src/notes/agc/agc_ibr.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detailed SFR Study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we will demonstrate how to use AMS and ANDES to mimic the system secondary frequency regulation, where system automatic generation control (AGC) is used to maintain the system frequency at the nominal value.\n",
    "\n",
    "This demo is prepared by [Jinning Wang](https://jinningwang.github.io/).\n",
    "\n",
    "Reference:\n",
    "\n",
    "1. J. Wang et al., \"Electric Vehicles Charging Time Constrained Deliverable Provision of Secondary Frequency Regulation,\" in IEEE Transactions on Smart Grid, doi: [10.1109/TSG.2024.3356948](https://ieeexplore.ieee.org/document/10411057).\n",
    "1. “Standard BAL-001-2 – Real Power Balancing Control Performance.” [Online]. Available: https://www.nerc.com/pa/Stand/Reliability%20Standards/BAL-001-2.pdf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import chain\n",
    "\n",
    "import numpy as np\n",
    "import scipy\n",
    "import pandas as pd\n",
    "\n",
    "import ams\n",
    "import andes\n",
    "\n",
    "import datetime\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reset matplotlib style to default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcdefaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensure in-line plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last run time: 2024-03-26 17:36:54\n",
      "andes:1.9.1.post24+g7a87ad5d\n",
      "ams:0.9.5.post1+g114d11e\n"
     ]
    }
   ],
   "source": [
    "print(\"Last run time:\", datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "\n",
    "print(f'andes:{andes.__version__}')\n",
    "print(f'ams:{ams.__version__}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "andes.config_logger(stream_level=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ams.config_logger(stream_level=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dispatch case\n",
    "\n",
    "We use the IEEE 39-bus system as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp = ams.load('ieee39_uced_vis2.xlsx',\n",
    "              setup=True,\n",
    "              no_output=True,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In [RTED documentation](https://ltb.readthedocs.io/projects/ams/en/stable/typedoc/DCED.html#id24),\n",
    "we can see that Var ``rgu`` and ``rgd`` are the variables for RegUp/Dn reserve,\n",
    "and Constraint ``rbu`` and ``rbd`` are the equality constraints for RegUp/Dn reserve balance.\n",
    "\n",
    "As for the RegUp/Dn reserve requirements, it is defined by parameter ``du`` and ``dd`` as percentage of the total load,\n",
    "and later ``dud`` and ``ddd`` are the actual reserve requirements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.34256, 0.     ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.RTED.dud.v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05, 0.05])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.RTED.du.v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dynamic case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Following PFlow models in addfile will be overwritten: <Bus>, <PQ>, <PV>, <Slack>, <Shunt>, <Line>, <Area>\n",
      "AMS system 0x10a581460 is linked to the ANDES system 0x168f68580.\n"
     ]
    }
   ],
   "source": [
    "sa = sp.to_andes(addfile='ieee39_vis2.xlsx',\n",
    "                 setup=True,\n",
    "                 no_output=True,\n",
    "                 default_config=True,\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Device `ACEc` is used to calculate the Area Control Error (ACE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>idx</th>\n",
       "      <th>u</th>\n",
       "      <th>name</th>\n",
       "      <th>bus</th>\n",
       "      <th>bias</th>\n",
       "      <th>busf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ACE_1</td>\n",
       "      <td>1</td>\n",
       "      <td>350.0</td>\n",
       "      <td>BusFreq_2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     idx    u   name  bus   bias       busf\n",
       "uid                                        \n",
       "0      1  1.0  ACE_1    1  350.0  BusFreq_2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa.ACEc.as_df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic load\n",
    "\n",
    "ISO-NE provides various grid data, such as [Five-Minute System Demand](https://www.iso-ne.com/isoexpress/web/reports/load-and-demand/-/tree/dmnd-five-minute-sys).\n",
    "In this example, we revise the March 02, 2024, 18 PM data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17da0bac0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "load_isone = np.array([\n",
    "    11920.071, 11980.979, 12000.579, 12145.243, 12211.862, 12220.703,\n",
    "    12191.051, 12241.546, 12285.719, 12312.626, 12364.102, 12336.354\n",
    "])\n",
    "\n",
    "# Normalize the load\n",
    "load_min = load_isone.min()\n",
    "load_max = load_isone.max()\n",
    "load_mid = (load_max + load_min) / 2  # Midpoint of the range\n",
    "# Set to desired range\n",
    "load_range = 0.8 + 0.01 * ((load_isone - load_mid) / (load_max - load_min))\n",
    "\n",
    "load_scale_base = np.repeat(load_range, 300)\n",
    "# smooth load_scale_base\n",
    "load_scale_smooth = scipy.signal.savgol_filter(load_scale_base, 600, 4)\n",
    "\n",
    "np.random.seed(2024)  # Set random seed for reproducibility\n",
    "random_bias = np.random.normal(loc=0, scale=0.0015,\n",
    "                               size=len(load_scale_smooth))\n",
    "random_smooth = scipy.signal.savgol_filter(random_bias, 20, 1)\n",
    "\n",
    "# Add noise to the load as random ACE\n",
    "load_coeff_base = load_scale_smooth + random_smooth\n",
    "# smooth\n",
    "load_coeff = scipy.signal.savgol_filter(load_coeff_base, 10, 2)\n",
    "\n",
    "# NOTE: force the first 2 points to be the same as first interval average\n",
    "load_coeff[0:2] = load_coeff[0:300].mean()\n",
    "\n",
    "# average load every N points, for RTED dispatch\n",
    "load_coeff_avg = load_coeff.reshape(-1, 300).mean(axis=1)\n",
    "load_coeff_avg = np.repeat(load_coeff_avg, 300)\n",
    "\n",
    "fig_load, ax_load = plt.subplots(figsize=(5, 3), dpi=100)\n",
    "\n",
    "ax_load.plot(range(len(load_coeff)), load_coeff,\n",
    "             label='Load Coefficient')\n",
    "ax_load.plot(range(len(load_coeff_avg)), load_coeff_avg,\n",
    "             label='Load Average')\n",
    "\n",
    "ax_load.set_xlim([0, 3600])\n",
    "ax_load.set_xlabel('Time [s]')\n",
    "ax_load.set_ylabel('Ratio')\n",
    "ax_load.set_title('Load Curve')\n",
    "ax_load.grid(True)\n",
    "ax_load.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Co-simulation\n",
    "\n",
    "### Define constants\n",
    "\n",
    "Here we assume the AGC interval is 4 seconds, and RTED interval is 300 seconds.\n",
    "\n",
    "Between the interoperation of AMS and ANDES, there is a AC conversion step to convert the DC-based dispatch resutls to AC-based power flow results.\n",
    "For more details, check the reference paper and [AMS source code - dc2ac](https://github.com/CURENT/ams/blob/master/ams/routines/rted.py#L184).\n",
    "\n",
    "### AGC controller\n",
    "\n",
    "Since there is not an built-in AGC controller in ANDES, we can define a PI controller to calculate the control signal for the AGC:\n",
    "\n",
    "AGC_raw = kp * ACE + ki * integral(ACE)\n",
    "\n",
    "Note that, in the AGC interval, there is a cap operation to limit the AGC signal within procured reserve limits.\n",
    "\n",
    "### ANDES settings\n",
    "\n",
    "ANDES load needs to be set to constant load for effective load change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- time constants ---\n",
    "total_time = 3600\n",
    "\n",
    "RTED_interval = 300\n",
    "AGC_interval = 4\n",
    "\n",
    "id_rted = -1  # RTED interval counter\n",
    "id_agc = -1  # AGC interval counter\n",
    "\n",
    "# --- AGC controller ---\n",
    "kp = 0.1\n",
    "ki = 0.05\n",
    "\n",
    "ACE_integral = 0\n",
    "ACE_raw = 0\n",
    "\n",
    "# --- initialize output ---\n",
    "# pd_andes: total load in ANDES\n",
    "# pd_ams: total load in AMS; pg_ams: total generation in AMS\n",
    "# pru_ams: total RegUp in AMS; prd_ams: total RegDn in AMS\n",
    "out_cols = ['time', 'freq', 'ACE', 'AGC',\n",
    "            'pd_andes', 'pd_ams', 'pg_ams',\n",
    "            'pru_ams', 'prd_ams']\n",
    "outdf = pd.DataFrame(data=np.zeros((total_time, len(out_cols))),\n",
    "                     columns=out_cols)\n",
    "\n",
    "# --- AMS settings ---\n",
    "sp.SFR.set(src='du', attr='v', idx=sp.SFR.idx.v, value=0.0015*np.ones(sp.SFR.n))\n",
    "sp.SFR.set(src='dd', attr='v', idx=sp.SFR.idx.v, value=0.0015*np.ones(sp.SFR.n))\n",
    "\n",
    "# --- ANDES settings ---\n",
    "sa.TDS.config.no_tqdm = True  # turn off ANDES progress bar\n",
    "sa.TDS.config.criteria = 0  # turn off ANDES criteria check\n",
    "\n",
    "# adjsut ANDES TDS settings to save memory\n",
    "sa.TDS.config.save_every = 0\n",
    "\n",
    "# adjust dynamic parameters\n",
    "# NOTE: might run into error if there exists a TurbineGov model that does not have \"VMAX\"\n",
    "tbgov_src = [mdl.idx.v for mdl in sa.TurbineGov.models.values()]\n",
    "tbgov_idx = list(chain.from_iterable(tbgov_src))\n",
    "sa.TurbineGov.set(src='VMAX', attr='v', idx=tbgov_idx,\n",
    "                  value=9999 * np.ones(sa.TurbineGov.n),)\n",
    "sa.TurbineGov.set(src='VMIN', attr='v', idx=tbgov_idx,\n",
    "                  value=np.zeros(sa.TurbineGov.n),)\n",
    "syg_src = [mdl.idx.v for mdl in sa.SynGen.models.values()]\n",
    "syg_idx = list(chain.from_iterable(syg_src))\n",
    "sa.SynGen.set(src='ra', attr='v', idx=syg_idx,\n",
    "              value=np.zeros(sa.SynGen.n),)\n",
    "\n",
    "# use constant power model for PQ\n",
    "sa.PQ.config.p2p = 1\n",
    "sa.PQ.config.q2q = 1\n",
    "sa.PQ.config.p2z = 0\n",
    "sa.PQ.config.q2z = 0\n",
    "sa.PQ.pq2z = 0\n",
    "\n",
    "# save the initial load values\n",
    "p0_sp = sp.PQ.p0.v.copy()\n",
    "q0_sp = sp.PQ.q0.v.copy()\n",
    "p0_sa = sa.PQ.p0.v.copy()\n",
    "q0_sa = sa.PQ.q0.v.copy()\n",
    "\n",
    "# --- Co-Sim Variables ---\n",
    "# save device index\n",
    "pq_idx = sp.PQ.idx.v  # PQ index\n",
    "\n",
    "# get a copy of link table to calculate AGC power\n",
    "maptab = sp.dyn.link.copy().fillna(False)\n",
    "\n",
    "# existence of each type of generator\n",
    "maptab['has_gov'] = maptab['gov_idx'].fillna(0, inplace=False).astype(bool).astype(int)\n",
    "maptab['has_dg'] = maptab['dg_idx'].fillna(0, inplace=False).astype(bool).astype(int)\n",
    "maptab['has_rg'] = maptab['rg_idx'].fillna(0, inplace=False).astype(bool).astype(int)\n",
    "\n",
    "# initialize columns for power output\n",
    "# pg: StaticGen power reference; pru: RegUp power; prd: RegDn power\n",
    "# pgov: TurbineGov power; prg: RenGen power; pdg: DG power\n",
    "# bu: RegUp participation factor; bd: RegDown participation factor\n",
    "# agov: TurbineGov AGC power; adg: DG AGC power; arg: RenGen AGC power\n",
    "\n",
    "add_cols = ['pg', 'pru', 'prd', 'pgov',\n",
    "            'prg', 'pdg', 'bu', 'bd',\n",
    "            'agov', 'adg', 'arg']\n",
    "maptab[add_cols] = 0\n",
    "\n",
    "# output data of each unit's AGC power\n",
    "# aout: delivered AGC power; aref: AGC reference\n",
    "agc_cols = list(maptab['stg_idx'])\n",
    "agc_ref = pd.DataFrame(data=np.zeros((total_time, len(list(['time']) + agc_cols))),\n",
    "                       columns=list(['time']) + agc_cols)\n",
    "agc_out = pd.DataFrame(data=np.zeros((total_time, len(list(['time']) + agc_cols))),\n",
    "                       columns=list(['time']) + agc_cols)\n",
    "\n",
    "# output data of each dispatch interval's results\n",
    "gen_cols = list(maptab['stg_idx'])\n",
    "n_dispatch = np.ceil(total_time / RTED_interval).astype(int)\n",
    "pg_ref = pd.DataFrame(data=np.zeros((n_dispatch, len(list(['n_rted']) + gen_cols))),\n",
    "                      columns=list(['n_rted']) + gen_cols)\n",
    "pru_ref = pd.DataFrame(data=np.zeros((n_dispatch, len(list(['n_rted']) + gen_cols))),\n",
    "                       columns=list(['n_rted']) + gen_cols)\n",
    "prd_ref = pd.DataFrame(data=np.zeros((n_dispatch, len(list(['n_rted']) + gen_cols))),\n",
    "                       columns=list(['n_rted']) + gen_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> reinit OModel due to non-parametric change.\n",
      "<RTED> solved as optimal in 0.0175 seconds, converged in 14 iterations with PIQP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== RTED Interval <0> ======\n",
      "--AMS: update disaptch load with factor 0.795040.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--ANDES: TDS initialized.\n",
      "--Watchdog: t=200 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> reinit OModel due to non-parametric change.\n",
      "<RTED> solved as optimal in 0.0141 seconds, converged in 14 iterations with PIQP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== RTED Interval <1> ======\n",
      "--AMS: update disaptch load with factor 0.796370.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--Watchdog: t=400 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> reinit OModel due to non-parametric change.\n",
      "<RTED> solved as optimal in 0.0136 seconds, converged in 14 iterations with PIQP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Watchdog: t=600 sec.\n",
      "====== RTED Interval <2> ======\n",
      "--AMS: update disaptch load with factor 0.797038.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--Watchdog: t=800 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> reinit OModel due to non-parametric change.\n",
      "<RTED> solved as optimal in 0.0140 seconds, converged in 14 iterations with PIQP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== RTED Interval <3> ======\n",
      "--AMS: update disaptch load with factor 0.799885.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--Watchdog: t=1000 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> reinit OModel due to non-parametric change.\n",
      "<RTED> solved as optimal in 0.0143 seconds, converged in 14 iterations with PIQP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Watchdog: t=1200 sec.\n",
      "====== RTED Interval <4> ======\n",
      "--AMS: update disaptch load with factor 0.801533.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--Watchdog: t=1400 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> reinit OModel due to non-parametric change.\n",
      "<RTED> solved as optimal in 0.0136 seconds, converged in 14 iterations with PIQP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== RTED Interval <5> ======\n",
      "--AMS: update disaptch load with factor 0.801746.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--Watchdog: t=1600 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> reinit OModel due to non-parametric change.\n",
      "<RTED> solved as optimal in 0.0139 seconds, converged in 14 iterations with PIQP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Watchdog: t=1800 sec.\n",
      "====== RTED Interval <6> ======\n",
      "--AMS: update disaptch load with factor 0.801211.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--Watchdog: t=2000 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> reinit OModel due to non-parametric change.\n",
      "<RTED> solved as optimal in 0.0140 seconds, converged in 14 iterations with PIQP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== RTED Interval <7> ======\n",
      "--AMS: update disaptch load with factor 0.802259.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--Watchdog: t=2200 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> reinit OModel due to non-parametric change.\n",
      "<RTED> solved as optimal in 0.0139 seconds, converged in 14 iterations with PIQP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Watchdog: t=2400 sec.\n",
      "====== RTED Interval <8> ======\n",
      "--AMS: update disaptch load with factor 0.803289.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--Watchdog: t=2600 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> reinit OModel due to non-parametric change.\n",
      "<RTED> solved as optimal in 0.0144 seconds, converged in 14 iterations with PIQP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== RTED Interval <9> ======\n",
      "--AMS: update disaptch load with factor 0.803895.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--Watchdog: t=2800 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> reinit OModel due to non-parametric change.\n",
      "<RTED> solved as optimal in 0.0134 seconds, converged in 14 iterations with PIQP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Watchdog: t=3000 sec.\n",
      "====== RTED Interval <10> ======\n",
      "--AMS: update disaptch load with factor 0.804958.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--Watchdog: t=3200 sec.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> reinit OModel due to non-parametric change.\n",
      "<RTED> solved as optimal in 0.0166 seconds, converged in 14 iterations with PIQP.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== RTED Interval <11> ======\n",
      "--AMS: update disaptch load with factor 0.804369.\n",
      "--AMS: received data from ANDES.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<RTED> converted to AC.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--AMS: AC conversion successful.\n",
      "--AMS: RTED optimized.\n",
      "--ANDES: update TurbineGov reference.\n",
      "--ANDES: update DG reference.\n",
      "--ANDES: update RenGen reference.\n",
      "--Watchdog: t=3400 sec.\n"
     ]
    }
   ],
   "source": [
    "for t in range(0, total_time, 1):\n",
    "    # --- Wathdog ---\n",
    "    if (t % 200 == 0) and (t > 0):\n",
    "        print(f\"--Watchdog: t={t} sec.\")\n",
    "\n",
    "    # --- Dispatch interval ---\n",
    "    if t % RTED_interval == 0:\n",
    "        id_rted += 1  # update RTED interval counter\n",
    "        id_agc = -1   # reset AGC interval counter\n",
    "        print(f\"====== RTED Interval <{id_rted}> ======\")\n",
    "        # use 5-min average load in dispatch solution\n",
    "        load_avg = load_coeff[t:t+RTED_interval].mean()\n",
    "        # set load in to AMS\n",
    "        sp.PQ.set(src='p0', attr='v',\n",
    "                  value=load_avg * p0_sp,\n",
    "                  idx=pq_idx)\n",
    "        sp.PQ.set(src='q0', attr='v',\n",
    "                  value=load_avg * q0_sp,\n",
    "                  idx=pq_idx)\n",
    "        print(f\"--AMS: update disaptch load with factor {load_avg:.6f}.\")\n",
    "\n",
    "        # get dynamic generator output from TDS\n",
    "        if t > 0:\n",
    "            _receive = sp.dyn.receive(adsys=sa, routine='RTED', no_update=True)\n",
    "            if _receive:\n",
    "                print(\"--AMS: received data from ANDES.\")\n",
    "\n",
    "        # update RTED parameters\n",
    "        sp.RTED.update()\n",
    "        # run RTED\n",
    "        sp.RTED.run(solver='PIQP')\n",
    "        # convert to AC\n",
    "        flag_2ac = sp.RTED.dc2ac()\n",
    "        if flag_2ac:\n",
    "            print(f\"--AMS: AC conversion successful.\")\n",
    "        else:\n",
    "            print(f\"ERROR! AC conversion failed!\")\n",
    "            break\n",
    "\n",
    "        if sp.RTED.exit_code == 0:\n",
    "            print(f\"--AMS: {sp.recent.class_name} optimized.\")\n",
    "\n",
    "            # update in mapping table\n",
    "            maptab['pg'] = sp.RTED.get(src='pg', attr='v', idx=maptab['stg_idx'])\n",
    "            maptab['pru'] = sp.RTED.get(src='pru', attr='v', idx=maptab['stg_idx'])\n",
    "            maptab['prd'] = sp.RTED.get(src='prd', attr='v', idx=maptab['stg_idx'])\n",
    "            maptab['bu'] = maptab['pru'] / maptab['pru'].sum()\n",
    "            maptab['bd'] = maptab['prd'] / maptab['prd'].sum()\n",
    "\n",
    "            # calculate power reference for dynamic generator\n",
    "            maptab['pgov'] = maptab['pg'] * maptab['has_gov'] * maptab['gammap']\n",
    "            maptab['pdg'] = maptab['pg'] * maptab['has_dg'] * maptab['gammap']\n",
    "            maptab['prg'] = maptab['pg'] * maptab['has_rg'] * maptab['gammap']\n",
    "\n",
    "            # set into governor, Exclude NaN values for governor index\n",
    "            gov_to_set = {gov: pgov for gov, pgov in zip(maptab['gov_idx'], maptab['pgov']) if bool(gov)}\n",
    "            sa.TurbineGov.set(src='pref0', attr='v',\n",
    "                              idx=list(gov_to_set.keys()),\n",
    "                              value=list(gov_to_set.values()))\n",
    "            print(f\"--ANDES: update TurbineGov reference.\")\n",
    "\n",
    "            # set into dg, Exclude NaN values for dg index\n",
    "            dg_to_set = {dg: pdg for dg, pdg in zip(maptab['dg_idx'], maptab['pdg']) if bool(dg)}\n",
    "            sa.DG.set(src='pref0', attr='v',\n",
    "                      idx=list(dg_to_set.keys()),\n",
    "                      value=list(dg_to_set.values()))\n",
    "            print(f\"--ANDES: update DG reference.\")\n",
    "\n",
    "            # set into rg, Exclude NaN values for rg index\n",
    "            rg_to_set = {rg: prg for rg, prg in zip(maptab['rg_idx'], maptab['prg']) if bool(rg)}\n",
    "            sa.RenGen.set(src='Pref', attr='v',\n",
    "                          idx=list(rg_to_set.keys()),\n",
    "                          value=list(rg_to_set.values()))\n",
    "            print(f\"--ANDES: update RenGen reference.\")\n",
    "\n",
    "            # record dispatch data\n",
    "            pg_ref.loc[id_rted, 'n_rted'] = id_rted\n",
    "            pg_ref.loc[id_rted, gen_cols] = maptab['pg'].values\n",
    "            pru_ref.loc[id_rted, 'n_rted'] = id_rted\n",
    "            pru_ref.loc[id_rted, gen_cols] = maptab['pru'].values\n",
    "            prd_ref.loc[id_rted, 'n_rted'] = id_rted\n",
    "            prd_ref.loc[id_rted, gen_cols] = maptab['prd'].values\n",
    "        else:\n",
    "            print(f\"ERROR! {sp.recent.class_name} failed: {sp.RTED.om.prob.status}\")\n",
    "            break\n",
    "\n",
    "    # --- AGC interval ---\n",
    "    if t % AGC_interval == 0:\n",
    "        id_agc += 1  # update AGC interval counter\n",
    "        # cap ACE_raw with procured capacity as AGC response\n",
    "        if ACE_raw >= 0:  # RegUp\n",
    "            ACE_input = min([ACE_raw, maptab['pru'].sum()])\n",
    "            b_factor = maptab['bu']\n",
    "        else:            # RegDn\n",
    "            ACE_input = -min([-ACE_raw, maptab['prd'].sum()])\n",
    "            b_factor = maptab['bd']\n",
    "        outdf.loc[t:t+AGC_interval, 'AGC'] = ACE_input\n",
    "\n",
    "        maptab['agov'] = ACE_input * b_factor * maptab['has_gov'] * maptab['gammap']\n",
    "        maptab['adg'] = ACE_input * b_factor * maptab['has_dg'] * maptab['gammap']\n",
    "        maptab['arg'] = ACE_input * b_factor * maptab['has_rg'] * maptab['gammap']\n",
    "\n",
    "        # set into governor, Exclude NaN values for governor index\n",
    "        agov_to_set = {gov: agov for gov, agov in zip(maptab['gov_idx'], maptab['agov']) if bool(gov)}\n",
    "        sa.TurbineGov.set(src='paux0', attr='v',\n",
    "                          idx=list(agov_to_set.keys()),\n",
    "                          value=list(agov_to_set.values()))\n",
    "\n",
    "        # set into dg, Exclude NaN values for dg index\n",
    "        adg_to_set = {dg: adg for dg, adg in zip(maptab['dg_idx'], maptab['adg']) if bool(dg)}\n",
    "        sa.DG.set(src='Pext0', attr='v',\n",
    "                  idx=list(adg_to_set.keys()),\n",
    "                  value=list(adg_to_set.values()))\n",
    "\n",
    "        # set into rg, Exclude NaN values for rg index\n",
    "        arg_to_set = {rg: arg + prg for rg, arg,\n",
    "                      prg in zip(maptab['rg_idx'], maptab['arg'], maptab['prg']) if bool(rg)}\n",
    "        sa.RenGen.set(src='Pref', attr='v',\n",
    "                      idx=list(arg_to_set.keys()),\n",
    "                      value=list(arg_to_set.values()))\n",
    "\n",
    "    # --- TDS interval ---\n",
    "    if t > 0:  # --- run TDS ---\n",
    "        # set laod into PQ.Ppf and PQ.Qpf\n",
    "        sa.PQ.set(src='Ppf', attr='v', idx=pq_idx,\n",
    "                  value=load_coeff[t] * p0_sa)\n",
    "        sa.PQ.set(src='Qpf', attr='v', idx=pq_idx,\n",
    "                  value=load_coeff[t] * q0_sa)\n",
    "        sa.TDS.config.tf = t\n",
    "        sa.TDS.run()\n",
    "        # Update AGC PI controller\n",
    "        ACE_raw = -(kp * sa.ACEc.ace.v.sum() + ki * ACE_integral)\n",
    "        ACE_integral = ACE_integral + sa.ACEc.ace.v.sum()\n",
    "\n",
    "        # record AGC data\n",
    "        # agc reference\n",
    "        agc_ref.loc[t, 'time'] = t\n",
    "        agc_ref.loc[t, agc_cols] = maptab[['agov', 'adg', 'arg']].sum(axis=1).values\n",
    "        # delivered AGC power\n",
    "        sp.dyn.receive(adsys=sa, routine='RTED', no_update=True)\n",
    "        pout = sp.recent.get(src='pg0', attr='v', idx=maptab['stg_idx'])\n",
    "        pref = sp.recent.get(src='pg', attr='v', idx=maptab['stg_idx'])\n",
    "        # agc output is the difference between output power and scheduled power\n",
    "        agc_out.loc[t, 'time'] = t\n",
    "        agc_out.loc[t, agc_cols] = pout - pref\n",
    "\n",
    "        # check if to continue\n",
    "        if sa.exit_code != 0:\n",
    "            print(f\"ERROR! t={t}, TDS error: {sa.exit_code}\")\n",
    "            break\n",
    "    else:  # --- init TDS ---\n",
    "        # set pg to StaticGen.p0\n",
    "        sa.StaticGen.set(src='p0', attr='v', value=sp.RTED.pg.v,\n",
    "                         idx=sp.RTED.pg.get_idx())\n",
    "        # set Bus.v to StaticGen.v\n",
    "        bus_stg = sp.StaticGen.get(src='bus', attr='v', idx=sp.StaticGen.get_idx())\n",
    "        v_stg = sp.Bus.get(src='v', attr='v', idx=bus_stg)\n",
    "        sa.StaticGen.set(src='v0', attr='v', value=v_stg,\n",
    "                         idx=sp.StaticGen.get_idx())\n",
    "        # set vBus to Bus\n",
    "        sa.Bus.set(src='v0', attr='v',\n",
    "                   value=sp.RTED.vBus.v,\n",
    "                   idx=sp.RTED.vBus.get_idx())\n",
    "        # set load into PQ.p0 and PQ.q0\n",
    "        sa.PQ.set(src='p0', attr='v', idx=pq_idx,\n",
    "                  value=load_coeff[t] * p0_sa)\n",
    "        sa.PQ.set(src='q0', attr='v', idx=pq_idx,\n",
    "                  value=load_coeff[t] * q0_sa)\n",
    "        sa.PFlow.run()  # run power flow\n",
    "        sa.TDS.init()  # initialize TDS\n",
    "\n",
    "        if sa.exit_code != 0:\n",
    "            print(f\"ERROR! t={t}, TDS init error: {sa.exit_code}\")\n",
    "            break\n",
    "        print(f\"--ANDES: TDS initialized.\")\n",
    "\n",
    "    # --- record output ---\n",
    "    outdf.loc[t, 'time'] = t\n",
    "    outdf.loc[t, 'freq'] = sa.BusFreq.f.v[1]\n",
    "    outdf.loc[t, 'ACE'] = sa.ACEc.ace.v.sum()\n",
    "    outdf.loc[t, 'pd_andes'] = sa.PQ.Ppf.v.sum()\n",
    "    outdf.loc[t, 'pd_ams'] = sp.RTED.pd.v.sum()\n",
    "    outdf.loc[t, 'pg_ams'] = sp.RTED.pg.v.sum()\n",
    "    outdf.loc[t, 'pru_ams'] = sp.RTED.pru.v.sum()\n",
    "    outdf.loc[t, 'prd_ams'] = sp.RTED.prd.v.sum()\n",
    "\n",
    "# crop the output with valid time\n",
    "if t < total_time - 1:  # end early, means some error happened\n",
    "    outdf = outdf[0:t-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale to nominal value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "outdf_plt = outdf.copy()\n",
    "agc_out_plt = agc_out.copy()\n",
    "agc_ref_plt = agc_ref.copy()\n",
    "\n",
    "pg_ref_plt = pg_ref.copy()\n",
    "pru_ref_plt = pru_ref.copy()\n",
    "prd_ref_plt = prd_ref.copy()\n",
    "\n",
    "# scale to nominal values\n",
    "outdf_plt['freq'] *= sa.config.freq\n",
    "mva_cols = ['pd_andes', 'pd_ams', 'pg_ams',\n",
    "            'pru_ams', 'prd_ams',\n",
    "            'ACE', 'AGC']\n",
    "outdf_plt['prd_ams'] *= -1\n",
    "outdf_plt[mva_cols] *= sa.config.mva\n",
    "\n",
    "agc_out_plt[agc_cols] *= sa.config.mva\n",
    "agc_ref_plt[agc_cols] *= sa.config.mva\n",
    "\n",
    "pg_ref_plt[gen_cols] *= sa.config.mva\n",
    "pru_ref_plt[gen_cols] *= sa.config.mva\n",
    "prd_ref_plt[gen_cols] *= sa.config.mva\n",
    "\n",
    "# calculate frequency deviation\n",
    "outdf_plt['fd'] = outdf_plt['freq'] - sa.config.freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dispatch results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dispatch generation\n",
      "    n_rted    Slack_39       PV_38       PV_37       PV_36       PV_35  \\\n",
      "0      0.0  300.000015  886.758083  581.441996  100.000041  691.064282   \n",
      "1      1.0  300.000010  876.718388  584.638449  100.000028  697.501909   \n",
      "2      2.0  300.000004  878.253307  577.417843  100.000010  700.287697   \n",
      "3      3.0  300.000013  890.947281  584.514343  100.000035  702.331821   \n",
      "4      4.0  300.000011  889.750392  588.687192  100.000031  703.158245   \n",
      "5      5.0  300.000031  874.263333  605.201420  100.000084  693.333776   \n",
      "6      6.0  300.000005  867.072004  607.835193  100.000014  687.310032   \n",
      "7      7.0  300.000014  896.336094  585.196480  100.000038  706.910224   \n",
      "8      8.0  300.000035  886.178649  593.933667  100.000094  704.055610   \n",
      "9      9.0  300.000034  886.997589  594.558986  100.000091  704.665880   \n",
      "10    10.0  300.000032  888.608627  595.775162  100.000087  705.405297   \n",
      "11    11.0  300.000032  887.836516  594.996179  100.000088  705.003582   \n",
      "\n",
      "         PV_34       PV_33       PV_32       PV_31       PV_30  \n",
      "0   100.000041  100.000041  100.000041  150.000029  776.156366  \n",
      "1   100.000028  100.000028  100.000028  150.000019  782.027061  \n",
      "2   100.000010  100.000010  100.000010  150.000007  787.908606  \n",
      "3   100.000035  100.000035  100.000035  150.000024  781.170974  \n",
      "4   100.000031  100.000031  100.000031  150.000022  785.309682  \n",
      "5   100.000084  100.000084  100.000084  150.000059  794.893023  \n",
      "6   100.000014  100.000014  100.000014  150.000010  802.201903  \n",
      "7   100.000038  100.000038  100.000038  150.000027  781.955915  \n",
      "8   100.000095  100.000094  100.000094  150.000066  790.890858  \n",
      "9   100.000091  100.000091  100.000091  150.000064  791.714762  \n",
      "10  100.000087  100.000087  100.000087  150.000061  793.199850  \n",
      "11  100.000088  100.000087  100.000088  150.000061  792.351533  \n",
      "Dispatch RegUp\n",
      "    n_rted  Slack_39     PV_38     PV_37     PV_36     PV_35     PV_34  \\\n",
      "0      0.0  0.646491  0.749986  0.000315  0.751954  0.000107  0.751441   \n",
      "1      1.0  0.495286  0.617987  0.000369  0.840683  0.000117  0.839902   \n",
      "2      2.0  0.496112  0.619494  0.000349  0.840868  0.000109  0.840075   \n",
      "3      3.0  0.502245  0.623098  0.000334  0.842134  0.000106  0.841323   \n",
      "4      4.0  0.505116  0.623852  0.000765  0.842907  0.000518  0.842081   \n",
      "5      5.0  0.505488  0.624201  0.000381  0.843131  0.000130  0.842300   \n",
      "6      6.0  0.504488  0.625113  0.000327  0.842533  0.000104  0.841718   \n",
      "7      7.0  0.506811  0.627187  0.000302  0.842800  0.000094  0.841984   \n",
      "8      8.0  0.508527  0.625552  0.000400  0.843958  0.000140  0.843114   \n",
      "9      9.0  0.509927  0.627337  0.000356  0.843949  0.000120  0.843104   \n",
      "10    10.0  0.511719  0.628389  0.000754  0.844223  0.000516  0.843369   \n",
      "11    11.0  0.510659  0.627595  0.000363  0.844235  0.000125  0.843383   \n",
      "\n",
      "       PV_33     PV_32     PV_31     PV_30  \n",
      "0   0.750981  0.944241  0.991719  0.000054  \n",
      "1   0.839371  0.971355  0.991518  0.000049  \n",
      "2   0.839543  0.972193  0.992534  0.000049  \n",
      "3   0.840783  0.975206  0.996053  0.000049  \n",
      "4   0.841543  0.977278  0.998470  0.000384  \n",
      "5   0.841769  0.977842  0.999121  0.000049  \n",
      "6   0.841183  0.976953  0.998186  0.000049  \n",
      "7   0.841448  0.977947  0.999400  0.000049  \n",
      "8   0.842578  0.979693  1.001249  0.000050  \n",
      "9   0.842569  0.980189  1.001915  0.000049  \n",
      "10  0.842835  0.981410  1.003388  0.000381  \n",
      "11  0.842849  0.980879  1.002708  0.000050  \n",
      "Dispatch RegDn\n",
      "    n_rted      Slack_39     PV_38     PV_37         PV_36     PV_35  \\\n",
      "0      0.0  1.694768e-11  1.260009  1.447590  2.707094e-11  1.478411   \n",
      "1      1.0  1.405937e-11  1.150096  1.539569  2.292555e-11  1.521148   \n",
      "2      2.0  1.428441e-11  1.155382  1.539932  2.324915e-11  1.520921   \n",
      "3      3.0  1.456943e-11  1.162506  1.543554  2.368235e-11  1.525458   \n",
      "4      4.0  2.280103e-11  1.162839  1.545595  3.566951e-11  1.528533   \n",
      "5      5.0  1.351199e-11  1.164521  1.545688  2.202381e-11  1.528835   \n",
      "6      6.0  1.451064e-11  1.168029  1.543646  2.357922e-11  1.528493   \n",
      "7      7.0  1.484899e-11  1.172871  1.544379  2.409147e-11  1.530663   \n",
      "8      8.0  1.309544e-11  1.166239  1.547838  2.133837e-11  1.531192   \n",
      "9      9.0  1.391830e-11  1.171508  1.547709  2.265452e-11  1.532087   \n",
      "10    10.0  2.257188e-11  1.174060  1.548899  3.472444e-11  1.533794   \n",
      "11    11.0  1.375662e-11  1.172439  1.548403  2.235931e-11  1.533002   \n",
      "\n",
      "           PV_34         PV_33         PV_32         PV_31     PV_30  \n",
      "0   2.724694e-11  2.700628e-11  2.785464e-11  2.416912e-11  1.401280  \n",
      "1   2.307641e-11  2.286844e-11  2.323526e-11  2.007665e-11  1.385823  \n",
      "2   2.339130e-11  2.318522e-11  2.355653e-11  2.035583e-11  1.385091  \n",
      "3   2.382910e-11  2.361760e-11  2.401316e-11  2.074251e-11  1.389814  \n",
      "4   3.606447e-11  3.543425e-11  3.290835e-11  2.458191e-11  1.395947  \n",
      "5   2.215880e-11  2.195811e-11  2.229719e-11  1.928981e-11  1.395368  \n",
      "6   2.373003e-11  2.352779e-11  2.390783e-11  2.066712e-11  1.390486  \n",
      "7   2.425004e-11  2.403874e-11  2.445196e-11  2.111679e-11  1.390109  \n",
      "8   2.146520e-11  2.128093e-11  2.161481e-11  1.868424e-11  1.399991  \n",
      "9   2.280264e-11  2.259873e-11  2.295457e-11  1.985652e-11  1.398212  \n",
      "10  3.510768e-11  3.449799e-11  3.183233e-11  2.402413e-11  1.400231  \n",
      "11  2.250239e-11  2.230267e-11  2.264591e-11  1.959489e-11  1.399000  \n"
     ]
    }
   ],
   "source": [
    "print('Dispatch generation')\n",
    "print(pg_ref_plt)\n",
    "\n",
    "print('Dispatch RegUp')\n",
    "print(pru_ref_plt)\n",
    "\n",
    "print('Dispatch RegDn')\n",
    "print(prd_ref_plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AGC mileage in MWh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AGC milage in MWh:\n",
      "Slack_39    0.104306\n",
      "PV_38       0.200599\n",
      "PV_37       0.127391\n",
      "PV_36       0.095413\n",
      "PV_35       0.092986\n",
      "PV_34       0.089903\n",
      "PV_33       0.098902\n",
      "PV_32       0.076675\n",
      "PV_31       0.074863\n",
      "PV_30       0.085381\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "agc_row_index = np.arange(0, total_time, AGC_interval)\n",
    "agc_mileage = agc_out_plt.loc[agc_row_index, agc_cols].diff().abs().sum(axis=0) * AGC_interval / 3600\n",
    "\n",
    "print(f\"AGC milage in MWh:\\n{agc_mileage}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CPS1 score\n",
    "\n",
    "Following adjustments are made to calculate the CPS1 score:\n",
    "1. eps is the constant derived from a targeted frequency bound, where we use the average frequency deviation as the reference\n",
    "1. The $CF_{clock-minute}$ is used when calcualting $CF$ rather than $CF_{12-month}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPS1 score: 100.00372723390494\n"
     ]
    }
   ],
   "source": [
    "def cm_avg(x):\n",
    "    \"\"\"\n",
    "    Clock minute average\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x : pd.Series\n",
    "        Input time series, index is time in seconds.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float\n",
    "        Clock minute average of the time series.\n",
    "    \"\"\"\n",
    "    return np.sum(x) / len(x)\n",
    "\n",
    "eps = outdf_plt['fd'].mean()\n",
    "bias = sa.ACEc.bias.v[0]\n",
    "\n",
    "df_cm = cm_avg(outdf_plt['fd'])\n",
    "ace_cm = cm_avg(outdf_plt['ACE'] / (10 * bias))\n",
    "cf_cm = df_cm * ace_cm \n",
    "cf = cf_cm / np.square(eps)\n",
    "cps1 = (2 - cf) * 100\n",
    "\n",
    "print(f\"CPS1 score: {cps1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "System dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17ef304f0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xr8fIkSNx+vRp3HHHHdi4cSPkcjm++uorbgLm66+/RvPmzbF7926MHj3ak0MgRDRr1gyfffYZpk2bhqKiIkyePBlhYWHIz8/HiRMnkJ+fz3lHvPPOOxg7dixGjRqFl19+GXq9HkuXLkVgYCAXTRYwTlw9+eSTWLt2LWJiYtC3b18cOnQI//vf/8za/+STTzB48GAMGTIEzz//PDp06IDy8nJcvnwZ27ZtM0tab894/vvf/2L69Ol44IEHMGPGDISHh+Py5cs4ceKE2Ttgzpw5mDJlCmQyGWbNmuXEGXSezMxMPProo+jUqRP+9a9/ma33uuuuu8wmVwlCEs/GsCAI24ij8DEMwxQWFjIzZ85kIiMjGaVSybRv355JTExkampqBOUyMjKY++67jwkICBBEplOr1cwrr7zCtGnThvHz82Puvvtu5pdffpGM9AMHovBZ+mOPt7ff27ZtY/r27cv4+fkxbdq0YV599VXm999/ZwAwu3bt4soZDAZmyZIlTNu2bRkfHx+mT58+zLZt25hhw4a5LAqfNaSi8DEMw5w9e5YbOxu9j99nNoLUl19+abFuin5ENGYWL17MBAYGcn9yuZzx9fUVbNuzZw8XVTMxMVFwfO/evZn58+czDMMws2bNYhQKheDYwMBARiaTMStXrjRru3379szHH3/sjmE2amw9J9kodj/++KPk/rS0NGb8+PFMy5YtGZVKxbRp04YZP368Wflff/2V6dOnD+Pj48O0a9eOef/997nnI5/S0lJm+vTpTHh4OBMYGMhMmDCBuXbtmuQ7LDMzk3nmmWeYNm3aMCqVimndujUzaNAg5t1337XZf0vvjuTkZGbYsGFMYGAgExAQwPTo0YNZunSp2bjVajXj6+vLjB07VvK8SGErCt/48eMl9x0+fFjQV3ZMlv4yMzMFx9N7iLCEjGFs2FsJgmiUXLt2DR07dsSaNWvw1FNPQaFQWI2g5C4YhoFer8eiRYvwzjvvID8/3651bQTRkCgqKhJYEKZOnYpJkyYJcsK1adMGOTk56NSpE7799ls8+eST3L4pU6ZAqVTiu+++w/PPP49jx46ZufMCxvUpISEhgm0dOnTA3LlzMXfuXNcPjHALb731Ft5++22bLnPeyLZt2/DQQw9h+/btiIuLs+uYp59+Grt378bly5chk8mgUCjqtY/0HiJsQS58BNHEefbZZ/Hss8/ixx9/tBoVyl188sknFCWMaPS0bNlSkLTT398fYWFh6Ny5s6Bchw4dEBUVZZYc++LFixg3bhwA4O6778amTZsQFhaG4ODg+u88QTjB2bNncf36dbz88su48847ufvXXq5fvw6VSoWePXvi9OnT9dRLI/QeImxBChRBNFGioqJw+PBh7ntMTIwHe2PiiSeewODBg7nvzZs391xnCMLDyGQyvPrqq3jzzTfRt29f3HnnnVi3bh3Onz/PrbecOnUqPvzwQ0ycOBGLFi1CdHQ0srKy8PPPP+PVV19FdHQ0NBoNzp49CwDQaDS4desWMjIy0KxZMzOljSDqg1mzZuHvv//G3XffjXXr1jnk8fDWW2/hxRdfBGCcbKhv6D1E2IJc+AiCIAjCw1jLAwUYg7isWLECRUVF6Nu3Lz744AOBgJeTk4P//Oc/SE5ORnl5Odq0aYORI0di2bJlCA4O5lx2xQwbNgy7d++up1ERBEE0TkiBIgiCIAiCIAiCsBPKA0UQBEEQBEEQBGEnpEARBEEQBEEQBEHYSaMNImEwGHD79m0EBQV5RWhmgiCIpgLDMCgvL0dUVBTkcpqn40PvJoIgCM/gyndTo1Wgbt++jbZt23q6GwRBEE2WGzduIDo62tPd8Cro3UQQBOFZXPFuarQKVFBQEAAgMzNTkGujIaHVarFjxw6MHj0aKpXK091xChqDd0Bj8A6ayhjKysrQtm1b7jlMmKB3k3dAY/AOaAzeQUMfg739d+W7qdEqUKxrRFBQUINNLKjVahEQEIDg4OAGeUMDNAZvgcbgHTS1MZCLmjn0bvIOaAzeAY3BO2joY3C0/654N5FzOkEQBEEQBEEQhJ2QAkUQBEEQBEEQBGEnpEARBEEQBEEQBEHYCSlQBEEQBEEQBEEQdkIKFEEQBEEQBEEQhJ2QAkUQBEEQBEEQBGEnpEARBEEQBEEQBEHYCSlQBEEQBEEQBEEQdkIKFEEQBEEQBEEQhJ2QAkUQBEEQBEEQBGEnpEARBEEQBEEQBEHYidLTHSAIgmgqHM07ioPqg6i8VAmFXOG2dnuE9kDv1r3d1h7hXeSXq3HqVgmGdwmDXC7zdHcIgiAaPKRAEQRBuIGC6gL8+89/w8AYsO3wNre27avwxZ4pexCgCnBru4R3MOrjNJRUabHkkd54fEA7T3eHIAiiwUMKFEEQhBsoqimCgTFAAQWGtx0Oudw9HtQ7r++EWq9GhbaCFKgmSkmVFgDw57k8UqCIBkeFWof9lwswtEtr+KncZ7knCGuQAkUQBOEGDIwBABAgC8CHQz6ESqVyS7sDvhuAal01NHqNW9ojCIJwJTPWHUH61ULE39se7/yjl6e7QxAAKIgEQRCEW9Ab9AAAuZsfu0q5cZ5Ma9C6tV3CG2E83QGCcJj0q4UAgB+O3PBwTwjCBClQBEEQbkDPGBUomcy9i/hVcqOlixQogiAIgnANpEARBEG4AVaBUsC9PvykQBEsDBmgCIIgXAIpUARBEG6AdeGTwb0WKB+FDwBAqycFiiCIhgvp/4Q3QUEkCIIg3ABrgXL3GijWArX7xm5cL7te5/r0ej1OaE5Af1UPhULamlZVXlXndgjXQwIoQRCEayAFiiAIwg14SoHyV/oDANacXuPSen8+8LPFffpqvUvbIgiCIAhvghQogiAIN8BF4ZO5V4GadecsbLqwiQujXlcYA4P8/Hy0bt0aMrm0O6KmUoNzOOeS9giCIAjC2yAFiiAIwg2wCoy710ANjR6KodFDXVafVqtFcnIy4kbEWcxlVVZWhrVY67I2pVi5ciU+/PBDZGdno2fPnkhKSsKQIUMslk9LS0NCQgLOnDmDqKgovPbaa5g5c6agzObNm7Fw4UJcuXIFMTExWLx4MR5++GFu/1tvvYW3335bcEx4eDhycnJcOziCIMwhH1TCi6AgEgRBEG5Ax+gAuN+FrzGyadMmzJ07F6+//jqOHz+OIUOGYNy4ccjKypIsn5mZibi4OAwZMgTHjx/HggULMHv2bGzevJkrk56ejilTpiA+Ph4nTpxAfHw8HnvsMRw8eFBQV8+ePZGdnc39nTp1ql7HShAEQXgf9CYnCIJwA6wFihSouvPRRx/h2WefxfTp09G9e3ckJSWhbdu2WLVqlWT51atXo127dkhKSkL37t0xffp0PPPMM1i2bBlXJikpCaNGjUJiYiK6deuGxMREjBw5EklJSYK6lEolIiIiuL/WrVvX51BdCkNxzAmCIFwCufARBEG4AU+tgWpsaDQaHD16FPPnzxdsHz16NPbv3y95THp6OkaPHi3YNmbMGKxZswZarRYqlQrp6emYN2+eWRmxAnXp0iVERUXB19cXAwcOxHvvvYdOnTpZ7K9arYZarea+l5WVATC6Qmq17g0tb2AYl7TJ1uHu/rsSGoN34NAYZN451iZ3HbwQe/vvyvGRAkUQRJPjRP4JJO5NRKW20m1tqvVGIdrda6AaGwUFBdDr9QgPDxdst7YWKScnR7K8TqdDQUEBIiMjLZbh1zlw4ECsX78eXbp0QW5uLt59910MGjQIZ86cQWhoqGTbS5YsMVs3BQC7du1CQECAXWOuO8ZXfX5eHpKTk11Wa2pqqsvq8hQ0Bu/A+hiM969er3fp/etqGv918H5s9b+qynUpNkiBIgiiybEraxdulN/wSNsRigiPtNvYkMmEiijDMGbbbJUXb7dV57hx47jPvXv3RmxsLGJiYrBu3TokJCRItpuYmCjYV1ZWhrZt22LEiBEWlS5XMyd9BwCgdeswxMXdXef6tFotUlNTMWrUKIuBRLwdGoN3YM8Y2PtXLpcjLm6MO7tnF03lOngz9vaf9QBwBaRAEQTR5NAajGb8hzs/jKd6POW+hg3A6b2n3ddeI6RVq1ZQKBRm1qa8vDwzCxJLRESEZHmlUskpMZbKWKoTAAIDA9G7d29cunTJYhlfX1/4+vqabVepVG4XVGRymUvb9MQYXA2NwTuwZwwyuPb+dTVN5Tp4M7b678qxkQJFEESTg1WgwgLC0LlFZ/e1q9XijOyM29prjPj4+KBfv35ITU0VhBhPTU3FxIkTJY+JjY3Ftm3bBNt27NiB/v37cy/U2NhYpKamCtZB7dixA4MGDbLYF7VajXPnzlkNn+5NUAwJgiAI10CrmQmCaHKwCpRK3nBn2poyCQkJ+Oqrr7B27VqcO3cO8+bNQ1ZWFpfXKTExEU89ZbIszpw5E9evX0dCQgLOnTuHtWvXYs2aNXjllVe4MnPmzMGOHTuwdOlSnD9/HkuXLsXOnTsxd+5crswrr7yCtLQ0ZGZm4uDBg5g8eTLKysowbdo0t42dIJoqDCWCIrwIskARBNHk0OprFSgFKVANkSlTpqCwsBCLFi1CdnY2evXqheTkZLRv3x4AkJ2dLcgJ1bFjRyQnJ2PevHlYsWIFoqKi8Omnn2LSpElcmUGDBmHjxo144403sHDhQsTExGDTpk0YOHAgV+bmzZt4/PHHUVBQgNatW+Pee+/FgQMHuHa9HStLxAiCIAgHIAWKIIgmB1mgGj6zZs3CrFmzJPd98803ZtuGDRuGY8eOWa1z8uTJmDx5ssX9GzdudKiP3ga58BEEQbgGcuEjCKLJQQoUQRAEQRDOQhYogiA8yp6be/Dbld9s+rcbDAZkV2Zjz749kMvrNvdzIu8EAFKgiKYFGaAIgiBcAylQBEF4lA8Pf4hrZdfsLn8q65TL2m7l38pldREEQRAE0TQgBYogCI9SpTNmBv9Xz38hPNByzh29Xo+zZ8+iR48eUCgUdW431C8U97W5r871EARBEATRtCAFiiAIj6I36AEA4zuNR9eWXS2W02q1SL6SjLiucQ060R9BeAqGokgQBEG4BAoiQRCER9EzRgVKKaf5HIIgCEIa0v8Jb4IUKIIgPAqrQClkdXfLIwiCIAiCqG9IgSIIwqOwLnwKOSlQBEEQBEF4P6RAEQThUcgCRRAEQdiCPPgIb4IUKIIgPApngSIFiiAIgiCIBgApUARBeBQdowNALnwEQRAEQTQMSIEiCMJjGBgD95ksUARRv1AUM6IhI/N0BwiCBylQBEF4DNZ9DyALFEEQBGEZ0v8Jb8IhBWrVqlXo06cPgoODERwcjNjYWPz+++/c/oqKCrz44ouIjo6Gv78/unfvjlWrVnH7i4qK8NJLL6Fr164ICAhAu3btMHv2bJSWlgraKS4uRnx8PEJCQhASEoL4+HiUlJTUbaQEQXgdrPseAChllAeKIAiCIAjvxyGJJTo6Gu+//z46d+4MAFi3bh0mTpyI48ePo2fPnpg3bx527dqFDRs2oEOHDtixYwdmzZqFqKgoTJw4Ebdv38bt27exbNky9OjRA9evX8fMmTNx+/Zt/PTTT1w7TzzxBG7evImUlBQAwHPPPYf4+Hhs27bNhUMnCEJMZmkmblXcclt7Nboa7jNZoAiifmFoDp8gCMIlOKRATZgwQfB98eLFWLVqFQ4cOICePXsiPT0d06ZNw/DhwwEYFZ/PP/8cR44cwcSJE9GrVy9s3ryZOz4mJgaLFy/Gk08+CZ1OB6VSiXPnziElJQUHDhzAwIEDAQBffvklYmNjceHCBXTt2rWOQyYIQopbFbcw8ZeJHhGyZJDRGiiCIAiCIBoETvvM6PV6/Pjjj6isrERsbCwAYPDgwfj111/xzDPPICoqCrt378bFixfxySefWKyntLQUwcHBUCqNXUlPT0dISAinPAHAvffei5CQEOzfv9+iAqVWq6FWq7nvZWVlAACtVgutVuvsMD0K2++G2n+AxuAt2DOGG6U3wICBj9wHnUI6uatrAIAhbYaA0TPQ6i33r6lcB2/HnjE05PERBOGdMBQFhfAiHFagTp06hdjYWNTU1KBZs2bYsmULevToAQD49NNPMWPGDERHR0OpVEIul+Orr77C4MGDJesqLCzEO++8g3//+9/ctpycHISFhZmVDQsLQ05OjsV+LVmyBG+//bbZ9l27diEgIMDRYXoVqampnu5CnaExeAfWxnBVexUAEIIQPMk86a4uGbkJJN9MtqtoY78ODQVrY6iqqnJjTwiCIAjCvTisQHXt2hUZGRkoKSnB5s2bMW3aNKSlpaFHjx749NNPceDAAfz6669o37499uzZg1mzZiEyMhIPPPCAoJ6ysjKMHz8ePXr0wJtvvinYJ5OZB6tkGEZyO0tiYiISEhIE9bdt2xYjRoxAaGioo8P0CrRaLVJTUzFq1CioVCpPd8cpaAzegT1jOJhzEGv/WouQoBDExcW5uYe2aSrXwduxZwysBwDhXdAEPkEQhGtwWIHy8fHhgkj0798fhw8fxieffIKkpCQsWLAAW7Zswfjx4wEAffr0QUZGBpYtWyZQoMrLyzF27FjOgsV/CUdERCA3N9es3fz8fISHh1vsl6+vL3x9fc22q1SqBiuosNAYvIPGPgaZ3DhBoZArvHqcjf06NBSsjaGhj40gCIIgrFHnPFAMw0CtVnNrjeRyYZUKhQIGgylZZllZGUaPHg0fHx/8+uuv8PPzE5SPjY1FaWkpDh06xG07ePAgSktLMWjQoLp2lyAIC+gZY04muYzSwxEEQRAEQVjCIQvUggULMG7cOLRt2xbl5eXYuHEjdu/ejZSUFAQHB2PYsGF49dVX4e/vj/bt2yMtLQ3r16/HRx99BMBoeRo9ejSqqqqwYcMGlJWVca4erVu3hkKhQPfu3TF27FjMmDEDn3/+OQBjNL8HH3yQIvARRD1iYIwTHRQNjyAaJ+TCRxAE4RocUqByc3MRHx+P7OxshISEoE+fPkhJScGoUaMAABs3bkRiYiKmTp2KoqIitG/fHosXL8bMmTMBAEePHsXBgwcBgHMDZMnMzESHDh0AAN999x1mz56N0aNHAwAeeughLF++vE4DJQhnOZ53HKnVqcg8kWlmYa1PIgIjMOmOSW7Lj8RZoNw4RoIgCIIgiIaGQwrUmjVrrO6PiIjA119/bXH/8OHD7QpD2bJlS2zYsMGRrhFEvfHavtdQqC5E2pk0t7fdMaQj7om4xy1tkQWKIBo3lEiXIAjCNTidB4ogPMG10mvIrTIPMlJf6A16FNYUAgAeveNRqBTuWRy/8/pO5FXnoVRd6pb2AJMCJYPlaJcEQRAE4QlI/Se8CVKgiAbDxeKLmPTrJI+0rYQS8/vPh4+Pj1vau1xyGXnVedAa3JeQlLNAucllkCAI90KTIwRBEK6BFCiiwXCr/BYAwE/hh+igaPc1zADt1e2t5iFzNUq58aepM+jc1iZF4SOIxg258BEEQbgGUqCIBoMBRgtJt5bd8G3ct25rV6vVIjk52W3tAYBKbnQVdKcCRWugCIIgCIIgbENTzUSDgRXwm4KFhLVAudOFT28gCxRBNGYojDnRkKH7l/AmSFIiGgxckAM3utJ5Ck8oUGSBIgiCIAiCsA0pUESDgQ2B3xQEfFoDRRAEQRAmmsDcKdGAoDVQRIOhKVmg2DVQhTWFuFVxq8716bQ6FOuLcbviNpQq6Z99cU0xgKahoBJEU4Q8oAiCIFwDKVBEg4ENIiFvAoZT1gL19emv8fVpy8mpHeW/v/7XZhmyQBENgZUrV+LDDz9EdnY2evbsiaSkJAwZMsRi+bS0NCQkJODMmTOIiorCa6+9hpkzZwrKbN68GQsXLsSVK1cQExODxYsX4+GHH5asb8mSJViwYAHmzJmDpKQkVw6NIAgJaA0U4U2QpEQ0GJpSEIkRbUeguW9z+Cn8XPangspmmSCfIIxsN9LTwycIq2zatAlz587F66+/juPHj2PIkCEYN24csrKyJMtnZmYiLi4OQ4YMwfHjx7FgwQLMnj0bmzdv5sqkp6djypQpiI+Px4kTJxAfH4/HHnsMBw8eNKvv8OHD+OKLL9CnT596GyNBEAThvZAFimgwNCUXvqHRQ7H3n3tdVh8bij0uLg4qlcpl9RKEJ/joo4/w7LPPYvr06QCApKQk/PHHH1i1ahWWLFliVn716tVo164dZynq3r07jhw5gmXLlmHSpElcHaNGjUJiYiIAIDExEWlpaUhKSsL333/P1VVRUYGpU6fiyy+/xLvvvlvPIyUIgiC8EVKgiAZDUwoiQRCENBqNBkePHsX8+fMF20ePHo39+/dLHpOeno7Ro0cLto0ZMwZr1qyBVquFSqVCeno65s2bZ1ZG7J73wgsvYPz48XjggQfsUqDUajXUajX3vaysDIBxUkOrdV+UTcD4DHVFm2wd7u6/K6ExeAeOjsEbx9oUr4O3YW//XTk+UqCIBkNTskARBCFNQUEB9Ho9wsPDBdvDw8ORk5MjeUxOTo5keZ1Oh4KCAkRGRlosw69z48aNOHbsGA4fPmx3f5csWYK3337bbPuuXbsQEBBgdz11w/iqLyoscmlS8NTUVJfV5SloDN6B9TGYRFV3J7V3hMZ/HbwfW/2vqqpyWVukQBENhqYURIIgCOuIJ1IYhrE6uSJVXrzdWp03btzAnDlzsGPHDvj5+dndz8TERCQkJHDfy8rK0LZtW4wYMQKhoaF211MX5qTvAAC0aNkCcXED6lyfVqtFamoqRo0a1WBdgmkM3oE9Y2DvXwCIi4tzV9fspqlcB2/G3v6zHgCugBQoosFgMDSdIBIEQUjTqlUrKBQKM2tTXl6emQWJJSIiQrK8UqnklBhLZdg6jx49iry8PPTr14/br9frsWfPHixfvhxqtRoKhbl7sa+vL3x9fc22q1QqtwsqMpnMpW16YgyuhsbgHdg7Bm8eZ1O6Dt6Krf67cmwkiRINBtYCRS58BNF08fHxQb9+/cxcNVJTUzFo0CDJY2JjY83K79ixA/379+deqJbKsHWOHDkSp06dQkZGBvfXv39/TJ06FRkZGZLKE0EQBNE4IQsU0WBg10BREAmCaNokJCQgPj4e/fv3R2xsLL744gtkZWVxeZ0SExNx69YtrF+/HgAwc+ZMLF++HAkJCZgxYwbS09OxZs0aQXS9OXPmYOjQoVi6dCkmTpyIrVu3YufOndi3bx8AICgoCL169RL0IzAwEKGhoWbbCYIgiMYNKVBEg0FqzQJBEE2PKVOmoLCwEIsWLUJ2djZ69eqF5ORktG/fHgCQnZ0tyAnVsWNHJCcnY968eVixYgWioqLw6aefciHMAWDQoEHYuHEj3njjDSxcuBAxMTHYtGkTBg4c6Pbx1ReUiJQgCMI1kAJFNBj0jB4ArYEiCAKYNWsWZs2aJbnvm2++Mds2bNgwHDt2zGqdkydPxuTJk+3uw+7du+0uSxAEQTQeSBIlGgysBYqi8BEEQRAEQRCegiRRosFAQSQIgiAIgiAIT0MKFNFgoCASBEEQzkNLoAiCIFwDKVBEg4Fz4aM1UARBEARBEISHoCAShFMUVBdg+9Xt0Bq0Vsvp9XpcqLmAnDM5dc6TcijnEABy4SMIgiAIgiA8BylQhFOsyliFHy7+YHf51BOptgvZib/S32V1EQRBNBUYimNOEAThEkiBIpyiVFMKAOjTug9iQmIsljMYDLhx8wbaRreFXF531zt/pT+mdp9a53oIgiAIgiAIwhlIgSKcgp3JHN9xPJ7o/oTFclqtFsnJyYi7Nw4qlcpd3SMIgiBEkP2JIAjCNdBqfMIpmNpXMa1HIgiCIAiCIJoSpEARdUIGUqAIgiAaAvS0JgiCcA2kQBFOweZkopDiBEEQDQNy4SMIgnANJP0STkHRnAiCIAiCIIimCClQhFPQGiiCIIiGBc17EQRBuAZSoAin4BQo8qonCIIgCIIgmhCkQBHOUTuTSQoUQRAEQRAE0ZQgBYpwCnLhIwiCaFiQBx9BEIRrIAWKcAo2Ch9ZoAiCIAiCIIimBClQhFOQBYogCIIgCIJoipACRTgFBZEgCIIgvI0KtQ6JP5/C/ssFnu4KQRCNGFKgCOdgg0iQBYogCILwEj5OvYjvD2Xhia8OerorBEE0YkiBIpyCLFAEQRANjCaQCOpqfoWnu0AQRBOAFCjCKZgm8CImCIIgGhY6A72bCIKof0iBIpzCgNoofOTCRxAE0SBwt2pRXqN1+2SbTk8KFEEQ9Q8pUIRz1L6j5HQLEQRBECLSrxSi91s7sHDrabe2q9Ub3NoeQRBNE5J+CaegMOYEQRCEJT5KvQAA2HAgy63tHrle7Nb2CIJompACRTgFBZEgCIIgCIIgmiKkQBFOwfm1k/5EEARBiKDJNYIgGjMOKVCrVq1Cnz59EBwcjODgYMTGxuL333/n9ldUVODFF19EdHQ0/P390b17d6xatUpQh1qtxksvvYRWrVohMDAQDz30EG7evCkoU1xcjPj4eISEhCAkJATx8fEoKSlxfpSEyyELFEEQRMPCrfEc6NVAEEQjxiEFKjo6Gu+//z6OHDmCI0eO4P7778fEiRNx5swZAMC8efOQkpKCDRs24Ny5c5g3bx5eeuklbN26latj7ty52LJlCzZu3Ih9+/ahoqICDz74IPR6PVfmiSeeQEZGBlJSUpCSkoKMjAzEx8e7aMiEK2AtUKRAEQRBEGLozUAQRGNG6UjhCRMmCL4vXrwYq1atwoEDB9CzZ0+kp6dj2rRpGD58OADgueeew+eff44jR45g4sSJKC0txZo1a/Dtt9/igQceAABs2LABbdu2xc6dOzFmzBicO3cOKSkpOHDgAAYOHAgA+PLLLxEbG4sLFy6ga9euLhg2UVdYC5RcRl6gBEEQDQHGjYHMKb4QQRCNGYcUKD56vR4//vgjKisrERsbCwAYPHgwfv31VzzzzDOIiorC7t27cfHiRXzyyScAgKNHj0Kr1WL06NFcPVFRUejVqxf279+PMWPGID09HSEhIZzyBAD33nsvQkJCsH//fosKlFqthlqt5r6XlZUBALRaLbRarbPD9Chsv72x/waDMVSsXq+32j9vHoO90Bi8AxqDd2DPGBry+AiCIAjCFg4rUKdOnUJsbCxqamrQrFkzbNmyBT169AAAfPrpp5gxYwaio6OhVCohl8vx1VdfYfDgwQCAnJwc+Pj4oEWLFoI6w8PDkZOTw5UJCwszazcsLIwrI8WSJUvw9ttvm23ftWsXAgICHB2mV5GamurpLphRVF4EADh29BhqTtXYLO+NY3AUGoN3QGPwDqyNoaqqyo09IbwRcu8mCKIx47AC1bVrV2RkZKCkpASbN2/GtGnTkJaWhh49euDTTz/FgQMH8Ouvv6J9+/bYs2cPZs2ahcjISM5lTwqGYQT5hKRyC4nLiElMTERCQgL3vaysDG3btsWIESMQGhrq6DC9Aq1Wi9TUVIwaNQoqlcrT3RGw6Y9NuFF4A/3798fw6OEWy3nzGOyFxuAd0Bi8A3vGwHoA1CcrV67Ehx9+iOzsbPTs2RNJSUkYMmSIxfJpaWlISEjAmTNnEBUVhddeew0zZ84UlNm8eTMWLlyIK1euICYmBosXL8bDDz/M7V+1ahVWrVqFa9euAQB69uyJ//u//8O4cePqZYyuxq1BJAiCIBoxDitQPj4+6Ny5MwCgf//+OHz4MD755BMkJSVhwYIF2LJlC8aPHw8A6NOnDzIyMrBs2TI88MADiIiIgEajQXFxscAKlZeXh0GDBgEAIiIikJuba9Zufn4+wsPDLfbL19cXvr6+ZttVKlWDFVRYvHEMrDKrVCjt6ps3jsFRaAzeAY3BO7A2hvoe26ZNmzB37lysXLkS9913Hz7//HOMGzcOZ8+eRbt27czKZ2ZmIi4uDjNmzMCGDRvw999/Y9asWWjdujUmTZoEAEhPT8eUKVPwzjvv4OGHH8aWLVvw2GOPYd++fZxLORtIiX0Hrlu3DhMnTsTx48fRs2fPeh1zQ8PTa6D8VLQ+lyCI+qPOTxiGYaBWq7m1RnK5sEqFQsGtl+nXrx9UKpXA9SM7OxunT5/mFKjY2FiUlpbi0KFDXJmDBw+itLSUK0N4Hi6MuaffkgRBNDk++ugjPPvss5g+fTq6d++OpKQktG3b1ixtBsvq1avRrl07JCUloXv37pg+fTqeeeYZLFu2jCuTlJSEUaNGITExEd26dUNiYiJGjhyJpKQkrsyECRMQFxeHLl26oEuXLli8eDGaNWuGAwcO1PeQGxyefjUYyNpGEEQ94pAFasGCBRg3bhzatm2L8vJybNy4Ebt370ZKSgqCg4MxbNgwvPrqq/D390f79u2RlpaG9evX46OPPgIAhISE4Nlnn8XLL7+M0NBQtGzZEq+88gp69+7Nufh1794dY8eOxYwZM/D5558DMEbze/DBBykCnxdhYIxKMUXhIwjCnWg0Ghw9ehTz588XbB89ejT2798veUx6erogeBEAjBkzBmvWrIFWq4VKpUJ6ejrmzZtnVoavQPGRCqQkhTcFOGIYxiVt2hNIhOFpMJ4IKmJrrE0loIu34+gYvHGsTfE6eBv29t+V43NIgcrNzUV8fDyys7MREhKCPn36ICUlBaNGjQIAbNy4EYmJiZg6dSqKiorQvn17LF68WOBn/vHHH0OpVOKxxx5DdXU1Ro4ciW+++QYKhYIr891332H27NncC++hhx7C8uXLXTFewkW4MxwuQRAES0FBAfR6vZlLNz8YkZicnBzJ8jqdDgUFBYiMjLRYRlyntUBKUnhHgCPjq76srAzJyckuq9VaIJGCQjlYJxdXtmkb41gNBoNd7Tb2gC4NBetjMImq7r2XHKPxXwfvx1b/XRngyCEFas2aNVb3R0RE4Ouvv7Zaxs/PD5999hk+++wzi2VatmyJDRs2ONI1ws1QIl2CIDyJ2H3YVqAhqfLi7fbUaS2QkhTeEOBoTvoOAEBQUDDi4ixby+zFnkAiP+QdxcXSQgBAXFxcndu0F3asMpkccXFjLJZrKgFdvB17xsBeU8C995K9NJXr4M3Y239XBjhyOg8UQQC0BoogCPfSqlUrKBQKM8tQXl6exUBDERERkuWVSiWnxFgqI67TUiAl1uVcjDcFOJLJZC5t09oY5HKZoJy7Yexst7EHdGko2DsGbx5nU7oO3oqt/rtybLSAhXAKLogEWaAIgnAjPj4+6Nevn5mrRmpqqsVAQ7GxsWbld+zYgf79+3MvVEtlbAUvYgMpNQSakuM1QzHbCYKoR8gCRTgFufARBOEpEhISEB8fj/79+yM2NhZffPEFsrKyuPW2iYmJuHXrFtavXw8AmDlzJpYvX46EhATMmDED6enpWLNmDb7//nuuzjlz5mDo0KFYunQpJk6ciK1bt2Lnzp3Yt28fV8ZaICXCu3C3+mQwMAKrG0EQjRtSoAinMMAYhY9c+AiCcDdTpkxBYWEhFi1ahOzsbPTq1QvJyclo3749AGN6jKysLK58x44dkZycjHnz5mHFihWIiorCp59+yuWAAoBBgwZh48aNeOONN7Bw4ULExMRg06ZNXA4owHYgJcKEp98N7jRAJZ/Kxn9+OonPnrgLw7uGua9hgiA8BilQhFNILcAmCIJwF7NmzcKsWbMk933zzTdm24YNG4Zjx45ZrXPy5MmYPHmyxf22Ail5O+50a2tKb4ZZ3xnvq6e/Poxr74/3cG8IgnAHtAaKqBPkwkcQBEGIobk1giAaM6RAEU5Ba6AIgiAIS9CbgSCIxgwpUIRTcFH4aJqRIAiCIAiCaEKQAkU4BdOkAuISBEEQBEEQhBEKItEI0Og1uFVxy+1tAuTCRxAEQZhD3gkEQTRmSIFq4DAMgym/TcHlksseaV8uIyMmQRAEIYTUJ4IgGjOkQDVwdAYdpzwFqYLc+tZqF9QO3Vp2c1+DBEEQhNO4MzcSGaAIgmjMkALVwNEatNznvx77C35KPw/2hiAIgiAAskERBNGYIf+rBg5fgVLJVR7sCUEQBEEQBEE0fkiBauCwCpQMMijkCg/3hiAIgiDIhY8giMYNKVANHK3eqECR9YkgCIIgCIIg6h9SoBo4OoMOAKBSkAJFEARBWMad+fvIAEUQRGOGFKgGDuvCRxYogiAIwlsgFz6ioTN/80ks2HLK090gvBSKwudCqnXVWHt6LQqrC11Sn8FgQFZVFo4fOg65XFrXLVGXACAFiiAIgrCOO8OYE0RDpqBCjY2HbwAA5o/rhmA/krEIIaRAuZA9N/dg9YnVLq/38OXDNss092vu8nYJgiAIwhlkTciJTyYj5bSxoTeYLihdW0IKUqBcSJW2CgDQIbgDxncaX+f6DHoDLl66iC53dIFcYdnbUgYZhrcdXuf2CIIgCMIVNCUXPrlMBj1J2Y0KupyELUiBciF6Rg8A6BDSATP7zqxzfVqtFsk3khHXOw4qFZmPCYIgCOdpajKh3sBAIa9/Ta4J6YpNBkHAlUb+wymt1uK3jJuQ6zzdk4YFKVAuRG8wKlAKGeVjIgiCILwLxo3T6t5ggVLr9AjwqX8xxxvGSrgW/k+lsVsXF/x8CttPZaNzsByTPd2ZBgRF4XMhrAWKFCiCIAiCcD8qhUmbqdEa3NKmjDSoRoeBpzTx10M1RrafygYAXC4jlcAR6Gy5EANjfFiTAkUQBEF4G+4UAz0VREIu4ytQere0SepT40MYRKJxK1C+SlIFnIHOmgthLVCWQo4TBEEQhMdwrwblcTQ691igiMYHX4Fq7C58crKgOgVJ+i6ELFAEQRCEt9IU9CfGwuf6hOTPxk0j9+CDG+KsNEpIgXIhnAVKRqeVIAiC8C7cG0TC81KZwU3jbUo5r5oK/DvH0Mg1KLJAOQdJ+i6ELFAEQRCEt9K4xUBzGvvaFcI9uEsR9xikPzkFKVAuhA1jThYogiAIwtto9IKgCHcZDmgCv/HB/6k09p8NWaCcgyR9F0JhzAmCIAhvxZ2CoMdEMn7+HjdpUCR+Nm4auf7klmTTjRFSoFwI58InJwWKIAiC8C7cqkB5gUzW1CxuhCtpOmHMveCn2iAhBcqFUBAJgiAIgvAOocxdcq83BMwg6o/GrT7R/essJOm7EAoiQRAEQRDeIZS5Lwof0dhoWmugPN2DhonS0x2obyZsnQBVoMotbVVqKwGQBYogCILwPhq7KxIAMDx7QSOPPk24jcZ9I9EaKOdo9ApUiaYECoV7LUJdWnRxa3sEQRAEYYvGLQaa47Y1UCR/NjoECZkb+Q+Hbl/naPQK1LrR69C8RXO3tReoCkRUsyi3tUcQBEEQ9tAkovDxcJfFzRvGStQfjVx/8gp324ZIo1egOoV0QmiLUE93gyAIgnAhK1euxIcffojs7Gz07NkTSUlJGDJkiMXyaWlpSEhIwJkzZxAVFYXXXnsNM2fOFJTZvHkzFi5ciCtXriAmJgaLFy/Gww8/zO1fsmQJfv75Z5w/fx7+/v4YNGgQli5diq5du9bbOF0J405R0AtkMnLhI5ylSa2BolUnTkGnjSAIgmhQbNq0CXPnzsXrr7+O48ePY8iQIRg3bhyysrIky2dmZiIuLg5DhgzB8ePHsWDBAsyePRubN2/myqSnp2PKlCmIj4/HiRMnEB8fj8ceewwHDx7kyqSlpeGFF17AgQMHkJqaCp1Oh9GjR6OysrLex+wK3GuB8owGxR+jwV15oGgGv1Hj1okHD0CJdJ2j0VugCIIgiMbFRx99hGeffRbTp08HACQlJeGPP/7AqlWrsGTJErPyq1evRrt27ZCUlAQA6N69O44cOYJly5Zh0qRJXB2jRo1CYmIiACAxMRFpaWlISkrC999/DwBISUkR1Pv1118jLCwMR48exdChQ+truC6jcYuB5rjLAkXyZ+ODEeSB8mBH3AApUM5BChRBEATRYNBoNDh69Cjmz58v2D569Gjs379f8pj09HSMHj1asG3MmDFYs2YNtFotVCoV0tPTMW/ePLMyrNIlRWlpKQCgZcuWFsuo1Wqo1Wrue1lZGQBAq9VCq9VaPK4+YBjGJW2ydViri6lN62GrXH2i1Vk+x/aMwW54ArY7x+rSMXgIR8fgrrHqtDpemzqr7Tb06yDj3cANdQz2XgNXjo8UKIIgCKLBUFBQAL1ej/DwcMH28PBw5OTkSB6Tk5MjWV6n06GgoACRkZEWy1iqk2EYJCQkYPDgwejVq5fF/i5ZsgRvv/222fZdu3YhICDA4nGuxfiqV6vVSE5OdlmtqampFvfduikHu0rAlW3awsAowC7AOnDgEIrPWzcfWBuDvWi1pjbdOVYWV4zB01gfg0lUddf5vVVpanfvvr3IDLR9TEO9DpWVpvu3oY6BxVb/q6qqXNYWKVAEQRBEg0O87oRhGKtrUaTKi7c7UueLL76IkydPYt++fVb7mZiYiISEBO57WVkZ2rZtixEjRiA01D0Bjuak7wAA+Pj4Ii5ueJ3r02q1SE1NxahRo6BSSedZ3LPlNA7m3wYAxMXF1blNe0k4mMr5XPUfcA+GdG4lWc6eMdjLWyd2oVJnnNl251hdOQZPYc8Y2PsXcN/5PZ9Tjg9OpgMA7rtvMHpGBVss29Cvw2eX/0ZutXEdZ0Mdg73XgPUAcAWkQBEEQRANhlatWkGhUJhZhvLy8swsSCwRERGS5ZVKJafEWCojVedLL72EX3/9FXv27EF0dLTV/vr6+sLX19dsu0ql8oig4so2rY1BwQvt5SmBTC5X2GzbFdeBr2R76po2RKGXj71jcNc4lUql4LO9fWuI10GpEP5WG+IYWGz135Vjoyh8BEEQRIPBx8cH/fr1M3PVSE1NxaBBgySPiY2NNSu/Y8cO9O/fn3uhWirDr5NhGLz44ov4+eef8ddff6Fjx46uGJLbcOdaeE9F4eNDeXQJZ2lKYcwJ5yALFEEQBNGgSEhIQHx8PPr374/Y2Fh88cUXyMrK4vI6JSYm4tatW1i/fj0AYObMmVi+fDkSEhIwY8YMpKenY82aNVx0PQCYM2cOhg4diqVLl2LixInYunUrdu7cKXDRe+GFF/C///0PW7duRVBQEGexCgkJgb+/vxvPgHO4K7Es4B25ZQwk+RIugMKYE1KQAkUQBEE0KKZMmYLCwkIsWrQI2dnZ6NWrF5KTk9G+fXsAQHZ2tiAnVMeOHZGcnIx58+ZhxYoViIqKwqeffsqFMAeAQYMGYePGjXjjjTewcOFCxMTEYNOmTRg4cCBXZtWqVQCA4cOHC/rz9ddf4+mnn66/AbsIt1qgPCSU8ZVECmNOOEuTCmPuBZMdDRGHTtuqVavQp08fBAcHIzg4GLGxsfj999+5/TKZTPLvww8/5Mrk5OQgPj4eERERCAwMxN13342ffvpJ0E5xcTHi4+MREhKCkJAQxMfHo6SkpG4jJQiCIBoNs2bNwrVr16BWq83yMH3zzTfYvXu3oPywYcNw7NgxqNVqZGZmctYqPpMnT8b58+eh0Whw7tw5PPLII4L9DMNI/jUE5QlwdyJdz+M+C5Q3jJaoLxq5/uQV7rYNEYcUqOjoaLz//vs4cuQIjhw5gvvvvx8TJ07EmTNnABhn/fh/a9euhUwmE8zyxcfH48KFC/j1119x6tQpPPLII5gyZQqOHz/OlXniiSeQkZGBlJQUpKSkICMjA/Hx8S4aMkEQBEE0Pdzp0uYNbkEGd5mgiEaHcA1U476PvOCn2iBxyIVvwoQJgu+LFy/GqlWrcODAAfTs2RMRERGC/Vu3bsWIESPQqVMnblt6ejpWrVqFAQMGAADeeOMNfPzxxzh27BjuuusunDt3DikpKThw4ADnOvHll18iNjYWFy5cQNeuXZ0aKEEQBEE0adxpgfICoSyvXG27kAvw1FgXJ5/HhStyuC9wetOkcatPnnO3beg4vQZKr9fjxx9/RGVlJWJjY8325+bmYvv27Vi3bp1g++DBg7Fp0yaMHz8ezZs3xw8//AC1Ws35lKenpyMkJETgd37vvfciJCQE+/fvt6hAeVO2d1fR0LNbAzQGb4HG4B00lTE05PERrsFTFii+sFtY4SYFyi2tCKnW6PFNehYAObJLa9CuVcMNPe3tNHIDFDnwOYnDCtSpU6cQGxuLmpoaNGvWDFu2bEGPHj3Myq1btw5BQUFmPuSbNm3ClClTEBoaCqVSiYCAAGzZsgUxMTEAjGukwsLCzOoLCwuzmBEe8JZs7/VDQ88MDdAYvAUag3fQ2MfgymzvhOtwbxAJNzZmAY2+8Uq+Gr3B011oQjTe+wjwjt9qQ8RhBapr167IyMhASUkJNm/ejGnTpiEtLc1MiVq7di2mTp0KPz8/wfY33ngDxcXF2LlzJ1q1aoVffvkFjz76KPbu3YvevXsDkDYn2soy7w3Z3l1NQ89uDdAYvAUag3fQVMbgymzvhOtwaxhzL5DK3DVeTwyVv75LIff8uW5sNKU8UN7wW22IOKxA+fj4oHPnzgCA/v374/Dhw/jkk0/w+eefc2X27t2LCxcuYNOmTYJjr1y5guXLl+P06dPo2bMnAKBv377Yu3cvVqxYgdWrVyMiIgK5ublm7ebn51vMMg94X7Z3V0Jj8A5oDN4BjcE7sDaGhj62xoo75UBvkOkLKjRuaccTUcx0fAXKC861u/j7cgHu69zKrW02cv2JXPicpM7R3xmGEaw9AoA1a9agX79+6Nu3r2A769YhFwWdVygUMBiM5ujY2FiUlpbi0KFD3P6DBw+itLTUYpZ5giAIgiCs49Yw5h7LA2X6vPnYTY/0wR0IIio2IQvC1K8OuqWdppQHqgndPi7FIQvUggULMG7cOLRt2xbl5eXYuHEjdu/ejZSUFK5MWVkZfvzxR/z3v/81O75bt27o3Lkz/v3vf2PZsmUIDQ3FL7/8gtTUVPz2228AgO7du2Ps2LGYMWMGZ9V67rnn8OCDD1IEPoIgCIJwEsaNc+lNSSjzxFj1Br6A38glfA/T2M8vReFzDocUqNzcXMTHxyM7OxshISHo06cPUlJSMGrUKK7Mxo0bwTAMHn/8cbPjVSoVkpOTMX/+fEyYMAEVFRXo3Lkz1q1bh7g4UyDO7777DrNnz8bo0aMBAA899BCWL1/u7BgJgiAIosnjTjmwKa2r8MRI+RaoRi7fewTBGijPdcMteIO7bUPEIQVqzZo1Nss899xzeO655yzuv+OOO7B582ardbRs2RIbNmxwpGsEQRAEQVihqa2Baio0dgHf0zR2BdUTa/gaA3VeA0UQBEEQRAPAQxYocoFyPcIocY37/IrhRyCsLxjB58Z9fpuQsdilkAJFEARBEIRL4SsVbpB3mzRN7fTq3H1DNfITTAqUc5ACRRAEQRBNALcGkeB9NjQxC4m7aWqn1x33E9+q19hPL7nwOQcpUARBEATRBPBUEInGrkDRDL57cbcFqpHfvnT/OgkpUARBEATRBPBUEAl3CaCeWgvkCQG0Ka+B0tMaKJdCYcydgxQogiAIgmgCuFPQlsubjgXK0zS1s+sOBYpPY799SX1yDlKgCIIgGiDuFiKIho877xj+pLYnb1V3KI2eXkPS2AV8MTqDod7boDxQhC1IgSIIgmhgLNp2FjELkrH56E1Pd4VoQLhT0OYrFZ60QLlDefOICx8Yyc9NATfoTwIau4skufA5BylQBEEQXkKlWif4vuT3c3jhu2OoEG1f+3cmAODlH0+4rW8E4Qh8mYxxk8ArJec2BffBJjBEAe6wQPFp7OeX1CfnIAWKIAjCwzAMgw9SzqPnm3/gvzsuAABuFlfh87Sr2H4qG49/cUBQVnwsQXgbcoELn+fuUXe4unpCAG1KLmZi3KM/NR0LH1mgnIMUKIIgCA/z45GbWLn7CgDgs78uAwAyCyq5/adulaJGqwcAlFZrBceWVQutUwThDXhLGHN3NO1xAbRxy/dmlKu1tgu5kMY+R+Xp27ehQgoUQRCEh9l5Llfw/dsD13GjqFqw7Wax8futEuH2/Ap1/XaOIOqIJ+XPJuHC56Ua1Lr91/DL8Vsur7e0qv4VKGGY+HpvzqMIkl5TcCK7IQWKIAjCw2j1Qp+Uhb+cxrnsMsG2G0VVgv8s+eWkQBHejbuUGKlW3NF2XSbwdXrn/NEEeYq8UOa9UVSFN389g7mbMlxet8bJc+YsXnh6XQrfWvz7mVwrJQk+pEARBEF4mGKJGdW/rxQIvt8sNipOhzKLBdvP3C7Ff3dcwNaMW8gqFCpXBOENeFLAd8d6GbmTcaC/3HMVPd/8Axk3SurUvjcqUOU1JtdiV1s1tHo3J9L1xhPsQvgufFlF9A6xF1KgCIIgPAy7rqldywBu29V84xqoNs39AQCratdIsYoUy+Lkc/jsr8uYszEDT6456I7uEoRDeDaMef237adyTpRanHwOap0B8zefdPhYvlDvjeI9XygvqXaty11xlcal9dnCG8+vK6E1UM5BChRBEISHYQWC9x7ubbZvdM9wAIDWwIBhGOSU1QAAOoQalS2+fJhVVIW88pp67i1BOIYnl1W4Q4HyVym4z85YK87nlNepfW+0kPCF8i0uXgf12k+OK5yO0rTWQJkuVmMfqytReroDBEEQTQXZ5Z3onLsd8vQrgMIodBkYYIrmHBgFcOeN81jZIV/g0jMvqAtUiotAFfDJol9wr0aHexXAXcEtcLyk2KyNr97fhgAfJao0Ojx2T1t0bt0MiLob6DjErOzpW6Vo09wfLQJ96m3MBFEXF67yGi2C/FR2lZVSJPRukAhVCtNctN7AQKmo/yl9xsJnb4G/rkYcObTh4Y1n2HWQBco5SIEiCIJwB2XZUGx6HD3BALdNm+UAEtkn8R4gDkAcX17cDSxgvzMA2M/ZwDhLciVbLqP2q8IHstcyAd9mXJHzOWV48LN9AICzi8YgwMf8dbDpcBau5ldi/rhung/VTDQoXDGD/0HKeazcfQVfPtUfo3qE17kf9YWCtwZKZ2CgVFgpXB94oXzPXxamd3PiW1cgcJH0wvPrSvjP9kY+VJdCChRBEIQ7qC6CDAz0MhVkvSfhzO1yXMg1ue6oFDJM7NsGVwoqcDyrBADQJzoEXcKCcPh6Ea6LAkRMursNzmaX4Vy2dfefR+R7IddrAHWZQIFaknye+3zwahFGdAszO/Y/m08BAEZ0C0O/9i0EM+0EIebvywX4OPUi3ntE6IrqrBsdmxvt7W1nnFag3JFIVylSoFzNlfwKRIb4SU5yAN4axpyfB8yD3XABDbz7NlHxLKatm5E3gr2QAkUQBFGPqHV6fHcgC8NDStEJgEbZDMoJyzH3079xRWtKltsuKAATHx6Bq2dz8cqVIwCAnQ8PBcKCEHi7DK98uldQ7+SHx6P6WhFeWZ1utf0434MIgBrQCxdeXys0tX1TlFsKEIZX/ucXBwAAr4zugn8P6WDXuImmx9SvjEFMZqw/gicHtue213UdUl2Mn+5YA6XkTSw4G5bcEkeuFWHy6nS0axmAPa+N4LZ7/xodU6fY9ZoNCf4pZZOYu4Mj14pwOa8C/xzQzm1t+ipN92+gL6kF9kJniiAIoh7ZcCAL7/x2FqfC8/AxAAZyHLhahCv5lYJyobUzf5Ehfty26BZGwaNrRBA6tQ7kIvMtnWSc4e/XvgWmD+6I/Ao1BnRsifYtA7lIfD0ig3E2uwxa1PoT6U3rEDQ6A27zlKbcUvPAExVqndm2ZTsukgJF2CS7RHg/ucsCIZkHyg3eYwpZ/Vmgfj1h9Pe1Fl7aG/Wnhm514lMp8SysLybXToi1Dw1EbEyo29plcYfFtrFAChRBEEQ9ciizEABwLa8U8AUYmRzrD2SZlbuzbXMAQM+oYMwf1w2tmvnCrza6l0Iuw+aZg1BcpYFCLkP70EAARt/1Nx7sIdluaDMfrH6yHzQ/GhdKXc0pQqdWxn27L+QJcqnklNUg/Uoh2jT3R7vQANRo9SirlhYaGIbBjpsy7N1yBu890gc+SnLrI4RoRVpLXaPEyeqQqtYdQST4FjKdi3MUWRZovXuNDt/y1xBlcv45rdS4zwLFcrWgwm0KFH+spEDZD735CIIgXIBOb8ChzCKz2cqcWuuOHEah0gA5/r5SaHY8u8ZDJpNh5rAYTO4XLdjfItAHnVo345QnS7x0f2f4KORIHNcdvdoEQ1M7T3b2hikx7+q0K4Jjdl/Iw+NfHsDQD3dh1/k89HrzDzy0Yp9k/UezSrD9hgI/HbuFL/deNduv1umx40yO1T66gpUrV6Jjx47w8/NDv379sHfvXqvl09LS0K9fP/j5+aFTp05YvXq1WZnNmzejR48e8PX1RY8ePbBlyxbB/j179mDChAmIioqCTCbDL7/84sohNRrEAn1dZTJnXPjYY9whEAoEUBdrM/Z03xvXQPHPuyfzgLmCnNIat4eK95QiUx9r+BorpEARBEG4gJW7r+Cxz9OxcOtpbtuFnHKcuFkKAFDWKlA6yFGl0SPIVylIwHlvR9fMNiaM6oJz74xFj6hgRLcIgEJpdA0srzS5DBZWGtdD3dWuOQCgoMK0PmrDgevQGRiUVEmHHn78q8Pc572X8nHqZimGfbgL/91xAQCw4OfTSPjhhEvGYolNmzZh7ty5eP3113H8+HEMGTIE48aNQ1aWuWUPADIzMxEXF4chQ4bg+PHjWLBgAWbPno3NmzdzZdLT0zFlyhTEx8fjxIkTiI+Px2OPPYaDB03JiSsrK9G3b18sX768XsfXGOAL9XVeA+XEMaxbnbuF97qEbHekPm9fA8XvU0OUyfn37zf7r2FZ7fPNlZy6WSpwpebjKQWqoSu77oRc+AiCIFzA139nAgB+PnYL/320L2QyGXaey+X2y2W1ChRjdMuLau4viMInl7smTLhMJgM/DY3Sxw+oBnrf2gjsMEbVm1ZxDRqlAfcHt8ZfynzB8b7X5LhHad/CEdVNGf5ezeBxANgDVKjbo+fZG4hWqpHgktFI89FHH+HZZ5/F9OnTAQBJSUn4448/sGrVKixZssSs/OrVq9GuXTskJSUBALp3744jR45g2bJlmDRpElfHqFGjkJiYCABITExEWloakpKS8P333wMAxo0bh3HjxtXjyBon7hLK+M0o5DLoDIzbBVFXt2fJonXkuikHnDfKvFpeMA1vTPTrKCt2XcGrY7q5pC6t3oBnvjmMvZeMXgHX3h9vVsad9y3/8pAFyn5IgSIIgrCBwcDYVHD4ax/KanQI8VfhfI5RQbq3U0sorxn96DWM0eoUHuInUKDqC61PCFAN9Cr+E9j/JwDgGRmMT//LQBept4Ajbwa+H8NR4BkAZUqm3hQojUaDo0ePYv78+YLto0ePxv79+yWPSU9Px+jRowXbxowZgzVr1kCr1UKlUiE9PR3z5s0zK8MqXc6iVquhVqu572VlZQAArVYLrdb9CUZd0SZbh7W69DwBWqvV1anda4VVdh2v1ZnaZHMzqTXS59meMdgLP89RjYX2bGHpGJ1OL1km8edTpu06z9xL1lBrTP3R6vQW++fsdajv8ep05mtAXTWGTUducsqTpePUdfzNOIKBMd2/ao372nUl9l4DV46NFCiCIAgLaHQGTF69H2XVWmx9YTBCAqQz1xZUqFHOW/uUU1qDEH8VFznr6UEdsbNgH6ABSrVGC1R4kC+mDmyH7w5m4bH+0ZL1uoLjvRZga9q3kEFoVVLKZZg2qAPW7Mu0enyLAB8UV2mslhGjMegA/OhoV+2ioKAAer0e4eHCvEDh4eHIyZFee5WTkyNZXqfToaCgAJGRkRbLWKrTXpYsWYK3337bbPuuXbsQEOCu8M6mV31ycrLLak1NTbXYzvnz54DaCJB79+3D9WZwAsf6bdSfjMcY9DoAMpttm4/BcXJz5WBnEnanpeGC3ZfV9vhu3DLVLSxjOvbAwYPIPmN/f93BpVIZ2Ot/+swZJBedtlre+nUwF1VdeR9LcZHXf3vbtPde+vuGsG6p63r23Hkkl5+zq766cuOG6R47evoCIsrc0259YOsaVFVZjmbpKKRAEQRB1KI3MNh4OAt3t2uB7pHBOHWrBCdr1zBl3CzBsC6tJY+7kCO0JN0uqYaBYXCzVoGKbuGPHhGBQJbJAhXazBcv3d8ZQ+5ohWFdzJPYumxMYb3xvu5xs+1tW/rj2bH347EhGuw4m8MlzWW5I6wZRvUIx5ShnfDIyv3IL1djUOdQ/HEm16wuMeH+BtSXAsUiE0UWYBjGbJut8uLtjtZpD4mJiUhIMNnjysrK0LZtW4wYMQKhoe6JsjUnfQf3OS4urs71abVapKamYtSoUVCpTJMK/Ha6deuOrdcvAgBiB92HvtEhDrfjaL81OgNePrgTAODro0JNtQ73xg7iIlzaMwZn+K0kAyjKAwDcN3gIukUE2XWcPePb8cNJoCDHrAz/2AEDBqJfB/eHvLbG31cKgbNHAQDdu/dA3KD2kuWkrkN+uRqHrxVjbM9wyOUywVhZXHEfW6P5lUKsqO2/rTYdvZeu7LqC32+aAvmMGzeOe86wY425owviRsQ4232H2LvlDJB3CwBwpjIAK+KGuqVdV2LvNWA9AFwBKVAEQTRZ8svVOJ5VjFE9wiGTyfDriVt4fctpRLfwx77/3I/kUybrg6XFvgA4Vz2Wf31zWPA9uoU/soKMD3V97UyfVm9AoK8SY3tFumo4kgT5ST/mqzVGi1SLQB9MuacdNh+7hUOZRZDJgHOLxnIh1AHg97lDoNMzSNp5kdv23j96YsEvxmnv2E6hSL9qiizYqplvfQzFWHerVlAoFGaWoby8PDMLEktERIRkeaVSySkxlspYqtNefH194etrfj5UKlWdBXd7uJJfYdauq7A2Brnc5NspVyjq3K49xxtkJnc3ZW37ttp2xXUQKOFy58Zq+RiZzTIKF5zfunA5rxzbTmRj+pCOCPIz9oORma6/TCa32T/+dZj1/SFk3CjBq2O64oURnS2Wr0+USvPnpiNjsFq3QmjZkiuUgmTMAAA7zpmr4Lun3yqpAeQKqMT9aSDYugauPKcN8wwRBEG4gDFJe/Dct0exvzas+Fd7je5sN4urodMbBO5tORLJZlkybpRY3Bfoo0CIvwqRzYwvZF2t68aT90rPyLqaEH/pF0a8qP03J/TAuF4R2P7SEIHyBAC+SgUCfZWCLPU9Ik2z7K+N7Soo3y3Svhl4Z/Dx8UG/fv3MXDVSU1MxaNAgyWNiY2PNyu/YsQP9+/fnXqiWyliqsyFgMDAY+d80T3ejzkEEVArHrYCsUKh3QyJdPq4OmGFPMAFPL/t/4KM9+OTPS3j/9/PcNv6a0JvFjrlNsc/TzcduSu6/I8wpf1CHcGfcC6lLXFKlwdikPfg49aL5znqmIeaC+vN8HlafkyOvXG27sIsgCxRBEE2C8hotDAyw+ehNHL1ejFfv9UerqitoJQNOHNOjrTYS2uwz6FIrq104dRBdZDe442X5WiBXuLC4oEKN1385jcyCSnSRAR1CA3CtUCgsdAwJhCzvHHo3M7oChvnLcGnhaLfNLlqyBr0gcg/pGRWCVU/2s1oXf1aye0QQJt0dDY3egL7RzTGyWxj+PG90Y3qwTyQ+qmO/rZGQkID4+Hj0798fsbGx+OKLL5CVlYWZM2cCMLrN3bp1C+vXrwcAzJw5E8uXL0dCQgJmzJiB9PR0rFmzhouuBwBz5szB0KFDsXTpUkycOBFbt27Fzp07sW+fKR9WRUUFLl++zH3PzMxERkYGWrZsiXbt2tXjiJ3DWyJq1bUbzgizbBhzt+SB4n2295yX1QgXs1tyF7VLIfOOy8y5OwPCwBrr0q/j+eGdERHi51iFFsbVUK0jlpC6xuvTrwMwejfMG9XF7rq+2nsVV/Ir8d7Dvex2PxY3b+mWq9HqkXI6B0O7tEbLQB+7++QOZn6XAUCOd7afx+r4/m5pkxQogiAaPTq9AWM+3oPbtVakh+T70eHScuxgdYuzxr8dfF3jF9H3i7V/PFoB+BwA2HIVvM8s5QBWmR62gT7uffm3DjJ1SC4D/FQKrHjibnOXETt4qG8UVu2+gk6BGsjlMvz3sb7cvlkjOuPwtSK8OqYr+rVv6ZK+W2LKlCkoLCzEokWLkJ2djV69eiE5ORnt2xutatnZ2YKcUB07dkRycjLmzZuHFStWICoqCp9++ikXwhwABg0ahI0bN+KNN97AwoULERMTg02bNmHgwIFcmSNHjmDEiBHcd3Zt07Rp0/DNN9/U65idwVtyutQ1N5Iz42Cj8HlrHqiXRbnSDAwE6QfYyJ/2JdL1DhQ8VzCtXtir41nFGNfbMXdlS9fOHde0PpMTi9UaW0r+jaIqtG1pX2SSd7cbA0D8484oDOzk3Lo4S6Hz3//9PL7Zfw09o4KxffYQp+qub8gCRRAEYYVqjR6l1Vq7ZzSzS2s45QkAesiNs3vVjA8q4QdfpRwKuQxVGr2lKsDKBuy7zl+lAMMwqOGFTm7ur0JJtXFmWaWQgWGAYD8l5LUzgYxciZstBqGF3SOtO34qBVo180VBhRpfTeuPEV3DnA6M0LZlAPa9OhR/pZov6u7XvgVOvjUGgGsX6lpi1qxZmDVrluQ+KWVm2LBhOHbsmNU6J0+ejMmTJ1vcP3z48AaV06a+LVBVGh0C7FgvwfaiQq3D9pO3MapHhEMz2PaOQpwHCnC/O5K97aWeFQZj0eoNUMiNrrOLtp3FLxm3kDx7iF33m7fck3xXS/F5cOaR4x2jcj3ic2FLIRzywS5cfS/OoVyBFWrzMOz2Yqk/v2TUBpq4Xf/Pd2dx54QJKVAEQTQo9AYGD6/6G1fzK5Eydwg6tTb3h/9yz1VczqvAkkd6Qy6XmQWAUMH4cvlaPxYf6P6JIe1aIbqFP74/dAMymVAQ6xzWDJfzhAvxAeDBrpEordYK8nlce3s8Qq1EbtNptbiRnIzezgy8Dix/4i7cKKrC/d3qFhABAAJ9lWhkHjSNlvpUHjR6YNDSNDQP8MHf8+83289vmRVqXt9yClszbuM/m09JJg+1hFMufKwC5Q5rBa8JZ9vjX6u1tUm5fzxyQ1IgFCtM3qJoKHmBQ7SixWfOTNpYEobP55S7JEKmpxD322DHOj2twQBfucJ2wVocuQ3FRQ0GBgzDGK2icstKsTfiTgWKXoMEQTQorhVW4XxOOTR6Az7985LZfoZhsDj5HDYduYF9l43KTbYoAASrQLUICgRgjMaXW2Y0/Q/u3EpQdmR36RDjN4qrBcoTize+1O/tFIpH+7f1dDcIN1OfAk9+DVCp0eNWSTVqtJYtt4BJmPv1xG1uW1655aAsroCzGLtZ6LNHGJaCtRbe4k32KBVySRc+8XV1hcz4edoVPLY6HVUa5y0XSmsWKCfqszYua94CrsC9QSRsN+bob1nn7I1Y29bMDUcxfNkuwW+7Pn5LOr3BpRbUOgzbYUiBIgiiQVBSZQwCwc+5JOVKwLoZAEBOmVFIY4WSR+5qg8uLx3EKVFRoMABjMIjc2rJPDGiHmNaBXB3PDxMGW2Dz2ZwQRd57bmgnp8ZFEHVBqzdg8qr9+L+t5olKXaFAMQyDggrzdQX8pXy2hFlWQOTLSdaiWroCZ134LueV45Odl5x2gXJWcGX7+TdvUkalkEkK1+L1Ra5Yr7Pk9/M4dK0I3x3Isl2Y3zavf/zJI62ZC5/jKpQ1ubrahtJe39Ro9SiqdCzBuCXssVra447LV3B+OX7bSknb/fnjTC5uFFVjH+9+dLVLcGm1FrHv/4U5GzNcVqc7rWTkwkcQhEfIL1cjp7QGve1IsHnyZgkeWbkffVrI0c1QxG3PLKiEWqfH+v3XMaRLK8ggw7xNpsXZr/10Er3bhOBqfiUAoF1oAJQKOYZ0CgaygLs7RQCXgaJKDTfT27ZlAP58eTjKa7Ro5quETCbD+XfG4ss9VzGyezgUchnGJO0R9O/8O2PNQn8ThDv4+3IBjlwvxpHrxVg0sZdgnyuEiTd+OY3vDmZhzbT+GNnd5AIqiDxnI1a4VDeW/3UZXzxVf9GyuHWHDh73wEfG33ZxlQZvPdTTzqNMrTjrQsQqXnwhVaWQSyoRGtH5dqW1xFHLDj/nHV9F0otd+JzoizXLRHV9W6Bs7B+xbDeyS2twcMFItPSv27PfHsuOXu+YktUqyIE1hlai8Em54rqKLcduIr9cjV9P3Manj9/lkjrduR6QFCiCIDzCC98dw6FrRfjfjIEYFNPKbH+1Ro8lv59Dv/YtsP9yIXQGBscK5VAFV3JldAYGH+24iM/3XAWSpdt569czKKkyBnbo3caorEUHGV94gf7+AIwCHjubyAamYBNCAsZADC+NvAOAMRw6n5fu70zKE+Ex8q1EnXLF+p/vDhotEh+lXhQoUHyZz9bMtJSC9fdlc/dXV8JaPGwJVEevF2NFWibenNATnXn5hU7dKrVylGVqtHWzQPGF1MyCSkmh9Xx23RbxGwwMrhZUIKZ1MzPLkKPWrN0X8rnP/KrE94Srg0h4Om4G6xb+9+UCTOhdt7Wl9sxzaO2wbPInTPjr0RzF0sSLqy1Qt21YoctqtAj2cyzdhzvWPLKQCx9BEG6HYRgcuma0JKWczgF0akBbI/jbcvgKNqVfwmsbD0FpUMMXGvhCg9NZedxnX2jwzZ7zgu/iv5rqKpRXlMMXGkQFyoz164wPbrnSBx1bmdz1VAoZWgZYn7kL8lMh2M809+Rt+TCIxsHK3Zfx3x0XrJbR6Q149aeT3Hfxwn17Zq3tRTxJwK9aZ6OdQglXJ2fC6DsCuwbKljz1z68OY++lAjz37RHBdh8n+1fprOtf7TmM4QXF+Wb/NUkFasoXBwTfNToD3t52BmkX883KSvFR6kU88NEerP37mtm+usiffB3JJQqUlb7Ud7AAey0Zzlh5xefCHqHfnnb47qN1XQMlhatP+XmeO76YP87koM9bO/DhH+ctlpHCnYmzyQJFEITbYS1CADAh6wPg3a1mZZ4A8AQbpfwssJgfsdyRSamS2v9+ANaK9il88Mb47nh2nVF48lMq7AoVG9XcH2W1D39SoAhXo9bp8UGKUXl68t72CA+WDtfPD6EPGIVwvp7jytlYhVxsqeC1KyGsMTYUrACf+rXaskKqvfLtdVECbF+VtAKl1umx63weYmNaIcTf+CDij/XVn07i/m5hCLWQwNoSrNAqFtzt6f93h27gz/P5+Prva3ZFN1y+y5gM+p3fzuLZwR0FgSOkmjt4tRBXCyrx+ABhsmhx8BC+Nau0WmiplznhxGfNGuYtOc5c0Q17XPhsTVIAQsXHsSh8loOS1KdLXKtmlt+db249AwBYsesKXh3TTbLM2dtleOF/x5DASzRMLnwEQTRqbhSbhJWY0nTPdMI3GIjuj5FhJvcLtc6+6as2zf252bPWDgpKBGGLkzdN7mPW3GbEM8UavQH+UPD2u246VjyvILBA2RAApfpxd3vXZ0Pjy06mNVDOWRIsWaA+TLmAr/ZlYkCHlvhhZqxkmS/2XsX57HIE+iqwcmo/u9pnz6FY6bVHILxRVG2zjBTsGDcdvsFv0Kwca/GKDPHD8K6mqKSW1ktdK6jEqt1XBNucsdRYO6S+YwXYW70zkxRiZdIeZdAe9zl+mbqcHnvcBfmwSZ/5VKh1+O3EbYzuaTnnm5J3jDgsPX/CJr9cDR+lnJuwYHn9l1PILKjES98fN/XFjXo1KVAEQbidm8WmFz5jML6EJ6v/D+eZdpYOEdC6mS+GdW2Nn47eBACkvToCz3xzGFfyjfmafpo5CAu2nMLFXJOLgL+PAocXPGCqROkHKI0P9ohgP+SU1WBSv2i72o9u4c99bs9zASQIV/DS/0wCgTWDqFgoffmHE/hqWn/e/rr1o5jneicX+R2l55oUDFvCsdT+TvX8u2GFMWcFKpVSWoHadMSobLAuyFJkFVZx7nRVGh0CfGyLWuw54gvBAzu2tC/6mpOz7mwwCv7EkbXmLudViBQoobsie4f875B5JD9n1s9YG1ZuWY1gzZqnkLq3V+y6jLQL+Vj3zAD4S1hazVz47Aki4eAaqLq474pdga1xMbcck1btx/PDYzBreGdue+LPp7DtxG1sOnIDW2bdB8DocqyQyyR/mwYG4EXBB38J1z2LdwKAmXVVyirHKrR6A2NmNXc1tAaKIAi3c6PIZIEy6I2uHuUIQIWdfxFhYWjdqjX3PTS0Fba+PA7pb/0DKf8Zj24d2uDLGSMEx/gENAf8gk1/StOs2LpnBmDuA3fgzQk97Or/4wPboX1oAKYObIc2zf1tH0AQDlDGC1RiTbgSB2fYeS5XuL+OFqjEn09xn8XCSHqe/QrUW9vOQq0TWivqO9ywaQ2Uc+0oLCzakXK3Em/hK5saO63arNDKr99PpbBLOarrqdx9IY/7LLbY8YXpLcdvCfaZu/DV/pfsozMKlOVjZm446nB9jjUuscmOpMYA8OEfF3DoWhE+/cs8T6EUdgWRcDAKn6NWJEFbOvuv1aJtZ1Feo+NcjgFg+8lsbKvN+XY8qwSAMfhS7Pt/4UWetUiQgFp0EqR+f+LnnY/EJAfDMDifU4Y7396B1WlXzPa7ElKgCIJwO3wXPiVqw/fC/jURTwxsh3/d1wET+kZh5dS7ue1BfipEtwgAADNzv/g7n64RQZj7QBe7o+l1iwhG2qsjsPjh3nb3mSD4nLhRijX7MiUFML5galWBsiF52StDVah1GPPxHrz/u3DB9mGelUVpZTbXHuF459k8wff6jpbF9tbZZqRmrxmGQaUd4bP5SoilfEXi+g28mXMWvYGxS7iu67qPA1dN11lc1Y4zJqX8zO0yQf6uao34BrOsQTljgbJ2X5XXOJ/w11mkfovWhiV2YwSMCukFUfAEVyXS5Vud7FkzxbUv4QpsL1KTNC/875jZtt9P5yC/XI3tJ7O5bYyV8P9Sa5HFvyWVwryMDMD/bT2DcrWOe569//t5jPoozSyCbl0hBYogCLdx5nYpZn13FH+dMwlTiloFSl/7OHp6UAeLx/duYcBLIzrhwT6RCPBR4rPH70Jc70jJsuIoX80DHAuHShD1ydPrjuKd385i+6lss332hgi3JVTZa4H64fANXMgtN5ux5Qs1CithkaX6IbZkiAWk+tCf+G3aswZK3O3P/jRZDLYcv4VDmUI3vV0XhEqgJfjWgmqNHlfyK/DKjyeQWWBKwdBcNKEjFcZcqzcIlCNLilJdLVBjekrn9wKAkmphBEU24ThgLtCaLFDmgq0z6/FcfYswDIOU0zm4WVxls+yOs7lm26SsQNZ+g7GdQs22vfrjCTNLnj3KkdiCCxitm1//nYnLeUaF7HapyTVe/Nu/mFuOj3ZcEFi3uXpEChPf6mirZ/Zc1sIKtXQIEQctUOLcX1Kh2mUymZlCuDrtCi7lVeB7CdfSukAKFEEQbmP6uiNIPpUjyP+ggPGhyCpQoYE+6BJu7tt+V9sQTO9mwOz7O9ud1T4syBTggRQowhs5c9t6Th9rwpWttQr2uk1ZmnHmH11XC5Qz6z7sReo8sAqUNQFP3IX/pl4UfH/sc2GAm1M3pa+VWLEpqDDl5qrW6vH014fw09GbeOJLU/hxcZ9NFijTNqMFij9Lb2kc1s8lwzBm7nZ8/HmWd7GblNhyX8EL0y6+htaeyo5YRFhs3VaOWt5+O5mNmRuOYvDSXVbL5ZXVSArb7O+EEVwTy30I9DVf+/ZLxm2zbfb8firU5tfvu4PX8fa2s1zy56UpJguyWNkb/fEefPrXZXy1N9OsHo3IZc+RNVD29H30x3ukd/BuGHE9UhZgS9ZcQZVWbkJzi2ndcEiBWrVqFfr06YPg4GAEBwcjNjYWv//+O7dfJpNJ/n344YeCetLT03H//fcjMDAQzZs3x/Dhw1FdbdKci4uLER8fj5CQEISEhCA+Ph4lJSV1GylBeBCDgTFbcNvU2HepgEs+yId14dMzxpd4y2Y+eOuhnrizbXPBIuGXR93hcJtbX7yP+xzkSwoU4X2IZ1XFWBM8bVqg7BRaLQlB/JlchYS7jKkfdjUj4EJOOe5ctAP3L9uN7FLzKHJ8IdWaUPTridvo+X9/4LeTQsGUPcbaGXBUh7M3lxG77gMwJtZlo+Txn3/stWMVU/ZaCXP5MIJza+k6mYU+NzDYczEfJVVG69Fz3x5Frzf/QF6Z8PkbVZs0vBkvr52/yI1ZrEDxI++J22XPj5SuzQXJ0BuQVWjbAiRVv5jkUzl21cNy4GqhXeWkrDSAaT0b/76x9hu0936xx4ojlV/s9C2hQs8PHS+VvBoAjmcVm20TK0z854Yt/ejIdfP6xBRWaiQnPfmWSvE5EAetASxHfRTXaSkViSujkgIOKlDR0dF4//33ceTIERw5cgT3338/Jk6ciDNnjPHas7OzBX9r166FTCbDpEmTuDrS09MxduxYjB49GocOHcLhw4fx4osvQs4zxT3xxBPIyMhASkoKUlJSkJGRgfj4eBcNmSDczys/nsDd76TiemGl7cKNlFd+PCH4/mCfSKycejcUMqELX8dWgRgU0wq/vHAfVj/ZD4E+Cvzrvg4Y2LGlw222ohDjhJcjJRjd19nk+lOXNVD2rjOyVIxfv8qKBUpvYOzKZcNn3+UClFRpcbWgErFL/pKs0x5mf38cGr0BL/IiFwI8C5SVc+COnJuWgkiwFgJ23SV7rfj91RsYSWtHP1EIeC3vXNVo9fj+cBaeWnsI/6wNP556Nhc6A4MB7/0pqs/4XyCsigRX/hoocf/El4gViKUUB7aPX+7NxNAPd2HtPnNLiBhbaSXSrxbYrIOPNSsqH0seDgbGeD34lhCdgcEvGbex6YrcIYscH3t+pxUSzwmz1AK89u1Zq8ciVqCEURkt940fDMoW/K6yzwp+/8XnQMoCJVagpNxz5TLLAWCcyuZsBYfCmE+YMEHwffHixVi1ahUOHDiAnj17IiIiQrB/69atGDFiBDp16sRtmzdvHmbPno358+dz2+64wzSzfO7cOaSkpODAgQMYOHAgAODLL79EbGwsLly4gK5duzrSZYLwCn6u9Xn+KPUiPvnnXR7ujfuo0erx2V+X8GCfKOSIZj9bBPggrlcEsNn4UAzw80UrpS/uamsSDjqHNcOZRWMBAFqt4wtAVQo5VAoZtHoGLSjhLeGFSM12dwgNxN+XjbPl1tYxORM+3BH4bl/W1kAZGMapIAF8xHlg+AKV1Gy0dB2mz2x3rVkxHLZAWWrXyjFavQFRIX4Ct2WGYbgoaX4qOSrUphl4vixbVKlBoK+Cd5zxf3iwcGKIr6RdzC3HxkPGUOvnRcEKAKCcJ4hLB64woKRKg0/+vIRJd0dj32WhkmLNfc3aZdLXDox1M1v021k8M7ij5QNgW4GSWgNjtbyF3F5iLN0zegOD6euO4M/zeYJtr24+DUCO7adyMKm/KRWH3RYoOxQoqXMhvn/5lqOCcjXsRaxA8b9b+12LkyVbo4r3LNEaDCir1OHH2jQkgPE86vQG7hpJWZGsuaGyyGSWn3uuDmrudB4ovV6PH3/8EZWVlYiNNU8ml5ubi+3bt2PdunXctry8PBw8eBBTp07FoEGDcOXKFXTr1g2LFy/G4MGDARgtVCEhIZzyBAD33nsvQkJCsH//fosKlFqthlptumHKyoymTa1W65Tg5Q2w/W6o/QdoDGKu5ld45Fy4cgxqrR4+Srld65CWJJ/HuvQsfJ521Wxf2xZ+0GrUYB1EfpgZC3lAKJQyA7Ra85eFs2N4cXgMDmYW4el7oz1+HzaV30NDHp+7Ka4ynqviSg22ZtzCQ3e2EQjEdVkDZa8CZcl6xN8sFfGK305dg0ToDQyUvDb4eqMzgg8XRMJKPxxVoCy5BllDozPAT5QLyGhZMn72VYosULxO3SqpRofQAF5/2TLCNrpHBmH/lSKubr5gK74HjvJcrthd/Gun0zN4L/kcfjhyE1//fQ2P3N1GEPxC4L5mQYGSCiJhSRAvqdLCzkjvZtirWLMordzDfCz9rPQGRqA8sdtY8ivsV1r42GO91Ut0Sqzo8ftij7LBIraS8idtrLm9FVVqLO4Ts/CX09xnrZ4xC0M/be0hlKu1SJ03DH4qhaS10B4XvtwyNa4WSFvGXGyAclyBOnXqFGJjY1FTU4NmzZphy5Yt6NHDPHfKunXrEBQUhEceeYTbdvWqUYh66623sGzZMtx5551Yv349Ro4cidOnT+OOO+5ATk4OwsLCzOoLCwtDTo5lf9clS5bg7bffNtu+a9cuBAQESBzRcEhNTfV0F+qMu8ewP1eGA3lyPNtVjxAXGR6cHYPxN2/8qZ26VYa1PyUjwkO3ZF2vQ6kGePOoAgxk6NPSgGe6GKBnAKXcOM4yLdDKz1Q+JUMBQCZ4eQ6PNEBvAFoWncHvySfwUO32Q3/vhU5pO7mmo2PoAKBDOHBwz58OHVefNPbfdFWV/a4dTR3W3eSl749j3+UC7L1UgCDempS6ROGT2n/mdim6hgcJZuN31yZ9tYa1CRO9HRYoKaGaj87AQMnTM1wV5txat+o5FRUAY+AB8cj558pXZbwOrKAqPo+3JdZNiZVVcehzvgIlFqT564AYCQuUzsDgXLbJcnU+W2jFEkRoEytQVlz4pO6Pggo17lmyC5H+Cjz0oPkxUvw+ZwjGfbIXACBlUFLr9Ljz7VR0ah2I314azN23qWdzsZln8bCGpd+VlKWIr2xodQaL1qv9VwoQ01o68a89Ex1S54+/xWBg8Gj/aHz212UAQI0DWqlGtFZSOIFj+bin1h4SfD+eVYy72rWwUNqEVmcQKPIAcDbbaPQ4mFmEYV1a2xVEQupUW3NdtPUMchSHFaiuXbsiIyMDJSUl2Lx5M6ZNm4a0tDQzJWrt2rWYOnUq/PxM0pSh9kb797//jX/9618AgLvuugt//vkn1q5diyVLlgCQflCLzftiEhMTkZCQwH0vKytD27ZtMWLECISGmoeSbAhotVqkpqZi1KhRUKka5gJ4T4yBYRjM+T+jcFfRqjset+EmYIu6juF2STVwaC/3Xd6mJ+Ji29epT45izxiO3yjBN/uvY/7YrogM8ZMss/1UDpijJwEAJ4vk2F3dBttP52DTjAFYvusq/rqQjw8e6YWH74pCtUaPeQfMlZakZx8wRSfSVgG1S6NGjx0H+FjOLE+/B+/AnjGwHgCEbVgvJNZV6s/zeegTHcLtt+bGZEtpkdo//tN9mNwvGsse7cttEwszUlgLp20wMIIcNJboHhmMc9nS94ZYiOTXx776c0pr4K9SIMSOiJrOhDG3hSURhD0dEcF+Zq7KGp3BzFLCj3rox1qgWBc+xrIwa7IYCdsXK0D8kNfiQCJRIabE31KR/3R6g+CcnRVdL35blowTUoqGlJKw/4pRmcuutm7d5MOPpirlVvp52lVUa/U4c7sMGTdKOIF+xvojFtsQY8mlTmoMOtG5l7qn9l8pwBNfHrTSnu0+SSpQvH5uPHwDAT4mkd6RSHoaUYh0vkLlSOCFh1fux455Q22W0+oNkMmkFSD23EtZoGocWNfFwg+773ELlI+PDzp37gwA6N+/Pw4fPoxPPvkEn3/+OVdm7969uHDhAjZt2iQ4NjLSmK9FrGx1794dWVnGkJERERHIzTWPv5+fn4/w8HCz7Sy+vr7w9TVfMK5SqRqsoMJCY3AMfrS7D/64hPs6h6Fv2+Z1rtfZMZSphbPxVRoGVwtrkFNWg6F3tLI7JLcrsDQGtU6Px744hBYow6+KLLw4QhjxTqc3YN4PJ3A5rwLded29cPI6OgNYuSkX2SXV6C4Dkv/MwWPtBmD2d8fQFZVm/jfNKy4DFbVfNKZzo/LxA+w4v/R78A6sjaGhj82dSLkhnbxZyn0uqhS6Ba3dl4m0i/n4PL6fzSh7loSon47eFChQfCxNVloLHqA3MGaCv5RbkrVF/GIBUVxfcaUG9y4xTshce3+8dN95n9mmrFqgLO+SxNYMtkppvl+jN1eg+NfNj7NAmbvwiWEFZrECyw9ZbTAwIoVKOEr+LL6kC58Naf6PMzkY2T0cIf4qc0WjdphSio3Uvchf7G/JcmPNFc1HwiVvNy9X1yd/XsI3/xogWTd/vY0Yi1EpJbbzrxcD4TjZ++UgL1GxI+3xkVLe+Nd9wZZTeHlUF1NfHJgcqBG5zPOVdkfXNmbcKLFZRqM3QCWXS6ZPYK+olAVKK7qX7Rlj7BLTJK7XrIFiYRhGsPYIANasWYN+/fqhb1/hA7pDhw6IiorChQsXBNsvXryIcePGAQBiY2NRWlqKQ4cOYcCAAQCAgwcPorS0FIMGDaprd4kmgDhazcc7L+Kbfw2o1zZ1egMOXC3CPR1bcD7tLEVVQj/hm8VVGJNkzIvw6piueGFE53rtmz1MX3cEYSjGXt+58L2gBYQ/USgBfAYAloLaVfP2qQGsBlZZKr/aQh3yOj+OCKLBYWsCRSvK0bLot7MAgJ+P3UJUc6GluIXIMuNM7p2v9mZixtBOZtvF+Yn4GINIiBaii8owYKy6KplZoETfLVmuLMEpLbWR06SVQoeqhI/SehAClYTioNEZzGa++UI2GwhBKqCDGCmFBzAPfS62ioj7Y6pPwoVPzyC3zPJanj/O5CK//BB+nnWfRcE/mOeC+lRse6xPv24WUVDcb34flHKT2/fOc8IJdf77NUAizxK7phAAdl/Ix9X8CkQ19zcrp9ZZVqAsXQOp+Qi+uyMY6WP9RKHh7W0PAHyVcqh1BsnfcotA0+/9rnbNBb85S3XWaPVYtfsKJt4ZxZ0X1mIplzEwMDLhPeLgj8Se8lq9cb2jlEGJ/d1KKVDsOcgtq8GFnHKr1mWuP7wizqxhtIZDIUwWLFiAvXv34tq1azh16hRef/117N69G1OnTuXKlJWV4ccff8T06dPNjpfJZHj11Vfx6aef4qeffsLly5excOFCnD9/Hs8++ywAozVq7NixmDFjBg4cOIADBw5gxowZePDBBykCH2EXVaKEc39fNg91uu3EbTyy8m9czDWPUmQPS1PO4787TFrGa5tP4sk1B/Ft+nWzsuIZZP7s8td/X3OqfVfCMAz2XipAB1kOfGVaYzjxZhHcX7Vva+QyzQV/lT6tzLbx/3SB4YLvpcpQaPzDwPDqFfzdPQ1QkNWCaHqwAgc//84dvPxn4llXFo1Ob1PpsJQg1xqLk89xn7tFBHGfrUVf0xvMF6JvOX5TVMY80AQfsQJmzeJlTwJVVmnZfSEfd7+TitSz5p4tdY3Cx147thqVhEBuEClvlWod/uIFImAFRVZhsK5AMZL91uuFgjP/9FiLsMaW04ssUPz7T4pjtXmuLF0Xtp+P3NWGOydaPWOW3JX//deT2aZ6eGXSrwhzN7UM9OF+K1K3weDOrQTfx36yV/J3YM011tJ9KhURkx+lkIH0WkBbTiZSAURYfJWmNXJnb5dh+rrDOJ9jnEzgJ+rtGRUsyP1kaQ3h4WvFWJpyHoPeN6UOYC1QvrW3r71R+KSwx3VQozNIKkh8pKzVGp0BczYex8D3/sRTaw/hgA3LnhiPuvDl5uYiPj4e2dnZCAkJQZ8+fZCSkoJRo0ZxZTZu3AiGYfD4449L1jF37lzU1NRg3rx5KCoqQt++fZGamoqYmBiuzHfffYfZs2dj9OjRAICHHnoIy5cvd2Z8RBNEbIESu09czC3HS98fBwCs3n0FH02506H6S6u1WLX7CgBgyj1tEd0iAD8fM4Yp/+TPS5g+RDh7W1ghtEBd4CltBRVqnLldip5RIQ71oS4wDIOPUy9CZ2Dw6piuKKmdsWPzMV0xRELzeDp6tTH26YNtZwSK3oAOLfHlU/0xcNEOy43wdMbdrwxHeCvbwSEIoimi44IHmASPS3kVpv0i9ywWP5XCTLgRyzqOrIOQgh+17Ofjt/DB5D5QKuSS7nbiCFls8liujA0FypoyKLXPVkQ1Vmlho6bNWH/EzPXPUQVKLEBr9Ab4yU2Kr1SfdHpGkO+m55t/4DmehY8VJNlray14BltGfB75lgfxtRFbLqQsUIxAgTIgwsIaWLP+mLltCvugkMu4c6I3GARrs4x9M/XlPz+fwZQBHWrLmuq9p0NLbDx8Q3DcP+6KwveHbkgmixUn/tXoDFBLRHW15hooVvTYtW1iVzcxBpEFSiYzBq94//fzVo9jz//WE7fM9vmqFECNDloDg39+kY6yGh12nsvDlffiBNe2RmuAv8o5yxErM/kogGq9OIiEYz8SexQurd5g0f2O3Szl2rz7Yh6X3sEZvjuQhSfvNg9S5ywOKVBr1qyxWea5557Dc889Z7XM/PnzBXmgxLRs2RIbNmxwpGsEwSF+kfvzQshqdAaM/ngP9z23XLjg1x7KeBGOruZXChblggEKK9RY+3cmpsV2QFiwH4prXfg6tgoUhINlST6V7VYFKrOgEp/WRup5tH9b7kWiAJvQVoHnvzuKva/dz5UHgPce7o3xfSLhq5TDT6WAQi4TPFxbNfPBHWFBSOdFeYpu4Y8OpDwRhEVYIciS4MFXgk7fNlmv/VQKM+HYzLXLCRc+PpUia/7Oc7kY2yvSTDgzGBhU21jgrTNYd+G7VVyN6euO4KnY9phyTzuzsvycRkYXIPM6+IqAPd46jqqX4muk1hkE7llSLmF6A2MmDHautfAo5TLOrYgdr1U3R07hEfVLZIGy1WepPvLL2yszi8ux9x/rBaKQyzhLglbPmOUNkgpLLb63pNwmWbdHsZsoYB6pDZBOQmvNAiWeeGCVXFuhwRlGqGQbGMau4BVsc2dumbupmixQDMpqTONYuy9ToEAaDEILn71RLC/zJmtYCxRf0WXrlLL6BvkqBXnFjOVst6nRGySvCWC6/lKTEWXV0sfYy62SatuFHMCxLGQE4cVsOHAdT3x5wBj1DuBCAZdUabkZFb4AApjPNPFZknwOA9/biexSoZLFfwnklaux+ZjJVYUB8F7yeazYdQUD3vsTvxy/xZXvZEGR2Hk2D7+dvG2XW4or4M9uH7hayFmgokOMM3d6yAWC081i4/lsHxqAEH8VJzC89VBPrkyIvwpfPNUfD9/VRtBWWJClRVMEQQC1a1aszMiyAnBxpQYPLf+b2z53UwZO3TI+zzq1Nj5bxMKzsxYoVogRCzlscACxcKY3MDbb0umtC+Zv/noGZ26X4T+bTxn7IGqDXfsF2OeaaE/IYoctUBasOeyzWyqogc7AmLkOqWsF8SF3tAJ7iDU3LnH75hY5yxYDKasZC+fCxyui1RnMorJZwiwaIwOs2n0Fy3cZJ+gUcpnpnjEw+HKPMB+geAKTYcytlFKKDitcS91zUkqZlKu+dQuUsF52aZtNBUq0zs9e6w1bTiFx/7DvW/G99/3hLGG0PNE6RIaxz9U1+ZTJddK/1qTCt7SxSppkEAsHIvTx2SCx1IFrz8o5q6tF3dXQqm2iUcAwDN6oTdTGzhZ1jwhGxs0SaHQG5JbVoE1zf0xfJ5wNKpBIfPfT0ZswGBh8Xvuw/3JvJvrzphrKebNAeeU1gjVWNVq9IArQ3E0Z8KmdlewZFSxIwtc+NADXC6twIbccL/7vOPIeVNvMzO4K+DNOiT+fwoon7gYABPnIgGpABznXZ8AYOhiAWWjz+HvbI7ZTS4QF+6GZjxJyuQx3t2uBqOb+eHKNMWRrq2akQBGENXQGA5amWHbxYQUXqdnT1WlGV2I2FLZYXrI2QQQYw5e3axmAXm2CcZo3+601GOArV6BKpECxgqtYyNcz5utbzMdhsCpQnrltal+nt17WHkHKnhl4xxUoYbul1Vq05k0SKSWCSOgNBrMAFqyAqpDLOOsUOyZr/WaFS2sufCXVQpdxseBdwXt/cUobr74dZ3MxvGtri33gIxVMhH8vK+QyqLg1XgazAEti93YDYz2MOwv7fpJy4ZNScqQSvlpThjSiwC1stEAp65YARni+8yvsSzTLnn+FhNsaO1ZxOHGdnhGMX6MzmAWcscfVlT8mPwUDQCZQQtl7S+q+lLJw2/Pb/Pm4uasiizVLbKWmbhYoV0MKFNEo4JuRrxcaQ2M381MiMsQP1wurkF1ag+zSGrMHaUG5UIEqr9HilR9PCLYFGsrQuuIkZFf8AKUCquvFGCq/BAAIupmDO9U1UMh5lq1qCG27jPF7LCpxye8a9xB4pGM0fi42Wa92J5/AfeiOrhFBKKrUYtmBcgwZNATjekdCb2BsLrq0lys8BQoAN4sd5FMrHEHOuR2W1Wi5WejIEPNIRp3Dgsy2xcaY8q51bE3uewRhDZ2ewZd7My3uZwUJNty1FFwobJGQU2VF4Dh4tRBTvjgAhVxmFjRAb2BgMDBmSSm/P5iFEV3DIJabDPZYoGysgeIT+/5fWP1kP8t12eGaKCVcJ/58CkF+SiyI6w7APgWqsEKN0NqJIPHseHmN0CVNSljVG8yDT7AuUgq5jAuywCoK1hRHdkzWXPhe33Ka+yyTmff51xO3uc9SiXQBIM9KFD4+ttbcyWUyzqqi0zO4r3MLfLP/Gre/RBShVmcwmOWWEq+bAvgWKAkXPgkLVGUdXfjknAuf9XucgfBcnrAjpDfAU6Ak3vFsomWxu2JZjVbQz99P50jUW9svK3mt+MFr2EdMtdZ0vth7btkfwtC8BoN0wAwphdcRrEWjFK+r9DSkQBENnhqtHu9tN0WOYhMZBvgoEBHMKlDVgpfOthcHY8LyfSir0UGt03MzY+IZMQB45tKLCKvJBIyTvegPYL1P7c7Ltf99zA4zZz8Qyy97GviH+Lidxn8tAbwHIO5/76HFjH/imW8O4/Xx3TF1YHs7GrJOZqFwHdaN4lqFU1X7ooMCap0B1Ro9Z31qHqASrCWzhkIuw7wHuuDvywV49r76t6gRREOmuMr6LDWXH8iKsM+6+fCFjtO3SvHZX5ctHYI9l/K5Y8yj6jGokRBcd9RGsjNzIWNsK1CsUmYP+eVqbOMJ+mIstcWvXUq4+/6QMd/kK6O7QgaYKYJS9Ht3Jxd8QtyuuA2pKHx6g8FsPRYriCvlcu7do7ZHgbJggbJ0DMNYF2j1FuoTKzaWEB8nbkspl3Gh3fUSa+BulQhd+PQS66+kFB32PJ+4WWK2r8rONVA1Wj1+P5WNjJsl+M+YbpDLZfjfwSyEB/uar4GqtQxtzbBsOQGkw/nbA3tepAIncGug9OJrpJVcAybuD2DZLa6sWisI8c7q/3wlVGdgUKPVm03yWHKjtaaY2gPbV0ej/9mLeNK8LpACRTRIGIbBluO30Kl1M+w6nyeI0sM+xAN9lFA1Nz58sktruMWsD/WNQq82wVApZNDqGRRWaLhs8WJhRgYDWtVcAwCcM7SFHkIlgs3RYA89o4Jxq6SaW3PUKyoYgFH4YHNJNPNVon1oADS5l+DLVKOtLA8JmzJQpdHj9S2nXaNAiQJZsGucmtUqc0yt+ayoSsOtJ4sIti8qE8ucB+7AnAfusF2QIJo41nLuAEYL8bnsMquLs/mzyGzOo0Xbzlo+AEIrhlQ0P4MV7cIs5DhvAbvYHZDfhr0L2wHp2XgWe9ZAWVNEanR6+CsAA+OYVb9CFFRDLGhLhV42roESbmf7L5fLOAsDGynO2jm6VlCJ7pHBEi58ls+HNSske4rE50ocGMAS5gl9zYMvsNdRa2DMLIfi3F5SQnOKhGWlWW347uNZJSiu1KBFoGkmssYBC9Tz3x0DANzVtgW6hDfDgi3G9XeLH+5lNg4A2HvJPCUKny/2XsNDd0ZbLSOFNQsUOzkiNWkgZWXl8/flAhzPKsHM4TGS+2t0es6Fb3SPMBTmGc+1wIVPb5D8vd0oqpKsU8pi6Aj/+ekkJvSJdDj6n73cKpHutzOQAkU0HBgGuJgClN7EpbwKHEu/jvM+CrRt4Y8nFRVmxQeVhUIplyFQUYC2l49CqZDhSUUhBlWFQnb4CJ7zv4DSah02fLaLi27zaL9oPKkwutU181VAq66GvHZec6LmXWggylVUq28p5DIoZDKLL/b/jO2GXsNjoCqrwZfJ5zBjSCegNky4AsDl47cwd1OGsb5y4DvVYtynOANf6FAg4b9tjcIKNU7eKsXwLq3NXtyFlRpOgesZFYwzt8twq9YCFVhrgZIpjI+F4koNDmUa8yxIJSIkCKL++et8Hv46n4efZ1lOJM+PBMeue7CV84TvjmS2lsWJkOOsgOcnFR4PrAXCep/4WFu7Yc86C2sz2NUaPfz9FXavgdLpjUlXWQuWabs9FijGzALFTvIp5TJujYtGbxQ8rVnpnv/uGK69P96s39ZcGm0FPjBIWIYsRUjjk1+u5gJ+sIhd6owuiqYw5raEYqmxZ0i4wfEj890srhYoUNJR+My38c9LblmNIAm1WOG6KhE91xKX8hzPLcnezpIufLVjLRFFMARsr3F8tnbNdzM/aVG/WqNHda2C7a9SQMlaoHjnRqdnJK2Y0y1EF6yrBapaq8fZ7LI6RxC1hCstW6RAEQ2Hm0eA7/8JAOgC4F0VjD4bRYBYrwEA1L7rRqsA1BqoxrCfbwCvovY4Pe/4k0Bf9rPBtL0S/ubKE49BMaF4fEA7zKqd0QKA54fHcPmiQmsf8OHBfvjkn3eZH985VPBdU/vT9JFpJR9erDuEOMSr3sDgHyv/xo2iaix5pDceH9BOsJ9N4hvTOhBhQb44A6Cg1m0xpDaGqa7WyvbgZ/u449q1DLA4doIg6h+pWXQWX976qJO3SnF3uxbcLL099Um55LHCrFxm7j4oFm70jEmp4StzfD784wJaBtrj62xEJRGQwVL70mUsC3LVGj3gr7DLhQ8AanQGKCUEL/a8sbqmymIUPuF29lzJZeYWKHsEPLHlx9oxUlHp+BgkIt/ZYyicv/mk2Tbxu4ofhU+rl14zw0dnYOyInQjB+WQgrFPK4ia1TZwPi6+MVGsMFsvawpnAdNbW/bAunlKKsL1R6c5nm1uEjXUauLH6+yjA6v9CFz6D5PjZteZi6roGCjDeK/VlgdLpSIEimiKVtRHs/Jpjn74HZzWSAZD6SXQIDUCrZr44cr0YSrkMIf4qFFZq0LtNCNq28Mfh68XIF/nDBvkpUV6jQ1RzP7Rp7o/D14oR6KNAqs5c6eHTPTIYcb0jsWhiT/zf1jMY0zNcELXOluDQWhStjlXWfCD94B/7yR7kl6sxa3hnPDGwHZc8cMWuy9xCyw0HrpspUGwI855RIWbnLNi3dqGshJwW1zvSav8JgqhfxDmZ+PCVlkdW7se198fjzrbNBVE/xVRoLCtQBgPDra9QKuRmQpFUeVap8VHKJZUuQDoamiWsWaAsWfr5gr81AYxVKtgilvrLciGnDDGtTYE2+kSH4OTNUnMXPjstUOxxSrnMbA2UPevExEWsjdVW5Dg945ywKnVvSbnwmRLpMmaR5Mz6YjDPmdUhNABlNTrBvXN/tzAs5NoU9l0q0IPUb4c/5re3ncUHk/pw36u09rkwSuFMpDj2mkspRKwFSuo62qusWLIKVWtNLnx8C5TQhc+x+8MVCpRCJnNqLZk9aFxYLylQRMOBMd74l9EGT5a/aLa7R2QwzvJmWlY/0A8x3VrjlUWpxsR+tbrS18PvQdtuYfjhxxP48ehNYSW1VvKn7+iAR+5ug1nL/+a2iZk/rhuXYTymNtrc4wPaoWOrQAzsGIpdvHDmoc2sK1BmPvKsBUrUOMMwOHWrFFfzjS4FS1POo0KtxatjugEAtvDCg0opbddqZ406tQ5EsUiYYRWomPAQgHdaHuobhQEdW1rtP0G4m5UrV+LDDz9EdnY2evbsiaSkJAwZMsRi+bS0NCQkJODMmTOIiorCa6+9hpkzZwrKbN68GQsXLsSVK1cQExODxYsX4+GHH65Tu84ikwkVgmorQp2U25ytBealVaZni9gywOamAgCVXAax2iMur2cYTqlRKYzrXqytobIHa4qE1g4hzZq1g12nwXbRRym3GmFt0qp0/PnyMABGpYA93+ZBJKSi8DEWo+bJ5TJOQDb1ybhPfP352BvNEJCOSseHYewLpmEPYsVWIeMn0jXYZYES581VyGW4u11z7DyXh+eGdgIAtGnuzyVxFVsapRQNKeutuNwPR0zrqG2dM2vY4/7I4qOQQ6M3cNecVT5CA31QWPt+Zi2UUvenVBANKSxZqqo0OkkXPr61y9Z1Y9eSs0i5GjqKTGZ/Di1HseX26AiUSJdoONQqUIVV0g+35U+YrESvjumKsb0i4KtUoFuEMNQ2m5so0sq6ntZBvugVFWIxm32f6BCM6hHOfWdnJ1UKOYbc0Ro+SrkgiWxbO1zg2HxMgMkC1UJWjlYo5f4qi27j6rVMwbbUQ6eAijygIg9tVGXcdnYb++erLUVR7k20Qim6NatGtE+FoJ5IX6OGGR0axAkCHz3WF++KFtQShKfZtGkT5s6di9dffx3Hjx/HkCFDMG7cOGRlZUmWz8zMRFxcHIYMGYLjx49jwYIFmD17NjZv3syVSU9Px5QpUxAfH48TJ04gPj4ejz32GA4ePOh0u3VBbJW25orlKxHi3NZMMD8HnlhZGfT+X1wePKl1GeLk4vw1UCqF3CUpFzRWBB2LQpCdFij2ePYMifMTScEG1PFVmsbHCpasG5nUGiit3mAmrKt5FijWesgKyKys6yNRF4sjsqUtZcCo4LlGqDRz4VOYgkhIReEz64uEtYOvOPCT0bPrcsXCvZS7XvrVQrNtYqWKf89as/baokLKhcMCrPLM/v5YS1GAr4JXxrILn62w6iakf4+l1VqeBUrOKa/8Z43OhgVKnPssrzY5cl0eARo7lG1n0bswGS9ZoIiGQ60CJeUlrZDL0K5lAN5+qCcKKzV4YURnbh8/0SEARNS61o3vHYlP/zTmc3p8QDvBAuGo5n6Qy2XoHNYMF3ONbm+DOrXE/qvGoAoqhRwxrZshYVQX3CquRt+2zc361DMqBPd2aonm/j7cGihrjO8TCT9Vf7zz21kMiowCLgNzlFswR7nFVOgz4FEAj/KD4ukBLDN+3AAA7L5i03YVgLG1f/ADkGLcPoNfzy7jP4VCgUuL47hoXgThbXz00Ud49tlnMX36dABAUlIS/vjjD6xatQpLliwxK7969Wq0a9cOSUlJAIDu3bvjyJEjWLZsGSZNmsTVMWrUKCQmJgIAEhMTkZaWhqSkJHz//fdOtVsXxEqRNUFYygJlS4EqrbZsgQKA/x00Pg+llILn/5ch+G5M6mlSIoxCVd0EFWvrO9ioc6dvlQq28y0zVo+v3cfKuuK1pFKwArePUs65pYmtH1KJdPkRYrn2daZEun6chaHWAlU7NmsRXh1ReKTCevMxWHHhWzOtPxeIwB6kwn+ruKS3ttdA6RkGclERS0EwTLmgeGuZDIzdSoU4sAS/b9asvbawtlZRjK9KjnK1SSFmLXiBPibRnFWy6uIaZ8kdrqRKyylLfj4KKGtPfrXIAmVdgRLKCKwCacuqaw2NznbAEWexJ4KnvZAFimg4GFi/deMPljXnA8bZDqVCjmmDOiBhVBfBYXwFKshPiVa17nRdI4Kw97UROP32GNzfLUxwzPAuxu8RvOSx7UIDuGMHd24FAJg98g4sndxHUsjwUcqx8blYrI7vZ7ciMrJ7OHa/OgKR/R6CQRUAA2TGP8bKH2RA7Z+4LFO7nan9428XfwdkgNIP6DIWgLlbIUF4AxqNBkePHsXo0aMF20ePHo39+/dLHpOenm5WfsyYMThy5Ai0Wq3VMmydzrQLAGq1GmVlZYI/S0QEm55V4iAKZdWW1w+pFKJw0lot1DaEQL6A4oiAxJ4vPmqtDjUa43aFrG6zz/w6LfHGllPQaDSCQDcAUK02nSOr64LUGmi1Wmy8alQ8xWthpSipNJbxVci5fDk1Gh20Wi1nQVDI7BP6WHc9GRgoa4+p0Rrr0tbus6TUqdUam+Gr+VTWWHepUmu0Fmfl+7cLtrsdwFzIl4Hhoipo9XpobNyT1WoN1Bphf9U6A3d+9Xq98RxptVzAgxq1lttWXl0jrlIAP7BKuej3pOWF33ZECRJzzYGIfaYkysZrX1NrPWvGs0Cxt4GtaIrW0Fg4triiBlW1Y/WVm/JACY7V6c2uCR/xWkXWhVFKJrKXarXW7gAZjlJjZSyOQhYoosFQWqVGCAADZPjqqf54oEc4vqh1M5FZid3TiucK07FVoEAxYF3r+GuUhndtzYVFjeTlP2rdzAdfTbsHv524jadi27tkTBbp/iDkr2dzXzvM326xqJ9KjnOLxsLAAJ1fTxb4zWf83yhUa/XYlnELBZln8cV5BSJD/JCeOBIA8Ne5XHRq3Qwdea4RBOHNFBQUQK/XIzw8XLA9PDwcOTnmOWMAICcnR7K8TqdDQUEBIiMjLZZh63SmXQBYsmQJ3n77bbvGplXXgHW3qamqAN/15sz5S7A053nlwnmAl6MuOTkZV67JufLDIg1IyxYeW1Vtakut1cKSm49GXYPHOhnwQ62ysfanZIhFh4uXLtfOXitw+9YN6HXspI7zXMm8DkAOOZjaSSITt0pqsPrH3836kbrzT25bRWWlxT6kHzqCysuM2fHWOHT8JAAFdJoaFOZXA5DjxMmTCMw9gYIC47m+nnkV9sxLZ+fmGctfywSTX3vecguQnJyMrJvGunQatWT/t27/HeUVCotjE3Pp6jWrfdqxIxUlZeb19W5hwO6dO/CfvsCqswqUaW23V6PVCeo5f+4sCvwAQIHCohKcO18EwLK7ZFranlqFwXRdNFot8vJyAchx6tQpNMszRv+rKDX2+eCRo7h6msGmqwoMizRYrb93iAYGBjiYL8eFq8b7i6WguJTr+62cfLPzYS8Xbtp/rE5dDUCGM2fOIrn4DK5lGa99ZWkR17drly8CUKCqRvp+sIfc/ELJYzPOXsSNCgCQ49L5s2brzwDg1PV87NmbA/FvxVfOQG2QQa/VCOqurDb2k9FbfqbYIv3gIZSVy50+3honTp1xWV1kgSIaBFszbuHNracBAL4+Kgy+w2gB6l2bS2lyf8vJ6/gWKEvhuPnrDe7pYAqYwDfCtGrmizvbNscbD/ZAqGh9grt55r6OWPt0fwBGP+hytQ7lNVpOeWIT3/5jxd+IXfIX3vv9AjZcMv7c+dGkRnYPJ+WJaJCILaS2XE6lyou321Ono+0mJiaitLSU+7txw9yliyW4mem36B/YTLAvql0Hi8fd2ae34HtcXBzCI9tw3//7r5FmxyhUprQMWoPl/gc1C8Br/3yA+77khLnS0b5jR3SMMbpNx3TsAH+/uj8fI9oYn+k+FsKi9xtwr9m2Vl37cZ/9/S2vO+3T9y6MGjXKof607WT0bGgR3AxtooxRSbv16Im4uDi0amV8H3Xt0tni8XyatzCmrbgjJgb33mPsc0BwCOLi4hAREVXbf+EaXfYWGzx8JAIC7H9mh4ZHWd1/I/AOBIjuNQBoHx2FuLg4TJ8chy0v2hckRS9KTNy7V0/cO+AeAEBgUDBiOltPrj5o8GAMHTpMuFGuQOswo0dI7969ERcXh7i4OIS1Mr6n+/S9E99eb4brFTKsv2R9LVunDu3RoX1bAMCRAqH4q/A13S/+QSFW67EGo7Q/6XzLEOP67K7duiMuLg6hYRHGfkabrlmfXj0AAAaZ7XV6lghq3lxye9v2HXCpzHgeuvXoyQWR4FOolqFzn/7mO+TG/gQG+AuCp7D9DApwPndkt953wtfK79cZxvY0Tnx17dbNZXWSAkU0CD7ZeQmKWp/6yOaB3MLbj6f0xVOx7TH3AcsP5rAg0wMtuoX0j5JvpWrOS6g3pmeEabu/5TxQ9U2gj/Dh2Sc6BPd3C+fCl6dfKURxbVStZr5K9Ik2vgCu8XI1VOmND7nwYPsf8AThbbRq1QoKhcLM6pOXl2dmHWKJiIiQLK9UKhEaGmq1DFunM+0CgK+vL4KDgwV/Fsuq+K47wtfzhRzzZOEsfj5CpUalUoH1pnr7oZ5oFWxyP2ax10NGpZDD39f6Gk7WdRgwhlSXWgskxcxhMRb3sf2z5Ark5+NjluvqxY0nuM/WnOkMMhlUPAXy+eGW+8FSXZs/xlelgE/tmjMD5LX1GMfuo7TPosWut/FRKRDobzy3Gh0DlUrFrRwTy7IBtfeGlpFZHZuYGht5b1alZXL18ecBAnyVUKlUUKlUiAkPwfuP9JY8HoBZoCYWH5USfj7G82xgAMiM19JSVFeZXAG5QviuMxgYyGuPUyoVXJ9UtdeAkcnNgppYwkel4I4Tc6O4mvtc7eTaHQBcehV74H7vMnntb9Z4JYJ5MkhA7W+vLglqLa09q+TluwoN8pN04QOAy/nmOZ/Y/igVMnz99ABuO7vGyJ51hZYoq9HbFc7fEdhzrXeh2kMKFNEguFpQCbmMVaBMSlDnsCAsmthLoCSJ6RJuml3jrzHg489TUFoGmISFEd3C8PywjugUxGBYl1ZO97+ufDXtHsH3TrVh01kL3F/n8pBXZnyJhDbzQbdIy0JauIVzQBANAR8fH/Tr1w+pqamC7ampqRg0aJDkMbGxsWbld+zYgf79+3OCtKUybJ3OtOsIMplQgRIbtQ5dK7J4rFhYyS2rwfZTRhdgVgH5caawj/auMVDK5VZzMgHGAAGs4KR0IAqfj0KG9c8MkNzHrqexFI1OpzdwE0UsfPdla3EWtDpGsKbkob6mGf8gCwmIy2vMg0iI8xp1sNOafyyrBAAbRMKUB8pgYCyux2LLVWv1DkXhY8cpXsvGh10vxj/X/iLL30N3RqFruLSiZEmBUshMUfi0BlO4bkvnWG9guDLcNgsXkh+cwl5UCjkUdqztrUsYc1t5t/iw55sNfmItiERd0FpQom+VmJTGAR1awpKube1+U8nlGHxHK3z6uDEKsimptPP9Lq7SujwKH9sfV66tIgWK8HoO1woO7LybQuGYKbstz+pkLXT5iyM6Y2iX1hjZXTibnPDAHZjTS49ACw99dxAbE4qH7zK55LBud1PuMbojZBZW4mbtDFp0C3+LLzQAHh0HQbiChIQEfPXVV1i7di3OnTuHefPmISsri8vrlJiYiKeeeoorP3PmTFy/fh0JCQk4d+4c1q5dizVr1uCVV17hysyZMwc7duzA0qVLcf78eSxduhQ7d+7E3Llz7W7XEcQ5gxQyGXx5QocjM7hiYeWrvVfN6hFHI7VXQKnR6c0CWrC0DFRxdfGj8NmrQCnkcgzt0lpyH5ds1oLyptZZD3VsLYjEtpO30XvRn9x3fiLikABpT4Pc2gmqFgE+vNxGdRPyFDIZJ0RrdAbM3HAUR64XS5flhQN3JA8UG9Y7wMfye1NKgfITKVABPkpsniU9USAuy+8zm1xYb2A4q4K/hb4Yyxg/s+eYYaStiUqeYmZvvCNjjjLbvytrSpArAqSwsJE22fPPThrwr5X43LZt6Y/hXYW/mS+f6o/OYeZumCxqnfR4+EE/gvyUuFAiPThrvzP29yme6Ph/9s47PIrq6+Pf2ZbeQxoJIUBoSQgdAlJC7yCIdEERVKSJiAWliAI2REVQMBQFxVf4gSJFAhIQ6b1HSkIoCSWE9LLZnfePzU5mdmc3u8kmu5ucz/Pkyc7MnTvnzMzuveeec881loa/LAqKVRbNwscwPAOqDI+sOZABRdg8CSUL0kq0P6NmxgJLJAxWjG6Bad0aoEcTw6E2s3s3wo8vta2Q67ky4acddnPUNPKBJSnZUzPzSw0oT2d0LMkS6OUsx7l5wlh/3fVlCMLeGDFiBJYvX44PP/wQzZs3x6FDh7Br1y6EhmqSu6SmpgrWZgoLC8OuXbuQkJCA5s2bY9GiRfj666+5FOYA0KFDB2zevBnr1q1Ds2bNsH79evz6669o166dydc1B10jQyJhBKnLzRnBlUslnMeglpsDHmSVejG0v2e6HRpTOygqNQuJgV6jdo0alVrNGTwKKWPU28HHmIpFvHWlxChQqvTCmvjh18Na1dY9heOf648F2468++5hIFQ7q+T318VBxhkFWqORNSuorhTddZL2XnlguGxJObXauHdNF623y5CRA5R6QPhtn7Hyuhh6RlJJ6bvAX0+otqcTImvrR0nwjUN+nWLvammHWG30feN/zxxl0jK9qYD4WlJadD1zFUH7/dHqrH2f+Z5o3f6IXCrR+y43DnDDp881M3gdQ+nEtSnMvUuSZjkbGFvNM5KVUBuuq/sM5CL9qGgdj7EhCpXmrwMV4mV4cFwulXADVpb0bNlmT5Go0eiucaFdj2RgVInxU4702gOaBeHNXo0sssCjtWgustaUdj7Tg8xC3M3QxCkHeznBw0mO5KX9cXZeL3g6K/D1iGZwkLDoHxWAIS0MdywIwl6YMmUKkpOTUVhYiNOnT6Nz587csfXr1yMhIUFQvkuXLjhz5gwKCwuRlJQk6jV67rnncO3aNRQVFeHq1asYOnSoWdc1B92OnEzCCMJ1dD1UWro2qoW+kQEI5nUY5FIGaydownyVKjUOXHvIHdN2tgzVVxbGOutaeY2F8M3t18Tg+YYMM6DUA2VoQKtIpUZhiadA2xnnJwl6PbYB6vqYNhGdv5Cui4MMh9+O1Suj7WzKeUaBbgifuU0TP8RNt2Onm5hEUrJ9+vYTQeiVqYgttqyF80CV04Ay9BilEr5+pSF8EgmDLa/qe7NUbOlaUfz3VdyAKr1vEiM3nj9/2EFumneUb3AEeQinBzgpLBfBwV9I9/itdFy+r1nigD9PWdeg56+tpUUmZYx6fApKPFCNA9wE8wa1hqL2Xjb1Ev+y5xgzoErO1f09E/u54T9GY1MJCsuxDpSx5yqXMBTCR1R/vj1wAy0XxePmI82E6byiYi45gr9bydwkpma+tpM718OM7uHY/2ZpliJt8osilRqJD7IBALVFRmL6Rgbg03YqLH++mc162AiiJiHT6fRJJYygI68wMNndxUGGVWNbYUKHuryyEihKUmg9zVMim9fh0XbSKmNdN86AMhLCF2xkZNjYfJRjJYuWFxoYPS9WsVwIUrswTSKQHIHeUgxoZjwDHScHT95arg4I9nLGmHZ1BGW0nU2+UWBofo6p8D00xsLyBkUHcR3UBTuulOtaxjwnYl4fRxGDy9DTMvRuSXkd1+yCYt56WYyogaZSs9waV/zjYp1pGdchZo12nvkh6y4OMpO9o4DGU7Z9akfBPieFae3nix3rlllG2xanPMnDiNXHOD3reDtj9bhW2PpaB732WhMWqePtkUqMvj/cPDgpg0sLe2Pxs5qEINygQMm9jPYWryPbqAeqxICS6MupC/85rhzT0mCdKrXa4OK/hjBmRMukEjKgiOrPZ38lIiNPiSW7rgIA7j8tza5T37ekIZZYzoVuTzjKpXijZ0NBGnInhZRrGC/czQRgONMgQRC2g1wqQThv3oJcKhF0lgyNKGs7CvyOoEIqMTi3g98pfrYc3mdtREBEkH7IVakBpebmjch5YWmA8ZFhU7wBhrwtxWqWC3nShiDxF0BlTKxfVw7t54+GRArKaDubMl4iAq23pLx2lEzCcF443cVx+QbM58OjTUp+YAxjHiWViNfHnFA1Q51XmYThwhvzilQoUpV6oADgq5HNMa5dCGo7s5wcuSX3me8xEzNUOQ+USm30OfPnEwV6OJoVhTKnTyO9BFWm3pdiFWt03hlQ+h2/pbP4roNMgl4RAWgV6qVn8EkYRk8HuURi1GOj9ahpjRztvdO+01o5DL1iOUYyC/ITovARu898I8/BwAARoPleme+BMnxME/Yo/j2rCGRAEZXOg3zgToZ+GkxjPMrRrBKeVpKetKG/Kxjtl6+GeqAMoe08aDE24ksQhG0gkzL4v1diuG2phBHMzQnyFM8squ3jyng9BrlUYnBknW+UfTmiOV7pUs8sObXdmI+f1U9jreCF8MWXzN9Rq1mBLMbmchkbNS6LYpWamxyv/Q3MKxROljfZgBKsBab9Lzw3v4hnIEq185F0wu7MXPiT74HS7TC+3KkevF0UGNU2BAqZxGi4oykYy+amvTTf62lOmKAh0RQyiSAELSNX065r7/fg5rUxb0AT7p1WqVmMX3sCAHDnSen1xVJa80MfjRtQpR4obxcHszxQYvWaakD9fCIFO6d3wvv9m+D7ca1Ey7g6amTTfdf431m9UF8po5fURSZlEFnbA40D3IzO89bqrv1OlobwGe9TGQvh06KXFKcMD5Qx1GrW7LlKxn5L5NLSZCZFFUz8IrimxWoiCBHOpjzF4nMy9Fz+r2B0UAz+j+STXM0k6PuZmh/RAA8ngC1pHMmAEuCuEyNN6zwRhO2jkErgxRv8kEkYPM4pTf4wvFWI6HnajgK/w2Is851uCJCDkc4Sf8kHLdpxK7EJ4NpRZH7yg1uPcwWd/Yp6oAyhVLNceJ92jb48ZfkMKH7fy1BHTFu3VMLoeaDuZpg/J0lTl4S7nq6Xxd/dASfe644lQzXJAczp+IthzAOlDWvizz9+UmLsmIJUwuD31zvq7VdIpQj0KB3QyypQlpQXlpPwDCgxxDxQWm9KsVpt1DvH9wI5yAx7asXQPhu+0cTPIGjs/VKpWYT5uuDlTvUE60lqCXB35L6buvO+BQaUiAdKLIRPLpVg94xO+GG8yKK32rqkQgNKe7vlYivo8uB7oOrXEk/Vr5cUR+SZ8D1QxsZOlGrWbK+u8RC+0lDSLafvmlexsWtarCaC0KGwWIXn12hGk1RqFu9vv2S0fEZe6Q/2g8xCqNQs54EKdHcE2BLXKxlQAnQzBtlzogyCqCnojvrqdgAMTXjXGif8OQcKGWOww6EbCmisAynWCdGGYDEMo5c5TSHS8ZIwwix8Mglj0ENRkZ8qjQeqZN2cknkuuh1w07MB6nugdCkNcyv19mXmK7H5RIrAW7N8RHP4uzvgq5HN9erQFUcqAW9NKVbnmETgZTTFW2dofSXAeMiUNpSLP+o/qZO+p9KQCBIJg2iRJEdaQ0A7V1drQOl608o0oIwlkVDpZ4mszVuuhH/fNAaUuA5iaM/9nTcPiu/RMpaYpYmRtRgBIOGtrpzhl6ez7hT/O6v7feXPwdOVo6x5jtrfHDEDTMvOqTHQJZfXx9BdT047F1P390zst4v/G2XMW2to3qMxjCeRkBh9VuWFeqJEpZD0OBeN3t8j2Bd/5QFyCovxy4kUbk0NPg95CwgWqdS4+SiHW2E8wINvQNXMOVCG4Kc3JwjCPtBN86uXxcqA4aHdJ9PxQBlKpa1r5BhL4yxqQPGq1V20VKxTzjDCemRSCVaPEx8Vr0hYmlKlLl14VMRwYJhyhvCVEYYnkzDcyP3/ztzDxzuvCq45pEVtHH+vB1qEeOmfK2LMaq+tZ0CJJBkpC1/eWl+TOwsNILGkELrwr2DqosBA6Tvp7ih8DloDytVB855k5Zck4tDLMKj5r+tp0upcqDOHR3OsNImE7uLD/JTeR2+lc58d5FKzwka112/IW0DY1BC+D/obzj4JaIwkrSy6acL5XjPdQQAxb5upCWK0+ugOqvCNn1Af/eeuTdbSKdxXLyvggGaBgroNyQ0In68xkfnrVpn6E2HMsTioeVClJM8iA4qoFD76Uz9TUE5hMVYeuIF3/3cRI1cf0zt+WGdtjjO3M3C85McvyNMRUFMInxgt65Q21MNbBVtREoIgTEXfM8QIUvvKJIxox0jbUZHrzIEyNeTFmFdGrAr+Pt1Ovti8GjdHuV5ShtZ19Y0JwHgWvrLILyodpTY0Wd9UA4pvyJUlkkzCCKIlDGUok4oYqnK9ifa89Z10br7YOmHGqOfrAheH0vvQRWeBYlPSkpd3Tpr2PF0Zte+41sAtDeETlkvSJJDFqoSbgv3a96NIZFFlrUeBbyBpMfQ+OMgket8pvrdKF7Fbzl9o2dD6SgDg6awweAwoGWgouYDuO8T3cokNrJQXbs0mqfhzAvTfUaD0/mu9X2JzHMVCDXXh7zH2qvE9civHtEKAuyN+ntTO8Akw/lsyo3u4XpZAS0A9UcJiFBarcPRmOoqK1UhKL80q81yYiltLYc+lNAAaD9XDbKEXavPJFMH2T8duc9lpujby43mgKESNz8JBERgYHYSZPcIxb2BTa4tDEIQJ6IaUyCQMpsY24Lb5c234aDuAgix8MonBn8U63sIRZWMdMN25GLr7TDGgJnUKw8WSjKCAxvOh63kxRZay4I9SG0qQ0L9kdNwcypJIVkbKaK6ciG6iHigD90BsnTBj9IoIELwvuvfEJA9UBZtW3dNLPVAlBlRJtIRu51rFara16yBp0d4brQdELFvi1VThObrlGgeUeo9kEn3/orFMeWJGq9h8JjHKercZpvT7na2T5Y7/rMSy25X3MekmkdDC/y0S01lZrPUAlniwRJJc6L/bIgMIvDLGtNDOl1dIJegTGYBj73VHh/q+BssD+u+UdjPYywkMw1RKCJ/lVgQjahaqYuDeKaC41HW+/dQdbD93HwDgD8BfAiwc0BiPbl2Ga4E7ErNzIXuq2Q8Apw/koG+k5sfo3tN8+KVfgJ8EmNK1HlYm3ALSgBgJ4KqQwv+xK/AkSXMieaAEhHg745tRLawtBkEQZqC3GKZEgu5N/PHB75cBaEahxfpgYkaVXCpBkwD9ORerx7XSC10x1hEXMwz4RpNuiJVuJz3UxxlujnKB14plDV+zImE1/NF/BwPeFT83zcj16DXHTa63TA+UlIGhJWr4p4p3IPWNIkMdbb2OcxmCSRhhKJdueKWjkTlQWsoKBTPU6dW+FrqdWK1nQ2tAaV8lc9PLF+p04AHjmeO0BhegWW+o2xcHUc/XBR5Ocr3na2yBYf49P/puN9x4mINO4bUMlheT3XgZ8f3856DrOSlQqsq9ppvW2NFfW8r491DrgdI+X4VMopcCXS/UUMKgXZg3jidp1nOLqu2B51oFY/4fl9E2zNvo9+xamsYlaSxzpC66Zbe82gFf77+ODwY05WS2NGRAEeXjwEfA4S8Fu0YAGKHrtd4LNATQEQB0j50p+QNQG8Av2uNHgE66ZX/kfZbQa0sQhH0jlva3lpsDfFwUYBgGnk5y0dFg7S6+kaKQatJcvxATih+P3ub2dxdJaVxQbDjsSMyxwjeggr2ccDu9dEkK3U6Jdn6Ig0wCbULBBn6uBg0ofqiSISZ3rofVh25x23V9nJGcnocCngfKUaRzpO1kuhpJrAAALz9TV3ieCXOgTFlAV0xnsUxlhkKL9OYJldH/Yxih8adrGPC3o0M8cf7OU/06jF/CINr5d+k6mfu8XDThbs46z8CUuW/jY0Kx7ew9AOCyU/LvlbGBAP59CPN1QfLS/qXX1rmvxgxLftlADydBRsGyMCWBiSmGkJghZszoM0UmXWOjLFmVJXMftQMV/JA/mYHEFFIJg00vt8M/1x9jx/n7eKNnQ9T2dEJEkDuaBrnjHi9z5cwe4dh29h7a1PUWZMkzS08G6Buswu67GhlbhXphw0ttucNlpWovDzSUT5SPjGTNf1d/wK8p4NcUtyShuKYO4f6uow7YWk2Q5RiMh071BMe0f09dGyDTLZzbviUJBfya4jpKy9yR1+WugdqtgOiR1tScIAiiwmg7Rq91rQ8AmNu/CeRSCQ6/3Q3/vhMLiYQRn0dQso/fh9caY7rlxTpf1x/kGJRJzDDgR+19+ly04Jiul0NrUPE9WY5yqZ4cr3Wtj07hvohtVPZofr8oYRiedp2bAl7KcmPze/j3ZHxMqN7xOb3CBdtlz4EyLYRPzEjQNZY0C+mKn68/Kd94d00vi6MB4xYAXjOwFlhZC78aQux2LB/RHG6OGgNKb+6XkZusfYcmd6mv74XjddINeXi8nOVoWccLU2Mb4JtRLfSMFN1LG313TDBaDaEr39ciUSJiOsS/0VmwrWvcMIzxjIoAMLpdHdH9WmNH990w1SOoXS6Ab9hof3vEPKYyqQSxjf2wbERzhHg7QyJh0LquN5wVMsG961DfFwffitWbt1eWnroYU6OiywCI1mnxGomagfYXs9NsoN1kAMCsb//FOd6olquDDGcnd8OBXbvw1DcK83dc1a9HmDcCnUJ98dPEdvho7Qkc/O8RAGB0izpYLLKII0EQhL3zdp/GmNatAeeNKWudGe0+/twk7T5+eUOT4431I8Q6wvyOjm6dhjpiunOldDuxb/dpbFgIHfgdn4HRQVz2Qm1Yl4QxPbOgridETLayDCi5lBFd2BUQpgEX67CJeR0NGUbGdBKDgfD56YXwyfnvlfg1yxqlN3RvxO7GkBa1edfT9bwZvob23koZ/fBGU0L4NGF6DGb3biR6XPd5G5sbVlZSDblUwoULyhgWxaxhA29QdBAeZRdiES/Blq4huWhIJMJ5Gf8A/feAASOa6IHPx0Mi8fPxFL392vdPIRW+G6YaUNrvu0IngQ2gb+A7lmmM8+ZdMeJymBPCBwDeDoaP6WY9tQTkgSLKSclPZskPwIYjyQLjCQD6RZVOtowIEsbnB3uJN+5aQ2kYL5ucobIEQRD2Cr/vZCiUzVga8wZ+pYveajuF/A7I58OF3iJTEEsiYaxzZSjRgQH7olxy8a/BsizXeeSntjbmneEfcjHBw1JWWJVUIjGo3+nbGbxyIh4okYn2hm6vrgGmXezWEAzDCJ6fngeKp7vYc36nb2OTPGvlwZyMglovqERi3KNq6twxXXSPGpo/Z0pd/EWpdfvnYufq7tHPWli21xKM/nukdx0D77BWJt3QOF1Zp3drAAeZBEOaBwllKbmubgZQzTFhHWW9SnwRtZ915TB33lJLXxaTO9XF2gn6yyboZj21BGRAEeWDlxFPrWYx/4/L3KEPBjRF/2aBghGgyCB3hPo4c9tii8zN6dMIId7OXHktQ5rX1itLEARhz5iSMvotkVF0bacr3N8Na15ojb9mdtYrAwBNAt1E9xvr14gZBu8Y8RbpqqDVSWzh04WDIjC5cz0Ma6n/e756XCuD1+AbEixKO3HaLHza1MqGbid/lN+UOVdlDcbLpIYTPxTzskuIGXViE+0NdXZ1349iY1YpNM+CX0S3k8yfC6ZSs9j6mnDBVCnDGH03dOGv+VRWZ9mchBjauqQ6CzIDwntqKKtaWaGOupd+klMkXhBlf0cDPR1Lr6tTVNSA0vu+CLfFZNctw6D84Wja+nWNCV09Z/VqhIsLeiOytodgv1ioMJeFT0emIiNzLQHdlOYlA0B6i4mbHsLHQDMY8VavhujWWH/uJ82BImwH7heTwa5LqYJDg5sH4dvRLeHnVvrjIpUwSJjdFe/0bYxvRrUQ/cEd1rLU61Svliu+Gtkcaye0RpCRdRoIgiDsEVO6QCPa1MHht2O5TFKAsPPSs6k/GvHSNIuF9elizMuge+zdvo0xsq34fAoAaBniKdjWdqbE5lKN71AX7/VrImow9IoIwFu9GyE62APj2gvnKemGm2mvoZ0DxaVmNtBx5l+Pv05Ss9rumNtcfw2nssO2NMk6xOAbjmK3X7cTZ9S7p6NPWQaUhGEEiynrhvDx50Ap1SxahXojjLdYrlTCiBq+hpAKDFvNeV+UeBc71PcRlBUzHPnIJeKeT10PDf88Q4ZSWQkqdJOEiK0jxdVloKo1L7RGTD0fwdSCnGLTjUSujM4FXB1NCzEtb/p/sTTk/P18FDKJ3ndBe8/5t15hIISvXT1vo7Lw9dJ+qmgInzHMDYk1BTKgiAqRX6zG1J/PCvZ56qxUrYVhGLzapT4GRgehRxM/wbHfX+8If3dHwb7BzWuLjiQQBEHYO6YuWhrs5SzoSBjrmPH7v4Y6mK90rm/wfF27J9jLWbwgAHe5WKe3JImEGR1xLa/HNsDvU5+Bm04nUtC5Y0sNJe0cKCm3Do0hT07pZ74BM7JNCPxExua8eAugrnuxDQLcHRHNMxSlEgla1PESDS8qVpXqLWYo6i1gaqSDqLsQb1n3lAEE6dX1jBSe7ip16fwxfvkRbUIAQC90S4yMPGXpRolow1oF4/y8Xtj0snDRU10jWNfIGV1f31shEUnxzn8XDD3vshwN5mQAN2So9Gzqj18mtzf6/RBbSFn3O6+7HaDTBxKDAVNuY0DrvdV97wwZnXrvkEzEA2XAKDPUD9TCr1lbX1kG1Fu9G8HHRYFJncL06yvjlpR3kWijdVq8RqJmUBLCl1Wg0jtUVnwuADzXKhgfPxsJAKjl5oCG/uLhJgRBEDUdwUKXRvoBfAPIUOeP77HSRdcDZexaDKPvFSmdA1X+uTR6nWaRxAtA6RwoqYHOl1h9/M+6/am3+zRGyzqeeJnXOYtt5Idj73VHV152MO0crDre+p3n9vV89Pbx0fWSGct6Z+7Cnz6uDkbvO/8+ag09fqdSImEwvFUwtr7WAYuHmpe0iX9VD2e5nvGo9wx1jod7iBjjjP5C0mI66J1XZrZCo4d1yhovzH+fwt2FRqCYR1S3Ot13VnfwQAyGgclrUekiNxBuZ+ie6HsKNToxBsp0b+wnul8M/r0w1YB6PbYBTr3fQ/S7VxaVkISPDCiinJT8UOcrhT8aL3XUHxkQQyaVYEy7UFxc0Av73ugimOBKEARR3TGnPWd0OrqG4IdwlWeeRGpmgc51jcgksk/KGVBmX5p3zTLCvbQhfLw5UJr9pd0Zsc6Zbl268r/WtT7+N6Ujl3pb7JoAb90bkfv7bAvj83V1DQl+CJ0ufE8YINRpz8xO6BTuy20PiQ7Ec62Cjc5F4nsktXNLhCFxmvlYrUK9TJorxkcsKQUfvTlQOj1PF5HLiYXw8XV4pF1oDEDHBqWGa5iP8c61Usfw4t9HXcwxoNx11q4UCz/TXXPKlDlQujCM8ffGGNr6db9jhp6eocGM83czefKUlmngX5rYxpwwQ63auvd739WHemUNzRks6/eOPFCEDaH5yuWVGFBhvi5IWtIP8wY2NXaSHm6Ocng4G3f1EgRBVDfMadD5I/HGzuP3Y01ZrJTP/70SI7LXcB0SBmhVx1OwT9uJ8XbR9CbLk0HVWKeSBcsZMLpzoAx1oPi3y9T7qItMkG1NfLT8/f5Nyrzn/HOWj2hucJ2bXye315svxa+5cYC7IOT9s+eiysxYJpMymNuvCXo08UffSE2GXH5n1JQ5OwbTmJdhMOt7PPQ9IGKLDOvKpGvwaSlWsfhlUnsMaR4kmC8oBt+wGdw8CEuMeNvKCpXjy1ekE4wj9i4Mah6EdmHeeKNHQ00ZMxdLBkrncJXnu2VIH0MGsL4H0LiAfK9bmck8eG+09rlmFSgNFS8TS86XMhVaB4ooFyqVClIAeUWaL15tTyeTVtUmCIIgYJYLit9xrIxQFACoKzJyb+wn3dtB06H6ckQ03vj1PIBSOb94Phqz/+88ZvcSX4vHGHqdZt42y5Z2nAuUwjlQhrJsGfRAmXEf+XVrO6G6HX5TOrT8kD1j4VrtxEIBK9i+yiQMJnWuh0mdSxfR5d+y8iYmAIxndhSrWzTFu04SC212Rd0yhuqPqe+DmPrGQygBjRGz/+pDdAz3xbj2oXrGg0JWurZTWamv+TbCxYyyO/COcil+5Q1UmLtYsrCs+c/LUFiooeQhekk8GO13jdHz5OmWN2cRYm29WfkmGlC8k71dFHiSW4Q+Ef7A3fsmXc9SkAeKKBeJaVkAgH9uaFbC9ShjwiBBEARRinkhfKWfjYbwVWDukUSiCd8qi00vt0OXcF+MaaANodP3zsQ28sPpD3oKFlM1Rw7htvC41oDhFjAtKWDuHChz5lEIQvhE5oEApnm0FDyPk7keQl3KWvRYFzED05BxaWl0O/ti3jJd+cS8UoZkvPc032RZHGRSfDeuFZftUXfgl59+u6zFV80xeMTQfYamPAOtuC3L+K6+0qWe3j5DgwyGQm51n9udjDwAhj2Opix0LIZWb5PXfeIJ8NfMztj0cjv05607KkZDfzfU83VBSx2veUUgDxRhNkXFajzMKkBTKXA3QxMz704GFEEQhMkwZnRYhR4oIwaUifX5uCiQnitc/0Ym0V/UVWwtl44NfNE21AO7du0CUJpUQSNnxcdky8pUpt3WLiyrvTw/PIl/hmCkW8Jg8+T2SHqci5Z1PLHrkmky8TvK2uvk6sRsmdL55TsATAmZ41NR80YsfEs3iUR5Kctu161bLHQxp7A0pbyE0Rg2enOgDHhQbqfnmSipeZjjgQKAer4uuPU41+T607KEcw5N8SppDb75AyMQ4O5ocJDi7d6NMbZdKDp9eqC0fgP65Bbqp/MH9J+b1igylKyE/x1wKmMNJ93vJWDYE2YMX1cFarn5Qqk07r2SSyWIn9UFOdlZ8Jxl9mVEIQ8UYTZJj3MhgabxUpf8rHvRPCaCIAiTMae/yu9sG09jbloHZNOkduje2A8ze4SXyiPRX9TVlMnqhuallBfdKiQ6IXzabW0GQK1xY2gdKIGRwDBoX88Ho4ysbVWWTFod83Q6nWIG1OG3YwWhfbqyiNE7QnzpDt3i5oYkiT0bc58df95KA7/ShAFsGaa7KR4oMbl07SW+IVsV60OWKafIXC5z0A1ZE0t9rstzrTTrZXo4yTGnT2ODGYwlEgYh3s7c2lyAcLCDT7qBxYR19dOm0jcl6YRjmQaU/nehdajxtaPKqqcsjC1cXR7IgCLMJjWz1F3Ownj8OUEQBKGP7oKexuCPBBtr/02N4Gsc4I64CW3QJNCd2ydlhBKtHtcKkbU9yqyL7xUwpQNYFnrJBHSaFl3DQ1ue78HgD2TzO4H/loScmwv//mvbOlM8UMFezmhZpzTUSphNUfxaq8a0Et2vW7u50Zpi4WZ8kc3NUiZIWV3Gubp1lzXh31Baa74h1ieiNGSrbV3zO95i7J7RSbBdlgdKVz5zHSi6HhdDRuysnpqkE23qemFgs0CzrsGfd8f3QC0cFMF91ma01EVXP2XJ+mEDmmnWCWuuu5A2r7yxFP2AeCr0Oj7O+HVye9H6hCdXXripOVAIH2E2D7IKEAThSISfu4P1BCIqHbVajaIi8VGqqkKpVEImk6GgoAAqlfgPvq1TnXRQqVSQy8nzXF7M6QOYOlfF3AAY3XWj+NfpFWF8TkHpeaYt8msquiPEAg8UWP0sfSVGW16RgTAk3vmmJBkQQypy/3XDngzpzn8mgqQNBspXJJRO997F1PPB0VvpJcdErlWBOVCCDH5lGM7mGlBaWfSMZf56aDx529WrmAF18K2ueJxTKBhQAMr2QOmlAzfTqlXplDf0DKZ1a4B+UQGo5+tqtgdFKjD+Sz+P71AXeUUqfLX/P3w4OLLMcwHA302T+XHxs5GIqedj0FsKmBfCxzeUGgeUPoPvxooPJtgKZEARZpOaWYDgkmahT2QgGgY2wsg25oVEEPZDUVERkpKSoFbrz4eoSliWRUBAAO7cuWO3GR+rkw63bt2Cl5cXAgIC7FYXa2LOHeN3vE1NY24K/A6fVMKULzWygSQN5UW3L64XwmcgsYChazO8e1e/lqtomTJlEumE6i4ibIrhU5bB0sjIgvKmeIh0S6x+oRWiFuw1UZ4yqxdey0AHWPw6wu2yIlYMLY5s6DqmeEqNEerjglAfTbjq2PZ1sPFYiuj1jdGjthpPZQokmTEfS63ngRK/LwzDoIGf4XfDGIIMkjr1v9a1Pl7uFAa5VCI6h0j3MY1pr+nnuTnKMbqdfp+P/51wkJuexpz/3eEbrfwwUQEVSJZjSciAIszm8v0stC0xoPpGBQFRDawsEVFZsCyL1NRUSKVShISEQGKBSeLlRa1WIycnB66urlaVoyJUFx2ys7MhkUjw+LEmJCow0LywEsK82H3TJ/ub17EQeEcYBu/2a4LcomI83zrE5DosPgdKbz0gneMG0pyb0qcqr3dHItBR/HtryvXLWhA5yNNRb1/puWXXrwtfVrGQUaFhaNoirqX1AS/EhOKP8/cxvkNdo+fp6lrLzXjEisSAUay7vfeNzrh0LxO9mhr2hFQV4e4shvWOxLvbL+O1rvVNOufFjmHYcPQ2t10ZiRANeaBK9xl+7vzv2vOtgw2uW1bWdcXgz5vje2P5BlRZ74m1IQOKMAuWZRF/5QFe0kbu0Mhztaa4uBh5eXkICgqCs7PpaX8rA20YoaOjo10bH9VFB3d3d0gkEjx8+BB+fn6QSk1vXInyh/AZ65eY74HiXUPCwNtFgZUG5uAYQuCBssAcKF3DUndbt7+n7agZUp1/78oKKzKEWAifLoYSKQi9fOJ1ltZRMXSr5Mvq4qCvO798WfN9xM79cHAk5g1oWuYCq7pGb1kJBgyF8Okarw393QwmUahqPBUsQn2c8durHUw+p66vC5oFe+DC3UwA5g2qmMrD7ELus7keYv79NjfDZlkp3vnzv/gGtlTC4PDbsShWsXBxMGCi2Ei/06w7smrVKjRr1gzu7u5wd3dHTEwMdu/ezR1nGEb077PPPtOri2VZ9O3bFwzDYPv27YJjGRkZGDduHDw8PODh4YFx48bh6dOn5VKQsCx3MzQJJBhG+/LbxotMVA7aeToKhcLKkhC2iNaoLiuFLKGPeSF8fAPKgiF8Fe6y66yzZIGOzdnbGQaPsdDvZGrnQBnKQOjqIMNbvRvhzZ4N4e1Svt8xvn1gcMTewK3k7y4rC5+x52dK0hHdDrJCplnoeOnQKPi46o/mSw2ETpmCVp6yjCfA/AQVhpJIVOJSVeXml0ntsWx4FALKOb5oruFqLs2CS8MbswvE5wkagm8DmSKmYECmjGfFL6vruQ72ckZdEzKAWhuzPFDBwcFYunQpGjTQhGxt2LABgwcPxtmzZxEREYHU1FRB+d27d2PixIkYNmyYXl3Lly83aG2PHj0ad+/exZ49ewAAkydPxrhx47Bjxw5zxCUqgUc5mtEMRylT0prZ5yg6YR40x4UQg96L8mNOp1IwV8UCacy1WGIqAX+k2dxOuBj8EXNdWFZff+3IuDFdXo+tWJg5//4bWovI0OUD3EvD8oShmKbXAcAki1usyLMtgg2W599Lk0L4+J/N8qCaXhYoTTKh+x2pit8bc78TMfV9oFS6Y9fds+W6nu5cOkvD99Bl5ps30GXq8glilPWs1DrzL+0RswyogQMHCrY//vhjrFq1CseOHUNERAQCAoRZe37//XfExsaiXj3hisjnz5/HsmXLcPLkSb3Y+atXr2LPnj04duwY2rVrBwBYs2YNYmJikJiYiEaNGonKVlhYiMLC0h/erKwsAJqRUXsdHdXKbU35WZbFmsPJiAxyR4f6PniYqZkgKZcCKAaK1WqwRuSzBR0qSk3WQalUgmVZqNVqm0giof1vbVnKS3XUgWVZKJVKQQifPX9Xqgqz1oEyNY25mTKYa3CJUREvhhiTO9fDYQPpxlmWFQnrYrhjlQW/ZkPzvAzdyxk9wvEgqwBDmtfGkZvp3H6xTqMxHUx5Xcw1MBgTDENLXIsfotVUJ9Odlt+ntMfglccAAI4lCQj4l5jRPVzsNItT1ekJtAtCVyZeznJk5CnRwcwslALvsoVDzgUhfOYaxvaeREKlUuG3335Dbm4uYmJi9I4/ePAAO3fuxIYNGwT78/LyMGrUKKxYsULP4AKAo0ePwsPDgzOeAKB9+/bw8PDAkSNHDBpQS5YswcKFC/X2HzhwwOpzNypKfHy81a596QmDNYmajtHy9sU4/ogBIAVUGlfwmTNnkHqr7Lhya+pgKWqiDjKZDAEBAcjJybF6GnMt2dnZ1hZBlJSUFERHR+PQoUOIiooyWtZWdTCH7OxsFBUVIT8/H4cOHUJxcWl4SF6e6ZmoaixmeaBKPxvzFFijX8HveJszydwQbo6GuyVRwR4G14WqTNX52dIMGYmGHAnujnKsGN0SAHDsFs+AqgRvirkj+aakVTeEOaX5huErXeqJlvFyLg2v1L5H8VcecPteeibMLPnshaLiyjegEt6KxeOcQrOzUAoNqLLLmxMSLFirrSZ4oADg4sWLiImJQUFBAVxdXbFt2zY0bdpUr9yGDRvg5uaGoUOHCva/8cYb6NChAwYPHixaf1paGvz8/PT2+/n5IS0tzaBc7777LmbNmsVtZ2VlISQkBLGxsfDxKd/aD9ZGqVQiPj4ePXv2tNp6K4+O3gYSEwEALZ/phrvnU4Gb1+HsKAfygZat2oBt1M/g+bagQ0WpyToUFBTgzp07cHV1haOj4QxRVQHLssjOzoabm1uZo58vvvginj59im3btlWRdICrq6ZxcnFxgbu7+CirVoc///wT48ePx8svv4xVq1YJyiQkJKB79+6IiIjA2bNnBZ4db29vLFu2DBMmTAAA1KtXD7dva7I4OTo6wt/fH23atMErr7yCbt26ceclJyejfn3xzFD//vsv2rdvD5VKhc8++ww//vgjbt++DScnJzRs2BCTJk3Ciy++qKeDm5sbCgsL4eTkhM6dOwveD20EQGWQkZGB6dOn448//gAADBo0CN988w08PT0NnsOyLBYuXIjVq1cjIyMD7dq1w7fffouIiNLFJAsLCzF79mz88ssvyM/PR/fu3bFy5UoEB5eGQH388cfYuXMnzp07B4VCUaG5ueZ0GXQnWRtCLFGAMSxhcPHlcTM06dsMxAyU/W92QfyVB3ghJpSbcK+l1ANV4UsbhB9mZciANcUDJi3jORpM2wz9UXqxzqq5/VDTszvqY469dezWE+6zoTk/Cp4hXiiysKtYBrnKoKoHIarCA+XhJIeHk/l9l8r0QIX6OMPHRQE3R5nZ762rkUGWqsRsKRo1aoRz587h6dOn2Lp1K8aPH4+DBw/qGVFr167FmDFjBI3qH3/8gb///htnzxqPFRXrHLEsa7TT5ODgAAcH/UmScrncbju9Wqypw8Oc0lCclIxCPM3XjDLLS954mUwGmCAbPQfbwFwdVCoVGIaBRCKxetY4bcibVh5jaBPYVKXM2msZu1daHdavX485c+Zg1apV+PLLLwVecu25N2/exMaNGwXGi1j9H374ISZNmoSioiIkJydj48aN6NWrFxYtWoS5c+cK6ty3b5/AaAAAHx8fSCQSzJ8/H6tXr8aKFSvQunVrZGVl4dSpU8jMzBRcT/c5MAyj915V5vekPHNkP/30Uyxbtgzr169Hw4YN8dFHH6Fnz55ITEyEm5tmjsDMmTOxY8cObN68GT4+PnjzzTcxYMAAnD59mjNii4qKMHz4cMTExCAuLq5CepgVwieYq2L4xOndw3HuzlOT05BbIokEP6TNq5xJGviIJUuoX8sV9btojAu91NbcukyV1xHlh+cZCuEzJUGFYPFZXj3bpnTAzgupmNmzoZFzy5bT3BA+vtFk0jpTvDKmJLXQ0rOpP7advQfA8AAA33C++SgXADCkeRC2n7sPwLQ5WpZgcPMg/HIiBfVrVU0SA6XKNsLRxKjIOmFlIZdKcOy97pCUtNXmMKBZEPZcSkPbMOs6R8w2oBQKBZdEonXr1jh58iS++uorfP/991yZf/75B4mJifj1118F5/7999+4efOm3kjhsGHD0KlTJyQkJCAgIAAPHjyALo8ePYK/v/Vz/dc0kh/ncp9vPsrhJviWZiG1T9crUT5YlkW+Un90sCpwsOAI5MGDB/HWW2/h/Pnz8Pb2xvjx4/HRRx9pBgQA7NmzBx999BEuXboEqVSKmJgYfPXVVwIvzokTJ/DKK6/g6tWriIyM5IyVskhJScGRI0ewdetWHDhwAFu2bMELL7ygV27atGmYP38+Ro0aZdT75+bmxoVD16lTB507d0ZgYCDmzZuH5557ThD27OPjIxo6DQA7duzAlClTMHz4cG5fdHS0STpVFeWZI8uyLJYvX465c+dyEREbNmyAv78/fv75Z7zyyivIzMxEXFwcfvrpJ/To0QMAsHHjRoSEhGDfvn3o3bs3AHBh4uvXrzdZZkPzc1m12uS5YmoV7ztn5DwPBwm2vqK5L6bU3bBWqeFuzrw1/pxKVl0qmwSm62Sw7mLh+br1Ce4FAAk0c/AKlEIDqiw5zJkXWqQsDU/lh6pqmd0zHE38Xcquiy2VUa1SceUjA10RGRgOlOhi4GSB7CzPYNSew+iUKROeYahWFZd5Dn/eCsua/qx9nEu7mrrncZ/VwueqVCrhxvOosqpiKNWV399oFeKO3dM6IMjT0WT9KjJPuoDXnlpz7qioDvxnwhp7NzWois3XRQ29R28SK0ZGC65j6jOw5D2usB+MZVlB4wAAcXFxaNWqlV7j+8477+Dll18W7IuKisKXX37JJaiIiYlBZmYmTpw4gbZt2wIAjh8/jszMTHToYHp+fcIy/PegdK7G+TtPcSdDM7eB68xSFr4aRb5Shabz/rLKtS8t6GmReu7du4d+/fphwoQJ+PHHH3Ht2jVMmjQJjo6OWLBgAQAgNzcXs2bNQlRUFHJzczFv3jw8++yzOHfuHCQSCXJzczFgwAB069YNGzduRFJSEmbMmGHS9Tdu3Ih+/frBw8MDY8eORVxcnKgBNXPmTGzcuBErVqzA7NmzzdJxxowZWLRoEX7//XfMmTPHpHMCAgLw999/Y8qUKahVq5ZZ16sqyjNHNikpCWlpaejVqxe3z8HBAV26dMGRI0fwyiuv4PTp01AqlYIyQUFBiIyMxJEjRzgDqjwYmp+bcvcudu0ST5agy91cQNtcnz1zGoVJlhu1ntYU8FAAu3btMvvc+Ph4PC0sle3MqZPIuV4x2Z7w6gP05bqZJTx+9L9U7Np1F7rdGVP1MWVe6JmHJXN/BfWWXi8k5yp27bpaZj23UiTQrh5zMOEAvMxYJ1SZWXrurl27cPde6bZWh4wnwjJlkZZaWv7QwQR4lyGPxn7S6H39v0Tsyrtmkuy3s0vPO3PqFApu6r8jBw/8Dd3nfvt2qXz8JXOqgv/KcU555knnFkihHYguz3fQ0vB1SMsDtM/kccp17Npl/K4k3hP7nlQtZT0DS87PNcuAeu+999C3b1+EhIQgOzsbmzdvRkJCAhdKAWhG13777Td88cUXeucHBASIjn7WqVMHYWGaCYJNmjRBnz59MGnSJM6rNXnyZAwYMMBgAgmicsguUCI5vfRl+1+JCx7gxStTGmPCzli5ciVCQkKwYsUKMAyDxo0b4/79+3j77bcxb948SCQSvaUX4uLi4OfnhytXriAyMhKbNm2CSqXC2rVr4ezsjIiICNy9exevvfaa0Wur1Wr8/PPP+OabbwAAI0eOxKxZs3Djxg3Os6/F2dkZ8+fPx3vvvYdJkybBw8NDrEpRvL294efnh+TkZMH+Dh066IUXZmZmQiqVYtmyZXjuuecQEBCAiIgIbq5q3759Tb5uZVOeObLa/boRDP7+/tz8sbS0NCgUCnh5eemVMTb31hQMzc8NDQlBv35tTKrjWlo2PrtwFADQrm0bdAr3rZBMFYU/pzKzUI35Zw4CAJ7p0B6tQ73KOLts1tw8hPuZBQCAfv2Ec2x3XEgFLl/kth8XMOjXrx9mHN0rKKd7njEdygo5zTl1F7h5RVAv/3plXUvL9f03sPfeLQBAj+7d4O9u+rzS1tmFmLjhNEa2CUa/dnWQsPUiTj7SLB2j1eHXh6fwX+YTk2U6sOUiTj/W1NG9WzcEehiXR61m8cYxTQe1UaNG6GcgIYQul+9nYdklTYY93fdX+xx69+oJHDvA7e/Xrx/O7LqGQ2kpJutjLSoyT3r2iXhovYvW1FFMh1uPcrHk/L8AgB4dW6NrQ+MDa3cOJeHPlOsAql4XU5+BJefnmmVAPXjwAOPGjUNqaio8PDzQrFkz7NmzBz17lo4Mb968GSzLYtSoUeUWatOmTZg+fTo3Gjho0CCsWLGi3PUR5ePiPc1kXVcHGXIKS8MWZBIGDjKt4UQGVE3CSS7FlQ/LPxpfERykDLILKl7P1atXERMTI4i77tixI3JycnD37l3UqVMHN2/exAcffIBjx47h8ePH3LyflJQUREZG4urVq4iOjhbMXRLLRqrL3r17kZeXxxklvr6+6NWrF9auXYvFixfrlZ84cSKWLVuGTz75RPS4McTmjf76669o0qSJYJ92fk/Tpk1x6dIlnD59GocPH8ahQ4cwcOBATJgwAT/88INZ1zaXBQsWiHpp+Jw8eRJA+ebIip1nyjmmlCkLQ/NzpTKpyZ0tbWgpALQI9bGZuZhyuRxOvLAxB4Vl5omOalsHX8T/x12Dj4NI/XK5HMtHNMfMX88J9pmCSfNCeYMOYmVNf46lIWkOCoVZ96q2txx73ujCbTM6MsnlcsFEf1Pq5svjaMKz42cjlEpNf38dFKXlFAbut+4+jT5Sg8dtkfLMk+bPgbIFHfk68J+bgwm66b6T1qCsZ2BJucwyoEyZNDt58mRMnjzZ5DrFMtd4e3tj48aN5ohGVAKHr2vCS3o19Ye7kxzrjyQDAIa0qA1peslzoxC+GgXDMHBWWCcDjqXWTRLrFGt/h7T7Bw4ciJCQEKxZswZBQUFQq9WIjIzkUrmXd82ZdevWISMjg8vYB2j0Onv2LBYtWiTIuAdoOs4fffQRJkyYgKlTp5p8nfT0dDx69Ijz7GsJCQnR83TxkUgkaNOmDdq0aYM33ngDGzduxLhx4zB37ly9uizJ1KlTMXLkSKNl6tatiwsXLpg9R1Yb9ZCWliZYd/Dhw4fcOQEBASgqKkJGRobAC/Xw4cNKCx03Lw106Wdz1+upbCojBbGxjHCGDg1pURvfHbyJa2mWXyKgcYB+Vs3p3cPx9f7rGN2uTrnqNJSMoiKYu54OXwRzs/CZcy2+rubk9bHT7NZmETe+NV7beAZLhhpf+sIalJU1UpcRbUKw8sAN9GxaM/IV2EYuQMImuZORDwBoGiRsPNqGeQOcAVXVUhFExWjatCm2bt0qMKSOHDkCNzc31K5dG+np6bh69Sq+//57dOrUCQBw+PBhvTp++ukn5Ofnw8nJCQBw7Ngxo9dNT0/HH3/8gbi4OLRu3ZoLpVOr1ejUqRN2796NAQMG6J03fPhwfPbZZ2V6aPh89dVXkEgkGDJkiMnniKHNrpqbm1tGyYrh6+sLX9+yw9LKM0c2LCwMAQEBiI+PR4sWLQBosukdPHgQn3zyCQCgVatWkMvliI+Px/PPPw8ASE1NxaVLl/Dpp59aQkU9zOnr8rPlyaycDVMXRzlvor+FckAb66wZWm8JABzkFV+HSoxWoV74flwr1PUpzcw2s3s4+kQEoFGAm8n18G+PuQaLLsGeTnr7zDVm+Zn0zF4HypwskmZm+yvPNeyV7k38cfnD3lWWZdAczDWgfF0dcG5+r0oZHLBFyIAiDPI0TzPa7uWsEIx6Nglw5yUEqhlfFML+yMzMxLlz5wT7vL29MWXKFCxfvhzTpk3D1KlTkZiYiPnz52PWrFmQSCTw8vKCj48PVq9ejcDAQKSkpOCdd94R1DN69GjMnTsXEydOxPvvv4/k5GR8/vnnRuX56aef4OPjgyFDhsDT01MwF2nAgAGIi4sTNaAAYOnSpQYTGWRnZyMtLQ1KpRJJSUnYuHEjfvjhByxZskTP25Senq43p8fT0xOOjo547rnn0LFjR3To0AEBAQFISkrCu+++i4YNG6Jx48ZGdasqTJ0j27hxYyxZsgTPPvssGIbBzJkzsXjxYoSHhyM8PByLFy+Gs7MzRo8eDQDw8PDAxIkT8eabb8LHxwfe3t6YPXs2oqKiuKx8gCaE88mTJ0hJSYFKpeLerwYNGgi8iqZgTkdS2PE26zKVDr/jZ6k1dIx15mMbC+dh8BfelVdix613hHD+tkTC6A0umkNFPXevdq2PB1kF8Mq9XSqTmVXyDXNTDDr+YzHnUnyj35jewV5OuJuRj6jaHiXXqxn9C1s0ngDzDSjAdnWpDMiAquFcvp+JxzlF6CIyOTBDa0C5yFHLtXRyabi/a2k6VgrhI2yUhIQEzuOgZfz48Vi/fj127dqFt956C9HR0fD29uYMIUATxrZ582ZMnz4dkZGRaNSoEb7++mt07dqVq8fV1RU7duzAq6++ihYtWqBp06b45JNP9JJP8Fm7di2GDBkiukbUsGHDMGLECNHwNADo1q0bunXrhr179+odmzdvHubNmweFQoGAgAC0b98e+/fvR2xsrF5ZvjGg5ZdffsHIkSPRu3dv/PLLL1iyZAkyMzMREBCAbt26YcGCBYI5ONbGlDmyiYmJyMwsXXB1zpw5yM/Px5QpU7iFdPfu3cutAQUAX375JWQyGZ5//nluId3169cLwirnzZuHDRs2cNva9+vAgQOC98MUyts3tDUPFAA81yoYyY9z0TzE0yL1GevM64YQO/G8TrYW3qgL37401+Oji7NCho8GN8WuXcncPnMNDoEHqhIX0pXynouxgYNNL7fDj0dv4+VOYSXyEdakvJ7DmoLttIqEVXhp/Uk8yCrE9tc76jV+GbmafPmezgpEBXtg+Yjm8HNzKAnZ0Ibw0ZeKsD3Wr19vdK2eLl264MSJEwaP9+jRA1euXBHs0w1Pat++vZ6Hy1gI04ULF6BWq0WzAA0dOpRbn8Lf31+0nr/+0k8fr5tlzxB169YtM7xq0qRJmDRpkkn1WRNT5sjq6sowDBYsWMClqRfD0dER33zzDZchUYyy3itzMGchUoEHygZ/cj8fbtn1wqKDTc84yV+byOZHv1m+x8fy1Zv7bvANTnMNunLPgTJyWqiPCz4Y0JTbdpDZ+POs5lTUyK/u0NtpZ1y6l4m8Iv2F/MpDgVKFB1maNbwu3n2qd5wfwgdoJul2aFAyT4FrCOgLRhAEYS7m/HJ6OpdmjqoJYU2t63pj7YTW2P9mF9Hj7/UrDSkttiMDypIeKDHM9RIoeAZKZTo2yxMKBgAvdgxDvVoumNE9vDLEIsqA7wmuAT87ZmPbvzaEgP+duYsB3xzGhzuulFnWlMm8j7JLF0Decuae4FhRsRq5RZrlob2cRdI+ciF89K0iCIIwF3O8BSHezvhkWBS+G9uq8gSyMbo19kf9WuLzyiZ3rs995qfWtvXJ6/xmuVKyF5rZHvMNTlPO5Rvv5hjy5X0uXi4K/P1mV7zRs2G5zicqRmW8o9UJMqDsiBV/3wAAbD55x2i5cXHH0enTA4K1m8RIyypdVOf8nafILlBi8a6ruJaWxXmfJAzg7iiWN5/SmBMEQZQXc0L4AGBEmzroE6m/EH1Np55fqZElt/GQL37ShsrwJJpbJd9oMjsLnxll+R1xY1kUCdvC1gckrI1t/9oQAlx52Yb2XEoVLfPz8RT8c/0x7mbk49jNdKP1vRAnnAOy8VgKVh+6hT7L/0FGnmY+hoeTXHxCL4XwEQRBlB/66awQf057BoOig7BiVGmimMrMwmcJLJWl0BDmZgWsyDpQ5thb/MQnlkp1T1Q+lDjCOJREwo7ge5TWH0lGn8iSRSE3jwESdwMARrAsni9Z9F7ym/GX/6KEBRxKt5kEYJL23O8Y3HBgwagBLBQzoFQlJ9EXjCAIwlyoc1IxImt74Gue8QQALg623aWpbNNh4jNhUBaz6NpIP6uuGBV5B8vrgSLzyX7gPzf6tdLHtn9tCAEZuUXc56THJQtbqpTAtT+5/VKg9E0v45dKJvaN4I9IlVWPoyfgS7HJBEEQ5kIdEsvj7iQWbm47VHZWMweZFDN6mJ5woSKL+ZpzLj8UjDxQ9gP/EdNT04cMKBtGpWZxP1fzg6NSs3iar+SOPcwuRF5RMVrN+wNXS5Zo2tfzL7y74wZX5tUu9THxmTDRuotVasQs/RuAZiHC7IJi1HJzECSWAID+UYFYMChCXEBHT0DuKH6MIAiCMAg5oCyP+Hxd28HWsgRWJOLRnFMlAgOq/Nckqhb+PD2aD6UPGVBWJCU9Dz8cvoVradkY3bYOhrSoLTj+2d7/EHdBBt/wVHRvGij44WFZYN/Vh5CjNKzv1R0PUAwvbvtWgSt+v6lCuzAfBHg48s5lkZlbhEclZQN9PXDrbiYeZQOAs0CGKYM6Am5kJBEEQRC2jbuTbXdpbG2h3wqFkZbzXLKf7Isx7eogNbMAkUGmr81WU7DtX5tqzuzfzuNE8hMAwImkJxjcPIiz+NNzChH3720AwKqDSWgR6g0AcHeUwc1RjntP83E1NQtyqLj6iiEV1L/peAo2HU9Bl4a1sOGltgCAPZfS8N62i5ga2wCAxvvk7aIQlS86xBN+ZDwRBEFYnJqwnlNV42HjIXxyGzOgKpKmurxnBrhTn8Ke+PjZKGuLYLPYlj+5GpNXVIwbD3MAaNateG3jac540pLOm+N0PKn02K3HuZj3+2UAQFZBMfzcNZkertzP4jxQRawU2p+06d0aCOo9+N8j3HqkufarG0/jSW4RPvxTs5aUl7PCYKPTq6l/uXQliJpIcnIyGIbBuXPnrC0KYQeQ/WR5Gvm7WVsEo9haCF9F3kFzvVe/vRqD78e1Qoi3c9mFCcIOsK1vczXm1Y1n0GPZQYz4/iiS0nOx+1KaXpnb6bnc550XhWnKj/BSkvuXeIWupGZBzmgMKCXPmdg4UD+V6cH/HuFE0hO9/R5Ocr248a9GNsfodnUwqm0dU1QjCJtjwoQJGDJkiLXFMMjPP/8MqVSKV199Ve9YQkICGIaBl5cXCgoKBMdOnDgBhmH0vBfff/89oqOj4eLiAk9PT7Ro0QKffPJJpepAVAzKwmd5wv3d8NXI5tg4sZ21RRHFU2xReitSkaQW5p7apq43ekfQOmZE9YEMqCri0H+PAGg8S9rPutx8qDGgnuQWYecF8XWePhkWBf8SD9Sj7EIuhE8bvtcp3Fd0st+9jHxsO3tPb7+Pq74HqndEABY/G2UwtI8giIqxfv16zJkzB5s3b0ZeXp5oGTc3N2zbtk2wb+3atahTRziwERcXh1mzZmH69Ok4f/48/v33X8yZMwc5OTmVJj9RcWhOduUwuHltPBPua20xRBnQLAg9m/pjbr8m1hYFAOCkkJZdyAD0+hI1HTKgLI1aDahVen8SqLm/Cynpgu2BUX6QQI3E1KeAWoX5288LjvP/Yhv6wMdZxm07QJOZrwiauUyfPReN1nW99cT64XASxKZvPsktEhhQI1qHwFFe/h9VoprDskBRrnX+LJi+6eDBg2jbti0cHBwQGBiId955B8XFpQlZ9uzZg2eeeQaenp7w8fHBgAEDcPPmTUEdJ06cQIsWLeDo6IjWrVvj7NmzJl07JSUFR44cwTvvvIPGjRtjy5YtouXGjx+PtWvXctv5+fnYvHkzxo8fLyi3Y8cOPP/885g4cSIaNGiAiIgIjBo1CosWLTL1dhBWQEKtb41DLpVgzQutMalzPWuLAgBoFepVdiEDkAOVqOlQEglL8vg6sLY3kJeud+gWf95kIvAlf/s68I0jgDOav29Qsi3Gl8B0ANN1jvu6u+LMmz1Lt10VeJxTJCjzy4k7etX5ujoIDKhhrYINXJggACjzgMVB1rn2O3ctUs29e/fQr18/TJgwAT/++COuXbuGSZMmwdHREQsWLAAA5ObmYtasWYiKikJubi7mzZuHZ599FufOnYNEIkFubi4GDBiAbt26YePGjUhKSsKMGTNMuv7GjRvRr18/eHh4YOzYsYiLi8MLL7ygV27cuHH47LPPkJKSgjp16mDr1q2oW7cuWrZsKSgXEBCAgwcP4vbt2wgNDa3w/SGqhspeE4ggyiIiyAObJ7dHkIeT2edSEhSipkNjYJbkzglR46kqYMI6C7ajgz0Nlu0U7osQbyd0aVgLHw6OgKtjqR0d7GX+DylB2BMrV65ESEgIVqxYgcaNG2PIkCFYuHAhvvjiC6jVagDAsGHDMHToUISHh6N58+aIi4vDxYsXceWKJvnKpk2boFKpsHbtWkRERGDAgAF46623yry2Wq3Gzz//jDFjxgAARo4ciaNHj+LGjRt6Zf38/NC3b1+sX78egCZ876WXXtIrN3/+fHh6eqJu3bpo1KgRJkyYgP/7v//jdCFsEwrhI2yB9vV8UMfH/MQO9PoSNR3yQFkStiSleP1uwLA43Huaj35fHzZY/KVn6mJGt3DcfJSDoauOCo4NjA7E/P6NseSXfdiapAmpiwhyx88vt8PB/x5h+uZzAIBfJ7fXJI1wErriX+wYhv3XHuL12PoIcHfEByVZ/ABg/sCmaOBXmq3oIW/xXH9KMUoYQ+4MvHffOteWOgIF2RWu5urVq4iJiRGMoHbs2BE5OTm4e/cu6tSpg5s3b+KDDz7AsWPH8PjxY84YSUlJQWRkJK5evYro6Gg4O5d2PGJiYsq89t69e5GXl4e+ffsCAHx9fdGrVy+sXbsWixcv1iv/0ksvYcaMGRg7diyOHj2K3377Df/884+gTGBgII4ePYpLly7h4MGDOHLkCMaPH48ffvgBe/bsgYRixWwSCVlQhB1DHiiipkMGlCVRlxhQMkfA2RuJKQ+QCVeDxR3dfAFnb9QP9UYmLgqOvdyzFeCkQJS/C9YmaR5TvswdcPaGgzvL1Vs3JBgQmbP0TLgvLi7oBTdHOZ7kFnEG1Oux9QXGEwC0CPHE+/2boK6PS4XWhSBqAAwDKFysc20LeVRYltVr/NmS+VXa/QMHDkRISAjWrFmDoKAgqNVqREZGoqioSFDeXNatW4eMjAy4upb+LqjVapw9exaLFi2CVCr8Lvfr1w+vvPIKJk6ciIEDB8LHx8dg3ZGRkYiMjMTrr7+Ow4cPo1OnTjh48CBiY2PLJStRuVAWPsKeobeXqOnQ0KQl0XqgGM1tPa6TNryTTmYgNa8P1iy4dJXnUB9nhJa41MXsmTZ1vTG6XR2827ex0YQPbiXpyb1dFPhmVAuMbBOCad3C9coxDIOXO9VDD1r3iagBNG3aFEeOHBEYQUeOHIGbmxtq166N9PR0XL16Fe+//z66d++OJk2aICMjQ6+O8+fPIz8/n9t37Ngxo9dNT0/HH3/8gbi4OJw5cwbnzp3j/nJycrB79269c6RSKcaNG4eEhATR8D1jOgKauVyEbUL2E2HPkGObqOmQB8qSaDtkEo1R85fOWk8DmwXhn+uPue0hLUon44d4OePC3UwAQMLsrqLu8ZZ1NGF6UgmDxWauDj0wOggDo600+Z8grEBmZqbeorbe3t6YMmUKli9fjmnTpmHq1KlITEzE/PnzMWvWLEgkEnh5ecHHxwerV69GYGAgUlJS8M477wjqGT16NObOnYuJEyfi/fffR3JyMj7//HOj8vz000/w8fHBkCFD4OnpKQitGzBgAOLi4jBgwAC98xYtWoS33nrLoPfptddeQ1BQELp164bg4GCkpqbio48+Qq1atUwKKySsg4J6oIQdw5APiqjh0C+4JVGXeqAKlCokp2vWd6nt6YTGAW7o3yyQK7pqTEsE8jLfvNe/CdrX88b341rpGU8/vdgao9rWwayeDStfB4KoJiQkJKBFixaCv3nz5qF27drYtWsXTpw4gejoaLz66qucIQQAEokEmzdvxunTpxEZGYk33ngDn332maBuV1dX7NixA1euXEGLFi0wd+7cMheuXbt2LYYMGSI6J2nYsGH4888/8eDBA71jCoUCvr6+Bucc9OjRA8eOHcPw4cPRsGFDDBs2DI6Ojti/f7/RkD/CuvRo4mdtEQii3JAHlajpkAfKkpSE8CU/KUDXD/YAABRSCf6ZEwuG0YTK7X+zC1LS8xDbWNh41vZ0wubJ4qPF7et5o1MjCq8jCFNZv349l71OjC5duuDEiRMGj/fo0YPLuKdFd95T+/bt9TxcxuZGXbhwAWq1GllZWXrHhg4dCqVSs6abv7+/0XqGDBkiOD5s2DAMGzbMYHnCNnGg9fYIgiDsFjKgdGBZFrfT8xDq42x+lpkSD9SZu6WZwvw9HATZlurXckX9WoYTSxAEQRAEQdgylIWPqOlU+xC+f248LrsQj7jDSej6eQK+PaC/LkuZlHig1LzbWtuT1lUiCIIgCKL6QOYTUdOp9gbU9M0XzCr/0c6rAIDP9/5n/sVYTZplFVt6W7s1pjh3giAIgiCqD5SGn6jpVHsDqkopWadGzRubGdoy2FrSEARBEARBWByyn4iaDs2B0kEmYVCsNm2RTL0FOXkhfG/3aYxhrWrD19WhMsQkCIIgCIKwCmQ/ETWdGmFAZeYr4eEkN6msmpfdSmsg7b/6ACeSn+CtXo0gk2qcdlN/PoOzKU+RkVeEvCIV1r/YBl1LkkioIEGrUC/4uTlaXhmCIAiCIAgrQh4ooqZTIwyolPQ8RAV7mFSW73vKLVLB1UGGiRtOAQD83Bwx8ZkwqNUs/ryQKjjv872J6NJEBQYaAyrAnYwngiAIgiCqH5SFj6jp1Ig5ULef5KKwWIXl+/7Dnksaw4dlWah1QvXyiorBX35l35UHOJH0hNs+fisdgMajpcule1lIfqxJX66GBH7uFLpHEARBEET1g8wnoqZTIzxQt9PzEH/lAZbvuw4AuPJhbyzZdQ3/d+oOtr/eEU0C3QEA6TlFgvNm/npOsJ3yJE9TLldYTstfl+7jVRmgkMvgSIskEgRBEARRDSEPFFHTqfYGVG3mIf4+ehzFKhYhjMbwOXfhLBKOX4QfgJ/3HMSiwZEAgMRrDxHCPDBYlyr9MdgnIchJfcqV2z2jE/p+9Q8AwBM5AABHhWnzrQiCIAiCIOwNMp+Imk61D+H7y+FdbC2agt9Vr+Mfhzfwj8Mb6PBnD+7zottjga+bA183R4+9vbj9Yn/x0ulgvm6O5tu6cvtcv2vNfR4pSwAAODsorKozQRBAWloaZsyYgQYNGsDR0RH+/v545pln8N133yEvL6/K5GjUqBEUCgXu3bund6xr165gGAZLly7VO9avXz8wDIMFCxZw+27duoVRo0YhKCgIjo6OCA4OxuDBg/Hff+VYt85OycjIwLhx4+Dh4QEPDw+MGzcOT58+NXoOy7JYsGABgoKC4OTkhK5du+Ly5cuCMoWFhZg2bRp8fX3h4uKCQYMG4e7du9zx5ORkTJw4EWFhYXByckL9+vUxf/58FBWJRyQQRHVGUu17jwRhnGr/FchhHZDDOhr8y4UjVDIXwb4iqTPyGSduWyl1Ri40n1VyFxRKNMcKJU6AwhWswpUrm8Z64aS8tbXVJogaza1bt9CiRQvs3bsXixcvxtmzZ7Fv3z688cYb2LFjB/bt21clchw+fBgFBQUYPnw41q9fL1omJCQE69atE+y7f/8+/v77bwQGBnL7ioqK0LNnT2RlZeF///sfEhMT8euvvyIyMhKZmZmVqYZNMXr0aJw7dw579uzBnj17cO7cOYwbN87oOZ9++imWLVuGFStW4OTJkwgICEDPnj2RnZ3NlZk5cya2bduGzZs34/Dhw8jJycGAAQOgUmmyq167dg1qtRrff/89Ll++jC+//BLfffcd3nvvvUrVlyBsEYZ8UEQNp9qH8C1ssAV/3cgxWmZ693B8vf86t50wrSu8nBXYdvYuhrcOgdxBhr7LDuLGwxy81bURdpy/j2tp2VjzQmv0bOoPBkDXj+LxuGQO1aI2kZWpEkFYDZZlkV+cb5VrO0hMT8wyZcoUyGQynDp1Ci4uLtz+qKgoDBs2DCwvW0xmZibeeustbN++HQUFBWjdujW+/PJLREdHAwAWLFiA7du3480338QHH3yAjIwM9O3bF2vWrIGbm5tROeLi4jB69Gh06dIFr7/+Ot555x29MgMGDMD//d//4d9//0XHjh0BAOvXr0evXr2QkpLClbty5Qpu3bqFv//+G6GhoQCA0NBQ7pyawNWrV7Fnzx4cO3YM7dq1AwCsWbMGMTExSExMRKNGjfTOYVkWy5cvx9y5czF06FAAwIYNG+Dv74+ff/4Zr7zyCjIzMxEXF4effvoJPXr0AABs3LgRISEh2LdvH3r37o0+ffqgT58+XL316tVDYmIiVq1ahc8//7wKtCcIG4LsJ6KGU+0NqDrezkDJ3KQj73TDltN3sSxeGO7CN54AINTHGQzDYELHMG5fek4hAOCzvxK5feF+rtxnfkK/MW3rWEp8grAp8ovz0e7ndla59tGRR00ql56eznme+MYTH+0EaJZl0b9/f3h7e2PXrl3w8PDA999/j+7du+O///6Dt7c3AODmzZvYvn07/vzzT2RkZOD555/H0qVL8fHHHxuUIzs7G7/99huOHz+Oxo0bIzc3FwkJCWjVqpWgnEKhwJgxY7Bu3TqBAfXpp58Kwvdq1aoFiUSCLVu2YObMmZBKa16imqNHj8LDw4MzngCgffv28PDwwJEjR0QNqKSkJKSlpaFXr17cPgcHB3Tp0gVHjhzBK6+8gtOnT0OpVArKBAUFITIyEkeOHEHv3r1F5cnMzOTeEUMUFhaisLCQ287KygIAKJVKKJX6GV3tAa3c9io/QDpUFLVKZZHr0nOwDexdB1Plt6R+1d6A6tnUD1svP0WH+j4I8nRCyzpe3LEmge64mpolKL/mhdai2WVGtq2DVQk3BfuCvZy4z/wFeCUSGpohCGtx48YNsCyr15n29fVFQUEBAOD111/HJ598ggMHDuDixYt4+PAhHBw0Hq7PP/8c27dvx5YtWzB58mQAgFqtxvr16zmP07hx47B//36jBtTmzZsRHh6OiIgIAMDIkSOxdu1aPQMKACZOnIhnnnkGX331FU6fPo3MzEz0799fYEDVrl0bX3/9NebMmYOFCxeidevWiI2NxZgxY1CvXr3y3zA7Ii0tDX5+fnr7/fz8kJaWZvAcAPD39xfs9/f3x+3bt7kyCoUCXl5eemUM1Xvz5k188803+OKLL4zKvGTJEixcuFBv/4EDB+Ds7Gz0XFsnPj7e2iJUGNLBXDTdxlMnTyLnOltGWdOh52Ab2LsOZclvyfnP1d6Aql/LFaff78EZRe3qeaN1qBfkUgmGtqyNt7Zc4Mr+/npHNDOw4O5bvRoJDKjI2u6QSUunkA1pXhvrjySjT0RAJWlCENbHSeaE46OPW+XaDhIHZCO77IIl6A6EnDhxAmq1GmPGjOE8AqdPn0ZOTg58fHwEZfPz83HzZun3vW7duoJwvcDAQDx8+NDo9ePi4jB27Fhue+zYsejcuTMWL14Md3d3QdlmzZohPDwcW7ZswYEDBzBu3DjI5frZPF9//XW88MILOHDgAI4fP47ffvsNixcvxh9//IGePXuWcUdslwULFogaGXxOnjwJQDx9MsuyZaZV1j1uyjmGyty/fx99+vTB8OHD8fLLLxut491338WsWbO47aysLISEhCA2NlbvvbMXlEol4uPj0bNnT9H31B4gHcrHjKN7AQBt27bFMw0q/v7Sc7AN7F0HU+XXRgBYgmpvQAHChlMulWDLax0AAJfvl068rlfLBdEhngbrkEgYeDrL8TRP4/6b3Us4uv1O38ao7+eKXk39xU4niGoBwzBwlltn1FytVptUrkGDBmAYBteuXRPs13ppnJx4nmO1GoGBgUhISNCrx9PTk/us+4PMMIxRea5cuYLjx4/j5MmTePvtt7n9KpUKW7ZswRtvvKF3zksvvYRvv/0WV65cwYkTJwzW7ebmhkGDBmHQoEH46KOP0Lt3b3z00Ud2bUBNnToVI0eONFqmbt26uHDhAh480F9q4tGjR3oeJi0BAZpBrbS0NEFSjocPH3LnBAQEoKioCBkZGQIv1MOHD9GhQwdBfffv30dsbCxiYmKwevXqMnVzcHDgvJt85HK5XXZU+JAOtoE1dJDLZBa9Jj0H28DedShLfkvqVu2z8Bmjnq9r2YV4LBwUwX3mh+8BgKNcinHtQ+Hv7mgR2QiCKB8+Pj7o2bMnVqxYgdzcXKNlW7ZsibS0NMhkMjRo0EDw5+vrW24Z4uLi0LlzZ5w/fx7nzp3j/t566y1s3LhR9JzRo0fj4sWLiIyMRNOmTU26DsMw3Pwqe8bX1xeNGzc2+ufo6IiYmBhkZmYKDMzjx48jMzNTz9DREhYWhoCAAEFoR1FREQ4ePMid06pVK8jlckGZ1NRUXLp0SVDvvXv30LVrV7Rs2RLr1q2DhHI5EzUUWkeXqOnU6F9/J0XpJOxQ77JH1QdFB2FAs0B0aVgLYWYaXwRBVB0rV65EcXExWrdujV9//RVXr15FYmIiNm7ciGvXrnEJGHr06IGYmBgMGTIEf/31F5KTk3HkyBG8//77OHXqVLmurVQq8dNPP2HUqFGIjIwU/E2cOBHnzp3D+fPn9c7z8vJCamoq9u/fL1rvuXPnMHjwYGzZsgVXrlzBjRs3EBcXh7Vr12Lw4MHlktXeaNKkCfr06YNJkybh2LFjOHbsGCZNmoQBAwYI5rw1btwY27ZtA6AxMmfOnInFixdj27ZtuHTpEiZMmABnZ2eMHj0aAODh4YGJEyfizTffxP79+3H27FmMHTsWUVFRXFa++/fvo2vXrggJCcHnn3+OR48eIS0tzeAcKYKozjjKa3T3kSBqRgifMX6a2BarEm7iw8Flpx5nGAYrRresAqkIgqgI9evXx9mzZ7F48WK8++67uHv3LhwcHNC0aVPMnj0bU6ZMAaD5Tu/atQtz587FSy+9hEePHiEgIACdO3c2GBJWFn/88QfS09Px7LPP6h0LDw9H06ZNsXbtWnzzzTd6x/lhg7oEBwejbt26WLhwIZKTk8EwDLctFhJYXdm0aROmT5/OZcwbNGgQVqxYISiTmJgoWBtrzpw5yM/Px5QpU5CRkYF27dph7969gnltX375JWQyGZ5//nnk5+eje/fuWL9+PWds7927Fzdu3MCNGzcQHBwsuB4/LT5BVGemdWuA5PQ8QUIugqiJMGw1/eXPysqCh4cHHj9+bNcTdXft2oV+/frZbUwq6WAblFeHgoICJCUlISwsDI6O1g1PVavVyMrKgru7u92GTlU3HYqKikTfD+3vb2Zmpl7CjJoOtU22AelgG5AOtoG962Cq/JZsm8xqwVetWoVmzZrB3d0d7u7uiImJwe7du7njDMOI/n322WcAgCdPnmDatGlo1KgRnJ2dUadOHUyfPl0wUggAGRkZGDduHDw8PODh4YFx48bh6dOnFVKUIAiCIAiCIAiiophlQAUHB2Pp0qU4deoUTp06hW7dumHw4MG4fPkyAM2kW/7f2rVrwTAMhg0bBkATQ37//n18/vnnuHjxItavX489e/Zg4sSJguuMHj0a586dw549e7Bnzx6cO3cO48aNs5DKBEEQBEEQBEEQ5cOsOVADBw4UbH/88cdYtWoVjh07hoiICC5drJbff/8dsbGxXPrgyMhIbN26lTtev359fPzxxxg7diyKi4shk8lw9epV7NmzB8eOHeNWm1+zZg1iYmKQmJgoutI8QRAEQRAEQRBEVVDuJBIqlQq//fYbcnNzERMTo3f8wYMH2LlzJzZs2GC0Hm0cokymEeXo0aPw8PDgjCcAaN++PTw8PHDkyBGDBlRhYSG3OCZQuliWUqmEUqk0Wz9bQCu3vcoPkA62Qnl1UCqVYFkWarXa5HWYKgvtdE2tPPZIddSBZVkolUou2QJg398VgiAIgigLsw2oixcvIiYmBgUFBXB1dcW2bdtE1yzZsGED3NzcMHToUIN1paenY9GiRXjllVe4fWlpafDz89Mr6+fnZzRd7JIlS0RXsj9w4ACcna2z8Kel4K9NYq+QDraBuTrIZDIEBAQgJycHRUVFlSSVeWRnZ1tbhApTXXQoLCxEfn4+Dh06hOLiYu5YXl6eFSUjCIIgiMrFbAOqUaNGOHfuHJ4+fYqtW7di/PjxOHjwoJ4RtXbtWowZM8Zg5q6srCz0798fTZs2xfz58wXHGJEV2liWFd2v5d1338WsWbME9YeEhCA2NtauMx3Fx8ejZ8+edpkVBSAdbIXy6lBcXIykpCQoFAqrZ1NjWRbZ2dlwc3Mz+ltgy1Q3HZRKJZycnNC9e3eBB0obAUAQBEEQ1RGzDSiFQoEGDRoAAFq3bo2TJ0/iq6++wvfff8+V+eeff5CYmIhff/1VtI7s7Gz06dOH82DxO3QBAQF48OCB3jmPHj0yui6Lg4MDHBwc9PbL5XK77fRqIR1sg5qog0wmg4uLCx4/fgyFQmHV1NtqtRpFRUUoLCy06xTg1UGHwsJCKJVKPH78GF5eXnoDZfb+PSEIgiAIY1R4IV2WZQVzjwAgLi4OrVq1QnR0tF75rKws9O7dGw4ODvjjjz/0Gt6YmBhkZmbixIkTaNu2LQDg+PHjyMzMRIcOHSoqLkEQZsAwDAIDA5GUlITbt29bVRaWZZGfnw8nJye79t5UJx28vLz0kgcRBEEQRHXHLAPqvffeQ9++fRESEoLs7Gxs3rwZCQkJ2LNnD1cmKysLv/32G7744gu987Ozs9GrVy/k5eVh48aNyMrK4kI9atWqBalUiiZNmqBPnz6YNGkS59WaPHkyBgwYQBn4CMIKKBQKhIeHW30OlFKpxKFDh9C5c2e79XBUJx26d+9u9cWVCYIgCMIamGVAPXjwAOPGjUNqaio8PDzQrFkz7NmzBz179uTKbN68GSzLYtSoUXrnnz59GsePHwcALgxQS1JSEurWrQsA2LRpE6ZPn45evXoBAAYNGoQVK1aYpRhBEJZDIpFYvbMslUpRXFwMR0dHuzU+qpMO/DlPBEEQBFGTMMuAiouLK7PM5MmTMXnyZNFjXbt25VLgGsPb2xsbN240RzSCIAiCIAiCIIhKxz5nMRMEQRAEQRAEQVgBMqAIgiAIgiAIgiBMpMJZ+GwVbahgdna23c41UCqVyMvLQ1ZWFulgRUgH24B0sA1M0UGbHMiUkO2aBrVNtgHpYBuQDraBvetgqvyWbJuqrQGVnp4OAAgLC7OyJARBEDWT7OxseHh4WFsMm4LaJoIgCOtiibap2hpQ3t7eAICUlBS7bcCzsrIQEhKCO3fuwN3d3drilAvSwTYgHWyDmqIDy7LIzs5GUFBQFUtn+1DbZBuQDrYB6WAb2LsOpspvybap2hpQEolmepeHh4ddvgx83N3dSQcbgHSwDUgH26AsHezVOKhsqG2yLUgH24B0sA3sXQdT5LdU20RJJAiCIAiCIAiCIEyEDCiCIAiCIAiCIAgTqbYGlIODx3eaPwABAABJREFUA+bPnw8HBwdri1JuSAfbgHSwDUgH26A66GBNqsP9Ix1sA9LBNiAdrI815GdYyjNLEARBEARBEARhEtXWA0UQBEEQBEEQBGFpyIAiCIIgCIIgCIIwETKgCIIgCIIgCIIgTIQMKIIgCIIgCIIgCBOptgbUypUrERYWBkdHR7Rq1Qr//POPtUUCACxYsAAMwwj+AgICuOMsy2LBggUICgqCk5MTunbtisuXLwvqKCwsxLRp0+Dr6wsXFxcMGjQId+/erTSZDx06hIEDByIoKAgMw2D79u2C45aSOSMjA+PGjYOHhwc8PDwwbtw4PH36tEp0mDBhgt5zad++vc3osGTJErRp0wZubm7w8/PDkCFDkJiYKChj68/BFB1s/TmsWrUKzZo14xbri4mJwe7du7njtv4MTNHB1p+BPWOr7RJAbRO1TeWD2ibb0IHaJivowFZDNm/ezMrlcnbNmjXslStX2BkzZrAuLi7s7du3rS0aO3/+fDYiIoJNTU3l/h4+fMgdX7p0Kevm5sZu3bqVvXjxIjtixAg2MDCQzcrK4sq8+uqrbO3atdn4+Hj2zJkzbGxsLBsdHc0WFxdXisy7du1i586dy27dupUFwG7btk1w3FIy9+nTh42MjGSPHDnCHjlyhI2MjGQHDBhQJTqMHz+e7dOnj+C5pKenC8pYU4fevXuz69atYy9dusSeO3eO7d+/P1unTh02JyeHK2Prz8EUHWz9Ofzxxx/szp072cTERDYxMZF97733WLlczl66dIllWdt/BqboYOvPwF6x5XaJZaltorapfFDbVAq1TZWrg609g2ppQLVt25Z99dVXBfsaN27MvvPOO1aSqJT58+ez0dHRosfUajUbEBDALl26lNtXUFDAenh4sN999x3Lsiz79OlTVi6Xs5s3b+bK3Lt3j5VIJOyePXsqVXaWZfV+4C0l85UrV1gA7LFjx7gyR48eZQGw165dq1QdWFbzxRw8eLDBc2xNh4cPH7IA2IMHD7Isa5/PQVcHlrW/58CyLOvl5cX+8MMPdvkMdHVgWft8BvaALbdLLEttkyGZqW0yD2qbbEMHlqW2qbJ1qHYhfEVFRTh9+jR69eol2N+rVy8cOXLESlIJuX79OoKCghAWFoaRI0fi1q1bAICkpCSkpaUJZHdwcECXLl042U+fPg2lUikoExQUhMjISKvoZymZjx49Cg8PD7Rr144r0759e3h4eFSZXgkJCfDz80PDhg0xadIkPHz4kDtmazpkZmYCALy9vQHY53PQ1UGLvTwHlUqFzZs3Izc3FzExMXb5DHR10GIvz8BesId2CaC2SUxmW3iX7en7SG2T9XWgtqlqdJCVVzlb5fHjx1CpVPD39xfs9/f3R1pampWkKqVdu3b48ccf0bBhQzx48AAfffQROnTogMuXL3Pyicl++/ZtAEBaWhoUCgW8vLz0ylhDP0vJnJaWBj8/P736/fz8qkSvvn37Yvjw4QgNDUVSUhI++OADdOvWDadPn4aDg4NN6cCyLGbNmoVnnnkGkZGR3LW18ujKZ4vPQUwHwD6ew8WLFxETE4OCggK4urpi27ZtaNq0Kffjaw/PwJAOgH08A3vD1tslgNom3TK28i7b0/eR2iZqmypLB8D2nkG1M6C0MAwj2GZZVm+fNejbty/3OSoqCjExMahfvz42bNjATYYrj+zW1s8SMouVryq9RowYwX2OjIxE69atERoaip07d2Lo0KEGz7OGDlOnTsWFCxdw+PBhvWP28hwM6WAPz6FRo0Y4d+4cnj59iq1bt2L8+PE4ePCgwWvb4jMwpEPTpk3t4hnYK7baLgHUNhkrQ22TaVDbpA+1TeZhT21TtQvh8/X1hVQq1bMkHz58qGd92wIuLi6IiorC9evXuYxHxmQPCAhAUVERMjIyDJapSiwlc0BAAB48eKBX/6NHj6yiV2BgIEJDQ3H9+nUAtqPDtGnT8Mcff+DAgQMIDg7m9tvTczCkgxi2+BwUCgUaNGiA1q1bY8mSJYiOjsZXX31lV8/AkA5i2OIzsDfsrV0CqG2y1XfZVr+P1DZZXwdqm6pWh2pnQCkUCrRq1Qrx8fGC/fHx8ejQoYOVpDJMYWEhrl69isDAQISFhSEgIEAge1FREQ4ePMjJ3qpVK8jlckGZ1NRUXLp0ySr6WUrmmJgYZGZm4sSJE1yZ48ePIzMz0yp6paen486dOwgMDARgfR1YlsXUqVPxv//9D3///TfCwsIEx+3hOZSlgxi29hzEYFkWhYWFdvEMytJBDHt4BraOvbVLALVNtvou29r3kdom23gOYlDbVMk6mJVywk7QpouNi4tjr1y5ws6cOZN1cXFhk5OTrS0a++abb7IJCQnsrVu32GPHjrEDBgxg3dzcONmWLl3Kenh4sP/73//YixcvsqNGjRJNNRkcHMzu27ePPXPmDNutW7dKTRWbnZ3Nnj17lj179iwLgF22bBl79uxZLv2upWTu06cP26xZM/bo0aPs0aNH2aioKIulxzSmQ3Z2Nvvmm2+yR44cYZOSktgDBw6wMTExbO3atW1Gh9dee4318PBgExISBCk88/LyuDK2/hzK0sEensO7777LHjp0iE1KSmIvXLjAvvfee6xEImH37t3LsqztP4OydLCHZ2Cv2HK7xLLUNlHbVD6obbINHahtqnodqqUBxbIs++2337KhoaGsQqFgW7ZsKUhHaU20ufflcjkbFBTEDh06lL18+TJ3XK1Ws/Pnz2cDAgJYBwcHtnPnzuzFixcFdeTn57NTp05lvb29WScnJ3bAgAFsSkpKpcl84MABFoDe3/jx4y0qc3p6OjtmzBjWzc2NdXNzY8eMGcNmZGRUug55eXlsr1692Fq1arFyuZytU6cOO378eD35rKmDmOwA2HXr1nFlbP05lKWDPTyHl156iftdqVWrFtu9e3eugWJZ238GZelgD8/AnrHVdollqW2itql8UNtkGzpQ21T1OjAsy7Lm+awIgiAIgiAIgiBqJtVuDhRBEARBEARBEERlQQYUQRAEQRAEQRCEiZABRRAEQRAEQRAEYSJkQBEEQRAEQRAEQZgIGVAEQRAEQRAEQRAmQgYUQRAEQRAEQRCEiZABRRAEQRAEQRAEYSJkQBEEQRAEQRAEQZgIGVAEYUEWLFiA5s2bV/l1ExISwDAMGIbBkCFDTDpnwYIF3DnLly+vVPkIgiAI60FtE0FYFjKgCMJEtD/ohv4mTJiA2bNnY//+/VaTMTExEevXrzep7OzZs5Gamorg4ODKFYogCIKoNKhtIoiqR2ZtAQjCXkhNTeU+//rrr5g3bx4SExO5fU5OTnB1dYWrq6s1xAMA+Pn5wdPT06SyWlmlUmnlCkUQBEFUGtQ2EUTVQx4ogjCRgIAA7s/DwwMMw+jt0w2TmDBhAoYMGYLFixfD398fnp6eWLhwIYqLi/HWW2/B29sbwcHBWLt2reBa9+7dw4gRI+Dl5QUfHx8MHjwYycnJZsu8ZcsWREVFwcnJCT4+PujRowdyc3MreCcIgiAIW4HaJoKoesiAIohK5u+//8b9+/dx6NAhLFu2DAsWLMCAAQPg5eWF48eP49VXX8Wrr76KO3fuAADy8vIQGxsLV1dXHDp0CIcPH4arqyv69OmDoqIik6+bmpqKUaNG4aWXXsLVq1eRkJCAoUOHgmXZylKVIAiCsBOobSKI8kMGFEFUMt7e3vj666/RqFEjvPTSS2jUqBHy8vLw3nvvITw8HO+++y4UCgX+/fdfAMDmzZshkUjwww8/ICoqCk2aNMG6deuQkpKChIQEk6+bmpqK4uJiDB06FHXr1kVUVBSmTJli1TAOgiAIwjagtokgyg/NgSKISiYiIgISSelYhb+/PyIjI7ltqVQKHx8fPHz4EABw+vRp3LhxA25uboJ6CgoKcPPmTZOvGx0dje7duyMqKgq9e/dGr1698Nxzz8HLy6uCGhEEQRD2DrVNBFF+yIAiiEpGLpcLthmGEd2nVqsBAGq1Gq1atcKmTZv06qpVq5bJ15VKpYiPj8eRI0ewd+9efPPNN5g7dy6OHz+OsLCwcmhCEARBVBeobSKI8kMhfARhY7Rs2RLXr1+Hn58fGjRoIPjz8PAwqy6GYdCxY0csXLgQZ8+ehUKhwLZt2ypJcoIgCKK6Qm0TQZRCBhRB2BhjxoyBr68vBg8ejH/++QdJSUk4ePAgZsyYgbt375pcz/Hjx7F48WKcOnUKKSkp+N///odHjx6hSZMmlSg9QRAEUR2htokgSqEQPoKwMZydnXHo0CG8/fbbGDp0KLKzs1G7dm10794d7u7uJtfj7u6OQ4cOYfny5cjKykJoaCi++OIL9O3btxKlJwiCIKoj1DYRRCkMS3kjCcLuSUhIQGxsLDIyMkxerFBL3bp1MXPmTMycObNSZCMIgiBqJtQ2EdUVCuEjiGpEcHAwRo0aZVLZxYsXw9XVFSkpKZUsFUEQBFGTobaJqG6QB4ogqgH5+fm4d+8eAMDV1RUBAQFlnvPkyRM8efIEgCaDkrmTgAmCIAjCGNQ2EdUVMqAIgiAIgiAIgiBMhEL4CIIgCIIgCIIgTIQMKIIgCIIgCIIgCBMhA4ogCIIgCIIgCMJEyIAiCIIgCIIgCIIwETKgCIIgCIIgCIIgTIQMKIIA8PXXX4NhGERGRhotd+vWLUydOhUNGzaEk5MTnJ2dERERgffff59L1QoAEyZMAMMwBv+MkZycLCi7ZcsW7tj69eu5/QkJCXrnsiyLBg0agGEYdO3aFQCgUqng6ekpusr7l19+CYZhRNfnWLRoERiGwYULFwAAy5cvF8j1+PFjo3oQBEFUJ0xtJ5KSkjB9+nQ0adIELi4ucHR0RN26dTF27FgcOHAAYsmPL1y4gBdffBFhYWFwdHSEq6srWrZsiU8//ZRL6W2IBQsWCH6bFQoFwsLCMGPGDDx9+rQiKlsdag8JW0VmbQEIwhZYu3YtAODy5cs4fvw42rVrp1fmzz//xMiRI+Hr64upU6eiRYsWYBgGFy9exNq1a7Fz506cPXuWK+/k5IS///673DK9//776N+/Pxo2bKh3zM3NDXFxcVyjoOXgwYO4efMm3NzcuH1SqRSdOnVCQkICiouLIZOVfu0TEhLg4uKCAwcO6F0jISEBPj4+iIqKAgCMHDkS7du3xw8//IC4uLhy60UQBGGPmNJO/PHHHxg9ejR8fX3x6quvomXLlnBwcMCNGzewZcsWdOvWDfv27UP37t25c9asWYMpU6agUaNGeOutt9C0aVMolUqcOnUK3333HY4ePYpt27aVKd+ePXvg4eGB7Oxs7Nq1C1999RVOnDiBI0eOlDlwZ+tQe0jYHCxB1HBOnjzJAmD79+/PAmAnTZqkV+bWrVusi4sL26JFC/bp06d6x9VqNbt161Zue/z48ayLi0u55ElKSmIBsOvWrdM7tm7dOhYA+/LLL7NOTk5sZmam4PjYsWPZmJgYNiIigu3SpQu3/4svvmABsEePHuX2qVQq1svLi509ezYLgL1y5Qp3rLCwkHVycmKHDRumJ8P8+fNZAOyjR4/KpR9BEIS9YUo7cePGDdbZ2Zlt06aN3m+zlgMHDrDnzp3jto8cOcJKpVK2T58+bEFBgV75wsJC9vfffzcqm6Hf5HHjxrEA2MOHD5uiotUoLi4W1Z1lqT0kbBcK4SNqPNrRo6VLl6JDhw7YvHkz8vLyBGWWLVuG3NxcrFy5UnRVdIZhMHTo0CqRFwAXYvDLL79w+zIzM7F161a89NJLeuVjY2MBQBDmcP78eWRkZGDy5MkIDAwUjLodP34c+fn53HkEQRA1GVPbiby8PKxcuRLu7u6i9XTt2hXR0dHc9uLFi8EwDFavXg0HBwe98gqFAoMGDSqXzO3btwcA3L59GwDw5MkTTJkyBbVr14ZCoUC9evUwd+5cFBYWcucMHz4cERERgnoGDhwIhmHw22+/cfvOnDkDhmGwY8cObl9aWhpeeeUVBAcHc2GECxcuRHFxMVdGG5L36aef4qOPPkJYWBgcHBxEvT6mQu0hYQ3IgCJqNPn5+fjll1/Qpk0bREZG4qWXXkJ2dragoQCAvXv3wt/fn2uQTKW4uFjvT61WV1hud3d3PPfcc1xICaBpPCQSCUaMGKFXPjo6Gl5eXoJG4cCBAwgMDER4eDg6d+4saEy05ajBIAiipmNqOxEfH4/AwEC0bt3apHpVKhX+/vtvtGrVCiEhIRaX+8aNGwCAWrVqoaCgALGxsfjxxx8xa9Ys7Ny5E2PHjsWnn34qGPzr0aMHrly5gtTUVACaNuzgwYNwcnJCfHw8V27fvn2QyWRc2FxaWhratm2Lv/76C/PmzcPu3bsxceJELFmyBJMmTdKT7euvv8bff/+Nzz//HLt370bjxo3LrSe1h4Q1IAOKqNFs2bIFmZmZmDhxIgBgxIgRcHV11YtpTklJQVhYmFl15+bmQi6X6/316tXLIrK/9NJLOHHiBC5fvgxAE58/fPhwQby3FolEgi5duuDff//lRgMTEhLQpUsXAECXLl2QkJDATW5OSEiAn58fmjZtahFZCYIg7BVT24k7d+4gNDRU73y1Wi06iPb48WPk5eWZ3bYYQqVSobi4GE+fPsWmTZvw3XffISQkBJ06dcKGDRtw4cIFrFu3Dm+++SZ69uyJDz/8EB9//DF27drFGUc9evQAoDGQAI33JTs7G9OmTeP2aY+3bduWa28WLFiAjIwMHDp0CJMnT0b37t3x/vvv4+OPP8b69etx5coVgayOjo7466+/MGzYMPTs2RN169atkO7UHhJVDRlQRI0mLi4OTk5OGDlyJADA1dUVw4cPxz///IPr169XqG4nJyecPHlS72/lypWWEB1dunRB/fr1sXbtWly8eBEnT54UDVfQEhsbi9zcXJw8eRJqtRr//PMPN3rYpUsXPHr0CJcvX0ZhYSGOHTtGo20EQRCoeDsxdOhQwSDa9OnTK0XOgIAAyOVyeHl5YezYsWjZsiX27NkDR0dH/P3333BxccFzzz0nOGfChAkAgP379wMA6tevj7p163LGUnx8PKKiojB27FgkJSXh5s2bKCwsxOHDhzljC9AkWYqNjUVQUJDAWNRmuzt48KDguoMGDYJcLreY7tQeElUNZeEjaiw3btzAoUOHMGzYMLAsy6V7fe6557Bu3TqsXbsWS5YsAQDUqVMHSUlJZtUvkUhMDuUoDwzD4MUXX8TXX3+NgoICNGzYEJ06dTJYXtsAHDhwAAqFAk+fPuVG3Jo2bYpatWohISEB6enpFO9NEAQB89sJ7XwjPl988QXef/99AECbNm24/b6+vnB2dja7bTHEvn374OHhAblcjuDgYPj4+HDH0tPTERAQoJeNz8/PDzKZDOnp6dy+7t27Y8+ePVydPXv2RFRUFPz9/bFv3z6Eh4cjPz9fYEA9ePAAO3bsMGgU6ab5DgwMrLC+fKg9JKoa8kARNZa1a9eCZVls2bIFXl5e3F///v0BABs2bIBKpQIA9O7dGw8ePMCxY8esKbIeEyZMwOPHj/Hdd9/hxRdfNFo2MjKSaxQSEhLg7+8viDvv3LkzDhw4wMV+U4NBEERNx5x2omfPnkhNTcWpU6cEddSvXx+tW7fWG1CTSqXo3r07Tp8+jbt371ZY1ujoaLRu3RrR0dEC4wkAfHx88ODBA701qB4+fIji4mL4+vpy+7p374579+7hxIkTOH78OHr27AkA6NatG+Lj47Fv3z64uroK5gT7+vqiV69eolEXJ0+e5MIftVRGWnVqD4mqhAwookaiUqmwYcMG1K9fHwcOHND7e/PNN5Gamordu3cDAN544w24uLhgypQpyMzM1KuPZVmT1umwNLVr18Zbb72FgQMHYvz48UbLMgyDLl264MiRI4iPj+dG27R06dIFBw8exIEDBxAUFCS63gZBEERNoTzthLOzM15//XVkZ2ebdI13330XLMti0qRJKCoq0juuVCoFme7KS/fu3ZGTk4Pt27cL9v/444/ccX5ZhmHwwQcfQCKRoHPnzgA086MOHDiA+Ph4dO7cWeBtGjBgAC5duiQwFvl/QUFBFdahLKg9JKoSCuEjaiS7d+/G/fv38cknn+gtvgdoRqdWrFiBuLg4DBgwAGFhYdi8eTNGjBiB5s2bcwvpAsCVK1e4Ucpnn32Wq0OtVhv0WLVo0UI0ZW15WLp0qcllY2NjsWXLFuzduxcrVqwQHOvSpQvS09Nx6NAhjB492iKyEQRB2CvmthP169fHL7/8glGjRiEqKgqvvfYat5Duw4cPsXfvXgAQpDiPiYnBqlWrMGXKFLRq1QqvvfYaIiIioFQqcfbsWaxevRqRkZEYOHBghXR54YUX8O2332L8+PFITk5GVFQUDh8+jMWLF6Nfv36CcDw/Pz9ERkZi7969iI2NhbOzMwCNAfXkyRM8efIEy5YtE9T/4YcfIj4+Hh06dMD06dPRqFEjFBQUIDk5Gbt27cJ3332H4ODgCulgCtQeElUFGVBEjSQuLg4KhcKgm9/X1xfPPvsstmzZggcPHsDf3x8DBgzAxYsX8cUXX+C7777DnTt3IJFIEBYWhj59+mDatGmCOvLz8xETEyNa//Xr19GgQQOL61UW2jAElmX1RtyioqLg7e2NJ0+eiHYWCIIgahLlaScGDRqEixcvYvny5Vi3bh0WLlwItVqNgIAAtG3bFtu2bcPgwYMF9UyaNAlt27bFl19+iU8++QRpaWmQy+Vo2LAhRo8ejalTp1ZYF0dHRxw4cABz587FZ599hkePHqF27dqYPXs25s+fr1e+R48euHjxosCwqlOnDsLDw3H9+nXBfkAzp+nUqVNYtGgRPvvsM9y9exdubm5c++jl5VVhHSwNtYdERWBY3YBYgiCsSnJyMsLCwhAXF4cXXngBUqm0UuLFzYVlWahUKnz44YdYtGgRHj16JIibJwiCIAhLQu0hYavQHCiCsFEmTpwIuVyOrVu3WlsUAMBXX30FuVyORYsWWVsUgiAIogZB7SFha5AHiiBsjKKiIly4cIHbrl+/vk2EPzx8+BApKSncdvPmzSGTURQwQRAEUTlQe0jYKmRAEQRBEARBEARBmAiF8BEEQRAEQRAEQZgIGVAEQRAEQRAEQRAmQgYUQRAEQRAEQRCEiVTbGW9qtRr379+Hm5ubTaS8JAiCqCmwLIvs7GwEBQVBIqFxOj7UNhEEQVgHS7ZN1daAun//PkJCQqwtBkEQRI3lzp07CA4OtrYYNgW1TQRBENbFEm1TtTWg3NzcAABJSUnw9va2sjTlQ6lUYu/evejVqxfkcrm1xSkXpINtQDrYBjVFh6ysLISEhHC/w0Qp1DbZBqSDbUA62Ab2roOp8luybaq2BpQ2NMLNzQ3u7u5WlqZ8KJVKODs7w93d3S5faIB0sBVIB9ugpulQHUPU7t27h7fffhu7d+9Gfn4+GjZsiLi4OLRq1cqk86ltsg1IB9uAdLAN7F0Hc+W3RNtUbQ0ogiAIgrAkGRkZ6NixI2JjY7F79274+fnh5s2b8PT0tLZoBEEQRBVCBhRBEARBmMAnn3yCkJAQrFu3jttXt25d6wlEEARBWAUyoAiCIAjCBP744w/07t0bw4cPx8GDB1G7dm1MmTIFkyZNMnhOYWEhCgsLue2srCwAmpATpVJZ6TJXBlq57VV+gHSwFUgH28DedTBVfkvqRwYUQRAEQZjArVu3sGrVKsyaNQvvvfceTpw4genTp8PBwQEvvPCC6DlLlizBwoUL9fYfOHAAzs7OlS1ypRIfH29tESoM6WAbVFcdJBKJ3SzlIJPJcODAAWuLUW5kMhn2798PtVptsExeXp7lrmexmgiCIAiiGqNWq9G6dWssXrwYANCiRQtcvnwZq1atMmhAvfvuu5g1axa3rc0CFRsbCx8fnyqR29IolUrEx8ejZ8+edjnhHCAdbIXqqoNSqcSDBw+Qn59vZelMg2VZFBQUwNHR0S6T//Dld3Z2hr+/v+j7pI0AsARkQBEEQRCECQQGBqJp06aCfU2aNMHWrVsNnuPg4AAHBwe9/XK53G47jFpIB9uAdLANtDqo1WrcunULUqkUtWvXhkKhsHmjRK1WIycnB66urnbjMeOjlV+hUODx48e4c+cOwsPD9XSx5DtGBhRBEARBmEDHjh2RmJgo2Pfff/8hNDTUShIRBGFrFBUVQa1WIyQkxG7CdNVqNYqKiuDo6Gi3BlRRURHc3d2hUChw+/ZtTp/Kwv7uEkEQBEFYgTfeeAPHjh3D4sWLcePGDfz8889YvXo1Xn/9dWuLRhCEjWGPhkh1oKruOz1dgiAIgjCBNm3aYNu2bfjll18QGRmJRYsWYfny5RgzZoy1RSMIgiCqEArhIwiCIAgTGTBgAAYMGGBtMQiCIAgrQh4ogiAIgiAIgiAIEyEPlAUpLFbhYOIjtK/vA3dH+84mQxAEQRAEUVNRKpV4+vQpt+3o6Ag3NzcAmqQF6enpXLnMzEw8evQIcrkcSqVSsBYRy7IoLi42eB2GYSCTybh6CwoKwLKsaFmpVCpIjJCbm2uwXt2yeXl5ovWyLKu3dtLOnTsxaNAgdOnSBZs3b9Y7Z8OGDfjll1+QmJgIqVSKkJAQ9OrVCy+++CICAgK4ck+ePMG3336LnTt34t69e/D19UVERAQmT56Mzp07Qy6XcxkKZTIZXF1duXOzsrIMrukklUq5ZwEAOTk53L0oKipCfn4+EhISoFKp4OzsjNjYWIP3qbyQAWVBPtmdiLX/JqF1qBe2vNbB2uIQBEEQRI1i96GT2L3vbzQOcIdEop86+plnnkFkZCQATedu9erVkEqlonW1b98ezZs3BwA8fPgQ//vf/0TLubi4YMSIEVAoFACAv//+G0uWLDEo49tvv40ePXoAAI4ePYoFCxYYLDt9+nT0798fAHD27Fm899573DGWZfHo0SOsWrUKDMNg8uTJePbZZwEAV69exezZsw3WO27cOIwcORIAkJSUhOnTpxss+9xzz2H8+PEAgNTUVLz66qsGyw4YMACTJk0CoLm/EydOFBznp/Pu0aMHVzYvLw8vvfSSaNm8vDx07doVc+bMAQCkpaWhXbt2BmUYOXIkPvnkEwBAZmYmmjVrZrDs4MGD8fXXXwPQGEINGjQAoLm3d+7cEZSdNGkSVq9ezdXr5+cnWmdoaCh++ukn1K9fn6vr/PnzBmXw9PTkrsswDK5cuWJSWQC4du2aQWPLzc0NjRo14rYTExOhUqkM1uvr68ttr1mzBs8//zx+//13nDhxgjOKioqKMHPmTFy/fh0LFy5Ex44dcffuXaSkpODgwYNYtmwZpk6dCgC4f/8+Jk2aBF9fX3z66ado1qwZLl68iMOHD2P27NnYsmWLQAYXFxc0adKE205KSoJSqRSV18nJCREREdz27du3UVhYyG0/fvwYr776Km7fvo369evjxo0bovVUBDKgLMiW05ov26nbGVaWhCDMR61mcfC/R4io7Q4/t8pL/UkQBPDtt9/CxcVFb3+jRo24OVYsy2LZsmUG66hXrx7XYQaA5cuXi3aQHjx4gHr16nEd39zcXDz33HMG6+3SpQveeecdAIBKpdKb88XvuMfExGD+/PncsUGDBkGpVHJl+CPksbGxWLVqFbcdFRVlsIMUExODdevWcdvt2rVDZmamaNnmzZtj8+bNSM8pxLNjX0LhnUsGdVuxYgVnQN2/fx/vv/++wbKffPIJZ0Ddvn0br732mmg5hUKBW7du4e2334ajoyMePHiAffv2GaxXa4wAGsNs7969BssOHTqU+5yeno49e/YYLNurVy/u89OnT7Fr1y6DZWNiYrjPWVlZ+PPPPw2W1d4vQPPu/PHHHwbL1q1bl/tcWFiI7du3Gyzr7e3NfVYqlQYNVABo2LAhVCoVpFIpVCoVUlJSDJZ98uQJ95llWaNltV4kLcbKVgSWZVEobrsgX6lGXlGph6oYEoOZ5JSsRFBWLZEZNKCKIUVeUTGc5FIwDAOFQmHQgJJIJJy3Jzc3F/v27cO2bduQnZ2Nffv2Ydq0aQCA7777DqdPn8apU6fQokULAJrnGBkZiX79+oFlWc7wnT17NiQSCU6cOMH91ikUCjRv3hwvvPACXFxcBPI4OTkJZHJ2djboudNdW097rlQqBcuyUCgUiIqKgp+fH4KDg0XrqChkQBEEAQD439l7mP3beTgrpLjyYR9ri0MQ1Rq+0cFnxIgRAgPKmBdhwIABAgPqnXfeEYzC8pk4cSLXuSkuLjbaEff09OQ+syxrtKxuR+avv/5CUVGRaNmGDRsKtv/77z+DZXU7PdevX0dGhvjgpJeXFwDgkz3X4D9qCbKO/YZwJg0hPq56ZevVq8d9dnd3x5AhQwx2Vvnyenl5CYwZXVJTU7l1Z2JiYrBp0ybumG4Ht0OH0giVli1b4qeffjJYlu9piYiIwIYNG7htpVKJCxcuoFmzZpDJZGjdujV3rH79+gIDVLdebecXAEJCQhAXF2ewLN+DU6tWLaxZs8ZgWb5XwMPDA99//73BsnzviKOjo8C41i1br149bl+tWrVw4sQJGILvSXF1dTValm/ESaVSQVmZTIZmzZqJvh+enp6cwaFUKrFr1y7069cPcrkcBQUFSE5O5soyDIOmzZojYr4hQ/khgOsGZRRyD8BVE8sCQCKufNgbzgqZ4NnoolarkZWVBQD49ddf0aRJE/Ts2ROFhYWYNm0avvzySzAMg71796Jnz56C9ycsLEyvvidPnuDQoUP4+OOPBQNFfAO7LMLDw00uW69ePWRlZcHd3R1FRUWQSCT47bffKnUdKDKgCIIAABxIfAgAyCsyMExGEITFeP755/WMDwB6oUnjxo0zWIfWO6JlzJgxBj06zZs3h1KphEKhgJOTE9avX2+wXn6HSCKR6JUtLi7mOu66nacffviB61jK5XIEBARALtfMCeZ3VgFg//79BkfP+UYcoJmTYWg0WjsX4lF2IRiGgUfM83jn+WgMbWl85LlOnTr4v//7P04+YzRo0ABbt24tsxyg6SSa2lEMCQnB2LFjTSobGBiIF154gdvW7bjz8fPzw4QJE0yq19vbWy98zhAeHh54+eWXTSrr7OyMyZMnGy2jfV8dHByMhgbyUSgUaNOmjUllZTKZyWUlEonJZfmhiAzD6P3pltXdZ8vExcVx72SfPn2Qk5OD/fv3o0ePHvjvv//QtWtXQflnn30W8fHxADTG9pEjR3Djxg2wLIvGjRtXtfhVBhlQFsSeviAEQRCE9Vi5ciV8fHyMlpFIJPjxxx9NrpPvRTCGQqEQhJGVJYNuWWMdd2MGny7PPPOMyWX5IWeGKFaXGmMG7DKCsApOcimufNjbatc2lcTERJw4cYILp5TJZBgxYgTWrl3LzdvT7euuXLkSubm5+Prrr3Ho0CEApR7E6twvJgOKIAiCIAi7p1hVajWpyYIibAiGYeCssP0u99q1a1FcXIzatWtz+1iWhVwuR0ZGBsLDw3Ht2jXBOYGBgQCEHubw8HAwDIOrV69iyJAhVSJ7VUPrQFmQamxoEwRBEIRNo2LJA0UQ5aW4uBg//fQTvvjiC5w7d477O3/+PEJDQ7Fp0yaMGjUK8fHxOHv2rNG6vL290bt3b3z77beiqdb56eHtFds3hwmCqBLI/icIwp5RqckDRRDl5a+//kJGRgYmTpwIDw8PwbHnnnsOcXFxOHr0KHbu3Ilu3bphwYIF6NSpE7y8vPDff/9h9+7dgiUBVq5ciQ4dOqBt27b48MMP0axZMxQXFyM+Ph6rVq3C1avmJMOwPcgDZUGoA0oQBEEQ1oFvQJH5RBDm8dNPP6F79+56xhMADBs2DOfOncOVK1ewf/9+vPPOO1i3bh2eeeYZNGnSBDNnzkTHjh0FaevDwsJw5swZxMbG4s0330RkZCR69uyJ/fv3CzIu2ivkgbIg9INNEARBENaBPFAEUX42b94Md3d30WMtW7YUZMx8++238fbbb5dZZ2BgIFasWIEVK1ZYTE5bgTxQBEEQBEHYPUIDyoqCEARR7SEDyoJQCB9hz1TndKMEQVR/BF4n8kARBFGJkAFFEAQA/ZXfCYIg7Ili8kARBFFFkAFlQWgEnyAIgiCsg5rmQBEEUUWQAUUQBEEQhN3D90CR/UQQRGVCBhRBEADIg0oQhH1DWfgIgqgqyICyINT9JAiCIAjrUKxWc5/JfiIIojIhA4ogCIIgCLumWKXGg6xCbpullRkJgqhEyIAiCIIgCMKueZRTKNimLHwEQVQmZEARBAGAQlAJgrBfdA0mmgNFEERlQgYUQRAEQRB2jVrHgiL7iSDM58UXXwTDMGAYBjKZDHXq1MFrr72GjIwMi10jISEBDMPg6dOneseaN2+OBQsWWOxalQkZUBaEkpgR9gz1NwiCsFd0PU60MDhBlI8+ffogNTUVycnJ+OGHH7Bjxw5MmTLF2mLZHGRAEQRBEARh16h0PFA0B4qwRXJzcw3+FRQUmFw2Pz/fpLLlwcHBAQEBAQgODkavXr0wYsQI7N27lzu+bt06NGnSBI6OjmjcuDFWrlwpOP/IkSNo3rw5HB0d0bp1a2zfvh0Mw+DcuXNmy8IwDFatWoW+ffvCyckJYWFh+O2338qll6WRWVuA6gW5oAj7hd5egiDsFV2DiRxQhC3i6upq8Fi/fv2wc+dObtvPzw95eXmiZbt06YKEhARuu27dunj8+LFeuYp6Ym/duoU9e/ZALpcDANasWYP58+djxYoVaNGiBc6ePYtJkybBxcUF48ePR3Z2NgYOHIh+/frh559/xu3btzFz5swKyfDBBx9g6dKl+Oqrr/DTTz9h1KhRiIyMRJMmTSpUb0UhA4ogCIIgCLtGN4SPkkgQRPn4888/4erqCpVKxXnFli1bBgBYtGgRvvjiCwwdOhQAEBYWhitXruD777/H+PHjsWnTJjAMgzVr1sDR0RFNmzbFvXv3MGnSpHLLM3z4cLz88svc9ePj4/HNN9/oeb6qGjKgCIIgCIKwa2gOFGEP5OTkGDwmlUoF2w8fPjRYViIRzsBJTk6ukFx8YmNjsWrVKuTl5eGHH37Af//9h2nTpuHRo0e4c+cOJk6cKDCIiouL4eHhAQBITExEs2bN4OjoyB1v27ZtheSJiYnR2y5POKClIQPKglASCYIgCIKoenTnQJH5RNgiLi4uVi9rSl0NGjQAAHz99deIjY3FwoULMXXqVACaML527doJztEafyzLgtHpDOsOZri7uwMAMjMz4enpKTj29OlTzhgzhu41rAElkSAIAgANABAEYb/oOpwohI8gLMP8+fPx+eefQ6VSoXbt2rh16xYaNGgg+AsLCwMANG7cGBcuXEBhYenC1qdOnRLUFx4eDolEgpMnTwr2p6am4t69e2jUqJFg/7Fjx/S2GzdubEkVywV5oAiCIAiCsGsoCx9BVA5du3ZFREQEFi9ejAULFmD69Olwd3dH3759UVhYiFOnTiEjIwOzZs3C6NGjMXfuXEyePBnvvPMOUlJS8PnnnwMo9Rq5ubnhlVdewZtvvgmZTIbo6Gjcv38fc+fORZMmTdCrVy/B9X/77Te0bt0azzzzDDZt2oQTJ04gLi6uyu+DLmRAEQRBEARh11ASCYKoPGbNmoUXX3wRN27cwA8//IDPPvsMc+bMgYuLC6KiorhMe+7u7tixYwdee+01NG/eHFFRUZg3bx5Gjx4tmBf15ZdfIjAwEO+99x6Sk5Ph5+eH2NhYbN68GTKZ0DRZuHAhNm/ejClTpiAgIACbNm1C06ZNq1J9UciAIggCAKUxJwjCftH1OJ2/89QqchCEPbNu3Tq9BBUAMHr0aIwePVrvsxgdOnTA+fPnue1NmzZBLpejTp063D4HBwd88MEH+OCDD8qUKSgoSLAOla1ABpQFoQ4oYc/QeC1BEPaKrscpv0hlJUkIombz448/ol69eqhduzbOnz+Pt99+G88//zycnJysLZpFIQOKIAiCIAi7Rq3jgnKUSw2UJAiiMklLS8O8efOQlpaGwMBADB8+HB9//LG1xbI4ZEBZEMpiRhAEQRBVj0p3HSgryUEQNZ05c+Zgzpw5FqnLltdzozTmBEEAoBBUgiDsFxvuZxEEUQ0hA4ogCIIgCLtGN405uaAIgqhMyIAiCIIgCMKu0U0iwZIFVW248TAb4+KOY+eFVGuLQhAcZEARBEEQBGHX3M3IF2xTSF/14dM9ifjn+mO8/vMZa4tCEBxkQFkQhmaREHYMQ1lQCIKwU3QNKKL6cPNRjrVFIAg9yIAiCIIgCMKukUs1A0BSieY/OaCqD8W689sIwgYgA8qC0AA+Yc/YcrpQgiAIY2iTSAS4OwKg37PqhLJYbW0RCEIPqxhQhw4dwsCBAxEUFASGYbB9+3bB8QkTJoBhGMFf+/btrSEqQRAEQRA2jnYdKJmUPFDVDSV5oKqMF198ket3y2Qy1KlTB6+99hoyMjIsdo3k5GRB/97NzQ0RERF4/fXXcf36dYtdp7KxigGVm5uL6OhorFixwmCZPn36IDU1lfvbtWtXFUpIEDUPmgNFEIS9oi7pZEvpd6zaoVSRB6oq0fa/k5OT8cMPP2DHjh2YMmWKxa+zb98+pKam4vz581i8eDGuXr2K6Oho7N+/3+LXqgysYkD17dsXH330EYYOHWqwjIODAwICArg/b2/vKpSwfNDPNkEQRM1hyZIlYBgGM2fOtLYoNR5tH5ubA0VOi2rD0zyltUWwGLm5uQb/CgoKTC6bn59vUtnyoO1/BwcHo1evXhgxYgT27t3LHV+3bh2aNGkCR0dHNG7cGCtXrhScf+TIETRv3hyOjo5o3bo1tm/fDoZhcO7cOUE5Hx8fBAQEoF69ehg8eDD27duHdu3aYeLEiVCpVACABQsWoHnz5vjpp59Qt25deHh4YOTIkcjOzi6XbpZEZm0BDJGQkAA/Pz94enqiS5cu+Pjjj+Hn52ewfGFhIQoLC7ntrKwsAIBSqYRSWTVfPv7vtSWuqa2jquSvDEgH28AUHdTq0lE+W9S1pjwHW8cUHexZP1M5efIkVq9ejWbNmllbFAKl60BZI4nEof8e4d3/XcSMHuF4vnVIFV6ZsDdcXV0NHuvXrx927tzJbfv5+SEvL0+0bJcuXZCQkMBt161bF48fP9YrV9G5gLdu3cKePXsgl8sBAGvWrMH8+fOxYsUKtGjRAmfPnsWkSZPg4uKC8ePHIzs7GwMHDkS/fv3w888/4/bt2yYPMEkkEsyYMQPPPvssTp8+jbZt2wIAbt68ie3bt+PPP/9ERkYGnn/+eSxduhQff/xxhXSrKDZpQPXt2xfDhw9HaGgokpKS8MEHH6Bbt244ffo0HBwcRM9ZsmQJFi5cqLf/wIEDcHZ2rmyRAQD5+VJo/VCWDDmMj4+3WF3WgnSwDYzpcO+eBFqntC2HzFb352AvGNPBUKNfXcjJycGYMWOwZs0afPTRR9YWh0BpEgntHKiqdEEt+OMy7j3Nx5wtF8iAIuyeP//8E66urlCpVJxXbNmyZQCARYsW4YsvvuAiyMLCwnDlyhV8//33GD9+PDZt2gSGYbBmzRo4OjqiadOmuHfvHiZNmmTStRs3bgxAM09Ka0Cp1WqsX78ebm5uAIBx48Zh//79ZECJMWLECO5zZGQkWrdujdDQUOzcudNg2N+7776LWbNmcdtZWVkICQlBbGwsfHx8Kl1mAFh65RCeFmletn79+lW4PqVSifj4ePTs2ZOz/u0N0sE2MEWHA1su4tRjzUrvlnh/LU1NeQ62jik6aCMAqiuvv/46+vfvjx49epRpQNlCdISlsUVPqrIk5KfEAQU1y5rkJbWEDncySgcMqvKe2OJzMBdzdJBKmCrR9XjSE6w7chszuzdA4wC3Msvr6qBUKsGyLNRqtSCyAzD+2yiVSgXl09LSDJaVSCSCsrdu3RItp3t9Q/A9VV27dsXKlSuRl5eHuLg4/Pfff3j99dfx4MED3LlzBxMnThQYRMXFxfDw8IBarca1a9fQrFkzKBQK7tqtW7fmZOHfE7H7ow3d094/lmVRt25duLi4cGUDAgLw8OFDwbla+fnnKZVKSKVSQf2WfH9s0oDSJTAwEKGhoUazczg4OIh6p+RyeZV1VPhzoCx5zarUobIgHWwDYzpIJBJBOVuluj8He8GYDvaumzE2b96MM2fO4OTJkyaVt4XoiMrCljypt29rPOhZTzMBMHj6NNMkT7oldChWVU70ianY0nMoL8Z1KOmqsuoqub9fXpQiOYfB/muP8FVMscnnaXWQyWQICAhATk4OioqKTD5fpVKZXF6lUplkDJg7mKVUKuHg4MBNmVm0aBEGDhyIuXPnckbT8uXLOaNIi1QqRVZWFgoLC6FSqQTX1c5Xys3NRVZWFnJycgTbfM6cOQNAE76orU8ikQjKFRYWori4WFS37OxsFBUVIT8/H4cOHUJxsfD5WTI6wi4MqPT0dNy5cweBgYHWFsUolMWMIAii+nLnzh3MmDEDe/fuhaOjo0nn2EJ0hKWxJU9q0uNcnL3zFL656cDDNPj6eCE55yk8PDzQr5/h5U8sqcOMo6UT7KvSe29Lz6G8mKKD9v5KpVL069e70mV683g8tLPoTHmeujoUFBTgzp07cHV1Nfl3wtqwLIvs7GzI5XLIZDK4u7tzxxYuXIj+/ftjxowZqF27NtLS0tC8eXPReqKiorBlyxaBU+PatWsAABcXF7i7u3PzwLTbWtRqNeLi4hAWFoZnnnkGUqkUDg4OkEqlgnKOjo6QSCSCfVr53dzcUFhYCCcnJ3Tu3Fnv/lsyOsIqBlROTg5u3LjBbSclJeHcuXPw9vaGt7c3FixYgGHDhiEwMBDJycl477334Ovri2effdYa4hIEQRAETp8+jYcPH6JVq1bcPpVKhUOHDmHFihUoLCzUCxmxheiIysLaOjzKLkSvr/4V7JNr7z9jmifU0jpY435Y+zlYAlN0kJj4TCtKqI8zbj7SZLCTyWQmD45rdVCpVGAYBhKJRBDZYcvww+G0smvp1q0bIiIisHTpUixYsADTp0+Hh4cH+vbti8LCQpw6dQoZGRmYNWsWxo4diw8++ACvvvoq3nnnHaSkpHDzp6RSqeCeZGRk4OHDh8jLy8OlS5ewfPlynDhxAjt37uSes/be8+UR26eVXys7wzCi75Ql3x+rGFCnTp1CbGwst60dnRs/fjxWrVqFixcv4scff8TTp08RGBiI2NhY/Prrr9wEMoIgCIKoarp3746LFy8K9r344oto3Lgx3n77bT3jiahcVvytH9bPLaRLacyrHZIqivLhX0elZksTk9RgZs2ahRdffBE3btzADz/8gM8++wxz5syBi4sLoqKiuEx77u7u2LFjB1577TU0b94cUVFRmDdvHkaPHq3nDerRowcAwNnZGaGhoYiNjcXq1avRoEGDqlavXFjFgOratavR1Ip//fVXFUpDEARBEGXj5uaGyMhIwT4XFxf4+Pjo7ScqnxuPcvT2adOYE9WPqnqy/HdIqWIhq0HjIuvWrRP1mo0ePRqjR4/W+yxGhw4dcP78eW5706ZNkMvlqFOnDgBNynVT06svWLAACxYsEOybOXOmTay9ZxdzoAiCIAiCIPgwIl1qGS2kW22pKg8UP2SvSKWGE2qQBWUBfvzxR9SrVw+1a9fG+fPn8fbbb+P555+Hk5OTtUWzKGRAWRDKIUEQBFGz4C9mSVQth2/oLxxqjYV0icqD76moqj4W/zJKlWlpwIlS0tLSMG/ePKSlpSEwMBDDhw+3+ppNlQEZUARBaKABAIIg7ARDHduS9XRNDhEibBvtAskAIKmi8Ew1790hA8p85syZgzlz5lhbjErHPtKDEARBEARBlMDvWPNpUcezagUhKhUVz5ipqhA+/rv1NM9+FykmKhcyoAiCIAiCsCuKDRhQnk6KKpaklHA/V6tdu7rCy66NqsoPwjeg/r72sNz1kBfUOlTVfScDiiAIgiAIu0KlEu8kOco13Zqq6rvyO9syKXWpLI1KMAeqaiwovnH+2V+JyCksNuv8/2fvu8PkKK7tT/fMbN7VSlpplTNCGYEkgkySQYR1wgEb2882tnEOD2M//5wfOOGIcXiO2GBsg7GNsQkyIECJJIRQzjltXmnz7qTu3x891X27ujrM7ITdVZ3v06fZme6u6urq7nvq3nsuqzXU29ub1X5JBAMb91zXDJM5UFmEFJGQGMoQKVpJSEhIDEYkXRhSScRQTNPzJCNBc2TCUkI966AENV+jy4eHtnVHUVEc3FwOhUKorq5Gc7PhvSorK8sb+csUmqYhFouhv79/yBT/pdA0DdFoFG1tbWhtbUV1dXXO6/JJAiUhIQEgfwaHhISExECR0MTJ/aWMQOXpcUYJlKxBlX1oWv5zoE619w34GOPGjQMAk0QNdui6jr6+PpSWlg56sicC7f/IkSPN8c8lJIHKIuQKvoSEhISERO7hwp9QFM7v6nmChBJGQtIGyDZauqPm53zY9X2xpOO7TMi4oigYP348xo4di3h88AtRxONxrF+/HpdffnnOQ99yAdb/q666CiUlJXlpUxIoCQkJCQkJiSEFkQdqyqgy08jOlz89TvoxFFfuBzuOt+U3j+hMb8zx3UDmUigUynkoWTYQCoWQSCRQUlIyJAkU638+x3roBTpKSEjkBNKDKiEhMVQg8kAtnTrSfI7lS4mLeqBkFHT2QUMk83FJWf5TEREEkWp6EiJIAiUhISEhISExpCDyQF0zf1z+PVDUwJcMKuuI5Xl8WRHdSEhBVUk41a6EhBOSQElISEhISEgMKWgCr0BFcTjvfvRtJzvy3OLZhTjx8OXTA6WqihmSKR1QEiJIApVFyPBniaEMOX8lJCSGClitnlHlRZg7vgqjy4swf0KVtUGejN7+uCU6IA3t7IN6+FxqJ2cVjJiHVIW8E+WFlXBCikhISEhISEhIDCmw3KNISMHjn7kUSU1HUVi1vAZ56kcsQUPMJLINSqDyMcKsuZCimGRKEmMJESSByiLkAr6EhISEhETuwWr1hFUVIVUxazCZOVB5snptBEpa2lmHfXxz3x4N4dNTTcurKiGCDOGTkJCQkJCQGFJo7OgH4Cx6yhYy8+aBSroUpJLICtbsswrR5uOamiF8MqZdwgeSQElISEhISEgMKTD79orZY4Tf5wsyhC+3KC+yAqXy4eFjHqiQahX2kI5FCREkgcoiZBE9CQkJCQmJ3IMZupUl4kyEfBm9p3uswqvS0M4+qNpiPoY3qbMQPhIOKqmxhACSQElISEhISEgMKTBFNpb7ZIGJSOTH6H12b5P5WZrZ2UeCSO/lg6BqGg3hkzLmEu6QBEpCQkJCQkJiSIGFc6lc5IclIpGffoyvKs1PQ2cpkoRAiWp/5ao9lciYSwIlIYIkUBISEgCkiqSEhMTQATOm+cj5fD/HktS6lpZ21kEJVD5cfEkiImEJksjrKuGEJFASEhISEhISQwrMruY9UAz54jLUwJdmdvaR7/HVWB0o6YGS8IEkUFmEXMGXkJCQkJDIPcxQK94DlWcxJ5vIgTS0sw77+OYhhI+EhirSqpPwgCRQEhISEhISEkMKzJjmRSQU7vdcwxZiJpF1JPLsgWrvNVQVqQdKQkIESaCyCXmzSQxhyJeFhIREpnj+QCsu/8EafOLPm/NCXphdzXucLOnp/MAeYibJVLah5VmFb8fJDgBAdzSR13YLhf54Es8faEVfLFnorgw5iAsoSEhInHUYzi8JCQmJ3OKJHfU4froXx0/3orU7hjGVxTltT9PFIXz5hgzhyy2oSEc+VPhKi0IAgLGVxThxuhfA8CbGt/51K57c1Yj3XTQZS6VLJS3I4ZKQkJCQkJAYEOgKdjyp5bw9NxEJJc+1e5J59pCcbUgkrUGNJnI/r1jI4LwJVaZ3c7heV13X8eSuRgDAnzaeKHBvhh4kgZKQkJCQkJAYEPrjlnGbFwKlWcn+FFYIX36sXpkClX00dfbjTI+Ri8R7nerb+3LaNptXYeLaHK6XuJcsekwYUVLAngxNSAIlISEBQOZASUhIZI7+RL49UGICxSBlzIcmWrqiWP6953Dlj9Yimkg6RDo6+uI5bT8hLKQ7PK8szfOq7+hHcnieZs4gCVQWcbbZn79aewh/eP5IobshISEhIVFgsOR7AIjnwRKzQvjs3+d7ISjfMtvDHcdP9yKp6ejoi+N0T8xBoHKtepgkHqh8C5LkG0dbe2x/9yZcNpQQQopISGSExo5+fP/JvQCA910yFZGQ5OISEhISZyvaUiFXQH48UIysqC4qEvkyejUZw5dVRIkns7s/gaNtvbbfc81RGYEKkTpQw5UXH2jutv0dz/1tO6wgrV6JjNAdza0bXUJCQkJiaID3CuQzhI/3OOVdRGK4WtcFQowIRdR39JufK4uN9f5cjzcL4Qup6rAPa+dHUiqZpwdJoLKIfFdALyRocbt8SItK5B6y6rqEhEQmiHHqaF39uY8FYhzNTUQiXz4oyhXlq3DgoEp7Gw+3mZ9HlhcByH0In2YSKPrt8LywSW6hQ3qg0oMkUMMA/fFkXlb8KKi0qHxpSEhISJy9oGFXALDp6Omct8kW7kKuBCo/sOVADVNDO5+gBIqpxCkKEEqFauZ6wdbmgUp9N1xtHD5V8WSPXERNB5JAZRGFmHp9sSTmfP1JXPb9NXltNyHjviUkJCQk4PRAlUZCOW9T9ymkmy+jl6rCDVVDW9N0NHf2+2+YB+xv7DI/JzRjXqmKYl7nnHugdCoikQoHzWmLhUNSs9+3PVJEIi1IAjXEsbexEwDQmOeH3y+eO2B+liF8EhISEmcv+AKn+QiIYHY0Hzpv5kDlvgvoiSZybtDnA7c+tBUXfvdZPLO7qdBdwckzlmgEi3QJKYrlgcrxeNtkzFPfDVcTh79Ph8FUziskgZLICM/saTY/D9eHi4SEhISEP06csSul5WNRza0OVD5r9zRxC5dBW2zu7MdnH9yCVTsast+pDPDotnoAwJ9ePlbgntjHkMnhK4p1nXMtImErpDvM60DxHihZByo9SAI1xDEYhCvkPSchISFx9uLE6fwTqEe2nAIgUuEzkI/3Eu99Cmpo/2b9YTy6rR6f/MtruehWxhgM0SS9RAqOhvBZOVC5bf9IqjaSzQOV2yYLBqcHqvD25FCCJFBDHINhug+Gh66EhISERGHAvwLy8U6YXlMOwBIXYMjnmiKfCxz0rI9xtY0GCwbDq7wnaiXimCF8qmJ6oHIdwre7wUiLgK5bOVB5HJfO/vyViMmVB6qtO4q+s0ATXRKoLKIQzqBB4IAaFA9dCQkJCYnCgA+rykcOFPP+LJo0Qvh7Pt5LmeY/yUVHd7x4yJIuZ+rCimIVTM51ztmI0ggAYN6EKuKBys/1+turJ7Do9qdx1+r9eWnPcd+meZq/XnfIlg8PGN7oi+98FtfcvW7YF5mWBEpi4Bje98hZg8FAxiUkJIYeeEMpHzkjrAlexhxmId3c98GhRhuwyeEgPJEr0MtpCjooCkJMhS/H15V5TsaNKM37O/EHT+4DAPzs2QM+W2YH/PxNZ1rWt/fhe//Zix89vR/HiUd1d0Mn4kkdJ073oS8+vL1QkkBlEYUoRDoYip/K2hcSEhISZy94QpAPgsAMaYcKH0v8z3kPnCFQQds0w8QGGQr9Ltd13eY5ZB4oldaByuHcSiQ1xFJtllEp/jwNSz7D9wDnWKbjgWrvtfpK+01FXSSBkhjUGAxeA7mYJiEhIXH2gje88vFOMAvp8jlQqf+7+nMvMc6HKgb1erV0RXPQm4GDGsWFAH+5RDlQufRAne6NmZ9Li0J5lcQHnPXUco2BeKB6YlauGi1jQBcVhnselCRQwwj5lNqcO76qIO1KSEhISAwu8CvZeZExN8O77N/XVpWYn0+eya1YQ0LLzOBleTZA4cP5aPu76gvrGePHgo2vQlT4cjleu05Z518cVokkfs6adEVXHrxRh1sMxcGisEEF+tPgOwebu83PUeJpipHVlH7pgZIIikJ7g/J5k89IKSABMgVKQkJC4mwG7xXITx0o43+VY1DlxWHzc6674ZAxD7hfJGT1eW9jYUnLzlMd5ufaquIC9sQ5b1gdKBrC9xIRmcg2WMjg5FGlttDQfIQ28gvRrd0xly2zh0MtBglinq8tbcEpASVQ/QmLKMWJN2rzsTMD7eKghiRQQxyUtBWKyEhFIQkJCYmzFwXJgSICAzwqUyQq1+8mh4x5wOZo2GFnX8Jjy9yDhmKF1cKahG4eqJCioKHDKFpcWhRy7JctsOs5fkQpAORVxjzGxYNSOfdcobrM8IS+btZoAMDYkuAnSu+tTUctokTPQ3qgJIYMChZKJ/mThISExFkLZwhf7ttk7zunCh/MRKhcdyOZ5D1QwVpMkP0KHcJHjdxC94X3ZLJxUhQF1y8YByC3ZIZ5oJiHMJ+FdPn8p32NXTlvk43v0qmjAKR3nnTeRONW3+OEQDlUKocZJIEa4qAqfPmcqvRFMbxvEQkJCQkDd955J5YtW4bKykqMHTsWN9xwA/bt21fobhUcjhC+vKrwOX8zDd8cd4OFLvFCFn6gq/TxDPOosoX++OAxePl5Y8qYq5Y3KJdeRRYyGAkZprGVA5X7ceEJ1LG2npy3ycaX5UClc5ZUIIKSptW7mxzHH66QBGqIwxbCV6C5KkP4hgcKncMnITHYsW7dOnzqU5/Cyy+/jNWrVyORSOCaa65BT0/ujZ3BDKcccv5yoETkxcpfyW0/XjjYmuqL0U7Q07Z5oNKtXppldPRZYgW8LHu+4QjhM2XMFVMsJLcEymiPhTLm850Y5+YBn9uXC7DxDqvpu2yf3dNsfqbkbzAJpOQaYf9Nso/169fjhz/8ITZv3oyGhgY88sgjuOGGG8zfdV3HHXfcgd/+9rc4c+YMLrroIvzf//0f5s+fX4juDmoMBqNX8icJCYmzAU8++aTt73vvvRdjx47F5s2bcfnllwv3iUajiEYt2erOTkM0IB6PIx4vrGx0pmD9Nv9P2HMdkslkzs+NGftaMuFoi70WY3Hnbwz8OWSC8lQ+DnsH6roe6HhUvS86gHmQjXN4bo/dY5DvOUnPIRqzEzhmmKsA9NSYJZJaTvrYH0/i/hePAgDCqtEf5nlKJNznEes7/T8TdPfbpe3jidzfQ+y+VZXUAgCCnwM1+/rJfUafBdGY97hlE0GvQTb7UxAC1dPTg/POOw8f/OAH8fa3v93x+w9+8APcdddduO+++zB79mx8+9vfxsqVK7Fv3z5UVlYWoMdDA4Uqgif5k4SExNmIjg5DwWzUqFGu29x555244447HN+vWbMGZWVlOetbPrB69WoAwP5jKmhAy/GTp7Bq1Ymcth2NhgAoeH7DBhzkhjEeN35bv349DvgMMTuHTHDwqHHe51RpONCpore3D6tWrfLcR9eBeNIyvTa9+hoSRwf2Fh3IOXS2WtcuGov79j9XWL16NTpiADVLO3v6ACjo7e3BwQP7AYRw7NhxrFp1NOvtr21QsKfRIMTNjQ1YteoUOjuMefTKplfRc9D/Gg3kOrzaogCwBDIOHDiIVdH9GR8vCLp7jfPbt2e32XbQc0gmjH0B4Bi53+sbrPm0d/8BrOrPb4izX/97e7NX2qAgBOr666/H9ddfL/xN13Xcfffd+OpXv4q3ve1tAIA//vGPqK2txQMPPICPfexj+exqWuAroucb+fQE0bbyEe9O28qHa/vshBxXCYmg0HUdt912Gy699FIsWLDAdbsvf/nLuO2228y/Ozs7MXnyZKxYsQKjR4/OR1ezjng8jtWrV2PlypWIRCLY/uQ+oP4YIiEF8aSO8eMnoK5uUU778PUtzwGJBK684grMGFNu++2O7WvQk4jjsssuw+xa8aIrfw6ZYPMTe4GG46gdOwYHOttQWlqKujqxJ5Khvr0PeHmD+ffC8xaj7rzxGbWfjXNY+/AOoKXB+EMNoa7u2oyOkynoObT2JoHN683fuhPGO6myogJzzh2PJ04cxKTJk1FXl/1opOf+sQOAMQ5Tp0xCXd0C3HP8ZZzo6cTSpUux4twxgc4h0+vQs/kUcHCX+ff0GTNRd805GR0rKL61Yy0Qi2HxooX4x5Hd0IHA5/CVzc8CScPbNKqmFnV15wMAHml7DThjhLZOmzEDddfMzlHv7Qh6DVgEQDZQEALlhSNHjqCxsRHXXHON+V1xcTGuuOIKvPjii64EajCESdBEw2y0GcQlmUxYUpfxeBwh5CeGWSMhCPGE+xhnw7XN0NDRjxt+9RLeuWQSPr8ytw8WimyeQ6EQ5Bxs13QQnuvZch0GO4Kcw1A+v6D49Kc/je3bt+P555/33K64uBjFxc76OpFIJGNja7CAnYOeWnGOhFTEk0msP9Ca83Njr9viIuc4MmlzNRT27ceArkOqnXDI8r75HWvT8Sbb37qiDnisBnIOOlk4S2p6weZkJBKB4pKVrygKwmFmrio56eO4lHQ5AEwZVY5IJGLOo1AoFKjNgVyHJL+AqebmPG1tpha/S4qsdoKeQ5wsnD+3r8Xch84nTc/9OfDw6382+zPoCFRjYyMAoLa21vZ9bW0tjh075rrfYAiT6Oq0XJrZdIN7uSQbegF2GZ988inksESCDY2Nlpt2zZq1qCnx3n4grm2Gvx1WcbpHxa/XH8Hc+IEBHy9dZOMcCg2vczhx3LqmhQrjCILhfh2GCrzOIZthEoMRn/nMZ/Doo49i/fr1mDRpUqG7U3D8e+spAMQgi+T+RcSEKkR1oNh7OF+FdFkSfpDm1u9v4Y5RWOEGqpSW1HToul6waBo3gYi2nhgRkchR46njzx1fhU+tmJX6Ln91oOKcCl8+2kyYqoPBzrOhow890QRmja20CUSUE8OTfj/cVfgGHYFi4G9gv5t6MIRJ/OrIS0Cvod1fV1c34OMFcUkeaO7G97a9CAC45tprUFaUn0u6qmMrtp02VFiuuPJKTB0lJqnZcG0zvPr4HrzQZMTZZmN8gyKb51AoBDmHF/+9G2g+CSC/4xsUZ8t1GOwIcg7ZDJMYTNB1HZ/5zGfwyCOPYO3atZg+fXqhuzQoMK2mHG09MVw4fRQ2HGi1VL1yCGZsi2q/mvLTOc7QZX1gSoBBjN5IyN7hQhuZPGlJajrCocIQKDfVtmvm1ZpEOVeS4kyw4spzx5iy3nmtA8UV0s2Hgl1Xqlgvm5NeLcaTGq65az26ogms+cKVtv7FbaRJE34ejhh0BGrcOKNYWmNjI8aPt+KCm5ubHV4pisEQJkEJXjbb9DqHooh1CcPhCCKR/FxShfjaQ7kOk2DHCFvnVgjjcziF3IigqsHDUAqJ4X4dhgq8zmGon5sbPvWpT+GBBx7Av//9b1RWVpoREyNGjEBpaanP3sMXzAi/YMpIbDjQiqSu41R7H/6zowFvXjwBYyt9QhQyaTNlm4k8UPmqA8WMyHTqQPGE5bk9zXjvRVOz2q904JAO13SE8xTJwsPNAxVSFZMU50rGnBGoIkJwC1kHKtflYc70xMzPPKkX4Whrj0m4jnI1qhKE/NH59K8t9fj2DQsz6t+xth78/dWTeOfSyZgyenCK7Qy6OlDTp0/HuHHjbOEhsVgM69atw/LlywvYM3+crSn4+Xi4ACjYqpiEhIQEAPzqV79CR0cHrrzySowfP97899BDDxW6awUFExJiK/dJDfjGv3bi20/swR2P7c5Jm2zFXlwHKidNOsDsxpDKVvD934W8x4nWzQGAvY2dWHj7U/jOE7kZNx48gdpVXzjvcXNXVPh9SFVMopwrx8yZXoNQsDkM5Nem4wlUrs2qU+195ufxI4wFDrcmm7v6sfInlrhHNM6TPWse0fk9aWTmi0r/8/ft+MWag/jm47v8Ny4QCkKguru7sXXrVmzduhWAIRyxdetWHD9+HIqi4NZbb8V3v/tdPPLII9i5cyduvvlmlJWV4T3veU8hujtkUKhAgHy1m4+wEAkJCQk36Lou/HfzzTcXumsFBbOZ2DNa03U8u9cI8X5ie0PW22vrtgxtkQdKzVPuCvMShNMI4WNkc2pqVZ0vOvyOX72Erv4EfrfhSBZ76g6eQPVEE/jt+kN4dk+Tyx65w7E2ce5kPgrpPrnT8CaLCHk+bJzHt9fb/s51CB8jOjaS49Lk0Vb7demPJx3bsCLEtN98WGI6eOXoaQDAM6Rg72BDQUL4Xn31VaxYscL8m+UufeADH8B9992HL37xi+jr68MnP/lJs5Du008/LWtACWHd7PnyBPHImwdKEigJCQmJQQeTSISYByq374T69n7zc01FkeN3K3clt/3gQ/iCtMb2ibiMVTThNE5ziSTX6TX7mnHvC0cBALu/eW3e8qoB67rNqCnH4VYrTCykKmb5klyZG5NHleFYW6/pjQGstIx8mDijK4pxlBDIXIfwMcITCakkZ1CMBEeEemNiAlUSCZnCFIDTq5Zu3wBgZNngDQcviAfqyiuvFK7i3XfffQCMSXv77bejoaEB/f39WLdunWedjcGCQgjX0DbzSZ/oiylfvK3QdbaGO+TwSkhIZALGAZial6bpKInkzrxgxuXE6lLheyFfhi/zHoVS7bV0RX3rIiY4AsUbyvleB+VVAPc3dZmfn9rVmNe+sKGrrbLnzBk5UJZ3MxdgRHYKEcQiy9M5aZOiL0VKlk0bCSCfBEqB4hOsGHUQqIRjm3iKOO1p7CTfZUagqIdrwcQRGR0jHxh0OVAS6YFO+wI5oPJG3MRytRISEhIShYTGe1V0HaPLnaJO2YIpYe5jweTaCDXD8Woso9stj4fvk5Uvpgt/zxf49l842GZ+7ujNbz03dl158m3kQBmfc02gwqpTRKKzz0kYsg1GGpjHL9cCdozwhFV/DxQvsb7lRLv5mXlfE0kN8aRms0PjvHszIKgXazAvnEsClUUU/DoXiEDl64Ff8PGVkJCQkHCAzwVKarot/yHbYd465/nh4WcQZgvM6KZCEHxOk9s+RSE2Vvbf8/0a9zLUu6O5Jw4U7LqWcgUtQ4olIjGAtBpPMM8gFavqjhqkZtvJ9tw0SsDC4iqKUwQqy/fMidO9aO+1lPdYWF4k7E+g+FymXjIv2D1/7HQvohzRyjSEjwpRFCo1JQgkgRrioOw81/HebsjX/JYpUBISEhKDD8zYo2FpLcQTk+13RNJDwhyg8tPZbZeHRohcccqj5BfCJxorinzbi161ep7alV8hCUYuFUXB+VOqze9VVTHJcq4Mar4oMgCMLjfy6ypKcpsHFktoaOw08vrKUuTRj4ing6OtPbj8h2uw8ifrzfM0Q/hUEsLn0iQfirfxiCHwcOH0USZpauzod3gzMyXgdE7mox5WppAEaoijUJyC3tsyB0pCQkLi7AV7B7DVez50J9uvCGZUqS6rakqeSqDSfoSIAqEXWHiSWwhfvuElW55OfatsgA0FJaTs71zXgUoIZPHnT6gCkHsb5/hpSzxiWk151ts83NoNXTdy9A61dAMAYkmLyPt5oNq6Y7a/+1LhhiFFwWXn1AAwSI+I3FKvV1DQED6vQtN7Gzvx5X9ux77GLtdtcglJoIYRCpcDJUP4JCQkJM5WJDmvCo9chfC52fdqnjxQa/a1ADAMyZApcuC9Dz9W+c554sGHXVHku2/Me6cqQDGp5htSkfM6UOIcKMX2W67AQt1GlxehJGKcdzbHnjoZNx42ctyeS8nUh1Q/CQng5cOnbX/TBRM2j+MJXThOh1p6HN/5Ie5SmJfHF/6+DQ++cgJf+9eOtNvIBiSBGuIolAofRf5C+CSDkpCQkMgHNE3Ho9vqsfNUh/+2TBjBjUBltWdERMI1hC+4rPhAwHKfqssi5rvYz9g2iw7nSfLdC5TYMm8LRaY5LJmCzSNVtXugVFUxBUNy5oFikvQkB4pN51xfIyZdX1YcMsl/NtukIXHsPKtSc7cnlvD1QFW7SImHVMVU3owlNRu5PWdsBQAgKqgZ5d9f4oHySHrbecrwnm46eqYguVKSQGUR/jw+tyhcHaj8tCNzoIYn/rbpBN71m5cycvVLSEjkBusPtOCzD27Bm3/xvO+7hdlnNAGfItvvCDPUyzWELzftOvqR6sjMMRXBQ/gYgQoQwueXTzVQ0K6WccINQOYy1JmCEuMiQqDCqpLz4siiHCjLq5hrAmWMc1FItWqKZbHJGA2JS31m8/CK2WMAnxwoNjaTR5Xavg8pxAOV1CwCrMD0pPVnUNeMzrugSn5f/teutNsZKCSBGuJQ8lqpwIJu+5ynEL4CE9ThjkKN7hcf3o6NR07j588dLFAPJCQkeDA5bk23VnoZ/r75JL70SghP7DDqBDGCFXbRFc/2O8IK9XJ5apkhfLl9N5leizRyoFgOilkzy2P7bAoJiEDbFl07P+P15JlevO57z+GL/9iWlf6w7oQUxRZaqCq5rQOl67rtWprtElXJXIKda3E4ZJ7nK0dPe+2SFqgXh51nMmkRRj8PVJLzmjKEVMX8LpG0QvhCqmJK0ffH0yfhNAdqd0On8D7u44r5PvxafdrtDBSSQGURhY4wK1Qodb5CEAo9vhK5RXd/fiVzJSQk3EHJCTP6GX749AH0JRXc+rftACxDvyicLw+Un4gE2y677fKgBmOQfBld19GVes6FBSF8J4iYgN+xsgF6eJH30M8D9di2Bpxq78PfXj2Zlf5YohzADYsnmt8XhVUzAuXFQ22iXQcE2zgUwAN1rM3IEyqOqCZZ6I+l77lxA72OjEyZRaBV1Xfx1CRQYbuXkuZAxWweKMX0QB1pTT8HahNHHvsEYYC8tHohIAmUxICRrzhpqcI3vCEvr4TE4AE1uvo5A4Z6JvrjSdMALQ47w8ByAdPQdnNAmTlQfuF0wEf+9Bq+8PdtGXmrkrq1ih8KEGJGf1swYUTqGNZ3HX32wrW5Ntzp+JzucYZQ+xGobL/7qQE+boRViPnqubWmpyPiEiY6ENDzEHmgcl3U9lCzsUDR0RfHBVNGAjDqM2UL9H6NMw8UCVn0yxlkOVRF3NiHVNUk3u29MXOcVEUx61odaEpfIY+39UTzTDQ32/rTbmpAkARqiIM+AAtVB6o/XwQqL61IFArDnUDpuo5/b2tAY6//thIShQYN++GV2hZNsgQHntvbbJIPdxW+7PaN1l8SwbSBfdr95mshrN3fin9sPomW7qj3xhx03R6yFCT5nxIilnNE85z4VXUvCedsgF6Xa+ePc/ze2u2dl5rtEEMamnnBlJH40vVz8NObFmNCdSnmjjfmXC6k1R/adNz8TBcBzOK9OSayoVT45KKJI0xhEj5EbSBI2FTtjM8J09vnL/rPdi/iSB3lU2v2tVj3papgdm0lACsXKh0kOcYalEB1xh1f5RSSQGURBbf/8sif6PMkmze6F4a7gV1oFH58C96BnGLtvhZ84R87cOe23BZllJDIBuiqdZRLBKc5Ch19cdMDpSosKd2OrOdAsfZ86kD5tdoRt/ZP15tCiVJYVa18GQ9jWxQyt4+s0Ce4nKNcR3dQQlfpUiy21YNYZjvHjIqDKIqCj18xE29JhfLlsjjyzlQtrIrisI0ksPWAXIt5MA/PlFFlJuHoiyezNr7PpeT2AWuOaTYPVOpHVxGJlAeKJ1CqipFlRrHhcVUl5txXFGDq6DIAwUUgKPiFA5HUPn+vGP1Mu6kBQRKobKLAFmiBUqAcL9dcQcqYSwxlHGzu9t9IQmKQwB7C5+4ZSSQ1k0woioIKgSGebaPXP4TPv11eHllkkHmBjoGqgqinBfNAmbkjCc00ZvlVda8it9kAtVOpp4CqrTV1usdF0fPJBsmgBjgPJUCIZKZg4/7+S6bavs+FB2rzsdN4dFu9bZ6w9sMhFaVEDdGrRlc6oOQ4bqrwWYWD/RYcTOVIzsMcVhXMGV9pHk8jHtmwKcCR/jkkk/4E6kCzMzQwqefXRpQEahihUCISfHx8riD5U25R4HqOw/76Fkfk41Zi6IAShH9vPWX7Lc6petHQHd74AbK/uEfb84KX54sPPU/4GHq6ruPhzSexdl+zrQ9AygNlikh4HcP6TOsuxVNt8yF8mRif6YAa8bTuUkRVMWWU4UHwUlGjnCkb4YY0B4qHWRw5B0vFjJCPrSy2fR8ySUB22tQ0HW//1Uv47INbsOnoGfN7RmoiIRWlhMhuPdGetXYZ2JyyC6D47G+KxNjfYSohSomkTjzR1vfxDMaOn0siT+xH7t/s3C/PNox8o2cRhbb/CpUDle9iexISQxFSBEViKIE+12urSmy/0bCcpKabxEBVgNfPGes4VvZDvdwNbSCYt4Jf1fYLNdp5qhOf//s23HzvJnRHEzYjL2gOFH1HU0OZ7cN7wdL1iqULNw9USFXM/nkVQrUb5gPvqyljLiDGzEuSi2g6SiYosl2TKU4IMVVcZAsSkZBi60NvLDvKtPTaPLqt3vYdbc/VA5VkBMqez3SmJ2bK3yc0nXiGFYRS3qpoBjLm/FwSFfMWzbd816SWBCqLKIR9RG/s/HoQdMGn3EKan7lFoe37QnvAcg1ZCFpiKGHVjgbzM78iTMPf+mJJdEcNQ09VFKGnNdu3NmvelUAFaJdf+PMjK3979YT5+fkDLTZPW8hW6DVYDhRVWWPGIB/Cl3MVPnL8kgjN/bHq+IgkpBk2H7O8KH4evCCwQkGdv7HnZy7GxCIT9rkbRJpehGgiKdyHzjE6XhaBMtq/YEo1ACCWJZcKHbOR5UWp9tPJgWIeMvuFef2csQiFrDGyPMNAJHXBntnThLtW70+rv/zzprM/mDqE9EANYZytOTq5TrA0QcY31wUSJfKPvM2jAuFsfT5IFAbH23rxkftfdYTfBQVLDgec+UIHW6zaLrRG1NiqYqGUebYf15uOGHVi3EL4lADGNp+T6FdXppeIJR1q6bHnQCkk3CtgDhTNJ2EGakOHPd8o10nx9JFbTa53Z1/c9EhtONDquj8dg2xEGzJPg0hdMZc5UBaBsn8fyiAHatPR0zjvjqdx6fefc8jSUwIVo9LirKhtiowwIuUnIx8UlMwdbunBweYu0wNLa5i5nSU7/2IuhK+kKGSF6nF1oOi9+bNnD6TZX/t5dwasESlFJIYwCm0eFcr8LITdK/nT8EOhQlDzhUI/HySGPnRdD7zQ8Jm/bsHq3U346iM7M2qLrvpSA4xXXWWGWHVZBMXhkM2TYSLLtzYzNPnCswzmYoVHu01ddnU5niTyoEbdoZZuM/eX1dFhbXpdHp00QQ1MRkS2n2y3t5lHD9TiSdXm5+WzakyvopcRT3NisuGBau815hxrm4JyqmwvoNKishSZqPC9cuQ0+uMaGjr6HTWQaAjfS4csYvpyqjgwI05sXLMxpoC91hgAXH3XerxCFiH8PLZbjrcb/eIYZhEJOTQ8UMb3qqK4ljQIAt4DteX4Gcc2E6tLHd9tOy1FJIYsCr3AXCivTK7DDBjo8A5vU1tiOEJ6oCQo1nms7IvQH0/i2rvX4+qfrAsk3HPqjEEuuqOJtN8NHb1x7G20jD+aH9QVta+qMwLFcmZKBXVfciVj/sZFE4S/W/zJQ0SCG0M/EQT6+zO7m0yFPKuejvGbV+FQ+q6kq/SMoPKXKddeeXZ4RbFLwmuajhuXTAIAvJQy7kWg8yobOVBMgW7x5GrHb6otAmXATdng5oFibT67tznwsSj54/Pqki4eu/6UkjE7Q9Ork60QPo9rUxRSPW1Xeo2plxIwCB8jSkmaA6X6C7x4gZ9LR1p7fLcBgO2n1bzawZJAZRFKgdeY88mfaFt5I1A5XIGSOLvmbyHgVrNG4uzErQ9tt4W/+eGZPU3Y39SNwy09ONrmNCh40FC6oCEwDA+SwqKA3VjhjUJWxoIZTItT+RsU2b63dV1s8DKYK+oe7fK5PS8e8ia09D03uqLY7ENlsSERfazNIKx82JbbMWjYHyNhLMGfIfeFdK2QK8CogwQA546rxIgyo6DrYYHxKupfNrxlrD+smCwFfXpm2+Zwy4Fi16esKHgx2B5CoHgjn3rzntzViIMpKW4WLnleijiaEvc5COHjcd7kak8Zc3qNz+fu7XBItc1hGoLJ50ulA9YmU6oUFeN1uzeyJf0eBJJAZROFEJHIf5MOFCSEL/9NSkgMCJI+SfB4bk/wle19xCPUG6B4OV0B/uZju9PqVydHAmjoUZwzUJjKFls1F+ZApdW6P5gB7apsGSBfpj9mPw+/kKOELWdFM997c1NGnps3jIJ2RyFSz8xDwHJMmBfPzXNw3wtHsOJHa7HtpFOdLB2YHqjU33/80DJ8fuVs/NfFU/G6WTXmdm4eT2qY90QHXs4kyRE6CpsHasAt2cGMcT736vwpIwGk9+z+6yZLbCTOheDxQiW/WnsYgHWdWYgcExjxCysNCi9yWxxWPUUkkjYCNdL2WyREZcw1kyAeO93rIKNf+Pu2wP1dv98o/Dsupf7Z2Rd33AsspPZn7z4fd7x5vvl9T4BnY7YgCVQWUWgDqVAr+PkL4cudC1+i8Bjul1SVT1sJDl4KZzzoimt/ACOBqsy19UQ9tnSCGU1Lpo60/Q04c2L6OQ+UCNmXMTf+H4gK35O7mmx/+4Wg8WNg5s0odi+Fl9fISrI3/uY9UKyNWWMrjL9dxu32x3bjSGsPfvbcQc8++4GFOLJxXDJ1FD5z1TkoLw5jTEWx2U83IQlKCHbVD4zMAURdUSRjTp6f2bY5aAFYCkZo0/EETSK5OXxNND6niRUptnKwjPYZkbr9sd2ehYzX7W/B1/+1E23d3ve3VwhfJKR63i903heHVZQTb1wkpJoy5vUd/eY8ftv5k7Bw4gjbcf6x+WTgOcLOn41Lc1cU7/zNS7ZtWFsLJlThA8unWaqRkkBJZIJCJeHni8zYQviGvbktMdxAjb3hrjgoEQzp1NCjc+bFVF7KvS8cwet/vFaotNfeFzM/B/FYUTDjhBkl1FDmQ2R2njJygagHh89hyboHSrMTER5mDpTHy6m82O4pSycHKpHUrfC31GnTlXg3sO4wz1nYzIHSoOu62QYTEfAjdW09Mc/f/UBzoHgoioJw6pq2dIkN9GwVmDX74+IJArgc6CxPqIQLgWLXIZ4MLt5CvU48YTrOiZ48f9AgphpHHLuIgMvv1h92betD923Cn14+ht94bAN4e6AiIdVz9Z+vd1ZRErbtO7rCyotqTKlIhlUFtVX2osSAoQAYBGwcLiVe0FePnRHm3DECx8ju5pTgRT4gCVQWcTbliNPbsRDGoPRADT8M92tqI1DD/WQlAoHlDwUBNVZ/scbwPPz46f043NKD//7rVtu2Xf1x9JMClkFEJygYCWDheKKaNTyo8XnfB5fhHx+/xPw729Nd08UGL0MQDxRbqZ4zrhKAPxmg92w8qdmKhtK+ZOqBoruxFfhcPyc07hx4vOU8IyzxqV2Nwt/pvMhG0V+NI6UUuRSRYO2GXQgUENwLZa/1ZO/oF/+xw/Y3C9XkvZmvn1NrbtPo4YFic/BQs3cuJbvOH718Bs5JeTcZIiGF5EA55wG9L0KKglHlFjGqrSq2Fdlm+X/hkKFMWcTJngeVZWdtVhKyBnA5d4z0pnKtOvqM3LNYIpm3HHlJoLKIszUJP29loPLTjIRETkBtFOmAkgDS80Dxq8i6rgvlngHnSu/2NHNleA9UU6flgehyEaSgxmd1WRGWThsVSA0vE1ieE/FbIUhRW5YrwYw0PwKQ4Or28GGE4ZC/14jvN1Xho2SEFSP2I3UDtTksj5j4d0Ya3OS0af+yISLhlQNlf35mdz7taTC8qHzoIJXtDkygOE8lBR9Ky3saGXGkXh1RTmG6YON6yczReOwzl9p+UxTFU6CLXXs1pdR4zweW4k3nTcDX3jAX40cY4YqjUsV5mYAG80Y/wbWVLoHixSPoeFoeKKPzV88ZAwB4dm8LFt3xNO7Z4O2VywYkgcoiCuGBopO9UDZZYVT48tKkRB4x3MMy7Suow/tcJYIhHcUofsp47Xum1xna1RsLrsTHjBO6utzQ0QfACKURQeQNMr/JkcfAP4TP/RgnzhjnU5UiUHzxTh6ULCQ0zZE3Ew7igeJCD5nHYfvJDtvxiwKQMWDgNgefA8XjqrmGJ4RXXgSA/+xowNE2KyQtG+F8brlIfB+zOZ2Smm6eX8SDQAXJOwTsIZz8XJgyqsz2d3lRyBbBw+ZDeZHleSkW1VVLE6xLIUVBSSTkuG+8wiP5ULmJ1aX4+bvPxy2XzTC3YePUk3rGsHvhnNpK27FiAb2UrE2+JAILj9Q03RF2yf5/dm8LuvoT+PYTewK1NRBIApVFFDqEr1BGWSHaHe7GdiFQ6Pk73EFfWrkukCkxNPDIFmfukht4A9UrWfrJnUbI1bJplmpWOnlQzKCkBIq1VxwWmw1hQdwV87RkPQfKw1MBWJ6Z0wIiCQAnz1iGP1P68suBStpC+HTiLTG+M71JHkYiOwTrd30qZySR1GztB82BGii8cqAAy4AVeQ74sNFsSK77iYNY22VvXKiQyyIud496pIIqHtpC+LhxY4qV/3XxFPP4dF6xOTRnvEU8+DA2Efze3TwxZQqLLE9J8SCn7Hy8RGLYfGVKjGEXRUtewdMNbN6PLLfXnWJ92dPYaX7HyOZA6k5lCkmgsoiCh/Dlsy1y0+cvhE+q8A1rDPNrSl9SMoRPAgBqKpyJ1m7gSTcLOxKBkZyEppsGfjq5qswbE1YVjE4ZMcxAZsb0tAr78bw8UFnPWfFQawOsXAy3MaJkckylcQ38jHKeILC8MjOEjxU/9fBk8R6fNywcn2rbTryYQbouJefshoFaHH5ElNXyEYWa8iFtySxIbic5Dx1FrnKg6EJEuaDeExX6CAI6T3j1QpbzeP5kY2FDI8VnAWs+11QU430XTwXgfq7pLFyze5aN4bdvWICv1M3BPz6+HADvgbIflw+VE4HNV+bldqsBFTSEj40h9cQBFiGl14wVXy5EoXpJoLKIQq/gD3cZc3qXS/tTYqhBhvBJ8OhJI6yOJ0AtnHQxnVPMuH39uWMtz0gacy5BjKYwZ0SzVeDikP14ogKyucuB8g7hm1BteJUqip0FWQHrXEZEdKKe593HHSfbbX+zvDI+hChQDlTqbzY+GlHgUxSrDlcQ74MfWruj+H//2I6nBUIQus84stCsIIZvdjxQXiF81mev56eu62nVT2JEuCSiCnPqLkjVPgp6SD6XbePhNtz7whHEEhrO9BrXlUneJ3XdZj9R9UF27d3GvpkoI3oRbU3TzWLIbAynji7HRy+ficmpkELFw7YyQ+U8CuOynKf9TYaYhcgbDaSTA5VawAkpeNN5Vn21uLmIY/w/iwhiSA+UxABRGKOsIIV0pQE67DDcryh9vksPlAQAtPfGAz/L+IUqvjgu9Qiw/KiisGoS93TCwcywnZBqGkfM+GEeliLOerhuwTjHcUx1r6znQBn/u606M8PKbWyt/ImAxIdTyQOs1XbWBzZOQVT4WLfZvppuX+lnuUfZqKP6h+eP4KFXT+Cjf9os6A9S/RGPI/MsiDxQvMGajYVUXtmQIqgH/5N/eQ0XfGs1Drd4K9MxtKYWIvh8GwZLKdH/Yui6bssXO3m6D+/67cu447Hd+PPLx8zvK0sMYq/p9nOhY8rC4NyIPZVEn0BqT/GgizQzOQU+Bq/oniAeKP6XMCFbP71psfn58e0NrscQtRlSFXznrQvM7xOcqAntk8gb3dHrXNTJJiSByiJcq6LnEIPBDssXmbG5mfPS4tmFQqdADXdSTB8Puc5tkBg62NvYFWg73pjmawDtqrfC1SiBYkZZwAgkAMCTKW9FRFWIF8KYs8ygG8lFH/KSxQDMh0rucqDEv6s+uVeMDIaUYPLj1Hs3MWWs9qXyWVTOA/WEh5Fo1Y5SbP03akBp5nEsUudz0QI8tPnaQ/b+wNYPHhHu2lv7WaFnK8411M/o+Om6ju0n29MSLgEsMpGpByqp6fjPzkZ09ifw9O4m4TYUZ3pieOsvXwTgMn9JX4IQRH4KvXL0tPl5y4l287MZNsqH8JGXRMSHuFFi5dU1unt1qdgj6xXds+OU4Wn1sm/veMt829+UcL9l8UTctGwyAONZ9+i2evfOpmASKEVBVUnEFHph8/DZPc3G72RSiGqHtaZZQDxdSAI1jJBP+5M2lT8VPpkDJTF0YV/lkxNYwsCZgMVQ/Z6zVCUsRgkU80ClMedY3lNpUcjhgWKrwCUhKxQJEItLWDlQuQrhczHqfPK+KIFiq9in2vtc26NGLivAa+VAGd8zD4aogKjVb9j6bXmgdLNuV1hVPRX9qACGl5AIg1dok+UR8/ZAnWrvw+ZjFhmg48GkpmkO1z82n8Sbf/ECPvmX13z7J+pPph6oXfWW0IMft9Q0Hdf9dL3595Wzxwq3U00y63NAOMNqKR5LEYfFk6vBNBaSur1Ar8gDtadBvMBik5D3IP+UgLnWTbMnQdl+Y88St2LKADCjptz29yUzR9v+vjJFsgHgL8QT5wazLhZTuEyNxfaT7dB1Hfe9eBSAfdFIpFuRbv27dCEJVBZRgBBMGwplkhVkMV3an1lHoYe00O3nHDKET0KAeMDJ4Oe1pMY2C10pCqnEAAw+6Vg44MKJI6wcKDN8hhm5urkyDIjr1QSRE88EfiF8/h4otsJtGe2bXeTZjfasIzGiFE0ZZ4ygLpg4AoB3LhXvOVMUyzjfnsqx6oklPMMKaU2uA809jt95iFbmGfY3Gca52xZUdnvNXivPhs41Nh70OxautnaftwgGDyt0S/y7X07dkVZrPFbtFBf/ZVh/oMU2lt97+0LhdiwaLYgIi5e3j6EorJLrrtsWNqgNyRYq3I4ZpLg1AJtapBtR9oruYftfN98ZostQXmzP1eO9edNrrNBBvraTCDSEDwBOpxaZognNtXyDiBzSYuK5gCRQWUShQ6DyJuZQoHbtN7m0QCWGGMiULdS9KjH4EDTh3c+DRI1tFjoUCalphSAxUA8W80C9cuR0qr8W+aBGi9gDlZu3ohUKJ/6dtep2zgnigZo5xjDugniOAMsAZMYZM0oZ0fQyZq3usLA/q5/st4qisJmEL/JA0eLJNRVFjt950GvEzzXmwWp28S6UF4dx8/JpAOznRfPt2PF/+uwB87oElfzm4S9Pb8BtKlN1xRKXkDyGps5++7Fd2kxHhCVImOyI0ohJanXdXhuM9mHu+CoA7mF3QT1QPBkRwSu6x+9eA5z3foTbeHZtBSpSJMvL08v3mXli6xYa5C2R1FzLMYgWCqLSAzV0UIgcKIpC2WSFaFfan9lHoRcAzqZrKj1QEgyiIqUiMEPmPFKrZkZNOeaMM2rGiOoIzRhTnraIhK7rpoFcFFZRnzJ42POBiUjwBEqUQ5IrD5SX2AD93q3dxpTxHFKBsZVFtmN6tQdYBIrloDESFPEgPQy8B8rqp5ULc+H0UZ4eqO0klybINY2Q67L9lJ3YMFJwzbxa1/1ZyCJd+Y+Slf2xhHjWd/SbNcgyASOHmV5XalzzMus8gt536dw/QZQIl0wdabu+bFx5gjOy3CBObsSNtuVF2oPUcaLk4yRHcPzuNcCwfc8hAhWRsOL4/X+uPRcAzOeJG+JJzZELxxYUYkndNa9OJCJB885yAUmgsohCG6D5XNWmTeUvB4q0n5cWJSRyA+mBkmAIou4FWIbMjUsm4bWvr8S+b1+HZz9/hbmyy37Xdd30II0bUWLlWwQkUAnN8oYUh0J4c0pGmBlyLJdDVeyG12KuCClAPAY5kzH3zulwy73akfKQJDQgpIqFEmzt2RL9jf+Zepupwhd290A9uq0eb/3lC2byO9uHLbpqOlUGVDxzoBqJ5yQICWgjeTl8zpSXaAMDnwMHAK8dt8Idb14+nRw/gY//2an2FwTd0YQ579z6Q3PGRKBS8yLlQArqjWNeNhGYZzHI/ROkVpQtrFbXzTwe/lr6EbegHihTGt6DAJWSXMYzXPHpIB4swBLGAMQy5uz5UCaotUVBa7dVpEKE6Rz83ENbhfuJzq+rPz0Rk3Qx8CIDEiYK4YCiz5HhXgfKRqCkATrsMNyvKDUi5fSVYPCrP8TA7L2QqmBUuRW6xXsrqCFWHAqZhkXQ5zQ1PIvCqln/hYkEjK0swan2PkRU+6pvRJC4ovh4DDKFXw6U2a7r/sYv48t000D2CqWkXgAmQc2gcqvk8aQOXddtESmffXALAGDL8XYAFgkyixzruq32jWW0O/tEzykI+X7hYJv5mSd3WgDvgohAMaXB2qpijKksxojSCDr64uiOZh4y1dBuEcOJbrLcZLxEOEbyhdxyZRgYOZ03vgpff+M81+2y7YFSFLv6IjsXPgzOUs8M4IHyFJEIRoBmjinHoZYeRxhiEAIG2D3QokK6LE/Kj/Sz61YcVs28yqLU4sQ9Gw6jtdsieKwQNSD2QMUSGr76yA4cbevB796/FGVF2aU80gOVVZydIXzZqFWRLqT9KTGUIT1QEgxBi0s+s8eQZeYNGb5OTTRhGbFF4fRFJFqJx6Io7FSEY2FW54zQbX0JC4wmywOVXfgVgPXLgfrbqycBAFMrdFMu2ssIpfWbeE8b6wM1Gv3G+pIZo1P7ijxQqpUDJTA244QYBLmm40aUmJ/Z8Y629uBwS7flyfMwrplhTw1fFoZ10XTjPNgcCaIK6AZGUmsqil37YxEP8TEoEaT3gQjsfOaOr/IOb0tHxjx1aWa51Fsyj0lFJFLX8Pwp1bZtTOLmKtkebB5oAQmU20KLuVjhs38RWUAJCxZTIoKFirbuKP615RS6+q16Tex5SAVMGImn5AkAvnWDVSNKRPBauqP4y8bjeOFgm6dITKaQBCqLKHAKVMGMMi95y1whn6f62PYG/N9u1VF3Zbih8Dl8Zw+pOHvOVMIPiZQR1dHnXfRxBEsm525T3gPl8CClKSKxZm+z7dgsxC2pGZ4V1s+SkN2ryieO075m+97edNQwhtyeWX65Mkw9sCIStJCu8X9IURxEsaPPIJTUA+fniWD5LdQTQRPnvfpECbfh7fJsyubBSGgauvrjWPHjtXj9j9ehLWWQetnG7LxYTlE8qeHVlDH6zqVGfR82JgORjQ5WsNX7uvYQgY0Tp61cm95YAve+cAT7SM01Zsh7tQfYyY4f2CLGqLIi/POTyzFhRAn+33Vz8OKXXk/OwSIjRiFdMcGxPF8ubRFCy+5NcZ8sYu4Fyxtq/97KgfLcHcVEXS8i2FhUb+0rj+zArQ9txfef3OtozybpLuj73PFVNk+8yBu733a9s//WlQQqizircqDI51aP2gdZbZOGK+bRBL3t7zuwv0PFT589mLc2JYYf7OG2kkJJGIglNNx87ytY8q3VppS1CMygOG9Ste37ME+giGEYUhViAAbrz7MpAnXFbKN2C60jReuulIbsxlY+PVAMRWHxW1dxMQYZ2BhNLtfN1XIvIzRJPDW8obs8VfOGnn/URz7ZmQOlW9LqqkXSDjR3O/blQ6D8LqtdbEBHS1fUfBYxJTqv8CxGoFjYXnuvRfSXTB0JwDJw+7JAoLw8JSbhdJlRfFHq9lQ+zx+eP4I7HtuNj/3pVfM35nEUzVtbm2mo8JlkQwUumDISL375KnziypmYQEISddjHmxn2fBil38IHT+jc1On8pOEZFDcPVEAPFiWiYg+UU2TlqV2GV/3PLx83v2PjQRckeFEKwPkOnTzSGfa5r8maD34hnZlAEqhhhEIpewUNQckqCnCunTlOSDzbcTZRioC6ARJnAf6y8Rg2HGhFQtOx4UCr63bWSrX9e+ohAiyBBGbwZFIHCgAWTDRklMMkB+o08cIXhexGjJBADTAH6sVDrbakcgZmq80bP0K4n2XLiRtmJCSs2lfLb31oK6768VrHoiCVmuY9FkxmmnrgdpKCriKY14aE8Jk5UKqCElJTi/fq8Opyfpc1yXmgXjhozTF2LK/oAxbCxyTT2Up/JKSY4gPsfAbkgQogl836eeqMU8lNFAnDokZeOmzkgR1ts3KkkuZ5eJvBZmhbEBGJ1CYij8mCiVUIqwpeN6vGRpaY/eQkUKyf4nZ5RcWdp8RzzvLs+ZynC2Gz6kh5EyhWTHfSyFKh9Dpd6OmPJ9HZL/a4s/GwPU8EQ3BBirwz0JC/+ROqHNuzkE43Fb9MIEUksojCREBZM6tQq9qFIG6FOFM/V7+ERFCcTWRRwhvUGBQlXzO4JfwzQ4ut7DLlKVMeObX9rvoOXJLylniBrWQvnFidOr4VesPCAxdNqgJw2vbsF4XwKT5Exgv7m7rwnt9thKoAe791vZmkbiTew9Y3Z7vM6HX+RvNOwor9GP/eaigM3v3Mfnz7BquwKk2k5w3R4ojxN80R8VOAY9fEDJvSdPP6hUMK5hEDsDuasBUf5Rcs/SKTbPXBjpzGg6+ccPTTi0MsmjTCdpxE0mmQs3cj7wUp8nN7EGgBDH2Wf9ciiHqx18cqRmt31Dy/ho5+x/YWifYJ4Uvdky8dbsOnX3+O57aMlInm5cOfWI7u/gRGVxTbiCYj224hfG7EjVeYc5NtT2j+xNRoz/ifby6oB+qTK2bhstljMH9ClTBfil7XlT9Z55r6wfpLnyfnpko1MBSFVNx6tf1aUMJ1yYzRNm85YMz1/ngS19+9wfM80oH0QGURuSoaGBSF8kAdEoQZ5AKFVhz0S6IcTvjzy8fyT8iHOasohPS/xODFRdOMFdQeYnR6qZixKcMTKGaYsDnVn1ppZbV9jrT1AAgewtKYMjZZ/R9r5Viz6kOF7G0C4uejGcKXwXRnBTc13R4aRo/lTqDg6B8DJSAhVRxu1NxpN+6s0CxnDlRRyCI3TAjALyqDETy66k89BSFVMceYJ2NHW3tsf/vpNtDQM0qe6LG9vAtsfBhxEnkI3DxQovF38zwkiJfPDcs9FgBYv0aWRVCU6hs7v8Mt1pix99ojW04ZffcJ4Tvdzep9+ZvLXmGIxeEQRlcYUt+UELOQSLcQPrfQQX5s3cJGT6SUCf08UIoLYfNTvKT9XTy52tWjR+fLidN9ZiFqHqL5xROor79pHsZWlti+o+c3XqDiGEtqaOrsz2ouuyRQWcTZJCJBjetYUnM81HPefgGs7bOIP+Fr/9qJtftbCt2NYQuZAzW08ctf/hLTp09HSUkJlixZgg0b0l/VDAsKz/71leOCLQ24hdIwQ4sVMGVKaMxIe8eSSQCCJVHHk5pJXJjkr2XIWQYp8wb5CQj5yYl7IUn6S0kENSjdnsmqR7uUSIYVccL707ubbH/TRH/eOLbJN3PhlG5gNiatA8UMXXZ8dlwHgSJhaABwqsf7xeTVF0aIvRYHGYFmBZRFYg+MZB1J2QEs7C9BvH2AMb8X3f40fvrMAUc7bmIKFF7iGnRusrH7zINbsOX4GYwmYgO7UyGhLGfGz0u2Yo6RCxjk/R9UMhywCmIzrx3fDT/59H6OObstkDDJ/CM+NtpAQ/j8EGRMADEJHVdlJ0tFAtJLvfeiWlOrdjRkPQ9KEqgs4mwiUDxoYb18oBCn6lcHYbghX55FhkKQ4kJB8qehi4ceegi33norvvrVr2LLli247LLLcP311+P4cXfyI0KxIDGaqkrxsCSn7d+zVf+RqX2ZkVCaIlCMIHjVDGru6sczu5tsOU5mDhTxQLE8AmZ09vmIJQzEA9VPZKipR4calG6GP/vW1wOlGMeoLnPmbNDt6Co8H/JFCVTIJBt+BIoL4dN17EyFHJljzAgU6YdRX8p+LD+T0JNAmR4o9/0jITspNEPfqGx16gDHUuSOGqq7SSjVz58zhJh+8sx+QT+N/708PTRnjEdUQKCOn+7F5/++zTZPWOgbIzu8fDiPiOlt9dwMgOXpChLuX5LqI1MODCIicf9LR/HHF48CsN8fgLtsO9v9uvnjPPvjH8Lnubsv/HLNGBihpNszzx2DyJtGty+NOAlUeVF4QDl6IkgClUUUOoSvkEZZ0JtjINBdPucLhZb5lhg+KFS4rcTAcdddd+HDH/4wbrnlFsydOxd33303Jk+ejF/96ldpHack5HzJ723scvVOuoXSrJgzFgAQTRkHT+8yPFElqdwcZuh6FbD8+J8245b7X8V9KeMspCpmEcsQqUl0pNUwkJmH4QOXTAEAfPG6c4XHZV3NZHGEhiRRL4wthM9HxlzU7IEmY2EoElLM/i2c6BSjiAnqLamK4jDwx5M6S6ICuM1dzvwb1cyBUsxzGpkicTNqjBpCohC+/rhmnv/sWmO7dEQkeDBy5rU4aJLCVIFgU0SCkAS2zcYjhljDynm15nduynz8PLfyh9zPhRJOx7kwAhVSbaT2cEuPeW/Q7dj/fqFtfrlIFCzX6kyvf5gYE+BguVv8YgDvbWvo6MM3/r0L//voLpw802vOYwYq207BxnWEQNiBwi2Ej4avDgRBd1+7z1AB5efHhdNGmZ9FgjWUtJYKPFCvHW/PugdKikhkEwWwrwdLXkU+CBRFYUKgpNWbSwx3rww9PZkDNTQRi8WwefNmfOlLX7J9f8011+DFF18U7hONRhGNWqFunZ3GirybBPee+nacIyjEyQybZDKBeNzKI4koxve9MeN7Fr7S1R9HPB6Hkpp5sbh9P4rXjrcDAJ7aaUhVqwqsbfUUMdvdhMvPMYw95p35n6tn4AOXTMGk6lLXYwNA3KNtNxxosjwXvdEY4nHDw9ZPVLS0ZALxuPNe0jSjz4mk5mi3qaM3dQ4pj0o8bnoDKHr6oyhSjW2iMeMYIQXmeDCMKg2ZbTAbrj9mnW97t5NAQdcRj8eh61qqn0nTgzBhRBHi8bgZktTbHzOP1UEMc7bKrgOeY5v08DwyYqGn+iOEZp1vfzSG/mhqLFTF3OfkGWNMmVdnZGkYs8aUY19Td+raGdvNGltuhog2nOnBmMpi87dYPEUkPM8nJWSRcM6nnn7jHouoiiMsk3rxWH/YHFZ05xyxtZg6/6Tmvh37nl2z8yaO8J3vrI9M7EDhroGWNMZD043nzhlCxJs7etHMhc9uP3lG2GaUjavicY0BqKmxjXNjm0gmhf3LNtixmYKfAvs8uGnZRLxy9DQAYN64ckdfFN26xhHV+Uxo7Y6io0dwLw4AkkBlEYX2TxRyVdvNGMgVpP2Ze0iPm4SEHa2trUgmk6itrbV9X1tbi8bGRuE+d955J+644w7H92XdpwBUOr5/es16HHB+jaQWAqBg7XPPoYpE+u1qUwCE0NDchlWrVqG5xdhuRM8JrFp1HMeOqwBUHDx8BKtWHXI5M8MU6O7uAaBA0TWsWrUKALC31Th+karjaH0rAAWlvU3AKODZZ54BAOxwOWosavRlw4YNOFwO/PuoimPdCm6Zk0SZj/Xxwl6j3wCwZt16HDBUktGbsPr79FNPQcB9sLfB6POp+lNYtcounLCpxfhtzgjD4Fq9ejVOt1htMTz59DMYUWTfJxrtx2uvvgrAIC9Fqm6OEwC0pY6zddt2lDVuAwA09lr9ZThx/BhWrTqCQyeM7Y8cPYbT3QoABVtf24zoYR3RPmPs7ln1Mq6aqKEkBBzrMo4VUXR0dbQDUKDpxjm4IZYwjiPC6fZOAAqOHzuKVasOC7fpT1r9f3zVkzjeY/wd7es1z31+pYp13ar5Xq4/eQK9qfN58eVX0LHP+KGrzRrnp555FqNIZNZrW7YCCKGzo8M2phQtzcb+27bvQHnTdttv/zxq/NbV1YXWJGznbBDFlJdskzG+HV3GuGx65WWc3gtXbEvN/5bWNtd+MZyqbwCgovXEQaxa5czzoqhvtM+5xoZ6rFp10vy7Ow6wcX9i1X9wqsf6++l1L4LNwfNHa9jSpuJMS5Owf/tS9//JE8exatVR1/6cOWONbaTBGtsDh43vDx86hFVx73Pyh/tNz/p+LHVPVPY32s5H04CrJqiYXK5j98Z12M3t39pvHX/bq6+Yn8eV6mjsM67975/aPMD+2yEJVBZRaINzuHugCp14L0lbbnE2ja/0QA1t8M96IzdF/Pz/8pe/jNtuu838u7OzE5MnT8ay8xfhryePOLafc95STB5ZinPGVtiO+d8vPQ0AWHn1VbacgLL9Lbh3/xYc6VJQV1eHP5zYCHR14KKlS3DV3LE4+NxBrD51GJOmTEFd3TxhH9mxy8rLgf5eFEUiqKu7FgBwUU8M939vLWKakjKegTdeej5iR1/DypUrEYm4hwZ9Z+c6dMajWLDkIlw8fRT++xuGoa9MOg91F0x03Q8Anurahh1nDDGH2ectw5Wpwr7tvXF8edMaAEDd9deJVfReOoZHju7DuPETUFe3yPZb56aTwMHdGF87FkAjVq5ciae79wCtdgJ86RVXYvJIo7bM6Y3HgYN70R5T8IG3XIVf7VkLAFBDIXOcAGBVx1bsPNOMufMXoO7CyQCAfY1dwLaXbMeeMX0a6urm4NCaQ3jy5CFES0ahVI8DPT143cUX4eIZo/C/W9cAiOPpUyrWN4fxlw8tQ7SlB9i5E3FdQc3okTjcdQa6Ds/rcNvG1a4P16LSMqCvDzNnTEedSxhmNJ7E/3vlWQDA1ddcg39trQd27UV1VSXq6pYDALY/uQ/rGo6Z+8yaMQ19pzpxoqcdixZfgGvnGwsOT3ZuA9qMa3rJpVdg5hjDk7B69WosWLQI2LcLNaNHoq7uQmFfHm83xnf+ggWoWzbZ9tve1QewruEISssrcP25Y3DP80cBABXFYZvE+fxFi1F33nj8YM96oL8fl1/6Opw3yRnCyaDsbMR9B7ajeqR7v9g5jBlbC7S1YOFC6/q7oan6GHb9Z5/59+RJE1FXZ0nnd/TF8dVXjXl+zbXXYVdDJ7DjFQBAV+U0AMbCwDVL52DLU/sxeuw41NUtdrSz86n9wKmjmDVjOuquF19jAHioaRP2d5wxxnaJ1fevfec5AAnMnDUTdVd7y7j7gT1nAGD5jFF48fBp8++6ujoAwDN/3w60NGL+vHmoWz7Vtv9bPI7d3dePn+9aBzVchHe/8Qr8dNc6AMDY0SPQfKoTmg6MmzAROOG2iJQ+JIHKIgq9Xl9IguEXR5xtSPtTYqiB3p9y/g5N1NTUIBQKObxNzc3NDq8UQ3FxMYqLix3fl5WIDd6P/2UrAOB7b1uImy40coxoXkJRUZHNWB5daUn26krIVM0qLo4gEomgOGK85jVd8SQ7gF1em207tsppJlw0owYbjgKRSMTzmCzM6NjpflwyyzqOBtW3L1Rk7GBLH1bON7ZXQ1aoTklxkZC4RlL5ZYriPGeWCsbyJCKRCC49Zwye2GG/phpC5r5J3WjjhsUTbOOtabAdPxJmYXVWu4og1y2a1BGJRMxzbOiImnk7JanrdvW8Wvxjs+GR6I9r2NnQbS5Uvm7WaPMZosH9OuhEHj0SUhx5cMdTeTPhcMj1eihqyPa5NxUyebi1x9wnHLafY3FRGMWpEMMkGQsaTEi/BwBFsXLu3PrC8s8U1dlfLWWBrZgz1pYnSMkTABxp60UkEjHDDUuKvOdwkfmb//3DbtPiSNh325Ii+30VDtnPqVy3bKrj7VFoxFsVI9exspQVONbFcwBM1dH9GgN0bO3jP6OmHNtOdqCytMj3nNLBeVNG2giUNUeM/gYZQ4oKAF8/P4lrrr0cJcWWiz6sqiiJhNAbS6LLo0xEJhi0IhK33347FEWx/Rs3zltFpNAodMSTR6jzsEMhFNuGu81b6Pl7NqnwSQ/U0ERRURGWLFniCJlavXo1li9fntaxFgmECyjued7yTtH5wif8n1trxfvFkxriCWNbJkLAPDR/3XRCKChAv2MknyZk88njlcVhVJYEW3tlK/uKotgSuIMklNO8FSpRzAiiorhHfShEnIEH60cxif0TqaY1dPQ59ikKq7ZoC75GD/vtjsd2422/fAF9saRwzM/0GPkbrGDuiNKIKWoQMUU/7C/0eFIzVcRGlRebIgNejxJWYwiw8pNE8JKopnLS+xq7zM9vXjzB2obbnwo5NJIitnQs+IT+V46ecbTn6Gdq6EWLxSJ1QBGYDDyT9feLnmHdcavHRJGOjDm/6MzvQ4UQOvviNjGRjj7rurJ6bX6FdEXCCxSqqfpn/579PXdcFQaKCSnBFUOkRjzurHxBUNlzirBqlG+g97OmWyUd6LhlA4OWQAHA/Pnz0dDQYP7bscMt0npwoBD2Z6ES0/mm8uH9sqnwSfsz65Bjmj/IsR66uO2223DPPffgD3/4A/bs2YPPfe5zOH78OD7+8Y+ndZwSTmr3jYvG2/4+SMoIUKNG4d7a1NBI6rpZr4cZEd391go8U0mjoDLTlvKe/W12RSp8DhArXLlh0igjBC6p6TYDMMhiDVVOs5M8438vo591X7Qos6/JIAG2+k0CI5oWX+XrX4n6xeO14+3YcapDuA0TjGDKexpVtwuJDdmEppuKdiVh1VJNc+0BcKjFmkOTRlqes2mjy2zbeanwUZLa1Z8w3/XFAvl2hkhINVUUt51sN7+nY7GnodO2j4hwufVFpIhH1QG9Hq/svmAFVf2IRciFWIggqpHl2g+uXZHKHVNaTGq6jVCzYsSqApSk7lm3QroJU90wGFHkbTmNLFgMFN98ywJMGFGCH924yFXVMJHGGLpBURSzztaPblxkCq5sP9mR8TFFGNQhfOFweNB7nSgKnwNVuLaDrM5kEwXR4JNGb04x3MdXd/ksMbTwrne9C21tbfjmN7+JhoYGLFiwAKtWrcLUqVP9d+Ywo6Ych1t7cMmM0Zg7vgqPb28QbkcXx3jiYFttJYZWJGWQzhxbbv6+5Xg7ls+sse1PQ5x48sVA66oUR4Kvu7LjJDTdVqfGyxvCQGu20M3NQpte71vT0Hb+xIq+98c1IBUhJDKi6eEtr5U3eeQLeCaSGlSe8YIUsCWesqbOlIocqzvEjVEiqWHHKcMALC0KgXXZaygZ8TtnbAXOm1yNE6eNkMBR5UW2grx+turSqSPx6rEzSGg6aY94KblrkdB0TKg2CFtFcdj2PcPp7phjHwCoW2hfSKDwqgNl80B5yFX3xZJo7rRIWmWxtxmcjoy5qAisG/h7TDSfWdsJTccLB63Fj5dToW9h4ul79dgZYTump82nT1bhXvv3buUTMsHV82px9Twj1Hnj4Tb8LFUXjMIifANr7+GPX4LeeBJVJRFT+fGskjE/cOAAJkyYgOLiYlx00UX47ne/ixkzZgi3dZOKjcfjOZVepNDJ0zobbbJjeB0rEScvP4G0Z66g6Vx4gYtMbZBzCIpkkhRWzON1NdvXknlvM1sIch00ztrQ8ny+nlK6yO5cKgSSxICMFWD+ZgtBrsNQPbeg+OQnP4lPfvKTAz7OH25ehh2nOnDdgnG49wWnoASDVwgfNTQSmk4MJsOweuOiCfjcQ4Yi3M5TzhVYWpCTeV341XDqdbp+gbuBy4P1LZHUDMKSQtzHkNF1HdvIajE9f6sujfv+Xh6o8pTBPG98JZBqQpTDmyB5JozM8R4oHkumjsRfNloFleOajpDIA5UaC3YpmYEHWMWU+YiSeFJHfXu/ub9bCN+LB1vxwCvH8bmVsxFNikMPee9nsaDwKAWtSWR5AJ2/M1w4bRR2FxuDS8eReqB4Eq2Z19UjnNAkjQIPVOpcwyEFc8cLZCxT6IsncYaENo6tKnHdlvbHK8Jnf1MXdpxWzALKQXLC+VBDUe0os66YrtvCWBkiXBHovljS4SFeu69F2B4Pi5y6XJcsOwj4e4kJ8ZhePB/PoB/CIRVVqXN+w6LxeMJlcWpAbWT9iFnCRRddhPvvvx+zZ89GU1MTvv3tb2P58uXYtWsXRo8e7djeTSp2zZo1KCsrc3yfC5yqt2Qp/eQu04GXRGkDkUjd/Npr0I7lZ227tdUuwbnxlU3oOuDettc5BMX2ZkNOFADWrluHPaXe22cPxvjWn7LLjA5FeF2HY0ft13T37t1YdWZXHnpljG9jY2Og+yYbc6kQ2H7amr+vbNqE0/u8tx/s8LoOvb29rr9JWJhWU45pNYaHyCsXwxbCx9kVRo6wYUhrmm4W84ykSktEQio+9Lrp+MMLR4QEICoodMqvjleRnKexlU5BDDcwEpfQdLxGVsi9ivqKfqer/8y+8ww7g5enwjC0a6tKTAJFjdNZYytwsLnbZkg+nBJz8MuX4YlJIqkhmTr25FGlKA6HcLC528wfYufQQ2pb1aQUFpdNG4X/7LSELRKaZvbzgqnVeGZPquAo14dPP7gFp3ti6Isl8c6UUl1RWEUROUd+HlwwZaTneTFjNqFpwpAunkCNqSxGuMm69tY5WJ/5HC8WxeJ1XanHjgc7dkRV8cZFE5BI6viff2x3bLe3scv0zNEiyO5tpvrn4YF62683IpoIAfDP42LgPUKXzHTatSzsLpnUhV7bkKrYrt3cbzyJBz5yES6ZMdqMiGIe45jPooVfCF+2dcL4e0nTDYLMCHc2hcnK0wg7TgeDlkBdf/315ueFCxfikksuwcyZM/HHP/7RJgnL4CYVu2LFCiHhygXW/nMnNrXUAzD6P9CQPiaN6SVReqCpG9/bZhRwXLz4fNQtzE/I40NNr2J/h6WgsmTpUqw4d4xjuyDnEBQ9m0/hwUOGQX/55YYEaj7ApDfHT3BK4g4VBLkOr63ai3WN1urpvHnzUHdJ+mFJ6YKN75ixtairO991u2zOpUIgvLsJv99neAKWLlmK5eeMLXCPMkOQ68AiACSCQ2ScxxIaisKqzXgTrQSHVUNh7STxZIyttIzDiSPdV5taup0r37wB+IHl07C/ycineceSSR5nwR2HraBrui3JneZ3ieBYBachfLr/irhlDDp/o3kqjDrS82UhZ9RgnTyqDLsbOk1D7J73L8VH//Qq3nex/fnIJ8bHkzqKUscpLwrjoY9dgt31nbhw+iijn5wXiXmfAOA9F01BRUkYT+1sxLN7m5FIWmM4prLYJBq8XX06lduz7WQ7bjjfkIqnog6ifpb4hGWahrxm+fTo+PPXoqwoZCNdDEkPAsVIshdHVVy8JIAVFhkOKYiEVNy4dDK+8sgOk4xXloTR1Z9AS1fUDCcNUn7Fi7QBhneSDw0LlgNlb7tCEEpIQ2BFBC4SMnLhWCgwALzndxvxu/cvxcpUqBzr/8UzRnn2h223hwiFAIRAZdkDxc/BhKYhpIbSyiMLilyV2Rm0BIpHeXk5Fi5ciAMHxIW83KRi/WRWswmapBcORzxd0enA6xxCYesS8vKTuYTCnZtIVpQiG9chTORgvWRXcwU1j+ObK3hdB9WhCpTfMVZVf5lYIL/3dDYRCln3qhpKT6J1MMLrOgz1cysEigQv+VPtfZheU25bFRatbhvGjY5+ov1NjXEGkRH44sFWx3d8GzPGVODBj15s/h00RDNMQviowXy41ZtA8f0Uh/B5CR+w4zhP2MyVIQSKGlhMYVBk7C9MqQpePa8WO26/1gwHZOA9UPGkhqRmHDukKhhRGrF5GvhToONeEgnhnUsn40hrD57d24x40hLiKAqFfMUNqHBHUVi1GfkVxfb7k+83D2rIszGlXeen7oiyiOlBoCF8mz28kJaCnbuxy4ZHdM57UmIolJiMG1GCEymp9k+tmIXv/ceomMtIpigsztmmk7QlNR3vvedl9EST+PX7ljj2CQU4Lh+iJvK40NDJhCChjx3jt+9fgqvvWm9+f/uju0wCxfbzCz9t6TZSYDr77LLvQURbMgHfH3Z6TCBjoDlQFLkiUINahY8iGo1iz549GD8+ePx1vkEv99kmcpAPBUAaz16Icz3bEv/7BKE9ucRwF5GgkDLmEjxYyB0F89RQg1FkVzADl+W88DLjbBfRrONJAACcPNMn2DJ9hIjhHbMZ8N5rt7woESVCh1PKcl4GliljLjq2IMeC2oasbyICRUmuaNxGlNqJSULTPFfUvQRBGFioVzyp2QiR6b1y7GHgTG/cVF0sDqtmaCcAzJ9gl6T2I1CiHCjFwwNVVRKxkS4ReNntIB4otzwdADjaZnhgaE++dN1chFUFNy2bjI9eNsMUQ2Fy7EU+oiBGm8b/dE42dPTh5cOnseNUB/626YRjH0+BkxRqyu0L/qL5zI6T0DSTiJ4/pdr8nQmPzBpbiQUTrWtK7/+gIXEXp7yiztA65oHy3D1t8ASKEb29qWuTTcKWTW8WxaAlUF/4whewbt06HDlyBBs3bsQ73vEOdHZ24gMf+EChuxYIhShqW8g6Ovk+34Kc6SA1el882IofP73PM0Y7CBROiL+rP+GypURmsK4PKzAqIcHAewUAS7yA3tui0HBmTDMDmzfGvGwRkTLVijnZCS9lxlhS023t+Knw8QYyNV5ZbSPmRRCBna7I0GZkiHo65oyrwsiyCOaMq8TIlOfOTqCMz36r+PMnVOErdXNs+3l5zHgjUWhEp/r58GsnzetbHFY9vTE8Gjr6cXlKin7x5GqH52V0hdNbSUE9iX45UK+bZXjYrBA+Y3texY4XEgkUmpkafpEiXlmqMO3ilHw1YIgHHPxuHb739kVQVcWUjf/x6v0AYMsLc4NIrIMqRIpqC80d718zaQonJS8y8sMhizCycawmJP0GUouLKmXSeRq0NlVVKSsaLQ6fzbbKNL+Ioml2O3Lm2IqstRXxuW8zxaAlUCdPnsS73/1unHvuuXjb296GoqIivPzyyxlJxeYLdH4VwtTOZyFdZ4hF/toWte+HhEuRueGA99yzET9/7iD+sdm5EjYQ5HvMBic9zQ3+5+Gdhe6CxCADXUFmYF7gY2x13cWGCREvBeC+4ipa6IolnJ7m0YLwv0xg9cvugfLzwHIir7b3C8tfuX6Be74vM8I3Hj7t+I0RGkoiRpUX4eWvXIUnPnuZ6fGhBIp5S/xCgRRFwUcvn2mFTyV1vHa8HUDmHiiWnzS9phz1KS9SUVg1vRN0JLv67cY8I35vWDQeNy2bjN9/YCl++/4l9nQDVUFViXfILfUksvboghs1zlkIGFVgBJyG+foDLba/mf0SxLPoKPaq6aYQx9gqd5GTi2fY8+GDhHaxNul8oIqSogWIIIWmHeGbAjJnjaFFxEeSe5OS8s9dPdv8TPsqmu8imCGhvDpijjxQlSUR/Pq/LjD/pt5aABhVlp1nEOAv4Z4pBi2B+utf/4r6+nrEYjGcOnUKDz/8MObNm1fobnmCPlAK4awoZFjQQL0fQUBPL532tp1ox/z/fQq/W384a+0PRtC6HpmA92DySb4SEhK5g6jGEFvpbk0JPbg9g8KcB4o3zL1C+ETqXNnKP2D9evi1k7aQLb/nt3MV3PqbGaxeYWdse1E7bivyxWEjr8gUTCBtxgMSKAZmrD69uxF/efmYeXwefFSVaNznpcLtqNd6RKmVY03S3nCmx06gmDR2RDVkzK+aW4uxlSW2MLnziMfGDWFCKkUGNZ1vb00JV7CxYiFk/LXg57Kpwucx9yyvm33nw6095vEqBZ5cBiaqweAXukj7Q9ukHijR/RNknvDnKfRA2XKgjPbnTxhh/k7n1PJZNfjzhy8CYM87SyTFXmlHf1zGNlc5UABw3YLxNpVD6pkOkkcWFH4S7pli0BKooQi7Byo/1nah84IY8k3e+gWrpm740j93IJrQ8J1VewbU5iDnTzaZ4GzgyAAJWbooRNhrPjHMT09igBCFhz2315CqZl6X5QKpY8AybuIuxpIZfiOYg6IV9GzNVZYnNH5ECR7bVm9+70eg3GrRALSorbv5wsQeyoudBnLCpVgwA7O1aB9Z3SaR0IcIvSlWU1YUQleqUPEnV8x0bOf0QDmPz7bpJKFiE6pLzTHaecbahx+3lw4ZOVD8fKAeqJYA4cTME/rtJ/aQHCh6POsPNubsu83HjfcSf835PgUpQmsp4tmP9et1hwAY95CXoiBPbF4fIFSVdae+vQ+3/HETbn90F46kFO8A2ApEs/4HWYBwhG96FNKNJTUkmRhESMHd71qMS2fV4MOXTrdtz0L+4iQciZESvxwoq96V/XszBDUHBAqwrvddq/dj9e4m8/ts5i3laoF/yKjwDQXYCNRZ5oHKd9N9seAEij5sNU3PWB1xsBvAG484w1XSAZ8DtX5/i1ncTkJCIrcQEQJm8PmRBl8PlMct/GLKyKbIlveZEb7DLT2YP7HKFKdIm0AJ8k+8CBQbN1EzTJXNzaCkkt0A0BtLmARCJPQhwlVzxmLtvhaTAAPAxdOd5DdIDhTbhhnCLDyMnX91EXm/ceMWdcmJo8bpW0gejRuoZ6s1pdZG+06JZWkqF6koZB9H3qvIe25MAhWgDhR/XdtSfVpx7hjP9xUfxsZCLb3A5ommw6y99c6llpQ/fx5BDf8g155t8vSuJpQVMWKq4obzJzq8aYB1fjYPVMDCtCFBqCKQuzpQZrupEgx/3XQCfyWCHNlU4Vs0aYT/RhlAeqCyisIamgMh2V398bQ8AA73ez5C+MhnXsHHC9TdfuKMLPCZDvKZ2zbI+amERE4hMrzYc9USDxCHHLFFoQMp1T43YyloZES2FuOqS408hlhSQ3On5enwqaMrlDFv6OjD5T9Yg58/dxAAUOwRfuUmIkET/kXeKcDpgTrcYnkbxlQEKyLMrhPLlVEUNxEJ+9+iPBW+kCszLJlYAn0Tuj2veWOUemK8iCjDRy+fYX42CQM55FVzLU8OI3i80h+fW8O/w00RiQAeKP66snGuW+it0syHcvmRCgCYXesUM3iaeEr48wjqpeRPU9QXdppjKot98xuN31jYpLFtR1/cvF5+xM7M9XJZvMiZB8rluNn0QI0LUDA5E0gClUXQ6z2UPFAvH27Dwtufxtf+lXlie769Xwm/NzABrTcxkFWNQqocFgrDPawun5AjKeEFunK+cKKxYspWj00PlEt4EvPsMJLCGyVmDhQ3Cen9/Z//vsz8nM7z1QuTR1kFfE+RIr9JH8Ujxyq4puP3G47g+GlrAezc2krX/VWXkMX2Xku5b3qNuBA780DtbzLklJnhOrG6NHAuBX+dXEMvHaF17kIC5t+pcwupTi+b2/OaP8Z5k60V+SD5OlfPqzWJFhsPalBXlxXhpzctxmdePwuXzqoBYCcruq47lBddC+kGKJDMR1uwkH6/nCaeoPqFtQHGefBhgUwJEnB6oKIBF3e9wioZWM2weFLDmn0twv3sfWUhfEa9rrqfbnBtz9kf43+e6LI5lesQPv67bEa+MIXGbEMSqCwiXzlQrd1RvOs3L+GRLSdt32dq7N71tCHp+ZeNxzPuU74JlN8LmILGKHs9BFq6omgktTJ4DHcuIRqavHqghvn4Skj44RtvnIfrF4zDO5dNBmA9555PKZa5rW6zMCxmSDpW8V2ee5SojCertNm67xVFwQTB6q+fjSkK4aPhcADw9iWT4AaRp6KjN46r71oHQFxkmIEZ9kdTeS5sAc6NvIrAewrdSAr/PhJFcvCGJPvbFBgguwT1QNH+BSWFbO4xwsDPqLcsnojPX3Ouea60SU0XeKA44nEw5enzMvTPpIgLvwnzQJX6EijOAxVwQTXiQbT48xCJSoigKIrtthTLmFtCHMyz56UyyK5RNJ5EPKnbFi38yGLIJTwyVyp8DKL5l83wPQBm+GO2IQlUFpEvFb67Vu/HxiOn8bmHttnaybeUOEU+2qbnylcx9wKVHHUjepqmY9l3nsHFdz6L3pisf8SQT69bfx4L99a39+G36w8Ja3jkA2+/wD/vQOLsw4cunY5f/dcSs14N/5xrd5mvTKyBiRe4reK7KZ8BdtKVzQUxkYEkquNDIQrhqyDS0DU+dYvY6dNmNhxsMcfTayGNef/qO/rR3huzFPjSSALhyZab8crbiaJRcchdq+x/p8Hrdt34+RA0zIyCeTdiAg+UCJT4JTVdGBpGCWODx+Ilg1ljSrV7t/Y0dALw90DxJCWo0pvXdumkE/CgQyKSPmfy2wlNM73QM8e410di91pnfwKdnKR9Vam3F8YSkRCH8OUqF5qvBwZkv/Bttsoy8JAEKkfIpdnZ7VLgNNOXXiZGMr9PPnKgMm2vO2qNl5vjij4E3VSJBruDZMYYcUjKQJBPr1BvGsIgA8U7f/MSvrtqL77yyI68tUlRyMUOicGPMCdkwMKf3JLeWXgVE9dxqPCl/uef2/R5GLKFZHnXBUoHotyORLohfLpuexb5PZesCD5rQ/sx3Q9APXF3rd5v1YAKKCABANNH25/FRS778iSEr1MEuIfwhU2D15/48teAKj4GjVwJ8x4on+Gwe6B0YVgoXcBiYXLzuNwpCuY5o2TsDAmnm8oVp+WRqQeKhuzxiMbtc/maAMIUIkwd7Xx/m2NO6qh55axNrLZCZmnuHuBPgHxFJHLkgRoheNZk2wMlZcyHAOwqfLmzkNwSHwvrgcpv4+moRJ1HFFjcXtz0oZGrWF8Jb/Tk0fPHckbWcmFBuYStjlmWckwkhifC5sqzMU/Y884tRIkZxMx77pQxN/738kCFVAXfest8XDR9FD70uukDOwECkefG713llDGHLYfG7+4RqbXZC/m671tKwn2e2N5gGv5Ba0ABRtI/hasHirtOonePU1GR5UA5z9HtNcwfQySZ7wdHCJ+fQU7OTdeta0rn8K76DvMzG+cRpe7knV0Cal/REP3aKm+xAH5hIEgOlB8YwV4wUsOdb52Pn950/oCPycBytnrIIrCXeEpRWDW91zSS5ubl03zb4j1QLxxsxQsHW3NaBwoArpg9xvFdtj1QFH4FhdOBJFBZBL0suTSP3EIJMiVtA+E+7J7Kjwqf1UY6hO2CqSN996MvZ1cVoGFu84rOOp/E+HRPLHD8eLbAJzbnC3w4i4QERdiUIzbuh7iPEc9W5k+kFgb4ZxhfooAhyQnsvO+SaXjoY5dg8ijvlfx0kIkHylGLRtdthnJXv3forUUYrQNRD/fpnhi/iwlq4JcVh9Iuogs4z9k9B4rbz0PKmoERE9aGPQfKxQPlIVYR9BnP2ou65EDxoAa3puumjRAJKThnrBGG9uyeZvxizSG0R2m9IvcjKx7EOEieS2UJT6AGbkyz9mtKgHdcMNFGwAcKRvBsBMqH/DIxCjrfv/qGub5tUbXHXfUdeO89G/HeezaaETy5IlCiRaEzHh6/TMGUJEX12DKFrAOVRdAVmVzaR+4eqExD+DJHSFGQIA/HfCGdsGMab+9mMCfIAb1UgAY1BngJRLvn085v743jHb9+EY9++tKsHrehow8tXVEsmlTt+K1QtdPyfb9IDC3wIXxBwnfodm5PMH7W2TxQOXruiYxUVovJDfxi4D822wWT/HJgrYKr1nePkkK+XivyTHodAGoqis0CtumsXPOLnG778mMuWrxzqrUxD5RThc/tseIlDV5VEixck13HU4yk++ZA0X7pNhn2cSNKcKC5G/e9eBQAsHCkNV4RjzmuCsLM2JwP6lWrKA5bpCALBIoJNeTCacLmzbFUUXtV8Sd97Pe9jYaKZE1FcSDyT0Uk/rOj0fF7rupALcxRjSYeX7puDt61bDJqipL4QpaOKT1QuUIO7aOITR7U+j5TmywTzxXbJcS5ffOFdFT44mRg3AxXe3iI9Zmq8g12GfNsvAx45PuMt5/s8N8oDfzppaO45M7n8OZfvICDqRo5FPn0QLnnY0hI2GF6oLgQPjdDaCrnMeIFWVxD+IJ43gcImn9QUWyt2XZ4rDL7eWiZVLYbLBEJ6zhUee9jV8zgdzExoiyCy84xjj+huhTfeHQXAH+FNwpVVWwGtVu4G5+bISKxbkWRw4IQvqAeKAD41g0L8NbzJ+KN5wUTtGlLee26UuTDPweKeKA04FCL8fwNqYptHgDAjjOEQHlY6uIQvhSBCughHMg6wXmTqzFnnFg+P5qDFF52v7NCzpoePHSyKWW7sMLHfqAhfCJhjFx5oOaNd+a8sRpn2YSqKpg5piKrYhiSQGUR+ZIxd7v+BfFAMSnVPERe2XJI0jBAaZiK2340p4pu85H7XxW2PxiRixXkQnlosoWv/3uX+XnHqXbH74U6vXRy+CTOPjCDl5F+U8jAxZMxaWSp7W9ebMLtyZAMEDY1UNBjn0OKkvYn3C1Ot/WxkKrg62+ch++8dYFnmyxkkT7umaH94xvPw/gRpaLdTFy3YBwAoDeaMD0cbgIebqD5NW6eQ15OXcQBeMPVqgOVOkfym9tiqIh4v+/iqfjJuxY7yIwblk4dZfvbb8aEuBA+JhjR2h3zJOtenj5hCF8yPQ9Ul4sIlxeYQX/t/FrXRYzJ5dl/mfDzZglJR3ADmxdsXK6dH2zeWracLgylr/LITRsIptWUO87rv68+JydtZRuSQGUR+ZIxdzt2pm1uOd6ecV8st2+ePVBpNBfXxOSIgioE0VPZcSq7HpFcYqD8SbR7IQhGrgRY3PJACgGvHAwJCSrH3BdLmopabkYiH9btLudsv7eYpydX3ifAbryXhEOmUejl/XV7n7zv4qn48KXThYplFPai9iwM0iBsQQxt1udXSMHWdy6d7LsfBb0mXivqU4j3UHQdnDLmqRwo5mnoU9DY2Y//94/tWL+/VdhGEMPbD7zCnd+ccYbwGZ9XzqvFbStnu+7npcAmqu91KLXIkIkwRlD86r8uwM/efT4+9LrprgSvIgf8Yv4Ee3gbX9BXBDZ+UXO+B/OcsrHd3dAlXOALSrTTRSSk4uFPLMcv33sBIiEF/33VOVhx7tictJVtyByoLMLugco//Gpr5AIqWbXINWgL6ZxrMlAIn3+tqMHui0lX+jOaSGL17iYsn1njXliyACet6UA2hHJ4IlboEEzane2nOqFpek4NV4mhCxrWQkNPp7iIO4S4sCeeQLmF8LHnaC7zPqn4w3ULxmH7yXZE4a1E6fYMDhpGxOcjK4rlgfLLIwOscLAekoifbugP9by55S0D9ud2sBA+4++R5Jn9+rs2uOaFRUJKVgqJpktQWKFYQ4HPIushxQilevAjF+Pdv3vZ3kZI9RxnKnTA0NRphKoxZdWgSKcW1vgRpXjzeYbX0m0ccvEo5+cNf5+LYHqgUvM9qHd5BKkT1ceFAE8e5e2xzQbqFo7HVXPHOopQD2ZID1SW0N4bw8uH28y/CxH6lCmHWXGuU0bSD6ypcB4JFEU6uSuJQCF81veDNT2lO5rAXav340BTl/ndQOTX73/xGD79wBbc+OsXXbcpxDzO1lzijzOYPFAA8O9tpwrdBYlBClVVTEPopcOGV6GiOIzRFcXC7Xkjic/XYXPfISJBEvtzha/UGQpgigK8/5KpZlteSnzbXHIhg9q8fA0igAhxBMhl4g3Xq+emvyJOPW9eRqyNQAUSkTD+n0a8cF6iGhXF4azkffCEI8j7hnqMTLKeGttLZo7GTcvsXr24T24zI5iimmDXzh/n2x8AOH9KNQDg+oXBtnf0weVa5uIW4tsKQobYGLEFg6D39vQaaz7xOZTpFJEeCIYSeQIkgcoa3vCz57GrvtP8O7chfGLBg0yNXV7aMx24Va/ONdJpL5FmCJ+rB6rA+UB3rtqDnz17ACt/st78jrraj7T2pNXHx3c0AAAOcQX3KAriSc3SOAeVCv/837bhpt++lPdFgG0nhk54KMOJ07340B83Y1/74CKjwxHM8Pnuqr0AvL0Y/G8j+eKULrsm8kCgzp8yEhu+uAI7br8WiqKYwglei2BJlxzBoB5bmwcq9T97PwfxPPAENJN6QfSaeHkOqFEsIiX8V2yboJcsW5LQtCA9ECxknIb4JwTezq+9cZ5te/8CyU57g4lEVQfM0fnRjefhc1fPDiTtLYJrra2MjuYNL/l5N1ghfOl5oOh14YsDez17zmZIApUlMClLhlyGC9Ej21diMmtzIItTbtWrcwJyfkHb03UdT+1qMv92lTEnJKvQRMkNrx494/iOjkN3NIEHXjke+Hj8c1U0DwoxFtmaS/xipts8f/i1k3j58GlbUUc/vHioFR+9/1U0dAQPG+HPyutl+NzeJuxr7HL9vVD4wt+3YcPBNvxyz9BaKRyKiHDz4zyBDD8DbyTxuRMM/P3MDNFcEigAmDyqzMyhMD1QHl4T9pzmFc+Chhry+TfU+K+pcAlXJuCllb2ktd1AOeBAPFA8qWJ/sxA5EWgxWqYoOFDwRnSQKWOpIZJwUbIjn090uaCoqqhNujjGSpAENfJnjqnAf199DsZWehfdde/DIPdAcQQq6KID3Y4XeEmnBtrZBDkquUKeRCToSkymdudA7ntTuSXvMubB2ntql72eAd9P9lCnx3M7dKFplSi8gTdCfrX2UODjBbnuhQhnzNZcSteT5RY2JMJ7frcRT+9uwpce3pFut0y4Ga07T3XgQ/e9imvvXi/8vZBo7gomiSsxcPDzY7SH4e/MgbL/zY7E3xFbT7Tbfs8HGDH0eoaz3/h8paBEjxq5um4vRDprbIVoFxv4UCKezAYBff549dvmgUqjDhTgbsxPrLZyVvpi2dHXfu+FU21/BwmJNkP4NN3KgSL9572BfuPM9qWPdka282Xk37h0EqrLIg7CpCrpv7dm1HiLoSiKXQ4/HQ9UujlQlH++cLDN9hsvty9hQI5KjpBNu1PTdNfK6/QlRL1ej2+vx56GTtEuDtBwh8DiDKnNTCnVvBfSDdbeRqKiBNgJx9O7GrHg9qfw5M4Gm2fKPYQvg45mEaIVWz6PIB1vIv/yFZ1fIYQXsjWX0iViX//XTrxwUKxi5YYTZ3rT2p7C7WW4O+B9WwgM1RrTQxG80eIlfsAbSXyej1sODFulZhLT+UDIrHHlnu+SMAlUsPPgYVfhs3I6yopCgY7Bj3UmIUxXEm9KYA9UgBA+6kV0Oyzl09mqdTdldBl+874lrv0S9sP0QJFCumRH/lr4EQRhCF8yPaIwULxl8URs/cY1+NOHL7J9n0nz7mqZFmj4aDoeKKY6GXTRwWu7TBYQzgZIApUjZNPY/tSDW7Hw9qdxPFWN2i3viT0nXzrUhk8/sAXX/3SDUM+fB32G+SVx8shrHSjyOVMvBS3A+9E/bUZvLImP//k1uwdqkJboEZFG/uWYjpBEkE1PpalslA1kL4SPE5EIcMKrdzf5bmNDGl3lw6fcwl8LoaYZFLlUa5Owgzdoblo2xXVbp4y5+NXOPzb7U96JNwUsppoNMIPQ6z5ni0XF3HkEDuEj3hFN19GfyukIWgw3HYU212MQEubtgaLbOX8fXW4XDukn+Snu4WTW97VVYuGRTEAN/iDPUytH2rre3jWgvMddpMLH5kq+vSR8AdhMnowfWG549bxqNVEyHCQcj801ltsclFgqigLF5YUmc6DEkAQqR8jmyv0ze1sAAH/ZeMzxGyUujEzRXI7fbTjse3z6sknXeKUrTPlEUCOzn0uGdM+Byo+IxMuH2/DGn2/AluPOfCY/iFZs+fNJ5zHHh2CI3oevcB68bEM0ptkK4ePnci7yuQYS0qaqCu5/6SgW3f6UbT7kIhxW13UzV2AgyFU1egknqOFzyYzROM+jlhBvoPMEwC2Ej8kVB1kJzxYsFT6vHKhUcdQQH8IXrA2+pEi658kbqsdPp+9ptqnwBZQxF91fIVWx5YJdRRQB3WxjRVHww3cswnmTq/HfV7nXXEoXM8dYIWedAbyWVIUvSNFmP0NdHMKXKjKdZy8JP5cy4RjvXDoZT3z2Uvz0pvNdt0nXA8UW2s1+pSGA4nZ4mQMlhhyVNPC9/+zFm37+fKCY4kxsoNbuqKeRx8IQRA8P+j0lAA9vPunbLn1mp+vup9Wr00FXfxw/feaArcZJOgjaT74gnFs/k4JxzAVu+u3L2HmqE/91z8a09+2JOucdbxSnI1cbZNOxWVy9DArKE3VdR2NHf0bHceS75eDC8spU6SCkKPjGv3ehJ5bEF/6+zfw+F4IsH7l/My7/wRr0xjLvL5DbgqsSdlBj0q+AJvWsvPvCKY7ngFUHyj63GDEI6pnJBpgR2N3vPhfNED7uvAMnxNOwdF03Q3MzXUnv6Ev/vqEeKC8VP9qncpdipZUl1veUVLqNh6oANy6djH9/6nWYN6FKuE0mmDTSqkMWpBi4SuZdEMl8v3AzUSFdJuEeyrOXhA/zzGRtSVEUzJ8wwpPY20VG/E32tyy2e5PTCW10O3rPAN5zwxmSQKWBX687hB2nOvCvrf71W9I1gdbsbcbSbz+Dr/1rZ1rHpMamJYhgfXe41V2imoHeXl7KSPa+pNzxrC5Dmmf8nSf24CfP7Md1aSTK03d/pkam2/kFkjHPqEUxetJM7L3vhSNCY31AHqgAG/fFchvPKBpqOv63P7oLF9/5LP4RYCHA79hBQjPz6WChhpMtPDXLBErXdTyzpwn1Hf22WnWZQPKn/IEa3X4rwJUlEfzoxvPw1bq5+O5bFzh+F83rfY1d5n3FE5VcguVdbfbwwlsiEnbDMnghXeuzrllGqBdp40FJ68oM6kAFFZGg57R06kjhNhWEWFFiFiSEL9tYNs3o4xU+inm0H0nNWtDi+/bFa88xP/stTluqftbYsiiJfNUqYlBVxUaictV6ugqZ/LMind3dps27uHpdEgYkgQoIWliMLzImQrrhQt9/0qj18ZeN/jLU1L6yyXmmfkh3pZ1unW6Yj5XU6b1de28cH7pvEx7fXg8AeCllyGWa4BpNBCMg/PPAtQ4U+Z6NaZDr7IdjbT1ZOc7tj+0Wfu8ghOk8LB0hfM6dC11I948vGWGr3/vP3gEdB7DfK7mWbQ6C8iJiHGZhccANvcQoKREUKuyJJgLf9zKEL3+wyT0HkNF+x5JJ+MjlMwJ7of9vzUHzM5/PkUuw1XYvrxeLHHCo8AUWkaCLE1b46sp57rkmPB78yMX49IpZ+MHbF+FjV8wMvB/DSZI/ek6tu/JfW48VBlxRIvZAjSeqejaj3WU4gr4fM8F9H7wQT916Oa4OMJbsMuw81UFkzO3bfGj5NPPzsTbvUElKyBhYeGa2xDLSAW0zV68U+hzwEpIx+8EX1S4Szynhvi7nQD2PEhbOegK1dl8zbv3rFl8Vog//cZP5OYiXJl27sy8QKTP+p/k/9HNbyqWergFmO16a/bZc9N7b/d/aQ3hubzM+/cAWAAgkbuEFUThbEATJgWLk93fr7fljug78+eVj+M26YFLhW46fwRU/XItb/vhqRn0NAj4vKh0Dl1+wE+2Z6zpQQq+qoM1MiJxXDtRgIFBFLlXXs02g9jZaqn78y/VMTwzz//cpvPHnzwc6lgzhyx+o8qqI+KYDtlhCbyO6sHP5Of7ehGyBeVk8C+mmfisKZxrCZ33WdKutdEL4zp8yEl+49ly8c9lk19A6L7xu5mgAwKJJIxyeNIq3nT/J/OxGKj9ISIbdaBefz4iARWUzQXlxGOdy9bnc0Npt2SRW0WZ3afquqLcdZuVAWWPAQhqXuHjvcon5JDwyZwSKXOPzp1T7bs+H7J03eUTgttwIwaJJwY9xNiH9p8Iww833GsRoVHkxvvGmea7bUV38XEg7BzGaWLs2b4lNjUYLfCxb27RAbZqGqltsPY/TPfYHYxAC9cLBVowfUYIZYyocbTy6rR5vvWAiVpybXmhF0iWOi37Pho+XqE7quhli+ebFEzB+RCm88OeXDW/i8wGlsU+19+HRrfV478VTUFUS7AXIe7cGJiIh8kClccAsQTR/MyEVPOniC1uKIviD1DbJFI6QQpd7JsEtkAyUtNDz5hd/NqTm5l6Por2xhIZ3/uYlLJ5cnVGitERmGFEaQVOn4Z143yVTB3QskZ3N6kq97YKJGFGWO4ObR5DSF8+k1DBHltlrXwWdfwqXA2UqteUxzOvdF01BbVUJzp/ibdi/c9lkvHLkNBZPqXb1Hp5TaxEW+m5wI1BLpo7KoMfZx9Vza/HMniZouo4ndxo1GUXRqAtGath5RrWRSRHYo7CtJ4akpiOkKqa3siIDkjtQrJxbi+2p+oGlOWq+sdPKAU6nDhRDOoqSbocvT8OLdTbhrB4VasQ3dQZPVA9i/Ke7YB5EVU4kEkGNrUxD+JIuHq0gfTFzoHx24xM8/cZwV30H3psSWjj6vTcIt/ngvZvwxevOxT9fO4WHPnoxRlc4BQ/49wsbI57w2XKgXFbK6DgFERI51Z6ectOnH3gNW463Y+uJM/jN+5YG2uftv3oprTYo+LERPTxzHcInIt658kDZchIGQSga7Q/tqcbd00XE4FQUJ9F9dFs9zhlbgbkuYVg0PI8fRzoO/fEkWrujjnCNlw63YeuJdmw90e6aoyGRfcyurcT+JkNkZ7GHAl86oIt/sYRutpNPsBVyLw9UVWkE9R39iCY0LJ06Eq8eM/Kl0vEcK4rxXtKpByqPHtTicAjXLxzvu11FcRi/JvWV3HDvB5fhtWNnbJLzbuMRJNQrHzBlx3UdtVXFONXeJ8yrfc9MDaNmX4Ar547zPN4EEsp46kwfpowuMwlUUTj/z/QPXTod1eVFGFsRQf+hTf47ZIBLZ9WYi7BBFgD4OZGOgh7ddc64SnNhTUYeiDE47rICYeMRy6uUzoP5R0/v9/W4pOOl0nUd9S5KYyeISB2zddwkt7UMCZTNkEvTThUVthOBN1hjPjkXbFXHDz94ch8ONnfjl2uDhdUlzTESf09/41+2dJsgeQYvH7ZLgPvNmS3H2wEATwesRSQiuwPJURHt++pR90TvXIGXngfE5/r8gVbU/XQDdrjMFX5O2gjUIHCluCpC6s77O5HUcP1PN+C/fm9Xb3zhYCs++6BR842BlywX5fcx0Hfrm37+PC79/hpbGQTAvoLJG72apuc8zFMie6CXihme+ZYotpRb/Rcil88cjQUTrfChdFRGrcU9634YDPd9plhx7lh8/ppzUVpEazGJtx08BMrKkWZT75JUaCNFecSQZ/eTmS8rCpthnexZxhZjCyG1XV4cxvsunorXn5u7ENgxldbCcJAFAH4bPgzWC3Q+fW7lbNx69Tm494PLAu9/tmFw3GUFAjUG0l2ZOtPrHaubjk2x0aPWzs93OR8o1JikRtize5uNZM007Zmkh4HlBzMHyme7NftazM+6rvsSLpFx6bVHUHlmJnnKH1+UA8WTarpNJq9hFo7jB10PJmAhulbp8CeeMIn2fWJHQ/ADZgDRNRXlI4rO9b9+vxG7Gzrxpl+I83e8SLLb/Z5Px5TbfSq6v/c2dmFfUxdeONhmfnesrcf00lJ8/M+bcen315jSs14eZjoHDqRKCjy6td62DV3ZpWUB4kkN1969Hu//wyvuJymRMbJJSxVBpIBZPyfPpCJIHSiNPINpvk069yfblOZA5VupLddwWzDLZ10vL9CcJXa5B+rMYOI7jICzd/pwrVVE788gOXz8nEjLA0U+jy4vwq1Xz047TeJswvCccQRNXf34xXMH0NotMF7J8zvd+hB+qlVur4Yv/mMb3vbLF2z7nyJqPTyimtUvlp+R0NxDcj71wGue/RJBVIw3KKyXlPd+baRmRFc04UvyXj2aXgHXuIuwB3+ZWD/5/saTzhwo3sim1ywTQ3vrifbA2/7s2QO+2wxUbIA/h3RWd3MJUXhnuqeq67ojr4ceoxAiEo6QQgFpB8TKmqLk/6//e5ewnad2NaGxsx9r9jUDsM9tvg+icYhy40/DRqjYze6GLhxo7saGA62GVPruJiy/81lsHKBUuoSBoHmQmYKF8OXb8GTP1XtfOIqGDvG7j81TVVFQRjwul8xwei/cwJ7xB5u7zXfmYBCPySbczJZ8hip6wZQdJ57qgSp5UgKu67oZzTJcCVSYFmQOsADA27Lp5ECdiVn7Tqsp99hSAjgLCNTn/rYDP3p6Pz4tIBbUiE73pvYz6NzCWv726km8drwdm0hoVFDSwrpoDzez75uJup1NQCFoDlTqf3Pc0jBw3/CzDbZzoHK6DP8iq+BB+hRPaogmkvjqIzvMBGTAqVLH2uUNSTpu5uon9yAaiExqUtNRXhx8VXDzMf/QORGBSquQLvd3Nt+5W46fweZj/iR4T0On4ztReGfQecmwencTPvvgFtdjuL2I3EjpkzsbsGavQUi8QtZ6ogn86Kl92N/kFGXgz8vN22tb0GDzlWzLCBRVaQPgaFOUM8k/L0Sx7bwEMt0nGhcv3sSSGm65/1XUd/Q7wgwlMsOHL52GmWPK8fmVswd8LHaVaWh54UL4rPZW/Git8N5mU4snPLVVJYHbYYftjibMHNd8e9tyDbfn/aJJ1fntiAtM2XHdel4MdJ3OJFBJ3fZOTidUbSiBEqBMPFAlRcHHZXqlNZ41gpxyCTuG54wj2NNgGBV8PgqQfk6LbV/fHChv0BdZuma5LeSOszUzeTZR542XnRoXGLaiyuAiXDjNSj4/cdq+6vjDp/Z5hqwxw9OriXhSw19fOYG/bDyOW+63ZMN50pMUGKSAfdXdJFDcnKBCE259oeISNPE7ntTS8hiJiubyEBG6dK4/fdBqmu6qQMcb6n6IJTS89Zcv4u2/esn3PN78ixcc34nmmeh+q3KpmQIAD77irKcWpLAl730BjJDCj//5NXzwvk3ojyc9QzG/8sgO/GLNQXzwXmdCMX9erip8gvwlOq/6U33k5+c1P1kvDGXVPO5vkZhGNO7eT1ufyUe6AOHmDZZID7PGVuLZz1+Jz1x1jv/GPrDUUq3v1u03wqrzH8Jnfe6Pa3hqV6NjG6voKrBs2ihUFoexeHK1zRvlhwunGUp0mq6bYcG8MNBQh1v5lUmjvBVi8wWrzIluFjIfqAeKLX5pum5bNBoseV/ZBvUmZpIDNbYy+KKDRHoYnjMuIKgBmu5LxG9FXGQb2QgbMVaDJmGzPewy5k6Djz+Tm377EqZ96Qm87BJa45ZTRXGqvQ8XfHM1vvLIDntbAR1QfgQr5hFm5Cc4ARjkpk0QpmkmD5uJyykCxRl5lMCxrvLPeVGu2M5THTiYyh3588vHMPcbT+KJ7UbeEK3pkdC88774ORCkLpjfHDxxutfTI0kXDZK67uqB+uRf0gsLpUThTI9ILNwboj6Lhs7r7EXkMkgh3ahg3KlHpr037rl48tIh4x471e4MTeLPyxbCR763h5MavzBjF7DmqugcaDgwOybtLn9viYwZnkTS28+2P9lVRDwlBg9EiyPs+cRLhecaPIk5ftqpVsrmvaoqmFBdik1fuxr//MTytBY6aagXI2mDJEo5a+jsFy9QVQwS2Wm6wGpe0yyG8FECOVjyvrINWkQ7HMBbTN8L5+ZZYfNsw1lNoKhrNF13pR8hEJEiahjxhf6CQKR4F4BbmN63m377srBfXiGBDA9sPIauaAIPbLSv7FOVHS/4hb9RQsOv1AcJS9R0HVWkeCDbh62Gl3DKPbwR3C8ITZrMyTnTc9h6vB1t3VG88efP4+q71gGAWSPqUw+8hsaOftuDLJHUbNfqAq4gHm9wB+HUIkOeLQTsPNWBy36wxlYAmgddqeqNJV2lSjccCFbHStSvTPK02D68V3JXfSf+uF8VGlw8RMWuKWFxI1D9CSeBSnjMTf47r7PlPTNu966oNMGvSfFmNi6icA662MDuda8QPtH97hXC10m8kXRXSaCGBujVZuHNhZIxZxDNHbYuyDykJZFQ2lLKtN7UtNFGPkdt1dkRljRYZKdZPzSdlj4Z2DHDZDGUvSvcSjgMB8TJ/RHEA0XfbWVppA0AwIVjjLYuO6cmrf3OVgx7AhXxcOsONKfFC6JfbRXEySRPV7iBGnS8Ee23QidS/KPHcOsKf7pmQqjKfvc+Bz9vichoZGBkyOsImm6vEcFCnphxy1anTKl3rg1qNAaROn9mT5NZo4X/DQB+yolAJMjDnm7/f2sO4tFt9TYPg9E2vSbiMxfNwRGp1eR/vnYKgDf5oTHjrd3RrK3OUjLM56AFgabruP+lo5jz9Sdt37/t1y/jtTYVn3pwm+8xRGUE6HC5vYhE4We2+03THXOnuSuYumLQED6/umysj6KV3NOcx0/TdPzkmf22Y9MQQREJ5w1a2ge60EDvGZHnTmLwwJwqqUup6zpRL8uvsc0b96LwbSoiMdB2kpqVKzNl1PBKjF+SWoj7+OXTMTH1/htMZMKsA0UiMAYqViTKgRosohm5wKSRll0za2yF7/YjyEJyOiGvAHDxWB0P3rIM//feC9La72zFsCdQo7gK67qu46+vHMeehk7bam26ZMqWV+CRBEtBVxLo7Z4ujxPVfrK17bHvrnpn0r4thM/FqHNLNA4qIuHrgaJ94IzYIB6opKbbXrYsBI4nUG45UFS0gXWFN3BpuOSccVW28AHeOH7wleP2gsdJewhfPKlj56kO/PCpffjsg1tsDz3APn/chs6LxPN9jyY09HLRHnQM+mJJ2/hdOYC6FvS4mXgmkhrwDYHCHDtdU13PY0qJQpboeLkZZqL7iX9O8Nc6iOQ8IAjhc1m4sIfoOgkbO47IaHjf7y1JcV0HHttej8MtPeZ3v9twBIvueNpUhBSdL38+bs+E99+72fwsPVCDG/xMoQsFXouMuUAgDxS3QJcJGC9M6tbi1XBT4fvpuxbhlnOT+OzrZ+J7b1+Iy86pwffetrDQ3TJBa3FZIXwDOyYNx2e2wnC7rhQ3Lp2Mb9+wAM/cdkWgMEXqUe5yCfF0g6oAS6eOzLkC6HDBsCdQPB7b3oAv/XMHrv/pBhuhCVLUj8JmHAuMkCOt3Y4ETxqmRfcInAOlONtLl/gVCVYbRYU7edCXnshwH7gHiiShc9ciSA4UjbGmx2Peg+KImvpet33PQCWv2fXgr0ucI5qdHgQKsI9TQtMcf9OH236H5Lb/NRFdBzaPeUWiG3+zEV/eFLbJ+dNr0htL2l5s77lwirBNilv/ugXTvvQE6sm8PtDUhbtWWx6PjAhUmh5ZioPNXdhxskPoTaPj6KZmJLqf6NzsjiYc21DC4fUa5+cIvX7UY6Zx9zcdTwB4dm8T4knNNx5eh+4IDd3T0IneWBL/7x/bjbYEQ+3MgfK/HgOV1JfID9g8o3MxHZnjbKCq1J6f0y0w8mgdqExBQ/jY/BxunoraqhIsHKUjElJx2Tlj8KcPX4TziHhRoaGQEH8zhG+A18AkUPrZ4YEqLw7jvy6eGsj7BNjFNK6dPy5X3ZLAWUCgdO7FToUU2okBzCtP+cGeh+Q0Hj7+59dw8XeftX33v49aq+rUgA8sHa472xPVo/J6mIhUiPiQod5YAic88kyoQWmtMLn329gnuAeKJzdmCF+qkXECKVtdF18T0wMVToXwCfJCeFgeKO4cbKFcmi1fhg/9Ko2E7F6FJE/wdJvR/LPn7FLudg9UcALF+kiNomgiiT0pgrZmnxXSR4/bG0u4qvCJ0NIVNaXmP0Pkwlf+ZL0tTy6od4YiXclyiqvvWo83/eJ5RygbYB8vN8NMFHJIr/v77tno8CTRa+/Vc0doHNmYjj29VzRdxy84mf/frDuMnz97wDf0Kqm5G8fs2ovmkJenzA09AVQjJQoHXoWPXuN8y5gvn2nPr/jnlpOObUyP0UBC+BTL0LZU/YavoT0YwR6zdhGJgR3TyoHShm19r4GALp4OZ2I5GDDsCRT/8qdehe+u2mN+5g2uTUdP48Zfv4hd9R3C41LDw81Dwiup0eR3ezFct96L26Rt+3k+eIhuKLuIhFGb47IfrMEBQS0btj3bgz4gveD3e9wjb4Y3PCNh8TnYPTyMQBn/l6Q8UG51oER95ftM+5XU7KSbvw5FYZUriKo5SKKX0/NUex+OthqhV25DJ/aWOFdue6JiEkO7fLonllYOVEefdb941azKLIQv4IIC9zclXmJlL+uzm2EmEp+g93eXwAMVNIfRS5yBdsdrMYHh/peP+cqFx5Oa6wtU8bhvnaGrns0AAL7MqXMORxw9ehQf/vCHMX36dJSWlmLmzJn43//9X8Ri6StN5h+pha7UX+x5pSr5Nz5HldtV/6aMKnNsw26BgeTLMC+zzQM1zOpADXZQL2A2rik95vaTHfK6CkAXROR6QW4x7AlUa489jG78CCshjz7IeRJ0469fwqajZ/DR+zcLDTq7dyGYkUiN7Fv/utXcL6jBmBSsGscERpRXCJToZcmLG7A6N8+lCocC9tAkmxiGYr2YvUIRs+GBYogIvGiqKpZr3p0q1GqKSATyQLFt7N/z8vHUuyLK0+I9Tvw4x5Le3pkrf7TWdi5ex78zFffO+khFLNp7LQOPei3o/l/8x3bbi83vJcd36bm9TcLtMhEXCEpI+Pnml3vlVQeKhT14efWsv91FFij++dpJvHjI8vgxAQZV8Sbx9sUVd++jHzn1CvMzV+eFYaB6qr9JPLO7Cd1R/zpgNM9quGLv3r3QNA2/+c1vsGvXLvzkJz/Br3/9a3zlK18pdNd8wd/OLPoi394n1uZjn74Un14xC4BYuIXdUwMhd2yO07xF6YHKL6hKb7ZkzNniWH9cM5/Nw62+10BAPVDpRJVIpI+zatbpuo4eUmhyMlE3cVvpbemOCsN67F6ggASIbNfWE8N/dhq1KejK9Mgy9+Q99lKhxvrPOLU3IJgEtu24ARTfbCvk5DzY96d7Ynjd957Dd57YLW4jjRwofrx5cit66YdV1XZNGjr6bYSVFXRl19mL0LEhcBjonIeLehj54xmFAzmPk01EQgskjgH4h/CNqSzGuBElqXacx6Te1V+tO2IpEXIEj7dVPnr5DADAJTNGO47Jh+Z96L5XHdsAufVAee0nem3Q33k1SkagEpqOvlgSn/rLa/j3VkPJkM6jcVUljvno1u5tf9uG9/xuo3lN2JgV+yhX8vOsXKSkpAMxgeQ6RSzh7oFiEPVB14E/PH8Ec77+JG65/1V87iF/1cOzAddddx3uvfdeXHPNNZgxYwbe/OY34wtf+AL++c9/FrprgcGeaXtSC0uFEv9YOGkErltg5Gd4Fc4eSAgfm9p3PLbbzDeVoV75BfV0Z0vG/IbFE81jsmdlRF5XE/SZL9cLcovBUW0tT2DGEcNrx9vJb+IXSUlYFa/ScsZwEPCGF+tLD+nTmd44jrT2YHqNU26V9YOSQBG8DFCvFWfAfl705qO7UbLAPBUsjOt3G47gq2+Y52gjHQ8UT0h5oiFy1yuK3Rj8wB9ewQO3XGT+zYpFirx4bn1xhjLZPUj9HiF8Opxjxv8dxHjRuP1E/QkpiumVE51Xa7dFoA639uCx7fV4y+KJtm2n15Q7VgYXp5KRRcek9xGDKN8pnRyoNywajye2N2QkInGsrQf/8/ft5t/GuYivn+h8iiMhoD+BhKbh3heP4IkdDXhiRwNWzBnL1XnSHYsttjIAgr71xpOoCqmmgEdRCOhLui8q8Cp8V547Fk/saLBtEwopvvMnltR8x9JNEOabj4sXQoIiqDDOUEdHRwdGjRrluU00GkU0aom3dHYa5CUejyMe9/fuZQNaytut6brRbop8Tx1VllEf2D4D6b+iG32IJTTHccwcvWQi4zb2NToVZ3Ut4eh7vq5BLjDozyF1HRPJpClwpCWTtv6mew7MwRKNJxCNG7aQqhR2DAbrdSiLKIH7NFjPISiC9j+b53d2Eaik7mrQUaOdhjwVR0K23ypLwujqT9gMBC8Cpeu6STJ4m4IRAT4B+6ofr8XhO98AAKiK6OiMW8mwgNh4ZVAUb3KQ0HR8/E+bUVESxo9uPA+A3dB/fFuD634MSS391SQ/pwJ/fAongRILYfCE52+vnjA/X3ZODdbtbyF1oNz7wsbZO4RPNx/eoj5CdxIuXvwjCIHqiyddjVE2b0KqYq6siuZiS1e/7W+mzEbHa9m0kTbCrMBayeJVEVm/eLDaUxRe58ifV5jEy6eLr/1rJ145anmVRCtvrD2R58/0QCV1tBHC+Z7fvYxbr5pt/p3UnIstfgKe/bEkqkoi2HTUWGRgzdP5JAo/ZZ9FNa3CquLqNWeIJ3Q0tPd7buO3OJQpjgUodjzUcejQIfz85z/Hj3/8Y8/t7rzzTtxxxx2O79esWYOyMmf+Ty6w47QCIIT2M+1YtWoVtjQbf5dp3Vi1alXGx129enXG+zb3AUAYvf1RRx/iiRAABevWrsHIDGvfnuk0jkGxbu1ajOKON5BzGCwYrOdw9KgKQMXBg4fR16cAUPDCC8/jmKAcV9BzOHLCOOahw0fR06gDCKGlqXFA8zhbGCzX4W3TFBztUhCq345VTdv9dyAYLOeQKfz639ubvXfT2UWgNA0nz/S5/sZw9V3rzc8tXVH8nRjiNRXF6OpP2BKrvUL4YkkNxSkFuJsunIz7Xzpm/sbslG6OQFGbpqoI6EwRZmZYerXHq9HxeOVIG57cZYQOfustC1BaFLIZUQ+Rc6VIuoTYBY1n9iuoSo1BngSwXCF2WkIpdk13JLtTiVNLkSm1vVe+VlLsgeKN3DOEaIuICx8aSf+OBQzh64kmhIRx24l2vOu3LwMwCBTLbTJyY+zk5iQnZW0RFes7EdFhoZKicxOReBGZ9vJA8V7JkJmX47qLDXTvpk47URDNS3bNRaIvtE4YPY+dpzpt56/pOmIJdw+UCDzZ7EmkcjNs6n32cE/z2JouvN9DquIb6nj8dC8efs2pcEYh9EBlIaor3eLghcTtt98uJDgUmzZtwtKlS82/6+vrcd111+HGG2/ELbfc4rnvl7/8Zdx2223m352dnZg8eTJWrFiB0aOd4bG5QPGeZtyzbytGVFejru4i9Gw+CRzajfG1tairOz/t48XjcaxevRorV65EJJJZzZiTZ/rwna0bADWEurprbb/dtnE1oOu4+qrXo1aguhoE39m5Dojai1xffdXrTRXXbJxDoTHYz2HnU/uxpuEopk2fjh1dDUA8hssvuwxzxlm1itI9h6NrD+OpkwcxYdJk1Hf0A2jDxAkTUFe3KIdn4o3Bdh3qMthnsJ1DugjafxYBkA2cVQTqub3NePFQm/A3akjQWjkA8O0nDLW+opBqqcoE9EDFkzoaO3rQF0+ivNg+3OwIvZxK2vmp6uIAZ7SnPvuRES/DiklPA0BXf9xBoNyPKT5+UALl14ZIQY+hvdfucg0LEkYTAg+UVYxRMYtFstpf3l46Jpvu3t+EpuPp3ZZwAm/karqOhg7LqOc9UP1xDUda/RPve2JJVBQ7x/iHT+0zP9s9UM4Qs9Yuu0oYS7ilhr+IzDECJfJ0iDxQoiHt9ygPwN83rD5IJh6QIPOQNSc6Vyb3zRdkBuz1vwzPIaem5zO3++JJW26aAh06FBuRs9/n1h+arpv7Koq1XUjxJ1Br9zW7/qaQBHsezV3eXqsg6Pfwkg82fPrTn8ZNN93kuc20adPMz/X19VixYgUuueQS/Pa3v/U9fnFxMYqLnW6USCSSN0MlHE69exQFkUgEWir9ORJWB9SHgZxDWQkrdq7bjhFNJM25XVSU+fFFizclRUWO4+XzOuQKg/UcwqnFYyiqae8UufQ16DkUFxlzOakrqKkw7qveeHJQnP9gvQ7pYKifg1//s3luZxWB+tHT+1x/C0IiisKqJdtNthcZkwyxhIYrfrgWAHDjkkm235iZxuc0bSG5WbRbmqZD18Ur0uYxleCy6Bd+91n8+cMXuZ47VXCxe6B0upEDD206jv/sbMQv3nOBmTDvlwN1oLkLl8w0VmN5g52vpi3KgdIEBIoWTyxOGcjMaPUkUMwD5bHN1hPttr95MmDktVnzQtOdbf7++SOuxzePE02IRQQIDA+URQCcBNROoEShciIPFBtnoQdKMOd7Bbl5PNmgiHOenIGE8O31KERsfmd6cAUhfCmZ+7imORQIacHtzv4E/vuvW33bojja2ouJ1ZZgzaRy4ESPfWzYIVbvbsIrROAiQa5nWSRk5kuqquJLNHu9Qn1T/4vug6CiOJm2PdhQU1ODmpoa/w0BnDp1CitWrMCSJUtw7733Qh0i6l/mlOZyQP2KMecS5gKNZtQeLEsZxvsbu81tqkuLhPsGwdJpo2xKsoAUkcg32HAfbOk27ZJs1YGipUGumD1mYAeVkMgAQ+PpnyWcOC0O3wOCEahISLEV5wMMgnTjr19y3YcaSXxeAHupiYwNFpJETT23cB4KXU/PAP3EnzcHWvGnRnkiSetAOZ+G/+/hHVi7rwV/fcUqqOo3vt/49y4cbDaMYN7AZSvwrFWRCl9C0x3nbYosqIppILPrEcQD5dXlg83dtr/9hEQ03UnwgoAXn+D7CBjeiJBqeRT4czvFhfCJPD080VEUa5wPtfQ48pVEIXx8KCpgeKD2N3Xhq4/sQGPKI/fioVb84fkjtkLEtF9d/cGSPL2GU/SbRu5ZHqaMeVJ35E8NxOMLGGNLx+viscbxRGP4kfvtaoaaZnkUy4gHO6wqvve5W306Cr+FjUzxwsFW/42GGOrr63HllVdi8uTJ+NGPfoSWlhY0NjaisbGx0F3zhXNOW4tLhUJViTWfd9dbYTXsfhtdXmSTZE4XX3vDXMd3kkDlF30x41oea+sxn78DrQNFIyPoO15CIt84qwiUFxo6+rHhQIunepThgbLqGgBWnSE30MR63tAy61QIDB1m5FG+lNR1X2NOUdILgSoKq4EIF/UK2XOg3PeJc7kcfnj+gGF08YQyzu3rVgzYyS2N7UKqYhrIrPitF5l54WCb7zY8/IitIbka+HAmROIYgP160ByoBKnOzsB7Z0xPDznsCwfbHKRjRKll4DzMCUSIjH9eDAUwyMNb/+8F/GXjcXz2wS0AgP/+61Z88/HdeGSL/ZisX38keYJuONzSHaiWF0WQHKiEpuNXaw/ZfvMSbQFSuRxP7MbJM+Lk1GhcMz125cUhFKWeunSeuz13Nh45bV7PMuKJDOKB8rrndjd0ormz3+ZZziZyxMsKiqeffhoHDx7Ec889h0mTJmH8+PHmv6ECdlnYO6eQhmc4pGLqaENEgy5qsHu3omRgATIzxlTguvnjbN8VD4CQSaSPeROqAABVJRHTzhiwByr1rjvdEyOF4+V1lcg/5KxLoaGjH+/7/SsOlz9FUVi1VdYG/OtU0DwXniixXYWFBAVKcJqm28Kelk4d6dgvFGBlmqI4rLoaYrbEdmJoBc2BokVbg6x0s7H9+J83277nx83NA8UboaxrYVUxVzKDhPCt29/iEH3wg58ghBsR8oOm6/iWQFKakkojB8ryoPiRVX4OM/DjN7rcytug3kRAHMLXHXV+1x/XzLCzrSfbARjCLIBBgijSKbD4o6f3eXqgvEL4PD1QgrFjRUfd8M3Hd+N3G47gQ/dtEuZd9MWTJlkqjYRMGV46hm6n8ut1h8znQ2nEIlBBVPj8cMfju3PmgRqOdVluvvlm6Lou/DfYwcKxWVcHgwcKMAxrAIimnssaEQPKBrn70vVzMGdcJUaWRfDzd59vLpRI5AdMsCOW0Ihy78CuK9v/pcNttjB9CYl846zKgQoC5n0QIRJSTVLxj80nsWLOWPgtfCyaNAL/2GwoYfHGilnrIvV9SUQ1k+5ZSBjdJanrttVzFpZGcaytFxNGlDq+d0NxJIQugeeA72/c5oGyPns9tlhejaYHLO7r8mBl7bFjRIQqfJorcVAVxVRCZB4oP5LRHU2kRXj86h3pghyoIHjpUJtZcJmC5uWEVMUmOe5nWFtqd/bt+skxFQUYUWolW/JeG1HYqSgHipIVP0MznZdgPCmW92YQDTUbFpEHis0PUShmhw+BYtjf1I3pNeWOnL3+eNIkSyWRENj07Ys5c6BEYIsXVIRGVZQBK921dEaRrM2N8R8bji6ooQzu1hoMOVAAbJEBz+1twqcf2IIlqYXBgRTRZZhWU44nb718wMeRyAxs4TJOatINlEAxr2V1WYR4oCSBksg/pAeKg9f7pCikYucpI2SPFbb0M4o7iIIcvy2z1VidnTnjqszfGGGxESjNCuGLhBShGh1grMwERbFHCB/1dlHDMkkYkVc8MyVQDF7Pua0p8YzxI4xVqxljjGIRPCFQXUL4+NNgW4VpCF+AHCjAIAjp2IBeQiKsvUwI1F2r9wu/pyQgpCpmWEOQdizvJkegyDlMr6mwjfN5k6pdt2XoEXig7AWndTxJyCBv/6fzEpw2uiztMDF2vvE0PVD7uBBIL4hWuPviSVOVrox4oCgJ9SJDbP4XkYdTEBlzP6hq7nKgghYXl8gv2KLDYPFAFZHn8o+e2o/eWBIbUqHc0ige+mCLnd3RhPm8GygvZgvE8YRmhiCLhKUkJHKNQU+gfvnLX2L69OkoKSnBkiVLsGHDhpy252VQiBJa/UK3qCQ6b1QkNQ26biWJ05bZd5Q7aLoVwhchkuoDQZFHCB/try0Hinz2zoFy5nF5JQU//NpJ/HvrKVSmYt9rKw0ixef0KFAc5y4KkWMPalUlHigzt0y3bcPDIFDZ80BlKiIRpL2QYpHpeNI/T44Z3nx/GAkcURrB9BqDvL5z6STzO9G2FKIcqCRHfj/xFys8kx+NdJKLy4rCaRMIM4QvaSWpMzBvruj+dyt9IIIop6g3ZoXwlRSppgeKXkOv545p7BIjIZ7UBjyfskHC3CDK65QoHDgRPjy+zShnUWiSwhYuTp7pQycnHlPovkkMHOx939xl2UGZ1vViMEuSEIEdOVckCoFBTaAeeugh3HrrrfjqV7+KLVu24LLLLsP111+P48eP++8cEGMr7fU5vAwKUe6NH4FqJAU++Vyn//fwDtzyx1ctY4Ou1msaNE03i24CBnFhxl+2CFRxWHU9Z0qgHB6oFLzc8ZppqNP2vGPQH9/eYJKc8uJUYr+AYPLhYPGk7gghY3H/YVVBScpAZkYr61uRi8uxN5YIFnZobp9vAiUO4QPgKPTKgxnk/HVn4Y2XzrIknVmOQpwjBqLzFXk+eWJAh8AvB8sLfiSRgt0n7HzZPVtWTHOKLA/UpJHBQ2Cd/XKeQ0dv3CScRg6UsQ0dQy+PjSjhv6s/MWDvkaooOfRAyRC+wQS2OMFuMWbEFjp9iz3rd5zscLzPCu0dkxg4+PfrqAEqKwJWfmU8qQ0aT6rE2YlBTaDuuusufPjDH8Ytt9yCuXPn4u6778bkyZPxq1/9Kmtt8HlEXoRIlHsT9Vlpbeu2avCIauI8u7fZyvEh3yeSOh7bYc99cYbwZYNAuRfSZUZQfzxpKxpLjVcvpwE7blAPFGAY1TGTQIVT7Tk9S3yPT7X34f/W2NXTGGEJqYoZWsWIBzummypTbyyZlhiHXwifptuLEVN85LLpgdthiFIPFAnhA+CQB3f2xZ5TxiBSmwu7FNP187gxeKm88f1Mh2CKwgXdwBe/NudXkT2nCBDL4acDsQhFzBzb0kgIYZYDRT1QHoQjLgjh648nB+w9MjxQufEUyRC+wQX+Mc0W4i6eMTr/nSFg+SxlxSFHzpMoVFtiaIF/34tsqHTBFrJ13XqWSxU+iUJg0M66WCyGzZs345prrrF9f8011+DFF190bB+NRtHZ2Wn754aJ1ZYLOcLdeH2CRHgGnrDE43H0R70TzKmhGY2LjQpmbNDj98diONFmVymLJZPoS4U5hFUFCqERmZKpsOouOfyHF46g/nQ3/rPdLjcdjVOBBS/DL4l4PI4nT1hj7NfPRFIzx6w0RW5jceM4iQTzHmmBVk5jqe1VBVBTFbWiiSRisRiiceM6uxG6rt4oEsngRnq3T+2iWDyBeEI8tz5x+XSMqUivYCQlHwp06KSvvf0x0S7WvrGEMZ6c8dyfGhNN0xCPxxGPx8Hqx0fjCfO7eDwuDNejmDIqFafuYUjzx4j7ED+KI5yCnxfYOzuRNM6rN3XPVhAPFHSrn6KiwkEhyq9q742hO3VNisMKRFMukRpzEeiiCUN/PD2CL4IC3bxHso1Y3Ht+SBQGbMYww7PQst6zxlYCMBYJeMKUDREJicJiVLn9vZaNaxoJ2xeSAOmBkigMBq0KX2trK5LJJGpra23f19bWCgsX3nnnnbjjjjsCHVuL9oGtyUV7u0HX546eOAU3Xtne1mL77fEnVmFbmwLAMsTGlOho6beO13qmwzx+TzQGkW5db+r7Syra8FrqWL/7zyaMKtZtxz59pgPrnn8eQBjxaD+aGhvM/oQUDQlPTTwxTrc2u54vAHzy92swuhi2bTa+8ira21UACk4cP+66/8HDR7F69WFsaLKmWbTfGnsRmpqb0duvAFDQdNI49sn6eqxadRL7ThljferkSc8+MzQ0GefW292N9WueBRCGpgOPPfEfbGk1jpWMRW39KVJ1xDQFz7+8CYc7lUDtAMCBw0c9t31tyxYc73Yer0jVseG5p3HrHOD5RgX/ORlMZpeGSLW2tuCZp58Cu52ff3EjgBDCio6E7hzrXbt3Y1X7LnT3hEDP/cRJY+43NDZg1SqDNB89aYzT4SPH8O/Hj+B3e1XMqtLR1KbC7TqOKtaxYnQ3/ng6hKZm+z1DcaqxxXaMw0eOum7LY99J+75e0LUkAAVNzc1YtWoVXm02zqmn47TZ3tEjR8zPvf32OZEOenqd8/tk02lsSbYCCOFMSxPCE5z7xZM6nnhiFUSP5O7efgAKWhrrzT7GEsnUp8wNh9bmZiTagVyso52sH/zFZc8mMLtV57ywAw2nGiiKWP26pGarcwbIvJbhgMoSe+5sKAseKEqWGlLF2aW3UqIQGLQeKAY+sVzXdWGy+Ze//GV0dHSY/06cOOF6zPFjrPpJNaOqbb+NHlMLN0wcby/Kt/La6zB/0Xnm3w98eBmqqyps2xSVlpmf45r4Jo8mje/fft0V5ncbGlXs6Km0bVdRUYllF10MABhRWY7JEyeav5WXeHswrphdI/x+0gTvIpBaSTVWLFtg++6QXovq6hEAgClTprjuO2HSZKxcudL2XUV5mcvWBkaNrgEzDRfOOQcAUDO2FnV1dTj33DlGnydP8jwGw4iRRnhKdXUV3nT9teb3K65eifkLFxnbVJbb9hlZYXgn5yw4D1OnTg3UDgDU1AqsYoL7D4QwZeo0x/flJUWoq6vDO99Sh7s/cp3rdfJsu2YM3lB3vfn3ovOXAABGVYiTdWefOwd1dXUoKbHn+tSOM+bC+HHjUVdXh7q6OsybY4z5+EmT0Fu7EPs6VDxxIoSi0nLHcRkqy8ux5ILzAQAHOt0fMaWVI2x/pzPeejh4InJJkXFvjBo9BnV1dZgzz5jPk8j9PGPmDPOzpmReKyZcVOz4rrS8Ai+fNsa6M1Ql9EABQNfYRcLvE6lFlJnTrHtNhwLfGgo+GD1mLCZ63L+Z4MOvm5o69pisHldiYFA4os1C+ApNoFiIcDypOcQFOvulF3M4oLbKeiZmxQNFQpm7U1EM0lspUQgMWgJVU1ODUCjk8DY1Nzc7vFIAUFxcjKqqKts/Bn4liyqKFXOyw2v2t7j2qThiXx1W1BCSqRX+q+eOxfJzxuKdS+0GiV9CPwUz9BgOt/ba/k7ogJ4yporCIUSIIENpxNuZeM7YSuH3oZC3sRgJW+fIsO5Aq0ViFfcplNSASCSC0pAxBiNKI67S6wwJzcpPqiozxiOpG8dRUw9O1aNN+7GYelkI5aXF5ipsEiGz37zsdHWp0WZU8z43Hv0BrvOBZqOoMn3Wl0ZCiEQiiEQiKC4uwh8/dJHnMaaMEhBQRUFxcZGpiJhMGUsVJS5zQlERiUQcoZss+EwNqVafUvNK0xV0x6zwtD6XcFTACLEoLoq4/u56jDTGOx3jiuWH6TDmEfPKlZAcqDC5DwYSwidStExoOlpSuZB7G7vhtgj7x5fF4jisP+++aKqZB6XpAxdqiCZ06AIP5UAwMlV8OY3HnkQeUd/eh98/fwQnzxjvFjcRnXwhYhIoHfub7OUC9jS4h+FLDB1QcpMNT5HIMzlnvNi+kZDIJQYtgSoqKsKSJUuwevVq2/erV6/G8uXL0zoWHx9bRQkUtwLnlVvDq/Alkjq+9M8dtt8+dKldEEAkHOHaTx/3dlLTrXypkF04wC+W/WNXzBR+L8rZsPVJVYTnwMbJSzktljRk2ktStum3bljg+wDtJbkTpamQDj5Znl9sclt8ei1VVyqsKlAUqxZUfzyJppQ6Ir8CyyTU+9OUMe9L9dsrFpvlslCjRVQ36Obl01yPUSIonsy6yVZzzcRal4GxxD107nvntmyOxZOa7XiiornmPqoSaEVQJFoRFH6iHRRuIhL0ngnyXg+izicSg6BE570XTnb1QPnlNI0qL8IrX73Ktw9B0RdPZl2Fj4Xs+BVzlsgv2O3Y2Z/Atx7fbQmTFNgDFSHPl2NtvT5bSwxF0Hd+rnKVKooHbTaKxDDGoCVQAHDbbbfhnnvuwR/+8Afs2bMHn/vc53D8+HF8/OMfT+s4/CpbFYnLpUpcvscJ22/++o4+8zMz6PjVkX6PlXoevKAFw6jylJR0UjMJVCSk2iTE/cjXmMpiXHmuM6wmiFqWm/gF4C37vrexC7O/sRpnYkbfikL+hvWZnnhqW9WS0Bb08YIp1eZnv4cya5PNgz+/fAw/ffYAACcpNkmb7izM6wVTZa3I3aPH+Iofgbr9zfNxPjk/iuOnnUYGK47JxiFqKhN5EyhGfsMcwaCgKnz0eF4eoKKwGijWnSdBbvXIBgrm9WSaGaL8D013jtf7L7GHFL7v4qn40Ou8FRNF8urxpGYS87ecN97VA+VHZiIhNZDBO7E6mAx7jBSizBaqSo3zjEsX1KCC293I6r0VChESwsfuv5oKw4t5ztgK1/0khg7oc9Wr7Ek6WD7Trh4p8+UkCoFBTaDe9a534e6778Y3v/lNLF68GOvXr8eqVavSypUAnOSikoQ20VowFCKPTiSk4o43zzf/pqvwbiuu2fBAsdpJhgdKN/tCiUOpwBDncbilx/FdzIdA6bp3SNNMj5fcwWa7Uhofvida0ac1oCJmgrFTdps+iP0enowYMDLwm/WHzd/468y8k0lNT6suEfN2eV0HZiBTI1jkUfKCiJCbHqjUOFjqgwq+dP0cx/amB0qz5hL9m4LW3Aj68isJhwKtNPJerIGqyrmBTTtG0GIpxUJKZDVdd8yjcm5Vs6IkjG+8aZ5nW6IxTGi6uVBTFFYRdhkaP09rWHUWkBbhi9ed67sNYDybsu2BYufJ1w2TKDAE06ayOCxcwMkn2HNi09Ez5vy/5wNL8dbzJ+L77xDnBEoMLYTSeFcHBb/wKXOgJAqBQU2gAOCTn/wkjh49img0is2bN+Pyyy9P+xhh7majOVBuHqiwquCzV51j8wREQio+QMKrHt1ab352u3/TsU/c8oOYkR9P6sQDpdhqH5xT6x8DLHJz+3mgdOgmCaQeLOb1OHdcJT6/crZv24CRG9PWY0lsP/6ZS53tEbLDzs9RBwqK3fvmk1fFDGcRAeBX9KtS5Lqpsx9/3eQuROIGL4OESa7aCdTADRg+hI96oD4uCN1k45EwCZS7B8pcISY1yPxQHAlW5JkngwOta+QGWiT3WFsPnt3TDMBOnpdNG+UgfTy5dvMQU7AFjjedZ4mKxBOW57gopLqG8M2utS9G8PdrWFUD9SGoNHU0oWV9zCNhy6MgMbhR6PA9UR8UBThv0gj85F2LccGUkS57SQwl5CKEj5Z0UBSpwidRGBT+CZoHRLibi+ZAuXmgQqqC21bOxvfeZq2CsYc9k1v940vHstpPdw8UMwDtIXyUF0ZCKr51wwLR7p6ob+83P4tIoKIopkE+a4xl4PWS/JWV892VCykiIQWdfVatm+qyIvz2fUts2yRJWJlXaBldUfV7dvIeGgre2GSG//0ZXlsvo4SNI109c/NYpfM6YGSWEUQWoub2UmFGM/PsMRInMqbDRGbYKxyVetIqisO+pJbH3z9+SUYhfLPGVmD+hCrPbdh113UdV/xwLfY2GsnqxWEV//nvy/DTmxZj5bxaB+krDtuvTcTNdSTAN988Hxu+uAKA4Y1hnt5IWHGdr2UcYarkCVRIgaoqnsWrAXtRyUc+6Z4vahTmzi6BKiIhnxKDB6L7kV/FLwQWTaq2/a3rTuVdiaGNbItIAPa5K2tASRQKhX+C5gFvXjzR9jc1mstc1OvYjU5JDbtpRavr9KH/g7dnFnoQVhUHoaD9TZAQvrCq2gwlo7CuGKZgnmADUU4NhapYOVDVZRHzGL1Rg0ApMEK2gqAopJrGPsM188fhPRdZyoUsjIsZi4DYGKOXIOgquui60ToVf/rwhQMOBfB6mDMPFF09S0eowg3sEK3dUQDAjlMdAKwCsqs+exneev4EXDDauI4sPJHlIDECJTKmI8Qg9gpHpZ602qqStEI13rFkEpZNG+UI4QtyKQ42d+PC6aM8t2F94QlaUVjF3PFVeEvq+cAblDy5TocUhkOKSabtnmP3Y3RzeWW8QigbY9+cP9LE+BHu+VAJTTfvnUyNkOoyu9oizWmRGDw4f0o1blxiL//glzebD/ALSEunSq/TcAMlTdkKtaPPUZn/JFEonBUE6hNX2sOYqJegvS/Gbw7AutFpyAwr+tclSKCn9/A7l03GX27xlqMWQVEUXDN/nON71t9EUse3Ht8NANhV32EzesIh95Vpdi5+zy7RzwosFb6SSMgMeeyJWmMQNAwtElKFKof0Jcps6IiqunqgFMUejufnuWC/ih60pZEQLp1Vg+k15bho+ugBr5B5GchRU7zAOt81+9xl80VYOHGE4zv+vFg+Fvt+3oQq/OBtC1CTKrOS1HTc/cwBc3tGFEQ5SOwYCc3HA0XOaXR5UVoGOZufPE8OeozPX3MuvvaGuXjA5Z4rM9Uc7d/z3kKHB4rLT2PE94Ovm4ZxVSWYM849bDZM5m9S04XeRx7dUftzhRe/Yf3zI3J0MSfiYSRrxAMVNOyPB99HU7VR5kANKkRCKn5443n46U2Lbd8VGvz8dC29IDFkQadZbjxQhZ/HEmcnzoqZV1YUwutmWaot9IY73RMTihkwI0TkgRKBfyx4hXKd65OvtIJTy2OhRH3xpOk1aOjod8QWu63uBH1oiUInFMUy/IvDqkmWeomCWlAhhEhI9fW4UDVDRpKYkdfSFSVtWgZ7UCeOiECFVAV/+vCFeOa2K4wE/wE+4L1WdUU5UOni9zcvdXzHxokZ9CyXhs/5MutgabqpQghYREFERE0hD83PA2WdU9AcKLP/qV2njbbXuKLH8DpeRXEYt1w2A8tn1WDCCGeB3ZuWGR5OniA6jH+/EL7U9v/7pvl46cuv95TODYcUMx8IsOYoG88rBQWTnR4o8Tzxm6NU/CTiMdeSum6q8GU6J/n92JieOtMv2lyiwLhy9ljzc2wA9c6yBf6+HgykTiK7oHZJtsLtqCKy9EBJFApnxdMqrCq2MDB6v6mKgg8KpInZczwwgeKMVa+b2i90gu8PL59uHsdmYKquniC2mXuQH/td8J2NQFnKeDRsLqgHqiSi+opqmCFFIdUcp6aOfjR29OO+F4+a233jjfMwYUQJbn/TPPCHdAx9yqAUXRNWI4r9NtAVMq8XBMsbKybz6M1EbIDCLQ9gbGUJblo2mdvW+H/eeCMXqLPfyDPjzzekiOs/UZVHHmyxIZHUPeXs6RyIkGsXBIzo8Z5iutARNPSDD0P82bvPx6RRpanf7P0v4giSMweK965YfyuK4hmmFlYVoeADe4b85J3OMN8eokr47OevcC1ySsdWNKfpaXoVSk0mdfO5yJPFoHAIbUgDeFCDycwDQFtP1GPL/EBRFNscLXRhX4nsg75T69v7PLYMDvqMkwRKolA4K55WiqLYjEO6Mm+Egzn3UQUhfF43Kv+LlyHtZ2TwtqKbcUP7EwkpuFYQ/gcED+ET9gWKrW4ObxjTArV+4HM6vBAJWR6ormgC/7fmIGkTmFZTjhe/fBVuft108AyKV11kEBnhTpLhP0hlpNbTrVefY287QDgBFSPgyVAQjCovsv3NzoHJbnf2GYa4wwOV+j+R1Ey1QcAygtl1pqIrbB4nNA0Pv3bSdrx7P7jM/Ew9EfubutIL4UttW8YpYtKQNjfP5Q2L7QSUJ1BVJWHzmvIhiGVczS5fFT5u7sdchBJCKVIuCp9j977Ie0U9AjPHVNjGlKophnyeSZQgez1rkrqVmzW2qth1Oy/QEZg2uswzZFCi8KALM+nUKMwlIiFF+FlieIA+V9+4SLxgmC6unmcJV0kCJVEonBUECrAbVjSsRVUUobw1+44SBq/7tMRhjLkPrd8qG28EuhEUvkCdWxHXgXhVNF23FMRCYhnloKpJJWmECYWICh8APLa93nVb3rjmVRe9cqAcBCrAWL3vYqsOWU1FsS30LIjnhc4NN7LnBd4oZnPVJFApDxR/3ROa8fffXj1pO0+eQIUE/UsknYWFp46yzrufhHQaHj17H8cLQuv4/meCuePtCny8V0gl3kU2Lgz8/cJfC96zyo+7mweKzVvRXOLnJgW7z9h+9L6vIGqhNs+zKOyWfPaazzQ3670XTcHU0WX4wjXBShIwUM/+Y5+5VHqghgBYnbDPvH5WgXtigL6PM3keSgxu0Od7tnLc6AKgrAElUSicNU8r6oFaRBLxNU0XEgxm/9kMAo8b9RNcvR0vQ9pPDjmZ9CdQ33/7QpshtXa/uxgB287vMSM6vUTSUuoKqYrrec3jDFkR3DxQIudCRLXn0bT3xp0bpcATKFcPlEsIn982jn3IGPDFTYN4XmgoWSZKWPwqLWuSFYhmUvH8oQ92ittic5wZ0/bQUOMzla1noARjdLnlwYhw5BcQK/xZ/be2ZSGNNyyegLGV1jHd9uavFx+GqCqKSfB58Zd0PVD8734Eig9PCquK52JGPGEnUEUuhqVfCF9QEkMJ1NTR5Vj3Pyvw6def47OXHaMrLG9oZUlEEqghgE9eOQsHvnM9Pn9NsILLuQadM3L+DD/QZ1S2QjRnjbVKqjD1WQmJfOOseVpRw2okCYEyxAqc27NVDWo0sU88Wbhqzlgs4NTRvAxxv5eEM0fFuf0lM2pshmeDR2zxQEQL4ppmkzrmPQsMjwmK4vJI1wMV1DXPG9duYSBiDxSndBZgNYuOOwvXYgiygkpzidwKo3r1wlGFnYXwpQgBm+qOBG3VPeQMIAV4BSE1jZ1OUQBKoM6bXI3JqVyjT1w5y9E2FQBxtm99/v7bF+Ge9y/FnW9bhIlE3MUthI9vh5e816G7ziNewt8xXj75PXGXJPywzSC0julXc8n0QCmMQNnzyszjcwT3a2+YazsO75VzQ0LTzZyEYptX3nu/dy+z5LA/eeUsXD23Fr94z/mpfsrV4KGAwURUilzuF4nhARqa7CaMky5GV1iLa9muZSchERSD5ymaY/Ar02yl+6OXzxAazcxIpkYV24zPe7lNEPbiZhgD/nky5VwuiIgAlRaFbCvRX6mb69iGgXoHvCASmbB5oELOvA72lx/ZKYmoaYVnhEPuBIq/lrxtze/nVUiX71KQcEfVRpgUHGzuNv/ujTkl7nnEkgPzQPVw3qALpowE4CzEyofG0Wm0kBSwZOfMVvLsOVDu14x6cC6YUo0NX3w9jn7vDRg3oiStHCjaz9KiEK6eV4vSopBNNc9NaZFvZ0K1PVRQ191J8Ypzxzq2peDzlHijk5cdF/UpnVc7q/HGjAw3w5LeRyFVwS2XzcAzt12Bt10wEX/80IUYN6IET916OV780uttx79gSjVm11bgA5dYIaimsAkha34huV+53vJcVJWEcc8Hlpq5DV6qfxISInzjTfPMz5VSxnzYgebsZipW44U3LBqf9WNKSATBWfO2+9/UQ5rFfd/9rsXYfvs1OKe2UpiDYRWfpR4ocW7DlFF2+WXAvorPw01Vj2H5zNG2v0UGTVHIHuImKib6hkXjsWBiFX727sXsQK5t8rkPd75tIQAjTClBPFBBDOOJ1U5Z+Mc+dYnvfhSRkLsUdtwleZ/uK4KIHPEeqCDnZ/dA8d4g6+8ZNeVC8jvQFdeHN1tiDjcsnoAPXTpdeCynB8r6zOSrv/e2hQ6CYc+Bcu9fSSSEX7znfHx6xSysJEm9ora94EZa/ep7ifb93fuX2u4FHeJp//23L8QEbp7yXi4+xI8f3x+8Q1wwm45ZJoWSq0uN4rR0tdbNA8Xm4qyxFbjrnYtxxWyjBMK54yod51ddVoSnP3cFPrfSueBD2/K7cnT+8s8mr4UjCQkRFk+uNj+/hSt6LzH0sXhKtfk5mx7GO9+2EBdMqcatV6UXdiwhkS2cNcs9y2fVYPc3rzWVvlRVQVVJxPzMQ2QAMluB356vpg4MTIVPVRW8fs5YPLe3GQCw6egZZ/9CdvELkaH+mdfPwpxxVjiP16OLJwJTU8IISU2HkpK/DilKIC+SyGCtTI11UIRU97pWu+s7Pfflx1eHRQB5ZJIDRTfh9w/yevjg66bj1WNnUvu7hPB5HIga6J+/5lzzfPlryM9TOkWY50EVhEryOV5eeOOiCXijgEekU9zQrQkPlXDSjn3nc2or8ecPX4TZX/uP+R1/ftfOr8W7UrWhvOAnInHdgvGorSpGU6c9PJGeu1c92aKQavNGMrBVWj5/ioGeT2lR+oRF9LyjIXx+Uax0/xGl9vtahmBJpIvaqhJ8/+0L0RdLYrZPjUSJoQf6HONTHQaCd184Be++0P85LiGRK5xVy4W8TDKDl4w5AIyrMsKCWG4BNezfuGi8kFQMJAeK75MoLCysKraQI/6YlSVhG3kCgJoKu/w1fzxq/bPjJTTd9EDxynjuffc20IIgwoXwUcNsX1OX5758H3WXnCAADgM2iCKc6mLMAnbjU9N1LJ060vb762aNxhgijpBJCB8l7NRz4PAkcX/TK9CXIlAhgQqlXRQjs0eElwfWsa3LmPOFb0UQXS/af1135kAFDSXlCZToWvEEgt/OywN13weXIawqDil7tj+9Z9wEJbxyy3iwXonu4XRC+ADgv2Yl8YWV5+DccXaDV0oKS2SCdy2bYpSkkBh2oM8TKgwkITHUcVYRKDcsn1nj+I4aZr+/eSnuvXmZGWpAjYRrXGoveRniIgNmxbljbH9T23FkmZP4qIpiC3FixtZDH70YNyyegDVfuNKxzzffssC1TyHVngHF+hhPaqYhGw45PVCi0xR9ly6BCquqjRyIiry67st7oDwIVDMnjhDMAxXMQ5PQdNz9rsXc8VWOGKZ/C55DVmmp4esnyU6vCyvaKiLF9O9MCB5/DD+4hfAFqU0k6h89nK4770U3KXGeOAQpEivKG6Tn7kWgDK/4dY7C2Wz+2kgTIbKnzliCMZnU8hEucKQRwgcAy8bo+NjlToM3aEkDCQmJswMJskjptogtITEUIQkUgHEjSrDpq1fjcaIkRxfe508YgRVzrITzIAa07mE4iQyx371/qe1vShjueJNTICKsKrYVenbMi2aMxt03nY+aCqfxOaG6FA/cchFm11bg81weBG+Imh6oJPVAqZ51bBj4LT4xN+lJFERhP2FVsYlfpPPgdRCC1PGFYiEDDOFzkBTyOZHUMbaqxFY3KqTY50Ymsq7fJkSYCh3w580byrWlVrt9JITPawwyJVDp1HZy2/a7b12IS2aMxr03LxP+7rYvb8Tzl9StFsmxth7b3/x4Bs3vsYXw+fD+orDqEDJh9xglcPQeobK9F5D8Aj+wYRHN8WIbWZMkSEJCIjug9fakh1piOEESqBTGVBabOVGAt5w1fQi4PRCqBV4jBp5MTKwudXhN6Mo1rXkAGIaQqio2khX0wbR8Vg2e/twVWDrNLjoRVlWbh4IZzgmNqPAp7nWgKKhRO6I0jDnVVj/5xHwA+MjlMzBlVBkumWGJZzAVvhsWG+peXoSUB99HRlJEno4YJ0WdvgeK98hZv9HQR+v4KsZXl0JVgNHlRaguE+eGne6JubY/oiyCvd+6Dnu/dZ392C71oRheV2uNYWu3ceBppe0AADLfSURBVPyQojgMeJsHKsMQvnSMcLcxnzyqDA9+9GLb4oWzHe/+iWTMWUguD77WlUMgQSD+InpMpEs6HR4ygQfKbQFi/oT0cwpEzzZK5H9/8zKMKI3gf64dHHWCJCQkhi7efN4EXDF7jHyeSAw7SAJFQA1Qr1AUasu4GYohVcE7lkwS/sYbYqKmvEJ/WJuZKHzR/omOyf+d0DQuByrAlFHoR/tx7/nAUswdX4WHP7Hc/K6mohjrv7gCX7jW8ooxQsmIaNSl5o4IvKeArYCJrhRfN8ivDtTkUaW26+VFuJjSnX17gzCvvu0KPPqZS13n2aGWHuH3DCWRkDNHx8ebVixQkA2pznN2K9qaDoLIwZvbDiDsy69/ohC+dAVNzLaCeqDS9Cryx2XnZFdrtD7/9KbF5ud0VnTZXBNdGzoPL54xGlu/sRJvPV8qoklISAwM1WVF+OOHLsSnVswqdFckJLIKSaAIvEKz7Nv5e6AACMPoAKCZU+0S2Y9eOT+hLBAovttOQmWF8NlzoPiQNYExRj9zPy+fWYP//PdlWMKJK9A2jc+K7X9aLO9vH/OWROf7+NmUzKlonJfPskvG03G45/1L8cAtF5l/L51ajfs+eKGnB5L+xcQiQoL5MnNMhVDufSDg+yIylOdPsCf9q4ozhM/ugbL/dvVcu1x5NjCQqA6RR5MiLJDDT4d0fOetVrhkUIW5IGGuFDwvY2TJjYjR50o65xKkSDSDogQvZC0hISEhIXG2QRIoAmpgeNkOXiFcFCLv1BsWjcdTuxpt34lICC9//K6lllIXa3MgK/e85yMcUmz9YCSE1oEK6oGy1c5Ko4t2+eyUNDdntM4cUy6seUVBDb+6heOwLBWuyI/zzcunmQWVRfuOG1FiKi8CwM9vOg8zx1T4EugHPnIRLp4xCr987wWObXip8WyCvzYig7mGK6oskov3UuH77lsX4C2LJ+DhTwSv6zWxuhQPfuRi198HYqjzXjiGj10+A5edU4NLZ9V4qgz6YRQJxRV5C0Xfpeu1c/MG00WU2bUVjt/5z35Id+oN5PkiISEhISExnCElUQjoSryI1DAEyYFyw4XTRmH5zNH46iM7rbYEhxjDKZD9z3Xn4qFXTxj9TG3/5sUT8OeXj+Gi6aP53X3h8FYoiq0fVMY8SQrpBlmFt3mgAml6GSgShI45EvkDhEeVE8GJsZVWvgs/zu9aNtkpOEDngEvXaZeEXq2ZNTZlR9pGLsvkBPG08Ma9qiqC3Cl3A31sVQl+etP5gfrz+ZWzcbStFz+6cZFnSGy6Co0Ubl7eL9dZwis8cUjLa+OzbaVAkCJdZUWewJ5IqexRDzOVvqd9SidUMl2FPHroGWPKcbilx6EWKiEhISEhcTZCEigC24prUA+Uh0XsJvE9Z5x/scD/feM89PTHMVttMtqxGfbG57GVJVj7Pyt8jyWCXwhfiKyCJ1LuMDVgWI+iGAZfS1cUr5s5GkBvoD7Zcm9S7fAeGzePAwWtzeO1ii46Fz7cThQkSQ1R/vii5qg9nY7Bmy54siMqrswb9yHFvWAxMLD+fiZghfh0CceF00bilVRx6XJRYhcHR46Xyzl9+NLp+P3zR2zfjR/hHWb5hWvOxTt/85Ltu7QJFNefPQ1GoWgaxatw89JsK4e5ZrSdL157LkqLwlg2bSQgvCssvPeiKXjklQNptSUhISHx/9u79/goynt/4J/dze7mvrnfSEgCCBhIQC6GAF4C5SoFilDwRIXawylW/KEIVKCV0CKXUq16qp6fWtHavg60gtoW8JAeCKhcCyg3QYQgigEEgUQCySZ5zh/pDjO7M5vZZDc7Gz7v18uXO7vPznyfnQ0z331uRKGEXfhk9I4R0NsCpTYTn0ll4VKnygQJKbHhePWBPugR7zmTmz+4x2A2KXNG+euuiRbCLCaPm0O1j8xsMmHdwwPx5KjuKP1+d90xKdcfMns8B6gnBXfcolzHyyGb2U4e7uUap6Kc+wx8TeW1kyP1Mu6ver5H77pRreUxaYTKZ3Xo6yqP9wR7rIvaOfVGvv6QfB0sLXrHQKmNp8rPdOAXY/Lw/x/oq/qe23MTsHP+UMWYPuWPHc2GpxmP1syT8m6V4c2MAZPz9TTLk2erxYy7uibrWk7g6R/k48N5Q3w7GBERUQhhAiWjaOzw8iOrnln4gKZfYu/JT8evJxZIz5ngeWM+oqf6YrxyLZ1OWotny4nbTabGdNx6EoDz1bXISojEjLs6+zTjmfyG7UYLlPJ4apNrvHx/X/xyXA9pW742kryeO05elB7HhId5TA/vfjyzSb0DojwkPb/q6510pLXc9622xtRXskVYgX9NIqGjFS2QfE2ghMoC0t54jPfT+FuS/zgwLO/GZBk/HpyLERoLZgNNY+V+P/XGOm7yw6ktgu3O11Y+efgROlpkXXyZRMK9vK/z1fg6EyEREVEo4VVORu/Nrd4b4nCrBS+W9MEk2XTmJpPyBmvpD/Ixb0TzrTT+vvH2+FXeZFJO/qDyzQgzm3XdGHlbw8hrTCpdI93jVPtVPtoehuJuN9YKkt9Ua92c7ln4PdXugIrudib1PNrbWBKtFjm1x/7m0aqo4zvTli1Qt/9rMo8fD85VPO9rlzd5Dq0ngdLbAiVPkn73b/rGebnI/y7kaffrXhYBlt6rEY98AhNl+RvHam4WQjn593b/L4Y1Wz7YLZNERERGxTFQMnpvbr3NUqbGfcyM/Dg/7JepKymR32T5sqisFvd7I/dqqH0WFpO+FqgWxySLwSrNNKgso1VzReuV7PPU+tVdayyVnmTHvcyKe/Pxs7UHAagPnZOf3sC2QCm33de40npPW90ovzatH/aeuoTBtyTBYjbhlW0nAfjeAiWfXEHPxAh6x0B1S4vBB/OKkRht09U1UM8+e2fFYe3DA1Hy2k7MHtZVtYz798w1vf2gLkl4fkpv3JKiHDMpP196xgTeOM6Nx/FROlrGOAsfERGRKrZAyei9YWhNlywTlL8a62718vcYKPf1i9y6q6klHhaL55pBalGN9NLdyRvFNPL/Oo7TLQnQyh3lYdksnvsBgFiVGdPcyRNirXOjnBQCmNy/o9d9ymMI5E2pezJx6Osrzb5HrQufu4Gdm2Z5HNI9xWu55sSGW1HcPQVWixmjZN1W7T62QPmacLlzn3VQLishUtc4H3fexj31zY7HwdIR+I87O6vH4/Y9e+vHt0uPx/XugLyMWM3yviR6Wud5eJ762l5sgCIiIlLHFigZ+Y2J8DIIStkC5WMCZQI6JUfj3wfnIj7K5vPUwv7iOYlE8+NgwswmXTdVLW3RUEzOobELrdY3efpn1WiB0hOXvBXM/TNwbXprpWquC19Lbv5fLumD2X/+BM9N6e21nHssqbHqU3zLNXXh817mpZI+2HDwLO4pSG92f3rJPwerD59JRqTAwE6JGJ6Xiu4aXdzchVuV+w9EK6rFSwIFeO+mKP+OhlvN6JTsOTZPTh6/3sV9Ac9W5q6p0fjs3HcYf1sH9biYQREREaliAiWj936hdS1QTeV/PibPp/f5m0cXPrdt1Sm+zd6nvHbJTPA+9bNmTCpTtbvTSmvlCa+iC58iIfKtu5fWuZWff/cS3dM8b+rl+1Sb2KE5o/LTMSwvtdmunu7npk/H+Gb3racFKi7Shn8r9N7K5iv556DnM4mwWnDN2YBbHKKp+9+D/Zp9j4vJZMLTP+gprb3m62QKeo/h8vHpyz6919fW5ZauQ+f+/V/300E4cf47FGQ6dJUnIiKiJkygZPTeMMh/yfW2DpT6QXwrrqb1I6A8u8KZTSZFbFpjoLzd7K36UX+sP1CJR4foW/9Hbf83jt/0f/cwtLrwyZ/XSpr0fPTy86mVWKityfXeI4Pwj0/P4eG7PbtpyXfT0u5nesbJuYerp7tgsKYxl7fI6PlMNj1+J/7n0NdwXDjcouPZLM13zfSXs1XXA7p/X8dgSu9z+z5E28PQKyvOX2ERERHdNDgGSoO3eRoUrRQ+/krrjzEw/rgBdB987pY/ebRImUxNv5R7xC/bLO6Wgt9M6qWYRtwXinpptkCpnxhFlzCNm2U9H72eSSTU1oHqlRWHJ4Z305jZT717ob+5J7dq5+E/7shRbIdbLR7fp9TYcL/H5s6qcb60ZCVEYmpRNnyYdE5Bnhj7MvFCy44V2H9WFd8/Hw7FHnlERET+wQSqBRQ31m3fAOWXLkhpDuVNssc05iaTIuFwtboE8ibMrNIC5Z7INnqufQsASIq245Hiznjse7cgSpY4KD8rHWOgVGJwZ9HR1VCrfEAX0nWLpVhl0oc5w5Stg/Yws6LOVosJw25Vn1TAn+Rjd1rSrdFXFvniswFKoO67vamb46yhLWuB1UvRAurDvyjskkdEROQf7MKnwVtLkfye3pcbGMA/i5QGoguS2WzC8LxU/GXvV9I0yspucaaAHdv9GIB2ktjopWlw7r/W09r62TfSc2YfW6DkZSxmE1TW7VVtgfK+T3mLQeA+P/muJ/bNVD1XJpMJnZKicPLCVQBNCdRh2Wx9v53cO6AxusiTJj+vEa1K/n3yZfFZX/xqXA/cP6AjblUZB+dPLT0/nJaciIjIP9gCpcHbvYbZ7SbbX/vVKxC/oJtNwOJxPTBvZDf86d8LPV53jbUI5K/Y8o+yNTfxigV5ZfvppTFYXhmDsnVJbdY/+bgTXeOMFF0+my3eYvLvYtU1p2Y5V/IEAHarBdfqGqRtX8bUtIaeCUP8Sd7IFW4LTB3DLGb0yHAEPAH1NmW6mvG9MwAA0wbmtPiYge72GGpqa2vRu3dvmEwmfPzxx8EOh4iI2hhboDR4uzGJi7RJNyUJOhakVOzXD534YiP8f9pMJhMibWH46d1dVF+/WlcPwDMB8Ed95DG4aLVA5emYulorEVtxbwFe3HICk/tn6YpHXxc+3/YTyJvrxOgb05Y3qDWdqQgPM+OH/bLw7sdfAwBsYW3TShEpuyFP9PFvqCUaZF0/A9UC5Q96/p58/dHmt5N7Y/m9BS1KguaO6IajZ6ultcCoybx585CRkYFPPvkk2KEQEVEQMIHS0FzLwnNTbmvRft0XxWyJQHTFaW6froaYSzXaLRv+pDa74fQ7cjGzuPnxJVqJWGK0HU993/v08XqmqJfHpqf1pK0W0pXTO1NjmMWMcNnMDG3VAhVmMWP3gqFoFG3TulFbf6OVrSUL5bYVb+vPufg6BtJkMrX4M36kWP0HlZvZxo0bsWnTJqxduxYbN24MdjhERBQExr2TCDJvY21aonzO3ThbdR1dU2Nava9AdHnSu8dbUr0v8ukvasnLwnv0rZ2lXCfHt+N6bd0zeY4D07WwcCvWDWsprQWH1cjHIwVylkB3KW0w259Lbf2NJigjLxCrZwZERQtoIIMhD+fOncP06dPx7rvvIjIyUtd7amtrUVtbK21XVVUBAJxOJ5zOtvlByt9ccYdq/ADrYBSsgzGEeh30xu/P+jGB0rDfx8Uwm5OTFIWcpCi/7Mtf42hWTeuPH72xB4D+hPFqbb1/Dt6M1sw0qOgy5+N+OsRFYFTPNFjMTV0a5eODXM1wYT62KMkbdQKxiKsanT34ADRNJOFiDeQgrSCqdTY0X8gAkmXdMLVwNr3gEEJg2rRpmDFjBvr164dTp07pet+yZcuwePFij+e3bNmiOwkzqrKysmCH0GqsgzGwDsYQ6nVoLv6amhq/HYsJlIZ7CtKDHYImf3UDK+yUID3WO2ZmdH46frb2oLQdqHu51owVMrWixcdkMuHl+/t6LdOqtaXaqgXKh7Jaa2i1J/IWKCK50tJS1QRHbs+ePdi+fTuqqqowf/58n/Y/f/58zJ49W9quqqpCVlYWiouLkZgYmmPLnE4nysrKMGzYMFit1mCH0yKsgzGwDsYQ6nXQG7+rB4A/MIHS0BaLiQabfLyL3gQqJtyK0u/nofRvRwIVFgCgc3JTa92Q7ilY+T/HEBep/w9a2YXPjwnLvxIh+eemZ9C/clyV/8LxxlsXvvsHdMQfd56WtuUJlNrYs/ZgYt9MvFR+AkNV1sYKRbfnJuD0xRr0yooLdighb+bMmZgyZYrXMjk5OViyZAl27twJu13ZStivXz+UlJTgzTffVH2v3W73eA8AWK3WkLxRkWMdjIF1MAbWIfiai9+fdWMCFYL8NT5L3l2rwYd9WsMClwX8/dHB+Ka6Fl1SmsaK3Zoei81P3OXTeJnWTDPvTl5Xq7QWlvqxtFiCMYmEl9PpnvTJW53aahKJtpYYbcc/f/69dtPCtuY/BqChUSCsndQnmJKSkpCUlNRsuRdeeAFLliyRtr/++muMGDECa9asQWGh59IPRETUfjGB0mDk3+Ftfkpg5F3dGn3o4RTIm9CeHTzXauqU7NvEFWYd06HrFRtuxbwRt+Dop0cRG2H12L+uMVBBmETCm+xE5bgL+fdJzyxwoSoUkie9X1eTydRuWwuNqmPHjort6Oimf5c6d+6MzMzMYIRERERBwgRKgxHHaf/63gK8WP45lozv6fd9+9QCJZ/G2++RtJ6/xxxNH5yLDVWfqu9fz0K65pYndC39HnpLhB4sysGF7+pwd7dkAMpZ+Pw8+SQRERFRu8MESoM/F4j1lx/2z8IPdS4C66vsBM/ZoDIc4fj6ynWP543+S36gZ71TJE06dh9uvRGQrwldS6P31qJoCzPjyVHdb2zLzqcRfzggMqKcnByflgsgIqL2gwmUhk7J/ply3Og2zroDF7+rU51i/Zkf9sZ9r+70eN7o42QCve6SPMnQs3v5IqYtWQS1JXzpimc2mzCmIB3fVNeia0rr1ykjIiIias+YQLl5e0YR9py6hPG9OwQ7lDZxa3qs5mtFnRMxokcq/ufwOcXztjBjN1O0ZhpzPcyKacyb3798nSVfc8+WRu/rD+O/+7c+LTwSERER0c2FCZSbfjkJ6JeT0HzBm0RHla59VkWXL+MlU/6cha+5/euZEVHeYufrLHwtnbWPPYuIiIiIAoMJFHn1/4begovf1WFs7wzpOcN34QvwtOGRtht/NmE6EjR5i53PCV0AJpEg4zLi2EsiIiJSYgJFXsWEW/Hs5N6K56Ltxv7aBHracEeEFc9M6gWL2aRIprTIE06fx0D5HF0TtkARERERBYax74TJkGIjjP21ka+PE6gJA+/tq3/dF3k8Ps/C18IMyl+LLRMRERGRkrH7YpEhxYZbpcf1vqzA20bCwyzNF2pD8jFjvraItXgMVIveRcEWZTfWd5eIiIg8GTaBysnJgclkUvz35JNPBjssAhATfqMFqvp6fRAjUWeXrbtU3xD8VEIx6YaP72UXvpvDbyf3Ql56LH45zv+LZBMREZF/Gbov1i9/+UtMnz5d2o6Ojg5iNOQSJksILtfUBTESdfIWqPrG4GcS8i58vkbj6yyHkTYLauoaMLhLko9HomD6wW2Z+MFt+ruFEhERUfAYOoGKiYlBWlpasMMgL9IdEcEOwYN8nFFdQ/C7GFplk0g0+JjQ+doCtenxO7HtswuY0OfmWMeMiIiIqK0ZOoFasWIFfvWrXyErKwuTJk3C3LlzYbPZVMvW1taitrZW2q6qqpIeO53OgMcaCK64jRj/2p8U4nBlFQbkOLzGF/Q6NDa2+titroNsnFhtndO3/Zh8O25qtBWT+qQDaITTeeO4QT8PfsA6GIOeOoRy/YiIiJpj2ARq1qxZ6NOnD+Lj47F7927Mnz8fFRUVeO2111TLL1u2DIsXL/Z43gSBDRs2BDrcgCorKwt2CKocADZuPKirbFvX4Z4sE87UmHD52C5s+Mw/+2xdHZr+1D7cuRuXj+lphWoqX+90+vX7a9Tvki9YB2PwVoeampo2jISIiKhttWkCVVpaqprkyO3Zswf9+vXD448/Lj1XUFCA+Ph4TJw4EStWrEBiYqLH++bPn4/Zs2dL21VVVcjKyoLFbMbo0aP9V4k25HQ6UVZWhmHDhsFqtTb/BgMKVh38ecb9UYdZOzYBAH448k7kJEbpLm+32TB6dHGLjinH75Ix3Cx1kPcAICIiam/aNIGaOXMmpkyZ4rVMTk6O6vMDBgwAAHz++eeqCZTdbofdbvd43mQ2heyNiovVamUdDKA1dSifczfOV9filrQ4n95nMsGvn9vNfh6Mor3XIdTrRkRE5E2bJlBJSUlISmrZ7GD79+8HAKSnp/v0Ph+X3SEKiJykKOQkNd/y5M7XWfiIiIiIKLAMOQZqx44d2LlzJ4qLi+FwOLBnzx48/vjjGDt2LDp27OjTviy8/6QQxq8vERERkbEYMoGy2+1Ys2YNFi9ejNraWmRnZ2P69OmYN2+ez/viL/gUyvj9JSIiIjIWQyZQffr0wc6dO/2yL3bho1DG/ImIiIjIWMzNFwltkTZD5ohEujB/IiIiIjKWdp9A/XZSQbBDIGoxtkARERERGUu7T6BuTY8JdghELWZmBkVERERkKO0+gSIKZUyfiIiIiIyFCRSRgXEWPiIiIiJjYQJFZGDMn4iIiIiMhQkUkYExgSIiIiIyFiZQRAZm4igoIiIiIkNhAkVkYGyBIiIiIjIWJlBEBsZpzImIiIiMhQkUkYExfSIiIiIyFiZQREbGDIqIiIjIUJhAERkY8yciIiIiY2ECRWRgHANFREREZCxMoIgMjPkTERERkbEwgSIyMK4DRURERGQsTKCIDIwtUERERETGwgSKyMBMzKCIiIiIDCUs2AEQkTamT0Tt04EDBxAfH+/xfFxcHHJycgAAQgh88sknmvuIjY1Fp06dpO1PPvkEQgjVsllZWUhMTGxd0EREBIAJFJGhsQGKqH0aMmSI6vOTJ0/G6tWrATQlULfddpvmPsaMGYO//e1v0nZhYSFqa2tVy44bNw5//vOfYbPZUFVVhdzcXM39TpgwAa+++ioAoL6+HqmpqR5lnE4nrFYrRo0ahT/+8Y/S8+np6airqwMAWK1WpKWlwWq1AgDuuOMOPPvss1LZgQMHwul0qsbQv39/vPTSS9L20KFDUVVVpVo2Pz8fr7/+urR9+PBhXL58WbVsp06dkJ6eDgC4evUqtm/fjrAw9Vuh7OxsZGZmAgCqq6tx4MAB1XIAkJiYiO7du2u+TkTtCxMoIgNjAkXUPqWlpcFs9uxF794qlZGRobmPhIQEj7JqCdS5c+cQHx8Pm80GoCkx+/bbbzX3+9133ym2vZWtrq72KOtKoFzHdnElLi579+5VlJVzOByK7f379+PSpUuqZeUJ0CuvvILly5ejoqJCtezvfvc7PPLIIwCAiooKlJSUqJYDgBUrVmDevHkAgKNHj2Lw4MGaZTMzM7Fv3z4kJyfjnXfewYwZM6TX3FsFX3zxRUyaNAkAsHHjRjz44IOaZZ955hlMnToVALB161ZMmDBBUdaVyALAkiVL8NOf/hQAsHv3bgwfPlwz3oULF2Lu3LkAgIMHD2LQoEGaZWfPno3S0lIAwIkTJ9CrVy/Nsg8//DBWrlwJADh79qyihdTdtGnT8PzzzwMArly5IiWr3sra7XZ88803is/B3ZgxY/Czn/0MQNP3c/To0Zplhw0bhqeeegpA048FxcXF0mthYWHIzs6W/iYHDBiAyZMnAwBqamqwcOFCAEBjYyMqKiqwefNm6W+6b9++uP/++6X9uj5rNfn5+XjooYek7SVLliA6Olq1bKdOnTB27Fhp+4UXXkBjY6Nq2aysLNx7773S9ssvv6z5A0t6ejosFou0/dFHH2nGGxcXhx49ekjbO3fuRENDg2rZmJgYFBQUSNu7d++WfjSprq5W/NCRmJiIYcOGSdvvvfcerl27phnDyJEjpe0NGzbggw8+QHV1tccPItHR0RgzZoxmfVqKCRSRgSVG2YMdAhEFwKFDh5rtUmc2m3HmzBnd+zx58qTma1euXJEeR0dH49NPP9UsGxMTIz22WCweZZ1OJ7Zt24Y777zTI+E7cOCAlAScO3cOV69elV5LSUlRlH3vvfc0b/7cP5s1a9ZotlbFxcVJj7/44guEhYXhlltuUS0rT8xsNhu6dOmiOdZUXje73a65z8TERKxfv15KaK9fv47z58+rlgWguImtq6vDhQsXNMtev35deux0Or0ms/L9NjQ0KM65t7KNjY0eibBWWSGE4py6kyfEQgjNG2D3su7HcWe322G326X3ffjhh5pl5S2BDQ0NXst27NhREa+3stOnT5cSqNraWjz33HOaZUtKSqQEqrGx0WvZ8ePHKxKo0tJSzYRk7NixigRqzpw5mn8XQ4cOVSRQCxYs0GyZHTt2LKZNmyZt33XXXZoxDBs2DJs2bZK2R4wYodk6PGjQIMVnOn78eFRWVqqWHTBggCKBeuSRRzT//SsoKFAkUHPnzsXx48dVy3bu3JkJFNHN4tUH++H3H57E0gn5wQ6FiNoBeeJgsVh0dzczmUweZZ1OJ06ePInu3btLrR8u3bp1kx43dwz5DVBz5DdW3jz99NN4+umndZXt2rUrjhw54lEHNQUFBfjss8907XfUqFE4ePCg5uvylpbi4mIcPnxY8bo8oUtLS5MeFxUVKZJZp9OJDz74AHfeeSesViuSk5Ol13r37o1jx45p7leeoHbv3t3j5lNeVp6gZmdn48SJE5plY2NjpcfJyckeLYHysvJWlpiYGHzxxReqZRsaGhRJQkJCAtauXQstrjGEABAZGem1bFZWlvTYYrFIZYUQqKioUCSs/fv3lx6Hh4dj/vz5UnwnTpxA586dpVac3r17S2XNZrNUVk1eXp70uLa2FiUlJaivr1ct26dPH8X2lClTNBMdeSsRAEycOBE1NTUe5a5du4bc3FxFi3iXLl009+veKt6pUyePVmsX91bF3NxcREVFAWg6vykpKVJiLP8cgKbkS+vHBfeWzcLCQoSHhyMpKcnjBxFvrfitwQSKyICG5aViWJ7nuAMiIjK2uLg4RdLhTWxsrMeNo5aoqChFUupKZLt16+aRBEZERKBr16669mu329GlSxddZa1Wq9dueXJhYWGKZEaNKzEym82K1iBvIiIivHbhk7PZbLrLms1m3WUjIiKwdOlSAE112LBhA0aPHq2ajIeFhUllm2O32/Hmm2/qKgsAf/jDH3SXdY1rVOOqg8vRo0d173f//v26y3rrGuhuzZo1usu+/vrrXs9BIHAacyIiIiIiIp2YQBEREREREenEBIqIiIiIiEgnJlBEREREREQ6MYEiIiLSaf369SgsLERERASSkpJ0DzonIqL2g7PwERER6bB27VpMnz4dS5cuxZAhQyCE8DpdNRERtU9MoIiIiJpRX1+PWbNmYeXKlfjxj38sPS9f90hNbW2tYoFQ14KTTqdTcwFMo3PFHarxA6yDUbAOxhDqddAbvz/rxwSKiIioGfv27cOZM2dgNptx22234ezZs+jduzd+85vfeCxYKbds2TIsXrzY4/ktW7YgMjIykCEHXFlZWbBDaDXWwRhYB2MI9To0F7/aQsItxQSKiIioGSdPngQAlJaW4tlnn0VOTg6eeeYZ3HXXXfjss8+QkJCg+r758+dj9uzZ0nZVVRWysrJQXFyMxMTENond35xOJ8rKyjBs2LA2W7TS31gHY2AdjCHU66A3flcPAH9gAkVERDet0tJS1RYiuT179qCxsREAsHDhQtx7770AgFWrViEzMxN/+ctf8JOf/ET1vXa7HXa73eN5q9UakjcqcqyDMbAOxsA6BF9z8fuzbkygiIjopjVz5kxMmTLFa5mcnBxUV1cDAPLy8qTn7XY7OnXqhNOnTwc0RiIiMhYmUEREdNNKSkpCUlJSs+X69u0Lu92OY8eOYfDgwQCauo2cOnUK2dnZgQ6TiIgMhAkUERFRM2JjYzFjxgwsWrQIWVlZyM7OxsqVKwEAkyZNCnJ0RETUltptAiWEAABUV1eHbH9Op9OJmpoaVFVVsQ5BxDoYA+tgDHrq4Bqo6/p3uL1YuXIlwsLC8MADD+DatWsoLCzE5s2bER8fr3sfvDYZA+tgDKyDMYR6HfTG789rk0m0tyvcv5w8eRKdO3cOdhhERDetL7/8EpmZmcEOw1B4bSIiCi5/XJvabQuUa0rZ06dPw+FwBDmalnFNd/vll18iNjY22OG0COtgDKyDMdwsdRBCoLq6GhkZGW0cnfHx2mQMrIMxsA7GEOp10Bu/P69N7TaBMpvNAACHwxGSXwa52NhY1sEAWAdjYB2Mobk6hGpyEGi8NhkL62AMrIMxhHod9MTvr2uT2S97ISIiIiIiugkwgSIiIiIiItKp3SZQdrsdixYtUl0BPlSwDsbAOhgD62AM7aEOwdQePj/WwRhYB2NgHYIvGPG321n4iIiIiIiI/K3dtkARERERERH5GxMoIiIiIiIinZhAERERERER6cQEioiIiIiISKd2m0C99NJLyM3NRXh4OPr27YsPPvgg2CEBAEpLS2EymRT/paWlSa8LIVBaWoqMjAxERETg7rvvxuHDhxX7qK2txaOPPoqkpCRERUVh7Nix+OqrrwIW87Zt2/D9738fGRkZMJlMePfddxWv+yvmS5cu4YEHHoDD4YDD4cADDzyAy5cvt0kdpk2b5nFeBgwYYJg6LFu2DP3790dMTAxSUlIwfvx4HDt2TFHG6OdBTx2Mfh5efvllFBQUSIv1FRUVYePGjdLrRj8Heupg9HMQyox6XQJ4beK1qWV4bTJGHXhtCkIdRDu0evVqYbVaxauvviqOHDkiZs2aJaKiosQXX3wR7NDEokWLRI8ePURlZaX03/nz56XXly9fLmJiYsTatWvFwYMHxeTJk0V6erqoqqqSysyYMUN06NBBlJWViX379oni4mLRq1cvUV9fH5CYN2zYIBYuXCjWrl0rAIh33nlH8bq/Yh45cqTo2bOn2L59u9i+fbvo2bOnGDNmTJvUYerUqWLkyJGK83Lx4kVFmWDWYcSIEWLVqlXi0KFD4uOPPxb33HOP6Nixo/juu++kMkY/D3rqYPTz8Ne//lWsX79eHDt2TBw7dkwsWLBAWK1WcejQISGE8c+BnjoY/RyEKiNfl4TgtYnXppbhtekGXpsCWwejnYN2mUDdfvvtYsaMGYrnunfvLp588skgRXTDokWLRK9evVRfa2xsFGlpaWL58uXSc9evXxcOh0P813/9lxBCiMuXLwur1SpWr14tlTlz5owwm83i/fffD2jsQgiPf+D9FfORI0cEALFz506pzI4dOwQAcfTo0YDWQYimP8xx48ZpvsdodTh//rwAILZu3SqECM3z4F4HIULvPAghRHx8vHjttddC8hy410GI0DwHocDI1yUheG3SipnXJt/w2mSMOgjBa1Og69DuuvDV1dVh7969GD58uOL54cOHY/v27UGKSun48ePIyMhAbm4upkyZgpMnTwIAKioqcPbsWUXsdrsdd911lxT73r174XQ6FWUyMjLQs2fPoNTPXzHv2LEDDocDhYWFUpkBAwbA4XC0Wb3Ky8uRkpKCrl27Yvr06Th//rz0mtHqcOXKFQBAQkICgNA8D+51cAmV89DQ0IDVq1fj6tWrKCoqCslz4F4Hl1A5B6EiFK5LAK9NajEb4bscSn+PvDYFvw68NrVNHcJaWjmjunDhAhoaGpCamqp4PjU1FWfPng1SVDcUFhbiD3/4A7p27Ypz585hyZIlGDhwIA4fPizFpxb7F198AQA4e/YsbDYb4uPjPcoEo37+ivns2bNISUnx2H9KSkqb1GvUqFGYNGkSsrOzUVFRgV/84hcYMmQI9u7dC7vdbqg6CCEwe/ZsDB48GD179pSO7YrHPT4jnge1OgChcR4OHjyIoqIiXL9+HdHR0XjnnXeQl5cn/eMbCudAqw5AaJyDUGP06xLAa5N7GaN8l0Pp75HXJl6bAlUHwHjnoN0lUC4mk0mxLYTweC4YRo0aJT3Oz89HUVEROnfujDfffFMaDNeS2INdP3/ErFa+reo1efJk6XHPnj3Rr18/ZGdnY/369ZgwYYLm+4JRh5kzZ+LAgQP48MMPPV4LlfOgVYdQOA/dunXDxx9/jMuXL2Pt2rWYOnUqtm7dqnlsI54DrTrk5eWFxDkIVUa9LgG8Nnkrw2uTPrw2eeK1yTehdG1qd134kpKSYLFYPDLJ8+fPe2TfRhAVFYX8/HwcP35cmvHIW+xpaWmoq6vDpUuXNMu0JX/FnJaWhnPnznns/5tvvglKvdLT05GdnY3jx48DME4dHn30Ufz1r3/Fli1bkJmZKT0fSudBqw5qjHgebDYbunTpgn79+mHZsmXo1asXnn/++ZA6B1p1UGPEcxBqQu26BPDaZNTvslH/HnltCn4deG1q2zq0uwTKZrOhb9++KCsrUzxfVlaGgQMHBikqbbW1tfj000+Rnp6O3NxcpKWlKWKvq6vD1q1bpdj79u0Lq9WqKFNZWYlDhw4FpX7+irmoqAhXrlzB7t27pTK7du3ClStXglKvixcv4ssvv0R6ejqA4NdBCIGZM2di3bp12Lx5M3JzcxWvh8J5aK4Oaox2HtQIIVBbWxsS56C5OqgJhXNgdKF2XQJ4bTLqd9lof4+8NhnjPKjhtSnAdfBpyokQ4Zou9ve//704cuSIeOyxx0RUVJQ4depUsEMTTzzxhCgvLxcnT54UO3fuFGPGjBExMTFSbMuXLxcOh0OsW7dOHDx4UNx3332qU01mZmaKf/zjH2Lfvn1iyJAhAZ0qtrq6Wuzfv1/s379fABDPPvus2L9/vzT9rr9iHjlypCgoKBA7duwQO3bsEPn5+X6bHtNbHaqrq8UTTzwhtm/fLioqKsSWLVtEUVGR6NChg2Hq8PDDDwuHwyHKy8sVU3jW1NRIZYx+HpqrQyich/nz54tt27aJiooKceDAAbFgwQJhNpvFpk2bhBDGPwfN1SEUzkGoMvJ1SQhem3htahlem4xRB16b2r4O7TKBEkKIF198UWRnZwubzSb69OmjmI4ymFxz71utVpGRkSEmTJggDh8+LL3e2NgoFi1aJNLS0oTdbhd33nmnOHjwoGIf165dEzNnzhQJCQkiIiJCjBkzRpw+fTpgMW/ZskUA8Phv6tSpfo354sWLoqSkRMTExIiYmBhRUlIiLl26FPA61NTUiOHDh4vk5GRhtVpFx44dxdSpUz3iC2Yd1GIHIFatWiWVMfp5aK4OoXAeHnroIenfleTkZDF06FDpAiWE8c9Bc3UIhXMQyox6XRKC1yZem1qG1yZj1IHXpravg0kIIXxrsyIiIiIiIro5tbsxUERERERERIHCBIqIiIiIiEgnJlBEREREREQ6MYEiIiIiIiLSiQkUERERERGRTkygiIiIiIiIdGICRUREREREpBMTKCIiIiIiIp2YQBH5UWlpKXr37t3mxy0vL4fJZILJZML48eN1vae0tFR6z3PPPRfQ+IiIyNhc15HLly8HOxQiw2MCRaSTK9nQ+m/atGmYM2cO/vd//zdoMR47dgxvvPGGrrJz5sxBZWUlMjMzAxsUEREZzt13343HHntM2h44cCAqKyvhcDiCFxRRiAgLdgBEoaKyslJ6vGbNGjz11FM4duyY9FxERASio6MRHR0djPAAACkpKYiLi9NV1hWrxWIJbFBERGR4NpsNaWlpwQ6DKCSwBYpIp7S0NOk/h8MBk8nk8Zx7F75p06Zh/PjxWLp0KVJTUxEXF4fFixejvr4ec+fORUJCAjIzM/H6668rjnXmzBlMnjwZ8fHxSExMxLhx43Dq1CmfY3777beRn5+PiIgIJCYm4nvf+x6uXr3ayk+CiIhC2bRp07B161Y8//zzUi+KN954Q9GF74033kBcXBz+/ve/o1u3boiMjMTEiRNx9epVvPnmm8jJyUF8fDweffRRNDQ0SPuuq6vDvHnz0KFDB0RFRaGwsBDl5eXBqShRgDCBIgqwzZs34+uvv8a2bdvw7LPPorS0FGPGjEF8fDx27dqFGTNmYMaMGfjyyy8BADU1NSguLkZ0dDS2bduGDz/8ENHR0Rg5ciTq6up0H7eyshL33XcfHnroIXz66acoLy/HhAkTIIQIVFWJiCgEPP/88ygqKsL06dNRWVmJyspKZGVleZSrqanBCy+8gNWrV+P999+XriMbNmzAhg0b8NZbb+GVV17B22+/Lb3nRz/6ET766COsXr0aBw4cwKRJkzBy5EgcP368LatIFFDswkcUYAkJCXjhhRdgNpvRrVs3/PrXv0ZNTQ0WLFgAAJg/fz6WL1+Ojz76CFOmTMHq1athNpvx2muvwWQyAQBWrVqFuLg4lJeXY/jw4bqOW1lZifr6ekyYMAHZ2dkAgPz8/MBUkoiIQobD4YDNZkNkZKTUbe/o0aMe5ZxOJ15++WV07twZADBx4kS89dZbOHfuHKKjo5GXl4fi4mJs2bIFkydPxokTJ/Df//3f+Oqrr5CRkQGgabzt+++/j1WrVmHp0qVtV0miAGICRRRgPXr0gNl8o7E3NTUVPXv2lLYtFgsSExNx/vx5AMDevXvx+eefIyYmRrGf69ev48SJE7qP26tXLwwdOhT5+fkYMWIEhg8fjokTJyI+Pr6VNSIioptBZGSklDwBTdevnJwcxVjf1NRU6fq1b98+CCHQtWtXxX5qa2uRmJjYNkETtQEmUEQBZrVaFdsmk0n1ucbGRgBAY2Mj+vbtiz/96U8e+0pOTtZ9XIvFgrKyMmzfvh2bNm3Cf/7nf2LhwoXYtWsXcnNzW1ATIiK6mbTk+mWxWLB3716PCYqCOcESkb8xgSIymD59+mDNmjVISUlBbGxsq/ZlMpkwaNAgDBo0CE899RSys7PxzjvvYPbs2X6KloiIQpHNZlNM/uAPt912GxoaGnD+/Hnccccdft03kZFwEgkigykpKUFSUhLGjRuHDz74ABUVFdi6dStmzZqFr776Svd+du3ahaVLl+Kf//wnTp8+jXXr1uGbb77BrbfeGsDoiYgoFOTk5GDXrl04deoULly4ILUitUbXrl1RUlKCBx98EOvWrUNFRQX27NmDFStWYMOGDX6ImsgYmEARGUxkZCS2bduGjh07YsKECbj11lvx0EMP4dq1az61SMXGxmLbtm0YPXo0unbtip///Od45plnMGrUqABGT0REoWDOnDmwWCzIy8tDcnIyTp8+7Zf9rlq1Cg8++CCeeOIJdOvWDWPHjsWuXbtUZ/kjClUmwTmNiUJeeXk5iouLcenSJd0L6brk5OTgscceU6xIT0RERETq2AJF1I5kZmbivvvu01V26dKliI6O9tuvjkREREQ3A7ZAEbUD165dw5kzZwA0zXTkWtfDm2+//RbffvstgKbZ/RwOR0BjJCIiImoPmEARERERERHpxC58REREREREOjGBIiIiIiIi0okJFBERERERkU5MoIiIiIiIiHRiAkVERERERKQTEygiIiIiIiKdmEARERERERHpxASKiIiIiIhIp/8DaEKiLH8OEcYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(10, 10), dpi=100)\n",
    "\n",
    "outdf_plt.plot(x='time', y='pd_andes', ax=ax[0, 0],\n",
    "               title='Total Load [MW]',\n",
    "               xlim=[0, total_time],\n",
    "               legend=True, label='Load ANDES')\n",
    "outdf_plt.plot(x='time', y='pd_ams', ax=ax[0, 0],\n",
    "               legend=True, label='Load AMS')\n",
    "outdf_plt.plot(x='time', y='pg_ams', ax=ax[0, 0],\n",
    "               grid=True,\n",
    "               legend=True, label='Gen AMS',\n",
    "               xlabel='Time [s]')\n",
    "\n",
    "outdf_plt.plot(x='time', y='freq', ax=ax[0, 1],\n",
    "               title='Frequency [Hz]', grid=True,\n",
    "               xlim=[0, total_time], legend=False,\n",
    "               xlabel='Time [s]')\n",
    "\n",
    "outdf_plt.plot(x='time', y='ACE', ax=ax[1, 0],\n",
    "               title='ACE [MW]', grid=True,\n",
    "               xlim=[0, total_time], legend=False,\n",
    "               xlabel='Time [s]')\n",
    "\n",
    "outdf_plt.plot(x='time', y='AGC', ax=ax[1, 1],\n",
    "               title='AGC Power [MW]',\n",
    "               xlim=[0, total_time],\n",
    "               legend=True, label='AGC',\n",
    "               xlabel='Time [s]')\n",
    "outdf_plt.plot(x='time', y='pru_ams', ax=ax[1, 1],\n",
    "               legend=True, label='RegUp',\n",
    "               style='--', color='black')\n",
    "outdf_plt.plot(x='time', y='prd_ams', ax=ax[1, 1],\n",
    "               grid=True,\n",
    "               legend=True, label='RegDn',\n",
    "               style='--', color='black')\n",
    "ax[1, 1].legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Frequency regulation performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ACE [MW]', ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFjk9iB19OhRPfTQQ2rQoIFq166tNm3aKC0tzfm+MUZTp05VSEiI/Pz81K1bN+3evdtlG7m5uRo7dqwCAwNVp04d3X333frxxx8ruisAAAAAagi3BqkTJ06oU6dO8vLy0ieffKI9e/Zo1qxZ+t3vfues88orr2j27Nl688039fXXX8vhcKhXr146deqUs058fLxWrVqlZcuWafPmzTp9+rQGDBig/Px8N/QKAAAAQHXn6c6dz5w5U6GhoVq4cKGzrEmTJs5/G2OUlJSkyZMna9CgQZKkRYsWKSgoSEuWLNETTzyh7OxsLViwQH/729/Us2dPSdJ7772n0NBQffbZZ+rdu3eF9gkAAABA9efWGamPPvpI7du31/33369GjRqpbdu2+stf/uJ8/8CBA8rMzFRMTIyzzMfHR127dlVqaqokKS0tTefOnXOpExISooiICGedy+Xm5ionJ8dlAQAAAICScmuQ2r9/v+bNm6emTZtqzZo1GjVqlJ566iktXrxYkpSZmSlJCgoKclkvKCjI+V5mZqa8vb1Vr169YutcLjExUXa73bmEhoaWddcAAAAAVGNuDVIFBQVq166dpk+frrZt2+qJJ57QY489pnnz5rnUs9lsLq+NMYXKLnelOgkJCcrOznYuR44cubaOAAAAAKhR3BqkgoOD1aJFC5eyW265RYcPH5YkORwOSSo0s5SVleWcpXI4HMrLy9OJEyeKrXM5Hx8fBQQEuCwAAAAAUFJuDVKdOnXSvn37XMr+85//KCwsTJIUHh4uh8OhlJQU5/t5eXnasGGDoqOjJUmRkZHy8vJyqZORkaFdu3Y56wAAAABAWXLrXfuefvppRUdHa/r06RoyZIi2bt2q+fPna/78+ZIuXNIXHx+v6dOnq2nTpmratKmmT5+u2rVr68EHH5Qk2e12xcXFacKECWrQoIHq16+viRMnqlWrVs67+AEAAABAWXJrkLrtttu0atUqJSQkaNq0aQoPD1dSUpKGDh3qrPPMM8/o7NmzGj16tE6cOKEOHTpo7dq18vf3d9Z57bXX5OnpqSFDhujs2bPq0aOHkpOT5eHh4Y5uAQAAAKjmbMYY4+5GuFtOTo7sdruys7P5vlQFaDLpY7fu/+CM/m7dP4D/h/Nv8Tg2FYdxCcClSnr+det3pAAAAACgKnLrpX2o3tz9CR8AAJdjbAJQVpiRAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAary5c+cqPDxcvr6+ioyM1KZNm4qtm5GRoQcffFDNmjVTrVq1FB8fX6hOcnKybDZboeW3334rx14AACqSp7sbAFS0JpM+LlR2cEZ/N7QEQGWwfPlyxcfHa+7cuerUqZPeeecd9e3bV3v27FHjxo0L1c/NzVXDhg01efJkvfbaa8VuNyAgQPv27XMp8/X1LfP2o+oralySGJuAyo4ZKQBAjTZ79mzFxcXp0Ucf1S233KKkpCSFhoZq3rx5RdZv0qSJXn/9dQ0fPlx2u73Y7dpsNjkcDpcFAFB9EKQAADVWXl6e0tLSFBMT41IeExOj1NTUa9r26dOnFRYWpuuvv14DBgxQenr6Fevn5uYqJyfHZQEAVF4EKQBAjXXs2DHl5+crKCjIpTwoKEiZmZml3m7z5s2VnJysjz76SEuXLpWvr686deqk7777rth1EhMTZbfbnUtoaGip9w8AKH8EKQBAjWez2VxeG2MKlVnRsWNHPfTQQ2rdurU6d+6s999/XzfffLPmzJlT7DoJCQnKzs52LkeOHCn1/gEA5Y+bTQAAaqzAwEB5eHgUmn3KysoqNEt1LWrVqqXbbrvtijNSPj4+8vHxKbN9AgDKFzNSAIAay9vbW5GRkUpJSXEpT0lJUXR0dJntxxijHTt2KDg4uMy2CQBwL2akAAA12vjx4zVs2DC1b99eUVFRmj9/vg4fPqxRo0ZJunDJ3dGjR7V48WLnOjt27JB04YYSP//8s3bs2CFvb2+1aNFCkvTiiy+qY8eOatq0qXJycvTGG29ox44deuuttyq8fwCA8lGqIHXgwAGFh4eXdVsAACixshqLYmNjdfz4cU2bNk0ZGRmKiIjQ6tWrFRYWJunCA3gPHz7ssk7btm2d/05LS9OSJUsUFhamgwcPSpJOnjypxx9/XJmZmbLb7Wrbtq02btyo22+//ZrbCwCoHGzGGGN1JQ8PD3Xp0kVxcXEaPHhwlX/AYE5Ojux2u7KzsxUQEODu5lQbxT1gsDLioYeAe1zL+be6jUWXY2wqH4xNAK6mpOffUn1HaufOnWrbtq0mTJggh8OhJ554Qlu3bi11YwEAsIqxCADgTqUKUhEREZo9e7aOHj2qhQsXKjMzU3fccYdatmyp2bNn6+effy7rdgIA4IKxCADgTtd01z5PT0/de++9ev/99zVz5kz98MMPmjhxoq6//noNHz5cGRkZZdVOAACKxFgEAHCHawpS27Zt0+jRoxUcHKzZs2dr4sSJ+uGHH/Svf/1LR48e1cCBA8uqnQAAFImxCADgDqW6a9/s2bO1cOFC7du3T/369dPixYvVr18/1ap1IZeFh4frnXfeUfPmzcu0sQAAXMRYBABwp1IFqXnz5umRRx7Rww8/LIfDUWSdxo0ba8GCBdfUOAAAisNYBABwp1IFqZSUFDVu3Nj5qd9FxhgdOXJEjRs3lre3t0aMGFEmjQQA4HKMRQAAdyrVd6RuvPFGHTt2rFD5L7/8woN6AQAVgrEIAOBOpQpSxT3D9/Tp09XugYgAgMqJsQgA4E6WLu0bP368JMlms+mFF15Q7dq1ne/l5+frq6++Ups2bcq0gQAAXIqxCABQGVgKUunp6ZIufAr47bffytvb2/met7e3WrdurYkTJ5ZtCwEAuARjEQCgMrAUpNatWydJevjhh/X6668rICCgXBoFAEBxGIsAAJVBqe7at3DhwrJuBwAAljAWAQDcqcRBatCgQUpOTlZAQIAGDRp0xborV6685oYBAHA5xiIAQGVR4iBlt9tls9mc/wYAoKIxFgEAKosSB6lLL6HgcgoAgDswFgEAKotSPUfq7NmzOnPmjPP1oUOHlJSUpLVr15ZZwwAAuBLGIgCAO5UqSA0cOFCLFy+WJJ08eVK33367Zs2apYEDB2revHll2kAAAIrCWAQAcKdSBant27erc+fOkqT//d//lcPh0KFDh7R48WK98cYbZdpAAACKwlgEAHCnUgWpM2fOyN/fX5K0du1aDRo0SLVq1VLHjh116NChMm0gAABFYSwCALhTqYLUTTfdpH/84x86cuSI1qxZo5iYGElSVlYWD0YEAFQIxiIAgDuVKki98MILmjhxopo0aaIOHTooKipK0oVPBNu2bVumDQQAoCiMRQAAdyrx7c8vNXjwYN1xxx3KyMhQ69atneU9evTQvffeW2aNAwCgOIxFAAB3KlWQkiSHwyGHw+FSdvvtt19zgwAAKCnGIgCAu5QqSP3666+aMWOGPv/8c2VlZamgoMDl/f3795dJ4wAAKA5jEQDAnUoVpB599FFt2LBBw4YNU3BwsGw2W1m3CwCAK2IsAgC4U6mC1CeffKKPP/5YnTp1Kuv2AABQIoxFAAB3KtVd++rVq6f69euXdVsAACgxxiIAgDuVKkj9+c9/1gsvvKAzZ86UdXsAACgRxiIAgDuV6tK+WbNm6YcfflBQUJCaNGkiLy8vl/e3b99eJo0DAKA4jEUAAHcqVZC65557yrgZAABYw1gEAHCnUgWpKVOmlHU7AACwhLEIAOBOpfqOlCSdPHlSf/3rX5WQkKBffvlF0oXLKI4ePVpmjQMA4EoYiwAA7lKqGalvvvlGPXv2lN1u18GDB/XYY4+pfv36WrVqlQ4dOqTFixeXdTsBAHDBWAQAcKdSzUiNHz9eI0eO1HfffSdfX19ned++fbVx48YyaxwAAMVhLAIAuFOpgtTXX3+tJ554olD5ddddp8zMzGtuFAAAV8NYBABwp1IFKV9fX+Xk5BQq37dvnxo2bHjNjQIA4GoYiwAA7lSqIDVw4EBNmzZN586dkyTZbDYdPnxYkyZN0n333VemDQQAoCiMRQAAdyrVzSZeffVV9evXT40aNdLZs2fVtWtXZWZmKioqSi+//HJZtxFVQJNJH7u7CQBqGMYiXA1jE4DyVKogFRAQoM2bN2vdunVKS0tTQUGB2rVrp549e5Z1+wAAKBJjEQDAnSwHqYKCAiUnJ2vlypU6ePCgbDabwsPD5XA4ZIyRzWYrj3YCAODEWAQAcDdL35Eyxujuu+/Wo48+qqNHj6pVq1Zq2bKlDh06pJEjR+ree+8tdUMSExNls9kUHx/vsr+pU6cqJCREfn5+6tatm3bv3u2yXm5ursaOHavAwEDVqVNHd999t3788cdStwMAULmV51gEAEBJWQpSycnJ2rhxoz7//HOlp6dr6dKlWrZsmXbu3KnPPvtM//rXv0r1AMSvv/5a8+fP16233upS/sorr2j27Nl688039fXXX8vhcKhXr146deqUs058fLxWrVqlZcuWafPmzTp9+rQGDBig/Px8y+0AAFR+5TUWAQBghaUgtXTpUj333HPq3r17offuvPNOTZo0SX//+98tNeD06dMaOnSo/vKXv6hevXrOcmOMkpKSNHnyZA0aNEgRERFatGiRzpw5oyVLlkiSsrOztWDBAs2aNUs9e/ZU27Zt9d577+nbb7/VZ599ZqkdAICqoTzGIgAArLIUpL755hv16dOn2Pf79u2rnTt3WmrAmDFj1L9//0JfDj5w4IAyMzMVExPjLPPx8VHXrl2VmpoqSUpLS9O5c+dc6oSEhCgiIsJZBwBQvZTHWAQAgFWWbjbxyy+/KCgoqNj3g4KCdOLEiRJvb9myZdq+fbu+/vrrQu9dfCr95fsLCgrSoUOHnHW8vb1dZrIu1rnSU+1zc3OVm5vrfF3UAx0BAJVTWY9FAACUhqUZqfz8fHl6Fp+9PDw8dP78+RJt68iRIxo3bpzee+89+fr6Flvv8jsvleRuTFerk5iYKLvd7lxCQ0NL1GYAgPuV5VgEAEBpWZqRMsZo5MiR8vHxKfL9S2d5riYtLU1ZWVmKjIx0luXn52vjxo168803tW/fPkkXZp2Cg4OddbKyspyfRDocDuXl5enEiRMus1JZWVmKjo4udt8JCQkaP36883VOTg5hCgCqiLIciwAAKC1LQWrEiBFXrTN8+PASbatHjx769ttvXcoefvhhNW/eXM8++6xuuOEGORwOpaSkqG3btpKkvLw8bdiwQTNnzpQkRUZGysvLSykpKRoyZIgkKSMjQ7t27dIrr7xS7L59fHyKHYABAJVbWY5FAACUlqUgtXDhwjLbsb+/vyIiIlzK6tSpowYNGjjL4+PjNX36dDVt2lRNmzbV9OnTVbt2bT344IOSJLvdrri4OE2YMEENGjRQ/fr1NXHiRLVq1Yon28OSJpM+LlR2cEZ/N7QEwNWU5VgEVGaMTUDlZilIVbRnnnlGZ8+e1ejRo3XixAl16NBBa9eulb+/v7POa6+9Jk9PTw0ZMkRnz55Vjx49lJycLA8PDze2HAAAAEB1ZjPGGHc3wt1ycnJkt9uVnZ2tgIAAdzenSirqU7Oqjk/9gPLH+bd4HJtrx9gEoDRKev61dNc+AAAAAABBCgAAAAAsI0gBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQBqvLlz5yo8PFy+vr6KjIzUpk2biq2bkZGhBx98UM2aNVOtWrUUHx9fZL0VK1aoRYsW8vHxUYsWLbRq1apyaj0AwB0IUgCAGm358uWKj4/X5MmTlZ6ers6dO6tv3746fPhwkfVzc3PVsGFDTZ48Wa1bty6yzpYtWxQbG6thw4Zp586dGjZsmIYMGaKvvvqqPLsCAKhABCkAQI02e/ZsxcXF6dFHH9Utt9yipKQkhYaGat68eUXWb9KkiV5//XUNHz5cdru9yDpJSUnq1auXEhIS1Lx5cyUkJKhHjx5KSkoqx54AACoSQQoAUGPl5eUpLS1NMTExLuUxMTFKTU0t9Xa3bNlSaJu9e/e+pm0CACoXT3c3AAAAdzl27Jjy8/MVFBTkUh4UFKTMzMxSbzczM9PyNnNzc5Wbm+t8nZOTU+r9AwDKHzNSAIAaz2azubw2xhQqK+9tJiYmym63O5fQ0NBr2j8AoHwRpAAANVZgYKA8PDwKzRRlZWUVmlGywuFwWN5mQkKCsrOzncuRI0dKvX8AQPkjSAEAaixvb29FRkYqJSXFpTwlJUXR0dGl3m5UVFShba5du/aK2/Tx8VFAQIDLAgCovPiOFACgRhs/fryGDRum9u3bKyoqSvPnz9fhw4c1atQoSRdmio4eParFixc719mxY4ck6fTp0/r555+1Y8cOeXt7q0WLFpKkcePGqUuXLpo5c6YGDhyoDz/8UJ999pk2b95c4f0DAJQPghQAoEaLjY3V8ePHNW3aNGVkZCgiIkKrV69WWFiYpAsP4L38mVJt27Z1/jstLU1LlixRWFiYDh48KEmKjo7WsmXL9Kc//UnPP/+8brzxRi1fvlwdOnSosH4BAMoXQQoAUOONHj1ao0ePLvK95OTkQmXGmKtuc/DgwRo8ePC1Ng0AUEnxHSkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWOTp7gag6mky6WN3NwEAACfGJQDuwIwUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFjk6e4GAJVVk0kfF1l+cEb/Cm4JAAAXFDU2MS4B7sGMFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEVuDVKJiYm67bbb5O/vr0aNGumee+7Rvn37XOoYYzR16lSFhITIz89P3bp10+7du13q5ObmauzYsQoMDFSdOnV0991368cff6zIrgAAAACoQdwapDZs2KAxY8boyy+/VEpKis6fP6+YmBj9+uuvzjqvvPKKZs+erTfffFNff/21HA6HevXqpVOnTjnrxMfHa9WqVVq2bJk2b96s06dPa8CAAcrPz3dHtwAAAABUc57u3Pmnn37q8nrhwoVq1KiR0tLS1KVLFxljlJSUpMmTJ2vQoEGSpEWLFikoKEhLlizRE088oezsbC1YsEB/+9vf1LNnT0nSe++9p9DQUH322Wfq3bt3hfcLAAAAQPVWqb4jlZ2dLUmqX7++JOnAgQPKzMxUTEyMs46Pj4+6du2q1NRUSVJaWprOnTvnUickJEQRERHOOgAAAABQltw6I3UpY4zGjx+vO+64QxEREZKkzMxMSVJQUJBL3aCgIB06dMhZx9vbW/Xq1StU5+L6l8vNzVVubq7zdU5OTpn1AwAAAED1V2lmpJ588kl98803Wrp0aaH3bDaby2tjTKGyy12pTmJioux2u3MJDQ0tfcMBAAAA1DiVIkiNHTtWH330kdatW6frr7/eWe5wOCSp0MxSVlaWc5bK4XAoLy9PJ06cKLbO5RISEpSdne1cjhw5UpbdAQAAAFDNuTVIGWP05JNPauXKlfrXv/6l8PBwl/fDw8PlcDiUkpLiLMvLy9OGDRsUHR0tSYqMjJSXl5dLnYyMDO3atctZ53I+Pj4KCAhwWQAAAACgpNz6HakxY8ZoyZIl+vDDD+Xv7++cebLb7fLz85PNZlN8fLymT5+upk2bqmnTppo+fbpq166tBx980Fk3Li5OEyZMUIMGDVS/fn1NnDhRrVq1ct7FDwAAAADKkluD1Lx58yRJ3bp1cylfuHChRo4cKUl65plndPbsWY0ePVonTpxQhw4dtHbtWvn7+zvrv/baa/L09NSQIUN09uxZ9ejRQ8nJyfLw8KiorgAAAACoQdwapIwxV61js9k0depUTZ06tdg6vr6+mjNnjubMmVOGrQMAAACAolWKm00AAOBOc+fOVXh4uHx9fRUZGalNmzZdsf6GDRsUGRkpX19f3XDDDXr77bdd3k9OTpbNZiu0/Pbbb+XZDQBABSJIAQBqtOXLlys+Pl6TJ09Wenq6OnfurL59++rw4cNF1j9w4ID69eunzp07Kz09Xc8995yeeuoprVixwqVeQECAMjIyXBZfX9+K6BIAoAJUmgfyAgDgDrNnz1ZcXJweffRRSVJSUpLWrFmjefPmKTExsVD9t99+W40bN1ZSUpIk6ZZbbtG2bdv06quv6r777nPWs9lszsd4AACqH2akAAA1Vl5entLS0hQTE+NSHhMTo9TU1CLX2bJlS6H6vXv31rZt23Tu3Dln2enTpxUWFqbrr79eAwYMUHp6+hXbkpubq5ycHJcFAFB5EaQAADXWsWPHlJ+fX+gB7kFBQYUeBn9RZmZmkfXPnz+vY8eOSZKaN2+u5ORkffTRR1q6dKl8fX3VqVMnfffdd8W2JTExUXa73bmEhoZeY+8AAOWJIAUAqPFsNpvLa2NMobKr1b+0vGPHjnrooYfUunVrde7cWe+//75uvvnmK95dNiEhQdnZ2c7lyJEjpe0OAKAC8B0pAECNFRgYKA8Pj0KzT1lZWYVmnS5yOBxF1vf09FSDBg2KXKdWrVq67bbbrjgj5ePjIx8fH4s9AAC4CzNSAIAay9vbW5GRkUpJSXEpT0lJUXR0dJHrREVFFaq/du1atW/fXl5eXkWuY4zRjh07FBwcXDYNBwC4HUEKAFCjjR8/Xn/961/17rvvau/evXr66ad1+PBhjRo1StKFS+6GDx/urD9q1CgdOnRI48eP1969e/Xuu+9qwYIFmjhxorPOiy++qDVr1mj//v3asWOH4uLitGPHDuc2AQBVH5f2AQBqtNjYWB0/flzTpk1TRkaGIiIitHr1aoWFhUmSMjIyXJ4pFR4ertWrV+vpp5/WW2+9pZCQEL3xxhsutz4/efKkHn/8cWVmZsput6tt27bauHGjbr/99grvHwCgfNjMxW/I1mA5OTmy2+3Kzs5WQECAu5tT6TWZ9LG7m+BWB2f0d3cTgGqD82/xODYlx7jEuASUpZKef7m0DwAAAAAsIkgBAAAAgEV8RwrFqumXSgAAKh/GJgCVBTNSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYxHOkIInncgAAKh/GJgCVGTNSAAAAAGARM1KARUV9QnpwRn83tAQAgOJn7hibgPLFjBQAAAAAWESQAgAAAACLCFIAAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGARQQoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABY5OnuBqDiNZn0sbubAACAE+MSgKqIGSkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEbc/B8pAUbfuPTijvxtaAgDABYxNQPliRgoAAAAALCJIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYxO3PgQrG7WgBAJVJUeOSxNgEXA0zUgAAAABgETNSQDkp7hM+AADchbEJKDvMSAEAAACARcxIVWN86gQAqGwYmwBUF8xIAQAAAIBFBCkAAAAAsIggBQAAAAAWEaQAAAAAwCKCFAAAAABYRJACAAAAAIsIUgAAAABgEUEKAAAAACwiSAEAAACARZ7ubgDKBk+Kr9qK+vkdnNHfDS0BgLLD2FS1MTYBV0aQqoIYmAAAlQnjEoCaiCAF4JrxqSUAoLJhbEJ5I0gB1QCDBQCgsmFsQnVXbW42MXfuXIWHh8vX11eRkZHatGmTu5sEAKgirI4hGzZsUGRkpHx9fXXDDTfo7bffLlRnxYoVatGihXx8fNSiRQutWrWqvJoPAHCDajEjtXz5csXHx2vu3Lnq1KmT3nnnHfXt21d79uxR48aN3d08oFSu9TsHxa1fHT8NtHKsqmP/cW2sjiEHDhxQv3799Nhjj+m9997TF198odGjR6thw4a67777JElbtmxRbGys/vznP+vee+/VqlWrNGTIEG3evFkdOnSo6C4CZaY8xqbqel4u6bGqrv2vCWzGGOPuRlyrDh06qF27dpo3b56z7JZbbtE999yjxMTEq66fk5Mju92u7OxsBQQElGdTywRf6kVZu9aT+LUOjOWxfnEYsCqXynD+tTqGPPvss/roo4+0d+9eZ9moUaO0c+dObdmyRZIUGxurnJwcffLJJ846ffr0Ub169bR06dIStasyHJuSYlxCeagpYxPjUuVT0vNvlZ+RysvLU1pamiZNmuRSHhMTo9TUVDe1yrqa9AkNKp/y+COoJs2I1STV7VxVmjFky5YtiomJcSnr3bu3FixYoHPnzsnLy0tbtmzR008/XahOUlJSmba/vFW3nzeqlooam/idrvrc9XOt8kHq2LFjys/PV1BQkEt5UFCQMjMzi1wnNzdXubm5ztfZ2dmSLqTP0oqYsqZQ2a4Xe5d4/YLcM4XKimtPUXWBqqKo3+uifqcbP/1BkesX9f/Kyv+JorZr5f9qTWflXFUSF9d118URpRlDMjMzi6x//vx5HTt2TMHBwcXWKW6bUtUemxiXUJVZ+XvLyhhS0v8XVsY7FM1dY1OVD1IX2Ww2l9fGmEJlFyUmJurFF18sVB4aGlqmbbInuXd9oDKqjP8v+L92bcri+J06dUp2u/3aN1RKVsaQ4upfXm51m4xNgHtU1v8T/F+7NhUxNlX5IBUYGCgPD49Cn/JlZWUV+jTwooSEBI0fP975uqCgQL/88osaNGhwxUGupHJychQaGqojR45U+uvaqxqObfnguJYfju2VGWN06tQphYSEuGX/pRlDHA5HkfU9PT3VoEGDK9YpbptS+Y9NFaGm/r7XxH7XxD5L9Lum9LukY1OVD1Le3t6KjIxUSkqK7r33Xmd5SkqKBg4cWOQ6Pj4+8vHxcSn73e9+V+ZtCwgIqBG/bO7AsS0fHNfyw7EtnjtnokozhkRFRen//u//XMrWrl2r9u3by8vLy1knJSXF5XtSa9euVXR0dLFtqaixqSLU1N/3mtjvmthniX7XBCUZm6p8kJKk8ePHa9iwYWrfvr2ioqI0f/58HT58WKNGjXJ30wAAldzVxpCEhAQdPXpUixcvlnThDn1vvvmmxo8fr8cee0xbtmzRggULXO7GN27cOHXp0kUzZ87UwIED9eGHH+qzzz7T5s2b3dJHAEDZqxZBKjY2VsePH9e0adOUkZGhiIgIrV69WmFhYe5uGgCgkrvaGJKRkaHDhw8764eHh2v16tV6+umn9dZbbykkJERvvPGG8xlSkhQdHa1ly5bpT3/6k55//nndeOONWr58Oc+QAoBqpFoEKUkaPXq0Ro8e7e5mSLpwecaUKVMKXaKBa8exLR8c1/LDsa0arjSGJCcnFyrr2rWrtm/ffsVtDh48WIMHDy6L5lUZNfX3vSb2uyb2WaLfNa3fV1MtHsgLAAAAABWplrsbAAAAAABVDUEKAAAAACwiSAEAAACARQQpAAAAALCIIFUCJ06c0LBhw2S322W32zVs2DCdPHnyiusYYzR16lSFhITIz89P3bp10+7du13q5ObmauzYsQoMDFSdOnV0991368cff3S+f/DgQcXFxSk8PFx+fn668cYbNWXKFOXl5ZVHNyvE3LlzFR4eLl9fX0VGRmrTpk1XrL9hwwZFRkbK19dXN9xwg95+++1CdVasWKEWLVrIx8dHLVq00KpVq655v1WNO45rYmKibrvtNvn7+6tRo0a65557tG/fvjLtV2Xgrt/ZixITE2Wz2RQfH3+tXQEqVJMmTWSz2VyWSZMmubtZZa66jy+Xmzp1aqGfq8PhcHezytzGjRt11113KSQkRDabTf/4xz9c3i/J33lVzdX6PHLkyEI/+44dO7qnsZWFwVX16dPHREREmNTUVJOammoiIiLMgAEDrrjOjBkzjL+/v1mxYoX59ttvTWxsrAkODjY5OTnOOqNGjTLXXXedSUlJMdu3bzfdu3c3rVu3NufPnzfGGPPJJ5+YkSNHmjVr1pgffvjBfPjhh6ZRo0ZmwoQJ5drf8rJs2TLj5eVl/vKXv5g9e/aYcePGmTp16phDhw4VWX///v2mdu3aZty4cWbPnj3mL3/5i/Hy8jL/+7//66yTmppqPDw8zPTp083evXvN9OnTjaenp/nyyy9Lvd+qxl3HtXfv3mbhwoVm165dZseOHaZ///6mcePG5vTp0+Xe54rirmN70datW02TJk3MrbfeasaNG1de3QTKRVhYmJk2bZrJyMhwLqdOnXJ3s8pUdR9fijJlyhTTsmVLl59rVlaWu5tV5lavXm0mT55sVqxYYSSZVatWubxfkr/zqpqr9XnEiBGmT58+Lj/748ePu6exlQRB6ir27NljJLn8kbNlyxYjyfz73/8ucp2CggLjcDjMjBkznGW//fabsdvt5u233zbGGHPy5Enj5eVlli1b5qxz9OhRU6tWLfPpp58W255XXnnFhIeHX2u33OL22283o0aNcilr3ry5mTRpUpH1n3nmGdO8eXOXsieeeMJ07NjR+XrIkCGmT58+LnV69+5tHnjggVLvt6px13G9XFZWlpFkNmzYYLULlZY7j+2pU6dM06ZNTUpKiunatStBClVOWFiYee2119zdjHJV3ceXokyZMsW0bt3a3c2oUJeHipL8nVfVFRekBg4c6Jb2VFZc2ncVW7Zskd1ud3kafceOHWW325WamlrkOgcOHFBmZqZiYmKcZT4+PuratatznbS0NJ07d86lTkhIiCIiIordriRlZ2erfv3619qtCpeXl6e0tDSX/kpSTExMsf3dsmVLofq9e/fWtm3bdO7cuSvWubjN0uy3KnHXcS1Kdna2JFXJ38+iuPvYjhkzRv3791fPnj2vtSuA28ycOVMNGjRQmzZt9PLLL1fpS9MvV93Hlyv57rvvFBISovDwcD3wwAPav3+/u5tUoUryd151tX79ejVq1Eg333yzHnvsMWVlZbm7SW7l6e4GVHaZmZlq1KhRofJGjRopMzOz2HUkKSgoyKU8KChIhw4dctbx9vZWvXr1CtUpbrs//PCD5syZo1mzZlnuh7sdO3ZM+fn5RR6TKx3HouqfP39ex44dU3BwcLF1Lm6zNPutStx1XC9njNH48eN1xx13KCIi4hp6VHm489guW7ZM27dv19dff11GvQEq3rhx49SuXTvVq1dPW7duVUJCgg4cOKC//vWv7m5amaju40txOnTooMWLF+vmm2/Wf//7X7300kuKjo7W7t271aBBA3c3r0KU5O+86qhv3766//77FRYWpgMHDuj555/XnXfeqbS0NPn4+Li7eW5RY4PU1KlT9eKLL16xzsU/Ymw2W6H3jDFFll/q8vdLsk5xdX766Sf16dNH999/vx599NErbqMys3pMiqp/eXlJtlman0VV4q7jetGTTz6pb775Rps3b7bU7qqgoo/tkSNHNG7cOK1du1a+vr7X1HagrJV07Gzfvr2efvppZ9mtt96qevXqafDgwc5Zquqiuo8vl+vbt6/z361atVJUVJRuvPFGLVq0SOPHj3djyypeTfvZx8bGOv8dERGh9u3bKywsTB9//LEGDRrkxpa5T40NUk8++aQeeOCBK9Zp0qSJvvnmG/33v/8t9N7PP/9c6JOIiy7evSYzM1PBwcHO8qysLOc6DodDeXl5OnHihMusVFZWlqKjo12299NPP6l79+6KiorS/PnzS9bBSiYwMFAeHh6FPqW79JhczuFwFFnf09PTOQgXV+fiNkuz36rEXcf1UmPHjtVHH32kjRs36vrrr7+W7lQq7jq2aWlpysrKUmRkpPP9/Px8bdy4UW+++aZyc3Pl4eFxzf0DSqOkY2dRLt7d6/vvv68WQaq6jy8lVadOHbVq1Urfffedu5tSYUryd15NEBwcrLCwsBr1s79cjf2OVGBgoJo3b37FxdfXV1FRUcrOztbWrVud63711VfKzs4uFHguCg8Pl8PhUEpKirMsLy9PGzZscK4TGRkpLy8vlzoZGRnatWuXy3aPHj2qbt26qV27dlq4cKFq1aqaPzJvb29FRka69FeSUlJSij2OUVFRheqvXbtW7du3l5eX1xXrXNxmafZblbjruEoXPnl78skntXLlSv3rX/9SeHh4WXSp0nDXse3Ro4e+/fZb7dixw7m0b99eQ4cO1Y4dOwhRcKuSjp1FSU9PlySXPzyrsuo+vpRUbm6u9u7dW21+riVRkr/zaoLjx4/ryJEjNepnX0iF396iCurTp4+59dZbzZYtW8yWLVtMq1atCt3+vFmzZmblypXO1zNmzDB2u92sXLnSfPvtt+b3v/99kbc/v/76681nn31mtm/fbu68806X258fPXrU3HTTTebOO+80P/74o8vtJquii7eJXbBggdmzZ4+Jj483derUMQcPHjTGGDNp0iQzbNgwZ/2Lt5J++umnzZ49e8yCBQsK3Ur6iy++MB4eHmbGjBlm7969ZsaMGcXe/ry4/VZ17jquf/jDH4zdbjfr1693+d08c+ZMxXW+nLnr2F6Ou/ahqklNTTWzZ8826enpZv/+/Wb58uUmJCTE3H333e5uWpmq7uNLUSZMmGDWr19v9u/fb7788kszYMAA4+/vX+36fOrUKZOenm7S09ONJOfv88Vb25fk77yq5kp9PnXqlJkwYYJJTU01Bw4cMOvWrTNRUVHmuuuuq9J9vlYEqRI4fvy4GTp0qPH39zf+/v5m6NCh5sSJEy51JJmFCxc6XxcUFJgpU6YYh8NhfHx8TJcuXcy3337rss7Zs2fNk08+aerXr2/8/PzMgAEDzOHDh53vL1y40Egqcqmq3nrrLRMWFma8vb1Nu3btXG6VPWLECNO1a1eX+uvXrzdt27Y13t7epkmTJmbevHmFtvnBBx+YZs2aGS8vL9O8eXOzYsUKS/utDtxxXIv73bz0/0F14K7f2UsRpFDVpKWlmQ4dOhi73W58fX1Ns2bNzJQpU8yvv/7q7qaVueo+vlzu4vOSvLy8TEhIiBk0aJDZvXu3u5tV5tatW1fkGDdixAhjTMn+zqtqrtTnM2fOmJiYGNOwYUPj5eVlGjdubEaMGOHyd2tNZDPm//8mNAAAAACgRKrmF24AAAAAwI0IUgAAAABgEUEKAAAAACwiSAEAAACARQQpAAAAALCIIAUAAAAAFhGkAAAAAMAighQAHTx4UDabTTt27KgU27makSNHymazyWaz6R//+EeZbnv9+vXObd9zzz1lum0AqAnK8xx9NZzDUZEIUqgwl55YL12+//57dzetUpo6darzGHl6eiowMFBdunRRUlKScnNzy3RfoaGhysjIUERERInXGTlyZKFBqjTbKa0+ffooIyNDffv2dZYVN2gX1dbiREdHKyMjQ0OGDCmjlgKAe6SmpsrDw0N9+vQp8v28vDy98sorat26tWrXrq3AwEB16tRJCxcu1Llz5yQVP3YXt82LijtH22w2ffnlly51c3Nz1aBBA9lsNq1fv16S1LFjR/3hD39wqTdv3jzZbDYtWLDApTwuLk7R0dGSOIejYhGkUKEunlgvXcLDwwvVy8vLc0PrKp+WLVsqIyNDhw8f1rp163T//fcrMTFR0dHROnXqVJntx8PDQw6HQ56enpViOyXh4+Mjh8MhHx+fMt2ut7e3HA6H/Pz8ynS7AFDR3n33XY0dO1abN2/W4cOHXd7Ly8tT7969NWPGDD3++ONKTU3V1q1bNWbMGM2ZM0e7d+921i1q7F66dOkV913cOTo0NFQLFy50KVu1apXq1q3rUta9e3etW7fOpWz9+vUKDQ0tsrx79+6SOIejYhGkUKEunlgvXTw8PNStWzc9+eSTGj9+vAIDA9WrVy9J0p49e9SvXz/VrVtXQUFBGjZsmI4dO+bc3q+//qrhw4erbt26Cg4O1qxZs9StWzfFx8c76xQ1S/G73/1OycnJztdHjx5VbGys6tWrpwYNGmjgwIE6ePCg8/2LMxqvvvqqgoOD1aBBA40ZM8b5iZ104RO1Z555RqGhofLx8VHTpk21YMECGWN000036dVXX3Vpw65du1SrVi398MMPxR4vT09PORwOhYSEqFWrVho7dqw2bNigXbt2aebMmc56eXl5euaZZ3TdddepTp066tChg/NTvezsbPn5+enTTz912fbKlStVp04dnT59utAlefn5+YqLi1N4eLj8/PzUrFkzvf766851p06dqkWLFunDDz90fsK4fv36Ii/t27Bhg26//Xb5+PgoODhYkyZN0vnz553vd+vWTU899ZSeeeYZ1a9fXw6HQ1OnTi32mFyri228fOnWrVu57RMAKtqvv/6q999/X3/4wx80YMAAlzFPkpKSkrRx40Z9/vnnGjNmjNq0aaMbbrhBDz74oL766is1bdrUWbeosbtevXqlateIESO0bNkynT171ln27rvvasSIES71unfvrn379ikjI8NZtmHDBiUkJDjHN0k6cuSI9u/f7wxSQEUiSKHSWLRokTw9PfXFF1/onXfeUUZGhrp27ao2bdpo27Zt+vTTT/Xf//7XZbr+j3/8o9atW6dVq1Zp7dq1Wr9+vdLS0izt98yZM+revbvq1q2rjRs3avPmzapbt6769OnjMjO2bt06/fDDD1q3bp0WLVqk5ORkl4Fp+PDhWrZsmd544w3t3btXb7/9turWrSubzaZHHnmk0Cdw7777rjp37qwbb7zRUnubN2+uvn37auXKlc6yhx9+WF988YWWLVumb775Rvfff7/69Omj7777Tna7Xf3799ff//53l+0sWbJEAwcOLPQpoCQVFBTo+uuv1/vvv689e/bohRde0HPPPaf3339fkjRx4kQNGTLE5VPKi5dVXOro0aPq16+fbrvtNu3cuVPz5s3TggUL9NJLL7nUW7RokerUqaOvvvpKr7zyiqZNm6aUlBRLx6WkLl5+eHFJT09XgwYN1KVLl3LZHwC4w/Lly9WsWTM1a9ZMDz30kBYuXChjjPP9v//97+rZs6fatm1baF0vLy/VqVOnXNoVGRmp8PBwrVixQtKFILRx40YNGzbMpV6nTp3k5eXlDE179uzR2bNn9cgjjygnJ0ffffedpAtjs7e3d5FjEFDuDFBBRowYYTw8PEydOnWcy+DBg40xxnTt2tW0adPGpf7zzz9vYmJiXMqOHDliJJl9+/aZU6dOGW9vb7Ns2TLn+8ePHzd+fn5m3LhxzjJJZtWqVS7bsdvtZuHChcYYYxYsWGCaNWtmCgoKnO/n5uYaPz8/s2bNGmfbw8LCzPnz55117r//fhMbG2uMMWbfvn1GkklJSSmy7z/99JPx8PAwX331lTHGmLy8PNOwYUOTnJxc7PGaMmWKad26dZHvPfvss8bPz88YY8z3339vbDabOXr0qEudHj16mISEBGOMMStXrjR169Y1v/76qzHGmOzsbOPr62s+/vhjY4wxBw4cMJJMenp6se0ZPXq0ue+++5yvR4wYYQYOHOhS5/LtPPfcc4WO7VtvvWXq1q1r8vPzjTEXfvZ33HGHy3Zuu+028+yzzxbblqL2bcyFn7Wvr6/L71idOnWMp6dnkfXPnj1rOnToYAYMGOBsz9X2AQBVQXR0tElKSjLGGHPu3DkTGBjoMkb5+fmZp5566qrbKWrsrlOnjpk2bdoV1ynuHL1q1SqTlJRkunfvbowx5sUXXzT33nuvOXHihJFk1q1b59KHxx9/3BhzYezo16+fMcaYPn36mPnz5xtjjHn44YdN586dS9wGoCyV/xcZgEt0795d8+bNc76+9BOv9u3bu9RNS0vTunXripwx+eGHH3T27Fnl5eUpKirKWV6/fn01a9bMUpvS0tL0/fffy9/f36X8t99+c7nsrmXLlvLw8HC+Dg4O1rfffitJ2rFjhzw8PNS1a9ci9xEcHKz+/fvr3Xff1e23365//vOf+u2333T//fdbautFxhjZbDZJ0vbt22WM0c033+xS5+KXdyWpf//+8vT01EcffaQHHnhAK1askL+/v2JiYordx9tvv62//vWvOnTokPNYt2nTxlI79+7dq6ioKGdbpQufMp4+fVo//vijGjduLEm69dZbXdYLDg5WVlaWpX1d9Nprr6lnz54uZc8++6zy8/ML1Y2Li9OpU6eUkpKiWrWYoAdQPezbt09bt251Xrng6emp2NhYvfvuu87z46XjyNVcPnZLF8bb0nrooYc0adIk7d+/X8nJyXrjjTeK3e8HH3wg6cL3oC5egt21a1etX79ejz32mNavX6/hw4eXui3AtSBIoULVqVNHN910U7HvXaqgoEB33XWXy3eBLgoODnZO61+NzWZzuZxBkst3mwoKChQZGVno0jdJatiwofPfXl5ehbZbUFAgSSX6Uuujjz6qYcOG6bXXXtPChQsVGxur2rVrl6gPl9u7d6/zJh0FBQXy8PBQWlqaS9CT5Ayh3t7eGjx4sJYsWaIHHnhAS5YsUWxsbLE3hXj//ff19NNPa9asWYqKipK/v7/+53/+R1999ZWldhY1UF/8WVxafqVja5XD4Sj0O+bv76+TJ0+6lL300kv69NNPtXXr1kIhGgCqsgULFuj8+fO67rrrnGXGGHl5eenEiROqV6+ebr75Zu3du7dE27vS2F0aDRo00IABAxQXF6fffvtNffv2LfIGSt27d9fLL7+so0ePasOGDZo4caKkC0Fqzpw5Onz4sA4cOMD3o+A2BClUWu3atdOKFSvUpEmTIv/gv+mmm+Tl5aUvv/zSObNx4sQJ/ec//3GZGWrYsKHLl1W/++47nTlzxmU/y5cvV6NGjRQQEFCqtrZq1UoFBQXasGFDodmQi/r166c6depo3rx5+uSTT7Rx48ZS7evf//63Pv30UyUkJEiS2rZtq/z8fGVlZalz587Frjd06FDFxMRo9+7dWrdunf785z8XW3fTpk2Kjo7W6NGjnWWX3xTD29u7yFmeS7Vo0UIrVqxwCVSpqany9/d3GeAr2ooVKzRt2jR98sknlr+jBgCV2fnz57V48WLNmjWr0FUH9913n/7+97/rySef1IMPPqjnnntO6enphb4ndf78eeXm5pbb96Qk6ZFHHlG/fv307LPPFvoQ8KLo6Gj5+Pho7ty5Onv2rCIjIyVduIIlOztb77zzjnx9fdWxY8dyaydwJVzLgkprzJgx+uWXX/T73/9eW7du1f79+7V27Vo98sgjys/PV926dRUXF6c//vGP+vzzz7Vr1y6NHDmy0CVad955p958801t375d27Zt06hRo1xmQIYOHarAwEANHDhQmzZt0oEDB7RhwwaNGzdOP/74Y4na2qRJE40YMUKPPPKI/vGPf+jAgQNav3698+YM0oVbg48cOVIJCQm66aabXC5JLM758+eVmZmpn376Sd9++63mzJnjvAHHH//4R0nSzTffrKFDh2r48OFauXKlDhw4oK+//lozZ87U6tWrndvq2rWrgoKCNHToUDVp0uSKA89NN92kbdu2ac2aNfrPf/6j559/Xl9//XWhPn/zzTfat2+fjh075jLLd9Ho0aN15MgRjR07Vv/+97/14YcfasqUKRo/frzbLqXbtWuXhg8frmeffVYtW7ZUZmamMjMz9csvv7ilPQBQlv75z3/qxIkTiouLU0REhMsyePBg5zOY4uPj1alTJ/Xo0UNvvfWWdu7cqf379+v9999Xhw4dXK76yM3NdZ4rLy6X3kG3NPr06aOff/5Z06ZNK7aOn5+fOnTooDlz5qhTp07OwOXl5aWoqCjNmTPHGbYAdyBIodIKCQnRF198ofz8fPXu3VsREREaN26c7Ha784/w//mf/1GXLl109913q2fPnrrjjjucn1hdNGvWLIWGhqpLly568MEHNXHiRJdL6mrXrq2NGzeqcePGGjRokG655RY98sgjOnv2rKUZqnnz5mnw4MEaPXq0mjdvrscee0y//vqrS524uDjl5eXpkUceKdE2d+/ereDgYDVu3FjdunXT+++/r4SEBG3atMnlu2MLFy7U8OHDNWHCBDVr1kx33323vvrqK4WGhjrr2Gw2/f73v9fOnTs1dOjQK+531KhRGjRokGJjY9WhQwcdP37cZXZKkh577DE1a9ZM7du3V8OGDfXFF18U2s51112n1atXa+vWrWrdurVGjRqluLg4/elPfypR/8vDtm3bdObMGb300ksKDg52LoMGDXJbmwCgrCxYsEA9e/aU3W4v9N59992nHTt2aPv27fLx8VFKSoqeeeYZvfPOO+rYsaNuu+02vfHGG3rqqadcHqz+6aefupwvg4ODdccdd1xTO202mwIDA+Xt7X3Fet27d9epU6cKPaKia9euOnXqFJf1wa1s5vIvjwBVXLdu3dSmTRslJSW5uymFfPHFF+rWrZt+/PFHBQUFubs5VdbIkSN18uTJQs8Hq2r7AIDqqDKcPytDG1D9MSMFVIDc3Fx9//33ev755zVkyBBCVBn45z//qbp16+qf//xnmW734mxfUTcfAQCUTHmdo6+GczgqEjNSqHYq44xUcnKy4uLi1KZNG3300UduvdFCdZCVlaWcnBxJF+7gWJZfiD579qyOHj0q6cJdDx0OR5ltGwBqgvI8R18N53BUJIIUAAAAAFjEpX0AAAAAYBFBCgAAAAAsIkgBAAAAgEUEKQAAAACwiCAFAAAAABYRpAAAAADAIoIUAAAAAFhEkAIAAAAAiwhSAAAAAGDR/wdzUvT4moTwKQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_freq, ax_freq = plt.subplots(1, 2, figsize=(10, 5), dpi=100)\n",
    "\n",
    "outdf_plt.plot(x='time', y='fd', kind='hist', alpha=1, bins=60, density=True,\n",
    "               ax=ax_freq[0], xlabel='Frequency Deviation [Hz]', ylabel='Density',\n",
    "               legend=False)\n",
    "outdf_plt.plot(x='time', y='ACE', kind='hist', alpha=1, bins=60, density=True,\n",
    "               ax=ax_freq[1], xlabel='ACE [MW]', ylabel='Density',\n",
    "               legend=False)"
   ]
  },
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   "metadata": {},
   "source": [
    "## Settings to Improve Performance\n",
    "\n",
    "Long-term dynamic simulation can be memory-consuming, as time-series data is updated by default. To reduce the memory burden, we can configure the TDS with `save_every=0`, discarding all data immediately after each simulation step. As a trade-off, a separate output array is utilized to store the data, with a resolution matching the co-simulation time step.\n",
    "More details about ANDES settings can be found in the [ANDES Release notes - v1.7.0](https://docs.andes.app/en/latest/release-notes.html#v1-7-0-2022-05-22).\n",
    "\n",
    "The case has been tested with a complete 3600s duration. However, for demonstration purposes and to conserve CI resources, the simulated time is truncated.\n",
    "\n",
    "## Limitations\n",
    "\n",
    "1. Although the code is designed for generalization, the demo is implemented on the IEEE 39-bus case with generators set to synchronous machines, and its application to other cases is not fully tested.\n",
    "1. The load curve is synthetic, based on experience.\n",
    "1. Within each interval, generator setpoints are updated only once, without considering smooth action.\n",
    "1. In ANDES, certain dynamic parameters are adjusted to facilitate co-simulation, disregarding their actual physical implications.\n",
    "1. The used case comprises synchronous generators exclusively, necessitating further adaptation for the inclusion of renewable energy sources.\n",
    "\n",
    "## FAQ\n",
    "\n",
    "Q: Why ANDES TDS run into error?\n",
    "\n",
    "A: Most likely, the error is due to power flow not converging. Possible reasons include: 1) load is too heavy, 2) step change is too large, 3) some devices run into limits.\n",
    "\n",
    "Q: Why in AMS RTED, load and generation do not exactly match?\n",
    "\n",
    "A: The RTED is converted using ``dc2ac``, where the generation and bus voltage are adjusted using ACOPF."
   ]
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