQILJjcZYLS7fbZ3FRFA7Gp7UxRy6ms78vJqjAcrZpo6nRklF8WsZpzdIV1ENeUSkbpLAorxxOnuJsyelE67iVMb8FCVygEtOW9RcxoRxTB7UhKrNCeeBIb31u7CemO9cpE1861ib6TjK4sbVohP2vt+ME8kuekh3AMqcCjLJjo7Dl6kFjaZz9qq1Zsjs5BJVbQVLKO9xfNxeoyVv3YmddwVdbxSvuDDC/8hp/YgI+3hgD/6+933TEnpwhQawTcF3CECFuDFtrviTsZSLnY6s+28sHohGWtLLVERWoxM9NSXw85eSK8LBr0rkW/Frp37c/q93DusCd08l5CSSZdLdF/q9V8atB+S33LyCDSCOR6vRSt3rwY/h975wEeRdW2/9/sbnrvvRcIoSeUAFIFpDcpothBRbG/dl9fG/aGFSuCWABRitJ7T0ggISGN9N7rtmz7XzNrWAIJoPL+P/g+n+vi2rB7ZubMmTMz59znfu7b7og3owPhyb2hxIaNuuBQquIq5hy5mTp7Zz557rkOv3/UdIo2jGzNuwlXgy2txQINuTL8Iorp/Ue617kbHPl1DM11rnj1M1Lt24iLwRYXg40kTlx2QIZRJzB9xBIUphY+in+RGnuzGLZDzWkM2+zJq4lm4R1aft+moKxczqLrltGjtz1GmWXSJpZvZ6VkN19PN+edmLRe/JA+llGHPsVefB6PnA72XWvo6Y1yxHeQ+E96PkvP6Pa/Bel5LQ4TpO8EcUwmQwRT1FrzPWTe1hzt25k9qMQwf4rfS/8XP6V9ifsU923q8J1Y3thej7N1MO9H3IFJqsgfdfzju7PlEaTUcn+HWuzyktkob8QQuAAbtSv78uOZNeg0Pl72fLI5lGC3MubPqCLXdytOFXJ2po+jn8shJmkLUQd0ZCOKKdEOBbt5zv5OXt1mFibPfCAMo7UMh1I18gIVdsVqPKvMosNtVgJlQXa0RDoiRDlgY3AjLKUfDa6F5NYdoCLpTo6OyeSdKLNlurPMigWO3aRFjPYILC4mPDebisBAcrt1nDyL7j0yQWB8WhbT1/4oubCKbqznx+aiI3wXcSuu4e4ccY7ii8JchiuVZ4sp7TX4+JklBoTCxbRq+pBitxb3IkeUfrOl7/+bjJSrCUhRqbUMm/YAKdvNwNK5IQIbO/cnS321qVmJu5szn77+COI2K37awvrf9zMioS+P3SuObwQGTLiH0CCLRqRSpZHSe8aNsCwknbv/CxgpO49w4GiaJDDbHrPu/jf/eex2esVcOv3rghM474t/gJRLtdAlft/7wVa+yx/TZSk362bcrC032rkFRaEu8YHZ/ik9dE0CRkEmCQhKz0PxoSl+98fnueXP3e5sGQT0xiubh35une+I2E73uu38bqimxXc2QW7ubEg0Aym3DE3kG394/eQxij0Wd2gTuV5NrOxRKffW1mDg3+WFPBMUwfSaZEavHkLJDcU8GrYdF0FB6sfrcWlp6ZSJIRgMfFd1Are2x3n6RvMK2N7sU/jpLZOaI45OvOQbQP4fq7JpJ57k0+ynuKniGV53/Pqso464rWhxqmo18UbVeGQmPW/9+yU0tpdPvfu4+RRaMQkcuH/1JCK7+eDpeeGkrbRcoLS4irn91zK31z3EtzazusgsVNkei9xc2ecfyrDaM4T4t/Lkf55DoddLk/uVJZM43XT59Me/2a3/Rzcf43eCsYafUaQe4t4pt7OsvhJvt0ypTqIY4vLyvtyW/h0HYp5kQ6Ml5enW8B3EuBR3WndRLO2TmDk8v/cEJT0cqLkpiJ6v52KtNDB6wWRe3P0VsbV5HV6esvxMftb2w73uZvTupUyaYXFVEQ8i07XiUGhe3RQnKT9X30TJIguwdm5FRu3YyrA9u8m0GcTvsa9Jjk7todUKZGQKtDSZeKxhHuPvHskEY382evXj5pwc5nptpSZaR4+yOdQ55EggihgDv3Xi4R4T2RVqvg+85SZ8ZQK+Chm+MrARTFQawb72DA8VyNhZKONouUVoNyZhE987ZLDzp73or5/Nnsq+bK+I+x+99lf64I6OJuL7dz7BEx21jhwxcZ3jnUT0msPXRybh51zFlBs9+fxrOT7OZSyZvI7UACUhXwfhFF7Bkb1PUOXaRP5FnJnaWSnt53KHU3fcZJfOixYdBETXlfaY8eP39Ew7SZtPGJ+ZnuTB6ntZdMMdLM4YzMg+SylxDuPLvk9LxQNKc1iU/zYNGm8Oyr0YLDcL/onRqtUSZpco/f3CgHfBzjIBWKc0YdS0ceqrH1CILx0xfWxkAvkhRh4/bIMhoCNL6Vav1eS4tfHZNhNJVQvx8TER082ISilwIlVApxf4fcZHVDiZn6Hdaxs5eLQaQ3A38lv9SaqLJqMp5L/6jrrSfeiv7s9eruaZXj8iF4wojm5HqK/m+9vvJO8Pa9BWg46jyijW51fw2gHzirijTSutWkde7vkTz/v248uGpWjbnFif+ip3z/8VlXUbQ8pqeWXXcfpV1Enb5MT04KcFt3dazVaTjt1FxXxXVkWjaiRrs8xpgWLc1juFSnUjs51XYkTG5sMfUmltYk7lFH53vhvjhAvZBe3bag4vIzjeB4VXHdS6Yd1yE1ZdzyslHYuSvaIcronrb9uATH4hCHmnEMOdYb8i08sYtf8prJus0EQfR+dTjNOBmWjsm8jI28LGOyzaFI8cehJXQwN3au7itqSNrLJ/i359jLi4XPgOrqoWyMz6c3arf/Xa///ezt/fSGSECdkfAJN4Kx86LEfUgnoy9idcrbvW7ZJVFPJt6/UEV41AFn2SiX0PYFuZTFjfjzi84hf8lErSH4+kzcUyrtQY62gSzCLsdnt8GRVl5I19I/EPuZCl0doKN/42AQU6iZElMrPa4/vWXCoNKj4vmURvtTeGNoHyAzLkVjrGLNjUgQWq11qz67spoggeQSMM3B+8hRKrZt6suF6yr23MFWgpljFywNt42qWzKWoBx/3MqYdtrbWEbK3keEl/rotv4cBxJxxtmnlr/tOoHS/UOFToq7Brtei0mDSBfJE1nLnb38NkY4d+zCzez5xJlebKWbn//+4zf+Z4Y/2SGWO1lez845zocx+ebS7szB3E1H6n0GjkbM/swfjI03hN3o3avhnXrX6sDQ3mnZIfUYaOwnS+QrboxNVcSlZrAxmlw7n5dNdOh53VU7RPdlAb0MrlJPp158NQW9b2MGsW9rByY4x9IFZiRzkn7vpoGf7lpajs7Vn25LPorC7UZRn720YGHzp4gdBs+26qMg4zavJY7lAG8V1wPI+VnmFRk+XearJWExD0mVRcyH6FfJ8GUqoL8SsZjiLYzBr8vwKkiOcqAiC71r4npee0x74jqXz0zS98/e4Tkj7J3sMn+WnjHglIaY+2Nh0vv78KZyd7/nXfPImRsnvte9jbXXocJe7jfCDldE4hL7y9grWf/0c6hN5gYNi0JWz7/i1cnLtmy1zuPfIPkHK5LdVFuZefKaVI6UNQkBF3N1CJavMqE0rpE8SVkP9NEeFUzkLP7ziTc5hf+93NIDd3kk9EIJOZsB7aiLX8NNOynWj06ChQKLZBt8ZXmBG/gGy7AMY3VLPNzZv7Cg/Te8conpyznmzbeh4/XcGzm3ZjcnDGcN0kTLILk661mUk8HnonnuEy1gf04rbyMzzT0EqZtQ1Lff3Z6WRejXU3tbEsbzn9y5t5rWE2kwo2s9LhaXr1NOLxB2W5fXD1UP0DBGkz+OG2OznT7UIRo66u4damM5xGSWhjK+N/f0hiunQW4mT5yDEZC3svZ8ag2wlXtrBF1MD4I37Q6/hPnwHYGdqYqc0goKWE25d/IrEEKgcu4J1Mi+PG/6b+1Nm5iA4gz0Usx2bfL7w1dCiLdEGEeB+RiorMj90FakaXp/BqwHoa25wQB5Hl5TJ6uBSyIHxXp80jq6/mw6Dr+M/OdGq95FTcEU6vN3MlodknbuzOi18sxbbnAIzeHZkD6owT7Gt9FzudNTETfiHYv+P1dTzzO4LJQGbxZFZbDUI960LHnnPZKO96foHvgLCzjk7nVja/QCAkcz35sXsojxjOzrBJEu19V/YnFA5TY61zpM3K/NKOX+XEapd43kzoT4KNQM9LaLUVGXTcf+YU/sVhrK1uJb06gobRa7FtSOHDtGpaY0fx2ql56Ez/PRD2f6Lf9uhuwNu76yMfPSbjeu1ivIdM5OvDU/B0qKXvIA927hYYGb2dbmMzsD1jQFdqR1nNALQFAzk4IhN5QtcTk3atFHEq5yu3Y76jBby63DYIzc9jwZfLMckVrAz5gFPqHjxRfydretkSUP4ad/R/SNrVC8M/lz6HJ/3IGPVutmri6GNth+I843a1qoxgx3w+dptDda/rz1YjXQdHtCaW7z7IlAwzCHjjjAnEC6kMLZiE9TkkJbVMx5iIVdjp9Dy1ZgyFxugOz1KVWuDECYGaqK/5eYB5Ijv/VD4zyiZztLGXdK/+X4uZQQcZ4JmN/NRRZCVn2DZ5ColDzJM7MWqKFdzdYs2Gw/40/TEps1Zoec/je/41pDvLipdzMu8WHulmRb3LMV7Yn8roAgs7r30/2TGxrL15AaZOhLdXtuawP6kCpfVIPkuxrOA9PuQYh0yt3NW2irymfpxIfYgzfqe5N/t+vg9YivMwixvf+ddNdvA30t360L84BMHKRODwrtko7dtWJcloaxaIn3AAD//qC7rCBwYv4m3SMAU3E39sDl7lMeh8itCGp+J4ZCotTpXsqUomeaZFy+mZ/Q9gQxuz9bcw/0Ae290XMSTBKK32dxanMgTq6v73gCni8CimmwmvTkR+T2fJqK4WGO93nJG+5klmp9HWRvmxY2QJ3yAIRhJmrSeo+jduiniIJ7bl0a+qluxFISiDzCKtRp0Rj29LsXFppFnrRI1CYGK8mqXpd+PmcWHbiutM8b8swk+fzxf3P0hlgCUFrB10fr5yGJOazelDVYly2lq4oJ9UFgSSunsQNm4m8oeV8HCgWT/HyiTjsZoEJpd1o+KojPCAvfSPWEGrlTMfDHqVNpkNjUYY/Hsiu3KHIZebMBgExvfYwOThZ9BZdZ6SZt+yH7nBDFYaVWEsOzOAO7Z+iNEvlMyIeXyTZ7FW/d/+XHNQqHkmZjXs/pHlox4jVu/GlqzBjO9xkswKL4obAlg04iR1Q3/Hvk7OivzpvN/8AdbesegdunabtC85yH/cJnDvtkoCWlTkuTqT4uNFprc7zvaVJHACjwY5x5yiaTLYY91kS7+KeuIqzWm16V6uvBUSwsr+GVgjY7xDMFGKC5m1oXlnWPCV+V0pxvZJUzg21PIcbv++nQV+vtBs+++y3DS6TYjmmdIY3u45lPiGWlaXW1LQmhQNBMqNGAQN8prJpMWspyhHgad8JjIrE8lCHovnmXWu/htxNTFSxPN7eukXklbJY/fOxWgy0dyiZP/RVLbtTZKAE22bjidfWS5pp4j/zzpTTPdIs8C56KxTXFYlMU9EjRRRa0XUSLFSyCkuq8bP2x2rc8wazm3P84EUUVx2ym1P8/SSmxk20Ozas/tgCl+/9+QVuQz/ACl/sxnvfkiH+DIbNtggfXYWYtaJSCk1GpD+6UW2ifhPRC3/eOG3f1opBORygTaduEZkZt9JRcRP8Y8//n/27/bfpX39QSSUqIJ/88TO21xcaRQFGMUVjoe6/YzfoeXcP3EJ010CuKl0kJSr2KdfI69Xfotf1XR0DhdS2b1q17OslydrPIdIrBSNXM4LqQexLRjI7RN+wsUIZ975HlGIXT90IibnztF+oaGGpxzaGFM9j8emm1kacxpqWeNmRnytMBFnI+P2uoNMzVlJcfVgPjRE0O2MhkTHKQwbaji7cmM0IZ3X2LqvGK1czYFRY9g79vJfkLqcFFK0dURVKAhpfVxaFeoqRPeAucErmDNiPh4aNYfzzCs65Xo902NiabK248ayk1hFyBi6bw+jt23BGBTJj85PkFIfdYHzyF+9wldzas+xJBlqtcC0wMMMS3uFzaGe9Le/gdgA86CptCaO6uz9+AhOvGD7teSQFB9n5OAhuUThEtN7xDSfC8Jo5COncF7cmSvRREvmBRCxqpTdwQGc7Al3rPvUbGN+/gjcZGRVQXeCy6/D4JnGxEmn4RzLQ/uSQ8g19RzPvptVYZ4oRrZccOhR27YwbN8eTtsM4YeAVzo4Op1fOPmojgcq5xJ373gWNvVgdXACG5NXUXODZeIRt9qJLFUw86bfwEhbgag/gX34VmczrVRBZeUZFnRvZO6JoyzWuPGL4WaO1cXg7ma2Db0S8T+Z2nO59RcnG8MK/43L9QP45vBUnG2a8fK0Iq/MjnuGfkntiGqiVvrgH17I5m3/kZiBRx9MkUTtLhaH1BVUGFX0tvYg+iIis53tQ2Sh3ffeW7g2NJAXPJlPdeZ0nMGqTfRSfMmX0a/zhtUa3J0L+KLv05Q6h7F4/7P4UMNSwwAWChcy6nIaVQx1T+awrDfbhj1w9rAak8AqpREfpYrjK9dR7mDHhDlTeD5zH6EKi56KuMFheTaPRhxmdGEVYfufRSE3MXRIx0mruJCQlpPPx9PN6RsP7W6mtdQs+mhrY8Lbx4Sft6ivffH2u9zrd7Wm9lRWCmTlyHC1buGJ2LXICzKQZ6ZwfNBgtkyzuIloc9X0FLypPuJIcaN5QhfsWsRkUxJ1ccUMqSrn7ZLHOBi3kh/zOnFZ0+mQpR1C0OtJ69efDbMvZJEc0lTy/t5iXD378fYRszC8jVzDs4MOUyg/wRDVKXYkv0ytxp9w2WK61eXwec8f8I/t2lZelZHLMfcSBpwZhJObgsho80qhUtZGk0JLk6wNb509Hgbz5FuMpjyB5kIZEX0ziYy70Ja30ijwBT6MityHV1ko8Ym3YbRRoey5B6fkSTS4FLBOW0bpyH5n9/ni/kXS33O0UxmR6Ex52BiJIXUl42pO7bnYeYoaKekZMvzs6niw+68XbRLFgc184/MswdXxEHGKmaGfsdx9KCGpbkzMK6JgXgANsU4Y24x4rSglpERFgasTLj2Oc7A5nufDqlnS9JCUVtRZhGx6hV6qvfw4fT65Ay22xEc1VRzWVnJbfW/uqzWzIZvyZTQXCAT3OENMggUAOn2oHyVZ4ZI2yuIR6yUR70C5A6UGM+N7clMUd28cga4Vpgx/DBvqOBQ4jpRsz60AACAASURBVO3hNyLeOUN3HmD9KQt7fOnMx7ENGIBgkCPkRmFTG4bGLxOjfzmCXRsKfS12rQekfeuV3fj0dDi37lmBIXYgn7c9RF6rP6EhJkIvktJ2uf3watVIEVnxicfNY7JZwQcYnLWMpfE3c53gxcaMoQwOyeBoUSxW8jbmL/6GVqc6XPb4sMfTi38pj6P1tjDgOmsLWVsLutIkbgx5imJ7N8INFdxWvZepDcnoWzxYpxrBvcNL0cuMyI0yJlXKiVe5obVzR94mw17bxlPdzxCkcGCCfQiOQucrSjet+JLInBxMTq4ILY00OzvzwVMd08zE+j35wrOdCs22112oLuWuAANj9P35qO9IWuV2klTBJyWFuBj0tMhr8a81u0qJqYgHgpbjlJyAIcyskfaFfAdfzrlybk/nt6mpIREMXS/yXG5//LPlBNeBoLiQkiim7bz03rccTzXPdZ56YD4jEvrw0PMfUVhaiae7CwtmjWPTjsMSkPLMa19wNOU0NtbWBPl7sfTphVIZ0bXnrU9+ZP/RNNp0OinNZ/mbj3fJUDkfSBGPLQrcivuvqKojPMSf159dJDkFXYn4B0j5m60oAimiQ4vo1HIl4mrWSBHFGcvKBXq5FbCg5jnmDpnKeNcQ7qgRUJt0DBwwgH0nl1IvN+tWnB+2TdmkhCbybOBNZ39atu8Y610d+KH7CV7an8aSI6e6pNWdu7/U03v5xft5lP1VbPe25DtGK0wMtJFhJ8D4vDUMKdtJRsEs1lrVICsZjMo/hp49Og60iopl2GUe5s7G58iPjGL1nRZBtItdUzu1ivvffgM7tZq1Lo9jGjEBe7uu+4HYfiMVK7l5/DysDXpOZZlF4B5wdmJHUAT9morp7tMofTfv22+Iys6kJXYULzc8h9Ek69R55K/0uasZSKmsEsjKNk9AntEuoaCtnpbwOxnpax4IpleOxi9lBac9JvOt4lEiwo0EBZokyrbILprgn8Rwn7ROm+V7nJiXpsZHqaYm3hWv4418ENcbf1MWowtTMHazDNDP3cHa8t54FsQjE1PsvFMZNzFXAjvbY8vmeGRVIeycchKH2I6OPfYqpaSNIk6ORTaKbXSElMPfVYgpYN1TPiexbwa/9R6IzGU8T59Mwm10Gno7E/1/cMJQ4srYedPo72ZL+J8AUcRj9sg5wg21P3GyIYGRg7WSY0/fgAm8deYWKY2wM3ebv9LHxG2uBSClvb1dx/vx3RGzq0N7vDrtffKd2xCOeaJTeFOVNJm0qFKUsy7t6NVVm4lWxvO+/YqCiCgOjxyFWnTsOi9Gbd/CsL170Dl58YLzd7QZrXF2NqFt1PBC9QzmTr+XzwtbiQrYydaIOZy2682j6c/RprfldVkI93EhBaexRUmUSwoakzWvXPch8vYcAGCH2kShAZbUVPKlmydqhYK1u1NQeHVkJHxis5OVISU8eKwKZfazBPibiIq8sC+rNQIbDB9jY9ASmTMGa5c++PqAi/OVeUee21xXK5AiAvRHj8po0wnMD91FH90h5Mf3UBgewaq7LXnaVc0toO7OgH2tnKoyM5fiAtOQOTVyj/3XHM6+h5tH5LOxtAFfmQWUOLcNZA21yI7tQDAaSIkfyG8zb+zQo8TUiUEHS7jexZ+PDw9Ba7Clm2cBN0Scopd+PTUt4ew78W8S40/zzO770Qq2fDPsNwL9u75eDbUGjtj8hx96heOut0WGQO157jB2RgW3NvTm5oaeWBvlaGoFalJluPnUMnDyvk5vkcVCFLe5b0HnYuCGDc8jGGWouh3CPnsoda6ZfG6rQ9PXbCNuY9DwzKEHpdSn+3W98D11PR69Q/HyurL97FoFUiSRezG9xwCP91iHh03XIpSyzGRKa1VmVgomJox/mSJBT1Ll9dxxKpPSST5U9nfB5+tigso0FLg6U9ZfyzDtPj5RTWKjYzm32FkA2vMvruuubxlWu4qtCWNJmmJ2kxMjW9fAb6piRreGsrTcrK+ia4HKRDk2dmpGzv/9bNn9a8ejbnbkzMgino7ZIU2a73aOoVDfyhZlIVqM3JE0kInHexMemUh//0+kbT8c8DK1dj6MPrCDVcfN4HAPv1QWT1qHUTMG64x4FOel6GhcCtH55WFr/zMKWRW6lp58m2jP7ORNVA64ibdLF0mLlaI7lJW48vc342oFUsTTKq8QyMmV4WnTyL+EZ3kpOIHxtiGsT7sOJ5tmWrTOxHjn0ePun7BplrEiZybvtX6GTeAgTLJLD1BE0f4UnQwvfTORmko0bS5kFM7iA18nViUkE1PTiIdaS62dDQVuTmgVHVesh9r6Mcima9qpd2Ul9yx7V7pC+lEzkR/bjqBq5bfpM0kZOPjslfOoqWbxe2+DrT06cVGtkxDatHzWdIqs0BG8YErlpsgHKbd2J1Sj4quSIlyNxfjVmNnjFYHpHNWm4FE9F5s/NNrucf2ekxPu/Ju95Z/Nr7YW+AdI+ZtXRARSEgYZsbH5+w9TsSpXM5Ci0QqIVHjxRfuC/VM8Eu2HbeR17HIzo62OBjX7Dqyk2OP+LltVJn+DaT0sgocr1p9i1phMFDIl2cvWYeXogn7IhAuZAeftUXYmnVl9erOocACPT4giTpnHqyU/0VtVRLlDCBVOgfSsSZYGW0dOLyHFdgMZ9c8Q3t1aYnacGyJj6OTBFl6onIHGxoa3n3+xU4r0+Sc16/tV9Eg/RYFVT1aGLSOu38VXwurqBHwb1vLshBtQyWxJzUxji1bLU30tKT1Gd/NL4on/PI9ol7u1+4vsbBou6a6cDwD91a57NQMp505AHrZZin/RTrYNfIybvNZKp7u3ajJ9kz7ma/elZNokSCvionhwY6PAyTTzy/6xHmaXk/PjeH0zLq1hDCmrRG8jQ6E18uj4BOYeW0FESKjkEHV+nKiPZE3RCBzczjCkLBy5SYbBI5txU0+jkJmv9++rxyPXOLLzriQczgO4r9/yGwkH9pFhM4Rv3F+9wNHp/OOJue2nD9azpO5mut8/g0U10Tg1u/HigURUbkasmxRMnj0Z/xBPgrpgwHXVL0KL0rmjyCwC/Zz1aF6NaiN11S62O71BVnMwvr4mukdfudXcawFIaWkVHaK2Ejxay9qjlsFToFsRt07biJAk4Olew28HH0TR6sGeG09iG9m5vfWl7kcRRLn9s49xa6iXirZZWZE8KIEjI0ahdDDn6XrW1nDPe+8gw8QPgW+SrI/Hw8NIbHcTRxJlTKt5h3XDqritaDrXRX/BKdc4CoUIpjSs4YQqlkYrOT3PEZo9t04mdQHeDqW843MHzd0s4n/FBoFtaiN2RgNqmZxQbTVvH3FE7uHc4ZQWevzIKQ81/95gS1nTLfTva+g0RU3cSK0SUKoFPD2uXH/qrH2vViBFrGtpmcCZPBnetg08Gvwtin0baHF25v1zVkJ1mMitdmfuIQ0HS83v0QlRByn3amFB3RHutJpKgtU2bqdrery4jay2AtnxPQhGI0mDh7B1qnlVtD0KMgr5l86K3enxFDQEMi4kEwffDCbpN3MsczEnVbFox2/i1hXLpffZwdEf4NaJQHP7/sSU5cyKF3h7xKV1u3x0DtxfG8+4pvBL6qQcNNhQbtLgH53D0N1349wUQJtHEdZ1IdS4pfCOrxs2IeZJk4u2jkePPY1S48mLrR40Fj3JsKGmLpnBl7o/u/r9WgVSxPPJzpZRUSVwvW8KovZYVyFOEOX7NvBN8CsE1wwgKPZnBnls4m3DvTx+9CSl8a4IpWoCKrWUOTnw1W1TWZT2Or6GCp4wTqNcU8cNXnd0uX9Z4m4mFiwlMbIP2+68+Wy5aoOa71pziNS6812RBcguPyTDoBFImLYLZ89GNK327PtpAoLCxOLbf5BcWSbYBxNjZWYti+mUG5T56FtlLP92vsTmnjLhJWyVBWetkKck/8Zn+832y/eNeJdetiOwLjZPpFsda1HJcrARgnBpOsfz274QHLNpMtmy8/jPjC48xaroz0hrDMff30R0J0DyX+lnVzOQIo7JDh+WSWLiS1w/4nsnLSPd+/NzqkVYeFKvo9hO2Y3TIS/2OfvwmDEfnVMn3umdNY7JIAnPojOQVTyZrLIb+OS6Y+yKyWHxiTP8p94WWWM9svICCQApdnEkz92JFD8/mvxisYrraPl9/iHatcaMgREYeicglBVI+ntNrq58+PhTZ8f6PU+eYMaaHzD6BGGIu1Botn2/yZn7mDh7OG8eC2O23UEWxc7nlEMwrjotK4oSGVg5QSqa3ucXclJcCXA3v2t16BkZsRL9wI76kX+lv/yzzdXVAv8AKX/zejz9chux5zEc/s4ur2YgRTyvjEwZNTUCk+02sC9oL23d5/KLj3klf1BLLm8kV9LgcaFye3ub+DctY9Aw84NE1AN5c30BN05N5MW9J1iSlI3husmYHC6dSy9o1HzbkEar6kV8vBu40WEHbg75yLUXrrpsO/46tbavsUf7gTTx7mwFISdX4NbUW/E0lLH8wUep9r34wFVki4isEa1gw9te3xLQ2/uSkwZxZagpezffTuopodi/ZKaxMDyCWhsHZpedQBFhnhl7VVZw77L3MFrb8YzXRkmYURTMFIUzr0RczUCKeH4lpTLy8gX6y44xv+wpfhj8GPd5rpNOfX35NEanLON5n804eNvRs4elTY4mytBoBO6N2kyIY9UFTSWU57PLYQRzT1tEfp+aFssTGz6U3KHOj/xWP77INbuNREWaUGrTiMntiZVBjtGtiLFTTqA3ytj33VR0MiOJ/zouKb+3x7lslLc8V2DwDZJcFS4VBYUCw1Jf4fcBdbw9pBePF/XlrW3Hpc2eG5FA5eAYAv5k6p5XVTGLst7EWmgjs2U+PQYXY6/Tk/7TGV41vi1lNImAsLX1pet3qfq3/34tAClSf9uVQfeEXH47btEhGhuzmYDRmcg2++AeJCd9283UOyjJXnJhSsLltIdzcxMLvvgM97o6auSB1NmH0b3lD9q4QkGSCKiMHM3sVd8QVFxMse9wlgkvStdjQLxJemZVVMpQpucQ4/oSGZ6P8JLXRzTJXakyeRJtPMMbDOVOY9cdI79RySD3FHYoBnJwyN0dqv2d0oT6j0s/pTqdOyr6co7ureQUMyp8JYLJyNMrhlPv1I3BA/+7IMnltOvVDKSIoKiYNqrXCyyM+I1uh96R0g/PF9xU5hkYmyNny5kB0inf2ms7Ko8slGmDeDtuHxtr3C+5sCBuJ6suQ568VzrG/tHXs+/6cWebcF9LBe/m1mBfO4oSrYYe7goKFYcYoT7J5qMfsG9WKsMrdzB2y28ctp9K1YSHLrnSbrP7PezsjlPv6o7OztYsim80py+LnxmB/nzf20ua5IrRU+3Fs79ORNFohaN7E72vO46Tp5mBeW7cLO/FfSHriMoaSWSmZUJT7XKYl2K74eFsTl3zVpVx//EXaWwN5v1mBUrNc/TpdeWeX+11upaBlIZGgdQ0GV42DTzaY/1FbymhNJ+ygjKJlWJv3cSUwQ/zhTCPhQcLz25X4uzIigWTGF27haGl21Fp3JjiNo7YMjUDgzoy+s49WFtOLjNP3Eehuz+rHn/47E8GTHzQlCbpnBzIve3s9405MlpKBCL6nSayfyZlOaGkH4ijNqCR+6auw1Nmy61OHYWwRVByq7KQ2b8m0K8kCLeoXYwO+BHRIean2PuIqc4nLd+DJo0L9/XfiSzbLDyZF70Xt8QTuBeY2RM6Z1uaY8KQ20Xj2mTWa6hyPk3BgWcJE+x41upb6Tvx+ddVKtPlPLvOLXM1AyliPYuKZBQUCcTa56Cw+4RuoVNZk2zRQbxpzmpM/sV8nzWdN9TfYevX/7KaQNXsRElmCC2lMpQqb5Q6R96cuIu0gEI+PnCaGS7dJBfF9pA11tFSVIW8ogAnYwN6QcZnix6mIaTz8bprQz0PvP0GgsmEfvhUTI7O0vNRBLVFUObX2fM41c9c1+t/30zCwf2XZMQbT+wn6JahGAQZO47djLdmA28MjmCXS2+C2xr50Govji2eVBsKEJLmYeNnZhL+okjijfB0THH3XVbb/FPo2mmBf4CUv3mtPlmhxdnpyr28r3YgpVUpcDxZhrWgpafHYoribmdVsJkCfmfNbmblxdLWiT5KezN71G3lvoHdyLAPooe6lO7HaljVP4Xsj9ZjFd0PY+jlC71qU/Zw05DbeGinmSoaEptL94EnkWsbUGgaMDU3Y2pt5auMl/FTf8Ah7+ckRf/OQqSjh29/nTj1DjZOnUXq4EFd9gxRPPT+d97EQankZ+dHKImeQmTE5U0q8k7ncWi8QLq4mlJfQ6a7F3GNRUT7WgCguGNHmLjhF8rd+vGu7bu4upjo20W9/0r3vdqBFJEhdOioHJPeyBvV41nV/24e8t0inep3JeMZdnoDr3j+SM9YI54elnuvsFhGYaFAf/dcZofsv6BpxBfnJuse3JFkFvltsLXhqxFe3HH6MOJqxbnR0ObMh1lTURtsCAwwnb2+hdkHCCkeip3eCoNLBZHx2RTsGkmxZz1ld1vEg8V9tSvA59gP4nOX1yXA1asTR6fzKyqu9hYeLOCulvuIvW8GU2oDcdVH4aFRMc3mdxzVVqR5DiYvpC9a287p/ufu06mpjntOvoqT0Eph0zi2B47inuDf6F9ey+Jt8RyRX09IsJGw0Cv3HBOPf60AKWdSmogK3cbRnKmo28zMkMVDV+Agq8dJq2T/4YdRaJ0oiNhGP+t9qO3s0Ng7oLa3l/6u8fGlwd3iknP+9XRpaOC2Lz7FpbGROqsAPnL7kBa5G8HGXG43voNzjTl/uD0MNg684L4GDfb07mnE/Q9hbPF3UUPo7rKF3DdlDrubNmNr03x2uzute/LGRVwkKupV9PZMptVkz5sj3u8gR5uoNZH6B9Hmkcx0hustOgbiAZKtirk/bBcJJTXE7n6SoBAIv8L95a88y65mIEU8HxEUFVNHIx3LuKfifoTWJr68fwkVAZbV2oaSFm7MsWP9afPK5T39N+HTmsP4kBB+qCwlQHHphYX2tpOXFyI7eVD67+aZszkRbwZnCvTNPHiwkIHWc7FXuVHlfYpW/Qq02UNYK4+jeU45k35aQ//U42xwewj5uEvn8Vsd2MT48g8uetm+mXErm/r4ckxThcqkp2eZP//aMQZ7tVlTJbB7Pt0HpCO3trC8vjK4EaHIwdfBjQGHbjm7/0qnfbyaEI/7H1hhcFMed6W+QXVTNF9X24D3IxdNR/or/Uvc5loGUsT0nkN/sAke6r4eX7uGizaDPHEX37o+TFD1QMYnPMoR6x6MO2jW5hGZKF8vmEy0Poe5GWaL110nXuDecWncdNqP6NCuHd+a6nTcvHMCOpmc1195rUMdPm8+jegutSF/Dj568/NX2yBQnSLDyb2JITN2krp3IJV5QXw77Cibe6Uz2yGCoE40GWLST9GYpiG02LxYF9N/ObGOR2iyduOUz1CGOpkXZIS8J2gkgJyoTcT+3Iat3pm2YG/k9S1YVTUg6MznrHOyQRkVRp2DNY6//5tcj8mskD2Mt5eJHjGXN+a7nH53tQMpokbi4SMyyUl0msNCrGPmsPa4edxtZ61i4uPv45DkyQFbXx5R1GKwuVDwtb0djEYZlfmBlGaF0VBl1jUUQ2Wl4aVp22hwKGX18TLiXMM7AMgGk4wdFf3ZX9VbSkOe1fguCepNVDu4s+LxR9B24tU+YeOvxB89fAHLRFaahzztCHUeHnz66BOSBfSCL5YTWpCHIX40Rm//Li+byIYfN8iHFH9PPimdQJ96P0x52awfWc0PvoP5xvVdjFYmtIlhZBkHscelgMP2pbTKzc+4f4CUy7kjrq0y/wApf/N6ffdzJ8KWf2OfVzuQIp6amEIhplJMtH+Q1ISZfBFuHgC+WfATUa0W2qZW0KOW6XE1WMQPrVuLWR2Vz/eewxjfkEpaTSM3FG/i8awa9IMtubOX04SysgIe9tej1s1mblJ/BJOAu38V/ccckwZm5cU+nNoxjLTAYnoU76Wwzy0EBnY9WVTs28QNlR9wsPsA9txq9nzvLKat/ZHeJ1I4Y92X70LepX8/41nx2kvVu7hExvGBpznqHCkVtTdomabNAHdLLmk7FXGj6wPst5vVwRnjUvu/nN+vdiBFPAeRkSIyUx5pWsyesD48EGZ27VmZm0BgcS1rPJ+5wCFJ22YWRJYLBv7dezXWsgtTMNYJAdx32LzCtj/IjyqXSkbZgekcS0aNwZqPs6dQq3WV6O19elsGTOJAou7UGpzqp+OitpNElmVGBSe6FaGZYRaEFVM4YtLTENN6RG2Udzy/psYutEtHp86uWXaOjBnpD7FqiJz3B8cyvmUoTxSfZDTbOhTPlMeQ7jGQ3ND+nYIqNmoVdyUtxYdqypvjKXG/j19dMnnd96jkqOKa/DxGG1sGDzIi/5Msl0v1tWsFSBEnu/7yFZyunEGTWqSKm3jlxk8oTHNCWxNGTel1FHmlc1fekk5PWXyi5EdFk5QwVHL8Egdl7SGCKLcv/xjn5mZabLx522U5RkcXrK2hqclcbqbb7wyu/V5iFIjxi98zHGIsfj4mup0nnFlXL+B4bDNrx6bxeZkzwZ4p0jbFqlB+sbVhfif6KGcrYzJiq8vGybaWbz3nIjh54tVagaeqghaZiVuizNT8945XE2rTcSD5le1+vgjO465j1ZD9jCSYLAo9/0/H1Q6knCvS/rL+Luxq8lk/dx4ZfSwrtqKGyR1HBX5MMduw3jX4Z7bXuSF3Osw9Jr8/3cSysnzkqWbrVlHcdsfEqeitFAzfdZKh7mPxrIsix28v0U1fsPnoMnbcmYvC1cjt739AUHUZK4Pfxz3BnGZ0sRABotICHQqTFmtTG47WWpxsNTjatBFWuYtBdetQKmxYvuRRGr1cSdJUkaytRq5TcNOxOG5Ij5V0p6zs1MQOTsMn3Ox+0WCCV01h3Bi6h7GbnjpbhTPOO/l6yBBJTF6MiOp0bs1aRmFjT9YUB+ARNxebK8ioaz/wtQykiOcg6luIOhejfU4w1t/8vOgqBI2KquMppPM18VErsA9I50jzOPoU1/PDvPE426i4J/llbIxakrIXssbej3fH7+HNYzfi49u1/7VWC2M2zcPVUMsHTz5Ds4vr2SqsVZ6hRK/kw9LxDFBZnjule+WYDDBizlYO/ToafZs1D85fi42HkZkO4RecQkRuDvO/+VL6/vteS/AuMqdqjh3yGC6KOjb6z2RKWz0m63qy7fvSYHucuDXumAKC0Hlb6iNuk6oxoqtqZEhhKTYGA0dD3Ol+8lM+9XyfPKs+ksC9o8OVe/5d7UCK1I9yZJRXCszmJUwDB7Eu0czijQvKIHzeRn7OmMzLut+w9zC7L50fLfUuFGeFUn4mGKPOwjKpdGvgYGQWm3rnEtBay5ocLQEOHTVParRurM4fddZuWtSZC/ZpY9CW+yU3qNSwHmxc2NEC3lal5tHXXkJuMKAfOgGTi4elSiYT8r2/IlMrWXfTAjJ79bqk0Gz7xkJtBc85N7I8rjs3NnRntDIMwSDglmbHmZ451PmaiDRVUlboztIeFltnZ20b4wurWTP/1Yveg//8eO21wD9Ayt+8Zv8XgZT6eoG0dBljtZ+ROS6Kz6OHSa34Y/I6bKzNrgHl1i086ruVMmslI1tDmd7cjTiVn2RVZFS8xyHXMKLrldwR0EbqVz9jO3giJtsLhRcv+tI3GEg/tZNRC8bRtziQx7aPwVZnhZ1zC/Hjj5BX5E55YjyJEcfxLdDiPDJBco7oMorymHz0HoyCwI6ewzgxczw6G8sDX9wuLC+XW776QhLke8d3Fd0Gu/+pwZtSCfuD09njaWbezC1NQRbZUZDr4aUv49TawtueX9HsGsbAuCu38iEe81oAUnQ6gcNHZYxr/hrBPRcvk5n222j4gXplP4qjp3Yq8Jx6SkZDg8CMoIMM9Oy40i9uvxZvFh8xT1i/6RvFkLItuEdaxIqNJoEvz9xAQas/Dg4mCSQ7H2AQc4ZNSe+j0i7Eq9WsI3EwIYM4h32IebZh+WfOdrES5/584PAOgYFGIv+EILVoJas8cJipmlfouXgGGoWcT0omEKd0w7E5HVdDMj4OJ5DLzbR5MSRQxV0EVfqhtbNHrtdx89F3iTDmUaeMJNf5aSld4z3HnfzkX8LDB8ppKfg3UVFGAvyu3KCwvT7XCpAighP2tcspaJ1JdasP4Z65zE/Yijq/jYzExyh3rWB23f1Y+51jk6nXIbRpQKNGVm9JIxPzrhOHDONk3ABJhPrWzz+RQBStvSdLHb9ApXAlTkzTczAh2l2LwKoYEY5l3OqxlspCJZ8Kz0opPYMGdg5unT4hsiCfINZ+CFNczG5WK/UJ9BS0BAnmFd2uoripiTi3zsWYkxwjqVMFsdMlnsmqCOyMFgeEB9zWctyrlWc3CrTq7iI+znDR4/z/+vFqB1LEdmgXab+t7S161f3O/tFj2Hd9x1TCicca+SFxLDYKNVN7bON5nxJ+q7GlQ37VZTaq6PB2JlvPrMZ3sDWpqPX04uebb6GtTEtCaDm6ECWkeRGbauB991EIw5slbZWn/v0sCqOBT/r9RvAfLjwXO6RSBa0tAqK8j729qYNToMlopOfmJwhTn6TA1Y8fHluCQa6QWCkZbfVktTXgXOXEPXuGEVZnnuDIfUqJH5GKq5OGf5lCudF1N4NO3o1Ts3lSley6i42DLeKQ3YuTuKnwC9Kb4tiRn4DXsM7Fwi+z2bosdq0DKY1NAidTZXhYN/F4rJmRcbGQFWaxUjuLIaL4eMyn5Lj1ZHWvB7EyaFmYvBQfTQVnykaT2jiHObNX49ui4tmixThfgjjV/dd/Eak9wbe3L6Q42jLZ3qkqJU1Xx7+qEpjVZGEk12XIUFUKBEQXSqk9jXZqFt2+mgWO3fCUd3Qm8y8tkVInrXW6s6kZ35f7EpA/CVeHYq6P+ze77/GlTgAAIABJREFUXOPoo5/OqbgNuORriDkegT7Qj/ZVsGaFgjWuHnzn5kGFiHQD7ho1b578nZ41tTid2MCTvttxdpPR95zFlUu15+X8fi0AKWLatJg+3V+9nbARBn5ONGuBTB28A2f3MxyRB7DEVoVJbnlvGHRWVOQFSQBKS53FhVNpreVgVB7bY7Mo9qjHt1VNQkUzj1VGYCXvqM1VovJiS7mZWSc+Z3p0N51Nc1cWVTP92F3YmNRsmjidk8PMrmRijNyxjev27MLk7tPpIq285AyyU0ep9vZhzYLbeeCdNzDZOaAfZdbR6TL0bWwoOcbCqeZ5z7kxPqMH17f4sqmfLTmag+DQwqwTmUzMLWFgi5Hy2Bvp/ZCFZXc5feOfMld/C/wDpPzNa/R/EUgRm0ykmAc3JKEcU8/nPcyaKHv37aXB/XoOOBTzgs8eVIqOAECQxoFpLTE8eeYQgc5JZDX35EtTCU81OWIKvHCF4XIujSz9GImmJubPGomtxoOnfx+Hf6MrcisdcpcG2mq9SY7ahKJ2IN0GW2iEXe07YO8n9Ksy5xLX2Lny+/SZFPcyv9xF8ddF77yFa0sza50fQz14omQX+2cj3eYQK0MHMbChgB4etWitzTRnMcQV7Affeg2tmIvr+zsx3Y34eP/5Y1ysTtcCkCLWXxTKcylOYlzb2+TI1kinFG26hV+tn8N3aHSnDkmic4/o4BNkX8PibhsvaIatWisWpKik7z9PCGNmfQomd8vqx8/Fwzhe1w0rKxMD4kxdaobItErsE5eSb/M4SmsNPTQfMabo2NnjGT390ftH8ELd8+iMVgwcYLyoo1Nn1ys9XWBR1s28M8KfTwbESHaiYW1uhLY5E6n1IFLnRr/KEjz1xyVQRSHXnt1Nlqw7TsYWAiijVeNLlt2zGBRmoPJB17UkerfyxCY5Ks3tDIzvaGH7Z/tzV+WvFSBFTCXTpX1JiWwSZU2BTIzdSIB1DUJOD1JbezJMeS/e/fpjdOy4atl+3oJaiVCQiawkD8FgZkHpFQqJjSSGwcmdd5w/pdroLaVPiWlU7VFfL+N0JpKQn5OVihad+RqJA3XXLsQ+VUoBu8T3ODIkkE/05pS3mxwSWNZyaYeE/HoVgzyTMRisqVMFUqrzI19wI93KhWMODpS6NpIeWI6j0YoZjd2Z1xArWdgOD/+GNgW8siIWWeRgyanuaohrAUgRmXKiSHuccivzmt4go3dv1s/rOJgel1lKqJCOFdY8qonhuYZ8QqwuP6Wn/VrsrezDtop46b/i6v/8xpcJb0vDIJOxbMo84p01ZNv6M645lbwTN3L41jZ8K0qIysxg+O5dNMi9WTf0B/yuALBqamlh5NY7cDI2srP3dRyZ1zFdqN6oIUvbSEhSAFMSe2OjV2CwVhE77gD1nhpOmmRMVwYTXGg+n+1eezkUZ55QidE9dy83VXxPUmM8u9W3EhB5cRDxr/bXax1IEVmUIlNTdJB6IPpXAhzqLt4UJhNVJ9LI07/EtMEPo5HZ8NqwD5mR/hV9649R2xTF0bKnKehxigdjkxhY3MJi3YPILyF+7rPlQwY0b2D9uBlkjLSIXSdra9inKZeeNQ/XWJzC1DUyatMs7L493XLYNz6d8fYdRUw9amq489MPsdVoMARFYuz1B9hmMvFdWTBBhePoE/EdreGFRGS6caynC1m2A2i2t4AxekFguZfP2XZxNBlYUL6fx2vWY2ds40RDDEJSKR95fHhBuuVf7VfnbnctAClifU9lyNBV1zNyyDE2HjPrx827eznrKgfzIoewdzS3oZiyU5oVSmVhIEa9pWOc9qsgu1sentqd9CkuIrK+Gb8WK7bb3MNJ25FSyk5XIb5zwsJMF7C/VYePMKfkeUkv5esHHqLKzw8rnY6HX38FW7Uaw4DRGL06T9VR7PoZQavmZP94+qYcx+gbhKF/10Kz7XWrTN7OkpG9qAkMQm9njwIZCgQUgozgKg/6pEdiH/E7d69ejcxoQhwPGuNGcKQhlimPj7wSXeaffVxFLfAPkHIZF6OhqYWnl35OWmY+7q7OvPSvO+nfy4yo/18FUiorBQqyNYTEb+RoVCzB2lpuyw3j7YBCvvcxr8iPKKzkgaRMtof782v3YGoczHoON5UqmFmj4oiTLfclHsSl19DLuAqdFxGa6lAc2kKVox2z5o0lz9mdh3eMIq7ILBImRnbYZxhsF112Tr+ptIiex98nTHtK2v5ERE/2zZnO0O07GJB8TErp2dLvnb+sKdFyIovuuu9xUlfjoaplw03zKQkJlY7V60QK09f+yGmbBFb7v0rCf0HQ8VoBUkTnj7RENS9XTWa/v1nYLKH8IEujt3bpkCSKHB46KsOgF3gs5mc8bTuKGVY3NPGD92jcNBqsrWsY0KigXO1BmcqDeq0TJSpvZDITcf1MEiPlYmHdWEnk0UcIb6qRitXZBKL3i8I93ANsbUmpj2Rt0QicnCCu359fwReFAh2ObGCEbjmRD3a0ND23XoFtTkRrPLi1SMcQVQGhdqewkqulIlqdI4lWz5DtLFAk1FDTVsmPfsU02cGjP8YRFNlHcoT6b8S1AqSI5958bD3RsXJya/0YGZGJplJDxsmFhAqPEhPu3eVA7Nx2E/R6hLI8ZIXZCEqzdonJ2ZW1fi+R2NxL0tLq1/dC0Ep0QxNBM1F/SoyubIXPPVZjSjafD9nD7WWeuCqU7HWo5B21ZSLQ1fUUDHp0ZQ0oKvNQNBQgiCjSHyG6CH02YgDf9ggm384y+O2r8uGkfRV9K+oYum0J/YfYXlFR4r/T964FIEU8PxHctS3JZEn9/dJA//Mlj3Q47YqsbJy0nji3AsaTPCGcZ/91iUYSJ8ubShM4UttDKikC8KLFdUamwPDWtUxp/oQWayuchsXhal8hCbT+1BrKlIyfsVebgWWpnjYDSRn1Gi4uV+aZYJV/mrFJjyDDwDdz76K0T0eB0PbjNjcY6bmuGwF1ZrDSf+ReXo/051XO0OukWcR0deBRcnpaHDp6pm5kdtNmDjQOJM35Hlz+PO50WV3vWgdSxJM8kydQWiZjuHcaEwKSLnne4vNrVdlg7g7egJN9JcnuQ4irP4xa68KBwhdx6enEmrZ9vNszn8mntMx1uveS+7Tb/ytjKj5ib5+hHJhrEabN1zXzq6qAIcpA3i2zpHeLaT2l++QSi1mMj0fvJXqAtWR73B6i7tQdn36EU0uz9Iw2xI8ioymcEw1h+Nk1EmxfxeFGayLLEugz5DUe9ltIkn/XVrkeAkyoSOHVsq+xEnRo9fbs1cQycN96dtvfxH7fuxgQf+VB5GsFSGlnNw0f8hOe1i7olLY4B2ZwxBTGvYJBYg6VZoegbLKwSpS2WnZ0z2JXbBYzq/J5db2ZRamSO7Pd4TYOOsyU3if2Xci9iZmyIoDSlRalqCnnueMz6TlX5+zGV488St/kJMZt3iil0RqGd633JCvOQZ6eeLY/Gbr1wxgRe8m+LD+xH1lF8QUWyu0bik6NY7b8JsFCkluQCO4JAj8XXce9L1x6/5eswD8FrqoW+AdIuYzL8eSrywnw9eSBO2aSnpXPoy9+wuaVr2FrY/1/Fkhpt6ntFfIp97kkotPbEhc8klNu4kgQnjqayeNqN4wevsjqKjFWFXPIuo2fI33ZFB1Mk601i5Nz+Q+hSIIBFwmt0YqT9ZEUtnrTzz2PaGdzLnV7yBpqkKXsQ9BqeOyGwXzdJ4LZx+OYk9SPVhsNtZ4fYR/2yCWpp+fuU0wtIWkfo8s+xsVYh0phg71eK6X0LI9YSXhc1+KSl+pSootDcUYT4/NfpVtbslT8wPBR7L1hAhN++Zn4pGP85nQPpf3m/FdSLq4VIEVsl1PpArdn34Wf3qxrUmQVw7aEj/H363ow054TPtQrncmBFpaIuH2typG9wQJKGzlOKSaSyjsCFKKNucg69nC/vMGSU3k6Ttn7OcANlMjN4KpowTzYK4tTDWEUKX3+VurMyeM6Hsufg9FKzbIF8ymzltHQ1kq1zECJrZwqewub6dx+d3+BgttqG3nbz4M1gRamSnsZR62Oe7bcQ9++V2bC1Fmfv5aAlLbkA9w64TfkdiXUV46iODGSXFUq1zuUYwizpH5d6t4++0yqrYCKIjJ8Z7KydAJymYmBA0yI/auzEJ+neXkCYpqRyBCSXUKvRmQ5HGx5hRVx5pW2L1JrmCbrfIIgav4UK30oVHpJn6VKT+wUWrxsmuihTyJUdQKv1hxsWy0pSscCvFg2pBdbw3ylVEcxbk0ux7P0+S4Fuy+3ba5kuWsFSBFB4dREDa9UTZJO/+Wlb0qfTvkVjN2wAU91BW8O6UmNrQveSc8S5VTKKN9UwhwrL9lc4iruD4UjOdUQLg3cY2NNZx3k6hsE0jNkeGmLWNj6DIn9bmCwoZXUNh+uO/mhtG+TgzNGTz9WtdxCrnV/Bl5nc0X1klwOruG6ss9RKmz5/JFHaXXrnNklmvu4r/cnpjAAEybOTDtMf4dKxueY04Xf6X6K5lAzc9WprYnRx7+kvz6bHXUJVAbdLTmP/TfifwOQ0twMKSfluFi18lTPny6rmSoKynG0DyLcb+/Z8ofynkPRPUz6/9umjazrVsftx2wY4zP/kvs0piYzNetJTgVG8+tii2tYg1HDNy3ZiAsC6wo7vo9rUmVoas0X9tOF2+nrYUkPEV3x7vz4Q8lO3ujqSWq3hWyrGihpm50bMpOBENejjNXLOTyqGBujHmuTgVa5Iy1Wriht3VDZeaJQK3k4ZwVBVEibJ2ri+DW+Hzev/pyAkhK+dH8deg/A1+fKvzOvFSBFbJekZBm9uq8lYLx5wXRz+g3MTTNQnWFhi4nfKzwM/NAnhTXdUrE1GPnm1/3ccKYMrcyWffbz2OcwB8HelqBAcfHg8sZbXXUykYk85uASQnWnyYqOIaCiTALXDH2HYfQ3L1J2FoLRgHzPrxIrRYxLCc2270MuMlAzkyUL5Ua3jnMBuU5HYGmJeX/RfTFGWvSmPsiawSuvX3rB45I30z8FrqoW+AdIucTlMBpNDJl6P3vWvY+drXnCv+S5ZcyaOJyRQ/r+nwVSxHYQ8/trjF/QGGZNnRVs8tPjptby5eF8rnOP7GBbJjWcyYRoX0ZlMdvsdfRzDcHTpWub4Uq1G0dqenCyPoI2k2UVwt26mUFe2cR75GAvN4v9Cm3a/9fefcfHeD9wAP/ksiWSiCRC7NXSoih+KEW1Re3W3qOltTdB7T2KmkFr1ipqtChVSo3ae4UQQRJk5zLv7vf6fiMnIYlc+lya3H2ef6ruue/zfN/fx91zn+c7YCnClJBgbHmnJAY0+R/yRzoj0DkCC7fGwrV61pYcexoQj+KX1qFO9M+w0iVih+twWNRrLId+/NtNfPh7XNyBZqFLZFGPPArBPjYGrhGhWFZgMYrUy/wKRoacS24KUsIjLFDyyAI5O7vYxNOLZ436wcoqff+oKAucPa+CnSoeEyqtl++7Fl4cJ4LL4V5UIdSJ3g57bQROObSAzjmffPrq7GQBJyedQfPdpDRP1ACBT1QIeAy5BHPyJm7ua9fUZni+GbVd8FMLFD/9AxpEb0xztyhba9xwc8ZNNxfcyO+MG+7iz84IdHw535D4N1nuWRjKPI1BvrB8sIosg6jomqhYIT/yKrji2KsnmKuClLv38bbTKRRDIgKtE3HWzxlt1PuhqfiyC7oh/8bEvmKYzrzrnyNOa4O339Jm6gZchKwZXdspz+Gu38+YWD9S/tWUXR9ApXGWT/4drOPgYBmLRJ0l7kcXQHAGK/mkLM9RG4ZKCcdRRXMEXtHXYJUYi0BHeyyr+hbWvVcG/f7QwMm1HwopMOzDUMv09s8tQYo4/8tXLTDwems46sLh07Mfyh8+gQ/uX9BX7XLB1jjl2QV3g52h0SZ9hhRzCEK9ApfxtrN/mgQanSXW32uAWxFFZfhW4V2tnCA75RYZaYFLVyygS9CgU8J3sLW+BmdbV+RzcsQDu/dwO6Ek/KM95GejmENMTDyt5CZCwnJ7x6CM+gx83Ypg8+B+0GWQFOr+ckbNE2WhsY/F7g6+GI9gxNpHYJ1lTZRHGCre+Q01oi/pT3FfSB08L5J6okklz98UghThIeYcE0/vvy67G0UdknpRvmk7G2iLxoUOyd32PG2F1nWeI9Ey9fUx+FgxVC3ycinc9MqM9g9Gu5MdEWNlg7vly8GvTFn4ln0bUXnzYn54Unueut0DMaoEnHR4hEM2d2HzxAG9jtXGI5cQlHCcjgLqCNhCI0MzMQzaJSwUMXZuchLYxxovWYa4N3PII283IeakE8MmrXXxKJ73CprZBcDD5RryOfrB8sUqKq+e7xN1MawvWRtBJfPBKiERIyeOg0qnw5TCe/BeLbtMLyzwJtuUr+emIEXck6gT/0BwCU94aMIRpbPD+3uSht9Z2ungWAiIKRaNAcV2w99eDffoGOzcehiln8bgZJ4WOOLYAcjrjKJFdXL1I6W2uxfC0PtOTzjoXvQGFfOd1GuZ4dLxYfGOiLr9ACX8k4bIriq1CvGqN8/VWEh9HS0fTU331MWE81oR4hQspt8nQWuFCZe6YuXCjB8cK+XBcrJPgEHKG6yDnoai84BpOLh5rn7P+Su2wsXZET3bNzHrIEX0Cj9963csa5I0eWeVwFCsua+Fp8Ob5yJJjz1Ra4mLYaVw+ulbCFC/fMLq7KSTP3QDgywge4u82Crlu4sabjf1T+4s71yB6s4lXPXIh7Zf1MOTvHmwYWtB6Ko3yvK/KrE08pMLT/BuyK8IrNNbnodSm3iyHHLpAVr4T4BHYlKKnQAbrP7fryhczDiP2HJTkCI84o8cQuugmdJmTxFvWNR6803bP2dUEBO2vuPsh0dqN4QlvOz37eamhVchvPaDQ4k2lav6hKjw6BEghuYosUzilb/DMCYg6UldjK0rnloXwwPVW7hnWR5qVVK9LHSAizYI7gn+cNMEwN7yCUKco6GK8UBoQhXctq2KBzbl5Y+tvI46uLsDhb2U/cH0ql9uClLiohNQJ2I+yjrdxvGntVD+9iFoanwsZmbO8mXhc7sJ/KILwt1Ni3fKK/eZkXxCqthoXPPdh3CdJ2JDMx7XLdpcBIYuzi+DGvEDQ60GxKShYnSH+LP4u6TrSYuiCTdRG4dQJu4MLGODsNVlOJw/bAjrDELMLGNl8Y25KUgRoXDNP4bIOUtSbjfy1cfD6r0Q75L0UEF8vz0MAAIeW0D7oj1KOj5Gybwvewwlv/9ORCHZ601cppUqaNMdkhMdbYGLl5O+Oz3tQuTbA2Nf71Xpmk+LihWUv1Y1UdGoc6APXBMDcbzC/xBRqhCcwsLgFB4mlwUXfxY9C8QT3mfuHvjLrSE8brXEsWa3EeGqg6+tB0b770DriFN6OjFXx5mo93EhjwuKFk76IWeMzVSClOTJrT9wv4rPXumpmZ5bSEQMngdWxVPXp2hb9zniLV8fojrheF2ULlzqjfTiGvxoXxe4aR6n2jcwfwE07PghHuWxRoWnDrjiHp3q9QpBIRhz/Aoa+6buiSx2ilY5YYGbD0ItC8gAulhRoFCh1JN0i6GTYlLk2PBoWEeegk7rAAtdHnjYRKGQzXN42gbC0z4AeWzC8c/zoqh0bu9rdXlkVRp7aqzIcNXHNwJksENuClJENe5cvwb/2hpccfbEoM32OO8aguNlb0LtEIyiajv87RmNSFsd3gkOw8btJ+CraYyjju1g5+YIr0IWme7xa4ipCAmf/X0JXwYNlUGb5t3q0BYtm6oI0TvTN9ILdyILwjfCCyHxTrDRxaGmejeCrYrghu3LyazfdOxS8RfT3SXWykn29HO3C4eHXTjcbcMQr7XEz/4fYtXClw+F33QMvm64wIOAIPTzXoC2zeqha5vUk7ofPXkJI6Ysw7pF3ni79MvpHww/Sup3MEh5g6BolP7eC7Bn3Qz9nkvX/ALRU6V/z1bYtc/wuQ/+baPlpPef832CKbX2oPnFBBS83B2JSGegYxZOWtwcFvK0QKkSFvphOeLpVmCQDn73dQhJPf2F/ghl486hc9gkxNrGY/RHVdEmrBtsyvz73h1PnwPuKVZQy0KV0n3LQ794lD6zEP+L3gs/m4q41/57WL1h8jYlj5+Ty4q49xjNjnaQp7iv/k/IUzzpyVNG2z0/Ha7devmDwCGPDsWKWMpupDbZ9D0m5rzQ6XTIm/6qkG+qhnzdzx94dvkeQiwLIt7i5VAeO1vIFTNSbTogLl4nfzDFxYvQBHBxBlxdVHBxgUHD2zJ1cia0U8V17aB2toVDdBzmufgg5kVI9W+qaGsL1K+jgvWb54HN8mHE0qJimd34BNHuQPyL9hdXv4sTkM8ZUGXis0SEgOHhwOMgLQKDdRA/fJI3O10UPNxUqFTtX17MWa6labzRaccsVA7/TVbG36ES7tccCF2R0mlWLjEB8L2vhd8D4MW8xWnuJ1Zur1VdBefUi128tq8Ilk/8o0FMUi92OXxHzN8k3ufslPR+8f9KL4OefCIxd+7i4+NfwhKZu2e6ka88zpQcjpmfJQ1fO391FPKr43AjrCqOJhZFlJMzyhauDKtXVnAxjStF+VqER+jw1wnDQ7IKcUGIVIUiDtZItLBBgoWt/G8ibBCryoPqVVQokP60I6kqsme/FgUT/PB23GmUizupDxXbfVEPv5d6+b0uwpPGt5+hWoAHXHT5EaNzQLjWCaGJTojQ5IVa5SxDlCiVC8TCiqVLqlC8SOY+58QJ6URy/MQfls8fwjb0ARwjA2Af9xy2mmjY6tSw0cXARhcLW11Sj+e/HVsh7vPBRvu3oXxrG7dEcf/tcOMZLhe5hmPlruOuKxBlm/pLrv69IPQ4VBDXXL6ARzEXFC4EiO9DY26Pn+jgenwNaqt/wVSPrUhMMZ9OWse1twM8CwAe7iqD21b8S4qN1ckHdmq1FuoYQB2jQ0xMxg9AjRmk3PfXQdwPZPdWvJiF/Hf4X2+nL9zAtAXrUbpEYbz3TqlUQcqaLfvx54kLiImNk/OcMkjJxtYKfhaGdn0nyqE9ydusJZvg5uqMXh2SZq3mRgEKUIACFKAABShAAQpQgAIUoIB5CLBHyhvaWTxRrt28P/ZvmgOnF/MO9B01H22a1sNHdaqYx1XCWlKAAhSgAAUoQAEKUIACFKAABSggBRikZOJC+HbOD8ifzxkDeiat2tN/7ELs2zgbDinWoc9EMdyFAhSgAAUoQAEKUIACFKAABShAgVwuwCAlEw0YEaWG9/SVOH/1NpwcHTB+SFfUrvZySatMFMFdKEABClCAAhSgAAUoQAEKUIACFDABAQYpRmjE+l8MxqZl38LT/fVZ8bNyuGu37kOsFHT73kPY29mie7vG6Njqo1RFiclvuw2ajtLFvTBhmPGWAszK+fM9yguMmroCH1SvgGaf1FKk8Gch4Zi3fCv+PnMFVlaWqFOjIsYN7gprK0ucv3Ib3QfPhE2KGTPFnEF5Uyyxq8hJsJAcJfDz3qO4dP0upozsqch5ZXSNiQOoY+Lwnc9WHD5+ARYqC4zp34nDJxWRz7mF+D8Kwlcj5mH/T7MVOcmERA181u/Gzn3HkKjRokyJwpg8sicKerhiw/aDWLByW6rjxMYlYM2C0Xi/0luKHJ+F5EwBpe/JfvvjNFZs2I2w8Ci4uuSF98DOqPZe0oT29x8GYsLcHxH8LBQqlQq9O36GVo3r5EwYnpViAkrfk2V03x8bF48Z32/E8dNXYG1tha+7tUCLT2srVhcWRAEKZF6AQUrmrTK9p9Jf2uKmsHgRT1R+twyePg9D2z4TsXLOCJQu8XKW8zVb9+PY6csoWsiDQUqmWyr37qj0l/bFa77yBrDpxzWh1WgxYNwi1Kv1Hjq0/AgH/zqLU+euy55Y3MxHQOkgJaNrTKj2914oZ1Lv07W5DPC4mb6A0kFKeEQ0Nu86jE6tG8LRwR5L1+6Cr18A5k/s9xpmeGQ0Pu81HnvXz4RdTlhywPSb+z+rodL3ZMvX7UbzT2qhkKcbzly8iWGTluLojoWwsLDAl8PnokWj2mjasCZEePx572+xfdVkuUABN9MVUPqeLKP7/iU/7kTQs1BMHNYDwc9D0eHryVgyfTDKly1uusCsGQVyqACDFCM0TMov7cvX72L6og0IDY+STyfEk4s6NSrIozZsOxRd2nyKk2ev4llIBCq/WxpjB3V54xkNGLsQLRp9gIZ1qsp9xQ/g0dNWyJ4qp89fZ5DyRsHcv0PKL23xVGz0dB/4+T9BYqIG7Vs2wJedmspKfjViLurVqoyjJy9CHRMrezTNGf81nJ1eXTc3tcm6bQcQ8OQZvAd2wpZdhxESHomvu7bI/XCsQaYFXg1SxOfYX6cuIzExERXLl8KscX1l4LFy415ERqnh/ygY4ZFRUKvjMH5oV7z7VokMj5XyGrt19yEmzPkBm5dPyPT5ccfcL/BqkCJ+PPyw6TfExicgfz4nzP32axQu6J7la+ymrz/GTPfBzh+mvoa16qdfEREZjaF92uZ+SNYgQ4HM3JOJ3nc+G/bInsQPHgXheUg4urVthJaNPnijbo3Pvsbvm+fCOa8D2vWZJHtBvVWqiHxf827eWDx9EIp6FXhjOdwh9wqkvCcz9n2/uMZmjP0KJYsWlGBrtx1AUHAIRvbrkHsBeeYUyKUCDFKM0HApv7TF8smxcXHyS1T0GJmzbAt2r5kmj/rex73lBLZiGWWNRouO30zBiG/aZ9jNOCEhEY07j8L6Rd4oWCA/xJCeHkNmYnT/jvKHzKlz1xikGKFNc1qRKb+0xTVw8dodVKlQVj4Ba9p1DH7bMEt2ORZBiuj6uWjKQFhaqjB7ySY4OubBN90yDkWGTlyKBrUryx4qP27eJ7+obW2sZRDTtnn914aW5TQfns+/F3g1SLlw9Q4qlCspC+4+aCa6fPEJPq1XTf7I3fTLH9i5eqoM6P48cQEiJPnxu9EZnkRNWuCvAAAVJElEQVTKa2zjjoO4cccfUdExuOf/BMUKF8C4QV1QwD3fv68IS8ixAq8GKSJQ8/RwlT9IxVDDmNg4jBvcJcvXmOidcv32fUwekXp4WqJGg8adRmHdwjHye5SbaQtk5p5MBCniHmzT0vEyKH4eGoFmXcfgyPYFsLGxThfo7oPH6O+9QC5AILZDx85B9FgZ2qcN/jh2Xn7vigdo3ExbIOU9mbHv+1v2GIclMwbDy9NNf83t2n8c308bZNrIrB0FcqAAgxQjNEp63UjF+O1azfrhzL7l+iDl8Lbv5A9esU2av1Y+xf38s7rpntWi1dsRrY7FmAGd5D7iR67oadCvRyscOHKGQYoR2jMnFplRN9JO/aZi7KDOspunCFLE9fRpveqyGvsOn8aRkxcxa2yfdKt14uxVLFq1HRuWjIOVpaUM+eLiE5DH3hbih48Y9tO/Ryt8XPf9nEjDc1JIIKOhPQtW/gwXZ0d0b9tI/sgNDA7RD/0SYZ4Yfig+29LbXr3GxBxQ+//8R94Iiie5P+38Q/aiWjF7mEK1YTE5USCjoT3iGtm445Dssp6Vayzoaah8yLBq7gg5BCPlJua4OHDkHyycMiAnsvCcFBbIzD2ZCFLGz1qN3Wun648uHkosnTEERb080jwjEch9OXwOOrf+RD+fk+idN/jbxXLoRWxsHBZPHyyHLHIzbYH07smMcd8/beF6+VBryFdt5MMzcb255nPC91MHmjYya0eBHCjAIMUIjZLyS/v0hRsQT1tjY+PlkcTEnWf3+8g/ix4pF35fKcfVim3qgvUoU8IL7Vo0SPOsxBCLg8fOYdmMIbKXgXhyO27mKqxd5C272DNIMUJj5tAiU35pP3wcjBXr90D8cBCX0pUb97By3ggZyokgpVfHz1CjcjlZE3GN/H70DOZN+CbNml256Qfv6T7wmTtCTtCY1rZ1zxG5DPirT3lzKBVPK4sCKYOUmNh4rFi/G1dv+UFlYSF7v7VrXh892jeWP3Lj4hLQv2creaTQ8Ei06D4Wf+1clOlrbOGq7VCpLGQPPbGJ8K5ms29w+tdl+s/HLFaDb8vBAq8GKdv2HsGhv85Bp9NBzHcifhwsmznE4GtMDHfsNWy2HLaT1gp77ftOwrC+7fQThOZgIp6aAgKZuScTQcq85VuwbpG3/ojNu4/Fgsn99UMoUp6K6AkqhlSL3sbJn33i9S4Dpsl7ODFHivg+HT5pKZbPGooSL4ZhKFAdFpEDBVLekxnzvl9UXawkOvP7jbh2+z488ueT0wKI793MTA2QA+l4ShTI1QIMUozQfMlf2va2tmjcaSQ2Lhknv0RFz5G6rQamClIuHlylP4OMgpRdB/6G+GGzYvZQ5LG3k+8RP55X/bRXzr0iNjE/hkajQfEiBfHLj6+PCTdCVVnkfySQ8ktbPP3v+sWnchiO2Np8NREThnXTBym9OzZF9cpJKwpkFKSIbvUjJi/DgikD0rxxTK6qGMbh5x8o50/hZroCKYOUOcs2y1V1RE8n0UtpztLNcvLE5CAlPj5B9ooTW0ZBSnrX2PZf/5IB4MThSSuOiSGM4rPy5N6lpgvMmskebsmr9hw9eQmLf9yJH+aPlCuCHTlxEVt2/6kPUjJ7jYkeAV+OmIse7Rrpe+KlpBZD1CbPX5vmvClsEtMUyMw9mQhSRM+4tQvH6BHSC1JE0CdW5hH3YmJYdfImFgPo8M0UHNoyT/93ovee6HXctc2nponLWkmB5Huyuv+rZNT7/rS4vWeshDhuo/pJPY+5UYAC2SfAIMUI1slf2nLiz76T5SRkYljE6k2/Yfm6XTizb4U8quiRkpkgRXRB3rD9kHyq4ZAnKURJa2OPFCM0Zg4tMmWQIiYt/m5Sfzl/xbnLt+WT2A2LxxoUpIjeTUMnLMG8id+gVLFCqWotVuwRZYtr73HgM3w1ch4mDO3Gp7k59NpQ6rRSBiljpq+Uq4SJ+ZzE+G+x1HrbZi97pGTmR25G15hYQUVMyiiG8ohu8GIiUDG3RVqrrShVP5bz3wukDFLERLPiO0z0QBFDCcVnXHxCokFBinhY0WfkfLlqT3o/KoZMWIza1Srgi6Yf/vcAPINsEcjMPZkhQcr0RRsB6F6b+0QM9WnwxRD5OVauTDH58Kz3sDmyp13N99/JlrryIP+NQPI9WeUKZbLtvl/0itp76IQc4r/VZxJXu/tvmp5HNXMBBilGuABSdiMVT25/O3wKri5OaNOsHrbu/hM7Vk8xKEj5sPUgOfHZixFA8r3iRlAEKyk3BilGaMwcWmTKIOXXP07hO59tMuio/E4ZxCckoGPrhgYFKSOmLIOYN0AMr0jexOSyYhiaGLoh5ioQm1NeB/Tu2ATNP6mdQ2V4WkoJpAxSbt8LgLhGRDgsVlGpUaUcLGBhUI+UjK4xcc7HTl/BzMUb5RxQ77xVHJOG9+CSoUo1Zg4tJ2WQIn50Dhq/GPcDAmW7d/n8E+w5eMKgIEUMO5w0b02qzzFR9Y2Lx8kJREUQ3K7vJBzcMo9LHufQa8IYp5WZe7LMBililcTPuox+7Rob/GUbGTSfOn8d363YhuiYWDlETcxR1rN9E2NUi2XmIIGU92TGvu8X16p48KXRalG1YlmM6tcRHm4uOUiDp0IB8xFgkGKEthbBh1huMXkSWSMcgkWaucDwycvwcd2qaXZdN3MaVl8hARH6iqE444d0VahEFkOB1AJiyfZB479PNcEnjSigtADvyZQWZXmvCvCejNcEBcxTgEGKgu0uurf73n+Er0d/h6M7FipYMouiQJKAuMZi4uLR9quJWDpjMEoV9yINBRQVEPOTiO5vI6csQ40q5dE+ncmvFT0oCzMrATGZsA46bNr5By5c9cX8iWlPfm1WKKys4gK8J1OclAW+IsB7Ml4SFDBvAQYpCra/mJhMfKiKpYk/rFlJwZJZFAWSBES39cN/X0D7lg3wddcWZKGA4gJiYmvRNbl65XKYPqY3h0AoLswCRU+UboNmwKugO2aN/UqufMKNAkoL8J5MaVGW96oA78l4TVDAvAUYpJh3+7P2FKAABShAAQpQgAIUoAAFKEABChggwCDFACzuSgEKUIACFKAABShAAQpQgAIUoIB5CzBIMe/2Z+0pQAEKUIACFKAABShAAQpQgAIUMECAQYoBWNyVAhSgAAUoQAEKUIACFKAABShAAfMWYJBi5Pa/dus+5q/Yitv3HsLezhbd2zVGx1YfyaOGhkdizHQfXL5xD64uTpg8oieqVCiDhEQNfNbvxs59x5Co0aJMicKYPLInCnq4ZviakavC4ilAAQpQgAIUoAAFKEABClCAAmYvwCDFyJeACEOKF/FE5XfL4OnzMLTtMxEr54xA6RJeGDVtBbw83dC/R2tcvXkPQyctxd51MxAXl4DNuw6jU+uGcHSwx9K1u+DrF4D5E/shPCI63deMXBUWTwEKUIACFKAABShAAQpQgAIUMHsBBinZfAkMGLsQLRp9gAa1q6BW83748+cFsLezkWcxYNwifN6kLurVei/VWd309Zc9V3b+MPW1s83otWyuGg9HAQpQgAIUoAAFKEABClCAAhQweQEGKdnYxAkJiWjceRTWL/KGSqVC5wHTcHDzXP0ZiCFALs6O6Nm+SaqzEr1Trt++L4f+vLpl9Fo2Vo2HogAFKEABClCAAhSgAAUoQAEKmIUAg5RsbOZFq7cjWh2LMQM64UFAEPp7L8CedTP0Z7B0zS/QanXo37OV/u+Cnoaix5CZWDV3BAp5uqU624xey8Zq8VAUoAAFKEABClCAAhSgAAUoQAGzEWCQkk1NvWXXYRw8dg7LZgyBtbUVgp+FoV3fiXJoT/I2a8kmuLk6o1eHpB4pYeFR6DVsNob2aYva1d5NdaYZvZZNVeJhKEABClCAAhSgAAUoQAEKUIACZifAICUbmnzXgb/x896jWDF7KPLY28kj6nQ61G7eH/s3zYGTYx75d31HzUebpvXwUZ0qiIxS48sRc9GjXSN8Wq96qrPM6LVsqA4PQQEKUIACFKAABShAAQpQgAIUMFsBBilGbvoDR/7Bhu2HsHzWUDjkSQpRkrdv5/yA/PmcMaBn0qo9/ccuxL6Ns2FhAfQZOV+u2tOofuoQRR0Tm+5rRq4Ki6cABShAAQpQgAIUoAAFKEABCpi9AIMUI18CH7YehOehETIcSd5qV6sgg5WIKDW8p6/E+au34eTogPFDusohPFv3HMGkeWugUqV4E4CNi8fh5t2H6b5WsXwpI9eGxVOAAhSgAAUoQAEKUIACFKAABcxbgEGKebc/a08BClC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{\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenu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                       {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",

From 11cac971585a248c7e9fd9a2ad723b529cb453d2 Mon Sep 17 00:00:00 2001
From: FLomb <f.lombardi@tudelft.nl>
Date: Wed, 8 May 2024 17:15:16 +0200
Subject: [PATCH 102/140] Updated code based on new plotting functionality

---
 docs/notebooks/example_figure_joss.ipynb | 75 +++++++++++++-----------
 1 file changed, 41 insertions(+), 34 deletions(-)

diff --git a/docs/notebooks/example_figure_joss.ipynb b/docs/notebooks/example_figure_joss.ipynb
index ea8cd0e0..b6c2ea2c 100644
--- a/docs/notebooks/example_figure_joss.ipynb
+++ b/docs/notebooks/example_figure_joss.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "0465f589-cc6c-40de-9df7-5731e699b4d0",
    "metadata": {},
    "outputs": [],
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "8d506451-7951-4d54-b9b6-df76f1fe6762",
    "metadata": {},
    "outputs": [
@@ -56,15 +56,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "You will simulate 5 day(s) from 2024-05-08 00:00:00 until 2024-05-13 00:00:00\n"
+      "You will simulate 1 day(s) from 2024-05-08 00:00:00 until 2024-05-09 00:00:00\n"
      ]
     }
    ],
    "source": [
     "uc = UseCase(\"JOSS-paper-figure\", users=[LMI, LI])\n",
-    "uc.initialize(num_days=5)\n",
+    "uc.initialize(num_days=1)\n",
     "\n",
-    "my_profiles_list = uc.generate_daily_load_profiles(flat='False')"
+    "profiles = uc.generate_daily_load_profiles(\n",
+    "    cases=[1,2,3,4,5]\n",
+    ")"
    ]
   },
   {
@@ -77,13 +79,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "id": "13d1fe35-2c1c-4641-9448-ea9a54e4e095",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/plain": [
+       "Text(0.5, 0, 'Time (hours:minutes)')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -93,37 +105,32 @@
     }
    ],
    "source": [
-    "# Creating a custom plot function based on the default 'plot cloud' functionality\n",
+    "# Creating a custom plot function based on the in-built 'plot.shadow()' functionality\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
     "\n",
-    "import matplotlib.pyplot as plt \n",
-    "from matplotlib.ticker import FormatStrFormatter\n",
-    "import pandas as pd\n",
+    "plt.rcParams.update({'font.size': 22})\n",
     "\n",
-    "def plot_cloud(df_profiles_list):\n",
-    "    plt.rcParams.update({'font.size': 22})\n",
-    "    fig = plt.figure(figsize=(16, 8))\n",
-    "    ax1=fig.add_subplot(111)\n",
-    "    \n",
-    "    normalised_load = df_profiles_list/df_profiles_list.max()\n",
-    "    ax1.plot(normalised_load[0], color='#b0c4de', label='Single day(s)')\n",
-    "    ax1.plot(normalised_load[[1,2,3,4]], color='#b0c4de')\n",
-    "    ax1.plot(normalised_load.mean(axis=1), color='#4169e1', label='5-day average')\n",
-    "     \n",
-    "    ax1.margins(0)\n",
-    "    ax1.set_xticks([60*4,60*8,60*12,60*16,60*20])\n",
-    "    ax1.set_xticklabels(['04:00','08:00','12:00','16:00','20:00'])\n",
-    "    ax1.set_yticks([0.0,0.2,0.4,0.6,0.8,1.0])\n",
-    "    ax1.set_ylabel('Normalised power (kW/kW-Peak)', fontsize='large', labelpad=20)\n",
-    "    ax1.set_xlabel('Time (hours:minutes)', fontsize='large', labelpad=20)\n",
-    "    ax1.legend()\n",
-    "    ax1.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n",
+    "fig, ax1 = profiles.shadow( figsize=(16, 8))\n",
+    "ax1.legend(['5-day average', 'Single days(s)'])\n",
+    "ax1.margins(0)\n",
     "\n",
-    "    return\n",
+    "ax1.set_xticks(ax1.get_xticks()[1:])\n",
     "\n",
-    "# Calling the plot function\n",
-    "my_df_profiles = pd.DataFrame(my_profiles_list).T\n",
-    "plot_cloud(my_df_profiles)"
+    "myFmt = mdates.DateFormatter('%H:%M')\n",
+    "ax1.xaxis.set_major_formatter(myFmt)\n",
+    "\n",
+    "ax1.set_ylabel('Normalised power (kW/kW-Peak)', fontsize='large', labelpad=20)\n",
+    "ax1.set_xlabel('Time (hours:minutes)', fontsize='large', labelpad=20)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "991cb76c-f108-40f3-b12f-7220f9589039",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -142,7 +149,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.8.19"
   }
  },
  "nbformat": 4,

From d4c1c9491e6302f314c718ee93453655f3b41123 Mon Sep 17 00:00:00 2001
From: FLomb <f.lombardi@tudelft.nl>
Date: Tue, 14 May 2024 11:15:46 +0200
Subject: [PATCH 103/140] Custom line colours

---
 docs/notebooks/example_figure_joss.ipynb | 18 ++++++------------
 1 file changed, 6 insertions(+), 12 deletions(-)

diff --git a/docs/notebooks/example_figure_joss.ipynb b/docs/notebooks/example_figure_joss.ipynb
index b6c2ea2c..3801d4c4 100644
--- a/docs/notebooks/example_figure_joss.ipynb
+++ b/docs/notebooks/example_figure_joss.ipynb
@@ -56,7 +56,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "You will simulate 1 day(s) from 2024-05-08 00:00:00 until 2024-05-09 00:00:00\n"
+      "You will simulate 1 day(s) from 2024-05-14 00:00:00 until 2024-05-15 00:00:00\n"
      ]
     }
    ],
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 13,
    "id": "13d1fe35-2c1c-4641-9448-ea9a54e4e095",
    "metadata": {},
    "outputs": [
@@ -89,13 +89,13 @@
        "Text(0.5, 0, 'Time (hours:minutes)')"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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bu3fvmmFVREREREREREREFR8rcEkXs7VQmDZtGjZv3lzq6xMSEhAaGorY2FhzLYmIiIiIiIiIiKjCU97EjEid2QLcoqIijBs3DgcPHjT62vj4eISGhiIuLg4AIJVKzbUsIqpCAgICIJFIEBAQUN5LISIiIiIiIjIbVuCSLmYJcAcNGgQAyM/Px7Bhw3DmzBmDr42Li0PPnj0RHx8PQRBgbW2N1atXm2NZRGSE0NBQSCQSg7/i4+PLe8lEREREREREVQJ74JIuZglwN27ciKCgIABAdnY2BgwYgBs3bui9LjY2Fj179sS9e/cAADY2NlizZg1GjRpljmURERERERERERFVeKzAJV2szTGJnZ0ddu3ahZCQEFy+fBmpqakIDw/H8ePH4e/vr/Gau3fvomfPnkhKSgJQHN6uXbsWw4cPN8eSiMgEW7du1TvG29u7DFZCREREREREVPWxApd0MUuACwAuLi7Yt28funfvjrt37+L+/fsICwvD8ePHSwQ9t2/fRq9evXD//n0AgK2tLf79918MGzbMXMshIhMMHTq0vJdAREREREREVC3I5XLI5XLxe1bgkjqzbWIGADVr1sSBAwdQp04dSCQS3LlzB+Hh4cjIyBDH3Lp1Cz179lQJb9etW8fwloiIiIiIiIiIqp2ioqLyXgJVcGYNcAHA398f+/fvh4eHByQSCa5cuYLBgwcjLy8PMTEx6NmzJx48eACgOLzdsGEDq/2ICElJSZg5cyaaNm0KR0dH1KhRA126dMG8efOQm5tr1Dx//vknRo8ejRYtWsDFxQU2NjbifHPmzEFiYqLW6999911xo7Y1a9YYdM9XXnlFvGbv3r0Gr1VZYWEhDhw4gPfeew8hISGoU6cObG1t4eTkhICAAIwcORKbNm1S+a2ssvT0dNjb20MikaBhw4YG3fPRo0ewsbGBRCJBq1attI7LyMjAvHnz0KdPH/j4+MDOzg6enp7o0KED5syZI/5CTpuJEyeW2Pxu27ZteOGFF+Dv7w87O7sSG+MJgoATJ07g008/RVhYGOrWrQt7e3s4ODigbt26GDJkCJYvX46CggKDnisA7Ny5E0OGDEGdOnVgb28PPz8/jBgxAocOHQIAHDlyRFzn559/btB848ePR6NGjeDi4gJHR0fUr18f48aNw8GDBw1eFxERERERVW/qAS4rcKkEwUKioqIEZ2dnwcrKSrCyshJ69eol1KlTR5BIJIJEIhHs7e2FHTt2WOr2ZGEZGRkCACEjI8Poa3Nzc4UbN24Iubm5FlgZlVZISIgAQLDgHwta7dq1S3BxcRHvr/7VqlUrISEhQfD39xcACP7+/hrniYiIECQSidZ5FF+2trbC0qVLNc5x+/ZtcY7g4GC9a09LSxMcHBwEAEJAQIBQVFRUqtegZ8+eetcNQOjWrZvw8OFDjXOMGDFCHHfs2DG99/z555/F8d9//73GMRs2bBA8PT11rsne3l74559/tN5nwoQJ4tibN28Kw4cP1zhPXFyceM0rr7xi0OvRrFkz4datWzqfp0wmE8aOHatzntmzZwsRERHi95999pnW+RISEoSuXbvqXdvw4cOF7OxsnWvThX9WEhERERFVD9nZ2cKOHTvErz179pT3kqiMGJqvma0HrrouXbpg8+bNGDJkCGQyGY4cOSL+BsHOzg6bNm3CwIEDLXV7IjLBoEGDcPHiRTx58gROTk7w8fFBt27dMGbMGPTs2dOs94qKisILL7wgVlJ27NgRY8aMgY+PDx48eIC1a9fi3LlzGDlyJGQymc658vLyIAgCmjZtip49e6JFixaoUaMGrK2t8fDhQxw9ehTbtm1DQUEBJk+ejFq1amHQoEEqczRq1AhhYWHYv38/jh49ips3b6Jp06Za77lq1SqxQnjy5MmwsirdBxtycnLg5OSE0NBQdOjQAfXr14eLiwuys7MRHR2NjRs34u7duzh58iSGDRuGo0ePwtpa9Y/wCRMmYNOmTeK6evToofOeK1euBABYWVlh3LhxJc7/9ddfmDJlCgRBgK2tLZ5//nkEBwejVq1ayMrKwvHjx7F27Vrk5eVh4sSJsLW1xUsvvaTznu+88w727NkDf39/jB8/Hs2aNUNeXh7OnDkDOzs7ldfD1tYWPXr0QJcuXdCoUSO4uroiPz8fd+7cwZYtW3DlyhXExMSgf//+uHDhAlxdXTXec+rUqWI1tbW1NcaNG4eQkBDY2dnhypUrWLZsGebNm6ezMlshMTERXbp0QXJyMgCgXbt2GDp0KBo1agQrKyvcvHkTK1euRGxsLDZv3ozs7Gzs3r0bEolE79xERERERFQ9afukJZHI0knyhg0bBKlUKlhZWYmVt//995+lb0sWVlYVuHK5XMjKyqq2X3K5vDQ/nlJRrsDV9dWrVy/hwYMHZrlnYWGh0Lx5c3HumTNnlqhgLSoqEmbOnKmyBm0VuPHx8cKlS5d03vPixYuCt7e3AEBo3Lixxtd469at4r1mzZqlc75WrVoJAARra2shOTlZ9xPW4cCBAzqrNWUymTBjxgxxXatWrdI4RvHc3N3dhby8PK3zXbt2TZwrLCysxPnLly8Ltra24usUHR2tcZ4bN24IPj4+AgDBxcVFSElJKTFGuQIXgDB06FC9//5HRkYKqampWs/L5XLh22+/FeecO3euxnGHDx8Wx7i6ugpRUVElxjx58kQIDAxUWaOmCly5XC5W3kqlUmHJkiUa75mXlyeMHj1anOuvv/7S+Vy1YQUuEREREVHVVFBQIFy5ckU4d+6ccO7cOSEqKkqlAnf37t3lvUQqI4bma2XyWeklS5YIEolEcHBwEPbu3VsWtyQLK6sANysry6BQsap+ZWVllebHUyohISGCh4eHMGrUKOGHH34Q1qxZI6xbt06YN2+eMGDAAJXWBP7+/iaFlQrbt28X5+zcubPWwFoulwudO3fWG+AaaunSpeJcx48fL3G+sLBQqFu3rgBA8PLy0hqEnjhxQuXj8pYmk8mEgIAAAYDQp08fjWPefvttcU0bNmzQOtf777+vMwweNmyYABS3R7h9+7bOde3fv1+c67vvvitxXjnA9fX1FTIzM/U8U8P16NFDACA0atRI4/nBgweL9168eLHWeaKjowVra2udAa7y+1VbYKyQn58v/qyaNWtm1HNSYIBLRERERFQ13bt3TyWwVf9i4WP1YWi+ZtBnfaVSqUlfU6dOBQDk5+djwIABeserfyyYiCzv22+/xcOHD7F+/Xq8++67GDNmDF588UXMmjUL//33H86cOQM/Pz8AwL179zBp0iST77llyxbxeObMmVo/Zi6RSDB79myT76eg3FogKiqqxHmpVIrXX38dAJCSkoLNmzdrnGfJkiXi8ZQpU8y2Pm2sra3x3HPPAQDOnDmjsbH9hAkTxONVq1ZpnEcul4stBZydnfHCCy+onE9PT8f27dsBAMOGDUOjRo10rissLAx16tQBAOzbt0/n2EmTJsHZ2VnnGGMofpZ37txBSkqKyrm8vDxxUzl3d3dMnDhR6zzNmjVD//79dd5rxYoVAIrbAL311ls6xyq3k4iJiUFCQoLO8UREREREVH0o2gO6u7vDwcFBfJyt10gbg5JSQRAgkUhM2gVP8SY0ZQ6qfhwdHZGVlVXeyyg3jo6OZXavrl276jzfsWNH7N27F+3atUN+fj727NmDs2fPolOnTqW+55kzZ8Tj3r176xyr77yyS5cuYfXq1Th16hRu376NZ8+eIT8/X+PYpKQkjY+/9tprmDt3LmQyGZYsWYIxY8aonE9PT8eGDRsAAA0bNkSfPn0MXp82OTk5WL9+PXbu3ImrV6/i0aNHyMrK0vjn5rNnz/Ds2TO4ubmpPN6uXTu0atUK165dw969e/HkyRPUrFlTZUxERIT4vIcPH17ifXbixAmxB5OdnR22bdumd+0uLi5ITk7GjRs3dI4LCgrSO5dCYWEhtmzZgm3btuHSpUt48OABMjMztfaHSkpKgpeXl/j95cuXxf8x6t69O2xtbXXer2fPnti5c6fW80ePHgUA1KpVC4cPH9a7/rS0NPH4xo0b4i9AiIiIiIioelP8ncbV1RWZmZnivipWVlYoKipidkYlGFzqauqbh28+Kg2JRAInJ6fyXgb9f82bN8fLL7+MpUuXAgB27dqlEuDm5ORg//79Wq93dHRE3759xe8fPHgAoPg/WuohozovLy+4u7sjPT1d65jCwkLMmDEDf/31l8F/5jx79kzj43Xq1MHQoUOxceNGREZG4tatW2jSpIl4Xn3zMlN/U3ry5EmMHj3aoI20lNeuHuACwPjx4/Hee+9BJpPh33//LVEtqlyZO378+BLXx8fHi8f//PMP/vnnH4PXlJqaqvN83bp1DZrn5s2beOGFF/QGwsrUf5aK9xdQHLLr06BBA63nsrOz8fTpUwBAQkIChg0bZvC6AP2vCxERERERVR9FRUUAij/9qbwRtiLAJVJnUIAbERFh6XUQUSXRs2dPMcCNjo5WOff48WOdwZa/v79KOKiorja00tjJyUlngPv222+LbQ1sbGzQr18/dO7cGXXr1oWTkxNsbGzEdSpaHuj6j+O0adOwceNGAMXtEn766SfxnPJ9XnnlFYPWr01cXBzCw8PF16NRo0bo168fmjRpgho1asDe3l4MiH/77Tfxz2Rtax83bhzmzJmDoqIirFq1SiXAzcnJEVtC+Pn5oWfPniWu1/Ua66OoeNVG+eNB2mRkZKBXr15iAOvj44OBAweiefPmqFWrFuzt7cX/yVm3bh3Wr18PoOTrkZ2dLR4b8h7T9csiU14TACgoKDDpeiIiIiIiqjoUFbhWVlYlAlwiTQwKcENCQiy9DiKqJJQrZU0NtZydnZGRkYGcnByDxisHcuoSExOxaNEiAICvry8iIiLQuHFjjWOvX79u0P169uyJZs2aISYmBitWrMA333wDW1tbnDx5EteuXQNQ3CPW29vboPm0+eabb8Tw9v3338e3336rtaJX0btWlzp16qBPnz7Yt28fzp07h+joaDRv3hwAsHXrVvFe48aN03gf5R61v/32G958802jn5Mp/vjjDzG8HTt2LJYvX661/cGJEye0zqMcyBryHtP1/lJ+Tdq3b4/z58/rnY+IiIiIiEgTXRW4AD/FTiUx2icioyg+Rg4UN1xXFhAQAEEQtH4pV98CxUErUPzR9ydPnui8b0pKis7A+ODBg+JvMT/44AOt4S1QXPFqqGnTpgEoft6KytXFixeL582xeZmi7YS3tze+/vprne0YDF27cmuElStXajzW1D4BUG1zYExLB3NRvB7W1tb4/fffdfau1fV6+Pj4iMd3797Ve9/Y2Fit59zc3MQQV1vfZCIiIiIiIkOwApeMxXcGERlFuaVK06ZNTZqrc+fO4rG+TaEOHTqk8/zDhw/F40aNGukcu2fPHgNWV2z8+PHix++XLFmC9PR0sa1C48aNNbYgMJZi7fXr14dUKtU6Ljk5GZcvXzZozmHDhsHV1RVAcdWuIAhITk4WX8fOnTtr/fkFBQWJIfLevXsNfh7mong9vLy84OHhoXVcXl4ejhw5ovV8YGCg2Dbj5MmTetsY6GsXpPg0yuPHj1mBS0REREREpaatAlfx90FW4JI6BrhEZLBbt26pbIA1aNAgk+ZT7pf7yy+/aP2PlCAI+Pnnn3XOpfxx+Tt37mgdFxsbixUrVhi8Rnd3d7z00ksAgCNHjuCTTz4RNy97/fXXTd68DPjf2u/evavzP9RffvklCgsLDZrTwcEBI0aMAFBcRRsREYG1a9eK/6OgrfoWKK4E7t+/PwDg6tWr+Pfffw26p7koXo/Hjx9r3WQOAH799VekpKRoPW9vb4/w8HAAQFpams7N2GJiYvQG+xMmTBCPP/74Y/5PFRERERERlYpyBa7y3ylZgUva8J1BRPjtt99w8uRJnWMuXryI8PBw5OXlAQD69u2LLl26mHRfxcZUABAVFYX33nuvRCgmCALee+89REVF6ZyrU6dO4vFPP/2kMdhLSEjA4MGDdfY61WT69Oni8R9//AEAsLW1xcSJE42aRxvF2p8+fYp58+ZpHDNv3jyxx6+h1NsoKNon2NraYvTo0Tqv/frrr8XWBa+99preEDc1NRXz58/HwYMHjVqjJorXQxAEfPTRRxrH/Pvvv/jkk0/0zjVz5kzx+N1338Xp06dLjHn69ClGjx6tNxwfMWKE+J7fu3cvxo8fL/YT1qSoqAh79+7FV199pXedRERERERU9cjlciQlJSEpKUll02V9PXABVuGSKoM2MSOiqu3w4cN4++230bBhQ/Tp0wetWrWCl5cXpFIpHjx4gEOHDmH37t3ibwn9/f3x999/m3xfqVSK5cuXIyQkBAUFBfjpp58QGRmJMWPGwMfHBw8ePMDatWtx9uxZdOnSBYmJieLmVuq6du2KLl264PTp07h37x6aNWuG119/Hc2bN0dRURGioqKwatUqZGdnY+LEiTqrMdW1b98enTt3xpkzZ8THhg8fjho1apj6EgAA3nnnHbHv67vvvouIiAj069cPtWrVQkJCAjZs2ICzZ8+iTp06aN26tThWn+DgYAQEBCA+Ph7//vuv2EJgwIAB8PLy0nlt27ZtsXjxYrz66qvIycnBmDFj8MMPP2Dw4MFo3LgxHBwckJGRgTt37uDMmTM4evQoCgsLVSq0S+uNN97A8uXLUVhYiD/++AMXLlzAiBEj4Ovri0ePHmH79u04dOgQnJ2dMWTIELE3sSa9evXCq6++imXLluHZs2cICgrCuHHjEBwcDDs7O1y5cgXLli3DkydPMGrUKGzYsAGA5t98SyQSbN68GV27dkViYiJWr16N//77DyNHjkSHDh3g6emJvLw8PHjwAJcvX8aBAwfw5MkT9O7dGx9//LHJrwsREREREVUuSUlJYhs8mUyG+vXrA9DeA1dXSz2q3swe4Mrlcmzfvh379u3D+fPncf/+fWRkZIhVe4aQSCQGf0yYiMzn7t27ejd7Cg8Px/Lly1U2iDLFc889h82bN2PMmDHIzMzE2bNncfbsWZUxrVu3xsaNGxEUFKRzrnXr1qFXr16Ii4vD06dP8c0335QY8+abb2LmzJlGBbhA8WZmygGuOTYvU+jfvz8+//xzfP755wCA3bt3Y/fu3Spj/P39sWXLFvz2228GzyuRSPDyyy9j7ty5Kv1fdbVPUDZx4kTUqVMHkyZNwoMHD3Dp0iVcunRJ63g7OzuzhNqtW7fG4sWLMWXKFBQWFuLkyZMlKsS9vLywdu1anDx5UmeACwCLFi1Cbm4u1q5dC5lMhr///rvELyBmz56Nfv36iQGui4uLxrl8fX1x7tw5TJw4EXv27EFaWhqWLFmi8/7Km8IREREREVH1ofyJvfz8fPHY0Apcc7Tso6rBrAHurl27MH36dNy/f198jCXfRBXfvHnzMHjwYJw+fRqXL1/G48eP8fTpU+Tn58PNzQ0BAQHo2rUrxo4da3LbBE0GDRqEGzdu4KeffsLu3buRmJgIR0dHNGzYEC+++CKmTZsmbiSmS0BAAC5evIhffvkFW7ZsEXvh1q5dG926dcOrr76K0NBQxMfHG73Gvn37isfNmjUTN7Qyl88++wzBwcH47bffcOrUKaSmpsLd3R0NGjTAsGHDMGXKFLi7uxs97/jx4zF37lzxey8vLwwcONDg68PDwxEbG4u1a9di9+7dOH/+PJ48eYK8vDy4uLggICAAgYGB6NWrFwYPHlyqNWoyadIktG3bFvPnz0dkZCQePXoEFxcX+Pn5YfDgwZg6dSp8fHz0tv4AAGtra6xZswajR4/GkiVLcObMGaSnp8Pb2xtdunTBtGnT0Lt3b6xfv168xtPTU+t83t7e2L17N6KiorBmzRocP34ciYmJSE9Ph729PWrXro3mzZujR48eGDRoEFq2bGmW14SIiIiIiCoX5WJGRdWt8rF6BS574JI2EsFMCeuvv/6KWbNmASgZ2ip+Y6DpVsq/TVCcl0gkKr1BqOJ59uwZ3NzckJGRIe50b6i8vDzExcWhfv36sLe3t9AKiczrl19+Efupzp8/X6W3KlUNs2fPxvz58wEU93xu27Ztua6Hf1YSEREREVVuJ0+eFPdnadCggVjcERERgaysLHTr1g1Pnz7FrVu3ABR/4k9RFDlgwAC2VKgGDM3XzFKBe/LkScyePVsMYG1tbTF27Fj07t0b3t7e6Nu3LyQSCXx9fbFq1Sqkp6cjLi4OERER2LNnD4qKiiCVSvHJJ5+YvaqNiMhUgiCIG4g5ODhgwoQJ5bwiMreMjAyxf2/NmjXRqlWrcl4RERERERFVdqzAJXMxS4D76aefQi6XQyKRwM/PD3v27EGzZs1KjHNwcEBoaKj4/cyZMxEbG4tJkybh6NGj+Oqrr+Dr64tXX33VHMsiIjKL1atX4+bNmwCKWxLo+ng9VTxxcXGwsrKCv7+/xvPp6ekYNWoUnjx5AgB47bXXYG3NPT6JiIiIiMg0ubm54rFygGtoD1wiBZP/hpqSkoKIiAixFcKGDRs0hrfaNGjQAAcPHsSQIUOwd+9ezJgxA4GBgejYsaOpSyMiKpXU1FScOXMGBQUFOHv2rPixegcHB3z00UflvDoy1vnz5zF69Gh0794dISEhaNSoEZycnJCWlobz589j3bp1SE9PBwA0atSIP2MiIiIiIjKZTCZTCW2VA1ltFbhsmUDamBzgnjhxQtwZr2fPnujUqZPxi7C2xj///IPGjRsjKysLs2fPRmRkpKlLIyIqlStXrqB///4lHp8/fz7q1atXDisiUxUVFeHo0aM4evSo1jHt2rXD9u3b4eTkVIYrIyIiIiKiqki5+hZgBS6ZxuTmGormygBU2iNootz7Q523tzeGDRsGQRBw/PhxJCQkmLo0IiKTeXl5ITg4GP/99x+mTp1a3suhUggPD8eyZcswevRotG7dGnXq1IGdnR0cHR0REBCAkSNHYt26dTh37hwDeiIiIiIiMov8/HyV7w2pwGUPXNLG5ArctLQ08djHx0fjGHt7e+Tl5SErK0vnXB06dMDKlSsBAGfOnIGfn5+pyyMiMlpoaCh/21mFuLi4YNKkSZg0aVJ5L4WIiIiIiKoJRZWtgiK0Va7EZQUuGcrkaN/e3l48Vn4TKnN1dQVQvMu3ripcxTgASE5ONnVpREREREREREREZU49gFV8rxzssgKXDGXyO8Pb21s8fvr0qcYxyjt/X7lyRetcyu0YcnJyTF0aERERERERERFRuVMUPSoCXIlEAisrK0gkEnEMK3BJG5MD3GbNmonHly9f1jgmMDBQPN61a5fWuXbs2CEeKwfDRERERERERERElYW2Clzl/rfK/1Q/JlJm8jujTZs2YhuF48ePaxwzZMgQAMVv1t9//x3R0dElxsyfPx9nzpwRv3/uuedMXRoREREREREREVG5U++Bqy3AVVTksgKXlJm8iZmtrS26d++OQ4cO4cGDBzh69CiCg4NVxvTr1w/169dHfHw8MjIy0LlzZ4wfPx6BgYHIy8vDf//9h4MHD4pv0s6dO6N58+amLo2IiIiIiIiIiKjM6euBK5VKAbAClwxjcoALACNHjsShQ4cgCAJ++OGHEgGutbU1FixYgEGDBgEAsrOzsWjRohLzCIIAe3t7LFiwwBzLIiIiIiIiIiIiKneKytuCggIAmgNciUQCiUQCQRBYgUsqzBLgvvjii5DJZACK32wFBQWwtbVVGdOvXz+sWrUKU6dORWZmpsY3oqenJ9atW4f27dubY1lERERERERERERlTlMF7qlTp/D06VMA7IFLxjFLgOvm5oYZM2boHffSSy+hV69e+Pvvv3H48GE8ePAAgiCgXr16CAsLw2uvvQY3NzdzLImIiIiIiIiIiKhcWVlZQS6Xo6ioSAxvAcDb21s8rzyWPXBJE7MEuMaoVasWPvjgA3zwwQdlfWsiIiIiIiIiIiKLUwSwUqkUcrkchYWFAACZTAYHBwfExsbCyckJNWvWFK9RhLdE6libTUREREREREREZEaKAFdRYasIcHNyciCTySAIAh49esQKXDIIA1wiIiIiIiIiIiILUAS0RUVFAP63URlQXI2rvokZkSYMcImIiIiIiIiIiCxAfWMy5ZBWPcBVP0+kYLEeuJcvX8ahQ4dw9uxZJCUlIT09HTk5OQgICMChQ4dUxhYVFeH+/fsAit/YdevWtdSyiIiIiIiIiIiILEq9hYKCVCoVjwsLC0ucV7+eCLBAgLt//3588sknOHfunMrjys2b1UkkEoSEhCAhIQESiQQ3btxAkyZNzL00IiIiIiIiIiIii9MW4KpX4KpX3LIHLmlithYKgiBg2rRp6N+/P86dOwdBEFS+dJWAW1lZ4Y033hDHrlixwlzLIqJyEBoaqtLXpzKZOHGiuPb4+PjyXo7RKvNrbw4ymQzNmzeHRCLB7NmzzT7/1atXIZVKIZFIcOTIEbPPT0REREREVYu+ClwiQ5gtwJ04cSKWLFkihrA2NjYYMGAAPv/8cyxcuFBviDt69GjxeNeuXeZaFhEZ6dKlS5g1axaee+451KxZE7a2tnBwcECdOnXQtWtXvPbaa1i8eDHu3r1b3kslKuHnn39GTEwM3N3d8fHHH5t9/tatW2P8+PEAgDfeeEPciICIiIiIiEiZIRW4QHGIqwh1XVxcWIFLGpmlhcKGDRuwatUq8U02ZswYzJ8/H97e3uKYadOm6ZzD19cXgYGBuHz5Mq5du4aUlBR4eXmZY3lEZIDs7GxMnToVq1evLnFOJpPh4cOHePjwIaKiorBs2TIAwO7du9G/f/+yXiqRRqmpqfjmm28AAG+++SY8PDwscp8PP/wQK1euxPXr1/HPP//g1Vdftch9iIiIiIio8lMPcNW/l8lkCA8Ph1wuh42NTVkujSoRswS4ylVOM2fOxLx580o1T4cOHXD58mUAxR9TDQ0NNcfyiEiPwsJC9O/fH8eOHQMAWFtbY9CgQQgKCoKPjw8kEgmePn2Kq1ev4ujRo4iOjgYArdWH/Gg5lYeffvoJGRkZsLe3x9tvv22x+zRu3BgvvPACNm3ahC+//BLjx4/n/2gREREREZEKbXtBqQe4hYWFcHR0FMdV13Z4pJvJAe7Vq1dx584dSCQSNGjQAN9//32p52ratKl4fOfOHQa4RGVk4cKFYngbEBCA3bt3o3nz5lrH3759G0uXLoW7u3sZrZBIt6ysLCxcuBAAMHLkSIt/gmPq1KnYtGkTEhISsGHDBowdO9ai9yMiIiIiospJUwWucnsEmUym8Tq2UCBlJvfAvXjxong8cuRIWFuXPhNW/rhrenq6KcsiIiOsWrVKPF64cKHO8BYorkD8/vvv0aNHD0svjcgga9asEf+7MWHCBIvfr1evXqhbty4A4I8//rD4/YiIiIiIqHLR1gNXUwsFZazAJU1MDnAfPXokHjdq1MikubgTH1H5iImJEY9DQkJMni80NBQSiUTrf3j++ecf8fw///wDALh16xbefPNNNGnSBI6OjnB3d0fXrl3x66+/oqCgwKD7Xr16Fa+99hrq168Pe3t71K5dG6GhoVi+fDnkcjkAiPc1R4V/Xl4eFi9ejEGDBqFevXqwt7eHm5sbWrVqhbfeegu3bt0y+R7K0tLS8Mknn6B169ZwdnaGu7s72rZtiy+//BIpKSkGz/P06VP8/fffmDBhAtq2bQt3d3fY2NjA09MTbdu2xdtvvy22ydBkwYIF4uv49ddfG3TPL774Qrxm0aJFJc7fvXsX7733Hjp16gQPDw9xPY0bN0ZwcDBmzZqFo0ePap1f8T7y8vIy6Gd77NgxTJo0Cc2bN4eLiwtsbGzg7e2NFi1aoF+/fpg7d67On59EIsELL7wAAIiKisLNmzf13pOIiIiIiKoP5Qpa5b8ba2qhoO96IpNbKNja2orH+fn5Js2VmpoqHnMDM6Kyo/wfjEePHiEgIKBM779q1SpMmTIFubm54mO5ubmIiopCVFQU1q9fj71798LV1VXrHL/99htmz55d4rk8evQIkZGRWLNmDbZu3Wq2NUdGRmLs2LG4f/++yuP5+fm4fv06rl+/jj///BNz587FnDlzTL7f2bNnMXjwYJVfmgHA5cuXcfnyZSxduhS7du3SO09sbCyaNm2q8X8S0tLSkJaWhsuXL+P333/HF198gU8++aTEuJdffhnvv/8+srOzsXTpUnz44Yc6f0tcVFSEpUuXAgCcnZ1LtBtYvnw5pk+fXuK/IYr13LlzB8eOHcOSJUuQlZVVYv7k5GRERUUBALp3716ix5QyuVyO6dOnY/HixSXOPXnyBE+ePEF0dDT27duH06dP63xNQ0ND8dtvvwEAtm7dig8++EDrWCIiIiIiqp4UhSzaKnK1VeAywCVlJge4tWrVEo+Vq/hK48KFC+Kxj4+PSXMRkeEaNWqEq1evAgB++eUX/PLLL2V2771792LTpk1wdHTEjBkz0KlTJ9jZ2eHSpUtYtGgRMjIycOrUKfzf//0flixZonGO1atXq2xaFRYWhmHDhsHLywvx8fFYuXIlDh8+jMmTJ5tlzXv27MHzzz8PmUwGKysr9OvXD3369IGvry/y8vJw7tw5rFy5EhkZGfjwww8BwKQQNy4uDn379hVbBDRt2hQTJ05E/fr18fTpU2zZsgWHDx/G0KFD4ebmpnOugoICFBYWws/PD71790br1q1Rq1Yt2Nra4smTJ4iKisLGjRuRm5uLTz/9FF5eXpg+fbrKHK6urhg7diyWLFmC+Ph47N+/H+Hh4VrvuXv3biQlJQEAXnrpJbi4uIjnLl68iNdffx1FRUWQSqUIDw9HWFgYvL29YWVlhcePH+Py5cs4cOCAyi/5lO3bt088fu6553Q+/z/++EMMb11cXDBixAh06NABNWvWREFBAZKSknDu3DkcPHhQ5zzq99q7dy8DXCIiIiIiEikCWIlEAisrK5VPhSqLj4/H06dP4e7ujsaNG5f5OqmSEEx069YtQSKRCFZWVoKfn58gk8k0jlOMady4scbz2dnZgqenpyCRSARra2shPT3d1KWRBWVkZAgAhIyMDKOvzc3NFW7cuCHk5uZaYGVUGt9++60AQPzq37+/sGnTJiElJaVU84WEhIhzafL333+r3K9ly5ZCUlJSiXHR0dGCs7OzAECwsbERHj58WGJMSkqK4OHhIc61YMGCEmNkMpkwYcIElXuGhIRoXJvyuLi4uBLnHzx4IHh6egoABG9vb+HUqVMa50lKShJatWolABCkUqkQHR2tcZwh+vbtK65p1KhRQn5+fokx8+fPV3l+2l77lJQU4dixYzrvFxcXJzRp0kQAILi5uQmZmZklxly8eFG8zwsvvKBzvkGDBoljz507p3JuxowZ4rkdO3ZonUMulwuRkZEaz02dOlWc49ChQzrX0rJlSwGA4OHhIcTHx2sdl5ubK0RFRemcSxAEwd/fXwAgODo6CoWFhXrHG4N/VhIRERERVV63bt0SduzYIVy6dEnYs2ePsGPHDmHHjh3CwYMHxWP1r9zcXOHQoUPCjh07Sv33capcDM3XTO6B27hxYzRu3BiCICApKQnff/99qeb56aefkJaWBolEgu7du+utIqPqo6ioqNp+lZV33nkH3bp1E7/fs2cPRowYAS8vLzRo0AAjRozAjz/+iDNnzpj9YxzW1tbYsmULfH19S5xr1qwZZsyYAaD4YyWaqiL/+ecfpKWlASiu7lSvFlXcY/HixWjYsKHJ6/3xxx/FStBNmzZprfj09fXFxo0bIZVKUVRUhF9//bVU97ty5Qr2798PAPDz88M///yj0rpGYebMmRgxYoTe+Tw9PfVuPhcQEIA///wTAJCRkYHt27eXGNO2bVt07doVALBjxw48fPhQ41yJiYnYs2cPAKBDhw7o0KGDyvk7d+4AAGrWrInBgwdrXZNEIkFwcLDGc4rqcaD4PaOL4n59+vSBv7+/1nH29vbo0qWLzrkAiBv+5eTk4O7du3rHExERERFR9SCoVeAqKI69vLzQvn17tGnTRmwDx/2gSBuTWygAwHvvvSd+NPmzzz6Ds7OzyseZ9Vm/fj3mzp0rfm+OfpFUNRQVFWH37t3lvYxyM2DAAJ39PM3F3t4ehw4dwieffII///wTOTk54rm4uDjExcVh8+bNAAB/f3/MnDkT06dPh42Njcn3HjRoEJo0aaL1fFhYmPiLoWvXrpU4v23bNvF45syZWuexs7PD9OnTMXv27FKvVRAErFy5EgDQtWtXBAUF6RzfrFkzdO7cGadOnVL5mL8xtmzZIh5PmzYNDg4OWse+++672LRpU6nuo0455I2KiirRt1axnlOnTqGwsBDLly8X20UoW7ZsmfjLiClTppQ47+TkBABISUlBfHx8qfovx8fHi8eenp46xzo5OSE/Px9Xr15FQUGBxjDcGMr3i4+P1/leJiIiIiKi6klTgCuVSsVCplu3bomFXLr2F6Hqy+QKXACYNGkSunTpAkEQIJfLMWvWLDz33HP4999/xco4dUVFRThx4gReeukljBkzRnyTDhkyRGcvRSKyDHt7e/z4449ISkrC8uXL8dJLL6F+/folxt27dw/vvPMOgoKCkJKSYvJ9FVWc2tStW1c8Vv/zRC6X4/z58wCK+5l27NhR51w9e/Ys5SqL3bhxQ3zOHh4e2LZtm94vRQAfFxeHvLw8o+955swZ8bh37946x3bq1Emlv6wud+7cwaefforQ0FDUqVMHjo6OYnN9iUQCe3t7cayif626kSNHihtOLl26tER1dlFREZYtWwag+Ofz0ksvlZijb9++AIp/lqGhoVi8eHGJjdr0UVREOzo6qqxbE8X9YmJi0Lt3b2zdulXlFxbGUt5wU1uPXiIiIiIiqn6UK3CVQ1lFgKvpMUWfXOXriQAzVeBKJBLs2rUL3bt3x61btwAU75g+btw4AMUfjVVISEiAv78/njx5Iu44rnhTBgYGYs2aNeZYElURUqkUAwYMKO9llJuyqL5V5+HhgVdeeQWvvPIKgOLQ9Ny5c4iIiMCaNWuQkJAAADh9+jTGjBlT6spShRo1aug8b2dnJx6rB6AZGRli+Fa/fn29v6ls0KBBKVdZTLnSc/fu3UZXh6emphq9QeODBw/E40aNGukcK5FI0LBhQ1y6dEnnuM8//xxff/21wR/PefbsmcbH7e3tMWnSJPz444+Ii4vDgQMHxIAUUN28bOzYsXB2di4xx6RJk7Bx40YcOnQI9+7dw9SpUzF16lQ0a9YM3bp1Q3BwMAYOHKjzfaL4b4kh4fX333+P48ePIykpCcePH8fx48dhY2OD9u3bo1u3bggNDUXfvn31BsEKrq6u4nFubq5B1xARERERUfWiXIGr6e+tir/7swKXtDFLBS5QXIV08uRJDBs2DIIgiKGsIAh4/Pix+AaUyWRITEwUgxjFuOHDhyMyMhKOjo7mWhJVEVKptNp+VQQeHh4ICwvDN998gzt37qj0mN2/fz9OnDhh0vzK/yEzVnZ2tnhsyJ8dio/rl1Z6erpJ1xcUFBh9TVZWlnhsjuf4448/4osvvkBhYSGsrKzQu3dvfPrpp1i6dCnWr1+PrVu3il8KuvoxT5kyRfzzfcmSJSrnFi9erDJOExsbG+zZswc///yzSo/imJgYLF++HBMnTkSdOnUwduxYJCcna5xDEfJrC5qV+fn54eLFi3jnnXfE9gcymQynT5/Gzz//jOeffx61atXCp59+KgbDumRkZIjHutpbEBERERFR9cIKXDInswW4QHEvwM2bN2P37t0qVVgAxFBX+Q0oCAJ69OiBXbt2YePGjQZ/9JeIyoeNjQ1+/fVXtGjRQnzswIED5bYe5bDSkI/BKwe+paFcQTpr1iyVP9cM+SpNf1fle5r6HPPy8vDll1+K8546dQoHDx7EF198gVdffRWjRo3C0KFDMXToUISFhRm0voYNG4p/3u/YsUNsf5CYmIi9e/cCADp37oy2bdtqncPGxgbvvPMO7ty5g5iYGCxbtgyvvvqqWDFdWFiItWvXomPHjioVyQqKNga5ubkGtamoUaMGfv75Zzx69EgMbkeOHCkGus+ePcPcuXMxYMAAlf+B0kS5jYi+/rtERERERFT9aNvETJmmClwGuKTMrAGuQr9+/bB3716kpKRg165d+OabbzB79my89tpreOutt/DFF19g3bp1SE5OxtGjR6v1R+SJKhtra2uEhoaK32uriiwLbm5uYlVqXFyc3v/AxcbGmnQ/5X68iYmJJs1lKEVTe6C4b60ugiDofI6nTp0SK3qnTJmCzp07ax0bFxdn8BoVVdkymQzLly8HUNwTV9fmZdo0bdoUkyZNwtKlS3H37l2cPn0arVu3BlDcTuLbb78tcY1yr2Zj+tBaW1ujc+fOeOedd7BhwwY8fvwYGzduhJubGwDg8OHDKpXImijfrzQBPRERERERVU3Kfz/VVG2rTFMFLpEyiwS4Cu7u7hgwYAA++OAD/Pjjj1iyZAl++eUXfPLJJxg1ahRq1aplydsTkYXY2NiIx5r6mpYVKysrdOjQAQCQmZmJc+fO6RwfERFh0v3atm0rhnsREREGfcTeVMoh6+HDh3WOPXv2rM42Ag8fPhSP9fXT3bNnj4ErBAYOHIh69eoBKA5ulYNcNzc3jB492uC51HXu3BkrV64Uvz927FiJMYqAFwCio6NLfS+pVIoRI0bg888/13k/ZTdu3ABQXA2u3AKCiIiIiIhIQVMPXOVQlz1wSR+TA9y0tDQcOXIEGzduxJYtW3D8+HGTP6ZMRGVL8bF3Q8hkMpVwTzk8Kw/PP/+8ePzzzz9rHZefn48///zTpHtJpVKMHTsWAPD06VPMnz/fpPkMMWzYMPF44cKFOlsEzJs3T+dcyi0ndFXzpqWl4ZdffjF4jVKpFK+//jqA4irnWbNmiZuXjRs3zuTe5soVtpo2XnvuuefE49OnT5t0L0Pup5CcnCxWYnfq1KnC9K0mIiIiIqLyxx64ZE6lDnAPHz6M4OBgeHt7o3fv3hg9ejRGjhyJkJAQeHp6YvDgwbh48aI510pEFtKxY0dMnDgRp06d0jkuKysLr7zyCm7dugWguLpyyJAhZbFErV555RV4eHgAAP79918sXLiwxJjCwkJMmTIFd+/eNfl+H374Idzd3QEAH3/8MX755RedH3PJzs7G0qVL8e+//5bqfm3atBF7zN67dw+TJk2CTCYrMe7333/Hhg0bdM7VsWNH8X8SFC0K1KWmpmLo0KEae83q8tprr4mV2X/88Yf4uL72CbNmzcLJkyd1jlEO3jX10u3bt6/4vHQFuMnJyZg9e7bO90FhYSH++usvnfdTUP73JTw8XOs4IiIiIiKqfpQDXOVPsVpbW5cYywpc0qfku8YAn3/+OebOnQtA828EZDIZ/vvvP+zbtw+LFi3CpEmTTFslEVmUTCbDihUrsGLFCvj7+yMkJARt27aFt7c37OzskJKSgosXL2LLli148uQJgOL/CP3222/lvnGTp6cnfvvtN7z88ssAivuxbt26FcOGDYOXlxfi4+OxcuVKXL9+HaNGjRJDTk19hwzh6+uLDRs2YPDgwcjPz8fMmTPx559/YtiwYWjRogWcnZ2RmZmJuLg4nDt3DocPH0ZeXp74Z2ZpLFq0CO3bt0d6ejr+/fdfXLx4ERMnTkT9+vWRkpKCzZs349ChQ6hfvz7c3Nxw6dIljfP4+Phg5MiR2LBhAzIyMtC2bVu89tprCAwMhLW1NS5evIgVK1YgJSUFEydOxD///GPwGmvXro1hw4aphMhdu3bVW6G9ZcsW/Pzzz/D390dYWBjatGmDmjVroqioCPfv38eOHTtw4sQJAMWtO959990Sc9SqVQvdu3fH8ePHcfz4cchkMpX/QVLIz8/H/PnzMX/+fHTo0AFBQUFo3rw5PDw8kJWVhdjYWPz7779iwNugQQOd7R+OHj0qHitXShMRERERESlr0qQJbG1t4eDgIAa4rMA1zePHj+Hi4gIHB4fyXkqZMDrAXbFihbiLuaIMXP1NpXgTFhYW4vXXX0fDhg0REhJihuUSkSUEBgbiwIEDEAQB9+7dw8qVK1X6jqqrXbs2fv/9d4wYMaIMV6nduHHjkJqaitmzZ6OwsBAHDhzAgQMHVMb06tULCxcuFANGFxeXUt8vLCwMx48fx7hx43Dz5k3cvn0bP/zwg9bxUqkUtWvXLvX96tevj3379mHw4MF4/PgxYmJi8MEHH6iMqVevHrZt24a33npL51yLFi3CnTt3cOHCBWRlZWlslTBixAgsXLjQqAAXAKZNm6YS4BqyeZnivxf37t3D0qVLtY7z8vLCqlWrEBgYqPH8+PHjcfz4caSmpuK///7D0KFDtd4LAM6fP4/z589rvV+rVq2wbds2rT2eBUHAli1bAABdunRB06ZNtc5FRERERETVl0QigZubm/h3GU0bT2uqwGWAq11qaqr46cvBgweX82rKhlEBbm5uLmbNmqXyZmrUqBGGDRuGBg0aoLCwEDdv3sTmzZuRnJwMiUQCuVyOGTNm4Nq1axZ5AuUlMzMT+/fvR0REBC5cuIDbt28jPT0dDg4O8PHxQefOnTFmzBiEh4frLH//559/8Morrxh8388++0xlgx1t8vPzsXz5cmzcuBHR0dFITU1FzZo1ERgYiJdeegljxowpdQUiVT379u3DgwcPsH//fpw4cQLXrl1DXFwc0tPTUVRUBGdnZ/j6+qJNmzYYMGAAhg0bptJPtSJ46623EBoail9++QWHDx/Gw4cP4ebmhubNm2P8+PGYMGECnj59Ko43tXK4Y8eOuHHjBrZs2YLt27fj9OnTePToEbKzs+Hs7Ix69eqhdevWCA0NxZAhQ0wKcIHizbxiYmIwb948bNu2DXFxcbC2tkZAQABeeOEFvPHGG/Dy8tI7j4eHB06cOIGFCxdi3bp1iI6ORkFBAWrVqoWOHTvi5Zdf1hh+GqJHjx5wcHBAbm4u3N3dMWrUKL3XnD9/Hvv27cOxY8dw8eJFxMbGIi0tDRKJBJ6enmjZsiX69++PSZMmia0yNBkzZgzef/99pKWlYeXKlRqfg7+/P+7evYt9+/bh5MmTuHLlChISEpCZmQlbW1vUrl0b7dq1w/DhwzFq1CiNH21SiIiIEPvfvvHGG/pfHCIiIiIiqlaMCWA1VeCSdsqbdxcVFVWL/UgkghHvqH/++QeTJk0SA8mPPvoIn3/+eYkgMD8/H1OnTsWKFSuKbyKR4MiRIwgKCjLj0svP/Pnz8dFHH+ncTEghKCgIq1evhp+fn8bzlghwY2JiMHz4cHF3dE169OiBTZs2oVatWgbfW9mzZ8/g5uaGjIwMuLq6GnVtXl4e4uLiUL9+fdjb25fq/kSlsXPnTrFn788//4x33nmnfBdUxWzbtk1sJfDWW2/h119/LdP7f/zxx/j6669ha2uLpKQk1KxZ02L3GjVqFDZu3Ih69erh7t27Gls2mIp/VhIRERERVV5Xr15FfHw8mjRpovKJvbt37+LGjRuoW7cu2rVrBwC4efMmbt26hYCAADx79gypqano1KmTyYVAVdX9+/dx4cIFAED37t3LvbWjKQzN14yqwD148KB4PHLkSLGVgjo7OzssW7YMN2/eRFRUlHhtVQlwb926JYa3vr6+6NOnDzp06ABvb2/k5eUhKioKq1evRlZWFo4dO4bQ0FBERUXB29tb57xvvvkmevXqpXNMs2bNdJ5PTk5GeHg4EhISABRvgDRhwgT4+PggNjYWy5YtQ2xsLI4fP46BAwciMjKywlVSElmK8uZaPXv2LMeVVE3KG8hNnTq1zO8/e/Zs/PHHH8jIyMAPP/yAH3/80SL3uXPnjtg+4dNPP7VIeEtERERERNUHe+Aap6ioSDxOSkpCfn4+AMDV1bXKZlxGBbgXL14Uj9X7L6qzsrLCrFmzxI/QKl9b2UkkEvTt2xf/93//h969e5eoQJ4wYQI++OADhIeH4+bNm4iLi8MHH3yA5cuX65y3ffv2pf7ossKsWbPE8Hb06NFYtWqVyseA33rrLQwaNAiRkZE4f/48vvvuO5M2VyKqCHJzc3HlyhV06dJF43m5XI4PP/wQ+/fvB1C8uZa2XqpUOkePHhVf3/DwcDRv3rzM1+Dh4YEPP/wQ77//PhYsWIDZs2db5DfW33zzDYqKitCiRQtMnDjR7PMTEREREVHlpwhg1dtqanpcUw9c0k45wL137x7u3bsHoPh17Nu3r852eJWVUc/o8ePHAAAnJye0bdtW7/gePXqUuLYq+Prrr/WWZ/v7+2P9+vXi67R+/Xr88ccfcHR0tNi6bty4gfXr1wMA6tSpg7/++qvEm9bZ2Rlr1qxBo0aNkJeXh/nz52P27Nlwd3e32LqILC07OxvPPfccWrRogb59+6Jly5Zwd3dHbm4uoqOjsXHjRty5cwdA8ScEFi1aVM4rrvxyc3MRGRmJwsJCXL9+Xax2lUgkWj+dURZmzpyJv//+GzExMfjmm2/w22+/mXX+a9euie2BFixYUCX/x4CIiIiIiMoWK3CNoxzgKvK51NRUFBUVoaCgoEr+Pc2oZ5SRkQGJRIIaNWoYNF65/6Byg+HKztDeGoGBgWjatClu3ryJnJwc3LlzB23atLHYutavXy/+C/76669r3T3d19cXo0aNwsqVK5GTk4Pt27djwoQJFlsXUVm5ceOGzt7PNWrUwMaNGy3672F18ejRI/Tv37/E4++99x46d+5cDisqZmNjg+joaIvN36pVK5X/WSAiIiIiItKEFbiWo/g7WUBAAFq3bg0A2LNnDwoLC8tzWRZlpX/I/yheCEOTbOVd4Kryi6iLcgPi3Nxci95rz5494vGAAQN0jlU+r3wdUWXk6emJHTt2YMqUKejYsSP8/Pzg5OQEOzs71KlTB2FhYZg/fz5iY2MRGhpa3sutclxdXdGpUyesWrUK3377bXkvh4iIiIiIqFJhBa5xFK+TektToOq+blWvprgCKSgowK1bt8Tv/f39dY7/888/8f333yMxMRFyuRw1atRA27Zt0b9/f0yYMEFn+wVBEHD9+nUAxcG5YidDbTp16iQeX7161ZCnQ1RhWVlZYfDgwRg8eHB5L6XaCAgIqLL/YSQiIiIiIjJVaSpw5XK5SjEkaaaowFV+rap65bJRFbhknLVr1yIjIwNA8QZl+jbTOXv2LGJiYpCdnY3c3FwkJiZi586dmD59OgICArBr1y6t1yYmJiInJwcAULduXb27oterV098o9++fZtBDBERERERERGRmWgLcDVRVJIqt2tjTqOdpgBXoaq+bqzAtZAnT57g/fffF7//+OOPtY6VSqXo2rUrgoKC0KRJEzg7OyM9PR3nz5/Hhg0bkJqaiidPnmDIkCFYs2YNXnrppRJzpKeni8eG9Ci2sbGBq6sr0tLSIJPJkJ2drbVnLgDk5+cjPz9f/L4q9TQmIiIiIiIiIioL7IFrOl0VuAxwldy/fx+9evWy2DUSiQSHDh0qzdIqhIKCAgwfPhyPHz8GAAwdOhTDhg3TOLZHjx6Ij49H3bp1S5x77bXX8MMPP2Dy5MniBmWTJk1C9+7d4efnpzI2KytLPLa3tzdonQ4ODkhLSwMAZGZm6gxwv/32W3zxxRcGzUtEREREREREVJ2VpgKXPXANowhwlffoqurBd6kC3Ly8PERGRho0VvECGnqNIAiV+kWXy+WYNGkSjh07BgBo2LAhli9frnV8o0aNdM7n4uKCNWvW4NGjRzhy5Ajy8vLw/fffY8GCBWZdtz5z5szBrFmzxO+fPXuGevXqlekaiIiIiIiIiIgqM309cCtzJlZWqmMLBfbANSNBEDB16lSsWbMGAODn54eDBw/Cw8PDpHmlUim++uor8XtNvXCVq2fz8vIMmjc3N1c8dnFx0TnWzs4Orq6uKl+mqqr/UhERmQP/jCQiIiIiqrzYA9dyFK+T4nUDWIGrws/Pr8q/IKUlCAKmT5+Ov/76C0DxRmKHDx9GQECAWebv2rUr7O3tkZeXh4SEBOTk5MDR0VE87+7uLh4/ffpU73yFhYViH1sbGxs4OTmZZZ2G0PTRACIiUqX4M1L5f0qIiIiIiKhyYw9c01XHClyjAtz4+HgLLaNyEwQBM2bMwKJFiwAAvr6+iIiIQMOGDc12DysrK3h6euLBgwcAijctUw5w69WrB0dHR+Tk5CApKQkymQw2NjZa50tISBDf8I0bNy7TPyCsra0hkUiQn59fpsExEVFlkp+fD4lEotLXiYiIiIiIKgdjKnA1bcBVVYNIc6iOm5ixrMdEivB24cKFAAAfHx9ERETo7W1rLLlcLm44BqhW3ALFb9SWLVsCKH4jX7x4Ued8Z8+eFY9btWplvoUawMrKCg4ODsjOzi7T+xIRVSbZ2dlwcHBgBS4RERERURWiKdhl1a1xdAW4VRX/VmgC9fC2Tp06iIiIQOPGjc1+r6ioKLFnbd26dVWqbxX69esnHu/Zs0fnfLt37xaPBwwYYKZVGs7Z2RnZ2dkoKCgo83sTEVV0BQUFyM7OVulvTkRERERElYcxFbjK11T1SlJzqI4tFBjgmuCNN94Qw9vatWsjIiICTZo0Mft95HI5Pv30U/H7QYMGaRz34osviseLFy/WWuF6//59bNiwAQDg4OCA559/3oyrNYybmxusra2RlJSk0qSbiKi6KyoqQlJSEqytreHm5lbeyyEiIiIiIhOoB7j6KnCreiWpOVTHFgpsrFdKb775Jv78808AxeHtkSNH0LRpU6PmOHXqFK5evYrx48fD3t5e45js7GxMmTIFhw4dAgDY2dnh/fff1zi2ZcuWGDVqFDZs2IDk5GRMnjwZK1euVOmfmJWVhbFjxyIvLw8AMGvWrBLtGMqCtbU16tWrh/j4eNy5cwdubm5wdnaGVCrlH1ZEVO0IgoCioiJkZmaKG0wGBASw/y0RERERUSVlTJConIMorquqQaQ56KrArarK7G+GL7/8Mu7fvw+JRCKGkZXVxx9/jD/++ANA8b9kb7/9NqKjoxEdHa3zuvbt28PPz0/8/tGjR5gyZQpmz56NsLAwdOjQAfXq1YOTkxMyMjJw4cIFrFu3DikpKeK9li5dioCAAK33mD9/Pk6ePImkpCT8+++/uH79OiZOnAgfHx/ExsZi6dKliI2NBQC0bdsWH3zwgYmvRunZ2dmhfv36SE9PR0ZGhkqPXyKi6sja2hoeHh5wd3eHra1teS+HiIiIiIgshMVrpaP8KW5W4FpAVFQU7t69WyXeoMePHxePBUHAnDlzDLru77//xsSJE0s8npWVha1bt2Lr1q1ar61duzaWLl2KgQMH6ryHr68v9u3bh+HDhyMmJgZXrlzBrFmzSozr1q0bNm/eXO79FW1tbeHt7Y2aNWuisLCQ7RSIqNqSSqWwtrauEv+dJCIiIiKq7rT1wDU0YKyqQaSp9AW4VRU/m1mO+vTpg+3bt+P06dM4c+YMEhMTkZKSgvT0dDg6OsLb2xvt27fHwIEDMWrUKK1tFtS1aNECFy9exLJly7Bx40bExMQgLS0NNWrUQJs2bTBmzBiMHTu2Qu1sLpFIYGNjAxsbm/JeChERERERERGRSYzZxKyqh4/mpAhwraysNL5uVTX4ZoBbCkeOHDHLPM7OzhgyZAiGDBlilvmU2dvbY8aMGZgxY4bZ5yYiIiIiIiIiIuPp28RMfRypksvlAFCiKLGqt1CoOCWYREREREREREREVYixLRSqagBpLtVxAzOAAS4REREREREREZFZ6QtitVXgVvVKUlNV1wpctlAgIiIiIiIiIiIyI32bmLHvbenoC3C1KSgowMWLF5Gfny8+5uzsjHbt2lWKnwUDXCIiIiIiIiIionLCHriG09dCQdvr9vTpUzx+/FjlsYyMDDRq1Aiurq7mXaQFsIUCERERERERERGRGbHS1jJK20JB8bibmxuee+452NvbA/hfIFzRlVkFrq+vLwoLC8vqdkREREREREREROXKkBYK7IFruNK2UFC8nra2tqhZsyZsbGyQl5fHAFfdkSNHyupWRERERERERERE5aa0ASyDW90UAa6xLRQUFEGv4nrFfBWdUS0UPv30Uxw+fBh5eXmWWg8REREREREREVGVpK21guJ7VuDqpqiYLW0FroLi+spSgWtUgPvVV18hLCwM7u7uCAoKwieffIKDBw8iNzfXUusjIiIiIiIiIiKqVErbA5fBrW7aWigo6OuBW1krcEvVQqGgoAAnT57EyZMn8c0338Da2hodO3ZEaGgoQkJC0KNHDzg6Opp7rURERERERERERBWetgBXVwWuIAiswNVDWwsFQ183xbjKVoFrdICr/kIIggCZTIaoqChERUXhu+++g7W1NTp06ICQkBCEhoaiR48ecHJyMtuiiYiIiIiIiIiIqHp49uwZkpOTxVzS1BYKVboCNyYmBpGRkYiMjMTRo0eRlJRUYowi0D19+jROnz6NH374AVKpFO3bt1cJdF1cXMz2JIiIiIiIiIiIiCqK0lTgKp9nBa6qyMhIle+NbaGgrkpX4DZp0gRNmjTB5MmTAQBxcXFioBsZGYn4+PgS1wiCgMLCQpw9exZnz57FTz/9BKlUirZt24otF4KCguDq6mqWJ0RERERERERERERVl7YWCtqoB+eKALdKVuCqq1+/PurXr4+JEycCAJKSklQC3du3b4tjFX08FIHu+fPncf78ecybNw9WVlYIDAwUA93g4GC4ubmZ9MSIiIiIiIiIiIjKQ2krcNXHkWalrcBV38SsSlbg6lO3bl2MHTsWY8eOBQA8evRIpeXCjRs3xLHKgW5RUREuXLiAixcv4ueff4ZUKkVBQYE5l0ZERERERERERFQh6asgJVXaeuBqC3C19cCtlgGuulq1amHUqFEYNWoUACAlJQVHjx4VA90rV66ovICK48ry4hEREREREREREakztgJX2/WkWXVroaC53thCvLy8MGzYMPzyyy+4cOECUlJSsGrVKrRs2RIAf9tARERERERERETVlyIbi4+PrzThYnkwdRMzRQBcWV5ji1bgapKeno5jx46JrRUuXboEuVzO8JaIiIiIiIiIiKoEfZW22nrgKleG3r9/H/Xq1bPgKisvbS0U9FF/nStLFwCLB7jKbRMiIyNx9epVjW0TFJycnNC1a1dLL4uIiIiIiIiIiKhMaasQVQSL9erVQ1JSEgAgNze3zNZV2ai3UFBgD1wDKTYuU4S2yhuXASVfME9PT/To0QPBwcEICgpC+/bttf4QiIiIiIiIiIiIKrrS9sC1tbVF48aNcfv2bRQUFFh2kZVYaTcxq6w9cE0OcO/fvy9W10ZGRuL27dsq59VfuLp16yIoKEj8UvS/JSIiIiIiIiIiqkqM/Wg/ANjY2AAobkOakJAAAKhZsyYcHBzMv8BKytQWClW+B+69e/dUAtu4uDiV8+qBbePGjcXq2uDgYAQEBJi0YCIiIiIiIiIioorM0EpQTecVAW5aWhrS0tIAAO7u7ggKCrLASisHiUSi8pqa2kKhSvfADQgIQGJiovi9picfGBgoVtcGBwfD29vbPCslIiIiIiIiIiKqBPQFteqUx9na2pY4n5+fb56FVVLW1taQyWTi96WtwFWo0j1wFWXbCra2tujYsaMY1nbv3h2urq5mXSAREREREREREVFVYkgFrvrj1ZlUKtUZ4CqwB+7/p3iiTk5OmDRpEsLDw9GjRw+4uLiYfXFERERERERERESVlbZNzLSNEwRBYwVudafeMkH9e32bmKmPq2wVuJrjagNkZ2fj999/x6BBg+Dp6YkOHTpg5syZ2Lp1K1JSUsy5RiIiIiIiIiIiokrD2IpZTZuYAcWtA0ozX1WjXnFrbAsFbT1wq2QF7h9//IHIyEgcPXoUjx49Eh8vKirCpUuXcOnSJfz2228AgObNmyM4OFj88vHxMe/KiYiIiIiIiIiIKiBtPXCN2cQMKA5zCwsLLbTKysvYFgoK6hW4VTLAnT59OqZPnw4AuHnzJo4ePYrIyEhERkbi/v37KmOjo6MRHR2NxYsXAwDq168vhrkhISGoX7++mZ4CERERERERERFR1aDcHkBxXN0rcNVpa6GgjbYK3MrSQsHoHrgKTZs2RdOmTTF58mQAQGxsrEqgGx8frzI+Li4OcXFxWLFiBQDA19dX3PwsODgYzZs3L/2zICIiIiIiIiIiqiCMrcDV1sNVPaisrpRfFz8/P9jZ2ekdp4mmCtyioqIK/zqXOsBV16BBAzRo0AATJ04EACQlJYlhbmRkJG7fvq0y/v79+1i3bh3WrVsHAKhRowZ69OghBrrt2rUz19KIiIiIiIiIiIjKnL7KUH3jHBwckJGRUe0rcBXPv3v37vD09CxxXt8mZtoqcAHg1KlT6NGjh7mWahFmC3DV1a1bF2PHjsXYsWMBAI8ePVIJdKOjo1VevCdPnmDbtm3Ytm0bJBIJ+3sQEREREREREVGlpC9I1NUDFwBat26N+/fvo3Hjxnj48KFlFlmFGNpCQTHO2toanp6eSE1NRVZWlsXXZyqLBbjqatWqhVGjRmHUqFEAgJSUFLHlwt69e3Hr1i0A7OlBRERERERERETVi3oFaUBAAAICApCdna3yeHWlL/hWH2eIdu3a4dChQ5ViIzPNW7ZZWHZ2Ns6dO4ezZ8/i7NmziIuLM7iknIiIiIiIiIiIqCIztgeuNszLDGNsBS7wvz64lWEjszKpwH327BmOHTsmtk+4ePFipXhxiIiIiIiIiIiIjGXuoJYVuOapwFW+XrkPrlwuV/m+orFIgJuWloZjx47hyJEjiIyMxJUrV1TKkZVfdPUXtlmzZggJCbHEsoiIiIiIiIiIiMqNoT1wFfRtzkXFjN3EDPhfBS5QXIVb5QPcp0+fiv1sIyMjce3aNZUXRltgKwgCWrRogZCQEISGhiIkJATe3t7mWBIREREREREREVG5MrYCl0GtZsZWNGujqwK3IitVgPvo0SMxrI2MjER0dLR4TvmNpqnCtlWrVmJYGxISgho1apRy6URERERERERERBWPvkpQ9sAtHW2vR2kqcIHiEFcul1etAHfKlCmIjIzE7du3xcfUA1t1gYGBYoVtcHAwPD09TVguERERERERERFRxVbaoNbYALK6MKa3rTHjpFIp5HJ5hd+ry6gA96+//lKpqlV/0hKJBG3bthUrbIOCguDh4WG+1RIREREREREREVVSujI1TdhawTjGBuBSqRQymaxqVeCqs7KyQrt27cR2CEFBQXBzczPX2oiIiIiIiIiIiCotY1sgaNvErLrTV9FsaAWz+vWKPrhVqgJXKpWiQ4cOYkuEHj16wMXFxVJrIyIiIiIiIiIiqlR0tRs1tgJX/drqGuiaaxMzdVKpFEAV28QsPT0dTk5OlloLERERERERERFRpWZKuwNW4JaOoa0mKmsFrpUxg8sqvH3w4EGZ3IeIiIiIiIiIiKismFqBW92Z2kJBXWWpwDUqwNXmu+++M8c0AICkpCSEhoaabT4iIiIiIiIiIqLywB645mFqeF3WPXATExNx8uRJFBQUmGU+swS4H374IZYuXWryPIrw9u7du2ZYFRERERERERERUdnS1QNXG1bg6mbqJmbq4xQUFbjmDnAvXbqElJQU3Lx50yzzGdUDV5dp06bBw8MDw4cPL9X1CQkJ6NWrF2JjY/nbBSIiIiIiIiIiqpSuX7+Oq1evAgAcHR1hb2+PNm3awMXFRW8QyQpcy9AW7CoqcC3VQiEvL88s85ilAhcoTqrHjRuHgwcPGn1tfHw8QkNDERcXB+B/6TcREREREREREVFlIZfLERsbi9zcXOTm5iIrKwupqal693sypIKUFbimV+Cqs3SAa67KXrMEuIMGDQIA5OfnY9iwYThz5ozB18bFxaFnz56Ij4+HIAiwtrbG6tWrzbEsIiIiIiIiIiKiMiMIghgiNmnSBHXq1BEfV/6nOVsrkOGbmJVVCwWFwsJCs8xjlgB348aNCAoKAgBkZ2djwIABuHHjht7rYmNj0bNnT9y7dw8AYGNjgzVr1mDUqFHmWBYREREREREREVGZUQ4QXVxc4OjoWOJxoGSQyApc3YwNvrWp1gGunZ0ddu3ahbZt20IikSA1NRXh4eFiMKvJ3bt3ERoaioSEBADF4e3atWsxcuRIcyyJiIiIiIiIiIioTClX4EokEjEwVHxEX1sIq681AOlmaAWuumrVQgEo/q3Cvn370LBhQ0gkEty/fx9hYWF4/PhxibG3b99GaGgokpKSAAC2trZYt25dqTdAIyIiIiIiIiIiqkgkEonWfZ4M3cTM0HNVnakVuGyhoKRmzZo4cOAA6tSpA4lEgjt37iA8PBwZGRnimFu3bqFnz564f/8+gP+Ft8OGDTPnUoiIiIiIiIiIiMqUcshqTAWuNqzAVcVNzMzE398f+/fvh4eHByQSCa5cuYLBgwcjLy8PMTEx6Nmzp7jznq2tLTZs2IChQ4eaexlERERERERERERlSlsLBX3BInvgamfI8zb2dVawdAVuhQ1wAaBFixb477//xEbNJ06cwMCBA9GrVy8kJycDKO6bu3HjRgwZMsQSSyAiIiIiIiIiIipT6h/VVw8WtX2UX1elbWmrS6siU1soqLN0Ba65WCTABYAuXbpg8+bNsLGxAQAcOXIEDx8+BFAc3m7atAmDBw+21O2JiIiIiIiIiIjKjTEVuAqaxlX3NgrmqMAtrx645mKxABcA+vbti1WrVqm8OHZ2dti8eTMGDhxoyVsTERERERERERGVKeUWCoDxFbhsoaCboWF2dnY2rl27htu3b2v8eSgoKnAfP36M7Oxs8y3UzCwa4ALAyJEjsXDhQgiCAHt7e2zbtg0DBgyw9G2JiIiIiIiIiIjKlHr/W7Y/MF1pKnBv376NuLg4xMTEICUlRescdnZ24vHdu3fNsNpiimDYXKwNGaQoJzZVfn6+QeGtRCJBYWGhWe5JRERERERERERUFtgD17K0vU7qr5FMJhPPKR+rX+/l5QUXFxdkZmaaNYuUSqViX11BEExug2FQHKz8Jivtl/ILacgXERERERERERFRZaNehav8mKHXKmMPXONzQuWetnK5XOscEokEfn5+pV6bNsrFsOYIhg2u5zU1VGUwS0REREREREREVZl6v1X2wDWdrh626o8rxiqqXwHVMFfT9ZaocFZuoXDr1i1kZGSYNJ9BLRQiIiJMugkREREREREREVFVp/5JdEPDQUNaKJDhlEPboqKiMg+/le8XGxuLx48fo2fPnqWez6AANyQkpNQ3ICIiIiIiIiIiqg7UK2wVlZjK/VCVz2u73thz1UVpKnCVj8uqAlcxl5OTE7Kzs1X68JaGebdEIyIiIiIiIiIiIpUWCqbOU50ZEqyqh7CG9sDVdK05NWnSxCzzGBXgXr582Sw3JSIiIiIiIiIiqmoEQRArPq2srMRwULkKFGAPXGMY0gNXnTE9cC3JXOGwUQFuu3bt4O/vjxkzZmDv3r0oKCgw6eZERERERERERERVhSLAlUgkkEqlWjcxU2dID9zqGuAaQlcFrr4euJZsoVAuAS4AJCUlYdGiRRg4cCC8vLwwbNgwLF++HI8ePTJpIURERERERERERJWZ8iZmyhW46gFeaXrgVlf6+gZrot4DtzRzmMLc9zMqwG3UqJH4RhQEAdnZ2dixYwcmT54MX19fdOnSBV999RVbLRARERERERERUbWkCA9LU4Gr6Xx174GrYEyVsnoPXEPmtUR4Xi4B7q1btxATE4Mff/wRwcHBkEqlYpgrl8tx7tw5fPbZZ2jfvr3YamHPnj1stUBERERERERERFWeth64hlbg6pu7OjJ2EzPFl4JyCwVNr3uVbKHQpEkTzJ49G0eOHMGTJ0+wZs0ajB49Gu7u7iovkqLVwqBBg9hqgYiIiIiIiIiIqjxdPXAN6cOq61x1DXCNpVx9q/59Zd3EzNqUi93d3fHSSy/hpZdeQlFREY4dO4Zdu3Zh586duH37tjhO0Wphx44dkEgk6NChAwYPHozBgwcjMDDQpCdARERERERERERUESj3wNXUQkHBmB641b2FgiH9ZJVfZ/WWCco9cPVda27l0kJBF6lUitDQUPz000+4efOmSqsFa2trna0Wpk+fzlYLRERERERERERU6WlroWBqiFjdK3ANDUPVA1x9FbhVsoWCoZRbLTx+/Fhnq4XFixertFpYtmwZWy0QEREREREREVGlotxCQVcPXGOUpoozNzcXqampePbsWanvW1EY2wNXvYWCvgpcSzCkatgYJrVQMJR6q4Xjx49j586dBrdaGDRoENq2bVsWSyUiIiIiIiIiIioV5Y/wG9NCwZwVuFlZWYiIiBC/DwwMhJ+fn0HXVmSGtlAwtgduWbRQqLAVuNpIpVKEhISUaLUQEhKitdVCx44dy3qZRERERERERERERlPugWtlVRy9GdqH1dhzmmRlZal8n52dbdT1FY2x4Wdpe+Cak7krcMs8wFWnaLUQERGhs9UCERERERERERFRRaZcgavcQkFxTsGYTcwMOadMPcCs7EzdxKyoqMigOSpyBW6ZtFAwlKZWCzt27MCuXbvKe2lEREREREREREQ6aeuBqx4qamuhoImxIaD6vRRtBaRSqUHXV3aKlgkSiUT8eSgqoTUpixYKpir3ClxtFK0W5s2bh5s3b5b3coiIiIiIiIiIiHTS1QPXkIDQHCGieoB79+5d7N69Gzk5OSbPXR6MrcBVBLg2NjYAVF8PS7RL0ETTmk352RoV4I4fPx7Lly9HbGxsqW9IRERERERERERUVSmCOuUKXH3hnTkrcNU38VJITEw06PrKSFMLBUWAq9xCQd+15mLuHrhGtVBYvXo11qxZAwDw9fVFSEgIQkJCEBoaikaNGpllQURERERERERERJWRcgsF9QpcZaXpgWsobT1wK+seU8aGoZoqcHXNYckWCsqtGxSb25VGqXrgCoKApKQkrF27FmvXrgUA1KlTRyXQbdKkSakWREREREREREREVBnpCnBLWwVqag/c6kBXBS6g2he3PNZlKqNaKEilUo1vFkEQ8ODBA6xbtw7Tpk1D8+bN4ePjg5deegmLFy9GTEyMWRZbkWRmZmLz5s1444030K1bN9SsWRM2NjZwdXVFs2bNMH78eOzdu9eo9P7OnTt499130apVK7i5ucHZ2RlNmzbFjBkzcOnSJaPWl5+fj4ULF6JXr16oU6cO7OzsULduXQwcOBCrV6+ulv8yExERERERERFZknKAqKuFgiWDRG0tFKpyBa7yOfUKXOXHdF1rrtdHeR5z9cA1qgI3PT0dJ0+eRGRkJCIjI3H27FkUFBRoXOjDhw+xYcMGbNiwAQDg7e2N4OBghIaGIiQkBC1atCj1osvb/Pnz8dFHHyEvL6/EuczMTNy8eRM3b97EqlWrEBQUhNWrV8PPz0/nnEuWLME777yD3Nxclcdv3bqFW7duYfHixfj000/x6aef6l1fTEwMhg8fjhs3bqg8fv/+fdy/fx+7d+/G4sWLsWnTJtSqVcuAZ0xERERERERERIZQfFRePcAt7wrcyhrgKhgSemvaRE55Y7OyqMDVFuCawqgA18nJCWFhYQgLCwMA5OXlISoqSgx0T58+XSKABIoX/ujRI2zatAmbNm0CANSsWRPBwcFiy4WWLVua4emUjVu3bonhra+vL/r06YMOHTrA29tbfE1Wr16NrKwsHDt2DKGhoYiKioK3t7fG+VavXo0pU6YAKP7tzOjRo9G7d29YW1vjxIkTWLFiBfLz8/HZZ5/Bzs4O77//vta1JScnIzw8HAkJCQCANm3aYMKECfDx8UFsbCyWLVuG2NhYHD9+HAMHDkRkZCScnJzM/AoREREREREREVV+giDg5MmTSEtLQ/369fXmV4oAUSqVqrRQUJwDjA/1jB1fVStwdVG8Rvn5+YiOjgYA8WdQWFho0LWWeH3KpQJXnb29PUJDQxEaGgoAkMlkOHPmjBjonjp1CllZWSWuEwQBjx8/xubNm7F582YAgJeXl0qg27p1a1OWZlESiQR9+/bF//3f/6F3794qDYkBYMKECfjggw8QHh6OmzdvIi4uDh988AGWL19eYq4nT55gxowZAIrD261bt2LIkCHi+fHjx+OVV15B7969kZOTg48//hhDhw5F06ZNNa5t1qxZYng7evRorFq1CtbW//sxv/XWWxg0aBAiIyNx/vx5fPfdd5g7d67JrwkRERERERERUVVTUFCA1NRUAMC9e/fQokULnYGqotrT1ta2RICrq52lISGisRW4VlZWVaKFZmmCb4lEAk9PT9y/f7/E45rGKt/HnMwV4BrVA1cfGxsbdO/eHR9++CH27duHtLQ0REVF4fvvv8fAgQPh6upaYrGKEvKnT59i69ateOedd9CuXTtzLsvsvv76a+zbtw9hYWElwlsFf39/rF+/Xvx+/fr1yMnJKTHup59+wrNnzwAAM2bMUAlvFZ577jkxZC0sLMQXX3yh8Z43btwQ71mnTh389ddfKuEtADg7O2PNmjWwt7cHUNwOIj09Xc8zJiIiIiIiIiKqfpRzrKKiImRmZhp0jXoLBeW5dIWImhhbgavcQkB9XVWV8mtUs2ZNhIeHw9fXV2tuZ0mWaKFg0WchlUrRuXNnvPvuu9i5cydSU1Nx/vx5zJ8/H0OHDoWnp6c4VtGTQl9PkIpAed26BAYGipWyOTk5uHPnTokxyiHvzJkztc41efJksdXBjh07NLaqWL9+vfjavf7663B2dtY4l6+vL0aNGiWua/v27QY9HyIiIiIiIiKi6kxfEZxyrmVlZaUSIBpSDWuOClxFCwX1ALeyMzTkdnR0FDcwU7+mPCtwTVGmMbREIkG7du3wzjvvYMuWLXj8+DHWrl2Lli1bir+dqGpcXV3FY/XQ9caNG7h37x4AoHnz5qhfv77WeVxcXBAUFAQAyM7ORmRkZIkxe/bsEY8HDBigc13K55WvIyIiIiIiIiKiYuqhXkxMDI4dO6axZahivLkrYKt7Ba6x61Z86hyAQRW45g5wtVXgVpgWCoa4cuUKfv/9d4wYMQK1atXC2LFjcePGjSoZ3hYUFODWrVvi9/7+/irnr169Kh536tRJ73zKY5SvBYrfBNevXwdQ/C+ovjYUuuYiIiIiIiIiIqKS8vPzkZ6ejkuXLmk8rwhwJRKJ1h64pa0CNbYHblULcA2twNUV4JZF/miJANekTcz0EQQBFy9eFDc1O3bsmEqpuaaFN23aVKw0rezWrl2LjIwMAED79u1Ru3ZtlfM3b94Uj3VV32oao3wtACQmJoo9duvWrSuWimtTr149SKVSFBUV4fbt23oroPPz85Gfny9+r+jbS0RERERERERUVWkL3RRtCtTJ5fISPXAVbUMN2cRM1zlTWyhU1gDXWA4ODuKxIYGtJVsomItZA9yioiKcO3cOkZGROHr0KE6cOKES9Km/EFZWVggMDERQUBCCg4MRFBSEmjVrmnNJ5ebJkyd4//33xe8//vjjEmOUw+waNWrondPLy0vjtaWZy8bGBq6urkhLS4NMJkN2drbWnrkA8O2332rdPI2IiIiIiIiIqDpR3zReQTmkVVR/qge4ukJFTSGiuVooVFaWrsC1ZFWucoBfbhW4MpkMZ86cEStsT548KVaBAiXfdLa2tujUqZMY1nbv3h0uLi6mLKFCKigowPDhw/H48WMAwNChQzFs2LAS45T7pSi/ubRR/g2C+q6Hxs6lmC8tLU2cT1eAO2fOHMyaNUv8/tmzZ6hXr55B9yEiIiIiIiIiqoy0hW7awlHlylzlAFf5nKZrLdFCQT28rMgVpqZSDs6VczFjwllL9MA1F6MC3Pz8fERFRYmBbVRUFPLy8rQu0NnZGV27dhUD2y5dusDOzs48K6+g5HI5Jk2ahGPHjgEAGjZsiOXLl5fzqkxnZ2dX5X92RERERERERESG0FeBq6i8VBwDQGFhIQDDNtYyRVVroWBIBa5yZqXcVtSYClxLvD7lUoHr7u6OgoIC8Xv1G9eoUQM9evRAUFAQgoKC0L59e4u/KSsSQRAwdepUrFmzBgDg5+eHgwcPwsPDQ+N45YpX5SBcm9zcXPFYvXLZ2Ln0zUdERERERERERJppq8BVrn5VD3ANqcDVxNiAsaptYmYIa2tr9OnTp8RzVn9dy3oTM3MxugJXkRoDxZtlKaprg4OD0bx5c7MvsLIQBAHTp0/HX3/9BaD4tTl8+DACAgK0XuPu7i4eP336VO89UlJSNF5bmrkKCwvF/sQ2NjZwcnLSew0RERERERERUXWiyMCsrKxUPqavrQJXVw9bQypw2QO3JEMqcAHV1qMKhhSWmrsCV3295pi/VOWxEokEzs7O6N69O7p3744ePXpU+/B2xowZWLRoEQDA19cXERERaNiwoc7rmjZtKh7HxcXpvY/yGOVrAaBevXpwdHQEACQlJUEmk+mcKyEhQfzNT+PGjcvkNxBERERERERERJWRehiqrweu8nn1ALeseuBWtQrc0mRX5b2JmbnmNzrAVfRsyM7OxoYNG/DGG2+gTZs2qFmzJoYNG4aff/4ZFy5cqPRvCkMpwtuFCxcCAHx8fBAREYFGjRrpvbZ169bi8dmzZ/WOVx7TqlUrlXMSiQQtW7YEUPyHxcWLF0s9FxERERERERERaa/+1NdCQXm8uVooGKqq9sAtjfLYxEzbvcusB+6uXbvEDcwuXLgg/uYAKP54/44dO7Bjxw4AxT1Vu3XrhuDgYAQHB6Nz585ay8srK/Xwtk6dOoiIiEDjxo0Nur5Fixbw8/NDQkICoqOjER8fr7XlQlZWlrgxmqOjI0JCQkqM6devnxjM7tmzB507d9Z67927d4vHAwYMMGi9RERERERERERULC4uTqXdJQDcuXMHcrlcZwWusS0UDDmnzJwtFGJiYpCVlQWguAVn06ZNYW9vb/K8pWHpClxzt1DQdU9jGVWBO2DAAHz//feIiopCWloa9u3bhw8//BDdu3eHra2tWJ0rCAKePXuGffv24aOPPkJQUBDc3NzQq1cvfP7554iIiDB4o62K7I033hDD29q1ayMiIgJNmjQxao4XX3xRPJ4/f77WcUuWLEF2djYAYMiQIWK7BG1zLV68WByv7v79+9iwYQOA4v4gzz//vFFrJiIiIiIiIiKqDrRV4BYWFuLatWtITk5W+Xrw4AFycnJUxhoS4JqrAle5T6+pFbiZmZm4ffu2+NwSEhJw//59o+YwB0tX4FqqhYI5K3BL1QMXAJycnBAWFoavvvoKx44dQ3p6Og4fPozPPvsMoaGhsLe3Vwl0c3NzERkZiblz56JPnz5wd3dH9+7dMWfOHOzZsweZmZmlfhLl4c0338Sff/4JoDi8PXLkSIm+tIb4v//7P7i4uAAAFixYIFYwKzt9+jQ++eQTAMVNsj/77DONc7Vs2RKjRo0CACQnJ2Py5MkqVdJAcSXv2LFjxQB91qxZJTZEIyIiIiIiIiIi7ZSD0latWqF169ZwcnJCUVERBEHQWIGrq4WCQm5uLqKjo5Gbm1vinCEBoOIeQMmg2NgAUbG/kq2tLTw9PQGoPu+yYugmZpoYUoGrfh9TaavALbMWCrrY29sjNDQUoaGhAIp/yGfOnBFbLpw6dUosuQaAgoICREVFISoqCj/88AOsrKzQpk0bseXCsGHDzLU0s/v444/xxx9/ACj+Ibz99tuIjo5GdHS0zuvat28PPz8/lce8vb3x+++/Y+LEiZDL5Rg2bBhGjx6NsLAwSKVSnDhxAitWrBAD1y+++ALNmjXTeo/58+fj5MmTSEpKwr///ovr169j4sSJ8PHxQWxsLJYuXYrY2FgAQNu2bfHBBx+Y8lIQEREREREREVV5uqopAwICIJFIkJycLBYyauqBa8gmZllZWbhz5w4ePnyInj17qpwzJABUDlh1tWowhGIuOzs7uLi4IDU1tdL10TWmAtfcAa45K3st1pTWxsYG3bt3R/fu3fHhhx+iqKgI58+fFwPdEydOICMjQxxfVFSES5cu4dKlS/j9999LVI5WJMePHxePBUHAnDlzDLru77//xsSJE0s8PmHCBOTk5GDWrFnIy8vD2rVrsXbtWpUxUqkUH330ET788EOd9/D19cW+ffswfPhwxMTE4MqVK5g1a1aJcd26dcPmzZvh7Oxs0NqJiIiIiIiIiKobbaGe8uOKoE4ikYiPK4e0iiBVUR1rSLCqXARpqNTUVCQkJIj3MPUj/IoA19Qg2FRlVYFrKRWqAlcfqVSKzp07o3Pnznj33XchCAIuXbqEyMhILFu2DNevXwdQeXfEM9W0adPQp08fLFq0CHv37kViYiLkcjl8fHzQu3dvvP7662jXrp1Bc7Vo0QIXL17EsmXLsHHjRsTExCAtLQ01atRAmzZtMGbMGIwdO7bc/wUkIiIiIiIiIqoMDA3+dIWNpvbA1ZeZXblyRWxRamtrW2UCXIXShK/lUYFrzL0NVWYBrrKYmBixEjcyMhIPHz4slwS8tI4cOWKReRs3box58+Zh3rx5Js9lb2+PGTNmYMaMGWZYGRERERERERFR9WNIBa6CRCLRGHqqV+Dq6oFrCkVA7OfnBz8/P62b2xtKeb2WCjkNYdLmXwZU4Jo7k6zQPXB1uXbtmhjWHj16FE+ePBHPqfcFISIiIiIiIiIiqmhq1aqFR48eAfhfdaqmKldtm5gZ0gNXE2MDwICAALi5uSEnJ6fE2oxR0SpwS8PYFgrmyCkrTQ/cy5cvIzIyEkeOHMGxY8eQmpoqntP0JBSPubi4oHv37ggJCbHEsoiIiIiIiIiIiAymnGO1bdsW+/fv1xqEKvfAVX8c0N1CQRdj2zeU9ry6ihLgmhKIGtNCQXEvcxeaVogKXEEQcOHCBbHC9vjx40hPT1c5D2j+jYSrqyt69OiBkJAQhIaGon379hYrIyciIiIiIiIiIiotW1tbNGrUCLdv39Zagat4XFMFrq4WCoaEhoYGgMqbqpXmegVLt3wwljk2MSsLFaICt6ioCOfOnRMD2xMnTogNknUtUhAEuLm5ISgoCKGhoQgJCUG7du3KPcUnIiIiIiIiIiLSxJgwTrkCV3m8+rWKLExftafivLkqcI2lXIFbWXvgqr92+nrgWuL5lXkFbnh4OE6ePKnSQ0NXYOvh4YHg4GCxwjYwMJD9bomIiIiIiIiIqFJS5Fql6YGrIJVKsWPHDkycOBErV67EoEGDNOZlRUVFsLb+X3RX1hW4FaWFgoI5KnDLYhMz9XnLPMA9cOCA+NsETW8CLy8vBAcHixW2rVu3ZmBLRERERERERESVmrZQVH2MpkJHOzs7lXFWVlYYOnQoBEHA4MGDtVbiymQyWFtbl7oC15AANzs7GwUFBfDw8ChxTrkdRHlU3iqYswJX3xhzPE9LvFal7oErCAJq1qyJkJAQ8atVq1bmXBsREREREREREVG50RbGaftEuqaqVXt7e5UxUqkUNjY2KCgo0HlvmUwGBwcHvWvRtyZdDh8+DAAICwsrsU5FD1wrKyvxeZUnUytwzdln2JA5DAn9DWVUgOvt7S1W14aGhqJ58+YmL4CIiIiIiIiIiKgiM+Tj8BKJBHK5HIIg6Axwrays4O3tjaSkpBLzKyssLNR7T11rVad+vSKgBYC8vLwS61QOoxXHlb0HbmnHmEOZtVB4+PBhqW9ERERERERERERUmegL3bRtVqbcA1e5ihYoDkRr1aolBrjaqltlMplJa9XXQiEvL0/jehUqSg/c0lQWK1SkCtwyC3DLyvXr19GyZcvyXgYREREREREREZHBYZymMFZTCwUvLy/x+0ePHsHW1rbEdSkpKeI/09LS8OTJE7i7u2tdY1paGoqKivDo0SM4OzurVNhqWnNubq7Wc8D/KnQ1hbuVRUWqwDWFWQLcGTNmYMGCBeaYClevXkWfPn3w6NEjs8xHRERERERERERUGvqCWvUKXMV45dBTU4CraI8AALdu3ULr1q1L3OPu3bu4e/cuEhIS8PDhQ2RnZyMtLU3rWm/evAm5XA4HBwfY2dmVCIXVn0t+fn6J56PpOVpZWZVryMkKXDMFuAsXLoSXlxe+/PJLk+a5cuUKevfujdTUVHMsi4iIiIiIiIiIyGwMqcAVBEElwLWzs1MJd62srFSqX0NDQ5GYmAhfX1/k5ubCy8sLKSkpYoCalpaGzMxMODs766zAdXJyglwuh5OTEwoLC5Gfn68SWhpbgauphUJ59MA1haGhr+LnU1Gfn9laKHz99deoUaMG3nrrrVJdf+nSJYSFhSElJaVSlC4TEREREREREVHVpq36U9PjyiGt+uN2dnZiz1mpVKrSfxYAmjVrhjfeeAPfffddiTV4enrCw8MDjRo1QvPmzbWu9dmzZ5DL5WjZsiUuX74MQRB0BrjKa6jILRTKogJX+WdnKktU4Jq1C/GsWbOwatUqo6+7cOEC+vTpI1beOjk5mXNZREREREREREREJtMXxilX2Spr1KgRXFxc4OfnBzs7O7H6tXbt2gCA7OxsfP/99xrnNTYA1BZUKrdJyMrKQlZWVol1axpf3puYKZQmwDX2mopagWuWn8CUKVMgCALkcjleffVV7Nq1y+Brz58/j7CwMKSmpkIQBDg7O+O///4zx7KIiIiIiIiIiIhKTV/1p3qlrbbQs379+ggNDUVgYCCA/7UvaNmypco45b60mu5hyFr1nU9MTERERASePHkinjO0B255BJwmVa4aUYFr6r0UKmwF7p9//omRI0cCAAoLCzFq1CgcPXpU73Vnz55FWFiY2IDZxcUFu3fvRlBQkDmWRUREREREREREZDalrcBVp2hfoN4SIScnR+s1hgaA2u6tuF5ReWttbV3inLLybqFQWFiI7Oxsk1ooVIQ2reZYg1kCXIlEgtWrVyMsLAxA8ZtwyJAhuHTpktZrTp8+jb59+yI9PR3A/8LbHj16mGNJREREREREREREJtFWTamJ+kZluigqcJs1a6byuKYA19wVuIp/+vv7i5uiVbQWCjKZDAcPHsThw4cRExNT6nkqQgWu+uOlYbafgI2NDbZu3YouXboAKG6cHB4ejtu3b5cYe+rUKYSHhyMjIwMA4Orqij179qB79+7mWg4REREREREREZFFKMJNTSGdIAgGB7hlWYGrqU2CYqyuAFcqlZZ5JWtubi5kMpnKY6ZuYqaLJZ9fhanAVXB0dMTu3bvRokULSCQSPHnyBH379sX9+/fFMSdPnkT//v3x7NkzAICbmxv27t2Lbt26mXMpREREREREREREZmFIP1NdPXCVFRUVieFkkyZNVM6VtgLXkHBXvQJXIpGIc2sKdxUtFJSfS1n1wFXc21TqPYr1qfIVuAoeHh7Yv38//Pz8IJFIcO/ePfTt2xcpKSk4fvy4xvD2ueeeM/cyiIiIiIiIiIiITKKv160ipCsqKoJcLjeohYKi/y1QnKONHTtW/L4seuAqKAe4Fa2FgqYA19QK3LJqoWCJuS3yE/Dx8cH+/ftRs2ZNSCQSxMTEIDQ0FAMGDEBmZiYAwN3dHfv27RNbLhAREREREREREVVk6mGcRCLB06dPsXfvXhw/fhxZWVkQBEHnxl+K9gkAYG9vj9WrV6N169YAdFfg6goAS1OBq29u5RYKZU1TRXBpGBr6mrOFgimbrmljsQi9cePG2Lt3L1xdXQEA169fF3e5c3d3x/79+9G5c2dL3Z6IiIiIiIiIiMgkhnwcPi0tTQwcDQnvFBW4NjY2Yjjq6OgIQHcFrqF0VcwqevQq1qgYK5fLNX4p5ivrHriaAlxLVuAqmLMC15C2G4ayNsuKtGjbti22b9+Ofv36IT8/H4IgwN3dHQcOHECHDh0seWsiIiIiIiIiIiKz0hTGaQrmDKnAdXBwEB/TFeAaW4GrK6hUD0YVYy9fvozLly9rvKai9MAtTYBbnhW45mRQgJuQkFDqGwQEBOCHH37AW2+9BXt7eyxbtgw1a9bUO6efn1+p70lERERERERERGQqfRW12ipzdVXBGhvgGrNOXWtVjNO0iZk2Hh4esLa2aP2nRubaxMzW1haurq549uwZatSooXd8pa7ADQgIMEsSnZ+fjxEjRugdJ5FIUFhYaPL9iIiIiIiIiIiIzMWQClxBEMwa4BobAOoLcJUpr9PZ2Rk9evRQOW9jY2PQPc3NnD1wg4ODUVhYqPO5mHMTs3KrwDXHAiy5mxsREREREREREZGlaKumlEgkGqt0DemBa29vLz5mSICri6a8Tdtj2ipwpVKp1pCzrHM9c/XAVVynL4i25PMr0x64pj4BBrdERERERERERFSZaMuz9D1uzh64+u6pTlf1r7YeuPquK2vm6oFbHtQDfXOs26AANy4uzuQbERERERERERERVQX6KnDLuoWCpnOagkNdFbgVKSA1Vw9cQ1miAldbf+TSMCjA9ff3L/UNiIiIiIiIiIiIKiNt1ZT6euDqqsA1toWCsYyppDU0dK7MLRTKmvprZI51V5zaaCIiIiIiIiIiogpMPYxTrsAF/hfeKVfXqittBa4uhgar6hW4yqFtRWqhYK5NzAxV0StwK85PhoiIiIiIiIiIqALR1B5B+XFlAQEBCAgIQLNmzWBnZ6d1zrS0NACAi4uL+JgiwF25ciUePXqkcy26qLdF0DWP+tiKFOAqWigor8mSFbjmDHBZgUtERERERERERFRO1IM+9Qpce3t7SKVSnaFdTEwMAKBx48biY4oAt6ioCKNGjdJ5T02MqcDVNDdQMQNcd3d38THFa2QJZdGeweI9cImIiIiIiIiIiKorTRW46sFqeno68vPz9c4VHR0NALCzs0NmZiZcXFxUwsmjR4+atE5jqknLqsLVWIoWCr6+vmjdujUEQYCbm5vF73vq1Cl4eHiI3zdo0AA+Pj5GzWFI32RjGRSt79mzp9Q3MIeUlBScPXu2XNdARERERERERETVi7aKVU2tFZKTk8Vj5Q3K1Oe7ceMGgOIWCidOnACgu7rU3BW4pWmhUNabmCkqcKVSKVxdXS0e3iq/DmlpaeLX3bt3zTKnqQwKcAcOHIjQ0FBERkaa7caGSEtLwxdffIGGDRuWe4hMRERERERERETVk64euIrjgoICAEDTpk21BriPHz9GWloaJBIJfHx8IJPJAABZWVniGH9/f5331rdO9cpP9XVXhh64igrc8lhT48aN0axZM5V1GENf6F8aBr8Kx44dQ69evdClSxesW7dOfFNawo0bN/DWW2/Bz88PX375JTIzMy12LyIiIiIiIiIiIk0MqcBVhKKFhYUAVPu2qrt58yYAwNvbW2Wjs3bt2onHuqp3DV2nvvEKFT3AlUqlZXI/5dfB09MTnp6eKuswdU5TGfSTmTJlCqysrCAIAs6dO4exY8fCx8cHM2bMwKFDh8SyZlPcu3cPv/76K7p164bWrVtjwYIFyM7OhiAIaNiwIfr162fyPYiIiIiIiIiIiIwlkUiQm5uLa9euITY2Fg8fPsTt27cRHR2Na9euiX1tJRIJbG1ttc6jKFJ0dXVVebxly5b4448/AAC5ublGr09TSwdzVuCWVwuFsgqVlV8H5U3oSvN8LVGBa9AmZgsXLsTUqVMxe/ZsHD58GACQmpqKRYsWYdGiRXBxcUG3bt3Qo0cPtGnTBi1atEDdunW1vmGfPn2K27dv49q1azh37hyOHDmCO3fuiOcVT8jDwwMffPAB3nnnHdjY2JT6SRIRERERERERERlLOXRLSkrCgwcP8PTpU8hkMtjY2KCwsBB2dnbIysqCnZ0drK11R22KT7RryrlCQkIAlAxwjQ0AS7uJWUWqwFXugVvWlF8HU0JXc1bgGhTgAkBgYCAOHjyIyMhIzJ07F4cPHxafxLNnz7Bv3z7s27dP5RoPDw84OzvD3t4ehYWFyMvLQ0pKisb2C8ovSM2aNfHmm2/i7bffhouLS2mfGxERERERERERkckkEkmJT6BLpVI0atQIgiDg6dOnYvWtrtBPkYlpKnp0cHAAoD3A1cUcFbjmDBxNVdY9cNUrcBX3r1QVuMpCQkIQEhKCa9eu4ffff8fGjRuRnp5eovcHUFylm5qaqnUu5bEA0K1bN7z66qsYM2aMSh8QIiIiIiIiIiKisqYtjAMAJycn1KlTB1ZWVrh06VKJQFQTXRW4ygGuIAg6N07TRVdgqOv5VKQWCmXdA1eZuStwzRGMGx3gKrRq1QqLFy/G77//jn379mH37t04cOAAYmNjDZ7D0dER3bt3R79+/TB06FAEBASUdjlEREREREREREQWox7mKUJYxT8lEglkMpnJFbhFRUWQyWTimNJW4Boy1srKCk+ePIFUKq2QLRTKqwJX8RqVZhMzbT//Mq3AVWdra4vBgwdj8ODBAIqrbi9evIi7d+8iMTER6enpyM3NhY2NDZycnFC7dm34+/ujVatWaNasWYUqzyYiIiIiIiIiIlKnnl/J5XK4u7uLny53cnKCIAjw9PTUOY8hFbhAcRWueoBrSJiorwpYPUQsKChAXFwcACAoKEjv/GWlvHvgmmMTswpRgauNp6cnevfujd69e5t7aiIiIiIiIiIiojKjHOAp2hp4eHjAwcEBXl5e4uNNmjSBXC6HtbW10RW4crkcVlZWsLOzEwPh3NxcuLm5AfhfiKneg1fbOvU9H+WAsTQVpqbKzc3FvXv3VJ6PlZUVatSogZSUFBQVFZV5D1zl108qlaKwsLDE4+a8h7HMHuASERERERERERFVJcr7ONnb28PDw0N8HCiuqPX09NS5FxQA5OfnA4DK3k+FhYWwtbWFRCKBvb09cnNzVTYys7Yuju9kMplB6zTmOSlXuJZVf9tbt24hISGhxON37txR+d7Kykp87pamHGSbWoGroF6BywCXiIiIiIiIiIjIBA8fPkRGRgaA4tDUz89P56ZfCupjDKnAVW6hoAhwgeL9otQDXMVYQytw9bVQUB5r6IZd5tzETFHdWrNmTbi5uSE7OxvJycni+Ro1asDd3R2enp5l1kJBPcBVvC6mtFAwJwa4RERERERERERUreXn5+Ps2bMqjxUVFYlBnnIoqtx6QLky15CP+2sLcBUUfXCVA1xFiGmuClzlFgrqz6ssKO5fu3ZtBAQE4MmTJyoBro+PD/z9/ctkLQrqrSSM6TusjTkrcCvO9nJERERERERERETlQNHaQCqViu0R8vLySvTA1cWQoM7QADcnJ0d8TLkCV9vc2ipwNVUHFxYW4tGjR4iLi8PDhw+1jrUU9U2+1Ktsy6rvraY1Keh6DU2Zq7RYgUtERERERERERNWaIkS1t7eHj48P0tLSVIJVQHOQp16Nq48iwFXu7aqvAld9rHL4q742fRW1giAgOTkZ9+7dg42NDWQyGXJzc8X7amPOFgrqa1UPbMuqbYIybRW4wP82rzOUpp+F8uOlwQCXiIiIiIiIiIiqNUWIam1tLQamhYWFJcI4TR+tVx9jbAVucnIycnJy4OnpqTHAVfRk/X/svXd8HPWd///aol31LtmSJXe5Y0yxsSnBGAgh9HBAviGFBAIJ7RK4QC6N3IXkQgIkOY6EeiGFEBJCSYhzhGJjAzY2Lgj3blmSZfWyklba9vtDv8/Hn5md2Z3dHUkr+fV8PPTQ7pTPfKbtzr7mNa93OBxGKBQyFHBV4omPog9iWpGtm0pcQCLEE3BHw4EbT8AdbSjgEkIIIYQQQgghhJATGjMBV0WIoWZuzGQF3Lq6OjksMzMTgFbABYZcqeFwGIFAQE6j75vaBzMikYgUbPXrki4RCungwLVa3C0WdmbgUsAlhBBCCCGEEEIIISc0RgJuIBDQiI3qa6MiZlYQWbtqLEJFRQWam5sRCASksKsXcN1uNwKBQJSoHAuzvhnl4kYiEbS3t6O+vj5q+pKSElsLnKVjBq6dDlxm4BJCCCGEEEIIIYQQYjNGAq5wqgrMMnDFcCvCo96Bu2DBAkybNg3r1q1Da2urXLZewM3IyEB/f7+pgKuKoqrzUy9M6h24YlhfXx+2bt2K1tbWqLbz8/MxdepUzXJSYaxl4CYbLcEMXEIIIYQQQgghhBBCbMKKA1dFFeNSycAV8xQVFWnEU5/Pp5nPLNYhUSKRiEaQFOKpyPv1eDwoKCgAMCRctrW1oa+vL6VlGvUBSK8M3FjiPB24hBBCCCGEEEIIIYSMMolk4IrXgFacE8Jjohm4AFBYWAjguNuzsbFRMz6egGvkwDXDyJUrKCoqwpIlS2RfX3vtNU0xNztIxwxcI1IVcO104I68pE0IIYQQQgghhBBCSBqhCrhCXA2Hw5qsW4E+WzZWAbFdu3bhq1/9qixUJgRcIciKecrLy1FVVYXs7GwAyTtwrRQxM1onIVSqw9ScXn2cRCrot5dedB4NB64RdhQfU9tJBTpwCSGEEEIIIYQQQsgJjSrgqg5QVTDVC5zqcMBY8Fu8eDF8Ph8aGxvxyiuvRAm4AqfTiVNOOQXl5eUAEBVbYNWBq/ZDP1wQy4GrF1JdLhdCoZBc7nBk4IplCZE4XRy4ok/JrnNaZeBu2bIFv/vd7wAABQUFuO+++1JtkhBCCCGEEEIIIYSQEUMVcJ1OJ5xOJ8LhcJSAC2gduPGco8JJu2PHDgDAwMAAgOgMXEFRUREAoKurSzNcCLg+nw/t7e3Izs5GZmZmwuupOnDdbrdGoIxEIlHrIKZJNXtX3wfA3Jmabg7cRIuYmWXgjqqA+/bbb+PnP/85HA4HbrnlllSbI4QQQgghhBBCCCFkRAkEAgCOC6VutxuDg4NSuFQf9weMi5jp33d0dMhhs2bNAhAdoaCntLQUANDe3q4ZLqavr69HfX09nE4nLrzwQng8Hs0y42XgqgKuy+WKWg/9vBkZGRgYGLBVwDVCXW66OHCTFV7jCdTJkLKkre7A+fPnp9ocIYQQQgghhBBCCCEjitC3hDNW/LfiwDUT7D744AP5WrSnL2Kmn6esrAxAtIBbUVGBgoIC5OTkwOFwIBwOo7+/37BvKkbio4gqEKKwyL81E3DV7TBcEQpmMQ6jid0ZuKNaxKyiokK+9nq9qTZHCCGEEEIIIYQQQsiIIgRK4f4U//UOXIHRY/V6oe7555+X40QkQjwHrsjAVd27AJCfn4+PfexjWLFihYxOMBMEE3HgxpvXavG0RDAqopYuCEczkF4O3JQjFObNmydfHzp0KNXmCCGEEEIIIYQQQggZUcwcuP39/dKxauRQNXLgRiIRHDx4EM8884ycTi/gxnPgdnZ2RrlsBQMDA/D7/abu4HgIATUjIwNerxf5+fkyTiGeA9dO1GWNtpi7dOlS7Ny5EyeffLIcJrJ4E83A1ZMWGbinnHIKZs2ahb179+Lll1/G/fffn2qThBBCCCGEEEIIIYSMGGoRM/X/vn370NbWhpqaGjmtWUyBOnzz5s1S+AUSd+AGAgG89dZbhgJubW0t/H4/3G43rrzyyoTETyMH7uTJkwEMCcNmDlx1XVIlXoTCaFBWVibFc0Gqwqs+NzkVbCnr9p3vfAeRSAQ7d+7Ef//3f9vRJCGEEEIIIYQQQgghw0YoFMLg4CAGBwcxMDCASCQCp9OJcDiMyspKZGRkIBKJIBAIwO/3SyEvGAxKQVN14ArHZiQSkcJrZWUlAOsO3Pz8fPkY/xtvvIEPPvgALpcLbrdb8wcAfX19sviaXhQ1Ex9VAdftdmvGh8NhuQ4C0U/9clJhOCIGhoNUIxSsDrdCyg5cAPjsZz+LNWvW4KmnnsJdd90Fn8+He++9N22qxhFCCCGEEEIIIYQQImhvb8e6deukmLllyxZ4vV4sWbIEmzZtwpw5c/CJT3wCTz31FIAhsdfhcKC5uRk7d+5EOByOcmyqCAF34sSJaGxsRFdXFyKRCAYGBgCYO3CdTicKCwvR3NyMBx54AADw/vvvY8mSJXKa7OxsvPXWW4hEIggGg5rc1niIYmVqH8T7cDhsGqEw3A7cdMSuDFw71tMWAbeurg7f+c53kJWVhUceeQTf/e538dhjj+G6667DsmXLMGPGDOTl5ZkenEYI+zYhhBBCCCGEEEIIIXbS0dEhxVshuA0MDOD999+H0+nEjh07MGPGDGlODIVCcLlc6Onpke/7+vriOnAnTpwIYMi129/fHzdCweVyoaCgAM3NzXLYqlWrNAKu0+mUmbUi+sFINDQSHkOhkBwu2ogl4Ip+Dg4OppwFKzAScNNRzLUju1Zl1B24U6dOjTpA6uvr8fDDDyfVnsPhGJZwZEIIIYQQQgghhBBChO40ZcoUnHTSSdi6dSsCgUBUjIBewFWdqHoBWBUhhYBbWloqYxm6urqiBFy9cCkEXJVf/OIXuPPOO5GVlSXncTqdGgFXYJbPK1D7L9y1YppYRcxaW1tx8OBBLFu2DKmSjhm4RiRbxGw4HLi2ZODqcTgcUTsh0T9CCCGEEEIIIYQQQoYDIWSKHFh99qtADBfOVVUA1QueRg7c7Oxs5OfnAxiKbRBioBBG9bhcrqin0o8ePYpLL71Uvnc4HKYOXCvrLfqt9ldk4+rFxuLiYtlXIUKnyniPUDAjlXZsE3ApxhJCCCGEEEIIIYSQsYAQYoXDVvw3ixBQBVy9sBfLgZuVlSUdtS0tLVHt6pfndDpxzjnnRPV3x44d8rWRgKuOM2pXv96iDdF//boJsrOzcdFFF2HSpEmadU2F8R6hYLWgXCLYEqFw8OBBO5ohhBBCCCGEEEIIIWTYUQXcSCQixUw9qgNX/Q8cz4zVC3OqA9dMwM3IyIDf749ansvlwvTp0+X722+/Hf/zP/8j2xN9Ev1NxoELaEVF4cg1cuCKaVW3bqqMlQgFux24qWCLgDtlyhQ7miGEEEIIIYQQQgghZNgRwqeIUDATcPVCqZnICUATw5CsA1cs87HHHsP+/fuxdOnSKAFXuGfD4XBcB65efAyHwzIyQi/KhkIh0ygJ1a2bKnZHKOzduxf79u2LOU15eTlOO+20hNpNJwfusGTgEkIIIYQQQgghhBCSriQaoSDEUjU2QThXE3XgmuXfClwuFyorK3HOOecgJycHADA4OKhxzwqhNRAIyGUCQFNTU0wxU7ThdDo1fRcZuGaI7ZJoQS8jjATcU045BQAwb968hNtraGhAMBiM+dfY2KhxT1sh2SJmeuwQqm1x4BJCCCGEEEIIIYQQMlbQRyjEK2IWDAYRCAQ0YpwQ9oQgGc+B29TUBADweDwxXagul0sKs0LABQC/34+cnBzTImbhcBhXXnklAOAvf/kLMjIyosRltYiZEK1VV7He0SvQ93NwcBDr16+PioEoKirC4sWLDdsQGK17RUUFLr74YimYJ4Jo79RTT0VhYWHU+FWrViESiSAQCJg6rY2wy4GrH54MFHAJIYQQQgghhBBCyAmFECr1Gbh60U0VOfUOTn2cgir4GQm4W7ZsAQBUV1fH7JsqMqoCbn9/P3JycuB0OmX0gyq4+nw++bqxsdEw8lTvwBUIcTGe21R1+nZ1dUWNb2pqgt/vR2ZmZsz5AXO3c7JkZmZqtpfabiAQMBWnzRh3GbhmtLe3Y+PGjaivr0dnZyf6+vpQVFSE22+/fTgXSwghhBBCCCGEEEKIKULIFKKhPpNWvFczcIUrVo/ecWkm4L7//vsAhmICYjlwVSev1+tFRkYGAoGAbFNEKOgduN3d3XK+tra2uAKuaEM4ctXxesR2EOM7OjoAAJMnT8a0adMAAO+88w5CoVDMqIJYAm6yxMvUFdvPbP+ZkU4ZuLYLuH6/H7/+9a/xxBNPoLa2Nmr8jBkzogTcgYEBXHXVVfD7/XC73XjxxReRm5trd9cIIYQQQgghhBBCCIkboaDPxlUduCI7Vp+Bq7YhogVUAVcwd+7cmH1THbhutxtZWVlRAq7L5ZL5rgLVEfv9738fV111FT796U8brreao6sKi1YjFDo7OwEAEyZMQH5+vux3KBSynBlrl4AbDyHGj5SAa9ZOKthaxGzt2rU46aSTcPvtt6O2tlYezOqfEV6vF5MmTcLq1avx5ptv4vnnn7ezW4QQQgghhBBCCCGESPQCrsvl0ghtQkSNlYGrFgAD4jtwBfEcuEYCLoAoAVfvwNVHGrz00ktRWlx3d7cUnlUHrhBdzcRXdd1CoRB6enoAQJM5a6Xo12g5cAFzcdqMZIuYDUcGrm0C7quvvooLL7wQBw4c0Ii1U6dOxRlnnBF3/i996Uvy9QsvvGBXtwghhBBCCCGEEEII0aBm4Kr/BfpoBX00gBAyjTJwAeMiZoJ58+bF7Jvq5I0l4KrrAUAToRCPzMxMQ4epWfyB6tb1+XyIRCLweDyarNvREnDjIQTcE96Bu2/fPlxzzTUYHBxEJBJBcXExHnroITQ3N+PAgQNYt24dgNgdXrp0KSZMmIBIJIK1a9cmvFEJIYQQQgghhBBCCImH6jgVxcD0Aq7QsIQoOTg4iPr6etP21Hn0DlwRMSCYOXNm1HJU4jlwnU4nXC4XwuEwfD4f3n33XWzcuBH79u2LakstbKb2d/LkyZooCLEOZi5VVcBV4yGMprEq4NpFPAfuSEcopK0D9+6778bAwAAcDgcWLFiA7du34+tf/zpKS0stt+FwOLBs2TIAQwfk9u3b7egaIYQQQgghhBBCCCES1WWqOnCN4hE8Ho+MGlAdsGIadR7VOWvmwC0sLERubm5MMS87O1u+zs3NNXTgejweAENiaXt7Ozo7O2VhMYHD4UBzc3NU+zk5ObKImX59YxUgE8sTAq7qvgXGX4RCqg5cfRGzVEhZwG1ra8PKlSvhcDiQlZWFv/3tb5gwYUJSbS1cuFC+3rVrV6pdG1ZCoRC2bduGZ555BnfccQeWLVuG7OxsOBwOOBwO3HDDDZbaeeaZZ+Q8Vv6+//3vW2p3YGAAv/rVr7BixQpUVFTA6/WiqqoKl1xyCX7/+98nnN9BCCGEEEIIIYQQMh5QRUohzuqLmKnjFyxYgJNPPhlz5szB7Nmzpe6VTAZudXW1pn0jcW/u3Lk444wzcM455yA/P99UwF28eLH8W7hwYZSLODMzM6b+YyS4mgm46rqZCbhi+bFE4LEUoWBU5M0KZtOn4sB1Jz3n/8/bb78tMz+uvvpqTJkyJem2ysvL5etjx46l2rVh5dprr8WLL7442t0wZNeuXbj66quxY8cOzfCGhgY0NDRg5cqVePzxx/HCCy8kLbYTQgghhBBCCCGEjEXUAmYiRsDj8Rg6cIEhobK8vBz9/f1wu91oamoCEO00NRNwxSP8AFBVVRXVvh6n06nRyIwiFMTwiRMnAgD6+vqksHrVVVfhpZdeQjAYNFyOPh5C7Y+Z4GsUoZCKA3c4xNt4DtxkIxSSNUHqHbijKuA2NDTI10uWLEmprZycHPm6t7c3pbaGG/3dhOLiYpSUlGDv3r1Jt3nHHXdgxYoVMaeZM2dOzPFHjx7FRRddhLq6OgBDruYvfOELqKysxIEDB/D000/jwIEDeOedd3DJJZfg7bff1mx3QgghhBBCCCGEkPGMvoAZMJSTetJJJ2Hy5MnYu3cvmpqaUFRUJEU/o2JlekFODA8EAlL0y8rK0kQi6ONGrQiZRg5c/fIjkQi6uroAAJWVlQDMnbCqiKrm3wLWMnBFP9JFwI0njAoBfaQiFPTTW3EmxyNlAbevr0++Vg/IZFCDlfPy8lJqa7hZsmQJ5s6di9NOOw2nnXYapk2bhmeeeQZf/OIXk27z1FNPxZVXXplSv+666y4p3n7605/G7373O82dnjvvvBOXXnop3n77bWzatAk//vGP8YMf/CClZRJCCCGEEEIIIYSMFYSQJvQSIbjl5OSguroa7733Htra2lBXV4dgMBg1nYhd0IufQsB8/PHH5bCsrCzpAAWO612JiIJWBdyenh4AQyZDYEhINRJTRT+FsKi2Y+ZSVd2odhQxGw4Bd7gcuKkWXrOyXeKRsoCrWrpVN24y7N+/X74uKytLqa3h5lvf+tZodyGKHTt24PnnnwcAVFRU4Mknn9SIt8BQ+PWzzz6LmTNnwu/34+GHH8bdd9+NwsLCUegxIYQQQgghhBBCyMiiRiioGD0y393djeLi4pgO3M7OTrzyyisoKSnBwMAAVq5cKafVRzPk5+cn3F8rAi5wXKBUn7TWuz71GbRChBYi48DAgGEf1HVI1wgFM4Q21tPTg61bt0aNdzgcmDx5MoqKiqKGA8k7cMX84jhLRcBNuYjZ9OnT5es33ngjpbZef/11+fqUU05Jqa0Tkeeff14eJDfffDNyc3MNp5s0aRKuvfZaAEMO6ldeeWXE+kgIIYQQQgghhBAymugjFGIV1urp6cHevXvx3HPPyexbIXyGw2E4HA689dZb+Mtf/oKzzjoL3/72tzXz69ubNGmSZpnJRCgYFdeKRCIYHBwEoH1C3sh1qs/AFRnAAGQbesS0oVBItun1eg2nSTcHrth+gUAAR44cifqrq6vDrl27ouZL1Tmr386pRCikLOCeddZZyM/PRyQSwdq1a/Hee+8l1c4bb7yB7du3w+FwoLq6GrNnz061aycc//jHP+TrT37ykzGnVcer8xFCCCGEEEIIIYSMZ4QAKURLVQDUuy67u7tx991345vf/KaMoFSdu5FIBB0dHfL9nj17DJf54IMP4uMf/zhuvPHGhPtr5sDVC4tivVQBV5/7GolEDItriW0RDodjir5qe2o0BGAt63U0HLi5ublYvHgx5s6dG/UnisoZ9TmdMnBTFnDdbjf+3//7fwCGOnjdddfh0KFDCbXR3NyMm266Sb7/yle+kmq3xiS//OUvMXfuXOTm5iI7OxuTJ0/G5Zdfjl/96learGEjIpEItm/fDmDowIjnYF68eLF8/dFHH6XeeUIIIYQQQgghhJAxgBAo9QKkEHBVAa63t1e+bm1tldMBx4U6s9iBv/zlL/L13Xffjddee02Ksak4cI2ExXA4LMVVNbbBqHCX6gxVc31dLhccDoeMSDCaR42f0Pc9XR24ADBx4kTMnDkz6k8UfDMi1QxcvQN3VCMUAOA///M/kZeXB4fDgYaGBpx22ml4/PHHTW3XKq+99hrOOOMMHDlyBA6HAxUVFfjXf/1XO7o15ti4cSN27dqF3t5e9Pf348iRI/jb3/6GW2+9FVOnTsWrr75qOu+RI0ekyFtVVRX1IaSnurpa3gHYu3dvyoHMhBBCCCGEEEIIIWMBoVfpHbgCkQs7ceJEjRtVvNYLckamu5KSEnzqU5+ypb9WBFxVg/N4PFIXipWBq66HcOGaCbhi2mAwiM7OTjQ2NmLnzp3YuXMn9u7di0AgkLYZuFYw0sVEHzs7O7Fz504cPnw4qbbsyMBNuYgZMFRw7I9//COuuOIKhEIhdHR04NZbb8U999yD5cuXY+7cuXLajo4O3HfffWhoaMCaNWtk4bJIJIKsrCy8+OKLUVXsxjsulwvLli3DOeecg1mzZiE3NxednZ3YtGkT/vSnP6G9vR0tLS24/PLL8eyzz0rHs0pnZ6d8XVpaGneZGRkZyM/PR0dHBwKBAHp7e00zc4Ghu0nqHaXu7u7EVpIQQgghhBBCCCEkDdA7cI0iFIChgmNqzqtws6oCaiQSkcKqir7Alx47MnBVQVAVXTMyMuB2uzE4OBg3A1f0IxKJyO0RS8AdHBzE3r174fF4sG/fPs36pLMD14xYLluxPXw+n1zXwsJCFBQUJLQMNWs4HA7L94lgi4ALABdffDH+9Kc/4Utf+pIUE3t6evDqq6/i1VdflRukvb0d999/v5xPbKDi4mI899xzWLJkiV1dGhOcffbZOHTokMzcULnpppvwk5/8BF/+8pdlgbIvfelLOOusszB58mTNtD6fT76O9yEhyMrKkjktPT09MQXc//qv/8J//Md/WGqXEEIIIYQQQgghJF0RblWjp5fVCAW9IKh34IrphAM3JydHRi7oC3ylghUHrmq6c7vdct3iRSio7Xi9XjgcDuzatQuVlZWarF8xz8DAAJxOJzIzMzF9+nR0dXWhra0NfX19sp+jJeAmQ6x+VFRUwO/3Y3BwEEeOHEEgEDAUxM36I9pWt2OyAq4tEQqCK6+8ErW1tbj22ms1lfzMrOhCnf+Xf/kXbN68GRdeeKGd3RkTzJw501C8FeTl5eHZZ5/F8uXLAQzdBXnggQdGqHfH+fd//3d0dXXJvyNHjox4HwghhBBCCCGEEEJSJVYRM4HqTNXPpxc+hbBaXl4up7Uq4NqVgSsEXLfbDYfDYVo4yyhCQWh0QsD1+/1oaGgw7KcQZ4uKijB//nxpMPT7/aMeoZBKm0YisNvtRk1NDebPn2/ZLGnUH1WwTbaQmW0OXEFVVRX++Mc/oqGhAS+88ALeffdd1NbWoq2tDV1dXcjOzkZJSQlmz56Nc889F1dffTVmzpxpdzfGFS6XC/fffz/OPvtsAMCrr76KRx99VDON6p41sroboVr88/LyYk7r9XptvXtECCGEEEIIIYQQMhpYiVCIRCJwOBwaMTIUCqGrqwsbN25EMBhEOByGw+GQDtzy8nIcPHgQgPUIBSsUFhYCgBRV9QIycFzAFevkdg9JfrEcuPqCbWVlZXJ99U5TvYAr+iS0ov7+flPRWCVdBVyrbVvZb0bTOJ1OhMPhpHNwbRdwBZMmTcK//uu/nrAFyexm2bJlyMzMhN/vR11dHfr6+pCdnS3HixMHOF4VMRbBYFDm2GZkZCAnJ8f2PhNCCCGEEEIIIYSkG/oiZip6EVAvxn3zm99EQ0MDTj31VEybNg0ANAKuwKpj04rouGzZMgBAbW0tWltbDcVEYeYTAq5ZhIIQpoHobNaMjAyUl5ejubk5ar3FtEKcFTqUcAePpgPXjgiFVNqI1zYwZM4Mh8NJO3BtjVAgw4fT6URxcbF8rxYtA4Dq6mop6NbX18fN5Kirq5MHTU1NTdpV/yOEEEIIIYQQQggZDmI5cPXv9WKkcMHW1dXJ6AEjATfeU8yJCIYTJ07EggULAACrVq2KcsMC0Q5c4YY1cuAKjLJYzfJZ9csUwq0QqoUjWd8vPelaxMzqdMk6cMX+SNaBSwF3jBAOh2XBMUDruAWGDqT58+cDGLobsmXLlpjtbdy4Ub4WHwKEEEIIIYQQQggh45lIJBKVgSvQRygA5nEA2dnZsi0hkibiwE1UdDz//PMBAGvWrImbgav+NxJwhUirj1BQ+2M2XGwPIVC73W65LLFdkxUpR4OjR4/i5ptvxnPPPTesy9E7mBOe345OrFy5Ej09PXY0RUxYv369zKytqqrSxCcIPvGJT8jX//jHP2K2t3LlSvn6k5/8pE29JIQQQgghhBBCCElf1CeW9Q5c4LioKaIG4gm4Pp9PDksmQsEqs2fPBjD01HUsAVcfoWDUfyFUO51Oy0KyPh5BFb/Fuh47dgxtbW3SkSz61djYiIaGBvT19cn50yUD94EHHkBTUxOeeOIJS20n4sBV+2MlXiIWtgi4l156KUpKSrBkyRLcc889FHRtJhwO43vf+558f+mllxpOd91118nXjz/+OHp7ew2na2howJ/+9CcAQ5b3K664wsbeEkIIIYQQQgghhKQnQsB1u92GkQmq6OZwOGRerh4RryAEXK/Xqykwb7UQvFXRceLEiQCGHKNGYqDegWsWoaAXqwXt7e24/fbb8corr0RNB8QWcEWcwqFDh7B//37U1tbKcZs2bcKmTZuwefNmvPvuu7Jds6iGREk1u1aYJeORTFauPgMXGGUHrujApk2b8NBDD+Gyyy6joGuBdevW4YknnpBB00b09vbi85//PN58800AQx8A9957r+G08+fPx7XXXgtg6IT+8pe/HHWi+nw+XH/99XKZd911V1QcAyGEEEIIIYQQQsh4ROgkQuiMhxBGjdqJRCLSPJedna1x3VqNULBKRUUFAKCpqSmmA1cIq0IwLC0tRXV1NWbNmiWnFUK1KjD+9a9/RV1dHe6//37D5eszcFUBd+bMmSgrK0NJSQmAIVG0o6MDgUAA7e3tcjq/3y8FTLscuGaCdDLz28VwZOBaO1rj4PF4ou5IBINBqbI/9NBDcLlcWLRoEZYvX47ly5fjnHPOQV5enh2LHxUOHjyIp59+WjNMvcOwZcsWfOc739GMX7FiBVasWCHfHzt2DLfccgvuvvtuXHjhhTjttNNQXV2NnJwcdHV1YfPmzfjjH/+ItrY2AEMH4lNPPYWpU6ea9uvhhx/Ge++9h/r6ejz33HPYvn07brjhBlRWVuLAgQN46qmncODAAQDAokWL8M1vfjPVTUEIIYQQQgghhBAyJtAXMAOMHbhimJmAGwqFNBEK2dnZ0okKROfrporqwNX3W+2nWC8hUDudTixatAiHDh2S8+hdxkC0E9XMgavPwAWGROLS0lK0trZi/fr16Ovrw6pVq1BYWIhIJKLRDdUIi3TAqqBqV4RCsg5cWwTcrq4urF+/HqtXr8bq1auxfv16DAwMaFZqvAm6hw8fxg9/+EPT8bW1tRpBFxg6eVQBV+Dz+fDSSy/hpZdeMm1v4sSJeOqpp3DJJZfE7NekSZPw2muv4eqrr8auXbtQW1uLu+66K2q6M888E3/5y1809n5CCCGEaIlEIujv7zfMnieEEEIIIWMPIweuXsBV3yfiwFUFXLuLmAkBd3BwEN3d3Zo2AHMB10ww1S9XH2lgJFQODg5KwVMVwAUulwsulwuhUAjBYFBum6KiIrS1tSEYDMr+pIsDN1lBNVFSzcC1RcD1er0499xzce655+K+++7DwMBAyoLuiVBY64ILLsArr7yC999/Hxs2bMCRI0fQ1taGzs5OZGdno7y8HKeeeiouueQSXHvttZYDsOfNm4ctW7bg6aefxp///Gfs2rULHR0dKC0txcKFC/GZz3wG119/vW15I4QQQsh4pba2FnV1dTj55JMxefLk0e4OIYQQQghJESsRCqp+ZZaBGwqFEA6H8f777wMACgsLExJwBVZFx8zMTBQWFqKzsxPNzc1R/dRn4ArNx0gwNXsdi927d+PQoUPIyMiAw+Ew3H5OpxNut1sKuILCwkJ0dXUNi4CbKsPhwNXPA6SegWuLgKsnVUH34YcfjspuTTeWL1+eck5Gbm4uLr/8clx++eU29eo4mZmZuO2223DbbbfZ3jYhhBByolBXVwdg6IKVAi4hhBBCyNjHigNXHR7LgdvX14dnn30WAHD55ZdrBNx4RcyS0ZQqKirQ2dmJY8eOwel0GhYxM3PgqgKkup5VVVXymlfflsqRI0c074UgqSIE3IGBAQSDQXg8HuTm5qKqqgoNDQ2G/UmVsZKBm6oDd0QsmELQve+++7Bq1Sp0dnZi1apVuO+++3DuuefC6/VqDqTh2HiEEEIIIcmS7jeWCSGEEEKINWI5cPUCYKwIhVAohGPHjmFwcBATJ07EGWecMawOXOB4jEIsB67I3o0XoSCYPn06AK2w6PP5orQ5vWvXSMAVEQrAkGB50UUX4ayzzkJ2drapoGwnwxmhMNoO3FF5hn5gYAA9PT3o6elBd3d32gUYE0IIIYSoUMAlhBBCCBkfxHLgAohy4MaKUBBZtBUVFXA6nRrRNl4Rs2QduABw7NgxTRvNzc14/PHHARxfLyEYiv7rBVhVkHS5XJoiZj09PaZFzMT8RrGcWVlZqKiogNfrRVVVlWbccAm4qZpAk3XExsKoT2J/jGoGbjx6enqwZs0arF69GqtWrcKHH34Y1WF15ebPnz8S3SKEEEIIIYQQQgghJxCJFDEDAL/fb9pOV1cXAKCsrAwOhyMpB24iTJo0CQBw9OhRnHzyybKfah0pEaEgBEO9EUFEKKi43W709fXJ9z6fL25fjBy4DocDJ598MgoKCqTYrC4DGF4HbjLo413NspETceAaFagTgndaZeD29PRg7dq1WLVqFVavXo2tW7dqBFv9ys6bN08WL1u+fDlKS0uHo1uEEEIIIYQQQgghxIBwOHxCFDuPF6GgFxZjOXB7enoADAm4AJJy4CYiZFZXVwOAJk9W1JQSxMvAFaiCZEZGRlwHrorT6TQ9VsR667fbSDhwU41QGBgYiFncLhVSzcC1pVdCsBVFyrZu3arZABRsCSGEEEIIIYQQQtKTw4cPo7a2FkuWLMGECRNGuzvDSrwIBf37WAJuS0sLgOMOXFXAHQ6HqYglaGxshM/nw1e/+lXMnDlTM43egWskmKriayQSgdvtjhJwzRDbxkzAFcvXx6WmawauKqgODg4iJycnZtujlYFri4BbXFwsV9hoRebOnasRbMWdCUIIIYQQQgghhBAyutTW1gIAPvjgA1xyySWj3JvhxYoD16g4WGZmpiZOIRQKydgBowgFq07LZBy49fX1OHDgALq6ujTuW+C4sCrWT/RfuHUDgQDC4XBU1m+sImZGebjxHLhqJAMQLeymSwauGjFhVrAOSC5CQSUtMnBDoZA8wB0OB+bOnYtzzz2Xgi0hhBBCxjQulyvpu+SEEEIIIWON4SjolG5YLWJ29OhR7NixQzpw9QJuMBjUCLix2tSTrOgoHLhHjx5FU1OT4TSdnZ0AjgupAwMDqK2txebNm7F//34MDAzA7/fL8ZFIJMot+8wzz+CLX/yifK/P0TXKvxUIobajowMHDhzA9OnTDeexW8BNtj3VeRxLwE0GOzNwbQ03EVXosrKykJ2djZycnGEJbSaEEEIIGQmGKwOLEEIIIYSMDvGKmAHAunXr8PTTT+Oyyy5DY2MjACA7OzuqLSF4FxcXR+XnWhVpExEey8vLpUC6Z88eOVx1TQuhUAimfr8fzc3Nmn45HA50d3fLYUba3cqVK+XrQCBgGAdgRFFRkXzd1tYmX4t+C9KliJkqypvFZQCJ9ddo36eagWubgBuJRKTlevPmzXj44Ydx2WWXoaSkBEuWLMG9996Lf/zjH5Yq2RFCCCGEpAOxLk4JIYQQQsjYQ7hNYwm4quApMBJwgaHrRY/Hk7LAZwWn04lJkyYBAHbt2gUA+H//7//hlltuwRe+8AVMnz4dF110EQDA6/UCGBIohQB70kknYerUqZo+RCIRqdVNmzZNzqfm4OodurGK3Xk8HixatAiAVqzUGyPsFnBHyoGbSIRC2mXgHj16VBYwW716NXbv3i3HiWp4mzZtwoMPPgiXy4VTTz1Vxiucc845pgHBhBBCCCGjif7CPl2cAoQQQgghJDmEgKZ3hKroBUsAyMvLM5zWTNidN2+epf4ken05ZcoUHDp0CHV1dQCOxzecfPLJqK6ulv0U6+f3+6XrWAzTL1MI1nPmzME555yD3/72txoxM5EIBbV9VewcLgE31QxcqwJuMkXMVNIiA3fChAm47rrrcN111wEAjh07FlPQ3bhxIzZu3Iif/vSncLvdUtA977zzcPbZZ5se/IQQQgghI4l6oRkKhRipQAghhJBxSUZGhqFoOR4RYqQqQuodk+JR+muvvRb5+fkoLS3FO++8Y9ieiB8Q8/7qV7/CkSNHcPrpp5v2IRXR8eyzz8bbb78t38+ZMwfz5s3D3r17Nf0QTlohSjocDkPhNRKJSAF36tSpUuBWowUSceCqfRgpAXf37t3Yu3cvLr744rh9UxkYGNA4jWNFKCRD2mbgCoSg+6tf/Qo7d+7E0aNH8dxzz+GWW27B7NmzZdyCCEresGEDfvKTn+Diiy9GUVERzjrrrOHoFiGEEEJIQqgXgHrnASGEEELIeOFEuUkdDoelqBjLRSqEvIULF+LTn/40TjvtNI3wlp+fL19nZWUBOC7WTZs2DUuXLh2WDFwAOP/88zXvnU6nLEimtqcXcN1ut6xd5XA4NP1TBVwhSKtuVH0GbrzjRVxDq8vIy8vTuJ4LCwvjrKl1vvGNb+CJJ57AE088YXmeRx55BCUlJejt7ZXD7HLgGk2TFg7ceCTi0A0EAli/fv1IdIsQQgghJCbqxRcFXEIIIYSMV4ZLwA2Hw1i3bh16e3sxZ84cTJ48eViWYxX12k69Ua934ArH6eHDh+F0OuHz+dDe3i6nz8zMlEXAvF4vtm3bhlmzZmnaiCX0peLAXbZsGTIzM+H3++F0OlFaWmro6hSibiAQQDgc1oi8ah8ikQiOHTsGYCieQRQeEw5cv9+PrVu3aua16sBVxUqv14sLL7wQgUAgSnROBXVb/uY3v8FXvvIVS/P96Ec/0oi3gLUM3ESw04E7KrdYysvLMWPGDBw5cgR1dXWoq6uTB0aq2RWEEEIIIcMBBVxCCCGEjFeGS8Dt7u6WwmdDQ8OoC7iqoKiKa2YCrsvlQjAYRCgU0ghvwnUr5u3r68O+ffuS6lOiDtzMzEw8/vjjePPNN3HqqafC4/ForlOFy1Y4cIEhYVI4a/XLjEQiaGlpATDkwN2xY4ecB4BGuBYkk4Er5hvOIsF6Q2hPTw+amppQU1MTNa1evAViRyicEA7cSCSCzZs3Y/Xq1Vi1ahXeeecdTcYEIYQQQkg6ol58rV+/HhdddBF6enqkC+HUU09Fbm7uKPWOEEIIIcQeVAE3GAzaJuiq11LpYNhT+xBLOBVCnsfjwdSpU6WLVaAKuGK4aE/837JlC84991zD9n0+H3bs2IGKigo5bNOmTejo6IiaNj8/H4sXL9b09/Of/zwuvfRSvPvuu7K/6rplZWVp4gr8fj9yc3MN3Z+BQEC6btUIBWG0FPOox0Q8EdYoQmG40C+jra0NJSUlAIArr7wSb731FrZu3YqTTz5ZM51avExglwNXf0MASFMHbiQSwZYtW7Bq1SqsXr0a77zzjrSWi/FGzJ49G8uXL8fy5cuHo1uEEEIIIUkTCATg8/nQ1NSErq4uAEBTUxNmzpw5yj0jhBBCCEkNVZAbLgE3HVDF1lgOXFXAzczMhNvt1gi4qptVCKViWE5ODvx+P7q7uxEIBDRCqmDbtm3w+Xyy8FhfXx8aGxsN+9zf3w+fz4e8vDzNcH3Ul/o+OztbFi0LhULw+/3wer3o6+uT6y6m7+joQCQSgdPpRHl5uRSnhZgp1rugoEAuN5kiZsOFXhBtampCSUkJenp68NZbbwEAfvvb3+Khhx6S0wSDQcOn6+zKwDUiLRy4QrAVmbbvvPOO/GEjxhuhCrbLly/HhAkT7OgOIYQQQogt6K9hAoGApgpvuv0oIYQQQghJBvWaJhAIaATK4VjGaGHkjDRCXO95PB44nU64XC6N8KYW4CorK8OCBQuk8Ll06VL8/e9/1yxPjz7yQLzPyMjAsmXL5LgNGzbA7/drrj8Fan/0j/57vV44HA643W4p4ApRXi/g+nw+AEPC87p163Do0CEAQGtrK1atWqUpgqb2ORZGGbjDhei/oLOzEwCwZs0aOUzvtjVy3wInQAZuSUkJBVtCCCGEjHvM7tYTQgghhIwXjMTC8YKZgBvLgWsUF6DqWXl5ecjOzpbvjYqj6dE7WMX1pcfjkU5X8d7v9xuKfmrbaoSCw+FAZmamFHAHBgbg9/tNi4YJATQ7OxttbW2ynYGBAfh8PjQ2NmLixInIyMhAdnY2BgYGNNEPRoykA1efZStiKIT7FoDM9RUkI+CmmoGr7vNwOBzXxazHFgG3s7NTo94LKNgSQgghZCyjv7bRC7jp4CQhhBBCCEkV9Zrmww8/REZGBqqqqjB16lTb2k2H6ybRB714ZlbETDhww+EwzjvvPDz77LP4l3/5F5SVlcl5VbeyEObEPGbrrIrCqgNXLxYL16uRgUBt+9ixYxohU3XgAogScI0cuEKELioq0mwLsR5utxuTJk1CJBJBcXGx4Xqp7ev7OFzoBVzhwN28ebMctnPnTs00QsD1er2499578cMf/lA6lc1IRsBVbxSo+zYUCo2OgAsMdY6CLSGEEELGE+rFVyQSoQOXEEIIIeMSVZQSgl5vb6+tAm46oC84psfhcCAcDsvrPa/XC7fbjWAwiAkTJuCqq67CFVdcgf3798t5VAE3GAzC4/HEjRAwc+Dq83KFAGvkila3bSgUki5dITq73W4pGqoCrn7ZYn+Lwrwia1f0SRW9VVE4FmIZIxGhYCbgqq7b5uZmTXEzIeBmZWXhu9/9LtauXYtVq1ZFRVHYid6Bmyi2CLjPPfccBVtCCCGEjFvcbjcCgQAduIQQQggZ10yaNAmlpaX48MMPk87qNCMdrpviOXCBoYJigoyMDCnGulwuKfCWl5fLaVQBNxAIaATceA5c4b4V15d6cTSWA1eIgG63G4sWLUJ/fz8yMjKQk5MDp9OJjIwMKQj7/X7k5ORo5t2/fz8GBgakACoyfMX6CDeqcOAm4hgd7QiFtrY2NDc3AxgStAcHB3Hs2DFDARc4LpzbHaGgv1Egisolc27ZIuBed911djRDCCGEEJJWiIuvjIwMQwGXEEIIIWQ8MWHCBCly2eGeTAfRVsXMgasKbqqA6/V6pfNUCJjCjStQBVwhzMUT+/RF45IRcEUbhYWFqKio0BRWEzm4qgNX9N/hcOC9997D+vXrEQwGkZ+fD+B4hIIQNYWYKbZZugq4RkXMRGTC5MmTEQgEcPToUY04qwq4DofDkoBrB06nE6FQKKlzK7HABUIIIYSQExD14pkOXEIIIYSMN9RrGiH6RSIRWx+BT4frJjNnpIoQcIWLVe/ajefAVac1W2d1uw4ODloWcLu7u1FbWwu/369ZF724KqIOxPy/+tWvsGDBArS0tCASiWD9+vUAgCeffDIqA9dMwA2Hwzh69Kjh+uiJt/56QqEQamtrsWHDBs3fgQMH4s6rCu4AcPDgQaxYsQIAMHfuXHi9XgDQxCPoHbhiO8WKUEhGlDZy4ALJ3RyhgEsIIYQQYoLqwAWiBVxCCCGEkPGAmRiYqoCbrkXMrDhwVQEU0IpvZg5ccZ0YT+xTrydjOXDVa1AAePvtt3H48GF89NFHGmHZSMAV+b2C3t5e/OUvf4HD4UBBQYEcLgRcIWaK/+FwGKFQCJFIBJFIBN///vfx9NNPY+PGjYbrpF9+rPXXc+zYMRw+fBjHjh3T/G3fvj3utbfegfvSSy9JIX3x4sUy+9eKAzdWEbNEMFtvsZ9GLULBiPfffx8rV67Ehg0bsHfvXnR0dMDn8yE3NxdFRUWoqanBGWecgUsuuQSLFy8erm4QQgghhKSMmYCbDj9ECCGEEELsxE4BN90wiwMwEnBdLpcmdkCIoaFQSOPAVa8H9QKu2fZThycToeDz+aLEaJGvKtYvIyMjqr1jx44BGCpU1tXVBQBoa2sDEO3ABYYcqaKvr776KgBI9+7evXvR3d0t+zl79mwpZsdbfz3C+VpQUCAL53344YcAhrZ3rMJpegeuYOnSpfjmN7+JV155BYC5gAuMbAYukCYC7ptvvolvfOMbckMD2pXr6OhAR0cHDh48iH/+85/4wQ9+gFNOOQU//elPcd5559ndHUIIIYSQpBHXMOpjVePthwwhhBBCiCo4CReucGDa0W66YCVCQRTFcrlccLvdUfOEQiFNQTBVDLbqwFWzchOJUBB4PJ6oPF+RryqGZWdnS8FQcOzYMenOFezYsQPAcTFTCLnAkKAZDoc1178TJkxAfn4+du3apWk7KysLs2bNsrT+esT65efnY/LkyQCAbdu2WSr4JRy4hYWF6OzslMOffPJJ5OTkxI1QUMV54dw1ItYxYxU1hiPheVNeusJdd92Fj3/84/jwww/lTooX2ByJRLB582ZccMEFuPvuu+3sDiGEEEKILZg9VpVuP0oIIYQQQlJBFQOB8RuhYMWB63A4UF9fLwVEfX7pFVdcgblz52LBggWyHasCbqoOXDWbV3XgChwOB8rKyjSFzYDjDlxVqKyrqwNwXLh1uVzy2jcQCERl306fPh0TJ06U/SstLQWgdZUmKuCK/ojlAtaPwY6ODgDQxEJMmDAB8+fPB4C4EQrqNGJ4LEYrA9c2B+7Xv/51/OIXvwCgPeFPPfVULFy4EGVlZcjJyUFvby9aWlpQW1uLzZs3IxQKweFwIBKJ4Oc//znC4TB+9rOf2dUtQgghhJCk0TtwKeASQgghZDyiv6ZxuVwIBoMpO3BjLWM00LtWjVAfyW9vb0ckEomKUACAT37ykzjppJMMHbjxxEdV6O3v75cCZiwBVxVd4wm4IhdXdQoDx8Vio2JdIv7A5XLB4/EgEAhgcHAQbW1t+M53viOny8rKkuvl8XhQWFiI1tZWzf5Vt4nYfrEwWn+XyyUF5Fg0NjZq+g8AN954o1ymcOBaycC1IuDGI9ZxPuoZuG+++SZ+8YtfSCE2Ozsb9957L26++WZNLoie5uZmPPnkk3jggQfQ29uLSCSC//7v/8all16K888/346uEUIIIYSkjOpCIIQQQggZr9jtwE034hUxA4BDhw4BGBIQ8/PzMWfOHOTm5uLIkSMAgJ6eHmzfvl0TVyA4duwYXC5X3KfS1e3a1dUl21IdqIBWwNUbCfR5vqpoKqIwjLJju7u75TXtqaeeis2bNwM4LoA6nU54PB709vZi//79eOyxxzQOYDVSwel0GubdqtvEioAr2jdy4MYTO4XgXlZWhsbGRvh8PnzjG9+Q440iFMS2jEQi2L59u4xhiFXELFFXsTqPIJUMXFsiFP793/9dvp4xYwa2bNmC73znOzHFWwAoLy/Ht7/9bWzZsgUzZsyQAvC3vvUtO7pFCCGEEJIS4gJNfzFNCCGEEDKe0ItS4zVCIV4Rs+7ubmzfvl1OU1JSgpqaGkyYMEHzRNaBAwcM4xi6urqwc+dOmcUaLwNXzCMEVX1mrZmAGw6Ho8RoIwHX6BpWdeB+6lOfksP1DlwAePDBB+Hz+VBZWYnp06fLZRsJuOq6qsKllWPIKELBqtgptktubi5ef/11NDQ0aKIjYkUo9PT04MCBAzL3OJYD16qAa8WBOyoZuEeOHMEHH3wgQ5BfeeUVzJw5M6E2ZsyYgZdffllu1A8++EDe2SCEEEIIGS30EQpm4wkhhBBCxgP6x/FPtCJmwWAQH330EYAhR+eECRPkuIKCApSVlWHSpEmoqanBtGnTMGPGDHi9XpSVlWHu3LnIy8sDcFyQtOLADYfDUjg0c+AGAgHpDBbzxMvAdTqdhgJuIBCQ/Vu+fLkcLjQ5l8ulKXLmcDjwgx/8AJdeeqncRokIuFaOAaMMYKtip9h2Ho8H5eXlqKio0IwX69Lb24snn3wS+/bt08yj/rcjQkHFzgzclAXc9957T76+8sorMW/evKTamTdvHq666irDdgkhhBBCRhOPx2N4oZ9uP0oIIYQQQpJhuBy4sZYxGsSLUKirq8MHH3wAAJg8ebIU9oQgWlRUhKqqKsyZMwczZsxASUkJgKFrxZkzZ8on0YXbM14GriiiJRDLU987HA6Ew2E0NTXJ4aFQKMpNrM4rhFAjE4Iq4JaUlODrX/86Lr30UkyaNAmA1oEr+pifny/bStSBa2W/p+LAFds6IyMjqtgbcFzAffzxx3HzzTejpqYmSsAV62bFgRuPtM3AFRXsAGDZsmUptbVs2TL88Y9/BADNgUkIIYQQMhqoEQqnnHIK2tvbZbEJXqsQQgghZLxg5ua0M0JhtOnv70d9fT2A6AgFYMih+dnPfhbA0LVfYWGhoWgnhqnjRHsihkB14AYCAdTX18PtdqOqqkoKsgAwbdo0TJ8+HZFIBAUFBRrnKzAkLJ5yyino6OgAMKTB9fX1IRQKRe2zOXPmIDs7G9nZ2cjPz4fT6TQUcAcHB6WAmJ2djWuuuQatra0yS9bpdCI3N1dOn5OTg0gkIsVVVcB1uVyGYr+IcIhEIprhR48elXEFgtzc3JgCbrxj0KqAu3v3bjnMTMDt7e3FgQMHNMt0Op1S3AZSy8BVIzESJWUBV83gyM7OTqkt9c6Dmk1BCCGEEDKaOBwOTJo0SV687dmzB01NTWn1o4QQQgghxC5ScQqaMdrXTatWrTIsPAYM9a2trU2+P+WUU6QIKabXFyZT2xCvjQTcPXv24MCBAwCOP+YvBMKcnBzMnz8/Zr/Va9CysjJs2LDBMEIhLy8vqi0jAVcU7BLLV7cBMCScCmcxcFyrE22FQiG5Hc0cuKJfkUhEDu/p6ZHuZjOMIhTiHYMiz9dMwBUibWZmptQwGxoaABwXd4Vw7PP5ZAayis/nk9OmkoE7qgKuWqhsz549KbW1d+9e+bqsrCyltsjYoK2tTX54ZGRkYOLEiYZ3wgghhJDRwOwCzOojVIQQQgghY4ETwYGrCoFGRcyEA3XChAmoqalBIBAwLMJmVDQslgNXFYYHBwej3J2JoLpd4+X5mmXgtrS0yNder1e2o7pqi4qK5DTCgSuEUCsRCmq/xHDVKTtx4kQAQGtrqya2QO2v1RgP0a7H44npwFX3/+bNm+U8mZmZUYXOSktLkZWVBZ/Ph46ODgwODka5o80wi5IAUhNwU1bKZs+eLV8///zzUvlOlMHBQTz//PPy/Zw5c1LtGklz+vv78d5776G2tha1tbXYtGmTfJyBEEIIGQuk048SQgghhBC7GI8OXBUjB64QcLOzsw1jEvTDYkUoCG0sEAigu7tbsxxVkFQLj1lBzYWNJ+AC0UXRgCHR1GicKsoWFxfL4UYOXHVase5mOcpiuDiWcnJysGjRIixatAiVlZWaeVQHrt0ZuEJUB4Dm5mYAQwKu1+uVyxVtTZ8+HYsWLUJVVZWcx0yoTgSxzUdFwF26dClKS0sBAEeOHMHXvva1pNr52te+hrq6OgBDSvfSpUtT7RpJc8SJ4XK5ZPyG3RX/CCGEkFQwuzCmA5cQQggh4wkzd+l4cuCqGF3LxRNwY7WhF3CBobzaQ4cOabZBOByW4qsa0WAVVdTUFzEz6p9RhIJwm2ZlZWkiDlQHrirgCr3GKANXdeDqjxW94GnUX9Xpq+bpqtPFOwaFWO52u2NGKBjh9Xrh9Xrlug0ODiISichlpyra6vev2H+qmGyVlAVch8OB22+/Xa7M448/jmuuuUbmScSjoaEB//Iv/4LHH39c0x5/GJ04eL1eaZ+3s8IlIYQQYhdm1yXp+qOEEEIIISQZ7I5QUEmn6yY7HLhGYqPT6ZSOz8OHD2P//v1RyzHKj7VKIhEKDofD0IErRE4h4Ir2BHoBNycnRyMG68XjeBEKYlqx3qrruLCwUL7W99WqAzdeBq5R9EFlZSXmz5+PBQsWaARcYGj7iGUb7f9UMnBH1YELAPfccw9qamrk+xdffBE1NTW4+uqr8eSTT2L9+vXYv38/jh49iv3792P9+vV44okncPXVV6OmpgYvvfSSXMFZs2bhnnvusaNbJM1RD2q77u4RQgghdhIvAzedfogQQgghhCSL2ePvqUYopOu1kpFr1UjAVYlVxExtb/78+SgpKUFBQQGKi4sxceJEWSxMRCgIB26y/bYi4ALGEQoi0iEzM1O2o+4nvYCblZUFl8ulEfVVMdZqBq7q8FXbnjNnDsrLyzFv3jzTdY2FEHDjZeAKvv/976OhoQHPPfccCgsLowTcbdu2GQr8yWBnBm7KRcyAoZ3++uuv42Mf+xjq6urgcDjg9/vx8ssv4+WXX445r3rQTpkyBa+//rrlYGAytlE/bKzeWSGEEEJGAz4ZRAghhJATiRPdgWs2nTrMyJ0JAJMmTcLChQtRX1+PefPmYcaMGdi6dSt6e3s1DtzRilDo6ekBMKTlCfFW78BVow2Ey1a0pY9QMMvA1Qu4qvNYRTWEmq2rGZFIJK4DVx+hIDJ9RYyB1+vV9Om+++7DtGnT8LnPfS7m+sTqkxmjWsRMMHnyZGzatAnXXnttVGfFAaH+6cdfd911+OCDDzQBweTEgQ5cQggh6QgduIQQQgg5ETDLwD1RHLiJCLjxHLjqONW1293djZaWFrS1tRnOYwUhaqqiaywR2KhImqhH5PV6DfNp9dELBQUFUcY7Kxm4ep0nnuCsx4pOJNYFsB6hIARcMa3b7ZZ/ghdeeAGAdj9aFdtjOaPTQsAFgJKSEvzxj3/Epk2bcNNNN6GiosL0ZI1EIqisrMTNN9+MTZs24bnnnkNJSYmd3SFjBIfDQQGXEEJIWkMHLiGEEEJOBIaziFk6iblG13aiqHpWVpahyKgX5hIRcPft24ddu3Zh69at2LFjBwBt/IBV1OUIETBWBq4RBw8eBDCUP6saLYVIK+Z7+OGHccstt2DSpEkAYOrAtRqhYJSBGwsrDlyxz4DjEQr6fpgJuMKBm5GRESVa5+XlGa6D/nWiqDnCibZjS4SCnlNOOQVPPPEEAKCxsRF79+5FR0cHent7kZubi6KiIsycOROVlZXDsXgyRtBnrACMUCCEEJJe0IFLCCGEkBMB/TXNeP+NbhSh0NvbC2DIgSvEPaN5xLYyKmKmfy+m9fv9AIZiCwoKCuB0OmUx90RQl6NGMZgRa9wll1yicceq5jpgSNubPn06PvjgAwBa8dGoEJvVDNxAIGDJ0WrlJoIQcFWHcFtbm2Y9RMSCQC/gut1uuFwujQM3Pz8/almJXv/HcuACQwK8UUaxGcMi4KpUVlZSqCWGqHev6MAlhBCSjlgpDkEIIYQQMtYxi1AYrw7cWEXMcnJy0NXVBcB6Bq6ZA1e//WbMmIFzzjkH7e3tePfddxPut9BPwuFw0g5cAJg+fTrmzp0blYGrF2SNjHdqETM1A1e/rkYO3I6ODhw5cgThcBiLFy+Oua6JOHBVYXTdunWaaYTjWKCPUMjIyIjaf3oHbiLEOs7F9hL7L60EXEKsQAGXEEJIOqO/eKMDlxBCCCHjmeEoYpZOGDlw1QiF7u7uhNqId62oz6sVrtBkBEK9gBsrU9as/cLCQsP+GRU9ExhFKKiRC/rrYqMM3KNHjyI/Px9NTU3mK2gyvxGqgJuTk4OcnJyoafQiqRBwhRDt8/kQCAQ0ruvMzEzNPKpjOBUHrujr4OBgwjm4FHDJqKHe4RvvXw6EEELGJvEiFAghhBBCxhPDWcQsnW58Gwm4RkXMrLZhpYiZGO7z+bBx48bkOo4h0TQYDMZ14Mba3iIiQLhsQ6GQjBEwEypVR2wyEQqi+JnVImaxHLh9fX248cYbUVFRAWBIFJ06dSpWrFgRNe369es171UBd9u2bRgcHMSBAwc0UQv6bZvIsRtv2rQUcBsaGvDBBx9gz5496OjogM/nkxm4s2bNwuLFixmvQADY9+VACCGEjCTp9EOEEEIIISRZzMS6cDiMgYEBhEIheDyemA7NRJcxmsSKUMjOzpbjY920TyQDV3Xg1tXVyemSdeAC8TNwY21vEREg+qUKuEbojXfJFDETjlerAm4snejhhx/GH//4R/leFCIzwsyBGwwGMTAwIJelGgrF8FjrY0a8CDaxjX0+HwoKCmK2pZnP8pQWGRwcxJNPPon//d//xdatW+NOf/LJJ+Omm27CTTfdBI/HY3d3SBrDDFxCCCHpjtkFGB24hBBCCBlPmGXgtre345///CeAIeFpxYoV8Hq9CbebbsRy4GZlZVkqDJZMBq7RshNF70w1E0RFYTIjhICrOnABmDpwI5GIYYSCmoFrFqGQqgPXaBvqHcyxBFy91qjPwFX7KhBF54YjNk1sx61bt2LPnj04/fTTLc1nbatZZM2aNTjppJNw5513YuvWrVGBx/rXkUgEW7duxR133IGFCxdi7dq1dnaHjBEo4BJCCEl3knE2EEIIIYSMVYxEtmAwiN7e3lHojf3o1y8cDss81czMTEORVpCIgKt34KqOVbP2E+27WRtClBUxA8uXL5fjjCIUHA5HlANXXVcxzixCIV4RM+HAtbrOqgN3YGBA/oXD4agMXVV41mPmwFUdzPptmooDVz+9nqqqKtmnvr4+y1EKtjlw//SnP+Hzn/88AoGAoepeUFCA7Oxs9PX1oaurK8oCvWfPHlx44YX43e9+h2uuucaubpE0xqiaISMUCCGEpAuxLs7owCWEEELIeELvwBW/0c2mS7TdZOYdTvTXcmoBK6/XG/Naz+gJrXhFzNR1t8uBG6894cD97Gc/i/POOw99fX1YvXo1AMhH99UCY4C5A1eME9MmEqGgxjQk48Dt7++XLnBgSJA9cOCAZtpkIhTEPlcLsQmEgKuum9X9Fu84nzJlCiZPnoxXX33V0vQCWxy4W7duxec+9zlN4O+8efPw05/+FB9++CH6+vrQ2tqKuro6tLa2oq+vDx9++CF++tOfYv78+XJDDA4O4rOf/Sxqa2vt6BYZI9CBSwghJN2hA5cQQgghJwL6CAU9if5mT9drJTPBDtCKgbEcuFYycMPhsGYb2OHAFY7aeG2IfeX1enHWWWdpirOpDlwAGgeuWXtGRcxU8dNKBi5gfmzpyc3Nlf1UaWlpQWtrq2ZYrGxmswgFsQ6q41pg5MAVpOrAjTfODFscuLfeeisCgYAU4h566CHcfvvtpjslIyMDJ510Ek466STcdddd+J//+R/cfffdCIVCCAQCuPXWW/HOO+/Y0TWSxug/xAAKuIQQQtIHKw7cdP1RQgghhBCSCGZFzOJNl8oyRpNYj8xbLdRmNULBLFogWWbMmIH6+np0d3cbLlugX25mZqZ8rY9QEI/xq+tulmk7MDCAvr4+uFyumBm4+uvleJm9epxOJ84991zNsHfffRf19fVR07rd7pgapIo+A1cUrTv//PPx5ptvAkgtAzcVl3osUnbg7t69G+vXr5cr9fTTT+POO++0vEMcDgfuuOMOPP3007LT69atw549e1LtGhkj6KsZEkIIIekGi5gRQggh5EQgngM3nUTYVDBzXAqxL5YD16iNeAKuWXvJbk9VlIznwBXLUQVcEaEgBFyjCAWzZapxE7EycPVGPfE/letot9utWb463Ay9A1dsByHgejweuFwulJSU4NprrwUQO0Ih3j4zK4KsJ1FxOGUB9/3335evly5dis9//vNJtfO5z30Oy5Ytk+/Xr1+fatdImmPmwB0vXwiEEELGNla+j/idRQghhJDxgJnbMt50ibY7mtdO6jrpxTXhuBRin5g2WQFXdaWKeAIjETdZE5tVAVcVCc0cuMDxCAW1XX1ur5HxzkoGrpEDN1kRNyMjw7DoV6wM3JycHM17sW9EOxkZGVIAFuvo9/uxZs0abNu2DZ2dnVi/fj3WrFmD7du3Y82aNdi0aVNS/VdJdBukLOCqld8uu+yylNpS59dXlCPjD/WuhPphRxcuIYSQdIOOW0IIIYScCMQrYjaWM3BjXc+Jmk4ZGRmWC1bFysA1cuA6HI6otpPVP1THqRUHrn4eIeAKE51Rpq1KJBKR8+sL0ieTgWt2fMUjlgPXbDt4vV6UlJREDRfrrAq4Yj/6fD50dXWhsbERnZ2dCIVCGBgYQGNjIwCgsbEx7rFt9++HlAVcYbsGgAkTJqTUljq/2i4Z/6gnLwVcQggh6QAzcAkhhBByohDPgWvXtU+6OHD1/RCPzOuFQCMnspGT1ixuKxwOS3er0+lEOBzWaB52OHBjZeCq+024jIHEHbjAcQFY7bNqyDM7howcuFZzhvVkZGQYCrhFRUUxBdOTTz45aphw4EYiEdTX16OpqQnt7e0AgP7+fjmd6L9+/cT66BmuDNyUi5hNmTJFvj527FhKbanzq+2S8YnqwFXvRoVCoaiQaUIIIWQ0YQYuIYQQQk4EzDJwXS4XgsHgsIlTI4EVAdfj8Wiu84z6bzQsEQeuKoCaiYDxSMaBe9JJJ6GsrAwTJ06UmovIwBX9UAXsWBEKYpzH49FEGqgOY307QnhNxYGrRigsWrQI//3f/42VK1fGjGVwOBy4/vrrsWHDBqxYsUIOF+309fVJcV04sfv6+uS8oVBI7jd1mwSDQUMhOm0zcJcvXy7dsn/7299SakvMX1BQgPPOOy/VrpExgv4Lgg5cQggh6QAzcAkhhBByomD0+LsqSho9Pm/HclT27t2L9957L2lRM5V+NDQ0yOGq8Hbo0CH09PRg48aNOHjwoGkbZjf7VXHU6XRKwdSsH1ZJtohZXV0dtmzZYlpgLJYzVn8MLF++XJPtq1+m6kKORCJSMFXF4ERRHbiZmZk455xzMG/evLi5umVlZdixYwdefPFFOUyNjcjLy8PEiRORlZUF4HikBnBc6BXTi/U3yuJNhBHPwM3MzMTNN9+MSCSC9evX4/e//31S7fz+97/HunXr4HA4cMstt8Dr9abaNZLmmNnrKeASQghJN+jAJYQQQsh4xsg1qAq4QnAbTgfurl270NbWJnNG7SaWcNrc3CxfDw4OymJmDocDO3fuRFNTkxTwcnNzAUDqVkaOUlXfCIVCcDqdUsC1O0LB7Lp07ty5ALRxpZmZmVG5tVYcuACiipiJ92aOZb0LWRWyk8XtdkvhVOyjeI5XMTwrK0uzn9QiZi6XS+4jQCvgqg5cFTMB16oDVz99PFKOUACA+++/H2vXrsX69etx4403orOzE7fddpvlzv7yl7/E17/+dQDAsmXLcP/999vRLTLGcLlcCAQCFHAJIYSkBbFcFUbTEEIIIYSMJ4wcuKn+Xrdy7TQSN8r1/RCuTrfbjUgkgoKCAvT29gI4HveZn5+PCRMmYM6cOQCGts8nP/lJwz7rxUsxvd6BO5wZuBMmTMCSJUswMDAQs8DYoUOH5DBVIFVRi5ip66O2JabT92vv3r3o7u7Gvn375PBUnMdiXwkBPZZgKhzHvb29CAaDMkEgEonInFsh3orXQLQDV41SGLMOXGBoA/7zn//EZz/7WQQCAfzrv/4rFi5ciJ/97GfYvn17lP09FAph+/bt+NnPfoaFCxfijjvuQDAYxOc//3n885//TNpKTcYW+pNMnDAj8bgEIYQQEg8rRcwIIYQQQsYTZg7cZCMU9NObza8OHy5NSF1GWVmZZpxewI1EIsjNzdVsj/z8fGRnZ0f11ai/Zhm4+iJmyWIlAxc47lLVozqEhUAt1sUsA9dMxDfLFhZOZQDYsmUL+vr6NO0ng9vtlvtK78A14uDBg/D7/RgYGEBvb6/88/l8Mi4jOztbroPegWsk3ArGpAN3+vTp8rUI/t2+fTv+7d/+DcDQQVBYWIisrCz09/ejs7MzKjvC5XJhzZo1WLBgQdzlORwO7N+/346ukzSAGbiEEELGKnTgEkIIIWSsY/bUkfraLgHXDNXIFSuH1Q7OPPNMmXUqEIKdEHCB43EDgry8PMvLEPqGiCcQmcJGj+Ing5UIBRV1P/j9ftTW1mL//v1obm6WQuTZZ58d1Z76Wo3RULOCBwYGsHHjRixatEizbpMnT8bBgwfR3d0tlzFv3ryo/iSCWsRMH71qtB3EtNOmTcOSJUtQWloKYEh36u3thdfrhcfjiXIxC5EYGDo2XS5XlNlwpB24tpwVhw4dMtzBqq24tbXVcF411Pjw4cNRgdF64o0nYwdm4BJCCElnYt09T7RqLCGEEEJIumLlesauCAUzVDEslYzUWKjCrB69AxcYut4rLi6WztG8vDzDOAIjVK1LOHBdLpehkzMZrDpwja5Zjx49is7OTvT19aGnp0fqbHqBWt9PvYjf39+PRx99FD/72c9QX1+PL37xi7j44os18xQWFmoE3FTd1YlGKIjl5uTkoLCwEMXFxQCG9otYX4fDIY8J9clwsd9E++MiAzeRBVqZjz+GTizEQS1O5C1btuDCCy+kUE8IISQt4PcRIYQQQk50ki1ipsds/lTdjIlgdG1nFKHgcDhw6qmnIhgMIicnBxs2bMDAwEBCy1AduHZGKKhCaKLXqn6/H06nE0VFRZgzZw5aW1uRmZkpRUyz9lTROBwO4/LLL8fatWvlsNdee8208Jl+/yZ7HKkZvR0dHXj33Xdx5MgR036L/Fq9cKxffn5+PiZPnmxY5EwQDoc1Arzq0k2GRA0htgi4v/71r+1ohpxg6A/S7OxsdHR0YGBgAD09PcjPzx+lnhFCCCHWMnB505kQQggh4wkz8S7Zax+rGbiqWDZc11ex2hURCnqhOiMjA0VFRQkvyywD164IBTU+IJar1Wi/CQE3JycHubm5UojUO59VsVLNwAWAAwcOaMRbYChe1exJa7F/UzVGqA7pSCSC9vZ2OU7ftiqWC/ezum4qwoWrbldVoBVCvIpZ/aZEHbhWsUXA/cIXvmBHM+QERRzUCxcuRENDAwDGKBBCCCGEEEIIISOBWQauiprpmmzbsRhJATeWAzcjI0PqEUJ0FSQiYusduABkBq4d6+d0OnHBBRcgEokkHDnh9/vhcDjg9XoNxVuzY0AVipubm6PG9/X1RWk5djtwAWDixIkAgMrKSlRWVmLLli2G/VZFY72Aa4aaLRwIBOR7VXi324FrleEJFiHEAkZ5KvogcUIIIWS0YAYuIYQQQk4EEhUkh2NZ6RihYDatFdQaP+FwGE6nU4qIdhnWsrKykJ2dHXMao/3W398Pp9OpyZM1cvHqa1AZCbhXXHEF/vCHPwCAzNNVEaK1mVs1GcQyiouLkZ+fb7qvxDHldDqjxpkdh2I/AdECrdhvwok8ZjNwCUkUo4OaP4gJIYSkG7EEXEIIIYSQ8US8CIVExcdkHLjDRay+qA5cNQM30XYEYt7BwUE0NjbKYSJHdaQwWgcRoeDxeOI6cNW+qhEKQsCtqqrC5MmTAQA+n09uux07dmBgYEAKuGL/6guhJYPIIfZ4PDGzgPXLtILD4YDH40F/fz8CgYCmTXHsezweTfvJMioZuITYBX8QE0IISResXEzxhiMhhBBCxjrp5sAdjesrkYGrPkIPGBvOrCBEvnA4jN7eXikM2unATQSxTYPBIEKhkHTgqhmx8VCnaWlpATAk4BYXFwMAjh49ikcffRTz5s0zLRrmcrlSFrHFvvJ6vVIgBswFXKMCfEYZuOJ/LAduJBKJK+BadeAmqn9RwCWjRqyDmj+ICSGEpAt04BJCCCEnLvv27UNvby+AIXFv5syZUsAZjwx3ETMzRjMDNxwOW8rATaR/mZmZOPPMM+Hz+QAMiY47duwYcQFX7X9jY6N0A+vdq1YcuOo0woFbXV0tBVwA+Pa3vw0AePbZZ5GXlycLtwncbrcUYJNFOHCFgGuGVdevXqQX20XtpxoDIc7/7u5ubN26FR6PBzU1NVHiv1XowCVjEv4gJoQQki7QgUsIIYSc2HR3d2Pnzp2aYVlZWZg2bdoo9Wh4MLueMRLvUhUf0zEDNxQKyeWrRavMiphZpaSkBCUlJQCGcmd37dplWxGzRIlEIvjwww/leubm5mq2uZVCaGJ7RCIRjQO3qKgoatrW1lbk5eVFDTdywyaKEFatRiiY5fuqrxNx4GZmZsp+HDlyBACQk5ODKVOmaNq224HLImZk1KADlxBCyFiADlxCCCHkxEQIQB6PBwUFBQBgazGmdGS8OnBjtTk4OKgRcFWR2i69Qt1+o+HAVXNoZ8+ejVNOOUUznZkQalSUDDjugq2qqjJ0pHd1dcl2zXJ0k0V14CaSgWu031ThFtAWMRscHNRsP3Hu5+fn47TTTsPcuXNRWFgop00WOnDJmCGVO1qEEELIcEEHLiGEEHJiI77nhYDb1dU1Lr/70yEDV+92HE70ukMgENCIfWof7dIr1CJwqoAr3JzDjXrjYcaMGVIEFVjJwBXTibaysrJQXV1tOJ0QcI3mB+xz4CYSoaASKw9XTO/xeAyLmDmdTlRWVgIYEpM7OzsNb0AwA5eMG2KdsOPxS5EQQsjYItbFl10/YgghhBCSvqiCzYlALEFJbIOGhgZ4vd6Y7bjdbkyePBler1c+bu/3+wEAu3btQnZ2dtQ8nZ2d8vVIO3D1Au5wuKyNhMDq6mrMmTPH9mUZLVcfl6A/pq1k4OqnO+ecc0zzoIWAqxYZA+x34Krt69dJ3BSIJRqr65qXl4cZM2YgJycHADTrpjqnVbFbrI9RBIjdBkUKuGTUMPphzB/EhBBC0g0+HUIIIYScmKi/Wcfzb1WzdSovL0ddXR28Xq/GQbpv3764bYZCIcyZMwc+nw8HDx6Uw/fv34+srCx7Op4kRg5cIfapDlyzDNxUIhSA427YqVOnjpgDV70Zoea8CmJFEaioIun5559vOp0QcPVtxSsoZgUh4OozcPUkkoELDMVnTJkyRWb3qq5w1TWtbgMjAdfqutGBSwghhBBiA7EuvsbzjzhCCCGEDHGiO3Dnz5+P3NxcVFZW4tixY3K4y+WSBZv0dHZ2or29XT7mrrogy8rKMG3aNEMHLgAcOHAAwOg4cIVDODMzM24GbjKox5AQcEfiuFKFd3WZsRy4Kvrtpoqh55xzjnx9zz334He/+x2WLVuGF198UZOBazZ/sohjS2Tg6h244XAYR44cQXt7OwBj0VhvKFQzcIXzVs3AVY8Jqw5cqzADl4wZ6MAlhBAy1qArlxBCCBn/nOgOXLfbjRkzZgDQXvtkZGRg/vz5hvPs378f7e3tUqQUwpfX68XkyZMxZ84c6XDU093djdbW1mHfxkYOXOHqzMzMHDEH7kheT+pFYzscuBMmTJCvr7zySpx99tnYvn27RsB1OBya7WlHBq4+QkHf72PHjqG2tlYON4t5EKj90wu4AnX7GUVCpJKBa3VbnBi3kUhaMh6/+AghhIwfrFx88buMEEIIGb/EcuBGIhEcPXoUdXV1I1qAaziwcs2jboNYLkrVBQmMrNs0FrGu2Xp6emR/VQHXToxyZUfSgWsUJ6AuP15fjJyohYWFUeNF+62trVi7di06Ojo07djpwG1ra9MUYxN9EG7qnJwc1NTUoKysDIA1By4AQwE3HA5LgTeegKvvj11QwCWjBjNwCSGEjAXotiWEEEJOTGI5cNvb2/HBBx/gww8/xJ49e0atjyOFej0US4QT4/QOXCu/9Ufqmku/nLa2Nvk6IyPDNELBLgdurGHDhZForO7HeA5cMb8QRwGgoKAgan6x3/fv34+f/vSneOqppwwFz1Q0H9GHhoYGfPTRR1HjhZhaUlKCOXPmICMjw3LbTqdTFunz+/2mERQCsT7qTZxEM3DpwB1mQqEQtm3bhmeeeQZ33HEHli1bhuzsbPnBfsMNNyTc5r59+/CNb3wDCxYsQEFBAXJzczF79mzcdttt2Lp1a0JtDQwM4Fe/+hVWrFiBiooKeL1eVFVV4ZJLLsHvf/97zQdSOsEfyYQQQtIFZuASQgghJzb6wk8qqjtPdQGORaw4cNVxsdyadjhwRzoDt7OzEwCkpjMcGbhA9DaIRCI4duyY3EbDgV0OXIEqVKptzZo1C9XV1dLtKlAL2NkdoeB2uzXnnmhTrKsQV/Xj1ddqxAMwtB2E4Nvf3y+HCweu/uaFWIbRPrRb32IGbpJce+21ePHFF21r74knnsDXvvY1zQECAHv27MGePXvw+OOP43vf+x6+973vxW1r165duPrqq7Fjxw7N8IaGBjQ0NGDlypV4/PHH8cILL2gyS0aaWF8S/EFMCCFktIn1PcUbjoQQQsj4x+haQAwzEoPGOnYIuGYOXDFPOtwg16+nKHaVmZmpWb7dEQf65W7fvh3Hjh3D5MmTcfLJJ9u6LP0yxX4wc93qhUkhbFo1/xUUFGDRokUIhUJYunQp1q9fDwDIzc2NyphNhXXr1qGurg7AkFs6FApFnZNCZBbiaiLHlVkRs2AwCLfbHSX40oE7BtCr68XFxaipqUmqrd///ve45ZZb0N/fD6fTic985jN4+umn8Zvf/AY333wzvF4vQqEQ7rvvPjzwwAMx2zp69CguuugiKd4uXLgQDz30EJ577jn88Ic/xPTp0wEA77zzDi655BL09vYm1Wc7MXokgRBCCBkLjJcfbIQQQgiJJl4GrtHrsYhVYUuQSAZurG04kqjrqOoO4XAYhw8fBjAk4HZ3d0tXp16fSFVg1rfX0NAAAFKQHE6MnNDJZODGo7CwEN/61rdwzz33ABgSPq3Gb8QjEAjgzDPPlO/dbrdGYBavhQM3VnSC3oErnqaPRCIaIX/KlCkoKipCXl5ezAxcVUi2SqL6lyUH7po1axJq1A4+9rGPjfgyE2HJkiWYO3cuTjvtNJx22mmYNm0annnmGXzxi19MqJ2WlhbcdtttAIZOmJdeegmXX365HP/5z38eX/ziF3H++eejr68P3/nOd3DllVdi9uzZhu3ddddd8uT/9Kc/jd/97nca2/idd96JSy+9FG+//TY2bdqEH//4x/jBD36Q6OrbQqyDe6x/ARJCCBk/0IFLCCGEnJjEysA1mm48k2wGrvifSAbuSEYo7N+/H93d3XL5TU1N6O/vH5anlc0E4eEkVoarFQduovtCFDZTnanqeqaSgfvGG29o3ou84nA4rBFW9REKsY4rtYiZWF8h4Pb39+Okk05CY2Mjenp6EAgEoraTKhIHg0FkZGRYiiRRsbotLAm4y5cvH9EfKg6Hw7CCWzrxrW99y5Z2HnzwQflhcdttt2nEW8HSpUvxgx/8AHfffTeCwSD+4z/+A3/4wx+iptuxYweef/55AEBFRQWefPLJqMyP3NxcPPvss5g5cyb8fj8efvhh3H333ZrqgaMJfxATQghJF6xcTJ0IP9gIIYSQExWjDFyjCIV0rTFjleHMwE0mQmEk8fv98Pl8AI5HKAj065mqwGzW3kiQagaufp1FzICekpISzJw5E/v375fLFW2nmoH7pz/9SfNe6F1CwNU7cPUCrtn66I9/UcRMjTiNlZkrhoVCIY2gG2//Jrr/bfWwC7Xa7M/KNMko/GMZIbgCwNe//nXT6b785S8jJycHAPDXv/41KitXtCW23c0334zc3FzDtiZNmoRrr70WANDX14dXXnkl6f6nAjNwCSGEpDPMwCWEEEJObKw66cbL71erAm4yDtx0KWKmX8dwOCyjJfUZuHZf7+nbG4lYiVQycAHzbZGVlWW6zLlz56K6uhrAkAPXavxGPPRREyJfV5+3bKWImcBoHwsB1+/3x3QwC/Q5uIkev7Zn4FoVXcWjBVZFuVjTj3d27Nghs1bmzp2LadOmmU6bl5eHc845BwDQ29uLt99+O2qaf/zjH/L1Jz/5yZjLVser840kg4OD6O7uRldXFzo7O2XuCDB+vgAJIYSMb/h9RQghhIxfrDpwx/r1QKIZuIk4cPUC7mhvK7329H//93/485//DOC4cGcmWtqZgTvSmcCpZuDq1zk7Ozvm9MKhqwq4qgPXqM14+P1+AMD3vvc9/OxnP5MisjjWzCIUjDBbthqh8Prrr0vTpdh+Lpcral6xHJ/Ph/7+frn8UXHgikyJWH+BQADf/va3pXW4pKQEt912G1555RWZKRIMBtHd3Y39+/fjlVdewW233YbS0lJEIhE4nU5897vfRSAQiCoQNl756KOP5OvFixfHnV6dRp0XGDrItm/fDmDogDrllFOSbmskCIfD2LBhA3bt2oUtW7Zg7dq12Ldv34j3gxBCCDHDigN3tH+EEEIIIWT4sPrU6Hi5HhgOB24ibtbRyMBVC8ULAVffH7tIRDC1i1QduGbvkxFwjZaTCKK43EknnYQZM2bI4fq4jkQzcPVmQjUe4nOf+xxCoRDC4bDULvWI2IQPPvgAb7zxhtTmrGK7AzceV199NX70ox8hHA7ja1/7Gg4cOIBHHnkEl112GaZNm4bc3Fw4nU7k5uZi2rRpuOyyy/DII4/gwIEDuOuuuxAKhXD//ffjmmuusatLac/u3bvl61juW6Np1HkB4MiRI+jr6wMAVFVVxay2BwDV1dXyxNm7d++If+EEAgFpLxcnR19f3wnpxCaEEDL24PcVIYQQMv6J5cA1mm6sYkUPSDYDNxEH7nBfX/X19cHn86Gzs1P+qcsUwl88B26yTJ8+HXl5ecjLy7OkASVDOBxGZ2en1FsERg7cKVOmoLCwEBMmTEBxcbFhe2b7K1aEAnBc1AwEApg6dSqys7NRXl4uo0FjtW2GEHBVvctKhEKisZ16Ib+lpUXTjn7e6dOnW18JhURvWFgqYhaPn/zkJ/jrX/8Kh8OB//qv/8K9995red6cnBw8+OCDmDBhAu6991688sorePDBB/Fv//ZvdnQtrens7JSvS0tL405fUlJiOG8ybWVkZCA/Px8dHR0IBALo7e01zcwFhk4UcbIAkIXXkkXcvXA4HKiursbg4OC4vINJCCFk7GLFMcLvK0IIIWT8Qgeu8TgrDtxIJCKf2I7Xtp7h2J779+/Htm3b4HQ6pXs0GAxqtJQ5c+ZgYGAg7vKT7d/UqVMxdepUAENxAHv37tW0aYeAvWHDBrS0tMDj8eDCCy+UbaoRAIKysjKUlZUZthNPXCwvL4/ZDxFDEAwGMWnSJCxYsABZWVkpraOIUMjMzMTg4KAcrkYoqIJurAxcfVEydX31Au6RI0cM2xBUV1dj165dsn8C2x3cqTYQDAbx8MMPw+Fw4KSTTkpIvFX5xje+gZNPPhmRSAQPPvjgCRGjICodAtHVDo1Q73D09PSk1Fa89vT813/9FwoKCuSfCKROFtV+rt4B4iOphBBC0g26bQkhhJATE2bgHifRDFxgaPvphUMrDtzh2J5C83C73cjKykJWVhZaW1sRDoeRkZGBn/zkJygqKtIsfzhjDlQBEjjuGk0VYbYbHByUT2kDsYtwJcJXv/pVVFdX46tf/WrM6VQHrtmNkGQduGrEAaCNUFC3o5UIBSOMBFy94KvH6KbGqGTgxuLtt99Gc3MzgKEYhVQQ87e0tGD16tWpdo3YyL//+7+jq6tL/ql3IJLByL4/1r/0CCGEjC9G6wcGIYQQQtIDfbF2dZjRdGMVK08dWXXg6gVcu4TDVBEaRFVVFS644AJccMEFsm9lZWWm+ahG7+3Y3/qIA/17O9r1+/0xHbixMBNbzzzzTHzhC18wjVwQCBFUL+CmYoyIJ+C63W4p4OrNgnqMHLjitZGAG6/fqRzfI5aBe/DgQfm6qqoqpbYmTZpk2O54RY0s0Futjejv75ev8/LyUmorXnt6vF4v8vPzNX+pEAqFohy4ets6IYQQkg7EKmJGCCGEkPGL1QiFsZ6BawWrGbgOh0OOD4VCUji08lt/OPUAvZAcCATwy1/+EgAwceJE+fi92o/huN6LRCLo7+9Hb2+vZrgdDlxVMAe0Am6iQrp+XySaByxE1mAwaLo/E93PQutSBdZIJIKJEyciIyMDM2fOlNtVjU9IdD/qBdz6+vq4/U7FgTtiGbhtbW3ydWtra0ptqfOr7Y5XCgsL5Wsr207dJuq8ybQVDAaltT4jI0MTJD0SmOXg8AcxIYSQdMHqxZRdmWWEEEIISS9U0Usv0o7HCAU7HLhivBATrbQ9EuifAn7hhRdQW1uLrKwsnH322YYi/HA4cN95552omkaAPQ5cfRuquS8UCsHlcll24JqRTAyB6JddDly9wCrE73Xr1sn9q8+/BWJn4Kqv9e2r+0sV+lWSceCOeITChAkT5Ot//OMfKbWlzh8vEHk8MHv2bPnaiuNYnUadFxgKTRZB3PX19XFP/rq6OvkBVlNTM+IfpvGqeY71L0BCCCHjBzpwCSGEkBOTEyVCwQpWM3DV8aoDd7QzcPX9ePbZZwEMRQLk5eVZEnDt6IMQA51Op0Zk9Pv9CAQC8i8ZR24sATdVB67ZeDPUmAMhvKaSgRuJRGQ7Il8XGKoBJc5Pl8sFp9MJl8ulecI/0eNKH9HQ3d0d90ZEMg5cwYg5cE877TS5wLfffhsvvfQSrrrqqoTbeemllzS5t6effnqqXUt7TjrpJPl648aNcadXp1mwYIFmnMPhwPz587Fx40aEQiFs2bIFS5YsSaqtkcAoAxfgD2JCCCHpAx24hBBCyImNKnrpi5eNpwgFux244ne++ki/lWul4byeUvvR0dGB1157DQCwaNEihEIhzT40i1BIVWBWnaiXXHIJAGD9+vVoaWnB5s2bo5Z1yimnaITIeOhF3/7+fukmTTQD1wyrjmpVBFWF5WT3sVr0LTMzEwCQk5ODs88+W673JZdckrBoqmbgCnetaF/Q39+PYDBoWsAMGCMO3JNOOgknn3yyXJHrr78ezzzzTEJt/Pa3v8X1118vVfNFixZpxM3xyrx58zB58mQAwM6dO3Ho0CHTaX0+H9auXQsAyM7Oxrnnnhs1zSc+8Qn5Op4beuXKlfL1Jz/5yUS6bQtmGbiCE+EOJiGEkPQm1gUqBVtCCCFk/COuBYzEmfH4+9WqgBtPrBIioerANfrdb8ZwO3B37tyJYDCIqqoqlJWVRcU9COy83lMdpKq4OXHiRNOM5URjSo0cuJFIJKlicvrs3ETRu4vVNgWJ7Gex7YDjDlyrkZyxnvo2Euv1EQrA8TxhMxF/JBy4tpQB/OUvfylXxO/348Ybb8SSJUvw+OOP48CBA4bzHDhwAI8//jiWLl2KL37xi/LAcjqdePTRR+3o1pjguuuuk68ffvhh0+meeOIJGcZ8+eWXy7gEs7Yef/zxqFBsQUNDA/70pz8BALKysnDFFVck1fdUUO9+qR8gLGJGCCFkrMHvLEIIIWR8ov5uHc8RClb6n4iAa+TATcahaCeqgFtXVwcAmDx5shRujbaBXQLu0aNHsXLlSqxZswaANgJg6tSpuOSSS3DppZfKP/GUdKK5uGJ6ISZ2dnZizZo1+OCDD9DV1aUZlyxWHbgOh0OKuMI9m0oGrhoHoQrgiR67RujduE6nEz/72c/wt7/9Tca79vX1Gc4jGBMOXABYtmwZHnvsMbkzIpEINm3ahFtvvRU1NTXIycnB5MmTMXv2bEyePBm5ubmoqanBrbfeio0bN2ruaj322GNYunSpHd0aE/zbv/0b8vLyAACPPvoo/vrXv0ZN8/777+O73/0ugKG7GPfdd59hW/Pnz8e1114LYOgD4stf/nKUhd7n8+H666+XB/9dd90VVRBtJBAfkupBPta/9AghhIwv6MAlhBBCTmzowDUeF+86SHXg6gXc0crAVcV4IeBWV1dH9dPKNki0f83NzRonqz5jVWhp4k+MV2MDrCD0n+LiYmn60ztL8/PzLbWVagauw+GQQrWZEJ2MA9fj8ZhGXMTDikgv4hRmzJiB008/HaWlpQCGBNxY/R0TGbiCm266CZWVlfjKV76C+vp6TUf6+/s1wwTqylRVVeGxxx4blcf5k+HgwYN4+umnNcNqa2vl6y1btuA73/mOZvyKFSuwYsUKzbDy8nI88sgjuOGGGxAOh3HVVVfh05/+NC688EK4XC68++67+M1vfiMF1//4j//AnDlzTPv18MMP47333kN9fT2ee+45bN++HTfccAMqKytx4MABPPXUU9IVvWjRInzzm99MaTski/oYBR24hBBC0hmrd+0JIYQQMr6gA/c4Rr/b402rRiiMZgZuZ2cnNm3ahP3792PWrFk4cuQIgCEHbiQSQSgUkk8wu1wu2Q+7XMN6Y53qwDVCCLjJOnC9Xi/OOOMMBINBbNu2TcZ1+v1+bNy4EWeeeWbS29rqsa46cPVFzNS8WauINvTxBlYdwXqsOq6FgNvf3z9sGbgjLuACQ1mqO3fuxCOPPIJnnnkGe/bsiTl9JBJBTU0NvvSlL+H2229HTk6Ond0ZVg4fPowf/vCHpuNra2s1gi4w5J7VC7gA8IUvfAF9fX2466674Pf78Yc//AF/+MMfNNO4XC58+9vfxre+9a2Y/Zo0aRJee+01XH311di1axdqa2tx1113RU135pln4i9/+Qtyc3NjtjdcGBUxG+tfeoQQQsYXVhwihBBCCBm/WHXgjvUiZoLhyMDVzzMaGbgtLS0YGBiQQq0aoSCiBRwOB1wul6aAlV1FzBIVcMX4RB24QsDNyMiQDtjy8nKNobK9vR39/f2GsZwq+nW1mjerol8PvYCbCMLUmJmZmbBoGy8DV1/ETJ1eFXDN2gJSc+BaxVYBFxiqAvfNb34T3/zmN7F3715s3LgRe/bsQWdnJ3p7e5GTk4PCwkLMmjULixcvRk1Njd1dGJN89atfxQUXXIDHHnsM//d//4cjR44gHA6jsrIS559/Pm6++WaccsopltqaN28etmzZgqeffhp//vOfsWvXLnR0dKC0tBQLFy7EZz7zGVx//fWjmkFjdCdTvAco5hJCCBk78DuLEEIIGZ/EcuCOpwiFRHNErTpwt23bFlMEN1uG3dtTFPISyxAC7pQpU/Dhhx/K5U2bNk0WOFP7kyp6AVcfoaAnWQeuWI5aQGzSpEkoLy9HKBTC6tWrEQgEkrrhoD/urWwbowxcozatYObAFQxH9AUAlJSUADgu4ArszMAdFQeunpqamnEr0C5fvtz2D5Wamho89NBDeOihh1JuKzMzE7fddhtuu+02G3pmP8zAJYQQku4wA5cQQgg5sYmVizoeBdx4IpjX68Xg4GDcJ3kLCwvR1NQkHbhZWVkJOXDtRugPgFbAnTp1KrZu3SrHCeeqYLQduCKb16o4qDpw9e1lZGRoisvFI54D18r8aoSCKionQ7wIhURJ1IErMnDNlmfkwC0oKEiqb2YMq4BLiEpXVxe2bt2KYDAoD341A1d/ohBCCCHpQDwBl99ZhBBCyPhEdY/G+62q/p4dr1xwwQVRRiwjampqMGnSJITDYbhcLgQCAUu5p8O1/UKhkFxuKBRCe3s7gKFaTKqQ53a7Nes2XBm48Ry4brdbbq/BwUFNrIMZra2tMirBTCAWIqORgBsKhbBhwwaUlpYaGjETdeCqRcwGBwflOqnz7tu3DytXrsScOXNwxRVXxGzPjgiFRMcB2gzcWDdt1GNl/vz5qKioQFZWlqV+pYUDlxCVpqYmdHd3a4apX4QAHU2EEELSBwqzhBBCyImNKhTFe/x7LAu4VgWxRARNkbGalZWFUCiUUO6p3ddgqoDa19cHYGhdCwoK5Do5HA5kZ2dripjFE+Csoo9CsCp+Dg4OIhAIWBJwGxoa5Ov8/HzD93pU0wABAABJREFUadTicnqam5vR2tqK1tZWzJw5M664mGiEQnZ2tpynoaEBpaWluPXWW/HOO+8AAHbs2IGnn34aV111FU477TQ0NTVh6tSpsi0jB656TFnpj5kAa+TGFcOF23znzp3Yu3cvvvjFLxq2rTpws7KybDt2VIZdwN29ezfq6+vR2dmJvr4+5OXl4corrxzuxZI0xKzKX7zHUQghhJDRJN4FIb+zCCGEkPGJcCpaif5bvXp10o5Nh8OBKVOmaASr0WA4BOhEBLbheiJXFSxFlqkq3grRLiMjA/PmzUNnZyemT5+O6upqW/qnF0yNBFQ9qoBrBTHdzJkzUVhYaDhNrAgFdf/4fL6UjwU1QkEtxvbiiy/i5ptvxkUXXYSdO3fK4TfccAM2bNiAhx56CDfccAN++9vf4pVXXsGll14K4LgD1+v1Jrz9E9lvTU1NOHLkiMy+VYXYYDCIlStX4rLLLovpwLX6OZAWDtx//OMfeOKJJ7B69eoox+WMGTOiBNyBgQHccccdCAaDcLvd+J//+Z+4lnIyPrDyKAohhBAyGsT7XrLyKCAhhBBCxi6JOHB7e3tTWtaBAwdGTcAdiWuZ0fzdLyIUHA6HFHCFyKnm4zqdTng8HmRnZyMrK8sWQTscDkvBNDMzE36/HxMmTIg7n8fjQW9vr0b8jIVwGcfKJxYuUSMBWR3W2dkpXxs5zYHki5jddNNNAIDXXntNM+2GDRvk62eeeQYAcNlll+HGG2/EVVddJR24qhs5UQeu2XpEIhG5bwKBADo7O5GRkSGHq5jlB6sOXKM8XCMS7bOtAq6wE69btw6A9RPT6/Wis7MTL7zwAhwOB84//3xcd911dnaNpAFGJ75ewKWYSwghJN0wu7hK5FFAQgghhIw9Yjlwja4BZs+ejeLi4oSW4fP58NFHH43bawq9wGYlA3e4IhQcDoeMUFAFXDFOn4FrRxEzNb7hvPPOQyAQsPR4vciP3b59O/bu3SuHO51OzJs3D0VFRYbLiVUgLZYDV+1nR0eHJu6gtbU14XXXZ+CKYR0dHZbmFzz99NN48cUX8fDDDwOw34ErxhUXF6Oqqkq6j4VJw2xf2eHATRTbWt24cSOWLFmCdevWadwoGRkZlu4u3HzzzfL1888/b1e3SBoS68Aeq5lBhBBCxh+jldNGCCGEkPTAyIEb63u/qKgIpaWlCf0JIXE0ryeSdTEmQiIRCnYj3KUOh0M6pfXbXexju2v0iGgDl8sFt9ttORtVOGn7+vrQ2dkp/9rb23HkyJGo6YUAG8v9aVXAFSJ3MBhEc3Mz2tvb5XqobuV4CAFXzNvY2Bh3HpUf/ehHcLlc6OjowMGDBwEk58C1GtuZm5sr0wDEeL0D1+w8HTMO3GPHjuETn/gEurq6AAxZvW+55RZ87nOfw6JFi+RdjFidO++881BUVISOjg6sWrUK4XB42FRrMjqIA93r9coKfmZfhPwxTAghZLQZiR8zhBBCCEmOSCSC7du3w+fzAYDML7WzeJCRA1ddvp5kNIx0egp1JDJwzdazpaUFW7duleLk/PnzbeuDGqGgd+Dq47BiCbjJ7CshHsdyxhoxd+5cTJgwQSO2NjY24siRIzEF2FjLEcJiPAE3HA5HPWmmiuDqfzOMMnD//ve/x5xH5cILL8S///u/4ze/+Q12796NLVu2AEjOgSswc+Dqj1F1P4+EA9fq+tiikN57773SYl1VVYVNmzbhF7/4BU4//XS5w+LhcrmwbNkyAEB3d7cmzJiMD1QBV+B0Og0fUUiHLy9CCCEEiB2hAPA7ixBCCBkNenp6cPDgQbS0tKClpQWNjY0JO/ziEcuBa7eAO5oM57WM1Rvi+/fvR0dHB7q7u3H48GHL2a9WEA5QNQO3oKDAsJaB1+uVfdW7L5PBijPWCKfTidLSUpSXl8u/vLw8AMb7SywnlgYnjk+jDFxVwDVqX4i+sY5/PaIvAwMDCIVCePnll6OmmTdvnuG8QsCfO3cuAGgEXJVUM3D16NsxOwb084+EAzdlAdfn88nIA5fLhb/97W9J3yk5+eST5WsKuOMXcQKIDFwBfwATQghJJ6wUMSOEEELI6CBEKI/HIyvGmxUYSpZYDlwjEhXpVNLh9/BoRiioomIkEkFfXx9qa2s1BbWSRc25VR24euetw+FAdnY2Tj31VJx++ulSPLS6DkYI8ThRB64RscwDiQi48Ry4wq2s1ikS+8dqhILqwA0EAqivr0dbW1vUdEuWLDGcX+iKQuCtr68HMKQnJSraxnrqO1kHrn4bjgkH7urVqzEwMACHw4HLL79cI8ImSkVFhXx99OjRVLtG0gxxUIpMEWDoxKADlxBCyFiG31mEEELIyKPW3RF5ocO1DKsO3GQE3HT4DTySDlyzZYVCIc00u3fvxuHDh7F27dqU+yDEyd7eXrz11lsAtAKuKlQCQ+7ciooKUzE0mSJmVp9Oj4XQTvTLFxER8ZaTjICrLkNdthXxVI1QENEYNTU1mmlOOukkw3kXLFgAAFEiejIO3ESEd/1xapaBq9+G6jISdeBaPZ5SPoLq6urk64997GMptSXs4ABkjg0Zf6gnnP4uGyGEEJIuxLsgpAOXEEIIGT2M3LF2/qZUBZqRyMBNB0bCgVtbWyudmKWlpZg8eTKAaEFMOGXtIBwOIxKJ4I033pDRDHoBV+2jXdth7969aGpqAmCPgCv6pd9WqvgaSzwU4+JFKITD4ah2VBczYO1JNeE6HhwclPt88eLF2Lt3r5xu9uzZ+Pa3v41wOIydO3fKmAXhvD3//PPhdrtl/9QM3FRiE2I5cNVp9A5cKxESaRuh0N3dLV/n5+en1JZ6gmZnZ6fUFkk/jO68eb1ezV2kdLj7SAghhKjEu7jidxYhhBAy8sRyx9rZfiLLSKUQ+4niwAWAhoYGNDQ0oLa2VoqJonCWQH1yt7e3N6U+CPGvp6dHDjOKUIhHIseZz+fDrl27ZASEHcX1zJavunxjrUciDlyn06mZLlEHrhqh8Mgjj2DTpk0AhqJT1fOkoKAA999/P370ox+hrKwMAFBdXS31xYqKClx99dVyerPtaMWBa2W/6ac1E3D121B16tqVx6sn5VsAYgMDwLFjx1Jq6/Dhw/J1aWlpSm2R9MXhcGD58uVRHwiEEEJIOsEMXEIIISR9sSrg9vT04PDhw5ZEkkmTJsHr9eLQoUMaQcvpdBq68/SM9QiF4by2UUW7iooKHD16FJFIBKFQCC6XK8oVqr7v7OxETk5OzPb37duH3t5euFwuzJgxQyO8GTlOOzo68OGHH8qiZuq627EdhNPX6/Vi9uzZqKysTLlNKwJuLFLJwBXjrWbgAkPnk2D37t0Ahpy1WVlZUpQvLCyU0wgdUF9X67777sOBAwfgdDpxzTXXGLpnEyXWMa9uZ7MiZvptmJGRgbPOOgsul2vYzqOUBVxhdweANWvW4N577026rdWrV8vXIu+CjB/UEysvLw+FhYVob2+nA5cQQkhaYvXHDL+zCCGEkJFHFZJifVfv2rVLPsYej/b2dhQUFMi8TiDa1RjrEepUHLjjFaPrqfLycjQ1NSESiUghTBXEIpGIFEABrXPWiO7ubuzcuVO+dzgcGhHQSMBtaWnB4cOHpYBbXFwcd10S0StE8bKsrCxMmTIl7vRWMMvAtSrgWo1Q0BecB6IjFOLhcDjwiU98AitXrtSYNWfNmoXMzExDAXfFihX4+c9/jk996lOatubOnYsNGzbI96KgmVXi7Tf9zSD9U+MqqmNcj5VjKJF+6UlZwD377LORmZkJv9+Pf/7zn9i5c2dUyLAVPvjgA2zYsAEOhwNlZWUpFUMj6Ynd4dKEEELIaMKbjoQQQsjoYfT70ug7WQguEydOREFBgWFbfr8fhw8fRiAQkMKhmL6kpMTy79RUMnDHuwNXbdvpdMLpdCIUCkUJuEJkHBgYkNMbCY4qqvgIHBdPgaG6TS0tLVHbd8aMGYhEIigvL4fL5cK0adNszcAVfRA5sHYQLwM3FQeuus1EnIVqshPzJHKcOhwOzJs3TyPgFhYWaravKuBecMEF6O7ujrseVvJr42E2n/581I8X28mOp8kT7XPKAm5WVhauuOIKPP/88wiFQrj22muxdu1azU6IR19fH2688Ub5/oYbbki1W2QMEKsaJX8ME0IISRdYxIwQQghJP1RHYKzvZPHbsrKyUvNIt0p3dzcOHz6McDgs262oqEBVVZWcRv+71a7frOkg4A4nRkKYy+WKEnBDoZDMTRUOXCHkJSqWiel7e3vx4YcforOzU+Po/dnPfoYZM2agpaUFwNCj+1biLxLZV1ZF1UQYzggFfYF5p9OpWY7eeWolAxdAVARBbm6uZln6aAw7t5e+L1YcuPEQx5GdcaBWz31b/P0/+tGP4PF44HA4sH37dpx22ml47bXXLM27c+dOnHvuufjoo48ADBVCu+eee+zoFklTzO5wGI0jhBBCRovx+kOKEEIIGQ9YdeBaQX20XIhLekFvuITWdPgNPFoOXGBICItEItLtKba7KrjGE8v0+0TsQ9GGy+WS7muHw4Hp06ejrKxMZvDq5093B67ob0tLC3bt2oWGhgYAyUcoqDcuRPv6fNmmpibs2rULfr8fgHW3uT6CICsrS+OYTmZbD6cD12gaFTsF3BF34ALAtGnT8Oijj+LLX/4yHA4HDh48iE9+8pOYOXMmPvGJT2giFXw+H37961+joaEBb7/9NlatWiU3isvlwu9///uEcyPI2EB/8NOBSwghJJ1hBi4hhBCSvlgtYmbl+1wVE4UwE0+gsvv7Px2uJ4ZDwI3lwAWixUNVwM3OzgYQP0LBTMAV/zMyMqTT0+v1wuFwyIJZQjxOZp1iIQRcOx2l+m22YcMGzbbTi6Wx5jfqq8DIgetwOLB3714ZbRHPsWzkwM3KyoLD4Yi7P+0mlubkcDii3PxCGDb7PAkGg6PiwLXtSLrxxhvR39+Pu+++G8FgEJFIBHv37sW+ffs0nTp27BhuuummqI56vV489thjuOSSS+zqEkkz4mWMqK/T4cuLEELIiUskEolbIIHfWYQQQsjooQqtqQqPQowKh8OWHbjjKUJhOJdtpAPoHbiqGCYET30mq5Vl6KdX5+vr6wMAeDweANpH9/WCoh3xWcJlOlwOXHW7TZ06FW63O26xNPU4VxGuWoGRAxcYyqutqKjAwMAASkpKLPVZL+AC0ZnFyWLVgZvIfrNyPg4MDIyKA9fWEom333473nvvPSxevFgOM7orJoaLcaeffjreffddfOELX7CzOyTNieXAJYQQQkaTY8eOyQt9M4dBOjzySAghhJyoWI1QSMSBCxwXDkfagZsOjFSEgt6BKwRUh8Mhs3FVUTVRx6aaqyva1Qu4brc7KlLAzvUf7ggF9fibP38+5s6dKx3LZohtrt+eQsAVfQ2Hw1EOXGDo6fvp06ejsrIy7vkh+qq6gq06quOR7LlnNJ+RCBxPo7JLwI3VLyNsTwc+7bTTsH79erz//vv44x//iHfffRcfffSRpoKgy+VCTU0Nzj33XFxzzTVYsWKF3d0gaYiZYKue+BRzCSGEpAP9/f3y9dSpU2NOy+8sQgghZOSxGqFgBfVmrRDeRiMDVzgfR5qRuJax4sAVwu7g4KBGIEs2A1d9NL63txdAbAHXqK9Gw0crQsFMwLV6zJhFKAgBNysrC4FAQOPAVZdZWFiYcJ+HQ8AVpHKumO1DK23a7cAdNQFXcMYZZ+CMM86Q73t7e9HV1YXs7OykdjoZ//AHMCGEkHRBPNo1efJkUzcDHbiEEELI6KEagqx8J8d7xFoIVkJcGikHbjpdT4x2Bq7Yl6FQyBYBVxUK9Q5cl8tl6MC1S0QfjggFsc2SFXDjRShkZ2eju7tbZuDqycnJsSw6GmXgxnMIW8UsyzZeXxKdz2wd/X6/rQ5cq9gaoRCLnJwcVFZWUrw9gTE7QejAJYQQkm6Ii24rrgl+ZxFCCCEjT6IRCvHQO25HyoGrMlrXFOmSgetyuRAIBNDZ2anpk1XHpliGPgPX4XDIp6u8Xi/cbjccDoe8zrOayZqMA3c4IhTC4bDlYrsq8SIUhMCq5jyL9mtqapISto0ycH/84x/D4/Hg0UcfTbg9Fav9ibffEnXzDwwM2HLOpI0DlxAzzA5S/gAmhBCSLlgRcNPJMUMIIYScaOgrxwOp/abUOw717/Xf++PJgZuMGJgKZhm4TqcTBw8eRF9fHyZPniynt+rAdblcCAaDMR24GRkZ8vpOiPT67FM7SMQMYBWjCIVkBFz99hTithBYRbtiGdnZ2SgqKkqqr0YRCsuWLcPzzz+PM888M6E2BYk6aWO1YTTfSGfgWmVUBdzOzk4Eg0GUlpaOZjfICGEm2KqPAdCBSwghJB2gA5cQQghJb6wKN1an0wu2ZkVMVXeiHcRzEI8EHR0dw9KukdAGmDtwgSHRNRKJoKCgADU1Ndi7d2/CAq7qwBUFuUQGrnDgiumNsDMDd7iLmCUiXIr1DQaDaGxslMPFtjETcM36YAUjAdfhcMDlcqVcjCxRB66V9tQ4FTP0Gc3Jkui2tCVCIRgMoru7G93d3XLHm9Hd3Y27774blZWVKCkpwYQJE5CXl4fPfOYz2Ldvnx3dIWmO/uRR36fD3UdCCCEkEQcuBVxCCCFk5LGziBkQLebFy8AdDkbjmqK9vR1NTU0AhnedVcHLzIGr7st58+ahoqICgPUIBXHdJpYlxGCn04nOzk4AQyKlmQNXkKouoa6TnQ5cdZulIuCGQiFs2rRJ/gl3sj6jNlVntsPhMIxQsFP3SaSPVpy7Vm6ojJYD15az8+tf/zqKiopQVFSEu+++23S6zs5OLFu2DD//+c/R1NQk7xr09vbi+eefx8knn4y33nrLji6RNCReZILZ3TlCCCFkpBmOx94IIYQQYh/6wldA7AzceAJPPAFXv4zx4sAV4h0ATWyBHcT6jW/kwFUdkPpprCxHLdIViUTkf4fDga6uLgBDImU8ATeR9TJiYGBA9j+dHLiZmZmYMWMGSkpKov6mTZuGvLy8qGUlugx9f1UBV30t1mMkSCQDV/0vmDVrlub9aDlwbflV8vLLL8uT4s477zSd7vbbb8fOnTsBGNux+/v78alPfQo7duxAZWWlHV0jaQhdtoQQQtIdOnAJIYSQ9MbIgZsKqmCrisKC4crAHW2EEDVx4sRhLTqvF7zMIhTEdg6FQkkLuGIe1YErBFzVgSvEVb1wFy9CIR5qwTQ79Y9UBVxgyNlsBSGAp9p/NULBrqxhOzKbE3Hg/uQnP8GDDz6Iffv2oampyXYB1yopO3APHz6MhoYGOBwOzJo1y/Rg2L17N5577jn54VpeXo6f//znWLlyJX784x8jPz8fDocDPT09uO+++1LtFklDmIFLCCFkrEAHLiGEEJLeGAkwqThw9QJuvOXaxWg7cNUCYnZj1YGr9kFoRuFwWOOotYLIVe3t7UVLSws6OjrQ3d2NUCiEnp4eAFoBt7S0FPn5+cjNzUV5ebnlQl3x9pPf75fLshNVOxmuwnOqyGp0kyTR5Xk8nqi2BXYc71a2g5HOpM5n9lkihpeXl+PMM89Efn4+APsjFEbMgbt9+3b5+qyzzjKd7je/+Y2mgt369esxZcoUAMAnPvEJnHXWWfjYxz6GSCSC559/Ho888kiUvZqMbazc4aCASwghJB0QAm6sx974nUUIIYSMHqohyO4MXKPiVvEiFJLVL0b7CVU1imI4l7F27VpUVVVJl6+ZA1fsT1F8TEyjGr70qFrD0aNHUV9fj4yMDBw5cgRNTU3o6enR6FFi/2ZmZmLOnDkoKyvDGWecgc2bN8t2jLC6r4SAa7emZYcD18oyjLZ1vEhMs7aM9J6RduAmsrxYYq8Q/sesA7eurk6+jmXF/vvf/w5gqIPXX3+9FG8FZ511Fi644AIAQxXwNm3alGrXSJqjP+n4A5gQQki6IARcs+rEwOj/4CKEEEJOZNTcVDtQv/MTETOnT5+O4uJinHHGGSn3YTR+Ew+ngCvW5/XXX8e3vvUtTc2kWBEKDodDE6Gg9jPWchwOhxRPMzIy4PV6EYlE0NbWBmBIUHU4HFKIGy5NYiwLuGKbCxE9lWUkGkPi9/vR3d0tIyiskGwRM7VPZhm4+unFZ0QiAm4kEsH5558Ph8OBr371q5b7ZUTKZ2h3d7d8bWY3b21txbZt2+T7a6+91nC6j33sY/K1yMol4wezD0f1Q5liLiGEkNEmEolYcuCq0xNCCCFkZDF6vNuuCIVEHLglJSU466yz5OPVyTCav4NFfEGsm9ap8vbbbwMAWlpa5DCjCAWxL51OpyZCQUxnhrqPxXSzZ8/GqaeeCq/XKwu1iUgDNYrA6H08B67VCIXhEnAB+29gGC1DvDeqYZVK20bbsaurC6+//jrefvttvPHGG2hqajJtx84MXLM+GpGMA7elpQVvvfUWAODPf/6z4bJGLEJB/MBRF65n7dq1skOZmZkaoVZFLVzW0dGRatdImhLvsQdCCCFkNBE/IgBrRcwIIYQQMvJYFXCtYjUD12j5qWKXQJYMw+nAFW0LQVNFLC8UChkWMRMFtMS2CYVCcW+sq9txYGAAvb29cLlc8Pl8AIYEXCEQ9/b2yn7Fc4rGW0e9Y1QIxsMp4Kqi93AtQz0m47lT47UFAJMmTTIcDmjNoeL9xIkT4y7Djgxc/XTiONKvo/hdkEgGbkNDg3zt9/vlsQFof3NYIWUBV73LpN5NUVm1ahWAoY2wdOlS05NO3TiqMEzGB2YnOB24hBBC0glxDSIu8OPB7yxCCCFk5DHKwI01XTyhK9kMXDsFtPEWofDee+8BiC3gqhEKahEztbCZXuTVoxfmmpubsX79enR0dKChoUGKaKKg1gcffIDdu3fLeazqEPrpwuEwVq1apRHlVOwWcK1GSqSCWRGzVI7NBx54AG+//TY+/elPa4arbQYCAc04q45rK8Tbv0ZCtX5ah8MhPxesCrjbtm3Da6+9Jt/39/fjzTfflO/b29uRl5c3cg7cyZMny9cbN240nOavf/2rfG3mvgWGohYEeXl5qXaNpDnJhGATQgghw424CI/lvgWOX+AFAgEMDg5qquwSQsYeg4ODsnALz2dC0h+jR8jtKmKWipgZDocRDAbhcDgsRTEB4zNCYWBgAD09PQCGPl/1iG0cDAY148UN9FAohIGBAQSDQflnhj5Cwe/3SzE4NzdXXtvl5+fD6/XKz3i32w2Xy4WKigpNe4kUKxNt6/d1bm6uLNhmF0YRCnYL70brri9GlmhbZ555JubOnQuv12s6rX7/JupOTbR/VkRgMweuuF6IR2dnpzwHgOPF+NxuN0KhEEKhkOHNDTNSFnAXL14s1fhXX30VR44cQXV1tRz/8ssvawqdXXTRRaZtbd++Xb6uqqpKtWskzYiXgatWOqSYSwghZDRYv369fKIonoAr2Lp1KwBg4sSJWLx48XB1jRAyjGzatAmNjY3y/fTp0zF//vxR7BEhJB7pkoGrf6x91apV8pH6+fPnY/r06XHXZTRjmYZLCOzs7AQwJGQaCbi///3vUV9fj8suu0wOU59+GhgYwD//+U9s3bpVujMvueSSmCKgEHDFvqmpqUFBQQEGBgYADOXizpo1C16vFw6HAxdffLFm/kQduEJ8y87Oxvnnnx9zXjsYiQzceC7fZDQbIzerfniyDtxEohziOXDjfZYIkX5wcNCywKyPhli+fDny8/OxevVqtLS0GEY1mJHyGVpRUYFzzz0XwNAJ9vGPfxx///vfsXv3bvzmN7/Bl770JbkRZs6ciaVLl5q2Jez1wNCJRcYnqeTLEEIIIcOJ+jSQ3o2hZ8KECZrvMHVeQsjYor29XfNeVCwnhKQvdkcYxHPgxitsBQC9vb2aPNREP0vGU4SC+FwtKiqSGbSC1tZW3HfffXjyySfR29sLYEgcKygogNvtRk5OjtSR1MfWVTejithuoVBIinpOp1OKY6IvZWVlch6XyyWdvXqx0OoxJfa13VEJsVAzgoezffW9Pk7BCOE8DwaDUdPqxWajNoSAKwwUia6fnQ5cvYhrdCxaOVcjkUiUgCuOGTVCxCopO3AB4Ec/+hHOPvtsRCIR7NmzB5dffrkcp67wfffdZ9rGhx9+iP379wMACgoKMHfuXDu6RtIIs8gEo9BoOnAJIYSMNKFQSH7/XHzxxXEduDNnzsTMmTPh9/vx+uuvM7+fkDGMOPcXLlyI2tpans+EjAGMMnDtKmIWK05A78BV0X92WP0sGW8RCgcPHsS+ffsAAIWFhVHb4cCBA/J1d3c35s6di5NPPhkbNmyA0+nE7NmzcemllwIAcnJy8P777yMcDpu6HvUCLjC0TX0+H7761a/K6UpKSuR0O3fulP1yOp1YvHixZQeuQDhws7KyYs5nJ2pRN6M+2dG+uhyz8eq4xsZGbN68WQ7Ly8szjE/V99XIgev1ehEMBmM6XIfbgRuviNng4KAl4TUSiUTddBACrjjfEhFwbbnFsnTpUjz++OOaOxz6lf3yl7+Mz3zmM6Zt/PrXvwYwtMHOOeccO7pF0hSzDxg1QoEQQggZacSFo8PhsByfAGgzzyj6EDK2Eec+z2VC0h/V1WdHETNVwLXiwDVqV//ZoX8sPB0ZDgdufX29fK26XgFg0aJFUtwFhgRc4WA16osQ6CORSFyxKxQKweVyySiFXbt2yXG5ublyX4lsXUE4HEZbW5vlY0UfoTCSDly9c3O4IxTE9tcvT90XIgpA0NPTA7/fH7MgmB6xP0RExnA5jAVm+zrW9kxUwAUQU8BNtDicbWfojTfeiE2bNuGzn/0sqqurpf393HPPxR/+8Ac89thjpvO2trbiqaeeAjC0EVUHLxk/mDlw1Qxcs2kJIYSQ4Ubc6U9EvAWOX4ABFH0IGauIa09xQ4bnMiHpj5EAY1cRs0QcuLEE3LHgwLVbwA2Hw/Kx8RUrVsj8WUFpaampgPvAAw/g0Ucf1cRQuFwuOJ1OhMNhU9FMdeA6nU44nU4cOnQI69atk9OIp8aFGOlyuZCdnY0ZM2bIfifKaAi4ZrEEdrcv/gsBV0QjGB2rIuN4/vz5UoBVj30zsdTMgQtY2x9Woh3UcVYcuLHmF58LVgXcPXv24I033tAMM4pQsHre2xKhIFi4cCF++9vfJjxfTk6OpoBZvMw5MraJdYeDDlxCCCGjhbhwTOYRQrfbjUAgQNGHkDGK+PEkbuCISBVemxKSvthdxKywsFAKhcXFxVHjrYis4jogMzMTfr9/TAi4dkcodHd3IxwOw+PxICcnR+PGBYYELCMB1+fz4eWXXwYA7N69W44XgqzVCIW8vDy5PUUG8VlnnYUzzjhDzi+cpS6XS/O5L4gn5I2mA3ekBFzxVL0q7AcCgZgFyDIzM+F2u6XD2ShL1qzPiThw7ThPrDhw9TdrxLHi9/stCbjf/e53o4apAq7D4bBcDA2wWcBNlqysLEyZMmW0u0GGGSsZuLHyhAghhJDhRFw4qpEIVqGAS8j4QB+JksznASFkZNA/0akOS4aCggJcdNFFiEQils79WA7crKyspATc0cBOB25vby/Wrl0LYEgQB6KLRH7jG9/A22+/Ld/39PQgMzMTa9asMWxTPOl04MABRCIRTJw4ETNmzMCsWbPkNKqAW15ejnnz5mHatGl4/vnnAQzFfk6YMAENDQ1SNBPirZpFavX4aW5uxt///ne57cZThIJen3E6nVK4PHLkCNavX4+WlhY0NzejoqJCunOBoe9QNYrIqvMViHbgJpqBm6gDN14RM30srBqxNjAwgEAggP/7v//D/PnzUV1dbbjcpqamqGFGGbhWjzt7ywwSEgMrdzhYxIwQQshoIS4+E41QUOehgEvI2EY4vQCez4SkO4nmlVoRutxut6l4G8+VCWgduIC2QKoVxnqEQmtrq3w9ceJEAEBfX59mmv/93/9FXV2dfC8cuOqj5mqEgnBGRyIRBINBBIPBKFevQBUSnU4nGhoaAAwVngWO190Jh8NSvBXrre4rs32dn58fJaB6PB7k5eXF3C52Ivo2kkXMhIh+6NAhBINB6YYOhUKa70qPx2N4TawXm2O5eEcqA1fFqptfrJvoayAQwNGjR03brayslK/FDQ19hIJRsTQz0sKBS05M7M4sIoQQQlKBAi4hJy7qNajb7cbg4CDPZ0LSHCMH4kj8noz11KhewBXD4jl6x0uEgmirpKREPmXd29sbNd1FF12EHTt24MiRI+ju7obH48HGjRvleFXAnT17Nrq6ulBXV4fKykq0tbVFiXuBQAA7duxAXl4eqqur4XA44PP5pHg8bdo07N+/X84nMnDFnxgWj4KCAnz84x+PEi3tip+wwnBHKBgJi+I6t6+vDw6HA1OmTMEZZ5yByspKvPfee3LeeA5cs76q4nkiEQrD6cA1mkbNwJ03bx527Nhhukx1XT73uc/h8OHDWLNmjaED1yp04JIRw+wEUe/00YFLCCFktKCAS8iJi3qdqnfYEELSEyPXXLzpUkH/WzVWhILH49E4O9MZOx24oq3s7Gw5zEjAvfHGG3HvvfcCAGpra7F27Vrs2rXLdJ6srCx4vV6Ns1mlra0NPp9PDs/JyUFPTw+AIREtNzcXQHSxs0QduMCQSJmVlSX/RlK8BUa+iBkwdJ2rDs/OzkZmZiYKCgo08+oFXIFZBq4YLr5vHQ4HPB4PgJE9b+J9lgjntlroVOz3WNqVWIdrrrkGWVlZAKIzcG0vYvaf//mfmvff+973Yo63A/0yyPgjVsA8IYQQMtJQwCWEqAIuz2dC0hsjF95o/J40EnDdbrd08wcCgbgZqaNpZLJTwDVy8+ojFIChx8nFOjc1NeHcc8/VjFcduGrfxPbRuxaFWFteXo7ly5dj9erV2LZtGwAgLy8PLpdL4ygVDlx9Bq4gnQtYjpSAC0QX7xLjPR4PIpFI1DWzmQPXjMbGRhw6dEi+d7vdUREV8fo6HA5c/TRq/wSDg4OG06iI88HtdkcJuMk4cC39Qvn+97+vWSm9uKofbwcUcMcf+hPEKHSeDlxCCCGjBQVcQk5c9BEKAM9nQtIdVXiMJeDa7cDVt6tiJOBa+SwZTxEKjY2NCAaDWLBgARwOh6EDt7CwMOb1ln4e0Td9/qugq6sLAFBcXCzdv0LUzcvLk87JcDgsXY8i+kB14KazcCvQO7uHK0JBbCcgWsDNyMgwFR4TycA9cuSIdEcD2jgKKxEKVrFaTC1eBq56jgwMDMRdbiwBVzhwhy0DV9iGzcbZxVg4acjwQOGWEELIaEEBlxCif0SSEJK+WI1QsAu9M9FIGBaPggsBF0j/zxI7Hbjr1q3DV77yFQDAL37xC9x5552GDtyioiKUl5ebtqOfRy0uuXfvXgwMDCA3N1dueyHg5ufny/URAm5+fr7cF3oHrirgimFAemtSI+XAVbWZjIwMOVwUiFMNearYaiUDV3Vf+/1+ZGVlYfr06cjMzIwbPXL48GFs3LgRTU1N6OzsxPTp0zVt1tbWoqOjQzOPz+fDtm3b4PF4kJmZiZqamqQycJ1OJzweDwYHB9Hd3R21nfSMigP3Yx/7WMyDIt54QgBm4BJCCElvKOAScuJCBy4hYw+rRbHtcuAatalvV+/AVYfFYrR+B6viUbIC7j333INXX30Vb775JtavXy+Hb968GYBxBm5hYSFKS0vx+uuvY9KkSZg3b55mvH4e0bf29nYpznV2dmpEsIyMDOTk5Mht6PP5AAw5cFUB1+FwyAxTtYiZyMVNd0YjQkF1noo4EHWcmYCrb0dPT08PAoEA+vr6UFFRoRFwzYTN/fv3ywJ1Pp8P7e3tcpzf78fhw4ej5unr60NfXx8CgQA6Oztx8OBBWWDMaP3VaAY9paWlaGxsRGNjIzIzM20RcG114K5evTql8YQAxhlF8aYlhBBCRgo7BFwWPSJkbONwOOSPKgq4hKQ3qgNwNARQKxEK6rBYpIOAm0yEQltbG376058CAH7+859jz549clxraysA4wxcUfzqggsuMGxXP4/+czkrKwuLFy+WBa+KiorQ3NyscYMKAVc4cCORCLq6unDo0CGUlZXB6XRqMnBDoZB8AiOdDYp6gXM4HbjieFQduHoBVy96GzlwzSIUVJdtOBxGZmZmlLCpXz8xz4QJE6LGqW7yJUuWyOFCKBa5tfqCdWZufqMbNeXl5WhsbER9fT1mzpwZvQEN+ioK3wHGRcyskv63F8i4RD0RxsJdLkIIIeMfOnAJOXHRPyoK8HwmJN1JNELBzgxcux24o4UqoCW6fZqamnDmmWfK96+++ir27dsn37e0tAAwduCKz1nBypUrsXDhQhm/YObAFYKex+NBcXExysrKUFZWhqKiIiky6gXcgoICjTO3u7sbXV1d8Hq9UREKY8FINhoRCkJ8BIYEcXX5s2bNAgBUVlYCOL5v45kaIpGIRryMRCIaB66+DwIhvqrZuaLfoj2XyyWPDfEnjgPRRixiRceK6I/6+nrTPqp9BcwzcMX8w5KBS4gd6E+GeI+8EEIIOfEIh8PYvn07ysrKMHHixGFdVm9vL3bt2oXOzk4A5gJuJBLBjh070Nvbi+nTp6O0tFSOE/O0t7fj/fffj5rX6XSipqYGhYWFOHz4MPx+P2bPnm2pf/X19WhoaEhwrbTLnjlzJoqKipJuI1n6+vqwa9cuzUV8WVmZzCsbLXp6erB79+6oC3iXy4WZM2eisLBwdDo2RvD7/dixY0fUj7PKykpUV1cbziPOs2AwiKysLCxYsGDYbuI3NTUZPkIZi927dwMYcnEdPXoUhw8fxqFDh7BmzRrMnTsXmZmZCAQCOHToECZMmCB/wBYWFlo+l/V0d3djz5498jgsLi5GTU1NUm0RciJiJGDFilBIlUSKmGVkZMhrg0OHDsHn88miXrHaHi0HrupiNqO9vR2NjY3weDzYuXMnHnjgAY3jdseOHZrpYwm4ei6++GJcfPHF+OUvfwlg6Ht6/fr18vrJ6XTi2LFj6OjoQH19Pfr7+/HMM89g9uzZyM7OxoEDB9DQ0IDe3l60tLSgr69PLjc/Px+TJk3CokWLpLN36tSpUsA1epQ9nR24evfqcBUxU8XVnJwcnHPOOQiHw2hra8PevXvltpoyZQoKCwvl96JZBq56o8XhcERdgxkJuEaxFqpIa3ZOmm0TI7E+EQeuw+GIEnBjES9CQYjOFHBJ2mF2UKonCDNwCSGEAENVaQ8dOoRDhw7hsssuG9ZlHT58GI2NjfK9qF6sp6urCwcOHAAwdOGnCrhinsHBQTQ3NxvO73K5cOqpp6K2thYAUFFRIS92Y7Fjxw5LlW5j4XA4cPrpp6fURjIcOXIkSnxuaWnB1KlTR/UJnIMHD+Lo0aOG45xOJ0499dQR7tHYorGx0fCmQkdHh6mAW1dXpznPKisrNeeQnezevVsWF7GCeKwWAJqbm+Hz+eT7Q4cOoa+vDzU1NWhubsahQ4fQ0tIiH5tsbm7GlClT5COliXDo0CHNcdjc3IypU6dGOdMIIcZYrRw/XMvWi0WhUEiKS263W14b+Hw++Hw+VFZWoqSkZET6Z5VECpjNmzcPx44dixr+qU99Cnv27MG2bds0w2NFKJghHJKDg4PYsmULfvGLX+ArX/kKrr32Whw5cgThcBjt7e3o7+/HwYMHEQ6HMXnyZLS3t6O7uxsdHR1wOp3ysxwYcuA6HA4UFBQgPz8fDodD7hvVgRvPlZkujKQD1+v1wufzwe12o7i4GA6HQxoe1ONfvfGtxl2I7zO9gAtEb+9QKBQl4BrFC6gCrr7fZsezUXRDPLHXzBk7YcIEAEPXuGI6M1RH/ogVMSPEDpiBSwghxCri4mYkEC7CiooKTJs2zVRUVd2G+out4uJiLF26FH6/P2q+1tZW1NfXR83j9/stCbhiufPnz09Y2Glvb0ddXV1CF4d2Ivo+ceJETJw4EVu3bkUkEkEwGJS5daPZr0mTJqGsrAzAkLDc0NAwattqLCG2UXFxMaZMmYJgMIiPPvoIgUDAMK8OiH6Ucji3s1iWcGbFIxKJoK2tDQBwyimnwO12o7W1Fe+88w4OHjyIQCCAWbNmweVy4dChQ8jKysKiRYtQW1uLcDic9LqIflZVVVl6FJMQosWqW9IuV2U8t5+4BnC5XMjIyMD06dORm5uLnTt3ore3N+Yj5aNlZNq/fz8AawKuKt56vV4sXrwYl156KebNm4eVK1dKAXf58uVYvXo1enp6MDAwYMmBK1Af1b/11lsBAF//+tfx6U9/Wn7WlpaWor29HVlZWZg1a5Z8SiInJweFhYUYHBxEMBiUyxV5u8CQM1oVDtUMXPWR/nR24I5kBu7cuXPR2NiIM888Uw5XH/03Qlyr+v1+WXCsoqICubm5GiFV/90ZiUTk/ne5XJobIgLVrWr0xJzZua7vu9GyxXSxzkWfzyfHHzp0KOZ2AOzPwKWAS0YFIyt6ItkfhBBCiF2Iu+MlJSUxnTHxqukKIVBPOBxGfX191DxWnB6qOFRdXZ2wgBuJRFBXVzdq36/ix2pRURGqq6vx0UcfIRQKIRAIjKqAK/ZlWVmZdIyGQqGUoipOJMTxlJubi6qqKoRCIXz00UcAhraj0Y8qfQbkcB6TYlmVlZVRGXlGqI76qqoqZGRkoLq6Gtu3b5c/6CdMmKCJWRHHsx39LC0tpYBLSBIYPf4/kg5cgV7AFY58l8uFiooKHD58GL29vTGzcEdDwA0GgzJuxuv1xpy2p6dH837BggVYu3YtVq1aBZ/Ph8WLF+PJJ58EANxwww1Yu3YtQqGQjDOwisPhQHV1tXQ3qsMF2dnZ6O/vh9frRVlZGaqqqtDe3g6/34+CggI0NDSgpaVFxjsId2gkEoHH49GYBFQHLjA2XLjD7cBVBdqMjAyUlpYiLy/PdPl6vF6vLCbX0dEBn88nv2PV40yNFwgGg9LxK/oQCoWi9odYZiQSSciBq+97vOxn/fko+iWKoAFDNzRina/qDYERzcD97W9/a6kxO/n85z8/4sskw4s4KLu6unDw4EE5XBVwGaFACCFkpLFavCzZIiRmF9ZW2lOnSaa42mg7SNQsQGBoHYwuyEcao33Oa5DkETluwl1tRcAdTlIpSKieM2L+UCgEh8Ohea9Om+wxo/bTyhNqhBAtVl1zw+nADQQC2LFjB5xOp4wM0EeqGBUzC4fD2LZtm4xIEo/7j+TngCrALV68OOa0+hucIgdUCFoVFRV44IEH4PV6UVxcjJKSEjQ3N6OlpcXQgbtlyxYpCufl5cHlcmH69OkIh8OYNWuWRsDNz8/XCHJOpxNOpxORSMQwyz4SieCFF16Qw8TNefH0jyrCmQm4o339FIuRilAwywSO993n8Xhw5plnoru7GwMDA+jq6sLcuXMxZcoU+eSZmoHr8Xgwa9YsLFy4UO4LM6es+l4v4Kp9ipc1rc+djfdZMmXKFEyfPh19fX2yjkM4HIbP5zOtm6D2NV6Egq0C7g033DDiBzAF3PHLnj175N1JccEq4IUrIYQQPWaCkJ3tA8Mn4AqSceCKZRoVarDCaIuS+m3rdrsxMDAQtzLxcJOKwEeMfyC53W4EAgHT82SkHLhqrp3V/WvWF30+nV7ATRWj45DXwoRYx6zw0EguW8SvqE/IWBFwv/vd7+JHP/qRfF9aWopHH3102PprhPp5I7JnzdALuEIUFd/n2dnZmDVrlhxfWlqK5uZmtLa2SgH3e9/7Hh566CHcfPPNOHz4MDZt2gQAOPXUU6ULEwBmzpyJDz74QGaR6wVgcUNNdVyKdcnIyEAkEpFi+uLFi6XgJp7+UQVb0Y5wjFLA1V47JiPgAkNPXhUVFaG+vh5erxfV1dVS9BdtiG3tdDqRm5urOQbN8mHVdTYqvGc1A1d9ws2KA9fpdKK0tBTFxcXyibhAIICuri5MmjTJcBuo53tGRoZ0MYsbF8OegTtSFxTpfLKQ5BHHj/q42Je+9CX8/e9/x8UXXwxg9H9opkp/fz8OHTqU2EnodmPatGmWHyXt6+vD4cOHNcuoqKhAcXFx1LTBYBA7d+5EfX09KioqLD16W1lZqalU3tjYiI6OjpjzlJWVob293fQHjcfjwfTp0+FyuXDw4MGEHqOxm0AggPr6evT09MgvdiMyMzM1XzLA0IXJeeedB6fTiTVr1siLCpWioqKkihNUV1dbysIk5ERB/R5IRwE3ke8psxuViThwx6rQaCTgqsNHCzpw7SdRAXe4sNO1rq+kbXfuIB24hCSPer6oYk2s82g4HLhGn21WBFxR0LS4uBh+vx+tra145ZVXcM455yTVt/b2dlkosry8POp3jBGJOJP1Am5eXp587B0Y+p2kFo8Uj8yrEQqXXXYZLrvsMhw9etSwrkB/f78UZ++44w7MmzcPn/nMZxAKhWSEjeivEO/E789wOIxXXnkFfX19mDt3rhTJvvzlL0uhTET8qL+JxTi9gJvOGMUADEf7LS0t0q2sd0ADiV0vGU0r+m8kZJoVllNFX/V7WfQ7XgauOlxdXiI3gxwOB3Jzc9HR0aE5LvXoBVzxW1+cJ2oGrq0O3MmTJ1NUJbYhDs69e/di165d2LVrFy666CLNuLHK3r17ZY5QIjidTtTU1FheRl1dnWbYsWPHsGLFiqhpGxoasGbNGjQ1NaGqqgqVlZVx229ra8PHPvYxAEMfOps3b467Xz788ENNLo4R2dnZyMnJiapOOtI0Njaivr4e9fX1cYXkqVOnRgnrZWVlyMvLw5tvvmk4j8vlwmmnnZZwv3p6erB06dKE5yNkvKJe9Ay38CN+RAyXgGs2TyIO3GQF3NEWJfX9Fz+a0lHAJdYxc+AC5vt2pBy4yexboyxL4PijuHpHj/7cTXZdjD57xvq1MCEjhVFNFSvT2oX4XBCfB7m5uTIGIZaA29LSgjvvvBOvvvoqAOCXv/wlgsEgPvvZz2L16tVJ96e29v9j77vD3KjutV91aYu29+beu3HDBhuwKaEmJIRwE1IJyQ3c3JBKckkgEPKlcNPzpZAQEkJJyBfAFFNs3HDBvbBre4u93l60q92VVl36/tjnd3zmaEYalW143ufx45U0c+bMzKnvec/7O85Iy7a2Nja/VoNkCNysrCxG7hkMhigPXZp37tq1iyloybsWgGSOZbfb4fV6mZ0E+Zvm5eWx59rT0yNJX2yfT58+jT/96U9R+c7Ly2P3FwgEoNPpJEpPGpcYDIZxH5uohUigqglAlwjo3fC+xzzpHc8Dl0esssV74PKfAXUWCvxx4u9Kz4SsN/hj1bQd4rgnJycH/f39GBoaUmxfxAVlInDpuSZDhKsa2VB0NQ0aUoFYMPmgEj09PSgqKhr3iWaq4IOiKHmh8Ojp6YHT6Uyos+CvkZGRgebmZsWtqH6/nzWEdrs9Jkns8XjQ2toqSYtfVZU71+12o729HV6vF9nZ2SgsLJSod4ERwpRM+ylts9mMmpoa1fecThgMBrbKZTAYUFBQEDXg6OrqQigUwrx58xgxXVdXB6fTieHhYdbJZGRkYPHixQBGOhwKQKKWjAcuPMPx3k6sQcNEw1gSuGPlgSsGKdAUuOMH0ZsXGH+yezJB7hnFe7fUz9G2w9FCqgQuDyUPXP46qYCfvGrlT4OGxKC08DLadUgMvk3tQ2FhISNwRQEI3z7eeeed2LJlC/utr6+PkZ1DQ0Oor69nu/yys7MVt2eL4G0GeI/XWJB7VsPDw8jIyIj6XiRw+/v7sXnzZvT396OgoADNzc0S39qrrroKzz77LP74xz+yti4zM5MJaHJyctixBQUFaGtrg9frlagoTSYT8vLyJARuOBzGvn37UFdXh02bNjHFstyOUavVygJqARfa7eXLl8Pn8yEvL0+iwOUxkQWM4q7adOe1uroawIXnlZ2dzfxb+eulosAVLRQAeX9bJQKXzuF9cHlLjXgKXNE/WW5BSO7+6HwiY+V25BLiKXB5ezS1aurJORvQMKkhWikAwPnz51FUVDTpB63UYJSWlmLKlCmqjnc6nUk1fqWlpSguLkZzc7NihecbvNzcXMyZM0cxXafTGRUlnd/WIHdud3c32tvb2XGlpaWYOnWq5BiXywW32y0Z6Nhstph5GU3Q1pjMzEzk5eVh1apVUVuMtm3bBrfbjbVr1zJrit7eXjidToRCIVZ2c3Nz8YEPfADAyMSUBmOzZ89W3ZHSM5zsZV+DhnRjLAlcasPi2cykqsBVuq6aa75fFLgTgcDlfc/kAmBoUI9kFLhktTBaZVKtol4JogIXiI50r9PpmK0CkFz94svhZF2g0aBhPMHPc8YqiJlcmtSX87sRebILuFDHe3p6sGvXLslv/f39rK3xer1oamqSkEJ5eXmyhCqPQCAgS3TFU2aKz+U3v/kN7rnnHrzwwgu4+eabJceKBO7w8DDbWdnf34/8/Hx0dHSw35ctW4ZFixYxqwhg5LnQNXkCNz8/H21tbYx4FgnclpYWdHd3AwBaW1uxd+9eAMC+ffvwpS99ieVHBF1DJHALCgokYjIgejwwkQlctdaLycJoNDLfYDkk0vcpHcsTuLEsFMQxhUj6imU8nio5EolAr9dLAuqK/r5yC0JiXeHJWDUKXL1ez87x+Xzw+XySeYdaC05ttKBhzCAWfgpkBow0xMuXLx/3iWaqSHRwkIp/DEXfBJQrfCgUUt0YyD37eBNccfuE3HF8Hkdj8JQo5DoPEXLPgvdO4oMKyaUTiURGtQxo0HAxYKwI3ETIvFQVuMDF5YHLewNOJAJXySN1so9BxgNqCVy+nplMJraFdjQgp66OB7UKXEB5UpkoxHKolT8NGpJHPAI33deSU+AajUasXbsWg4ODUfEwqD3yer1RAbmmT5+O0tJSACNEbFVVFUpLS5lIR01bw0e15z1hEyVw77nnHgDALbfcEvUcaachYcmSJQiHw3C5XDAYDDAYDCgsLGTtZm5uLm6++WYJgZuRkSHpC2bPno1IJMIsJ/x+v+Q9ms1mtquVCFwKGkff0fOhZ1BWVsaIZCLMxHZbbkyVbhuC0cRoK3DjIdb8dWhoSELkt7S0oK+vD7m5uZKyf+7cuZjBvKhMkK0GQVTYKilwlRTVtAs3Hk8Sz5eeFgdcLldcApeUtvyiwdDQkKSdSKsHrgYN6YQcgfunP/0Jy5cvT8o7dCIhUYIyEf8YAt9oUYNFgwilKIyJ5EWOwI3lIQNEr4QppTtaPj2JQA2BK9cp8ZM4ObJHNERXe4/JlAENGi4GiHYuo4VEAh5pHriJgb+/iUjg8lvXgImttploSNRCQa4sjFaZFD31EoUcKU0EDU38AKSswBXLoVb+NGhIDEoWCrEwGgpcXkmfn58vG1ian7OJWLx4scQmobq6GjNnzkRHRwc8Ho+qOQLNqzMyMhgplsw8cPr06WhsbGRp8l6+ogL3tttuw5tvvgmXy4VQKASDwYCKigpYLBbodDqUlZVh2bJlqKqqQktLC8rKymC1WiWiLlEhy7e1wAUFLjBC1trtdvT19bE8+Hw+RugSgTt79mxGIFI7S4pLSleuf5hMCtzxJnBjzV+PHj0qCezV3NyMwcFBWCwWyfetra2M0JXzwKWyJy74igSukgI31jORC0iqFMQslgcuAEngPhFiP280GpGRkYHh4WEMDg6isLAw4XGERuBqGDPwhV+n00Wtprz00ktsq8ZkVR8kSlCm4h8jRl2kqJo8+I4qHuTUvOK2BhGiZ0s8AjddUZtTQToVuHLKLblrxIKmuNGgQR5jpcAlolgk82IdmyjG0wN3PNtbyju/4DiRCFylZ6q1x/EhN0GKFaCO6o5erx9124pkLBRiKXBFnzyewE0FSuVQK38aNKiD0phe/D7ddSoQCEi2TatZNKLf5PwyMzMzJRYJZAWQyByBCFybzQa3280s4+QQCoXQ3t6OYDAYdcysWbMYgfvII4/gkUceATDSXhEpes011+Dyyy9HTU0Nuy+fzweDwcA+6/V6mEwmGAwGPPbYY2hvb2cktUiI0b1SnBJCJBKRELhdXV1RBC4AnD17FsAFoo+3siCCUK/XS9rsWLtGJwNEC4XxInDlyiZxPGVlZTCbzRgYGIDFYkFlZSVKSkrYceFwGIFAACaTidkZqlHgut1udHV1weFwME6JFi0sFkuU6lrMMzBSljwej0Qxnui9E4FLVpFykNu1a7fbMTw8LAkQB2gEroYJCJHA5RW4gNS3ZrIOXhMlKFMlcPnGQG4FjLctSCYviRK4csfJERfjSSiIzykWgStnpM775fAdg0bgatCQXowVgavW/1bMR7J1Vq6NjYVU/TzlrjtWkCOoJgOBqyE5xCI25Z75aJXJVN6vOCbgt3XSOIYWe9KlwNXKoQYNqUEMTqTm2FRw/PhxBAIBnD9/HoB8kEMRNMbg7QQIWVlZMBgMLMAjEZHJELhWqxV6vV5iWyOiubkZ7733HoCR+XcoFGJbufn5+Q9+8AN86lOfwowZM9DV1cXytHz5ctx1110ALlgUZGVlMcIWGGk7iWS0Wq3MT1VcyCaEQiGYTCbW1lLezWazRIE7c+ZMRuCSavLcuXPsXoARFbLFYoHP52MxaXgCl7es4aEpcNUjVtmkdzd79mxkZ2fD4/Ggu7sbc+bMQVVVFTvOZrOxuXRmZiZbeCAoKXBPnz6N5uZmDAwMYHh4GMFgkNU9j8eDzMxMSR4J/Lz9/Pnz8Hg8sNlsyMnJgcFgSFiBS+VSJGJ58Apw3ju3s7OTKXcTfXejOmJwuVxob2/HwMAAvF5vQoObyy+/fBRzpmE8IWehAIysrkx2EitZD9xkLBR4426lVdZEFLjJWCioUeBOVA/cWHmIpcANBoOKHrg0kEjkfSZTBjRouBgwHgrcRPKUCJQGuu93D9yJSuAqkeLagpp6yPWl9DzllOpjWY6TuVY8D1y+b0+XB65YDrXyp0FDYlCqK6OtwKW2gEhNtQrcf/zjH3jqqaeifnM4HBgaGoLZbEYgEEBTUxPKy8sxODgoIZbk0NPTA4/Hw2wEiMDl8ylCJMQCgQBrf8Tfzp49ixkzZjD7hKysLGRlZaGwsBAAsHr1anR0dKCkpEQyjiIFrhzkFLjhcBhms1ninQ+MEJXkgdvV1QWLxYKenh4AI2rh06dP4/Tp04hEIhIbiR//+Md45plncP3117P8xOsbJlNQ0/FW4MbywBV5AaW8VVVVwev1QqfTwWKx4NSpU7IWCiJnRGU0KysLBQUFzL6DiFT6XeQljEYjFi1aBIfDga6uLrhcLpSUlKCyshJFRUVR+RO9rpUI3NraWrz77ru46qqrotJQUuACiCJwx02BW1dXh9/97nd4/fXX0dDQkFSDSavaGt5fEMsCVUZacRFNyycjkvXATVaBC4BtNxlNBa5aD9zJoMBN1kKBHwzFMkdXWllWwmQv8xo0jAbELctj4YE7mgpcpTbv/e6BO1EJXKV3rrXHqSGWhYKc8mkyKHD5NGhRPN0KXHpuE1ntpUHDRIQ4rxjrNtzv9wMYmRvEU+Dq9Xo8/fTTAKSBxoAR/gS40N7U1dUhOzsbdXV1qKmpUSRinU4n9u3bJ/mOVzUqjTHEsQzlH4gmcIksJQI3OzsbU6dOZcfn5uaiuroawAipSM/EZDJFkYz89UTwClxegMRbKLS3t+PRRx8FMPKuZ8+ejdOnT6O2thaNjY0SBW5NTQ02btzI7BSSIXAncps8URS4SvwDEP08xXpptVqxcOFCACOBzsT0lAhcWvwsKSlhym4AaGxslPCIcs+kpqYGNTU18Hg86Ovrw4IFC1BWVibJH6/AlWtL6LcZM2aw71588UXcf//9UcfKxVwQCVzCmBO4wWAQ3/rWt/Dzn/9cdlWFwBsEa7g4ISpw8/PzGYE72TEeBG6sTjpVD9x4kdkT9cBN1CN4NKCGwJV7FjzpoDQASOZ9aoSBhvcLvF5vlC9ZshDbs8HBQbS3t8seGwgE0NfXh+HhYXR1dbHV/KysLBYZNjMzUxKIQ0y7tbUVubm5bNuVHCKRCIuADIxMcpTyxB8zMDCA3t5eNDQ0ICcnB0ajkfnLGQwGyUSK9+6i30lRMB4EbiAQQG9vr6xy2OFwRH1PAVyoDaVgFXIErvj8KKADwWQyoaysjKl8kkUoFEJPT4+kPSel0mRUNY8FqC7HKjOdnZ1wOByw2+2szPb09MDhcLA6yGNwcBAOh4P59TscDnR0dKRtPDA0NIShoSE4nU60tLRgYGAAwWAQLpdL8ZycnBzmO0kKNp1OB7fbLWkL6F7a29vR29sLl8sFvV6PU6dOoampCT6fD1lZWRLfRUJubi5sNpvs9Xt7ewFoHrgaNKQLSvMv0Ws1XSAPWbIHjNWn8NvD77vvPvzkJz9hv9ntdthsNlitVgwMDMBqtTLy0+/3K7YJRLaaTCbk5+fDYrGgrKwMDQ0NAJQVuCKBS/knJTAAFBcXo7u7m7VTPIHLzwt5ItFms2HatGlwOp2orq6WXRhXEheFw2FG4A4NDbF88RYKfACsT3/606z97u7uRkNDA3seGRkZqK6uxsGDByXz5fcTgUuLoeMljorlNy0KndTkTW7uTeP2cDiM8+fPo6ioCDabLUrxTruRxd0xscYXvDViovdImDp1Km699Vb861//ilr4IMjt8qOxwrhbKNx555147rnnYrLUcsq0ZH0jNUw+iMQ+T+C2tLRICNzJWhbiWQ6IiLX9QAkiCcr7synlB4jfOIh1UafTqSZwYx3H3+NECGJGeXjjjTewefNm6PV6lkdaCdy/fz9KSkrw0ksvsfP46JhKimMxwqkaJFMGNGiYiNi7d29MsiQVOBwORrqJqK+vh8PhQH19PQuwIUKn06G0tDRm25OVlYWmpibVebJYLLDZbDh58iRqampkyZsjR44gEAgwSymr1Yr6+nrJdXbv3i05p6amBhaLhX2uqKhARUWFKoVwunHixImoqNPAyJZKUuWIqK6uRmlpqeQ7Pu/0t8fjwaFDhwCMtH9HjhyJUm5OmTIFN998c0ok7pkzZ9hkVoRmoSAPNXW5ubmZBQGhSYjT6WSLE3I+j8DIlsNIJAKn04lwOMy2IKeCYDCII0eOYGhoSLIo0NTUxLbeysFoNGLp0qUsCEpjYyP0ej127dqFq6++mnnWUV9/6tQpdHV1sWv4fD6cPXsWgUAAbW1tskStyWTC0qVLY+ZfU+Bq0JAclMir0W7D6XqBQEByrVgELrWTZrMZ+fn5kt+mT5+OyspK5ObmoqurCxUVFcjJyYm7s4/mNLm5uVi5ciX7Pp6FgtjXEmm6Z88els/c3Fx0d3ejvb0d4XBYQuDKLcoSZs6cyf4WlZMEObFfKBSCxWJBJBKRLBybzWZkZ2czko6uccUVV6Curo553TY2NkosFDIyMlBYWJgQgTuZFnV1Oh1MJhPjTyYKgSsXP4YQq17KEap6vZ6932PHjiE7OxsbNmyIeo+0G5nsRtQIxmLxJ6IHrph/fhc0KYDFQGuEWApcWiyJpWaWQ1pK6d/+9jc8++yz7OJz5szB/fffj6uuugrFxcUwm83Q6XSYMWMGzpw5A6fTibNnz+Ltt9/G73//e9TX18NgMODBBx/Ed77znXRkScMEhhyBC1ycHrhjocClxiDeNeQI3HgWCskqcCeChcKuXbtkI8HSAKW/vx/Hjx9nAxH+Ocv52QCaAlfDxQ1q0/Py8tLmI5afnw+XyxVzh0Zrayuys7ORlZUFu90Oq9UqqVek+KupqYnZlpWUlMRU4BIMBgP6+vpgtVpZwBG/34/c3NyoAE02mw02mw12ux2BQAA2mw2VlZVsgpSVlcWO93g8CIfDKC4uht1uh8/ng9frRWZmJioqKmT9udQglTaG3ml2draEVO7s7GSKIVIJeb1e+Hw+ZGZmSghXnU6H6dOns885OTmoqamREIShUIiRX3a7HV6vF06nE36/X3ECmOg9ZGRkSAg2g8GAqVOnppT2+xVq6vLAwAA8Hg/y8vLY+87Pz0c4HFacyOj1elRVVaG1tRXhcFhybqr5zc7ORjAYRFFREQwGA7KyslBRUaE4Iaf+v6CgAHq9nqXhdrsRCAQQCoVYe1FQUACLxYKCggL4fD5UVFQgEomgtLSU7SCrqKiQtB+RSITV84KCAsVxj16vZ0F2+HM1aNAQH2otFEZLgUvesaL6NhKJ4Lvf/S6OHj0Ks9mMb3zjG6wttdvtUX0PtTXUR7ndbkb4xCJwleZfai0UjEYjE9h4vV5kZWWx8VZRURHOnDmDH/3oRzh58iTbZp6ZmSlZlI31PBOxUAiHw5gyZQqamprQ3d2N4eFhVFRUwGw2Q6/XIzc3l+3yItJMr9ejoKAA7e3taGxslChwI5EIeyc+nw8Oh4N9fj8ocAGMK4GrtEggErBA8gpcAJg3bx6am5vR19fH3q9okWAwGFhdVFvX5eqIktBUyQPXZDKxsbHb7Wb8CQ96P3wdnRAeuORFAgBXXHEFNm/ezCTtcsjNzcXSpUuxdOlSfPnLX8Z3v/td/PCHP8R3v/tdNDU14U9/+lM6sqVhgoHfogFc2PZBBO7F7IGbTBAzkcCNp8BVmxfgwn3E8rblr63GA3eiBTGj8vfHP/4Rl156Kd599118+tOflhzLbwfnFbhKiuNk3mcy52jQMBFBdWvZsmUxxwDpRjAYRG9vL3Jzc9HW1oYVK1ZI+pL+/n7k5+fjmmuukZ1MJIr+/n7s3r07ihBcvHixxP4gEokw1fCyZctw+PBh5ObmYsWKFXjzzTeh0+lwww03sON37doFp9OJlStXoqSkBPX19Th16hSqq6uxePHilPOdDOidzp49m03egJF2q6enB8uWLUNFRQWAEc++hoYGTJs2DfPnz1dMU6fTYdGiRZLvgsEg2xr5gQ98AHV1ddiyZYskD6new9SpUyVeaUp5S8c13y+IVZezsrKQk5ODWbNmYfbs2ez7tWvXxk33wIED6OzsxKJFi1BTU5NyPt1uN1wuFxwOBwoKClBeXo7ly5crHh8MBvHaa68BANasWQO9Xg+3243BwUEcOXJEcqxOp4Pdbkd5eTnWrFmDxsZGZGdnY/r06Zg3bx62bdsGt9uNtWvXSlR1gUCAleHVq1er2pk10ckCDRomKkQCd6yuFwqF2N88KdjQ0IBHHnmEfXY6nfjWt74FACgrK5P0p8CF+QSNKTweD9sBoBTnhK7Pny+mF0+BS2OicDjMviPCid9J88orr2DTpk0ARnYfqVWqyrV7PCEtKnDtdjsWLFjArK8qKirY883Ly5MQuHQuPbPBwUE2t8vMzGSWDH6/H++99x7cbjdro5V2NE2mIGbAyPtzu90AJp4Cl8ovj1hjK6VFh8rKShQWFuLNN99kaYtCKj6wO3+NWP1uPJW6Gg9cg8HA6pDL5UJTU5NEsCDmVckDN9GxZ8rGU01NTTh9+jSAke3HTz/9dEITN4PBgB/84Af41re+hUgkgr/85S+y0Rk1vD/AF0xRgRvL42eyIFELhXQocGN5uPDb+eNdg88z3Ue8+xFXhuMFMUv0+YwGKA/UyS9YsADz5s1jAxMe/JZtujfeA1dT4GrQMP6IpU5R2gaVCvg6G8t/TC5PsZQBo9UWpCNdNfeWruc81hNxDdFQU1ZSWZAd735Pyb5N6Xu5vxOxpUoU2nhAgwZ1iFVXlOpwuhW4NJ/gSU3yjSWi8J133mHWK+TnykNU4A4PD0sI3HgWCkoKXCVyinw5ybKAditFIhG2g6KkpERyzrZt29g5qRCdSgQu5ZXms+JYgN+FNGPGDPY9KSB5AjcjIwPhcBhGo5GR0zqdDrm5uSgoKFBcOJxsCly+zE0UAldOFa6mz4+16MCXZyqrgNRCgb+OeN1Y15NT4CqNecX88wH8AoEAI9N5yHngEoFLO4ESfXcpsyjvvvsuu/CNN94YVdl5xFKYPfjgg6iqqkIkEsHDDz+carY0TDDIFXw5C4XxHtSnivGwUIjngZvMZIPOUeOBy19jMlkoUCdPjWh5eXnUsTQAA6TPWWnFWyNwNVzMGO/6HY/4S1cdS+b+eK9rtfmYCG3DWF1badtaOvKQSLmcCM98IiHWMxvv+s4jVvlJFeKCTazf1eZPzbU0aNAQH0oWCvxvo42mpibodDqJqpN2lMyfPx/l5eXw+Xx4+eWXAcQmcCloExG4QOxxQzwCN56FgqjADQaD7Foip0Npmc1mRQWrGhGfkoUCpR8Oh6HT6aK233/84x/Hpz71KXz84x/HkiVLmCcukd5DQ0MSy6RIJAKTySSZL1922WW49NJLZd8BMLk8cIHxJXCVYrgkK9aKteggisxEIZWoxJU7T0Q8lToJQOTqHq/4JY/7QCAQ5S0NyCtwCwoKAFzgGcZcgctHZObNs3lQpmL5mJnNZnzoQx8CMLLtQNzCpOH9gXgWCpMdiU5o4q2QykFsGON54KpV4MayUFBqAHlD+UgkInucuGomXmusEYlEJI0sEbhyeeItFPiVOnHlj5DM++SfmTZp06Ahcaht40bjuvwuhES2islhtNrF0SCFYrXlqV6Hn4iP9TvVCLQRjPb9j/dzlhvviHVYKW9jocDVoEHD6CGddZNvD3hfWyJw8/LycNVVVwEA/v3vfwMYsZ8Rg0S6XC4MDAwwAtfj8cBgMLB5llJ7lKqFAilwSZzCz8WVRHlyFgqrVq1CUVERFixYEHX80qVLUVJSIktI8/mLt/uzuroajz76KG677TYWgDocDjMSWrRQGBgYYB6/cs9IDpoCVz3iKXD5Z6mmz4/FZ/BpBYPBKH5C/F+8bqzrDQ8Pw+l0sjpL58kJ0OTyT4sWgUCAqW15yBG4pCanYMBjTuDyDRCxySIyMzMRiURkAwbxmDNnDvu7rq4u1axpmECQK/gigctH8pysk6fxUODG88BNRQ0azwNX3D4cT50SS6k7VohEIqzsAZB4Vm7YsEFyrJIHrhKxnYoCN9HzNGiYaBjPBRoiX5JVyCUCpftTM1Dk8xFPKTzeJJdcXkTw36eSXyUFZSKK5XhpT/SJ2ETEZHlmqShwxXFnrIUYTYGrQcPEgloFbrrrlFhXS0pKmB88cGFrdG5uLvPjprmDy+XCsWPHJOnt27cPO3fuZFHpRQuFRD1w1c4NlQhcnU6H4uJi2WvKEbjFxcVYvXo1I6B5VFZWYuXKlUy1q0SI0b2IFgpi3uk8ukfeQoHEgi6XC/39/RKlsBp17WTzwJ0IBK5YxpJV4MZadODH97xKnO6fJ3D556BGgdvW1oZdu3Zh165dsteVA1//icCNRCKyYlU5CwWRwCWMGYHLR1wdHh6WPSYnJ4f93t/fr5gWX8k6OjpSzZqGCQpRkc0HfUg12vR4I1GCMp0WCvEUuMnkR40HrhhULVaaE2EizRO44jag5557Dn/84x/xzW9+E4CyBy49a3ELUaoErhbITIOGxBFv4W+0Fox4xZ7cteWIn1hkkHhMusicdBCratTF6WrX090/TIR+Z7JBTVlJ5bmmm6hMp9pdqR6K10pEgasRsho0jC7iWSjJHZsKxHFFVlaW5DOp+XJzczFv3ryoY81mMxO+5eTkMBKI8paqhYI4NyQSKRQKob+/Hx6PBx6PB4FAAF6vl81tiMC1Wq2KNgMWiyVq/uPxeOByuWQViIR4ikYlBS6dxwtoiMANh8OMwB0YGGDzO+ITjEYjsrOzJenEgqbAVY/RUuCGw2GJl6zb7cbQ0BB8Ph+Gh4fhcDiiSNHi4mLk5OQgOztbUm5jPZPS0lLY7XbYbDZJQGI6T2mnDn/dYDAosQ0RlfV0DH8OABQWFgK4YKGgZEehhJSNPiorK9nfnZ2dssfMmjULbW1tAICDBw/KBgsCgDNnzqSaHQ0TFGLB1+v1rJHlKxp9N1kHu/HITBHJbLkXSWKlFSvxs5pnSp2haCQfzwOXz49cmpSfRJ/PaCAcDrPFpoyMDEleiouL8bnPfQ6/+tWvAMgrcPn70BS4GjRcwHgRZWpVmqOhwBV3IShdL52K0omEdFkoxFJQjuXz0hSQI1BTlycyMZ5OBS5/3Ggr/LXyp0FDYlDbd4yWApfmA0QSEojAzcnJwdy5cyW/ZWdnY/HixXj00Ufx/PPP40Mf+hA2btyInTt3MjsAslCgeVY8AjeWAreurg4NDQ1YtWoVjh07BqfTiRMnTkCv12NoaAi1tbVwu92YMWOGJJiayWTCn//8Z+Tn5+OWW25haVutVsn1WlpacPToUXbdK6+8MooQ45+Z0jiIV+DqdFIP3M7OTpw/fx6lpaWS9HQ6nYTApQBsRPCtW7cOb7zxhuyzk4PmgaseSv2V3Bw5EfI8Eolg27ZtmDp1KiKRCM6dOwcAOHnyJILBIAKBAAYHByVlxGaz4fLLL0dnZycOHDjA0owl4LDb7Vi/fj37vHnzZsVjxXECPfdgMAi73c7mAnJBzGJZKAwODrIyy6cfDynLUvhVpYMHD8oeQ1sHAOCZZ56RPcbn8+HZZ59ln3liWMP7B/wAmcja7OxsVsH4QjwZMRYWCiIJquQZw5vBq8XFpsCVG2AAF1Th/I4BfjWbGmMlD1yNwNWgYeJgtNqbZOu50nejRd5MJgWunCVDupCIMktri0cX4/2cE1HgEojATUaBm458adCgIRqx6uNYtC+RSAR5eXlR1gG8AreiokJC8GZmZkKn06GsrAz33nsviouL4ff7odfrmbJ1eHgY3d3dcLlc8Pl88Hq98Hq9GBoaYn6dTqcTLpcLfr8/yn+TJ3AbGhoAAEeOHIHX60UoFILBYIDFYoHZbIZOp2M2hqTAtdls0Ol0KCwsjPK1tVqtknkhL3YJh8MYHByUfVb0jgKBgOx8X0mB6/V6cf78eQBgZB6vwKVnL3fdnJwc1NTUIDs7W7LjVwkiEa4UrG2iYCIEMQPkrTDkxF+x6qTJZEJ5eTm7p4GBAWZFYjQaYTabYTQaodPpYDQakZmZyXb5i7uS5fIYD7HG5OFwGB6Ph5Vbuk4gEJAsIsQicHl7h9zcXJZGb29vwmOilJcZ5s6di/z8fPT19WH37t3wer1RjdhHPvIR/PSnPwUA/PWvf8WGDRtw5513st+Hh4fx6U9/mlVOvV4vYcQ1TH7IKRx4Es1iscDj8UhsOJTUDhMZY0HgKlkoxFPgJpOfdChwJxqBGwqFcPLkSQDRClwCbWuSU+BGIpG4HriJkuY0KdQsFDRMVsSyBhira4tbnsT8pFuBmwiho0aBO1qE7mi+j9FW4KZDsayRscljtBS4o2WTkUz68SxY5IjcZK8xWsdr0HCxI56FQrrnH5ReXl4eiouLo+YDPIGr0+kwd+5cvPvuuwCkdgsdHR1oaWmBTqdDY2Mjs257/fXX8frrr0Ov1+ODH/wg7HY7Dh48iPr6ekn70Nvbi4KCAjidTjQ2NuKSSy5BaWmp7NyQCF6TyYTly5cjMzMTc+bMwblz59h8hydw6Z5EXsdisUjmhWIQcqWg5PTs9+3bh8OHD6OmpkaiqBUVuHT8zp072TGUl3A4DL1ej0gkEpU/nU4Hs9mMrKwsmEwmzJgxA319farIWP6+TCbThPfE5e9pvBS4gJSzSVaBC4yIPru7u7F//36JMG358uWwWCxwu91YtGgR7HY7zGYz+46QigWGXq+P8l+m/30+H9566y12LD13qlMWiwVer1eWwJXzwNXr9SgsLERXVxd6enoSfndpMYYjSwSXy4UXXngh6vcVK1bg8ssvZwTFpz/9acyfPx933HEHPvShD6G6uhrPP/88gJEH9eEPf1hSoTVMfvADZGqURQIXQFIy8okE0d4gHtJB4MZT4CaStqggVWNEHm/QxKeZrLF5OvGvf/0LTz31FIALK8wiaJXW5/OxxpgfDFFjLCpwkyVcxluNpEFDOjFeCzTxiNHRrF9yaSuR2mq2avO/pyvfk1WBmy4CV1PgqsNYLsaMZ9lWs2tIPC6RBaJkdtdMNtGCBg3jjVh1a7TacbmFRZG34AlcAPjUpz4Fm82GsrIyzJ49m9V18szU6/WwWCxRvrPhcBgdHR3Q6XQsTb1ez7Zj+/1++P1+tpjMHwNI54bBYBDBYJDNxUwmE/R6PTIyMhCJRDA0NIRvfetbAKTzI/FeDQaDZB4np0qkuRL9C4fDLI+Uj/7+fgQCAfbZ7/cjFArB5XJBp9Ohuroaer1eEszN5/OxbfRkLZGTkyPJT3l5OSwWC6ZMmSJ5Bmrmnvy8jriJiQw5n9mxAn89fqEgWQUugVeP82lRerRAYDAYFDkRMS01kBNl8SINHryFAq/A9Xg8UWIsOQ9cQOqDO+YKXAC4/fbb8dxzzyESieDRRx/F7bffHnXMH/7wB6xbt46tLJ06dQqnTp1imaWMV1VVMf/JiwkbNmzAjh07VB9/9uxZ1jApoaGhAb///e/x2muvoaWlBaFQCBUVFdi4cSPuuusuLFmyJLVMpwAlAneyBzFL1OM1FQJXrQduMtt81XrgyuVHKc2JosD94x//yP5WUuBmZWWxRntgYACZmZkSbx56PhqBq0HDCMaz7FLboqTATTeUSMZYJKeaOj5aeR+N9iXd21ZjKXA1jC3UPvN0KHBHyy4kFQWumLfxKINaudegQR3Eehtv4SQd/Szf3+t0OphMJixatEhyjEjgfvGLX8TnPvc5bNmyRTbNpUuXYu7cuXC5XHjxxRclv+n1emzYsAEdHR0YHBzEihUrUFpaiv3792PHjh3Iz8/HtGnTMDQ0FCWU4eeGg4ODqK+vR1ZWFubNmwej0QiDwcAI3O3bt7NjeQJXnF/qdDrJnI8ItaysLAwMDGBoaAhvvvmmRIlrMpkQDodx9OhR+P1+NDY2orOzU0JM22w2OJ1O9PX1obi4GOvXr2fzrDNnzmD//v3w+/3MgzcUCqGvrw8VFRWw2+0snUsvvRQbN25kBFki4iF+vkt+xBMZ42nxoFTXUlHgAvLB2YnIB6SWBCKUdsaqgTi2j3UulUtegQuMLGaEQiFJPpQIXPLB7enpYVYQapEWGdwNN9yAEydO4MSJE3jmmWdYRnnMmjUL27dvxyWXXMImWzyhE4lEcOWVV2LPnj2swmlIHn/4wx+waNEi/PSnP8V7772HwcFBuN1unDlzBr/97W9xySWX4Pvf//6Y5omIL51Oh1AoxCr4+4XATUa1kmgQM7lrqFXgqpkQiBMXNZ1evEkcPwCYCAQu30gqEbi0Ig1c8FTivWsISl47GoGr4WLGRFPgJlsvk72eHCaCAjcZqG3f+b/TofQdDQsFTYGbOCaLIjQdClylciIu0sQqq0rX0BS4GjSMLuIp5dO9gCkSuPX19di4cSO2bdsGAMy7kwhcQDrXkxPZWK1WmEwmLFy4UHK97u5u+P1+DA0NSdLMzc1FJBKBy+VidgKi2pTm26R8BS4EUyopKYHBYIDVaoXZbJbMwckbl/LIC1Z4BS55g4bDYWRkZCAcDqOzsxNer1cy7wsEAujt7ZX49IZCoSjib2hoCAMDA8jOzpZcc3h4WHIPYvA23pZCFKipESMR+PnuRPe/BSZGEDNAWo5TVeDywrREFbipeOAmcqyowCUbD6/XKynjdIxc3ngCN9F5SloUuAaDAfPnz4973Lx587B//37s27cP27ZtQ3t7OyKRCKqqqrBp0yZJsLOLGf/+97/jHlNcXKz421NPPYW7774bwEhhvP3223HVVVfBaDTinXfewZNPPgmfz4fvfe97sFgs+OY3v5m2vMshEAjgrbfeYlEBV6xYIekkeALX4/HAZDLhvffeQ3l5+aQqE8kQuIkO7uWuMRoKXDon3QrciWChkJOTg97eXpYPJQLXZrPB7XazAROt/vGEgpLXTqJetqNFMGnQMFYYb5JxNK9/+vRp/O1vf8PXv/515OTkyBI6sRS4ardST0YFroaLFxNRgSumrwbx8iD+rnTfWv3SoGHsMV4WCgQicO+44w4cPHgQW7dulVgZ8AQu33bIBewym80wGAyYNWsWiouLkZmZiZdeegnt7e3YuXMnqqqqYLVaGVmUm5uLcDgMh8OB/fv3Y3BwEOfOnUNXVxemTJmC4eFh7Nq1Cw6HAwaDAfn5+dDpdFi/fj2WL18OvV7P8llTU4Oenh4cOnQIANDV1SVpp81mMyOiSJ24Y8cOOJ1OFsS+o6MDbW1t7H5zc3Mxa9Ys9pnOt1gsmDFjBqxWKy699FL09PQAGCG96+vrkZGRIXlu/HMqKyvDnDlz2Jirra0NRqMR119/Pd566y1UV1dj3rx5kvY5EQKXP08jcGODtxhQq8BNxEJBSYFLBKkcl5CqB67a83gPXJ7AJYsPHnIeuABQXV0NAHjvvfck9UQN0kLgJorVq1dj9erV43HpSYFbbrkl6XN7enrwpS99CcBIQfz3v/+Nm266if1+55134tOf/jSuuuoqDA8P43/+539wyy23YPbs2almWxGDg4PMLgEAa3CBCz41vALXbDbD4/Ggs7Nz1PI0GuAbpdHywOWJQbUK3ESQjAI3kTQnggKXf9Z8kDIeOp0uSoFLW4b4+0i3hYIWxEzD+wHjVb+VrpvqAsltt92G48eP48SJE1FbG9XmK5F2cDIpcHmkW4GbrvvXFLiJQe1i9ETozwlKFhyxEG/BRe6zXLqxriU3uVWTp4u5/GnQkAjk2iFeKR/ruFSuyfdVZrMZx44dk/xOxCi/60+OwOXznJubi4yMDBgMBsyYMYMd43K5GClUUVHBzikoKGBEEj9P83g8cLlcGBwcZIKpUCiEwcFBFviJjufnNWRzCQDnzp2TqGzNZjMLNK7X6+Hz+TA4OMjIKZ1OF+UZSxYEg4ODsNvtEnWwXq9nu3LpOnR8MBhEeXm5JC1+Tks7eY1GI7vm5Zdfjs9+9rMYGhqC2+2WvOdk57IagRsfcn1cIoS5HHhhGi+You95RSu/qACMnQcuWXiIFgperzeKwFV6Hhs2bMBjjz2GrVu34tZbb1WdT2CcCFwNo4ef/vSnjHD60pe+JCFvCatXr8bDDz+Mr371qwgGg3jooYfw9NNPp3ztQCCA3bt3swYeGFHXypHDLS0tAIC5c+cCkBb87OxsAEBjY2Pca545cwbt7e1Yu3ZtVEN74sQJtLS0wOfzwel0oqSkRPK73+9HXV2dYrRMtaAonkNDQ3jllVcQDodx6NAhZGRkYN68edDr9ejr68Pjjz+OQ4cOwWw2s44dGGkw1q1bh3/84x/wer2w2+0oLS3FwMAAuru7JQ0Hr3b1eDzIyspiK7OhUAhlZWXs+VGD2tjYiKKiIvj9fvzyl79Ef38/S++dd97B9u3bYbPZ8NnPfhYNDQ1wuVz4y1/+gszMTPj9fgSDQdx5552oqanBl7/8ZYTDYbS1tSEcDuP8+fM4d+4cQqEQWltbcd999+H48eNRz8hoNGL58uUoLCyE2+1GdnY285XNyMhAUVER64T0ej22b9+OnTt3xiQ0S0tLcdddd2F4eBgdHR0Ih8MwmUyoqamBXq9HXV0dXnjhBej1etxxxx1swNLe3s7SOHfuHB5++GFJw5+RkYHz58+z4GVvv/02fD4f+vv7UV9fD4PBAJPJBJfLhe3bt6O4uBhmsxmrV6+Omnw1NDRERYyVw5EjR5h5/+WXX468vDzs2bOHbcHikZubizVr1rCFkBMnTqCxsREejwdz5sxhwQ2WLFmC3t5e9PX1Ye3atdDr9ejs7MSxY8eiBkIzZ87EjBkzYuZRg4ZYmAgkoxLBkiqoTXvppZcAKJMsqSryJqMCN915jrcNVsPoYyzq8kQiKpXqsVLelCwUNGjQMH4YbdsEHqKYxmQySbZNv/nmm/B6vbBYLJIds3ycFLlA02azGWvXrkVzczNKS0uRk5ODJ554AsFgEDU1NbjuuuskhJ3RaMTixYvhcrlQUVGBtrY2Nr8MhUIIBoOS8X4gEEBGRoZkvmwwGHD+/HmcOXMG586dkxy7e/du9Pb2oqurS3L//AKrwWDAsmXLYLVasXDhQrbbFhjhAETLBB68hcLSpUuRl5cHq9UKnU4XZaeptMvUaDQiGAxKdnmKEO0W1EIU6UxE8HkcDxGQnABJ7nlPNgVuvHNNJhOrY/ziRSIK3MsvvxwGgwGNjY14/PHHE1Lhjt8+Zg2jgueee479/ZWvfEXxuLvuuguZmZkARialvEI2WTidTrhcLoTDYfbP7XbD4XBEDXjPnz8PYMRWA7hA4Pr9flbAldSRPE6fPo2hoSE0NTVF/dba2opQKASn08mCuPH/BgYGMDw8zKJfJvuP0nE4HAgEAqxCDw0NweVyMZP1HTt2wOVyoa+vT/KMgsEgmpub0dXVxaJyUrp8dE7qiGnFx+l0smsR0TowMBB1rNlsZlFOe3p6JOkdPXoUgUAAg4ODOHHiBFwuF7umz+eD2+1mA4KTJ0+iu7ubeRgNDw/D7Xaz+zAYDDh58qTk3uif3+9Hb28vPB4Pyytdo7+/n60u070cOXKERUpV+tfe3o6Ojg4MDAywNGnVORgM4uTJk/D5fPB4PDh27Bja2trQ1tYmse9YtGiR5Hn4/X44nU4Eg0HWKQ4MDDAPKYvFwjx5+L89Hg96e3ujlH7t7e2Sd6H0j+7J4/Ggu7sbw8PD6Ovrkz3W4XCw+trR0YFAIICenh5WDkOhEPx+Pzo7O9HU1ASn08nU7F1dXYwopn/BYFBCamvQkAySsZBJ9/X5SQWPVMkiPmDo3r17cfjwYezdu1eyWMlfR+76cjYK7wcFrhzhmooCVy4tTYE7fkhGyToeSEWByy8AyZ2r9v7kykyi5Ukrfxo0JAa19S7dClw+LTHY1be//W0AI4HLaDefeJ5oc8cHpqZ/ZrOZiXKGh4dlCcVIZMSf1mq1sqBkANj8kieT/H4/TCZTlJ+t0+nE0NCQxP5g/vz5rP8NhUL46le/Cr1ejxtuuEFCRJESNiMjAxkZGTAajewf8Qz0nIiQ498B72dK5DIpfAlE0MqlZbFY2PNU2uEk+gLHQ2VlJUwmE6ZOnarq+PGE+C7GGnJ1Te55p0KkAlIFLk+Iiu87XQpcfoFWzjYxVhAztQSu3W7HpZdeCgD45z//GSXai4WJv7SgQTVqa2vR3NwMYETZGqvhyc7OxmWXXYYtW7bA7XZjx44duPbaa1O6PhW67OxsrFq1CgcOHGCEogg+nwAkHrirV69miqdwOKyq8sk1WvzqZiQSQVFRkSRKaEdHBzIzM5GbmxtleK4We/bsgd/vx9q1a3Hu3Dm0traisLAQc+bMgdfrxZo1a5CXl8f8gQhFRUX497//jf/zf/4PXn75ZVgsFpSVlSErKwvLli3Dtddei0OHDsHhcGD27NkoLi6Gw+FAbW0t2tra0NfXh1WrVmHmzJmMnHY6nVi9erXkXtra2vD000+jp6eHvZ9Zs2bh6quvhl6vx89+9jNJvsrKyhAOh3HJJZegtLQU3d3dLCppKBRCZWUlCgsLMTQ0hClTpuD06dM4c+YMpk6dihkzZuBPf/oTdDodtmzZwjruRx99FK+++ipKS0tx2WWXYWBgAPPmzcOUKVPwzjvvYHh4GNOmTcMNN9yArq4uNDY2ssbuhz/8IS677LKo5/6xj30MLS0tWLlyJaZNm4b29na43W5kZmZi2bJlKCoqwp49e/Dee+8BuND58+Xk5z//OQoLC3HNNdewZ1VfXw9ghOjcunUrurq6UFlZiauuugo9PT2w2Wwwm80wm80YGhrCkiVLGEFKW4GAC6uPdL1ly5YhLy9PsRwZDAY0NDSwFWQ632Qy4fLLL2fH7dy5E4FAICp9+rxy5Up4vV7U19dLVkPp3dPxs2bNQlVVFfr6+nDkyBFtsqhhUkMkYESk6rldWVnJlCk02AKA9evX47HHHlO8drzvlIiidBO46UhHjbp4IhB5sTBZyMjxhtpyko7ylK6+J5l04r1rtQrcVK6R6vEaNFzsULJQ4H8brWuK1yMcOnQIWVlZjMgVz4vVf/J51+v1bH5MuwJF8PMF4AJJRCKNwcFBRCIRZGZmIhAIQK/XSxS4Op0OPp8PW7ZsYd/df//9uOuuu9DT04OWlhaUlZXB4XDgj3/8I7q6utDT04M9e/agra0NFosF8+fPh8ViiSKybTabZMepHLFHRC3FJLFYLPB4PPB6vbDZbOwYo9HI7B744FYmkykugZuoAnfp0qWq+YeJBDm+ZbQhV9diWVaoqZNy74lX4BLpL5e+aHuQTgUuqW4BKYErKnAPHTqEoqIiVs+UgpgBwB//+EfMmTMHwAXLRjXQCNwJiBtuuAFHjhxBT08PMjMzUV5ejksvvRR33HEHrrjiCsXzTpw4wf5esWJF3OusWLGCNdgnTpxIG4FrMBhgs9kkBs9ihaXJsKjA9Xq9ksZAbQMq53XEk1a0vZ5fCbVarbBYLMjKyoraqqEWmZmZ0Ol0LIKnyWRCZmYmsrKyEIlEYLFYkJGRwQzaCVOnTsXatWsxbdo0lkeTyQSz2Qy73Y78/HzY7XYEAgFUVFSgrKwMVqsVra2tTLFKaVssFthsNvh8PtjtdhQUFLDrDA0NsY6NGlSr1Yri4mJ0dXVJBgQ9PT2oqKhAJBKB3W6H3W6Hy+WSeCK1tbUx3+K8vDx2XEFBAVN5lpeXY+3atYzAnT9/Pl599VUEAgG2naagoADFxcXss8lkYjYPvM/SpZdeirVr10Y99xkzZqClpQWtra1YunQpXC4XTCYTbDYb8vLyUFhYyFTHwEgE1+rqajZQMJvNqKyshMViYe9+eHiYlUN6psBImczIyEBWVhYrL1arlW1ForyGQqGojoyeOa1MK4HqC5V5fkWeP89gMDCVN58+gfefirWoYbVaJXnXCFwN6cREJSGSLedKO1TEbYVK4BW46VCUJopU3ke8vKZr26rStvR0KnBH+5z3I2KVnVQUbaPdRiSTfrL3o4YsSrQ8aeVPg4bEoNZ+J50euDQ/5UlKwle+8hUWZZ4/Ty4tpXzxBK6444cg+mvS/2TrEAqF4PP5kJmZiWAwGEXghsPhKN/ORx55BHq9Hi6XC2azmc2H6Hr8bk8ibfPy8qKsDGn+oqTA1el0UWSc1WplBC7B4/FAr9fDaDQyMY6SApeQigI30WPHG7m5uXA6nTED3I8WeJ9kQqoeuHJEKtkDAlJCVK7+8N64ibxHuTaE/85kMrFySQQuP68GLswX+vr6mHVnLAJ39uzZuPrqq/HGG29geHh4/BS4x48fx3PPPYd3330Xp0+fhtPpVFw1UoJOpxuXVYSJgldeeYX97XQ64XQ6UVtbi8cffxxXXnklnnrqKZSVlUWdd/r0afa3Gtk/fwx/rhx8Pp9kO4PcKoG4DUT0KiEMDAwwso8UsUT28elSI63Gg0Ys8KL6kFYy5Y5JpTPnVzr5Cio2aKLFQ01NDQCwTpE3gSdjd7HD4f/n/ZN4E3iRtJObWND5dXV1kmPpnfCrXKFQCB0dHeyYc+fOYfny5ZK06f9Tp04BGImqyDdSRCjzhCpdg7ccoOfm8XjYc1Mi1un51dbW4gMf+ACA6MaUt+AghSyVYbvdHvXexa0TvPk+f5+8zxJ/D7wCV1S8xuvE+IEg7wmllEdRgcsTxnSMXBsqrorKdbwaNCSD8d7mr0T0eb1e+P1+ZGVlJZ0+DcjWr1+PHTt2SNLm86BGpcp/r1aBmy6kg1iNlVY68qt2Ep5q2qkcEwtnzpxhHv/JQK/XY+7cuSgtLU0pH6mA70/27dvHxvBmsxnLli1LqS45nU4cPXoUDQ0N6OjoQHd3N4LBIBYsWJCWPKfj3HhBD0dTgatBg4bEoHbhJJ1jFH68D0A26PaNN96omJ9Y1g78OIBX9skRuPx8hOZAPJFLYhcCKXBF31R+vrB06VLWBorb0sliLxQKQa/Xsx2iJNoR5xI0xxXnRAT+GfIELgAcPHgQZWVlzC6QiGeaH9O1zGYzU1wqjQNTJRQnOtatW8esDMca9A737t0bpZCV88BtaGhATU1NTFETnSvyIJSeyDeJSJd1g5wCl8DXoWPHjjGy9uzZswCk8+pYBC4AttCjtEgjh7QRuK2trfjiF7+IV199lX2nrSAnhry8PGzatAmXXHIJKioqYDAY0NbWhq1bt+K1115DJBLBtm3bsGbNGuzbty9qgM+vAKpRlPJKTbnVQx4//OEP8dBDD8U8RiRERa8SqhhEHC5YsIBtK8/NzQUQ7XurllgSj+M7CVqVU5qApjLA5gkwXlbPexAB0QQu3S9NhEilzBPCSmRbZ2cn3nrrLWzcuJEdyxO/cvnj6+Irr7yCF154geWptLQUnZ2d6Onpwa5duzBjxgy2TSUUCkn8Uc+ePYvjx4/j5ZdfxpIlS9Dd3Y1Tp05h3bp1+PnPfw5gZLsx3whSw+R2uxEKhbB9+3bs3r0b2dnZ6O/vx6pVq7B161a8/fbbGB4eRkNDA3uO9JxEEIG7b98+/OQnP2EWESUlJVi4cCEAaZmmwHLUiNrtdgDygwf6PhaBGwgE8Le//Q1PPvkkfD4fdDodvvOd70QNVtRGPuW3fMi9ewJfPuh/cSAo1jseYrqa356GdCGWNcBYXJvaeP7azz77LAvQOX36dLz77rtJpU8E7g033CAhcKnuE5TuW1TgKkHp/IlcP9OtwJVLdzIpcM+ePZtyYNTW1tZxJXAJw8PD6O3tZZ89Hg+6urrYuCWZMVRHRweGhobg8/ng9/vh8/lw9uzZlAlcQjLkajwLFrUq9HQocLU+WYOGxBDLQmGsrskHhybQtmgesea0yVgo8OmRfyY/B/D7/di5cyemTZuGwsJCBAIBFBcXMyL02LFj8Pl8kvnCCy+8EJUnfu5BW8aBEfEVBaUGRuYWdrsdg4ODyMzMZHkX2zVxDsL/nZeXx4RDvIBIr9cjKytLEj8GuOA/TN/HslCYTKraRKDT6caNnM7OzmZCCRH8Yi95OQMjO35pHq8EXpQm8iCEWApcPh21UDqWriEG/7NYLPD5fGhra2OCxLq6Ovh8PlkCV0mQOG4E7tmzZ7Fhwwa0trZqg44k8cMf/hDLly+P8o8BgPvuuw8HDx7ErbfeivPnz6O5uRmf+cxnJGQ5IFU40gpWLPBbIoaGhmIee//99+O+++5jnwcHB1FVVSU5RqxEogKXVlPIl5TfGi9H4BKZpQbxFLh8vuJ9nwjiKXAp/7QiQ6DJCnV65NFK75/fHiJui3n99dfx7rvv4l//+hcuu+wyhEIhdk2550X3Fw6H4XQ68cQTT0h+X7RoEVwuF1wuF5qamtDd3Y2Pf/zjiEQicDqdEpVZc3Mzfv/73+P06dPYtm0b+573+J0+fbqk8eQtCpqbm7F7927J9UOhEPbu3RuVb5vNptjYkXr8/PnzLCAe4eabb0ZNTQ0jcAsKCuBwOCQDgeLi4iiyR1wppIEH1Q2eDD9+/Dj++c9/Sq67cOFC3H777QCiFbLxOlZS8ooWCvEUuOKWoUgkEmWszkPMjzZZ1PB+glhfDh8+zP5ubGyEw+FAfn5+wukSgbthwwZcdtllqK2thcPhiBqkiYhHascjbNNVP1NJZyIocNNF4CZD6iV7reXLl0vGWGrQ1dWF+vr6cW+PxetbrVbk5OSgq6srbe+itLSUbfuk79Nh9SG37VEJsRZc5NJUupaaayRK4GrQoCExxFtMTOfOFjEtJfLqyJEjGBgYYN/RgpXb7WZtHpE2SmMEms8rEbiRSAS//e1v8frrr+Oqq67Cvffei9bWVpw7dw5bt27FsWPHcOzYMUQiESxfvhydnZ3Yu3cvtm7dyuKgUOyUoqIiVFdXS64PXNi5SXMUfku7SHqtW7cOQ0NDyM7OZvaB8XYdARfmJdOnT0dzc7PkfteuXQuj0cgELqQCBi4ocEVBixyB+35V4I4nVq5cCZfLFdXHWSwWCRdVVVWFs2fPYmBgQJU4jywD6W9AfYCydCtw5QjcSCSCyy67DG+99RaAEfvI/Px89PX1oa6uDitXrmTHqlXgJuJYkBYC97Of/SxaWlrYjZaWluLWW2/FsmXLUFJSwkgqDcpYs2ZNzN8vueQSbNmyBUuXLoXP58Nrr72GAwcOqPK6TQcsFovEX1MOYgPNe5UQsRQOh1mgKD4YDE/g8o2AWisNseHg1bBKDUU8Cb4ayClweZ8W+o48fzds2IBVq1YpErhEvvGetSKBS4MBeo7Umep0OlkFLn0fDocZGVlYWIj7778f58+fR1lZGTZs2IDt27fjjTfegMvlYopgnvQERgjcWA3vnDlzsGrVKskzJQKXSGIAKCkpwZw5c7Bjxw6cPHkSwEjjt2HDBqaYy8zMlF3QAEY6An57xYoVK9DX14fGxkacOnVKEjzvoYcewtatW3HmzBno9XrU1NTghhtuiCJwxQafyrucApe+q6qqwpQpU7Br1y6cOXMmqrFPRIGrhsAVyxUdS8+CVuwBKYHLK3b5dOJtFdWgQS1Ga9t/ItcWry9OqvjFqERABG52djZ27tyJ9957DwsWLFBtoSCqSce6vo3GQk2s951OBe5kbZtycnISHvtS/zze9yxen5Qm4m/J1Hk6x2KxQK/Xp43ATQWiAlctYSunmEt3njRo0BAbsRYTR6seRSIjMU5ojCEnnhkeHkZra6vku8HBwaiYKAS+PamurobH48GMGTNiWiiEQiGcOHECr7/+OgBg69at+OAHP4j29nYcPXoU+/btY8ceOnQIixcvhsPhQFVVlUSEQxYQRJoRUcXPESiAE29nIKf8NBgMbE4vKm1FgRdBr9dLFjxzcnIkZFZ+fj6ysrJgNBrh9/uTtlB4vypwxxOkulaDrKwsyYJGvHQJIg/CpyfXP2dlZcHtdkvGLolek8Cny4vKeNEdfV6yZAm2bduGY8eOJaTAJZ5EKd6GHFImcOvq6rB9+3Z2g5///Ofx85//XJUCVENimDt3Lj7xiU/g8ccfBwC8/PLLEgKXl6qrmajyBYWXticLpS3/ImlEkxRewZuTkwMg2kJBrQJXJBXVEGfp9sDlSWOe2A2Hw2hubgYwQnCuWLGC/U7vLBgMIhwOyypwxedJnRrvs0IKzlj+QpFIhA0Apk2bhvvuuw9btmxBfX09SkpKEA6HsWPHDvh8PnR3dyM/P5916jU1NWhubo4r77/55ptZp04gU/Xh4WFWLisrK3Hrrbdix44drDysWrUKX/ziFxmBm5GRodjYmc1mVFRUMPXtlVdeiZaWFjQ2NqK+vp4NkCwWC6qrq7F+/Xrmp3zJJZcgLy8vpgKXzgUulFdeyUx1Z+rUqbjpppuwa9cuNDU1SQaN/LtQ64FL5yktLsh54PIELu+ByxO4vGKXT2eykyQaNPAIhUL417/+hb/97W8wm81R1jWJbE/iQfWdJhjk3eXz+dKqalVS/o1n/UxGbZgKRmMb7HgocJPBRHjfPEZzUSbdaSbzzBJV4MazWlCTVrJ50qBBgzzUWiiksz0Lh8OYOnUqQqEQNm7ciN/+9reS3wsLC/HOO++gtrYWRqMRc+fOhV6vR29vL86fPw+z2YzFixdLAqHy+SotLUVBQQEWLFigaKFw/vx5nDt3Do2NjZLvt23bhjNnzjBxDKGlpQXFxcUoKSmBTqdjcUuACwSTxWKB1+tlBC4/7+EFRgaDQVEVySOWp2h5eTnKysqwdu1aZGRkSMgwuS3wRBaHw2GcP3+ezS2JwI1lofB+98CdbFDTH8qVAf67qVOnoqqqionZeFxyySVwOp3IzMxUFUdJvA5BbCvE4H/88ZFIBIsXL2YELs8BTEgP3HfeeYf9vWzZMvzud79LNUkNMXDFFVcwAlcMQsX7hfK+ZUpwOByy5yYLJQUu/UafqYDypDF54fb19UkqTKoKXDkDbaX8JgOeUFOyUGhtbYXX64VOp2PEoRyByz8jXoErErhEKJ49e5YpfylNJQUuKTvp2VNjwZO/Op0Oubm56OrqQmtrK2bPns0I3PLycvT19cW12sjJyYlqAGllKRKJMJ+o7OxszJo1S3LcvHnzMHPmTPaZJ7RF6PV6CYFbVVXFGtaGhgZW/mlhIJa1BJ8m/xuRNSKBG4lEGBGdmZnJ7qOpqSmKuJdLWw78++PPVavApVVpvgzxdUJJgcsPzjRoSAXjrcCNRCJoa2vD0aNHFY9LRoEbCoWYyobaBFJW8gtt8RS4aoic0Xp2o5HuWCpwJwqhqQbjWQ/SDV41JVd+U1Hgiguo6X7HyeRJzIOSMlfJrkFT4GrQMH5IV1+kBrTbLTs7Gzabjc2tpk6diry8PHz2s59Ff38/XC4XU646nU50dXXB6/UiEolEBXES25OMjAzJXERU5505cwYej4fNg/Ly8tDf34/9+/ejra1NNt8nT57E7373O9TV1Unm17Q70mw2w+v1svk537aZTCYmMuFjvcQiRZV2+VG6drtd1tZKTn1JfsDkoa7T6Rjxq9Pp2DxavAbw/vfAnSxIpK/k35WcZzJv9SF3bjJ2aYkocHkrEZq3kw9uQ0MD+vr6MH36dACJWSiobbNSLsn8VoCPfOQjqSanIQ7oJQPRgcdmz57N/hY9V+XAH8OfmyzEQa24MmE0GqHTXfD74SX3RCD39/dLyKRUPXDFPMTKbzKQU+DStkCyG/jTn/4EYMSLlchdOo8I3FAohKGhIeatGAqF4Ha7ceDAAbz88sv497//jc2bN+Pdd99l2w+8Xi9aW1sT8sAVCVzaCkMELhHpR44cwcmTJxmBW1JSwn4DlBXbdrtdVslKAxBSWGdmZrKGjTB37lyJcn94eFhx5cxgMEi2p5aVlWHGjBkARqwlXnnlFQAXCFw5xFLg8oOmzs5O9vxra2slClybzcZIZ6fTiVdeeYUFVKN3wRP2SqDnD0gDkymRzEoeu0pRSDUFroaLAfEI2kS2J8mlKRK4gLoVcyWySqnvGS0FbjrVwgQ51VM6rpNOcm8sFbhiOsmcM97tsRyhma68jVXZTkYxrlYZnwg0Ba4GDaMLtRYK6VxcE9OiccDNN9+MQ4cOYcqUKYhEIpg6dSrKysrYOTQGLy4uxvLly2XzLOadxh0dHR0SS4bOzk6cOnWK7fBct24dAMiStzTfPnbsGE6cOBEljqJxDhG4Yp6am5tx+vRpDA4OsnmjGgWuOB/h75Gf98Q6j5+v1NTUYNasWZg5cyYWL16MdevWwWq1QqfTMes/zUJh4iKR/pAvA/S33M7xdNbreB64/O9yPFNBQQELznbo0CH2vVoCd0wtFPht+yRn1zB64JW1omp24cKF7O8DBw7ETYs/Jh0RgGMpcOlzJBJhBZQncIloC4fDzF8UkA/EFOvahPFQ4PLX/OAHP4jBwUH84Ac/wPe//30AIxWbzhEJ3GAwiBdffBE+nw+bN2/Gtddei8cff5z5GimhtrYWhYWFjCCQU+DSvfIEbmFhoUTlSZ0plak9e/Zgz549LJ3i4mLk5+ezld5LL71UNm92u122AczJyYHH42Gq76ysLGRmZqKoqIgtAs2bN0/yvnJzc2MalJeXl7PPJpMJ06ZNAzDiEfyDH/wAQLQCN9YkT0mB29XVhQ996EPst0ceeYQNcDIyMpCdnY3i4mJ0d3fj61//OgoKCrBt27aEVnzp+QcCAdUeuPRO+cURcUsHgVfs8ulMFMJAw+THRFDgKvUXZDGSDIHLn0NtgsViYTsb5CY7fL7k8qqE0aqP6UhXzb2l493H2wY70ZHOZzxekCPTY6nbkiWrqQ7xaY0HxGuLqjFNgatBw8THWPYd1O9TW0Fzq4yMDCboiUQiyMvLk3jx0xic5g00dygqKlJsT2ie6HK5UFdXh8rKSgDAn//8Z+zbt4+NUVavXo3NmzfL5rekpASDg4Nsu3lZWRkOHjyI3/72t2y+BFywUCDw84mhoSH09PQwBa7ctnYRSh64cgu1cufx6dM8ieZ1JSUlLFCWTqdjuxDl+iUtiNnEQLIKXHpvhYWFOH36NGw226jUcaXFW7k80T0YDAZmgwmMCCKbm5vR0NDAjuUXSORAO5V9Pp9q3itlAnfevHnsb97LRcPo4O2332Z/i6rZefPmobq6GufPn0ddXR3OnTuHKVOmyKbjcrmwa9cuACMdzvr161POm7hCITaURqNRUjB5AtdqtcJqtcLr9TIbBV7FGOt64t+AdGVEqaNIx5YKnlDjV1iIhP7FL37Bjl2zZo2ELAWkQczo/O7ubgwNDeH06dMAgPnz5yM3NxculwvHjh2TXL+urg6XXnqpKg9cUYHL3z/9mz59Ojo7O+F0OiUK7/z8fMybNw/hcBgmkwm33XYb5s6di7a2NmzZsoXZDGRnZ8v6tpaWlqKzsxPt7e0ARgYker0eH/zgB/Haa69hxowZWLJkCTweDx5++GH8/e9/x3XXXRfzud9www3o7+9nCxd2ux133nknjh8/jszMTHg8Htxyyy0AlIlbJQWuwWBASUkJiyJpMplQV1eHvr4+tLW1SSwUDAYDPvGJT+DNN9/E8ePH4XA44Ha7E/Jc4suEWgUuvT+yTdDpdGxlXISo2BVtObTJooZUMRG2jisNfLKzs+F0OlMicE0mk0RxYrVa4fF44PP5VKWTiAJXPGcikVsi0rVtNdbi2sWiwCVMlPZY7btN5F6V7i1d7ziVd62Uh1TeZbIK3IlSBjRomOiIVVdGQ4H78ssv45ZbbsHdd9+Nj370owCkBC4/DjEajRK1K+WBRBfV1dUsQJdSvmiuT8G7CPv375eMaaZMmYKsrCxmh1BeXs7mXFOmTJHEBykpKUF5eXlUoE0lBS5wIcAZ5T8RD1y5/l1pYZCuJaahRK7xClzNA/f9A7kykJ+fj3Xr1knEo+mce8iVD75PjqXApXxMnToVAGQJXKWAavxuYbVWbylryS+//HLmM/HGG2+kmpyGGDhz5gz+9re/sc833HBD1DHUmQDA//7v/yqm9Yc//IEZot90001RXjzJQPTtFBt1g8HAJrtixEngAqHL23KoJXBF5amaxnq0FLj8hL67uxvAyHuprKyMIreVrAg6OzvZVpkXX3wRu3fvxpYtW6KOoy39RMCK20f4ji8YDEoIXJ7Mo38ZGRm4/vrrcc0110iuk5mZifz8fHzta1/D9773PVRUVOC+++7DJz7xCclxvLE9QafTsYB1NJAhq4WVK1fik5/8JP7jP/4DOt2IQf3MmTPx4Q9/mK1IycFgMMBqteKxxx7DpZdeCmCEYPnwhz+MP/3pT9i9ezd+/etfMz8anuwU88anyT83vV6Pm2++Ga+88gp2796N66+/HsCIRw0NmjIyMqDX67F+/Xr86le/Yo0zbwWSiAKXVLVK5/ILBvw98QpcuTLPK3b5dOj+NQ9cDZMZVK95pQsPameT8cAVA5gRlAKLyOVLLRk5WuT3aKQ7FgQTr9AcK6T6rNKhwB1v8k6ODI21JTmZtHl//nQinWVdzJsSSTxRlNMaNFyMkOtnR7Mt/djHPoZQKITf/va37Dr8nIAnOYHoeDD0Hc0zaOeikgKXdkb6fD5JPAuaz+Xm5uJTn/oUCgsLcemllyI/Px933XUXU+oCkFjgARd2TItzUFGByz9To9HI1MV8rBc1ClwRopgp1nlKRDFP4AIjC/g8Z6ARuBMPqSpwgZGyzAcTSyfiKXD5fIjzabonslXkgwtSXVUicOXSjYeUCVyTyYT7778fkUgE27dvx5tvvplqkhcdfvnLX0q2q8vhyJEjuOaaa1jDevXVV2PVqlVRx33ta19jDfJvfvMbvPTSS1HH7N+/Hw888ACAkQb5e9/7Xqq3AECdBy5NsDMzM6M6WyJwP/zhD7MOMB0KXKVj0kHgynngygX6ou3+ovKRX0XicfjwYQSDQZhMJqaiLikpiTqurq5OYqRNeZED3+HzClyRzNPpdBJ7DqvVytRn/ORLqQGOReASSKkrpzBVQ3zy90rH8xFSAXUTTSULBX5ySedSvfJ4PBILBZ7Ep8Us8jEG1A0Y+OupVeDy75l/HkoKXLmgaulUuWnQAIwfmcFbKIiDO77uJop4BK4aUlgkwOL1PUpbtpNFKvU8Xl7TNWmOtS19LBW44jljcS3CRCFwRagtp6mkma53rJR+rDyoVe/GSzOWOllT4GrQMDpQ2w6lS6lHClc+LV6BS/NcuSBf/HwlHllE+SWVrN/vZ+dTPBFghCh64oknYLPZsHLlSnz9619HRUUF4wgWLlwYRdQaDAYcPnw4KuC52WxGX18fDh8+jMOHD+O9995DY2MjGhsb0draiu7ubhafJBEFLt2POF5IhwLXbDaz48lGgUc4HGZjQ6Ut7BrGBon0b3JlQA7pVODGCmImll8lBS7ZOTY1NbHvqH2wWq1obm5m9evo0aNwuVxMwManGw8pWygAwH333Yft27fjlVdewW233Ybnn38eV111VTqSviiwbds2fPnLX8b06dOxceNGLFiwgAW8am9vx9atW/Hqq6+yl1pTU4MnnnhCNq3i4mL86le/wqc+9SmEw2F88IMfxO23345NmzbBYDDgnXfewZNPPskmnQ899BDmzJmTlvsQK5EckUfqVHHbBgCsWrWKSc57enpQVlYWZbTOgy/kYmOghjxLh4WCHKHGd+6EsrIyDA4ORpHbIilA2L9/PwCgqqoqimDlUVtbi2AwKCFDxcpPnSRP4BYWFrL8EvFJaeh0Okn0RrI74MlVg8Gg2MjIWSiIBC4pcOUUpqIVR6xr8OWDSBs5v1uR1JVTzsit7vLkAW1xGB4ejrJQoOsWFBSgo6MDfX19KSlwlTokOQUunUvvR0mBy78vuTIldk4aNCSCibDNnyZOOTk5kolJuhW4Op2OEbhKql8+X0rfK01I0k3gitdOhshUQ7ilo/0YTbXwWLZvk7ktTQcRGS9tcfKebgI3FcSzFkqmTmiErAYNo4uxUuDyIGJQzkKB5iRGo5GJKMQdCHL5F7/nCVxgZD5DAabNZjMTAtE4h3YFlZWV4Y033kBTUxNOnTolSdNkMkns4AgWiwV+v58FQuvp6WGxS9xuN1wuF0wmE8xmM7NFVKPApfkNPweOReAqeeDy4J8Xjcd8Ph8bq9Hv9Nx0Ot2oKTc1qEOyCly18+hUoaTylvtdFMHR56lTp0Kv12N4eBjt7e2oqKhg9cxkMuHEiROS+9fpdFi8eDGL1RFLuMgjLQSuTqfDv/71L9x999148skncfXVV+Omm27Cxz/+caxcuRIlJSXaqocK0CpXLFxzzTX485//LAngJOKTn/wkhoeHcd9998Hr9eLpp5/G008/LTnGYDDgO9/5Dr797W+nJe9ANCEqbhvR6/WMwBUtG3Q6HT760Y9i165dOH/+PCPmYhG4ahW48QbbcpX+8OHD2LRpE1vlnDp1Kg4cOICuri5s2LABPT09KCgoYEQ6Vbrjx49HqZ71ej2Ki4slBC6/MmsymaK8Gw8ePAgAqK6uVrx/YGSr/vXXXw+32w2j0Ygvf/nL2LRpk+QeT506hc2bN+ONN97AwMAAAKkClzo06lD5YGYAmDcTT9ryz1XsvOUaQDkCl0hH8Vw1xCddk39udB90Pk9YypHaIuRWhnnbAVKIezweRurwBG44HGbblPr7+6MWERoaGrBu3TpZr3C9Xo+PfvSj2LBhg4RslXs+dC3RAgOIrcCl4/lBUyyFugYNiWA8SDLx+tRfZGdnMwLXaDSywX26FLj8hIEmbnL1R4msej9B7r5SUeCK6aZDgZsI0qEi5tMZq2uPJkQFOSEVZbMSWZEupKLAHY1rJZrWRCsDGjRMVKhdOEnHGIXmZoR33nkHBQUF6O/vBxCtwCW7Aa/Xi7a2NmYRaDKZFMkiMe9E0FK6oVCIzSMyMjLYHGPGjBmYMmUKmz+UlpairKwMLS0tUUHP58yZg/nz50ssC4ERr9z58+ezz+3t7ZIgyKFQCOXl5bjiiisQiUTQ3d2taq7Gz+34+01FgctflzgFnsAl0HMzm83v23HYZEGyClw1dpjpQDylrxoPXIvFgtzcXPT19aGzsxMVFRVsHmGxWNhxFBuIj58ExN55LslrIjcWC2azGU888QT++7//G5FIBC+99BJuu+02TJkyBTabjXljqvlHisCLBY899hgef/xx3HXXXVi5ciUzIjeZTCgsLMQll1yCe++9F/v27cOWLVtikreEL37xizh+/Djuu+8+zJs3D9nZ2cjMzMTMmTPxhS98AQcOHMBDDz2U1vuIZaFAn5UIXAKpHKlAx5KSyyksCXIK3EQsFP79739LtqicPXsW+/fvx1tvvcU6PIfDgXfeeYflNxwOy1pWXHfddVFbaUQCl1BRUQEALFKoGITuO9/5DgwGA2688UaUlpYCuLDaGgwGsXPnTknlj0Qi+Oc//wmv18v8eDMyMlBdXS2rwKXnUVBQwNLIzMyEXq+XELi8UvZLX/oSjEYj7r33Xsm9EXS6EXsM3i5CSYELqFNGiwSuTqdj9yGnwBW/izd5lCMPiMAdHh6WlGOePKXnJqfAfeWVVxQDPYbDYezZs4ddiydb5e5bVODSP6VIq0qksNx2EA0aJhP4es4rcAkUpRiA6oBjPNRaKMSbGMipDcdKgZsOpeNoK3Dl2uZ0k1mJkHrJIJ3vaTwhR6anO2/pIk8JyZyvpv+Xu0Ysuw+110g0Txo0aIgNOQVuOuFwOFhgZEJdXR2am5vZfFFOgWswGNDV1YXu7m4JWaNWgUtzJxq/hMNhNg+12WxsTG+xWFBcXIzS0lKUlpYiPz+fiTosFovERmHevHmYNm0a8+skFBYWYtq0aezflClTWHrl5eWoqanBzJkzUVlZqTjfkLsnUYGrxBMQEvHABSA7xqPf6TtNSDj+SFaBO1bexYkocEURG6+uJ76AhCQ0j+DLIPlQi/VoTBW4wIjXw5133om9e/dqK8gJYvr06Zg+fTo++9nPpjXdmTNn4rHHHsNjjz2W1nSVkIqFAp3Dm5ED6j1wxePUkICxCNy6ujoAwMMPP4ytW7di+/bt6OnpiVqtpFVXuejn69evxze+8Q2sW7cOL7zwgiQ//P88gTtv3jy2dQWIJnBXrVqFv//97zh58iQ6OzvZNhq73Y7BwUG0tLRInoUcKbd7926YTKYoBS5PBPIWCkTgGo1GSQNFfy9cuBD/+Mc/2MqzUhTHsrIyRkzn5OTIKnD5PMcagNF59Nx5MlgpYJncd7EmcGI7xlso0HVFBS4RuE6nM2oRgcrUV77yFXzrW99i1zp+/Dg2bdqEwcFB9p0SgRtLgavT6STfieVAblEjnVtYNVzcGG8FLnChPaDBEzAyYKJBUzo9cKmv8nq9CZG3gPJiyXiqEZUQr11IlwJ3NJFsflKxlRlPIng08X5S4Ip5StbyJJFrjNbxGjRcrIhVV9JZj9555x3J/Ay4MObg42LIBTETd5PGInDFvBOB6/V62W5PEuTw8WTkBB8U5NpkMqG0tBRDQ0MwGAxsbinGYZHbHSvmKRQKxRSMiOfT/ITitRDkbCT4vPPHyeWF/8wrcB0OB9xuN+rq6jBt2jS2sK8UQErD2CFZBe5E8MAVf1dS4JJoDYgmcKkM6nS6KMJ2XBS4jY2NWLNmDfbu3Zu21XQNkw88ucf/T7+pUeDSJJk6OzlilBBri3yqClwi21asWMEUzzyBS4QAT+CK6efl5SEnJ0eyDV9cddTpLnjyZGVloaioSJLG1KlTo/JGDQBvS7BmzRoAQFdXlyQquujPWFBQwMhz8RkpKXD5aKlyHrikmqd3ptQA8srx3NxcyQBGblKoxgOXygcphPn7kvNITpTA5T2reAsFfiGCV+AS8d3f3x/VuNfW1gIAli9fjuLiYvaPVsAHBwfZOfQs1Spw+QESfxyBD2KmpMDV2mwNkxF8uaV6yStwdTodI1tHI4hZrDQTJYSUfk9n3UyWSBptBa5cGum6/0QG+BNBgTvebfHFrsDt7e1Fe3u7Yj2MZfehdA21+ZsoZUCDhskCufY91twi2baMJ28/85nPALgw3+DntjTv4glcmktQHmIFMRPzzguevve970kUuDwBK7frlRS4JpMJt99+O/7rv/4Lv/71r5ndmyimEufmYpo8gcsLRmKB5o/8gqgSKSt3XSUPXP4YGp/5fD40Njais7MTTU1NqKur0xS4EwiJ1D3+fY3VuxMXNGKVOSUP3FgKXJ7AFUVn46LA/exnP4uenh52o6Wlpbj11luxdOlSlJSUyAas0vD+gxoFLnVsSqt8ogJXrYWCkgJXSW3IHyN2PoFAAGfOnAEAzJ07l5Gqvb29rDLOmzcPR48eZdtm5Ahcq9XKVktp5VHOH5gI3JycHNapUr7kPHApDZ7AXbhwIXbv3g23240zZ86grKwMwIj1Aw+ecOQVuH6/X2Iqz5MVlHclD1x6rrFWY3U6HbOHoHvlFbhyZHwiFgpyaYkWG/zneOoffsIqeuCS5yUgfZ4AJASukgJ33rx5kmtR+QqFQqx+KBG4cgpcnsBVGlDxxuhy9ZIfWGrQkAzGW4EbiVzwwOUVuEC03UEiUKPAVVqQ5J/JaJOHY51urPedTFuSCCk2VkhUgSvXxySCiXDPclBa6EuHAlcu3XQgWQWux+PBd7/7XQAj9lf87+O5u0CDBg3yGKv6SRZoGzZsYOKa3t5enDx5khGqXV1djATirfLEviQWgUtwu904ffo0BgYGMGPGDDQ0NODkyZPweDxsLsoTTnLzBZq/mUwmWCwWzJs3D+Xl5SxvRqMRNpuNjXPUKHApcLbaQM2iwIRPO10KXBqf8aIvnU4Ht9vNxoOaAnfiQE1/X1VVxcqMGEdHLq101P+8vDwsW7ZMUg/4cZmchYKowBUJ3EgkwuYeRETHUuCqHQulTOCeOHECO3fuZDf4hS98AT/72c+0inIRQkllStDr9ZJtJjzoHLERVhvETEmBy1do8Rix0r/wwgt46qmnMDw8jGAwiKysLFRVVTGCjVfgyhG4IonscrkYcRaJRGA0GmXVyVShc3JyJEbzZWVlsqtO1OlVVlay76qqqlBTU4Pa2lp8/etfZ42dXIR0pUaD0ubVrPScYnngKgXaEr/jFbh5eXlpJXDJP5u/L3EynehkXCQ2eQUu5Y23UAAuELinTp3C//zP/+Cyyy7DiRMn8Mgjj6C3txc6nQ6zZ8+WXCcjIwNWqxVerxculwsAFNXMSgrcPXv2oKGhAWvXrpUcR+CPlws6x/+uQcNkAl/Pqb3jF43D4fCoELiJpinXF010Ba4aQjJditlYW+sniwJ3NNIZD6RDSRovbSW1WarpJgI5coJ2ygDA0NCQ5DhRgZtIeUpUgatBg4bkMRoKXJr/FRYWMv/Kuro67Ny5kx3T2dmJkpISAFIPXJH8kYsBQvki5S4JcoaHh/HRj34UP/7xjxEIBFBfXw+HwwEgmsCleQtdIxAIMAu8cDjM4rXwpGgiBC5wQdSjJAoRQfNHcQ7Gi5rkzuGfFQCJ3SD/nACpApeH1+vVFLgTCIn0h0ajEdOnTx/tLEWBF5uJiwx8GVKrwOXnCDRvkOM/xlyBu2fPHvb3smXL8Nvf/jbVJDVMUsRS4EYiEfj9fuzevRuAsoUCnTswMIC+vj6cP38ejY2N7Pfc3Fy2vV+NAhdQXr0T8/ulL30J7e3t7Pfly5dDp9PJErhz584FANaJhkIhRr4RFi9ezLauh8NhxaBdtN23sLBQsso0c+ZMxY5Rp9NJjq2oqMDcuXNRW1uLAwcO4MCBA7LnXXLJJVGNjdlslmwDpgaLSMXZs2fH9MAV86ikwJ0yZQr0ej1sNhtsNpvsFgL+bzUWCvwAIpYCl4hYJSN9ufSVCNzh4WE2cBAVuIWFhQBGysXTTz+Nd955B93d3ez5zp8/X7bs5+fno729XRKQTi5/Sh64v//97wEAN954I9avXx/17MTjeUxU1ZeGyYXxUqnxpIqS1xmvlk0U5E2tROAODg4qBhal9oMWZ3w+nyQKrYjRqovpeCdqFJOjqfRNBxLNX7J2E8liorTFsSwU0qXAFVUr6QblaWhoCJFIBNnZ2VGLAm63O2r3yfHjx9nfYnvBPwPez3G8F1o0aLiYodZCIR5++ctf4tVXX8Vzzz0nsWEikOq1qKiIEbhiXJTp06ejsLAQFosFZrMZnZ2dMBqNUXNUk8mkqCgtLi7G7NmzGfF4+vRpNhdtb29HXV0d88DlA5MB0vgXpJIlCwWyPwiFQhJbAp4YpbESnx4PUuDyilo1BC6AqDYzUQUu+fYGg0FkZGRIBE+i+IuebzAYZLsmNWHh+GM0+8rRnHvMnDkTS5YsiaofQGwPXIfDIeFXzGazZFczcIHHEAOxx0PKBC4pEAHgIx/5SKrJaZjEECuR2PC/9dZb7G+xcxRJxcHBQfT29uLs2bNRtgLXXHONhEzkzyPwFUBposAThf39/Yy8/dWvfgWz2cy2z8UicPnyzweh+sc//gGLxcKUjeFwWHbLDABcf/31yM7ORnV1NWbPno3vfe976Ovrw6pVq2KuTmZkZODNN9/EyZMnYTab8bnPfQ6VlZUoLS1FTk4OvvzlL7PObN68eZg/fz6WLFkiS34S+BXcn/70p9i9ezfmzp0b0wOXJ3P5581Dp9MhNzcXd955J8xmM2uo+MaciNVEgpjxn2MpcJXSi6UqEzsaapC9Xi+7Fr9VKhQKRQWda25uBjDSaP/yl7/Exo0bZa+Xl5eH9vZ2pvhRCmImp8DlV5zperEUuBqBq+H9Clr44AfqvAI3UQ/cI0eO4IEHHgAQTeDS5+effx6f+MQnZEncSCSCBx54gJFCOp0On//857Fw4UL2ORbGmxhK9rrpUuCOhzpzPBW4E1V9qVYpnkra6X7HOp0OX/jCF9ji5rp16yS7BW+66Sa8/PLLmDt3Lt5++212Hk/giu0FXaOurg5f+cpX8I1vfAMPPvhgzHwlq8DV+mMNGhKDHIHLI1579eUvfxnASKDhP//5z1G/k2CnpKSEiTVELF++nM0Vzp07x64nqk1jWSgYDAbMmjWLfU/pFBcXo729HZ/73OfYb7xaEJASuDQvofkRze3oO7pmXl4es4cQxzFiHkkMIhLFsSBnHRiPwJVT4FqtVsyZM0f2eHF8RvPMcDgMp9PJvtMwvphs/RvlNy8vj9WNzMxMuN1uxk2J1gc8gdvb28sWEGinsGipOW4KXD7oEq1Iabg4IRJEIolEjSgArF69GufOnYNOp0NhYSGmTZuGSCTCOkWDwYDs7Gzk5+czq4C2tjaEw2EEAgHWMBNIESFWCL6TEBsMvrKRP2llZSXuueceyXGUp66uLkbYko+py+VCIBCAyWRiCtyqqipcd911ePvtt1lnR3mT6+iys7NRVVXFVmSWL1/OfoulwAVGvG89Hg/rhK+66iosWbIEVVVV+NWvfsXuq6ysDNOnT5dYDfAeuGLa9C5mzJjB1KixLBT4BkdJgavTXbB9EAOnUX5iWTPwkFP9iu9eLB/UYKpR4PLPge6N99Wk72irNj2D3Nxc5OTkYGBgQJLW7NmzcffddyteizoCUYGrZE/BK2p55TetysdS4Mp54AKTp0PVMDExkRS4/Cp5JBJhA/dEFbh///vf2d/i4suiRYvw9NNPAwDOnz/PAkny8Pv9EkIoEong6NGjcRW46cZoKmPTnXa8SfhoQ1RpJgI1lhOJpjMeGEsLhdGC1+vFk08+yT7v3r0bp0+fxpw5c9De3o6XX34ZwAgZSxOsSCQiUdSJBC7l+bnnnoPH48FDDz2EBx98UOtDNWgYR8Sqd93d3UzkQASs1+tlYgcCP1948cUXo34HLgh2ysrKmF2aCJ5IjGULZDKZFMUUIug4kTSeP3++hOgV0xDJIn5OwytwP/nJT2LXrl248cYbJQGslfI0NDSElpaWhCwUALC5UU5ODtxuN7KyshTneXIK3FgwGo2MsKbzbTYb3G63YmwEDWOPyTQWVcKaNWvQ3NzMBFuxPHDffvtt/PSnPwUAye47OQXumBO4vD8FkQcaLk7EUuDyk+vbbrsNAwMDOHHiBICRRnX9+vVYtGgRpk2bBmBEBTF//nxMnToVS5cuBTDiLUT+Pfz1CDyBKypwRZJRzC/5nokBpoALixR8QDAiQ0OhEAYGBlBYWMjUk/n5+VFKyUgkomihwAcPU+Mny3/PE30igTlnzhxG4Obm5katloqyfXoWIslJ54iqWyWVqNI98qBrxiJwYw0KYilwKa1Y20D57+TAf0/3bLFYJAME4AKBS15TwEh5EQlcUmwrgQZNVIaULBTkFLj8tc6fPy85jhDPAxfQJp8aUsN4Ebg8qI/hlRaRSIRNqhIlcLdu3QoA+Mtf/oJbb71V8tuiRYtw3XXX4bXXXlP0auev9+tf/xr33HMPfD5f3Gcl/j4RFLhq2spU8qu0Y0Lc4p4Mki2bF6uFgohYSqlU0uSRbgXukSNH4PV6UVpaylS2b731FubMmYNt27ZJjiWilh+nAsrtBXlcAtLI9HLQFLgaNIwu5Np3GufKEbGnTp2Kig/S39/P/u7r68POnTslW/SBC2PtsrIyybyJBy+IUZoT84Qqj3h2AkQYVVRU4KMf/ShMJlNUPvi5Ec3TaH5In0UF7vz58xkpHStPVqsVkUgEQ0NDqK2tjQrWpgQ+ZkkkEmHPsaSkRPFc/j7U9j1Wq5WJYCg4G302mUyS2AgaxheTqX+jhQ/Ks81mkyjBY3ngAsAvfvELANJ5SSwPXLXxaFImcC+//HKUlZWhs7MTr7/+Or7xjW+kmqSGSYRAIIC2tjZEIhF0dnait7cXnZ2dMJlMMJvNEiKQCK7S0lLJNg1+kMz7gz7zzDO47bbb2Ge/34++vj60tLQgIyMDjY2NqK+vZ79HIhHk5eVhcHAQhw4dQlZWFiNYh4aGJB15RkYGurq64Pf74Xa78fDDDwMYIV8PHjyISCSC/v5+BINBRqrx57711lvIysrCwMAA6uvr4XQ60draCmCE1PP5fGy1d+/evejo6IDBYIDFYoHD4WDPCBhRXYZCIQQCATQ1NWF4eBgmkwlDQ0OS1UMAaGhoAAB0dHQgNzeXpUcB0hwOB06dOoWenh7Jc83KykJfXx9MJhMaGxvR0tKCrq4uOBwO2Gw2OBwO9Pf3w+12Q6fTobm5Gc3Nzcw/zmg0wuPxsM7Q5/NBr9cjGAwiNzcXPp8Pg4ODCIfD6Onpidqq4nA4JEpRfvVXp9PB6/Xi4MGDMJlM6OjogMvlgtPpVJwcDQ4OwuFwICsrCz6fD4WFhZIOv7W1FT09PQiFQlHq62AwiL6+PhQWFqqyUOAb04yMDEl56Ovrg9PpRF9fHzweD957770oE31gRNnd1taGvr4+2d+JFO7o6EB9fT0sFgv0ej2ys7ORk5ODYDCI3t5euN1u1NfXw2AwoKurCx6Ph5H0wEhQya9//evo7+/H9OnTJVvHe3p64HQ6YTKZ4PF4mJrA4XDA6/WitbVV1vcrGAzC4XBInoNOp0NBQQFMJhOsVqvEl7qnp0cSCTZRZGdnayvlGlSDr9tU7kQFbqIBx370ox/hxRdfxNGjRwEA1157bdQxOp1O4rsm15bwCj5aCPT7/YhEInj++efh9XrxzDPPRPm0jwaBKw5CkzmfMNYK4nRhrPI30Z9DPMSyH+J/S4YYVzonXQTuwMAAfvOb3zBLrKuuugrz58/H22+/jYcffhhPPfUUW+gk8O0CT+woWa7wJM3GjRthMplQU1ODxx57LOrYyV4WNGiYjJg1axZMJhMbtw4PD2NwcFDiiVpaWsqO5+3wgBGChf8duLDbraSkRFV7FWs3IVka8IhH4FZVVeG3v/0trrrqKjgcDrz66qtxCVx+9ycJUILBYNQuSCXrOv5zdXU1mpqa4Ha7UVZWhqysLOTn5ydkTVBYWMief1VVVZTtnHjPcnlSQk1NDbq6uhCJRDBnzhzJOJC3YtQwfhhNUcJo7mCLld9YClwe8RS4Y+6BazAY8I1vfANf+cpXsH37drzxxhu4+uqrU01WwyRBXV0dW7msr69Hf38/IpEIM3t3Op3Iy8tDJBJhk2sKZkUFWSTIePAFuba2FsPDw7DZbBgcHMTp06cZaQoA+/fvBzAygCd/ktbWVoTDYfT29uLo0aNsQq7T6WA2m2EwGLB582Y2oB8cHMSLL76IoaEhdHR0ALiw/Z6vnE8++STL9zvvvIPS0lLmUxSJRLBlyxY0NjbC4/GgpaUFwMj2EZPJhOHhYeh0OrZVr7W1Fd3d3TCbzdi3bx8GBgaQm5uLnp4e5OXl4b333mP3eO7cOfj9fjgcDvT29qK5uRlWqxUmkwl9fX3o6elBQ0MD62QJAwMDrJN94YUXJB6Rw8PD6OjowMDAAHp6euB2u3H06FGcP38eXq8XGRkZcLlc7LpNTU1obGxkSuN169ahs7NTQraSnxKhsbER/f390OlGgqOJDe25c+eYxQaR8larFYcPH4YcvF6vJLhdIBDAmjVr2IDl2LFjqK+vZ+RzJBJhg5hAIBCXwM3JyWG/8WUwOzubEbgmk4mVJyLeT548GWVJAYy0k9u3b8epU6dkr0f1pbm5Gfv372dq51AohKGhIZw9exY9PT0IBoNoampi54VCIclzdzqdbLvGtGnTcMkllyAUCqGrqwtGoxHFxcXo7u6WEKwNDQ0suBIf0ZZw7tw52d0VZH0CjPgLUn2jZ5Is9Ho9rr76atnnqGHiYrwVuHwfo0TgqvHA7e/vx7e//W3Wvq9atUqiuONBbWowGJQd4NH1zGYz69uIwP3rX/8KAPj0pz8tSxBPBKgZZMu973R64KZTgasG6bJQSAYTWX0pl7dUCFyl4LKp4vDhw5IgrjfddBPmzp2L73znO+ju7pbty6ie+v1+yXhUXPChvPOLsNSnnzhxAl/72teitjSL58bDRC4DGjRMRMi1Q/n5+RKbg3PnzuHEiRNsXpSZmYkVK1aw3/m5JDCisuV/j0QiLM5JUVERfD4f7r33XuzduxfXXXcdHn744ajxs0iKim2e2raPP66yshJmszmuJRr9RmpbnW4kWJnP50MwGIzKixKByxPCNpsNU6ZMgd/vx/Lly6MCqCmBF+8sXryYjZsWLVokO+cQr6sWubm5LL3KykrJHEJUU2sYH0zm/k0pz3IK3IKCAlxxxRUSf32ed+F3XNMYd8wVuMCI8feePXvwz3/+Ex/5yEfwz3/+UyNxLxLQANdutyMnJwehUAgFBQWwWCzw+XyYNm0aTCYTysrK2KCXtmEQeIJMJHBpYM17eObl5SEYDCIrKws5OTmwWCyw2WxsVY+2qObk5GD27NksHWrYBwYG2CpsMBhkxCtwYbLucDgQCoWY1+5ll12Gjo4ORCIRTJ8+HXl5ecjMzMTQ0BCys7NRVFTE7qmmpgbDw8OorKyE0+mE1+uFyWTCzJkzUVRUBL/fj2nTpkGn08HlcmHlypVsu0dJSQksFgtycnKg1+tRXV0t6XhI7R4KhdDe3g6r1Qq73Q6bzYa5c+eiubkZDocDJpMJS5cuhcfjwS233MII48LCQhadMzc3FwaDAVOmTEFZWRl6e3tRXV3Nftfr9ejr60N2djaWLFmCgYEBRnRnZGRgeHiYve/u7m4MDQ3BbrdjxowZUavC1dXVMBqNyMnJkdhUrFy5EsDIQoBON+LjW1RUBKfTidWrVysqMUOhELq7uzE8PAy9Xs8axnnz5jHy2G63IxKJYMaMGXA4HIywNZvNip35+vXr0dTUhNmzZzMiki+fDzzwAJ544gn4/X4sXLgQRqMReXl5WLRoETo6OhAMBnH11Vfjueeek2zJKigogM1mg91uh8lkihq0zJo1C/v27YPH40FxcTGLjp2bm4uioiJ0dXXBbrcjMzMTGRkZGBwchNfrhdvtZuQvMLI4MjAwwBZSZs+eDb/fD4/HA4PBgLy8PMmgKy8vD7m5ufB4PMjPz5dV4HZ3d7Nrm81mplrPzMyEyWRCIBCAx+NBXl4eaw+sVqvqwR2P3t5ehMNh+P1+jcDVoApyClx+oJSoApe8y6dPn47//d//xaWXXip7HLUlAFhUZhHUf1EfBYyQRPyuiq1btzICV0mBmw7EUxGoOZ8wmgrc0VwAUJN2Orxrk01jokxuYilw0wXxXtNF0tM4c9WqVfjJT36CdevWQafT4fDhwxLlbVlZGa6++mo2RgOi2wfxM+WZrrF8+XJ897vfxe233w6Px8PGoHLnaNCgYXQQr911uVzo6elBf38/E5KIcwCRwO3r60MwGMSOHTswNDQEm83G2oOioiKcP38emzZtwhe+8AUcOXIEd999NzZt2hQzX6Itndq2gSdp+/r60NnZyeY54jhZDP7Fe+CS1RuvwI035hDznEwfxwtG5CyX5JBMu8mfYzQaUV1dzdrkmpqahNPTkH6M5hhnvPpaOS9rnU6Hr3zlKygoKMDzzz8PQFmBC0DiSz2mBC4APPvss5g9ezZ+8IMf4LrrrsONN96Ij3/841i5ciVKS0u16H/vU1BBmz17NiwWC3p6erB06VJ0d3ejra0N5eXlmDZtGrMrAKTqKODC6oNOp4sicKnx5f1tly5dysjU8vJyzJ07F0uWLMGOHTsAANu2bYPX68Udd9yByy67LCrPW7duRX9/P44dOwadTsf8eO666y4sXboUV1xxBTo6OnDq1ClUV1djYGAAGzduxOrVq+F2u3HixAkMDg5i79696OzsRFlZGa6//nrs2bMHwIjyMRQKoby8HOvXr0d9fT1KSkoYWcnj1KlT8Hg8qKioQDAYZMRgVVUVWlpaMHv2bFlFx759+/DWW2+hoqIChYWFyM7OxoYNGwAAb7zxBvr6+uD3+/G9730P119/PXbv3o3+/n7MnTsXdXV1MJvNuOaaaxTf6/Hjx5nlRGlpqWQlGhghOd5++21GjsyZMwfZ2dmYP38+U2XyCAaDsNvtWLFihWRbUklJCa688koAIx3uddddp5gnHj6fL2rLEwBMnToVU6dOBQCmcL766qslhE5TUxNTNYsNvt1ux5IlSwDIG4p/4QtfwBe+8AW0t7fj0KFDyMnJwerVq9nvg4ODqKiogM1mw89+9jP2fU5ODhYsWACTyST7PGtqavDXv/4VbrcbmzZtwuHDhxEKhbBw4UIsXrwYoVAIfX19uOSSS1BWVoaOjg4cPHgQXV1d6OzsBDDiLf3cc8/h5z//Oes4vv/976OlpQV/+MMfoNfrUVxcLBlMLV26lNmELF++XDayLinqly1bhoqKCnbvBQUF0Ov16OnpiQocV1paioULF0a/uDjYsmUL88nSMLkw3gpc4MJEQQwkQn1Ob28vHnzwQWRlZeGuu+6SLFj09fXh8ccfx+bNmwEA1113HW666aaY16N2RanM0oSPJ3B9Pp+E6Hn++eeZNxvZ7vBRpuke0oVkPXCVkG4Frly6Y6nATcd5qRK4EwWiGhpIv4VCuid0NM6cOnWqZPy3ZMkS1rcTRGV+PAKXQATutddei5tuugmZmZnweDwxrYM0Ba4GDaMLuXbI5/Nh+/bt6O7uZnFMMjMzowhc2ilJcDgc+P3vfx8V1HrhwoXIzMyMaseKioqiAoAptXXUnqpVmfJka0NDA7xeL0tbKaYF/U3zF/LLDYfDMRW4IkRFbzJtPimB8/LyVBO4yYC/d6PRiIyMDCxbtiyt19CQGkbTQmG0EC/PIlfA7x7jBWtKHrjAhRhAABRjaohIC4ErNiCRSASbN29mE6FEodPpVN+AhvEFHxiLb9jl1BU0sObJNAIVXpHApe0u5GXKX5ffFs+XQfpeqXMQpetE4JJikO/0eJ+icDgskcjTpJu21dBWuurqanZ+LA8kSp/AV2iaCCgZvFNANLn0+fumYF5iALN4HWc8/yHeEJ9XR8czpJcbJPDPVC3iHcs3tLECw8V6DrEiQirlmc7hCSRghMBVGnABI6S/Xq+H1+tFX19fVP0Rr8cHoiNLB/J4IhKWCG4+LbKYIPD1RKlzEq/NHy+uGL5fFGgaJg/4ssIvEhYWFqK3txdLly5ldcPlcuGhhx4CMGI38sgjj7BzH3nkEcmii6imEZGKApe3cjh37hzLE2H58uVYvHhxWutDqmmNhwI3Xfc/VosL6Wq3xrv9k1PFputdtLW14Te/+Q3C4TDsdjs2btyYUnoEyhfNHeTGmSJoYUdJgStarogqXzo/lsI/0ec20Uh8DRomOmLVLdrNZjAY2M63/Px8VFZWSo4TfbH7+vqYPRmJJgDg+9//vmQ3S6ydLUrzP5ojq63rJpMJ5eXl8Pv9bEcntUHibkelNI1GI4xGIwtiplaBKxLCyfSlq1evxtmzZzF//nz2HMW00wFRgath4iGZcQQJmOLVs9FCvLLOB5UnK0qXy4VgMCgJYC4qcImLIm6JyuyYKnBJPUn/a5PwiwdyBC5PRMpNruUG1tShiAQukVOkwNXpdKyw8+VNqYMkBINBRoqK0UfJe5UIWdp2QvfCl2f+HnlLho6ODng8HphMJpSUlLCOX4wuKELsHOlalFelDo6OVSJOxZUd0d9IbYOkdKxowB+PhI1FhsYjf2OlpwS5iSchUQJXbjEp3nMXCVy+XMs9I5vNhtLSUrS3t6OlpSWq/vALFnwaoVCIlV+RwKXvqa7IQQ2BK96rSAjzx6RKlMi1GxomByaSAtdqteKHP/whtm/fjuuuuw7l5eX4whe+AJfLhb6+Prz66qt4/fXXJQTu66+/DmDEM3P16tW44YYbYl6LJ3CVlHdyClyahBH+8z//k/39z3/+Ez09PUyJO96YCArc8WgLaDyb6LXTtYA13pB7F7HecyL53rx5M7Zv384+19TUYNq0aWl7z3I2KkoQFbgiYatkoUBjSKrT1A7IBShNFlofqEFDYpBrh6g9yM7OZp64eXl5UbuCKfZGSUkJC/BMIoivf/3rmD17Nrq7u3HzzTcDkJKzSkStGPhXzKvadlOv16OyshKZmZlwu92wWCwsv7GCmPEgAhdAQh64SgrcRMhX3o84GQWuWjs1jcCd+EhmjHP8+PEoixM5pHtBQEQ8Ba7X68Xp06cBjMT9CQaDzMaTfifQcyACd1wtFNLlY6VhcoH8dcStFXLbMmhwSx0nbefgCUYlCwVS4PKFnVfaigpcntQ9deoU1qxZwwgtACguLsanPvUpGI1G9n08BS4/odPpdMyf9fXXX2eT/ylTpkgI4EQUuPRMgMQVuKKSl79/WnGlfMfKj1y+lAhcXlU70RS4sQZOaqObxiJw4ylw4/lSyV2rqqqKEbjV1dUALpSf5557Dk899RReeeUVFBUVsTR27NiBkydPAgAbIBGBOzAwgI997GPYsmUL86O+5557JG20WL7loKT+5bd80PtP5l3yiLedS4MGEbxKncqN1WpFSUkJPvrRjwIYKY8f+MAHsHjxYhiNRlRUVODQoUPYunUrC4pZW1sLnU6HJ554QhL8JBaIAFLaMSSnwPX5fGwgl52djd/85jfs+OPHj6Onp4edNxkUuOlSzMqRgeOlwI2lqlJ7fiqYyOPoVPPGB7QBLowL0/WOE1HgisrZeBYKogKX6jSlI0fgagpcDRpGF7HqFs2l+DG5WMcikQjbQTl37lx0dXWhr6+PxbGwWCxYs2YNgAvCCGobRDWuXL5onsxfP5F6LgpvaO4s3hcdQ0GJ+WuYTCbJnCZZD1wlojfRe1GTxqJFi9Db24vy8vKE09YI3ImNRPp7PvCg0nvNz88fNavWeOU0Pz8fpaWlzKIlKysLWVlZcDqdkvzyAchFLoZiLtHfapCWEs5HWdNwcYFIHJ60lFNh8xYKVqsVXq9XlgBUslDgjdhFKwNRgSsqwV977TUJeQuMBGbq6uqSeH4SgSsqcPl88oQpKXZ5XH/99RLLgkQUuMBIJxsOh1UpcPnOVO7+CfzzIqSqwBUtKsZagUvXUiL6Yilw1RK4sUjneApcue9jPSO9Xo+qqirs378fLS0tzMeX7uN3v/sdAOBHP/oRLr/8cpY+efkCI4MdYMSLi/L47LPPst/fe+89ZnnBXzee6lWNAlezUNAw3gpcXgVLfYyISOSCb3pdXV3U9u1ly5apJm8BSBS4cmVWToFLATTpex50jKgETAeSeS/jWQ8nQluQrAI3WUyEe5aD0s66ZOq8WC9j+cYmA16FHw+JeuDSfSZC4CaLiVYGNGiYqIjVDvE7P5XqZ0tLCwuIPGvWLGzfvh0Oh4Nt929vb8fu3btlz1VD4PJjZUIiQXpFtazJZGJpi4TW2bNncerUKcycOVMyXzUYDBKCSE6QIXcPIoEr930iUEpPDjU1NQkFHxM9cDVMPCRroQCM+E/T/HY8EGsn64oVK+D1euF0OrFy5UrY7XZs374d4XCYBX3PyMiQcFnAhXk1L4gaUwXu+vXr05GMhkkIkn0n4oFrNpslBC5wocCKBK7X65XIy3kCl3yN5AhcnkCrra0FAHzzm9/Ed77zHaxYsQKnT5+WRA22Wq1sMh5LgcuTcKTABUZWbQ8dOoSuri4cO3YsaQUukcepeOAS+JUdJR9TJYjWDnK/U954wnqsFLh0rWQIXLXXibUappRncVBE6ZCJP+VbBClwgZHBpJzqm88TpU8Tz7vvvpsFh8vIyIDNZpMlgXp6etjCA9WbeKpXzQNXw0QGlRXeGocnb+T6o//5n//B97//fYly1mQy4f7771d9Xd5CQckDlwggq9UqsVUh3/R4BO5o1IdE0oo3MRWRTgWuUhuYDJJR4I7FdZSuO97tn7ijTul+krlf0cKKxjrpesdKwXLlICpwxT5TyQNXvEYsC4VkFbjjXQY0aJgsiNUOUftiNpsVCdy6ujoAQHl5ObMiczgcTIGbl5cnK9ihoMRK1xfFEsCIwCIYDEZ58MZCLAWuOJ8IBoOwWq1wOp0sSCsJNXgLBWq7ErFQSMb+QOleUklDTdoagTsxkUz/lurOzlSRTDkljiQSieDNN9/EV77yFdxyyy1RdZnnRsZFgavh4gXfifCdmJyqT7RQ4MlRJQWux+NhhC2lS5YAvFUCEVFyJDJ1zosXL5b4IHk8Hqbwtdvtknyr8cAlxS4wEoTKZrMxUpXOT0SBq9PpYDKZEAgEGLEQzwNXLn2xgeAVzuJvSlBjoaBEbMdKL90KXCXIlQO582I9h2TyLKcIp61LYiA58Vo8gcvXH977hx+QARcmnllZWRLy2G63yxK4HR0dmD59uiSNeB1qMh64qVooaJPXyYfxUuCKpAqpTWLhjjvuwB133JHSdUUCVw5yFgoAWOBMJQJXTj08UTGaE7LxxsXaDo2mnYWSAjddzzoZD1zKk0jwxFPg0nh1NDxwNWjQkBhiEbhyFgoDAwP4/Oc/jyNHjgAAqqqq2LzunXfeYcfPnDkTV155peJ1lca9cgrcvLw8ZGRkJEQwimN1nsAV203eb5ZAgh+6psViUbRQULo2f6z4fSIYKwI3EYWzhrFDKgrcRDmCdCNenkVxE4nM5syZg+effx5Hjx6N2iHO8wyJKnBTprOfeeYZ1mBcdtllqSanYZKBJ+7kSE++kxGDmIn2BEB08Cev18sIWyr0ch64lB5di46NRCJMgTtv3jwAQG5uLoARf12aYGdnZ0On00WRdrzXq0jg8gpc2ubBq1J5qFHg8s9D7nfx+3jb8vl7UfIxVcJE98CNdS0gNpk0mh649B1/DZPJJHlXcvnW6/VsRX5wcJApw4ELCnL6jU+DJpgZGRmSDoEvmzw6Ozsl26qA+B2qUtnhFbjpDmKmeeBOPkwUCwWTyRQ1SRithYF4Hri8hYLRaGTlmyx9RJXgaCpwU0krlgJX7n2nS4GbrvtPVoGbrIXCZFfgioj3LhK5X5EUFRW5yYLylYiFAh2jNogZQVTgikQwD02Bq0HD6CJWXeF3fvLHNzc348EHH8Q//vEPFhBszpw5EmEOgVS58a6v1E+SAClZ8lI8NhAIsDF3LLUeCZRoTkIErs1mg8vlgtvtxvDwMLOACofD8Hg8knaQv3a8fl8N0pGGEqqrq2G1WlFcXCyrmNYw/pjMCly1BC5woc7xO6p5iBYK4+KB29zcDKfTCZ1Oh02bNqWanIZJCjUWCvxWFkBegZuVlYXKykqmOuQVuLzvK1koHDp0CC+88AL+/ve/M5VjZ2cnTp06Bb/fjwceeAD9/f3Q6XSYNWsWgAud8datW5mfCu9/C8RX4BoMBklHT+mQEjkZBa6cckytB65c+pRvucYjXsepxkKBT2OiKXBjGe0nSuAmmmfxPRJxE4/0tVqtKCgogMPhQE9PD+x2Ox5++GGJ8fmLL76IBx54AA888AAAqRcfv5BBSl0RL730EmpqatDb24sdO3bg5z//OQYHB7Fx40YsXrxY9hwlBS7VSfqb///9RmBomLgQiRuj0TgmJDKvwFXy8eQVuPS/x+NRVOCSok8peFKq+U0UStcdq/o5WRW96dheOp6Qs1BIF1FPhEpOTg4GBgbSbqEgjjNjIVEFLkHJAzddZLQGDRoSRzwLBWBk8fQb3/gGC1oGAI8++iimT58Ok8kUFWQRuCD6UYIaqyEidMhuKZH2TlTgOhwOJuYQr2k2m1k7dPDgQcm1ac6dmZnJYhe53W7U1tbCaDTixIkTGBgYQF5eHqZNm4b58+dL0k63ejbd/d3UqVNZ/BANExPJvPN4PMpEA4mbjEYjIpEIgsGgZEzFj6f4OfSYe+DyJFYini4a3l8QLRREIkbsFHmbgEAgAI/HwzqdP//5z3j00Uexfft2iQcubU3nCdyf/exnAIDLLrsMc+fORSQSwcGDB9Hc3BwV4IkG23xn3NPTA2DE+0hOEcyTzHwQM7I7ICxbtgzABSI7GQ/cRBS4dGwsqwX6jicXkvHAlTt2onjg8qCGkf6OlXeCGgI30TzTuykpKUFXVxduuOEGVR64AFBWVsYI3O7ubmzevDnq2EceeQS33norAOlEkl/Rq6ioYIM3Edu3b0dHRwd6e3vZd11dXfjMZz6jeJ/8vaqxUNAI3IsX40VC8RY9Y6XAjWehQHnivTJjEbh0HB/hOt1IhweuElJ5zqPlgZuM4icd6qJUMFHav3ikbTLtPdUJu90uIXDTBaqLiXjgUp4SVeCqCWKmKXA1aBhdqFHgUh3961//ilOnTsFgMMBiseCGG27At771LXR1deHAgQPIzc3FV7/6VTz22GMsDTV2LEB8C4VUCVy73Y5AIACv14tp06ahr68Py5cvlxzLC05o/lpZWQmbzQaHw4Hq6moUFhZK4mnwJBO1P7RLyGg0ory8HOFwWPIc0tFHjpeiUsP4IxkLhfFW4Ko9jq/vpMAVbUXlCNwxV+CWl5ezv0mur+HiA6/AFYlQl8uFv/zlL+zYffv2obm5GXa7HZFIBENDQxgcHERBQQEA4PTp00yJ5PF4mAUArzolCwXCoUOHsGDBAoTDYfT19Uny9vvf/x7XXnst+8xvhykuLsbtt9+ORYsWAYhWXYoKXJ640+v1eOCBB5Cbm4vq6mp2Ph2n0+niEpv894kocKljjrctH7hA4CayepqIBy4R6kr54L9PpwJXPJ5fwVJroRALvOG/iHhkrF6vx9q1a2E0GvHBD35QEnAtFoFbXl6OkydPoquri+X98ssvR0VFBZ555hl2fFNTE3Q6HcubaKFwxx13YPbs2bjqqqvgcrmwc+dOHDhwAHv27EFXVxcLznDffffhf//3f9Hf3y/boYqDQSDa5wdIXxAzOe9sDZMD4/XOROWdaKEwWtDpdGxCw7eBPOQUuICyhQL1e2Shks77SGdaSvV8IqpyUkn7YrVQEK+vpMBNBkSK5uXloaWlZdSCmCXigUv1NFUFbiwCV4MGDaODWO2uqMDt6OgAADz55JP4j//4D3YcP9Zdv369hMCNV4fj2Y/xhE68c+RA1zebzXG9eOk+rrnmmqhdCHIiu3379uHEiRNRz5B/HkQSp3thWWsbLz4kOsbheZfxVuAm4oErErhiXeTn68A4eeCuXbuWXfTo0aOpJqdhEkIsdKLiqb29XdLw816acpXZ5/Mx8szn8zHClg/cRb64hJ6eHuj1eni9XgwNDbHvjUYj7rrrLkawApAYvNfU1GD16tVMtSUqCsVgXSJJXVBQgJkzZ0ruiY7jCdZYVgj834l64MZamRItFHgSMd0euGrvM50KXPF4Pu14ClmCGgVuMh64FDBg6tSpqt4VfVdRUQFgRBFLCtlNmzZJFiAA4NSpU5LJomihYLPZsG7dOmzatAnTp09HWVkZLr/8cgBAd3c3y8tdd90FYMRbV45cl1Pgym35oHNT9SriFwU0TC6MtwcuETdj6YHLD8rkyBveA5f/P9EgZunMdzoVuErfp6LAFdNOpwJXLZItL+kicCca+HylS4FLNj9Ub9NVxpPxwKX6JtZX+izes5ICN5aFgqbA1aBhdBCrHRI9cOmzGCdCHHPy88R441kl6zDRA1ecJ6qF2tgQ/FxFbZA0pXuTu1a8HaWJYqL2dxpGD4n2b3Jz0LFGMn2ykoUCcCEuEU/aBgKBmEI3OaSswC0uLsbVV1+N1157Df/v//0//OhHP0JhYWGqyWqYRJCThvMF3u/3s0F1ZmYmrrzySpw4cQJlZWWIRCLo7OzEokWLUFNTg507dyISibDOllfgUgdIn3lbhu7ubhgMBrS1tUnyVlhYGNVJ8B1zQUEBs0bgV0hFpS19x3dg/DYYOo8/nyftRssDV7y+3Ln07EWf4lhQY6Egt1I7nh64oVCI2VqotVCI1SDHWg1LxAOXypYauwtaIe/q6mKTwtmzZ0cNxmpra9nuB6PRKCGt+PR5/+iCggIYjUaWD4vFwhY2gsGgrPeX3MqnnIVCuhS42uRVQ6KQU+CKdWw0Jgo6nU5C4Mot9IiEEB1P/nVKCtzJFMQs1nGJQM5CId3twWSZMI53+yd3/XS9CyJwycqK6k26FLjpIHCzsrLg8/miFGeRSCRq4RRIrwJ3spRRDRomOiKRC7FXxEUWMWC2ODeprKxkuzmTtd4R7QLEsblaqG17+TmGWrJLnFvIzesIqca40KDh/UzginNj2jEeCARYe1NfX4/BwUGcP38eHR0dbH5eV1fHLD3VPpu0PI1f/OIXyM7OxuDgID7xiU9oRv4XGeS2rfMFORAIsE7UZrNJSCGR2CMCjia6ch641EnxStvTp0/jlVdewaFDhyR5o+BiPHgLhaKiIkZyqVHg8nkXbSPofzkLhdHywI1F4IoK3GQJXCXCUY6UHU8PXD5ttRYKsQZRsfxo4ilwebsBUqrGIn2p7BGB63Q6WWM+Y8aMqEHW3r17mT+uxWKR1Dnx/qjuGI1GlJWVsd/y8vKQkZHBJrEOhyMqX6FQCENDQzhw4ACrd3IWCpoHrobxVuDyBK4S0l2u+LouN+4hAoj3wAUSV+COF5J9XhOp/k5GBe54Pz+1FgqpKHCJwE33fCERAldU2vIELv+Zv0/eJ5cWXKheyxG4hGTLkgYNGmJDqR3ix/c0hlYicMW5wM9+9jNkZmbik5/8pGoLBaVdgUTo8IuTo6nAVau+BZTnbLEUuBqBqyFZJDrGobklz8+MNWLZKSqBFLjhcJgpcEOhUJTFJy+EIoyZAhcYIRj+8Y9/4GMf+xjeeOMNrFixAj/+8Y9x9dVXaxX9IoCowOWJSJKPU8G32WwSUpPfYgKAFXgafA8PD0d54MoRuC6XCz/60Y+i8iZH4MopcIlwlVPgxrJQkFPg8unF824Rla7JeuDGUnXyFgpqt8Ak4oHLE3tK9X2sFLiEWPeZqAI30TzzHQ29I1qJU8oTfZ+dnc2ic5OpeXV1NVpbWyXHNjU1oampCcAFApe/Jg0QxbpTWlqKlpYWABcm0Lm5uejs7JQlcMPhMH70ox/h+PHjyM/Px7333iup20oK3GQ7Ws0DV0OiEL0vYylw01muKE2LxYLh4WFVClz6P54CdzSCmKXzGShN5NKRX9H+Ip1IVkk1WtdJ93VHC0oWCsmAD2IGIO0euKLiLhaUFLgUnFlOgcur42liR+loQcw0aBg/xFLA0lhd9K8miOP8uXPnYtu2bZJ4FEqg6wwMDOD1119nY5FTp05hcHAQ06dPl8yLE7VQSFSBmwiBm4gCNx0ErtauaUgEqdrypQNWqxWDg4NxhRWiAtdgMEh2oLvdbrZzb8GCBXC73SgoKMDatWuxc+fOKAFhPKSFwP3+978PALjxxhvxt7/9DSdPnsQHPvABFBUVYeXKlZg+fTqys7MTalS++93vpiNrGsYAsTxww+Ew/H6/hMDlSU2RwCX1FHWuLpdLoiLU6XSyBO51112H1157LSpvcgRuVlYWPvvZz8LhcCArKyuuApcnleRIavrM/6/WAzceYZtuD1y1HXAiHrj0PmIRsBNJgat0jlL6iXrg0run8qRGgUvf6/V6VFRUMIUe7938yCOPoLe3Fzk5Odi2bRt27doFAMy/mb9XinbLE7gGgyFKgUv/d3Z2Rq0MAiP14Pjx4wCAX//617j33ntjKnBT3WKlTV4nL8ZbnTHWQcwIZrMZw8PDeP755/Hmm2/i9ttvxz333AMgWoEreuCKBG4sBS61K8kimXPjvVOl75Opv3KqT/o+lfZgLM9NZ7uV6vtON+Ta5kTrPB+7gPofqrfpQioKXCJ3iMBtbm7GN77xDezYsQP9/f349Kc/zQhcs9nM+n/RX1ODBg1jB6V2V7QwANQrcGlOyJ8b7/p9fX2SHUD89XU6HbKzs5koiB/bx8NoKnDFe6P8j4UHroaLD8kqcMczgJm40KsEOQKXdqFHIhG4XC5EIhFkZmayOkrHGo1G1fWckBYC98EHH5RsDaBMdXd345VXXkkqTY3AnTwQVXPbt2/HJz/5SQDAL3/5S5SUlEgsFOQUuLyFQiQSYZ2r2+1GIBCQVeC63W4AwJVXXolXXnkFmZmZku1twIVAGWJ+b775Zpw9exY9PT1R3rZAfAWuqIClxoW/J1JA8r8rPTuCaKmghGQ9cNUSbMl44MbK71grcNXepxoFbqIeuHRdXgmrVoGr0+lQUVGB2tpaACMELp23bt06rF+/HgDw+c9/ngU8MxqNUQQuX44pAKDBYEBJSQk7hhS4pEiXU+DKTUj5+sAv1NB3/DGJQkxPg4Z4EBW4tKDBYzSIMF6BCwAnT54EABw4cACf+cxnkJGRoeiBS3mNR+CKysd03MdYeOCmQuDyY8l0L+iMNiGarvZvvMGPYYDYu2sSAT8+o/5ntBS4qXjg8gudP/nJT9jf+/fvx4033ghAGsBQjQduogpcDRo0qINSu1tbW4va2lrMnj07LoErzk344MxqhSCUj6qqKsydOxdZWVno7+/H8uXLYbFYcODAAQBAaWmpqh0ChNFU4IqWU9SujZYHribOuLiRaH84URS4wIiivr6+Hnq9HvPnz0dVVZXiObyFQl9fH5qamtDW1oZIJIKsrCzZneuJBjFL2xMhcktUS4jfq/mnYXJB9MB95pln0NPTg56eHuzduxeBQEBWgcsXWN5CIRKJSDo3h8PBSCi9Xi+RowMjNghEfImIpUzlSS5epciTR7xVAN+hx7JQ4MtwPAuFWPmNdQ6pNWMFmZIjcJNR4MrVSd6/OF0K3FRX2OQsFNKhwE3UA5dX39KxiShw+cljTU2N7Hn8MRR8TK7skOKJyjYFPgOkClwAsgrcxsZG9jfVNzmiWPPA1TBeClw5C4V0KkPjQZyI+f1+vPPOOwCUFbhK52ZmZkrOG2/Ee15jQZSni9hLBMm2Q+msA+PZBsqR6eJvorpMDYaHh9nfogduOu6XFiyB1Dxwi4uL8dhjj0Udf/r0aUZCk3URn04sD9xEofWBGjQkBrEdamhogMvlgtPpZPOfRBS4attzuZ2kFosFJpMJJpMJVqtV0tcnK1YZCw9cntAVrzfeu6w0TH5MZgUuMJKfQCCA8+fPRx0np8ANh8Po7u6G1+tlIozc3Nwo7ovf1TymFgqXX365VqEvYoh+PqdOnWJ/9/f3w+Vy4fnnnwcgVeDyZKcYxEyn08FqtcLr9aK3txdFRUV48sknMTg4iEceeQQA0NvbC+CCglAueI1cuRQJzlAoJCFi+coTzwOXP058FrwCV+3qUbIKXP5YcZU0GQsFufTkrg+MDBr4xifWfcVS4Ka6wpZuC4VYQcxidSqiUi4RD1y9Xi8hWauqqmTP49MfHByMqcDl7UHy8/NZXkQFbnt7O4CROkvP78iRIyzNtrY2tLS0wGq1Smwi6Dp072L+EoFG4E5ejPc7i2WhMBrlitLklXiEH//4x9i7dy86OjoARHvgEpQUuEQSud1ubNmyBWvWrEk576k8A7UKXLW/xzonFmmYKtS2S+M1nh2Ne04FciStXL7UPi8q10ajkZX1dFkoRCIRid1RIgrczs5OPPTQQ2hoaAAwUqdnzpwJs9ks2YXS0NCA3/3ud+wYghjhnkeyCtyJ8P41aJgMkKsr/HyNxqt8W6NGgZsogUvHydkKxrOliwW1sSHo/lLxwDWbzaydprkLQSNwNaSKyazABUa8+wcHB+F0OqPqBw8SuUUiEQwPD8NqtaK8vBx5eXmw2WxRz4HnUMbUQmH79u3pSEbDJAVPDEYiEQmB63Q62dZSAFi8eLFsheRXLol0yszMZASu3W5n5ezMmTNoa2vD3r17AVwgoOTsEuS2qfDBnijPPMnFd7rxCFw5ApXvbBNtfBJR4PIWDnLpiwpcXl0cLz/xJpL8cyECd6w9cJXSSSRNNQRuMnkWFbhkpQAkpsCtrq6OuwIpkqn8saIHrl6vR0FBAXp6eqII3GeeeQZbtmxBf3+/7HUoPwCwYcMG3HfffVErhqm+y0Q7MA0TD+OtwJWzUBgN0DXILxMAvvjFL+L//t//i7feegtvvfUW+57qGgUpIygFMaPAn5/85Cfx0ksv4fjx4/jYxz6WlvwmgkQVuOnGeCtZx1qBO1EmxuJ9y+UrmefKq1dFAjcdhCVPoKohcKleOp1OPPjgg+x7qtMf+tCH8Oyzz2Lq1Kno7u6G2+3Gn//8Z3bMaCpwNWjQoA5y7S5FfgcuzFf49kGNAlfteFacB4q7Uvn5gJr0RKi1FqNxeCoKXP5zKBSSpCU3300UpaWlOHHihOx8XcP7H5NdgVtRUQGPx4NAIIChoSFJOVZS4Pr9fjbm4Xdr07H0WfwuHtJC4Gq4uMGTnn19fZLgYv39/SzidmZmJh577DEcPnwYgLwClyfNsrKy4HA44HA4JMHIuru7sW/fPvZ5zZo1AID//M//xHvvvYelS5fCZDLh+PHj+PrXvx6VX16Bq9Pp2LWpkxUVoTypJA4I5Eg5uc5WbeOTjAI3EonETJ/PczITTCULBTGNsfbAVUoHUD+RjtVQxtrOoNYDl64hp46Vu55Op4PdbseCBQvgcDhQWVmpOIj82c9+hm9/+9u44YYbYipwefuRcDiMhQsX4vDhw1i6dCkAYO3atfj5z38OALLkrdlsRmlpqWTLyN69eyVBHkQLHE2Be/FivEgoNR64o1Gu7rrrLvzpT3/ClClT8Itf/AIlJSVMeTswMACbzYbZs2cDAGbNmiU5V0mBC4yQXS+99BIAYPfu3WnLeyLpKNVnpTRSec5KCtxUrbWS2eqfanlJB4E7EdpA/l2kQ4FLFgoWi4UtVqTLQiESiTAyWKfTye7IEjF9+nR87nOfg9vtRm5uLhwOB0wmE+sbv/nNb2LhwoVYvnw5nnrqKTQ3N2PBggU4f/48NmzYwNKhepxOD9yJ8P41aJhM4NshChwEXBBRUP3U6/UwmUwIBoM4ePAghoeHo+quWgXumTNnoq4vKmZHQ4Hb1taGM2fOSL6jtjQVBS4FU+IJbEI6PHCtViuuvfbahPKo4f2Dya7AzcnJQW5uLnp6erB//35JvT5z5gwcDgcGBgZgMplw+vRpOJ1O5OfnIxgMIjs7O4oz4i0UEvXA1WqQhpRBRGwoFMKPf/xjyW9Op5MRuPPmzZMQpkoKXFKukgrC4XBISOHOzk60tLQAAL797W9j3rx5AIBp06bhZz/7Gd577z1UVlbigQcekGxHJ4idrLjNRfRkFZW69JsSIUp/h0KhqGvFQ6IeuJR/uWPlVlbTSeCKz2ciKXBH00JBVGKLoMkuT+TzzzCeAjcSieCb3/wmTp06Jakv4nlr167Ffffdx66lxgM3HA7j0ksvxZe//GWm9F20aBG+9rWv4ac//Sk7d2hoCGazGefOncPJkydRUVGBpUuXsk7I5/PB4XBEWUVoBO7Fi/F6Z6ICN5aFQjpBac6YMQPXXXcdKioqYDKZ8L3vfY8d09DQgLq6OnbssmXLJGnEInC3bt3K/haJ31Tym473pFTPJ4qCdLyQzjownm0gv6OKIJafVBS4ZrOZlf10Wiio8cHmodPpcNNNN2HOnDmYOXMmTpw4gXPnzrHFeavVim9/+9s4efIkpkyZglWrVuGee+7B5s2b2fnABQWu3L1c7HVCg4bRhlxb5Pf7JQIhvV7P2geqr729vejp6ZGcl5WVBZfLpXo8e/bs2ag5kDjfES33Em0T5ERB586dY/EvRNjtdtVpi/MKo9HI5gpycx8+P8lCzeKahvcnEi07E0GBazabYbPZ4Pf7kZeXh+LiYvT09EQt+ni9Xvh8Png8HuaV29/fj8zMTIRCIZjNZolgEJBX4I6phYKGixtEOtXW1qKurg4AUFJSgq6uLjidTka+EiHLk6BUeHt7e7Fjxw7WIZECF4gmcFtbW9Ha2gpgJMiTSCIBsRsJOQUukWC8B66c1YKSAld8HpQXUqqOlgcuXUfOp0junGS2wCh54IpKKTUKXCLB+WNHU4GrduuTHJQI3HjKatHSgFeqxnungNS6I9Y2LlF1q6TAlbMHoXzSeWSNAIyokohIovOoczEYDJg5cyZqa2tx/vx5yTV5L+lkB3hqvb40TDyMtz/aeAUxi5W2+Ezy8vIwZcoUnDt3DkA0gavTXfB+/+AHP8i+z8nJGZc6Ee+dxnrX4s6DRK9F7Vu6FLiJ5CVZsvv9YqEgQuxr5H5XA57AHQ0LBUpLrY2KEimtNLZTyqMaCwVNgatBw+hArt3lF1PC4TBee+01vPHGGwCigxcWFBRgzpw5MJvNaG5ujiJwxbG3y+XCiRMnEAgEcOzYMbS3t8Nut6O2thalpaVpt1CQE8DQ/S1YsECyjdtkMklsneKB5qiUV4vFAoPBgEAgoAUx0zBqmEwKXJ1Oh/Xr1yMSicBoNGLatGkoLCyM4gays7PR0dGBOXPmICMjAy0tLZJYQSaTKYrvkFPgagSuhjEDEbEOh4N995vf/AYf/vCH4XQ6GSlLnQxPkNIENhgMYnBwkHWoOp2OdUJvvvkm9u/fz9I+duwYgsEgjEYjiouLo7YdUmcZb5snTUpodVYkueQUuEodMg9+63284F5K5/LXV7oHnuxSYzSfTgWuuKIcL7+i2lpOfT1ZFLhikDsliB64QHxVNRBNfCgR3OKKPn+OkgeukrKqoqKCfSZfQP7a/LXmzp2L2tpatojCb7dK9V1qk1cNiYLKSqLkTaoQy6pawugjH/kInn32WeTl5WHJkiVRv990003YtWsXrFYrzp49CyA93prpfCbx+tZU0pRLY7K1B+l41hNBgRvrt1QUuLwHbiQiDT6WLHgFrlxgwUSg1A+JxK6owJULYqZBg4axAd/u8grcuro63H333ew3kcC12+0sFoScZZ7Ynre3t7Mg2i6XC16vFzabDW63Gx0dHbIEbioWCnLtEbWZeXl5kjF7ohDH6iaTSdE+LhkBkAYNPBKd400EBS4QrRqXU7nb7Xbmi5udnS3xyVdaxOEVuLFsG+UwagRue3s7Xn/9dbz77ruor69Hf38/XC4XsrKykJeXh5kzZ2LVqlW45pprJEF7NEwu1NfXM5n4wMAAgJHAD9S5uFwuRvTk5OSgr68PJ06cYMfm5+fDYrHA7/fDZDJh7ty5aG5uRm9vL5xOJwDg9OnTkmt2dXUBGCGazpw5g1AohI6ODpw8eRLd3d2MzBoaGsKePXsQDAbR0NDAJvhdXV3o7OyE2+2G1+tlSl+v1wudThfVkfMVjSqb0WhkCkkRVGHb2towderUhBoetQpc/tj6+nrs37+fEY40KDEajaitrWX5GRoaYqR4qgSunCJHjQIXAPbt2yc5lsiJVBvos2fPsrLBLwTEQiwCl/IzPDyM3//+95JzhoaGWEAwESdPnkRvby+6u7sRDofR1dWFrq4uGAwGmEwmxe1NZ86cQXd3N1Pnud1uOJ1OGAwG+P1+lJaWoq+vj3UkZ86cYVYi2dnZMBgMrOyeOnUKvb29OHfuHDweDwYGBpCRkYGmpiZ0dHSwa5SVlcHpdLLBKDBSnv7jP/4DwAgpFgwGkZ+fjyVLlsBkMrFJ+J49e/D444+zOnj27Fk0Njbi3LlzuOeee2Cz2XD11VfDarUiNzdX8qwikQieeOIJdHR0YP369Vi/fj2mTJmCrq4utLW1wWw2Y86cOQBGLFPOnj2Lc+fOoba2VpHM0ul0KCwsRH5+PjIzM1kbEws6nQ5VVVWqAyrQNfr6+lR3snq9HjNnzpQtK+8njLc6g9r3WBYKY63AFa+v0+lw2WWX4YYbbmB1Ujzu4x//OP7rv/4La9euxb/+9S98+MMflkxGRzOfSscm804TVeCKSFc5Gg8FbrIQF6MnCvj+Ppa9QjyQB67ZbJaU/XSV72QsFID4pLTS73S+5oGrQcP4Qa6u8B64TU1Nkt+IwKWxLL8TRty9BkS3bzT2KykpwcyZM5GRkYG8vDy0trYiEAhEkTOpWijEUuCmakdAxBEJmXgCdzQ8cDVc3Ei0f5sICly14O/NYDAgKysravctT9iKnxMN4p12Areurg7f+c538PLLL8uu3tDNvP322/jDH/4Ag8GAm266CQ8//DDmzp2b7uxoGGWQapb3urVYLJKBLClzs7Ozce7cOfT392N4eJgdT/D7/QgGg3C73fD7/SgqKsKMGTPQ09MjS8YUFRVhaGgIbrebBZ/w+/3IzMxkHTQFQSMlEzASaM3hcMDv9zOSGBghm4eHhxkRShMMfiu82JhQ5eNJOXE7nRjtVERubi6cTieqqqokkxpxci+Crul2u9Hf3y9p4EwmEzIzM1kwHWBksJIIgUtbeUtKSqJ+M5lMsFgskpXlePm12WzweDyygbL0ej17bmpRWlrKiEhg5P5oQMZfUw5FRUXo6enBlClTFNPPyclhCm1ahOBhsVgkqnOCyWSCy+WCwWBgeerp6UFmZiYLzCcHr9fLyvPw8DA7l1fvNjY2soB+/O90HpVHSousR4LBIIaHh+FyueDxeHD+/Hnk5OTA7/ezd0LlsKioSBKwDBgpyzabDbm5uUzd1N3djdbWVjgcDvaMent7sX//fkak2+12TJ06Fa2trZg+fTpLr62tDe+++y4AYPPmzZgyZQqbfA8NDaGhoQHXXHMNgBFCube3F4cPH47Kl4jOzk6JHYQauFwuzJw5U/XxPNmtFgaD4X1P4I4X5DxwRYwGKaJGgRuPEIpn9SAGekoF6XwGkUgEb7zxBp588sko0tFkMuGrX/0qrr/++oTT/PGPf4yjR4+y3QJGoxE333zzpCGzxnsRI12QsxSQQ2NjIx599FFJPwUAc+bMQWdnp2R8BVwgHSwWi6S/93g8Kb/jYDCI+++/H0DiFgpK38vdv1w+qU8cHBxEfn4+5s6di9OnT8Pv9yMUCuGSSy7BH/7wB1X3oRG4GjQkBiULBfq+s7NTcryowFUicJXaP5oHZmRkIDc3Fy6XCwUFBYzAVcoXWfSlqsDldy2kGgyMF87wVmmAsgJ3svdvGsYPiZadiaLAVQO+nur1eokCV+Q35AjdcbVQ+OUvf4lvfetb8Pl8ErKWH4iIDUAwGMS///1vvPrqq/jRj36Ee++9N51Z0jDKmDlzJpYuXYry8nL84x//ADBCSBqNRmRmZsLtdktUghRUqaioCEuXLkVeXh4A4Pz58+ju7kYgEMD8+fPR0dGByspKbNy4Ea+++ireeustACOK3S9+8Yswm8244oorkJ+fj7y8PFaeaMLARwalADB2ux1TpkxBS0sLzpw5g7a2NlRWVmLatGkAgI0bNzKShZR29DcgH7xq+fLlWLdunWQAMHfuXPT09CA/Px+XXHIJU0UqYc2aNRgaGmLP4rLLLoPX62VEnRIuvfRS1NTUAIgmT1euXIlwOIwVK1bA5/PBaDSis7NTtTIVADZs2MCiM4vQ6/W49tprsWbNGrbaRM9LCWvXrpUlb4GRspHotseioiJcdtllsNlsGBgYiNqGqdfrFZ/hihUrMDg4GHPrUXZ2Nu6++250d3fL/p6bmxvlYcmnv3//fmZmvnjxYrbVSYmoXrZsGRwOB06cOIHe3l5UVFSgtbUVGRkZsFgsCIfDmD9/PqqqqgCMBAUki4RNmzZJ7mXp0qUSlajdbofX68Wbb76JcDgMr9eLjIwMTJ8+HWazGY2NjWhpaUF3dzeqq6tRXV3N8tnT04NQKIQZM2agqKgIZrMZL7zwAoaGhrBixQpMmTIFPp8Py5Ytw4kTJ7BlyxaWj4qKCqxZswYGgwHr1q1j37/44ovsb7/fj+nTp6Oqqgp6vR4dHR2IRC54JZO3bnFxMQKBAGbNmiUhg4GRRYzm5mb4fD6Ulpay72fNmqX4jvr7+9HS0oKCggIsX75c9hgeg4ODqK+vZ58rKytlFzd49PX14ezZs6rVupMZ4z2457dPj6WFQqxryfm6JgJagKIx1Vgj1jvdtWuXxJuex/79+xPOr8vlwu7du6O+5+tcMhhLBW46QNv0J4KFgmg3xf8GAIcOHYoKAgQABw4ciJn+rFmz2I6UQCCA++67DwcPHkwpz62traydTWRBDohPWMd7FyUlJSgoKIDD4UB/fz/27Nkj+X3nzp2avYIGDaMMJQsFXsgCqCdwPR4Pzp49iyVLlkjmcXLjOVL98+SqqFjlla6JQFTm8XOdVBW4fDwTUgorEUnjPcbTMPnxflbg8jAYDLDZbJg5cyZKS0uRl5cnyyHxlp3jpsD96U9/im9+85tRxG1RUREWLVqEoqIiRuj19PTg+PHjkoGf1+vFf//3f7PBnIbJAbvdjry8PJSVlcHtdgMYIbZ0uhEPW/oOGCHEaFUzOzsbFRUVbOtyf38/I3AtFgsKCwuRlZWFzMxMnDlzhhG4CxcuxMMPP6zYgZSVlWFgYAANDQ3su8LCQjgcDpSVlWHZsmXIzc1lnrtWqxUzZswAAMyfP1+2kZAjcKmDkzOMNxqNyM/PR3FxsSp7EKPRyMhben5qYLVao4gsEeXl5exvp9PJBixqGkOTyRQzLxkZGXFVtzxsNltcNXKioPwVFxcndJ7BYJA8cyVUVFRI/GHVoqioSKIOXrJkierrOZ1O2O12VFRUwGKxoKioCCaTCe3t7SgqKvr/7J13eBXF/sbf09IT0gskhB4IvUqRIk0UxQ6iPwELCirqvWK9KHCv5Xq9XkW8ilcQe8OKDRWkN+k1BAglCem9nuSU/f2RZ8Y5e3ZPT3II38/z8HByzuzs7O7M7O4733mH16m6ujqkpqZCp9Mpzl5ITk62+04+lWz8+PG8jYlWJePGjeP1+tChQ8jOzkbPnj2RlJSECxcuAGh6QGbeYbW1tejTpw+qq6ttouXr6+t5HR04cCD//rPPPrNJExkZidjYWP5gzaLdRQGX+Qv17t0bY8eOtTmO0tJS7NixA2VlZTYP2v3790doaKjiub5w4QLq6uoQGxtr007UCAgIsBGToqKinG6n0WguGQG3tZBH4CoNBDXnC4erD1tK2zizemD9pS+mmHsjSioJWuxe8uabb2LSpEkAgGXLluGNN97wSKxizwoajQYnTpzAP//5T6xevdomksobWuKl0xcvuHKf1dZEySZBFDvZLKP/+7//w+LFiwEA119/PY4dOwYAWLFiBSZOnGiT59atWxETEwONRoPLLrsM27ZtQ3l5udcCJ6uPAPDwww+7tI2aUOtqBK4YXbNixQqYzWbMnDmT/759+3aMGjUKAOxmB7laJoIgHCPvL4uKirBmzRqkpKRAkiQ+I4zBnhGcCbjHjh1DdXU1Dhw4wAMn2G8A7IRPNgtUvjCjGIFrMpm8jsBl+SutReIuooDLRCTywCWam4vNA9cV5BG4ABAaGorIyEi74xWPhwXBtUoE7r59+/Dkk08C+HOk/s4778T8+fMdRjbt378fb731FlavXg2g6SAef/xxjBs3DoMGDfJF0YgWgFVUJsizqefXXHMNVq5cydOFhYXxiEE1Q2d2YxIXkBKFr5CQEKc3P/nNRS66ykccGWr5KnngysPflfbnbzc5b0z0CfdwZ4E3OSyt+JDmaDaDu4vkiTcHsU3I08nTsMiBwMBAhIeHo7q6GufOnUNaWhr/PS8vz+ah78SJE/yzuHid+D3QJN6wRf8YFosFer2eD/rI27GI3E9I6TjkuCtoOVpETg13R1QvZlorOkOSJBw5coTffxz5X7a0B667EbhqAq44LdNTPPWxVfueiXddu3blg6BsQMMTwZkJuKGhoejRowcfqDKbzV5dN28Ea0/364s24A8RuAwxAldMw+pAhw4deB3o168fF3BvueUWuxlIJ06c4PeCv/71rzzq2lWBUw0mxkRHRyuW1xFq3rZqv8vTaTQaBAYG2lkyjRgxwq58npaJIAhl5PfZyZMn49ChQ7j66qvRtWtXu8Gh2tpa7N27l0eyqgm4rM3K+ya5sMmeDQMCAhwKuCxvbz1wWbm9jb4F7N8LHQlJ5IFLeIvSs1V9fT1OnDihuJgps9r0Nz3FGeI76alTp6DVatG+fXs+oCRJErKyshAfH28n4Lq8voovCvr444/zhh0TE4PNmzdj5cqVTqelDho0CO+88w62bt2K2NhYaDRNi0I98cQTvigW0UKwSse8IZmAe9lll2H48OE8XVhYGBdj5COHctGKCbhWq9XmBUAtmk5ELapJvHnKFyhT2k7+vZIHrpIw4683OW9M9An3kJ9fd24+8rYgTq0Qb3qe1DN5WrYvuTiptJie1Wrl37NBlbNnz9qM1jOPWtZmz58/j7Vr1yI7O5svNvjWW2/ZTZetrKzkAzssAo0dH4vAZWVREnDVzq8j4dzRIIwrebkiyrt7QyY846OPPuKflRakay0PXHlahqtit2ih4CvcOQeOysnKJN6TxYhhd2ECLpvVIYrXLR2B6+n90Zfl9FcBTyyXUh2YPXs2gCYfXCX7KHF7vV7P+2G2wJmnsLKw6DpPPHCdReC6Ym3x+OOPAwDmz58PjUbDxSFXBVx/v/4E4e8cOnQIALB3714+a4oNMAFN9xpmqxAUFGTjI+uOB64YuQr8Kaiy+588yIKl81UEri8EXHGwiwm4ziJw6f2R8BSl+1tOTg5yc3NRUFBg9489F/h69m5zIB6bwWCATqeDXq9HeXk5SktLUVhYiMbGRpw/fx4lJSUoKytDdnY2f7dWa3dqeB2BW1xcjM2bN3NR7JtvvuFThlxlxIgR+PrrrzFu3DhYLBZs2rQJJSUlTj01Cf9AHoEbERHB60NKSgp27doFwDYCVy7EuCrghoWFOS2PeHNhgwLiPpQEXFcj9dyJwPW3mxxF4LYcakKpK8ij0cUIXHHAwBtvR/m+HEUMyyNwgSYBNzs7G2fPnrUZrWeLvaWnpyMjIwOlpaVYuXIlYmNjcc899+DBBx/E119/bZO3xWJBVVUVz1+r1UKSJH4TEx+klcoqlrE5I3A9iaqmCNyW2S97yJswYQK38GluXKk/apF9ziL6mtNCwVcwwUy00fHGs5ddQyYGsrxaIwLX0219ZaHgyb59iaMIXNFCgYmSYh248sorsX79evTs2dNh3qxvDAgIgNFo9LmA6w7O2ql4XZUsFMT0//jHPzBixAhMmTIFQJO9gtFodDsClyAI1xDbpyh+JCQkcNuwGTNm4Pnnn+e/9enTBwBsdIbS0lJkZGSgsLAQsbGxPF/5OgpsH/JZYcwCjM1EU7JaEP92FflzpPw92VvkVhBKz61WqxXHjx+3KQ9BuIvS8w2LvBUtAkUMBoPN2ib+inhsOp0OI0eOxNGjR1FfX4/S0lKYTCbU1NQA+LNPqa+vh9ls9shCwetWuHXrVr6zKVOmuC3eMkaNGoWrrroKQFPhN2/e7G3RiBaCiTBlZWUA/ozA1Wq1Nj6cYWFh/GVMHoErF63YyKLFYvEqAlecMi6KVeyFxJWprUoCrppgJH7nbyKp0vkmmg95PXQVRwKukoWCLwRcR/YAjiJwz5w5YzNqyBapi46Oxn/+8x/uB1hSUoLCwkLs27cPADB27Fjccccd3F9YjMCVPzyKEbhy6xWl8rryvXguXL5ZOrCZUONSisBtzX6PRbykpaUp7t8fRDGlcrgagWuxWBSnl3mCN5YCYh5KAi777MsIXG8FXIYn/WRrCLj+iNrxKNUBoGkgxVXveCZ6eCvgMoGUPTu6E4GrJNSKuHNdAwICcP311/MXNIrAJYiWQaPRICsri/8dFhbGF7SeP38+/95kMqFz587o3LmzzRomR48eRX5+Ps6fPw+j0cjboHxQSP7MyJ4FWV9WU1PDF+/WarVcaFWyLGOClaN1POTPqr60UBDzl1soiM+tbDFjwLNBMoIAlO+hrF5FRUUhNTXV7l/79u0vSs0iMjIS3bt352v1WCwW/pyTkJAAg8EASZJQUVHhkYWC18M34uqO48eP9yqvK664Aj/88INdvoR/o9PpUFpaym92oaGhqK+vh1artRndDAkJ4V5C7EYh5gHYCrhs6qS4kBa7QTpCbs0gN8GWjzaK3znKT4wEdDRSQh64BGAbreOthYL8BVP87E7eclFZHhkg/iYvjzwCF4BNBC5bGBBoGsSZOnUqZs2ahQ0bNqCoqAh79+7F+fPnAQDLly9HRkYGt1JgEbhMoLVarTYRDOx45f2GvIxiOjWxV34uyAP34kYUE9XEm+bo71zJ05kgpJanPAIXaBqply+Y6Q6enANXPHDFQVVRwHVXgGLPBr62ULjYhDB/EPDE+83333+PL7/8EkFBQaisrMTNN9/Mf1OqA67kC9hG4AK+E3DdERec2WYptV9HEbhKMAHXlzYoBEH8idgmjxw5wj+fPXsWQNM9RRxQUvPbbmho4G25trZWVcCVCyxMoGVWkOnp6fxZJDIy0k64FfuLAQMGIDExEQkJCarHJ5/x6UsLBbE87J1Y6f1W7J979Ojhk/0Slx7ye6k4S9rfNBN3UXp2i42NhdFoRENDA6qrq1FdXY2wsDBERkYiLCwM5eXlOHHiBEJCQlBaWgqgBRcxq66u5p+VvOfcQdyeRWMQ/o9Wq8X27dsBAJ07d7a5SfXs2ROxsbEICAhAYGAgDx9Xi8CV2x0ATZYMDFcqtrMIXHcFXLFRyvNSeskiD1wCsD3HnlgoiG2BffbWA1ctClvueasUPSyWhw2qlJSU8L7abDajsrISQFObZXmmpqaiqKgIa9euBdA0TYbZrDBBqqqqysYDl+1PtFFgx+rIA5e1d9FPSI2WsFC4FCNwWwMW8anX6x22h+Yoo9Lgivw3TyP6xKmb3gpc8n27k1ZeTrPZzKOAfGWhwJ4NmBjI8vWVhYI3MxWac1++2ndzkJ2djXfeecfmu6ioKC7iKlkouIpcwPV2ETNngziOcNSGRcSXTUf5iLCgA1ePzx8EfIK4mGCWW4899hi++eYb/n1BQQGApmdOEbW2aDKZeL/kSINQi8DV6XSIj49HWlqa4vMn6wtE4VWv19vMVFWipSwUWNCDkhcn6+s7d+5MEbiET2ACrjzI7mJF6ZkjMzMTVqsVOTk5CA8P58+30dHRCA8PR3l5OUpKSqDRaPgzcItZKIgd47lz57zKi0VnASD/24sIrVaL77//HgAwdepUm0g6g8GAp59+GnPmzIHVarWJKnQkcIk3JjGdK6txy8UntQhcMULPXQsF8sAlnOGpZYVSpKfSdH9vhQmlhcqUyioXIdmUMHYjOnHiBP9djMBl+aSmpgIAF3DT09O5WMsE3NzcXJt+gz2Qy29kalG1YoSDPEpeDXejYz1ZxOxSjMBt6b5FkiQu4Hoi3niKJ1G+ziwU5L+L0y+99dD05TkRxWRfLWIm98Bl4rWvrCM8wVMRzRfn2h8icNkLRUxMDF+UuKKigqfxJgKX0ZoRuGq48nwHKHvgyqEIXIJoXiSpaaX3jz76SFF4ZTrFuHHjAAC33nqrXRqz2cyfSwHbGZfydq0WgctQe97v2bMn+vXr57afp3g/ycjIQFFREQDfReCK+xGncpeUlODMmTM2fudyP2CCcAel2SxtJQKXIfYXOTk5CAgIgNVq5c/FsbGxCA8PR1JSEjp27Ihu3bohLS2Nt2dXn3m9Pltdu3bln7/88kuP85EkyWb7Ll26eFUuouXQarX48ccfAQDXXnutnZel+Jnd+Jzd8MTViSVJ4obzkyZNcloesYMQIyaUBFy1KeRK+Sl54LLvRfxVwCUP3JaFXX93RxWV7AyUXhC9FXDVInDVrALk7SgmJgZAk7fYhQsXbARccdpYp06dAID74/bq1YuLtezFf9OmTfjtt9/46L/VauX/WLkd3eRFywRXHwa8jcB1xwNX7DvaKq3V7ykJuHKaM6rNlbxdEXrU8JVHKMMXEbisLBqNxkYw82bRNZYni+Zk/3trocDwpJ/01APXG/wpApOJjsnJyVz8YNdJzQfZVXxtoeBJBK78XDuyDFH67Aqs/ZIHLkE0H2wNFgB44403bH5jAu6CBQvwz3/+E3fccYfd9uKaE4CtSCtvi/LZb2rBEHJCQkKQmprq9vuXOKPs9OnT/FnaFUtBV2Dv4+Hh4QgICOB9aGVlJY4dO4b8/Hzef4m2TgThLkoCbluLwBX7i8bGRt5OWVASs1rR6/VITExEt27dkJ6ezgOaWkzAvfzyy/kU98zMTDz33HMe5fP8888jIyMDQFMnMnr0aG+LRrQQpaWlyM/Ph0ajQVJSErKzs5Gfn4/CwkLk5eXxiA2r1corplzAVYpuEwXcv/zlL3jllVcc+gQx5EKlGDko/i/uw1MPXFY+EfLAJQC4NDighFJbaA4B15sIXKApkpZx6NAhGwuFdu3a8e1GjBiB+Ph4GAwGxMTEYPr06XyQR8xj9+7ddoM94j4lSbLx55LDzpOSDYujc+HOi7IjoVsJ8VxeCjYKrYEojLD7Skt64DqyMnFFEFLKU/ydCVy+isD1RBhSE3CDgoJsfvNlBK64iJk3EewtKYS1FQsFdhyiQMteLsR66K6AqxS96q7FgBq+9MCVR+AqvXTKAwXE30TcjcD1h+tPEBcTkiRxUfPGG2+0WxCM/W0wGJCenq747CYXcMV7jvz+w57llN4Hm0OE0ul0GDx4MLp06cL/paWloWPHjj7Jf+bMmRg9ejQmTZqE4OBgpKSkIC0tja91UV5ezvtnisAlvOFSiMAtKyvDyZMn+QKKgYGB/B3WbDbzQCXxuUGr1SIsLAxAC3rg6vV6zJkzB6+//joAYPHixaioqMDf//53lx7q6urq8Mwzz+C1114D0HRx77zzTp95uxDND6ukCQkJOHfuHM6fPw+TyQSz2WzzMC2+iDmLwGVCjeh/FxgY6NLNUS0C11sPXLFRieW1Wq12fzvLszUgD9yWxVcRuPLBDIYnNz1XPHBdicC1Wq1IT09H+/bt8e677yInJwe1tbX8JTUiIoJvl5CQgJUrV2Lo0KF86lhGRgY0Gg2Sk5Px2GOP4eWXX8b58+d5JK1aBK6aBy4rpzsRuI6myTraRi5ku7IPoO3bKLRWBK5cwBVnVshpTjHPFTsdT4RlX0UoeoLa+RIFXBFvPHDZ1Fe5B64rtkmO8Gagy9MI3LZioSAKtOy6iPXQGwsFdo58HYHL8vPGA1e+rfi9u9eFtRF3B2AoApcgXIdF4CYmJtppD3JBV6ltsUFH1vYtFgtiYmJQWlpq9+wm/1uv19sJwL4mMTHRbesFV0lOTrbx4TUYDOjRowdCQ0NRXl6O8vJyslAgfILSfdnVoBt/h+larM0wDAYDAgICuAbGxFv5OygbJHc12McnKumSJUvw2Wefobi4GJIk4dVXX8XHH3+M2267DVOmTEHfvn0RFxcHnU4Hi8WC4uJiHD58GL/88gs++eQTFBUV8QNISEjAkiVLfFEsooVgHpgpKSnQ6XSIiYmBxWLhq9KLoyysocqnuiqJRmLlZtu6cnOUC7i+8sBVE3AvFgsFisBtWeQR365yMXjgsv0zC52cnBy+gqZWq0VISIjd8Ys3JdFmJSUlhefB0rPoW7aNMwsF9r0o4DZHBK6aBYWj9KxMFIHbPIiRe2rXpDmmJXuTp9ogn1KevopQ9OU5cCbg+lMErie05v3Rn6bQi9Nm2Uw7Vg89sVBwJOD6ahEzTyJwnUXKuxqBq4S7Ai49mxGEe0iSxGd6JiUlwWAw2DwLuiLgMgFWFHAjIiIUBVz5s5xaMMTFDlusuKKigp8zEnAJb3BkoXCxR+CmpqbCYrHYBB1UVVWhrKzMJjCJvdPL3+vZM2+LReACTY38l19+wfjx43knWlhYiNdee41H1gJN6rTc24GtQgc0rW67bt06tGvXzhfFIloIFoGbkpKCoKAgdOrUiYeKHz16lFdmi8XCr7dalB9DNFJnlZ6FmTvDmYArirbueOCKN+2LUcAlD9yWpS174DJP1+7duwNoWoSMCbghISE2bVxJwBWF2cTEROh0OjQ2NiI3N5dPNRFFT1cicFmkMrvHuOqB66k45Op1ZRHLFIHbPIhTp1mf7i99r1oErjMLBREm4JaVlWHPnj02v4WHhyMtLc0tv093cOaBK/fgYw+gbADXHRx54HqDN/WyNSNw/QEmxIeGhvLpfUxotVgs/HdvPHBdHaAwmUw4dOgQ2rdvD61Wi9zcXPTv358HA3jigStHfv2UInMd1QlfWCg4yosgCHskSbKJwJUkCcHBwXxWR1JSkl16OawvE59XWUSc/Llbvr2rHrgXG6GhoTAYDPweHBAQ0KYEaqJ1aWseuIGBgejVq5fNd9XV1di0aZPN4twssEcegcueFVpUwAWA/v37Y+fOnbjzzjuxc+dO/r3Y0ckfxMWHpJEjR2L16tVcECAuDg4cOIC33noLwJ8RuADsKigAm/BxtSg/8W+l6ceeiGFyQUcU1tzxwJULuOxhXs3g3t9u5BSB27K4MjighKseuJ7UM1cicOV1Qy0C12q18gjcsrIyPPvsswCaHvpEEU0ciBHLztIYDAZ06NAB2dnZuO2225CYmIipU6dyw3e2T7PZjM8++wyffvqp4uINtbW1mDJlCgYOHGh3fI7Ohacvyq6ed6X+g/AdooDrjjDqLd4OAIh5yFHywJ0zZ45i2vfeew+zZ892eZ++8MBlYpt8QRPx7/r6ev4C7ApqEbi+il5vyXueLzxw/cFCgbUt0QOXXXtRcPXEQoHhagTu3XffjQ8//NDmuxkzZuCzzz6zKas7K7M7skpQS+euSO9pBC4JuAThGqIHblJSEiwWCxdvASAtLc0uvRxRpOzYsSPS0tJQUlJiM/sLUF7cTLQEvNhFKDkDBw5EXl4eJElC+/btW7s4RBtArp34q2biC0RNjAUmhYWFISwszO5e7+6ihD41mu3Rowe2bduGb775Bm+//TY2btzoMApDp9Nh/PjxuO+++3D99deTqHQR8tNPP/HPaWlpdiKhXIRVi6JT88AF/hzxtFqtbhDJWIMAAJJ3SURBVN8cxZuv3ELBXQ9c+XdigxTx1ygc8sBtWXwVgevLRcxc8cCVIxdgxbJERESgR48eOHnyJLdAYHY58v3IH4JZP6DRaNCzZ09kZ2cjKysLWVlZGD58uE3UqlarRVZWFg4dOuTw+GpqatC/f3/nJwIt96KsJGC3RVqr32PCjyjguiKMtgSeRuAqWSiw39nCJbW1tSgpKcFXX33lloDrDp564IppXEVNwAXcj14U8Uaw9jQC1xv8QcCTe+AqWSiI19eTlcnZfcEVD1yz2Yxvv/3W7vsdO3bwz6ysrL240w/Jz7X8Hqx03xVxdM1YebypwwRBOIbN/k1MTER+fj7/PjY21iVbFdH2JzExkfvfMljwkdJzXFuNwAWabC1dWTycIFxFTcBta4MfgL2VosViQefOnRUtFNydyeTzlcI0Gg1uvPFG3HjjjTAajdi/fz9OnTqF8vJy1NbWIiwsDFFRUejWrRsGDRpEfioXOWzV+aeeegrJycl2/rLyCFxmg6Ak2Mr/lnvgKqVzhmihIBdrRYHZHQFXPk33YrFQoAjclsVXEbhiW2gJD1w5ShGk7Aas0WjwxRdf4KuvvkJgYCBqa2vt2rc8gpcdh9hHzJkzB6NGjcKrr76KiooKGI1GOw/cgoICAMDQoUN5tC+jrKwMs2fPxoULF1yeuu3JImaecKlE4La2hYKjqdPNIYp5c5ys7biShyjgDh8+nItW+/fvx+DBg7Fp0yaYzWanC796cw5ctVDQarXcKsvdRZtYnmyqvq8EXIan/aQ7+Ou931NEiwQxAleSJB7hxvwmXcHTCNw9e/agurra7vvc3Fw0NDQgMDDQIwsFNasEeXm9icBl9ZgicAmieTCbzTYCbl5eHm644Qb89NNPeOihh1BSUoK8vDwuyAYEBCAvL88mD/YuK+YpX39CvkiukvDkbhQdQVxqyO9xbcUDVwnWN0RFRUGSJISEhKBLly4A7M9Dqwu4IkFBQRg5ciRGjhzZnLshWhH2UD106FAAtj6zLMqO4SgCV+lvJQ9cX0fguiKyyX9zFk3lry9x5IHbsrBz7G6dddVCwZceuOL38vzk4rE8bVxcHIYOHYqAgACUlZXh1KlTip66Sh647Lfg4GAMHDgQ0dHRXMCVe+AWFhYCaBKxrrnmGpsyWq1W3HvvvWhoaEBhYSHi4uKcnhdfTIF3BSXxnfAdYgQuoyX6XleEFkcRuK4KzeJxTZgwgX8eMGAAoqOjUVZWhpiYGD4Y8uSTT+Kxxx5TzdsdnEXgKkVestV21SIqLRYLZs6cieDgYLz33nv4+uuv8eCDD/IBGvYQq9fr+UuyJ4uiOTuG5t7WU/xBwHNmoSBJkmoUtiv5ioMXcg/c1157DZ988gnWrl2LxMRE7N69m79DJCcnIzc31ya/7OxsdO/e3S4C1xvUhF02cCmmUUovwsrj7oAGQRCuUVtby5+vYmJiAABjxozBuHHjUFxcjIyMDNTW1iIrKwtA03Onmr0Pa+NyAddisUCv19uITUqBRRSURhCOkT+LthUPXCVEr/+kpCRERUXxQWb5u6G7M5maVcAl2j5s1DI8PBz19fU8Ckin08FisSh64Mr9bQHHEbgA3FrETJ6PfHRH/N/TCFwxHzUB199EUorAbVk8jcB1dxEzX3jgijhaoIG1Q3EKjCjQKkXYKwmYalNmWPRdQ0ODjQeuKOD27NnTrsxarRadOnVCZmYmLly4gD59+jgVQORRVc3VJpQE7LbMpRKBK8dkMvE6yigtLUV5eTlKS0sRHh6O0tJSVFVVISQkxKZdy/Opq6vjeaWmpvJjuOKKK2z2cd1112H16tWoqqri373zzjuKAi7DnXOgNkjExDall9XAwEDU1dWpClb79+/HmjVrADTN3Hnttde4eBscHMyjE4AmMdhoNPrEQuFiicD1BwGXIS5ixgSPxsZGmM1mh3XAGWoCrtVqxV/+8hcAwDPPPIPnnnsOy5Yt49stWrQIy5YtQ2NjIywWC86dO4czZ87YCLgGgwE1NTUoKiqyiUpv166dTVkbGxt5+wwKCkJhYSHPQ379xIh5dwfi2EuZq3XYn64/cWlQW1tr4xkbGBiIwMBAm/uKK4SGhrrsh+1LWF9kMBj481ZDQwNCQ0MRHByMqKgoxMbGchsYnU6H2NhYu3zYIl05OTl2a6ewdi+KTeyz2M+QgEsQrnEpeeCmp6ejsLAQnTp14r/J7/Uk4BItCmt4YWFhqK+vVxRJxbTsQVipoYo3RDECV/RK8cRCwdceuPJt5A/0SpGK/gB54LYs7Bz7wgNXabDAk3rmSRS2mI4NysinlbHflIRZNQ9c8Tf2PxNwWQSueIzFxcUA7BekYHTp0gWZmZn45JNP0KlTJ6xbtw6bN29WPS6LxYKsrCyMHj26RQTcth6B21qCg9IiZmqDbr5EfPiSJAmnTp3CH3/8YZMmKysLpaWlMBqNKCoq4nZSqampSEhIUCzniRMnYDKZeCRq9+7dsWDBAvTp0we1tbU2+7j++utx1113IT4+HoWFhRgzZgzOnTsHi8Vi1+/4UhhyFH3pzNN0w4YN/PN3332HXbt2AQDmz5+P8PBwm4dYXwi43uCpB+7Ffn+VR+CKAi7Q5DVeU1MDwL2IV0cRqvX19Thy5Aj/fs+ePdi9ezfWr18PAHj44YfRvn17LF26FBaLBf/5z3+4gAv8KZCaTCYcP34cwcHBNm0gODgYEydO5H/v3LkTJ0+eRHZ2NsrKymwGHBzZ67gbgcvaCHngEv6I0WjEpk2bfPJ8otVqMWnSJJc8Z30JE3BDQ0P5syV7pktJScHAgQPRq1cvlJSUAGh6JhsxYoRiXidOnABgHxXI/layUBAXTiQBlyAcI94vxX6nLUbgAk19BVu4TOwb5c8Z7izACpCAS/iAkJAQu+gn0UqBwQQecYEyEXm0rNwD15NFzMTOwZceuOL/F4uFAkXgtixKgxiuoOSB2xwWCq62JbZ/SZJ4lK1YBpYPi7CXD9CoeeCKv2m1WkiSZCPgih649fX1PEJEKQIXaBK6fv75Z5SXlzuMQJRz+vRp3HHHHS6ndxfywG1eRAHX2f6bQ2Rmg4QsYicyMpL/Fh4eDqPRiHbt2iEyMtIlsctoNEKn0yEiIoI/+On1enTo0IFHEAFNbaKhoQExMTHo0aMHunbtCoPBAJPJhAsXLvDFzrxBvKYrVqzAxo0bAQD79u0DoBwxIJ8SDwA5OTlYsmQJamtrsXPnTv79Sy+9BLPZjPj4eHTt2pXbp8jzaq0IXE/rS1uLwA0ODkZQUBAfZK+srOQ+5J5aKDDYNT579izuvfde/v3x48fx8ssvo7i4GDqdDldffTVMJhO/zyQmJgKAnYDLCAgI4KJzRUUFj/Bl/XFNTQ00Gg1CQkIQFhbG221QUBCio6N5VLhYbvlnV3BmoVBYWIhnnnkGVVVViIuLw8KFCz3aD0F4QnV1NX+3YoE4omVNu3bt3Mqntra2xQVc0epFvk4Ee36tra1FVVUV9Ho9f84UkSQJFRUVqKqq4pHHVVVVaGhoQFVVFUpKStDQ0ICysjL+O8tH7M9IwCUIx4jPOOJ7UVuMwAWa3nOVoozlQYB6vd4mkNEZJOASXhMXFweTyQTAXrhl085YQ1USeBg6nc4mH1944Io4slBw1HG0FQGXPHBbFk8jcB154PpyETO1OqCUHxtcEffP2rVcwJUP0DjywGVTz1he8ghclo5F8+n1etUXiptvvhn//e9/bfZzxx13qC7ulJ2djQ0bNqCwsLBZX5YvlQjc1kL0wG2NCFwxUlyn02H06NE8TWhoKC5cuIDevXujS5cuKC0ttRGGlKZqs3+jRo2CXq+HyWRCbW0tRowYgejoaJ42MzMTJ0+e5MKxTqdDamoqTp8+jTNnztgJuN6cg7KyMsyfP9/u+7i4OLvvlASrl156Ce+++65d2vLycgDA4MGDbbwGGUwI8MYDtyXxRT/iD88N7DiYKBoSEgKNRoPg4GDU1NTg559/xoEDBwBAcSqyM0QLBSac1tTU2ESXm0wmbNu2DQBwxRVXYPLkydi6dSsqKipgsVjQvn17AMDhw4dtysr626SkJAwcOBCSJOGHH34A0HSPCggIsGmzPXv2RMeOHTF48GC+7+zsbJvzoHRufBWBu2zZMrzzzjv875SUFPTq1UsxLUH4GtZPR0dHY/jw4Th27BgfFAkKCsKYMWNcymf79u12kewthZqAy9436+rq8Pvvv/Po2q5du9rlcfz4cZw5cwYFBQXIzs7mCyPm5uZyITgsLAyVlZXIzMxESEgI+vTpA8A2cs7dadAEcamhFIGrpgu1BZQCmgD75wa9Xu/W859bAm56ero7yT1Go9Hg2LFjLbIvwntEAVe+cBMTSS0Wi40HrloErvhZHoHrSQNX6hxEAVYuxiqhtoiZ2uJE/urnQhG4LUtze+B6Us888cBlaUUBV2ybYjtQsjpxxQOXlYv5p8k9cJlIFxgYqHq80dHRePrpp/GPf/wDABAREYH3339fta5v2bIFGzZsQGVlZbOKqxSB27yIHrjOytBcQr04gq60P1Ye9rtaOZgYC9i3Dfk2LC9xmy5dunABd9y4cYr78MQDlwlr3bp1w0MPPYTy8nKUlZXh+uuvt9tGyUKBTYN/4IEHkJaWhp49e8JkMiErKwu5ubkYMGAA8vPzAagLuJ7anFxsEbgMf1rEjPXLTMD95ptvADQdJ/OsdSdfti3QJLQ+88wzMJlMSE5Oxvnz55GcnAyTycSjfq+99loA4IsIWa1W/j6ybds2NDY22gm44qwwdv8ymUx8kT2GTqdzeq5FD1x3rwsTcNWELdY2evTogZMnT2Lz5s3o1asXReASLQKrl6yeihGk7kSTsrTsWa22ttamnTUn7F4TEhJiZ8/FBFwRcXYIQ7SECQoKQkBAADQaDYKCghAUFMRnIVRVVcFgMNg8h4ptlSJwCcIx4vORvL22RZQsBcXPooDrznlwS8A9ceKERw8w7kLi0sVFXFwcv1HLRSsWjcemQzvzwBU/i3+zqCRPG7lS2LroLXopWCi05Q7SH/GVB25zWSi4Ux9EEVI+eir+xtq3Jx64FovFRsAVo6REz02186nVam2iDjt27Ojw3MTHxwNomiZHEbj+y86dO5Gamsoj7uTILRSUaI5p6UpRBGrR3gw1X1qGuHCKs3atJuACwNq1a+2ONT8/H127dnX7HFRWVuJf//oXAOCGG27AggULkJOTg4MHDzq0UPj9999hsVhQX1+PzMxMaLVa/OMf/0BUVJRN+s2bN6OqqkoxAtcXFgpA0wv7nj17+CrkQNO1GDlyJPbs2WMnruXl5aFbt25u78eXEbj+IOCxgXkxAhdourYA8OCDD3ocgSsyaNAgdO7cGf3798ePP/4Iq9WKSZMm2YkhooCbmpqKdu3aobKyEs899xy/R8gFXPZZtORhbUYt4kXpGsi/czUCl52zkpISrFq1yuY3i8XC7UhefPFF3HTTTfjpp58wceJE9O/f3y4vgvA1vhZwjUYjcnNzeYR+S8DstUJDQxUtFBwtzMtgfUOvXr0QFRUFvV4Ps9mM0NBQdO3aFUOHDsXhw4cRERGBpKQkPnuE7Yfh7BmAIIgmxGfntqxPOIvAlVsouIrbPU1zPVT600Mr4R5hYWEoLy9HTU0NKisruTcf81Iym81obGyE0WiEyWRy6IHLEMXVhoYGmM1maLVaGI1Gm9VS1WAPJex/pUbjqpglf8CXi75ms9mmTHIx21+QC+JE8+JLD1xRJGV1TW3FbFfKpLQfhlJ+LG1tbS0aGhqg0WhgsVh4u66vr0dFRQUaGxthMpnQ0NCAxsZGvqov0BRFV1tbi7q6OtTW1nIxmA3ONDQ08Iffuro6FBUVoa6uDtXV1fxhOTAwEPX19YqRHQ0NDTYeoRqNBkVFRU7PSV1dHTZv3owOHTooTgkXYccIQDVvg8FgIyYajUYYjUaUlZXxbYKDg2E2m7lA4gpMRHEXnU7XIlEhzTFwtX37dlx++eXo1asXjh8/rphGjMD19f4dId5HfB2B62iaFUNJwGWi43fffYfvvvvOLv9JkyZh6NChTo9NLP+KFSt4nzNhwgSb35XONVt87YMPPsAHH3zAvx88eLCdeCuWn7WZ5ojA/eyzz3jUqKtMnTrVrXPF9gVc/B64ShG4Go2GW9yw+u6uyKgkiIq/ye1I5IgCrlarRf/+/bFlyxY+6wL4sx8Q2yLLi/W38nYmP9dKZWPfu3td2DkrKSnBPffco5gmLS0N06ZN44L0X//6V/z4449u7YcgPEEu4IqDcp4KuCzC1WAwtMjCRKzPCA8Pt7l/iJHzQJNdSk5ODrc+UMpDfl9l5a+pqeHP3KGhoTb9S3x8PKqrq2387wmCUEbJA7etLmAGqEfgtqiFwuzZs91J7jK//PILCgoK/E7wIlwjOzsb3377LRoaGtC5c2cYDAZERkaioqICJSUlqK+v5wtIBAUFIS4uzmkErijyFhYW8oeMHTt22KyGrAbzRUtMTETHjh0V9ycKY87qnvjgLheA5SuPi/n7E/5WnraOryJwxbyqqqp49JOj9M7K5Gg7pQgClnbfvn0oLi4G0PSgvm/fPpw5cwZr167lL8fBwcGIiIhARUUFxowZw4+/vLwc77//Pp8qzfbFfAkbGxtx8uRJAE2em5999hlPx9q/1WrF5s2bFcuel5eH3Nxc/ndtbS2WL1+uei7ESI0rr7wSGo0G99xzDxISElS3OXv2LD9OVlY5Go0GvXr14i/u2dnZKCgo4H2SmM4dMSAiIkJ1ATdn9O/f3yeLWrU0TADMyMhAYWGh4rVRWsSsJTxwGb6MwFWaTuaOgHvHHXfg8OHDKCsrs0l74cIFHDhwAMXFxW4LUGzl7i5dumDixImKZRGZMWMGLBYLIiIibMQ0tan2rD2xtEoRuN564LI+q3fv3ujUqRPMZjN++eUX/vvIkSO5uJydnY0jR47w43YHf5194ymiYAEAM2fOxA8//IDo6GhcdtlliI+Pd+tY1aJX2SCeWJeV2pIo4AJNdS0+Ph6NjY0oLS1Fp06dEBwcDIvFYheBC/zZVsQIXFfK68oxqKXv27cvrrvuOpSWlioKPDqdDvfffz/0ej1WrFiBmTNnAvhzSjdBNCdyAVdcaNNbAXfIkCEeRei7y+nTpwE0DZbII3DFAdaIiAj07t2bDzKKsPuOfCV4MXiB7WPAgAE4ePAgT2MwGHDZZZf57oAIog0j3i/ZPbMtC7hqgVPy2anNaqGwevVqd5I7Zf369fjb3/6GwsLCNvPAeykSGRkJi8WC8PBwhIaGQqfTITExEdHR0TCZTIiNjUVeXh6CgoIQEhKC5ORkPn1ZJDk5GbW1tQgNDeUrcIeGhqK2tpZHkYWEhLg0StGlSxcUFxejY8eOMBgMSElJ4b8ZDAa0b98esbGxPFIwKSnJYX4dO3ZEXl4eLydgL4CJN/6goCDFaKPWRKPRICUlBY2NjYqrsBK+JSkpCdXV1U6jOuVotVqkpKSgoKCA1zV5fWdtQKfTORQclcpUVFQEvV6PmJgYm98GDBiArKwsxeiElJQUnDp1CkCT12y7du0QGhqKhoYGGAwGhISEoK6uDjqdDvHx8YiOjoYkSfz4w8PDYTQa0dDQwKPr27Vrh4aGBtTV1SE2NhZlZWVcKLBYLAgKCuKRVqI/mfiCIcLymDhxIvbs2YOpU6eqpmWwhSqApoeJ7Oxsh0Jn586dkZeXh9jYWMW8mY1EY2Mj7w/i4+NRVVVlN30X+NOf0RFslNpoNNq9XDiDiR0VFRXNLuAyIbOwsNBnC3ns37+ff/7mm29wzTXX2KVh0dmOFjETy9gcqEUNysvDrp9aOdyJRlAScOPj4/H+++/bpf36669x0003uW1FwCLjAeDNN990KkADTVNQn3rqKVx++eUu3QPZMbP2JB6PaKHg6bUTj+Hhhx/G3Llz+eKLLM+ffvqJL474wQcfYPbs2V7t0xv8JQLXZDLxes0EjwEDBqB///6YNGkSampqsHPnTo8FXCUrKjHqTSlfvV5vE6WbkpKC9957D6Ghodi8eTNKS0u5sCL2lfK2IhdwXY3AVUrrDJ1Oh7vvvhuJiYlOI7pvvfVW3HPPPaitrW2VxaCI5od5McvRaDROn1fcwWw2K85Uku+/rq6OtydW5xobGxWn8zY2NipaQen1ei7gVlVV8fzlC86qHbsr50Sv19sMuIjHV1FRAaApeMCRgMtQOga1QVh2DqqqqgAo23hRcAxBuI6S/VhbbkOuRuCGh4e7ZcHSKmYte/bswVNPPYWNGzfy79gBDB8+HC+++GJrFIvwkBEjRqBTp06Ijo7GVVddZVMBz5w5wxekGzdunMPo2dTUVKSmpvK/IyIiMH78eJSUlHBhBwAmT57s9WiNuOKwK/Tr1w/9+vWz+U7u2TtlyhSvytQSDBgwoLWLcMnQvn17Vd9OZ8ivk/zmNnHiRLfFPKBJ4Jk0aZLibykpKTYDHSJdu3a1Wbm3b9++OH78OJKTk/mK2VqtFlOnTgXQFJVeWFgIi8WCgIAAvqBSeHg4ysrKMHToUCQmJnL/y+HDhyMuLg5Lly7FN998g+TkZLz//vs4dOgQsrOz8fXXX/PyO2tnTz31lMvn45133uErjgNNAzWLFi1yeXs5+/btQ15eHvr06YPOnTvb/W6xWPDTTz/xv5OSkjBkyBCHedbX12P9+vXQarVu9zGnT59GRkZGi/jvSpKEJUuW2ESm+JL58+dj/vz5qr87ag+t7YErt1CQf88QPXDluBKBqwYT4NwVJdlgBGA7tdZRHu6ca1GMY9dPrKtyCwVPkR+DRqNBz549kZGRAcBWaGBpPIn6dSUC9+TJkzZevEBTv9O7d2+n27Ykf/vb3/hn9vzlqzUw1ARSVpfV2rI8AlfchmGxWKDVam3amrsCrrxcYpndjcBVyssRQUFB3KqIaFs0NDRg48aNqtZJrjwPuEJlZSW2bdtmd9+vr6/H0aNHFes1mwkFAAcPHoROp+PPdgCQmZmpOutIq9Xy9yN2bGFhYTbtuKamBlu2bLFbzLWhoQG//vorqqur7fLVaDTo168fevfuDa1Wy2dKyI/vyJEjAGAzUC6WTY7SgrLOInCZSBwUFKS4TgVBEK6h9Ox8KUbgKj1TuDProUUF3IyMDPztb3/j3mziTaRPnz54/vnn+WqzxMVDhw4deAWVN0K1kQd3UPOgbW1c8RMlCF8gbwOtXd/Y/sWFYZRW15Q/KCutEAz8eS9gkeEs2kH0HwN8v8KvvHxqLyiuIp4XJeR9lyvXUVwEzV0fULXr0Fwwn1p3pwI5omvXrigvL7ezBWBIkoTQ0FD07t27RaewiwsQWK1WxWhqVxZPEVGKwFU7Fk8FXHdREnCdlc1VxLIz8UApAtcbCwU1EfrDDz/ELbfcgn/+85826UUB11OxUu285OTkoG/fvnbHo9VqcfjwYS7isnK3FpIk4cyZMwCafJXl/a449dGTCFxxO7UIXCWcCbjshVCn07kl4MqR35eUPHCbq49h9Y8E3LZHRUUFFziVBjFKS0t9sp/y8nLeRsT9MBsA+b7Dw8MREBDAv09MTERlZaVNRDCbgajmXd3Q0ICYmBiUlZVBo9HYBOMATcfO7m9iHjt27MA777yjeiybNm3CW2+9xY8rKioKpaWlNsfH2gpbJ4XN3GLPS6zNGgwGmEwmxechtUFY+Uw1pQjc1n4eJ4iLCdb+TSYT7w/9RddpDtR0MPZZfKZo1kXMPOH8+fNYvHgxPv74Y7uX0C5dumDp0qW47bbb/CbygHCd4OBgREZG8oWNHImtnl5fuW+nv9QTX4jTBOEK/jaIIXr3KIlOougoIk8r9wBiAi6LxpD7jyl5l3kDE4oZ3gq48uORo9FooNVq3Zo2JKZh4oSrqF2H5sBqtXJhKjc31y1rD284deoUTpw4gdLSUmRnZyveH5p7Wjp7YWwtD1xnwj6LoPTEFoC9HLsbgesK7OFdp9MpDn4wAcFkMnl17ZSOYfDgwVykFPFGwHWWft26dWhsbERkZCS6dOkCoMlzt6SkBD/88AN69+7tNxYKrC0/88wz/HtfPXup5SP3Q5bjSMBl50tJiHEk4LL24whxMSSltI6umbvXk4nlJOC2PZhFQUJCAoYNG8a/Z7NsXBmMcwWWT0pKis1srtOnTyM2NpZ/v2XLFlRWVgJomoUwZswYAMDRo0dx9uxZPnBuMpn458mTJ9ss0nrs2DGcOXMGJpMJI0eOdFomMcq4sbERCxcuBNA0Yy0xMZGnt1qtOH78OPLz8/HEE09g4cKFSEtLA/DneezSpQt69+6NdevWAfgzel2SJGi1Wr5QrjjLQ7SGEWH3HfHYgCYxOyYmBpmZmXwfJOAShOewe+KePXv4d225DTmLwBX7I7+xUCgqKsJzzz2H//3vf/wBnBU4ISEBzzzzDObOnetWgQn/Ijk5mV9XpQboSiSRM8Tt/KmRUwQu0VL4WxsQhUElMVJNyJSnVYvAZQIuS9dcAq582l5+fj769u2rml6n02HEiBHYt28fX6hDxGg0IigoCG+88QZ69OihmIco4LoTgQt4LuC2RASuGFXoKw9cV3A0pVntb18g5qkU9adUNlc9cF0R9sV9WSwWh89RrN2466vpzELB0Xl1RbASp63KxTXAN2KW2jGo4U0EpBixqSQSbNiwAUCTF++SJUsAAK+//joefvhhrF+/HgsXLmxV4ZYhtmUxMo/hiwhc+bZiBK5aXXYm4IqrWrsj4MpxJLq6e33c7XsoArftwp4Z5BHtrH6y5ylvB+jV2pF8gEQUK8XPUVFROHv2LMrLyyFJEioqKiBJEkJCQmAwGGzagF6v532so7ZRVFSERYsWobq6mtfxqqoq5ObmIigoCF9++aXNQmCSJGHWrFn45JNPkJmZic8//xwjRoyAJEmor6+HJEn8PLLnw8DAQNTV1dloDeLzlpJND0MtAlej0dh4uQcHB5OFAkF4QUJCgk3wjEajabGAj9bAnQjcZlvEzFWqqqrwr3/9C8uWLbPrTNu1a4cnnngCDz30UIu+5BHNQ3p6On/gUKp4vojAVarw/gBF4BIthTwKvbURp+YrCbhqwqFcuJS/KEdHRwNoeiCvrq7medbV1QHwvYA7b948rFixApMmTcKxY8eQl5eHo0ePOtzm0KFDTvP96aefMHbsWMXfdDqdQ69TOWLkl3x1dWc4iwj2JaI42Br3dl+Jip7g6iJm8jRqHriOfLLEfFi9MJvNLgm47kbgilHVrloouBNx6GoErjd2Bp4KuJ564C5btowLtWpMmDDB7vP69ev5wkH33nsvBg4c6Pb+fYUoHooCrrfRwWqDLUx8dUXAFS1LxG0A9UgWbxcxE79Xmpruaw9cgATctgi7R8r7IbGu/vjjj9Dr9bjsssv485C7qLUj1qcxsVZ8lhA/R0ZGAmiyLPj8889x4sQJPt2ZrWnCKC0tRXl5OTp37myznoCcDz/8EIcPH7b7XqvVYtiwYfjkk09s1gcAmtpzcnIycnNzsXv3bmzYsAFnzpxBRkYGampqEBsbi65du9oIuLW1tTYiuNxCAbB/LhUH3OQRuBqNhp8PlgcJuAThOT179kTPnj1buxgtxkURgWs0GrFs2TL861//4iN2rIDBwcF4+OGH8fjjj9utTElcvNx+++0wm802L2AivhBf/U28YlAELtFS+FtdU/LAVbJQcOaBKx+BbNeuHSIjI1FRUYETJ05w/zEm4LIIXV9x8803o0+fPjCZTJg+fToMBoPqQm7FxcW49dZb+d8vvfSS3YIjb7zxBr755hucPn1adZ+eXEsm+robSduSEbiiD50nC+x5Skv6U4q4s4iZuA3z5hPzYIhtyRW/Y71eD5PJ5HTqLbNQUPP/U0O0LnDVQsEdxEWrWP1RE3C9wVMB15Pj3Llzp8Pf09PTbSLN0tPTMXz4cOzatQtA0/H/8ccfrRqJKw7GiHVaHjEr/84Z8m3ktluuRuBaLBYEBQWhvr7eJgJX9Nj0xgNXXl7xWrh73BSBSzBYu5JH4LIZjKz+ms1mFBUV+VzAdTUCNzQ0lD+HsYXBfvnlF+zbt091n7NmzUL37t1Vfz9//jyAJk9tsf9rbGy0a69imYYNG4bc3FxUV1ejqKgInTt35n0zs39gAm5QUBAPGjMYDLzfdybgigJKcHAwwsLCuGVETEwMDAYD4uPjUV1djcjISJv+0Z9s/QiC8D/Ugv2UFjFrcQHXYrHgf//7H5577jkUFBTYvHjo9XrMnTsXzzzzTJsOkb5UYS/NzRmB62/TxxkUgUu0FP42iOHMA1ct8lOeVn4D0+l0SE5ORkVFBTIyMngUK5t6yIQoX6HT6dCxY0dkZ2cjPDwc/fr1s1l5WcRqtWLWrFn8peCWW25B586dbdIcOnQI33zzjaK3prhPhqvX0tNI2taIwPX1QnOu4oova3NH4DpbFMnZIgXuROCy/bki4IqR60rWH2p4ElXtzrkWhQZ2zGqLmPnaA1cNUUDzxAOXnbMjR44gKSnJLk1kZKTd9d2+fTvKy8vx3Xff4e6773bb6sLXsP2z6FtfWSio4U4ErtlshtVq5QIuE6UAW4sesVzuRuAqlc9X0cfO8CYCnPBvHN0j2eAEw51+Wo6rAq5aBC4AXH755TCZTMjMzER4eDhefvllAE3Tn8XnlpqaGlRXV+PEiRO48sorVcv05JNPAgCuvPJKm4UjN2zYAKPRiFGjRtlEugLAvn37cPjwYezduxfZ2dkwm8248sorUV1djYyMDH6+2AA/O6+S1LSwKfteLuCqWXuxNOPGjYPZbLYRVC677DJFu0B/eB4nCMJ/cRaBKz4btOgiZh9//DEWL16Ms2fP2jzkaDQa3H777Vi6dKndSy7RdnC0Ajfg2JfQVfzVQsHfoiKJtou/DWKIwqAvPHDF7zt27IijR4/i+PHjuOKKKwD8+eLjawFX9EkTvdKU0Gq1CAgI4C/W8lWWAXDf2zNnzqhGUHrSb3gqxLZGBK64cnVL4EoEbnN64IoRuGoWCiKORthFaw1XI3AB2IhYSoiCgacCrnhdfSXeiUKD6AMp36c3YpanFgpMKHQHcYGcDh062HgnOkKr1SImJoYLvszjsbUQBVzAdgAC8J2FgrseuAaDgd9zWJ1mfZuj7d2NwHXFA9dVCwV32whZKLRdnAm44jX3ZhDHVQsFMer27NmzeOaZZ1BeXo6YmBi88cYbiIuLQ2lpKe6//340NDSgc+fOyMrKsqnTf/zxBy677DL88ccfmD9/PpYvX47MzEw899xzPEK2Z8+efIHYPn368JlUkiRBp9MhJCQEsbGxduclOjoawcHB6NSpE7Kzs5GRkQGNRgOj0Yi1a9fCYDBg6tSpNhG4LN+wsDAUFxcD+POeohaBK0buy/3qRcTnRYY/vZMSBOF/REVF8b5FHKSSv9c5C/CQ47GA+/3332PRokU4evSo3ej0tGnT8Pzzz6N3796eZk9cJLCXV7UIXF8IuP4mXjFoFJZoKfztgVEUBpXEKyXhUBRD1BYxYxG4APDDDz/AaDSioKAA5eXlAJpPwGVT290ROpWuA5tCWFVVhaeffhoBAQEwGAy44447uODrSb+hJsSaTCbs2rWLR5oATdGWw4cPh8lkwo4dO7B//34EBATAZDLh5MmTNosHqB1Xp06d+PTN6OhoDB061GkZWzsCl8EelMrLy3Hu3DlotVpUVVXhxIkTPCpHr9dj0KBBLgtsjigsLFSdBqokWGm1WuTn56OoqAj19fU2llJMXGXXu6KiAvv27UN5eTmqq6u5pQjj6NGjqKmpwaFDh/g+Ro0ahXHjxtmk02q1CAwMRENDA959912sWrUKQJMlSVJSEgwGA6699loefW40GrFr1y4+rd9gMODNN99ERUUFAKCsrAwlJSXo27cvioqKbPZ15MgR1NbWoq6uzi6iisHqIRPjYmNjUVtbi+zsbHTt2hW//PILACA3N5fv79dff7XzJwSaIlqHDRuG8vJyHDlyBL1790ZhYSE2bNiA6upqm6nJO3fuxPHjx/m1SEtLsxuIEUVed0UUsR0q9VVWqxW7d++2a4OhoaEYMWIEwsPDATRd982bNyM+Pt6t/YtoNBp0797dowAKdtxyEaOwsBBnz55FY2Mjzp49i9raWsXn/OrqauzZs8dOeK+qqsLx48cREhKChoYG7N27FxUVFSgsLERYWBg6dOgAq9WKyspKdOnSBYGBgaisrMTevXuRm5uL8+fP89XgP/jgA2g0GvTo0QM5OTk4cOAAX5RS/pzI2mV5eTnWrVvnNAL3yJEj2LdvH8LCwnDhwgWcOHECZ86cQXl5OXJzc2G1WhESEsLba3V1NY4dO4bAwEBYLBbExMSgrKwMVqsVpaWlOHXqFCIiIhT73traWpw+fRopKSlISkridby+vh7r1q3jfZWn0+mJ1icrKwtZWVl8oE1NwBVpDgFXLQK3uLgYf/3rX7ngCgBffPEFpk2bhri4ON7vz5492+49rnfv3khLS0NmZibeffddfPrpp2hsbLR5VhE9wUXvSzbLQaPRKA78BgUFQafT8XvkkSNHYDQa8d577+Hw4cM4fPgwXn75ZW53cP78ed72O3fubPd86SwC190ZUQRBEM5ISEjAlVdeafeOwPqnnJwc5OXlwWQyNa+Au3nzZjz99NP8wV70iBo3bhxeeOEFDB8+3N1siYsYJuA2VwSuvwq4FIFLtBT+1gaUPHCdLWImflazUGDiIQAcO3bMbsGM5nqJZVNunUXc3XLLLVi9erWqAX9ISAjat2+PvLw8m2mCBw8exJdffsn3xXDHAxewF3DLy8v5yxWjsbER5eXlMBqNqK2t5S9ztbW1dmKbGgUFBTxKpqCgAEaj0akw21oCrtynktWpsrIy/kDEbAZMJhMaGxvR2NiIgoICrwTciIgIAE33P2Yj5GyRvYiICISFhXFfQZ1OZydyaTQahISEQJIkFBQUoKGhAWazmZdbJCgoCBUVFTa2A4cOHbITcAFwAbekpISXs76+nnv6HTlyhAu4paWlqK6u5gKVXq9HQUEBz8toNMJoNKK0tNSuTGazWbW8QJOIIK+HAQEB/JwYjUa+HWsr7LopUVRUhLq6OuzatQsWiwU7d+6ExWLh0Vdi+9BoNDb5ZGdnOxRw3Z3GzARcnU6nKDbX1NSgpKTE7nvWZpmA29DQoHr+3CEnJ8cjAZdFAsojcIuLi2E2m1FZWcnFHiaeiBQXF/OoOBHmwczaIhM8WX0RI8nLysqQlJSEwsJC1NXVoaCgAI2NjXzVe6vViqqqKjQ0NPD2zfpHuVd6eHg4F/LZPrRaLdq1a8cHCRhmsxn5+fk8bVFREV88kvUlQFN7Yp/Zvs1mMxoaGnDo0CHExcXxY3bUHoqLi1FdXc1FbNbXs+MymUwoKCggAfciJjs7m7ep8PBwxehOXwq4rF4qReDm5ubaCbm//PILKisr0b9/f9xxxx1YuHAhAGDt2rV824kTJ3IbBJGAgAD87W9/w8aNG/HFF1/wdt+nTx9ceeWVaGhowPvvv4/q6mp07NjRZhCS9R3BwcGK74fR0dHQ6XR8ZsK5c+ewYsUKbN261S4PdjxmsxlhYWEoKipyWcBVsgFzhD88gxMEcfGg1OezgSkWyOAsuEaOWwLulVdeifXr1wOwHbEePHgwXnjhBUyaNMmtnRNtA1c9cNvaImYUgUu0FP7WBjzxwFWKwJWn0+l06N27Nx5//HHU1taivr4eBQUFqKioQGRkJNq3b+/T4xCFY1cicF955RV06dIFt99+u+LvWq0WDz30EPbs2YPExEQUFhbiyy+/xMGDB3kaX3rgshexyMhIDBgwAAcPHuSCnslkglarRWRkJDp16oSRI0fCbDZDq9Vi1KhRivspLCzEyZMnERcXh6FDh2Lr1q1ckHMGe0FtLQFXbqEgTmUfNWoUAgICEB4ejuTkZJw7d86lY3JEYmIiJk2ahMjISBQUFKBTp052D2lyUTklJQVz5sxBVVUVj84WYRHt7HtWxri4OIwcOVKx/tfW1kKSJOTl5eGbb75RFf1CQ0NRVVWFnj17ok+fPjAajQgODkZkZKRdtCTbL7uWoaGh6Nu3LzQaDS6//HJs27YNe/fuRe/evbnNCSMgIACVlZUYMmSIYgQpG1TQ6XQYOXIkF75XrVqF1NRUDB48mA+QsHaj0+kwevRoOwuEHTt28GgvcSo9O5ZevXohLy+Pp588eTK0Wi0qKipw8OBBxTrA/HgtFouiCOkIJrqoCflsf8HBwXwhn/3796Oqqgpms9lGwE1JScGIESMc7k9tULyyshL79+/3uI7LLRQYrE316NEDYWFh0Gg0iI2Ntdue9UsdOnTgtjJAk1hpMBgQERGByy+/HOPGjcPp06dx5MgRREdHY8iQIThx4gSqq6vt7A5iY2PRuXNn3HbbbdBqtdi4cSPy8vIwYMAADBs2DJIkoby8HJIkITEx0aY8QUFBmDRpks0U9cDAQJSWlgKwfZ9h3pcajQYDBw5EaGgo8vPzccMNN/D2xwZZ2PmvrKyETqfj+bP6l56eDkmSEBwcjKioKMXruXXrVuTm5vL6y9pcUlISOnfujLNnz3rdVxGtC7t+Q4YMUV0LRi62svuuO4vayPcn3l+sViv27NmDJUuWcLE1ICAAkiRh8+bNAIAnnngCM2fOxLlz5/DGG2/Y5PmPf/xDMUpWr9cjMjISN9xwA6666ioYjUb07t0b3bp1g0ajwYkTJzB27Fhs27YNISEhNoMrbHBNbaZGTEwMxo8fj9LSUgQHB6O+vh6PP/44gKZBmtDQUHTo0AH79+8HANx5551ISEjA7t27cfr0aRurBvF8sEFXJQsvV6BFywiC8Jb4+HhMnjyZe41XVVW5ZU/l1p3ht99+4y9JGo0GCQkJWLRoEW666SZoNBqXo3tcwZupY0TroDQqyW6g3ohO/iZeMSgCl2gpfBHJ7kuceeCy9iAKjkpCr1IErkajwZw5c9CrVy+UlpZix44dOHnyJCoqKnzezuQCrrMI3KioKCxatEj1d51Oh/T0dIwcORKjR49GQUEBvvzyS5w9e5ZHsXoTgasWORIUFITw8HD+gsVe/phvb1BQEIKDgxEcHIygoCDFxZUYOTk5CAgIQFhYGI9ocUVA8BcLBYb83AQHByMkJISLa74QRYKCghASEqIoxqrBroNamVk+oqdnYGAgQkND7SILgT/vsaxuqAm4rG5otVpER0ejoaEBgYGBPJJYFLeYAMfaB6s/BoMBSUlJiI6O5scsL1NoaCgaGxtVy2uxWBTroV6vh1arRXBwMBcyWdmYuCk/b2ylcTFyUxRzQ0ND+THo9XqbiAeWrxJBQUGora11W8BlEbhq15ftjw0mALBps+w7s9lsk8ZdnB2fM1hdYHWR9dPsvAYGBtp4BcsRhWqxDtTX1/P6z76Pj49HVFQUYmJikJiYiAsXLqC6utomuhVo6gODg4ORlJQEnU6H0NBQ6PV63lcxgUfNO9pgMNi1UaV0og91cHAwAgICEBwcjLi4ODthmMHqNOvbWRsMCwvjAq5cvGIwkZydW/FvX/ZVROvBrl9ERITqO4ySUGs0GhXrjKv7E/M0mUz4+OOPAQBr1qxBSUkJAgMDce7cORQWFiI4OBjTpk0DACxfvhzTp0/HmDFjADQNJih5/gNNbchgMMBkMiEoKAh9+/a1maEUExPDI8hNJpONtQyzxlITcIGmNhQdHY1+/fohOzsbYWFh6NWrF26++Wa+XUZGBrKysjBy5EhIkoQ//viDR70Df84SlQva8plN9B5HEERLEhgYiMDAQD4Q3mwCLoOJuIWFhViwYAEWLFjgSTYO86cHlosHJWN3hk6nw5QpU7wSXv3N/5NBEbhESyHWL38QcFndF0Umpfag5IGrdCyiB664nVKEri8RIyQ1Go3Xi33Jy5uQkICoqCiUl5fj5MmT6Nevn1cRuEoeuMCfL2riYj0skkyn08FqtapOqxSRL/Yj/9sRrR2Byz7Lr6V4viVJcuuYXN2/2iJG7i72JU8nti1nD3bsvKstaMZ+Zy+1QUFBfBEZ8Xtxv6LgCPxZH1xZHE+tvGr+jCxP8bqIx6SUnzxSmZWJLayq1WoVF9dzVgc8FXCZ5YIzAVc8drEsYjSrs4XpHOFtHVcTcFm/Jo9YlaN2jV1ZAIxtw+oWy0uMotPpdDxKWr5/cSEiV1GKwJXXR0f9JtufvC3p9Xq7wRA15AJufX29z/sqVzEajThw4IDXFh4tTWlpKfco9iVsFosnSJKEAwcOAGjqg5SsVQDg9OnTyMnJQX5+Pr9//fTTT6rPPMHBwejQoQOAJm/psrIydOjQAQaDAUePHgUAZGZmYvPmzdi5cyesVisXTIEmi4PAwEBkZ2cDaIqWf+edd2zKffPNNyM7Oxv9+vXD7t27VYXW48eP8wFcZvnBqKqqwtGjR3HmzBlYrVbs27eP94+sf3VkZWQwGBAaGoouXbpg0qRJGDp0KF/IjMGicoGmtshEYjULBQDcwoh9Bug9jiCI1oH1ge7cuzxexMzb1WiJtoNcfJHjamSSGv4WfcigCFyipfC3KHSxvouegvLflTxwnUXgAuqCrSfTCR2htl9PkR+3RqNBr169sGPHDvz666+wWq04deoUtFotamtrkZGRgfz8fKf5ZmVlITg42K58cnFBLuCK3uRMlGkuAbe1I3DlzyJifRPrWWuJIu7CBkeYmOSqgMtELfl1ZgImE2UCAwNhNBoVBVy5eOaOgOvsHq0miLE2KF4Xtl+22I0cpWvJInBZGZUWDnJFwAVsFyVzBSbgOrNQUBNwAwMDeYCENx6YogiqFpHqCLZvVmdYmdQEXPk+fCHgyiNwxWPT6XTQarU2dhmevIsovcew/bI254qAy5BH4Or1ejsBWo78+Zk9L7emgJufn6/o1ezvnD59mi9k50tKS0sRGRnp0XO+xWLh/UJdXZ3NTAeR2tpaFBcXIy8vz8aewxF6vR56vR4nT54E0LSYXmJiIj8HxcXF+OWXXxQHgwoLC23+jouL45YijJ49e6J37978GBwdf319PS+zeA2sVisaGxthNBoRGBiIuro6m/psMBhsFvKUo9PpYDAYbO5v4eHh6Nu3L44cOYJu3brZbcPEEDaIx2CzPMSZY6yMbF8EQRAtDfO/d+f93u23YRJsCTlyP0tf468CLkXgEi2Fv7UBsb6LL7zy35UekpUi6tUicMVIX/k+fIG8TN5G4CpZHaSnp2PHjh147LHHvMq7R48eWLdunc137gi4oqigxsUcgStvI0oCLuB9dKKj/TdnBK4zxPOuNPWWiXHFxcX4/fffERQUhOTkZD4tXBRw5eIZ25adO1fai7sRuPLIS/GY1KJRla4l8+WW13ulCFy2rbwsLELMUwHX0whcthq70WhUFXpcQX587g6iyyNwAdvrojRYIt+nvBwiju5n8qhqs9nMI6pZnkr9miRJfD0GT9sbOxbgTwGX/e2KgMu2EftaV8viTwIua28JCQno0qVLi+7bG6xWK2pqatCjRw9uv+It+/btgyRJGDRokNNFKpVgi4kCwOWXX66aLjQ0FLt378aPP/4IAHjqqae4pYGcI0eOwGKxYOjQoQgLC8PGjRsBgAub27Ztw7Fjx1BRUQGTyYSIiAg8++yzsFqtCA0NRU1NDbp164avv/6a2ypcdtllGD58OBdEmUe4yWSCwWDAiBEjVO0chg0bhoqKCoSEhCj2fQMHDsTPP/+MkJAQXH755Tb3M3HhPiUMBgO0Wi3at2+P7t27cx/hgIAAxMTEKJapS5cu6NWrF6KiojBu3Dhs2rSJPw8xAdfZ7DCCIIiWQqvV8jUhXMUtAXf16tVuF4po+zSXuMLwt+hDBkXgEi2Fvwm4APiDsKMIXFc9cOUP0PK/5dFZvkIusHkbgaskbM2ePRubN2/mqyWLEbdsESlnZSwoKMDJkyftoovMZjNqampw4cIFGAwGXLhwATU1NVzAra6uRnV1NRISElosAldNvGpu1K5lc1souFImT7d1JwJXq9XyiD8lAZdFs65Zs4Z/FxoayhemVbJQYN95EoHrroCr1HbEqGGltulKBK6SgMtEfXaOfSXgOlvETMnGRH4MQUFBMBqNXkXgikKFNwIuu+6irZm4QCBDfg6dReAqIe/jRQFXvJ7s2rFj8kboViqX/H7mjoWCVqvl5ZWL2mrHLr/X+YOAy9pMRESE4iJ1/kpISAi0Wi169OjhMKrTHbKzs2E0GtGuXTuP8qypqUFERAQMBoPquZQkCVu2bOFCLACsWrVK1cIiOzsbHTt2xC233IKYmBhs3boVu3fvxsyZM9G1a1d8/PHHePPNN3n68ePHY/78+TCZTNiyZQtf1C8tLY0LuP3790dycjLS0tIAABcuXOA2MqGhoUhMTHQ4OKu2OBvQJCx36NCBr53jDmJ7DwsLQ2RkJO+X1DzCJUlCeHg4oqOjbWYuiRFuzp5NCYIgWpKwsDC33nHdehuePXu22wUiWpe1a9fiww8/xJ49e1BQUICIiAh069YNN9xwA+677z6fjFI78sD1Bf4oXgEUgUu0Dv7SBuTeqs3hgdvcEbhyCwVfe+ACTVE3bIojAHTt2hVnzpwBADz55JN49tlnneYbHR2N8vJynDp1Cv369ePfZ2VlYdasWXZTEtesWYO8vDw88MADkCQJ999/P1+ExBUBl00xlE9ndoQ/ROCKIonaS1lzeeAqReAyvI3AdfX+EhAQwAVcpd9EgoODUVtby/0SxWss98BlopI80tuT9uKJB668fAxHAq58Ort8BXXmT6pUD5iAyyJqXYUJvp5aKAB/HrO7+5aj1+vR2NjoUT1n9UcUftlgglartbsW8n14E4HrSMAVI1rZeWK/swhcpTzdgR2nXq/3yEKB9f2s/3RWFvHeZrVa/ULAZdfXW/uz5ub06dM2Vg+HDx+G1WpFeHi4z+5Dp06dQm1tLfR6vUOvVjWqq6tx4sQJRERE8MW05Lz//vtYunSpzXdFRUV45ZVXHObdvXt39O/fH08//TS3X3jqqafwxx9/AADS0tIQFxeH66+/HgaDgddF1if27t0br776KgoKChATE2NzjwgICEB1dTVfkLKiogLnz5/3aFDSnSh2JUTP64MHD9rVS7Z4mvx5kdmsiM+eSoOPFIFLEERrw547XMW34UyE31BTU4Pbb78da9eutfm+uLgYxcXF2LlzJ5YvX44vvvgCw4cP92pfLRmB6y/iFUARuETr4C9tgNV/X0TgqnnRqlks+ApfC7iuLO7UrVs3LuD26tXLpXy7du2KvXv34vTp0zbfb9iwgUe/BQcHo66uDiaTCRs2bMCFCxf48e3fvx8NDQ2wWCyoqKiwWWRERJIkVFdXIzw83CZyz5Xz0loCLkMUb8TyilOqWysC15M2624ELtD00q3msyi+nHfq1Am9evXCzz//jM2bNyMhIQHBwcG8LjmLwHXkGd0cEbhAkzgaHR1ts40jARdoWuVcXLRNvq2agMsEWHdFVFctFEQBQn4M7JhbU8B1FIGr0+k8FnA9jcBlK8aL+bGyyRcyc6etKdVV8ThFr0xXInDFz+Iq9/J9iLDvAwMDbQbN/CECV23BLX/AZDIhIyOD/22xWLB+/Xps3bqVR20OHDgQs2fPhlarxZo1a7Bt2zabejZ16lRMnDiR53H+/HmsXLkSFRUV/LuoqChcc801iIuL8+g6VFVV4ZdffsGpU6eQlpaGxx57jC8cxiguLrb5+/7770dRUZHqjKDt27ejsLAQ3377LYxGI99+48aNmD59Og4dOgQAeO2112AymXgEq1g32aDII488goMHDyInJ8emXzIYDDZt6+zZs177IntiQQE0XavAwED+/KJEYmIitwMSnx/Fc0gRuARB+CvizCJXIAG3DWKxWHDLLbdwv8SEhATMnTsX6enpKCsrw6effort27cjJycHV199NbZv3+6ykKBEc0fgKu3LH6AIXKI18Je6Jl8kyJMIXGceuPJjbW4B11eLmLFoMKX+SvQUTE9Pdynfbt26KQq4e/bsAQA888wzePbZZ7FkyRIsXboUu3fvthFpc3JyUFNTg/vvv9+lRdPuvPNOTJgwwe4aO6K1I3BFxEXkHHngerLAk9L+fbmoK4vkFaP/XK33THCRR+BarVYbMWbIkCHo168ffv75Z3z00UcAgKlTp3LrBbZfeSSekoWC/Bx6uoiZ0uCHWGalqGKWh3wBtrq6Orz88ss2i/IoReCK5RFhQoPRaHSrjri6iJl4PcVyWCwWnwq44j7dQU3AZe1JTbCV/+3NImZiHWQLvIn5GQwGHpXH2rI8T2copROPU+z3XI2MYXnK/1eD+fsywYzVjfr6epsBNF/0Va5yMUTgsvZhMBjQt29f1NTUYPv27dxvFgA2bdqETZs2qebxwQcf4IMPPnC4n8rKSnz22We4+eabFRfMckZeXh42bNgAALjiiitc2ubee+91aF2xbNkyvPzyy1i3bp2NL359fT3mzJkDoKm/Gzx4MHbt2mXTf7P7i8lksru/i/2tKODqdDreJ3Tv3l3VusAZMTExHm2n1+vRtWtXdO/e3W4Q7+zZsygvL7fpL9lznNJgLkXgEgThj1AELoGVK1fym3p6ejp+//13G9+hBx54AAsXLsQrr7yC8vJy3HfffdiyZYvX+73UBFyKwCVaA39pA65G4LIXT089cMWp6b5+oRQf9MW/PUU8B3IPRIYYnefqCyFL9/HHH/OXQaApYgj488XwiiuuwNKlS7F3716bY8nPz8eePXu4eKvWVzPhcOPGjTYRuO4sYtbSHriOBFz5cUqSZLcwk7e+yo6ibL2JwGWiDQC3InABe/HPbDZjyJAhWL9+PXQ6Ha655hp0794dK1asQH5+PiRJwpkzZ+wEXLUIXPGcqZ1DdyNwlV6sRV9fJT9aNQH39OnTdiuqK0XgiuURYQJsQ0ODWx6yTGQODQ1V/N1ZBC4TKoHWFXDZcYiCji8sFNwVcFkErGihIB6fVqu1iRIU7UzcQSkCl/nZAvYLIcoRj0FNuFVqD2yRNJ1Oh4CAAFgsFhsLBfFe6YmXsadcDBG4RqMRR44cQU5ODo4ePYqsrCxUV1cjIiICW7duxblz57BgwQLk5OQAAOLj4/Hvf/8b/fv3BwCsX78eS5YssfGV12g0uOmmm/C3v/0NWq0WdXV1mDZtGrczGDNmDICmaM+UlBQ+gAo0+QXfeuutdrZ0+/fvtyv7Rx99xMsBNA2wvvDCC9i2bRuApkFeRyLpLbfcgjVr1uDcuXMAmsTaqKgoFBYW8uOYN2+ena8y0HRNGxoa0NjYyPtEdr3l6VjbMxgMvE9ITk5WXcysuTAYDDAYDIiLi+NRtoyKigqUl5fbDPApReCyfkEpuIAicAmC8AfkgQaOIAG3jWGxWGy8lD788ENF0/iXXnoJGzZswMGDB7F161b8+uuvmDx5slf7bombnz+NkFIELtEa+IuAK7dJUGsPbCqpJx64LL3SPnxBc3ngAupi8HXXXYfXXnsNffv2dfmFfNiwYQCaXurPnj1r81uHDh3Qu3dvAECfPn3Qvn175OXlAQD69euHU6dOob6+Ht999x0AYNq0afyznKKiIiQkJODcuXPIz8+38Zl0hr9YKAD2kY5iPRMHBZQWsPJVGTxFjJBiuCvgygU2k8mEAQMG4I033kBsbCySkpIQERGBFStW4OzZs3j44YdtXoLlEbhy4VYUydw9h0oLebFjFPfNMBgMqr6+LA/xN4vFghMnTtil9SQCl1kQuNpOXbVQUPPAFQVcdxdQk+ONgCsXdEQLBaVnHXEfrtgOOLqHySOSWZ5MzBfTsfuKPALXVZxZKMjL5AmOovPFfbGIR3Z+6+vrvV6MzlOaMwI3NzfXxjJAp9MhKioKpaWlNvfjTp068UXDJEniXrSMvXv3YtGiRXbndeDAgbBYLEhJScGuXbuQlJQEoMmmIDc3l5/z8ePH46677rJZmIz1HWzRxICAANx222147bXX8P333+P77793eGyvvPIKv1czjhw5wj+npaXhmmuuwW233Wa3OPP8+fNRW1uLq666yul5b9euHV5//XWkpqaiY8eO2Lp1K4CmPk58j2OzcOSRtQ0NDTb3CJPJBJPJhFOnTvFtzp07h+zsbN4fsfStcX9n7S8rK8tuBlFOTg7OnDmD6upqfh84f/48zp07xy2lgD/bM/s/KyuLPydVVVXZ/OYO/vI8ThDExY1Go3Fr0JQE3DbGli1b+A1u7NixGDRokGI6nU6Hhx56CHfddRcA4NNPP70oBFx/ulkqTQUniObGX+qavBxqEelMwPXEA1eevrkFXEfWB67AIjxEEUNOcnIy3n33XYSHh7t8Lfv06YN33nkHBoOBrxINgL+gshe+gIAA/Oc//0FeXh4sFgs6deqERYsWITMzEzt37gQAHkWkRHx8PLp27YqsrCxceeWVCA4ORkpKChYvXuy0jK0VgcsQo/ucWQ848j/1FF9H4LqzeBJDLQJXtJRgebJ82QBvRUUFv4YlJSV4+eWX+WCB3EIB+DM6Xn4OXfXAlYsUSoMo4pSyGTNm2EV+mUwmtG/fnvtKrlq1ChMnTuRWI0FBQfyl3h0Bl9Vhdz1kXY3AVRJwWX1kAsmqVavw888/u7xvOQ0NDbj66qvRp08fj7YFbC0UlKxyAgIC7M6R+NkVD1xHEbhiZK2agCv3wPUW0UJB7TgcHYOr1glsXyx/g8GAxsZGvq/c3Fz07dsXJpMJ8+bNw7hx4zw5HI9oLgG3trYW8+bNw7Fjx/h3QUFBfMEscSB3xowZeP755wEAx48fx9y5c23Eu7KyMkiShMTERPTq1QsNDQ1oaGjAqFGjkJubC6Bp5klCQgK0Wi0OHTpk1y/W19fbrAGSm5vL/ekZPXr0wJgxY1BfX4+wsDBIkoTt27fzQTG2sNn+/ftx8uRJmwVLRWbMmIG//OUvuOyyy+x+MxqNCA4Oxrx585CUlOT0JZ5dl8bGRjshVkQpslbcVkxXVFQEk8mEyspKAE33gKqqKkRHR/O2IN43WhLWJ5aVlaGsrMzmt7KyMpSWlqKxsZFHLefn56O0tBR6vZ4PxrH+n/0vz0f8zRUiIyNRUVGBDh06uH9ABEEQCiQnJ6Nfv344fPiw07Qk4LYxxAf+q6++2mHaq666SnE7T7nULBRoug3RGvhLG5DXf7kwyyIJ2RQ8TzxwWZrmXsRMPKfeTqtnAq6jaF53veC0Wi0SEhKQmJiIoUOH8u9LS0ttFr5hLyyiNcPAgQORmZnJ7QMmTJjgcF9jx45FVlYWt2c4ceIEbr31VowaNcrhdv7igatk16G00j2LOvLF/n3tgQvYR6q6kjd7AZUvYiY/TiUBly2G06lTJ2zcuNHmAZJNWxXLouaP7KkHrloUfGxsLKqrq/l0YTmZmZmYMmUKXnvtNeTn5+Pw4cM8j0ceeQT//Oc/ATQt3CY/B2J5ROLj4wE0RbC5Iw56E4HLhEh2rpXECnf56KOPcP/997u9nSMLBbEPDgoKshORXLEdcOSZLJ4PUWQSf2Of5R647g6YqHngsvIr7dddXInAFf18Y2Nj+cDD0aNHAQDfffcdZs2a5XEZ3MFsNuPUqVN45plnbCJeW5oXXngBL7zwgsM0Wq0WS5YswcSJE5GXl4eMjAzExsYiPT0dJ0+ehNlsRk1NDQICAnjbTE9PR2NjI06fPo3y8nKb/pstkBUfH4/Y2Fjk5uYiICAAY8aMQXJyMiZOnIji4mLs27cPBQUFGD16NLp164aqqiocPHgQhw4dQkpKik0ZCwsLUVZWhj59+ijOIgCa2pvFYkFAQIDNQltqiDMtxDbCIrXFQSHA3hpB/I19rqmpQWxsLNq3b4+oqCjuNRsdHY1u3brh0KFDrTa7plevXmjXrp1iGyovL4fJZEJwcDCfiRQQEAC9Xo+OHTuie/fuAIC4uDgAQO/evREdHW2Xl16vR/v27V0u0/Dhw1FcXGxn6UAQBOEp4eHhLlvUkIDbxhCn64gv+kowH6ecnBwUFhaiuLiY3+TcobnEFSX8JfoQ8K+yEJcO/iLgOorAZX9bLBYu3HrigcvSy6OzfI0r1geuwhb58daOQZ4nYL/whnyqstK09AceeAADBgyAyWRCQkICkpOTHe7r7rvvRlpaGpKTk/HGG29g586d+OOPP/hsDTUuFg9coOmFtr6+3ieRe44EXG8icJUiHp0h9zRksONUisANCAhAeHg4qqurcfbsWQwcOJCvYj5z5kwMGzaMC5pyAU0pApfhqQeumJ9Go8H//d//8cg6+YPt3//+d2zevBn79u3j0Xns2ickJOD666+HXq9HdXU1Zs+ebbOtIwGXRakfOnTILZHf1UXM1CwUTCYTxowZg/j4eAwZMgQdO3Z0ed8iFosFU6dORWlpKbKystCjRw+3tldbxExe9qCgIFRVVdn0S44ixx0NQij18UzwYu3YlQhcX3jgyoVqZ5GoShG4rsDKHRQUxAcqw8LCcOTIEWRnZ+Po0aN4+OGHcfjwYZ9GGTuisbER33//fbOJt2FhYVi4cCFGjRqF+vp6/Oc//8GOHTtw55134uabb4YkSXjnnXfw1Vdf2dyLO3TogMWLF6Nz584AmgYW9Xo9pkyZgtTUVABNAmxycjK6du2K4uJiFBcXo7y8nA9shYeHo2vXrrBarTh79izMZjNqa2t5v1JeXg4ASE1NRWJiIiwWC5+FEBkZia5duyIkJATFxcXo2LEjYmNj0aVLF9TU1CAnJweTJk3CVVddZVMHjhw5guPHj6OsrMyhgGs2mxEQEOBS1LNaBC7QdN+QC7hyCwXxN/aMVltbyxcLi4yMRGhoKMrKytCuXTve/lpLwA0MDOTXXU5tbS3y8vKg0+n4ArF1dXVobGxEamqqzaKxQNMxqOXlDgaDwS3BlyAIwhG0iNklTmZmJv/syk2qc+fO3OQ/MzPTbQE3JyeHm/ZfahG4/lQW4tLBX+qdowhc9rfFYsHJkycREBDAFxVS8sCtrKzE8ePHUV9fj+zsbGi1WqxduxZA0wtQQUEBrFYrzpw549MXy+zsbP6ZRc7++OOPXg1GHTp0CA0NDcjKylK8GRcXF3OfORbl6ozKykpkZ2fDYDBwHz6r1coj9M6dO8cjnlnUFvBnRC7zeMvJyUF5ebmqwCRJEnJyclBRUYGCggL+4vfxxx9j+/btDst46tQpAMCPP/7Ip7C2BNXV1WhoaOAekjk5OXxaozwC12w249ixY8jOzkZVVRVqa2u9FpyLiopQVFQEnU6HX3/91eYFnEVwxsTE2HgtOiI7OxsWiwWVlZWorKxEWVkZzGYzKisrbQZolTh48CAyMzORl5eH3bt38+8bGxt5HYiLi8OFCxcQHByMkydP8gViAGDhwoV4/vnnkZGRAaCpPhw/fhxZWVlo164dKioqEBERgfLychQXF6Ourg4bNmywOYcnT55EZWUlMjIyFM9tSUkJLBYLqqqqbATZ7OxsFBcX4/z589yjOTMzE1lZWTAYDNi2bZtde2J/f/vtt3b7CQ8PxzfffIP8/HxYLBZ88cUXNnW4oKAAxcXFCA4OtrM8YCJeWVkZxowZ4/JU8qysLABNCyQxMUiE+XyeOnWKty2LxYLjx48DADZu3Ijs7Gw+lZ61KU+IjY1FQUEB7r77brcj/tl+MzIyeL9bU1ODzMxMBAcHc+/IuLg4FBcX4/Dhw/w5sLGxkYtm8inrtbW1KC8vtxHNWN+m1+t5HWWLQ2VkZHCBODk5mVspAE19XlFREYxGIz755BPU1taisbER7du3R1VVlUvPslVVVcjOzoZOp0NNTQ2ApnpRXV3N6ygrt3xxKhGj0cjvJZWVlSguLsaxY8eg1WpRU1OD48ePQ6fT2VgHiOeqvLwc+fn5yMvLg16v53XdZDLxejh69OgWW1SMtf/p06dzD1kRT59BampqIEkSrFYr96nu0aMHunXrhoCAAP5dcnIyZs+ejaCgIAQGBqKiogIGgwFlZWV8cKG0tBQmkwmbNm1CVFQUKisrUVVVxb1ri4uLkZ2djbKyMuh0OlRWViIxMZFfg9LSUlRVVeGHH37g90P2LtShQweUlpaitLSU9xM5OTlYu3YtGhoa+ABXcnIycnNzIUkS9u/fz593xH6qsrISNTU1qKysxIULFxTbdFFRERoaGpCYmAiTycT7AzVYvdXr9cjJyeG2BwDw008/8f6qvLwcNTU13EuZHSO7xx87dgwWiwW5ubl8phSr56yu1dXV8brdWgKuI1iZLBYLjh07Bo1Go/isSRAE4c9oNBqb2arOIAG3jcGmAAFND/DOEB/sxW3lMH8pBntgyMnJQefOnVFXVwej0cgfeH0NW9BDo9E02z48gZXLZDL5VbmItgera1qt1i/qWmNjo81CO/X19TYvdiaTCXV1dTaDSkBTX8LKbzQaUVdXh7q6OhQVFcFsNiM7OxuSJPHIl9zcXB65kpWVhaKiIp8dQ1lZGaqrqxEaGspXnHdVVFWDCTBqGI1GLu6Ii7k4oqGhQXU6tVartcmnsLDQJjI3ICDA5joVFBQ4FKiZsAmAixo1NTU2wrAjTp8+7fXUb0+IiIhAaGgoQkNDkZOTw+07QkNDeR0DgKNHj/IXWG+vNaO4uBiSJNm8SIuEhoa6LBRnZ2fbRNsZjUabldIdceHCBS5CKIkAwcHBKCkpQWlpKQIDA3laRl5eHhfnNBoNzp49C41Gw6d1senIQNMxM/FcpKCggNcbR9TW1toIo1arFcXFxQgKCuJtPD8/n58PeT8C/BnxqtTerFYr9u/fz89dQUGBTR41NTUOz2tISAhqa2sV9+uMrKws1boANAk2Yl9ZUFDAo0Dr6+thtVq97udY/vn5+XYL/7hKfn4+TCYTiouLuZAYGBjI/y4uLkZJSYnitoGBgarnt7q6mpevrq6Ot0MmgJ87d86mDYSEhMBiscBisfBrXlJSwiMsmcDE7FMqKytVB6lEjEYj37fofWoymSBJEq/rtbW1DkVL8b5VXl7OxcWqqio0NjbaDBQq0djYiPz8fBQXF9tF2rJ6qOat2lzodDqcP3/eadk9QRwcZPersLAwPvBXXV2t2Ieweici9kkA+IKHlZWViv07O7+FhYUoKiqySxMQEMBFVpPJhIKCAhQVFSE0NJS3SeaxXVFRwevNhQsX7AYsRIxGIwoKClTFWY1GwyNhnQmljY2Nqvcute/FAYoLFy7Y/c5sB1g68fmO9WWSJPnFs6cci8WChoYGu2eU5nwnJQiC8CVGo9FmbQ5ntmkayRembYTfEBAQwKfGiFNp1Lj99tvxySefAAA++eQTzJw5UzHdkiVLsHTpUt8WliAIgiAIgiAIgiAIgiAucXJychxa3lEELuESTz31FP7617/yv9n03ZiYGL+Z0k20TaqqqrhXs6NpjAThLVTXiJaC6hrRUlBdI1oKqmtES0F1jWhJqL4RLYEkSaiurnbqsU0CbhsjLCyMT881Go1OV7MTp/yEh4erpgsMDOQLATAiIyM9LyhBuElERATdNIkWgeoa0VJQXSNaCqprREtBdY1oKaiuES0J1TeiuXFlzQxy+G5jiKKqmjeZCDN7l29LEARBEARBEARBEARBEETrQwJuGyMtLY1/ZosAOUJMI25LEARBEARBEARBEARBEETrQwJuG6Nv37788549exymLSws5KtQx8fHIy4urlnLRhCeEBgYiMWLF9tZeBCEr6G6RrQUVNeIloLqGtFSUF0jWgqqa0RLQvWN8Cc0kiRJrV0Iwnf8/vvvmDBhAgBg3Lhx2Lhxo2ra1atX46677gIAzJkzB6tXr26RMhIEQRAEQRAEQRAEQRAE4RoUgdvGGDt2LBITEwEAmzZtwv79+xXTWSwWvP766/zvW2+9tUXKRxAEQRAEQRAEQRAEQRCE65CA28bQ6XR49tln+d+zZs1CUVGRXbonn3wSBw8eBACMGjUKV155ZUsVkSAIgiAIgiAIgiAIgiAIFyELhTaI2WzG1Vdfjd9++w0AkJiYiLlz5yI9PR1lZWX49NNPsW3bNgBAZGQktm3bht69e7dmkQmCIAiCIAiCIAiCIAiCUIAE3DZKdXU1brvtNvzwww+qaZKTk/H5559j5MiRLVgygiAIgiAIgiAIgiAIgiBchSwU2ijh4eH4/vvv8e233+LGG29ESkoKAgMDERsbi8suuwwvvfQSjh49SuItYcPatWtxyy23oFOnTggKCkJ8fDxGjhyJl19+GVVVVU63v3DhAj777DMsXLgQV1xxBdLS0hATEwODwYDIyEj069cP99xzDzZs2NBsx7BlyxbMnj0bXbt2RUhICGJiYjB48GAsXboUBQUFbuWVn5+PJUuWYPDgwYiJiUFISAi6du2KOXPmYMuWLc10BJcG3tY1OeXl5Vi+fDkmTZqEjh07IigoCNHR0ejVqxduuukmvPHGG7hw4YLfHsPp06fx2GOPoU+fPmjXrh3CwsKQlpaGBx54gNvdEJ7hq+tUU1ODZcuWYdKkSUhMTERgYCDi4uIwbNgwPPfccygsLPT7YwCorjUHFosFR48exXvvvYcFCxZgxIgRCAkJgUajgUajwZw5c1zKR5Ik7Nq1C8899xymTp2KTp06ITg4GEFBQWjfvj2mTJmCZcuWoaKiotmOheqaf+Oruvbee+/xbVz5t2TJEp8fC9U1/8ZXdY1hNBrx/vvv47rrrkNqaipCQkIQEBCA+Ph4jBkzBkuXLkVOTk6zHAu9G/g31dXV+Oqrr/Dggw9i5MiRiIuLg8FgQEREBHr27IlZs2Zh3bp1cCXuMD8/H99//z2WLFmCa665BklJSTZ9WXND/RrR7EgEQVzyVFdXS9OmTZMAqP5LSUmRdu7c6TCfBx54wGEe4r8JEyZIRUVFPjsGk8kkzZ071+E+o6OjpbVr17qU37fffitFRUU5zO++++6TzGazz47hUsBXdU1k9erVUkxMjNM69+qrr/rlMbz99ttScHCwal46nU5aunSpT8p+KeHL6/T7779LCQkJTvuXr776ym+PQZKorjUXN954o8NrNHv2bKd5ZGZmSsnJyS7dP2NiYqQvv/zSp8dAde3iwBd1TZKa7puuPq8BkBYvXuyzY6C6dnHgq7omSZJ04MABqXv37k7rWVBQkLRs2TKfHQO9G/g/r7zyihQUFORSPzR69Gjp/PnzqnmtXbvWaR7NBfVrREuhB0EQlzQWiwW33HIL1q1bBwBISEiw80zevn07cnJycPXVV2P79u3o1auXan4BAQEYPHgwBg8ejO7du/NR1OLiYuzatQtr1qxBfX09NmzYgHHjxmHv3r0IDg72+jjmz5+PlStXAgDatWuHu+++G4MGDUJtbS3Wrl2LH3/8EWVlZbjlllvw66+/YsyYMap5bdy4EdOnT0djYyMAYOrUqZg2bRpCQ0Oxf/9+rFq1CpWVlXj77beh0Wjw1ltveV3+SwFf1zUA+Pvf/47FixcDAAwGA6699lqMGTMGiYmJsFqtyMnJwe7du/Hrr7/65TF89NFHuO+++wAAWq0Wt956KyZMmAC9Xo/t27fj/fffR0NDAxYvXozAwEA88cQTPjmOto4vr9PWrVsxZcoU3h8MHDgQM2fORGpqKiorK/Hbb7/hq6++QllZGWbMmIG1a9fiqquu8qtjAKiuNScWi8Xm7+joaMTExODUqVMu51FWVobc3FwAQGBgIK644gqMGjUKHTt2RGBgIE6fPo2PP/4YGRkZKC0txfTp0/Hpp59i+vTpPik/1bWLA1/UNTkLFizA+PHjHabp2bOnx/mLUF27ePBVXcvJycH48eNRXl4OAIiPj8ecOXPQo0cPBAYG4ty5c/j0009x/PhxGI1GPPzwwwgJCcE999zj9THQu4H/c/LkSRiNRgBAhw4dMHHiRAwePBjx8fEwGo3YtWsXPvroI9TU1GDr1q0YN24cdu3ahfj4eLu85HXWYDCgT58+OHDgQLMeA/VrRIvS2goyQRCty4oVK/hoXnp6ulRQUGCX5tFHH7UZ/VQjKytLqqmpcbi/s2fPSl27duX5/etf//L6GNatW8fzS0pKkk6ePGmX5vXXX+dpunbtKjU0NCjmZTQapU6dOvG0y5cvt0uTmZkpJSYm8jQbNmzw+hguBXxZ1yRJkj755BOetn///tKpU6dU0xqNRqmwsNCvjqGoqEiKiIiQAEharVb67rvv7NLs3LlTCgkJkQBIer1eOnHihNfHcCngq+vU0NBg0x888sgjktVqtUv322+/SYGBgRIAKSEhQaqqqvKbY5AkqmvNzfPPPy89+eST0po1a6QzZ85IkmQb4ehKpNrOnTullJQU6fXXX5fKysoU05hMJpuZLtHR0VJ5ebnX5ae6dvHgi7om32b16tXNV2AZVNcuHnxV1+6++26+zeTJk6Xa2lq7NFarVXr66ad5uri4OMlkMnlVfno3uDiYN2+eNHnyZOnXX3+VLBaLYppz585JaWlp/Nzeeeedium2b98uzZ07V1qxYoW0Z88efj3Zds0lfVG/RrQkJOASxCWM2WyWkpKS+A1l3759qukGDBjA0/3yyy9e7ferr75yWaRzhWHDhvH8HE1hvvbaa3m6t99+WzHNm2++ydNce+21Lh3DiBEjvD6Gto6v61pJSYkUHR0tAZA6dOgglZaWNmfxedl8eQyPP/44T7NgwQLV/b7yyis83cyZM31yLG0ZX16nzz//nP8+cOBA1ZcLSZKkv//97zzt3//+d785BkmiutYauCt01NTUqIoHIlarVRo0aJDPxDeqaxc/F4uAS3Xt4seTutahQwe+zbFjx1TTmc1mG6uiI0eOeFVWeje4OHD1+f3gwYP83IaEhCgOBKjRnAIu9WtES0MCLkFcwvz++++88x87dqzDtO+++y5PO2fOHK/2e+LECZ5X9+7dvcrrzJkzPK/OnTsrRscxxOMdN26cYpoxY8bwNBs3blTNy2Kx2IzGnzt3zqvjaOv4uq69+OKLPM27777bDCW2x9fHkJqaytOw6BYlqqqqpNDQUAmAFBoaKtXV1XlzGG0eX16ne++9l//uzEM5JyeHp01LS/PiCKiutQU8ETpcRez//vKXv3iVF9W1i5+LRcClunbx40ldMxgMfJvGxkaHaUeMGMHTurMWghx6N2ibiFG4hw4dcnm75hRwqV8jWhotCIK4ZPn555/556uvvtphWtHTUdzOE06fPs0/JyYmqqZbsmSJ09VuxbJMmTLF4Qqjo0ePRlhYGIAmX8va2lqb36urq7Ft2zYAQHh4OEaPHq2al1arxZQpUxTLQdjj67q2atUqAE2eyzNmzPC6fHPmzHG62rYvj+H48eM4f/48AKBXr17o3Lmzal5iXaytrcXmzZsd7vtSx5fXiXmSAkBaWprDvJKTkxESEgIAyMzMVPUJpLpGeEtERAT/XF9fr5qO6hrRUlBdI9QQvUpPnjypms5isSArKwsAoNfrVe+59G5w6eLqvc9XUL9G+CMk4BLEJcyRI0f456FDhzpMm5iYiJSUFABAYWEhiouLPdpnUVERnnzySf73zTff7FE+DHeOQa/XY+DAgQCaHhSPHz9u8/vx48dhtVoBNC1UpNPpHOYn7k8sB2GPL+tafn4+HwTo06cPQkJCcOrUKTz44IPo1q0bgoODER0djcGDB+Opp55CXl6e3x2DO3nJ01Bdc4wvr5MkSR6X4/Dhwx5vS3WNcIR4XVJTU32WF9W1S5M333wTvXr1QlhYGEJCQtCxY0dMmzYNb731Furq6ny2H6prlybXX389//yXv/xFsU5JkoRnnnkGRUVFAIC77roLUVFRHu+T3g3aHo2NjTYDAN7e+3wF9WtES0MCLkFcwmRmZvLPjkb5lNKI2ypx7tw5fPvtt/j222/x9ddfY+XKlZg3bx569OiBo0ePAmgaibz//vs9LL19Obw9huY8H5c6vjy3e/bs4Z87duyIDz/8EP3798d///tfZGVlwWg0ory8HPv378c///lPdOvWDR9++KFfHQPVtebDl+dWnCHgKHIIAC5cuGDzYnrixAmn+1aD6hqhRnl5OT7//HP+99SpU73Kj+oasWfPHpw4cQK1tbWor69HTk4Ovv/+e9x///3o1KkTfvjhB5/sh+rapcmSJUvQvXt3AMBvv/2Gzp0748knn8S7776Ljz76CM8//zz69u2LF198EQAwa9YsLFu2zKt9Ul1re3zyySeorKwEAAwaNMjhDM6WhOoa0dLoW7sABEG0HhUVFfxzbGys0/QxMTGK2yqxbt06zJ8/X/G3jh07Yv78+Xj88ceh1Xo3juTLY2jO83Gp48tzm5+fzz8fOXIE33//PSwWC0aNGoXp06cjMTERFy5cwKeffoo9e/agvr4es2bNQmhoKG688Ua/OAaqa82HL8/t6NGjsXr1agDABx98gIceekh1KuZ7772nWg53obpGqPHoo4+ivLwcADBt2jT07dvXq/yorl266HQ6jBgxAqNHj0aPHj0QFhaGiooK7Nu3D1988QXKyspQXFyMadOm4eOPP8bMmTO92h/VtUuT2NhY7N69G/fffz++/PJLFBUV4aWXXrJLN3HiRDz99NO44oorvN4n1bW2RXFxMZ544gn+96JFi1qxNLZQXSNaGhJwCeISpqamhn8OCgpymj44OJh/rq6u9mifOp0OEyZMwKhRo5yKt0uWLFH1HGL48hha43xcKvjy3DLxAgD3S1u8eLFdXXnkkUfw+OOP49///jcA4N5778WVV16J0NBQu/299957dgJccx4D1bXmw5fn9uabb8bChQtRVlaG/fv34/HHH8e//vUvOxF348aNeO6552y+q6qqUtwf1TXCU1asWMEHFCIjI51GqVFdI9S4/PLLce7cOSQnJ9v9ds899+Bf//oX5s6di88//xySJOGuu+7CqFGj0LFjR8X8qK4RjoiKisJLL72EuLg4LF++XDHN77//Do1Gg6ioKAwYMEA1L3o3uLRobGzETTfdxO01rr/+etxwww0tsm/q1wh/hCwUCIJoFubNmwdJkiBJEhobG5GdnY2PP/4YAwcOxOrVqzFmzBg8+uijsFgsrV1U4iKDeZExxowZo/gwr9Fo8NJLL2Hw4MEAgNLSUnz00UctUUSijRAeHo7XX3+d//3vf/8bQ4cOxSuvvII1a9Zg5cqVuPXWWzFp0iQYjUZ07dqVp/V2dgFBiPz4449YsGABgKa6tXr1anTq1Kl1C0VctHTr1k1RvGWEh4fj448/xrhx4wAARqNRMWqSIFzh3//+N7p06YLly5fjjjvuwI4dO1BdXQ2j0Yjjx49jyZIlCAgIwG+//YbRo0dj3bp1rV1kwg+wWq246667sHXrVgBA165d8e6777ZyqQiidaG3C4K4hGGrrgJND+fOEFf8DA8Pd3k/BoMBKSkpuO2227Br1y783//9HwDgP//5D5555hk3SmyPL4+hpc7HpYgvz6387/vuu081H61Wi7lz5/K/f//9d6f7VoPq2sWBr8/t7bffjnfeeQeBgYEAgH379mHhwoWYPn26TYTa0qVLce211/LtvFmAheoaIbJ+/XrcfPPNMJvN0Gg0+N///mezMJA3UF0j1NDpdDYzC7z1wqW6dmny7LPP4rHHHoPFYsHLL7+MDz74ACNGjEBYWBgCAwPRq1cvLF68GOvXr0dAQABqampw6623erxYMkB1rS0gSRLmzZuHjz/+GECT/d769eu9erZqDqiuES0NCbgEcQkTGRnJP5eUlDhNX1paqritO+h0Orz55pto164dAODVV1/1yrfHl8fQGufjUsGX51b+8MYibNUYMmQI/8wsFzyB6trFQXOc23vuuQenT5/GU089hcGDByMyMpIPTN1+++3YsWMHnn32WZu8vFlgg+oawfj9998xbdo0GI1GaDQavPXWW7j77rt9lj/VNcIRI0aM4FN5s7OzbRZqdBeqa5ceeXl5+Oc//wkASEtLw6OPPqqadtSoUZg1axYAoLKyktvFeALVtYsbSZJw//3345133gEAJCcn4/fff/fLWSdU14iWhgRcgriESUtL45/Pnj3rNL2YRtzWXcLDw3H55ZcDaBph3LVrl8d5+fIYWut8XAr48tz27NnT5m82GKCG+LuaL6krUF27OGiuc5ucnIwXXngBe/fuRXl5ObeG+eijj3DZZZcBAI4dO8bTDxs2zJPi25WD6tqly++//45rr72WR9n897//dTjjwBOorhGO0Gq1iI6O5n97M+BOde3S49dff4XJZALQtEiZ2iKgjMmTJ/PPu3fv9ni/VNcuXiRJwgMPPIAVK1YAADp06ICNGzfaWFT5E1TXiJaGBFyCuIQRV6/es2ePw7SFhYXIyckBAMTHxyMuLs6rfYtTPcRFqdzFnWMwm804cOAAgKaXkvT0dJvf09PTuW/lgQMHnPrzivvr06ePW+W+1PBlXevduzf0+j/X4KysrHSYn/i7M7HXEb48BnfykqehuuaY1urXSktLceTIEQBN0+AGDhzocV5U1wgm3rKIx+XLl2P+/Pk+3w/VNcIRVqvV5hnNmygvqmuXHnl5efyzK89fYv0SF3RyF3o3uDhh4u1bb70FAGjfvj02btyIbt26tXLJ1KF+jWhpSMAliEuYKVOm8M8///yzw7Q//fQT/3z11Vd7ve9Tp07xz96IJuIxrFu3DpIkqabdunUrfyAcM2YMQkNDbX4PDw/HqFGjADSt5rlt2zbVvKxWK3755Rf+91VXXeVR+S8VfFnXgoOD+cIqQJMnqSP27t3LP3szQu3LY0hPT+ereWdkZODcuXOqedXU1PAFHEJCQjB27Fh3in3J0Vr92ocffsgjje644w7umesJVNcubeTi7bJly/Dggw82y76orhGO2LVrF48AT05ORkhIiMd5UV279BCDNZhw5Yjz58/zzzExMR7vl94NLj7k4m1SUhI2btyI7t27t3LJHEP9GtHiSARBXLKYzWYpMTFRAiABkPbt26eabsCAATzdunXrvNrvH3/8IWk0GgmAFBAQIFVUVHiV39ChQ3nZvvrqK9V01157LU+3YsUKxTT//e9/eZprr71WNa+vvvqKpxs+fLhX5b8U8HVde++993iaMWPGqO7XYrFIgwcP5mk/+ugjvzmGxx57jKdZsGCB6n5feeUVnu7WW2/1uPyXCq3Rr124cEGKiorifdqJEyc8zouVjeraxc3q1av5uZw9e7bL223cuFEKCQnh27722mvNV0iJ6lpbwNO65gyLxSJNmDCB5z1v3jyv8qO6dvHjbl3buHEjTx8VFSVVVlY6TD9u3Die/tVXX/WqrPRucHFx//3383OXmJjo9XOUCMu3OaQv6teIloYEXIK4xHnzzTf5DaB3795SYWGhXZqFCxfyNKNGjVLM5+DBg9Jrr70mVVdXO9zf7t27pQ4dOvD87rzzTtW0ixcvdulB8aeffuLpkpKSpFOnTtmlWb58OU/TuXNnqaGhQTGv+vp6qWPHjjztG2+8YZfm5MmTNjfr3377zeExE034qq5JUtODUHp6Ok+7ZMkSuzRWq9Umv06dOkmNjY2K+c2ePZunW7x4cYscQ2FhoRQeHi4BkLRarfTdd9/Zpdm1axcXc/R6vZSRkaGaH/EnvrxOZ86ckc6dO6f6+9GjR6W0tDSe14svvuiwbFTXLg08EdU2bdrkU/GW6tqlgbt1bceOHdLbb78t1dfXq6apqamRbr/9dp5vYGCgdPbsWdX0VNcuDdyta42NjTbP1FdffbVUU1Njl85qtUpPP/00TxccHCxduHBBMU96N2h7PPjgg80m3kqS5wIu9WuEP/KniSBBEJckc+fOxTfffIPffvsNx44dQ//+/TF37lykp6ejrKwMn376KZ8uFBkZibffflsxn/LycjzyyCN46qmnMGHCBAwZMgSpqakIDw9HfX09zp07h40bN2Ljxo18KlPv3r3x73//2+tjuOqqq3DnnXdi9erVyM/Px5AhQ3DPPfdg0KBBqK2txdq1a/HDDz8AAAICArBq1SoEBAQo5hUUFIRVq1bh6quvhslkwoMPPoh169Zh2rRpCA0Nxf79+7Fy5Uruqzp37lxMnDjR62O4FPBVXQMAnU6H999/H1dccQVqamqwZMkS/Pbbb5gxYwYSExNx4cIFfPLJJ9wfKiAgAB9//DEMBoPfHEN8fDyWL1+OOXPmwGq14oYbbsCtt96KSZMmQafTYfv27Xj//fdhNBoBAEuXLrVbwI1QxpfXad++fZgxYwZGjRqFcePGoXv37ggKCkJBQQE2btyI77//HmazGQBw99134/HHH/e7Y6C61rycPXsWq1atsvnu8OHD/POBAwewaNEim9/Hjx+P8ePH878PHjyIqVOnctuEK6+8Eqmpqfj2228d7js2NpYvCuopVNcuHnxR1woLC3Hffffh0UcfxaRJkzB48GCkpKQgNDQUlZWV2L9/Pz777DO+wrlGo8HKlSt9sgI81bWLB1/UNYPBgOXLl+OGG26A1WrFTz/9hB49euCOO+5A3759YTAYcObMGXz++ec4ePAg3+6FF15A+/btvSo/vRtcHCxatAhvvPEGgKa+5uGHH0ZGRgYyMjIcbjdo0CBuNyDyyiuvOFxbRV5no6Ki8Oijj3pQ8j+hfo1oUVpbQSYIovWpqqqSrrnmGpsRSvm/5ORkafv27ap5iNOkXPk3c+ZMqaSkxGG5XB1llyRJMplM0l133eVwn1FRUdK3337r0jn5+uuvpcjISIf5zZ07VzKbzS7lRzThi7omsnnzZik5OdlhfvHx8dLGjRsd5uPqKHtzHMObb74pBQUFqeal0+mkZ5991qW8iD/x1XVas2aN0/4sJCREevHFFyWr1eq0XFTX2h7u3v+Urr0Y2ebOv7Fjx6qWi+pa28MXde2bb75xedvExETphx9+cFouqmttD1/UNcYXX3zBbYYc/QsMDHRqnUDvBm2LsWPHenTvW716tWJ+qampbuWTmpqqWjbq1wh/hCJwCYJAeHg4vv/+e3z33Xf44IMPsGfPHhQVFSE8PBxdu3bFjTfeiPvuu8/hCrJjxozBrl27sGHDBuzZswcnTpzAhQsXUFdXh8DAQERGRqJnz54YOXIkZs6cabfKq7fo9XqsWrUKd9xxB1atWoXt27cjPz8fQUFB6NSpE6ZNm4Z58+YhKSnJpfxuuOEGDB8+HG+99Ra+//57nDt3DkajEUlJSbj88stx9913k2G8B/iiromMGTMGx44dw8qVK/HNN9/g1KlTKCsrQ7t27ZCeno5p06bhvvvuQ1hYmN8ew/z58zFx4kSsWLEC69atQ05ODqxWK9q3b48JEybg3nvvxcCBA31W/ksFX12nK664Am+//TY2btyIw4cPo7CwEFVVVYiNjUWXLl0wdepUzJo1Cx06dPDbY2BQXSPUoLp26TBx4kR899132L17N/744w/k5OSgtLQUFRUVCAkJQXx8PAYNGoSpU6di+vTpCAoK8un+qa5detxyyy2YMGECPvzwQ6xbtw6HDx9GWVkZLBYLIiMj0atXL1xxxRW4++67kZKS4rP90rsB0VJQv0a0FBpJcrAsI0EQBEEQBEEQBEEQBEEQBNFqaFu7AARBEARBEARBEARBEARBEIQyJOASBEEQBEEQBEEQBEEQBEH4KSTgEgRBEARBEARBEARBEARB+Ckk4BIEQRAEQRAEQRAEQRAEQfgpJOASBEEQBEEQBEEQBEEQBEH4KSTgEgRBEARBEARBEARBEARB+Ckk4BIEQRAEQRAEQRAEQRAEQfgpJOASBEEQBEEQBEEQBEEQBEH4KSTgEgRBEARBEARBEARBEARB+Ckk4BIEQRAEQRAEQRAEQRAEQfgpJOASBEEQBEEQBEEQBEEQBEH4KSTgEgRBEARBEARBEARBEARB+Ckk4BIEQRAEQRAEQRAEQRAEQfgpJOASBEEQBEEQBEEQBEEQBEH4KSTgEgRBEARBEARBEARBEARB+Ckk4BIEQRAEQRAEQRAEQRAEQfgpJOASBEEQBEEQBEEQBEEQBEH4KSTgEgRBEARBEARBEARBEARB+Ckk4BIEQRAEQRAEQRAEQRAEQfgpJOASBEEQBEEQBEEQBEEQBEH4KSTgEgRBEARBEARBEARBEARB+Ckk4BIEQRAEQRAEQRAEQRAEQfgpJOASBEEQBEEQBEEQBEEQBEH4KSTgEgRBEARBEARBEARBEARB+Ckk4BIEQRAEQRAEQRAEQRAEQfgpJOASBEEQBEEQBEEQBEEQBEH4KSTgEgRBEARBEARBEARBEARB+Ckk4BIEQRAEQRAEQRAEQRAEQfgpJOASBEEQBEEQBEEQBEEQBEH4KSTgEgRBEARBEARBEARBEARB+Ckk4BIEQRAEQRAEQRAEQRAEQfgpJOASBEEQBEEQBEEQBEEQBEH4KSTgEgRBEARBEARBEARBEARB+Ckk4BIEQRAEQbjIpk2boNFo+D/Ce+bPn8/P548//qiY5ty5czbn/dy5cy1bSKJNI9atTZs2tXZxCDewWq3o168fNBoNwsPDkZeX19pFIgiCIIhmgQRcgiAIgiAIolU4dOgQ3nnnHQDA5ZdfjqlTp7ZyiQiCuJjQarV47rnnAAA1NTV48sknW7lEBEEQBNE8kIBLEARBEESbQR6p6ct/S5Ysae3Da3M8+uijsFgsAIClS5e2cmkIom1y8OBBLFmyBEuWLMFrr73W2sXxOdOmTcOgQYMAAB999BEOHDjQyiUiCIIgCN9DAi5BEARBEATR4mzevBkbNmwAAAwZMgTjx49v5RIRRNvk4MGDWLp0KZYuXdomBVwAeOyxxwAAkiTh2WefbeXSEARBEITv0bd2AQiCIAiCIHyFwWBA165dnaYrKipCdXU1/9uVbaKjozFu3DhIkuRVGYkm/vGPf/DPjzzySOsVhLjkoTZ98XPLLbdg4cKFuHDhAn744QccOHAAAwcObO1iEQRBEITPIAGXIAiCIIg2Q4cOHXD69Gmn6ebMmYP333+f/+3KNoTvOHLkCI++jYqKwk033dTKJSII4mJGp9Nhzpw5eP755wEAy5Ytw3vvvde6hSIIgiAIH0IWCgRBEARBEESL8sYbb/DPM2bMQFBQUCuWhiCItsDs2bP5588++wylpaWtWBqCIAiC8C0UgUsQBEEQBNGCmM1mbNmyBVlZWSguLkZ0dDRGjhyJfv36qW7T2NiILVu24Pjx46ipqUFcXBxGjRqF9PR0j8thtVrxxx9/IDMzE4WFhTAYDEhKSsLo0aPRoUMHj/N1RkNDA7744gv+94033uh1nvv27cPRo0eRn5+PsLAw9OjRA2PHjkVgYKDHeR44cABHjhxBYWEhdDodEhISMGTIEKSlpXld3ubGarVi586dOH36NAoKChAcHIwpU6agR48eimmPHDmCgwcPoqSkBPX19QgODkZsbCy6dOmCAQMGIDw8vMXKnpmZiX379uHChQvQarVIT0/H+PHjHV7L48ePY9euXSgsLERoaCjS0tJwxRVXICAgoMXKnZGRgf379yM3NxfBwcHo3LkzrrjiCoSFhbVYGVqKnJwc7Ny5E4WFhaitrUV8fDx69+6NYcOGQaPRuJ1faWkp/vjjD2RlZaGqqgparRZhYWFISUlBz5490aNHD5fy7d69O/r06YOjR4/yfmb+/PmeHCJBEARB+B8SQRAEQRDEJcbs2bMlAPyfq2zcuNGl7dTSvfbaa1JSUpLNb+zfmDFjpFOnTtnkY7FYpH//+99SdHS04jbjxo2TsrKy3Dr2qqoq6cknn5RiY2MV89RoNNKYMWOkvXv3upWvq3z33Xd8X+Hh4VJjY6PTbc6ePWtTxrNnz0qSJEk//vij1KdPH8XjiIyMlF577TW3ymY2m6Xly5dLycnJinkCkHr27Cl99tlnLuU3duxYvt3ixYtd2sabOma1WqX//Oc/iuV/9dVX7Y711VdflTp27Kh6rAAkrVYrDRs2TPrkk09cKr8rpKam8vxXr14tSZIk7d+/Xxo5cqRiGeLi4qQPP/zQLp8dO3ZIw4YNU9wmPj7e5eskbrdx40a30u3evVsaMWKEYhmCg4OlRYsWuVTHXS2DyOLFi/k2Y8eOtftdPM+u/Js9e7bD/X399dfSgAEDVLdPSkqS3njjDclisbhU/oyMDOm6666T9Hq9w3LFxMRIc+bMkYqLi53m+be//Y1vN3r0aJfKQRAEQRAXA2ShQBAEQRAE0cxYrVbMmDEDjzzyCPLz8xXTbNmyBSNGjOB+vA0NDbjuuuuwcOFClJWVKW6zadMmjB07FtnZ2S6VY+/evejRowf++c9/oqSkRDGNJEnYsmULhg0bhhUrVriUrzv89NNP/PPIkSNhMBg8yuf555/HNddcg6NHjyr+XlFRgUceeQQPPvigS/mVl5dj9OjRWLBgAXJzc1XTnThxArfeeituvvlmNDY2elT25sBkMuGaa67BX//6V4flBwCj0Yirr74af/nLX5zWHRap/fnnn6um2bRpEzQaDf+3ZMkSt8r+5ZdfYuTIkdixY4fi78XFxbjjjjvw2muv8e/ef/99jBkzBn/88YfiNkVFRbj11luxatUqt8riDu+99x5Gjx6NnTt3Kv5eX1+P5557DjfffDMsFkuzlaO5qaurw3XXXYcbb7wRBw8eVE2Xn5+PBx98EFdeeSXq6uoc5vnzzz9jwIAB+O6772A2mx2mLS0txXvvvee0XgPAuHHj+OedO3eisrLS6TYEQRAEcTFAFgoEQRAEQRDNzOLFi7ltwLhx43DllVciPj4eRUVFWLNmDfbv3w8AKCkpwaxZs7Bjxw7MmzcPP/zwA99m8uTJSEhIsNsmNzcX8+fPx48//uiwDLt27cKkSZNQU1PDvxs4cCCuvvpqdOzYEWazGYcOHcIXX3yBiooKWK1W3H///YiIiMBtt93ms3Px22+/8c+XX365R3l89NFHeOaZZwAAvXr1wnXXXYcuXbrAZDLhjz/+wKeffsrF1f/+97+YPHkypk2bpppfQ0MDJk6cyM8pAMTGxmLGjBno06cPzGYz9u/fjzVr1vDz99VXX2H69On49ttvPToGX/Poo49ycXzIkCGYOnUqkpOTUVNTg/379yM4OJinXbRoEX799Vf+d8eOHXHttdeiZ8+eCA8PR11dHQoKCnDo0CFs3LgRVVVVzVbuo0eP4q233oLRaETHjh1xyy23oEePHjCbzdi+fTs+//xzLn4+9thjmDRpEgoKCnD33XfDYrGgY8eOuPnmm9GjRw9YrVZs374dn332Gd9mwYIFmDRpEjp27OjTcm/atAnPP/88zGYzUlNTceONN/Kp/ocPH8aHH36I6upqAMDatWvxxhtv4OGHH/ZpGZzRqVMn6PV6VFdXo6ioCACg1+uRmpqqmD4hIcHuu4aGBkyePBnbt2/n38XGxuK6665D//79ERoaiuzsbHz99dc4cuQIAGD9+vW48cYb8fPPPytaH+Tn52PGjBloaGgA0LQA2eTJkzFy5EgkJSVBq9WioqICmZmZ2LVrFw4dOuTyMY8cORJarRZWqxVmsxm///47brjhBpe3JwiCIAi/pbVDgAmCIAiCIFqalrZQ0Gg0UlhYmPTTTz/ZpbVYLNK8efNs0j/yyCMSACkiIkL6+eefFbe59957bbY5cOCAankqKyulTp068bQRERHS119/rZi2sLBQGjduHE8bFRUl5eXlOT85LlBSUmJT5u+++86l7eQWClqtVtLpdNLrr7+uOF374MGDNrYTAwcOdJj/E088YZP/tGnTpPLycrt02dnZ0pAhQ2zSvvPOO6r5tqSFAgApMDDQqdVBTU2NFBQUxLe5//77JZPJpJq+oaFB+uqrr6TnnnvO5bI4O1Zxar9Go5EASA888IBUX1+vmHdgYCBPf+ONN0opKSkSAGnBggWS0Wi022bDhg1SQEAA3+bhhx92WB6x7K5aKGi1WgmA9NRTT0kNDQ12ac+fPy917tzZxtLB0Xl2tQwiziwUGKtXr+bpUlNTXcqb8Ze//MWmbPPnz5eqq6vt0lmtVulf//qXTdq33npLMc9nnnmGp4mLi3PYd0mSJJ05c0Z69NFHpRMnTrhU5h49evD8n3jiCZe2IQiCIAh/hwRcgiAIgiAuOVpawAWgKN4y6uvrFb1xf/nlF9Vt6urqpMTERJ72ySefVE0rCpQBAQHSjh07HB5nTU2N1K1bN77NwoULHaZ3ld9++83m+OSev2rIBVzA3tNVzqpVq2zSq4k/+fn5ksFg4OmGDRumKAoyioqKpA4dOtgIc0oCniS1vID7wQcfOM1/w4YNNuKZI1HRVbwRcAFIM2bMcJieDWiI/2677TaH2zz88MM8bWJiosO0ngi4rgjD4rkGIK1bt87rMog0t4B77NgxLrAzwdwZTz/9NE+flJSkWL9Gjx7N0yxbtszl8rjKDTfcwPOfNGmSz/MnCIIgiNaAPHAJgiAIgiCamWuuuQZXXXWV6u9BQUG46aabbL679tprMXnyZNVtgoODbbZR8wKtq6vD//73P/73ww8/jBEjRjgsb2hoKF566SX+93vvvQeTyeRwG1fIysqy+Ts5OdmjfNLT051OR585cyZCQ0P532rn591337U5tuXLlyMwMFA137i4OLz44ov876KiInz11VeuFr3ZGDp0KO644w6n6QoKCvjnzp07Q69vXUc1g8GAV1991WGa//u//7Pb5pVXXnF5m4KCAuTk5HheSAWio6Nt6oES48ePR5cuXfjfanXQX3n99dchSRKAprb68ssvO93m2WefRVxcHIAmq4Tvv//eLo1YB7t37+6j0v5JSkoK/yzvcwiCIAjiYoUEXIIgCIIgiGbGFWFt4MCBbm8zaNAg/jkzM1Mxza+//ory8nL+90MPPeQ0XwC47rrrEBERAaDJm9cdH0o1RBEtKioKQUFBHuUza9YsRW9NkeDgYPTv35//rXZ+RO/gAQMGYNiwYU73P2PGDERFRfG/xYXZWgtX6gsAhISE8M8ZGRk+WeRp3LhxkJpm9kGSJLcWMZs4cSKSkpIcpunbty90Oh3/e9KkSUhMTHS4Tb9+/Wy2Ubv+njJ9+nQbX2E1hg8f3mxlaE4kSeK+3QAwb948hwMbjMDAQNxyyy387w0bNtilEevgrl27vCypPe3bt+efc3JyuAhNEARBEBczJOASBEEQBEE0M66IgvIFhNzdpqKiQjGNuPhQnz59XI561el0NgLxnj17XNrOEWIZw8LCPM7nsssucymdKOQonR+z2YwDBw7wv6dMmeJSvgEBAZg4cSL/2x8iK51FVTMGDBjAP1dXV+P666/H6dOnm6lUznGlngcEBNgI5p5so9Y+PMVXddBfOX78uM3Aj6ttA7C9Pkr9hlgHX3zxRaxcudInEf4MsW8xmUyor6/3Wd4EQRAE0VqQgEsQBEEQBNHMOIsWBGAz3R9QXhHe0Ta1tbWKaQ4fPsw/9+rVy2meamXIzc11a1slRCHF0+hbwLXzCTg/P7m5uTZl6tu3r8tl6NevH/98+vTpVo/y69q1q0vpOnXqZGPNsWnTJqSlpWHMmDF44YUXsH37djQ2NjZXMe3w5Fq60jbk26i1D0/xVR30V8R+A3Cv73DWb9x77738s8lkwty5c5GcnIx77rkHn3zyidd9jTwyuq6uzqv8CIIgCMIfaF3TK4IgCIIgiEsAT8RKbwROkdLSUv55zZo1Tq0H1PBF9KDBYOCfzWazx/l4cm6UBFYxwhAA9+50BTGt1WpFVVUV2rVr53a5fEV4eLjLaVeuXIlx48bhzJkzAJrKv3XrVmzduhVA0xT30aNH4+abb8aMGTPcyttdWqpt+Fpg94cyNCdivwHYDzC5ilK/MXLkSDz33HNYtGgR/66oqAirVq3CqlWrADR541511VWYNWsWBg8e7NY+5dG8Yr9DEARBEBcrFIFLEARBEATRhvGFxyngmyg2UQQyGo1e5+ct8ohI0ZvTGXJBq6amxidl8hR3FiNLSUnB3r178dBDDykKc3V1dfjll18wd+5cdO7cGW+//bYvi0pcBDR3v/G3v/0NP//8s533N+PUqVN4/fXXMWTIEFx11VVuLUInt0zwVHwmCIIgCH+CInAJgiAIgiDaMKIoGRER4VaUqUh8fLzXZRGnnZeWlkKSJI8jgn2B3IfXHZFaLv564+nbGkRFRWHZsmV47rnn8PPPP2P9+vXYunUrTpw4YZOutLQU8+bNw7lz5/Diiy+2UmmJlkY+mOGqRYc7TJkyBVOmTMHBgwfx888/Y9OmTdi5cyeqq6tt0q1btw5Dhw7F7t27kZqa6jTf4uJi/jkmJsatwQ2CIAiC8FfobkYQBEEQBNGGiYmJ4Z+nTZuGDz/8sNXKIoovjY2NKC4u9okw7CniIleArfDjDDGtVqtFRESEXRpPxOmWXnApPDwc06dPx/Tp0wEABQUF+PHHH/H+++9zSwUAeOmllzBjxgybBaiI1qU564rYbwDAiRMnmk0IHTBgAAYMGICnnnoKZrMZu3fvxpdffon33nuPWzAUFhbikUcewTfffOM0v7y8PP7ZFcGXIAiCIC4GyEKBIAiCIAiiDZOWlsY/X7hwoRVLAvTp08fm71OnTrVSSZro0KGDzYJH8oWbHCGm7datm6JYK0YxuhrdW1hY6HIZmoPExETcfffd2LJlC/773//y7yVJwqefftqKJWvb+FtdEfsNwFYUbU70ej1GjRqFV199FadOnbJZPO2HH36wi85VQuxX5H0OQRAEQVyskIBLEARBEATRhhk7diz/vHv3bjQ0NLRaWXr27GnjR3n06NFWKwvQJBYNGjSI//3LL7+4tJ3JZMKGDRv435dddpliusjISP7ZVQFs165dLqVrCe6//36biNuMjIzWK0wbpznririIl9VqdWmbwYMH29iCbN682aXtfElsbKyNbYfZbHZp0OfYsWP8s7sLoBEEQRCEv0ICLkEQBEEQRBtmypQpCA8PB9AU2ffuu++2Wlm0Wi1GjRrF/967d2+rlYUxdepU/vngwYP4448/nG7zxRdfoKysTDEPkR49evDPruRbV1eHNWvWOE3XkojHYDabW7EkbRvxPO/evdtp+t27dyMzM9OlvEUhtqqqyqVt9Ho9rr/+ev63GI3dkojnBXBeB0+dOsVtFwDbASyCIAiCuJghAZcgCIIgCKINExkZifvuu4///fTTT7sdSWk0Gn1WnilTpvDPmzZt8lm+nnLXXXfZRCg+9NBDaGxsVE1fUlKCp556iv8dHx+PG2+8UTGtGJl76tQpG09ZJZ5++mkbYbg5yM3NdTkKU5IkmyhpNT/RTZs2QaPR8H9LlizxRVEvKcS68vXXX6OyslI1rclkwoIFC1zOW7xulZWVyM3NdWm7J554gluD7N692+1F7CRJUoz4P3/+vMt5HDlyxObvjh07OkwvRgonJiaiX79+Lu+LIAiCIPwZEnAJgiAIgiDaOIsWLeKryFdUVODyyy/H559/7lTIO3ToEP76179i2LBhPiuLGNV3+vRpnD171md5e0JCQgL++te/8r93796NGTNmKApoubm5mDp1KnJycvh3L7zwgo0ALDJhwgTExsbyv++++25FH+LGxkY8/fTTWLZsmUcLn7nDypUr0atXL7z55psoKSlRTWexWPDUU0/h+PHj/Ltp06Y1a9kuZW699Vb+uaysDHfeeaei+FlcXIxp06Zhz549LteV9PR0myjcJ5980qVI3D59+ti0jaeffhoPPPCA00GGkpISvP322+jTpw927txp93u3bt0wZ84cbNu2DZIkqeaTkZGBhQsX8r+HDRuGxMREh/v+7bff+Ofrr7++2dsTQfx/e/cXU/Mfx3H81c4fU9MfaVllSqklRJLJsnFhamTDWmu50Z0o5kYYozaGMbF2GKZhzZmTf2WGrYskNSvCbMmhtmbrIGulTPG7aL+zXz9y+vmpTno+rk7r8/1839+zb+fi3fe83gAwUoZnlCgAAADcho+Pj2w2m5YtW6b29nZ9+PBB6enp2rlzp1asWKHo6Gh5e3uru7tb796907Nnz1RTU+N8Uu53TnIPCwtTYmKiHjx4IEm6du2atm3b9tv2/xX79u3T3bt3VVdX56xp5syZSk9PV0xMjHp7e1VfXy+r1TpgiNKaNWuUlZU16L4mk0l5eXnavn27pP6ncGNiYpSZmam5c+fq27dvamxslM1mU3Nzs4xGo/Ly8pSfnz+s19vY2Kjs7Gzl5uZq0aJFSkhI0PTp0+Xr66vu7m41Njbqxo0bevXqlfOY5cuXKzk5eVjrGs/mzZun1atX6+bNm5Kkq1evatasWcrIyFBYWJi6urpUV1en0tJSdXR0KCgoSKtWrdLp06dd7m02m5WRkeFce+nSJVmtVoWGhg4Ynpaamqr9+/cPOPbgwYN6+vSp7ty5I0kqKirS+fPntXLlSi1cuFABAQGS+v8x1NTUpPr6ej169Eh9fX2D1tPb26vi4mIVFxcrODhYS5YsUWxsrKZMmSKTyaS2tjZVV1ervLzcGZng4eGhQ4cO/fQ6P3/+rFu3bjl/zszMdPneAAAwVtDABQAAGAdiY2P18OFDpaamOrMz7Xa7LBaLy2MNBsNvrSUrK8vZwL18+fKoN3AnTJige/fuKSUlxTkYyuFw6MSJE4Mes3btWpWUlLjcOzc3VxUVFSorK5PU/xX2H+WJmkwmWSwWzZgxY9gbuH/r7e1VVVWVqqqqfrpu8eLFunLlyojUNJ5ZLBY9f/5cdrtdUv/fZ0FBwXfrAgMDdf36dec9NRQHDhxQdXW1M5Lgy5cv3w0E++fAur8ZjUaVlZVp8+bNzgbwp0+fVFpaqtLSUpfndfXZ0draKqvVKqvVOugas9msM2fOuMyzLS8vV2dnp6T+gYmJiYku6wMAYKwgQgEAAGCciIyMVENDg4qKir4bDvRvJpNJS5cu1dGjR4c87X6oMjIynE/u1dTUDMhZHS1+fn6qrKxUYWGhQkJCBl0XFRWlkpIS2Ww2mc1ml/saDAbZbDbt2rVLEydO/OGahIQEVVZWauPGjb9c/1BlZmZq9+7diouLc9lcCw8P1/Hjx1VZWSk/P79hr228CwoK0v3795WWlvbDr/4bjUatW7dO9fX1io+P/097T548WbW1tbJYLEpOTlZISMig9+O/mUwmnTp1StXV1UpJSXF530dERGjLli2qra1VUlLSd7+/ePGi0tLSBsSL/IjZbNb69ev1+PFjbdiwwWWdZ8+edb7OyckhPgEA8Efx+Paz4CEAAAD8sd68eaPa2lq1tbXp48eP8vT0lL+/v6KiojRnzhx5eXkN27nz8/O1Z88eSVJ2drZOnjw5bOf6FXV1dWpoaFBbW5sMBoMCAwO1YMECRUdH//KenZ2dqqiokN1uV09Pj4KCghQXF6eYmJjfWPl/q+fJkydqamqSw+FQT0+PvLy8FBgYqPnz5/+va8X/8/btW1VUVKi1tVUGg0HTpk1TUlKSywzYkdDZ2amqqiq1tLTo/fv3kvqHJYaFhWn27NkKDg4e8l4vX77Uixcv1NLSoo6ODnl4eMjX11eRkZGKj4+Xj4/PkPZpbm5WeHi4+vr6FBAQoNevXw/r5xcAACONBi4AAABGXEdHh0JDQ9Xe3i5PT0+1tLTI399/tMsCMAbl5OQ4I0+OHDnizJ0GAOBPQYQCAAAARpy3t7d27NghqT9Ts7CwcJQrAjAWORwOnTt3TpIUEhKiTZs2jXJFAAD8fjRwAQAAMCq2bt2qiIgISdKxY8fkcDhGuSIAY01BQYG6urokSYcPHx5yti8AAGOJcbQLAAAAwPhkNpt14cIF3b59W5Jkt9udw80AwJWvX79q6tSp2rt3ryZNmqT09PTRLgkAgGFBBi4AAAAAAAAAuCkiFAAAAAAAAADATdHABQAAAAAAAAA3RQMXAAAAAAAAANwUDVwAAAAAAAAAcFM0cAEAAAAAAADATdHABQAAAAAAAAA3RQMXAAAAAAAAANwUDVwAAAAAAAAAcFM0cAEAAAAAAADATdHABQAAAAAAAAA3RQMXAAAAAAAAANzUX1Z6Z3URICMJAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -112,6 +112,8 @@
     "plt.rcParams.update({'font.size': 22})\n",
     "\n",
     "fig, ax1 = profiles.shadow( figsize=(16, 8))\n",
+    "for ln in range(0,len(profiles.columns)+1):\n",
+    "    ax1.get_lines()[ln].set_color('royalblue') # edits the defualt colour of the shadow-plot lines\n",
     "ax1.legend(['5-day average', 'Single days(s)'])\n",
     "ax1.margins(0)\n",
     "\n",
@@ -123,14 +125,6 @@
     "ax1.set_ylabel('Normalised power (kW/kW-Peak)', fontsize='large', labelpad=20)\n",
     "ax1.set_xlabel('Time (hours:minutes)', fontsize='large', labelpad=20)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "991cb76c-f108-40f3-b12f-7220f9589039",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From ff3b1870d2492f13d1701221ab45d2a3e4c569f6 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 12:55:53 +0200
Subject: [PATCH 104/140] Fix pandas deprecation warnings

---
 ramp/core/core.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 3aa8b747..85a5089c 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -312,8 +312,8 @@ def initialize(
             # format datetimeindex in minutes
             self.__datetimeindex = pd.date_range(
                 start=self.days[0],
-                end=self.days[-1] + pd.Timedelta(1, "d") - pd.Timedelta(1, "T"),
-                freq="T",
+                end=self.days[-1] + pd.Timedelta(1, "d") - pd.Timedelta(1, "min"),
+                freq="min",
             )
 
     @property
@@ -1424,10 +1424,10 @@ def windows(
         )  # same as above for window3
 
         self.random_var_1 = int(
-            random_var_w * np.diff(self.window_1)
+            random_var_w * np.diff(self.window_1)[0]
         )  # calculate the random variability of window1, i.e. the maximum range of time they can be enlarged or shortened
-        self.random_var_2 = int(random_var_w * np.diff(self.window_2))  # same as above
-        self.random_var_3 = int(random_var_w * np.diff(self.window_3))  # same as above
+        self.random_var_2 = int(random_var_w * np.diff(self.window_2)[0])  # same as above
+        self.random_var_3 = int(random_var_w * np.diff(self.window_3)[0])  # same as above
 
         # automatically appends the appliance to the user's appliance list
         self.user._add_appliance_instance(self)

From 8a59b6e4ca8d4e86cd18ad87c71043461c11bf12 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 12:57:01 +0200
Subject: [PATCH 105/140] Update the figure generation with a fixed random seed

---
 docs/notebooks/example_figure_joss.ipynb | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/docs/notebooks/example_figure_joss.ipynb b/docs/notebooks/example_figure_joss.ipynb
index 3801d4c4..50db22d5 100644
--- a/docs/notebooks/example_figure_joss.ipynb
+++ b/docs/notebooks/example_figure_joss.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "0465f589-cc6c-40de-9df7-5731e699b4d0",
    "metadata": {},
    "outputs": [],
@@ -56,12 +56,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "You will simulate 1 day(s) from 2024-05-14 00:00:00 until 2024-05-15 00:00:00\n"
+      "You will simulate 1 day(s) from 2024-05-15 00:00:00 until 2024-05-16 00:00:00\n"
      ]
     }
    ],
    "source": [
-    "uc = UseCase(\"JOSS-paper-figure\", users=[LMI, LI])\n",
+    "uc = UseCase(\"JOSS-paper-figure\", users=[LMI, LI], random_seed=20240110)\n",
     "uc.initialize(num_days=1)\n",
     "\n",
     "profiles = uc.generate_daily_load_profiles(\n",
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 3,
    "id": "13d1fe35-2c1c-4641-9448-ea9a54e4e095",
    "metadata": {},
    "outputs": [
@@ -89,13 +89,13 @@
        "Text(0.5, 0, 'Time (hours:minutes)')"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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wcGnNmjUVcUkKsIqqwBVFUSrSGWvshyiK3nx5vGJegevso3///lJmZqZfrmkwGKT27dsrc0+fPt2mgtVoNErTp0+3WIOjCtz09HTpwIEDTq+5f/9+qV69ehIAqXXr1nbv8fLly5VrzZgxw+l8nTp1kgBIGo1GunTpkvMX7MT69eudVmvq9Xpp2rRpyrrmz59vd4z82uLi4iSdTudwvr/++kuZa9CgQTbHDx48KIWEhCj36dixY3bnOXr0qNSoUSMJgBQdHS1lZWXZjDGvwAUg3XPPPS7//qekpEg3btxweFwURentt99W5nzjjTfsjtu4caMyJiYmRkpNTbUZc+3aNalz584Wa7RXgSuKolJ5q1arpTlz5ti9pk6nk8aPH6/M9dVXXzl9rY6wApeIiIiIyH/SMkstKnAvXdcHe0lVxr7jpgpcuYp515GiYC+p2nA3XxMkyXVurlarfcmIAZj6GbpbgSQIAgwGg8/XpMDJy8tDbGwscnNzHVa9OaLT6ZCWloYWLVogLCzM6djiEhHDp2f4stQq7fcPGiM8NCCtqm0kJyfj0KFDGDRoEG699VYkJCRArVbj4sWL2LBhA1avXq1U0jdr1gypqalo0KCBT9dcsWIF7r77bgBAjx49kJqaavffCUmScPvtt2PXrl3K9c37pXrq66+/xkMPPQQA2LZtG3r37m1x3Gg0onnz5sjIyEDt2rVx8eJFi+pQ2Y4dO5RzR48erVS/BorBYEDr1q2Rnp6OgQMHYv369TZjnnnmGXz00UcAgMWLF2Ps2LF253r++efx3//+F4CpWte6Avfee+/F8uXLERYWhsOHD6NVq1YO17V+/XoMHjwYAPDOO+/gX//6l8XxyZMn47vvvgMAJCQk4Pjx44iKinLzVTvXt29fbNu2Da1atcKpU6dsjo8cORK//fYbAGD27Nl45JFH7M5z/Phx3Hzzzcp/e1555RWbPrjm369vvPEGZs6c6XBdpaWlaNu2LdLT09GuXTscO3bM49fmyb+VRERERETk3JmMUly5UZ41NWuoRUJdbRBXVHXsP6FDcYmIlo1DcCajFBq1gB4dw4O9rGrB3XzNrWRIDm0kSfL6Qw5l3B1PRBXr7bffxuXLl/HTTz/hueeew4QJE/C3v/0NM2bMwO+//45du3ahadOmAIBz585hypQpPl9z2bJlyuPp06c7/CWPIAh49tlnfb6ezLy1QGpqqs1xtVqtBH1ZWVlYunSp3XnmzJmjPJ46darf1ueIRqPB7bffDgDYtWuX3X8rJ02apDyeP3++3XlEUVRaCkRFReHee++1OJ6Tk4Nff/0VADBq1Cin4S0ADBo0CA0bNgQArF271unYKVOm+C28Bcq/lqdPn0ZWVpbFMZ1Op2wqFxcXh8mTJzucp127drjzzjudXksOoUNDQ/HUU085HWveTuL48eM4f/680/FERERERBRYRtH55+SY/JOn3DGQ3Scqnts9cH0NVRnKkjfCQgT8/kHjYC8jaMJCKq5vZq9evZwev/XWW7FmzRp07doVJSUlWL16NXbv3o3bbrvN62vKFbUAMGDAAKdjXR03d+DAASxYsAB//vknTp06hby8PJSUlNgdm5Fhv8L7oYcewhtvvAG9Xo85c+ZgwoQJFsdzcnKwePFiAEDLli0xcOBAt9fnSFFREX766Sf89ttvOHz4MK5cuYKCggK7/37m5eUpv6kz17VrV3Tq1Al//fUX1qxZg2vXrqFu3boWYzZt2qS87tGjRyMiIsLi+Pbt2yGWbSsaGhqKX375xeXao6OjcenSJRw9etTpuL59+7qcS2YwGLBs2TL88ssvOHDgADIzM5Gfn6+szZpcMS07ePCg0je5d+/eCAkJcXq9fv36KdW69mzZsgUAUL9+fWzcuNHl+rOzs5XHR48eVX4BQkREREREFc9oNP1cpdUI0Bsk5XNyn7qs6Epkglvh3ApwN23aFOh1ENklCALCQ7n5T2XRvn17/P3vf8fcuXMBACtXrrQIcIuKirBu3TqH50dERChvtQeAzMxMAEBMTIxNyGitdu3aiIuLQ05OjsMxBoMB06ZNw1dffeX2L43y8vLsPt+wYUPcc889WLJkCVJSUnDy5Em0adNGOW69eZmvm1Tt2LED48ePd2sjLfO1Wwe4APDAAw/gn//8J/R6PX744QebalHzytwHHnjA5nzz9hTffvstvv32W7fXdOPGDafHGzd27xcyJ06cwL333usyEDZn/bWUv78AU8juyk033eTwWGFhIa5fvw4AOH/+PEaNGuX2ugDX94WIiIiIiAJLrrgN0ZoCXAd1IWRP2Y/X5nt2i6IElYp5TUVxK8BNSkoK9DqIqIro16+fEuBa9/W8evWq02DLundtQUEBANhUgDoSGRnpNMB9+umnlbYGWq0WQ4cORY8ePdC4cWNERkZCq9Uq65RbHhiNRofzPfbYY1iyZAkAU7uE9957Tzlmfp0HH3zQrfU7kpaWhiFDhij3o1WrVhg6dCjatGmDOnXqICwsTAmIP/74Y+WXao7Wfv/99+OFF16A0WjE/PnzLQLcoqIipSVE06ZN0a9fP5vznd1jV+SKV0fCw133ScrNzUX//v2VALZRo0YYPnw42rdvj/r16yMsLAyqsv/n8OOPP+Knn34CYHs/CgsLlcfufI9FRkY6PObLPQFMPXGJiIiIiKjiFelEZFw1oFBnSmxDtAIKi9lCwRvmAS7faF+x3G6hQEQEwKJS1tdQKyoqCrm5uSgqKnJrvHkgZ+3ChQv48ssvAZg2ytq0aRNat25td+yRI0fcul6/fv3Qrl07HD9+HN999x3eeusthISEYMeOHfjrr78AmHrE1qtXz635HHnrrbeU8PZf//oX3n77bYcVvXLvWmcaNmyIgQMHYu3atdizZw+OHTuG9u3bAwCWL1+uXOv++++3ex3zHrUff/wxnnzySY9fky8+/fRTJbydOHEi5s2b57D9wfbt2x3OYx7IuvM95uz7y/yedOvWDXv37nU5HxERERERBd+VGwZczynfvCwyTIXsPCOMbAPgNqUHrtnPj0YRUKuDs56aqGK2tyeiakN+Gzlg2hjKXPPmzZ1uTmhefQuYglbA9Nb3a9euOb1uVlaW08D4jz/+UHqjPv/88w7DW8BU8equxx57DIDpdcuVq7Nnz1aO+2PzMrntRL169fDmm286bcfg7trNWyN8//33dh/ba58AWLY58KSlg7/I90Oj0eCTTz5x2rvW2f1o1KiR8vjMmTMur3v27FmHx2JjY5UQ11HfZCIiIiIiqnzkVgnxMWp0ahmKiDDTz1tFOgnnL+tx6bqBPV3dJQBqten+MQCvWAxwicgj5j2x27Zt69NcPXr0UB672hRqw4YNTo9fvnxZedyqVSunY1evXu3G6kweeOAB5e33c+bMQU5OjtJWoXXr1nZbEHhKXnuLFi2gdvIrzEuXLuHgwYNuzTlq1CjExMQAMFXtSpKES5cuKfexR48eDr9+ffv2VULkNWvWuP06/EW+H7Vr10atWrUcjtPpdNi8ebPD4507d1baZuzYscNlGwNX/d7ldkJXr15lBS4RERERURUTGa5CTKQaWo3pZ52SUhEZV/VIyyzFjTzHrfUIFnvMqMuSRPYQrlgMcInIbSdPnrTYAGvEiBE+zWfeL/fDDz90uPGYJEn44IMPnM5l/nb506dPOxx39uxZfPfdd26vMS4uDvfddx8AYPPmzXjppZeUzcseeeQRnzcvA8rXfubMGaebr73++uswGAwOj5sLDw/HmDFjAJiqaDdt2oRFixYpfWIdVd8CpkrgO++8EwBw+PBh/PDDD25d01/k+3H16lWHm8wBwEcffYSsrCyHx8PCwjBkyBAAQHZ2ttPN2I4fP+4y2J80aZLyeObMmW5vlEdERERERMEn/+gWE6lCs4ZaNKyjQViIKRYzeJHf6kpF3Mg14kauEdn5xhpRxSsAysZlrMCtWAxwiQgff/wxduzY4XTM/v37MWTIEOh0OgDA4MGD0bNnT5+uK29MBQCpqan45z//aROKSZKEf/7zn0hNTXU612233aY8fu+99+wGe+fPn8ddd93ltNepPY8//rjy+NNPPwUAhISEYPLkyR7N44i89uvXr+P999+3O+b9999Xevy6y7qNgtw+ISQkBOPHj3d67ptvvqm0LnjooYdchrg3btzArFmz8Mcff3i0Rnvk+yFJEv7973/bHfPDDz/gpZdecjnX9OnTlcfPPfccdu7caTPm+vXrGD9+vMtwfMyYMcr3/Jo1a/DAAw8o/YTtMRqNWLNmDf7zn/+4XCcREREREVUMQRCQUFeLFo1CEBnuXUGOKEo4dKoEx8+ZPo6llSDjqnvFNlWR+Y/pcgUuN4GrWH7fxEwURfz6669Yu3Yt9u7di4sXLyI3N1cJfdwhCILbVWZE5LuNGzfi6aefRsuWLTFw4EB06tQJtWvXhlqtRmZmJjZs2IBVq1YpPWabNWuGb775xufrqtVqzJs3D0lJSSgtLcV7772HlJQUTJgwAY0aNUJmZiYWLVqE3bt3o2fPnrhw4YKyuZW1Xr16oWfPnti5cyfOnTuHdu3a4ZFHHkH79u1hNBqRmpqK+fPno7CwEJMnT3ZajWmtW7du6NGjB3bt2qU8N3r0aNSpU8fXWwAAeOaZZ5S+r8899xw2bdqEoUOHon79+jh//jwWL16M3bt3o2HDhrj55puVsa4kJiaiefPmSE9Pxw8//KC0EBg2bBhq167t9NwuXbpg9uzZ+L//+z8UFRVhwoQJ+N///oe77roLrVu3Rnh4OHJzc3H69Gns2rULW7ZsgcFgsKjQ9tYTTzyBefPmwWAw4NNPP8W+ffswZswYJCQk4MqVK/j111+xYcMGREVFYeTIkUpvYnv69++P//u//8PXX3+NvLw89O3bF/fffz8SExMRGhqKQ4cO4euvv8a1a9cwbtw4LF68GACgUtn+flMQBCxduhS9evXChQsXsGDBAvz+++8YO3Ysunfvjvj4eOh0OmRmZuLgwYNYv349rl27hgEDBmDmzJk+3xciIiIiIvIv+R2Vnr65zigCBqPppNAQFUpKRZTqq39FqiAAarkC11j9X29l4tcAd+XKlXj88cdx8eJF5Tm+xZSo6jhz5ozLzZ6GDBmCefPmWWwQ5Yvbb78dS5cuxYQJE5Cfn4/du3dj9+7dFmNuvvlmLFmyBH379nU6148//oj+/fsjLS0N169fx1tvvWUz5sknn8T06dM9CnAB02Zm5gGuPzYvk91555149dVX8eqrrwIAVq1ahVWrVlmMadasGZYtW4aPP/7Y7XkFQcDf//53vPHGGxb9X521TzA3efJkNGzYEFOmTEFmZiYOHDiAAwcOOBwfGhrql1D75ptvxuzZszF16lQYDAbs2LHDpkK8du3aWLRoEXbs2OE0wAWAL7/8EsXFxVi0aBH0ej2++eYbm19APPvssxg6dKgS4EZHR9udKyEhAXv27MHkyZOxevVqZGdnY86cOU6vb74pHBERERERVR5yWwVPkyu5XYJKENCgthrnLokez1FVydu2sAK3YvmthcJHH32Eu+++GxcvXrTYdR4whQiO+kTKx/zRR5KIvPP+++9j7ty5ePjhh9GjRw80b94cUVFR0Gq1qFOnDm699VY8+eSTSE1NxZo1a/wW3spGjBiBo0eP4umnn0br1q0RFhaG+Ph43HbbbXjvvfeQmpqKJk2auJynefPm2L9/P1599VXccsstiIiIQEREBG666Sbcf//92LRpEz7++GOv/r0ZPHiw8rhdu3bKhlb+8sorr2Djxo245557UL9+fWi1WtStWxc9e/bEO++8gwMHDqBbt24ez2sd1tauXRvDhw93+/whQ4bg7NmzmDdvHsaMGYMWLVogKioKGo0GtWrVQteuXTF58mR8//33uHz5MoYOHerxGu2ZMmUKdu7ciYkTJ6Jx48bQarWIj49Hly5d8NJLL+HQoUMWXxNnNBoNFi5ciBUrVmDEiBGoV68eQkJC0LhxY4wePRp//PGHTduN+Ph4h/PVq1cPq1atwp9//oknnngCXbp0USrWIyMj0bJlS4wYMQLvvPMO/vrrL49/WUBERERERP7jrK5Q/slQ8rCfqzxcpUKNyrMEmFXgMsCtUILkhxLZHTt2IDExUXl7dUhICCZOnIgBAwagXr16GDx4MARBQKNGjTB//nzk5OQgLS0NmzZtwurVq2E0GqFWq/HSSy8poYi/wxHyr7y8PMTGxiI3N1fZ6d5dOp0OaWlpaNGiBcLCwgK0QiL/+vDDD5V+qrNmzbLorUrVw7PPPotZs2YBMPV87tKlS1DXw38riYiIiIh8d/pCKa5mG9CsgRYJ9bQWx85klOLKDQOaNtCisdUxZwqLRRw8pYNWIyChnhbpmaWoE6dBm6Yh/l5+pbDrSDEMRgld24Qh87rBq3tG9rmbr/mlhcLLL78MURQhCAKaNm2K1atXo127djbjwsPDkZycrHw+ffp0nD17FlOmTMGWLVvwn//8BwkJCfi///s/fyyLiMgvJElSNhALDw/HpEmTgrwi8rfc3Fylf2/dunXRqVOnIK+IiIiIiIgqiqeljXIFrlyNWmMIpqpjADAag7uUmsbnFgpZWVnYtGmTUjK+ePFiu+GtIzfddBP++OMPDB06FAaDAdOmTcOePXt8XRYRkd8sWLAAJ06cAGBqSeDs7fVU+aSlpeHcuXMOj+fk5GDs2LG4du0aAOChhx6CRuP3PT6JiIiIiKiS8bkHrqq8DUN1Zn5/1Gq5hUJN6fpbOfj8E+r27dshSRIEQUC/fv1w2223eb4IjQbffvstWrdujYKCAjz77LNISUnxdWlERF65ceMGdu3ahdLSUuzevVt5W314eDj+/e9/B3l15Km9e/di/Pjx6N27N5KSktCqVStERkYiOzsbe/fuxY8//oicnBwAQKtWrfg1JiIiIiKqRpSY0U7SqrSv9bICVxDM5qgBBAFQyxW47IFboXwOcC9evKg8Nm+PYI9Op3N4rF69ehg1ahS+//57bNu2DefPn0fTpk19XR4RkccOHTqEO++80+b5WbNmubWZGlU+RqMRW7ZswZYtWxyO6dq1K3799VdERkZW4MqIiIiIiChY5HeTe9xCoSy8NG+h4Ictpiot85embGJmrL6vtzLyuYVCdna28tjRzvRhYWGQJAkFBQVO5+revbvyeNeuXb4ujYjIZ7Vr10ZiYiJ+//13PProo8FeDnlhyJAh+PrrrzF+/HjcfPPNaNiwIUJDQxEREYHmzZtj7Nix+PHHH7Fnzx4G9ERERERENYgcv4oehq/mLRRqBLniGOUVuOygULF8rsA13xlbFO3XT8fExECn0yE3Nxc6nc7hbtrmu61dunTJ16UFVH5+PtatW4dNmzZh3759OHXqFHJychAeHo5GjRqhR48emDBhAoYMGaL8Rseeb7/9Fg8++KDb133llVfw6quvuhxXUlKCefPmYcmSJTh27Bhu3LiBunXronPnzrjvvvswYcIEqGrMvzREnklOTq7Wvz2taaKjozFlyhRMmTIl2EshIiIiIqIK5uxHO2/bH8jtA8z3MKspP0KyAjc4fA5w69Wrpzy+fv263THNmjXD1atXAZjemtyjRw+748zbMRQVFfm6tICZNWsW/v3vf9ttCZGfn48TJ07gxIkTmD9/Pvr27YsFCxZUaDuI48ePY/To0Th69KjF8xcvXsTFixexatUqzJ49Gz///DPq169fYesiIiIiIiIiIgoGu8V18iZmXvbAVamEGtYDV4BabXrx7IFbsXwOcNu1a6c8PnjwoN0xnTt3xu7duwEAK1eudBjgrlixQnlsHgxXNidPnlTC24SEBAwcOBDdu3dHvXr1oNPpkJqaigULFqCgoABbt25FcnIyUlNTXb6mJ598Ev3793c6xvx+23Pp0iUMGTIE58+fBwDccsstmDRpEho1aoSzZ8/i66+/xtmzZ7Ft2zYMHz4cKSkp7PdIRERERERERDWOyssAV5JbKJhX4PppTZWR+WtTlb3oIp0ISZKcvutckiToSiSEaAWo1TUo6Q4AnwPcW265BWFhYSgpKcG2bdvsjhk5ciTmzp0LSZLwySef4L777kP79u0txsyaNcui7+3tt9/u69ICRhAEDB48GP/4xz8wYMAAm1YEkyZNwvPPP48hQ4bgxIkTSEtLw/PPP4958+Y5nbdbt2645557fFrbjBkzlPB2/PjxmD9/PjSa8i/zU089hREjRiAlJQV79+7FO++8gzfeeMOnaxIRERERERER1RRGpQK3vI9uTaE2i8BOZ+jRukmIw7GnM/S4lm2ARi2ge7swhrg+8LkJakhICHr37g1JkpCZmWl3h++hQ4eiRYsWEAQBubm56NGjB6ZNm4Y5c+bg448/xpAhQ/Dcc89BEAQIgoAePXrYBLyVyZtvvom1a9di0KBBDvvINmvWDD/99JPy+U8//RTwthBHjx5VrtmwYUN89dVXFuEtAERFRWHhwoVKH+JZs2YhJycnoOsiIiIiIiIiIqps5OJRTzfkkreAUtWQElzzPWLCQwWoym5cYbHzPgrycYNRQqmhGt+gCuCXXazGjh0LwPQF/d///mdzXKPR4LPPPlMC2sLCQnz55Zd47LHHMH36dPzxxx/K+aGhofjss8/8sayAiY+Pd2tc586d0bZtWwCmnr6nT58O5LLw008/KX+pHnnkEURFRdkdl5CQgHHjxinr+vXXXwO6LiIiIiIiIiKiykZ++7/HPXDlFgoq7zdCq4oEwXTPOtxkqrp1FXyL5vku81uf+CXA/dvf/oZPPvkEn3zyCYYNG4bS0lKbMUOHDsX8+fOVUFGSJJuP+Ph4rFixAt26dfPHsiqFmJgY5XFxcXFAr7V69Wrl8bBhw5yONT9ufh4RERERERERUXVjL2f1NnuVA9+aUIAr2Um3VW4G30ZPS5vJIZ974AJAbGwspk2b5nLcfffdh/79++Obb77Bxo0bkZmZCUmS0KRJEwwaNAgPPfQQYmNj/bGkSqG0tBQnT55UPm/WrJnT8Z9//jn++9//4sKFCxBFEXXq1EGXLl1w5513YtKkSYiIiHB4riRJOHLkCABArVaja9euTq912223KY8PHz7szsshIiIiIiIiIqo2BGUTM8+CRqN5C4UaVoFr/qfoIqA1P8wo1zd+CXA9Ub9+fTz//PN4/vnnK/rSFW7RokXIzc0FYNqgrEGDBk7H79692+LzCxcu4MKFC/jtt9/wyiuvYN68eRgxYoTdcy9cuKD02G3cuDG0Wq3TazVp0gRqtRpGoxGnTp1yuXMgEREREREREVF1Uh7genae0kLBvAK3miaU9l6Xys37JjpvkUseqPAAt6a4du0a/vWvfymfz5w50+FYtVqNXr16oW/fvmjTpg2ioqKQk5ODvXv3YvHixbhx4wauXbuGkSNHYuHChbjvvvts5jDfiKxOnTou16fVahETE4Ps7Gzo9XoUFhY67JkLACUlJSgpKVE+z8vLc3kNIiIiIiIiIqJgkqtr7dWsyU95mr3KlaVqVY0qwFVeq1DWkNVZAa4oShaVzdU14K4oDHADoLS0FKNHj8bVq1cBAPfccw9GjRpld2yfPn2Qnp6Oxo0b2xx76KGH8L///Q8PP/ywskHZlClT0Lt3bzRt2tRibEFBgfI4LCzMrXWGh4cjOzsbAJCfn+80wH377bfx2muvuTUvEREREREREVFl530FruX53sxRVdh7WXIPXGctFNj+1r/8sokZlRNFEVOmTMHWrVsBAC1btsS8efMcjm/VqpXd8FYWHR2NhQsXIjk5GQCg0+nw3//+169rdscLL7yA3Nxc5ePChQsVvgYiIiIiIiIiIr/xNsAtO0GlEmpWO8qyl6oySxMdhbhsn+BfAavAPXjwIDZs2IDdu3cjIyMDOTk5KCoqQvPmzbFhwwaLsUajERcvXgQAqFQqp4FmZSZJEh599FEsXLgQANC0aVP88ccfqFWrlk/zqtVq/Oc//0GfPn0AACtXrsRnn31mMca8elan07k1b3FxsfI4Ojra6djQ0FCEhoa6u2QiIiIiIiIioqBzFs4KboyxRw4n1SrAYPRqWVWHnXvjTuWx0SrYZUGub/we4K5btw4vvfQS9uzZY/G83PdCrVbbnCMIApKSknD+/HkIgoCjR4+iTZs2/l5aQEmShMcffxxfffUVANNGYhs3bkTz5s39Mn+vXr0QFhYGnU6H8+fPo6ioCBEREcrxuLg45fH169ddzmcwGJQ+tlqtFpGRkX5ZJxERERERERFRVeBt9awc4KpUAgRjzYkm5btlvnmbKAG2SR8rcP3Nby0UJEnCY489hjvvvBN79uyBJEkWH87+UqhUKjzxxBPK2O+++85fy6oQkiRh2rRp+PLLLwEACQkJ2LRpE1q2bOm3a6hUKsTHxyufm29aBgBNmjRRAt2MjAzo9Xqn850/fx5Go+nXRK1bt65ZJf9EREREREREVOOVV+B6FsIqLRQsKlGrZ5Br71UJQnnrCEcvmz1w/ctvAe7kyZMxZ84cJYTVarUYNmwYXn31VXzxxRcuQ9zx48crj1euXOmvZQWcHN5+8cUXAIBGjRph06ZNaNWqlV+vI4qisuEYYFlxC5j+8nTs2BGAqSXF/v37nc63e/du5XGnTp38t1AiAMnJyRb/oFclkydPVtaenp4e7OV4rCrfe3/Q6/Vo3749BEHAs88+6/f5Dx8+DLVaDUEQsHnzZr/PT0REREREFUcoS8U8zRrl0LKm/dhl/nrlx6KDBNemNy4DXZ/4JcBdvHgx5s+fr3w+YcIEXLhwAStXrsTLL7+MqVOnupwjISEBnTt3hiRJ+Ouvv5CVleWPpQWUdXjbsGFDbNq0Ca1bt/b7tVJTU5WetY0bN7ZonyAbOnSo8nj16tVO51u1apXyeNiwYX5aJVUHBw4cwIwZM3D77bejbt26CAkJQXh4OBo2bIhevXrhoYcewuzZs3HmzJlgL5XIxgcffIDjx48jLi4OM2fO9Pv8N998Mx544AEAwBNPPKG8k4GIiIiIiCo3e2Gr/JQoAgdP6XDiXIkXE5v+qK75pKMKW7n6WHLQKsHIFgp+5ZcA1/yH5OnTp2PBggWoV6+ex/N0795deXz48GF/LC2gnnjiCSW8bdCgATZt2hSQ3r2iKOLll19WPh8xYoTdcX/729+Ux7Nnz0ZhYaHdcRcvXsTixYsBAOHh4bj77rv9uFqqqgoLC/H3v/8dXbt2xQcffICdO3fi+vXr0Ov10Ol0uHz5MlJTU/H111/j0UcfRatWrVz+ooCoIt24cQNvvfUWAODJJ5/0eQNJR1588UWoVCocOXIE3377bUCuQURERERE/uEsWJVD3SKdiMJiEVm57hVoKBW4qFlVuObv9FSVJYqOWiVYV+BW14C7ovgc4B4+fBinT5+GIAi46aab8N///tfrudq2bas8Pn36tK9LC6gnn3wSn3/+OQBTeLt582aL9bvjzz//xJw5c6DT6RyOKSwsxAMPPIANGzYAAEJDQ/Gvf/3L7tiOHTti3LhxAIBLly7h4YcfhsFgsBhTUFCAiRMnKtecMWOGTTsGqnkMBgPuvPNOLFiwAACg0Whwzz334P3338cPP/yAH3/8EZ9++immTp2K9u3bK+c5qj7cvHmz0k6FqKK89957yM3NRVhYGJ5++umAXad169a49957AQCvv/66y57jRERERERU/VXXH38dV+CawtxDp0tw5GwJSvWWA7mJmX9pfJ3AvNfq2LFjodF4P6V5tZT1Jl2VycyZM/Hpp58CMP324emnn8axY8dw7Ngxp+d169YNTZs2VT6/cuUKpk6dimeffRaDBg1C9+7d0aRJE0RGRiI3Nxf79u3Djz/+qLSTEAQBc+fORfPmzR1eY9asWdixYwcyMjLwww8/4MiRI5g8eTIaNWqEs2fPYu7cuTh79iwAoEuXLnj++ed9vBtUHXzxxRfYunUrAKB58+ZYtWqVRVBr7dSpU5g7dy7Df6o0CgoKlHdEjB07FrVr1w7o9R599FH8/PPPOH/+PBYvXoyJEycG9HpEREREROR/3lbPKlGlUN6Gobqz3mdF/lSSJOQWGHEjz4gGtcszQZsWuNU14a4gPge4V65cUR77unGXWq1WHltXjlYm27ZtUx5LkoQXXnjBrfO++eYbTJ482eb5goICLF++HMuXL3d4boMGDTB37lwMHz7c6TUSEhKwdu1ajB49GsePH8ehQ4cwY8YMm3F33HEHli5diqioKLfWTtWbeQ/rL774wml4C5gqEH2ptifyt4ULFyq/+Js0aVLAr9e/f380btwYGRkZ+PTTTxngEhERERFVQX7d/LmG5ZMqq1tntEpsrT8n3/jcQiEkJER5XFLiRbNnMzdu3FAeB7p6qjIYOHAgfv31V7z44osYOHAg2rZtizp16kCj0SAmJgatWrXCuHHj8N133yEtLc1leCvr0KED9u/fj08//RRJSUmoX78+QkJC0KhRIwwdOhTff/89tm7digYNGgT4FVJVcfz4ceVxUlKSz/MlJydDEASH/zH89ttvleNyD9GTJ0/iySefRJs2bRAREYG4uDj06tULH330EUpLS9267uHDh/HQQw+hRYsWCAsLQ4MGDZCcnIx58+ZBLHv/hnzd5ORkn1+nTqfD7NmzMWLECDRp0gRhYWGIjY1Fp06d8NRTT+HkyZM+X8NcdnY2XnrpJdx8882IiopCXFwcunTpgtdff92jjR+vX7+Ob775BpMmTUKXLl0QFxcHrVaL+Ph4dOnSRXlXgSOfffaZch/ffPNNt6752muvKed8+eWXNsfPnDmDf/7zn7jttttQq1YtZT2tW7dGYmIiZsyYgS1btjicX/4+ql27tltf261bt2LKlClo3749oqOjodVqUa9ePXTo0AFDhw7FG2+84fTrJwiC0kYhNTUVJ06ccHlNIiIiIiIKArN+tda8rsCV56wp5bewvX+CVaJovWkZWyj4meSjH374QRIEQVKpVNJTTz3lcJw8pnXr1g7HTJgwQRm3cuVKX5dGAZSbmysBkHJzcz0+t7i4WDp69KhUXFwcgJWRN8LDwyWY/rMmpaWl+TxfUlKSMp8933zzjXL8m2++kb7//nuLNVh/9OrVy+X32kcffSRpNBqHc/Tv31/5vgUgJSUl2Z1n0qRJbt2LzZs3SwkJCQ6vB0BSq9XSW2+95e5tc2rXrl1S/fr1HV6rSZMm0sGDB13e+zNnzji9T/KHIAjS66+/bneO3NxcKTIyUgIgNW/eXBJF0enaDQaD1LhxYwmAFBUVJeXl5Vkc//rrr6XQ0FCXa4qMjLQ7f2ZmpjJm5MiRTtdiNBqlqVOnurwWAGn48OFO51q2bJky9u2333Y61hv8t5KIiIiIyHdHz+qk7QcLpStZeptjBUVGafvBQosPVz/fSJIk/Xm4SNp+sFAqLjFKOfkGafvBQmn/ier5/9t1JaZ79OehIovnD5/WWdy3sxdLLI6nZ5ZaHM/JN1TksqsMd/M1n1sodO/eXXn8yy+/4P333/eqD25RURHWrFkDAFCpVOjTp4+vSyMiN7Vq1QqHDx8GAHz44Yf48MMPK+zaa9aswc8//4yIiAhMmzYNt912G0JDQ3HgwAF8+eWXyM3NxZ9//ol//OMfmDNnjt05FixYYLFp1aBBgzBq1CjUrl0b6enp+P7777Fx40Y8/PDDflnz6tWrcffdd0Ov10OlUmHo0KEYOHAgEhISoNPpsGfPHnz//ffIzc3Fiy++CABut1qxJy0tDYMHD1ZaBLRt2xaTJ09GixYtcP36dSxbtgwbN27EPffcg9jYWKdzlZaWwmAwoGnTphgwYABuvvlmpUr/2rVrSE1NxZIlS1BcXIyXX34ZtWvXxuOPP24xR0xMDCZOnIg5c+YgPT0d69atw5AhQxxec9WqVcjIyAAA3HfffYiOjlaO7d+/H4888giMRiPUajWGDBmCQYMGoV69elCpVLh69SoOHjyI9evXW7xLw9zatWuVx7fffrvT1//pp59i9uzZAIDo6GiMGTMG3bt3R926dVFaWoqMjAzs2bMHf/zxh9N5rK+1Zs0a9hQnIiIiIqpivK6gdVLVW1OorCpwrStuRfa89S9/pMVt2rRRKmf/85//2B3jqgL3tddeU8Y4qoyjyoMVuNXL22+/bVF5eOedd0o///yzlJWV5dV8nlTgApA6duwoZWRk2Iw7duyYFBUVJQGQtFqtdPnyZZsxWVlZUq1atZS5PvvsM5sxer3eorIWPlTgZmZmSvHx8RIAqV69etKff/5pd56MjAypU6dOSiXusWPH7I5zx+DBg5U1jRs3TiopKbEZM2vWLJsKUnuysrKkrVu3Or1eWlqa1KZNGwmAFBsbK+Xn59uM2b9/v3Kde++91+l8I0aMUMbu2bPH4ti0adOUYytWrHA4hyiKUkpKit1jjz76qDLHhg0bnK6lY8eOEgCpVq1aUnp6usNxxcXFUmpqqtO5JEmSmjVrJgGQIiIiJIPBv79R5r+VRERERESu6cqqYO19GAyidMRJBW5RsW0FrtHoRgXuIVMFbkmpqFTg7jtePf9/e7FcgXvYsgL3aJplBe6Jc+U/pxYWG5XKZ1bgOuduvuZzD1wA+Oc//6k8fuWVV/DRRx95dP5PP/2EN954Q/ncl0o1qn6MolRjPyrKM888gzvuuEP5fPXq1RgzZgxq166Nm266CWPGjMG7776LXbt2+X3nSI1Gg2XLliEhIcHmWLt27TBt2jQAgF6vt1sV+e233yI7OxuAqbrTulpUvsbs2bPRsmVLn9f77rvvKpWgP//8s8OKz4SEBCxZsgRqtRpGo9Hjfxdlhw4dwrp16wAATZs2xbfffmvRe1w2ffp0jBkzxuV88fHxLt/h0Lx5c3z++ecAgNzcXPz66682Y7p06YJevXoBAFasWIHLly/bnevChQtYvXo1ANM7NszftQEAp0+fBgDUrVsXd911l8M1CYKAxMREu8fk6nHA9D3jjHy9gQMHolmzZg7HhYWFoWfPnk7nAqBs+FdUVIQzZ864HE9ERERERP5Tqpew70QJjpy1/3E0rXyvJrvVtt72wDWfooaU4Vq/TkebmOXkG3HgpA7Z+cYKWlnN4HMLBQCYMmUK5s6di507d0KSJMyYMQM//PADnn76aQwdOhS1atWyOcdoNCI1NRWffvopFi9eDEmSIAgCRo4c6fStuFSzGEUJO/8qDvYygqZnp3Corf9VDICwsDBs2LABL730Ej7//HMUFRUpx9LS0pCWloalS5cCAJo1a4bp06fj8ccfh1ar9fnaI0aMQJs2bRweHzRoEP773/8CAP766y+b47/88ovyePr06Q7nCQ0NxeOPP45nn33W67VKkoTvv/8eANCrVy/07dvX6fh27dqhR48e+PPPPy3e5u+JZcuWKY8fe+wxhIeHOxz73HPP4eeff/bqOtbMQ97U1FRMnDjRZsxjjz2GP//8EwaDAfPmzVPaRZj7+uuvYTSa/sM9depUm+ORkZEAgKysLKSnp6N58+YerzU9PV15HB8f73RsZGQkSkpKcPjwYZSWltoNwz1hfr309HSn38tERERERORfulIRkiRBJQgIDSn/2VmSTMd0pRIiwx3/TG3vx21vS5aqa8MARzVc1puWl/3Yh6IS0wkatYCIMBUKdSKMRsnhPOQev1TgCoKAlStXWvzgunv3btx///2oU6cOGjRooDx//vx5NGvWDNHR0UhMTFTCWwDo3LkzFi5c6I8lEZGHwsLC8O677yIjIwPz5s3DfffdhxYtWtiMO3fuHJ555hn07dsXWVlZPl9XruJ0pHHjxspjudJWJooi9u7dC8DUz/TWW291Ole/fv28XKXJ0aNHlddcq1Yt/PLLLy4/1Go1AFMQrtPpPL7mrl27lMcDBgxwOva2226z6C/rzOnTp/Hyyy8jOTkZDRs2REREBARBUD7CwsKUsXL/Wmtjx45F7dq1AQBz5861qc42Go34+uuvAZi+Pvfdd5/NHIMHDwZg+lomJydj9uzZuHLliluvQSZXREdERFis2x75esePH8eAAQOwfPlyi19YeEp+/ebrICIiIiKiiiH/CBIWKqBr2zDlo11z9wo1rENI969b89JIV3dK7nlrMJj+rFtLjU4tQxGmrSElygHmlwpcwPRD7I4dO/Dwww9j+fLlAEx/ESRJwtWrV5W/FHq9HhcuXLA4DgCjR4/GvHnzEBER4a8lUTWgVgno2clxxWF1VxHVt9Zq1aqFBx98EA8++CAAU2i6Z88ebNq0CQsXLsT58+cBADt37sSECRO8riyV1alTx+nx0NBQ5bF1AJqbm6uEby1atHD5H9+bbrrJy1WamFd6rlq1CqtWrfLo/Bs3bqBRo0YenZOZmak8btWqldOxgiCgZcuWOHDggNNxr776Kt58800YDAa31pCXl2f3+bCwMEyZMgXvvvsu0tLSsH79eiUgBSw3L5s4cSKioqJs5pgyZQqWLFmCDRs24Ny5c3j00Ufx6KOPol27drjjjjuQmJiI4cOHO/0+KSkxvS3KnfD6v//9L7Zt24aMjAxs27YN27Ztg1arRbdu3XDHHXcgOTkZgwcPdhkEy2JiYpTHxcU1990CRERERETBIHcetP5RUP7UImd198drD7NZ5efQ6prpOnhdtgU8pj/1hvIKXPIfv1TgyuLj47F06VKsWrXK4od4wPSFlT/Mn+vTpw9WrlyJJUuWuF05RjWLWiXU2I/KoFatWhg0aBDeeustnD592qLH7Lp167B9+3af5ldZb13pgcLCQuWxO7/8kd+u762cnByfzi8tLfX4nIKCAuWxP17ju+++i9deew0GgwEqlQoDBgzAyy+/jLlz5+Knn37C8uXLlQ+Z3ALBnqlTpyr/h2XOnDkWx2bPnm0xzh6tVovVq1fjgw8+sOhRfPz4ccybNw+TJ09Gw4YNMXHiRFy6dMnuHHLI7yhoNte0aVPs378fzzzzjNL+QK/XY+fOnfjggw9w9913o379+nj55ZeVYNiZ3Nxc5bGz9hZEREREROR/kmj609mPz86KZX3tXysIXrfRrXKs75X1tj1yD1w5wNVqyk6o5vl2RfFbBa65oUOHYujQocjJycGOHTtw6NAhZGVlITc3FxEREahduzbatm2LpKQk1K9fPxBLIKIA0Gq1+Oijj7B582YcPXoUALB+/Xr07t07KOsxDyvdeRu8eeDrDfMK0hkzZuD999/3aT5Pr1lUVGRRkWyPs9eo0+nw+uuvK/Nu2LABPXr08Hgecy1btsTgwYOxdu1arFixAleuXEH9+vVx4cIFrFmzBgDQo0cPdOnSxeEcWq0WzzzzDJ555hmcOHEC27dvx44dO7Bp0yacPXsWBoMBixYtwubNm7F7926bKubatWsjIyMDxcXF0Ol0Lqtn69Spgw8++ADvvvsu9u3bhx07dmDHjh3YsGEDbty4gby8PLzxxhvYvn071q9f7/SXDOZtRFz13yUiIiIiIv+S37Zv825MN1NVewGuq6DRvDDR/PzqGlA6el3WwbixLEw3lNX/aNQBW1KN5NcKXGtxcXEYNmwYnn/+ebz77ruYM2cOPvzwQ7z00ksYN24cw1uiKkij0SA5OVn53FFVZEWIjY1VqlLT0tJc9iE6e/asT9cz78crt4IJtISEBOXx6dOnnY6VJMnpa/zzzz+Vit6pU6c6DG8B0/10l1yVrdfrMW/ePACmnrjONi9zpG3btsrGmGfOnMHOnTtx8803AzC1k3j77bdtzjHv1exJH1qNRoMePXrgmWeeweLFi3H16lUsWbIEsbGxAICNGzdaVCLbY349bzZgIyIiIiIi78k/AqodpFuuQlVvqmdtfuysKSW4VkTR/ufWFbhKh4nqmnBXEJ8D3OzsbGzevBlLlizBsmXLsG3bNp+r3IioctNqtcpje31NK4pKpUL37t0BAPn5+dizZ4/T8Zs2bfLpel26dFHCvU2bNrn1FntfmYesGzdudDp29+7dTtsIXL58WXnsqp/u6tWr3VwhMHz4cDRp0gSAKbg1D3JjY2Mxfvx4t+ey1qNHD3z//ffK51u3brUZIwe8AHDs2DGvr6VWqzFmzBi8+uqrTq9nTq5Ej4yMtGgBQUREREREgeeoB645T1soeBs0VteNzRy9LNHqgCRJMIoSDEarFgrkF14HuBs3bkRiYiLq1auHAQMGYPz48Rg7diySkpIQHx+Pu+66C/v37/fnWokoQK5cueL2WL1ebxHumYdnwXD33Xcrjz/44AOH40pKSvD555/7dC21Wo2JEycCAK5fv45Zs2b5NJ87Ro0apTz+4osvbDZyM+eqpYN5ywln1bzZ2dn48MMP3V6jWq3GI488AsBU5Txjxgxl87L777/f580pzSts7W28dvvttyuPd+7c6dO13Lme7NKlS0ol9m233Qa1mu8RIiIiIiKqSHLVp8qqCa69qk97caLPPXAdzFsdWd8rSbQdYzSat1Aoq8BVTgjY0moErwLcV199FYMGDcL27dthNBotNiiTJAl6vR6///47evbsqVRhEVHldeutt2Ly5Mn4888/nY4rKCjAgw8+iJMnTwIwVVeOHDmyIpbo0IMPPohatWoBAH744Qd88cUXNmMMBgOmTp2KM2fO+Hy9F198EXFxcQCAmTNn4sMPP4Ro/d4RM4WFhZg7dy5++OEHr653yy23KJtCnjt3DlOmTIFer7cZ98knn2Dx4sVO57r11luV3lByiwJrN27cwD333IPMzEyP1vnQQw8pldmffvqp8ryr9gkzZszAjh07nI4xD97t9dIdPHiw8rqcBbiXLl3Cs88+6/T7wGAw4KuvvnJ6PZn535chQ4Y4HEdERERERIFR3gPXu/NteufCdQWuuz1hqwtHLysqwhQpCoIAdVlYW6KXlEpk9sD1L483Mfvuu++UTXAEQYAgCDZl4vJfAIPBgEceeQQtW7ZEUlKSH5ZLRIGg1+vx3Xff4bvvvkOzZs2QlJSELl26oF69eggNDUVWVhb279+PZcuW4dq1awBMf88//vjjoG/cFB8fj48//hh///vfAZj6sS5fvhyjRo1C7dq1kZ6eju+//x5HjhzBuHHjlJDT2cZUziQkJGDx4sW46667UFJSgunTp+Pzzz/HqFGj0KFDB0RFRSE/Px9paWnYs2cPNm7cCJ1OhzfeeMPr1/jll1+iW7duyMnJwQ8//ID9+/dj8uTJaNGiBbKysrB06VJs2LABLVq0QGxsLA4cOGB3nkaNGmHs2LFYvHgxcnNz0aVLFzz00EPo3LkzNBoN9u/fj++++w5ZWVmYPHkyvv32W7fX2KBBA4waNcoiRO7Vq5fLCu1ly5bhgw8+QLNmzTBo0CDccsstqFu3LoxGIy5evIgVK1Zg+/btAEytO5577jmbOerXr4/evXtj27Zt2LZtG/R6vUWbD1lJSQlmzZqFWbNmoXv37ujbty/at2+PWrVqoaCgAGfPnsUPP/ygBLw33XST0/YPW7ZsUR6bV0oTEREREVHFkOMoqwJcj6o+7eVabqsp5bewDbubNdRCqxVQN1aNI2klMBqB7DxT+a1GLShV0fZCcvKcRwFucXExZsyYodx8SZLQqlUrjBo1CjfddBMMBgNOnDiBpUuX4tKlSxAEAaIoYtq0afjrr78C8gKIyHedO3fG+vXrIUkSzp07h++//96i76i1Bg0a4JNPPsGYMWMqcJWO3X///bhx4waeffZZGAwGrF+/HuvXr7cY079/f3zxxRdKwBgdHe319QYNGoRt27bh/vvvx4kTJ3Dq1Cn873//czherVajQYMGXl+vRYsWWLt2Le666y5cvXoVx48fx/PPP28xpkmTJvjll1/w1FNPOZ3ryy+/xOnTp7Fv3z4UFBTYbZUwZswYfPHFFx4FuADw2GOPWQS47mxeJv/35Ny5c5g7d67DcbVr18b8+fPRuXNnu8cfeOABbNu2DTdu3MDvv/+Oe+65x+G1AGDv3r3Yu3evw+t16tQJv/zyi8Mez5IkYdmyZQCAnj17om3btg7nIiIiIiKiwHCnB64rAjx8d79VW4Zqn086uDkatYCm9U2FM2qV6S5mXDW9W1Rjp/9tNS1QrjAeBbg//fQTsrOzlR+CZ86ciVdffdWmku3dd9/Fo48+iu+++w6AaVOZrVu3om/fvn5aNhH509q1a5GZmYl169Zh+/bt+Ouvv5CWloacnBwYjUZERUUhISEBt9xyC4YNG4ZRo0ZZ9FOtDJ566ikkJyfjww8/xMaNG3H58mXExsaiffv2eOCBBzBp0iRcv35dGe9r5fCtt96Ko0ePYtmyZfj111+xc+dOXLlyBYWFhYiKikKTJk1w8803Izk5GSNHjvQpwAVMm3kdP34c77//Pn755RekpaVBo9GgefPmuPfee/HEE0+gdu3aLuepVasWtm/fji+++AI//vgjjh07htLSUtSvXx+33nor/v73v9sNP93Rp08fhIeHo7i4GHFxcRg3bpzLc/bu3Yu1a9di69at2L9/P86ePav8dyY+Ph4dO3bEnXfeiSlTpiitMuyZMGEC/vWvfyE7Oxvff/+93dfQrFkznDlzBmvXrsWOHTtw6NAhnD9/Hvn5+QgJCUGDBg3QtWtXjB49GuPGjYNG4/g/kZs2bVL63z7xxBOubw4REREREfmd5LAHrm2A6LAS1MME13podchvRVGyuYfWnB1NqKvB5RsGSJIp0G4Q7/Eb/skFQfKgTvz+++/HokWLIAgCxo4dix9//NHhWFEU0adPH6SmpkIQBMycOROvvfaaXxZNwZeXl4fY2Fjk5uYiJibGo3N1Oh3S0tLQokULhIWFBWiFRLZ+++03pWfvBx98gGeeeSa4C6pmfvnlF6WVwFNPPYWPPvqoQq8/c+ZMvPnmmwgJCUFGRgbq1q0bsGuNGzcOS5YsQZMmTXDmzBm7LRt8xX8riYiIiIicS8ssxaXrBjSup0XTBuX/n7xUL2HPsWIIgoDoCBXyCo1o2ywUtWNtG7PuPFIMo7E8GuveLgyhIY5b7hmMEnYdKQYA3N4pHCWlEvaf1EGjFtCjY7gfX13FMBgl7DuuQ0ykCu2ah9oczy8Scfi0DmEhKnRr5/nPJX+dKUFeoRFtmoagThyDXWvu5mseNYHcv3+/8tj67bs2E6tUmDFjht1ziYiCwXxzrX79+gVxJdWT+QZyjz76aIVf/9lnn0VsbCxKS0udtrTw1enTp5X2CS+//HJAwlsiIiIiInJN3k/a1xYKXp8r+DhBJZCVa4TBKOFGWf9aa0rdZxV/nVWdRwHu1atXAQCRkZFOd+aW9enTx+ZcIiJ/Ky4uxs6dOx0eF0URzz//PNatWwfAtLmWo16q5J0tW7Yo93fIkCFo3759ha+hVq1aePHFFwEAn332GS5fvhyQ67z11lswGo3o0KEDJk+eHJBrEBERERGRa442MSs/Xl5Z6yh/tA5/Xb1N3dH72Ktqj1cv9/d2W7XvEVxBPKpdzs3NhSAIqFOnjlvjzd++mpeX59nKiIjcVFhYiNtvvx0dOnTA4MGD0bFjR8TFxaG4uBjHjh3DkiVLcPr0aQBAaGgovvzyyyCvuOorLi5GSkoKDAYDjhw5gnfffReAqa/U66+/HrR1TZ8+Hd988w2OHz+Ot956Cx9//LFf5//rr7+U/u6fffaZ0z65REREREQUWMomZk76t7rqHOpT9a7g8RZolY7a7AZIkuSwV7CvOWzVvkvB59FPngaDAYIguP0Dq1pd3lvEYDB4tjIiIg8dPXoUR48edXi8Tp06WLJkCW655ZYKXFX1dOXKFdx55502z//zn/9Ejx49grAiE61Wi2PHjgVs/k6dOsFotP/WIiIiIiIiqlhiWYJrnd96Espah7CudoqyPi5fy/0dpioXs+gOomj5OVB1X1d1w9IhIqry4uPjsWLFCvz+++/Yu3cvrl69iqysLBgMBsTHx6NTp06488478dBDDyE6OjrYy612YmJi0LZtWzz11FOYOHFisJdDREREREQ1hKsWCuZjHPG2slSuVK3qHQLMw26jnQDX3jiv5mcQ7BMGuERU5alUKtx111246667gr2UGqN58+Yu34pEREREREQUSOUtFCyfNw8bXf7UYh1MevtjThX98cj8xzqjUQK0VT2Srp4C3KqYiIiIiIiIiIjI/8orcJ2V4Jb96WCIxkHFqSvydI56xlZFRtH2OfkeV59XWTV5VYF78eJF9O/fP2DnCIKADRs2eLM0IiIiIiIiIiKqAcSydNGXDPWmRiG4lmPApeumvZtcFdI6eidiFS3AtWAU/f8qlA4K1eEGBZFXAa5Op0NKSopbY+XfRLh7jrMd74iIiIiIiIiIiABAKqsYtdnEzHyMizmiIlSIigjB9Rwj9AbXKaMyohpGV/YqcBXV8PVWJWyhQEREREREREREVY5RbqHgJN2SPKyN9bZStKruEWLRA9dOBa7Pr0rw0zw1nEcVuE2bNmV1LPlNVf3HjYioIvDfSCIiIiIi5xz2wLVTgusqznI37pLcnK8qMhodH6uOr7cq8SjATU9PD9AyqCZRlf1qTBSd1eYTEdVs8r+RKmflBERERERENZgo+t4D15qnZRRVPdg0f732WuD6WldSxW9PpcGfCqnCaTQaCIKAkpKSYC+FiKjSKikpgSAI0Gi8aldPRERERFTtlVfgWj7vSQ9cb8nXMA9wq/q76IxGewmu6Q9fg9gqfmuCjgEuVTiVSoXw8HAUFhYGeylERJVWYWEhwsPDWYFLREREROSAXDEqOPu/zG4GkErLUFdBo5PjVTGktOyBG7x1kHP8qZCCIioqCoWFhSgtLQ32UoiIKp3S0lIUFhYiKioq2EshIiIiIqq0HPXA9aatgbunyHlnVW+dYI/dCtwy3u6JVR3vUzAwwKWgiI2NhUajQUZGBozOumQTEdUwRqMRGRkZ0Gg0iI2NDfZyiIiIiIgqJUmSlJYFzt605mlVrMc9cD0cX+m4qMCtgkXF1VKFNdb7+9//josXL0IQBGzYsKGiLkuVlEajQZMmTZCeno7Tp08jNjYWUVFRUKvVXv9Wh4ioqpIkCUajEfn5+cjLywMANG/enP1viYiIiIjKGEUJl68bUGqwjRStYwSvcgU3T3EWCFf1sPN6jgHNG2oRoi2/Gb729ZW/FlWxvURlUmE/GaampuLMmTMM50gRGhqKFi1aICcnB7m5ucjOzg72koiIgkqj0aBWrVqIi4tDSEhIsJdDRERERFRpZOcZce6y3uZ5lUqw2cTMnOhpBa6nSWMVj7msX+3Fa3q0aMSfRSoblvZQUIWEhKBevXqoW7cuDAYD2ykQUY2lVquh0Wj4i04iIiIiIjvkt/eHhapQO0atPB8bpXLr/0P7+/9my/NZTFsNqkwNVrGMv3r+VoNbE1QMcKlSEAQBWq0WWq022EshIiIiIiIiokoqIlRAs4auswNBEDyqppXzSVenODtcFUNK63vEVgeVEzcxIyIiIiIiIiKiSi3QwaLbFaaSPL56vnNOdNBzopq+3CqDAS4REREREREREVUrFZU3VvVg0zqutclvfQzO5fvj62ZoNR0DXCIiIiIiIiIiqhI8DUzdDQ49LMC1O746ZJSiaPm5/JqqeE5d5THAJSIiIiIiIiKiKiHQrQs8DWGrfCsFq9fr70pZ5e5Ug3A7mBjgEhERERERERFRpeZxrii/dd/v63BcklqVK3DlINqmAlcZUKHLISuairpQQkICDAZDRV2OiIiIiIiIiIhqOCVvdRVA+hD4CoJQZXu8ystWqQCj0U4PXB8JAQrSa5oKC3A3b95cUZciIiIiIiIiIqJqxOMCXA8rRt0dX117wqoEwAjHAW51e71VjUctFF5++WVs3LgROp0uUOshIiIiIiIiIiIKCm8KaatyuCm/XJVKbqFgeQOqaGFxteNRgPuf//wHgwYNQlxcHPr27YuXXnoJf/zxB4qLiwO1PiIiIiIiIiIiquHkFgXuVsp6Gqp6PN5uD9yqm3aq5FYHjipwfUypq/CtqRS8aqFQWlqKHTt2YMeOHXjrrbeg0Whw6623Ijk5GUlJSejTpw8iIiL8vVYiIiIiIiIiIqrBPM0RJdH1GMsTPBwPmBZVRQNK8x64gO0mZlQ5eBzgWv82QZIk6PV6pKamIjU1Fe+88w40Gg26d++OpKQkJCcno0+fPoiMjPTboomIiIiIiIiIqAbxMSAVXJSQujquLMNJD9wqmuECANRyCwWb3M/0p7v3hwLDowD3+PHjSElJQUpKCrZs2YKMjAybMXKgu3PnTuzcuRP/+9//oFar0a1bN4tANzo62m8vgoiIiIiIiIiIqj+3WyjILQE8nN+rAlwX7QeqApVZk1VRlJSeuL7y9utAljwKcNu0aYM2bdrg4YcfBgCkpaUpgW5KSgrS09NtzpEkCQaDAbt378bu3bvx3nvvQa1Wo0uXLkrLhb59+yImJsYvL4iIiIiIiIiIiKqXyhIAKuuoZgWp5nmteRBdWe57TedVD1xZixYt0KJFC0yePBkAkJGRYRHonjp1ShkrSRIEQVAC3b1792Lv3r14//33oVKp0LlzZyXQTUxMRGxsrE8vjIiIiIiIiIiIajZJMlWBupu3erMRWVXOcst74Ja/CqMIqNXycd8iXKVimkmwT3wKcK01btwYEydOxMSJEwEAV65csWi5cPToUWWseaBrNBqxb98+7N+/Hx988AHUajVKS0v9uTQiIiIiIiIiIqqiAt2ewN3WDOU9Ye0d9NtyKoz5klWCAFGSykJbASWlIs5d0gNw//5QYPg1wLVWv359jBs3DuPGjQMAZGVlYcuWLUqge+jQIYskX35sNBoDuSwiIiIiIiIiIqqCvOmB60n26G4Gaz5ndQk3VSpANAJi2U3ILxKVYzGRKgdnUUWo0Ltfu3ZtjBo1Ch9++CH27duHrKwszJ8/Hx07dgTAHe2IiIiIiIiIiMhWoItb3Y6knCykChbgKgSh/B6IZbmtsezP2Cg16sf7VgNalTd4qwwCWoFrT05ODrZu3aq0Vjhw4ABEUWR4S0RERERERERETrlfgSsAkCDJJbh+CGht56/6zN8Zb+qDKylhqxzgajXV47VWZQEPcM3bJqSkpODw4cN22ybIIiMj0atXr0Avi4iIiIiIiIiIqopA98B1c5yzZVTlKlMBZhW4ZS9ELOuloPbh/fty0F2Fb02l4PcAV964TA5tzTcuA2wD2/j4ePTp0weJiYno27cvunXrBrW81R0REREREREREZGHvK0Z9SaErdLFuGavV23dQqFsiyoV298Gnc8B7sWLF5Xq2pSUFJw6dcriuHVg27hxY/Tt21f5kPvfEhERERERERER2RPw6lazTc+cr8PxiMpaZVpYLCK3ULR77Eq2EboSydQDtyyoFZUWCmUVuGrvE2r5TGf3jVzzOMA9d+6cRWCblpZmcdz6C9K6dWulujYxMRHNmzf3acFERERERERERFQzud17VqkmlaBWCV5X5Dpeh/WaKm9AeSy9BKV6++u7nmPEjTwjmjbQQCW3O7DqgetLCwXyD48C3ObNm+PChQvK59ZhrUqlQufOnZXq2sTERNSrV88/KyUiIiIiIiIiohqpggpwXXK6jkqa4crhbe1YtU0Afj3HCFGUIErlrRLkHrhGo9wD14cK3KrcXqIS8SjAPX/+vMXnISEhuPXWW5Wwtnfv3oiJifHrAomIiIiIiIiIiADve9u6y913+ltU4AZmKX4hb0QGAC0bh0Bj1Q7h5LmSsnFmm5iVVd7Kp3KrquDzuIWCnNRHRkZiypQpGDJkCPr06YPo6Gi/L46IiIiIiIiIiMjTHqqehqpuV4o6WUZlLMA1v232XqP8nCQCao1VCwW5ApdltEHndReLwsJCfPLJJxgxYgTi4+PRvXt3TJ8+HcuXL0dWVpY/10hEREREREREROTxW/LlMDKgGaQcglbCjbrMCnBhrxOCqqwi1yiV3yM5uFV64PpSgavcGx/mIM8qcD/99FOkpKRgy5YtuHLlivK80WjEgQMHcODAAXz88ccAgPbt2yMxMVH5aNSokX9XTkREREREREREZIenga1gtYGXI0og7MWagqE8wBbsbgAnh7qiJCE81FTnmVMgIqGe+SZmVeXVVl8eBbiPP/44Hn/8cQDAiRMnsGXLFqSkpCAlJQUXL160GHvs2DEcO3YMs2fPBgC0aNFCCXOTkpLQokULP70EIiIiIiIiIiKqESpLluhiHcUlIk5dKIUkAi2bhCAq3Os3wftE3pDMUaCtljcuMwJ149Q4f1mP3AIjDp/WQW+QLMZ4Q74sK3B943EPXFnbtm3Rtm1bPPzwwwCAs2fPWgS66enpFuPT0tKQlpaG7777DgCQkJCgbH6WmJiI9u3be/8qiIiIiIiIiIio2vI0APQ2OHQ13N5xe9fKzhdRUGQqYb2eYwxagCuVVdE6KqJVlR0QISE0RIXasWpk5RqRX7Z2AFCrK0tqXnN5HeBau+mmm3DTTTdh8uTJAICMjAwlzE1JScGpU6csxl+8eBE//vgjfvzxRwBAnTp10KdPHyXQ7dq1q7+WRkRERERERERE1YCnUaKcqfo7gjSfz151q2jWfFbuKRsMoosewPLzotH0Z+smISjUlUBXUh7gqnypwGX26xd+C3CtNW7cGBMnTsTEiRMBAFeuXLEIdI8dO2bR3PnatWv45Zdf8Msvv0AQBBgMhkAtjYiIiIiIiIiIqpBAvwXf3aDR3XWYbx5mqAQBrspBCa5SgVv2wlQqAfVrqXHuslkFbnCKh8lMwAJca/Xr18e4ceMwbtw4AEBWVpbScmHNmjU4efIkgMq5Yx8REREREREREQWf2xWdXlZ+epNL2WuhIJXnnzAYvVuLP8iVwI5aKMjdEcwz5kizdg8qB5ufUcUKSoZeWFiIPXv2YPfu3di9ezfS0tL4zUBERERERERERIHhInZSDruZ37qKsUSzNDeYFbjyMhz2wJVbKJgFzlER5XGhxsfST25i5h8VUoGbl5eHrVu3Ku0T9u/fD6MxiL9+ICIiIiIiIiKiKsftAlw5mAxQcOiqENE8sAxmBKb0wHVQwqlSm/40rxjWqAW0bhqCvAIRtWPVgV0guSUgAW52dja2bt2KzZs3IyUlBYcOHYJoFuXL5eiCINiUprdr1w5JSUmBWBYREREREREREVVBFdUD19Vl7K7DzrlSJemBW16B66oHruXzdeM0qBvnx3X4b6oayS8B7vXr15V+tikpKfjrr78sgllHga0kSejQoQOSkpKQnJyMpKQk1KtXzx9LIiIiIiIiIiKi6sbNElxPWyL4wl42arTogStBkqSgtA8VlUzO/nG5hYIxYKXKgZm2pvEqwL1y5YoS1qakpODYsWPKMfOA1l6FbadOnZSwNikpCXXq1PFy6UREREREREREVBNUlgpOZ+uw2MTMKg8zioAmCN0I5DfEqxy1ULDTA5cqH48C3KlTpyIlJQWnTp1SnrMObK117txZqbBNTExEfHy8D8slIiIiIiIiIqKaRo6f3K1iFax6Irhb/Opuqwbz+exV+1oXtBqMEjTqii9HdbmJmdxCIUAVuPLXgZuY+cajAPerr76yqKq1/ksjCAK6dOmiVNj27dsXtWrV8t9qiYiIiIiIiIiI3ORubuh+D1z3ZrQeZgjSRmaii+BbDnaLSyScOFeCwmLJpp2CSiWgRSMt4mO4oVmw+NQDV6VSoWvXrko7hL59+yI2NtZfayMiIiIiIiIiIlJUlpaq5uuwF45atyQwBmkjM6ksjHVUgRuiLT+QlesoZZaQlWtkgBtEHgW4arUa3bt3V1oi9OnTB9HR0YFaGxERERERERERkddMFbOuY18lg3VZguveIetK3fRLesTHiGhSX+tyLf4kumghodUIaFhHC72hfL0JdbWoE2cKa6/lGJF5Te91C4TKErhXdR4FuDk5OYiMjAzUWoiIiIiIiIiIiGyUt/P07nx/B4mu1iFX4Go1AvQGCYXFIgqLRdSP11hUvQaa0gPXwSZmgGlzNb2h/POwUAGR4aYTcgu5u1ll4OTLZ6uiwtvMzMwKuQ4REREREREREVU/7va09ZSz+cyrbuXK15YJIWjdNAQqZTOvim2lYFRaKNgPjSXJdK/MD6vsbdDmJeXrwF3MfOJRgOvIO++8449pAAAZGRlITk7223xERERERERERFS1VVT+58117GWj8jwaDVA3TgOhLIGr6BhTXofgJAG07uHrbZUzBY5fAtwXX3wRc+fO9XkeObw9c+aMH1ZFREREREREREQ1kXVPW3sbjVmOF8yHO+Qs4DU/JJYNVJWVsyrLqeAEV2mh4ODlS3aOqcye8FcFLQtwfeOXABcAHnvsMSxdutTr88+fP4/k5GScPXvWX0siIiIiIiIiIqJqxNPq0EDlhubrcFaBK2eh5UFogBbkgKtNzOwdcxT2UvD4LcA1Go24//778ccff3h8bnp6OpKTk5GWlgYAUKvV/loWERERERERERFVcZ7mnp5WvJaPd36C06NmBx0Fp8GrwHW/B66zQNpT8lxGkX1wfeGXAHfEiBEAgJKSEowaNQq7du1y+9y0tDT069cP6enpkCQJGo0GCxYs8MeyiIiIiIiIiIioOpAD0QBN73ZlrxsVrQAgiaY/bSpwPV6Zb0R5EzMPeuCq7bRQ8FVugRFHzpb6Z7IayC8B7pIlS9C3b18AQGFhIYYNG4ajR4+6PO/s2bPo168fzp07BwDQarVYuHAhxo0b549lERERERERERFRTVSBbQCsq30lSVJ64MrhqBCkBFd00QMXsA1pA7WJWV6hMTAT1wB+CXBDQ0OxcuVKdOnSBYIg4MaNGxgyZIgSzNpz5swZJCcn4/z58wBM4e2iRYswduxYfyyJiIiIiIiIiIiqCTn3rCw9cC04aZMgV74GrwLX8vr22G5iZjvG23Wzna5/+K0HbnR0NNauXYuWLVtCEARcvHgRgwYNwtWrV23Gnjp1CsnJycjIyAAAhISE4Mcff8To0aP9tRwiIiIiIiIiIqqhPA4O3dxkzNlx+ZBoHuDKLRSU8ys2wpWv57gHrgQIlvfLPNBVHvpp2aLIPrje8FuACwB169bF+vXr0bBhQwiCgNOnT2PIkCHIzc1Vxpw8eRL9+vXDxYsXAZSHt6NGjfLnUgIuPz8fS5cuxRNPPIE77rgDdevWhVarRUxMDNq1a4cHHngAa9as8egv5unTp/Hcc8+hU6dOiI2NRVRUFNq2bYtp06bhwIEDHq2vpKQEX3zxBfr374+GDRsiNDQUjRs3xvDhw7FgwQKI8q9giIiIiIiIiIiqCk8rcAOUF5r3jbVeknnkolLJLRQCux5HlM3UnPXARfk6AdueuL6wnor5rXc0/p6wWbNmWLduHRITE5GdnY1Dhw7hrrvuwrp165Ceno4BAwbg0qVLAEzh7eLFizFy5Eh/LyOgZs2ahX//+9/Q6XQ2x/Lz83HixAmcOHEC8+fPR9++fbFgwQI0bdrU6Zxz5szBM888g+LiYovnT548iZMnT2L27Nl4+eWX8fLLL7tc3/HjxzF69GibPsQXL17ExYsXsWrVKsyePRs///wz6tev78YrJiIiIiIiIiIKnkAHn9Z9bB2uw9kxsx64gGXVq7vz+5vkogeuvfWYt1Dwd+sHg1GCRs3GCp7ye4ALAB06dMDvv/+OgQMHoqioCNu3b8fw4cNx7NgxXL58GYCpb+7ixYtx1113BWIJAXXy5EklvE1ISMDAgQPRvXt31KtXDzqdDqmpqViwYAEKCgqwdetWJCcnIzU1FfXq1bM734IFCzB16lQAgEqlwvjx4zFgwABoNBps374d3333HUpKSvDKK68gNDQU//rXvxyu7dKlSxgyZIjSW/iWW27BpEmT0KhRI5w9exZff/01zp49i23btmH48OFISUlBZGSkn+8QEREREREREZH/yEGju9GfpxtzuVt0KoezzobbrXoNUg9cdzlqoeBzE1ur889fNqB5Qy1CtAxxPRGQABcAevbsiaVLl2LkyJHQ6/XYvHmz8k0eGhqKn3/+GcOHDw/U5QNKEAQMHjwY//jHPzBgwACorLo7T5o0Cc8//zyGDBmCEydOIC0tDc8//zzmzZtnM9e1a9cwbdo0AKbwdvny5RYVyQ888AAefPBBDBgwAEVFRZg5cybuuecetG3b1u7aZsyYoYS348ePx/z586HRlH+Zn3rqKYwYMQIpKSnYu3cv3nnnHbzxxhs+3xMiIiIiIiIiosqkovrNWoe/9qpeg9VCwZuKYpWjcl0/uJ5jgK5UxC2twgJ2jerIrz1wrQ0ePBjz58+36J0RGhqKpUuXVtnwFgDefPNNrF27FoMGDbIJb2XNmjXDTz/9pHz+008/oaioyGbce++9h7y8PADAtGnT7LaTuP3225WQ1WAw4LXXXrN7zaNHjyrXbNiwIb766iuL8BYAoqKisHDhQoSFmf6izJo1Czk5OS5eMRERERERERFR8Lnbn1Xw03v/C4tFXL1hwLUcAwzG8skEi3DWck1KBa7Z0yp/9yJwk3w5V7dNlCQUFosOe9T6M3guKOK+TJ4KaIALAGPHjsUXX3wBSZIQFhaGX375BcOGDQv0ZQMqPj7erXGdO3dWKmWLiopw+vRpmzHmIe/06dMdzvXwww8rrQ5WrFhh0ytXnkv+7dIjjzyCqKgou3MlJCRg3Lhxyrp+/fVXt14PEREREREREVFV5LKFQtmf5kGlUZRw+EwJTmeU4tT5Upy9qHc6h3yuWJaC2qtkrXQtFMoWdCNPxIUremReM1gc9rV3Lxsl+IdbAa5arfbp49FHHwUAlJSUYNiwYS7HW1eNVmUxMTHKY+vQ9ejRozh37hwAoH379mjRooXDeaKjo9G3b18AQGFhIVJSUmzGrF69WnnsKiQ3P25+HhERERERERFRZeUqEBRFSamUleBbYKo3SEoYCwClerPZ7CzEKJqqWItL5E3MzIaXPRYrvIeCfH3ndy63wAgAKCgyBnpF0GoY63rKrQBXruqUJMnrD/kbxd3x1UFpaSlOnjypfN6sWTOL44cPH1Ye33bbbS7nMx9jfi5guq9HjhwBYArcu3bt6vVcRERERERERESVibtZ0cFTJdh1pBh6g4fZkp0OB6LVO/2NRslpJerZi3ocPKXDmYxS05R2AtzKVoLrajnutqzwRAgDXI+53ULB11C1OgWz7lq0aBFyc3MBAN26dUODBg0sjp84cUJ57Kz61t4Y83MB4MKFC0qP3caNG0Or1Tqdq0mTJlCr1QCAU6dO1bivDRERERERERFVHUpq4SL7Ky4xpa4FxZ71WbUXVBqtA1yxvJWAvWXI19ZqBIRoBdSPL3+Hua+tCLyl9MB1Mc5RTutr1Grvvmq1DHA95Vavgk2bNgV6HdXOtWvX8K9//Uv5fObMmTZjzDcPq1Onjss5a9eubfdcb+bSarWIiYlBdnY29Ho9CgsLHfbMBUztL0pKSpTP5Y3XiIiIiIiIiIgqG40a0BsAycOSV4seuGWtGARBgCRJMDrY4Utpj1B2vONNoYgIs6qZDM4eZjbXtxaMej6NmgGup9wKcJOSkgK9jmqltLQUo0ePxtWrVwEA99xzD0aNGmUzrqCgQHkcFhbmct7w8HDlcX5+vk9zyfNlZ2cr8zkLcN9++2289tprbs1LRERERERERORPSuWrk+zPvF+tymqgN5GhXIGr1QClesBo1h7W3jrky6vsvN89aBW4frqgt/PYu+8B6MpQ7bndQoHcI4oipkyZgq1btwIAWrZsiXnz5gV5Vb574YUXkJubq3xcuHAh2EsiIiIiIiIiIlKYtzxQCfBoFzN7maJcgRtS9pZ/0UF7UCWcFeVr286mUsl7Q7m3Hn9zlJm6XE8AKofZxdNzblXgyg4ePIjOnTsHai1VniRJePTRR7Fw4UIAQNOmTfHHH3+gVq1adsebV7zqdDqX8xcXFyuPo6OjfZrL1XzWQkNDERoa6ta8RERERERERER+5aT3rMxRiwN3OKuo1ZptuiWHxObDBaFs7ycnc8kqOrtkWFo9eFSB27VrVzRr1gzTpk3DmjVrUFpaGqh1VTmSJOHxxx/HV199BcC0kdjGjRvRvHlzh+fExcUpj69fv+7yGllZWXbP9WYug8Gg9LHVarWIjIx0eQ4RERERERERUWXlqsWBO8wrbOUKXK1aUKpqza9heV75Y5Wda8vrCdYm8q7uh8NNzJR1e3thL88jCx63UMjIyMCXX36J4cOHo3bt2hg1ahTmzZuHK1euBGJ9VYIkSZg2bRq+/PJLAEBCQgI2bdqEli1bOj2vbdu2yuO0tDSX1zEfY34uADRp0gQREREATF8jvV7vdK7z58/DWPavTuvWre3uCkhEREREREREVBm4U93qtALXi9hDrrZVq8v72hol+6XA5gGnvTUqT1WyFgqujjMtqhw8CnBbtWplKgkv+ygsLMSKFSvw8MMPIyEhAT179sR//vMfHDx4MFDrrXTk8PaLL74AADRq1AibNm1Cq1atXJ578803K493797tcrz5mE6dOlkcEwQBHTt2BAAYjUbs37/f67mIiIiIiIiIiKoaUbT83Jus1DyIlQNhtUqAWi3YvYb5teRzVXZKcIUA9JJ1h6vKWbcrgr1cuL0wO1hVyFWZRwHuyZMncfz4cbz77rtITEyEWq1WwlxRFLFnzx688sor6Natm9JqYfXq1dW21YJ1eNuwYUNs2rQJrVu3duv8Dh06oGnTpgCAY8eOIT093eHYgoICZWO0iIgIJCUl2YwZOnSo8nj16tVOr71q1Srl8bBhw9xaLxERERERERFRMLiT+ZlvYiaPdzcrtBc0yu0SVCpArbJ8zvqdzEp46+IdzkHLLl2U0oaFml6gvfDZb0vgu7+95nELhTZt2uDZZ5/F5s2bce3aNSxcuBDjx49HXFycRXWu3GphxIgR1bbVwhNPPKGEtw0aNMCmTZvQpk0bj+b429/+pjyeNWuWw3Fz5sxBYWEhAGDkyJFKuwRHc82ePVsZb+3ixYtYvHgxACA8PBx33323R2smIiIiIiIiIqps5J619rgbHZrPIHdkUKuE8gDXTpsG0yZmpnNVDpI2+fmKDnBdXU4+3qC2BrVi1GjWUGtx3NfKYevN3gBurOYNjwNcc3FxcbjvvvuwaNEiXLt2DRs3bsSMGTPQunXrat9q4cknn8Tnn38OwBTebt682aYvrTv+8Y9/IDo6GgDw2WefYcWKFTZjdu7ciZdeegkAoNFo8Morr9idq2PHjhg3bhwA4NKlS3j44YdhMBgsxhQUFGDixInQ6XQAgBkzZthsiEZEREREREREVJm41wPX7BPBdJK7WaG9aeVAWK2CTQsF62BSLEslXRWZVniAK7fsdbGuEI2A+vEahGoDVyUbwOLeak/jr4nUajWSk5ORnJyM9957DydPnsRvv/2G3377DTt27FCCREmSsGfPHqXdQuPGjTF8+HDcddddGDBgAEJCQvy1pICZOXMmPv30UwCm8u+nn34ax44dw7Fjx5ye161bN6VlgqxevXr45JNPMHnyZIiiiFGjRmH8+PEYNGgQ1Go1tm/fju+++04JXF977TW0a9fO4TVmzZqFHTt2ICMjAz/88AOOHDmCyZMno1GjRjh79izmzp2Ls2fPAgC6dOmC559/3pdbQURERERERERUKTjdxMxdFj1wTX+q1YISPjq6hCSZznXUgkBuH1DZOijYC5RFUbJ5Hf4InoPVB7g68FuAa01utfDss88iJycHq1evxm+//YY1a9YgJydHGZeRkYHZs2dj9uzZiIiIwMCBAzFixAiMGDEC9evXD9TyfLJt2zblsSRJeOGFF9w675tvvsHkyZNtnp80aRKKioowY8YM6HQ6LFq0CIsWLbIYo1ar8e9//xsvvvii02skJCRg7dq1GD16NI4fP45Dhw5hxowZNuPuuOMOLF26FFFRUW6tnYiIiIiIiIgoaJRKUsdlnOYbjCmjfOqBa1uBazSKUKkEm/GuKl0FZVwl66EgM1u3+RJ9bVtrfj5b4HovYAGuObnVwn333Qej0Yht27Yp1bmnTp1SxsmtFlasWAFBENC9e3fcddddGDFiBLp06VIRSw2axx57DAMHDsSXX36JNWvW4MKFCxBFEY0aNcKAAQPwyCOPoGvXrm7N1aFDB+zfvx9ff/01lixZguPHjyM7Oxt16tTBLbfcggkTJmDixIlQOWrMQkRERERERERUxfijAtd8BrmlrsUmZqL9Prflm5jZnzfY4aXD4NteBa4EqF0P85hpgzcP+lqQQpAqPPq3JLdaWLlyJbZv327Rs1X+5hIEwaaXKwVXXl4eYmNjkZubi5iYmGAvh4iIiIiIiIiqsb3HdSgpFXFLqzBERdgvSDt9oRRXs035UWS4gLwCETfyRNStpUavm8OdVu9eyzHg6NkS5BeJSKhr2shLV2ra26lz6zBczzHi4jU9Ll03QG+Q0KdLBBrXM41LyyzFqQulKNZJaN5Ii86tw2zmv3BFjwtX9GhQW4ObEiqufeifh4shSRJubR+OEDv9bQ+e1KFQJ6K4REJ4qOm4+di8QiP+OlOCsFAVurW1fV2u5BYYceRsCQAgLFQFXYmIWtFqtG8R6sOrqj7czdeCXoIpt1rYtGkTrl69ioULF2L8+PGIi4uz2AiNiIiIiIiIiIjIEesKXE/TpIJiEQXFEopLRBSXiJAkUy/Y0BABEWFlLRREoEgnoqC4vF+DIAiQygpL1Q574JatqcI3MXN+QUkZV/6c+X0U/Ni4VsUeuF6rkBYK7rLXamHFihVYuXJlsJdGRERERERERERBogSRgqkq9MoNo00Yml9k1gQXnm0aJqC8h26taDUa1TVFZuGhKmjUAurW0iA8TIUbeYUoKCrvjyufK5WFnq574Lq5oAokKf9bds/Me+D6ODd74PpHpQpwzanVaiQlJSEpKQnvv/9+sJdDRERERERERERBJgA4f9mAvEKj3+cWRQmSBISFCoiNsu4CC0SFq6DVlFfiWpwLU8jsqAcuglB9al5967AFrmQbKouinXF+WE+wqpCrA48C3AceeADJyclITk7GTTfdFKg1ERERERERERERKSze4l9W/Vo/3lQVay49s9TqPLky1nn5pyAIkDsHOGqDYDpm+lM0y48FoXx9DitwBdvq1orkbA8zmwDXnyW4ZlQswfWaRwHuggULsHDhQgBAQkKCUiGbnJyMVq1aBWSBREREREREREREMjlerBOntqmUzcoxIL9I9Cp3lKtq1bbFtwo53LXpt1v2qcpRD1wv1uMrd8Ni63H2zvN2fyrz4JwVuN7zqoWCJEnIyMjAokWLsGjRIgBAw4YNLQLdNm3a+HWhRERERERERERUs5lXu9o/7l2lq6kHrqkPrLMKXEGuwHUQejquwC07z/rECuIsQJYkyWLd5u0h/Bk8swDXex4FuGq1GkajbY8RSZKQmZmJH3/8ET/++CMAoH79+kqYm5SUhHbt2vlnxUREREREREREVGPJEaizQNCbsFA0NbL1vAJXKO8l6yj7VapPPV+W19y6lp0euPYrcL1bg/ntYIDrPY8C3JycHOzYsQMpKSlISUnB7t27UVpaajNOkiRcvnwZixcvxuLFiwEA9erVQ2JiohLodujQwT+vgIiIiIiIiIiIqjWlwhVQkklnfW3Ne7u66n8rT2wUTQGsRu2kArfskPUmZnKLAZUKdikzBqt9gJNb4KwHrj9DVyeFzeSCRwFuZGQkBg0ahEGDBgEAdDodUlNTlUB3586dKC4utjlPkiRcuXIFP//8M37++WcAQN26dZGYmKhU6Xbs2NEPL4eIiIiIiIiIiKotwb0KXDmw9KRyVJQkCIKgbFRmj7KJmVkFrgDXYXEwKnBhWSTscIhNgCvaHeqz8vYWbILrKa964MrCwsKQnJyM5ORkAIBer8euXbuUQPfPP/9EQUGBzXmSJOHq1atYunQpli5dCgCoXbu2RaB78803+7I0IiIiIiIiIiKqJswjPzkAtBdKKvmppz1wBVNwqVbBaQ9cVdkFzDuMCmahssNTg7CBlzuXkiQJIiSoLJ4rf+xz0azZBGyh4D2fAlxrWq0WvXv3Ru/evfHiiy/CaDRi7969SElJwZYtW7Bt2zbk5uZanCP/pbt+/TqWL1+O5cuXQxAEGAwGfy6NiIiIiIiIiIiqAVcbhnlLFE1za5z0wFV5u4mZ1biK5uxeSSJgnuCev6zHxWsGxEWp0KiOKTr0x7pVwahCrib8GuBaU6vV6NGjB3r06IHnnnsOkiThwIEDSqC7detWZGVlATCVUbOEmoiIiIiIiIiIrJkHpBb9cK3YCyrdyXklUSrLpQSonfTAVZWlkNZtBiTJdG1HFbiqIJSfuhuzlb1shcEowWCUcOWGiPrxTtJsN7Do1j+cdPXwP0EQ0LVrVzzzzDNYtmwZrl69ikWLFqFjx46QyvqMEBEREREREREROaLkkk5iJE8TJvNNyez1wC3VSzh9oRQlpaaBohL4lvfAlSRAcJTglj0tBql40Tpzy8k34sS5Epy/rEdWrqkfREykGl3ahKFTy1BlnGT1py/k6mXWb3ouoBW49hw6dEjpkbtlyxaLClwiIiIiIiIiIiIbZlW3ztoVeNkCVwlWVYL9jOpqtgFXsw3l49Sm0FdutyBKEtSC4LACV9nErCJ74Dq5VsZVA/IKjcgvEmE0ShCgRohWQESYynKDNj/Gdcz+vBfQAFeSJOzfv18JbLdu3YqcnByL49batm2Lvn37BnJZRERERERERERUBQlmO4b5sy2BvCmZKAEXruiV5yPDVIiPVSvHa0Vr0KQBEBEqIOOqAWoVcOWGASV6CeGhTgJc+UEQqk/tBafGspA2PkYNlUpAi4QQNKqjLRtvO4e3wbP5XMG8B1WdXwNco9GIPXv2KNW127dvR15ennLcOrBVqVTo3Lkz+vbti8TERPTt2xd169b155KIiIiIiIiIiKiKkxw8tmFVgutp8ChJkkWACwC3tg9XKm+jwlWIj1GjpFRE5jXTuBt5IvR6CeGhgMZB/1ylAtez5fiF3RWVLSQyXIWwEAH1a2kQorWX3PpxHRXayLV68SnA1ev12LVrl1Jhu2PHDhQVFSnHrQPbkJAQ3HbbbUpY27t3b0RHR/uyBCIiIiIiIiIiqiEkyaz3rIsCXCWWcqNQNyJMQN1aGqhVAurHm+Kya9lGiJJpQy+lbYMKaJmgVfrGAoBKZUSpXkTdWhrERTtPKSu2hYJ3FzOv2FWm8LYC1+yxKoghdlXnUYBbUlKC1NRUJbBNTU2FTqdTjlt/Y0RFRaFXr15KYNuzZ0+EhoZaT0tEREREREREROSQnDmZR0/OeuBCKBvv9hUE1I5VIzJchZaNQwAA2fnFKNUDolh+XZUAxEWrERetVs4MDzNAFCXUidNA5aCHQjArcO1Rsm15uQ579/qvTQVb4HrPowA3Li4OpaWlyufWgW2dOnXQp08f9O3bF3379kW3bt2gUrE+moiIiIiIiIiIfGeeRPkzD5T37TIPGU1hrAS9UYSu1HHVrzvrkPv1VmgFrvzAQU9bUZIcrkcQBIvcz+tlm/fADcI9qC48rsA1/wI2btxYqa5NTExE+/btA7JIIiIiIiIiIiKqmcyDRPMs0m6YKliNlNys/JQDXLOnVIJpc7MDJ0pwPceAEK0KNyWEuL9wi4WVXSYI6aW913/pugFXbhjQpIEGgOD4HvkxJXe0wRu55lUPXEEQEBkZid69e6N3797o06cPw1siIiIiIiIiIgosi4pO/01b3lKgfFKVCigxiBCNpqrRohLJaWjsLJxV9lar0B64jo9l5Rldrtdi4zg/LLyytZGoSjwOcOUvWGFhIRYvXozFixcDAOLj49GnTx8kJiYiKSkJXbt29WufDCIiIiIiIiIiqnnMs0PLHri2uZPyNn2Pr2F7hkoQAMl0Tans4t5WkQYzInN2aaHsqMMxPga35q+7sqSEF67ocTnLgNgoNdo09bKiuoJ5FOCuXLlS2cBs3759MBgMyrGsrCysWLECK1asAABER0fjjjvuQGJiIhITE9GjRw9oNF4V/BIRERERERERESl5oquiQcHD3FEebr6Vk3llralnrPMg1lnWGYwKXKcpttkxXakEo9FqcFkJrnUVri/FmoJ8b4NcgnvlhgF6g4TrOQY0b6hFiLayRMuOeZSoDhs2DMOGDQNgqsDdsWOHEuju3r3bYoOzvLw8rF27FmvXrgUAhIWFoWfPnkqFbq9evRAWFubHl0JERERERERERNWNvY3LPInc3GqBK9qOVamgBLdiWSWuym7Vr/uLMIrA0bQSqFVA0/pahIepnJ/ng/K2EI7HFOlEfLokG22ahuCTfzSwOe5r5bBlT+HKF5QajRJQ3QJcc5GRkRg0aBAGDRoEANDpdEhNTVUC3dTUVOh0OmV8cXGxcuyNN96AVqtF9+7dlQrdPn36IDo62vdXRERERERERERE1ZKolOC6GGjqfuB2sacyzmxelTyHVP6hcpK3OruWViNAJQgQJQk5+UYAQGiICs0bBi7AdUQUJUhlq828ZoDeABw5W4r8IhHREab1lFcfl58nubshnANKFbL3U/iF+WsyiMFbhyf81tMgLCwMycnJSE5OBgDo9Xrs2rVLCW3//PNPFBQUKONLS0uRmpqK1NRU/O9//4NKpcItt9yiBLqjRo3y19KIiIiIiIiIiKiqkmwfOgoSvQ0Y7eXCgtwDF2VtFHyYX6MWcHOrUBTpRGTlGnEjzwhRDGyU6ahdg/ll1eryx4dO69D7lgiLsf6sTRUqPqt2qKBYRLFORPqlUsRGqqFWAfVra6BRV85q3IA1pdVqtejduzd69+6NF198EUajEXv37lUC3e3btyM3N1cZbzQaceDAARw4cACffPKJRX9dIiIiIiIiIiIid1soyOGlpz1nzQNadVngKIplrRREQGVnFzPlGRfXigxXITJchRK9hBt5xgrrh2sdOhuNknJtvVnv20OnSpQA19799XW55X2Ag1uDK0oSLl41QJIkhIYYkB9ZXoabUE8bxJU5VmHZt1qtRo8ePfDcc89h5cqVuHHjBvbu3YtZs2ahY8eOyjhTY+hgF1MTEREREREREVFlYLmRlulPhxW43l7DzuZoglDW+xamwNOXClxzcghsDPDb9x2la+bXNa+f3Ly3CKV6/2ZyFv2LK0lxq9FYHiLXiVUrbSNKDZU3jwxYBa4zx48fVypxU1JScPnyZZ92sSMiIiIiIiIiourPZYBr/nzZYHciJ3utGcz7wMq9cO0U4JaPc30ZAOVzBLyA0U4oDVgHuOVruJZjxLqdhRjRJ8r+a/JiueYvUQ6ug123aTS1IIZKJaBBHQ0EAcgvEmEwBnddzlRIgPvXX38pYe2WLVtw7do15ZgkSQxviYiIiIiIiIjILnuBn7+zJKmsMaz5rOqyXczkDczg6F3jHi5FXnqAW+CWX8/qc1E0b6FgeezQaR1G9IlyeK5P66gk8Z+x7MarVYDBCISHmhZmMNawCtyDBw8iJSUFmzdvxtatW3Hjxg3lmKT89kOweS46Ohq9e/dGUlJSIJZFRERERERERERVmFIpG6h5rSpw5cpbOfBUOUkh3a0sVZn11g0khy0UzEJbvVXbgNwCy0WZvyZ/9cANNrkCWaUSYDRK0JQ1OzZW5wpcSZKwb98+pcJ227ZtyMnJsTgO2P5mRJIkxMTEoE+fPkhKSkJycjK6desGtfn2d0REREREREREVKPZDUbdbYvgZupo7xplBbimPrhlA/xRoymHwAHvoKD0m7B83mhW+isHuPVqqXA1W0R2vmWK6WvVbHioCrVi1NCoBbvtJ4LBsgJXgqYsirQOsysTjwNco9GIPXv2KIHt9u3bkZ+frxx3FtjGxsaib9++SE5ORlJSErp27QqVqsL2USMiIiIiIiIioirMQSZpZ6CH85b9aR5nqVQCJMnUckBm77rycx5X4AapGaxRLH+9ctuA+vEaXM0uRU6+qTzVXnDr7XLbNw8FABTrRJ/m8Re5163cQkGtrmYtFIYMGYIdO3agqKhIec5ZYFurVi0kJiYqFbadO3dmv1siIiIiIiIiIvKYeabkqJpTHuLpxmLlG35ZzSVZtjrwR+gqrz3gLRQchN1GUVJer7yJWf14NQ6fAXLyjZZ9fv2Z41WSSFAsC2pVKgEGQ3kFbrVpobB+/XoIgmB34zFJklC7dm0kJiYqFbY333wzA1siIiIiIiIiIvILJVt0EeBKHpTgZl43IOOqvux8y5BYQnnPVOvjzp5zpqI3MbNm/nqUFgq1TRGhwQgUFsu5n2SRgPurcjbYda7y61erTI81ZRW4oiRBFCWoKkuvBzNe98CVJAl169ZFUlKS8tGpUyd/ro2IiIiIiIiIiGo45d3fKA893c1MJTfGpmeW4soNA+Ki1Ra5sEplCoLlFgqCINgNXT2N++SAUKygBNf69RuN5fF2qcH0Z2ykChFhAop0kk0f3ECtI1jMNzEzGCWoVVAKVg1GIKQSdnv1KMCtV6+eUl2bnJyM9u3bB2pdREREREREREREltztgWs21hnzDb0kyaoHrmAqwTUPjZ2FrpKbJapygWdF9YI1v1d6g4RL1w3K53Lf11CtgLhoNYp0BuTkG8v7+qI83PSXYPfAVTYxU5e/frkfrsEoIURbSZJmMx4FuJcvXw7UOoiIiIiIiIiIiGwoeZ9gvtmYeyGbq7BQ7kMrSWVBp3kP3LJKTLliUxD8Ez5WVAsFe2u9esNgcVBuoRAaokJclAqZ14DsfBFR4eVlqGXNFHxWWWJRpYWCUP65Rm2qxq2sG5lVwqJg4MiRI8FeAhERERERERERVTJuV4K6OcymAtfsmEooC1nttHCw4OGGaeqyElwpwJuYKcxelFEsX2eT+lpTlTGA8FAVakWbdvNav7MQ8ijz++23KtxgV+DKVbdlCa7eIEFdtpFZTr7pi5JXaESpvvKEuX4JcKdNm+aPaQAAhw8fRv/+/f02HxERERERERERVV1ybiiYVeCqHCRaSmWuILhVrWs0ll9DhGTZQkElWISNgqq8YtcX5RW4kl9bE1izN7N8uVoxakSGq5S1hGgFNG2gBQBsP1SMfSdK/L4eTzd7CxQ5tJc3LzOKQIjG9Djjqh6Xswz460wJ9h3XBW2N1vwS4H7xxRd4+eWXfZ7n0KFD6N+/P65fv+6HVRERERERERERUbXiogeu9fOiKEFXIqKw2PRhtHqLvEUFrmjdA9d2btFO4Cqf424Waz5vINsomAfflgfKH5aUmhLpsBABk0fE4s5ekQCAdTsLTNW6EjyuMHa5Lj/N49W1JUlZgKassazBIKFR3fIus1ezTW0m7H2tg8VvLRTefPNNfPzxx16ff+DAAQwYMABZWVn+WhIREREREREREVV1ZqGtEni6KOaUD5eUijhzsRQHT+lw8JQO+0/qLDYiM5r1wDVtYlY+sVUBrmkzLz9W4MrXDTTzW2V9+3Slcg9cAVqNgGljayEmUoXrOSKWb8ozja0chbN+I98DbVkFrsEoITbKVJEM+KfK2t/82gN3xowZmD9/vsfn7du3DwMHDsSNGzcAAJGRkf5cFhERERERERERVQOSqwrcsgMR4SpEhqugVgvQqE3hJACU6iUYjOXjlU3MYAqHzecVVJZ9XwXBfsWsco67FbgqQQmKAxoWOliPBCiLlvu8hmpNT0SEqfDi5NoAgAOnSlFYXL5Av7XADWJlq/ml1UqAW/Z5WWl0oDeX84ZfAtypU6dCkiSIooj/+7//w8qVK90+d+/evRg0aBBu3LgBSZIQFRWF33//3R/LIiIiIiIiIiKiKq68l235c66qQjVqoONNYWjdJATtmoXitg7hpp62KH9r/OUsA3YfLcbx9BLkFxoBybqFguVFVA4CXG+Y98ENNPOqYvMWAoBlBa6sR8dwpcdwcYnoqtjZg3X4aSI/0ZZtXCa31fC0FUZF8kuA+/nnn2Ps2LEAAIPBgHHjxmHLli0uz9u9ezcGDRqE7OxsAEB0dDRWrVqFvn37+mNZRERERERERERUjZT3dXWQBjoJ4eTes3IbhOx8o7KJGQCIsAwZtZryza0AIDxMsGi/oFzSix6x1msJBEfrkdvaSgBKSk3PhYVYRoRhZYFukU6yPNFfawtSSmp+VbkqW67Alb8m9r7GweaXAFcQBCxYsACDBg0CAOh0OowcORIHDhxweM7OnTsxePBg5OTkACgPb/v06eOPJRERERERERERUTUg2ckQvanmLK96LZ/XvALWOkwVBAFtm4WgVRPTR904jd8qcOUK18BuYuZ48uPnSvHtb7nK/ZRbKMDq8+JS/y2wslTgyrdFrQS4lhW4lTC/9V8PXK1Wi+XLl6Nnz54AgLy8PAwZMgSnTp2yGfvnn39iyJAhyM3NBQDExMRg9erV6N27t7+WQ0RERERERERE1YxUlq457IErj7NXgVtWYimZB7hmoa0ISanCNJ9RU9ZH13R9s/GiZBGSelJUKrdnqIiw0CY4lYD9J0pw5qK+bC2WLRQAswBXJ3lVYexK0NoUmF1XW5aKyhvZKd8fVl/jysCvm5hFRERg1apV6NChAwRBwLVr1zB48GBcvHhRGbNjxw7ceeedyMsz7WQXGxuLNWvW4I477vDnUoiIiIiIiIiIqBowr7r1pAJXstrxzLrvrHUFrtJbwM61ywNX0zMFxSImvpyJt7/Ncvt1mBMq4O369maWW+CWGspTyqn3xintBGRyoFtc4r8eD5WkAFchV+DmFRqRllmq9MIVvQzlA8mvAS4A1KpVC+vWrUPTpk0hCALOnTuHwYMHIysrC9u2bbMb3t5+++3+XgYREREREREREVUzrgI1Z8GuWglNy5+zfmxTrFp2PaXlQdn4Y2kluHLDiD3HdN71wFVZzh9I9u6JHEi/93Q9jB0QY3M8tKwnbnFJYMLMSlCAq/T5LdVLuHTdgOx8o834SlKA6/8AFwAaNWqEdevWoW7duhAEAcePH0dycjKGDRuG/Px8AEBcXBzWrl2rtFwgIiIiIiIiIiKyYbZxmU89cK36zkqSZBHQSbDcHK1YJ+J6jgHZ+aJSlSm/3f7CFQMAoETvXcKnshMm+52dpUlmrx0A1A6SwbAQ05/FJaL/etdWkhJc+R7ERKrQqnEIYiLVAKyqsctUyxYK5lq3bo01a9YgJsaU4h85cgQFBQUATOHtunXr0KNHj0BdnoiIiIiIiIiIqhslzLV/2GkPXMGyB65o1QNXkiSLjPHYuVJkXNXjSpYel66bAltjWaB34aqpf2ypXioPfT3I+gSrlgyBYNVBwoL8utW2TX8BAGH2KnD9ujh/TubBZc2uKwhAvXgN4qLlcmjbe1FJ8tvABbgA0KVLF/z6668IDQ1VvjHj4uKwfv163HrrrYG8NBERERERERERVQNK1S3MQkk3ykKtA0zbHrgSinSixXjBLCnTGyTl4nKQZyx7l33GFTnQhdI71RNqq5YMFUmSJMiXVavtj6kJPXDlQF8Ose2FtZWlB67GnUHnz5/3+gLNmzfH//73Pzz11FMICwvD119/jbp167qcs2nTpl5fk4iIiIiIiIiIqh85fHUYBjophpWLTaWyTPLSdYOplYAgAGXtFGx64FqdK1fgnr9SqozRGySH13S4TCVM9uAkDylT27lZkssKXDnAlSuMJb9WzQatB669ymylH7HtLnbBCNjtcSvAbd68uVu/2XClpKQEY8aMcTlOEAQYDAafr0dERERERERERFWb0ofULJryqgeuVWhaYuqCAJVQ9pxkOa/l2+1NB4wioCsVcS27PNkrKRUhSeV9ZT1ZS0Uwv5RpneX31FEP3FCt6Sxdiei3ytmKfM3OlPdRlitwy5638+ULZIsLT7gV4Mo8+Ua0Jih9RirHCyciIiIiIiIiosrtxLkSZOUalc+VWMlRD1yrPrfmVFZvlZdbH8REqpCTb3T6FnqlAtcoYcehYosxe4/pkJVnRGiICh1vCkV4qOuOpc7W6S/O5pZfq1pt/0aGBrgHbtDjQbM0Wb4H9pYU9HWWcbsHrq/BqyRJDG+JiIiIiIiIiMht2Xnlla5xUSqnG3M5Imd15S0UTJPIb48P0ZaHqRZVopJ5z11TwKc3SjidobeYX182j9EooaC4krzn3oxFVXHZn0qA6yAZDAsx/akrsdNXwut1VJYSXMt80lkFrrGSfDndqsBNS0sL9DqIiIiIiIiIiKgGupxlwOUsAyLDVWjVWGsR9MlvYe/aNgzhoSqcyTD1nnWUBTqLCK1bKMjhnEZdXpnrKGRUq0wBn65EwtUblgFum6ZaHEsztVaQPAz8KrrUUQ4plR64Dipww8qqiItLzSpw/ViYGawaT/k1mL9qZRMz0bYHbmWpRXUrwG3WrFmg10FERERERERERDVQ5nUDdCUiinQi6sSpUStaDcAyMJRDVvOKWE+prCot5Q3J5H6vkCSbeeUVqFUCJBHQlUq4nmO5b5PBWL4ed/M+ZXwAE8Lye2UVSsJ1Ba58T4pL/FuCKghCUN+hb35tpTJb/r6wE/9XyR64REREREREREREfmWWkaVn6nEt3NTzVpIkZF4zhaVnMkqhUgnILzIFio4qZZ0Fo/I5cngp/6nVmFXgWi1LCUFVAATTpl7Xcy1DTb1BMruui9cqr8W9YYEhlbePcNhCIbRsEzNd+SZmlSTL9InyEsy+AHIFriRJkCBYfG3EqtRCgYiIiIiIiIiIKBDMc8HiElGp+hRFIK/QFOZezzUqPWwBQKt2Y16rfrkqq3BX3sQsRFs+3llrBkEASvUScvKNynyiBOj1kvchZyA3MXP0vCSVVxY73MSsLMAtta1K9oXgZF0VwfzrI78uddn3kgBTawlBZX98MDHAJSIiIiIiIiKioGvaQAuVWUprNErIKTCFpc0blh/TqoHasc4TXHvBm/xWeVEsb58AlFfgStYVuJJU3jNVMA9sTcfDQwUU6iSUGsqv6e5b7j1tueAL6wDWvKjUUQVuSNk9KTEEZoWm+1rxdcj2NsFTqwQIggCh7OtrfktEBrhERERERERERFTTySFpXLQaUeHl8VmpXkJ8jCmobVxP6/N1zDcxE8XyMC9Eqyp7XrLJFCWUt15QqQSIogS9KVNGeJgpwNXrzapUK0ngB9hvIyFJEkRj+edqlf0QVVvWA1evD3K7hwAyf10qVVlAL0qAWVWyWEkSXAc5u6XVq1cHeh1OZWVlYffu3UFdAxERERERERERVTxH/W7tjzX9aS92UwnllbZyMCcIAjRlxbySBOQXGpFbYPoo0UtAWVsFQQAESDCYhZ/hoaZYrcQswPU07/PnW/T1BgmlerMJ7VSbmqqEyz9XOyhkDjHrCyxXK/tlrRVYeWyPo9egVkGpwDVXSfJb9wLc4cOHIzk5GSkpKYFej4Xs7Gy89tpraNmyZdBDZCIiIiIiIiIiChzrmFY0a1/gDesNq5SQVZRgFE3VqCoVoNWUP38svRRHzpbgr7MlSLtYCr1BUlovqFQCSs1aCoSXbfSlN3vO3bwvEC0Udh8txp5jxUpvX+tryYxmPRQcVuBq/p+9N4+XpKrP/59T1dvdl7mzbwzDviOyuTGCKAIi/lAwoqKCaETUgFGjGL5Go3HDqNGASiRGoyhRiAaCSxgWFYVhYGQYmGEWZmW2u/ftpZbz+6PqnDpVXdVd3V19b8/M581ruN21nHNqudW3n3rq+XjTDTOxISaap9sQ6q4JFDJjjFU4bnmbFDGLJeACwMMPP4xzzz0XZ555Jn7yk5+gXC63bFDPPPMMPvShD2HJkiX4h3/4B0xMTLSsL4IgCIIgCIIgCIIgCGLmiHJFyrzSOkQ/uWhYBq4sYuYVMNMYQy6ro79HRzajoSunoTOnqU/Rg4HJImamItbm3EJfZdNx4HIe3u90oAqPJdeFGzUUddmoDNy0ErpqWtXba4SZKg4mug2eU7ruHl/Lv51xM41bTSwB933vex80TQPnHI8//jiuvPJKLFiwANdddx1+97vfwbKs2o3U4IUXXsDXv/51vOxlL8OJJ56Ib33rW8jn8+CcY/ny5bjgggua7oMgCIIgCIIgCIIgCII4QAiJAGgG5qpglu2Jkow5Iua8WSksW5DGyUflcMpROfR2eZKZpjmDcIRfZ1paB1KuymuUuYx5iPvIfdKZuaqrttr+4nDyfwVahANXY0yKu6aVnIjZpgZc14EL7NxrYMtOQy5nt4kDN1YRs3/913/F+9//ftx44434v//7PwDA8PAwbr31Vtx6663o6enBy172MrziFa/ASSedhOOOOw6LFi1CJpMJbW/fvn3YsGEDnn76aTz++ONYuXIlnn/+eTlfhCwPDAzgE5/4BD7ykY8gnW4+rJogCIIgCIIgCIIgCIJoT4KuSLsRB26VaAJdZuBymK4wJ4pXBdfXFQuuk4HLwAGY7qDSKS9moGxxKQaGFQ6bDixFORavooZiceE+jm6PMSCVAqwyUDad7U1002bKgRvR70CvjpEJR50vlW1YFpDSZ84pHCSWgAsAJ598Mn7729/iwQcfxGc/+1n83//9nzwpx8fHcf/99+P+++/3rTMwMIDu7m7kcjmYpolisYj9+/eHxi+oJ/js2bNx/fXX48Mf/jB6enoa3TaCIAiCIAiCIAiCIAiizeERTtuox93rbldk4LqOUpsDtnTgMl/74qWaAcsYk9Nt14GbSWvSgVs2uFeca4aKmKkPxwvXaOj+4942aDWey0/rDCVwN24iGe/sjGfguojCdIIFQynMG9Tw/LYybJu7OiXDvjEL4/liYGVgydwUhvpjy6pNU3dP55xzDs455xw8/fTT+OY3v4mf/exnGB0dlQIsY0y+Hh4exvDwcGRb6rIA8LKXvQxXX3013va2tyGbzdY7NIIgCIIgCIIgCIIgCOIgQUhGWgMhuGHCqJqBKxy4OvO3Lx24Wsh6NochHLhphpSrqpUNx4HLI/oNHaZwA8dbvCaqA7dabitX5lcTcBljrojNvSJtCbpRZ8rYWi3iQtM0ZDMMhSKXy9k2R7FcudLeEau9BVzBCSecgNtuuw3f/OY3cf/99+Pee+/Fb37zG2zatCl2G52dnXj5y1+OCy64AJdeeikOO+ywRodDEARBEARBEARBEARBHMgEdFoeZc1ttHm3HTvgQlWFzOgIBYBzJoufpVOOQxUASobtRTfEFXCV15xzvPCiid5ODYN9er2bBcCf1SrHEBFBISJt9Rr7NeUOxTBnSm5tLSzkxsDhC9OYnLLxkmNyyKYZTNM/f3jCwvbdxrQL0E1LxZlMBm94wxvwhje8AYDjul29ejU2btyIbdu2YXR0FIVCAel0Gl1dXZg3bx6WLl2KE044Acccc0zoziIIgiAIgiAIgiAIgiAODaLEMM+BG78tL4u2cppw2to2h2F5ObC+CAX3TSrEgWuDe0XMUgwpkYErIhQ4r1vY45xj35iFnXsN7ATwspM662zBQS1iZtUovCXiI7QaCq7YPsPd5kQjcGdKE5b3BMK3Pa1r6Mg650ouowGB8l6F0sxUNUvc6zs4OIjzzjsP5513XtJNEwRBEARBEARBEARBEAcZUWJe0wbcQLtCqDUtYHTCEeK0CMOryLc1LOCuB8ZxzJIMXnNmtxRH0zqTDlUnQkEUSIs3NFU0LhvNq5nCGayOIWwsnMcXxkUOcJIO3Jn2ccp9EjEO4ca2o7IWZmj8NeKKCYIgCIIgCIIgCIIgCKL1BMU9oaGxOtSrak96C8HVtjnG846tVNcZMunKdYSwu2WngT3DNh56suhk57pCaSrlCZxlg3vxDPbMODRV120wA1fdJ5x7+1WrouAy5u0v0xVweYK22Rk24EbqsGrMRtV2pnkDpi9tlyAIgiAIgiAIgiAIgiCCRDltk43ARTaj4fCFGeQLNkplG6OTKQz26OjIajhiUUZGBgCKA1dxn1q2rWTgMrlM2eR1Z+BWK7bWCL4iZq6YG9W02Aa9hjCeDtkHzeKIyTOXqVsrV9krWBc+PyyiYzogAZcgCIIgCIIgCIIgCIJoO4STtJ76SZ7A5ils6vrzZjlSWKFoY3TSliLsnEG/RJYKyYctG4Bpe/Ozaef15JQt+63l3KwcZ33bF0VYETOxD4Kt8zgOXCgZuG4hr0Q1yxnScGt1K45F0MU801CEAkEQBEEQBEEQBEEQBDHjBIVM+bh7g/pmNQmuVtthWajFEpdO13SKYaDXyRjYuc+SLcY24Cacpepz4EZk/3rLOj+r1TBjDEhpTkOGlaADN7GWGqSGMCuOe9RiSYjtjUACLkEQBEEQBEEQBEEQBDFjREpq4mn3ejQz8Qh8rT5rtK277tSS4U0rlW1Yljd/0BVwCyVH3AX8TtjpRIzLGUP1rZcZuBEF3AAn63eq5LyWEQoJmlJnLANXbHvEfGFKtmboOEZBAi5BEARBEARBEARBEAQxY0QVx7IbEXBj9ynaDm+cc6CvW0ex7Cl5UyUuhb207rhwe7scaW1k0lVQG4hQSIJqDlzV9so5lwJvlQQFTEzZMmLBTDADd6YtuHJLIp3XzoxIB27iI4oHZeASBEEQBEEQBEEQBEEQbUeNelOhxC0mJiMUqiwzfyiFjFLYbKrIYYsiZmln+qxeDeN5G6MTNvq7NVgNKLK2zbFllwHD5LI42tFLM+jMxfddqo5RUYArbCgcnktYr6Lgcu4VOZMZuEk6cGc4YzZqy8X5UysDd7rHTw5cgiAIgiAIgiAIgiAIYsYJmmHjFNuqRjWRjcdUh/NFr42pog0RByuKnM3qd3IIRsYtt92Yg1OiHqaKHMWSDcviMEyOQsnGyIRVdfUganSDEB+jNlHM12qogik3YsFI0IE70xm4taIztFo3AGJGdCQNOXAJgiAIgiAIgiAIgiCIGaGqyNpAe1KXq+XAjenunSp6ymhvl4bOnLNG2lXUBnoclXNiylmOx8xOFdENnHuZtZk0w6w+HfvHrFABcXTCwq59Zuim5ZVxBnN4mS9CQSliVkMYF/NFEbMkRcuZMuDW6tcrXtf6sdQDCbgEQRAEQRAEQRAEQRDEjFPhwI2R1dostdoulBQHbolLgTbtOnAzbpRCMzEDQlBN6QxpN7IhrA7Zjr0mxiZrO3Nrxke4/dVy4OrCgWsk6MCdaQtuDYSwHlUHLm5ER9KQgEsQBEEQBEEQBEEQBEHMCNWEMJlTW4fox0IecQ9bX/Yb0TbnHBt3GD4Bd3zSRspV0hbNdV6IKAWzTpeq2q0oQKZpyvhDHKBizPOHUujuqFRfRyZs7Bs1I8VHMUAxv3oGLpfzn3iuhE07DXzkrYOYO3hgS4m1zikvQmFmM3qDHNh7nSAIgiAIgiAIgiAIgjgoqMxqba69aquLtquJw3f+Ztz3fuUTU8i6jttFc9IwLSCV8gu4ccccjDUAAI0xKZqGFdESomJft47BXr1ivmWb2DfqRTJEZuAqgnE1dN1bc3TCxhd/sA+vOnVJ9ZVqIFqc8QiFKAG3RoRC3IiOpCEBlyAIgiAIgiAIgiAIgkiUQslGvlA7SNS2OcbzNjKpMJus86OW0BhGTYGwRgYuB1AqOwu97XW9+M/7x8E5UCxznHZMDqcencPjzxSRdnVUyxL91qfscc6lWKg6cMMExFqis66JNgMzVLFYabtWfEQqsN+LperLHwjUyj6W+7+9DLjNC7irV6/Gf/zHfwAA+vr6cPPNNzc9KIIgCIIgCIIgCIIgCOLAxLY5/vJ8SbpSay27c68BADijnEN3h+csbcalGUe/Hc/bePaFMo45LCuzZ72+ucymvezcHvz41+NyPB97x6CMThAOXFnoq84xO0XFvKxf7xH+6I2qJT6KcUcNRYiTml4tQiFEOE8iv3aGM3ClgBuhgmtKcbkwwiI6poOmBdwHH3wQ//zP/wzGGN73vvclMSaCIAiCIAiCIAiCIAjiAMW0vEiB3q7KR/1VLJtD0xhsm6NU5uju8OaJGAGtjhDcsEXDM3A5bvv5KMbyNno6NbzlvF7ffMPwXmdSDOef0YVf/ymPJXNTmD3gyWmimJlluhEKtU3HFWPyHLhMCpxhDtDa+a1CfAxEKCgrcO7tV72as5kxX4RC0rSZwVXiOaDba4QNmND9mKYpXx9//PHNNnfAYFkWnn76adxxxx24/vrrcfbZZ6OzsxOMMTDG8K53vStWO3fccYdcJ86///f//l+sdkulEv71X/8V5557LubPn49sNotFixbhoosuwg9/+EPYca8oBEEQBEEQBEEQBEEQdSAcpSmd4YTl2ar/jj88i2zGkacic0cb1RFrFEgbyzsd/nltsWJ+2fRWzqQZPvDmfrztdb340vVzlIEpGbju2OvV/Ti8/cWYmhEbloFbva1a+a0CMV+vtmO5V6BNkIgBd4YduLVczELUrnUcpzvDt2kH7vz58+XrbDbbbHMHDJdffjl+/vOfz/QwQnn22Wdx2WWX4ZlnnvFN37FjB3bs2IF7770Xt912G+666y7MnTt3hkZJEARBEARBEARBEMTBiKVkusZBiGbByAUhNNYj+rGYz7hbSl/dnZUDVQXclO44ia95Y3/FciID1xTLxy1iJl5whGfgNuDAjVpXXdyXgVuriFnTts9oZqyImfuz5j5sM99j0wLucccdJ19v2bKl2eYOGCyRTu0yODiIWbNmYcOGDQ23ef311+Pcc8+tuswxxxxTdf6uXbvwute9Dlu3bgUAnHTSSbjqqquwYMECbNq0Cbfffjs2bdqERx55BBdddBEefPBBdHV1NTxmgiAIgiAIgiAIgiAIFSGO6rWqZLloVURLoDWuzf1jnq6Ty1R2UC4LF3F0XipjgO4KuCID146pTKpNWtIRW8NFK7Jro/JbtfD81uDiYj/r1dMtKgTcJI6DKlxPN8NjFtY8X8KOvSZGJmw8s7mEYw/L+I5vMIYiyAGbgXvqqafiqKOOwoYNG3D33Xfjc5/7XBLjanvOOOMMHHvssTjttNNw2mmnYdmyZbjjjjvw7ne/u+E2X/KSl+DSSy9talw33HCDFG/f+ta34j/+4z+QSnmH+UMf+hAuvvhiPPjgg1i1ahX+6Z/+CZ/97Geb6pMgCIIgCIIgCIIgCEIgBckaAiHgL5ZV4cCVGa7x+5YRBDWW27XfE3AnpyrVUiHIVtsGBiCdcgZvKj4/znmk6BvEccS6Wb8aA0N0ES0pDkdm4PqXCxUhudJf9QSFigzcGY8/aJJ9YxZMywbnzv4dnbBQLHF05LwNYzEjFKabRMzQN910EzjnWLduHb7xjW8k0WTb88lPfhJf+MIX8OY3vxnLli2b6eEAAJ555hnceeedAJxoi+9+97s+8RYAuru78aMf/Qi5XA4AcMstt2B0dHS6h0oQBEEQBEEQBEEQxEGKyHSN68AVywVdp2FFuGoiHJKco5reuXu/V9NpPF8p4AoHbjpVve+UcOCaot+Y8QBynJ5Y6I9QiM7AjRqR2N2GCTy9sYSxyfAcAOnArarg8pZEKNR1LBOmbDjnRH+3hlzWGcdk0b+PpAgemcc8MxbcRA7F29/+dlxzzTXgnOOGG27A5z//+YqIAaL13HnnnfLuyrXXXovu7u7Q5RYuXIjLL78cADA1NYV77rln2sZIEARBEARBEARBEMTBjSySFUN1Uh24lQKuW9wrwbEJdg97utXYZKWGJTJwUzVEaFXgFcJovfmuviJmQh+sksEapYGm0wwaY+CcYzxvSUdzJu2t4MvAreIu5qyyiFmSzITBVRzTdIrJwnlB93VUDMVM03SEAgBs3boVN910Ezo6OvDNb34Tn/70p3HrrbfiiiuuwNlnn43ly5ejp6enwg1ajSVLliQxtEOK++67T76+8MILqy574YUX4gc/+IFc76qrrmrp2AiCIAiCIAiCIAiCODTwipjFzcB1lgtGKHgO3Ph9x100X/BE2627TaxclceK07waQYYQcKtFKDD/fLHddRhwHQeusr+qFjGrsT9SOsPJR2YxVfJWTqecAmwqamRDJLwyPqIR8+zohIWRCWdfz+qLkanRQsoGd6IhNC/3eNc+E7MHUujucATdag5oIH5ER9IkIuAedthh0kLMXKV/+/btuOWWWxpqjzEG0zRrL3iQ8e1vfxtf/OIXsW3bNti2jaGhIZxyyil4/etfj6uuugqdnZ2R63LOsXbtWgCArus49dRTq/Z1+umny9d/+ctfktkAgiAIgiAIgiAIgiAOebwiZvGW1zUAjEkBVCBEskaEQ5/+5q5/z4MTmCpx/NVre1Eo+SW4f7h9P45emsX8IUcqkw7cmhEK3nyx3bYNoIZWqUYJSAGXeYONiK91141utyOnoSMXPZ9z7jmka+zXYD+GCfzrf43gfW/qjy3Ob9hWlmL46IQtHctRRcJahWVxR7h2Hd9ZxZX8wi4Dxx+eBeBFKLSbA7cFaRbOSaieiE7uSH3/DkUee+wxPPvss8jn8ygUCti2bRt++ctf4gMf+AAOO+ww/OpXv4pcd9u2bZiamgIALFq0COl0umpfixcvhu7eStmwYcMhu88JgiAIgiAIgiAIgkgWr4hZTAeu5siWVoQDN6ZWWIHammlxfP3OEXz37lHsHTFRKFVmFGzbbcjXZSNeBq7qwrXs+M5M0aplc7mOpnlxElaIBVfEKjQbbCC23DA59gyb8t++UdPXL0OlA/lnv5vAPQ9NYM+wKfdRNdSEVcPkmKEIWSnIO/uYIZtmGOp3xHrfNldxQDsLOD+mW0dLxIELTP/ADyZ0XcfZZ5+NV77ylTjqqKPQ3d2N0dFRrFq1Cj/96U8xPDyMvXv34pJLLsGPfvQj/NVf/VVFG2ohsqGhoZp9ptNp9Pb2YmRkBIZhIJ/PR2bmAkCpVEKpVJLvx8fH69tIgiAIgiAIgiAIgiAOCepx4HLOpWhpWsF57ot6IhQillWzTotljmKpUsfavsfEGcc7r0VRsmoitOgrk3bGLoRARyOLN2jL9gvVcRygzRQCcyIbnMYnCjae3172zV80J40l89JVxfNnXyhj7mAK/T06jluWrdIX90URmNbMmTaF2KxmGs8e0LFv1PTt66iCejNNIgLu5s2bk2jmkOQVr3gFtmzZgkWLFlXMu+aaa/ClL30J733ve2WBsve85z14+ctfXpERPDk5KV/nclW88godHR0YGRkBAExMTFQVcL/whS/gM5/5TKx2CYIgCIIgCIIgCII4dPEycOMtLx7HD7pOPRExvmCpZsuqTCgCbqnMUSxXComhDtwYsa2ZNMNU0Y0m4DzavRkyUE+kFk+zuzEMIW1E5bLWixrZ0N2pIa0zlAyOqaIdul+ClMocpoWay4bGQMyQ/9OLxHDeR51STHHYcs4rxPKo86vVJCLgLl26NIlmDkmOOOKIqvN7enrwox/9CLt378bKlStRLBbxxS9+Ed/61remaYQOf/d3f4cbbrhBvh8fH8fixYundQwEQRAEQRAEQRAEQbQ/wuGpx8g+4PBcnhUZuK5K1mxkAIPfgVsoeULlYfNS2PKiY7fdvserx2TEzMAFgEzKEV5tEaFQh7gn9pXoRQiGQYFQda42YcB1+lSE8cPmp9HbpWP3sImN28tyPF7gbuX6D60uYN3mEj769lmx+hF92Zx7heqmWQAVjmo1EkO+8o3Te23z2jnB00VLMnCJZNF1HZ/73Ofk+7AsXNU9WywWY7VbKBTk656enqrLZrNZ9Pb2+v4RBEEQBEEQBEEQBEEE8TJway/LueLADTxiLwRAVo96pQhuqgiqOnCnShxFNwP3XRf34Rs3zgXgd+BKAbdK30JszaT8DuI4DlymOnBVAVET0/37Qt2WpgVcxSEt3M1e9q7bn9tHVFd7R+0KwT2qH8BzvsZyJ7eASqE8fDkWcf6EzZ9OEsvAJVrL2WefjVwuh2KxiK1bt2JqagqdnZ1yfn9/v3y9b9++mu2ZpilzbNPpNLq6uhIfM0EQBEEQBEEQBEEQhx5WHQ5cZzlPGPvjXwoV8xvRzILa24TPgctRcB24uRzD/CFHHts7asG2OTSNxS5iBgDpjD83de2mEk4/LicF3i27DAyPWzjpiCxSOsPIhIUn1xdhGBwTBY7tu8uYyNvgnKNscJQMjp5OzecAVbenGRGRcy+KQWNMtqUHYyxiCK22zeX+Cp0v+tEYUrqzT0VRs5mKUgjrVp2maU6UBeduJEaMmxDTATlwDxA0TcPg4KB8rxYtA4DFixdLQXf79u0wDAPV2Lp1Kyz3t+bII49sKgCbIAiCIAiCIAiCIAhCIES6OEXMAEeQ7MyFL5zSWeS8epgseAJuvmCj7MomHVkNXR1ebIGIVhCP3FeLUBBzMq7KqusM4E6xLrUg2869BoolG7v3O40+v60M0+TYvsfA+KS/cpvNOcYmbTkeierAjbPBVRBCM9M85604VuLYxenEtitjL1S4krWb0oXI7RZ6q3PMzRIUjBmr7cINyxw+oDNwoxgeHsZjjz2G7du3Y3R0FFNTUxgYGMAHP/jBVnZ7UGLbtiw4Bvgdt4Bj2z/++OPx2GOPwbIsrF69GmeccUZke4899ph8fcIJJyQ+XoIgCIIgCIIgCIIgDh0KRRubdhowLY5SuQ4HriuELZ2XwqlHd1TMTumIdHiG4YsmUFAzcIfHPdG0I8OQTTPomiNG5gs2OnOadOCmqjkw3b7SaedFb5cnNIcJfCL+Vd0ezjlSKQ0L5uhYNDsFBqBYdsaqOkCTcuDaNsfOfY6QPNijycxXXfc7cOMIlKbFYdkc6Qi1V2btal6chmn7c2bbgeC2agyw4QnQ7UDiAm6xWMT3v/99fOc738GaNWsq5i9fvrxCwC2VSnjTm96EYrGIVCqFn//8575MVwJ49NFHZWbtokWLfPEJggsuuEAKs/fdd19VAffee++Vry+88MKER0sQBEEQBEEQBEEQxKHEvjELY4qbVGMM2Uw9witDJt28shdWmIoxf4TC/lFLTs+knUfmuzo0jOdtTBZszB4Aym4GbjpGFaus69I1TA7miyIIrOuOKZdhPhE2m3aydjtzGgzThmUBpiUcoP54Bmfcje+nrbtN5AscugYsmZeWbQkHrl2HaGnaHC/uM/HM5jLOP7OrIm5CLZYmHLh8hiqDidOBK2OKwnPghs30tzddJBqh8PDDD+PEE0/EBz/4QaxZs0YGLqv/wshms1i4cCFWrlyJ3/3ud7jzzjuTHNYBj23b+Pu//3v5/uKLLw5d7oorrpCvb7vtNuTz+dDlduzYgZ/+9KcAgI6ODrzxjW9McLQEQRAEQRAEQRAEQRxqCMlnsFfHscuyOPmobKz8WKEUtTrZUXXg7ncduNm0J4Z2dTgSWb7gjMgURcxiiI3CgWtaPFRAFniCJlAyOEYmLIyMW47blzlO1VxGA2PAjj0GLKuykWYjMJ/fVgYALJitQ9eYFG69DFz/+Kt1Z1nAR7+xB1/50TB+8uvxivmycBjznMymKJI2Qxm4TBHVWYQYKxzSM1VwLYzEBNxf/epXOP/887Fp0yafWHvYYYfhzDPPrLn+e97zHvn6rrvuSmpYbc0f//hHfOc730GxWIxcJp/P453vfCd+97vfAXDE7o9//OOhyx5//PG4/PLLAQC7du3Ce9/7Xpim6VtmcnISV155pezzhhtuqIhjIAiCIAiCIAiCIAiCaIRshmGgR0dHNp7klLSQFyVw+hy4Y0LA9cbYlXPWmyo6y5VdOaWaCC0zcIWAa3qZsmHin9DKLBsYGbeQL3DkixyTBS7F2q4OR8AtlGyZhauu26zOXXKjITqzGqDkwAohl3PuFTKrgWkBw+POGB95aqpiPlcEay8Dt4nBN4M7Ftl9lR0pIh7skP0QFdHRahKJUHj++efxlre8BeWyo+IPDg7iU5/6FN7xjndgaGgIgFOEq9pdgrPOOgtz587F7t278fDDD8MwDKTT6SSG1xI2b96M22+/3TdNjYxYvXo1brrpJt/8c889F+eee658v3v3brzvfe/DjTfeiPPPPx+nnXYaFi9ejK6uLoyNjeGJJ57AT37yE+zfvx+AcxH63ve+h8MOOyxyXLfccgv+8Ic/YPv27fjxj3+MtWvX4l3vehcWLFiATZs24Xvf+x42bdoEADjllFPwiU98otldQRAEQRAEQRAEQRDEIc5MOSqj4HDGVCpzrH6uJEVbABh2X2eUKAPPgesKuCIDt4pyJtbNpBQHLvO7N9Wn0cVLy+KwbCCb0dCVYzBtLvNxF81J48kNjunOML11DYtjPG+ht1vH5JSNiSkb/d0aOuos8Ga6HWmOfivdpprSjGXFiwgwFYewZVXOF2KtprWBgOuiSpMswoKrzZBIW41EBNwbb7wRpVJJFtL6zW9+g7lz59bVBmMMZ599Nu6++24UCgWsXbsWp5xyShLDawkvvPAC/vEf/zFy/po1ayoygFOplE/AFUxOTuIXv/gFfvGLX0S2N2/ePHzve9/DRRddVHVcCxcuxP3334/LLrsMzz77LNasWYMbbrihYrmXvexl+K//+i/KGiYIgiAIgiAIgiAIYsZpVYTCQ09O4cEnCr5pngPX61QKuEVHtTOs+BEKqbSSgSvFP2d91cQpBVwOABy9XU4BtfEp7mTDwnHz9nTqGBk3fWLnph0Gdu41UShxrOGOwJvLanjJ0bma41MRorCuV4qZmsZg2xw253Ks1bZejXgIc+3awjXMmIxQENnA0y2Oyv4UV3AUrIqLulpERitpWsDdv38/7r33XjDG0NHRgV/+8pd1i7eCk046CXfffTcA4Nlnn21rATcJXvOa1+Cee+7Bn/70J/z5z3/Gtm3bsH//foyOjqKzsxNz5szBS17yElx00UW4/PLLkcvF+6U87rjjsHr1atx+++342c9+hmeffRYjIyMYGhrCSSedhLe97W248soroWmJRiATBEEQBEEQBEEQBHGI4mXZ1qfEtkoHEwLqvtFKa6jIec2kPUFORChMug5cw3XgxolQyLrqmmmpj9/7fzpjcvu3HIFULaQl5nF4cQYTBRvjeWf8O/c6mQ7jeRtzB93+zPr3nnDKqvm3Al1zxmtZiKxjtWAoBcO0sXfUhpraaYY4cGWEggav+JfYBzaXLueywdHVweS5Y1ochZKTJ9yZY9IlnATqdolWKzJwhYu6jUJwmxZwH3zwQViWBcYYLrvsMixdurThtubMmSNf7969u9mhtZQVK1ZEnsxx6e7uxiWXXIJLLrkkoVF55HI5XHfddbjuuusSb5sgCIIgCIIgCIIgCKIdUfVjDi+fVtOA04/N4U9rvTpE2bQmVbxghIJwqqb12n0KkVc4cDlXIxS85WwpXjrTGYMSucDc5Tl01/W77UVTFl/bPeyvcdQohhRwK8V2XWcwzOoZuLoOiJ02UfBUWzOk4JrNgZEJG1MFjtkDzo4UXa7dVMLWTkMuO2cghSMWZwAAazaUUCw72z3Un8JRSzJ1bWMYwdFVk4Q1mQdcOU/ss+mWdpu2YO7YsUO+PuOMM5pqq6urS77O5/NNtUUQBEEQBEEQBEEQBEFMDw0X2RKP6rcoQ0G4KK+9tB9vOa/XN6+v26vXJARcUcRMOHDjRCgIAde0ORiEEOv2r6iAls3BOYdtcyng6joA7neGDvXryKQZ0mknJiGdYshlnHab3U0i9kDXWEWMgHDkCoEZAE45JlexjNjeibxnLx7PV4bb2jbw1R8N4/P/vh/D447YK9otG9zncC2UbLdvLsVbwDseLSEQdyEnCxd1+xhwmxdwp6a8KnOdnZ1NtTU5OSlf9/T0NNUWQRAEQRAEQRAEQRAE0d7I6IWE2vM5cDmXEQYpnaEj6+9lqN+z13a5xcD2jFh4YZeBpzeXnfWqRCiIQQuXr6lk4ArhVo1QsCzvPXfHmtIZOPyO3aE+HYcvzODYw7J4ydE5LJqTlmIrU/ZUI/qiWsRMC7iLdbcTEfEAACctz+LT75nlW0ZsryraFkochYDYqkY87NrrOYg5gL0jJtZvLWPri44LV+6XwEaFOXsbQYi0Mtu3iiIap4hZs0/l10vTAq4ae6C6cRth48aN8vXs2bObaosgCIIgCIIgCIIgCIKYXlpVjKwhlCgDXQM6cn4ZbKhf9zJwO5xXv3+qgHd/dpdcpqczWjoT25oRDlzLm8ZFBq6i81k2l/m7gCMUiuJeXJ0eeIRf05J5ZJ9zLkVVx4EbiFAQxbuUsTAwLJ6XQsoNYT16aUY6cEW8g2DdlrLv/cSUF7EgHMTdHZpPGJ0q2rBsLveTHTDcWgkbcH2F29yfQS3Wi7VItu9maFrAPfzww+Xr3/72t0219Zvf/Ea+PvXUU5tqiyAIgiAIgiAIgiAIgmhvpJOxBcIvhycI6jrD/Fk60ko1qKF+742IUFA5+cgMDptfu3xUMAMXCM/AtWwvwkC4PFM680UojEzY2LjdcCIGhIDLIBXc+bNTOPXoXEXbYQyPW3hyfRHPbC7JomF51yWr6Z5QLBDZu89vL0vnKwcwVbDxxld249Sjs5g7qKNUduMyAsfso9/YIzOEAWA8r8ZHOD8HenWcdnQWRy5Wcm15eOQEINzAySmpcRzfnpBd2e9M3aBoWsB9+ctfjt7eXnDO8fDDD+MPf/hDQ+389re/xdq1a8EYw+LFi3H00Uc3OzSCIAiCIAiCIAiCIAhiGuBxlLFpINi9EOF0DchmNCyZl5bz+ro0KcjpwUBYAK85oxssZHqwr7SIULA4NC2QgauIgJalOnCd5XTXgSsW27nXQL5oY9c+U67LmCc8prT4IuKeYRNTRRujExYmp2yMjFswTLG9QDbjb0iNmLA88yxGJiykUgwLZumwbE/43T9WaY/dtc+LSlAduIYShWBz5hOPOSALpwknsqbs90RduIomG7Uf42bgTmeMQtMCbiqVwl/91V8BcAZ+xRVXYMuWLXW1sWfPHlxzzTXy/fvf//5mh0UQBEEQBEEQBEEQBEG0OUlrYEXDlo/27xu1pPgnXJUXv7wbAJDN+AW8E4/IojPnV/T6u+PJZrKImQkIhVBm4Crbt3fUwvqtJeQLtuw7pVU+rq8xpyCajFBQBuoTHWsJjOprLoRS5/3C2Sn0d/tDcBfNSSGXdbc5pJ/uLg2L5qSkU3fvqCPQnn1iBxbPdZzKolgZ4M/IFcIx5xyWzSuK1nkOXOdnSve220ogB1e0L+8zVIs2DsRghM1T25wOmhZwAeAf/uEf0NPTA8YYduzYgdNOOw233XYbyuVyzXXvv/9+nHnmmdi2bRsYY5g/fz4+/OEPJzEsgiAIgiAIgiAIgiAIYhpoNgkhKePutZ9/EV/64TDyBRtTRVsKqUJ0vORV3bjhbYO48W1OYS4hyA326rjnK4v8Y2LVxyVESJGBa1i8ogCWEAENk+PFfQb2jVrYM2LJIlqiSJovd5Y5pcpkhIKm7N8aY6qFcPV2ZHUEzcWMMQz2+kVd7v4D524hOE2K4ROuUD7Ur6HPFbtVAXdSiVMoG5VxCmL/ce5tv62I1sKdnKQDN04Gri5F9fYJwa0d5BGD2bNn4yc/+Qne+MY3wrIsjIyM4AMf+AA+9rGPYcWKFTj22GPlsiMjI7j55puxY8cOPPTQQ7JwGeccHR0d+PnPf46Ojo4khkUQBEEQBEEQBEEQBEFMI41mhCaRLbp/zMKufUJAZFi2IINMyhEWhYDLGMPFr+jGui0ljChiIxAeoxCHjJvKIAqEAZ4QaQWcuON5G7bNoTMGCxwp3RVJFbGQMWecvggFRcCNDfe/5NwTQ9Mpf0yBty0hzlgR4cCd/lN+jRdHLs5i2I1TGJnw1NYJxYGrZuqaIY5a6VhWtlnXGAzw0OXrxRPU3QziatEYIkIhzIHb9EgaIxEHLgC8/vWvx09/+lP09fXJaRMTE/jVr36FL3/5y1JVHx4exuc+9zl8//vfx8aNG8G5E0Y8ODiIu+++G2eccUZSQyIIgiAIgiAIgiAIgiAOEZ7aUJSv5wzqmD+Uki5OPUIBCwpyf/v2QQDANZc4+lbwUf8wZISCVfn4vfzpKojjeQs298ajBzJznT6df0L01TUmtVhdY55ztebI/DgCrpcJHLZp6RCrJ+NeCK8j4Horzh3UMdiroacrxIE7pUYoqFnAXltiXIAj3srYCM3bR4lm4LLK18H9GHRRRzGd/tzEBFwAuPTSS7FmzRpcfvnl0N3fECHQqohpnHNomoY3v/nNeOKJJ3D++ecnORyCIAiCIAiCIAiCIIiDjnzB9oljBzLNRi8AwJ/WFvD/vrsX3//lmJwmHtkPZuB6HYe39fqXdeOuLyzE687qit2/jFAwucxttTnHVNH2inNxoK9bR1+3jt4uDbP69MC4mHShqgJisWxjPG+hWLJRLHPkC05RsnzBRr5gYTxvRRbTCmbgAoDtiqfpFKuIUFC3JZj1KqIUNMZ8Au7yRY79uLfLNW5GRCgIAVcVkYPjVKMUNOa5ppPIwFW3BagRjRGSS+zNVBtLalS1SSRCQWXRokX4yU9+gh07duCuu+7C73//e6xZswb79+/H2NgYOjs7MWvWLBx99NE455xzcNlll+GII45IehgEQRAEQRAEQRAEQRAHHSMTFtZtLgEADl+YwbxZiUs7DZGEENsIT64v4u++tbdiuhBwxeP3uu4fWbViVoN9OvaPWZUzAshCZGnhwOXSKrlv1MKufaZ0yw70ptCRVSMF3KxbpWYYtwHoTrsaA0pljtXPlVAs29g9bMIwgY6sgUyaYdtuAwDQmdOwaE4aS+ala46XAxBaaFpn0EJsncEIBbGeus3qvuzpcoTonk7n58iYKuB6a/oycAMOXJHTYNlelAJrVQaucoYGHcACIWzboQruzNCy3/KFCxfiwx/+MBUkIwiCIAiCIAiCIAiCSIhiyROVSuX2EZgapdktEEJmLsNw/eUD+PIPhwF4gqFwnKb0CGk5AcU5rQsHrveo+869JmYP6Ng7YmCoPxXajSPUMjkOKSq784plG5wDKY0hndIAcHRkNXTnNGQzmnSyFiPOA58wyR0nq5im6+HxENKB6xuo95MxIKUIv72dzpveGhEKMh+4qgNXGZ/mHbMkM3BF16xKBm61CIUZMuAmG6FAEARBEARBEARBEARBtA4e8XqmkWNpVBBtcL2xSUckfPVpnXj9y7pxxnE5AEDZFBEKXuarSs1801oLwHNwCteqZXubYVgc45MWNu0wMDFlh7YntdtA0Szx3jKdn10dDPNm6Zg7qGP5ogxOPDKHZQvSGOytz5ep5tCmIiIUdJ15ojIgd5Q6fF0pYiYE3K4O5+d+RcAtlGpk4Mo+nB+2UmTNKWLmLt+iDNxg/3K65o2nXSABlyAIgiAIgiAIgiAIgmgKGaEQo+hX+HqN9Tued9S9vm5H4hJiqnAnR2Xgxo18iDOutCJomkJs5MDOfSY4B3bsMUI7CrYtHbias7jhKogpnVXEGIixVxOa1VkcTmyDN2YtNEIBAGYPeBvEAUBukyP6qm7mXne/d+WcafkCl6J5SYlNMCx1HCImQekDjoDtFTHzsnaTyMCVLYiIhirLCgE71IE73RkhLokIuPfeey8mJiaSaIogCIIgCIIgCIIgCIKIQBXs4rhEW41lczyzuYS9I+aM9D826SiDvW4WqxBwpQM3IgNXECU4x9mzYs10ymtDOE2Fe9OLRfD3I2IS4P5kDODuShocwdYwOLbtNjE8YYYPSD7qH+88UHNoNT1ajFy+KIPTjs1JAZWr8Q5BAbdLCOeexDhV5BX97dxrYuN2A+u3ljE6IWy2YvzeT5E7q6kO3NpxxDUJF2Pd7auY7vZbw4I7nb9+iQi4F198MWbNmoUzzjgDH/vYx0jQJQiCIAiCIAiCIAiCOATYP2phdMIp2NUQTRY/Ew5cISRmhYBbw4GbJLquRCeYfuVW9MsrntP3Rcs6Aq54zxw3asngyBcs7B+1lZzYxlygnHM5Ns11+OrVcmA1Z4BSpFScsb4IBXe/axqQdhMd8gVnp6sCbrnMYZg2yobtFSoL9Glz7mXUKsXSagmpdSG2o8o+lPOqiL7A9Aq4iRUxsywLq1atwqpVq/DVr34Vuq7jlFNOwYoVK7BixQq88pWvRE9PT1LdEQRBEARBEARxAFM2OLa+aMC0ODJphqXz01W/SBIEQRAuM2+6jYRznkRNsLqIilAQDlzxuH5UEbOmIhRklAFDOgWUTaBQ5simvNQBTQspCibbZr4+1AxcDsWN6tQvCx0PR3whUewT8XGrCrFhBMcshGb187qvW0OhZIFzoLtDw8iEjXzBhm1zlBVN3+Ich83PIJdlOOmIHIbHLWzaUXa2QWTg+iIUlP2S5DkfciCCDuZgtEM7kIiAm8lkUC6XfdNM0yRBlyAIgiAIgiCIUPaMmNijPG7b361jsK/GN0mCIAjCX8SsDRQmVRhtpNiU5zptTPodCzhwpYBr+B24wbzXmrEDdexbDiCdZiibHMUSh86Yc2wYg8bcImBOYq1ch8ETUp1t5z4HLueecKkxdc34A/PFbcCJZAC8fRElaovxBdvi3JluKpm0fV06XtxvweZOIbORCRv5ou3LvwWc45DLMuSyGro7NUwVbbndAqeImRCZvSJrSZ7nUgxuwpHNGJv2+JJEBNyxsTE8+uijWLlyJVauXIlHH30UpVLJtzEk6BIEQRAEQRAEITCt4Ps2UCEIgiCIulGfsDdNPu1FnsYjMnCFgBiVgSs/dWqMN4YB1+k3xZAHx8i4hWKJeW7kqIgI5jk9pVApHbjO+pbtzNA05suJldELrv4Z9xNUjVAAqgu46qClvueOWY1GyGadxmyboyvnvM4XOAol/6jWbipj5z4Thy/MOE0pAqoaDyG70jz3st3AjYEam+S8Vt5wzituIrTDDRJBIgkg2WwW55xzDm6++WY88MADGB0dxQMPPICbb74Z55xzDrLZLACh1jv/hKD71a9+FW94wxsqMnQJgiAIgiAIgjh4CVaUbsS1RRAEcSiiikrtIDCJPFPOAbMRB24DG2HZHGOTFiyLY7LgrB+MUPjFykk8+pdCZAauFAojNMx6hsW5XzgenbBkVq33cVfZoNo3g7cvNOHAtf2Cq7dkzHEF3hiW524FgFSVB18qxqa8Ud21qku2q8N5ky/YKJYrt/fbd43K1zpjXh8iQsF1+Yp2vQiF5k90rpynQHOC6EwEPiWWgasiBF0h6pZKpbocurfccgtMc2aqFxIEQRAEQRAE0XqCgm2iBUoIgiCIaYMr13PTnJ5r+Rf/fT9++9gUvvKhOXJaT6cr4KY8ee2T/7pXCqtROevNiHGqyJnNOG8eeXIKPZ0azjy+A5pwq3YwZNNOVqzar1ifMWeCWsDL5p7zVGcBQZn5flS14FoWl8Kt4XYvdkVNB24IGgMWz/HkRDWntlM6cG0UiuFqftlwpjNNdRJ7Tlsv8oJVOJObQe4/VZhm/vm+44HazmZH15weObclAm6QOIKuyNCd7gwJgiAIgiAIgiCmH89VxGDbPNHHIwmCIA5m2k01Ue+/mVaNol8hqLmvcfntY1MAgFt/MQLAEW9FRIIQUr0xiSJmER3XGFdcfS7rCsdbdjmGxHNf2omRCe7coGQMmXTlOpqy0Y4D133tCrZSzKwitFbbDM6BTTsNWJZTQCyYgRt0JQcRY7LdjtxYXxyzLIsrzgdef3aXFMa57WTgAkC+GO7ABYD9o46KrDpsnZxg5kYoeC5kMc5EU5ZqOK9jUWd0RRIkEqFQL6VSCRMTE5iYmMD4+DgMw5iJYRAEQRAEQRAEMUOIL6XCGUUOXIIgiJi0XYSC97qVeeZbdxt46007cPeDE3La89scPWmWUgRTdeACios1QgRtRsgTzlHOK4XjQolLETObZpVF2lil41PdlwxOdIBlu4KuO131fAYSCEIRkUVjkxYM92F3XWOOwzXClRxECLmOUZiBAThxeRYLZqd9MQedORGhwFEohd+Z3TPiCrhK30KgtmzucyFryv5tBVFbz+JacKeRaXHgTkxM4KGHHsLKlSvxwAMP4KmnnoIduMWuOm+PP/746RgWQRAEQRAEQRAzhBBsMymGYgmwrBorEARBEG0JV1THhgTcmI7I2+8ZxZ5hC9+4c6Ri3hGLPXuruDEYpCIDN+bw4uq7A71+i+9EgWN03ATTGHIZVhEd5ItQAAMY9+cbM2DnXhMPPD6FpfNTOPP4jvDxVLXg+l+K46Nr1fNvoYyNu/9zE2QDebxeHAMQyMAthQ9sr+vAZYqALY6NWsRM0xRhO4GbvDzwM6hd19OD3DfTKPC2RMCdmJjAww8/jAceeAArV67Ek08+6RNsgzEJxx13HFasWCH/DQ0NtWJYBEEQBEEQBEG0CUKwJQcuQRBEffCAKDfT2Ioy1spE0GoC75GLM/J1tIDrn15LfKuriBmAwxdm8PCTBTmtWLIxPG5joFdHX7cWGhmqOnDVImYA0J3T8INHnaiIDdtMnBnmdaxTSCwrEQpx8m8Zc2MNIuZzDuiKENyRdZTYR9cWcNgCR1RP6U60hmCv68BVuxfHRs391RiTYnHLhFJ1F/DKye3w+yVIRMAVgq3ItH3yySdhKbfQSbAlCIIgCIIgCEJFCLbplHg/g4MhCII4QGmHCAUxBg5/Iah616+1WlcuOgX0qCUxBNyA47SR7N1qLFvgD7l9akMJm3YYWDQnhXNe0hma9S5iBMIiFIQYCvjdojbnscesnh6cQxYziyPgBmMEpHM1IIQzxlyhl8siZqpTOpdhmCx4IxmddB24isNW09ysXdvZPqddb7vj5ORbNse6zWX092hYNCckcDj4u9JMdEbjqzZMIgLu4OCgdNiG3VE49thjfYLt7Nmzk+iWIAiCIAiCIIgDFPFlTGQVUhEzgiCIeLSBZutDFR1bKSh3d0YLuEcs8gTcbISAG8dxGkY1sVSdt3yhXzTctMPJ592+xwmeDYuXEFvkOXC9eZMF5YORhb4E4AnncfCKmLGaEQpRqPptsOhaZ4jInsv6BVzTFEXKmNwWXWMwLQ7OvaKmuuaJyHaMDdw7YmE87/wLE3Ar9FsWacBVnM1xK921nkQEXMuypNrOGMOxxx6Lc845hwRbgiAIgiAIgiBCqShi1sLCNwRBEAcTNUWlaUbNJ+VA3fZEuXaN9cIE2Ite3oVTj86hq8MTDqMcuBX1uqT42LyfknOO+UPREhsHYIRkvatdTxRslMrevpyY8lZQb3LWc/SDp4ohM3AZUjrD7mET+5RM2gVDKfT3OMqub69w5R8gNUCBxgAbIfsYQDatwZnrH4PsVNmq/WOWjFtgDL68XdvmVYuuxY1i4gkc95mocRZ9+6IBGGPQNA0dHR3o7OxEV1cXcrlckl0QBEEQBEEQBHGAY9lcfvFLp0QG7kyOiCAIgmgUNUKhlZRNfw+nH5fDjVfOwrkv7fJNDxNwNVb56L8gSsaLI/TJOdxp/+2v7w1dLj9lh96oFGMan7Txu8cK+MG9Y3Le5JT/g7HTFannz0rJMdXSIFWRlYHBNN1+mRMp8cIuA2OTFsYmLYxOWNItXLmhrGrkhBBaTz06i5OPzPrmpQO6tnDgpnRv/2UzTgNlg8sbApk0C0RHVN3Uhojaf1JWbqN7JYkVMRMnhW3beOKJJ/DEE0/glltuga7rOOWUU/DqV78aK1aswCtf+Up0d3cn1S1BEARBEARBEAcYSrkMRcBto29JBEEQBwjt4MZtNkIhbgauKMAl6O0K9ySGRSgE82+B1gjO73lDP3o6Nfzrf436pu8dtdDTVTkIIXzu3OcIpyXDmzcREHDnDuqwLB2L56bwqX/diz+tLeCk5RlcuqIncjy+YwPAMIUD1xFxRazD/KEUdu0z5XwAAHOjEdz/RIU6GfegbgdzpoxN2vja38zF1Z/bhc07nY3pDURfSBFZYzhmaQb7xiwM9elYMi8tx5PLaMhl/OvZNoCYsQ9hbt3gjYYKQ3YT567AtDh27zdhWo4APW+WnojDG0jIgbtr1y78+Mc/xvve9z4cddRR4JzLf6ZpYtWqVfjKV76Ciy++GIODgzjrrLPwiU98Av/7v/+LfD6fxBAIgiAIgiAIgjhAEGKtmsFnhTxaShAEQVSnDfRbTxiLKcQ2SlDA7YsQcMMcuHqI+5bXGHCcXRv2KH2YsLxv1AoV28X6YRrf5JR/edtm6O7QMVUC/viXAmwbeHpTGZxHC/k8QsDVdObrc3a/4+9Uc3ojEicC7btuWtceKkTgxXM9v2g2w3DjlQO46d2DAPxRErmsJo/XQI+O2f0pzO5PoadTLeDGfH1FoswOuycsD7cvwYFVzHemV+8qir0jFl540cCOvQY27yxjbDK5x4sSceDOnTsXV1xxBa644goAwO7du7Fy5Ur577nnnpPLmqaJxx57DI899hi+/OUvI5VK4SUveQlWrFiBV7/61XjFK16Bzs7OJIZFEARBEARBEEQbohYo0XWvQEmtfDuCIAiiPURbFV8GrlsbqRFqrVbhwO0Ot2OK4pgqYQ5c2W/NkdVHT0ixtdGJ8LuU4iNPzXp1ja7+ImZws+IZMJGPH4jrKxDKuRRodQ0QW64xJkVU06o8hqbJZewB537Rc3TShqbZ6OvWsHOvY63dPWxggZIHnMtq6Mxp6O/R3D4UkThGlqymAbZVO0JBnW9ZPLJonVismT83os7VYKG6JOOhEotQUKlH0DUMA3/+85/x5z//GV/60peQSqXw0pe+FL///e9bMTSCIAiCIAiCIGYY4cDV9cp8u0SLdBAEQRyEBF2VM03wMf1WERRwl85Lhy6Xju3ATWZcQXpDohLKSrSsxhhst3PpwFVkZMPgyKRZRYSCaTvLq9NN21dbrALfucIhC4Q5Aq74LIZ8GoZzDst23qu7rFDyFywTGQobt5fl9Bf3m7BtjmyG4aglGVz26m7sGbGkG7cj64rEZoiAW+VYiGXsGmKoKpbGEk6l+5lVuHvjZuDWOoWSjDhpiYAbpF5B99FHH52OYREEQRAEQRAEMU2Uyrb8ki++COqaE6MgvsxOFW30dGqJ5cURBEEQrcfv8qzf0RqnWBgAlFwB9/Vnd+H1L+vGccsyoct15hi6OxgmC554VtWBGxWhEBBZq6HqdAO9lbciDdPbSboO2EoObLCPsivgBouYmaYNBh3jeb+bt5qwaSsDs2zPSatJFdYRtzWNQdcZLIvDMB33akdOQzbNUCzBr1Qy/3jTKYbOnIbuDg2m7URI5DIaVpyWwjObS7BsYP4sHb2dzkEwFJdvHAHXGSuvKaaqReKqCbhJaKrMzfwNKritvIExLQJukDlz5mD58uXYtm0btm7diq1bt6JYLAJojwBugiAIgiAIgiCSY8ceAy+8aFRMF44o8WX26Y0lDPToOHZZtmJZgiAIIoQ2kFCkjsP5tDhwTz++Aycsj/6c0DWGn35hIT70ld14frshp0XRzC3DsAiAhbPT+OBbBvDsCyVs3F7G5p0mVj9Xgq4xXPjybuQ0BgMcO/eaePTpAi58ebfv0fuSwdENIF8MCLiW68DNV0YrRElpqoDLbcAQEQrMm6e7enNaEXA73N07f1YKY5OWU8wsIjK4r1vHUUsyKJRslA2OY5Zm0dXhNDpZsGFZHEvnZ5BXBGnDBDKKgTqWA7eGXuhz4FrRy4rxe+7nkF+jGNEOcUjy92FaBFzOOZ544gmsXLkSDzzwAB555BFMTExMR9cEQRAEQRAEQcww+aLr+GFM5vwxBgz1O26c2QMp7B42YVm8IvOPIAiCiKYN9Ft/NmkDA4q7inDgZkMiEoLkMhq6lSxaPSSfp6Z/sImibP/fq3swPN6Jr/9kPzbvNGHZwJ/WFrFkXhqvPKUTxTLw7f8aBQD0dGk+cbJkcMwZ0GEIly5z9rGIUBgPOnOriJXBCAVRxExX8mHF63QKKJYBU4l7ELlGXHHAMha+T4QrNWy/MgZkMt5aZdNxGQvXdbVDIYZaK0JBzWIOW1bcaLDd3lhgK5z58Y62jFio6CTW6g3REgGXc47Vq1fjgQcewMqVK/HII49gfHzcNz+Mo48+GitWrMCKFStaMSyCIAiCIAiCIGYA8ff/YQvSmDer8ivIYfPTmDOg48n1xekeGkEQxAFHuz24LA247r/Eq4K5GK6Am4kh4AaXiypoBaCp8bJIJU/061eOdw+b0DRgZELJsTX9jtGywTHQ62U+ZDMMhZIXfxB04JpWdP+cA2s2lrBlp4nFc9NSnNWYKuC6Y3WLvxlqkbGQNoPTpJM1xCmrnqtp5eO/bHCgw8vZrfY0PtNEu5GLAAhm4FYuHLl6SBpCnGiH6SYRAVcItiLT9pFHHsHY2JhvfhiqYLtixQrMnTs3ieEQBEEQBEEQBEEQBEEclPgMr20gMAmtTHVo1kPcrNmyK2BmYipZ6ZQiUoY5cGuNy/3ZaCw7Y15xMMH2PSYYA9ZtLslpmbTjrhU8+EQe37vH0dQYHOGzUHJEXo1VFjez/JG4/nk2sGaDU2jsD3+Z8iIUdO94iXiJtB5dZEzdWSwQR8ECP33npOJiZowhpTuCc7AgXTWE2FwzA1cRbasWMWvwPPURITxXjDHB389EBNxZs2aRYEsQBEEQBEEQRChxBIa4FZ8JgiCI9kJ9dL2V1/BS2Y1QyISosSFkVAE3xIHbqOBcD6mA6rZll4HVzxV9ufBGwIG7YZuXYaC6Vk2Lo1C28cRz/qdVTCs6e1g9HqYBGK6yqWuetpiqcOB66/jyYlURl1W+jrMfpYBr+kX76kXMnJ92DQuuKmSHZeAG+9AC41bny01po79JEhFwR0dHwRirEG5JsCUIgiAIgiAIgiAIgkiOYK7pTBMcT916aMysWSH6pWMqWWqEQpgDt5mM24qmwnJfURndYNuQ7lpB2eCRLtqU4pQ1LeDnD0xg43Z/UdCwuABvXEouLPfybTXNmycduO5+LZVV+6o3fiETR9WD8yIUwuYxd3scJbhcji/gVmtXJa4DN4lfmaibzq38dUwsA5dzToItQRAEQRAEQRCRVHXnJFTxmSAIgpheaglrSSGLmMV04PojFOqXaWM9PVKjCFdQwD3msAye3VL2TSub3BehoJJJMdm2ZXGsfq5UsYxpRo9VLeblCLjcHRfAbSZfA0B3h7Nf84Xw/NiobSyWbWzeWcaL+01MFW3MH0phoEeX66k4Ll/uOXBF+1X2tTh2tYuYea9DRe2gYM8C7xXiFFeLQ5I3WBIRcH/84x+TYEsQBEEQBEEQRCgkyhIEQRy8cEU442ggA9f9WTMDVxQxi+vAVZbT9cr5Xr+ty1BIKcJxT6eGf7puNi792x2+ZQyTO4XIwtZPM9c1G72MERIXIAi6ow0h4KaYFDlFvESXK+AWyzZMiyOlM/8x4a7DmvmP1e79FnJZG8PjNgpFCxu3G1i2IBM6nrR7HGQGboybt2HF0cJQXbfVxN7I862OP1aiTplqxdiaJd5tixpcccUVJN4SBEEQBEEQBNEw7VjxmSAIoh1RRaJWCkZxsach08GyuBTl1GiEaqRrZODWIo6w7DlIK7ebMX8GbkeOoadTq4iAKJvcVzhMJa17kQWmzX3xBVlXI7Xs6Axc1Yhq2Z7TN60zKXiKeImUzpBz3c0795rYPWxidMJCvmBjIs9hWk4+RnB3iOPf3cFkP4LgfhE5u0LAFeL5yLiFvaOmL7t2YsrGHncMo5MWNm4vY3TCywe2bY6RCQuWzZEv2L6+8kUbw2OW/3cFjrA7WbDxzOYyvv/foygb3Pv7I2T/Nfv7leRvQyICLkEQBEEQBEEQRCQJ5gwSBEEQ7UNYAbN6Ra84i4v4BADIxhVwa2TgTof4nVKcv9k0A2NMOl0FhsFDi24BQDqlQXPbyBdsKch+48a5mN3vKMFRzlzAX8yLc6/QV0pncp4aL9HlirDb9xjYuL2MXftNjEzY2Ddqyn40Ldy13NulyX6CiMVFpIQQcKU4bXFs2FrGtj2mnP/0xhKe317G9j0GXtxn4vltZfx5bRFTRUd53rzTwLrNJWzcbuCZzf5oickpG8++UMK+Mf/O2e++f+K5Ep7cUMbPH5iI2HPVmYnCqyTgEgRBEARBEAQx47RjxWeCIIh2xGd4naExFEo28gUbk4XKZ9VbIWqVFQFXddZWIxMzAzfSYctrzK8xDwBSKU9209wxpAJKnGFEi7DplCdyjk86+7ozx3DC8qwUQy2LR54Ihul3oBqmaJdJMZgp41k0J42h/hQGe3UM9uro7dKQyzJ0djg/+7t1mW+rtgsAmsiN5Y44HhTIGfME7XKE41gcZ8N01tcYw7L5GXR3aNA0BtMGim4BtN3DzsbsGTHldi6cncbcwZR0aYtiaWJcJYODKV0/8tSUb746Vm96e/xhklgRsyB/+tOfcO+99+LPf/4zNmzYgJGREUxOTqK7uxsDAwM48sgjceaZZ+Kiiy7C6aef3qphEARBEARBEAQxw8TNNyQIgiBqo8pJM6Et7Rk28fz2csV0OZQWZOB+86cjAFxBM2ZBMn+EQqBPZce18rNJdf6KFIdgnEPZtCNzbFNpDYw5wu143vnZ5zpdhRhazYFreIkDsG0vJzadDl++q0PDUUu8/NrJgo2RcQv9PRqyaYa5gzp0nfkOsdiVYh9zeFm5AsacaSJCoVi25XQV4egWbabTDKccnUM2y7BmQxEAKtzKpsmRSTNoGsPS+c6GbdrhFFUL1jIzAsLxM5srz+NYRMQuBH8f266Imcrvfvc7/O3f/i2eeuopOU39xRgZGcHIyAg2b96MX//61/jsZz+LU089FV/+8pfx6le/OunhEARBEARBEARxAJBUxWeCIAiitUyV3MffNSZFxIEeHRt3GACSF5X3jZp4YJXjlFQFyVqoAm4qIJomNsYq+e1BcVI4XYNuYMPyR1EM9DCMTLj7GJ5gPZZ3lNo+1wGrSwE3OgNXFYYNxcWcSbFY8Uaa3IhqSzF3WeedF6URWIo52buAkoEbaEmsI3J0RfcM3n7wohwYbJvDtDkyYL5sZFn4LFBgz7KdzFsG7++N/WOWjH8Iri/GFFfkb+UNlUQjFG644Qa89rWvxVNPPSVF2yirsTr/iSeewGte8xrceOONSQ6HIAiCIAiCIIh2ghy4BEEQzcMjXk8z82al8NJjO/DSYztw+ELV0tmYBTdKJCuVG9vITI0MXEFkgkIC+1ZtQwicRy/NVCwjnLEvP6kDl7+mV86zuSeiTrgO3P5u4cAVgmb0QFXHabGBHOFIQlYXAisPSXQQi4uibl4RM/9ydkD8VXV3XXOmmzK715luuqK+Gpkh9lmwwJ4Qf9XzYZO48VC5SZHMROHVxBy4f/M3f4Ovf/3rALy755qm4SUveQlOOukkzJ49G11dXcjn89i7dy/WrFmDJ554ApZlgTEGzjn++Z//GbZt42tf+1pSwyIIgiAIgiAIYoap5wtOu2TNEQRBtCszfZUU12lVe2OMtWxgagGzesgoilfQ9eprsQkts55iVkJUfMtremCYHEcsSuOO/xlHscRhOBoi5s3SfW5hzjk0V2zMOwkC6Ot2rLdeBm70Z6car1AseXbUbEaLFV0Ry3nKOQAmx8nhKLg8ZN2UexykGziwjNiMYD4vY5Dtm0rxNQOeKKsK9nIsym4xTGcC48wXrbBpRxmnHJX1nRS+iIjwrZ52EhFwf/e73+HrX/+6FGI7Ozvx8Y9/HNdeey3mzJkTud6ePXvw3e9+F1/84heRz+fBOcc3vvENXHzxxTjvvPOSGBpBEARBEARBEARBEMRByUyKSxXinlQz62unlvipFjCbO6hXWdKPz4EbXC3GGD2Bs4ECaCHrCVGxK6fhgrO7pOtWFah1nWFiyhNauV3ZR6UDN3oMPgeu4mTONOnADVtbU44/Dy7DAMa9WAtRxEwLbJyIThCREup8IcJbARetyMRNKwqnGIsaoSCFXx0+AXfjdqPu36OZKLyaSITC3/3d38nXy5cvx+rVq3HTTTdVFW8BYM6cOfjUpz6F1atXY/ny5VIA/uQnP5nEsAiCIAiCIAiCaAN4uNHGRztWfCYIgmhHAk+Ftw0N6rc1KSsi5Nf+Zm7s9RbM9mId5s2K9i9OV7qPzHN1f4bFGKR0xx07f8hRnJctTCNYs22wz5knBEvL5pHngVrwSwjFGnNEzDjnThwHrmjGF6HAEXoiCNFZiMlRGbjipyYduK7DV4lQEPNEzq8aocA0EaHgtW0EohcEY3kbO/ea0a7s0HzjeGdNWxUx27ZtGx5//HEwxpDNZnHPPffgiCOOqKuN5cuX4+6778Zpp52GUqmExx9/HNu2bcPixYubHR5BEARBEARBEERijOct7B2Jtjt15jTYnKNYqv2tLZ1iWDgnVfFoL0EQxIEJD/ysb60oUUxk4C5flK4qxAY5akkGt980D+N5Gyccng3tMw5JXaGFwCkF3Iy/5Y4Mw7kv7UJ/t4YjFqXxyFNT6OvS8Px2fzuL56Sxd9SUkQhqNEIQtehbqeQJuBm9ua3y3XR1f3oRCjJVwVseTqSCKHonncERGbgyQiGQgWtZ/iJmJYNjfNLCgqGUr2id58D1HL279jo7Q1cU3Jcem8Pj64p4emMJF5zdHW/jFYLnUStvqDQt4P7hD3+Qry+99FIcd9xxDbVz3HHH4U1vehN+8pOfyHavuOKKZodHEARBEARBEESbEPcx1HoqPk83W3YZmJyK/rJcL10dGmb1xX8kmCAIwufAncn+A9dp5xof7QZtFOHAVR2WcVm2IBM6XR1j1OdNnKdBqmXgMreNjgxQKAMvO6nD7c9ZS9cYchkm3aiL5qYwq09HX7eOXftN9HU5QqMeGOCC2To2bjOkkFntM0ktcGa6i2kaoKdYQxm4obtEuGWZuwL3TfaRco9hKaqImRRcnffqDU5dYzAtfxGz/aPeDdVctlLAFeMdnbClaJxSPnJfeUoHHl9XxJZdhu94N5qBWyHo1rFuLZoWcHfv3i1fn3322U21dfbZZ0sB98UXX2yqLYIgCIIgCIIgiKQRXyrnDqYq3FPb95jyy2dKZ1gwO/rr1p4RC8WSLZcnCII4UKila9Yr4IYVRVMpuwJnWORAEsR9HL5RLn5lNxbOTuPClzsOTyEuahp8Au7iuV7kg6Y85s+U17rmfP5s32PKKICoDFzOuU/AVdtONevADZmmacwRrSGOqRJp4M4Q/Ypc46gIBZtXCrzBImaa5hUj6+7Q0NvlKbNin4n5hrIfBpSbpkP9Kd946mU6I5+aFnCLxaJ83dnZ2VRbHR0d8nWpVGqqLYIgCIIgCIIg2oNaX86D89pZ0hTf1Yb6dVkJXLBrnykF3pTOsGhOGlFM5G0US+2VX0kQxAHIDF5DkpY9o3RU6cBNUMCt59pbVd8VTs8q87o6NJzzkk4pXgoBN6UzX0br4rkpLydX2buqmDu7X5eCs6aLnNcqGbgh5tyU7rYuYwqiNzCY8BNnWc65F6MQQOT2lk3Rnn++2B8yA1fJDdY1Bg5bCtZqlm3wSRbhWpaCsO1sbm+X7jtvO7OiEByPdGXXc660dYSCWqhs/fr1TbW1YcMG+Xr27NlNtUUQBEEQBEEQBNG2VPvSTxAEUQXV9TeT15B6jKucc5QMjlxGq71wAOGOTLfAgdus+7bW+mExBYN9OhaU0hjq16X7FgCWzPNu+qnLq/ts6fy0PP4iXsCKeJKDc38RM0Fft9ZwTJE890IETqa5CQq8UsgU8RpCxC6Vw2MfxE1QW4l7EIjXtl3pLA7uA+HAFdPFT8b8oraIXTBN1AWL+RmepEO3/t+cAEcffbR8feedd6JcLjfUTrlcxp133infH3PMMc0OjSAIgiAIgiCINkB+fTkILLjVMgN909o0w5cgCKJVVBO1tuwy8MSzRby4v06lDF5eaisiFGK1GGOhqAzcsHkpneGw+Wl0d2i48nW9WDI3hY+8dcC3fernyaxeDT2dDJ054HVndUmXqnCgWlb0x2aYuNvvPj0SEWXs34agAzdsIe4VRwuZ7EMIuKK4WnAZzjk450oRMyb71TVv0E9t8D+1bwf04GAGrioIi+gijXmu7qAgrArzocd2Bj7jmxZwzzrrLAwNDQEAtm3bho985CMNtfORj3wEW7duBQAMDQ3hrLPOanZoBEEQBEEQBEEcIBxqeuehtr0EQSSHqidNZwZnbELMlbv2OYrdll1GxbyoomgC4cBNNkKh9n5LctdGOXXfcWEf7rh5gYzckfEIyuKdHRquuqgPP/38Iqw4rcsr8OWmBoSYbAE450lYPm53ZxMOXPenL2JAZPqqoqkvjoDJnzJCwfAXK1OxuXd81P3AGMNAj7PRhsF9/VQIuJqIl3DeW1K0ZbDdAeu6d06JAmd1U9OC21izYTQt4DLG8MEPflDu3Ntuuw1vectbsGPHjljr79ixA29+85tx2223+dprdYg0QRAEQRAEQRDTg3y8Muaf+G0oR3hUyQxUp8X9NtOO2gtBEO3NTF83Qp6i973n1a7igVn7xyw8vbGEfCH8kXrAE/taUsSsySZrrh7zWAUXC36eqMdcvPYcuNEZuHaIgNvbpQEsPA4hCPMOaug41fEwxpwIhYjlgMoiZnbIwDmvjFAQ4wgWBxVrB9sRywu3rW077TIG2JaIoPDOKdOKin2oDecca54vYu2mUktvqDSdgQsAH/vYx/CjH/0Izz//PADg5z//Of7nf/4Hr3/963HBBRfgxBNPxOzZs9HZ2YmpqSns3bsXa9aswf3334/77rsPpZK3kUcddRQ+9rGPJTEsgiAIgiAIgiCItoT8KgRBJMFMiLmRXcbIBQ0Kbc+9UIJpcWzbbeKYw7Kh67TEgRtnmdr6ZlWEmFlPG2I5XxxBIFdWuElFBq4dkYELDlghJ0hPp+YrANbI9rGQd7rmnQC+gmDKkl6EgrPAQI+OXEZDT5eGfaOWE59gQ4lQCO8/KBLXilCwpCDMPAezxuQ5ZUfkBUehxmMUyxyTU7bcnlaRiICby+Xwm9/8Bq961auwdetWMMZQLBZx99134+677666LudcqtpLly7Fb37zG2Sz4b+0BEEQBEEQBEEcgMT4EtxoxefpptqXcd/3bRJoCYJoFe1yjUz4Ohd13ZQCbirJCIXqfcZF5v42+cEVdMMytXgX87cvHbiuVmjbiDwngtmu2TTQkWVI68xXzCsKuX9i7CfGXLew+BeyT9Ipz4G7Y4+BiSkbpxyVgaZp2D9WcNy33HMUa4EDFJn1G5ghC57JiAW1iJnnwFVvCojzTG6Pbzuid4DPHR0YYZK/qk1HKAiWLFmCVatW4fLLL684SCKEWP0XnH/FFVfg8ccfx6JFi5IaEkEQBEEQBEEQRKLEeeQ0DsLE0s5iNUEQRBjqI/PTgShilsnMTBGzRjeTAZ4btc42gn1yrsYFOD9FzqsVEpPgdi3nHbM0jfe8oQ/vuLAPjMV3M0dtu+/Yy/FAuoWjlMuUayMtGRwvvGhgeNzC6IQ/75bbSqGxgGrpOV+5b58EhWoh/Aq3rSVdt1AyhJnvpkC5Dgeu6jaPMkAnTWICLgDMmjULP/nJT7Bq1Spcc801mD9/fuRdCM45FixYgGuvvRarVq3Cj3/8Y8yaNSvJ4RAEQRAEQRAEcYBQq+LzAQELfUkQBJEo6iWyHa+XSQ+pFQ7cpAeZ1HHwDK9KBi5DIC7AeZcSAi6v4kx1xcrBXh1nHN+Bnk5HBsykPTmwmkBda49zzuVCGlNEayhjUhpJua5h1e0qYh6EWGtxRaQWjuSIeA4ZkRAoQqY6o51YBkfw1ZgX46FpjggunMylcjAEN7zPwCaFHvtW3NxIJEIhyKmnnorvfOc7AICdO3diw4YNGBkZQT6fR3d3NwYGBnDEEUdgwYIFreieIAiCIAiCIIg2QsYOHASqZkIGXK+9hNohCOLQoR1FWwDQ4oTghlDruiocuNkEHbhJfS5VXb+Om3qVBbTUdZkvJiEYoWBFWEA5B0xX2BTZs6bryE3Fjmp1nxaJqGKmvpUOXPhjB9RtSacclbZsVo7Zcc1yXxEzxsJ/Bgm2pzp37UB7tuLGBYC07jiVjZAx1cIZqxJvYVfOT4qWCLgqCxYsIKGWIAiCIAiCIIiaMMZaWsG51bDINwRBEK1hJq+YFZe5QOGouNRavCUO3BjUc8MuahvqPT5SqKzSjtALxf7gdnQGr3CbCsHWMB2FMZtmsbavYjwh0Q4C4Za1eeU8QVq6XSvnydxamysu2WCHat/eGzMQI6GuxrnnRGZaZRG4dIqhWOaVGbgxz2d/Bq67LpL/3Uw0QoEgCIIgCIIgCCLIAazJRtIuhW8Igjj0mOkIBXHdinsdDDpEoxyjUUqiYTo/4+a2xmG691u9nxmqg5SxQAauK0aKPNmoYmScA6bMe3XEXEs6cOPZg+WsgHAqt4er7lgv+EHdv2rzKUWEtwPjZooALJysQf1WRe3DNINtMRljYNuKaMu84m26uw/EvigZ8U8K9XjaIQJug2b0qrTcgUsQBEEQBEEQBBGHuBWfZ5JqjiXfI6/tOXyCIIiWExStgkW2LMt7fF1dISo3VBYxS1LAheiz9jL1CpxyXh0iXvCzRd0XjDFYFsfYpAXL4pgsOAqkEB6FIOkIq/7Bin2v6wyG4QQhMMaQTrP6xMUYzmAhOssbk2EOXEWFNCyOrKLQ+oqYyXMi0F9QLHUnhMUfCOHb5twRhSEiFPyZu8LJXOHADdvIMHjl61b8DdBSAXfHjh14/PHHsX79eoyMjGByclJm4B511FE4/fTTKV6BIAiCIAiCIA5yDqYM3KSI/cWQIAgiSIhgNK3dR/YZLtwFHbeGyesSY8stEHCTotbnWqO56T7nKQNMi+PZF0rozHrKdzrlF3DD+xdxAY5gCnjZubFgIgNXbTOY0euOWWNuFFLAgatm4CrOX9PkyKa9Cm2aLMrGpYCrByy4vv2t9GHZHJbNfcvrmuO+tW3AsrxIBot78wHPyVxPBq4QyoVALIcUPN7tnIFbLpfx3e9+F//2b/+GJ598subyJ598Mq655hpcc801yGQySQ+HIAiCIAiCIIgDBdeC286aZjVHFot4HQaJ2QRBNEoz10jLch5/r8gWTYCwx+eBykflQyMUqlwUhQNX1/wim6552xHmPq1KDdev02btZQLN+WhIxJMWXM/K2dvlFDLrzGrQdSbFSCGGiniFoLAKePteYwy25SyjMc+d6nYRPZwI563cNO7MY4z5c2cRsU80Bl1zRGeRW2sF4hK47blk1XgG0W5UH+UyR0dOdS47P02h2HIOjQG24koGPCE8MgM3fNO9cdR3OjdMohm4Dz30EE488UR86EMfwpNPPgnOuS/TKfiac44nn3wS119/PU466SQ8/PDDSQ6HIAiCIAiCIAiirWlnsZogiPannmvIi/tN/GltAY+tK6JYrmLbjEmFSMUCP12Cgm0wrzS4epCSO9aNOww89kxB/nt8XRGlso1Cycafnyli64tG7LGrxaaaIm4DtZy6gfeaIpwuGEpj2YI0jj88i8Fezz6bVhzJQZEccHQ3ses1LVqQrlrELGSAFSFHTI1+QIUDN9ieKKgmhNWgkGwr64uYg7BTy9cHA1avL0rh11nXWVo935gG6cBNaX4B14g4L6vB4c/AjYp3ToLEBNyf/vSneO1rX4vnn3++QrjVNA0DAwNYtGgRBgYGoGmV3a5fvx7nn38+fvaznyU1JIIgCIIgCIIg2oGYLqa4FZ9nkqqOJV8GLllsCYJoDVFGuVqM5x2Vz7I4JqcaF3DDelQdsMEhBR/xDwq6tbZAOCNTgUf/TYsjX+AYHnOyYbfvMTy3ZVwSulSHuzDjN17x2aJYX31FwPTw16bFI46L81PTnGgCwHXgArHU/9gREVrgM7yKiJuSgqmzgIggEIKrzVXncLA/f1wB556TFvAXIhPrmhaX5yeD5+71IhRYxbph2xhFNQdukn/PJBKh8OSTT+Id73gHDMOQJ+hxxx2Hd7/73Xjta1+LY445Bul0Wi5vGAaeffZZ/PrXv8Ydd9yBtWvXgjGGcrmMt7/97Tj66KNx0kknJTE0giAIgiAIgiCItqWdxWqCIA4uVHditdzUmoTkuopH6cOwAqJqVN9RYqHMwE0xnH1iBwBg7aYyxvMWbM59At7q54pVRcdcRsOxyzJ1Cd+xHKoRiHiDeu/pqUXeVA+kuq3COQpU7mPA7w7VqkQmVBOagzECzI06QnA6nJgGGaMR0oZ47RRf4zJCISjWbt9tSFFXjM0TtivHOLtfl27asLFv2eU4s70ia/C9F1EURoSAG4Yn0HLY01R0NREH7gc+8AEp3mqahn/+53/GmjVrcOONN+LEE0/0ibcAkE6nceKJJ+LGG2/EmjVr8PWvfx26roMxBsMw8IEPfCCJYREEQRAEQRAE0QbEfVS1FUU/EqdKQRoy3RIEMR0Etce4YqS6WFMCbhQRrsMKB25AbKw1fOGMTKecIlmMMSlwBh/XN0yOshH9bzxvYXLKbri4WBRN34yLGI+TL+u9TykCrvratBD62cmVDFzxmokM3DqGF/n5JoVW75/tPpUf1b5wDgsHrth3nR2auy2uYJ9mFa5rf9fKTgsZX1fOa48DyGac95Ys5sbcfpzlgw7cuBm46o2RenKT66VpB+5zzz2HRx99VA7u9ttvxzvf+c7Y6zPGcP3116O/vx9XXXUVAOCPf/wj1q9fj6OOOqrZ4REEQRAEQRAE0QBTRRvb95i+Lyaz+nXM7k+8DvIBSbNfzuJ+MSQIgkgKNbkgzLEZF58bU0G8HZu08OwWT7UtlQMO3Dq6Ni0us0mzmcoCVZbtPYLf06lh2cJMZFubd5QxMWWjbHJkUrWv4VwRJ6OI9VnAeahQPDllY8dew4kM4OHiIeAvDpfS/MtomuNgFW7WQLdePIHuHf96Pr7CFlUzcFV3sdqu34Hrb0UIz0KoFeNaMJTCQLcmz4+ODKsotudzffv6qJy4fFEa84dSjhPZ5mAoOf25p6aMUBDjMeOfmOoNZ/X3Sp4zIWNslqb/+vrTn/4kX5911ll1ibcq73jHO3Drrbfij3/8IwDg0UcfJQGXIAiCIAiCIGaIXftM7Bv1V/SYLNgNCbg8ptXpQBA1q41N3bxaX5DJrUsQRFJwHu+aot6QCyt61dQYlNdjkzZCSh9JosTjsG3IF7yB5hQBVwh7qgO3M6ehuyO642xGcwRcwxNwm70WN7P6jr0G9o/5lVchdmpKy6oAqrpudY0hpQFl29mnVTNwGZPFwbys4jghuDXmc88lLH7KZiOaF9EPUsBVzsuOXMTxC3F31zDggjGGrg4m+5Civ7uecOCKYnDlgIDLRF5ElYxbwHM5h403SZqOUHjxxRfl6ze84Q1NtaWur7ZLEARBEARBEMT0Ir4YzerTsXiu83xh0l/4D0TqyU2M12CyzREEcfDT6GXI58C1E7j4VGSp+qcvmpvG8kUZLF+UwZFLMlg4u/7PknxRxCf481+FMdO2uZehWkPhyqQZpoo29o2aMC2OkQm7atGzWPtZjCNkYTWmIMypG+aaVdcNvuYAdN0/XWxz6HZw7zhrWrSjuLrD2G1KiQZQbxio28eUWIsde0xs22NgeNz27RsnA1eM2d92NeSpFbD5VsSJRKyvTg8WMRMZuOU6MnDVdtXtC0ZGJfk3Q9MO3L6+Pvl67ty5TbWlrq+2SxAEQRAEQRDE9CK+cnR3ahjs0bFtt9F0m3ENKQdqYS/1iyU5bAmCmC7iXjKTysANvUaHuIDnDaaQSXsTd5lmeN9VHtIQDtxcRgt9ysHmajRA9QtvOgVsfdHA6IQN2wZ27zcwWbDw0mM7qq5XDU9Irr6cI+T5x1dNRFdjCdQ1VQeupnkipGWFHxevYJfrwJVtxTtrQoXTkPYZcxzBzjSOnfsMZNMMe4YNdOb860oHrut4redc9DlwlTEGhebKFb2XUsAVDtxUhAO3ctVQ1GMfVSguCZoWcJcuXSpf7969u6m21PXVdgmCIAiCIAiCmEGajDY4UAXZIDzgIkqmzWTaIQji0KHishHzOqIKTU1l4IYIrr48UjiCXzqgOAm36FTRxrAbHTA5ZcOwwjNiAU/AzQaibTVfETPXZVrjuiyLZ1nA/nGnXdOssoJLteu9GEekFlvlIl9N9I3MwFUcuBpj0HTnMX8j5Hhy+PNeo5zKVR24IdPUGAaxEIMjhC6Zm8aLwybmDKRQLIcLtEKEFmOO41L1O5KZUyQtPN2gJsEMXFHEzKgjA1fAKzJw3TE2MK5aNB2hsGLFCumW/eUvf9lUW2L9vr4+vPrVr252aARBEARBEARBNEgrKylHcSBk4ApqZtxOzzAIgjgEaTxCwVuxGQduFOp1L6VXfn7oioD77AslPPtCCdv3GCiVncGwEIVKdeCqaG7bVh0RCmr2KotRoCzObhbjCDsmvqf9Q+ZbctyVT2/49523surAZQzQmbdNYX2Iw6xprtDIxXGK+SlVI9w96Dgd6NUxdzCFRXPSMo/Yf/OTeREKrnjeaJqH76ZBjc1Rl5UZuO5+F3nII+OBXwrp6g2Px5Bth42/HTNwc7kcrr32WnDO8eijj+KHP/xhQ+388Ic/xB//+EcwxvC+970P2Wy22aERBEEQBEEQBJEANR9NrLOdA5Uk3bKyiExyTRIEcYjSSISC3WQGrmXxSgepkoGrRicI1AxbAOjp1NCRdf51d2ro79Yr1hEZuNk087swFQeuHRARo8i4/VsWT+zzSEY51NifYf0JF7QQEGuux/37kNscmu5vS4VzZxnAcSc38nh/nM9/tYhYmHM42F3WLUZXKNk1247qz1kRMhMi7DO1bHAUyzaKZdtfwM/N3hVC8tFLHf3xyfUlbK0zLspxOVfmOrTi752mBVwA+NznPoezzjoLnHNcffXV+Jd/+Ze6gnq//e1v4+qrrwYAnH322fjc5z6XxLAIgiAIgiAIgmgS9YtZq9VGdgBZcMO+m/m+sJFDlyCIVtHgNVJ13TbjwB2dsLBhWxlPbihhYsoT4dTrWqiAq/nnn3hEDkvnp7F0fhqL56SlQ1ZFOnCz/nmaIizaUqSskYHrjsm2w92qFcR4HF6rInDW0sXEMVCjJsI+Bqs9CaPLImbh84W2qGtMKWLmb6/a9vmy3UOW5epMZfmwrFqxWJ8r1I+7506conZqjESwXQZWMa4dew08vq6AJ54t4olni3j6+ZKcZ8oiZs5ai+emcPSSNDiA/3ssX7VPOU/psFqEQpI3fhMRcNPpNH7961/j7W9/OwzDwIc//GGcdNJJ+NrXvoa1a9fCsvxnkmVZWLt2Lb72ta/hpJNOwvXXXw/TNPHOd74Tv/71r6HrlXddCIIgCIIgCII4MGllJtx00gpdOckK1QRBHJrEvYwkVcRssmDJBoXACjguWcC51oe5SnUlKiBM4A3Di1AICLiaiFDwBLRaEQqaxqTAaLqO1WY/l1i1DNygcqngiMjOAmHCNeDEEQh3strcgtlpdHVo6O3RZIRC2PHkALgSLxHpwK1zJ3CuOJi5014wf5iH9ePS2+Vsz/ikK+DGOYFDbpCGfn66k0TbQtSfLNpy+zl3pqnny6K5ThDu/rEIJTwKHihiJobYAgtu00XMAODwww+XrzVNg23bWLt2LT760Y8CAHRdR39/Pzo6OlAoFDA6OipFXbHDdV3HQw89hBNOOKFmf4wxbNy4MYmhEwRBEARBEAQRgufUmb7H/ZXvg+1JlS/jQPijo1Ec6HESBEHMHMFrZBz9yyn6pGTgNlHELKzwF+eQQiMQXhBKFcyqxQaoqBm46nVTU0Q8sVm1iphx7ozBsrwc1KrLuz+rFjFjnqu3HlTBVRVw1Sde5g6mkM1oFc7iw+anAaSxZkNRceCK4+tfVjpwGXNEY9QXH1GxaIhQC3h/JzBVXA1zJzNFwM3bbsxD/PFUnPtuqG/QuVx2z78jFqexfmtZzmPw9onIE2aMoavDzcGd8AYTdz+pv1e8hX/BJCLgbtmyxW+rlpZpZ+CmaWLfvn2h6zJ5stt44YUXXCU/ei/Vmk8QBEEQBEEQRHsRfMTyYOAg2hSCIA4wGnHuB/XFZhy4hqJ+qkPRNIbZA47MNH+oUm5SHbhRrtMgUyIDN+DAldmzigNSq6XgumOwLC4FbMaa05lUUdq2uX8M6suK/c/lmIPZwFEEjztjgKb7HcX+FbzjI4uYuevVkfgT1qwSEcB9SmdFPm0IIkJh97CFr/xwGB9752DNPtVohji5vGXDzRcOcXrbltj33jRRcG1kvD4HLg84cIPbnORDNokIuEDjj/6ErUePEREEQRAEQRDEzNJInl/TVHsssg2oNSrfF2JSeAmCaCOCOaOc80rBMSZq3mrwujjUn8IZx+VCRUk1LTOuaDlZjIpQcPvn3iP4ta67NufSsRrMA05VS/Ks0q7aZ/Cjq9pnhkga1TV/NrD8HFTeVouHl9tjhc9XM3Bt7gmgsT9llb5D928glsE31oj91tflbfBY3saOPSGW7hio+1t1/nLOpQNcCLjcFZSZxqT7Oq2cg10dzgkwMuGd3NUzcL11Q2qYteRvgEQE3O9///tJNEMQBEEQBEEQRBvS7BeRRipftztNb0sMBxFBEESQxs1zldMsu3ZubFj/Mh4hLEoG0eKs6sDVY/YrIhSyGeYTzbzoAiXnNUaEghiD6li1bQAhAm6cXa32aXN/M+pweGBsllJIq+oxqLFNYl0jxIFrKcqipgGm2E/Vm6wKQ8AFC7eIWMS5AASEVlS6qa064yd8+5VX7iLD1YMZYxVObwal6J3qRSIakAABAABJREFUwO10IxTG7boc2RwRhdXaVcC96qqrkmjmgMKyLKxbtw6PP/44Vq1ahccffxxPPfUUCoUCAGef3HHHHXW1+fzzz+O2227Dfffdh23btsGyLCxcuBCvec1r8N73vhennHJK7LZKpRL+7d/+DT/72c+wbt06DA8PY/bs2Tj55JPxV3/1V3jb294Grd4rNUEQBEEQBHHIEPUlpJWRZtXcLu1AzXGpj8vW2EcHkZZNEMQME5Z9GkS4VIXj1rY5tr5oyBxQXQfmzUohpTPsHTWRTTP0dlWqmoaZzBMacR24UUXMvAgFLh2VNSUO7i1jSpHPzQOOWVQtCGOOsKxm8ar9RSEc0Y4DtzKCQN2fUR8njMErYmZVHhdVpNaYJ1wGP5+qfVyxavZfZXKwiUCyQlWK5drnThXzLwB3+9wJIv82nQqIve5P4b72MnCBHjdCoWRwFEocnTkvZyJsdAxOzvOufYbPxS4E5VZ8xicWoXCocfnll+PnP/95Yu195zvfwUc+8hEpAAvWr1+P9evX47bbbsPf//3f4+///u9rtvXss8/isssuwzPPPOObvmPHDuzYsQP33nsvbrvtNtx1112YO3duYttAEARBEARBHJwEHxGtW7+NNmsdsCQlYrerWE0QRHsSKp7GWC8oGBbLHLuH/Y+uaxpDX5eGDW7Rp5ed1FnRjlqczHFjBnqPeWnsysVbcCLvCrhZzde0cPDatpLzWuO6zKE4VpXtiMoD5jE/u5wcXSGSK2KeukxgHdGnrrOazuHQBl1EBq5hVm6EGpuhacxXnNQ//jo+z1hgHEKwDIlQCHXlutPOObUDD6529K9SHAGXVb7mIeox517+bToV7gwOc+Bm0gyZNFA2nBzczlxtw+PeEQu67hSb8waAlim4JOA2iGX5g40HBwcxa9YsbNiwoe62fvjDH+J973sfAEDTNLz1rW/Feeedh1Qqhd///vf493//d5RKJdx8883IZrP4+Mc/HtnWrl278LrXvQ5bt24FAJx00km46qqrsGDBAmzatAm33347Nm3ahEceeQQXXXQRHnzwQXR1ddU9ZoIgCIIgCOIgZwZE13aPWKglttYz/HbfVoIgDhzi3AhSC1gdsTiN/WOepjGet5Ev2DBNjpJRvbH9EUWe4t6LOvGIHApFWxaykkQIbbv2OyLzYI8eeMrBXYarztLqfasRCn4Bt7k7aRoDbHhRDsExir5VRBE1XfO7kYPiZDUtUNOYLwM3eBBUYVrTGbiSgRv3I0g6ggPbUyjZMEwus26DAq46lrDz84Kzu7Fzn4kN2wyUDI6ywUMLjgXx3TDg/oimUplj36gpR622JzJwwZSbGcKB6y7T16Vh76iNkQkbC+dUL5bGGFAocTDGMdSvY9+o//eiFU8UkYDbIGeccQaOPfZYnHbaaTjttNOwbNky3HHHHXj3u99dVzt79+7FddddB8ARb3/xi1/gkksukfPf+c534t3vfjfOO+88TE1N4aabbsKll16Ko48+OrS9G264QYq3b33rW/Ef//EfSKW8w/yhD30IF198MR588EGsWrUK//RP/4TPfvaz9W4+QRAEQRAEcQgR9gjioUyrIiQIgiBq4XN1MhY7usBzHTrRCGo8wpZdBvIFGzYPZLoGipxNFW1s320o/XsDipt13tOpoaczXpzjvlELhumInH3d/nWE25YrIl6tCAXOAc3dbNMGUprz+RblwBXUuuZrGgDLX8yqsg3/e58DN2a6ZdihFofHDMnANZUN07TAMWriY6xkcKzfWsbohIU5gynXgetvUAqmAcRSNvcEVsPkWLe5hJOPytXsOzRv1m1vyy4DpsnR5cYhZFIs9NiJ4yQK14lF+rp1V8ANv0mhYtlcup77unVMTnEUy7ZnwG3BnwkUgtogn/zkJ/GFL3wBb37zm7Fs2bKG2/nKV76C8fFxAMB1113nE28FZ511lhRZTdPEZz7zmdC2nnnmGdx5550AgPnz5+O73/2uT7wFgO7ubvzoRz9CLuf8Ytxyyy0YHR1tePwEQRAEQRDEwYkvA7fJLyJeW/FyYQ/UWIGwRzwJgiASJVAQKjgtcjUZM1A5T0xzMs696UFhs1DyOkq74lsrL9c79jru27mDeoXIqckIBS4dlTWLmMFz4Eq3LEvCgeu0WdmM6gDlKBRt+a9YtuWYNd9nR0g+bZUsVl0K0rxivqnokGpGrIh8COuvYguYfwUGIF+MVrxFe8KZG4VteY7bksFlbm09qDcvnGJ2zvv+Hh2DvTrmzfLrYcJFLB6o1wMnzECPc1KJaJFqv19CakunNZ94HtyvSf5+kIA7wwjBFQD+5m/+JnK59773vTLq4L//+78rsnJFW+IEvvbaa9Hd3R3a1sKFC3H55ZcDAKampnDPPfc0PH6CIAiCIAjiEOMAFVeTgCtfYJvlQBerCYJoA+q4GKniXRAtJE8WqBQ2RexAd4eGzmy4nJTkvSsh4AohTm3bF6Egs11rZOByjpTYVnU7axsuqyLHUkUI3rDNwOr1Rflv1z5n23TdK6YV2na1fuEVpTNDBFBx/IQGHJWBWy9O4TbntYj9lREKENN5uIgpHMO245AFAKNGbEdwzLWKrh23LItjDstKJ656XjiOay++QmWue55t221WrKcyMWVj24sm+nt0dGSYcwNhGj7UScCdQZ555hm88MILAIBjjz22qpO3p6cHr3zlKwEA+XweDz74YMUy9913n3x94YUXVu1bna+uRxAEQRAEQRCAvzhIsxEKsauTV3EZHQgEvyRWX7ilQyEI4iDFH6FQOS1yPVnErPLi48+T9aYHhU3hNEzpISJaCy7cO/c6cQ3zhyrTP8VmWIr7MVaEghRwPXEzyoEb96adpoqZgf4EU0VXNNQZ0innXzajYbBXR1eH81MthqWuG9k/gxSkTSssZ9ddTBXoUednlRhPxLLBfRRXHLZUB65ZY+HAOELfs+onoDo3mIErEOfZ9j2Gb3qw5XzBaYAxJ9ojLNahFVAG7gzyl7/8Rb4+/fTTay5/+umn43//93/luhdccIGcxznH2rVrAQC6ruPUU0+t2VbYOAiCIAiCIAiCCMf7olh72bhfYg9UsZogiJlHY4CFeKY/q4r7Us2TtZXGohy4ms4AgwOssu8k42MqHLhK21qIEF0rQgEIiVAAsPVFE9te9FTE+UMpLJ2fjj1OFuLq9WZ6+xYAjl+WRXdIBvAxh2Vr9hPc1xpTHLghLmJx/ERvtptL29QhCtzUte3ozFevoBmvmGbZSgauUT2EeGTCwtPPl8DBMdCruwK0CF92260SERKcJM5x4cAVY5oXcOBGITZnoDeFTMrZr8FNrVYArVHIgTuDPPfcc/J1nBxddRl1XQDYtm0bpqamAACLFi1COl39YrN48WLobljKhg0b4rsiCIIgCIIgiEMCXwVsVjm9EWp9sW/FF54kiVukJw5UCI0giEZo9PrIq+TECleqZXNf+3ZAVzNcXSv46DnQmptR1SIUwrejVoSCk92bSjEptuayGjjnsJV/IgM17k07LSDShmGrH6oxUPuu9nEhXKSmWZmBKzKMmea04Y/RiDceT4QNX7biczHkczw4Lps7JkQRoVA2eNV9t25zCWWTY+uLRnhhNJERofQfNlCR8SzO62B0xbxBRyPbN2qhoOT8BvWyoAAcFp3Rik94cuDOIGrxsKGhoZrLz5o1K3TdRtpKp9Po7e3FyMgIDMNAPp+PzMwFgFKphFKpJN+LwmsEQRAEQRDEwU8zYiMZBapDu4cgiHrwPVpfR4RC1QxcRXRTC5cFi5jJCAV3BQYm+076WsY5x04p4OoYm/QPJrgdWozPKZs727p8YQZ7RkzM6tPx0mM7pBDHATz5XAmmxWWRsTgEYxl845Tb43+fFDJCIUREFPESGvN/Fmus8c914X0V63v5w958wNle2UMgDkIIqNmMK+DGiFCooSODV9uzgVnSmRxw4HZ16ujr1jA2aWP7Xm9QFbEN7gR53vDK87EV92jJgTuDTE5Oyte5XK7m8h0dHfL1xMREU23Vai/IF77wBfT19cl/ixcvjtUHQRAEQRAEcWAT/JLXUgeu7KTxPlpKjS/g9XxhI/8tQRDNUK8AJ/S9MJeqV4QrUMTM8l+MRYSCrjO/M7MJCiWOJ9eXUA4IpsPjNoplDo0BcwZCMnAD21Er/xaAb6y6xpDSGTJpJ4s2m9GQy2jo7HDanZyyY3/eqfvP112YoBvXgasI7izC4cuYt92miYpjITJwNcacdbkQX71l64n78cRZTy61A+Kl77WyroptO27hnBBwDbvh00isV9OAq7wWcRPCAawutHiu8zT7tt1G5L6xAwKuqp238qYsCbhELP7u7/4OY2Nj8t+2bdtmekgEQRAEQRDENNKoW6eeLzMUK0AQBBEfFsOCu3fUxOadZQyPOapVtegBESEgqHTgiuXd/kPHFGfkHj/+9Rju+r8J3PPwpG/6DreA2dxBHSkhtAVdt8rGxOk3zC0c3B9dOWfjdu0zUTaic4N944iI/1HfNurArbW8jFCwKk8C4TRlDJgseAez2ZuN3I1AGM/byBdEH8y3PI+wyjLGpNDtCbjVx6AxhlXPFfFfD0xi77AZqKzqz8IN79NblDHvRoRwAKssmuPl4EbtJxlHokQoRGXgJgkJuDOIGllQLBZrLl8oFOTrnp6eptqq1V6QbDaL3t5e3z+CIAiCIAji4CVu9e2W9D0DfcZBfj+M2Cnq5Lh5vwRBEI2gxn5GXTNNi+P5bQZ27TMxnnfU11TQdQhFgLT9LtKoImbB7NBqY6jFph3Oo+qrnyv5pov82wWzo+v7pJVtSYdsVxDxuSYyWIFKJ2+PW2BsYsquur0qXpxA2Ez458W99oe0FZykMSaLsplWSAauK7gzDXhxvyW7Z4xV0zt9+D6rlG0plBwBt1hyTpiU7l8mTLyW47IdK3BH1tnXtTJwwTh+99gUCiWOh58qVM5mtT+f1QGJ45oLCLg84MANWxfwYiNSrgXX58ANjCPJGCnKwJ1B+vv75et9+/bVXH7//v2h6zbSlmmaMsc2nU6jq6ur5joEQRAEQRDEoYt44pJH2WpitnEwE/UYaTUoI5ggiHqo55phWaJwE8PCOSnoDJgdEkcgrlcW94tte0csKYxy7hVr0jTPTlr5WH8yV/rnt5UBAAtnpyJdjUctyWBkwgIDMNCroxaeA9cRO8Nyc4f6ddg847k000yKulF4Arh/Z6jjFq9r1FmroNb+TCkRCsFzQzpw3S3n4FgwOyXHFHsMwQkcKBnCxaph6fyMV2hOiXsQ+ze4DV4BOZGBW/2c3vail0fb161F/C0R3UZweVGML5vRKsa32HXgbt8THcxrBxy4TfxZVBck4M4gRx99tHy9efPmmsury6jrAsDixYvR2dmJqakpbN++HYZhIJ2OvlO1detWWO7tmCOPPJIeVyMIgiAIgiB8yK9CTf6Z2Ig82a6iZpsOiyCIQwjftbmGy8/LvQWWzI3WB4SL0yli5rVVKNnYuL3sW9ZxfYa4f5u8PqrC5tikhfv+mAcAnHmCV7sn+HHU06nVFFd9iKftXaGahayqaUyKkXGRGbgh+yCY7R5Xe1GbinzqgwGaq1sbIREKppKBK+jq8Ofp1hxOSKatrQi43Z0MC4ZS0qXsSzcQwnHg/LRtgDGOzpyztGHwqufP+q3eOShcxU77ythYZf8q6rJREQocwCLVgcvF+ANtue9TMkKhcl4roAiFGeTEE0+Urx977LGay6vLnHDCCb55jDEcf/zxAADLsrB69eqG2yIIgiAIgiCICmpHLcZu40AnSe8DicIEQTRCrMzXmK5PrwhX5aPsHVkNA726/LdsYTo0z7XehICoMQDA758qoFjiOHxhGmcen8VUwSloVihx5At2wzf5bEXABep3w0bhc2JG9t38xT5su3W1iFkA0w0xVoVqzxVbb9/ez3zBlvnAmbS/obDogIpoB1fwFBEK+SJH2QwELisUy14LhsVDC+hVyxgOFlaTEQrpStF3wVAKjDkREXuGLRRLNvJFGyWlyJ44jwwL7nwLxbINy+ZKhIJfKE8CEnBnkOOOOw5LliwBAKxbtw5btmyJXHZychIPP/wwAKCzsxPnnHNOxTIXXHCBfH3fffdV7fvee++Vry+88MJ6hk0QBEEQBEEcAgTdOWoRkPoair9ojHo8M0qS46IH4AiCaAT1GhwsnBTEe9S7+gVHLcIVbGvpvDSOPSwr/80dTPk6T+qJCfWaOJ53Bn7Eogw2bDOwdnMJW3aWsXF7GU9tKGLLrhpVryJQRUjOa++XuAhRNJgZHPah0Ui8TtDFq5KSRczCsmbd8UEYtr3OGz1sO/aYeO6Fkiyalk2x0G3iPHzcjLlRHBzozHmS5L/9ciyyTzXd1wyJW2DyfwCvsn85HCFdOJMzFRm4HJk0Q1+3M33t5hK27DKwbksZq54tYmzSksuNT1p4flsZW3YZeH6bgY3bDWzZacgYjVZ8xMfyhT/00EMt6Lo6r3rVq6a9z5ngiiuuwJe//GUAwC233IJvfOMboct95zvfQT7vPEJwySWXoLOzM7Stz372swCA2267DR/96EdDs2137NiBn/70pwCAjo4OvPGNb0xkWwiCIAiCIIiDiCpulkapq1hKO1LnI7B1NEkQBFEXTjGq6ssI12et5TTlUXArYIQUj9xHDsKFV7NAxkAd45RbGKsjxzBVdMQ+XWfQXbGyUGruymnDcRondSlnigNXFV6d/e8EpHrOzDrbrnnsXKcnvIxigSViFTSxbH19A5WHs2jY6NI1pHSG7k4nSyMq/52FnB+cc1fo9mcLb32xduYsAJQMO7R4H4tz+nF/BIMoYhbcx4M9OkYnTBRKHD2dmpxfKHH0dTvjKRocmub8PaBrDJxzGCaXY23F3zOxBNwVK1ZMa0YqYwxmmP/7IOSjH/0obr31VkxMTOBb3/oWXvOa1+CSSy7xLfOnP/0Jn/70pwEAqVQKN998c2hbxx9/PC6//HL89Kc/xa5du/De974XP/jBD5BKeYd5cnISV155JYrFIgDghhtuqCiIRhAEQRAEQRBBqjmAqtHQ1+wDVNX0f3Gt/v2p3bVqgiDalDquj0JM0mtGKDgL2JxXOGpFoaeKdeIPIxaqGXaq6IyhM6tJp+iiOWmZWdqoe1Q6cG1nN9baL3ERY39xv4kX93taVqlsY9tuE4wxLJ3vZKvG7TIsAzdss1NK7TaRSytQHbhqO+o46ipmxhi4GxMwb1YKE3kr8lgI6RoAimUbf/xLAYCTLZsv2OgYSiGbYfjy9bPxt9/c6xZ646GfnaouXQ4zXzPPeRv12SsKjakF03Lp8GX7e3QAJkoGxxGLM7IInRiHLR3cwECvhsVzM7LonnRht+CJokSLmNWyzjPG2rYgQb1s3rwZt99+u2/amjVr5OvVq1fjpptu8s0/99xzce655/qmzZkzB9/85jfxrne9C7Zt401vehPe+ta34vzzz4eu6/j973+Pf//3f5eC62c+8xkcc8wxkeO65ZZb8Ic//AHbt2/Hj3/8Y6xduxbvete7sGDBAmzatAnf+973sGnTJgDAKaecgk984hNN7QeCIAiCIAiCOFSolfEY9uU4dqMEQRAxUJ2cNSMUhJZUw3mpOjNVB25UMa+qeaPVu4pEvX4Wis4gOnPMEZXBoTHmi3poBLGaYTlv0hECXr30dGrQGKvIueUQ28WlgpikA5cxQFcE3HJAwBVxA0xzThatgQ+pYJarMBWrsUpqU+rNXhnBFNFuT6eG7o6MbKds8IrCYoDfgWuYSofq5lY5J9TNFvm3mgbp6A4OcKDH+YUQ56HGGCzOpcNZRHCI4n+2zb0sYju0yUSILeDGFV5VtTu4Tlgb1ZZvZ1544QX84z/+Y+T8NWvW+ARdwHHPBgVcALjqqqswNTWFG264AcViEf/5n/+J//zP//Qto+s6PvWpT+GTn/xk1XEtXLgQ999/Py677DI8++yzWLNmDW644YaK5V72spfhv/7rv9Dd3V21PYIgCIIgCOLQJPi4Z6P5tL68xlousJB12olmHxFWafu4CIIg2pJ6ro9esa54GbgAZLbpEYsymDNYRTIKiKnNXrfVMU65EQkdWc33SDqD6+yM0RnnHBu2GVKE03WGjqzTR7HkFELryiVTFqqvW8fpx+d8QiPgFPta83wJk1PejNjX/pj7U2NM7hcjkA8bdOCqx9nLuY83oIIbayGMmTK6ITBO9W8FFviwPOWoHHo6NYxO2jhycRp93bqvOFi+aIc6vtVs4aBILVi5Ko/NuwwcuTgTuQ0cngM3raPis1xsS2+3o4oLJ7gs8icduFw6cIUzV4jBQuQNK7TWLLHOVtu2a/4zDAOf+tSn5MGcNWsWrrvuOtxzzz3YuHEjxsfHYZomxsfHsXHjRtxzzz247rrrMDQ05B58DZ/+9KdhGAYsNZTiEOGv//qvpdh63HHHoaenB11dXTjyyCPx/ve/H4899hg+85nPxGrruOOOw+rVq/Ev//IvOOecczB37lxkMhksWLAAF1xwAX7wgx/g4Ycfxrx581q8VQRBEARBEMTBxnSIq22q39ZF7EI1rR0GQRAHMzVurAlHaK3sU7WYlyjwpFfJF6h23Wr45lSkAxeem7OOG4lTRY59oybyRRv5oo3xvIW9o068QbHsxABUzfetE11jSKcq/+mKwAo0l6EeJZZqrgu3HBRwLf9yMigW8e9Dqo7YySknYkPND+bBxhRhX/bmLpxJM6R0Bl3zBHtdZ0i79wmmigEFPLAdgCNSB28omybwu8cL2LTDxIvDlXGs6j4Xaa3plDctuC/6upxfmMmiEK3d/mzvpyiCx5jzXjhw5T4P3ZLmSCxC4bLLLsMvf/lLcM7xkY98BP/wD/8Q6u7s7u5Gd3c3li1bhje84Q34p3/6J9x888245ZZb8LnPfQ5/+ctf8POf/zypYbWMFStWJO4YPvLII/HVr34VX/3qV5tuK5fL4brrrsN1112XwMgIgiAIgiCIQ42k/tT1O3BjWnDblAQNuG2/rQRBtCe+CIUakQL1FFQSEQCi8FXty3VzebSAUmQLgQxc14HbmdN8TlHxKDsP1/n8bbtOyGxGw+x+Hdv3GJgqcBQNjmLJRleHhq6OZBy4UQTFzXrEW/XzptYTMLrmCIdBd6oVcIOqQn2tSCBBd4eGgR4dw+Ome0OAKdEQzjkWFqEQjFZQxxEkk2YwTC4dr0HU4mz5Ise+UQsj4xZ6OjWs21LGD/93Qs4PFuETiOgHsY/SqaA/2NvnPa6AO1XwC7jihoiTgcuhMbd4HOcyTqGVGbiJnK1f+tKX8N///d8AgC984Qu45ZZbYj+a39XVha985Sv44he/CM457rnnHnzlK19JYlgEQRAEQRAEQTRIUhEK9XAoaprtGhdBEET7U+ua6UUo1G5LCzgIq63jiaoh02p3JVGLbqnipnBi5jKOUMYRT8hUEUJeSgeG+h2L6kTBwov7nCpYus6QC8lbTZSofNh4q8ZeSZiljYD51BPwnQXinAdh5NzoCaFNcu6JiTxwNNTjGMzA1UJc1AxA1s0iniqFq6+mMrlscLy438TYpIXdwyZWPVvyLVsuV66vphmIiBCfAzewX3o6nfNl5z4LkwVbLiDOcVHETNdUcdyZaQX2eZI0LeCapolbbrkFjDGceOKJ+PjHP95QO3/7t3+Lk08+GZxzfOUrXzkkYxQONTjnGB6zZKXGF/ebGJ2g404QBEEQBHEw0ojzqF2J2hTfNh7YZmOCINqVsOzSKAduHTmnQZdhrdiFJCiWvYFPTNn42Df3oFi2ZYRCTslDZZo3JjvGZ4Rw9+qak32rCbuky9J56ZaIbJEIFboBquWpMgZoroJbKvsXMAMRCv5jGl9xF8Kv54T1lFg1TkFtLvjEusZY5P4WMQ2FCAeupdhqy4aNiSnn33i+UvCdmLJg2Rx7hk3sHTF97l1nfednWo8uKDfY6+2on/12oqJ4nm07srWmeftG05w42WB/SdL0r+SDDz6IPXv2AHBiFJpBrL93716sXLmy2aERbc543sazL5SwaUdZ/ntmc0mGYxMEQRAEQRDtA5sOC26bU8th1ojTqt3FaoIg2gufhFZDgBRikh5D+dEC9syqhc9Crlv1OEYFQcHx8XVF/M8jkzJCQTg/1Uf2o/oPIjQ/XXf2U3eHJldbMDuNjmzrFWo57sCTLPUQZxVhJg1m4JpCTNScdjSfOzb+YMSyPv3WxRFwKxVc30iYI8AHVpUThAO3EJGBW1RMtmUTGBk3MTZpY/+YhWygZtnOfY458PntZWzYVsbeUUsZE4chHLjpiiFLOnIazj+zEwAwlrekSCvOKc4dd7OusQp3s3Q9w1s2KZo+Yzdv3ixfL1q0qKm2Fi5cGNoucXAi7galUwxD/Smk3LtGUVUFCYIgCIIgiGkk4NyqYgCKRZyvitNphpppptX5RRDEQU10ETPnZ5zLTXCZmhm4NfJ341AsVwp2e0YsFISA6zpwmVsITIjMdoxOpYDrrrN0fhoDvSnM6kuhp0uL1UYSiGt9vQbcOMOTn89uEbNghILYB5rrklUduPVEXnjubLl25DjV80I9h4LxDep6MkIhKgOXq/1xdOV0dGY1dGY1pAPF9kqGjQnFmVt0zyXRgmE489KpSkew6MaygOULHWXYKXrGZd/OeDxxXpyTWiADtxUf8U0LuPv375ev9+3b11Rb6vpqu8TBiTj5O7IajlqSQYd7d43SMwiCIAiCIA4e6in8K7/otvv9/MgIhfDXBEEQScFDlLeo6yyvo4hZQAerGqHgZaR7K4Xl4taiGPL07eiEJU1dOdddKZqsx9UYdB/3dGpYMjeF2QM6GBCrEFqzcLeSlxx/k58LUdstRGrD4Ni228DG7U4QrBU4/o1m4GqBHe8zQwcFXDnDf9yCefrq8plM9Qxcn0bEGWYP6Bjs0zDQq1XcvDAMp9CZoGxy33iFyJ1ST/jAmEyLIyVEcYvLgn1qBrBwHuu62C5eOdaEaVrAnTt3rnx93333NdWWuv6cOXOaaotof4KFMcQvkGm1+1/sBEEQBEEQBz8VT0pKV82h+7dakltO+i5BEM0SFMM457Bs5x/nXDoX9RjKHQuoQ3HFvmaui8EIBQDYO+IpYDk35kALCJBxPoZkMbYQ1ykwfWlAvmidRiIUmN/FG2ybMUBnwsFq46rP7MJ7P/8iJgs2TNP/PL/YF2GFxqoh16uQ0qOPRVDWCUZ0qHgRChEOXGXLLSVjlsPLOj5iUQqAI7iWFGe3IWIluPNPxExk0pXjsbnTvmFynz6lnnecc/nPiaXwto/DEc1V93GSfzKlmm3gtNNOA+BsxIMPPohf/OIXeNOb3lR3O7/4xS98ubcvfelLmx0acYCh68JyPsMDIQiCIAiCIEJdXg21I17U0U67SsSNOMxqt9muW0sQRDsSdmnmcISqpzYUpXtV1xlyrrMxlkgXWKhazEvodauBj4xCSHzizn2ORTKdChGew/JVIxBCn66Hi40trDWFpzeWYNkch81PB57MiL93vM+b2utorgt03QteWOyWnWXs2mu6bfkf868XVmW94Lkghlss2dilCKmeCzl4ngHZtKMQT0Vk4KquVlUvsm3PUdvTqQMwYQTOqbLBfcdACLopRQ0VY9o3amLfqDj/XAHX9AZv234XLmPe+SV2UcngeG5rGYN9eui2NEPTDtwTTzwRJ598MhhzKq5deeWVuOOOO+pq4wc/+AGuvPJKJ9eEMZxyyik48cQTmx0acYAgfpnEow1WK6+kBEEQBEEQREO0oiDHgUrkV9noJzLraIQgCKI2jjPTe58v2L56MpbFkS84alcc3a6/x5OHujs1pGvY/ZJwGIY5cPe4DtyOrCbbFsKj7DOG6cvLwI1YoEWfZZbNMZ63kC/YKAa2r9HLvvf5WzloBia38an1noC7c5/li1DwCfqoLwO3Qkf3xXf453XmvB1uKsJrtQzcjFtQTBSvC2Irx9u0lPMAXG5jd6czMVjITfxOyAxc4cBNeQPq6dR8Qj8A9HY52+G0L7JvuRy3EHBT7uZm08yLZeAck1PJOxObduACwLe//W288pWvBAAUi0VcffXV+Pa3v42rr74a559/Pg4//PCKdTZt2oTf/OY3+P73v4/HHntMnoi6ruNb3/pWEsMi2p3A76YuLeozMBaCIAiCIAgilGaz+1rhWp0xanzhZ5FvGm6SIAiiNtwTLHs6NXR3ati1z6toFcd5uWhOGvOHHImoWuSCKiIGXcBAfS7TYoRgBwCdOeYVr5IRCuFRAmGIR+vVbfE7cFtz9a1W1CvJJ1E27yzjuRfKjmPZXXhkwhMN942aWLfFEXT7ujUcsSjdcPHM4GqWxYF0+LFIpxiOXJLBhq1lf+5tRNeMMWTdYnWFCAeuHTD5CUFXFXYdB64oOuZhWlzejODcm58OCLinH5vzbUtZEd/F7xa3vT45mCOMa4BpA9v3mFi2IIVtu22UDbslf/MkIuCeffbZuPXWW/H+978fgPMLvWrVKqxatQoAkMvlMGvWLHR0dKBQKGB4eBiFQkGuLy4Amqbh1ltvxVlnnZXEsIgDBHFeSwcuZeASBEEQBEHMODPhtPW+6Lb334ONfgn2t+H8bPNNJQiizVA1TfXJCPE9OqUznzgFxL+BFicr199wfYsHUbNKg/R0alIsEw+j1/MZITQ/Xa+c5rRRx0DrQBUVOffvooYduEEB1ebYtc9EqWzDtOwK1ykA3Pv7SUy6slsmxfyfW6y+eCNZt8hVEFmEKC4QmbzV8n/V1UQGbhwHLuA9ta1GKwgHbslwFk6nGCzLEepN0+mwaHA8urYg56sEb3IIVzDg5OoCzvnjCf/Oz84OHeN5x+1sWMzbzhacX4kIuABwzTXXYMGCBXj/+9+P7du3y+mccxQKBd80gXoCLVq0CLfeeisuvPDCpIZEtDnBX3TKwCUIgiAIgmhfmo1QiPPFVS7TpqJmrWE1mnVIEATREO5lZutuAwtmO4qTHiLgRsYINEDUZ0AjN96mQopWveW8HuwZsXDRy7s8wVUW3/KWs21e1VksHbgR12LLAtZvLUPXgEVzUtIF2izqfrC5P+qikQjasOHLLtx5A70a9o3ZOP/MTpTKwINPTGHnPk/dPG5Ztv6OFcS4+7vdonLKvGKZY/3Wsm/58byFHXtNcAD7xyyUTY5chmHuYLgEKQqKRWXgBt3S4qlt0z3GDF50g+HOy6QYTM2J6di530S+YGPzzjLKhjN/wVB1OVTXHTGWc8ByXbucV8Z3HLEojSees9xlK8/nJG9IJybgAsCFF16IdevW4Zvf/CbuuOMOrF+/vurynHMceeSReM973oMPfvCD6OrqSnI4RJsTvOOTIgcuQRAEQRBE29G0DnkQ/WmX5BexdherCYJoT9Tv0WnXBFU2OMYmHOVK17zpctEW3E+qlssal9EJf37i7H4df33ZgHy/f8yZL6ITVAG0Vq8yA1dx4PrFVe4VrEozLJmbjIBr+4ps+Qto1RWhoO7XwBMbQUfqnME0Lj2nF6cdk8P/PT6FB5+YkvNeflIOPV3+bRPCpHhdC+aq0AxwhO6AkC72o2CqaGMi7xy7QolD0zjG8hybdhgY7NMqtk/kze4bDc/TDJr8LNsZghBy02kg6+b7iiJmHTkNpsVRKgOTUzZKZVtGdhy5JI3jDq8tamdSQMlwHLi65kR62Nw/dl1nyKSdfQN1nO0aoaDS1dWFT3ziE/jEJz6BDRs24LHHHsP69esxOjqKfD6Prq4u9Pf346ijjsLpp5+OI488MukhEAco5MAlCIIgCIJoHyoKnMiiIa2DTUMfrSSJR2UJgiCqoep6S+alsWfEEc9EwSxN8x51F2gJKrjq9Tk8Azd+W8PjfsEunfav7H9cnfnarqUbe0XMKh/3H+pPoadTw8iEhdEJq0IQbQY1piHYbl2HoUqRsYr8XhtI6c72DfZ6irXGgFl9elXnb9whaczZNo15QnpHVkNPl4ZlCzK+ZdUCbpNTNnq7NWiuQzWYUQs4wj0A7NpvwTB5hYO8IkLB4kjpngM3k2LIpl0HrgkcvjAj98PwuIWS4RT0EwXyZvfHk0LTKYaSwWGaHBYDxiYsDPbq4Nx/U0E8cRN2Sib590ziAq7KkUceSQItURPKwCUIgiAIgmh/Gs1sbeRLfbuS5LYcDPuDIIiZgzHn0fOBXh0jrkgFOIJl0IGrJRih4A3A+dHMgwnDE35lLq3754vH1YVgWJeAK4uYKeu4P/u6NcwdTMEwOUYnrESfrlCbshQHbjAPtx6CEUZivzA4zk9V0B3s83biwjk6Unrl5w0D6lYW1YJk4nVPl4bBXl0WvxNMFmx5Y2FPp4a+bl2O3RR5vUr/PV0aMimgbAI795pYOj/tay9QwwyWzZHSmczCzaSBzqxnCOzp1GQsw7xZKewZMZFNM3mDIKXFOxaOkMxhcYa9wxaKJRt9PaYj4CqxHi2MvfXRil9jgohF8BqZcj9kTHLgEgRBEARBtA/M94NAFfGVxVgmAFkXCIKoi4AzU3yPtm1PsEw1WMSs7v5DLLj19DUadOAGnZeBC6SaLR7UEzjnmJiyMRpw1eqqmO2uIx2pCYjQQSxl0LbtLzZXTzZ6tRuGwQJtzJ3GAQz2ejLfS47OuYJrdL9xx6SK6LVWCXP8imNrhhj2GBiGXFfs9j1GxfwKAdc9bQzTazuTZrLw2Iv7/TZfMRwRr6AHbhREIURgw7BhWhwcwECvjtkDKcwd1MEYc3KYW3AehdFSBy5BxEH88pMDlyAIgiAIon2I/IusXgduA3/atfpLUMO0YFxtu60EQRwQpAJilK6zimnVin01g+NPbJxghEKmIkLB+SnGz+A8um5z7rpmveX3jFjYuN1fTMtZ13sdrMOjtUB4U9uyOcAS2PdBwVTsFyHec3iREUvmpXHWCTkAwMI5KewZrswsYKy+4xYsxCbWj9qyMMd3UMD19c+AoX4NO/cBa54vQdcZzjguJ4+7EOOFS7dscnSCSe0o47bd3aFh2LCxa5+JebNSeHJ9EWed2CG7MVz3b0pnsW40CCe7abm5t5xj0ZwU0imG4XGrsjhdiOqe5LnVcgH3ueeew/bt2zE6OoqpqSn09PTg0ksvbXW3xAGIrkffkSEIgiAIgiCml+CXjnqcQ43S7hm4FbnAAaZjHxEEQQDe9UYPxiW4jst0iknBKkn9Nur67GXOxmvHtJzCVipBBy53lUrfVFc1Do5jbNIZQDajSQG7r1uXIifgCYEBA26iIpua1ypjE4TQl9Az8LayXxwHKJf7StcYPv+BOVj1bBGbd5ahsXhiZS2c8407rlPGnP0f0W4wc5kBSLvqo2FW7gfG4Dpwy/jZ7ybws99N4Kb3zMK5L+2S2woAnR0M5QmOySkbfV2al4HrCv89nRqGx23s2m/i/h/sx++fKuA9b+jDKUflZN8AfOdENYST3TS579wpGxyPPl3AscuychqkKN66v2BaIuDed999+M53voOVK1difHzcN2/58uUVAm6pVML1118P0zSRSqXwL//yL8hk/CHIxMGHV/VQfPB4857ZXMJxy2pXBSQIgiAIgiBaS/CLX6NfTUjb9NPuYjVBEO1J8JoRFKOEoJvSWyPgSgKuQyGmxRXHRicc9y1jwMtP6sAjTxVw7aX9vmWkdqc8vq8xwEal6JovOArb4QvTGOip/oy80CCEOzb4iH4zqHm0VqDheg6Dun0VGbjSmQww8SSzHcjftRyVW81o9bdf30b7cohdEb0eB25GceBmQk7IoX7/MfvT0wWc+9IumIpJuyunYXTCwsonCpg7qGHRHCczIZNm4Bzo7nQ6fvjJAp7eWAIA/Nsvx3DaMUUsmZeGIc/ReNssIhkMiyv6FfCf/zuOXz2Sx4NPTOH1Z3cDzIuxqNgv7erA3bBhA9797nfjj3/8I4D4J0Q2m8Xo6CjuuusuMMZw3nnn4YorrkhyaMQBgK4x5LIaiiUbk1MUhEsQBEEQBDGjRLhN6/3SV+9jmnWvNI0EH78Nok4mwZogiFYQvAanAmKZcMDO6tOxfY+NrpyGbCa5C5Kve6VZIbQFHcFhbN9j4A9rCgCcx94vOLsL55/ZKR2Nwb40TXHPhsQeWBZHoeQs0JWLtrmKfRd04CaJL0LB9gTWao7V0Hbcn6EZuEpxNw1ChOaRy1QUMWPhr6uhFu3SGGBVyVCoaJMB6ZQGm3MYJpeCqzK7QsAd6HXeqyJ4T6cOwDnRdg/bmD/LmZdOOwpqd4czSCHeClY9W8Lq50o4/nDn/Ip7k0E4wg3TO3c4GB58YgoAMDHF/dtbLVciARITcB977DG89rWvxfj4uO+Ckk6nMTg4iN27d1dd/9prr8Vdd90FALjzzjtJwD0ECPu7/NjDMlj9XLFd/2YnCIIgCII4ZCFB0iOJXUFxCwRBJEGUA3fJvDQWz0217FoTvN9m1XA3cs7xo/8dx6//lMf2PV4ua3en01ImXSm82vJGope2Gybg5ou22waryNH1jSEweNGWnaAF1/YJuBzqVjV8JAJPbAix1imi5bo/FQ+cbXMnJxjRRcfq3WKm7DMRpxAllQcNtgxAOg2UysBkwcmonSzYmNWno9MV3If69NA2LMWBK84VgeHOy6b8DlzB5/96NrbvNfDtu0Zhc2DKdWmnUvFiJYRruGw40RGcc2jgGAlkNzNU5gq3It8+kQSO3bt344ILLsDY2Bg458hkMrj++uvx5z//Gfl8Hrt27QJQ/Y+UV7/61RgYGADnHA888ABsmxyYBEEQBEEQBDHTBP+Gr/s7SSA260Cm1hcyn6spdqONjoYgiEORoDNTDxYxU1SeVlx31SxwtXW1QFQY3//VGP7tl2NSvO3t0nDSERmce1qnr11/XwGHI7xtUpcvGc6bjmx1iUs6egMCbpKXYb+QCjjyM5NxBrFRHtmP6kPXPOHQViIURB4xj1hfJe6YZOyEKghHOnBZxbnXmdWc7FzuxCgUShwjE54QGhTeJ9ynsn0O3A7/MuWys0zajTqYM6Dj2MOcNxec3YWzTuzAZa/ukctPuS7tuDnN6bQq4IqNcwqpBbcXgM9m3YoHihJx4H784x/HyMgIGGNYuHAh7rvvPhx//PF1taHrOs4++2zce++9GB8fx7p16+pugzgwCbPvUzVegiAIgiCImSX459h06q/t/rdgIgVh3J9tvqkEQbQ5QcE0qUJZcRHXay9CoXKZ36+Zwg/v89dHuvKCXlz8im6sfq7oNlS5XjA2Qc1zVSMDZFxArW0PiKLBbNkk8GXg1ivaRhAUrW1lO8Q2q85fSylyxkKKmLGgXTQGQvTWGPME8Cq3KjXN757NpBlOOzaHnq0MazeVfNsTto+EgGv6HLj+k6tkOD/TuiMM6xpwzSX9WLogg74uzW2bIZNyRFeRk5zS493cEBEK67eWMVWyccLyLMK8ptLJrdxwaMVne9O/2pOTk7jzzjsBOCLsL3/5y4aF15NPPlm+XrduXbNDI9qcevPTCIIgCIIgiOkj8m+1Ov+EC9SgqYr8klpfF23JQWA4JgiijRHXGCEyCfSWVCyrMgAXq0oRsw1bywCA15zeiUvP6cZxyzJO8aca2CECX5jpK+isrdWebEvk0yb4oePPwPWcmzzBfuT26gwaY2DcdeDKft35Irc2gT5Trv2TMUcA5bxKFTMEjoVyrnbmNOcc4ZX745yXdMjX+YIzU5xXTMm4FQjXt+93gAGDvboviznlzs8Xue99LUSEwur1JTz3goH/eWQSv3pk0pvvOn9DP+9bYE5s2oG7cuVKlEolMMZwySWX+ETYepk/f758LWIXiIOYkMIYLDCPIAiCIAiCaA9a4VQ60PDcQslZcMnUQBBEXQQuGcHHweM+Ht4sQSHVdEXDMAF3yhXOhgZSuPbSfjl9smBheNwGB8dgj+eutGyOPcOWdEzKp9cj8lyl4zQwc9+oiamSt8OE4NdSB67i0Gym3WCRL38fzkwNnlCqfpZYSgEz2Yab4Sra4yF6TDVm9erIphiWLshgeNzCZMGuum6cwmnB/fPBNw+gv1vHPQ9NYmLKxu/XTKFU9o5ZZ6BAXdkVd4UDNwrnnORyv+haNe+wR/DmSLEMfOfuMfleOsTFe678erbgo71pAXfr1q3y9ate9aqm2urp8bIpJicnqyxJEARBEARBEMR0UCvrrha8jm+J3pfpA1PUZKHOhIhlWzoSgiAOVoKXR11nUpxzilq19uqiioAqZpUiZiJ7tDMbFFgt7Bl2noNnynYNj1vYvLMs3+s6APdxee9xdXVM7nKKvlcq21i/1WtDRbiUNRlN0JoiZpbtiqgNHBIv67hyZdWZzDSnfScD1+9a1RSlmilthrvoomEANI2ht1vHQI/uFfGq5sDVvB4ZGLo6Ku8sBCMUclkN553ehXsemsT6rWV8+tZ9XnvMOQ/OPD6HP611YjcMQzhqq29OMF83HZHTHCQdUEyH+jXM6tUxVeLYttuEabk3D5RMpCb/ZKpK0wLu+LiXY9Lb29tUW1NTU/J1Z2dnU20R7U8wfN15ffA8NkcQBEEQBHGgUu3L7KH8d9qhvO0EQbQXqkB09NIMRics9PeEqKfTAOe8aoRCwXXgBh2UpuldVcvKa9MtEpXLapjdr8O0OKaKzsQw92ZY1ILhaoyaxjB30NsvuYzmiYlq7amEUD8/bVsJwW2iE2+bufvTea9pnkhbMjhGJ2zkMhb2jZqYmLI9p26ICNzocHyu3hjLHbYgg/4eHXMGvGMQzIxVx9TTGW4h1xigM+CoJRk8s6mEiQL3BFzlnAsTvNPBQn96jcG7qMJvJg1cdVEfjj0si5LBcd2XdgMASmXuZg37NS7xuq2KmM2ePVu+3r17d1NtvfDCC/L10NBQU20RBEEQBEEQBNE8wUdN66WexzQPlNzYqHHWaWoCcGhHUhAEUT9hLsrBXh2DvdMj3kqRSlHy1EJTqgv26Y0l6BowVXQcuB0BB656/VPbEJO7OzQsnpvGC7sMOc9zzXrLe5mvSvvu/EyKYdmCTOi2yPiBkMJUjRJa5Mo/pFhEOZ0BLzJCY0zm3OYLFjbtKGNs0sL+MQt7R0xkFQGSaQBCjLP1fuyK3OCUXlkczbeccNVmGOYM6FJYZax6fEGUgMvcvAgODt1VMg1X6M+kNYRmdLoEoxDCbjKEkVHWmz2QwqI5aRy+MOO63Z1zsFjyco5bfZe36XSUJUuWyNcPPfRQU22tXLlSvj7hhBOaaos4sDlQH5sjCIIgCII4GKj2p9ih/Gdakn+jHihiNUEQhMC2OUplW4qtpgWUDFsKtJrmRThMFW186Ku7cd2Xd2M87wq4AQeuekU1FAeuFC8rclRZxTLqa1W/tWPcPUzAHFtBsFCagCfQi9gkuWk60JVjyGYYclkd3R0aert0ZDMaOnI6ert0dHfo6O9xhPDeLmfaormphj/LdXenDfTqGOpP4eil2fDlqkR5MMbAoWTIKgeiu4oDVzSZcl+I3GWmRe93IFzArTcDd3afJreJMWefA0ChbMvxq9p9K/5WatqB+4pXvAK5XA7FYhG//vWvsW7dOhx77LF1t/P444/jz3/+MxhjmD17dlPF0IgDg0P5j3+CIAiCIIgDBbV4TFPtxLLgOj/a9u/EGnpAWDQYQRBEK5juK4xtczzxXBGTUzY27SijZHBk0wy5rCYLTan5tyMTnqV297BjlQxm4KpuVcvmMsdXXGu1EIFVOE7VaZ4Q6E2L8/RHWJ5us9hJNobwwm0yQgGArmuY1adj0RwdRy7JYNGcNNIpoLuDYahfx75RC7mMhgVDKSwY8iTAF/ebjY3H3f8aA5YvykQ7ZiOiFsKOhzotmFcr0EThMc6hBxy0e0csX0xGkKCAq8c0qy9fnEZKBywLOPqwrDz3AKAjw1Ascb8DV6EVrtymHbgdHR144xvfCACwLAuXX345RkdH62pjamoKV199tXz/rne9q9lhEQcA4Rm4yvy2/cudIAiCIAji4CbsrzASJJNFfrejP3kJgqiDmbpmlAyOslHZuShwpWkMcwY8gTBf8JYdHneLmAUduIEIBZnxGuhDjROQ105F/BWaqSqw8YAIHAZrgQU3qN/6Pjqb7EesbrnbrqsxBkrbIlogTrGuej/a683AjeqTc16xr4K759WnebWxGBzxuK9b90VDAP7jHtZthQM3FW+7j1iYwSffNQs3vWcWTlye9W1TznXgFmUGLgO3nW1gaM3vadMCLgB8/vOfRyaTAWMMa9euxWmnnYb7778/1rrr1q3DOeecg7/85S8AnEJoH/vYx5IYFkEQBEEQBEEQzRLh/IlLPcu3u6gZZkDwEfOLbbwFCIIg2gf5+H5g+lFLMjjrhA6cdUIHlsxLy+ljkxaCBDNwfUWsOEfZENOdn6HX2hDN1RNrlYiFwPJhiFnBYlrNENoUQ93irWwnZPxqxIRPH3bXEUJ7Os3kclHt13tzVs0ZjpOB67zxT68oRBdo58NvHcBrzujEVRf3eeu56uX8oRR6ugKCrMZQLRQhHcgeSFVTl1WY4wgWcQnqtueyzoCKZe7bnmDLbVXEDACWLVuGb33rW3jve98Lxhg2b96MCy+8EEcccQQuuOACX6TC5OQkvv/972PHjh148MEH8cADD8iTT9d1/PCHP8Tg4GASwyLanZCLcvCRBzJ6EARBEARBzADVMnAbbJL+rCMIgkiO6f6uLETVTIphx14TDz9ZwFkndOC0Y8MHInJvVao5cAGgbHLksgjVCsR7LSj+wYst8OkJorBZnAzcRCMUouc1/fnpNiAdx+r2KssLATeTCqyfAHHPO7/QG75S8Klr8f6Nr+rBG1/VI7OVAed4ilbqjURoNAM3SKgDt2QD3Zqr0TsxIK36gycRARcArr76ahQKBdx4440wTROcc2zYsAHPP/+8XIZzjt27d+Oaa67xTQOAbDaLW2+9FRdddFFSQyIIgiAIgiAIokmmUyNo95v3tb7ks8g3VdpsdDAEQRySNOqcbBZVJH1mcwmmBTzyVAFvfFV36PITU2ECbnQGLuDENADVr7Viu30CrvtaVyMUAsuHtxXdT6ME3bwi9qHea72a4Rscp9hvWuCZ+r0jFkbGLeweNjF7QK/IivW1X+d4BKqIWU0cT0I4Vx3bhuUJo8GcXPW4hx3TtLI8Y5X7LXqc0VENOXdsz2wuY94soSC7blyubmNyn/KJRCgIPvjBD+IPf/gDTj/9dDnNs3azioqBYt5LX/pS/P73v8dVV12V5HCINifsPE4wHoYgCIIgCIJokPAMXHdenV9G4jzGeqCRxJf+Vji/CIIgWoUnHAOz+z0v4F+eL4UuH+bA7chWl6Bsy5+BWyG+Ko/fq0JpWBEzW4kZiKIVRcz4/8/en8dLctRX4uiJzKz97rfvvb0v6pZau9ACCAmEWQTGBhmDsXk2MGDGxn74Zwa8je0ZY8zYY88A5g2/4WE/fmbAHrDM2GAwYjGLzCK0C0loaalbvW9332rPzHh/REZk5FpZVVn3VnXH+Xy6by1ZkZGRkbGcOHG+MQrcrtPmfyUSn4ApSimAWsPG4qqFhRVmgqsntQpoAx7yM47AjbrVxPWI9Tso+PtD+f43TUmB6yOmDfl9GIErKXC5KrmTMYlcl/JZdoGf+8YqHj7EngGZdO9FTKdUCVwAuPHGG3Hvvffihz/8Id7znvfgpptuQjabFYQtpRSapuHyyy/Hu971Lnzzm9/E/fffjxtuuCHtrCgoKCgoKCgoKCgodAk+YdkI/nVQPHCjIE82+11NrKCgMJjYrOZR3rYvKyNXQ5S2QJDA1bWgF6llh7/n5FeYXYLfTsB7fNBTIAmBm1afU6nZKNfSZXAJl/BKsH3lM1zUcGBnFpftznqI9l4soGoJ+zkSw6kSAiyvWTDN1iQ7h6zW9lsiaBqJHavIxxtG8sLwH6l7PHDd1//0nTUAvX82U7NQ8OOFL3whXvjCF4r35XIZKysrKBaLGBsb69VpFQYIoatqSoKroKCgoKCgoLDpiJ3MdhiI5YIgNFO8ljASQkFBQaFfIXvKysRr0wxvxFbL3iBmxbwWUNT6fxnoe3wEq2wnIKtmwywFYgOhOeBkZFoE7nOnm4HPCFiZ0RTOw3/Pr1fXIBSto0M6towZAJgatJVNAE2gUA5D0uNl0lS2OJD9Z5dW2yS7IywUjBYeuFmPApe97qQfl8vUlOp9Ia8BhD0jlCJVwlxGzwhcP0qlEkql0kadTkFBQUFBQUFBQUEhBfAJt1Aq9fRkG3COHoJEvA499oJgtBUUFDYanRJv3cKWVuMsiT01rfDj/QrcQj4kwzT8rbBECEk3rC8KI2tl0rcV0tjuTinFepVlfNsWA0NFDZUqRaNJsbze/ub3sPw3mhRzS6ZLmocVqfiKxC+gtlE+MmQSM67YZiYMce7JMZdhnR7XRf9Xa9pt5YEf5yds/YSuH7Ly22iDBfWXmxyY7egZl6wvO/e92qAwTVYuy+sWbJvAMIBqzWYkb5fYMAJXQcEP0fFInykBroKCgoKCgoLC5iNVC4MOJ4n9iF5YO6gxr4KCwiCAt3+aBlgSaRulwF3zEbjFEP9by2c+G/RADb4Xn3ksFHjegj1NrALXQ0bSrhbWanUK26bQNIK92zIsrTGgUrfx3OkGQDvvQ3QnX9W6jWdPNsTnhnS9Qp3L7RV0pNb/ysWSNIhZxiDYOZ0JfG7oBLu2ZnB6rgnT9H6XNFCo30Ih5wtSFsiLLr8mnrRiz+c7SFYSz0wYODXLLmB+yYJlUdTrFEtr7OHIZggopdB1gseP1HHTFfnQ+tkONpXAXV5ehmma2LJly2ZmQ2GTocQHCgoKCgoKCgqDg17604ZFF28HtYYdCCBjWtQb4MSBpgG5bGtFjGVTNBosQzxCeuQkX3YGU2NcBQWFXmCTFsVkrtVjoWC5X/zomRr++P83j/f8wnhAgVsMUeBGWSjEqWd5+9toUlQdv1nT8nrCyvmNI2Xlb2wKhHQVsbBsirrTPyyvs7yU8iSVHRayf+3okIbpcQMNedt+jni8iN3fsc9acYWupWV7+SKEYO/2LCyLJupDw5B1eN1GBPkvI58lqDW8FSLjU9xunzJAANi2gYyhodGksKR6SaTC0DS26JDkHk2M6FgZM8Q4YmLEZYL/w5vH8em7VvDN+yugAL77SAUHd2dBCEEuSzBU1GDZQL1hw7RoKmOnVAhc0zRRqVQAALqux1olrK6u4gMf+AA+97nP4fz58wCAYrGI173udfiTP/kTHDhwII0sKQwowrY8KCgoKCgoKCgobA7SDMrVa8uAk+ebOHne6z+4VrFxeraJmUkD48NBk7y927PYviV6SmTZFA8/XYtUmKWBXkSqVlBQUEgbru+qz0JBah8/c9cKVss2Pvg3C4Hfv+CqQuAzseBGCEApqJNuVPAt7icLALNLJmaXvBJOzcMnJLCa6GILsG1T/OiZOuoNL1FdKnhJTfkUeguv1ijoOsGBXdnA53PLJgjxBvjiRIo32Fi6/W9cv5kE3IfWbwURdgsKeYnA9f2eY2JEx8SoAf1MA9WajYeernn61vllt55YFnDkVCOg4g1DxiC4bHew3AFgx3QGf/D2LTi/cB6PH6njscMNbJ00kNGZTcTuGQOmBRw+1Yi8tnbRvQkDgPe+970YHx/H+Pg4fuu3fivyuOXlZbzoRS/CRz/6UZw7dw6UUlBKUS6Xceedd+K6667Dt7/97TSypKCgoKCgoKCgoKDQJWJs8xKjneO7mWJWHCWWphEYOvvHFbONJsRnhk7ENsb1iOjpHE2TigmmZzIclX+lulVQUOgxOlVOdgtbIsS8Fgru6y2jXobyF145jH/9v3fhro/uxNt+ajSQpt+j1Q58HtyVMT6iI5/VkDFIgIQjIf6scUrUMMVuUlTqFPWGDUKIyEs+y5SyfmyfyqCQ17Bve9BSIAr+MoiC/1t+HbrWvxY93LOW969xl/hzLx8BAMxMuDdXJnA1wvp9/kmlboNSCo24fX424/5W17kdRTql81tvmRCvV8u2uBjh9JHiTUhFgfvFL35R+IX85m/+ZuRxv/Ebv4GnnnoKAKuE/tXmarWKN7zhDXjyySexffv2NLKm0McQt9+z1Szo4aKgoKCgoKCgoLCxCFOFhvkO9hN4lvduy2DrJJvm3P9kFfNLJiZGNI/66/yiiSOnGgH/xag0dZ1g55SB4+eYwjcN4kSRvQoKCoMEmRCV2055h4K0ax1vfPkwfvVnx0AIgZ4Nb/CEXysBLAQ5AE6wyorc4aKGGy7Pi2MeeLIautCWhE/ohn/ggatGShquuiQXe+xIScMlO7LId2g5kARJ7CfCjt8McM9ay/ZaHYTl6edfOYwzc00MFYioDzJx71c1WxazSZgc03Gpo1qWd+dsn9Kdc6dxJcDumQxefF0B33+0irWyhclRFqSNAp6bkEZ5d117jh8/jtOnT4MQgssuuwxXXnll6HGHDh3C5z73ORDC/ECmp6fx0Y9+FHfddRf+/M//HCMjIyCEYG1tDe9///u7zZaCgoKCgoKCgoKCQh+gHYsAEV08rZllRARuHojEioieHgY9gTmiJzhvi8O91mF9yoorKCj0HTarubAlAZZMfskWCpzU/MVXj+DdPzfeUj3qtzngydptyGFlj3MtTIHbgvXSOlxN49fqt0zwo2v7oTa/EwSuFt+3bJaSGwAMQ2M7YSjzwY3Lgq4R7JgyPKStYXjvOYF7HXxxwZCI3azkmbt3GyN126ljrbDNsZRYq9iuqpuQ1H2qu1bgPvHEE+L1rbfeGnncpz/9aVF5isUi7r33XuzZswcA8JM/+ZO49dZbcdttt4FSijvvvBMf+9jHkM/nI9NTGHxErQyFqbMVFBQUFBQUFBQ2DlH+g0D/7pJqZzLKydhWClw5WJDeO+EUKFWKXAUFhf4G96slxGehICkoKzX2Oso31A9voDEq2lx/ex5nJ5AxCKp17/Hyb1rCOTVTA4c3xEfPNLBesTE9YaCQIzhxzhS2PaVCfzTe/HJdpTTZVJI2DoSwfpjaFE8da8DQfDfeB+q7EJmQ1X0eGbJXM4chvd6/I4OT55te3+AusV0icAN5jnjfCboehpw4cUK8jlLfAsBXvvIVAOyB+6Vf+iVB3nLceuuteOUrXwkAKJfLeOihh7rNmsKAoN8aEwUFBQUFBQWFix1hE41Og6B0MoHsNUmsCaVO8t94JokR1yJfY6+DtikoKFyc2GwPXA2+IGYSmctVqUMtVKkcfp9aEdQsoQ0A4N1C7/W0DdoqhMFPEvvRaFKcnTedoJgmzi9aWC1bMC3mszpSbKXADfr4JkGSIGxhafPrJq1uQcQOlY0AAZDPMoK5VrfRMFv9wvmdk9lCViZw4akovD56Fl2l73fOMLLVTnGg4SpwqVufUkvdRdcE7urqqng9Pj4eesz8/Dx+/OMfi/c///M/H3rcbbfdJl5zr1yFiw+u2XOfyjsUFBQUFBQUFC4ShE3s+naEFjPh9w8ruQK31RZKmSiRt+GmMd9V9K6CgoKMWsPGsbNNnJptprq9Oy1wxaKmRXvgcgK3mE/WwgWCmDkfiLbXx1iFpSpbKIQtnLUiKLUWBK7H49em4t7MTBi49tIccj30tU0En/VQWPC2fltP1AiwbdIQeeV1K6rW8895SU9PRJsJ8Lop2x5dujuDF1yZxzvvGBX1Jc1HbGqcrSKYFkuXkN708V1bKJimS5VHrTJ/73vfE5Upn897iFoZcuCypaWlbrOmoKCgoKCgoKCgoNAFSMjrThfZk0wge6dc8Z5ceOC2o8BN4IEbfcb4A/qPqlFQUNhonJkzcW6B8SvFvIaJET38wE1STsoWiLKFgmkFCdxWvrAcXguFoA1Akks0ooqpzXKK6tpMX5AtnuehooZiPjl52247nygIG4Jl5CnTfu1cnEXRTKazSjxScn9XrlGPB66wUJDqha4R3HHbEHZOZ8T9bMcDvxXykiK4l4svXRO4IyMj4vXc3FzoMd/5zncAsAp08803I5PJhB4nDwZlYljhwkRko+y1v1FQUFBQUFBQUNhohFoopJdWL9CWB64jTZKjX4emKX0tb8fsNzWTgoLC4ENeUJJVn/0CQQxq4UHMbJuiUmevkxK4vOH2q2D9nrdxdgJGxOJaUgJX0xgBkUSBa9vJg6NtJEyLYn7JhK4R2A4xqbXofDdzw7NQB/vud9QCse27l3J4ML+XrckVuFr4jXftOtIrAMMgnMaCJS6G59V5G+OxnPg8Xf0awO7du8XrBx54IPSYL33pS+J1lPoWYFYLHMPDw91mTUFBQUFBQUFBQUFhgCCmNqnNq8ITkpU5lk0jJ3qe3ySYrHuIghZJer7uP65GQUFhgyFzV3G7AzaLeJNJVI+FgkMYVusuCZrUA9f2EbOcVBPnSpBGIdeCwG3xex+XGIDs8WtT6lpJ9HglL8mipKax75smxYnzJso1CgrugUtiy8BNf+NXJPk5W5V98Ifu8YR4SXqeFn92wpTZlLrEe4v127ZAABgG0DQBasFVBKcsTOx6zeD5z38+CCGglOJf/uVfcPLkSc/3X/ziFz2Bzl796ldHpvXEE0+I1zt37uw2awp9DlGRfe1FKxNxBQUFBQUFBQWF3iJ04tjhHK+drbDdwI1SHfzOPz+VCdu4bZRyOaRuoRByHgUFBQWg9e6AXp97vWqjXLU9ikhZBWmHWChw+4SMAWQTbo3nafrX0PxcQRw3MDmqY8dUBgf35ELTJq0W6MTW+/CT+NXQ/N4k5T1dfiP9ezpS0rBlVMdQUcPoELPdGBvWMTVuwNBJJOfSN/ArcaOO8ytwCTA1Fu6dYVvxClz+OaXp2R0QQmA48lgrws83jTN1TeBu27YNL33pSwEA9Xodr3rVq/CVr3wFhw4dwqc//Wn88i//MgghIITgwIEDuPnmmyPTuueee8TrgwcPdps1hQGBitCroKCgoKCgoNBfiJtn9nKRvZfRm/3ghKyVcALniW4eoY5T41oFBYU0YMYQuL1WTj56uI7Hnq3h0WdrePZk0z2vtBhnygrcpkPg1hz/2zZ8YYWFgvMTnmyU/UHYJRNCsGdbBpOj4YReq1Jqxe/674XJPVZ7baGQYPFT0wh2TGewczqDS3ZkcfneHC7ZkcXkqN4ySFc7Kue0we83AVdhJ+uHZbHfz79yGMU88KJr8g7nyL7ji7LyThvZY1m2vmjHBz82X0AgOFovCPuuLRQA4M/+7M/w4he/GJRSPPPMM7jjjjvEd67MnuD9739/ZBqPPvoojhw5AgAYHR3FFVdckUbWFPoYUfU5/a1zCgoKCgoKCgoK3cL1cWvvdxulAIpV+obkWdfYRC92Aif5MGqyajcF1Y7iehUUFKIQtUjU+/NS1OruydcrtvQd+6tpxOuB6xBm5SprF4tJ/W+BAMko3vv9QxOnKKVlJ1PK8q+jmvWmLzxT2wpc/qLH/Ibw5vUofr1WBf0CkR8n0JoNll9qU1BKA4sTYf371kkDv/aGCSytmZ7juIWE7I3s2UzkCEwB1pdnEpZOLCFLKAxuzWCF715Kg89NhcC9+eab8Vd/9Vf4tV/7NVgRe5B+5Vd+Bb/4i78YmcanPvUpAKwwX/KSl6SRLQUFBQUFBQUFBQWFLiBPQjaEcOyRlRYlwQR54JpOtlBGKnDl161IA+kAZR2moKAgE0TxHri9U076m6KGHMCLuv60chto2YwsXa9yBW7ynFGJFJYzkMT/tWXaCdMgDvEW1Q4HFLjO+37wwA37nr+XA6618/uNAD+n5vjYnjpnYr1qYa1MMTLURMYguHp/DoWcdzEgNqvE2zeHKaQ5qa1JZGsSnFswcfRMM5LEZXYjzpiihx16aqLvd77znXjooYfwlre8Bbt27UImk8Ho6Che+tKX4rOf/Sw+8YlPRP52fn4en/zkJwGwApUVvAoXPvwP4UZunVNQUFBQUFBQUAgizXHYRnngciQlRvnkLpYo4Wn6Pk9r26WCgoICh9xexVkobFQeAEbM8rxwsjWsXW2aFIdPNgAApTYUuP42lhNkst+ufGBb3uIR/rp+tFLIcosIP7SEl7lRBCnPpdajhdA0Ibxs4S12/rppugsC8udEsl5gn1M3YBjYIgMLVEY8YwH/PeDB35I+Z0trVqwClxB4SOGCvIjhU5V3g1QUuBzXXnstPvOZz7T9u1Kp5Algtm3btjSzpaCgoKCgoKCgoKDQJTq1UNjMc5AYZQAPZNJJsKBI1S4JfdkS/TzRVlBQ2HgkaZd6QQzKbZGmEdg2RbNJYehEkKphFjLHzjbxqX9ZAQC86JpC4vNxtaLfA1e2rwGA2SUTp2ZNlPIapieS0VhxgS1l8HP4lZPLaxZOnG+iWg+/F+2Wf7vNfPL8+95rXi/WsGPaSb8XIBKDy50eMgbBtQdyWF63cfRMA08cqWN8mNkjrFct5DMkVux3bt7EifMmMjpghNshs99St/8/cqoJXWM+z0QD9mzNYHQo+GNeVtxf2I+VdQtDRYLFVZa7u35QxstuLGL31kyC0kiOVAncTlEoFLBnz57NzobCBsP1R476fgMzo6CgoKCgoKCg4MI3eR4ExG0rDrsMHuAkyVZlf6IJ+NtEk2JCSE8CnSgoKAwe5JZgs1T+cnOUyxBU6xQNk6IgfUftYOP2+JE6KAUO7s7i514+nOhc8kKYHGRK/svPdG7BQr1h4+y8iWsvDXqkxl1LK6uDKFLw/KIpPICZotNLrLdS9m44QjxwaUp9+a6ZDJ473cCWsfQoRI0QTxkSAhgGszdYWLGgaQTZDCv/9YqN/Kgu6kOYMvvsgolm00ZG1wLWC35kM8x7t9bwPmjnFsxQApdbM2QMgowRLMtshiDrfP7DH9cBAM+eXMF/+bUtqe4w7wsCV0FBQUFBQeHChW1TnJozI7eg9QIZg2DHtCFW2AHmT3VuwYz5lReaBmzbYiCf7XWY4Y3B4qqFxRVm9kUIMDNpYChim+N61cb5BdMTBXpqXMdIKUbS0IdYr9g4v9jePd8xlWED+4scocKcLichSeaPfJJZrdtiO24rjI/okdHHOfwcadOkmFuyYNu0I6KkE9WugoLChYvlNQsLKxaGihpmEipE4xC3tXsjlJOEMKKqWnd9cLlC1ZIaVENnQcyOnWEqxr3bM4nJQir+k0hUThJLi2eeRS7CyO04haVIP6FXMB8qnl+wsLxmi8/WHPJ2+1QG2yZ1PHG04SVw22Rw2w4A2qH9kGsnkF76WycNjJY05HMpVjrfPScSK2tZjHzeOZ3BydlmoOwCVh+UCouP3TMZXL4nG3kuCmDrpI5iTsclO5hCdq1i4+T5JhoRcxW7hfiQEAIjhNh185vOmEERuAqbhqg6rDxwFRQUFC4srJZtnDrf3PDzlgqah9Q5eb6JxdWE0Qok7NuebX3QAODIqQaaUjCShklxxd5c6LEnzzWxtOYtq2qd4ur9g0XgnjjfxPJae/dc1wl2z6S75e1CgVBI9XCQxiflTZNidikZ+b64amFytOCOHRPMLw+famB+2cRaxcaBXdHPuH+Cm80QNJoUY8PpPAt+/z8FBYXBxJHTTdQbNs4vAhMjeqhKryWkxmDzFLgu6ckXMzmpxVWItgjixRbMTYviqEPgbp9KTjFxMo2nxT+T/xLfcQBbQDP0BApc/qLFoZx4Wy1bQDn4falAkMtq8J+y3xS4fs9ff7l1i0I+XUGDRhj3I9YjHDsFW1J4jw5pODkbJHnlomc7WdwPh4oadN/N8t8qjRAMFTXRl+s6YQRuxLBDBNuLJHCBTIs6mcbYSRG4CpuOAdqZp6CgoKDQAfiAP5fVMDPRewJwftlCpWZ7yEqAKUQAYMuY0TJC8krZxvKaFRlpfhDBr398RMfSavy1NZ3J2fQ4GyrOLpmhnnf9Dq76nh43WqpGVp17bpqDd529QJR1QGdpOUklGPSNlDRcujuLRqP1fbBs4NRss6PndHnNFtthk0ahBoBrD+SxUrYwORLelsmX2M4Yl8qzTwUFhYFCo0lRl7ZimxbtiMD1k5SUhlsF9DIwpEx6cgKXtZemaGubzl9dg0OkUhw9ywjcHQkJ3PWKjcVVNl4DJNWorz3XSFBJmpTcbkW6cezZmsFQQRMqy3KVYn7ZZfK4BQPx8ZdJ2/iw42ybYmHFQlPqf0p5Erp9v1Xf6f8+qizbyd9GgDgELihlQcCcfPDxu67z7+VxBPvrHyHwayWExF5PFImacapt1G5BLryOClxHABgRVT/N8k30dP3Jn/yJ5/0f/dEfxX6fBvznULjwEDUsJ+Ip3cjcKCgoKCj0Crw5z2UIdk73XtlYrVNUanaAcOTvp8Z1jLdQz9kdKDf7GZZNBSE34RC4cUoATmhNjeuglBG4g9gv8wH39ERr+4fTc+yeb5bqaRCwEXM8QgimEnrsNZoUp2abrmeib4LnTdj7llIqtr8mWZzgaWYzyfOXCEqCq6Aw8CjXvB1HWv1IUquANCGrODmBu7JuYWXdChyjay7xVXMCfSUhcG2b4omjddQblHnMhpBusk2EbXuJtzh7CU8aPIkWnVc2Q7Bti5vvhRXLQ+Dy3/u9dNu2UJBeL6xaeNZnE0QIwY2X5zu2cXLVzHy3TH93LpzA9ffd3KYiqxP3Wvhv4D2W/5b7KUdbHLivw2Ixcf9am1KYIQrvVn7KrRS4FOl09YlGH3/8x3/sYfX95Kr/+zSgCFwFBQUFBYULAxs9gNSd1XH/BMqyvd/H4ULbHSIrDJNcPye0dJ0MtCKVTwL0BJMsfozyNk0ffVGiIZnQItqKbkAiXrdCX5SRgoJC26jWbCz57Jk67Uf8w6WVdRvZDEEpT0LJwl6MVTze92M6anVD7OAB2Pb0s3NMbWsYCBBd26daL9RbtqswHinpLECYOD/323XzYUsFQ0jyNtv2EYNJ4VdZ8nGT/Hm75K0fnPDO5zQMFTQsrVmwLIpaw4ZlETRMKojqVmfylx1XCts0PnB8L5XcSSAHriNwyVx+fzVdyhzPq/CH8KZl2exQXSOt+9PQ8QCBoTM7kEYzSOC2JIiBWA/ctNDW8nGUhJ9/lxYGKdqtQheIaDB8z6iCgoKCgkJb0CKIuHbIvAsNnJBlUZRbXz/fiqYTwIrYrjYIEKR9AgUTP0YpcBnCJnau4qW/aoPIT0ieXQVWsN5zJU0c2dL2BNdjodD6V34VkYKCwuBgccXC08frgc/T6kcOOWlPjRu4NMarO03IbVHGILhkR/C8J524BppGcM2BHP7t4QoIAV50TQHDxdarxJyY1DTmmatpBHXHNkecXmp7/VYAiQnyNux7ZPiHifzncjLtDCVJCMHBfYW3jOnYPZPBj4/UsVpmgfDOzvuMWNsloOV+ZZNJ2jgw2wQSsFni9SNjELFJJdBHSmNT2Uc3yuIg9Py+99mMS+AW897v+GJAZPoRClxGTqc3kE5E4N52222xlb7V9woKcVBVJxy2TZ1tBf1TQHZC/0NKqbvNwOlBul2lTAty3uQtGxcT0qhX8ipkP9VRhf5E0i1saYErJQID/jbIPFfNkFq2NhW2pD5OchvcsiLAgCpwKaWirUqkwHUqaNK+7mJBnGonKeIUQN2gPY/Z4Ge6pFJSUFBQaBfVOussdZ2glNfQMClqdbvjfoS3U6WCBstm/VGjydIMO64Xw6okYzbuUWpowMtuLOJlNxZxy7XFxOcQYwznJBpxSUc+XpEX3zwKXLSjwO2s7/HPW8V8ViZwO4jnJfdDvAz51n1uRbGy7taprEGQyxKMlOJP5r8+4YHbohqKrzdpKhdGd5oWleoXgV9wq8H7nr+glAJOPQrr7+VxfVQdz2YIKjUEYmgAyYKYhSlwOVGflgoiEYF79913d/W9gkIYIutwn6o7eoGz8yZOzTZx5b4cSgW3YT63YOLomSYMHbju0s59cNLE3LKJIyebng40DKYFHDvbgGkyxf6OKQNDRQ3FvIZrD+REB3jiXBPnF83A9Z2abeLsvIlrDuSQz6Yb6XJl3cJTxxpiUHVuwcL0uNY35PJGIZfVcN2luUTRW8Mwt2Ti8KkmKKXI51haF6OiUaF/wSPPygN8mcyL8q+6kCFbInBE9bOyX65stzBo3bJM4CeZaCkF7mCDUtr2YpHm7NmMVeDyFz1qNi6+1khB4cIBJ8i2jOrYvzOLp47VUat334/snM5gclTH4qqFp48FFb49RQLVqtjR5BtTJBV1WJIC16ZOm+0j6uQxiu2TYCb2wO2Q6PZPa/h7ec7YzliSz5PkeXTDR+Dy+TC3VpgY0TtWXbseuBsvomgHsgdu06Q4u2CikKuL+6tzCwUK/p9Q2/ovh1soRM7rE4hgeeDBhi+QGaVUWgwIT59Iv5fB72ercydFuuyIgkIn6MPGZKNw9EwDTZPi6Jmm5/PlNQuUstWn9Wp/zCRX1uyW5C0A1Bu28EuklApT/0rNFh0VwIjapklxes577SfOsc9PnPNtHUkBK2V3Rdy2geU105OniwX1hi0ivnaCpTVbDKpqdRvV+sVXhgqdYaMVuHJgIpnMS6LATXG3U1+gHf/fdonPfgW/ZkJIex64/dHtbjpiLRTaTiyYVhpI0qbEHaJpbGKeqgdupxd5oTQ2CgoXEXh/yfvKtPoRfzPibx56ubAkCL+YY7gnbqcB1rgvPyfbCJEUkraXvNW04M6YpOUrB2RrB1EWCvLn7bT1QhEr5ZuThJy4zUhBtIBk4zX3BFHni+9Y/NYFGw1B4IIR2vK4Q9MIhgpMgevh751y8dMSHo/aDvtTXp9NXwxj+Vxx9yUT8jxU64xZZtfQfUefYghVBYX2EFV/L7Rtq0ngf5g9b/ukHHg2dm/NYNtkdNOxtGYhn3M7ga2TBhZXbceovseZbAG+9WHbFgOzS07LTIEXXFW4aNYRHj1cR61up3ovNvu+KgwANriOCAJXGoC1S+ZdaPD4/7Yg4fx+uR2TdpsM95qTHe/Wm0G7UgXAURqF3rroLbQaIS0JXH907LQxqM+XgoKCS7ZxxWO3/Yh/TrgZHtk0bPXOB77F3L9LJ2k76Y4zAFhwxhqOAAje6yXwKkmB5OXbKdFNfOMGXSKaOdoZSmq++8iEWuw1t07I+tSbnYxV/YR106R44kgds0sWFldtYc/AsdlCJm7Fx/tvQoDhIsGe7VmcPt9ENqN5Ap3JOHqmgTPzLPBbLkPcwGcRYz6RTuRYQd7B5z1AfhtVxynCLRS4t3NaUASugkIfQm4k+mVAL3va6jFb7zUfOSImR8CmX4y7oilFOkUweuqFjDR4K//gUvlFKrTCRke5FUHMpLopLAQSknnyQO9CgOz/22qCxYnvttQffYhWg3k/eN/m905WcCGqTpvPRa+2cLaTXJjyRdPYRL1XpH0/bllVUFBID2J84/QzvL/puh8hnj/R5+1BI5NEtcrbzDDCKgksn58oV2KyDPi6GOIrT+JdoI9DpwtwfnsEocCV57jtBMsSFgrsvWm5fVLGZ6HQUfrOX3692Qybr1sWRb1J0TRtNJoEtUZ4xSzkNqez0ogzJ5eVt4R5I3Ni1x8ArpgjKFcpmk2KZpNfD0HW0ADYQrEbh6jv3R183s+9O9PCyyprkNAgZtUGZfc4pWGGInAVNg1RE3o12PUSBv3mBdzq9vhzKzeAYTzfRm7ZsIUCz5ODDTt/PyANhXtgy0p/VVEFhdABWLtk3oUG10LBbfOi2gG/Xy4ZUImg1UYAM0CaeDt+yRebP7ofYcqlfq4KFOFEcVx/p2kEGiHxZEubC1BhlhNJ0GfDPQUFhQSwfURklIIvKSJ/taEKXPY3qvk6PdvE/IoTaEseU7RxDlN44LL97oy4c5WWfgWuTb07OZOWr0tGt9ef+8eKwgPX0x8mT9NV4LIMcfvEjEHEWMPvnxonmGoFXSe44WAetQbFqdkmSgUN0xMG9m7LBI41dKCQ25zBMfc+ZkHG3QDusmJZtkSglOKyXVmYNsHCsgnDIb1HSzrGRzQsrLSnXPbfQmGB4lvU5fctrh5pGsGV+4KexbU6FerqNPp5ReAqbDpUBPsg5E6pX8bzSSNIB+0gaOyEbyPnx7xYWafsXM/Gnb4/kMLk2/9bqtRqCi3Q6Ra2TuEqKSUFrmwhkAAXWttgSwrkVtfmRofubZ56DVl1nASy4tiyL16yn6PX1gGpoB2CVH4tjWk0koy0Tzpe7Zdxm4KCQu/hzi18Fgodjo397W6rZqcXzXPcmK3RpDh+riliaXTtgSsTo85rP1nLg1zJ2UrugdtZixzlgeuxUGhHISuujZXh/DLzT5CJ0wCB20H6MjIGQcYgKBU0FHIEpTzBcLG/Bjbch14M14n0D6wuehZkAei6hkKeoN7QUHTKr5DTYNtOn66FE6WeIooY3+hiId/7ud/rOgqjw8EHotawMVJKr9wTEbif+cxnUjthUrztbW/b8HMq9AfSUAgOOvrSAzfhRM6fXRZZ1Fk6C7uWDZwYhq3C9knxbhjEimY3pKuv0KyL+WFV6EvEKXCTknkXGtyAIdKHEY+uLcrKUeDGH963aFeBSwhTwtg2hWVTZC44Gj8FSKqYdrAh3YQkwQ0jW+VPZEW+JrUXYZOzXme9rwlyBQWFWLjKPPY+SsHXLvzNwkb2v3FzPsumoAAefroKABiSCcE2MrletbGwYsHm7S7xcgBeApcECLVqnSlLk6LdBVn/Yh5/L6fTlgeuZK0h141Ld7mK2IyPmUvNxqqPF2M1wvJl247aVnzmKla5Apt9IJPp3guSY12EwfWTpogiIKKeXzthGe6aCdKrq+s2Zibif9cOEhG4b3/72zdcJakI3IsHfdiWbDrkTqpftqcn9rAMWbGKm/xv5P3nCjTWifLWfwMz0AeIMoJvB0qBq9AuNtoDN2wA1j6Zx/72m41Np/DbIsQe6w/+NaDtZCdevrrGJ1i9ydNAYYOf206QKG8hBwlLJXg9s9Mg7UnE61a4MFoaBYWLC0ELBfa3UwVuVEPgH4ok3RnZTR5Ct4tT4OljdRw5zRSkw8XOVsWfO93A3JIJQoCJER26RqDxIGay3yjhO6rYe+65W2/YOHEuWSHLwVi7hVwm7dgsaRIhyef22QxBLusOUAghMHQi7CXasVDo5346DvK8lNOqfGcM4N3Jy48Lu5eEeAPjtepPo4OYsb8BD1zn+FZzCI0QvOO1o/j0XSuizs4tm7h0dzZgDdIp2rJQ2KhJjNpSf2HAtCiLCpgNnzlF1qcUtngPOuRtv/3CHYT5yoXBTzjLi1z8nsvXt5GPu9jmJK3yXqwVrZv23O8V2C+LDAoKHN6t8BS65npcDnpgrk5hSdffysfU8ilwOQaNzHYH88k7Gl0jaIKqdi0Cne6SSjqG6AaSANdLotLgSeXFHV49onxwe13vY3cpKSgo9DWEMs8ZW+iCcOxSgdvCQqGXzRIFm8evVWwsr1kYLmpiPEABPPhkDQBTjP7Sa0ZwfsES3yVKn1JU6+zoLaMGpsZ1TE8YePpYXaQTCJjsvC/mNeyczgQCfsVhpKS15YsaB/l+tNOfuRYKVJqPBhMwDJfA7STLYfWin4duOrdQ4ApcX72n1MsNUhCsrFvQNIKVsoX1Kuu4NQ0oOJxTq3KLKw8hAAkEMUu+YLJ/ZwYf+PdbcORMA//rX1Yxu5SuIiARgbt7925Fqiq0jfufYFsrbrqi0FYjq9CfDS0NmxXFHKcRApuybaiyEbknrdbJpQo5iFnCy7ngkGYAGn6PFdGhkBQbNZSQt7hZFhsgulum28tEP7bHnaAdD2BXrczeD2o72QlpL+w3utz+eiFBngP023ygrexIt9QKUXh1rJiLQ38Vl4KCQsrw27N1q8CN6nk2skeybBvHzjSRzWqglGJ8RMcVe3MAgNWyhWdOMOuCT/7hNuycNnB+odpW+k3TJcQu2ZHB9ASjpGSfWD8xLu+a2D5lYHRoc/yw9E4tFPj8y44nA7MGQa3unKudQAQDKoBz56VUELhEI4HFYv5+ec3Gk0cb0DVgrWLjtGOjUSpo2DWTASHOPWpREO6ist8qg/0NBjFzvk94Swp5gulxVq8XVuyOgxqGIRGBe+zYsdROqHDxoVyzkc0EG1lRjX0PAn+OLIvCtBj5d7FFgpaf8X4hD9rdAq1pgG253jVyGp42bANvrRxBUrTXpE8KeIMgrruLyxblyO+xYnA3FZZN4yOo9xia1l7E140AIQS6TmBZFI0m237VbPpsAVqm0cMM9giWxRZUmmbwmaw1KCyLTRxMixHbTZOKYyllnwNAtW7DtChsSsUxpuWUY0ja8rk5DD04MNa1ZP25Py2ADZrbjcjM89rO7/ixjSYNvVbTooF+OewZ6CS//YY42yObsmcrKfhkqKcKXCqptuQtl6K/dz8zZQWu0ybUGzZyIYKDRpM9G6YVXif8aJpUWHA0TQrb1+b4nw3+Kiz9pM+MgoLC5kDetg24fYH8PLczTvL3L60WzeSvmyYN7XvbheW0dznn/WrZdrayEyyv26AA8llg10zGo5RNMme1bVYuDZNC14lH5BW2O1KeQ1K6+cFFx4Z1jI/osCxgZiL5ZnaZnI4jA+WgcJ3uGGua3nGKJQjj/utLCIEIUsc4AyJ8cYEgX2SaFARAPsuClvGd3vmchlJBQz5LUKmHV8Qkl6/7Fgw4kgYxk/M9XNKQywL1BvPB9VxPF2jLQkFBoRO0elbCHqZqneKRQ3WUCiygyDX7cygVLqx9r3Fb8uRGo9+osVA/JAn8snTHw8embmNIfcdsNMIb3/7rzHqJTre/yuA/1TQAlrJQ2Ewsrlg4dKKxqVvbNUJw5SVZjJSi1RBuQIyNe950jU1CHjtc837eJqk2KNV7YcXCMycaODvfxPJa9HatSt2GrgEnzjWRzRAxwTx6pol6wztiXVq1Mbdkod6gOHqmAV0noW3HesXGqTnT07AU8jr2bPUOM7MZgusP5mMnsnPLJg6fbIbW6b3bs9i+JdnQdXnNwrkF5tHXiQL38KlG4LvziyaWVoNlSwjBrhkDxbz3RPt3Ztua4PUb4tqV9YqNB59qT3XVC0QGK4n5jWVRPHm0Lt7zMcEzJ4L3HABW1y2cmTdRKmgi8nocmibFEV5/CA3U91Jew7WX5ty8O3/kPHFkDILrLs2rnWwKCn0KPxnH+5BGk+KBJ1kbqWkEV+/PYaiNuWygaaOxb3H4ZAOzSybyOQ3PuzTX1cKPyYlnR0RlWRS1OkUhT9BosO8yjhdtO+O65TULTx9roNqwYZrMZlEek3n8UMW4kf0VilwkI+J6hYxBhBq5HfD7YdN4MpCXK9CeOIKX3dySibkls+38bRZ0jQgCl0Uo895jWXkNuPVizzamtuVlOD6s44p9OaxXbDx2uBY7dpctOvwlzOujTSlsm0r3rT0SnDj/towZOD1rOosgiX7aEhcWI6bQN0jicRpngVutU/Gg2Dbz4LnQEEV6UUp9q5n9QR+EqVpCj3P+up5yrvk4T8KzjWADLy8sguTFNiVKw0JBkPS8U7vwHs+BwWrF3vQ2wqYU69X+aKdkTI4GCWVdJxgbujCHPqtlC5TSWILJ0AkKObfV43fNsmmAvNU0glLBSzBFoVqngU69WgsSnY0mmwTGYa0cXadX15P7iHFfNAAYbeOej4/okQP0Si08X7Kfn4zV8oXXOA4VtY7JRE0jPd326o9cHoVqw3vQSCn6ngPt95cZg6CYZ2qgsAl4uWZ7tlePD0fXz6ZJUa1fePVIQeFCgX9ukcsSlHyLebZNsZ5wLutvb5JsnKOUYn6F9Y+1uo16G7sjwmBy+yEDKOVZDtadsUXNR+B68xGf7vGzTdiU4uw8IxgNnaAojUk4Gcfmwt7f2jZlAawGdAgnz4vjyECZ1G1n8TkTslbMgrcRERytH8e/xGE6KXUDlLF/4X0y5SQvieCYYoYnYQpvP+QuW+6nxUJNwiLkp8hl2F++62fDg5gpKCRFosoZwQcSQkBFHEKGNH1D+gVRpFdoELA+QBj5GQY+8eYeUJRKql3qfiaOTzGPreA2vkEC42KBn0zvCEJlzf7a/VJJL2Js22Jg77bMhp/32ZNNzC+3XunfjBqyb3s2UCbtKEWSDPT6EVvGDIyUNFx1SS6SLFstWyjmNeRzGm44mIdluSrBF15dCJBO1RrbWp4xCJ5/ZSGQ3rGzTZyZa2L7VAY7pgyhOnrRNQVR5g88WU20/Zxj50wGu6bZMPX8ooXnToerI6PAm6WZCSNWHe7H1kkDMxPhxxfzBOWqjSv25cRE6MipJmaXLeyZMbBjmtW3M/Mmjp9ttpXffgS/W/Jjk80Q3HRFsA5sJggh8QtZ1PMngMlRHVfvj1ZUzS2ZePZkA2PDOq7cl0x5dcu1wc9sm+LeHwdVy/u2Z7Fvezbw+aPP1FCupafaUVBQSB+2sFAg4u91l+XF98+caGB+2Wz7OfYr9AOQ5tG1OvWIp8wuYybxvtrQCEoFDWsVG+UqxdQYhHVOGIHbCrksQbnmltllu7M+Ba57rH/e6eqIBlN6I8eDiVPgyuPUdnaMDfkWDQYlBpFGAA3EtVDwkbMu2c3fszofWBv1vU8qbvFPCzSNeOL4ZOAqp3l+20nPVfTyjCXKViwUgavQE3TNt1JEBpu4UCC3K3Get30zcE8mwA2Y+YeJbe2Ia+81eCfgaXz7pXw3CGJ7UhfX7Y+e2jd19CKE19d54wdq7sC6PytBP/p9bRTiLp34F9Xk72LSbHWb47Y2Jr0V8mYPns80gy8mQfS2fPacaYRAc2ZeGg+2IT2DabSzCt0h7BZ2uijTbgyAKFzEzZGCwgWLpMGNEjc7MTs0ow5b9+28MbsMwmk66/IaYUG1AJe45ererETgtlxEc1DIMe81Po/I5yIKjUrEuDyfFATf4DWmIhgbjScDO3W+8JflIJC3gDOGIl5vYDbWYq+pb5xKxO/CxXBxV510LKnrgG1603dJ92Tlys9h9GC3qiJwFXqCtCctF2I0aFm16CFzfQ94v1x5mBIn9DifOpPawVU0e7MsFHjjK1soDEb/ljpSEOC6CtwLcIFFIRmSEiLUt4I+CNho0rBbUP/oNgZJBriJf4BoAp+rKTyfxScVmo9OiLekfVYnGKR63BUS7ryJTYJSHDvbdLfd6gS7t2ZSnVwSJKsa/ongRkMmHRKNkwesDVJQuBjRaodiuwvd/q7cn+zCioW5JRPHzjawXqFYWDFx749ruOXaorBjMS2KWsPGiXMmLJsilyHYuy2TmHwybYqmCfzVF5exZ1sGv/iqEZF/brckK3CTtsEcfN6Q9/UD7riLBsY0LqE7mPZ3suglbEcoR6f97SCS2oCrtmX1i4AQZr3Ir8YvDOPQCIEt1Tr/1bd63OLGzLpG0AT1CAj9SuBW4IdxG4w0xYg9JXDX19dx5swZrKysoFartaXQue2223qYM4VeI4qclBE3uWI+KO4PL0SCyLOq41Gk+iW4G5OfVkj6+Apyz2P67Z01eSwUNlSBy/4SDYPZ+6eAVBSTnKSXDPkVNhcDOm7rb1wEZdruo9vqeDKgE6ukSEw+9zQXg4W1ii38DjmGihq2TqY/BZF9E0PXITpsKMV9VzdWQUHBB8r9YiPI0U6V/8Et4ezv8bNN1Bo21so2ylUbn/inNQBAw6T42ZcOA2ABXM8vWB6Lq4kRHWPDyeyETJPi/JKJcpXiyecaWF23MTHCfssVuJmQRThOwkVB7CJ3XgwVvdv+5V/6g1cltfLrV8jzL24NGXYtY8M6Tp5vdtRfbdti4Oy8iV0zG2+p1ilEoHOH2OaK3FjLP8IUuFnpmUtk6ZGwSAXpKgkIkyrt/TvcDCF2ovLHXSH10dNTTz2FT3ziE/j617+Ow4cPd0QSEEJgmoMTPU8hCCqbPkcdE1M1ZM4PuDAtFDykrfQmoMDtE3IssZpJkHvB3/JriRLg9nobtmc7ThtqtQsJHQ8kJfCfyob8ChcnEqtUU9qCvBnolza4FUQ+xSQnzkMh4retftdunuRTEkej00F5dqSG3uDJ3qDUk3aQhnUA92LMZzUYBrBesTdm0S9MHMD/dniz0qxKSbKQime9goJCLKo1G+tVG1PjLi3SaFLMLZmhbZWuE8yM69B1ZhvQSpnXbv/lb5/86TYdUmlmwsCzJ11veJ0QTI8bmF0yYVo0EMisHTGUaVGUpUCgR882sWc7IwXrIohZ8vQ4eHkZOsHMpIHxES+hLK6VAucWTc9nVO7TB3AwKfvdWk6/GEYGDhc1XL0/h3y2/YBje7ZmMD6stxW4dbPhKnABiBhINPDc+IuKEGbJcdUlOTRMinFncSLJmE9WQYcdzn1rZ5csEYyW/20VxEwo550xr/DAtalz3u479NQIXNM08R//43/ERz/6UZGxsAwm9UhRGGx4CLo2yVf5wdN1AsuiF2QQsygVaiCI2cZkpzUitjAEDhPbQoK/5ddie6I6tlZrpwGZZNS0dIjMQUQa28LDAtUpbA7a2TavcHEhSZVwx2st0kpKJMVMrKR5WYs8eY/vJ4Rdf1ib2u/e0BsJPn7LZVkQvKSR2NuCtDYQVuLCwiXi563uU5q3Uc2DFBT6C488UwPA5pxcZXpqtolzC3Fisiy2bzE8c7YoZV6nCzFhFgqUUqEKHB/RsbLutqeTo7oYl5uW61nLYbWRAdMCVsvu8cfONEFvYK+FB66swG1jfda2gZGShqGC5hH7AF5LgeU1xnLyY2yb7c5tNQ/tV8j1g3sUR5GB7QRe9ZxDI4lV1v0CHsODe+AS5zN/nBW/rRYvT3+w3rja4bdliAJXzYYFaW4ZWI6Lm5yTGM7xaXJZqRG4b3vb23DnnXdGkrYA39rkX1VyC0ENaC4cJLE4jVJ0yuJIgxO4XUbT7Ed4VLcRZC573x/PhZ1wVu2qM4mYqAjfOaHADb+mXl6pfEpNJhkGcxzQMeQonp1C+BwrCwWFhEis4O8jJB3oDSLavQ0tN1606ui7RQf1ppf3bZDqcTcQRdjF9fLxm671jthudT9anbdfH/GLdaFZQWEzUK3ZgEPg8p0DIyUdxbzbwKxXbaxXbOED65lbRJBx7RK4YcfVGkyRaErzYV1z1bgAUGtSsY3csiiaJicJCVP/taXABVbL7smePFoX18wVuNkkW9ZF/m2UqxSLqxbWyjYIiQ++W21QkIqNmQkdY0M6FlcsNExX6TyIXTCR5sWWiMkyiFeSLjSfhQJX5No2Ra1Okc14+QdeZK38nFs9b3zxI+wW7N6aQS4b/ELXCLZOxNOnGb6IYrJ7runeufJa1QYh7NnKGJ2R9anoq//2b/8Wf//3fy/eX3755fj0pz+NU6dOodFoiAHTgQMHYNs2FhcX8dBDD+FDH/oQDhw4AEopNE3DBz/4Qdi2DetCZOsuMnjJyfZHnpQyB1xDGD9feKNXr4WC/Np7rf1CHiRVRcnBikSj6N+uGxLVkX3RTQ7jIRfrxdxfytuTOoVfZd0vdfRiRL+oFVUd6EMk3Ebm+UnLKJWtTxmZQkLyLo403Ox6Fnb6uDZ1s/PbD+DjN00jPW+nPNsipZP561Sn9yWNscNmt9UKCgou5DmX7KHJ+6ktYzou2ZEV/6bGGHnD1a3CpzWGjHTTbC9vPLmVsoVjZxo4drYplJtMBUhgmm6i9QYVaj/TooLwzGfbF1uYFhVbxgGgXKP4/qNVAO61e4KYxbSttk3x2LN1HDpex/GzTZxfNEGIu4sv7JqXVi2cnm2iUqM4OdvE08frqNXt0MCovUTOsTEwWqkuE4LnnauoyeA4HfQMchAzCmccSYAnj9Vx7GxDKLH9VmyR/G3MreLP6GrZQs1ZkAg7vJjXsG97NvAvSQBW/lzYlMK0qOCybBuo1m0cOtbAoePsefjxkTpW1tvnPVOpNn/2Z38mXr/sZS/Dgw8+iLe+9a3Yvn07DCPIUo+NjeH666/H+973Pjz55JP4/d//fViWhT/6oz/CO9/5zjSypLDJ8DTgXUxgdCE77y4//QjqsxHgg4WAhUKfTQBbzu+lLd2icRXKW+/fwG+7zVwMZP/bQY3UmSZSiGEmVj8vxAUWhWRIasnRb+1YEgxaO9FOGUd69UUe39nEtJvbLp+zEzW0q/pO/z62SnPAqk4k0lggskWAHznhLhIMQeL8RdXTTWifkqiQxQ7GXmdGQeEiRVMiQOWt0WELQYBrG9B0dlYniUzf6XPMk1xcYQRPveHuSDV0pqqVfW7rDWY5aFOKRtO1Wsh1QOCWq0wxCwCvfXEJAHD4FPPb5efMZYJ9dBgs27UMkFW7YUHftk0aKOY1FHIaSkUdIyXvv9GShkKebFgfe+XeLLaMGbh6fy6V9PgluwrcVJIdaGgaga4BW8YMTIzozOaQANU6qzPLEUrZLvUGApNj6VpOaBrxKOE1IUaU/aOJWBSoNdrv4bu2UHjuuedw6NAhAEA+n8dnP/tZFIvFxL/XdR1/+qd/Ckop/vzP/xz/63/9L7zsZS/DW97ylm6zprCJ8KhLW2yt9E+ECBwVBYKR+y4k+L2IbAropH3P4I1C1GAm8jg4Ck3L/SyMpI7yAk4bInopn0T2iXJxo5HGhNC1UPC+V9g8XCiEUT9h0Iu0F/lv9ah7dl50eo5+bk/azFs/X8pGgU9UeUwDoIflQhHft3dYN2nEeLUjEKiKoaDQJ2hG2NxKWhQPeOAurkKlCYg4dwEy2YPvP4xvwaZU8k4FxTMnmlhYdtV7S2smnniujnMLJi7dzX6jaQQZ/vuEc+mjZxpYlwKY7d+RBVAWhG5dKHDDMh/8SBbQbN3CgqyBIOB/CwBjwzp2b2XB0vI5LUCcVuu2h3TvNQp5DZftzqaWHu9CXA/cQR9pdg9eBFvGdJg2xeyC7bHJcIVh3EPB+V1E2bURvxc7pjIdBYtrhaxB0DQpZpcsLDkKYsumzAKFAhMjOkyLYmHF6mjM23WO77//fgBsUPO6170OMzMzkcfaMeEP//iP/xi7du0CpRQf/OAHu82WwiYjCSkX1TnKBxgXiQIXcMvJT+z2y2TW3362Avc3kn8rgplJgwiPWLuH1yq2/ftsHfqkeDcM/Pq78iD0EbgX4PqKQkIkteRot/3oJ/SLD3krtJNL/8C4V9coJ9uJilb8toOK0ypwVTcIS3NAqklH6Oa55aRt2GS9lwitM6K+h38ehV7c2nbSHJQ2SEFh0CAH+pIt/1opcIWFAp9bxLRvHbefzu8Miaji5KlpAw3TG+S7WqPQNWarUHPUi9kMEXlLOlZfXHU9fjMGMOb4ApcdUrfJCdxMskZdFtDweT0BaRkMKhMjjBzAoSQAeeei835QLyRF8DKwbXj8juKemyTEd5JuM+657QYZp50oV203XoyjRKfO93yM0skcuutsz87OitcveMELQo/hGazVapHpZLNZvOENbwAAHD58GI888ki3WRsY/MRP/ITwzkny79ixYy3TPHz4MH7nd34HV199NUZHRzE0NISDBw/i3e9+N370ox/1/JrCOsHEIJLCz2m8LevCG7z6H1i+vhEgdjcmOy3B89WqyZQVuH5PJH4t8u30kv3SQCplVpCXr2j0+6VgBxC86DTRKanC3Cz0iweuQv+gE7JcGjPHH9eiQ2ftfvuqiKi0+g1hVx+2q0HkXTWNHgUuR9p8pGzlEnqP+ug+dBqRXkFBIX00ZDVn2NzET+D6/C0FgRvTwbUdxMz/XvqgUvNuJ5cFTg2TusIKJ2NZwyXCYnR0Ak2Tot6wUW+y96WChtEiS5QTuJxEzoZ54IZdjySg4VvLxS7NGBghQdIGvd0MeOD240Bng0GkBYaA2AryPMe9+UkU70m+7BWBKz8bPK+mRUXQwaxBAs9qO+g62+vr6+L15ORk6DGlUgmUUqysrMSmdfnll4vXTz31VLdZu2jx13/917j22mvxoQ99CE888QRWV1dRLpfxzDPP4OMf/zhuuukm/Mmf/ElP8yB3ElHVkkYwD/LuMtk0/EIjcQMEbp974LqkQHxv4660uqu+/LeCyI0YRNCI12kg4FPl3Ylx0aCdgVwU+LOrLBQUEiNCzdLXGLC1nrbI/HbJ1ITWOUk/bxfdVJue1LlBqsddIA3rAK4Q07X+ff779RlP6jGuoKDQGWQCN2z3qL/J0jTXu7LRpIJ8iffADaYfB7/6VxZFcWVt2I6WpslIT10ngtgdKWmCXE4SVJyTtHyOUMprGB5y1IQ1Fq+F+3i2CubEIQtoCHELttU8JBtC4Ar0aV/SCpzWcC0UNjEzfQJRPyUCxG/HRSkVt5wJGhMknOB5C/NhTgNioUKyCqE2YJosW56FlQ46+K49cEulknhdqVRCjxkdHcX6+joqlQqWlpYwPj4eelwmkxGvz549223WBhJf+MIXWh4zPT0d+d3f/d3f4V3vehcAQNM0vPnNb8YrXvEKGIaBH/zgB/j0pz+Ner2O97///cjlcvi93/u91PIuQ+4jjp1pYGk1GGHv+NkmCvmIB4eyCi4rNiw7PGLloMLvRcRJzSCB2x9D93bzQeCuOvH+mqcgDyLkBjut4HdhEKrui5S45ejen9K9MWJbSH9U0YsSSZWTvUafNFMKSGBPFHJsUo9zdiyNJvR4+0pIoM9IaqFA/YttId9tFsLOT0K+U8SbC3fy7n7Wq3KhNPweUVEvOzt/XJ1sFxfr2ENBYbOxsGLh/KLX9JYrWoH4HRYyMgaBaVEcPtVwFYNxFgo8/TYbHv4703LzV3EIXOqkJ5OgTZNCI8D+nVlQm2Lvtgy2T2Vw8jyT08YRppZFceR0UyJw2XkKeYLRki7yX65SQXpnMwTVmo3j55o4fq4J0wKymToKOW9hNE2KuSULhZyNXY6/LSGtAyAbIRYLg96nEl89iVNuXywIe3Y04u0r/XZcccRr3IKz/6teWTvJixuybQYfS2QzLoHbSeyjrgncnTt3itfnzp0LPeayyy7D6dOnAQAPPvggbr/99tDjnnnmmW6zM/B4/etf3/Fv5+bm8O53vxsAI2+/8IUv4I477hDfv+1tb8M73vEOvOIVr0ClUsF/+k//Ca9//etx8ODBbrMdgL9NXlkPErjlmo1yNX4wqxE38AVr6C+chs5fRlxgHCB2+6C3CpuYtjqWELfRosJ4jv2JVGiH72RKBXw1mnee9CLtNDsdSHLIv3O3f3SVJYUBRtKgeP3QjrWLQd3qnKRla8G/Jj6eo+dF1AHxFrUFNg3ISYaWzQXSvaRxX10FrqSaSbnChN2D8AUA71//5y3P01624pHgnL0qLwWFixEnzjVRrUcPWMMs3cKe+WKeoFoH1ituWrk4NWq7/Zd/fmi7DRf3pg18B4jgXhoBoBMcO9vE9qmMFPsi+pTL6zbml11ymx9byhFkMwQZgyl8V8qW8P/NGMDskoXFVQuVqg3Tolgtax5fYYCpehdWTOSyGnZMMeqJIDq+jaEzgnxyNFq1NahTOD9hO6jXkSYix6N+GwXpg0RigwTn7pUC15AYVt3newywZ8cVQbXfwXdN4F555ZXi9YMPPhh6zI033ojvfOc7AIDPfe5zoQRuvV7H3//934v3MjGskAwf+tCHsLq6CgB497vf7SFvOW6++WZ88IMfxG/91m/BNE184AMfwGc/+9nU8+InIbMZgj3bXIV10wSePdkI7UwIcchNrpjUAMsCTs+aQpIOsIdj66TR0cM3t2SiUqNomhQLK6YgoIp5DWPDwQ5jed3C6dlmy8iXugZMjRuYmWSP1vyyBdNkkRUzBsFq2cJ6xcZISRcPbK1uY61i4+Gna8gaBBRuVEJKgXqDopDTYFoU88uWWBVdr9ioOAMRywJWyzYoKDIGwXAxekmplNdQKni/X6uwiI/DRQ3jozryjjH9StlCuWKDAjg7b2KtYsG2qFBGjwxpGCp4y+vMfBPfe6QKkAoopTi4OwuNAOtVG8+eaGD31gyaJhV5n10ycfR0HfPLNmoNG+cWLMyvWLhmfw4raxZsygYoGgFACHQCLJdNzC/aMKXIpiNDGiZGdOzd5kYLrdZtLK+xsqw1KVbWLdTqOhpNikrFwsq6hSOnmshl3N0DhbyGqy/JwjCiy9CyKU6dN3FusQlbWpsYKmoYKQXrz8iQhvGQepUWFlcsrFXCR0LDJVYuAiEDyVrDxuySlWgVUO5ohEUGpTh2tukZ6OZzBDMTXXcxCi2QanR0BQ9EiQ4qeZJITdt+spTGDbiJOHWnxdbPhHm7eevna9koWE4fuRFBzCInQt3ehxTvo2qqFRQ2B3xquntrBrms+yA+d7oZsOkT638h7db+nVlMjlqe3S6jQ3Fko7PQ3a4CV2wtD/nO+St/Z1psvjm/7BVNkQS75fiW/lJBw+6tGTzyTBUAUCwwC4ZiXsPKuo3Vsi0I2lyGiDZ3pKQhn9Owf2cWxbxbaKdnTUF0EwLw7BJEWyNefzCPepNiSJqrVus25pasgReMbJQCdJDgoXGcuT4hXgmuj7/tOPib/2e9srCQiXri87qllKn4u7E07Hp2fcUVV2BiYgKLi4v4/ve/j1qthnw+7znmTW96Ez70oQ8BAD7zmc/gJ37iJ/C2t71NfF+pVPCOd7wDJ06cAMDUoy996Uu7zdpFhzvvvFO8fu973xt53K/8yq/gj/7oj1Aul/GlL30J1WoVhUIh1bz4O4mMQTA15la3mrOCGDYRlBVdhLDfNpoUs0vebS8A8xCZGm+vGtcbNp492QAAzC1ZWFiR0iUEl+7MBKwbnniujtnF4PnDMDZs4vK9WRBAbFsZG9axddLA08fqAJgy9bLdjGg8t2ihVrexsMI63HqTIpchYgWy1tCQyxIsrloiD5RSnJ5zO+hy1Ua17hb62IiOKP5RI8D2KbfMbJvizLwFQoAdUwaGSzp2TBmwKfDsiYZYhV5et7FeYSusvGHKGBr278x40n/s2Trufrgq3i8sW9i33cBahaVzZt7EzIQ70GlawIlzDayV2fc/fo7dm+VVRt7WG+zaNMKiNpbyGk6ca2Jl3dviDRcJclkN9vXuSvjxcyaqNe9AptEETs81cWbexFqFwqYWnnLuC8dIScMlO7KIwuKqhccO1zDnq5PyfZVxdoHgBVfmE0XNbBeWTfHMiUbkxFWbJ3j+VXmx0BE2kDxxzvSsvCeFoRNohA3gzsw1A9+PFDUU8mp0ciEjbPt4GMRER7EXPUM79gIBcrpLgiraA1faOdOmCkmuKp2ooXsZ4O9iqcZpWAcIBW4Pg5iFIVSBG3gR+jbyd2ne9yRF0O2OGQUFBRecQJkY0T0k49IqU5/Kc9e4wM2GTrBlLPncs91mw/+4y0QntzAiIao+9n0wPT71iAuWxNPJZwkqVRtVJ+58MacBhKmOV9aBlXWXwM1miCCf8jkNIyUNk6OGh3idW7I8fTG1ne3lBF5xiYSMQTyCLQA4ed47T+mVcrLX8F/XxTKWiAMh7lwScAUAMglq29JuXpKMeE0yJu4VgctzLnvg8uvLZgg0jSRSxkchFXnU7bffjjvvvBPr6+v44he/iDe/+c2e75///Ofjtttuw3e/+11QSvGOd7wDf/EXf4HrrrsOtVoN3/3ud7G0tASA3cSf+7mfw9atW9PI2kWDJ598EsePHwfASPV9+/ZFHjs8PIyXvOQl+NrXvoZyuYx/+7d/w0/+5E+mmh9/ZfQ3tJogkeLVEgTA/h1ZzK94SbilVQvVui18gdoB/42mEYyPaLCpjqGCjvUq+2Jm0vA0sM0mxckSQaWmYXxYw+hQ+NNeqVEsrFoYLmnYMqoDhPktNU3WSW2d1HF+0e2stm0xHHsIYGHVQqmgoVy1sVaxMFJiytbVsoXhooZtW9ij2jSZGjefAygYYTYxomF2mSl7bQvQdGDHlgxyPh7RpsDyGutpL9vjftloUqxW6rAsYGxYw3BRx7YtBiybKZWbJlP1Np1y2z2TRT5HsLBiQdOIyJuArzPKZAj2bc9idsnCetVGLkNwcE8OaxUb1TpT3RZzGnSNsk79OafcLWD3jIFzCyaaJlMIjxR1bNuio9awkTEIRoYICAiaFjPVL2QJJkZcFexq2UY+C4wPMwV0tU5h6EwRxAcrQ0U2EJsY1XDyvIVqzQps//HDNNnvMxmC6XEDxTwRK95bJ3UPSXV23oRt056J+Gzb7RT89+LsvAmbUti224GEjXn4yvv4sI58Lno0UWtQj5+1pgGX7c5i1af+nV00YVoU5oCvlA8S1BhQgaMTsqnVb5ImReQXvkYvKQk7cFxVyHUp4s0F72tZELPetFQsXRodIFVShvcLVN1QUNhYiCDLAeEQ+yvPSdNctAlL349qzca648e7WrY8OyXLVRuVmo1iQUMhp6FY0CR/XG+aYbYESYgiTu5+7Ydl/PN315FztDkl51zFnAbAwmrZRl3ywA2Uqe8cGnGvW9PYPdg1k8GWMd2zM7cVLGmeMjWuB4jQQcHurYaHiFYeuAyEVS8QMGWqHfKw8ieCiXSjy83jt++L2RBUQPeo/MPUwgSYGNWxz6n3SZTxUUiFwH3zm9+MO++8E5RS/Nmf/VmAwAWAv/7rv8aLX/xiLCwsAACefvppPP300wC8hbtr1y587GMfSyNbA4nXvva1eOSRRzA3N4dSqYTt27fjlltuwS/+4i/iZS97WeTvHn/8cfH6+c9/fsvzPP/5z8fXvvY18du0CVy/Mbn/gdE0yYsspObKHedQUcOQzxKg0aSo1u2OJnr8N4YOTI5mYOgEe7dnceJsEzal2LM1g1zWPV+twQza603ghsvyuO6yfGi6R0418NBTVeRzGnZMZ0AAnJkzUalSbJ/KYO+2DM4tuOTXvu2MRJ1fsqDrwN7tWRw700DGYB1UMa+hkCMYHdLFsQCwYyqDbVsMZI0qNEJw8zUFPHOigfllE+UaRSlPcPX+XGArv2lR3P8EU8befHVBqEHLVQvHzjTRaFJMjRsYc87XaFLMLpooV22UChpMC8jowDUHcpgcNfDgU1UQQjx5A4CsT/qbz2q44YoCjpxs4OiZBoaKGm69roiFFQuHjtdRrtqYX7aR0QkmRnV8+0GWx1Ke4NpL82iYVVSqFEQDxkc0XLU/D0KAuSUbN1yeRzZDMLdk4tFn68hlWPnwlfGlNRu1uo1rDuQxXNRwdt7E0TMN2NQlPceHdeyayeD6g3nc9YN1VGutt+jYlEViLeQ0XLE3h+1ThijbvduykmE5xdl5p6Pu0WRNHpD578X5BcvJa/B38ko8v97pCSPWc2px1fIQuISwezbh+83SqgXTopsecEih90g69ozzk+tXDFogqnYet7aJNN9WtqRIq+wCiuF2ftuDSjdI9bgrpKBitgWBKylwu0gvDEnz5z+vCLbXrw/5gLVBCgr9DBrSFgHh/Uuaw9dWi5eWTfHYkbogKU/PNjE5aoAQNt997nQDi6s2MhkNlbqNWoNvxabCokakFSB0qbuFO+aaeDr//N11AEDd2VRXKmggBCgW2EWsSh64uQyRgriFt8KaJpG8cLePb5vMhAYpi8yfc++2jOttqZ/7Dfmshj1bMzh+jhVwrxSggwZulwkwwtP2KeD988kkQQMBVufjqlnPFLgyges8GxrRMD1uiMWZJMr4KKTyBLz2ta/1EIimacIwvElfdtlluPvuu/GOd7wDDzzwQCANSile/vKX4zOf+Qy2bNmSRrYGEl/5ylfE6+XlZSwvL+PJJ5/EJz/5Sbz85S/H3/3d32Hbtm2B3x06dEi8jlPfhh0j/zYt+Dspf7seMKb2/14cGJ5+ktXM6MzxNIh4aDTirv74nyM+ticx+QFYI0AIRGRQbgEBZyWplTWbKCPqfe8PukE0lyD3+6pwlWXcFprg944yhZBARFN2XcTzG00KREIdIlMmA7jX0UiRYLVCsVq2WPl6pFlBhRIvP3/OqO2UJXXK1abg8ew04l4X/62syublQnzHyPdDTkN4uraYLtm2Y//hZNJTn335D/u8FwgjZHidlgMAhpFSYttKi7GUXIeSRPlU/G3vkWZ0dIULBG2QbvwRbetZjTnW39Z6vkvwe/n7fqnSpkXRdCastToLqCoHwmnU2ZbSWt2G5XjEb4RVSNOkMFv48ntA2PZY11eR7VyR4U+t0U76IZAX8nQtueVKp5CHL/IdIL46RaVjkuSkF2SOgoLCxoFSGjnWDRsXp6rAFXkI/77RoLAce7q8o8OoOGrcat2GZTuCpoKGXFYT/REAEQuEI0joAnoCD16/8IqjmCeSApft5BRBzDJE/E4LKUP+uRurQfIpb7NcRT9yATSgsrr6AricVLBna4bFDbIAQ9O9ym7KuBm5344TzrbimGT0SoErp8rP4R+vbbqFgq7ruOqqq1oed+WVV+K+++7Dvffei29/+9s4c+YMKKXYtWsXbr/9dtx4441pZGcgMT4+jttvvx033XQTduzYAV3Xcfr0aXzrW9/CV7/6VVBK8e1vfxsvetGLcO+99wYsJpaXl8XrJAT45ORk6G+jUK/XUa+7PqE8WFoU/JUxoMCV3oeJHV1yLWJFzz8KbwNypyw3EJGTS8r+EdK6oSUOg2vZjkeRk1calqxDfIqtJZxcFIyuexz7C3Gc/Fr+jR7TGMiro5YN6Lp7LCEEmsZITTfYqa9TdhhpTfOuWPlXt9ar7I5um8pg9XgDq2Wbe5I7CfHCct8SsK0E8qBEENdwj5fJbE4K87zw5OVBiL+c+F95ixHRiEgjcA8i4CGd4RuHSL/dyI457FQaASyEb+31KA1s9/g4yPc97tBWg1WFCwhtDkAGcbA6KPW41cJnEnRqoRBFyHVTdqEeuG38Pg0P3EaT4uFDNbEYeOR0Q7zOOl7r88sW5pdNLK7qWK9S3HB5vufkc6Vm49Fn620ToVPjBi7dxRiCJ482sFpO5kPV6XP72GF33Bi2SJsawtIN+cxdsIgnriNPk2L+k5xT9aUKCulAHtdHjXU9z5mYH3b/0Lfqv/hCWS5LcNmeDB4+VEOtyWaObLcpRdYg2LM1g51TBo6ecWNO+BW3AUJXUuBGkbTsO4TaEpbybILMA3yfmm2i4Zw+n9UC8yx/28qFTex1iOVCQojfXQCKVUXgBjE1brBdwHynrLxLFACoxAmRaMU30C6B23me4yBlVcyd+bMpngfxXLaf/qZo0G+++WbcfPPNm3HqvsR//a//FTfeeCOy2WDwo/e973148MEH8cY3vhEnTpzA8ePH8cu//Mu46667PMetr6+L1/4gcmGQg5atra0lyuMHPvCBlsdx+PsI/4MmRxeMInuFwjEGnYxp5a28YepPf5ryqk+cVw0hxKPA5RfAV4/810kdUpj6OiX+Xvflhz/gnu0ozjHPnGjg/3xrDQ3TxtSYgT/+lXASn5uEy4FlGNnM0qVSPv0NzOm5Ju55rIrvPFSBrgHVOsV1B3J4/pUFTwNYdgjcHVt0HDrOjOd//+NzmBjRcM2BnEhZlLcN1JoU3390XXj0AkDToswvyfaqxGwK1OsUX/thGV+5pwwCIJsBnndpDhlD86xwuWXm/ctUQW45E0Lw6LM1/K+vrIBSioN7cqHlx2FTYHbRwg8eq+Dxw3UcO9vEyJCGn3v5SGSd7NUELG4Cz5476lFVh9VzS3oO4hAWVTMUHZAtCp1BTex7h0GzUBBoY+IpFgjbT96Xjvd9KJ+WcKbST+VdrdtiImHoxOnrWJBRvv3T0N3+07S8qtZeKU0rNVssAieZgFCwiT7vnwF3sdXQSewkUtNIwCYnKRpOWYyP6KkQIa0QZskFQFRIfw74LpWW6XaTKf85+aBQQUFhw+ARMvhFRWL3nXR8xLEdocVCd0PylM05AY5sm6Jap6ixuM5sZ4cGT/A1CqDpuLRlDPba74lr28mUfpZFvUG9HZSKGjTC4q0AwENPs+hmWQMYLRFUnTU6EkGo8Xkc4JRlTHC4OPiJ4kFGxiCYmTDQMKkIuq3A4O6Udd/zub9bfePHLIyPIc4u4fD0OTbCQoGP0fyLLa4yvv3xwOCaiFxAeNGLXhT7/U033YSvfe1ruP7661Gv1/HVr34VDzzwQCKv27Tw+7//+3jf+94n3q+urmLXrl2Rx7eyUADkrWw08LkgGKPUQAmVknF5YybZPH8k8L147/zXUn0L3tCwLf5E/iwqXelz/iDbPjWkqzql4nNh/eAc9L0fVXDG8VqdX27g2ZMNTI0HH29NA2yfTQQnknXCtrbw7S3+fB06zjyYsOpOAL/zUAXv/cUJj5l8ucZ+uH0qA4D5wp6aNXFqFti7PYORkg7bpuLW2gDOzDUxu+Rdgqo1qGd7o1yOz51t4vScd6AxMaLhqn05z0qWX8nN//LtSCxdpsD9wt3rwqP46WN13HpdMVB+HLZN8bV7y1gt2zg9VxWfv/g6k90/3U3bn5e0Eae6c58TeeQazE8rxTtHUgVuN9tCFAYLSRVi4ns1Vu0d2nje2rbA7fS+heQpcTbDFJRtXGNc29guinkNz7ssL4jQGy7PI+945Z+abeLEuaYIZLkRzR4/x0hJw1WXxC84AsDKuoUnnqt7P3QSue7SnMf3P03wfO7f4QgUevT8JxTgBheoxeetGjDn+DTzn6CiKHWWgkI6cAkhEq3e84yLXbFPt2g1ThKWBAYjnnJZDdWahccO17GwbIEAMAyWTiFHhBgHcIUqIyUdCysW7rqnjFffXPLsSkwyZ26aFF/67nrg85ESW3wbdxS45SpLZHJMdwRFtuca/dAIEfN8Qtxdtx0rcC+QNnH/zqBoT8EF5zo5J/Tk0bprmYjWRD7nD2TBmh8a6Z3dlUiXuHnlCnfRtiTwpo7CBSBEvzhwxRVX4K1vfat4/y//8i+e74eGhsTrWq3WMr1q1SWdhoeHWx6fy+UwMjLi+RcHvyFzqB9ejCcPbdFQB7bjtwF5W6XshxSptqLsYeOREaMgpyF7pMoqW3+68vmIfwXYd41y5+VfiZxd9JKZq+VwPb5/ZYunqxG2skup+52sTgbcFeLXvbiED79nmhmO28D5Be+5yxXWQm2fChLI9QaEdYWstOa+ud5jKSiIl2i0ARCIQFrXHMjixdcxNflamXqujfvzytdAwlbBnMb15Hl3S1K9GV+xKA0vYz6Bl9Fr1VHcsxJGpGohz53fKzgKYVuaw48bVOni4CFVlYjCRQk5iFO37ZW7MNbifLH56SoLgXS6uSa/DUNY3oj/hTRf6LcmMDT/m9B49GpxL2oiRP03chPRxfBVQUGhQ8TFehC+4CHChjSax1biCK6izWYIKIBhHribUjH3GuKBjzSCsWH2WvaD5ZY+3/9RFc+dliwWbFfs45+bP3a4hk/+8zKaJsXDT9dw/BzLyM5pA9dflsNtzytg37YMAGBixLsLY2qMvae+nY4BxaPmnbMmnW/4kVRoonBhgNeTQp7ZdDRN6qrLSWvlbBQ5Ko93ehlATq6lwgPXp8B1g5i1n74icAcIL3vZy8Trp556yvPd2NiYeD0/P98yrYWFhdDfpoWghULwmKZp47uPVPFHfzXnUYa0ZaHQCYErTgSP92cU6USRkCQhrj8tpSzQCfvMCQ4W4bnmJ2N52WlOofHjbKmT9JK5FLNLjDjkW1yiCFx/QDKWLsunzglcTiz7CVyH1LxkZxbXH8xji9N5y8Qn4CpwJ0eCWy7rDeaHa0sKWJtSrIUQuLUGdcqTK3EpbLB7tugQuJftygr1Eb9m7vEk10F+Lv8qmEZckv3UrEtEN1oQuJYNFEIWT+eXraD/U2xK6SHsPPzeeTx/Q47z3+sotLt1yV/nFS48JB1L9xF/khiDthDRjsi5fQVu/G6CqCJKq+j65la0WY971gS2SS6Eld+GKIUj+sO0y6XtOb2v/Pq1q4oTOigoKCRHXKyHMCV+qmMWab4TBleBC4CyudyBXTnccHkeB3ZmsH9nFjscUQylwME9Wdx4eR67ZlyhzNl5dw5z/GwDIyU2B2PB0bzXBAC1ho3/8JFZfPbrq/j6vWUcOtEQ3/3335zGO143hlfdXBIWO/kcQSHrlsaWMQOEENeCLaKg/EHMOm3LOiV+FQYTnPPYMWUEeAmg9cKz+Damvul67ypTqAcuf0TFnLvz/l0RuAOEqakp8dofeOzgwYPi9dGjR1umJR8j/zYt+FcTwlbMzs5bOHHexBPPNfGFu8N9eKMVuE6l7yBvckcie3+GbjeHo+QEEOPowNKQSGChwCXuj8I8cOWLIL6ZDX84A4QqIaJ8dY1gftlC02Tn5535WisFrm+QwvPOFLhUzpbolJvOKnAxxz6YGncI3FmfAteJnDpcCjYv9SbzteXeffxEYQrcpsnywi0euI8wBbDk2DhsnTSE0pcHY+ErXHIdFEHMRGPpWDg4qumVdRs1ybewFYFLKUUuF7y++RUzxgO3RzOwmGTD1LZhA0m/bUdketIBrdToLbKmkBYGjBgdpMH3AGU1FO2Q6+2Qv3FNWewaZ8J2oZ/IfnnBNxJ8oM77agxGPe9ZnxSCJMrsNBDncxmGdhegUoEYE6oeUkFho+CKY4LfhbUDidr+hGi1UMTnHFnJD9XQnSBhIMhmNI9VHbdZMCU/2de/1N2J+28PV/Gxf1jEDx6teizjZDHHXT8oi9ePHa7hmeOMwP3Dd0xgZsLw7Frgvx8bcQtvclT3xpKJInA1Is273Rgu7Spp4+6fwoWDsJ3CPN6AHLQ8n42vP1G2IfKvNspPmQeNt2n4GKUTsVPqHriPPfYY7rzzTtx///04dOgQlpeXUS6XW/9QAiEEphk00r7YIStr/arZa665Rrx+4IEHWqYlH3P11Vd3nbfZRVMExABY4A8ZFqU4ca7pkY+vrLv3eG7Je79bbV2JinaZCFLn4frKkkhlCHUkuBpBbEfOlZyAY0vgfKYR5+GMIHAFUepbiRFBoihP08mrxkhK0wIWViycd+wTxkc0seK65tgYrKxbQhELMOuBXIYELBS4/QM/ty25hWs6gWVTsXLEo2dOjRkAGjh8siH8d02LiuikI0NBBW7FIXdtyi600aRomhTlCNeP9ZoNbl9DnXzZNsXSmmvTsHWCNWO1BlBt0IAFBDczZ6/dtC2bHds0KQ6fcleeAdcuIgo2DY8aOb9kBwdonHyOTbFzCOIlpG5qYguJe/awgaS7tSy+N5M7u1gSRzxMsckpXADolaKunzAol8Z3fySZcEYe0umANqKQuqkXcnvUjRo6jTG6v2kMzZs0Lkmi/ugG7ZLcceXXK1KVhvQ7vUJY8qH9WYiSp63zbBIzPyhtkIJCvyKOOAwbF7ciJttBVH9QrduYX7ZQa7AJRdYggWedi2c4iSXn0RTWC8Cv/uwYfuqWIbzvo+exXqV49mQTz55s4nUvGRL+tVykc2rOxD2PVUQ637zffX3tARYIXd7pyfO/b3sWZ+erMHRg99YMs8TjyliNgNoUZ+ZNLKywOZqmkRAFLvUmmhBxCmqFCw9eUpO9HirqmBrXMVTQMDMRH1iVB/GO6+v1zmKzJoKop8Tb5lhSYHYuAO7EQiE1AvfUqVP49V//ddx1113iM7W6nC6+853viNd+1eyVV16J3bt348SJE3jqqadw7Ngx7N27NzSd9fV1fO973wMAFItFvPSlL+0qX2EEmB/Lq5ZH4QgAFYm0O7/k9Q51SakoCW67uQymDXi3ZESdKrE6yUmDr7BYlH9GEMLfSulzEtnJk2/QwH8nqyRNk+Jj/7CIhRX3qd8yqqNUYD9aLdt4+Okafvdjs4HVpx1TBv4/vzUj3jM7AyIsFPzXzHlkTmryCKi88bz74Sruftj1VHaKAqV8sLQefbaBcpXiVS8cwsf+YQnfeaiC19xSAgDhqStjvcx8cJlvEsS1cb+orRMGhooaSgWCcpXiH7+9judfwQcf7r3lkHdL/Ot9FZxdsAJ5B5J54NakRYrxEQ1Lqzbml81AFOxeb9FMQqTKnYN/ICl7YrX0FEr43F0MpF6/II7AV+gSA6iU84jtk6oLkewaeVTfuPPGKS03emEnjfsWWNCNO7brsyVHu+ow/2EbUadj/YI38JxxGdD49p5W6Xaco+gsDFCzoqAw8Ijbgh86TpdEIN0iav3s6WMNj+Apm/HG/bApFTE7DCOYD07uZjME+ayGA7uy2Ls9ix8fcW0J/9//7TzueEkJz7+qAFCKuWUTp843hfWejKsuyWKE756UFgl5/l9zSwm//LpRrJVttttSImczBotzwmOUyNckk8G2zeZ77ZZqUqGJwmAjfDGW/bUsilyGoJDXElso+J85mbTNhDxTqUFKWp5byzF4Nj2I2dGjR3HrrbfirrvuAg8cNEgTnkHAM888g7/9278V71/72tcGjvmFX/gF8fojH/lIZFp//dd/LVTRd9xxB4rFYld5s2y3Ud05kxH/xobdp4RXzqGiJr7PZtw0VtbcBl9slweNbOD5552sWsjEnrwlI1KBK/7zbiEP5IkQkXe29Z+6HR8Npmv7nhN5ckspDaiM5Q7w7IIpyNtCjqCYJ3jewbwwvl+v2Hjw6RpsyrbhFHIEBcf64PSc6fGctZ1zEc0tG5u65yXcfNsZKJQcAvfKfTlMjmrIZ930CzmCbAa48YocdI3g9942gZ3TBm69Ni/Od/hUE7ZN8c0HKrBsZrgPAOPDBHfcVsLkqNssleu2KHzq5Iurbws5IJfTQAjwgivd9OecQYlrMyHdI+5DYwFnF6Q6B2B0SBO+vk2vrW8ATZMKErmUJ/gv72L2Jg0TqNZ9BC5/0eMmMVSBGzIo9Q8k5Y6jHQuFJMSx6gUuAiS81/20Lf5CRVvDrg5uRCf3rps2gIS87iS9ruZ7bdTbQSDmwrK2EfPhIIHc4xPEQNhiSb9rlZ80I9K3A+IbByooKHSGOEVtWNvdixgO/iRl8lbTSMi2cPd9WL65sCYjWS/s2RrU5n3pe2X86BBTTXGLvbKza3bPVgM7pw38xpvG8OZXjQhfUHlRVt5ksntrBkPOXJPAnWvt35HBrhn3Hw961rTcua7c17TT77QjNFG48MD3SPP4NYnGY0LAxOpOo0lxftHEyrqN8WENxTzBcJF4g5qnCI8HrpRh06JSWxRU1SdFKgrcd77znTh58qRgw7du3Yo3vvGNuOGGGzAzM4NSqZTGaS5I/I//8T9w00034ZZbbok85pFHHsEb3vAG1Gqs8X3Vq16FF77whYHjfvu3fxuf+MQnsLa2hv/5P/8nXvnKV+KOO+7wHHPffffhP//n/wwAMAwD73//+7u+BlERNWD3jMvKLqxYWHbINl53h4uaOCabcVvhehOo1mwU8jxwF5evhp8zlQkZ8W3JiCAiuBq11Tnlh9SmEH54GoEnEJonXelDHqWQH+uP6MkbIU0jODvPynXntIHP/PF2sRXmiKOEXqtQnHKCi/3aG8bxhpcNAwBe/zunsFq2cXq2iT1bMyJ9QrjlAxHnkgc7lLqE5ZCj8h0b1vHe/9cELt+Tw8SoS9Y/+FQVjSYFIcCrbx7Cq28ewv/+2gp+8JgruZYDhnHP3LERHZfvyeEFVxbwkc8uYnHVRqVmOyQ4cbyWKMpVlrFCToNGWHm84vklPPhkDWtVirWqZNMA733jjaV/hfiGy3P47785g7/49Dy+fl9FrGpHQSbAP/mftmJ63BAK4pWyjcmx2J+nirichkXW9fsCebyC29ibFLdi2GvVsYILUcaKGU0dg16kifMv9UVdX7NQ4AaVjUkJzrTIqjTbHzeYVDDRsOvq9SJW2/fLRwRuRNsceooel4vb7/tKpo8WkJSATEFh4yEHgg7A1y6lbf+SpD8YKmiBXS7+PkXYJDkZFgpcSUm4Z5ukjpJw7xM1XH8wj7UKm9+trLMC+c1fGPcIT3Tf3JOdO1y8wYRQ7INiXsfYsHvc4oqFxVULluklz/l9aKdcw/pWhQsToQp5hxUSgsEE6fgFTEdONwJz/xPnTJgWwd6IZ6YbeAQIDp3FrkHKo3jWHNFfG5W7awL3qaeewt133y1O+qu/+qv46Ec/inw+3+KXCgDw7W9/G+95z3uwf/9+vPKVr8TVV1+NyclJ6LqOM2fO4Fvf+hbuuusu2E6Lt2fPHnzqU58KTWt6ehof+9jH8Pa3vx22beNnf/Zn8eY3vxm33347dF3HD37wA3z6058WRPAHPvABXH755d1fRMTA2F952V/3U/+qx8KqhZ15zfVipdEPaTdKF3ni4/rKEvdh96t6ncmtUNNG5clZpaTgtgTuP79xtZyu/HvnY0GqyvmVO8Azc4yc3TpheFYm+aroWsVCzbEBkKOUzkzogsDlsG04RChEwcr5JWA+S/wsRccDV2xp8F2Y7HXEMeILaPbAU65tASeGJ4Z1cQ/4toZqjQrynBPe6xWuwGWKZ/6bfI5grUpFQDR3YUFawXayMbfs9VzetsXwnLcVgctXrvNZRrwTQlAsaFgr21j1BZDruRo1asIKdyAm1xG/F6LsA93JecOgImdfPEh6r/2Kt0HCQNVjGt9PcUT2rQl+K08g3c+i89MxSPD1RisRo84WpiQKtVztk7oTd1s35JEMGQOmmnybZIBfjZf0NqVC5nRybJ/UIwWFQUXcFnz/OCZtwjBKaVfMayI2SM6nvuX2e/y18I+VwHdG5iQF7s5pl4y6dFcGr7ipiL/+4gpOz5o4t9DE6JCBpklF/JPtUxk8e6IhhDdMJEOl3bVyfqin/aQxylgekE1WHBIC8BlSWwpc6bKVB+7FBx4Iz+b8axuDXF51Go6N50hJh6ED9QZFuWa3DFreKfztjK4zBbFpedXtHLbdnidv1wTuD37wA/H6hhtuwCc+8Yluk7woceTIERw5ciT2mFe/+tX4m7/5G2zfvj3ymH/37/4dKpUK3ve+96FWq+Gzn/0sPvvZz3qO0XUdf/iHf4g/+IM/SCXvYRURgBuEC3AH7/w3lAbsDxZWLE/HA0Q31ERSq7aLsBVWjURPLFzigcRPgojcwbEJrrz1xN/xUl/+PYrbkEm4rCg9M8cIyK1bdM+AYMQhcFfLNupN1tLtklTRWycNPHuyiTPz7iqU7czF5dNx1TE7IQsOxlF0vG2jCO8wZRC3duC4/4lg1LLJUV38iHf83Nhf5IlCBMsr5AizvuAErjP44d8LxXLIRHt+2Zvp7VOsKeQDqEYz3puDK3AZicx+M5QnWCuzwHFh6NVEXjx/Id/51bbycfyjTiO7JrkcNefsPQbNmmCQ/MsGKKvhSJj/xM+pIyGIO574/oZ92ep8abcbad3HpOQxDem/e4W21934xNuTSFq58Z8s+hS96g/tiPaQRo0le5ONROgXcl9B4WIAjRnrBtqLmO86QsQ8ULZ4k0lYDkE6R+SNC2BkBe4l290531t/ahQaAXZvNXDsrImjZ0yMD9siJs34sIahAsFzZxowTQpdJwEimZPPRDq3mD94jvPmPeMwTJbt3RlB29gGzyFvOR+kMaRCBwi5vVzEZcb4WPvh5yh4Hdw1Y2B0SMfsoonDpxowe2yhwMHtH756zzquvTTnySPPXzsx1bomcOfm5sTrN73pTd0md9Hhwx/+MF73utfhvvvuw6OPPorZ2VnMz8+jXq9jdHQUe/fuxYte9CL80i/9UqhtQhh+/dd/Ha985SvxiU98Al/72tdw8uRJ2LaN7du34xWveAV+9Vd/Fddff31q1xCmuAS8z6DmNP2yqtTy7cH+x2+v4epLcuJ76k8k5tyd5NfTQXseIr+ilIoJWawCF87DSJ0tIsRtaPx2CTwf8mfyg1yu2vjOAxXUmhTX7HfKxAYqNRtfvWcdz5xgVglbJwwPOTfsKF3XHZuBjAFMjbtNwtZJ9vrMvKvApZTi7HwTuYyG3dvY722b5e3EeRNziybOLbJRgqYBGcOrwPVvpQ9T2vkVuE8eDQa9mxwzJAUu+1upUeSyXB3NVozXK+wMzEKBCOuJfFYDYAmLhTALhUbTxmOH6zh53mtyu2Mq45zXIXC9At0AuMq3mNfEZTJlsuWxV5DP36u5Go1h8IivA5OPE97KHWxn8pw3BMq37+JB0mqjqkLvwb09k9yTqOe906lR1P3tSoArL761OE/a5xZpRJywpaVSz9t9fp5kdyxw2AY8j2Gn6NXUux2latghievVBnMHPd/Bo6BwkYBvXQ71wPWPWVNW4Eb1X7KQiQtJZL9t0c5LUyg5jaZDPmUk0nV6wsCH3jONrEFg2RSmxQQyx86amF82sV7VUKmx302O6jB0gmJeQ7VmY2rCQC7rnozHWHEzxOO3+K8saMHG51MUVCiFuaUg0N5ivlADK//bixKaxnmj5L8RqnrnvX9XruF4PffcA9exEyvmgUoNeOpYA1VHdU+cBQnqeaaSoWsCd2hoSLyenp7uNrmLDvv378f+/fvxzne+M9V0L730Unz4wx/Ghz/84VTTDUPURMLzNkSWY/mEit9/tIr7nqhi2xajZWR1f4CvTvLr73iiBspxCkcZHgsFyo7npG5YPim8HbG7tZ0F+Hr4ECM5R35WxwuvprApxTfvr+D+J1316s6ZjKQ0JRguMl9YTl7u3poRBCcAbJ1kROU5SYG7vGbjC3ezoHa//UvjAFgHXWvY+JsvLYtVIwDISi0GbwQDBG4IcTpcat3rTo64keRcCwUb2YwuTc6oZKGgeUj1nBOkTVgoiCBmbkY+9eVV/J9vrwXOvX0LI7a58reVhcK68OElIs8lZ5Djt1AQ2IQZmC7ukXty/0AyzGqiW7QzmVZIB0qVkD46IQ03HTGqx8ifhCwwhsFv6xN+jDTZg7fvS1qeqZV3CunIY5Gk+Rqk6gJsDCe5Yc1T1IJmlNAgISGQahugSFkFhQ1H3Fg3YFkX8l03iEqDz5+uuiQnCCUZstVLWBsU5oELADcc9NpY3vt4BQ89Xcfhk00srLpzlC1jBhomsH0LI25vvDza/jK0/5eUsX5oGkHGINDqRKgcCeFkbHuF6re8UbhwEXaLGYEb3Lkdm44QmVHPX16HuF1BO6RwO/Bfx4uvK+Ib97F4RTLHoGmMD2v3ueiawL3yyivF6/Pnz3ebnMIAIorgDOuwPJH4nCdxbIhgeZ2lcnbexPapDLi/QKuq3JEC1/dj3pmL/PoJSZutIBK02GYuEbh+BXGUAlf81Dm5RghsCiyuuazp4qolOvmzC0waetluFuVz7zZXgUs0RkC+/qVDODNvYqig4TW3uAssALDNISrPLZqwbQpNIx417krZxtgws2VYXrM95C0AGNIgwd2eEC7BlTv06XEDP3VLCasVG9WqjVyW4PCpBmaXHC/ZDJDLauI3OU7gNijGCCNJedxobpFQzBPomntPCs4KdMWJ6sq9eeV66Cdvd00buGJfFiNDfgIXsZhbYgdMjLi+vaUCS2PVZ6EQFswnTcRtoRcdmGya7jvQDimnbqFUQxcP2lVbq/F379DO89YJ4b/R986zBtzFydPId9wOo81YO2m3bQ0s3KWZmQiENQm92p3RVn2mIf1mwkWFzWq/BmoRSUGhD+EncGRECRuAdBbHoxY/w0hlWWMU+tzLCtwQD9ww7HSs9GTyFmCq32aTk8CxSXjaShEME3weHf6brEECdoq0gzlH2I5KhYsHmuMH7d+5HQc/p2P7FPhc4NQrCwX/YOHGy/P41/sqoPARuASw0H4f3zWBe9ttt2FiYgJLS0v4xje+gd/7vd/rNkmFAUPUFm5vp0c9h1C4CtzrLs2j1qC474kaFp3OpdUWi24IIn8UZv4wR6mDKP+vRcdB4Kp0bIfBZQpcEppXWTJP5EQA1Gru0ZWaLQjh+SVWaL9w+whyGWYf4L+OGy7P42dmMh7vW47pcQOaxgjK2SULWycNnF+QyOIVC3u2ZmDZwFqZfZ7LAvWg40FrCwUfbrm2gDNzJkaHNFy9P4/5ZRPv+cgsABZ8TZPUtBlnMFKp2iBc5OokXJbUr4R4g5ix8nLub8x2KY4rLsni+svyLnHsFFkrBe6ccx+2jOoiz6UCexGwUHD+9nr+Fbdg4qnTvs94pxay+N91XtSks/eQt9sppIwBL9Skkx2/53lX54xJJvHkK6V2Q1bPdp0IkmeLWS712SpWTBlsxKR4o3YItBsgZzN2Lqj+UUFh4xFnF+Zvrtu1qEkK/yMfRirLuxhlgtcOIa/MEA/cMOzd5p0P7t5q4LbnFfD6nxgRQZwyLdKQy8g/f4gqpkzGGztGnsu1teYWo/RVuLAQ9sxxCwUuKkuixPZzFLLYDdh4BS4FYBiMf2lKpHEUl9IKXbuJZDIZ/P7v/z4opbj77rvxr//6r90mqTBgiFImePzrfNsqAZfAzRiuR+riquU08DR23pNUNRGeYe9PXZ+S8CRl9WxcZy6bq3MPWTdyKEKDffkbFJ58xUfgWhZFpWqj2mA+wtNjuvM74q4qacQbCC0Ehk6YVQEgfGDPLbhy08VVS/x+1SEit4y6HrpVKV/8XP4tDWGrq+K1Q9LalHpsFYYKmkvUAshm2HfVBnUbKScRV4GrOdYX7HOXwHWCmPkGCX7Fj6EDWd0l3gEg63g/tSJw51dYmW0Zc9fASgUngFzF99seKY444pRBYTYXrTq1NKAmqBcfWm+LT1/pvVEYlGrs3/nRCuHRuJOcKOzc4aXUTRvQrQdumggUS0je5HFJrxfuOn2e/AQFS6M3D2XcvdrMvoEiZJyX8Ldp+mG2c75BaYMUFPoVLhka8qVvzJr2wnhU0Ge/KtAPTvCuli185LOLuOexqjeIGffAbaHA3b3VJXANHXjtrUO447ZhTIzogsDNtkhDHtf726MoC7ZSXvN4geo66WhhlZPXgzh+VGgP/iB6gKNSbdMn1i9g8i8CcAWuZdGezM/93BKoG99H3uUrRH4bTeACwPve9z789E//NCil+Pmf/3l861vfSiNZhQFDki2GsiqFRxPUdYKhIquKS6sWZOo2alsGJ5u64G/FX91noRD2EFHn+1Z9ByefbZmcBXsdmlcf+Uac85c9BC7zfZlfZuTq9LgO3ZFLaiR8C3xUQ6BpLul48jxrQWYXZbsGTn5SrDlerpyYBLwrVbzRkf1VPX6HIQQ+V9lSCowNu8TwUJF18nwQwLfyVOs2iMbLn31XliwUNOLWkaJDvtbq1MkXv2b2u6XVEGUs4dfC/uadAUzclopG08biCktratxV4A47dXitHKHA7bkEN+QjTtZKDK5/8UP2UE4bisDtPdJSTioEMeiThXaIubY8cGMP8h4blp+WZH/rrLSHLu5jmN9f5LGdn6Z9xCzchcF/PzaybZbr4ek5E9+4r+xZpE4T3NIpqlxiYzVsNFT/qKCwYbBixrr+cXra4yp/QCWOUAGF1Lbz/PzDN1dxftHCXfeUPW23mdBCYWxIE3OlF11TQDZDBInEBSutFbhS/+0jxaLa0d1bDVy9P4ddMxlcsiMDQ5MI3NizeaE8cC8eDBU0XH9ZHlddksO1l+ZRKjCxlm3BfTYSsJdyfbVtl6TVfApcIChESwNh42/dObfHnrJDkVfXFgoAy+Q//uM/4l3vehc+/elP41WvehXuuOMOvOUtb8ELXvACzMzMIJvNpnEqhT5ElAIw1mcILhmYyRBBfi2uWp4eLnIQ7jt3J/n1dzxRk0ubsof77ofK+LeHysjn3Jbjkh1Z/Pobx5jS1vFjtamr2iXESZcGH055FVNeqWlatocordRsWBSYX2FP/M7pTKixvU6iBwkchABbHPXu57+1inseq+DQybr4/tDxOv72qxTveO0o1hwl6VAhvKUMU/vSiHsnXx8hrDyH8oQHNXUIXHerDV8J/tEzDcwtWaCUYnRIx/K6jRXHL7norOzyelZ0LAyW1mz8+acXcNMVORw728Qjh1Zx+d4sHjnkXqecRyKRwFln5a9pAvWGjf9x5xJuubaAW68r4jsPlvHwoRrmlk3YlDXEI0OaKPNSniXy7Mk6/uDjs3jnHWN4+ngDX/ruGvZtz+KqS3Kh5dgt4h4BPeQeRUfmTC9PreqhwoWDpAqxQSbzBybv7ZJ6CN63TudHPS+iDpSIady3qIl83AI1lQ7o1c6LTuHPzkZYCMhn+N2PzYJSoFa3cUNMwJy2z9FG/aDSeCxqh07keTrIWyCNNsrc7+OnoKDQGcScL2Ss6yd1WxGTnechOA8MO7//+0PHm6HfC/K1pXqW4NffOIbTsyZ2TjMrPa68TazA5XmC3Bw5QqyInxJCMDakCyGQZbs/bk+By/6mOU9R6F8U8hoKzuuMwcRa/sD3reDuNqW+OTD7q2vEiTtEYdleQrcXoIATqJCKhRegcz4rFQIXALLZLD71qU9hfHwcH/3oR/GlL30JX/rSlzpKixAC02wRRUihbxC5BVtqnHnnJDfYXOWYNVwF7uKq5R2nxnQKQHcErps33/chvzkzb+KpY8EO9OFDdfzUrUPYuy3DyEBxDu/D6e3w3PMEJ1NAzcczlh0LhSXH3mDHlDdwGSd7iRZ+fhkaIdg5zR7784sWzi96W8RaAzh0vIF/vb+M0SF2T0oFDbdem8cPHqvh0l0ZKS32V165CvNalV8Sx2bCpkx5PTmmYX7ZxthwuAcuAJyeY3k8M++eSNeA4ZLGGmAnI8W8WwG/cV8Z37ivjKkxDXPLNp46FjTxvfayrLC44PUz76xAWzZw5zfX8NUflvHVH5bx7Y/vxgf/ZsHz+y1jOnTNJZ1nJljrX60D9/64htGhNXz7wTKaJvDsySZ++XUjANLvIeIGmvyzMI8fXkd6ERygVT1USA+9mmj0CoOSTyCoVh8EdOLdR8P2RCY4R/h5nb9xx3aQfidIs/kJI7uD59u4iuJf/G2FzajLYeXBPzpyujdj/I7LpVW6PSi3AWpWFBQGHrEqTv9CTpuLoa3g3SFJ2TwoRBUonRqO9gf1ho/0lV43nSlcKwUuAFx3II9tkyYWVizoGrNlODNPUKknI3DlMhIeuAkUkYQA9SYjrXJZrWWMmzC4O00HaACpkAp4sPJG031OktQC2UJBthGUn39dB2yT8VEt63+H8C72s5HketXC7KIJ0wYWVi3U6hTnFkyH4E2G1Ajc5557Dm9729vwwx/+UCLX1PDkokBER+dtZ2ngM+GBq7vbz5fXbLGlm9IkCtz265j4iU95GKXEoDYVvqrbt+h4x+vGAAB/9YVlzC9bWFmzgG0ZoS715NP5jFIa9MC1wycb9Yb3wGqNrQ7xLYdjw5ooI53IQcxIy0mLpgFX7svh//p5HSMlRiYeP9eE7gwUHjlUw+NHGlgtW6KRGy5quPKSHAo5Dbu3uk0GCfFXjeBvPZN6AncA847XjuGJ52rIZVne+SBAHoz4J86vemER2QxBLuN6K2mECAWsDH/EVQD4wLsmcfyMiaZpY9WxO+DpyA3444eDil0ZL7ux6Pnt9ISBX/vZMZycbeIrPyjjkWdqHp+bWqNH7WHMQFMLaYs9A0VKW3pwdQI1xrp4kJQYUqOB3qNd8ioMcROkjX6uvTY87G8nw8push11Pk9Z+BeBKQVJbeqfDiIDwm7IyYMfpT0/aKduyluAkxZAlBK7XfyPOxfx0NM1vP2nR9v6nZpOKSh0B0uoOIMPsavWY3/T6EvD0gfc3ZlhqkD+vXhtA/c/WRPv+VyZQwihEpBP3CKBKxrXKjbWKq64pVUgNHlRNspXNAyaRnD0NDvPrpmMc/3tFWwvdgoqDAYMnQm1LNsWz247tUeur3KsIoCpcJugPQtk5gc/9bEzTUyNsWfi/IKJesNGLsOCtB/YniytVAjcI0eO4JZbbsH8/LwgZhR5e/EgajUtrArIHYDc8XDyzabAallSr7ZgcDupZYK/9a3oRRERFGy7HwBs3WLgFc8vAQC++G9rjMCVPE/9nTKASL9eKn3oesSSANHHPHApyg6JPFLSvQpcS1rBbTHJ5SrXg3tyuHIf29L/8KEaZhdNGDqwvJbB40caWCvbwht4qECgAdg6qSNjaJ60AK+/atSAhL/UuM2EU2SjQxrGhnRUG7ZHQSwPJHbNGDhx3mVCX3BlHsfONpn/LXHTNQwCXfOqTf2+Nm96xTAmhg3oOwieOdEQPrh+CwUAmFtyz2n5PHGvvTQrFL8yOb1zxsC+HRl85Qdlj7cwEFxFTxtRkTsBbznIR7GVSV5/0pvKi8HwBnWKFzVSVooouBhktUeinDurY2E7RNqGvx6GZCDpdvW0W8o07mNY3x52DODNf6+Gwp2m6x//9BJxZ+hZucTsNgEJ0urEd1wvn/lKzcYX/20dAPD4kTquOdAbWyUFBQUvqjVbWAXE2fsl9XZtF7z/qDUoVsoWdI14hCpRw2/Tpvj+oxX3vendLdNM6IELuCTvUFHD6JCGXMadzxXzxBPvJAxyHi2LCZtqdfabpMRqzQnEXW/YWFlnc6R8liCXjU+gFzsFFQYDhu5YKNgUBqQJdwvINo/c/9qvFHc9aXs/HpoY0cVzynmKoSLjQMo15g3N26gkSIXAfec734m5uTkx8Nm6dSve+MY34vrrr8fMzAxKpVIap1HoU0RNBOK2dAOuAjdrMHP1UoGgXKVYXrOk4yNUI12ocXh++U8DCtzA8WxbPOBd/RwdYgpWWcVJCPE0LJo0YfCTWZTSYKdE3CBcWxx7gXKNeeJWHRJ5dEjzbAXi6cp+sK1UQ37bA07sFhx/37Uqha7xIGZ66Gp02Lnk7buh984pHlm1a1MKAqYm5pAtFHZvy3gIXL6KLCueCQEMDchlgYq7WB3ArpmMZxDCbhcRn/GOwqbA3LJbD+XXADA1JimRfS9mJsNtEmptNMztIC5Vv6qAfej9bS+CA6hB1sWDxN6TA0w0D8pydLv9YafPaTun6abs5D4kaoF1sxCaN8/3vT1/twqxtBSlseeImXSnrXgJELMJF//DlHFh6MSaxI8fH3F39ZydT2YhofzkFRS6w+KqhaePuc9e3NzUXeDin6fTQBKwAI7rFQvZDFP+8fgiflWg3DYtrVooV92nv2lRT1vA3SaTKXDhnA/YvyPrsZ1LfBFgZXP4VAMr6xaLZ1LUEs8fbJvZAs4tWyLPGiG44fJ87DX0Yp6iMBgwdC/X0S68ClzvdzwgvF+klSb4o713W8ZZJLHE+XbNMFvK5TUbtbrtCQrfCl0TuI8//ji++93visbn137t1/CXf/mXyOXUyvLFAj8RypHPapiZMKDrBOUqZxmd30gK3JFh9sNtkwYOn2ri6RN15AwieQS5adrUhmkDjYYF07RhNoGG6T7VXA3K1YSWTYWSlH9fa9pomDZ0XWMByiwbluNFZDv5siTGy7Ipqo6tQSlPxHfDRZbu0pqFesNGs8lk+B6CklDYlB3H0zUtioyhwbIpLIsd22jaqNRYiMVaw7VrmF+2YVrAWsUSZZjPUtSbNkyLwrQpmiYVgdNsfg029VwTh2lTWBbLAy830xkQULiruKtrJoiT73yWomkyEtmm0u+ca6k3bVQbjOBsNtlnRGPHEede8PzaNoVF2TVWG5bzOTs30dxyy0oc6MSwt2JZNleMUScYnssMZQ2Cim+q44jMAADbJjRYlOXRsihsuGXEy4sTuNW6m87R0147hfFhAovaMC0bFmXlY9vM/sPQCKbGNcwteXubWp19Lytd/fcnChqJHkhSysqhaTKFgTwI4udigfjYueVULMtdSOlka5Jt09CJJa+HUX2R1eKi/eUSd/2bDX2TR5TdEjkbjUHJp4wLfUeRHNQpDty/K+yh99fDiPU778GRGWqZlbbQTZWTJ/KJq0EfVxf/fd6I5zHsFL16pmL9mUOO36h+ZWnNwn/5m3nx/uiZZj9XEwWFCwZ8V6Ous92e48NBkYVfFCT6s5TyQIirrsvoLA5IuRauCpSx4ASv1nU2Vjct767HZhsWCvIx7XhtytcAsPF73bmWXFZDKa9heiKeTpoaNzC3ZMKmFHVHhWvoBLYzr6zWbWQz0TFCbKkfVri4oOsEmm93bZJqwG09bGk+6d9pygOXNc3e78AhxD0fv5ZSXoNGmHCMP9V2ElIAKRC499xzj3h9ww034OMf/3i3SSoMGmLq2v6dWQCu8kB+NExOHDkq1Ut3ZXH4VBNPHKnjhoN5lCs27n+iJlYkag2Ke39chWkyM3SLUgwVdDzqeJXWGhTzKxYIWICplXVGaBXzBBMjOlbLzPN0vWqjVmefV+oUTx1t4I0vH8bMhI6lVQsLyybOzLnqiLklCwuOAnOtQnHfj6sAIFZFP/XlFXzqyysA2Arny28qCh9bedvl4oqFz31jBecXbeyaMfDm24dh6AT/+J01PHOCBUjbNa0LhematOr6Hz5yXqy0fvHfykIJ/ORzddEpj5R05LIEc0smzi3oOHW+iZPnTU9jUGvYmF+2MVTU8PDTVfF5vcmIy7PzznVW3UHPdx6qImOwyWsuC1QcIrlat7Gw4rRC3/Xed0KAZ483QAiBYRCUKxZmlyzU6rbjd8vyPO+U63CR4NkTdbHCtrjittRrFfe1pgFHTjVwftFi+cmwazhyuoHVsi0M/WXMTOo4t8C+uPeJGio1GwsrjBC3wRTg2YyGSs3Gk8+F+95+wdn2yHHktAlKKY6fMfHcaVbGq2Ubw0UNK2WKkaIeIHAfP1yHaREc2JnB1LgBy6Z49Nm6sOeIg64TXH1JLnSLk21T/NU/LePUrAlCgHe8dhRvec0oKy/ngas3bNz3RA2X7MhgetwdJD34lFsHknZc3CJnftnCvT+uhh6zsGJhbsnEatnC/p0ZT9pPHauLgHxhaDQpjp1teuptJkOwb1smVZuHtDA5quPgnot4wbKF6p9DkRW9h1DUEySaeW6EcrQbnk7OSidKxF6RhP62Mqzt5J/0jPuPUbeGwX/chqxJxJwj9fMn3QkAeILwdHiatvHJLy5jXRrTzS5ZiWyV3HqkWlAFhU7An53JER0HdmXjj/X9Ji0GV14A3DGdwcnzTebpSUhQVSqRlUurbOI3NebOY+Rt1jyafRICVxYb6B3EU+a/tm23/b7qkiy2jMVTSbZNMTmqo9GkYrFY0wimxnWsV2ysVagQksSlwa6h/XwrDDYM3X1+2ukG5TEPrz/+cRB/Jo6eaeD0HMH1l+WFKjdtULDFG8BV/PLnlmsjACQSdQFA14/C4uKieP2mN72p2+QUBhDteNPIAe74CkTGANZrFJftZh3roeNNmBbbci7LydfKluisKFjHJVf0eoMpgyhlNgS8k+NEJFe2yiP8Hx9pwLKBf/jmmvjM/+xQuB1mMe9eZNj2k6bprpgCEFtk4KRxfpHl4eR5U+T92JmmOObkrCW8bvMZgi1j7PeVGtBwCFy+cqrrgMH7TUJQLEhBzBwFqX8lJ2Owlaxa3ft5Ka8hnyXIZKRr4RYXGYKREgsYNjWme9OKuOe5rHufm00bGYPd51JR85Bw2Qy7jqGShuGijq1bDGgaQSFPMFwiGB/WMDGq4/rLctA04Or9WZg2q2uyncVQQYOhsYB4fuyaNjA+rGFqXEM+R5DPEjSbbACRzwD5HIGhM386CoqhYvCiViWf40IOGC5ooiHmdcM0AdMZ2Fx1SRYEQD7LOh9enpRSLK+ztGp1moi8BVhjLxPZMlbWKU7Nmk55Az983CVV2fVqzncUK+s2CCEY8ykQNMLucRJcuS8LQ2f3Jgq8Hlaq1BPIDUAseQuE19tmk6Jh9ucEdmltc41+B25e338cfCQGWezRVt7DRbXBNN3Dg0n4PgxT2ya120irSot0uriPbeWFq7f68Jn0WgW4GdyIKh5WF5NOUtpF2NZn+VT+rHjKpTdZAgA8dIh5O125zyWQ/OOwMAxyG6Sg0A9I4mfrBvz1/SbNjBB+LvZXBBD2TaRk9e+iM16WbdvqEoHLFbj5BARuMU+Y5+aw3tHOMd6m+nfHtYJc/vI2eI0QMZ9t5UGqPHAvXgi1eJvjFtkyMspCYXxEE89+o0lRSdAntwu5zsoWkNw+gb/n+1mTWkV0rcCdmpoSr6enp7tNTmEAkaSjEwHDnPcyqZPRmS3BlnEd48MaltZszC9bmHKUgrtmMtg+ZeC5Uw3MLVsgBNg5bWB13cLurVlcfzAPADh+rolHDtWga8C1B/I4fq6JY2ebuHJvDjddkccTz9WxVrGR0QkW1yw0mhT3P+kqLq87kMeZeRPbtxjYs819sI6ebYiMX7EvixdeVQAAPPKM+9sXX1fAzKSBf/z2GnSN4LLdWVx7II9SQROEWtb3tB3cnYWuucRsPgvUGsCKQ/DtmDLwe2+dxNs/eM7zu5ffWMRlu7PYOqm7AdgIGwScnTdx7GwTk6M6inmCk0M6psZ07NvOrme9auPHz9WQzWi44bK8SJNvn7/vyRq+/UBVDCAMDfjVnxnFvu1ZABS6b/mTUhra2Og6wel5E6dmXXL69S/NYrioB2wxKKUwMpoYUFBK8Z2Hq8hmgD1bMyjmNVx3aQ4/89IhGAbBFbuzePy5Ggo5HTccdK+hWrfx3Ok6HnjKPScA/OJrRnBwZ9b1KAbwo2drePJoHVsnDdx8dRHDRQ3PnGw4FgoE0+M6rr00hz/91AJml2yUHOJ+6zjBz71yBFPjBuaXLSysWDiwM4vj55qY2aJj+6SBS3dn8ZoXlfCunx2HrgG/8l/P4uR5CyOl8AFiNsNW/aJw5HQT88tm5IS36iOBZbJZ1wiuP5jDuQULR880BDF65b6cx/OH158kGB3S8fwr83j8SB3rFRsH9+QwNuStF2fnTfzr/WVHEcglgd6tITddUQhdTT+3YGKoyFTzl+7M4kfP1tFoUlx7IFyBvFlomBSPHKr1JVmzkUiqNBz0cur19qpUIPrizvMZe4kbffkh59ssJaJcLgES0PeChh2UMropBkqxIZL4uFPQlBncNtcr2t4i7Qa9beNEDs4tmJhdtKBpwH//v6Zxx++cgmWhbxclFRQuJCRqKwVH5D04zU1fog0RFn9cXRg89sipJr72w3WcdVS3E6OaCNLMlfuUUrEzM5OAwCWE4NoD0XONpBBzB0ISqRVl8lV+TTTXg9RsQVrx4JT9PgRTSB+cwKVy3IEE9UCO02OJxRLvMVNjBiZHdPzI2Q2b1L4gKTxzBupey5YxQxC4Ig4TXA4kCbomcPfv3y9ez87OdpucwgAj7oFi3rKu148c0MkwCMwGC2R1+d4sfvh4DecXTaH21HUCXWMR+jTCSKmMrkHXKQxn+zvAPtc1tmXfMIijNmX/slkNOv88Q5DNMA8iTpryfGiat1Ph6TacDnN8yBDflSQ17p5tGaHOrdRspu7MaSzvOttyXvat7KxXbeGzOlpihNXRs6bIT6mgIZfTMFzUhPpSI0CxqKGQ15DLBeWm/BrYdbDXmQy7fgDIWYChM7I0GxL1UyNMtcnzNTWho1Bg/9pF1iCeVd58Tg89ZxAEunOf+f0DAE1naRoZTVyDfJ/yOQ2lggHAS+Du35FD3qeWzhgaMoYGQyeOLw1TExvO/crnNAwVdUe9auO0Yy2Rz7Pf5bMaDN0W9VEjBLrGGmuer4KTt3yGmZbzgRbvILi6XNPiB0Gc5IzqWPwrhjyyqyhNJ2+AdyDbzTYReTuYrgXTMgzi2Id4VfLy+Q09nDQmznOcMTT23OoEmkVbltNGgy/xsEUMumn2DoKI6J+iuWAwyGXajkKBiv+SIWyA6a+HcWR3a7I/nUF0EuVV6zRo6OvQYxN+lgbc8k52cf6jNqLdiCuuXu1bkCzxpYw4nxH/F77r7/JmPfx0DR/45LxYVN23PYOP/fZWPPYsU98e3J1FIa8hawBVy6ukU1BQ6A2StHX+hWjRdqXZPvIxM3HmABaADOAf1lJK8cihqiBvAWBsWEfGAKyG227IsWRyCQjcbsGJJu4pmnzxy33NiTTizPP4DsVWClweN6Of5gAKGwNu99H2uFAa37oK3GD90TSCEJFvKvDnme+aNkM2orrPV7K0u5Yz3Xbbbdi2bRsA4Otf/3q3ySkMIMK2rPnxxHN1/Mn/M49v3V8GADQa7vBdXhHhXpKziyZsp7fzbzfRDQAEgcArlDpKQmebBiFwAoq5nR3Lp/tPtjjwKxnldKsOgTssbTOX1YC7ZjIYKbJWpsKN6flE1jlGVkYCzE/3tOO1Oz2hB3yESgW24irbNuRzjr1hxJMrG/GHrVhqCRopbn8AMMuETlVnxEdotcNvEUFa+j53Atux177vQj4bH9E895gjbEVY/oyXDx8UzS6y1jZraKCgTlAt98RyXfMXLV8Z55YU4ny8PrcoF7ENJOKe1WpugD0AWK/SQERN0mbHkAgx5Ah/vkC991A2oY8iPLkyS/M9P/021fVuSd68fGw2krYPPdmSuIEYhHvMlYXdlHHa96ebYpPzsmlkeli9jZDgyiSAPyhOP2GjsxRqodDC87Drc0TUF4JoBXKsYjhBod11zzrWKiwwq2kBz55s4tRsE48+y3ZrXXcpG9/ybZRmEgVuH9cjBYWBQIKFPP93Pdmy7yQmB9mmCJ/P1b06FEwMa6LdkD1wm+bGEbiif5PmlknKh8/BCbxCFKK5Fgr+OYsf/JqzxqCOIBU6hWuhACyvsYFDknmHbIsi6mDEz7QIziFNUOp64Dalvt+fpQ2zUNB1Hb/7u7+L9773vbj77rvxjW98A6961au6TVZhgJBkXPmpL6/AtoGP/+Myfu4VI2IF0dCdyktZFb5kB9O1LUq+kvyBa1oUc8s2TNNGuUoxNqwHVvb4NnmxOijIX+pmlDJC7vyi6VFA3P1QBVsnDcz4omlaluvj6fdd5dg1Ywjv20rdVcs+driGp47VccmODNZ9HqbrFRunHe/SrRMZDzkMAMUc84v1+u46quSIVkieQPIGS1bBRhGjHDYFcpIP7pYxo+MJvZ+fi4u06oewU/B9LpOrAf4vJKO7pjPBD8HqAydh+WDEth3lMgDLmeBls95EsxmAUOIZuHDiWNP4ogJ8v/FO1njZ25RtrVyr2Fhai57Nzi0xVfa2yfDmusLV4SMayjUebM/2+Nzy+pLm9pC4pAhY5ymvfALugkvYKigHH8eJhR0xge2vGaxc/2wKdBATIh0kWEDrJwxKPoHBJZvbRdLgEH6VkjcR55iY+5vYAzeBQjdRPUpx0YB5lCU730bAb0vVEgGVV6rZCUXcKXo2T2qTmE2rOaKUCqL2j39lC/7n55cwt8ysunig3WsvZduX2SSOeogYBQWF3iBR3+ZfKOnFgrOPFOYLSf7xsKys5SjmNWSc3Xw1KfihiFWS3YDRinMK04aT72TjOT7HApGuizg7aoUCNz4NTngpAvfig6EzElfTgKoTRD2TgL10PXCp5DcddawzR055YOR/Pgy+eOuzMARc0VvSLHRN4ALAe97zHtxzzz34/Oc/jze96U34/Oc/r0jciwh+xVwY/AE0/vRv5gFIDyFhuqF8lq/GAZTy1Up2yOnZJr5+b1mk86Kr87hku0vQyR5ltk1BNO+Ekz8TaxUbn/zSSiCPd35zDRkD+Mh7vF7OcuTgoWK4AnfndEZ0MBXn+CeO1vEfPsJsRV58XQE7pryP22rZxpl5R4E7aWCo4C3AYoE4Clz3PAUn/kVLBS5cotCrwI1vpAiYFQGfXm0Z0zue4PjrQxxpF/htyJZ/nr+ohpgAGCl5abRdM+FNHKWssdQIgSUptD0ryiS4qs3tOrjSmx+nyQSu71zc+7ghtj2xv/PLJj7+f5YTq2LzOYJ9O4IRdKuOAreY1zBUsLFeZcHKZAK3FwpcnlSUAlcsnkg3kZ8/qhMFgsrxJKrxzQAhrP7YtPOI5hcC3Db2wi6EQbg6yplYQhK126HHtKFSSpynDuHxnfWMITZOkRuW+8DuD6kvgP9ljytO0nLwHCblaSPKkU9iLKkDoikzuG6A3AQHSwsW7V6/fPzJ8018/9EqqE1RbVAsrFjIGMALr8rj//kSO/AfvrmGM3MmNAJcs99R4GaCSrrI8/EsD0IDpKDQh+CPTtwcxL+46I5vU2wg+ZhWzG+YFUHYeNjfNlyyI4us7v2OwhWGZDdQgWvZAAUV8yXTolhcsTAxqrtqSQmyrY2YvznzJpIwiFmjDa9fhQsLhBBcdUkOpkWxtMasC/1Cu/Dfsb+ykCiKp4riHNKA6MPhKnDDdt+4z1eyTKRC4ALA3//93+PgwYP40z/9U7zmNa/B6173OrzlLW/BC17wAmzduhXZbJB4ULgwEEfkcGhs4RAAI1CfO+MQl2Pc3IT94Y2/TWVimH12btG7RLe0ZnkeNq665dvi5c7ast1OZH45uNRXyjMv3KbpVf8CrvVBLuM+fAAwUtLwEzcUsHPawHBRw+iQY6HgKHAPHW+IY+eWTIz4FLZrFQtnHQJ3ZlwPBGgq5TUQwv5yjDqkXCRZLpFdosGSkpVfh/l22hS4Yk8OWYNgfFjH5XuyHS9B+0nmONLOD9kLhpNkAL+3/LU3Y4QAN1yex9EzLOhcowG84WXDoenbNvOP8ShwKRWqbdtZYfbbLxTyhBG2mkuQEOfkBI7fa0CBy9Lg0WL5AOa5003hw8ytQ8Jw8nwTi6s2jp5uhH7PV+NzWVYH16smVsuOuZaDnpCgcYo7Z3BGqVftHbao4IdfOd7PE1jitGusw92cgaVofzfl7O1jUPIJYMAyy9BJTQxptjo6L6Rzh6ltxXO/Qc9ykrFJW+m1yLcbhCKd8/UKadzvxCeSIG8b7FUZhbWHHrI2pi7E5Snsqw/970U8frju+ezyvTnkspogVO5+uAIAuHRXVozxDB8RE4cB2rCgoNCXSBKAUHRNdvLftJ0H3w40211vDYAHOPz3PzOK0ZKOMdlCgStw6cZ64PpVytw67sipBhZWLIyv6LhiX3AuI9ssygu6RHN3B1qtFLjCQqGrS1AYUJQKGqbHDQCMM0nihSzPe22pDsYdm3IMM+ek7kuhwJXmxUKs5LzfUAWurntVb5RSfPnLX8aXv/zljtIjhMDkEX8U+h5JKpvME5YlRetbfmqUpeG8N6SqZPrInnWfh2ytERIgSSKOAO9KJz9JJcTr9ldeP47PfmMVs4tW0OqgynqWQl7zPIgEwCtfUMKVTofFCdqmyWwXTp5363ClTrFeDRLD5xYcAnfCQC7Lpn/8kgo5ErBQmBh2FaBhkMkuoXaUDk7i27llTMNLrh/F9LiB84tmx1FY/avd7QyEZENxXQdspygJwolplj5TLP/0rUM4eb6JbVsy2Lc9fOHIpozt93jg2q6yljpetiND3pMMFTSRASIxuHw1GTTYAQgP3CY/N/vL1ddX7Mvhw++ZiSyLT315GX/71VVPQAMZ3Ls5nyUYKWk4PRf0W+ZEfZr+PnHEIV9IARB8RqX8hCFA8oYQQf0CjbB1qc0kbDZd+Zrw/mx6PrvFAGQ/jSKOa6Y3iksK80veLB4rrEz9eQnjpYUitBeZgpSvhAUT6H9bTGjSgD+LTTP4Xa/O2eoYftzymoX/8+013HJtIVHCcmktrbI++ear8xgf1qHrBK97yRCAIKHynjePi9d8G3AjiQeugoJCV0jSVvrb654s/klpaYSIOC7+4TCl7mLXxIiOkZIGjRCxKFSXdvNxqmRDFLiSFRvfxQhA2AdGWcFF7fjUCBFz/mZMW9g0qUgjoywULlrIVGOiWiATuDwIXoSQrBdzZD8odXedezxwPWqw5CRyKgQu9yPjf92tTGpwcjEgmQLXpSa/8xCzQSgViLTdgv2Vt19YvgeOE6sZg00Eqg3qGa3btqTA5QGi+EMpHVquBp/QTIaRc7OwAkRrucJ+Wcj6tqX6iIuhAmEB1ChQrVOcPO+60FdqtjhvNgM0msCxs02YFru+yTENtQZQKhKsVygyBiszjQBFSQU6PmJI5RmETJqGBftq5dsptq9LzWOn3aWfYG0riJm0HVLXidvYETcQVlh63sloePtjC2W3s6WHE7g0uA1T9jwGmC+x7ZSOHGSLELaabCPY7uWcAUfTWZHgX59xAthtjfC25djuWG+cXwxf1Ko5wtx8lkBzZM8r69467JKp6bXJcQoFzekHqM9eoJWRPEuXpwHPsf3YnfB2rZedfmIMyrh2UPKJgcpqAMksFJx+mbb3fIUdS32sa5JnvFMk/XkabYZnfJMwvT5sqjyQy2VD6rhzkqZPcWpaNHTLbVcI6WPirvcjn13C6TkTJ841ccdLwnfsRIHvfnn7a8dw2W7vYrHs1bh/ZwaX73WVae1YKCgoKHSHsAXB6GMdclTMhdLLg0jLmS9Q/nlIZ8kFH1lnIx0hcrvBMmfabl+zEQpcDst2lcNJ5nVhdjWEsDkiV1LGbRvn7WTGILHiD4ULG3qb994bxIx9Fil+68EcOQxGnIUCn+smZHDb2NQcD9HoUSr+KVwkoPwPC8rA/8kBk+RVj0/+8woARji55AyFZXmNzPnKIj+GE6ATI4x2rNVtz0RJqCoRJIps262j5VqwbuYMDcNFdrBfgbvmnDefI57O3E8saYSwLfZgBO6pWUmBW6NYc4jgqVFGyB0+yZi3yVEdukPWDjtkLV9N1TRgSFLgTo5qzrkCl+BkiufJJc7ksue+naxMguVAJZmJ+LrD/lLOIyf0koKvtFFKPapsr7o6mB4nU+Pg2f6juY0lpdwagTjH0OBqnTOJ595P/Jo0zVlRDyHy+ACszoOYORk4O+eqr+PAvZPnlqzQSK3cAzefJcLGw6/A1X11NQ3Eegg6AzsKbz0TCysx90h0tD4LhX5kRTaq00+CzRrWJiXYN7+EusPA5N+n9IlD6KPbZUUK/LybggtZMN0syOVSrdtYWbfEv7WKFUnG9Wos3A4pAQRJgo2oz/5r9ytO5WA83SLgSxxzLFe+AcBppx9eXI1fhQsTKvB7ng8JICQHFfKTK1xF1mzh+wh4d1UpKCh0jlgLBc+CD3Xbx4QNrG1TNE0aOkYHnPbDScsy2fzYsth5/PMMCpek1TQCy7Hp4sdVajYsm6IutZ+GDs/8W/4Xlad2IbaZy3FvSOv4JmF8FJuLurtuTTOYf34eLuBR6tuLG+3YMAJy30kDsVUCafdQKCSfkitwZa6rU7FSKgrc73znO2kkozCgoJQpKZ54roHz0jZvQye47lLmByaYLwn5rCZWVOaWLDSaTQwVNBg6q9x8RY4TdesO8To5ouH8ooV6w7tQQG2mHNU0d/VUXoHhh1ZCCFxNo0JtueZX4FbdIFFeNSt/2tgfQtgx5aqFr/xgXWwrARih+8wJRthuGddwet5VTrJAYcTJA8HZBXfArxHiidY85hB0rVeRXKVqYGLj+HaGdaouKUel1511mp7V1jYbXtmPRlboEMkPN1SB6zlPeL7lcmH+us65bCoIYFZfgqQz9+DlBK4ISkDYYoSNYEArTsYvLFv42g/L2L5Fx7ceqODoWVYBtm3x66C9mBrTxTPxf39+Ce958wTmlk3876+uolK38c37mcdePqeJevOv95fx3BmW/rUH8njlC4qea08DcQQCLx955ROQydnodD2DQwS3tvUTekGMt4tBm9gPkqfjIOUVcNottM91Jq5C3i4vcO6QQ73qx4TPcsv8tKmE7eo+Uu/Lr9xTxg8fqwYO0zXgDS8bwsRIfkMFDJ1cm0xgbgQxzk/hJ3DrDYqhFs4F7SJuMa1So56qs7DqjtF2bGl/OsQJ6LDty7ICN+cjeANelgkwYM28gkLfIElT51fst9M81ho2Hn22Dstiu5AP7skKoREH3yEKAIdONHD4VAOmSTFcDAaKXlmzUHf0P0dPN0EpxfK6hVqDDeCPnW3igSdr2D7lnuPRZ2uRuSWE4MCuDKbGuqN8ZEGU/J7PKaMQRZ4RIse9oXjwKW+/augE1x/Mi4WyjbCJUOhfyArcRDvMpFg6/tgqfoi4O73YTSllltd3efHW56CwsRYKL33pS9NIRmFAYVO2EuhXH5oWRblGkcu6q4kyuALXsoHVsoV8loACMAxGVvGVOf68lR0v2slRHUATtYY7ESGEwKIuqcafDfcBdp+Ici3Y01AQDJfYCct+Ba7zvpALf/DlSeLEsIa5JQuHT7H9L1NjOuZ8QdMu2ZHFo8+6Aam2ORMHjRAnSJmJUoGINLdKCk2+3SSqEZLVilQQnd5juW9nWEMVJrrttMuUFbLt7noRWabB1em4QFgEwcbQD1EuvgbbptIAhVLx/rLdGTxzoomrLsm6W40dgpKfQ+SRBokMPpF76lgTQNPzna4B27dkEAddJ9g6aeDUrIl//u46fu7lw7jrnjK+9L11z3H5LBF2DEfPNHH0DDvXtx6o4IVX5Z1rjz1VW4hTKMj3Qa5nslo9Ml1xjJOWIEn7bwrL25c0ifFBg9zmxKEPb19bGKT8s+cvyQiX/fEEFon53UYT2p5FQOnzzbwVUcEkLRs4O2/iukshdmn0G/xBZHoN/6n8FgqMkIhfwEyKVu3QvT+u4l++X8b5RRPXXZrHatnGX352SXxfLCa8YZIKLVaBK5ENAQVuyCSu1fkUFBQ6g39XVxg8BG7E51GoVF2VK6UUK+tWgMAFmCUbV/oL+yICjA17j10pW2LcbOiud7jh5L9psvZnaZWrdF1LgzBQSrFWtjE11vpaksDiHrjEjf8RF4PMs6hG2E7W4aIGTWM2iuPDeqh/rmlRR23M3qdut6MwUPDvJm4FWUTQKoC28Hfu4fCIeeCy84QF7ePt04YSuAoKttTAZgyCw6caODtv4smjdRRyGurN4G9Mi+LI6QZsm3VGuayOqy7Jo5hbQ61uQ9c1/OiZOq7Yk0Uxr6FSZ7V6y7hbbWsNtyMRxA+IeK2LwbakwK2ESU9dv1O/Ry5/X8h5Fbj+7dOEENxx2zCePFoHIcCumQxuvDyP3/hv51Bzrv8nbijgxsvzYnBfymvYMW0IxeL2qQyu2Euxfycj9TQN2L01g1e/sIh92zMuORnZCInLEfck4EXr+HaG+xiKlGI9TpNACymrpJBXZf2dtmuhEPydTKpGcWq8cTQ09xyAo+Amrk2DbTOl7ateWMLtL2DpnplvMl9eeNW5uu52ElEKXD9ec0sJO6YMEZk6ChoheNMrhvGXn2OTzWPnmjh+rimul58vnyP4qVtKyGeJ8HH+7NdXsbJuC79dbm+TRuCaOAsFV4FLPYM3oa6NU+D6Fl98Qve+gqzw32z0I2HkwcYJ/lJDLwM89Rrt5rxbD1xxXn7ikOfWHVDHnyy156nLXST+vFDqbuf8y/dO49oDzNf0Q/97EV+9p+wJ0uVNI502Nyxf7aTqUAYd/75T8EsPU+CmDXG/ZEKGUvzL91nshe8+UsVvvwWYX/LOoCyL7+iKWKj3ZbUukdF+hS3gJW3ZLjQXGW6rFL4W4IHfl19BQaEzxLV1nu9kBW6Cdtv/aIb1AzalmBo3MDNJcPPVBRTzNVRqNq66JCeszzjkdvF5B/NYK9vYtz2LB5+q4dCJJjTn8NOzbB5QyLH5yPS4gQO7vF7cp+eaOH62mYrIgBfF2QXL6dM0sODRBM2YETovy7Eh3UNs8zniFftygd88+kwN5Zrt7OLrbi6qcGFAb5PA5/WlWqeo1Vl/H7WG41oopN/Ryqd0LRTiFLjJ8tA1gfu5z30O7373uwEAV111Fb73ve91m6TCgIFSwHL8Z9eqNgpZAtNkWz40okVOanSdmTA3LUaUjQ1psCkVKxT/el8Z1TrFWtXC7711C6qO9cHYkCYCmVVqrg8uJ9yIFiSKmISeva7Ugj0ZhUvgrle9D49M4MrKQVklyjE6pOFF1xSQy2q48XKmetQNAjiD/Zc8rwhNI7hmfw5bJw3kMgTHzzVZUCyNNSIzEzqKedfrlhCC3duymBjV3ABeLSwJ+FZaTz5978MaCXnyE+txmgAeu4l2zcc1d9Lib7T5fYxUcbY4lSC2dfccnGgkzs/51n+NEOSzBM+7LI+HD9XF8dC8KlPmU0U90a05Mpnwm/W6lwxhccVqqU7WNKY6f95lOfzomTpOnTdFgLzrDuTwo2dZvgpZglxWw2tuGRK/vf+JGh56uoZTc02MO6v8No33oE2MFgRAmKdQKyN5+XjdOUg8Zn2octVCFP4bjc2e2PczwZ4mNruck6CbLCb5bZJmw8ffer9L2u6EtC2bNXlz+1GWAe5dxnYQsc/4Ih0flFP4FF19psj1Kq57fz5+Cn+k8V564MaCBslkM05ChiDhLXsehwUQkhdu/QrdthS4CgoKXSGJGMWvwI2aP8WlzxHmh+7a+nnTDVPbyYtDuiRk4O2MZTIBzl33sF14l2x3BT9+8N2aafjg8nkpTytrEBi6v68LLlZGWafFla28Q9L2lZ3CxYmiFA8oake0DM7Z2JKndTFCMNVTCwUHFK4CVw5i5n8ONswD9/jx41heXgYhBLfffnu3ySkMILjykAAgTsWbGNEwPaZj2xYD+7Zn8flvrQV+l8lo+OkXD+GRQ3VUajZGhnRQG8g5FbzqKG6//sMKfu+tLvE6XNJQzBGsmJR5mjnntKUBtp8oolKPvF4PJy6HS5zA9T7B64LAhWdGSaTf+iE/j2WJEM5lCXTNVWrKgwRNksdw4pY43qqUskj3gjCM4i5lMkUoVb0Hy6pkP+RJSrcKHb0LCwXemFLq3TbBysEZjLVQ4EYhTJ3tGWAQrlRlrzXCor2ysmWeuZxw59AJYMu/kxA2uTN0dxLXiozn93R6nBGwx842haL2RdcWBIGbzwUT2jlj4KGngVOzpkvg2kFbik7gJzb8eSbEqwQHpLKPqRCW//72MUEYZhOxWRiUsW0/EVlJsNHbzrtFmAIxCnF9WPxJYs67AUh6rjaKoiV498wH3jJBlxXb4jauEMSZ2rk4aYyxEfDfJz+x8fH/s4SRkoZrDuRx+FQD1ZqN8WEdv/Hz4yiE9GeJzun8jdoSzeEnk02zvaLh5HPGCF+gjrNQ4PXFbymhoKCQPpL0F/I41rYhghsnaV558pwsWi1beOpYXXyvEWB8xBu/RAgcQsaONWeOyshRIn7H1Xtzyxb+v/+4jDPzbB7AVbdhYyseEDoNBe7WSQOjQxpOnmfxanZOG9A04pnDWDY8gacBadck8d6LuHmAu6XdnVO1G8RK4cJCIafhpisKsGyaaHyQzRDceIXroWzowd0wHHELKt3AX8ONmACm7ZLIXRO4w8PD4vXOnTu7TU5hQCGTiwCQzWjIZghyGYJCPryRzhnA2LCBXLYByyZC9WiEEF6WTYUNw3BRQzFPsFJm/ji8caecRCZup8g7CNt2V2Aq1RAPXAqMlqIsFNgvi3nN+zDGEUtJV3olopkQN2AZJ8B4UnxLv2EQQeyGQQ6sFhXsSzb29sOzXTQkv+0gzG4iKbj60q/ApfAOBoLndAuORIzaRLkImwZvmjxZy6KCSLed15wYJwEFLgArfKCYjbC4TaJGZWmzA7Y4AQgefKoGy2aTwusvy4vjwlYkd02zk5+aNXHtgXwowdwJuBVDVP41p47yZ5pDkLMx1+y/vx2TTBuAXkYubRubTIy2KoN+KKILHe3umoiyP4k+vg1W2JenTiCfb7PsLPzkPR94hxF0ps1/0/vHsZ0tvoHfyr9PL0vRcE7i34315FHmIXDvj2uez6/Yl8NrXzyETpDkuiiCZDL3dWwFXt58m3M+YkLoqR9+Ba7znV8FHHq+1llSUFCIQdK5jLxY25YC12n3s1kiyNelVe88kwfPdm3wnEW/kEanLi0OyePhrNPWPH3c9STcvyODHVNuHBU/+FzKSomZKuQ05LMaclkiyCh5cGdaQds7edekfL1JFLjyHGKQLa0U0gHrV5PXg4xBhOo1DvzZsdNmcAGXK6JUCLfkHT9RivVW6JrA3b59u3hdLpe7TU5hAMG3oBNCJFUIV9ERERSskCW4an8GDz7FBu18UKtprsqUUoQ+bD/z26fE69GShlJBB2ALApdSiqNnm/jc11dg6MBbXjOKrZOGIFhs5xgKoBIMIA1KKYYcaf35RQt/99UVvOU1o/jfX1vBrOOVVswTLykpXb8fcYpT2YNWnnDrmpuYeO+8FhYQpMWAQlwvjdx2EuXb6Sfl2lFyhaGb7S4yySynQ21INhLBE8gKXBpxfuHXLAYIblkRzb3HpmPtQYhr80HAvHUJiPPeXR3n98k/4Q/zwOWeuUB8QC/2Pfu7ZYwtay+ssPq4c8bAzhm3CQ9bYd/lfP/Dx6p49JkaZiYM/MVnFrBtSwZ/+R+mUch3qHKiwA8fr+LbD1awd5uBv3zvVu918vvgK4848p3D9t3ffh6zbYTxfStsNjHaz/cnDbDWekAgZbTd29KOyjjsSP9nofUi4YJHq5wkVuCmyFISpyLwgbdHgess0lmmd/Iv8tH96VMBSVj+acF/Gq56nRzVcPvzS9i1LYP/+fklQW7snGbBOh87XOucwA37LORDnhdDZ/fUr8hthXqLyOixBG4bClz3nvVLLVJQ6E9YFsX5JcvjLzlc1Dw7G+PA+/rTc03PzslW4EfmMgQ1V3iL/TuzqNRsnJ03RXvBx/uCoAxT4DY5gUs887DnXZrDo8/UUKtTDBU1vPU1o9B14Pyi6UlTBlfgprlLjJPBWsj4N8yKhquZNc07T4mdJ0tz9ySxMxQUuoEWI2zrFryaL6/ZWFlnD4BlscCHuk7E97z9SdrXd/043HrrrdCdFuJHP/pRt8kpDCA4uUgIwUrZwj9/dx1f/Ld1PHyoDtsGPvFPywCAQp7gmktcxWAuI/u88pU2GrrlnA/wJ0c1GAYRXij8c0qBx56tYb1KsbxOceg4I4k1SYELAOsV1zP3jS9jE4RrL82CEGB60iXDvvw95i30tR+6ixJjw3piX1f5mze9kqnUf8H5K1R78E4s+JZzuUxEWtT1ZI3b/i6TylG+Q1G+nbJPH5Hy1unct5vONsO34FAvESA3bqEKXMjHh+dcrAbLHktOmvLCsWVDWFjYtnM+QWwzQpefghAiEZbe822dMJDzxhXA7/zSZOJBCb/OqTEdk6Pu3qQbL88jn9VwyfYMCjmCvduCUt/LdmcxVGC2D+tViiOnm1irUDxzooGnjyeIoBKDhw/VUK1TPHWsiSO+yOzEuS4K78DRX/Zh8Acs6OcgLrqoQ32YuQ1GWqRbv+JCJFDCFiH7mY/fbAWOZbuLfVlpoZm/NuVJ8gZltZ3TyMfSsA9Thl8RzlWvY0M6brgij9e8aAj7trv91lteMwoA+Ob9lYCVVSt47KMQX1copYKw5WPJsKjQoedx/roK3PDzyOPYvG9MywncJApcBQWFZJhdsnDsTAOnzjfFv0PHGu525RZtHSc7z86bWC1bzmetG0jXGsz9TCMEMxMGppydc40mV4nw79nfUA/curs4JAs9pscN/PufGcNvv2USb3/tKKYndM/5w+ZEwgM3RQJXtOua9z3g9fbksCPyFzcPcOfuyYQfCgrdoJe7KXk9X1m3RLtyZt7Ca993Cv/83TV3rrvRFgrT09N41ateha9+9av4p3/6J/zFX/wFtmzZ0m2yCgMG7oH7/R9V8KNnXDIna5TxzEm23WOooKFYkAa1WVm9SISXbiZC0fBLPzkMSgFDJ8L/hClwKWxKML/sjsD921X44JwfMz6s4fK9WfzhOyZw/GzTyY+G333LBP7b3y1ibtlCtW5jtcyepJ++tYSsQUJVRaHcjXTcbc8r4uDurOd6Afg8cL06L6JJykziKmpla4UwhNkz+BWeUb6dNg1Pu5VCNDovnfe24r5Rbyfv3U4T/TsgeqwmBluyhYIIbOYeZ1oukW7ZEKy2CJZH3JUzTu7KdgwcQ0UNv/vWSYyUdNg2xVrFxs1XF3DMqXctVQGam9+//cA2zC9bMHSCGWfw9gdvn8SJ800RYEDG6JCOv//T/z97Xx5vSVHf+63uPuvd99kXZoZh2BkEAYMiKCBGNC5xS1zjM8Y1eYmaRCMajYlPk6Av7obk4b4CagQRAQVBWWQbmGGY9c7cfb9n76XeH9VVXd2n+5w+211m+stnuPee011d3V3rt771/a3HfY/n8Ykbpl3f8bJdDyiFCCrol5ZMbsv1QyidK06u2U8+bvZOzFcSWuWbVAtq3Ta/XHDaumXNxgkPSp2Fp9pOrOUa5Qd7y6HfwosfYRw2/XrQDAGurICSg8vIqsqY/btQfq1UCS6HZ+G41SBg3u1T9u4R2SNx85oY9hxkY8ZLz03h/9zI+tvXfeg4vv/PGwIVroHwleCWf8TJ01RCwULWhGHRiu/JWyYLJdaZedW1HJUVuOxnLarfE3D9KEKEpoLXyfa0gvaUgolZE5ZFUbSnpNVaku0b4y7rA1UhWNsfniaRRT18HsvbgZJOodqBqgslC1//2Tz6u1X0dKiuxcC2FBFtU0wjjqBBugGvDR7lOwF9JhOcVG6mP7uXUJXFQIbPdZw5FwDb/aGSFSD73knbtNyfRYjQbMgLBs1GbycT/63t17BX8sYu6hS/fDCH83YmXceH7esbJnAB4Prrr8c999yDhYUF/Omf/iluvvlmxOPx6idGOCHgBJaiOHictc7rB1UcnzBFgCUAeNauJNpkkslujFWFiMBjFmXeuF78zZ/0YiFnIZszoalAW4oTuJQRjxbE5AAAsnbAM2HgbtcITuD2dzM/22RCAQi3cAA62xW0pQiyeYqjYzoWuf1Dkm+Zl7IviNjy2ub1f5VVGnxFVPYjZYpFIm31cYzhufE7paybrkSo8m9MS1KqehW4AStNroaLLG/gHlXKpHy/smrZb/WWoDqpxtWSqkLEO+DHalKapmShYEh+uEB5JFleNmRSXuSJMDXOQLeKyTkTyThxRVatNiiRvXmScQUbBt03ThQ2SAxKJp1UcO6pybLP5zMhJUc+oHDqGEurnMBVQFw2IUBl9TSHGKxxCxF+7gpUuYo2YDmDmDWBpGoEYhC+8l5Pc2Cvra2G22skj2EI9joscGs+v9Lx7mXOpQWB2zfVV4ErTV7lrLciz2Eiq3vht8DbUtjXOD5p4FP/OiE+VlVHWfaWl3Tj+KSBF13SjlRCwWte2Ilv3LaATJ5i75Eizt5e3nf5QdxbiPuiFJIClxnY+ynHfG5FvFhO5vvtGAPc5cMbOIWTOlEQswgRmgfusd3frWFdv4aFbAG5gqO2ryYq6elQRbDfWmBRYHLWxBd/OIdiyRK7Nde/bUAEH6bU3tUH4Ivfn8VvHme+39s2xjG7aGJkykCuYGHXlgQKRTY2T0gWCqR86iy+ExYFPrfHhSrNVOB6bezkuaOfhYIT+8LpwavPe9hPPvcFKgc9ixChEcgLBs1GTGPq+a3r4rgnlnNfF059VgiBhfBxapriKLJ9+3Z897vfRVdXF37+85/jggsuwG233XZCbjmMUA5OgM0smljIslXGV13e6ZrAbFmrIZ0krgBlIsi8TZLxaJOyAvdZu5L4wgeG8KJL2h3vUlVxLBSKFkCBfNF0kUh5m1zi7b1XgdvfE5O2v3PyjZXXAdtrlAfYAICEzey5gqqIB1D+TEjA7/x6QLl9AqSK67JQIE4wKG/Ez6ALy1u6yybBAb6dlk0QE5vYDLJgWArIfjSu/FPJI9VXgiv9SvzJdX5fTnRW51411Rm8mZLi2TKp89wsz4XglCE/Ba73OJYHKpGZlQcl8vuutFW/UjK9neUvsREFbrFEXQFp+LYQV34Uu22Q8uz42wan7VWOr2R+UARJjPq60FjubfC1YpVlVyBUtuusW77HByRSc9oh6lKtY8tG3qF8KU64qYpbbSWCmNkE7lI0B41cg4r/Lc3Cz+/2uAMPaNIifW+Xin/7yyFc+ew2AMBbX9qN5+1OAwAekwQAYRFSgCv6r7Ykf3e1lVVuoRCkwJU/95K83EKhGCaI2SptfyJEWGrwBba43b5446m0qipRSvHo/gKOTRiYnLOwf1jH00d1/M1nJ0RcF7ZrzUJBp7jlHseaL66xIMNf+tEc/usnCzhwXEeBBzFzWShIRI89fuZtEIcfv+kEA6NNs/ryCjHkZHOF4HmF22Ki8jXcFnfs96gtjNAqiAWDFotxBrtVnLophtM2MeuohaxVVq7DBhxsigL3Yx/7GADgJS95CW688UY88cQTuOaaazAwMIALL7wQ27ZtQ0dHBzQt/OX+4R/+oRlZi7AEoGCN7LFxNiLevFZDT6eKtK1kBYBkgisXCU7dFMPwmIGtG2L2Z5xsYx1DTGrk+7pU7NycACApJ1U4Fgp55ml7bMId3jhXpMjmLYxMGti8NgbLYiTu759mq54DXYqIzkns/xE76tVAj4bDowb2HGSTh2QcIISW9f6ViCUXSeE5T/bAlVWYVEqTELmDoyLQFkHlTsy3A/d8FuTbSW2bAK4kXU5lnxyBVO70LZlY9uNvpUEOvw/vcXxApCmAATfJ6Nh5UJEGi5wqv29aRmoT+8KyqtoLSh1bAG4XIt9rEJqhmvIjzWQC1zCZLy4nIE5ZF0d7OjhjXsLWSwYrxFmgcVkoVBmIyYH0vM9lJXKkQXYky4HlIkZDC99W4PurBSux/HlRL7EZdNqjTxfwo7sXxQJoJm/hvJ0J7NgUvMOqmeWwLClbwLMcr4IQhyCIeURacUHgsr/lvnwlwr143LqM8suMz7j7C63K7P2cHQnc/XAOj+4v4k9eVOM1Q072ORmftndzmVblBVJvutU8cGMuBa77GK7O1d3D1opYBc1PhAjLCm49EPfYFwi0jMF1ggvLOD5p4PiUAVUBfnR3BvuOlDDU4+48xqZNfPO2BdHH3vlgFlvWMq4kHiO+OwkJYWkDwPoBraLYRp4/mVZzAoF5x/Fyu5n18S73221Yfd7jCI0q7bqMEKEZ4GWriU4j/tdRCS48PYn+bg17j85iPmu6OAtgiS0UrrvuOlHZHN8ziomJCfz0pz+tK82IwF1FsNWh0/OsQ1nTFwOhFKmEQ+CmEoylJQBe9rwOHJvQkbK3lSmKQ+ACjp8c4KgjAKcTiGnMJwhgRC2l5QTuQtbC9d9h0Y03rdHwd2/sw02/WnSCoXVrgtlUFPfWzAG7g+UELlf7Evj7CvpVtkperH5b/djOEiL9TYTCh6fP1aiV5j7eyZji4zMUZDFg2Ry1IE997mWpoEr37iUw+QqZXzA3RfJ+lN+pDBFBVVL5ygMEeTGB/23afriU50laFec/uVLaC9EmwhlgmTWsKsvv26KAd4OX7NFYCd7nIVsofP77s7jp7oz4e92AhhuvWxs4ufdaJpRbKNiENtzkZpAvM4flrRMoL48rCfJCw3Jh2Z9Lje3DCua1fLHa8kvhLErWdJ5PQfrCD+fw9FF3gMLpeRMvurjd97oy/CyGwgxQm1mXmuGBK4Nvm9c8yi5OyAUFwmpF+1BP/+zqS5uaG39QSnHnQzk8ddhdhjTVv6/kOH0rW7Q/OFJHoE2fdL3XotQhe+Qxpl6DqxAvC0EevZU9cG0CN4SFgrSOHyFChAoQC2x2/Yp72ulW7cCncHZ3nntqAlNzJkYmDVgUGJ82cOdDWRFYe3zW3cj8908XALBFq0f3F7F/WBfBiuMaEfVeUdxzm3zRQjKuQCEEXe1sIKr6dAaEELY1m1KYFkWsCb2hPF/yLnplC0yEIc8dxK5H6bOwOw+5eCnMOREi1Atetlo9l+NtEB93LEhzZ8LcnJYuiBlHkPKjHhuF1bbF8mQHI2koZhdYqRvoZqxXMq4AsD1kE4ozqaQOGfbI0wXsP1pCJm+iZACnbY4jJkX9bE85LJ0pFLgEaRHEjDIFrq3+XTegYmTSbacwNs060rFpdoymssAZTmfIyGV+RqetPOSqEe7by7fTc1Qib7g+EAUAANB0SURBVNzHeQYRnJyEe4IpJt6wFYyeasCDZ3nTqwS/Fdkg305GEBMQH+/SpYYgcEHLOm0RUDboMcjLWRUmc8wbim0rcgYIfFAiJUdY2eM2F5b8ovjClb0IYNHyNs8hLairk6DSwKwS5AGY5bq4+36qlYr3vLoHN/9qEbu2xPGz+3Iu1ezjB9hiBffpHZk0MLtgobfL3w/MS9j62TEohGVOfh6Whzz3Qn7uZY9lBU5ghT/xCszbUqNaX7/qH9EKv4G6xlri5HJCMJO38Mwwm3S+85XdmJwz8d1fLKJYWjq5edDi51L2TXL7KgeXkcEJOu6j6p3ArjSwscfSPMS7HsqVfdYX0K9w8MmNd4twJcgLpezvyscLD9yE0xlV9MH19LMFocD178xk24Skh+R1FLgrvFGJEGGVwDQdiwC+eOINiN2qJtmyqIjB8r7X9mLTUAx/9/kJ3P9EAU8eKuKO37nbwL4uBT0dCp455giP/vJ1vXjrx0dhmsCxCRZLpqhbIjAb3x0IwLEno8DZOxLYP1xCNm8F2t2pKmAZ5QuMU3MGDo3oNfWnqkpgGNyi0L0QRwiBZVH87smCq+/mO/sUaW4fVrjCLBRoqHMiRKgXfmryVoDPGfmudNNyxhIcYS35mkLgPve5z13Rg9UIrQX3qJmxo3cO9mowLYqUpDrglgfcpBkASiULuYIlvG9LuoVcgboJ3DZFuo6tflEIUklO4FqgFhUd3sahGEYm3b1USWcdIVffvu1l3dBU95Y4Aggj+LaUuxd0FLj+HYgvgev/qOxn4JwnTrXJLuFoLSlJhVG9rQCurMD1v5aMIN9O2UZADsi8PApcnic32eciPn1Xm93cql8zaAkFr0OmOgMERmDD9iPmpD3zw3WO915ZAQRh7G3//Uh/vhrO7sPvCXjS4CtzFdr1au/p1E1x/NlLuzG3aLoIXMuiOG4r2D/93kF88P9OYHTaxPC4Hkjgeglbr6WCvNghR7+1xLvzz6NTJx3leJBn80rAUnX6lbDcXvNhmwcadqUhQmOw13iC2oOSTjE8zvrL0WnTpUDk31EKPHmoCIuyLZqvuLwTTx0q4ru/WGReob4LY2wR9+DxEmIawbEJ3V7EkQ7ifVqV7DcLrv61URCHwPW6gfkFMXPloxVVtI7qJILILFGTYZhOBPH/+oe1KJQoFnMWCkWrYhbkyO21wu8M72eUUuj2u0pJgXWLVa5XKFEcHtHR0Wbi4HG2uBHkgVtRgcuDmIXYr0lC1JkIEU528LZCVYkY28ebJlGrjNkFC7rBxrXr+tlFh/rYz2/fvgDdhAiODbAga+mEc/7aPhUbBzV0tSmYWbAwaat0O9Ns/K2pxGXVIots5N2rQeNqVSHQQcu8NSdmzZoXkQyTIpunaEsRMVfk6OtSMTVnuMb8MuT8hbWOs6gjNopimEVoFZZqNyUXbGmqgkSMoKhTLNq2I47YK1xaTWne7rrrrmYkE2EVwzCA+SwrdWt6NVDqHrSmEpyQpNIWePZ9e1oFAUG2wJSQ8qpph02m8hU/gHXKfNJpWkyFe3zK9t8d0vDbJ8rzl8lRYa6eSnBiCOKnPNnl33Ok+QCfuCdLXsWHC0Q+zv2V3FC4CCu4+FtHmSmI3OqrkOXX8ic5gXKZvkxYMomOncYyMLh8JZmifNtnJRWna5Ua/g2h2M7jY6GgKu4JE7dkME3KniVloyW57IjrwiaCQ5CsllV90CVDIYCJxrxW+bX5AsW8TcJOzZkolFjwwbX9GjYOxRiBO2HgnFP90+L2CzGVbTstt1CQCFzpefh5YcngRLr8bp1ObeVNYdUl6vTDYLnXUFfCM2gFVguBEvb5v/vTY9g/rIu/LzoziV1bEjBNis99dxazi+66fM4ONtP0+rx68funi/jBLxddnw10K/jMewfF3/IiWCiQin8uCeSsckWoV4HLbZ+8kb5lP/VW5aveer8UC7RFp5hhTZ+GeIygULLw8N5CxTIglzXToqGij3v7iUpnlHQKXefXYu24aVVW4BZ1C5/5xgzyRfcxQR64iYoWCnaaNSiMI0SIEAzhfyu1zWVBzFrU1o3auzsHelRotgBpqJdV8qLtAnPO9gRKJsWeA0VsGtQwJ1mY7dgYx5OHSqIt4e3m6dsSOO/UJOIxYu8YdO6Dx+5QFSLF0/C/QVkQIyOTYx+ctjnhmvfmChaGx40ykc/UnImZBROppIK2lApVslBQCMGOjTFsHHLTSjPzJo6M6Xb+nM+rNelCaGRRMYeIPHAjtAqy4ruVkBcmutoVTMyayOasMp4jDKLqEKFhWJYlFHgxFejpVGBZbiKUK3AJgVDg8hF2THUULRalUCXRHw+kJPG3UBRG8vIKN5+1cHySdRBr+zQkfeKrzC6YImBEWsoL4FbEAhJhy/POLRTgHgBUIpZIwO/ydeXzvCuTRP6Mun9W2nLvHaD4DViCVppkGwEKSaG7DLPmSh64FYOYiV+CiQL+3PlAi1rOxNtF5tvXVgixFbhOGpbn0RCZlA/IFKXusmbVMCjx+iG778edl2ppcAJ3wR5ADk9w+xENmkqwYYgFF+QqPT8s2gO/ftsv2qvI5c8NcFt1OAHK/HNr+ay0LzcxWQmCpF6mIGYrkdSuhhX8Ok8ICGLO57uxaQP7h3UQArTbPvJj0wYoGJnEydueTgW9nQo2DGp48XOY361QDRrl6klKKcbsRdREDOi2Pfkm56za60aYIh2y2IcNaBUuDSLUSvIuIQBI8C3xpvuclQbvkAJoMYFbcshUTlqGuZysXg2rwiV+Nwf/z0qGo35NxIgISldJgTuXsQR5y+uON68y3Apcdyff362BAFjMUUzOVYlkFjWYESJUhe5jb+P1wG3VTmEuaOhpd+r5ml737rVNa2K45uJ2/OEftEM3Kfq6VFywK4E/ODuJS89LYz5jlomHNg5qSCUVF3nL7sMt6KlmMcDPl/viQsmCYTJbt+4OBamk828uYyFXtFAoUde/fIkJobjCVg6yRhT2fFMJxfWvq8N5DokKbaIXLgXuMoqJIpwccHZ6Nnfwtm6ADXz6u9lPhwOg6LR3mPOFFEH1LKUCN8LJiyNjOkZnFIzak7eeTmJvywCSkrcYtyGQlZ28wnBz9mLJwsSMIewQePogOdeWjHiM+ekm4gT5IsXXbplDvsC+s0CRiJEyT5FhKchZQlLgUlviSQgwOWeyvHsG8TIBJQ8A+K/jMyYIYXmemGHXKelU3P/IlIHxGQOLOROUAuOzBmbmTWgqgaKwY2cXWSTChZyJXJ4RzqPTBjSNiO00PFcVLRQ8f/sdyzvBxZyFw6POsy4U7VUgWzVE7aBqy9FlClKTUhwdMzBhPwNCgMEed0PoApF+EODomA5NI2hLsVXqfJEK03BHgUtFBFk5UMD0golc3kJHWsH0vImudkV40M4suLc8KOADKUZ8FkoW+ro0lHQLB46VkMmzLVbTCwYmZ008tr8gJoOjU7ogk4MwPK5DN4CnDhXR363CtCgWshYsy1G/7h8uoqdDw+a1Grrby5v28WlWDrN5dq+ZPMVfXz+GqXnnedx81wJm7WCE37tjEaoKHBguwbQo1vZpuPjsNACIwEZ8QDYzb+KTN0yJhRhKgVyJoitN8NiBIo7ZCyzzGTb4y+YtQSQrCtDfpUHTHILENU4jrKxOPVNEvkgR1wim5o2yetndoYiFIhmEAIO9aqBXYSPgdalQYnUppjGlWRjFWLOxXGPbsNddqaRWWKz0/LtIuYBjHt3POspdW+J43VWd+NAXp6Ab9pZySX34/U+uL5vsJqoocPn5r7qiA6+/ugsvet8xAPVtgw+6j+VWQ5cCFLjxOFcLlb+HVuVVlMda6r28eLwED5H7N8ZjPuRJCAUuwMpPKhF8bCCky3m39JZ06iJ8VJUAOq24nZiX454OBZ9+7yDe+vExAAjsu+MVPHDTSQVr+lWMTpl4bH8RV1xQfSq20tufCBGWE5yclAVAXnFEq4ZI3J5PtmPhFgoAi7sy1KvBsqhoLxSF4NLzUmhLqehuZ5nualMBsPG3QoDeTjcJ7F2ksmxRSLXdfPw5TMwYQmzF27NUkpQJKjgpO9CjoafDuaeOtIFEDBjo1rBzcwKphCJ2tgZdO+0hpc/ZkUShREXgtSDIQiPZ3i9ChFZAbisOScFT4xrB2n6tpthDMjYOaejuUEQ8J0fpS9HVrgLQsZiz0CHZhYbt6iMCN0JDGJvSMTGnCoKmryuGuQUT8RgRJs0AU+AWihZA5G3UfADNFJclnWIuY2FyzmFmRiYNdp6NeEwRE4H2FCNwH3iSTUo70sxjKBknws4hHmMeuDyAWTLO1a1EbP/Q7D5yIWtiZt4QwcR4JeKdWZnSQqqII/b9cx9gTVOcz+aZUTy/j2zewpxnm2q+YEFVCfIFoKBbyOQszMwbUBVgdtEhL+WffuDWC8Iv2GdywUm2fNFCfrJcHsWfRzPUSzwvXe2Vg5Z4oSrO9tPxaR2ZnImSTtGeVkSAGO+qNFCufJ6YDVa3xGNOcDC+gq6prMEuFJlCtVCiGJsxoRLg2LgurpnNMwKeRyPv6dKQKegApZicNTA5a6C3y8Ri1sL0goGFjIV8wcJ8xsLIlIGSzZsTwspdNWXAXMaEYd9Kb6eCQomKQSPHxCxBKmHgyKiC83elytKYnjdwcKSEXIFCscnmh/c5HZVlAY8+U3Rt7fr2z+Ut0SVBlPIFG5vvh0WB2x8oD1bzrF0JLOYojo27Py/p1FWf+rpNDHQ7ZcSlcKMUI5MGKKWYmjPQ06lidqGcRUonFWxaEyv7HGDKqh0bfaT5DYLXpZLutAHxGMFA99J0rSthUl9r87DaBuGrLb+gbDHn8QMlnH9aEa+7qhOEEDx9tIR/+X8zAICzdySFAob7tvIJnab5K5Xkbe3U46VHqZNOIq64CTiJFAvzKJtapJu4iYRA9sD1V+ACLFCMt14ytVRrClK9qS7FBhtepuTyEEYFpyoEmsrKWq0LAH5He5W1RZ268sYIeSoU1H7gfXYiTrBlrdPPHBkN3qnCEfexWdi6Lo7RqTxu/J95/PqRHHo6Vbztpd1lO8AEZ7MC2voIEVYqBIGrBLc1rerLc7YYIy3FT9myNiaCAp+1PQHFtmnhC6GEAD0dKgghyNtzw1TSSbOzTSnrZ7zZp7adG7/3oN18XIk8u2gCbpcjV6BwDj5/7EgrQjkIsHZzMWehs10Vu2Or2aLJxFciTtCWUtBWPj0pP48rIq3aditGiFAPFIk74fNbjmRCqRp4NQiEEHS2OefKu2m5Apd74ApECtwIS4E1/TEULVVsod6+MYbBXg3JuOIigtJJgkIJzHzd/ow3+EO9GtrTFLmiCU1RBJkGAM86PSW23gHAJslf56XP68DxCZ0pK0sW1vbFsGEwhisubMPeIyV0tSk4eLyEI2MmsvYqYTpJsG4ghs60gkOjOgCKgW4VGwZjUBSCjrTKPH4SQK7IrrOuX8MZ2xLYucktA5EXZDSVYLBXQ2+nisWchf4uFcQ+YKBHQ0EHsjkTvV0qBnpUzC1aQu2hqYwUUxSCjYMGijrzUlrbH0NMI9ANNmHsanMUi0EghGDn5jjmMxYIgP7u8kant1PF5jUx38mKZVHRIVfrmMPg3FMTmFmwMNRbW+MX0xQ8b3caizkLXe0q1vTHMLtooreTeUx1pBVfcpo3jl5fYw7+nviKeDKuYCHnrCAP9apIJdI4OFKCbgDFkompOUtET13Tp2FNv4b1AzEoBNi1NY58kZ3X06GiI61gdtFCNmeir1OFQoDpBUYudneo6GpnAzYKinSSrcp5g+b5oViiGLE7lfUDGjI5S3jYcqQSCvJFC+mUinUD5USmKId5C1ddTDA26XRSMY3gnJ1J4cE1v2jh0Gg5+Z1KEuzcksB9j+cBAD2dKnZtiWNkmpHS/d0qUnGC+5/IYz5LsWlNDOsH3N1MTCPosZUFubyFbN5CX6c7z/1SZ8m8oqm4Xk8n237allaRTrKFn/mMhVSClN13vmBhdtGsHF28AXSkFWxdF0dRp5hZMFEoWmWRfk8WnOgEw4q/PSmDP/9tFuMzFh55uoiLz0rhlPVxfPXmOfH9s3YlxbZx3aCgcLaUxwKaank7ql/fIc63CWBOwOlVdohXQlDfsxxljRBpm67nGckEXa1BYepFPc9gqcnAIM9goHp9iscIDJOGJ3CFutj1J4Dyd6IbVPQJMY2IRetSBU9ankY8xgJsbhrScHTcwLN2JX2P75QUNV6fXAJgx8YYfvNYHkfHDRwdZ5XkzFMSuOKCtkp3GSFCBAklnaJkUEE6qtJwukyB2yICN8/nlx7R0tc/tg4PPpmHBbZLzTQpYjGCvi4V6wc0bFkXF/0jpRR9XRoANvHs61aC+z/pF0UhVS0GNg5piMeJa9cawJ4P9+qVEWRTJ2zRqHwstdMKfrjnnprEQtaqiQRzLBRoFMQsQsuhKIw7WZDiuUzbc7pmjum43aJFWcwZAPjxrxax/uU9Imj4sitwR0ZGcNttt+F3v/sd9u/fj9nZWWQyGbS3t6Onpwc7duzAs5/9bFx11VVYu3Ztq7IRocXYvCaGyQWCnG1hcPb2JNYPxpCIExRKjuItESNiW7vj3cNa40ScETrFUhw9HSoogHseZQletjtddk1embasjeG1VzJ10eiUgUMjJbQlFZy+NYHTtyZQ0inGpk0AplCxdrVrQj1xaNRRzG1eG4OqMGXFxIyBVFJBzl4V3bExjl1bEq5VFPtWXPcnqzK8yBeZqva0LYmybTHVYBjURWJX8wHq6WBkYhBUhWD9oH9eTYsKtTLvmBvZDp6MK1jXX9+y6Snr61dMMiuI8s+976m3SxWNJsfafgVr+zUsZE08caAICiIU020pBefuSLpI154O9rOvS0VfVwojUwYOj5TQ360hphEcHCmhs03BQA/zmG1Pq+hsY+W0u8J78uLBp/Io6RTn7EhiYtYoWyWMacDjzxTR3aEElkWnHHZULIdnbUvif31yTPy9bkDFyKSJdELBc85O48afzgMABntUXHBGClNzJhayFi46K4VdWxL4m89O4KG9BZy6KYErnx08IT02oePomI6hXq1i/eEY6tXQ16UirrHysabPeU+pRPl9T84ZmF00YbWIsCCEiE64pFMWXX0JyaUw2+ZbjpAXXkZL7ZMKFNQVHOmRp4vYtCaGJw6yieFbXtKF805NiEBm3BKBK/z9yDbAHYjJj1Tj53OFUUxjacvqRz//97L8N7H+OIvF9Zc6Oa/8XrzPSFWcXTuGvF3f/rCVTUK99yby1MIKycuhn09sNf/uuEaQA3UpuINgmBSf+fo0knEFr7yio+x7b3mVLRTkAEF6QPR0ACjZgRh4+b7+fw/hiYNFXHSGv5wsnVTwhQ8M2Wri8vvfvjGOd76yG5pK8JN7MzhwTC8LkAa0zrczQoQTAQ8+xcQEfDxbKVBWq2pSThIIyYhpBF0dKubsMWjJtitKJQja0irWS4IDSqlrXrF5Taw8/57+k9q/O7El/POXiCvYNBR+HiYI4QAC3JIG1GGCMaeTStnOgmoQXqFWOJI4QoRG4eVOii2Y0/E6ZVnMbuxXv8/h2ISBPYeKuPRcxneFjW3SdAL3qaeewt///d/jJz/5CUyPFIlvfQaAO++8E1/+8pehqiquvfZa/OM//iN27drV7OxEWALwrdyaygKXKYR1lPEYwT+/awCDPRoOHCsB9ue8bPIxrUKcAa5pAdde2o4jYzouOct/YOwNaiWbuLui1xMgaYtmR2wPXG8HK471pC9PgDvaFf9BtPQRqdI3NbKCWBaYrIXbSPyyt5rmD+4gc34K3fBpOVsdqKsBr/YOZY8by27zhN8ynGdc63NlxD2FRf0jZXLvr0pRNMOWw55O94Patj6GkUkTx+x6xFXLqaQitnBRSkW6nba/FffbCrwnxan3gXkWxsb2liqPOkDxfC5DrRDMrlVYNqXmMtfTFa9QrROELAEL1wTI2ZOJqN89mUd/t4pCkQVN4JYKfFFQWCgE2ANwyCSll1STiUuHwGUWR37q97D1cam2wFaCnNdSAIGrEAJNY2pj5inMPncm3K3LXy2PpMowpunghLes3g77DrmquVhBFcvxm8fyePwAswN6xeUdZdfxWigwApf9Tqmj2lvMscA+foSrzi0U7PLd1a7iOWeXCwxk7Nzsb95L7LHw7tOS2LoujscOFHHgmI5iaZmiYUaIsMoxx2NbSHW3jPBrUWMnLBR8SErhv009C0SeToEQgk7Jam5Nn1Zh8cbeaQjiGvc2q390ggl7+zn2U856NfuGesHTcwUwX0Vz0QirH2Iu38QBnJMmG0NcfFYK37tjEVNzTgCFsJdrapX77Gc/i/PPPx8333wzDMNRh8krRPLvAGAYBn70ox/h/PPPx+c+97lmZifCEoErGXi/KW9f7+tU0dPBgo4ROH6Z7ED7h+IMoE2TIhFX8Nev78MlAYNjuQ3nSfEOR/UQuKkE6xCL9uDbz++HHytfICcpIYj3e3GONFCo0nPyfrueTs6bdJV4Vw3B7zZWU6dJpJ/+ZHT4m+H3LRPBzCenSh7sw02JLJXLPS9Zta4my6vffmSlZhf+SmRo2HLoVV0P2tusuI3Dok3MphLs6XjvjW8fnc9UnpDKETmDQDw/+bogvwc/VYA4l7crrZLgytcKcS9NxwonFX2xitoTYNVlF6COGhYAfrengOu+MgUAOGtbQrQ7XBWpG9Q+h9sDBCxyEoKYLRgqU+BS9xZzwCE5veRZ1eyHODxsis2siy4LBR+Sm5N+RgUVZzNRz73xXDNbmubmxw9FHw9cGZXugZO+YRS4jx8oit+9AcuA8vKqGxQlnfVNxyd08c4OHivhwacKvlsm+WcJHz/bRsFJ4ZKP3ciqa38iRFgG+FkoAO65WastFNr8CFx7CEzB25BgUcGWtTE897wkXnhhGprqJ0GB51zqsuxq1lyNE1ZB4iF5OF3NvqFe8PTk/nQ1iYkirH44c8vmpekoy1m53jjEBtVT80bNnX3TFLif/vSn8YEPfMClsqWUYmBgAGeffTYGBgbQ1taGbDaLyclJPPbYY5icnBTnFwoFvO9974Ou6/irv/qrZmUrwhKAKxlUuzQpCimbKDBSl5dc9sNR0EmTnxAVxdWIe1YsiNSDKYQRTDLSgQSu1MmjvML6koHS79UIsUY6ufJOtHW9mB/BuZqM40X2AxjcWu7FXc7kQUTl5y9Wju2Bh0KIrTx3Bm/s8/B5kY8PmnyrIcjKsOXQ+5wGe1jlfvpoCW/66AiytmVKW1IBUZxJM88j92teyFau0I7PVaWj3F/y++P3IEerLbuPJVDAcYgnukyk6nKNbcOqDFcj1yxjpedffv7co3bz2hjmbPugRJzg2ue2i2MSclAy6pwTRODy70q6vy8pt2LgyklOwMlkWC3dn187uxxqaPlSjgLXfQwhdkBLW3HsWaNuLRq8SCsnxZwkTbiCmIU7l5O+3rJGKcVvHstjas5hLn77RF78rhsUmkZc5afoIWSLkoWCpjkWCqbFvDTzRQsxzW0xxPORCCCja4InCV5XKilwl3RhMEKEVQqv+IAoAOymolVNXb6CApfPJSllbQ7Pn19tVghw3s4kpudM3zgeXpEA2/3GfydV5yZhEbRTr5IHbrP7ESGEkQjcyEIhwlKi0tyyXhBPHdowyAaTU7POeCbs9ZpC4D700EP44Ac/KDJHCMGb3/xmvOMd78D5558feN7DDz+ML3zhC7jhhhsAsEbp/e9/Py677DLs3r27GVmLsARwgnGxvxXikIzU/if3nJysUQUBQ1wK3GpwWSh40tQ8CtwBTxAvb0AlPyiE4OqL23DrfVlc+ex02TX98lGt87Ks5nVyre7DCCEun+LV5MHGsxrA39b07MRAkLpV49VIYD7I4SvvfCDGG2WhwK2VwJW8c/xIWi2EhULYcqgQghc/pw0/vTeLF16YtoO7sS3RPOAKwCwUFELKCFjuF12NwA3VQfJ3ygd0nuB6jmWFX/rsyyUQ4DqD69ZfSmAlTOnDFmPvtvJVA8+kaaXDglMX/vV9g4F+6JwgoxQwLApddwitIDDlabkvqaMuctLVuMLXh+xdiidJXYtujadH4JB4fs9IBIUzfdqzFtxwXUkucRtV4EHMAhW4we+GE6VeBffvny7iw1+aCrymbgKaZ5jn64Frz5fa0gq62hWMTplirOO3i6XkKd/NAC8nXNXrp8Bd6ncWIcJqgd+uq3IFruBvWzaXyRc4gVuePh+rU3uXihIvJ0E5CAE0KRBzNXD7MqC5QpugnXqO4ILtpN5zsIRFHgS6RRYKzt+rbeAYYbWjkj1fPaCU4uiYjmMTBgolis49eeHdLQe2D4umELjvf//7YVkWCCHo6+vDj370IzznOc+pet7u3bvxla98BW95y1vwspe9DFNTU7AsCx/4wAdw++23NyNrEZYAwkJB6nh4UysUuCgnsviQVCGQFBDVryd3wpYFQHU6crmR5wGG3veaHnS2KZhZMLFtffVASSDAc85J4fzTkmhLEVhW0HZ85/dqfUuQKXwYeAcdLSdw4UwWVhvZ4qfA1VTiqGFreHjOvUtEAEJ44HoWI7g3rXfAVquXMRGdiePJK9+brCIKQthyqCjARWelsH1jHP1dKhJxgve+ugeLOQv/8f05AEAqTsSCiWORw/7uaucWClU8cPk9Vaj3xEP8edW+8nMpO1eQu62f/oZTE5/AOFnve4WAF395S2W8AhkrKwkZoWUTVBVGhdw6wI+U5e0QJ7h4Or5b4EOUFd8+N/zpzYN0sSAPXPkzX8/fVmSrwQUR5/zWdfIlHwuFsFeLBywAPPgU2/6xYVDDKdJ47le/Zypc3aBlO6+8aeiSB25Cc2Iw8H7CT0jAx7nJeONMhTw+BiS7iBrtRiJEOJnhvxPNo8CtQWhTL3K2ct5vh6cnFFDF9k9RCFJJIoQz7Wl3eq4xpn2MdzzcDATt1JMDixVK1BXjoiPdXAa3XP3b1OQjRKgKYRnSpEndXMZC1rZbsSw2zrAsilQCyBeBmXkLPZ2KUMBXQ8ME7uTkJO6++24QQqAoSmjyVsbFF1+MH/7wh7jssstgmibuuusuTE1Nob+/v9HsRVgCOAQu+1tRPFs9PGXf65dJiExyUZgWLeuEvZBVouw8+9o+jX5Pl4ot6+I4PFIKZT/ACej+btVWkFDfXlf+qBoxaAVsSQkD7ymtDGImLhjwPFcLZAWuKpGctQzgZILQklSf1d41/5pvKS7bxh8wOKqaH0mtKjyfpXvjK/eVFbjuPAWB1wGuYFcUoD2t4PStCUHgWpRCUd3XFEHMarZQqEA6e/42PB64MhltWdT1fpbUQmEZ2CX5vpZrsSU0AcQJo9ZlpSVYbfmVCcRKfp0yCanrTrAxr3rRdY7w6vQjcNnPpMcDVz42zLNsltK5WXVDNyi+d8cCcgWK8WnG+vkR45wE/N4dC7jg9GTD1w2LWi4h253U8pSfOlTEV26ecxGML7iwDS97XkfF80osrpjbdkD6tVIe4gEK3Mf2MwL39Vd34qqLHEuQF7/vKPIl+KpYvMRo0XDEAvEYEYpq3o/5K3Dt41vggRtkFwGEt6iJEOFkg189VT0bTnjwX6B17TH3wPXGWOGBjAGn/oq/fdJRCFsg2r4xhlRCwVCvf2dsWY4give7zby3oJ16MoHM28qYRnDOjmRTdyawawUT8REiLAWaOX+cnjex70gRCgHaUsS1M66vS8OxCQMLObMsgHjF/DWaqV//+tew7Jp89dVX10zecjznOc/Bi170IgCAZVm4++67G81ahCUCV+6o9iBYJqb4RMGtynV3DorCVk3F9rXKoj12rictUyK0ZIgt1B7fzDBpg1ZWudSiwHV8gmrvhbynNNss3gv5XlaT/y3gPF+2gs0+k201aiGkhR0Dqe39eX2qZAWuRamr3NcCnnfTcvIj3xufhFJKRZvsRdj78D4nvjgjbx0u6k5d8doHdNnRdKsFMVPFCmeFg4jrh3iuwgNXyqt3oVSooZcwuPdyTbSX2+rkROcXVguBIjzxUL6dVIaiENE2FXWKkhmsLuUQNgG6+3NKHeI4YSsUHQ9c57iabEb8suFpW5cCh0Z0PLq/hP3DOhZy7Lp9XW6WgBCgp4PddyZPMTLp3gvfkuzWkaY8tqkFP7kng0eeLuLJQyXx779/Ol/1vEKVIGaV8uFHauaLFvYdYazwOTuSruNjNrGq6xSPHyjiH782hUMjJXzoi5P47HdmXMfe+D9z4vdknAjfZ6MCgavbfr7JJhAV3qa6EoEbIUIEf/j1A2XiH/+1o6bBsigK9kKV10LBL9gXr/zUR9XH2wVVIRX7Yb4rVCGoa3dhNfCsBdkYsLmMk9dmk7csXe/fEYMbYWnBy3sz5o/D42zQTEDQnlJclohtKXYd7qVdSdDkyl+jmRodHRW/X3755Q2l9fznP9833QgrF4dHS2J7mq8CF8DIpC4INQLJC5S6CV9+/tiMnxGYB56JoCB1yjoc9rOWKIKuSWYltaQ8MKjQt8jy+0oT6mr54Wg1qeompldXp+mXW5nUr+XZKQoR5dYZrIQ7z3V9j/WBo+wNnxc5XUqdIANB9+YXDLCWcui9B80+wWvPwK/pVZ86ClyzbJBtmBTHJ3Ucn9QxNm1gMWeVEa+5guWcJwa+7mMcItz5wlvP/YIutArCnL71lxJYLaSijOUmmmvFKsuuE4xMq/6seTAu3ZCCOlUKYhagwGVB0Gyyzp54Bm2Br4ZmFWk5nUZeYdH2cV3Tq+Jv/qQHb3tpF87Znig77o+e76hRc8WlWzGq30IhvCqNe9m++Dlt+OvX9wJwJhuVwIOYyYrlsNn1IzX3HCzCtIDBXhVr+tzqNE7C6ibFd25fxPFJA2/9+Bh+81geuaL3ftjP9hRBKqGgI83OnZlnlcdvy2QrPHA5Kipw+S+rsK2PEKGV8NvZXObbGvhHc5CT2sE2jwKX2xuYJrMVWz8Yw5o+tvjnV51lktKPsHQ8uqmYZ0/awY+aaqEQsFNP3jHn2BY277p+1wr6O0KEVsNR4DbW+ZoWFeOlUzbEENOIK34TV+5zJX9YNGyhsLi4KH7v6upqKC35/Gw221BaEZYG49MGdJP5kMUURxHHy3s2b4lBqaoAIKTMA5c3zPEY2w5+bFzHun6t4kRSISxYi7MtBc41XMc5HZ58rTCots1QTqrS6qc8yKinEypX4NaeRr1YbZ2mrJrlgx15S1WthLTwbbZ4Ga5+fpmNh8JW8LgfNC84tb5HefDELfrke5PVuIZR7mVZSzkMUuBSyia9mbz7eTjPnf3SZRO4ugEUihSpJCdSKf7iU2N4Ztgt4XvVFR3YvZMpqvYeLuJdnx7HK57fgXe8osdRVXvz6FJWExFYQT5SVkNTSltKHgoOezWyqk1Atfs+OZ/K0oE/f0MQuNXLekwjKBQpSjqFYVQ/j5NkfkST4dliLhNqAjVUv0prpktZxYo2cdfTqeJZp6ew/2hJWMeIfBFmEzDUq2J8xhSEZyv7z3oeQb2LTNzGYOfmBC45OwV8g5UBr2VN0HlBpGelfHDSV7ZQeHQ/Y2L9CHRH8V2e6lnb4rj8WW0goCAKi42wf7iExawFTQVOWR/HLx/M48Bxxuz67QLj5buSLUlYeFMIsouIECFCMPyEOX5BzDia0RzPLpr4z1vm8MrLO7F5bQzZvCWu623nuHJvIWuJrdHOmNSPoPX/3QuL8l2rED60lebLtcISgih3mvKW8lYET3NdSyGuGB9hxjMRIjQTzYprkiuw+WdMIyI+DAelEF7XYRbFZTRc9QYGBsTvhw8fbiitI0eOiN8j/9vVA67i4RYKhDg+rXzQSylFMqFgdtHEvN3h8OBEvFPYsTEu0jR8gkj4Qqh57Tx4t13YBJdZg0+QyyeugkpFJgMrkXHyIKOebS7eM1pNqroUuKvMQgHSu+OQBza1+gcrxDOoCnG+9/3I5CdX4CqE1Ewm8qJjWc79yfemqooT0MtXgSulVdVj2nNtrmSnwCffOYg1fSr+5EWdjg+tIAZYxpIJgnZ7W8jotKOon89YgrxNJwk0u37uO1oSx3ztlnlYFvC9O9jioLP1zJMnn3IaZKHg912zsZyLHcupag1tgbtKie3lIA0bgeNlW/3FyDYHnPiqNFESqloPSWaaVNQvnqZQ65bKH1ylZ9m059wkCS5XkSZipCrzGfMhHb1ZaRYafk72+WEejRyMLCkFCKtGOPoGMQv5LhI+qtTHbAL3bI99gnwNw6DCzoLj3FOTGOhRQRSCvi4VG4di6OvSoKmsD922no09p+Ys3Pd4Hr98KIuj4+5FRp6PRBMVuPwd+t0rR022IxEinETwDWLmJTJDkqJh8YUfzOKn92bxZ59gu4QffZp5crelCKbnDIzPOP+m5vhKkNO38rbJtCjGZwxMzhlCYCSPaf12yfGvLQtY169hTV8MG4di2LQmFi5AdwjIuw+CFbi12RLWi9O2xJt+fxEihIUcdyYMDJNiYsbA2LT7H4+d0J5SyuqUadHlU+Bu27ZN/P79738fH/vYx+pKh1KK73//++LvU045pdGsRVgi8MGnJhS4RKzg8Z+6QTE5ywrx9JyJgR7V8cy1K0lbSkFMI9ANWtXywLsyIoIoBUQgtWqYrPAOSfYh8e2jQhKdXs/OmlFGprWWrCGS6f9qsx2Ssys8cF0K3NrSk+1Awp7vVemyASUjOCi1/XnrIMbdQcwkVbsNArZgYRiAbljwrs/VUg7LLBSkIC9nnJLAv75vCAePl8SEs3y7E8GGoRj2Hi7h2ISBU+wJMvcBGupV8a2Pr8edD2Xxj1+bFm0D4F68MS0KYq/0lCtwy0mBcgsF53fLqs/CJCyC8nAyodUq5wjB4LWGK2NCKXAlRW0tClwvgVvyCZzG05EVuLWQ4b5d7jIUrZLN4yUSzu6hskVV+yf3CC56SesWsm/1PBNKqeNME+J8mYiV7RCKJRZBOQhFQXo6n8ntQ6VyIGwF7HJpmhR7j9gEro8CVwTNM5miZXbRaYjP3BZ3eTEDEgGhEKSTwNp+FaNTJn56L9v997PfZPHtj68T/Qwvx8lmBDHzkLKRB26ECLXDzyvSO8Zzq1rrr7v5goUPfn4Sjz/D2iDTAi7/i6Mi/VPWx3F0zMDMQvkAkLcha/s15AqO6ujAMSZc2DjEiFjXrjJfCwV+KkUiTjDYo2HjUHOJzUo79YQC15ICKbdwTN3ZpqKzTa1+YIQILYDggkLO6Y6OMVvAILSlFWGrIosPO7gC10fsUDF/NR3tgz/4gz9AZ2cnAGDfvn34+Mc/Xlc6n/jEJ/DUU08BADo6OnDppZc2mrUISwQ+meMrn4z0crf8sidnb6eCwR4NPZ1s/UAmlDj5Vc002qtKqLblw6yFwfVRcVabTFbb7sLyFuLavtfxv6dWQU6+1WRxqyCr/WRCtdb7UQjxLZ+VUKbAtesFV3QTUt87dBYW/BW4ILJlSPn5jZRDvjjjVVHywRvPhXxbGwdZ/R6WlEzD46xz2zDIBp188Dk153jlypPYyVlTCiQYXA9kf2AZRFI6t1pBuRzkktghsPSXFqj1vlcdv7vK8msIL9rqx2qSp22YrYqxgK3ecp3l53Oiz+uXWxVNqqdNEuCK/CfCKJr58xEWCq0rPPVZKNR3rrBC0FhbyxftC1UmHFx9HY/5dzqV2mShSi2xzmxyzoRusB1f6wfKC3dMUuDyMWkqQfCGazpxyjq2gCi/Dtl2ixCCay5px7mnxrFrSxyqyvqkI2NO38UD98XjzWcswhC4q2UHQIQISwW/mAfeNrdZLfCdD+UEeSuDUqa+3bUlju4OFb2d5f8GexkJSQhrbwZ6NHR3qEgnWVvi11+EIUZb0b1U2qkndvhR2nILhQgRlhuybWEY8HFSW0opawMGejSs6dWc3aqSuDBtK3BrjZ3QsAJX0zS86U1vwmc/+1kAwEc+8hHMzc3hYx/7GNLpdNXzc7kcPvzhD+Pf//3fAbAG4s1vfjM0reGsRVgicOUOtysgxKfTlJb10kkF5+xICIWE3AHwTsusYqEgVKJ8FUMyXSeECGJDEMLcQiHE/fBjXCSYz4kuorNCT8o7xCYJcFtPgNSoOF2pcIJcOZ/V4ztbq6VEmQeutCDAvKvqtNIQAfkchbrqWZzm+fMlcBsoh95ggLw/4/fhPBcncU7OctKW/a7b37H2fV2/BkLYFu7JWQODvTEcn3Qfzwe/XvgtoPh1tITwZ0/RvOG8T37snyfzRJspzP0+X/0PZaXfAX/EJrdCCOGJxxWjJZ0KorKS9QInhQ2vAlfnC6hOnxu3hUFyELNaCM2V4oHL85+IO4EngiS4wkLBJh1FfluYv3pJ4lqeofMMFPsnQVGn1Qlc6dnJkMdoQfAqcPk2xMEezbf/5GVTN53FiH/6iwGcsyMpgpPJi3l8jKnaHvVb18VwwekpTM0ZuPF/5rHvqI5H9xex1SZ/ef1INsFCwbuoGA8IDhghQoRgVLLM4mjWItr+Y6Wyz/7qdb3Ysi6Gh/fmEdcIdmyKo6ejfLz69NESiiUDit3u9XWp2LwmBlUlOHi8JNorl4WCX//tnVu0gDx1AoKXX1++nmkFHxchwokAYX8YklflPNOGwRj6uvznrd7qYlkUHTaBWyjU1v83hSW97rrr8O1vfxuTk5OglOLf/u3f8I1vfAOve93rcPXVV+Oss87CwMAAVFWFaZqYnJzEY489httuuw3f/OY3MTExIRqNoaEhXHfddc3IVoQlAg+aogoyh5RFq/ehVRxbA59Oq6oC15Mu53sVhX3HP1dU9kctnY3sgeu9nt9x/LpB8JJdtaLcU7W1HabL9H+V9s0yVRdWKe0HrwdumEcf9L5YIC1SRgrXkheWjkNUuvx9iVN//DykGymHXisS7/Zbpz46191gE7hPHCzi+79cAAA8tJf5hXFyNxEj6O1UMD1v4Tu3L2CgJ4aFrFP5f/abLE7bHAMFcGxCR1tKQUdawePPFDAy5RC9o1PMpiEoqIVlLYG1QZ3qtkYQSCgtIWq99GprU1ZbfkUQsxBEU0xSyYZR7moBqlpO5MWkMWtM8tetBc2qP67+u4F3WPQhIQP4W0Fal1kotAJ1XII4DXVNkBW4ALMRWMg6RHW18+rxjRVqb/saYzOsIA0FLOjx8mYYUkA9Ow3RX0nH8zGhqhLAcBYgAGDbhjj2HdXx5Zvm8M3bFtDZpmDCtvmJN8NCwQO/gG0ckQduhAj+kNv4RFzBYE9rttsfPF7CzXdnXJ/d8R8bQQjB8Ukdqh1wK6hlcOxaJDs/xYkBYZhsfuAnZpLhTb8V/rOWJIYqu770GR9nRPxthBMVvLyHDckkLwoHpikJsQBuocAuVKuFQlMI3O7ubtx22224/PLLMTc3BwAYHx/Hv//7vwtlLcDUuobhHs3Lnnk9PT249dZb0dXV1YxsRVgi6J5C6yW9AB+1B3GrZjkUT+EOhCCznErA0iIuBlch3HA93L1ISbsUItX6yUqdmJ9BfS0o9xetL53Q15N+X7WrqxKD6wo2V2sQM48HbphnryjEpTDigzQexIwQUp+FglJOzpZ54HKi1af+NFIOxbYPTuBKCnfmU+vkgWPzGta9jEwa+Pz351zpbV7LmA5CmKJqer6EH92dLbvuXQ/ncNfDwHk7E/j9viJuuz+Hi89K4b7H82XHbl2n4eKzUmWfe8nnVmGprBpWMoJu/UR4Jiv+Huz86VZ1JS2HbHPAVZaVlLvieK+Fgk8AtJgP2dtwb7IMZJZfIK4gxDX3lljvOKWZ8CMl6zo/RAIlHwUuUNlCwbSoID072twdr7zIHoSEV4E7wxiDNX3+0xY5wB4fk1Yiji2fLcCK7bd++tY4br0vi0KRolA0pWBEzfHA9abAn2fkgRshQnjwcW53h4rTt/qbcTcyXxqZMvDNW+dx6/3lY1M+3uN1VlODryXsy4gc/MsRYCxkTdz3eB7zGRMdbQoUQvwVuN48tECB6yes4pDnDpysiiwUIpyo8M57q8EUO2OD626ZApcyz36A+WzXsmOgaT4F55xzDu677z68+c1vxn333Sc+lweuuu6O6upMeCkuueQS3HDDDdixY0ezshRhieB44LK/FeI3qWCsrujAKPX1rRUWClUIV1mNCDidIvcX5aerCoFlUUFehaobZYRpua+SN61K6VKp864H5auu9aVTzwVXLX9LndtwT9BquyH27t1/hztPVrzaeeL5IvUNehw/Z+czrwK3koVCI+XQu7Ai35tpMTWByISNLWtjeNvLunHwuHvr2ZpeDefsSNjnE1xxQRrJBEF3u8LIbwDn7Uxi75ESHtpbwMikgaePOmkcHmW/n709gYEeFfkixW8ey2N0yvAlrhXbbqXlHritTd4XtMJge8lQ47VXW5Mi8rtKuBUzhJcth1DJ6tRR7lYYFcYESeb+nHudyu1RTCLUvKhEaDarnroVuPWXOk5sJ+Okat44iVhYSiKuhltzFqchynMoAlcQ9OxvTjhWUhofPK4jX2Sq5E0BgXZoBVsbry8sDw4y1BtA4AoFLhUWH0KB64k+R6l3suUeiw71xfDtT6zD7KKFex7J4cafLYjr1KMmroaghRFAKrurpP2JEGGpUIls5Ag73v3dnjx+v6+AP3tptyBgvv6zedx6HyNvNwxqeOXlHZjLWLjoTEcooBusPalE2sj2ZfI4XPOcU9JZG5CME38FrucSrZif8fz57fTkc2FKnR07rd4RGiHCciG0oNAGrxNahfm9qC9cDGUBne02gVukNc2Pmmo0e+qpp+Kee+7Bj370I3zpS1/CnXfeWaa4laGqKi6//HK8/e1vx8te9rIoevUqBR8s84JJlPKOxqKsYKoqI1TliZDL90d41lauMK6JCNwBmvy2vIsONMT9ePujoHNcys5KHrgeIq9WlHXaLV7xbMRyYKVALj2NWEKoSn3blFyLCPYgzbIkAreONPkpLgWuZwDIB4SmiTI0Ug5FPRIKXPfnDsklq9YJXntlZ9W01w3E8Irna9h9WhJJKUDMiy4B/usnc/h//7OAbN5Jd3SK3dw7XtGNnZsTKJYsvOh9x1AoAXOLFvq7PXmvsROuF0Lp22qpr9+1l/yKAQi49Yh7aD34M+bkaryGoFuyarFiEDP7u2MTOu59LIeLz0xBUQgeO8CCu8ihCxKak7ZASC5KNyieOlzE1LzTkJ1xSsLX3qjVKPIgZrKFQtkiL/sZ8wYxs79vRX7rUvXWyQUWPSpk3k4XShTzGRO/3VMoG7M9Zgf82bw2Vt7n2JxppXzw8jsyZeB/7s1g3xG2cDcUoMAVCwYm+wc4ZKvfdbiAQJXGjHJf0d+tob+bKetkArcpClxRjt3PNVLgRogQHqFECSGq62LOwgf/YxIAcM6OJC6yd3JN2/3PGafE8a/vG/LtG0s6Mw6LVVDgCl9ZQlzjcG8MC0L8dwYEoRU7JKuJnTgJLSwUIgVuhBMUte7eDKPA5WkWdYrfPJ5HT7uCDXbA73xRWlkPgaZHCiOE4OUvfzle/vKXo1Ao4OGHH8b+/fsxOzuLbDaL9vZ29PT0YPv27di9ezeSyWSzsxBhiaF7tlKwSKDuQsg7Wk0BdLgrhNsDl/2spsD1TkT87BgArgx0ImaG4c3KFhKCzpGJ4gqdmKMOrrOz9ShAW73Q0YjlwEpErR62Qef6/R0ERQEgvKHZT6bApfV74NrpON5T5VYMzjHlnUAj5dAhJ9lPnjq/XiNFkpPdfkbxGwOUWwAzigfYtt7eTgUzCxaOT+rYvjHun/dWK3CXQyi1Aub7Nb/6FcM2h8NqW1g2LLdashL4MbpOBdFayXohlWDf7R/W8eEvTuFDb+nDYI+GG348b6dXrsCtlZSiAO55NI87Hsi5Pu/pVPDht/TXlBbQ+PsTAbxi1TvDSl6mrUK9d1cL/1vyeNkmJKL6M9+YwT2PllvacGxdGy97B2FeSTrFDjoyquPT35gRn6/t8/e5jEtqct5PeW0vZPGLZfejmkTgeoPeAmwnSXe7grkMSzSVbP6gKCHVFdlWDpAWAZp+1QgRVjfCxDYJ0z7+v/+ZF78vZE3pd1bnX3tlZ+DCZslgK1GqSgKFGY7vrWyhQMoUuLJC11cB6/m7lQrcoLT5eN0wqj/7CBFWM8R4IYQFp2lRyVow+Lj2NNtpahkWcnkLoMCGQfadRWvzwW06gSsjmUzikksuwSWXXNLKy0RYZhjcA0iKSO8dsItOySdImdtCgX9fRYErKXGoZMfg3fIuyLMGJDCBK5EhjuF5BBpQ4Ia8TivQCpP8pYBb4V0/Ie0dnIQmcO1t+4BTLygFKJhFQH0qWD65dBZMvOpi55jy8xsph44XEJfgsh+qvUDi2RVSW9o22e1X5YMI3M42BW0p50aGejXMLJRwfLJ8x0ctnXAjcFTIrb3OSkbgrZ8Az8TvFjI5C+MzRkMKy4EeFV3tjQdfEbtR6rBQKBpUBH6qdN6zdiWx52AR49MGRqZMPPBkAUlpvSRfsPDMMFNKzi+yRiibdz5byJoYnTLQ3135fucz7NwNgxrWD2h45OkiZhcsjEwa6O5QlrQ8cQ/WRMyxUCgnJNnfZQrcFnafDQhwazrXMMsJUa5CHR7X8cCTjLw9bXOsrOyYFkU6AfzuiRwOHtfQ06GiPa1gZNKAaVF0DpeEr64Xm4c0vOTSdpf/bE+HAk0Dfv1Irmxh4Ogos2gbnXKs2obHdTx1uITZBRPZApswZfIK5jImJmcNEMXdxzs7TSj2Dzu2Pa9+YSfuejiHjYMaejubFyiJvwf+XC1b2RZm8SVChJMdYRS41drgX/wuix/8clH8vZBzBoqL0u/PSO2BojBf74WMhVKJgoKyhc9ABa6TT5nM1coUuES0tb4kUJ2CklpQTQEsdgJWymeECCcA+NjAouULq17Ic+5KCtx0UsHLL+vAodES7n00j460grO2J0RcgELRCk3MRsOECA2DB02p5IHLCVneYVkBK6fOFrbK15TJEnkyogry2M5TgEKxYtphBbguBW5whbVCDDIqwW3VUF8atcBloXACdM6NeOA2I/AcrxdUCvZVT7r8XN5REOJ+P4Q4UW1Nnxl6I+XQWy+93qvifusgFTjZ7Ufgrh/w76IGPATQmj4VTx0Gbr57EQ/tLbi+KxQpLjk7hR2b3MrcZmM5FLj8Wsu5zlLrtVfnkpD/IuDRcR1ziz6rJTUgk7dw7qnNI4X0EEQsh/DANZydNJWsFzrbVLzi+R2Ynjfxb9+axWPPFNHf7TRCcxkncBXbDsbUqPyzTN7CfMZEUaewLOrfb1JH3fOHf9COP35BJ/72Pybw2z0FPHOshGftCrdri7+vRssb93+Nh9g6L5SpeotXiyTUW/edtqNyAjJRyolGbifx2IECijqQiAOvu7qrrG95eG8BMwsmTEtHJm9hNK5g67oY5jMmLItias5EPOb/rAgheM+re8SiPqUUv32igGPjBobH3fE0TAuYtUn/+ayT3vS8iQPHHOJFNwHDNLGQAbIF5uOoSLvFVIVNvkyTYnLWWQzcOKThT1/UyYILNWFM5H3kslK4pFN33RVihRNgFSxChCYijC1YUPt2fFLHkVEdN9296Pp8QWo/Fu3fZxcsAG5xwNi0Oz1NDe5rZK9emcz125nAx+5+JJBXsRumj68VshjKDwrf1RoFMYtwgkMez1i0nE+SweuDt476obNdxZq+GJKJIhJxBZ1tGmIaEwsUSkB7yKlqROBGaBhcucO3XiqK20LBsuyOC47ClneS3gG/o8CtfE0RAA/ldgxykvV0LmG3zbuuU6HOOiua9bKB0nWWoLNsxHJgpYB6ygRHrc8vLJnvhXwdPhCTp1/1bOuV1UHsGu4tWwROGfP1wG2gHHptCPi98HsT99MAMe3nHduWUjDUp2B82t0gbF7r7rq2rovjzofymJyzMDlXLEvHMCleeGFb7ZmrBWKi3drLNBM3/o8dYdkn02v6Nfzj2weQDtgyPD5j4LqvTGF+0YCiKHj1CzsC730VPZKawG0Hhno1JBO1Ff6STjE6ZfgG+aoHPBVTWCiEJ3B/9pssYjaHrFUYFfIUt2+MQyHA6JSBsSn3MZvXMtX8iK2Gn56z8O/fZlvgLYsiFVdw5UXtMEwgQHwpPEw5sXXOjiR+u6eAm+7OIJ0k2LnZP+J4K1CyydhknFQsx4QQoZws2pxhPYrXWlFLqZO7nbAEt4vA1dwK3Mf2sxvdtTmOrevKd0scHCnBsCg2r4lB0whiGsHmtYzANSxGjCYT5YXg6JgBSilM01F4WZT1faZFMdCjIREnQsldLFE8caCAYxOm2BVCAGwY0rCQs6AQ5rXekVKQyVuwKMVvn8jbPrwEci96+taEaxu1jPaUEio6fK2QFbclg6LFPVWECCcEnOBg4evk8UkdP7org5vuXnQJhS48I4nf7SlgwbZKMS2KTJ79zuvnhqEY8gVLeOMCrO8v6RS5AsXYjIGU1J61pwg621SXYKnSOFwhko+mT9/I2rs4DJMirhH0dDR/Qlhtp543Foe6SndpRohQDe7xUuVjw/jfylAUOxQABSgoYjGCkkGRL5oRgRth6aALLzF/DyCLSh5jnsLt7SR4p1U1iJmkSpA7YcUj/y3fAl+9cnmPCDwlJNHprGhWvbT/ZVzXaX1n6SbAV3/nLN9DrY/PWz7DWyg4vzs2Hqyhlj9rNC/yZ1RK1/LzwG2gHCpCBWVfy+P100hwIaXKuR/4kz788K4MNg5pSCcV5IsWdp/mVuFddFYSpkWRSiiu7dnHJw3c8ON5TM6ZrffAtX8uJYHbyLVKOsXXb50Xik0vRqdN3P9EHpc/y59O+PlvsyKwEGBh7+ESrr64+nVX23i/krKaD9oGelR0ttWmos0XLYxOGdX93msEJ4TDbMOWFe5e0rQSknGC3acl8eBTBddzufTcFNYPMCJv11YTisIm2VNz8k2amMuYNtFcfi0KR4HLFa0XnZXCl2+aAwD8fl8Rf3RZ9XsT+WqwvOk+QcyC4ChwW98I1GWhQNzteBjwe4lpTj/gfRYvuLBdvHcZnWkVpRLF9g1xLOYsqCrB+oEYRibZwsXa/pjvAtGxCQOmCVcZERYhFtDbqaK3S8WuLYzIX8xZ6OvKANBFfU0lCdb0xjA+bSKmEZx3qrvP2HuohELJKrPr6kgr6EgvjaxMVkHHYwQlnQr7DY7l6FciRFgNcIKDBR/jHW/849em8fTRUtlx55/GCFyu4M/mLVHnOCm7YUDDzILpJnD7NAyP6zg+oSMeI6IPYPkiuOD0pMdCIXgcLgcx8/PAVRWCod7W0jbVduoFxd2IEOFEg8wdWBaACkN8saARsj4ott0nr28JjSCL8v6/EiICN0JDUAiBbitUVNXZ0u2SnltM48C2jLjPP2Wde6nBzyPXD/KkWigS7Q9d5JnXJL7K/chpO38HbCWRPq5kNRAqUmql/FTIW0twQihwnUZQaeB+vIR52JV++ThuocAWMrhitbZ8+OVF9dQny6LiWn47eBsph/wcPtkVFgrNCGJWxTalp1PDlrUaOttUrLMJp6RHuqepCnZsjGPdQAxb1jpEwuyiiRt+PI+5RQuFYmu3NS+nX3Q9l37qcBG6wYJD/ePbB1zf3Xz3Im7/XQ6P7i8GEriP7XcrnQ2TBitwT1DygStr/CZb1cDPsazq/lqhYD9j3nfGQwTduvCMFA4e1/ELKWBYLEQEXQD4+J8P4ODxEqbmTWRyFo5PGjh7m6OM7e3U8P4/6QUFsGkNq5Mf+sIkZhctGAYNrO+UUsfOwZ4Mb1kbw1uv7cLXbpmHblAs5XZy3d6tn4wRR7XqN/mGHLjNcj5EixXoDRabasWOK3BlYl/2rX32mUm86BL/NoK/Y/5cqi3Mc6gKbAK3PC0uCJDrnGlScQ0eBCSuEdFf+aliXP3OUrwnCSI30gUTNoFbapIiP0KE1QbLosgWKBIxEmohsZpfK+Ae75omxcHjjLx92fPYLpCf3JPB7p0J9HSwwfNC1kShZGHU3kGSiLFxb0xjwYc0laBQssS1c3kL81kTxRKFYVB0t/N0LMQ0Vp/57oFM3nKUegG3FyYQUjNh2Upj3qXmCvbu2IAxjbe/iAjcCCcymGqex1kKbpN4vQ5joQDYAizC6ju1UDZ+CYOaCNzTTz+9lsPrBiEEe/bsWZJrRWgMRHGUOzGVOJ2l3GlarJFXFU+AMZWgt8u9pBFagWv/pJYTnIh3JC4FaRkZWzFZ32MCBbgub9rghMMMMsLmZ0ksFKTfVyuBK8NF4NZ4Qy5ivgaCRR58afYflDrPtj4VbHneglbDfRW4DZRDr0pWBCWUFk0IIQ2pwqxqJ1cg4p38udPobleQSgD5IjA2Y2DLuhb64Np5qHofTUSYK80tmnh4X6GMMPvdHhZ86JztSZy+1b0lfWbexO2/y+HBJ/O4/bdZaCpwwekpFHWKR54uwLSAPQcZgXvujgQe2V+sagVAARw6riOTr+P51PlMuzpUXHh60tVWL+Ys/G5PPjSZNDlnYsOg/1DJbKBOqVLXZ1nuv+uFYQJPHmQT1FjI9J61K+kmcEP66sVjBKdtSeD4pI59R0roalfKFjLb0wr6uzWcavtPp5MEs4vsuVUKVGrwMYWUlw2DjATWQ7432ad7PmPiwafK60AQkgmCZ5+RYqpIocCt/pK5xUCxBNz+2yxGpwxsWtManUQjrUwt5/oRuElJgXv2toTv4oMz6XEvCsjvPahacxsE3zrK+x7pdZiWU1ZEfuOk4nZkIgKUrAxfbv58f/VwDoOSyq6oU2SyJrZvbK2He4QIy419R1nAQVUlOP+0ZFUyRPaWDYKcxui0AcNkiyXvelUPTAvYtj6GZ5+ZEr7ac4smfr+vKP6OxwkOHi/h1M2s/s1nLBwecTy4E3EFR8cMzC6Y6O4wRXDdI6MlbFoTg2XPUSdnTZhmUbRTfqIfSsP5+jYTzxzTMTUXHPy32ucnwi7NCBGCoBDAQvUg2GaNClwWr8mu83D6/0KxRQTu3r177Ul6ayeoDStRIiwdqKM2URT2t2myACV8smSa1Pbn9N9aLoN3WqZln0dYB2Fa1DXr4OnrBkXRXuGkYJMDyw6IxFQa7vRFICjLziN1/va9Pcl3zQvDtCRPUBqoGubn1luul9qT1mU5cIKtrtauwHV+r+VU+Z3xAaRFnfJWj2KvbOWbEFeZsqhDFusmRclwF0jdtOsJrUye+OVPViuZlqO0FIsmthq4GjHje19gdVE3nDyz9sJRKPLPnPz458/ZdutgsFfFkVETh0Z0nH6KgWSMQFWVut5BxfvwkNwrBZ+4YbossJuMc3aU+4metT0BQpiNwif/m0XreOGFaYxMGdhz0Nl+2NmmYMemOB7ZXxSkmx8oBY6NG/jPH8/XfyN14h//vB/POTst/v73b83gzodyFc4ox/PPT5WR3MyjszEFLh9PmU0gcCmA3zyWF21MGE9eQhjJurZPxeg0e4HJSlYBPmVckNghFks5wWmYwf0l4Pjqy1v1+QDXMGonL//l/03j/ieC64Af3vySLvzpi7qg65KFAicM/E4gjoUCBUS9+YNzUjh7R7jAa7XAG0iy3vOrdWyCEJXIdHmbcH+Pf8GV369s52GalW1JAKcuyGnwdp1/JBMclkXFggW3hIlrpGL9dMaa1QnlZsPv/lN2fQ1qI//ytT0tzlWECMuLrO05a5oUmZyF7o7KnaKzqyy4EVs/qCFbsNDfpeLAcUa8bhjSWAwJBXjp8zoAsEU+AJjLWKCUolhieUnbfRBvV/JFR6GqqkBbkkBTWBCzVEJBMqFANyiIQpArMIsWi1Jk8xb6ujQk4syihe8i27U1gZFJA8k4wVymVJevbyPgPr+JuCL6cEVhtlB+8OarBZbgESKsGCgKABPga8kybySDC1jCeuDyObNFWYBzPkaqZadozdKAVpG3jjfXCpv9RqiI/ceKyNnzoolZE3uPFqGbjIyRo/+u6dPs4GbOuX4rd3ygXShZ+O2ePBTbG6xQchfqYxM6MjkLTx91tvFq9oD98KiOQtFCLKZgXb+7iBMCPHGgiIWsiWeOlYTXnjcrE7MmYirB+KyBuOYO5MFR0p3tOJTSqpP4ZvBFS724sZzbwluB2j1w62Nw5fMUPrHUKeYWLXS1KXVaKHiv4f6bUmfB4pnhEp4ZLvf5AoDpOQNj0wHGpzZ6Ot2DN1kl+9sn8mV54vcznzFd34fBsQkDmZxZ5ks20KOhr0v1JQaDAg1OzBoi4j1HMsZ64K/cNI+v3DSPzjaC9722F5uGYjhlffNUTSJLS9mFCRLHv0AVSxYe3c8a6HNPTZS1UT0dCl7gE9ytq13Fn7+8Gw88WUChRPHEgSLuejgnJjHnn5aEogBXXdSGI6NsUlRNgTu3yF5kZ5uCnZtbryYbmTRwfNLAA3sKgsC1LIoHn2LP4+ztiaq+pqNTBo5NGJjPWGXkjqzmrJd8VRWmNjVNCoTYMloJlFIxCQVQ5vlZCa+5qhM/vz+L7nYVG4dqGxaaJls9VRT/ciiP6eSt9EEKaEodla1MGgoCN6QCV1hKmBQP77PV4qcmqqq65jImnhnW8cCTBbz+qk6xw0gmtoPa73iM4AXPSuPohIF80cLwuOF6J61APQuLlDqLcNXO52MfmbSVldED3UHqdHvhTSE22cHGZ2EW+JxgttKx1P1Tdal6gZjm7hDjMUmB61M/+e4RyyJY2kYbvg/9Ldd242e/yZS1M3sOFpAvArOLrbUAihBhuSEv2GTz1QlcJzhY8DGaSsTi610Ps4XbjYM+ft22h/1izkK+SDGzwDLT3ubsoAMchWxfl4qBHg27T0theNxAIk5w+ilxnLopgcOjOp4ZLqFQojAM1o6VdCZIOmtbwrWboadDRU+HitEpgwmGWh2swQM+bjt9a9wVgC0I3mcdWShEOJHBOBCKx/YXcNb2JPYcLFaso6E9cLmAw2Kjj4RteVZolYXCG9/4xloOD43bbrsNY2NjkfJ2FUIhznZHVWFRev1ACIvIKb9hv4KeShAkE4pYhbAo9S3QbUmFbcWVRrv82m1JgkIRACjaUsyzyDApFIWgLUlwdIxleP2AhuFxA4M+K425gsUmehV8T2IakEywVcuq5K1C0NlWX0+XTipIxBUUS1ZLoo560dWuYHqeQFPZO1tNWNOnYXzGwPm7kiiWgG0b4kgmCNrTTHFZ63Yffvhgr4aZBQunrC8f+PlBfoYb+lSkkypyBRNtSVJ3WYjHCNqSCrK2R1VXO0ujv1tDJm+hu13Bmj4VTx4igeQIIcQ3aIwXswsmdm1NYP/RErZtiCOmMaVeJueMsBWFoKdTxWKODU67O2q+JQBAmx0Z3DtzzRYo+rocZUM8xuqyaTFrBBmdbSoUxfDtWHdtTWD/sC4G3gtZiuMTBjrTTdizLqGaqmw58OShEgyTRS/+zHsHa+pjX3VFJ151RSdKOsW1f31MEDkbBjX8n/cMiuO+cStTjDEP3OD2km9F37Uljk++c9D3mGbiV7/P4bqvTOHRZ5xFvkMjOhZzFlIJgs+8d7Dqavn37ljAF34wB8OnXJmSUqZeNbd3db9R8MnYCy9Mo72GQExnb0ti2/o45hbNimWEfyNXVWHNEqJsOSRssOc1IAUxk0hTTiDqBq2pkh0b11HSKXo6lFB14Pikjj/9yCj2Hi5iUWrvkjGCQoXgZFxRcck5Kby4Q8XwhIF//eYMGx+1olGoI816FpmKPhYKvA8CgN4u/3Jmmqyc8Koh+9oSe1IUlA8/X3ReBbktg8tCwaRlFhcJmcD1qZ+8GFgWXRGBFS/bncZlu9Nln//Fp0ax97AefuEiQoRVCMtyL+plC9XLe7WAWxzT8yY+//1Z7DnExgJ+i5R8PG0YbNcSB7d/4X0ez2NM5VugnUUpzV7YakuxcX6hxPzceWCiZAVvX1UButoUlHTa8kBlHKb0zOMhrZO6O1R7QZuiLaWE8iqOEGG1oqdTwegUG0jMzJsVyVuFkKqLTs6xXIFLAeoEP2xZELMbbrihlsOr4he/+AX+/u//HuPj4xF5u0rxs99kUdKZgmvr+hhe+lzG4ugGdSlddmyM45ljJWGQDgRtayM479QELAoMjxsYmXS8hs7clhD+QoAd/EU6V07v4b0FlHQL6YSKC07XhM+ZXPeet7sNgDt4TCZnYc/BIqjEDa8fZB5JfrjorMBH4wJB/V5Bmkqwe2eCTYaWwENhqFfDQLdqT0hXV72MaQTP2lX+rs7eXt8WVn77vZ0qBnu0UOQnUP4M//gF7bYXNKm7LBBCcPaOhCjLPA3uLwkAG4fieP1VGoK4kWrXNk3gwaeYgrarTcEFkn/o2duTLkUUT+vMbSpMK25H2K35tgC46/Lsgon9R0s2KUuwkDWxeW0Ma/s1bFkb860HHWkFF56e9OUDnn1mCm+9tguGRfGX/zqBw6MGJudM7Gzybo/lsFCgYErTW36d8f2eb0k8e7u/T2UYxGMEp29hNgkAcLbHciGsMtKPlGsluDXEkVEdf/KREQDO9scztyVCbXXi96br5VyTGUL9Uw0VvT7rAH8HMY3UT0rVeJ5pKwgUxf9UuT4kNEdZWdFCwV4U9lfghssXBTA+Y+CLP2ILDGeFrAPr+pnyf3rexP/6pzHn+vHKBK732nF7dF1Nmd4oGu2j6wliNr/ovAQtQHLi1A+5z6N2UFuGQAsFocB1PnPKEXEdw48b8izEx2NECjJYfo1qwTOXAmH6iri8cBEhwgkCSin2D+tiCz8oRaFoIWmrQGfmTTzydAGbhmKuWCklneLpoyWUDCrapkptmGFSfPO2eZdtkqYBD+/zWOpQyhaZPO1Bhy224GNbIViSiF3HGo39bE8qUAmQKVI8+nQBo/aOt/a24IyqKrN02LExjm0bmrtDaWbBxNExJmJoSxKcuikOQoh4fnyHRBis69ewplcV50WIcCJj67o4xqdNWJSK8XV/t4btG8rFXLVwJk4QM0bi8nlRyxS4zcIDDzyAv/3bv8Wdd94pPuPb7C666CJ88pOfXI5sRagDU/MWtDhTo3a2KaJBV1V/H1B5hhe07ZQQApWUD7o11a10qqR60lQC3WDeuYQowqfHEB0+sfPkTkPuxHinHFPrV1g1C4Q0MCGvA1HHzCCTM43YLyiK0pStRrxuVIKm1X8hIk2p/YjSoHrAP6/XD0tONx5janmK8jaiUj2oVGbbUiyhNX0aDo8amJ43mj5xFzZAzU22IiileOyZIo5NVLbEeM7Z/gtQYfGcc1KCwJX9ZAGZYAi+d0opStzXdIkUG13tKs7alsDjB4oYmXQ/n0tCPg+e10oK3EaiRft5fdYLSh2fPi2kuFwus2HIJL+6J4g6T3fqd2xMImEr+c5zsl8mDWUiK+wiyZOHHFsWb7kNAiEEf3BOCjf/KoPJOTZb7+tiOzgqXVe+XUqBuK0INUzacgHut29fwH//ZB5r+jV87q+HXDuhKKX4hy9PCWubS89N4c0v6Q59nZLPu7jyonb88K4Mtq7TAtVvjjKb/RQBakOUdb9gtlx5K8gSqYybFkUsRtDboWDGthpIxAhMm2zx63t5vpgC162yazX8lOxBEG1QROBGOIFQKFJX8KySTjGfZQQut1vJFSjGZgwXgTu3aGIh617Fq7T1f2bexPQ8O37HxhguOTuFU9bFfb0mX3BhG554pogdm2J46fPa8f07MrjorBTGp3XBUfD2kKttKZy2ji84JhMEiQRBJk+RK1I7ngtBT0cw5RI2gHc9mJgxhHiqUATyRYp0kvguzoVBND+McDJBVQHLcI/5G60Dqm0pymxTpCBmJQtAuEnFkhK4Tz31FP7+7/8eN998MwC3N9qZZ56JT3ziE3jJS16ylFmK0AS87qoO6Aa1vSYZvEWbD6AV6ZtaPWNrIdCcqPTuz/kkIJAEks7j5fNEC+QVITzkbcGrTIxcF+R7tCjQXJOBcJDroMuCuAkvgAdmmJoz61YLB6GWSXkzwZVZV13UhmsuKfezbUsp2LounPVHEP7osg6csyOJmEawaY172BBagWt/n4wvXYP6z+8awMHjuuudpBIktBVKvAJ50kgAMw5fr88GwInmaj6vzYRpsiCeQQtU8p3x58mClAanKTxwfQjcsFvJGaHNjj1vZwIvuDAcgQsA73xVD668qA2mCTx1uIj+rrCMuHO/QoHbIgtcPj6hlOKmuxZR1CmOjOp4YE8ez3+W0w6MTZu491HHl/zxA0X7PJHlivCzUDh1UxyfeEc/DCO4X3SU2QQgUlk3qRQMz/9d8oV0XwUut1CQA3haLMmBXlUQuLEYEfXKT10mAtrSlR2IR14gixDhRAGv2zGNoL9bxaHjOgyTBdM6a1sCk7MGjoyVW4fw2LzdHSo2DGqIx0jFMUW2YAny8lm7kvijy4K9vs7cxnbtpJMKNJXgnB0pPPZMAePTugjgy/t9oZOg5Yu5hBCctjmO4STBQLeG9hRBLEawpq8CgevT5jUL3l0r/Jny/jGsfUKECCcjxFjBbHzXnZwmt2CxLIqUrcBtmYVCvThy5Ag+8pGP4Bvf+AYsi3mn8Mn4Kaecgo9+9KN43etet+q2a0dgOHNbAk8eLLoH8gHkq0yGVlMukZDqP/9z2U+vys6rCvGCV0zZQiFabDx5IZfpk6F9IoRAIQSW3anUvJ+6KXlgPy3qfubNyMlQD+vy9h3Ry1QcDaMKKdEq8MH4hkENZ9VpFVINikKwfaP/tr5ECGUkBQviByydhQLAlDlnnJKofmAA+MTGMMuJeT7RasxCwU6rSUWR7zCJhZyQOYsO4YJa+S1SyFvl/Y6VwctKpWBWlFLHQkEmcO3RqmE4C7HVwNPZubk2CxFNJdi1hZWbfNFiUcUlctYvKfkzCjf538o2YWLGxMSsU4Ae3V90EbiPPcO2Csc0RgIahj2+ERmvnL5fEDOAKdznFs3A8ZFY4CD2zpEa1Ob+Clz2kz9KtwKX/VzTp2HfEWa75QpiVsFCwbSc75eq5a6lLMZtYkePPHAjnEDg7X9MI+hIKzAtCsOg0BRWd7mHexn5aPCFYCICj1VCNs+UvAAw2KOFOkcGF3GYFiTLBsdygClw3apcgN1DOqkgHmc/q/XJvB1t1mKuDK+qlz9Tvisq8rGNECEY3P6Jz7WawQUoxGlbDAtIJGQFbrg2qqUE7sTEBD7+8Y/jy1/+MnRddxG3Q0ND+PCHP4y3ve1t0LRlcXKI0AR0pB3zZdd2c89xfHJHXJ9VTrsxBS472DvRE6b3AdeWFZdeX6MIJx/UCmX6RAWxAystcTBcAd5WWBZ1P/8mvIA1/U5f841bF/DCC9uaRswvhwKXUn+14lJCJjkrgas9lspCoRngZLOfAtfx16z/fkRZb0KZodRRJ2lq/YPMWs8zLQC2Wt73TOneYpKKNsjCRFYaymWFB6mSt6xWAkVzy5xrfTogOQIiGoBEjYrhWkAphUWBR58u4JcPZl3f3f9EHt+8bV76mxG455+WxP1PFNhCi/3d8UkDj+0vVCQ1njjIFLve9kUQ/hUUuKDOWEv2ta3WVgp/Wul76lhlutIDHFXv2l7nPhYyVkWVPA+pa5kUWGIFWi1Xi1dogyJEaDUMk2JqziwjAXs7VaRCxoTwA1+0VBVG4poW20HCiVG+i6ScfKy8y0Q3WH55/5zJOwpcOXjw3KKJbMFCZ5uCTC54QbFYYucqBMKKSbYokj1w5TzxNofX22rdajPtlLzQpWdmmI6KWBcLvs2/ZoQIJwqcxRX77yYqcFm6FCl7fFvUK5zkQUuq7cLCAj71qU/h+uuvRy6XcxG3XV1d+MAHPoD3vOc9SKUa8+WLsPwY7NGkjlhSywWQr7JKp5ppunfMXUulkZW0MhxVrf+1nUjyjkQlTHTtCCcmKqnKT1QoBDCx9FYA8vVhX7/Zz3+gW8WZ2+J44kAJI5MGdIM2jfhcrmbCWGYVhSA5TRooYaMUKJlLr8BtFJyc9lO/CXVfAz4jzfa9Mzz+fNVQc5kV/aMDSyhw/dMOtFAImjBLRJWfAhdwlFDVYDRhccPVDoa4LLX/F8YbuhEcPF7CD+50ghf+4R+043/uzWBi1sRXb54vO/7ZZ6Zw/xMF4eFIKfD1ny1gMReOMZB9dfn5QPD4SA7yR+CUdeY5W/laLrsFG2UeuFJ2eF2UCaW9R4oVF+FlBa4TgHJpO70w16vUBkWI0GqMThkYHi9nFaYXzLqDAwNw2ZtwtbxhOORoLGBhmP8d1Mcdm9AxOuX2G+EK3M52lrhuUDx5iC1M5YtWRQ9dnkdVZUExAYhgxt3tiu1hyQarbgKX/eQWOtXmkaokXLAs2lSfWf7MEnECI08lBW5koRAhQjXwqsvHk82omopCRB03DGbtBiyjhUKhUMD111+PT33qU5ibm3MRt6lUCu9973vx/ve/H11dXc28bIRlxFCvJgbWiovAdZdw/pX8aTXfMa/3bC2VRt6GLYNPNoMDIdnnw1EzRR64Jy9cHrjLmI+lhBMtfHmu77ZQcD5vVqf5qis68OTBaZgW84fctKY5FdwhAZqSXGg0g6RqBIJgqKIQ4+TiqiJw484AK8hCoSEP3Cb73slKm3pzVek8P3WuUCWECLTJywqzUPA/RpcGsLLSSS7fxTBqRCoFQ2vGSDfEAxXKUtTu2VsLKAUWcyzdvi4Fz9vdhtdf3YkztyXwyNOFsqyu6dPwB+ekcf23ZxmhbDFbB07eXnFBuqJKOR4jeOUVna7PxO6mgNMs2wPXWbxnP02z+qP0U6NRz09ZAGCaVJDE6wc0HJ808Nxz0xUVuM4iP13RfXvkgRthOcH79baUgrakAsOkmFkwQy+iBcHpN1i/YNltEm8veBtAKVOMqh5FblCs3kzewqERHfEYQVwjWD+ooaSzi3XZClw579k8RSrB7s+7SMXR0aYgGXMIlx2b4sgXKNb2azbh6l6kYvmvTYHLd81wC6FmhQrghDDAbCeyeR8P3FW0KypChKWGUNPbCx/NEuvwuVAmbyFttz2FUvgdeU0hcE3TxJe//GV8/OMfx9jYmIu41TQNb3vb2/DhD38YQ0NDzbhchBWEZ52eCJSVcy9NQPLAJc4k0Otx64UcpIJFnw9fa5zVTPfn1S0U+AXZJEdOK8LJB7mcnCzFwCFQl0fxI9ddl6q/CWkrCkuzt1PB1LyFo+M6Nq1pLMAXh5/isNWQAzUtl4rCIaoq3DsFSraIZzVZKFRU4ApyqP70m6nApXDU2DGtOpkKQPJtrk196PLAlQI7uC7pc31B4FrB91ySSFfiGQOoKiMA9dAKXPYz0cBs2CXArXBZQuC6Z1cAvBY0Crq9wrxzcxzvelUPAODKZ7fhymeXBzIEILYRA+y5yMq2972mF20B5EUQqETA+MG0KEApFKKAEHmxwnkYQc/TL7ifZbE+iV9O7pt5UVJUgssvSEMhwMuf34kDx0osPR+VPB9fuhYSlqrxrqGvaKUVR4QI1cDnUP1dKtYPxpAvWphZMBtedJQVuKpKRF3g404WpZ0Tmg6BW81C4ft3LOLXjzhBG+MxIghbbqEg16WS7d3f361i/UDtY8FcwYnro/oocA2pf6wEQghiGhsnlfTm7QyT3xPrB02Rp1KNnvkRIpyMEHPiJnNCaVt1m81ZaE86aeaK4RrXhgncb3zjG/jIRz6CQ4cOuYhbQghe//rX46Mf/Si2bt3a6GUirFCcuimBRWaxVj6QZ0I+AE6BdwWFqpK2rHytdcUjiISqNungRLH8dTP8TiKsTpyMFgp8DLpcFgru7axSJpqhwLXT6OlUMTVv+W4NrBdCfbfEymVBUlUY8BtmcICxIOWsqkje5cSZMMnHmxYVbWyxZLlIIhn5ooWSTTglV5MCVyJPKFiZ5Eo4/hyEX6dFq/oAe2FRlna+aCFfsKCqRBCYtaKkU/GMCWH5827F9HrP6jrLc6HE7ks3KLIFSyiPvcgVLBRKFrI5E/NZk+1UscnApH+MO1e544oD02TXzWRN6J4iMzNviHuYXXQeqKayMmmawHzWRCbvnKjY5VNVnbEGhaROb2CkW83r1e943bCgEMYallqgnKQAdLvpSkoWJgopDybH8uRWmeaLlkuFVk+dtKTn4udpzOuJ44HLP2eEjGGy8qRppGzxyTQpdIMiX6TI2CrhbM5CPk9RKlEUihT5giXmG4WiBcOkIPZCxAVnpBFTZfLE5/6Icy3D/lcoUSzmTBimm9RQ7YU/TQ1Iy5N3mTSJ2QsRpklR1Jl3cSZnolCykIyTqkpG/txKJavqLgd2rytXdBAm/60A7/8a8QZvNeQ+diXBEb2wvAWJY2qFHGDQtOT2yflNU+3AiybAQ5GW7PbDotRVnjQVmJw1ce9jjLzdvEZDUacYm3b6kM52VpnkoKGs33YTmux+Hc9Yfq+c4zBNKhaNcnlHpORS4HrqYJgqycnmRtXNMvi9qSpBjO9s4BYKeqTAjRChGhqJx1QJaZu0LeoUFoB4jC3gZEPaWtU9rP3xj3+MD33oQ3jiiSdcxC2lFNdeey0+8YlP4Iwzzqg3+QirBGyFlP1eadDoVuC6PwuCnFytKifZR1OG6WP34IWjEo4UuCc7Kvk6n6jgCyetCKYQBq7qJi/4NOEF8DR6u1RgWMetv8ng0PHGSNxLzknjst1ppw9sOJe1waiyDe7wqI6RSf97HJk0sJD1Zx1VlWDrurjYxr55bQzFEsXYNGNmCiULR0YNzGfY+UUd+MEvFwMDYixmWYFqRA251BDqN4ONbR7bX0TWQ1KrKoFpUfx+X6HmiddcxsLYFHs3DzxZACHOKn+tIIQIlfPxSR1xDZjPWDj31AQUhWBkysDhkZLrnKk5E1NzBp4ZZn3lxKyJJw4UAj0BMzmKuYxTXgZ7VFHuTtuScB3rVxplBe7R8RJ+cs9iWR89Y5O2pgl87jsz4vNEjIjKdccDOew77L6X/m4Ngz0aTj8lLoJyCXV6E8qctB7t2xbxhd/Dozo0jbgI5ul5AwM9TXQso45SORknODqm49iEjphGcN7OZBkRtOdgCQtZEwphhMzIpIEOW42mqdXjEfhmwX4Yo9MGZub92xCKcuJnet7EoREdxZKFZ4aZD2VHm4r1A87zKRQpDo+y97vnAFMIZHIWphdMZPMWDo2q4ntxTokRsJmchR9OLqCnXcXOrXGkE4q/By5xznvyUBFziyaeGS5hfNaErlMk4wT93awcEUKwea2G7g4N5+5IBI4fZxZM7DtScqnZ29MKNg3FcN/jeYxO6ZiZt7CQs6AQiu4OFYUqnnfjs+zZzi5aeODJfMVj2X0R7NoaR1d7A+bcLcCBYyXhIbqUmJ43MTnLrhuLEWxdF1uxcS3WD8SweW1zdgQ1C0Ip61mIoZQF/qp3fsR3YOSLFh7YU0DWbi9lYlhViVjwARjh+NShEkq6BdNkC4L7jpQwPW/iueel8dWbZ2FZwJa1Gr74wbUAgKvfOyzSS9r9gLyjxrKA8RkT+UJBeOTK1+f5NC3mO852q5TvWFEU9y7Rck/46s+J94/1LuL6QXgGq8SlYpaJ64jAjRAhGOW7y5uTrqYqiMcILAoMjxuIqWwBRx47Vjy/1gvefffd+Lu/+zvcf//9AJxGjFKKyy67DP/0T/+Eiy66qNZkI6xSdLUpmMn4byVNxokwj/cVQFQjcBsgzxxVlPvzagpcoPw+Iv725EUtivETBYpYjFum68vKe19dRv3gdXuwhw3UhydMDE/kGkrznsfyeO65TkDOpQyEQ+FMRoIUuPOZYFmol4yUYZoUJd2CZj+0hYyFvEQ25IvMs45PCqopj/n2/lVloSAF3SqWrLLnpakE3e0KCsX6VDPpBBGTREqDVdJhkC9aor9TFQIQRrIXdYpUgmDBpxykpW1b3I6jkntuIk6gKE6/yrd6dtiEqd+p8j3x52lRirkFy/d+LR70xdMPF3XqBMLyKdK5ggWLUixmLXS2qUwtbR/XkAeuTx6rlWBKgZhEis4tNn81TBee0goW7MUR3aDIFSxBYANsQYAv0mj2Ft25jClU1vU+G6685wszftBUgnSS2Xl0phXENALDZFuZpwynvOeL1EVwJOKMbCm66hQTCyiE2H7L7rfQ1Uagquz+LQso6BTj0ybO3Kb5KowTcRY8iRAWoG0hx0gh3aCs7ugsT3wfSL5IkYxbKBk0ULG8mLXK2v9MzsJsxhQEVUFn5ZRSVCVvASdCvGnBJZYJgkUpFrLWiiNw5zLLsyIs7wrRdQrDYEqnlYj5rAlgZWWOt/W8PXZZl5j1WwjxdAtFpqaNaawvlH3PYypQgKMYnc+arp0vFMCNP1sAABwc0fHMMQPpJMFLn9shlO+DPSomZt0dhmyhYFEglzPR1V7+3GWbn6JuVbQ62jTkbki9i2JhdnLGWkLg2pYTiuMpb5jONRRP8LUIESK4URbTqUmkECEQ/bRC2Pg4W3B2HVVDTUO3q666Cr/4xS8AuCep559/Pv7pn/4JL3zhC2tJLsIJgO4OFdYY+90b7GugW8WRMWdLJ4cIalGlDrgDGNVWYXiFKwtiFmI7pKwSBiILhZMZiqvcnhyDHLH4sUwMLrcx8U6Em/H4eRo7N8WRuFxBRxsRqox68JWb5lAoUkzMmujrcmYeYSbazQCljsIiSEXBldRnbku4iB2ADUQsi2L3aUnXc3j8mQIWcxZO25yARSmePlqCaTnq0LN3JDGfMXFkVEciTvCjuzKgAF57VWfZNQBG9N32W0aUr6ogZtIz5b5UikJw0Zkp13GcIEsmFOzeWXtk7kMjJVfk7AtOT4X2pTNMit/tyWMha4n+bdeWuJig8ffPf+7YFMdAtzP049fWDQpFATYNxfGCAB9VjmeGS5iYNbB5TQzdHSoe3V8oO8av/PPnGY8p2L4pDtOi2LklgeecnRbH3PlQFk8cnEZvl4p/+LMBmCbFb/cw5eFTh4vIz1i49Lw0rriA5XFmwcTew0URkEveOVBNnV4L/Nok1/fif6ydufTcNIBpAOGIulpA4VgUJOPERSx4d07IY6CEvUW3qFNhwVCvOpk/Cv5z19YEejrcdf+pw0XMLrC6kUoquOB0d73JFy38fl/Bt0495xz39Y5N6Nh7uIiFrIUta+PYfVp5PZuZN3HvYzlMzhrobFexfUMc5+zwr4+KQnDK+jjO25kUivOSTvHgU47K9eKzUnjmmI7JWUOQ5GbwephQK24YignVLaUUus4Ub31dGgyTkUGgQG+Xhkuksu+HmQUTP703h2SC4JKz0xX7FV6Xl2v3TCXwMnruqUmkk0s3qH7iQNG1y+TsHcnAYFXLhfmMiT0HixXL1nKB9/mOnZKz6MjKe50KXLuM8vZp0xoNulGuwAUcEpIvkHR3qPiDc9KYlxaP9to7Mi7YlcS6AU3Uk0vPS+MHv1x0X9tjoQCwsT4fB3nbgfNOTWIhZ+HoqI5EXMHWdbGqamkvsR1mOCgCFrbIQoETtcyihn0fW0UL6hEiLAdaZaGgEKCvS8XpWxNQVTb2BdhOtzCoicC9/fbbxSCWEIKhoSF86EMfwite8QoQQjAxMVH7HQRgcHCwaWlFaC28K7QcQ30aRqYMxDQWvXP9QAzHJ3XR6VVTCKgNkKiOhYK7Inj9nPzPbc1qS4TVh5OEs3XBMWxf3jywqktdnzUjXYANZs89NYEzTkk0pFT6n3szODJmYHhcF9ttAZb3pSo71SIJB0Vip5QGmvLLAYc0zfnd7VvHfpfVaEYFP10n2Fqo21oRkNXC+YL/ThPA7edXD8r7nPDneoOlAEz5yOuvswW08rvmfaMa4v3w/JnUHSCqWpnnk0XdoA656iGqvUH55GfBy6Ksdna29Np5sqj426iiTg8DvxIddJ/8Y4WwMQ9XK5e8Rr9NAA9ilogTF2HnbbdFkDlCkIgTLOYoiiXnGdb7bHh5cYIO+RzEF8wD0nD8NGnVRS87JlrwtcDKiuxJXQl+3sam5xzLKp+8VUrW2w6oiu0RbVD7POZTTOCUq2rb0PmCl2ErlivVMVGX67RhaSWsBtvIeuFVTTYjYGSzIerBysua73tTCGCisTGi3E4DQHtaxeyC29ucE452UycWUVIJBapKfGMYbFkXc/Upf3ZtF0o6dS0QeRW4ABs/8UVs71gqmSCYZ9yKmA9W66MTMSI8dAGgLcSiBb9uMxW4prBQkOIYmM6OodU0HosQYTnQKgsFeae4qjJhA2BhsVUWCoCjRBgfH8e73/1uvPvd764nmYrpG8bSeyVFqA9iYugZWWoqwe6dSTHg3Lw2hnUDGlSFdZrVtm00EsQs2EIh2NJBPpd/TRQSWSicxOC+VpTSk0aJHeQfvdR5sNAKCwV7u78gHxpLb8NQzCZwDew+zfl8yR6dRFJVU+B6I7HLpE+QbYxpAdwO1bTcZDD/PSUp+AoByhFFInDr8dtcLqgqEYsJ+QIjs/3yH0SSh7+O8zshpKZ0iL2l3Evg8n5ZKHDtiZz3XauevtLbj/vmV3GUPEF59fs0KRG4QgHkIXD5xJKTtcQmQy2LQuMBnaQJrvd9yH0+H0Y2osANbYni2jHEfmoqULJaoMClTn1KJRQX8VhGWEn1XxAEusVUoKifwOWWKZViIPCcBBUpudzzSUyl61mWHSwooA1RVdJQEE6vctW0yq9ViQD0tgMKC/rOlIWUqxfZ53znRLVt6JyMChMgUZXa6pUESwp2udTtv987XWkQ720Fkss8S4pCMD5jYGbBxNFxHePTBsamDaR8iEmFAOv6NezcnKiwsOz+uyOtYG6RBep76lARIMDxCR1zixZyBYrH9hcEgdueZmkOj5fzBJvXxFzXHJ8xcfXFbLfGU4eZ5/bRMR3FEsWaPk30F51twZWQOIoC0b5UK8eqSrD7tCSKJbZo4/ecvOB94WLWYs+gyrFb18VEPvIFC8MTRlkZ4n0Ps6hgn+kGcMwmvyP/2wgRKsM7fmkWF+DsFKeIKUQspmRz4bZi1L32Igcti3ByQygZfAbf3k6Od1Bhyr+sSqp1XixUhA1aKERdWwQ+djtZykKQ/chSgk985Sw0U4HLf1bzba2GjYOsCz02obvLxxI+u0oklayy9ao8+cSFE2QyVGmrnWqPVnQXaeacH4s5JGfQ1j+2KMsnEjXc3ApAPAYUS0C2aKFLU+E3bwsiycNCJr/qUaipqrOlnsBehCSOclr+WabAlVSQPK0w12PnuAezfooseYzIFbimSYV3s7c88DIkE7uqrWSN+Wwx9b4PmaDRqyxu1AJX9amSHF98jqnMc7bUWJzEMlDqpJmIERcZEkRYKQoRZG2p5Ciw6rU04W+Ak6r1TGpUaYHUrELgWpTCpBQKIYF1RFXYojsQgqjzUQf7qTX5tfhxldL1tgOsblGUTN4Os0UIQhQoRK6XIRW4VW5JXlhZSZDHEkuuwLWc9kQ3aJnKeiXA2fGyzBnxgaPgBw6N6LAsage/NEEBpALaj2zeQm+nivWD/jYDXgVuIkbQliLI5Chm7UCW+RLz75YtMBSFiACMxyZYI3jB6QkcnzCxeY3GlLQJlqdMzhLHyJhftJDJm9iyNibKpjeA2YahGI6N61jTxzooCmKPldj3YcqxphJoqfDtayrBF2ucZ1AJ7WlF5G9sxsDUXLDwjXt+88XQbMFZAIwQIUIwynbINWl7JW9LKAVUjYgAz4ut8MBlF1p5nV+E5YWj3Gluuq5gRjVWmKBATPzvSpMNNqng1z05t9FHcMDVoCcLg+so8pavrWf113P9Jjx/70KQ2WB/tnGITU5+/OsMfn5/BsmEgjdc07Vk/K1FHVsDP5LK7Sfn/s5Ri5WnKxRB0hZ5XafS5IVIZAWBpgG6Dk/QITdKAYrLlY64RlAsURSLFGjzV96YFvuXL1gVg8Z5QQhTHrm2p9ZJhAmvO41/xn7yMhC0hdlrQRBG/atKJJl8vIvA9UkmIRS4rPwZJgtiIz+zBXvw6iJw7Wjkmn0tPwWusFCQyKtGPXC9491KzYV8Bb4gwu6BVqwX9cKxh3D7lZfbADj1nD+HoiFtoa3j2bCAe+yfRZnq1VeBKyS4wWkpClPjVSMyKbXLVyUFruK08fX0X2XkN5WuZf+oRI5ant1octtp2YMIVVOgKCxgGqXVSTuuwNVDkLL8eiuNCJRtPJY6loDoH20CdzmtoYLgtMGM6F9Jtm28GlFQfOf2eew9XML6AQ1nnJLAljUatqyLu47P5CgOjzLPfL2iXzRPn0I3KEoGxakbYxidNpGMM6Kxb9FETCUiD+v6NRR1C5/6fzNYzFpCXXrJWWk8/1lp5ItsjNJre3FzO4a2pII1/Q7dwUndzjYFGwY0WBTYMOimQzYOauhMK0KZa1rUtVDVioWIVELBmdsSKBQr1/WZBRMzC05gRMBZwO3v1tDd7s6cogA9nSpUheCsbQlxHv88QoQIwfCOyZvpgQvY42jb4goAcoVwY5eaCNwbbrihttxFOClgSRP5ZqKRSiI8+jyDeDNAjRZ0XTbgrD8fEVY/uBr0ZCkGQer15ciDywO3Cel6J0aNKnDP3p5ATGOD51yRBbraP1xibeISjItlJaLfVmg+SfLblm9WaLdlJRdvKw2TIkYATXMmNOxYIK4S6Dp1eZPKMEwn4jw/f7XAIeHY334Tt0zWxKe/wSaVteIFF6Txv/6oW/xdjw2DqjhkJY807VV1Bb1vR4HL/lZClFvRv5Zt1y9///IncclC4cBxHbf8KoNv375Ydg7gVubyLHNSV/fxwGV+s86CRpgAf9UgE7auQKwBx3t37vCy08ygNBycxI7F3AXSuzVZts5ISArmRjxwHf9b517rV59zArfycZzAVUjwjixVdRRy1fovx97BSaxsvCgpcPkJtStwGfkqPKYVVp51O8Fqaln+fgwDZWuaXnDieKWpTBvdoVAv5B0osRgBCiuP3AbcBIFp1beI1yrwfmF4wsCj+1mgsIMjBjJ5C1vXxZHwtD85xUS+SJGIh1vsOHRcxw0/mXe9l7V9Kv73n/SBgKI9rYj6fsYpCXz15jmMTTuNnKYC55yaRGebik5P7E1OVHZ3qBjqdToUXi8Nk6UPlHuxE0LQLQVl5LtNRBDuFpHsfvfhhaoSzHj8gnmd72pXMNgbTO20pRS0rbAgfhEirGR4q3qz2mfehlDK0uRtUD6k5VZNBO4b3/jGGrMX4WQAV380uz9rJL0gH09h91ChAvIgE0Ckvo3gqEFPlrIgOpVlnOg4HaTz0JuxbcWbRKPz3PWDMfzgnzdgPmPiv346jzseyCFXWLoHJyv7/EgqmWSt5Tt5i7z4nbJ/jl8f+6ko3M/c8TX1QvYAXWX8rVgVzxfZe/UjvB8/UKyLvAWAux7O4S3Xdom/6yE5VMnzNmbnT1VshZ+9fZurScsUuFy1JxFMVa8X4LUp/018+mBeRg2T4vBIKVDRqijA6VsTrvsDHN98WYHL2wpCWPkUQdsk0sw7Oa8XFRW4roVf9pMTzs0MSsPyQaHr7mtwWJ5MinquEhFwUDepqJP1ELj8EnziEeTbLMZbFdLiNgPViExKIQKdBdURhTj9Vz1Ky7IAcD5kWiVlrxPEzKmDgOyBywgnogAgBBTV+6C49M6qQW63VxK8z2Wprws4bcBKs5cA3D7fpkURWwa5gGVRLGQtFHWKjL0LgloUxyZ0HJ/QMT7jWBFoKjAxa+Gz350NTO/sHXFsWx9HT4eJfJGpZXNFC6k4U2HPLViwKMU9j+bK+pHRaRN/fT0Lin7ezjiuenY7CCFYyJi462EWTeztf9SNM0+Jo6NNRXtKwcx8udyXe+YSAt/v+XiFx7qoBNOi9uKRu24vB9psW4Z8gWJ63gQBhGq3WmyZCBEi1Abi6beaFsTMTieTZ5YuvE2ppsDnWGVudBFqxS233IIbb7wRDzzwAMbGxtDZ2Ynt27fjj/7oj/D2t78dnZ2dDV9DKHeaPDhrZKuVUBF6g5hxC4UKSRMCMeMgpDnEUYTVC8dO4+QoB2IL6jLa5bRqnldLRPGwaE8rLi+yXIGTZa0vL5wYYsRAsALXb8VYJmC94G053yJPCIFlWbAsIn3HCUEi1JLFkj9zULTJIoLVtyjGJ/6cLPebuPHgKJc/K42/e1NfqHQpgNd+aARTcyaeOaaLslkPyaEojheyKghcZqvA7R3kY2V4PXC1ENd31L3+2/WDIAdkms+yY//kRZ1444sdAnti1sDBYzp6pa2d/JlzdbGs9Obkh2L75HJ+Rj6mUZ9XwF2bq5Vhx0LBzkuTg5gBgG5wqwn3OKdMgStZZ3CPNcNwAg7G63g2si2HplZXpFTqO8Nu+xcLSBWC/ClS0FlG+Aan52cHV74g4QTpowHHuI732NLwcqAblp0hVt8UxUmwGqEoL3pUK0WytclKQiW7nlZC9nlfqfYSHNzn21t/lwojUwaOjuk4MmYgX2CZsCzgvifyODTirMzu2hJDTFOw72gR1CrvrwyLLeQeOq7j4PES8kULB47ryOYtzC6YaE8rYov/sQkDe4+4PWo3rdGQihMcmzSQzVM8fqCE0zbrSMQJbvutgclZCzENWNOnYj5rYb7KwmmhRHHweMm1wM2bI6OGcmmaEP0MsPTB+GQk4wo0lfXv+464g51pkSNChAhNhXe40SwugLclk7MGjo/rwjoh7Lw0InBPUGQyGbz+9a/HLbfc4vp8cnISk5OTuO+++/C5z30O3/3ud3HRRRc1dC15a9hKgVBhlKlR3N8Hncu/VcjqIxwiNBe8qJws5SBo8WNp88AftkTUNKF9EUQDV5A1catplz0pyRWtJfPAdXxl/b/3RkV3fUeDv3MUls4Ex7IJEa+3qqo4KsBbfp3Bg3vLoydzVbKmLa81Rz0QvqE2CSeTVQeOlXDr/Vk8bN/z6ackalrIPGdHAnc8kMO3b1/AYLeKtf0aRqcM/PLB2mZhs9J2Sj6BU1QAJoSqi+W9XGnkfZ9hLBTCRLsXV5HetwjIZDLbCQBY16e5yqCwLZKyKUhpO2+/f7qI//vdGfH95JyJM05JoL9bEWW+JBFjTVHgVkmCLU6wg4QC1853sdkKXDjqsZjt0cwRaBulEJeas9SQApc3oOxHI2M/vkBebds/2w7P+qdK13MIXAq9RrbOS6ZaFkRkaP6UKuXTayfG88kDzlFQqKrd5hJG6Ib1wDWC4xMJOLsjVlYj2yqbtWowTRZw6/f7CkjGFbSniWsr/UoC9/leLvsLQR5YFKmEgmSCgFLn8440QUdawc7NcRSKFIl4EhsHNWxa4w5SNjyu4wd3ZpAtUCRiBImYglRCwfS8iQ7brqC/W8NcxsRdD+cBAOkkwZ9c3YnfPVnAK57fga52FZZF8eEvTWIxx0hKiwITM6zPOH9XEn1d4d5jMs7Uv5pKkE6y6xdKbEddLcpwtpjjzB2Xe767ZW0ME7MmTIu6vHCXk1iOEOFEhLd5aFbdH+rVUCxRe6cPqboQ7sXK7MkiNATTNPGqV70Kt956KwBgaGgIb3vb23D66adjZmYG3/rWt3DvvfdieHgY11xzDe69917s2rWr7utZ0gRhpUAM4ssUuNXtHhhpK7F2K+e2IiwDxBbd5c3GkkGOjLlc8PoOAk3ywPWYVDZzntvZxtilXJ4u2bMrlThp5/90KnkPyt6YXvjZJFgWa+sdlZezNbstrQDTJu5/ogCgEJjf9pSyYhVQQUh4CFx5svdv35rBk4eYLyAhwBlb4+UJVMDunUnc8UAOTx4s4ckm5VezCR9eJEyr8rt2PENrUODWGe2eR9kGgKl5VhDWDvgPQ+Vc8Hy3pVhBPnhcx8HjbuXW8LiO113VKcqXeF9qA2OTgNurlhp/pjFOmBpUbP9vBihlaQKMJC5CJnDdx7oUuBIZyC0UkvUocO3LWVVU43y8VekKYbf9W5S1P4RUJinkSVAQ6Smrb+VX4vfsRJA8/lmAQtKyqGMn5lHp8XaaT9LkXV3V2kP+flg9phW3SHt9r1cKnEWEpb4u8NN7M9g/7LQVZ+9IYtuG2trppQB/rcu1cF7SmeVMX5eCzjYVF56Rgm5QfPO2BQDAe1/Ti2Sc+cIenzQwPFbCeTuTOG9nypXOsQkdP7wzA8tixOy2jTFYlC1cbLDb+ovOTOF/fpMV57zmhZ14zZVdeM2VXa60rrigDTfdncHD+0riM0UB3vWqXqzrD0dfHJvQcXSM7ejYvpG99/EZFQeOlZAv1qDA5YtHDeyUaSYGezUM9mooliw8tNcZc0UK3AgRmguveKhZYq62lIJdtlXYoZEi20mpACHWagFEBO4Jia9+9auCvD399NPxy1/+EkNDQ+L7d77znfjrv/5rfOYzn8Hs7Cze/va341e/+lXd1+OD+RXE3wYGsgiTV1n9I/vhRjg5wSfdJ4sCl08uV0IQMyrVvmY8fy853UyfX1mBu1QSXB7lOUhhKNscBH7nF8TMs0VeVYizhdmzFVVVgJdf1oHf7cmjr0tFMu4/G5pdMLFhTayu6PDLCa5aLNkkHJ/s5QsW9h5hE8vLn5XGun7Vte0/DF5wYRuyBQsPPVXAb/ewSVgyDrzi8tqsjX5016JQSnECVpG8RcO8a2EvFGIy6wQJDT7Gr74m4woGelRMzjos2Pr+6s+MX++CXUkoCpCMEbFgki9R/PDORRwdN1AyIPx+HYKz+v0EwWuhUHFhxud+46qz/d20mju5FkHMPHW/LLCcpMJPSNvxRRCzeuwlqPtnI8Rc2G3/1G5/FFQbvzGVOSsDteXF79mpnj4jSCEp55+fw7fF83ZaIcwCwrbV972mF/LW75LBgkMFwenfmO/1ShFVyH3FUqKoWzg8ysjbznaChQzF3sNFXHpuemkzEgLLTb6XDArDYAsEikKgqQQLWROzC/ZCW5+G2UUTmkrEziW/BamOtILuDoLZRYrDozrO3J6ERYHpWQN3PpDFs3YlcfaOJB7dz/q755+fws7N/oX6Zc/rwE13Z8Tfr7uqE2duS4QmbwFnoUuuR/x34QsfQrVqWfYuC6lurwR42//IAzdChOZC9bRzrehXEzGFEbg1sLIRgXuCwTRNfPSjHxV/33jjjS7yluNf/uVfcMcdd+CRRx7Br3/9a/z85z/HlVdeWdc1OQmyklb+gkgo4YlVaQue4g5idrIQdxH8sZIiAi8FHAuF5SPaRAfZ5LpXHsSseffY2WYTuAW6ZOQ3V3Z5B/EcVoWJc6VJtXeLvKpyBW65+lJVCNb2aXjBhW0445QEutr9O4JDIyWMThnL5vFXL5IxR0Vpmky5mMlZeOTpAiwLGOpV8dLntmMxZ9WsyolpBK+8vBPnbE8IAnfHxjjeem13TensO1LEg08xGwc+ANQkol2UA5/slVtihFfgysHRguD9duvamCBwNRXo6XQXQL/k+PVSSYLnnZfGhqEYNg3F7OMpbv9tBos5iqNjJXRvT8KyHAVurBUT2oAk+ce8zxCes6b9fJs0RqKWQ05q3iBmHgLIkupwQrJQ4EGxvFHkw4C3b9zrO1CBy3+p8ArCbvvn7Q+qKHAB9vxNE7Asy+VjK/Il7+yQvvK226ZVXh+CFJJ+NiWqyha+uNemojKPXkXKfzXFpbwgNrdoVYzJQCmFYbJ65ReAbbnA361u0CUN8rnnYBG6wZSgL7igDT+8M4O9h0tLmgc/UAp85aY53PFAVpRFi1KctS2BXVtLrjLX1e7468sgBFjbr2HDYKzsO/e1KPYdKYmAXjIScVZGO1IqSjqFYdkKb8re04FjOiiAeAw4cLyEqTkTk7OmaHPmFk38bk/elaZuUKQSjMB94KkCers0fO8XC2LHxb6jOn54V0bsANiyLh5IOm5aE8O5pybwyNNFvPoFHXjzH3Zhz6Fi2TXlZ7JhMIa1EsHLF6rkMZJ3vBTOA9e9e3Ol1C0vmbRSiOUIEU4UyHxRq2IiJeIKCGqrvxGBe4LhV7/6FUZHRwEAz3ve87B7927f41RVxXve8x685S1vAQB861vfqpvANUPYEiw1gkgoJ4hZhS140neRAjfCyeaBKwidZRRKimcuzbSbwbV6B7vN3K7oELgryAO3gue3mJD4fKf4kLSWRW0C161Ckwccld5RUOCrlQ5OwpV0is//YBYTs+5Cc86OZEWrijCQt/T2ddeeyGmbEw6BKzz6nOft5K/8XRNCoBBSkez3Qr5Py/bvClrw8ZJlW9bF8LsnGVndnlKgBjw0ub2VSWZVCgDF879jYxwP7yviu7/IoCOt4lm7UkKh6iU4a4E37xUFuNJlFIURJ7xeGiZTQqMOv1k/lCRPXU7Ux2MEJZ2WKfjkNiAuLUY0osB1LHA5UVn5uEp9Z1jlIaVsYUwh1cuoQgATrA8zzfCTIt7eiWcpKXCZjUz583XOte/HNdmzz7EDIIGyySC7ZQqK6p6nqsoUu5QCb/zoaNV7UBXgDdd0YfdpScRWyOjVtIAf3rmIh/eV+6MvBbatj+HMbQn88M4Mfv90EX/4V8eWJR/V8MTBEk7dHHe1pVPzFL1dqu/8anTKqErgFooUMwv+q6az0xYIAVIJC4RQGAZw0z0LePKQ256mu0OFblCRr2ScKVsIcRYnZAz0aBiZKmF43MBvHs0L8paDWxd0pBVsGtKEN64f3vnKHjy6v4ArLmjDfMZCJle5oRidNlwELl/okhW4qQRBTCNCndtW4foc3rHUSgpqzHcc8N8jRIjQPCiesV0rEI+z9jRMDAqOiMA9wfCzn/1M/H7NNddUPPZFL3qR73m1wqpAEiwXgnw8w3hxEUWacBCArKD7irD0cAZsy5yRJcLKCGLGfsrVtxm0n7cqN9OrlitPdcOOOp+qckITUI2kqhT9uxJh5yXxVUXawqy4fSDDDmjCBL5aiRjsYe/16aMlzCy4M5+IEVz57LaagqH4QVEILtudwiP7i3jRxe01n//sM5L44V0LyBeAU9bH7Lyw7yyrehR4VXX8oNUQhKeqONvUTcvZKh4GOzbG0d1GMJelIq9Vr6dyxa//9xeflRLk0GP7i3j5ZdRXedUI5Ilx4CTZ/lglLABQj22pMT1nNrXcF4rlCygxjRO4HhsAKRBhv704MDNvYqiXZbaeIGZ85wIV7UP9z1jYcVRT4NoeuOEUuMyjwLJ4XoOPl7/h78h5lk7+FMIJYf98Wj7PQlV58EfKiFjA9sB1yO8w5WLXlrjw2q4G0wIe3V/Ay5/fEer4pUChaOHR/ctD3qoKax/O2JrAYI+KidmVsQUkHgOuvqgNV1/cDtOieM+nJ5AvUixkLGzfGEcirmB4XMfaXhWnboqjI+U03hTAo/uL0A2KYskSi4x+yNhq47aUglM3OguFR8Z0TM+zZxHXWHt576P5MvIWALati2FdfwzdHSqKJaaw3bEhjvN2Jnzr/v7hEp44UIJuAI8+w977K57fjne8ohvHJ03kiyxP/d0qC5hWYRFp24Y4Nq+NQVMJjo7p9nkaNg666QvTAh57poBC0YIheUU7/YBzrKoQ7D4tiVKJgigItH1yp1997rhc8I7JIkSI0DzI3FareICUTeBqNTQwEYF7guHxxx8Xv19wwQUVj12zZg02btyI4eFhjI+PY3JyEgMDAzVfkw/mV9LWDSWAhHIUuMHnqpJtQqTAjeAocE+OksAV6NW2Rrc0D6ItcZ55UxS4HmK4mWLQtiQRk/z5nInertZ7ylQjqSopLyt9J9skUMq8FE2LQqUEqkpcnqpECvRY6XHWG/hquXH2jiS+84tFQd4+a1cSn3gH6ycVhd0X39LZSB/4ggvb8IIL22r20QWY4ukdL+/BQs5CT4dq58UhiCq9a34sV1eFdRxQFWYNwLeo656377cIw9ReBO94ZQ+OjOnoaCu/V0e1KRFhgvxnZJy3LTj31CRee2UHvvXzRRwaLcG0HO/DWjzFgvIS9LcM+bHx9uvUTXEAWRybMGCYFpiDa+MolOQxF7tyPEaQzZdP5GVlKPeanMtQxGKsUDSkwBUWCkEHsh+VrhB2YUcsIIVU4AJ88SIwW2Xg7Rp/lixoGmvzmMo8OJ9+iyRyvVLsdlIhxCUwCNMevv7qLugGxe6dSZeS0IsHn8rjQ1+cwqERfUXtdNg/zOpkd7uCb3583ZKNqUemDBybMLCmT0MqoeDdr+6BShh5t5yYz5jYd6SEdFLBaVtYEJtNazQcGTPw43uyAJwAX6kEwSsuN9DT4W7IRiZ1bFkXRzZf2Rc5k7NwbMLAQtZyBX6cz5iYWTBBAKwdiOHBPXk8/DQjWy89J4W/f0s/JmYM3HpfBm1pFakkgWk5dV1Vgfa0f1/V1a7glPUxHJ/QQRSCTUMxvO6qTiiKgo1DtbeBnIzN5lnl62xTkEqWp5OIKyiWLGTzllhQ52Mkb71RFYJUMnxJNCUf65UGvuMgQoQIzYd7d1WrLBTYdWpZIIoI3BMM+/btE79v3bq16vFbt27F8PCwOLdWAve+x7MwDSY1q+Qru9Tgkz+LUhHEAHA680pcHPGstpwkvF2EAPD3f7IUA96BFEruurOUGJ0yMDFjYDFn4dgEy8NCzqq41S4MSjrFweMlZPNMpRHTCH79++Y1XKoCWCbwD1+YrDjRbhYW7e2EswsmvnbzbNn3+SJFyaDo7igKYo8jk2PfzS1aODTi8SG1gH1HmeJrbMbAtO19RwjF5JyBtpSChYwFTSXQDRYoplCkGB7XA+0cFnPsXE0h+PUjWf+DViB0TxUo6ib+6yfuZ839BY9NlOoOInJwxA62k1bw6P7aSNxs3sLxSRO6QdGWUtDZpmBsxsDMvAmFEOw9zMpCd7uOidnyOn1ohKmxLAsYmdJdW/SDMDFrQDcofrsnj2PjuiAV+YJuvkBxZEyHqhAcHmHEgGlRHB0zYJgsP+0pE/c86i4L+QKQyZso6Y4FwULGwvgMIyF0g+LgSAkP7XWe8/S8iak5NoWdmrPwzk+PopBn+chkTXzlpvK6EQjp9VHLqWNj0wayBQslnV3vkIdoPzKmI5u3MDJpoFCiWMxZGJtm+3ezBYr3fHq8oYBqMor2bJ0CuPlXCyjqFJ1pBQt2Xh8/4HhEZvJMlXvweAmGSZFKAPkihA/xfY/nMDZdWztvmEC2wII1dneoGJvWMeqTxuwCa2fb0wpmF/0phqk5E+MzBuYWTd+yKdKatxh5GmKSw7+fy5h44Ml8GYlPKUR5OTymC1KG+ybzoJDZAsWhkRImZw1k8xTzGQOapuDwaLmaVDdYWUklCRbzLO1cwcLB4wZyBQtxjTLiKcEJXLa1/bd7cnjyUKEsPRmHR3VQCuw9XKh477ytml208J7PjIVejGk1ZhZZuUwlga//bN71XSuzWCxRFEoWOtoVdKVVNpYgBHsOVn7erUbJoMgXLaRTKo6MsX5WXkjhuz4yORO5IsXXf7bom45KgO8NqhUV8AWdYnQqPL3XngKG+lT89N5F5AtUtCl87pROKuL3ICTiCjav0XDmtjjSNtH6+/1FJGLhVORBmJlnOxm62k3R38hYyJqYz1jIP1VAMsEsSybtej4yadRNvjD7FmkhZoWB7ziIECFC8yH3o61awEnGCNh/4RERuCcY5ubmxO/9/f1Vj+/r6/M914tisYhi0Rm0zs+zQdjj+6aRSrOtWsU8wQJtLoubz+ZhUYqudAwLC+G3YJkWRT5bgEUp9mfKv89nk4Dpn9dC1kAxn0c+ZyAd05DLGlgIYiUinPAo5HVkMzrybTEsLITb8ruakctbyGbYBKdCk9BSzCyamJpl5MfxSTYAX5hXGiZFLYtidNpCqWQhk2/+gNfUSzAM4MhI05OuiEyG4IHH/QN7AEwdnEj4t3ezs4xU8GJkyhRKu3zBEr5109MK4vZkM6YRZDMKpubZVs5K0A2KRZ9AKqsBilUSfsOzMxoeyvk8SwJMTSt1K/Vn7O2sbUmCwwHvKgiGQQWJHI8TtKcUzLQpmPM8746UgvHJ8vxNzVuCvDo+VsTcXPXrj47rKOkWRsaAhQzFYp75KRbztpWICUzMuIkDSilmFy0x10wnCR7V/Mn8bEaFXmT9br5IMTJewmLWIRRk8PLJ39Oho853OQo89ESD27cJMD3jsK/TMwraPOqtqXkLhSLF7IKJfFbBsVGCXN6EapVQNIDDxxvLgh9UBdhjq+ZSScLqaECzNjmhMEsAqwhDIj+OHMtjZqa+cZsWI+hMK5hqUzA6HlzuVRpDKqBML+YtjE6GJ5CLuTjyWROlgv/1YiiiVCginzORzwFT08FpEULQFouXB7fsjCObKSELYGaGlfV80cLYdHUiLJUgmJtl96obVHhmJ+IEPW0xJFQVZslCqaBjet7C5FT1e55dNEWw4GrgdeDg0erHLjWKeQUPVuinWoVUgiAZD15EWC6kkwRH7XqhUhMdcQO9XQrW9rNxZr5o4dCI/4JaoURRNICDw+GulYy7rZYsiyLn4bETGrC2J46DR3QcG5V2PxkKZmdZO9PRp2E2wzrDhQXD/2JGFnqpgNGMk+9cRmmKeo0QgpQa801rZsHE1Fx5nlSV1fOGr21ZyOdYtPjAe18GxEgJMxkD8biyovIVIcKJgJJOkc2wfkuxVCwsNLYQ5QdiGtCLi2wOWWKkVbVdsBErdYIhk3HYymSy+jahVMoxalxc9F/lBYBPfvKT+OhHP1r2+d+89Szx+7vCZjJChAgRIkRYhfj1cmcgQgQJdy13BiJEiBAhQoQIESI0DYuLi+jq6gr8PiJwI4TC3/7t3+Kv/uqvxN+WZWFmZgZ9fX0njTdohOXBwsKC8Gru7Oxc7uxEOIERlbUIS4WorEVYKkRlLcJSISprEZYKUVmLsFSIylqEpQKlFIuLi1i3bl3F4yIC9wRDe3s7ZmeZ31uhUEB7e+WI1vm8s52poyM4am0ikUAikXB91t3dXX9GI0SoEZ2dnVHHGWFJEJW1CEuFqKxFWCpEZS3CUiEqaxGWClFZi7BUiMpahKVAJeUtxwoKOxWhGZBJ1amp6uZa09OOQVhEyEaIECFChAgRIkSIECFChAgRIkSIsLIQEbgnGHbu3Cl+P3ToUNXj5WPkcyNEiBAhQoQIESJEiBAhQoQIESJEiLD8iAjcEwxnneUEFXvggQcqHjs+Po7hYRbCdHBwEAMDAy3NW4QI9SCRSOAjH/lImYVHhAjNRlTWIiwVorIWYakQlbUIS4WorEVYKkRlLcJSISprEVYaCKWULncmIjQPv/zlL3HFFVcAAC677DLceeedgcfecMMNeMtb3gIAeNOb3oQbbrhhSfIYIUKECBEiRIgQIUKECBEiRIgQIUKEcIgUuCcYnve852HNmjUAgLvuugsPP/yw73GmaeKzn/2s+Ps1r3nNkuQvQoQIESJEiBAhQoQIESJEiBAhQoQI4RERuCcYVFXFP/zDP4i/3/CGN2BiYqLsuA9+8IN45JFHAADPec5zcNVVVy1VFiNEiBAhQoQIESJEiBAhQoQIESJEiBASkYXCCQjDMHDNNdfg9ttvBwCsWbMGb3vb23D66adjZmYG3/rWt3DPPfcAALq7u3HPPffgjDPOWM4sR4gQIUKECBEiRIgQIUKECBEiRIgQwQcRgXuCYnFxEa973evwk5/8JPCYDRs24Dvf+Q4uueSSJcxZhAgRIkSIECFChAgRIkSIECFChAgRwiKyUDhB0dHRgR//+Me46aab8PKXvxwbN25EIpFAf38/nv3sZ+Nf/uVf8MQTT0TkbQQXbrnlFrzqVa/Cli1bkEwmMTg4iEsuuQT/5//8HywsLFQ9//jx4/j2t7+Nv/7rv8bzn/987Ny5E319fYjFYuju7sbZZ5+NP/uzP8Mdd9zRsnv41a9+hTe+8Y3Ytm0b0uk0+vr6cP755+OjH/0oxsbGakprdHQU1113Hc4//3z09fUhnU5j27ZteNOb3oRf/epXLbqDkwONljUvZmdn8bnPfQ4vfOELsWnTJiSTSfT29mLXrl14xStegf/7f/8vjh8/vmLv4ZlnnsHf/M3f4Mwzz0RXVxfa29uxc+dOvPOd7xR2NxHqQ7PeUyaTwfXXX48XvvCFWLNmDRKJBAYGBnDhhRfi4x//OMbHx1f8PQBRWWs2TNPEE088gf/6r//Cu9/9blx88cVIp9MghIAQgje96U2h0qGU4v7778fHP/5xvPjFL8aWLVuQSqWQTCaxbt06XH311bj++usxNzfXsnuJytnKR7PK23/913+Jc8L8u+6665p+L1F5W9loVlnjKBQK+O///m+89KUvxebNm5FOpxGPxzE4OIjnPve5+OhHP4rh4eGW3Es0N1jZWFxcxA9+8AO8613vwiWXXIKBgQHEYjF0dnbitNNOwxve8AbceuutCKM7HB0dxY9//GNcd911+MM//EOsXbvW1Za1GlG7FqHloBEiRDjpsbi4SK+99loKIPDfxo0b6X333VcxnXe+850V05D/XXHFFXRiYqJp96DrOn3b295W8Zq9vb30lltuCZXeTTfdRHt6eiqm9/a3v50ahtG0ezgZ0KyyJuOGG26gfX19Vcvcv/3bv63Ie/jSl75EU6lUYFqqqtKPfvSjTcn7yYRmvqdf/vKXdGhoqGr78oMf/GDF3gOlUVlrBV7+8pdXfD9vfOMbq6axb98+umHDhlB9Z19fH/3+97/f1HuIytnqQTPKG6Ws3ww7XgNAP/KRjzTtHqLytjrQrLJGKaW///3v6Y4dO6qWs2QySa+//vqm3UM0N1j5+MxnPkOTyWSodujSSy+lR44cCUzrlltuqZpGqxC1axGWChoiRIhwUsM0TbzqVa/CrbfeCgAYGhoq80y+9957MTw8jGuuuQb33nsvdu3aFZhePB7H+eefj/PPPx87duwQq6iTk5O4//778b3vfQ/5fB533HEHLrvsMjz44INIpVIN38c73vEOfPWrXwUAdHV14a1vfSt2796NbDaLW265BT/96U8xMzODV73qVfj5z3+O5z73uYFp3XnnnfjjP/5jlEolAMCLX/xiXHvttWhra8PDDz+Mr33ta5ifn8eXvvQlEELwhS98oeH8nwxodlkDgI997GP4yEc+AgCIxWJ4yUteguc+97lYs2YNLMvC8PAwfvvb3+LnP//5iryHr3/963j7298OAFAUBa95zWtwxRVXQNM03Hvvvfjv//5vFItFfOQjH0EikcAHPvCBptzHiY5mvqdf//rXuPrqq0V7cN555+G1r30tNm/ejPn5edx+++34wQ9+gJmZGbz61a/GLbfcghe96EUr6h6AqKy1CqZpuv7u7e1FX18f9u/fHzqNmZkZHDt2DACQSCTw/Oc/H895znOwadMmJBIJPPPMM/jGN76Bp556CtPT0/jjP/5jfOtb38If//EfNyX/UTlbPWhGefPi3e9+Ny6//PKKx5x22ml1py8jKm+rB80qa8PDw7j88ssxOzsLABgcHMSb3vQmnHrqqUgkEjh8+DC+9a1v4cknn0ShUMB73/tepNNp/Nmf/VnD9xDNDVY+nn76aRQKBQDA+vXr8YIXvADnn38+BgcHUSgUcP/99+PrX/86MpkMfv3rX+Oyyy7D/fffj8HBwbK0vGU2FovhzDPPxO9///uW3kPUrkVYUiw3gxwhQoTlxRe/+EWxmnf66afTsbGxsmP+9//+367VzyAcOHCAZjKZitc7dOgQ3bZtm0jvU5/6VMP3cOutt4r01q5dS59++umyYz772c+KY7Zt20aLxaJvWoVCgW7ZskUc+7nPfa7smH379tE1a9aIY+64446G7+FkQDPLGqWUfvOb3xTHnnPOOXT//v2BxxYKBTo+Pr6i7mFiYoJ2dnZSAFRRFHrzzTeXHXPffffRdDpNAVBN0+jevXsbvoeTAc16T8Vi0dUevO9976OWZZUdd/vtt9NEIkEB0KGhIbqwsLBi7oHSqKy1Ep/4xCfoBz/4Qfq9732PHjx4kFLqVjeGUandd999dOPGjfSzn/0snZmZ8T1G13XXLpfe3l46OzvbcP6jcra60Izy5j3nhhtuaF2GPYjK2+pBs8raW9/6VnHOlVdeSbPZbNkxlmXRv/u7vxPHDQwMUF3XG8p/NDdYHfjzP/9zeuWVV9Kf//zn1DRN32MOHz5Md+7cKZ7tm9/8Zt/j7r33Xvq2t72NfvGLX6QPPPCAeJ/8vFZRX1G7FmEpERG4ESKcxDAMg65du1Z0KA899FDgceeee6447rbbbmvouj/4wQ9Ck3RhcOGFF4r0Km1hfslLXiKO+9KXvuR7zOc//3lxzEte8pJQ93DxxRc3fA8nOppd1qampmhvby8FQNevX0+np6dbmX2Rt2bew/vf/35xzLvf/e7A637mM58Rx732ta9tyr2cyGjme/rOd74jvj/vvPMCJxeUUvqxj31MHPuxj31sxdwDpVFZW2rUSnJkMplA4kCGZVl09+7dTSPeonJ2YmC1ELhReVv9qKesrV+/XpyzZ8+ewOMMw3BZFT3++OMN5TWaG6wOhB2/P/LII+LZptNp34WAILSSwI3atQhLjYjAjRDhJMYvf/lL0fg/73nPq3jsf/7nf4pj3/SmNzV03b1794q0duzY0VBaBw8eFGlt3brVVx3HId/vZZdd5nvMc5/7XHHMnXfeGZiWaZqu1fjDhw83dB8nOppd1j75yU+KY/7zP/+zBTkuR7PvYfPmzeIYrm7xw8LCAm1ra6MAaFtbG83lco3cxgmPZr6n//W//pf4vpqH8vDwsDh2586dDdxBVNZWO+ohOcJCbvv+8i//sqG0onJ2YmC1ELhReVv9qKesxWIxcU6pVKp47MUXXyyOrSUWghfR3ODEhKzCffTRR0Of10oCN2rXIiw1FESIEOGkxc9+9jPx+zXXXFPxWNnTUT6vHjzzzDPi9zVr1gQed91111WNdivn5eqrr64YYfTSSy9Fe3s7AOZrmc1mXd8vLi7innvuAQB0dHTg0ksvDUxLURRcffXVvvmIUI5ml7Wvfe1rAJjn8qtf/eqG8/emN72parTtZt7Dk08+iSNHjgAAdu3aha1btwamJZfFbDaLu+++u+K1T3Y08z1xX1IA2LlzZ8W0NmzYgHQ6DQDYt29foE9gVNYiNILOzk7xez6fDzwuKmcRlhJReYsQBNmr9Omnnw48zjRNHDhwAACgaVpgnxvNDU5ehO3/moWoXYuwEhERuBEinMR4/PHHxe8XXHBBxWPXrFmDjRs3AgDGx8cxOTlZ1zUnJibwwQ9+UPz9yle+sq50OGq5B03TcN555wFgA8Unn3zS9f2TTz4Jy7IAsEBFqqpWTE++npyPCOVoZlkbHR0ViwBnnnkm0uk09u/fj3e9613Yvn07UqkUent7cf755+Nv//ZvMTIysuLuoZa0vMdEZa0ymvmeKKV15+Oxxx6r+9yorEUIgvxONm/e3LS0onJ28uLzn/88du3ahfb2dqTTaWzatAnXXnstvvCFLyCXyzXtOlF5Oznxspe9TPz+l3/5l75lilKKD3/4w5iYmAAAvOUtb0FPT0/d14zmBiceSqWSawGg0f6vWYjatQhLjYjAjRDhJMa+ffvE75VW+fyOkc/1w+HDh3HTTTfhpptuwg9/+EN89atfxZ//+Z/j1FNPxRNPPAGArUT+xV/8RZ25L89Ho/fQyudxsqOZz/aBBx4Qv2/atAk33ngjzjnnHPzHf/wHDhw4gEKhgNnZWTz88MP453/+Z2zfvh033njjirqHqKy1Ds18tvIOgUrKIQA4fvy4a2K6d+/eqtcOQlTWIvhhdnYW3/nOd8TfL37xixtKLypnEQDWp+7duxfZbBb5fB7Dw8P48Y9/jL/4i7/Ali1b8JOf/KQp14nK28mJ6667Djt27AAA3H777di6dSs++MEP4j//8z/x9a9/HZ/4xCdw1lln4ZOf/CQA4A1veAOuv/76hq4ZlbUTD9/85jcxPz8PANi9e3fFHZxLiaisRVhqaMudgQgRIiwf5ubmxO/9/f1Vj+/7/+3deVhU1f8H8Dc7iCgoIiiCKyqioiIlCOKaK6aZkLmlmftSWqmZyy/LLMs1l5LU1ExNzdwoF0hDFhVwRcNdUdkUEVmHub8/eLjfGZmNYWAGeL+eh+e5l/mccz/3crjiZ+6cU7euwraKhIaGYvLkyQpfc3FxweTJk/HJJ5/A2Lhs7yPp8hzK83pUd7q8to8fPxa3L1++jEOHDqGwsBC+vr4YPnw4HB0dkZSUhF27duHcuXPIycnB6NGjYW1tjaFDhxrEOXCslR9dXls/Pz9s2bIFAPDLL79gxowZSj+KuXXrVqV5lBbHGikye/ZsPHv2DAAQGBiItm3blqk/jrPqzcTEBF26dIGfnx/c3NxQs2ZNZGRk4MKFC9izZw+ePn2K1NRUBAYGYufOnXjnnXfKdDyOt+rJ3t4e0dHRmDJlCn7//XekpKRg+fLlJeJ69eqF+fPno3v37mU+Jsda1ZKamopPP/1U3F+wYIEes5HHsUYVjQVcomosKytL3La0tFQbb2VlJW6/ePFCq2OamJigZ8+e8PX1VVu8Xbx4sdI5h4rp8hz0cT2qC11e2+ICBgBxvrRFixaVGCuzZs3CJ598ghUrVgAAPvjgA7zxxhuwtrYucbytW7eWKMCV5zlwrJUfXV7bYcOGYc6cOXj69CliY2PxySef4JtvvilRxA0LC8PSpUvlvpeZmanweBxrpI2NGzeKbybY2tqqfUKN44xU6dq1K+7evQtnZ+cSr73//vv45ptvMGHCBOzevRuCIGDcuHHw9fWFi4uLwv443kgVOzs7LF++HPXq1cPatWsVxpw6dQpGRkaws7ODp6en0r74f4PqJT8/H2+99ZY4vcabb76JIUOGVMixeV8jQ8QpFIioXEyaNAmCIEAQBOTn5+P+/fvYuXMnOnTogC1btsDf3x+zZ89GYWGhvlOlSqZ4LrJi/v7+Cv+YNzIywvLly9GpUycAQHp6Onbs2FERKVIVYWNjgzVr1oj7K1asQOfOnfHdd99h79692Lx5M4KDg9G7d2/k5uaiWbNmYmxZP11AVOzIkSOYPn06gKJxtWXLFjRu3Fi/SVGl1rx5c4XF22I2NjbYuXMnAgICAAC5ubkKn5ok0sSKFSvQtGlTrF27FqNGjcLZs2fx4sUL5Obm4tq1a1i8eDHMzc1x/Phx+Pn5ITQ0VN8pkwGQSqUYN24czpw5AwBo1qwZfv75Zz1nRaRf/N8FUTVWvOoqUPTHuTqyK37a2NhofBwzMzM0atQII0aMQFRUFEaOHAkA+P777/H555+XIuOSdHkOFXU9qiNdXttX9ydOnKi0H2NjY0yYMEHcP3XqlNpjK8OxVjno+tq+++67+Omnn2BhYQEAuHDhAubMmYPhw4fLPaG2ZMkSDBo0SGxXlgVYONao2IkTJzBs2DBIJBIYGRnhxx9/lFsUqCw4zkgVExMTuU8WlHUuXI636mnhwoX4+OOPUVhYiG+//Ra//PILunTpgpo1a8LCwgKtW7fGokWLcOLECZibmyMrKwvBwcFaL5YMcKxVBYIgYNKkSdi5cyeAoun3Tpw4Uaa/rcoDxxpVNBZwiaoxW1tbcTstLU1tfHp6usK2pWFiYoL169ejdu3aAICVK1eWad4eXZ6DPq5HdaHLa/vqH2/FT9gq4+XlJW4XT7mgDY61yqE8ru3777+PmzdvYt68eejUqRNsbW3FN6beffddnD17FgsXLpTrqywLbHCsEVD0hlNgYCByc3NhZGSEDRs2YPz48Trrn+OM1OnSpYv4Ud779+/LLdRYWhxv1c+jR4/w9ddfAwBatmyJ2bNnK4319fXF6NGjAQDPnz8Xp4zRBsda5SYIAqZMmYKffvoJAODs7IxTp04Z5CdPONaoorGAS1SNtWzZUty+c+eO2njZGNm2pWVjY4OuXbsCKHqHMSoqSuu+dHkO+roe1YEur22rVq3k9ovfDFBG9nVl85JqgmOtciiva+vs7IyvvvoK58+fx7Nnz8SpYXbs2IHXXnsNAHD16lUx3tvbW5v0S+TBsVY9nTp1CoMGDRKfsPnhhx9UftpAGxxnpI6xsTHq1Kkj7pflDXeOt+rn77//RkFBAYCiRcqULQJarE+fPuJ2dHS01sflWKu8BEHA1KlTsXHjRgBAw4YNERYWJjdFlSHhWKOKxgIuUTUmu4L1uXPnVMYmJyfjwYMHAAAHBwfUq1evTMeW/aiH7KJUpVWac5BIJIiLiwNQ9J8Sd3d3udfd3d3FeSvj4uLUzs8rezwPD49S5V3d6HKstWnTBqam/1uD8/nz5yr7k31dXbFXFV2eQ2n6ejWGY001fd3X0tPTcfnyZQBFH4Pr0KGD1n1xrFVvxcXb4qcd165di8mTJ+v8OBxnpI5UKpX7G60sT3lxvFU/jx49Erc1+ftLdnzJLuhUWvy/QeVUXLzdsGEDAKBBgwYICwtD8+bN9ZyZcryvUUVjAZeoGuvbt6+4fezYMZWxR48eFbf79+9f5mMnJiaK22UpmsieQ2hoKARBUBp75swZ8Q9Cf39/WFtby71uY2MDX19fAEWref77779K+5JKpfjrr7/E/X79+mmVf3Why7FmZWUlLqwCFM1Jqsr58+fF7bK8Q63Lc3B3dxdX805ISMDdu3eV9pWVlSUu4FCjRg1069atNGlXO/q6r23fvl180mjUqFHinLna4Firvl4t3q5evRrTpk0rl2NxnJE6UVFR4lPgzs7OqFGjhtZ9cbxVP7IPaxQXrlS5d++euF23bl2tj8v/G1Q+rxZvnZycEBYWhhYtWug5M9V4X6MKJxBRtSWRSARHR0cBgABAuHDhgtI4T09PMS40NLRMx42JiRGMjIwEAIK5ubmQkZFRpv46d+4s5rZv3z6lcYMGDRLjNm7cqDDmhx9+EGMGDRqktK99+/aJca+//nqZ8q8OdD3Wtm7dKsb4+/srPW5hYaHQqVMnMXbHjh0Gcw4ff/yxGDN9+nSlx/3uu+/EuODgYK3zry70cV9LSkoS7OzsxHva9evXte6rODeOtcpry5Yt4nUcM2aMxu3CwsKEGjVqiG1XrVpVfkkKHGdVhbbjTZ3CwkKhZ8+eYt+TJk0qU38cb5VfacdaWFiYGG9nZyc8f/5cZXxAQIAYv3LlyjLlyv8bVC5TpkwRr52jo2OZ/46SVdxveZS+eF+jisYCLlE1t379evEfgDZt2gjJycklYubMmSPG+Pr6KuwnPj5eWLVqlfDixQuVx4uOjhYaNmwo9vfee+8pjV20aJFGfygePXpUjHNychISExNLxKxdu1aMadKkiZCXl6ewr5ycHMHFxUWMXbduXYmY//77T+4f6+PHj6s8Zyqiq7EmCEV/CLm7u4uxixcvLhEjlUrl+mvcuLGQn5+vsL8xY8aIcYsWLaqQc0hOThZsbGwEAIKxsbFw8ODBEjFRUVFiQcfU1FRISEhQ2h/9jy5/Trdv3xbu3r2r9PUrV64ILVu2FPtatmyZytw41qo+bQpq4eHhOi3ecpxVH6Udb2fPnhU2bdok5OTkKI3JysoS3n33XbFfCwsL4c6dO0rjOd6qh9KOtfz8fLm/qfv37y9kZWWViJNKpcL8+fPFOCsrKyEpKUlhn/y/QdUzbdq0civeCoL2BVze18gQ/W8SQSKqliZMmIADBw7g+PHjuHr1Ktq3b48JEybA3d0dT58+xa5du8SPC9na2mLTpk0K+3n27BlmzZqFefPmoWfPnvDy8oKrqytsbGyQk5ODu3fvIiwsDGFhYeJHmdq0aYMVK1aU+Rz69euH9957D1u2bMHjx4/h5eWF999/Hx07dsTLly/x559/4vDhwwAAc3NzhISEwNzcXGFflpaWCAkJQf/+/VFQUIBp06YhNDQUgYGBsLa2RmxsLDZv3izOqzphwgT06tWrzOdQHehqrAGAiYkJtm3bhu7duyMrKwuLFy/G8ePHERQUBEdHRyQlJeHXX38V54cyNzfHzp07YWZmZjDn4ODggLVr12Ls2LGQSqUYMmQIgoOD0bt3b5iYmCAiIgLbtm1Dbm4uAGDJkiUlFnAjxXT5c7pw4QKCgoLg6+uLgIAAtGjRApaWlnjy5AnCwsJw6NAhSCQSAMD48ePxySefGNw5cKyVnzt37iAkJETue5cuXRK34+LisGDBArnXe/TogR49eoj78fHxGDBggDhtwhtvvAFXV1f88ccfKo9tb28vLgiqLY6zykUX4y05ORkTJ07E7Nmz0bt3b3Tq1AmNGjWCtbU1nj9/jtjYWPz222/iCudGRkbYvHmzTlaA53irPHQx1szMzLB27VoMGTIEUqkUR48ehZubG0aNGoW2bdvCzMwMt2/fxu7duxEfHy+2++qrr9CgQYMy5c//G1QOCxYswLp16wAU3WtmzpyJhIQEJCQkqGzXsWNHcboBWd99953KtVVeHbN2dnaYPXu2Fpn/D+9rVKH0XUEmIv3LzMwUBg4cKPcO5atfzs7OQkREhNI+ZD8mpcnXO++8I6SlpanMS9N32QVBEAoKCoRx48apPKadnZ3wxx9/aHRN9u/fL9ja2qrsb8KECYJEItGoPyqii7Em659//hGcnZ1V9ufg4CCEhYWp7EfTd9nL4xzWr18vWFpaKu3LxMREWLhwoUZ90f/o6ue0d+9etfezGjVqCMuWLROkUqnavDjWqpbS/tun6Ocu+1Rbab66deumNC+Os6pJF+PtwIEDGrd1dHQUDh8+rDYvjreqRxdjrdiePXvEaYZUfVlYWKidOoH/N6haunXrptW/f1u2bFHYn6ura6n6cXV1VZob72tkiPgELhHBxsYGhw4dwsGDB/HLL7/g3LlzSElJgY2NDZo1a4ahQ4di4sSJKleQ9ff3R1RUFE6ePIlz587h+vXrSEpKQnZ2NiwsLGBra4tWrVrBx8cH77zzTolVXsvK1NQUISEhGDVqFEJCQhAREYHHjx/D0tISjRs3RmBgICZNmgQnJyeN+hsyZAhef/11bNiwAYcOHcLdu3eRm5sLJycndO3aFePHj+eE8VrQxViT5e/vj6tXr2Lz5s04cOAAEhMT8fTpU9SuXRvu7u4IDAzExIkTUbNmTYM9h8mTJ6NXr17YuHEjQkND8eDBA0ilUjRo0AA9e/bEBx98gA4dOugs/+pCVz+n7t27Y9OmTQgLC8OlS5eQnJyMzMxM2Nvbo2nTphgwYABGjx6Nhg0bGuw5FONYI0U4zqqXXr164eDBg4iOjkZMTAwePHiA9PR0ZGRkoEaNGnBwcEDHjh0xYMAADB8+HJaWljo9Psdb9fP222+jZ8+e2L59O0JDQ3Hp0iU8ffoUhYWFsLW1RevWrdG9e3eMHz8ejRo10tlx+X8Dqii8r1FFMRIEFcsyEhEREREREREREZHeGOs7ASIiIiIiIiIiIiJSjAVcIiIiIiIiIiIiIgPFAi4RERERERERERGRgWIBl4iIiIiIiIiIiMhAsYBLREREREREREREZKBYwCUiIiIiIiIiIiIyUCzgEhERERERERERERkoFnCJiIiIiIiIiIiIDBQLuEREREREREREREQGigVcIiIiIiIiIiIiIgPFAi4RERERERERERGRgWIBl4iIiIiIiIiIiMhAsYBLREREREREREREZKBYwCUiIiIiIiIiIiIyUCzgEhERERERERERERkoFnCJiIiIiIiIiIiIDBQLuEREREREREREREQGigVcIiIiIiIiIiIiIgPFAi4RERERERERERGRgWIBl4iIiIiIiIiIiMhAsYBLREREREREREREZKBYwCUiIiIiIiIiIiIyUCzgEhERERERERERERkoFnCJiIiIiIiIiIiIDBQLuEREREREREREREQGigVcIiIiIiIiIiIiIgPFAi4RERERERERERGRgWIBl4iIiIiIiIiIiMhAsYBLREREREREREREZKBYwCUiIiIiIiIiIiIyUCzgEhERERERERERERkoFnCJiIiIiIiIiIiIDBQLuEREREREREREREQGigVcIiIiIg2Fh4fDyMhI/KKymzx5sng9jxw5ojDm7t27ctf97t27FZskVWmyYys8PFzf6VApSKVStGvXDkZGRrCxscGjR4/0nRIREVG5YAGXiIiIiPTi4sWL+OmnnwAAXbt2xYABA/ScERFVJsbGxli6dCkAICsrC3PnztVzRkREROWDBVwiIiKqMl59UlOXX4sXL9b36VU5s2fPRmFhIQBgyZIles6GqGqKj4/H4sWLsXjxYqxatUrf6ehcYGAgOnbsCADYsWMH4uLi9JwRERGR7rGAS0REREQV7p9//sHJkycBAF5eXujRo4eeMyKqmuLj47FkyRIsWbKkShZwAeDjjz8GAAiCgIULF+o5GyIiIt0z1XcCRERERLpiZmaGZs2aqY1LSUnBixcvxH1N2tSpUwcBAQEQBKFMOVKRL774QtyeNWuW/hKhao+/05Xf22+/jTlz5iApKQmHDx9GXFwcOnTooO+0iIiIdIYFXCIiIqoyGjZsiJs3b6qNGzt2LLZt2ybua9KGdOfy5cvi07d2dnZ466239JwREVVmJiYmGDt2LL788ksAwOrVq7F161b9JkVERKRDnEKBiIiIiCrUunXrxO2goCBYWlrqMRsiqgrGjBkjbv/2229IT0/XYzZERES6xSdwiYiIiCqQRCLB6dOncevWLaSmpqJOnTrw8fFBu3btlLbJz8/H6dOnce3aNWRlZaFevXrw9fWFu7u71nlIpVLExMTgxo0bSE5OhpmZGZycnODn54eGDRtq3a86eXl52LNnj7g/dOjQMvd54cIFXLlyBY8fP0bNmjXh5uaGbt26wcLCQus+4+LicPnyZSQnJ8PExAT169eHl5cXWrZsWeZ8y5tUKkVkZCRu3ryJJ0+ewMrKCn379oWbm5vC2MuXLyM+Ph5paWnIycmBlZUV7O3t0bRpU3h6esLGxqbCcr9x4wYuXLiApKQkGBsbw93dHT169FD5s7x27RqioqKQnJwMa2trtGzZEt27d4e5uXmF5Z2QkIDY2Fg8fPgQVlZWaNKkCbp3746aNWtWWA4V5cGDB4iMjERycjJevnwJBwcHtGnTBt7e3jAyMip1f+np6YiJicGtW7eQmZkJY2Nj1KxZE40aNUKrVq3g5uamUb8tWrSAh4cHrly5It5nJk+erM0pEhERGR6BiIiIqJoZM2aMAED80lRYWJhG7ZTFrVq1SnBycpJ7rfjL399fSExMlOunsLBQWLFihVCnTh2FbQICAoRbt26V6twzMzOFuXPnCvb29gr7NDIyEvz9/YXz58+Xql9NHTx4UDyWjY2NkJ+fr7bNnTt35HK8c+eOIAiCcOTIEcHDw0Phedja2gqrVq0qVW4SiURYu3at4OzsrLBPAEKrVq2E3377TaP+unXrJrZbtGiRRm3KMsakUqnw/fffK8x/5cqVJc515cqVgouLi9JzBSAYGxsL3t7ewq+//qpR/ppwdXUV+9+yZYsgCIIQGxsr+Pj4KMyhXr16wvbt20v0c/bsWcHb21thGwcHB41/TrLtwsLCShUXHR0tdOnSRWEOVlZWwoIFCzQa45rmIGvRokVim27dupV4XfY6a/I1ZswYlcfbv3+/4OnpqbS9k5OTsG7dOqGwsFCj/BMSEoTBgwcLpqamKvOqW7euMHbsWCE1NVVtn5999pnYzs/PT6M8iIiIKgNOoUBERERUzqRSKYKCgjBr1iw8fvxYYczp06fRpUsXcT7evLw8DB48GHPmzMHTp08VtgkPD0e3bt1w//59jfI4f/483Nzc8PXXXyMtLU1hjCAIOH36NLy9vbFx40aN+i2No0ePits+Pj4wMzPTqp8vv/wSAwcOxJUrVxS+npGRgVmzZmHatGka9ffs2TP4+flh+vTpePjwodK469evIzg4GMOGDUN+fr5WuZeHgoICDBw4EB999JHK/AEgNzcX/fv3x4cffqh27BQ/qb17926lMeHh4TAyMhK/Fi9eXKrcf//9d/j4+ODs2bMKX09NTcWoUaOwatUq8Xvbtm2Dv78/YmJiFLZJSUlBcHAwQkJCSpVLaWzduhV+fn6IjIxU+HpOTg6WLl2KYcOGobCwsNzyKG/Z2dkYPHgwhg4divj4eKVxjx8/xrRp0/DGG28gOztbZZ/Hjh2Dp6cnDh48CIlEojI2PT0dW7duVTuuASAgIEDcjoyMxPPnz9W2ISIiqgw4hQIRERFROVu0aJE4bUBAQADeeOMNODg4ICUlBXv37kVsbCwAIC0tDaNHj8bZs2cxadIkHD58WGzTp08f1K9fv0Sbhw8fYvLkyThy5IjKHKKiotC7d29kZWWJ3+vQoQP69+8PFxcXSCQSXLx4EXv27EFGRgakUimmTJmCWrVqYcSIETq7FsePHxe3u3btqlUfO3bswOeffw4AaN26NQYPHoymTZuioKAAMTEx2LVrl1hc/eGHH9CnTx8EBgYq7S8vLw+9evUSrykA2NvbIygoCB4eHpBIJIiNjcXevXvF67dv3z4MHz4cf/zxh1bnoGuzZ88Wi+NeXl4YMGAAnJ2dkZWVhdjYWFhZWYmxCxYswN9//y3uu7i4YNCgQWjVqhVsbGyQnZ2NJ0+e4OLFiwgLC0NmZma55X3lyhVs2LABubm5cHFxwdtvvw03NzdIJBJERERg9+7dYvHz448/Ru/evfHkyROMHz8ehYWFcHFxwbBhw+Dm5gapVIqIiAj89ttvYpvp06ejd+/ecHFx0Wne4eHh+PLLLyGRSODq6oqhQ4eKH/W/dOkStm/fjhcvXgAA/vzzT6xbtw4zZ87UaQ7qNG7cGKampnjx4gVSUlIAAKampnB1dVUYX79+/RLfy8vLQ58+fRARESF+z97eHoMHD0b79u1hbW2N+/fvY//+/bh8+TIA4MSJExg6dCiOHTumcOqDx48fIygoCHl5eQCKFiDr06cPfHx84OTkBGNjY2RkZODGjRuIiorCxYsXNT5nHx8fGBsbQyqVQiKR4NSpUxgyZIjG7YmIiAyWvh8BJiIiIqpoFT2FgpGRkVCzZk3h6NGjJWILCwuFSZMmycXPmjVLACDUqlVLOHbsmMI2H3zwgVybuLg4pfk8f/5caNy4sRhbq1YtYf/+/Qpjk5OThYCAADHWzs5OePTokfqLo4G0tDS5nA8ePKhRu1enUDA2NhZMTEyENWvWKPy4dnx8vNy0Ex06dFDZ/6effirXf2BgoPDs2bMScffv3xe8vLzkYn/66Sel/VbkFAoABAsLC7VTHWRlZQmWlpZimylTpggFBQVK4/Py8oR9+/YJS5cu1TgXdecq+9F+IyMjAYAwdepUIScnR2HfFhYWYvzQoUOFRo0aCQCE6dOnC7m5uSXanDx5UjA3NxfbzJw5U2U+srlrOoWCsbGxAECYN2+ekJeXVyL23r17QpMmTeSmdFB1nTXNQZa6KRSKbdmyRYxzdXXVqO9iH374oVxukydPFl68eFEiTiqVCt98841c7IYNGxT2+fnnn4sx9erVU3nvEgRBuH37tjB79mzh+vXrGuXs5uYm9v/pp59q1IaIiMjQsYBLRERE1U5FF3ABKCzeFsvJyVE4N+5ff/2ltE12drbg6Ogoxs6dO1dprGyB0tzcXDh79qzK88zKyhKaN28utpkzZ47KeE0dP35c7vxenfNXmVcLuEDJOV1fFRISIhevrPjz+PFjwczMTIzz9vZWWBQslpKSIjRs2FCuMKeogCcIFV/A/eWXX9T2f/LkSbnimaqioqbKUsAFIAQFBamML35DQ/ZrxIgRKtvMnDlTjHV0dFQZq00BV5PCsOy1BiCEhoaWOQdZ5V3AvXr1qlhgLy6YqzN//nwx3snJSeH48vPzE2NWr16tcT6aGjJkiNh/7969dd4/ERGRPnAOXCIiIqJyNnDgQPTr10/p65aWlnjrrbfkvjdo0CD06dNHaRsrKyu5NsrmAs3OzsaPP/4o7s+cORNdunRRma+1tTWWL18u7m/duhUFBQUq22ji1q1bcvvOzs5a9ePu7q724+jvvPMOrK2txX1l1+fnn3+WO7e1a9fCwsJCab/16tXDsmXLxP2UlBTs27dP09TLTefOnTFq1Ci1cU+ePBG3mzRpAlNT/c6oZmZmhpUrV6qMGTlyZIk23333ncZtnjx5ggcPHmifpAJ16tSRGweK9OjRA02bNhX3lY1BQ7VmzRoIggCg6Hf122+/Vdtm4cKFqFevHoCiqRIOHTpUIkZ2DLZo0UJH2f5Po0aNxO1X7zlERESVFQu4REREROVMk8Jahw4dSt2mY8eO4vaNGzcUxvz999949uyZuD9jxgy1/QLA4MGDUatWLQBFc/OWZh5KZWSLaHZ2drC0tNSqn9GjRyucW1OWlZUV2rdvL+4ruz6ycwd7enrC29tb7fGDgoJgZ2cn7ssuzKYvmowXAKhRo4a4nZCQoJNFngICAiAUfbIPgiCUahGzXr16wcnJSWVM27ZtYWJiIu737t0bjo6OKtu0a9dOro2yn7+2hg8fLjevsDKvv/56ueVQngRBEOftBoBJkyapfGOjmIWFBd5++21x/+TJkyViZMdgVFRUGTMtqUGDBuL2gwcPxCI0ERFRZcYCLhEREVE506Qo+OoCQqVtk5GRoTBGdvEhDw8PjZ96NTExkSsQnzt3TqN2qsjmWLNmTa37ee211zSKky3kKLo+EokEcXFx4n7fvn016tfc3By9evUS9w3hyUp1T1UX8/T0FLdfvHiBN998Ezdv3iynrNTTZJybm5vLFcy1aaPs90NbuhqDhuratWtyb/xo+rsByP98FN03ZMfgsmXLsHnzZp084V9M9t5SUFCAnJwcnfVNRESkLyzgEhEREZUzdU8LApD7uD+geEV4VW1evnypMObSpUviduvWrdX2qSyHhw8flqqtIrKFFG2fvgU0u56A+uvz8OFDuZzatm2rcQ7t2rUTt2/evKn3p/yaNWumUVzjxo3lpuYIDw9Hy5Yt4e/vj6+++goRERHIz88vrzRL0OZnqcnvxqttlP1+aEtXY9BQyd43gNLdO9TdNz744ANxu6CgABMmTICzszPef/99/Prrr2W+17z6ZHR2dnaZ+iMiIjIE+p30ioiIiKga0KZYWZYCp6z09HRxe+/evWqnHlBGF08PmpmZidsSiUTrfrS5NooKrLJPGAIQ5+7UhGysVCpFZmYmateuXeq8dMXGxkbj2M2bNyMgIAC3b98GUJT/mTNncObMGQBFH3H38/PDsGHDEBQUVKq+S6uifjd0XWA3hBzKk+x9Ayj5BpOmFN03fHx8sHTpUixYsED8XkpKCkJCQhASEgKgaG7cfv36YfTo0ejUqVOpjvnq07yy9x0iIqLKik/gEhEREVVhupjjFNDNU2yyRaDc3Nwy91dWrz4RKTs3pzqvFrSysrJ0kpO2SrMYWaNGjXD+/HnMmDFDYWEuOzsbf/31FyZMmIAmTZpg06ZNukyVKoHyvm989tlnOHbsWIm5v4slJiZizZo18PLyQr9+/Uq1CN2rUyZoW3wmIiIyJHwCl4iIiKgKky1K1qpVq1RPmcpycHAocy6yHztPT0+HIAhaPxGsC6/Ow1uaIvWrxd+yzOmrD3Z2dli9ejWWLl2KY8eO4cSJEzhz5gyuX78uF5eeno5Jkybh7t27WLZsmZ6ypYr26psZmk7RURp9+/ZF3759ER8fj2PHjiE8PByRkZF48eKFXFxoaCg6d+6M6OhouLq6qu03NTVV3K5bt26p3twgIiIyVPzXjIiIiKgKq1u3rrgdGBiI7du36y0X2eJLfn4+UlNTdVIY1pbsIleAfOFHHdlYY2Nj1KpVq0SMNsXpil5wycbGBsOHD8fw4cMBAE+ePMGRI0ewbds2cUoFAFi+fDmCgoLkFqAi/SrPsSJ73wCA69evl1sh1NPTE56enpg3bx4kEgmio6Px+++/Y+vWreIUDMnJyZg1axYOHDigtr9Hjx6J25oUfImIiCoDTqFAREREVIW1bNlS3E5KStJjJoCHh4fcfmJiop4yKdKwYUO5BY9eXbhJFdnY5s2bKyzWyj7FqOnTvcnJyRrnUB4cHR0xfvx4nD59Gj/88IP4fUEQsGvXLj1mVrUZ2liRvW8A8kXR8mRqagpfX1+sXLkSiYmJcounHT58uMTTuYrI3ldevecQERFVVizgEhEREVVh3bp1E7ejo6ORl5ent1xatWolNx/llStX9JYLUFQs6tixo7j/119/adSuoKAAJ0+eFPdfe+01hXG2trbitqYFsKioKI3iKsKUKVPknrhNSEjQXzJVXHmOFdlFvKRSqUZtOnXqJDctyD///KNRO12yt7eXm7ZDIpFo9KbP1atXxe3SLoBGRERkqFjAJSIiIqrC+vbtCxsbGwBFT/b9/PPPesvF2NgYvr6+4v758+f1lkuxAQMGiNvx8fGIiYlR22bPnj14+vSpwj5kubm5idua9JudnY29e/eqjatIsucgkUj0mEnVJnudo6Oj1cZHR0fjxo0bGvUtW4jNzMzUqI2pqSnefPNNcV/2aeyKJHtdAPVjMDExUZx2AZB/A4uIiKgyYwGXiIiIqAqztbXFxIkTxf358+eX+knK3NxcneXTt29fcTs8PFxn/Wpr3Lhxck8ozpgxA/n5+Urj09LSMG/ePHHfwcEBQ4cOVRgr+2RuYmKi3JyyisyfP1+uMFweHj58qPFTmIIgyD0lrWw+0fDwcBgZGYlfixcv1kWq1YrsWNm/fz+eP3+uNLagoADTp0/XuG/Zn9vz58/x8OFDjdp9+umn4tQg0dHRpV7EThAEhU/837t3T+M+Ll++LLfv4uKiMl72SWFHR0e0a9dO42MREREZMhZwiYiIiKq4BQsWiKvIZ2RkoGvXrti9e7faQt7Fixfx0UcfwdvbW2e5yD7Vd/PmTdy5c0dnfWujfv36+Oijj8T96OhoBAUFKSygPXz4EAMGDMCDBw/E73311VdyBWBZPXv2hL29vbg/fvx4hfMQ5+fnY/78+Vi9erVWC5+VxubNm9G6dWusX78eaWlpSuMKCwsxb948XLt2TfxeYGBgueZWnQUHB4vbT58+xXvvvaew+JmamorAwECcO3dO47Hi7u4u9xTu3LlzNXoS18PDQ+53Y/78+Zg6daraNxnS0tKwadMmeHh4IDIyssTrzZs3x9ixY/Hvv/9CEASl/SQkJGDOnDnivre3NxwdHVUe+/jx4+L2m2++We6/T0RERBWlfJYSJSIiIiKDUbt2bezbtw/du3fHs2fP8PTpUwQHB2P+/Pno06cPWrdujVq1aiEnJwdpaWm4cuUKoqOjxSfldLmSe5MmTeDj44OzZ88CAP744w98+OGHOutfG0uWLMHx48cRGxsr5tSiRQsEBwejTZs2kEgkiIuLw549e+QWURo8eDDGjx+vtF8zMzPMmzcPs2fPBlD0FG6bNm0wcuRItGvXDoIg4L///sO+fftw7949mJqaYt68efjiiy/K9Xz/++8/TJ06FTNnzsRrr70Gb29vuLq6wtbWFjk5Ofjvv//w559/4tatW2KbHj16oF+/fuWaV3Xm6emJQYMG4dChQwCAAwcOwN3dHSNGjECTJk3w8uVLxMbGYv/+/cjMzESDBg0wcOBA/Pjjj2r7Njc3x4gRI8TYnTt3Ys+ePWjcuLHc4mmBgYH4v//7P7m2X3/9NS5fvoy///4bALB+/Xps3boVffv2RefOnVGvXj0ARW8M3bx5E3FxcTh//jwKCwuV5iORSLBt2zZs27YNDRs2hK+vL9q3bw97e3uYmZkhJSUFkZGROHLkiDhlgpGREb755huV55mXl4ejR4+K+yNHjlR7bYiIiCoLFnCJiIiIqoH27dsjKioKgYGB4tyZt2/fxsaNG9W2NTEx0Wku48ePFwu4u3fv1nsB18LCAidOnED//v3FhaFSU1Oxdu1apW2GDh2KXbt2qe175syZCAsLw+HDhwEUfYRd0XyiZmZm2LhxI5o2bVruBdxiEokEERERiIiIUBnXpUsX/P777xWSU3W2ceNGXL16Fbdv3wZQ9Pu5dOnSEnH169fHwYMHxTGliWXLliEyMlKckqCgoKDEgmCyC9YVMzU1xeHDhzFt2jSxAJydnY39+/dj//79ao+r7t6RlJSEPXv2YM+ePUpjzM3NsXnzZrXz2R45cgRZWVkAihZM9PHxUZsfERFRZcEpFIiIiIiqCTc3N1y6dAnr168vsTjQq8zMzODv74/vv/9e49XuNTVixAjxyb3o6Gi5eVb1xc7ODmfOnMGaNWvg7OysNK5ly5bYtWsX9u3bB3Nzc7X9mpiYYN+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79u3o169fiQZvNWrUwNtvv40bN27gxx9/RI0aNUrsXlT6lEol1Go1vLy8ynopRETllqenJ9RqtdReiIiIiIiISKJXZauQFz6t1hhW51LlYFcLhWPHjqFjx44lvRYTbm5ueOGFF/DMM88gNja21O9PJUOj0db0y2R2//sBEdFDRy7X/n9hup+ZREREREREOqJeD1yZTIAgCAWbIxe2PxVQGObyg30Vm10JWlmEt/o8PDzQvHnzMl0DOR//JYiIyDL+jCQiIiIiIkuMNyrT9cFVqQuTWv5KUXmwBJKIiIiIiIiIiKgCEfVaKACAXK49UGv0qm2FwsIQFuBWbOUywL1161ZZL4GIiIiIiIiIiKhcKgxwtQGtoiDhU6tFg/YKVDk4JcD97LPPnDENACAhIQGRkZFOm4+IiIiIiIiIiKgyMa6o1a/A1RGEwhBXZBPcCs0pAe5bb72FxYsXF3seXXh77do1J6yKiIiIiIiIiIio8hE12kBWVpDQ6vaJ12hYgVsZOa2FwvPPP4/169cX+fr4+HhERkbi+vXrzloSERERERERERFRpWO8iZkuyNWIetW5AjcyqyycFuCq1Wo89dRT2L17t8PXxsXFITIyErGxsQAAuVzurGURUSUSGhoKQRAQGhpa1kshIiIiIiIiKjPGHRFkBQmuRmPaH5cqPqcEuEOGDAEA5OXlYfjw4Th27Jjd18bGxqJXr16Ii4uDKIpQKBRYuXKlM5ZFRA6IjIyEIAh2f8XFxZX1komIiIiIiIgeSrqQVtc6QZAqcM33umUL3IrNKQHuunXrEBYWBgDIysrCoEGDcOHCBZvXXb9+Hb169cKNGzcAAC4uLli1ahVGjx7tjGURERERERERERFVOiYVuELh87oNy2QC2Ai3klA4YxI3Nzds3boVEREROHPmDJKTk9G/f38cOnQIdevWNXvNtWvX0KtXLyQkJADQhrerV6/GiBEjnLEkIiqGjRs32hwTFBRUCishIiIiIiIiImOFPXC1CW3hJmaG4a4uv2UFbsXmlAAXAHx8fLBz50706NED165dQ2JiIvr27YtDhw6ZBD1Xr15F7969kZiYCABwdXXFb7/9huHDhztrOURUDI899lhZL4GIiIiIiIiILNBV2eoCWl2Qq7+JGVvgVh5O28QMAKpVq4a///4bNWvWhCAIiImJQf/+/ZGWliaNuXLlCnr16mUQ3q5Zs4bhLRERERERERERkR2kjcoKkr3CFgp6CS4Y4lYWTg1wAaBu3brYtWsXAgICIAgCzp49i6FDhyI3NxeXLl1Cr169cOvWLQDa8Hbt2rWs9iMiJCQkYMaMGWjSpAk8PT1RtWpVdOnSBV9//TVycnIcmuenn37CmDFj0Lx5c/j4+MDFxUWa780338TNmzctXv/aa69JG7WtWrXKrns+88wz0jU7duywe636VCoV/v77b7z++uuIiIhAzZo14erqCi8vL4SGhmLUqFH4448/oNFozF6fmpoKd3d3CIKABg0a2HXPu3fvwsXFBYIgoGXLlhbHpaWl4euvv8YjjzyC4OBguLm5oUqVKujQoQPefPNN6R/kLJk4caLJ5nd//vknHn/8cdStWxdubm4mG+OJoojDhw/j3XffRd++fVGrVi24u7vDw8MDtWrVwrBhw7B06VLk5+fb9VoBYMuWLRg2bBhq1qwJd3d31KlTByNHjsSePXsAAPv375fW+f7779s13/jx49GwYUP4+PjA09MT9erVw1NPPYXdu3fbvS4iIiIiIiJHSQFuwWODFgowPEeVgFhCoqOjRW9vb1Emk4kymUzs3bu3WLNmTVEQBFEQBNHd3V3cvHlzSd2eSlhaWpoIQExLS3P42pycHPHChQtiTk5OCayMiioiIkKE9ud8qd9769atoo+Pj3R/46+WLVuK8fHxYt26dUUAYt26dc3Os2/fPlEQBIvz6L5cXV3FxYsXm53j6tWr0hzh4eE2156SkiJ6eHiIAMTQ0FBRrVYX6T3o1auXzXUDELt37y7euXPH7BwjR46Uxh08eNDmPb/55htp/Oeff252zNq1a8UqVapYXZO7u7u4bNkyi/eZMGGCNPby5cviiBEjzM4TGxsrXfPMM8/Y9X40bdpUvHLlitXXqVQqxXHjxlmdZ9asWeK+ffukx++9957F+eLj48Vu3brZXNuIESPErKwsq2uzhj8riYiIiIjIkpibeeLhM1nizbv5oiiKYsK9fPHwmSzxSnye+N/1XPHwmSzxXrJSvHlX+/zV+LwyXjGZY2++5rQeuMa6dOmC9evXY9iwYVAqldi/f7/Un8PNzQ1//PEHBg8eXFK3J6JiGDJkCE6dOoX79+/Dy8sLwcHB6N69O8aOHYtevXo59V7R0dF4/PHHpUrKjh07YuzYsQgODsatW7ewevVqnDhxAqNGjYJSqbQ6V25uLkRRRJMmTdCrVy80b94cVatWhUKhwJ07d3DgwAH8+eefyM/Px5QpU1C9enUMGTLEYI6GDRuib9++2LVrFw4cOIDLly+jSZMmFu+5YsUKqUJ4ypQpkMmK9sGG7OxseHl5ITIyEh06dEC9evXg4+ODrKwsXLx4EevWrcO1a9dw5MgRDB8+HAcOHIBCYfgjfMKECfjjjz+kdfXs2dPqPZcvXw4AkMlkeOqpp0zO//zzz5g6dSpEUYSrqyseffRRhIeHo3r16sjMzMShQ4ewevVq5ObmYuLEiXB1dcWTTz5p9Z6vvvoqtm/fjrp162L8+PFo2rQpcnNzcezYMbi5uRm8H66urujZsye6dOmChg0bwtfXF3l5eYiJicGGDRtw9uxZXLp0CQMHDsS///4LX19fs/ecNm2aVE2tUCjw1FNPISIiAm5ubjh79iyWLFmCr7/+2mplts7NmzfRpUsX3L59GwDQrl07PPbYY2jYsCFkMhkuX76M5cuX4/r161i/fj2ysrKwbds2qR8VERERERGRM2h0FbgFv2rICg5EUW/DMoFVuJVGSSfJa9euFeVyuSiTyaTK27/++qukb0slrLQqcDUajZidq35ovzQaTVH+eopEvwLX2lfv3r3FW7duOeWeKpVKbNasmTT3jBkzTCpY1Wq1OGPGDIM1WKrAjYuLE0+fPm31nqdOnRKDgoJEAGKjRo3MvscbN26U7jVz5kyr87Vs2VIEICoUCvH27dvWX7AVf//9t9VqTaVSKU6fPl1a14oVK8yO0b02f39/MTc31+J858+fl+bq27evyfkzZ86Irq6u0vt08eJFs/NcuHBBDA4OFgGIPj4+YlJSkskY/QpcAOJjjz1m83//UVFRYnJyssXzGo1G/PTTT6U5P/zwQ7Pj9u7dK43x9fUVo6OjTcbcv39fbNOmjcEazVXgajQaqfJWLpeLixYtMnvP3NxcccyYMdJcP//8s9XXagkrcImIiIiIyJLLN7QVuIn3laIoiuKdJKV4+EyWeCE2VzwXo63AvZ+iNKjMpfLH3nxNEEUply8xuioud3d3bNy4Ef379y/pW1IJS09Ph5+fH9LS0ixWvVmSm5uL2NhY1KtXD+7u7lbH5uRpMHhGQnGWWqH99U0teLg5vVW1WZGRkTh79iz69u2Ljh07IiQkBHK5HImJidizZw+2b98uVdHXrVsX0dHRqFGjRrHuuXnzZjz66KMAgM6dOyM6OtpspaIoiujatSuOHTsm3V+/X6qjlixZgsmTJwMADh06hB49ehicV6vVCA0NRUJCAgIDA5GYmGhQHapz5MgR6doRI0ZI1a8lRaVSoVGjRoiLi8MjjzyCv//+22TMq6++iu+++w4AsHbtWowaNcrsXLNnz8bnn38OQFuta1yB+/jjj2Pjxo1wd3fHuXPn0LBhQ4vr+vvvv9GvXz8AwGeffYY33njD4PzEiRPx66+/AgBCQkJw6dIleHt72/mqrQsLC8OhQ4fQsGFDXL161eT8sGHDsGXLFgDAwoUL8dxzz5md59KlS2jVqhVUKhUA4L333jPpg6v//frhhx/i7bfftriu/Px8NGnSBHFxcWjatCkuXrzo8Gtz5GclERERERE9XK7E5+NBqgr1gl1Rs6oC91NUuHozH/4+cmg0QHqWGk3quiE3X4Mbt5WoFqBAo9quZb1sMmJvvmZXMiSXy4v1NW3aNABAXl4eBg0aZHO88ceCiajkffrpp7hz5w5+//13vPbaaxg7diyeeOIJzJw5E3/99ReOHTuGOnXqAABu3LiBSZMmFfueGzZskI5nzJhh8WPmgiBg1qxZxb6fjn5rgejoaJPzcrlcCvqSkpKwfv16s/MsWrRIOp46darT1meJQqFA165dAQDHjh2DuX9/mzBhgnS8YsUKs/NoNBqppYC3tzcef/xxg/OpqanYtGkTAGD48OFWw1sA6Nu3L2rWrAkA2Llzp9WxkyZNclp4CxT+XcbExCApKcngXG5urrSpnL+/PyZOnGhxnqZNm2LgwIFW76ULod3c3PDyyy9bHavfTuLSpUuIj4+3Op6IiIiIiMgRmoIeCrpfo3V/ajSQflcUAOn37JIv36SSZFdSKooiBEEwGxbYq/Abht8xZD93VwF/fVOrrJdRZtxdS69bTbdu3aye79ixI3bs2IF27dohLy8P27dvx/Hjx9GpU6ci31NXUQsAffr0sTrW1nl9p0+fxsqVK/HPP//g6tWrSE9PR15entmxCQnmK7wnT56MDz/8EEqlEosWLcLYsWMNzqempmLt2rUAgAYNGuCRRx6xe32WZGdn4/fff8eWLVtw7tw53L17F5mZmWZ/bqanp0v/UqevXbt2aNmyJc6fP48dO3bg/v37qFatmsGYffv2Sa97xIgR8PT0NDh/+PBhaDQaANqw8s8//7S5dh8fH9y+fRsXLlywOi4sLMzmXDoqlQobNmzAn3/+idOnT+PWrVvIyMiQ1mZMVzGtc+bMGalvco8ePeDqav1fm3v16iVV65pz4MABAED16tWxd+9em+tPSUmRji9cuCD9AwgREREREVFxSW1udT1wZdoDjf7vj2yAW2nYXepa3OCVwS0VhSAI8HDjT5zyolmzZnj66aexePFiAMDWrVsNAtzs7Gzs2rXL4vWenp7SR+0B4NatWwAAX19fk5DRWGBgIPz9/ZGammpxjEqlwvTp0/Hzzz/b/TMnPT3d7PM1a9bEY489hnXr1iEqKgpXrlxB48aNpfPGm5cVd5OqI0eOYMyYMXZtpKW/duMAFwDGjx+P119/HUqlEr/99ptJtah+Ze748eNNrtdvT7Fs2TIsW7bM7jUlJydbPV+rln3/IHP58mU8/vjjNgNhfcZ/l7rvL0AbsttSv359i+eysrLw4MEDAEB8fDyGDx9u97oA2+8LERERERGRQ3SbmBU8LMhvIWoKn+ReypWHXQHuvn37SnodRFRB9OrVSwpwjft63rt3z2qwZdy7NjMzEwBMKkAt8fLyshrgvvLKK1JbAxcXFwwYMACdO3dGrVq14OXlBRcXF2mdupYHarXa4nzPP/881q1bB0DbLuGrr76Szunf55lnnrFr/ZbExsaif//+0vvRsGFDDBgwAI0bN0bVqlXh7u4uBcTff/+99DPZ0tqfeuopvPnmm1Cr1VixYoVBgJudnS21hKhTpw569eplcr2199gWXcWrJR4eHjbnSEtLQ+/evaUANjg4GIMHD0azZs1QvXp1uLu7QybTdgBas2YNfv/9dwCm70dWVpZ0bM/3mJeXl8VzxXlPAG1PXCIiIiIiImcxrsCVWiiIhf1SBbAIt7KwK8CNiIgo6XUQUQWhXylb3FDL29sbaWlpyM7Otmu8fiBn7ObNm1iwYAEA7UZZ+/btQ6NGjcyO/e+//+y6X69evdC0aVNcunQJv/76Kz755BO4urriyJEjOH/+PABtj9igoCC75rPkk08+kcLbN954A59++qnFil5d71pratasiUceeQQ7d+7EiRMncPHiRTRr1gwAsHHjRuleTz31lNn76Peo/f777/HSSy85/JqK48cff5TC23HjxmHp0qUW2x8cPnzY4jz6gaw932PWvr/035P27dvj5MmTNucjIiIiIiJyNqVKRGqmGumZ2tZypi0UTENdgJ+Mr+hKZ3t7Iqo0dB8jB7QbQ+kLDQ2FKIoWv/SrbwFt0ApoP/p+//59q/dNSkqyGhjv3r1b6o06e/Zsi+EtoK14tdfzzz8PQPu6dZWrCxculM47Y/MyXduJoKAgfPzxx1bbMdi7dv3WCMuXLzd7bK59AmDY5sCRlg7Oons/FAoFfvjhB6u9a629H8HBwdLxtWvXbN73+vXrFs/5+flJIa6lvslEREREREQl7XpiPq7G50u9bnW/P8qkTcxEgw3L2EahcmCAS0QO0W+p0qRJk2LN1blzZ+nY1qZQe/bssXr+zp070nHDhg2tjt2+fbsdq9MaP3689PH7RYsWITU1VWqr0KhRI7MtCBylW3u9evUgl8stjrt9+zbOnDlj15zDhw+Hr68vAG3VriiKuH37tvQ+du7c2eLfX1hYmPT/BOzYscPu1+EsuvcjMDAQAQEBFsfl5uZi//79Fs+3adNGaptx5MgRm20MbLUL0n0a5d69e6zAJSIiIiKiMpGvNKykNemBK5q2V9A9TxUXA1wistuVK1cMNsAaMmRIsebT75f77bffWvxIhyiK+Oabb6zOpf9x+ZiYGIvjrl+/jl9//dXuNfr7++PJJ58EAOzfvx/vvPOOtHnZc889V+zNy4DCtV+7ds3qx1o++OADqFQqu+b08PDAyJEjAWiraPft24fVq1dLfWItVd8C2krggQMHAgDOnTuH3377za57Oovu/bh3757FTeYA4LvvvkNSUpLF8+7u7ujfvz8AICUlxepmbJcuXbIZ7E+YMEE6fvvtt/kRJCIiIiIiKnXGv4UIMsM/NWLhIGf8vkrlAwNcIsL333+PI0eOWB1z6tQp9O/fH7m5uQCAfv36oUuXLsW6r25jKgCIjo7G66+/bhKKiaKI119/HdHR0Vbn6tSpk3T81VdfmQ324uPjMXToUKu9Ts154YUXpOMff/wRAODq6oqJEyc6NI8lurU/ePAAX3/9tdkxX3/9tdTj117GbRR07RNcXV0xZswYq9d+/PHHUuuCyZMn2wxxk5OTMXfuXOzevduhNZqjez9EUcT//vc/s2N+++03vPPOOzbnmjFjhnT82muv4ejRoyZjHjx4gDFjxtgMx0eOHCl9z+/YsQPjx4+X+gmbo1arsWPHDnz00Uc210lERERERGQP4zqSwgrcgh64GrGwAhdsoVBZ2LWJGRFVbnv37sUrr7yCBg0a4JFHHkHLli0RGBgIuVyOW7duYc+ePdi2bZvUY7Zu3br45Zdfin1fuVyOpUuXIiIiAvn5+fjqq68QFRWFsWPHIjg4GLdu3cLq1atx/PhxdOnSBTdv3pQ2tzLWrVs3dOnSBUePHsWNGzfQtGlTPPfcc2jWrBnUajWio6OxYsUKZGVlYeLEiVarMY21b98enTt3xrFjx6TnRowYgapVqxb3LQAAvPrqq1Lf19deew379u3DgAEDUL16dcTHx2Pt2rU4fvw4atasiVatWkljbQkPD0doaCji4uLw22+/SS0EBg0ahMDAQKvXtm3bFgsXLsSzzz6L7OxsjB07Fl988QWGDh2KRo0awcPDA2lpaYiJicGxY8dw4MABqFQqgwrtonrxxRexdOlSqFQq/Pjjj/j3338xcuRIhISE4O7du9i0aRP27NkDb29vDBs2TOpNbE7v3r3x7LPPYsmSJUhPT0dYWBieeuophIeHw83NDWfPnsWSJUtw//59jB49GmvXrgUAyGSm/74pCALWr1+Pbt264ebNm1i5ciX++usvjBo1Ch06dECVKlWQm5uLW7du4cyZM/j7779x//599OnTB2+//Xax3xciIiIiIiJjhZuYFT6n0ehKcAuf4+cHKzanB7gajQabNm3Czp07cfLkSSQmJiItLU2q2rOHIAh2f0yYiJzn2rVrNjd76t+/P5YuXWqwQVRxdO3aFevXr8fYsWORkZGB48eP4/jx4wZjWrVqhXXr1iEsLMzqXGvWrEHv3r0RGxuLBw8e4JNPPjEZ89JLL2HGjBkOBbiAdjMz/QDXGZuX6QwcOBDvv/8+3n//fQDAtm3bsG3bNoMxdevWxYYNG/D999/bPa8gCHj66afx4YcfGvR/tdY+Qd/EiRNRs2ZNTJo0Cbdu3cLp06dx+vRpi+Pd3NycEmq3atUKCxcuxNSpU6FSqXDkyBGTCvHAwECsXr0aR44csRrgAsCCBQuQk5OD1atXQ6lU4pdffjH5B4hZs2ZhwIABUoDr4+Njdq6QkBCcOHECEydOxPbt25GSkoJFixZZvb/+pnBERERERETFYiGJ1a+0VWsKniv51VApcWqAu3XrVrzwwgtITEyUnmOPQKLy7+uvv8bQoUNx9OhRnDlzBvfu3cODBw+Ql5cHPz8/hIaGolu3bhg3blyx2yaYM2TIEFy4cAFfffUVtm3bhps3b8LT0xMNGjTAE088geeff17aSMya0NBQnDp1Ct9++y02bNgg9cKtUaMGunfvjmeffRaRkZGIi4tzeI39+vWTjps2bSptaOUs7733HsLDw/H999/jn3/+QXJyMvz9/VG/fn0MHz4cU6dOhb+/v8Pzjh8/Hh9++KH0ODAwEIMHD7b7+v79++P69etYvXo1tm3bhpMnT+L+/fvIzc2Fj48PQkND0aZNG/Tu3RtDhw4t0hrNmTRpEtq2bYu5c+ciKioKd+/ehY+PD+rUqYOhQ4di2rRpCA4Ottn6AwAUCgVWrVqFMWPGYNGiRTh27BhSU1MRFBSELl264Pnnn0efPn3w+++/S9dUqVLF4nxBQUHYtm0boqOjsWrVKhw6dAg3b95Eamoq3N3dUaNGDTRr1gw9e/bEkCFD0KJFC6e8J0RERERERMYpm6xg9zKZXlqrq8AVBL0Ql/FchSaITkpYv/vuO8ycOROAaWira5ps7lb6DZV15wVBkDbaofIpPT0dfn5+SEtLk3a6t1dubi5iY2NRr149uLu7l9AKiZzr22+/lfqpzp0716C3KlUOs2bNwty5cwFoez63bdu2TNfDn5VERERERGTs1OVc5ORppMetG7rD21PbPyH6XA40etlbu8buyMjWICYhH/4+cjSv51bq6yXr7M3XnFKBe+TIEcyaNUsKYF1dXTFu3Dj06dMHQUFB6NevHwRBQEhICFasWIHU1FTExsZi37592L59O9RqNeRyOd555x2nV7URERWXKIrSBmIeHh6YMGFCGa+InC0tLU3q31utWjW0bNmyjFdERERERERkyqQ0Uq/yVpABUBue4yZmlYNTAtx3330XGo0GgiCgTp062L59O5o2bWoyzsPDA5GRkdLjGTNm4Pr165g0aRIOHDiAjz76CCEhIXj22WedsSwiIqdYuXIlLl++DEDbksDax+up/ImNjYVMJkPdunXNnk9NTcXo0aNx//59AMDkyZOhUHCPTyIiIiIiKv/0WyfIBMP8FgAb4VYSxf4NNSkpCfv27ZNaIaxdu9ZseGtJ/fr1sXv3bgwbNgw7duzA9OnT0aZNG3Ts2LG4SyMiKpLk5GQcO3YM+fn5OH78uPSxeg8PD/zvf/8r49WRo06ePIkxY8agR48eiIiIQMOGDeHl5YWUlBScPHkSa9asQWpqKgCgYcOG/DsmIiIiIqLyy6gEVz+f1fbDLRygX33LLaoqtmIHuIcPH4YoihAEAb169UKnTp0cX4RCgWXLlqFRo0bIzMzErFmzEBUVVdylEREVydmzZzFw4ECT5+fOnYvatWuXwYqouNRqNQ4cOIADBw5YHNOuXTts2rQJXl5epbgyIiIiIiIi+1lroSATLJ6iCk5W3AkSExOlY/32CObk5uZaPBcUFIThw4dDFEUcOnQI8fHxxV0aEVGxBQYGIjw8HH/99RemTZtW1suhIujfvz+WLFmCMWPGoFWrVqhZsybc3Nzg6emJ0NBQjBo1CmvWrMGJEycY0BMRERERUbkmGpXS6oe2CrnhWEEQpBCXFbgVW7ErcFNSUqTj4OBgs2Pc3d2Rm5uLzMxMq3N16NABy5cvBwAcO3YMderUKe7yiIgcFhkZafIfRaq4fHx8MGnSJEyaNKmsl0JERERERFQsxr+qCnp9EuRy1txWVsWuwHV3d5eONRqN2TG+vr4AtLt8W6vC1Y0DgNu3bxd3aURERERERERERA8FhVGAKwiGAS9VXMUOcIOCgqTjBw8emB2jv/P32bNnLc6l344hOzu7uEsjIiIiIiIiIiKqNEwrcAuP5TLL56hiK3aA27RpU+n4zJkzZse0adNGOt66davFuTZv3iwd6wfDREREREREREREZMggwDWuwAX0euCyTWBFVuwAt3Xr1lIbhUOHDpkdM2zYMADab5YffvgBFy9eNBkzd+5cHDt2THrctWvX4i6NiIiIiIiIiIio0tCPYV1dBIOqW+NNzMAK3Eqj2JuYubq6okePHtizZw9u3bqFAwcOIDw83GDMgAEDUK9ePcTFxSEtLQ2dO3fG+PHj0aZNG+Tm5uKvv/7C7t27pb4cnTt3RrNmzYq7NCIiIiIiIiIiokpDV0jbrrE73FwFgx63Jj1wpf9jGPxSxVPsABcARo0ahT179kAURXzxxRcmAa5CocC8efMwZMgQAEBWVhYWLFhgMo8oinB3d8e8efOcsSwiIiIiIiIiIqLKoyCJlckAmcwwsGUP3MrLKQHuE088AaVSCUC7u11+fj5cXV0NxgwYMAArVqzAtGnTkJGRYbb3RpUqVbBmzRq0b9/eGcsiIiIiIiIiIiKqdAQz6axJD1xBkEJctsCt2JwS4Pr5+WH69Ok2xz355JPo3bs3fvnlF+zduxe3bt2CKIqoXbs2+vbti8mTJ8PPz88ZSyIiIiIiIiIiIqpUrOWw+j1wzQW8VHE5JcB1RPXq1TF79mzMnj27tG9NRERERERERERUYZn7RLuOXK+lgmD0J5vgVmwy20OIiIiIiIiIiIioLOmHt+YKbPUrcKlyYYBLRERERERERERUzukX35oLcPV74IpG41iAW7ExwCUiIiIiIiIiIqpAzHW4lcsKQ1w3V/bArUxKrAfumTNnsGfPHhw/fhwJCQlITU1FdnY2QkNDsWfPHoOxarUaiYmJAACZTIZatWqV1LKIiIiIiIiIiIgqHFtVtIIgoHVDN2TmaODraVizaaV1LlUATg9wd+3ahXfeeQcnTpwweF7Xp0MuN23IIQgCIiIiEB8fD0EQcOHCBTRu3NjZSyMiIiIiIiIiIqqY9ENYCwW2Hm4yeLjxA/eVjdP+RkVRxPPPP4+BAwfixIkTEEXR4Esw15xDtwiZDC+++KI09tdff3XWsoioDERGRkIQBKv/uy+vJk6cKK09Li6urJfjsIr83juDUqlEs2bNIAgCZs2a5fT5z507B7lcDkEQsH//fqfPT0REREREZIkd+a0J9sCtHJwW4E6cOBGLFi2SQlgXFxcMGjQI77//PubPn28zxB0zZox0vHXrVmcti4gcdPr0acycORNdu3ZFtWrV4OrqCg8PD9SsWRPdunXD5MmTsXDhQly7dq2sl0pk4ptvvsGlS5fg7++Pt99+2+nzt2rVCuPHjwcAvPjii1Cr1U6/BxERERERkTm2NjGjysspLRTWrl2LFStWSAHt2LFjMXfuXAQFBUljnn/+eatzhISEoE2bNjhz5gzOnz+PpKQkBAYGOmN5RGSHrKwsTJs2DStXrjQ5p1QqcefOHdy5cwfR0dFYsmQJAGDbtm0YOHBgaS+VyKzk5GR88sknAICXXnoJAQEBJXKft956C8uXL8d///2HZcuW4dlnny2R+xAREREREekzDHDtS3AFluBWCk4JcPWrnGbMmIGvv/66SPN06NABZ86cAaD9mGpkZKQzlkdENqhUKgwcOBAHDx4EACgUCgwZMgRhYWEIDg6GIAh48OABzp07hwMHDuDixYsAYLH6kB8tp7Lw1VdfIS0tDe7u7njllVdK7D6NGjXC448/jj/++AMffPABxo8fDxcXlxK7HxERERERET3cih3gnjt3DjExMRAEAfXr18fnn39e5LmaNGkiHcfExDDAJSol8+fPl8Lb0NBQbNu2Dc2aNbM4/urVq1i8eDH8/f1LaYVE1mVmZmL+/PkAgFGjRpX4JzimTZuGP/74A/Hx8Vi7di3GjRtXovcjIiIiIiLScWTPE91IFuBWbMXugXvq1CnpeNSoUVAoip4J63/cNTU1tTjLIiIHrFixQjqeP3++1fAW0FYgfv755+jZs2dJL43ILqtWrZL+uzFhwoQSv1/v3r1Rq1YtAMCPP/5Y4vcjIiIiIiISC3oosP3tw6fYAe7du3el44YNGxZrLrlcLh2rVKpizUVE9rt06ZJ0HBERUez5IiMjIQiCxX8VXLZsmXR+2bJlAIArV67gpZdeQuPGjeHp6Ql/f39069YN3333HfLz8+2677lz5zB58mTUq1cP7u7uqFGjBiIjI7F06VJoNBoAkO7rjAr/3NxcLFy4EEOGDEHt2rXh7u4OPz8/tGzZEi+//DKuXLlS7HvoS0lJwTvvvINWrVrB29sb/v7+aNu2LT744AMkJSXZPc+DBw/wyy+/YMKECWjbti38/f3h4uKCKlWqoG3btnjllVekNhnmzJs3T3ofP/74Y7vuOWfOHOmaBQsWmJy/du0aXn/9dXTq1AkBAQHSeho1aoTw8HDMnDkTBw4csDi/7vsoMDDQrr/bgwcPYtKkSWjWrBl8fHzg4uKCoKAgNG/eHAMGDMCHH35o9e9PEAQ8/vjjAIDo6GhcvnzZ5j2JiIiIiIiKQ6qidSTB1bXAFVmDW6GJxfTtt9+KgiCIMplM/OmnnyyO041p1KiRxTFfffWVNG7RokXFXRqVoLS0NBGAmJaW5vC1OTk54oULF8ScnJwSWBkVhYeHhwjtfwvE2NjYYs8XEREhzWfOL7/8Ip3/5ZdfxOXLlxuswfirW7duNr/XvvvuO1GhUFico3fv3tL3LQAxIiLC7DwTJkyw673Yv3+/GBISYvF+AES5XC5+8skn9r5tVh07dkysXr26xXvVrl1bPHPmjM33/tq1a1bfJ92XIAjiBx98YHaOtLQ00cvLSwQghoaGihqNxuraVSqVWKtWLRGA6O3tLaanpxucX7Jkiejm5mZzTV5eXmbnv3XrljRm2LBhVteiVqvFqVOn2rwXAHHw4MFW59qwYYM09tNPP7U6tij4s5KIiIiIiPRl56rFw2eyxOjz2XZfk5Gtveb4BfuvodJjb75W7B641atXl471q/iK4t9//5WOg4ODizUXEdmvYcOGOHfuHADg22+/xbfffltq996xYwf++OMPeHp6Yvr06ejUqRPc3Nxw+vRpLFiwAGlpafjnn3/wf//3f1i0aJHZOVauXGmwaVXfvn0xfPhwBAYGIi4uDsuXL8fevXsxZcoUp6x5+/btePTRR6FUKiGTyTBgwAA88sgjCAkJQW5uLk6cOIHly5cjLS0Nb731FgDgzTffLPL9YmNj0a9fP6lFQJMmTTBx4kTUq1cPDx48wIYNG7B371489thj8PPzszpXfn4+VCoV6tSpgz59+qBVq1aoXr06XF1dcf/+fURHR2PdunXIycnBu+++i8DAQLzwwgsGc/j6+mLcuHFYtGgR4uLisGvXLvTv39/iPbdt24aEhAQAwJNPPgkfHx/p3KlTp/Dcc89BrVZDLpejf//+6Nu3L4KCgiCTyXDv3j2cOXMGf//9N5KTk83Ov3PnTum4a9euVl//jz/+iIULFwIAfHx8MHLkSHTo0AHVqlVDfn4+EhIScOLECezevdvqPMb32rFjB2bPnm3zGiIiIiIievjkK0WkZKjh5iLA30du+wIbilCACxbgVnDFTYqvXLkiVc3WqVNHVCqVZsfZqsDNysoSq1SpIgqCICoUCjE1NbW4S6MSxArcyuXTTz81qDwcOHCg+Mcff4hJSUlFms+RClwAYosWLcSEhASTcRcvXhS9vb1FAKKLi4t4584dkzFJSUliQECANNe8efNMxiiVSoPKWhSjAvfWrVtilSpVRABiUFCQ+M8//5idJyEhQWzZsqVUiXvx4kWz4+zRr18/aU2jR48W8/LyTMbMnTvXpILUnKSkJPHgwYNW7xcbGys2btxYBCD6+fmJGRkZJmNOnTol3efxxx+3Ot+QIUOksSdOnDA4N336dOnc5s2bLc6h0WjEqKgos+emTZsmzbFnzx6ra2nRooUIQAwICBDj4uIsjsvJyRGjo6OtziWKoli3bl0RgOjp6SmqVCqb4x3Bn5VERERERJXD5Rt54uEzWeLhM1lido66yPNk52iraY86UIGbWVCBe+w/VuCWR/bma8XugduoUSM0atQIoigiISEBn3/+eZHm+eqrr5CSkgJBENCjRw+bVWT08FBrxIf2q7S8+uqr6N69u/R4+/btGDlyJAIDA1G/fn2MHDkSX375JY4dO+b0vjkKhQIbNmxASEiIybmmTZti+vTpAAClUmm2KnLZsmVISUkBoK3uNK4W1d1j4cKFaNCgQbHX++WXX0qVoH/88YfFis+QkBCsW7cOcrkcarUa3333XZHud/bsWezatQsAUKdOHSxbtgyurq4m42bMmIGRI0fanK9KlSo2N58LDQ3FTz/9BABIS0vDpk2bTMa0bdsW3bp1AwBs3rwZd+7cMTvXzZs3sX37dgBAhw4d0KFDB4PzMTExAIBq1aph6NChFtckCALCw8PNntNVjwPa7xlrdPd75JFHULduXYvj3N3d0aVLF6tzAZA2/MvOzsa1a9dsjiciIiIiooePSl34e3S+qui/U+uutLDdjFmOjKXyq9gtFADg9ddflz6a/N5778Hb29vg48y2/P777/jwww+lx8X5qDFVLmqNiKPnc8p6GWWmS0sPyGUl/9PW3d0de/bswTvvvIOffvoJ2dnZ0rnY2FjExsZi/fr1AIC6detixowZeOGFF+Di4lLsew8ZMgSNGze2eL5v377SPwydP3/e5Pyff/4pHc+YMcPiPG5ubnjhhRcwa9asIq9VFEUsX74cANCtWzeEhYVZHd+0aVN07twZ//zzj8HH/B2xYcMG6fj555+Hh4eHxbGvvfYa/vjjjyLdx5h+yBsdHY1x48aZjHn++efxzz//QKVSYenSpVK7CH1LliyBWq0GAEydOtXkvJeXFwAgKSkJcXFxCA0NdXitcXFx0nGVKlWsjvXy8kJeXh7OnTuH/Px8s2G4I/TvFxcXZ/V7mYiIiIiIHk76dVDFqdViG4SHV7ErcAFg0qRJ6NKlC0RRhEajwcyZM9G1a1f89ttvUmWcMbVajcOHD+PJJ5/E2LFjoVarIQgChg0bZrWXIhGVDHd3d3z55ZdISEjA0qVL8eSTT6JevXom427cuIFXX30VYWFhSEpKKvZ9dVWcltSqVUs6Nv55otFocPLkSQDafqYdO3a0OlevXr2KuEqtCxcuSK85ICAAf/75p80vuVzb3yg2Nha5ubkO3/PYsWPScZ8+fayO7dSpk0F/WWtiYmLw7rvvIjIyEjVr1oSnpycEQZC+3N3dpbG6/rXGRo0ahcDAQADA4sWLTaqz1Wo1lixZAkD79/Pkk0+azNGvXz8A2r/LyMhILFy4EHfv3rXrNejoKqI9PT0N1m2O7n6XLl1Cnz59sHHjRoN/sHCU7vXrr4OIiIiIiMgSUVOMawv+dKgCV3ctw98KzSkVuIIgYOvWrejRoweuXLkCADh+/DieeuopANqPxurEx8ejbt26uH//PvLy8gBA+qW/TZs2WLVqlTOWRJWEXCagS0vLFYeVXWlU3xoLCAjAM888g2eeeQaANjQ9ceIE9u3bh1WrViE+Ph4AcPToUYwdO7bIlaU6VatWtXrezc1NOjYOQNPS0qTwrV69ehBs/Fesfv36RVylln6l57Zt27Bt2zaHrk9OTnZ4g8Zbt25Jxw0bNrQ6VhAENGjQAKdPn7Y67v3338fHH38MlUpl1xrS09PNPu/u7o5Jkybhyy+/RGxsLP7++28pIAUMNy8bN24cvL29TeaYNGkS1q1bhz179uDGjRuYNm0apk2bhqZNm6J79+4IDw/H4MGDrX6f6P5bYk94/fnnn+PQoUNISEjAoUOHcOjQIbi4uKB9+/bo3r07IiMj0a9fP5tBsI6vr690nJPz8H5agIiIiIiILNMPT9XFSVILLrX1uy9VPk6pwAW0VUhHjhzB8OHDIYqiFMqKooh79+5J31xKpRI3b96UghjduBEjRiAqKgqenp7OWhJVEnKZ8NB+lQcBAQHo27cvPvnkE8TExBj0mN21axcOHz5crPllsqL/GMrKypKO7fnZofu4flGlpqYW6/r8/HyHr8nMzJSOnfEav/zyS8yZMwcqlQoymQx9+vTBu+++i8WLF+P333/Hxo0bpS8dXQsEc6ZOnSr9fF+0aJHBuYULFxqMM8fFxQXbt2/HN998Y9Cj+NKlS1i6dCkmTpyImjVrYty4cbh9+7bZOXQhv6WgWV+dOnVw6tQpvPrqq1L7A6VSiaNHj+Kbb77Bo48+iurVq+Pdd9+VgmFr0tLSpGNr7S2IiIiIiIgAJ1XgOnKRYHgtVUxOC3ABbS/A9evXY9u2bQZVWACkUFf/I7aiKKJnz57YunUr1q1bZ/dHf4mobLi4uOC7775D8+bNpef+/vvvMluPflhpz8fg9QPfotCvIJ05c6bBzzV7vorS31X/nsV9jbm5ufjggw+kef/55x/s3r0bc+bMwbPPPovRo0fjsccew2OPPYa+ffvatb4GDRpIP+83b94stT+4efMmduzYAQDo3Lkz2rZta3EOFxcXvPrqq4iJicGlS5ewZMkSPPvss1LFtEqlwurVq9GxY0eDimQdXRuDnJwcu9pUVK1aFd988w3u3r0rBbejRo2SAt309HR8+OGHGDRoEDQa6//flX4bEVv9d4mIiIiIiIrXA5cx7MPKqQGuzoABA7Bjxw4kJSVh69at+OSTTzBr1ixMnjwZL7/8MubMmYM1a9bg9u3bOHDgAAYNGlQSyyCiEqBQKBAZGSk9tlQVWRr8/PykqtTY2Fib/zG7fv16se6n34/35s2bxZrLXiEhIdJxTEyM1bGiKFp9jf/8849U0Tt16lR07tzZ4tjY2Fi716irylYqlVi6dCkAbU9ca5uXWdKkSRNMmjQJixcvxrVr13D06FG0atUKgLadxKeffmpyjX6vZkf60CoUCnTu3Bmvvvoq1q5di3v37mHdunXw8/MDAOzdu9egEtkc/fsVJaAnIiIiIqKHS3ECXIkjPXB1FbjMfiu0Eglwdfz9/TFo0CDMnj0bX375JRYtWoRvv/0W77zzDkaPHo3q1auX5O2JqIS4uLhIx+b6mpYWmUyGDh06AAAyMjJw4sQJq+P37dtXrPu1bdtWCvf27dtn10fsi0s/ZN27d6/VscePH7faRuDOnTvSsa1+utu3b7dzhcDgwYNRu3ZtANrgVj/I9fPzw5gxY+yey1jnzp2xfPly6fHBgwdNxugCXgC4ePFike8ll8sxcuRIvP/++1bvp+/ChQsAtNXg+i0giIiIiIiIdAw+jV6MBFc3TflouEilqdgBbkpKCvbv349169Zhw4YNOHToULE/pkxEpUv3sXd7KJVKg3BPPzwrC48++qh0/M0331gcl5eXh59++qlY95LL5Rg3bhwA4MGDB5g7d26x5rPH8OHDpeP58+dbbRHw9ddfW51Lv+WEtWrelJQUfPvtt3avUS6X47nnngOgrXKeOXOmtHnZU089Veze5voVtuY2Xuvatat0fPTo0WLdy5776dy+fVuqxO7UqRPkcnmx701ERERERJWPfmRrb36rVlse6MgeZtJQVuBWaEUOcPfu3Yvw8HAEBQWhT58+GDNmDEaNGoWIiAhUqVIFQ4cOxalTp5y5ViIqIR07dsTEiRPxzz//WB2XmZmJZ555BleuXAGgra4cNmxYaSzRomeeeQYBAQEAgN9++w3z5883GaNSqTB16lRcu3at2Pd766234O/vDwB4++238e2331rtk5qVlYXFixfjt99+K9L9WrduLfWYvXHjBiZNmgSlUmky7ocffsDatWutztWxY0dpwzFdiwJjycnJeOyxx8z2mrVm8uTJUmX2jz/+KD1vq33CzJkzceTIEatj9IN3c710+/XrJ70uawHu7du3MWvWLKvfByqVCj///LPV++no/++lf//+FscRERERERHp2NPKIP6OEkf/y0FyuuGG0qzAfXgpinLR+++/jw8//BCA+QbKSqUSf/31F3bu3IkFCxZg0qRJxVslEZUopVKJX3/9Fb/++ivq1q2LiIgItG3bFkFBQXBzc0NSUhJOnTqFDRs24P79+wAAQRDw/fffl/nGTVWqVMH333+Pp59+GoC2H+vGjRsxfPhwBAYGIi4uDsuXL8d///2H0aNHSyGnTFa0f78KCQnB2rVrMXToUOTl5WHGjBn46aefMHz4cDRv3hze3t7IyMhAbGwsTpw4gb179yI3N1f6mVkUCxYsQPv27ZGamorffvsNp06dwsSJE1GvXj0kJSVh/fr12LNnD+rVqwc/Pz+cPn3a7DzBwcEYNWoU1q5di7S0NLRt2xaTJ09GmzZtoFAocOrUKfz6669ISkrCxIkTsWzZMrvXWKNGDQwfPtwgRO7WrZvNCu0NGzbgm2++Qd26ddG3b1+0bt0a1apVg1qtRmJiIjZv3ozDhw8D0LbueO2110zmqF69Onr06IFDhw7h0KFDUCqVBm0+dPLy8jB37lzMnTsXHTp0QFhYGJo1a4aAgABkZmbi+vXr+O2336SAt379+lbbPxw4cEA61q+UJiIiIiIi0qcfnantKMFNuKct2om9pUQV3+J90k9wpFyXyi/RQcuWLRMFQRAFQRBlMpkok8mkx+ael8vl4v79+x29TbmXnp4u/vHHH+L06dPFbt26iVWrVhUVCoXo4+MjNmnSRHz66afF7du3ixqNxuo8v/zyiwhtIbtdX++9955d68vNzRV/+uknsVevXmKNGjVEV1dXMSQkRBw0aJC4YsUKUa1WF+v1p6WliQDEtLQ0h6/NyckRL1y4IObk5BRrDeQ8/fr1EwVBsPv7sEaNGuK6desszhcRESGNNUf/+/6XX36xurbY2Fhp7IQJEyyO++6770SFQmFxzb179xaTkpKkx8OGDTM7z4QJE6QxsbGxFu93/PhxsUmTJna9X3K5XPz555+tvk5bjh49KgYFBVm8R+3atcUzZ87YfO+Tk5PF9u3bW13vyJEjxZycHOlxRESEXWvct2+fwTzLli2zeU1oaKhd72FgYKC4bds2i/MsWrRIGrtx40azY+Li4uz+Hm/ZsqUYExNj8X4ajUasXbu2CEDs0qWLzddZFPxZSURERERUOZy+kiMePpMlHj6TJV5LyLM5Xjf22H/ZBs8np6vEw2eyxNNX7P8dIS9fIx4+kyUeOZttezCVOnvzNYcqcHNycjBz5kwpvRdFEQ0bNsTw4cNRv359qFQqXL58GevXr8ft27chCAI0Gg2mT5+O8+fPO3Krcm3u3Ln43//+Z7YXZUZGBi5fvozLly9jxYoVCAsLw8qVK1GnTp1SW9+lS5cwYsQIaXMdncTERCQmJmLbtm1YuHAh/vjjD24kRwCAnTt34tatW9i1axcOHz6M8+fPIzY2FqmpqVCr1fD29kZISAhat26NQYMGYfjw4Qb9VMuDl19+GZGRkfj222+xd+9e3LlzB35+fmjWrBnGjx+PCRMm4MGDB9L44lYOd+zYERcuXMCGDRuwadMmHD16FHfv3kVWVha8vb1Ru3ZttGrVCpGRkRg2bBhq1KhRrPt17twZly5dwtdff40///wTsbGxUCgUCA0NxeOPP44XX3wRgYGBNucJCAjA4cOHMX/+fKxZswYXL15Efn4+qlevjo4dO+Lpp5/GY489VqQ19uzZEx4eHsjJyYG/vz9Gjx5t85qTJ09i586dOHjwIE6dOoXr168jJSUFgiCgSpUqaNGiBQYOHIhJkyZJrTLMGTt2LN544w2kpKRg+fLlZl9D3bp1ce3aNezcuRNHjhzB2bNnER8fj4yMDLi6uqJGjRpo164dRowYgdGjR0OhsPyfyH379kn9b1988UXbbw4RERERET289Ipu7WmhoGNcrCu1UChCUa3oyI2p3BFEB/4Gly1bhkmTJkkB7v/+9z+8//77Jh9FzsvLw7Rp0/Drr79qbyII2L9/P8LCwpy49LIzbdo0LFy4EID249SPPPIIOnTogKCgIOTm5iI6OhorV65EZmYmAO2GONHR0QgKCjKZa9myZXjmmWcAAC+99BJ69+5t9d5NmzZF06ZNLZ6/ffs2unbtivj4eADa/pkTJkxAcHAwrl+/jiVLluD69esAgA4dOiAqKqpIQVx6ejr8/PyQlpYGX19fh67Nzc1FbGws6tWrB3d3d4fvTVRUW7ZskXr2fvPNN3j11VfLdkGVzJ9//im1Enj55Zfx3Xffler93377bXz88cdwdXVFQkICqlWrVmL3Gj16NNatW4fatWvj2rVrZls2FBd/VhIRERERVQ5nruQiK1e7d0pVfwUa13G1Ov7I2WwAgEwQ0LWVh/R8cpoal27kwcdThlYN7fsdIV8p4sTFHABA99bF22CanM/efM2hCtzdu3dLx6NGjcIHH3xgdpybmxuWLFmCy5cvIzo6Wrq2sgS4giCgX79++L//+z/06dPHJMCeMGECZs+ejf79++Py5cuIjY3F7NmzsXTpUqvztm/fvsiVbzozZ86UwtsxY8ZgxYoVBlVkL7/8MoYMGYKoqCicPHkSn332WbF6cxJVJPqba/Xq1asMV1I56W8gN23atFK//6xZs/Djjz8iLS0NX3zxBb788ssSuU9MTAw2bNgAAHj33XdLJLwlIiIiIqLKQ79y0pFKWI3RWN0jR/rasgVu5eDQLj6nTp2SjmfPnm19YpkMM2fONHttRffxxx9j586d6Nu3r8WNkOrWrYvff/9devz7778jOzu7RNd14cIF6Z41a9bEzz//bPIRYG9vb6xatUqq5po7dy5SU1NLdF1EJS0nJwdHjx61eF6j0WD27NnYtWsXAO3mWm3atCmt5T0UDhw4IL2//fv3R7NmzUp9DQEBAXjrrbcAAPPmzcOdO3dK5D6ffPIJ1Go1mjdvjokTJ5bIPYiIiIiIqHKyYw8zi4rbBoFtFCouhypw7927BwDw8vJC27ZtbY7v2bOnybWVgb29M9u0aYMmTZrg8uXLyM7ORkxMDFq3bl1i6/r999+l/zE+99xz8Pb2NjsuJCQEo0ePxvLly5GdnY1NmzZhwoQJJbYuopKWlZWFrl27onnz5ujXrx9atGgBf39/5OTk4OLFi1i3bh1iYmIAaD8hsGDBgjJeccWXk5ODqKgoqFQq/Pfff1K1qyAIFj+dURpmzJiBX375BZcuXcInn3yC77//3qnznz9/XmoPNG/ePKt9comIiIiIiADDvrcaTTHmKfjTkapaVuBWDg795pmWlgZBEFC1alW7xuv3H0xPT3dsZZWEfv+KnJycEr3X9u3bpeNBgwZZHTto0CAsX75cuo4BLlUGFy5cMNm8T1/VqlWxbt26Ev2HlIfF3bt3MXDgQJPnX3/9dXTu3LkMVqTl4uKCixcvltj8LVu2hFqtLrH5iYiIiIio8jFsoVBmy4AoMtCtqBwKcFUqFQRBsLviSC6XG1z7sMnPz8eVK1ekx3Xr1rU6/qeffsLnn3+OmzdvQqPRoGrVqmjbti0GDhyICRMmwNPTcrNpURTx33//AdC+7+3atbN6r06dOknH586ds+flEJVbVapUwebNm/HXX3/h5MmTuHfvHpKSkqBSqVClShW0bNkSAwcOxOTJk+Hj41PWy610fH190aRJE7z88ssYN25cWS+HiIiIiIio3DLua+uQgksZwj58+NnPErR69WqkpaUB0G5QVqNGDavjjx8/bvD45s2buHnzJrZs2YL33nsPS5cuxZAhQ8xee/PmTanHbq1atWxuqlO7dm3I5XKo1WpcvXoVoihabYKdl5eHvLw86fHDWlFN5ZNMJsPQoUMxdOjQsl7KQyM0NJT9k4iIiIiIiBxUrBYKugDXgWv0x/I3uIqLAW4JuX//Pt544w3p8dtvv21xrFwuR7du3RAWFobGjRvD29sbqampOHnyJNauXYvk5GTcv38fw4YNw6pVq/Dkk0+azKG/EZk9LS5cXFzg6+uLlJQUKJVKZGVlWeyZCwCffvop5syZY3NeIiIiIiIiIiLSo98D144UVSYIZit1pWdYgfvQYYBbAvLz8zFixAhp47bHHnsMw4cPNzu2Z8+eiIuLQ61atUzOTZ48GV988QWmTJkibVA2adIk9OjRA3Xq1DEYm5mZKR27u7vbtU4PDw+kpKQAADIyMqwGuG+++SZmzpwpPU5PT0ft2rXtug8RERERERER0cPK0R64ggyAma03ilKBqz9YowGuxOchXymidnUXVPGVW76OypUiBbiJiYno3bt3iV0jCAL27NlTlKWVOY1Gg0mTJuHgwYMAgAYNGmDp0qUWxzds2NDqfD4+Pli1ahXu3r2L/fv3Izc3F59//jnmzZvn1HXb4ubmBjc3t1K9JxERERERERFRZaKxowRXJpjNb4tEP+zNyNIgJV07850kFQPcCqRIAW5ubi6ioqLsGqvrq2rvNbZ6sZZnoihi2rRpWLVqFQCgTp062L17NwICAoo1r1wux0cffYSePXsCALZu3WoS4OpXz+bm5to1b05OjnTMjZ2IiIiIiIiIiJxPf/8Qu1ooyARY61hb1NhMrXfzfCU74lYksrJeQGUhiiJeeOEF/PzzzwC0G4nt3bsXoaGhTpm/W7duUmuE+Ph4acMyHX9/f+n4wYMHNudTqVTSRmQuLi7w8vJyyjodwQ2QiIgs489IIiIiIqLKR7RjEzNLAa3UQqGICa5a794McCsWhypw69SpU2GrY0uSKIqYPn06FixYAAAICQnBvn370KBBA6fdQyaToUqVKrh16xYA7aZlnp6e0vnatWvD09MT2dnZSEhIgFKphIuLi8X54uPjoVZry+YbNWpUqn+vMpn23w00xdl6kYioktP9jNT9zCQiIiIioopJNNjETLT56XO5Qd9asaAi11pNrmX6t9GvwFWpRag1IuQy5nwVgUMBblxcXAkto+LShbfz588HAAQHB2Pfvn02e9s6SqPRSBuOAYYVt4D2X19atGiB48ePQ61W49SpU+jcubPF+Y4fPy4dt2zZ0qlrtUWhUEAQBOTl5ZVJ5S8RUUWQl5cHQRCgUHC/USIiIiKiykQjGoa0xgSZ4VjdQ92n9Ipag2dcR5evFOHhxgC3ImBZTzEYh7c1a9bEvn370KhRI6ffKzo6WupZW6tWLYPqW50BAwZIx9u3b7c637Zt26TjQYMGOWmV9pHJZPDw8EBWVlap3peIqCLJysqCh4cHK3CJiIiIiCqZ+ynWtyiT6SW0BqGrroWCA/fSr/RVmwlwqWLgb4XF8OKLL0rhbY0aNbBv3z40btzY6ffRaDR49913pcdDhgwxO+6JJ56QjhcuXGgxIE1MTMTatWsBAB4eHnj00UeduFr7eHt7IysrC/n5+aV+byKi8i4/Px9ZWVkGG1QSEREREVHlEHdbafW8/n4Y+m0PdEdFr8A1DGwZ4FYcDHCL6KWXXsJPP/0EQBve7t+/H02aNHFojn/++QeLFi1Cbm6uxTFZWVkYP3489uzZAwBwc3PDG2+8YXZsixYtMHr0aADA7du3MWXKFKhUKoMxmZmZGDdunHTPmTNnmrRjKA1+fn5QKBRISEiQevESERGgVquRkJAAhUIBPz+/sl4OEREREREVky6PDQ12NXhsD2dsH6SrwmUFbsXFxnpF8Pbbb+PHH38EoP0fwSuvvIKLFy/i4sWLVq9r37496tSpIz2+e/cupk6dilmzZqFv377o0KEDateuDS8vL6SlpeHff//FmjVrkJSUJN1r8eLFCA0NtXiPuXPn4siRI0hISMBvv/2G//77DxMnTkRwcDCuX7+OxYsX4/r16wCAtm3bYvbs2cV8N4pGoVCgdu3aiIuLQ0xMDPz8/ODt7Q25XM6N8ojooSOKItRqNTIyMpCeng4ACA0NZf9bIiIiIqJKxNdTW0cp2tjITD9W1S+adST4NcekAlfFALeiKLXfDJ9++mkkJiZCEASpmrSiOnTokHQsiiLefPNNu6775ZdfMHHiRJPnMzMzsXHjRmzcuNHitTVq1MDixYsxePBgq/cICQnBzp07MWLECFy6dAlnz57FzJkzTcZ1794d69evL9OP57q5uaFevXpITU1FWlqawSZtREQPI4VCgYCAAPj7+8PV1bWsl0NERERERE5grvWBRgPI5bav1TihhYJQcK2uAlcmCNCIYrEDYSo9pRbgRkdH49q1a6yu1PPII49g06ZNOHr0KI4dO4abN28iKSkJqamp8PT0RFBQENq3b4/Bgwdj9OjRcHd3t2ve5s2b49SpU1iyZAnWrVuHS5cuISUlBVWrVkXr1q0xduxYjBs3rlxsjOPq6oqgoCBUq1YNKpWK7RSI6KEll8uhUCj430kiIiIiokpGF5TqxzAaEbCU3+oHqwZFs7pNzIrcA1f7p1wOaFTFr+il0sPPZhbB/v37nTKPt7c3hg0bhmHDhjllPn3u7u6YPn06pk+f7vS5S4IgCHBxcYGLi0tZL4WIiIiIiIiIyOkEobD6VVtZazuJFfX61hY5cC0owVWrtRPI5QKUKhEaBrgVRtmXYBIREREREREREVVWuspZFFbhWg1PRbOHxVaQ30JRUPrLCtyKgwEuERERERERERFRKdC1P9BorI/TEUVzPXAd66FQeE/tDAq5YPCYyj8GuERERERERERERCVEP3iVybThqbXqVzNtbwuu0T4q6q4Zuk3MdAEuK3ArDga4REREREREREREpaAgv4Xa3upXM8Mc3cRMN1zaxMyeNg5UrjDAJSIiIiIiIiIiKiH6bRB0Aa7VCly9c/ohq/R8EUtwdetgBW7FwwCXiIiIiIiIiIiohAmC3iZmdvfANT0uagsFHd0mZhomuBWGorRuFBISApVKVVq3IyIiIiIiIiIiKlOiUUiq24DMWnhqK1Z1uIWC0Xi5rgLXzhCZyl6pBbj79+8vrVsRERERERERERGVK4Kg13/WzvC0JPrU6ipw1SzArTAcaqHw7rvvYu/evcjNzS2p9RAREREREREREVUKxoW2umpYu/cw05vAeS0UdI14izkRlRqHAtyPPvoIffv2hb+/P8LCwvDOO+9g9+7dyMnJKan1ERERERERERERVXgCAJlM10LBykDR/LHuGkHmWIRr3EJBF+CyB27FUaQWCvn5+Thy5AiOHDmCTz75BAqFAh07dkRkZCQiIiLQs2dPeHp6OnutREREREREREREFYZxRKproSDaWYJrblRxKnAFQXC4jQOVPYcDXOPmy6IoQqlUIjo6GtHR0fjss8+gUCjQoUMHREREIDIyEj179oSXl5fTFk1ERERERERERFShCIXVsNb6z+qf0s95NQUPHN3ETK0X1NYIlEPQBbgswK0wHApwL126hKioKERFReHAgQNISEgwGaMLdI8ePYqjR4/iiy++gFwuR/v27Q0CXR8fH6e9CCIiIiIiIiIionLHQg9c0d7qV9H00OEAVy8trhfsinylaHZtVH45FOA2btwYjRs3xpQpUwAAsbGxUqAbFRWFuLg4k2tEUYRKpcLx48dx/PhxfPXVV5DL5Wjbtq3UciEsLAy+vr5OeUFERERERERERETljQBALrPdf9bcxmX6xw62wDVdh7SRmghRFCE4mghTqStSD1ydevXqoV69epg4cSIAICEhwSDQvXr1qjRW9w2hC3RPnjyJkydP4uuvv4ZMJkObNm2kQDc8PBx+fn7FemFERERERERERERlyTimlTnYf9ZgPzOpBLdoa5EXbF6mHwCLouMVvVT6ihXgGqtVqxbGjRuHcePGAQDu3r1r0HLhwoUL0lj9QFetVuPff//FqVOn8M0330AulyM/P9+ZSyMiIiIiIiIiIiozgqBf/Wp5nGHVrWk1rqyIiauLQpDWYe5eVH45NcA1Vr16dYwePRqjR48GACQlJeHAgQNSoHv27Fmjb0TtsVqtLsllERERERERERERlTjjgFQXvtobnJoLc4taMOtqJsDViIC8iPNR6ZGV5s0CAwMxfPhwfPvtt/j333+RlJSEFStWoEWLFgDAnhtERERERERERFRp6AewgiBILRTU1kpwLVyvu0QoYprn6qILcAWHg2QqWyVagWtOamoqDh48KLVWOH36NDQaDcNbIiIiIiIiIiKq1HQBbpGCU12AW8R7u+ilgIIMgFoXJDOTK+9KPMDVb5sQFRWFc+fOmW2boOPl5YVu3bqV9LKIiIiIiIiIiIhKha5wUVf5am0TM3NVt/rHjtZA1ghUIClNjZBqLoXrkW7m2FxUNpwe4Oo2LtOFtvoblwGmgW2VKlXQs2dPhIeHIywsDO3bt4dczu4bRERERERERERUuejC1/QsNY79l4NGdVwR4GM5BzPogWs0h73qh7iiXrBo8Ol3WUEFrp2dHKiMFTvATUxMlKpro6KicPXqVYPzxoFtrVq1EBYWJn3p+t8SERERERERERFVJsYbj3l7yCCXC1CrRajUIu6nqE0CXEuZqi5ikxWhDalx61LtHCID3ArC4QD3xo0bBoFtbGyswXnjwLZRo0ZSdW14eDhCQ0OLtWAiIiIiIiIiIqKKyNVFQMdm7khJV+NKfD4yc6z0UoBhzqYpSFudso2UYDo/lV8OBbihoaG4efOm9Nj4L1kmk6FNmzZSdW14eDiCgoKcs1IiIiIiIiIiIqIKRErO9EJXuUyAf0HVbW6eBiq1CIW8cIBB2wQz+aozAlzd7az14qXyw6EANz4+3uCxq6srOnbsKIW1PXr0gK+vr1MXSEREREREREREVJEZh64KuQB3Nxly8zTIyNZY7INrbkMzZwS4gsxwTirfHG6hoOuZ4eXlhUmTJqF///7o2bMnfHx8nL44IiIiIiIiIiKiispahwIPNwG5eUBevtEgMxuX6c/ljABX10eXHRQqBllRL8zKysIPP/yAIUOGoEqVKujQoQNmzJiBjRs3IikpyZlrJCIiIiIiIiIiqrDMZa725LDm2ikUZRMzk3vrWigwwa0QHKrA/fHHHxEVFYUDBw7g7t270vNqtRqnT5/G6dOn8f333wMAmjVrhvDwcOkrODjYuSsnIiIiIiIiIiIqz6zko/bksIYBrvPCVpluEzP2wK0QHApwX3jhBbzwwgsAgMuXL+PAgQOIiopCVFQUEhMTDcZevHgRFy9exMKFCwEA9erVk8LciIgI1KtXz0kvgYiIiIiIiIiIqHSJoojriUp4e8pQvYr1iM1aWGucyxq2TdA+0ug1q5UV+fP0puuJSchHoL8ccpkT+jJQiXG4B65OkyZN0KRJE0yZMgUAcP36dYNANy4uzmB8bGwsYmNj8euvvwIAQkJCpM3PwsPD0axZs6K/CiIiIiIiIiIiolKUkqHB3WQV7ibDYoBrrWZWsKMEVzT6E7Cv9YItbq6FsySnq1HNv8gRIZUCp/3t1K9fH/Xr18fEiRMBAAkJCVKYGxUVhatXrxqMT0xMxJo1a7BmzRoAQNWqVdGzZ08p0G3Xrp2zlkZERERERERERORU9nQ0sGuMyTWi3rHpPM7YxKxODRekZGiQm6dBZrYG1fyLPyeVnBKL12vVqoVx48Zh3LhxAIC7d+8aBLoXL140+Ia8f/8+/vzzT/z5558QBAEqlaqklkZERERERERERFQsCnnhsVojlkgbgpIKcOUyAbWDFLh6Mx9ZOdzIrLwrtfro6tWrY/To0Rg9ejQAICkpSWq5sGPHDly5cgWAcxsyExERERERERERFUVaphp3k9UAgAAfGaoFGMZoMr3AVq0G5FZ601rvgSuaPdY+NvxTEAS7Wi/Yw9tDu+DMHA00GtHg9VD5UiYNLrKysnDixAkcP34cx48fR2xsrNO++YiIiIiIiIiIiIrrxh0lMrM1AIDkNMEkwNUPW5UqEa4uptmW9R64ttcg9cAtuJcz0zN3NwEymQCNRkSeUoSHG7O58qpUAtz09HQcPHhQap9w6tQpqNXq0rg1ERERERERERGRw1R60ZVGFCGKosUCRJXaQlSrVzlrTDAaA5jpmSvq7l9wjZUqX0cJggC5DNBo7OvVS2WnRALclJQUHDx4EPv370dUVBTOnj0LjUYjnZf+1UAQTErDmzZtioiIiJJYFhERERERERERkV2MQ02NCMgF8+dVJVSnqJF6KGj/KKkaWQa45ZtTAtwHDx5I/WyjoqJw/vx5s/07jANbURTRvHlzREREIDIyEhEREQgKCnLGkoiIiIiIiIiIiIpMozFKNa2EnJYqcHXPmgtedUW51rJT0bgC18kJrrbvrWgzwM1XiniQpkZQgBwKOVstlLYiBbh3796VwtqoqChcvHhROqcf0JqrsG3ZsqUU1kZERKBq1apFXDoREREREREREVHJMM5vNSIgtzBWpbKRgNqZeVqaRQqCnZyd6qazVYB7/noecvM0yMiSo0ldN+cugmxyKMCdOnUqoqKicPXqVek548DWWJs2baQK2/DwcFSpUqUYyyUiIiIiIiIiIip5et1AAVhvM2CphYJxYaP5MbbP6f6UOTnB1U1nUm1sJDdP+2akZGisjqOS4VCA+/PPPxtU1RoHtoIgoG3btlKFbVhYGAICApy3WiIiIiIiIiIiohImFmxaZvycfimtYQ9cJzWRNVP1CxQGrE5vocBuCBVCsXrgymQytGvXTmqHEBYWBj8/P2etjYiIiIiIiIiIqNTpV9/qihmtFala7IFrZfMxcz1wTWYxesLZAa5uYTYKcKmMORTgyuVydOjQQWqJ0LNnT/j4+JTU2oiIiIiIiIiIiEqdfqApkwFqNUzCVP2Hlloo6JgLXgU7GtDqTpXYJma6EJmdEco1hwLc1NRUeHl5ldRaiIiIiIiIiIiIypx+z1mZAKhhvUpVpRaRkqGGn5cMMif2JdC1cRBLKMDVtUdlAW75JnNkcGmFt7du3SqV+xARERERERERERlT63rOysy3OgAMe+DG31HiYmwebt5VmR1jb+5qbkMzbT9e7XGJbWJmx2ZrVHYcCnAt+eyzz5wxDQAgISEBkZGRTpuPiIiIiIiIiIjIEboeuDJBr0rVJMEtPMzM1l5wL0UFR9nKTkURJhuqOYs9bRyo7DklwH3rrbewePHiYs+jC2+vXbvmhFURERERERERERE5Tqp4lQmFFbh2hKjGBbLSFWZ74Jq2LzBfgau/HptLcIjATcwqBKf9tT///PNYv359ka+Pj49HZGQkrl+/7qwlEREREREREREROUzXUkAm6G30ZcdmY85ucaCbOzu3oKWDk+eWCnAZ4JZrTgtw1Wo1nnrqKezevdvha+Pi4hAZGYnY2FgAgFwud9ayiIiIiIiIiIiIHGLQQqHgOeOQ01zlrHGFrLXNxwrntZ6epmVqkHhfaXGe4tBtuMYAt3xzSoA7ZMgQAEBeXh6GDx+OY8eO2X1tbGwsevXqhbi4OIiiCIVCgZUrVzpjWURERERERERERA6TAlyZdiMzwLTNgLng1VkBqyAIUouF+DtK6Xld4OpszG/LN6cEuOvWrUNYWBgAICsrC4MGDcKFCxdsXnf9+nX06tULN27cAAC4uLhg1apVGD16tDOWRURERERERERE5DBdCwVBEArbFtiRcprkq7oKXDNjzYW9ulBY0LtGPzjOVzo3apXaQ7AJbrnmlADXzc0NW7duRdu2bSEIApKTk9G/f38pmDXn2rVriIyMRHx8PABteLt69WqMGjXKGUsiIiIiIiIiIiIqEl0FrlxWuNmYxqji1lzkKVgowbX0vFUFl+Tla6SnMnM0FgYXjbRBm1NnJWdzWg9cHx8f7Ny5Ew0aNIAgCEhMTETfvn1x7949k7FXr15FZGQkEhISAACurq5Ys2YNRowY4azlEBERERERERERFYlGr3etPSGnxR641m5iZnM0Ue+cuW4Jzu6Bq5uPBbjlm9MCXACoVq0a/v77b9SsWROCICAmJgb9+/dHWlqaNObKlSvo1asXEhMTARSGt8OHD3fmUoiIiIiIiIiIiIpEIwWygl6bAaNBZkJPZ7aoDaqigItCgItCQICPHF7uMjSp4+q8G0AvEGaAW64pnD1h3bp1sWvXLoSHhyMlJQVnz57F0KFDsWvXLsTFxaFPnz64ffs2AG14u3btWgwbNszZyyAiIiIiIiIiIioSXU9YmQCo7anALfjTeJMxcxud6UjZqX4Frl7lb2hNF4TWdLF/0UVQ2B6iRG9DxeTUClyd5s2b46+//oKnpycA4PDhwxg8eDB69+4thbdubm5Yt24dw1siIiIiIiIiIipX1HotEWRmWh2YPNYLXmH6tNnWB85uh1AUha+NCW55ViIBLgB06dIF69evh4uL9l8K9u/fjzt37gDQhrd//PEHhg4dWlK3JyIiIiIiIiIiKhJduwRtD1zzVaq6h/qbmzmrhUKpZbsWwmkqX0oswAWAfv36YcWKFQY77bm5uWH9+vUYPHhwSd6aiIiIiIiIiIioSHShrFwmFG5iZiHl1O+Na1JVa6Ey12CImUre0mKpupjKlxINcAFg1KhRmD9/PkRRhLu7O/78808MGjSopG9LRERERERERERUJBq9ClxbIadGBMSC5NWRtgjloIOC2T68VP7YtYmZXC53ys3y8vLsCm8FQYBKpXLKPYmIiIiIiIiIiByh0euBq2OpB6618FPalMzcSTObo1nrmVsSBBk3MasI7ApwRVGEIAjFamisa6PApshERERERERERFRepWWqcT9FW1goEwTIZNosy1KkJYqiXWFueVQeqoDJNrtbKBQ3eNV+M1ew72IiIiIiIiIiInqoJKerpWMvj8KI01oLBUtjpIdmktLCYkfL15c0XaWvhiW45ZpdFbj79u0r6XUQERERERERERGVOXVBfhtSzQW+XnKkpGsb4mqM0lX9qltbwaujla6l1kLBTBsHKn/sCnAjIiJKeh1ERERERERERERlTl1Qjermqk03bYWp9vTANcfctKUdpNraoI3KB7tbKBAREREREREREVV2am3BLeQFqVlhmwHDcbrMs0J3H7Dw2qh8cSjAPXPmTEmtg4iIiIiIiIiIqMyp1dpEVi4zrMC1uImZRpTCXEtjzFXxFs6r3wRXd650eijISqtXAxWLQwFuu3btULduXUyfPh07duxAfn5+Sa2LiIiIiIiIiIio1EkVuHLtn5b6xOqCV425k9IY3RyWg9KybF8gVRezh0K55nALhYSEBCxYsACDBw9GYGAghg8fjqVLl+Lu3bslsT4iIiIiIiIiIqJSU9hCQVeBq/3TcgWu3nEx7627vrTqYm1VF1P54FCA27BhQ4iiKH1lZWVh8+bNmDJlCkJCQtClSxd89NFHbLVAREREREREREQVUmELBe1jmblWB4CUtmpEy8GttVy0PHQv4CZmFYNDAe6VK1dw6dIlfPnllwgPD4dcLpfCXI1GgxMnTuC9995D+/btpVYL27dvZ6sFIiIiIiIiIiKqEApbKBj2wDXerMyg721BAlqUIFT/EikkLuVwlwFu+eZwC4XGjRtj1qxZ2L9/P+7fv49Vq1ZhzJgx8Pf3N6jO1bVaGDJkCFstEBERERERERFRuafWiFKIqqvAlbLUooSzBXOZ3cSsGPM6i0xmvT0ElQ+K4lzs7++PJ598Ek8++STUajUOHjyIrVu3YsuWLbh69ao0TtdqYfPmzRAEAR06dMDQoUMxdOhQtGnTptgvgoiIiIiIiIiIqLjU6sJjmS7ALfjTpAJX1P1ZUH3rhPuXdgGu7j7Mb8s3hytwLZHL5YiMjMRXX32Fy5cvG7RaUCgUVlstvPDCC2y1QEREREREREREZUpdkNLK5YK0eZkUclraxMzkwPSkuUBW2kCsCOt0Fqk9hHE6TeWK0wJcY/qtFu7du2e11cLChQsNWi0sWbKErRaIiIiIiIiIiKhUSf1v9RIzmaBrM2AYcpoLbk02Oiui0trgTOAmZhVCsVoo2Mu41cKhQ4ewZcsWu1stDBkyBG3bti2NpRIRERERERER0UNKrdb1v9VLUC1sYqYj6v1pHITqHpoNZMtBeFoeqoDJthKrwLVELpcjIiLCpNVCRESExVYLHTt2LO1lEhERERERERHRQ0aqwJUXPiez1CjW1uMiKPUeuLoAV1NKN6QiKfUA15iu1cK+ffustlogIiIiIiIiIiIqSbfuqwAYVuAKNipwVSoRGdkaqNSiaaZrJdKy1Vu3NEjtIcpuCWSHUmmhYC9zrRY2b96MrVu3lvXSiIiIiIiIiIioEsvI1iA9Sw0AcFGYBriWwtmkdA3SMjW4cUeJ6oFOitpKvQeuCI1GhExWWrW/5Igyr8C1RNdq4euvv8bly5fLejlERERERERERFSJ6frfAkDt6oVBbOEmZuavy8nV9h9QKkWTlNdaD1zBzJOlXQmrkBe+PqWKdbjllUMB7vjx47F06VJcv369pNZDRERERERERERUZrw8ZPBwK4zMpBYKRj0U7Io7dT1tzQW4Vi4zu+lZCRAEAS4u2pvlq0rnnuQ4hwLclStXYsqUKWjUqBHq1KmDp59+GosXL0ZMTExJra/cysjIwPr16/Hiiy+ie/fuqFatGlxcXODr64umTZti/Pjx2LFjh0P9e2NiYvDaa6+hZcuW8PPzg7e3N5o0aYLp06fj9OnTDq0vLy8P8+fPR+/evVGzZk24ubmhVq1aGDx4MFauXAmNht2piYiIiIiIiIh0LCU4soL0zLgHrmi045hoZQ6r99XLjsqiH65rQbFxvpIVuOVVkRpziKKIhIQErF69GqtXrwYA1KxZExEREYiIiEBkZCQaN27s1IWWJ3PnzsX//vc/5ObmmpzLyMjA5cuXcfnyZaxYsQJhYWFYuXIl6tSpY3XORYsW4dVXX0VOTo7B81euXMGVK1ewcOFCvPvuu3j33Xdtru/SpUsYMWIELly4YPB8YmIiEhMTsW3bNixcuBB//PEHqlevbscrJiIiIiIiIiKq3EQL1bK6trCihVo4a8WyVgNZcxeKlk+VFFddBS4D3HLLoQBXLpdDrVabPC+KIm7duoU1a9ZgzZo1AIDq1atLYW5ERASaNm3qnBWXA1euXJHC25CQEDzyyCPo0KEDgoKCkJubi+joaKxcuRKZmZk4ePAgIiMjER0djaCgILPzrVy5ElOnTgUAyGQyjBkzBn369IFCocDhw4fx66+/Ii8vD++99x7c3NzwxhtvWFzb7du30b9/f8THxwMAWrdujQkTJiA4OBjXr1/HkiVLcP36dRw6dAiDBw9GVFQUvLy8nPwOERERERERERFVDrpetRpRhCiKJr1r9R8WpYK2rGNTBrjlnyA68Bn/rKwsHDlyBFFRUYiKisLx48eRn59fOJnU1Fk0eAwAQUFBCA8PlwLd5s2bO+s1lLrnn38e169fx//93/+hT58+kMlMO1HcuHED/fv3lzZge+aZZ7B06VKTcffv30fDhg2Rnp4OmUyGjRs3YtiwYQZjoqOj0adPH2RnZ0OhUOD8+fNo0qSJ2bU9+eSTUog+ZswYrFixAgpFYU6fmZmJIUOGICoqCgDw9ttv48MPP3T4PUhPT4efnx/S0tLg6+vr8PVEREREREREROVJcpoal27kwcdThlYN3aXn1WoRR//TfmK6S0sPyAtKcmNv5eP2AxWOX8hBWqYGNQIV6NHGEy3qu0nXxt1W4tZ9JYKruSC0povB/VIy1LgYmwcvDxnaNHI3WIOvlxwtG7ihNCTeU+LGHSWqBSjQqLar2TFHzmYDAGQyAV1bepTKuh4G9uZrDgW4xnTVprpA9+jRowYtAKwFutWqVUN4eLhUpduiRYuiLqPUJScno0qVKjbHnTlzBm3btgUAeHp64v79+/D09DQY88Ybb+CLL74AALz00kv4/vvvzc41d+5czJo1C4A2pNW1rtB34cIFtGzZEqIoombNmrhy5Qq8vb1NxiUmJqJhw4bIzc2Fp6cnEhMT4e/vb/P16GOAS0RERERERESViaXwVKMREX1em3d1buEBhVybb11PzMedJBVOXMxBaoY2wO3e2tPgWkcD3KQ0NS6XcoB7P1WFq/H58POWG4TP+hjglgx78zWHNjEz5u7ujsjISLz33nvYu3cvUlNTcfDgQXz00Ufo27cvvLy8zG7iJYoi7t27h/Xr1+Pll19G69atERQUhJEjR+KHH37AuXPnirOsEmdPeAsAbdq0kSpls7OzzW729vvvv0vHM2bMsDjXlClTpFYHmzdvNumVq5tL934/99xzZsNbQNv2YfTo0dK6Nm3aZNfrISIiIiIiIiKqrCxvYlZYkGhtT3gR2rDX4DldUaOZ8dJzZdy5wFXBFgrlXbECXGMuLi7o0aMH3nrrLezcuRMpKSmIjo7G559/jsGDB8PX19ck0BUL+oc8ePAAGzduxKuvvop27do5c1llSj89Nw5dL1y4gBs3bgAAmjVrhnr16lmcx8fHB2FhYQC0rSx0LRD0bd++XToeNGiQ1XXpn9e/joiIiIiIiIiIDMn0+uDqFG56phfwFjMDLcYH5YtMF1CXwa3JTk4NcI3J5XJ07twZr732GrZs2YLk5GScPHkSc+fOxWOPPWZQySoIghTmlsU3a0nIz8/HlStXpMd169Y1OK9fadypUyeb8+mPMa5SFkUR//33HwDt+24rBLc2FxERERERERHRw8ZaHqXb/kg0U4Grf53aQoIrmCnB1T1X1imYbmllvQ6yrEQDXGOCIKBdu3Z49dVXsWHDBty7dw+rV69GixYtzO7iV9GtXr0aaWlpAID27dujRo0aBud1G5wBsFp9a26M/rUAcPPmTWRna/uR1KpVCy4uhn1VjNWuXRtyuRwAcPXq1UoTmhMRERERERERFYe1sNVcPqsf6ipVIjKyNdKXo20JRAB5ShE5eRrk5Vvp1+BMuiCZ2VC5pSjtG549e1ba9OzAgQNISkoCgEoX3t6/fx9vvPGG9Pjtt982GZOamiodV61a1eacgYGBZq8tylwuLi7w9fVFSkoKlEolsrKyLPbMBYC8vDzk5eVJj9PT023eg4iIiIiIiIioorAWX2rbDIhmA1z9mPV6otJ8n1wrsZd+bpqUqkZsYj68PGRQqkS0a+wOD/dSrb+kcqhEA1xRFHHq1CkpsD148KBB0Ggu2W/SpInU67Wiys/Px4gRI3Dv3j0AwGOPPYbhw4ebjMvMzJSO3d3dbc7r4VG4y19GRkax5tLNl5KSIs1nLcD99NNPMWfOHLvmJSIiIiIiIiKqqMzVGOr2MdPfpEwXa0nxll7M5e5aGLrK5UCgr9ziffTTsZw8w6wsJ1+Eh30xT5FJLRRYgFtuOTXAVavVOHHihFRde/jwYYNKTePAViaToU2bNggLC0N4eDjCwsJQrVo1Zy6p1Gk0GkyaNAkHDx4EADRo0ABLly4t41UV35tvvomZM2dKj9PT01G7du0yXBERERERERERkRNZCTBlUpsBM5fpgtyCY3dXGdo3LVrqqmGKSmYUK8BVKpU4duyYVGF75MgRqQ8rYBrYurq6olOnTlJY26NHD/j4+BRnCeWKKIqYNm0aVq1aBQCoU6cOdu/ejYCAALPj9Stec3Nzbc6fk5MjHRu/b47OZWs+Y25ubnBzc7NrXiIiIiIiIiKiispctwOhoKBWvz2CVHhrlLl6exaj5YFxflsKeW4l62paKTkU4Obl5SE6OloKbKOjow3CQuPA1tvbG926dZMC2y5dulTaEFAURbzwwgv4+eefAWg3Etu7dy9CQ0MtXuPv7y8dP3jwwOY9dP2Cja8tylwqlUqqjnZxcYGXl5fNa4iIiIiIiIiIKitrxa+ygpRTv0LWUgsFewNcwUwPBXM9dokcCnD9/f2Rn58vPTYObKtWrYqePXsiLCwMYWFhaN++PWSyyt9oWRRFTJ8+HQsWLAAAhISEYN++fWjQoIHV65o0aSIdx8bG2ryP/hj9awGgdu3a8PT0RHZ2NhISEqBUKuHi4mJxrvj4eKjVagBAo0aNKt0mckREREREREREjpBSLnM9cAviLbMtFPT+dFEIqOJrZ4BrfF+9+UszpWEP3PLP4QpcQRCk4LZWrVpSdW14eDiaNWtWIossz3Th7fz58wEAwcHB2LdvHxo2bGjz2latWknHx48ftzlef0zLli0NzgmCgBYtWuD48eNQq9U4deoUOnfuXKS5iIiIiIiIiIgeVmZbKOg2MTPbA7cgJwuSo3Z1V4MNzBxlHKKWSqbKmr5yr0jfUYIgwNvbGz169ECPHj3Qs2dPhrcAatasiX379qFRo0Z2Xd+8eXPUqVMHAHDx4kXExcVZHJuZmSltjObp6YmIiAiTMQMGDJCOt2/fbvXe27Ztk44HDRpk13qJiIiIiIiIiCoraxWo8oJdzPR74Jpc5+inm6WN0UzbMpRFqGrp5Rt/Ap9Kn8MBriiKEEURWVlZWLt2LV588UW0bt0a1apVw/Dhw/HNN9/g33//fSj+cl988UUpvK1Rowb27duHxo0bOzTHE088IR3PnTvX4rhFixYhKysLADBs2DB4enpanWvhwoXSeGOJiYlYu3YtAMDDwwOPPvqoQ2smIiIiIiIiInqYFFbg6oetYsFzBWNQ/DYEGqMJSiNfk1rxVv4or8JyKMDdunUrXnvtNXTu3BlyuVwKc0VRRFJSEjZv3oz/+7//Q6dOnRAQEIBBgwbhs88+w5EjR6BSqUrqNZSJl156CT/99BMAbXi7f/9+k7609vi///s/+Pj4AADmzZuHzZs3m4w5evQo3nnnHQCAQqHAe++9Z3auFi1aYPTo0QCA27dvY8qUKSbve2ZmJsaNGydtPjdz5kyTDdGIiIiIiIiIiB5W5gppZdZCzoInBTjW8sBc79my6IFrC4PdsudQD9xBgwZJH7fPysrCkSNHEBUVhaioKBw/ftxgg7P09HTs3LkTO3fuBAC4u7ujS5cuCA8PR0REBLp16wZ3d3cnvpTS8/bbb+PHH38EoG0n8corr+DixYu4ePGi1evat28vtUzQCQoKwg8//ICJEydCo9Fg+PDhGDNmDPr27Qu5XI7Dhw/j119/lQLXOXPmoGnTphbvMXfuXBw5cgQJCQn47bff8N9//2HixIkIDg7G9evXsXjxYly/fh0A0LZtW8yePbs4bwURERERERERUaVibqN33SZm+i0UdMGmM/PNsuiBK71aBrXlliA6qRY7NzcX0dHRUqAbHR0thY7SzfT+B+Di4oIOHTogPDwc4eHh6Nmzp1SJWt5FRkYiKirK4et++eUXTJw40ey5+fPnY+bMmSbvmY5cLsf//vc/zJkzx+Z9Lly4gBEjRuDSpUsWx3Tv3h3r169HjRo17Fq7sfT0dPj5+SEtLQ2+vr5FmoOIiIiIiIiIqLy4/UCF2Fv5qOqvQOM6rgbn4m4rceu+EsHVXBBa0wUAcCkuD8npauw9ngWNCHRo5o6agS5o39S+gsXsXA1OX8mFi0JAp+YeAIADp7IRczMPPl5yhFRToFEdV1Tzd6j+0mH5ShEnLuZAJgjo2srD5LxaI+Lo+RwAgEwmoGtL0zFUNPbma077DnB3d0dkZCQiIyMBAEqlEseOHZMC3X/++QeZmZnS+Pz8fERHRyM6OhpffPEFZDIZWrduLQW6w4cPd9bSKoTnn38ejzzyCBYsWIAdO3bg5s2b0Gg0CA4ORp8+ffDcc8+hXbt2ds3VvHlznDp1CkuWLMG6detw6dIlpKSkoGrVqmjdujXGjh2LcePGQSYr+q6IRERERERERESVibUKR5m5DccK/tQ4sXLVpMyyFKtiLd6KlbllzmkVuLao1WqcPHlSCnQPHz6MtLQ0w8UUVOgKglDpeuZWNqzAJSIiIiIiIqLK5NYDFeIsVOAm3FMi/o4S1aso0KCW9tzFuDwkp6mw53g2AKBDMw/UCFSgg50VuDm5Gpy6kguFXEDnFtqq1n0nshB7Kx++XnIEV1OgUW1XVAsonQpcAOje2tPkvFot4uh/rMAtCfbma6VWgimXy9G5c2e89tpr2Lp1K5KTk3Hy5EnMnTsXLVq0kMbpNkUjIiIiIiIiIiIqbeY2MdM9Z1BtKxoWp5q7zvqNTJ8yKcAtBxFZOVjCQ69kI3wLLl26JFXiRkVF4c6dO2YbRBMREREREREREZUGawWFsoLcSn+ICECjl+gKNuaweF+DNRTMVYoxGSO58q9UAtzz589LYe2BAwdw//596ZwoigxviYiIiIiIiIiobOnCUzOndNsIaYwa3hanQtZcHKYLgHX3K+3qV3M5XXmoAn7YlUiAe+bMGURFRWH//v04ePAgkpOTpXO6b0T9bwbdcz4+PujRowciIiJKYllEREREREREREQO0wWqKrXh8xpN4bG2AtfxufWvceaGaPbSz2tFkRW55VGxA1xRFPHvv/9KFbaHDh1CamqqwXkAZtJ7Eb6+vujZsyciIiIQGRmJ9u3bQy6XF3dJREREREREREREDtFlp+YCTFeF9sl8VWHCKorGFbKOpa/SbYz66gKAXGbasqGslIc1POwcDnDVajVOnDghBbaHDx9GRkaGdN5aYOvn54ewsDBERkYiIiIC7dq1g0xWavuoEREREREREREROczVpSDAVRoFuHoVuM4oXdVV4MpKsweu3rHNrJZhbplwKMDt378/jhw5guzsbOk5a4FtQEAAwsPDpQrbNm3asN8tERERERERERGVO9YqTXUBrkYjQqUWoZAXVMg6MIcxcxmZtAlaacZnjOrKPYcC3L///huCIFhoaCwiMDAQ4eHhUoVtq1atGNgSEREREREREVGFJpcJUMgFqNQi8pWFAa6uB64goMjVqQYdFMqgAteAjdfAAtyyUeQeuKIoolq1aoiIiJC+WrZs6cy1ERERERERERERlQprPXABbRWuLsD1dDe8xvjYofvqle3qWijoCiJLo/+srRYK+usT2RC3TDgU4AYFBUnVtZGRkWjWrFlJrYuIiIiIiIiIiKjUWQtws3ML++CKogiN1FpUG34WO97UVeByyyjS41CAe+fOnZJaBxERERERERERUdmxkb66Kgo2MlPpDdS1UNBN4VAPXL1bF7QrVRdMoGuhUBr1robrMD3PmtuyVy7z/P/++6+sl0BERERERERERCTRbWSmLAhwRUCqwIXgeLsD40pf/fYEpbmllK39q4xfF9solD6nBLjTp093xjQAgHPnzqF3795Om4+IiIiIiIiIiMgWW7mkLufUbVwGFPSsFfQC16JuZCYCarWIlHTt5LLSTHCN1uGMMeRcTglw58+fj3fffbfY85w9exa9e/fGgwcPnLAqIiIiIiIiIiIix1iqSJXaGoiFf4qitn2CABS710Byugaagl3MFArdJmalk5baqsKlsuW0Fgoff/wxvv/++yJff/r0afTp0wdJSUnOWhIREREREREREZFdbEalugpc3UCxMMyVFaS7jsSt+pGpCEClLry6qr/czkWVgvKwhoecU3vgzpw5EytWrHD4un///RePPPIIkpOTAQBeXl7OXBYREREREREREZFVumpXS7Wo5toaiKK2fYIgFCHnNJpO15rB21MmbZhWWqzdzfh1Mc8tfU4JcKdOnQpRFKHRaPDss89i69atdl978uRJ9O3bF8nJyRBFEd7e3vjrr7+csSwiIiIiIiIiIiKHWOomUNgDVxthZudqkJGt0Qa4AESIRW95IBZuiCYTBOlepR2W2rV+JrilzikB7k8//YRRo0YBAFQqFUaPHo0DBw7YvO748ePo27cvUlJSAAA+Pj7Ytm0bwsLCnLEsIiIiIiIiIiIip9APVTUaEbG3lUjPUgMQpBYKDs1n9FjKTsuiHa2VwJiblpU9pwS4giBg5cqV6Nu3LwAgNzcXw4YNw+nTpy1ec/ToUfTr1w+pqakACsPbnj17OmNJRERERERERERETqO/iZlGLGx54OUuwM9bJqWfRanCFaHXT7ec7yfGPLf0Oa0HrouLCzZu3IguXboAANLT09G/f39cvXrVZOw///yD/v37Iy0tDQDg6+uL7du3o0ePHs5aDhERERERERERkd1sVsDqBbiiWHgc6CeHh5vjEZtxqwbd5mj6z5dW9aug99qMMbAte07dxMzT0xPbtm1D8+bNIQgC7t+/j379+iExMVEac+TIEQwcOBDp6ekAAD8/P+zYsQPdu3d35lKIiIiIiIiIiIgcZmsTMym8BaBUiZDJBLi6FF5VlNBVV9ULFG6KVpocuR1bKpQ+pwa4ABAQEIBdu3ahTp06EAQBN27cQL9+/ZCUlIRDhw6ZDW+7du3q7GUQERERERERERHZzVYwqQs5RVGUxuYrtX96uAkOB5uCcUormj5f6lmp2Sa4pb0IMub0ABcAgoODsWvXLlSrVg2CIODSpUuIjIzEoEGDkJGRAQDw9/fHzp07pZYLREREREREREREZc1S9atQkKJpREClFpGbp0G+WptuursKUs5ZlLxTW4ErSvcv9Qpca5uYGT9moFvqSiTABYBGjRphx44d8PX1BQD8999/yMzMBKANb3ft2oXOnTuX1O2JiIiIiIiIiIicRspUReBibB7upaghagC5DHBzEYqU3OpX20otFPQHMCwllGCACwBt27bFpk2b4ObmJn1D+vv74++//0bHjh1L8tZEREREREREREQmlCoRmdkaZGZrkJOnMTlvcQ+zghMaEcjN1yarLgoBNQIVhhWzRQ1dC66TlXL1rcESzG1ixpLbMqewZ1B8fHyRbxAaGoovvvgCL7/8Mtzd3bFkyRJUq1bN5px16tQp8j2JiIiIiIiIiIiMqdQi/r2cC7W6MJRsVNsV1QIUtnvg6rUZ0GhEACKq+itQ1V+B1EyVNM6RuFPQG6/WGN4HKL12BaXdsoEcY1eAGxoaatpYuQjy8vIwcuRIm+MEQYBKpbI5joiIiIiIiIiIyF7ZuRqo1SIEQYBMBqjVIrJztSmplJVaiMBkBdmYtl9t4VBr/WPtJYpiYQVuiX5e3sY6zD0nGj8WYblOmUqCXQGuTnFKpgXpm5xl10REREREREREVPryldpcysdTBl8vGRLuKaXNw2zRBbX61bu6DceKHGcWlOCK0NvEDEJhjlbUeUtQeVxTZWd3gFvc4JXBLRERERERERERlQWVWoRGU9i71tVFKOxpW9C6QBddWfoUuv54UdR+yWR6FbgFExQlAhM1gErXjrc4gXARaV+zyHS2nLIrwI2NjS3pdRARERERERERETnd3WQVriXkGzznoijcLMzewLVwEzP9ClyhoI1CYeTqSICru+zUlVw8SFUDAOQGPXBLJ1HV3dKeu7FGs/TZFeDWrVu3pNdBRERERERERETkdBlZGpPn3FwLG81qjAJJS9WvxoW5goXnHaF/qQgRgkyAr7e8XG0qxsC27JVhW2QiIiIiIiIiIqKSZRzQAoCrXgWuxtwAM2QWWys4J211kQtoUscVVf3lTpnPIUZtIKxioFvqGOASEREREREREVGlpQtofb0Kg1FXF8GkhYIuvLQnjxULBuo2MdOfp6hkBQsqT9W3APPa8oABLhERERERERERVVq6Alsfz8IYzEUhQJAZnrdFZpSiCXp/nr6Sh6Wb0xCTkO9Q31r9sNZgfieFwnavA/bfj4Fu6WOAS0RERERERERElZamoAWup4cAL3cZPNxkcHcVIC9IT3UVurbCS4PCWLEwfJUJwB97M5CdJ+LYf7lFXqdcVnalt9aqftkDt+zZtYnZ9u3bMXDgwJJei0VJSUm4fv06OnXqVGZrICIiIiIiIiKi0nXzrhK3H6ikx8HVFKgV5GL1mrjbStxL1l5TvYoCmoIEUi4T0LqRGwBt31pLFbiWwkyT5wVtqPvP+RzpKXfXoleo6lfgOlIRW+KM1lAu1vSQsasCd/DgwYiMjERUVFRJr8dASkoK5syZgwYNGmD79u2lem8iIiIiIiIiIipbd5NVUKlF6etestrmNff0rrmXooJYUIErEwqC24Ik1qQHro159QNcEYAAEScv5WLJ5jTpeZXGzhdmZs7CHrilX4lbrgJjMmF3C4WDBw+id+/e6NKlC9asWYP8/PwSW9SFCxfw8ssvo06dOvjggw+QkZFRYvciIiIiIiIiIqLyRxRFKAuKbxvVdgUAqNS2E0b9ilq1BtBdYtzDVheYaoxCV0vxqX74CwAZ2SLOXTPMx5TKooegcr0bl7eNzPQx4y19dgW4U6dOhUwmgyiKOHHiBMaNG4fg4GBMnz4de/bsgVpt+18/bLlx4wa+++47dO/eHa1atcK8efOQlZUFURTRoEEDDBgwoNj3ICIiIiIiIiKiikGp0oa4giDAz1sOQBvg2tokTKOX4Go0ohSoyoxSUd1Djb0luNALd0VAbSZMtidgNlxD4Zp0FcFlkt3qqpHNnGJgW/bs6oE7f/58TJs2DbNmzcLevXsBAMnJyViwYAEWLFgAHx8fdO/eHT179kTr1q3RvHlz1KpVC66urmbne/DgAa5evYrz58/jxIkT2L9/P2JiYqTzuv8hBgQEYPbs2Xj11Vfh4mK9vwkREREREREREVUe+UptPuSiABTywufVGsPH+jTGDW1RGKqaVOBaaKFgrfpVkAFQa8fq58i+XgLSs0SoVGKRE0+ZXFsNLAilH+Ja38TMuAluya6FTNkV4AJAmzZtsHv3bkRFReHDDz/E3r17pb/A9PR07Ny5Ezt37jS4JiAgAN7e3nB3d4dKpUJubi6SkpLMtl/Q/2aoVq0aXnrpJbzyyivw8fEp6msjIiIiIiIiIqIKShfguroIkMkEyOUC1GoRSpUIhdx84miuOFdXKWscUuoCXbVx31prAa7+vAXXNartgv5dvfHjuhQoHe2Bq78eQYDGKB0t7Z609tyP+W3pszvA1YmIiEBERATOnz+PH374AevWrUNqaqoUwAqCIB0nJycjOTnZ4lz6YwGge/fuePbZZ/H/7L13nOVWff7/HOm2aTs7u7O9unvdcMHGiwEbG4Kxafk52AQSwGBKMCZgJ7SQOAQSEgIOCSkYcHBCNSGAgZgvEIJNs7FxYc267XrX2/v0couk8/tDOkdHutK9d2buztw7+7xfr93RXElHR9IdlUePns9rX/ta5PP5qXaNEEIIIYQQQgghhMwTyk4g4GZ8mTMTCLi1kjxNMdYSIoxHAGBbUWVWRSpoB24jEQo6aiCMZshlBQp5f4TjyMgy6zdo9sf4bJYtuGY0RBwKtnPPlAVcxRlnnIFbb70Vn/70p/GDH/wAd911F370ox9h27ZtDbfR2dmJiy66CJdffjle9apXYf369dPtDiGEEEIIIYQQQgiZR5gOXADIWEAJQKVGzqwyClpCwLYBzwnHxR24WoyV9XN1k9pQaQ3ZDNARCLiuJ+FMs1SUZQvAiTlwp9fUUaXRbUWax7QFXEUul8PLX/5yvPzlLwfgu24ffvhhPP3009i1axeGhoYwOTmJbDaLrq4uLF++HOvWrcMZZ5yBU089NRLWTAghhBBCCCGEEELIRNHD7oMVAIaAGzhxaxUK8wIHrmUBliVgSqBVGbjG754MHbi1lCrftSshvTBvN5cVKOT8xlzHF3Gng1nEbLb1MrW8xJ5Tr51zZizgxlm0aBEuu+wyXHbZZc1umhBCCCGEEEIIIYQcA+w/ElpnO/O+OKoKlzlO0hw+SjsVwogkCKj1u2kqrSmeGoXPVFRCNiOQzyoHLlCppM+e0hwAwLaqx7ei2bUV+zTfSfhqEEIIIYQQQgghhBAyd5SC+IRFC2z0LVACbgMO3GCUbYmIw1YIUSXMmp95DRYfM0VfNU8uI7RL2PUkntlfbqwxRCMZ1PBcvqyeFI9AwXbuoYBLCCGEEEIIIYQQQloKlX+7dFFGi6wNCbiBgitEtGhZPD5BocRSs/BYLf3UHKdmyWYNAdcFxiY8jE82pghnMyJxeLY13LA4WzXUb+eepkcoEEIIIYQQQgghhBAyE+IFzAAgE6hY+w47WNmfQT5XrcoqB65lRSMJ4vEJCtvynbTSa6w4lwjalAgduPlIhILfRi2R2eSkNTkMjrroLFg4EMRG+Bm4Dc0+J7SaI3dk3MWhwbByXP9CG73d9hz2qPnQgUsIIYQQQgghhBBCWgbPk6g4gYBruFLzhpi7/4hbNR/gC7GAL9iarlsrRcFVQmlEb60hnlqGVVWJxTnTgRss303uXhW5rMCyRRn0dCZLdI2Iys2glQXjejyzr4IDA47+98zeKYQQtwkUcAkhhBBCCCGEEEJIy6DEWyEEssa744sW2Np1m+ZwdQPB07JERLRNEyiVICtl41EBpbLE4WFHi77xImaeBPYedrB9b7nhKAXAWP5ciqkJG6HVHLdxlFi+sMd33TZofm4rKOASQgghhBBCCCGEkJah7CcJIJeNFh6zLIHli3yRLk1UNB24ZoSCnSbgBtN4ntRt1nKjWhZwYNDF0KgH6YUxD7lcOJPr+q/17zvs4Jl9jbtBw+WLqs+ONmqJjSyvVfXRBV3zV+acv2tGCCGEEEIIIYQQQtoKKSV2H/BFz1xC5SblqvVSVEQzAzfiwK1bxKyx/gmEhdKU+JvLikjUg2tYQBvNwk3r16wxheW1miNXdUdvsxbrXzOggEsIIYQQQgghhBBCWoLRCQ+Do/478UlFyrTgmqK4eoaLNZKBm6KIKo1XetDCXy0tU4hQwFRt5rLRqAcVAeE3XKOxBphtLTJpea0m2KYh2jnItw4UcAkhhBBCCCGEEEJIS1CuhGrhmmXVFlwVi5DqwA1G2FY0NsFKUcCUS/fQkNNQ/0yNUC0rmxGRvN6K0VSjzt7IMqY+y4yZ0jJbTdCNCe+t1r1mkGBGJ4QQQgghhBBCCCFk9nGDDNu+Hhsd+VoO3OT5PSPH1jYUXCtFoVSC8JFhV4u8tYyctrbsAq6RgQv4Qm7FkXAM1XYq7lUpVfE2Q1CdZTUyqb/tIojOYwMuHbiEEEIIIYQQQgghpDVQAq6dUnUszMBNiVDwwumsiAM3ub01y7Lhst36/Vu1JINFC2z0LbC1sqnyb7PBT8eIUJiP4merrVO8P7JdMh+mAB24hBBCCCGEEEIIIaQlMCMQkohk1iagxDtLABlDBE5rr6vDQkfewmTJg5T1hcyOgoUV/RkIIeHIqHCbDZZnZuCmZfUm9z0cFqHRd85pF0F0PjtwKeASQgghhBBCCCGEkJZAuWDTM2uD6VIzcMPpent8sbXiACv60yWwuPDXqBDoOtEIhUywCKcBJ28zGJ1w8eDjRYwXJS4+twPdHfa021IFwOJa7diEh/HJ6IetJujq6An9+9z15WgxYwH34Ycfxhe/+EUAQG9vL26++eYZd4oQQgghhBBCCCGEHHuoXNnUCIUUoVERycC1BI5bmWt42Y3qflL6E6us2+oIhdAePJUiZmpSIVC3qli5IvG9n4/hc98eBgA8urUT739Df+MLq9MHACiVPTz6dKlKsG1VfVTM46DYGQu499xzDz71qU9BCIG3ve1tzegTIYQQQgghhBBCCDkG0Rm4dRy4adEEYQTDNN6n1+Jv7XnVkpVbWDlwlZBbMRy4M3WDps1fdiT2H3b077sOOMkTzoBiWUJKCcsS6O22MDbh+fEQLabgzkfHbZwZa9OOE35BTj/99Jk2RwghhBBCCCGEEEKOUVy3tgCrtNU0Z6vpwG0U/er9FJRJCcAJ+poN7JEZ5cA18h2mJC6qvqOuAReQwPB46PQ9Mjyz3Iak7aUE6q6CwIb1efR0+jKim5I/PNcwQqEGK1as0MP5fH6mzRFCCCGEEEIIIYSQYxQlwKY5cJWwKz1fKB2fjKqJ5UpQxGwGlsVGtV8l1IYOXP/zSkTAnZmamDa7BDAyFoq2AyMupJR13cP1lxcuUK2HEqYtq3Z8xVxjzeMqZjMWcE877TQ9/Mwzz8y0OUIIIYQQQgghhBByjNK4A1fit0+XMFFMtoPa07DgNpxXG6QIKIeqyr5VQqcbSzPwPKnFzzrN+t0RDcQ4SBlx4DouMDzmYWHP9AqZ6aUZ20AJ1Jkgj9jc9q2E7k7Qv9bqXXOYcYTCOeecg5NPPhkA8O1vf3umzRFCCCGEEEIIIYSQYxSdgZuiQ5rOWiXeFvIWOox/vd02FnRPQ/KKCYE1J5OAqlUWL2KWzwks7ctEpzcoV2Sq8NzI8hUj49E2Dg5OP0YhSS9WqamZYF8oDdpr1QiF+WvAnbmACwAf+tCHIKXE448/jn/6p39qRpOEEEIIIYQQQggh5BgjLGKWrMYlfXz2SXmcc0pB/zv9+Lx2jTaCmIZzU8pQyIwXMessWDh+VTYyrWKy5OHXj0/ikaeK2Lm/UtWm7lOd5XuexPCYFyzPn/rAwMwLmZnbIO7AVeJ5ixlwNfM5A7cpAu4f/MEf4LrrroOUEjfeeCP+5m/+Bq47s/BkQgghhBBCCCGEEHJsEUYoJI8XQkTiBSxLNBRPMBXqtSalhBFzq5236me5IiNuUFNQHDMye8dTXLiHBlx852ejKFdkaobuyLgHJ5De1izzxeKDTRBwTaojFPyfDUdNzBJm9MR8ZcYZuACwc+dOfOhDH0JHRwc+/elP48///M/xmc98Btdccw02btyIE044AT09PchkGl/c2rVrm9E1QgghhBBCCCGEENImKFdrrSJkllWdPzsTtHNzCtEAjhOqmMqBmw1kr7IjtdAspYwIuKrIGlAdRaCm+/PPHsLgiIfzNxTwBy9dkLj8oVF/5o68wJKFNp7cAew73AQHbiQD1/+p5Dy1T7wWU3CrNO7W6l5TaIqAu379eq3Cqy/n7t27ccstt0yrPSEEHKe5Tw0IIYQQQgghhBBCSOvieVIXyLJrRCBYAlDvfWea8m55lHpOTilDdyoQZsSaDlzAF4YlELhoo+OAdCF0cMQXZx97ppSqRRbL/ph8VqB/od+B3QeTtTTHlRGx2LJ8V636PGMnx0gokVpHKKhpWlQgpQN3isQr5aXZvQkhhBBCCCGEEEIIAcL8WyA9QgFAEJkQiIvNcODGdKxGWtTuVDucXzlxtYBrAXCjkQMVQ2OtVwzMFHurxpXV+gOLe30Bd9eBMFN398EKFvfaGBh2sSOWtQv4YnMlEGjzOQu9XdVrrYq0ZXWEQtDvVpP5gv6o/dBq3WsGTRNwKdISQgghhBBCCCGEkOniBsqgFcu5jWOOUu7XZjAVYVJl9ZppocqpqkRa3c2UCAU3try4tlZx0t2upaCdbMaPUACA/QMuyhWJbXvKeMfHD+CE1Vnc+PuLEuevGBEQpbKHcl5VKAunUS5jO9jGqrBcq0UoKOaxAbc5Au727dub0QwhhBBCCCGEEEIIOUZRubZ2HVHWjgi4R0G2ayBCQYnNZgavcuAqcTXJsVqKRShs31vGkWEXZ51YmFIXS4EAm7UFujotFHJAsQzsPezgJw9OAACe3l3RruaT1+bQvzCDQ4MOtuwqV7UXdwN7ngxF6hZ34Oru6IiHFutgE2iKgLtu3bpmNEMIIYQQQgghhBBCjlGUMKicnmkII16hmQKumVVbjzBCIZxeFzGLCbhKTpRSohIrYqYKjx0YcJJf/U/RItUyMhm/x0sWZrDroIPdByrI58I+KbespbZpyuopAVdpn6ZIq+IsWj4Dd647cBQ5ClHPhBBCCCGEEEIIIYRMDeX4rJV/C/gRC4pmRigoGimGpQTcrCHg5jIqQiGMggBCwfOx7WVdpA2Iul6nWoCrVI46gBd0+xttYMTV/QCA0Qkv6EuwnJT24q5a1TchhBZ/LR2hMLW+Hm2U49bchvPNhUsBlxBCCCGEEEIIIYTMOep1f7uOq9YyHbhNKWLm/2xU8pMI82HNCAU1rHNuVeSAJyGlxPd+PoYf/WpcRy2YYq5AsrO1Eg/KDSg70eV3FfyNMjLuRWIaBkd9pdnSLtrk7aW2vZrTSxBFwwiF1hRHzTVr0S5OGwq4hBBCCCGEEEIIIWTOadyBGw43N0LB/1mvRSnDvF6ziJlyvpa1Azcc5zgSP7hvHPc8PImlfQmJpsa05vqPTSbbXcuBA1cts7Pg/xwZ9yLzjIwrB26yBVcVi1NRC8q5qly2Zl90hEILOXAjTtt5nKHQlAzcNAYGBvDAAw9g9+7dGBoawsTEBPr6+vDOd77zaC6WEEIIIYQQQgghhLQZygVq1RFwuzosDIy4sITQwuVMmGp8AZDiwA2ctSrnVo3xJDBWDFXPJAerJQQg/WldQyAdm0gRcJUDN6sEXH+jDY+5Effp0Fgg4AbbNL6uGRuoONWOVRWpYE6v2kgxBc85daKT25qmC7jFYhFf+MIX8NnPfhabNm2qGn/CCSdUCbilUgm/+7u/i2KxiEwmg29+85vo7u5udtcIIYQQQgghhBBCSIviesqBW1uJW7MsiyV9NjK2aKoDF1q0rNOmIbJmI0XMog5cVWxNSmB80si+lb5gawq5ashxoupo3IG7/4iDzdtKYQZukAFsOnDN7o+MucE6BesWW5WMLVBxpN72uo/x4meozvRtBSIGXGPFW6iLTaGpAu7PfvYzvOlNb8K2bdsANB4YnM/nsWrVKtx2220QQuCOO+7Am9/85mZ2jRBCCCGEEEIIIYS0MPq1/QYKkxVyzU8FnYro5wZCq1lETWXbqgxcU/AcN4TYSkXCsgDPNZbtSUiE4q+eNvb72/92P0bGPeRy/u/ZrL8dOvNhBq6ZITwcRCgoUTzJgQv4onJShqyppesM3HjFs1akDbo4FZr2bf/e976HF7/4xdi2bRuklFq8Xb9+PZ7znOfUnf9Nb3qTHv7GN77RrG4RQgghhBBCCCGEkDagUQdus1FLa9RZKgFUvGiEAQDkAptkxYlNLyXGjSiEciDgjk162L63gu17K3hsWwlbd5WxfU8lMq/rRttSmbblslqmv/wOw4FrisUHj/id0Q7cKgHX/0AXMQu2QVKchWVFp2kFzK5MJwqjXWiKgLt161a8+tWvRrlchpQSixYtwic/+UkcPHgQ27Ztw7333gugtgX9wgsvxLJlyyClxM9+9jNUKpXUaQkhhBBCCCGEEELI/EKJlfWKmLUCyj2bFKHger4YbQqeZgZuqSJhWQJDox5KZf/feMlDsexhvBiNTIgLuHHygYDcYThwzdzcHfsdFMvSyMCNanOZTLyIGSI/LWFGKATr3kJFzEzMVWshjbkpNOVP4qabbkKpVIIQAmeccQY2b96M97znPejv72+4DSEENm7cCACYnJzE5s2bm9G1o4bruvjtb3+L22+/HTfccAM2btyIzs5OCCEghMAb3/jGhtq5/fbb9TyN/PvLv/zLhtotlUr4t3/7N1x66aVYsWIF8vk8Vq9ejSuvvBJf+tKX4LXqXxshhBBCCCGEEEKOSbQDt5m5to0QLE6JfnUjcKVMLmJmDJfLMuLsNTNwSxUJW4Qu1/6FGZywKod1K3JYsjCaH+HUiStQDmDlwB2b9HRsgiX8aIRPfnkAwyoLNza/jlCIyURhBm742fC4h72HHFRaqYqZmYE7d7046sw4A/fIkSO46667IIRAR0cHvvvd72LZsmXTauuss87Ct7/9bQDAE088gbPPPnum3TtqXH311fjmN785191I5IknnsBVV12Fxx57LPL5nj17sGfPHtx111249dZb8Y1vfGPa+4oQQgghhBBCCCGkmejX9udAiTPrODXyKr4Sm7OGspYz4hTKjgwzYyUiztpyWcLqDEXSzoJAZ8FCxY0WDQMAp44DV0coBJnAUoYZvFdc1IXv/XwckyWJ+x4t4oqLuqvWTYnlfl/CkUo3Nqd/8pkSRsZd2LY/fbyvc0FESjYduC2kMTeDGQu499xzD1zXhRACV111FdatWzfttpYuXaqHDxw4MNOuHVXcmId90aJFWLx4MbZs2TLtNm+44QZceumlNac59dRTa47ft28fXvKSl2Dnzp0AfFH8DW94A1auXIlt27bhtttuw7Zt2/Dzn/8cV155Je655x50dXVNu8+EEEIIIYQQQgghzSAsYjb7GbgSjYl+SuhVwqrpurUs4Rcn84BSORRwpfSdsQoVoaBEYEsIXzCV1UXLXMPtqoRZEyUaWxbQVQDGi+G4a1/Wix/dP45SGSiW/XnTMnCrHbj+TzOPWARuXNfz13/X3jIW9dro62mg6twsMPdy8tFjxgLunj179PAFF1wwo7ZMIXF8fHxGbR1tLrjgAmzYsAHnnXcezjvvPBx33HG4/fbbce211067zXPPPRevetWrZtSvG2+8UYu3r3nNa/DFL34RmUy4m9/1rnfhZS97Ge655x48+OCD+Nu//Vt85CMfmdEyCSGEEEIIIYQQQmaKEivbIQNX+foyhoArAGRtoOT5Iq3Km5UAJqoE3FAktaxQPI4LuI4h4E4Uq+Mw87lw+Uv6Mhjf5xctK+QF8jkLG9bn8chTJZSDduMiZzYWoaD64clqwVfl4VoC2H2wggMDDg4MOHjuWZ1V/ZoLBB246UxMTOjhzs6Z7bCxsTE93NPTM6O2jjYf/OAH57oLVTz22GO44447AAArVqzA5z73uYh4CwDd3d348pe/jBNPPBHFYhG33HILbrrpJixcuHAOekwIIYQQQgghhBDi486RAzdOraUrYTApA1cIX9AtVSTKFcOB68lIBm65Iv18WpX5G7hxJYCKE12e6YyNFzgDorENl53fhdu+MwwAuOC0AjwvLLJWUe7dlAgFN6Z4eh4wOOohY0ffQBdCwLIERoNCaeWKxNZdZcSjepf22VhoOHP3HnYixdXi9PfaWNQ7dSdvXKgVQkTiMOYLM36mYcYemG7c6fD000/r4SVLlsyorWORO+64Q39J3/rWt6K7uztxulWrVuHqq68G4Avwd95556z1kRBCCCGEEEIIISQJXcRslh24QmUo6A/qz6MzcGNicz4QVCdLUmf5SgBjhvhaKkcFxpoOXM904FYLk3lDQL58YxcKOT/G4Y0vWwhPSl2kTMUvVEco+D896cdDqCU4jsSBIxXsOVSJuICF8B24qr3BURcHBx0cHor+27G/ouepOBLP7C1XTWP+274vnH4qmFqtaCS8uE2ZsQP3+OOP18P/+7//iw996EPTbutHP/qRHj7nnHNm1K9jke9///t6+Iorrqg57RVXXIH//M//1PO94Q1vOKp9I4QQQgghhBBCyLFJxZHYc8iJ5LkmoXJl5yJCQer/GpgOgBPosdmYslbIWQA8jE146Cz46qgnqyMUdJEwS0AI3/EqpazOwDUcuUkRCqOTLkoV//PFvTY+dO1i9PXaWL8ii6FRV0c8qAgFKyZyKgFaQODgoItS2Rdo9x72FyyELzhbeejfhRUKzUpA7evxHbfFsod9hx3tpgYQyfpdtyIbWb7jSuw6UKla7+kS1+LnCzP+k7jooouwYMECSCnxs5/9DL/85S+n1c7//u//YvPmzRBCYM2aNTjllFNm2rW241//9V+xYcMGdHd3o7OzE2vXrsUrXvEK/Nu//VskqiIJKSU2b94MALBtu64Afv755+vhRx99dOadJ4QQQgghhBBCCEng4KCDvYfCzNS0f1JKWELowlqzRdy52cjSXSfZgVsIhM7xohe6XWU0/qBckVqkVi5dlTlbJeAaQmiSA3dkzMPQmIehURcHBhxAAGuWZYM2UeXAjWNZoag7OOJiYCRoRwnMwu+TEpwztoiIwErAXdBlYUV/Bkv6fEXbM5zD0sj6XdGfifxbvjic3ovnMEyB+D6cbzEKMxZwM5kMfv/3fx+Av3GuueYaPPPMM1Nq4+DBg7juuuv0729/+9tn2q225IEHHsATTzyB8fFxTE5OYteuXfjud7+Ld7zjHVi/fj2+973vpc67a9cuLfKuXr0a2Ww2dVoAWLNmDWzb/yvesmXLvPtiE0IIIYQQQgghpDVQOa7dnRbWLs/W/Hfq+tzcZODKBp2bKgNXOXCNDFohlAMXGJvwtKjoO3DD1ktlicW9NpYuyqAvyIn1HbjVQqsbiVCoduBmMgKFnMCSvozO41VteJ4MHbgpEQpCCNh2KFoX8hY6C5beFpYQqDhSu6dX9NvJxcKEmh56ffQ6BNNYCSqk6bZ20yNyG2eepijMOEIBAP7qr/4KX/nKVzA2NoY9e/bgvPPOw9/8zd/g2muvRS6XqznvD37wA7z97W/Hrl27IITAihUr8Md//MfN6FbbYNs2Nm7ciOc///k4+eST0d3djaGhITz44IP4+te/joGBARw6dAiveMUr8OUvf1kL5iZDQ0N6uL+/v+4ys9ksFixYgMHBQVQqFYyPj6dm5gJAqVRCqVTSv4+MjExtJQkhhBBCCCGEEHJMojS+7g4Lq5fWNpy1ArWiVNW6uAlFzACgkPN/H5v0tJYopcTIeFgMrFSREBBYtMD2i5lJaIfr2GRUxTTzZ8eDcYW8QLEULr8jZ2Fxr42OvIWJoqf7JmXoEC6lCLiAX0RNdbYzL9DTaWF4zNXTlypSx1sUcha6CiIiLAOhi1eJtOZo5ay1rOqFW5ZfFM3zJBxXVm3PeihDYnyu+WZTbIqAu2TJEnzta1/DK1/5Sriui8HBQbzjHe/Ae9/7XlxyySXYsGGDnnZwcBA333wz9uzZg5/+9Ke6cJmUEh0dHfjmN7+Jjo6OZnSrLXje856HZ555BqtXr64ad9111+HjH/843vKWt+gCZW9605tw0UUXYe3atZFpx8bG9HChUGho2R0dHRgcHAQAjI6O1hRwP/axj+HDH/5wQ+0SQgghhBBCCCGExGnlGlNTFfyUWzRjKGtCAB15w4EbiJmOKzE8Hi6hXJFhEbSsQKkcxgeMTvgN93YJDI9LvRzXldg/4OfSLl+UwTNB0a+sDQQvWGs364EBF8WyhOfWL2JmWVEXrGVFRWkhov0FfAF5PHAUe4FreP+Ag4wN9Hb7CzRf9FbDCfqtvw1toOw1x4Gr1m++vWjetFjol770pfj617+O3t5e/dno6Ci+973v4e///u+1bXxgYAAf/ehH8YUvfAFPP/20X+FOSixatAjf/va3ccEFFzSrS23BiSeemCjeKnp6evDlL38Zl1xyCQCgWCzi7/7u72apdyEf+MAHMDw8rP/t2rVr1vtACCGEEEIIIYSQNqTFxTQt+kGJnPWVZuWMzcUduHn/9/Gi1K7QwZGoMmk6WpVDVkr/3+h4IOB2+5KdctPuH3CwPygs1tsdLjNjCy3SKiF3ZNzFrgMVPLPf0XnCqohZfM0EEImssIQfy6AE0FDADefpKkTlxENDLvYddrBlVxmTJS9Yn1CU9oy2krADZbdekbsk9Bwt/HCgGTS1rt+rXvUqbNq0CVdffbXOV1UCrYn6TEoJy7Lwe7/3e3jooYfw4he/uJndmTfYto2PfvSj+vekLFzTPVssFhtqd3JyUg/39PTUnDafz2PBggWRf4QQQgghhBBCCCHHEkriUoLm2KTEvsMOSuUg3kBFKEx42nG674gTaWNkzMXQqK/gqoxa1d7IRFzA9T8vliRKZX/hq5ZkcfnGLjzvWR3IZYVuw45ZXCuOVzcDFyLuwBXIZmBk4AIDI24kf7erw88yVtvDcaQWGNV2MLeVysON90+hBGjXTRxdExkTh/USWvyhwVRpSoSCyerVq/G1r30Ne/bswTe+8Q384he/wKZNm3DkyBEMDw+js7MTixcvximnnIKLL74YV111FU488cRmd2PesXHjRhQKBRSLRezcuRMTExPo7OzU4xcuXKiHDx8+XLc9x3F0jm02m0VXV1fT+0wIIYQQQgghhBDS6q+zCwR9lPXdt2pdnKCK2eCoi+17yxges5HPCRRUhMKkp52tB2IC7uiEh8FAwM1lQweuJ6WOJljYbQOo6GJpZUdiInC3rlqaxYJOgQNBpIISYONFwjwPCREK0fVTDlz1sWUHrmAtjPojdgSRDYoV/Rns3F+BROCwDeavGCKs6/muYE+qDFwkolzCzjQcuMcKTRdwFatWrcIf//EfH3MFyY4WlmVh0aJF2Lt3LwC/aJkp4K5ZswadnZ2YmJjA7t27UalUkM2mB4Pv3LkTbvBo46STTmro9QBCCCGEEEIIIYSQ6TKftAclrGYDUbJUlijkLRRy/u9jkx4W99qYLGWwaYv/pnTGBhzX10aX9GVgW8CyRTYODjh+obOJUP3s6QocuEH+QLkicXjYX+jK/gyKgdNViFAAzVgCpbKHyaDAWckJ82xVhEIcEXfgCn8e04Ebx9BrfeHZk1oALlckLCHg6TfyhXbgpkYoKAF3Bhm4qukwDmN+0dQIBXL08DxPFxwDoo5bwD8Inn766QAA13Xx8MMP12zvgQce0MNnnHFG8zpKCCGEEEIIIYQQYtDqYpopLDYqMbuBqVblzirxsZALi5hlMwLHrcxp4bp/oR0sT+CkNTkcvyqnHbsAMDzqN7JogdCipueGAu6hQX+ha5dntGgLhAKuZQnsPOBg/xH/39i4p8dVAgdusezhnocncGjQ1X2xrHC9bUv4Am6d3FrLUs5hPx9XTVZxQqetyr6tV8RMCcjTycBt+S9Xk6CA2ybcd999OrN29erVEfet4vLLL9fD3//+92u2d9ddd+nhK664okm9JIQQQgghhBBCCImiaiPNF/9tqSxRDgTcnk4bUkoMjrgYGnG1Y3bcyIw9POyLpYt7fQG3HLhnN20t4m//47B2zKr8274eWxc3cz3f4Xpk2EW54guqq5dmtTArRCgiCyG1CNrdYaG7U2BJIBqrCIXbvzeMH/1qAv/8X6FJ0LaF3jeeKzE24ep9ZgrFCjXOEiKIfoDeueVK6MZVzlu1TazUDNzpRyioOeJCc6vHdkyVpgi4d911F0ZHR5vRFEnA8zz8xV/8hf79ZS97WeJ011xzjR6+9dZbMT4+njjdnj178PWvfx0A0NHRgVe+8pVN7C0hhBBCCCGEEEJIe6EFvzoqs5QSR0Z8QTafA7o7LQyPe9i5v4xn9pUxPBZk4454+PTXBwAAQ4Gz1s+1BfYdcfHGD+/Fu285iB8/MIn7H/MNe+NawLW0i9VxJSqOxMHAfbtqiY1sRiBjhKKqmASNEFi9LIvVS7M4YbWf6VAKBNyHnvTjHFwjriBjC73ek2WJJ3aUMTLuT2DbIlHEBQChVEUptbu2XJG6WFnDDtxAgPZmEKGg+zRfnhLEaIqA+7KXvQyLFy/GBRdcgPe+970UdBvk3nvvxWc/+1kUi8XUacbHx/H6178eP/7xjwEA+Xwe73vf+xKnPf3003H11VcDAPbt24e3vOUtcJxoUPbY2Bhe97rX6WXeeOONVXEMhBBCCCGEEEIIIc2m1cW1Rl2bRwJHbU+nhXwWqAT5srYtkM+GK/mtu8fw0JNF7X7tLITjdh4w9JpguaoAWC4rkDEE3AMDDvYd8qdft8IXZHXmK8IIAimDKAXlgJVANuiP6oNnFBlTWALo6rCwoMtGIedPv3RRBn0LbCzotCLrZPbXEqFIqzpULHvaBex56mcw/VEoYpa2z+aZAbd5Rcxc18WDDz6IBx98EJ/85Cdh2zbOPvtsXHLJJbjkkkvw/Oc/Hz09Pc1a3Jyzfft23HbbbZHPNm3apIcffvhhfOhDH4qMv/TSS3HppZfq3w8cOIC3ve1tuOmmm/DiF78Y5513HtasWYOuri4MDw/joYcewte+9jUcOXIEgJ9L8vnPfx7r169P7dctt9yCX/7yl9i9eze++tWvYvPmzXjjG9+IlStXYtu2bfj85z+Pbdu2AQDOPvtsvP/975/ppiCEEEIIIYQQQgiZF9QTmSWAASXgdliwLQE7mGlBp4WhgsD6lRk8s9cXXL/x4xEtnvYGDty0ZVYCATObFbAMUfNH94/j/sd8I95ZJ+YB+AXL/JlVNIGEki0tLegCucCdW6pIeJ5EkkYqpS8Cr1ySQf9CGwcHXSzstn0hWCTHKOh+q6xcw7q8fV8Fq5dmtLiqRN60InY6RzhBXJ4qwuzUPKIpAm4ul0O5XI585jjOvBZ0d+zYgb/+679OHb9p06aIoAsAmUwmIuAqxsbG8K1vfQvf+ta3Uttbvnw5Pv/5z+PKK6+s2a9Vq1bhBz/4Aa666io88cQT2LRpE2688caq6Z773Ofiv//7v9Hd3V2zPUIIIYQQQgghhJCZ0Gg8wVyhdEUjyrWKUtlD2QEytgwduN0W7IyA0JEBEgLAyy7qRt8CGx+57QgOD7twA2Gyp8tCV0FgvBgVGCuBGdcNnLy5jNACreNIHB7yMDjqIWsDL7mwK2hLoLfbxtplWd2OJ4HFCzPagSul7+ZVy7j2I/t0lEIalrEuatsoh7FC/WYJAYlwusW9No4MhznAynmrnLiOKzE64aG7Q0TEXBW54HrTcOAGP6u04Xmm4TZFwB0eHsZ9992Hu+++G3fffTfuu+8+lEolHWoMzH9Bdzq86EUvwp133olf/epXuP/++7Fr1y4cOXIEQ0ND6OzsxNKlS3HuuefiyiuvxNVXX41CodBQu6eddhoefvhh3Hbbbfiv//ovPPHEExgcHER/fz/OOussvPa1r8XrXvc6WGnedUIIIYQQQgghhJAm06L6LYDael+p7OGhJ32da2GPhYER5cC1YQszwiCcR7lWK04ogGZsgf4+G+P7fMX2tONyeGx7GZWKr3SWg+nyuTAD1/WAw0P+9OeeWsCCLuXiFVjRn8GyRRkcHPT747jAkoU2OvIWJksepJRawAWAXQeiMZvK8Wob8lC8AJmALwJPJKV/inCdBYD1K7I4MuxCCAFPSrjBSCn9bfD49hIODjhYvjiD41fldDNq+W4zHLjBz3mm3zZHwM3n87j44otx8cUX4+abb0apVJqxoHvFFVc0o2tHjUsuuSSyPtOhu7sbr3jFK/CKV7yiSb0KKRQKuP7663H99dc3vW1CCCGEEEIIIYSQRpmhfHLUEXVUv8mS1BrQ+KTEZMkfLuT9uAItesowJkAVFitXpBZws7afLbsjEHDXLc/ise1llAPhUgmquYxALqdcqcBIUBhtUW8YwZDkPFUZshkjqSFTw7tXLPnt9i+0cWTERm+3jVLZ0+vity9w3IosnvZ8gblY8vT+tE0B14hasOB/LgMR2PV8AVdtm4mYA9m2G3fgup5EsRRONz7pwXGBfG5qjwc8z9+PhbzQDuBWpmkZuCYzFXRvueWWquJbhBBCCCGEEEIIIaR9aekiZkZkQByzuFalInWmbca2YNtCxw5Iw2OsRNSIgJsR6DKExjVB/IHj+EEETiB4ZowIBdeTGB73ld2ugoW9hx2s7M9EYimUW9cxnL66HzWUPyVEW5bAhvV+tu6OfRUAYeyBEEBHwcIZJ+SxbU8Z+wPRVy1bd8MKhVjlzFV6rIxtWzcWxGsbbuN6bNpSwqTRh8mShx37KjhpTU4v21xmGo8/U8bwmIt8zsK5p+RT83lbhaMi4MZpRNBVGbozdbUSQgghhBBCCCGEENIsTGGx7EiUgsiDjO2LjyKQMV3Xg+dJVJywMFfFMQVfgYwRadDd6SuXarzKwM1nhRZePQ8YHveXt6TPxvikP2zKZ6pF5eDNRGqlpQuTk+VqDa4qQiFh9jADN/xFmVgtIWDFBFzVlprGiQm1OgPX9Z3OaWKq60kt3iqHc6msirQlr2MaY8F2LJU9eF64v1qVWRFw45RKJYyOjmJ0dBQjIyOoVKa4lQkhhBBCCCGEEEJIW9HKHsdadkLTgSsBlAMZy7Z98dEKnKcDIx72HKogY1vIZUNxVgm02Qzwkud04acPT+LiczpQyIU5uZDhcrKZUKCcLIX96F9oV7lXhf7PjFAIt3Qtn6TpZNXtGXEQRtORcWpjCSG0EVOJsJbl//Nk6OL1pN8PEeunwhRPa4mpymFsCYHzT+sAADz4+GSkb7qLdfyh0lj1drCSzoqAOzo6ip/+9Ke4++678ZOf/AS/+c1v4HnRL4npvD399NNno1uEEEIIIYQQQggh5CjTTi9bJ5k/zeJanie10zVrC9g20F2wkMkI2LaACIRMJVIWDZdrxhZYsiiDb/3dKhTyAj//jS8+quJljqfEUL9tk84CkLEEShWphVHAFzPjDlw7I7S4Wmvbm1my8fU3IxTSECKcX8UnWLqgmzQcuEp49X+PO21tK+yvW0vAVetnjJ9u8oG55p4H4Fh04I6OjuJnP/sZfvKTn+Duu+/GI488EhFs4zEJp512mi5edskll6C/v/9odIsQQgghhBBCCCGEzDJaBWpRC64QfphrmtZpFtdyDDFXibaFPHDi6hwW9liAlNh32InFGITTW0GmLADtwC1XPEgA6gX1fUdcdBai1cc6cha27CqhkLdQLEudGwsgzOCVoQCs0g1MDa6rQ2CyGAqriQ7c+O9JEQoJG0rpzZYQECJaxExvPqMt14tGPdiWv21dVwLZ5C9KosO4gf7G8TzZdhGuTRFwlWCrMm0feeQRuMbjCQq2hBBCCCGEEEIIIfMX5ahMEsbMz9KEs1YpIpXUD1O0VXEIAJCxfOFSeRZVjIBti4jAqrDtqECaVwKu4/+us3WtcJxiQZffoOdJuK5EqSx9ITNhs9mWgLAAuL54agn/583X9cNxJT5/5xC27XEwWZLYvreMwVEP555S8NffijZYa7+Y45Tz1rJ8EdXzAC/Y156nvh/+ZwK+UGsKsbYt4LiyZiEz7XzOhPOpPqivVSPfovhX0P9Otsb3L42mCLiLFi3SDtukP8QNGzZEBNslS5Y0Y7GEEEIIIYQQQgghZA6RUuLRp0sYm/DQVbAwWY6+4g8Aew87yGcFCjmBnfudKu3ItgVOXZdDb/fcvMee5FY1Uc5PyxKo6BxWX6i0baGFShHEGWRsoV2xikwmmlcLAIWcr3pWKr79V3khMxmB5Yuzkfl7uy10d9ratape+xei2nUaEYol8KW/Womd+yt49oYObN5WQj7I5y2WJN780f0AgC/+5QqsWpqtKWNqsTRhnG354vK23WXsPFjB0oU2jl+VAwDs3F/B7kMVWEIgn/XbWbs8hzXLrMj8QNTtHEfth0jEgs7jjU5by2AbX0SNRbYMTRFwXdfVT1mEENiwYQMuvvhiCraEEEIIIYQQQggh85hyRWJswjf1jRdT7JMSGJ3wMDjqoSNfLRG6rsTwuDdnAq5JkoCphNX+Xhs79pUBBDm1GQuFvIA3rARevwHlQlXOVwDIBqtmulZ1hIIjIWFk4Noiki8L+AKuJUJh0nScxvvsZ8r6w54Eli/OYPliXwK0BJANtOFiJdxf2/ZUfAE31pj5e1W8gjGRbVsoVSRKgUN5dNLT6z485upl+32XGBhxsWZZKFJrAddwO8dRRczi+cDA1AqRxR8wtEMVs6Zm4AohYFkWOjo60NnZia6uLhQKhWYughBCCCGEEEIIIYS0CEnuxRX9Gaxe6otzE0UPew45cFwZOCgFzjghrzNed+6vYP8RR+elzgUR0TKxiJm/kv0LbRy30l+vQh44fnUWGUtoMdUKEg1sC4AEshmgFOTa5jKWnkahIxQC0VM7cO0wjkHRt8C38ErpC6Cu4TytcuBa4WdJGbGZoO3R8XCj54Lc2XhbVsL2kNpxHPyEL1pL6f+0IOB5wKFBBwcH/H0PCZywKovJssShQUcLvbrPgShb24GrpjX6otYL0YFammwb6rfNE3ClzrXw8NBDD+Ghhx7CLbfcAtu2cfbZZ+OFL3whLrnkEjz/+c9Hd3d3sxZLCCGEEEIIIYQQQuYIL0F4zdhC55R25H3h0nGkjh/oLFg6/1T99Oa4qFStxTsq49YWWpDNZiwtGro6A9c3N2ZsXxTMZIQWKjMJCpxy4Dquv/6mgBvXTRd0Bg5c+AKk9MLc1u5OCwcG1LwCnQULlvCDIeLrZVnhvlHOWCAUbmtHKER/lwhFXxG4jQWAbFZol6tZeKxQsFAJVrISF3B1hEL68pOKmNXrYxLx72w71DNrioC7b98+XcDs7rvvxpNPPqnHOY6DBx98EA8++CA+8YlPwLZtnHvuuTpe4fnPfz66urqa0Q1CCCGEEEIIIYQQMoskiV+WUcArayhPAoAlRESA06/6z6EDtx7KgZuxgGLJ72guEyqeOs5A+BZcO3Dl+jm4wWv/wfSmo7VgFCqrVKR2n2YzVkRJ7e4UsDP+dvU8/5/pwF22KINFC2x4nkQmI3z3rhFXYCJEKICOjIUbXYnrtSIU0tBxEZ6EsPyc2/6FWZx7Sh6TJanbt0ToLC5XCbiBA9et5cCNbstgDWM/g99qiLJVRczSJ20ZmiLgLlu2DNdccw2uueYaAMCBAwdqCroPPPAAHnjgAfz93/89MpmMFnRf+MIX4nnPex46Ozub0S1CCCGEEEIIIYQQchRJcs5awhRohS68BQHkclFFUIm9c+qC1GJntQNVSj/6oViW+My3BuE4/udhMTGpRVLLCh2oQJh7a05vCqK5rJ9VKyVQdkIRO2OLiKC9sNvW4qeUMnDgqvaCZWWiabiWsU4mlgjdwJPl8POK24CAGx9nthuI1trJK4BMxoLteMhmjJiJYH8rwVihYhFqO3Cj0yZhFm9LIx7TcMw4cONMRdCtVCq4//77cf/99+PjH/84MpkMnv3sZ+MXv/jF0egaIYQQQgghhBBCCGkSSc5Z04ELRB2TuUxMwBUqQqHpXZsWSoAcGHaxY39Fi3u/3DSJ//v1hJ5OibSmUKhEU0sIHL86F3EaZw3HbrgsgXxOoFiSEQeuimBQ4u7yxTZsKxR2PU/qiII0g6z6PEmcTIogUI5YEVNw478ntWkJoQu2mcKx5xnxEsEy1Xej7ETb0A5c44vguBJPPFPWfVM/zSJmQv9swCqc0v+4S7kVOSoCbpypCrr33XffbHSLEEIIIYQQQgghhMyAJOE1XvhKCYYCYbEsRRihMHcimkC1qHdg0MFkKVSnS+WoUp3JVAvPti30+uTsqFCqREcrJogWsoGA68iI2Ol5wLuu6cOvH59E1hawLN/JXAmcunq5KbqlCITS+Ga1rKgAqtARCvF2Eoar9E4Rxjvo4mmBc1nFHqjvhBJqK050e+oM3DCWFyNjHkbG3ch0Qgh05MNexfuiBOeaRcyO1QzcqbJ06VKccMIJ2LVrF3bu3ImdO3eiWCwCaA/VmxBCCCGEEBLiuhJbd5exqNfGkoXhLca2PWXkskJXIieEEDL/SM7AjcqAZg5uXMBNe9V/rtCFyQLdcO3yLHq7bTy9u4xfbCrq6XK2EgqjwqwSMC1LRAqXlV0Jx60WSPNBpETZEHAztoAngaV9frbt6LgH2xK+QGz5EQpKhEzznar+JAmcSQXVVFExYcWnT1mAsXARtOtJGQry0nffukbsgeeF+9t1fdFefVeEVS2Iq8GugoXjV+cAAPmsiHyHtGBco5tx4rEfrfLdq8WsCLhSSjz00EO4++678ZOf/AQ///nPMTo6OhuLJoQQQgghhBxl9h9xcGTYxZFhVwu4YxMe9h/x34+kgEsIIfOXJOdsXPQr5ANVUABdHckZuLWyT482SSKlepW/q8NCT6dV9cq/FkFj+bf+Z6pYmDGDBMYnvSrF1d82LoplT4uyWdsUFcPogYwtYEH4EQpGEbNaxI2SUsraEQrxEZHIh+iofDYUrQs5AelFhWPXlboomW2JQLD15/Wkn2mbC35Xwq75fVJ9z2QEejpjynK8m7G+1TKIVjlwa7bcGhwVAVdKiYcffhg/+clPcPfdd+PnP/85RkZGIuOTOOWUU3DJJZfgkksuORrdIoQQQgghhBwFkm66y0473A4RQgiZKUnJB3ZMa+vrtrB2RRbHr8qhvzdagSoU/FrkvKEcoirOIFiXYjnaP5Vpq52wsYhbP/IgVBVty28zHi+xYrGNHfsqODQYRgX4EQoyWiQsKD4mLODwsIt81oosL06t4nDZBDVQnbfjQmi8vyYLe2ysW5FFZ8HCskU2Dgy4RoRC4MD1ov0Rwo+D8MdJvQZJTmydElGjD2qaqXx94t/ZVvnq1aIpAq4SbFWm7c9//nMMDw9HxidhCraXXHIJli1b1ozuEEIIIYQQQmaReLEaYG6zDAkhhMweSZJPleAmBDrzFno6rOoiWSlZrbONRHRdTOcogEgeLgCUAkFX9du2/PxbIVRbMlK8zbYFXE9WbZv1K3O477dF7DscWnwztrk9QodvZ15gZNSPO8gpRS8tA1fNHduunpdWxCyYLzaqVgau9ICOvIXuYL96EtEIBSMDN2MJlIx18TwZybtNcmLLOjER/kTRfjcSqVD1XWuDS5amCLiLFy+mYEsIIYQQQsgxip1gz5nLV2EJIYTMHm6C8hrPwK1F+Op8s3o0dQRQJQSGBcX8n5Mlf4JCDiiWgRODTFalgQkRNGQ4Sc2sVttKfri5foUfM7TXEHBtW2jx0px/ZX8GPZ02Dg87dSMUTCE1TtKDVx2hUEvBjRE6ZAMXtScNJ62f6esYGbj+T+HHKUhocRdIdmLHUiQSiQu29SIlVD+T1qOVaYqAOzQ0BCFElXBLwZYQQgghhJD5j3kjqAqSUMAlhJBjg8QiZmkiWsLnSuxtpdfYpZRabFUPKYuBgHvtyxdiyUIbg6O+Mqm6rdZZQEBCQkogG4lQEH4Rr9jGUQLu4SEvmN9vyywIBqiiaBb6FvjZsxU3JbM2IE3IlBJY2G0jY4fiKpAeoRARdGNiqSej87iGA9ePUJBa4FerbVv+sOPKyLWCzsY1HbhKpE5Zx5rT1Pg+ubFx8aJmrUjTMnCllBRsCSGEEEIIOQYxsw5dL3w1khBCyPynEYdnLX0sdIrO4XlDxR74gxFhUZ3jVIRCPiciztrdBx1YIlbELCAaoeC3GxdI1y6PSnOWGRFsTGtbAlYQ0ZDJCENwTZY31efx/eNJYNliG/mcgDMZjtTtxdox9eb4spRLWLtujSJmnge4LnRMghWI2RlbBG5kYO+hCo4Mu/CkxOCIix37K+jMC2w4Lo9cVujvTS1Ht+7SFL4+cXdzO1hwmyLgfvWrX6VgSwghhBBCCIHrSWQhYg4amXqDSQghpL1Jij6w4kJfDSelMqnOZYSCiRBC599aQcEtIIxQ6Mip6mBAsezBdYGuDkuLukL46yuln2Wr6O224clwfRWFXFgADIiO91xj21nQGbsZW9QVvEMnbELEhRDBPgrHOU50vlroDNzYskzXsETgwNVZwv7nGVsgkxGQ0sPIuAcx4WFswsOuAw72Ha6gq9PC07vL2HBcviFdNR6zkJb9axLfdm1gwG2OgHvNNdc0oxlCCCGEEEJIm6OcNmYmopSN3RASQghpPxIFwoSM1TRaoYiZ0JXH/N/j+bcAUCyrDkocHvLgejIiOh+3MofRCS+SPWtuh4U9NlxX6vU1WbMsGwq4htvULApmi2Cc8IVhKf2Yhj2HKvA8P1t21ZKMdv2GWbTRZan9NToRVczDDNz4tqnub9hWdBovcBhbloDnSew/4uiCaUrUty2gf6HvAF67PAspgc3bSn5shfSLw40EfZMyuU81aaSIWexhQRvot5jCnxQhhBBCCCGEVGPe+Cjh1qwsTQghZP6S5JxNL6xVPSIsXtXMXs0MLeAaYqqKUNh32MHhIQcDI55+QNlZsCKxCorB0XDj9HRafgZuwjZYsyz0V5qisW1Bn2TtwIFrBQ5cKYHxoofdByo4MOBg7yH/Z5y4MJ62ndME3EiEQkrbwtiHQvjREWoxh4eiFwRCALmMhb4eG0v7bCxd5DuT1fQVJ3Ttqg9r1sSbxvem0W3SSlDAJYQQQgghhDQNddPLCFxCCDk2SDreR1yknsThIQeHh1xUKuluXd9ROjcnDz9MIFx2/LV/ICxiZgeO0smyNDJaw3bCV/ilLnTmj5PwpEx0J69dntXDhXy47Qo5C4t6bSzps2EFGbiW5Yu8nqwWzytOuA56OY0KuGlF0SIZuMnzKjzpi60Luixkg41XCva53kYiFKkd1xe0/T6FIvDeww48T07rWqIRs261K3nqy5ltmlbELM6vfvUr3HXXXbj//vuxZcsWDA4OYmxsDN3d3ejr68NJJ52E5zznObjyyitx/vnnH61uEEIIIYQQQo42xo2Puul1jRLPbXBfRAghZJrUE7+GRj3sO+xgsuThwICD5f1RKcoUBf0ogKPQySkghBmhUO3AzWYESmUAMlx301XrR8v64u5VL1yAf/jqAC59dgeA5CJmALBuWSjgnnF8Hrbt5/AWyx4W9dpGNIHvds0GDlwzYgEAKoYBN62Imfp1ca+FI8MeclmgXIEW1+Mu6SRBNC60hxEKEsISsCygf6EI+lTt7LUtgQokXE8ibwk/K9+4Whib8DA87lVl7CaSMrLW99KNbZR2uE5puoD74x//GH/6p3+K3/zmN/ozc8cODg5icHAQ27dvxw9/+EN85CMfwTnnnIO///u/xwtf+MJmd4cQQgghhBAyi6ibXrOCd1vcGRFCCJkWXh2bZMV4oOcmFjwz2pLAXOi3qp6X6mncgeu4UoujOUNJ8/UuEWqIgQVXwhcQX7qxC6cdl8PiXhv//X8jgbArEZdF16/M4oRVGbgesG55Fhk7LKTW35tBPisghP/Pd+AKrF6SwZpl2YjL13x4ajqBTdSvb7xyIX67rYjuDgv//ZOxGhm41QKpLmKW0LYAAqewP1+5IjFZkjg44OLwkAtPAvkgbkIJ016wSQo5gWLgbHbccIc0koGrpqk3bbkiMTji6nWbS+f3VGhqhMKNN96I3/md38FvfvMbvfJpG8Ec/9BDD+FFL3oRbrrppmZ2hxBCCCGEEDILmJf8oYDb+jdDhBBCZk69w72UtZ/jKWFSTTvXJDlwv/6/I3q8KhIGQ3g0YxG0cBrMf/yqHDryQYCtEPC8aoUxkxH4/Zf04nnP6vBdtsa4VUsy6O7wF2BZoUDZ3Wmhu9OK9NMxBVwdTRFdlvq9b4GFjWd0oKPgT5jklDXXJxEZnUYVbhPCF7+llCg7ErsOVLD/iJ8dvP+wg0NBLq7jSl/AD5zEmYxAPud7caXX4PdB64sNTAu/6JsiY0eaaGma5sB9z3veg3/8x38EEKrzlmXh3HPPxVlnnYUlS5agq6sL4+PjOHToEDZt2oSHHnoIrutqxftTn/oUPM/DP/zDPzSrW4QQQgghhJBZRLl/zFy+NrgvIoQQMk2SiphFxhtRAxDJZwTL8otfukoRnQOUaxYIH0LaFnB4yMHn7xzW05mxCmGEgv9TiCBPV0ZFQU9KPc3AiIvli2MxEqoD8J2rphCbMSzJlogKrGq6bMZ37FaMemGhAzd9nT2j/XKagGuK08G4tCZNB27GFr47W0ot7HYFQrRUBU+9wAULqefT2w/+dvPXu/HvRL2HAU6wjWxboK/HxsFB59gRcH/84x/jH//xH7UQ29nZife9731461vfiqVLl6bOd/DgQXzuc5/D3/3d32F8fBxSSvzTP/0TXvayl+Gyyy5rRtcIIYQQQgghRxnzvocOXEIIObao9/q5bOB8YAnARWs4IQV8MRnwRUflFtXIcCAIUNCioUCygOh5vqDpehLb9pSxZKEdEYLNGAlLxJy0hnipYgksIeBJqfuZywgUS9EIBdVmfJt6xgeeJ5EJ+qEzcBO2RxrxjFrXk77gK1Rmbzh3d4eFXEZgZDwMAnZ04TRlBPVHSSkj/a6dgVu/n5E+B+2uWZZFMcg1boGvXV2aEqHwgQ98QA+fcMIJePjhh/GhD32opngLAEuXLsWf/dmf4eGHH8YJJ5ygBeAPfvCDzegWIYQQQgghZJYZm/BwcMDRDhegNW7IZ8LYpL9OSf9UURtCCDlWqefAdc1zQMr5QBfBmqPzhRDRrimR07YEjsQFXHM6FbVgqmsJSqIngWWLbD0qngUsLFMMrS9FCiObFwByWSWIGoJ60IwqHndgwMHYhJcoLANAWYmpVRm4CauWsp+k9MVlpduq7SKEQD4rcOfPRvHfPxnB9r0lf5mVaEOWEBBBObOoczttSxh9Eckf10KvWxtcp8zYgbtr1y78+te/9ndGPo8777wTJ5544pTaOOGEE/Dtb38b5513HkqlEn79619j165dWLNmzUy7RwghhBBCCDnKmDeDI+MuRsbTb3bbDceV+O3WUsSxZJLLCjx7Q8cs94oQQlqHuhm4DTznsoIqYvUKoh1VDCFQCbOWBRwZCc9pZ56QNw24+tX/JJHTPG9I6ccHmI5UEyuIEQAAq4EqbsqxrB6WKgFXSgnX82MR7GBZoxMeRifKejnZbNgHT4YRCo6OUIj2sZaeXO3uDfoXLFu5jIUADg27eHy7nz/7q98Wce4pHSiWomKzCIrAQUZduCmbbUaYbul2eGloxg7cX/7yl3r4Va96FU477bRptXPaaafhd3/3dxPbJYQQQgghhLQHfT22/qdoZwduxZHwpIQQIrJuC4P1K1fmWHAghJA5Rhs+U5S+tAdgJlZKwa25QCAU9IQAjgz7KunZJ+Xx13+0JDKhDCJ7RVyARDxCQWW5Rj8bHHVxZNjF6ISHhd02ujstLO0Lz59mzIKJ+lwVHsvYoWiqXLmLe20s7cugb4GNvgU2RBC7UKn4wujYpIdKReqibCoD1+9nuFwRGQ7WLb7NlINaZQcH/VvSl0F3p41FC2xkDAXy4KALKYHJstQNSglUXIlS2cNE0cPgiIvBURej4x5GxtzUaCYZc9HqPqZ8mdTnKq+41rStxIwduAcOHNDDGzdunFFbGzduxNe+9jUAwP79+2fUFiGEEEIIIWR2Wdxr45R1ef37LzdNzGFvmkRwT2dbwIbjwnWTUuLeRycB+K/CWk0JpyOEkPZDCbSdeYHxYrUQVi9iAWiBCAUEh3ulJ+pjv8BAIOCec2oB3Z1WRLw0pzPbgtEWEBWE/fkk9h1xsWNfJdKP1Uuz6CxYmCj6G62n00I2QblTrlkl4FqWQMYGyl7oys1mBE5ck9PzPPDYJCqOv7+GRj0cGKigt9tGZ8E/gVXMrugNEksmqBNToLaHEmsLOYHVSzPI5yzsOhAuoOL4xdz6LT9KFQKYKHkYHPEwWfJQqkhs21NB2ZEYGXNRdiU6ChaOW5lDPaZi1p1CbbQ5Z8YCbrFY1MOdnZ0zaqujI3z1qFQqzagtQgghhBBCyOyQ5r5SNS7mI0IIWJZfYdv1JLJzVDWdEEJmk1LZw2QpelxXea7Hrcpi/xEXyxZFMwCkrO+s1QW3WiRWXLk9hQAOBwJuf68dPacF61UVoRA4c00xWq2X6WxV8QGFnKUjEABg2WIb+azA/iMu1q/IIpsBli3KYEFX+KTQjrltLQFkbYFyReoohDhmH8uO36FsVmiB2HH9t0pyWT/DVu+KhNNbfH8K4RdQU4KybfvCvWv0rxLLu91/xMXiXhuHh1w4joTj+O3Yll+UratgIeNIlMoSAgLDYylfjrTvVo2cXrPfSevTisxYwDULlT311FMzamvLli16eMmSJTWmJIQQQgghhLQ6hoGnbYlX2DaxLXWDOqtdIoSQOcF1JR5+qpQaG5PLCpy8ttoh2YirVr3+786RkiZU7mowbGbgKgfuol47WlgLqMpoNSMGzDVR66WKj0mE7tmVSzJYvrhanlvQFQrhJ6yObtdMLCfXEmFsgeMmb0N/G8tIv/t6bCxeYKEjJzBZlti5v4IT1+SSC5chQcs1FvXkzrIetoMHnMoNbAmgFBNwDw1WAOSwZVcZFcdPALYEUMhbWLwwgxPX5FCqSOw7XMHYpMRE0YPrytRYCd3HlJiHeJeFaC8Bd8Yv+pxyyil6+I477kC5XK4xdTrlchl33HGH/v3UU0+dadcIIYQQQgghs0C9+5756sJVN5FpuXyEEDKfKDth5ndXwYr8W7Yog0IuKjG5nsTohKfFuVpowbAFDqdCiFDAFUJn4PoOXABSolwxhFABCDNCIWFdlAPXFAyVuBkXYxshExMxhWUUI0t5qJike/oipsCyxf7M2/ZWqqZRGbkmSaf1Yjn8buRzMYewVS3gDox66Oqw4LgSZUfCdf3+WMK/bvCk/33LZoR2KKt9EVmHqoHGMOKKjw0B98ILL0R/fz8AYNeuXXj3u989rXbe/e53Y+fOnQCA/v5+XHjhhTPtGiGEEEIIIYTMiFo3dXZwN+W2yCu/hBAyG2RsgWedXIj8iztEAWDzthIe3VrE8FgourWsTiaifVO5vpblF/sC/DxazwMGRjwcHHThev50AqYDVyBJi45n4AJhvEBcjG2EeO66LUT4UDHFgSuMeTxTUBZ+RAMAbN/jmzJNEdjM4I1HJSWdI49bldVREa5ejtCid0+nP273gQrOPCGPHfsd7D/iYLLkwbL8dVPbSzXf1eHPs3V3GSPjURFXO2pj/Ug9f5uf13HrthIzFnCFEHjnO9+pn6rfeuutePWrX409e/Y0NP+ePXvwe7/3e7j11lsj7aVVLySEEEIIIYS0FmZF5whtdGM0HVQGYdrNMiGEzCumeKgbn4zmxQKASLFJtooTUi1eCZxSSlQcfzifE/CkRKnij7SEQE+njWyQGatRcQzGyihBUj34gwQqSsBNcLjWo5YDN+2hopm/K/VnfneVA3d74MA1+15Ln5PmNEYevhKYlQPXtoBysN1W9PuK8MFBXwiHDPe/JQSswAHteeECli4MbcoTCYXypkI0QsFf8lx/7xphxhm4APDe974XX/7yl7F161YAwDe/+U38z//8D1760pfi8ssvx5lnnoklS5ags7MTExMTOHToEDZt2oQf/OAH+P73v49SqaS/HCeffDLe+973NqNbhBBCCCGEkFmkSr9to2y5eiRm4Na5WSaEkPlIo347VcyrHVGCq+lEzWV9YVHFIfQtsLB2eRbDY652t6a9li9VUTTjTDmTCIV4DmxjGbhGf2KTLFnoy4N7Djk1l9vIrjcdyaovQkA7cLs7LPR2WRge9/DY9hIsK/xOqZ+elJAyjN7o7bEhIXBo0KmKLYpv73oZuOaMunheG3xRmyLgFgoF/OhHP8ILXvAC7Ny5E0IIFItFfPvb38a3v/3tmvNKKbXivW7dOvzoRz9CPp9vRrcIIYQQQgghs0Hr3/dMm1qrph24FHAJIccAUz7UT2WGOX5jwxQB/SJmfk8cJ+xRPiswUZJwDaFQTWdHqn5VuzqV5mjp6J0wT3g6EQqZ2Pv0liW0uzctA9d0CZv9EcJfNyAUWfVyUvqmBE/9Bg6i+04VpdNFzCyBUqAN57ICy/szGB4v4+ePTGD73gocR+LEtTk/gkL41eQcz4h6gOEwjq1fXLitR5JW2wb67cwjFBRr167Fgw8+iKuvvrpKuZZKOTf+xcdfc801+PWvf43Vq1c3q0uEEEIIIYSQWcB8HdFkvoei6QxcRigQQo4BpipyecYMykuZVs6sFc4XEcescuAGAm4244uQnicNwVCEGa9KXRNKzIyuaVxkVMKmEKHwOhXiwqolws/SCmtahoKr+wMRjTyIzVtVwCxFaBfCcLEazla1/SwBVIIiZ1kbePYG37h590OT2LHfwZ7DLhxHwtZFzELx1m9fGA9NY0uPd7FOQTyjm427dVuApgm4ALB48WJ87Wtfw4MPPojrrrsOK1asSLUhSymxcuVKvPWtb8WDDz6Ir371q1i8eHEzu0MIIYQQQghpBdrhzigN40Y9ji4YQwcuIeQYohGn43RfSZ8rJ6R5jBcIj+vlQGjNZUNXratdt2FEgC5iBkMUNM4Nah41ruIo923tjNk07FjsQkTAbcCBG98/On4hlqBQJeA2SChUh8XgSsE657ICG9bnccFphcg8pYqEsHyhWQKBWB46fMOHpunL/fXjk9i6q9JgH43s4ja4TmlKhEKcc845B5/97GcBAHv37sWWLVswODiI8fFxdHd3o6+vDyeeeCJWrlx5NBZPCCGEEEIIaQHaydkyHejAJYSQZOJCbDu8om4Sd+CqiAHXC8VPywqFXssSVfMnOXDVZGq+eJZto1Rl4Fr1z0kiIuCG8wFAJuhYJTavEq7jbdTanwLRvF01n4pnyGX9kS9/fjdOWJ3FV384CgAolqRfVE3428d1Q2ezsOpn/E6WJP76C4fgesCrL+vBqiXZ9E4G/VTu3RTTcktxVARck5UrV1KoJYQQQgghZB7TbjfmU0HHQySMowOXEHIsMZVD/VRPC9MwoTYVgfBcZkZ/VowIBSklXFeGoq0QWiw1i3CFIqcRIaEFUxFtdxoFzIDqwmdCCP2Zk3JOMkXVuGAZF3/XLMviwICDdcujsmF8N0njJGleC9gxQdu2hBZwsxnVZ+C1L+nF48+U8chTJZQqHoQV7gtPSlie0MutVzh0sujpcT99eAJXPLc7cTpzvyQVnGtVjrqASwghhBBCCJnfpGXg6vFtcGOURq3XgG2jGA0hhMx7akTKpE3bKKrNOT9fSBnpQzkQWj0PuPfRyUguqymImnqluXWklNGs3Fi0wHQKmAFJAmkocKY5VK2IBTf8TAjACuatOH6f1yzLYs2yGg7WGjG0pohtflbWYng4Mp8TWLTAX3ip7Gfy2pZERfpCrbDD+dMycNVvpnu4VE5LWza6LvzltQtNzcAlhBBCCCGEkHlJwj1eI3l8hBAy32hE8mrn51pm3ytBJmwmEB3NcREBV73qH/wXunnVTxW7EM2pnW6EQrUD1xA4XZn48DHqwFXOYQmBMEIBmNpbJUmCe1KEgmWFEQrmOmdsgcUL/IlLFU8Lvx78bWS2rx3GsXOuas0Urp2UbRCZT5gO3Nb/wh5VB+6ePXvw61//Gk899RQGBwcxNjamM3BPPvlknH/++YxXIIQQQgiGRl1s2VWOXBQLACuXZLB6ae38KkJICxCrrq2Yzxm4Uko8vbuCp3aWAQAPPTkJAMjaAutWZPUNqiWAE1bntMOIEELalSlFKKRl4NZpZM7OF5HzlSpMZr72L3D68Xk8/GTRnzx2vjPdreao+zcXI4Ku1aTzohAChbyFYslXWy1LIGO06nrVIm+0iFlsnCG4Oq5MdQannddFLELBim0gc1vGzMNYvNCXJktllYErIKWEJyVksDXjArWJzis2hN1aD1bNfqqM3XZ44NB0AbdcLuNzn/sc/v3f/x2PPPJI3emf9axn4brrrsN1112HXC7X7O4QQgghpA0YHHV1FpjJ4SGXAi4hZE5RN3rxW9mK41fMlgCkJ/VrtY4jMTbhobvTvyt0AQyOuBRwCSHzh/Z563xKqJfupQwewFm+qAj4D+cKOeEf62WywxQAIHwxtZBTjt2wmpklBAqB7BUXdKdDV0GgWDL7IGAJAS/I6o2LsMJYmDQevAoRdcU6DoA68lxNw6oAujst3RchBLo6QgHXjm2741f51/qlSrR9T5rbSaRm4OpYCuNeIl6MLdp53c2qz1qZpgq4P/3pT/GWt7wFW7duBRBakMMsExkZBoBHHnkEN9xwA/7pn/4Jn/vc5/D85z+/mV0ihBBCSBugLs6WL85gRX8GY5Metuwsz30OGiGkIdL+Vv1rf9kWN0ZTRUqJjA2cvCaH047373S37qxgvOjhlHU59C2wse+wg32HnTnuKSGENIepvGY+1VfSkwp/zSbh8qPiZikQHTMZAREIkhLVGnbckbtuZRbnnFyIfJaxBTZtLaFY8uB5MnG+qdBZsHBkOGo1tW3Ac4BNW31l17IE1q/IYnGvHXH/xvU6U5CuKX6mEJzt4UngY7cfwdI+G2+/qg+uK2HbAhlboKIcuDHV+uS1OQgAkyWJ3m4LFUdidMLDrv0VFPICS/sygAjzgj1PwvNk2I4ERic8DI6E28Jx/Yenx9V56b+d3hRqWgbu17/+dfzO7/wOtm7dGqnYBwCWZaGvrw+rV69GX18frPijCgBPPfUUXvziF+O//uu/mtUlQgghhLQZmYxAR95CLjNPrR2EzHPihW10ttzsd6XppN1k25ZAX08GfT0ZFPICuaxAPmuhkLMihVoIIWS+MG8zcIM+q77blhGhELg/1wRvhplu1kLe0uKoOlcICBRyVuSf6YgNBdTpd3fZogyEEFjQFb7hod7+qDgSFUeiVPZwaNAJ1kctO2xD99sYn1YEzeyvTsSITbr/sIOf/2YS37x7DHsPO8gb610KHLJrl2cjPxd02Tj31DwAYPeBCvI5ARkUMRse8+C6MtI/oNqFOz7poRx7Xnp4KDlHwSy8agr3rU5THLiPPPII/vAP/xCVSkVftJ122mm49tpr8Tu/8zs49dRTkc2Grz9WKhU88cQT+OEPf4jbb78dmzdvhhAC5XIZf/AHf4BTTjkFZ511VjO6RgghhJA2IP6Kcjs9DSfkaDM06mLH/op+RV8IYOmiDFb2H9VyFlOikb/VwVEXO/dVkM0KnLI2N+3CLe1EWBxlTrtBCCFNYUrHsngGrhpIOfSLqglnF7NbpriqHLjZrD/Fol4ba5dlUXE8nLo+h3XLcyjkRdUDzHrLUSLxTATcXFbg/NMKkRiGU9flMFmSkBIYGnOxY19FZ8OmiZXqc9v2hVGnxosjcYe0uV+lRMQR/H8PjOONL1uo56sEEQnLF2Vw0tocctmw4y+6oAsPPlHCM/sqePZpBRTLsYJxwm971wEHK/ozcD2JbLA11WTxbNx4sbNwHcx1F1WftSpNceC+4x3v0OKtZVn41Kc+hU2bNuGmm27CmWeeGRFvASCbzeLMM8/ETTfdhE2bNuEf//EfYds2hBCoVCp4xzve0YxuEUIIIYQQ0vYcHHQxPulhsuT/myh62HuoUn/GOaDqPtR4JfbQoIvxooehURejE1MocT3HpGXgJt3rNXoDTwgh7Uwjh7qqY2SDRczmkrir1M/A9c9XykXqFwcTsC3/rbGuDksX1wIaF6LDmIaZnTcytohEEggh0Fnw+9VZCLLYvWh0QVzAVF3IBAphWoTCjx8Yxx/evA/b91aS108Chw0B91e/LerhihPOks0iIt4CfowCAOw55EBYwnc8B3EWEgAE8NTOMkplDwcGnMQiZa4XF3CT1yPiwI191srMWMB98skncd999+kv3W233YZ3vetdiTEJSQghcMMNN+C2227TSv69996Lp556aqZdI4QQQkiboV87owWXkCpW9PuOFaD61cG5ppHMQs+4sZpXf9oJ995xp9m8Wl9CCGmAqToa5/rSz9RRzQJjSsDNZgQqjsS2PWV/XEr1sXqCbNwFO5MiZvXQorMbXVY8/7Z6+uS98NdfOIKxSYkvfX9Yf2YK0Q89OYkfPzChx41OhhcryskMAPls9UqvWpKFZQETJWB8wvNzenVZuXC7WRYwWfISxdn4tVGtKAhN0G65IvGbLUVs3lbCZLHFLrICZizg/upXv9LDF154IV7/+tdPq50//MM/xMaNG/Xv991330y7RgghhBBCSNujbrQ68hZ6g5w715UNiaazTfy+NS1CoAW7nkq9134T50m5OSaEkHYm7Y2EWtO2FToDN3SslowM3JHx0PYZd5CmNJU+XiuT0+loY8Qzba1YBm78FKWijSp1am86bvL6feP/xiK/F0uhEDoQOHOzGUSygBW5rNDRUEeGvbCYXOw7Z1kCjiMTH2THXblpEQowROdcRsASAlJKjE96GB5zcWQkbca5ZcYC7v79+/Xwy1/+8hm1Zc5vtksIIYSQ+U1VBq76fC46Q0gLY4d1SnQmbrsgU39pT6YiTrSlkEEIITNAHfYKOQvnnFzAyiWN5bbP5fFSxgYsIwM3kxHh9aoFLFpgTeshnZpDCZCz4sDVy4pGKAgRfYOmkSJmQEqEUMJnxXI45dbdvnN5+eJMqmi9LihqdmjI0QKuakGJsaqPj20r4bHtJQwMu5goehga8zA6ERVe9x6q4KmdZew6UMEz+8LoKVM7z2UFnnVyHhuOy6NvgX+R1aoF+GYs4Pb29urhZcuWzagtc36zXUIIIYTMb6ocbrFXvAg5ljH/DGwrLJTSSjEKaU6iyL1tm/45pzmVFObn8VeA6b8lhMwnpvJGgjSezncUrPD1/ZTp5zpCQSFltMBYORAhsxmh+9bd4Yu3tTZD/WvY4E2NGfW2Nuqhr5QSriu1A9fTArW/Tuq6IpPxf9aNHpDV6ycEUPBTntCZ938WS+HbQlt2+QJurQKs61b4Au5vnir6wrkEIP2CbBUncBEHG2x4zMXQqItdByo4MuyiUpFVzuGxoofDQw52Hahg76FKxEFt0pG30Ndjo5Br7bP2jAXcdevW6eEDBw7MqC1zfrNdQgghhBBCiI9yn6Rl1LUD7dtzQgghjTAbGa9Hm3iEQiOrUteUG4zXIvFR3EDxh74iZpDQtSeC6TNBX5w6EQqmdmsKucol+4k/9s2ZngzjGLYGAu6K/kyqc/mlG7vQ0ymwf8DD07vLESesKqyWy1pYuSTrO3kBOJ7/RpJEGBGh8FIiFWT81b+qFUz5fI6ZsYB7ySWXaLfsd7/73Rm1pebv7e3FC1/4wpl2jRBCCCFtQrwSbxtf6xPSdOIFPOzYK5GtQNq9kJmB26L3Q/WZQuajviet49olhJB2pN4bCYnTNth2/Pg525gOYAmgVJa479EJDI76J1vlTk2aJ41aLtypbp/pYsYiZDNJEQrhtFbg2K00EKGg5tPzS6kF0t7uUGosBkXgtu3xIwxW1HDgrlqaxeUbuwAAE0UZEXCdQEjv7rSwoMtCT6e/DNeVcJUgHdua8eukeAG5djtFz1jALRQKeOtb3wopJe677z586UtfmlY7X/rSl3DvvfdCCIG3ve1tyOfzM+0aIYQQQtocJigQUk2jGXVzQaOC5bEWj3KsrS8hZH7TUBEzY2IpZZV41oqYfbvzZ6O4/X9GsHmb7xzNGgJurfWvdx5U41WO/dF+0KdycB1XImMLnHViAeuWZ7G8P+MvW4Z9yAQC7nSuL8pOOE9nwUI20GmLJf/z0Ql/hXs6rZrbb2GPP2Op4sHzpN4nKkJBxRyopbleuC2dWHjtyLiLvYcd7D3sTKkwWat+R2cs4ALARz/6UVx44YWQUuLNb34z/vmf/3lKFyn/+q//ije/+c0AgI0bN+KjH/1oM7pFCCGEkDZDR+C22yNxQo4mMaeIEnDbooiZ6WgyX7mck87MjKpXPhMcPKbjODJpO64wIYTEmUbxxrjLs75rderdagYiZsHdtKUcGZ+1q+epp2QnrUv8PHG0r3lVDq5yo3Z3WljUayNri6rzWqMRCkmYAm4+J1DI+RcrxbKfv6uWn0najga9Xf58pZL/6o66flCu4Hw2GpYspdTirhcTcCdLEiNjLkbGXBwacFAqRy+c4tt+OkXpZpOmCLjZbBY//OEP8Qd/8AeoVCr44z/+Y5x11ln4h3/4B2zevBmuG1W6XdfF5s2b8Q//8A8466yzcMMNN8BxHLz+9a/HD3/4Q9h2nT1KCCGEEELIMYptuGnaiXYVMafU7XgRs9a+FySEkGnRkNBlFMqKfNwG54KkLpYciR0Hyth/xMHh4XQ3Z6OH/dnKCLat8JphYNjF07vLODDg6mVLQHfanoIDV0coBL+XK744KgSQtYV2yhbLEuVK2F7GFjXPjSp+oViRkMaekNKfSTmhPWOceqAdf7AtPaCzw4YVE6bbNAIX6eETU+D444/Xw5ZlwfM8bN68GX/yJ38CALBtGwsXLkRHRwcmJycxNDSkRV3l1LVtGz/96U9xxhln1F2eEAJPP/10M7pOCCGEkBYgrZhCO1zkE3K0qcrAVUXMWsiBm5qBm/bKLP+2CSGk7ZjKodszDvzmfGmimRKF5+r0YPYrqQ/lisThQRdDoy56uuyqeZJIakev5ywFsSrHq+sC2/aXdQFUIcLrCf2Gz3QeEAeTlv2IW+1UzgcC7mTJi7hzk7KETXq7/QZKZT8D15L+NlPmWjW/6wrYtogUdI0ZcOFJoCMv4LoCpbKszvaNO3Br9mzuaYqA+8wzz0SewMS/kI7j4PDhw4nzqmk9z8OOHTuCbJT0zVZvPCGEEEIIIfMSnVGnipi1oAqadjPUxpECadFwiTfmaW00rTeEENIexB8+6gd6LSznhAW5qsd1d1gQQqC7064d+dXg+nlB9mzcodxszAxcJXauWpLFZMlDNuNEzscqQqGSEKHg1hB1HVfiwGHfpJnNCEAAhXzowC2VA+OmVd9xvMB04Boat4pH0IK0J2FbvjCtNqEbe7LtSQlLhMtU4QD1zsmtmlvfFAEXmP4KJs3XqhuLEEIIIUeHqlN/7DVkQo5l4n8flnLgNl6P46jTyN9qu17j14tDSPq8yljVnqtOCCERpnQcNzNeG5gtfPtqrkJwg+VDouxEhcBcBoEQKLCwW2BkPP0VmMgpocaqKHPi0dazVSxCxXDBrl6awXjRw8CIC4nwPGbVKJJaqkQ/Uw5rCWD3QQcTRf+iRDlkO1QGbklqB24mUCBrRigEGbiO479plJEICuH547P6IbaKhwj6IQHX83/PZX1HsOf529gyYiTUtEn9aHWvaFME3C984QvNaIYQQgghxzitfuFESCug8uxayYEb3gxF/4jT3pxrnZ43l3a7GSSEkKkwlXzv1CJdKSeA2T5eDoy4yGUFujuqS0MNj8UE3KBwlhCBQNmEvnrSL0olmlKZKh3l8DUf+lqxZarVUeJrkoBbLEc/MwXhYsmDE7SfzfjtKQduqezpDNxcnfgEwC+ypmTZkXEP/QttCIRF0LJZBOsjdUwDpL9O6rqokAsEXOVytmJ9np30iqbTFAH3DW94QzOaIYQQQsgxSpUBNy03k5BjkLibM15Ruh0Iinq3NTMRF9p93QkhxGQKNcy0GNdKYtlE0cMTz5QAAM89qxNA2E8AGJ2InmB7uixA+MW3VCRBIyRn4Po/vVkSEeOuWiGE/hdHZeI6CREKpZiAWwoyb9U1iopYUA5ZVcRssiy1e1cJxLXW2bYEOjsExiclRidc9C+0YdlCO7MzQSddN3ygrVAidT4ngHE/gsGMUKib7dvi9x9Ni1AghBBCCCGEHD3UzZa6YTk06OD4lVlddKQVSM2AlTJyQ9SqN0eJpPQ1aR3CWiDqg6PTJUIIaXVSHbgtwHgx/QmolMDIWGhXXdAlcMVzu7UIadvhod10oSoaXd/Z2j6heJmyPENdVwJuxZUoVyQGRlwMjrj49+8OobMQte2WytFt6DihSCtEKOAWS1I7cLMNOHABoK/bwviki4zlRyqsX5HF4UGVsetP40mJwREXw2MuKo6EJYR+sJ2zfTne9SRGxl0Uyx7GJjwcHHSx/4jjR0dIicFRD8sXH2ULdBOhgEsIIYSQuSfmQmBuJCHpqAIeALDnkIO1y7Nz15mpcAz/PbeVYE0IIWlMJQLXiNaRjVhwZ9H96KXpt8GyJ4r+wPkb8vjwW/vxi01F7NxfgRVLTyhXkgTccIrkB33RcVa9ql4zRKjYpcB9qhanM4dhPCC2w2mf3l3GwKiLT3xpoCpSAghjJtR6VIwIBQAo5IMM3LIh4AbXL/VE674FGew+5CKTsbB0UQYLu20cHAgydm0/FsKTEvsHHIyMuShVJKQIRWpVKM91gcNDLsoVidEJD3sOVdDTaWH/gANIiad2ltGRF+jttqPbpEXP2e0jNRNCCCFk3lJdqbgF7RqEzBHqtUH1V9G3IFRwk9w/c4HuY0oGbLyXrXpzlERVQbJG5sHU5yGEkFZnKsfDqR7mZ1M8SxJwzfOXEnC7OixfgFaFsmLzVBKiBkxqrct0zi3TQQm2yp2q828TFqwE3IoDTJYkJoteongL+ONNVHxB1vbjGUIHrmcIuI2t7boV/oPpwREXnvS3voqcyNgWjl+VRf/CDBYtsNHTZaOn00LesKf2BLnGQgA9nRa6uyx05AU6CxYW99ro6RA6CsOMhmj12w86cAkhhBDSsrSRxkPIrGFbAuuWZ7Fjf6XlhVDzZqjFu3pUafX9RAghU6KRDFwvdHxK2VoPtLyUg7L6tBjEA3QWLP8z5ZaNrYQqbjYV1Bye59uSj7ZoaMcycK3YAqWUYREzY9pyRaJYTm9XCZ/qAa5qX+XcagG3nBChUGedT16bA+C7Z2WgH3te+KB46aIMli4C8lmBQq6CsUkP2/b6oby5TLhfshmBlUsyKFUAWwDLFmVw8tocjgy72HvIgQDgtFE9AQq4hBBCCGkdWunqnpAWQd9mmn8fKc7WuaKuQClr/trSJG5/VL85YA6HjmQe1Agh84epHLuTjpG11MrZPFyaDlwppV/US/8OTJZCAVd9ZnLyuhw68xbWLEuOMPJjI1K2VsqbKkcL1X5YxCyxGwBCB2654sGTEuVKurqpMnDVtiyrDFxVxMyIUIgXMauHEnCPDLt++zJ04FpGjoBtREqpN5K6OoSOpXA9PxvXtvxxYxMeBkZc/3MLgAijJdoBCriEEEIImXPSrnFTL34JOcZpVWGwVrf450wIIfODhiIU4sf8eJHHOSQq4BrnrqBzk4HztKvDz+9V04hAPOwqWDh+VS61fb+EVp0+zFoRs+gCrFiQqrn+yq1bCtbfqSFuKsFWTePGM3ADB+6kGaEQjKu3yutXZGFbQNkBBsc8SEA7cU0XtBKLBUwB19IOXOn566bmqTgSTzxTxoEBV9cTMNeRGbiEEEIIIXWI54C1qDZFyJxQq1J1q9xkpHXDvBkyp2mnhzNTySmMF2DkoYwQMp+YyqFbTWoKhrWOibOagWssJOkti1KQ79rdEVc7p7acpHNdfD2PuoAbWwVdxCwpA1cJuJWoKJuE40hIKbUAqn6qmIRCPsyYVWJvtkEHbi4rsLLfV1j3H65ASqn3mVn0bdECG4sW2FjQZevt2ZEX6O3yV8S2/YgKtQ3UtUi54jtzRXwdW/ykTQGXEEIIIS1NOwk9hBwVkqpYz34vGqKqX2kdnQ9/1rWqi8cn5XGMEDKPaEh0NA57Zs5qKxApYiarByMRCsbr+ypooRmiq44EOMoKblzAVW/wJC1VOVrLQTBsJRBlT1lX7TZW2bGqOJoScFUMQ4eZgVuOxis08hbRuhX+Mvcd8SMP9PoYs+ayAqeuz+OUdTkdhdBVsJDLWnpaX8ANHLnBfJ4nfbFaINK2olXP2BRwCSGEENIy6ItKs/BRq15FETLLtNLNbxV1/k5lbJp2/LNOu9+seSPaYlnFhBAyW4QRASLick09Hs7i8dI1IxQSxhfLYZ6qiWhUQauxLp6UmCx5s3Z9GxeI7RoRCkro/MVvivifX45jeNTfUP29Nv7oqoWR+SqO766dKPrTOMplG0QTFHJBBm6p2oHbyPXM+hV+vvCBI05kW8UFacDvf9GvYYauDksvp+L4Tlsl+koJQPr737b8fjiu2U7rxHwkQQGXEEIIIYSQNiNeLGuuSSxWg+hNWmv0lBBCyHSRhihbf9qgaFZKG3OJ50ns2O+ERbIQnL+CV+yLgQO3u8Py81dVBm6D7dfaPFt2VbBjX0X/niRINpOq87LOK6ueNmMUBbt30yR+8uAEAKCzIPDqyxbgqx9diYvOygMAKo6Hh54q4sCAr4AqIdS2ohEKxbKHUjmagdsI61cGAu6go921QojE756UUu+zzoKFjrwqxhZz4EoJCd+BrT6rlfPbarCIGSGEEELmnPhFfks7DQmZZdLEUaA1boQbId7Pduk3MDXBonqe6O+EEHKsIazGssRn0/2495CDz317CADwkgu7oyOlRFEXMbOiGe5TvECtPvdJVIJ82dnKSq/KwLWiS5RGHyZL0Q4PjQWiaJAFvGxRBrnAWVupSHiuhBB+lEElEHAzgfs1r4qYGRm4alwjK716qS/glsrA2KQScJOnlTLM7e0qCBTyfh/LjoSwfOFzQbeNnk4rmF4CQgYRCmE7rX7/QQcuIYQQQloa6h7kWCfpZrZlC/2lOX3QOm7hZpG0NvEM3FbdTYQQMh2mchTXztbgd30+aIFTwWPbS3rYCzoaGHDhuGH8gypiVl1st/bRPTX+fQ7O53ZMsE0qYqaGF/faSKIzH0qHuSDHVgmfHXmB41flID2Vc+t/XlAZuCWJciWagdsI+Ww4rDKJ4/EPJqrwXGeHhU5VQK0SZi+v7M9g1ZKMIZwH69FGDlwKuIQQQgiZc6qcDXzvmpCGaJU/j6lqs63S70ao19eGbkfbaYUJIaSJqGiCutMd9Z6EDIyEwadx16kSG4XwRUgdoaA+nApxB27CcNwR22xSIxRUP4xOnXVSHn/yukW46oU9kWm6CuFMuUCY9Tx/HVTe7MFBBwDQ02mhXJFaOC6WpXbHZnURs/r9tm2BTKBYqgiG9Cx6aJdvR05o8bjihGJ8MGXEkSwAuJEMXP9nqz5wpoA7TVzXxW9/+1vcfvvtuOGGG7Bx40Z0dnbqTI43vvGNU25z69at+NM//VOcccYZ6O3tRXd3N0455RRcf/31eOSRR6bUVqlUwr/927/h0ksvxYoVK5DP57F69WpceeWV+NKXvqSfMhFCCCGthLpwomuNkJCkCIVW+xtJez3WfCVWJt25zjPiDrOWdUoTQsg00JFXDRzb0s4LaYf/2TxeHhkOVbuxycCBG3RACYGFnIAVCI4SKuorOdc3Tq1X/WebuD6sBEz9sZGhYAmBKy7qxppl0bRVFaEAALlglBsUBMvYAkNjHkYmgnYF8OvHJ/HkDt/lXK7IaWXgWpZAJpheFZVLE7slQiE2lxXoKIT9LZZD7cuTEnZMRPakhOu1x0UJM3CnydVXX41vfvObTWvvs5/9LN797ndjcnIy8vlTTz2Fp556Crfeeiv+4i/+An/xF39Rt60nnngCV111FR577LHI53v27MGePXtw11134dZbb8U3vvENLFu2rGnrQAghhEyXWhe07XFJRcgcwT+QOWMqDh3uJkLIsYZ++GiJxAeRqfPNgsq597Cjh0cnoua2cpB/W8gFL9kHQiWEYTRoUGyOr4k0xFI5S2eGqgxcoQTMcCXiq9PdGZ2p0xBE81nlwA1jEbbs8jfaogUWchkLQgjt1AWA0XFPT5u0vMR+CwRiq0SxLJHPCqQmMEigEkQh5LICtuXHLbie77DuzAeTeWEMg1+YTkBKCdeNxjO0qAGXAu50cU2fNYBFixZh8eLF2LJly5Tb+tKXvoS3ve1tAADLsvCa17wGl112GTKZDH7xi1/gP/7jP1AqlXDzzTcjn8/jfe97X2pb+/btw0te8hLs3LkTAHDWWWfhDW94A1auXIlt27bhtttuw7Zt2/Dzn/8cV155Je655x50dXVNuc+EEEJIU5mlQg6EtCUJRbTiWautQlouoHoFVf/eah2vQV3HWRsXlyOEkCkxheu16txY9XntA+TRPn66nsTAcCjajk+GC5QScIJw13xOVBeinGpcUAsU8FRvietzWUzQjbwcE/zSWRA6ExiIRShkVQauP9a2gV0HKgCAU9blseG4HM4/rQMHBip6nmEl4GYav9K3rTBPt1TyBdx4302cQKJT+y2XFZgsSRSNiAxPRgVc2/Lnc10JZEXLvzXTkID705/+9Gj3o4oXvOAFs77MqXDBBRdgw4YNOO+883DeeefhuOOOw+23345rr712Su0cOnQI119/PQBfvP3Wt76FV7ziFXr861//elx77bW47LLLMDExgQ996EN41atehVNOOSWxvRtvvFGLt695zWvwxS9+EZlMuJvf9a534WUvexnuuecePPjgg/jbv/1bfOQjH5nq6hNCCCFHlWjho7nrByEtS4veZDTarbb6s27CAyYexwgh84mpOGmFaK1j4GTRi5yDxiejDtxKIASqbFczA1cLiHXWv9b2iUQXzBKWFUYMJKUQVD18lQLdnZZ2J5uRBNmsP2xm4O4/4juaVy4JtSdLCGQzfg7tiHbgquU10mehHbiligfA0u7hOOXQUI1csN/ygYDbaYjPUkJHKHjSF5QdV6LsSHSYq1+/e3NCQwLuJZdcUrfKXjMRQsBxnPoTziEf/OAHm9LOJz7xCYyMjAAArr/++oh4q7jwwgvxkY98BDfddBMcx8GHP/xhfOUrX6ma7rHHHsMdd9wBAFixYgU+97nPRcRbAOju7saXv/xlnHjiiSgWi7jllltw0003YeHChU1ZH0IIIWQ6xF+tm83rDkJaHf33kTSuRe4y0hxVpuOqRbp6VNGZv/p3RH4nhJB2ZirHMu3ANfPbjXNC/Fpvto6XqqCWYiwm4LpawFXi8/R79JstRSzty+DENTkAar2n3dy0sQSg3iGP58jKIKR+x74KIBx0dQh4UmJBdyjgmg7cvCHgAkDGghZwVyy2w/UTvphacSRGxv2lZ6fgwLUsIBP0VRWai8dBKEpGzm0mKyA9IJf1f+/MWzjrpAI2bSnCkxI5KyzClssIFEth7nGr3300tYiZlLLmv0amMac9FlCCKwC85z3vSZ3uLW95i446+M53vlOVlavaUtvurW99K7q7uxPbWrVqFa6++moAwMTEBO68885p958QQgg52hxDlwWENIyuldWmfx/tdL0/lexGPU8brR8hhBwNzIePRvTrnBMXcMcnVBEz/5zqBlmqWVtox6cXWHCDVNy667Kgy9bDh4fC+M25qpVlirZq0DynVRxgsuQBUuLgoN/fy57diSV9FvoWWFi9NKunVS5a1/PF0bFJD8Wyv036F8YcuEHcQiXwZxayUxBwRbgsJbCm1DALBV4B2EFWcS4QmkcnPD2fH5sQ7lMVB1GOfSda9alrwwJuo6KrytdIcs4kZ+KYAQABAABJREFUXcjUmn6+89hjj2HHjh0AgA0bNuC4445LnbanpwfPf/7zAQDj4+O45557qqb5/ve/r4evuOKKmss2x5vzEUIIIXMBtQ5C0tHX0BEXU2tdO+s/4Vi3zFdF59vfeVKF9fheabX9RAghM2Eqx/GIA1cfL0VqO7MVLWBmogLAWDHmwFXFuTKi+pzW4CH95LU5PHuD/1K+JyUqgQA5V8Jgkgva/MwMlVAC9qnr8vjaR1fhjr9eha4OM0LBn7EjB5x5Yh77j/iC76qlNjJ2eN4TCOMMFN1dVtWy07B1hAJwIHD4KiHacaUuogYAxXIY0aCWrxy4+w47enlmBu7AsIutu8uoOFILuK1+ym5IwPU8r+6/SqWCP/uzP9PhyIsXL8b111+PO++8E08//TRGRkbgOA5GRkbw9NNP484778T111+P/v5+SClhWRb+/M//HJVKpapA2Hzl0Ucf1cPnn39+3enNacx5Af/CfvPmzQAA27ZxzjnnTLstQgghpBWg8EFIlHb4i4j/2UZeiZ1nAm4SqYetY2DdCSHHDo1l4KqJYz/nmGIlKtiOmQ5chMWwspmgkJcMM3AVjax/Lit0ZIASCJMKhs0GttHfpBgCFYdgdsmy/GvxjB1d2Wzwuyv98fsO++Lq2mXZyHQQodirMIXgelhWtQM3YwGPby/h+o/vxxs+vE8L46VyUFDNSBBVUQ93/O8oHt1a8tfPC/vgScDzJMYmpHYIK1r1lN1QBm4jXHXVVfjud78LKSXe/e5346/+6q8SX+Hv7u5Gd3c3jjvuOLz85S/H3/7t3+Lmm2/GLbfcgo9+9KN49NFH8c1vfrNZ3WppnnzyST1cy32bNI05LwDs2rULExMTAIDVq1cjm4398cRYs2YNbNuG67rYsmVLYgaNSalUQqlU0r+r3F5CCCGkGSS9omxWvyXkWKamU6lFmOqNaDu5cWfSVx110ZSeEELI3DKtDNyE+ZLaqZWBu/ewg32HHf1GiiUE1i7PRF7Zb5RiMebAnUh24PoRCkGfEvJ8G0FlwJYdiS5EzyezeV4Qhm5qGQ5Z3RcZ/QmExb7iKFHa9Xwj4V4l4K7IRdoVAshlzPmAjpxAsdzYmvsCrt/apq0lHLcyh9u/N4ytuyt6mn1HHKxdltVtquk78xYuPKOAx58pAwB++3QJp67PwZMS+Zzv7B0d91AseZgsS0yWPIyMuxgec7HnoIOuTgsTMWe2bQl0dfi5wFJKLO61cdzKXEPr0iyakoH78Y9/HN/5zncAAB/72Mdwyy23pOavxunq6sInPvEJ/N3f/R2klLjzzjvxiU98ohndanmGhob0cH9/f93pFy9enDjvdNrKZrNYsGABAKBSqWB8fLzm9B/72MfQ29ur/61Zs6buMgghhJBmwCxJQtJpmb+PhDiBlEn84RbpdtOJ3ey3nNJOCCEzYRpCZpJgmEiNCQ4ccVAqeyhX/Nfdi2UPh4am9+Z2PAN3ohgtYKUduNkwQiF+ymp09eMZq/Fz38IeOz7LUcEydljSvku6lrBT1MJMIMqq7bQviDdYtzxYF6GWGY1Q6Oux9chG3rKzDPfvoUEPn78zKt4CfgwCABRLvtiaNUTnE1bncOmzOwEAuw5WMDLuT3N4yIXrSmQzAlL6+3+y5H+3Ko4fz1BxoL9r6t9kycPhofB7eODI7CcHzFjAdRwHt9xyC4QQOPPMM/G+971vWu386Z/+KZ71rGdBSolPfOITx0SMwtjYmB4uFAp1p+/o6NDDo6OjM2qrXntxPvCBD2B4eFj/27VrV0PLIIQQQhpC3xDELLiEEE3kfqeGU6kV8V9BbZfeRqlXxKyxV4nbc90JIWQ6DI26GBkPNB2RkOVe45CYdLhUH524Ooc1wav60z2sVmXgToZOy2gRs7CwGWRYv2kqVAm48NvMZgTOPCGPDetnx8FpRRy4wYCZgRs1mwJIF3CVKOu5EqWKxKGg6NmaeIQCwvUHomJ1I1vRjFBI44gScCvRPivHcU+nP3DPQ5P4+BcHMFH04AT7t7/PxvGrsli8wMKK/gyEAIQIrcgiKIYmBDAw4uKZfRWdtQvMzfXXjAXce+65BwcPHgTgxyjMBDX/oUOHcPfdd8+0a6SJ5PN5LFiwIPKPEEIImQ0oe5BjncRiWXGnZ4tQMwP3GEBrEw04kgkhpN1o9JxzcDA05BVyqmqW0c40lvvkjjJ+tXkShXx6IbRGKMYcuIeHnEj/HKOIWVxv1qvQ4ME9W+XADU8OGXvqgvB06ciHyynkq5eZtCnTIhSUK9ZxJXbur0BKv/2+mJtYCdWKvh5rSvvdtqLzP/esDnz07f340afX4LLzfWetEnBLJRWhECw7mGdBd1Ty3HPIgRtosB05gYULbHQULNiWCB40B98tqN+VqO+7fEfHE5TuWWTGGbjbt2/Xw6tXr55RW6tWrUpsd75ixkwUi8W6009OTurhnp6eGbVVrz1CCCFkNkl627hVBSpCWoFWEwbr/pm28x9yyivD06nGTgghxwJKqFzRn0FnwcLwmC+01Tp3xR+AmUwUPXzx+34dns+szaVO1whK7OvpEhibkNh3xMPVH9iDt/5uL1zPF+uAoIiZEJCQ0Vgc2Xih3XxKhILA7J4X1q/Ion9hBrkMkM9Zug+KJAeulbKKZgbu9r2+9XX5YlufI8MMXBERYBf22NVO7BoIIbBscSgKL1+cwXPP8oXbRQv8z7UDN3DGZjLRdevtigq4rivhBIXPLEvAtgSOW5nFiatz6OqwMDDiwraAnk4bG44L3dG/2VLCkWEnFgU1+yf2GTtwjxw5oocPHz48o7bM+c125ysLFy7Uw41sO3ObmPNOpy3HcXQhsmw2i66urrrzEEIIIUeLqVzQEXKsoW8Rkv4+WkQYTLuPUTd08ZvD+SRo1qpMPkvmKkIImRXCSJnGDm4d+VBySiqaZVKrzSd3hAXVB0bcoI3pnUjKjn9C6swLrAwEwsPDLv7vgXEUS552aGYzxjFcX6ZObZlZw63q9zloZ5ZPDkII9HRaWryN48nqjN70Imb+T8cFntmnBNxspHiZ+tnbHQqwC7unLj8WjP6aYmz/wriAG8Ze+Av3fyyICbjjkxKOB+zc7+DWbw1i94EKMrbAgm4bvd02ertsdHVY6O60/N+DfzmVDjHH1y4zFnCXLVumh7///e/PqC1z/qVLl86orXbglFNO0cONOI7Nacx5AWDNmjXo7PSfRuzevRuVSjTcOc7OnTt1zvBJJ5006wcQQgghpB48MxGSTqteujXar3bSb+eT2EwIIc2g3qE+TaCd7rnrse1lPTww6ius3nQduOXQgXmKkUF7cNCFJ0OxNWsLnYHrITi/TbX/sRghPwNXRD6bK8x9ofaX2ae0DFztwHVlxIEb3+cCwLM35PXvlmHpbbgInOHgNeMQ4g7cycBVrUXn4EdHIboSY5MeHEfip49MYnhM4j8DV3d8Z8QfDqT1d7ZduDMWcM877zwAfsfvuecefOtb35pWO9/61rciubfPfvazZ9q1lufMM8/Uww888EDd6c1pzjjjjMg4IQROP/10AIDrunj44Yen3RYhhBAyVyReIM31FS4hc0zo2EkYN7tdSaVeP+Y2NW72SMvApQhMCJkPTFewkhIoVSSeeKaMsUkvWeCtMf+eg6FB7eBAkFk7XQE3iDOwLYGV/Rn8fy/04ygHRtxoETMzAzd2TG9UgIzHgUnpz9sKD2FNE19yEbM0B27gKvYkntnrC+vLF2f0RjGduJ0FC6++rBvrV2RxxXO7pnwuNIugRRy4vb6A+5stJWzfW0apEkQoBA5cK9YXxVhRaoEeAEYazLRtlTz/GQu4Z555Jp71rGf52SBS4nWvex1uv/32KbXxn//5n3jd616nq/qdffbZEXFzvnLaaadh7dq1AIDHH38czzzzTOq0Y2Nj+NnPfgYA6OzsxMUXX1w1zeWXX66H67mh77rrLj18xRVXTKXbhBBCSNNJdmoE42a3K4S0Fe0qDLZTv6f6yjAhhMx36h0Ow+Nm+Nl3fjqKX2ya1Fm2qfMmnCAqTvjZUBChMF0HrmrLDsS+JcHr+ENjEo7jIUhYQEZFKEjok5Y6DzR6OtCTyehPPwO3dU6Esmog3D5xlIBbKvvbTABY2lc9sdpG553agX//8xVYtTRbNa4epoC7oCtcxrLFYTmvv/73IxEH7p5DDnYdcLDnkP/v2RtCl/XgiAMnrK+nhet4VFV8z1gpYvZs78IZC7gA8K//+q9afC0Wi3jzm9+MCy64ALfeeiu2bduWOM+2bdtw66234sILL8S1116LYrEIKSUsy8K//Mu/NKNbbcE111yjh2+55ZbU6T772c9ifHwcAPCKV7xCxyWktXXrrbfq6ePs2bMHX//61wEAHR0deOUrXzmtvhNCCCHNhvoIIQkkRES33AOOlFw//fs8tOAmbvv4fuExjRBCAAC/2eK7NfccdJInqHFeM0W3wTH/hDJd8axcVg5c//eOvIVcoAeOTUp4wbJyGSvMcQ+cs9M9pquueloInl47zUbHOUzDgatY0mcHBd9Uo6pt/+dMrlPMZZl5tssXZ/CWVy0EAGzbW8H+w/53KmMDo+MuRsY9jI67GB13ccVFPXjpRr/m09CoBy9B+Zd19kuL7K7mCLgbN27EZz7zGS3iSinx4IMP4h3veAdOOukkdHV1Ye3atTjllFOwdu1adHd346STTsI73vEOPPDAA3pjWZaFz3zmM7jwwgub0a224E/+5E/Q09MDAPiXf/kXfOc736ma5le/+hX+/M//HACQyWRw8803J7Z1+umn4+qrrwYA7Nu3D295y1vgONGD49jYGF73utehWCwCAG688caqgmiEEEJIK9FCBgVC5pR2cIDGexir/9KW6Bu7tAmMEfpmOJ6n1/xuEULI3FE3BDc62UyPga4hug2OujNqU0coBMdrT0r0L/QV3NFxT79in8kE+pY58xQXWhWhMM0+H23cBFHTSlELMzGz7foVvrM2KQMXiIrDU61ZHHXgRjv0+7+zACev9d21T+zwIza6Cv70yxZlsLw/i2WLszhhVQ5nneRn8RbLsqH7iqp1aZFze6b+JI1x3XXXYeXKlXj729+O3bt368+llJicnIx8pjAvQlevXo3PfOYzbfM6//bt23HbbbdFPtu0aZMefvjhh/GhD30oMv7SSy/FpZdeGvls6dKl+PSnP403vvGN8DwPv/u7v4vXvOY1ePGLXwzbtvGLX/wC//Ef/6EF1w9/+MM49dRTU/t1yy234Je//CV2796Nr371q9i8eTPe+MY3YuXKldi2bRs+//nPa1f02Wefjfe///0z2g6EEEJIM0i6oGsDrYqQWSHpBkG0mAW3Xjfi41vp1dGjSfzmnRBC2pnpHsuqhC8pEZfx0o6XUsqIA3coEHCTnJSNoCIUlEDpeUD/Qht7DzsYnfQQ1HpH1lDLVHdnfG3arHaahEqI8LTAHG7TtCJmuZgDd92KbOR3tW41i7U1uP7mPjaLmCnOPSWPp3b6zu4FXQIr+v2+LFpgB2KtxLJFGUyWfBW5VJI1ozeqIi/U5y2yv5om4AJ+lurjjz+OT3/607j99tvx1FNP1ZxeSomTTjoJb3rTm/DOd74TXV1dzezOUWXHjh3467/+69TxmzZtigi6gO+ejQu4APCGN7wBExMTuPHGG1EsFvGVr3wFX/nKVyLT2LaNP/uzP8MHP/jBmv1atWoVfvCDH+Cqq67CE088gU2bNuHGG2+smu65z30u/vu//xvd3d012yOEEELmGuoehFTTIvcSGpliq0m/IT/6fWo28Ru4dlwHQgiZCY06KJMycGeyTDdBwJ0uZeXAtZUDN8xwHR33tBtVCZVS+rKmuS4NZ+DGnrWa541WOockFjGzk1cyExNwuwoCxZIHVfItPpf5wHaqq2xmHxdy1QLuFRd149dPFDEx6eL04wuwgg1uWaE4DQB9C/z9WyxLXaTO7FO9fZG6v2d5HzZVwAWArq4uvP/978f73/9+bNmyBQ888ACeeuopDA0NYXx8HF1dXVi4cCFOPvlknH/++TjppJOa3YW25I/+6I/wohe9CJ/5zGfw//7f/8OuXbvgeR5WrlyJyy67DG9961txzjnnNNTWaaedhocffhi33XYb/uu//gtPPPEEBgcH0d/fj7POOguvfe1r8brXvQ5WmieeEEIImWWYF0lIOrXcqi10/1cTOd1qM21A5KY+Pk4NzN/VJ4SQdFIyFBLfLEkZ6cmoa3JsEti6u4zjYs7PRqm40Qxcz/Ndmn7bYRGzbNZwkAbLt4QpC9Yn/raM8h0L0SICbrA6KpvX7FOaAzceoeC4EgcHXZwWm86Um6SUkTfwGxXAN6zPo7tD4PhVyft69dIsPvuBFRgcdfGtn4wE4ryInIullFjY7XdaApgsRzd8qRT+npqBm/J520YoJHHSSSfNW4H2kksuafqrXyeddBI++clP4pOf/OSM2yoUCrj++utx/fXXN6FnhBBCyOwj1EVyK1zgEtICRG4gtLO1tf5Aqu5xWuSmZyboTdzADaeI7Zd2yC0mhJCpUu/QVu/UNJVTl+m+tS3A9YDbvzeCs07M4/nnNP4W932PTuKTXxnAkWG/Qe3A9YDl/b40NjHpaTeqKWAGHlwdMTDVI3vcgdtqZ4ak/ZFWxMw8r/V2W4AQcL3q8VERdXqidSFv4U//YDFOWF1HrI+3K6DFaQm/GFouA5QdYGwiajf+5/8axGc/uLx2cy0Sh0QLJiGEEELmHH1BW8PJRsixSJpA22p/H3Vv1ut+ML85xlaXEHKMM51iT2nR7mXjNfoV/aH9c9PWUmLxrTT+78FxLd4CQNaIUFixOHjFvhJOn82EHVICZIqmmYo2IBsOV6EsuC2A6kbSZmzkhe2V/Rm9s6uuA4xVTCtw1gi2PfWHoRaqN3Fnh//B0Fg0gmNozMMz+yqRz+L9tVpkf1HAJYQQQkjLkHR5ROGDEJ+k+4e5doPEifcxvHmd9a40jVTHVA0nVTuvLyGEpDFVB+lUZK9yRWJgxMXIeFRgqzjhsCpSpXh6d1R4q8XIWNR5mQneR5cS6O600dMZlccmJj3sOVjB/sNOmBGbYDioSTw7PfnjOUOfo72kCIX6vVy+OBPMJ1PP/4BxLT/dInj1xscFYqt6XFfeF+mHR6sDf0tl9dZMcvupEbjtnoEb58knn8Tu3bsxNDSEiYkJ9PT04FWvetXRXiwhhBBC2okaYWit9oo4IbNJ2te/RcwgU6C6aMixBI9jhJB5RcNFvMJCYBESDom7DlRwcMDB0KiFyaKHjoKvwpWNzNIFXVGR9cHHJ3Hy2lxDfRmOCbg5oxiXbQGdBYHRiaDfAA4MONixv4KhMTfov4BlB+/lN7r+wU+1+koobZkM3ADlwDVFW9tOmdhg+eJ0T6h5nVIlsDb5IibSvBAQQkCYwcMIHbjD49VF8IrlOtcokQJ2IuKonk2OioD7/e9/H5/97Gdx9913Y2RkJDLuhBNOqBJwS6USbrjhBjiOg0wmg3/+539GLtfYHyEhhBBC5g/MiyQknYQI3JYhzZVV6/XMdqORw1P8FWAe0ggh84mpHModFzg46KDiSIzGRLOkdpyguJgEUKpIdBT8z1XRMcsCjlsZdeBu3l5quD9x4S6XDcXljC2QscMDtirUVchb6LcFJot+oTNzmkaInwNa7VSoz9GBtq1iE4QQNR24V17UhQefKOLKi3rw/+4dQ7jnwjZFIKRKKUMBe5r9q/cQNCmCLb7tuzr8lRsKhPzuDmBpXxbb9la0AzcNtV1UEbq52o9NFXC3bNmCa6+9Fvfeey+Axp805/N5DA0N4Rvf+AaEELjssstwzTXXNLNrhBBCCGlhalUjbiWHAiGzzVRfG5xz0oqWxc1XrdbvWSBehZsQQtqVescyKSX2HXYwWfIwOOKiVJnaQd8zzLLlYF5LAOtXZPFHV/Xi4KCH//6/UTzxTBkA8H+/HkchJ/DcszpT24w7cLOZqNs0Y6hjyn26sNtCV4cFx5XoW2BH+jUVtIAZKIBCtJaY68lQJAeiBdySuOl1iyGlxMh4sEHS3hYKRikH89F6G0XGniInxTf0BA7c0aDPuaylRfxi2f9MxJ/CItae9OMZhHf01qUWTcvAfeCBB3DBBRfg3nvvDRR2f2Wy2SyWLVtWd/63vvWteviOO+5oVrcIIYQQQghpX8z7gxa24KbdxoiU8fNBwA1dtuHOELGR1GsJIfOJqRy7VfGx3m4bnfnowbDi1Hp0H7px/Wl9cU2JiyevyeH043IQAAZGPDzw2CQ++u9H8KHPHMZkKVlhLVckJkvRZeZygQMXQMYWuqgZEDpwTReqwMwzgH0ZUzl/G2ykQVxPYmDYxfBYdURAPVRf1Po2UsDMjykI5keK8zWlMF2jxM+p6X2JdSQhvqGr09+pSnQu5ARygaFbRSik7ddWKbLcFAH3wIEDuPzyyzE8PAwpJXK5HG644Qbcf//9GB8fx759+wDUfkrzwhe+EH19fZBS4ic/+Qm86T7aIIQQQkjbkfQUm8IHIfVpGx20bTpazVRv2Ou1Qwgh7U6946EE4LoSAsDxK7NYsSQafVBOcOSaha5cQw5SRcyUuOhKgXzOwtJFviD3vn8+pKfdticsauZ6Eo88VcTbPrYPH/uPI1XLy9mhumjbUUeuikqw7eRjd8NvU8QFzKP4cG/PIQdP7Chh87YShkYbE3HDiAL/p9KrGylg5jcQ/JRp2ynafvzzhvvX2OSRLsWX0dPpf6C+T7ms0A7ceIRCfHkRAVeYbwnO7om9KREK73vf+zA4OAghBFatWoXvf//7OP3006fUhm3b2LhxI+666y6MjIzg8ccfn3IbhBBCCGlvKNoSEiXVgJtyUzRXyLSb0jQL7jylKgN3znpCCCGzg+MGr9Ibx/mxCQ+eJyEBfPPuUew+6ETmSYpU2La7jPt+O4nnnN4B1whOV05e9Vq/60pMFj2csDqLAwNRoXLrrjJOPz4PAHjvpw/i4Sf9jNwtu3xht7MATBSDdtRr/fAFWzNCIZcTWLU027iQmUL8XO3JKoNo0zBFyHhRrnooQVqtbyMFzEwkAJkQX2AJwA0mMMXOo329nxih0BldqXzOQj7rf6n0tkvpl6WK8TWxj9NhxgLu2NiYjjywbRvf/e53py28PutZz8Jdd90FABRwCSGEkGOEtKfXzMAlJJ1GXyucbaqLmCXf9MxFdtx00T2NF6NJtBs10A4hhLQ5pgD31M5yleNz+15fMD085OC270QL2wPJDtzP3TkMAMjnSnjRBd36cxW3oF7rrzgSOw9UcPzKLM4+qYAfPzCOJ3f6y9uyu6zbf+TJ6gJnGdsC4AXDhgPXQiRCoStvoafTgm2FRTin8xp9/Fwdz2ptJq4RO2EO18I/R4fTWoHG2WixtkZXw0tx6DYN46mpWUTNXLeVS2ICbtaMUIgmAMTP78LSI/z2g6Zn+7w+4wiFu+++G6VSCUIIvOIVr8CznvWsabe1YsUKPaxiFwghhBAyvzGvkejAJSRK2/x91LmL8Y4x9VIaN5OEEDJfSHpwNT7pi19dBV/0LORCmcmMNDCpVdRsbNJDqeLhs98axENPFKsiFBSWJbBskY0/vKIXN/5+HwBga+C03XvYgQSQywKnrs/peTrzAs87uwOnrMvi7JN8p6524JoZuBnlRBXGGyZTL1yVGLUgVJ5uc0+MZuyEO81E0u4OgULe366NEHEYJ5z3phOBkETDmyrpbaDgs94uG4t7w+9mPoewiFmlTgauMWzN4cXYjAXcnTt36uEXvOAFM2qrp6dHD4+Njc2oLUIIIYS0OU266CNkPpLmbJ1r4jerabc5rdbvWpg37/WIvzkQmaOdVpoQQhKIC2meJ7VD9rTj8zjzxALWLA9f9FaCapwkB64iYwnc99sivvajUfzJPx1EpRJ14AK+QNfdaaE7eC1+SZ+/zCPDLlxPYud+f7n9vRmce0pez7eo18blF3bhD1/aq0VaIChiZvye1UXMYuuvO5Da/UTUfKrUU8MZulPALPzmNOrAjf3M2ALFksTQaGMKcO1SdMZ6xjNwG2q9cQG4Vj/UOMsCVi0Nv5sLOu30DNxYg5bZD4E5i7GasYA7MhLa4RcsWDCjtiYmJvRwZ2fnjNoihBBCSPuRlPFJyLFM1IHbun8UdW+uZO3fjwWOwVUmhLQhnicxNulVvYbveVILg+p0pPJpLREKoPls+IBx32HfPnvSmixeurFLC6JxwczMvM3YwOhEGMlwaMhvwzYcsqqQlMpqzeX8cUeGXbz8pt34y88dBgD091l49oYOPd/yxZlIG4B/PhICkQxc23TgJm2kKZLktm32edB03TqN1TDTF962DSzssdERuKfHJhoTcCPbsMZ4T8rodjxKlzMyWKYwK40F2LbAyWtyWLXExunH53DFc7uRi2XgHhx08L/3j2M0tv4iNjxXV2MzzsBdsmSJHj5w4MCM2tqxY4ce7u/vn1FbhBBCCGkPpir8EEJCWj1LttWKrc2E+A2bfls08rpo64rshBDSCI9tL2Nk3EUuK3DuKQVYgf3w0a0lPLmzjKV94ev1ykmrnIxqWEoJxwWcQAf7h/csQ2fBwsNPFbH/iFvlwB0ZDwUz2xaYLIXjH33az7U1Y1kFBCwrjFXIG+7ZojHvoh4bZ5yQx+03r8Bj20ro6Qw9jNLQ6CwRzcDNBJNVOXDTCnamYE4npZzy/FPBM0Rwdxq5RcsXZ3Dq+hx+u62khfl6iDpSZuStlGlcB+jt1OC8vm4b7ZO6TspYAoWcwKXP7kJHXqCzQ6AQc+C+718O4vCghwODLi59dlfYiM7ADd24U+lXs5ixA3ft2rV6+Kc//emM2rr77rv18BlnnDGjtgghhBDSJpgXP0kFIuaB8EPITKmKJmgxnXDKQvIx8nfdavuJEEJq4bgSI+O+fbNckREn53jRVzxNd2KigJuJimLZDNCR9z9TObPxDNz9Rxw97LoShwfD3zdtLQKAFpIB6BxZ9VE2k3y8XdxrwxLA2mVZrFuRhRBAPmeht9tGdyDmSum/Xm9GKKh4hYwd5t5Oq4hZRMA1HaLNPw2a+8pt1IEbIx84mSuOjAjCaahTvx8t4M8bfaiZPF/TT40N1BTt7BToLFiwbaC329/3YREzv4HDg/53e9uecmTeiGhrRijU6Va5IlEse/rfTB+6z9iB+7znPQ+FQgHFYhE//OEP8fjjj2PDhg1TbufXv/417r//fgghsGTJkhkVQyOEEEJIe0Ktg5Bk4n8bcVdPqzg/07pRFaFw9LvSNJJu3tMI3UZqnnCm+eBCJoTMb1RBMoXv5DRPOP4P9Uk50FlNAdeyBBwX2H3Qz6Ht7bb1sVCLuxWJL/+/YTzyVAm5rMC9j07q+SuuxHgxPGCOTfjDtlFXyxKAsMLYA9cDugoCY5P+tBedVYDjAmeeGDqI1TF47bIMlvRl8OSOkm5PCBGJUFBuXCtueZzhcfxonQekjAquU83AVeRzFiwh4Ek/21gJunUbiLiLRdLoGW26KW23WJfVvFlLYO3ybDiZEGEGbuyBQj4b3fGqcJkS4Bth72EHz+yNCsF9C2xsWJ9PmaM+MxZwOzo68MpXvhJ33HEHXNfF1VdfjZ/97GdYuHBhw21MTEzgzW9+s/79jW9840y7RQghhJA2Ie2arFUEKULmkkbcGiq/b64w+xjvR+hSoXpJAZcQ0urEsz/dlChUnYFbCV22Jj97ZALb9/rq7sLuUAzLBvrZ1344jN0Hk22ijgtMFKsXbMXPLxBanHVcv6jZ2KTf5gvO7UJXQWjhDTCOwSK6DupjU4TOmEXMkopSNkj8bXspZZih2sRzQnw/pe23OOY52/X8/ZnLChTLEqWKRD5Xe369T0RyVrAIdn183NG7ZgldwFXXI/EvEIBckPkbF3Dj3+fqJfjUOq+rHGEryOP1PNlwtnAaM45QAIC/+Zu/QS6XgxACmzdvxnnnnYcf/OAHDc37+OOP4+KLL8ajjz4KwC+E9t73vrcZ3SKEEEJIm5F0QUfNg5BqWunxRiPCZDsXMUvta1KWYcJrlXwYRQhpF+ICU7yQWfxwmBShAECLt4DvwFUoDS1NvAUAx5GYLFYfeG0rPJ4K4btjVXuOK9HdEcpbKuvWdNCqY7kVPyYHI7KGwzeb8cXh+PE7Kfu8JjEFVxf7avJpwYvpgo06cBW7Djh4encZv358EgcG/H0XzymuhQAgEyIXtAPXm955XxjO16n0JanQWIJ+G2bglqIbMB/7Ppvfo6me09euyOLME6bvujWZsQMXAI477jj8y7/8C97ylrdACIHt27fjiiuuwIknnojLL788EqkwNjaGL3zhC9izZw/uuece/OQnP9FP7W3bxpe+9CUsWrSoGd0ihBBCSBtQ74Ku1Ys0EXI00d/+qgyFhGlamHaOUCCEkGMFJ6arpjo5DeEUCKMRkljYE6pfuw86kXG2Vb0Mx5EoVqrbsSz/n+v6IpqAX/BM9aPbKFDW3SEwMi6r4oaMrmPVkiyyGQsLeyxIKSMidDYrdAEzU7Sd6iVplQPX+LyZ58G4YOu6sqF4JTV2ouhpkVIVMCs7yfNEkQlDRvuGAGtuu0ZF0IalUjO2ISVCocrBLcIM3FJFRgq/ZeMCburbRVPu3oxoioALAG9+85sxOTmJm266CY7jQEqJLVu2YOvWrXoaKSUOHDiA6667LvIZAOTzeXzmM5/BlVde2awuEUIIIaTNoFONkCgNVaxuITW0hs7ctkzFcaVv6swbVbTULiKEkIZxY67KeAaoEg7tJHtjQG9XKKy+4OxO/M8vx/Xv171yIU5YncV7P31If1ZxgclStXI8WQKe2VfGqiW+jCUswBb+8h0H6DIcuN2dFkbGvUjhM7Uq6rX+fE7ghNW+gidltIhZLiO0ONxMZBAp3OyWlQieywrtnHW9MAqiXp+klNqZrITrRhy4SXECiW/TSYmndparRzRIw2YOsw8xkVXE8gcEwgiFckViZDz8zlU9kEg4tzdKM/d1UyIUFO985zvxy1/+Eueff77+zAzwj4b4Sz3u2c9+Nn7xi1/gDW94QzO7QwghhJA2IO1iaKpPtwk5lmglYTTyN5riUqk90/xBr27C+vFtAkJIu+GmJB2I2Hg7JhR25MODvxmhcOn5nfjja/r076uXZvDsDR344l+uwOql/nSlikQlcH92dRjF0QRQrvhirSV8MUsJtJ6UkQvKnkDMjZyDYlm28dNTb0/Yz0xGoC9wDpuH7qlGKEQdwIbIiemJgWmoqIusHeb+NhSjIML4BdXXTCBcx+MzkjDfEkpaH+XqlTLMV+4qNC5DNrqdpYzt3IR549EZQgB5XVQPGBoNv+zxbScQn7f+TYpZAFVHScxwnzfNgas477zzcN999+FXv/oVvva1r+EXv/gFHn30UZRKYYU/27Zx0kkn4eKLL8arX/1qXHrppc3uBiGEEELajLj7lmZcQkJa2oDbQAfi+Xxz3uepkFLAJuF+MRlacAkhbYISnTK2gOPKagdu7FimhK5MzK3a0yEwWZKRaQBfcF3SZ+O1L+nBnkMuLji9AwCwamkWL39+D/7tv4cwYeTf9vVYGA8Kk9m2L9wGJlYIS8AOcmqllBg2HJTKPWt2SztwExRcCWD98qz+/bT1ORy3sk4FrwaImhirC6k1C+XAtW0B25bwHODhJ0up0y/utXHy2hyECMRvVF+HJ0TaVqFmEcb0CbHwcL3wu3VGk/JgG0VHKCToxoVc2NsDR0wBNzrdjO9JWi1CIc5znvMcPOc5z9G/j4+PY3h4GJ2dnVi4cOHRWiwhhBBC5hsUPsixTB2HeqtR1a0W7edcwEMZIaRdsJWAm+bAFcqlqaaPjjcNjElRBFddugB9PdGZ4oWj8jmgq8MGEAi4ll9YTAaVwHzx0S9YNjLuomK88h+PSwCq3bORpcloIbbODtscFc6nReDGT25KYK7+rOEm6uLqKAvf8Xx4yKn51seR4XDHxh24isb6F33LvqqdWFYykCykNtB8TcyuVu3fmhm4Apblb4NdB8Pg5YoTXXnl9I4/uK21iZK230x3+VETcON0dXWhq6trthZHCCGEkDaDOg8h1aS9rhl39cwljSxexqZqpziBaWXgJnxGCCGtjjp2ZSyghOoMXBPPk9q9GXfgVowCWK98QbceTsoJV2Rj6lRHzsKCrrBd2wocuNJvRw2ffnwO9z46GXGMhoWrqs+V6rNIvAGiGbgRMbmRLPoamC9h+IXFmnfN63kSj20vY2zSV2FtW+DktTmsX5FNnN51JR5+qggpJTxPQgBwgw2jBE61nl4jFlxjhqR9qtbT0UK/mJL4rWj8ksFfJyHqv9nnPxDwIx1GJzzs2J8u4Fahv8ezey3T1AxcQgghhJCpknbx06y8KELmO630N1IlNAc/k26m250pO29aaD8RQkgtlGs2Hn+jEIi+Zm7HlCUnEMD+5b3LIhm4tXLCs5loI/kssNCY17Kg3ZJKpPOLqglkbIF1RgSCEpZNt2fcIRp/EJozBOR8LvlcNZMEBCllKDI36VQ4UZIYGXe12Koyg3NZkfwvF11nIYTex0qEV1M0pN/K2tfryrlqOoSnQsP3AjWemqpRVsyCq1zjaps9szcUcB0nMmlDjtu0LglR+8HFVJg1B24SQ0NDcBwH/f39c9kNQgghhLQC8YvZ+aPzEDJtal3sJ72WORc00oVI7p9sUzEz7ZhU51jFQxkhpF1Qx2YlbjmuxPCYi217Knh6dwVHhh10FXyhVLlzk1yVlUCwy2UaPwLGHbj5nIUF3abiJ8IM3EAUk0Yfnnd2J1YvzeAF53YlxgLo198TuiQlkMuGyzIdxTM+X6nzHqAFz2adF5QwWshbOP24HPK52gqpqWEqgVYLuGpfiejnjSBE7e3kuBIHB10IAOedKjFelNi6qwzX84XzjO0LzCesnmHucI19nInFfNiWv2M6C/7EW3cbDtxGCsBNgVoPLqZCUxy4juNgZGQEIyMjGB8frzntyMgIbrrpJqxcuRKLFy/GsmXL0NPTg9e+9rXYunVrM7pDCCGEkDZiKg/VCSEhrSgMVr+yGM0UiGfQtQNTuXmvWZiaBzNCSKujBFzlnPSA/UccTJY8VBwPk2WJUsVXIXX+bUxVklLqCIW4k7XW6/OWJSLniEJeRIpMTZRcLRSqV+TNyIeMDbziBT1YvyJrvPURtldVxAzRt0LiAnIS+qHpFM5lpvvSPA004wGs2gcZG3XFW78vQq+zcu2q7aIETrWPGumfEtPNdsztq4YrDjAw7GC86OHIsIsjQy4mih5KZQ+jEx4GR10cGHCqIjsafWmntjifHPOhvrddHf5A2chQdhwZXf86bxcldypx1hnRFAH3Pe95D/r6+tDX14ebbropdbqhoSFs3LgRn/rUp7B//35I6W+U8fFx3HHHHXjWs56F//u//2tGlwghhBDSJqRddLWhzkNI02kkf3WuXbgNOXCDn/MoQaFhaoq6hBDSgigxz3UlRieidlbP9QdVYapa+bfxwmSKtIdcpksynxWRa0FLBEXMvDCzVel9KvLBDboaionVTto0kc/MwDXF3PD8FY6fyqnMdF+aImczzglK8Izvg1qoWIm4A1etv5LFG3Hg1jv/q14pp7AlgNEJT3934oL+dC9npPEzjMiIjos/aMjYAkL4GbhxKm69N6Cm1r/p5P4m0RQB99vf/ra+cHzXu96VOt073/lOPP7440Fwc/UKTE5O4v/7//4/7N27txndIoQQQsg8oC1ftSZkNmgxMTTp+j6ti+30d51muEr6PO44JoSQdkQ5cItliXLF1286CxYgQ9HQjWWnKspGAahcmoCbUvEqa0yv3Lcv2diFZYssnHZcHgK+8KhzRRMiH4DQXWpHMnD9n5EsduOQLSHwgrM7cN6phdQiYDM+d9UQkaeD2gdTebslLFLmD4cicHS6KdYw09s80YGrBFwLGJ+Uej/1dkclybho3HAGbsI8ScVfbeO7aln+tMqBayJl+F0y25pKP5IeXM/06zPjDNwdO3Zgz549EELg5JNPxmmnnZY43ZNPPomvfvWr+qJm6dKl+MAHPoCTTz4ZmzZtwsc+9jGMjIxgdHQUN998Mz73uc/NtGuEEEIIaSOqInBbTJwiZE5IcBGlTNLSKAPHsaxvtpNoTQg5NtGRBIGYVwnE2I68QFa5cqUqYpZcmKpcDg92jcQSAKGgu6DLwviknwugIgHWL89iSa/li4A6QsE/nxwcdDA85mLXgQqGx/xX8g8OZLRrOJqBW30QjghzEvidC7uQz1kx524gTDa2KunLQFBcTeX3NuGc4Ka4oGthBdmvavlKeFUOXCvI159qfFDS5GrdVcE7yxIYm/TQ0+nv295uC0OjYTU8v/jc9G8AVL6wMNV9o2O2ZcZO+MtRGbhxSmWJXDZsN74coMF9WOc7OBVmLOBu3rxZD1900UWp0/3Hf/yH7mxnZyfuu+8+rFu3DgBw+eWX46KLLsILXvACSClxxx134NOf/jQKhcJMu0cIIYSQVoeiBiGp1PrzaFZV42aReAtkZONZiN1UtRmNvAKpb+oi8x2V7hBCyFEjn/OzUj0ZOiX1q/cxB66d4sDN2Em56P7P+Fkg0DXR32tj32El4AaxCEooVtmsALLB6+/liu8QLpYlJkseRsYF8rnQxhl3hwJhhAAQjTeoF/WT5CydClIGrlc075X6cB80Po9y67qejAjJStBUYmw8jzYJU25NNFXHtr8l/GGVN9tVsFDIWSiWvWC6eAPhcmr2w9RqRXqhuIwt9LJtC1URChk7FJuLFYmeOsttpE/NZMYRCjt37tTDae5bAPif//kfAP4X9XWve50WbxUXXXQRXvSiFwEAxsfH8eCDD860a4QQQghpA9IumMMiCrPbH0JakaQbkVbRBWs5Sqr/rtU8R7FDR4mZbu82XGVCyDGGOjZnMwLPOimPU9flseG4PNYsy4SFzaoycKNtlMr/P3t/Hnc5Ul8H46dKutuzr/303j3TM9OzMzMYGGwzrAN2XiAOGJtgTEyI919CTGI7jhNsx6+x87Od4CUxeCOAQyDYMQGz2obBmG0GPPvWMz29b8++3F1S1ftHqUolqaSruzxLd+t8Pt3PvVdSqVSSajl16nwDRWfmIFT+3+mJIDFpoSCJxIBABibGKG47UsaNh0Qer99fxP5dBRzaXcTMRKBTlHkOTarpn3ULhQxK2x5imIUVuLrlwSAUuMoqogsFbqQdjnng+nnL1E77DC4BybS/vIeNljipbRHceqSktkc540EQ3Xq+dKWynHjQLRRmJiz1POtKch1Lay5OXXRw9pKL5XXPuE/0vIOayO2bwF1fX1efJycnjfssLi7iscceU99/4Ad+wLjfPffcoz4/+eST/WYtR44cOXLkyJEjR47LGpkiXm8zM6hOn5LH2GVcRmxmpyWPoYGZGhhfRheYI0eOHBEQAlTKFFPjFiZHLVg08A+V1ZtSxkbIw5aTvKxf1ZfG9fZCgStRKQWEMSCX/vuWPADGRyxMjQf/RoYohocoxoYDmss0aWgOYpYe5Gwg4MIiQPn3DgCybKI2FmnQiXACM4Er7B6ypRctY510lTYYMikauXDLEj7J5RL182Q+abdNKtEkuEmHyjKbHgueucN7Cqoc5HOs0oN49i4uetioM1QbHs7MaxH7kvIS+d5P/6BvCwXXDTKcxI5/5StfUZksl8sholbH3r171eeVlZV+s5YjR44cOXLkuAzQqR+T0yA5cphxOfjJRgnbbgKtXI4wKqWVnGmrc5MjR44c3SGpmuI8ILzksn2lJo164DpSgduFUtU/cblI8OLbSqg1OIYrFJ7HlY1DQODGD5fksudxFO3grFG1KWC2QuAwTDTKrKmlYtqPXbRlsg1gPEwIDqJJUArcLjxwJW8n/XhlrC7b1rZznkim6tD3MBGTu6csLK9ZWKsKpjn6rAS+uzKNSF475iB8nLCnSD5Wv/cyL2MjFO9+xzRcD7jraAlv/38vAi2ulOQ6GAc8n4QHklW64XOGlejCw7nzNZnQN4E7NjamPi8sLBj3+dKXvgRAZPzuu+9GoZAU0S+4eJ0YzpEjR44cOXJc+ch9InPkiKOX5ZpbjSyqpahK93LiMrMojGPH6GTBIDOTI0eOHNsEi4q6TRK4iR64mgI35oHr/40SdbqX6t23VlBtAK7LoC9Qpynth1z27npAsagRuBHSV5BpwXZ9gi2prtfb4V7aLpmcp/m7DiyImbwHXShwLU2BK/4GlhfRvDHGVRkmgQDgJFDs6ntTSnBgzk4kcGVeaGRyoFeo5yhCmKq8hsj7gFi9dn8RB+cK4JyrYH1tJ36DYs8t53A93lUQuX7QN4F78OBB9fmBBx4w7vPJT35SfU5S3wLCakFidLQfu+AcOXLkyJEjx+WCpP6rvlwpR44cydjuV2RpTQzM0tQuLYdjac3D2AhFuUi6GmwmYa3qYXHVw8gQxdxUb8OaZpvhwqIbC5xCKbB72kallJzRbss9r8ly5Mix42FYBi9/tijxfVyFAlEPTAX/949+YR1rNVGhFrogtfT6lPty2OikII0QjzqSFLjRvmQ0R4xxzK94OH62DY9xXFh0MVSmGC63/XQBxw0y15PNgiof7Vp6VC20HY5zCw4YE4SrzFs3HrgqsBj3yVepwJVkqqbK7bhKjnMQ4vvfJpTNSIWq9o9rZaAT/LI8YgrcjGMBoyNHj5yqJLKbDsfFJRf/5nfm8V23V1AuBrYTlBJQ/31oO2YCV3/m9K399AX6JnBf8IIX+DeM46/+6q9w5swZHDhwQG3/xCc+EQp09prXvCYxrccff1x93r9/f79Zy5EjR44cOXJcRkhSaeTIkcM8ENkJqnXOOU5fdACY8yMH1a4nCNe2wzE+SjEx0kXI7AScOO+g3mS4tAxMj1s9KWDOzbu4tGxe+ccYcGR/seOAPazmMu0g/uSTUTly5LhswTUClYf/ygm5z3ythj/+5Jo6xDYtvE5Q0XJdggsCxqXyM9jTosIXd92gjNTtHQoayyUDrSl1aGRObr3GsLzmYqRC4Gnt1PxK0C4wJq691zZXHqYIb0Ngtay4tOziwmK8zbK6aFLl+VmEpLUtCkIYOHigwOVAlqQJkj1zLSvwiygWgkK0dasLRc4Ppp1MulXR36OezIQQla9Wm+H//+ElXFh08edf3MBbv2c09MxLorftcAyVB5LtjuibwN2zZw9e+tKX4r777kOr1cKrX/1q/NZv/Rauu+46fOMb38DP/MzPqE7Nddddh7vvvjsxra997Wvq89GjR/vNWo4cOXLkyJHjckCnvlrOeeTIkYrtfEX0sdYNB4ux7SMViusPFnF23sXKugsQolRIUrnTK3RVFGPINsqMQA7up8ZEABwA2KgxrGx4PS3lTFoenCNHjhyLqy5si2Bi1EKzzTC/7Kk6dHyEYnK0/4mtfmGye5W/ywk5xgTBF3jgit//7sF66BjboArtWOMTogjE6EoNQglGKmIC8LbrSqFtASkZbldUO5FgRyQJw406w0iZolgg2D1t49DuAlY2GNZrniCTI8xzVwJcf2fZpvTjBd9oSdUzCXnqdqPAVVYUUknNpYo3UIsmKWKjiCmnE/C8G0qoNjwMV4KbamuPuyyjs/MuhisUQ+VgPw5gfsVFuUgxN22hXKRY2fDgehyzE7Z/bk1hTeAHist2DVFIC4Vf/ZOlUD/A9QBCxEwDpb7CnIeDnekIWXJot6fd5lhYdZV/8egQRTHjqqS+CVwAeM973oPv/u7vBuccx44dw+tf//og07IgCcEv/dIvJabx8MMP4/jx4wCA8fFx3HTTTYPIWo4cOXLkyJFjhyNpsHA5BGjKkWOzkeaBuxOCY+kDo9Eh8whkdsKG5wHPnqF9+9vpGERaMo3pcQuzk2JodH7RxcqG5rzYpw/xDhBK58iRY5vRaDEcOy2W5X/n7UM4e8kNqTwvLhHcfWtlu7IXh6Hi0v1LPSbsCoCAaD1+rh3av1AgiapVE6mmT4CZAj3JJf6jwzRmb5M0eTbsE4FKgRv1t9WO87hQU46NUOzbVYDHHUHg+hOE/dblsYBjPbTdzbZotCbHqLIvArrzwFV+s1zYGMgyKxZIYPSrSOeQNNqIWH/dsHulRDE2HJ6g0K0uJAHdaDE8dKyJ77x9SKVdqzOsVj1YlMD1OK7dV8CTJ1oAgPFhS+Rb4+lNkw8SYyM03L7LfbSdDu0u4OQFN9bH2Kgz1c8R95CDgxu9cqPQ83R+Mbzy5yIhuH5vtodhIATu3Xffjfe///34iZ/4CXhevDAA4Ed/9Efxlre8JTGND3zgAwBER/QlL3nJILKVI0eOHDly5MiRI8cVi8jKv21ByNMvRQHkB7sO+8AZBuhZwSMRsjnvPMg0ISAgNBuErHkw/ZgyLs9VuTlyXL2IkjxS/T82bPkkIe97VcIgkGT1Iutw6nN8nseVTyqlwnpgZT3MeBUMguJkQjdQUHIuqLGoT6zybjVM3ilVqZ+nO28oY63GMDspMqHEAlECVyd2/c+S0jK1sUE6XXjOIpzvfm5x01fgzkzYYQK3CwshRbgyYP8uGzPjFggFxoYDwj1anqnpAcobWX6PQk/nyP4iWg7H7ETwgKSVSb0VTFS7Hg8Rq8zQ9qeltWfahkUJxkfMjLfjcjz/pgquP1jEdfuLKNgEf/AXK3j2rIO1GlMKYkoA+GV+5pKD8RGKZotjl+bHn2S/pL/7zTZD2+GoNrLNSA8gfIDAO97xDnz729/GW9/6Vhw4cACFQgHj4+N46Utfio985CN43/vel3js4uIi/viP/xiAeHF1BW+OHDly5MiR48oGT5MY5shxlSNN0bIT3hn5/mYNyDIobmJQSt4ginrwW5JCq2f/wx1wn3LkyLG9SCLCJkdpx322A4keon6F5vGwp6v0Qtdh28kK3Cii9SznwjJBh/QcNZZTZPKsUqbYPW3HltFH2yr9d9mscMhV5NHMZbuWpLyp9qZHAa7rcUX+TYzQkO97Nx64lmY3USwQTI1bmBy1YFnKKCIU6CwNvTyzc1M2Ds4VQirqNAcIaRsBCBJct09S+fD/Ev8KCMwB0CgloQCl6vnwt59fcLGw4qLZYmi2ORothnHfs//E2TYY46iURCDWoh0ofx873sKzZ9tYWNX8ibXhjek9mNTiAdQyErgDUeBK3H777fjQhz7U9XHDw8OhAGZ79uwZZLZy5MiRI0eOHJcxdtKAJkeOnYjtDI6VFBgmCg4omY7Mbj+5lsrZUPo9wDUocFWaPZSrTj6o33rKWY4cOa4kRIkwx+NY2WAYHxmgr8wAkFTtqbl2ysFB4HlcKUotIrxLozAFlkzyJdXn8qWFAiVApWyhVBBpjQ6LhsbzOOaXXTAOzExYaLVF0LGlNQ+UEJxbiJPJ1TrD0rqHUoGFtq9seNioM0yMctg84nNrIDH7aReUQpUS8C6Dda1VPSyvC8VtsUBgWQRHDxWxvO5hdIh25YGrK5n1bFCiEY1+e50lqJhULyfZVACd+/LU0IcQQUpdNNsMQyVJOgcK6dR0Sba2N7qP7BOUSxQz4xSrVYbpMZG5c4seLiy6uP26EqoNjpEhimo9/P5u1BhmJ9LPqed5ZIhifmWbCNxeUalUcOjQoe3ORo4cOXLkyJFjG5AkwM09cHPkSF6CB+wMYjBQL2XbP3Qdfbzcg1LgMoMCN4pB8eP5ZFSOHFcxIu//pSUXl5YccM4xVKIol8hl0d+x/JbH8/QVDARnLsVJ00qRZG+nVFtHQh64s5MUQ75aUpZPrcnw7Fnht9t2C5hfdlFvciz4nsKnLsTZwFqDYWHZRbEgAqFJLK56WKsyjA55mJv2z6PlxfRdfM56YXELBUrF56xksMc4njzRVsHGyn7Eq/ERS6lDu4EkSxmPTDZq10QBMGQIYqYdxzO0y0nWE6ZVPMdOt3Fh0fHJ7uBeONoErpoQTpsYzlDM8l5IR4bRIYrDewt46FgLt11fxhfuFwH6Wg4HpQSWxUEJsG/GxoZGviaVl37dah8C9SzWmpcRgZsjR44cOXLkyJEjRw4DMjiMbCcxmKa40cE5Tw0s0i2iBG6vZaAICKMCN/1Y0+BbpXI5MDE5cuTYMjCuk04cji9YJZCqv50wJRcgqU6Xqy1cj6s6cGnNxSe/Uo3tK/1nTYhWkSGfWS7KiBKAauVi0bj/ba0hPEQJhKcoiFDlRonCcolhrcZQKVEVsBIAyoVA2RncIqL9r+erv4pdtpcWBeJ65WS4Lgfz/ZF3TVqYm+qPxqMaMR348hJQGlheEArA62yhoCOL00SiNYdBgSt9o0eGLEz4ViMeC2wkAHP+CBHXQwgP5ct43kiG9D6NKA+CyVGKN75iBH/xxaqyb5B9hqjNh/6IJNkvBecgKvhc1kcrJ3Bz5MiRI0eOHDsC0Q5OzHssR44cIewEb9UkX8H4jv7fQXngev1XDB4LCIh+PHD1343qonw1QY4cVz1iBKU2O6dIqB1cSaggY359pvuQ/sYHl1Bviu/T45YKrjU7aXdf5xP9XGEPXYsAUZ2iXMJuW8DeWUFvXbe/GAuqubzuodliGBmiuP5AUf3+7acauLDkXyN46G9goSAIdj3FrhS40jfYb7eo7zmQlbSTE422JQKA9QupwPUYVxMLirj180Z8DwVTwDgdKkApSSe4O5HfphhsjIm056YsVEoUrm/b4ersd0R5O4guhhp+cFFWngcU/AwGtkv+vgnkrDw+lC4h8XLoMsMDC2KWI0eOHDly5MjRD3YAF5Ujx46FiRjUBxnbBeXp1wV/G13y2AtiFgo9pKUijRNiVuB2n6Tx2J1AtOfIkWObkSA5DdWJW5mfBCTZWsm8Ud/vVBK4hBA8eryt9juyr6A+mxS4sj6sNRgWV10srrpoOwGZSYh2LhrOSJSUBYJ8lIqGJeqG64pONurfaGTfqDVAv22tp1kodIM0r/ZeIJPRFbiKkPT3CZPXQNVXOkfhMcHghvooJg/clG2ASckakMtUI/I5R0SBa1gJQ8Jke+p9I+F91OSBv1mWiyx7qZyX3wd0SzIjkwL3P/2n/xT6/u53vzt1+yAQPUeOHDly5MiR48pE52XKW5OPHDl2Inb649+XB24fGEQQM09FUA//ntkGocuT5nVZjhxXL0LKPISXancZz2prkLLigHPAcYL6s1QgaDkcP/nGCTz8TEvtWyrEPXBlW7Gw4mJhRXweLlMc8VWxMn25b4hgleyxAcUCQbMttjEORKnjLPYHlmUmd02rMLppyqJkaLekX5Rk7ReSCNeDmIWClyFMXjeaDI880wQAfOftQx3zZppwTvPzB+JlopS/xLcz0MrQNXjg6krcKAndDUL55JKo5WqVjqddLweUTUOQby1vkXxEn16ibRuohcIv//Ivh25ClFyNbh8EcgI3R44cOXLkyJEjx9WO1EHnDliarwZ/HQaWMiBN9Lde4Q4giJmXdVCs+dWZYPpVv2+5ADdHjhzR+k5Xug5iVcKgkJQFXcHqaR6+jHG0fDL3e79zBG2H42uPNBLTn5uy0Xa48pxdr3lotLhRkUxJuN0oFgimxgqoNhh2TVp45kyg/C0VKUhd2OKkKnCjE3aaH2yURIyRaz1W5jIdueqDdknaKQWuyWegj/ys1zx4nqAEJakbJT+rDYbVqgfXExYOprxFFa+9IFr2Okmr9y8YA1wv+B53JIivCsogwI2r4P0NtgW02lypj3UPXJfxWL8gyy3NunLJhK48cDmPZ1DfNigMmgzOkSNHjhw5cuxcJC2rUp3qLc1NjhyXD3ZClzmzB66GQeSbRSRrvQxFvIRlqWrQ3kO+Uq8tr8xy5LhqEaqjeJgUlFzKTiBwO4ESwAPQ9kSQsVY7UOIOlwm+/xWjIESoaoF4nTgyRHHTNSUAYhXENx9rhFSVOoErPHCDBAgBDu0JLBpOXnAUoVa0iVI4cq7rMAWSrCH0DZLEjPZLjfelh3ZMNlsmK4g0qMnG5JhwXUGftDx+zhF5iihvpU/vxSUXq1UPCyserjsQ99/1fJ/ajrYQSeXvI3q8q8huElKqCgI3rnLV75EkurPE0jBNLBMSqGUdl+PE+TaW18WMhes/b9QCwDpMTEcnf/1EoyrcbpCJwL3nnntSSdVO23PkyJEjR44cOTohFsRse7KRI8eOgmnpZmyfbRz0qyWhnRS4iV96g67A6RVSDRVTFUUHZOafO0KKX/JxUo4cOXSvzjCBQ4w+ntuFxKXu8ncLgCeILN22YHSIghCCUpHgTa8cwwNPNPx0kus/ixJYFoHn8cBTl4bzoB8dTalok4DALRCh1PSAaoOj2fZQLBBUSqJxilkFIPJdU+DKCUI5Many0+NtkqdgEdse1+PwGI9NIjbbTBHjBZtoq0UG05bIe8U5R7PFQnmSZ5iZoFirimdzterB88wBzTyPAyScN+OqFLkt0UIhvEGStCpfPqHMOFckKhD4EwPA3pkCltY9TI727jURJfplwDRb2k5wYGbChusJT+Bo36dT0LfkE2fbLROBe9999/W1PUeOHDly5MiRIwmdPXB3zsAmR46dBDXc2U4C1x+sdOQoNQuFQDETV0llxUA8cHn6stRoUJNEYlf3RUwj2nvIY44cOa4QxCwUdM+ALc9N1wgmsog/OSW+N3wCcHQoYLK66bYVfAJXD5ClE7ghgixSvxaLBDVhzRry2336VODD+7zryxiuBIkk1dGcA1S1BWE7AaWQTs5KOiIWCroC9pnTbdx4uKS+N9sMDz7dCvV9R/yyHaQH7k2Hi3jiRFBOVFeJAhgZsnBgTsxunp0XKl0R9IyHFMTMoMDtZc4yOonhqrIifp6DbdK+A9D6EwDGRihKRRHwLGSh0MXzGFbGB+eVEwEFm+CGg0X1jEXNIzyTB678a1B0dzvB25WFQo4cOXLkyJEjx8CRsKwqa5+GMa4UII4rVBwWFX5o0fQIMc+ON9sMjImOWcEWJ66UNl8513bCwRhKBTIwj7OdBKHy4OAQkaIHpSLZTriRAWcn9HzdSWooDN52rNFixoGObREUC+kkZ3Rro8lCgXkcVx+xiD/1Fofr9SZXabv9Mx6mwTQwOPV/1Pc3n4zKkePqRWhldWQZNd9JDG5CnR4OWBnkVypFx4Z7YxcLNtBsB3W6sFAIPEIpSV4FMTdlo93msG2C8RFqbCebbY5iIegnxtpNjbikUYmtVORGvncLpcD1r6tcoqiUOBothmoj3AbWGsLHl1ICSkRfo1oX+wyyf2jb4bQCpav4qzdXgRJZ+AtzztFoic/VBkPbYaAdjPC7DVisK7Kj0PteskxNE61ZrNgCkl4/LjhWloe0r5Dq36T+V5YujUnlnrUGyAncHDly5MiRI8dljSdOtLFe89BsM5y84Kqe0f5dBaVaAICVDQbP45iZCK+XXq16uLgopvMJIbh2XwEFm2BqzAqpIgaN9ZqHx463Qr/ZFsHzbyxfcSTu8bMO5ldEGZdLFHfeULqsl5W3HY5/eLoZ82FNQ6lIcdfRzbnuQQz9z1xycOaSk7j9xsMlTI3FDfhMnn4nLzg4v5CcliyCJ0+0EvfpGj0UQpIHrkrSPJZPxeX7VOfIkWMzoTcXQskoPhNE1f47oxZJDNoY+bneEpnX+1ud0tAhiUSTqhLwLRUSVnpMjVmhdkm0Q+GKu9Vm+PaZtmaLEMmjdtIoeRn1UI2SfL2CUoJbri3hW0824LjhWFOSnJwcpRiuUJy+GLSlg1LgAnHroIgAN1SMetA1jwGnLjq4uCRu2NlLLs4vuigWKCyLxPrY0eQSyy3yu2yf7QiRCkTsSPoNaBo5r0xbPUmSwPULwY2s/olaKOirg6Kk8iBWTeUEbo4cOXLkyJFjRyCuish2nFQveEx0bjkXy6c8X1ELiIFTy1fZUkpCnWDPE6oGqZbzGFAAUGturiKm1gg81ixLqA2kqrNyhRG4tWbQw262xH0YVDCO7UCjxcD86MOmqMxROC5Hq83gMXMU5zQkBl4J7dP/s1pvBgoffYDreWJAU2swI4HLDYPimv9O2hYJDbpKRYpiARgqUfVu9oOiTULPVrdICgwTiyrdY/FGB6w7SGOXI0eOLUaUaAov1SZgPL6MfDuQlAMO4Oy8ixPn2zi0u4BSgQrfWX/7mInAzXC+oh0mLgkFuE/mSgWuLLpOhLBp60ZdtNeUEBSLBNMRgpFr9bOuNAXCxGXW6zHmK3IgJUJ5LH1oa00Oi4qTNNviaosFospGYqAK3EhakqA0lbFObHPOUff7x7ZFQKkoN0qAVherkqKYHrMwP2JhrSqWxjj+ChldgUspiQcwjSak3St5JU6GFTvKioH7tgiSdPUv3vaZ06j/ftS716TAjanZwWO/Z+3H5QRujhw5cuTIkWNb0anLktan4ZyrztyNh0oYLlM0WhyVEsGBuQIOzIloxRcWXZy56IADOLS7gD0zQRfoxPk2Liy6qNYZRoYortlbwInzTsxjc9CQPlkzExauO1DE/Y83YjP7Vyq8y5zAlUvvRyoEt11XTt2Xc46vP9rwP3d/rtSlf5vA8x/aXcDu6eD9kGrapGWBJlWNfLavO1AMkb4yrb2zBRzWIon3g394qolmm/XmgcvSFbid0DEyeYr9RY4cOa4uRMlZ/ZvrcRw71cJQieKuG3du4/i+/7MKANg9beOuG8soFggefFqY0I4O6x642WtkSSQ6IQsFsY1SIlS1GftGprpW2g/MTVu4Zm8xvoPftlGd8YvAdDndrKaJ7kv9JfoFG2g7wCPPNGPHFO24ddEgFbgWDQhkkcfw9vDzGoghGA/u73X7iygVxERqrcHiymUdapu53KQq+eFjTdSaLLRChvnWDTqZr5JNeDR0H9yFFRe7Ji2Mj8TfLWWzoBhc7Yo5QBGILYA4gRsrN8aVojq2imcAfYEBPgI5cuTIkSNHjhzdI6mjb/KlikInlWQ6SR1cuW+iAjKyTKpHa87MUP6bfn6uaJVe5KI2mxzfbCjiL4MaZmCWCSnJDKI0k94z+T4l3TO5DJhGVO36sRKbwmMqZVD3pdBtPvu+lZf3Y58jR44+EPXf11Wd1bqojNZrEXZok2CqLxkTq4Bcj8NjglTW9/M0FePyenAx6/V4ELNuUPBJShkMTSe+KOkuFoFpLm6tJtIdqcTzxzkPqSHl4SxCRA66OyhJa52gjV5rsbi5BG50BZEsO3NfNPCZZUwrH+pPyBMYCct4Cp37AVJxKwl9qufRcP2B/Uj8BBOjwQGSyO8E/d5zQBUMVflicFwOz39fPC94byTpHR0/RB9hnejutl+RSYH7oQ99qLtUB4C3ve1tW37OHDly5MiRI8flBUUekaDrLQnRkHaAiE4Z5byj2k520jgXHbTNCrjVr/rvcsZmk+ObDbX0vsvBVD+rY01PyVYoOyVJnXTPgsFOkJlOBPdOCea1Gfk0CHBz5MiRI1T/80gUMw7Nc3OTsbzu4djpNo7sL2B2QtBBjRbDI8+24Hkcz5xuqTyOD1u4/foSFlc9PKp59utV5rpPkI4NB0ybuo4MbVQhamGjfaY08r0j+xf+em7ewVDFQrlIjAQu49ohhrRJwsx6vxOzkpjVLRJuOVLChUUXi6uu2hYjcAdsr2VZJCBKI0HMTAgUuOI7JaIdJYSAUh70BwzHmoKMmRBVulqUwIV4X8S28M1ItB0h4pncM2PjwqIbU85qu4XzGc2P/9fSvJrf/39W8NAzLeyetvGiW8pY8GM8UEpwzd4CPI/HLCqM5+jhdmYicH/kR35kywNN5ARujhw5cuTIcXXAtATb9N0EaTlAqfY54UDGOEDjM/jRJV96MCbPG6ziQUeS/+YO4bYGiuglXe5WEW6H4FdR6MsUu0WmwwZQnEnvoVLgJgRsi3oFin3Dx0psxnCinyQTifgktUyP58mymiBHjhxXNkJBzBCutqUH/1bg6VNtcM7xzOm2InCrDWZcZVFrMjgusFZlcDUFbrkklv8DwHPn2gAQsqZKXUofwdgwjX2XSuSpMSvkq9opvWiTXGtyDFWE/3q5FD9Y59F1ajCafxXYqsebFD0sSswCwFCZYGSIYnE12Ed4zAa+r4PujxYsQJo3xPrOURWE/xtjPKRS9TxfSQptJVwfRLPsowd9+vg2Uz6jFggSwSR0xgbYn00hJDyJa6u+EPB/7qsCELZQL7olsNFijKPlcLgeUEK87xANihf6LSO68sDdqtnyyzkqcY4cg8KlZRcnzjk9mdlzLiqUVtsslxkbtvDCWyo4uDvsP9doMjz2XKuj0XfBJrj9uhJKxcG1Io0Ww2PHzeeem7JxZL/wLJJelYCoK/bvspXHZS/wGMfDz7TQbIXLinGOk+cdVEo01iFJK9uRIYr9uzrn5+KSi9WNYCqw0eJYWvdACbBrwlIRWXUUbIrDewtdNd6VEsXt15cUyXBuwcHpi66xPp9f8bC8Jsp2dNjCvtngus8vuKozNTlmYW4q3nzo9ylHjl7QS/Mf2CKQkEWCxxDrxDEmfkqaFdc7iZZF4Hnc7/BtkgI3QgJeTd2f6FLSyw0sgXzfTJiej60gBgMLBfN2FUldszKIPttbgV7KoFM+OyVpIr31z6YlnTly5Lg6ofe9OddJQb+uINiSWR7TGEAGWJsYoTh6uCTy5W+Ty8P1MdqeGRuEEKxueDhzSYwdbrm2tzFAqUhx7b6iIoI5h8rD9QeKePy5VtrhIeg8klzSzzlw67VFI8fE/L6iRUXATVkOUeItIAd56PfM+YrkUZLfOqdoUYIhjWRWKt0CQbO1OW2q3h8mEQVuiO/kwW9cn33wn2PqiyNkOZkI6qwTofISdYGGzIKpHxTlZaPnkdeYqMDV+i7yPMZ8GcbfjAG3XVfEuXmKUxddNFvCB7iTSKGftzwTgXvw4MGcVM2RY4uxsu71HIm07fBEghEANhoMy+tejMBdr7NMURodl2OjzgZK4FZTzr287uGI/3lpLah9OedYXvf6InAbTR4jbwFRhm2Hw/UY9mi/y0jiScjqr7MR2a/Z5n4jCLRcbiRwHZeh2WIYNiwBSkKjxdBocYxURHor6yxxMk7PUzR/+vdqnWNuKn68fp9y5OgKyaufxOaUaikgYILPlJLYTDshUGRsdBIklj6X6W3uUv9EBe7mnXLboPvZMc6zKyF2KLq1v9i0pbHm1Z29IWFw1Um9oi+lBMLkfIzg3gwFbh9pJr2DesRtoL/ljjou76c+R44c/SCJaAr9tjVZiUFXVMpqrmCL5fWCwBVjI4kPf3YdxQLF/l2CTjq0pxCyUJDIWmXqTelGg2F0mIL6RGc3nKXeHlg08LhN4rIY9x1wI0pbU3r9QE+nYAdE88G5AjbqTI1lx4YpKiWKYoGo/sXUmIXzCwzlBBVxP9CVsvKj8QxElAvj8D1wg+9A4OUqvxdTWMaOKmoat1BQeTQ8DLF3JmkVUcaVX5xz4YdMwgrtgmFsDgCuK+6n2sqDvEf7DsS3gOhnVU8mAvfkyZM9JJ0jR45+IF/4w3uLmJ3oTuKzUWewLDGjeduRkvq97XDc/0QD5+bdWCcCCAZdU2NWopLy2Ok21qrewNVTMj8ToxauPyDO3WpzPPJsM5RXed6Duws4fdHpu6cjDy8VKW6/LiirlXUPJ887sCjBC26uqN9rDQZKRSX+vOuDJRMe43joaTFD/Pwby6El2CYQIir8m68tolKiOHG+jYeONVEpUTz/xjJmJ8PV85MnW6g1GI4eKoUieqfhkWdbaLVZqIMoG9zrDxYxEYnEaVsE9aYg0stFiu+4qaw6GJ4HNNsMtk0wVKa462hw7c02x6PPNvOloTn6RuytydCzCQgYEloO7SA+OGK+aiBpaZfewRedRL6pwbauRg9cywKYm6yEuFzgJgS/6gSuZFbdHCP+mgagW/HkWNqyQROCYCYktB8hJFnZOsDXajAWCuFU+klTv0/9Wi/kyJHjykGo3ouSuRDtyolzLr5RbsC2gBsPl4y+rZuZN6LVhbYl/D5dVygKdSsD1wPe/5er+KevHgMgFK7hBLs7P9Ev0z/2tutKoj7ttIReQ6gqJ8EMXNJxnIlrV9vlIRGlrZrMU2vh0/ORBl2dOjJE8cKbg/EWpQR33FAKtSOH9xRwYJct/IAHLKosaMNNEmkH9edVLwePBwSkHFdalk9gcplu7/lUCmC/cyGzlWQzwhP6JrKs7A4+/iodHvyNPHYAACuh7NtuON8cvLMCV+vXdVtSXVko5MiRYwvhv9g27b4StKiorIo+2SZhWxy2PxvJDAyuXgknnVMO5AYtntIVPPLcqiLVKlwWaRj6zYZqoEm4nGVlz8Fjv9sWQalAQ2XLGFcKHsvfJw0WJeAWx1BZzrRSFGyCQoGgXAqnDQClAkWrLQzRsz4P6l5pN0uWZcGQDqWis8Y4gWWJ67B83yXLktcu0tWPlelf7kuic2wfOpE5aZt1Ba4TWW4VPodc9xVX4JrOJ9/nTVXgRkhAOTN/Jcr05CXJgBmXvQLXC9rLTNg0Ca7AIAhRmUTcA1dO5CUpcMODLP2djCKLqr5X9GehkJRoOO3Y+C3p96TkLu/HPkeOHH0g2k/marJLbGs7HEVbjNHaTKxsGzSBG12F5/rBlnQ7B4mCTdBoBfYJOoErceK8sD04vCe8GjKpPUmCTo4F/YXu0hA7Bx+p9iVpnlx2RdQKEoTHoJJY5pG2oFvo11CMjL+ipKyJpB108DKJ0SGKCxCro+RqTSNJqpULZ0G5yWfY9u8V42LcbBIydWuhoL5rFgpGGwNpfRBJX/6VeUsiVWPWv5GHV35PssySynTd31b6RUf7DoO4izmBmyPHDoWqO3pot6ONkYRc3gDATOCqaO7JacsKebPG3iYfOca5WM7gexkBgyOSo0s/JVQkToiyokpZFO/giP11QhNAB5Esi1TonItzERBj54AayNhOMHkYybbLTHCFZxwZE+WsjieaT1foPPKZyEemOfpErM7q3NXRFbjNtngGbYsC8EJ8mactZ0pSYuhnsyLv/GZAKXD9jrnW97tiITvSSX6qlwuSlt53Qi/3Nu2YTXE4ixK4He5ZtD3T38mdjs3KazRoXe5ElyNHDr2f7C8ICsHzOKCRe46BMO0Xjhv+3nYEgavGQ9o2WS86HofnAe12PD9Pn44HMOsFer8sGI+S0N9M6RjUuowjcWWk9HS19AEZtHG4ylN4/NdPnV4q7pwGYWbCxsSoaOSl+CgY1wX7EUhCUgQwk8StHFcWbL/fzc3+t0B2K6JoH12/d2kcRfjtCnaU/fmsvrTRe6/vQGl8IiY6scHReZVZ6BxdPg5bo8nPkSNH11BBQXo4Vlaq0QpQELjJywhU451SM6hlDAMmNUxLREONOReNZyy65YAIXMM8oZ+fSNTYlDIKyO0MmTKd119GYSRwDWRsJ8gGS29ogvybnyxCSDDT6e+rOlIQnaho/nTl0uWuqsuxPej01KR64GqRefXP4rjgQPleUkpixHA0fc55x8BNg4Bu+XDFwy9ju4Of6uUC9axlHMVFPVV3IpLyZmltm2kSMToRGn0PdWwm4dxt0Xp6n8LQXwqlmVE5FMlSjhw5cijwSH9cr3M5C/oEZT/GSHsTCNxomvJ7dCIOCHxM2w4H4xxtNz54XFkXv0UJ3G7bunCdK/trpv3Sa9doEDMgnfRjTHjgRgN4BasrEsZLqbkw5Sv43I+9wGbAzrByVIL56ltFdkYUuGkEblZECXt9ojxM5sbHuSZ0WlEXW1ijKeND7ykhoXMN+4plOdGiHh2ukcXRvoPqC/b+bucK3Bw5djh68brxNKIinJauwDUcp7xmks8pG9NB20JGViv4+Qg+MxaJEK8tpxjEeY3KP71hkEtDUlTKlAAMyV48pvPK+ysN00HM10QNZGwn6ApmiaifUChPEXJaBnxSy6oSytwYcTtHjh4QfSyz1H6SYKU0nRBlCRNbofNp2zoFbuoXnHNt+XZ4bVU/Hbudjk5+qpcLlP1FlwrcXqBb/UQRtCGbd34rMpkafYWYNtgB4tYgm41eSWF9cmaz1MLS83gr7lOOHDl2NpKCmBEAnIh2kUP4ojbbbCAE7vK6h9MXHXXu6CTcEydaeOEtFaPsUNaLLV95246od3Xsnu5XgevXkUxbAm9Qu3YKGRBqD0iYWIviuXNtFRxbpktDYxpdBBBKsi9kJUu3C8TQF1UWCggLquQYUVkPch6ziFBpGCYJTIgpcDVSXb8/hQJRz6aevsyr/KOEA16wotecwdCf2IboYZUiQa0hrEUqJRKaTI6qfROFIyTbWEfH1aD5yJHjskTUPL2rYxNIRqL9xnm8EY/6/ZigKvUBD76lojfU7pLwoEcf8KuGvs/RUJKFgrITICREgCaR4+I3uU+W84Ybg+B85msykbGdENguBL+ZZthVnuRxkYEmV6SYeQCqKxpzH9wcvaCf11hNPNF0X1JF7hoe/ujpOTZ/qb/+rmwFCbjdkGWsd6QvZ0TtLzphs4drgyjNpBUplBLVLpjuW+CBG570MJWNrlAZNLpNM8s9TLIM6vWcOXLkuHoRUtwaNrqekDaODPkKXLf/CmZ+2UW9ydBsiX8mUni9yowiHtleS3sqJyE/k2Minkfocvy/WSfY9PGpbCjU3HYXDWjIQsE/MBqcCxBj4ItLLhyXh1Sj+rmE9ULwnaP765IgoXLt7tithspp6Hb72mjOQ/YAso88OhQUlFSmxpBxJUulFN6j4seF4dpnABgu+2PPhNdEFnl4pWjyfiqb2hhdv+fRx6hQEAnL90IvN1ezbQudy5zVrrCpCtxqtYrz589jbW0NzWazK6Llnnvu2cSc5cix8xFEde7j2JiqNKyijL6STBEcyWnrSykHiaQGUZAyYqAVzI6ScEM/gBPHBmXQfY+039MUuH7U+k4+tfr26LJaAvM1KQuFLghSUxmxCBmrg2vqRWFZIb7L8bpFfTLbkD+5zITpPa8cObqEqc7qBBXxXvvNpPxT+yX4PwNRlf/mKnBVfghR59pMcmu7oUi+K0WB26v9RS/3NuOgZ1AwB1ABmGu+b9H+ylZbg5gHm52Rls+ohULXt010B4yTQzly5Lg6EfLAZTz4rlYviO8ymJTjijEF48Azp9shQrdcFOKJ4QrB+IiFk+edWH+FEqDhk6/7ZguYHKP+7wRDZYIHnmzC87gSp0RR8InGZkts9xIUuHsS1Lf1JsPFJYbbjpRQKqY3CIFfLfcFR4E4JNQiddEQBrum17yH9xRwvkCwUfOCficBHn22BcY4Tpx3BPHNe1/xoR922Shw9R+lqIeHJ3LlMzw5RnHtviI455iZSKcYO60unpmwMVymcBlQsMN+sntnbIwPU1gWsLzOsLzudewzy0lo5k+SJJW/4EZMats4uQ+ELUb06+IIgpglIasa2YSBE7hPPvkk3ve+9+Hzn/88nn322Z7UcYQQuG6KRj9HjqsBfQzYVBCzmIUCCakrPRZWfgWDsOSzplkw9IOkqoISwIMkB/3f6OCIjiQimjP/PCQ8s6fnwZTXLHnSN2urQlQAsbQgZt1cbzTwGuc8RqKY8kUpAfOCjiXTFI7MM5eZ5Zu65wrcHJuBtL5E0JGUHSwCagX1nITywM1SqWqqi81S4Ab2CZuT/k7FFeOBG7W/6ADjgCgj0hQ/pqWOmwGLEjjgxiAg0QkQuY9xkNRHOQwa3dzDbsv3cvA8zpEjx9bCFJMCQCyQ0VCZKsKp7XKsbjCsbIQ7I7WG+Lu4CuyZ4VivmTsr61WGsRGKSolgbDgs/RwboljZ8MC1/rtuhdNS/rjibzth5cxeUwAzDpy+6MC2CY6fc3DzNSXjsRLRMZQ+2a63fV2Ni1MUvPqVCDJc7ktQsEVMknqTgXGg1WYqb4Pwr7V3mAduEkLtl7bSUp/IZVz8XrCpr2ImicG8u2kOdaVttSHLX6QwXJETEeHfoycKCTvkJLTpNdF2jL6XcsWyHiMGEH13qdpuOxzT4xbOL7qYGLWEBy6T6YXzFhOqGH7rhIERuK7r4t/9u3+H9773vSqj5mXAZNM7mTlyXAlIG7B1giTczKoS3ZYgrJZM80eV0CN6DhJR/9XgfFLVGvFCGtAgMKmcBaEqGnBdMRuom+OFFNgcZDwpEFyHxkGZDo8uTc2CaGcoFE3U1JnRyV0v6MyF7o0X7BsOFBA/R46diWab4eR5p2OE1EFifsVFtZ7M7rseUG14ePIkx9On2igVCPbM2lhc9fDYcy0QAF/6dt14bNsR9cOJ8w4oJbCo6HTPr3io1hnqTfFQLq2JieFUD1zp8cUD8mllw8Njx1uZr7XZYri07BqVihYlqJQJag0Gj4l9CwWKobJI/+QFB602g0WB4YoFiwIHdxdUZ3W7cG7ewcpG+IKKBYJr9hbUYKbRZDh50UkkvE+cd+B5HI4LXFh0QAjByfPOQPLnuBxLa16sjhsdoqFlfYDo+O+atGFR4MBcQS1V7ZS+UuBEsN32F7IWvrTkqWAyveL42TbqvtJqbNjC5BjF9LhQdp266KDZ5ri07MX6Fo2WUIhZlGC4QtWgv5cANL1AT7PeZDiV8hxKXFpxsV5laLY5hkokNigX6bgoFUiin193eRR/Ly66WF7rXPlSCuzfZccIF4nlNQ8XltwYQVywgWv2FvsOIpMjR47+4DGO5845IY9OvV8QbU3UG0tF/6NYIGi2RZ/o4qKL5Q2GiRGKiVELi6suqo1AKSiDnu2atDE9IeqMjRrD2XkHrrSoS1lpwLho584tuKg3RFv+6a9WUW9y/MsfmMTUmEjTSai6Du4uJJYDQRDkKQ2BNZ7ImD4WJQmfTdDrRNkGERPty8Mf9fMd2lPA4T0FjA1bYIyj0RQ37nk3lFLjxKRBP2zHWygYFbhc/SYnPykh6rNtQU06SH6BMY7j5xxMjFLMTtg9c4CdSlxZ/qXYT9oWgeNyPHumDcsShP3eWRuTo1YoHV0SQuRKGl95rQvjKqWAzG+7HNcfKMJxORZXPWzUGVpthoWVQJCqOAztXL12hwZG4L7tbW/Dxz72sUTSFgirv6Lb5PYcOXII9PM6pPmcygqJc55opp9K4G5SII6k9HTvV9kAWzSYKR2chUI0P8KyQVoDSKSR48peosMY2qjA9Sty2VhEQXpS4CKUHz1fcc/fIGFLkuYR4jfkAxVpeLLaR+TYfiytelhe3zr2ljGOk+fbHfdbrYoHlEB0eCwLWFn3sJoxr6UiUCoQlIoUtQZDo+mhWoBSpcglgFnVE+Wi2M9xOZwu2O5Lyy5WUvLcaPGQv9cQISqP9abo9G3UmRrolYoE1+4rZj7/oMEYx6mLZqJ1ckx0ygFgfsVLve56k8HzOFpthro/GJIKon6xVhVlFsXianxp58KKWPZGqRggjwx1LtvldQ+Lq/GVYQWbZFbgSgy67ZREXbPN0Oz8mqWi5vskbtRFZb9RZ2CMY3ndg+syNJoMjWby8c22hZbDYVmibSunLJndFA9cAAsdnkNAtIWntDqpYFkx9VqjJeoQ1w3eT8siMcI+mAQOPwfRp6LUw32yrbhiTuLsgpM4KTY27MUiwufIkWNrsVYNEzhRiEBQ4rNuL1D2+yhDZYJmG6g1GBZWXbQdjqkx6gsogEYzqLMuLDmYHLUwVKEhQgoIFIemlkoFSOaivt+oeSjaBF99uIG1qsjc1x5p4LXfPSLSSlgWfmhPnMANjXUyzEGHRCech8iykGCkQzrc8MUYuFn7vHvaxoVFV02W2xbB7KSNSkmUt/y9XAwmKLsl3/Th0eVioRAiuXnwV01EEN2KiAQrNf3fLi57WFhxsbAC1VcMpd8lYjFYOnAB+nnKJYJGS/RzdEyOWqGxeCCoIoG1iS8i05+joTJR/S/HFe9SpURh20ylcfpSNpFCtxPbA2ndP/zhD+OjH/2oOvmNN96IX/iFX8ArX/lK7Nq1C8ViEYQQXHfddTh27BhWV1dx4sQJfOlLX8L73/9+PPPMM7AsC7/8y7+MX/zFXxxElnLkuOwh6w9TsKxOCKKtG1SiNCAlo0Sjp45LTjvwLxzs6CtpECTbOKHADRpNNQPaZzaSyG492fBSEfP+QNi/KQ3h2eFggksKi01Fq5dDVqjAYlqjC4R9pUx5shTxy0PnlMQuEC/2gXkS59h0yOdhYtTCrsnNlwF4HldKkWv2FhI7bucXHNSbHG2Ho1yiOLK/iEbTg22LTtLsRHJeCzZRHezRIYqFVU8dIwcWqxvC22xuKt71UbP2mkf4xKiFW64tJQbtSEKpQFApEUyN2ZgYDSrT5TUPq1UxQBodtrB3VgwORoeoUmO4Lke9xXDtviJcD1hcjSvsthPXHyiCEODcgotag4WCWcp6b2rMwozhXjWaDI7HcdM1JRw9XFJLEgeBC4suFlY8jA5TTIxStNscF5c9FGzg5sNi2SaHUAEDwMykjeWIYjcN8h6MDFG1VJQDMXVvGvoRnqY9A/t32RgZogOZPPM8jlqT45q9BSyueuCcKw/0Gw+XUCnTxLyUigQbdYZ/+ZuX8LK7KvhXPziFseGtVY6LiengOZydNA9zHJej5i/JPLJfqKyigcxqDQbXEwT50UPiGRoqd0/Yy8ZS3qcs7/PqhodLy26qsEW+e/t3FTDkB3K5uORhveYNPEZBjhw5uof0wBwuU+zbFdRFx06LGRz9Lb35miLmV1x4HrB7RvRZrjtQxFqVwXXFBCXnwG1HSigWCKoNhqcsgpbDsbTqQjpPhn38xV/dbz8Kvb8vlZRDZRLq9zx0rIn/57tGsHfWTmzHDhkUuKr+0sdsKdAFO5yniIm6UODyyN8k7J2xMDo05Nv/EJSKRAVlkytXOed99cd039idTuBKhC0UxB/GdQWubpkUX43ZbIX7eb0Wn27r8eSJYEXcyoaHM5ccrPorxJTVgqF4rz9QxHqVgQOo1hnOLTjGflPMQgG6yCpIeLhiqdU5sj8rSd5Dewuo1Vk4rZhwShNudYmBELjvec971OeXv/zl+NSnPoWhoaHE/ScmJnDnnXfizjvvxDvf+U68+93vxq//+q/j3e9+N5577jn8yZ/8ySCylSPHZY2Ir32XxwYVaxRE+z2uwA2WRCRhs4i6pDwTRYoGZaETzP0OVBLLmQtylZBwg83TyPGMJGtgXK6tQNAyYVzJoM2SZwWNELGSdE8L4qRvD6wX4qrj2CzoFRKY6GqAvHWlAukYaGAQcD2OsWFBnF1/oJg4KWVbBBeXXGw0GEYrFFNjFholirkp4S0lSZQs8JjooE2MWuoaKSGYX/FSVxhEN42PdE9wr1UZWg7H3lkb+3cFg5qTFxww7oBxYGyYquV54fO5sG2CqTFLdUS3G/qrPjUmiK6FVQ+1hrkzXilR43M1NmLBccW9HCoPltgrFigKtoO9s2LZY7XO8MizTRQLBDf4zw1jwmYBEP6Cy2udA19EMYh3ppcWSxeEREEpUctb+8X4qAvLYpgctbC4GlakDldoRyX47//vZQDAff/QwLv/hTlPWzVsrZSE/YMJjssx6pPLR/YXjcRGqUAwOkRRKiank4boHHM39ynLpJHcY3yEqnpqdYNhvYbeR8k5cuQYGKQHZqUcbhPPXHLRaIXJnWKBYHTIwlDZUxO6tkUwPW6h2hB1UcEm2Dsr+hSTYxznF1zUWwxLqwGJZgzE6glDUlPdqxNukl8sFkioCmm2Rf/FY8Jmx4S9s+ntYpowKNgnWOHJI9cStlPI3ooE463kbTLN8ZHkTIYWRxrGcFlwOY2P9GBcCgYFLtEIXKHAFSWlq3VhSKNrz1dLkOiM85AX9HqNodZgYJyEfjetkrUtgim/LbcocG5B933WxuOR50JaQkiydWqMYnmd4TV3j+BR315NWovIZEo2QQ0JHIV85wDo2ezmeep75Pbcc8/h6aefBgCUy2V85CMfSSVvo7AsC7/2a78Gzjl+4zd+A//jf/wPvPzlL8db3/rWfrOWI8dlDd4Hg8tSlLSyAuJAbOYp7TgJtdxmsxqiyPVKMpkxrj5LawOJqB9rT6eNHM782V+hiOXa78nkeOYgZgaPHs41vx0DFOnejwcuC/8eypP2OTprHyhww/kNn4sYf8+xc7EJVpR9nTOtyhuIb2aGcyd9H8TpMl3BYBYWbB4yXETSrepnUrJr7EBxi1TwnPGDukQxNWZhpEJwdt4F40KFuV4TlW+zzdFoMbQdHhoEzkxYseWy/cA42O1iwBVVsUqcXww8sBdXXZxfcFFrcgxXCPbNxpVbF5fcRLW8CaYFOVnqGrFfwo4pE6rhtDbvbc3b0xw5thdrVQ/zK8mTfUNlEpqo1SEVuFG/U13sEcSYIKF6TB/XyHR0L26LEtx5Qxlff0z4EDleXKAh7V705e5RqHMyTaBCgnOWikCrDTx3ro0Hn25iYdU8+EtTlOrCIUDELThzSRDCBVuod/WJfbmMXR+LhjxwE89kgCxfQz2fqR3QT8r7Ew15CQHgdiJkadSbDM+eaQvxkDYObfqKU0qJsuiwrOA+e0o4NJj8FAsEtx0pKfstidUNseJktGLhuv1igtm2CU6eb6PZTi7vqFLYdPep9p6K7+IdvfeFw1ha9/C6l4zg6VNiQqPtmC80qubVz91PR79vAvf+++/3M0Pwute9DnNzc4n7shTG55d/+ZfxP//n/8SZM2fwq7/6qzmBm+OqRz+D3VQPXLkEgMcHByavU9PxYt/BNkRqFizyO9XUoJ5f2+kdHbmtXz8dk6WASVGbRnLLDkgnewm1NXYN4rpMVaUVUcVmQZCf8LEmBWTYQiFMxpqOS1Lg5ks3LwP0OAPe8+m6fCRU38ZQR/WSRiw/KcdR2j9p0vH4FPWGaZCy7a+UqQMqN/WSt0147qKTYmlKo16ykNROdItak6HZZmpppo6VdQ+7p21cWhZrYY+dbscm7Ao2UFwJ8rBRY5i8cfA2KKGr7OIem1QvbSfsgb2yLpb5c85x6gLB7IQdCrjlehzPnRP7z0xYmSwLNqMu6zbJpP036/01Cnt2+gRQjhyXGU5fdIz+6jqmxyxUDKtK1NLyyIRdrK4gJFbn6mMQpXCMkKSVMoXtn1ZaKFhaZWhpYwCpIIxCxe7gXLPRIyrI7b5ZG8+dc/GlbweG9ffcUcHfPSS+jw5R/L8/MRNPGOE6Sh8/LKy4qp0DgPFhSykjRV6CMZEJnep7fVLN9CmeXvY2RqqDge7bCPdyInD9i1vVVK3GVb1Et1AgBuFQ+Jr74TZGhmgs6KxtEyyueRgZotilTfievCAY96RbGzz34d91G6YoByAFcMUCwf5ZG5QARX/uRq6aCeJ+yQSTryc6Yd5NmfRN4M7Pz6vPL3zhC437yItpNpMjHxSLRbzhDW/A7/zO7+DZZ5/Fgw8+iDvvvLPf7OXIcdki8Cvt/tgg0JaJICCaAjdyXMpMpUTgl9R9vrIgeupgNi/4HFPg9nE+1RAnzUzzCIGbQn5lV+CG989yDNHKISuC/IjEo42SKU+EEC0Ym5xBDYhzGWE0KX+bpszOcUWgm44yMFgiIlWRN8DzmCZo9PNnORfX1A7b3eXPen5VLSSU81Zch8ljzIguMzOIvBOI4B4WBcolit1+cDXX5Tg774CxYBAwOSr8WzkHdk9bYmBEhS+zbRF1zMAHhIZ72I1tj0mBK6/JsggOzBUwMuSCgyhvPE+X9iDchnRNfvLOz6GOLPXRdkygdFNXbMdKihw5rhbIpdF7ZuxQoDEAQkXqBT7hUUgSNEmdqvrk/vek916mUzCwNtRPW1ko6Apc3W6OJagMtTGd7PMLUk78fnCugJMXXFUvF21gUrOCecfrx3HbdWXj9eliFn28Ex3HRNsxSZJaCQrcTgjZ3g1IsKAO572vkL2cLBSi1zY7aatVokvrDBeXXFBKsG82rMDVbQ/1vxJJ/eNeoca5XZZtMGaNv7zRNl8GsLW0iRZ5nVIV3/YD26nnXHuX246wfXjuXBuUEqxVve699CPom8CtVqvq8/T0tHGf4eFhVKtVrK2tpaZ14403qs9PPvlkTuDmuKrRTyWXqsCFqGA4B7xILaU33kmILjsYFJLaQ90Tianfwvv1M8DiLNyB0tMU1xomLOVHU+VLM5Lbpg4Fl9+J+XoCi4LsFxtd/qGsEIwEblAO0XscIpwlqa0zTNBmMy+nDspViq0mJHo9X1/ZTCFA0vIjl7kPAp3IIdNWfZCwE6GUAmmTQFuXnUSYJ+S0HyOTW1sFjwMFAEMlooKhtR1BxgLBAG981ML0GAPjHLdfVw4pVPVjNmtAaHw2M4yATQpcOYgv2OKaKRH+1BcW5dKQPjJqQDfPYdo+fQ/4N3kCJi3dbVft58hxhUAue981aatAqRIXFt3UZfGSmCxEF0kY+v6xVYCGdExEsB1REurJSNGF3J4oVIH0wPVJKIuoc44MUfzUGyZw4oKDz369hluvK4WWppeLyRWlp9moR1dN6nANYqLoykpdxVwwWBDp0JMPxjbxY0x2dknQy6nX+nVi1EK9yVA2rMDZaYiWiYhdQDC/4mKswnF+UZCaIxWiYhoIBW5YABRewRoU3KD6iYntrCZKMoFGV7Xq6fi/SesTqcAvyH4YUUNh1Tdru+F0oK18XV73VGBSSgnOzns4MGf7z188qHgW9E3gDg8Pq8/1et24z/j4OKrVKur1OlZWVjA5OWncr1AIPGQuXLjQb9Zy5Lis0c+sYbDk3bDRV69y8NiMVRYLBRpRZw4KqvMRIUZ171e5TaqIJfoicP2/cQ9cDkpEHayn76WQ3LLh6mihYGSV/HyAGAdmWclhHbGZ0BT7B33CIHou/Th9GVHoXKpzk48cc5jRqZMSJV64pnToth6MLWPS0jWzuua89ILEYxPenSSYrmE7kHb+bvI2KCXMZQsi2jHOiXEgzhGQBTZNJ+iktyHnHB7jfas59DwA4XvUTVOvX5f0cJQksyR30yYBeoVeZ2TBZhD3m3E9O3UiJ0eOqwWq/kpzqkl4Tx2XY2VDBDU1IWodl/TeO8pLN17JqHxx7vvGkth2xv0xVMq4hXMo0olyHgpMNTlm4bvvGMLeWRv1BsPKRjAQiaqSdQTjpfCJo+ME0zJ7znnoWvbO2LAtAosCk6MdCFBTcZsmnVO2xQ7XCD75TES9jTvhwJyNoRLBxAB96zcL0WfFtoBykeLGQyWcX3SwUuVoNL3QJDIlcWJUHw/3Y4vWKZ/dUhJRgZOpz7N7ysah3QX1nJSLFM0284VO4gA5meA44ckATsR3zoPnZXLcwnpVFIJx4qeLPkTfUwD79+9Xny9evGjc54YbblCfv/WtbyWmdezYsX6zkyPHlQOleux+VKAbbkchFbhCRRk5pRpoJZ8zq01Ar4ieWa+cA2uIcIdgU8gWf/aXwLyk0xggLqNPrXmQHES4NCpwI2RsFlgRAiiwUDDNRAd5CmbseeicRPfpiuRjs5+LHIND8PztTCZtO/IVmYQf2JJ547lSJILGyL87CDJ/afcocVMXyshuEVVRJBF6UVK8W8Kv30eTdSADArIgXQlu0UC1oauc+kWqOj3D8bqFglxS6GlEgPGcmXOXghRl2QCT7O74LZLg6u9i7oGbI8fgwDlPt6ST+yUcv7jq4dKSg6dOtsLH6f3lpEpXF44oK4b4btHgwtFsWpSo8Z6p3ZZjC48FVhCUBue0fOGGbRMwBn/CMDi+lKbANdjPyXzqcCNtGOccHOFrsS2xgmNuyjbG8dAxNhxkcFBaI70+79SmJcGiBLum7NiKmp2IaA7l5MHUuIUDcwUMleQ4MdhHj08TFQ4B4edhUN38pHY22qePQonROA97JvPg2GKRYN+uAvb7/4YrQWpyJXOgwDX3EXXSemrUUu8wY53zmIa+Cdybb75ZfU4iZ5///Oerz//rf/0v4z6tVgsf/ehH1XedGM6R42pEP21O4Fka3yb8Y4mwJAg1qHHjbhOCxr6PDBqQNEAO+TNFFFyDIHqSCFmupa/PIKaR45lJTAM5z7moxAnM6iB9GVRWEK1jpufLqLDWyJW4Ajd4npIGiL0ohHNcXej0tsbUEBwYJOm33eRGbHKqy/23C6kK3K3LRiZkLbOum44BPIcEwaSYaeDHNVVGFu8/K1K/DxLhFS7Z09fzLVVnUQVblFwflMIbyP48dqO86ndSspfDu/LANRyXI0eO/qGPc0z2MCYrnlab4dyCg7PzDs4uONiosdh4KVoHSvGGfIE5uNlCwWAdoE+aMR7v3+txS0zVg66YVKsySTDWsCyfwLVEPjyGEAGZZqGge9uafGnVqsWIGpH5fb+01aA62g7HuXkH88suVjY8DJUJrt1XxJ1Hy5BXnWr7lKHeHIQC97JCpEx09bdlEbVdjQ/9AooqW8PPAA/FWxlINhVhnNBaJpxG74IxHlG/J+RRnziQm+S74EQJXK1/o79LtiVIbo/xWN+imyLpm8C96aabMDU1Bc45/v7v/94YqOxNb3qT+vyhD30IH/rQh0Lb6/U63va2t+H06dMiU5TipS99ab9Zy5HjsgXXZoT6slAwHStVnggvWwnNoqURuJu0rDepIdXVoMFS/nCD3M9yyMTJbx50qkwWCkZyXIv4mgbV0GjXurDq4Uv/UMdG3TPmiUQayyyINqQqwqzh/urkeJSI1p+nJAuFrPYRObYf3fh+DfJ8nfcTf9N82rKi12sjffeKkiejulnqtdXerFnQrQVGr+n0glhxJRBg0TNvdSkrBW4CQWsKRJMENZk6SAWuStugNuuS7Gy1IwRuJM1NsRxAtucw08C9z/z1dXiWsu55Y44cObJAEouUkI6qT4mTFxycuuDg1EUHS6se1moM88tuaB+ixjXye7DiD0Ds/U0LhqavxuC+9Vtou0XA4Y+fDJcQeJYGHri6EEP66FIiyoN5QKUUJFRKUZMyrW3S+zTyum3fyDM2ZuLBGCwLTl8U5f3s2TaePNHCkyfbmJuyUCnRxDgn8jyJ2yIISHee6kl8pSDaT9PJaovGx5eyP6IrW4GwunozBD7RyRCo7+mNYJoNY5IyVn+3CBE7KgWutFAwcCTys0WpslzwdH/GHh6jgbgo33vvvQBEQLNPfOITse0veMELcM899/hLERje/va345ZbbsFb3vIWvOENb8DBgwfx53/+5wDEhX//938/du/ePYisXTZ42cte5vt6Zvt38uTJjmk+++yz+Nmf/VnceuutGB8fx8jICI4ePYqf/umfxkMPPbTp15Sjd4R8G3t4sVODmJGg4kla0pLugSv35wMlGRIrTG32WJ5PVqK9et8YzxspLLkUKUrgppHjul9v6jkNdfYffWIV//BUC5/4cjXdA7eLa9UDwOnHmjqiQTnoDbD4zdOI8yRfzqz2ETl2DrZKrdWN2k3fTZsI7xppkzumZ7Tbzl8ash7aqfwHMUG1Weglb1txFVmf6W4ffb1+7BWEBAFQdNWUnqaaIIwOFAyw1ITh5pZsN4+frqRqaRYKJ847OL8gArVF1aXR5KPLGTcrr5nTzHrO6H3axAnmULpXLoeQI0dfqDYYLiy6oX+Lq27md7KT/62JPHJcQeY4LseIv+S67XJjPR39KYlUCgjDeB6isSmiYxmLit89zhOCU8q8BCIZptXjFgVAAkWwvmwcMKuCVb61mCGm8WbBMrdhzLdQyGpRIJev63B8zlxxhv1OyGllLEnJrArhyxF6cVEansDQJw2ifIPcbb3KcG7eiRH3g26i5fk8BpxfdHFpORxYMOm26+NZxhJWHSWIMJS4C0AxwQM3UNOHV7HKusQzWCh084j2HcQMAN785jfjYx/7GDjneM973oM3v/nNsX3+8A//EN/93d+NpaUlAMBTTz2Fp556CoAoKFmIBw4cwO/93u8NIltXNf7wD/8Q//pf/2s0Go3Q78eOHcOxY8fw/ve/H+9+97vx7ne/e5tymCMNegXXS5sTVBbxo+VMKoBQELPAyDs9IqK+ibEOxv5dIFmBG2yPzvQlzVZ3d974gFmej/hMt8nHyVRE3frA6mmsVcVJTl5wjcfLjkw08FwaFKHsJ8gN5ICCui7Nw0hZLwTHkfDusXPtQK4pRw4A6eSbmphRP2x2bswwLZ3eKe9Up7aoE1G/lUR00koBGTpY5WULy5bArMDV4Xrx7UnlLgfzg1TgmpYOJhKUBujRxKUC99Kyiz/55BoA4GXPH1bpKEf1AdyD0OBL/ZhyQAbucyfYEfT6yuyUOiNHju0C5xyPP9cyBgu68VAJU+OdBy9JqwckkuqI84supscszE7aWFwVQY9qDYax4fA5A3UoCdmncURJYUngxk+oRDn+MdG2RU4WMmbu++sWC0q0oQt6iMifUiNbRKkI5fYkhBSXIQKXh/IWbcM4706B6xrucdvhKBZIKjnWzcSsTtZ7V4UCN/hciJD00lYDQCjYndgm/q5seFjZCN9YpvW9BtW+Bs83x8nzbQCA4xYytYFSoKXbL+jvXvTZ1jkVqVyXkxlNJ0gTWhp6v5cSzULB4+B9eCEPhMB97Wtfi0cffVR9d10Xth1O+oYbbsB9992Ht7/97XjggQdiaXDO8YpXvAIf+tCHMDMzM4hsXbb4y7/8y4777Nq1K3Hbn/3Zn+HHf/zHAQg7ije/+c145StfCdu28dWvfhUf/OAH0Wq18Eu/9EsolUr4+Z//+YHlPceAoFU8PSlwZacj4VhloaBVLGnWADqivjGDsgBKGiTqalDVEJPw34GMVaIELoIlTSGriRRynGRUyXYSzqRaKPjK5yzLkGMWCinks96RsSLXEfj+BpmOkjFZ7SNyXH3oxmdM7OdPVvC4R1Sv5w79lnru/s6npx+zUJDbI5GnB33+QcNEvA5i8mwzkLX4ui3nQRFipoA4+sCQg4tJ1gwDQytBvTQoCK/87tJ2DQrc58466rcs6e2wR6rne6/f116P7XbfnRqYMkeOrQbTiLbpcQuEEFTrDM02Q6PFkGX0YvIk7wThZQ6AAKNDFHtmLHAOzC97aLZEeivrHqoND1PjETUKERNf63WGVpthvSZUsWlBzCgRZJIH7gcqC2+XCtz1Gos46/qnVGOLwPLAi0wk1psMKxsMhBBYFlfLxYGA3HM9jpV1T40bNuoMS2sBgac3U/JzQVkoRBW4/irIjOUeDYIGAI02Q8vhaiLROO7ppm7Wxpt6sNGrAXbMVzkQgrm+0lmW7+5pS1htaGW7sCJ26sYCMCuKBYLDe4uo1sV7XWswNFqaAjflFlnUn7iIBCpPGqNHV0ZxDuydsUEIcGHRxfv/zwq+97uGZUoqPWFfIlTMehCzfjo7AyFwLcvCLbfc0nG/m2++Gd/85jfxjW98A1/84hdx/vx5cM5x4MAB3HvvvaFgZ1czvu/7vq/nYxcWFvDTP/3TAAR5+5d/+Zd4/etfr7a/7W1vw9vf/na88pWvRL1ex3/4D/8B3/d934ejR4/2m+0cA0RIgdsLgSsrH0PjJ2ZrSUjRCiTPOMWPF35IYrkNx6DW7yVWmGp2mKuykIPfRJVVF2AJZIr0kopaKKSR45ZajpGeobT7I84d/03vyDCeTM6bjpF55hEFcyhPmk9UoMCN5zdJaZvVPiLH9qMbRd0g0VHBmTIzv2W8xAAnhWJZVmlnSL0L9cmWoUN+AqI+fcfNuK6YpYvaYD43N2/edOjBLBS0+pb4k2QhwiChvIIgZoPLn141EITVKFlumz6QlgPnZU2F03biy3ij9yBsWZStj6Fb+2R5DrMor7JOUmyG2jULEZtqrzDAvOTIcVlCewmuO1CERQlOXXBwboEZl9yb0ImoM03ScIg2ngDYM2Oj3uRYXHUxv+JifkXsc27RRb3hBXZw/n/tNsfCqgeXcTx4rBUjbE2KT7EUXOaDx8QltkXAATRbDKsbDENlK3Y8IPruUtTDIoIex+W4sOiKIEwcWF5nOLLXRqFAcWBOUEknzjuKqKs3GU5fDCbuSLSMePh69DaMUqEwKhWJMVi0CSaV9dlLLhotQdiLPMTTytIOqHzJMmaapcWVbKGglYnJJkNuj9o+lYsUR/YXQ/vWm4JYlcHpgMEOP/bOiGdwYcXFM2faoQmGNCTZMKqvURGGJh6Tn2cmbdx1tIRvP9XCx/5mAwzA864rhdpg5tuXeB7H4qoHj/lBzCIB9rrpGw+EwO0Wd999N+6+++7tOPUVj9/6rd/C+vo6AOCnf/qnQ+StxN13341f/dVfxb/5N/8GruviV37lV/CRj3xkq7N6xaDZZqg3zJXFcIWgVAxqeMfl2KiZR1tth2N53UWzLSJRX1gSDeFz50QjyDnH48fbaLQZbjtSQsvlKNkE5VKQvk2ByXErFhVSh+6B22hxzK+4WF710GgzVOsMxQLB8lr6msxag8FjwteuYBOMDVFYFoHjclQbwfUxzlGrczguQ60p8lQpieU3lAAjFQri1/pLqy5WNzx4HsdwRVzz0pqHtsPRcjhWNxgIARotBtsSebiw4GKjLo5hrPOgpVIkOLC7gKJNsFEXjclz59q46HvmtB0OyxKz5vUmV4PplsOxvCY6VWtVBs45Vjc8bDQYHj/eQtMfqNabDJwDY8MUu6ddlIoEo0MULYdjadVDsy1IYY+JZaVPnPBwbt5NnN2vNhjaftocoiNSLlKcveTA9UTnanSIwvVEeQCiTOWMuONyVOvi9+Nn2lhc81CtM1RrojEdrsRPrHvgtl1x3bIxdByOjZqHeotjeV08I2PD1Cf1xfGyrAoFce05dh623DqxRzZB6+t1DZM6n5h+jB7X4/lCyJrpjCfbbjLGdP5uVz9stn1CtMOdRICR2Ifu0u+LfCZQ7ZRpSS7zJw0pJaHBZtIplQeuYfA6SHSjoDd54K5usNBvLYfj9CUXBUumn5z/7Xz2u1WzRvfeqgkY/TwBobTdtUaOHDsH8r2IBhzqhN4UuMFJCQEmRinajoXJ0YA4XVrzUG8EPq1ytZ+qP7XsDZUpSgWC8VHLSCTrwYVNddauKUul1zJct+4hKvMurXDkdVMq2qXJUQsth6PedHH0cBE3Hi6pc65V5ZjAQtvhGBkS1yvHJrJOqjaYmtxTAZ20duPWa4s4v+Bg36ydWYFrshESKmtBBE+MWhiq9Fchq0lCXCUWCtrnJOU3oIm/0mLnaOT3QPpSCdDf7yzjHDHW5SGBGIe2So5E9w8+y5XMAHDvi4bx7adaAIBLiy5wXSn0Dkvv33/33+Zx7LSDsSGCf/qa8dTVeJ2wLQRujs3Dxz72MfX5Z37mZxL3+9Ef/VG8+93vRq1Wwyc/+Uk0Gg1UKpWtyOIVBY9xPPyM2WMJEI3Td9xUVhX/Uydb2KibCdxjp1s4fdGN/b7mq1dOXXTxlYeEp/E1e2wc2lMAIGZ49c7Fnhkb4yOitjVVqMT/nQNYr3l4/LkWnj7VUsdWSiRkUG/CuQUHrsdxdl4QraPDFvbN2jh1wVGNJiCW0KxVGdZqTBl8UwuY8jsyYyMUYz7Jd2nFw0bVA6FAqUDhMajrfckdldA1nl9wMFSmOHnRxeq6h2892TQuoYmCUuDwngIO7ylged0D5xzHTjtotDieOe3g8B5RJdabHDceLirvp3qT4alTLZy66KLRFCcqlwi+/kgTn/9mLXSOSpHgDS8fwUadYc+MjesPFnHsVFuVMQCUihR/+aUNLG+YnwXOOWoNhkeeaarfjp1uo9HkOLzXxlMng7Rmp2ysV8VyKwColKh6NjwW3CP5FwCabY6NBsPzbyyrCQa9sZNl3WyJ6wbEjPxTJ9u4sOSi1RZk+kiF4oaDRcxMBB2tWiM45tYjpZjnV44cSTASRV2QR52QlkSU8OiH/0jqoOoDgCToip6sCsDNRtcWGClpdJNOL4gFo0zaUY6TMzPQMv1echUcK9UqJo9b6VFo0Wy3XA+GMSiEgnf4E73dPH56X0iSJMvrQb+m5XD85w8t4bHjbbz0zgr2zBiGIQN494BO73tvaW4VuiFi9evccar9HDm2CaY3p1sC1+2gwDWu2OHh9tu2CPbO2rjpmpLapdZkWFhxERs6aqeRqw/2zNiYm0qma6i2Ms500ZUSxcSohZV1N+xJqx0P+J6c/m8s0k5RSjBUJtg3W8C5BQfEvy6ZXtsRAhhCCG66poizlyiKBTHmqNaZGG/5nx95tokLiy72zNhK2am3YcsbDLun7VDe0qArh02YHrf9Jf+Ge5hhtUbsEM4Dr/oreHijrxBN8l4GgjbKZCsooSz2NnliUb3frrbSJ+XW6iS0yUc/+szoE++6en6kQvHD3zuGD3923cjxcCYmrY+dFu/Eep2j2vAwMdq7yCkncK8gPPHEEzh16hQA4KabbsI111yTuO/o6Che8pKX4HOf+xxqtRq+/OUv43u+53u2KqtXDDwvGLDoRBXnHBt1BscVAbDk7JVUag6XaahDwDmHRSmKBQrb0hpNQjAzIV7TZ8601f6Ox1EsEhQsMbNYKhC0HEH6ORoHbKpPJfFWsAnGhi0UbBfFAlC0KcaGLeydtTuSbof3FrG64cH1xICs6KdVLLgAsVAuEv8aPD9iJwcvEVVJTo1ZcDxgbIhgetxGvcVRKjDULYpSkWB8mGKjHjCylTIwMSzKwaLAzKQFixLMr3potQksKs5nW8Q4U865MBhvtJgfQZWiUgYsylEueXA8hkpJENG1BoPrMbgux8iwhUo5uLcF2wVKFFNjFqbGbJxfFJXxzISF0SGCE+ddNNo8FF2y1uCK1K74S5coATb8Wem5KQuXlsPss8e4mimn/rUxxjE2QuC4Ih3XY7ApQalAROPqE7HFAgndv0N7ghlwmZ4kzVsOR8lf6aIr2EaHKGYm7FAnlxBhSk+I6BDKBl3OpE+MWpget+C4onPqedw4259j52CrBvs9z7j38fikkZ+pBOogyyQhrVRCVBsQXg5czE4R+nWbj+0qW7mE0EQIeJzDBoltS3omlQJ3E2xrei0ffSJV9ncWVzULhTbHY8dFX+bR4y3c+6Lh2L3Tv+6U56sTvvwPdRyYK+Ct3zuOp0+18LG/3oDHOF71gvj1DQqXS9nkyLHdkHVoQPBkOy5KZCalG7VQ6JifSPoyLWV3pu3bOS4JUSsZQcxnJ/7vpsk+naSSZJy0CJCbZPClqj8uIxTgLldtj1zpN1QS4zGdUFVlxIB532JBjg2kBQFjQWyPujZe0VXLSegk3lFWcoYKUxNLd4RMh0ObiL2CFbiToxZmJmx4HleEuo7os59mvxhS4G5iuyXf76yrktTkBdMCtfHkidOpMQtrVQuXli2MjYiZdlkOw77Ce91fZS1T4Bx4+lQbDzzZCqUVWo3dw2OUE7g7EK997Wvx4IMPYmFhAcPDw9i7dy++8zu/E295y1vw8pe/PPE4PZDcC17wgo7necELXoDPfe5z6ticwO0eeiV+65FSaNvXHqkD8BtktVRQ/L3hYBGVctAqe0z4ojguw/45GyMVS6X7oluEMvrkBQeAqADGhi3snbaxZ8bGi28fwkiF4rlzbXzryWbIX9EYaIuIiurafUXsnrbRdBhWNzwc3FPA3bcMYaSLZe8rGx6ePNHCcIXi1iMlNNsMbYfj9uvLKk8Xl1ys18Ry/bbDUClR7N9l4+y8i12TNq7dV8A3Hmv4HRkXM+MWXnrXMJ4928bfPCAUuN9xtIJXvWgkdv5yieDsJQcToxZ2T9s4eqiEaUNkWY9x/PU3q7j/8SamJyxcs6+AlXUPB+YK8DxBjt9xQwU3Hi7i/scbWFkXlS8lwKG9BeybFYrWRkuQ8nfcUMZQmWLFV9D+/Num8fwby3j7r17AqQsOigWCUd+ewPPEUg5CCQ7tFlUu58HSqV96xwx+6jcvhfJbbwayp+FyYMMxNmLh4FwBtQZDvcUxVCLYPW1jftlDvenBsoi6FxLR5xIAHj7WRK3Jwh7IWqAySgluOBj2MGq1Gb79lAeLEhyYs1EuUjTbTCkIbIvg6CFxridOtLC64W27cjDH5YVBLq0yLvGPKAZM50763g2SOqjdKmoHGqRxAOh0W7JGF97KIU/0Xsc8cLewcDkX/3FEFLh+ngJ7hWz5Uh64GVafdJXHSL6CSOmd4WqExOe+XsWffXYN5xfCCtxo+oOArgbK8hx2U9d0UsGubHj46/tFf+/7XjqKP/6/a/j2U2L1zLX7Crjtungb3BHdvPtXLoeQI0fPCNdl4iUp+qyHWGLdORiwJAezLuXXz6v7ZEahAh4zfV+iSe91Yiw9j4QCMntGn1fOVRqmuix0bf7mqPKY+gIZSZBRIvInCeFq07dwM4wfdZ/Rum+lpzxwNW9VKXaS/sQ3HiplsijoNIFpqbYhvi204qQD9P6bmxJU7kqBbcXHgTp033nxPTmtgETfXAsFiwpBk5uVwNWuQc9OUh6LBTHOrTW48laWGPUtQ9b9SQ79eZvXJrFHhwg26tyo1N3xHrg50vHpT39afV5dXcXq6iqeeOIJ/PEf/zFe8YpX4M/+7M+wZ8+e2HFPP/20+pymvjXtox+bIzvSZp4oJdrSDrGDbKyjnQHGtKicWoOlJ7uuzda02lz5K+lLO6n2PanRj1a6irgD6XogECUkouWhV2ChMpLBvrg04A/yRKnokJzSDPBPXUqYLo80HEmdLH35jOdpwduIKEeZN4uSIHCLymqQcaadr+1wNTA96BOzh3YLG4mFFU/NWDoujzUOegCF6cl4D2CjwULnV4N9Hg+ixv0GUXYAswz8idaYSpiuV0e0QZMz+twwo29SJeTYOdhqD9xe1ZF6QKKtQjcDtU7oVL4pAtydBcM9MJHLOyGokiy/zh3h7kq6m+fQ9TiaLY5Wm6HtCZVRpUTVSgjX5ai39FZG+NG32txfvUKM5IPIBxd+8lwoXBnnWFjzMDc9GB+FZpsZl9mKjJh/rjUYLEsEL3E0gvahY63YvqFVGR0U6uJLen5NSQ2qzsg6mNIVYPc/0cCjx4PrbrUzhSzsCcZ087Y3Rw4A5ndAquw452i0eEfyT1qTJRGJpklZnnDutDwG6ZBochkUuNpyd0M25bgKgLFujy4dJ4QEFgGUgFKCcpHAtoCjh0q4sOj6gawDBbGs90vF8FhXzxOHWJ0n8yTShwqK7XkctkXUyr9OVn4SnSYw05b2dwU/mVqTa8rsHdlj2xLQSB8wrZyJNuYfpC2aCcVCmMDNRiwHPA1X/3XOo/6ejvgKXFOcI/mMvvMHJ3D/E018/dEmqvXgOeqlLAZO4D7yyCP42Mc+hvvvvx9PP/00VldXUavVOh+ogRAC1824vuEKwuTkJO699158x3d8B/bt2wfLsnDu3Dn87d/+LT772c+Cc44vfvGLePGLX4xvfOMb2L17d+j41dVV9XlmZqbj+aanp43HmtBqtdBqBZ1SGSjtakdAyMbfPkoAhjDBJZdwxHz6ONRssK2FtdR326gFrVTLCQJsRRVmQWWanne5n+rAdM/fJqqZSOS73FeRf/5veuXFfLKGEEFO6hFMT10IPuuQWZfkbFpbKjtgYqmOzGCQIZ+ODDpfBpJL3kuLEpxbcMC4UMjO+KrfQ7sLABqYX3VBIFQ3MjCAfj+k7QAhQLkYz3S1xkIqIr3zFTWMl88OCIlFek2CPuuorq3DgdFZ1mBWPX6cJiLIkUOhYycl8owlTaRkO1mmn4JTRx/WPh7eRAVuhFxJuybOoWQ1203GmNQIpNuXXN9vE8c8Rps7g9Kq2yxkfQ4553joWBMbNYYT5wPbo72zYtWH4wGnLzl49Nlm6LhTF8S+4yPFkK9e9HzPng0ifa9XmbLxWVx1MT3WvxzogSeasC2Ca/YVgnY6Zf9zC45qn2+5pqSCr+p4+2vH8dG/XkejxVVQTgBqte9Aif8un8es9UqaWk9fBv3hz6yFrIeyePOb0M0kaOi97O10OXJcsdDfW0pFEGXHFfV0ViQtlTfWCVo/Jul9lGOVYAwU75NH900CpSbdbTg7aqm44QQ6CSnHYJ6mPD4wZ4uxqSUCE+/bZePCoisUuP5+biSoV2jsB+KnydXS9mAcQ2BZAHNFXVnkXK1OzErg6uc2KS9pSj+qGzGDLKbzC8F4tJvgdlca5KMvyyCtLCw1ht38zmyxQFDXXu20e6uvftJ31MVa5gPFH33cLFdON9vhdp8j4FlKRYpdvmir3mQ99w+AARK4Z8+exU/+5E/iM5/5jPotj4KaHb/+67+O5z//+SgW43L1d73rXfjWt76FN77xjTh9+jROnTqFf/7P/3morAGgWq2qz+VyueM59aBlGxsbHfP3K7/yKx3TvNrANJItCkoBeEFFoFdc0YrO80lFpaL18YVv1vAf378IxnjoRT877+KDn17H7ATFndeXgSEKSkhIgbte9/CW/3gOS2seKiWKX/oXM7jzaFlTx3I/X+K7dmhmRAcZ6vgIsft3D9bxrSebYAy45doi/u0PTYWOk/leWnPxN/fX8D/+aj3k1XT2UtBg/v7/Xsan/r6q1Hm3HClietTF3z/SiBmR63BdYHqCYm7ahuuvDyJEqKG++VgTy+se6s0KPvyZNTzv+nKMsOSc49NfreKJEy0MlQkuLIkbcnB3QXW+ZPCwbz/ZwuSohZfdNSTUWG2GT9xXxUadwbaBe+4YAiACntmUoGAj5F1cDSlwtc4dgmeORlS0xP8vyz2khsa0E/EfJ3DFTUub0c+bgJ2J6Hu6ZefLiF7qosRzp3Tawz/Kcw/u7J3U7OZjBnLqbYGJ6DVhMy4xySpBbItPPCUd1y88JlZntBwO2w4G1oQEA9LxEdFe6wPUYoGCc6FinZtM7ppLP3XbIhiqaDZMnji2H7geR73BMFShODfvqoF92oRDsxWU33rdCylwAWDvjIVbjpQwfb+Fs/NuxEKh+/cjCSZFeCYLhQxpdoLeNzsVCUQrV+B0C3XqNHK7x205clzNsCxB4Ep0qjdtG5jsEGxIf92Yb5Mj+uQJxK9alRl+UU16/U71ECXpgh3OAUsqIBMII6mClW2l65ePRQOCV9odUCrGmoyLfx4LSFc5+ahflsy/Tq5yiNUjUsTiuBz1FkPBttRkWSEjQyXHiqUigdswENTSwjAljSx1/eykjUaLq2ubGrMGp+69DHHLtUVcXHIxNyUKOK0MdRuNzW6bdk/ZauXLSIUqC0IT5LMdshPkvCtiX15buSQsNVwPWN3wMD4cnFe+5wWbYHZSBktngde0Siv78zQQAvfEiRN42ctehrNnz+akbY948YtfnLr9O77jO/C5z30Od955J1qtFj772c/igQceyOR1Owj8wi/8At71rnep7+vr6zhw4MCWnHsnI7qcXQeVBJckcA0NmoQkIwkRjaScpbz/8UZitFTGgEvLDCcvOJiesJXCVe597LSDiz7J6LgM9/1DHXceLcfItcBOoPuKNWbHECM9xd/Hn2upQc6jx9uo+yocTymYxd8Li54xuMDZBReeJxr7T3+1FiI7nz3j4FLFC/2WhMVVhlYrmPXinOO58w5qTY6HjrXx0DGhgnromRZefHsldC0e4/jqI8KTd0Wb77j7tmAi5HbN6+6xZ1t42V1D8DzgzCUXq1VxsV4beNCf+S+XCAgFfuh7xvDBT6+r8qo2uFL7EoRn66Oq71AQBGRV4Prpafvq5zMhao+h1FmG86WpCXJcveioXlQ7Bl+UEr3bc8k0U+rd1LwM4Nntt2tvuobtwCDOv9Oqgm7z08tzeGRfMLl3YK6AtSrD6IIDxjlGhsJe5W1HDGjvuKGMYiFYRho9n7wX+2ZtnLroYHbSxsKKi4kRirtu7Dxxn4bTl8IrXbpVWXMebw++63lDuOtoGWPDQeDM4AQJyevtUg8PzmY8a2mTw/oy7O+6vYJSkeD0RQfPnnXEAG2TH349W5fzBFCOHINEljrbtkjf9aZ+rhBSTqxEFJHvoVVxalv6Sx22pzPlLT2Qlzw/88NWEAK03WCiUAqO5OJQKseaflqPPNNSeSgYBsPyF9fjoUlBzsS2kSGKjTpDtc5Q8cm2gp2dzJIkWLFAUGsYrk22MwaxSTdEnQhefQWb3naJ8REbB3cX1Pd0q4I4Udr1qrqMmBq3MGWIhWOCvpLUlJukLBp/JgTjIxaW1jy896Mr+MW3T2OoTAHOFNdRsIPA9HWNi+ilLAZC4L7jHe/AmTNnVAZ2796NN77xjbjrrrswNzeH4eHhQZzmqsdNN92EH/7hH8Yf//EfAwD+6q/+KkTgjowEQZ6azc5LQxqNoKYbHR1N3bdUKqFU6iEQwxUOpX40TPBEVY6BWT2JNcjSA5cQ0Uh6nlC2bNTFsWPDNOSBq2OtFjBvlBA1mFhYdkPH6pYEgEbgap2cbidgYuM7RfLJRlr80GiH070w76BYpGo7pQQcgan3D/+jMbz2u0Zw4nwb/+F9i3A94MKSC9siQtlkAX/w87vxo++5iHqTo94UteBv/NQMrtlnNl3/0fecx3oNWK0x5UFLCAkpiCSa+jJP/+/iSnjq+rbrSvjlH50JRUmdmbDx3nftwr/+L/NYWvfAIToXC6vhY+W9LBeFEuu6/UW8+x3T+L9f3sBDz7RRq3shMkufXVeTBspj2d+NILN00dSYdlJlxtR1GVS2O420ySGQZQn/YE+Yfdd6i/lqwqgPaP/ZSEtCPd9ygNHHeZLqUV3Nn4SdPPlhtifQv/j79ZBOv4iWV9Ip5MoBddwm5yO2vePxQruV+IxEpaObVZaGm2Q6VdiGJ64ok+SI9EZsG4KYRS/V8Eh1hwzPYZbgNVmLVrajU6MUv/oTswCA//yhJTx71sk0sdwzdmAdkSPHjgNJ/dpf0oZJKGnBJrvkJtDY6obwdq4l2imOF6FBesRQJ4QIXGZObNeUmAi0KDBcsbCwLMaLFg2OlQpciwpLhHKJ+l7CTHkLSwsFL+JBSiDGtZZGVHMIcnXEX0myUWcYKovKNKt9AhAQuBYl2DNjKzJYwvIvIK8uB4voSuI0P2B95e+WjTsywBSvR5+I7myZFV4to6vMnznTxh3Xi/6PJxXtFjAz4VsotHjM8qObkumbwH3yySdx3333qYv8sR/7Mbz3ve/NtIQ/R/d4+ctfrgjcJ598MrRtYmJCfV5cXOyY1tLSkvHYHNkhByumgGH6kgHxl4d+D6WjLV2hFgAniFo4M2Hh8J4CvvWkmZRf85WdRP0ncMknHL/7jgo+89WaInCjHQ5PzQD1QBpECIno7C7nYgmhtLTeO2vh/IKHc0surtlTDNSk/rllkJcbDxUxO2nj/KKL2QkLF5Y8nLroqM7B/l0FHNlfRKUINNpBGjcfKSkfmij2zhawXnOwXmVwXI6CLQii1ap5TVG0YxVdGnlkXyFE3kpcu68ASoQlwnqVYXyEYnlN3kuKxVWGts+ll0tB56hgE1TK4ku1EV7CoROuykKBiNn7UBRbJM+w6wgUuBqR0WE8G1XgRj28TOlzkzw3x1WHKOeUhG8+1sCHPyv81UsF4L/+zJwifrpFR3/ZrUDiy+Rv7jQw2yEdXVNx6bYumdLYojJPsSTsuF+v6RvBw/cvxIkaMyR+59FdkhQg0YwMaILDlLW0WxclLqJV/u2+yrjkD8j1yVE1/hvgs7FZz1laspKs0L2L5fU6rnnivRNMxFDyvsHN3xk1Ro4c24+kdyfkGb1JE2DcT7xT+okBIzWYBEI6LJLugQv4fXJN4BPF4T0FHN5TQMEmqDcZzl4SAyvbDoJM25aWFnwLASLruXAd6IWUluKv4wFFX7DJIa6d0IDArdYZqnVx3qKd/cbIc9kWcM3eIjzG8c3HAoFa2sR5p3Y2RzKiz3aajYcex4XSQDS23dDzFbbdSm95k95ryccA/jNMRFByubL45HkHd94g+kRth8MxeDZnznvPR/r46le/qj7fddddeN/73peTt5uI2dlZ9TkaeOzo0aPq84kTJzqmpe+jH9srOBezCVn+bYWR9VYgMGKPb4t6q3CNrIxCKnApIeo4Gajk0O5C6rKNdT+4mfTPlXma9xW4L/H9Vlc2GNaqXmwQ2c+ARyXFwxWePvioNaXKFji0W8wZnZ8XedbVo602V5XcgV2ilbcowazvr3P6oqtI6IN+OtPaMonpcYqinVyl7Z4Sx6zXvBBZuboR70FxrimFFIEbVjBLv9soijbF1LjIx7x/D5fWxfUe3h0+ZqhEQ8/DsOzINFjI5D8a7VP8LvNqUjRla3w6RaTVEQRVC0u/zBYK8fzm2HnY8g5UhxOe1IIVthzh9Z312H7Pr9439b33pzdx4Ohvy5Iyx87o4OrIaoGxHcR0rEyJeaPeZul/B5YPmW5sQ3eTpKnKLOgC3LCSqx+EiA3E25NOt40DMYJATkpKRdVGXR/ZJ6RjWkfcAXqbk+U5zLR0NuNjKq9ZVyDJiSfX61fNn7Ktx+Ny5LgakFSHDhKmSbmkCWuPxaPOM3/sFPTxgwZEpmMSCOkYGaIol8TKzpEhqsbZsh7lnKtAZ16CB67Kuz8JJ1cO6JYIUkCjk14qODTnyuaOMR5S4MrCYLoHLpcKXIJyiWDS95OlVARLkyrFLPC8cP0bHWPrE2vRft1WPCNXKiglob5cVg/cLPtvFeS7FVXCyuciS5A6ve/xupcEK+Hbrngfmi2u+giUiIkMQEw8uA6HxzQhVBdl0rcCd2FhQX1+05ve1G9yOTpAV9ZGVbO33Xab+vzAAw90TEvf59Zbb+0rX4xxPPxMSwXY6ATbInje9aVUc+nLAV4KKat7qwCArB9MZK809iYkaITml8Vbfmi3nRgBFQA++Ol1nFtwUW8y1BsMdxwto+1wLK8HatZdUxbmlz385w8tAUQQiXtnC6FrIIR03enXlx8YlztzoO6raofLFLunbQAt3P94HYurHl71ImGvYlGiPGKHy6JDItOZnbABtPHZr1XV+Q775OnUhI2zC6KcZift1BlAcW5hXyCXqTLOsLJh7tEsr3vYM2OrCv70hTCBe3DOTOBSCuyatLG42saHPrOOX/iRKUXgXrOviG891VL7los0ZKcxVBKfaw0Gz+P4wjdrcF2Of/zSUVxY8vDEyRa+76VjoETMNrcdaCpmTe3F0xtHk99WpyUj0ckKonXikpAPIncmshIygz5fJ3iRZ6nl9BYACNCJuhTVhem4ATRJWQcFWct/22MLmCZp5KaM3n+bfgmR/m+nolXbuyRUB8Gqc8TvfZKdQPR00Wcr6/VuFqJLD/V3+Lf+1S71WSpS10MrXrj2f0L6/WRukIWSkhHVD9T6aUWlwOWb9v6mvXt505sjh0C0GhhktWBKS1gohN/AjTrDA483QShw67Wl2NJtY/2fMmbUUSwQ3HqkhNEhCsfluP9xoT4dKlM87/qSsiogRIxHGeOJvrqXllz8zv9eRstf2WhrLJHMh1oRyIVCt9FiOH3RFUSxRVCwiNHrVJJk3L9Qzrgfu4XgpsO9WzSqMpRjE0JUUDYgbEHRaWyUozvok9JpEw2BoI139gTZQshn5uKSi+FyMBnOIu9l7Dj/L48oLX70+ybwpW/VUG1wJQJjXhC7xrZJaAJkvc7wzOkWKAXuuqE78WvfBK7uu7pr166UPXMMAl/60pfU56hq9uabb8bBgwdx+vRpPPnkkzh58iQOHz5sTKdareIrX/kKAGBoaAgvfelL+8pX2+WZyVtAVOS1BkfJbFd62UBXSUZBIwSXslsw7MsYV7OwBX+2cNkn/fbPFXBwdwEf/9uN2HESf3N/XX0ulyhGfQJ0dIhifMTCDQeKmF9u4BuPCRuGf3iqiXtfKMhT3QO3a2G0QfUChGekqg1xHcMVokjPpXWOpfUmbBu494XDoBTY8H1hJ8epOp5SEawFCKvxrj8gHpxdk8HU6t4Z20ikS0gCt1pn6jo36lzZGUSxtKZZUwA4vxS2UEhS4FIi8vzEiTY4B7787QYc/xzX7A0fUy6FMyyjiteaHI8918LfPSg6Yi2X49tPCuL36MEWbrm25Ht1Bh64+rV3uo2yodXVUllJPdkZIwm2GYBGEHfIR44cOlhkFrytRYoeiAA3TZE3gPSjicUGjgQdTxRWPe6cji7QOTdZeKrtsIUIZWurCK6ocjZKzEYJ3OjxGZ+T5B+6B0EwGRteTpjteOGBKz7/P981FAoOJCfr9SWG0Qmbbs+nQ5807ub4VNVQxjQCBW7wm7JQ6HGJZL6KJUeOPpH08mgv9qCao/BEVtCXlunXm0yMyzw/bklk7ERSXvgOMcwAAOPDFizLDSlf600Gx0XIAxcQ9W4SKXz/k01F3gJAsSDGkJwHdRqRRrucgxKORius8jX5ehL/vFy7RtNEZi+I2rsBPjHnaZ8j+wY/BHnM0T0sqomIUiYa9AmKQU6G94vxYYqz/udqI3g4lDUhkkQgQivPueBuOASPM1wmeP5NZXz5HxpwXK54FdnVsWgw/tYnOTyPo9rozm6pbwL35ptvVp8vXbrUb3I5UnDs2DF8+MMfVt9f+9rXxvb5wR/8Qfzmb/4mAOC//Jf/gt/93d81pvWHf/iHqNVqAIDXv/71GBoa6itv8vm2LYLvuCl9FuGJE22xjL2vM+4MME0WH0V0htXUyEjIGR9CgkpQ+sFOjVm462gZ/78fmIBFCX7noyux4w/vKWCkQvDYc23UmlxVRFNjIrF/+YOTuOOGMloOxx99YhVtB2g6fsWjS/e7vCmhWWNdgev/ZQyo+XkZKhNcs7eIf/rqUZy+6OCrjzRxcSmwf5ATAMNlGloKc/2BIn76+ycUUTkxYuHFt1UAADcdLqJaZ5gYtfDCm8up0VrHR0RZNJ3gXpz3SeFKCXjVC4cxOULxlYcaOHHBxfKaG7qYNV859Jq7h/HqFw0n2lpQSvDi2yr4xuMNbNQEEQuIhmJsJNzCVSIE7oi/3LTWYFhZD5RKz50NelT1ZnhWWXGwFME96HAfo5ML+jFJJRh7fuW9T2tzroSX/ArEVgs6My1XRnz5tdNmvec1hQBJTXMLO5WmUxHDl+1+jUzn32kqFjUmyJivbvOf9RlOUlsqOp53uJ8diF5V3UeltwPxUIieW3QKsirKOUQkc5G/8M5SkaoHY5Xe+GkK1W1Xn/tIy4VpAKssFFy+6e9v2NMzUBHlyHE1I0ub0PeEoqGNNr17+m+tNguPD7l5P1XXZ2BwR4YoXnhzWR1//+NNP7aKjK9C1ASTxzgKCS2ZVCFKFCyCW64Nq2OjRKk85/S4hb2zBZxfCKti9NWo+vVGfUd7hVoBoWWMEsXfhqxtEvjbHRNv4HKDXmypBK7/Vydwd0KJj49YmJuycWnZVfYlHELodex0GwsrLi4sxSORnjjvoNVmqDUYmMdxftHFUJkK7sW/Pse3UBAWCeI32yKgWqTBw3sKaDuBgK6bMul7seA999yDqakpAMAXvvCFfpO7KvG7v/u7+NrXvpa6z4MPPojXvOY1aDaFgvLVr341XvSiF8X2+7f/9t9idHQUAPDf/tt/wyc/+cnYPt/85jfxH//jfwQA2LaNX/qlX+r3EkIdfOljk/Qvq0/n5YA0mb1sTCQpkbavxwDpkSftEmpN8cZL4vHOG8qYTfAFuuvGMu7w5feNlqdsC8ZHxP6zEzbe8PJRvPneUWVEv1ET5IgcTvXA3waDNJ7sbVP3r2O4QmFZwC3XlvAqX/0rfX4pCYKbDJUsTYErnpe7b6vgTa8cw5teOYZ7XzSsytaiBNftL+CuoyWUO9hxjA2JC2cs8Hc6t+j42yhuO1LCK180gjlfqSstKGRepHLolmtLuPNo+iRFuUjxqhcMh46bHKMYraQTuFKBW28yMUvvY2UjKFwVSEBfkoLwqpROSmpj5E3/b1I/Jpqm7PCYBEZy2xVidZ1jQOjUR45ZKOgK3AH09lKTiHQq+2meEt8l4itusqTNk5fVbzV4xh53lt02vdPunyBJQRrc36Cz3lXyGUjMtLwliU+iAqykdGoNhkeebeHvH6rj4WdaqNU7mBr2AU3UmoioX60n1WeR5liqt/R2LbSkNpSm+XMWZB0gZnpWMz6srn8LbI0oCCwUsqURP3f67I3J+z5HjhydMcj3xZRUpwkvx40H+o229YLn7K7yI0QbZ0uRhlYfqjFpSpPhuOFzFgzBxOR5AMCiVKVfsIla/Rk+AADxbRV4+BqzqIs7wSSk0slcK02Bm6MvhEnz5Jtpas52Srtl6mev1zwwxrMR+/rYmwUzFHL1DeNcCZ30oICAeBeDgODd5btvBW6hUMAv/MIv4Gd/9mdx33334a//+q9x77339pvsVYUvfvGLeOc734kjR47gVa96FW699VZMT0/DsiycP38ef/u3f4vPfOYzYP7dPXToED7wgQ8Y09q1axd+7/d+Dz/yIz8Cxhj+yT/5J3jzm9+Me++9F5Zl4atf/So++MEPKiL4V37lV3DjjTf2fQ29LFm7EurRtKUDUZJM7msZWiyhwOX+LKnYXveVqxMjVuh4Ew7tthUB2mhyRf5OjIYzRohoYFc2mArmxTUG17QcPgvUzKp2HnFdwbKAoRJV5bR3xrczaHCsVV0/iJlU4AYeMSaiUUegRiKps38AMFQJyr3eYBgfoTi/KHoyYyNUMdiTfpktrYuRF/GvQyqHRiqdK3RKgdnJMNk+NWapaKsqT+Xw95FyMOhbXDWP/Fq+r46lddDEOYMIs51uI9E8rCQ6ef7IwpbnlbuZAhJGSZIcOwtZlCnbgagCVwUS7AGp5GdKspteJqEebPL5d/ybk1K+pk7vZtcFcYsC842MKVo3KVvRs8cCcyU8Z9EVO1F87K/X8cSJYEXG3JSFe1/Y3woqiU6kYScICwXZPpkVuGuaB250qW0/MN3vflVV+vFpz4mceNJjFYQsFDbhGUvKzw6r0nPk2DZkeUcG9b4E9TY3klR6XtoOV+INSffE8tFnnaHS06of2Xd/9HgLIxWKtstjhO35hfC4IzqGBMS1UCLEP+cWHH9ZuCBwVzdcHDvdDo8H/eHVUFmQvVxLZxDKV9Pq1iiZK2K8xGMq5GOU/hBSY2eYud+JxW3qE8qJ111TFp5/YyV2TLlEUaszHD1UBOPAcLmtxiu2P+nhOFAKXHnZNg2XmecB1ILiX7p5HfomcAHgXe96F+677z58+tOfxg/8wA/gz//8z/HKV75yEElfVTh+/DiOHz+eus9rXvMa/Omf/in27t2buM8/+2f/DPV6He9617vQbDbxkY98BB/5yEdC+1iWhV/8xV/Ev//3/34gee+GEFDkTpezDTsRkvA0zTzFPHDlvqYgZkw0LOs1Dw8/08TiqoeGT8iOj3QWyh/aU8CFRVHjNNtc2S+MD8cVu6PDgsDdqIuORrPNcXbBxcy4BRwswvM4Hj3eQqPFMT1u4YaDyUbFZy45OHa6jUN7CuCco95iOHvRBSEERw8V4Xgcjx0XFgIjQ4E1QrlIMT5CsVZl+NsH6tg9bWPFV7wOVYiq4IxL/XVopGMnAte2CIo20HaFCqjeauPZM44qJwICQrhSLa9VOS4uudi/y0axQJTKJmqDYAIhwHTEYmF63EKlJIhmeT1R1XCxKJY5eQy4sGA25221Rb4uLLpotDiu3VvQVGckU4ckmPGLkApIfofVnhkIkJ1GDObYXmS3UAh/b7d55mOj0Acw6reUBzNNBdgtBkWumK5hO5B2DzL7o8oP21A3bOWgIY2A9ecIu0L0kZXe+PtmLJxb9LCxSQpcov4L+mqd7j9HcmBXpcCt6gpc7UA9zR7y2+2xg3wemYqCHvwmr9ftcRIqi/o5R44cnZGFWBpU2tHVHqb3WBG4hAgFLk+IG9HHyx+ai/PTkSv8GONYr5nbDbnqaf+shZuuKeGFN5tXG1bKFBs1DwUbWK9xjA0RjAwRbNQ5igWCphYTh3tinFkuEuUb2u/16VDiE63+jZK5su2NxhbYqWKGywX6uNtKGRpLjmSnWSiEoD2PcnK5VCBGFXrBAixLkLWci8/Uf+Tl/nJyRF+FY1vivZfPo8s4yv7+W67ABcSg6C/+4i/w4z/+4/jgBz+IV7/61Xj961+Pt771rXjhC1+Iubk5FIuXebSqTcRv//Zv43Wvex2++c1v4uGHH8b8/DwWFxfRarUwPj6Ow4cP48UvfjF+6Id+yGibYMJP/uRP4lWvehXe97734XOf+xzOnDkDxhj27t2LV77ylfixH/sx3HnnnYO7CDUDlp3BvRI6pmkK3OgycmWKbSgixoUH7Ee/sBEjMUaHReLRAeHEKMXqhtj54FxB2SY0Wlz5pI4bZk/H/CUuG74C91NfqeL0JRd/92ADt11fxlceauD3P76i9v+Dn5/D0UPxCKELKy5+9D0XAQC3XFvE3bdW8GefXcfpi6K22r/LxvOuL6nl/0PlQF3MwbFr0sJaleH3P74aSne4QtWyjE5L8fXGt9OzR4jwpWu7HJ/7RhWLq0FBT4xavtKWoFIOSNbf//gqCjbwgXeLCZOCHSddTbCoqPSnxgiW10Uup8ctWBZBqUDQaInfKhG/KUpEJ2ujxnB2wazAPTPv4q+1oHWvuXsYL7mjIgImwNxJiedP/A3xtx1eyGACwr83BhWvhPK8ugImaa5I7NAOVOATJciddo/LjzvBKMpVk3GDS99UJXUQ4F422MQxcU/IOiiIKWMHnH7HibCEdLJaKLT9id1/8vJR/P7HV5WP7CBAEr/4P3V4nvWlgtE+kckDVy7lTWt7spLvpuIe5HOYlg/ZBuoD2GJRKnA3fwIhpChUSqcroYedI0fvSHoDNtPvVCdMkxjctsNVfz3J97Kft/fcvINnzrThuhzMV/+dvNDG4T1F3HFDGQ8da6p9J0ctXLMvCK78sb9eBwB89x0V3Hi4hLGRuAiIcxGouTFGcW7BxcFdFmYmbeydKWBh1cOhPQUc3lPAwoqHi0sOzi+4aDtcKBF1FeyAbkNgIxckqLc/RBscxcq1Q3udIx36uDtLQNDQ2HSHlLlOLosPAYFrWx0yqb2/8vqlzaHwwCWhwIK2Lb7btiB2PQ/B2D2hLkjCQAhcACgWi/jABz6AyclJvPe978UnP/lJo/9qFhBC4A6yV7rDceTIERw5cgTveMc7Bpru9ddfj9/+7d/Gb//2bw80XRNYF1MqwYu8adnZMpiWbkgE6lHecV/GgIUVL0behklPgX/xj8fx1YcbeP5NZZw418bMpI2JUUs1tM1WYFswYWh8x3xCeKMuPHCXfEWPzMPjJ1qh/R9/rm0kcJ8+FSzjnF92wZj4K3F23g0NaG69tqw1qgQvuWMIz5xZi6U7UqEGBa75YdG9pjo1wJLA3ahzRd7OTVkYHaKYmQx8d8ERIlkdF8qUf6hMMxE8Mj8vuWMY//B0E0MlgrkpcY5yMUh7V8RmAQSYGhUE7obP0b7+nmEcP9vG48+JPBzXApoBwNKa9BEmIJQDXmfvWRPBGpBO6RJceQuJ/4OJpM07QzmM6PBcyGepVJAErrYcsctnKm01+HaRG90EGBLLC/3Pm5inbhAOlhTfnh6MKvm4QYKEBhTZViRkQbfPYfSs+v003VAZMCyaQLQ+bvuqDunp7g50koyHsifPnPkUXFfghvMtg3rpYNzcVvWnfueZ7nnW5zHLMyQHe1RrzosRFU7X6EDEhn7N29scOeJIqEN19PvqxNp0rvelw1nhvk0e4xyux43t6SBaq0srHjxP1Etth4OBw3E4Vje8WNwN2yYhUYpcBj42ZMG2SGr9qJNbtuWPbX0SamzYwkaNYbhCAULgsSBtqxMp1iXShFTy950ST+BKw3CFYL0mhD2VUucoZhw7pz8rYXrGZbtdNKhvxTHx3+Uvqu33hNxY7wPIFba25RO4jIP60vGohVwnDIzAfe655/C2t70NX//610P+lzmuDnQjiZcKvivh8QiWbsSvPOrfKl9OU0RRxrkKmnVwl43T84KU0xtbmc5hf3bz/KKL7/nOERzeI2ZPx31ilnEoZa7JfkGazG/UORhjaDSDG7FeYzh9URCF1+4t4LnzDk5dNC/lP639XmuKSqrpc4vlEkGzxXHKV+P+0GtGMTpMQ4ra6w4Uccu1RTz+XJiQHC7TIIhZSqMr6hepmuvsgWtRgnIhXPY//oYJnLrgiBkyAhU0oGgDDY3HfvTZlspbloiwlIo83XRNES+4uYyTFxxYFgUlJNR47ZkthI4jAHZN2qrcAODWa0t4+2sn8J/+ZBEPPt2KBUVptYOXT+Wsw7tFDXV0JwWieselB642wJQdU/069GNy7CykKUQ383yd4Pp1ZKlEUWuykAfuVkUJ7juyK5A4cCTaNrE9fuhOI211dLoDvZLtm4UIJRr8qH8YcEEniYviHr0dEkhIRxK4oyNyOSxCvoKDgjF7HaRinPNEq6hSwXzBrmsI1RPnsTsiSpaIH1MOyNhnNT5DEZjiG0jC2vW6DUWULV+dEs3b3hxXO7IIPQfVreCRvyp9+XtkwzNnHFxccmFTgrFhEiIYecIxmfPiH7dnxsYtR0pYr3k4fcFBsy366fqklN7fZ4yrGBuVSueeECHw47eIup9SEipzSoUAqWiLfaVVoG2Oxd0zTPE79L4XJeKfB+CJE21/xaYYS+/Y5fyXCa7ZW8TuKRuFAklVq+pj+WDydIeUeqQryJFdgcsRPHeBAtdv+12ZVnAaQgDP4yjYQsgluQf471I3D+JAxirHjx/Hi1/8Ynz9619XlUJO3l6d6OZ9vBKeEFP0SwkV8dPv3HdS4Epvu+fdEKhd2xp3Gn2n/HdeFWTZ908FhL0BYCZwpVK3WvdQa/KQAmZ1w1PE7EvuFIFRTicQuCe13xtNjpUNT+Xr1mvDit2ZCTvkU8sYh0UFIRrF6HBAkkbLUIeeb0LMweF0UBpWAREAsxO2MOSn4bIsRAabj0gCV/PnTYNFiercAIH6ilJgXfMAHI5YKBBCYsHPRoeFvUMhMt12ZK/4QXa4ZOOgXUYiTN7CnZY1yc3q+rWTRFW4JKIaz7FDsVUEbka1m3yOKjIAkBMPOtHruTudPx7cor/zAsnFGxDo2W7Adnenkvxck7aZEwkft1kwcoyGTHabj6yDjrTyIIQohZaR3EcyESDS5qpPIFfSAKJ9HMRYKGpNqNqTFAkuj3xmCQrcYgKB67jp7/h2P/sSaWMaOfFkGzxwoxOu3Z+38z56ye6UMXGOHDsVm/mOaLoSv0+uiXC0/doOg+tyeExM8BT9yqPVZvjGow0lGuktD4IUKhUIKiWiVmK2nbjyV59o4wgEIcO+eMjcniK0jak2LagnJUkKiFWVcl+xEpKq8w0CamydMDgjBEod2mozNFsM9SbDwqq34yadL0dUyrQj0WlafbRTitwkFJOkq12I75+cjkjI9sfqjm9hIsV7lj+0Fwpcn+Rlekya7vI9EAXuO97xDiwsLKiKavfu3XjjG9+IO++8E3NzcxgeHh7EaXLsYHQzi3UleXSZvM8kaOQ605Z5cA5FgB7cHdQYMhiZ3EeHUrrL75T4yxm48o5MtVBocKzVwjXG8XMOHFeQhS+8pYwPfnoNpy4kKXCDkQkHcE6qhssE1+wt4FtPCp+lUgEYGRKm3XpFSYggRKMYHgoKKFqGOnS/GEI7d8r0RhwAZiYsdS8CRar4GzUtf/gZ0ZkaKqcvKdLPRShEgAIIhTX1Cdww8RxPbNdU+J6ND1MQChQiD9l1B4o4ft5VHS7dRqLTqyWvWw8C3ukdjpJwetYZB/Rcd03u5NhS7NT7Ir2iSv7god3r8uMEhFR6EdW4gimYSJdIO7ZTuqaO7g69XTFslU2C+dzJpaRvyeo1u+WIlBk3VMi6In1Uaye/+K0ahkr9F3rovoXeFWMW4+B6PydcsiYLBUB4xBqS6R6m/KZN2MjDBvCwBtccpFVUBG7/k1AmdEpyxzzXOXJsE0x1KBBRavZ5juh4VlfQhuoWn9i9dn8RQ2WCsWGKhVUPRRuYm7IxPSF60E8cb6PlAKcuuj2/w2ps5ItIbJsoUcpjx1s4dbGNSpFiZsIKE8xcBBsDgIphbKZfCvz05XHgYhxssrWbnbTQdjmmxizcfn0ZZ+edSEL9wUsRUom8iNWQMjbMyrqHs/OOUllql5Jjk9GlS8CWIHrvOedw/bFHoZMC11ee6wnZ/ljd8cSKpDiByxXH4DFfFQ/fD7uLB7FvAvfRRx/F3/3d36lK4Cd+4ifwX//rf0WpFPfMzHHlwhQFMgkmBd/lirTAZNFZFcaBz3ythrOXHPz3n9+NIU19yjiw6itwD8zZod8losWl0ufB9+GyIHAljEHMJIFb87C2ER49PXVKEJUHdxeUNcNqleF733kGlgVcs7eA0xddDFcILi6Fjz19STTKIxUSIqFlJ0EoUGWlJT4PGRS4Ra3CTJuZYgxKdqqTw0mghKCsDSJ3T1uqsyU9kuTyz6RKe7hidbRqkPkmALjmjyVmpElsvyjmJsPV8nCFGBW4Nxwq4vPfrKPZ5vi/X66KwAQUuPuWCm6/zhw5NjhvkK8oEi1w1XOmaHNQ388raXnwlTBJk2MAyEgAybpMWp2ELBS6PGUnNTpP6CwNgtBJGjj26uO73TCqVDqUb1Iam35N0TwmZbDHfHQ6LO15S9se7Je8hxxcA8DIEFWXN7/C8Kd/tY63/qOJDql3hyxdNR7po3gGMhMA9s6Yhxsmj1geTbQLDLzJ8Qs5LVk5QNOXBkuVmU4SdHXaLp7PQRJSOXJcadjMd0KlbRDYEEKwXmN470eXcc2eAvbOjsCyhD/syBBD2Y9wXyoQlU7D6b3uU4dxSaAStWTdcTiePtXC/l02Wi2OWt3F9ER4PKMTuCMVC7UGS/cP9gnisAI3KBeZNiHCH3XIV2oO+n4ECtzkfSxKMDoU7ld6WxBgMoeA3h/XVdo7Agbhk1TgRsVc6hBD/1fuKfsBriN4ChWcWQqnvECBK4OYAcmxfpLQt4XC1772NfX5rrvuwn//7/89J2+vRmRVaMQPuayh/N5MhtZymbp/oa7L8LVHGjh9ycV9366H9q3WGRq+FeyBuQLe9MpRAMD3v2JU7RMjyeTbq814jg0HIwhCzArcmXHx2/I6w2o1zIyeuiBUtAd3F1ApUdxxvXiXWw5Hvcnx+HNtbNSZIm9tC5jwSWI5qzpcsXDHDSWlQLnuQFEt79cVtZRAGNzreZugoUAg0TLU4TEesg3oRKxSKmaC5Z265dqS8g8UlgcEzF+KWkxYNnFot22816ZzAVD3hnOf1CXAG14+CgLgh793LLhOLc3xUYo5X4V7zR4b1M+bbuswVCbYPR1YKHz76SYcD3Ac4KFjrcwqP73BMPlI6VAEjHZP5Ock8/W8c7QzsV2+X53IUanALftKeZ3c2ezOXrRMNsNCQVfI97MUf7vQ8R5s03OlnToE04qE6IB74JNMHZKTjqjRsow+d6Z3VA6uLQrYlKileoOCXjame2h6ZvXi04OSRS2NDswV8Cf/YTfe81Oz+PWfmlGrbwalUA2VX4bnMOt9z1LvMH+wF/LA1SwUennGOtVDHZPcgfVHjhxbiaR3hCR+6R9MOykB8PlvVLG4yvDAk6JfTrRtQjQiVho9/lwTT5xowRlAVErVV5efOfD5b9TwwJMtfP2RRtCHZ2ERScgDN2HFBGDqKwkGV/fARUSwEoqN1OXkbycEtj3Z9rcUkbZ1/curHURjPHda31blLfIuAMnWT6ZJUzlpXbADBa7ncXjSjsEnbRnngRrX4z43QEKrirOg7+7f8vKy+vymN72p3+RyXKYI1DWdHz9FHu2wl7gX8BRbBCuiclxaCxrmqHXBuYVAvTpcoXje9SXcdLgYqjyilR41/D47aeHp0yKt3dOWcfZo7y7BTq7XGC4smg3aDvkK2t985y4srHhYXPXwr377Umy/fbsKYIxjdYPh7CVXXcO+2QL+4jf24euP1tVSGkJISFFLIx64Nx0uYveUpZYfAEEZmmamJAmiVi90ePYoFQHCXvGCCg7tLuD5N1UEQaQtgeD+NHJBM7P78X8yjpfeNYxLyy5qDZZJgSvzEiIQiMjDy+4awq3XinurtxvyCikh+Knvn4THuB8FVlTsehCY2UkLI/4S2vUqC71LLYd1HDBSAzHeyeMxCHoQDoDgweCBO+AOWo7NwVYFEcj6HMjnUarX2m62iPImEMNDmOlqB1Ak3fpWJu6zQ14ks1KfJG7LmsYgkdW+QdXNXaffn2pEKpREWtmP08/XbAWDCkLEgMCkYO0X+iA8lJdOx2kNimnfa/YWcc1eoN5kKNoENXCjRyxP+JyGrZrgMcGT7aGuwNXa6359cDshFEB0Z1QZOXLsGMTqBpKyrce05fsmFK+Bz6wTUdSG+lz+xy8+UMe3nmrhTz+1jsmRYLsp9kcWcAY17pKrDBdWRWJfuL+Of/GPx+GAgzEeWinRdngQh6BMgTWDv40GvQ/AIcYDXCNT9RW5+oQU4bINHkwtxVTbnO1m6v6jncY9OQaDgPcJpjF2SpGbJktlV6bQRcA9+b5JCsNxOTyuBTnVVvYqla6mwO32fe+bwJ2dnVWfd+3a1W9yOS5TLKw4ODvvYKTCYhEmR4Yo9u8KJI0mj7/LFXICz0TqRdWJ5xYDL9lz82Ff2YtLoocvfFlFAUVnfpIUuAF5LghKiX2z5td7pEIxOkSxUWd44rmWOlZPXxK4FhVKz12T5lrs0G4bC6uikT+7IAlckbHhCsXosIVqPQhupqs3KSEhD9yhMgGh4VnUNAVuVDFq8iHWQQkBpYBFKSolKshHLyAiiZ8mAVDQFLh7ZgrYPW2jWmeCwM3Q6Fjq3oj0pQLXoiLQnLy3KimNwSVEHD8+bKHqn48jvJRj14SNkYqczQufu+2EZ5bN+fM7XoYGI+nyAgsF8dwH3j9xBZXJFD5Hjk6QEzVlfxFP2xnwM6Q93NFkeXyX/k9nIMA6EdK6t/mOaSozKmv1tigJm91pJ6bPhgLUB9yDRFJysg1IKsukctF/brZFhV2wfQLXJkBrcBcQSonohHO2c7jaeJ+mDHwIgvbMdXnq893LBE6W5zDLPkC2+sAzKHD1/ptuBZMZHYjYxGLZIYPiHDm2G1lWGg38ddHfS4JYBSO/ERJ81gU9K9UggZ7qDcjJwqACYZHKotpgsGmgwP3Wkw3c/3gTr7l7SO1TSlAemsB8VSWlYTGX7kQnx8inLzpqzChjjvQDffWfPgZMq9ctRZ6ltz05BgedJN1pZW7q53BfUU47eOCKSRnx0XEYLiy5WF4X73OrzfHkiRZWq0HH6PyCC8aBht9vW1731CR8t32dvgncI0eOqM/z8/P9JpfjMsWpCy6qdQbOCZbXw7N2y+seZicspaqSuBLInbgvaICof+uFhUCGcTISGEwqYafH7czLQKKVDiEkRODunzX7ABAAs5OCwH3MJ3AnRihWNoJOxMHd4aohKbrnod0FNJriuFX/+BEtuIr0XyJEnFhXJVOKkAdu0SYh3ySgkwcuV8pecf3GLAbXTWQHg/tm4STwMKYif4xxUD8vEhMjIkPKazjDDVLKWl+tKgMmyVlxtZ8KogawyLF6x4RzHlPgRu0nRocINuriGN0rMS1/TMtbJ4VZVOGmCAnEO4gmT6EcOw9bNdbP2jGRM9By+Z7T4wDG8zhabQaPpXQWEzZstyrgcuFfus3nplcFXZ6g2/ucXWmcje3OaqGgI1DgiuO7UYdkQkrdn6W89InDTitVpM+84xlU9jux3UhT4EqFjXbNumf9oIMx6siVYzlydIfNeGOiHufRvr7cFlKlEgLGkwUXjssxOdZ9Ja/35ZnPmFWKBA1/XPDRL2zg+18xAo+J5du/9oElrFUZ/v5hYe1XKgT5NFUvSfW1vjoPAIg2VpL14UadqTGj3Ykcy4CQPVLG5OREm2dqe3JsCkL3JqMgYKtgEhVyzmFZJNEyUe+vycloxoUdppxodzyOtSoLAo0DaLQZHCcIktZoMazXGKbHLTAWVul2Qt8E7j333IM9e/bg4sWL+PznP4+f+7mf6zfJHJcZGOOK3No7Y+HafUW17eQFJ7QdCMjOGOnTI5bWPKyspy/1GB+lqNaZUkoAwOgwRavNQ7OcKxseqvVkHbvHOJbWxbqLyVEL1YZoAJrtOqLW7F9+sIalNQ97pm3c9+0qHniiqbY9faqNn/iN87jraAXXHyji2bPCALdYAE6cdxSh22wz4UnEoYhxQoDpcUtVOpeWXHieWJboagW9VnPxv/9mXXisUGByVFgqVJtM5VQGPIsGyDpxro2T5x3YBYK5SSuxEjtzycHiarjszy04+F+fX8NwheLikouCDQyXLVGunOPCoouFFQ8bdQ/r2szzcpXBtsPnWlr3cGHRBaUEq5GAa47LsbDiolZnuFAicD0z0SuxUWdY2WBYXffwxHMtXFpyUbQJ1usMRZuCEFHGF5fd0DPx5IkWltY8LK55WPaftRsOFkGIWL46v+LGzju/6mFl3VXPZaMlPITBORZWPQyVCdZqDIsrHkaHKY6fbcNlHJQQzEyI+2TbBPUGg20THJwrhBQ9FgXml8PrMidHLdQaYnav1oxbdDSa2uDaEu/tapXhS9+ugwCotzgswnHqohN6ByxLBKDhAO5/ooFzCy5uP1JG8RqCS8semi2Gap2pRowQQYBfWHKxUWNdzeTrIATYPW2HiOpz846audwOTE9YmBwdNGuSjoVVF2sb4fuxb1Y8D5xznJl30W7z0O+dsF2d1k4dbEmEyIj17R48cE9fdPDTv3kRtQZHwQb++esm8PwbRVC/tCSMvm49IonDC026dZrY173idsggYxC+vVtJOKUtJ++1SDNn35B+LxYK+nPSdGR7HVgoDBIk8jkY13DRv+kwmHe0ZifqgRs9ke3PL5uDmGmfO+Y6yK88NlP5RiYkE9PNQlwrAjfYmRCCog203d6UdD09Kxp2SJWRI8e2Qb0CaVVRn+2R2UIhvj2alb+5v4ZzCw4O7ykm2uDsmbFxw4GicVsS5Comx2VoORQcQmnaiIg6FlY8XLcfWFzzsObHQpGxTYYNwaV1RD3lZSBjQsL9Nb0JODhXwMSoBQ5gadUDpQS7p/vvSweBxElIXJN2W/VVwpITyOfBNhem9mynlLnqO0QESRYlyRPRkn9ZdrFWo6iUghg6wQQBcPO1JdSbHp4+DZRLBLdfV8KDT7fUs1pvMDXO8TjQzRvRN4FrWRZ+7ud+Dj/zMz+D++67D1/4whfw6le/ut9kc1xG0CXxU+OWCq4ECIJPBoqSGPRLe/xsu2Ok3/mVzr9xznHstJM6WK41GVbWA6XpxAhF2+FYXich9UXL4fi7BwVh22j5BKxGwngMOHbaxcWlKl7/khFFglaKBMtrLjzGUa0zbNSD2RsdhACVEkXL4VhZ98C4IHubLe0cHvDUyZb6PjZCMTZE0XbiAz99ef7MBA0phJvNAoYrFC++tYyvP9bE9QcKeOaM2L5RYzF1SaPJ8cyZNqhcokOBuSnA8SgqZQ9r/nKC5XWGukY0MsZQbxJFJnuM48wlR+2/sm6+H/U2sFb1UCyQmH2HjlabodXy0GhxNFoe6k0PwxVxwFrVBbiNi8seqo0w0b+87qHWZFhc89BscbgeR6VEUS4RLKx4WFqLG9yt1xnWqwxrNaZUhJbl4eQFB2s1Bs6FSnZylKG8JkhkSYJ6HkepSGBZ4vPQqog2XioQlApAyxEE1/I6h02FjxMgnsdyiaDe5Kg3gnvSaDKciii+OYD1Oo+RwIBQGEcVARYlWK95+OTf1QAAJ887OLJ/CtW6h1qDqXskUSwQtB0xOTK60rsBoONy3HhYrKevNRhOXXQ6HLG5WK8xTN64dQQuYxzHzzixya6CTbB/VwHrNYazl4IysSxB9ndCloHNIJGVS5Cd8bLugdvlub74rRpqDTmIAZ47186WJ0XoBKsE+oaR8JLnSThkh3RqdZiKwrjsLLKtYyIDROq5NUTVFpmVtVnzESHv9dUNqeeLEgGGXUIeuBg8gdupTIxqLO0z04PCdDiVVOC6Lo9dLE/8kgLthFmfhW6Qlg1JmkRtnAo2QdvlPS+FzoLYJJH/Nydwc+QQiJGoA6wXTCpbIJgA0zd7PsFz/Gwbf/Y5MaB5+2uLid6XhQLpSpEnz88Y8PG/3QA4xx/8u91CPOLjNXcP4/PfqGFxzUPL5fiDvwgGwnfcUAIhwAtuKideXxRCRRyvbCgJr2SslCl2TYnBHaXAatXLtJqxEwL/2+zHUCqUlYxzxR3swG7XFQV9omOnNU16n9CyCDw5UW6RxBXRsu+14fM0/uFqdTEgFLnn510wnxkuFykcV1pIin1aDseGL2Trts0eSAzbd77znfja176Gj3/843jTm96Ej3/84zmJexVB+t8AyVEgQw9mn8qCKGQFvH+ugGhb13Z5KFDX2LCFiVGK0xoRVC5SzE1ZYIwr75K9M7axQl9a95Rf7eSohb0zNlzOcWnJg20JtSoAXNSIsekxC5USwemL4rebry2AEuCx4w48j+OmwyU02xsAgFe8YBiHdhdwaHcBa1WGs4sOHEdcY8EmaDQ5qg1PBcXyGFcS/l2TBdQaHG95zQhsi6JUIFhc9TBUISjaBFPjNvZMifwdOVDAg081sVZjWK96mBqzcMcNJXiM4KZDxVBrtmemgMlRin/++gncfl0Dz7uhjIUVF19/rIFigWB63MLspIuCJZ6F6/cXUSkLdbNU++6aEEG3jh4sYdeEjbbD4XgcJ887GB2mqDcYLBu4bl8J1+wVM86eJyrQ/bsKmBk3k2YMHJwLpcvkqNWxszMzTvHsWQeOC4wOUVBKsLLhYXqc4sj+IkAcNJoMY8MUh/famJ2wcOu1ojNz8oKDi8suZidt7JmxMTZMlS3D2DDFqKYU9RjHWo2h2eao1RmKBYLZSQsbDQ52yYFlicH4rkkLB3YXQSnH+XlXNQAyeFmjxUCI8BbcM2PjDS8fRavNcXhPARMjFAWbwPUJ/tEhcc/rTR4ixiXBa1sE+2ZtzK94aLQYDs4V4HkcFhWTAdUGg22JIHp7ZmwUbaESXt3w4HpcTVwAgkw9tLuA3VO28Pfx3+VqQ5DWUnk0XKbKT7kb1Joci6tuqGMr3/OCTbB3ZsDh1ztA1iO6rcVWgCPooB6YE3XCes1TXpNeZOFBJ+/j7UanTrYkQoZKYkdHI3eydrAffFpMWpWLQLMNOFoZbRU5Oqi7sFPImLRJzW4J0O0YKBnz2G1GunwO9dOIep2Hjk4iFaIWCvp+cmle0Rbtgz3oapDo+SNqMoOxZE9bvWz1ifROPshy0tgZsA9hr0R71v1NkPWueWKc9xRoTqWUcGjSO5lbKuTIIZA8EbUJ70h05o2Iek7PguMI2zddIOMxnihAarVSlhQmZYMDpy85ahJ7fpmpfutwmeCmw0V8/hs1LK16uO9bNTz+nJjgftnzK3jVC4ZxYE6Mt6qN5HNHcys4gHBANBBgcozi+gOCoJ6ZsPRNKq/9Qk76d8sFWxbA3LBve47Ng7rnuqBvhzRVuuXhzdcUsbzuYa3KMTpEUu0jRyo0NHnBGbC64SmPZwD433+7oT7blj+JA6Dij3E8DhWLRox/shfKwLp/H/3oR3H06FH82q/9Gr73e78Xr3vd6/DWt74VL3zhC7F7924Ui90tA8hx+YAxMcgmhMCKSBBMSxgHGeCIaYTKvhk7RuA1WixE4I4OUeybtUMEbqlIsG9XAY7LMT0v9r3rxrKxkT+34Kgl3bsmbVx3oIiVDQ+u28JwheJ51wuy7+FnAruE8RELs5MWXCZ++0cvHsHclI2f/b0FtBxgekK8hkNlgjtuKId8Ydse0Gh5aLeF/H6ZMFQbnpDqw68MIQZ0+3YVML/iYW7Kxo2Hi/jqww24jGP/bAGlIsGeGVuRowBw7wtHsLLh4XNfq2Kl6uHI3iKuPyi2S5IaAOamLMz63rpH9ovt1+4rouVwnL7kYu8MxT13DWHPjI0Liy6GyhT1piCGz/tlPzVhY3yEYt+uAvZpAe2GKy0c2O2h7XCcOO/g4O5gCbgcoIwNW7jrpkrsXvSCfbsKuO368G9fe0T4Pk2P21irMtSbNibGbOzfZav7CQDFIkXTEZX63LSNqTELzbYg0A/tDl9XEh59tokLCw5GhywQMIyPWLjj+hJKRYoT421cWBRe0tJHeN0PaMA5R7FAcMu1Qo1asAnGR6lYhuoLDEeGxDKOlY2whYJ8R4oF8YxUGwyNlnjup8ctlIsUM5NWSMk5N2WL53DeweqGmDDY0GwVmm1gdtKOLdc/v+ji5Pk2SkWCVptjuEIzlUsUy2seFlfd0Pst6wv5rG8lZD2y1USafr59szY4d7Fe89S70asNTScV6MCRMZsxC4UulGuf+PIG7vt2HY+fEATuC26p4CsPNuA45oFINyrDbpFUviQ8vknIQDiNnYSOZRNRMW8lQsvuOccf/d813P9YA3ceLYXq8STF1MDzI89HAXgaMYuMXXQDwSgVuJL8TFtx0gs65avTdjmh1Mn/Fghsm1zDAo3wveycFmBWzqc9h2q3ATyqikSI3I9Coft6TKKjXYnaseukc+S4OpBhkmZQTZVev+t1PNf6sG1XxJvQbedcNyBwKQkHJe4UyyIpH0+dDFYdbdQ9VS+PDFG1ou3isof5FbHh1iNF3H2LGGPt9cfRksDt3OQLBVc0pwSiDpudjNNMg+wfBLFJwr+Pj1CsbniJ9n+WJSzwlAI3r0c3FXpwXvXb9mQlBr3vMDpEUbSBc2M08dkBxHh6j0FIdPqSExor67AtolYllySB63HM+2Ili3f3HA6EwLWscK+Fc45PfepT+NSnPtVTeoQQuKZeXY4dCZ+/NT544gUIR6kf6OxbqLMe3x71YSM0UJZEgzYFg26S2MDoXqfy3HL2RD+X7te6uOaqAdfoMEWxQDBc8ZcPesDTpwThMDtpxa6B+b6ohEbyCm1pJpfXFeSLMSnTJyripskflvuVCUHgYxS7ZsNvUvlLSTzQmGxI9TIkMAd6U9sjaejnzTIY7AeUEt+n2fdxQjhaeDif8tkR3yXhRDJO/8pr4f6MNSFBOam/ER89UytHKYFFiL8MNVDglkuCLdB9Ypl6rrU0EailKEVMua72lV4+THQEdaxVvVjnLKYa6vEd15egSESfr61EmpfmZiJqPaPKhcW3m77vNHR6SyRhX/EnscRyo84d7Gab4Q/+YgWO3204tKeAQ3MFfAWNkPotE6GT8L0XJJ0ta9o7ZVBhFLB2mbeswb36hVQ4ffQLYonqyoaHf/baCVOOukpXEbId8h8l7/Xd/e4QOOGJ3ohp2ZI+hkXbV7EO2EIhqD+iFUvyMY8/18Jf3reBN75iDJMjIj/BJD03vnMEgVrVcc39jgynHvBBZmTzwBUnjPY35f3pJ4hZt0fukCojR44dg82cUIymLcdk8kXU3/21KkOtIezYJBwvmPh677vm8I1H6/jUV6vYqPGeCVwZUwUA1moMzE9/dIjimr0FtSqEceDma4r4rX81h797sIb5FRdn5h0QCCu/+RUXrTYPiYoAhOJp6O1WaII6pcgDDqD/ilr2GaNKyT3TNgoWwdiIecAgJz872S/mGCx4As+wnYg+j/2MNSkhMSslCcvyg4YDsP3n9eISw8WlJmYmLLz41qGuzjUQAlePYq6TXzsl8EaOzYVoryQZFdmodeRjvw3g3EkG5upUkZ/keEfn5+Q+nmoIks+nX4ZsODwDwSBN4QHgwqKLlt+eTo6IZfelgnjJPQY8+qwgcHdN2LEBgCSwZIWgBka+Atfz+wqUkFBDKm0tdENtExEr+xnUJyU5j+9nJH5D6ZvzGo62Gk9D/13UG2FFYXBvzccOCpQADKJMPe2c0eJiXERsJQjuveo8ZMyjHLTK81ASv7ehAT+F8UWhRCwB0oPPjQ5RlH3lot7BYpEJBtdjuLjkYmzYUtcaJaDlvgE5z1XAO4nVKsPsZDhfUaKz13fcTOjL92zrh6iqge9+RVtfiKrJgroq/Ne0f3rCfpp95S47suZLXk9ZU3arJW4pmX302RYcVwR3/Jc/MImbrynis18Tfs1OwlxwnPyOvMv9NFAZCKnEOlHlp4/zbxJMeeaGz9tBIun5kDYaQFz9GM3bZpVzlMjtdJoYEWD4XXrcFwqiH2HbAy7pDsmZ7v+ffXYNzTbwh3+5ine9ZQKAPylMgj6CCcpCoQ9yM5Q37XM3z2HWEkzLZRDELPy7UhlvAknQaRVFPv7KcbUj6Q3ISjT2ci59npIQoK2FbfiTT67hTz+5Flq55npc9XFKRYJyifrkDker3YuFAg8Fvd2oMVX/DFeE7drspKXUt7dfV4LrMZxbECvMzi+IDtNalWF5zUPbAUrF5NgTIVFR6PfODO4gaqgk+0ZKifLcNUGOxXILha2BzqvwbRzLGRF5HqOip25AaTIfYMlA3xQx+6uz864/xs3+VgzMQkHekLzTcPWB+c+cIALD20yD0UEGiemkzoqSsURnySIkRicfXwAhD0z50TRboxO4l5YZLi0LBndyTAy8QITX6HqN4aFjwlrBrMAVf2Vjo4gt/Ro49wdMfrlCEG7cJxwty1eYGhhczrkoExIoqaP7mZZpS4WvUAWG7yelYhIndCkmcl+7HqptZ0x4KQWE8OZW8tRf4urxQBUbXcoEaINRbVuWZ0aHItP9BAgNSLkg6k3QFSKAiowZyrNP/JYKFP7TgJFhqnx16iEFbvgd+aNPrOHhZ1r4se8bx8HdBRFpM/reavdFXmdUgaurzKPHBeeO5z0LrMgzBehEdG9p9gPT8p+thFSvB+XiTyAYntGdCJWtDu+JvMcVTfHxhW9U4TKh9BuumG/+Q8cEYfcdN5Vxz51iFlvaMDgR8iQaVGozEe2gEn20k+V4/++231fD+UnKNmMSmzxp0GwxfOOxBp4+1cbXH2uo3x1DkKxe8pHVty16KvUI8PDnrMfrkASuDA6jBx8dSDOZ0A6k0QhUW67S0oJ/pJ6GEI3ANajfufnzINGdNYP5GZKQ/cKoB65UeSVNImVBp7oqVtI7ZEycI8d2I0s73+/rErTRwbn0s0YnEDmCehIQ7ZMrbc5sIaSRqyYbhvirz51r49c+sISab3Gwd9bGz/3wtAoe3na5WqkBiMC7sn4aHRK53TMTELhH9hdRa3J4fpwVuSy8WPDQbDOMjVjGpeKUEJxbcLQCDMR7aastAY30HUDd7vUo9JHjCNmm7hQu8UqFXrxsk/uC3UIXxgHoi3uQq2rf8ppRDFco/ugTa2pbWRK4AKzIAze/7IXOnQUDIXC/9KUvDSKZHJcpmL+cnhjG1qbnf5AvLdeUjCbEZuUkf2uYgQ2I2OQcmgglE4m3VgvIrZkJCo8BI2XhPyTVrsNlgvUasLgmEpqdtGKzVEoV5i/zDyoaQTQySAVu9LiAYLVoQLjGrse/fkoCVW1wPSJKp0mBq+wGiL6s2ycKEShwJORsdBSKs9S2MQ5QPb1NruV1mw/GZAUMw6gSmgI3yCuQfamFnHVTam9NOR7lcWW6pvKnVDwT1x8sotnmmB63cPPhIh72JwPqIQ/ccB4ffkaQXV/+hzp++B+Nq/sfTV//yzhQjfj6rNfiGYvdqh47aFFVvMyDvm07sNUTlCzCeqm6Sk4gRF7qrLlTXNRO6UFBlK28nGKBoGAL4uOzX69nTuOuo4HXaclXubhdKvxUHdvVUWEkKn8yJBy6Jzvo/gCds7NVNgkmfP3RBj791Vrsd8eLFHn0/g74lY5ZKEQaEzk9l6Q+SRNDyCW1BRu+DUGwLSnIWLeoNhgWznv47jsYykUryHQC9s3aeOaMUGid92MIWJ0PCy9hjU5E9ZDvgBTgXT2HnerALI+yZ5jEBwKCvTcP3A47dEhyu+d8cuTYKUi0q9mEtKWITq5G7vTuu5qFgvTMlvNALYOFwhe/VceJ84Eidn7Fwzt/+xJ+6527cGCuEBNWrNeYIiknRkWlOzdlQwbPuP5AEeu+4Gi4QlWclKGyi7bDMTVmhWKnBPnmOLfgBOMWGu+vJqHbyd80yFN2S7YVIqtXdlJf+IpEiKPYvmyYENVV9MM9WL4o65q9RRw9VMT/+NSaCqQshSkWDawtJRZW/SDmXYjuB0LgvvSlLx1EMjkuU+iWP8kWCtpPijDt/y2W4qokAi3qd2tcpu5nKIvXomlJd2C9EBwoFbj3vqiCI/tKqDYY9szYqNWZWloofIWCxnZu0g4RaXrxWFZALsptBFARHSkloTLwfGaXEHGs45orTc4C9av0ZVXLAf0onUZfXM1jl2p5AgJCWF2Ln+k0BS4hwT1hDIDVvbq1V+gEtH4NBv5WZVLee6YRsVkgZ31NAz5VFnqkciSVmyB+RWC6Ag7vKaBcpKiURIINLXqtXo7G32n8/ZFlLq+LMY71erhEsihwe33Dda9giYAs3/qeVnRiZauW/kTJoKgHbnRlbtaO0VZ3oLL06/W61abAG18+ijOXXHiMo+1wjI9QlIvJMyVTYxZe9vzAQ0ouU4z6TxrnZgwWP/2gE9EvJ84yp9dfdvqG6XLSxMRpl7ZZr86aP6F0aI+Nmw6XMDNh4c8+uw7Ow0vYg4my7jKSmROMFAgx/MwRTyep7tR/lwRusUBDeQLEO9MvCOf41pNigu8v79vAO14/ETtPFHo/5bnzrp+XzqU7aAsFEwb5qKXlMrpSSmJQ15jW5sTIKUOfO0eOqxGJE6mbOEnq87dqTBPtf7zm7qH/j733jrfkqK/ET3W46eU8OY+kmVFAWYgskDAYMMFe23hZDF6ME/YavIQFbJKxje01YHv9Mw6sDRgTFtkgQBJJEhISynk0o5Emz7wcb+5Qvz+qq7q6u/revum9J+mez0d6d+7trq7urnjq1PlC1wi+IyyefAuFlOEJcrx2pKywUDjrBZj++Wv68OKLc/iLL87hxJSNj//TLD73vzZifik4Ll8pOkLdx5W0w/0+e0QpxYwXVE3e4VSv/Qg/Ng3R8WrsuS0ukperLhZXXFAKEWytUb/STWNGIFh3F52FXCTEmGGdkebUEyrNeXWoGQ9cLgDjgpRMhsDy7Ae5xaGmk8hYoWoBK3kXfTG7DFVom4VCF89dCAsFQmIHk6pgY+0YX8YZmMvQtGh0ZFUHI7Zo1yJwpUzzlRIV0cjJrb6s7q3IUhAqV26gJ+efkDKBgT4tMEgPExoWooNzmXSWs+04LCgXIUT4KSm9bANpUkHiAmwyYtk0oIKUn4OvwOUkH7y8sHJAaihLOeT3wAct7J7IqgWtkssoVy0D0W3WjLBmvwsFboN5NDwGlysn5Um3vJItvtNIlKUDI4K5GlgeNPkErn+OI1YTCU5O+QOWFT7wIUECWvYxlwnDvKcq780S5EsUi3lFgQotkDSrWA0HxgP8Z7YG/G3QX5Gq24+OIEQa8ffEH6u/A4F4qvhn7qxdJtl0neD83Wm8/PIelCoUywUH525PY2QgucyQK3DjPXB9Gi3w2NpIgKgWNClQs/Pz607r12876pT7JHnuVNXhSuurL8jhHa8fRKXq4ovfZYHMVEomGvrbboQtF0SdpdEAZqp8cagIXNNg6aoWxtuFk5O+yktFJnPICjO+rVf4u8U8XEIkctOJBjUJbEdu8AXJu4jqHcfyUo9tqJ8WDxKkt5HArZet9dg8dNHFekStutRysxlu312ASgO3sAL3JZf04Krzszh4rIqjZ6xA22AabN7B1aSqfuvsLBvQnL87jfN3p/GJ3xzDf/vIWTx9xoLrUiysBMflKwUXZ2ZYW77ZI3CvvjCHOx8tYWJIx4kpC4USu06vgjyqT8ay+SWRVC9Jn2mzY5wjJy0sF4JEdaPWapmUhqF+HQvLXRPc1YBcjpIS/asFmQN47GgFjjcXacZCgXvg8mDy2ZSGFa+sZrz5uU6iYwUAmFqwMTGafI7TMjXy5S9/GcPDwxgeHsaLXvSiVpPr4hkIQWKplIL8g0rg1IYRaBKVpvwbJ0ACxGGIZFZZQXDIqmHON6hIvGVvu3lPjgTSFQQuKHrS/gljg0aEhJatDPhvMoHjq2a9e5BOd1z/ey7VVxGxgoQlsgcu+41vcVQrcCWPXbHN3j9QkxQ4sso2DHnyFPah4cHhOq241CUC2nWp8HKO3HeIgAca39YfJnp1aQktTHiLz8rn5ttUyCR+xtvpVCpHlba6Bhw/60/KZxYcpXpbpQp2pZXuTWMmAIhtV4H74+UzdO1GIRYFaLS+dZrQV0Ix+FgNhIkTfu+8LvPno9eoq0nSXS3Uup4ckC28vagZcA/ciIVCnQXEVh9JyyS6lIH1oqZTXr7BvHX6HjhRz4l7OVBMpYkt7HGoR/iFxSVa4H36hS9usbsWs8yD2pgGCSzexRzeOKQ88V0+9SATELyPMHTiL1bEgJO8lkVrHpf0vjrdltWq19zDMhrEjGWqGQuF4LWTH9vO8XUXXTyjsQZ1INz+V0MLyIO9rJHgajy5bUiZrNEU/qwKAnfSI3C5mnZ8iP11Xdb+LoR2xp2ctlGssPxsGDW9PBC8/sV9uGxfFmNDBvp7GJk51N/A4Foxr0s6rmxVxMVVzQO9OkYHDYwNGdg8bjacjrJv7qIjkIVBIojZWmZIAp+3Og4V5O3ooIFtGxrXuIp4PpTdZybt32XO+6zrUQsFgM3JlUFvYtCyAvf48eNYXFwEIQTXXnttq8l18QyES7naM7qVXBX8hxOk7ehbkxBocpANX4Hrfxe2dKi1HT4wYfI+F0ou/vbrC1hcdgTxVCizH/tzupg8EgQJwJy02jk+rEdIaKGc1HyS0SfUaJDA1UIKXM8OgAcxC+ddBlfRcu8m/ky5xF+p3KW+WpVIKk3AuyZRTVBrN0yCnJJWsnl6nYT8TJkoLzo5BrytTbyhFwQa+y3pSp0ZarQNacYnUpBJbQ3Q3GjamkYiakxCiFDg3vloGa9590kAwHCfDsuhWMo7AZLMdoBPfXEelsVsPQxdw8iAhrOzNnSd4MUX5/CCi7L4q3+bx5te1icUu5vHdBw+AXznJ3n89LESPv4bY9i7NRW8B6E2S/RYovcXWIxgigQqFN7NpdkK5Euu5pwgYqEQIs38wG4EFmoTIesdAQWuoj41+tpTksKvEQgerQ0PM9IGAuDxkJIQgeuNjEn6DtaijvJJHSduCSEwdNbOyZPkMCneKOGe9NYcl+InD5dQrrjQdYJXXNGjVDjFpauybOD3wYlBOedum4VEVLp4LbWqUoGr17ZQIMQPdKZUp7ZQ3gNjzRqZSEwMJzgmzgufv6dGfbiTXHetF3W66GK9I872Rm4XWu2rwuMFSqkv9kC0fevvYY1EyuMbAwSuQUDhL2BXqhTlqiuso0plV+x884ONEeQyBMUyxVLexcIy+32kX8PcMrMaAFgAbblvHBtiFzlve5oFIwNgKnx4Yq1bQv/WNJpY0NJoMNcw+LPevsFEb655RYd8b+uFTHw2g7MuKk5hTeG9fD8gHsE526K+z0nAg7hzBW5GEhLwIOOaBm9ndBBcVJUULRO4fX194vOWLVtaTa6LZyBkYiXeQkGaQHl/21GJZQIjDqrOWtXB0JhBuAyZAOOE7xPHqzg7y2dP/n1mUwQbRgwc97YiEsLyqXlbTbZtMEWDtmdLqoYCN5pv2UKBE4vyPbncA1dqKFSWw9zHlqdJpeN0vfZ5fJCi8sAlWjRgnOqZq9TRzapbm4VsE+C6PtEevm1KmSQpoMBt0OxcDjRDCAmtAHt/JeqGiP+F0iG+AlcepG7faAo1ddFbRCiW432eVgKByFys8EBlFsWNdxZw453Mp+sf/nNJHHXlgSxue6AEx2UBFG67vygI3LC6sdk5pmq7jVCbroGHgio/q4EwcRK2K4kQB0nzVoOQ6QSSbFcOKHAlD+RmFa2pFCeIgt+rVK3BBcbWnknS7MZdhYTawfUAVV5URHfchBmQnnGHihwnyfh7BxiBZjsUVUnJxJ9vo0U/aTnkxx07awufQwDoy2l47Yv6IJw7Yq4fuYx0HF8MNnR4uy/83+w2ELiqd1kPMgGx4vmkc3VtrTSCFgrBA+V/JX3uqsfZzqJWKxtctWOEFmhN7zlUG1xEily7xm9xY+511Hx00cWaIEmf0+oYKHx6eAGpagUnupzA5TuEhMCHsDmXS30bv9sfKuE17z6FP/y1Ubz44hxuf6gk0pAXAwd6dRTLNiNwPQXu+IiOuWX/2hPDRqwYRsQ9CcR6qHvrLN/e302jZuJxpRg7JLtEBO0KltpOIr+LBPAID3cNxTgq+GPZxubzKsgxhZgHrl+p0ikexExtoTC9aAe4snpomcDdtGmT+FwoRCMAd/HsB/dDDW/jB6SKQOXv2ldreWGvRbrK9UQoWaXf/W3J3r9rqTakysUnT2dmGENw8blpvPuXhwEAK0UXk3O2F6iMgUjBolwK7N5s4gO/Ooy9W9kKaPix+NYMBGHvXmZ9QPwJswaxRYEHInMpYBB/NVcVNI778YkGx/WPM2qcx4KYMd8j34LAP04jRChzfWIyCvme5WBi/Bnx++8kZL9V346ARAYwvIwT+O8mCekvI6C4JeoBROA7DSCKiTknbwkJdobbN5h4/68OY7BHx8ZRA5/+9wXc90QZAHBgVwrv/28jKFYoPvuVeTz2NItCe+42Ey+6OId/lEha7nMbRjZNcPG5GXz9Tzfj+ltW8K/fWRYLFEC07rRiocDLMg9qt5advly31sRCQeSD/eXlVLbHkI9fb0iSL67A5TsC+HnNEn+ZGAsFZTIdenDha6kWhmqer9jBspYItlfNVcRO1V9eftLSqNY0CEoVWtdCoZ2BCfmVCuVwMBk3+PzCJyZY/KpICtxwH9WGmLDBhSo3OtFWPaGKQl0qLBRi8kQgqVMt9TF+Rur8rjg+mQdusslakmIRtxNHkNTNWCjISSlOXy9tQhddrHeo+uGOISQ8CduncB/MtBeIUuyq8OZbhFIRDwRg496P/MMs3vzKfvzbTczTfeNIkLoZ7NVwdhb40X0FfPsOxsNsGDFQKlMcO8vmp5ftz8TOU5rx/JTnK/09OgZ6tZr9RPBk9qfZxfkk1olJsBbxNJ7LCM9f1kKMo0K4PWglW0EPXBqw8uIWCoYeXewFmAK3kZ1ULbsJvuAFL4DuMUQPPvhgq8l18QyEr5QkkYKvUumEVztaunYCUicQFEpBknH4ZFx8Yq4b/Xxmls1Adm1KYfO4ic3jJjaOGkiZQVWsRkjEziCX0TDQ6xtbB64lTTB4lnyVKhXb/Km0YsuTcFzqEY5EBMpSKp6pRASGSCGjhvWCK9L3yUuZzGSNGCfLibiPWggreTmR26g5faPg79txQ89bQeAS72H5efTSSNjghxttuQNTfQorq8URhJ1L4OcF3rvIpTWMjzBPqL1bfV+onZtY2Rzp1zE+5Gdk+8YULtidFv8e7tewe4t6+8j4sA4QtuJ/vneOTOBG8tpCFQ939nHbVFcL7Wy3kiKsNOD13A+i59WRGnYnynQRSHZdgAea1LX2TLBS9QIIxXzdavEKJKvqZ+pE+lhP76Rp1OhfOwXuNSgPmIUCsg0euI0uJISV32ESOaKcjElH/t4KWyiEHqrK574x+FcLpBTTYFBKYSkIWN431YJRw+KkmbKibDNWqTL5BEjwe78N6ty14xYe1pOCv4su1gKrUQfCc1w+J7NsivklJ3YHEO+nqmJXhb9Ym01F6zQnbwHgv76qP/Abn0Nef0tefLd51MSrr+6FYQD7d6ZwzrZU7LhK7GSV5idJHx0hQDrN9g0mVcaqeIFGELYWawueFQOv9Y0IP7ROnnmEwG1hEqBrxBNWMUsRKs3JuAJXI0R4YQP+OLVYpiJ+UhK0PBUeHx/HddddB0opvvGNb2B2drbVJLt4hoF6ZB4h8SudcjvdTg/cJCtxsqetULLK+SGc/KB105LnR0yNR0VU0C3jhvSbfx2eHCE+ASPfOx/8h4OniU6V+HkM2CR4akBKqb/tl/A0fY/aWlYIFP5742mFO3NH0Z641E/fJ0D93337hNottPyzHExMzm/HLRS89PkW1DChzSE/Kx7EzVeAJ8tk2GJKFTAsbkukDN5JsIz5AeVEXr1nuG2DT+Bu8Uz+iQaMDflldfOYga0T/nG9OS1wnozxQUOU5+3eMadn7FiSrJHtIGHooYUHv66vUa+vqLudRrh9E37TNPh3nSxk10WtV8fbQa5yB/zJUL1zVUinOUFUPw+qd9o0UV/jtGYnLqu5aKC+PvtbzzOuZi6Tze2ahuVtVeWDZCB5EKlkik32t2459I6zQ9esWlRSHtW/juqYSojADXvqOm30weW7c4D4djzuuarUJQEQSZ1qR3c10JjPSUCTnpOwPCaps3FWXj5JnSRD6usCMfcTk5/1Minuoov1go7WifCilvffd35SwCc+PxeZP/HD+Q6hSjW0KAcgm4mnZj789hG84KJc4LuB3mCD+7oX9WDbBhObxgz8+bvG8ZtvGgKB3z6Ft287Me1XLchjgWyaxahIqoxt9X20a24YmH+1llQXTWCtxDhhRBW4zZcGXfcUuG5wVy//jf0lGBmQVPQUGOhlH2cWkq/2tuXxfeYzn0FfXx+Wl5fxlre8BdVqtR3JdvEMgUN9civOQkGpwG2DBy5XnNQi0ORGQhwmHc4/JukUShUXN99VwLdvz2Mp78JxKc7ORQlckZYWFK+IjlN6ILZQcQYvLKch8hrKW5jA4b87HsHIlZqhSwbO5ypa7tnCGxxfgRujkOEEbuiWCCHifchlQtVYKy0UuBJ4lbbMCwsNsYWbSOXWv3dhEwL/WYk0EubRCDG4gXuLSUPllUO8dyYTAgT+oIpnbftGmcA1RF5lBe7mcQM9ISJgewyBOzaki2uMDurIZQhcF/jTf53DX35pDg89WY6c0yz55JMH3l9R15tKrmWo2rJOIyxo4G2EsBkRiy3xdVWdbofZtLjr1YA8iWjHdva06bd7tkrlF3NeeCGtUQQWKyOJ1/iNf69oe9Yzwm0ksDYkkrBQkJouQeBKC0y1LH3aAf4Mwr6n1TpWHnHPTP5e9sAFgFde1YPtUqRkVTlvBPVIw3AWVVHSAZ+4rAW+ZVhF4DYFeQEzQTkU47I2FARH7IQIft9KELN6kMeVtX7voosugggsRLapI+D1jbc9k3PqVRt+vXRYgcubcRrcRQIA3/izzRjs1TAyoOP5F2YjacpqPgDYvcVX2/ZmNWgaUKm6KJZd5Isuhvo0DPfr2LWZ7bRT7SBIYjGzeczEyICOXFrzFLjePcafEvi96Xa/TWKOTpSDLuLRTquCdiLpWCwJdJ3NX/gu6MCuba/c6lpwjGQ5vop+qbCKClwA2LNnD7761a9iYGAAN998My6//HLcdNNNz5gJSBetgfKAWSSquFQFZFGpcpu+doIVvyBJSCLHC6JIeM7Gp3X/E2Xc9mAJdz5axu0PFjGz4KBSZWlsGguuqADBhkHTiOgg5XkWjSGmfEWwTygyQtR/ptQjz32LBS99l2/5lwOK0KifLfUIS28rvrxabNTYlu142wNkha/o8P0sKlXZMuTyooUUl6vlk8PzYEsKQI5AEybKuP98/TQav174WmJtIZSWykKC++wEFh+kf/N3sU1S1m72yqemEYwO+mV16zgLbjDczy50ybmZgPWCjI2jhpRPgj2e1cKP7i3i23cU8LdfW4ic04oPLqDwRF6jTl9cdjUJ3NDF5TaCbdEJDrwb3UG9ao8ywcBe1L+whUKTXHNamgRVYogmcQnFzx17zQ0kTAJt0dqNp5RXbrAf9wmzzpQ6vlWVewsCPoGm8sClMZ9j0WA55LsSMp4bTbXq2x4lupy0IMrBg7HxCX5vTsdbpO20rRK4gWfiqsdvMkqVGAJXBNhU/05QR50qj48avKW2V5ME5dwJLaRx1LVxqXXZuHFITP7889bJrLiLLtYYqjYUCFaZVmuLso2Mqa+Dff7itAhi5rUNpmShEM7TYJ+Oz//hRvzjBzcgk4pOCGQFrqEDQ316QGwyt+Tg6BkLh09U8fCRMh58soK9W1PY4HnpxrVfUORFxvaNptjZx+eidU+Sfm+2qW5lZ5+M9UIgPlcQroedjm+TFJF8tZAtQ2fxf1zXF9KJ63gFX1XPeB1ezicncFsOYgYAH/vYxwAAr33ta/GFL3wBjz76KF796ldjbGwMV1xxBXbv3o2+vj4YRvLL/eEf/mE7stbFKkD2am1EDdCONjgJ6RogUQWxGF15E9vhawyAz8z68vbJeRvHJ9m/Rwa0wMRRqV4hPikqH8DnXHEBoDhZJ6fDfmMkKqia/OXq2lqRRYWq1EtTJnhFEDPFzbguP5cFWJO3bhItSCgTxTMXx0qfw0reJO+2HeDpcwsFQ/cHNPKtu55NAZ+Y+v7LpKGOyH9/wQ6Ml7vw7WqEwAm9OEbq+8HtuD8v0YLH9WQ1/M5/GUKx5CKX5f477B5/7XUDcFyKgV4dmga8/XWDeOpUFS++OIfzd6fxgbeOYH7ZASHA0TMWBvs07NhkBhSKf/Bfh/HjB0vIF118+eZlzC+7gZX48DNsBPHlYW06/bAieDUQXqCSb92VVnfFIkeD5NB6gtPm9yurWCqWG1GZh5/BkVNVfOV7K7BsioFeDe9+83BT1026sFOPZ1mP76gumiTb2wFOkqWktSdOoFWr/qBYLHKhDY2UAjwp7g3bm9NRrjrCo1dM0GOIt1rvveKlye+LIKgYt1v0WnUlAjhuDBPIT5wCVyIj4uCrU2vnKemrCWQtQTlsZ/3y2+Hg9yoFeLtQd0Hnmdh+dNFFG5GkCrS63hE+Pa5aPv/CLK7Y58ea4FY/YleFUbvNDNskBH7r8xueTaMGWwSXrAqLJdZAGVwd6FCUqxQ9WXZNpYd3wvZDBJyl/q7JpBYKzbTBgWDZrc4NFQKaLjqHdavADeerJQ9ceDaLrHxvGDHw8BHmSsD6bCLifFy+P4N7Hi9j64SBgR4dgIWVYnK/pbYQuB/5yEckj05emSmmp6fx7W9/u6k0uwTuMwfM50MdcEm1HbSdClxflRffEsjXqbWNPwlhOC35k8wsODh+ls2oxgaNwESK3y9F0N5AxbPEEVNOTGeoEcDhiVA/WJl8L0whSj0Frp8xx40a1bPt+AS27YprEkJ8T1qVOo1KHruEwJLuiEDyGpbyrmqs5d/59ZyQ4rLjFgpe+rbsRczn+14ewtuJZG+bRvOnaTHBz3jZCC2EaFrUh5g/X5nkDChypePP2ZrCcsGBzgli76Cdm3xPXEKYqfql52XEFvZrr+wBANz1aAlbxg3oOhv4yXnbMm7il68zUakyAtdx2aTezMryocSPRnmP/N6bfd7tQjvbrcQIKUjkNoK3u4Bfp9frnN1XX8Yf40gKXHEepYnOVUHTCAydLcyUK9H+J5A/Chw6bglF4dySixOTFp5/QWPXDCO6I8W/Xvw56s98R8VaQLWdstFJWKcVxJzAlT1wuWZApcBt9FE2Wg55cK6+HMHsItvCSghLiPW76oRo6C+H41LRFhqSgoOAiKjHrSpw44jGuFdXrqrVIroIsqY+jxDfQsGlgB0aZCRRs8Wh3cUsSTlXvRfAD07SnALXT6uRe1onc+Iuuli36GQ/ShEdr+/aZGD3JgN9OX/yxXcI8QUsLpjhdf1ll2Vwy31l/P4v119Inhj2qZxtG7lVmt8G8/Z187iBcoWiVKGBvoLb+MXZtSWBpyXyzql9kvi1ibZaPqXV16iaf3XROYQf8Xr1wG1lF4uhe6I2T2Dzuhf1YTpufwwAAOQuSURBVGrexsSwIa7D73v/jhRKZRcTw7pYlG8kiFlbCFwgfnDezKC9uwXomQXZbyxuhUUuBnLH0ir4alxS30KfCJG+8/7Ws2OglGJmwa9c+RLF40crAJg3aGDrfeCa/l9d87xRvO2JlNJA8J7g9bz8aEQ5cXbBJj8UMmHKZoiOwwPLscmdRogX9Ezof8Q9EcJUny6VfWBlsjL6olzKzyWRRpgQmVCW8l6HwJXVpIH773B7IIKweQtfukZgwyfg5bxwlXlQ/djY9dg2iihZJcpJ6Hhdj0axFopM8cziyU0nVEfCdUWv83x1vh3Eib9GOqUhkyYoVygKJRe9EoHbbDUPtxO0yefdLvC6tZosqWqrmFyXfV/g5trU9dTV2qL+tS9fpk5gO1RJTIW/CftUVq3mrlmTnJV+r3eL65GMT5rntRjD8a34ST1w27FLQAWeLift+nM6gPhAj/XA81uV1K4mv0ci9dXUn4g3i4rlJyD3+3FlthxnoaDXr8Om7AEXejbNPKkA4an4LoykhHySkhwXANdswUKhHuq1I+ux/eiii9VEEh/XenAcGiFkATb2ZopWdp2q5aJqUVgWDdT3P/y1EVBKcfSMFdhexy0UOER76J26Y0MKn3v/AHZvTaMenrc3jd/7pSEs5V1cdl4ai3k3MDeQSWLTIChV/PEWv0cgGGQ5afshi7SSjj/DsToaQbPWdep8tHZ+F40hyGO0J9ZFO9BOYtnQuQKXWdxl0hpefnkPpudtwRkYus8C9PZoIAQY6Ge5WMm7SOpu2xYC98UvfvG6eRFdrD74Nn5ObqkQaNilBr9VJPLArfMdCSlN47bwLhdcrHirI2mTbWe8+/ESAGB8yAicx8kmRtR6jRUYGee4bHWGV2BBsIUuK08Kwg0fQAFXzjMiacgByjQNcJ2on61QdWnBd6RrclCx6LNwXSrODRNqJJQfoc6PJhMicL334OXRD1CnOLGNCCtwmRKY+nnR/YEGV5mzYG/su0abvqAq2f+H4Lk9Yp9Dzo84j0eTlcquXP/kdxlW+4YJ8XqLH3Gq+jAGezVMVhwUy3yRIPgMG0U4qJ3T5PNuF9ZCgasiGeS6zN8zr4NJ85aURGwXkjT1tjdLkj1wKW1+oY/A25ZYpQEFpmg/Q7BC7JfVakCohAV1peji//t/C1j2ghfkMkREmZZTWFNCRnFxUuO3Wkl0ov46rm9pk5IUuL6FQjSTjeaj0XLIy09vj5cHK7h7IXx5eSIs/+WQy7ApWSgArP47buvBsuSgZFW7/jMqVf2xi5xdU2EVJYMQEgziEVJIqwLj1UMzxer2h0qYnLXxupf0YcuYgS/dtBwgXwgBLtidZgFAa+RDKHBDwdtMszUCNzwWUB8T/oL96cYg6aILhkjbkLCxmF9ycOhENbYu7d6SAqUUx87aSJmO6MPl+j46qGN2kbGlREomoyBw5R1HIIBpJpv8aBrBz724DwAwOWdjMV8NzAVsiTiydXYFWYEbDobbCMS0lzbexzfTRrWXwCXKz110BvITXi/qWwCR9qAVawddIyCeUI/P0USZ9dIV8YWoT9X29bBJcr68ygTuLbfc0o5kuniGwnWp2IofJoNUpIeKZGoWTfsmkuhHN4ZI5eB2CX05DRtHdRw+YaHCrE0wHqPApeCkn7fCqRNULU855/EI/j0ErycThKrAV9Q7P2ChIJ3PCVbuUwsnSsbycwmiHaO4jmL1mROYnCCWQTQCTSKfa3XqwQY9SKTLHsCdRJjA1bToxElW4IJ4q2sh9WNSBMh4hQI3PFNUjal8VTcR+SOILkbIeRcB/ELPs172tRCBHDfQGejRMTnnoFBy4FIz0eSzFnwFOP/b3PNuF8LtxKpAQVrLddkJqZKfyXN2WQGvakubGV/zLcwVlVIw9FU4mFKYVGoU6gUrEgn08e078vjunYXAcQO9Ol52aW71GPY6SPoOktT3TsyTZIJWnhjXDGKmaCOToP4zYH+5By7fNlsN7aKILIxFEgp+z+9B1+IX4Vq1UJCJB9f127pwmeXgHri9WU0sbgPJiAA+dnBdPxK7jG/8aAW6RvBbPz/U0D1QShOVw2LJxY1evTs+aWHfzjR+8nApctzh41X83i/VzgN/ThEPXG/hMryDJinUS031z+miiy4Qv4Akf65RYVaKbs22ZCnvIpchqFRdmIavUqh6Ywnmxx7t84GoAtfQiRjH18p7PcjzJuqRqo7H4BoGgeHljfcVqmC4cjqJryvlOakHLrsObYg89edirSs4VUGku+gc5PnuevG/BVQK3OYzpwsFropvYeImzfPJhbcDmhBgwBsn5tfCQqGL5y5kP9Q4VYncGbWz4vLOtVaaqn5I1YELP8kYwvD4JJuRjQ+zyXWhWIBhAMP9OjaOGkHSQZCQfgWV0+aepS4UgYg8yJ68qvxSz4dV9kYMEObcHkFM+KhSTcvfW8DmQiMSoUojnawreeyGGzsCiBcSICujlxaEI79PljYfWHjfd3hlNLzowAht6lkMswZXZaGQRP2tQjBwmZQP/g4RLLMqApuXo0AQMw0gSrI9WEcinsp1biD8c9zRPJBCocxGjfw+wrYdSSHUxVzN3uTzbhfWYoFepfKW63KYnE9KmMcRMp1CEgLQdqNteUt0FFETeIouydtmGLxaswrcJO9APuL+J8oAgOuu7EGh5OKOh0t46nSVEbhxJ60DNKxI72D+5febklSQnMBXWWg0OvlrNPvCQqGHtYvVqguQBOnQwB/xnP1gN8H+ntkksTbBTh7/QokwkcqvWc8DN5chWCn63xsJPLkJITA0oOoClRDBubTi4v5DzJ6qWG7RFyIGsn/vwoqL+w+xevi21w5guF+H61J85t8XMLPoYCnv1rwXEQQoRFyn2mShoOpD61oorLP2oosuVhtx447gjsb48/nYecu4iW0bfG+e2UUbh09UUbUochk/gfN2pPHEsQpsz4omFbJFkK/VGwqqahok4CPLA0s3Cn6KRtiuNSot9Kd0wPbaZm6rINtDNCOWkefPYn7ZEIHb2Nha2GIkPyVRPrroPJIunKw2ku4yTQLugeu6VOwiFul6T0C2KuFzt74c+1uuIvE4rkvgdtEyxDZ+RCdFysjKvE9rwwBTGQwqAVQVlKcVV3mPeQrc8SEd+3amsXXCxHk70jh8vAqX0lDwHZ4WESpYIGhLwPMca6EgiLewB67P9IVVqoKckO5FJpDD6kFOvLPVWp+oZb65weP0QKcb9NiVIRP5PLgKECRrxbHS57Bf8moFrQp7wIoIrpJiOUDgEgLqxiun6yGopiTS92oGt1ZgAXlBgEjfyyRSxEIh9B7qK3DDF1cfN+hFyS2WqO/LrFB9J4U/MPQI/VBZX22IurWKE2PVJJ0/F+ZzHVROrPdJe00C1xu0aBpRL/Y02BAQ+FuV7ni4iKl5doEzMxb2bEkFjqXwCVu+HVylCmw4AypQoFBycecjJRw/a+GRI4yo+sVr+zC76OCOh0t4+pQFl9LV4tfbAlWgUhU6cU8BdarUoKVMBYGvIJ7bWW94UnaYwLW9voOqn0E9QpyrjM1AADMG3ia2qsCthHyfq4LA9evGqWkLD3hk58Ne2e3JBu9IeLzVyY5pElRtKpS8HHJQs7jAahGE+gue3ziE63e5QtGX0/DmV/aLxcMb7yzg4LEqnjpVxRUHsgCAR5+q4OnTVRg6wfm703j0qYpQ3EUUuC1aKHTRRRdrh7jA1rxfEbspwdoa/j0fS5hm0BZBHtn09wYTNQwEGv+wkCMpqIh5wSZflPq7iwyDwPCIW95X+DsPg4rWJIvugd+perzabvjzydbTUglouugcAjuJ15EEl6u5kwgC60GXBHeuJLID/Pqh6z5Jwh9JLqPDNNhunaV8Mga3S+B20TIok9mBaFpUgev9dSnFUt7BoeNVFMsunj5dBSEkMOHQdYLztqcw0JvcMDNJY16PDAgTRXENy0lPgTsxbAhCVt5+our8hDLZ+4lPvlwX0HgU5hgSWh48yB25bG3At8jwCQcJETkaJ2Nj/GzF6icJ/pv75srHhfPGSWg99Lpk+wXu/QvEvQf/s64TUCnSdrNBwhqFyvZDZCt037Iq2fcobqy1ly+n3sLjD93itgmF7RBcj3jnVh3y+1INQjWNxAZeiVwrTHDHHD/QyxW4bNuZePNNzl35ddxQeei0IjsWrd1Oa5cOkP7sb7jtBJITUQFLkNVAgnw5Nm9/EZTJtvDA0x5P+83bCgB8m4Krzs/gsv2ZwLF8m3NfjmC5QJve9pwku3c8VMS0FBBzqF/Djo2m2MmxVHCxuBJUHq4lBVSrXKl+U5WrTi4uyOpUGSKIlILABQUasnmRFmWToOoECVyXBj1qk1oxcPB7NA1Ff+B91bKFQojUVC1ifPDvZnByKlg5+ntYoDYO00B9FRb4vVCmTpYgE+5JF1JUz7Mmgauo3887Jx3YCXXxuRkcPFbF8UkLlFIsrjj4H381FYklwJFOBQcTQoHb7HvxhgI1zw7dY5eI6KILhrimPWkViYtzkZYIXFld6yvuveOM+HEZ7xc4VgoufvqYb+HSrP0YP0sWxPAdAqZOxO4I3ldMzdk4dLzqBSuW5h3w7yvRdWXyOSnpi8bGNfPLDp44xhYN29LMddvKNUMTdssdhbxg0opISNMIdMPnhxyJi3W9nTSGvAgvjUeH+3VMzTuYX+kSuF2sEhzJA7fWYHIp78J2aGzj7jgUJ6fsxghc7j1Wo8dQdRDBLcnecXXUvCenGIG7YdiQSBT/96B/kH9VOQiZITp0//c4H195y7h8D4JwltKRxZuAT0br3qoqn5So/Gw14pOKMvErr0qFA1HxrTmERJ89kfMjtYr1OnWdsCkgv6dmg4Q1ivD7Zs+MffaJeF/hxeErBhu7XpwPUOCZeaCUKgnscEAyocDl50nHqhYYuH1HkvxHPI5jjpMJXDkXzVIKsj2E7Gu4VgrcWp7QnYKs5OfgdVnV9jyTdV62tGAj1z9fvdI4rrmsB9l0Cf29GjRCMDVn48gpC0v5EDkqkWu9OQ3LBadp1Vwtcpx/xwL9ARftTaO/R8N1V/WAEIJMiqA3S7BcpCiW3XVDxqjUOOE2st58U/zcgXuS1alyHjnZ2bKaWkK97PN2ipOh/Tm/wUqSj/Bz5Pfjk9TSPXqLjcJCoclFB46wV7BlU/YMva+rVSrI26svzEIjwHLRwYuel8WDhyvivJRJAhOiOHCP2Ih1g6TILVcba3CT7vhQvYtLzg0u6owM6F4e2Bh3YcVRkrfn7Uhh64SB4b7g2NVoOYgZ+6uqW3EprpMmo4su1hyqfiuMWgtyvrd18Bjer7iUBnbucMse3vabJrdFiNbWTEqDofvjOHmcnzIbXFyUIM/hAArHdcV9GAaBYbDP/LozSw4TXMhBuKkqx2oIMROlcCn7XG+RU/61kVvk5C27RvLz4hAIIt1tODsOee7WDgV1WxHgKlorDLJQTxWPRg9xAAQAJWy8MTXvYGEp2ZinYwTumTNncNNNN+Huu+/Gk08+iYWFBeTzefT29mJoaAh79+7FlVdeiVe+8pXYuHFjp7LRRYdx78ESsjlTEEgR8lNeBfRK8oYRAzMLLGInL9C2Q3H0DCNIv3d3HmVV0BmeJGGrl305HYsrDlaKLh45UsZy0VVOYJYKDqre1sBb72dGbSsFF2Wvk+3LEWRSGlxK8ehTVcwvO8rGnKexUnJx8GgFK0UXR05WA1u7eYcwt2hjasHBYJ/uqWCj5NOTJyxUbYqBXg3LBRf5oouTU/4EwJWIXdn6gD/jmXn/Zu87WIKmERw7awU847g6lt/P4ZNVaKf8PDgua6w4efzUaXaTvTkNxbKLJ09W4boUR05Z0AiQSRNUqpRNSLgHrlKBS8RnFTEpHxu+r/llF3c/Vgps7ekkIh6+JDpxkq0q+HvhlhqNtvXy4cogZqERjSp9Xo7438k5Bw8+WYbjUJyesUEIcO/jbJursOiAr9Q8fKIaUGsZOsHhE2xb6L6d0ecRewMSuIXCA4cqWCm4mFty8PLLczhyylKekssQbJ0wFb8wTM/bmF1ycOSkBUL8+5Dr2WogbRLs25kWnToFMDVv48Sk1bCqMG0S6DpJ5Oto6AS9HvmjIs54+ZMtVuT8lKsuDh6tKskD2fblgt0Z9GQ7O5pK8pwcOYhgG65JCLBjo4kdG01cvj8L0yC4+acF/Om/zMF2aCRPfCLWl2XeH61uey6UXEzNBzsk26EoVlyxaPebbxrCOduCdg5migBFGtk6vh7sMZJWu7jDKIDFFQdT8zZGBvSAEkFGsewGgjn09WjIpuPLKCf5wupUrojKlyim521Rn+R8Jn2siR+/dyBXYeWymrASqlRdpqgk0WcUIcRDvystFLxVO94PWHZrq0vhMje94GDjCJsmFCuuGB/0ZAg+/s5REELwk4eLgXMmhvVkgXiIv6Bdjnjv+vcRtleokVzkmrWKq4rAvfi8IIHLlXZ8cYe/06F+DQvLfh7f+YZBFErRZ8/Ln22zPiNtEgz2JRco1EK9XRTrob3ooov1gKRj2DDiRCSaRmAaBJZNxaIXE+rwxXX2Xcr04meIuuhXSgLmg7voLSZvGDVw2b4sCiUXJ7TmK2/YVk9e6DcN31aKt2kVbzF5+wY2TlouuDh03CdK6yEsOAl/1ym04xpd0nZ1IT/uNdtJGYNGRE31YOhM/HZs0sKDT1ZQKDmgFBjqN9CTDe2G9cZwZ2YsUR5PzVjKdCPXaS2bURw8eBAf/OAHccMNN8BxgjJgORDSj370I3zuc5+Drut43eteh49//OPYt29fu7PTRYfBfRgJ8VQX4Y5OQSyYJkG+5AJepCMCNvHqy2lYWHGwUqg/CVnKu8ikCFaKTNVbtSlKZXWnZ+oExZKLdIqISbnl+EFrbBuwNIqKTYVPYhwG+zT09WgwDUaq8pymTMJWW7wO2vYiOLsuRS6nwXbYg+jP6VjMuCiWXTiUHbOUd5nHrB7dAkkIQS7DCObjkxYGenWYOsEjTzkYH9Yxs+CAEMLScihMw181Ng0itun0ZjUs5Z1AnuVrjA1qWMwT8UxMndlbmAZBqeyv4FaldkXTCPp7dPTnNJyW0spm2MqyRggyaQLH4fcRbRVl7jSb0WAV2PZ7PugwdBKIKt4JZFKMVOMEUk+WEeqARPJIq9rZDEFBeibhYAT1sGtLCg8eKmHjqIEe6ZkIUZX3zotlF/09ukifr8ibBhETw5EBHaen2UvRNZZPlwJwKRxpsGiahHnx8Oi4BpvAGgb73nbYosjiihNRwPdmNSwssxNNgwifrzDO3e4TUUdOsTw9cqSCy0Nb1TmW8sDooK7cEgywyT0rr/59pFNaoJ6tBmyHBiKsU0oxs9AcwdfIFmfbkdQd0vdyXQaY/6QWZn/A2shSRd2WOi5rtytVti240wQuR62Vbf5sZAU8IO1WaHShRHEt3pZEyBsqWSh4bWbTBC4F7jtYxvW35useqqpLabHtv3UlQNuQ4FEkeVo3/DiPnz7GFpa2bzDwjx/aGFE3HT1TxW/86WTAwqInQ/DPf7gRY4PqIatSnQrfg/ShJyv4pQ+dgWkAH/61EWiEBMYkSQItJi2HXvMrSPqMSWCaQKUKVHiZotGdSJFXHSIPOLlqGtFywRcCm96q78EKqV3/7v8tYuOojsv2ZXDDjwviHW8YNWIVYhtHvXeUoOimhEI6+H2lKn1ONpfxkfARhO0iRgd1bB0Pli8eKZ4/V95u5NIaljQXrssWImVFtAxB4DrAL3/oDADg3W8exmte2Jsoj03V/nXSZHTRxZojpi0IWOjVOj1EhspIcQK3ysdofkp87MD7d3k3pYxcxidwN44YSJmMGCaEND3CDS/siH6UEJiGBlNn1+NrfWVvsawnw+a16ZjxfSzkOX7CoVpwfNfY5TjaQQAGhBEtp9ZFXciCrXWmwFWJyZqFrrO5RqniwrGpEH2lTFZu+7xFfYCNWXlMIT7Xn19aAwuFz372s3j/+9+PSqUSIGvlQZ6/QsNXgWxcf/31+M53voM/+7M/w7ve9a52ZqmLDmOoT8eBczJIGUQd6dn7y7ZBs8+y6Gbf9hSyHon1vHMoSmUX9z3BVv8O7E5FKlKh7OLpUzbSaQ0X7k7hoSNV2LaLjaM6Tk87yKQJdm+OqvochwaCQZ2cZOo+ANi5ycBgn46Dx6q48+EyRgYIPvxro8r73TBioDejgRCCctVXEKdNElBynpyx0JvTMDGsY+emlJikpkyCwT4NlSqFRoBSlZMWwJ6taQyFjO113SfMrjyQZeo0QrB/Vwoa8ZS5JNiZlSuM3kqbBDmvQdi+0cTEiK7c/q17vkgX7HVRrVIQDciY3jvZS1GuUiwXXBw968+kNo8b2DRsMHWRRnDZPh2OwwhBTshdtj8D26FCIagk6qSvchmCc7ZlAj6BKU+x2EmYBsFl52VQtZgiMZ3SMDXHRj3cfoDniBDggj1pf9Cmsa1QjeCSczM4d5uJlKkFn4nEwV3oXSOTZoO5wT6d2VV4fsi8rF22L4s9W03oGoEhvqMRojBcPp+3l6Vvmr69xtNnLMwv2RFCeuuEibEhVnZSKRIhXDh2b0nhQ28bxic+Py++2zxu4PUv6Ysc++hTFbiU3Wet51e1qSDWATahXs2V2ydPVZEvskUFYekAvx/btTmFgZ5k7//wyapQaWkawUV70rHHnpqxMbNgi3uXbzlcl9MpIhY85LfO26aBXh27NgXbxKoNzCzYLU0UGkGSawgrmZjBfStvXbTTnMBV9FXCQiHLCdzmr3fc80vXNARUppYdVP6qCFwRIGUdKnBliEmwKl+Kl0UpcGLS70OOT9o4crKKc7cH68FtD5Rg2b6/uuMAhTLFnQ+X8LoXR9sSwFdpmqG+Yu/WFAZ7NeRLjGyzbLYz4YoDWZbtRiS4DUDuwzJpgpShoVJ1UfG24tcsyzH54ffIA4QBvm0Ov+2k0YvjIJc5U2fBb87OOjgzbQeyxVW5cpl8wYVZHD5ZwcXnpAWBUVOAS/wtx1UrODBplwdurQfN73V0UEN/j46ffUFvhBgXQYlsANQ/J2USfOp3xvGX/zaP9/7X4dhr9Pdq2LPFxLFJiy08usDBo5XEBK6A6hHUIUvWWXPRRRfrErWGk7XiLqRSBIUyfNGPdIhoJzz7mZAORFxXDv44MWxEjlH1+YUS2xkpj/H7e3Sxk0fYvXECly9uegMrviuzUnVxYsoSbS0fGzUac0QWacnzpFoIxIsJ3WOx7AZ2CGZSGvbtSEXngW2YBnSDmK0u5Ee8jmKYRdBqWdAIwbYNBiwbyGVsDPfr0AjwguflYBqeWMy7Rk9Gw0Cvhn070phZcvDTx8qJRIxAGwncv/iLv8D73ve+CHE7NjaGCy+8EGNjY+jp6UGhUMDMzAwefvhhzMzMiPPL5TL+x//4H7AsC+9+97vbla0uOgzDIMimNWg6AWyFv600x/NXM331SzpNAlsjU6aG3hyb5G0cMSPb2/MlF9PzLlImYXL0jAXL1tDfY2ApR9Hfo2PzeHBLqgpV25fLbxxLYbhfF8Txni1pXLgnWzeNrEJRypExNaRNdm/hrZ+axlScmbQGV2JUMylSM025A+vJxm/Di0ujHtHYm9UBxW3nsoCmOUjP+ozGQI+O3h4/DymTACEywtBJ7BZZjmCDzsjfOEVmJ6HrBFkpr+EAWvKqNn9/rUD1/mSP1fA14lSvADDYG2zG65dchlzoQENHLFmelKS+IERKDvRoGB6IdjPZjAXLpkintJplPum9dAq8/MqkC3X9clGvzqrSAtggudZ5qTp1IPw+iKfG5V7B8gDZ0KPXSptEDNSTeka2ArFwW+MYVTRkeeGvHeAK3DCRSuGrZnpzujimGVD4ir3ffOMg3nRNv/jtn7+5gC/euCL+nVKMwETgLdtf7G42oEm7oAxUxn9D+EM8+HPp72G7HO57ohwhcO9/gil0/8cvM6XiF76zhM/fsIT7D5VjCdyqCPAVLGOjAzr+4L8OY8+WFO45WMZffmkejx+r4ooDwZalkadbr+WnNEg6pgwiEZUSsRmTUNi2h6Mi+Sr6EYzZB7FdtkXbD56/iWEd737zMN73N2yMXg4RrBtGogX3VVf34IWFLBbzbHcCAWqWWwK/rIe9d+XnV47ZRRCHpEdzkmVsUMffvldt4yZbKMjvNWUQXHJeBl/62CYAwMNHysrzCSH41dcMYHTQwMNHyvg/X1+M3Gst1Fojka+hOqeLLp7raDWIGbfteuzpCm5/sBgYK1k2xaXnZcTYnI/fN4+ZYiEtbXp1N6SKBbyxozQ33DZhBNLhQarDWFhxIhZcs4s2dmw0A/MEvitD7GzSeZ782CanptjCEiFEBGAMzwGa8bNN0gbFjWvml4P3V7UcLOVdDPSF5tFtaOcC+ey2mx2HzOd02hqxUchlIU6klBRcaJcymSCMW3fx+k5psPwaOoFpEoz0e577CccIbSFw77vvPrz//e8HwDJNCMHb3vY2/OZv/iYuvfTS2PPuv/9+/N3f/R0+//nPA2CDvfe+97146UtfiksuuaQdWetilRA0TvchT8R9Q3j/dy3UataLZBm2ZIgL+NEI+DlcHbRtQ7wvZ1IkmW+Hs7rO2rMAwtsd2rX9Yb2ayPP7ixK4ncuk7LG6FuDXb4XQGw1tcV4uqBNjnTddFfKwFcikrbwYxcUPjdSDgIF/PYVCg/UtvCWNENm/LXoxbnvD/lE77Xbh23cUcPysFbsYsZRnC0Sa1r62IDxJEASuYlu25U26uO1MtUk/UUb0sM+RqPQhOxilhYJEMq831Gr/AuqimGO4qvmq87O4+acFfPnmZXzv7qCH6kmvH77E8yO9+NwMPn/DEu58pIS3f/ysMt2VInt5kQVDqc7yAFVHTlr4z9vy+I03Diq9o+PQCIde9e7TNABN2kVTrlC4lCaKwOyrmdjBgqQOr/0Rvx9tlsDlW/iEn6MmBdcAUA5ZInJSIHy1JGVAhiBwQz63FcnKQaWWrwX+nvJFFw8/WQ6U2Q0jhiCfuadwrcVRTuCGLRTMhNuM5aN4Wo0QuLVQL5X1ptjvoovVRrgNhfi3/I8a53sJ/N8bFnHsbHRLTr7k4rorewJpbt9oYtgjYsJti2yzkE5pAQXuvp3pQF6dmOEHJ4fHhgxsHDXwxLEKqhZF1aLCyg/w55O+NZX3vUZw+f4M7n6sBIApdg3dX9BvVIHLb8mlDRK4kHez+Sfwcc/4kAHHpZhbcpAvuRHRTDuGiOvGouo5iPX26FlZ4KKJVtNSf+aQ/bL5Ma7rtxdJ58VtIXDf+973wnVdEEIwMjKC66+/Hi94wQvqnnfJJZfgH/7hH/D2t78dr3/96zE7OwvXdfG+970P3/ve99qRtS46DH8CpN7PpdoOq2v+8ZEJgEQ6qBpXTmTwzs0n2Fz8+IESChUXQ6EgEQTA887N4PDxCgqeT+5KwcXIIAuCdtsDJaRTBPc8zjq0HRtbJ3D5NpZa270bJWnWEuH7aNs29nW6AuqTmcFyuhpZXCu1XaQuN5VG8An94N4ictn5yHEupbjq/GwgON+6hEQACb5TGqg2Ug/ktrDe6nOjriEBFQT/myCgBFPJNXatZlCqUNz5SCnRscP9urLtb2pxDsE+hhOqYVJIVuT2eavlqoCYScGDSYX9u8P+ckoLBeEL6quWVROd1YSyiDTYXlDqE4wvvjiLH95bQKFEUShF2fRdm01s8rxUz9uRwuigjtlFRwTui8PooB77iDaOGti+wcDxSRv3PF7G215Dm3qa9cohpRT/8B+LANi7JMR/p3/2hXlsGNZx3VU9OHHWwnLen6WfmbG9iOU+bAc4NWXBcSjOerY+hkEi3aY/WW/8fqoWxe/8+aTwLQd8qx7TZIsRYRXsq6/2LABCr96lIWV7jaJBiB/ELExqyoRu0iBm/kXZn8U8i3swt+Sgv0dDLqPhzIztE7iCZI9/oSmxmBJU6YeV83HEhdyn8rqf1BJClX4SiCys8661iy46jSQ7f2r9xknJU9OssfjV1wygJ0NwdtbGN27J4/S0Lc6Xx4I8/I9paky8FFMXS1Kwbj4m1KU2w1VUfEdYCxD0ZtlOT07gyuDp8WvL7Zyhs/glywUHrsvaYa46DM9D6/WRTe+W8gY24VP4s8umCXRdEwTuQMheMDx/bgZdD9zVRTt9ZtuNtnrgSgmoTOoIiV6DUv+8pPWoZQJ3ZmYGt956K1gkey0xeSvj+c9/Pr7xjW/gpS99KRzHwS233ILZ2VmMjqp9SLtYPwgrWOLKvetS3+NQanjDBbVeh6uRYAHnHdyhYxZu+mkhNp/f/HH9gDIcu7fUt2BIinrkiYz1FpVRRjhr7SKb12uDrkcUuHUKeBsQVFFSJYnVSbRr4rdzk4GjZ3z261sxdW/jiIEDO+N9YNcDZNJWfh2ceG7kFcnvs15ZJ6ED6m5jUzK43m+hY8PErmqS0G5UJRLmL35vXFmNbJviyGkLWyfUw5JWagMnPwMWCnL+pMlPb9Y/pqlrwVeahgncsPWFijyK88BdD0j6DuKKK3+m2zea+PwfbsR0TNDQvVv9PtjQCT73gQ04eiaevHVciidPWtgSCkIVzsZn3jOB1/9PFnKzXUrIMFaKrgh6uGOTCUIIdm02cWKKFYrJeQf/+p1l5bmbRg18/DfYuJdSis9dv4gzs8GVhLDPL+C3F80EMXvkqUqAvAVYbANKGfFctajY0vf21/bjuit7MT4cM3VoUIIbXqzgqEq3nPQ9he0GHBf48s3LOHzCQjYN/P6bhwP1LeBTGQPZQkHOZy3VbhxSXt1vmJCOwVrbqnTRxTMFzQ6lXZdiMe/Cdthuil/5mX7oGsFywcE3bsljesGRSFhp0UmyWgnU0lA+rjyQxYOHKxgZ8EVHmuZlmFLlIjKfR3Org1RI2S+LoFyXSkGO/Yu7LsXsoo0zszYsm6InowmLBUKCQZ3r7haTPscpnpXnhbgDDq4YNgwi4hEs512UQ5Eu2zFfDnrgrqMJ6LMU8hNeb4K1YN5aKwukTrnSNBKZA7rUjwmwagTuj3/8Y6G+/Zmf+ZmGyVuOF7zgBXjVq16FG264Aa7r4tZbb8Wb3vSmVrPXRYdRz39LHlT75CwRHVT4eFFuY+qPCCREg8GNTkyxxn3TmIFrr+gR37suxRe+60+Wrjo/g3O3p/GlG5eEWmXLuI6XX84UJZvHDGEG3wpEBaxF4IZVreusQZMRsVBoU18XVhKtF/jkFvsbF0W2nQir1Vd7PJHEby8J3vzKAdxw+woeepKFEu/NkoAP6C33F3H8rIWpOXv9WygoOmJXUlQ0UmcD1jF1zguXs7qEr1x2Qn/jyhER/+s8Kh4BYhr+VvYwqhaF5bCFC584p42pOsIIqTw4oWo7gCv1H7LnFPeraoYMA9j1ONGTrmGZQKD22hLbtm3pQIVSZTWhIovkxQ35b2wa8G0q0ibB6KCBzWPJdrsM9um4+Nx433fHoQHFcuTaXt76e3QYOnv/AdIwwcNNuoYnE46/dF0/CIBXXNGDF1+cwyf/75wYd+gauy+AjVMWVlxMzkkzdgqc9cjboT4WLNQ0mM84r9OEsAy1osDlnsMvvzyHN76kB1/+fh65NLt+yiRAiQUyBVjdkMnbyGOj8nOq37jEEbiBIGYNEp786GrVxZMn2LiwVAGeOmXhoj1+GRJq2hr+7n5dDHngNkjgUtqchUIyD9zwv9fTSKqLLtYf6m1x5nBd5i8LAJvGTNFf9/fo2DRm4MyMjW/+eAXPvyAb6Bi4BYzYcROj0H/jy/rQ36vhUnlMRJidgeMgMMflcEJWhKmQsl8em+ZLwRgrHPmSi6pNxc6KdIoEfufXTwIxV3JpQ/OkOMEI78MMnSCbZjFRLJuiXGmsH0iCrgJ3dREUbK2vJ560TUiCJMHxNG8owku169KG4/+0TOCePet7kl1zzTUtpfWyl70MN9xwQyTdLtYvwpO4cGFdWHYws+BgqE8XakaiyVtbg41yvQ5ATl+OwslVKhfuSeOtPzsQOOe7dxYwu8h6hde/pA9XHMjiW7evYH6JZWjfjug5rSJJRxZHdq9HhImGdhmQJ/WiWm2Etx+tggA3kPhakDWN+EHWQl9Ow0sv6REE7p6tqUD9KldcHD9rYXrBWfc+fTJpK2+H5bEHGzG7l0nbeoOXSLJ1CVxZXQFAj2+Tw4tLq/EOrAaIj9gj2lD5ZEK1Iilcef50TfKgbUGhycmhWhYKZszoKyWRRusNtYpt0L8+eqDj+BO8sDdwq4h7Uqr8pgyfwOXPuqEnXaccVryJZtpkh/I8ZNIaTNOfoB7Ylcan3z0BAFguOHj9/zwNl/rjGlkl/q8f2YSerIbFFQePH62ILBDCrsHblm/etoJ7HlcH1IrDkydZO335/ix6czoMvkiOqEdtJlR/IzuoQmnXa1vCwd04ZA/c5ArcIFlyZtYO5OfpUxYu2J2G61JoGgkEJIuDsFDw3lmsajfB+KAVCwUV6qWy/lqPLrpYIzQ5dnApxNwxvLvjwM4UzszYOHrGxuZxG5ul3/2FHgQWX0moUpoGwaue3xv4jlIKzSNQVTb8jrQACEiLYKHxgkYgSE/TJNg45i9e2Q7Qm9Vw7vY0No8bGOzVMNDr/65r/gy9HoIiLf5lY+fJEApcnc3DLtqbEUR0vujilKcobofgaT3PuZ+NCJCk60ywJheFVumNJPFO+NyRjxGZAneVCdyVFT+i8sBAaySYfH6hEL8dvov1g7BSMbwN/Lc+NQUA+JPfGhOTJV5glYP7GNKBQ65YfCJECBEE7ubRqEpny5ghOmEeoKw/pwsCt78nXtnTKmpVx/A9thr5sJPoFNlcb6vBWsGPBBtaZuhgFuWk14LYbBeBCyAQnGHzWLCb2eZ5TM8s2uveA1cmbeXFKm470LSFQj0FboM+ZDx9WSlZL6AE8TRyq1HWkmxXDqg85UlBC9cNl2mZQC0rfDZNQ6GAbRBUOjeTDr5I+fpGPQLXCpax9bbY0WhzLW8dD3sBtxP18pUyNRQrLiybNqSkTPr4OeHIA13J+UkbBCVEFzPk58F3isrlM7wQEEZPhpWz0zMOTs80LsM1DeDS8zIolP1zKfXzyMdatdSqQKi+1l10AgxTbSsgk5yVhAsZYUUX960c6iNYWKG452AZvTmCy/dnoWlymxSfJn8v1CPWG1mIAoIkRXirc7LzPSKlxilx5b1rsdDFcx1xVSCpbuToGQvfvoNxEVsngrtFfu11gyIA5/S8HdhxINoWkzTUJnLoGoEFqlzEFQrckIWCUOB6v2vE99gdGdBhSFvAeBqDfTp2bIzuONXlKXG9dpx/oM0JXcLviN+zId3fsBe50zSIIHDbMYIICCnWz/TzWQv5cTca56PTIA3skKyHJMHZ5YC7LqXMi7pBKqplAndsbEx8PnbsWEtpHT9+XHzu+t8+M3BqyoKRqigb7iUpQMfUvI3N46wDlImMo2csGLo/U7Zs1qDffFcB3/1JAT/7wl5cep6/vYQQAo0QuJSKlTpCgLMz/jaXMAyJNBgfYjWkr8f/LmyO3g4IXqpGIxUR2a2zBk1GuEFrV+MbiMTYniTbAhEsL6zA7SSBG1j86Nx16qHVa1MEiavBUFDBHd4iytS8g0efqmCoP8qU7dxkBhQBawV5cOpvFfN/b6SjDwQxq1OOwgrdJOWOaya432vcexRN0ypWuKrFHlotAleABEnLdtY9TSMwdaaok7dmlwUxo0mkVXMVgVKKqu1vTZSRluqFysuU5SGoqFkP7aJKWUPCv/HvY16UrBCKUx83izj1rxxgRVxbItJ6stHz66FeORQErnePchvCyj9X6PoJyYQgf048HUP3J+thIoCfdcGeNPbvTGHHplRgYpAU2zeaGBnQUfQIXAICChqpr2HiPRrDwP/s32k8Ul4TH1aPVVuwUODNMw/69pZXD+Lv/t8CLBu4/aESShUXpqH7ZGwNcly+30qVinyaTSxA8LagEQ9cUcdUP8aRU+uhweiiizXAiUkrsPB1atrC/JKDUsXFv920jPN3pXD5/iyOnbFwZsZGJq1h+0Z1Wq5L8eBhfzfDRXuD8RrGhw387i8O4bNfWcD0giPqaqnsYmGFtULLBRcnp6oolJIt+PNFF861FkouDp+oBhZjphccLOcdZFIE01kN+aKL09MWlgsuzs5aWCm6sGwmkDg9YyOdArJpLTDmDKt4w2hETESkHRviuyTDPGlxS4YjLBTiz0l6jaR56GL1EY7zsdaQc9OqvQNJsNuSzx35Gq1Lo+Otemh5GL17927x+etf/zo+9rGPNZUOpRRf//rXxb937drVata6WAUsrDjoW2ItrkZIYPLAFRAAm4zIgX8MnaDq0gDJy3HkVBU/uJetbB46UcUXP7oxol5zHb+hL1dcLBdZ2pvHo0WaR7Nm57J0eKRxoDMErmwkH4vQT+vaAzeU13ZtfwishK+j9lwMLnjx9N5nJ3175LKyNgrcEEnQJCgNlpeIAtcjcAslio//85wyjQ0jOr70sU1rrsqWB5n8syOphhsZgwQsFOqc2JQCVwPgSLYf/Ps6bdBqiKC5qrCWcq0T2Qir8gBGvlgODUz0LIkskwOdNQuhwA3db9BCQf0s0pJPbwDPcEFdVSI217Je80GyZUU9+GshaZvMCTo+FpL7SjOlgVOMMnFICPO3tWy/LAofZUWdEfn2/hoGwbnbU3j+BbmmAmxxyItTlAJmaD08vCARRvgR1XpmhBBBhEYtFCQFblLFqpS1fNHFokeiXH1hFq+4ogeve88pWDZw8OkKrrwgJ+p3rW2LYWI9ie2CMku0/RYKkWt00cVzGIWSv72eY2HZwVLBwR0PFfHQkSq+/9MCPvrrOpYLjvjPtoPE7N98bQF3P1bC5fszmFlgHfnbX9uPKw9kI9fct4OpVxdXXLFT4MysjYK35d92KJbzrmQLkKy2cgJ1dtGJtOdnZiw4DsVSXke5SmE7wEqJYqXo3zulwNS8i2LZRbnKdhnI6YRVvNHr+5/r5VheyG1ksV21u8BxqdjdpnpW8ldtJ3Cf4eOrZwLidh2tB8jjgFYFBnI5HejT/HgCEsT4kHjCG3cNLBRe+MIXor+/HysrKzh06BA+8YlP4EMf+lDD6fzxH/8xDh48CADo6+vDi170olaz1sUqgBAWsXioX0cuQwIdwskpv0NZKbgBm4Ut4waKFYpdm/3tG2dnbZQqLh4+Ugl895OHS7h8f9a3YPAuwTvFGc8eYaBHQy4TZRbf+poBnJ6x8eqr/eBmA5JtQieVfrWqY/i3dbYgFQAhJLBFu11EZrtXVNsFXXjg0sDfToOry9cCwjaiHRJcAK96fg/yJRcvv7wn8HNPVsMrr8rh/kMVmAaJdFonJy1MzjHv7Nho56sE2SJGELiSdUsjRJRM2tY7LWJZkqBxCJOVfl0NHRgaZK+mhUIt5Zo8+A/cSxPb8mohbRIUy1Tps2kaRPKgbS5926GCCMukaxG46vP9wE4KA7w1gnJrJAkfU1sxzEm5RlUGSZAkgBqH7xmo/r1eIvVy70cg1wLHUwS366dDzyFlsmAt/DmJ5yWVmVpbgpktFU2Qw3iE253wu6pLDkuT+CQSXDNJELMEhOc9j5dw010F9GYJrjw/i6dOM1/fiWEdowM6NI3geeek8eDhCh44zAhc/v5rTSQJITAMwLZZPholcGU0Y6Hg+5RHz4ktC0Q+hq75ImgXXawGOClpGgRbxG5P4Js/zuPkFKvsLgVOTNkY6vXrBF/IsR2K629ZwTd+xGwhT03nxTFXHMgprzk6aKAnS1AoUSyusIFhxaIoe2OLbeMmRgZ1VG2Wl42j0U6/alGcnbWxYUQX7aEeCnC0ecxEOkVw7Kwl5tHjwzpGBgzYDtvxU2FNHnqzBCMDOo6dtbBpjEIjwK7NKYwO+td2RAwH9bOMI3ZVkHe5+HP85ONVuRmzbX9+qcqDvBjajnYt0Fa2nFoX9bBhxEDKJNAJwVD/+lKs7dqcwlA/U7a3GqNBnq+NDugYHzLQkw2mSQg7jhAWG8KhQKbB3VMtz44Nw8Cv/uqv4rOf/SwA4I/+6I+wuLiIj33sY8jl1I2ejGKxiA9/+MP49Kc/DYBVyre97W0w4gziulh36OvRsGEk+r5OSgrclZIr1IwaYYPZTCp43sKyg0IZeOwpRuCmDDbJ+vDfz+KTvzWKq85n5UnzDNa5wfvMAus4x4Z0pbJtqE/Hn//ueCTPHAM97W9IZLVxHOS8ag2SQWsBeT7WLrXwerUgCm+Xp6HvO3dhiO0Uq/5E2kTocQL6BRdlcfWF6j7g56/px4ueZ2PrhBnxFnvbx87g+KSN45PWuiFwwyoBoPE60IiFQngLW5KSEN6SFhdQIrDVLUG67YDVAvHRClTb9HgeAio/b8JlGkQQOi5lgbcamcwAYe/SYCGRVZf1LBT4xDJuq+FaQNX+Jc0XJ/GNDpaBJH2oUH3anNxq75MVZckM5olSipTpl4dUSN2aMggKku9h2QpFMgciJLL4pU2PVG77KY0qQsIK3DCxqLCxjgVBfBAz2VKhHuFJKcWf/esc5pfZ8zp/dxqnPUutHZtMMc46Z1sKDx6u4InjjOmwEtq6pAwC21PfctI3TGTHjQ/kusufXdWibSFWxVOpkQylqzBm6aKLdQA+Vk8ZRBCls4u2IG85Hj1Sxoue56tpeXv7l1+ax013qWPvbJ1Qj0M1jS3SFkr+4k7VoiKQ5eZxA5m0FvF1lfHE8QryRRdLBQcHdqVFuhyEEGyZMKBrBNPztojLsXnMxGCfjqpFcWLSQs4TEm8YMbB5zMBywV8AHhkwAm2544mf4qwSAgrcemID76+8eJioyVEsTvFdR3qMpiqJt2gjkG+/6xneeegawdjg+uT2UibBRJvmm3K50jUSsRHk3xMA/P+UNh4HqS00zEc+8hGMj48Lhd5f/dVfYffu3XjPe96D733ve5icnITjyZYcx8Hk5CRuvvlmvOc978Hu3bvx6U9/WlSeiYkJfOQjH2lHtrpYBRASbEjLVRf/8u0l/M1X53HHQ0Xxfb7gRJSFRGOd5xe/u4S/+doC/vO2PI6escQg+bUv7hPH3nC737HyMs4VuNPeNpexYSOxirVfIm0H+zpgoeD9TdrJrLeIjPXQLrWwnMx6egZ8ACW2lzewNagVrGXAIn8g1vlr8eeruha3WDgxaUV/XCOwibBH7EkLUY1ALjv1yN84IiAJ+DOtF1DCX6To/Av3g3rUP5arCYGAALdtC1ycWCvLKj+vzzENEiBZG1LL8XMkAjesspXJIj3OQiFE4HKst/lFuK2qRyhZUmCXjuUp/G/FJJGXQUtWODciiqxTDivcLoRvkZN+C/jehshQX6HJ8lWtqo+rlYdWi4hIlyAQxIwjbAkSvl69fwcvJi2m1PTAra1EP3bWEuQtwBZQeB3slXZkcVJn0vPGFeWxji2ErBIWNiBJy7B0mFz3k9ooJOmTI2U+Wc666OJZBTEuk8ZWcj171y8MAgAefqqCe58oizq1VHDwF1+aE+Ttrk0mfvNNg+I8XWMesiroGpDhbZjFd064KHltdy6kulPV43zRDfxl6RJxfC5NxL/le+v1AgWHVbSaRiL2S0fPVAP/Tc6xA+KI0oaIJO/QcpUi71lHJI3ZAPjPhJHtrPOMs5pot81gwLauvUl38RxGQKwTU2YJYb8RDaAusxjVtMbmem2hmwcHB3HTTTfhmmuuweLiIgBgamoKn/70p4WyFmBqXdsOrobJK9FDQ0O48cYbMTAw0I5sdbEKYJNtv8R9544C/uXbS5HjeMMO+AWUAPj+3QX887eix6dM4IUXZQFQ/L8f5vHwk2U4LoWuEVE5+CrizDzrjMYH9cSkijwxUa2OtIwE2y0DhM4zbNRdz8MzMYjy45qDr/TywYVs/9FJiOTXgsBdxRfAn6+jIA8ZgVvC8ckm97C3EbIvsGh3xEShwdXSBMb2qmNZPpKkz9SE4ScaPjdA7JLVKWrVBH6TncyIPHFiKkgnQLQ+6tn2mEZwa/utDxRx5YEshvuT9xF8+2TKjBJtsoIxLuJsWnjwJr7kukA9grmV7ef1r13n4vL7lwg5Ev05SRI1ITxweRAzqS7L7zxMhgpvXu+9c0JA3s4Xlwfi/deu3RMA98ANl9/6M+hGlEx8HFauUPzoXn+RXiYzKop6cPhEFac9v8sHDlcCv1UtqlwsGB9kD3920YHj+AHJkihwgeY8cDkoDdb9ikWRjgZ/bzjNuse0dokuunjGgLddcp8rgpOmgK1S7IUf3lvCvh0pXLgnjb/+6gLOzrI55EsvyeEP//soqhbFf9yygrNzDs7dHl9RNY0g7dkkVasUjktRLPu1ri+rYUlSwtatj94B8u6cXkW8FkPXYHCLnlBTpJHouHpuKWyozxBH1MrtZj3fXlP0W367G0cMy5Dz7boUR05a4h3Gea0HFbP1r9FFF2uBsIJeeQzh6lsKSnybvkbWKNqmZb7oootw55134m1vexvuvPNO8b08mLOsoKJK3lp29dVX4/Of/zz27t3brix1sRogwUb13oMlAMCVBzLYsyWF2x8q4vikjZ8+VkFPVsdF56R9ApcAdzzMjr98fwb3PO5H/MykWDTw17+kDzfcnke+RPGm950WndPVF+TwxmuYQvfsvKfAHdKbUnFm6igwmoFQjdXIT3CVZj3Rl6sH+bbXk4UEb4D5QKiemrFdWMvt0qup/hUKZ4W4ars30L7h9jz270zhZ57fCwAoVVy8729mxMRdxkV7M/jwr40EypDtUHzgb2dw9EwVv/Dyfgz16/jH/1wUCz8q7Nqcwid/a0wMSgO+wN4/+PmNFldd3v5Vp52KBA1MkH5Y0VA3oEQj7FWL4BYKtfwmVbsWWGCM5jOouvdwMKGDRyu40VPfpE0CXddEQKk//8I8zt2Wwt+9f0PiawrlpILkSRK4QZ4Qxd3DakPV/sUtNsXllytLWw0Q0QhUWUlJFgqNIGk5lP2Uw3kIKHDDBC5X4Nq+kgsAMpJqvdPbPOX3TBH16Y1aKITP97/wPXnjwZ+H7SA2oGWp7AaEHpNzNn77U5NiIS0M26aCBJfVtcMDOnSNLcDNLDqxdghhyIHWqjEq8rrrB5QRIZrG+rykyv6m6v46aC+66KITWC44WAwFvjZ0gp4MwelpGzOLNioWxYkpNsA641mpmHpUqXrwWBWOS3F21kEuDVxzeQ9+5qoenPBUoB975xi+edsKDuzOxOZH14C0Z4tT8RT6Rc8+wdCBVEoDVQTqVkHTiOhKe3IE48MG+nJawGJsZMDAhlEjEMMlPHfUNEUAVDAieLBXx9S87ROtMePQ8SEdFIy4Hhuszcb25TTs3pISC5emQTCSYMFbHq86rk/Ab50wMTqgPr+T82Sa7DV10UVdyAKdegpcjbCy57psQaURlXlbh9LnnHMObr/9dlx//fX4+7//e/zoRz+KKG5l6LqOa665Bu985zvx+te/fl0ROF0kA1Pgss+npy3c9SgjYd/+ukHs3ZrCpjEDf/7FeQDAD+8t4sSkhWsu7cEDh8o4eKyKI6dYZ/nff24Q2dQSbnuQEbrZNIGhMz/Ci/ZkcPfj5YCnz52PlvC6l/SialERbXh8SA+QJLVwybmsU948ZnSkU2iU8HuuFn25zq+nR+ATd8Hvn9UWCtwiYDUI3Br3ef5uPyrwt36cFwTuI0cqePSpSvQEALfcX8Svv2Ew4Kn95Ikq7nuCtUfX37qCsUEds4tqJQLHfU+U8eTJKvbv9PIg5ZO/es7/NmiNGiBt650bbpMSlTuZbEZ8QAn+zDX4RE2nUeXbyhvcPt+uvMnpcGKt5E04fvJISfz24kuYZ/N1V/bg8acrODZp49CJKuaWHIzETCrCKCuCT3HIBF5cHRcWCqGFhnUhOEnw+uIO4ev3HVHgxlxc6YHMn6+FSJ1pBzjxyu9Trn61ojALYpkHMaux6KFsD9pdmSmNvKt6CxARQrfGsQQsoOXVF2YwNeegT1KaFUou+no0PHi4gpUixZlZG5vHGJFx92MlOC4w2Kthx0b2XX+vhgcOlbFSZCQrD4aTDinJBvs0zC25ODtr+4tKdcqj7NPbqA1I+Ki0SVCqUPGO24GafcO6aDS66KI9OHS8GrEW4lhYcTG36KBSBdIm62ymPLsUXSNKFerhE+y417+0D/t3prGYdwME8ZXnZ2PtE3i6XEnPLVZKFXY+P0/Oba1+Rg/t0Bru1zHUpwfaGtbu6RFvfU0jgbgrKpHCcL+OLeMmlgsuLI/hjVPX6jrBJkXAtTg05R0q9c1iTEpIJCZGHNq9mNltKrtoF4K7LeOOkRb4xWI3XX0LBRmEELzxjW/EG9/4RpTLZdx///148sknsbCwgEKhgN7eXgwNDWHPnj245JJLkMnEr2518QwAYQUwX3Lxlo+cBcD8ZXdvZo3wYG+wozk+aWGl6OI/bs0LBcXGUQN7tpgYkKwMMikCQ2cdyWtf1ItrLs9h79YUylWK3/7UFJYLLvJFVxAyvVmCXEZLrMDt69HwobePwDRIR0g5X/0Wn7j8U6Nk0LMS6+gZ8MZVBDFL8D7bAR5UZy09cFflWvz5Ku5zw4iBv/r9cfz+X03jxKQl1FfHPU/cK/Zn8M43Dorj/+hzszg1bePEpBUgcI9LHrrT8w4Wlllb8bFfH8Xm8WjX97//bR6PPV3F8UlLELiySoC/eqHAbSGIGamzaBT5uQHiLLLooDh2at7GV763DF0neNtrB33CukPgZGQtC4VAACTFYe2qemlvflAsuSiVXdzvkfxveGkv9mxms7HnX8AC8H3+hkUcOWnh7sdLeOnF6oB8hkEC9yW2bSpIHjkgWj0C17bkLaF0TWcYqkvL9iKxB0kQNhqd9MBNkLSwKqihxG/lGuFgbXEEbio0CefezJyg4ApxvkUXqGGhQNrD3waCmEFhoRB+dxEFbvI88OfykotzGB00cPG5/lzg0acqWC44mFt2cPysjZ8+WsYrr2Ljw3sP8vrah7e8ekCc8/aPn8FK0YZlA1Xv3co2FbrOSJG5JRfHJ63E5dG3UJBsQJISuGELFY/ATazA9f7W9MANXWMdDaO66KJtsB1/AYWP81aKLgqePR+lzA5vqE8Xv+c8D+xMSosdu5sGIyCLZVfYFcxIC/25tIvTM+p4DI7jj9WXVlxMLzgoeRYK2TSJ1sVQPbYdCtcFlgouTB04MxsUvJWqNLDjjC8OR3xvCcBpZ10jyt0JvB2T2652e8o2Almswnc6NjKm7loodLFeEYx3om53NOL9Rnz1dzimVD10dDNbJpPB1VdfjauvvrqTl+liDUHAGt1JqeN5x+sHRaHtDxG4lg1845YVOC4w0KPhv79+EM87Jw1CSIDszXgKXENnBOvogIGdm9jkuq+HYKXAVBlTC57/7RAryo2Iabl1QmcIXK9DqnFMI0GNnq2Qn8F6UiGLAE/ee1ytsYKYsK3S9QLX7oAaLQ68nsYF0Nq/Mw1NAwplitklB2ODhghqdu72lGgLAGDXZhOnpm0cn7RwxQE/uvDxs8FBt2Wz615xIKucgO/dmsJjT1dxQjpPVT6FB26DBVbuyOu1UxEP3CTpSwNi9jd+C/7jR6uYXmA38sChMl5xeU+CKzQPqwHiI2yhoPo+KVTkJyfO/v17K/j3762I73dtDnrdUUpxybkZHDlp4c+/MI8//8K88hqmAXzyt8Zx6XmMgKpKWwmbAd/2Xa0tFl9VJFnAUllgyGiU/GoESbawc6SkLfFJCDL2e9AaoBYqofcvPzM5iF+YDE1L3ryAHwyNb9GV0am+kvKtBiTq26qR4AIEEO2nIo+xxa5k9+YUjp+18TdfW8DffG0h8JtM+ALB4H98419aUs/pGjDUpwOw8Nmv+GnVK4++YtuVPHCDx9Qr+yIt73k2ExwxjK4HbhfPJfC6Z+hE9NWnZyxB4BJQbBgxMD5kiN+5pV8uw2KnXLE/jbsfD+7iMg1gZsHG8IAuCN/ZBUe0+Zm0hmxaTeBSSoVdwVLBwcyCjSJX4GaIZyMTf0+VKsXCCjvPMEjA7xYAyhUXxyejbKwRGltozNafPQcSDYAK+O1YKrSotVYQV6ayAjf5+e1u25LYW3XRRRLoCeZ6PGBZwGqKkobqwCq6kXXxrISn/OBG7Ts3mfjZF/SKn8cG/SK2c6OBo2dt/MctbNJ8wZ5U4NghSYGbTXkErtef2ZJaZuOIgZWChbOzNmYW2Eh9fJid24wdQiea7bgtnbF5WE/s5Spivd42X+EWClyPaOy4VfEqkqiRS4cIwE5CPN+Ya5kG28LFlLU2xgYNEdRs24bgFqvtIuhZcJAd/jcAbBg1Yifs273tuPJ5PpEvWUzwstCCArdeOSKEgBAiykEispi/P++ftTxw5ejMcsCgTqGq2NIchlwUZH/8VqBaEDlvewp3PVIKqFQuPieNoT6N9WdSFl9xeQ++9eM8SpX4fFg2cNNdeUHglis8iFntd1a1KSbnohZTVc8rlm8Db+eiju1QzC85gXpHCNte2SzhnPQdVUPesJ1AkpRTEtGXFI0UQ0688kA0cp5kS4KIhQIn7rkC17NiCBxXx5Og1babIqj6lPOr8i6OXI/6eaxHXhDvf/miC6I5gbowt+QgX3SwfYOBtOk/U46dm0wM9mnK+rOw4qBYZmxGoeTgkSNlZNMaCmUXWycM3HcIcDyyY7CP7fQqVdzYrdK8vK4UXbE9OmkZ9p9lsP1LbKEQatNl1FJjd9FFO+G4FPPLTiBmQV9OE4TnakC1ABgItqXY8cDbjUyKkannbEvjJRfn8PUfreDoGdZ2pE0N/b06xgYNjHlCoJlFR/QPvVlNfK9CX9YbqwAYGzJg6owgzpgEM4s2FpYdIQTJZUiANC1VXDG3TRlEWBH05TSUqzS2jwpbFmh8oRrxHrhpBYFrrKkC139uvFw1NH9v0zzlwK40CmXamWDmXTwnkSRAPSE8kBkgOnrSWP/dJXC7aAkEBBohWFxhPcZASHHbl9PwG28YBAXFmRkbR8/aonO5+Nxs4NjBPlmBq8Ew/BVCebK9adTA4RMWvvr9FWydYEV4fLhBBW6Lyq66yQuVXvwxSYyun+1Yr5MNmbgDkitsWkUtb9hOI6karS3X4s+3Bne4fQNT1v7bTUu446EinjpVFd/L4ITuvY+X8dmv+CrJx4/y4yXydyK+y+PpPn60KtLh6o43vLQvQg41SuYHgxbWPz5AgCThbyMKXPWplPoekcDqELhWG7bPt6vqXXpeBtsmTOzYZIidG/mSi0PHq5H6vXuLiW/+xZbAAqKMh56s4P1/O4MHDlWE1Uc5AVkNsAnp06erke/PzLAOshGCMSmOn7UwNR8lvZYGDZyzLT7aNketO6rXbgj/0E4QuDHXruWBW7Ub8xtLCk68cksE+aEFPHBDAcFSoeB1ZZWFQkydbtd9hNM3pOayXjRyIMDf1gXR2PhxYdlBvkSRk+7zzIwlyNK3vHoAW8aDbb6uQ+zI4ODbcOeXmA8mAJyatqGREkAIxgc1ZNME7/g55pV+etpCsexict6Brldx0V61nRt/Z0t5F/lSssWZOPA2oR0K3CTobjPuoh04PW3jVCh4rGkQXLYvs2riExWBK/exXPUmj634OZzABSh6c1ogjVyWiQV2b0kJYrRQclEss7o+1Kdj79b4vpGRuxVoGtvFdc9jTPXrAjg1bWF+yfXjErhAsRyslLyO9mRZMLBmEPTcJHC8CbPsjSsUuFKbvpYKXA5KqRSvoYHz2nT9gV4dA731j+uii6SQ62OcLYiu+fad/jG0IRuRLoHbRWsg7L8lz/h9sFeP/L5lwsDSioOBHWls32RipN/A3JKNXZuCg/Kh/rAHLoHufUUpheNQ6DrxCBvWSZ6cYpNRHimzGYVkJwYgSQi/JKs0z3as19v2PXA9C4VVmgitJokauXYNtU+7wRcvaqn39mxN4Y6HS3jgUAUPHGKqhpRJxKKNOM4b9E4vOPiPW/PB62jAK67owT99c0mkGYedm0xoGrBccCPpnLMthRdcFPRAbVTtT0KD7HrQNZ/gbkCAK15grTZI9v/kpEQnUU1C3knb9NtWDwSpHUxI1wHT0JD27BQK0qQq/Lx0ncROdC7am4ZpMMXgu/5iCrpGMO3tClEpFmVQsMmD7Ge3UnSFuq9iAb/3l1OoWC5ecXkPzt/des3kxFFvTkPKILBsipWiG1BkJ0XQ6qL++fza68YD16ZN9T/yOZZN8b//bR5T8zZ+8dp+XHkg6yuNvTIjNxNmDQJXqDM9nkSQD1KdqbWzh6ANbXcoAbm+hrftKg6vb6kgYduECdsB5pc0pEwtECRwftmB4f2zJ6tjYri+Oqo3pwOwA8HQRvqZ92U2o2FkSEfVZsGFJoZ1FMuumGjVKv8DPeygnzxcEgGOEhO4oT411SCBm2RXTLjMP1d3c3XROfAA0n05DaZBsLjiwrIpShWKXGaVCFzFGCKgJvWaCHn7Mq/X2Ywmvndcv20G/M/yApX8uZ7angdS5LYtK96CuKkTELDYLBph+Rjo1SLBUFeKLhZXHBDCyNamdpGGxAGOJCAS3rjePcn3E/bSXU3I8w0+zm2ET+4uTnWxXiFXYT2mP1b73ZKG+u8ugdtFS+Ay8KW8WoFLCOtEj521kE4RbBg1cOHeNA4eRWQSEj7X0Im3SsG2Ejsum3hffWEON95ZwPSCv09kdIBbKCTNeEO32TASdS6BTve5Oeher3ONqJ+o932nBzyrSKLGXnsVLs6fYy0F7pte1od0iqDkKSEogAv3pAXpxrF9o4kPvm1EaZlw3vYULj0vg5RJYDvAa14Yv9Q+0KvjY78+iieO+6rIH95TwJlZB/PLTqSsNq7AbUxxH/DfTZB+OKhUnHqXIqjuXA0C1+J+nqn6d0LE/0JlsZNtRaxauXYblU5puGxfFnc+UhKKbw7ZPijumrs2mchK21APHq0gm2bkr2UDjzzFFi4Gesu47qrWfYp5AL4tYyaGB3Qsrjh4/GhFGbU6kFWFn3L4udRbtOQ+yOlkQaYbQt3t5NIB3Ie2KjUXdT1wA4n6H+96tISb7ioAAJYLi7jyQFYiqrXw4cLnFogqtPnx1UgQM4UHbuTf3sC/DW23PLGW81hvQUKcFFOXwhjs03HOVoJyxUU6peHc7X4gxYpFxc6A/h498Fsc+Bhwx6YU7jlYAUCxdUMKKwUHE8M6No6kQF1LpOe6wOQ8QblSO2goj4ouR6dvVkXO27+kCybhRTkZSRZNuiRHF62CUirGCHu2pJDNaCLI4ErRXTUbBe4tngq0SdF6GLBQqPoLLvw816WB8/hnQ+JV5c/1FmsmvIBpPMDYkyfZOCDr7SjYMOLbE22ZMLFtItgBui4LVEYIsz5INfE4w56bvD/PZTQsF4J+CnLbtR6CmIH6Ox0bDQzcRRfrETKfE1emNSLNIQOEb/LrNETg7t+/v5HDmwYhBI899tiqXKuL1kDAGmI+uA2TsJbFTNrLFgWlFNWqP1jmDXi+xBRA8lbegV5NdKKGziaztkORMgkyKYLXvbgX//ifTFWXSQG9nkpivagPkihwG/HEfLZivbyvMPjAhq9kr9ZEaC0tFHxV7Cpciz/fGtfqzWn4pWv7E6X38jpBuH7h5cnSufrCHK6+0Ffanp62cGa2hJWS2/K25YY9czVfU5dIgesdIwLv8UWH0MmUBj3SCqXOv3AR8b0G8aHKRZsEuA0FQ2u0RfrAW0fw0JNl0VYsFxzMLDq4+Bz1lmwOiugWRl0nMA2Ct7yqH1UbmFlwcNNdBVQtty31kueR72xRWRQ1gyR5S6TC7uD1OeRgV4lfdkz69z9RFp+fPm1hYcVBxVNiCfuBGAVumBjg5J4lgphFCe9Ot81hT3I5v6ZqZhE6PrzgUje/or9TbytW/RYHOUCYZXtbiAlrRzXipyMHJiXef3Fe7ADwi9f249ztKXzpxmU8fMTfCRLMsHc7ocYlrKAVFgpJPXBroQZRLvund9FFI3BcipWCK6p2pUrhumz3Y8YjJXtzjBhcWHakrfkEPdnmGbiVohuxKurJaAHLGyAUhEuaOKkCYfltKBHzSccNk8Dsb5wCtx6ByxXIU/MObrm/KHaLjQ0ZqNo0OLdTLcSAQNMIdMJtYGpfj1LqPSv/u5WiI+bPi3lXkMkbRnRk0yQYXyajYcuE6QUIX7v5lzw2E3ElGshPt3nrYr0iCZ+jSRYKKR4vgTTGiTRE4D7xxBOrMjBYr6ROF2qQgAI3uD3kiWNVnJ62sFJwsQIgnXJEJ6YRguWCg0c9ldHckoPrrsxhKe9i85gpOlFdZ1s9eeeuacD4kH+d4X5ddAbrxks2hjyRIf/SLfLrC2Ey0yfkO/uiiCx/WmW0+85qPavVJItbQW8Pa2fyhahfZitBzBo9vhECN1x2VKeGFbjcv7UWVooubr4rryQf0imC667qDWxfVl2vZhAzyYBTSbzWzF1yqO4zrn4nKZ69OS1grzGzaOPJE9VEgY7CWxj5v/t7dGwcNXBmxsZNdxWUQUmagSBwvcLFrxfn8cuRqJ4qSKyfPlbCEU+R9PRpJnntiAduvawFFLgSgct/byAtAuDElIU7HirhJw+XAr997vpFLHvxAFLeEIUPyimlgfKfifHAjShwFXUmrqq22pwGiEwaDmJWu95QSgHqfddqP0mVH2tCDhBmeSS6rhNB4oq+XFqsSGJRYBoEl+/P4unTVjyBGwNxlJc+P++Oh0tYWKlfqWeXHJy3PRX7DOaWHNz/RBnDjwXH3TOLNp5XZwGpiy5UOHraEjZAMnqzmmjbe73AXfPLbHcSx0V7M02RuLOLNg6fiPrBZ9IaLjmXlWPhgRvTf3CLF1n9xndZZNLMpgVgJKkq+JmsutVlBW6d/qpXut+P/eMsAGB8SEO54uL0lIWNI7XtX1zXC2ak1d6RxjG76AiVL8epKUuopA2d3S/A2i6Vr25YBbwmkKY6zexwbNaHvIsuOo2k8zXenvKxIKW0oXliwxYKnSJv2xV1uovVBQF7d74HbrAFdlygr0dHqcI+p9NEKCAAiOjehs5Wb3lAotEhQ0wY+CSTewsVQlt+V9NLtlJ1E/mXOWJPSI2D5HyvF+K5CwD++6CUYinviMHjag0ZCmU3EECmEfRktYAyITFCCk6Rl5Lrl+cE16bgCiOK+WVb1HEZtkOxXHBB8w4OHY/mNZMmiYLmcGRjjrdsinKVoifDVjuL5ej9Ba5rkoDqjPtPLuZt5KXAFgAL/nV6JmrbUAtzSzZMg2Cl6Nat86WKf718iQKIn/D3ZjVRNotl6m1vdFCsuChUXAwjOImQySvXZR6wfFIWhy98Zwlf/+FK7O9n5xz8zi8MKX+zFAFIEqHF4UCtgVRsMWhTJa83iBsfMiJlQNcJQFgfSamabGwFfGsl71P53yQTRyDe1kOVu+l5Gx/8PzMRdWOuBZVWPSQZOJuSoispzxi+v0/80yyOnGJ1X9OAV17Zg+/eWRB2CoC/hVaGTC5EFLjevydnbfzFF+dwesaLkq5IJwJBRLZeTuQ1xB6pTVDdj7DWQMA9QShb62VHtVATvUb9PAP+8ytbVCxUmAZ7P5rmq7zk8ki8jFJK6y5i7dvpEyGqZ5EE/d6C4J2PlHDnI6U6RzOc3pfGZfuyke8pgOtvWcGxs1GyDWCLpM+/IKf8rYsu4sCDB2bSmugfNAJsHvMHpEP9OkYGdKH0LFdZjJJi2W2KwOWBvUzDtzoolFyUK2zsqWtELGKGPdTP2ZbC3JID26FYygfHVcJHPKTANVQWCjGLVak6tk+yhcTYoI6BXg3nbkuhUHaZn7/UpqjaMsdlgYuYhUL9xo6Pp02DCOKnJ6uJNq+/h3n7p1Na7IL6eoDc9vPbTjJt2b8zjekFR3gPd9HFekMSS0zH9cefmg4Uyy7j0UjyMVxDFMFb3/rWRg5PjJtuugmTk5Nd5e0zFAELBWmrBosuSTHcr2Ns0MDknIW0qQXsBXinM9inY+uEKTqksSG/aHJi5vhZNmFaWHExNecTJ3KAi6ResnHG0rVQrroi2nhS1LqKrM5tinBbZaRSzC+uU1hPj0AuHo89XfG/7/B4iA8+T05ZODnVXBoDvToO7KrvGRiGSo00OWfj6dNRZUQc+nt0zC85mFmwYRosCq+tIJ8sB5iaYxPPex4vR37XdWDjSPLuSdcJ9mwxA32I41AcOWWBUoq+Hh2GDiws11Y9EY1gz5aUmLgUPAJ1ZsHF4RNsNwHHU6esZL6QEk5N255vOEFvncH18UkbpTLLr6aRiGJPxnC/Lt7f6RkLp2eAo2csVKouXJe1L5tGvcxSRBSdSytOQEmiwj2PM8LhygMZDEsBJ+eXHfz0sTLufbwEQE3gqrY/xoEtCoqs+t+30D6omuyA2DCuTW+muatzzlt+pg8/fqiEq86PRvD2CdXg87Ls2j6dSeFbKJDAX5fSmgFUlJcm6mP41/cdKsOlbLfMZfszWC4wG5Krzo+SUa0ilosP+UIDQNpTYlnSQmwjz3Zh2cGRU8yz8Geu6sEl52Vw8TkZZDNMdbW44mCwT8dQH6tvgsxEsAxHFLjeuz4xZePElE/KjQz4jYxvPxU8t11dZ9jeatuEiZdfnsPZWRvXXlHDpsa7QeotPBC5EtdAO5XEMvHDYRoEhkG87HGRCMRf+foure0/15fT8EvX9iGb1gQRG85vvTv+xVf0wdD99rAWTk7ZeORIBStFGts+cdWd3CY/+nQFJ6dsrBRd77x1NLDqYt2D9xG7N5uRHZUcukYCvtRPnqxiZsFuKhgm4BOtG0cNbBlnxNxPHyvBcSiqFkU2TQS5aYSyNDpoYHTQwONH2Tg9aKHgeeCmiCBlHQdISeO2lEmgERKYg8mf6ylw+3qY3Z/tAH/+e+PIF108eLiEIyddaPVjL4rxmUaSWRnxxdyJEUMoaTNpDTOeavr8PRlkmjHSXWUEg6Cyv0nm74N9Ogb7EjzYLrpYB4gr0pT6vxXLwNyihadOWw2JNRqafn7+859v5PC6+P73v48PfvCDmJqa6pK3z1CIIGbedjAesRfwlQ6yabxtUzHalVccDT1eSbthxAhMYA0dKJR0vPzyLA6fsPDONw5jZsFuKBDYUD+LBlqPtJDBgl1QaITUXZUFgJ4MqRmhdahfw9ySDpfSSGTS9Yjztqdw9IyFLePtjX24dcJEqULR37N+Bh26RrBx1MDiij+i0nVgpL+z72li2EDVojU9+eJAKVOIcwVFoxAlVbo2T8vQiTIKueraW8ZNFEouNo4aODFlw5QCSACAbVGYJnvfVTs6ILc8crEnqyUiAapS0CBZhVuuutB1wHb4d0xda+hE2VZYFgUFhaFDBErr93YUVCwXw/06CmUKy6bQNbZiSgiQMrTEc+SRAXZ+f4+GVJ1B9oYRHbOLbAIx0KOBKPLsumyCU6pQbN9oMr867/2lTQLbYeRFSVIOUyBCqj9+tCrUNCrkiy6OTzLy+QO/OhIgMFaKLt7wP0/hxJSNBw6VlaoPvvBTi8BVkqwtspbNjCpaHYlIThBKDPXr2LHRVAZ003X2vrgilqsK22Gh4Lg+ERRW4LLfa+8E4cFIeURy16VYzrusjlD/Xc0u2njyZBU/eYgR/tde2YNfe90gnj5dxeSc3RkFbh1iUy5HvAyWKhTT8w6yGVKXMeSnL+Vd3Hw3U9nu3mLif75lRBzD1ecHj1WwsOxg2VvUjhvbhi0JUiFB0UsuzmLLuIGdmyQCt3Y2Wyf5mf+B+Ew0gqsvzGJmwcamsWi/L/tOuor8NZudwH0kVeB67emKFEvB1P2dGXznhewRriIR4qDrBOfvTmNDAwuL4YWozeMmfvcXhxOd+72fFvDIkYpyARRg+eVWEf/tZwewbwcj1D77lXmcnMrDcujaBEPt4hmNsM1OEoTtXzgoZWMTeWEom456r1YVFkspg6DkEbhpk4rdI3E7s3ifGQhixhW4KSIWK8tVN2gNo5OAZQIQ3KVVb5E+ZRD8+usHQTSCbRMmHj5SFnkJP0JVfeREDiEExbKLlEmQTTHC2pL6/bTJSGixu0B6DsF4Ks8MLsXfdS174K5ljrroon3YtoHxGnGLYBPDBraMGxjq0+G4wOkZCmK5DU1A2svEJMQ999yDD3zgA/jRj34kvuMD7Kuuugp/8id/shbZ6qIJEPjboQEEzNJ5H+i4fsPsuFRsxyYEgU45rt8ZGdAjBOfGUUYk/tK1BjaNGozAbaDgE0ISRTaWwTvlnizBBXta9xfLpDScv7txpeRaIZfRmlJ21sPW9eDHpMDOTVHvqE5DVdaTolh28eDhctOTeD8Ilv8dL/MbR42a76lUcfGAp7jLZQi2bWDRdjlJ8fwLsmLA9tCTZRRKLnZvZmSKvD3UcSh++pin9Dw/m2gScecjJVBK8by9mQBByC0Pzkzb2L0lhZTBPLfP2ZbC6GC067v3YAlVi+KCPRmxsGNZLv795hXYNnD5/iwul+J43v1YCbZDhfouCeRrtBLwg2Ol6OKRI+ydD/frAWUswBTQhETJCSu0Ve9P/mUu0fX2bk1F1Gd9OQ17t6Vw6HgV7/nMdM3za3ngchASH5inHVAlQ2N+a4kAickur1+qXSBCgeutc4rAVnbrZIwjTQY5UUsIW8xwvX7ZjMm061L8zdcWML8cXRz69TcM4ooD7PN9B8u4/tZ84HfuYViP2G4HkqTNn+nCiotP/t85XHtlDvsSjAWOnrHwT99cEv++9NyYMQBXeMqZEQpV/6twADu5buga8JoX9qJi0UQT8nbN2QPBw/j/+H8Jri8TNUmy5BPsjeRSDf78OIFLwMo574O49618LfZsPWLXBVCj6+V1s5mAf83cHy+ndg0i1lYQX2LRR+2s0EUXNRG22UkCEWgspMB96lTUT3di2Ij4snJffXn8ljIJShWWplznwgv+HJx0lRfnLW/DVDrlB8XOF4OWYGkzar8ljzuTjDs2jRminbYd1n9rmiqAbLQmOy71jgVOTFo4Mam25dI0gkvPy8Qqkf3j6mZ3XYHCHxM9Q7jnLrqoC76TIA6mQXDxuf7c98HDZcwvOQ2Nj1eVwD148CA++MEP4j//8z8BBBuz888/H3/8x3+M1772tauZpS5aBWHRN13KVlaH+iUFrtfp8s4MYI01n0gS6bMeErHVa8g1acIg/HM63HHxAUJXLd7FeoTWwgQTUG9bV0X2VYEPeKnrD8Z4OlrIB0weHIcHuHIdTjrp1TTWjoQj+LouIz+I539Yb5sW+54KNQAAsVVrRRHoS6gsGmh3OGHQjMJanR77q/IopvCfr/yzrNy6fF8Gh09WE/kNGzrwX17Rp/ztF1/Rh8/9x6JINwzLphgb1LF1YpXXjBVlmkM1mWKBBVq7ZL1X69bor2SSiFLff68dHri8jOh6uD6ysuzUUPlWLSrI25EBXQQutWzfCgUAznqfs2nmab9ni4kL9gTJ0U50n3FPR3WtbRMGdm82cXrGQrkazH9s+hSYmmfHpUxmR/Kqq3vrnxTKRy1PQnkRaP/ONNIpDRXLCXko+ovfnYBYyFD8VuuaASUroFQ+q0+snY/w51rg5E++yAqy6c2fuILOV+AGz+P9QT27Ad5vObV8Ktv4Xnz7FPXvFOrgkIFFn64Et4sGEbbZSYJ0DIG77C2m8EUUy6YBhTyHsFiSvWelNPlChaFH1bscVKF6lRW48uK2XC9Mg0R2Q4wN6phd1APz2TiEbXpsm40hdZ0kIsFdymzPshlGJMvPUNOYd69lszFsseyKOh+3I+6ZomL1F/1oU2PpLrp4NkHT2C7fRhT0qzKbOn78OP7oj/4IX/rSl+C6wYnwrl278NGPfhRvfvObu8TYMxCEEBHIZ8u4ESQ4pK1qgnB1fdsE2UJB10nAX7Qugav5xIRPMnW2/Lhia1FHL9NFF02Bl//mFbhRtssnm2rXLV+96w/GuBl72Dc4EAiJhH8jIqJ5PUUUh0ZYeK/wffMtsgR8m5Z/fFw6/DyOfo9wcRxmA5DNRNu3RvotPkB128TgqvIs4PERhESvxydEV12QwRte1teyL+lLL+3BSy+N98h89KkKlgtOTaI4oNBUlItmoPJ1FinFsET1go4kvnbM9yqlEIfu7UTh70tW07VKxjgx/ZeuE1g2rRmosCqJgr7yx5ugaQQf/YcZ3PpASfThlPrK7l+6rh9vedVAaxluBgmKia4TvO21A3j4SAVf/f4KLDsZSciJtBdfnMX/+tXR2OPCaclq1P27UjiwM4Whfi2S1X070rh8XwbFsov//nO1n13cbbZaRmTCles+awlwg568/sqdaPMT5idyWGjBKQnCClzT6zv4VmOhwA3xR5rou2qnz7dZqxZI44hq1aJoUvD7sRwamwDfYi1beqXk87roogGobHaSgJe5cIBnTkaevzsNSpnKLEzyOo5vj5AKWSgAjNy1vTpXa/ygEvJUPQ/cbIogl9EwMWxgat4OzBVTIYsvwLdLaRSUUtgOG7uaWvyuUhmuyxb2Ljk3i5RJcM/jJd/ndljHzk0pMX6q2lQstMY9i0asBNcS8jhMjIm6HFAXz1EwoRPWj4XC9PQ0PvGJT+Bzn/scLMsKELcTExP48Ic/jHe84x0wmg233sWagwA4M81mNltCW6zlLWvyQJb7+RFCREfV6MqDTIQI/5yOK3DZ324f08V6hFC51wlIFAfVZDNp3ZJ/F5F0vT3E4WzIx6rSjVPUxl+bK2eD33PrFk1jvmH1lPoqBTNTRbA26y0fOYNXv6AXb3/tIFyX4of3FnHP42WYRnKCsSdL8JZXDTSkwP3UF+YCgd6yaYIPvHUE+3amUahQ/P31izh/VxpXHAiSsBQsKA9BSIELBFUc3Xl+W1GPcHKlXSdhyBYKoJKarg1kjL81NjRZ9a5Zy2fXkrZt8nbFV/r596za0r0aiFV61iDwuTLTduKDRMng7VqSe4truQgILjkvA12Pthkpg+DnXtKL/h4d5+9O45Ej5UB+RcJ1rrsWkBW3lLL7JAmaFtUCSxhJ74mXx0LJFwUAftR6EZNBCAs4aVA/D4CkwG3X9ok6EBYKMep7W1LYKi0UahC/XXQWjMjz/61pjXnKOk77dunIIKQOCaqw2UkCWS1etWhkoTplEr+PcKhHFLM6x733dZ0EVL88zXKVolx1vWPi80Ap8MiRCh4/WhV1gAfXTnmODVy1KosKTJNE2nQ+PpKVueFdMBoJqpQp9a0eXMoiy4ffebiNcSXCnD+zlOHPi/kzkNXIVh0LhWcK5PlGV4HbxXMdrI8AGqkCHWFOl5eX8alPfQqf+cxnUCwWA8TtwMAA3ve+9+F3f/d3kc22PxpxF6uPU54Cd2souJW/ldp//5QGB8D8s6GTgDKiHpkrqyZWi1hNqkbsoou1QCMBWZTni3P9k0WZr6eIl34XFined+H6Iv9TNamJU9TGQVb/ymALR0QocOsp9VUKZkIItowbOHbWxvyyi/+4ZQVvf+0gHJfi9oeKAWViEswtsYjNF5+TzEM7X3Jx452FyPffu7uAfTvT+PaPV3ByysbJKTsQUIndB2UvlSBCbnPllqmTVZ3j1yK6RT6IQpnd7PUiide2CglfpyUFbkym/QmbQoHLd5a4wQmcJQX/bBb+1lj1NWsrcL0JrbRGy4NGWbYrnhNXqapIzvXigcvLYEra1psEqu3qKtCQZDVsy0GBmu8yQiaHz0Xnxjouv5wkqI0LECfnJ5LHhPmLVRInOz2AlCAu2b+58tbUWUCgoLe7/w+V97sKSRY6wlDtakmKtFT3Ve2QXG6DFgoso10P3LWB61I8+GQFZSmYrEYIDuxO17RQ4Ziat/HUqWrH8jc2ZGDvVnV8hzibnXrgi9iUUtx7kMUw4LEcDJ2IPob7rR85WcXcUrAipSJBHdm/F5YdLCx7tig1Aumenrbwle+vKH/ry/G8RH9Lm0Hl72NPV7CUZ9fjfr1Hz1RxdjZaofZsSWFIsmYQdZJ4Hrh1SFa5zeHDgZRJUCj7n+W/lWr9YG7PNATH5mubly66WCuwcSKJ7FithbYSuOVyGZ/5zGfwqU99CouLiwHiNpvN4vd+7/fw3ve+FwMDA+28bBdriP97wyLyFts6Gw5yJAhclwQGyXwArJGgB67cndcbO4j0XNnaoLOtP+1GyuxiHUMuly5N5D4QgEoNpYrsqz7Xtz7wt1Sr64s8MVClm3RCzaFLbYEM16Ug3kCaUn/rWT0Fbnhy/7bXDGJq3sb/+X+LyJfY/c0sOKha7Pe/e9+GRG3CF29cwm0PlDA9b8NJyAwurbBMp02Cv/6DCdz1aAn//K0lHPcCXfCJBsC8H3tzQZ83jXAFrn89WbllGiSR+rBdSEawdZbkUyFMRPGy3AriJsC11CacXBVBzAIWCq3lR0zOQ4WVT2preeBaHoGblibQ6RBhJlsoyFu6I+jAy60jwFX+zifASSwUKKXSdvXaI2xxrbi2Lc5rNcT1NfK2kyhZG02L56nG7cROvJOqWv2EwunS2N/iECbWda+smgYPLOQnRKX1EKL539UCLy+17G/aWbRlKwTVFeWt6OHgT0B7Ah920TjKVRogbwFW9pbzTiICdynfZBCDhFhciW/o42x26oEQgpEBHbOLPsnJCdpw2SxXaIS8BYCRwWhw1JQZ9IQ9PWPj4NEK9u9KY9+OoMUBt/IbG9Jw1YGcyMNgnya893kdlts4U9dEHl2XBsZUXMG7uKJ+J0sFF8NS0GGxu5QSaIRE1HRRBa53vBdMFFDbSPDvimU/gWeVArcrjuriOQ4+Lm+kBrSFwHUcB5/73OfwiU98ApOTkwHi1jAMvOMd78CHP/xhTExMtONyXawjTC24MFJsMnrejuCqLh/oupQRKYZBYNtU6YFr6CQ4MK6n+BMeuFTyomzHHcVjtYKlddFFMyCEDRpdym1FGqsQKrKjll9nGNz6wA5t9w6fKk8O1BYKTP6V1CeWyNvOJbg0SAbK7Y4y/wrimBACXQe2bzLZlmAKLOddHDvLJgsjAxrO2aZWs4RxYFcatz1QwsyiEyGb47BUYAcO9mnYszUFy6H4528t4cQkmyjNSxOL42ctHNgdJHCZp1Jwd4PsU8cnAc1YbjSCJORjzUOazFo4wEjS68kCxEZR10LB+6srCqLu+eZRb2slV9NRQPgANgsnxrpBS6LADW3pBIIKQQ6bE70qBW7jWW4YScYA/BBTENAJyiZq35vqeILogJyXDarIa/jfvr9s9LswxCGtPmSpjDFlFBWfa0H0HeDnJPN/bGfci3Ro0YC3baapoVRxAm1gUPlWn5gFagcJrbuAUDNlNVLS4o0KQhUfsvAJeOd2serg7UkmreHic9I4MWnj9Iwl2tCk5+/ZksLYUPtYuqpFcd8T5ZoK8jibnSQ4Z1sKe7easB3gnsdL4vswIVmu+OdccSAr+qOIpYxJcNm+LJbyDh57uoJ8ycVf/ds8KhYr4//60Y0YG/QpjKl5dmNX7M/i9988DAB46MkyCiXX5yN0AIQIdT67V8miIPSO+Lvgfy/am0FPVhMq6bC9idwXcvuuWuB9rjwOVi3G8Dpd8hYGagVze6aASI0jb3uf4bfURRdNg5f9Rup1ywTul770JfzRH/0Rjh49GiBuCSH4lV/5FXz0ox/Fzp07W71MF+sYv3RtH1734j5sGAkWJz4B4ESCoXsErtTJ8cGwoZNI51cLclC01fLPoQnViF10sVbQNOax2ZR/WkgBBkAZ2Tf22iSooucJhutlwANXkW6cojb+uuoJuOt54JKI56f6ZsSikHRd0akC6O/RsJR3sZh3cGKKzaonhpJ3ods2sB0KMwtOYmUaV8sM9rKJ3DZvl8PckoN80cXJKX92f2zSwoHdvjUDBSO3CfzFJ4Btw+PgnnDraarPOOfONbKqpOMtFBpfCKn3LGsqcKXvHDdIFoaDvzQKeXusDKNGcCZxbe4JaPoZTIX8eSl8a46aFgqr2IHWupQpFLg0UQWwGvDAZRf3/zayk76Gg0K8hUKbnqlYDJeuLXScSgku4n9D8nYlfFwTAtzIezG8opr2mmjH9ZX1gW27DSpwHc8zudPlmN+P46rJ5arCp1M+z7bao8juojFwwt30fK5l/9IkkP3p21nGeKgZthuJRvoBIN5mJykIITANRl5zFbJsjSCTk7mMFrEB+MnDRfzbTcsiH1ceyIhgmLc9UELFs6yqWBRf/O4yfv+Xh8U9TS+wzmf7BmknaKjpIoTA0IL2A7pOkPKeDX9Hhme74jhMSOAHDmN/ed9hOzTQ9PG5LbfNqWuhoBhfq8jcVGix9NlinwCwNoo3b90A4V08V8HbgEba3qYJ3G9961v40Ic+hEcffTQwmKGU4nWvex3++I//GAcOHGg2+S6eQbhwb0aQt8Wyi8ePVmDbLBrwyWkL2RSBRthAs1zxJ3lsIM1XfINzkHrdk1BDyAPxDvdprsjrs6fz7OLZBdlapFHIiyIcjajOuXJW5EXkKVhfghYK0boUp6iNvy77G9mexutrHaVb+Ht5q60QCVBgsJcTuC5OeArcieHkXSifWMwuOfiX7yyhN1v7ofZkCHq8Ywb62N+erIaxQR0ziw4++5V5nJr2TXj/8ksLuPrCHIb62AigWnXxk4dKsByKl13aI47jkxRda0y5t7Di4MkTVWXZ0jRg77aUuHYYIvlaTWctRWyLTe7hE1Vx6ck5GwsrDg4frwKETVqn5m04DkU2reGuR0s4NW2BUuAH9xbq9kWEEIwO6j6B5PGBZ2dtzCu2iq54qmqVOkfTiFiQePJkFUckL8S7HilidLAPh49XUao0zszkSy5OTVuYW3KwadQQZYv3Z6emLBGQFAAmRlgEbICRQUBMsCTb78fl4CuUUjz2dBX5IrvfsEd1HKoWxaNPVQKERyZNcMHutJJ0AKIEPKUUjx+t4uiZKmYWHEzN25hZcJAyCTaPsTprCg9cdv4TxyrKrbKpFMHerWbg3k5OWTgtPSsQVr83jhrsWdDAT1JG2R/Hobj7sVJgLJEvuVjKO9g4auCuR0o1n1esv3LsGfGQ39OCtL2axqR32wMFPH3KAgVThM0uOcikCPpyOlKGr74tFF089GQ5+JxC0AlThus6xZ2P+Ko9WbEfVu+7LsUjT1UC24kBBLZvs/PYX0PyOj58ogqXMjL+9JSNslTG8iVXeGVyEABbJgxsGTcD5MJdj5ZBCNCf07BcdPHkiSpsh6JqUWRSGpbyDibnWPoLy+ydNroAU636ZfHux0qYGA7e36NP+VLGu6Rnd+Q0azPyZRf3PF5CJsUa+m0bTGwaXfuA0YdPVCPtomEAB3amkc00xuAcPFbBUsz29kZAKcXJKRvFkPWBoRPRbx+ftGLV+gTAhhEDA706FvMOzs7a6M1qKJQoVoqs3c2mNSwss/SJxtqLsOgF8HdKxPm9Hj9rKf1YwyiUXZyesQL9Ne/XHj9aUZKAxbKLOa8+3/N4WbTdp2csPPxkBbbDxiWvfH4PcjXe1dySg2KZndvfo2HAW4BezDui/+vJanjwcDlw3n/cuoLlgv+MDx2v4oFDZVRtisMnWCe0bcLAiSkbN96Zx5X7M9A0gpPTFg4dY+W+VHFFfXj6TBWVKkWxTNGb1XDoeBVT8zZKFYrNYzosm2J+2cZ9T5ShEYKlvIMzszZyaU2UBdsGnvLqFCjro4tlF8cnLaRMgpWCi4PHK5iZd/CDe1i8gkxKQ2+OROakU/O2IJoBP5Cm3A/IY2L+fSpUTJol2NcT5N1RSa3auuji2Qo+j22kDjTcm9966634X//rf+Guu+4CIG+xonjpS1+KT37yk7jqqqsaTbaLZzBGJA+gpbwrBqqO67GrREN/jy62uFg2hUYAl/pqbV33J67sy9rXlAMO+dtQOtv6dzuZLtY7OInaVBAzxXbzeoG/gucH/82VW5Et29JxqhV3VTCxWlBZHwCSN3ZoohLrgasgv+V76u/VAdhYzjs4MeURuIoJWBzGh3TkMgTFMsVNisBkKvRmWQYGevxM795iYmbRwffvKUaOv94LsgYAjx2t4pb72USGUuCayxiJW5G23nIkedQLy07sBNZ12O+xBK5QX9a/jpLUT5A/FfpyGpYLDlss9L5Lp+D/mwLlir8rxNQZQWQaGipVV5CxtUFRKrswQ4U5nSJKEo5/k8uo72qo3/f+47tXKAXml11MztkolJsjLFaKjndvBEsFVxC4fTlNKBPl/M4uOti5iX0WFgoKD1weuIxSf6tpOsXyv1wIEjUaIeiJuW+O5YIjoo5zFMsU+ZIriIB6sGzmD813AbkuI5DLVYrlYjAgjm2zAFfzy+q9xeUKRaHkijKSThHMLTnBd0sZabHRI8jCum1WpilMg7VHPMo8kdJImQQrJRcbIS0+6SRIlNQpjM34JFek98TP9pVyhNUB6t/P8bO2yB8P2Oa6BOWKg5ShAZTdb77sIlXHY9tyKVxKoNfIe/jrkvc+whjo1THYqwnfyo0jJjRCMNyvY2HF9X1JKQV1vXvlN0aDlhHi2mDvdcu4CU0j6MtpWCm6QsV7fNLCQK8G1xMj8Dq0VHC88a8XOV5a5EgKXaprlk0jbQmvd4YebGcMSR3IgvyyfMwt2mtO4LoujRDtAFC1mN9oIwSu7VAR3KpVWDZFvhRNq+qy7ymAqlW73V3KO+jr0WALhTZ79prG/l21XP89OaxcqQhcYSsX8yhmw21PDJYLTmSLP4EXh8Sbg6muzRWR37xtBSvF6HVWShTfv7uIl12Wi23L0yZQ8NYUTEMKyqn79TltBpXl5SoV5O3+nSlML9iYXXTxyFP+IqapAzs2mTg9Y8OygVPTNrZMGPjaD5ZF2zU2ZATaJ+o1Ui6lGB82sLhiI5vWoGtscTKXZg/apRRVr57qOut3XZeiYrm+SI1wS0DqxXsAKPs6YL+QNlngtv7e6EuU2wEeBFcek44O6Dg1RTAojaVSJusHOCke7gc3jxuYXnAw3kbLjU5DXmxtxKqtiy6ejeDz3o4RuK985Svx/e9/H0CwEbr00kvxyU9+Etdee20jyXXxLIFM4HIydXTQwPYNBNk064jO353G0TOst5qctXFqxoJOCPp6NOgaga4B5YqLo56ybbng4syMjS3jppgUyZADDq2eAjd47S66WG/gdaCWn2U9yG27q/DoikNU0KHukAIWCkoP3OC160FlfQBEt8iK42PaCZWSN6zABdhEkytfN4wkHzBrGsHv/PwQ7nqshL6cVpOMOnyiinsPlpEvsczIg/nf/vkhnLOtILzssmmCf795GYUyC67GIasJZcUOJ3BT0nb4JNwC39a4ZdzExLCfn6l5B6emrZpb8JOg+RIbj+0b1f2H7VBRXmYWbTx9qor+Xl0ERqGUCoKkFk7PWJhZYBPxbdLWTULiFVRzSw6yaRJL4O7fmcbuzSnxfr/2wxVUqizPPE/ZtIYDu9LK8+Pw9OkqZhYdFjBUIuKHB3Rcvj8jnkfVonj4SDkQ1EzlgcstFGzHJ8P59uG0SUT+TYPgor3M2kPXogsqYfByNNinY8+WFA4eraBQdmuWL3E3vP3jXo46sHtLCmOe1cniim9fwrcUWw4NPI/L9mXF5yeOV5AvurCdoIUCJ1n27UzDtimePFkVbW64HMs+3JpGsHuLCccBLjsvI56F41A8cLiMPVtSuOTcjGjTdD2ozurElnhZ8Xf+rjTml2y2CE/VCyf83n/2hX0oeSrPigWh3uVZdD2SdMuEiQ2KnQqHT1SxXHBw3g4TQ30KJaJL8cChciS4Hn/OmZSG83cH68AXProJJ6csHDtjYXzYwEV7mWr7eXs1UR4tm+LCPRn0ZKsoll3oOnsGe7amAp6a+aKLJ45XAuXu/N1pWDYj+58+XRWq7I2jBjIpDRfuSSOX0ZDLaFguuFguONAIxc6NZqBcJQUTOrAyfGBXJvDbzCK7od4sCaQ91McILwLgeXszcClTHrfaPrcDch4uOS8DAuD4pI3ZRbvhMQuvsxohuPjcxtrCMDgxZuhM6Q8wcn5+2cG2CRMUFD0ZDaMDeiRY83LBwVOnLfRkNVywO42TUxb6cho2jBjYsdFE1aKeZylwyblpLBcojpyqKu9XDgJrxPQf/L4P7Eojm44fmB0+UcHckoOtEybGvABhjzxVQani4rztKfT3RMcfZ2dtnJy2UCy5uOkutkCcTTMF6Gtf3IcdG1L4xOfnsJh38e3b83jNC3vFQvBQn45XXtUj2i45vokM1fflKsWH/34aADA6pOHdvzwEywE+8fkZzC26KHsc7iuvymH/7gyeOlnF6VkH9z5RwqkZHbOL7P1ddSCNl12akzxvCUoVF/t3psV46+WX5VAou3jwEFP/Pu+cDPq8Z3HsrIWBXh0bRw0sLDsoVyl2bDSRTmkwDYJLz2N1sGpR6HoZBMDl+7LYudHEE8eq6M1p2Ls1BdMgcBwXjx31yWeO87an0ZvT8NjTFeFpG7ZNuGxfJkBmEkJw0V7W9qjGFpmUhisPZJ5RBGjA270Bq7Yuung2gu88VcXGiENDBO73vvc9odQghGBiYgIf+tCH8KY3vQmEEExPTzeU4VoYHx9vW1pddBaD0iqjPxlgAxDTIEibzMuJT1gdl2Kl4KI3S+C6jOS1HQrH9SdSbLJKMT1vKyfgcqT6VfPAbUCN2EUXa4GGo3/L58pCL6+N53OMRMrJOGVraFQWHphGjo9R1MbBV84GT+ATJHmioJF4Xzler+WJlYhQDt+H9vikJZQiKgVNLZy3M43+Xg2bxkzs2GjGHveTh4u496C/vXBAamM3j5v41dcMin/ff6iM667swfW35kUEZUqpmJQCCGzbs0LBb7iarB5425wyiQiuxf7tBn5XgpejGunLCwftbGJTCs9S+bt8SUM2o6Evp6E3QZRwGYWyi5Wii3SKJD7X0NnWyrhnTghBJu3nz9QJKmDbs32PPvV91YLmLZQCUb9bQyeAzo9jf3kwRE0jAfsADlUQM0sqI7LnbiN5lX3xUyYRREYjBI8fTZ2NQUyDiOfNf/N9DP18h/PKj3EdKsjplEnEmCOTIrBCz5SrUlWFnVLWzmgGI8A5OUu9ZkQjLA9x5H8cGvHYDSP8nvSQFQ5PlxCgaruing71atA0IJ3S4FAaCPzHVeOgrJyo3j/3k6RU/TsnXOVyyPILL7/ROpAyCcaHDMwvO0LtDLCyn/LyCriiTPD/LEJhht49D4omt2vM09T/zVfuE1EnUyaBprH7S6c0truswTrg3w9QrrJnGT6fkx6plBb4rcdrh2yHet8Hn9taQt4tl/H6EE4ANmr7JJcDuT9qBlWbkWLplObttAH6e1jbDgJQl5WTvl5d/M6h6wTmlA1KWT40r83JZjSkUxpSJkXK1EAphaZpSKd4fxnNhxxgzIhZ3+X3nU1rNcsUpeyeBnt1QVBy0jad0sR3MhbyLrJpDQc9O4IXXJjFx39jLHDM6Vkb/3HrChaWXXzjlnzgt1xWx2te2Cv+/dPHSvjOHXlf/EKA17ywF5fvDy5mfPHGBRw+wRrYc7emMTLI7Ci+8JEtAIDbHyzi+/cU8NbXDOHIqSo2jRuMwD3o24hsmzDwyd8OBko3DQLbIUin5PrHyHSu9k6H6o+hM3uHUsUV81JDZwvl/Dhd99+PrgHw+uzBPh1D/Z6FlaV+NwO9rE3oy2k+gRsa8KiIWN72xOGZRN4C8GNu0G6A8C664NPUjloosAuwSd/U1BTe9a534V3velczydRM344LvdrFukJfTgt0HPJkwCdW2e+DfTpSpoZcmqnYhvt1vPjiLPqybAtnueJHCx0f1rG4Eq+60SXVnU8ydbYDa0SN2EUXawFfvdr4uXLt4Vu3XYkIqXvtSP3jCpnwcerP4rsYRW0c5MWcwNUVg8I4kln+TanAdSG2wz1yhE0aBnu1mgoYFZKqi7eHyN1aal2NADnPaoFvH6YUga32hRLFUt7GQK8hWSgQQbokCdYVF+CE/7uTBEEnm/a44F5JIBSUzdS3hJczTQKU2IKm8C9uIq+y/UUtsj0cSE3TfNJf6YHr+HXGEjYDml9eGsyqHwk9mB8V4cERtugIv1NKpXT4wo5EkvLt9WFLF36+7UoK3BQJEMSuyJ+XCRrkb/nWW+8nAfmxEEKgaQSuyxR4cQQulY6X0Ur14KSRuHcp7fAiQ9W33BYKZv88/zcNALd8jQtM49cddVlUlUOWX14+at+10opF9BU0UmbC2ajVrvG88XgOWqiQ87LKvSvdWotbNWCaBOUqDWzP5qgIW5Pg97K1CYW/Fb/mAtsqwQmXNfjv0XGZUpj7rtaD7bC6kmmRvGX5CrY5ACPMZxcdEXR0peiyd1GlWC66ojLaLsVi3sVgr+bVX/Y9X/zhgb2qFvDwkTIqVYqjZ6qxO0MAVjf4PPvQ8arwe3ZdimNnqtg0ZsCyXWFDp8L9h8q46a4CWzTS/MW2i/amsWtzKvY5PHCojG/8iBGzF5+biRzzllcN4Bdf0Y/rb1nB5Dybp0/N2bjr0TL+9msL+PLNyzB04CWX5HD9j1ZQCHlVP3CojJ97SR9uvb8o6ta0l86520y86OJspH974fNyeOHzcgCA0zMEuzczlWsuTfD9e5hXw64tUXYzrsQTQpgdUckFBUR63N4jZfgL/+VqVAiga357bTl+vyzbUsX51PJ7681pmF7g6cVk9FkM/jSnpaC+XXFUF89V8GDbjfBLTRsiyUHLunjuoq8nWNr8iY0U0dhrkzVCYOhMSWHqbKA5NmiIwQXRfJVBLqNhccWNDxogVu981W6niVV5FbmLLtYjBPnZlAmu/5GTejRUh2tfO/hvHp83/L2cltJCQZpkJ0FsEDOpLQqnrQIfnwc9cL1+Dv5Og6dPMxZjbFhvmFhMqpDeMGLANHyfw0GFl5qfRyCXYbOFJWkbc9gn8rYHSti1OYWnvYAcpkF8/8cEeRekWOghyhPwOMjKvCTHrOY43lGUk6QIk4JJQBKWAQ4+cZQnis3kVS7XtQhnmUx0XAoTxI94L1sohBS4LqUBCwVHIiMagb9QQAJ/kzxjfiUVUWQINa2Xf2n0y4PCRcs2zxMV92nqQFmyaODZUj1T3n6IhaAat2DoQNWtvXAl2uPYA+LPjYNcr0O2vkx9KKnnLc8H1DAINE0DqAvNC4ZI5OsTgLoUFCT2/cepwTkIYXERXOqXQ8B/PnEKxVqPQNX+xrXJvkiAe5qSyG+8TIQ5bP5MOaHT7OIW85ymygBo/F2kQypM/m/HZXnXPauc9aTA1QNkGPtbLFOxgyQJCiUXjksxoFCSNp4v9lcm6VImwfyy4wVkBCyb+WCfmbUxOWcHFgAqVReDvSlYNiOV80UXT5+pCluRfIn5QXM/5KpFMbccfSGc/OX5KJRpwJvbdigKZRd/f/0iPvqPzfn//ujeIt740j7l7qFKleKW+31v/asuUNt+pEyCX7y2X/y7alG8/RNncWbGFnZNX/zuMgBg64SBX3g5O/Yr31vG6RkbX7pxOZLmpedl8KZreuG6tfu3TIqp3a88kMV1V/ZgZnEGDx+p4PnnK/IaI+6p2hSL3rupVtk7zaT8Y3qyGgzv/ZQrnMANJs3ba9vxA8PKSnDdU5nLfu5y0NuBXt97vtHgfc8GZNN++wqwuUs61dhYoYsuni3g45BGxvYNE7hdwrYLGf2hbaOOpI7g5CsvmLwP5ZNBjQS3fcirb2YdZZNM/PBjOu6BG1IUd9HFeoMqEFdShBW4chJJFkeCyloitnjW9sCtoZJKeA8q6wPAJ1ZkUqZW3SWC/I7+RikN+NACwPig0fCiUXgbcBx0jWDrhCnI4loKXF0jItjZUkFW4AZv5K++vBD4d8pgW/ldJPTAVZBi8r/t5uaTEay2CMNJqOhTIYk6NA5Jr8ZJIMvyg300k1fHoShXKI5PWRjqq11wdY3VP0fyDQWAtBzELCWRWNRT4vLfTCLKX6MRs2MVuLXqTKj8qhYbeD58L3sitvqXquq8atL7taWgUQAj9GQFLif6alYlWd0faRdZunGL1jLC5yYhiOPgB3sMfq9Kqxp4BgyaRkCE5thXtvrtr/q6YuGnlhpcB1w7WL+cFsZhstqWX1W2DAnmL3hNmcDhz8p21OVG2HQYrZGnXImt8uMWqr8YApcf0+vxWtQjwptpO9oF1WIZJ3N5kDDTIDhvR21P2xOTFlaKLhynud0IkXwpRCA8+JbtAFsnTCznXRg68I0fLePhI0Fv020bDOzekkLV80f+31+eD6jVAeDDbx/BZfuzmF/y/JOt6MKA7w8bzFcmpWHvthSeOlnFU6csTM4l63Au25fGf3v1gLjGn/7LHE7P2PjqD1Zw6Xl+BvdsMXHejjROTluYW2Lv4Z8+tCFx0LuUSfD/vW8Djk+yNB86XMYXvrsMl1L89s8P4YoDrBCODuj4w8/NQNcI3vqzA7hgD3vPGgF2bjJw/yG2u6lWGU2bbNcnD9L3O78whCdPVmNVxSoUPJU3fy5Vi4o2dO/WFLJpTbyDikKBC7A5atViAVB5XQyr4S/am0apQpHLME94WaGbTWu49LwMLJuKgKLPJYwOGujJ+jt10mbj1kFddPFsgc+TJa8DDRG4n//85xvKUBfPfvSGOh55m204wiYvoHwiVGvgxQfEXGEbiSQv/VMQxR0emIYVxV10sd4Qtx20kXMBHr3X/3eSqhVYjJEU+BFVm3QhtYWCn4ckiLON4Cv78sC7Vt1VeenKE7pLz0vjgt1pzC45SBnAxeemG1408tVe9W/uv7y8D1+8cRmbxgycuz1+cqJpEJHqi2U2mSAEKHoK3JdcnMHjxywYOhsccK/x51+YbUgNGmc1wP9dyxYi0buUya3QT51sc+MIrCRoRB3aLFLepEYm9prJq+0CX/vBMg6dsFCuUOzbGd0e66fPfG/5ffnWCP4xwkLB26pdkVSCKZNg2RNyNUoYRRS4CYg+DiIWc/i1/d9U1iwpA6hYQKVCkZY8aTnk92uFyJUwwSxfl+VFtlFgf2vdQSKiOg4t1A/ZkkCVP7Ybg11DkGySNYX/yPyzeQR3nq4KglCvcb/hcijnN1aBW6OxEe1vZJdF1Adc0yQFsEMjW6gp9Ul7XfP94uVdYUKB26TwhRMaFYUCl5PpYdJD9vOsWMGxs+Os7XZt1WIZLwcVb76QMpg/aC1kPBsTp45aM3G+FApcnkfbptAIcN/BMvIlR5C3E0MaoBFMzTk4MWljpeiialHcd7CMqsXayuF+A4WSi+WCi3sOlvGyy3pEvXBcFkhTbsvDAb6qFsVdj5awmHcxMqBjbtHGfU8wb/zfeOMgXvsi329WRr7k4uDRKnIZgvN3++38Cy/K4ivfX8Gt9xdxq6S0NQ3gcx/YiCc879vztpvYuSk5IQowSwAeWPPArjR+4RX9Xtr+M73qgiy+/VdbI98Dwfa9Vv+WMtnCs+34762/R+0HLG0ICIDbS/Xm2PYB16ViFwaf03KLHTGOjVhH+f2yKsgnP6Y3x75TOX1wz+znKhq1IOuii2crBE/WKQXuW9/61kYO72Id4Jvf/Ca+8IUv4J577sHk5CT6+/uxZ88evOENb8A73/lO9Pf310+kBsKBW2Tli094sg6KF0yxHTPUgWuhVXm+vcRxo52nvL0urPTtFFRbsrvoYj3Bt1Bo/Fx55S9I3sYH/gpcWzpE9s+NqMW04HHRdBojxeK2wPJ/B9Q+CQhclQcuwAKAfOY9LEjG6RkLx89aDS8aNaKQvu6qXlx3lXqCJoMQIJMmImL5Ut7BQK8uLBQu25/FtVf24uJzM8imNSysODh4tIKerCaUI8kIXPa30wrc1YbsZ9oomiHdxKNOeDlO4FrS820mr5bt4tAJxpLcdFcBv/mmodhjw/fFFYBKCwWv/61W/UJkGmpfySTwxxBeXhIQfeHiK9s3cJJNeIG6fpk3DYKKRVGqukin9Niy7Tg+ie0HxoL3N0j0scWvYI74U5NVnuE21WhkMSDm9TdDE8r1mikC4y/DywH3v6XwngPxApfB9/91vUXAWC/IULR69THBPALJFfOqvoU/c5f6EtxafaZQAIfKnq7537lcnevdeHCBILpo0Ah4HVNbKHDVX5Q04n2B7JntOJwIXwcKXFnN7L0Ty3bFIsKh4xWl6phjcs7GySnLUzWS2PZ3dFBHX07DiUkr9hhD9xej9MDYhAKEPcvbHyrh9odL4rfBPg3v/W+j0AjwJ/8yi+kFF//3hkX80zcXsez50F9+XgaX7c/i+KSF62/J485HinjkSA+qNjCz4IJowO0PFbGU9zNmO4x0H+h1QSlw3xMl3HB7IZLn4X4Nhk7wvZ9GfwPYc65UXWQzGjIpP9DXrk0mzt1uolKlov2aW3KxlHfxns9Oo+SNGS7fn8X9B0s4NRN9CaZBsHncaLtfqWVTHJ+0QQD091Rjj1vKM19ihzr49u0rTI3rWVfIzxLw6wj3V+b1YaXIOtNcmsAItSO8zkUUt0a4vWZ/T0zZQqX7XCZju+iii+ahCZ6sQwrcLp45yOfz+JVf+RV885vfDHw/MzODmZkZ3Hnnnfjrv/5rfPWrX8VVV13V9HV6ssHCJgfE4JMYPlDgg2dbTISCaQVSoiwN25Gj6YaO1wA4/jU7TuB6Y+iuAreL9Qo+AaFNKgI5EQH4REKtwF8y5HohK5LCK4oBqwWVB26DCtw46wN/q61Ud2vci6Yi40Kq5PDnRtscfv12CjY1T+nX10OwlGeTmGxaEwqtvhxraFXPM2n0ellVFueBq/KKFOeHrqe+hneM+J+cz841us0Sjeyc5OpQDqL4VAumpHT1r5v4cgKnpv0EKlUXZ2dtEMLSGh3UlR6f/L5UFgqyBy6l0sKsUHp7aTXqgRuy6vA9lhtIQ/biD+VDJtN4gLiysFBQl23HpZKFQvA3dp5P9AVKQvjWaxSTRoK1Nfu7Ck5I9ScLailnZMHqrqVYfI+dcIgFtBgFboLFD1X9qqeYr/UIVH2LSpUrX98CjeRR04hP0gvTbt5v8q8JUime/xqZqgFOzqoI3DjVH8AWUCqWr9zVNZaHtfbBVS2W8fdYtYBsmnnbFsu10ylVXJTKLEZGsaxjuaB+wPmSi/EhXXjRxsE0CRwHWFxxRECt5aKLOx8u4sSUHVmc3L3ZRKHkgBBg+wYT0wsVTM37D5cQYGxIx+yijZTJrFqW8hTv/ZtpgLLnsGuzibEhPWasRnESwN2PM9J4w7CO83aksJR3kC+62LrBxOJK/UDfKZMEnw0BLt8X3Hkxu+TixjsLmF9ix23fYOB1L+nFf96yEpuuoaPt2/4rFkWp7EDTSOz7BFgbZNkuqjYwu2gLFadtQ3keIQTFMhUBygC/HOayGkwz2EfwPoB7tHKE1aLs344IgGnoJLKY0kUXXXSRBHxs0shwuUvgPgvhOA5+4Rd+ATfeeCMAYGJiAu94xzuwf/9+zM/P48tf/jLuuOMOnDx5Eq9+9atxxx13YN++fU1dK2yhILbN6cT3jPUKZHirc3iFMzxHN3QiIs2qoBHAgT+4T0o0NQvZbL2LLtYjRBT2ZglCbx4qR+lOWtxlMpZ7S7Lzw0p7afKmIOYa9fFVWR8A/gKSrhO4wnc7/mb4b7J6Tj5aTt2VyOFGoItrNHZeLfDn2ZfTsZS3sZh3kPF2P2oEyGWYVxvPs8w7aJx4qJMf+V2EiRN5K3PYK7IpkNXViDVLNLJzgml0Ar4CV95i2lheXZfiqZO+52HFAn7lD8+If//8NX34rZ/3Fbnh+6oKCwX/ugELBUoFWcQJ56YVuKHgZ77CO76QhhWvjuir+e8yYegpcCERZBV1Xn1yifqerjpXaEnHhYg+uY6xD+yPvwgcfX9J7DgCixxtQoAQDV86pMbl5YCP3YIWCv75cjJx718ojut44AIhBW5Cxbxagcv+BjxwxXfRfPikevQ3EaCOBFWtcrkXi1stWijUJHAVpBFXlnNloO75Kzey0NQJqNoE4YFru8imdVAmfMVQn47xYfUUdWbRxnLBhWVT7N6SwtYJM/A7pUxx6boURW+7/IYRI+IlP7NgY37ZQb7g4K+/uojFfHxDvnergRc9L4eTUzbO25ESFjTMHoGpXAmA//KKPuzabGDnJmYnMLfk4IFDZRw+YaEiCUun5x0M9+nIpJnfPX+PN/00jy/euBS49utf2ouXX96LI6equPfxErIZDVvGTezaFLxvGWxRV4v0FXu3pYWIBgCOna1iYogplft6dLzweVlBSuo6wQsuzIljz87ZKJZdbNtgYnSwvfRBocQsKFImwbnba3sguxRYWHawbYOJoT4dpkkic1GOTIqI2AD9PTo2jhqwHQrLcpE2GenKF/DkujTUp+P83WnYNmuH+kMBu7dOGOjv1cTCTy5LunPDLrrooinwsUkjPFaXwH0W4h//8R8Febt//3788Ic/xMTEhPj9t3/7t/EHf/AH+Mu//EssLCzgne98J2677bamrhVehQ0oX0KTGN63iQAPdQjcepNjTQxKveM7LI1tVnXXRRerBaGobJJQkj1RGw3aJ5OjmhYlUfzjpOspOqtawcRU4JPB8PGu1M6IaOEJFLgyWRn2BRZp1yBiaoGESKR2gD/PPs9r7RP/PAddY+nnMkS0k4JEUChd6xEMsqosTJywYFBMkea6AFQEboLnVSsHnWxy26HAraU+bhVjQ+yBLiw74hoNBwZzgadOM/YgnWJlnKvtLRv4wb0F/OabBkX+w6pXS6H24xG3KdguGL5dlZNOTStwhXIzeH4SCwWxgCVFc68gSAi7rl/X+YS9bMlElw/+75JkDyEUuAG/TH5dCoTGPfLnWgRsI3YcEVuaFoqdTIg6rh/MR66PnCBXK3B9ESr3yuU3ShBPaih3PISgUl/Xra81GhLZJoffE1G0++L6NYLp+o7BwfuTyz0vK24nFLhWtE5ysPdDJQuFYN7WCqo2gb9Hy1tfcimggZGPIwPxDV02TTA5Z+POh4t4XBHk89S0hR0bUxgf1nDP4xUM9WnCK55jueCiWHGRSxNB3nLfYkqBvpyGX3xFP55/YRbjQzq+9oMVTAzrGOjRRd4mRkz88nV9+PYdBbzhpb34L6/oD6g1sxkLb3nVAECBbRtNzCw4+IPPTmO56CKbJujNaThnWwqGzvrQ2/++JJ4LIYwovPJAFiMDOmYXNXZOlmB0UMeerY351AKIPNNCyUUuo2HfzjSGvECtXK2aSWuBaxACTM3b6MnWfjfNwNDZ885l6qe9adRAJsWI7w0j9WmMmUVWAXuyBCMDOhyHIuO9o5QpEbihutTfE58PTSPieXXRRRddtAI+1mnEmqZL4D7L4DgOPvrRj4p/f+ELXwiQtxx/9md/hh/84Ad48MEH8eMf/xg333wzrrvuuoavJwcbCG+z9T0ovYIZUiOofG39tOp7pPlqitVR4MpbsrvoYj2iVcWPqIG08aB9cr3QSPyCR8BCQZF2nKI2DqIdCAcx49cP5SsOmoIIJsQnJwMEbgw5XQ+NqosTpenlYcdGEwePWVgu+IlvGTfElhzVNQVZU+dR1yNN6tndNFIaV3t9rCUFbiiAVcvqYwXO25HCzT8tYmbREddo1AO3VHZx7CxjSH799YOYGDbw/AuysGzg5/7gFBaW2e88cE1YeWirCNxQsKSKF+AqJQjcFhW4WnIFbhh2qLxSyAQpZWMR6nu5iuPDgWq8c+QgUhoJ5oud5xN9ci4beUvN2HGEr9OKhYLwdeUkJ9iYzifHiUTg+udzCxdNY37DjMj1xnw16oNRgxzlUClg69XXWiS574EbzH8j15cSi5xPSLDc+/1x/DVqgT/nqh1NwF9UUZ0XJH4b8lfuIPhzLFcd/PkXlzG74MCljGi+fH8Wo4M6imUXt9xXhGEAGVXkJ7D7mlmwhad3HFJGERtHDRyfrG83AAAvvjiLj7xjDABwdtbG0TNVjA4aQuErgoxJ5d/QWdAuHsAr3P9xC4X+Xh1bJ0xsGjOYR7EL/Oi+IhaWHfznrXkArJ2ZmneQNoG/+N1xnJ5hSuMzszYqFnByysbckoOKRdGXs3H4RLxXLCHAxhEjEqck8oxMgkI5uEhQLLPPPZngvfDdFZVq+8tR2DqnFhoNHsrvjS86yo2DqbAF6qKLLrpYTfB5bCPijC6B+yzDbbfdhrNnzwIAXvKSl+CSSy5RHqfrOn73d38Xb3/72wEAX/7yl5sicGUFrkwQGLrkocml4SEFbr0JM58Mx/mHhU/vtDI2bkt4F12sF6hUpI1A9kQVW2uTErgyMauRWAWvvNCiUmcJ0iXhHEFlfQBA2vJc+3rhdMJzAqZlCqJZNT6/fjstFDgp8fPX9ONN1/TDdoByxcXhE1VsmTCVxDQ/j23/rY96bbbu2d3UU3jVelyB4HHhAzvY5DZLNAK1I9W3C3u3pqER5hF5ZsbGjx8sYbngNETiViwK2wF6c0RsS3ZcNmG9YE8a9x4s471/PSMm7JZNcf7uNH75lQMA/Ij3Mmkrk3jVqq/24983MiGX4atnvfMTEH0c8hZ5+Vw5H4EgU8LHF/j2HQUcn7QCE3rXpcikCa69okekEV6YltOWiT4i5SdCsCpeXaJgbTXIyWYh122qUHrKsGxPpShZKBDCVLYEgEMpdEoi9gQq1CRH+TGKd9+KYt73u40S8qryVbPsSW2n3HbJ5b5V4pSXz+/8JI+fPloK/DblebWqLRTY37/+6jz++VtLqNounn9BDrs2N67YbCccF/juTwq4+7FSICgjwO51+wYDP7ingPueqKgTUGCgl+DqC3KR7+85WMbsoiPI22uvyEXa54UVB3c96hvuXrjb37pvhcqZ41CxcGtIz1xO09CjO1SEiprH/tAIBvs0zC+7+Olj/NpBInr/rjSWCi7mll2Uyg568xootbG44qBUodB1ikLJDfi6qlC1qCCW46AKlFfwbCfCBHr4XtoJ0Q8n6EMbDR4a7pvkK3AvW/a5O7froosuVh9dD9wu8N3vfld8fvWrX13z2Fe96lXK8xoB37YLBLfZapICV3jghpR19dRK9TzSworbTvoPMQVec6q7LrpYLYQJjGbPpzRZ4K/AuVL90whghRZw/N+Cx8XlIengPI609tuZ2tcLXzei/PUYXFfsD5bV+I21ObX8FpvF/9/enYc3Ua1/AP8mabq3tBRKgbLJUjaRTbyAlB1ZpCgioFcBRRQQBAUV+KGAF8X1IqAIVxAQFAFBVkERimKBsm+yL4VSuu9pkzbL+f0ROk3abG3T/ft5Hh4mzZkzZyZvJsk7Z86RxvkEpB/oWWoDVGoDXBQmvc5Mxv7M42jPvYI9Gguyl4wRRcg8GW/HLrsfUsVNNOaxNlO9syhdZAisqUBcsh77I7NwO9ax3mSWPPyQGxQFEs492nvg5GUNktP1SE7PL5uQmo2nenkDUOb39jMZA1cmk0GpALT6vB64D34k542BW4Qf5HkMBmFyh455T1ebPVMLPCXFqyz/eWkIBSEgHiQZ8xISiak6HL1gniAzVbemMdGidDFNeOY/b9YD16TXap68eM7vzVp4G0WarM3KIS3OWcVaQjRvLFIh8uvNm8gt7zWWErXyB5NFavOS3A++K9lK4JZWD9y8BRufLQZReJzighcAzbZv4QNVJixv3zTuLV00KIq6tYw/0VTZAqpsy+/7urUKd8ENrOmCmEQ9EtMMSEwzbvzgiSw8FepdvIY4SWKqDhHnje8zDzcZBjzmhahYLc5dz0F8ih5f/5yKhFRje0f280FtP8s/Ei7dzkH4KWM9/2rrgbdfDChUZvffmfjvj6kAgA4t3DBrbEChzxV1jh6TPo7H3XgdlC5A+5D8Cb7yYq5gb+a8i3Z5TL9fWOrBaSlBWsPLmMAFgJq+cgzu5iU9p3SR4bG2HvD2VECdI6DOkaFxXSV8vY2DVMckyuHvI0fDIBcE17GckNfpBe7Fa6FSG+wO7aO00L68cYML9sC1tC/OYjr8nj1FvVtBGsPdwutT01cOD3dXCAMQYCXeiIhKU/5cUY5/X2YCt4q5cOGCtPzoo4/aLBsUFIQGDRogOjoa8fHxSExMRO3atYu0PTdl/qdtwR8CBXvgFZrEzEagCjgwBm7ByZFK8Te/tXExiSoSRQl7eMoeTGplHAM370e4YwFv3gPX+GPe2KYC5ex8Qc/vperYTkhjKRYonveb2/RCka1dsd5TFQ/ak/83RxIUFrchJYmLtp4jdZq3z/i/ccxa87+ZTdJmZZ8LsjbxZB5HbocuidI65VpKGBaVtZnqrSnOe7NeLRfEJeul5G33du4Y0cfX4fUzswyITtQhpJGrsSeZScJ5SHdvhDRyg1qTvwMLVichLdOAq3dy0aqxuzSEQsEfwEqlDFq9MXmbNwaua8ExcIuQGLd0a7sjsVXw+oDUi9clP9GY1w4ZjPEshJCSM1cf3IrcuJ4L3hhZU6r31wgV/jiRLT3vqpSb9Ni20APX8GColfxrPebttPFGcyRRbfUZG0lIe8x64BbcXoE/5PWaVBbo9al4kMA1TfYCtn+M5J+XBPQGYbFHucUeuA72mLe0ZXmBc6FxG8b/Lb000meLpbvA8npXF9iQadzLS9gDt++jnqjpq0ANL3mhCb2i47XQ6gU6mCQd8/z7iRroGJKDBoFKeHrIMOurRKRmGhCXrEP9QOsTX5W2y1H5k33N+HdNtHnIDZejcnDueg7uJeQnqFs1dsWEYX5Wz8mPJ2ulBG7rxpaTmO2au+PVp2sgKVWPzq09LCYx3ZRyjH2yBk5e1qBlI1ezsVd1eRf2Hvwpp8AY33lMv1/YSuAaDMYJmRVyoH5tF+lc/twTvnimt+Vz+f1ELTzcZAiuo4S3hxxZ2Xr4eMrh7+uCOjWVqFfL8k94IQTuJ+qg1wuocwQ83a2/Dy31qs1WG5c9C8xxUpoJ3PyLxA70wHXgbgVTuQXGuDUNBblcZvVCARFRWcj7rlSU3yFM4FYxV69elZabNGlit3yTJk0QHR0trVvUBO5f57JwN8n4BUqnN04KoHQxziibmmGATm/88pCRpcD9JB0SUnRQ55jfzmJNXiCnZuotfrmOT9EhMzv/Ezw6QWt1zKySMpjdclcqmyAqsbwvpip1/piXRRGfrEOu1oCTVzQw6AUysoy37NkbRw0AUjOM72/AeMu1p1vhL8uA/fdP3vOaXOHQPmRrDEhI1UMu10Odk38+SEw1/gITQpgsW/+A1OoEElJ0kMlkiDifLf39+t1c6A3A/SSdlDTIzBbQ6gTSMg2o4eP4OUerA27eMyaDYhKL/vpYkq7SIyXDeN71fDAxh84gkKUWcFEYf6Blaww4d00DpVIGnc44M7e7qxwCxh+m529oCk0qaSpXK6DOMcDDTY7r0YVvb41L0kGdk7+NgjKy9BACuBuXa3WcuZxc462bvl5yJKbpcPVOrpRocXOVIUtTzJmAbBACSHowwUl0nLZYmeKEVOO+51xSQ+lAbuR+ohbpWQZcuZOD1Ez7vWnvJ+kKjYtlMAgcv5RteQULdHqBbI1ASoYWOp3x8fkbGqtJMG93GdIygbW70rHzz0wkPHj/HDyRhStR+a9/Xs/cD9ckSgnclHQdvt2egsxsY3L8bpzW4jidlhgMQJrKIA1Lkfe3vLEeY5Msj/mYozXGu7enDMkZOqSkGx4kUwUSUvVIzTQgJcM4ZmROrvH2Y7kciE3WPqjXuH/uLsDJy/nHVfcgGxefYvw/J9eA3X9nIlcrjIm1B+/9lHQ90lUGnLoqg1ZrnFjVx1MOD3c5crXGY5CtMeBOrPF/hdz4PjTtq5uZZUBMog4uChkuR+Xf1m0qS21Mlien63H7fi5iEozfgVIz9EjL1CPqvhbutu+YLiRNZYDBAKhzDNDpjZPlZWkM0GhzkZSuRWyiDrlaIEOlh0wugyZHICnVOD5oljr/4odcJkO2RkCRa4BMBigVxrEyb9+3Mk6nyB8GIOJctsULe2mZesQl66F0ycXFm8aEXWqm8XXNzRVwsRBX6hwBVbYBmlwBN1fzN/T9RB0SUo3jiubkPrh4IwTiH8RIcrr5+zEpTY+kND1uxuTC64p5XfcSdEjNMCBbY4CHuxxCADlaA7Q64x0QWp1x2JK8ybG2haejqJLT9cjIMsBNKUNUnPlxzM4xfgalqwzw9Sq8nxqt8fPbRSGDr7cMaZkCH69Lhq9X+d1ClvDglv/GdRU4e1WNhFRdoeR4/dpyNA12wc7DmTbrahikgCrbgNRMA345lFHo+cwsA2KTddDpgVOX1YhNsvx5eydOC3WO8WJVltogxYxKbUCuViA1U4+oWC1ycgXSVAZ4uAEJKTrpu0l8il46P+r0sPidJTndOH75kQe9j3295ejU0g3BgcbxcE2/b5hKStU/SMZq4aqUQ6XOv3Bs60K4TCaDt4ccGVl63I3TShN2WZKu0iMhRYfUTD1SMoz7kZyR1wPX8hAKWp1j382KIjPLuG1HxoDMS/JmZNn/nitE/kVaS8ONEBGVt7zfvRxCoRpLS0uTlmvVqmW3fEBA/q1HpusWlJOTg5yc/B9u6enGL6PXbqUiKdX8MqirElBl5n8KK4QSHm5ypGTozcZsys3RIyPDPBmQpTJ+kdHmuCEnRyBLlYssFZCQWLhNsUk6ZGbnf/tzk7uWyhiEphQKGTId+NFNVB7UWXpkqXKQpQKSk4u+fkycFrlaA2Li8v/m46mAQWv/o0KlMeB+gvHLdFaWAoH+Lg/a5A7ozX8I5L3P1dla6LXm79lstQFZKmMCw8YpSZKrFYiJNf64jY4p8KRMhlyNAompxvdsjsYF2VmWfyEYDEBMfC4gBO7F5v89LkVvdRzu1FR5kcZNMxiA2GRjZTcdXsu2vISUJQqFsQejpUlHjMlGmZR4c4SrKxAdW/j4qdR65FqfT0WSmCS3e4uQl7sciUkyxCblX7hzVQIZ6aXXS0ahkOGGa/HGh4xL0CFLo8d9B8unZBigzhHQ58oRF28/dtIyBVQqLfTa/IR2XKIGySlF/6xzdTVONKWz8xGmUeugyzUgJRdIScv/+827asTG57+Xdbm50Olg9n7JVAGnTMaVTE5SFHmCUYVCBnW2cSUB4H6SHhDALTvrebjJkJxscldQrouUJASM72Wt1njhBQAy0rXQmbw3MjJdcPJCVv76wriPeeQG4J9rxu8sXh5yKdmjzjFArTF/H/n5yI1Jl0w5kjMENCYXlxQKGbKyzA9KjlYgKc2xLmUpqXJ4ucuQmS2QkWVAttqA1LSSJeays+RQyGVIzRTSLdR6vTFBCACJycbPAgBI8HOFQZf/maDPFdDmaJGe8eBYCzk8XGXIlClwI8r6Z0d8Yi70eoH7cZaf1+QIKZlUkDpbYfNioMpLAX2u+bYLfgc1tl2J+BTLSaAstZASsAVlq/XQ5AJpaTJ4PEh05R1DANBkK5ClUiApRQ0IIKkYn8fZGgM0ObbPz3EecrOhTQAgI9sAncl5XZ9rfD/HJwHxSUVvh7Ops11wLSobicly41js+vwxcWUGF9y8o0ZSsu3zvYdSD3dv4J8bWosXonQ6IU3oGe8KxMRZri9dlf/5npRc+PMpLU0O9wfHNz1dD7WLHF5KF1y/bTxPpD1IgAKAJtsFOerC24lP1CIn14CYB+fJtHQ91Go94hMVyMmW436c9UCWyWTwcHGVErbq7BzkeCph0OmQkWHjGOm1yFJpkaWyXgR48P0pzvKHt9wgkJGR/x4SQkCj1kCvF7hup97i0ngpkZFh+4qfJlsn/TZ09H2lVMqgUuW/9/O+g2ZnaaHLZWKXiMqPEALqbA20uVnQ5aqkv9nCBG4Vo1Llf6q6uxe+taogDw8PaTkz0/oV70WLFmHBggWF/r5ynuVJ0oiIiMj5Dlez7VYkB8q7AURVGM8xRERU3WVmZqJGjRpWn2cClxwye/ZsvPXWW9Jjg8GAlJQUBAQUnhiAyJkyMjKksZp9fR0f95GoqBhrVFYYa1RWGGtUVhhrVFYYa1SWGG9UFoQQyMzMRL169WyWYwK3ivH29kZqqnHmVY1GA29v27POqtX5My/7+PhYLefm5gY3N/PB1fz8/IrfUKIi8vX15YcmlQnGGpUVxhqVFcYalRXGGpUVxhqVJcYblTZbPW/zlN9o9lQqTJOqSUn2B7tKNhkokwlZIiIiIiIiIiKiioUJ3ComJCREWr59+7bd8qZlTNclIiIiIiIiIiKi8scEbhXz8MMPS8snTpywWTY+Ph7R0dEAgMDAQNSuXbtU20ZUHG5ubpg3b16hITyInI2xRmWFsUZlhbFGZYWxRmWFsUZlifFGFYlMCCHKuxHkPAcPHkTfvn0BAL169UJ4eLjVsmvWrMHLL78MABg3bhzWrFlTJm0kIiIiIiIiIiIix7AHbhXTs2dPBAUFAQAOHTqE06dPWyyn1+uxdOlS6fHo0aPLpH1ERERERERERETkOCZwqxiFQoH3339fejxmzBgkJCQUKjdr1iycPXsWANC9e3c88cQTZdVEIiIiIiIiIiIichCHUKiCdDodBg8ejP379wMAgoKCMGHCBLRu3RopKSnYuHEj/v77bwCAn58f/v77b7Rp06Y8m0xEREREREREREQWMIFbRWVmZuL555/H7t27rZYJDg7Gpk2b0K1btzJsGRERERERERERETmKQyhUUT4+Pti1axe2b9+O4cOHo0GDBnBzc0OtWrXw2GOP4ZNPPsHFixeZvCUzO3fuxLPPPovGjRvD3d0dgYGB6NatGz777DNkZGTYXT8mJgY//fQTZs6cid69eyMkJAQBAQFQKpXw8/NDu3bt8Morr+DAgQOltg9//fUXxo4di6ZNm8LT0xMBAQHo1KkTFixYgLi4uCLVFRsbi/nz56NTp04ICAiAp6cnmjZtinHjxuGvv/4qpT2oHkoaawWlpqZi2bJl6N+/Pxo2bAh3d3fUrFkTrVq1wjPPPIOvvvoKMTExFXYfbty4gbfffhtt27ZFjRo14O3tjZCQELz++uvScDdUPM56nVQqFZYsWYL+/fsjKCgIbm5uqF27Nrp06YKFCxciPj6+wu8DwFgrDXq9HhcvXsTatWsxdepUdO3aFZ6enpDJZJDJZBg3bpxD9QghcOzYMSxcuBBDhgxB48aN4eHhAXd3d9SrVw8DBw7EkiVLkJaWVmr7wlir2JwVa2vXrpXWceTf/Pnznb4vjLWKzVmxlkej0WDdunUYNmwYGjVqBE9PT7i6uiIwMBChoaFYsGABoqOjS2Vf+NugYsvMzMTWrVsxZcoUdOvWDbVr14ZSqYSvry9atmyJMWPGYN++fXCk32FsbCx27dqF+fPn48knn0TdunXNzmWljec1KnWCiKq9zMxMERYWJgBY/degQQNx9OhRm/W8/vrrNusw/de3b1+RkJDgtH3QarViwoQJNrdZs2ZNsXPnTofq2759u/D397dZ32uvvSZ0Op3T9qE6cFasmVqzZo0ICAiwG3OLFy+ukPuwcuVK4eHhYbUuhUIhFixY4JS2VyfOfJ0OHjwo6tSpY/f8snXr1gq7D0Iw1krL8OHDbb5GY8eOtVvH1atXRXBwsEOfnwEBAeLnn3926j4w1ioHZ8SaEMbPTUe/rwEQ8+bNc9o+MNYqB2fFmhBCnDlzRjRv3txunLm7u4slS5Y4bR/426Di++KLL4S7u7tD56EePXqIO3fuWK1r586ddusoLTyvUVlxARFVa3q9Hs8++yz27dsHAKhTp06hMZMjIiIQHR2NwYMHIyIiAq1atbJan6urKzp16oROnTqhefPm0lXUxMREHDt2DFu2bIFarcaBAwfQq1cvnDx5Eh4eHiXej0mTJmHVqlUAgBo1amD8+PHo2LEjsrKysHPnTuzZswcpKSl49tln8fvvvyM0NNRqXeHh4Rg5ciRyc3MBAEOGDEFYWBi8vLxw+vRprF69Gunp6Vi5ciVkMhm++eabEre/OnB2rAHABx98gHnz5gEAlEolhg4ditDQUAQFBcFgMCA6OhqRkZH4/fffK+Q+bNiwAa+99hoAQC6XY/To0ejbty9cXFwQERGBdevWIScnB/PmzYObmxveffddp+xHVefM1+nw4cMYOHCgdD7o0KEDnnvuOTRq1Ajp6enYv38/tm7dipSUFIwaNQo7d+7EoEGDKtQ+AIy10qTX680e16xZEwEBAbh+/brDdaSkpODevXsAADc3N/Tu3Rvdu3dHw4YN4ebmhhs3buCHH37A5cuXkZycjJEjR2Ljxo0YOXKkU9rPWKscnBFrBU2dOhV9+vSxWaZly5bFrt8UY63ycFasRUdHo0+fPkhNTQUABAYGYty4cWjRogXc3NwQFRWFjRs34tKlS9BoNJg2bRo8PT3xyiuvlHgf+Nug4rt27Ro0Gg0AoH79+ujXrx86deqEwMBAaDQaHDt2DBs2bIBKpcLhw4fRq1cvHDt2DIGBgYXqKhizSqUSbdu2xZkzZ0p1H3heozJV3hlkIipfK1askK7mtW7dWsTFxRUqM2PGDLOrn9bcvHlTqFQqm9u7ffu2aNq0qVTfp59+WuJ92Ldvn1Rf3bp1xbVr1wqVWbp0qVSmadOmIicnx2JdGo1GNG7cWCq7bNmyQmWuXr0qgoKCpDIHDhwo8T5UB86MNSGE+PHHH6WyjzzyiLh+/brVshqNRsTHx1eofUhISBC+vr4CgJDL5WLHjh2Fyhw9elR4enoKAMLFxUVcuXKlxPtQHTjrdcrJyTE7H0yfPl0YDIZC5fbv3y/c3NwEAFGnTh2RkZFRYfZBCMZaafvwww/FrFmzxJYtW8StW7eEEOY9HB3pqXb06FHRoEEDsXTpUpGSkmKxjFarNbvTpWbNmiI1NbXE7WesVR7OiLWC66xZs6b0GlwAY63ycFasjR8/XlpnwIABIisrq1AZg8Eg5syZI5WrXbu20Gq1JWo/fxtUDhMnThQDBgwQv//+u9Dr9RbLREVFiZCQEOnYvvTSSxbLRUREiAkTJogVK1aIEydOSK9n3nqllfrieY3KEhO4RNWYTqcTdevWlT5QTp06ZbVc+/btpXK//fZbiba7detWh5N0jujSpYtUn61bmIcOHSqVW7lypcUyy5cvl8oMHTrUoX3o2rVrifehqnN2rCUlJYmaNWsKAKJ+/foiOTm5NJsvtc2Z+/DOO+9IZaZOnWp1u1988YVU7rnnnnPKvlRlznydNm3aJD3foUMHqz8uhBDigw8+kMp+8MEHFWYfhGCslYeiJjpUKpXV5IEpg8EgOnbs6LTkG2Ot8qssCVzGWuVXnFirX7++tM4///xjtZxOpzMbqujChQslait/G1QOjn5/P3v2rHRsPT09LV4IsKY0E7g8r1FZYwKXqBo7ePCgdPLv2bOnzbLfffedVHbcuHEl2u6VK1ekupo3b16ium7duiXV1aRJE4u94/KY7m+vXr0slgkNDZXKhIeHW61Lr9ebXY2Piooq0X5Udc6OtUWLFkllvvvuu1JocWHO3odGjRpJZfJ6t1iSkZEhvLy8BADh5eUlsrOzS7IbVZ4zX6dXX31Vet7eGMrR0dFS2ZCQkBLsAWOtKihOosNRpue/N998s0R1MdYqv8qSwGWsVX7FiTWlUimtk5uba7Ns165dpbJFmQuhIP42qJpMe+GeO3fO4fVKM4HL8xqVNTmIqNrau3evtDx48GCbZU3HdDRdrzhu3LghLQcFBVktN3/+fLuz3Zq2ZeDAgTZnGO3Rowe8vb0BGMe1zMrKMns+MzMTf//9NwDAx8cHPXr0sFqXXC7HwIEDLbaDCnN2rK1evRqAcczlUaNGlbh948aNszvbtjP34dKlS7hz5w4AoFWrVmjSpInVukxjMSsrC3/++afNbVd3znyd8sYkBYCQkBCbdQUHB8PT0xMAcPXqVavjBDLWqKR8fX2lZbVabbUcY43KCmONrDEdq/TatWtWy+n1ety8eRMA4OLiYvUzl78Nqi9HP/uchec1qoiYwCWqxi5cuCAtP/roozbLBgUFoUGDBgCA+Ph4JCYmFmubCQkJmDVrlvR4xIgRxaonT1H2wcXFBR06dABg/KJ46dIls+cvXboEg8EAwDhRkUKhsFmf6fZM20GFOTPWYmNjpYsAbdu2haenJ65fv44pU6agWbNm8PDwQM2aNdGpUyfMnj0b9+/fr3D7UJS6CpZhrNnmzNdJCFHsdpw/f77Y6zLWyBbT16VRo0ZOq4uxVj0tX74crVq1gre3Nzw9PdGwYUOEhYXhm2++QXZ2ttO2w1irnp566ilp+c0337QYU0IIvPfee0hISAAAvPzyy/D39y/2NvnboOrJzc01uwBQ0s8+Z+F5jcoaE7hE1djVq1elZVtX+SyVMV3XkqioKGzfvh3bt2/Htm3bsGrVKkycOBEtWrTAxYsXARivRE6ePLmYrS/cjpLuQ2kej+rOmcf2xIkT0nLDhg2xfv16PPLII/j6669x8+ZNaDQapKam4vTp0/j444/RrFkzrF+/vkLtA2Ot9Djz2JreIWCr5xAAxMTEmP0wvXLlit1tW8NYI2tSU1OxadMm6fGQIUNKVB9jjU6cOIErV64gKysLarUa0dHR2LVrFyZPnozGjRtj9+7dTtkOY616mj9/Ppo3bw4A2L9/P5o0aYJZs2bhu+++w4YNG/Dhhx/i4YcfxqJFiwAAY8aMwZIlS0q0TcZa1fPjjz8iPT0dANCxY0ebd3CWJcYalTWX8m4AEZWftLQ0ablWrVp2ywcEBFhc15J9+/Zh0qRJFp9r2LAhJk2ahHfeeQdyecmuIzlzH0rzeFR3zjy2sbGx0vKFCxewa9cu6PV6dO/eHSNHjkRQUBBiYmKwceNGnDhxAmq1GmPGjIGXlxeGDx9eIfaBsVZ6nHlse/TogTVr1gAAvv/+e7zxxhtWb8Vcu3at1XYUFWONrJkxYwZSU1MBAGFhYXj44YdLVB9jrfpSKBTo2rUrevTogRYtWsDb2xtpaWk4deoUNm/ejJSUFCQmJiIsLAw//PADnnvuuRJtj7FWPdWqVQuRkZGYPHkyfv75ZyQkJOCTTz4pVK5fv36YM2cOevfuXeJtMtaqlsTERLz77rvS47lz55Zja8wx1qisMYFLVI2pVCpp2d3d3W55Dw8PaTkzM7NY21QoFOjbty+6d+9uN3k7f/58q2MO5XHmPpTH8agunHls85IXAKTx0ubNm1coVqZPn4533nkHn3/+OQDg1VdfxRNPPAEvL69C21u7dm2hBFxp7gNjrfQ489iOGDECM2fOREpKCk6fPo133nkHn376aaEkbnh4OBYuXGj2t4yMDIvbY6xRca1YsUK6oODn52e3lxpjjax5/PHHERUVheDg4ELPvfLKK/j0008xYcIEbNq0CUIIvPzyy+jevTsaNmxosT7GGtni7++PTz75BLVr18ayZcssljl48CBkMhn8/f3Rvn17q3Xxt0H1kpubi2eeeUYaXuOpp57C008/XSbb5nmNKiIOoUBEpWLixIkQQkAIgdzcXNy9exc//PADOnTogDVr1iA0NBQzZsyAXq8v76ZSJZM3Flme0NBQi1/mZTIZPvnkE3Tq1AkAkJycjA0bNpRFE6mK8PHxwdKlS6XHn3/+OR599FF88cUX2LJlC1atWoXRo0ejf//+0Gg0aNq0qVS2pHcXEJnas2cPpk6dCsAYW2vWrEHjxo3Lt1FUaTVr1sxi8jaPj48PfvjhB/Tq1QsAoNFoLPaaJHLE559/joceegjLli3Diy++iCNHjiAzMxMajQaXLl3C/Pnz4erqiv3796NHjx7Yt29feTeZKgCDwYCXX34Zhw8fBgA0bdoU3333XTm3iqh88dcFUTWWN+sqYPxybo/pjJ8+Pj4Ob0epVKJBgwZ4/vnncezYMbzwwgsAgP/+97947733itDiwpy5D2V1PKojZx7bgo9fe+01q/XI5XJMmDBBenzw4EG727aGsVY5OPvY/vvf/8a3334LNzc3AMCpU6cwc+ZMjBw50qyH2oIFCzB06FBpvZJMwMJYI1N//PEHRowYAZ1OB5lMhv/9739mEwOVBGONrFEoFGZ3FpR0LFzGWvX0/vvv4+2334Zer8dnn32G77//Hl27doW3tzfc3NzQqlUrzJs3D3/88QdcXV2hUqkwevToYk+WDDDWqgIhBCZOnIgffvgBgHH4vT/++KNE361KA2ONyhoTuETVmJ+fn7SclJRkt3xycrLFdYtCoVBg+fLlqFGjBgBg8eLFJRq3x5n7UB7Ho7pw5rEt+OUtr4etNZ07d5aW84ZcKA7GWuVQGsf2lVdewY0bNzB79mx06tQJfn5+0oWpf//73zhy5Ajef/99s7pKMsEGY43yHDx4EGFhYdBoNJDJZPjmm28wfvx4p9XPWCNbunbtKt3Ke/fuXbOJGouKsVb93L9/Hx9//DEAICQkBDNmzLBatnv37hgzZgwAID09XRoupjgYa5WbEAKTJ0/Gt99+CwAIDg7GwYMHK+RdJ4w1KmtM4BJVYyEhIdLy7du37ZY3LWO6blH5+Pjg8ccfB2C8wnjs2LFi1+XMfSiv41EdOPPYtmzZ0uxx3sUAa0yftzYuqSMYa5VDaR3b4OBgfPTRRzh58iRSU1OloWE2bNiAxx57DADwzz//SOW7dOlSnOYXagdjrfo6ePAghg4dKvWy+frrr23ecVAcjDWyRS6Xo2bNmtLjklxwZ6xVP7///ju0Wi0A4yRl1iYBzTNgwABpOTIystjbZaxVXkIIvP7661ixYgUAoH79+ggPDzcboqoiYaxRWWMCl6gaM529+sSJEzbLxsfHIzo6GgAQGBiI2rVrl2jbprd6mE5KVVRF2QedToczZ84AMP4oad26tdnzrVu3lsatPHPmjN3xeU2317Zt2yK1u7pxZqy1adMGLi75c3Cmp6fbrM/0eXvJXlucuQ9FqatgGcaabeV1XktOTsaFCxcAGG+D69ChQ7HrYqxRXvI2r8fjsmXLMGnSJKdvh7FGthgMBrPvaCXp5cVYq37u378vLTvy/cs0vkwndCoq/jaonPKSt9988w0AoF69eggPD0ezZs3KuWXW8bxGZY0JXKJqbODAgdLy3r17bZb99ddfpeXBgweXeNvXr1+XlkuSNDHdh3379kEIYbXs4cOHpS+EoaGh8PLyMnvex8cH3bt3B2CczfPvv/+2WpfBYMBvv/0mPR40aFCx2l9dODPWPDw8pIlVAOOYpLacPHlSWi7JFWpn7kPr1q2l2bwvX76MqKgoq3WpVCppAgdPT0/07NmzKM2udsrrvLZ+/Xqpp9GLL74ojZlbHIy16q1g8nbJkiWYMmVKqWyLsUa2HDt2TOoBHhwcDE9Pz2LXxVirfkw7a+Qlrmy5c+eOtBwQEFDs7fK3QeVTMHlbt25dhIeHo3nz5uXcMtt4XqMyJ4io2tLpdCIoKEgAEADEqVOnrJZr3769VG7fvn0l2u7x48eFTCYTAISrq6tIS0srUX2PPvqo1LatW7daLTd06FCp3IoVKyyW+frrr6UyQ4cOtVrX1q1bpXL/+te/StT+6sDZsbZ27VqpTGhoqNXt6vV60alTJ6nshg0bKsw+vP3221KZqVOnWt3uF198IZUbPXp0sdtfXZTHeS0mJkb4+/tL57QrV64Uu668tjHWKrc1a9ZIx3Ls2LEOrxceHi48PT2ldb/88svSa6RgrFUFxY01e/R6vejbt69U98SJE0tUH2Ot8itqrIWHh0vl/f39RXp6us3yvXr1ksovXry4RG3lb4PKZfLkydKxCwoKKvH3KFN59ZZG6ovnNSprTOASVXPLly+XPgDatGkj4uPjC5WZOXOmVKZ79+4W6zl79qz48ssvRWZmps3tRUZGivr160v1vfTSS1bLzps3z6Evir/++qtUrm7duuL69euFyixbtkwq06RJE5GTk2OxLrVaLRo2bCiV/eqrrwqVuXbtmtmH9f79+23uMxk5K9aEMH4Rat26tVR2/vz5hcoYDAaz+ho3bixyc3Mt1jd27Fip3Lx588pkH+Lj44WPj48AIORyudixY0ehMseOHZOSOS4uLuLy5ctW66N8znydbt26JaKioqw+f/HiRRESEiLVtWjRIpttY6xVD8VJqh06dMipyVvGWvVQ1Fg7cuSIWLlypVCr1VbLqFQq8e9//1uq183NTdy+fdtqecZa9VDUWMvNzTX7Tj148GChUqkKlTMYDGLOnDlSOQ8PDxETE2OxTv42qHqmTJlSaslbIYqfwOV5jSqi/EEEiahamjBhAn755Rfs378f//zzDx555BFMmDABrVu3RkpKCjZu3CjdLuTn54eVK1darCc1NRXTp0/H7Nmz0bdvX3Tu3BmNGjWCj48P1Go1oqKiEB4ejvDwcOlWpjZt2uDzzz8v8T4MGjQIL730EtasWYPY2Fh07twZr7zyCjp27IisrCzs3LkTu3fvBgC4urpi9erVcHV1tViXu7s7Vq9ejcGDB0Or1WLKlCnYt28fwsLC4OXlhdOnT2PVqlXSuKoTJkxAv379SrwP1YGzYg0AFAoF1q1bh969e0OlUmH+/PnYv38/Ro0ahaCgIMTExODHH3+UxodydXXFDz/8AKVSWWH2ITAwEMuWLcO4ceNgMBjw9NNPY/To0ejfvz8UCgUiIiKwbt06aDQaAMCCBQsKTeBGljnzdTp16hRGjRqF7t27o1evXmjevDnc3d0RFxeH8PBw7Nq1CzqdDgAwfvx4vPPOOxVuHxhrpev27dtYvXq12d/Onz8vLZ85cwZz5841e75Pnz7o06eP9Pjs2bMYMmSINGzCE088gUaNGmH79u02t12rVi1pUtDiYqxVHs6Itfj4eLz22muYMWMG+vfvj06dOqFBgwbw8vJCeno6Tp8+jZ9++kma4Vwmk2HVqlVOmQGesVZ5OCPWlEolli1bhqeffhoGgwG//vorWrRogRdffBEPP/wwlEolbt26hU2bNuHs2bPSeh999BHq1atXovbzt0HlMHfuXHz11VcAjOeaadOm4fLly7h8+bLN9Tp27CgNN2Dqiy++sDm3SsGY9ff3x4wZM4rR8nw8r1GZKu8MMhGVv4yMDPHkk0+aXaEs+C84OFhERERYrcP0NilH/j333HMiKSnJZrscvcouhBBarVa8/PLLNrfp7+8vtm/f7tAx2bZtm/Dz87NZ34QJE4ROp3OoPjJyRqyZ+vPPP0VwcLDN+gIDA0V4eLjNehy9yl4a+7B8+XLh7u5utS6FQiHef/99h+qifM56nbZs2WL3fObp6SkWLVokDAaD3XYx1qqeon7+WXrtTXu2FeVfz549rbaLsVb1OCPWfvnlF4fXDQoKErt377bbLsZa1eOMWMuzefNmaZghW//c3NzsDp3A3wZVS8+ePYv12bdmzRqL9TVq1KhI9TRq1Mhq23heo4qIPXCJCD4+Pti1axd27NiB77//HidOnEBCQgJ8fHzQtGlTDB8+HK+99prNGWRDQ0Nx7NgxHDhwACdOnMCVK1cQExOD7OxsuLm5wc/PDy1btkS3bt3w3HPPFZrltaRcXFywevVqvPjii1i9ejUiIiIQGxsLd3d3NG7cGGFhYZg4cSLq1q3rUH1PP/00/vWvf+Gbb77Brl27EBUVBY1Gg7p16+Lxxx/H+PHjOWB8MTgj1kyFhobin3/+wapVq/DLL7/g+vXrSElJQY0aNdC6dWuEhYXhtddeg7e3d4Xdh0mTJqFfv35YsWIF9u3bh+joaBgMBtSrVw99+/bFq6++ig4dOjit/dWFs16n3r17Y+XKlQgPD8f58+cRHx+PjIwM1KpVCw899BCGDBmCMWPGoH79+hV2H/Iw1sgaxlr10a9fP+zYsQORkZE4fvw4oqOjkZycjLS0NHh6eiIwMBAdO3bEkCFDMHLkSLi7uzt1+4y16ufZZ59F3759sX79euzbtw/nz59HSkoK9Ho9/Pz80KpVK/Tu3Rvjx49HgwYNnLZd/jagssLzGpUVmRA2pmUkIiIiIiIiIiIionIjL+8GEBEREREREREREZFlTOASERERERERERERVVBM4BIRERERERERERFVUEzgEhEREREREREREVVQTOASERERERERERERVVBM4BIRERERERERERFVUEzgEhEREREREREREVVQTOASERERERERERERVVBM4BIRERERERERERFVUEzgEhEREREREREREVVQTOASERERERERERERVVBM4BIRERERERERERFVUEzgEhEREREREREREVVQTOASERERERERERERVVBM4BIRERERERERERFVUEzgEhEREREREREREVVQTOASERERERERERERVVBM4BIRERERERERERFVUEzgEhEREREREREREVVQTOASERERERERERERVVBM4BIRERERERERERFVUEzgEhEREREREREREVVQTOASERERERERERERVVBM4BIRERERERERERFVUEzgEhEREREREREREVVQTOASERERERERERERVVBM4BIRERERERERERFVUEzgEhEREREREREREVVQTOASERERERERERERVVBM4BIRERERERERERFVUEzgEhEREREREREREVVQTOASERERERERERERVVBM4BIRERERERERERFVUEzgEhERETno0KFDkMlk0j8quUmTJknHc8+ePRbLREVFmR33qKiosm0kVWmmsXXo0KHybg4VgcFgQLt27SCTyeDj44P79++Xd5OIiIhKBRO4RERERFQuzp07h2+//RYA8Pjjj2PIkCHl3CIiqkzkcjkWLlwIAFCpVJg1a1Y5t4iIiKh0MIFLREREVUbBnprO/Dd//vzy3r0qZ8aMGdDr9QCABQsWlHNriKqms2fPYv78+Zg/fz6+/PLL8m6O04WFhaFjx44AgA0bNuDMmTPl3CIiIiLnYwKXiIiIiMrcn3/+iQMHDgAAOnfujD59+pRzi4iqprNnz2LBggVYsGBBlUzgAsDbb78NABBC4P333y/n1hARETmfS3k3gIiIiMhZlEolmjZtardcQkICMjMzpceOrFOzZk306tULQogStZGM/vOf/0jL06dPL7+GULXH93Tl9+yzz2LmzJmIiYnB7t27cebMGXTo0KG8m0VEROQ0TOASERFRlVG/fn3cuHHDbrlx48Zh3bp10mNH1iHnuXDhgtT71t/fH88880w5t4iIKjOFQoFx48bhww8/BAAsWbIEa9euLd9GERERORGHUCAiIiKiMvXVV19Jy6NGjYK7u3s5toaIqoKxY8dKyz/99BOSk5PLsTVERETOxR64RERERGVIp9Phr7/+ws2bN5GYmIiaNWuiW7duaNeundV1cnNz8ddff+HSpUtQqVSoXbs2unfvjtatWxe7HQaDAcePH8fVq1cRHx8PpVKJunXrokePHqhfv36x67UnJycHmzdvlh4PHz68xHWeOnUKFy9eRGxsLLy9vdGiRQv07NkTbm5uxa7zzJkzuHDhAuLj46FQKFCnTh107twZISEhJW5vaTMYDDh69Chu3LiBuLg4eHh4YODAgWjRooXFshcuXMDZs2eRlJQEtVoNDw8P1KpVCw899BDat28PHx+fMmv71atXcerUKcTExEAul6N169bo06ePzdfy0qVLOHbsGOLj4+Hl5YWQkBD07t0brq6uZdbuy5cv4/Tp07h37x48PDzQpEkT9O7dG97e3mXWhrISHR2No0ePIj4+HllZWQgMDESbNm3QpUsXyGSyIteXnJyM48eP4+bNm8jIyIBcLoe3tzcaNGiAli1bokWLFg7V27x5c7Rt2xYXL16UzjOTJk0qzi4SERFVPIKIiIiomhk7dqwAIP1zVHh4uEPrWSv35Zdfirp165o9l/cvNDRUXL9+3awevV4vPv/8c1GzZk2L6/Tq1UvcvHmzSPuekZEhZs2aJWrVqmWxTplMJkJDQ8XJkyeLVK+jduzYIW3Lx8dH5Obm2l3n9u3bZm28ffu2EEKIPXv2iLZt21rcDz8/P/Hll18WqW06nU4sW7ZMBAcHW6wTgGjZsqX46aefHKqvZ8+e0nrz5s1zaJ2SxJjBYBD//e9/LbZ/8eLFhfZ18eLFomHDhlb3FYCQy+WiS5cu4scff3So/Y5o1KiRVP+aNWuEEEKcPn1adOvWzWIbateuLdavX1+oniNHjoguXbpYXCcwMNDh18l0vfDw8CKVi4yMFF27drXYBg8PDzF37lyHYtzRNpiaN2+etE7Pnj0LPW96nB35N3bsWJvb27Ztm2jfvr3V9evWrSu++uorodfrHWr/5cuXxbBhw4SLi4vNdgUEBIhx48aJxMREu3X+3//9n7Rejx49HGoHERFRZcAhFIiIiIhKmcFgwKhRozB9+nTExsZaLPPXX3+ha9eu0ni8OTk5GDZsGGbOnImUlBSL6xw6dAg9e/bE3bt3HWrHyZMn0aJFC3z88cdISkqyWEYIgb/++gtdunTBihUrHKq3KH799VdpuVu3blAqlcWq58MPP8STTz6JixcvWnw+LS0N06dPx5QpUxyqLzU1FT169MDUqVNx7949q+WuXLmC0aNHY8SIEcjNzS1W20uDVqvFk08+ibfeestm+wFAo9Fg8ODBePPNN+3GTl5P7U2bNlktc+jQIchkMunf/Pnzi9T2n3/+Gd26dcORI0csPp+YmIgXX3wRX375pfS3devWITQ0FMePH7e4TkJCAkaPHo3Vq1cXqS1FsXbtWvTo0QNHjx61+LxarcbChQsxYsQI6PX6UmtHacvOzsawYcMwfPhwnD171mq52NhYTJkyBU888QSys7Nt1rl37160b98eO3bsgE6ns1k2OTkZa9eutRvXANCrVy9p+ejRo0hPT7e7DhERUWXAIRSIiIiIStm8efOkYQN69eqFJ554AoGBgUhISMCWLVtw+vRpAEBSUhLGjBmDI0eOYOLEidi9e7e0zoABA1CnTp1C69y7dw+TJk3Cnj17bLbh2LFj6N+/P1QqlfS3Dh06YPDgwWjYsCF0Oh3OnTuHzZs3Iy0tDQaDAZMnT4avry+ef/55px2L/fv3S8uPP/54serYsGED3nvvPQBAq1atMGzYMDz00EPQarU4fvw4Nm7cKCVXv/76awwYMABhYWFW68vJyUG/fv2kYwoAtWrVwqhRo9C2bVvodDqcPn0aW7ZskY7f1q1bMXLkSGzfvr1Y++BsM2bMkJLjnTt3xpAhQxAcHAyVSoXTp0/Dw8NDKjt37lz8/vvv0uOGDRti6NChaNmyJXx8fJCdnY24uDicO3cO4eHhyMjIKLV2X7x4Ed988w00Gg0aNmyIZ599Fi1atIBOp0NERAQ2bdokJT/ffvtt9O/fH3FxcRg/fjz0ej0aNmyIESNGoEWLFjAYDIiIiMBPP/0krTN16lT0798fDRs2dGq7Dx06hA8//BA6nQ6NGjXC8OHDpVv9z58/j/Xr1yMzMxMAsHPnTnz11VeYNm2aU9tgT+PGjeHi4oLMzEwkJCQAAFxcXNCoUSOL5evUqVPobzk5ORgwYAAiIiKkv9WqVQvDhg3DI488Ai8vL9y9exfbtm3DhQsXAAB//PEHhg8fjr1791oc+iA2NhajRo1CTk4OAOMEZAMGDEC3bt1Qt25dyOVypKWl4erVqzh27BjOnTvn8D5369YNcrkcBoMBOp0OBw8exNNPP+3w+kRERBVWeXcBJiIiIiprZT2EgkwmE97e3uLXX38tVFav14uJEyealZ8+fboAIHx9fcXevXstrvPqq6+arXPmzBmr7UlPTxeNGzeWyvr6+opt27ZZLBsfHy969eollfX39xf379+3f3AckJSUZNbmHTt2OLRewSEU5HK5UCgUYunSpRZv1z579qzZsBMdOnSwWf+7775rVn9YWJhITU0tVO7u3buic+fOZmW//fZbq/WW5RAKAISbm5vdoQ5UKpVwd3eX1pk8ebLQarVWy+fk5IitW7eKhQsXOtwWe/tqemu/TCYTAMTrr78u1Gq1xbrd3Nyk8sOHDxcNGjQQAMTUqVOFRqMptM6BAweEq6urtM60adNstse07Y4OoSCXywUAMXv2bJGTk1Oo7J07d0STJk3MhnSwdZwdbYMpe0Mo5FmzZo1UrlGjRg7VnefNN980a9ukSZNEZmZmoXIGg0F8+umnZmW/+eYbi3W+9957UpnatWvbPHcJIcStW7fEjBkzxJUrVxxqc4sWLaT63333XYfWISIiquiYwCUiIqJqp6wTuAAsJm/zqNVqi2Pj/vbbb1bXyc7OFkFBQVLZWbNmWS1rmqB0dXUVR44csbmfKpVKNGvWTFpn5syZNss7av/+/Wb7V3DMX2sKJnCBwmO6FrR69Wqz8taSP7GxsUKpVErlunTpYjEpmCchIUHUr1/fLDFnKYEnRNkncL///nu79R84cMAseWYrqeiokiRwAYhRo0bZLJ93QcP03/PPP29znWnTpkllg4KCbJYtTgLXkcSw6bEGIPbt21fiNpgq7QTuP//8IyXY8xLm9syZM0cqX7duXYvx1aNHD6nMkiVLHG6Po55++mmp/v79+zu9fiIiovLAMXCJiIiIStmTTz6JQYMGWX3e3d0dzzzzjNnfhg4digEDBlhdx8PDw2wda2OBZmdn43//+5/0eNq0aejatavN9np5eeGTTz6RHq9duxZardbmOo64efOm2ePg4OBi1dO6dWu7t6M/99xz8PLykh5bOz7fffed2b4tW7YMbm5uVuutXbs2Fi1aJD1OSEjA1q1bHW16qXn00Ufx4osv2i0XFxcnLTdp0gQuLuU7oppSqcTixYttlnnhhRcKrfPFF184vE5cXByio6OL30gLatasaRYHlvTp0wcPPfSQ9NhaDFZUS5cuhRACgPG9+tlnn9ld5/3330ft2rUBGIdK2LVrV6EypjHYvHlzJ7U2X4MGDaTlguccIiKiyooJXCIiIqJS5khirUOHDkVep2PHjtLy1atXLZb5/fffkZqaKj1+44037NYLAMOGDYOvry8A49i8RRmH0hrTJJq/vz/c3d2LVc+YMWMsjq1pysPDA4888oj02NrxMR07uH379ujSpYvd7Y8aNQr+/v7SY9OJ2cqLI/ECAJ6entLy5cuXnTLJU69evSCMd/ZBCFGkScz69euHunXr2izz8MMPQ6FQSI/79++PoKAgm+u0a9fObB1rr39xjRw50mxcYWv+9a9/lVobSpMQQhq3GwAmTpxo88JGHjc3Nzz77LPS4wMHDhQqYxqDx44dK2FLC6tXr560HB0dLSWhiYiIKjMmcImIiIhKmSNJwYITCBV1nbS0NItlTCcfatu2rcO9XhUKhVmC+MSJEw6tZ4tpG729vYtdz2OPPeZQOdNEjqXjo9PpcObMGenxwIEDHarX1dUV/fr1kx5XhJ6V9npV52nfvr20nJmZiaeeego3btwopVbZ50icu7q6miXMi7OOtfdHcTkrBiuqS5cumV34cfS9AZi/PpbOG6YxuGjRIqxatcopPfzzmJ5btFot1Gq10+omIiIqL0zgEhEREZUye70FAZjd7g9YnhHe1jpZWVkWy5w/f15abtWqld06rbXh3r17RVrXEtNESnF73wKOHU/A/vG5d++eWZsefvhhh9vQrl07afnGjRvl3suvadOmDpVr3Lix2dAchw4dQkhICEJDQ/HRRx8hIiICubm5pdXMQorzWjry3ii4jrX3R3E5KwYrKtPzBlC0c4e988arr74qLWu1WkyYMAHBwcF45ZVX8OOPP5b4XFOwZ3R2dnaJ6iMiIqoIynfQKyIiIqJqoDjJypIkOE0lJydLy1u2bLE79IA1zug9qFQqpWWdTlfseopzbCwlWE17GAKQxu50hGlZg8GAjIwM1KhRo8jtchYfHx+Hy65atQq9evXCrVu3ABjbf/jwYRw+fBiA8Rb3Hj16YMSIERg1alSR6i6qsnpvODvBXhHaUJpMzxtA4QtMjrJ03ujWrRsWLlyIuXPnSn9LSEjA6tWrsXr1agDGsXEHDRqEMWPGoFOnTkXaZsHevKbnHSIiosqKPXCJiIiIqjBnjHEKOKcXm2kSSKPRlLi+kirYI9J0bE57Cia0VCqVU9pUXEWZjKxBgwY4efIk3njjDYuJuezsbPz222+YMGECmjRpgpUrVzqzqVQJlPZ54//+7/+wd+/eQmN/57l+/TqWLl2Kzp07Y9CgQUWahK7gkAnFTT4TERFVJOyBS0RERFSFmSYlfX19i9TL1FRgYGCJ22J623lycjKEEMXuEewMBcfhLUqSumDytyRj+pYHf39/LFmyBAsXLsTevXvxxx9/4PDhw7hy5YpZueTkZEycOBFRUVFYtGhRObWWylrBixmODtFRFAMHDsTAgQNx9uxZ7N27F4cOHcLRo0eRmZlpVm7fvn149NFHERkZiUaNGtmtNzExUVoOCAgo0sUNIiKiioqfZkRERERVWEBAgLQcFhaG9evXl1tbTJMvubm5SExMdEpiuLhMJ7kCzBM/9piWlcvl8PX1LVSmOMnpsp5wycfHByNHjsTIkSMBAHFxcdizZw/WrVsnDakAAJ988glGjRplNgEVla/SjBXT8wYAXLlypdQSoe3bt0f79u0xe/Zs6HQ6REZG4ueff8batWulIRji4+Mxffp0/PLLL3bru3//vrTsSMKXiIioMuAQCkRERERVWEhIiLQcExNTji0B2rZta/b4+vXr5dQSo/r165tNeFRw4iZbTMs2a9bMYrLWtBejo7174+PjHW5DaQgKCsL48ePx119/4euvv5b+LoTAxo0by7FlVVtFixXT8wZgnhQtTS4uLujevTsWL16M69evm02etnv37kK9cy0xPa8UPOcQERFVVkzgEhEREVVhPXv2lJYjIyORk5NTbm1p2bKl2XiUFy9eLLe2AMZkUceOHaXHv/32m0PrabVaHDhwQHr82GOPWSzn5+cnLTuaADt27JhD5crC5MmTzXrcXr58ufwaU8WVZqyYTuJlMBgcWqdTp05mw4L8+eefDq3nTLVq1TIbtkOn0zl00eeff/6Rlos6ARoREVFFxQQuERERURU2cOBA+Pj4ADD27Pvuu+/KrS1yuRzdu3eXHp88ebLc2pJnyJAh0vLZs2dx/Phxu+ts3rwZKSkpFusw1aJFC2nZkXqzs7OxZcsWu+XKkuk+6HS6cmxJ1WZ6nCMjI+2Wj4yMxNWrVx2q2zQRm5GR4dA6Li4ueOqpp6THpr2xy5LpcQHsx+D169elYRcA8wtYRERElRkTuERERERVmJ+fH1577TXp8Zw5c4rck1Kj0TitPQMHDpSWDx065LR6i+vll18266H4xhtvIDc312r5pKQkzJ49W3ocGBiI4cOHWyxr2jP3+vXrZmPKWjJnzhyzxHBpuHfvnsO9MIUQZr2krY0neujQIchkMunf/PnzndHUasU0VrZt24b09HSrZbVaLaZOnepw3aavW3p6Ou7du+fQeu+++640NEhkZGSRJ7ETQljs8X/nzh2H67hw4YLZ44YNG9osb9pTOCgoCO3atXN4W0RERBUZE7hEREREVdzcuXOlWeTT0tLw+OOPY9OmTXYTeefOncNbb72FLl26OK0tpr36bty4gdu3bzut7uKoU6cO3nrrLelxZGQkRo0aZTGBdu/ePQwZMgTR0dHS3z766COzBLCpvn37olatWtLj8ePHWxyHODc3F3PmzMGSJUuKNfFZUaxatQqtWrXC8uXLkZSUZLWcXq/H7NmzcenSJelvYWFhpdq26mz06NHSckpKCl566SWLyc/ExESEhYXhxIkTDsdK69atzXrhzpo1y6GeuG3btjV7b8yZMwevv/663YsMSUlJWLlyJdq2bYujR48Wer5Zs2YYN24c/v77bwghrNZz+fJlzJw5U3rcpUsXBAUF2dz2/v37peWnnnqq1N9PREREZaV0phIlIiIiogqjRo0a2Lp1K3r37o3U1FSkpKRg9OjRmDNnDgYMGIBWrVrB19cXarUaSUlJuHjxIiIjI6Wecs6cyb1Jkybo1q0bjhw5AgDYvn073nzzTafVXxwLFizA/v37cfr0aalNzZs3x+jRo9GmTRvodDqcOXMGmzdvNptEadiwYRg/frzVepVKJWbPno0ZM2YAMPbCbdOmDV544QW0a9cOQghcu3YNW7duxZ07d+Di4oLZs2fjP//5T6nu77Vr1/D6669j2rRpeOyxx9ClSxc0atQIfn5+UKvVuHbtGnbu3ImbN29K6/Tp0weDBg0q1XZVZ+3bt8fQoUOxa9cuAMAvv/yC1q1b4/nnn0eTJk2QlZWF06dPY9u2bcjIyEC9evXw5JNP4n//+5/dul1dXfH8889LZX/44Qds3rwZjRs3Nps8LSwsDB988IHZuh9//DEuXLiA33//HQCwfPlyrF27FgMHDsSjjz6K2rVrAzBeGLpx4wbOnDmDkydPQq/XW22PTqfDunXrsG7dOtSvXx/du3fHI488glq1akGpVCIhIQFHjx7Fnj17pCETZDIZPv30U5v7mZOTg19//VV6/MILL9g9NkRERJUFE7hERERE1cAjjzyCY8eOISwsTBo789atW1ixYoXddRUKhVPbMn78eCmBu2nTpnJP4Lq5ueGPP/7A4MGDpYmhEhMTsWzZMqvrDB8+HBs3brRb97Rp0xAeHo7du3cDMN7Cbmk8UaVSiRUrVuChhx4q9QRuHp1Oh4iICERERNgs17VrV/z8889l0qbqbMWKFfjnn39w69YtAMb358KFCwuVq1OnDnbs2CHFlCMWLVqEo0ePSkMSaLXaQhOCmU5Yl8fFxQW7d+/GlClTpARwdnY2tm3bhm3bttndrr1zR0xMDDZv3ozNmzdbLePq6opVq1bZHc92z549UKlUAIwTJnbr1s1u+4iIiCoLDqFAREREVE20aNEC58+fx/LlywtNDlSQUqlEaGgo/vvf/zo8272jnn/+eannXmRkpNk4q+XF398fhw8fxtKlSxEcHGy1XEhICDZu3IitW7fC1dXVbr0KhQJbt27F//3f/8HDw8NimS5duuDw4cN4+eWXi91+R73wwguYO3cuOnbsaDe51rRpUyxZsgSHDx+Gv79/qbetuqtXrx7+/vtvjBw50uKt/y4uLnjmmWdw5swZdO7cuUh116xZE8ePH8eKFSswaNAgBAcHW43HgpRKJVauXImjR49i8ODBduO+WbNmmDp1Ko4fP44ePXoUen7Dhg0YOXKk2fAilri6umLEiBE4e/YsXnzxRbvtXL16tbT8xhtvcPgEIiKqUmTC1sBDRERERFRlRUVF4fjx40hISEBaWho8PT0REBCAkJAQPPzww/Dy8iq1bf/nP//B+++/DwB4/fXX8dVXX5Xatorj9OnTOH/+PBISEqBQKFCnTh106tQJrVq1KnadKpUK4eHhuHXrFjQaDerVq4eOHTuiTZs2Tmx50dpz7tw53LhxA4mJidBoNPDy8kKdOnXQoUOHEu0rlUxsbCzCw8MRExMDhUKBBg0aoEePHnbHgC0LKpUKERERuHv3LpKTkwEYJ0ts0qQJ2rZti/r16ztc1/Xr13H58mXcvXsXGRkZkMlk8PPzQ4sWLdC5c2fUqFHDoXru3LmDpk2bQq/Xo3bt2rh9+3apnr+IiIjKGhO4RERERFTmMjIy0LhxY6SmpsLT0xN3795FQEBAeTeLiCqhN954Qxry5PPPP5fGnSYiIqoqOIQCEREREZU5X19fzJo1C4BxTM2lS5eWc4uIqDJKTEzEd999BwAIDg7G5MmTy7lFREREzscELhERERGVi+nTp6NZs2YAgMWLFyMxMbGcW0RElc3ChQuRlZUFAPjss88cHtuXiIioMnEp7wYQERERUfXk6uqK9evXY9++fQCAW7duSZObERHZYzAYEBQUhHnz5sHHxwejR48u7yYRERGVCo6BS0RERERERERERFRBcQgFIiIiIiIiIiIiogqKCVwiIiIiIiIiIiKiCooJXCIiIiIiIiIiIqIKiglcIiIiIiIiIiIiogqKCVwiIiIiIiIiIiKiCooJXCIiIiIiIiIiIqIKiglcIiIiIiIiIiIiogqKCVwiIiIiIiIiIiKiCooJXCIiIiIiIiIiIqIKiglcIiIiIiIiIiIiogqKCVwiIiIiIiIiIiKiCur/AYawMxZEJhL1AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -143,7 +143,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.19"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

From a61018124289fcfcbe10a5cab33b913751ca0f92 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 13:07:37 +0200
Subject: [PATCH 106/140] Lint with black

---
 docs/notebooks/example_figure_joss.ipynb | 22 +++++++++++-----------
 ramp/core/core.py                        |  8 ++++++--
 2 files changed, 17 insertions(+), 13 deletions(-)

diff --git a/docs/notebooks/example_figure_joss.ipynb b/docs/notebooks/example_figure_joss.ipynb
index 50db22d5..2d9bc0bc 100644
--- a/docs/notebooks/example_figure_joss.ipynb
+++ b/docs/notebooks/example_figure_joss.ipynb
@@ -64,9 +64,7 @@
     "uc = UseCase(\"JOSS-paper-figure\", users=[LMI, LI], random_seed=20240110)\n",
     "uc.initialize(num_days=1)\n",
     "\n",
-    "profiles = uc.generate_daily_load_profiles(\n",
-    "    cases=[1,2,3,4,5]\n",
-    ")"
+    "profiles = uc.generate_daily_load_profiles(cases=[1, 2, 3, 4, 5])"
    ]
   },
   {
@@ -109,21 +107,23 @@
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.dates as mdates\n",
     "\n",
-    "plt.rcParams.update({'font.size': 22})\n",
+    "plt.rcParams.update({\"font.size\": 22})\n",
     "\n",
-    "fig, ax1 = profiles.shadow( figsize=(16, 8))\n",
-    "for ln in range(0,len(profiles.columns)+1):\n",
-    "    ax1.get_lines()[ln].set_color('royalblue') # edits the defualt colour of the shadow-plot lines\n",
-    "ax1.legend(['5-day average', 'Single days(s)'])\n",
+    "fig, ax1 = profiles.shadow(figsize=(16, 8))\n",
+    "for ln in range(0, len(profiles.columns) + 1):\n",
+    "    ax1.get_lines()[ln].set_color(\n",
+    "        \"royalblue\"\n",
+    "    )  # edits the defualt colour of the shadow-plot lines\n",
+    "ax1.legend([\"5-day average\", \"Single days(s)\"])\n",
     "ax1.margins(0)\n",
     "\n",
     "ax1.set_xticks(ax1.get_xticks()[1:])\n",
     "\n",
-    "myFmt = mdates.DateFormatter('%H:%M')\n",
+    "myFmt = mdates.DateFormatter(\"%H:%M\")\n",
     "ax1.xaxis.set_major_formatter(myFmt)\n",
     "\n",
-    "ax1.set_ylabel('Normalised power (kW/kW-Peak)', fontsize='large', labelpad=20)\n",
-    "ax1.set_xlabel('Time (hours:minutes)', fontsize='large', labelpad=20)"
+    "ax1.set_ylabel(\"Normalised power (kW/kW-Peak)\", fontsize=\"large\", labelpad=20)\n",
+    "ax1.set_xlabel(\"Time (hours:minutes)\", fontsize=\"large\", labelpad=20)"
    ]
   }
  ],
diff --git a/ramp/core/core.py b/ramp/core/core.py
index 85a5089c..f05e0c43 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1426,8 +1426,12 @@ def windows(
         self.random_var_1 = int(
             random_var_w * np.diff(self.window_1)[0]
         )  # calculate the random variability of window1, i.e. the maximum range of time they can be enlarged or shortened
-        self.random_var_2 = int(random_var_w * np.diff(self.window_2)[0])  # same as above
-        self.random_var_3 = int(random_var_w * np.diff(self.window_3)[0])  # same as above
+        self.random_var_2 = int(
+            random_var_w * np.diff(self.window_2)[0]
+        )  # same as above
+        self.random_var_3 = int(
+            random_var_w * np.diff(self.window_3)[0]
+        )  # same as above
 
         # automatically appends the appliance to the user's appliance list
         self.user._add_appliance_instance(self)

From 8acb3183d304d47b39a97eae4fe76876b8da92e6 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 13:42:04 +0200
Subject: [PATCH 107/140] Add pre-commit hooks configuration

---
 .pre-commit-config.yaml | 15 +++++++++++++++
 dev_requirements.txt    |  2 ++
 2 files changed, 17 insertions(+)
 create mode 100644 .pre-commit-config.yaml
 create mode 100644 dev_requirements.txt

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 00000000..9dc163c4
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,15 @@
+exclude: '.git'
+default_stages: [commit]
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: check-added-large-files
+
+  - repo: https://github.com/psf/black
+    rev: 24.4.2
+    hooks:
+      - id: black
diff --git a/dev_requirements.txt b/dev_requirements.txt
new file mode 100644
index 00000000..7acd232b
--- /dev/null
+++ b/dev_requirements.txt
@@ -0,0 +1,2 @@
+pre-commit
+-e .

From 6e90793f9d1c94ceacc124428a8fb98cce496f55 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 13:43:24 +0200
Subject: [PATCH 108/140] Update CONTRIBUTING.md with developper setup

Pre-commit already getting rid of trailing spaces
---
 CONTRIBUTING.md | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 0de3761e..7051a32d 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -27,12 +27,24 @@ By contributing to RAMP, e.g. through opening a pull request or submitting a pat
 
 To contribute changes:
 - Fork the project on GitHub
+- Follow the "Setup" steps below
 - Create a feature branch (e.g. named "add-this-new-feature") to work on in your fork
 - Add your name to the [AUTHORS](AUTHORS) file
 - Commit your changes to the feature branch
 - Push the branch to GitHub
 - On GitHub, create a new pull request from the feature branch
 
+### Setup
+
+1. Create a virtual environment and install the dev dependencies with
+
+        pip install -r dev_requirements.txt
+
+2. Install the pre-commit hooks with
+
+        pre-commit install
+
+
 ### Pull requests
 
 Before submitting a pull request, check whether you have:
@@ -54,7 +66,7 @@ Please try to write clear commit messages. One-line messages are fine for small
 
 ## Testing
 
-Testing is used by RAMP developers to make sure their new feature/bug fix is not breaking existing code. As RAMP is stochastic some tests are only qualitative, other unit tests are ran by GitHub Actions. 
+Testing is used by RAMP developers to make sure their new feature/bug fix is not breaking existing code. As RAMP is stochastic some tests are only qualitative, other unit tests are ran by GitHub Actions.
 
 Before running the tests locally, you need to install the testing dependencies
 
@@ -73,7 +85,7 @@ To run the qualitative test, you'll have to run
 from the root level of this repository.
 
 If you already ran this script, you will be asked if you want to overwrite the results files (if you decide not to, the results are not going to be regenerated from your latest code version). You should compare the results of your code and those saved from the latest stable version thanks to the image which is displayed after the script ran.
-   
+
 Ideally, the difference between reference and new results should be minimal and just due to the stochastic nature of the code. If more pronounced, it should be fully explainable based on the changes made to the code and aligned to the expectations of the developers (i.e. it should reflect a change in the output *wanted* and precisely *sought* with the commit in question).
 
 ### Unit tests
@@ -82,4 +94,4 @@ Run `pytest tests/` form the root of the repository to run the unit tests.
 
 ## Attribution
 
-The layout and content of this document is partially based on [calliope](https://github.com/calliope-project/calliope/blob/master/CONTRIBUTING.md)'s equivalent document.
\ No newline at end of file
+The layout and content of this document is partially based on [calliope](https://github.com/calliope-project/calliope/blob/master/CONTRIBUTING.md)'s equivalent document.

From 4a2843cf1f44b8f1430e6d59daec2773e8cc4250 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 13:43:58 +0200
Subject: [PATCH 109/140] Add multiplrocess to the install_requires in setup.py

---
 setup.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/setup.py b/setup.py
index f833a97a..d8dfc540 100644
--- a/setup.py
+++ b/setup.py
@@ -23,6 +23,7 @@
         "openpyxl >= 3.0.6",
         "tqdm",
         "plotly",
+        "multiprocess",
     ],
     # classifiers=[
     #     "Programming Language :: Python :: 3.7",

From 7bf997b794884f7eec53f6d84dab4900de5169dc Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 13:51:26 +0200
Subject: [PATCH 110/140] Add black to dev requirements

---
 CONTRIBUTING.md      | 2 ++
 dev_requirements.txt | 1 +
 2 files changed, 3 insertions(+)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 7051a32d..c2dc9f49 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -44,6 +44,8 @@ To contribute changes:
 
         pre-commit install
 
+   This will mainly make sure you can't commit if your code is not linted with black.
+   The pre-commit hook will check if your code is linted and if it is not it will simply lint it for you, you then only need to stage the changes made by the linter and commit again, as simple as that :)
 
 ### Pull requests
 
diff --git a/dev_requirements.txt b/dev_requirements.txt
index 7acd232b..e3639560 100644
--- a/dev_requirements.txt
+++ b/dev_requirements.txt
@@ -1,2 +1,3 @@
 pre-commit
+black==24.4.2
 -e .

From 3e4ca1dcb73aebb074803655661845e9aac13c28 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 15 May 2024 13:52:16 +0200
Subject: [PATCH 111/140] Add test requirements to dev requirements

---
 dev_requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dev_requirements.txt b/dev_requirements.txt
index e3639560..d39a2e2b 100644
--- a/dev_requirements.txt
+++ b/dev_requirements.txt
@@ -1,3 +1,3 @@
 pre-commit
 black==24.4.2
--e .
+-r tests/requirements.txt

From a54dc56f0eaef338e4650253123a624d9a25856d Mon Sep 17 00:00:00 2001
From: FLomb <f.lombardi@tudelft.nl>
Date: Wed, 22 May 2024 14:12:57 +0200
Subject: [PATCH 112/140] More literature and examples

---
 joss_paper/paper.bib | 53 ++++++++++++++++++++++++++++++++++++++++++++
 joss_paper/paper.md  |  6 ++++-
 2 files changed, 58 insertions(+), 1 deletion(-)

diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib
index 6069919e..5d70bc27 100644
--- a/joss_paper/paper.bib
+++ b/joss_paper/paper.bib
@@ -220,3 +220,56 @@ @misc{ramp_zenodo:2023
 	year = {2023},
 	doi = {10.5281/zenodo.10275752},
 }
+
+@article{crest_model:2015,
+	author = "McKenna, Eoghan and Thomson, Murray and Barton, John",
+	title = "{CREST Demand Model}",
+	year = "2015",
+	month = "9",
+	url = "https://repository.lboro.ac.uk/articles/dataset/CREST_Demand_Model_v2_0/2001129",
+	doi = "10.17028/rd.lboro.2001129.v8"
+}
+
+
+@article{mandelli_lpg:2016,
+	title = {Novel procedure to formulate load profiles for off-grid rural areas},
+	volume = {31},
+	issn = {0973-0826},
+	doi = {https://doi.org/10.1016/j.esd.2016.01.005},
+    journal = {Energy for Sustainable Development},
+	author = {Mandelli, Stefano and Merlo, Marco and Colombo, Emanuela},
+	year = {2016},
+	keywords = {Electric consumptions, Load model, Off-grid energy systems, Renewables, Rural electrification, Stochastic model},
+	pages = {130--142},
+}
+
+@article{barsanti_demod:2021,
+	title = {Socio-technical modeling of smart energy systems: a co-simulation design for domestic energy demand},
+	volume = {4},
+	issn = {2520-8942},
+	doi = {10.1186/s42162-021-00180-6},
+	number = {3},
+	urldate = {2024-05-22},
+	journal = {Energy Informatics},
+	author = {Barsanti, Matteo and Schwarz, Jan Sören and Gérard Constantin, Lionel Guy and Kasturi, Pranay and Binder, Claudia R. and Lehnhoff, Sebastian},
+	month = sep,
+	year = {2021},
+	keywords = {Co-simulation, Domestic energy demand, Granularity, Modularity, Scalability, Smart energy system, Socio-technical simulation, Transparency},
+	pages = {12},
+}
+
+
+@article{pflugradt_loadprofilegenerator:2022,
+	title = {{LoadProfileGenerator}: {An} {Agent}-{Based} {Behavior} {Simulation} for {Generating} {Residential} {Load} {Profiles}},
+	volume = {7},
+	issn = {2475-9066},
+	url = {https://joss.theoj.org/papers/10.21105/joss.03574},
+	doi = {10.21105/joss.03574},
+	number = {71},
+	urldate = {2024-05-22},
+	journal = {Journal of Open Source Software},
+	author = {Pflugradt, Noah and Stenzel, Peter and Kotzur, Leander and Stolten, Detlef},
+	month = mar,
+	year = {2022},
+	pages = {3574},
+}
\ No newline at end of file
diff --git a/joss_paper/paper.md b/joss_paper/paper.md
index 238ef8ed..ce7188c4 100644
--- a/joss_paper/paper.md
+++ b/joss_paper/paper.md
@@ -64,7 +64,11 @@ The urgency of the energy transition is leading to a rapid evolution of energy s
 
 # Statement of need
 
-RAMP is an open-source, Python-based software suite that enables the stochastic simulation of any user-driven energy demand time series based on few simple inputs. In fact, the software is designed to require only a basic understanding of the expected user activity patterns and owned appliances as inputs, to be provided in tabular (`.xlsx`) format. Then, it leverages stochasticity (using the `random` package) to make up for the lack of more detailed information and to account for the unpredictability of human behaviour (see Figure \ref{fig:example}). This way, RAMP allows generating and visualising synthetic data wherever metered data does not exist, such as when designing systems in remote areas [@lombardi_generating:2019] or when looking at future electric-vehicle fleets [@mangipinto_impact:2022]. Moreover, it features several degrees of customisations, allowing users to explicitly simulate radically different but equally plausible behaviour scenarios as a key ingredient to robust system design.
+RAMP is an open-source, Python-based software suite that enables the stochastic simulation of any user-driven energy demand time series based on few simple inputs. In fact, the software is designed to require only a basic understanding of the expected user activity patterns and owned appliances as inputs, to be provided in tabular (`.xlsx`) format. For instance, a minimal definition of a user type (e.g., a certain category of households) requires only information about which energy-consuming devices they own, when, on a typical day, they tend to use them, and for how long in total. Then, the software leverages stochasticity (using the `random` package) to make up for the lack of more detailed information and to account for the unpredictability of human behaviour (see Figure \ref{fig:example}). This way, RAMP allows generating and visualising synthetic data wherever detailed metered data does not exist, such as when designing systems in remote areas [@lombardi_generating:2019] or when looking at future electric-vehicle fleets [@mangipinto_impact:2022]. 
+
+This reliance on simple inputs distinguishes RAMP from comparable tools. For instance, other popular open-source demand simulation tools, such as CREST [crest_model:2015] and demod [barsanti_demod:2021], are based on extensive and context-specific input datasets from the UK and Germany, respectively, which populate the occupancy model at the core of their approach. Similarly, the Load Profile Generator model [pflugradt_loadprofilegenerator:2022], also openly available, implements a desire-driven behavioural simulation grounded on a psychological model based on German household data. Due to their data-driven nature, these models are not readily applicable to other similar contexts, such as different European countries, and are arguably inapplicable to cases, such as remote areas, with unique characteristics. Moreover, the heavy reliance on historical data serves poorly the need to accommodate the modelling of future, entirely new behaviours or devices. RAMP trades off the capability of providing detailed information about users' occupancy and activity, typical of the above data-driven models, for substantially greater modelling flexibility and context adaptability. Previous attempts at generating demand time series based on a similar approach as RAMP exist [mandelli_lpg:2016], but they are neither open-source nor capable of simulating non-electric or more sophisticated energy uses.
+
+In fact, RAMP features several degrees of customisation, enabling modellers to switch on or off features tailored to the needs of specific energy uses. For example, to represent the highly variable load profile of cooking appliances, which changes completely depending on which meal is cooked, the software allows defining many possible meal types with an associated cooking cycle; then, it leverages stochasticity to diversity which meals are cooked when. For heat-related energy uses that may be influenced by weather parameters, such as outdoor or groundwater temperature, it is possible to provide a time series of such parameters and define how they affect the default energy consumption. These and many other customisation options allow users to explicitly simulate radically different but equally plausible behaviour scenarios, including behaviours that may happen in the future and that have no relationship with past data, as a key ingredient to robust system design.
 
 RAMP has already been used in many scientific publications, for instance, for the simulation of electricity [@dimovski_holistic:2023], heating [@stevanato_modeling:2020], cooking [@stevanato_long-term:2020] and electric mobility [@secchi_smart:2023] demand time series at scales ranging from districts [@pasqui_new:2023] or villages [@villarroel-schneider_open-source:2023] to continents [@pickering_diversity:2022]. It has dozens of users globally and has recently become a multi-institution software development effort, actively contributed by TU Delft, VITO, Sympheny, the Reiner Lemoine Institut, the University of Liège and the Leibniz University Hannover. The joint development process has brought major improvements to the code structure, syntax and efficiency, more extensive documentation, and a web-based graphical user interface for users with no Python experience [@hart_gui:2023].
 

From fd2384650e6475c583226802200cdf4987581f7e Mon Sep 17 00:00:00 2001
From: FLomb <f.lombardi@tudelft.nl>
Date: Wed, 22 May 2024 14:19:10 +0200
Subject: [PATCH 113/140] Updated figure

---
 joss_paper/example_output.png | Bin 818499 -> 184607 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/joss_paper/example_output.png b/joss_paper/example_output.png
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From c633e192242f9fc8e58ea4c2f43dafcc39fa66f9 Mon Sep 17 00:00:00 2001
From: FLomb <f.lombardi@tudelft.nl>
Date: Thu, 30 May 2024 13:28:46 +0200
Subject: [PATCH 114/140] add two authors

---
 joss_paper/paper.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/joss_paper/paper.md b/joss_paper/paper.md
index ce7188c4..731138d0 100644
--- a/joss_paper/paper.md
+++ b/joss_paper/paper.md
@@ -35,6 +35,12 @@ authors:
   - name: Sergio Balderrama
     orcid: 
     affiliation: 7
+  - name: Johann Kraft
+    orcid: 
+    affiliation: 2
+  - name: Gokarna Dhungel
+    orcid: 
+    affiliation: 2
   - name: Sylvain Quoilin
     orcid: 
     affiliation: 4

From 572dd70a9ae66a90a26fd8f38b728f0c3a5953df Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 30 May 2024 21:03:21 +0200
Subject: [PATCH 115/140] Fix formating of certain citations

And fix trailing spaces (pre-commit hooks)
---
 joss_paper/paper.md | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/joss_paper/paper.md b/joss_paper/paper.md
index 731138d0..c11d9ac5 100644
--- a/joss_paper/paper.md
+++ b/joss_paper/paper.md
@@ -12,7 +12,7 @@ authors:
     corresponding: true # (This is how to denote the corresponding author)
     affiliation: 1
   - name: Pierre-François Duc
-    orcid: 
+    orcid:
     affiliation: 2
   - name: Mohammad Amin Tahavori
     orcid: 0000-0002-7753-0523
@@ -22,27 +22,27 @@ authors:
     affiliation: "4,7"
   - name: Sarah Eckhoff
     orcid: 0000-0002-6168-4835
-    affiliation: 5  
+    affiliation: 5
   - name: Maria C.G. Hart
     orcid: 0000-0002-1031-9782
-    affiliation: 5  
+    affiliation: 5
   - name: Francesco Sanvito
     orcid: 0000-0002-9152-9684
     affiliation: 1
   - name: Gregory Ireland
-    orcid: 
+    orcid:
     affiliation: "2,6"
   - name: Sergio Balderrama
-    orcid: 
+    orcid:
     affiliation: 7
   - name: Johann Kraft
-    orcid: 
+    orcid:
     affiliation: 2
   - name: Gokarna Dhungel
-    orcid: 
+    orcid:
     affiliation: 2
   - name: Sylvain Quoilin
-    orcid: 
+    orcid:
     affiliation: 4
 affiliations:
  - name: TU Delft, Faculty of Technology, Policy and Management, Delft, The Netherlands
@@ -70,9 +70,9 @@ The urgency of the energy transition is leading to a rapid evolution of energy s
 
 # Statement of need
 
-RAMP is an open-source, Python-based software suite that enables the stochastic simulation of any user-driven energy demand time series based on few simple inputs. In fact, the software is designed to require only a basic understanding of the expected user activity patterns and owned appliances as inputs, to be provided in tabular (`.xlsx`) format. For instance, a minimal definition of a user type (e.g., a certain category of households) requires only information about which energy-consuming devices they own, when, on a typical day, they tend to use them, and for how long in total. Then, the software leverages stochasticity (using the `random` package) to make up for the lack of more detailed information and to account for the unpredictability of human behaviour (see Figure \ref{fig:example}). This way, RAMP allows generating and visualising synthetic data wherever detailed metered data does not exist, such as when designing systems in remote areas [@lombardi_generating:2019] or when looking at future electric-vehicle fleets [@mangipinto_impact:2022]. 
+RAMP is an open-source, Python-based software suite that enables the stochastic simulation of any user-driven energy demand time series based on few simple inputs. In fact, the software is designed to require only a basic understanding of the expected user activity patterns and owned appliances as inputs, to be provided in tabular (`.xlsx`) format. For instance, a minimal definition of a user type (e.g., a certain category of households) requires only information about which energy-consuming devices they own, when, on a typical day, they tend to use them, and for how long in total. Then, the software leverages stochasticity (using the `random` package) to make up for the lack of more detailed information and to account for the unpredictability of human behaviour (see Figure \ref{fig:example}). This way, RAMP allows generating and visualising synthetic data wherever detailed metered data does not exist, such as when designing systems in remote areas [@lombardi_generating:2019] or when looking at future electric-vehicle fleets [@mangipinto_impact:2022].
 
-This reliance on simple inputs distinguishes RAMP from comparable tools. For instance, other popular open-source demand simulation tools, such as CREST [crest_model:2015] and demod [barsanti_demod:2021], are based on extensive and context-specific input datasets from the UK and Germany, respectively, which populate the occupancy model at the core of their approach. Similarly, the Load Profile Generator model [pflugradt_loadprofilegenerator:2022], also openly available, implements a desire-driven behavioural simulation grounded on a psychological model based on German household data. Due to their data-driven nature, these models are not readily applicable to other similar contexts, such as different European countries, and are arguably inapplicable to cases, such as remote areas, with unique characteristics. Moreover, the heavy reliance on historical data serves poorly the need to accommodate the modelling of future, entirely new behaviours or devices. RAMP trades off the capability of providing detailed information about users' occupancy and activity, typical of the above data-driven models, for substantially greater modelling flexibility and context adaptability. Previous attempts at generating demand time series based on a similar approach as RAMP exist [mandelli_lpg:2016], but they are neither open-source nor capable of simulating non-electric or more sophisticated energy uses.
+This reliance on simple inputs distinguishes RAMP from comparable tools. For instance, other popular open-source demand simulation tools, such as CREST [@crest_model:2015] and demod [@barsanti_demod:2021], are based on extensive and context-specific input datasets from the UK and Germany, respectively, which populate the occupancy model at the core of their approach. Similarly, the Load Profile Generator model [@pflugradt_loadprofilegenerator:2022], also openly available, implements a desire-driven behavioural simulation grounded on a psychological model based on German household data. Due to their data-driven nature, these models are not readily applicable to other similar contexts, such as different European countries, and are arguably inapplicable to cases, such as remote areas, with unique characteristics. Moreover, the heavy reliance on historical data serves poorly the need to accommodate the modelling of future, entirely new behaviours or devices. RAMP trades off the capability of providing detailed information about users' occupancy and activity, typical of the above data-driven models, for substantially greater modelling flexibility and context adaptability. Previous attempts at generating demand time series based on a similar approach as RAMP exist [@mandelli_lpg:2016], but they are neither open-source nor capable of simulating non-electric or more sophisticated energy uses.
 
 In fact, RAMP features several degrees of customisation, enabling modellers to switch on or off features tailored to the needs of specific energy uses. For example, to represent the highly variable load profile of cooking appliances, which changes completely depending on which meal is cooked, the software allows defining many possible meal types with an associated cooking cycle; then, it leverages stochasticity to diversity which meals are cooked when. For heat-related energy uses that may be influenced by weather parameters, such as outdoor or groundwater temperature, it is possible to provide a time series of such parameters and define how they affect the default energy consumption. These and many other customisation options allow users to explicitly simulate radically different but equally plausible behaviour scenarios, including behaviours that may happen in the future and that have no relationship with past data, as a key ingredient to robust system design.
 
@@ -82,4 +82,4 @@ RAMP is developed openly on GitHub [@ramp_github] and each new release is archiv
 
 ![Example output (normalised by peak demand) for the simulation of the electricity load of three households in a small village over five days. The thick blue line represents the five-day average, while individual days are plotted in a lighter colour. \label{fig:example}](example_output.png)
 
-# References
\ No newline at end of file
+# References

From c939cf9f7904440d136ace0d38808128a5349f3b Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Fri, 31 May 2024 10:52:27 +0200
Subject: [PATCH 116/140] Add 2nd affiliation of author

The author was master student from this institution working at RLI
at the time and he is researcher at this institution now
---
 joss_paper/paper.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/joss_paper/paper.md b/joss_paper/paper.md
index c11d9ac5..f459c88a 100644
--- a/joss_paper/paper.md
+++ b/joss_paper/paper.md
@@ -40,7 +40,7 @@ authors:
     affiliation: 2
   - name: Gokarna Dhungel
     orcid:
-    affiliation: 2
+    affiliation: "2,8"
   - name: Sylvain Quoilin
     orcid:
     affiliation: 4
@@ -59,6 +59,8 @@ affiliations:
    index: 6
  - name: Universidad Mayor de San Simon, Centro Universitario de Investigacion en Energias, Cochabamba, Bolivia
    index: 7
+ - name: University of Applied Sciences Nordhausen, Nordhausen, Germany
+   index: 8
 date: 6 December 2023
 bibliography: paper.bib
 

From 75e2871c7e81a40163e60c23c0e52d32744fc85e Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Sat, 1 Jun 2024 00:19:25 +0200
Subject: [PATCH 117/140] Add author orchid

---
 joss_paper/paper.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/joss_paper/paper.md b/joss_paper/paper.md
index f459c88a..4493f9b3 100644
--- a/joss_paper/paper.md
+++ b/joss_paper/paper.md
@@ -12,7 +12,7 @@ authors:
     corresponding: true # (This is how to denote the corresponding author)
     affiliation: 1
   - name: Pierre-François Duc
-    orcid:
+    orcid: 0000-0001-8698-8854
     affiliation: 2
   - name: Mohammad Amin Tahavori
     orcid: 0000-0002-7753-0523

From 85575fc29e8f6d80e5ede8b6ef4a6013fd0770bb Mon Sep 17 00:00:00 2001
From: "Adam R. Jensen" <39184289+AdamRJensen@users.noreply.github.com>
Date: Sun, 2 Jun 2024 20:30:10 +0200
Subject: [PATCH 118/140] Editorial suggested changes to JOSS paper

---
 joss_paper/paper.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/joss_paper/paper.md b/joss_paper/paper.md
index 4493f9b3..7f1d0f11 100644
--- a/joss_paper/paper.md
+++ b/joss_paper/paper.md
@@ -68,17 +68,17 @@ bibliography: paper.bib
 
 # Summary
 
-The urgency of the energy transition is leading to a rapid evolution of energy system design worldwide. In areas with widespread energy infrastructure, existing electricity, heat and mobility networks are being re-designed for carbon neutrality and are increasingly interconnected. In areas where energy infrastructure is limited, instead, networks and systems are being rapidly expanded to ensure access to energy for all. And yet, re-designing and expanding energy systems in these directions requires information on future user behaviour and associated energy demand, which is often unavailable. In fact, historical data are either entirely missing or poorly representative of future behaviour within transitioning systems. This results in the reliance on inadequate demand data, which affect system design and its resilience to rapid behaviour evolution.
+The urgency of the energy transition is leading to a rapid evolution of energy system design worldwide. In areas with widespread energy infrastructure, existing electricity, heat and mobility networks are being re-designed for carbon neutrality and are increasingly interconnected. In areas where energy infrastructure is limited, instead, networks and systems are being rapidly expanded to ensure access to energy for all. In both cases, re-designing and expanding energy systems in these directions requires information on future user behaviour and associated energy demand, yet this type of data is often unavailable. In fact, historical data are often either entirely missing or poorly representative of future behaviour within transitioning systems. This results in reliance on inadequate demand data, which affects system design and its resilience to rapid behaviour evolution.
 
 # Statement of need
 
-RAMP is an open-source, Python-based software suite that enables the stochastic simulation of any user-driven energy demand time series based on few simple inputs. In fact, the software is designed to require only a basic understanding of the expected user activity patterns and owned appliances as inputs, to be provided in tabular (`.xlsx`) format. For instance, a minimal definition of a user type (e.g., a certain category of households) requires only information about which energy-consuming devices they own, when, on a typical day, they tend to use them, and for how long in total. Then, the software leverages stochasticity (using the `random` package) to make up for the lack of more detailed information and to account for the unpredictability of human behaviour (see Figure \ref{fig:example}). This way, RAMP allows generating and visualising synthetic data wherever detailed metered data does not exist, such as when designing systems in remote areas [@lombardi_generating:2019] or when looking at future electric-vehicle fleets [@mangipinto_impact:2022].
+RAMP is an open-source, Python-based software suite that enables the stochastic simulation of any user-driven energy demand time series based on few simple inputs. In fact, the software is designed to require only a basic understanding of the expected user activity patterns and owned appliances as inputs, which are provided in tabular format (`.xlsx`). For instance, a minimal definition of a user type (e.g., a certain category of households) requires only information about which energy-consuming devices they own, when, on a typical day, they tend to use them, and for how long in total. Then, the software leverages stochasticity (using the `random` package) to make up for the lack of more detailed information and to account for the unpredictability of human behaviour (see Figure \ref{fig:example}). This way, RAMP allows generating and visualising synthetic data wherever detailed metered data does not exist, such as when designing systems in remote areas [@lombardi_generating:2019] or when looking at future electric-vehicle fleets [@mangipinto_impact:2022].
 
-This reliance on simple inputs distinguishes RAMP from comparable tools. For instance, other popular open-source demand simulation tools, such as CREST [@crest_model:2015] and demod [@barsanti_demod:2021], are based on extensive and context-specific input datasets from the UK and Germany, respectively, which populate the occupancy model at the core of their approach. Similarly, the Load Profile Generator model [@pflugradt_loadprofilegenerator:2022], also openly available, implements a desire-driven behavioural simulation grounded on a psychological model based on German household data. Due to their data-driven nature, these models are not readily applicable to other similar contexts, such as different European countries, and are arguably inapplicable to cases, such as remote areas, with unique characteristics. Moreover, the heavy reliance on historical data serves poorly the need to accommodate the modelling of future, entirely new behaviours or devices. RAMP trades off the capability of providing detailed information about users' occupancy and activity, typical of the above data-driven models, for substantially greater modelling flexibility and context adaptability. Previous attempts at generating demand time series based on a similar approach as RAMP exist [@mandelli_lpg:2016], but they are neither open-source nor capable of simulating non-electric or more sophisticated energy uses.
+Reliance on simple inputs distinguishes RAMP from comparable tools. For instance, other popular open-source demand simulation tools, such as CREST [@crest_model:2015] and demod [@barsanti_demod:2021], are based on extensive and context-specific input datasets from the UK and Germany, respectively, which populate the occupancy model at the core of their approach. Similarly, the Load Profile Generator model [@pflugradt_loadprofilegenerator:2022], also openly available, implements a desire-driven behavioural simulation grounded on a psychological model based on German household data. Due to their data-driven nature, these models are not readily applicable to other similar contexts, such as different European countries, and are arguably inapplicable to cases, such as remote areas, with unique characteristics. Moreover, the heavy reliance on historical data serves poorly the need to accommodate modelling of future, entirely new behaviours or devices. RAMP trades off the capability of providing detailed information about users' occupancy and activity, typical of the above data-driven models, for substantially greater modelling flexibility and context adaptability. Previous attempts at generating demand time series based on a similar approach as RAMP exist [@mandelli_lpg:2016], but they are neither open-source nor capable of simulating non-electric or more sophisticated energy uses.
 
 In fact, RAMP features several degrees of customisation, enabling modellers to switch on or off features tailored to the needs of specific energy uses. For example, to represent the highly variable load profile of cooking appliances, which changes completely depending on which meal is cooked, the software allows defining many possible meal types with an associated cooking cycle; then, it leverages stochasticity to diversity which meals are cooked when. For heat-related energy uses that may be influenced by weather parameters, such as outdoor or groundwater temperature, it is possible to provide a time series of such parameters and define how they affect the default energy consumption. These and many other customisation options allow users to explicitly simulate radically different but equally plausible behaviour scenarios, including behaviours that may happen in the future and that have no relationship with past data, as a key ingredient to robust system design.
 
-RAMP has already been used in many scientific publications, for instance, for the simulation of electricity [@dimovski_holistic:2023], heating [@stevanato_modeling:2020], cooking [@stevanato_long-term:2020] and electric mobility [@secchi_smart:2023] demand time series at scales ranging from districts [@pasqui_new:2023] or villages [@villarroel-schneider_open-source:2023] to continents [@pickering_diversity:2022]. It has dozens of users globally and has recently become a multi-institution software development effort, actively contributed by TU Delft, VITO, Sympheny, the Reiner Lemoine Institut, the University of Liège and the Leibniz University Hannover. The joint development process has brought major improvements to the code structure, syntax and efficiency, more extensive documentation, and a web-based graphical user interface for users with no Python experience [@hart_gui:2023].
+RAMP has already been used in many scientific publications, for instance, for the simulation of electricity [@dimovski_holistic:2023], heating [@stevanato_modeling:2020], cooking [@stevanato_long-term:2020] and electric mobility [@secchi_smart:2023] demand time series at scales ranging from districts [@pasqui_new:2023] or villages [@villarroel-schneider_open-source:2023] to continents [@pickering_diversity:2022]. It has dozens of users globally and has recently become a multi-institution software development effort, actively developed by TU Delft, VITO, Sympheny, the Reiner Lemoine Institut, the University of Liège and the Leibniz University Hannover. The joint development process has brought major improvements to the code structure, syntax and efficiency, more extensive documentation, and a web-based graphical user interface for users with no Python experience [@hart_gui:2023].
 
 RAMP is developed openly on GitHub [@ramp_github] and each new release is archived on Zenodo [@ramp_zenodo:2023].
 

From 6e4699d6be0ef5834046c745077b509023c00592 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 3 Jun 2024 21:00:37 +0200
Subject: [PATCH 119/140] Update multi-cycle notebook

---
 docs/notebooks/multi_cycle.ipynb | 46 +++++++++++++-------------------
 1 file changed, 19 insertions(+), 27 deletions(-)

diff --git a/docs/notebooks/multi_cycle.ipynb b/docs/notebooks/multi_cycle.ipynb
index 51198d3c..316e2a58 100644
--- a/docs/notebooks/multi_cycle.ipynb
+++ b/docs/notebooks/multi_cycle.ipynb
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "488c1fe3",
    "metadata": {},
    "outputs": [],
@@ -187,22 +187,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The app Fridge has duty cycle option on, however the switch on event fell outside the provided duty cycle windows\n",
-      "The app Fridge has duty cycle option on, however the switch on event fell outside the provided duty cycle windows\n",
-      "      2020-05-16  2020-08-16  2020-12-16\n",
-      "0          0.001       0.001       0.001\n",
-      "1          0.001       0.001       0.001\n",
-      "2          0.001       0.001       0.001\n",
-      "3          0.001       0.001       0.001\n",
-      "4          0.001       0.001       0.001\n",
-      "...          ...         ...         ...\n",
-      "1435       0.001     200.000       0.001\n",
-      "1436       0.001     200.000       0.001\n",
-      "1437       0.001       0.001       0.001\n",
-      "1438       0.001       0.001       0.001\n",
-      "1439       0.001       0.001       0.001\n",
+      "      2020-12-16\n",
+      "0          0.001\n",
+      "1          0.001\n",
+      "2          0.001\n",
+      "3          0.001\n",
+      "4          0.001\n",
+      "...          ...\n",
+      "1435       0.001\n",
+      "1436       0.001\n",
+      "1437       0.001\n",
+      "1438       0.001\n",
+      "1439       0.001\n",
       "\n",
-      "[1440 rows x 3 columns]\n"
+      "[1440 rows x 1 columns]\n"
      ]
     }
    ],
@@ -356,7 +354,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: >"
+       "<AxesSubplot:>"
       ]
      },
      "execution_count": 11,
@@ -367,10 +365,12 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -392,14 +392,6 @@
    "source": [
     "One can see that the switch on events are longer and more than one duty cycle run within the switch on event"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b27e1a44-193f-4ca9-808f-a83597f026d4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 2b5eefbfcb91b9723e1db6efb5d8374028d88737 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 3 Jun 2024 21:05:17 +0200
Subject: [PATCH 120/140] Lint with black

---
 ramp/core/core.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index f05e0c43..8637eb64 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -373,11 +373,15 @@ def calc_peak_time_range(self, peak_enlarge=None):
             )
         )
         # Rand_peak_enlarge is rounded to be at least 1 -> if rounded to 0 peak_time_range would be empty
-        rand_peak_enlarge = max(round(
-            math.fabs(
-                peak_time - random.gauss(mu=peak_time, sigma=peak_enlarge * peak_time)
-            )
-        ), 1)
+        rand_peak_enlarge = max(
+            round(
+                math.fabs(
+                    peak_time
+                    - random.gauss(mu=peak_time, sigma=peak_enlarge * peak_time)
+                )
+            ),
+            1,
+        )
         # The peak_time is randomly enlarged based on the calibration parameter peak_enlarge
         return np.arange(peak_time - rand_peak_enlarge, peak_time + rand_peak_enlarge)
 

From c56b550a952d8554bd877ee884d4f86ff417262a Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Mon, 3 Jun 2024 21:32:35 +0200
Subject: [PATCH 121/140] Fix failing test for appliance with 0 func_time

---
 ramp/core/core.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 8637eb64..d1833d43 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1184,7 +1184,7 @@ def __init__(
         self.random_var_1 = 0
         self.random_var_2 = 0
         self.random_var_3 = 0
-        self.daily_use = None
+        self.daily_use = np.zeros(1440)
         self.free_spots = None
 
         # attributes used for specific fixed and random cycles

From 2e2aa33127e14ad93f78b750c8ebcabfcd1b893f Mon Sep 17 00:00:00 2001
From: Francesco Lombardi <f.lombardi@outlook.com>
Date: Tue, 4 Jun 2024 11:07:20 +0200
Subject: [PATCH 122/140] Update README.rst to clarify 'active development'

This PR addresses a comment by a JOSS reviewer that the statement about 'active development' at the bottom of the README may lead to misunderstandings.

The statement is now more explicit and clear.
---
 README.rst | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/README.rst b/README.rst
index 2ef6da3f..216df0d3 100644
--- a/README.rst
+++ b/README.rst
@@ -310,5 +310,4 @@ Unless required by applicable law or agreed to in writing, software distributed
 
 
 .. note::
-
-   This project is under active development!
+   This project is actively maintained and developed. This means that while we provide stable and reliable software releases, we keep developing new features and improvements for upcoming, upgraded versions    of the software.

From d8f3c35aa465f784d8cf36d163888c6b159b2e1b Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 5 Jun 2024 15:36:09 +0200
Subject: [PATCH 123/140] Let the file to convert being added to system path
 also on posix

---
 ramp/ramp_convert_old_input_files.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/ramp/ramp_convert_old_input_files.py b/ramp/ramp_convert_old_input_files.py
index 7b740c3d..b8c45259 100644
--- a/ramp/ramp_convert_old_input_files.py
+++ b/ramp/ramp_convert_old_input_files.py
@@ -94,8 +94,7 @@ def convert_old_user_input_file(
     output_fname = fname_path.split(os.path.sep)[-1].replace(".py", suffix)
     output_fname = os.path.join(output_path, output_fname)
 
-    if os.name != "posix":
-        sys.path.insert(0, os.path.dirname(os.path.abspath(fname_path)))
+    sys.path.insert(0, os.path.dirname(os.path.abspath(fname_path)))
     file_module = importlib.import_module(fname)
 
     user_list = file_module.User_list

From 0c87d2ce09412e12efdcae9babda294e550cbded Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 5 Jun 2024 15:47:48 +0200
Subject: [PATCH 124/140] Attemp to fix the online documentation

---
 docs/source/contributing.md | 99 +++++++++++++++++++++++++++++++++++++
 1 file changed, 99 insertions(+)
 create mode 100644 docs/source/contributing.md

diff --git a/docs/source/contributing.md b/docs/source/contributing.md
new file mode 100644
index 00000000..ed618f4c
--- /dev/null
+++ b/docs/source/contributing.md
@@ -0,0 +1,99 @@
+# Contribute
+
+*(this section is freely adapted from the [calliope](https://github.com/calliope-project/calliope/blob/master/CONTRIBUTING.md) project)*
+
+We're really glad you're reading this, because we need volunteer developers to help this project come to fruition.
+
+Some of the resources to look at if you're interested in contributing:
+
+* [Join us on Gitter to chat!](https://gitter.im/RAMP-project/community)
+* Look at [open issues](https://github.com/RAMP-project/RAMP/issues)
+
+## Licensing
+
+By contributing to RAMP, e.g. through opening a pull request or submitting a patch, you represent that your contributions are your own original work and that you have the right to license them, and you agree that your contributions are licensed under the EUROPEAN UNION PUBLIC LICENCE v. 1.2.
+
+## Open an issue
+
+[Open an issue](https://github.com/RAMP-project/RAMP/issues) to report a bug or for a feature request, please try to provide the information summarized below
+
+- OS: *your_operating_system*, *your_distribution*
+- RAMP version or Branch: *RAMP version* or *branch_name*, updated on *update_date*
+- If applicable: Attach full error message
+- If applicable: Share screenshots/images of your problem
+- If applicable: Share used input data
+
+## Submitting changes
+
+To contribute changes:
+- Fork the project on GitHub
+- Follow the "Setup" steps below
+- Create a feature branch (e.g. named "add-this-new-feature") to work on in your fork
+- Add your name to the [AUTHORS](AUTHORS) file
+- Commit your changes to the feature branch
+- Push the branch to GitHub
+- On GitHub, create a new pull request from the feature branch
+
+### Setup
+
+1. Create a virtual environment and install the dev dependencies with
+
+        pip install -r dev_requirements.txt
+
+2. Install the pre-commit hooks with
+
+        pre-commit install
+
+   This will mainly make sure you can't commit if your code is not linted with black.
+   The pre-commit hook will check if your code is linted and if it is not it will simply lint it for you, you then only need to stage the changes made by the linter and commit again, as simple as that :)
+
+### Pull requests
+
+Before submitting a pull request, check whether you have:
+
+* Added your changes to ``CHANGELOG.md``
+* Added or updated documentation for your changes
+* Passed qualitative tests if you implemented new functionality
+
+When opening a pull request, please provide a clear summary of your changes!
+
+### Commit messages
+
+Please try to write clear commit messages. One-line messages are fine for small changes, but bigger changes should look like this:
+
+    A brief summary of the commit
+
+    A paragraph or bullet-point list describing what changed and its impact,
+    covering as many lines as needed.
+
+## Testing
+
+Testing is used by RAMP developers to make sure their new feature/bug fix is not breaking existing code. As RAMP is stochastic some tests are only qualitative, other unit tests are ran by GitHub Actions.
+
+Before running the tests locally, you need to install the testing dependencies
+
+```
+pip install -r tests/requirements.txt
+```
+
+### Qualitative testing
+
+The qualitative testing functionality allows to compare the results arising from a modified version of the code against default ones, for the 3 reference input files provided within the code itself.
+
+To run the qualitative test, you'll have to run
+ ```
+ python ramp/test/test_run.py
+ ```
+from the root level of this repository.
+
+If you already ran this script, you will be asked if you want to overwrite the results files (if you decide not to, the results are not going to be regenerated from your latest code version). You should compare the results of your code and those saved from the latest stable version thanks to the image which is displayed after the script ran.
+
+Ideally, the difference between reference and new results should be minimal and just due to the stochastic nature of the code. If more pronounced, it should be fully explainable based on the changes made to the code and aligned to the expectations of the developers (i.e. it should reflect a change in the output *wanted* and precisely *sought* with the commit in question).
+
+### Unit tests
+
+Run `pytest tests/` form the root of the repository to run the unit tests.
+
+## Attribution
+
+The layout and content of this document is partially based on [calliope](https://github.com/calliope-project/calliope/blob/master/CONTRIBUTING.md)'s equivalent document.

From 0922874da89566ff39a62501a8e81083f30782d9 Mon Sep 17 00:00:00 2001
From: FLomb <f.lombardi@tudelft.nl>
Date: Wed, 5 Jun 2024 16:26:31 +0200
Subject: [PATCH 125/140] fix path issues in test_run.py

---
 ramp/test/test_run.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/ramp/test/test_run.py b/ramp/test/test_run.py
index e9d892af..ee6d2ca1 100644
--- a/ramp/test/test_run.py
+++ b/ramp/test/test_run.py
@@ -91,7 +91,7 @@ def series_to_average(profile_series, num_days):
 from ramp.example.input_file_2 import User_list as User_list2
 from ramp.example.input_file_3 import User_list as User_list3
 
-TEST_OUTPUT_PATH = os.path.join(pp.BASE_PATH, "test", "results")
+TEST_OUTPUT_PATH = os.path.join(".", "ramp/test", "results")
 
 remove_old_tests = False
 for file in os.listdir(TEST_OUTPUT_PATH):
@@ -108,11 +108,12 @@ def series_to_average(profile_series, num_days):
             os.remove(os.path.join(TEST_OUTPUT_PATH, file))
 
 for i, ul in enumerate([User_list1, User_list2, User_list3]):
-    of_path = os.path.join(pp.BASE_PATH, "test", "results", f"output_file_{i + 1}.csv")
+    of_path = os.path.join(".", "ramp/test", "results", f"output_file_{i + 1}.csv")
     if os.path.exists(of_path) is False:
         uc = UseCase(
             users=ul,
             parallel_processing=False,
+            random_seed=True
         )
         uc.initialize(peak_enlarge=0.15, num_days=30)
 
@@ -121,7 +122,7 @@ def series_to_average(profile_series, num_days):
         pp.export_series(Profiles_list, ofname=of_path)
 
 test_output(
-    os.path.join(pp.BASE_PATH, "test", "results"),
-    os.path.join(pp.BASE_PATH, "test"),
+    os.path.join(".", "ramp/test", "results"),
+    os.path.join(".", "ramp/test"),
     num_input_files=3,
 )

From 522b4363609eaa6989a299546d1c768c1310a0b5 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 5 Jun 2024 20:50:05 +0200
Subject: [PATCH 126/140] Set a user's appliance name by default if empty

---
 ramp/core/core.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index d1833d43..b5b97350 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -699,6 +699,8 @@ def _add_appliance_instance(self, appliances):
                     f"You are trying to add an object of type {type(app)} as an appliance to the user {self.user_name}"
                 )
             if app not in self.App_list:
+                if app.name == "":
+                    app.name = f"appliance_{len(self.App_list) + 1}"
                 self.App_list.append(app)
 
     def add_appliance(self, *args, **kwargs):

From ff026c99d45f8796af0ee3d36de2fb9fc5e1a843 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 5 Jun 2024 22:03:32 +0200
Subject: [PATCH 127/140] Add appliances name to example files

---
 ramp/example/input_file_1.py | 113 +++++++++++++++++++----------------
 ramp/example/input_file_2.py |   1 +
 ramp/example/input_file_3.py |  89 +++++++++++++++++++++++----
 3 files changed, 140 insertions(+), 63 deletions(-)

diff --git a/ramp/example/input_file_1.py b/ramp/example/input_file_1.py
index f50ba444..2c7ea658 100644
--- a/ramp/example/input_file_1.py
+++ b/ramp/example/input_file_1.py
@@ -55,45 +55,50 @@
     func_cycle=60,
     fixed="yes",
     flat="yes",
+    name="indoor_bulb",
 )
 Ch_indoor_bulb.windows(window_1=[1200, 1440], window_2=[0, 0], random_var_w=0.1)
 
-Ch_outdoor_bulb = Church.add_appliance(7, 26, 1, 150, 0.2, 60, "yes", flat="yes")
+Ch_outdoor_bulb = Church.add_appliance(
+    7, 26, 1, 150, 0.2, 60, "yes", flat="yes", name="outdoor_bulb"
+)
 Ch_outdoor_bulb.windows([1200, 1440], [0, 0], 0.1)
 
-Ch_speaker = Church.add_appliance(1, 100, 1, 150, 0.2, 60)
+Ch_speaker = Church.add_appliance(1, 100, 1, 150, 0.2, 60, name="speaker")
 Ch_speaker.windows([1200, 1350], [0, 0], 0.1)
 
 # Public lighting
-Pub_lights = Public_lighting.add_appliance(12, 40, 2, 310, 0.1, 300, "yes", flat="yes")
+Pub_lights = Public_lighting.add_appliance(
+    12, 40, 2, 310, 0.1, 300, "yes", flat="yes", name="lights"
+)
 Pub_lights.windows([0, 336], [1110, 1440], 0.2)
 
 Pub_lights_2 = Public_lighting.add_appliance(
-    25, 150, 2, 310, 0.1, 300, "yes", flat="yes"
+    25, 150, 2, 310, 0.1, 300, "yes", flat="yes", name="lights2"
 )
 Pub_lights_2.windows([0, 336], [1110, 1440], 0.2)
 
 
 # High-Income
-HI_indoor_bulb = HI.add_appliance(6, 7, 2, 120, 0.2, 10)
+HI_indoor_bulb = HI.add_appliance(6, 7, 2, 120, 0.2, 10, name="indoor_bulb")
 HI_indoor_bulb.windows([1170, 1440], [0, 30], 0.35)
 
-HI_outdoor_bulb = HI.add_appliance(2, 13, 2, 600, 0.2, 10)
+HI_outdoor_bulb = HI.add_appliance(2, 13, 2, 600, 0.2, 10, name="outdoor_bulb")
 HI_outdoor_bulb.windows([0, 330], [1170, 1440], 0.35)
 
-HI_TV = HI.add_appliance(2, 60, 3, 180, 0.1, 5)
+HI_TV = HI.add_appliance(2, 60, 3, 180, 0.1, 5, name="TV")
 HI_TV.windows([720, 900], [1170, 1440], 0.35, [0, 60])
 
-HI_DVD = HI.add_appliance(1, 8, 3, 60, 0.1, 5)
+HI_DVD = HI.add_appliance(1, 8, 3, 60, 0.1, 5, name="DVD")
 HI_DVD.windows([720, 900], [1170, 1440], 0.35, [0, 60])
 
-HI_Antenna = HI.add_appliance(1, 8, 3, 120, 0.1, 5)
+HI_Antenna = HI.add_appliance(1, 8, 3, 120, 0.1, 5, name="antenna")
 HI_Antenna.windows([720, 900], [1170, 1440], 0.35, [0, 60])
 
-HI_Phone_charger = HI.add_appliance(5, 2, 2, 300, 0.2, 5)
+HI_Phone_charger = HI.add_appliance(5, 2, 2, 300, 0.2, 5, name="phone_charger")
 HI_Phone_charger.windows([1110, 1440], [0, 30], 0.35)
 
-HI_Freezer = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+HI_Freezer = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3, name="freezer")
 HI_Freezer.windows([0, 1440], [0, 0])
 HI_Freezer.specific_cycle_1(200, 20, 5, 10)
 HI_Freezer.specific_cycle_2(200, 15, 5, 15)
@@ -102,7 +107,7 @@
     [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
 )
 
-HI_Freezer2 = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+HI_Freezer2 = HI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3, name="freezer2")
 HI_Freezer2.windows([0, 1440], [0, 0])
 HI_Freezer2.specific_cycle_1(200, 20, 5, 10)
 HI_Freezer2.specific_cycle_2(200, 15, 5, 15)
@@ -111,32 +116,32 @@
     [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
 )
 
-HI_Mixer = HI.add_appliance(1, 50, 3, 30, 0.1, 1, occasional_use=0.33)
+HI_Mixer = HI.add_appliance(1, 50, 3, 30, 0.1, 1, occasional_use=0.33, name="mixer")
 HI_Mixer.windows([420, 480], [660, 750], 0.35, [1140, 1200])
 
 # Higher-Middle Income
-HMI_indoor_bulb = HMI.add_appliance(5, 7, 2, 120, 0.2, 10)
+HMI_indoor_bulb = HMI.add_appliance(5, 7, 2, 120, 0.2, 10, name="indoor_bulb")
 HMI_indoor_bulb.windows([1170, 1440], [0, 30], 0.35)
 
-HMI_outdoor_bulb = HMI.add_appliance(2, 13, 2, 600, 0.2, 10)
+HMI_outdoor_bulb = HMI.add_appliance(2, 13, 2, 600, 0.2, 10, name="outdoor_bulb")
 HMI_outdoor_bulb.windows([0, 330], [1170, 1440], 0.35)
 
-HMI_TV = HMI.add_appliance(1, 60, 2, 120, 0.1, 5)
+HMI_TV = HMI.add_appliance(1, 60, 2, 120, 0.1, 5, name="TV")
 HMI_TV.windows([1170, 1440], [0, 60], 0.35)
 
-HMI_DVD = HMI.add_appliance(1, 8, 2, 40, 0.1, 5)
+HMI_DVD = HMI.add_appliance(1, 8, 2, 40, 0.1, 5, name="DVD")
 HMI_DVD.windows([1170, 1440], [0, 60], 0.35)
 
-HMI_Antenna = HMI.add_appliance(1, 8, 2, 80, 0.1, 5)
+HMI_Antenna = HMI.add_appliance(1, 8, 2, 80, 0.1, 5, name="antenna")
 HMI_Antenna.windows([1170, 1440], [0, 60], 0.35)
 
-HMI_Radio = HMI.add_appliance(1, 36, 2, 60, 0.1, 5)
+HMI_Radio = HMI.add_appliance(1, 36, 2, 60, 0.1, 5, name="radio")
 HMI_Radio.windows([390, 450], [1140, 1260], 0.35)
 
-HMI_Phone_charger = HMI.add_appliance(4, 2, 2, 300, 0.2, 5)
+HMI_Phone_charger = HMI.add_appliance(4, 2, 2, 300, 0.2, 5, name="phone_charger")
 HMI_Phone_charger.windows([1110, 1440], [0, 30], 0.35)
 
-HMI_Freezer = HMI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+HMI_Freezer = HMI.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3, name="freezer")
 HMI_Freezer.windows([0, 1440], [0, 0])
 HMI_Freezer.specific_cycle_1(200, 20, 5, 10)
 HMI_Freezer.specific_cycle_2(200, 15, 5, 15)
@@ -145,61 +150,61 @@
     [480, 1200], [0, 0], [300, 479], [0, 0], [0, 299], [1201, 1440]
 )
 
-HMI_Mixer = HMI.add_appliance(1, 50, 3, 30, 0.1, 1, occasional_use=0.33)
+HMI_Mixer = HMI.add_appliance(1, 50, 3, 30, 0.1, 1, occasional_use=0.33, name="mixer")
 HMI_Mixer.windows([420, 450], [660, 750], 0.35, [1020, 1170])
 
 # Lower-Midlle Income
-LMI_indoor_bulb = LMI.add_appliance(3, 7, 2, 120, 0.2, 10)
+LMI_indoor_bulb = LMI.add_appliance(3, 7, 2, 120, 0.2, 10, name="indoor_bulb")
 LMI_indoor_bulb.windows([1170, 1440], [0, 30], 0.35)
 
-LMI_outdoor_bulb = LMI.add_appliance(2, 13, 2, 600, 0.2, 10)
+LMI_outdoor_bulb = LMI.add_appliance(2, 13, 2, 600, 0.2, 10, name="outdoor_bulb")
 LMI_outdoor_bulb.windows([0, 330], [1170, 1440], 0.35)
 
-LMI_TV = LMI.add_appliance(1, 60, 3, 90, 0.1, 5)
+LMI_TV = LMI.add_appliance(1, 60, 3, 90, 0.1, 5, name="TV")
 LMI_TV.windows([450, 660], [720, 840], 0.35, [1170, 1440])
 
-LMI_DVD = LMI.add_appliance(1, 8, 3, 30, 0.1, 5)
+LMI_DVD = LMI.add_appliance(1, 8, 3, 30, 0.1, 5, name="DVD")
 LMI_DVD.windows([450, 660], [720, 840], 0.35, [1170, 1440])
 
-LMI_Antenna = LMI.add_appliance(1, 8, 3, 60, 0.1, 5)
+LMI_Antenna = LMI.add_appliance(1, 8, 3, 60, 0.1, 5, name="antenna")
 LMI_Antenna.windows([450, 660], [720, 840], 0.35, [1170, 1440])
 
-LMI_Phone_charger = LMI.add_appliance(4, 2, 1, 300, 0.2, 5)
+LMI_Phone_charger = LMI.add_appliance(4, 2, 1, 300, 0.2, 5, name="phone_charger")
 LMI_Phone_charger.windows([1020, 1440], [0, 0], 0.35)
 
-LMI_Mixer = LMI.add_appliance(1, 50, 2, 30, 0.1, 1, occasional_use=0.33)
+LMI_Mixer = LMI.add_appliance(1, 50, 2, 30, 0.1, 1, occasional_use=0.33, name="mixer")
 LMI_Mixer.windows([660, 750], [1110, 1200], 0.35)
 
 # Low Income
-LI_indoor_bulb = LI.add_appliance(2, 7, 2, 120, 0.2, 10)
+LI_indoor_bulb = LI.add_appliance(2, 7, 2, 120, 0.2, 10, name="indoor_bulb")
 LI_indoor_bulb.windows([1170, 1440], [0, 30], 0.35)
 
-LI_outdoor_bulb = LI.add_appliance(1, 13, 2, 600, 0.2, 10)
+LI_outdoor_bulb = LI.add_appliance(1, 13, 2, 600, 0.2, 10, name="outdoor_bulb")
 LI_outdoor_bulb.windows([0, 330], [1170, 1440], 0.35)
 
-LI_TV = LI.add_appliance(1, 60, 3, 90, 0.1, 5)
+LI_TV = LI.add_appliance(1, 60, 3, 90, 0.1, 5, name="TV")
 LI_TV.windows([750, 840], [1170, 1440], 0.35, [0, 30])
 
-LI_DVD = LI.add_appliance(1, 8, 3, 30, 0.1, 5)
+LI_DVD = LI.add_appliance(1, 8, 3, 30, 0.1, 5, name="DVD")
 LI_DVD.windows([750, 840], [1170, 1440], 0.35, [0, 30])
 
-LI_Antenna = LI.add_appliance(1, 8, 3, 60, 0.1, 5)
+LI_Antenna = LI.add_appliance(1, 8, 3, 60, 0.1, 5, name="antenna")
 LI_Antenna.windows([750, 840], [1170, 1440], 0.35, [0, 30])
 
-LI_Phone_charger = LI.add_appliance(2, 2, 1, 300, 0.2, 5)
+LI_Phone_charger = LI.add_appliance(2, 2, 1, 300, 0.2, 5, name="phone_charger")
 LI_Phone_charger.windows([1080, 1440], [0, 0], 0.35)
 
 # Hospital
-Ho_indoor_bulb = Hospital.add_appliance(12, 7, 2, 690, 0.2, 10)
+Ho_indoor_bulb = Hospital.add_appliance(12, 7, 2, 690, 0.2, 10, name="indoor_bulb")
 Ho_indoor_bulb.windows([480, 720], [870, 1440], 0.35)
 
-Ho_outdoor_bulb = Hospital.add_appliance(1, 13, 2, 690, 0.2, 10)
+Ho_outdoor_bulb = Hospital.add_appliance(1, 13, 2, 690, 0.2, 10, name="outdoor_bulb")
 Ho_outdoor_bulb.windows([0, 330], [1050, 1440], 0.35)
 
-Ho_Phone_charger = Hospital.add_appliance(8, 2, 2, 300, 0.2, 5)
+Ho_Phone_charger = Hospital.add_appliance(8, 2, 2, 300, 0.2, 5, name="phone_charger")
 Ho_Phone_charger.windows([480, 720], [900, 1440], 0.35)
 
-Ho_Fridge = Hospital.add_appliance(1, 150, 1, 1440, 0, 30, "yes", 3)
+Ho_Fridge = Hospital.add_appliance(1, 150, 1, 1440, 0, 30, "yes", 3, name="fridge")
 Ho_Fridge.windows([0, 1440], [0, 0])
 Ho_Fridge.specific_cycle_1(150, 20, 5, 10)
 Ho_Fridge.specific_cycle_2(150, 15, 5, 15)
@@ -208,7 +213,7 @@
     [580, 1200], [0, 0], [420, 579], [0, 0], [0, 419], [1201, 1440]
 )
 
-Ho_Fridge2 = Hospital.add_appliance(1, 150, 1, 1440, 0, 30, "yes", 3)
+Ho_Fridge2 = Hospital.add_appliance(1, 150, 1, 1440, 0, 30, "yes", 3, name="fridge2")
 Ho_Fridge2.windows([0, 1440], [0, 0])
 Ho_Fridge2.specific_cycle_1(150, 20, 5, 10)
 Ho_Fridge2.specific_cycle_2(150, 15, 5, 15)
@@ -217,7 +222,7 @@
     [580, 1200], [0, 0], [420, 579], [0, 0], [0, 299], [1201, 1440]
 )
 
-Ho_Fridge3 = Hospital.add_appliance(1, 150, 1, 1440, 0.1, 30, "yes", 3)
+Ho_Fridge3 = Hospital.add_appliance(1, 150, 1, 1440, 0.1, 30, "yes", 3, name="fridge3")
 Ho_Fridge3.windows([0, 1440], [0, 0])
 Ho_Fridge3.specific_cycle_1(150, 20, 5, 10)
 Ho_Fridge3.specific_cycle_2(150, 15, 5, 15)
@@ -226,29 +231,31 @@
     [580, 1200], [0, 0], [420, 479], [0, 0], [0, 419], [1201, 1440]
 )
 
-Ho_PC = Hospital.add_appliance(2, 50, 2, 300, 0.1, 10)
+Ho_PC = Hospital.add_appliance(2, 50, 2, 300, 0.1, 10, name="PC")
 Ho_PC.windows([480, 720], [1050, 1440], 0.35)
 
-Ho_Mixer = Hospital.add_appliance(1, 50, 2, 60, 0.1, 1, occasional_use=0.33)
+Ho_Mixer = Hospital.add_appliance(
+    1, 50, 2, 60, 0.1, 1, occasional_use=0.33, name="mixer"
+)
 Ho_Mixer.windows([480, 720], [1050, 1440], 0.35)
 
 # School
-S_indoor_bulb = School.add_appliance(8, 7, 1, 60, 0.2, 10)
+S_indoor_bulb = School.add_appliance(8, 7, 1, 60, 0.2, 10, name="indoor_bulb")
 S_indoor_bulb.windows([1020, 1080], [0, 0], 0.35)
 
-S_outdoor_bulb = School.add_appliance(6, 13, 1, 60, 0.2, 10)
+S_outdoor_bulb = School.add_appliance(6, 13, 1, 60, 0.2, 10, name="outdoor_bulb")
 S_outdoor_bulb.windows([1020, 1080], [0, 0], 0.35)
 
-S_Phone_charger = School.add_appliance(5, 2, 2, 180, 0.2, 5)
+S_Phone_charger = School.add_appliance(5, 2, 2, 180, 0.2, 5, name="phone_charger")
 S_Phone_charger.windows([510, 750], [810, 1080], 0.35)
 
-S_PC = School.add_appliance(18, 50, 2, 210, 0.1, 10)
+S_PC = School.add_appliance(18, 50, 2, 210, 0.1, 10, name="PC")
 S_PC.windows([510, 750], [810, 1080], 0.35)
 
-S_Printer = School.add_appliance(1, 20, 2, 30, 0.1, 5)
+S_Printer = School.add_appliance(1, 20, 2, 30, 0.1, 5, name="printer")
 S_Printer.windows([510, 750], [810, 1080], 0.35)
 
-S_Freezer = School.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3)
+S_Freezer = School.add_appliance(1, 200, 1, 1440, 0, 30, "yes", 3, name="freezer")
 S_Freezer.windows([0, 1440])
 S_Freezer.specific_cycle_1(200, 20, 5, 10)
 S_Freezer.specific_cycle_2(200, 15, 5, 15)
@@ -257,13 +264,15 @@
     [580, 1200], [0, 0], [510, 579], [0, 0], [0, 509], [1201, 1440]
 )
 
-S_TV = School.add_appliance(1, 60, 2, 120, 0.1, 5, occasional_use=0.5)
+S_TV = School.add_appliance(1, 60, 2, 120, 0.1, 5, occasional_use=0.5, name="TV")
 S_TV.windows([510, 750], [810, 1080], 0.35)
 
-S_DVD = School.add_appliance(1, 8, 2, 120, 0.1, 5, occasional_use=0.5)
+S_DVD = School.add_appliance(1, 8, 2, 120, 0.1, 5, occasional_use=0.5, name="DVD")
 S_DVD.windows([510, 750], [810, 1080], 0.35)
 
-S_Stereo = School.add_appliance(1, 150, 2, 90, 0.1, 5, occasional_use=0.33)
+S_Stereo = School.add_appliance(
+    1, 150, 2, 90, 0.1, 5, occasional_use=0.33, name="stereo"
+)
 S_Stereo.windows([510, 750], [810, 1080], 0.35)
 
 if __name__ == "__main__":
diff --git a/ramp/example/input_file_2.py b/ramp/example/input_file_2.py
index b8479ba8..cbe59067 100644
--- a/ramp/example/input_file_2.py
+++ b/ramp/example/input_file_2.py
@@ -30,6 +30,7 @@
     time_fraction_random_variability=0.1,
     func_cycle=3,
     thermal_p_var=0.2,
+    name="shower",
 )
 HH_shower.windows(window_1=[390, 540], window_2=[1080, 1200], random_var_w=0.2)
 
diff --git a/ramp/example/input_file_3.py b/ramp/example/input_file_3.py
index b7f165f3..2414e4a9 100644
--- a/ramp/example/input_file_3.py
+++ b/ramp/example/input_file_3.py
@@ -34,72 +34,139 @@
     thermal_p_var=0.2,
     pref_index=1,
     fixed_cycle=1,
+    name="lunch1_soup",
 )
 HH_lunch1_soup.windows(window_1=[12 * 60, 15 * 60], window_2=[0, 0], random_var_w=0.15)
 HH_lunch1_soup.specific_cycle_1(p_11=1800, t_11=10, p_12=750, t_12=60, r_c1=0.15)
 HH_lunch1_soup.cycle_behaviour(cw11=[12 * 60, 15 * 60], cw12=[0, 0])
 
 HH_lunch2_rice = HH.add_appliance(
-    1, 1800, 2, 25, 0.15, 20, thermal_p_var=0.2, pref_index=2, fixed_cycle=1
+    1,
+    1800,
+    2,
+    25,
+    0.15,
+    20,
+    thermal_p_var=0.2,
+    pref_index=2,
+    fixed_cycle=1,
+    name="lunch2_rice",
 )
 HH_lunch2_rice.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch2_rice.specific_cycle_1(1800, 10, 750, 15, 0.15)
 HH_lunch2_rice.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
-HH_lunch2_egg = HH.add_appliance(1, 1200, 2, 3, 0.2, 3, thermal_p_var=0.2, pref_index=2)
+HH_lunch2_egg = HH.add_appliance(
+    1, 1200, 2, 3, 0.2, 3, thermal_p_var=0.2, pref_index=2, name="lunch2_egg"
+)
 HH_lunch2_egg.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 
 HH_lunch2_platano = HH.add_appliance(
-    1, 1800, 2, 10, 0.15, 5, thermal_p_var=0.2, pref_index=2, fixed_cycle=1
+    1,
+    1800,
+    2,
+    10,
+    0.15,
+    5,
+    thermal_p_var=0.2,
+    pref_index=2,
+    fixed_cycle=1,
+    name="lunch2_planato",
 )
 HH_lunch2_platano.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch2_platano.specific_cycle_1(1800, 5, 1200, 5, 0.15)
 HH_lunch2_platano.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
 HH_lunch2_meat = HH.add_appliance(
-    1, 1200, 2, 7, 0.15, 3, thermal_p_var=0.2, pref_index=2
+    1, 1200, 2, 7, 0.15, 3, thermal_p_var=0.2, pref_index=2, name="lunch2_meat"
 )
 HH_lunch2_meat.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 
 HH_lunch3_beansnrice = HH.add_appliance(
-    1, 1800, 2, 45, 0.2, 30, thermal_p_var=0.2, pref_index=3, fixed_cycle=1
+    1,
+    1800,
+    2,
+    45,
+    0.2,
+    30,
+    thermal_p_var=0.2,
+    pref_index=3,
+    fixed_cycle=1,
+    name="lunch3_beansnrice",
 )
 HH_lunch3_beansnrice.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 HH_lunch3_beansnrice.specific_cycle_1(1800, 10, 750, 35, 0.2)
 HH_lunch3_beansnrice.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
 HH_lunch3_meat = HH.add_appliance(
-    1, 1200, 2, 10, 0.2, 5, thermal_p_var=0.2, pref_index=3
+    1, 1200, 2, 10, 0.2, 5, thermal_p_var=0.2, pref_index=3, name="lunch3_meat"
 )
 HH_lunch3_meat.windows([12 * 60, 15 * 60], [0, 0], 0.15)
 
 HH_lunch_yuca = HH.add_appliance(
-    1, 1800, 1, 25, 0.15, 10, thermal_p_var=0.2, pref_index=0, fixed_cycle=1
+    1,
+    1800,
+    1,
+    25,
+    0.15,
+    10,
+    thermal_p_var=0.2,
+    pref_index=0,
+    fixed_cycle=1,
+    name="lunch_yuca",
 )
 HH_lunch_yuca.windows([13 * 60, 14 * 60], [0, 0], 0.15)
 HH_lunch_yuca.specific_cycle_1(1800, 10, 750, 15, 0.15)
 HH_lunch_yuca.cycle_behaviour([12 * 60, 15 * 60], [0, 0])
 
 HH_breakfast_huminta = HH.add_appliance(
-    1, 1800, 1, 65, 0.15, 50, thermal_p_var=0.2, pref_index=0, fixed_cycle=1
+    1,
+    1800,
+    1,
+    65,
+    0.15,
+    50,
+    thermal_p_var=0.2,
+    pref_index=0,
+    fixed_cycle=1,
+    name="breakfast_huminta",
 )
 HH_breakfast_huminta.windows([6 * 60, 9 * 60], [0, 0], 0.15)
 HH_breakfast_huminta.specific_cycle_1(1800, 5, 750, 60, 0.15)
 HH_breakfast_huminta.cycle_behaviour([6 * 60, 9 * 60], [0, 0])
 
 HH_breakfast_bread = HH.add_appliance(
-    1, 1800, 1, 15, 0.15, 10, thermal_p_var=0.2, pref_index=0, fixed_cycle=1
+    1,
+    1800,
+    1,
+    15,
+    0.15,
+    10,
+    thermal_p_var=0.2,
+    pref_index=0,
+    fixed_cycle=1,
+    name="breakfast_bread",
 )
 HH_breakfast_bread.windows([6 * 60, 9 * 60], [0, 0], 0.15)
 HH_breakfast_bread.specific_cycle_1(1800, 10, 1200, 5, 0.15)
 HH_breakfast_bread.cycle_behaviour([6 * 60, 9 * 60], [0, 0])
 
 HH_breakfast_coffee = HH.add_appliance(
-    1, 1800, 1, 5, 0.15, 2, thermal_p_var=0.2, pref_index=0
+    1,
+    1800,
+    1,
+    5,
+    0.15,
+    2,
+    thermal_p_var=0.2,
+    pref_index=0,
+    name="break_fast_coffee",
 )
 HH_breakfast_coffee.windows([6 * 60, 9 * 60], [0, 0], 0.15)
 
-HH_mate = HH.add_appliance(1, 1800, 1, 30, 0.3, 2, thermal_p_var=0.2, pref_index=0)
+HH_mate = HH.add_appliance(
+    1, 1800, 1, 30, 0.3, 2, thermal_p_var=0.2, pref_index=0, name="mate"
+)
 HH_mate.windows([7 * 60, 20 * 60], [0, 0], 0.15)
 
 

From 14f1d61ef177be4b651aa9551a10d5c55f66ec70 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 5 Jun 2024 22:06:10 +0200
Subject: [PATCH 128/140] Reselect a switch on even if falling outside duty
 cycle windows

---
 ramp/core/core.py | 28 +++++++++++++++++++++-------
 1 file changed, 21 insertions(+), 7 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index b5b97350..7d2a82c2 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1888,14 +1888,28 @@ def rand_switch_on_window(self, rand_time: int):
                     self.current_duty_cycle_id = 3
                     duty_cycle_duration = len(self.random_cycle3)
                 else:
-                    print(
-                        f"The app {self.name} has duty cycle option on, however the switch on event fell outside the provided duty cycle windows"
+                    # previously duty_cycle3 was always considered as default if neither duty_cycle1 nor duty_cycle2
+                    # got selected. If the switch on window does not fall within any duty cycle we do not assign it
+                    # to duty_cycle3 by default, rather we pick another switch on event and we notify the user we
+                    # did so. That way, in case this warning is shown too often, it can indicate to the user there
+                    # is some peculiar behavior for this appliance
+                    warnings.warn(
+                        f"The app {self.name} has duty cycle option on (with {self.fixed_cycle} cycle(s)). However, the switch on window [{switch_on}, {switch_on + len(indexes)}] fell outside the provided duty cycle windows: "
+                        + "cw11 "
+                        + str(self.cw11)
+                        + ", cw12 "
+                        + str(self.cw12)
+                        + ", cw21 "
+                        + str(self.cw21)
+                        + ", cw22 "
+                        + str(self.cw22)
+                        + ", cw31 "
+                        + str(self.cw31)
+                        + ", cw32 "
+                        + str(self.cw32)
+                        + ". Picking another random switch on event. You probably see this warning because your window of use is the same as the duty cycle window and the random variability of the windows of use is greater than zero. If you see this warning only once, no need to worry, this is inherent to stochasticity."
                     )
-                    # TODO previously duty_cycle3 was always considered as default if the evaluate proxy did neither
-                    #  get selected by duty_cycle1 nor duty_cycle2, for default is kept but not silently anymore in
-                    #  order to see wheather this is an issue or not
-                    self.current_duty_cycle_id = 3
-                    duty_cycle_duration = len(self.random_cycle3)
+                    return self.rand_switch_on_window(rand_time)
 
                 if (
                     indexes.size > duty_cycle_duration

From 4b4fce7f5fa98478d2ac692ff3661b6dc75728a5 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 5 Jun 2024 22:13:58 +0200
Subject: [PATCH 129/140] Fix fridge duty cycle

---
 ramp/example/input_file_1.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ramp/example/input_file_1.py b/ramp/example/input_file_1.py
index 2c7ea658..ae9cb1cd 100644
--- a/ramp/example/input_file_1.py
+++ b/ramp/example/input_file_1.py
@@ -219,7 +219,7 @@
 Ho_Fridge2.specific_cycle_2(150, 15, 5, 15)
 Ho_Fridge2.specific_cycle_3(150, 10, 5, 20)
 Ho_Fridge2.cycle_behaviour(
-    [580, 1200], [0, 0], [420, 579], [0, 0], [0, 299], [1201, 1440]
+    [580, 1200], [0, 0], [420, 579], [0, 0], [0, 419], [1201, 1440]
 )
 
 Ho_Fridge3 = Hospital.add_appliance(1, 150, 1, 1440, 0.1, 30, "yes", 3, name="fridge3")
@@ -228,7 +228,7 @@
 Ho_Fridge3.specific_cycle_2(150, 15, 5, 15)
 Ho_Fridge3.specific_cycle_3(150, 10, 5, 20)
 Ho_Fridge3.cycle_behaviour(
-    [580, 1200], [0, 0], [420, 479], [0, 0], [0, 419], [1201, 1440]
+    [580, 1200], [0, 0], [420, 579], [0, 0], [0, 419], [1201, 1440]
 )
 
 Ho_PC = Hospital.add_appliance(2, 50, 2, 300, 0.1, 10, name="PC")

From 510c1647237365ada7e3c2f10995301cf46e1e1a Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 5 Jun 2024 22:31:02 +0200
Subject: [PATCH 130/140] Extend duty_cycle window to upper bound

---
 ramp/core/core.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 7d2a82c2..725031b1 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -1870,20 +1870,20 @@ def rand_switch_on_window(self, rand_time: int):
                 evaluate = np.round(np.mean(indexes)) if indexes.size > 0 else 0
                 # selects the proper duty cycle
                 if (
-                    self.cw11[0] <= evaluate < self.cw11[1]
-                    or self.cw12[0] <= evaluate < self.cw12[1]
+                    self.cw11[0] <= evaluate <= self.cw11[1]
+                    or self.cw12[0] <= evaluate <= self.cw12[1]
                 ):
                     self.current_duty_cycle_id = 1
                     duty_cycle_duration = len(self.random_cycle1)
                 elif (
-                    self.cw21[0] <= evaluate < self.cw21[1]
-                    or self.cw22[0] <= evaluate < self.cw22[1]
+                    self.cw21[0] <= evaluate <= self.cw21[1]
+                    or self.cw22[0] <= evaluate <= self.cw22[1]
                 ):
                     self.current_duty_cycle_id = 2
                     duty_cycle_duration = len(self.random_cycle2)
                 elif (
-                    self.cw31[0] <= evaluate < self.cw31[1]
-                    or self.cw32[0] <= evaluate < self.cw32[1]
+                    self.cw31[0] <= evaluate <= self.cw31[1]
+                    or self.cw32[0] <= evaluate <= self.cw32[1]
                 ):
                     self.current_duty_cycle_id = 3
                     duty_cycle_duration = len(self.random_cycle3)

From fb91034e590dc5e8390ff1b80011b523830baabb Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 5 Jun 2024 22:35:35 +0200
Subject: [PATCH 131/140] Change duty_cycle selection method

Previously the mean of the index range was computed and see if it was
falling within a duty cycle. This method compare the boundaries of the
range with the one of each duty cycle to know if there is an
intersection. If there is one, the duty cycle is chosen.

Note: this method does not take into account the case where the indexes
range spans accross more than one duty cycle.
---
 ramp/core/core.py  | 25 ++++++++++++-------------
 ramp/core/utils.py | 14 ++++++++++++++
 2 files changed, 26 insertions(+), 13 deletions(-)

diff --git a/ramp/core/core.py b/ramp/core/core.py
index 725031b1..1578c935 100644
--- a/ramp/core/core.py
+++ b/ramp/core/core.py
@@ -28,6 +28,7 @@
     random_choice,
     read_input_file,
     within_peak_time_window,
+    range_within_window,
 )
 from ramp.post_process.post_process import Plot
 
@@ -1867,24 +1868,22 @@ def rand_switch_on_window(self, rand_time: int):
             if (
                 self.fixed_cycle > 0
             ):  # evaluates if the app has some duty cycles to be considered
-                evaluate = np.round(np.mean(indexes)) if indexes.size > 0 else 0
+                indexes_low = indexes[0]
+                indexes_high = indexes[-1]
                 # selects the proper duty cycle
-                if (
-                    self.cw11[0] <= evaluate <= self.cw11[1]
-                    or self.cw12[0] <= evaluate <= self.cw12[1]
-                ):
+                if range_within_window(
+                    indexes_low, indexes_high, self.cw11
+                ) or range_within_window(indexes_low, indexes_high, self.cw12):
                     self.current_duty_cycle_id = 1
                     duty_cycle_duration = len(self.random_cycle1)
-                elif (
-                    self.cw21[0] <= evaluate <= self.cw21[1]
-                    or self.cw22[0] <= evaluate <= self.cw22[1]
-                ):
+                elif range_within_window(
+                    indexes_low, indexes_high, self.cw21
+                ) or range_within_window(indexes_low, indexes_high, self.cw22):
                     self.current_duty_cycle_id = 2
                     duty_cycle_duration = len(self.random_cycle2)
-                elif (
-                    self.cw31[0] <= evaluate <= self.cw31[1]
-                    or self.cw32[0] <= evaluate <= self.cw32[1]
-                ):
+                elif range_within_window(
+                    indexes_low, indexes_high, self.cw31
+                ) or range_within_window(indexes_low, indexes_high, self.cw32):
                     self.current_duty_cycle_id = 3
                     duty_cycle_duration = len(self.random_cycle3)
                 else:
diff --git a/ramp/core/utils.py b/ramp/core/utils.py
index 8274de85..cac6936d 100644
--- a/ramp/core/utils.py
+++ b/ramp/core/utils.py
@@ -138,6 +138,20 @@ def duty_cycle(var, t1, p1, t2, p2):
     )
 
 
+def range_within_window(range_low, range_high, window):
+    """Compare a range with a window to see if there is an overlap
+
+    The two cases where there is no overlap between two windows are when the
+    range boundaries are both lower than the lowest window value or both
+    higher than the highest window value
+
+    """
+    return not (
+        (range_low < window[0] and range_high < window[0])
+        or (range_low > window[1] and range_high > window[1])
+    )
+
+
 def random_choice(var, t1, p1, t2, p2):
     """Chooses one of two duty cycles randomly
 

From 2ea65326a24affb0eec75afeffb1178bacab4215 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Wed, 5 Jun 2024 23:14:39 +0200
Subject: [PATCH 132/140] Fix failing tests

---
 tests/test_input_file_conversion.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/tests/test_input_file_conversion.py b/tests/test_input_file_conversion.py
index 68f30e78..5c3b6674 100644
--- a/tests/test_input_file_conversion.py
+++ b/tests/test_input_file_conversion.py
@@ -86,7 +86,9 @@ def test_convert_py_to_xlsx_command_line(self):
 def test_define_appliance_window_directly_equivalent_to_use_windows_method():
     user = User("test user", 1)
 
-    params = dict(number=1, power=200, num_windows=1, func_time=0)
+    params = dict(
+        number=1, power=200, num_windows=1, func_time=0, name="test_appliance"
+    )
     win_start = 390
     win_stop = 540
     appliance1 = user.add_appliance(**params)
@@ -108,6 +110,7 @@ def test_define_appliance_duty_cycle_directly_equivalent_to_use_specific_cycle_m
         func_time=0,
         window_1=[390, 540],
         fixed_cycle=1,
+        name="test_appliance",
     )
 
     appliance1 = user.add_appliance(**params)
@@ -149,7 +152,7 @@ def test_provide_no_appliance_window_when_declaring_one():
 def test_A():
     user = User("test user", 1)
 
-    old_params = dict(power=200, num_windows=1, func_time=0)
+    old_params = dict(power=200, num_windows=1, func_time=0, name="test_appliance")
     win_start = 390
     win_stop = 540
     appliance1 = user.Appliance(user, **old_params)
@@ -161,6 +164,7 @@ def test_A():
         num_windows=1,
         func_time=0,
         window_1=np.array([win_start, win_stop]),
+        name="test_appliance",
     )
     appliance2 = user.add_appliance(**params)
 
@@ -177,6 +181,7 @@ def test_B():
         func_time=0,
         window_1=[390, 540],
         fixed_cycle=1,
+        name="test_appliance",
     )
 
     appliance1 = user.add_appliance(**params)

From 75cb11ddf003ba714207ea7db4ed50d60682e636 Mon Sep 17 00:00:00 2001
From: "F.L" <f.lombardi@outlook.com>
Date: Thu, 6 Jun 2024 15:22:48 +0200
Subject: [PATCH 133/140] Minor docstring improvements

---
 ramp/test/test_run.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/ramp/test/test_run.py b/ramp/test/test_run.py
index ee6d2ca1..11e86408 100644
--- a/ramp/test/test_run.py
+++ b/ramp/test/test_run.py
@@ -1,8 +1,8 @@
 # -*- coding: utf-8 -*-
 """
-Minimal qualitative test functionality. Compares outputs of the code to default outputs,
+Minimal qualitative test functionality. Compares the outputs of the code to default outputs,
 for the 3 reference input files. The judgement about whether the resulting changes are
-as expected or not is left to the developers
+as expected or not is left to the developer making the commit
 """
 
 # %% Import required modules
@@ -84,7 +84,7 @@ def series_to_average(profile_series, num_days):
 Here the visual comparison between default and new/current results occurs.
 Besides the difference naturally occurring due to different realisations of stochastic
 parameters, the developers should check whether any other differences are brought by
-by the tested code changes. If any differences are there, the developers should 
+by the tested code changes. If any differences are there, the developers should
 evaluate whether these are as expected/designed or not
 """
 from ramp.example.input_file_1 import User_list as User_list1
@@ -110,11 +110,7 @@ def series_to_average(profile_series, num_days):
 for i, ul in enumerate([User_list1, User_list2, User_list3]):
     of_path = os.path.join(".", "ramp/test", "results", f"output_file_{i + 1}.csv")
     if os.path.exists(of_path) is False:
-        uc = UseCase(
-            users=ul,
-            parallel_processing=False,
-            random_seed=True
-        )
+        uc = UseCase(users=ul, parallel_processing=False, random_seed=True)
         uc.initialize(peak_enlarge=0.15, num_days=30)
 
         Profiles_list = uc.generate_daily_load_profiles(flat=True)

From f25fbf4b8b3d536e4495da3b3ab674b1319b052f Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 6 Jun 2024 23:13:38 +0200
Subject: [PATCH 134/140] Remove use of matplotlib join method in test_use

---
 ramp/test/test_run.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/ramp/test/test_run.py b/ramp/test/test_run.py
index 11e86408..6135d75e 100644
--- a/ramp/test/test_run.py
+++ b/ramp/test/test_run.py
@@ -72,7 +72,6 @@ def series_to_average(profile_series, num_days):
             axes[n].set_xmargin(0)
             axes[n].set_ymargin(0)
 
-    axes[n].get_shared_x_axes().join(axes[n], axes[n - 1], axes[n - 2])
     axes[n - 1].legend()
     axes[n - 1].set_xticklabels([])
     axes[n - 2].set_xticklabels([])

From 0fbb0214aa4c1d089d1d5ccd9f15004c8b5fc2ba Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Thu, 6 Jun 2024 23:58:05 +0200
Subject: [PATCH 135/140] Fix contribution link

---
 .readthedocs.yml           | 4 ++--
 docs/docs-requirements.txt | 1 +
 docs/source/conf.py        | 8 +++++++-
 3 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/.readthedocs.yml b/.readthedocs.yml
index 415a6dca..45b817cf 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -8,7 +8,7 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "3.7"
+    python: "3.10"
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:
@@ -23,4 +23,4 @@ sphinx:
 # Optionally set the version of Python and requirements required to build your docs
 python:
    install:
-      - requirements: docs/docs-requirements.txt
\ No newline at end of file
+      - requirements: docs/docs-requirements.txt
diff --git a/docs/docs-requirements.txt b/docs/docs-requirements.txt
index c338880b..8c2350b0 100644
--- a/docs/docs-requirements.txt
+++ b/docs/docs-requirements.txt
@@ -13,5 +13,6 @@ ipykernel
 sphinx-autobuild
 sphinx-copybutton
 sphinx_wagtail_theme
+myst_parser
 nbformat
 nbconvert
diff --git a/docs/source/conf.py b/docs/source/conf.py
index cb331cbf..1a2df393 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -57,7 +57,6 @@ def copy_contributing():
 copyright = "2022, Author List"
 author = "Author List"
 
-
 # -- General configuration ---------------------------------------------------
 
 # Add any Sphinx extension module names here, as strings. They can be
@@ -76,8 +75,15 @@ def copy_contributing():
     "sphinx_copybutton",
     "sphinx.ext.autosectionlabel",
     "sphinx_wagtail_theme",
+    "sphinx_copybutton",
+    "myst_parser",
 ]
 
+source_suffix = {
+    ".rst": "restructuredtext",
+    ".txt": "markdown",
+    ".md": "markdown",
+}
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]

From 37c9d2171b7b2bda546fbea58614b2e1f09cdc61 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Fri, 7 Jun 2024 00:04:51 +0200
Subject: [PATCH 136/140] Remove contributing.md from docs/source

---
 docs/source/contributing.md | 99 -------------------------------------
 1 file changed, 99 deletions(-)
 delete mode 100644 docs/source/contributing.md

diff --git a/docs/source/contributing.md b/docs/source/contributing.md
deleted file mode 100644
index ed618f4c..00000000
--- a/docs/source/contributing.md
+++ /dev/null
@@ -1,99 +0,0 @@
-# Contribute
-
-*(this section is freely adapted from the [calliope](https://github.com/calliope-project/calliope/blob/master/CONTRIBUTING.md) project)*
-
-We're really glad you're reading this, because we need volunteer developers to help this project come to fruition.
-
-Some of the resources to look at if you're interested in contributing:
-
-* [Join us on Gitter to chat!](https://gitter.im/RAMP-project/community)
-* Look at [open issues](https://github.com/RAMP-project/RAMP/issues)
-
-## Licensing
-
-By contributing to RAMP, e.g. through opening a pull request or submitting a patch, you represent that your contributions are your own original work and that you have the right to license them, and you agree that your contributions are licensed under the EUROPEAN UNION PUBLIC LICENCE v. 1.2.
-
-## Open an issue
-
-[Open an issue](https://github.com/RAMP-project/RAMP/issues) to report a bug or for a feature request, please try to provide the information summarized below
-
-- OS: *your_operating_system*, *your_distribution*
-- RAMP version or Branch: *RAMP version* or *branch_name*, updated on *update_date*
-- If applicable: Attach full error message
-- If applicable: Share screenshots/images of your problem
-- If applicable: Share used input data
-
-## Submitting changes
-
-To contribute changes:
-- Fork the project on GitHub
-- Follow the "Setup" steps below
-- Create a feature branch (e.g. named "add-this-new-feature") to work on in your fork
-- Add your name to the [AUTHORS](AUTHORS) file
-- Commit your changes to the feature branch
-- Push the branch to GitHub
-- On GitHub, create a new pull request from the feature branch
-
-### Setup
-
-1. Create a virtual environment and install the dev dependencies with
-
-        pip install -r dev_requirements.txt
-
-2. Install the pre-commit hooks with
-
-        pre-commit install
-
-   This will mainly make sure you can't commit if your code is not linted with black.
-   The pre-commit hook will check if your code is linted and if it is not it will simply lint it for you, you then only need to stage the changes made by the linter and commit again, as simple as that :)
-
-### Pull requests
-
-Before submitting a pull request, check whether you have:
-
-* Added your changes to ``CHANGELOG.md``
-* Added or updated documentation for your changes
-* Passed qualitative tests if you implemented new functionality
-
-When opening a pull request, please provide a clear summary of your changes!
-
-### Commit messages
-
-Please try to write clear commit messages. One-line messages are fine for small changes, but bigger changes should look like this:
-
-    A brief summary of the commit
-
-    A paragraph or bullet-point list describing what changed and its impact,
-    covering as many lines as needed.
-
-## Testing
-
-Testing is used by RAMP developers to make sure their new feature/bug fix is not breaking existing code. As RAMP is stochastic some tests are only qualitative, other unit tests are ran by GitHub Actions.
-
-Before running the tests locally, you need to install the testing dependencies
-
-```
-pip install -r tests/requirements.txt
-```
-
-### Qualitative testing
-
-The qualitative testing functionality allows to compare the results arising from a modified version of the code against default ones, for the 3 reference input files provided within the code itself.
-
-To run the qualitative test, you'll have to run
- ```
- python ramp/test/test_run.py
- ```
-from the root level of this repository.
-
-If you already ran this script, you will be asked if you want to overwrite the results files (if you decide not to, the results are not going to be regenerated from your latest code version). You should compare the results of your code and those saved from the latest stable version thanks to the image which is displayed after the script ran.
-
-Ideally, the difference between reference and new results should be minimal and just due to the stochastic nature of the code. If more pronounced, it should be fully explainable based on the changes made to the code and aligned to the expectations of the developers (i.e. it should reflect a change in the output *wanted* and precisely *sought* with the commit in question).
-
-### Unit tests
-
-Run `pytest tests/` form the root of the repository to run the unit tests.
-
-## Attribution
-
-The layout and content of this document is partially based on [calliope](https://github.com/calliope-project/calliope/blob/master/CONTRIBUTING.md)'s equivalent document.

From 0ec1e67a7b978f31aa72ae5abf0ea3d0c21ed0ed Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 4 Jun 2024 11:35:08 +0200
Subject: [PATCH 137/140] Update changelog

With some pre-commit linting fixes on previous releases
---
 CHANGELOG.md | 39 ++++++++++++++++++++++++++++++++++++---
 1 file changed, 36 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index f19e403c..cb968823 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,8 +4,42 @@ Release History
 0.5.2 (dev)
 -----------
 
+**|new|**       Addition of the 'coveralls' badge to the README
+
+**|new|**       Addition of a random-seed functionality to ensure reproducible results if needed
+
+**|new|**       If a `User` instance doesn't have any appliance, its `repr` method prints the user name, and number of users with a message to mention no appliances are assigned to the instance yet. Before it raised a `ValueError`.
+
+**|new|**       Documentation template was changed to sphinx wagtail theme to improve the navigation through the documentation sections
+
+**|new|**       Add issue templates for issue creation on github
+
+**|new|**       Automatic testing of the jupyter notebooks of the documentation, to make sure the examples are always running through
+
+**|new|**       Adding depreciation warning to back-compatibility `Appliance` method in `User` class to let users know they should use the `add_appliance` method instead.
+
 **|new|**       Introduction of an Appliance parameter to model productive use duty cycles: `continuous_use_duty_cycle`
 
+**|changed|**   Expanded and revised documentation, with a particular focus on more and clearer usage examples
+
+**|changed|**   Updated requirements for contributing, including pip dependencies specific to developers
+
+**|changed|**   Expanded test coverage
+
+**|changed|**   Updated .py example input files to match the latest formalism of the RAMP code
+
+**|changed|**   Python version was bumped from 3.8 to 3.10
+
+**|changed|**   Improved the way to run the quantitative tests and the instructions to do so
+
+**|fixed|**     Windows compatibility of path to convert .py to .xlsx
+
+**|fixed|**     `rand_peak_enlarge` is rounded to be at least 1 so that the `peak_time_range` is never empty
+
+**|fixed|**     Running .xlsx files form the command line
+
+**|fixed|**     Ignore profile of appliances if any of their functional time or randomly allocated time of use are 0
+
 0.5.1 (2024-02-08)
 ------------------
 
@@ -41,7 +75,7 @@ Release History
 0.4.1 (2023-10-XX)
 ------------------
 
-**|hotfix|**    added option `-o` to the terminal command line interface to enable the user to provide output path to save ramp results. This option is also accessible to python users using `ofname` argument of the `ramp/ramp_run.py::run_usecase` or the `ramp/post_process/post_process.py::export_series` functions. 
+**|hotfix|**    added option `-o` to the terminal command line interface to enable the user to provide output path to save ramp results. This option is also accessible to python users using `ofname` argument of the `ramp/ramp_run.py::run_usecase` or the `ramp/post_process/post_process.py::export_series` functions.
 
 0.4.0 (2023-02-17)
 ------------------
@@ -60,7 +94,7 @@ Release History
 
 **|fixed|**     the way in which the random switch-on time is computed in the `stochastic_process` has been changed so that it is sampled with uniform probability from a concatenated set of functioning windows, rather than for each window separately (which led to short windows having higher concentration of switch-on events and demand peaks)
 
-**|fixed|**     the default value for the `peak_enlarg` parameter has been changed from the mistyped value of 0 to the intended value of 0.15 
+**|fixed|**     the default value for the `peak_enlarg` parameter has been changed from the mistyped value of 0 to the intended value of 0.15
 
 **|new|**       added a paragraph describing the algorithm of RAMP
 
@@ -96,4 +130,3 @@ Release History
 **|changed|**   changed the way in which the probability of coincident switch-on of several identical appliances owned by a single user is computed. Now, it penalisse less the probability of maximum coincidence for off-peak events
 
 **|fixed|**     `s_peak` value is now set by default to 0.5, rather than 1. This fixes an unwanted behaviour in how the `random.gauss` function worked
-

From 1e4cc7e375a77feb26d4f439724ef028304e036c Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 4 Jun 2024 11:39:00 +0200
Subject: [PATCH 138/140] Bump version number

---
 ramp/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ramp/_version.py b/ramp/_version.py
index 3d187266..72251527 100644
--- a/ramp/_version.py
+++ b/ramp/_version.py
@@ -1 +1 @@
-__version__ = "0.5.0"
+__version__ = "0.5.2"

From b31aaf88679dca7c40267a4d57be49dcd1b458d5 Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Tue, 4 Jun 2024 11:41:58 +0200
Subject: [PATCH 139/140] Fix README formatting

So that twine check dist/* does not output errors.

Pre-commit removed trailing spaces in the file as well
---
 README.rst | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/README.rst b/README.rst
index 216df0d3..414a7966 100644
--- a/README.rst
+++ b/README.rst
@@ -12,8 +12,7 @@
     :alt: Documentation Status
 
 .. image:: https://coveralls.io/repos/github/RAMP-project/RAMP/badge.svg?branch=main
-:target: https://coveralls.io/github/RAMP-project/RAMP?branch=main
-
+   :target: https://coveralls.io/github/RAMP-project/RAMP?branch=main
 
 .. image:: https://github.com/RAMP-project/RAMP/blob/main/docs/source/_static/RAMP_logo_basic.png?raw=true
    :width: 300
@@ -110,7 +109,7 @@ To have a look to the python files, you can download them using the `download_ex
 
    download_example("the specfic folder directory to save the files")
 
--  ``input_file_1.py``: represents the most basic electric appliances; it is 
+-  ``input_file_1.py``: represents the most basic electric appliances; it is
    an example of how to model lightbulbs, radios, TVs, fridges, and
    other electric appliances. This input file is based on the ones used
    for `the first RAMP publication <https://doi.org/10.1016/j.energy.2019.04.097>`__.
@@ -267,7 +266,7 @@ we use a more compact formulation:
     random_var_w=0.35 # 35% randomness assigned to the size of the functioning windows
     )
 
-At this point, we can group our different users into a "use case" and run the simulation, 
+At this point, we can group our different users into a "use case" and run the simulation,
 for instance for a whole year.
 
 .. code-block:: python
@@ -276,10 +275,10 @@ for instance for a whole year.
    whole_year_profile = use_case.generate_daily_load_profiles()
 
 Here is your first load for a community including two types of housholds,
-for a total of 23 individual users. Of course, more variations and many more 
-features are possible! For instance, you can simulate loads even for 
-an individual appliance or user. In addition, you can use in-built plotting 
-functionalities to explore your results. Check out the documentation 
+for a total of 23 individual users. Of course, more variations and many more
+features are possible! For instance, you can simulate loads even for
+an individual appliance or user. In addition, you can use in-built plotting
+functionalities to explore your results. Check out the documentation
 for all the possibilities.
 
 Contributing

From 9ddd6a4f6fdd2cae4b5daf8e48f1f31ec730097f Mon Sep 17 00:00:00 2001
From: "pierre-francois.duc" <pierre-francois.duc@rl-institut.de>
Date: Fri, 7 Jun 2024 13:56:59 +0200
Subject: [PATCH 140/140] Allow all python version below 3.12

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index d8dfc540..83fa52ff 100644
--- a/setup.py
+++ b/setup.py
@@ -13,7 +13,7 @@
     version=__version__,
     packages=find_packages(),
     license="European Union Public License 1.2",
-    python_requires="<=3.11",
+    python_requires="<3.12",
     package_data={"": ["*.txt", "*.dat", "*.doc", "*.rst", "*.xlsx", "*.csv"]},
     install_requires=[
         "pandas >= 1.3.3",