RUL Prediction .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run to Failure degradation simulation of NASA Turbo Jet Engine Fans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='index'>Index</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ <a href='#data_analysis'>1. Data Analysis</a>\n",
    "    + <a href='#info_about_data'>1.1 Info about data:</a>\n",
    "+ <a href='#noise_removal'>2. Noise removal and Normalization</a>\n",
    "+ <a href='#training_and_validation'>3. Training and Validation</a>\n",
    "    + <a href='#3.1'>3.1 Training Performance of different models</a> \n",
    "    + <a href='#3.2'>3.2 Random Forest</a> \n",
    "        + <a href='#3.2.1'>3.2.1 Random Forest Validation Performance</a>\n",
    "        + <a href='#3.2.2'>3.2.2 Random Forest Validation Prediction vs Actual</a>\n",
    "    + <a href='#3.3'>3.3 Linear Regression</a> \n",
    "        + <a href='#3.3.1'>3.3.1 Linear Regression Validation Performance</a>\n",
    "        + <a href='#3.3.2'>3.3.2 Linear Regression Validation Prediction vs Actual</a>\n",
    "    + <a href='#3.4'>3.4 Logistic Regression</a> \n",
    "        + <a href='#3.4.1'>3.4.1 Logistic Regression Validation Performance</a>\n",
    "        + <a href='#3.4.2'>3.4.2 Logistic Regression Validation Prediction vs Actual</a>\n",
    "+ <a href='#testing'>4 Testing</a>\n",
    "    + <a href='#4.1'>4.1 Random Forest Testing</a>\n",
    "    + <a href='#4.2'>4.2 Linear Regression Testing</a>\n",
    "    + <a href='#4.3'>4.3 Logistic Regression Testing</a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import QuantileTransformer , PowerTransformer\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from keras.layers import Dense , LSTM\n",
    "from keras.models import Sequential\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "cmap = cm.get_cmap('Spectral') # Colour map (there are many others)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import explained_variance_score\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='data_analysis'>1. Data analysis</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>23</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>-0.0007</td>\n",
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       "      <td>100.0</td>\n",
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       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
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       "      <td>5</td>\n",
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       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 28 columns</p>\n",
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       "   0   1       2       3      4       5       6        7        8      9   \\\n",
       "0   1   1 -0.0007 -0.0004  100.0  518.67  641.82  1589.70  1400.60  14.62   \n",
       "1   1   2  0.0019 -0.0003  100.0  518.67  642.15  1591.82  1403.14  14.62   \n",
       "2   1   3 -0.0043  0.0003  100.0  518.67  642.35  1587.99  1404.20  14.62   \n",
       "3   1   4  0.0007  0.0000  100.0  518.67  642.35  1582.79  1401.87  14.62   \n",
       "4   1   5 -0.0019 -0.0002  100.0  518.67  642.37  1582.85  1406.22  14.62   \n",
       "\n",
       "   ...       18      19    20   21    22     23     24       25  26  27  \n",
       "0  ...  8138.62  8.4195  0.03  392  2388  100.0  39.06  23.4190 NaN NaN  \n",
       "1  ...  8131.49  8.4318  0.03  392  2388  100.0  39.00  23.4236 NaN NaN  \n",
       "2  ...  8133.23  8.4178  0.03  390  2388  100.0  38.95  23.3442 NaN NaN  \n",
       "3  ...  8133.83  8.3682  0.03  392  2388  100.0  38.88  23.3739 NaN NaN  \n",
       "4  ...  8133.80  8.4294  0.03  393  2388  100.0  38.90  23.4044 NaN NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_file = \"train_FD001.txt\" \n",
    "test_file = \"test_FD001.txt\"\n",
    "RUL_file = \"RUL_FD001.txt\"\n",
    "\n",
    "df = pd.read_csv(train_file,sep=\" \",header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Section-0</th>\n",
       "      <th>Section-1</th>\n",
       "      <th>Section-2</th>\n",
       "      <th>Section-3</th>\n",
       "      <th>Section-4</th>\n",
       "      <th>Section-5</th>\n",
       "      <th>Section-6</th>\n",
       "      <th>Section-7</th>\n",
       "      <th>Section-8</th>\n",
       "      <th>Section-9</th>\n",
       "      <th>...</th>\n",
       "      <th>Section-16</th>\n",
       "      <th>Section-17</th>\n",
       "      <th>Section-18</th>\n",
       "      <th>Section-19</th>\n",
       "      <th>Section-20</th>\n",
       "      <th>Section-21</th>\n",
       "      <th>Section-22</th>\n",
       "      <th>Section-23</th>\n",
       "      <th>Section-24</th>\n",
       "      <th>Section-25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.66</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Section-0  Section-1  Section-2  Section-3  Section-4  Section-5  \\\n",
       "0          1          1    -0.0007    -0.0004      100.0     518.67   \n",
       "1          1          2     0.0019    -0.0003      100.0     518.67   \n",
       "2          1          3    -0.0043     0.0003      100.0     518.67   \n",
       "3          1          4     0.0007     0.0000      100.0     518.67   \n",
       "4          1          5    -0.0019    -0.0002      100.0     518.67   \n",
       "\n",
       "   Section-6  Section-7  Section-8  Section-9  ...  Section-16  Section-17  \\\n",
       "0     641.82    1589.70    1400.60      14.62  ...      521.66     2388.02   \n",
       "1     642.15    1591.82    1403.14      14.62  ...      522.28     2388.07   \n",
       "2     642.35    1587.99    1404.20      14.62  ...      522.42     2388.03   \n",
       "3     642.35    1582.79    1401.87      14.62  ...      522.86     2388.08   \n",
       "4     642.37    1582.85    1406.22      14.62  ...      522.19     2388.04   \n",
       "\n",
       "   Section-18  Section-19  Section-20  Section-21  Section-22  Section-23  \\\n",
       "0     8138.62      8.4195        0.03         392        2388       100.0   \n",
       "1     8131.49      8.4318        0.03         392        2388       100.0   \n",
       "2     8133.23      8.4178        0.03         390        2388       100.0   \n",
       "3     8133.83      8.3682        0.03         392        2388       100.0   \n",
       "4     8133.80      8.4294        0.03         393        2388       100.0   \n",
       "\n",
       "   Section-24  Section-25  \n",
       "0       39.06     23.4190  \n",
       "1       39.00     23.4236  \n",
       "2       38.95     23.3442  \n",
       "3       38.88     23.3739  \n",
       "4       38.90     23.4044  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns = ['unit_number','time_in_cycles','setting_1','setting_2','TRA','T2','T24','T30','T50','P2','P15','P30','Nf',\n",
    "#           'Nc','epr','Ps3 0','phi','NRf','NRc','BPR','farB','htBleed','Nf_dmd','PCNfR_dmd','W31','W32' ]\n",
    "#delete NaN values\n",
    "df.drop(columns=[26,27],inplace=True)\n",
    "columns = [\"Section-{}\".format(i)  for i in range(26)]\n",
    "df.columns = columns\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataset statistics  for each parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "\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>Section-0</th>\n",
       "      <th>Section-1</th>\n",
       "      <th>Section-2</th>\n",
       "      <th>Section-3</th>\n",
       "      <th>Section-4</th>\n",
       "      <th>Section-5</th>\n",
       "      <th>Section-6</th>\n",
       "      <th>Section-7</th>\n",
       "      <th>Section-8</th>\n",
       "      <th>Section-9</th>\n",
       "      <th>...</th>\n",
       "      <th>Section-16</th>\n",
       "      <th>Section-17</th>\n",
       "      <th>Section-18</th>\n",
       "      <th>Section-19</th>\n",
       "      <th>Section-20</th>\n",
       "      <th>Section-21</th>\n",
       "      <th>Section-22</th>\n",
       "      <th>Section-23</th>\n",
       "      <th>Section-24</th>\n",
       "      <th>Section-25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.0</td>\n",
       "      <td>2.063100e+04</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>2.063100e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>2.063100e+04</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.0</td>\n",
       "      <td>20631.0</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.506568</td>\n",
       "      <td>108.807862</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>642.680934</td>\n",
       "      <td>1590.523119</td>\n",
       "      <td>1408.933782</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>521.413470</td>\n",
       "      <td>2388.096152</td>\n",
       "      <td>8143.752722</td>\n",
       "      <td>8.442146</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>393.210654</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.816271</td>\n",
       "      <td>23.289705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.227633</td>\n",
       "      <td>68.880990</td>\n",
       "      <td>0.002187</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.537152e-11</td>\n",
       "      <td>0.500053</td>\n",
       "      <td>6.131150</td>\n",
       "      <td>9.000605</td>\n",
       "      <td>3.394700e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.737553</td>\n",
       "      <td>0.071919</td>\n",
       "      <td>19.076176</td>\n",
       "      <td>0.037505</td>\n",
       "      <td>1.556432e-14</td>\n",
       "      <td>1.548763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.180746</td>\n",
       "      <td>0.108251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008700</td>\n",
       "      <td>-0.000600</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>641.210000</td>\n",
       "      <td>1571.040000</td>\n",
       "      <td>1382.250000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>518.690000</td>\n",
       "      <td>2387.880000</td>\n",
       "      <td>8099.940000</td>\n",
       "      <td>8.324900</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.140000</td>\n",
       "      <td>22.894200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>-0.001500</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>642.325000</td>\n",
       "      <td>1586.260000</td>\n",
       "      <td>1402.360000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>520.960000</td>\n",
       "      <td>2388.040000</td>\n",
       "      <td>8133.245000</td>\n",
       "      <td>8.414900</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.700000</td>\n",
       "      <td>23.221800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>642.640000</td>\n",
       "      <td>1590.100000</td>\n",
       "      <td>1408.040000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>521.480000</td>\n",
       "      <td>2388.090000</td>\n",
       "      <td>8140.540000</td>\n",
       "      <td>8.438900</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>393.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.830000</td>\n",
       "      <td>23.297900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>643.000000</td>\n",
       "      <td>1594.380000</td>\n",
       "      <td>1414.555000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>521.950000</td>\n",
       "      <td>2388.140000</td>\n",
       "      <td>8148.310000</td>\n",
       "      <td>8.465600</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.950000</td>\n",
       "      <td>23.366800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>0.008700</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>644.530000</td>\n",
       "      <td>1616.910000</td>\n",
       "      <td>1441.490000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>523.380000</td>\n",
       "      <td>2388.560000</td>\n",
       "      <td>8293.720000</td>\n",
       "      <td>8.584800</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.430000</td>\n",
       "      <td>23.618400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Section-0     Section-1     Section-2     Section-3  Section-4  \\\n",
       "count  20631.000000  20631.000000  20631.000000  20631.000000    20631.0   \n",
       "mean      51.506568    108.807862     -0.000009      0.000002      100.0   \n",
       "std       29.227633     68.880990      0.002187      0.000293        0.0   \n",
       "min        1.000000      1.000000     -0.008700     -0.000600      100.0   \n",
       "25%       26.000000     52.000000     -0.001500     -0.000200      100.0   \n",
       "50%       52.000000    104.000000      0.000000      0.000000      100.0   \n",
       "75%       77.000000    156.000000      0.001500      0.000300      100.0   \n",
       "max      100.000000    362.000000      0.008700      0.000600      100.0   \n",
       "\n",
       "          Section-5     Section-6     Section-7     Section-8     Section-9  \\\n",
       "count  2.063100e+04  20631.000000  20631.000000  20631.000000  2.063100e+04   \n",
       "mean   5.186700e+02    642.680934   1590.523119   1408.933782  1.462000e+01   \n",
       "std    6.537152e-11      0.500053      6.131150      9.000605  3.394700e-12   \n",
       "min    5.186700e+02    641.210000   1571.040000   1382.250000  1.462000e+01   \n",
       "25%    5.186700e+02    642.325000   1586.260000   1402.360000  1.462000e+01   \n",
       "50%    5.186700e+02    642.640000   1590.100000   1408.040000  1.462000e+01   \n",
       "75%    5.186700e+02    643.000000   1594.380000   1414.555000  1.462000e+01   \n",
       "max    5.186700e+02    644.530000   1616.910000   1441.490000  1.462000e+01   \n",
       "\n",
       "       ...    Section-16    Section-17    Section-18    Section-19  \\\n",
       "count  ...  20631.000000  20631.000000  20631.000000  20631.000000   \n",
       "mean   ...    521.413470   2388.096152   8143.752722      8.442146   \n",
       "std    ...      0.737553      0.071919     19.076176      0.037505   \n",
       "min    ...    518.690000   2387.880000   8099.940000      8.324900   \n",
       "25%    ...    520.960000   2388.040000   8133.245000      8.414900   \n",
       "50%    ...    521.480000   2388.090000   8140.540000      8.438900   \n",
       "75%    ...    521.950000   2388.140000   8148.310000      8.465600   \n",
       "max    ...    523.380000   2388.560000   8293.720000      8.584800   \n",
       "\n",
       "         Section-20    Section-21  Section-22  Section-23    Section-24  \\\n",
       "count  2.063100e+04  20631.000000     20631.0     20631.0  20631.000000   \n",
       "mean   3.000000e-02    393.210654      2388.0       100.0     38.816271   \n",
       "std    1.556432e-14      1.548763         0.0         0.0      0.180746   \n",
       "min    3.000000e-02    388.000000      2388.0       100.0     38.140000   \n",
       "25%    3.000000e-02    392.000000      2388.0       100.0     38.700000   \n",
       "50%    3.000000e-02    393.000000      2388.0       100.0     38.830000   \n",
       "75%    3.000000e-02    394.000000      2388.0       100.0     38.950000   \n",
       "max    3.000000e-02    400.000000      2388.0       100.0     39.430000   \n",
       "\n",
       "         Section-25  \n",
       "count  20631.000000  \n",
       "mean      23.289705  \n",
       "std        0.108251  \n",
       "min       22.894200  \n",
       "25%       23.221800  \n",
       "50%       23.297900  \n",
       "75%       23.366800  \n",
       "max       23.618400  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='info_about_data'>1.1 Info about data:</a>\n",
    "- Section-0 is MachineID\n",
    "- Section-1 is time in, Cycles\n",
    "- Section-2...4 is Opertional Settings\n",
    "- Section-5...25 is sensor's data \n",
    "\n",
    "\n",
    "- Data Set: FD001\n",
    "- Train trjectories: 100\n",
    "- Test trajectories: 100\n",
    "- Conditions: ONE (Sea Level)\n",
    "- Fault Modes: ONE (HPC Degradation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Names \n",
    "MachineID_name = [\"Section-0\"]\n",
    "RUL_name = [\"Section-1\"]\n",
    "OS_name = [\"Section-{}\".format(i) for i in range(2,5)]\n",
    "Sensor_name = [\"Section-{}\".format(i) for i in range(5,26)]\n",
    "\n",
    "# Data in pandas DataFrame\n",
    "MachineID_data = df[MachineID_name]\n",
    "RUL_data = df[RUL_name]\n",
    "OS_data = df[OS_name]\n",
    "Sensor_data = df[Sensor_name]\n",
    "\n",
    "# Data in pandas Series\n",
    "MachineID_series = df[\"Section-0\"]\n",
    "RUL_series = df[\"Section-1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "grp = RUL_data.groupby(MachineID_series)\n",
    "max_cycles = np.array([max(grp.get_group(i)[\"Section-1\"]) for i in MachineID_series.unique()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#delete columns with constant values that do not carry information about the state of the unit\n",
    "#data = pd.concat([RUL_data,OS_data,Sensor_data], axis=1)\n",
    "df.drop(columns=[\"Section-0\",\n",
    "                \"Section-4\", # Operatinal Setting\n",
    "                \"Section-5\", # Sensor data\n",
    "                \"Section-9\", # Sensor data\n",
    "                \"Section-10\", # Sensor data\n",
    "                \"Section-14\",# Sensor data\n",
    "                \"Section-20\",# Sensor data\n",
    "                \"Section-22\",# Sensor data\n",
    "                \"Section-23\"] , inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above vizulization its clear that \n",
    "- Section-4 (Oprational Setting-3)\n",
    "- Section-5 (Sensor-1)\n",
    "- Section-9  (Sensor-5)\n",
    "- Section-14 (Sensor-10)\n",
    "- Section-20 (Sensor-16)\n",
    "- Section-22 (Sensor-18)\n",
    "- Section-23 (Sensor-19)\n",
    "\n",
    "Does not play a vital role in variation of data and there std is also almost 0 so, these sensor data is useless for us hence, we can drop this coloumn data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Section-1</th>\n",
       "      <th>Section-2</th>\n",
       "      <th>Section-3</th>\n",
       "      <th>Section-6</th>\n",
       "      <th>Section-7</th>\n",
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       "      <th>Section-11</th>\n",
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       "      <th>Section-15</th>\n",
       "      <th>Section-16</th>\n",
       "      <th>Section-17</th>\n",
       "      <th>Section-18</th>\n",
       "      <th>Section-19</th>\n",
       "      <th>Section-21</th>\n",
       "      <th>Section-24</th>\n",
       "      <th>Section-25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>554.36</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>9046.19</td>\n",
       "      <td>47.47</td>\n",
       "      <td>521.66</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>392</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>553.75</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>9044.07</td>\n",
       "      <td>47.49</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>392</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>554.26</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>9052.94</td>\n",
       "      <td>47.27</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>390</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>554.45</td>\n",
       "      <td>2388.11</td>\n",
       "      <td>9049.48</td>\n",
       "      <td>47.13</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>392</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>554.00</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>9055.15</td>\n",
       "      <td>47.28</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>393</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Section-1  Section-2  Section-3  Section-6  Section-7  Section-8  \\\n",
       "0          1    -0.0007    -0.0004     641.82    1589.70    1400.60   \n",
       "1          2     0.0019    -0.0003     642.15    1591.82    1403.14   \n",
       "2          3    -0.0043     0.0003     642.35    1587.99    1404.20   \n",
       "3          4     0.0007     0.0000     642.35    1582.79    1401.87   \n",
       "4          5    -0.0019    -0.0002     642.37    1582.85    1406.22   \n",
       "\n",
       "   Section-11  Section-12  Section-13  Section-15  Section-16  Section-17  \\\n",
       "0      554.36     2388.06     9046.19       47.47      521.66     2388.02   \n",
       "1      553.75     2388.04     9044.07       47.49      522.28     2388.07   \n",
       "2      554.26     2388.08     9052.94       47.27      522.42     2388.03   \n",
       "3      554.45     2388.11     9049.48       47.13      522.86     2388.08   \n",
       "4      554.00     2388.06     9055.15       47.28      522.19     2388.04   \n",
       "\n",
       "   Section-18  Section-19  Section-21  Section-24  Section-25  \n",
       "0     8138.62      8.4195         392       39.06     23.4190  \n",
       "1     8131.49      8.4318         392       39.00     23.4236  \n",
       "2     8133.23      8.4178         390       38.95     23.3442  \n",
       "3     8133.83      8.3682         392       38.88     23.3739  \n",
       "4     8133.80      8.4294         393       38.90     23.4044  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='noise_removal'>2. Noise removal and Normalization</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df))\n",
    "gen = MinMaxScaler(feature_range=(0, 1))\n",
    "df = gen.fit_transform(df)\n",
    "df = pd.DataFrame(df)\n",
    "#df = df.rolling(20).mean()\n",
    "pt = PowerTransformer()\n",
    "df = pt.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=np.nan_to_num(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.84688137, -0.31975365, -1.3916867 , ..., -0.76399955,\n",
       "         1.42665355,  1.24248381],\n",
       "       [-1.82422371,  0.87202066, -1.03174892, ..., -0.76399955,\n",
       "         1.03734696,  1.29226848],\n",
       "       [-1.80166973, -1.9503069 ,  1.01385485, ..., -2.35059786,\n",
       "         0.72414823,  0.465476  ],\n",
       "       ...,\n",
       "       [ 1.25582441,  0.18308976,  0.01311911, ...,  2.66650739,\n",
       "        -1.90974435, -2.75272539],\n",
       "       [ 1.26682846, -0.50209065,  1.01385485, ...,  1.14636389,\n",
       "        -2.49938469, -1.91568423],\n",
       "       [ 1.27779988, -1.45465135, -1.75831429, ...,  1.68933126,\n",
       "        -2.19492018, -1.99819889]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grouping w.r.t MID (Machine ID)\n",
    "# col_names = df.columns\n",
    "# def grouping(datafile, mid_series):\n",
    "#     data = [x for x in datafile.groupby(mid_series)]\n",
    "#     return data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='training_and_validation'>3. Training and Validation</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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>rul</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rul\n",
       "0  192\n",
       "1  191\n",
       "2  190\n",
       "3  189\n",
       "4  188"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def RUL_df():\n",
    "    rul_lst = [j  for i in MachineID_series.unique() for j in np.array(grp.get_group(i)[::-1][\"Section-1\"])]\n",
    "    rul_col = pd.DataFrame({\"rul\":rul_lst})\n",
    "    return rul_col\n",
    "\n",
    "RUL_df().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='3.1'>3.1 Training Performance of different models</a> \n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array(df)\n",
    "\n",
    "y = np.array(RUL_df()).reshape(-1,1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20) \n",
    "\n",
    "forest_model = RandomForestRegressor()\n",
    "forest_model.fit(X_train, y_train)\n",
    "lin_model = LinearRegression()\n",
    "lin_model.fit(X_train, y_train)\n",
    "logistic_model = LogisticRegression()\n",
    "logistic_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error >>  413.2798822257767\n",
      "mean_absolute_error >> 12.600563230090641\n",
      "\n",
      "mean_squared_error >>  1522.8995858356257\n",
      "mean_absolute_error >> 29.3338649556834\n",
      "\n",
      "mean_squared_error >>  1717.120255925549\n",
      "mean_absolute_error >> 28.601909747467403\n"
     ]
    }
   ],
   "source": [
    "pred_f = forest_model.predict(X)\n",
    "pred_lin = lin_model.predict(X)\n",
    "pred_log = logistic_model.predict(X)\n",
    "\n",
    "print(\"mean_squared_error >> \", mean_squared_error(y, pred_f))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(y, pred_f))\n",
    "\n",
    "print(\"\\nmean_squared_error >> \", mean_squared_error(y, pred_lin))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(y, pred_lin))\n",
    "\n",
    "print(\"\\nmean_squared_error >> \", mean_squared_error(y, pred_log))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(y, pred_log))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(logistic_model, open('logistic_regression.sav', 'wb'))\n",
    "\n",
    "pickle.dump(lin_model, open('linear_regression.sav', 'wb'))\n",
    "\n",
    "pickle.dump(forest_model, open('forest_regression.sav', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc of Linear Regression >>  0.6768692656306913\n",
      "Acc of Random Forest >>  0.7226974982380021\n",
      "Acc of Logistic Regression >>  0.015992246183668523\n"
     ]
    }
   ],
   "source": [
    "print(\"Acc of Linear Regression >> \",lin_model.score(X_test, y_test))\n",
    "print(\"Acc of Random Forest >> \",forest_model.score(X_test, y_test))\n",
    "print(\"Acc of Logistic Regression >> \",logistic_model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='3.2'>3.2 Random Forest</a> \n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='3.2.1'>3.2.1 Random Forest Validation Performance</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error >>  1348.2407267506662\n",
      "mean_absolute_error >> 25.587678701235763\n"
     ]
    }
   ],
   "source": [
    "forest_pred = forest_model.predict(X_test)\n",
    "print(\"mean_squared_error >> \", mean_squared_error(y_test, forest_pred))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(y_test, forest_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='3.2.2'>3.2.2 Random Forest Validation Prediction vs Actual</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(y_test,c='k',label='Actual')\n",
    "plt.plot(forest_pred,c='red',label='Predicted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='3.3'>3.3 Linear Regression</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='3.3.1'>3.3.1 Linear Regression Validation Performance</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error >>  1549.6005631838705\n",
      "mean_absolute_error >> 29.427134311728\n"
     ]
    }
   ],
   "source": [
    "lin_pred = lin_model.predict(X_test)\n",
    "print(\"mean_squared_error >> \", mean_squared_error(y_test, lin_pred))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(y_test, lin_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='3.3.2'>3.3.2 Linear Regression Validation Prediction vs Actual</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2f/ViLfOdoigdFUVpryhKW0VRxmjhPyqK0kVRlOaKopypKMo+LXyvdtxcO/9j1DfhlbBWkRpftFQJNi/5pFvwpzv/MHjyySdp164dM2bMSEl+YVhv+eXjjz8GSq2czGT7c/zpp59CdU/tlzvvvJO2bdtaOixLJXKFapKk+kXIxBfPqJZ54oknbF2jGsnE+wD4+uuvAeK8L0ZZVj3tXbt20bFjx5g7i6jy3rdvX0o8S6aTxo0bp9Whme5IMN0ranNGuA8fPpzLL7/cc/xkesJR9KbcNhLO5J67UbhfeeWVvPLKK76vjZpMMXG1S3fu3LksWrQo8PZ7Xvnf//7nGidTP7y5whdffEHjxo3jvEZGQc4I9/Hjx3tquDp2DXjz5s3MnDkz0LXJ8Msvv4SeZqrIJWsZq4+NMWznzp18+OGHntOrVKmSJ922W92FUbe///4711xzTezY7sOajXMo2cQNN9zATz/9FLnaJmeEe1js27ePXr16+b7OabPcIKRTUG7evBkhhKPZpZFsEAZeyli1alXXDsLFF19M3759Pas29u3bF+dG2g2vzz1Ine/duzeytHONAwcO8PLLL8fWGfghUzo5UrgHwKrxT506NQ0liSesRqXrDP0s7w8z/3Rhtwm48b5WrFgBlPr/Dws3gRpF3WaD++F08fTTT3PBBRfENuHwyr59+/jggw8c46SqfqVwdyCVPRiveWXii+dFLVNSUsJDDz3Enj17UlWspDC7chBCMHr06Ng97t27FyGE7w+gGfNz19Nfs2YNY8eOpXz58rblk0TDN998w4svvgjg21z01ltv9Rw3avkihbsDmfQCZcuEqh0TJ05k2LBhvhp/mIRRN2PHjo391s0I77nHm9PTAwcOUFJSYvsci4uL4447d+4c54k0DMx5e2lTxl7ogQMHMuqdiIpOnToFdkfi5jzvkUceSdn8mhTuWUYmvlxehLu+IlQXil7vY+PGjdx9992R37dT+sZzQcuRn5/PgAEDbM9v2rQJKK3L33//3TG9KHt9xns89dRTY7/z8/MtdxLKZcJsd0uWLOHaa6/lxx9Ts/SnzAp3v/7c04lTjy9K7OrkrbfeitvI14+1jN96Puuss7jhhhsyYsWfkSDP47333kuJVUyyaTvFe+SRR8Iqji3m7elyhcceeyyl+eX8HqrpXKkWFmPHjqVmzZqu8YIKhjlz5jBv3ryYW1k3wXXmmWfG5RelywbdFjiI1UIY+dtdG5YQTmenIlOtYs4///x0FyEwTnXq1VopLHK6575jx46kVqql88Uz5m3eaCJsevTowX/+8x/LvP0QheOwZJ/BokWL+O9//5sxi5jSma/d5K1bPIl3MkkDkNPC3cnzX5DJpShxyj+M4XQQvNZJ2IuY9u3bl7DjfNDn07NnT+6//37X1YDmshcXFzN48GDLc8byJHvPQe8r3e1V4p9UC/6cFu5OeKnoTHmB3Cb09HKuW7eO7t27u07GhU3Y9XTVVVfRunXrOMuDqF6M119/PeYz34hxdx6r+k932wijPuzu4a+//ko67XTh1eGbn1XGYZHqDmWZFe5mrrjiirTm72at4aVnf+edd/Lll1/62sBbX5Tjt0xeMW9h5iXNzz//HAjPi6cT55xzDs8991zg64UQLF682PP+sen8KKT7g5QKnCySjES1m1km1XFOC3ezIHGq+I8++sj1ei/pRI1Xkz2vHHHEEQlhYd7f3XffbZmmU1nLlVObpXGDabsyHThwgKlTp0Y65DXaPFvl065dO1q1auUpLT9tMhn69evHXXfd5Zh3LmK+x99//92X+4fvv//eMb1kkGqZHObTTz+Nbcprxq1n7nTej+B0Ys+ePbz77rvuEU2E3WithLsdDz74IP369WPy5Mlx4QsWLCAvL89x3sXrphxe4gVVhQU1yXX7KEydOpUbb7wxUJnc6Ny5cyTpRkHfvn3p2bOnZ1VT+/btIy5R6pDCPUUUFxdz4okncsIJJ/i+1q/wHDJkSKBVcMOGDWPAgAHMnz/f97UQXi/Uj3DXF4To97tkyRKuu+46xowZE3e9VR0aBbJXIRvWh9QuvWxA93mfDehl/eyzzxBCpNU0OtXPWgp3G8K2UNHjeV2M42dVpJXlhr6hth90QWmn6xZCOHpNDMsU0o9wN6dz5JFHMm7cON5//33Xa4KUN1MmVIOQrjL/8ssvvuaBouChhx4CSp3iecWpzpYuXcq0adM8p5VqtUzOL2KyI13WMkH0+E5l/f7772NCMBUvr9G/io5Tvj///HPcsZcechDh7obVpGky6YdV12VBD37SSSexePFiTjnlFKpXr56yfC+55JKkLX+cnk+bNm1c4zghrWXSSJgfgGQW6zj14o06wjB8oBjZv39/0mnoPuH9NGSjcA9rub6VN0o3tU0y+SV7jRthCIZU9eR/+uknIPlVxl4wrmd4/vnnQ0+/Xbt21K9fP9C1sueeZSSz8nHt2rWer0nHkHratGl89dVXKc/Xj7VMMmoS47PwujGJGS/5RrFZh99298MPP/Dmm2/6usYrO3fupGrVquzatYuqVatm3ALBMPE6CQ/pv++c7rln0pDX6kEbbcDDbgjJ3Lvx2kmTJsWdMwrcxx57jPnz54fem7US7lE8S2OaXv1+mD8mXiboMmFRUPv27bntttsiSbt69eoMGzaM6tWrM27cONt4mfQ+eiUby6yT08I9GZzskf/888+Y69pUbLLhVd0SVkM03tP9998fd27Dhg2x31dffTVHH3205XVe8apzj8LpUtQfD5109+Ag3PkLKx5++GFA9RhqJhPu34l9+/Zx5ZVXsnXrVsvze/bssTVhzmRyWribG5VfB/x2QnX+/PnUrl07ITwqgrj8DdsLoheCuPw1H2/ZsiVmomgUSMaPiNX1CxYs8FVWq7y94FY3+/fvT3Bn4GeNQljlyESM9X3zzTczZ86cNJamlFdffZUnnnjCdh/kbt26edazG5+LW/tauXKl90IGIKeFu7lyzz333DSVpJQg9tOpnsTze20Ql7+6OkPfzkynfv36sUbvZUJVJ8jkWRT1Om3atATLHH0kkg4uvPDClKoWrNqCVdgdd9xBjx49UlEkV/ROhNXoRgjBokWLAqVr97HQGTRokOXK+LDIauEe9VAzLD/lGzdutO156phfQOPejanupaVSGCxcuND2XNTPNxm1TFTPJEjdz5o1iz59+lhao7z00kuOq3TLCkFGlV6vs4u/ZMkS1/hRbkKT1cL9wQcfTHcRPD38Rx55JLZSzmtjeeGFF1zzM+sBk+3tBxVY55xzjue4fpbbe1FHPf30057ztkrfL2716uaArkWLFr7S88LZZ5/Nxx9/HHNeNn369Ljzuj22mSg+UE5pWq2aXrZsWdw8TjoIqx5mzZrFs88+6+uaKDtSWS3c/Zgl+SXds+S33HJL3LHeALdv387MmTMB2LVrV6qLFSnm1YN+1DIQrg7ziSeeCHSdedEWxAuPbdu2BS6TF95//32OP/74uLBMEZ5t27Zl4cKFceVp3bo1hx12WKB0x44d69m7YyoMEa6++urAZYiCrBbudpW1dOlShBCsWrXK8Vo/C2S8fN1vueUWS6dKYS6GevXVV+nVqxdTpkwJJT0jyS6tF0JwLODHebJx+bZ59avdohe7+nzmmWd85OwtTTv81JFTXHMb9VOOr776Km6thKIosQVDmUphYWFgYW5m9OjRkftl9/M8ypcv73g+1erVnFvE9O6778asYtwWbRQXF3tO18tDHjNmjOf0kuW0005zXASVTC8hmUY4U/v/lMc0d+/ezZ49e6hcuXLgPIMSdk+qqKjIMjyql7pbt24AcVtJpnvEqd+r0SQy0y17wipfhQoVLMNnzpxJw4YNZc/dD1aVNWDAAE/qCrcH+tRTT/Hee+8FLlvYeLFCMNbHJ598EniyxqkRVgKqBLjOCTvdd2j+W4BU7Dt/1FFHWYZHYS0zd+5cjgQaGMLSLdh1fvzxx9gm6kGoAlT0EX/9+vWucVLh/yU/37qv3KtXL1q2bBko/2TIauFuhy4sktGz3XHHHYHzd7IA0dm2bVtku8GAqqJo27atr2u87Au6DUhG0++Utld3ukFe1Ctd0onSt0xULAbWmcLSXT4hhKUfHz/sAlbbnMvLy+Pss8+OCwvqojpswlrEFxY5KdzDwMuDUhSFpUuWJPQydu7cyZtvvmk7TAdYs2ZNkiVMxGmZ+/79+0MxLQyqPInarDFZMsUU0i/mtufnPlq1akWDBg3cIyZJkLpqaBNeUlKS4D44mW0SAd5+++244zxAN1y2q88gLrVTTc4I94qoKgMju3fvto3/zTffOKbnpUGuWbOGh4C9qA1CZ+TIkZx11lmxYXqqelNmPzBGKlSowAUXXOApnSgE12WXXeb7GiFE2nuiZoJMOvuJq6dfEXsBl0z6Rpr+9Rf1Pe796odUPbOKwFmou0654eSR8oMPPog7HgvMBTpqx5WAuqZrunfv7r2gaSKrhbuxEW0G9MGg3tgnTJgQOG1zT9PqBerbty+Xa7+N2ja/GwLYpR8Weo/+1VdfdYyX7Etpdw+635EaPtMLUy0TFnpd+ilDkGc7GfBqwLhx48ZYmfyUaxmqaidqomrbjwKvAxd6iDtjxgwAZs+ebbsZjR5eoB3rY5o5gL6ipBHgT9mZPrJauBvxKzjc0F8YHauXxqqR9ESdwHPaidHoc9orVi+IU0/dyOmnn+4rL/O91ge+AA72lUopQ4cOpRDYDhxloY7666+/2Dl4MJdYTACbn4MdlYAx48dzmo9yKYpCBWASsMvjEvN168xabneCCLdTfMTtDgzxnUM0CCFS4vJXURT+rf1+xMd1999/P7Vq1bI8p6tn9NLqb0EnQ5z1wPdALWAncKzhXDJ7NkRBVgv3ZCvLyyy7E1amlLoY7e1wXePGjT2XvQqATdwRI0Z4SsM8bC0qKqJ27doJukq7xnkZqgBxXqLh/Dz0IW4bC2F9zDHHUO2ppxigbfOns3Xr1pj3Tbd8mgAV//qLu1zKaOY44J/AwT4n0P0ILN8LlxzUiVZ8ATyu/Q5TgJyIqvbQaQq0crkmqCD/888/PX/Ig3AQ0MdjXLNwt6IQqArcZAirs28fj6Oq0x4gXlULcOKJJ3osQThktXC3w2sDGzx4cNxxIXAdcA3qgzU/HDMHDhyINQCnHM0vnHFjZicOQrUcuOz330Pt/XTv3p0//vgjwXuhH6ysT4w8TvwciK7ksrqL5cuXW6ZhV09OO0T5raXETQOD0xS4yiLcad9ZS0aOjP1UUIWsV4II908//dQyfBqq2kPnR2CpZUx/ZeiFel9G5WX79u1p2NDrLEM8AmDHDhgzxlagzQA+xJvAOyhQKWDY6tUMAX4ChhP/MSkHHPPiizQzXSOtZXyi60UrAtbLCqxZANwL6H04N8sQo3D3ixdh3Uj73zdix09/I/5DluDL3uKax1DVNXYMAQYZ09TT8tmYjwNWEP+hGDlyJB2BH4CaqC+R8SPbRTu29qhiKJOioFum+32OVs9vJqoeuJrPtMxlwjTJeS6q/vdrStuE4/U+8eLgyojZcMGIl/UYuuebLoCupPOr7krwUTRyJNxyS9xIw8gRPtIuMPz2VJ9Dh8K771JOi6sLVeM71QX42yef8IqPciRLTgr3p55S10fuRdWRBcVN/B4oLo59AE4EX/peL0TxTW/evHmcyeShwCrAuH+O0dSsHPbD1Jd85BvruVu8LE71PB5oAbQE3kYdMXz55ZfcgSq8J6H2yAYY0tJfcDu9dVVgJIDBgiIM4a5rco/CfdTniIXZ6MWoqi2nEVPQXqDf69w2XnTquPQzHTcxHZst3GYDidt/xFMd+PbddwF/nTk3FKC8l32EH34YBgxICLaqhVRq3V2FuxDiMCHEDCHEUiHED0KIoVp4HSHEJ0KIldr/2lq4EEI8LIRYJYT4TgjRyTmHaDEPsQ5D7Qk2RPXiZoWVqsWywRpeiinAO4Zr7gaMvhIP1/7ssOzx2MRtg9qbC8Lq1fHLQ+pp/3sZwnTvguWAA8DtNuWpaTo230OJxW+re3JT8eicTuJKU32Ye4ghzE1Vdgfq82losFX289KdBeQbdu3pClxkSGM6cKuP9CQqXMMAACAASURBVBLw+QE0olsm+cvOn8gpcDg3e/bshH139XZRFXjfJe0Sw7U1gR7AQIt4Zu+bBZq+fiRQxyK+/rGtBIzRyuLG4cC5kycbCue8VmOLaScngbsLlCjx0nMvBv6jKEpr1HZ8lRCiDTAK+ExRlBbAZ9oxqB2mFtrf5UAw93oe8NoojT7yLkMt2MWo22tZpqv9v9Emr36owsFuohNUldBEw/E64lcT6unl4zzM1ctT5Y8/0DXkPwDORo3eOcThXLKOh4yvgpNaxuhGSqDqR/ti3wM0rgC2GlW4tQrdsmqrwTvhYdp1N7tcWwtVD932uutiYV8BE0z5diQJAi74UhQlzt9QA9TRaxe828yHgZ2PJSdhUxlVKFc74YRYWOJUeil2TuLaAE4zHK8DowEvu8k+AzQzOmIz9OK9uD4TwL///W/XeFHhKtwVRflFUZSvtd87UOdUGqJqIXSn4y9QOjI+DXhRUZkL1BJCOMmQyDEuVrYSF52x7gmPRDVtNPO+dm6wR8djxg+DWY8/G9U+//R//pPlNi+FAPo8/jjPoL6svjj6aPVDZIPdPjAXkNhjMgtNc12+/vrrcccK6hd/IKWCvrvJIqYKmopEozKqnj1+WUki5ryNIwMvk9wQr35qp/2/3TpqDN3vX8Vff+Uj4k3hjCQ1/A5pNe+JqJ2Meag283atNag6pz/qfeqdoCqo9WOe8I65tXBIazdgvYOpf5zmPHS/SG4dKp24MhvqycuaWEFiB+UY1AnX0iQzZEJVCNEEtVMyD2igKMovoH4AKNWANCRe1b0Bi46DEOJyIUSREKLIuOtQKmmJOkFVhH1P2MmJ5zgPwv2aa66JO16Mak6m92C7Gs7VNG1GbXzslTXbeD8OlQCYPz9OeNpRAahk0EG/SPKjgxLgLlSdqZ24MpuMnmD4XWHXLjp4zMso0K8w/D7BOjqgzjf4RX9Vy+/YwcnEj86Mz6sP7maDoOqLr6FUbaAoiuOI8Abg7y5p9kSd7DZ/3PKA1tpv42S4lYD5r0seoI6CQVVzNUe17JqFvTVTqhw2OIlLYfjfiXjHa+BdRahTz+GcnXANx+GxO56FuxCiGur81TBFUf50imoRllDfiqI8rShKoaIohV43nw2b5VhPuCbzLTVf++ijj8YdN0Md+hz08ssJ1zotkbYiH7WhFPq6yprWwBTTDj5esWtEVjp3iO/NmBvLu4bflWy8e3agdARkVss0p3QuQKCu9LTDjxnkaKz1i8Lmdz6lZoMDsH4p+gJ/Ag9TqtMEHIU7wOdYm+uV09YEzEJdVWmVp/6mGRc9GYX7eahzCvc5lkDF2Fr1bVK6oq7/uAB1JeezwEPaDkyZZr2xEHWVrhHznI6bW3A39ZvdathU4Km+hRDlUQX7K4qi6F52NunqFu2/br+1gfiPUyMgktUJVj2OXqiqDrO1Qqp6DU08xiuxcDxk56rYruz3oA7xFhjCmqKqNOzc8rrxEs6jFXO5nib+JTdiFOh2k8mOz8XwfI2+PRZRqg7Rr7caZh9BcmaJRsYAg0ksrzF9q3WPF6J+YMw9wprEq56aaP9LSkoosRAo5nw3WeRVe/To2O8WOM+nxM2HKAosWMC/gFeIt213wu4TJITgRdSVnJcAuvPfZIT7P8x5eLyuNdYTrPr11mtVSykxtMGDfO45kG7Xcl6sZQTqB3ipoigPGE5NQTUSQPv/riH8Qs1qpiuwXVffhI2VcH8BdYbd2LAvQ23MXl0UJNNz97rQ385jZB7QzWM5rHoN96L2CIM6E/4X9npkK5zcgRnLf2+AshhfDrsxhf7RsFo9e3GAPK042fDbSbhb0dDwvzLqhG0+iZPVBai9oPnz5/NRwN2FhMmM0Gmuxfhs6mzeDF26+DJtBXtVm93IIxlhN9xHXD331sAS1PUBTmUY7zFdNxdrzU3H6R6peMm/O+r8Wm8hxCLtry9q2zlRCLESde5Gb0tTURezrUKduParxvKM380ogqw8q4E6DNmyZQvTgW99Xt8CqGfayBrURmbl8Og21CF1F9xfBmMPWxcyei/6SQ9lS1ad08Xwu5fFeS/Tgk73+NWcOf4KFBF2k85+UID/Q52wvZ/ED3chpSpC80tZk9KFdc6ZeOuW3Ei8SqqmRft0ow32HYgWNhPCbr1kJ/wKyqqogh2gsSH8OIu4Q/E+4nainenYqW0fh9qRi3JC1dXaTVGUL7Av5/HmAEUtrdUq7NBx0lGHtYBA19legLUAc2MFgIV3yiNRfZoYEZR6nDuE+Fl1K3oYfu/QrtdrxOyiVKcN6jD1S+LVOV5wqr+mlL5MOm6WPafhPOn4iWF/1TC4HLX35bVHX55EvXw5ITwLUShthzcCum3Otajmnm7X6DjZlcfhsVzmD0UQAePUrdpl4RgvD3+9bzN+RpMK/tWSawAro8VkRK+TcNefv9PoKlmyeg9Vv/5WknlQfoesbrg1vhtwN8uzwm1KVn8pnfTqdrU6yOWad0xhdl4kT0YdAZnjey1HUMz7urqxjUS1SzI9raMNv50mepO57+ou53tYhDV1cT8wm3jVVBCa4d423eiMOglaDhjmEE/BvadvVcfdLMKS8TZrzCO1/iBVck646zdkZTL4H7zpiGonUSavWJlD6Su/QBUEcfvNePiQjcH7ZGgQnEy4hpG46s9u39KPgLUe8gsyUgqLf2GtT09G8Hq91iyYvF5XvH+/6yIsq32XOrtsU9cD64+CHVaC95+A9ZJB7xShClsnUz1Q1UW/usRp7HI+DF5BHYl3B663idPUxmleGGS1cG+wfz9XE+81T59IvckifmTK/4gw9u537tjhOnk32nTsdaGGmSCTn23xJrB1mniI86h7lMiwG6klsxovqHD3ypw5c1xf6KA9yI99xE3Q1aKqH/ztKmBNa/conrDyO+TFJYFfbnA5X3+Tld1TOGS1cB+7fn3cUNdI6vcaj46/9u3Dg/uiBJy8WjgJGs86XhPptg5IBZ8kcW1Q4W5lymeFl31qU6EesMvDrsft1fQSVOdxUZHMhK8bT9uEV925U50riWBDk6x+H50Kb6U/y+wtmp0J8lI6bU5g5VYhWdJt15sK/KrsjHXi1VOkm4M2p+vcnkE6dL9u2LnptSJKHzlma5cwsduZ7ejZs8Gvv3+PZLVwP+Dza5fNNxuG4DSOcj4LIT0zqVpWHTVRGWB6VekEfdZerkuncA+69iJVBHFHEQqfRfE2ZrlapjjCTaUzCfPuLV4xP9y5yRakjGA16gtKkBaazZ0QsP+A/CelpcgiIpJjWd2OklGzJLWZQoqpQfgbgEtSgx/7bFDXIQR9KXvg3jP3s/G2JLvJauGezPfOn5cIiSQYTl4prWhO8HZd3cO1YVmbOJGJev2MRvbcw0U2QEkmkkdyL2VQ81dJGpHCPZFkqkQKd0kmkk9y7XqIexRJpiGFe7j0T3cBJBIL8inDL6UkVLK7HSXxxTsyxGJkA1arBiWZhxdb9UxHjop9InvuiVhttiyx5tN0F0DiCQXVQVY2k0urw7OZ7Bbu5bK6+BKJRCJ77lZUkD13SY7h5MpWkpsUb90aSbpZLdwlklyji3sUSY5Rsj+IW0B3pHCXSCSSHCSrhXs5qZaRSCTZjtS5SyQSicQrUrhLJBJJGtn822+RpJvVwl3auUskkmxn/YYNkaSb3cI93QWQSCSSJImqiyqFu0QikeQg2S3cpVpGIpFILMlu4Z7uAkgkEkmSKNIUMhHZc5dIJBJrslu4p7sAEolEkiRyQlUikUhyEamWSUSqZSQSicSa7Bbu6S6ARCKRZChSuEskEkkOkt3CXaplJBJJliMnVCUSiSQXkROqiUi1jEQiyXoi0kBktXDP6sJLJBJJhGS1fJQ6d4lEku3Ur18/knSzWrivq1gx3UWQSCSSpKhRvXok6Wa1cJ8RUaVIJBJJykjXhKoQ4jkhxGYhxGJDWB0hxCdCiJXa/9pauBBCPCyEWCWE+E4I0SmSUkskEkmukEZrmQlAH1PYKOAzRVFaAJ9pxwCnAC20v8uBJ8IppjVS5y6RSLKectEoUFxTVRRlFrDNFHwa8IL2+wVggCH8RUVlLlBLCHFIWIWVSCSSnCPD7NwbKIryC4D2/yAtvCGw3hBvgxaWgBDiciFEkRCi6LeAu39LO3eJRJLt7GzUKJJ0wx4PWMlbS92JoihPK4pSqChKYVBTIEWqZSQSSZazrWPHSNINKtw36eoW7f9mLXwDcJghXiNgY/DiuSCFu0QiyXLyy5ePJN2gwn0KcJH2+yLgXUP4hZrVTFdgu66+iQKplpFIJNlOfoUK0aTrFkEIMRE4DqgnhNgA3ALcDbwhhLgU+Ak4U4s+FegLrAJ2AxdHUGaJRCLJGRr/85+RpOsq3BVFOdfm1PEWcRXgqmQL5RmplpFIJFlO7YMPjiTdrF6hKpFIJBJrslq4y0VMEolEYk1WC3eJRCKRWCOFu0QikeQg2S3cpVpGIpFILMlq4S7t3CUSicSarBbu0v2ARCKRWJPVwl323CUSicSarBbuEolEIrEmq4X7sT17prsIEolEkpFktXAvH5HDHYlEIsl2slq4S1NIiUQisSa7hbtEIpFILMlu4S577hKJRGJJVgt3aQopkUgk1mS1cJdIJBKJNVK4ZwF70l0AiUSSdUjhngVI9ZNEIvFLdgt3OaEqkUhSSJ90F8AH2S3cJZ7Zle4CRMQn6S6ApEzxcboL4AMp3MsIJekugEQiSSlSuEskEokPdqe7AB6Rwr2MIGcnJJJw+C7dBfBIVgt3UUYmVMOwlpFqGYkkHLJF6mS1cJdIrk53AWw4K90FkETGynQXwCNZLdyz5QuaLNLO3ZrbgBXpLoSkzDEEWJXuQnggq4W7VMt4pziENDKNx9JdAAfKRMssI++fmd3AB+kuhAeyWrj7YXNeXiTpvm4THuaXPQzhfiCENOyoFvC6c5PMN5M/WIMuuijdRZBEyNZ0F8ADZUa4787PjyRdu75LvxDzyPQJ1aALpH4KtRSZRX758pGmnw3CJZe52+Fc35SVwpnsFu4+hoX7ypXjrQiLYibTepVR9tyDcC/wZboLYSCZSbIlnTsnBkassrgt0tQlbux3OJcpyqqsFu6KnxdIURgVXVEAmHn44bHfPyaRjlkQZ3rP3S9vASNDSCfMieZk0tpy8MEJYSURC/cOBQWRpi/JfsqOcAdWA507dWKPiMb+pFzNmnHHHwZMZ685XYe4HTymqUR0z0GYFHJ6rUJII5nasWqHftumXw5t1y7S9MPiYDKrYxEmC23CvbSlDWEWxIYyI9wFsHPnTr766iv+SjLfVcDkNm1c4/0RMH0/YuE74MWQ04wap8Y/+8ILPaej39PypEqjkoxw39ikSUJYSUl0Iu10YG2rMD5p0bMJiMaUIRiPJ3v946UpFNrE8fKulY94TgayXbj7eIEUoGrVqlSoUIENSU6utgA21KgRS1fHLCCM5zZVqJBUnk7c6SGOW4MbHTDvtqbjXzxc4yRIV/z9757zNqaz3zAy2eE5hXDYY1DHpYJ3ALyMxP79b74K+BHI1QnbZDs5Q4YMoWXLlrHjwFZxKRhJZ7VwDzppVT6EIXP16tUTwvJN5pbGx7fHIr4dfkunx9dVL19bxFnTsaNjGmOBX33mC9DpggsAaAMcDkz3eb3XSe6dLucHnHJK7PdGn2UAqFypkmscu0m0/v37s75DvIIsap27m2jYCfC///GXS0fmHIuw04FOwYqVwPDhw0NKyT/K2WcHus6ty1i3bl0AGjduTFvgqkC5RE9WC/egQ98nfQhaKx577DHOPVe10q5UsWIs/KAGDSzj/wm8du65HOcx/aDCff/hhzMKGN60aUKcuqOD9s2due666wBYCqwPcL3XidW3LcL22cQ9wtCz8oouLHs4xLEz3RQVK3JoUVHseAPqKDFKhEvPb4H+w+Uj8zqqIF9tCHsH7V6fey5o8XgC6Ag88MADQLC2kSzi5Zc9xzXqz7dWqeItfSHYB9Rv3z4uXK/xb2vU4H3PJQifrBbufnTuFQxqkaK2ZmWCNQ+ajveh6nevvPJKKmpC/fR//tM1nRFVqjB83DgG3XdfLMzJNvxnw++Fdeq4pv/+e+/Fft8D/Gqhz6tcubJrOn758W9/CzW9HUDv3r3Zo40GnJhAfB1efXVyXmZ2aD3cdQ5xGjVsaBmu1KhBnmHUdhjQq1evpMrjRm2XdqG/GV4G/wedfLK1G9uLL/ZZqlJ+A37UVJdQ6iYiLBVWK2C8Rfjn2v9f6tUDi1FLO5uJ6ELgsoBlKX/mmZbhihCWo2hIzRxYVgv3Gh564LrusNFJJ8XCJk+e7Cn9/HPj11BWBpZO0mw9rD4sDh+bihUrMsjwsoxzyPd6Si1KvLyceR5W31b0oPP32+Cqn3oqzZo1o1y5ckyaNAlFUdjm4ZnY3VN+zZo0bdqUypdc4lq2QffeS6dOqvKgqKiIUwxqGTP3Af92KdNN7dpxNc5WDBUNozQ3ypUrfbXCXNRyAHjttdc46cQTHeNV0D7wXjpADz30UCCVnBNXXXUV27dvTwiv7KMOnXjggw8o7BE/zjoHKB6vivx6dzstM3LGrcbMddqkadPQLcDCIKuFe0cXPTJo9ubTp1P+hRdiYbrOzI1rrrkm7vjtyZMZYO6pG4fHhod+3333UaOa/cJ8pwZU5eCDecoQb1c558dUVctHj9XeNEz0Qg1DL8uMnTCuP2YMVatW5cCBA/xTq5cF/fpxGlDs4SW++eabeerJJ2PHMb333/8Ot90GmzZxqcP1d911F3Xr1qWV3aThd99REfVj+axLWbZWrsxjwJ13epmejsdSgBraxaemUzM8tFs7SoCzPeiSq/lQC1WqVKnUFYQPayUzRYbfde0+tiHNRfTt25fu3buXBmzYwGuKwvFDh8L27ZS/1LrlOJkE62fcVF6x+IZ4VmNNRQhaNG/uuxxhEYlwF0L0EUIsF0KsEkJEtnbIqnrMVakA9OoFHvVow+IyiM/huOOOc7x2v0H18d///pcTTzihtAxJsN5BUM6ePZuDtUU0+fn5zJo1iwkTJiSskPWiA/ZdTotJyFbt2jEF5xekbu3aALRp04YmjRsnRihXDv7v/+Cgg7hD64FVMz8/ITjppJPYsmVL4r1p9c5BB3k2e9XLW1hoZ+DmmoDtqS+//DKurfY844y485v08nqgnKZqsJrQN6I/y/YdvK2E2ApUAHj++fgTW7bA4MGe0njeqKYz3VOsbYU40WzbxgJ0VHr06EHLFi0A2B/ATLH2oYfGfg/R6qtatWqxuTkzuyLydWUkdOEuhMhDddh3CqoRxblCCHej8JAwT7HarRRtpP3FMXIkU4zHpoZYq1Yt23MAimmorNuyXjt0qBpgaIzD9DAbjKk7CeYepqFpz549qVq1Ki1M8erVq+eY35tvvhk3LwGw6bjjSBjj2PSIdEaOHMkXX3xB3kcf2cYZNXIknTt35pRTTuFwFx2s/uEaOHBg/Amnns/48bB6NTRowPnnn8+ECRNYscLZOfD9999Ply5d6NatG1xxBRgmZVe46PPrmPTfN954Y9xxYWEhZxgEer5JF7zx9NMd0zeSp01QelHFAdQ2LayzQh957Af1w2qkbl144glPeT32mAc/nQ7C/RG8rdmwxGNPuMBm1DR79myGDBkCQDWDCteKRx55hK5duzJ58mQ6depEv37xnqRO05713xzmpHbn5cHu3XAgOscgUfTcuwCrFEX5UVGUv4DXgNMiyAdshjxG1g0YYBn+s/Z3Wrt28G9NI1uvXuzLvv+ww3wXp7VpYZPes+hg7j1VqRL/oTAxbtw42h55JABHHHEEDW0m8pg+Xc9I/W94cR70OK+gc9JJJ1HPpK5q0KkT3U49NT7iM884ppOXl6cOl489Fi6/3DLOIYccQlFREbVq1Ur4oNhifnmdFpGVLw/NmgHw8ssvc9FFF9GihflzF0+HDh2YN2+e+iF98klYXro0qqXLtWZBe8cddyTEMS5+MVPXw6Q5oD5fk6rQjpoO7Qtg4c03M14TYtWrV08YsRx77LHeymTG4nnedNNNtG7dWj04/3zbS69FtbKJEqd5uqra6LCGywexsLCQr776ig4dOrBw4UJq1arFicaOXZcu0KgR5e64w/ajU6NmTahcOfFjGiJRpNyQeMunDVpYHEKIy4UQRUKIot9++y1YTqbhLcC8uXNBV2Pcfz8j37YyooNNmzYxb9483liwQH2Z//c/GDaMOV98AUD5/HzvQ0g7dY0+Mdi1q/pff9AuD/Swww7jQa2HVq1qVcrp1y00GGwpiqpuMqZrYIDNR80Ri3TeeOMNfvzRZvzj1lvq31/9f46VNXWSOEygRoYflYKpburXr2977vAAHQk3muqrZv/xD8vznW+/nSvfe4+VK1dSt25dZsyYwbp1qq3QmjVrmDp1amhlGTt2LA11tUXPnvCr9fTtL7/8wkcOIz5HvOqwneI10sbyLh9zK55++unSg+rVYf166N7dts0c5nE0lAxRCHer2ku4Q0VRnlYUpVBRlMK4hu8rp8Ss6h90UOnBkCG2D/Oggw6iS5cuqgVEXp7ae8/P5+BDDvGWt/7QnBrLySer8cx6ZSHArI544w3o1s05T7u8DjsM2rVTP1J+ueEGMOsFh5XOPFSuXJmmFnbzgNpDdqJfP3XY6TTBG0QHm6y+0myN46bzTpdfntscfD+6LLqK6aP79rV9ThUqVKC5NvqtVq1aTEXWpEkTqtjNUbmZEXt5njZxDj74YNcRR6T06weffQb/+Y/vS2MjUAcjihg//0yeeUQcAVEI9w2opr46jQi2aDA4qXwZjXl5zbdcOdWG+H3DEoczz4wXWn7uoUIF+O47cDKPs3vp7rwTXn3VX55z5qjXecFqlOJHoB91lPrfqJdO9vk+a7KdcRsaR73jkKKoaiC9N1elCvzxh/NHp3JltQf8l82UsbGOwpy8+/57dT7DqmfvNMnr9szmzlX/63MdffoEK5+Z8ePVyXmnchjWidC7d3BVyezZsHRpfJie3623gteOY0hEIdwXAC2EEE2FEBVQzU+nuFyTfRx9tPrfZgGDJcbevhBqT8HqvNv1UeOWT7duao/fK16FsVW+bdpASQkEUTN5xWu92qxAdsWLcG3ZEs47T/1drhx4mAilQYP4XvkkF2vrhx5yT9MLzZolqsVeeUVtF/qIwqlOrdqD/j7Vrq1ee1ZIW4wPHRo/ArKaswirF92jR6lqR0efy+gUlkMH74Qu3BVFKUbdlP5j1FXpbyiK8kPY+Tjy0kuqKiCkBROWtGypNkJdr+wF3V2CW88gVSMPs8mYnu+gQapudMSIcPPr0wfq1YvvlXkRrFHUh5UJplv+hYUwc6a/a047LfF5m+/Hrg78fsyN8cc5LZOLAP3D9OWXMGaMvdoojA5KMu918+Ywf37yZfDKP/4B69bFz32kqJMWyVStoihTFUVpqSjK3xRFSTQdiJozzoBvvw13JjrAwqAEdOHu1pNTFNXa5Nxz4/XoyQi5Zs3ihfXVV8OiRdZx69aFWbPAzkrHL/rk3oAB8NtvwXvAOk714NUL4tq14HXyTtc/V6umPhc/uCxwiovjdF9e5qV0C6Lnnwejyd+sWXDttaoqJ2ratoWI/BjF+OabxLAM2q8ggRR7DdWJZmPRXMD4Uh5zjNoj8RLXCV242zVEY3iFCqW68DAQAu6/HzQrHM47D+wmSsPuWZx9tiqcevcOJz27+tuxw32S14jXj+1FF6mLea691nvaQYSN3hmx2NmJzZvdr2/dGv78M1H3fdRR6t///ue/TG5cfnncmoCUoHc6PHjydMVuAveyy9R6vPRS+PlntY2EkR+k7EOUe8Ld40pUW2zcCfi+1go9PbsRRap06lBqnmkkqkYnBBx/vHs8Xe/qhoXvGcDdUmH16vg61heQ2D2PQYNgwgTVAdVInxsD6tvgeXCCFitT1arwwguJJq5+hGeSHk9989RT7nHAci1G0lx3nTpRGZSCAliwwPqc0bTRw8Y8vshmtUxaSXbI72UY7eUaK/SVpmbbY/MkTJRf9q+/hg0bnPNI5UfGyMSJzuf1BTCPPhos/WbNwLhqUBfudj33558PXheNG6vXGtdimF0b6ILYaFd94YWqaSuU5u3RF1KZo0oVMJo++yU/39JzZGSkWHWUW8I97H0l9SF+WL2hGjVUwWpcpr1tGyxb5j2Nf/0ruTJ07GivS9dWdVqtMowMXYC1aFH68bPj5ZfV+GGZ9nlVy4TF3Lmwf3/pS37ppWoZDH5JLMlkfXJUOFmwBOmAGdEFesQ+99NN7gj3d9+FTz5JPh1jIznqKLjnHnjRxeOFn4bVsGG8Xrh27cRG5tRbnDABdjl5g0+CSZPgrbdSbo8LpEeAuallwiYvL7GnWFYEt9N9Wvkrql8fFi92T1fvqPj5QBcUwO23hzunlYHkjnDv3z95lYwZIeD6692HflGrMa64Qv1/+OFqI/Y7r+B15WrdumB20BU16VIBQelH1sGLYCSkW6AnOy8VhKu0zeisvFS61Ud+Ptj5Z586VTV9dnGMl5DfzTe7j5jCRlcJpmhknP0TqgMGJKd3M6N/IEYF8FQc1ktrTmfwYM9uV+Po1UvtleofB0k8p56qLnDxYwWjM3CgalpqXmzUs6f7tbrXQbcFcPpczMkn+y+fFfok+uuvh5OeH/r3L/2Qb9rk79qWLe0trQ4+OHlVZaqYPFldxRrU3YpPsl+4+/R+6EqVKv57k23bqrbEXr37pYrpfrerLmPk5cUvTffKn3+qNuNmFcuSJaWToU4ceaS3Nta4sWqGZ2UaaeSuu7zZlrdrp6qiUqWGChPdDUU2U7dutCutTWThU85AHngAZsxQX55vvkntCrhcId2qCj9Ur25tZdG6tTfHUX449FB3YTxqlDpR64VMFOx2k6ctWqi7cun2+fqEfza1lTSS/T33dsmmqQAABYRJREFUTKBixVK3v7p9cxB03W8qzbMkklSjt/PRo9U/u4VnFSrA55+XHk+frq4qTvUcSZYipUgmMWGCaltttcAoV0nnhKokPVSuDMXF6ijCTy+8cWM5f+QDKdwzifr1VeucsogcapctUrW2oAyTgQo4SZlC9/CXDtt6iSSHkT13iTemTVPNuMKmWTNVHWX2bS+RhMnixfYbm+QoQskAnWdhYaFSVFSU7mJIJBI7onD8JUkaIcRCRVEKrc5JtYxEIpHkIFK4SyQSSQ4ihbtEIpHkIFK4SyQSSQ4irWUkEok7c+eq+xJLsgYp3CUSiTtHH+19G0RJRiDVMhKJRJKDSOEukUgkOYgU7hKJRJKDSOEukUgkOYgU7hKJRJKDSOEukUgkOYgU7hKJRJKDSOEukUgkOUhGuPwVQvwGrAt4eT1gS4jFyVVkPXlD1pM3ZD25k4o6aqwoSn2rExkh3JNBCFFk589YUoqsJ2/IevKGrCd30l1HUi0jkUgkOYgU7hKJRJKD5IJwfzrdBcgSZD15Q9aTN2Q9uZPWOsp6nbtEIpFIEsmFnrtEIpFITEjhLpFIJDlIVgt3IUQfIcRyIcQqIcSodJcn1QghnhNCbBZCLDaE1RFCfCKEWKn9r62FCyHEw1pdfSeE6GS45iIt/kohxEXpuJeoEEIcJoSYIYRYKoT4QQgxVAuX9WRACFFJCDFfCPGtVk+3aeFNhRDztHt+XQhRQQuvqB2v0s43MaR1gxa+XAhxcnruKDqEEHlCiG+EEO9rx5lZR4qiZOUfkAesBpoBFYBvgTbpLleK6+DvQCdgsSHsXmCU9nsUcI/2uy/wISCArsA8LbwO8KP2v7b2u3a67y3EOjoE6KT9rg6sANrIekqoJwFU036XB+Zp9/8GcI4W/iQwRPt9JfCk9vsc4HXtdxvtXawINNXe0bx031/IdTUCeBV4XzvOyDrK5p57F2CVoig/KoryF/AacFqay5RSFEWZBWwzBZ8GvKD9fgEYYAh/UVGZC9QSQhwCnAx8oijKNkVRfgc+AfpEX/rUoCjKL4qifK393gEsBRoi6ykO7X53aofltT8F6A28pYWb60mvv7eA44UQQgt/TVGUfYqirAFWob6rOYEQohHQD3hGOxZkaB1ls3BvCKw3HG/Qwso6DRRF+QVUwQYcpIXb1VeZqUdtWNwRtVcq68mEpm5YBGxG/XitBv5QFKVYi2K851h9aOe3A3XJ/XoaD1wPlGjHdcnQOspm4S4swqRdpz129VUm6lEIUQ2YBAxTFOVPp6gWYWWinhRFOaAoSgHQCLUn2doqmva/zNWTEOJUYLOiKAuNwRZRM6KOslm4bwAOMxw3AjamqSyZxCZNjYD2f7MWbldfOV+PQojyqIL9FUVR3taCZT3ZoCjKH8BMVJ17LSFEvnbKeM+x+tDO10RVEeZyPXUH+gsh1qKqgXuj9uQzso6yWbgvAFpoM9UVUCcspqS5TJnAFEC35LgIeNcQfqFmDdIV2K6pIz4GThJC1NYsRk7SwnICTcf5LLBUUZQHDKdkPRkQQtQXQtTSflcGTkCdn5gBnKFFM9eTXn9nANMVdbZwCnCOZinSFGgBzE/NXUSLoig3KIrSSFGUJqjyZrqiKOeTqXWU7pnnJGet+6JaP6wGbkp3edJw/xOBX4D9qL2BS1F1ep8BK7X/dbS4AnhMq6vvgUJDOpegTuqsAi5O932FXEc9UIe83wGLtL++sp4S6qk98I1WT4uB/9PCm6EKnlXAm0BFLbySdrxKO9/MkNZNWv0tB05J971FVF/HUWotk5F1JN0PSCQSSQ6SzWoZiUQikdgghbtEIpHkIFK4SyQSSQ4ihbtEIpHkIFK4SyQSSQ4ihbtEIpHkIFK4SyQSSQ7y//LaVUTg79QpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_test,c='k',label='Actual')\n",
    "plt.plot(lin_pred,c='red',label='Predicted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='3.4'>3.4 Logistic Regression </a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='3.4.1'>3.4.1 Logistic Regression Validation Performance</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error >>  1778.72304337291\n",
      "mean_absolute_error >> 29.09571117034165\n"
     ]
    }
   ],
   "source": [
    "logistic_pred = logistic_model.predict(X_test)\n",
    "print(\"mean_squared_error >> \", mean_squared_error(y_test, logistic_pred))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(y_test, logistic_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='3.4.2'>3.4.2 Logistic Regression Validation Prediction vs Actual</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(y_test,c='k',label='Actual')\n",
    "plt.plot(logistic_pred,c='red',label='Predicted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import LinearRegression\n",
    "# linear_reg = LinearRegression()\n",
    "# linear_reg.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"acc of Linear Regressor >> \",linear_reg.score(testX, testY))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='testing'>4 Testing</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_model = pickle.load(open('forest_regression.sav', 'rb'))\n",
    "lin_model = pickle.load(open('linear_regression.sav', 'rb'))\n",
    "logistic_model = pickle.load(open('logistic_regression.sav', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>Section-0</th>\n",
       "      <th>Section-1</th>\n",
       "      <th>Section-2</th>\n",
       "      <th>Section-3</th>\n",
       "      <th>Section-4</th>\n",
       "      <th>Section-5</th>\n",
       "      <th>Section-6</th>\n",
       "      <th>Section-7</th>\n",
       "      <th>Section-8</th>\n",
       "      <th>Section-9</th>\n",
       "      <th>...</th>\n",
       "      <th>Section-16</th>\n",
       "      <th>Section-17</th>\n",
       "      <th>Section-18</th>\n",
       "      <th>Section-19</th>\n",
       "      <th>Section-20</th>\n",
       "      <th>Section-21</th>\n",
       "      <th>Section-22</th>\n",
       "      <th>Section-23</th>\n",
       "      <th>Section-24</th>\n",
       "      <th>Section-25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.02</td>\n",
       "      <td>1585.29</td>\n",
       "      <td>1398.21</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.72</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8125.55</td>\n",
       "      <td>8.4052</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.86</td>\n",
       "      <td>23.3735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.0027</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.71</td>\n",
       "      <td>1588.45</td>\n",
       "      <td>1395.42</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.16</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>8139.62</td>\n",
       "      <td>8.3803</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.02</td>\n",
       "      <td>23.3916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.46</td>\n",
       "      <td>1586.94</td>\n",
       "      <td>1401.34</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.97</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8130.10</td>\n",
       "      <td>8.4441</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.08</td>\n",
       "      <td>23.4166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.44</td>\n",
       "      <td>1584.12</td>\n",
       "      <td>1406.42</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.38</td>\n",
       "      <td>2388.05</td>\n",
       "      <td>8132.90</td>\n",
       "      <td>8.3917</td>\n",
       "      <td>0.03</td>\n",
       "      <td>391</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.3737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.51</td>\n",
       "      <td>1587.19</td>\n",
       "      <td>1401.92</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.15</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8129.54</td>\n",
       "      <td>8.4031</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.99</td>\n",
       "      <td>23.4130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Section-0  Section-1  Section-2  Section-3  Section-4  Section-5  \\\n",
       "0          1          1     0.0023     0.0003      100.0     518.67   \n",
       "1          1          2    -0.0027    -0.0003      100.0     518.67   \n",
       "2          1          3     0.0003     0.0001      100.0     518.67   \n",
       "3          1          4     0.0042     0.0000      100.0     518.67   \n",
       "4          1          5     0.0014     0.0000      100.0     518.67   \n",
       "\n",
       "   Section-6  Section-7  Section-8  Section-9  ...  Section-16  Section-17  \\\n",
       "0     643.02    1585.29    1398.21      14.62  ...      521.72     2388.03   \n",
       "1     641.71    1588.45    1395.42      14.62  ...      522.16     2388.06   \n",
       "2     642.46    1586.94    1401.34      14.62  ...      521.97     2388.03   \n",
       "3     642.44    1584.12    1406.42      14.62  ...      521.38     2388.05   \n",
       "4     642.51    1587.19    1401.92      14.62  ...      522.15     2388.03   \n",
       "\n",
       "   Section-18  Section-19  Section-20  Section-21  Section-22  Section-23  \\\n",
       "0     8125.55      8.4052        0.03         392        2388       100.0   \n",
       "1     8139.62      8.3803        0.03         393        2388       100.0   \n",
       "2     8130.10      8.4441        0.03         393        2388       100.0   \n",
       "3     8132.90      8.3917        0.03         391        2388       100.0   \n",
       "4     8129.54      8.4031        0.03         390        2388       100.0   \n",
       "\n",
       "   Section-24  Section-25  \n",
       "0       38.86     23.3735  \n",
       "1       39.02     23.3916  \n",
       "2       39.08     23.4166  \n",
       "3       39.00     23.3737  \n",
       "4       38.99     23.4130  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv(test_file, sep=\" \",header=None)\n",
    "df_test.drop(columns=[26,27],inplace=True)\n",
    "df_test.columns = columns\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>rul</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rul\n",
       "0  112\n",
       "1   98\n",
       "2   69\n",
       "3   82\n",
       "4   91"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rul = pd.read_csv(RUL_file, names=['rul'])\n",
    "df_rul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 31,  49, 126, 106,  98, 105, 160, 166,  55, 192,  83, 217, 195,\n",
       "        46,  76, 113, 165, 133, 135, 184, 148,  39, 130, 186,  48,  76,\n",
       "       140, 158, 171, 143, 196, 145,  50, 203, 198, 126, 121, 125,  37,\n",
       "       133, 123, 156, 172,  54, 152, 146,  73,  78, 303,  74, 144, 189,\n",
       "       164, 121, 113, 136, 160, 176,  94, 147, 159, 232, 155, 168,  71,\n",
       "       147,  71, 187,  54, 152,  68, 131, 112, 137,  88, 205, 162,  72,\n",
       "       101, 133, 213, 162,  73, 172,  34, 110,  56,  68, 177, 146, 234,\n",
       "       150, 244, 133,  89,  97, 134, 121,  97, 198])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RUL_name = [\"Section-1\"]\n",
    "RUL_data = df_test[RUL_name]\n",
    "MachineID_series = df_test[\"Section-0\"]\n",
    "grp = RUL_data.groupby(MachineID_series)\n",
    "max_cycles = np.array([max(grp.get_group(i)[\"Section-1\"]) for i in MachineID_series.unique()])\n",
    "max_cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.drop(df_test[[\"Section-0\",\n",
    "                \"Section-4\", # Operatinal Setting\n",
    "                \"Section-5\", # Sensor data\n",
    "                \"Section-9\", # Sensor data\n",
    "                \"Section-10\", # Sensor data\n",
    "                \"Section-14\",# Sensor data\n",
    "                \"Section-20\",# Sensor data\n",
    "                \"Section-22\",# Sensor data\n",
    "                \"Section-23\"]], axis=1 , inplace=True)\n",
    "#df_test = df_test.groupby([\"Section-0\"])\n",
    "#print(df_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = MinMaxScaler(feature_range=(0, 1))\n",
    "df_test = gen.fit_transform(df_test)\n",
    "df_test = pd.DataFrame(df_test)\n",
    "#df_test = df_test.rolling(20).mean()\n",
    "pt = PowerTransformer()\n",
    "df_test = pt.fit_transform(df_test)\n",
    "df_test=np.nan_to_num(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.7938502 ,  1.04936462,  1.00459956, ..., -0.43585161,\n",
       "        -0.26636316,  0.42059548],\n",
       "       [-1.75983242, -1.22056415, -1.03515135, ...,  0.37849296,\n",
       "         0.89958112,  0.64708667],\n",
       "       [-1.72609272,  0.14086219,  0.34489676, ...,  0.37849296,\n",
       "         1.36106152,  0.96601877],\n",
       "       ...,\n",
       "       [ 1.83603259, -0.26773967, -1.03515135, ...,  1.88097691,\n",
       "        -1.83688403, -1.50936271],\n",
       "       [ 1.84590292, -1.71931856,  0.34489676, ...,  1.88097691,\n",
       "        -1.59013861, -0.80455247],\n",
       "       [ 1.85572407,  0.59502963,  1.00459956, ...,  2.57907106,\n",
       "        -1.33736329, -1.7132478 ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='4.1'>4.1 Random Forest Testing</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_pred = forest_model.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_pred = np.array(forest_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_pred = forest_pred.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_pred = forest_pred.reshape(forest_pred.shape[0],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13096, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[186.22],\n",
       "       [220.59],\n",
       "       [192.87],\n",
       "       ...,\n",
       "       [ 12.55],\n",
       "       [  9.65],\n",
       "       [  6.66]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_forest_pred = []\n",
    "count = 0\n",
    "for i in range(100):\n",
    "    temp = 0\n",
    "    j = max_cycles[i] \n",
    "    while j>0:\n",
    "        temp = temp + forest_pred[count]\n",
    "        j=j-1\n",
    "        count=count+1\n",
    "    final_forest_pred.append(temp/max_cycles[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_forest_pred=np.array(final_forest_pred)\n",
    "final_forest_pred = final_forest_pred.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181.85838709677415"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_forest_pred[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,10))\n",
    "plt.plot(final_forest_pred,c='red',label='preduction')\n",
    "plt.plot(df_rul,c='blue',label='y_test')\n",
    "\n",
    "fig.suptitle('RUL Prediction using Random Forest Regressin Model', fontsize=35)\n",
    "plt.xlabel(\"Engine Number\", fontsize=35)\n",
    "plt.ylabel(\"Remaining Useful Life\", fontsize=35)\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error >>  3179.903974548361\n",
      "root mean_absolute_error >> 56.39063729510743\n",
      "mean_absolute_error >> 49.34931822665034\n",
      "scoring function >> 327970.27580986905\n"
     ]
    }
   ],
   "source": [
    "def scoring_function(actual,predicted):\n",
    "    d = []\n",
    "    for i in range(len(predicted)):\n",
    "        d.append((predicted[i] - actual[i]))\n",
    "    scores = []\n",
    "    for i in range(len(d)):\n",
    "        if d[i] >= 0:\n",
    "            scores.append(math.exp(d[i]/10) - 1)\n",
    "        else :\n",
    "            scores.append(math.exp((-1*d[i])/13) - 1)\n",
    "    return sum(scores)\n",
    "\n",
    "\n",
    "print(\"mean_squared_error >> \", mean_squared_error(df_rul,final_forest_pred))\n",
    "print(\"root mean_absolute_error >>\",math.sqrt(mean_squared_error(df_rul,final_forest_pred)))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(df_rul,final_forest_pred))\n",
    "print(\"scoring function >>\",scoring_function(np.array(df_rul),final_forest_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='4.2'>4.2 Linear Regressor Testing</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13096, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_pred = lin_model.predict(df_test)\n",
    "lin_pred = np.array(lin_pred)\n",
    "lin_pred = lin_pred.flatten()\n",
    "lin_pred = lin_pred.reshape(lin_pred.shape[0],1)\n",
    "lin_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_lin_pred = []\n",
    "count = 0\n",
    "for i in range(100):\n",
    "    temp = 0\n",
    "    j = max_cycles[i] \n",
    "    while j>0:\n",
    "        temp = temp + lin_pred[count]\n",
    "        j=j-1\n",
    "        count=count+1\n",
    "    final_lin_pred.append(temp/max_cycles[i])\n",
    "\n",
    "final_lin_pred=np.array(final_lin_pred)\n",
    "final_lin_pred = final_lin_pred.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4nlDx0U5HcazTeU18XUq5w24DIcQZoIkAgK77C6RNqKaUsgepITdfFmJMUuKw6i99P6/YlUvbj7Sqt3wS1nUbFioURQD4rP5GIjxXheYOgMRqL3weydClXgCfl1Ka9lkS/9elSIZ9FWNsKBaEENNBS90qfiClXG9Thu+DnKlu0MPJfm7XrwAAKeVdIJciQCG8n3H5PQzjChY6mFynTgjxmOH2lBBC5TPQO0+vAviElFJmpqi5gSCqhRBnCiGeQDJ+GyBL6DUOu7jDqYOeGGjrOQRucFm8O7THZssPflx7/LyU8g27nSU6wY/bbeMRPcb0NinlphD3HSZLkMz9AaQOkK1QyV0BGmic4eIzf3Cxjd7pPshyq8zTpz0+PAPff5eUssNhG/1YzhFCGHPrAMCHtcdvSimX2+1QStkHmpWOI7bnZ6KtWKa9ZHV+6oMbt3XZ7drjwEupJsTS1YmnFQDcrpZwk/MmgQmznY7iWKfrmtgNyl3ghP4b/yppRRpbpJQrQCExAE1MGFffCKv+0vdzqNBWDEsjYV23YfFXJNvGzxne+yCSky8PS/scMWbofZoHpZRbLLcEIKXcidR+0sdNNvsIaLIIoAmZmx32OQgXbbkQYg6AxYmnwxibx8UKp34dw/iGhQ4m1ykDNST67VRQJ0+d39sA/D8Ai6WUuzJRyCznWZG6tOYIKA72UQCna9v1AjhHSrndYX9uZouOQTLBaYeU8lWXZV2tPV5oMmulL0H7GNzxqPMmzgghpoLiXhVOzpdMcoz2eL3DbA4AQEq5DcDrFvuw4kUX22zVHrtxiWQKfQZRLb/oaSnXgHg9lgKpSWYVR2mPn3H53W63yyU2Oc2sJ7A9PxPLWOqz3K6OVaIebU88rRNC1NttL2iJ5guEENcJWrbzIaN4AFqVR+FmudkO0IoeURNKOx3hsU7XNfGStEkOqXGi9vhpD/vX28dFhvf0+utSIcQ3hBAV8I7eVh8CWvbccvnYCAjlug0TKaW+XOwcIYTuprtQe6wvR+tIQqheoL30L5cf1d1Q84QQEw3v6/2kF1w6x9z0k/TzdrWH/rbdecswgeBVV5h8oB40g8FODv88DUoUtdbFthtcbHOY9rgo0Un3SgmACaDOurKINmjvrzb5jBlut3PCOGvkVrzJBLog42Wg8xaAI0z2YYWbDmmP9thPxztd3AiarSsCJSX8B4DtiXP3OQBLpZQbI/x+x2MppewxaC9mx3OW9tjN9exlu1zCzbkJOJ+fel02AlrpwW0Z9NU0JoNWPEghMYi8HpR3wEufzUzkMrIpixyObtrpqI51uq4Jx7ZRCFGJ1DCa/xZCjAk/sEA/PpMN790HSjA6AyQu/S+AHwohngTlgloKWk7YNsRESrlFCHE/ko7MzwE4XwixFNRPWAoSdFyv0uGRsK7bsLkVSTfChQCWJQSGjyRe2w3vkyqzkLrindu2Wt9OgFbAeU17TW+73fZ/1oHCfIttttHPv+ke+nXl2mPjecswgWChg8l1NkspG9SThI1yGmiprctAcYgCFLc4Abx8lRkrQMmoFCOghJp7AawC8IybGX8NN8v36ssIjgPN8PmhCgmhA6lLtAJUfje43c6Jau3xkJvcHBlkovbYy+/XLdSOs2Ru4rcNpNMh4Qkp5etCiAtByweqQVM9KNfJxQAghNgMsqb/SUoZloCm8HosAfPjOVF73O5yP04hM7lIWMdTr8sKEKwuS/0yIQ4EDULrxmztTKmLbdK11HpY7XRUx3qi9rjd5T78XBNe20aAVvXwQ8pvlFL2CiHOAvAwgJmJl8eDQhtUeEObEOJxUCJju5DOS0AD0pMSzwsBnJy4AUC/EOIFUEjCHVLKfp+/wYywrtuweQC0PPF4AJ8UQlwOSrSprsM7XLp5dCYanrttq42hTsa2Wn/uap9SymEhRAdSlww2op+7tfB3fboRaBnGNRy6wsQKKeWIlHKblPJJKeVHAHxPe/vTQohLXexGT/TkprNoRJ85CjPRZVR8S0p5pnb7NynlJ6WUX5ZS/sGjyAEkY1XtGOe8iSv0Osz4X7ntEIXVCdO/P8yOXRToZfXScdS39XNt5DRSyjsAHAhabtnMljsbwNcAvCmE+GMiF00cCDORYNyIoi5DIpnoPUiKHAOgJYvPBQkEE0FLawp1AzmLvJCR/zVAOx3JsfaJn2OXqbYRAJDIWXUwgCtBK4UYmQQanD8mhHhBCDHbZBskRPxTQEk2X8DY31UK4DRQ7odmIcQpfn9ErpAI/1CJtatAq4/4DltJ4LdPY9zOuB+/7b9TvyaMczdrJzuY3ISFDibWSCl/ilS74M+FENVW2yfQZ2rG+/ha/TPZPKufSfRj/JLeWfd4a9H202n4jkqXZXG7nRPt2uNxiVCabEU//l5+v75tezhFyQrcrlABKeVmKeXlAKaCrPaXg/Kx6K4oAeALSE2yli3o/73b2bNszp2SafTjuSNAXdZk2O9ZSFrBBwGcKqW8WEp5v5RyrZSyw8QxFVZdllY8tNNRHetsuiaMTpEDff7GH5rtXErZJaW8Vkp5EEiY/RzIpWZMnH0CKH/XRIv9jEgp75BSngSayf8oKMRKLXOrmAXgUSHE0Sa7iRu6mPE9JN04q6WUr5ts74TxXPDbp2k3PNf7Sn7bfzP08t7l9/r0UB6GcYSFDiYfuByAsgxWgZaGs0O3/c2y3MqERHJCfRbEMVt6nrJTe+wm14MjkpYD1OOCG11+dL8wvh9jY4f3D2m/UbBbe+zl9+vJDndbbpV5dCeVXUyxYqLXL5DEm1LKX0spzwUwBbR6w5vaZh8TQpzgdd8Ro2ftd7sawYFRFCQm6HXZ1ESOhTDQVzW6Q9Ly2k5Md94ka3HTTkd1rLPpmtiDVIdEZO2IlHKLlPKvUsovSCn3AyWCfEDbpBGpq65Z7addSvmQlPK/pJRHgvpN14BW3gDIQXB1yMXPRp4DsDnx+FDtdT9uDmBsG+u2rZ5jeG7cj34dueonJYTHCQ6b6fvN5v4Pk0ew0MHEHinlu0hdCu7/JVbIsEJX3icIIbwOBHVHhx8VPx94SXtcK4QIa7lO/Xi7nUEKa6bpTQB69vKTrDb0gN7hDXOmQ09MtkAI4RiGkrDSH2mxj2xDj4V3cnABqUnUfCGlHJZSPgJKSKcLnH7zCETFCu2xW0t57K3nAXgFyQEdAHwgpP3qIvsKy60SJJZ29JPLIytw2U5Hdayz5pqQtHTtKu2ltC23mXAdnIvkCiKAj/pLSrlVSvltAD/RXj7ZTTuTy0gpJYDbDC8PI3V5Yy/72wlajUjhNl+LviJaJ8YmwfXTT3Kzyprer1sghDDmm2GYtMNCB5Mv/AzJ2aJyAN+02fYFw3OzdcitONvwfJnpVsxqpDbgXwlpv89rjz+eWJ7NksTg/ZNhfLGkRGNN2kuXhrD8qC6chJnvQT9O45HMDG/H6SDXgtk+so3N2uP5Lrb/WFhfLKXcg9TrPtsGn49ojxcIIWw7sInByUWRliiHkVJ2AHhZeymsukx3IrlZGeU/QvreTGLbTkd4rLPtmtBXq/isEMJPCK0vEoP1h7SXgtRff9ceF2FsotU4YhQ6npRSvh9gf3p/9DyXfYoLtMdL5diVdPS2+0AhhJs20k0i/+cA9CUeF4KSCzNMRmGhg8kLJC37qMfLf0kIMcVi2zVI7Ux9XQjhZNmDEKIKwNe1l15K7IsxkOhM/UJ76WIhxMkWm3tBt4jWgezQdlyC1CVpg/Jr7fEiBO+I6+EwoYT4AICUch2AF7WXfmQ325bIN6Jbj9+UUjrOMmcQfcbqU3YbCiGOBa36YItH0UqvL1ott8oMjwJ4T3v+e4eB1E+RGo7HjOUG7fEZQojPhLBPfXB0ot2GCTfHf4bwnRnFZTsdxbHOtmvit0gmfqwB8H9BdxhW/RVgP0Ae5CyTUr5ryDnxoYC7vFl7fAgop4olQohzkOq++JPJZk8hdaLJNqxICHEoAMfrTErZCcr3orgysXIUw2QMFjqYfOJqJEMBKmDv6tAtl/UAHhZCWC6rJYSYDOBfSJ39+InF5gzxOwDNicdFAP4hhPiY04eEEAcLIW4SQlxkfE9K+Q6AB7WXrhZCmA5ihRCnIbXTHBhJS/I9rb30SyHEl+06h0KI2TarDOjhIZ8UQtSHUc4E+vl5IIA7hRAVJuUrBVnKF1p8NhvR48xPFUKcb7aREOIwUCJRN23hG0KIC5xWUhFCfATJZRYB7ythRIqUchjAf2kvLQDwtBBigb6dEKJGCPFbAN8A5xpy4n6kzrz+RQjh6OgSQjQIIX4uhPiOydtN2uNPCCHOMNkGQoi5AB4HtWlxwKmdDv1YZ9s1IaXcBuB/tZcuEUL8zkXdUyWEuEwIcZfJ23cKIb5tNcGj7WMOUgV6Y/31rURZbHOZCCHGAfiR9tIrUspeq+0ZS55CakjIb4UQpuFMQojjkCqMvIlUdw6A0fP959pLHxZCXG12DSVW3vk73CfsvhrJnCATQNeRY54qIcTRQoi7hRCnu/wehnFFNq8KwDChIqVcJ4S4F8kZ3i8LIX4upRyTVFFK+c9Eh0Y1+CcB2CiEuB1k+9sBshNPS7x3PlIzUv9GSvkvD8X7rBDi0x5/T5nzVtmLlLJbCPFxkLOgKnF7UAjxMmiguhoUXzoOtMLFQlC8skryZZUj4nIAS0BZ8YtBAsq9iX2+Dwq/+ChohqIAwF2gJfXC4gJQ5vl6UOfgt6AwlrsAvJX4TZNAszOnJ8r6AoCbTPb1AEiMKQUlGtwohHgNqQnrdkkpPVtEpZSPCiH+CHK1ABSi9ZYQ4iaQI2IYFPbxRQDztI/eKaW81+v3pZnHQOePOlduSwhb/wCwF3Q+nQngs6Bz5DY4zJSB8nj8FcDvhBCPgvIFNINmKQtBSd3OAh1H1WFcAeCJcH5SeEgp70kcD/XfHw3gdSHEu6CZ7SrQf6/6CP8B4GFtF16WJHTLE0IIL0t2/lVKeYnzZtEjpZRCiE+CzomZAEoA/AHA5UKIe0DXUxsoHGMKaLWek5GMj7/WZLf3gEI5poHOr38JIf4CCrPYC2AyKGHphaAlzd8AuQByenULp3Y6omOdjdfEDwAcAaqnAOBLAM5J9EFeBC1vXQDKQXQwgMWg9rEEqY5UxVTQMf2xEKIJFF73FqgtGUayL3MRksuEdiLVpQjQcf0SyG3zBkjYfx2UiLInUZ4jAFwM+n8U2S6OZyWJ8/0iACtBYaYVoLryLpCI8T6oLvgwqA1TgkQfgM8lRA0zbgSFo6i8H98GcIoQ4mYA60HnwEmg/3oC6HyZDWCGQ3m3CyE+BWqDS0D9oBeEEE+BrpdmAF2g/vIMkPP1g0gmRf2L40FhGC9IKfnGt5y6gWaXZeLW4vGzh4IGiOrz19psWwhaLk16vF0PoMBFWVp87Hv0FuD4LTHsa0kI/4m+vwaPnz0Q1Ph5PQZfstnnMaBOmtM+ngCFhDgeV8N590OH3zQbwNsefkuTzb4uBXVErT475hpw+3+AOu23eSjnfQBKbPbX4PUcNZyPnq5nh/0uAi135/Sbbgd1smzL7eP8fAfATIt9XeTyv2/RtnN1nXr47wtAIprdudUNEu7KDK+PC+H/WWLzvW5ut3g9Vm6Pu+EzP7T6TpNt60GDTK+/5RqbY9Tn4vNbQPVYk/baRWEdA5//7y3a93i6ruGinQ77WEd1TcBDu2Hy2RJQKIDX3/iSyb6aPO5jH2hJY7vrwe3tv21+Y4u2XUauWw//h17WK9K5P5Ag0eryeHdaHUvDPmvhrp+yGZQc2fG/0vZ9PJITgl5uZ/o9T/jGN7Mbh64weYWUcjVSE2T9P6uQFEmrKHwDFB/9KJJJ0swYAinYJ0opvyHHJn9iLJAUbnI4yA682WHzLtCswHlIzdBv3OfLIPux1Wx6J8hi+SHY/6++kFJuBs1qfRPAdptNh0HW4Ott9nUTaHbxJiRdLqGcX1LKISnl50AzfetsNt0Imi36hJQyitn80JFSvgaakTKb3QRoRvQKKeX5oM6TE18FhQh0O2y3CzR7eYSU8j2HbTOGlHJESvmfoA70n0D/cS9oNvwN0PKQh0op/4bUJLS9UkqnY5CXSCm3gzr4nwew1mHzfpAt/RJQzgez/TWBzuFVNvu4HcACSauWxAI37XTYxzqxz6y6JqSUA1LKz4OcGs8gdcWZMZuDnBXfB/AJk/evBXAnnJcF7wU51w6VUj5t8v69IJeH0/kmQau3nCilNHXRMO6RUr4EcoHejGTCTyMDoPrg0ETd4bTPPaBz/TcwdyQNgSY3jpBSbjF5327fy0CTWD8GtYl2tAK4G5Qr60kv38MwTggp3fTvGIZJJCc7DjRbr5asbAUNzl+UUnZlqmxxQggxDyQSTAFZNbtBMwPvAFgtpRz0uL9G0GBhGmiWajOAZ6SUPWGW2+b7BUh0mQ/6TcUA2kEdxRVSyrZ0lMMNibjro5DMNbMLwGtSyrcyV6rgJJKpLQbNYLWDltt71uu5lNhXESiM5QDQrPI40EBqD8gKvkrSCjyxQQhxLmiAA9Bs8eJMlidXEELMAg0k6kChDz2g82QdKKGv65wFQoiFIKdaNciptBXAc1LK9pCLnZOEeaxdfl/arwkhxEQAJ4As/9WggWgbqC15U0q51+V+5oLCXWaBwhJkYj/vAFjpti8jhKgDtWuNifIUgoT4FlDbtsP604xfErm0loAclBNB9cEWkNtln+UH7fc5ERRKOwt0PmwF8HwY/2GiD3Q4qN2cDAp/6gJNAq0FsIYnB5moYKGDYRiGYRhLEjlJVK6A/0s43Rgmb+FrgmEYJvvh0BWGYRiGyTOcVqnQtrsYyQEdYBMyxjC5DF8TDMMw8YJXXWEYhmGY/OM3QohB0KpDLxutw4llBb8Byk2ieDDXw5gYxga+JhiGYWIEh64wDMMwTJ4hhPgbaFlsgPIZNINyDhWDcgA0Gj6yCcAx0mQ5boaJA3xNMAzDxAt2dDAMwzBM/qHPVleAEuZa8RSA83lAx8QcviYYhmFiBDs6GIZhGCbPEEKUg5ZXPg2UEX8KaFWacaCVabYBWArgXinl8xkqJsOkDb4mGIZh4gULHQzDMAzDMAzDMAzDxAZedYVhGIZhGIZhGIZhmNjAQgfDMAzDMAzDMAzDMLGBhQ6GYRiGYRiGYRiGYWIDCx0MwzAMwzAMwzAMw8QGFjoYhmEYhmEYhmEYhokNLHQwDMMwDMMwDMMwDBMbWOhgGIZhGIZhGIZhGCY2sNDBMAzDMAzDMAzDMExsYKGDYRiGYRiGYRiGYZjYwEIHwzAMwzAMwzAMwzCxgYUOhmEYhmEYhmEYhmFiAwsdDMMwDMMwDMMwDMPEBhY6GIZhGIZhGIZhGIaJDSx0MAzDMAzDMAzDMAwTG1joYBiGYRiGYRiGYRgmNrDQwTAMwzAMwzAMwzBMbGChg2EYhmEYhmEYhmGY2MBCB8MwDMMwDMMwDMMwsYGFDoZhGIZhGIZhGIZhYgMLHQzDMAzDMAzDMAzDxAYWOhiGYRiGYRiGYRiGiQ0sdDAMwzAMwzAMwzAMExtY6GAYhmEYhmEYhmEYJjaw0MEwDMMwDMMwDMMwTGxgoYNhGIZhGIZhGIZhmNjAQgfDMAzDMAzDMAzDMLGBhQ6GYRiGYRiGYRiGYWIDCx0MwzAMwzAMwzAMw8QGFjoYhmEYhmEYhmEYhokNLHQwDMMwDMMwDMMwDBMbWOhgGIZhGIZhGIZhGCY2sNDBMAzDMAzDMAzDMExsYKGDYRiGYRiGYRiGYZjYwEIHwzAMwzAMwzAMwzCxgYUOhmEYhmEYhmEYhmFiAwsdDMMwDMMwDMMwDMPEhqJMFyCbqa2tlQ0NDZkuhme6u7sxbty4TBeDYSKBz28m7vA5zsQZPr+ZuMPnOBNnsvH8fvXVV/dIKScbX2ehw4aGhgasXLky08XwTFNTE5YsWZLpYjBMJPD5zcQdPseZOMPnNxN3+Bxn4kw2nt9CiM1mr3PoCsMwDMMwDMMwDMMwsYGFDoZhGIZhGIZhGIZhYgMLHQzDMAzDMAzDMAzDxAbO0eGRwcFBbN26FX19fZkuiiVVVVVYu3ZtpovhmrKyMsyYMQPFxcWZLgrDMAzDMAzDMAyT47DQ4ZGtW7eisrISDQ0NEEJkujim7Nu3D5WVlZkuhiuklNi7dy+2bt2KxsbGTBeHYRiGYRiGYRiGyXE4dMUjfX19qKmpyVqRI9cQQqCmpiarHTIMwzAMwzAMwzBM7sBChw9Y5AgXPp4MwzAMwzAMwzBMWLDQwTAMwzAMwzAMwzBMbGChI89pamrCWWedBQB46KGHcM0111hu297ejt/+9rejz7dv345zzz038jIyDMMwDMMwDMMwjFtY6Igpw8PDnj/zkY98BFdeeaXl+0aho76+Hvfdd5+v8jEMwzAMwzAMwzBMFLDQkYO0tLTgwAMPxIUXXoj58+fj3HPPRU9PDxoaGvCjH/0IZ5xxBu6991488cQTWLx4MRYtWoRPfOIT6OrqAgA89thjOPDAA3HCCSfggQceGN3vLbfcgq9+9asAgJ07d+LjH/84Dj/8cBx++OF48cUXceWVV2LDhg1YsGABvvnNb6KlpQWHHnooAErS+h//8R847LDDsHDhQjz77LOj+zz77LNx5plnYv/998e3vvWtNB8thmEYhmEYhmEYJp/g5WWDcMUVwKpV4e5zwQLgF79w3GzdunX485//jOOPPx4XX3zxqNOirKwMTzzxBPr7+3H22Wfjqaeewrhx43Dttdfi//7v//Ctb30Ll1xyCZ555hnMnTsXn/rUp0z3f/nll+Pkk0/Ggw8+iOHhYXR1deGaa67B6tWrsSrxm1taWka3/81vfgMAeOutt/DOO+/gjDPOQHNzMwBg1apVeP3111FaWop58+bhsssuw8yZM30fIoZhGIZhGIZhGIaxgh0dOcrMmTNx/PHHAwAuuOACLF26FABGhYuXXnoJa9aswfHHH48FCxbg1ltvxebNm/HOO++gsbER+++/P4QQuOCCC0z3/8wzz+DLX/4yAKCwsBBVVVW25Vm6dCk++9nPAgAOPPBAzJ49e1ToOPXUU1FVVYWysjIcfPDB2Lx5c/ADwDAMwzAMwzAMwzAmsKMjCC6cF1FhXJJVPR83bhwAQEqJ008/HXfeeWfKdqtWrYpkOVcppeV7paWlo48LCwsxNDQU+vczDMMwDMMwDMMwDMCOjpxly5YtWL58OQDgzjvvxAknnJDy/rHHHotly5bh3XffBQD09PSgubkZBx54IDZt2oQNGzaMftaMU089Fb/73e8AUGLTzs5OVFZWYt++fabbn3TSSbj99tsBAM3NzdiyZQvmzZsX/IcyDMMwDMMwDMMwjAdY6MhRDjroINx6662YP38+WltbR8NMFJMnT8Ytt9yC8847D/Pnz8exxx6Ld955B2VlZbjpppvw4Q9/GCeccAJmz55tuv9f/vKXePbZZ3HYYYfhiCOOwNtvv42amhocf/zxOPTQQ/HNb34zZfuvfOUrGB4exmGHHYZPfepTuOWWW1KcHAzDMAzDMAzDMAyTDoRdyEG+c+SRR8qVK1emvLZ27VocdNBBGSoR0dLSgrPOOgurV682fX/fvn2orKxMc6mCkQ3HlckNmpqasGTJkkwXg2Eig89xJs7w+c3EHT7HmTiTjee3EOJVKeWRxtfZ0cEwDMMwDMMwDMMwTGxgoSMHaWhosHRzMAzDMAzDMAzDMEw+w0IHwzAMwzAMwzAMwzCxgYUOhmEYhmEYhmEYhmFiAwsdDMMwDMMwDMMwDMPEBhY6GIZhGIZhGIaJHw88AHzta5kuBcMwGYCFjhjT0tKCO+64w/fnr7766hBLwzAMwzAMwzBp5J//BP70p0yXgmGYDMBCR4xhoYNhGIZhGIbJW7q6gJ4eoK8v0yVhGCbN5KzQIYS4WQixSwixWnvtbiHEqsStRQixKvF6gxCiV3vv9xkreAh8//vfxy9/+cvR59/97nfxq1/9asx2V155JV544QUsWLAAN9xwA4aHh/HNb34TRx11FObPn48//OEPAID3338fJ510EhYsWIBDDz0UL7zwAq688kr09vZiwYIFOP/889P22xiGYRiGYRgmFLq76b61NbPlYBgm7RRlugABuAXAjQBuUy9IKT+lHgshrgfQoW2/QUq5IMwCXHEFsGpVmHsEFiwAfvEL+20+//nP4+yzz8bXvvY1jIyM4K677sIrr7wyZrtrrrkG//u//4t//vOfAICbbroJVVVVWLFiBfr7+3H88cfjjDPOwAMPPIAPfvCD+O53v4vh4WH09PTgxBNPxI033ohVYf9AhmEYhmEYhkkHXV10v3cvUF+f2bIwDJNWclbokFI+L4RoMHtPCCEAfBLAB9JaqDTR0NCAmpoavP7669i5cycWLlyImpoax8898cQTePPNN3HfffcBADo6OrB+/XocddRRuPjiizE4OIiPfexjWLBgQcS/gGEYhmEYhmEiRjk69u7NbDkYhkk7OSt0OHAigJ1SyvXaa41CiNcBdAL4npTyBbMPCiEuBXApANTV1aGpqSnl/aqqKuzbtw8A8OMfh19wAEjs3pbzzz8fN910E3bt2oXzzjtvtEwAMDw8jH379qGnpwdDQ0Oj7w0ODuLaa6/FaaedNmZ/jzzyCB5//HGcf/75uPzyy/GZz3wmURYXhQmBvr6+MceaYczo6uric4WJNXyOM3GGz28mnRy9axcqAKx+/nnsSdN38jnOxJlcOr/jKnScB+BO7fn7AGZJKfcKIY4A8HchxCFSyk7jB6WUNwG4CQCOPPJIuWTJkpT3165di8rKysgK7pbPfOYz+NnPfobBwUHcc889KCwsHH1v3759qKysRF1dHXp7e0fL++EPfxi33norzjrrLBQXF6O5uRnTp0/Hnj17sN9+++Gyyy7D8PDw6G8sLi5GWVkZiouLI/89ZWVlWLhwYeTfw+Q+TU1NMF6XDBMn+Bxn4gyf30xaGRkBABw6bRqQpvOOz3EmzuTS+R07oUMIUQTgbABHqNeklP0A+hOPXxVCbABwAICVGSlkCJSUlOCUU07BxIkTU0QOnfnz56OoqAiHH344LrroInzta19DS0sLFi1aBCklJk+ejL///e9oamrCddddh+LiYowfPx633UZpTy699FLMnz8fixYtwu23357On8cwDMMwDMMwweDQFYbJW2IndAA4DcA7Usqt6gUhxGQArVLKYSHEfgD2B7AxUwUMg5GREbz00ku49957LbcpLi7G008/nfLa1VdfPWbZ2AsvvBAXXnjhmM9fe+21uPbaa8MpMMMwDMMwDMOkCylTk5EyDJNX5PLysncCWA5gnhBiqxDi84m3Po3UsBUAOAnAm0KINwDcB+BLUsqcXWdqzZo1mDt3Lk499VTsv//+mS4OwzAMwzAMw2QXAwPA0BA95uVlGSbvyFlHh5TyPIvXLzJ57X4A90ddpnRx8MEHY+PGpCHlrbfewmc/+9nR5yMjIygvL8fLL7+cieIxDMMwDMMwTGZRYSsAOzoYJg/JWaGDSXLYYYdh1apVo89VMlKGYRiGYRiGyUtU2ArAQgfD5CE5G7qSSaSUmS5CrODjyTAMwzAMw4SKcnQIwaErDJOHsNDhkbKyMuzdu5cH5yEhpcTevXtRVlaW6aIwDMMwDMMwcUE5OqZNY0cHw+QhHLrikRkzZmDr1q3YvXt3potiSV9fX04JB2VlZZgxY0ami8EwDMMwDMPEBSV0zJoFrFxJq7AIkdkyMQyTNljo8EhxcTEaGxszXQxbmpqasHDhwkwXg2EYhmEYhmEygwpdmTULeOklEj44hx3D5A0cusIwDMMwQWhrAy67DOjry3RJGIZhGIVydMyeTfccvsIweQULHQzDMAwThOefB268EXj99UyXhGEYhlGw0MEweQ0LHQzDMAwThN5eumdHB8MwTPagh64ALHQwTJ7BQkfcGBhAkb5uOMMwDBMtSuhQ9wzDMEzm0ZORArzELMPkGZyMNG4cfzwOKigAzjor0yVhGIbJD9jRwTAMk310dwPFxcDUqfScHR0Mk1ewoyNuTJmCkra2TJeCYRgmf+jpoXsWOhiGYbKHri5g/Higupqes9DBMHkFCx1xo66OhQ6GYZh0wqErDMMw2UdXFzBuHLk6Kis5dIVh8gwWOuJGXR2K29qAkZFMl4RhGCY/4NAVhmGY7KO7mxwdAFBTw44OhskzWOiIG3V1KBgeBtjVwTAMkx7Y0cEwucu99wILF/IEURxRoSsACx0Mk4ew0BE36urofufOzJaDYRgmX2BHB8PkLq+9BqxaxddvHFGhKwDl6eDQFYbJK1joiBssdDAMw6QXdnQwTO7S3U33fP3GDw5dYZi8hoWOuMFCB8MwTHphRwfD5C4sdMQXDl1hmLyGhY64wUIHwzBMemFHB8PkLix0xJfu7tTQlfZ2YHg4o0ViGCZ9sNARN6qrIQsKWOhgGIZJF+zoYJjchYWO+GJ0dEhJYgfDMHkBCx1xo6AAA5MmsdDBMAyTLljoYJjchYWO+KInI62poXsOX2GYvIGFjhjCQgfDMEwa4dAVhsldWOiIJwMDwNBQqqMDYKGDYfIIFjpiyCALHQzDMOmDHR0Mk7uw0BFPurroXgkd1dV0z0vMMkzewEJHDGFHB8MwTBphRwfD5C5qQMzXb7xQ/yuHrjBM3sJCRwwZFTqkzHRRGIZh4g87Ohgmd2FHRzxR/yuHrjBM3sJCRwwZqK6m2MSOjkwXhWEYJv6wo4NhchcWOuKJMXSlqgooKODQFYbJI1joiCGDkybRAw5fYRiGiZ6eHrpnRwfD5BYjIyxUxhVj6EpBATBpEjs6GCaPYKEjhgyw0MEwDJMeBgeB4WF6zAMlhsktlEgJ8PUbN4yhKwCFr7DQwTB5AwsdMYSFDoZhmDShD47Y0cEwuYUaDAMsdMQNo6MDoJVXWOjwT0sL8OyzmS4Fw7iGhY4YwkIHwzBMmlCDo7IyFjoYJtdgoSO+WDk6OEeHf37+c+CcczJdCoZxDQsdMWRQJVxioYNhGCZa1OBo0iQeKDFMrsFCR3wxJiMFOHQlKHv2AG1ttOABw+QALHTEkcJCoLaWhQ6GYZioUYOj6mpgaIhuDMPkBix0xBez0BUWOoLR3k73fAyZHIGFjrhSV8dCB8MwTNTojg6Aw1cYJpdgoSO+dHcDxcVASUnytepqer2/P3PlymWU0LF7d0aLwfigqysvnTgsdMQVFjoYhmGiR3d0ACx0MEwuwUJHfOnqSnVzAOToADhPh1/a2uh+z57MloPxzuLFwI9+lOlSpB0WOuIKCx0MwzDRY3R08GCJYXIHPbyBr9140dWVmp8DSAodHHrhD+XoYKEj93j3XWDjxkyXIu2w0BFXlNAhZaZLwjAME1/Y0cEwuYtydNTWstARN7q7xwodqp5mR4d3pOTQlVxlYID6Jvv2ZbokaYeFjrhSV0eNtpqtYBiGYcKHHR0Mk7voQgeLlPHCLnSFHR3e6e5OJttmR0du0dFB952dmS1HBmChI67U1dE9h68wDMNEBycjZZjchR0d8cXM0cFCh3+UmwNgoSPXUEIHOzqY2MBCB8MwTPRw6ArD5C7d3UBRETBhAgsdccMsR4eqp1no8I4udHDoSm7BQgcTO1joYBiGiR4OXWGY3KW7m8Ibysv52o0bZqEr48bRcrOco8M7asUVgB0duQaHrjCxg4UOhmGY6OnpoXt2dDBM7sFCR3wxC10RgsJX2NHhHeXoqK9noSPXYEcHEzsmT6Z7FjoYhmGio7eXOs8TJiSfMwyTG7DQEV/MHB0ACx1+UULH/vtz6EquoYSO3t5kQtk8gYWOuFJcTJU5Cx0MwzDR0dtLg6TycnrOjg6GyR3UrD8LHfHDLEcHQO47Dl3xjgpdmTuXHB1SZrY8jHuU0AHknauDhY44M3UqCx0MwzBR0tsLVFQkhQ4eLDFM7qA7OoaHgcHBTJeICYOBAZq5NhM62NHhD+XomDOHjm9XV0aLw3iAhQ4mltTVsdDBMAwTJcrRUVZGz9nRwTC5gy50ACxUxgU1COfQlfBobwcqK4Fp0+g5h6/kDrrQkWcJSVnoiDMsdDAMw0SLMXSFB0pMHPnMZ4Af/CDTpQgflceBr9940d1N93ahKxx64Y22NmDiRKC2lp5zQtLcIY8dHUWZLgATISx0MAzDRIsSOoqLKSkpOzqYOLJsWTzzGrCjI54oR4dV6MrAgPmqLIw17e0sdOQqeSx0sKMjztTVUWWvlj9kGIZhwkUJHUJQ+AoLHYwdbW00yMo1OjpSO8txgYWOeOIUugJw+IpX2tqASZOSqzpy6Eru0NFBucQADl1hYkRdHd2zq4NhGCYalNAB8MoNjDMLFwI/+1mmS+GNkRHqHLPQweQKTqErAAsdXmFHR+7S0QHMmEGP2dHBxAYWOhgmPXCsb/6iCx3s6GDsGB4GNm8GVq3KdEm8sW8f1XFxEzpGRsjxykJH/HDj6IhjKFaUKKFjwgQK1WShI3fo6ABmzqTH7OhgYgMLHQwTPQ8/zPlw8hl2dDBuUTNpmzZlthxeUQJH3IQOda2y0BE/nHJ0AOzo8IoKXRGCXB0cupI75LGjI7RkpEKI8QCOA7AIwGQAVQCElPLzYX0H4xEWOhgmet56ixr8W24B/vu/M10aJt2wo4NxixIKNm0ih4QQmS2PW1S5u7vJlVJYmNnyhIUKb2ChI35w6Eq4DA+TE2DiRHpeW8uOjlyio4P+s9LSvBM6Ajs6hBDThRB/ALATwKMAfgrgCgAXA7jI4jNPCSG2CyG2CSH2D1oGxoIpU+iehQ6GiQ5lf/3Tn8gKzeQX7Ohg3KIEg85Omh3NFXQnR5xsz/pgmIWOeMGhK+GirvtJk+iehY7cYXCQ6rWqKqCyMl51uAsCCR1CiLMAvAngCwDKAQjtZsdtAKYmbhcGKQNjQ0kJVUosdDBMdKgBy7vvAk1NGS0KkwHY0cG4RRcMWloyVgzP6OWOU/gKOzrii52jo6SEXmdHh3tUP0c5OiZP5tCVXEHV2VVVlF+FHR3uEEKcBuA+ABNBwsYAgKcA/ALABoeP3wsgUQvhY37LwLiAcwcwTLS0tgJz55KoeNNNmS4Nk27Y0cG4RRcJcilPBwsdTK7R1UUJM0tKzN+vqWGhwwvt7XTPoSu5hy50sKPDHUKIcgB/AaBqkH8B2E9KeYaU8usA3rX7vJSyF8ATIIHkICFEnZ9yMC5goYNhoqWtDaivBz73OeDBB3mWI58YHgYGBtjRwbgjV4UONcgBWOhgcoOuLvOwFUV1NYeueEHVAXroSlsbMDSUsSIxLjEKHezocMXFAKYDkCDB4iNSyu0e9/Gy9vgwn+VgnGChg2GipbWVOk2XXEKD3ttuy3SJmHShBkUsdDBuUB3OgoLcEjri6ujQ8ziw0BEvurvNw1YU7OjwhlnoipQsFuUCHLrii3/XHl8mpZQ+9rFOe7yfz3IwTrDQwTDRopZcO+QQ4LjjgD/+kToATPwxCh0cusLYoTqc8+Zxjo5sQHd0lJbSKjh8/cYDJ0cHCx3eMAtdATh8JRfg0BVfHJK4b5ZS2oap2KCnHK/yuQ/Gibo6Osl5lpFhokE5OgBydaxbB7zwQmbLxKQHNSiqqKB7dnQwdnR0UN6AAw/MPUdHQUHycVzQhQ4h6PploSMedHXZOzqqq1no8IJZ6ArAQkcuwI4OX0wGha1sC+m7Ay9zy1hQl0h/smtXZsvBMHGkvx/o6Uk2/p/8JDUmf/xjZsvFpAd2dDBe6Oig+qGxkRwdueL86uigPERAvGYDdaED4Os3TrgJXWlr4yXh3dLWRmKnOqaTJ9M95yTLftjR4YtEYCMqAnx3vfaYZdWoUEIHh68wTPiouFXl6KioAC64ALj3Xo5dzQc4RwfjBV3o6O3NnXa5o4P6EsXF8XV0ACx0xAk3oStSpibaZaxpb6ewFSHoOTs6cgej0NHTQ4nU8wS/QscO0IopBwuhznrPnKQ9bvG5D8YJFjryj3vuAX70o0yXIj9QYoYSOgAKX+nvB/7618yUiUkfZo6O/n6eJWTM0YUOIHfCV9rbqdxVVfETOgoLk0uQstARH5wcHarN5vAVd7S3J52rAAsduURHB9VtxcUUugIkEzHnAX6FjqWJ+0oAZ3n9sBBiIoDzEk8HACzzWQ7GCRY68o8//pGEDrYURo9ydOgdgMMPB44+mpOS5gNmjg6AxA6GMWIUOnIlIakqdxyFDpWfA2ChI0445eioqaF7dl66o60tmYgUoOS9lZXcz8wFVP0N0H8G5FX4il+h40Ht8fVCiEqPn78ZJJJIAI9IKblliQoWOvKP5maypf3jH5kuSfwxc3QA5Op4+21g+fL0l4lJH1ZCB4evMGaoDufs2fQ8VxwdcRY69MEwCx3xwU3oCsCODreo0BWd2lp2dOQCutChHB15lJDUl9AhpXwcwIug8JU5AJ4VQhzg9DkhxAwhxL8AfFTtCsCP/ZSBcUlZGZ3YLHTkB729wHvv0eP77stsWfIBJXTojg4A+PSnqQPNSUnjjVnoiv46w+ioDue4ccCUKbkldEycGE+hQx8Ms9ARH9wkIwVY6HCLMXQFYKEjVzBzdLDQ4YpLkFwidiGAt4QQfxdCXA5gqtpICHG6EOIrQoj7AbwL4EyQQCIB/FRKucrPlwshbhZC7BJCrNZe+6EQYpsQYlXi9m/ae98WQrwrhFgnhPign+/MWerqWOjIFzZsoHCJxkbg6afZlhk1xmSkivHjgfPPB+6+m5OdxRl2dDBe0DucjY25IXQMD1OnOK6ODhY64sfAADA4aO/oUG0295HcYQxdAWjlFQ5d8U9rK/DWW9F/D4eu+ENKuRbAv4NWTBEAihPPbwAwHyRkCACPAfg1gI8BKEm8BgB/lFL+wO/3A7gFJJoYuUFKuSBxewQAhBAHA/g0gEMSn/mtEKIwwHfnFix05A/r19P9t74FDA0BDz2U2fLEndZWiu9WjYjOJZdQp/n229NfLiY9sKODccvISFIwAJJLzGY7auaPhQ4mV1CJFu0cHWoFEXZ0uINDV8JlaAj40IeAU06JPpcbh674R0r5IkjU+AeSwoa+Cov+mnp9F4BLpJRfCvjdzwNwK8V+FMBdUsp+KeUmkLPk6CDfn1Ow0JE/KKHjvPOAWbM4fMWOxx4DVq923s4ONctRYFKVHnEEsGgRcNNNnJQ0rrCjg3HLvn1UD6gOZ0MDsGVL9i/zpxxpcRQ6jHkcWOiIB2rZYDuho7CQQjFY6HCmv5+uC2PoyuTJLHT45Wc/A155hc4/5QyOijx3dBQF3YGUcgeAjydydFwA4EQAhwGYCBJShkDixksgd8ftEScf/aoQ4nMAVgL4hpSyDcD0xPcrtiZeG4MQ4lIAlwJAXV0dmpqaIixqNHR1daWUe/+hIUzZtg3LcvC3MN6Y99xzqJk0CS++/jrmHHMMpv/971j2z39i2K7BzzGM57cfijs6sPgTn8CeE07Amv/5H9/7OWjtWkwoL8fLFuWZdvLJmHfDDXj197/HvoMO8v09THYy6+23sR+A51eswEhpKSY1N+NwAK+/+CI6AsyYhHGOM9lF6c6dWAxg3Y4deL+pCdMGBjBvcBDL77sP/SppeBYy7t13cRSA1e+9h/FtbZi9bx+ee+YZc3HXJdlyfh+5axd6S0rwdqIs89rbUd3RgeVZUDbGPxWbN+NoAGs2b8Yum//y6PJydK1dizUR/N/Zco6HQXFrK44H0LxrF7Zrv2lWZyf26+nB8489hhEl8jOOjF+3DouuugoDU6agbNcurLzvPnQd4Jjm0jcntrVhe2cnNjQ1obijA8cDWP/aa9jW0OB7nzl1fkspI7mBHBwVUe0/8R0NAFZrz+sAFIIElp8CuDnx+m8AXKBt92cA5zjt/4gjjpC5yLPPPpv6wlVXSQlIOTCQkfIwaeSkk6Q84QR6/OKL9L//7W+ZLVPIjDm//fCzn9Gx+cAHgu3nzDOlPPJI6/c7OqSsqJDyC18I9j1MdvI//0Pn0cgIPX/hBXr+5JOBdhvKOc5kF2++SefGPffQ8yeeoOdNTZktlxPPPZc8p6+7jh53dATaZdac342NUl5wQfL5V78q5aRJmSsPEw4rVtB5+vDD9tsdc4yUp58eSRGy5hwPg3feoeN5++2pr//xj/T65s2ZKVcu0tMj5UEHSVlfL+VTT9Hxu//+6L5vYIC+46qr6HlfHz3/6U8D7TYbz28AK6XJWN5SkhdCPJC4fc+ngCKllD1+PusXKeVOKeWwlHIEwB+RDE/ZCmCmtukMANvTWbaMomaLdu3KbDmY6GluBpQyfMwxwPTpHL5iZGgI+O1v6XHQRFptbWMTkepMmEBhRHfemVcxkXlDTw+Fq4hEZCbn6GCsUCEfeo4OIPvzdKhyq1VX9NdynXzI0dHRASxcCKxalemSpA83OToAWnmFQ1ecUaEVZqErAIeveOF73wPWrgVuvplCm4Fok1KrEBVVd5eWAsXFeRW6Yuc9/Bgot8VxxjeEEBsTt5ujKpgfhBDTtKcfB6AC8B8C8GkhRKkQohHA/gBeSXf5MoYSOjhPR7zZtw/YsQPYf396XlAAnHMO8OijPMjW+cc/aAne6dODN9CtrfZCB0BJSbu7Sexg4kVvb1LcADhHB2ONUeiYNYsEsmxfeUUvdz4IHX198cqp1NxMIsdzz2W6JOlDCR12q64A1Haz0OGMytNjlowU4JVX3NLUBNxwA/CVrwAf/CAJR1VV0YrdxnYHoAm4PBoT+A2ybAAwG9oysulGCHEngOUA5gkhtgohPg/g50KIt4QQbwI4BcB/AoCU8m0A9wBYA8oT8v+klFmeASxEWOjID959l+6V0AEA555LiaQeeSQzZcpGfv1rYPZs4DOfIaEjSKe2rW3sLIeRo48G5s+npKRMvGChg3GLscNZUkJiKwsdmUFKcmQZhQ4gXtevWj71vfcyW4504iYZKUCODl5e1hknoYMdHc50dgIXXQTMmQP8/OfJ1xsa0i90VFbmlaPDLhnpMEgICbQyS1RIKc8zefnPNtv/FJS3I/9goSM/aG6mez2p0XHHAVOnUvjKpz6VmXJlE2++STNb115LWdcHB0nZVktueUFK59AVgGZtL70U+OpXgddfJxsxEw96e4GKiuRzDl1hrDDrcDY2Zr/QYVx1BYiH0NHbS3W4mdBhFDBzGeVYyCehw62jo6aG2v+BARIeGXM4dCU4V1xB1+DSpannZUMDsGFDdN/Ljg5bEUO1ZNNstmFyARY68gO1tOycOcnXCguBs88mR4ea5chnbryROrBf+EJw2+W+fbQ0pJOjA0iKTI8+6u+7mOyEHR2MW3JV6OjooLju0tJ4CR1ms/7q+o2TUJnPQocbRwfArg4nrBwdEydSiDSHrtjzj38Af/kLcOWVwOLFqe81NFAbEFW4nJWjg4UOAMA7oJVTDhVCHJ+m8jBRMG4c3VjoiDfNzcDMmakzzACFr/T0AI89lplyZQutrcDf/gacfz65MILORqjOkZOjAyBR5bDDKEaTiQ9GoYMdHYwVHR1AUVHq+dLYCGzbRjPK2UpHR7KTHEehw8rRERc4dMUa1Xaz0GFPezuJgMYlZAsKSCxiR4c1u3ZRnrYFC4Af/GDs+42NdL5GlSuGQ1dsQ1ceRzIRaZMQ4gUA74FCWhSHhZCQVEopPx9wH4wTdXUsdMSd9etT83MoTjyRBvX33UfJSfOVP/+ZOrCXXUbPg8aXqs6RG0cHACxZQmVgm2x8MAodpaV0z44OxogSDNQKPQDN5kkJbNkCzJ2bsaLZ0tGRnMlloSP3UAOo7dtpxbEiu25/TOjqot/p1M4qRwcnJLWnrW2sm0MxeTILHVZICXzpS1RfPvOM+fnY0ED3LS3JPmmYWIWuZLuTMETsarzfAPgKgCkACgGcbHhfAKgHcGEI5WChI2pY6Ig/69eTe8NIURHw8Y8Dd9wRr9hjLwwP05KyJ51EiUGB4EKHilt14+gAgFNOoUSoK1YAx7NJLhb09iY7ywDNcJWWxmugxISD7oxQqCVmN23KbqFDlbuigsIh4zAbmG9Cx8gI8P775Pr0y9NPk+X9Yx8LpWiR0d3t7OYAWOhwS3u7tdBRW8uhK1b89a/Agw8C110HHHqo+TZK6Ni0CTjyyPDLwI4O69AVKWUrgNMBvJV4SWg3mLzm98akAxY64k1rKzXWeiJSnXPPpVmOJ55Ib7myhX/+kxTzyy9PvhaWo8Ot0HHSSTSby+Er8cFMOCwrY0cHMxYnoSNb0cstBM0GxsHRYZawMo5Chx6WETR85aqrgO9/P9g+0kFXlzuhQ7XdLHTY095u7VytrWVHhxlbtpB7+MQTgf/8T+vtdEdHFHR0UJ9Ed5PkWY4OWw+blHI1gAVCiCMBHAGgBkAxgB8AkAA2ALg96kIyIVBXByxblulSMFGhEpGaha4AFDZRXU3hKx/9aNqKlTX8+tc0k6X/9spKqvz9zkZYZSK3oqaG3CTPPgt897v+vpPJLsyEjvLyeA2UmHAwEzqmTyfHXZTLCwalowOor08+r6qKh9CRT46O6dMpF0xQoaOlhdyR2U5Xl/OKKwAnI3VLW1syp5mRyZNpJREmla9/nVxUt9xCLjgrqqqoDxml0GFsdyZMoGtkZIRcqDHHVbCelHIlgJXquRBCZVR5V0p5VRQFY0Kmro5U13yJ0cw3zJaW1SkuJrvpffcB/f3JXAL5wNtvk+X26qtTz30hgs1GeHV0ACQ43XRT/v0HcYUdHYxbOjqA/fZLfa2wEJg1K7sdHe3tqR1lFjpyi717KRFiUKFjYID2UVxMuQdEFhuy3YaujB9Pv4cdHfa0t1tPotXW0vHLk0Gza95+GzjzzLF1vhkNDekVOior6b6ri0SPmBPkrMziWo4ZQ10dNU5sMYsn69dTI6Os0Gacey7F5T31VPrKlQ3ceCOJCpdcMva9oEJHaSn6RDlWrXL5mVNOoU70K6/4+04mu2Chg3GLWYcTyP4lZo3lZqEjt2htpXNs/PhgQsfWrTSY7e+nVdyyGbeODiFoooKFDnucQleGh5NL0DJEZ6d5fW+GWmI2CqwcHUDehK/4FTquStw4bCVXqKuje87TEU/Wr6fK0i7L+KmnUoV3331pK1bGaW8HbrsNOO8884zWQYSOtjaguhp33UU5pHbtcvEZztMRLzh0hXFLLgodQ0MkCOiJCFnoyB2GhqgNrKmh0M0gQoc+45ztoR5uc3QAdGyy/fdkEimdV10BeBLViFV9b0ZjI11fUqanHMrRkScJSX0JHVLKqxI3FjpyBRY64k1zs3XYiqKkhHJU/P3vZEPNB/7yF5p9UkvKGgmSMby1FaiuRmsrTWhs2ODiM5MmkY342Wf9fSeTPUhJzg12dDBOjIxYz/A1NpJKqgbe2YTqCLOjIzdReaTCFjqy3QHhNnQFoGOT7b8nk3R3UwfHbtUVgFde0RkepuPmxdHR2xvNMbQTOtjRwcQKFjrii5Tk6LCKodQ591ya4cmHgfbwMIWtHH88sGiR+TZB1oBvawMmTRrVjDZvdvm5JUuA5ct5MJzrqP+PHR2ME11dVE+bdXxV1n3XFUgaMVuaME5Ch1oOWhE3oUM5FcIQOvTzM9sdEG5DVwAWOpxwSroedPW6OKIEYrf5L/QlZsOGQ1dY6MgbWOiILzt3UoXlRug4/XRSc/MhfOXRR4GNG63dHAA10m1tZPH1SsLR0d9PTz0JHX19wMsve/9OJntQcers6MheRkbolmnMBANFNi8xa1buCROoIx+FzTqdqFl/Palm3IQONYCvriahY+dOjDZYXomro6O6OvuFm0yicm9w6Ip77Op7M6JcYpZDV6xXXRFC/I/+XEr5I6v3gqLvm4mIykrqgLPQET/U0rJOoSsAnQP//u/Agw8Cv/tdvFfg+fWvaVnEs8+23qa2NhmDarV8mhWtrcCCBd4dHXqejpNP9vadTPagBkPs6MhePvhB4NBDgRtuyGw5clXoUIMco6NjeJiEPrez5tlId/fY8hcV0S0u168SJJSjA6CVU9ysBGGkpQWYOxd4993sFzq85ujYuzf7V5LJFE5CB4eujMWvoyNsoUPlWMpzR4fdKOeHAHTJ/kc27wWFhY6oEYJcHSx0xA8ldLhxdAAUvnLHHcDzzwMf+EB05cok69YBTzwB/PjHtHycFXoj7VXoSCQj9ezomDgRWLiQwod+8APHzZksxUroYEdH9vDOO8DgYKZLYS901NXROZSNQodV6Ip6L25CBxBcqHzrLRIEjPWCV7Zto9vRR/vfhzF0BaDwFb9CxzHHkNCRzQ6IgQG65r2ErqiVZHL5fI4Kp9CVigo619nRkcSro6Oyks7DsIUOsxxL6vuAvBE6nEJXBKyXkRUh3Zh0wUJHPGlupsH87Nnutj/zTGrQ4xy+cuONlHz10kvtt/NruxwcpFkjLUeHpzbqlFOAl17iAXEuowZDFRWpr7OjI3vYtw/Yvj3TpbDv+ApBM3pR2JaDosptXHVFfy9XscrjEOT67emhJbj+/OdgZQOAn/wE+MhHgu3DGLoC+MvTMThIy8vOm0fHLJsdHSrJrJfQFSC7f1MmcXJ0AMFyncURr0IHEM0Ss1bl4NCVUW71+R6TrdTVAVu2ZLoUTNisXw/MmQMUFrrbvrwc+PCHgQceoPAOt5/LFbq7gVtuAT71KWDKFPtt/SbSUrMc1dXo30oPN2/24H5dsgS4/npKSnrKKd6+m8kO2NGR3UhJQsfQUOZt6U4d3yg6uWHg5OjIZaJwdLS3k6Ngx45ARQNAbdLOnXT++g0x3buX2veqqqSz0Y/QsXUr5bppaMj+5Vi7uujei6MDoN80a1Y0Zcpl3AgdQVaviyNeQ1cAurZWrw63HFbtTlkZ1Sl54uiwrD2llP/h5z0mi6mrA1asyHQpmLBpbnYftqI47TTgnnuo06PiA+PC009TZ+dzn3Pe1q/QoTp61dWjjo7ubnpZ9ZtsOfFEyvjf1MRCR66iCR09PcAvfgF861tAEQsd2UFPDw3OurupQ+el0xk2TkJHYyOJntlG3IUONbOpE0ToUIPsMI6NGizt3ZtMJu+VRMJsCEED/0mT/Akdym3U0ED7y2b3g/oPvOToALL7N2USNalj506orWVHh44fR0djI/Cvf4UryluVQwiq+/LE0cGrruQTdXWkumZDFnomHEZGKGbWTSJSnRkz6P7998MvU6Z59FHq5Jx4ovO2qpPj19ExaVJKEnvXeTqqqmjJ26Ymb9/LZA+a0PH008B3vwu8+iqSA6VcX5Ui19E7cZmu59wIHe3tydnTbKGjg85nPc9RnISOsB0daoY0jAGE2seuXf73sXdvMjQD8L/ErGrYlKMjm0UBDl0Jl/Z2GhTbuYo4dCUVv6ErfX3hphcwCz1UTJiQN44OFjryibo6ypbOFXp82LaNKkevjo76errPhvj1MJGShI5TTwVKS523LyujDpFX26XB0aEEeNdCB5DM08H5HHITg6MDSBg5ysroPMyGJJj5jN6Jy3Q919FBIQTGfC6KbF15pb19bGedhQ5rlJsgm4QO3WLoV+hoaaFGbubM7F+ONUjoCjOW9nb7sBWAQ1eMdHaSMFRW5v4zylkdZhtgJ7hUVrLQETVCiLVCiI1CiA2ZKkPeoeyPnJA0PjQ30z0LHcTataQ2/Nu/uf+Mn9kI1SlKODpUaK+nfIJLllA8dzZa1hlnNKFDPezvRzJnBwtYmSXbhI6qKmtLclTLCwZFlVuHhQ5rohA6ggwgjbGUQYSO6dMpwTc7OvKLtjbrFVcUtbVU3+r21nzGqb43Q4ndYbYBTkIHh65EToN2Y9IBCx3xQy0t6zV0paaG7MiZHgCEzaOP0v2HPuT+M37iS7VkpAMDwLRp1Gf25Og44QSa5X32WW/fzWQHVkKHmsXhPB2ZJdtCV+xszNnq6OjoGDubO348deBzvZOc7aErapASduhKaytGLWhuaWlJinEqGWm2huZ5zdFRVkZOq2wUOtrbgRdeyHwZnBwdavW6bDyGmaCz01vYCpBcNTFdQgeHrjCxhIWO+NHcTB0z5dBwS0EBjc7jJnQ88ghwyCHJpfTc4EfoUI6OiRPR309RMrNnexQ6JkwAjjiC83TkKuzoyG6y0dFhxaRJVB9ko9BhLHdBAc0G5rKjQ8rsdnSMjCTP3yCODrPQFcC7q6OlJTkQq66mEOhsFbq8hq4A2etS+cMfKMTVqzAVJm5DVwAOX1F0dHhPfj1uHAlGYYeulJWRE8sIOzqYWMJCR/xYv57CVgp8XMr19ZTjIy7s20ezH17CVgB/8aVtbTQAKCzEwAC1I56FDoA6MS+/nNmODOMPTehQ5g12dGQRqhNXVJT9QocQ5OrIBaEDoNdyWejo6yOxw2zWPxscHWqwDvh3dPT1UbsSVOgYGqLlZXVHB5CdwgDgPXQFyN4lc3fsIFEpk0mK3YauAJyQVOFU31vR0BC+o8OqHJyjg4klEyfSiIyFjvighA4/TJ+e+QFAmDzzDCWA9BK2AvjP0ZGwBCtHR0ODD6FjyRIq84svevwgk3GcQldywdExMgLccEPqwCouqE7cfvtlf+gKEH4nNwziKnSowXBUjo6gx0YXSvwKHVrC7FH8CB3bttFgWwkdan/ZKAwA8XJ0qBBZdZ8JvISusNBBdHb6W868sTF9QgeHrjCxRAhgyhQWOuLC0BCwYYN/oaO+Pl5CxyOPkEp9/PHePldbSx1fL53b1tbRWQ7d0dHa6rHtOP54ztORq/T20h9fUGAeupILjo7XXgO+/nXgX//KdEnCRw0W583LfD3nRuhQjo5syn1gtuoKwEKHFary7+0NtuqSLnT4DQdQA3fd0aGWlfcidKiBV644Orq6yMVlZte3oro6O3+PEpMy5ehQIUpuHR0cukIEcXRs3kwTEFGXQzk6sqm9iQgWOvKNujoWOuLC5s0kdnhNRKqor6eKUHX6chm1rOxpp3nr4ADJRtpLR6etbYyjQ4Uwe3J1VFYCRx3FeTpykd7eUVFDjYtGl5cdfZLlqNniTFqjo2LfPgrpmzuXhI5MdujcCh09PdkzWBgcpBM7jkKH3ax/GI4OINhsqRI6JkwI19FRWkqTXXEWOrq7kwlz3ZKtoSvKyZGp+lld406ODnWOsaODCCJ0DAyE50B0cnSoXEUxh4WOfIOFjvjgd2lZhUpgmmlbdxi8/TZ13rzm5wD8zUZooSu6owPwGb7yyivxDB+IMz09Y4SOnEtGqs75XB60WtHZSUJifT39V5my6UrpLgt/tq28YjfIyXWhw8nRMTzsz5Ghn2NB8nSoz86d61/oMHN0AN6XmG1pIdFAhb3kQuiKl7AVICl0hDWTHhaZDl1RAouT0FFUROcFCx3J+t5P6ErYy4w7OTqAvEhIykJHvsFCR3xQS8sGFToybesOA7Ws7Jlnev+sn/hSLUGXnqMD8JmQdGiI83TkGpqjI2eTkcZZ6Ni3jzqbma7nurpoAOUmRweQfUJHHB0dTkIH4E+o1MXqIAMIdWznzqXHAwPe9xGm0FFfT40ckAxjyHZHhxeqq+kazbZzOtOhK+p7nUJXAH9J3eNIby/15/w4OpTYnU6hIw/ydLDQkW/U1dEMQR7EZcWe9eupIz9lir/PZ3oAECaPPAIcdlgyBtkLXjOGS2nq6Kiro3vPQsdxx9GMCOfpyC1MQlfY0ZFF7NuXdHQAmavn7AQDnbA7uUFxI3Tkaj8iKqEjCkcH4G8AaRa6AvgTOpRdEaC2qqoqux0dXoUOJQZl22/KdOiK+n4nRwdA/Sh2dLiv781Q11k6hA7lOGGhg4kddXVkycxkFmcmHJqbyc3hJRZVJ9MDgLDo7ASWLvUXtgJ4Fzq6ukixNzg6CgqAWbN8tFHjxwNHH815OnKN3l6gomL0IcCOjqxC2YenTaPn2S50jB9PdVEuODomTKB+RH9/essUFm6EDj/Xb1dXclAY5JpSQsecOXTvJ3xl715qmBJ11CgzZ9Lgxm35WlqSbiNFtq5SAvgPXQGy6zf19ibPwWwPXQH8rV4XR/T8Ol4pL6cxWhhtwPAwXQscusJCR95RV0f3HL6S+6xf7z8RKUAVYHl57gsdTz9NwoPXZWUVkyaRWOS2kVadDoOjAyBB3rOjA6A8HStW5IW6HhvY0ZHdGB0dmcpF5GWGT628kg04OTr0bXKNKB0d06fT4zAcHUro8OPo2LuXBvDGiRAvS8wODQFbt+aW0OE3dAXIrt+kixuZdnRw6Ip7PNT3UpqY4sJaYlbVIezoQJHdm0KI/8nUdzMRoQsdBx2U2bIw/unvpxH1Zz/rfx9C0CBg27bwypUJHnmEKu3jjvP3+cJC6ui4baSVvXXSJIyM0MSmCl+ePZuK45lTTgGuvhpYtsxfnhEm/WgrUrCjIwvp7KT6rbKSBj7Z7ugAqJP72mv46ldpMu6WWyItmT1qcOUkdKg+RS4RZY6OefMoOXZQoWPcuKQbyY+jQwuvTEEXOg491H4f27eT2GEUOqqrsy/MQ9HVlQwDc0s2Ojra2nAPPoHv4SdY2/ZdFGaiDH4cHVL6dxnHAQ/1/U9/Cjz0EOWiH6WhgSa9oi5HHjk6nMSGHwLI0SBMxhR2dMSDjRspeZbfRKSK6dNz29GhlpU9/XSguNj/frzYLjVHh0rMrxwdDQ3Ajh00xlXjXVccdxyVv6mJhY5cobcXmDp19CFgEDrY0ZFZVDJSgASPXBA6GhqABx/EK69IjIxkeLDgtOqKvk2uEaXQEZajY8KEZKJsv6ErxkSkgDdHh3FpWUVNDfDuu97LlA7ikqOjrQ1rcRDW4wB07emDj4wPSaQEvv51mhhbtMj959rbaSLIzfGsraVZH73ezUdchq4MDwO/+51Jt7OhAbj/ftqgMIC85VboyANHB4eu5BssdMQDtbRskNAVILMDgDB46y1ypPjNz6HwkkhLS/KmQtR1RwfgLdcbAIqjPuYYTkiaS1itulJURLdccHSoAVSuDljtUMvLAjQzniuhK4OD2LNzOPPpL1S5zTrscRA6CgrM1eigoSt1dTSjHVToqKoikamoKFjoipH6evrtQYSOoI6OtjbgwgujOX/8hK5k40oybW0YAM2g9LQFrAw6OoBf/AK44w7PZcDEie4cGirXmYdztacnd/MZW+Kyvn/+eep6DwwYugoNDSQYBe2XO5Ujj0JX3AgdIsIbk26qq0klZKEjtwm6tKxCCR252toEWVZWx4vQocWtqlX/9BwdgM8QyyVLgFdfzQsrYSwwydEx2mEpK8t+oaOvL7kcZq4OWK2QMjcdHQnL/e7dWZDns6ODHA9FJsbfOAgd48aZD+D8Ch0DAzRAqayk8y7o8rITJlD5Jk8ON3SlqIiEPy9Cx6xZqa/X1NBs//Cw93IBNMq77TbgpZf8fd4OP8lICwtpQJ9NQkdrK/pBMyjd7YOB9wUA2LDB2+fa292FrQBJ95HLftSOHdTteuopb0XKelw6OnTNKaUaDWv1Lad2p7ycBM886G/aCh1SyoI03DISepa3FBTQcqQsdOQ269dTZ8NNkig76utJVs/Vyu7RR4EFC5IJB/3iJZGWC0eH74Skw8O0ggyT/VglIwXo9WwPXVHn+5QpdP3nqthpRk8PhfYpR0cmBd2ODhpIuRl8NTaiD6Xo6i0aFVEzht3ShHEROszwK3SomdEwhA4VugLQ9elV6JDS2tEBuF9itqWFRBHVwClqaug7/K4GotxVYQsLSmzy6ugA6DdlWejKqKOjIyShw2u4kRehw6Oj44036BLLltW0Q8POCZegvx+4775k85RSjSr3VNRChxBURnZ0MLGkro6FjlynuTl42AqQ20vMdnSQKOB3tRUdPZGWE62tlE+jomKMo2P6dNISfQkdxx1Hs20vvujjw0zasRM6csHRoTqk++9PApvKWxAhAwNpanpU5011NqdNoz8pE4KuPjvvxKxZ2AsanGaFoyOuQofdrL9foUO5o8aPp/876PKyutDhNXSlq4sG/EGFjs2bx4atAEmniF9hYMcOug9b6LDLveJEtq0k09aGgYSjo2ffMAm3ftEdHV7E3rY295NpSuhw6ehYt47uM17PhU1HB9UBNvk1Hn2UNCS1lkBKs6TcU0FX33LjJKysZKGDiSksdOQ+69cHD1sBclvoeOopGqCFIXTU1lJ2eTcDobY26ugJMcbRUVwMzJjhU+goL6fkllu3+vgwk1akHBU61EMgRx0dc+fSfRoGrb/9LXDwwcH67K5Q17Hu6AAyU8/ZCQZGysqwZ/LBALJgANDe7hzfnatCR9SOjqqq8BwdfkJXNNehKUrocBr0trSYCx1BVylRQkfYDgpdbPJKdXV2CR2tregvod/Rg/JgA1LlvOnt9ZarKMLQFSV0ZPt8gGf0a9eCO+6gw3XOOfQ8pRotKyNhPmpHB0B1Va66uT3AQkc+wkJHbtPdTQk4893R8cgjVIkvXhx8X15mI7TYZ6OjA6DwFV9CB0ANnOoEMtnLwAANEsrLU0IMctLRkUahY9s2unwi/yqjoyNXhA4Au6ccAgDZEbpiNchRoTi52kmOQugwOjoyGbqiBux2jo6+PvuB/fAwsGVLNI6OqEJXlKMjLqErxfQ7ujEuudSrH/Tf5SVPhxehY/x46gi5dB+pfPoZF3TDxqG+7+wEHn4Y+OQnk5fnmPawsTEcoaO0dGzYmQ6HrjCxpa7OX3IrJjtQcZZhOjq2bQu+LzsGB8kxERZqWdkzzjBPlucVL0KHZuc0OjoAEjp8t1GZXB2CcY8aBJWXp4yH2NFhjzokkU+c6rPrAF1XQGauLY9Cx55JVK9nfADgVO6qqng6OvwuDx1Wjg4pxwod3d2Ud8YtamBrJ3QA9uEr27dTmx2loyPsikCJTXEJXSmm39GDCv/5UAD/QoeX0BUhPCV1j62jw6He/Pvf6Td/5jM2xriGhnCEDqd2hx0dTGypraUrzUvDyWQPYa24AlCHoKoq+pnOT3wiGZAYBm+8QYOWoMvKKrwk0nLh6Ni2zaeuM3UqCx25gJPQkSuOjqKiZExwGgatqsmJfDxhzHyvhI4ccHTsGUcZjYeG0hDiYwcLHd72GZajo7ubxA517FVIgJc8HeoCswtdAeyFDqulZYHgQkdUjo6goSudnTQpkw20tqK/SBM6gjo6ysrIieU2IWlfH93cOjqAZK4zB7q7k6dexgXdsHEIXbnjDrqkFi9OXuJjqoqGBnJTBZkcdCt0sKODiSWqkXK7nCaTXSjPXxhCBxD90ouDg8ATTyQl/DAIa1lZhZf40tZWW0dHQwO5fn2ZZKZNozJkS2eLMUeN2DWho7DQIHTkgqOjtjbZkY2zo6Oykm45IHTsLpk++jij4Sv5KnQI4e/6DcvRYVy1YcoUuvcjdARxdNgJHRMmUNZtP6EeUkaXoyNo6AqQPeErbW0YKKQwqsChK21tVNc3NLh3dKjz0IvQ4XL1Ol1ryfb5AM/Y1Js7d1JqufPOSy56oj6SQmNjgE6kczlG4dAVJrYEVeOZzLJ+PQ2I/TTmZkQtdLz+OnUag1gvjTz6KLBoETkgwsBr6IqDowPwmadj6lTqCHJoWXZj4uiYOFHrtJWXZ38PbvduEvjSuIJG2oQOYzJSgOrMHBA69hRMHn2csdnO/n46f/NR6AD8hZ4ZHR3d3f5mZI1uJCV0eGkTnJKR1tVR5mw3QodyfOkUFPhP3tnWlhTysyl0pa6O7rMlf11bGwYKyV0USuhKdTUwZ457R4f6PrehK4Dr0BV9zit2jg6b+v7ee0m/OP98el5URKeqaegKECx8hUNXRmGhIx/xuAwUk2WEtbSsImqhY+lSug8yI6FR1NVFS7CGsdqKQiXScrom1MosBkdHaEKHsthzQtLsxkLoyLnQlbgKHcZkpADVc+kOC1P5FrwIHUPJgUXGHB1uMvbnutBhN1HgR+gwrrqiv+YFo9Ch3IZehI69e5NtmhkFBbQWup3QsXkzCe8qlMdIdbU/94O6BqdMya7QFSXo+M4kHiJSUuiK0ISOoKErSuhw6+hQ3xdB6IoSOiZPzv5m0jM2oSu33w7Mnw8cckjyNdOVqJXQEWSJWS+ODi9LDucgLHTkI+zoyG3CWlpWoYSOqCq7ZcvovqMjlKDzSStXkiweVn4OIJlIy8l2qRp/g6NDD11R/SVfYrxyqHCejuxGDYIqKkY7alVVOZiMdPJkmlIqLIyX0NHZSYO5iorka1ELumZ0d1Nd5UXo6CkffZyx2U43tvVcFTqkjM7RIQR9Vg10/MyWWjk6vIauWIWtKNQSs1ZYLS2r8Ju8U4n4hxxC50+YScpV6IofR4dquLdsCa88fkm4gQYEdSx6wlh1ZdIkSjzd1uZOoPIjdNTWpjp2LGhupsM9aVLMHB1DQ/TfmdT3GzcCL71ESUh1TFeinjmT6pJ0ODpGRmKfr5GFjnyEHR25S3s7dXjCFjoGB6MZfUhJQocQydnNgFS//DK1kMccE0IBNdzMRhgswWaOjrIycsGyoyPG2Dg6pEQwR8cXv4iZd98dRintUUKHChZOYzLSyMPg9+2jTpwQydeiFnTNcOOMMLB7T7JblnGhI46Ojv5+6txHIXSMH58afB+G0DF+PNUnXkNXsl3oOPRQug8zpDWIo6OujhrybHB0JI7JAKhj0V0yMdhxUuG2c+bQczeuDr+hK4BjBb9uHZmSS0tj5uhQ165JvXnnnXT/6U+nvm5ajZaWkuMqHUIHEPs8HSx05COq4mJHR+6hVlwJO3QFiGa2c+NGink9+mh6HrRTMzKC6ldeoWVlCwuDl0/HTXypasAT15CZowOg/qGv/pKKE2ZHR3ZjIXQAicmsII6ORx5B3eOPBy2hPYODJJoqW3yaBq1pdXTo+TkAEhH7+tI7OPchdOzZA1QW0sx01oeu9PVlOGOqD9zM+vsNXVHnXJhChxB0nXoNXbHKz6GYOZOSHZq5LIeHydlgJ3QEDV1R/v0wK4Pubkp8YBWyY0dBAdkMskHoSBzX/hH6HT3FE8MJXVFLibsROvyGrgC2/SgpydExbx7pd7FydBiv3QRSUtjKCSckQ5sVlk1vkCVmh4epPnITugKw0MHEkKIiGqixoyP3CHNpWUWUQofKz3HWWXQfNE/HG2+gtLU13LAVhRuhQwk1No4OgBozX/2l0lLaNzs6shsboaO/H8EcHZ2dGNfSEu2AXJ3ncRU69u0bGycdZT1nhUehQ0r6a+rL2wFkuaMjyGA+k7hJWBnE0QGEI3Tox37KlGhCVwYHzZNvvv8+vReVo6O8PLnvMCuDri76X3Unlxd8N9whoxwdsggA0FM8wX/fqbeX2qLqamC//eg1NwlJ/YauALbn6q5dVL3MmxdDR4dFvfnmm8DatWPDVgAbM2VDg/8cHUq4cOvoyLU63CMsdOQrfhspJrM0N1MjriyIYTA9sZxhkKWsrFi2jCrb44+n50EdHSqL1ZFHBtuPGW5ydLh0dMyeTRNivlKSTJuWe46Ol15yn809DjgJHeXlFK/rNf58ZATYtw9CSuDll8Mq7VjUeR5nocPo6MgBoWPfPhpfzqikz2W10JHGJLahkiuODv38nTIlmtAVwDx8xWJp2aYm4IEHEk+qq0lY8Oro2bGDclFFkStOF5v8MGtWduToUELHMLlWewor/fed9D5LeTnVg25DV8rKrJPRmuEiLF514Q44IH8cHXfcQfPLn/jE2I+Y5ugA6NrbutUx34kpbtsddnQwscblMlBZxZYtwHXXxT5DsC3r11Nj7KXxcUIlwIxiALBsGXDccUkbbVBHh2q0nTpxfpg8mRp3u8GpB0dHf7/PleqmTs09R8dnPwt8/euZLkX60IQONSOlIgJHHR2A9+mq7u5k/fbii4GLaUmGhA6VoyMtoStGR4fKf5NOEdGj0KH+lukTMxy6ouppFjrcY+bo8HNsOjooiW5RUfI1L6ErIyMpS6BbYid0KFeDQei49lrgU58CVq9Gsg32Gr7y/vt0Lfr9vB1Oq+k4MXs2lS/To28VujJEQke3qPTfdzL0WTB3rntHhxc3B+AqdKW5me7zxdExMkL5OT74waQOpGPZ9DY20oe3bg2lHKawo4OJNbno6Pjzn4FvfcvfhR8Xwl5xBaDWprY2fKGjtRVYs4bcHGoUGNTR4SdBllvcJNLy4OgAAiQkzTVHx+7dwGuvZboU6cPE0aH6FIGEDr3DsXx5oCLako+ODiV0ZLGjQ40P6qvpvOnvGY6iVM6oclsskwiAhQ4j+jmnjo1fR4fxuKvQFTeTPO3tNEAKw9GhViJJkFgMBJdeCoxM8unIiNrR4WfFFYVquO2StKYD5egYoiFajwiwvKwhgbrrJWbb2rz3s9R/auOMXbeO+kszZ8bQ0WFS3y9bRqeTWdiK2lRdVykEWWLWq9DBjg4mluSio2PNGrrPtUFgWAwPA2+/DRx0UPj7jmLpRTUjfcIJyZmBoI6OtjYMl5X5SzbmhJvViNraqHFIzLb191MkkTEvqmqjfAkdU6fSOZ4rzqWREeqcb9vmzV6dy9iErvT10esp27klMTAanDCBwoGGIxroZkDoGB4mF25pKXXsIu3gmg0Wx4+n13JA6Jg+hezK/e0ZWvavo4PqObuEzyx0pKI7OlSeiDCFjr6+ZH4RO9y6HmtqaKRpJXTU1SXrsQQ9PXRaLF8O/P7F+anf5xYldKh2NJtCVwLNUIRIWxtQWDg6kdIjy4OHruiOjvffT14HVvhxdJSU0LnrELqy//5UtcTO0WESunLHHWTQ+shHzD9iGeWmOpF+EpJy6EoKRVZvCCFOSlchpJTPp+u7mAS56OjId6HjnXeop3HUUeHvOwqhY9ky6sgcdRR1mAoKgjs6WlsxWFmJkNdbIdwIHSp7eYKBAWqsjbnPAjs6+vupsfLa0cgEHR1JUeb118mjGXd6e+ncLipCby/9/2pyJJCjI9FBaT3ySNQ98wzVeYcdFl65Fbt2UaHVuayEDin9J/JzQI0bZ8ygCcW9e5NpM0LHzNEBpN8t1dFB9Z7Lwddo6Eo9XU8D7b0ATH5H1LhZmpCFjlT0c66ggB6HJXQoQXLXLvPzWkf165xCV4SwXmLWYmnZnh7gzDOpGb/y5v3xUdRjupd+ZH8/taHTpiXrn7BXXVHHyg/KwZLpPB2trZATJ6G/lerinpEy+m2Dg0Bxsed9AUi6M1R+t40b7dsWfVUuL0ye7Bi6olYWjrujY2AAuOce4KMftW4CdPNXyiU7cybVI1EKHXkSumIpdABoApCOKUXpUA4mCmpqqOLs6ws330NUDA4mVxxJ54xcNrFyJd1HkYizvp5SQ4fJ0qXAokUkZwM0aA/B0THk1NHzi4uM4WhtTbFz9vebm0sqK2kz344OgAZkuSB06P9prgkd69aRI8WrS6q3d3S2Uz1U4UujyUjVm15IdDhajzqKhI7ly6MROnbvpjZAzdhPmECWi56eYNZvG4xCR2trREKHlObLywLRCLp2dHTQsXUpHo06OmbQ9v3tPpcoDgoLHcEcHQD972E6OgC6bp0SkSvhwE0eKzuh44gjxrysqoef/Qw49BCBy/BrPOBFqFBJq1QbV1MTbo6Ori7KbeCXmTPpWs0CR8fwpFrIxKHtHko0Lh0d5okeHPYFINXRAVAlbNe2tLVRxlCv2CR1Hxykrz37bHoeO0dHRwcJUYkx1RNP0OltFbYC2FSjxcXUWEYpdCjnWcwdHW5CV0Qabky6UZVlrrg6NmxIZh/OV0fHypXUkfLT+DhRX0+W0rCs8v39wIoVFLaimDQpFEdHZEKHi0RaxiRvytFhxuzZPpdBV7kEciUhqf6fvv565srhh8suA770Je+fcxI6Aubo6DrgAKqjo0pIunt3cvAEpGXQqhKRzphB95E1Pb29JF6Z5ZfIhNDhMmwFoKqnpASoqaeTqb8jQ6OAfBA67Fw25eV07boNHxwZoUG23jYFETqMx15dq25CA70k7DYTOkZGaKBv4eioqCCt5YffGcSDOBsPPu8hMbhq03ShI2xHR5DQlZISan+zQOgYmJisn3sGEy4OPxNFra0kaKtzUwllTglJ/YSuALZh8S0tlIti3jx6HjtHhxIpE8L2HXdQd/GMM6w/YluN+l1i1q3Qoayoeezo2IL0ODqYTKAnglLLi2YzKmwFyG9Hx6JF9nHTfqmvpw7Orl3JgXYQXnuNWjC1rCwQmqNj0MPAwRPqmnAKXTnkkNGnVo4OgNooZULyhO7oyAWU0DF5cu4lJN2zJzkC94ImdPT1he/oGBo3jlYriioh6e7dqbZkvbcVUTyJ7ugAIhQ6zJbnVCihI8IQnRR8CB21tUBpNbkNBjozJHS0t6cKYWYUF9N5nmudZLeODiB5cTuh6pCoHB166IoTbkNXABI63n+fRp9qlZf336dJJRuhAwC+fmUJ7vzhG/jqA6fgA79yeZqrNk31Maqrfc4GWBA0GSlAMxSZFjpaW9Ff1QCAjndPf+K/8TNRpMJtVX03cSI9t0tIKqV/oWPyZEt3sFpaVgkdsXR0JC6Eri7gH/8APvc5+5Rytgs0NTQAzzzjrxwlJe7c+pWV+evokFI2SCkb03FL5w9mErjJR5BNKKHjgANyZwAYJoODwKpV0YStAEmxa9u2cPa3dCnd60JHtjs6Skup0ndydGihK06Ojs2bfeQUzTVHhxKvTjmFZolyaeDT0eHvnOzpSXF0lJUl+xRhODqGx40DFi+mgOYo6mg7oSMilNChFnuwFDqkpKTLflGdNjNHh8p/E1RwdYtHoUP9LaW1VMf178vQdKfb/EATJuSmo0MI+0GAV6FSnXNGR4ff5WWthA67sErF3r30+9z8fzNn0gSHPnlksbSslKlCR3GJwB+r/xvvd1XiO99x/ioA0Ts6giYjBShPR6ZzdLS1YaCSJl4mTgSGhgswgGL/jg7j6ilOS8x2dZG718/qdip0xaTjo4QOZUouKyONLaqc22lHq+8feoiuF7uwFcBhgabGRuqTe11n3Eu7M2FC/godTMyJYmmvKFm7lhqg/ffPT0fHmjU0aIpK6FCzuGEd22XLyCJZV5d8LdtzdAC28aWQckwyUjtHx+zZ1F/wPI6uqqIeQK4IeuoHnnIK3b/xRubK4pXOTiq/VzXKbeiKX0dHeTk5OgBafSVsMih0KE3Vsul57DHKVvfWW/6+yGzQqQi7nnPCr6NjVOgYjKpk9rgtd5qWJQ6V7u5kbHqCZ58Fzj9fqwa8Ch1qNRR9kF1V5V30VflljEJHRQXt223oyqRJ7pyfZkvMKoeFyqidoL+fiqeEDgA4aupWXD7nEfzudy6j7HbsoOOu3EJh5ugYGKAJoaBCx+zZdDxGRsIplx/a2jAwgSYjldbQA59LzBrCbQE4LzGrvsdv6Epfn6lTsrmZ3lbFSWkz44B27d59N11e+lyfGY6hK1J6X+7YS7uTB6ErLHTkK7no6Dj4YOqo5soAMEyiTEQKhDsAkJJ6PXp+DiC4o6O/H+jpwWCUQoddxvCeHupMeXB0AD6cuULQjFeuOTo+8AG6z6U8HZ2d1Dn2Gr7S2zva47dNRurH0TFuHA1SjjyS7ORh5+kYHqbBRZqFDnWIa2roWFkKHU1NdO+1c6cwWeJvlBwROkpqqewD3Sx0hI5JeMMzz1A8/ejEaRhCh5/QlZ4e6/wykye7D11xE7YCeBI61PWrCx2orsaPp/4GM2YAl17qYuL5/ffpBFcrh9TU0DH2Kgib4SYkyQ2zZ9MPyVT7OzICtLWhfzxNRqYIHUFCV3TmziXnjtUfFkTosMl1tm5dMmwFMLgg44BWb27cSE14gcMo21HoALzn6fAqdLCjg4klquLLBUfH8DAtrXrwwWQ93rWL/G75xMqVVHE5ZVz3y5QpVCOHMQBYv55mjI1S9sSJwYSOxGcjd3RYCR3G7OWgfoKdowMIsMRsrgh6bW00MN9/f3Lw5IrQ0deX7Oh5PS9tHB19fQi2vKwa5FRUAAsWhJ+nY+9eEiMz5OgoL3dwrKvf63em187RocLC0nVt+Qhdqa0FSioTyUi7M9DOqesirkKHcnRoqMt09HINK3TFq9BhJ9JNmeI+dMVNIlLAWuiYMsWgaFgIHTU1qOzcht/9jqLNfv5zh+/bsSMZtgKE2w81E5v8EKjhDoHOTkBKDIwjhUNpDb4dHWZCx5w5yaSzZqj20G/oCmB6rq5bl5pLP6XNjANa+21SzZhSWkq6n63Q4XW2jENXUmChI18pKaETPBccHZs3U02oHB1SJpcpyxdWrKDl3pzkYb8UFdEgNQyhwyw/B0CNZn+//1Yt0fhG6uiwEzqM69GDfo6Vo0O1Ub6XmM0loWPiRHKiLFyYOwlJ9UFIAKEj9GSk+iDnuOOAV14JV9hVHdAMCR0VFTaO9cHBpHvNr9BhN1hUQkc6HB1SeupwDg3RaTh5MlBQKFCMAfT3ZEDocJuxX20TI6FjdGY5LEeHynXgFiehw23oiluho6qKxBmj0GGRiBQYK3Rg7158+MPAJz8J/PjHyTwMphiFjjBDqMNydMyaRfeZytORaIsGxpM4obob3YVV4eXoUBNmVuErQUNXgDH9qM5O+vtj7ejQVkxyK3QIYRPlNmMGTSJFKXRw6AoTa8JOBBUVKhHpQQelf0YuG+jvpyzWUYWtKMJaenHZMppBOPDA1NdVo+nX1ZEY+GQsR4dHR0d1NTV0vh0duRS6ojpSCxfS9ZoLPRd9gOa1Axnl8rL6IGfxYhphWGSx94WZ0DF+PPW4Mu3oeOON5IZRODrGjaMOYDqEjp4eGuS67HCqn6vGCSViEAM9GcjSx0JHeI4OICmCuEENOMyO/eTJ7h0dbkNXgLFLzHoROqqrR0/cX/6SDtsXv2iT8uj991NXdVNCRxh5OuLi6Egci/7yiQC00JXxU7z3nYaH6fo0C10BrBOSRhC60txM97F1dBiEbS8rHVtWo0VFdH1GGbqSB44Ou+VlLRFC+FjvxhIppTw1xP0xbrGbvc4mdKFDKdD5lJD0rbdopjMdQkcYsxjLltFMtNF9olrs9nZ/S9imI3Rl8mTq0enp5RWqM2ZIRmpVHCECrFQ3dSp9n51lJFvQV6JZuJCmplevJgdSNhOSo0OtuhKao0PvoKiEpC++SEtLh4GZ0FFQEPkKGkahQ18xfBQVtlJYGI2jA0hfWJgXwQDJplgJHaUFg+jvy0BCRC+DHBY6CCtHB+Bt0OHG0eG0NLKX0BUgVehQ4Qwf//iYzSwdHYnEk1OnVuC66yhXx1/+Alx8sWEHUqbH0RFU6Jgwgc79TAkdytFRRufAaOjKuMneBXm1vVHoqKuja8DK0RFB6IpxaVkgZo6O3l4SliZMwMgIXS9uzUW21WhDQ3ocHelacj0D+BI6ACwB4HXRRDNESPth/JBLjo5p06jSzUdHR9SJSBX19cFXeNi9m1q0iy4a+55qNAM6OiIPXQHoujAKHSaNv52jAyChw3MyUiB5nu/cmbTSZivt7cnemBqMv/569gsdes8ixBwd/f2goFsh/Dk69IHAzJl0XS5fDnz1q972ZYWZ0AFEPmhVAyVbR8fy5fR7y8qCOToKCsZev4qwnGtOeBQ6jH9LaeEQBvoy0D3y6ujo7iZxs8hvdzLNdHcn6/kElkKH2+vXzNFhu26kBerYWwkdQ0OpDjojg4NUFq+ODpVXaedOatS8ODqA0fby858H/vY34JvfpFVsUjT69nbatz7JEUWOjqChK0CAGYoQUEJHOZ0/o46OcbVA+1pv+zKZnAFAbdOcOc6ODiux2I6qKhKqTRwdBQWpaeZi5ejQ6s3eXtIN3J6KtnMMjY3A44+7L8fICNUBXoSO4eFkDG4MCRK6InzcjJ/1/+VC3CyE2CWEWK29dp0Q4h0hxJtCiAeFEBMTrzcIIXqFEKsSt98H+e7YUFOTG46OtWspPwdASrQQ+eXoWLmSGiqTzkeo1NdTb9vrmt06aoUI44orQHIw7HeJ2XQlIwXMrwsLR4ed4SKQowPIDUFPd3Q0NlKrnQt5OkJ0dJSXUyeuqCgxWBKCXgyao0MIcnWEufKKGlEbZ30jFjrMcnSMsbgvX07hOkFE+M5O6rxZzU5lqdBhdHSUFI6gvz8HhA4gt6zP6XZ0eBE67BwdSgGzC19RbZRXR8euXfTjlSrvJUeH9r0FBcB//ic9VfMzo6i2LCpHR1ihKwBNLmQqR4cKXSmhfs5ojo6yWu/tlElesVHslphtb6c61I94WVBg6hZft45OK72/FCtHh1Zvek0XY7sSdUMDtVduD9K+fdSwegldUZ+LKX4l+P/wsG0hgIkADgPwIQBTQC6OOwA85fP7AeAWADcCuE177UkA35ZSDgkhrgXwbQD/nXhvg5RyQYDvix+1tdnv6JCSHB3KIVBURDMbuTAADIuVK8nNEbWtbPp0un///TFLy7lm2TKyOJi5T8JwdAiBoTBmbKxwEjoKC1M6Uk6OjoYG+lhXl8f+l5r1yoU8HSoZKUCdnAULcmPllZCTkQLUcRvtj5SVBc/RAdDA/777xsa3+2X3broW1RKPijQIHYWF9LU1NTQ5nRKps2MHDbS++lW6YII4OuzEUBW6ErVVN2joSvEw+vszYCX2Um49PMOPzT0TmATPh5KjQy2foAhb6Jgyhe537UpNdKCj+nNehQ4A2LrVcmlZwEHo0PqRao7j+ecN+chVW6YLHWVltMMwcnSEFboC0O9/7rng+/GDcnSUUh02GrpSOsn7JJFJXrFR5swBHnmEHADGMGN98sIPJrnOmptTw1aAmDk6tGvXj9Bh6+gAKE+HMe+dGR7bndG2srMzWcfEDF+ODinlrR5uN0sp/09K+R8AZgK4EsAQgE8BKJNS3uqzDM8DaDW89oSUUqUpfwnADD/7zhtqaujkHhzMdEms2bqVOr3K0QGkb0YuG+jtpXwHRx0V/XfV19N9kGO7dCmFLCipXicMR4eyRUaFzdJoaGujDoM2OHLj6AB8uDpyJURLyrFW6oULKXmml9UGMoHqEAjhTegYHKTfVl4OKVM0D5SWGoQOL44OKc2FDpWnI6xlZnfvHhu2AqRF6FDHydSxrsLmFi9OSXLoGeXosKK+nv6kIEtdu8Gn0KHGjqXFEgODWS50pGG1ntAxcXSoyzSQo8M4wA4idJidv7rQYYVVqIId+hKzXoUO9T3atVpbCxxyCAkdKai2zCjWhhVCHXboSmen/75KENragNJSDIBmUEaFjpKJ3stjdz7MnUsn/LZtY9/Tw1H9YHB0SGkudLCjY/Qj1lXoIYfQ/erVFhsYUOcIOzpGSWtQpZRyEMDPhRDvAbgdwI1CiHVSyqYIvu5iAHdrzxuFEK8D6ATwPSnlC2YfEkJcCuBSAKirq0NTUxRFi5auri5X5a5vbcUBAF58+GEMeGkY08ikV17B4QBe7+9HR+I3HVZSgpLmZryag/+NVyasWYNFw8NYXVaGPRH/3nHbtuEoAKufeAJ7fLQ8BQMDOGHlSmw9+2xsNCmrGBzEyQA2vfYaNvv4LQe+8w6qKipcn99+KOrowAkA1i9fjm1K+Elw8DvvYHxZGV7Rvru7+zjs3r0bTU3rTfe3d+8EAIvw8MNvYvdu9wM3MTyMk4TA5pdeQosbFT9DFPT14aSBAWxoa8N7ieNSV16Og3p68Mrf/oYev86gNDDrjTewH4D+mhq0vfMO3nF5ThV2d+NEAO9u346NTz4HKU/G9u0b0dS0BUIsRkvLXjQ1NeMYAJ2bN2Ot2/329uLEkRFs2L075RwXAwM4sbgY2+6+GxtCqKcPX78eBSUleN1QroP6+jBh5068HNG19e67+6OoaDKaml7E9u01AA7Dk0++innzqIO13913Y0ZREZZ2dWFOby+m7NqFZT7KMn/zZhQBeM3is5Pb2nAIgBX/+Ae61WxZBEx75RXMA7B8zRr0uwgRff31uRg3biqWL08szy1no3+oAE3PPBPdsuImNLzxBmYLgedefdXxeye1tFD73NSEDg/CUZR1uC1S4uSuLmzZswebtO/fvfsIAJV45ZU3ALShsKcHJwLYsHr1aL1mx4Hr16OquDjl2indtQuLAaxbsQLvu5wp3W/1akwvKcELJqFqJbt34zgA65YuxfsW9UDN0qU4DMDKTZvQ5fL4lm/fjmMArH3iCVS99RZqJ07EiytWjNnujTemA9gfr722FBs20HxiyZ49VKYXX8T7Wt6TuXP3xxNP1OHpp5ehsJDCr2YsW4a5AF5Yvx7DmoB/ZEkJ+pqbsTrg+TDrzTexH4DnXn0V0s5m6YLJXV1UR9x3H7rVCiUe8XuOH/D226gZNw6r3ngHwEF4990VAI7C9m6JkdZWPP/ss66daNNffhn7A1i2di0GDZMmk7q6cDiAVfffj/YFC1LeW5BY5WOVz//kYADjtmzBisTnd+8uRXf3YgDNaGpKTqRt3DgOwFF49dW3UVnpYkWhLGbyiy/SOdPcjBV95QCOwIYNb6Kpybnf19bWiM7OWXj22efG/LUFAwM4saAAmx96CC2G3EJmVL35JhYCeKOlBW0u/r+JGzZgAYDXn3sOHR4E64zV4X6QUmbkBuBRACMA3gywjwYAq01e/y6ABwGIxPNSADWJx0cAeA/ABKf9H3HEETIXefbZZ91tePfdUgJSrl4daXkCccMNVMadO5OvfeELUk6dmrEieWVkRMrt231++Ne/pt//3nuhlsmUXbvou371K3+ff+EF+vzf/269TUWFlN/4hr/9/9u/SXnEEe7Pbz8MDUlZUCDl978/9r3TT5fy2GNTXpowQcorrrDe3bZtdEh+8xsfZZkyRcpLL/XxwTSifuDvf5987c036bXbb89cudzwzW9KWVYm5cKFUn74w+4/t2PH6J/a1kYP/+//6K3GRikvuCCx3SGHSHnOOe73qx3LMef4ccfRLQwOPVTKj31s7Otf/rKUNTXhfIcJF14o5axZ9HjpUvqpjz2mbXDiiVIecww9/v73pRRCyuFh71+0eLGUp51m/b6qpx5/3Pu+vXDddfQ9HR2uNj//fCn32y/5/LiGrfJUPOn686Fx+eVSVlW52/aVV+g3PvSQp6+ItA63o6+Pynv11SkvH3KIoekaHKQXfvQjd/v9+MfputJpb6d9XH+9+/J98YtS1tWZv9ff71ymP/+Zttm0yf13dnfTZ376UynPOEPKo44y3ezaa2mznh7txd7e5Gc17rqLXl6xQnvxv/6L6tuRkdQdf+AD4dRt3/mOlIWFY/fvh5df9nVe6/g+x885R8qDDpJ/+hMVoaWF7v/nAy+Y/AEOXHUVfWZgYOx7mzbRe3/849j35s+X8qMf9Vd+KaX80pekrK0dffr00/RVTz+dullzM73+17/6/6qsQV17LS2jv7epyd1Hf/5z2n7fPosNDjrI/f/x8MO0s5dfdre9Otcfftjd9gkyVofbAGClNBnLp2+aYCz3JO4PEUKElp5fCHEhgLMAnJ/44ZBS9ksp9yYevwpgAwCLIMc8wi4fQbawZg1ZG3Wr9bRpZN8cGrL+nBNvvultffsAPPMMMGOGdYJrW1aupASsKn9GlNTUUIyx39CVZcvoXlntzZg0yb9lPGjcqBsKC8nmaZWjwzCT5pSjY+pUet93QtJsz9FhtgzdgQdSDEe2JyRVYSJez0ltnVR9yVTAELriNRmpsq2bWU6PO47qgjA8vk6hK2MyhIZDb2/S9j4mtH9wkH7fscfS8+pqKoefkAiz8B8dP2FhGzdSlkUvbU5HB828uswZsGdP6mIgpaWC7OvpDgvxsjRhroWuWIQ3jMnRUVRENy85Ooz/s3ru5djYnbslJRROEHboSkUFbf/ee9RQWSQ9V6ErKVGpFjk2TjqJ7lPSXKgcQ8Ypa5WZOCgqfCiMvDtqpbNMrLySCJFVOeHLyqgp6SlInE9ewlfa2igMypiPCaCQpeJi84SkQUNXJk+m/zQRvqqWljWmlolVjo6AoSv6LsYwfz6NWTyWwxV5ELqSSaFjo/b4sDB2KIQ4E5R89CNSyh7t9clCiMLE4/0A7G/4/vwkzIzXUbFmDeXn0Buv+npKoGTX4NsxOEgd6iuuCKWITrz1FhVXVfaeSFciUoBsytOm+Rc6li6llsxsEKWYONF/3KuJ0BAJJom0Rr9fG9BL6Zyjo6CA+ky++ksqaWI2owQCvVNUXAwcdlj2JyRVA7qohA6vyUjtEhEuXkyqWtBjOjJCI2oroWNoyPtKMS7Rc3SMaXreeIM2WLyYnpvE/rvGTTJSwFs99/vfA7/4BfD22+4/09FB/6XLsJPdu1OFjpIygX6UstARJhYjkDFCB+BNqOzqGnvOFRbSa16Xl7UT6SZPtl91Ze9eEmi8rkw2cyatMuIgdFRUmHRFTHJsTJsG7L+/IU/Hjh2piUhtPu8LkySzvpkyhSrzTAgdiX6GOhdLS+m494jEOeulrbLrMxUW0n9tNgMXRjLSkZHRvt66dXTJGefrYpmjo7LSc15cV0LHpk3u6pIgyUhjSiaFDn0etM7rh4UQdwJYDmCeEGKrEOLzoFVYKgE8aVhG9iQAbwoh3gBwH4AvSSlDkJBznGx3dKgVV/REpEDwRI3vvUcdmNtvT4vIo1Yp27rV4we7umhpXbMVTKLCb6LXkRFaAtNsWVmdbHd0ANSZNLsmVDLSBMPDdIo6hQMHWmI224UOJVoZ/5eFC2lQHpE7IBT8OjrU1GZ5+egAKVRHh5XQAQRfZratjU5cK6EDiGzQqgsd6nQZrX5VotUwhA4nR0dFBQlzXuq5pxILxDU3u/+MF8EAY/Wn0vJCFjrCJkqhw2xUM2GC92SkdufulCn2Ezx795Jw4HViZOZM4NVX6UA4CB1jsEgcfNJJwAsvUNcAgLXQoT4ftK3o6gonESmQnKHIxBKziX6OcnSUlNBx75aJg+9loshpcmju3LGOjqEhEouDODpUTpqEI7W5mebAjKdlrBwdnZ1UBxQWes6Lqy9eZcr8+XTvJiEpOzrGkEmh40TtsWcpSUp5npRympSyWEo5Q0r5ZynlXCnlTCnlgsTtS4lt75dSHiKlPFxKuUhK+XBovyKXyXZHx65dVOkfdFDq60FXB0kkWkJfH/CnP/kvn0t8Cx2rVlEvIReEjnXrqFFNWU/OBL+ODinT6+gwCh3Dw2NWF9FnXOzwLXRMmwbs3Kn1FLMQs9AVAFi0iN7LREfRLVGHroTp6Jg2jQYgQVdeUbPBZskRIx609vQkj1NREVUFKUJHfX1yBQi/QoeUzo4OwJtbavfupJMmYqEjJXSlojD7Q1fKymgklu9Ch9U5l26hw28bOXMmtTWA5dLylkKHhSPj5JOpWh0dm1ktj11TQ+1r0HPI8xruDvhuuANiCF0pKaHTtUcm7A9ehQ67yaE5c0jo0EUmdb4GETrUOZRYxWfdOvMVkWPn6EjUm35DVyyrCiV0uAlf6eggV63ZyodmqEKyoyNchBAHAbhce8nlujlMqKj4ymx1dKxZQ/dhOzrUEmpz5wK//W3ky2D6FjpWrqT7I0JLYePM9Onmy405sTSxUoCT0OHX0dHdTTMN6XB0mAkdqhOmdSL1jogds2fTqep51mLqVAqzCiN+OSpUp8vYKVq4kO6zOU+HHrrS3+9+UKO2q6gYfaj6FGVlARwd6hyzGugcdxw5OoLMfCqhI8OODoAupRShY/Hi5JSfX6Gjt5fqc7vBIuBN0H3mGbovKIhM6OjpoVtK6EpFUfY7OgDaNlc6ybnu6HATuqImsLygBEbA0tHR3W3j6DAROlSejuefBx3Y1lbr0BUgeDsXZugKECDmNABDQ3QOJEJXCgpIFK6oAHqGEjMqYTs6OjtT+ztWkxdeUKtZtbSgv5+63MalZYFk3yk2jo7EtRt6jo6ZM2kjt0JHVZV7V1dBAV037OgIByHEHCHEdwC8CAoxAYBtAJalsxyMRm1t9jo6rISOujq6iIM4OgoLgZ/8hFSIh6M1+AQSOqZPN58FiYr6eqooVU3tlmXL6Fwyk+11/Do6VCconUKHPqA0SfLm1tGh+o3vveexHOp/z+aEpGY5OgDK0VFQkN15OnRHB+BegMuEowMgIWD7dh8nkkaGhQ59oDSag3DHDuoJq7AVwL/QoTprTo4OL0LHk0/S+X3CCZEJHWqckRK6Mj5DQofXRIQTJuS0o2NoKDnXkROOjj17rCdnwhA6/Dg6TK7T2bPp9txzSLpQ7ISOoP3QMENXACr8jh3pHYVroaB6onMSOhJPvEwUGcJtxzBnDt3r4StWkxdeqKuj9m/TJmzYQKZUM6FDCGozYyF0GBwdhYXOk2AKx6ZXCPcJSb0K1QDVOSx0pCKE2Ojx9p4QogtAM4AfA6gCIEDLy14hpcxib3bMqanJXkfH2rV0AapQFUVxMfUI/To6Nm2ihv2cc2g5lBtvDF5WC/r6ko5QX0JHOsNWgOSx9npsly0jN4eTijxpElXEXsMxVOOertCVoaHUVsdEaPHi6AB8TA6pTmE25+loa6PZgKKi1NcrKmj1lWwWOnRHBxCN0OEnR4fVIF2tZhQkT0cWOTpGHe/G/BxA8j/xKnQ4iUUKFbri5I6RkoSOD3yABPeIhY4UR8e4kvSHrqiVbrw6OnJY6NAvUV9Cx8AA3awcHW6PjZR0/tod+ylTqO20ui6ChK4AdAJauCIchQ6Ta+mkk8jRIbcn2jCzSRtV3jCEjrBDVwAfHbcAaP0cXegYNw7oHki0sW4nityE+yqhQ09IGobQIQTN8GzaZLniiiLFBZnLGISOcePcmyocc3QASaHDqc3yI3R4TZqcY/h1dDQAmO3hVg+gAiRuqL++F5QU9AGfZWDCINsdHQcdZF5b+M0lAdDsYWMjDc6+/GXg6aeT7pGQUW3kpEk0Eevadd7ZSYGNmRI6vBzbnTupoXQKWwGo8VQdOi+EYad0ixoEmtk5fTg6fAsdueDoMOQtSWHRouwVOtQ5GNDRYZaMdHR2qrzcu6OjosJ8KUCAOjoVFcHydCihQx9RK9KYowMwCB3FxXS+KIqLqfMVpaNjYMB5/xs2kCXvtNOop97a6n5iIKDQUTouA46O3l4SeeMudGgDYv0S9SV0qMyDZuecl7Cevj469k6hK4B1+EpQR4dF2ArgkIxUJbA0cNJJZOZofjXxXpSODjW6DAvfDXcAtAkVfUW3igqgp7eAzkm3Qkd3N4W+2vWZGhupf607OsLqazU2Ai0to9qwldARG0eHIXTFy6moVkW2rSrmz6drTIXeW8GOjjEECV0RHm+KjQD+F8DBUsroM0Ey9oS1tFcUmK24ogiy9OamTckG/ZJLqKb9zW/87csBFbZy3HFU+bke37/6Kt3ngtCxLBF55rTiCuB9UKkwCR2JDLPViAI4OqZPpygO30JHtjs6rDpECxdSvhe/y0BHSU8PzYxG7ejwKnTYDXKKioCjjw7u6JgwwVydy5Sj46WXSOQwJk+zsMTb4kXoAJzruSefpPvTT0/21N24Ojw6I0yFjkwsL+s1Y7/aNteEDm0Uol+iKZerV6EjaI4OQ46ejRuBpibDNiqJsFmd2tNDP8CP0KHW/fQjdNgIFSefTPfPvZgQb6PM0RG2o2PWLLpPp9ChiQxjQld6QBNFbtspN32msjJyNYcdugKkODqmTrVu2uLs6HBLQYEL85fbhKR+HR0sdIzhFI+34wHMBzAxsTLKt6SUGUhnzIzBLPFiNtDaSk4BK6HDr6Ojt5cGjipZ0uTJwKc/Ddx6aySdNV3oADzk+VSJSHNF6CgtTZ2RtUI1nl7zdKTT0WEmdARwdBQXUz/SSYgfw/jx1Fpmu6PDqkOkEpJmo6tDH1SEIHSoMfqY5WX7+92HaTkJHQCFd6xalVzi1iu7d5uHrQDU2REirTk6OjuBwRWrUsNWFBbLVtriJXQFcK7nnnqKZnbnzPEmdHh0RphFFJWUAAMogexIo6U4z4WOlAGX29AzO3FNzZS6qQMM5+5111F0bQp2QkeQyYDSUnItfeADlpvYOjoAU6Fj7lwa5D7/5kR6oa5u7OfHrDXtk7CTkc6YQfVhJoQOQ+hKitDhtu/kNtx37tzwQ1cA6mO3tWHdmiHT/ByK2Dg6AggdgItq9NBD6T4qoYNDV1KRUj7n8bZcSrlaShnfI5mr1NRQxTY0lOmSpLJ2Ld3bOTp27vS+YopSHpTQAQBf/SrVTLfe6r2cLr5OCOCYY+i563DPlStJETezmEdJVRUN0LwIHUuXAkcd5TziB3LL0aHbgwM4OgD6K30vMZurjo4FC+g+G4UOfVARpaMDcD9d5UboOO44qquVEOoVO6GjoIA6PBEMWgcHqao2OjoAoLWvHDj22LEfsljNwRavjg67a2t4mFZcOf30ZMx5UZE7ocOjYLBnDx1+fWxRWgpIFGCovcvVPkIhH4QOIVJOxNBCV6wcHfo2dhiEjq4uanZSxh92oSvqWvHj6ADIvfTlL1u+7ejoMBElhSBXx3MbZ0DW1JqH5RUWGtaa9oHKkxJm6EpJCdUT6Vwi3SJ0Zdy4hEY3aZJ7ocNtn0ktMatoa6P/JKholOhjNzebJyJVxMLRMTREF4gWuuL18Dk6OsaPp/8qCqEjX0NXhBCXJ24fSWeBmDRTW0s2Wz9LfkaJyplx0EHm79fX0yyJV1v8pk10r1s0jzySOto33ug9SaYDW7bQjIbK+eRJ6Ei3mwOgnokXt0xrKy0heuKJ7rb3K3S0tdEgI8yOjBVWOTrGjUtRNdw6OgCaFPYldEydmv1Ch9XMz6RJ1NnJRqFDH9Cp8nsROgoKgOJie6FDveg2IWlHh7PQoQQBv+ErdkIHENmg1XicAM3xjprsdHS8+ioNKk47jZ4XFwP77ReZ0FFTQ6eVQtUr/e0eEtoGxc9sblWVe9dCpunqotG6lvcrsNDh5OgA3M2WGs5dVZaURZZqaqjsZv2eoEKHA35CVwDK07G1uxotNUdY7zxoCLVJ7pVQ8N1w+8QhdEVWTfQeuuLkgp07l84ndR4rl6bbTJpWNDSgFZOwp63IdjG+WDg61LWbqO/9LADkKp2P08orIyP0P3p14+Sxo+MXAG4A8CXjG0KImxO3/4yqYEyaCCsRVNisWUMdDYulznznL1BCh+7oAIDLLgPWr0/GZIfEli0U6qkmEF0JHa2tFKCbCaEDoDgLt0LHvfeSmn3uue629xu60tpKDXbQxtcN48ZR62vM0WGYGfHi6Jg9m/57z8apadOyP3TFriO1cGF2Ch36oKKwkO69CB3l5YAQ6O2lU1KdA0rokBJJR4fbXpwbR4dawtlvQtIMCR0q0kYXOkYd7zXzUpe31DeIKkdHeTmdt3b1nGoLTj01+doBB0QidOzePda8p86pgXafYUp+8OvokNKdayHTmHjK0+LoCCB0pBgKioqozxZ26IoL/ISuACR0AMDzxaeavg/AXz4enaiEjlmz0i90JCZUjELH8DAwOKEmGkcHkHR1eF1e2orGRqwDWTli7+gw1JuRhK4AJHSsX28durpvH9XFnKMjBb85Oi4CcCGA08MrCpMRzPIRZANr15Kbo8DiFPWTSwKgRAklJWOXOTv3XIof/fWvPRfVDiV0lJTQ7l0JHZlKRKqor3efTOSOO2gZUZWPwYkgjo50hK0ANHI15q5RQouGapzdCh3Dwz7SymSzo0Nl2ncSOtavz77ZAuOAbtIk70IHaJCU0DwAJLWNwUF4d3S4EToACl958UUPSzglkDIrHR2tBxxrLmAqocPL7+zspH256WU6hYU99RSdv7oCMW8enc9O7gUfjg7j3zLq6OhI43SnH6HD1dqIWUIUQocaINgJHW6OjRuhA6ATJYrQFRuGhkjYtxU6LISKgw8Gagpa8VzfMdZfENTRocSmsB2fs2eTpSZdbiWtn2FcdQUAeirrws/RYVxi1i4c1QvV1WgupQSasc/RYbh2IxU6pATeftv8fT/1N0DlHhyMgeJkjp3QoXoXaZhCZTJGNjs6rPJzAMEcHbNnjxVQSkqAL34ReOSR1HjFAEiZFDoAym3lSuhQ8fdukntGgQpdcRpgbNkCPP88cP757p0W48fTDLqfZKTpSESqqK1N7UyaCC3K0eEmdEVFSnlOSDptGjWifpNPRolqVO1mf5QA9sYbkRfHE8YQB59Ch3ElkdHBaT/8OTrcdFAWL6aRsdd6qrOTOjMZFDpSkpGO0PW1d+YC8w9VV5M66GWmad++ZFJVJ+xC9Lq7Kcny6Ya5nAMOoP/TqSL3IXQYHR3qXBrozHKhI+LVekLFRugoLjYROoaGnG14TsvLAt4cHYnPqHKNETqmTLEPXYlgQsDs+h2lqIjqUYs+ZIGQOBEv4Pm9Nv05P/l4dOxcNUGYPZvqzHS5KrV+ju7oUKdsd3kt9Z3cCC+trbQD0z9NIypHhxBYN+EoFIkhu8V82NGRwDFHB+C88opfoUPVXdk2IRUSdkKH6l1MTEM5mEyhhI5scnR0dpKKbpWfA0guU+Z1inzTprFhK4ovfpEG4b/9rbd9WrB3L3UQfAkdc+emd2CvU19PA2unSu/OO+n+vPPc71sIb0ukKUxCRyJl8mTH0BUvjg6ly+3c6bEc6jzPxvAVNyvhZOvKKyEKHfqqqKZCh5tZYSm9OToA73k6zJb2MJJOR8f6lwAAe2stpvscZopNcXsMAXuh44UXaICj8nMoVLD5unX2+w5B6FD1Sn/XoHf3jl86OmgSwMuAMceFDnVuVlWZCB36BlaEFbqijl9i0GHp6JgyxdzR0dpKZdYvspBQOrvlmNku9KS9HSePPIsN7bXWRtGwcnRE4egA0he+ok2oGENXAKCnvIZEDjdhYm7DfSdMoDYhbKEDwLqigzGn5D3THLSKWDg6DEtD+3V0OFYT++1HOw5b6FD1VEzDV+yEjs0gN8fhQgibnhGT06jeVTY5Ot55h+7tHB3FxVQ5e3V0tLRYCx319bSe2803JxvOAKi20ZfQkamwFcB9WNAdd1ByRDUj4BYvS6QpMuHoMCYjNXy/F0eH6jd4HgsohSSbhQ67TtG0aRSzlW1Ch6FjErajo68PyTfc9OJ6e8m94GaQfvDBtJ3XPB0ZFDrMcnSMf/0FFGMAe8tnmH/Ij9ChHB1uUKErZiLCk0/Sn3nCCamvu11i1kOHc2TEIXRlpCiU9sgVKiGul1xIOS50qMvTt9Cxbx8dLzMVwGuOjtLS0T/eNnTFytERYSJSwEHosOpD7tiBk/A8ADKAWn5eOc78EJWjQ3Xe0il02IWulCf+Xzf9Jy+TQ/oSsyH2tZoHGnDA0FpboTYWjg7NjSUlXS9+hI7+fodjUVAAHHYYOzo8Yid0PJe4LwWwUgjxP0KI/xBCfE7bZroQ4nNBb9H9PMaRioqxiRczjdPSsgovq4MA1Bju2QNbH91ll1Ejcvvt7vdrgeqg6EJHW5tDn3XXLvpgtgsdq1dTZXv++d7372VQqUi3o8MsR0cAR4dqd7zqO75DtNKB+jF2nSIhsjMhaWcn9UQKC+m5l2X7enrCD11xu1oIQJ2dY4+N1tERsoPAzNEhXlqOmuJO7O0oMv+QX6HDi6NjcNB8gPbUUyRyGGfHp02j88aN0CGEK9Glo4M0LsvQFZSkT0To6PA+mxsjoSPlUvXi6Bg3zjyfmFehQzt3bR0dra1jRQELoaOvD/jCFwyrt3jEUeiwCz3ZsQOH4w1MGDdkL3QA/lf/i3LVFSB9QoeWo8MsdKWnZCI9cHOcNHeIlMDXv04L5JmiLzEbkqNjeBhY3zEF84bftp1IjZWjo6oKvb10vP2Erui7skStvGLWRgcVOmLq6LDoYQAAfgvgUgDFAGYA+IHhfQHgUAB/CVgGCeC2gPtg/KISL2aTo2PNGqrh99vPfjunZHJGrFZc0TnuOGDBAkpKesklgVb5MBM6AMrzabncVqYTkQLuhI477qBB4ic/6X3/Xh0dw8NUgafb0dHWRvHZg4PUEgdwdIwfT/1gz2OBXA9dAUjoeOqp1CmqTGNcaz5gMlJFitDhJRmp0WHixFFHAddeSwXQY2fscCt0qPM9RAv8mBj/gQFg5UrUTBi0bnr8hq64dXTo9ZyuMuzcSR3Ja64Z+xkh3K280tFB5bBKpq2h9FTL0BWU0v6mT3fcV2Da2713knNN6DAMhnWhI6Wa9eLosDrnvMyUWggdW7dSE6g0WUyZQvd79ybbB8ByMuC114A//5lWgL/wQudimOHK0bFxo/l777+PQozghCP68NxzFkKEvnKL+n1eiCoZaWUltQ1j1KaIcAhd6S7yMGPS2jq6mlVnJ3DDDbQ/09Rvc+bQ5F5HB10QIQgd770H9A8VYR7WkZPaWMEliJWjY8IEdPs8FfVq1PYSmD8fuOkmareMbQKHrphi2QpLKd8BcDGAfpCood8Uxtf93phMUlOTXY6ONWuoM1lkp8PBu6NDZYK0c3QIQa6O1attfJbu2LKF+kpqskIJHbbhKytXJmfBM4WT0DEyQkLH6af765R4dXSoGeZ0Cx0Adbwsspd7cXQI4TMqoLaWerjZ7Ohw6hQtXEiC0erVUZfIPcZcDpMm0YDGTY+rt3e055kRRwcAHH44HdM1a9xtD7gXOoDQB61jHB1vvAH09aFmSmG4QofX0BVg7LX19NN0b8zPoXArdHhYWhawdnT0ozR9lmIP5R4l14SOsENXurqsnQSFhfR9PoUOIegyTzlF1fVrDF+xcHSo+Z0gq/8GdXQAwEmnFGLtWvOom8BJ8aMKXQHI1ZEOR0d/Px1ou9CVYo9CR2Jfavxq2fecO5f6WMp5GUJfS6UxOgDNyZPQhNg4OoqLgbIy3+Yi13mL7RKScuiKKbbTDVLKOwDsB+BbAO4G8BSSIS0SQFvieZBbsNEkE5xsdHQ4ha0A1FHduZOmO9zgxtEBUHLN6urAS82qFVeUKcS10DFvnvsBTxSMG0cVpZXQ8eKL1PD7CVsBvDs63C6TFiaqM7lnT3KgFcDRAdAh9Ry6UlhIYlIuOzrUFFI2ha+YOToAdwKc22SkXhwdXoWOBQvoftUqd9sDNMKoqLDPwp8uoeMlSkRaPWu8tY6h/pMok5ECY+u5J5+kukYdYyMHHECiuZ0o5kEwUHMMVjk60h664rWTXFFB9VS+Ch1O4pqr5RQw5tzt60u6QVMMBWpywaXQoYwWkQodNTXUuJn1xXbsAEpLcfIHqaJ84QWLzwPernWdqEJXAPoT0iF0GNpT02SkBeNTt7VDc/io5sWy76nyrClHcQiODiV0jDo6LIiFo0PVm0L4Nhe5bnoPO4zurYSOhOBixte/Dtx/v8kbMXd0OEyZA1LKHQD+V39NCKHWNnpZSvlvURSMSSM1NcBbb2W6FERvLwkSn3ORuqW+nhrW3btTLZxWtLRQi2E3owlQB+cLXwCuv578dwn7n1f0pWWBpMvMUej4wAd8fV+o1NfDMkX6HXfQMfroR/3t26ujw+2AOkzU9OqePUn7uYWjwy6juI7vPI9eQ7TSRVsb/XinEIfGRmpIs0noMHN0APSbnOoSg9ChxstACI4Ot4PMOXOoJ+VF6Ni929mBFZHQMSYZ6fLlQH09aqaX42WLvGooK6P6OmpHhy50SElhVqeeqsUKGJg3j1xtGzdarwzmQ+hwDF1JBx0dwKGHevuMsqtl+2yglJZCR3ExnWqhOzoA98emszOlw9DfT2a4zZupL6EWWxrtv+grr0hpGbqSNqFDShI7jGLL++8D06Zh0RECFRVklD3nHJPPA8EcHYWF7uyVXpk9G3j2Wfp9AUKZHTFM6JguLysSD5xmTAYH6Zi4FTrmzqX7lSvpPgSho7mZTv0pYtDR0TE4SFWqi0i/7ETrT/hdAMh1jo6JE6mesBI6EoKLGTffTKfOmOsvnx0dTJ5gTLyYSdatowbFbmlZhddEjZs2UdiKm8bqy1+mcgRwdRiFjvJyancsG5vt2+mWyfwcCquwoMFB4J57SORwO6AwMnEi9eLc+hXVQCfdyUiBVEeH4fsHBqiD7LZx9i10TJ2anUJHe7u75esKCmh23DITWgawEzqcsFl1RWkbnpeX9eroKCggC+sbb7jbHqCBkZPImy5Hx/LlwOLFqKkV2LvXJvdpdbV7ocPLEr0A/T+TJqXWc+vWUQVtFbYCuFtiNgShIyV0JZsdHYB710ImGRigOBAToaOsjI53ZI4Ot8vLGkJX9t+fHjs6Ojo7adInU44OPceGkR07gKlTUVICLF5sERFs93k3KLEpCiFi9mz6j6M+vw0TOqahKzLxwEnoMIgmeuiKaV1bW0vnsBI6QgpdOeAAQOzXaCt0pLSZuYpWb/oVOjw1vSohqU05jKjm0bTbrYTamDo6/AodzyduVnMxTC6h1kAfGXHeNmpUzLmb0BW3y6AqNm1yDltRNDRQaMZ115EM6pH+fmrfdaEDcFhiNhsSkSqshI7HH6fOyGc+43/fXgaV+naZcHTs3m0ZutLf720Cyc+qugBI0MvW0BW3/8nChdQwuw0zi5oQQ1cck5FGkaMDIPFo1Sr3K6Rki9CxYwe56xYvRk0NjT8tV6LyInT09dH55UWAra9PFRGfeoruTz/d+jNq9GmXp8Njjg5lXtFJe+iKlP5WXQEiW5Y4VCxGICrvrm+hw8nR4Vbo0EQ6Kem6qK2lvyNF6Jg0idwLutChrpFMhq7o5dB5//1Rl9zJJ5M2O6aarayknGx+hQ4Tp05opGvlFUM/wzR0pa+AzhGnjoRhX+r06++3OMRCkEtQLTFrUwfcfDNFT/zXfwHLllkPG9atI/MbGhttQ1dSlmTPVUIUOlyZKubPB955Z6w6ZNPudHdTvWJ6nFUuIRY6kkgpl0gpT5FSXhl2gZgMUFtLtZWvUVjIrF1LF53qTNrh1dHR0uJe6AAos/EZZ1AYy23eFgZSYoYnoWPFCpqpzWQiUoUSOoyDqDvuoMHHBz/of9+qEXV7vmXa0WGRI2RgwNsiIoEcHV5y0aQLL8vQLVpEvWWnJI7pIiRHh9WqK3192hMvjg4vg/QFC+hzNp3IFDIsdBQXJ/JLL19OLyaEDsBmfKNEeDf4EYuMgu6TT9JqX3btxMSJNKsektCxZw/9LcbJ6GToSll6RITubqpj/Dg6cljoUHl2SkupTh9t8rw4OoIKHQY3kp7/adYsg9BRUEDtkx66oi4gk/BKFYFqu6y9A4EdHYm+2kkn0U9dtsywjRDernUjTmJTEFQnLmqhQ+tnjIyQ+UjVAcr10NMDqn+c2ilDn0kfvzqGrwC2ExiPPQasXw/86le0And9PfDFL9Lr6rzt6aGo73nzQJOGLS2WgnwsHB3pDF0BSOgYGiKxQ8em3VFVkGV1VlnJoStMjAkaHxkma9ZQhetmBKli6d04OtraqBKwW3HFSFkZ8Pe/U7z2RRfR8lsuGV1adqYEfv974MADgdNPx4z3lmPru73k3jDWOCtXAoccYp8sMF3U11OYin5OdHUB//gHLSkbJBY2FxwdJSXU8qjQlYKCMYNQr46OQDk6RkayJ7xM4dXRAWRHno7hYer56R0CJdg4nZPDw3RduHF0FBS4Tynf0UH1jZcTSiXLdBO+ImVGhY6eHkPYSnExsGiRc9Njt5qDEdWb9+roUO3H0BDF4tu5ORR2K68oZ4QHocNs5cXRc6ksTSKC34z96jM5KnTooStAcrDmydERNHSlv5/qlcRoR1UZpkIHQEKb7uhQ14jB0bF5c3J8mRFHx8BAyjK4xxxDVZxp+EpNTTBHR1RCR7ocHVo/x5jovKCAjn13N73vNXRFP/2sUq+NJiQFbOuALVtIsNq9m+a9Tj6Z7j/0IWpePvOZZMT3AQeAROO+PktXKjs6CJXuzLXQAYwNX3EhdFge5wkT2NHhhBBivBDiDCHElUKI64UQfxJC/Dms/TMRohqpbBhIrVnjLj8HQC1mba07R4fbFVeMlJfT4H7JEkqQetddrj42KnRc8xXK9zFhAtDWhhlrn8DufeXoO/J4apgPPBD4xCeAH/0IePnl7AhbAczDgv7xD+rxBAlbAfw5OpS3OJ2o3DVqQG9IxuHV0TFxYnKlXE94dS655K23yGHkOyqmrc29o+PAA+lgZYPQoXr8+sy/W6FDi8GQ0mHVFYDedBu64nWlpUMPpXPSTULS7m4qh5PQoQZsETg6UoSORYuAsjJ3QkeUjg4VFjYyArzyCnX07PJzKOyEjr4+GrSGJHQMlKUp/wULHaPPAbgTOqR0F7ridGwM566qP1wLHRahKypspbg4uNBRVGSTeNvqQt65k+4TQkdZGYkdzz1nsg8voqaRrq7oQlemTKGCj/kTQkb9hxMnjgoduu5dUaE5OtyGrvhxdJSVWa7aAZDeM2sWXfLnnQfcfTeJHv/8J3Vln3oKuDLh9T/wQCT73BbOw1g4OkIQOgAP1ej++1Pl4EPoYEeHD4QQ04UQfwCwE8CjAH4K4AoAFwO4yOIzTwkhtgshtgkhXMQoMJGielmZdnQMDJAnzk1+DsW0ae4cHaqS9Sp0ANTCPPww+fQuuAC4917Hj2x5noSVGU1/o9VbXnoJWLkSM37/fQDA9hsfBL73PRJ1Xn8d+OEPqXE6/njv5YsCM6Hj9tuphQtaRj+OjnSGrSiUPdgim70fR8fIiI8Op3IuhZynY9kymt2xyRNmj0pG6obiYgrszYaEpGYD4uJiGqx4EDqUzd3S0ZHYznXoileho6KCBtxuhA5lc3cSOgoLqcMTldAxMEDOtcWLASQvK0stQwkdbtRBv44O5Vx76imy0LtZ9eqAA2gQZ3acPAoGVkab0dAVdnSEg0uhI+XaVRtY0dND56bdOVdVReemXQ40C6GjrIya3LY2w2Tr5MmuQleU0HHwwcGFDlujaVUVia7GPqRqs5RYD3IDvPqqSXmCODqiDF0RIj1LzLa10XEsLHQWOtyGrmg5OoqLqXp3XGLWpk23yj1XVgZ8+MPAn/5E8zHPPw/ccktiJVTlorboaOS8o8MQdhZU6HClNRQVkfs7TEdHZSU7OswQQpwFSkj6BQDlAIR2s+M2AFMTtwuDlIEJgWxxdLz7LlnDvQgdxmRyVqhK1kvois64ccC//gUceyzJ2A88YL7d0BDw/e9jy81Po65wN8peaqLFqxNugBmz6H7rYR8CrroKePBB+t2dnTTFftFF/soXNmotXCV07NoFPPEEuTmCrgGmGlIvjo50hq0ojI4OA35ydAA+xgMROTqU9udrJkVKb6ErALmVVq7MfK4RqwGdm2WPNaFjzEoiCOjo8DPAVAlJnXArdACmg9b+fh9OJI3e3sRA6Y036HgkhA5Xjo6BgaR33g7VSfOYo0MCGNm6nfJzHHGEO1F13jy6X79+7HseBQPH0JWS8IUnUxzKPTJic+mqHnqQk0Snr49cjmH2SbwKHW5WTVKjdSdHh1ra1grD8tJGRwdAOQ9GsQpdMRE6ysposj5SoaOggOpPo2KphA5tye6TT6bz6MUXDfsIkqMjytAVgMJX0iF0aCuuAKn9C1+OjoRTUUVqTpvmQuiwcWla5Z7TKSwETjwRuPDCRN4h1eeOq6Ojpyclt5Fa6diPAdmTXmxcdW1kxLYfwaErPhBCnAbgPgATQcLGAICnAPwCwAaHj98LQNX6H/NbBiYkssXR4WXFFYVbR8emTVQBBBkwjx8PPPoocPTRwKc+RaEcOi0tNF3xk59gS/2xmLWgmjrOGjNm0P2Yxmb8eLKiFxb6L1+YGPOf3HsvVeZBw1YA92ECikw5OiZPTuboCMHRoX625zGLH0dHb69jx0xpf75mUlTiQi8rNJx4YlLQyyRWIQ4ehQ513DLm6ACAww+n/9mp4xtA6Ni9m/JzXn+99+IpRh0dL71ELxx7LAAXq0o6Wj40/CR0nTYNP8BVOP5TM6hsbvJzAMklZs3CVzwIHYODtLmZ0FFYSAOFgZLx6bEUq3JbXNMf+hBw2WUWn62qovogSMZLneuvB37wA+C++8LZH5Asm2FAbCl0CEFv2F2/blxE6rq2+w/VsbcIXQFMlpjt7Exu2NpK/0FRUcpu1UJzEyZELHQA5qEnSpzXhI7Fi+ncHpOnI6ijI6rQFSA9jg5tQsfM0TFunMccHRMnjvYnOzvpFLVNhj9jBp1wNm36aEi2jdAxhooKOl/j6ugwXLtqASA/Kx17WqV7/nxyFarwsK4uElQ5dGUMvoQOIUQ5gL8AUJfhvwDsJ6U8Q0r5dQDv2n1eStkL4AmQQHKQEKLOTzmYkFBLe2Xa0bFmDdUOarbMDfX1yRhrO7yuuGJFZSWJHYsWUUDiP/9Jr999N82uvv02cOed2DLhUMxqGCtaWAod2UZpKfW+Veaq228nH+JhhwXfd3ExtQRehI5MOjosHCV+HR2eFzcqL6cPe3F0/PCH1BCOZtYbSyBHh58EsSeeSPemmejSSLY6OvwIHW4TkgYQOr7xDdI7lQ3eD6PJSN9+m47zzJkAqCNfWRmS0OEzdGUVFuDdzUXkxnOTnwOg2U8haA1FIx6EDtXkmgkdQiSWPC0enx5Hh6qYLMq9fn2yuRuDp7URHdi2Dbj6anpsdnz94uDoMJ1ZdhIq3To6APtj45CjAzAIHeo6Vtf13r2mYvzGjSRSjh+fBqHDzJGhxPm6ZBd//Hgy941pBqqr6c9w494yEmXoCkCOjp07ox2NaxM66v+3DF3Zt4/qKysMkzP79tGpZSt0FBRQ7ocpUyx360voAKjvbSF05Lyjw+DGCrLSsWdHB5CcOHJodzh0xTsXA5gOQIIEi49IKV1Mq6fwsvY4hNET4xshqKeVDY6OhgZvq45Mm0YzSXq8qhmbNvkPWzFSVQU8/jjNpp5zDvCxjwGf/jTl21i1CvJTn8aWLeaNQWUlNThZL3QAyRUJNm6kBIJhuDkUbuyXCgtHRVD27XPot9TWUs9i+/bQcnQAAZaY9SJ0PPwwtWxr11puooQOX303JQh4cXTMnEnXYKaFjpAcHeqhnretoIB0vBShI0pHhxI6nMJXfAodTz4J/PWv9HKQgdKoo+Pdd6kzrU132U7k+nF0eExGuhN16BkoogIed5y7z5WW0rkc0NGhhA6rv6W0FOgvGpcVoSvd3RQ+Ydp2eVob0YFvf5sGcTNmpFXoGCNSAs5CR1iODsO5q6+6Mm0aTcyPcXQAyfCVvXvHJCKVkpruxsY0Ch1mOTpqasY0lCedRLnXUw5tkNX/gowu3aBWXkmJHwoZbULHuOoKYBA6APtrzTA5ozs63nvPJsLs9tuB666z3K06B9WEnWsaGy1DV2Lj6AhJ6HCtFRtXXgkqdHDoyhj+XXt8mZS+AjP1Fmw/n+VgwiKIbTAs1q71FrYCJJNm2g0CpQzP0aGYOJFyVhxyCPDQQ8B3v0sDuMZGtLZSg2Sletuq6tmEEjruvJOen3deePt2M6hUhOzoGByktryuDvjOd2w2VNOsvb2ZzdEBJFeHcMN77yUFDotVTrq7k31kXx0MJVJ5/V9OOomuk7Bi+f0QxNGhZhstHB1AYnCqh664XV7Wj9AxdSqdyG4cHaWl7mY9E72tnh7gS1+iKI0DDgjWBxrN0fHuu8ns/glCEzr27SMBxUsvs7QUOwrq0YtyyBNOtF1tYAxWK6+E5OgAaHw4UFjuc7kmj3R00IjaYkSrTv3ly03eDGtZ4pdeImXtG9+g5N/vvBNsfzpqpG8idOiLemWTo6OsjMy206c7CB2trWOEjtZW2q1ydAwO2hr8bAkUuqKFrSiOP57KklJtWS1R64T6YVE7OoBow1dchK709MBdjjMbR0d3t82pOH8+CdEWbNlCzY2XahIAicJbtpgm+YmNo8MQuuIHT46OyZPp2vIodNiGrvT3+68kshi/QschiftmKaVtmIoNeo/SRxY2JlSUTT9TDA3R7I1XoUMlarTL07F7N7UQYTk6FJMmAS+8QIPKn/xkdO01J3tfTgkd27aRyn/iicnGPgzcOjoGBqjlCEnoWLECOOoo4Fvfog6u7QSNPs0aYo4Oz6ErgDdHx+OP070QljP9en8tbaErAAkdu3eHO1PrlZAdHWZCx6i24SZ0xZC13TOHH+7O0TF5srvA4URv66qraEb4pptoDBLY0VEyTJVjVEKHmrb0EBwtJbBTToFEAfpPOdP15wAkhQ6jABGi0FFaCvQXltNgLuopT5Wx3+T46bk0xySRBMIROkZGgCuuoLru29+mENaWlvB+t/oBhgtWLREduaPD7tjYhK4AJkvMughdUaFmSugA/F/DgUJXtBVXFOp8TxFP/To6LHKvhIpp/FCIqOTeDqEr3d1wl+PMkNdMd3QA/vufVk5lRxobqQ5TodAa7OhIonLpuM7XPn++Z6FjcNBi/6qeiqGrw6/QMRkUtjL2rPX33QGXcWAUq1YBb7/to8OcaUfHpk1Uux90kLfPuXF0qNjAMB0dinHjxuQUiZXQ8f77JOSEGbYCuHd0qG0Chq7s2wd87WuUA3H3buD++4GFCx2iCvTRh8n3Z62j4/HHaQrwmGMsHR16uGwgR4eX0BWAhA6ABMJM0dlpPvM/aRL16O1mNNQJU1Hh3tHhFLrS10dCr1+hQ+UGsiu31RqmZlRVYVXbbFx/PfCFL9AqCWFY38uH91GH3kTosF1eFnDv6PCSnwOJfI6SLuLe413m51AccAAdFON1qS5wF2VxiigqLQX6RXnqfqPCZmlCfdWdyISO22+neIZrrqFjd+CB9KVmK9v4obubRouGVcOMoSsp9WEYjg43+UvU+p+JQjgKHS5CV1Qdnzaho7qarkG9Htqxw9TRoWbxU461Y2ZiCyycOqEyYwadN1E5Onp76bh5CV3x6egAggkdvua7VN/bJHwl5x0dIYeuAB7DV95+m/oPLoUOwOJYq/YqhglJ/QoMqrr0kExhDPXa4wzHTMSHL34RuOkmH5FAmXZ0KKu9V0eHakTthA5VuUYhdJigOiRWDcKMGdT+Dw6mpTj+USJSURElXg0Tt44Ow3rwfnjoITqtfv1rsuKvWQOcfbaLMagudJh8v1dHR1kZ9WV9Cx3d3c5q+9AQ8NRTwAc/SErOqlWmiXr1/kZaHR1z55L3NZN5OlSYiHGZZPVb7AQ4h1VXAIPQ4cbRYUhm5pkFC6hnbGfz9yB0DFdOxCWDv0FtrcTPf06vBRU6enuB8r52emIQOswc76OomAK3jg6PYpFKWA8APY2HWG9ohtXKKx0d1Gl0sYKWanKtdNySEmCgoDS53yjp6LAULtWk+YQJwGuvmdSbQYWOri7gyivJbvfZz9JragIhLPeXxQgka3J0aG4aM6Fj61ZtJnbCBDo5du+mF9vbLR0dKkcHkAZHB5CsP6W0DF0xFTr8OjrciE1BKS6m/lBUQoehn2MWujIqdDiFroyM2Obo+P/svXmcHEd5Pv7U7H1Jq10dK2mllWTJknzKl2wLn1/OcAQIR8wVICEkIRAwwSTkm4QA4Ugcwp2EcATzA3N9E8DBgMHHymDJ1mVZkmXLknVrpdVK2vs+6vfHO7XT09NHVXX1MTP9fD7zmd2Znp6e7uqqt5563ucF9IgOzgMoOoSa2sGQtOgVHYZTV6y79MWVV1Jjee45JaLDsUsTfViq6JjFGVDFlEsY0ymiAwC4xfL3Uc19pLBh0ybg2Web1CfRQtERV+68KC2rquioqaFj90pdEZ2r6dQVFxw/TgO5mxy5vT0XAyQaguj4nd8pWC0KjAgUHV1dwOtfD7z61RS/P/YY8JWv5MYBX/sEw4oOxtQ8WPMgW2J22zb6gpe+lCbAAwOOqyhHjuSCqEBmpKoqBMZyPh1xwW1CrEh0GFN06JhoWnHllfTs5dOhQHR8afdN2IHr8IWPD8yeEiNEx0h2AuOg6Ojrc5HTMkb3XkiKDivRMTquGA6JibgT0SFJWp07R31CNuuxADU1wDgiIjr6+lyPW/hz3H47cak7dtg2CEp0/NM/UYf9hS/kCEhBJIVIdHBO96o20SEzyZZZKbV59NiJjo4OWhiZba+M0f189myuv7KN0YcP0yaNjbmfHQnRIYiK/n76IQ6pK55Eh6pHh4vJrHF0dIRHdNjiHKfUFVFels9tzv+MHYODRHZk9zU9Tddvzhy6FIzpER1+3nOeWL6cvriUFR3Z+9wE0aFceWXPHiWiwzHmS1NXCvDb7HMTgFeqfpgx1gxAOBtOAHhM8zhS2LBpEzAxUeGbsl2A1laKYKKWLU1NAffeC3z1q1SVQSfYX7zYP3Vl/vy8YGTHDs0JpwQE6+1GARZNiVkRaL797eb33dxMbc2vLLCmcuCb3yTO7Gc/Az75SWDnTuDGG/O38Z2DzpuXC7oNKDoARbMpK0Sw6MeOPfAAHfOLXkSKDsAxfeXoUZI0V1RoBhhiUiSxal2AW26hmyRMYzcvuE1ENYkOuzGbtqJDl+i4+GL6Hq9OX5LoOHYM+Nuf3YCX43688bazs68HITo4z5qRDp2l32hjgFtbcynqjlAhOhTPoZU3VK5quWwZXWz7RFyB6OjpcSfEAdr9BKpz+w0THsct5pIvfCE9F6SvNDbSgKdzjEePkjv0m9+c30k3NNA5NmVI6jADsZp+ais6qqq8B4LKSmIJ/BQdlrZrrboCuFhELFxIRIcgFhyIjlVZcW9kqStA7njEzSWr6KipoeuTREUH4JA/ZBC2OMctdWVmBpiob6YX3AJYG2liFR1VV5OgUif21C4tC9APWbKkNBUdNgVfkErHysWr1q2j/kUQHZWVhSsvWUgrOtLUlVn82PL3ZxljassowDdBJAkH8HPOuUT9vRQyEHGCYx6tF0S0FZVPx8QE8I1v0I36lrfQAPdf/6W3L1EdxA22iivDw+T6/Y//qPd1fvCT9xUN0bF+PfD881RC1zTmzaPZjV+PLiY4CoqO8+fJW+Dyy6nE+N/8jXMc6lv5M5PJBY8GFB1AAKJDVtHxwAMk/25pAS67jAZfB6JDVFvOM85UQZBKOMKnIy5VR6kpOiorqbG7ER2joxR9+RAdnAN//ucAGMO/4T1gA7mG2tSkP0mamKB91/WfITWHjQH2VazLEh1Cn62APEWHahSSyVCFgoCKDq/LUl0NjM/ET3QIEqijg3i1gvgik6H2q3OMH/4wff4znyl8b+3aUBUdou+rrXVZWZZRdMi0uTlzlIgOKwEDeBAdPT2uY6QpooNzRUWHOB5ByjsQHa6TW888NhdERXR0dJB7ud/ijA4kU1cAYCTTSOO6G9Fhaw+C6BDNS9cjLhDRAVAM7kF0FK2iw3bvRqroqK6mOF0QHS5m0uIwPUmlVNGRD875AwC2gNJXLgLwCGPsYr/PMcbaGWP3A3i12BWAT+gcQwpntLcDixaNqRMdYpAK26djdBT48pcp4H3Xu2hl/8c/phtVLBepQkbRYUlbeeopGkgcy+QZQMkQHUAuUjIN2RIkGoqOc+coOHvPezwrpUnNQWcJQEOKjubmEBUdFy5QWZmXvpT+r6sjItFhAiy4PxnBgSP6+tSNSAUuu4w+G5chqVspV1migzGgpsaV6KitdVB0eKUEigahS3QAlL7y1FPO3+PneJnFj34E3H8/8I/vOoYOHM9rqI2NOc9UVcxW5L1wqiBtBTBIdGgoOvI8OlQVHYBziVlFosNP0TE+U0n/hL3SJqHoqK8n1eiWLQ5NLVuWWAmbN1PD++u/JvWGHevWkaLDRErt8HDBZNhKdGgrOmQm2H4kkAvRYVd05IngROqKg6JjaoriEDvRIa6jCiYmaG6vrejwSF0pmNzqmOJHmboyORlOzrFE6sos0THqkwNrI03ELSn4uFiJDofUFcbodxa1oiPbbwpSMDKPDiBXecVn3BkYIDUP4HKuU0WHI/4YuRKxVwHYyxj7CWPsLwDMUriMsRczxt7DGPtvAIcAvAxEkHAAn+Sc7w5wDCkccOml/clTdAwOkjx15Urgfe+jQeOXv6SJ2WteU2gMqAJRHcSJaZ+ZoejAoujYuZOed+0ybwg6MUGH4jUYNDfToFUUREdYkJlUArlBW2FSLVvd0dejA6D7oq7OsXC8rqJDK2WqpYUk0l6KjgcfpPYuiA6A0ldsio6BATqtK1bYJuUqCKLoyGSAm26KV9ERJHWlthZgTN6MlHPviihBFR0A+bGcP+9Yvm+W6BCVGhzQ2wv8xV8A114LvO+d2RVSG9EB6E2UZgmh3i59okNmXAqo6NAmOp5/Pp8BMkx0TAiiI0xFhyhx7KPoaGggouPcOeDQIdtGqoqO6WkqJ7tsGfChDzlvs3YtxQ6yFae84KPo0PbokGlzfiSQD9ExZw7tQjZ15cQJOr0mFB3i2isrOlRTV8Q+VD06olR0AOGkXEqkroimO1ti1m2cClHR4eU954sVK+iLHYJu7TgkCbAsnIyOUlcamaIDIKLjxAkikVz6b9G9ixAgNSOVBOf8GQCvAlVMYQCqsv9/DsAVICKDAfglgC8BeA2A6uxrAPA1zvlHdb8/hTsuvXQAJ09S25dGWIqO/n7gE5+gTu7DH6abcvNmWs196UvdjSxUsHgxBZpOwfDp0zRqWIiOXbvoeWwM2Lcv+NdbceoUdSpeRAdjRVRiNiyoKDoUvSDELmWIDilFh8OEfmaGmlxkHh2MUcDotZr0wAP0BRs35l676ipK6xJlCJFfhChQ6oquogOg9JUDB/JnmlEhaOpKltkQ4g57G8g7p4IF8TrJpogOwDl9RULR8eEPU9f/n/8JVLQURltBJkqzFXlnBvWIDpnJD+faZqSVlfnHqYSLL6aOwLpSKUl0cO7v0VFdDYxPVeT2GxaGhqhT86m6IhQdgEP6imrn9l//Re317rvdZ9Hr1tGzCZ8OB6LDqsrSJjpkFR0aRIe1b3EsMTsykgv0LKkr1oorQERER2MjkfHiRj59mk6qQ5uqrKQh3ZHo0FV0ROHRAYTj03HhAi0AZPsvz9QVUXlF0qPDSdHR16feFvy853yxciX1MQ7nTzsOSQIsBHFQcZGyRweQMyTdvt2zPPjkpKSiIyU68sE53wIiNX6KHLFhvQ2sr4nXzwL4Y875nwb57hTuuPRSukuUVB2mFR1WguPv/55MMZ54AvjVr3I5+qYgqoM4+XQ4VFzZuTNnmL9tm9lDkZX3lT3RoaLoUFQOyCo6fD06ADIt+NjHCl52WnGRgTbRAXgTHZwT0fGiF+VmboDjBFjMyQIpOvr6ApX8ne0D4khfcZuIVlVRhOJFvtmIjqy4Iw8Fig4gfKJDBDtOlVd8iI7Nm4Gvfx344Aez/rUOy0piDqETA81OJjHqSHSI+Zkrl9HSQjvxullFXo1G6oroq7UVHUB++ook0TE8TO3EK6OI2hKjIDRMosOn07QqOtavp82CEB0VQ0NknnTTTcAb3+i+ockSsz6KDjGpdCQ63FJnZMk1GaLDcu7Hxuh4rH1LAdEhGs6zz9Ik2fJ5QXQIRUeQqivSRIeokGRNXWlrc50ZO6ZNBvHoiCJ1BQhP0WExP/dMXRmBUuqKk6IDcBb/eUG7tKyAYN1cKq8UtaLDENFRV0fhmxbRMTHhW3FFKDocwxFhZJqmrhSCc36Gc/5aAOsB/COAzaCUFkFyTAHoAvA/AN4NYCXn/BtBvzeFOy66aBj19YpEx9y51MkGVXT09wMf/3iO4LjtNpJQ3Hdf/kqzSXj5FwiiI9vJjo5SJdvXv54WD1KiIyaoKDoUS8uqpK5MTrqUtRR44QvJS8YGp0BEBs3NFHh4fqcbFi92l3Dv30+RizVtBcgRHZb0FestEYsZKQBcfTVFblETHZOT1Am4TYj9yh7biA4ng/MCM1KxsRuES5gqa2ZFUxNw0UXKio6JCeBP/oTawj/8Q/ZFh2UlEyvCbkSHEGx5pq4A3tfFWlpAAWfO5OJvLUWHvcTs2JhnwGmFGGp9U1cmoG/0KQsfGZxV0ZHJkOl5EKKj4zvfoRPw+c97LxEvXUqzBhOKjqEhT6IjkyGuM68/FPev2yzMhKJjfJweNkWHvTtwVHQAdG5aWvLSf48coXmLmNRWVtJvDJXoAPLVV4LocIEj0SE+r2L4OTREHUiQ/lMGjY10nsMkOrLwSl2ZJTq8Uldqa2fbrpOiA1CPPwMTHWKx0cGQVNsrLAmwpK4EJToY07A6Wrw4J4uUJDpcx7o5c1JFhxc4589xzv+ec34753w+KJWlkXNezTlv55y/nnP+9bTCSviorOTYuFGR6BAVJnQVHX19OYLjox/NERw//nGuzGVY8FJ0CPY4y8Y/9RRNMq+5hngX00SHGAPFYOKG9nY6XK0JbylAVtGhMaFWIToAvQE2iKID0CTNvUx3H3iAnu1ER0sLtX0L0XH0KA3Era2aAcbEBEVbQVJXqqpothS1T4c48W6NwzTRIavoCKLmENiwwZ3oqKpy/M1799Ji+cc+ZpnIVFRQUG84daWuhjtOfOwLwQXwlXxASxXDOSk6BNGhpehobaU2I4gO2c4HckRHdXW2LQWSgknA57jtAfymTcDTT9t4atljPHgQ7f/938A730kDsRcyGVLNRKDoAGz3LuBPVJpQdDi03fHxQluo5cvpFpi9B+1EhwWHD1MoZs341C0RrUR0WG/k06f1iI6ZGbUBUlxXE2nQfujoCIfosClXRXxRVZXbRJz/4WF4p65cuJDXHtwUHSpEh4z3nC/a26lBulReKVpFh8HUFUCjq2csp+rwITo8U1cA6stSRYc8OEEndEhhAJs20dxGyTxOh+gQBMfKldETHAJiMHVTdCxePBs1CH+Oq68mouPpp80SmMePU/zhUsp6Fu3tRHLEYVGQCDT6lEgTsA3aMujvp77fL/4UgaTOSq6uokPLbEqgrY1mR04Oug88QPnsTpGIbQIsihAxpikZFdcsiKIDoPSVp57SdGfVhN+EOC5Fhymi49Chwg5NGEE4TATEqReq7FnYoi0jREd7q+tkxHPokSE6NBQdg4MU8AVSdDCWPxFXIDpkiuHMtqWYiQ77ZHfTJiKKnnjCspE4Rr8KKR/6EGaqq4FPflLu2NatC050TExQapNpokNV0eF0blyIDidFB2DxXhMNZ3g4z4gUyC8tKxAJ0WG9kc+ccay4IuBKdABqcajsNTCBAlmNIdiUq+PjRHJYu0vp1BXbvuyKjqVL6VmF6JDxnvNFZSUZD7ukrhSlomNyki6IQaJDS7wnSXR4pq4A1EhSRUeKYsGNN9JEescOhQ+1tqqlrgwMAJdeGh/BIVBbSx27m0eHreJKayt12Bs3UuctyA8TkJX3FVWJ2TDAmLf8UkBT0TFnjn8hH5k5qBt0FR2yGTuOWLyYGqzFWBQA/YBHHy1UcwhcdRWtOGejXFFaFtBMXRHXLIiiAyCig3PgsceC7UcFQYmOkZHZhjM2ZkjR4VbuVhVXXknPe/fmv97T4zqbdr2UIRAd9R3uM3pjRIfCeRQk87Jl1FdoKTqA/BKzhhUds6krCVB0VFXlVpg3bqRzlqcanTuXAn+vtv7gg8B99+HYW9/qudqfh7VrqdPSYqKycJmBRKromJlxXnlSJDpm59nWKkoORIcl7AEQIdFx4QK1g3PnPK+x49iTdKJDKDpMlDu2wiF1xX79C4iOsTHne822OCQyI8WijKicohJ7Bi4tK7ByZWkpOmzjjokCQFpdvSLRkaauhATG2ErG2A2MsYuj+s5yxg030LOyIanKAPPEE0Qu/OAH8RAcVrjJ+o8eLTAiveYammdfdx29ZjJ9JSU6FOAlvwQomNA0I5Wp7hgkdSU2RQdQ6NPx6KP0I7yIDs6BPXvAeU7RAcSs6Lj+epo5RZm+4jcRNaDoyDunUSs6gML0FQ+iQ1xKP6JDzOW0iI5hyrevW+W+uhuY6LAvW0pAEB2LFklWYHLDxRdTRz48XPypKy7k5chI/kS3qYli6wKiA3CXPk9NAXfeCaxciVOve538sa1dS/1XQT1bBUgSHQX9odf9OzlJG8vMarzOjS7R0dCQuyi2ie358zEpOkTqiri5fFJXCsYe8TtU4lCHlKTQ0NFBE0HTKkSH1BV7bJHn0SG2dToO276cuDhVjziRrWOE6CglRYetvzeVuqKcPSKIDpf+O01d0QRj7FrG2MbswzU5jjH2u4yx5wAcAvAYgGcYYycZY+/R/e4U/pg/n+KDrVsVPqSq6BBykZe8ROnYQsGSJYWKjqkp0nlmlzbGxihVRaQFL1hAb5kiOjhXJzpUna9LCn6KjpERCiY1UldUiI4oFR2BiA43090HHqADufVW589ZJsB9fTSOGVF0BCU66uqIbYzSkNRw6oo9jx7IrU5xjmg9Otrb6V4JgegIZEZ6is5n3eqlrtvEoegQfOGiRTSJC6ToAGgirpi6UlnpvWlNDQ1jM3Oaww1AJRQd9uB90yZa65j1mfLr3L7+darnfvfdlLoiCxMlZnUVHV75jSrLt6JdShIdY2OFY8uSJaSicay8YlF0iAXz2FJXxsZyB6GbuuJXTtqKqBUdgFmfDs6pI7alrthvkTyPDi9pqIOiw94tqhIdos35ec/5YsUKil9s91PRKjpCIjqU48Orrwb++Z+B17zG8W3RxYjuwjXmTVNXcmCMrQSwDcBWAF/k3FnHxRh7I6jaykXILzO7BMCXGGNf0Pn+FHLYtIlWXKRVdkLRIfuB7duBNWuCS9hNwEnRcfIkRWHZWd3evRQ0Xn11bhOThqS9vdTRyRAd8+fTQKaq6PjnfwZ+9jO940sc/BQdtjJpspAlOoJ4dDjVuZdBoNQVN0XHAw8AN9/sHokuW0aBz5NPFlRbDqToMHHf33IL9SPas0xFyCg6hoacfVAAaizZ8+zl0QFkdyHTyGylJbXBGJFa9hKzPkRHJuMghLBFW0HKU46eIPK8bq17x2gt1lCAxkZiBEJSdLS1GVB0AJS+oqjocLFOmYXoXyYaW8KvulJV5czcoVDRAVB8MThIiwcAHKv15O3/7/6O7vff+z21Y1uzhp6D+HSEkboibgbZ1BXAm+iwtBknM9LKSvJXcKy8YiE67KVlBSJTdAC5RqFjRgoYT115+GGKnQJDBHcmiQ5Rhs0ndUW0h9nUFcCZlLd5dDgpOpYuVSc6ZLznfCFWWGznr2gVHTaSMjaPjkwGuOsuV3mgOMzmZp/FLb8y2EUKXUWHdQn/P502YIw1AviKx3cwAO9ljL1C8xhS+GDTJhovDh6U/EBrK42wsg6mO3YA116rfXxGsWQJER1WksY2q9u5k/61Gr1ffz114m5VO1WgksfImDqrPjQE/N//C3z1q3rHlzj4KTrEewlUdIhgOFJFh9AdWgm9EyeotKxb2gpAje2qq4Ann5xVjSZC0QHQxGdqCnj88eD7koGMogNwZ6IkzUiB7HmVyY8ypegAyKdjzx46pwBFzP39nh4dzc0Ok20b0VFVRb9Li+joyio61q9w3aalhc6n470oyrKE4NGRyVBsGEjRISbimkSHF0RbGm9oyZWuDQOi03RhXdwUHYAlfcWrc/vHf6Rg5HOfU6+O0dBAg6oJRYdtQjxrlJu9TZWIDtHmgio6xPnySV0BPErMWsZIQXTYPToaGiJSdACk3AHUiQ7RGckSHTMzpKTykRp85zvAJz4ht0tPLFtGzyaluA4LOk6pK4xZ+im3FZOxMdpAQtFx7pz82B+4tKyAaJS29BXtMvdxI8TUFZM2MAMDuRLTnqRSqujIw4ssf7utL78DQCsADmAEwDsBzAPQAeAH2W0YgL/VPIYUPigIRPwgoi6ZQaa7myZZwugibixeTEuo1mMXREe2c921i8YSi2UHNm6k5+3bgx+CqmGTKtHx6KM0fxGBTNHDL00gZEWHifKykXp01NRQAGMlOn71K3p+2cu8P7thA7BvH44+Tzpzq6IjNjNSgDqpTCY6nw6HSUUe/MoeW9gNLzNSIDthijJ1BaDrPDaWY7dFKqKHosPxMjroZxsb9WKg0W6a3NVdtMR1G9+FXD+iY2CAZgIKEWZ3Nw15FRUBiY6GBurMrUSHxPVUIjrqs+0yLFWHT6c5MlJ4alesoHmsL9Fx8CDwxS9SOVmrnFIFa9eWvqJDh+hwSF05fJjuafuwGVTRIbWaL45j/356FuS8AxzHnooKOnhZouPAAerERLDrguFh+u2BecIFC+gY3cq868BhQccpdQWg5jtbXhYoJDocFiEGB52JDkCerzFGdIjAw2ZIqqUsTQJs/f3wMIUzqgtgVsydSwIfkyJXEWKISnueZqRjY+6K1iKFLtGxPvt8mnN+1mWbOyx/f4xzfg/nvJ9zfgLAWwE8k31vI2NsmeZxpPDAunU0ZkgTHWKQkvHpEMxAkhQdQL5Px9Gj1OtkWfidOynOsi4oXXUVjVt5ZfI0ETbR8dBD9Hz4sHnT71jgVSINCF3REUd52aoqChi15yuLF+fLjx54gHSol17q/bmrrgLGx3HkyV7MmZOb3GqnroilAQX84AekXs/D3LmkQojKp0MsbbhF7V6SYEBJ0TE+Dn/Z0Pg4Rd8miQ4g59PhU8PUk+iwKQi0J0o9Q6hh48hUuocbgYmOwUE6QAW1QHd3bh4WKHUFyJWY7e/Plc72gaj664XZ1JX6ZvojJqJjeLhwRZ+xXHosAHei46676KaQLSfrhHXrSNGhO/B5EB2M5arJFKwsyxAdJjw6xHJrFl5Ex4kTJGQA4Jq6Yk9bEYepS3TU1flXMQOQn7rS0uI543Ml2T3z2GwQjU+C6AD8i7z5oqKC2D2nCn+6cCAnnFJXAAdFh/0HifNmU3Q4mZECcvGnivecLxYvpk7NRnQUraLDlnYmlG+qojUrAi2GucC6llJX56PoAEpO1aFLdCwFKTUcE9UYY00Ars/+Ow3gG9b3OefTAO6xvHQNUhhHJkNlZkNRdOzYQV8QZ6UVK5yMGo8coR69qgrj4+TRcY2tpdXXA5dfbsan4/hx6rBd5hQFEESHbOz24IP0PDZmJtUmdsybRxGd2wxDI0WC82SbkQL+/I4n2tpybXx6mhrFS17iP7Jm79Ojz4xi5crc5iLAUJo/aJT8BYD//m/gW99yeOOWW8g1OSxZvhXWpQ0neCk6ZmaovUqYkQKSig4FBYAU1q2jWZvw6QhCdFiPD/oTpdHzo6ir9F4hMqLoUDyHVqIjkKIDyCc6JP1Wzp3zHytm21KNh/+FCfT3eyq0nBQdAM0vn38+63fiFKE/9BDw059SzqVsOVknrF1LjU93Jd2D6Kitze8PlVNXZBQdXrMXhz7Ji+iYnMz5yzilrhw54k50jIxYSBJJOPmzuELcyGfP+l5vT6JDVtGxZQttL9LHXCAuv4r1hysWLzZLdEimrgASqSsOi0Neig4ZokPFe84XmQwZutpSV4pe0WEjOoLAy+pIF9bh0Td1BUiJjizEpXQLezYBqACRIVs550486h7L3yZuoRQO2LSJyHWpiZWqomP9+ujcrv3gpOg4cmQ2bWXfPgoQ7EQHQOkr27erBwB2HD9O4hGplQ/QYDMxIXe6z56l1PubbqL/HUqRFx/8nDk1UleE6i6p5WWBgJUirYqO7dspCvHy5xC4+GKgthZHTlTmpW7V1hLJISwdpOA6O/bGwIALqXTLLfSGMNEJE34TUS+iQzQUFUWHn2zIzzNEFdXVpO5RUHQ43l6miI6ZGYz2T6CuetpzMyOKDgUjUoBuIzEXM6Lo6OujWb9E5zM9TT9HOnWlNoRlPis0FB0ALaQA2epu9gh9eprKya5YAXzgA8GOT1Re0U1fEQ3XhegQ0EpdkYmBRNt0U3TY7n+nqiuAQ4nZK6+k789WA5mZ8SY6AHVCT4nosKovPSquAB6r+KpEx403+hL9guhQKebiCqcKf0GgkLoyS3QIRaU9dpJUdCzNFsCSITpUlcq+WLmydBQd/f05AytQlxCU6Ahb0eGbugKkREcWIix2y9q72fL3ZpdtrD1+QmbLpQeh6JPy+pNVdHBOio6k+HMAzoqOo0fz/DkAd6Kjr488rYJAVd6nMtg8/DA9v/vd9FwSPh1+fgi9vSQVVZjAKHgBxqboCEx0CNPdBx6gAO9FL/L/XGUl+OVX4Ghvc55JXZ5xpiw0FR0DAy7fc3N2uIjCp8Nv5d+rTVqcCzmXJDoqK+nhdoJNEx0Apa9IEh3CjLQApoiO06cxOl3lO1GSIjq8xiVFRQfnISg6ACLrJDqfvj6alEqnrtSGrOjo61P26AAoFbS6OqsaraigRiKO8etfJxnl3Xcrp7kVYO1aetY1JPVRdAgUrCybUnSInEVJosOp6grgQHS86EV0vrMT264uGpvsRqSAfoloJaKjri53ziQUHY6r+H73usCFC9QefNJWAMOKjrCIDonUlVmPDsDZzN22ODQ1RU3X3jU2NdHtHhvR4aDomJwMvtgYOUTFtCzRNjwcfO1XdMMmi58op66UWOUVXaLjAshI1KE7BZBvVuoWvVqbQwSa5fLExo2kMJBKX5k3T87x+sQJkhgkxZ8DoJ5y3rzcADQ+Tn9bKq7Mneu80iEMSYOmr6gSHSrywQcfpHFNVOYrCaJDRtEh2qQkVIiOODw6AAOpK+Pj9EN/+UsiGy352V44t/5mDM/UYUVHLk9FxiuzAK6zY2+4Eh0LFtCKbRRERxBFh4XomJigybIT0SHO6WwQ77WE4lBaMjA2bKBZ/JkzRHRkMq4+NyqpK01NGkTHoUMYRR3qGrxDDdGEXVdcW1q83QQVFR1DQ3RJjHp0ABTpSlxLH/5pFrOkWXX2tyVM0VFbS4sHeT4dAwO0v7/9WyIxX/e64Me3dCnN8nQVHWJ2aPsRkSk6APfSjQ7lpb1SVwCbIalFQupWWtZ6mKESHUDuZg6SuiIjvRCrd0JW5AHjio5z58ylWl64QESY5ST7pq4AzoGETdHhxcXJesQZJzpWrKDzZ2mIeYsDxQRbv2kidSUKRYdneVkgVXRkka0dhSWMsSusbzDGVgEQS/2TAB5z2YfVgt0Ez5rCAY2NpG6UIjqE47VfLsWOHfScJEUHkFvtBqhON+ezSxtORqQCl1xCnVMQomNykniVMIgOzonouP12Os4lS0qE6JBRdGgYkQIlrugAyNV+2za5tJUsji6mgHBlQ84/umBSLgPXfAdvDAyQmt0xTeaWW4DHHqMNwoTfyn9NDTUMH6LDlsVSsAvAck69llDCUHRceSU9P/UUzahbWx3z6YQ9TqiKjkOHMIJ61M3xZgRraqhvc+XYxeTJra9wSkT3gPA4MKboWLGClDuAdGlZQCF1pSo7Sw2D6JiZofPnQ3S4BfCbNlFIMD6OXOcWpJysExgjVUcQRYeDo6Yv0VFVRXGRl6JDdmbjRnT09zsqOpzGluZm2jSP6LAgEUSHGLN9UlfEKn5Bl9/aSufJr/LDli10bSTiUOOKDsCcUZqIc2weLb5Ex7x5zh4dmcxse/Kquq1CdNTU5OxgAkPIjSzpK1pxSBJgiyeKxaPDNeZNFR15eMDy91cYY3MAgDFWA+DLILUHB/Ag59ztlFqTCEph2pZYbNpEVUWk8vDnz/cfDbZvp6Duiiu8t4saVkmhkMatXInJSfK3cEpbAWisvOaaYETHqVNESKgQHYsW0Xf7DTaHDxNvIzIUVq0qEaJDVtGhABWiwy+rwAuxeXSIVbLvfIcmKApEx5Emul9XDO2bfS3q1BXX77rlFjope/cq71f5IPwmxG5ljy1Eh+XPAhScUxlFRxhEx+7dpLxziVBFGwzVo+PQIYyiHnVz/W8UT8W6mDy5Lck6JaJ7wE50BFZ0VFUBF11EfxskOmZTV6qy0XMYAeiTT9Lg5bICPzlJsYPbZHfTJuoPn3wS1I737AG+8AXgHe9wH3R1EKTErMsMxJfoANwbh0jIlzXl8lJ0SBIdgEOJWQuOHKHDcYpDkqjoABzOt6+8K4utW0m9JjGzNKroEASOqfQVh/HUK3XFV9Exb95smxTNLaiiY/lyM3wlgBzRYUlf0YpDkoAiVHSkVVfk8W0AIhLcBOAEY2wLgJMArJH3lz32IbbjAHZrHkcKCWzaRIPbvn3+26K1VU7RccUVwfNuTcOq6BBs8YoVePppGjiuvtr9oxs3UqCmq0bUkfdVVBA34zfYiLKyL3whPa9aVSJmpDErOgD9CU5sVVdEkPX979OPvP567+0tOMrJsG7FmZxhj/JKysyMb4UGJ3CeC7pcDUmB8NNXZKpiKBAdvlVXsttHquiYN4/MCXfvJkWHhz8HEL6iY7SmGXX1/lGypwehH9GhqOgQi7FWRcfoaMCy3SJ9RSF1RVrRMV1JBxmGouNv/oZO/pvf7Pi2i73FLETmwJYtoN9+8CAxNEHKyTph3Tpi/HU6bJcZiN1nR4noUDXAnTPHu+pKFkL15kV0HHOsd0gLIMuWORPwkSs6JImOgq5RfN5rwW1qilbvJPw5pqdzl89Y6gpgjuhwWNDxSl3x9eiw7MtP0dHd7S+cMVZaVkC4oZeCoiMEoqOpiUglU5z25GS+T0tqRioJzvl5AH8KIikAQJSTtc5K/h/n/JdOn2eMXQtgdfbzT3POQ0o8TQHkxgKp9BU/RUcSjUgFlizJGTUeOUKrbEuWzBZz8Fpc2riRBpc9e9y38YJuHqMMq/7gg7SdiKNXrSIFSdGx33b4KTo0lAOqRIdnp++BoIqOsTFNUk0Ej729xHwJubwEjnZVo6WiD3OeeWL2NeWVlMFBIjsUr8vwcG4S6fhdy5ZRABQ20RGhoiPPoyOq8rICV16ZS13xqLgCuBAdDvpZUZ5SKbvo+ecxWj1HaqKkTXQIFk1D0WGtugIE7FMViA7V1JWJCQSUgrng4YeBX/2KyA6X4xYryG7XcPFiWqSdJToA2p9P6oIy1q6la33woPpnFRQdwn9nFl6KDhXnQeFfYoetTxL9hts6kpei4/BhZyNSIAZFh0TqCuBwz8koOvbupWsq4c9hTUkzmrqiW+rYDocFHe3UlQsXCiquAO6KDs79f4ZxomPBAvohFqKjaBUdIaSuZDJ0vUx19XayS6q8bJq6QuCc/wjAywE8nX2JZR+jAD4L4K0eH7/T8pmf6x5DCjl0dNCYI0V0+Ck6nn+eOtckGZEKLF5MUcqFCySLW74cqKjArl10/65e7f7RoIakIvBYtkztc35Ex8wMxaIvelFOOrhqFQ1Qbqs6RYOqKhoV3BQdIaeuAMEUHZmMEs8wi0DSxLlzcxGiQtoKQHHFijm9WY05QXklxVMG4A7ruOk6yN58MxEdgZbVPSDYJV1Fh4gwVYkOr0Y2MJBXns4YNmwgmf+pU3pER2Ul3Zs2ogOwrCj6gXPy6Mg0Op4nO7SJjvFxWuFV9OhgLEc0iImckcorkkRHQ4Nz+7FCTHby/C8U8PTTBXOKHDgHPvIRGrTe8x7XffgpOgBaTHnsMYBfehmVnb/zTveNdSFKzOr4dLiUQ3AiOgAHQ1KnTmtwUI3ocEpdmZigfTsQHV6KjvPnne/Dw4ed/TmA5KauuBIdXqyECGYVKq4AhhQd8+dT/xhD6oogOjhHThpqHS9tpImfogPwjj91vOd8wRgtalhSV1JFRz7cxF86sItGPVNXqqroYqSKjhw457/inF8BYDkoheVqAK2c87s4516CqJ8DeGf28e9BjiGFPxij8cCIomP7dnpOqqIDoJ75yJE8I9KrrvJOpV2+nFLZgxAd8+crBgTIER1uc7unnqLLIdJWgNyKTUn4dDitSgDE8PT1aaWuMCa/uOvZ6XvAbcVFBmJc1EpfYSwXQCoSHUePAivbJ6nBZclM5ZUUcdCKBJQU0XHLLaRAeO45pX0rH4QBRYeSGanXEopYETKWAJ3Fhg10Dw0N6REdQMHEWnmi1NMDDA5S1ZUwiQ6vZUsXdHfn5itAPESHn5oDsLUlDaLj0CFqto5jxY9/TAPexz7mmYbqp+gAKL44cwY49gd/R6vtMhdcFWvW0LOOT4eCogNwIDrcFB2qqSt2osNhJipDdABU/M6KkRG6BrETHa99LfAXf+E7RriOPTJEx9atFO9JzMCtRIcRRUcmQ2NwDKkrDQ3UrU9MgDru6en8C6qo6AC8iQ4d7zkprFxZ/IoOoSTM9vecmyM6TIr37GGPr4q5qSlVdDiBc36Sc/4453w359yXk+Ocf5dzfk/24SLCS2ESN95I/YqvUXRrK41sbnfCjh10p1xyifFjDAwhlTx9epbomJoissDPE40xsjsIQnToDAbt7XS63Sa9Dz5Iz1aiQwQyJeHT4ZRnClAvz7mWoqOpSd4fTjd1xW3FRQZiYqk9kLW3k4y7o0P6I5wT0bFibfagd+8GoFFeVlyrsIgOILz0Fdk0EdOpK36KDpOlZQWEISngS3S4XsqgRMehQwCA0elqaaKjt5cC+QLMmUM3tRPR4VVD0QXd3Tl/DiBYBaZZXH898Cd/knON9oBHRlEe8lJXNJb5xO8pWKCbmqL0kvXrgbe9zXMfsooOILuYUlGhdIzSqK+nPi8pRIeuosO6quFQXlqW6LCnr4gF8tiJjuuuIzNaH/LWdRVfxqNjyxYKaiUIYtF+GxsNKTqAfOP7IJiepntaIXUFyP4m0XFbg0dFjw7Am+gwXlpWYOXK4ld0iDzO7MkVHk8qXYIb3LLcdOCm6HAVzjY1pYqOFMUJEYhs3eqzoVhmchtktm8neURVlbFjMwah6Dh4kCLJFSuwfz/d1DLm7xs3kipWZwJ6/LjSvHMWfoPNQw8Rp2RNd21ro4GhpBUdYqKpoehQmTfqpq6YUHRoEx1f+hLwve8pfaS7m+6DlddlV8uy6SvKAYavDMAZ1kHb9XyvWUOzz7CIDodJhSPmzaOB3l6mShx4fb2nGWnBOZVRdJjGihW5/eqYkQLmiI7JSmmPDuF1W4BMhq6Ll6JDMXXFSnQYUXTU1QH/8R9S3hSyio6gqSuinRZcs3vuIcLgk5/0zb+TUXRcdhm1DynVaBDolpiVJDoc+0OTig6rMybgSL7qEh1epWXF/ioq1IiOmRk6R6pKVRm4kuyNjRRfurESZ87QKo9E2gqQIzpEyo8RCD+4oHAofcW5d+oKkL0n7R5n09MFKlgvRcfcuXRLxEJ0rFhBvz07CBWlosOWJy1DCMsibEXHzIxHFc45c1KiI0Vx4uqrKWjyDUSEbNDJp2N6Gti1K5n+HEAuwBRszsqV2LWL/pQlOjjHrHmpLIRfhq6iA3AebMbHac5nXyDMZIgQLwmiw03RIYIcDUWHKtGhM7gGUXQEJjo2bCCyUQGzRYgubaSc/CzRoRxghKnoYCzn0xEGVFJXgEICLogZqZeiIwyiI5PJqTo8FB1VVR5ZBrZoSwTLKkTHDKvA2HhGStHhu5Db0mJM0XHmTAiKDgVElboiSIq8azY6Cnz0o6RAec1rfPchE8BXVtLufBdSgkKUmFX18RGlYG0InLqiqugA8jtDhz5J9I9u48uSJXR7uxEdbmakjNEpUCE6LNyucbgSHYx557GJRqZIdCxbRveDkcm0KUWHQ5wjJqBeio48okOMyUIFa/PoqK11XpdkzN8jTtd7zheikWYDk6JUdNhISpNERxgeHWJ4FOfadaxLU1dSFCtqaoif8CU6vBQdzz5Ld3MS/TkACkiam3M/cuVK7NxJHY9I7/WC4G+eeMJ7Ozv6+yl4ME10bN1KnZE1bUVg1aoSITr8FB0REB1RKzr8is2EAaESXbkSRJTYUlcSYUYKUPB6/Dhw9qzS/qUg61TrVvZYkujIW4UXG0Wt6ADoOgOeREdzs4f624CiY2w5+VbIpq4AAYgORUWH1SvRiKJDAT09akSHbtUVx9SVr3yFku8/8xkp6b84J34B/KZNlCaqXIJYBevW0ReoTjLDSl1RVXQA+dfQgejwq7qSLSbnSHQ0NHinRKmWiJZR8+jCM23Si+jYsoUulCTZb1V0AO7e50pYvJiOL+jM3EG56lXRTTRhx9QVh335FaOSIToWLAjBckcQHdnApCgVHTaFaLEoOnwrjJWgosNTs8gYezjg/icB9AI4B+BJAI9xzjV0h4VgjH0TwCsBnOWcX5Z9rQXADwCsAHAUwBs5573Z9z4C4I8ATAP4C875AyaOo5iwaRPwxS9SR+q6Gu2l6Nixg56TqugAaAB65hn6e8WKWSNSmbThlhYiRFR9OoLI+xYvpljTabB56CE67ltvLXxv5cpcgQrTPoaRwk/RoZG6Yl2p9UMcHh2BFR0aEIqOjg7QDXH//cDICGpqKIJVMiMV9c8UIE10LF1Kzz095A5sEqYUHbW1nkRHJkOTkdnf6dXI+vtz1SRM40UvohQFl46pr8+HRzRAdIyuWA8cM0h09PQUvq5oRjo0RBO4uBQdY2OeHrF5ECuxs4qOkREqhSCZOlqQutLXB3zqU8DLXgbcdpvUPkQA7zfZ3bSJRJ/btwO33y61a3WsXUvPBw7k+goZmCY6ONcrLwv4Kjr8UlcA5xKzR47QAohXPFA0REdLizfRcc010gOwneg4f95A5WNridkVK/T347CgI8rO+6autDXTP2KcclCHDA56D3ft7VTVzw26SmVfiHNWCoqOkIiOsDw6fH3ZXvzicBaaYoSfouM2ALcGeLwIwBsA/BmA/wTwNGPst4yxlxk49m8BsO/nrwE8xDlfA+Ch7P9gjF0C4A4Al2Y/82+MsZAcs5KLTZuoExXpHI7wija3b6eRUgQbSYQYgOrqMD1/EXbvlktbEdi4MVqio6qKVhadiI4HH6TjcVqAXrWKBjFj5lpxYd486ontDoQlrOgQ87EoiY6jR4k3aGgAER0zM8CePXpmpM3N8m6vWUgTHV5Ea1CYUHTU1ACZjGfVFYA2i13R8bu/S7/BZf/iUrrCBNGxnMaKJCk6urvp2bhHhyTE75NRdAjSbJboAJRW2wqIjrvvpgv/qU9J70NW0XHDDfQcqk+HTonZyUl62H7A1BQ9rG1TmugYHaU+VEfRERLR4VVaVqCxUaE8NGJWdLiVkt6xQzptBci1fZF+YSRmshIdQeBBdCinrjgsDskoOrq6iKB0gq7Jvi/mzaP+LEt0FKWiI8TUlblz6VyIthAEoosR47dv6sr730/eTSUE2WiVBXxY97MJwP2MsS8EOXDO+aMA7F3WqwHck/37HgCvsbz+fc75OOf8CIBDADYG+f5ixI030rNnIOIVbe7YQayB4iQnUgiqfsUKPHuAYXSU/ElksXEjdfynTsl/Jqhhk5N8sL+feCWntBUgF9AUffpKczOtjtln/RESHVF7dFRU0NgYZerKkSOWhSeR0rB7t54ZqWLaCiBpRgrIlRbUhezKvxfRkZ0VeSk6ABvREYcZqYBHX+17KefOpR86SZXilYiOCxeA3l6MLl0NQG6ipE10KCo6nIiOKBUdgsOTIToAakuzqSuAEkOaR3ScPg187nPAm96k5PEjq+hobgYuvTRkomPJEmqIKpVXXGYg4pZ0UnTk3a5ORIe4CULw6JAlOk6cyK0PcC5PdBSFosMtdeXJJ+lmUCA6nBQdgSGIjqA+HQ7khFfqSh7RYa9T77AvGUXH9HSuT7SC8xCJDoACkmzqSlEqOkJMXXHKctOFILtEKOCbulKC8LbbBh4FoOj6lIdaAI0AOrLPyO6PAXgvY2ycc/7hAPu3YxHn/DQAcM5PM8aE9nkpgMct253MvlYAxti7AbwbABYtWoTOzk6DhxcNhoaGXI97yZLr8dOfDuGaa552/fxNDQ04s3s3Dln2wSYncfOuXTj5e7+Hwwk+J6umprAcwPk5c/Cd7zwDYD2mp7ehs1Nuqa6ycg6Aq/HNb+7DzTfLrSr/9rerUFXVjv37H9UyhK+tvRQHDtSjs3P77GuPPdaK6enL0dr6JDo7C3u78+cbAFyH//3fpzE87CDnLhIsOnMG6wE8/otfYEwEDwBWPfUUllZX4zcOhile7buv7xb0959EZ6ccA3T+/GoMDi5CZ+djSsfd3X0lpqYYOjt3K31OoLb2Bhw40IvOTo1SiRp45pmNWLNmCJ2d+wHO8YKmJvTcfz+evXgdgNvw7LNH0Nl5zHc/lx86hOrKSuxU7AOeffZiVFa2YWoqg717D6Kz05lJrOnpwY0ADmzZgtOKaUt+WLV3L9qrqvCoj2Ni9fnz2ATguSeeQJdlNnzx4cNorajA1s5OPPPMCjDWgS1bNjtKxRm7EUePXkBn5wF0nDmDlVNT2PzQQ+CWHDo2MYFbx8dx+Px5HLedT682bgqnT29EfX22TThg6dmzWAPgsZ//HJNz52JykgG4FXv2HEZnp3dV+KZnnsE1APYM0VLh88/vQ2end386MwNkMrdi165j6Ow8WvD+ioEBrOjrKziPK/buRQdj2Lx9uxQJ/+ij8wFchhMndqCzk2Z9Fy5UA9iE3bufQ0eHAZNBD+zcOQ/AlThxwrlvtyOTeQEOHz6Lfc0ncBmAHQ89hKHVq6W+69ChiwEswaFD3Tj1p3+KxRMT2P6KV2BUoW0988wKZDId2LrVua1b0d6+Dtu2zUNnp/c9FqR9X7NkCSa3bMEeyc9X9/RgE4ADJ0/itOUz/f1VAF6AEydy/dHTT9P4v337HjBGE8eVPT1YNjKCRy2frT11CjcAeObkSXRLHkdtVxd95okn0J0lU1fu3YvlmQw2b9s2m3Oycye1z717d2B01JmVGBtbgomJi/GTn2xBS8sELlyowsjICzA15d63AsD4+OXo66tCZ6eXrDeHPXvmArgKzz23G52dfVKfkYW45/bseQ6dnfn33KrhYbT39ODRRx7Jy8Vp/+EPsRrAFs4xIXne9+3rALASXV1PALgeW7Y8i3nzzgQ69qr+frwAwMHNm3FKMLQ2yLTx5Tt3YhWAzXv2gGeDx5Mn6wBcj+ef34/OzvwUguPH6b3t2/dj7tyzuKm+Hqf37cPznZ1Y8sQTuBjAY888g8ksc9HVdQ1aWyfQ2bnX8ft7e1sBXI6f/nQn1q/PV4oNDlZiaOgmTEwcQmenh5GHJi5takL9vn3Y3tmJoaFKADdh375wvisMtO/cidUAfrNnD6YPH8a2bQsAXIqnn96G4eFg0sCurkUA1uNXv3oCS5cGY98PHFiLmpqW2T75uedaAFyBxx7bid5efS+OKGIUY+CcR/IAcBGADwM4DWAm+5gGcGOAfa4AsM/yf5/t/d7s81cAvNXy+jcAvM5v/9dccw0vRjzyyCOu7731rZy3tXE+M+Oxg1WrOH/LW/Jf27WLc4Dz73/fyDGGhs99jo7zPe/h738/5/X1nE9NyX98dJTzykrO//qv5T9zxx2cX3SR6oHm8L73cT5nTuFrdXWcj405f2ZwkH7mpz6l/72JwE9+Qj9k58781//ojzhfssTxI27te3RU/ZzcdRfntbXy2wu84AWcv/CF6p8TuOwyzl/7Wv3Pq2B6mvOqKs7/6q8sL95+O+fXXcc5p/c+8hHJnd14o9YPf9ObOF+6lK7PZz7jseHICG306U8rf4cv/uRPOF+40H87t4b0lrdQ38g5/9CH6P50w8qV1Ndyzjm/+27a3+Bg/kY9PfT6l75U8HmvPtwUFi7k/N3v9tjgnnvo+A4dmn2pqkqyb/zudzkH+NZ7D3OA85//XO6YWls5f897XN78whfoeHp68l//wAc4b2qS+wLO+b/9G+2mqyv3Wl8fvfbZz0rvRhvf+x591/79ctsvXsz5u97FOX/oIfpgZ6f0d73tbfSRV/2fIRrYXE+uO+68k/PGRrlt3/9+zufO9d8uUPt+85s57+iQ3/7AAToJ3/lO3ssnTtDLX/ta7rUdO+i1n/zEsuHHPkYvWgOJ3bvptf/+b/njEPf7F7+Ye+297+V83ry8ze6917993HcfbfPEE/T/li30/89+5n0Ib3wj5+vWyR/yL39J+926Vf4zsujtpX1/7nMOb37mM/Tm0FD+6697HXWuCvjrv6amL2Kmf/on3SO2YGbGtzOUauMOA8m+fXScP/xh4ebHj9va7LJlnL/97fT3Jz5Bb46Pz26/ejWNvW548kn3Ziya+I9+5P8ztHDnnRSgz8xoxW6x46MfzesXvv51+vfo0eC7dguLdfC613G+fn3u/4cfpn0HDTGiiFFUAWAHd5jLR5aDwDl/nnP+zwDWAbCKG/+vwa/pZowtBoDss6BDTwKwFkhqBxDusk1CsWkTldYTVRgc0dpamCO/Pas2SGrFFQGhCshWXNmwQc6IVKC2lqoyqvh0BJX3tbeTvMyqaH3oIeCWW9zlq42NZGZX9KkrbmkCFy5opa0AeqkrqtUKg3h0AHSMUaWudHVR9kGeZ9qGDcDevcDUFGpqFM1IFa8LQG1bmC96flddHT3CSl2RSROpraWHT+qKl+9EgUeH+JD9eIBwU1dcwLmkGSlQ4NMhJX0/dAhgDKMtJJyUde33KrYwK8m2p6/46bNt6O6mRWKrGWiUHh2qqSvV1QZSV/YdpR397d9Kf1bAxcfTEfX1ah4QWli3jlwSZS+WOCBbmolX6kqBRweQf/8KnxQTqSu2tutXdQXIxRsibVaYTZtOXZFNW9KBb+oKkH+vc06l6BTSVoBc+21ooFvAiEcHY5QmbSJ1xaZclE5dAShfzJq6In5kFjIeHYCzR1zQlGxfrFhBP6SnZ/aQiyqdor+fTm52guHSzWjBdOqKtYspx9SVyM0WOOf9AF4LYBCUwvJixpipbvQ+AG/P/v12AD+1vH4HY6yGMbYSwBoAipaTpQExRnjm0c6fXxht7thBHbJbkfakINtzT3eswpNPqvlzCGzcSLyO3R/TDSaIDiDnC9LVBezfT0UTvLBqVS7AKVq41Vrt7Y2M6ADUc0ODeHQA9LOjMiPNKy0rcNVVNNIdOIDaWsXysppER3MzxWC+A6znbDcAVAxc5s3zJDrGxrwn73nn1C2ij5HoGB2lNuzr0QHoEx3t7RidpghWlujwKrbgSnT4RfM2dHdTE6u0JO5WVdH/UXl0MCZfUGqWNAtAdAyeHQE+8AGtchMjI/IT3fp6MvjM2rqEA2GGfvCg3PYKHh2OXgFORIe4CVTMSKur6Qvs5WVdiA4/jw4gNxkVCx5+BUCS5NHhaUDpZNhz/DgFR5pEh7jnjBm4L1lixozUNp56VV0RTXiW6Jg3L5/osHUqftx+ayt9TyxEhwhIjhxBJkO3R1F5dNjiCdNmpOIrgsLeBnzNSEsQsbhKcs57AHw3+28lyKBUCYyx7wHYCmAtY+wkY+yPAHwGRJwcBPDi7P/gnD8N4IcA9gP4JYA/55y7+AyXNi67jAY7X0NSJ0XHtdcmv5bpDTcA99yD5y5+JYaH1SquCGzcSAs2Mn5nk5M09pogOsRgI8p9uRmRCqxaVeKKDo3SsoAa0aHb6ZtQdERFdAgyLC8IFmaEu3erKzo0zUjnzJEs5xsW0aFi/OlDdCgpOtwamc21PUqI2FiV6GhqUiA6Vq+e/cmyEyXPS++0ygtQZ61IdDiVoK6ri0bR0dNDXZus0nC2LWks880qOjJzgQ/r2aGpKjqAkM+jtcSsDDTMSENRdAB0DSUVHV5ER3MzfbWV6FiyxJ9QFESHrIIxTKKDMbiPPU5Ehwhahau+JKzt1+jQsmRJcEWHB9HhFF/U1NB5m1VNWRUdvb15MdPkJJ1br66RMWczfIDaVnW1+SrvsxDyo6fJL1ApDkkCbPfu8DBZRAVZABMwSXTYBY+poiNaPGr5u0P1w5zzN3HOF3POqzjn7Zzzb3DOz3POX8g5X5N9vmDZ/pOc84s452s5578w8guKEBUVxAUoKTpGR4F9+4joSDoyGeAP/gC79tEooUt0AHLpK11dpPwwSXQ8+CANyFde6f25VatoMAp19SxsJETRoUp0BFV0REl0CEVHh7WXXbuWfsCTT3oWBcnD2Bg9NBUdguiIVdFhkOjwkpbnBW1ukUWMig5dokNJ0bF69exEKWmpK05ER319dKkrsmkrQLDUlZEeulhDTW1qHaN1HwqKjoIV5zCwZg3N0GSdv8MgOnQUHYAxooOx/BKzhw/LiW0bG0lxI1u2MkyiA/AYD8S9bic6GhqAyy9X+o7h4RwfZVTREUPqCmO2fqq5Ob+8rGVslq267UV0LFsWYpHF9espDvnSlwDO1ZSlSYCDokMoh4JC7NbaVejCTdGREh3RwKr5Mmuvn8IT11xDvIVb7Wy0tlIvKUbDp56i0THp/hwW7NxJN/T69eqfXbuW4hcZosOEvE/Yipw8SSstDz4I/J//4z/ArFpF1/DECf3vjh2NjcS+2SeVttUJGQQhOlQ7/aCKDrEQo+oNooMjRygmy5uYV1VRwJglOqQCDHGNAig6pMr5hqnoCJK6MjJiVtGRAKIjFI+O/n6SLVgUHaESHYqpK2fOuCs6okpdUSE6ZttSTQ09VBQdvXSzDXH9xHEdRUeoPh319TTgyio6RIN1ITqsbTMJig5xXH5Eup3o8PPnABRLRCNGosNJvbV1K3D99fk5ZxKwtl/P1DhVLFlCY0SQTkMxdQWwER0eqSsmiI7Q0lYACnD/6q+A3buBBx4oPkWHbeFEpZ/0Q5geHWnqSrRIeA5E6WL5cuItzp512UBEYWJEEEakxaDoyGLnTlJEKI6JAGjefd110REdtbVkjHfyJPDcc+TV4efPAeQCm6JOX2EsX34JkERlaKjkFR1TU9EMNkePuuRuX3VVNnWFywUYUrPjQnCeEEVHXKkrfooOzZX2IJDirByiLSmi4/nn6VmT6BgediHexMEaUHS0tRW+HpWio6dHk+gAlKVgo8NkNDU4zLRJVVWPDvGZULFuXWBFh2ibsSs6bPf/+DjNAf1iF0F0TExQ7BAW0VFRQbx4GPAlOsQ4MDxME2LFtBWAfqs1dcWoRwdAzKkuFFNXAPoteYqOgQFa8bIRHaKZ+TXRpUsp5rR70oVOdADAW95CTMunPlV8ig7bvWuS6HCy89HBzEyaugLEiO0FrAAA2JdJREFUS3RYQw1TXU8KCXg5LQPIDTLCp2PHDooMly4N/dhMYGYGePJJvbQVgY0bScji1RkcPQrcfz/9vWyZ+3YyEKz6gw/S/37+HECel1Nxwz6pFH+XuEcHoDeQ/exnwI9/LL/9kSMusuYNG4ALF1DLJtQUHYpEx/g4cVfKRIesG7AMBNsS1Iw0O5PTUnQUW+pKVRX9XlWi49Ahetb06ABcJiMVFXTAARQdw8P0iFvRYa344ofZ1BWA2q+Cnln8nulppj2JUAngI0ldAUh2eeCAnCROIXXFsfqDG9FRWak+CNiJDod0uvHxnBeDFzo6iDR79lk6DWERHfX14Vmzua7iV1fTwVoX26anlY1IgZAVHYB++opY0FFIXQFslY1EB97fX6CCVVF0TEzk2/KZ8J6TQnU18KEPAb/5DWqmh4tr8u2SumIKJtKbh4epb0hTV+LDLZa/j8V2FGUIaaLDOsgUgxFpFocOUScflOiYnCSyQ4Bzqsj58Y/TYvjKlcD3vge8+MXBOzhBdDz0EK2+ywQt7e0UaxW1ogPIl18CuYmMpqJDZZEtLkWHmzWJDD7+ceAd75Cb70xNUWqTo6Ije4PUjPerKToUU1es83lpomNmxqyJycgIBcqypIIoi2PN71OouiJdXray0tvsIyRIX0pbtKVEdFx0EUZHc6aDMnDyIMyDPcmecyVFR3c3Pcfp0dHbq3YLBVF0jExUgoEIQ5VqG3n7SKqiY3g4V6rMCy41Up2IjkyG+D2p1JWmJvWYaM6c3PWbnKR9uhAdfhCT0M5OepaJGUScokp0hAXPVXwrK7F1Kz3fcIPyd9jNSMfGDBGaooKRLtHhsnCgnLoijmF8PG9fsooOp/mACe85abzrXcD8+ai90FVcio4QU1cAZU7bEU5rKVVV1G2lqSshgzHWCuAt2X+nAHhZY6YwDF+iQ+hqz52jAf3ZZ4vOnwPQKy0rIAxJH38ceOwx4K67yAPtiiuAf/gHGmzuvpsq3P3qV4EPGe3tJBV85BFKW5GJnyoqaAJb9ESH1VAL0FYO2MqaS0FHxsd5vIqOU6doAPv61/23PXmS5uqOio4rrwSqqlA7ckHu92teFzvR4TvA2lPnTEBV7iN+o/UCKZqRSik65syJhUAOQnSIlUJXPP88TQIaGjAyQj9f9ic6eRAWbGAlOoRcSJLd9CI6olB0TE9TM1CxdqipsSk6ZDuN8XGMztSgtZ5+lO91c0HiPDoAtcorw8PUCG0DgxPRAdjuXcBd0aHqzwHkz15cltx1iQ5ZM1IgWUSH69hjzTPZsoXILUWVJ1Co6AAMDS1BFR0+RIeXoiMvdQXIBYGaig4gfz4gUrLzDMzDQkMD8P73o7a/G2PnNDupqCFISks8YU2RMgErJ6oLJ6KDMUmvtBJC5EQHY6wRwP8D0ASAA/g15zyCdZQUAvPnUycqpejYtYtmdkVEdOzaRYHCpZfq72PpUhrH7rwTuOkm4AtfIKLjq1+lce2xx0hxt3q1mWNub6fJR1+fXNqKQMmUmHVSdGikrqjaHeikrkxN0XNQjw5AfSCbns6lBH/+8/4Vd0TFFUdFR20tsGEDagfOhmpGqqXoAMwSHappIvayx5yHU142hrQVgG63ujqJNuxAdAwP+2QVZSuuAP7nyQ5lRYdsNJ+FuHfiUnSI/avMkaur9RQd/OAhjKIOC5qpwwqi6EhUeVmAJr2AnE+HC1PjRnQUqAy8FB2qEKkrIpVOvGaBKtGxeTNtLwQGXig6ouP8eTpXW7Zopa0AhYoOwJBPR0sL3ZynT/tv6wSXOMcvdaWhwSF1xYHoCKLoMOE9p4Q//3PUVExh/LCEQisJcPDXslb3MQETqStuYY90pb0SQWREB2NsBWPsTgDPID9t5VNRHUMKQiaTMyByhDXa3LGD/i4yI9IrrghuoHXXXcAb3wjcey/lwv7iF8C73+1sZBcUYrABqOKKLEqC6DCo6FAlOnRSV/wCERmI41RNXTl7liaZL385paT86Efe2wuiw3W1b+NG1PR1Y2xMItdd04zUOthKV10BwlF06BId4qJLEh15kyUvM9KYiA7p9AkHogPwmchGSXTIRvNZCEWHUx8ehaLDpQCIJ3RTVyaePgiODBa0ZfK+WwVCgZKo8rIAzeobG+UVHQpER6iKjjlziCkfG3OdhYyNyREdS5fS6uyFC9S/y5QBFYcsq7hJBNHx3HP0IzWIjpkZumx2RYcRooMxWg1LQupKAEXHwoWUQelEdAT1npPGvHmo7ViEsXNDudTHJMMhnkiiR4cX0VFOqSuevs6MsYcD7r8GQCOA5QDEqWYgJQcH8K+c8zRtJQa4lZQCQHdBQwOlrnR1kX5NxT0tRnBOio477gi+rw98IPg+ZCGIjiuuoIFHFitX0qCtM8lPDOyKjgBmpLpEhwq77ReIyMDqIaYCEVO9611kMvov/wK86U3uqQFHjtB7rgHLxo2o/coQxocmAfgwN7291C8oMoiJUnSopq6ItmgrH2KsvGyMig5pokNEvcjxCa7zvOFhaqQWokNlohS2okMQHU7DWRSKDjHBjCJ1ZXQfVb9ZuLwW2KWXuiKabOIUHYzlDEn94LLU6lbGVYroCKLoAOje91B0yNj2VFXRPPvUKTl/DiCZig7X6n/Co0P4c2gQHaIdhpK6AoRKdGilrmh4dFRU0M+wEx3z54d77e2oWbsS40cOUU74V78a3RfrwEXRUQweHUD5pa74Fd+8DURIBIUIw7nl/38D8GED+06hgfZ2n/Kp8+fnFB1FpObYu5fiwCBGpHFAEB0yZWWtEAHOkSNURKMo0dxM0Z2YPYqJjGKKRH+/Oh8Xt6JDl+hobwf+8i+J8HjkEXcV0NGjtK3rsW7ciBp0Ymx42mUDC6Rnx/lIFNGhq+iwEB2cy5uRcg4wL0VHGPIwCSgRHQ6KDteJkgi4s0THyIiaoqO+ntqIJ9HR20tLtZmMlqKjtdWZq0uqoiMvdWXOHJpkT0/7mhGNPEsE1cIlVXnfrQIXH09XRObRAVD6ym9+4/7++fOUc/rAA44BwdgYrWTby7hKKzp0Fn+ciA6H8rKyJPry5eETHSqlkFXhq+jo7QV++1vqrIQviwLsxKLR1BWAlEX79+t9NkDqSgHRIUp62xQddXX+ZYqBwoXPSErL2lDbXIuxpoXAt74FfPSjOQ+UJMLB88s00RGWRwdQfooO2dQVFvBh3c8TAF7FOX8v57qV3VMEhejYXK9Aays5bT7/fNH4cwwPA299K81RXv7yuI9GDRddRH4gf/Znap8TAU5Rp6+ISaVQdfT2Us8sM0JbEJVHhwlFR0MDzVNUU1cE0bFkCZWgX7iQVB1uOHLExZ9D4OKLUVvNMS6TutLbq5y2AmiYkc6dS5PYJJiROhAdk5M0z/YjOoBsW3GzOY9Z0SF1KVWJDktpWUA9dQXI9yAsQEtLfkUeIVNQIDqc/DmAaBUdgVJXACl5xuhBmrmI+bgO0WFfEfeDuNZRVK/B2rU0I7N/WVcXscAdHcAnPkGrB//xHwUfdyMrC4gOp0FicFA/dQWge98lnU6V6ADkjEiB5FVdcS0vC+Sqb/3yl8CNN8rl5thgv99CUXToenS4eF5NTFBs4MZj5pWXbWqi83LkCP1v8+iQFR0lgeioqQHGG1ootetf/zXaL1eFjUHgPBxFh+C0dZF6dBD8ZhKPIpiiYxJAP4BzAJ4E8BvO+TMB9pfCENrbaUA9f96FsZ8/H3jwQfq7CBQdnAN//MfAvn3Az39O+avFhIoKvb69JIgOMdD39tIKyYULWhPqvr7i8ehgTC8H89Qp+uyiRcQDve99wN/9HfD0087mu0ePArfd5rHDTAY1i1swdlyiVI1qXcwslBUdmUx+aUETMKjoEH/6VV0BxKTFxeY8Zo8OqQXSuXNptjM5CVRVyRMdF10EQJ/o8FR0ALk+QsOM1IvoGB+XEktoQ5w3ndQVzgFmlYJ53YucY/QwTcCCEB2qio5Mhq53JESHMCR97jmSMx45AvzzPwPf/CZdxDe9Cfjrv3Z1JXernFRAdIj6yHZFh07qivX6BTQjBXKTUVlFR0UFXZ+kEB2e5WWF/OLUKeBP/1Rr/3YFlVCMGVN0LFlCgYfOiertpTZkW9CZmPCOLQQhyznAGKN+4MIF2o+lY1Gouo32duB//1fsk4gOFa84E6itBcamKum+/Y//AP7mb7Sq7EQC28LJ2BidO9NEB0DXUSPsAuAueExTVyzgnN8W0XGkiBhWp2VHokOw6UBR5IF8/vPA974HfPKTwMteFvfRRIfmZor3i5rocFJ0aBAdOoqO6moa2KP26AD0iI6urhzJAZAC6FOfIpLsG98oPM6TJ/1X+2qXLcD4sRrwkVGweo9ZaV+fljvZwAAdb22twgDb2koeQaYgTrTs5KSujhqHB9Eho+jI8+mw//CYq65Ip64AdBFbW+WIjgULZj83OqrmOQQoEB0XXaSVuuImULRmGJkMWK3QUXRUV1MQPTUFVMnmvPX0YHSQSjKJ86/j0aFzvHkrzmFCMHX33Ucd4L330kz+ne8EPvxh39n/2Jg70VHQR9nzmkwoOjyIDtluQVXRAdBhJ4nocB0PrJPcG2/U2r9T+zXKoYv0itOnZ8ldaVy44DiR9ytdX1+fK3FfW4sc0TFvXp5Zl6qiY3SUhjuRERiLomMcRE5+97vAl78M/P3fR3sQsrARHTr9pB+sQ28QoqOurjBVs7Y2oj46IYi8vGyKZMCppFQeBJu+Zo3+XRYRHnmEKqS89rXARz4S99FEj5Urc8rFooRV0QG4BgBeGBujib0q0SFqiket6AD0iQ5r6mprK/CHfwh85zuFCtoTJygg8kxdAVC7gnwiJrbt9t4wgKJjzhw617W1JA7wlWN6znY1MDBAUYhsOhRjFDiaIjrsjWxighptDEQH5xpER7ahShEdlprbOooOz4mIvWyChhmpmy1KFEaaumakQLYtWaNfLxw4gFHQiW9tpclLkNQVlcluFClAACg2YYzy+f/7v4H3v58Gwv/4DymJgxfRUaAysN6/omqKCTNSxgpmR27H5YTf/30iui+/XP4QioboEDFoJgNs3Ki1f6cJqGdqnCpETV8dQ9Jz5xzjnIkJ70WUgspGYlHIti9VRQdA84HIS8tmMdsWLrsMeNWryF9HtyZ22LCRlGEQHeLaBfHpcBONlpuiIyU6yhS+RIeQeSTcn+PECRrs16whDyO36hOljKIvMWtA0aFqwWCFqjGTKUVHc7OeR4c9LevOO4k8+PKX818X5Jffal/NRdQZjG190ntDaWOHfFgHWxHAu8qVBUwTHTrqCZNEhz2iV5ygm8TQEIn1ghAdruoAG9GhakYKKCg6gFzAKRFhjozQb3dLXdFJY1OFbnlZINvvyCo6nn0WI6AZal2d2uTWCl1FRyRER10drWz83d8Bx44Bn/2skoGhNtGhk38kYCc6BANsgUrqyoIFdApU7Ctk2wLn0REdjl5xgui44go9Ugnuig6jqSuAHtGxf79j/qBM6gpgWZEXHbmN6FBVdADxEh3WFD38zd/QRfra16I9CFn099NFynYgOv26H3QN661wIzrKzaMjJTrKFIsWkcrTV9ERgT/HwYPAS15C45mXibodY2PA615Hzz/+cWwq8NixahV5MQQxLYoVBhQdQYgOVXY7SYoOgBSzv/d7wL//e34Ae/QoPfsqOhbSjTO+7Sn3jaan6WANER2+k0lR9ckUBgbUG4cL0SHaitcEvoDQsbNpqp4hBuFS1dAZKoqOsTFingMqOsSKq+fkx6roEIZ8PhClZb08OoDkKTpEP5On6PDrOA4cwGgltS1BdOikrugoOvKqQoSNT34S+PjHtUqDuBEdjr4RTkSHCUWHw/2vQnToQJboEMq7sIkOzum7CiDudY2ysgJuio5QUldUMDhIA/RllxW8JZO6AjhUXikBRQeQvfduuIHMxT77WYlVkRhgWzjR6df9EDbRkVZdSVHycKqdnQcRDWpKBmUwMUFxyuWXU6nb/n7glluAd787F4y7gXPgve8Ftm8Hvv3tnC9ZOWLVKjqXuuXcY4eV6OA8ckWHauqKX517WagSHRMTQE+P86Llhz5Ep+2//iv32tGjdJ+LIMYNIqge2/W0+0biQDVSV6wBlwhmpErMmiY6gig6RFSpYUYqPpf3o11KS0YBoSIynrpy5AjdvwaIjqkpl+wM0S9YFR0K/hxAMhQdKpNHx9QVGaKjjaRccSg6iiH/OxZFR00NDRyC6HC4/5NCdOiQXKrwHA/mzSO/Bk0jUiACRUdzM/0I1eDr6exY65Bz5Je64kp02GImFUVHWxtxxYLoqKpy7yfDQsGY+ZGPkBHtd74T7YHIwHbvhu3RoYs0dYWQEh1lDHtJqTy86lXk7hmATffCli3A1VcDf/u3wKtfDTzzDCn5PvQhMlVcvx74wQ/cy9/+53/Sdv/3/wKveU0oh1g0KPrKK1VVNEL09VEwOTERqaJDld0WA7EJM1KV1BWxaOREdNxwA92qn/scTRIBmncuW+ZvSzEbbB4/624AKg40oKLDavjoidZWuiimZp06TrXz5uV+t2bqyuzvTJCiIwjRUVND5JnjRMlWWhagn6w6UbKLNvJQVUXRu1XRoVBxBYhf0VFXp5ZqIAhVpdSVAwcwurADAP2upqYS9OgICG2iQ7GkcQHmzKHr55JOlxIdWTAGfPrTagYkNniZkbrFlkpgjHw6VImOffvo2UHR4Ze6EoZHR1UVkR2C6Fi2TKuabyAUtIUXv5gmCf/0T8mTK9viiWLz6EhTV1KUDZYu9SA6amuBO+4wbnrR10eVIl7wAuqIf/YzIjQWL6ZO4u67SaXR3k5f/8pXUvqtFVu3UlnNl70M+NjHjB5eUUJ4MBS1IalYPRcTmDJQdDQ300Akihv5QcRSbmnoH/oQtYEf/5j+P3rUP20FsEhGUUM3nxOEsiGAGan1u6SIDsCcqiOookM0kPp6fTNSJ0VHkREdjHlMmm1Ex/Q03Ss6ig7Ax6cjgKLDzYw0KkWHqhAgry3V1tKsxCv6nZgADh/GyHzSnket6Ig0dSUAYlF0AHRPeaSujI2FS3Q0NCSH6CgghA3DKaWgtZVuEWNtdMkSdaJj7166EA4DtGzqipdHx+Skul9uezsJKI4fjz5tBXBoC4yRV8fBg8D//E/0B+QFl9SVYvLoSFNXUpQFhKLDCLPtA87JGP2SS0iNceedpN57xSsKt736auDxx2l1evNm+sy//iutVJ85A7z+9cQ4f/e7tLpY7li+nNj3olV0ADlnTiUDgRzi8OgwoejgXH4C4kd0/O7v0hzz7rtpv0eOyJUdnA0wWD3wxBPOGxlSdMRGdOiakfb1ERNl2oxUNNgYiQ4djw7AY9J86BDtNBtwy5wnJ4jg3NVTwkp0KCxbCqLDrdxtVIoO1WA4ry0x5p/zdvgwMD2N0XlUEcKER4fKNSwmRYfT74pE0eFCdOSVDQ0JSVR0hGXDMDxMikYrcSD4AKM+HaoeHXv3Apde6iib0E5dsRAdOl7XYj4QF9Hh2BZe+1rqsH/xi+gPyAsRpK7U19P8JqyqK+Pj0cz9koCU6ChjtLfTDRrkRpLBiROUnvL619Nq2rZtRFx4LYhUVgIf+ACls9x+O/CXfwlcfz31e729RPAqZjeULKqrifgpaqLDruiI2Iw0Lo8OQD59RRAd9qorAhUVwAc/SKKMBx+k2EtJ0bFiLd2cTtAkoABNM9IwFB06qSuc02cVzUh9y8smwIxUStFRVUXH7kd0cE4k2Zo1sy/pEh0iWHT1ebATHQqKjpYW+klOiELRMTysLgTIS10BcqkPbnj2WQDA6BzK0amr009d0Um1KRaPjtFRRUWHuPGDKjo8iI6pKbqV0tQVMxgaKpx8eqbG6UBH0bFvn2tKjmzVlYLUFcvYLIYXVUXHsWOk6kiEogOgjqe9HTh7NvoD8kIEqSuC09b16BChi5uiAyif9JWU6Chj+JaYNYCZGSIqHnoI+Jd/oXnUNdfIf375cuB//xf44Q9pLHn8cfLmuPLK8I65GFESJWYNKDp05o1xeXSIiaYs0djVRZM0Eag54e1vp/c/+EH6X0nRcfEVdIM60fyaqStTUxSQxaromJlRS1gWEG2wt5caSFUVUFFhRtGRgNQVad7HpiBwnCjddx+wcyfwrnfNvmTJ9lGCp+EpECh1xctgLwpFh9PEyw8FbclP0XHgAABgtGE+amporqCbujIyon68xaTocCM6JiZsKYVOio4QiA5TY4sXGhvpp/jZHpQC0eGkoLJXqA6MxYvpWsreYN3d5Cru4M8B+KeuFBDBBhUdw8PU7hOj6ABI0SHkeElBBKkrgD+n7YWxMYq/UqIjJTrKGlEQHb/5DfD881QO+y//0t8Y0QmMAW94AxmWbtkCvOlN5o+z2FH0REdzc2BFR1OTXipT3IoOFaJj8WLv1dX6euDP/zzndaak6FhzKRELTmYvmqkr9oBLyYwUcDdH1TkIHUUHkCM6sgevXXXFruioqAh3FuGCvj6a7Ej3xX5Ex9QUVUdYtw545ztnX45M0aFgRipDdCRN0aFFdCxahNGZ2tlzr5u6Mjys3kRLwaMDsChoAHPlZQFqr729dHJjIjoA/2tUqkSHabGgcolZMTh7KDqUUlduuIEM7W64YXYbXUWHQGIUHQB12klSdAiphCWeGBqieYrplDPVynxWeK2lSMdhJYKU6ChjREF03HsvDTSvfnXwfTU3AzfeGHw/pYiVK4n0LoYA0xEGFB26lTrj9OgA5FNXTp1y9+ew4s//PHdsSoqOjmyNZiefjt5empgrLlnYB9tYFB266gkfoiOwomPOHONmzzLo61MU5vgRHd/6FqVLfPrTeeyJjr+D2D8gQXSIgNOQokMcZ9IUHQWpK3565gMHgHXrMDKCPKJjaEg9J1tX0TE1ZSMKEgg3osNxZdlJ0aG7fDtnTi7VIUaiw0+AUKpER1BFx9GjNrWPKtGxdy89uyg6lFNXWlqoQqIBRYdAIhUdSTGU6O+nBmCJUUU7Mz2ch0V0SKcQlwhSoqOMsXgx3ZhhER0TE8CPfkTlX01LulLkQ5SYLdrKK83N1KOfO0cTasVJaRCiQzV1xWTVFUBN0SFDdCxcSAvrc+fSPe6H2WBzwTIK6J18Ovr6aGBXHMm1iY7qaorITRAdunlNHkQHY96TkYKgzam8bAxpKwD9HCUe0YHomFUHjIwAH/0oMdA2NjuoosMzdWVqitrG5KSSGalbxRXrcSbajBSQU3SsXWttsmhqonmCamCro+iIIgUoKDj3V3Q4Eh3CPVo4Bepgzhxqt0DBoCX6xZToMAMvokNnaDl+nAy/f/5zy4tiUJb16di3D1iwwJV19UtdEeloXvdXUEXHsmXynzMFT0XH+LieJC0MiDQay/XTUerJIIhHR6royCElOsoY1dV0r4ZFdDzwAAXVb35zOPtPkYMgOoo2fUXMvI4eJQZAcUIdVNGh49ERR+qKDNEBAJ//PPDUU3Kx+OykfLqSSh45ER3Ks2OCG9Ehdb7nzzer6DCYulJb691EHVNXrIn/MRIdRhUdX/gCNcx//ueCE6JLdFRXkzDEU9EBUF8BSEXzo6MUJ3spOioryYYl0eVlAW+i49w5umdsRIfs5NYOHWKmGIgOQVYrER2c0wcHB4PNaqz9kIuiI+yqK0D5Eh21tfSbdBQdzz1H3iZ54g1VomPvXlc1B+CfusKYv+GvjohR/IyWlnAm7X7wVHQAyfHpcCE6wljMDeLRIaPoSImOFGUBUVIqDNx7LynQX/zicPafIoeiJzrEzOvwYa0JdZSpKxMTNCEKKlNUITpEdSS3iit21NQAHR3y2wLZc3D99cCuXbkVR4HeXmUjUiCAogOgziOBig63spRWFKxO2X+4TrlbQzBGdJw7B3zmM1TX+KabCj6ma0bKGAWNnooOIEd0SJxHh9jUEWEbaeoExK6pK05S7qwRqSA6xLkXExfVRdGRET2PDvHZpELchkpEB0CNemhI358DyG+vZZ664rqKbwhu91tLi76iA7C17TlzqH3IEB0zM8DTT7v6cwD+ig7Av5/SSV2pqSFOIY60FfH9gIuiA0iOT4c4jgiIjjR1xQxSoqPMERbRMTQE/PSnwBvf6F7OL4U5zJ9PAUzREh1iUnn4sFbd4KBEx/R04dzeDePjZgLR2loKaGQ8OsQKkqyiQ/U4gGyQvXEjRRrCME1ApK4owj7YKkkmTREduoqO+nrqvFwUHV4QgWqeogPI/fAiVnSIUqX8k5+iPz79aceP6Xp0ABQ0mlR0yBIdquouFXBu0Ix0ZsZ5pmohOqweHeIURanoSHKJ2UBER1BFR5ERHTr3ryxcV/ENwe1+a23VU3Q4Eh2M0cAs49Fx9CgdlI+iIyjRIYY81WZ6xRXAhg1qnzGFVNFRCDH06tiTpKkrOaRER5kjLKLjJz+hmOAtbzG/7xSFYIyMJ4vaowOgVeKIFR2q7LZMICILYU3iB7FYFAbRkbeSsnEj/WM3JA2o6BATLaUVPNNEhyqxwBi1RQeiwy/4z2SII8nz6AASQ3Qoe3QMD5MvBih45hwY/co3yQzmkkscP6abuiK+w5foOHaMniXO45kz9BynomNsjPgJXY+OPEUH4NxxHDhADW/FCiOpKzqKjmJIXRG3oVPbTBUdhJEROg5dKxIZxJG6AuQXblKBIDoKYoUlS+QUHcKI1EPR4Ze6AvhXNhocpPtQtcrhffcB//Efap8xhaJRdHR30wA/f/7sS2ESHdPTeuR7mrqSgxbRwRibNvAYY4x1M8aeZox9lzH254wx9aXcFIHQ3k7xkmmfn3vvJel8WiUlOhR1iVnrzCsGRQcgP5iYUnQA8tLEU6foOXSiY8UKGsDtPh2GFB2VlfSIJXVFp4FoEh0AndcCRYdoZDERHdPTdDqUFR3A7MWcnShl5gD/8A+uHwtCdEilrgiiQ0HR4WVGCoSr6BDEjeoqa4E6yI/oWLMGqKx0JDpUx/lS9ejwKhHtOOGyEx1lougIu/p1XESH7tDiqOgA5IkOoZS89FLXTWRTV/w8OnS4uLq6cNueF1wVHYJQSJKiY/78PAYwTI8OQC99JU1dyUFX0cEsz7qPagALAKwDcAeALwI4xRi7mzFmaL00hR+E07KYSJlATw/wq18Bb3oTEZ8pooEgOpJShUsJ1pmX4oR6fJweQYkO2WDLpKJj7ly51JUwFR2igsj4ePaf66/PJzo4D2xGap0X2CutuqK1lU5OVkmgjYGBnPGDKkwRHU6KDt0GGwDiemgRHdloq/E8EQxD73xvvlW/DaEpOqzGxYCSR4dQQbshTEWHmFgGrrriFf1mK64AyPPo0E1dST06srCnroSk6PA6LlMoF6JjZsa9PHJQRUdB2168WF7RsWKFa/uZnqbjNuHREZNgUBuuio6qKooFkqTosA0kYSo6AH2io6rKmbhKU1fkYbXi49mHH5y2s5IfNQA+COBRxliImYEpBEScajJ95Uc/og47rbYSLVatojgsKcS3EgIoOoIs2APqig4ZaaksVFJX6urCmxvX1FgGvY0bgf37c8u/IyNkYKKZutLUlE94SpfzbW3NkSxB0N9feBCyaG6m77cYHsiYkQIeio6pKdpfDJGoINUCER3/820AwNBb/tTzY7pmpICPoqOujh6Kio558/wnEFEoOlQD4spKarq+qSuTk8ChQ3lER5DUFc7dJ4peKHmPjqCKDnH9GCvYT7kpOioraWE8jAmXIAK8zEhVFoU491F0DA35S6b27fNNWwH8r7+MR0cQLi4OON53AgsXJiew7e4uyIEcGkom0TFnjrNpfpq6IoeV2cfvATgHIimGAfx/AN4J4AYA6wGszf79DgD3ABjKbtuT/ex6ALcCuAvA/uy+GYDrAHxN89hSKCAMouPee8lryaM/TxECklx5Zds2YPt2jw0aGnJSQEXlQFCiQ1XGJyMtlYVs6kpXF1VcCVrpxQ21tZYAY+NGiup27KD/xexYU9Fhn88rKTqA4OkrQdQTARQdeefUGlnoWOIbQmCi46GH0Lj7NwCyqSseGBnJeZWowlPRAVDbsBvAeMAhNnVEFIoOnTlydbVE6sqRI0SiZYkOqxmpTurK2Bh1A6Xs0eFEdDhK6MNQdDiQr1EQHdXVNNQmgegAFMYDRXgRi62tdKuoEH89Pbnr40h0AN6GpBMTpLjyMCKVLV0v49FRbIoOMVY4toVFi5Kl6LANJmErOsRQpwKv7Ng0dUUCnPNjAFYBuBdAK4D/BrCac/52zvk9nPNtnPMDnPOD2b+/zTl/J4CLAPw/UMrKvQDaOee/4Zx/lnN+GYB/zH4FA3AHY+yKgL8vhQ9EuUpTRMfRo8Bjj6VqjjiwciU9J9GQ9C//ErjrLo8NhPEjEDnRoZO6YtKjQzZ1JYy0FYE8Rcd119GzSF8RigpNRYd9sJUu52uK6AhSytVKdGSjfpmqK4BL6sroqH65WwMQl1LZjBSghvpXf4XGRTRr9ps0C0JIh5zzVHQA+aovCebgzBl5oiNpig7A1pbcol9LxRUguKJD93hLPnXFlEeHw/0fBdEhhCTlTHSI7kNlaBECMsCD6PBKXzlwgNgVCUVHXB4dcSNvccCKhQuTQ3ScPZs3mOhW05JBUI8OtxAjTV2RAGNsPoDvg1JNfsI5fwPn3LcVcs7Pcc7fCOB/ANQCuJcxtsDy/t9n9wsQ2ZHW7AgZtbXkq2OK6Ph+9urdcYeZ/aWQx4oV9JxERUdvrwQrLSbSCU9dManoUEldCZPoyAswWluB1atzREe5Kzr6+rQ9OmZ/pzWy0K0CYwCBFB1f+xqwcyca3/sOAP4TJdnz5ATP8rJAro9obJRKSZJVdNTVhTdB1zUjBagt+aauWIgOzvNTrGpqKE1AhegQ56HcFB2eREdvL+XlBpnV1NTQ0nVMRAeQLKIjr580CD9FB6Dm0yHSVpqaXDw6AG+iQ1Rc8SktCwRPXSlGRQfg0RaSkroyPEwPy2AilG9JTV1xgmhfqaLDG38IUmVwAO/X+PwHsp+dn92XFR+3/H2LzsGlUIPJErP33gts2pRTF6SIDnV1NBlOItExOCiRsx2TokOnvKxJRYelcqcjOCez4MgUHQClr9gVHVETHcJt3QTREUTRMTNDDxNmpKOjxUt03H8/cMUVaPz9VwCQIzp0J0q+qSuC6JA8h93d/hVXgGSakQK21BWR5udEdCxYALS0zN5f4vzLruJboavoEE29JD06xKpykOVyxqjdOrRdcVzlRHS4ruIHhIyiQ4foWLdOU9Gxbx+xjVnFlRNMpa6UnKJj0SIavGbZ3pggyBYL0RFEqeeHsIgOpep3JQBdouP3ss9Pc86Vp8jZz+wDqTZ+z/beswBOZd/r0Dy+FApobzdTdWXvXnq8JdXhxIaklpgdGJAIfMtQ0SGTg9nfT8cWtqKjgOg4eZICN8OpK0pmpED8qSsCJsrLFrOiAwA+8xk0NZOXjsxEKYiiY2yMFs8dIfoIiWhenHJZRUfSyssCtrYkJspORIclbQXIP/+NjWoeHbqKjkwmXGWMCWgTHT099BxUp+5CdIjvDLPqCpA8oiNMRYfTpdIZWo4fp35p6VKHtt3URG96eXTs3Uv3p0fwoJK6MjLibqZakooOIP70lYiJDjG8mfboABRSiEsAukTHCpAioyfAd5+z7MuOo9lntRlPCi2YUnTcey8tNL3hDcH3lUIPq1Ylz6ODc+p0fVcTy9SjA/D26QiztKxAwUrKxo30vG1bfKkrjY0k8Y47dUUgSNUVqxmpiFpiKC/b15ebJ0ujupoirttvB172Mmm/hyCpK+I7XMlRBUWHQ2zqiqQqOvJSVwBnF+Nnn80zIgXyz39TUzSKDvGZYiA6nNpn6IoOALjlFpK+2hBl6orfwkOpEB0mFR3Ll9P+CshQxmiA9lN0+Dj0q6SuiPQ0p32Mj5egogNIDtFhKS8bJtFRUUH3qmlFB6Cw4FQC0CU6RIQmIQZ1hQg7nG7HMdtzihDR3g6cOxdssJmZAb73PeAlLyH1bIp4sHIlkVZhSEF1MTJC7WN42Kecm5hUaio6dFcw4vboALwHMhE7CePgMFCwknLVVaRt3LYtp+jQmJgHMiNljJbeEqTo4FzTjNTayGJUdPT20mVUrrR7333EZDOG2lr6fNgeHYDHdygoOlSIjro6qtLqlUqmiyABcV7qClBIdPT2ktrAR9Gh49Ghc7x+ZolxQ5wfL0VHXh9lWtHxrW8BH/1owcvj47lywmGinBQdpsxIBdHhqlbyIjoGB8mp38OfA5BPXfHywYmxqFdg+Co6QvbpGBoC3vc+DwVFxIoOQL4ynx2poiMH3e70NCi1ZD1jbIXqhxljKwFcAlKFnHHYRETUQRQjKSQhSswGSV/ZupVcqdNqK/Fi1SoiE6wO4XFDDLxTUz4plitXUiK94uyov58CN1GdVhVxe3QAckRHpIqO2lrgyitzio6mJorAFTAz4yyhVQpsgxIdk5N0YQ0pOiYn8+w6POGr6IgpdUUjAwm47bZZkwtZv4ekKDrOZCMMWUUHEM5K19AQCZR0SNK8tgQURr8OFVeA/ImqauqKOPc6k90wlTEm4JW6Iq5P3vmuqqIBxpSiwwXj4+GrOYDyIDq8FFTV1XQOdBQdrm178WJ3omPfPnqWVHTIeHQAzsehUHU7cYhb0bF1K/DlLwOPPOKyQcSKDkCP6JiYoHvKT9GREh3eeMzy978xxqT3wxirAPAVy0tbHDa7GMFTY1JIQhAdQdJX7r2XgtpXv9rMMaXQw6pV9Jwknw4rO+65ynfnnTlncgX09wfLAkiCR4dM6oowdg8DjispGzcC27cT0aCRtiIUPIGJjnPn/LdzQ9DlLRvR4bRS7oa8yanV5ry/n9iCsCIjD2gTHTbIEh26E6U4FR1AOETH8LD+Ja+u9kldEUTHunUAzCg6ggTwxUJ0OPXjmQzxGgUTrro6c4oOFySF6JiZCXb/qiAORQdAXYgshz46SnNsT6JjyRLy6HCSrQqiw0fRoZK6AjjHU6miQx/innBd9D17lmICS8cRJCVRBnPnqnt0yLSBNHXFH9+0/P1SAD9jjLX7fYgxthTAfdnPOO0LjLErAIjLs1/z+FIoICjRMTkJ/PCHwO/+bnGyyKUEP6JjehrYvJk4hbe+NRyJth3WTtoz0K6uzlXaUEBQosO62C4Dk4oOmdSVU6fo94U5L3YMNjdupIv3+OPaRqRAADNSILiiI6iBSwCiI291KpPJRXFCU8qY3jEFQF+fFmdVANkV4aCpK3F4dADhTNKHh/XnxwWKDrsZ6YEDpLjKljsz4dGha0YKFIdHR22t+y3ouLJcVxe6okMcV9jwu3/tVXvCRFxER2urvKJDxMaC6JiYcIidliyhL3WSTe3dSweyYoXn95hIXSlJRUdjI/3okBUd4tK5Eh0OdcrDVnQ4+U77QUY0Wk6pK2pa5Cw4548wxu4F8GaQ8uKlAA4yxn4J4GEAzwDoy24+F8B6ALcD+B0AYorAAXyfc/6wbfdWK0v7eylCgMj91yU6HnyQFl3TtJX40dZGg4XVkHRsDPj1r4Gf/IRS7a0L5J/4RPilgKUVHZoISnSIOWicig6/1JUw01YAlwDj+uvp+eBB4NZblffpRXRElroSNE1E5ERNTwN1dZ4mhnYUTE4FwxOk3G1A9PYCF18cfD+JSV2RVHQ0N8uRk2EqOoaG9IPhmhrbpMxJ0XHRRSRFQDIUHcLaJ4nwIxQK7l2gJBUdnDuTPUFILlW4ruIHxPAwdd1uY7WKokOUll2+PNcERkdt3Y+1xKy9f9+3j9QcPuYrqoqOsvHoAEjVEbeiw4PoCKlLwNy56gUGZMKeckpd0SI6svhDAM0AXg4iLWoA/G724QaW3ZYB+CWAdzpscwkoNWYGpP5IETIaGykQ1CU6vvtdWiV82cuMHlYKDWQytGiwdy+lE/34x8AvfkGd8Zw5wCtfCbz2tRTgvPGNlL8eNtFhXeAIi+gQ5eJ0oaIyMKnoEAORX+pK2ESHY4Cxdi1FcoODRhUdSisJguhwi8j9ENSpljHq3M6d00pdKTA0tCo6YoDJ1BU/v4dIzEglFR0yag4guYoOx9SVgYHcfWGpuALEW15WfMZEyfqwoE10CJSAR8f0NH2f03mIkuhwXcUPCJEq5jZstLbKx7xWouOZZ+jvkRFbMxC5pV1dsylkAOge3btXKq879ejwaAuLFiVD0bFhQ95LSfTokCU6kkxGm4S2tzPnfAJEatwFQNxuLPvw+nsUwF8BeGV2H/b9vo5zfjPn/FbOucL6Q4og0C0xOzxMSoE3vMHcKneKYFi1CnjgAeAtbwF++1vgbW8DfvlLWon47neB178eWL2athVGfWFCOnVFE0EVHYDa5NukoqOqigZIP0VHmBVXAJcAI5MBrruO/tYsLQs4KzrGx30q8Ai0tlJunG7DMVHKVfx2C9GhXHVFfEgoOmIoLQsUj0eHr6KjrY1KdN5wg+++zpyRJzqSrOgoMCOdmqIDnZ4GDh1yJDqs57+pic7nzIzcdw4PUz+n6EE8+73FkLriBk+io6IiNDYiSqIDcL+HoyY6wlJ0eN1vLS3yqSvHjxNhsnSpBxkqViNOn85//exZIsp9jEgB9dSVUvPo8GwLCVd0JMmjI01dyUcQRQc45zMAPssY+xqAtwB4BYDrAFgLjDIA5wBsB/BzAN/hnGsUy0kRJnSJjv/9X7rR07SV5ODDH6bqoK94BWUfOKklswUUwh43ACQ/dQWgTl9mcjM9TQ+TwagXYz8zQ3FTLIoOgHw6Hn7YONEBuK8m5kF4tpw/r7dEFVTRAeQTHb2zf/qipoZW6GbFKFZFh2IJZROYnKT7zwTR0dQEPP+89zYmPDpcyZSqKjIbkkB3NxUQkkHYig7d0uuORAdA7XtkhBqahKIDoM1llCVBqm4Ui0eHGzyJjsbG0Px14iA6nGyxgpQWVkWYRIdXOxceHTJiwePHKWaqqfHoI6yKDiuEwbqPESlgJnWlmBUdjvedwKJFwI4doX6/p6JjbIz6W0vFFYDaGWPheevMmUP9+eTkbGaiL2QVHeViRhqI6BDgnA8A+PfsA4yxGlBaCwD0c87LhDcqXrS3A7t3q3/u3nuJ5b75ZuOHlEITt97qb6mwYAF1zlEoOqxy6aQqOmQ7fVlpqQrmznVPXTl/nga4KDw6ROnUPGJM+HQYNiMF6Hz7BgciJ+n8eV8jN8+DMKzokCU6AEuqk4joBwb0fktACM4nCjPSyclZWxPt/QNmiNHu7hyx64cwiY6hIf00QcfUFYAuqnCetsjlncxIxTkdHJQjOoJUiamvD4fUNgW/tCpPoiPEGWSq6DAHGUXH9LScwE6UlgU8+oimJnrYiQ7J0rKAfHwh49FRjESHr6Lj7FmHIMUcxP0wMEB/5/WTIm3GQdHhlSIVFNauXtarP/XoyEcorYVzPs45784+yuRUFjfa2ykgnChIJnJHXx+lRLzpTaH1OylCQmUlkR1Rp66YDn5FvfCoFB2yKy4q8FJ0iJWFKBQdgIshaWVlrjSTAvyIDqlB1kp06CCoGSkQmOiYPaeikfX3x6IrFvm4UaSuqJwnJ4jPBSVGxSJcElJXghAHnooOUVpWUtEhe06DKDpE6opUeloMkFF0FPRPVkVHTMdlCn6KqaiJDkGMmoQM0QHIDS1WosOzj1i82FnRsWBBgRLACbKpK34eHfX1lGFVbPBVdExPh2osYV2UK1B1+BAdYUF09V4+bnakqSv5SKenKQDQPIbzwvRCL/z2tzRAvfKV4R1XivDQ1hYd0SHYbtOKjqDVQwVkO33ZQEQFzc3uRIeImaJQdAAO52DxYuDpp6kWsSLcJLQiUIyE6Ojvp4hPd8YN5IiO+nrlqiuAJXCzKjpiIDpEoGSS6HCbyAYlOjIZM6oAl9jUFUk1IxVpULOwEh3PPkuzNstyn5OXjLgPZfvgIAF8QwPNSSYn9T4fNgKlrqSKDqOwpjKahJ8njhha/Hw6OJdUdAA0UDspOiTUHIB66oqbR0cx+nMAPv5dgigK0ZDUej8UEB0udcqDeC/JQKwxCUNcGYiY2+u4yil1JSU6UgDI3UwqPh2bN9OET6jbUxQXFi2KjugQY4NpRYdJoiOJio6oiQ7HYPPii7WYnYEBOq/2vNLIFR1z5wbTlXZ0ENlRVaVkRlpwTmtrKSoSJZAihmmiQ1RtcIKTGabOdwTtL1xiU1ck1Yy0utpH0WFRcwC51Axrs7emrsggqKJD7COJ8CM6HM2ZI1B0lDPRYXp12ZSi49w5OraODvrfl+iwrhbOzNBCgYQ/ByCfulJdTWSwm6KjGNNWgPx0zwIIokPXWK6nB/jpTz03GRrKxVqyREfYio6LLqJnP08sK8RailfYk6aupCg76BAdjz5KJEcUUssU5hGVomNwkJSbmUxyiQ5ZdjsMRYeXR4cgOoTPWVgQAYbJgc9NuKAU2Ao1xblzegdhIk3kL/5i1sAocOpKTw/9HSPRYcqjA/CfKAUR0jQ0BFeAif4tbkXH5CQF70FSV6amLBVTfIgOJyNY1dSVoB4dYh9JhJaiQ3wgVXQYRRhjD+DffmUVHdbSsoCkokNIEo4coQORVHSINudX6Uis1rt5dBSzogNwaQuiE9dVdPz7vwOveY3nBR8czHWlBXOhmIiOZctosUiH6PBCXR2NKVNTwY6vGBCY6GCMrWCM/T1j7AHG2HHG2CBjbFrhUQanOflQJToGB4GdO6nCX4rihCA6ws6jFp2uiYmLHVGnroSh6PBLXVmwIPzSzWHIh/2IDqlV88pKOkFBFR1BUFc3G+XqEB2z7aq2NhekxVBe1rRHB+B+PwdNXRHfUSqKDvE7gqSuAJaVTnFjnThBnbjFiBRwNttUTV0pZ0WHb9WVkBA10eF2f5VC6oqsosM40SF8mICcEamCoqOmRk6A6FbCuRQUHY5tIaiiQ5g2HzvmusnQEI0Vzc0uio6mpoKONUhKogwqKsi73DTREZaSKonQJjoYYxnG2N0AngPwUQAvArAUQAOopKzKI0XMmDOHblZZomPrVpIu+1X3SJFctLXRgKJao1sVotM1MXGxI+rUlbAUHePjzgNOV1f4aStAghUdAC29BfHoMLi8FVjRIUwLSiB1BXBPgzBBdJggRlWJjkyGrpvpCbro94KkrgCWttTURLOh7dvpf5fUFSuiVHR4mSUmAUn16BgbKz9FR9JTV5SIDnuJWVFa9tJLfY8XIKJDNrZw8zCKyQLKCDzbQmsrddC6io6jR+nZg+gQFamWLnUhOhwMZcNWdADA6tXAoUPy28sqOoCU6PDD/wfgg6AStSlpUeRgjG5uWaLj0UeJabzxxnCPK0V4ECUXw05fKQZFh2p5WdMeHYCzquPUqWiIjjCCTbfBVnmADUJ0mFB0WDA2Rn2lzPV3NCMViInoqKgwE5QVk6Jj7ly19EpZ0lMF4jwFVXTMtqVMhibc27bR/w5Eh32SquPRkaauWBCRoiOKVGBxfbyIjkwmfCUhEM7YMzPj334rK6kb9lN0HDtG50sQI76KDiDn07FvHy3HS5Jj4+NqREeppa54KjoyGZK36io6JIiOoSG6VK5EhwNjHgXRcdFFpOiQVV+nio58aBEdjLFXAniT5aUzAD4B4KUALgWwUuGxSvPYUxhGe7s80bF5M3DtteFKtlKEi6iIDjHwpooOZ4gVdieiIypFRxypK5ERHYYVHbW1ctJiR0WHQExER3NzMF9WAb80CBMrwqYUHbJqDgG3CUQQBFV0OJr0zZ1L90VFRc6xLgtTio5STl3xIuFKvepKRQVdI6/7t77eTF/hhzAmXGIs94tPZYYWUXFFnIuqKjp/nkSHVdEh6c8B5FJXZODm0VHMqSu+bWHRIj1Fx9RUbnLjQnTMzND94KroOHs2VqJjcFDeqkyF6CiHyis+ljeueKfl70cBvIpzLrlOkCKpaG8HHnrIf7vRUVpIev/7wz+mFOEhSkVHUxMNBmERHUHnjaoeHaZTV4BComNqiiZqZZ+6Mn8+8MwzegcRQuqK7IprEhUdJoxIgWgUHSb6izNn1ImOMBQdQT06ClJXAOo4TpwAVq4s6JCczEirq+khQ3RwbsaMNKlEh999HIeig3O1iW5QiBLRTgjiz6KKMIgOWWKxpUXOo0OkrQBEeLiSoZbUFbZoEfDcc8CrXy193CZSV0pW0QFQ6oiOouPkScq1B1yJDnE9haLjzBmKwWaNYbu7gZtvLvhcVKkrAKk6Fizw3z5NXcmHburKxuzzDIC3pSRHaaC9nRR3fi68TzxBHXJqRFrcEBOAMImO6elcNU2vwEoXfX00yPi5lPuhro7atBgL3SAG4DBSV+yVV7q7KfhdutTcd7mhpBUdBlNXnFbK3ZA0RUdvrxl/DqB4Uld6ehzTqj0RhqJDnKegig7HErO2tBXAvZ02NsqlrkxOUl+oO9lNskeHqDTgR3QU9E8hKzrCSIv0Qkp0ENEhq+iwwpUMbWigvr2rC/UnTlBDU1B0BE1dGR+ndpQqOmwQ5EZdnSvRIfpFoeiYmbFwKlNT1FBsrHlQQlgWQrAn69Mho+opJ0WHLtExHwAHsJdzfsLg8aSIEe3tFNz4EaaPPkqs9k03RXNcKcLBvHkkwwyT6BCDh/DoCEPRYWIeKxtsRanoEOrXYlR0cG6o6gpARMfgoE23L4GxMfqMYUWHNtGRAEVHMREdJlJX+vvVf3N9fXiKDuOpK0BBxRXAm+iQOadBjzfJHh3ifvQjOiYnLeV8gdAVHWGQ6F4oZaJDllhsbfVWdIyNUTxsJzo8ydAlS4DTp9Fw5Aj9H1LqitMxWOOtYkRoig7hz7FpkyvRIdqMUHQAlvSVnh4KamxEx9gY9RFhEx0rV9KcS6byyvQ0/ZbUoyMHXaIjW6gOPqKvFMUE2RKzjz4KXHmluaA5RTzIZKjfjpLoCMOM1ATRIVtWMoxg1M2jI0qiw7SiY2yMFkGMKToAf42xHaKcUEyKjoJzKl5gLBZzoziIjiCTJaHoCFL+Wseipa4uPEWH8dQVwFXR4XTum5rk+uCgHitJTl0R/Y4X0SHeyyOWQiY6ZI7LJJJCdISRNmkqdUXEwspER1cXER2VlcDFF0sdM6CWuuLk0SGGvJJWdAwPqzOoR4/mVmfPnXP8vF3RAViIDpfyXUEJYVnU1tIxyRAd4p5OU1dy0CU6ngdVWFHMfk2RZMgQHRMTwJYtadpKqaCtTd/EWgbWgTcsM1KTREecig576ooYZItR0SGuu7GqK4C8E5fMQWjCz8TQCtfUlaYmYhkjhkmPjro6ihv9zEiDKjo411dXyK5s2ZFkM1ITqStRKjqKlehwPN/iR4U0iyx3RYfJtEnZ9tvaSil9ecodC+ylZQWkiY5165SCBdXUFXs8VRaKDkA9feXoUWIK1qyh/8WFtcBT0SGCZFseZFDvJRWsXi1HdMiGPWnqij++n31ezxiLIAxPEQVkiI6dO+nGuPXWaI4pRbhoawtX0WHtdFNFhzOammjS6KToyGTUPQZ0YFrG6DXYVlbS71ImOlR9Okw51VqgY0Y6+zvFB2OKQk16dGQy3vfz6Chd5yDeOWKSokuOyq5s2ZHE8rJi8uOYuuJAdDiZkYrvl/HoCKroEN9dUkTHLbcAf/M3wI03hnJc5U50xKXomJlxrngGaBIdixcDXV1oPHwYuOwy6WMGgqeulIWiA9AjOlasADo66H+H9BWromPBAkrrToqiAyCfDhmPDlWiI1V0uOMeAEdAqo5PmjucFHGitZU6WS+iY/NmenYwH05RhIiS6GhspE7Vz/BTBaY9OvwmOGEoOjIZOj9OREdbG5WyCxumV9W8BlvG6PtCJzpiTl1xVXTEQHSMjdFxmEw39JooqZwnr/0D+uSorqAnLEUHY/rnxHHi/epXA+97nyMT6nb+ZVNXggbwojJFEj06Aik6PvlJX6bzN7/JtT0VRE10eBGV5UJ0+A0tgugQi4ACvoqO8XHUdncr+XMAeqkr1tS+slF0qMqQJYgOq6IjkyG+apboEMRKzETH2bP+RLXsuJemrviAcz4E4I0AhgH8AWPsM4yx6LW4KYyCMerQvYiORx8FLrlErsRRiuRDmFibJB+ssHt0AGYnEVGnroQVjM6d60x0RFFxBYg2dQWg4FbJjBRIjKIjsBlpTEakgHmiwy3oMkF0BFV06BIdYZWXbWigMVYHjhOAG28EvvjFgp1OT9OEyWmiKpu6ElTRIT5bUooOCQwNAbffDnzjG+rHlSo6zO1TRdEBuPt0HD9Oiw32a+JLdAgoKjpUU1eA/POWKjocMDVFZbg7OujaVFb6KjoAir3yFB21tQUnNkqiQ5SYPXzYe7s0daUQWuQEY2w5gB4Avw8yJL0LwH7G2IcZYzcxxlYzxpbLPgz+nhQB4UV0TE8Dv/1t6s9RSmhro+uqW73TD9aBVwwGJtNXok5dCUPRAdBvsHt0dHVF488BkEyTsWgUHUB5KDoKDCRjVHSEQXR4qQNMTJREwBk10RFWedkgwbBj6ooLxH0VJHXFRADvZJaYBHidHwFdoqO/n8ZTVd9k63GVG9ERphmpX6qYIDq8FB32tBVAgejQUHSopK4A+f1jqSg6XNuCWGFVUXScOkU35YoVJI9tb/dVdAAORMeiRQWkctSKDsA/fSVVdBRCN4P2KKi8rAADcDGAT2vsiwc4jhSG0d4OPPaY83u7d1NHmhIdpYO2Nno+cyYcLwh76gpgTs48MUGddJSpK2GtujU3Oys6oirhzBj9pqgUHXV1Ct9VX08XKAGKDhUz0kyGCKQkKTpMmZEC4aeuBCVGk6jo0IXKxNvLCDZqRUcSU1fEtZVRdKj2h2KiqfO7ZcremoRIJZ2aKvTSiZLoMD32AOqpK16KDieuQobomK6tRYVIlZCESuqKk+FvqSg6XPu52lrq0FUUHaK07IoV9NzRIa3o+OUvs28KosOGOIgOP0PS1KOjEEHTTQS9xVFIfPg9YPvbCBhjaxljuy2PAcbYBxhj/8AYO2V5/eUmv7dU0N5OLKaTC/Wjj9JzSnSUDqxERxgIU9Eh5rFRKzoyGfO+GfbUlbExmtdHpegAaOBLpKIDAObP11d0xJS6AlAAX6DoMKgwkUVvtiB8MXp0xKHomJoCJif1vtcJQ0PBnPlViA7Rh7l5dIyO+qcqmgjgyzF1RUyWdMa4OFJXAOf7a3g4OqID0BgPfDA8TGO0H2nglbrCuaaiY/FiOoaVK5Wra6mkrjilAtsn68UGKYJR5FvLQpLoGBqi76+qov+XLqXXBgaQCKJj7lwi5kwRHeJcp6kr3rCTFXYSQ/bzRsE5P8A538A53wDgGgAjAH6cfftz4j3O+c/D+P5iR3s7BXhOlRw3byZWMSrfgBThQxAdYZWYHRykoKCy0ryiIwyiQ8ajI4xA1J66cvo0PUdNdETp0aH0Xa2tekRHTY3RC6ZSdQWwER0JUHQUE9ERp6IDMBsABlV0qKSueBEdsgav5e7RoWvOXApEx+QkEX1REh2mFR0iVczPE0co3JyGlvPn6V5yIjo8VV91dUBbGwZFKVMF6KSu2BUdDQ3RGJiHgQIVpBMWLlQLWI8do4awbBn939FBclkbkz04mE8Q5ZWY7e52lDyL+zwKogOQKzErq+pRNoUvYuiakWYMPsK8JV8I4HnOeSF9l8IRbiVmZ2bITTxVc5QWolB0iA63VBQdpv05gEJFR1cXPUdJdJhOXamqcg/alMxIAT2io7/fKKnAufoEPk8lU2ZER9CJUlxmpE4TiKAYHo5e0eFmRgr498Hl4NGRKjro2X6sJkguVZhUEwLyxGJFBfWJTooOt9KyAJ2byUkP1dfDD+PIH/2R7OHOQid1xe7RUaz+HAK+cYiOomPJktyN1dFBExrbJGdoKJ8cmCU6TswAPT2xKzoAuRKzAwO5xUU/KMdhRYpS98a4A8D3LP+/lzH2BwB2APhLznmv/QOMsXcDeDcALFq0CJ2dnVEcp1EMDQ1pH3d3dxOAa/CLX+zFwEBuYnH4cAMuXLgOCxc+g87OkJb/U8SC2tqbsX17Fzo7fahiDRw6tB5VVU3o7NyGQ4caAFyHJ57Yh5oaB8mQJET73rWrGcAGHDmyG52dfYGOs7e3CsAL8NRTz6Gzs8t1uyNHLkYmMx+dnVsCfZ8d/f0r0de3HI88shmMAZ2dCwBciq6u7ejsjCbRfWZmI06cGEJn5/7A+3rmmTWoq1uIzZudDX9GR69EX18GnZ1PSu3vkulpNJw4ge0K/dr6gwfRVF2NbYb68MlJhpmZW3HmzGF0dh6X+gzn1+PYsQF0dj6DyoEB3ATg0LlzOOlzTEH6cCfs2rUcwCrs3fsoDhxwyEvUQF/favT1LUJnZ+E1Pnv2WjA2hs7Ofdr7HxqqBHATdu8+hM5Oj1JgLtizpwPASuzc2am0wnns2CIA6/HII49j8WIzzF9397WoqBhFZ+fTWp+fmGAAbsUzz/i3vaefngPgajz33FPo7MwPcU6cWAjgEjz00BNYvtw9wt2/fyUymeXYsmWzdqWY4eFLcfZsPTo7txe8Z7p9q+CppxYDWIsnn9yKkyedZ9fPPdcI4Frs2LEXtbXyBOu2bdR2Tp7sRWfnU0rHtXs3fXb37ifQ0xP+7OPIkVYAl6Ozcwe6unJsx/nz1QA24cSJA+jsPB36cQAA5xtx/PggOjufMbK/I0fWI5OhuMMP9fXXY//+gYLv/u1v5wO4DGfP7kBnZz4b1NXVDmA1fvWr36ChwTkPbCiTUW7jIyM3o7tbLhZ79lm6z7dufQrT03SfHzp0CSorG6V+d1JRUbEJhw/3oLPzoOP7a6amsODkSWyRPLdX7t6NzLx5eDK7/by+PlwJYPdPf4q+DRtmtzty5FIwVofOzh0AgFOn6gBcj877d+NF09M4ODCAU7bv3L9/BRjrwBNP6PeTKqioWIETJzrw618/iqoq7rjNgQMXo7a2FZ2dW333l8nciMOHz6Oz8znlY4mzD1cG57wkHwCqAZwDsCj7/yIAFSAVyycBfNNvH9dccw0vRjzyyCPanz19mnOA8698Jf/1L3+ZXj98ONixpUgeVq3i/M1vDmffL3855+I2OniQ2tC3vx1sn6J9/8//0P527Qq2P845Hxigff3Lv3hv9853ct7eHvz77Pinf6LvHxyk/z//efr/3Dnz3+WGK67g/NWvNrOvt76V85Ur3d9/5Ss5v/pqhR3+6Z9yPn++2kG8/OWKX+KNvj66Jp/9rPxn1q/n/PWvt7zwve9JXdQgfbgT7rqL85oao7vkH/kI51VVzu9dfDHnv//7wfY/MUHn+xOf0Pv8Bz/IeUOD+ud+8AP63n379L7XCatW0T2hi+lpOqaPftR/2wcfpG03by5877776L0dO7z38YEPcN7UpHWos/DqA0y3bxV86Ut0Dnp63LfZt4+2+cEP1Pb9b/9Gn9u4Uf24/vM/6bMnTqh/VgcPPeTcTg4dMjNOq+CKKzh/zWvM7e9Vr+L8yivltr3uOs5f9rLC17/wBToPZ88Wvvfv/07vnT7tvl+dNl5VRf2qDHbvpmP4n//JvfY7v8P5tdcqf22isHQpxVmu+OhHOWeM88lJuR2uXMn5W96S+/+55+jEfetbeZu96EWc33hj7v+REdrsk39xhv74/vcLdn3nnZw3Nsodhgnccw8dyoED7tu88Y00/spg5UrO3/Y2vWOJsw93A4Ad3GEuH9SMNMn4HQC7OOfdAMA57+acT3POZwB8DcDGWI8uoVi4kCRP9tSVRx+lFDfh55OidNDWFl7qilVKWQqpK2F6dAC539TVRRJWYZYWBUybkXpJaLU8Oi5ccHZJ9joIg8afMmUp7cjz6ACAO+7IWf1HiL4+s2krAEnfJyedfSNMeHRUVdE9ECR1RUfGHUbqStDyslK561nIeHT4lZgN6ikCFLdHh27qihjb0tQVNYRhRirbfltanLMijx+ne2j+/ML3wugjOKf+NEjqijVVuFjhG4csXEgny8lI0I6pKeDEifyJi/DqsBmS2j066urIw+XU0Wx+kkvqSlRpK4Bc5RWV9KVySV0pZaLjTbCkrTDGFlveey0AfU1tCSOTodw0K9HBORmR3nKLv7lTiuJDmESHdeBNshlpZSXl68bl0SEmoeI3nTpFaaVR3m+mPTqMEx0zM4U1eIMchCJkylLaUUB0xISwiA7AedJsgugAKIgMYkaqc/mTaEYKUFtSMSMN4tFhorxo6tGhhpToMLc/lftNcOh2iIorTmNwGESHuLeDlJctG48OQM6no6uLyA4r0VFbS0Gvjeiwe3QANBc6dcr2vRbERXR4+XSojHupGWkRgzFWD+DFAP7H8vI/M8b2Msb2ALgdwJ2xHFwRoL09n+g4eJBMh1Mj0tJEW1t4VVesna4YmE0rOkwN7J5O6lmEregQppFdXdEakQLmq674ER3KZqSAmiGpYTNSr5VyN5Qy0SGCQqf72cREGaDJWLErOmZmgpuRAjQBMlFeFpAzIzWh6BgepoWSJGFsjIhtL7O+oESHTptNiQ5z+1O537wUHU5GpEC4REfQqitloegA5IJWUVq2oyP/dYcSs04lwNvbgVNnsx1FAoiORYvo+7wUHSrjXl1deRAdrl09Y+ybln855/yPXN4Lirx9G9rhCIBW22tvM/kdpYz2dmDXrtz/mzfT8623xnM8KcJFWxsN9GGoFaydbkUFdawmFR0NDbm650Eh0+mHWXUFyE9dufxy89/jhdpaOTWoDAYGgLVrvb9LWdEBUENdvVr+IAymrugSHX5pAlGgr898xozbREmnOo0bSkHRIfZjQtEhM/EWE58gqSsmiKr6eiJ5VEpmRgGZEtHieFUnASYUHWGML05IGtGhUkjDD6qKjr4+YHo6vyzr8ePAy1/u/JkkKDrE70sVHR4QRIc9576jI3+Sg8LUFYAUHU/9NlvCxGGlIGqigzFSdZgiOsoldcWr6so7AFi5+D/yeC8ojBIdKYKhvR247z4KWBkjf45Fi4CLL477yFKEAVFi9uzZXHlhU7APvA0NZokOg/PYWBUd9tSVri7gZS8z/z1eiDJ1RXklQVXRcf480NubqxFnALpEhynyKAh6e3OyV1NwmyhNTNDYYYroCKLocFiE84XpSYw4/qCKDtXUFS+iIypFB0DnMUlEx9iYPNGhq+gYHye/BRUSfnycJrlRpSsmiegwOfYA6h4dQD4ZPD4OnD4draJDleiqqiJipiw9OgA1RYf9QnZ0AD/5CTGxGUpscEtd6R5uxFTbYlRmChMggnov6eCii4Bnn3V/X5XoUMkGLlb4pa6w7MPrvaCPFAlDezsFS73ZynSPPpr6c5QyBNFh2qdjfJwCc+vg0dhoNnXFJNEhw25HoegYHKRHHKkrUZuRSsvahSOcLNHx+OP0vGmT5Bf4Q8eM1OQ5DYIwPTrs97MOIeT1HcWu6BDHHzQgNpG6ErVHh9hXkhAF0QGoE3Qyx2US1dW0UJ0EosN0P6mq6ADyhxaRuu1GdITh46OausJYvuGvINdKXtExdy41XllFx5IlhSe1o4NOeJYsmZigh5OiY4ZncKblEsfdR63oAIjoOHzY2Zed8zR1xQleio6Pab6XosghVvVPnqSb5vhx4K674j2mFOFBrHqaJjoGBui51BQdpieMQL5Hx+nT9HfURIepVbWJCdqPH9Gh5DKvqujYsoWi+GuvldteAsXq0cF58RIdDQ36/kFJ8egwqeiQJToyGef7qrKS7r0oFR2m+nsnvOtdwCteAbz2tfKfkSEUGJMnlqywEx0q91xYakEvOBGJxe7RITxxVBUdVkPS48fpOQ6PDpWFFKvhr4i3Sl7RwRipOmQVHU6lIoVnx7FjwOLFs/eAk6IDAE41rYOT2DkOomP1ajo/p07lCsgIjI5SClaaupIPV6KDc+5KZni9l6L4YSU6hOw6NSItXYSl6HAjOkwqOubNM7MvIF6Pjvp6kqH29+dcvotV0SGCfT+iA6BBVup8zp1LszcVomPDBqPRum7VlbhXTEZGyHje5L0CRCN9b2yk1StVqK5sWZFURYdK6kpdnbsCs7ExOo8Osa8w0NcHfOMb9LdpogPQIymt51V1nEuJDjP7UvXEceLQ4yA6dDxarIoOmXG3GCA1Zi5aJKfoOHYMuOGGwtetRMcNN8yeOydFBwCcqnXO+zRhMq0Ka4lZO9HhFHN7oVwUHSVZdSVFMFiJjs2bKUC+7LJ4jylFeAhL0eE08AapomBHKXl0MEarf/395M8BFG/VFZnBVkw0pL8vk6GlNxnDi6kpYNs24MYbJXcuh2JVdIgUxGJVdOgQoyMjtLKrE/SLtmla0RFV6srIiPe5b2qKRtERdurKvn307FQa1AthEh3Wyg3FTnSYuH9lYZLoUL3fvBQdbp5lSai6Io5D/F7VSW5SIbXgIqPomJ6mC+mn6ADcFR1LKLf2ZIUz4xVX6grgXGJWtQ2k5WVTlC3a2mhecfIk+XPcfPOsX0+KEkRNDZFZpkvMOkkpTSs6SsWjA6Df0teXIzoM+mhKwZT6QGawFUG0siGpjKJjzx6KQA36cwDFS3SIksXFSHToEqNBgv5MxqykV5yfKFNXvM69jO+JSUVHWKkre/fSs0rFaSB8RcfixfR3MRMdwr8jKih7NnlAl+iwKzoWLXJvJ0lJXbEqOkoldUUqDlm40F/R0dVFix5ORMecOTQgZokON0XH/Kp+VGMcp2YKV504j4foWLaM7k2nyis6REc5pK6k09cUBaispMF62zZiDdO0ldJHW1s0qStJV3T4DbBhBqNz5+YUHY2N0QcstbW0CDI1FWw/oSg6AHmiY+tWek6JDgDhER0iwEuioiPo6qZ1AhEUphQdqqkrbvBLXZmepjZrsupKGBBER5IUHVaiI+lmpIA70RFl2gqQ79kUFKr3W3MzKSrtig63tBWAKp5UVsafumL16CiV1BUpRYdIXfFixtxKywp0dPgqOtjZbixBF06Nzy/4+Pg4qQajJjoqK4GVK80QHSLmNUEwJhkp0ZHCEe3twIMP0t+33hrvsaQIH1ERHabMSCcnKaCPOnUlTEWHNXUl6rQVQL/SgB2xEx1bttAJtCewBoRO1RUxOY0zkAiL6Mhk6H62T5pN5vg3NNC9LjPBtyKJREdQRYdK1RWvc++n6DB1/aIiOnQUHTL3sCrRMTVF5154XhWLosM+HsdFdABmFIWq91smQ4pWFaIDMNtHAPqpK2Wr6Bgfz/1oJygQHW6KDnR3YylO4dRwYaBpisDWwUUXmVN0cK4+vhYbjInTGGMZACsAzANQD4XSsZzzR00dRwozaG8HnniCOs0NG+I+mhRho60N2L7d7D7dPDpMpK6I2t9Rp66EregQeZdxEB0i2Ay6mhsq0fHkk/7bbdlCag7D9bBF21C5/mLbiYnoJzECgugwbUYKON/PplNXAAoqVQjGoESHDOkpC5NmpCZSV5qacpWdnGAqgA/To4PzfEUH5/K3++iovKJDpX8S17kUUleiJjrE7/ar1iUDnfZr5dA5J6Ljd37H+zNhER2qqSvi95aaosPznhbGcmfPugeBguhwW/Do6AAeeQTgHEND9EUFJNHZs1iKaTzZe13Bx+MmOrZsKTxHOkQHQPddXPFJFAhEdDDGGIC3AngngOsB6AjveNDjSGEewoTpBS+INlczRTxYtCg8RYfdo2N4WC0wdUIYREfcig6RujI8bNxHUwrWYDMIVIgOpcmkjKLj9GkKcN73PoUdy0FMkFTarVUlE1cgEZYZKRA+0SGCyOFhNaImiYoOE2akMitvfmakfmSzqeMN06Pj5EnqK8WirJ+KxYqwUlfERDMI0REGGemFpBAdVpI9KHTab0tLTtFx4QKdg6gVHUGrrpSKokO0Bc/FgYUL6bm7G1izxnmbo0fpZnS72Ts66Kbt68PgIN14zoqOcfysp7ogZjVFYOtg9Wq63ufPA/MtWTU6qSsA9YkmY+mkQTt1hTG2EMBWAN8CcCuAOpCKQ+eRImEQREfqz1EeaGujjtuUUShAnS5j+QNBQwPlNQadTIdFdHgdl5D4hTVhjTt1xZR8OFQz0tFRb3YkJH8OwH+l3AkmA3hdCEVHGIFMlIoOFSRN0VFTE3zBwKQZqZdHRzGkrgg1h0irVUlfkSU6VMtti3NabKkrSSI6TKau6Co6/ErLCtTVxZ+64uTREXW5U9OQSqG1KjrccPSoe9oKkFd5xc2jA93dWMq6MDLKZmNOgbgVHUBh+oquoqPUDUm1iA7GWCWAXwPYiHyyYgZAD4BjCo/j+oefIiysXEnPt98e73GkiAYiODNZeWVggDpcKwuuO3GxI0xFh5ufgjBKC1PRMTBAA3zUFVcAc5NyJ4LL7buUAluxdOE1q9m6lSKlq65S2LEcdIgOUyqZIOjro4lLGO02KkWH6qQxaYoOE8GwCtHhNVH1Ky9rKoAX6qcwiI49e+hZEB0qhqRhKzrmzwcqKtTHuDiJDuuYV+xEh85Ku1XRIUt01NebnSAGTV0ZGKDrWewVEqXaglXR4QYFomNwkPqqgnbf3Y2lTXRjnzqV/1ZSiY7qavl+xOR9l2To3hLvBnA5KO0EAO4H8EIAjZzzRZzzlSoPEz8khVm8+tXAww8DN9wQ95GkiAJhEB2Dg4UTDd2Jix1heXQA7sGteD1Mjw6BOM1ITSg67ASXHdoeHYA30bFlC3DNNaFcJFkTQytMGbwGQV9feJJ4txVhwMxkKaiiQ1fGbVrRYWKVVTZ1RUbRMT7uXuHC1PUTE4ewFB3LlgGrVtH/YSg6VIkO66qwjhdVXFVX7ArLYic6dFNXVBUdSUldGR2la+gUbxUjpMbMBQvo2U3RMT1NF1JB0dHY6BCzdHdj6Xw6EDeiIw4FjViIFp5uAiL2koWWsrYIoUt0vMHy91c456/inD/COS/x01U+qKxM1RzlBEF0mPTpGBgonGgkXdEBuHf6OisuKoib6DCl6JAJuAIRHefOOb8/Pg7s3BlK2goQTNERJ9HR2xuOPweQbEVHTY0+31XMig4Zjw7A/ZyaXKm0rjibxN69wOWX0wQVkFd0cB6+okOX6IhL0QHkH2s5Eh2trdRnTE7S/LimJjeXdkNSqq4AdN6c4q1ihFRbqKyki+a2Mnf6NJVB8iI6FiygjjKr6HAkLLq7sbRtGkCyFB11dWQv4KToUCE60tQVb1yafR4F8GFDx5IiRYqYEBbR4aboSDLR4dbph63osE5GS0HR4QVtM1LAffn2ySfpIoXk5CpbrcGKJBAdfX3REx3V1WYk1Lr9RdDVTZOKjuFhM6t+YuLtV6pYRtEBuE/ETSpywlB0TE4Czz5LRIeMyMv+Wc7DJzoaGlKiQwWmiY5MRu1cCsKsry9XWtbPdDoJVVeslY3KStEBkE+Hm6LDr7QsQBd4+fJZRYcjSdTdjSXLKgAki+gAnEvMpooOZ+iGInNAaSu7OeclzgWlSFH6mD+fggOTRIfTwOsXZMtCEB0mB3Y/oiNKRYdw7o8SJj06QlV0uM1qtmyh5xCJjmJUdMRBdJhQc4j9A3qpK0H6BpOTmKEhM8Gw6Hempry3kykvK47LCaYVHaaJjgMHiLDQUXSI/iYKRUcxeHQ4KaZKgehoaFCrjmUdWgTR4YekpK4A9JvLStEBkE+Hm6JDhugAZss2uSo6zp5F7ZIWtLaWJtGRenR4Q1Rhl8gYTZEiRdJRUUFKvmJSdNTXA1VVwfZjhZ/KQEdaqgJBdMybZ26iqAKTVVf8BlvtqiuAO9GxdSslr4bEEqVERyGamgoreJicKAVJXSlFRQfg3Zampujhdf7FsbhVXjGp6LBWhTAFUXHl8svpOtXVySs6RH8jcx+nqSvRwaRps879ZiXM4iI6dM1IgVTRUQBBdPhdyBUr3BUdw8P0WLQIS5cmk+g4cyb/3k1TV5yhS3TsAVVaWWXwWFKkSBEj2trC9+gwaUZqulym3+RbZ8VFBWIyGkfFFSDa1BVxDpW+q7qaIlinWQ3npOgISc0BFDfREaYZ6cREvklmKSk6/NJEZGBK0SHakpchqYw/SrF7dOzdSyn669bR/9aKGX4Q50dW0aHSPw0Okiqyrk6d6JieJoIqbqKD83gVHSb6SR1PHMGhnzlD9g7Cp9ILYRAdlZVqKX/W1JVU0WHB0aO04OF3o3d0AD09GOyfKSTHxL49iA7G4lmUAoDVq+n58OHca2nqijN0iY57ss/LGGPXmDqYFClSxIcwiA631BUTio6wiI64FR1x+HMA0aauMEbfpzzAtrY6Ex0nTgBdXaEZkQJ6VVdMBvA6mJkJP3UFyL+fTRIdokRp1IqO+no6d26VSVRgyoxUkINebUlMvIKkroh9mKgAEkbqyt69wNq1ufPR2hpO6kptrbqiQ1RuUPXoEN8TR9UVIHes4vyUQuqKCoSiY88eIntUFB0myFCA2oDqIkrZKzr6+503PHpUjq3KbjPUN1VIEvkQHUNDdP5VUqRMwqnEbKrocIYu0fFjAA+BVB1fZIyFtMaZIkWKqNDWZq68LOfe5WWTSHT4dfphKzriJjqiVHQAdL6VB1g3oiNkfw6gOM1IBwfpXgyb6LBO6kwSHYzp+R2YSF0BzEzSTZWXlWlLphQd9fXmzGTDIDouvzz3v7U0qB9UPTomJ4nwksHgYI5EUm2zYRtdu8HeFkymLakgKUTHk0/SsyzRMT1thgwFaCFF9fqXvUcH4Jy+cvSovz8HkCM6Bv0VHWfP5l9rUwS2LgTRYS0xm3p0OENrKOOccwBvBvAUgBsAPMAYk6DPUqRIkVQIRYeJFYrhYdpPmGakcSk6wiI6amrIYuKqq8LZvx9MqA9mZuRXlowqOrZsoajviisUdyiPIKkrcQUSfX30XKxEB6BXwcKEogMwM0k3WV4WkEtdCerRYWqiazp1ZWAAOHaskOgIy4wU8D7fVljz/FVTV1Kig55N9JM6qWJz55JPmQrR4RcvqCKIouPCBUp9KitFhxvRMT1NRisKRMfgcMZd0bFwIZYupXj29Onc23ETHc3NFA4JRcf4OD3S1JVCVOp8iDH2B9k/vwbgYwBuAXCQMfYggC0AzkDBqJRz/m2d40iRIoU5tLVRUGcip39ggJ7tg0d1NeWhmlB0yAQjKpD16AgzGD140MxKqg5MTMpFwCwz2NbVaXzX/PnAkSOFr2/dClx/PTWukFCMHh2C6AjTowMoNDM0+X0NDfEpOoJOYiYmaAJiQtEhk7piStFhKoA3nbqybx89W4kON+7TCTpEx/i43PZ2RcfQEE2OZKTtcRMd4v4qBaJjeFjdj5ox6rNOnKD/29v9P2MlQ00sukxMqBMd4j4VKcdlpehYtIie7TLk06dJeiFDdCxZgpmKKgyPV7orOrJEB0DpKyLuNGUyHQTWyiuCuE5TVwqhGxV+C1Re1r6vl2YfKuAAUqIjRYqYIcaNM2fMER1Ona6OI70dpajoAGhVKS6YCDa9rrvT9xlRdIyMALt3Ax/+sOLO5MF5cRIdvb30HLWiw2T6lU4agOrKlh2mFB3ivJhUdAT16PAzhDat6DBJdFgrrggIRYcMqaBLdMjASnQ0NBDBJZuOEBfRIa5z3IqOykoa++JKXQFoaDl3joQCMv28SdUXECx1RczJU0UH5EvLAkBFBUaWrgE/7qDoOHuWAuHq6jyiQyBuRQdARMfjj9PfKrGXgDC/LXVFR5C1Q2Z7OL0m+0iRIkXMaGujZxOGpF7sss4KrR1xenREHYxGBRFsBpmUR0J09PbSLEJgxw76P0Qj0qkpSsspNqKjHFNXdFa27DCl6DBZwcRU1ZWKCpoguaWumAzghUeHKcPGvXuJTLD6DLa20gKuTBuJiuhQNd1WOS6TyGTy76+4iA5AczxwgG77FT4dskpR00RHkNSVslZ0BCE6AAwtXQvAQZ3R3T37HUkmOo4dozFBh+gQVWNKnejQVXQ8ikJFR4oUKYoYJokOt9QVQC/n3oqpKWZMLmqFX+pKFIqOuKFaUtEOVaJDy4wUILJjwQL6WxiR3nCD4s7koVKW0oqU6DDzHaJdyUAn4LPDtKIjSakr4nj8zEhNQFSvkU3/8MPevcBll+UrN8QE9cIF/4le1ETH0FDu+LwQJ4lubQtxEh1Bxx6BIIoOID6iQyd1xU50lJWio6GBToA9deXYMXqWvJCDi6hOqxfR0dpKx2UnOubPl/qK0LB6NfWvx47pj3tacViRQYvo4JzfZvg4UqRIETME0WGi8opf6koQRcfwMOV3RJ26UuqKDkC9pKIdkSg6AEpfEUTH1q1Ub1K8FwJkJ5B2yExOw0TYRIdTqdKREfOKjq4u+e1NEB1JVnTIEB1+E9WmJu/UFXFrBYV1MhiU6OCciI43vCH/dXHbX7jgX1FS9Dcy7VPVs0iUlwXUTbdToiP42ANQGxkZ0SMW41Z06KSuCM+zUlJ0KN13ixY5Kzra2qQHoaH5KwAATXVTyJsSd3cDGzYAIGJ1yZJkKjoA8ukQIlcdoqPUFR0x2d6lSJEiaWhupoHTpKIjjNSV4WEajEwTHdXVNKDF6dERN6JUdGhJJq1EB0CR7ZYtoaatAPpERyYDVFXFT3SYvlcEnCp4jI6anSip9helqugwlboijicqM1LAzGSwq4uEXPbCSmKCKmNIqqLMCurRAaREhwpMTLhGR2lIKEZFh07qijiOUlJ0VFQQeSN13y1cWLgyJ1taNovBFmJHG8fO5b9hUXQAlL5iJTp0qvuYhpXo0B33yiF1JSU6UqRIAYAm+aLEbFB45ckHNSMdGgqH6GDMW8aXKjr8EamiA6Ai8ufOATfeqLgjNegSHYCZlUpd9PbStQjL5NY+oeOcrqnp1BWV/qJUFR0y6iAZM1KAzmkU5WXF7zYxGXQyIgXyU1f8oJK6olJue3KSttP16EiJDjNER5D7LQmKDl2io6eH/i4FRQeg0BbcFB0KRMfQHHLObhq0yAbHxsgIThieopDoSIKio62Nrv+hQ2nqihdSoiNFihSzMEV0+Hl0JFHRAXiz2+Wg6AgabEZOdGzdSs8hKzpUJO92mMo910FfX3hpKwARKHV1uYlSkPPkhmJWdIjjNqnoiMKjw7SiI6j5NOBOdNi7BC+E5dEhzqWTR4cMUqIjJTp0UlcA+q3C7LcUFB0AnQctRYcwrPDLYbNgsIFytht7T+ReFOSJg6KDc3okgehgLFdiNlV0uCMlOlKkSDGLRYvMER3V1c4Dd1Az0rCJjnJWdJhKXZFZWQpkRipmNVu2UENYv15xR2oIouiQDtpCQNhEB5A/UQpynrz2PzJCMawMTBIdQVe6TJaXFQSridQVP48Ok2akYp9BsWcPTTbspc91FB0yfbgK0SHUMbpER1xVV4DSIjqC3G+33w684hXApZfKbS/usSSkrgA06Y174m0KSoqOnp7c4HD6NMmrVBQdteQo2nT+aO5FF6JjdJTG1PFx+soknG8r0ZHJqN+7qaIjRYoUZQWTig63iUZQM9KhoXDMSAHvTl9MMKqqzH9vUmAidaWhQS5VQiuwbWqiC2BVdNxwA43wIUK36goQP9FhnxiaRtgTJdX0B5OpK6YUHVGakVZW+vdRbqkrMzP0m5Po0bF3b6GaA6Dz0tAgr+ioqcmv2uKGKImOOEl0p/KycRAucSs61q8HfvYz+b7LFBkqECR1BaA2F/IwGBmUFB3T0zmWU7G0LAAMjlFn2Xj2cO5FoRKxEB3t7fR86pRZpV5QXHQRcPgwjfVz5sj1bVaUgxmpa9UVxpjlqoNzzi9yeS8o8vadIkWK+NDWRgT59HSwvP7BQfeJRtIVHW6dvlhxUR1IigkmFB2yE0wtySRjpOo4f56+bO9e4HWvUz5OVRSroqO3F1i1KtzviELRAVBwKRNYDgzkUmp0ISbDSTQj9SM6ZH63W+qKuB9Ne3QETV2ZnASeeQZ4yUuc329pkVd0yE7igxAdqr87Sakr9fXxjHG1tWplpJ1gklj0g2lFh27qirhXSyVtBVBUdACkwJg/X4vomO2ju57LvehAdCxdSs8nT+bOdRIUHatX07l69lm9NlBXl/N4KVV4lZddAYADYNlnp/eCwmnfKVKkiAltbbSqd+5cXh+vjIEB9/SFxkYa1KemaPVRFXGlruiuuBQTamtzlTp0oEJ0aK8ktLZSA33iCUqWDdmIFCheoqMUUleshqcyfZJog0Ema4x59wWyGB6mVVYTE1iZ1BXZ0r5uqSumJ4qmFB0HD9LvdlJ0ADnu0w9REx3FoOhwIjrigAkvoyiJjqoqesSduiJ+a6kYkQKKig6AiIlLLskRHSoeHYNAdWYS1Seez73oQXScOpXbfRKIDlF55cknqQSuKtLUFSIivN4L+kiRIkWC0Ea+TIHTV7wmvEFX+YaHK1FXF04KiZ9HRyn7cwDRKjpqa0k5JOq/S0PMarZupRnp9dcrH6cqUqLDHVERHbL9hUob9EJ9vRlFR2OjmRVy04oOUSnECtOpR6aIDjcjUgEVRYds29QhOoRyp7qaHsVCdIyPU3uIk+gwUZ0qSqIDMNNHCARNXSl7RQdARMeiRUoD0NAQ0FQzARw/nvP66O4m5siyH0EiWFNXkkR0XLig1wbKOnUFwO2a76VIkaJIYSU6rrxSfz8DA8Dixc7vWfOXdVQZw8MVoag5AOr0+/ud3ysXRUeURAdA36ck7W9tBZ57joxIL788kggvaNWVOIiOqSmagIVNdDQ15YhR2fKmKlD1OzBFdJhSdJgKhisrSR3iR3TITFSt59Q6uTYdwJsqL7t3L6UjuXkOt7bmyBAv6Cg6ZPpDu6IDUCuLLK5pXGakAF37uIkOU4qOqLwTTBMdQVJXSk3RIdUWrIoOgCquKKStAHTvNtZNAxfGiTBpa6P9WUrLAhT7LViQPKJj+XIaG6am9FNXypbo4Jxv1nkvRYoUxQtTio7BQWDtWuf3gio6hoYqQyM66uryq5VZUQ6KDhNmpLb4wPO7AJqcKRMdPT2ULHvHHcrHqIMgZqQmcs91IL4zCjNSMdET5ykMM9JiVHTI+orIorrav+qKbOoKQBNxUcgICE/REdSjY+9e4OKL3fvflha51JXRUfl7WGynk7oCqJVFHhsjEksnlTMorKRXqRAdxajoCFp1pdQUHTL3M1paiAG1Kjquvlrpu4aGsvftBRBRIogOhzxJUWLWZDWtoKisJG7n0CF9RUe5p66kSJGijCD6dhOpK24rDKr5y3YMD4dLdJSzR0fUZqSAxve1tlJg098PbNqk+GE9FGPqSm8vPRd76op1xVkGSVJ0DA2ZDYb92pKsR4ebSsb0RNGUqatbxRUBkbrCfRzfwvLoEOcxiKIjLhK9FBUdxUZ0cK4fX5SqR4dUW8hkSGbR3U1pJ7qKjrlZhvHYMXo+e9aT6EiSogPIpa+kqSvOSImOFClSzKKhgQKfMD06VCcudoRJdHix26miwx+6qStKsC5BR2BECgQvLxtHICFMZYud6FAlRpOm6IiS6FDx6AAKS8yaVnQwFvw8Dg4CR454Ex2treT346ecUiE6xKRTVtFRUZG/72IjOkpF0WHK/FcGpoiO6WkiO9KqKwSlOGThQiImzpwhtkiR6BgaAppasze7IDp8FB2lRHTU1dFpE/YkpYiU6EiRIkUe2tqCER1TUxRw+5mR6is6wvPoSBUd+sEm5xETHfPnU221CCAk7zqmknEpOqIkOsbGcvc9kCo6BIQZqSnIpK6oenRYEUYA39AQbDL49NP07KfoAPwNSVWIDsbofMsSHU1N+f1DSnSoobaWDFGnp/X3IYjFqMrj1tWZITpEGwuSulKWig6ACImzZ7UqrgBZRUdzJQ2Ux47RQHb+vCvR0dOTU0smhegQYZCuogMobVVHSnSkSJEiDyJFURdilbAYFR1exkzlpOjwk4A7YXSUgtTIiI5NmyKLaFWqNdgRN9ERhUcHkJO+A+F4dKSKDnOKDqtHhxWiTzZ5/errg3l0+FVcAXJdgl9evwrRAcjfu4LosELFoyMlOtQ8UdxgOlXMD/X1ZvwNBHmZenQQlBUd3d05okNH0dEEIkiOHSMmg3NXogMgPwwgOtNbPwRVdAClTXQYsz5ijDEAlwJoBzAXgHS3zTn/tqnjSJEiRTC0teVW0XQg5MN+Hh1JJTrKWdFRW0sSxqkp9fK94rqrEh3KgeL8+fQcUdoKID+BdELcHh1h3SsC1olS3OVlp6dpu1JVdNTUmDEj9UtdMTlZDEoY7d1Lx+M1fwlD0QEEIzpUFB2qx2USdqIjrlVqa5UbXbLFNLHoB1OpK+Ke1iG7ytqjAwis6Jjtozs6KEdOrPJ5EB3PPUdrLCbHuSBYs4aedRY1ykHREZjoYIxtBPBBAK8AoNM9cQAp0ZEiRULQ1gY89JD+5/0mvKrlIq2YnATGxsItLzs9Td9jn+iPj4e/Oh43rMFmVESH8gB72WXAi14EvP71ih/Uh0q1BjvKRdFhJTpMTtqE94FMf+GnJlNBEhUdfqkUSTMjBcwQHZddRt4LbhCKjjiJDjuhlaauqMGEoqNYiQ4TqStlregYHqbVuYULlRvwLEnZ0QF0dkoTHfX10aVI+WH9euD73wde8Qr1z2ovOBURAhEdjLFPAvgrACz78AKX2CZFihQxo62NVoN1gy+/CW8QRYfYd5iKDsB5ol8uig6Arr3qCpEq0aEtmZw7F/j1rxU/FAzFqujIZMJf6bMTHbpeJl6QTQNQbYNeMCFLN11eNuzUlbBSj3Qng5wT0fHa13pvJxQdMqkrKvdxVIqOlOgws7JcrESHidSVUlR0cC4xlghCYts25bSViQl6NDYCWNBBA8hzz9GbCxcWbC+IjhMnHN+OFb//+3qfK4fUFW2PDsbYRwB8JLsPBmAMwA4AWcEsOIDNAHYB6EaO5ODZxxPZ9x/VPYYUKVKYhxg3dH06/FZVRceqo+jo76fnsIkOpwlOOXh0WBUdqohM0REDghIdExN6vidB0NdH/mphrzpZJ81BzpMXGhujJzqCGg1OT9P5MO3R4Za6wrm8GWldHbULJ0VHTQ2paEwhiEfHmTNEXnj5cwDyqSuqyixZCb2XR4fMfR/n2CLaZ18fta1iJzqi9E1IQuqKaPsio7MUINrC5KTExoJxOHRIy58DsCg6ACJMAEdFx7x5uWNLihFpUJSDokOL6GCMLQXw0ey/HMAXASzgnG8EsE1sxzm/nXN+Led8MYDVAD4FQJzOGgB/xDm/XffgU6RIYR5tbfSsW3nFz6Mjk1EzarMibKLDq9MvN0WHKlKiwxnid3p5K4SB3t5oUq2iWBFuaJAjRk0rOkZG9AmqMPwuvFJXJifJX0emnYo+2Mmjw/T1CzIZlDEiBUh919Rk3oxUVkI/a2hoQWMjXQ+Z/i1OoqO6ms5fTw/9X+xERxyKjqAkdpDUlU2bgAceAF7wgmDHkCQoLbhYCQlNomPWowMAtm+nxugQwDKWU3WUGtFRDHGYLnQVHX8MoBpEcnyXc/4BzrnntIVzfphz/rcArgRwJPv8EGOsWfMYUqRIEQIE0aGr6JCZbCSV6EgVHfQcpaKjGFYSglZdEfuIEkLRETasxpalpugA9NOO8oJoQ/BKpRBkguz5d0qtCGOiGAXRAdDKtpeiQ3gvRWlGCsgRdHGPLY2N5OcIpESHCurricwKSmIHSV1hDHjJS5LjF2ECSgsu1hwSRaJDEL15io4DB4g8cTmhpUZ0pKkr7rjN8vfHVT7IOX8ewMsAjANYDuALmseQIkWKEGBK0eE12VDJX7YiKqLDqdMvJ0VHmrqSj6BmpED0Ph1xKDrCIjriUnQA+pP0MIw9vVJXVCveNDU5e3SYnujqktoAsGcPsHixnCy/tdVb0SHuvyQSHXFWXQFSokMXQfsIgSCpK6UIpcWBAERHHhm9YEGu83RIWxEoNaKjmBacdKFLdGSL2eA45/yQ20aMMUez0+xn7gH5dryBMVZCfsEpUhQ3xLihS3QIltxrJVM3+BWBbFj5qOWu6DCRuiJrilZMKwlBPTqAeIiOKBUdYRMdcSk6dAPAMBQdXqkrqkRHY2Nh6koSFR0yag7AX9Eh+hnTRIcwNHTy6ABSRYcsypnoCJK6UopQikNqanIrX4qlZfMUHYwBy5fTC2VIdBRDHKYLXaKjBZS2cszhPet6g9eQ+6vscw2AWzWPI0WKFIZRXU2rY0EUHQ0N3oZ2uoqOc+foOSyio9w9OoKmrtTUyAfscaV06KAYiY6+vmgUHdYJnWx5U1XI9hfloOjwIzpkJ6pO5zRMjw5VH4OpKWD/fnmio7U1HqIjb7JkgSC4ZAi6lOgIPh5wHj3RIfo6U4qOUo8vZKE8+RbEhCLRUUBGi89LEB1Rmt6GiWJacNKFLtExk3128sS1rhEs8dhHj+Xvds3jSJEiRQhoawtGdPhNNHQVHefOAdXV06EFY16dftzBaBQIquhQmWBmMhTYFcMAW2xEB+fRpa5UVdFvFIqOsMxIVRQdJoLQoIoOcbymPTrCTF0JY6LY0EDtUbX9HzpEn1FRdHilroRNdNivc7F5dIhzF7eiQ7efHB2ldhaHoiOo7D9NXcmH8pi5cCGlnihe/IJ7VxAdHrVjS1XRkaauFEIMJ06Z8l2Wvy/x2Eer5e+QMu5TpEihg0WLwic6dBUdc+ZMhWa85Ta5mZ4m07FSX3EJquhQXUmvrS2OAdaEGWmURMfYGAXPUaSuADl1QBLMSJuaiEQLiqCKDtG/RVV1RceMNKqqK4A6sa1iRAqQoqO3l/ppJ8Sl6CgWokMgbqJDl/gOQ0HlhzR1JRwot4UbbgBuu035e/LKywJKio5SIzqKYcFJF7rhwAGQv8ZFDu89Zfn75R77sL7nU/08RYoUUSKIomNw0H/CKztxseP8eWDuXJni6npwIzpEIFLqKy5RKjrE9xXDAFtsio7eXnqOQtEBhE90CGLUL/1Bpw26wZSiI+rUlSRWXQHUJ4N79xJhtX693PYtLURyCMNqO0Q/o9I+gxAdqh4dcZuRCqREhzxMm5GmRAdBecy8+27ghz9U/h5XRUcZER1p6oo7tmefmxljq2zvPQBgKvv3WxljN9o/zBh7MYB3Wl56QvM4UqRIEQLa2oKVl/UzpAySuhIm0eEm4yuXQCRo1RVZI1KBurrkD7CTk6ToKaaqK3199FxKio7paf8yjjpt0A2mFB2mzUj9UleCeHQMD4en6NAhOtaskW9PLS307ObTIc6Pyn0sQ8QWrApnIevRwTl9R6rooOegREeU3glp1ZVwEJXKYGiIPEhn2/yGDWQud4l7MsKSJcSDrF4d7rFFhTR1xR0PWv5+hfUNzvk5AP8PpPioAfAIY+ybjLE/Y4z9KWPsuwDuB1AJMjTdzTm3qkBSpEgRM9raKHDQSS+RWVUNYkYahaLDPsCWi6IjjtSVpBMdqivldgTNPddB1IoO4fcQRuoDIL86blLRETT/PixFx9SUc3qGrkeHVSUzMhKOR4fYtwpUKq4AlLoCuPt0JDV1ZWqKrkFKdNBzOSo60tSVfES1ODA4SO1lNtXxsstIEubR8VRVAcePA+94R7jHFhWKyStNF7pEx2aQTwcD8EcO738QwFkQkVEN4O0AvgzgKwDuAJEcADAO4E80jyFFihQhoa2NnnXSV2Q9OkZG3POp3RAV0VHuio40dSWHoERHHNVl4lB0DA6Gm7oC+K+Oh5G6ElTRYXLi6DUB0EldmZ7OtUtRtSIJHh3Dw8Dhw2pEh5+iI2qiQ5acSwKJngSiI2g/WcxER7nEF7KIUtFRoACUaEDV1QjNJy4OFItXmi60iA7O+TSA1wN4G4C7GWN1tvfPALgNwNPZl5jtAQCnAfwO53yHzjGkSJEiPAQhOmQ9OjhX61ynp2mles6c6FNXkhCMRoHUjLQQpoiOUlZ0NDbSQtjERHipK0A8io4g5WXr6rzLbKtCTISc0ld0zEiB3DmdmCDiOQkeHYcO0fgg688BhKfomJz0JuTdiI6KCvquYiM6wrh/ZcBYsJXlUiA6Sj2+kEWUio5SKRMbBMWw4BQElf6bOINzvtnn/WcZYxsAvBLASwAsB1AFIjg6AfyQc17CpzZFiuKFLtHBubxHB6BmftfbS/sPU9EhZHzlqugIEmCUqqJDx8TQinIhOs6do79LTdERJHXFdBBtUtEh+uihIarMGNZEUWcyePo0PS9ZIv+ZsBQdAPX/bp9zIzoAOdPtJBEdVVX0iAtBxoNiJjrS1JV8xKroKEMUg1daEGgTHTLgnM8AuC/7SJEiRZFAmE6rEh1jY5RzLJO6AtBA41GyPA9iIhUm0QE4d/pJCEajQEUFUFmpPuiNj9NDdZJZV+c+MUkKdEwMrSgXM9IwiQ5ZY8ckKTqGhsxPukynrgC5ibr4naZTF3Q8OgTRsXix/GcEqRcG0eFVFWVwkPpMp0mqjBeVznGZhmgLcaWtCNTW6veTYZRz9kPQ9DaBiQlStJhUfxUzUkVHtCgGZW0QGKg2nyJFilJDaysNuqpEx8AAPcukrgBqedtREh3lqugA9FbVxGSpFBUdxZq60tAQ3eqs8HsA4jMjFWoyU0RHVRUpvIIoOkxPurxSV0ZH6X3ZyZI9dSVsRYdKXy/GHaEslEFlJTB3rvnUFcD73h0cpFVhp5x9GaIjCSS6uOZJIDqKSdFRUeGsAFXFxARd/1LyfQiCVNERLYohDguClOhIkSJFASoqSGmhWmJWdsIrK0W3QhAdYXp0AM7sdjlJS3VW1WQJLqfvSvoAW4xER19fdGoOIPwcfxlidHiYyA5TRIcoOxhE0RFl6srIiNq5t6auiM8DySgve/o0XUfVY2ltDU/R4QZBdDihoaE4iI4kKTqKiegAgvURAuPj5RFbyCJVdESLUk9dSYmOFClSOKKtTV/R4ceSy5oLWhFn6ko5mYXV1KgPeinR4Y64FB1R+XMA4RMdMooO3TboBSd1lyzCUHRYPSPsUK14E7WiQ2UyeOaMWtqKQEuLv6JD5RzJ3Lteq8LF5tFR7EQHY9GnAJkgOiYmUqLDCt0UWlWkig5CqaeuGPXoYIxVApgLoB656iq+4JwfN3kcKVKkCI4gREcYig4RwMaRupIqOrwRhOhI+gBbjGakpabokOkvwiA6gio6RCUQUxD9j5tHh8pENSqPjpoaSgFS6etPn1ZLWxFoaXFXdIyO5iZQspCR0HspOhobgZ4e7+9IiY4cghIdjY3Rp3+YIjrKYRFFBTKlnYMiDNVdMaK21p0gLgUEJjoYY7cB+AMANwFYBQWCIwtu4jhSpEhhFm1twN69ap+RTV3RVXTU1QG1tR61/gzAy6OjHIKRKBUdxSCZDKroCFo2UQe9vcDy5dF9n3WiF8ZkSaa/KCdFhxvRkURFh04K0OnTwHXXqX9Xayvw/PPO742Nqa/2B01dUfHoSM1IgxMdUaetAGnqSlgIW+3JearoECiGOCwItAkGxthcAN8F8DvWlxV2wRW3T5EiRYRYtIg8OmZmaEVOBmEqOs6dA+bPl99eF6lHR+rRYUXQqitANKtTVvT1AVdcEd33ha3oECabxaToSHrqSlQeHWKfUaWueHl0hEV0uJXBlfHoEP1fquigcyAWS1RRzERHmrpSiLDHzJERIjtSRUdxKGuDQMujI5ui8iCI5GCWh9JudL5baseMHWWM7WWM7WaM7ci+1sIY+zVj7GD2OcIM5hQpig9tbcDkJK0Oy0LWo0OX6DAtBXdCuXt06JAPQYiOyclcxY4kIqiiA4ie6Cg1jw7G/P0OwiI6dAPAMGTRXqkrqmaktbVEYIuJZZhmjg0N8pPBwUE6Fp3UldZWIvmc+pMwiQ6365x6dKihXBUdaepKIcJeBBH9XqroKH1Fh64Z6XsBXGP5/zcA3gJgNYAGznlG4RFW5ejbOecbOOfXZv//awAPcc7XAHgo+3+KFClcIAJNFZ8OVUWHaupKFIqOcvfoiNqMFIiWBFBFsREd09N0PUqJ6AD8V8fDSl3RmcRwnvzUFUEeRaXokCW1xXijq+jgnMgOO+JKXRkeJlWkG5JAdNTV5VKM4oSOmlCgmImONHWlEGGPmaLfSxUdqaLDDXdY/v4S5/xWzvn3OOeHOedJPV2vBnBP9u97ALwmvkNJkSL50CE6BgdJYu4XcFdV0cCuakYaV+pKuSk6dFJXMhn1QFlMPJI8yBZb6kp/Pz2XkhkpQJOYYkldGRujyW1Y5WXdUldU77+mpvA9OgC183j6ND3rKjoA5/SVMIgOzv2JDsD7tyeB6Mhk6LqHde/KIsgq/tBQPESHLhlqRZq6UohU0REdiiGFOAh0PTrWZp8HANxl6FhMggP4FWOMA/gq5/w/ASzinJ8GAM75acbYQqcPMsbeDeDdALBo0SJ0dnZGdMjmMDQ0VJTHnSJZOH26FsAN+OUvn0VFhRzbsX//atTVLcLmzY/5bltb+wIcOHAWnZ0HpfZ95swLMD7eHXr77u29GAMDrejs3Dr72tNPtwNYjW3bfovGxqnQvjsJGBy8FOfP16Gzc4f0Z/bvX436ernrbsWxY4sBrMXDD2/BggUOs7cE4Kmn1mDOnIXKv82K6enrcPLkMDo790ttH6SNnzpF9+2ZM8+gs7Nbax+q6O6uAXAjAGDXrt/iuefCuEeuwbFj4+js3Of47lNPdQBYiV27NqOqihv5xsHB9bhwoQmdnduUPtffXwXgBejqOojOzlNGjgUAurro2u7e/Qza2vKv7YULGzF//iA6O5+R3l8msxHPPz+Ezs792L9/JTKZ5Xjssc3GK1dMTGxAVxfQ2bkbgHf7fuSRBQAuxcmT29HZqcCEAzh5sgXAFfj1r3fh1KmBvPdOnbock5PV6OzcKb2/I0fqAWzErl1PY+7cwvIpExMZTE3dgp6ew+jsLCweeOrUEgAX41e/2oKWFuf+be9e2mbnzi04ciS+PvCd71yKdesG0dk54L9xSLhwoXDslcXZs9eiomLMtX8ICwMD69Db24zOzsfzXlfpw3t6NqCigqOz86kQjrA4MT5+Fc6cmUZn555Q9r9nz1wAV+H5559CZ6dCfnYJ4uzZlRgdXY7Ozs3SnymqeSbnXPkBYAzANIBOnc+H/QCwJPu8EMBTAG4B0GfbptdvP9dccw0vRjzyyCNxH0KKEsDUFOfV1Zz/1V/Jf+btb+d8+XK5bZct4/wd75DbdnKSc4Dzf/iH8Nv3+9/P+dy5+a99+tP0/SMjoX51InDHHZxffLHaZ1SuuxXf/jad10OH1D8bFX73dzm/4opg+9iwgfNXvUp++yBtfMcOOqc/+Yn2LpRx/jx9J8D52Fg433HTTZzffrv7+3fdxXltrdnv/MM/5HzpUvXPHTlC5+Kb3zR7PCdP0n6/+tXC95YupeNVwTXXcP7yl9Pf738/53PmBD5ER7z85Zxfe23uf6/2/fnP0288d079e7Zupc/ef3/hey98IecveIHa/g4epP19+9vO7/f00Ptf/KLz+/fc49+/fe5ztE1vr9qxlSLe9z7O583T++yqVZy/+c1mj0cGf/ZnnC9YUPi6Sh++cSPnL32puWMqBdx2G+c33xze/u+/n+67xx8P7zuKBZ/4BJ2LiQn5zyRxnglgB3eYy+umroglirD8NQKBc96VfT4L4McANgLoZowtBoDs89n4jjBFiuSjogJYtQo4KCe4AEDycVnpuEzpPQEhRY7KjLScPTp0zUh1UgaElDzJssmTJ4H29mD7iDJ1RZgHx+HRIUrphvUdfh4dJtNWAP3ysmGlgZisugLkn9Ph4fA8GlQ8Ok6fptTGlhb17xHjw/nzhe+NjamfH3G+3fonP/m7TFnkJFRdSQrK2Yy0HGILFaQeHdGhGOKwINAlOh4FVU1Zx5hpkWMwMMYaGGNN4m8ALwGwD8B9AN6e3eztAH4azxGmSFE8WL1ajegYHJSfbPjl3Ftx7hw9R+XRMTGR79w/MUHET0UiqV2z0DUjTYkOdwQx2VOFMGKM0qOjupomp8LUMAzIeHSYJjp0JzFhER1eVVeCenSMjIQ3UVQ5j2fOkD+HTjsS5IiTR8foqLpHh59ZsgmiIwkeHUmBIDq4RubZ8HA8k1bRtnWOWSCtulKI1KMjOggCOMlxWBDoEh3/nn1uAfA6Q8diCosA/JYx9hSAbQDu55z/EsBnALyYMXYQwIuz/6dIkcIDa9YAhw55u8ZbMTAgP3CoEB1ihS6qqitAfnBbTq7oumakQYiOpJqRjo8DZ8+aUXREFUTEoegA6L4P08xQprxsGIoOYSyqgrBWC93MMTnXV3RYy8smgeg4fVrPiBQgco+x6MxITREdlZVkCFruqKmhtjw5qfa5sKocyaC+nr4/CJFdTvGFLFJFR3RIehwWFFpdK+d8G4AvglQdX2GMrTN6VAHAqfLLldnHpZzzT2ZfP885fyHnfE322WEoTJEihRVr1lDn19Ult31YqStRKjrEZMHa6ZfTikuq6MhBtPtiSl0Rio6oiY7GxnDLU8qUlw1D0QGot8+wFR321BXRtoKkroyMhHf9VEjtM2f0SssCpLhrbnZPXQmL6HCbLInr7/Xbx8fLZ2zxg+54IFQgcREdQLD0lTR1pRBRKTpSoiP5cVhQBOGQ7wTwVQALAGxjjH2YMRbBNCRFihRRYc0aej50SG57lcmGTupKFB4dTux2OQUiQtGhIsU9f17v2iRdMnnyJD0XE9HR20sTvqiD/sbGcBUdcaSuOJGeMghrtZAxShGytyUxyVI9//bysmErOmT6lCCKDoD6IVOKDq9UIcCcoiMlOgh+qUJuCLM0sh9MER1pG8hHFIqO6uryieu8kPQ4LCh0y8si63D6Z4yxTgD3APg0gE8zxg4D6AYgWyeLc85fqHscKVKkCA+C6Dh4ELjtNv/tVTw6dBQdra3A88/LfUYXTpObcgpGrYaHMr95epomFjpqm6SvJBQr0TFvXnheGW5obAz3NzY2UjuZnnb2yglT0TEyokbkhTnxcmpLoq/SVXRwrv4bVWBVxngd4+Qk0NOjr+gAyKfDlKJDmOuGSXToHFepQnc8KHaiI01dKUQUio5UzUEo9dQVbaIDABhjrwPwDwCqQGksALAq+5DaBQAzBe9TpEhhHMuWUWAtY0g6M0ODR1geHfX14UrjBZzY7XJTdADy5E5fH137lOhwR9SpK1EakQq0tIQbKFnTAJwIjXJQdADUD9lTV8TxqfaPjY05kiNsRQdA3+NFdJzN1sILQnS0thJZYocuoeB176aKDrPQHQ/E+S1WoqOc4gtZRKHoSI1ICUmPw4JCm+hgjH0QwN1Ob+kfTooUKZKETAa46CI5okMEGyqpKyqKjij8OYBU0WEd9GSupZhUBCE6krqScPIknYOgAVEcio6o8cUv5lcqMg3rpDEqokN3ElNMig6AzmnYHh0AnRcv1cjp0/QcJHWlpQV49tn81zgPh+gQ45db/yCuR+rRIYdiVHSIa5ymrpiFtQJPGOrEVNGRgy6hXyzQIjoYY7cA+BfLS1MAHgZVOTkNIGBV6RQpUiQFa9bIER0DA/SskroyNSW3mhEl0VHuHh0i4JINNkVa0YIF6t+V9JUEE6VlgfJQdIg0t7DgZew4Pk5pD2ERHaoB4PAwVdIIo88wSXSICfrQUHSKDi+cOUPPQVNX7B4dU1OkOgtD0eGV55/J+BP6KdGRQzESHbp9hMDMDLXPcokvZCEq8ExNkS+RaaSKjhySHocFha6i4wPZZw7gaQC/xzmXtCtMkSJFMWH1auCBB2hA9iqBJ2S8KooOgIIUGaIjCiNSwDl1pZyCUVVDuCAVcZJugmWK6NAp2auL3l6goyOa74oSYvXNiehQJVllobtaOzQU3mqhU+qKrhmpOMbBwXAVHbJEhwlFR2sr0N9PE6TKbIQr+hcds1wvrwCZVE0/L6pyGlv8UMxEh66iQ9zLKdGRD2tbCIPoSBUdOSQ9DgsK3aorm7LPHCnJkSJFSWPNGuoAhV+BG8RkQ5Yll8lfFog7dSVVdLgjCNGh+l1Rw6SiY2KCyMKwEZeiI2yISYxTfxEW0REkdSWsSZeXokPHowOgNjMxEd4xi/3KKjqCpq4ARPgJiPMThqLDBNGRmpESdMcDQXTEMXE1RXSkZFc+wo4NUkVHDklPIQ4KXaKjGURy7ElJjhQpShvWyiteUJ1seEnR7Th/PvXoiAqqio4gHh2VlfRIItExOUkrzKaIDqBwJd40OI/PoyNsePUXYSs6dMxIw5p0hZG6IkxAw1Z0+PX1p0+TIiMIqSyUf9b0FdG/hEF0+F1nP9PtsbHyGVv8UI6KDtG2ymUhRRa6pYZlkSo6cij11BVdoiM7LMKhWnmKFClKCWERHbKKjslJWnFMPTqigeqgd+4cBZg6snDxfUlcSThzhogDk0RH2Okro6N0v5Qi0eHVX5STosOr6opu6kp3Nz3H7dFx+nQwNQeQU3RYS8yGSXSkqSvmoDu5LWaiI01dcUaq6IgOaeqKM54BVVdZavBYUqRIkUAsXUoBiB/REcSjwwtiZS716IgGOqkrQUgorxz4OGGqtCwQHdEh5PqlnLqSKjoK21FQjw6RMhK3R8eZM8GMSIHoFR0p0WEO5ajoSFNXnBG2oiPMPrrYkKauOOM72eeLGWMhe62nSJEiTmQyZEh6yCdJTdWjQ5boCOIBoYNy9+jQSV0Jcm3q6pJNdCw1QOdHTXSUsqIjSqIjiYoO4fdiRVBFh0hdidujI1V0lDeCEB2MxeN1ErS8bJq64owwFR2Tk3TeU0UHobqa7p8kxmEmoEt0fB9USpYB+ApjrMLcIaVIkSJpkCkxG5YZadREhxO7XU7BqI6iQ6e0rECq6DCHvj56LmVFR5SpK7qKjrBTV0yZkYq+WqSuxOnRwXlpKjrS8rLyCEJ0NDTQZC1qZDJ03GnqilmEqegQ92Oq6CAIkjCJcZgJaBEdnPNJAK8HcADACwH8jDGWprGkSFGiWLMGeP55YHrafZuBAeosZQdsWUWHWJmLiuioqgIqKvI7/VTR4Y5STl2pqzOjjkgVHcHhNVkOi+ioqiKz3CSVl/UyI1WdyFdX0+9LgkdHXx/9rqBEx5w5NPmMQtEhk+f//7d373Fy1GW+xz9PMsnMZAKBBBPuSZCIIMNdBRWICAoiQnS94AXFFdzX6nrd9bjq7uKedc+uR3fPLsoeAyK6siKuREFBxctw8ShREQUJCiYBQm6TkDuZJJP8zh+/Lqempy/V3XXt+r5fr3n1TE919W96qruqnnqe5zd9euN9nGZdGdNuoGP79mzKVgL9/SpdiVuSDTKDMmtldIzJa6+0OPS08yAzu6zy7eeAvwNeDiw3s+8DPwPWAZH7yzvnvtzOOEQkHUcf7XfITz4J8+bVXmbbttZONPKa0QH+wEUZHdGWjyPQkccdbDC1bBxXCZXR0blJk/wJc72Mjp6eZE4Yqz8LosiidKWvr/Vt1cx/Died0TF1qv//NToZjGNqWfDPc+CBtTM62mmYXC/Q4Vw8pSuadWVMpxkdWZk2TaUrcUtyn6mMjonyesEpDm0FOoAb8NPLhk0BXln5aoUDFOgQybHwzCv1Ah1bt7YWIW+1R0dazUhh4smNMjpq27XLH+x3a0ZHHGUrkHxjtUA3Z3RA/ak6t271QdYkUtfbOYlJMqOjVunKM8+0P+vRfvsln9Fh1nya1TVr/G2nGR3g9xVxla7U+3waGfEZjlECHSMjMDrqg3HVyhREb6anxweqihjoaDdYr9KV2pTRka689kqLQ7s9OsD35wh/1bov6peI5FiUKWaDk42o+vr8QU2UjI6BgXTTe6uzDMp0MNrKAUYQhOqkR0ded7BxBjqSniovEGR0zJiR7PNkpd7V8VY/e1rR6knMnj3+5CXJjI5apSvtBjqCE3FI9mSxWcAoCHR0mtEBviFpuHSl3dIeqJ/REZwsNQtoNQroO+e3lbLsW6JoJ/C9Y0e2V+fjyOjQNjCeMjrSldfM2ji0m9FxNxMzOkSkSx16qN+ZxxnoiHKVDzovjWhH+OTbOX/yUpYrLq2clMdRVtTXNzbrQ17s2werV8cf6Egjo2P6dN9bohs1y+hIQqv198H4kuzRUat0pd2yk/A4kypdCdYdpXQlroyOIHACyfToiHpVODxbUHUAUv0ZJurra/1zMg8ZHWpGGi9ldKQrrxec4tBWoMM5tzDmcYhIjpn5Ph2NAh3btrV+Yhgl0LFxYzaBjiC6XbaD0VZOyoeH/W23la6sX+9TzYsW6Ni8uXvLVqB+Y8ekMzraCXSkPetKJ6UrgawzOvr64vk/zpwJDz009nMeAh21MpGC9aoZ6Zh2Mzpmz05mPFEo0BE/ZXSkq5szOjopXRGREmk2xWyrPTqgeaM2yCajI/yhX7YDETP/t6ZVupLHQEecU8tCuhkd3diINFBvqs6kMzpaOQBMOtARnHi7UE5tJz06goP9YIrBpDQLagdTy8bRZyXOHh29vT6jb9++8ffHEegIxlWWIHoU7QY6iprRodKV2pTRka48HofFRYEOEYlkwQJYscJf6a6lnZONqKUraTYihfEnN2U8EImaPhxX6UreriQUOdChjI54tXoSk/TVwmBbCn8Od9qjA/zfmUQz10CUjI44ylbAZ3Rs2zYWpO4koBA8prpcKOrJUnAC3iijo0z7lmbKFugo24WUqJTRka5uLl1RoENEIlmwwF/ZeuKJ2r9v52Qjrxkd4Q/9Mh6IRD3Y3LDBnxx1cnKdxysJRQ10bN6sjI645S2jI/gcCm9LcfToSLI/R7D+ZoGOOBqRgg90wNgsRCMjY1PctqreezfYDlvp0VFNgY6JFOgQSLaB97Zt/rgl6c+8IsnjBae4xB7oMLODzWzQzF5iZmfFvX4RycbRR/vbWuUru3f7g7a4Mzp27/YnMVn26CjjwWi9uvRqw8M+yFFr2sSo8nglYdUqf+AZ13anjI54ZNGMNK8ZHdWBjk57dCR9ojgw0LwZaVwZHUEGYDDzyshI+2U59d67cfboKNO+pZlWAx3O+dc2y0BHqw2Lw7QN1NbTA5MnJ5fRMTDQXuCzW+XxglNcOjg8HWNmZwB/DrwMmBP6lav1HGb2ESA4LPmkc65J8rqIZC08xewrXjH+d+3WPE6fDk89Vf/3QZ21enSkq5WMjk76cwTPFfQcSDJ1vhWrVsFhh8V3IBScZCmjozO1MsBGR/1JRtkyOsKlFHGVriRp2rT6Qe2RER+kizujI9h/jIy0//rUe+8q0JGM3t7WTrhGRvy+Qxkd3Sepk+9t29Sfo1oeLzjFpaNAh5kdAFwLvCZ8d4SHHgj8FT4Q8ijwxU7GISLJO/hgf9BWK6Nj61Z/G3dGR9ADIoseHcGHfnAwWqYDkagHm3GUFYVPJPIy+8CqVfGVrUCyabiB0VF/AFeGjI5wUCw44SxzRkcczUiTPlFs9DquW+dv487oCAc6Os3oqH7vBttds/9zlB4defncy4O+vrHXNoqkA4tRTJvmg43tBOsV6KgvamZpq7ZvV3+OaipdqcHMDgTuwQc5LPS1GWh2OHdN6Ps3tjsGEUlPoylmkw50aHrZdLXSjDSuQEeedrJxBzpq9VWI25Yt/rabAx3Tp/uTifBJZ7ufPVHlLaMjqdKVLHt0rFnjb+NsRgpjpSs7dyZTutLbC1OmNH58ox4dmnVloqj7nkDwumZ54hq8d9oJZJfxQkpUyuhITzeXrnSSmHsj8Dx8cGMr8GHgMOfcTOCuRg90zj0OLK089kwz08e8SAHUm2K23auqzZqR5iHQUcYDkagZHcPD8QU68rKTdS7+QEcwZW+SgY6g8WI3l67UujqedKBj2jT/f9u7N9ryWZWudNqMNK0eHeFpcQNr1/rbuEpXksjoqBXoiHKy1NfnS+BUuhJNqydcecnogPbKV3bv9sGyvJRt5okyOtITZDHX+nwuurYCHWb2MuB8fOnJOuD5zrlPO+fWtLCaIBjSC5zQzjhEJF3BFLN79oy/PzjZaDVKPjDgD9LrnURkFegI0vicU0ZHPc7F06MjuBKdl0DHxo3+b48z0AHJHbQFNm/2t92e0QHjr46nEeiA6Nvn9u3+pKXdDItmqk+89+3z3xehRwfUzo6JO6Njv/18M8Okm5FG2d+Z1Q/oK9AxURkDHWW6iNKKJDM6FOgYL60+YlloN6Pj0tD3f+6cq3GNt6lfh74/ps1xiEiKFizwQYmVK8ff3+7JRrCzqXeAEByoZtGjA8Zmk4FyHYxEOcDYts0HvLotoyPuqWUDSQc6lNGRzHMGnwVRT2J27PAnPkl19K8+8Q7eN0Xo0QG1X8c1a3xAoNOgacDMl6/kIaMD6k+LrEDHRGULdOzapf9/PUlmdKh0Zby8HYfFqd1d8ZmV243OuSVtrmN96PuUr9eKSDuCmVcee2z8/Z306ID6fTo2bPAH4mkfCAQnDTt3ljOjI0rpyvCwv1WgI5q0Ah3dnNFR6/MirYyOqH06kk6Lri5dCU6uitCjA2qfDK5dC7NndzZNdbWZM5PN6GjlZGn69Nr7OAU6JipboEMZHfVF3Rb++Z/hgx+Mvl5ldEwUPubtNu0GOg7Gl6080sFzh1/OhHexIhKHo4/2t9V9Ojrp0QH1+3TE0eyyHeEGmWXN6Gh2Uh6UFcUxvSzkZwdb1EBHULrSzRkdtT4v8pjRkeRJV/WJd/C+KUKPDqh9wr9mTXxlK4E8ZXQ0K13RrCtjFOiQQJR95ugofPrTcP310ftLKKNjorxdcIpTu4GOIO4esT1XTeHrTls6WI+IpGT2bL+DqA50BCcbrR5sRMnoyCLQEe4boYyO2uLqn5K3HeyqVTB5MsyZE+96ldHRuSwzOqKexCSd0RF8DgWfS0Ggo8ilK2vXxteINDBrVvKBjqj/53qBDs26MlFvry+JjNr8N3hdixroUOlKfVGCXj/5iT8W2bJl7CJFI86pGWkteeuVFqd2Ax3r8DOmzO3guU+uWp+I5JxZ7ZlXtm71AZBW69KbBTo2bsw20KGMjvriCnTkbQe7ahUceqgPdsSp1WkTW7V5s0/9T7oEIUv1mpGaJXei02pKb9IZHdVTFXca6Jgxw98mfeDfrEdHEhkd4dKVdl+feoFY9ehIRqtNEfOQ0dFq1leYMjrqi3JxYEmoecKDDzZfZzDzkzI6xstbZm2c2g10/LZyO9fMFrS5jteHvv9Zm+sQkZTVCnRs29beFdUopStpNyIF9eiIciWlm3t0xF22AtGn7G3Xpk0+m6Obpyms14y0nSBrVK1erU27dKXTHh0zZsBNN8Fll3U+tkbqvY779sG6dcXM6FCPjvgVMdCh0pVkNDsOcc4HOs6sdI2MEugI9h3K6Bgvb8dhcWr30OD20PdXtfpgM7sCeB6+z8cy51yEhCMRyYMFC/ysK0EAAPzJRjuBjryWrpS9R0eUKykbNvjXpNMrI3nbwSYZ6Eg6o6Oby1agfkZHUmUr0HpGR9FKVwDe8AZflpikep/1Gzf6OvskMjp27PDvuZ074w10BOnvcfXoKNO+pZlW9wc7diQ7nXMUrTYsDlPpSn3N9pn33w9PPAGXXw5HHBEt0BH0k1NGx3h5y6yNU7uBjhuBSuIybzSzj0d9oJm9Ebg6dNen2xyDiGRgwQJ/FW7FirH7gquqrWqU0bFrl98pqUdH+vr6/N+9b1/9ZYIgVKcZBHlKmXSuuIGOTZu6uxEp1M/oSDLQkbeMjnqlK3kvWar3Oq5Z42+TyOgAn9URd0bHM8/4z8Y4Ah29vd2dhdWqdgIdAwPZvobK6EhGs4yOJUt8Jt9FF8HgoDI6OpGn47C4tRXocM5tBT6C79MB8Akzu9vMLjGzGdXLm9mAmV1kZnfggyRT8dkcS4Evtzd0EclCMMVsuHwliYyOoL5aPTrSVy9dO2x4OJ7/TZ4yOrZs8dtiEQMdZcjomDLFfymjI74eHWmpdzK4dq2/TSKjA/zn1O7d8QY6Wr0qXK9Hx8hIuQLoUbQb6MiSAh3JaLbPXLIEzjrLH4cMDsKyZb6RbSPK6KgtT8dhcWu7qtU5dz3wKcaCHS8GvgE8DZwbLGdmTwFbgW8CLw8t/ySwyDnX4JqhiORNrUBHpz068hzoCDI6pkxJfxxZiVInvWFD51PLQr5SJpOaWhaU0RGX6qvjZcvoSKJ0JQ3NMjqSCnQE62830FGdQQNj218rGR179owv9wzWqUDHeEUMdARj1qwr8WqU0fH738PDD8OiRf7nwUH/Hvv97xuvUxkdteXpOCxuHbXvcs59BHgXsBMfwAi+JuEzNgAOYSy4EdzeA7zQObe2k+cXkfTNmuUb2D322Nh97Z5sBAe/ta52BbN6ZNGMtLpHx9Sp5UovDg68Gu304uqfkqcrCUUPdHR7Rgf4k5q8ZnTs25d+6UqnzUjTUi97L8joSKp0ZfVqf9tuoMPMv+adZHTUC+jrJHeiIgY6Jk3y7z9ldMQr2Gc6N/F3wWwrl1zibwcH/W2z8hVldNSm0pUGnHPXAkcD/wQETUXDQY/APuD/Aa9zzp3tnNOUsiIFVGuK2XZ7dEye7D9ga2V0xDV9aTuqe3SU7WA0akZHHP+bnh5/oKhAR/uc86UrZcnoyCLQEeUkJjhITPJqYfB+KVqPjilT/Od9rYyO/faL/2Q1yOh46il/226gAya+d4OTpaj/53q9qBTomChKkD0sD4EO8O8/BTri1dfng8ejoxN/t2QJnHoqHHmk//m5z/Wfjc0CHcroqC1PF5zi1hPHSiqZGR8FPmpmc4FBYBYwAGwB1gFLK709RKTgFiyAn/7Uf+9cZycb9Rq15SHQEc7oKJNmO73RUd/kL47/jZl/vjxcSVi1yo8n7jR6SDbQsWOH/5+UJaMjzdKVnh7//o+yfaY11WVvb/FKV8xqnwyuWRN/NgfEl9EB9QMdrWZ0KNDRXKvTy27fXuxAh7aB+sL9ccKlw089BffdB5/85Nh9U6fCMccoo6NdrfaiKpJYAh1hzrnHgcejLm9mM51zT8c9DhFJzoIF8LWv+R3Q3r0+6t7uyUZ1KnogL6UrZczoaHZV7enKJ3YcPTqgeXf1tKxa5U+6kujHkuTfuHmzvy1LoCP4vNi3r/3+QK2Impae1tXCcCnFzp1j5RV5V+uzfu3aZAKLAwP+fRxkdHQSCKp+77bTjBRql650EoDpRq1eWd66FebMSW48Ual0JX7hbSH8mfqtb/nboD9HYHAQfvazxutURkdt3ZzR0XHpSrvMbJaZ/ROwMqsxiEh7gilmly/3BxrQfoS8XkbHxo3+BCaLg4Bgyr+RkXJndNS7qhZ3tk1/fz52sElNLQvJZnRs2uRvy1K6Enxe7NjhM8qSDnREvVqbZkZHuEdHf38xegjVy+hIItBh5oPkec7o0KwrE7VywuUcrFwJc+cmOqRIOild0TZQW73Z3265xWdvHHvs+PsHB/32sLVB7cC2bf54rmzHdM309PivPByHxS31QIeZHWRm/wysAP4KX94iIgUSnnkl2KkkkdGRRTYHjC+nKOOBSLOMjuFhfxtXoCNPGR1JBjr27PEBwriVNaOj08+eqPr7o6X0pnW1sLp0Je9lK4FaJ4Nr1yZTugK+T0eSPTpUuhK/VgIdGzb4z4Kjjkp2TFF0Urqik+7aam0LTz8NQ0MTszlgrCHpQw/VX2fS038XWV5KiOOWWqDDzGab2f/GBzj+EpjO+GalIlIQaQU6sujPEQhObsp4IJJ2RkdZAh0wcYrJOJQ1oyOtQEfeMjqqS1fy3og0UP067tjhgwZJZHSAD5QHs7qoGWkxtBLoWL7c3+Yl0NHOSaJKV+qrldHx7W/7culGgY5GfTq2bVN/jnrychwWt8iBDjObZmZXmtnXzGypmf3OzH5hZl80swsbPO6ASgbHcuCDjM/gcMCStkcvIpmYOdN/Pfro2EFfEs1Iswx0lDmjo9nBZhDo6KYeHdu2wZYtyQc6kihfUUZHss/ZakZHmqUrRcroqA5qr1njb5PM6Aimpowz0LF9u3/NeyJ2uWvUo6Ns+5Zm2gl0zJ+f3Hiiaiejw7lyHl9EVWtbWLIEDjsMTjtt4vJz5/ogRqNAhzI66stLCXHcIn1Mm9n5wA1A+LDW8IGKk4HLzOwu4PXOuQ2hx70T+BQwI7Q8ldubgX9wzv22w79BRDJw9NHjMzrajZI3yug47rj2x9ep4EO/jBkdzU7K424Um4eUySDFvYiBjrJldKQd6Gg1oyONZqRFLV0Jghswlm2RVEZHMMUsxJ/R0cr+rlFGh5qRjlemQMeePf62bMcXUVXvM595Br73PXjHO/wU29XM4PjjldHRrjwchyWhaUaHmb0MuBWYzVipSXXJiQFnA98xs8mVrxuBzwMHhJbbC3wZOM45d6mCHCLFtWABPPZY5ycbjZqRZtWjA8au4pbxikuzg83hYX+wENfrkocrCatW+dsiBzpmzIh/3XkzMODfk3v25C+jI8tmpEVQfTIYBD2SLF0JxB3oaCWYpdKV6IKT/qiBjoMPzkfpVjuBjiBYqUBHbdXHId/7nv8cfs1r6j9mcNAHOoJMrmrK6KgvD5m1SWgY6DCzKcD1+MyPcDbGw8BPgN+F7jfgNOAK4LPApaFV7QWuA45xzr3dOff7uP4AEcnGggXw5JOwfr3/Oc4eHSMjfoeUh9KVMmd0NCpdiatsBfKxgw0CHYcdlsz6ky5d2W+/6Kn0RRYuA8hbRkeazUi7oUdHkNGRZOlKIMuMjqlT/VS3mnWluUmTxvegaWTFinz054DOAh3aBmqr3mcuWeLf02edVf8xg4M+8B/MtlRNGR315eGCUxKaZXRcAhzBWDDjs8Ac59zxzrkznXPHAYcCi0OP+Sg+2AE++HEHcKxz7krn3IrYRi4imVqwwEfNH3jA/9xpRkc4Ar9xo7/NuhnpyEi5Mzoala7E+b8pQ6Cj2WvaiU2bytGfA8ZfHS9rRkdRS1dq9ejo6Ukucy+ujI6+vs4CHeD/dmV0RBN1f7B8ebEDHcE2VbYLKVGFMzr27IHbboOLLmoc0G/WkFQZHfWVtXQl3GT0P5xz73XObQwv4Jxb75z7M3yww4DDKut1wMedcxc65/4Q56BFJHvBzCu//KXf8bR7wDYw4KfcDB9Ixj2rRzvKPOtKlIyObgx0HHRQcjXzzV7TTmzeXJ5AR62MjqSv0LWS0dHbm3xmTVGbkdbK6Jgzp3a9fRzCGR2dvEa9vePft+0EOsK9ZQIKdNQWZX+we7fPKM1Dfw4YuzDSyvThKl1pLJzRcdddfj9Xa7aVsGaBDmV01JeH47AkNNu9nFK53Qd8osmyf1dZDnyQ4zbn3D92MDYRybEg0PHII/6KqrU5WXStjvR5CnQoo2Oi4eH4Ax1ZX0lIcmpZSL5HRxkakcLEjI5p05IPLESdOnLHjuSzOaDYgY6dO8dOBtesSa4/ByTbo6OdQIcyOqKJcsL1xBN+O8pTRge0th9T6Upj4YyOJUv8a/zylzd+zMyZPitTGR2tK2vpyiH4oMUjzrnhRgs659YByxhrVHp158MTkbw64AB/sutcZ6njtRq1BaUrWTYjLXOPjilTfOCqbD06ihroKHNGR9JlK+APAKNmdKQR6AiXrhStGSnA7t3+0HPNmuT6c8BYRsekSZ0Fw5IIdOzdC6OjmnWllij7g2DGlbwFOlopX1HpSmPBe2PnTh/oOP/8aJ91QUPSanv2+NdcgY7a8nDBKQnNAh1BD/e1EdcXXu7h1ocjIkUSZHV0crKR54yOsvboMJuYrh145hn/Fef/Jg9XEooc6ChjRkcQ6EgjDXnaNH+QPDraeLkdO9I5iC5qM9Lgs35kZDLgS1fSyOjo62s/4xAmBjq2b++8R0ewvrLtW6KIEuhYUen4l7dARzsZHQp01Ba8N+6+2wdFm5WtBAYHYdmyiZ/XwftPpSu15eGCUxKaBTqCGPieiOv742blnFvTaEERKb6jj/a3new4amV0BIGOPEwvW8aMDpjYgC+QRBAq2MHWmxIuaTt3+iyiogY6ypjREZSupJXRAc1PYtIsXSliM9LgZHBkZBKjo37GriQDHUFGR6dZE+FAh3PtBTqqe3Qo0FFf1IyOqVPh0EPTGVMz7WR0qHSlseB9e+utPiPrwgsbLx8YHPTvr0cfHX9/WrNiFVXUpttFk1ALKBEpgyQzOmbM8CUUWSlzjw6on9GRVKDDOX/VPAtPPeVvixjo2LPHH8CVJaMji9KV4CQm2PbrSav+O5h+c3TU//+LFujYtWsyw8P+PZ9k6Up/v3/fxRHo2LPH94TYscOPu9X/c3XpigId9dXb94QtXw7z5iXXyLZVKl2JX/De2LEDzjknejC/XkPSbdv8rTI6aitrRoeISF1xBDrCqeiBjRuzzeaAcvfogOYZHXH36IDsriYEU8sWMdCxebO/LUtGR3Uz0jQCHS96kf//XXSRT6GuJ+1mpMH7pSiBjnDpSvA6JpnRYeb3I3EEOsAHvds9WVKgI7p6+56wPE0tC51ldJTx+CKKnh6Y7KvcIpetABx7rH/cb34z/n5ldDSWhxLiJCjQISJtizOjo7p0Jcv+HOA/9Pfu9VfvyngwWi+6n1RGB2S3k+2GQIcyOpJz0klw++2wciWcdRY8/njt5dLK6AhKV4oW6AiXrqytdHRLMqMDfPlKXIGOXbvaD3SoR0d0UUtXuiXQoW2gvt5eH7C8+OLWHvOc5yijo1V9fT5zbe/erEcSr6h9qM82s+URlpsTfBNxeQDnnHt2xGVFJEeCQEcnO456pStJHwA3Ez55KOMVl+oGfIHhyvxbcTcjhewDHYcdltxzJJW1smmTvy1LRkd/vz/wTTOjA3zq9A9+ABdcAGee6b9/znPGL5NWRsfUqb5sJfjMLEoz0nDpShoZHeAzOoITnHaFp9vuJKMjKHsxG/ts1awrEzULdGza5AO8RQ90qHSlub4+OPHE1j8nBgfh5z8ff58yOhoLX3BKYz+WlqiBjj5gXoTlXOWLCMs7/FS0GbWfE5FO7b8//N3fwStf2f466jUjPf74zsbWqfABaBmvuDTK6Jg0Kd4T6zxkdBxwQLIHQDNn+pOupUvjXW/ZSlfM/EFYmhkdgdNPhx//GF7+ch/suPNOOOGEsd+nWboCsGWLvy1iRkcQ6Eg6oP2e97R28llL8HqPjHQW6Ni715/chj9by7hvaaZZoCNvM66ASleS8j/+B5xySuuPGxyEm28ePxW0MjoaC19wKlugo5VJuZJaVkRy6qqrOnt8vYyOPJSuBMp4INKoGemsWfE2gctDoCPJshXwNcMXXQRLlvgD3Li2qSCjoyylK+BPGjds8FkNaQY6wJex3HMPnHsunH02fPe78MIX+t+lWboCY0GuogQ6wj06tm71wbmkT/T/5E86X0ccpSvhgH64B4UCHRM1C3Qsr+SL5ynQEbwHVboSrw9/uL3HBQ1Jf/tbH6AGZXQ0k3WvtKQ0C3R8KZVRiEhpTZ3qm04FO6GdO/3BQtbNSMMnD2U8EOnrG59lExgejj8IlfUONo1AB/iGajfcAENDPisgDmXL6AB/wrx6tf8+7UAHwDHH+GDHy17mv267DV78Yh94Sat0BYoX6KjO6Ei6bCUu4UBH8JnYTo8O8I8/6CAFOhqJGuiYPz+d8USh0pV8Cc+8EgQ6lNHRWNYXnJLSMNDhnLs8rYGISDmFU9HBz7gCyujIWm9v7ek0k8i2yXoHu2oVnHxy8s9z3nl+W1+yJL5ARxkzOgYGxmY/ySLQAX5qy3vu8f/TV74SFi/292eR0VHEHh1r12bfhymqODM6gv2cAh31RQl0zJqV3Xu/FpWu5Mu8eX4/EW5IGgQpi/J5mbase6UlRbOuiEjmgkZtkMysHu0I9+go44FIo+ll45xaFrLdwe7eDevWpZPR0d/vm1l+61uwb18869y82W+fRbmqH4fp07PN6AgceijcdRc873lw2WX+vjR7dBQ1o2PXruJmdASBjlYDWtW9qNSMtL6gbNLV6eCXtxlXoL2sRJWuJGfSJN/nLRzo2LbNvw/jLLvtJlln1iZF/24RyVx46r28BDpUulK/R0c3ZXQEJ8xpBDrAl6+sWQP33RfP+jZt8tkcVqKuVwMDYyf5WV/VPegg+OEP4SUv8T+nkRZd1NKVoExx587JCnQoo6Ouvj4f5Bgdrf37FSvyF+gw84E8la7kx+CgD3QEAbO0eigVVdaZtUnpukCHmR1hZj82s2Vm9lsze1/l/qvM7Ckze6Dy1cE8ESISp3DpSh4DHWU8EKnVjHTfvu4LdARTy6YV6LjwQpgyxZevxGHTpnL154DxB6tZBzoAZsyA730P/vVf4RWvSP75iprRAf5k8OmnpzIyUtzSlWnTfHPhVoR7dIBmXWmk0f5g715YuTJ/gQ5oPdCh0pVkDQ76Uui1a/3P4RlYZKJgP6KMjvwbBT7knDsWOB14t5kdV/ndvzrnTqp83Z7dEEUkbPr0sQPAoEeHmpFmq1bpypYt/kAz7tKVLFMm0w50zJgB55zjAx31UrNbsXlz+QId4fKQPAQ6wJ/kvP/96fRKKWpGB/jXafVqP+CiZHQEn09BoKOdkyX16IiuUaBj1Sqf6ZGnRqSBdgIdkye3HjSTaMINSUEZHc0oo6MgnHNrnHP3V77fBiwDDst2VCLSSK2MjpkzsxsPqEdHrYyOpLJtypTRAb585bHH/NR3nQpKV8okbxkdaQtOjrds8bdFaq4XDnQULaNjZKTzQIdKV5prtD/I49SygXZKV8p4bJGW6kCHMjoa69ZmpM2mly00M5sHnAzcB7wYeI+ZXQb8Ap/1sanGY64ErgSYM2cOQ0NDqY03Ltu3by/kuKW8du48jvXrBxga+jm//vXRTJ8+h5/85Cc1l01r+x4e7gXOAOChh37J3r3bEn/OPFm3bj4jI0cwNHT3H+/77W/3B05h9erfMDT0dGzPtX37ZOBMHnroMYaGVsW23ih+9rOj6e8/mPvvvze1PhfPetZUzM7gM59Zydve9viE37eyja9Z8wL2338bQ0PLYh5lfj399LOBIwB44IG7mTo1ps6uBfHwwzOAk1m5chNwIEuX3kVPTwzpQak4jQ0b/Fn/k08uZWiohTPDjDz1VB9wOg88sIyVK2djNpWhoV+2tI7gM+6BB/xn3MMPHwE8m6VL76G/f28Swy6s5ctnA8dx1133cfjh49P8br/9YOC5rF//M4aG8nVGtnfvqTz55C6Ghh6K9Bm+fPnRTJp0MEND96YzwBKaNesM7rxzE6ee+ghr1pzKgQfuZmjoweYPLKHVq/3n3P33L2P27HUNly3SeWbXBjrMbDrwDeD9zrmtZvYfwP8EXOX2M8A7qh/nnFsMLAY47bTT3MKFC1Mbc1yGhoYo4rilvObN8w3GFi5cyOc/76/01duG09q+gxIagDPOOJWTTkr8KXPlnnt8mcqZZy78Y2rt1q3+9pxzTuC00+J7ruDq5uGHH83ChUfHt+IIPvtZmDsXXvrShak+7xlnwAMPzOeLX5yYg93KNj4yAsccM42FC+fEPML8+vGP/e2UKXDeeWeVqhErhDM4DmTyZDj33LOzHE5LnvWssavyr371CwpRdhVkfR111LH89Ke+5KbVfVDQWPPgg/1n3L2Vc9vzzjuTnq49Em9PsO898cQX/vGqfOAHP/ClHq973em5e91mz4be3v1YuHBhpM/wr33NZ7PqeD05p54Kw8MHs3ChTx+bN0+vdz1BY/b5849l4cJjGy5bpPPMritdATCzKfggx43OuVsAnHPrnHN7nXP7gGuBF2Q5RhEZUz29bNb9OWB86UoZ04vDDfgCQelK3D06gvTdrEpX0ixbCSxaBA884AN87XKunD06gjKA/fcv12wzgXAz0iL154CxIE1vb3FKrqqbkbaT/t7T49cT7OdGRvw0l3k7Wc+DZqUrc+fm83VT6Ur+DA76EtHRUfXoaKZbS1e6LtBhZgZ8AVjmnPuX0P3htleLgIfSHpuI1BaeXnbjxuxnXAHNulLrYDOpHh1m9aezTdpTT2UX6AD45jfbX8f27T7rpignjHEJmpGWsT8HjA90FKk/B4z97w45pDhBqupAR7snS+Gm27t2lTOAHkW4J0q15cvz2YgU2mtGWsZjizQNDvr32mOPqUdHM1k2hU9S1wU68L043gqcUzWV7KfM7EEz+w3wUuADmY5SRP5oYMDvjEZHk5m+tB2TJo0dhJTxgLTWwebwsN8ZJnFy1deX/g52dBTWrMkm0PHsZ8MJJ3Q2zWww60aZMzrKKPhc2rKluBkdRWlECvFkdIACHVGFZ7mptnx5PhuRgt+2W9mH7d6tbSBpQenTb36jjI5mGgUYiyyHyV+dcc7dC9S6TqDpZEVyKjz1Xl4CHeBPIsp61aXWwWbwv0niSmx/f/o72HXrfEZEFoEO8Fkdf//3sH69r+9u1aZKO21ldJRLcEDqXHEDHUWZWhbGPv87DXSEMxcV6KivXunK9u0+2J7nQIdKV/Ll2GP9RaulS/3npTI66ps0qfZse0XXjRkdIlIwwYnL8LC/IpKXQEdwwFXGA9Ja0f0NG+LvzxHIonQli6llwxYt8gdft97a3uODQIcyOsol/HlU1EBHkTI6zPwJ6c6dPhjfSUZH0KNj167xfaBkTL1AR9DPqFsCHWW9iJKm/n5YsAB++lP/szI6GssiszZpCnSISOaCnc/jlZk289CMFMZOIsp4MNIooyOp5ytboOOEE3y9ebvlK2UtXSl7Rkf486hogY5wj44i6esbmw1EpSvJqhfoCGbr6aZAh7aB5A0Owi9+4b9XRkdjWfVKS5ICHSKSueDgNwh05CWjQ4GOiT06FOiIj5nP6vjBD8am7m2FSleyHUdWwidHRWtGWsTSFfCvedCMOY5Ax8iITnLrKXKgY9cuXw4ZhUpX0jE46INKoIyOZvr7ldEhIhK76oyOvAQ6+vpg8mT/VTZZlK6kvYNdtco/78yZ6T5v2KJF/iDs9ja6SJU1o6PspStFzugoYukKxBPoUI+OaBoFOvbfP7+fd8G2HXU/ptKVdAQNSUGBjmaU0SEikoDgCu3Klf42L4GO/v7yHoxWl67s2eNneeimjI4nnvDZHFlOc3nGGb4RaTvlK0FGR9lO+Mue0WEGU6b474sa6ChiRkdQutLJ9LLhHh1l3bc00yjQcdRR+Z2WOHgvRi1fUelKOsKBDpWuNKZAh4hIAqpLV/LUo6OsV1yqMzqCq5lJBTrSnHVl82Z417vg5pvh5JPTec56Jk+Giy/2GR2t/v2bN8OMGeXLOJo502cWHXts1iPJTvD+LFqg45hjYGBgNLflB/XEXbqiQEd99aa5XLEiv2UrMBbEixroUOlKOo46aux/o4yOxlS6IiKSgOrSlSxLCcKU0TGW0ZF0oCONKwnOwde/7k+Qr7sOPvhBuP76ZJ8zikWL/AnQD3/Y2uM2bSpffw7w28r69fDa12Y9kuwEn0tF69Fx3nlw22335rb8oJ7eXnj6af99p7Ou7NunWVca6enxU12GG2Hv29d9gQ6VrqRj0iR43vP898roaEwZHSIiCQgyOlat8vW3PT3ZjifQ11feA5Hq9OEg0FHU6WUffxwuughe/3o49FBYuhQ+85l8XOE55xxfhtFq+crmzfmtV5dkBZ9LRcvogPyWHjTS2+sDpdBZoMM5f8VUGR31mU3cH6xd63/utkCHtoF0BOUredjf51mambVpycnphIiUWbDzGR3NT38OgOc/318NKKPgACzNjI4kUib37oWrr4aPf9yfZHzmM/De9+YnmAb+tb7wQrj11ugd+8FndCjQUU5FLV0pqvAJaSfNSMFndWjWlcaqAx3BjCvz52cznihabUaq0pX0XHihn2J2xoysR5JvfX3jM6m6QY4O9USkrMIH63kKdHz4w1mPIDvVGR3Dw/62SKUrv/oVXHEF/PKXcMEFcM01MG9evM8Rl0WL4KtfhZ/8JPpjNm2C5zwnuTFJfinQka44Ah1BQH/7dmV0NFMv0FGUjI4o/1uVrqTnNa/xX9LYzTd338W9LvtzRKSIJk0aO0jISyPSsqvXjDSp/0/cKZOf+ITPyFm1Cm66Cb7znfwGOcAHYnp74ZZboj9GpSvlVeTSlSIK99MIMjNapUBHdLUCHWYwd252Y2qmnWak2gYkTyZPLmZpYSMKdIhILgQHgXnK6CizWs1IDzhgbFrLJJ4vrkDHI4/AVVf5KzjLlsEb3pD/nff06b5R45IlY70AmilrM1IpbjPSogpe7+nT27/iqUBHdNX7gxUr/FTgeX7N1IxUJH8U6BCRXAiukinQkQ9B5/tw6UqS/5u+Pt+fYnS083Vdd50f/9VXFyvjYdEieOIJePTR5h3Tdu/2B9RF+vskPipdSVc40NGuYB8XBDo060p9tTI68ly2Aq0FOvbu9TPJKNAhkiwFOkQkF5TRkT/hxlQbNiQf6IDOG5Lu2gU33AAXXwxz5nQ8rFRddJEPLt1zT/OpbTZv9rfK6Cgnla6kKwh0dDI9ZTijQ81IG+vtnRjoyHMjUmgt0BHsV7UNiCRLgQ4RyQVldORP+Krahg3JTS0bPBd0Xr7yzW/Cxo1w5ZUdDyl1z3qWn2r2zjvnsG9f42WDQIcyOspJGR3pijPQsXmzL0/TSW594SD7zp2wenX+MzqC92KUQMfu3f5WGR0iyVKgQ0RyITgIVDPS/AhfVUs6oyM4SOw00LF4sW86eu65HQ8pE+98J6xb18eddzZebtMmf6uMjnIKTpDUoyMdcQY6Nm4cv06ZKBxkX7nS3+Y90NHb63tBKdAhkh8KdIhILiijI3+Cq2rOpdOjAzoLdPzhD/CjH8Gf/mlxp0i75BKYMWM3117beDlldJSbMjrSFUegI9jHPf30+HXKROFAx4oV/jbvgQ4zH3hU6YpIfhT0UFBEuo0CHfkTZHTs2OEPzPIe6LjuOj892uWXxzOmLPT2witesY5vfQvWrau/XJDRoUBHOSnQka44Ah1B9k2Q0aFmpPWFAx3Ll/vbvAc6wP+Po/SZUkaHSDoU6BCRXFAz0vwJMjo2bPA/p9Gjo91mpHv2wBe/CBdeCIcdFt+4snDhhWsYHfVNVetR6Uq5qRlpuoLPp04CHZMn+xNhla40Vx3omDYNZs/OdkxRRM3oUKBDJB0KdIhILkyf7lM/dYU6P4KDzSDQkeeMjttu8xkQRWxCWu3II5/hrLPg2mup25RUpSvlpoyOdMWR0QF+Pxd8nirQUV91oGP+fH98kHcqXRHJFwU6RCQX3v52uPpqf9VL8qG31x+QDQ/7n/PcjHTxYjj8cDj//PjGlKUrrvA9R4aGav9+0yb//1H6ezkFJ0hqRpqO4PUOMg/bNTCgHh1RVAc6ilC2AsroEMkbBTpEJBcGB+Hd7856FBJWndGR1+llV66E73/fNyHtlkDZa1/rszXqNSXdvFnZHGWm0pV0xZnRodKV5oL+UM75ZqQKdIhIOxToEBGRmoKDzbyXrlx/vb99xzviG0/W+vvhrW+FW24Ze/3DNm1Sf44y6+/3qfwKdKRDgY509fX5vkvr18P27d0X6FDpikg6FOgQEZGagmakw8M+U2LGjGSfC1oPdIyOwhe+ABdcAEceGf+4snTFFf7K35e/PPF3yugot8svhxtv7J4MpryLM9ARXM1X2Vl9wWuzbJm/LUqgo79fGR0ieaJAh4iI1BTO6DjooGSbwbU768odd8Dq1T4o0G2OPx7OOMOXrzg3/nfK6Ci3uXPh0kuzHkV5xBXoCKZRD69TJgr2Bw8/7G/nz89uLK1Q6YpIvijQISIiNYWnl02yPwe034x08WI45BA/rWw3uuIKeOQRuPfe8fcro0MkPUET0k7fc+Fmpgp01NftgQ6VroikQ4EOERGpKdyMNMn+HMFzQWuBjlWr4PbbfRr/lCnJjCtrr3897L//xKakmzYp0CGSlrPPhq9+1WdYdUKBjmjCpSsHH1yc2YWU0SGSLwp0iIhITUHpyvBw8oGO4KC/lUDH9dfDvn1+tpVuNTAAb34zfP3rPrgB/m/evFmlKyJpmTwZ3vhGmNThUbMCHdGEMzqK0p8DfKAjSvmlAh0i6VCgQ0REago3I026dGXSJH/QFzXQsXevb0J63nnFOhBuxxVX+NflK1/xP2/f7oMdyugQKZZwjw41I60vCAKtXVusz/dp03wQY+/exg2tVLoikg4FOkREpKbeXn9CvXFj8hkd4A/8ozYjvfNOeOKJ7mxCWu3kk+HUU8eakgaZHcroECkWZXREEw4CFaU/B4yV2Oza1fj0ShkdIulQoENERGoKH2ymFeiImtGxeLHPMrn44mTHlBdXXgkPPghLl/qyFVBGh0jRKNARTXjfU7SMDoCREQU6RPJAgQ4REakp7UBHf3+0QMeaNXDbbfD2t5fnQPHSS33a++LFyugQKSoFOqIpeqBj167JDZdT6YpIOhToEBGRmsIHYUn36IDoGR033ACjo/DOdyY+pNzYbz8f7LjpJl+yA8roECmaoEdHT0/njU27WVEDHWPTpEfL6OjpSXhAIiWnj1kREakpj6Ur+/bBddfBwoXwnOckP6Y8ueIKP3XhNdf4nxXoECmWIKNDV/IbC/Y9U6fCoYdmO5ZWRM3o2L3b/23WuGepiHRIgQ4REakpfDCel2akV18Ny5fDu96V/Hjy5vnPhxNOgPvu8z+rdEWkWIJAh2ZcaSx4febNK1bmS9QeHbt2KdglkoYCfXyIiEia8pbRcddd8KEPwSWXwOtfn/x48sbMNyUNvt9//2zHIyKtUUZHNMG+p0hlK9B6RoeIJEuBDhERqSk4GB8YGKs9TlKjZqSrVvngxoIF8KUvFesqX5ze/Gb/Os2YUd7XQKSogh4dCnQ0Frw+xQ10NO/RoUCHSPJ0mCQiIjUFV9XSyOYInq9WoGPXLnjta31Zy5Il5c5kOOAAuPxyOOaYrEciIq1SRkc006fD7NnwwhdmPZLWtDLrirYBkeSp36+IiNSUl0DHX/wFLF0Kt9wCz31uOmPJs3//d3Au61GISKsU6IhmyhRYvbp4WWtRe3Qoo0MkHQp0iIhITcHBeBpTy0LtZqTXXuu/PvpRWLQonXHk3eTGFwtFJKf6+31/HTUjba6In3NRMjq2bIE1axToEElDwWKlIiKSlqwzOu67D97zHnjFK+Dv/z6dMYiIJMXM9+lQRkd3qpfRsXYtfP7zcMEF/sLB3XeXb3p0kSwoo0NERGoKDsbTCnSEm5GuW+f7chx+OPzXfxXz6p6ISLXp0xXo6FZTpvh91a5dk/nDH3xPqSVL4Kc/9eWGRx0F732vz048/fSsRyvS/RToEBGRmrLK6NizB173Onj6aX+AOHNmOs8vIpI0BTq6l5kP2P/3fx/OV77i7zvpJLjqKh/cOP54v4yIpEOBDhERqWnWLHjJS+Css9J5vr4+H+T44AfhnnvgxhvhxBPTeW4RkTS88pVw5JFZj0KScv758Pvfb+Pyyw/gkktg3rysRyRSXgp0iIhITVOn+oBDWoIMks9+Ft7/fnjTm9J7bhGRNPzbv2U9AknS178OQ0MPsHDhwqyHIlJ6akYqIiK5EAQ6zj4bPvWpbMciIiIiIsWlQIeIiOTCi17k035vvtk3dRMRERERaYdKV0REJBdOOw3uuCPrUYiIiIhI0SmjQ0RERERERES6hgIdIiIiIiIiItI1FOgQERERERERka6hQIeIiIiIiIiIdA0FOkRERERERESkayjQISIiIiIiIiJdQ4EOEREREREREekaCnSIiIiIiIiISNdQoENEREREREREuoYCHSIiIiIiIiLSNRToEBEREREREZGuoUCHiIiIiIiIiHQNBTpEREREREREpGso0CEiIiIiIiIiXUOBDhERERERERHpGqULdJjZ+Wb2OzN7zMw+kvV4RERERERERCQ+pQp0mNlk4HPABcBxwKVmdly2oxIRERERERGRuJQq0AG8AHjMObfcObcbuAm4OOMxiYiIiIiIiEhMzDmX9RhSY2Z/ApzvnHtn5ee3Ai90zr0ntMyVwJUAc+bMOfWmm27KZKyd2L59O9OnT896GCKJ0PYt3U7buHQzbd/S7bSNSzfL4/b90pe+9JfOudOq7+/JYjAZshr3jYv0OOcWA4sBTjvtNLdw4cIUhhWvoaEhijhukSi0fUu30zYu3Uzbt3Q7bePSzYq0fZetdGUVcETo58OB1RmNRURERERERERiVrZAx8+BBWY238ymAm8Ebs14TCIiIiIiIiISk1KVrjjnRs3sPcD3gMnA9c6532Y8LBERERERERGJSamakbbKzIaBx7MeRxsOAjZkPQiRhGj7lm6nbVy6mbZv6XbaxqWb5XH7nuuce1b1nQp0dCEz+0WtzrMi3UDbt3Q7bePSzbR9S7fTNi7drEjbd9l6dIiIiIiIiIhIF1OgQ0RERERERES6hgId3Wlx1gMQSZC2b+l22salm2n7lm6nbVy6WWG2b/XoEBEREREREZGuoYwOEREREREREekaCnSIiIiIiIiISNdQoKOLmNn5ZvY7M3vMzD6S9XhEOmVmR5jZj81smZn91szeV7l/ppndaWaPVm4PzHqsIu0ys8lm9isz+3blZ23f0jXM7AAz+28ze6TyWX6GtnHpFmb2gcrxyUNm9lUz69P2LUVmZteb2Xozeyh0X91t2sz+unLu+Tsze0U2o65NgY4uYWaTgc8BFwDHAZea2XHZjkqkY6PAh5xzxwKnA++ubNcfAX7onFsA/LDys0hRvQ9YFvpZ27d0k38Dvuucey5wIn5b1zYuhWdmhwHvBU5zzh0PTAbeiLZvKbYbgPOr7qu5TVeOyd8IPK/ymGsq56S5oEBH93gB8JhzbrlzbjdwE3BxxmMS6Yhzbo1z7v7K99vwB8iH4bftL1UW+xJwSSYDFOmQmR0OXAhcF7pb27d0BTPbHzgL+AKAc263c24z2sale/QA/WbWA0wDVqPtWwrMOXc38HTV3fW26YuBm5xzu5xzK4DH8OekuaBAR/c4DHgy9POqyn0iXcHM5gEnA/cBc5xza8AHQ4DZGQ5NpBP/B/gwsC90n7Zv6RZHAcPAFyvlWdeZ2QDaxqULOOeeAj4NPAGsAbY4576Ptm/pPvW26VyffyrQ0T2sxn2aO1i6gplNB74BvN85tzXr8YjEwcxeBax3zv0y67GIJKQHOAX4D+fcycAOlMYvXaLSp+BiYD5wKDBgZm/JdlQiqcr1+acCHd1jFXBE6OfD8elzIoVmZlPwQY4bnXO3VO5eZ2aHVH5/CLA+q/GJdODFwKvNbCW+3PAcM/sK2r6le6wCVjnn7qv8/N/4wIe2cekG5wIrnHPDzrk9wC3Ai9D2Ld2n3jad6/NPBTq6x8+BBWY238ym4hvD3JrxmEQ6YmaGr+1e5pz7l9CvbgXeVvn+bcC30h6bSKecc3/tnDvcOTcP/5n9I+fcW9D2LV3CObcWeNLMjqnc9TLgYbSNS3d4AjjdzKZVjldehu8lpu1buk29bfpW4I1m1mtm84EFwNIMxleTOZeb7BLpkJm9El/vPRm43jn3yWxHJNIZM3sJcA/wIGM9DD6K79NxM3Ak/kDjdc656sZJIoVhZguBv3TOvcrMZqHtW7qEmZ2Eb7Y7FVgOXI6/0KZtXArPzD4BvAE/S9yvgHcC09H2LQVlZl8FFgIHAeuAvwO+SZ1t2sw+BrwD/x54v3PujvRHXZsCHSIiIiIiIiLSNVS6IiIiIiIiIiJdQ4EOEREREREREekaCnSIiIiIiIiISNdQoENEREREREREuoYCHSIiIiIiIiLSNRToEBER6XJmdpWZucrXDVmPR4ortB05M5uX9XhERERqUaBDRES6lpndUHVi1urXVVn/DRKPGv/bP5jZlDYff0mCQxUREZEOKdAhIiIiZXQU8KdZD0JERETi15P1AERERFIyAtzV4mMeS2Igkht/Y2Y3OOdGsh6IiIiIxEeBDhERKYt1zrnzsx5EFpxzVwFXZTyMPDoUeA/w6awHIiIiIvFR6YqIiIiUzaOh7z9iZvtlNhIRERGJnQIdIiIiUjb/G9he+X4W8KEMxyIiIiIxU6BDRESkRWa2MjQDx8LKfb1m9jYz+4GZrTKzXWa21szuMLO3mJm1+BynmdnnzexRM9thZhvN7Fdm9g9mNje0XNPpPqNOL1trXWa2n5m928zuNbM1ZjZS+ftuMbOLWvmbQs9zfuVv+23l79pVWef3zOw9ZjatnfW2YBj4P6GfP2hmszpdqZktDL1+KyM+5u2hxww1WK7W/+YgM/uwmS01s/WV13FF5bU9qsY6zMxeZ2bfrmybuyuPu6OTmWTMbLaZfSQ0jp2VWW2uN7PT21xnx9tIvdfWzE4xs38zswcr63Zm9kA74xQRkXxSjw4REZEOmdmzga8DJ1f9ag5wfuXrHWb2aufc9urHV63L8BkHH2D8BYlpwEzgJOD9ZnaFc+6r8fwFdcdyKnAzfoaSsMOARcAiM/sqcJlzbjTC+o4FrgVeXOPXh1W+Xo4vJ7ncOXdnJ+Nv4tPAn+Nf0/2Avwb+MsHni5WZnQV8DTi46lfzgCuBN5vZBc65eyrLz8Jvoy+tWv5ZVLZRM/sCcIVzzrUwjpcBX62sJ+yoytfbzexq4APOuX0R1pfYNmJmPcD/wmfwtBR4FBGRYlFGh4iISGcOBoYYC3I8Vvn5F8Ce0HIvBb4UYX3X4E/EwvvoP1TW+StgFBgAbjSzC9sfdlPHAT/Gn6w64OHKz78GwieslwL/3GxlZvYi4CeMP4HdBtxXWe/vQ/cfBnzbzF7dwfgbcs5tAT4VuuvdZnZoUs8XsxOB7+K3vVH8dvFjYEVomQHgO2Z2lJn1A99nLMjxOH4Gop9XHh/4U+CvWhjHScB3GAtyLMNvp+H/pQHvBa5rtrIUtpF/xQezDHgG//ffBaxsYR0iIlIACnSIiIh05rPA4cAdwLHOuQXOuZc6556Pn9XjG6FlX2NmZ9dbkZm9Fviz0F2/AE5xzh1dWecpwCHA5/EnazfE+6eM82V8psN/Akc6557nnDvHOXcSPvhxT2jZ99UqlQiY2SHAt4ADK3f9AZ8RcqBz7vTKeo8BjsGfkANMBb5kZkfE+UdVuRpYW/m+D/ibBJ8rTl8A+vHb3sHOuVMqr+FRwAX44AD4/9/H8Nkrp+ADIqc75+Y55xY6514AzGX8//JvzWz/iOO4FugFfgQ8xzl3XGU7PQYYBH4WWvZyM3tzvRWlsI2cgp9hZwfwbmCWc+4FlddhPvDaiH+ziIgUgAIdIiIinZmFL+94lXPukfAvnHMbgDcCvwndfXmtlZjZJOAzobt+A5zjnPtV9Tqdc3+G7zFxUMejr28W8Gnn3GXOuVVVY3gceBVjQYLJwGUN1nUNY2N9ADjVOfdN59zeqvX+HnglPksA4ADgbzv4Gxpyzj0DfDJ01582CtjkyCzgKufcXzjnNoZ/4Zz7Lj6DInAZPnj2IHC2c+6+quVX4wMKmyt3DQCviTiOg/CZFuc758Iz2eCcewg4F7g/dPdnzGxqnXUlvY3sB+wFXumcu8Y5N1K13j9EWIeIiBSEAh0iIlIWc6saOjb9irjeLcCV9foPVHpX/Fvorlq9B8D3SZgb+vnPnHPb6iwLvqfEkxHH2I5HK89Rk3NuK/6KfqDm32VmxwAXV34cBd5UKRupt969wLuA3ZW73tJChkE7FjNWujAF+ESCzxWXZcA/NPj9fzGW1dGDP957V73tqRIs+XrorpdEHMcefE+PPbV+6ZzbAVyBL30C37NmUfVyKW4j/9c5d3eE5UREpOAU6BAREenMTY1OyiruDX3/7DpXtcP9Nn7jnPtpoxVWrkjfEG2Ibbk+QoPR8N91bJ1l3sJY48fvOueWNXti59xTQHBC2ge8qNlj2uWc28344MabzOx5ST1fTL5YnekQVvmbfh266+Fm2xO+D0ag3v+y2u3NMiGcc/fj+24EamWLpLWNLI6wjIiIdAHNuiIiImUxgm88GLf/F2GZcOmHATPwU5yGPT/0/Y8iPvePSK6vRKt/14F1ljkz9P0PW3j+oPQBfH+F77bw2Fb9J/Bh/An+JHy2xITMgxz5WfNFWNPB8gdEHEfU/8kdjGWJPL/G79PYRrbgy3dERKQEFOgQEZGyWOecOz+B9a5ttoBz7hk/a+wfTaux2JGh75te0W5xuXY0/bvwM1cEav1N4JtSBt5uZlH/B0eHvq+eujRWzrm9Zva3jJVvXGJmz3fO/TzJ5+3AugjLhP83rS5f739Z7aE2lptvZj1V2UJpbCMrWpk2V0REik2BDhERkc7sbr7IBFbjvgNC32+OuJ5mJTOdaOfvGqfSYPWA0F0nVr5aNaPTsUTwDXzjzFMqP/8jcF4Kz9uOVv83rS5fa/usZWPzRWouNyO4L8VtpFG/GxER6TLq0SEiIlJcNRug5kg/8RxrJH68Urna/7HQXeea2cKkn7fgogZQdlX93Bv6Pq1tJO/vFRERiZECHSIiIvkQzs6ImsFQry9GLlRm3QiXKJzvnLM2vt6e0ni/C9wTuusfU3jaySk8R1L2a3O5rcE3RdtGRESkGBToEBERyYcnQt9HnfXiuUkMJGbrQ98vyGwU0X009P0ZZvaqFh4bznCYEvExB7Sw/ryZH3G5o0LfP+Oc2171+6JtIyIiknMKdIiIiORDuPHlSyM+JupyWQrP+PGyzEYRkXPuXsbP3vEPVtVJtoFwH4gDIz5usPkiufWCNpb7VY3fF2obERGR/FOgQ0REJB9uD31/kpm9sNHCZtYLvD3REcUjHDS40MyOrLtkfnwMCGboOBF4Q8THhbNy+hk/K8gEZjYVuKDl0eXH6yrNROuqbKfhqXrvqbFYEbcRERHJMQU6RERE8uEO4MnQz//XzKY3WP6TwNxkhxSLGxkrTZgCfL7ZyXHWnHP342dhCXwi4uO2AMtDdzULkLwfmN3S4PLl2cA7mizzAcZP/XpDjWUKt42IiEi+aSciIiKSA865vcBfhu46CfihmZ0UXs7MZpnZNcCHgA2pDbBNzrlnGD+byfnAN8ysYSNVM5tmZpeZ2Q8THWB9fwPsrXz/nBYed0vo+78ys+NqLWRmlwL/0ObY8uTfzeycWr8ws0uA/xm66xbn3O+qlyvwNiIiIjnVk/UAREREUjLHzL7bfLFx/uCce3cio6nBOXezmZ0LXFG56wXAr8zsMXy2xwzgBMb235cDt4VWEXW6z1Q5564zsxcw9nddArzMzL4K3A2sxk//eSBwDPBC4OXAALAu9QEDzrlHzOw/ab086HPAu/GlK/sDPzWzf8eXbOzGN+Z8PfAKfCDlv4A3xTTstH0Nn7Vyp5ndCNyKz8w4BHgt8LrQspuA99ZbURG3ERERyS8FOkREpCz68CeXrfh1EgNp4s+AHfiTwiDz8mjG93t4BngX8IOqx24hv96FPyH9GGD4KUevrHzl1VX4IMTUqA9wzq00s/cBn8f/nfsDH6+x6D78//gZihvo+DgwEzgPeGvlq5Zt+Gljn2qyviJuIyIikkMqXREREckR59w+59wHgNOB6/A9H3bir4j/Gvgn4Hjn3FcY399hp3NuR9rjjcp5fwOcBnyT5tkny/B/69kJD60u59zjwOI2HnctPmuj3on9w8DLnXPXdDC8PBjFN1P9R2BrnWW+B5zknFvabGVF3EZERCSfzDnXfCkRERHJHTP7E+DrlR9/5pw7I8vxtMLMBoAXA/OAWfgMh6CZ54POuTXZjS4eZtaD/xuPx2d2rMP/bT9v+MACMrNpwDn4Brn74UtN7nbOrexgnV2/jYiISDIU6BARESkoM7sD37gR4F+ccx/KcjwiIiIieaDSFRERkRwxM4u43DsYC3JA7Wk7RUREREpHzUhFRETy5XNmtge4CbjPObcv/Eszm4ufWvY9obuXOOceTHGMIiIiIrml0hUREZEcMbOvAG+u/PgM8HvgaWAKcDgwv+ohK4AXOueGUxukiIiISI4po0NERCRfwhkc04CTGiz7A+DNCnKIiIiIjFFGh4iISI6YWT9+ys5zgRPxU8geBAwAm/FTlt4LfN05d3dGwxQRERHJLQU6RERERERERKRraNYVEREREREREekaCnSIiIiIiIiISNdQoENEREREREREuoYCHSIiIiIiIiLSNRToEBEREREREZGu8f8BzL4UwioWXtoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,10))\n",
    "plt.plot(final_lin_pred,c='red',label='prediction')\n",
    "plt.plot(df_rul,c='blue',label='y_test')\n",
    "\n",
    "fig.suptitle('RUL Prediction using Linear Regressin Model', fontsize=35)\n",
    "plt.xlabel(\"Engine Number\", fontsize=35)\n",
    "plt.ylabel(\"Remaining Useful Life\", fontsize=35)\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error >>  2747.630311446985\n",
      "root mean_absolute_error >> 52.417843445214196\n",
      "mean_absolute_error >> 44.35622912326316\n",
      "scoring function >> 282257.88133331964\n"
     ]
    }
   ],
   "source": [
    "def scoring_function(actual,predicted):\n",
    "    d = []\n",
    "    for i in range(len(predicted)):\n",
    "        d.append((predicted[i] - actual[i]))\n",
    "    scores = []\n",
    "    for i in range(len(d)):\n",
    "        if d[i] >= 0:\n",
    "            scores.append(math.exp(d[i]/10) - 1)\n",
    "        else :\n",
    "            scores.append(math.exp((-1*d[i])/13) - 1)\n",
    "    return sum(scores)\n",
    "\n",
    "print(\"mean_squared_error >> \", mean_squared_error(df_rul,final_lin_pred))\n",
    "print(\"root mean_absolute_error >>\",math.sqrt(mean_squared_error(df_rul,final_lin_pred)))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(df_rul,final_lin_pred))\n",
    "print(\"scoring function >>\",scoring_function(np.array(df_rul),final_lin_pred))\n",
    "# scoring_function(np.array(df_rul),final_lin_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='4.3'>4.3 Logistic Regressor Testing</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13096, 1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_pred = logistic_model.predict(df_test)\n",
    "logistic_pred = np.array(logistic_pred)\n",
    "logistic_pred = logistic_pred.flatten()\n",
    "logistic_pred = logistic_pred.reshape(logistic_pred.shape[0],1)\n",
    "logistic_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_logistic_pred = []\n",
    "count = 0\n",
    "for i in range(100):\n",
    "    temp = 0\n",
    "    j = max_cycles[i] \n",
    "    while j>0:\n",
    "        temp = temp + logistic_pred[count]\n",
    "        j=j-1\n",
    "        count=count+1\n",
    "    final_logistic_pred.append(temp/max_cycles[i])\n",
    "\n",
    "final_logistic_pred=np.array(final_logistic_pred)\n",
    "final_logistic_pred = final_logistic_pred.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,10))\n",
    "plt.plot(final_logistic_pred,c='red',label='prediction')\n",
    "plt.plot(df_rul,c='blue',label='y_test')\n",
    "\n",
    "fig.suptitle('RUL Prediction using Logistic Regressin Model', fontsize=35)\n",
    "plt.xlabel(\"Engine Number\", fontsize=35)\n",
    "plt.ylabel(\"Remaining Useful Life\", fontsize=35)\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error >>  2454.3953412385013\n",
      "root mean_squared_error >>  49.54185443883284\n",
      "mean_absolute_error >> 40.50377638066752\n",
      "scoring function >> 210561.7210830336\n"
     ]
    }
   ],
   "source": [
    "print(\"mean_squared_error >> \", mean_squared_error(df_rul,final_logistic_pred))\n",
    "print(\"root mean_squared_error >> \", math.sqrt(mean_squared_error(df_rul,final_logistic_pred)))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(df_rul,final_logistic_pred))\n",
    "print(\"scoring function >>\",scoring_function(np.array(df_rul),final_logistic_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}