Vector_autoreggresion_(VAR).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from scipy import stats\n",
    "from statsmodels.tsa.api import VAR\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tools.eval_measures import rmse, aicc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This implementetion follows the methodology of https://www.machinelearningplus.com/time-series/vector-autoregression-examples-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(294, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coal_price</th>\n",
       "      <th>crude_price</th>\n",
       "      <th>ngas_price</th>\n",
       "      <th>gold_price</th>\n",
       "      <th>iron_price</th>\n",
       "      <th>aud_usd_closed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</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>2019-09-01</th>\n",
       "      <td>65.95</td>\n",
       "      <td>62.33</td>\n",
       "      <td>2.58</td>\n",
       "      <td>1510.58</td>\n",
       "      <td>93.08</td>\n",
       "      <td>0.6750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>69.20</td>\n",
       "      <td>59.37</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1494.81</td>\n",
       "      <td>88.53</td>\n",
       "      <td>0.6894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>66.99</td>\n",
       "      <td>62.74</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1470.79</td>\n",
       "      <td>84.98</td>\n",
       "      <td>0.6765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>66.18</td>\n",
       "      <td>65.85</td>\n",
       "      <td>2.24</td>\n",
       "      <td>1479.13</td>\n",
       "      <td>92.65</td>\n",
       "      <td>0.7022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>69.66</td>\n",
       "      <td>63.60</td>\n",
       "      <td>2.02</td>\n",
       "      <td>1560.67</td>\n",
       "      <td>95.76</td>\n",
       "      <td>0.6692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coal_price  crude_price  ngas_price  gold_price  iron_price  \\\n",
       "Month                                                                     \n",
       "2019-09-01       65.95        62.33        2.58     1510.58       93.08   \n",
       "2019-10-01       69.20        59.37        2.33     1494.81       88.53   \n",
       "2019-11-01       66.99        62.74        2.65     1470.79       84.98   \n",
       "2019-12-01       66.18        65.85        2.24     1479.13       92.65   \n",
       "2020-01-01       69.66        63.60        2.02     1560.67       95.76   \n",
       "\n",
       "            aud_usd_closed  \n",
       "Month                       \n",
       "2019-09-01          0.6750  \n",
       "2019-10-01          0.6894  \n",
       "2019-11-01          0.6765  \n",
       "2019-12-01          0.7022  \n",
       "2020-01-01          0.6692  "
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load data\n",
    "df = pd.read_csv('final.csv', parse_dates=['Month'], index_col='Month')\n",
    "print(df.shape)  # (294, 6)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "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>coal_price_x</th>\n",
       "      <th>crude_price_x</th>\n",
       "      <th>ngas_price_x</th>\n",
       "      <th>gold_price_x</th>\n",
       "      <th>iron_price_x</th>\n",
       "      <th>aud_usd_closed_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>coal_price_y</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0514</td>\n",
       "      <td>0.0175</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crude_price_y</th>\n",
       "      <td>0.0075</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.5577</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ngas_price_y</th>\n",
       "      <td>0.1811</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0875</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.1501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gold_price_y</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.1853</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0176</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iron_price_y</th>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.0517</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aud_usd_closed_y</th>\n",
       "      <td>0.0272</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.4427</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  coal_price_x  crude_price_x  ngas_price_x  gold_price_x  \\\n",
       "coal_price_y            1.0000         0.0514        0.0175        0.0003   \n",
       "crude_price_y           0.0075         1.0000        0.5577        0.0153   \n",
       "ngas_price_y            0.1811         0.0013        1.0000        0.0875   \n",
       "gold_price_y            0.0000         0.0207        0.1853        1.0000   \n",
       "iron_price_y            0.0016         0.0096        0.0517        0.0015   \n",
       "aud_usd_closed_y        0.0272         0.0051        0.4427        0.0077   \n",
       "\n",
       "                  iron_price_x  aud_usd_closed_x  \n",
       "coal_price_y            0.0048            0.0000  \n",
       "crude_price_y           0.0000            0.0000  \n",
       "ngas_price_y            0.0049            0.1501  \n",
       "gold_price_y            0.0176            0.0019  \n",
       "iron_price_y            1.0000            0.0000  \n",
       "aud_usd_closed_y        0.0359            1.0000  "
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grangers casuality test\n",
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "maxlag=12\n",
    "test = 'ssr_chi2test'\n",
    "def grangers_causation_matrix(data, variables, test='ssr_chi2test', verbose=False):    \n",
    "    \n",
    "    df = pd.DataFrame(np.zeros((len(variables), len(variables))), columns=variables, index=variables)\n",
    "    for c in df.columns:\n",
    "        for r in df.index:\n",
    "            test_result = grangercausalitytests(data[[r, c]], maxlag=maxlag, verbose=False)\n",
    "            p_values = [round(test_result[i+1][0][test][1],4) for i in range(maxlag)]\n",
    "            if verbose: print(f'Y = {r}, X = {c}, P Values = {p_values}')\n",
    "            min_p_value = np.min(p_values)\n",
    "            df.loc[r, c] = min_p_value\n",
    "    df.columns = [var + '_x' for var in variables]\n",
    "    df.index = [var + '_y' for var in variables]\n",
    "    return df\n",
    "\n",
    "grangers_causation_matrix(df, variables = df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284, 6)\n",
      "(10, 6)\n"
     ]
    }
   ],
   "source": [
    "# foracasting months\n",
    "nobs = 60\n",
    "df_train, df_test = df[0:-nobs], df[-nobs:]\n",
    "\n",
    "# Check size\n",
    "print(df_train.shape)  # (224, 6)\n",
    "print(df_test.shape)  # (60, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "#build the adfuller test\n",
    "def ADF_test(series, signif=0.05, name='', verbose=False):\n",
    "    \"\"\"Perform ADFuller to test for Stationarity of given series and print report\"\"\"\n",
    "    r = adfuller(series, autolag='AIC')\n",
    "    output = {'test_statistic':round(r[0], 4), 'pvalue':round(r[1], 4), 'n_lags':round(r[2], 4), 'n_obs':r[3]}\n",
    "    p_value = output['pvalue'] \n",
    "    def adjust(val, length= 6): return str(val).ljust(length)\n",
    "\n",
    "    # Print Summary\n",
    "    print(f'    Augmented Dickey-Fuller Test on \"{name}\"', \"\\n   \", '-'*47)\n",
    "    print(f' Null Hypothesis: Data has unit root. Non-Stationary.')\n",
    "    print(f' Significance Level    = {signif}')\n",
    "    print(f' Test Statistic        = {output[\"test_statistic\"]}')\n",
    "    print(f' No. Lags Chosen       = {output[\"n_lags\"]}')\n",
    "\n",
    "    for key,val in r[4].items():\n",
    "        print(f' Critical value {adjust(key)} = {round(val, 3)}')\n",
    "\n",
    "    if p_value <= signif:\n",
    "        print(f\" => P-Value = {p_value}. Rejecting Null Hypothesis.\")\n",
    "        print(f\" => Series is Stationary.\")\n",
    "    else:\n",
    "        print(f\" => P-Value = {p_value}. Weak evidence to reject the Null Hypothesis.\")\n",
    "        print(f\" => Series is Non-Stationary.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"coal_price\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -1.2434\n",
      " No. Lags Chosen       = 8\n",
      " Critical value 1%     = -3.454\n",
      " Critical value 5%     = -2.872\n",
      " Critical value 10%    = -2.572\n",
      " => P-Value = 0.6546. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"crude_price\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -2.0903\n",
      " No. Lags Chosen       = 1\n",
      " Critical value 1%     = -3.454\n",
      " Critical value 5%     = -2.872\n",
      " Critical value 10%    = -2.572\n",
      " => P-Value = 0.2484. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"ngas_price\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -2.9243\n",
      " No. Lags Chosen       = 0\n",
      " Critical value 1%     = -3.454\n",
      " Critical value 5%     = -2.872\n",
      " Critical value 10%    = -2.572\n",
      " => P-Value = 0.0426. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gold_price\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -0.8254\n",
      " No. Lags Chosen       = 11\n",
      " Critical value 1%     = -3.455\n",
      " Critical value 5%     = -2.872\n",
      " Critical value 10%    = -2.572\n",
      " => P-Value = 0.8115. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"iron_price\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -1.5085\n",
      " No. Lags Chosen       = 2\n",
      " Critical value 1%     = -3.454\n",
      " Critical value 5%     = -2.872\n",
      " Critical value 10%    = -2.572\n",
      " => P-Value = 0.5293. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"aud_usd_closed\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -1.5875\n",
      " No. Lags Chosen       = 0\n",
      " Critical value 1%     = -3.454\n",
      " Critical value 5%     = -2.872\n",
      " Critical value 10%    = -2.572\n",
      " => P-Value = 0.4899. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ADF Test\n",
    "for name, column in df_train.iteritems():\n",
    "    ADF_test(column, name=column.name)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1st difference\n",
    "df_differenced = df_train.diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"coal_price\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -6.2433\n",
      " No. Lags Chosen       = 7\n",
      " Critical value 1%     = -3.454\n",
      " Critical value 5%     = -2.872\n",
      " Critical value 10%    = -2.572\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"crude_price\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -11.2415\n",
      " No. Lags Chosen       = 0\n",
      " Critical value 1%     = -3.454\n",
      " Critical value 5%     = -2.872\n",
      " Critical value 10%    = -2.572\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"ngas_price\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -5.7901\n",
      " No. Lags Chosen       = 12\n",
      " Critical value 1%     = -3.455\n",
      " Critical value 5%     = -2.872\n",
      " Critical value 10%    = -2.573\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"gold_price\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -14.1439\n",
      " No. Lags Chosen       = 0\n",
      " Critical value 1%     = -3.454\n",
      " Critical value 5%     = -2.872\n",
      " Critical value 10%    = -2.572\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"iron_price\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -11.4252\n",
      " No. Lags Chosen       = 1\n",
      " Critical value 1%     = -3.454\n",
      " Critical value 5%     = -2.872\n",
      " Critical value 10%    = -2.572\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"aud_usd_closed\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -16.3887\n",
      " No. Lags Chosen       = 0\n",
      " Critical value 1%     = -3.454\n",
      " Critical value 5%     = -2.872\n",
      " Critical value 10%    = -2.572\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ADF Test on each column of 1st Differences Dataframe\n",
    "for name, column in df_differenced.iteritems():\n",
    "    adfuller_test(column, name=column.name)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lag Order = 1\n",
      "AIC :  -11.984892786282739\n",
      "Lag Order = 2\n",
      "AIC :  -11.987511145018754\n",
      "Lag Order = 3\n",
      "AIC :  -11.914340940437963\n",
      "Lag Order = 4\n",
      "AIC :  -11.818476068905916\n",
      "Lag Order = 5\n",
      "AIC :  -11.742464386496415\n",
      "Lag Order = 6\n",
      "AIC :  -11.702576973767373\n",
      "Lag Order = 7\n",
      "AIC :  -11.632165929060474\n",
      "Lag Order = 8\n",
      "AIC :  -11.601731222149292\n",
      "Lag Order = 9\n",
      "AIC :  -11.50163384313884\n",
      "Lag Order = 10\n",
      "AIC :  -11.409354758515219\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "# AIC test \n",
    "model = VAR(df_differenced)\n",
    "for i in [1,2,3,4,5,6,7,8,9,10]:\n",
    "    result = model.fit(i)\n",
    "    print('Lag Order =', i)\n",
    "    print('AIC : ', result.aic)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>VAR Order Selection (* highlights the minimums)</caption>\n",
       "<tr>\n",
       "   <td></td>      <th>AIC</th>         <th>BIC</th>         <th>FPE</th>        <th>HQIC</th>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>0</th>  <td>    -11.61</td>  <td>    -11.53*</td> <td> 9.110e-06</td>  <td>    -11.57</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1</th>  <td>    -11.91</td>  <td>    -11.35</td>  <td> 6.754e-06</td>  <td>    -11.68*</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th>  <td>    -11.93*</td> <td>    -10.89</td>  <td> 6.624e-06*</td> <td>    -11.51</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th>  <td>    -11.87</td>  <td>    -10.36</td>  <td> 7.020e-06</td>  <td>    -11.26</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4</th>  <td>    -11.78</td>  <td>    -9.793</td>  <td> 7.711e-06</td>  <td>    -10.98</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5</th>  <td>    -11.72</td>  <td>    -9.262</td>  <td> 8.165e-06</td>  <td>    -10.73</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>6</th>  <td>    -11.66</td>  <td>    -8.729</td>  <td> 8.686e-06</td>  <td>    -10.49</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>7</th>  <td>    -11.58</td>  <td>    -8.164</td>  <td> 9.546e-06</td>  <td>    -10.21</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>8</th>  <td>    -11.56</td>  <td>    -7.676</td>  <td> 9.737e-06</td>  <td>    -10.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>9</th>  <td>    -11.48</td>  <td>    -7.113</td>  <td> 1.073e-05</td>  <td>    -9.725</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>10</th> <td>    -11.41</td>  <td>    -6.570</td>  <td> 1.161e-05</td>  <td>    -9.467</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Order selection with select_order\n",
    "x = model.select_order(maxlags=10)\n",
    "x.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Summary of Regression Results   \n",
       "==================================\n",
       "Model:                         VAR\n",
       "Method:                        OLS\n",
       "Date:           Wed, 09, Dec, 2020\n",
       "Time:                     18:35:13\n",
       "--------------------------------------------------------------------\n",
       "No. of Equations:         6.00000    BIC:                   -10.9776\n",
       "Nobs:                     281.000    HQIC:                  -11.5825\n",
       "Log likelihood:          -630.085    FPE:                6.22390e-06\n",
       "AIC:                     -11.9875    Det(Omega_mle):     4.74478e-06\n",
       "--------------------------------------------------------------------\n",
       "Results for equation coal_price\n",
       "====================================================================================\n",
       "                       coefficient       std. error           t-stat            prob\n",
       "------------------------------------------------------------------------------------\n",
       "const                     0.001570         0.003667            0.428           0.669\n",
       "L1.coal_price             0.201340         0.063428            3.174           0.002\n",
       "L1.crude_price            0.000289         0.000880            0.328           0.743\n",
       "L1.ngas_price             0.045044         0.026351            1.709           0.087\n",
       "L1.gold_price             0.000011         0.000111            0.096           0.924\n",
       "L1.iron_price             0.053405         0.050679            1.054           0.292\n",
       "L1.aud_usd_closed         0.531343         0.144833            3.669           0.000\n",
       "L2.coal_price             0.093006         0.064154            1.450           0.147\n",
       "L2.crude_price           -0.000346         0.000865           -0.400           0.689\n",
       "L2.ngas_price            -0.024094         0.026512           -0.909           0.363\n",
       "L2.gold_price             0.000140         0.000109            1.283           0.200\n",
       "L2.iron_price            -0.043950         0.049916           -0.880           0.379\n",
       "L2.aud_usd_closed         0.314846         0.151319            2.081           0.037\n",
       "====================================================================================\n",
       "\n",
       "Results for equation crude_price\n",
       "====================================================================================\n",
       "                       coefficient       std. error           t-stat            prob\n",
       "------------------------------------------------------------------------------------\n",
       "const                     0.087000         0.277661            0.313           0.754\n",
       "L1.coal_price            -0.603578         4.803242           -0.126           0.900\n",
       "L1.crude_price            0.252569         0.066627            3.791           0.000\n",
       "L1.ngas_price            -0.278612         1.995523           -0.140           0.889\n",
       "L1.gold_price             0.001724         0.008416            0.205           0.838\n",
       "L1.iron_price             7.603873         3.837794            1.981           0.048\n",
       "L1.aud_usd_closed        43.731047        10.967926            3.987           0.000\n",
       "L2.coal_price             6.185413         4.858225            1.273           0.203\n",
       "L2.crude_price           -0.088917         0.065481           -1.358           0.174\n",
       "L2.ngas_price            -0.877258         2.007722           -0.437           0.662\n",
       "L2.gold_price             0.001778         0.008277            0.215           0.830\n",
       "L2.iron_price             3.881248         3.780011            1.027           0.305\n",
       "L2.aud_usd_closed         6.332028        11.459092            0.553           0.581\n",
       "====================================================================================\n",
       "\n",
       "Results for equation ngas_price\n",
       "====================================================================================\n",
       "                       coefficient       std. error           t-stat            prob\n",
       "------------------------------------------------------------------------------------\n",
       "const                     0.001654         0.008396            0.197           0.844\n",
       "L1.coal_price            -0.049337         0.145246           -0.340           0.734\n",
       "L1.crude_price           -0.001617         0.002015           -0.803           0.422\n",
       "L1.ngas_price             0.067870         0.060343            1.125           0.261\n",
       "L1.gold_price             0.000066         0.000254            0.260           0.795\n",
       "L1.iron_price             0.112451         0.116052            0.969           0.333\n",
       "L1.aud_usd_closed        -0.032596         0.331661           -0.098           0.922\n",
       "L2.coal_price            -0.032107         0.146909           -0.219           0.827\n",
       "L2.crude_price            0.006595         0.001980            3.331           0.001\n",
       "L2.ngas_price            -0.070165         0.060712           -1.156           0.248\n",
       "L2.gold_price            -0.000363         0.000250           -1.450           0.147\n",
       "L2.iron_price             0.049382         0.114304            0.432           0.666\n",
       "L2.aud_usd_closed        -0.319381         0.346514           -0.922           0.357\n",
       "====================================================================================\n",
       "\n",
       "Results for equation gold_price\n",
       "====================================================================================\n",
       "                       coefficient       std. error           t-stat            prob\n",
       "------------------------------------------------------------------------------------\n",
       "const                     3.044764         2.029296            1.500           0.134\n",
       "L1.coal_price             7.533348        35.104661            0.215           0.830\n",
       "L1.crude_price            0.275407         0.486945            0.566           0.572\n",
       "L1.ngas_price             5.387050        14.584347            0.369           0.712\n",
       "L1.gold_price             0.125428         0.061507            2.039           0.041\n",
       "L1.iron_price            28.968869        28.048650            1.033           0.302\n",
       "L1.aud_usd_closed       229.320625        80.159463            2.861           0.004\n",
       "L2.coal_price           -83.466449        35.506506           -2.351           0.019\n",
       "L2.crude_price           -0.256401         0.478574           -0.536           0.592\n",
       "L2.ngas_price            -9.805450        14.673508           -0.668           0.504\n",
       "L2.gold_price            -0.015820         0.060496           -0.262           0.794\n",
       "L2.iron_price             5.122554        27.626342            0.185           0.853\n",
       "L2.aud_usd_closed       -27.824719        83.749166           -0.332           0.740\n",
       "====================================================================================\n",
       "\n",
       "Results for equation iron_price\n",
       "====================================================================================\n",
       "                       coefficient       std. error           t-stat            prob\n",
       "------------------------------------------------------------------------------------\n",
       "const                     0.003283         0.004481            0.733           0.464\n",
       "L1.coal_price             0.210604         0.077524            2.717           0.007\n",
       "L1.crude_price           -0.000033         0.001075           -0.031           0.976\n",
       "L1.ngas_price             0.033357         0.032207            1.036           0.300\n",
       "L1.gold_price             0.000043         0.000136            0.320           0.749\n",
       "L1.iron_price             0.241145         0.061941            3.893           0.000\n",
       "L1.aud_usd_closed         0.693337         0.177021            3.917           0.000\n",
       "L2.coal_price            -0.067158         0.078411           -0.856           0.392\n",
       "L2.crude_price            0.001070         0.001057            1.013           0.311\n",
       "L2.ngas_price             0.008420         0.032404            0.260           0.795\n",
       "L2.gold_price            -0.000090         0.000134           -0.671           0.502\n",
       "L2.iron_price            -0.191763         0.061009           -3.143           0.002\n",
       "L2.aud_usd_closed         0.279017         0.184948            1.509           0.131\n",
       "====================================================================================\n",
       "\n",
       "Results for equation aud_usd_closed\n",
       "====================================================================================\n",
       "                       coefficient       std. error           t-stat            prob\n",
       "------------------------------------------------------------------------------------\n",
       "const                    -0.000609         0.001616           -0.377           0.706\n",
       "L1.coal_price            -0.053617         0.027954           -1.918           0.055\n",
       "L1.crude_price            0.001121         0.000388            2.890           0.004\n",
       "L1.ngas_price             0.007824         0.011614            0.674           0.501\n",
       "L1.gold_price            -0.000010         0.000049           -0.212           0.832\n",
       "L1.iron_price             0.035317         0.022335            1.581           0.114\n",
       "L1.aud_usd_closed        -0.054279         0.063832           -0.850           0.395\n",
       "L2.coal_price            -0.050760         0.028274           -1.795           0.073\n",
       "L2.crude_price            0.000317         0.000381            0.831           0.406\n",
       "L2.ngas_price             0.000931         0.011685            0.080           0.937\n",
       "L2.gold_price             0.000142         0.000048            2.952           0.003\n",
       "L2.iron_price            -0.026127         0.021999           -1.188           0.235\n",
       "L2.aud_usd_closed        -0.109922         0.066690           -1.648           0.099\n",
       "====================================================================================\n",
       "\n",
       "Correlation matrix of residuals\n",
       "                  coal_price  crude_price  ngas_price  gold_price  iron_price  aud_usd_closed\n",
       "coal_price          1.000000     0.278468    0.127831    0.078498    0.167689        0.181798\n",
       "crude_price         0.278468     1.000000    0.142255    0.120716    0.251680        0.321251\n",
       "ngas_price          0.127831     0.142255    1.000000    0.011041   -0.021514        0.106145\n",
       "gold_price          0.078498     0.120716    0.011041    1.000000    0.093545        0.186954\n",
       "iron_price          0.167689     0.251680   -0.021514    0.093545    1.000000        0.176004\n",
       "aud_usd_closed      0.181798     0.321251    0.106145    0.186954    0.176004        1.000000\n",
       "\n"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fitted = model.fit(2)\n",
    "model_fitted.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coal_price : 2.01\n",
      "crude_price : 2.03\n",
      "ngas_price : 2.0\n",
      "gold_price : 2.0\n",
      "iron_price : 2.01\n",
      "aud_usd_closed : 1.97\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.stattools import durbin_watson\n",
    "out = durbin_watson(model_fitted.resid)\n",
    "\n",
    "for col, val in zip(df.columns, out):\n",
    "    print((col), ':', round(val, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-3.23772997e-02,  4.86000000e+00, -1.24297717e-01,\n",
       "         2.83200000e+01,  1.46997304e-01, -1.78000000e-02],\n",
       "       [-2.43992161e-02,  2.28000000e+00,  7.77777010e-02,\n",
       "        -1.91700000e+01, -2.00361617e-02,  2.00000000e-04]])"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the lag order\n",
    "lag_order = model_fitted.k_ar\n",
    "print(lag_order)  \n",
    "\n",
    "# Input data for forecasting\n",
    "forecast_input = df_differenced.values[-lag_order:]\n",
    "forecast_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "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>coal_price_d</th>\n",
       "      <th>crude_price_d</th>\n",
       "      <th>ngas_price_d</th>\n",
       "      <th>gold_price_d</th>\n",
       "      <th>iron_price_d</th>\n",
       "      <th>aud_usd_closed_d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</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>2019-04-01</th>\n",
       "      <td>-0.010142</td>\n",
       "      <td>0.464068</td>\n",
       "      <td>0.045395</td>\n",
       "      <td>4.444215</td>\n",
       "      <td>-0.034227</td>\n",
       "      <td>0.008553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01</th>\n",
       "      <td>-0.002206</td>\n",
       "      <td>-0.213351</td>\n",
       "      <td>0.016919</td>\n",
       "      <td>5.752312</td>\n",
       "      <td>0.010867</td>\n",
       "      <td>-0.001101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-01</th>\n",
       "      <td>0.004505</td>\n",
       "      <td>-0.140472</td>\n",
       "      <td>-0.000939</td>\n",
       "      <td>3.643170</td>\n",
       "      <td>0.015609</td>\n",
       "      <td>0.001076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>0.003281</td>\n",
       "      <td>0.257019</td>\n",
       "      <td>-0.000167</td>\n",
       "      <td>4.259144</td>\n",
       "      <td>0.006029</td>\n",
       "      <td>0.000155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>0.003401</td>\n",
       "      <td>0.324995</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>3.545311</td>\n",
       "      <td>0.002226</td>\n",
       "      <td>-0.000620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>0.002782</td>\n",
       "      <td>0.192465</td>\n",
       "      <td>0.001777</td>\n",
       "      <td>3.148545</td>\n",
       "      <td>0.002812</td>\n",
       "      <td>-0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>0.002851</td>\n",
       "      <td>0.163076</td>\n",
       "      <td>0.002897</td>\n",
       "      <td>3.207767</td>\n",
       "      <td>0.003936</td>\n",
       "      <td>-0.000017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>0.003022</td>\n",
       "      <td>0.175381</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>3.304956</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>-0.000154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>0.002917</td>\n",
       "      <td>0.181726</td>\n",
       "      <td>0.001886</td>\n",
       "      <td>3.291892</td>\n",
       "      <td>0.004011</td>\n",
       "      <td>-0.000168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0.002850</td>\n",
       "      <td>0.182080</td>\n",
       "      <td>0.001987</td>\n",
       "      <td>3.272924</td>\n",
       "      <td>0.003794</td>\n",
       "      <td>-0.000151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coal_price_d  crude_price_d  ngas_price_d  gold_price_d  \\\n",
       "Month                                                                 \n",
       "2019-04-01     -0.010142       0.464068      0.045395      4.444215   \n",
       "2019-05-01     -0.002206      -0.213351      0.016919      5.752312   \n",
       "2019-06-01      0.004505      -0.140472     -0.000939      3.643170   \n",
       "2019-07-01      0.003281       0.257019     -0.000167      4.259144   \n",
       "2019-08-01      0.003401       0.324995      0.000119      3.545311   \n",
       "2019-09-01      0.002782       0.192465      0.001777      3.148545   \n",
       "2019-10-01      0.002851       0.163076      0.002897      3.207767   \n",
       "2019-11-01      0.003022       0.175381      0.002154      3.304956   \n",
       "2019-12-01      0.002917       0.181726      0.001886      3.291892   \n",
       "2020-01-01      0.002850       0.182080      0.001987      3.272924   \n",
       "\n",
       "            iron_price_d  aud_usd_closed_d  \n",
       "Month                                       \n",
       "2019-04-01     -0.034227          0.008553  \n",
       "2019-05-01      0.010867         -0.001101  \n",
       "2019-06-01      0.015609          0.001076  \n",
       "2019-07-01      0.006029          0.000155  \n",
       "2019-08-01      0.002226         -0.000620  \n",
       "2019-09-01      0.002812         -0.000005  \n",
       "2019-10-01      0.003936         -0.000017  \n",
       "2019-11-01      0.004263         -0.000154  \n",
       "2019-12-01      0.004011         -0.000168  \n",
       "2020-01-01      0.003794         -0.000151  "
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Forecast\n",
    "fc = model_fitted.forecast(y=forecast_input, steps=nobs)\n",
    "df_forecast = pd.DataFrame(fc, index=df.index[-nobs:], columns=df.columns + '_d')\n",
    "df_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "def invert_transformation(df_train, df_forecast, second_diff=False):\n",
    "    \"\"\"Revert back the differencing to get the forecast to original scale.\"\"\"\n",
    "    df_fc = df_forecast.copy()\n",
    "    columns = df_train.columns\n",
    "    for col in columns:        \n",
    "        # Roll back 2nd Diff\n",
    "        if second_diff:\n",
    "            df_fc[str(col)+'_1d'] = (df_train[col].iloc[-1]-df_train[col].iloc[-2]) + df_fc[str(col)+'_2d'].cumsum()\n",
    "        # Roll back 1st Diff\n",
    "        df_fc[str(col)+'_forecast'] = df_train[col].iloc[-1] + df_fc[str(col)+'_d'].cumsum()\n",
    "    return df_fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "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>coal_price_forecast</th>\n",
       "      <th>crude_price_forecast</th>\n",
       "      <th>ngas_price_forecast</th>\n",
       "      <th>gold_price_forecast</th>\n",
       "      <th>iron_price_forecast</th>\n",
       "      <th>aud_usd_closed_forecast</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</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>2019-04-01</th>\n",
       "      <td>4.523747</td>\n",
       "      <td>66.874068</td>\n",
       "      <td>1.123805</td>\n",
       "      <td>1305.344215</td>\n",
       "      <td>4.425570</td>\n",
       "      <td>0.718253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01</th>\n",
       "      <td>4.521541</td>\n",
       "      <td>66.660717</td>\n",
       "      <td>1.140724</td>\n",
       "      <td>1311.096527</td>\n",
       "      <td>4.436438</td>\n",
       "      <td>0.717152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-01</th>\n",
       "      <td>4.526046</td>\n",
       "      <td>66.520245</td>\n",
       "      <td>1.139785</td>\n",
       "      <td>1314.739697</td>\n",
       "      <td>4.452047</td>\n",
       "      <td>0.718228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>4.529327</td>\n",
       "      <td>66.777264</td>\n",
       "      <td>1.139618</td>\n",
       "      <td>1318.998841</td>\n",
       "      <td>4.458076</td>\n",
       "      <td>0.718383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>4.532728</td>\n",
       "      <td>67.102259</td>\n",
       "      <td>1.139737</td>\n",
       "      <td>1322.544153</td>\n",
       "      <td>4.460302</td>\n",
       "      <td>0.717763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>4.535510</td>\n",
       "      <td>67.294724</td>\n",
       "      <td>1.141514</td>\n",
       "      <td>1325.692697</td>\n",
       "      <td>4.463115</td>\n",
       "      <td>0.717758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>4.538361</td>\n",
       "      <td>67.457800</td>\n",
       "      <td>1.144412</td>\n",
       "      <td>1328.900464</td>\n",
       "      <td>4.467051</td>\n",
       "      <td>0.717741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>4.541383</td>\n",
       "      <td>67.633181</td>\n",
       "      <td>1.146566</td>\n",
       "      <td>1332.205421</td>\n",
       "      <td>4.471314</td>\n",
       "      <td>0.717587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>4.544300</td>\n",
       "      <td>67.814908</td>\n",
       "      <td>1.148452</td>\n",
       "      <td>1335.497312</td>\n",
       "      <td>4.475325</td>\n",
       "      <td>0.717418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>4.547151</td>\n",
       "      <td>67.996988</td>\n",
       "      <td>1.150439</td>\n",
       "      <td>1338.770236</td>\n",
       "      <td>4.479119</td>\n",
       "      <td>0.717267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            coal_price_forecast  crude_price_forecast  ngas_price_forecast  \\\n",
       "Month                                                                        \n",
       "2019-04-01             4.523747             66.874068             1.123805   \n",
       "2019-05-01             4.521541             66.660717             1.140724   \n",
       "2019-06-01             4.526046             66.520245             1.139785   \n",
       "2019-07-01             4.529327             66.777264             1.139618   \n",
       "2019-08-01             4.532728             67.102259             1.139737   \n",
       "2019-09-01             4.535510             67.294724             1.141514   \n",
       "2019-10-01             4.538361             67.457800             1.144412   \n",
       "2019-11-01             4.541383             67.633181             1.146566   \n",
       "2019-12-01             4.544300             67.814908             1.148452   \n",
       "2020-01-01             4.547151             67.996988             1.150439   \n",
       "\n",
       "            gold_price_forecast  iron_price_forecast  aud_usd_closed_forecast  \n",
       "Month                                                                          \n",
       "2019-04-01          1305.344215             4.425570                 0.718253  \n",
       "2019-05-01          1311.096527             4.436438                 0.717152  \n",
       "2019-06-01          1314.739697             4.452047                 0.718228  \n",
       "2019-07-01          1318.998841             4.458076                 0.718383  \n",
       "2019-08-01          1322.544153             4.460302                 0.717763  \n",
       "2019-09-01          1325.692697             4.463115                 0.717758  \n",
       "2019-10-01          1328.900464             4.467051                 0.717741  \n",
       "2019-11-01          1332.205421             4.471314                 0.717587  \n",
       "2019-12-01          1335.497312             4.475325                 0.717418  \n",
       "2020-01-01          1338.770236             4.479119                 0.717267  "
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = invert_transformation(df_train, df_forecast, second_diff=False)        \n",
    "df_results.loc[:, ['coal_price_forecast', 'crude_price_forecast', 'ngas_price_forecast', 'gold_price_forecast',\n",
    "                   'iron_price_forecast', 'aud_usd_closed_forecast']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BmNXOuX/XId+xwc/pzrnKZAmCAPRnCemrmNlBZvY7M3s3mHikIjJJzN+DZJ0T8zWi12uxrV3i5DERb2Z43Ezfg/DD+AjzE5YmXfCPFUJscsPG8EjQgE+2HJ2QNrwWyok1LJJ5NSF9ou3Ae2ny16d+jsXfTAC8kOYYSWV4bX+E7+2dD/zL/ESQRzd0YzaNsGdHF2KT6QBgZt/AP+4M1RvAYc/ms4PJhC4ys/0aqEyX44PGs51zy6MbnHOb8EP7hOkShX9XFfjeUbscM8sxs4uDCZdWBpMJRScdPT5IWpf/g38Lfl5tZo+b2TmZTkokIiINItN2XlSmbczaCNtZnzjnliZLEASF302RP2xvtQVWpWlvTQjSNWR7tDZt9xz80CLJ1LftXtc2RjbrKtHMNG33vRLSHoNvjwG8lmafYdu9p5nlp0iTrs4zrh8zOxA/DB1k1nbvYGa/MT9Z6DozK4+0xxYFyZoHZQOqvpwJv7R5ycxuM7MTzKygrsfP0DP4wH0Rfqz4KsFEmWGntcS2+2sEvfGB2WZ2RZr727oagh9WcgV+KMUqQczgr8HLZG33et1/7SxmdmYwOefHZrY1oe0efmFQl7Z7Y95LidSLguYisW80V9Qx377Bz8/SpvI90aPpATCzc/ENkOvxDbE2+GEpvsD3LlgfJG1Rx3LVR7pziW7bN0WaLzI8bqbvQfhhWkT6niLhTPbNMyxfQwvrb61zbkeadEmvnYh1qb6wCdSnfqLf9Nfpfcn02g563QwBluFvAu4A/gNsMrNXzex/zKzR3kPn3EpiX2JckrA5fP2Oc25xQr43gBvxj/l+G3gM+MzMPjGzh82sXyblMbMc/MSoUL2xH3ok+Dk46NkSFb6Ha51zWzMpQ2MK3svXgMn44YgOwLdLvsJfJ2vwjzlDHf4POuceA+7BN76H4G+ovjSzJWb2x+ALEBER2XkybedFZdrGrI26tukThe2tAtK3t8JgY1HGJa0u2233urYxsllX9RGtv3R1Hl4jecSeXkiUrs7rUz/1abufhO+48mvgxKDs24m13ddGkie2yYbjnwzcB/+U9dvAZjN7w8x+YWap6qHegh7/TwcvE9vuF+Hfh9XEvswI830clHsLcBL+qfGPzeyLIBg8MMVTp7URBsP/kvBkaihsu5+aJFCf8Xu4MwSdXR7DB/QvxD9BVIC/twvb7uETOXVpuzfKvZRIQ1DQXCQm2YdaQ+arSmdm7fDjezfD9wIpxo+N18Y518E51xE/oc/OlmkdhFI9athYxw97IP85TU+R6NK1nuVraHW+dhLUVN87vX7qe2075+YBPfC9RR7ETyxVhB8D8E/AYjM7sr7lTCN8zPN8MysCMLM8Yo/ZJg1eO+fuwjccr8P3/v4C38NiGPC6mU1N0+MoldOJ9dKYGO3FEenN8VKwPfooaLXi1fG4O8tN+Ef2t+Pr7UD8+I7tnHMdg2vlX0HaOt24OOeuxU+W9St8D7gNwNeAHwH/NrPxDXMKIiJSB/X5PKpvG7M2Mi1f2N56qZbtrUyDccmkK3Ntzidbbfds1NXOlrRu0gzNAg1XP7V+X4J27uP4IUPm4iftbe2caxVpu58YzZJwPivxnWS+DdyLfyIjBz8Z5p3AUjM7tbblyUDYdi82swMi68Mg+uRkde6cm4xve/4QmIIfNmUffHv6WWCmmbVOzJeOmX0dH4QHuDlF2z3sfGPAZXXZ/y7gCvw9UQV+KKuDgWbOub0jbfengrR1bbs3xr2USL0paC4Cq4KfdX0kK+wlcEDaVLGg15eRdWfge/euB85yzs0MHm+LysaYXukeo9o/8ntD9/bJ9D1YHfxszCBqYwjrbx8za5YmXbJrpy7qUz+rIr/X5dHYel/bzrlS59w059xVzrkj8Q3YH+J7IB9ArIdGY5iKD+K2BgYG6wbgexiV4WeMT1Xuz51z451z5zrnOgA9ic13cD7wP3UsyxV1TJ/4mGf4Hu5jfozEhlYe/mJmhckSWOoxVCE2L8RtQb2tTNIjJ+P/g865pc65251zZ+DHyD+J2HA215jZ2ZnuW0RE6iTTdl5dhJ9JST+PAqk+k8J2WU3DCeyfYn0226Ppyhzd1lht97q2MXb3tjvUrs7LiT1ZWRcN1XbvWod8J+Hb+hXAmc65F138+N9Qc9u90jn3snPuGufcsfie6kOBlfhe8Y814pAt04Pj5ATHxMwOIzYkUeKcQNFyf+Wce8A5N8Q51wXfweIO/JcOffET4dZFXdvuw4InS0OZ3n/VVnnk90z+V4Zt94nOuVuDtnbiU8/1abs39L2USL0paC4C/wx+djCzVGNHJxOOydgv4cOuipn1INbAfieyKQy0f+CSTCQT6F+HsjSUdI8+hdu+cs4ta+DjZvoehGMCnhiM47e7CK+dPBLGzk4QXgPvpEmTTn3q59/4R+TAD51RWw1+bTvn1jnnHsA/tgfQK+jRHgoba/XujRTcJISB1UsSfr7onFtbPVfKfS1wzl1J7H2o9WTAZrYPEAZ1zwdapVnCcb+/GfzPCYV/V7n4iaXqojZ1Gr0ZTPXl4Qlp8od5kk5UZWZd8Tcv9RbczL2Nr8uVweq6Ts4sIiKZybSdVxfhZ1K6ziypPpPCdtkBZtY9WYKgx2mq4b3Cz/n9zawuk981hNq03StJ8VlbD5m2MbJZV/XxHrG20bfSpAvbuPOcc2Vp0qWScf0EPb7D4WEyabt/6fwk7MnUte2+2fnh8sIgcgfivwioCrTWYxiU8FgOP9QfVG+7zw+eYq3tvj5yzo3ADw8CdWu750eO+zPSt9074wPYByQcI9P7L2igtruZHYJ/6iCZmtruLUnf9q+T+txLiTQUBc1F/LfTHwe/j6vDt+Bhj9P98WOiJXNb8HMt8ZPGhDPMH5Ksh6aZHQ1cXMtyNKQLzOzQJOVpD1wVvJzSCMfN9D34C75XcC7wR0szYWQwBluqBsBO5ZybT2xCnZuTldvMziDW6Hg8w0NlXD9BwDu8xn+Z8LhjOhlf2zX0ugd/LqHoY5abgp8N9f6GQ7AMMLODifU4Tzo0Sx3KXZdHoC/BTwS0EXjBObclzfIOsUc9q3qbOz+ZWTgB0Zg6PmJamzr9kNi5DUrcGHyZOCJN/vBaOSrF9jvSFTCVdO9H8HhueDOyMx71FxGRzNt5dREGxk5P1vM5GB7ipMT1gVeJBZNuSZHmBlKPr/0CsR6i99Q0/0oDj/Hcx8yKkxyjED+3DMDLzrkNDXjM+rQxsllXGQvq7+Xg5S+SldvMjiLWHsq07V7f+nko+DnczHrV8phhe6yD+ckzE4/RGfhpiuPX9LdcU9sdGqb9HrbRDwu+mBuasD5OI7Xdz8I/mVoJPFFD2/0zYhOoVvVOr8f9F9Si7e78/AMfBS+rtd0DN6U5Rk1t91vwXwrUSSO9HyINQkFzafKCIMrV+Mew+gD/MLM+Ye9xM2ttZsVm9tfgUa8w3xxiE4/8wcyuDhs2ZtbRzCYQG7v5FudcOCkGwCv4D9S9gclmtn+Qr8DMLgy2Jz4WtzOU4Gc+7x9+629mx+ED/u2DMmUUyEqnHu/BauCXwcvvAq+a2TfD4LB5PczsZ/ixsc9MPLaZLQ/GmJvR0OdVg7DXdF/gKQsmgjGzfDMbSqyx/U9iPZ/rpAHq5yb8Fz7tgDfN7EKLjfPdzMx6mtldZhadeKc+1/YQM3vTzK4ys4PClWaWa2anE7v23kq4+ftv8LN1cIz6ehX/eGwevqdJEf5m+m8p0j9rZg+Z2XeiXzyY2d5mdjOxHkl/r0MZwuD3c8650rQpvanBz++bH5sydA3+7/pg/Hv47aAnDGbW3MxOMLM/m1liD6KwTs83s7YkEfSgCv8H/iq4PgqCfR+Kn4AzVaMaYuOx32xm54XlNrNu5icZupDMHm3+l5ndG/zPqAqcmNl+ZvYHYr3X6/J+iIhIhjJt59XRk/j2Rzvg8SDQh5kVmdml+M+kr1KUbzswKnh5qZmNt+CJtqBst+DnyEgaeA7a+D8Kzu8Y/Oft6dGAYvDZdpWZzQnSNpSNwNNmdn7kc7QH8H/4OWIq8JM7NoY6tzEaoq7MbEbQdl/eSOeVyk34ofq+BrxswRw75juenIFvV+Thg5IPZHKABqif3wFL8HML/cPMrrTgC43gHuMQM/u1mf08kucNYCu+h/KT5nsaR9vfM0g9RnpvM5tvZteZ2dcjf9NmZr2B+4N0nwILIvk+JNaJYbhZvXubLyb2xMj9+B7RFcR6jCe6z8yeNLNBZlY1yauZtTSzHwLfD1bVpa0YBr/fcM6tSpvSezL4OdDin6DN5P4LYm33M8L7rxTCe8zLzexHkX0fYGYTgcFAqqeFw7b7lWb2g0i7v6OZjcN/ubgu7Vkn1xj3UiINwzmnRYsW58B/OJbgGwUu+H195LUDjk7I04ZYQ8LhG1Jf4Rvt4bq7UhzvjoR9b8A3Hhy+N87F4bYkeYcF25Y30LmHZbgc37vB4RtPmxPq47tJ8naNpOlaw3EmBekmNdR7EOT7Bf4RtzDNDnxjozQh79AkeZcH22ZkWHdh/qTnVEPe6xKulfVB2cPX84H96vv+17N+jsE3dMM05Umu8Wsb4tqOnFf0/V+Lb/SG6z4DeiQp52uRNJuC92V5Ytnq8N78PqEsf06TdkZC2o3BEl03Fcip5bFPjOQ7s5Z5jozkGZiwbUDwHoTbS4P3MFq+cxLynBx5j8uBz8M6TUjXOXhPovsOz30TfvihcFtxQt4D8V9ORP9/Rss5IlK3I9P87Q1Lsd4F57Ae2JJwvndncl1o0aJFi5bMFzJrayf9DEmx/9sS9rUh+Gxx+KD5KFK0+fCdyR6N5K0IPivD9tPj1NyOHYpvP0c/19YmnLMDbmqAugw/667DP20W1mf0c7QSuDJF/lrVK35M55TtZDJoY9S3riJtg+UZ1l2YP+k51ZB3MPFt9Y34XrDh65XA15PkKw7T1PI49amfg4CFaa5lB4xPyPPDhP1ujpzXl/he1OG2rsnOK/L+ryX2dxfWUd8k5ZwYSbMVWBFc17/L8H39SUJZXkqTdlKS8038XzQbaFHLY+8fqd+ra5lnb2L3R9ckbMvk/uvgyHtWgW9jLw+WzpF0LZNcH+G5l+LHLV9O8jb2XsD7SfKG5fozaf5PkqJdTwPfS2nR0pCLepqLBJxzj+J7ZIzHD51RDhTgews8ix8u4f2EPBvx33xegf9nvxn/QbQa3wOzn3PuFymO90v8zcMc/AdcPrAUGAP0wgepdraPg2P/Ed9AKsBPfPM40Ms593+NefBM3oMg311BvnH4QHMJ/kN9C3488DuB3qTubZAVzrlxwLHAX/EztjfHXwtv48fCO945V+/roD7145x7D/g6vsf62/hrvAW+ITcjKOdjCXkyvbafD/I9jH/MeiP+i6nNwb5uAQ53vjdJovOD8/swON6BwZLpI5+Jj3Mmfbwz8BP8kwN/x/fuMXzv9M/x5zTIOXeBqz5RTiphT5WN+J75NXLOLSD2t3FFwrZX8A3p3+LHINwelG85/lHjq4DXE/LMwj+d8FpQjg7E6jSa7lP8MEIT8cFz8NfVo8AxzrmZacq8An/9/y+xa6IE36P/dOfc7bU59ySGALfiH3tdhv8fko+/GZsCfMs597MM9y0iIhnKtJ1Xh/3/OtjH2/ggXC4wFx8QPI80j/Y7P/fF9/HtkLfxn5V5+LGsf0gthk10zk3G90Ieje/1ugXfDikJynEffmzosRmdYHLr8XOb3IEP2DbDB9deAL7pnJvQgMeqJpM2RpAvG3VVb865KcDh+J7kH+Hruxxf5luBI5xzGV/DkeNkXD/OufB+7kf4tvp6/P3pGuAtfHt6XEKeP+PbfTOCY+Xh23V/wD81GO0lHvUO/snA+4F38QHzNpFy3on/EmF2krw/xn8pE/aQ7oJvZ7ZPcayaPI4P1ofStd1H4YeceQb/pVM5vo6+wD9xejn+C6WttTz2MPz/m0piT2Gm5Zz7iiRDtATbMrn/WoKfx+B5/H18O2Jt97xIui34J37uxreTy/H19jRwknPuCVJw/knf3vj/4cvx/1PLgzJd5Jz7YW3OPYmGvpcSaTDmnMt2GUQky8ws/EfQzzk3I5tlERERERGR1IKhSQ4ELnPOTcpuaaYVrR4AACAASURBVERERPZM6mkuIiIiIiIiIiIiIhJQ0FxEREREREREREREJKCguYiIiIiIiIiIiIhIIK/mJCKyOzCz1XXN45zr2BhlERERERGR1MzsHeCAOmY7zjn3SWOUR0REROIpaC6y5+iQaUbnnDVkQUREREREJK19qHv7PRfAOde1wUsjIiIiccw5l+0yiIiIiIiIiIiIiIjsEjSmuYiIiIiIiIiIiIhIQEFzEREREREREREREZGAguYiIiIiIiIiIiIiIgEFzUVEREREREREREREAgqai4iIiIiIiIiIiIgE9viguZk9b2bPZ7scIiIiIrJ7UPux4alORURERKQust1+zMvWgXei7ocddthhgMt2QURERERk13fYYYexaNGiRdkuxx5GbXIRERERqbVst8n3+J7mIiIiIiIiIiIiIiK1paC5iIiIiIiIiIiIiEhAQXMRERERERERERERkYCC5iIiIiIiIiIiIiIiAQXNRUREREREREREREQCCpqLiIiIiIiIiIiIiAQUNBcRERERERERERERCeRluwAiIiIiIiIiIrLzOOdwzmW7GCLSRJgZZpbtYtSJguYiIiIiIiIiInu4iooK1q1bx+bNmyktLc12cUSkiSkoKKBVq1a0a9eO3NzcbBenRg06PIuZFZnZUjPbkCbNDDPbYWZbIst+td0uIiIiIiIiIiK1V1FRwcqVK1m3bp0C5iKSFaWlpaxbt46VK1dSUVGR7eLUqKF7mt8GfAq0ryHdjc658fXYLiIiIiIiIiIitbBu3TpKSkrIzc2lQ4cOtGjRgpwcTXMnIjtHZWUlW7duZc2aNZSUlLBu3Tr23XffbBcrrQYLmpvZMcAZwM+AKQ21XxERERERERERydzmzZsB6NChA23atMlyaUSkqcnJyan63/P555+zefPmXT5o3iBfK5pZHjAB+DGwoxZZbjazr8zsP2b2/Qy2i4iIiIiIiIhIDZxzVUOytGjRIsulEZGmLPwfVFpaustPRtxQPc2vB+Y752aYWXENaUcAi4BtwKnAk2a22Tn3TC23i4iIiIhkpLLSsaO8kpKyCkrKKygpq2R7afh7BTvKKtmyozzbxdwz7dgMW9dCi5pGchQRkYYUDUxpSBYRyabo/yDnHGaWxdKkV++guZl1x/cw71Wb9M65tyIvXzazB4DBwDO12S4iIiIiew7nIkHsMv9ze1lF7HV5BTuq1sWnKymvoKQ0li5uW1n19dvLKigtr6yxTKs2lOyEM2+CNn0Gd3WHDkdAt5P9cmBvKNQwASIiIiKya2mInuZ9gX2AhcG3AwVAazNbDZztnJtTQ/6a7lxqvrMRERERkQYRBrF3RALO25MFo6t+r6AkIeidLGi9vaySHQnrt5dWsKMWQWzZw6z5r1/e/hNYDnQ6OhZE73IiFGjoABERERHJroYImk8BXoq87g08DBwNrIsmNLO9gu0z8GOfFwNXAT+ozXYRERGRpqii0lUFopMFsLfHBaoT1/nfY72147fHenn77TvKK9nFhxdsULk5RlF+LoX5OTTL8z/X5edQlu2C7Ylyktx6uEr4/D2/vDkecvKh87GxIHrn4yCv2c4vq4iIiIg0afUOmjvntgPbw9dm9pVf7VYHr18EZjvnxgD5wK3AE0Hy5cD1zrmpweuatouIiIjsEsoqqveujga0tyf2uE4a8I6tCwPb0YD29mCM7dKKptMbO8cIgth+aZafQ2FeLkUFPqBdmBdbH0sXW1+Yn1OVN/o6PjAeW5+fW31s18Mfas6i1Vk4+T1du6/B1c/CspmwbBYsmw3bv4pPU1kGK9/yy8yxkFfoe593Oxm6ngz79YLchpqWSUREZNc1cuRIfvOb3/Dwww8zbNiwbBenXoYNG8YjjzzC9OnTKS4uznZxkqqoqOA3v/kNkydP5pNPPqGsrIxLL72USZMmZbtokiUN3uJ0zs0A9oq8/k7k9y+BE9LkTbtdREREJBXnHGUVrlqwOj4AXX1dSTBsSGxd9SFGticEv0vKKiivbBrdsXOMWAA6LycIWOdSFBecDoLWBblB8DoneQA7L/a6KLK9WZguL5f8XNulJwSSemp/sF+OGw6VlfDFQh88XzYLVrwJOzbFpy8vgY9n+AWgoJUfBz3sid7hCNCkdiIiIlJP99xzD6NGjWK//fbjvPPOo7CwkD59+mS7WLus4uJiZs6cybJly+jatWu2i9Mo1E1DREREGlU4Rvb2Uh+Yjga1t5cmBrCrrw+3hfl3BEHs+PT+dROJY1OQmxPrQV0tUB0fkE6+rnow2/fkju2rWZCuIDdHQWxpHDk50PFIv5z0I6goh1XzfE/05bNhxVtQvj0+T+lmWPKyXwCK2kLXPtDtFB9Eb38I6HoVERHZpdx+++388pe/pEuXLtkuSkrPPvssALNnz+aggw7KcmlkV6CguYiISBMVjpMdBqSjvarDdTvKYwHpuB7apfGB7qrtKdY3BYWR3tJFBbk0CwPRQRC66vfoujBoXRDrhR2uKyqIDSVSVBALbDfLyyU3R0FB2QPl5kHnb/il78+gfAd89m4wlMss+PQdqCiNz7N9Pbz/gl8AWnaArn1jPdHbdlUQXUREJMs6depEp06dsl2MtD799FMABcylioLmIiIiu5DKShebmLE8EoguDyZtTBhaJFmwO7H3dXQ87Wj6PX2cbIuMjV2UZGzsomCYEb8uui0+4B03lnaSdUUFuRTk5pCjQLZIw8pr5odiObA3FP8SSrfBJ/+KBdE/f89PJBq1ZQ389ym/ALTpEgTQg0B66/12/nmIiMgubeXKldx55528/PLLfPLJJ7Ro0YLu3btzzjnncN1111FUVBQ3FMU///lP7r33XhYuXEhubi4bNmxgxowZ9OvXL+UY2OnG9J45cya33nor//73v2nWrBknn3wyt99+e9oyb9myhbvvvpupU6fy0UcfkZeXR69evbjuuus455xzMq6LaDm3b9/Ob3/7W+bOnUt+fj79+vVjzJgx9OjRIy7PpEmTuOyyy7j11lu5+OKL+fWvf8306dP58ssvmTZtGuecc07a89+6dSv33nsvU6dOZcmSJTjn6NKlCwMGDOC6667jwAMPjEv/wgsvcN999/Hvf/+brVu30qVLFwYPHsyNN95Iy5YtMz7nUPQJy+jQI5988gmjR4/mpZdeYvXq1bRp04Y+ffowYsQIjjvuuLh9Ll++nG7dunHKKafw/PPP85vf/IZp06bx6aef8uMf/5jx48dnfD5lZWVMmDCByZMns3DhQkpLS+ncuTOnnHIK11xzDUcccQQAJSUlTJ48meeff54FCxawatUqmjVrRs+ePfnRj37EkCFDku77oYce4n//93/5+OOP2bZtG/vuuy9HHHEE3/ve9xgyZEjVuYWiv4N/ynhPoaC5iIhIGuHQInHjWScEs3ckBKaj6XYkBL3jJ4GsjFu/vayC0vI9O5ANkJ9rcUOGFEV6WhcVxNY1C7cX5MRNDBntiR33OjpUSYGGFRHZ4xQ0h+79/AJQsslPGLpslh/SZfWC6nk2roS5f/UL+MlIw17oXftCi/Y7r/wiIrLLmTVrFmeffTYbN27koIMOYuDAgWzdupVFixZx0003cfHFF8eN13z77bczceJEvvnNb3LmmWfyySef1Ov4zz33HIMGDaKiooLevXvTpUsX5syZwwknnMBZZ52VNM+aNWs49dRTWbRoEfvvvz+nnXYa27Zt46233uLcc8+tGgqlPqZOncr999/Psccey1lnncX8+fN55plneP3115k5cyZHHXVUtTwffPABxx13HO3ataNfv36sX7+e/Pz8tMdZtWoV/fv3Z9GiRey9996ceuqp5Ofns3TpUu69916OPvrouElQr7/+eu6++24KCws5/vjjad++Pe+++y6jR4/mxRdfZObMmbRo0aJO5xqOW/7UU0+xdetWLr300qptYdB6wYIFnHrqqaxdu5YePXpw3nnnsXLlSp555hleeOEFHnvsMS644IJq+96+fTunnHIKK1as4JRTTuGYY46hbdu2GZ/P1q1b+c53vsPs2bNp2bIlffv2pVWrVixbtoxJkyax//77VwXNly9fzvDhw+nQoQM9evTg+OOPZ/Xq1fzzn/9k9uzZLF68mJEjR8aV95JLLmHKlCm0b9+e3r1707x5cz777DNmz57Nli1bGDJkCC1btuTSSy/lpZdeYs2aNQwaNCijLyt2Bwqai4jIbsU5R2lFZcLkjZFAdDB29o6EIHU03Y4kPbhTBbN3lFeyB31ZnlazyPAgVeNbBz2yY4Ht5EHsaO/t+MB2biSt339+ribtE5EGUNgaDjndLwBb18GKN2ITi679oHqedUv98u+H/Ot9D48F0Q/sDUV77bzyi4hIVq1fv57zzz+fjRs3Mm7cOK655pq4DhezZs2KC3ACPProo7z++uuccsop9T7+5s2bGT58OBUVFTz22GNcdNFFAJSXlzN8+PC43s9Rl112GYsWLeKGG25g9OjRVYHpjz/+mAEDBnDzzTdzxhln0LNnz4zL9qc//YkHH3yQK6+8EvD3YCNGjGDs2LFcfvnlvPvuu9XyPPHEE1x99dWMHz+e3NzcWh3nkksuYdGiRVx00UVMmDAhLkC8ZMkSKioqql4/+eST3H333fTq1Ytp06ZVfZlRVlbG1VdfzYMPPsjIkSO566676nSuw4cPZ/jw4cyYMYOtW7dWe1LAOcfQoUNZu3YtI0aM4Le//W3VdfLUU08xePBgrrjiCk4++WQ6dOgQl3fOnDmcdNJJfPzxx+y1V3wbI5Pzueaaa5g9ezb9+vVj6tSptGvXrmrbZ599xurVq6te77PPPrz88sv079+fnMik6cuWLePUU09l1KhRDBs2rOq4y5cvZ8qUKRx33HHMmjWLwsLCqjzbt29n7ty5ALRv355JkyZRXFzMmjVr+N3vfqeJQEVERJJJNpxIYvB5R5LA9o6EXtY7kg5HEqyPBMNLyiuaTBAbIDfHqsa6LgyGGCmKBKqb5YWB6YRAdlWwunogPBrQLowMQ6LhRURkt9aiHRw20C8Am1cHAfRgYtH1y6vn+WKhX/51P1gOdDoqFkTvchIU1K23mojI7so5x6aS8mwXo05aF+bV66nCCRMm8OWXX3LmmWdy7bXXVtt+8sknV1t3xRVXNEjAHHxv7rVr13LaaadVBcwB8vLyGDduHE8//TRbtmyJyzN37lxefPFFevfuzR133BF3/gcddBC///3vOeecc5g4cSL33ntvxmXr3bt3VcAc/JAlo0aN4rHHHuO9997jrbfe4qSTTorLs88++zB27NhaB8znzJnDP/7xDzp27FgtYA5w8MEHx70eM2YMAI8//nhckDY/P5977rmH559/nokTJzJ27Ni4IHF9zZgxgwULFtCtWzdGjRoVV+fnn38+55xzDtOmTePhhx9O2sP/3nvvrRYwz+R8Vq1axaRJkygqKuLRRx+NC5gD7L///uy///5Vr9u1a8eAAQOqHbdbt27cdNNNXHnllbzwwgv85Cc/AeCLL74A/HsfDZgDFBUVVXu/mwIFzUVE9iDOOcoqXNz418mGAYkPXiesD4PV5bF0iQHuaP6mMJxIVI5RFZiOBrOrJoEMf0+Y8DHaa7tZkvzRAHezSKBbvbJFRDLUqiP0vMAvAOtX+OB5OCb65lXx6V0lfP4fv7x5D+TkQ+djYxOLdj4O8gurH0dEZA+wqaSco37zSraLUSfzbh1Am6L0w3+k89prrwFw1VVX1TrP2WefnfHxEr3xxhsAXHjhhdW2tW3blgEDBjBt2rS49a+++ioAAwcOTPqFQTjUyDvvvFOvsiUb7zo/P59BgwYxfvx43njjjWpB1P79+9O8efNaHyOs/6FDh9Y4pMoXX3zBvHnz+PrXv86hhx5abXthYSHHHnssf/vb31iyZEnSNJmaPXs2AIMHD076hcAll1zCtGnTmD17drWgeadOnTj22GOr5cnkfKZPn05FRQVnnHEGnTt3rnX533jjDWbMmMFnn31GSUkJzjlWrfJtoCVLllSl69GjBy1atODhhx/m8MMP57zzzqsWmG9qFDQXEWlEYRB7e2kF28rKg0kYK6sHteN6aMePh51qyJDEXthhusom1Asb/GSP8YHphMB1svX5sWB22Mu6WTRd2Ps6kq5ZkFfjZIuI7KbaHuiXXt8D52DdR74X+rJZPpi+bV18+soyP2b6yrdg1p2QVwgHnBDrib5fL8jNPFgjIiLZFY5H3r1791rn6dKlS4Md//PPP0+7z2Trly9fDsCNN97IjTfemHLfa9eurVfZEiffDIU9osOyR9W1bupS/ytWrADg/fffr/FebO3atQ0aNA/PNdUQJJnUSSbnU9frdePGjZx33nm8/vrrKdNs3ry56vfWrVszYcIEfvCDH/CDH/yAq666ikMPPZR+/frx/e9/nxNPPLFWx92TKGguIk2ec46Sskq2lZazrdSPfb2ttIJtpT7Iva20IvhZzrayiqp1fn1insi60gq2lVVQ0cSi2MmGE6kevM4JAtW5VeNoR4cRqepxnZe4Pj7o3Sw/h2Z5CmKLiEgdmUH7r/nluCugshK+WBTrib78DdixKT5PeUkQZJ/pXxe09OOgh0H0DkdCAz4OLiIiO0dd7iUSh62orcrK6k/numDMybocPxzju2/fvhx00EEp07Vv3zgTXbs042RmWje1Of/wvDt16pR0yJGoxuodXVM5k21PVSf1OZ/aXi833ngjr7/+OieffDK33XYbRxxxBHvttRe5ubm88sornH766dXez4suuoj+/fvz3HPP8corrzBz5kzuv/9+7r//fn7xi19w55131urYewoFzUVkt1BR6eKC2D5QXR4JVCcEussige7o9rJYULukrKIq4L0nj5GdGJSO7zkdHSIkvqd1fEA72nM7friRZgnrNJyIiIjsdnJyoOMRfjnxf6CiHFbPC4Zyme17m5dti89TugWWvOIXgMK9oGsf6HaKD6Lvc6gPzouI7AZaF+Yx79b0wbtdTevC+oW0DjjgABYvXszSpUvp0aNHvfZVUFAAUG0M8lDYSzhqv/32A2K9jhOtXLmy2rpwWI7zzz+fn/70pxmVtTZqKlNY9vo44IADAFi6dGmNacPz7tixY7WJOhtbeK7Lli1Luj2sq06dOtV6n5mcT13qC+CZZ54hNzeX559/njZt2sRt+/jjj1Pm22effaomR3XO8fLLLzN48GDuuusuhg0bxmGHHVar4+8JFDQXkQbhnKO0ojKhF3b13tlhwDrWOzs+qL09EuwOg9/bSiv2mHGz83IsIdicPCjdLCHAnbyHder8sZ7a6oUtIiJSZ7l5sP83/NLnOigvhc/ejY2H/ukcqCiNz1OyARb/zS8ALfaFbn1jPdHbdlMQXUR2WWZWr/HBd0f9+/fn1Vdf5cEHH+TMM8+s177CgOmHH35Ybdu6det47733qq3v06cPkyZNYurUqQwfPjxu24YNG3jllepjzPfv359bbrmFZ599tlGD5lOmTKmaIDJUXl7O008/DcA3v/nNeh+jf//+3HTTTUyePJnbbrst7XjonTt35tBDD2X+/PksW7aMbt261fv4tdW3b1/A18no0aOrjWv+17/+NS5dbWRyPsXFxeTm5vL3v/+dzz77LG7Sz2TWr19Pq1atqgXMAZ588slaldPM+Pa3v813v/tdHn/8cf773/9WBc3DL4rKy3evCYTrQkFzkSbCOceO8spIYLqc7aWVVUHtkoSgdTjcyPbS8qrfoz2zE4Pc2/egYUgK83NoXpBHUX4uzQv8UlSQ69cV5NI8P1yX53/mh9urr28ezVegXtgiIiK7pbwCOPAkvxTfCGXb4ZN/xYLon70HriI+z9Yv4L9P+wWgzQE+eB5OLNom/c2uiIg0ruHDh3PXXXfxwgsvcN999/HjH/84rsPR7Nmz6dmzZ9KgY6Ju3brRpUsXFixYwHPPPcfAgQMB2Lp1K1deeSWbNm2qlueCCy7ghhtu4JVXXuHJJ5+smhC0oqKC66+/Pmmv9RNPPJFvfetb/OMf/+C6665j1KhRtGzZsmp7ZWUlr732Gs2bN6+aFDQTb775Jg899BCXX3454OMJt956KytXruSoo46id+/eGe87dPzxx9OvXz+mT5/OVVddxQMPPBAXOF+6dCnl5eVVTwHcfPPNXHLJJQwaNIhHH32UI444Im5/H330ETNnzqwqc0MpLi7myCOPZMGCBfz6179m9OjRVdfJs88+y7Rp02jZsiXDhg2r037rej777bcf3//+93n44YcZNmwYU6ZMYe+9965K//nnn7N69WqOOeYYAA455BAWLlzIlClTGDx4cFW6cePGMX369Grl+c9//sOyZcs466yzyM+PfYG2fv16/vWvfwHxY7SHPfA/+OADvva1r9Xp3HcXCpqL7CIqK50PQCcErbeVlseC1QkB65LIONolQa/t6kHviqr97ilDkOQYcYHoZMHp+EB2LAAeBr9jv+fSPD8+X06OeoGJiIhIGvlFcFCxXwBKNsHKt2MTi65eACQ0vDZ+AnMn+wVg7+6xXuhd+0LLfXZa8UVEBPbee2+efPJJBg4cyE9+8hPGjx/PN77xDbZt28bChQtZtmwZy5Ytq1XQHGDkyJFcfvnlDBo0iJNPPpmWLVsyZ84cWrduzdlnn83zzz8fl75169Y8+OCDXHjhhQwePJh7772XLl26MGfOHL788kuGDh3K5MmTqx1n8uTJDBgwgPHjx/Poo49y9NFHs88++/DZZ5/xwQcf8OWXXzJu3Lh6Bc3/53/+h+HDh/PAAw/QvXt35s+fz8KFC2nVqhUPP/xwxvtN9Je//IVTTz2Vv/71r7z44ov06dOH/Px8li5dyrx583jooYeqgubf+973WLBgAXfeeSdHH300vXr1olu3bmzatIkVK1awePFijjrqqAYPmpsZkydPpl+/fowZM4ZnnnmGo48+mpUrV/Lmm2+Sl5fHQw89RMeOHeu030zO55577mHx4sW89tprdO3alb59+9KyZUuWL1/Oe++9x0033VQVNB8xYgTf+973GDJkCH/84x/p3Lkz8+bNY/HixVx33XWMGzcurjwrVqxg0KBBtGnThmOPPZaOHTuyYcMGZs+ezaZNmzj33HPjJgM9++yzeeSRR7j44osZMGBA1d/JxIkTM63qXY6C5iK1VF5RWT0gXa3HdWxIkcSe29V7cZfHTT65Yw8ZfiRUkJsTC0RHAttFBXmRntrxPbNjAfCEoHYkX1GBhhwRERGRXUxhazhkgF8Atn3lJxMNJxb9cnH1PF995Jd3g+DDvofBIadD3+uhWaudV3YRkSasX79+zJ07l7Fjx/Lyyy/z7LPP0rp1a7p3784PfvCDOgVCL7vsMsyM3//+97z55pu0bduWs846izvuuIPrr78+aZ5Bgwbx6quvMnLkSN59910WLlxI3759ueOOO1IOodGhQwfefvtt/vznPzNlyhTeeecdSktL6dSpE7169WLgwIFVvdYzdeGFF3LGGWcwZswYnnvuOfLz8xk4cCBjxoxp0DGt999/f9555x3GjRvHU089xSuvvEJeXh4HHHAA1157Laeeempc+rFjx3L66adz33338dZbbzFv3jzatm1L586d+cUvfsGQIUMarGxRRx55JO+99x6jR4/mpZde4qmnnqJNmzacc845jBgxguOPPz6j/db1fFq1asX06dO5//77mTx5MjNnzsQ5R+fOnbn88su54IILqtIOHTqUtm3bMmrUKObOncuCBQs49thj+dOf/oRzrlrQ/MQTT2T06NG8/vrrfPDBB8yePZu2bdvSs2dPrrzySi6++OK49Oeddx7jxo1jwoQJvPDCC+zYsQPYs4Lmlm7m2z2BmS087LDDDlu4cGG2iyJZUlnp2FpazuaScrbsKGdzSRmbS+JfbykpZ1P09Y5ge9X6MkrK9rCgdl5OXO/r8PfC/GhP7fjhR6qnz6OoIIeiaE/tYJuGIRERkd3V4YcfzqJFixY55w7Pdln2FE2+Tb55TRBAn+knFl2ffDIxAE74IXxn7M4rm4js8SorK/nggw8AOPTQQ8nJ0b2aJDds2DAeeeQRpk+fTnFxcbaLI3uguvw/ynabXD3NZZcVjsHtA9xl6YPeO4Lgdkn1oPeW0vLdcliS6LjaYUA6PqAd33u7KMW2wsgQJIUFfp+FeTnkKagtIiIisnO06gBHnu8XgA0rffA8HBN98+extPOegNNG+XHURURERCQrmkTQfNnarZx853QK8nIoyM3xP/NyaBZ53Swvtr4gN7cWaRL3k5Anmi43p8mNkVxeUVkVuN6cEMjeHOndXVMgvKxi14x2m1E1VEhRtFd2wrqaAtyFSXp6FxXkUpincbVFRERE9lh7dYFeQ/3iHKz9EB44Bcq3Q8kGWPoq9PhutkspIiIi0mQ1iaB5eYVj5VfbslqG/FxLCLTnpg3ixwXfUwTxw/zN8tMH8eP3lT6I75xja2lF8iFLSmoIekd6d28vq9jJNVw7zfJyaFWYT+vCPFoW5tGqMI+WzfJoVZhPy2Z5kfX5wfpwyadFM42pLSIiIiINzAz2ORR6nAH/fdqvmz9FQXMREamXxYsXc8cdd9QqbZ8+fRg+fHgjl2jn+fnPf87atWtrlXbSpEmNWxjZbTWJoPmuoKzCUVZRwdbSXSOYnBjEzzVj8w4fAN8VhzLJzbG4AHerIKAdC3znRwLcsdc+EJ5Py+D3gjwNSSIiIiIiu6Ceg2NB8w9egu0boGiv7JZJRER2W6tXr+aRRx6pdfrhw4czadKkPSKI/NRTT7FixYpapd0TzlcaR5MImnfeu4hHrziB0ooKSssr2VFeSWl5JaUVwc8k6+Jf15CvKo9PV7kLBp0T7cwgfouCXN9zO9Kzu3WkJ3fYs7tVs1iv7jBtqyBIXpivnt0iIiIisgfrfio0bw/b1kLFaw0+owAAIABJREFUDlj0HHzj0myXSkREdlPFxcW4XbFX5E6wfPnybBdB9gBNImhelJ9Ln4Pb77TjlVekC6zH1qcK4senqR7E31FWEbf/ZMeKpsk0iF+QlxPfozvsvV2YPugdHd6kZbM8cjU2t4iIiIhIern5cMQgmPOAfz3/SQXNRURERLKkSQTNd7a83BzycnNovotMeJ8qiL+jLLa+otJFhj/xge9mebnZLrqIiIiISNPRc3AsaL7iDdiw0k8aKiIiIiI7lYLmTcCuFsQXEREREZEk9j8G9u4OX33kXy+YCn2vz26ZRERERJogzYooIiIiIiKyKzDzvc1D86ZAEx2PVkRERCSbFDQXERERERHZVfS8MPb72g9g9fzslUVERESkiVLQXEREREREZFexdzc44ITY63lTslcWERERkSZKQXMRERERkSbGzLYkLGVmNj+yPd/M7jOzr4LlD2am+ZB2lmhv8/8+BRXl2SuLiIiISBOkoLmIiIiISBPjnGsZXYD3gSciSW4G+gCHB0tf4Fc7v6RN1OHnQU6+/33LGlg2M7vlEREREWliFDQXEREREWnCzOx44DBgUmT15cBo59wq59wq4LfAFVkoXtPUfG84eEDs9fwns1cWERERkSZIQXMRERERkabtCuBF59znAGbWFugMzI2kmQt0MbM2WShf0xQdouX9F6B0a/bKIiIiItLEKGguIiIiItJEmVlzYAgwMbK6ZfBzQ2Rd+HurnVEuAQ75NjQLvqMo2wqL/y+75RERkZ1q5MiRmBmTJk3KdlHqbdiwYZgZM2bMyHZRRGpNQXMRERERkabrQmAbEI3Ibgl+RnuVh79v3hmFEiC/EA4fGHs9f0r2yiIiIiLSxChoLiIiIiLSdA0HHnHOlYcrnHPrgU+BoyPpjgY+cc5t3Mnla9p6Do79/tHrsHlN9soiIiKSodtvv53333+f448/PttFEak1Bc1FRERERJogMzsU6A08lGTzw8BNZtbRzDoCvyJ+CBfZGbr0htad/e+uEv77dHbLIyIikoFOnTrRo0cPmjdvnu2iiNSaguYiIiIiIk3TFcBs59yHSbaNAt4C3g+WfwJjdmLZBCAnB3peEHutIVpERBrcypUrufrqqzn44IMpLCykXbt2HH/88YwZM4bt27cDUFxcjJmxfPlyHnvsMU488URatWrFXnvtBcCMGTMwM4YNG5b0GOnG9J45cybFxcW0bNmSdu3ace6557J48eK0Zd6yZQu33XYbRx55JM2bN6d169accsopPPvss/Wqi2g5X3zxRfr06UPLli1p27Yt5513XtJyTZo0CTNj5MiRfPjhhwwZMoQOHTqQk5NTVZ50579161Zuv/12jjnmGFq1akXLli057LDDuPbaa1mxYkW19C+88AKnn3467dq1o7CwkEMOOYRbbrmFLVu2VEsrUh8NGjQ3syIzW2pmG9KkmWFmO8xsS2TZL7K9tZk9ZmabzGyNmd3SkGUUERERERFwzt3gnDslxbYy59yPnXNtg+Xq6BAushP1HBL7fdVc+PKD7JVFRGQPM2vWLHr27Mkf//hHKisrGThwICeddBJr167lpptuYs2a+GGxbr/9di655BIKCgo488wzOeKII+p1/Oeee45vfetbzJw5k6OOOooBAwYwf/58TjjhBJYuXZo0z5o1azjhhBO49dZbWb9+PaeddhonnHAC7777Lueeey533HFHvcoEMHXqVL773e9SWlrKWWedxX777cczzzzDiSeeyLx585Lm+eCDDzjuuOOYM2cO/fr147TTTiM/Pz/tcVatWsXxxx/Pr371K1asWMGpp57Kt7/9bQoKCrj33nuZPn16XPrrr7+es88+m1mzZnHEEUdUlXH06NEUFxezdevWep+7SCivgfd3G378w/Y1pLvROTc+xbY/AHsDXYB9gdfMbIVz7tGGK6aIiIiIiMhuYN8e0LEnrJ7vX8+fAt/6dXbLJCJ7HuegZDebtqKwDZhlnH39+vWcf/75bNy4kXHjxnHNNddgkf3NmjWLtm3bxuV59NFHef311znllKTfOdfJ5s2bGT58OBUVFTz22GNcdNFFAJSXlzN8+HAeeeSRpPkuu+wyFi1axA033MDo0aOrAtMff/wxAwYM4Oabb+aMM86gZ8+eGZftT3/6Ew8++CBXXnklAM45RowYwdixY7n88st59913q+V54oknuPrqqxk/fjy5ubm1Os4ll1zCokWLuOiii5gwYQItWrSo2rZkyRIqKiqqXj/55JPcfffd9OrVi2nTptG1a1cAysrKuPrqq3nwwQcZOXIkd911V8bnLRLVYEFzMzsGOAP4GZDRc4Nm1hwYAnzTObcB2GBmf8A/OqqguYiIiIiIND09B0eC5lOh381+6BYRkYZSshHGHpjtUtTNjSugaK+Ms0+YMIEvv/ySM888k2uvvbba9pNPPrnauiuuuKJBAubge3OvXbuW0047rSpgDpCXl8e4ceN4+umnqw05MnfuXF588UV69+7NHXfcERfkP+igg/j973/POeecw8SJE7n33nszLlvv3r2rAuYAZsaoUaN47LHHeO+993jrrbc46aST4vLss88+jB07ttYB8zlz5vCPf/yDjh07VguYAxx88MFxr8eM8aPEPf7441UBc4D8/Hzuuecenn/+eSZOnMjYsWPJ0WekNIAGuYrMLA+YAPwY2FGLLDeb2Vdm9h8z+35k/aFAATA3sm4ukPnXYyIiIiIiIruzI88HC27dNq6ET97ObnlERPYAr732GgBXXXVVrfOcffbZDXb8N954A4ALL7yw2ra2bdsyYMCAautfffVVAAYOHBgXMA/16dMHgHfeeadeZRsyZEi1dfn5+QwaNAiIlT2qf//+dZroM6z/oUOHVguYJ/riiy+YN28eX//61zn00EOrbS8sLOTYY49lw4YNLFmypNZlEEmnob56uR6Y75ybUYu0I4DuQAfgl8AfzOzcYFtLYGvCeIkbgFYNVE4REREREZHdS6uOcFBx7PW8J7JVEhGRPcYnn3wCQPfu3Wudp0uXLg12/M8//zztPpOtX758OQA33ngjZlZtad/ej5a8du3aepXtwAOTP3UQ9vAOy15TedOpS/2HE4K+//77Sc/bzPjb3/4G1P/cRUL1Hp7FzLrje5j3qk1659xb/8/efYdHWeX9H3+fTDJppADSe+8B9MEKUhVUmiJgXXBl14qirK7r7m8tu4+oj72xIiq6NrCBWFEUZFUWVDAUQUCKgLRASAjpc35/3JPMhARJmeRO+byua67c58w993xngjH55OR7goafGGOeBSYC7wJHgBhjTHhQcJ4ApFe0ThERERERkRoraSJs+dw5XjcfznsQIqLcrUlEao+oBKfdSU0SlRCSy5S0Yvu4TxlVvq+7Pp+v2Jy1tszPX9Dje8CAAbRv3/645xWE56FWUHNJyvvelOb1F7zuZs2albgCP1jDhg3LVYfIsULR03wA0AhY5/+H7gXijTF7gNHW2hUneHzwV46NQC7QGyjYVaAPsCYEdYqIiIiIiNRMXUdCRAzkHoXsw7BpEXQPXZsAEanjjKlQf/CaqFWrVmzYsIHNmzfTtWvXCl3L6/UCFOtBXqBgVXWw5s2bA4FV1MfasWNHsbmWLVsCcPHFF3PTTTeVq9bSOFFNBbVXRKtWrQDYvHnzCc8teN1NmzZlzpw5FX5ukdIIRXuWuUA7nHC7DzAFZ2V4H2BV8InGmERjzPnGmBhjjMcYMxS4BngbwFp71H+9fxhjEowxnYCpwOwQ1CkiIiIiIlIzRdZzgvMCyXPdq0VEpBYYNmwYALNmzarwtZo1awbATz/9VOy+lJQUvv/++2LzBf3H33zzzWL3paamsmjRomLzBTXPnz+/QvWeyNy5xf8fk5eXx9tvvw3AWWedVeHnKHgtr776KkePHv3Nc1u2bEmXLl1ITk5m69atFX5ukdKocGhurc201u4puAEHnWm7x1qba4z5yBhzp//0COAuYA9wCHgUmG6tDf4KcSNwGNgJfAU8b619uaJ1ioiIiIiI1GhJEwPHP30CRw+6V4uISA03ZcoUTjrpJBYuXMhTTz1VrPXIsmXLOHz4cKmu1a5dO1q3bs2aNWtYsGBB4XxGRgZ/+MMfSEtLK/aY8ePH06BBAxYtWsS8efMK5/Pz85k+fXqJq9ZPP/10hg4dyhdffMEtt9xS7Byfz8eiRYtK3KizLL766iteeOGFwrG1lrvuuosdO3bQu3dvzjzzzApdH+DUU09l8ODB7Nmzh2uuuaZYcL5582Y2bNhQOP7b3/5Gfn4+48aNY+3atcWut2XLliI1i1RUqDYCLWStXWKtTQwan2etvc9/vN9ae5q1Nt5/S7LWvnDM49OstZdaa+OstY2ttfeGukYREREREZEap/0giG3kHPtyYX3lrjQUEanNGjRowLx584iLi2Pq1Kl06tSJiRMnMmrUKNq3b8/ZZ5/NoUOHSn29u+++G4Bx48YxZMgQRo8eTYcOHVi7di2jRxdvpxUfH8+sWbMICwtj4sSJ9O/fn8suu4wuXbrw1ltvcfnll5f4PK+++ipJSUk89thjtGnThqFDh3LJJZcwYMAAmjZtyvDhw/n222/L9Z4UuO6665gyZQqnnXYal112Gb169eK+++4jLi6OF198sULXDvbvf/+bzp0788orr9C6dWvGjh3L+PHj6du3L507d2b58uWF515xxRXcfvvtrFq1ij59+tCvXz8mTJjAiBEj6NatGx07duSJJ54IWW0iIQ/NRUREREREpBJ4wqHnxYFx8rzjnysiIic0ePBgVq9ezR//+Efy8vKYP38+y5cvp3HjxsyYMYOmTZuW+lpXXXUVL774It26deOrr75ixYoVjBo1im+++Yb69euX+Jhx48bx6aefMmDAAFatWsVHH31E9+7d+eabb+jYsWOJj2nSpAnLly/nkUceoVOnTqxcuZL58+ezc+dO+vbty9NPP80VV1xRrvejwIQJE3jvvffweDwsWLCAnTt3MmbMGJYvX07fvn0rdO1gLVq0YOXKldx99900a9aMRYsW8cknn5CTk8O0adMYMmRIkfMfeOABFi9ezOjRo9m5cyfz589n1apVxMTEcNttt2mluYSU+a2db2sDY8y67t27d1+3bp3bpYiIiIhIDdCjRw/Wr1+/3lrbw+1aagt9Tx5Cu1fBrEGB8c0/QP22blUjIjWAz+dj48aNAHTp0oWwMK2flJJNnjyZl156iS+++IJBgwa5XY7UQmX5euT29+T6SikiIiIiIlJTNOsDJ3UOjJOLbyAnIiIiIhWj0FxERERERKSmMAaSJgTGyXOhlv/1sIiIiEhVC3e7ABERERERESmDXhPg8386xymbnJYtLU52tyYREalWNmzYwP3331+qc/v378+UKVMquSKRmkWhuYiIiIiISE1Svw20PhN2fO2Mk+cqNBcRkSL27NnDSy+9VOrzp0yZwpw5c5gzZ07lFSVSgyg0FxERERERqWmSJgRC8zVvwbn/BE+EuzWJiEi1MWjQIKzad4mUm3qai4iIiIiI1DQ9xoLH6xwfPQA/L3G1HBEREZHaRKG5iIiIiIhITRNdHzoPD4yT57pXi4iIiEgto9BcRERERESkJkqaGDj+8X3ITnevFhEREZFaRKG5iIiIiIhITdTpXIhKdI7zMp3gXETkGMaYwmOfz+diJSJS1wV/DQr+2lQdKTQXERERERGpicIjoceFgbFatIhICYwxeL3OHggZGRkuVyMidVnB1yCv11vtQ/NwtwsQERERERGRckqaCN+96BxvXQppv0J8M3drEpFqJy4ujpSUFPbu3QtAbGwsYWFaRykiVcPn85GRkVH4NSguLs7lik5MobmIiIiIiEhN1eo0SGwNqTvA+mDtW3DmVLerEpFqpmHDhmRkZJCVlcXu3bvdLkdE6rCoqCgaNmzodhknpF8rioiIiIiI1FRhYdBrQmCsFi0iUgKPx0Pr1q1p2LBhYasWEZGq5PV6adiwIa1bt8bj8bhdzglppbmIiIiIiEhNljQRlj3kHO9ZA3vXQ5Pu7tYkItWOx+OhcePGNG7cGGst1lq3SxKROsIYU+17mB9LobmIiIiIiEhN1qgzNO8Lu1c54+S5cM497tYkItVaTQywRESqktqziIiIiIiI1HRJEwPHa94En8+9WkRERERqOIXmIiIiIiIiNV3PcWD8/UHTdsH2r9ytR0RERKQGU2guIiIiIiJS09VrDB2GBMbJb7hXi4iIiEgNp9BcRERERESkNghu0bL+PcjNdK8WERERkRpMobmIiIiIiEht0PV8iIh1jrPT4KeP3a1HREREpIZSaC4iIiIiIlIbeGOh++jAOHmee7WIiIiI1GAKzUVERERERGqLpAmB402LICPFvVpEREREaiiF5iIiIiIiIrVFu4FQr4lz7MuDde+4W4+IiIhIDaTQXEREREREpLYI80Cv8YGxWrSIiIiIlFndCM0P74S0X92uQkREREREpPIlTQwc71wBKVvcq0VERESkBqoboXnOEXjmNFj9GljrdjUiIiIiIiKVp2kvaNQtMF7zpnu1iIiIiNRAdSM0B8g6DPOvg1fHw+FdblcjIiIiIiJSOYwpuiFo8lwtHhIREREpg7oRmpugl7n5U3jmdPj+ZX3jKCIiIiIitVNwaH7wZ9j1nXu1iIiIiNQwdSM0b9AeOg4LjLPT4L2p8MpFkPqLe3WJiIiIiIhUhoSW0HZAYPzDG+7VIiIiIlLD1I3QPCwcLn8LxjwNkQmB+S2fwzNnwLcvatW5iIiIiIjULsGrzde+Dfm57tUiIiIiUoPUjdAcnL5+fa+AG5ZDp+GB+Zx0eH8a/HssHNruXn0iIiIiIiKh1G00eCKd48yDsHmxu/WIiIiI1BB1JzQvEN8cLpsLY/8FUUGrzn9eAjPPhJWzwedzrTwREREREZGQiE6ELucFxslz3atFREREpAape6E5OKvO+1wK1/8XupwfmM85Ah9Mh5dHw8Gt7tUnIiIiIiISCkkTA8cbP4Ssw+7VIiIiIlJD1M3QvEB8M7jkNbhoNkTXD8xvW+asOv/vLK06FxERERGRmqvjsMDPOnlZ8ONCd+sRERERqQFCGpobY6KNMZuNMamlOLeJMeagMWb1MfPbjDGZxpgj/tsJr1UhxkDSeGfVedeRgfnco/DRbfDSSDj4c6WWICIiIiIiUinCvdDjosBYLVpERERETijUK83vBXaW8tyngOTj3Heptbae/5YYmtJOIK4JTHwFxj0P0Q0C89u/gmfOhOUztepcRERERGoVY8xoY8xqY0yGMWa3MeZa/3x3Y8xiY8whY8xeY8zzxpgYt+uVcup9SeB46zI4vMu9WkRERERqgJCF5saYk4HzgRmlOHc0cBIwJ1TPHxLGQK+L4YYV0H1MYD4vEz6+A148Dw5sdq8+EREREZEQMcaMAJ4BpgHxQA9gif/u14GNQBOgp//296qvUkKiZT+o39Y/sLDmTTerEREREan2wkNxEWNMOPAccEMpzo0HHsUJ2M84zmnPGmNmA5uAf1hrPwxFnaVWrxFMeBnWvQsf/AmOHnDmf1kO/zoLhvw/OP06CPNUaVkiIiIiIiH0D+Bea+0S//iQ/wbQDrjOWpsD7DfGvMfxv3eX6s4YZ0PQpQ844+R50H+auzWJiIhInWOtJSMnn9SjOaQezSUtM5fUzFxSj+aSmpnD4aOB452HMl2tNSShOTAdSLbWLjHGDDrBuQ8AL1trNxpjSvrG+0rgOyAfGAe8bYw521q7MkS1ll6PC6HtAPjwNlj3jjOXlwWL/grrF8CYp6FR5yovS0RERESkIowxscApwFxjzAYgEVgK3Gyt3QM8BPzOGLMKSAAuBJ53q14JgeDQfN862LMWmvZ0tyYRERGpkfJ9lvSsgoA7l9SjORwuCL+DAvDDmcXvz/PZUj1HZk5+Jb+K31bh0NwY0wFnhXnfUpx7FjAQ6HO8c6y1y4KGrxljxuKE51UfmgPEngTjX3QC9A9uhYz9zvzOFfCv/jDkr3DGjVp1LiIiIiI1SX3A4CxYGQ6kAP8C/g2cA3wMvACkAx5gPs5flkpN1bADtPgf2PWtM05+A5r+092aRERExFXZefkczsx1VngXht5OwF0YghcLxXNIz87Dli77rrFCsdJ8ANAIWGeMAfAC8caYPcBoa+2KoHPPAVoDO/znRgMx/nP7Wmt/LeH61WP3ze6joW1/+Oj2QA/A/Gz49O/+VefPQOOu7tYoIiIiIlI6R/wfn7DWbgcwxtwFbDLGtAA+xelhPhOIBZ7ECdQvdaFWCZWkiYHQfM1bMOweLf4RERGp4ay1HM3JD4TbRVZ4F297EtwW5aiLq7nrRYaTEB1BYoz/Fu0lISaCRP/cP+ZFsjPFtfJCEprPxVmJUuBM4EWc1eTHvrSHcFawFJgI/AEYBuwzxrQG2gL/xQnLLwTGAINDUGfFxTSAcbOdVefv3wJH9jrzu76DZwfAoDvgzJvBE6quNyIiIiIioWetTTXG7ABKWiPUDojBCdQtkGOMeRb4qCprlErQ8yL45C/gy4P0X2HbMmg/yO2qREREhEDLk+Ov8HZC77Qi9+dyODOH3Hx3ln2HGfzBtzcQgPvH8dGBADwxJoKEaG/h/fHREUR4wn7z2o9HR1TRqyhZhdNda20mUNiZ3Rhz0Jm2e/zjj4Bl1tr7rLVHCKxqwRhzGMgLOrce8ATQEcgDfgImWGuXV7TOkOp6AbQ+Az65E3543ZnLz4HF98KPC51V5026u1ujiIiIiMhvmwXcZIz5BDiIs7J8MbAapy3L9f6wPBpnocsqtwqVEIk9CToOg5/8a55+mKvQXEREJMRy8nz+9iY5QWF3yW1PDh/NKQy/07JyXWt54vWEFVnxHV8kAI8gIcYbCMCjnYA8ISaCuMhwwsKMO0VXspAvibbWLsHZSKhgfN5vnDsHmBM0Xs9v9DuvVmIawIX/gu5j4f1pzkoNgN2r4NmzYeCfnR3pPe7+VkRERERE5DjuBxoAP/jHXwBXWmuPGGNGAQ8A/wvkA18Bk1ypUkIraUIgNP/xPbjgYfDGuFuTiIhINRPc8uRwcIuTzBJWfB8TgFeHlicJx1nhXTA+ti1KVEQY/lba4qc+IhXVZQS0Xg6f/BVWv+LM+XLhi38634SOnald6UVERESk2rHW5gPT/bdj7/sK6F/lRUnl63I+eOMgJx1yjsDGD6HXxW5XJSIiUikKWp6kFunzXb1bnpiClifRx67wdsYJx2l7klCKlidSegrNQyE6EcY+DT3Gwns3QfpuZ35PMswaCGffBv1vhXCvu3WKiIiIiEjdFhEN3ccEFvwkz1NoLiIi1V52Xr7T2qRwVXcJAbi/9Ul1anmScLwV3v72JokltD2Ji6q9LU9qEoXmodTpHLhhOSz6G3z/sjPny4MlM+DH951gvVlvd2sUEREREZG6LWlCIDTf/Bkc2Q/1Grlbk4iI1HrBLU9SjzrtTgKrv4NaoBQcZ+ZVi5YnsV5P0Y0uSwjAi7RB8a/6jo7wqOVJDabQPNSiEmD0k06v8/dugrSdzvzeNfDcEGfF+dm3adW5iIiIiIi4o21/iGvu/IWszYd178Bp17hdlYiI1BCBjS6dNiaFx8GrvY8Zu93yJMzgbG5ZxpYn8VEReMPV8qQuUmheWToOheu/gU//Dt+96Mz58uDLB2HDB86q8+Z93a1RRERERETqnjCP05Ll6yeccfJcheYiInVMXn5w8H3MLWgFeMFcWmagL3hmrnurvr2esMKV3CVvcnlsKO4lISaCuEi1PJGyUWhemaLiYdRjTq/zBVPh8A5nft86eG4o9J8GA/8M4ZHu1ikiIiIiInVL70sCofmu7+DAJjipk7s1iYhImRRscnk4KNA+XgBeEIKn+Y+PZOe5Wnu9yHAn4C5hdXeRADyo5UlitJeoiDC1PJEqodC8KrQfBNd/DZ/dDStnO3M2H5Y97F91/gy0OMXFAkVEREREpE5p0gOa9IS9a51x8jwY8ld3axIRqYN8Pkt6dl5hmH38ADynyDj1qBN8u7XJJUCEx5AQHRFoe3LMLTHGW+Kq8IToCCI8anki1ZtC86oSGQcXPOzsVL/gRkjd7szv3wCzh8GZN8Ggv0BElLt1ioiIiIhI3ZA0AT4tCM3nwuA7Qav3RETKzFpLRk6+P8zOKWxn8tsheKDtic/F4NsTZgpD7viCsPvYADzm2DBcG11K7afQvKq1O9vpdf7ZPbDiWWfO+uCrx2DjhzDmGWjVz90aRURERESk9us1Hj69C7DOop5fVkDr09yuSkTEFTl5PtKynBA7LSvP/zGXtMy8oHlnXKTPt/9jnovJtzEQHxXc0qT0AXi9yHAF3yIlUGjuBm8snP+gf9X5DXBoqzN/4Cd44Vw44wYY/FeIiHa3ThERERERqb3imzuLerYudcbJbyg0F5Eaqyyhd0nnZeX63H4JxEWFl7iiOxCAe4vdHx+tTS5FKoNCcze1PQuu+xo+/wcsnwlYZ9X510/Cxo+cVef6plVERERERCpL0sRAaL72HRjxAIR73a1JROqk2hB6A8R6PSTGeP1Bd/Bml97fXAEeHx2BR8G3SLWh0Nxt3hgYMcNZdT7/eji4xZlP2QwvDIfTr4chf3POExERERERCaVuo+CDWyEvC7JSYfOn0PUCt6sSkRqotoTe4WGG+OgI4qPC/R8jiI8O938MzJcUgMdrg0uRWkOheXXR+nS47iv4/J/wzdOAdW7Ln4afPoIxT0ObM92uUkREREREapOoeCckX/u2M06eq9BcpI7K99nCHt0F/bprc+hdfN4Za3NLEQGF5tVLRDQM/99Ar/MDPznzB3+GF8+H066BoX93eqKLiIiIiIiEQtLEQGi+8WPITIXoRHdrEpEKyczJ59DRHA4dzSH1aK7/OJfUDP/HgvsycwvvP5yZi3VvL8tCnjA3Q6hRAAAgAElEQVTjrNpW6C0iLlJoXh21OhWuWQZLZsDXTzh9zrHw33/BTx/D6Keg3QC3qxQRERERkdqgwxCIaQhHUyA/G9YvgFMmuV2ViOCs/k4tDLdzOJSRWzwIPyYcTz2aS3aee6u+PWGm5GD72JDb3+9bobeIVEcKzauriCg45x7oNhoWXA/7Nzjzh7bBSyOh3xQYdg9E1nO1TBERERERqeE8EdBzHKyY5YyT5yk0Fwkxay1H/au/g8PtVH/wXWIQnpFDWlZelddaltD72MA7PiqCGK9CbxGp+RSaV3ctT4FrvoSlD8B/HgOb78yvnA2bFjmrztsPdLdGERERERGp2ZIuCYTm2/8DqTsgsbW7NYlUU3n5vsDK76O5HMoICsKPsyI89WguOflVv/o71ushMcZL/dgI6sd4neOYiMKP9WO8JPg/FszHRYYTFqbQW0TqNoXmNUF4pNPLvNsomH897FvvzKfugJdHwylXwTn3Opv4iIiIiIiIlFWLk6FBBzi4xRmveRMGTHe3JpFKZq0lIye/SOh9vJXgwSvC011Y/R0eZkiM8ZIYE1Es9C4WhMd6SYyOICEmgshwT5XXKiJSGyg0r0ma94U/LoEvH4L/PAI+//+ov3sRNn8Go59w+hGKiIiIiIiUhTHOhqBL7nPGP8yF/rc68yI1UG6+jz2Hs/jl0FF2Hcpkp/+2K/UoB4M2w8zNr/qdL+Miw0kMWvmdGB1RPPQuEopHUC8yXC1PRESqkELzmiY8Eob8FbqNhPk3wN41zvzhX+DfF8LJv4Nz/wlRCe7WKSIiIiIiNUvShEBofmAj7EmGZr3drUnkOIJD8Z2FobhzvOtQJr8ezsRXyXl4hMeU2O6k+IrwwDmJMRFEeMIqtzAREakwheY1VbPe8IfPnRXnX/5fYNX59y/D5sUw6gnoNMzdGkVEREREpOZo0A5anQa//NcZ/zBXobm4Jjffx6+pWYVBeOHH1MoJxeOiwouE20UC79iIoBXh/kA81kusNrwUEam1FJrXZOFeGHQHdL3A6XW+J9mZT9sFr46DPlfA8P+F6ER36xQRERERkZohaUIgNF/7lrN3kkc/Nkro5eT5+PVwZlDrlKIrxvekZYUkFG8SH0nL+jG0SIymZf1oWtSPpnFcVJGV34nREYRr9beIiATRdz+1QdNe/lXnj8HSB8CX68yvfgW2LIZRj0Pn4e7WKCIiIiIi1V+Pi+CjO5yfKY7sha1LoeNQt6uSGqggFC8pEN95KJM9aVnYEIbiLetH+28xhR+bJUQRFaGNMEVEpOwUmtcWnggYeBt0Pd9Zdf7ramc+/Vd4bQL0vhRGzIDo+u7WKSIiIiIi1VdMA+h0Lmz8wBknz1VoLiXKzsv3t0/JLNJCZVdqZshCcWOgSVxUiYF4i/rRNE+MIjJcobiIiISeQvPapkkPmLIYvn4ClsyA/Bxn/ofXYcsXMPJRJ1gXEREREalCaVl5fLFhHw1ivTSI9dKwnpcYr34cqZaSJgRC8x8XQvYRiKznbk1S5Y4XihesGN+bHppQvGl8VGHrlOBQvGX9aJopFBcREZfou9TayBMOA26FLufDguth13fO/JE98Mal0GsCnPeAs4pERERERKQK7D2cxVVzVhaZi4oIo2FsZCBI939sUK/gODIwX89LXGS4Nt2rCp1HQGQCZB+G3KOw8UMnSJdaJTsvn90lbbTpP96Xnh2yULxoIB4UiidE4w1XL3EREal+FJrXZo27wu8XwTdPwRf3QX62M79mHvy8BEY+At1GuVqiiIiIiNRdWbk+dqVmsis1s1Tnez1h1I+NoEFsZCBgDwrVG8QEVrE3iI0kMTqCsDCF7GUWEQU9xsD3Lzvj5LkKzWugrNx8dqdmFgbhu1KLhuJ707Ir/BzGQLP4qMIQvIVCcRERqSUUmtd2nnDoPy2w6nynf3VPxj6YewX0HAfn/R/ENnS3ThERERGp1aK8YbQ/KZaUjBwOZ+aW6xo5+T72pmWXOuwLM1A/xlukJUwD/wr2hscG7rFO6B7uUcAHQNLEQGi+5XNI3wtxTdytSQCw1pKWlcf+9Cz2pmWzLz2LfWnZ7EvPZm9aVmFQvi+94qF4WOFK8ZI32myaEKVQXEREaiWF5nVFo87w+09g+Uz4/B+Ql+XMr30bfl4KFzwMPca6W6OIiIiI1Fqt6sfw+Z8GAZCb7+PQ0RwOZuRw8EgOKRnOsfMx2zk+4r8/I4dDR3PwlaNNhM9Civ+6pZUQHVF0FXtQ0N6ghFXuURG1tN9y6zMhviWk7QTrc35uOON6t6uq1ay1HDqaWywE358eCMb3+j9m5/lC8pxhBpolFF8h3jJRobiIiNRtCs3rkjAPnHmj06NwwQ3wy3Jn/ugBeHMS/HoLDLvbzQpFREREpA6I8ITROC6KxnFRpTo/32c5nJnLwYzswjC9IGgPDtsL7jt0NIfc/PI1Yz6cmcvhzFx+PpBRqvNjvR7/SvVjVq/HFm0VUzAX4/XUjL7sYWGQNB7+86gzTp6r0LycfD5LSkZOUBheNBTfl57Nfv8tJz80YXiB44bi9aNp5Q/FI/TXFSIiIsUoNK+LTuoIV30IK2bBZ/dAnr+H5H8ehc7nQevT3K1PRERERCSIJ8wUhtAdG5/4/IL2FQeDgvWDGdlOuF5i6J5NVm75wsqMnHwyDmbyy8HS9WWPDA8LaglTdNV6YkwE9SLDiYsKp15k4Dg2Mpx6keFVv+I36ZJAaP7rati/ERp1qdoaqrG8fB8HjuQUWwXuhOCB1ikHjuSQX54/lTiBCI+hUb1IGsVH0SQuksbxkTSOi6JpQhSt/OG4QnEREZHyUWheV4V54PTroNO58NpESNnkzL8/Da75EjwR7tYnIiIiIlJOxhgSoiNIiI6g3UmxpXrM0Zy8Ii1hUk4QtB/JzitXbdl5PnYfzmL34awyP9YbHkZcZDj1osKJ9TofC8eR/mP/uN4xxwVBfGykh1hveOk2SG3cFZomwZ5kZ5w8F4b+vcx11zQ5eT72H/GvAk9zAvDgVeEFwXhKRjY29Fk43vAwmvgD8MZxkc4tPqrIxybxUdroVkREpBIpNK/rGnaAMU/DC+c6433r4esnYcCt7tYlIiIiIlKFYrzhxDQIp1WDmFKdn52XX6z3+rGBe3DQnnq0fJufBsvJ85GSV7Ye7ccTHKr/VuDeL+FcTvGH5tnfv8GmLlOJi/ZSL9J5XGR4WM1oNwNk5eYXtkcp3ECzMAQPtE45FILPVUlivJ6iAXhclH91uBOCF8zFR4fXmPdURESktlJoLk47llOugu9edMZLH4AeF0KDdu7WJSIiIiJSTUWGe2iWEE2zhOhSnZ+X7+PQ0dzCdjCFAXtQ6H44M5f07DwysvM4kpXHkey8cq9oP5HCa6f99nmNaMfySIPHWCIzdnHP0y+w0nYtvD/CYwLhu7dgRXs49aIi/OG7x2k1c8yq+MDq98BjPeVcNZ2RnRdYBZ6ezb7CFeGBub1pWaRnVc57GRcVXiQELwjAG/nnmsQ7QXm9SP34LSIiUlPo/9riGHYXbPgAMvZBXhZ8MB2ueBu0wkFEREREpMLCPWE08gepEFfqx/l8loycPDKy8zmSnUt6QZgeFKoXHAcH7ulB8xn++3Lyyt63fT/1+crXk7M9awC40LOMlXmB0Dw333LoaG5IVmfHeD3FV7tHBtrQxEaGk53nKwzB9/uD8Yyc/Ao/d0kSYyIKV4EXBOCFq8L9K8Qbx0UR7fVUyvOLiIiIexSaiyO6PoyYAW9f7Yy3LIa1b0Ovi92tS0RERESkDgsLM8RFRRAXFQFEVeha2Xn5TvgeHLgHBfElBe5HsvP4PvVczk53QvORnv9yT/4ksq03BK+uqKM5+RzNyWdfenbIr13AGGgY66VRYQBetE1K46BV4pHhCsNFRETqqpCG5saYaGANcJK1NvEE5zYBfgR2WGv7BM03B2YDA4EU4B/W2udCWaccR89xsPo1JzAH+Pgv0HGoE6iLiFQj1lpsZey8JSK1jjFGvYFF/CLDPUSGe2gQW8bAO7sHPDQTco8Sb47y4xVhZHYc7qxuD17NXrgKPtcfuDur4wtXwmflkZGTV2ScXY7V7yUJM3BSvaDe4PGRNCpojRIXmDupXiQRnrCQPKeIiIjUXqFeaX4vsBM4qRTnPgUkA8eG668DW4DGQE/gE2PMT9bapaEsVEpgDFzwMDxzutOiJWMffHYPjHrM7cpERMjPzyclJYX09HRyciq+AZqI1B1er5e4uDgaNmyIx6OVoyJlFlkPuo6ENfMACFszj9geY4iNDKdJfMUunZvvKxq4l9B6JrDyPRdveFiRELxglXjD2Mhy90QXEREROVbIQnNjzMnA+cCtwNwTnDsaJ1ifA0wLmu8A9AcmWGszgP8aY14Ffg8oNK8KDdrBwNth8b3O+LsXofelzmahIiIuyc/PZ8eOHWRlZbldiojUQDk5OaSkpJCRkUHr1q0VnIuUR9LEwtCcnz6BowchpkGFLxvhCSMxxktiTOjbvYiIiIiUV0hCc2NMOPAccEMpzo0HHsUJ2M845u4k4Fdr7d6gudXA9aGoU0rpjKmQ/Cbs/9EZvz8NrvkSPBHu1iUidVZKSgpZWVl4PB6aNGlCbGwsYWH602oROTGfz0dGRgZ79+4lKyuLlJQUGjdu7HZZIjVP+0EQ2wgy9oMvF9bPh//5vdtViYiIiFSKUK00nw4kW2uXGGMGneDcB4CXrbUbjTHHhub1gNRj5lIpy/byUnHhXqclywvDnfG+9fD1kzDgVnfrEpE6Kz09HYAmTZqQkJDgcjUiUpOEhYUVft3YvXs36enpCs1FysMTDj0vhv/OdMbJ8xSai4iISK1V4WV6/pYqNwB/KsW5Z+Fs8DnjOKccAY5NQxKA9IrUKOXQ+nQ4ZXJgvPQBOLjVtXJEpO6y1hb2MI+NjXW5GhGpqQq+fuTk5GgjYZHy6j0xcLzjGzi0zbVSRERERCpTKP62fQDQCFhnjNkDvAPEG2P2GGNOPebcc4DWwA7/uY8DPfznNsPZGLS5MSZ4+U8fYE0I6pSyGna38yeY4GwM+sF00A+ZIlLFgsMttWQRkfIK/vqh0FyknJr1gZM6B8bJb7pXi4iIiEglCkX6MBdohxNu9wGm4KwM7wOsOubch4COQef+HdjoP95rrd0CfAXcZ4yJ8YfulwPPh6BOKavo+jDi/sB4y2JY94579YiIiIiIiHuMgaQJgXHyXC2qERERkVqpwqG5tTbTWrun4AYcdKbtHmttrjHmI2PMnf5zjxxz7mEgzz/2+S95KdAC2A+8DdxurV1a0TqlnHqOgw5DAuOP7oDMY9vOi4iIiIhIndArKDRP2QS7j10nJSIiIlLzhfzv3K21S6y1iUHj86y19x3n3DnW2j7HzO3yPybWWtvKWvtcqGuUMjAGLngYwqOcccY+WHyPuzWJiIiIiIg76reB1mcGxslz3atFREREpJKoOaycWIP2MPD2wPjbF+CXFe7VIyIi1c62bdswxjBo0CC3Symmbdu2GGPcLuM37d+/n9/97nc0a9YMj8eDMYY5c+a4XZaISMmCW7SseQvyc92rRURERKQSKDSX0jljKjTqFhgvvFnfHIuIiITI1Vdfzb///W8aN27MpZdeyqRJk+jYsaPbZVVbxhjatm3rdhm1gjFmtDFmtTEmwxiz2xhzbdB9U4wxG/33bTPGjHGzVqlGeowFj9c5PnoAfl7iajkiIiIioRbudgFSQ4R7YdRj8MJwZ7xvPXzzFPS/xd26RERETmDx4sXk5lbfX/Tm5OTw4Ycf0rZtW1atWkVYmNY0SNUwxowAngGuAJYB8UAT/31/BKYBlwCrgcZArDuVSrUTXR86D4cfFzrjH96ATue4W5OIiIhICOmnMim91qfDKZMD4yUPwMGtrpUjIiJSGh06dKBr165ul3Fce/bsIT8/nzZt2igwl6r2D+Be/55E+dbaQ9baDcYYD3AvMM1au8o69lprf3a5XqlOkiYGjjd8ANnp7tUiIiIiEmL6yUzKZtjdENvIOc7LhA+mg7VuViQiUqft2LGDG2+8kU6dOhEVFUXDhg059dRTue+++8jMzCw8LyUlhdtuu63wvAYNGjBixAgWLVpU4nWXLVvGjTfeSFJSEvXr1yc6OpquXbtyxx13kJqaWimvpaD3uLWWxx9/nO7duxMVFUWLFi246aabSnzeyZMnY4xhyZIlfPLJJwwePJjExESMMYXn/1ZP89K+f+CsCH/88cfp168fcXFxxMbGcuqpp/L8889jy/n/wrZt29KmTRsAli5dijGmxNYj33zzDWPGjKFRo0ZERkbStm1brr/+enbv3l3smnPmzMEYw913381PP/3EJZdcQpMmTQgLC2P+/PkVej0HDhzgL3/5Cz179iQ2NpbExET69OnDX//6V1JSUgrP+/XXX3nwwQcZOHAgLVq0wOv10rRpUy666CJWrlxZ4rVTUlK488476dGjB/Xq1SMhIYHOnTvzu9/9jhUrVhR5bQDbt28vfL+qaz/96swYEwucAsQbYzYYY/YYY+YaY5oCXXBWnHcyxmw1xuw0xjxrjIlztWipXjqdC1EJznFeJvz4vrv1iIiIiISQQnMpm+j6MOL+wHjLYlj3jnv1iIjUYV9++SVJSUk8/fTT+Hw+xowZwxlnnMGBAwf461//yt69ewHYtWsXp556Kg899BA5OTmMHTuWvn378tlnnzF8+HAeffTRYte+7bbbmD17Nl6vlyFDhjB06FDS0tJ44IEH6N+/P0eOHKm01zV16lRuu+02WrZsyZgxY8jPz+fJJ59k4MCBpKeXvJLxtdde47zzziMjI4PzzjuPfv36nXDzz9K+fwAZGRkMGzaMadOmsW3bNvr378+gQYPYvHkzU6ZM4brrrivXa7344osZN24cAE2aNGHSpElMmjSJiy++uPCcV155hQEDBrBw4UK6dOnCRRddRGRkJDNnzuTkk09mw4YNJV5748aN9OvXjxUrVjB48GDOOeccIiIiyv161q9fT58+fbj//vs5ePAgI0aMYNCgQWRnZ3PfffexZs2awnMXLFjAn//8Z3bv3k2vXr0YO3YszZs359133+Wss84q9suaI0eOcPrppzNjxgxyc3MZPnw4w4YNIyEhgddff50PP/wQgI4dOzJp0iQAYmNjC9+vSZMmMWLEiHJ9Duqw+oABrgSGAx2BXODfQAP/OWOBfkAfoAPwSNWXKdVWeCT0uDAwTp7rXi0iIiIioWatrdU3YF337t2thJDPZ+3LY629K965PdjR2qOH3K5KRGqp/Px8u379ert+/Xqbn5/vdjnVxsGDB22jRo0sYB999FHr8/mK3L906VKbmppqrbV25MiRFrBXXnmlzcnJKTxn2bJlNiYmxno8HvvDDz8UefwHH3xgDx48WGQuKyvL/vGPf7SAveeee4rct3XrVgvYgQMHlvs1tWnTxgI2Pj7efvvtt4Xz6enpdsiQIRawt9xyS5HHTJo0yQIWsG+88cZvXjdYWd4/a6297rrrCt/D9PT0wvl9+/bZ0047zQL2/fffL9fr/q33bseOHTY6OtqGh4fbhQsXFs7n5+fbadOmWcD269evyGNefPHFwvfkxhtvtHl5ecWuW9bXk5uba7t27WoBO3369CL/jqy19vvvv7e//PJL4Tg5ObnYvylrrf3444+t1+u1HTp0KPKeF9Q8derUYo/Zu3evXbNmTZE5wLZp06bYub+lLF9LunfvboF1thp8L1tZNyDR/+/k6qC5DoAPOMt/39Cg+4YC+yrwfPqevDba9nXgZ4K7E609vNvtikRERKSWcPt7cq00l7IzBi54GMKjnHHGPlh8j7s1iUidZa3lcGZujbtZW7HWVs899xz79+9n5MiRTJs2rdiq6rPPPpuEhAR+/vln3n//feLj43niiScKVxoD9O/fn2uvvZb8/HyeeeaZIo8///zzqV+/fpG5yMhIHnvsMcLDw1mwYEGF6v8tN954I6ecckrhuF69ejz11FMYY3j++efJzs4u9pgLLriAiRMnFps/ntK+fwD79u1j9uzZtGvXjueee4569eoVnteoUSOeffZZgMKPoTR79mwyMzO59NJLGTlyZOF8WFgY999/P82bN2flypUsX7682GMbNWrEAw88gMfjKTJfntfzzjvvsGHDBpKSknjwwQeL/DsC6Nu3Ly1btiwc9+rVi6SkpGI1DR8+nPHjx7NlyxbWrl1bpCaAIUOGFHtM48aN6dmzZ8lvkJSbtTYV2IETjh8rGcg6zn0iAa1Og8TWzrH1wdq33K1HRESkJvD54Mv/gzcnw+7VblcjxxHudgFSQzVoDwNvh8X3OuNvX4Del0KrU92tS0TqnLSsPHrfU3Jf7ursh7vOJSE64sQnHsdnn30GwDXXXPOb5/3nP/8BnBA8MTGx2P1XXnkljzzyCMuWLSt2365du1i4cCEbNmwgLS0Nn88HgNfrZdOmTeWu/UQuueSSYnPdunWjd+/erF69muTkZPr161fk/tGjR5fpOUr7/oHTazw3N5cRI0YQGRlZ7P7evXsTFxd33F7dFVHwebn88suL3RcZGcn48eN5/PHHWbZsGaeffnqR+4cNG0ZMTEyxx5Xn9RS8X3/4wx9KvVlpdnY2H3/8MStWrGD//v3k5OQAFLZx2bRpE7169QIo/CXJnXfeSXh4OMOGDSMqKqpUzyMVMgu4yRjzCXAQ+Duw2Fqbbox5BbjDGLMKJzz/M1B5vy2TmiksDHpNgGUPOePkuXDmVHdrEhERqe4W3wNfPeYcb/wYxs2GbiN/+zFS5RSaS/mdMRWS34T9PzrjhTfDNV+Cp/whkIiIlM4vv/wCQIcOHX7zvIKNIo/dWLJAwfyxG0o+8sgj/OUvfykMOqtSwcaYx2rbti2rV68ucfPL1q1bl+k5Svv+AWzbtg2AmTNnMnPmzOOed+zGoaFQ3s8fHP89Kc/rKcv7BU4wPnr06MLnKklwf/qhQ4dyyy238NhjjzFq1Ci8Xi99+vTh3HPP5eqrrz7u65cKux+nf/kP/vEXOD3OAaYBTwFbgWzgPeDWqi5QaoCkiYHQfM8a2LsemnR3tyYREZHqatWrgcAcnM20514Bw++D069zujtItaDQXMov3AujHoMXhjvjfevhm6eg/y3u1iUiUoecaLPLE51XMB98//Lly5k+fToJCQnMmjWLQYMG0bRp08JVyc2bN+fXX3+tYOVl91stbcq7Krk0719+fj7gtCApqeVIVThRnSXdf7z3pCKvpzTvl7WWCRMmsG3bNq699lquvfZa2rdvT7169TDGcOeddzJjxoxin89HHnmEa665hgULFrB48WK++uorVqxYwYMPPsjcuXMZO3ZsmWqVE7PW5gPT/bdj78sArvLfRI6vUWdo3hd2r3LGyXPhHLVuFBERKWb7N86C02IsfPIXOLQVhs8Aj+La6kCfBamY1qfDKZPhuznOeMkD0H0sNGjnZlUiUofER4Xzw13nul1GmcVHVex/wa1atWLDhg1s3ryZrl27Hve85s2bA7B169YS7y9YCdysWbPCuXfffReAf/7zn0yaNKnI+ZmZmezZs6cipZ/Q9u3bC9t2BNuxYwcQeE0VUdr3Dyjs1T1o0CAeeeSRCj93WTRv3pyNGzeydetWOnfuXOz+7du3A0U/fydSntfTqlUrADZv3nzCczds2MCGDRv4n//5nxJXsv/888/HfWyXLl24/fbbuf3228nKyuLpp5/mT3/6E9dcc41Cc5HqLGliIDRf8yYMvctp3SIiIiKOg1th7uXgy3XGMSfBhf+C92+Fw87POayYBYe2w8UvQGS9419LqoS+k5GKG3Y3xDZyjvMy4YPpUMEN7kRESssYQ0J0RI27lXaF+PEMGzYMgFmzZv3mef379wfggw8+IDU1tdj9r7zyCgADBgwonDt06BAQCEqDvfnmmxXexPRE5s6dW2xuw4YNrF69mri4uJCs9i7t+wcwePBgPB4P77//fuEq7apS8Hl59dVXi92Xk5PDm2++WeS80ijP6yl4v2bPnn3Cz3/Bv5/gjUGD7/v0009L9ZxRUVFMnz6dZs2asW/fvsLNQgEiIiLIy8sr1XVEpAr0HAfGv+lw2i7Y/pW79YiIiFQnWWnw+iVwNMUZe7xwyWvQ6RyY8hk0Pzlw7qZP4MXzIK3q/7JXilJoLhUXXR9G3B8Yb1kM695xrx4RkTpgypQpnHTSSSxcuJCnnnqqWJC5bNkyDh8+TPv27bngggtIT0/n5ptvJjc3t/Ccb775hpkzZ+LxeLj++usL5wtWND///PNFzl+/fj1//vOfK/mVwVNPPcWqVasKxxkZGUydOhVrLb///e9L3LyyrEr7/gG0aNGCyZMns2nTJq688koOHDhQ7Hpff/01H374YYXrOtbVV19NdHQ0r7/+Oh988EHhvM/n484772TXrl3069ev2Cagv6U8r+eiiy6ic+fO/PDDD9xxxx3FAuvVq1ezc+dOADp27EhYWBiff/55kQ1js7KyuPbaazl48GCx55s/fz7Lly8vNr9q1Sr27t1LXFwc9evXL5xv3rw5e/fuLfEXQSLignqNocOQwDj5DfdqERERqU7y8+Ct38P+DYG50U9B69Oc47gmMPkD6Bq0EeieZJg9FPasrdpapQiF5hIaPccV/Ub5ozsgUz/IiohUlgYNGjBv3jzi4uKYOnUqnTp1YuLEiYwaNYr27dtz9tlnF674ffbZZ2nXrh0vv/wynTp14tJLL2XYsGEMGDCAjIwMHnzwwSKrt6+66iqaNm3KwoUL6dKlCxMnTuScc86hT58+DBgw4LgbdYbKFVdcwWmnncaIESOYOHEiHTt25LPPPqNHjx7cc09o+uSW5f0DeOKJJxg8eDCvv/564f2XXHIJgwYNomXLlpx11lksWrQoJLUFa926NbNmzcJay6hRoxgwYACXXXYZ3X5TRp4AACAASURBVLt35+GHH6ZJkya8/PLLZb5uWV9PeHg4b7/9Nk2bNuXBBx+kTZs2jB8/ngsvvJDu3bvTt2/fwtYtjRs35uqrryYtLY3evXszcuRIxo8fT9u2bfn888+ZPHlysXqWLFnCGWecQcuWLRk1ahSXX345gwcP5tRTT8Xn8/GPf/yDiIjARuOjR48mLy+Pk08+mSuuuIIpU6bwf//3f2V/g0UkdJImBo7Xvwe5od8cWUREpMb59P/B5qC/tBwwHXpPLHqONwYmvAyn3xCYS9sFL4yAzZ9VTZ1SjEJzCQ1j4IKHIdy/6VjGPlisDYBERCrT4MGDWb16NX/84x/Jy8srXK3buHFjZsyYQdOmTQFnZfHKlSuZPn064eHhvPPOO3z33XcMHTqUTz75hFtvvbXIdRs2bMjKlSu57LLLyMnJ4b333mPXrl3ce++9vP7665X+up588klmzJjB9u3bWbBgAcYYbrjhBpYtW0ZCQkLInqe07x9ATEwMixYtYvbs2Zx88smsXbuWd999ly1bttChQwcefPBB/vSnP4WstmBXXHEFX375JSNHjuTHH3/krbfeIjMzk+uuu47vvvvuhD3ZS1Ke19OzZ09Wr17N9OnTiY2NZeHChSxdupTIyEj+9re/FfnFy8yZM3n44Ydp164dixcvZtmyZQwbNoxvv/22xF+6TJ48menTp9O8eXNWrFjB22+/zdatWzn//PP54osvuPnmohsmzZgxgxtvvJG8vDzmzp3L888/X2Qlvoi4oOv5EBHrHGenwU8fu1uPiIiI2759EZY/Exh3GwWD/1byuWEeGHEfnP8QGH9cm5MOr05wriNVzlR2X1K3GWPWde/evfu6devcLqVuWPYwLL43ML76U2h1qnv1iEiN5/P52LhxI+BsEhimjcVqrbZt27J9+/ZK75kudVNZvpb06NGD9evXr7fW9qiq+mo7fU9eR7x7Lfzg/+Vql/Ph0sr/RauIiEi19PNSeOUi8PnbGjZNgt9/DN7YEz9248dOS5fcjMDcWTfD0Lvr1Ebbbn9PXnfeaakaZ0yFRkEr3hbeDPm5xz9fRERERERqh6QJgeNNiyAjxb1aRERE3HJgM8z7XSAwr9cULn2jdIE5QJcRcNWHzuMKfPU4vHWV2p9VIYXmElrhXhj5WGC8bz1885R79YiIiIiISNVoNxDqNXGOfXmw7h136xEREalqmYfg9YmQ5d/nLzwKLn0NElqU7TrN+8AfFkPjoEXW6+fDS6Mh40Do6pXjCne7AKmF2pwBJ0+C719yxksegO5joUE7d+sSEZEqM3/+fObPn1+qc6dMmUL//v0ruaKqceDAgVL3Nu/atSt33HFHJVckIlKFwjzQa3xg0UzyPDj1D+7WJCIiUlXyc2HeJEjZHJgbOxNanFK+6yW0dFq6vDkZtix25naugNlD4fK34KROFS5Zjk+huVSOYXfDxg8hYz/kZcKHf3L+gzbG7cpERKQKrF69mpdeeqlU5w4aNIj+/fuzbdu2yi2qChw5cqTUr3vgwIEKzUWk9kmaGAjNd66AlC3QsIO7NYmIiFQ2a+Gj22Hr0sDcoDuh50UVu25UPFw218nVvpvjzB3aBrOHwSWvQduzKnZ9OS61Z5HKEdMAhs8IjDd/Buveda8eERGpUnfffTfW2lLdJk+e7Ha5IdO2bdtSv+4lS5a4Xa6ISOg17QWNugXGa950rxYREZGqsmIWfPtCYNxzHAy8PTTX9kQ4rZCH3ROYy0qFf491/qpLKoVCc6k8vS6G9oMD44/vgMxU9+oREREREZHKZUzRDUGT5zqr70RERGqrTZ85mVeBFqfAmKdD223BGOg/DcbPAU+kM5efA+/8AZY+qP/XVgKF5lJ5jIGRjzibHgAc2QuL73W3JhERERERqVy9xgeOD/4Mu75zrxYREZHKtG8DvHUVWJ8zjm/htE2JiK6c5+txIUx+H2IaBua++F+Yfz3k5VTOc9ZRCs2lcjVoD2ffFhh/+wL8ssK9ekREREREpHIltoK2AwLjH95wrxYREZHKkpECr0+E7DRnHBELl74BcU0r93lbnQpTPoOGQRuB/vAavHKROjyEkEJzqXxn3gSNuvoHFhZOc3YUFhERERGR2im4Rcvat/X9v4iI1C55OTD3CmdTTgAMXDQLmiVVzfM3aA9XL4I2QRuBblsGz58bVJNUhEJzqXzhXmfDggL71sE3T7tXj4iIiIiIVK5uowM9VzMPwubF7tYjIiISKtbC+7fAjq8Dc8Pugm4jq7aOmAZw5buQNDEwd2AjzB4GO9UaraIUmkvVaHMGnDwpMF5yv37zJSIiIiJSW0UnQpfzAuNktWgREZFa4usnYfUrgXHvy+Csae7UEh4JFz4LA/8cmMvYD3MugB8XulNTLaHQXKrOsLshtpFznJcJH0zX7r4iIiIiIrVV8Mq3jR9B1mH3ahEREQmFDR/Cp38PjFufAaMeA2Pcq8kYGHwnjJ0JYRHOXF4mzL3SCfiVvZWLQnOpOjENYPiMwHjzZ7DuXffqERERERGRytNxGETXd47zsrTiTUREarY9a+HtKYA/hE5sDRNfcVZ7Vwd9LoMr34HIBP+EhUV/cxat5ue5WlpNpNBcqlavi6H94MD44zu0s6+IiIiISG0U7oUeFwXGyXPdq0VERKQijuyD1y+B3Axn7I2Dy+ZB7Enu1nWsdmfDlE+dQL/At8/DG5dCdrp7ddVACs2lahkDIx+B8ChnfGQvLL7X3ZpERERERKRy9L4kcLx1GRze5V4tIiIi5ZGbBW9cBod/ccYmDMa/CI27uVvX8TTqAlMWQ4tTAnObFsGL50HabvfqqmEUmkvVa9Aezr4tMP72BfhlhXv1iIhIhW3btg1jDIMGDXK7lGLatm2LcbPHoIhIXdayH9Rv6x9YWPOmm9WIiIiUjbXw3o2wc2Vg7tz/hU7nuFdTadRrDJPeh26jAnN71sBzQ52PckIKzcUdZ94Ejbr6BxYWToP8XFdLEhERERGREDOm6IagyfPcq0VERKSsvnyo6C98T5kMp1/nWjll4o2B8S/DmVMDc+m74YURsOlT9+qqIRSaizvCvTDyscB43zr45mn36hERkVpr8eLF/Pjjj26XISJSdwWH5vvWORup/X/27js8qmpt4/BvpVFDCUUEpEWKIBFFUDoISBNBRQIoioi9HpVPj3pQsHAsB48eBEWwSxdRVJSOqKCIIgqKSAep0iGQtr4/VsJOKCGQSXZm8tzXNZfzrtkz8ySGZOadtdcSERHJ75ZPhblPe3W1FtD5RfeBcLAIC4PLn4Yu/3HLygAkHoCx8W7lBzkpNc3FP1WbwEU3evW8f8Pudb7FERGR0BQbG0udOnVOfaCIiOSOMrFQ6WKvXjbevywiIiLZsflH+Oh2r46JhZ7vQnikf5lyotEA6D0Booq72qbAp/+AGf+C1FR/s+VTAW2aG2OKGGP+NMbsyeKYycaYLcaYfcaYtcaYx4+5fZ0xJsEYcyDtctLHkhDQ7kkoVs5dT06Azx5060WJiEi2bNiwgbvvvpuaNWtSuHBhypQpQ+PGjXn22WdJSEg4etzff//NwIEDjx4XExNDx44dmTFjxgkfd8GCBdx9993ExcVRunRpihQpQp06dXjkkUfYsyd3/jSnrz1ureXll1+mbt26FC5cmEqVKnHvvfee8Hn79euHMYZ58+bx5Zdf0qZNG0qVKoUx5ujxWa1pnt3vH0BiYiIvv/wyjRo1Ijo6mmLFitG4cWPGjBmD1d8uEZGsZZxt/stkSE3xL4uIiEhW9v3lNv5MTns/ULgk9JkARWP8zZVTtS6Hm6ZD9Nne2LevwKQbISnh5PcroAI903wIsOkUxwwGqllrSwCtgD7GmOuPOaa3tbZ42qVUgDNKflI0BjoM9eo/Z8Hyj/zLIyISRL766ivi4uJ49dVXSU1NpVu3bjRp0oSdO3fy2GOPsW3bNgA2b95M48aNefHFF0lMTKR79+5ceOGFzJo1iw4dOvDSSy8d99gDBw5k9OjRREVFcdlll9G2bVv27dvHc889R/PmzTlw4ECufV333HMPAwcOpHLlynTr1o2UlBT+97//0apVK/bv33/C+4wdO5ZOnTpx8OBBOnXqRKNGjU65+Wd2v38ABw8epF27dtx///2sW7eO5s2b07p1a/78808GDBjAHXcEybqGIiJ+Of9qCItw1/dvgXUL/M0jIiJyIomHYFxv97cKwITDte9A2Zr+5gqUs+NgwGw4q7439tsn8E5XOLDDv1z5UESgHsgYcxHQGXgAmHCy46y1GbdotUAqECI/eXJG6veApR/Amrmu/uIRiL0MiujzEhHJBmvh8F6/U5y+wiVztBbe7t276dGjB3v37uWll17ivvvuy9Qk/uqrryhdujQAt99+O2vWrKFv376MGTOGyEh3SuHXX39Nhw4dGDhwIG3btiUuLu7o/QcNGkSTJk2OPgbAkSNHuPfeexk1ahTDhg1j0KBBZ5w/K++99x4LFy6kYcOGABw4cIBu3boxZ84cnnjiCYYNG3bcfd544w3Gjx9PfHz8cbedyOl8/8B9iLBgwQL69u3LiBEjKF7cnda4Y8cOunbtyuuvv07Xrl3p0qVLTr50EZHQVawsnNsO/vjC1T9PgBqt/UwkIiKSWWoqTL0dtiz1xjq/ALFt/MuUG0pWgv7TYVI/N3kVYNNiGN0WrpsM5Wr5Gi+/MIE4ndgYEwF8BzyYNjQ1qxnixpgRQD+gCLAeaGOtXZt227q08XBgFfCUtfbzHGRbXrdu3brLly8/04eQvLBrDYxoAsmHXX3xzXDF8U0RESl4UlNTWblyJQC1a9cmLOyYk6QS9sBzVX1IlkMPr8/Rh4PPP/88Dz/8MFdccQXTpk076XFr1qwhNjaWEiVKsH79ekqVyvycDz74IMOGDeO2227jtddeO+XzJiQkUKJECeLi4liyZMnR8XXr1lG9enVatWrFvHnzzuhrqlatGuvXr+fRRx/lmWeeyXTbb7/9Rr169YiOjmb79u0UKlQIcMuzvPPOO3Tp0oVPP/00y8fN+Jonu98/gO3bt1O5cmUqV67Mb7/9dvS50/388880aNCArl278sknn5zJly554JS/SzKoV68eK1asWGGtrZdX+UKdXpMLAL9+CJP7u+tRxeGhVRBV1N9MIiIi6eY8DV+94NWNb4POz/uXJ7elJMP0gZk3BC1cEnqNhWrN/cuVxu/X5IFanuVBYJm1dl52DrbW3gkUBxoB7wG7M9zcF6gOVAL+B3xojGkUoJySX8XUgJYDvfqHN2Hj9/7lERHJ52bNcjMCbrvttiyP+/rrrwHo3LnzcQ1zgL59+wJuDfNjbd68mddee43777+f/v37069fP+644w6ioqJYtWpVTr+Ek+rVq9dxY+eddx4XXHAB+/btY9myZcfdfuWVV57Wc2T3+wcwf/58kpKS6Nix43ENc4ALLriA6OhoFi9efFoZREQKnFqdICraXU88ACvPeG6UiIhIYC2blLlhHtsWOjzrX568EB4BXYZB+6e8scN74d3u8LM27c7x8izGmFjgLuDC07mftTYV+MEY0wZ4ERiQNp7xXftYY0x34BpA70RDXdN74ZdJsON3wMK0++G2+cG7M7GISC7auHEjALGxsVke99dffwFutvWJpI+nH5du2LBh/POf/yQxMTFnQc9A1aonPnOgWrVqLF269LisAFWqVDmt58ju9w/cLHqAkSNHMnLkyJMed+zGoSIicoyoolD3Src0I8CyiW6pRhERET9t/B4+vsury9aGa99yTeVQZww0uxdKV4Upt7oVIFKT4KPbYPc6aPVwjpYVDWaB+L/fAigHLE9bCzQKKGGM2Qpcaa091XThSLJe0zw1ABklGEREwRX/hbc6unr7clj4KjS/399cIpK/FS7pljoJNoVLBuRhTrXZ5amOSx/PePuiRYt48MEHKVmyJKNGjaJ169ZUqFDh6CzrihUrsmXLlhwmP31ZLSlXuHDhM3rM7Hz/UlJSALjwwgszrfsuIiJnIC7ea5r/OcttOla8nL+ZRESk4NqzAcb3gZQjri4SA33GB+z9WtCo2w2iK8K4XnBopxubN9Q1zru+4np2BUwgmuYTgC8y1E2Bt4AGwN8ZDzTGVAUuBr4EDgGXAvcCr6TdXgWohlsfPRW4CugGhNiK+3JSVZvARTfAj++6et6/oV53KF3N11giko8ZUyA3Dj7nnHP4/fff+fPPP6lTp85Jj6tYsSIAa9euPeHt6bOozz777KNjH330EQBPP/00N954Y6bjExIS2Lp1a06in9L69eupX7/+ceMbNmwAvK8pJ7L7/QOoXLkyAK1btz7hJqQiInIaqjV3b8r3/wU2BZZPgUtOvVSWiIhIwB3ZD+N6w8Edrg6LhPj33RLCBdE5jWDALBjbE3b+4cZ+Hgd7N0H8e1CktL/58liO1zS31iZYa7emX4BdbthutdYmGWOmG2MezXCX+4FNwB7gTdy65f9Ou604roH+N7ADeAjoaa1dlNOcEkTaDYaiZd315AT47CEIwIa1IiKhpF27dgCMGjUqy+OaN3cbuHz22Wfs2bPnuNvff/99AFq0aHF0bPdut9XIOeecc9zxkyZNynLGdyBMmDDhuLHff/+dpUuXEh0dHZDZ3tn9/gG0adOG8PBwPv3006OzzkVE5AyFhWdekmXZ8b/zRUREcl1qCnx4C2z71Ru74iWo1sy/TPlBTHW4eQZU894fsm4BjLkcdp14IlaoCtRGoEdZa+dZa0tlqDtZa59Nu77eWtvCWlvKWlvCWlvHWvtM2vrmWGtXWGsbWGuLpx3T2Fo7LdAZJZ8rGgMdh3r1nzNhxVT/8oiI5EMDBgygbNmyTJs2jeHDhx/XyF6wYAF79+6lRo0adOnShf3793PfffeRlJR09JiFCxcycuRIwsPDufPOO4+O16pVC4AxY8ZkOn7FihU8/PDDufyVwfDhw/npp5+O1gcPHuSee+7BWkv//v1PuBnn6cru9w+gUqVK9OvXj1WrVtG3b1927tx53ON9++23fP65NrQTEcmWCzJs+Lx5CezMvc2lRURETmjWk/DHdK9ueg9c1Ne3OPlKkdJw/RSIy/D3eucfMLodbCw4W04GvGkuEhD1r4Uarb16+sNuB18REQEgJiaGiRMnEh0dzT333EPNmjWJj4+na9eu1KhRg5YtWx6dMf76669TvXp13n33XWrWrEnv3r1p164dLVq04ODBgzz//POZZm/fdNNNVKhQgWnTplG7dm3i4+Np3749DRo0oEWLFifdqDNQrr/+ei655BI6duxIfHw85557LrNmzaJevXoMHjw4IM9xOt8/gFdeeYU2bdowbty4o7f36tWL1q1bU7lyZZo1a8aMGTMCkk1EJOSdVQ/OOt+rl030L4uIiBQ8P70P377i1bU6uVUPxBMRBVe9Bq3/6Y0d2gnvXAErPvYvVx5S01zyJ2OgyzAIT5tNeGAbzB7ibyYRkXymTZs2LF26lFtvvZXk5GSmTp3KokWLKF++PEOHDqVChQqAmym9ePFiHnzwQSIiIpgyZQpLliyhbdu2fPnllzzwwAOZHrdMmTIsXryYPn36kJiYyCeffMLmzZsZMmQI48aNy/Wv63//+x9Dhw5l/fr1fPzxxxhjuOuuu1iwYAElSwZuQ57sfv8AihYtyowZMxg9ejQXXXQRv/76Kx999BGrV68mNjaW559/noceeihg2UREQl5cT+/6sglajlFERPLGum9g2v1efdb5cM0bbvkwycwYaP0IXPW6W+8dIPkwTLwRvnkl5P92m9xel9RvxpjldevWrbt8+XK/o8iZ+OoFmPN0WmHg5pluYwIRKTBSU1NZuXIlALVr1yYsTJ/3hqpq1aqxfv36XF8zXQqm0/ldUq9ePVasWLHCWlsvr/KFOr0ml+Ps3Qwv1QPSfuf3nwFVLvE1koiIhLhda+CNtpCwy9XFysEtc6BUFX9zBYO1C2DCdZlXgbi4P3R6AcIjcuUp/X5Nrs6D5G9N74OytdMKC5/eDylJWd5FRERERETyuZKVoHpLr1423r8sIiIS+g7vhbG9vIZ5eCHoNVYN8+yq3gJungWlMizV+cObMK4XHNnvX65cpKa55G8RUdD1v1697VdYNMK/PCIiIiIiEhhx8d71X6dAcqJ/WUREJHSlJMOkm2DnSm+s23A4p7F/mYJRuVowYDZUzrACxJ8z4c1O7gyyEJM78+dFAqlqU7joBvjxXVfPHQp1u0Pp3N2ITkREztzUqVOZOnVqto4dMGAAzZs3z+VEIiKS75zXFT57wK2PeniPe+Ndp4vfqUREJNTMeAxWz/bqlgMz760h2Ve8HNw4DabcCr994sa2/QKj20KfiXB2nL/5AkhNcwkO7QbD75+7nXqTE+Dzh9w/RmP8TiYiIiewdOlS3nnnnWwd27p1a5o3b866detyN5SIiOQvhUu4JvmvH7r65/FqmouISGAtHgPfvebVdbtB60f9yxMKIovAte/ArCfg21fc2P4t8FYn6PEW1Lrc33wBouVZJDgUjYGOQ7161QxYkb0ZjCIikveefPJJrLXZuvTr18/vuCIi4peMS7T88QUk7PEvi4iIhJbVc+HzgV59dgPo/hpksSG8ZFNYGFz+FFzxEphwN5Z4AMbFw+LR/mYLEP2USPCofy3UaO3V0x/OvGuviIiIiIgEl9jLoGgZdz0lEVZ87G8eEREJDTtXwaQbwaa4Ovps6D0Ooor6myvUXNzfrQQRVdzVNhU+exC+fAxSU/3NlkNqmkvwMAa6DHM7HAMc2Aazh/ibSUREREREzlx4JJx/jVcvm+hfFhERCQ2HdsHYeG+iZUQR6DUWSlT0N1eoqtkO+n8B0Rm+vwuHw6QbIPGQf7lySE1zCS5lYqFVhlNrFo+BjYv9yyMiIiIiIjkT18u7vv5r2LPBvywiIhLcUpLcDPNdq72xq16DShf5l6kgqFAfbpnt/pvut2nwzhVwYLt/uXJATXMJPk3vg7K10woLn97vfimKSEgyGTb8TQ3y07tExD8Zf38YbSR+lDHmSmPMUmPMQWPMX8aY24+5/SxjzC5jzFK/MkoBUOkiiIn16l8m+ZdFRESCl7Xw+UOw9itvrM3jUK+7f5kKkhIV4abpUDPDRqCbl8DotrBjpX+5zpCa5hJ8IqKg63+9etuvsGiEf3lEJFcZY4iKigLg4MGDPqcRkWCV/vsjKipKTfM0xpiOwAjgfqAEUA+Yd8xhw4FleZtMChxjMm8I+vME1/gQERE5Hd+9Bkve9ur610LLh3yLUyAVioZe46DRAG9szwYY0z7zhxlBIMLvACJnpGpTuOgG+PFdV88dCnW7Q+mq/uYSkVwRHR3N33//zbZt2wAoVqwYYdrxXESyITU1lYMHDx79/REdHe1zonzlKWCItXZeWr077QK4WehAWeBtXGNdJPfEXQvznnXXd66Ercvg7Av8zRQMrIXtK2D1XFgzDzb/AGVruYZFsTJ+pxMRyTurZsKXj3p15UZw5XD3wazkrfAI6PwilK4OMx4HrFtf/r2r4cpXoEEfvxNmi5rmErzaDYbfP4dDOyE5wZ2C02eifiGKhKAyZcpw8OBBDh8+zF9//eV3HBEJUoULF6ZMGTWRAIwxxYCGwARjzO9AKWA+cJ+1dqsxpgTwEtAZaOJfUikwYmrAOZfAxu9c/fMENc1PZt8WWJPWJF8zDw5sy3z7xu9g9mDXmBARKQi2/waTbgKbthxficpu48/Iwv7mKsiMgaZ3u8mtH97i+napSTD1Dti9Dlr/M9/37zRNT4JX0RjoONSrV82AFVP9yyMiuSY8PJwqVapQpkyZo0u1iIhkV1RUFGXKlKFKlSqEh4f7HSe/KA0YoC/QATgXSALeS7v9OeBda23wLUApwSuup3f918mQkuxflvzkyH5Y+QVMfwRevQSG1XFNh2UTjm+Yp/vpPddEEhEJdQd3wth4SNzv6shi0GcCFC/vby5xzusK/T6DYuW8sfnPwUe3QfIR/3Jlg2aaS3Crfy0s/cDNsACY/jDEXgaFS/oaS0QCLzw8nPLly1O+fHmstVitdSoi2WCM0RrmJ3Yg7b+vWGvXAxhjngBWGWOaAa2ABn6FkwKq3tWuMZya5JrBa+fDuW39TpX3UpLhr5/cbPLVc2HT95Ca1QcIxs3Kj20DKz6GXWvcbMtZT7rGkYhIqEo+AhOuhz3r0wYMXDMaKpzvayw5RuWGMGAWfNDTLcEG7oPfvZsg/n03KTYfUtNcgpsx0GUYjGgCKUfci+vZQ6DLf/xOJiK5SE0wEZGcsdbuMcZsAE70CeRVQBVgQ9rv2iJAUWPMVuBCa+2WvEsqBUrRGKh5Oaz8zNXLJhSMprm18Pdqb8mVtQvgyN6s71OqCtRo4xrl1Vt5DYeKF8HEvu76H1+4x6reIlfji4j4wlqYdj9sWOiNtR8MdTr7l0lOrnQ1uPlLmNAX1i1wY+u/gTGXw3UT3TJt+Yya5hL8ysRCq4Ew52lXLx4Dcb3gnEb+5hIRERHJ30YB9xpjvgR2AYOA2cCTwIsZjosHbgHaAdvzOKMUNHE9vab5b9PgyAEoVNzfTLnh4E43kz59A8+9G7M+vnBJqN7Sa5SXrn7itWDP65p5bfiZ/4IBc0AbqItIqPnmZfh5rFc3uB6a3utfHjm1IqXh+ikw7T7v/93fq2B0O+g9Hs5p7G++Y6hpLqGh6X2wbFLaaR4WPr0fbp0H4ZE+BxMRERHJt/4NxAA/p9Vzgb7W2gN4y7dgjNkLJFtrt+Z9RClwanWEQiXgyD5IOgQrP8+81nmwSkqADYu8JVe2Lsv6+LBI1/yObQ01LoOKDSAsG3syGAPtn4I3L3f1Xz/B8ilQv0eOvwQRkXzjt0/dElTpqjSFK17K9xtLChARBd1HQEx1mPuMGzv0N7x9BVz9OtS7yt98GahpLqEhIgq6/hfeqwb1GAAAIABJREFU6uTqbb/CohHQ7D5/c4mIiIjkU9baFODBtEtWx70NvJ0HkUQgsjDU7eY2sgS3REswNs1TU2HbL2kzyee6hnny4azvU76uN5O8SpMzn2Ff5RI470r47RNXzx7sZqBHFDqzxxMRyU+2LIMpt3J0hblSVd262BFRvsaS02AMtPo/t2TLx3dBSqJbcnlSP9i93vXy8sEHIGqaS+io2hQu7Ou9wJ47FOp2h9JV/c0lIiIiIiLZd0Ev7zX96jmwfxtEn+VvpuzYs9GbSb52vps5l5XiFVyDvEYbqNEKoisELku7J90s/dRk2LMBvn8Dmt4duMcXEfHD/m0wrjckHXR1oRLQZyIUK+NvLjkzcT2hRCUY3wcO73Fjs56A3Wuhs/97FappLqGl/RBYOR0O7YTkBPj8IfcLNB98QiUiIiIiItlQpSmUqAz7NoFNhV8/hCZ3+p3qeIf3uo020xvlu1ZnfXxkMajWPK1R3hrK1cm99yllYuHi/vD9KFd/9QJceJ1bT1ZEJBglJbjm6r5NrjZh0OMtKF/H31ySM9WawYBZ8EEP2L3OjS15230QbVP9TKamuYSYojHQ4Vn46FZXr5oBK6bmqzWRREREREQkC2FhEHctfP2Sq5dNyB9N85Qk2LTYW3Jl85Ks39CbMKjUMG0meWuo3Chvlw9o9TAsHQeJ+90MvgX/gcufzrvnFxEJFGvh47th8w/eWIehULOdf5kkcMrWhAGz3VkEm753Y6tnw+4kX2OpaS6hJ64nLP3AnRIJMP0RiL3M7TgvIiIiIiL5X1wvr2m+ZSnsWAnlaudtBmvd86bPJF//DSQeyPo+MbGuQR7bBqq1gCKl8iLpiRUrC83vhzlPufq716HRLVq+UkSCz1cvwK+Tvfri/nDJbf7lkcArVhZu/AQ+ut1NfgW3zrmP1DSX0GOM2zV5RBP3D+zAVpj9FHR50e9kIiIiIiKSHeXrQIU42LrM1csmQNtBuf+8+7fBmnmuUb5mHuzfkvXxRWLceuTpG3iWqpL7GU/HpXfC4tHu60hJhDlPwzVv+J1KRCT7fp0Cc5/x6uqtoNPzWoY3FEUWcUvuzK4G3/zX7zRqmkuIKhMLLQfC3LTTDxePdhsKVb7Y31wiIiIiIpI9cfEZmuaToM3jbumWQEo8COu/TVtyZR5sX5718eGFoMql3gaeFeICnymQoopCm8fgk7RNQH+ZCE3ugooN/M0lIpIdm5fA1Du8usy50PMdCI/0L5PkrrAwaD8YYqrDiJt9jaKmuYSuZvfCL5Ng50rAwrT74NZ5+uUqIiIiIhIM6veAmf9y64bv3QAbF0HVpjl7zNQU+GsprJkDa+bDxu/cDOysVKjvzSSv0sTNhAsmDfrAohGwfYWrZ/4LbvhEszRFJH/buxnG9YHkw64uXAr6TNSGxgVFw35Q8knYsd63CGqaS+iKKOSWaXm7s6u3/QqLRrpmuoiIiIiI5G/RFdz64KvnuPrn8WfWNN+1xptJvvYrtylmVkpUhtjW3gaexcqe/nPmJ2Hh0H4IfNDD1Wu/gj9nQc32/uYSETmZxIMwrpdbbhcgLAJ6vutWFZCCI6qYr0+vprmEtmrN4MK+8NN7rp43FOp20+Y3IiIiIiLBIC7ea5ovn+rWsY0snPV9Du2CtfNdk3z1XNhzillqhUq4TTtj05rkZc4NvVnY57Zz6wCvne/qmYMg9jLXUBcRyU9SU+Gj27zluQA6v+D2jxDJQ2qaS+hrPwRWTodDOyHpEHz+kDulJ9ReCIuIiIiIhJo6V0BkUfc6/sheWDUD6l6Z+ZjkI7BhkbeB519LAXvyxwyLgMqNvJnklRpCeIi/NTbGvS8aldZ02r4Clo6Fi/r6m0tE5Fhzn4bfpnn1JXfAxf39yyMFVoi/MhABisZAh2fho1tdvWoGrPgY6nX3N5eIiIiIiGStUHGo08XtVQSwbAKc19UtvZg+k3z9t5CckPXjlK3tzSSv1hwKRedy8HyoYgM3c3/ZBFfPfQbOv8ZtFioikh/8PAEW/Merz20Plz/tXx4p0NQ0l4Ihrics/cA7HXH6w+5Fc+GS/uYSEREREZGsxfXymuZ/fAEv1oSDO7K+T7FyrkGePpu8ZKXczRgsLnvcLXOTcgT2b4FFr0LLgX6nEhGBDd/BJ3d7dbk60GNM6J8JJPlWmN8BRPKEMW5T0PBCrj6wFWY/5W8mERERERE5tRqtXRMcIDX5xA3ziCIQ29bNSLz9G3hoFVwzGi68Tg3zjEpVgUtu8+qvX4YDp/gAQkQkt+3ZAOP7QEqiq4uWgd7jNdFRfKWmuRQcZWIzz6JYPBo2/eBfHhERERERObXwCGhw3TGDBipeCM0fgBunwSProe8UaHoPVDhf+xdlpcUDULiUu564H+Y/528eESnYjuyHsfFuHzqAsEiIfx9iqvubSwo8neMgBUuze92pnTtXAham3Qe3zoPwSJ+DiYiIiIjISbV5DMKj4PAeqNoMqrd0exfJ6StSGlr9H3z5qKuXvAWX3A5lz/U3l4gUPKkp8OEAtzlxuq4vQ9Wm/mUSSRPQmebGmCLGmD+NMXuyOGayMWaLMWafMWatMebxY26vaIz53Bhz0BizwRhzSyAzSgEXUcgt05Ju26+waKR/eURERERE5NQiouCyx6DzC1CvuxrmOdVoAJSq6q6nJsPsJ32NIyIF1MxBbq+KdM3uc8tqieQDgV6eZQiw6RTHDAaqWWtLAK2APsaY6zPcPg7YCpQHrgVeMMa0CnBOKciqNYML+3r1vKGwe71/eURERERERPJSRCFoO8irf5vmNuETEckrP74LC4d7de0u0PZJ3+KIHCtgTXNjzEVAZ2BoVsdZa3+x1h5JL4FUoGbaY8QCzYF/WmsPWmu/Az4A+gcqpwgA7YdA0bLuetIh+PwhsNbfTCIiIiIiInml3tVuXfh0Mx7XeyIRyRvrvoZP/+HVZ9WHq0dBmLZelPwjID+NxpgI4A3gLuDIKQ7HGDPCGHMI2AAUB95OuykO2GKt3Zbh8KVp4yKBUzQGOjzr1atmwIqP/csjIiIiIiKSl8LCoP1TXr3pezfjXEQkN+1aAxOud0tDARQrD73HQaHi/uYSOUagPsJ5EFhmrZ2XnYOttXfimuWNgPeA3Wk3FQeOXQ99DxAdmJgiGcT1hOoZVv6Z/jAc3utfHhERERERkbxUvQXU6ujVs56ElCTf4ohIiEvYA2PjISGtDRheyDXMS53jby6RE8hx0zxtSZW7gIdO537W2lRr7Q/APuDFtOEDQMljDi0J7M9pTpHjGOM2BQ0v5OoDW2H2U1nfR0REREREJJS0GwwmrTWwazUsedvXOCISolKSYfJNsPMPb6z7CKh8sX+ZRLIQiJnmLYBywHJjzFZgClDCGLPVGNM4G/ePJG1Nc2AZUNEYUz7D7Q2AXwKQU+R4ZWKh5UCvXjwaNv3gXx4REREREZG8VL4OXNjXq+f9Gw7v8y+PiISmL/8Jq+d4dauHoX4P//KInEIgmuYTgOq45nYDYABuZngD4KeMBxpjqhpjrjHGFDfGhBljmgL3Al8CWGtXA98AzxpjiqY13a8DxgQgp8iJNbsXytZOKyxMu0+nJIqIiIiISMHR5lGILOquH9oJ37zsbx4RCS3fvwHfj/Lqut2h1SP+5RHJhhw3za21CdbarekXYJcbtluttUnGmOnGmEcz3OV+YBNurfI3gf8B/85we2+gErAD+BD4P2vt/JzmFDmpiEJumZZ0236FRSP9yyMiIiIiIpKXoitA03u8euGrsO8v//KISOhYPcftIZeu4oXQfaTbjFgkHwv4T6i1dp61tlSGupO19tm06+uttS2staWstSWstXWstc9Ya1MzHL857T7FrLXnWGvfCHRGkeNUa3bMKYlDYfd6//KIiIiIiIjkpab3QLFy7npyAsx9xt88IhL8dvwBE/uBTXF1dEXoNQ6iivoaSyQ79LGOSLr2Q6BoGXc96RB8/hBY628mERERERGRvFAoGlr/06uXjoVty/3LIyLB7dAuGNsTjux1dUQR6D0OSpztby6RbFLTXCRd0Rjo8KxXr5oBKz72L4+IiIiIiEheuugGKFPTXbepMPMJf/OISHBKToSJN8Dutd7Y1aOgYgP/MomcJjXNRTKKi4fqLb16+sNweK9/eURERERERPJKeCS0H+zVf86ENfN8iyMiQchad+b+ugXe2GX/grpX+pdJ5AyoaS6SkTHQ5SUIL+TqA1thztP+ZhIREREREckrtTtDlSZePeNfkJp68uNFRDL68R13SRcXDy0e9C+PyBlS01zkWGXPhZYPefX3b8CmJf7lERERERERySvGwOUZJg5tXQa/TvYvj4gEj80/wucDvbpyY+j6ivu9IhJk1DQXOZFm90HZWmmFhWn3QUqyr5FERERERETyROWLoW53r549BJIO+5dHRPK/Q7tg4o2QkujqYuWg57sQWdjfXCJnSE1zkROJKARX/Nert/0C3430L4+IiIiIiEheajsIwiLd9b0b4ftR/uYRkfwrNRWm3Ap7N7jahEGPt6DE2f7mEskBNc1FTqZaM7jweq+e+6xbqmX5R24znC0/w54NcOSA2+hCREREREQkVJSJhUY3e/WCF91MUhGRYy140W0cnK7tE1C9hX95RAIgwu8AIvla+6dg5XQ49DckHXI7QJ9IWCQUKQ1FY9x/T3Q57rYYiCqmtb1ERERERCR/avl/sHQsHNkHh/fCgv9Ah2f8TiUi+cmfs90kw3R1rnBL3ooEOTXNRbJSNAY6PAsf3Zb1calJcHC7u5wONdtFRERERCS/KlYGmv8DZg929fejoPEtULqar7FEJJ/YsxE+HACknX0fUwO6j1C/QkKCmuYipxIX7zayWD0HEna7y6G0/ybuz9ljq9kuIiIiIiL52aV3wOLRsG+ze180+ynoMcbvVCLit+QjMOlGSEhbtimiCPR8DwqX9DeXSICoaS5yKsbARTe4y7FSkiBhj/sjcbShnuF6wu7Mt6U33NVsFxERERGRYBBZBC57HKbe4epfJ0OTu6DSRf7mEhF/ffkobF7i1VcMgwrn+5dHJMDUNBfJifBIKF7OXU7H0Wb7iZrqu07ScN/j1hLMiZw220/VcC8TC2dfkLOMIiIiIiKSv8TFw8JXYduvrp45CG6cpok1IgXVzxPcGSjpGvaDBn18iyOSG9Q0F/FDQJrtp2q4p9+ex832toOgxYM5ez4REREREck/wsKh/RB4/2pXr1sAq2ZArQ7+5pLAS0mCjd/BWedDkVJ+p5H8aNtymJZho8+zG0DH5/zLI5JL1DQXCSYBa7Znp+G++8ya7bOHQEws1Ot++vcVEREREZH86dy2UKMNrJnr6pmDILYthKutEDKSEuC9q2DDQoiuCP2/gNJV/U4l+cnhfTChLyQnuLpwKej5LkQW9jeXSC7QXzeRgiAnzfbDe7OxTvsud6rmwR3ufh/dDqWqaJ1DEREREZFQ0n4IvN4SsLDjd1j6ATS80e9UEgjWwtQ7XcMcYP9f8EEP6P+lW6JTxFr4+E7YtTptwMA1o/XBioQsNc1F5OTCI6FYWXc5lZ1/wujLXJM9OQHG94Fb5kCJirmfU0REREREct/ZcXBBL/h5nKvnPgv1e0BUMX9zSc7NGwrLp2Qe2/mHe1/Xd6pmEgssHA6/TfPqVv8HNdv7l0ckl4X5HUBEQkTZc91pWSbc1fu3wLjekHjI31wiIiIiIhI4lz0O4YXc9QNb3QahEtyWTYT5GdakLlHJu75hIXx0G6Sm5n0uyT/WfQMzn/Dq2Mug1cP+5RHJA2qai0jg1GgNnV/w6i1LYerteoElIiIiIhIqSlaGS+/w6m9ehgPb/csjObPhO/j4Lq8uWxvu+BYuvN4bWzEVZv4r77NJ/rB/K0y+CWyKq0tUhqtHuw2CRUKYmuYiEliNbobGt3n1io9h/r/9yyMiIiIiIoHV4gEokrbOdeIBmKfX+0Fp9zq3/EpKoquLxECfCVCkFFzxX7fRa7qFw2HRa77EFB+lJMGkm+DANleHRbozzIuV8TeXSB5Q01xEAq/Ds+50rXTzn4NfJvuXR0RERE7IGHOlMWapMeagMeYvY8ztxpjyxpgPjDGbjDH7jDE/GWOu9DuriOQjhUu69YzTLXkbdvzhWxw5A4f3wth4OLTT1eFR0GssxFRPqyOh5ztQIc67zxePwIpP8j6r+Gf2YNjwrVd3+jdUbuhfHpE8pKa5iAReeAT0eAvK1vLGpt4Jm37wL5OIiIhkYozpCIwA7gdKAPWAeUBx4CfgUqAUMAgYZ4yp609SEcmXLr4ZSldz122Ka65JcEhJhkn9YMfv3tiVw6Fqk8zHFYqG6yZBySppAxam3OKWdJHQt+IT+PZ/Xh0X7/7dixQQapqLSO4oUirt1L7Srk454jYG3bvJ31wiIiKS7ilgiLV2nrU2xVq721r7u7V2jbX2RWvtJmttqrV2GrAS10QXEXEioqBtho0Bf/8U1i/0L49k3xePwOo5Xt1yIFwQf+JjoyvA9ZPd2QUAyYdhXDzsXJX7OcU/O/90E9/Sla8LV7wExviXSSSPqWkuIrknpgbEvw9hEa4+uB3G9oIjB/zNJSIiUsAZY4oBDYESxpjfjTFbjTETjDEVTnBseeA8YFle5xSRfK7eVVApw1INM/8F1vqXR07tu9dh8RteXbc7tH406/uUqw29xrklXAASdsP712gD2FCVeBAm9oXE/a6Oioae70FUMX9zieQxNc1FJHdVa+4+kU637Rf46DZITfUvk4iIiJQGDNAX6ACcCyQB72U8yBhTCBgPTLTWap01EcnMGGj/lFdvWgwrPvYvj2Ttjxlulnm6Sg3hqtcgLButoWrN3LHp9qyHsT1dg1VCh7Xw6T9g+wpvrPsIKHuuf5lEfKKmuYjkvotugCZ3e/Xvn8KcIf7lERERkfTTvl6x1q631h4AngDaps1CxxgTBUwCDgG3+BNTRPK9as2gdmevnvUkJCf6FkdOYttymNwfbNrkpRKV3ezxyCLZf4zzr4HLn/bqv36CSTe5NdIlNPwwBpZN8Oqm90Bd7QUuBZOa5iKSN9oPgZodvPrrl2DpOP/yiIiIFGDW2j3ABuBE6yiYDA3zKOAaa606YCJycu0Ggwl313evhSVv+ZtHMjuQtkzm0eU2irv9p6LPOv3HanI3NL7Nq1d9CZ8/pGV5QsGmJTA9w5kIVZtB2yd9iyPiNzXNRSRvhIXDNaPdBiLppt0LGxb5l0lERKRgGwXca4ypZIwpAgwCZgNHgIlAMaC7tfaIjxlFJBiUq+XOLk03799weK9/ecSTlADjesPeDa42YdDjTahw/pk9njHQcSjUucIbW/IWfD0s51nFPwf/hok3QGqSq4uf5X5OwiP8zSXiIzXNRSTvFC4BvcdD0bKuTkmE8dfB7vX+5hIRESmY/o1rkv8MbASK4tY4bwp0A5oBO40xB9Iup9gpTkQKtNb/hMi0jQITdsHX//U3j7jZ31PvhM0ZtqS4/Bmo1eHk98mO9AlRlRt7Y7OHwM8TTn4fyb9SU2DKANi3ydUmHHq8BdHH7Q0uUqCoaS4ieat0VYh/39t5/dBOGNcLDu/zN5eIiEgBY61NsdY+aK0tm3a51lq71Vo731prrLVFrLXFM1ye9TuziORj0WdBs3u9etEI2LvJvzwC84bC8ilefXF/uPSOwDx2ZBE3ISom1hv7+C5YMy8wjy95Z/7zsHqOV7cf7PYqECng1DQXkbxXtQl0fdmrt6+ADwe4T7hFRERERCQ4NbnbLesAkHwY5uqzNt8smwjzn/PqGm2g0/NueZVAKVYGrp/snUmcmgQT+rpNRyU4rJqZ+efkvK7u37GIqGkuIj5p0Aea3e/Vq76EmYP8yyMiIiIiIjlTqLhbpiXd0rGw9Rf/8hRUG75zs77Tla0N174N4ZGBf66YGtBnIkQUcfWRffB+D9i7OfDPJYG1ez1MuYWje4LHxEK3EYH9YEUkiKlpLiL+aftE5g1kFg6HH9/1L4+IiIiIiOTMhX1dkxYACzOf8DVOgbN7HYzv4/aPAihaBvpMgCKlcu85KzeEa99ym4wC7P8LPrhWm8HmZ0mH3cafCbtdHVnULaNauIS/uUTyETXNRcQ/YWFw1etwVn1v7NMHYN3X/mUSEREREZEzFx7h1kROt3p25vWSJfcc3gtj492+UeD2kYr/AGKq5/5z1+4EnV/06u3L3VItyYm5/9xy+r54BLYs9equL8NZdf3LI5IPqWkuIv4qVBx6j4Ni5V2dmgQTrodda/zNJSIiIiIiZ6ZWR6iaYSPBGYMgNdW/PAVBSjJM6gc7fvfGrhzu9pPKK41uhub/8Oq18+GTu8HavMsgp7Z0LCx5y6sbDYC4nv7lEcmn1DQXEf+VOgd6jYXwQq5O2O1mSOh0PhERERGR4GMMtH/Kq7f9Ar9M9C9PqLMWvng484z+lgPhgvi8z3LZIKifoQG7bALMeerkx0ve2voLfJrhg41KDaGDNuwVOZGANs2NMUWMMX8aY/ac5PbyxpgPjDGbjDH7jDE/GWOuPOaYdcaYBGPMgbTLCR9LRELMOY2g26tevfMPmHSTmzEhIiIiIiLBpXJDqHe1V89+CpIS/MsTyr4fBYtHe3Xd7tD6UX+yhIW593XVWnhjC/4DP7x18vtI3kjYk7ZkzmFXF4mBa9+BiEL+5hLJpwI903wIsCmL24sDPwGXAqWAQcA4Y8yxCyf1ttYWT7vk4m4VIpKvxF0LLf/Pq1fPhhmP+ZdHRERERETOXNtBEBbpru/bBN+97m+eUPTHDLc+dbpKDeGq11zz2i8RUW5TyXLneWOfPQArv/AvU0FnLUy9E3avTRswcM1od9a3iJxQwH6LGmMuAjoDQ092jLV2jbX2RWvtJmttqrV2GrAS10QXEYHW/4S63bz6u9dg8Rj/8oiIiIiIyJmJqQ6Nb/HqBcPg0C7/8oSabcthcn+waevFl6gMvcZBZBF/cwEUKQXXT4bos11tU2HyTbB5ib+5CqpvXoaVn3l1m0fh3Lb+5REJAgFpmhtjIoA3gLuAI6dxv/LAecCyY2563Riz0xiz0BjTORAZRSRIhIVB99fg7Abe2OcDYc083yKJiIiIiMgZajkQCpV014/sha9e8DdPqDiwHcb2gsT9ro4qDn0mQPRZ/ubKqGRluG4yREW7OumQ27tq19qs7yeBtXYBzB7s1ee2hxYP+ZdHJEgEaqb5g8Aya+287N7BGFMIGA9MtNb+kOGmvkB1oBLwP+BDY0yjAOUUkWAQVRR6j4PiFVxtU2DiDbDzT39ziYiIiIjI6SkaAy0e8Orv34Bda/zLEwqSEmBcb9i7wdUmDHq8CRXO9zfXiVQ4H+Lfg7AIVx/cAR/00BkHeWXfFjfDP/1shJJV4OpR/i7fIxIkcvyvxBgTi5thnu2PqYwxUcAk4BBwS8bbrLULrLWHrLVHrLVjgWnANTnNKSJBpkRF1ziPSDu18PBeGNsTEnb7m0tERERERE7PJbdDybS1k1OT3KagcmbS16benGHuYYdnoVYH/zKdSmwbuHK4V//9J4zrpY1hc1tKEkzq5z6oAAiPgvh33QdZInJKgfhoqQVQDlhujNkKTAFKGGO2GmMaH3twhoZ5FHCNtTbxFI+fGoCMIhKMKl0EV4306l2rYeKN7o+/iIiIiIgEh8jCcNnjXr18CmzS2tZnZN5Q9/1Ld3F/96FEftegN7TJ8DOw8TuYcgukpviXKdTNfAI2LvLqzi9AxQv9yyMSZALRNJ+AW06lQdplALA/7fpPGQ80xkQCE4FiQHdr7ZFjbq9ijGlpjClkjIk0xvQEugFTA5BTRIJRvaugzWNevXY+TP8/N8NCRERERESCQ/2eUKG+V8/8l17Tn65lE2H+c15dow10eh6M8S/T6Wj5EFx0o1f/Ng2+fOzkx8uZW/4RLHrVqxtcl/l7LyKnlOOmubU2wVq7Nf0C7HLDdqu1NskYM90Y82ja4U1xTfBmwE5jzIG0S/rtxYFXgL+BHbglX3paaxchIgVXy4FwfoZVmn54E74f5V8eEREJXXs3uYuIiARWWBi0z7Asy/pvYOV0//IEmw2L4OO7vLpsbbj2bQiP9C3SaTMGugyDmpd7Y9+NhIWvnvw+cvp2/AEf3+3VZ9WHzi8Gz4crIvlEwFf+t9bOs9aWylB3stY+m3Z9vrXWWGuLWGuLZ7ik377CWtsgbayUtbaxtXZaoDOKSJAxBrq9CpUaemNfPAKrZvmXSUREQktqKiweDa9eCokH/E4jIhKaYtvAue28etYTkJLsX55gsXsdjL8OUtJWty1aBvpMgCKlsrxbvhQeAT3egrMbeGNfPupmRkvOHTkAE/t6r2UKlYSe70BUUX9ziQQhbZcrIsEhsgj0GgslKrnaprpdwHes9DeXiIgEv52r4O0u8NmDkLjf7zQiIqGt3WAgbcbrzj/gp3d9jZPvHd4LY+Ph0E5Xh0dB/AcQU93fXDlRqDj0mQilqnhjU26D9Qv9yxQKrIVp98GO372xq0ZCmVj/MokEMTXNRSR4RFeA3uMhMu1T8iP7YGxPOLTL31wiIhKcUpLgqxdhZDPY8K03bsL9yyQiEuoqnA8N+nj13KFudqwcLyUZJvXL3AS9cjhUbeJbpICJPguu+xAKp82WTzkC43q5pUXkzHz/Bvw62aub/wPqdPEvj0iQU9NcRILL2XFw9RtevXsdTOgLyYm+RRIRkSC0+UcY1QbmPOXeqKer2z24Z++JiASDNo9BRGF3/eB2WDjc3zz5kbXwxcOweo431nIgXBDvX6ZAK1fLTYoKL+Tqw3vg/Wtg/zZ/cwWjjd+7ZW7SVWsBbR73L49ICFDTXESCz3lXQNsnvHr91/DZA+6FpYiISFYSD8GMx2F0W9j2izcefbZbBqznOxAW4V8+EZGCoGQluPROr/7mFTVKj/X9KLfXRrp6V0GdL4jLAAAgAElEQVTrR09+fLCq2gSuHsXRJXv2boCx1+rsg9NxcCdMvBFSk1xdvAL0eNOtHy8iZ0xNcxEJTs3/AXG9vPqn97TruoS21BRYPAZGNodxvWHlF25MRLJvzXwY2RS+/Z/bGyNdw35w5yKdwiwikpea3+82tARIOgjzhvqbJz/5YwZ88YhXV2oI3UdCWIi2cOp1hw7PevWWn92yNNok9tRSU2Byf9j/l6vDItwEgOLl/c0lEgJC9DeuiIQ8Y+DKV+CcS7yxGY+7RqJIqNm8xM2K/ewBNzN25ecwLh5evgC+ekEzs0ROJWEPfHw3vHsl7F7rjcfUgBs/ha4vQ5FS/uUTESmICpeEVg979Y/vwo6V/uXJL7Ytd03Q9A93S1SGXuMgsoi/uXJbkzszn33w50z47B86m/hU5j4La+d7dfunoMql/uURCSFqmotI8Ioo5HaOL5m+67qFD2+GbSt8jSUSMId2waf/gDfawl8/HX/73o0w52l4qa47JXPtV3pjIXKsFZ/Aq43dGUnpTDg0uw/u+Baqt/Avm4hIQdfwJvcBJoBNgVlP+hrHdwe2w9h4SNzv6qji0GeC2zSzILj8Gajbzat/fNdt2C0ntvILWJDh+1O3O1x6h395REKMmuYiEtyKl4M+490LSoDEA+6F5oEd/uYSyYnUVPjpfRh+MfzwJpChEX7+NXBeV9f0O3p8MqyYCu90heGNYOEISNid57FF8pX929xG0RP7woEMZ2NUqA+3zIH2Q0J/1p6ISH4XEZV5r6KVn8O6b/zL46ekBLcE396NrjZhbl3qCuf7mysvhYXBVaPgnAwzpec+DUvH+pcpv9q1Fj661avL1IRuw90Z2SISEGqai0jwO6seXDOGTJvHTLgOko/4GkvkjGz9Bd7qCB/fBYf+9sbL1XHLSPR4E+Lfh38sd5tBRVfMfP+/V8GX/4T/1IGpd8KmJZp9LgWLtfDje/BqI/jtE288vBC0HQS3zIWKDfzLJyIimdXtBpUbefWMxwveaxdr3eu2zT94Yx2ehVod/Mvkl8jC0HucawKn++QeWD3Hv0z5TVICTLwBDu91dWQx9/6gULS/uURCjJrmIhIaaneEy5/y6o3fwbT7Ct4Lbgleh/fC9Efg9Zbu5zddZDE3I/a2BZmXkShxNrR+GO7/BXqNhXPbcfSDI4Dkw7D0Axh9mXvMJW9D4sG8+mpE/LFrLbzbDT6523sjCVClKdzxDbR4EMIj/csnIiLHMwYuf9qr//oRlk/xL48f5g3N/DVf3B8uud2/PH4rGgPXT4Zi5VydmgwTbnCTSwQ+Hwhbl3n1la9A+Tr+5REJUWqai0joaHI3XHi9V/88Dr75r395RLLDWlg20S2r8t1Ib9MncDOv7v7erb0cEXXi+4dHQJ0ucP2HcO9P7tiiZTIfs3WZ+xDpP3Xgs4e07r+EntQU+HY4jGiSeTOsqGjoMgz6fQZla578/iIi4q8ql0KdK7x61uCCc9bosokw/zmvrtEGOj2vZTZKV4M+EyGyqKsT98MH18LeTb7G8t2P72bep6XxbVC/h395REKYmuYiEjqMgS4vQdVm3tiswfDbp/5lEsnK9t/dOuRTbsm85nJMrGuC93wXSlbO/uPFVHez0h/4Da4eDVWaZL79yD5Y/AaMbAJvdoRlkwrOG1IJXVt/hdHtYMZjkJzgjdfqBHd9B41udmukiohI/tbuSW/Plj3rYfEYP9PkjQ2L3JJ86crWhmvf1llR6SpdBNe+4/1c7N8C7/eAhD3+5vLLlp/dBJh0lRtlPktDRAJK7yBEJLREREHP99zMBACsa0huWZbVvUTy1pEDMONf8FozWLfAG48oDG0ehzsXpi23coYiCkHctdD/C7hjITS6xc24zWjDQpgyAIadBzMHuWUtRIJJ8hGY8zSMauVO5U9XtKxb+7/3OChZyb98IiJyesrWhItv8uqvng/t5ujudTD+OkhJdHXRMtBnAhQp5WusfKfW5XDFMK/e8RtMuL7gTfxI2O02OE9J+7qLlnEfKJzsbFQRyTE1zUUk9BQrA70nQKESrk46BON6wf5tWd9PJLdZC8unwquN4dtX3PqM6dJnxbYa6JregXJWXejyIjz4O3R9GSrEZb790N/wzcvwSgN472r4/TNIST7xY4nkFxsWwWvN4asXMv87uqA33L0Yzr9Gp7WLiASjVg9DVHF3PWE3fD0s6+OD1eG9MDYeDu10dXgUxH/gzhqU4zXsBy0yzLBet8BtnJqaetK7hJTUVPjodncGBoAJcxMENDlAJFepaS4ioal8HejxlntBAbBvM4zv43YaF/HD36vh/ath0o3u5zFdqSrQezz0GZ/hDIlcUKi4e8Nx21cwYA40uM7NbM9o9Wz37+TlOJj3HOzbknt5RM7Ekf3utOQ3O8LOP7zxklXckkZXveY2DxMRkeBUvLzbnyXdotdgz0b/8uSGlGSY1A92/O6NXTkcqjY56V0EuOxx9+F4ul8nw+zB/uXJS18Pgz++8Oo2j0GN1n6lESkw1DQXkdBVsx10GOrVm3+Aj+92s31F8kpSAsx5BkZcCqvneOPhUdByINz5HdTulHd5jIHKDaH7CDf7vMPQ/2fvvuOcqvI+jn9+Awy9SFNQREVF14a9K1bEXlAE1MWCfd11XetjYVeXB9uja1m7olKk6Np7ARULNlYUBUFRQQHpvc2c549zYu6EZPrMTSbf9+uVV+aeW/LLneTml5NToE3KBIlLZsHYQXDHdr776/R38qclj2Svqa/DvXv5cflJXMcN9ryg6kMaiYhI9tj7Imi2kf+7KAzFVVc4B69eWTInPOBy2Kl3fDHlCjM45q6SlcXj74RPHo4rotox/R1455/J5a2PgP3+Gl88InlEleYiUrfteR7sdlZy+asx8O5t8cUj+WXKK34olndvSY5XCdDlYD/W+MHXQmGT+OJrvAHsfaEfzuKPL8B2J0BB/eR6VwTfvABPHg/37AYf3A0rFsQXr+Sn5fPg6XNg+MmwZGayvN02cPbr0HOw70khIiJ1Q2FTOOia5PKXI/0EiHXBhAdLVvJudwJ0vybz9lJS/UI45QnYcPtk2cuXw7cvxxdTTVo8C54+G1xovNKqs+9VpwnORWqF3mkiUreZQc9bYPMDkmXv3ARf/ye+mKTuWzgDhp/qx9Jf9FOyvHlHP2HPac9A2y1jC289Zv49cvIQuHSyr8xv2ankNgumw+vXwu3bwDPnwc8T1GtDapZz8OUo/8PTpNHJ8oIGcOBVfqihTnvEF5+IiNScbv2g3bZhwcEbN8QaTrWY+jq8elVyeeNd4fj7VAFaUY1aQr/R0CKM5+2KYcxZMPPTeOOqbuvW+GEdV8z3y/UaQu8nfaMXEakVujqLSN1Xr4GvqGzdJVn2nwtg1ufxxSR107rVMO5WuHdPmPpKsrygPuxziW/Rvd3x2T1BYfMNfTfhP//XT6i7VQ8gEm/RavjyKXjkMLh/f/jkET/OtEh1WvQzDD8FnhmQ/LIIsPFucP57cNDV1TthroiIZJd69eGwyHjV378D096ML56qmvO1r9hNtBhu2QlOHQENGscbV65q0dFXnDds4ZfXrfQTq86fHm9c1en1a2HmJ8nlo26HDjvFF49IHlKluYjkhyatoe9I3zIBfGL1VF9Y8ku8cUndMe0t+PfevifDulXJ8s77wfnvw+E35tYQEgX1oOsR0G+Ur0Df/zJo2q7kNnMmwUt/9a3PX7wUZn8VT6xSdxQXw4SH/BwA372eLG/QBI4Y7Idjab9t5v1FRKTu2Opw2Gz/5PIbN0BxUXzxVNayub5Cd01oZFDYzE8C33zDeOPKdRtuB72H+h5oACvmwbBefli3XDdpDEx4ILm88+mwy+nxxSOSp1RpLiL5o+1WvsW51fPLS3+FEX1gzYp445LctngWjDoDhp7ohzBJaNoeTnwI+r+Y+5V8G3SGQ673Q7f0eqzkF1iANcvg00fh/n3h4cPgv0/B2lXpjyWSyW9T4bGe8PLf/GsqocvBcOFHsNcF/sccERHJD2a+0UHCnK98jpFL1q703zcW/+yXrQB6PQobbV/6flI+WxzoJ7dPWPC9Hx4xl7/fzf0Gnv9TcnmjHeHIW+OLRySPqdJcRPJLl4PgyFuSy79OhGfP960bRSqiaC2M/xfcsztMfi5ZbgWw5/nwp09hx1OyeyiWiqpfCNuf6H8IuGgC7HkBNGxZcpuZE+A/58H/bQOv/U/d6iYrNWPdGj+s0f37ws8fJcsbtYLj7/dzAGzQOb74REQkPh13hh1OTi6/fZOviM4FxcXw7IUwKzLWdo9BsHWP+GKqi3Y8BQ6JjHk/8xM/gXgu9kpYvRRGng5rQ6V/o5Z+4lMN4yMSC1Wai0j+2f0c2OO85PLk52Ds/8YXj+SeGe/D/fvBG9fD2uXJ8k32gHPHQc+bk0MB1VXtukLPwXDZt3DcvdBxl5LrVy6ED++Bu3eBJ46Dyc/7HxpEomZ9Bg9298MaFa1Jlm93gp8DoFufuvXDk4iIVNzB10G9Qv/30l/go3+Xvn22GDcYvn4mubzbWb5hhVS//S6F3c5OLk95yU+6mkuT1jsHz10M879Llp3wILTePL6YRPKcKs1FJD/1GOS7/Ce8ewt8OTq+eCQ3LJ0DTw+AIUfBb98my5u0gWPvgbNegw47xhdfHAqbwM6nwbnvwLljYZcz/PjTUd+PhVGnwx3bwzuD/JA2kt/WrPA9ER4+FOZ+nSxv3sFPjHbyEGjWPrbwREQki2zQGfY4N7n83h3ZP271l6Ng3M3J5S0Ogp636IfgmmLmz+/WRyTLJjwIH9wdX0wV9dF9MPnZ5PL+f/PzC4lIbFRpLiL5qV59PzZz262TZc9dBDM/zbyP5K+idfDR/XDPbjBpVGSFwa5nwsWf+sl5CvL8Y7XjznDs3b71ec9bod02Jdcvm+2/QN65PYzoC9Pe1NBI+ej7sXDf3r4ngov8/3c9Ey76GLY5MrbQREQkS+1/WbIX35qlMO6W0reP008f+e8VCW27+h+D6zWILaS8UK++Hy8+2vvxjev8pJrZ7scPfawJmx8IB10TXzwiAqjSXETyWeNWfub6xhv45aLVYaKemfHGJdnl5wnwUHd49UpYvSRZ3qEbDHgLjrkTmrSOLbys1Kgl7Hmun7zxzFdg+15QEPmi6Ip9t9mhJ8HdO8P7d2Z/izGpupULfSXCE8fBwhnJ8tZdoP9L/r1U14c1EhGRymnSGg64PLn86SPZOW/KwhnwVL/kkGNN2kDfkf57h9S8wqbQdxRssFmy7NkL/NCK2WrZXBjdH4rX+eUWG/vKf01+LhI7VZqLSH5r0wVOeRIK6vvl5XNh+Kmwelm8cUn8ls/zFXyPHAazJyXLG7WEo26HAW/DxrvGF18uMIPO+0CvR+Cv38ChA6FVyoSOC2fAmzfA/23rJ2368cPcGn9Symfyc3DvnvDF0GSZ1fNjkF4wHjbbL77YREQkN+w+AFpu6v8uXgdv/T3eeFKtWgzDe8OK0BCgXiH0HqYxqWtbs3bQ72loHBq1FK2Bp/rC3G9L3y8ORetgzFm+Nyb4RiYnPw5N28Ybl4gAqjQXEYHN94ej70guz5kEz5yrYSPyVXERfPoo3L1ryQo+gG794OLP/GSyav1RMc3a+QrSSyb6LzJdjwSLpCFFa2DSaHjsCLhvH5jwkP/yKblt6Wzf4m7UGbBsTrJ8ox39OPiHDoQGjeOKTkREckmDRnDI9cnlyc/Bz5/EF09U0TrfWjg6582x90DnvWMLKa+13dK38K/fyC+vWgzDesGSX+ONK9XbN8KM95LLPQZBp93ji0dESlCluYgI+MkL9744uTzlJXj7H/HFI/GY9bmfmPDFS2HVomT5htv7ST6P/7ev/JXKKyiArQ6FPiPgL5PggCug2UYlt5k7GV7+G9y+LTx/CfwyMZ5YpfKcg88eh3v2gG9fTJbXb+Qryge8Ax12iis6ERHJVdufVPLz4/Vr4++h5pwfxm/628myAy6HnXrHF5NApz3gpIeBMPnq4p9h+MmwemmsYf3umxdh/J3J5e17wR4D4otHRNajSnMRkYTD/gFbHZ5cfv8OmDgivnik9qxcCC/+FR46GH75PFle2ByOGAznjoNN94ovvrqq5SZw8P/ApV/BKU/4SY+i1i6Hzx+HBw/0/5svhsGaFfHEKuU3fzo8fgy8cAmsjvQW6LwfXPCB73FQr3588YmISO4qKIDDbkwu//wRfPtSfPEAfPwAfPJwcnm7E6C7JnHMCtseAz1vTi7PnuR7vxWtjS8m8LnSsxckl9ttA8f8yw9tKCJZQ5XmIiIJBfXgpEeg3bbJshcugZ8+ii8mqVnFxb4i9u7d/IRSRFoqbd8LLv4E9rpAFXw1rV4D+MNx8Mfn/fA3e18MjVImzJr1GTx3IfzfNvDq1fDb1HhilcyK1sH4u+C+fUt2NW7YAo6+E/74gp9HQrKKmR1rZhPNbLmZ/WJm54fyFmY23MyWmNkcM7su7lhFRADY4sCSDV3evCG+StCpr8NrVyeXN94Vjr/PV+5LdtjzvJI9iqe/DS/8Jb4eCmtW+Ir71Uv8cmEzP8dWw2bxxCMiGelKLiIS1agF9H3Kz3QPYeKYfrDwx3jjkuo3+yt4rKeviE1M2ATQdms443k/eWWLDvHFl6/abgk9/gmXfQvH3w+bpIzruGoxfPRvuHd3GHI0fP0fWLcmnlglafYkePgQeOM6WLcyWd71KLjoY9jtTFUgZCEzOwL4N/AXoAWwHTA2rL4baA1sCuwPDDCzM2IIU0RkfYf+PTk3yvxpvmdabZvztZ/E0YV5kFp2glNHaK6ObHTYjb4HQMLEoTB2cO3H4Ry89FeY81Wy7Lh7oN3WtR+LiJRJ315ERFJtsJmf6b5eoV9eMQ9GnAqrlsQallSTVUt8S+UHDvBdehMaNPFjLZ8/3rdgkng1aAzd+sA5b8J578GuZ0KDpiW3mfGen3Trju3grRth0U+xhJrX1q6Ct/4BD3aHXyNjzzdtBycPgVOHQYuOcUUnZbsR+Idzbqxzrsg5t9A5962ZNQFOBa51zi1yzk3FV6KfHWu0IiIJG/7BT9CeMHZw7Y5VvWwuDO8Na8JjFjaDPk9B8w1rLwYpv4IC3xij877JsnGD4fMnazeOz4bAfyPDf+51UcnKfBHJKqo0FxFJp/Pefly5hLmT4elzoLgovpikapyDSWPgnt19S2UX+V9ueyxcNMGPtVy/ML4YJb0OO8Ixd/rW50fdDu23K7l++Vx47zb4107+C+zU1+IfqzIf/PgB3L8fvHc7FK9Llnfr599P252gsTmzmJk1BXYFWpjZt2Y228xGmtlGQFegEIjOwjsR2DGGUEVE0jvoGqgfWnUv/80PEVYb1q6EEX38xJLgW7z3ehQ22r52Hl8qp0Ej6D3U9ypNeOHPMO3N2nn8WZ/DK1cklzvtBYf9vXYeW0QqRZXmIiKZdOsL+/4lufzda/DG9fHFI5X32xQ/MeHTZ8Oy2cny1ltAv6eh95PQqlN88Un5NGoBu58DF4yHs16HHU+Feg2T610xTH0Vhp8CN20Id+8Kw0+F16+Fzx73lbzL58U3hmVdsWqJnzj3sZ4w/7tkeatN4fT/wPH/hiat44tPymsDwIDTgR7AlsBa4EmgGbDcORf5NYRFQPPaDlJEJKMWHWGfyFjVH94DS36t2ccsLoZnL4RZnybLegyCrXvU7ONK9WjSGvqNgWahR4ArglF/hF//W7OPu2KBf5yiMKRgokdevQY1+7giUiWa2UxEpDSH3ADzvoMpL/nlD++Bdl1hFw3rmhNWL4N3b4EP7y3ZErZ+I9j/MtjnEt/qRHKLGWy6p7/1GAQTh8Gnj8LCH5LbuCI/xun8aZA6Z2ijVtB2K2izlR9Dvc1WvtVR682hfkOkFFNe9WNxLpkVKTQ/Ye7B10Jh04y7StZZFu7vcs79CGBmNwDfAQOBJmZWP1Jx3hKoxbEPRETKYZ9L4NPH/HCKa1fA2EFw7N0193jjBsPXzySXdzsL9jy/5h5Pqt8GnaHvKHjsSFi7HNYsg2En+yEBW21a/Y9XXAzPDIDFYRhBK4Bej2nuJJEcUK2V5mbWGJgEtHXOtUqzvj1wB3AgfrKh6cANzrnnI9t0BB4O28wHbnTOPVSdcYqIlFtBAZz4IDx6BMyZ5MtevNS3UN5sv3hjk8ycg2+eh1evgSUzS67bqgf0vNlXkErua9oG9r0E9r4YfhjrK8+nvArFpQzPsmoRzPzE36KsAFp1jlSob5X8u1n7/B5qZPk8eOVK+GpMyfJ22/oJrDbZLZ64pNKcc4vM7CcgXdeLSfhW5zsBn4WybqFcRCR7NGoB3a+Cl//ml78YCntdCO23rf7H+nIUjLs5ubzFQdDzlvzOD3JVx25wyhO+d6IrgmVzYGgvOPs1aLxB9T7Wu7eWHALmkBtg8/2r9zFEpEZUd0vzfwAzgbYZ1jcDvgCuBH4BjgKeMrPdnXOTwzYj8JXp7YHtgdfMbKpzblw1xyoiUj4Nm0GfEfDQwX7s5OJ1MPI0GPC2rzyX7DJ/uh8vMHV8wpabQs/B0PVIfbmpiwoKoMvB/rZujW91Pu87P3zIvO+Sf69cmPkYrtjvt/AH+O71kusatoA2W/oW6b+3Tt8KWnep270VnPOVBK9eBSsXJMsLGsABl2segNz3IHCJmb0GLACuB95yzi0xs5HAjWbWB5+X/wm4Lr5QRUQy2LU/fHy/713miuGNG6DfqOp9jJ8+gucuSi637arhNXLdVof6OayeD0P8zJsCT/XzQ81VV8/DaW/C2P9NLm9zNOz75+o5tojUuGqrNDezXYAjgb8CI9Nt45z7HrgtUvSCmU0B9gImm1kXYD/gFOfccuBjMxsGnAWo0lxE4tOqE5w6HIYcBUWrfcXb8N6+G1+jlnFHJ+AnZXr/Dnj/Tv8/Siho4Fsi7/83KGwSX3xSe+oX+mGU2nVdf93y+aEifWqoSJ/m7xf+UHIIn1Srl8Avn/tbCea78qYb7qX5Rrn9A82in/zY5dPeKFm+ye6+63tNtOKT2jYYaA0kBnN9Bz/GOcDFwAP4BjErgXucc0/UeoQiImWp1wAOHegbtYCfh+iHd2HzA6rn+At+gKf6JsejbtIG+o6Exut1rpdcs8vpfkLXRA+CH8fDf86Hkx7xDTKqYtFP8PQ5/N6hq/UWft6XXM4NRfJMtVSam1l94CHgorK2TdmvPbAt8GUo2hH41Tk3J7LZRODC6ohTRKRKOu0Ox90Lz5zjl+dNhdFn+jHx6mmKiFhNfQ1evhwW/ViyfIuD4MjbfEWmCPjhXJq2gU33KlletBYW/ujf14nW6fOn+eUV80s5oPOvu0U/rt+7obBZaJ0eqVBvu7VvnZ7NP+AUF8MnD8Gbf/djfSY0aAqHXA97DICCevHFJ9XGOVcEXBZuqeuWAH1qPSgRkcrY5mjotBf8/JFffv06GPBO1Ss+Vy2GEacmc4F6hb4hjYb5qzu6Xw2LZ/o5csCPWd9yYzj8psofc91qP/Fnoodj/UZ+OBg1thLJKdVVy3MZ8KVzbqyZdS/PDmbWEHgKGOWcS0w93QxYlLLpIqB5NcUpIlI1O57su+69e6tfnv4WvP4/foxsqX0Lf4RXr05O1JrQvCMcMQj+cLxac0j51GsQKrXT/MCyYkGyRXp0uJcF35c+dvqaZfDrRH9L1bJTskK97dbJv1tsHO9rdu638PyfYOaEkuVdDoFj7qyZCbJERESqygwOvxEeOcwv/zrRV37u0KvyxyxaB6P7w2/fJsuOvWf9H94lt5n5YVqW/grT3/ZlH9ztc7U9z6vcMV+9umTvxKPvgI12qHqsIlKrqlxpHoZUuQjYuQL7FAKjgRXAgMiqZUDqT28tgaVVDFNEpPp0v8a3Pp38nF/++H5f6bX72fHGlU/WrYYP7oJ3b4d1K5PlBfVhrwvgwCuhoX5vlWrSpDU02QM67VGyvGidb2GeaJEeHe5l+dzSj7n4Z3/7/p2S5Q2aQJsuoSI9MRHplv7WsFn1Pq+odWtg/J3+B8FE93Pwk2EdMRh27K0foEREJLt12gO2PdZPBg/w1t9h22MqNz61c/DqlclKVPBzeezUu3pilexSrwGc/DgMORJmhzmvX7kSWnT0r6GK+O9I+PSR5PKu/aFb32oLVURqT3W0NN8faAd8bf7LVCHQwsxmA8c650o0VYpUmBcCxznnIt/M+BLoaGbtnXOJb5vdgEnVEKeISPUoKIDj7/etnBMtSF++3I9T1+WgeGPLB9Pf9ud7/rSS5Z33haNu1zjLUnvq1fcV3G26wNY9Sq5buWj91unzp/lbtFI61doV/sva7DSpT/OOoWV6yvjpLTtVrfv5zM/8JFhzJ5cs3/4kOOJmaNau8scWERGpTYcOhCkv+3lKFv0EEx6CfS6u+HE+fgA+eTi5vN0JvuGM1F2NWkDf0b63wuKfAefHJP/jC+s3nMhkztfwQmSizw7dfC4lIjmpOirNRwKvRpb3AR7DV3aXGATUzBoAo4CmwNHOudXR9c656WY2HhhkZpcA2wP9gOOrIU4RkepT2AT6jIAHD4Jls8EVweg/wjlva/zsmrJ4Frx2DUx+tmR50/Z+zMEdT1FLWMkejVvBJrv5W1Rxkf8Sn264l2WzSz/m0l/87YeUudHrNw6V99Hx00ML9UYtMh9vzXJ4+5/w8X3gipPlzTv6bsRdj6jYcxYREYlbmy6w21kw4UG//O6tsHM/33OqvKa+Dq9dnVzeeFc4/r6qj48u2a9FB+g3Gh7pAasXw7pVMLw3nP1G2d/xVi2Bkacne8E2auXHMW/QqObjFpEaUeVKc+fcSuD3vvFmtsAXu9lh+RXgPefcIHyF+nHAKmCeJSs3BoX14Cccehj4DVgAXOGcS/l2KCKSBVp09BXnj/X0CdWqxTD8FBjwVsUScyld0Vr46D4YO7jkpIRWAOVD/+UAACAASURBVLsPgIOu8RWUIrmgoJ6fPKz15rDVYSXXrVqSbI0eHe5l/jR/jclk3UqY85W/pWq2UbICPTp++oLv4cVL1588d7ezfSu90irbRUREstmBV8LEEbBmKaxaBO/dXv5JHed8DWPOSv6Y3LITnDoCGjSuuXglu7TfFk4dBkNP9L0DVy6AYSfB2W9m7n3nHDx3ISyYHgoMTnoYNuhca2GLSPWrrolAf+ecGwu0iiz3jPw9Dii1GaBzbhbQs7RtRESyxsa7+JYnY870ywumw6gz4LRn/Nh4UjUzxsNLl8Fv35Qs32R3PxRLh53iiUukJjRq4a8pG+9Ssry4GJbMDBXp00oO97JkVunHXDbb32a8V/p2bbaEY++GzvtU7TmIiIjErWlb2O8v8PaNfvnjB3xDi7IqMJfO8a2K14Qp1QqbQZ+noPmGNRuvZJ/N9/ff8Z4Oc1YtnOEbR/V/EQqbrr/9h/fANy8klw+8Yv3GESKSc6q90lxEJO9sf6KvwBobOsz88K4fc/voOzRcSGUtnQNvXAdfjixZ3rg1HPZ36HaaushK/igogFab+tuWh5Zct3pZpHV6YriXqTB/uh8fvSxWz1csHHCFug+LiEjdsdeF8MkjflizojXw9k1w0kOZt1+7Ep7qG8ayxvdo7PUobLR97cQr2WeHXr5xwhvX++VfPocxZ0PvoX5em4QZ4+GNG5LLXQ72vR1EJOep0lxEpDoceAXMmwJfPe2XP3vMd+3b87x448o1Rev8bPNv3wSrl0RWGOz6RzjkBmjSOrbwRLJOw2bQsZu/RRUX+4qCRIv06HAviQqBjrvAMf+CDjvWftwiIiI1qbAJHPw/8NxFfnnSKNj7ovU/L8F/Zj57Icz6NFnWY9D6k3xL/tnnElj0M3wSfnCZ+gq8coXv8WoGS2f7HseuyK9vsQmc+LAfjk9Ecp4qzUVEqoMZHHev77o36zNf9upV0LoLbHVoqbvmBed8JfiK+bBiob9fuSAsz4cV4e/fvvWVe1EddoKj7oBNdo0ndpFcVFAALTfxty4HlVy3ZoWfBDTTuJwiIiJ1wU594MN/w9yv/fIb18EZz6/fE3TcYPj6meTybmfDnufXXpySvcyg582w5BeY8pIv+/QRaNUJ9r4YRp8Jy+b48oIGfuLPpm3ii1dEqpUqzUVEqkuDxnDqcHjoYN+VzxX7lgfnvAntusYdXfVxzk96mqjs/r3ye0FKZfiCkmXF6yr2OA1bwiHXwW5nqbWGSHUqbOJvIiIidVlBPTjsH34SR/BDKE57s+RY01+OgnE3J5e3OMhXkmqIRUkoqOcn9Xz8mGRvhDcHwrS34KcPktv1HKxGPiJ1jCrNRUSqU/ONoM8IePQIP57w6iV+0phz3s7OVgfFxbBqEaxcWLLiu7TK7xULkl0Qa8pOff2XHLWEFREREZHK2vIQ2PxA+GGcX37jej/mdEE9+Omj5PAtAG27wslDoF6DWEKVLFbYBPqOhEcOgwXf+7LoJOs79vY9FESkTlGluYhIdeuwE5z4IIw8zS8vnAGjTofTn4X6hTX3uIkK8IwV32lahq9c4FvE1xqDxhv4ccmbtPG3xq3Dcmv/9ya7wYbb1WJMIiIiIlInmcHhN8IDB/jluZNh4nDYbD8/8WfRGl/epI2vFG3cKr5YJbs1bQv9xviK8xXzk+Xt/wBH36HeCSJ1kCrNRURqwrbH+Ekr3/q7X/5xPLx0KRx7T/kSquIiWLko89jfiRbf0QryVYtqtwLcCnwFeONIBXiTDSIV4YmyyN+NWmqoFRERERGpPR128i2Bvxzpl9/5p89JExWf9Qr9EIutN48vRskNbbpA31Ew5GhYtxIKm8MpT0Jh07gjE5EaoEpzEZGast+l8NsU+PIpv/zFUGjeATp0K2P4k/m+whxXe7FaQaSiO9w33mD9iu/oNo1a+ckGRURERESy2cHXwtfPQtFqWPqrvyUcew9suld8sUlu2WQ3OOcN/3raoRe03TLuiESkhqjSXESkppjBsXfBwh/g54992bu31sLj1kup5G6dZjiUaAvw1n7STVWAi4iIiEhd1GpT2PM8+OCukuUHXA479Y4nJsldG+3gbyJSp6nSXESkJtVvCL2HwUMHweKfK75/Qf2USu40Lb5/rwwPLcMbtlAFuIiIiIhI1P5/hc+f8EMaAmx3AnS/Jt6YREQka6nSXESkpjVr5yeN+c95vitoiRbg6YY+iYwN3rCFJpUREREREamqxhtAr0fg1auh485wzL/U0ERERDJSpbmISG1ovw2cNy7uKERERERE8teWh8LFh8YdhYiI5AD9rCoiIiIiIiIiIiIiEqjSXEREREREREREREQkUKW5iIiIiIiIiIiIiEigSnMRERERERERERERkUCV5iIiIiIiIiIiIiIigSrNRUREREREREREREQCVZqLiIiIiIiIiIiIiASqNBcRERERERERERERCVRpLiIiIiIiIiIiIiISqNJcRERERERERERERCRQpbmIiIiIiIiIiIiISKBKcxERERERERERERGRQJXmIiIiIiIiIiIiIiKBOefijqFGmdmShg0bNu/SpUvcoYiIiIhIDpg+fTqrV69e6pxrEXcsdYVychERERGpiLhz8nyoNJ8NNAF+jjsWEREREckJnYAVzrmN4g6krlBOLiIiIiIVFGtOXucrzUVEREREREREREREyktjmouIiIiIiIiIiIiIBKo0FxEREREREREREREJVGkuIiIiIiIiIiIiIhKo0lxEREREREREREREJFCluYiIiIiIiIiIiIhIoEpzEREREREREREREZFAleYiIiIiIiIiIiIiIoEqzUVEREREREREREREAlWai4iIiIiIiIiIiIgEOVlpbmZ7mpkLt6vjjicukXPgzGzvUrY7JbLdjFoMMWslzkfcceSK6jpfZjajLpx3XYNKZ2aHmdmzZjbbzNaY2Xwzm2xmw8xsgJkVxh1jtjCztmZ2o5l9YWaLzGyFmU0zswfNbPu446ttkffVQjNrlWGbgWGbq2o7vmyQ8tnvzGytmc0zs0lmNsTMTjKz+nHHKXWfPguTlJNXnnLyilFOXpKuQ5kpH68Y5eQlKScvm3Ly/JCTlebA6Rn+zmf9Sll3Wq1FIZIfdA3KwMz+DrwOHAf8BrwAvAWsBfoADwKtYwswi5jZocB3wLXAxsA44EX8uRoATKxqEhpJZvtXMdza1gq4NO4gstzj4TYCGA/UB84AxgDfmNkeMcYm+UGfhekpJxepPboOpaF8vGKUk5dKOXnZlJPXYTn3q4eZNQB6Aw6YA2xrZrs45z6PN7LYrAamA73N7C/OuXXRlWbWBjgC+BzYJYb4ROoUXYMyM7PdgOuBNcAJzrmXU9ZvjE88V8cQXlYxs92Bl4AGwNXAbdHrt5kdCQwF/tfMVjjn7oon0lgUA+uAv5jZnc65hXEHlI2cc/1Ty8ysCzAIOAV4x8z2dc5NrO3YpO7TZ2FayslFapGuQ+kpH68Y5eSlUk5eDsrJ67ZcbGneE2gLvIv/hRT0q/Iw/DnpkWZdb/wHwNBajUik7tI1KLMTwv2o1AQdwDk3yzk3MN8TLjMzfGuEQuB659zg1MqVcP6Ox38RvNnMOtd+pLFZCzwMtAD+GnMsOcU5N9051xt4BGgCPBpzSFJ36bMwPeXkIrVH16H0lI+Xk3LyMiknryTl5HVHLlaaJz4Ih5JMOvuYWb3UDRNjtZn35zCG1yozm2Vmd6UbmymMPeTMrLuZ9TCzd8KYVi7TWE5ZYBj+Ip6uy+dpwDLguXQ7hnPTx8yeMrOpZrbczJaa2QQzu9DMClK2vzeciwGlHG+6mRXl0gdK+H87MxuSYf3vr4uUchdeZ/XM7IpwDleb2c9mdrOZNayN+GtbZc9XHVGRa1DGMUvNrH9YPzDNui3MbJSZLTCzZWb2frgelXres0C7cP9bRXYys3ZmdpuZTQnX6IVm9oqZHZBm29/PgZl1CPdzzGylmX1uZmdUyzOpWT2BbYFZwM2ZNnLOvQuMBhoBF0XXmVlTM7s6POel4XUy2czuTFx7w2vvhrDLY1ZyzL3u1f+0qtUgfAuoP5tZubsPm1kTM7vOzL4Kr4nFZvaumZ2asl0D82N7rsr02W5me4RzNb5qTyUWlwHLgZ3NbL/UlWa2mZk9ED6/VpvZb2Y2xsx2zHRAM9s7XJd+CfvMMrPXzEzDTeQn5ePpKSevosrmmKacfEiG9crJybucXPl4+SknL5ty8qpRTp7jcqrS3MxaAkfj37RjnHPfAROADYHDStn1buBWYCY+Ua0H/AkYZ2bNM+zTF3gFaBruP8EnwVnHOfcjfuykY82sWaLczDYH9gaeAVZk2L0hMBw4HJiLH+/sY2A74F7W/0Xs/nCfNkEHDga2AF4PceWLYfhucDPx48c1B67A/7IodUQVrkEVeYyt8O/Bk0m+J+sBLwMnVcdj1KCZ4f4kM2tX6paBmW0DfIFPKBLP80v8teQdM+ubYdfWwEf4ru5jgfeAHYDHzeyGDPtkiyPD/Wjn3Noyth0e7nsmCsysA/51NwjoDLwNvIrvhnsJcFDYdAzw3/D3eJLj7T0OzK7aU6hZzrlZwEP4a+ll5dknfJ6/C/wDaI8fi3I8sAcwwszujBx/Lf7LT0Myv68Sr71hlXgKsXLOLcbnLpB8PQAQEvb/AufiK/Cex4/jeSLwkZmV2D7s8xf8uTwZ/z5/BvgWP8TETTXzLGqemY0Nz00qQPl4ZsrJs4Jy8jygnLxUysfLTzl5GZSTV41y8vLJ6pzcOZczN3xS6PAfjImyP4WyoWm2nxHWLQZ2jZQ3w0+E4YD/S9lnSCh3QO+4n3MZ58MBq8Lf54XlMyLrrwtlhwMbhb9npByjPv5NWZhS3o7kF5MDUtaND+U7pYnpqbDuxLjPTznOnYssdw9lQzJsn3hddE93HGAysFmkfHNgQVjXJe7nm0Xna0b0OLl2q8Q1aL33XGRd/7B+YEr5m6H8LqAgUn565PWW9rzHfQO6ACsj193HgXPwX/gtzfb1gElh+0ui2wA7A/PwCUT7NK89h/8y3DSybndgKVAEdIv7fJRynt4P8Z9Wjm03CdsWAQ1SXiPDo88/rNsK2CayPDBs2z/u513OcxP9XOsYXk9LgDZpntNVKfveHcrfAJpFyrfBj3XqgCMj5fuHsrfSxFEA/ILvlto27vOS5hy5cmz3P4nXSaSsBfAr/stcr5TtD8VXPswkkhMAB+DHtFzM+tf0QqBH3OekCudyLPCXuOPItRvKx9OdE+XkVTt3LrLcHeXktXG+ZpTnsyRbb5W4DuVNTo7y8YqcK+XkmZ+vcvLynSNXju2Uk5d9jsaSpTl5TrU0p2QXrISn8JMTnBBt0ZHiHufcZ4kF59wy4GL8C/fsDN31XnLOjayGmGvLKPwbrl+krB/+l8u3Mu3knFvnnHvGObcmpfw3/EQY4Gfdjnog3J8TLTQ/wdHx+AvhCxV9AjnuT865GYkF59wPJF+n+8cSkdSEyl6DysXMtgQOwX+5u9o5V5xY55x7Ev+LfdZyzk3HXy9+wScCZ+BbJnwFzDazW1K63R0DbA+McM7d5cInZjjWF8CN+NaF6bqaOfz7bnlkn0/wrfEKgAuq87lVszbhfm45tk10rS0AWpufff0Q/LV9QPT5AzjnvnPOfVttkcbIOfcLfozS5sDfStvWzJoCZ+MTyQvD53ziON+SbHlxSWS394Efge5m1jHlkAcDHYDXnHPzqvI8YpSIe4NI2Vn4CrvbnHNjohs7594E/g1sjG+9l3AVYMA/nHNjU/ZZ45x7rZrjrnVm1szMnjOzuZHuwztF1g80sxfM7B7zQ4T8ZGa944w5ZsrHS6ecPF7KyfODcvIMlI9XiHLyclBOXmXKycspG3PynKk0N7PNgP3wH1y/T2gREsnX8IPrn5BuX/wHaAnOuW/wXSFaAOnGC3q+SgHXMucn8ngZOMTMNjI/C3RX/IdfUVn7m1k38+P/3Wtmj5kfny3xIbdVyuaj8P+H08yscaT8DHy3miGu7O5Ndcla/C9jqaaG+w61F4rUlCpeg8prn3D/cmriFYyu4vFrnHPudXx38FPwCfqX+KSpPXA58Emkq2ii++yzGQ73frjfPc26L5xzU9KUjwj3640Zl0Us5b4824L/YnJo+HtYhtdIXTMYWAVcbGZtS9luV6AxMMH5Ltqpngz3+5qZQWgW4l8vBcCpKdvnbDfQiMRrx0XKKvSeMz8ubPdQ9mC6HeqIAnwrsc3xXfu/AEYlXitBD3yr3jbAtcDDpQwpUmcpHy+bcvJYKSfPA8rJy6Z8vNyUk5efcvLKU05eflmXk+dMpTn+l03DzwK9JmVd4hfmTLNlZxrHb0a4T/01C+CnCkWXHYbiu1edSvKX4KGZNwczKzSz4fgX483Ahfguan/EdxEF/4vi75xzq4AngFZAr8iqc/AXgnwbM/DXDF+CEr+q1smJh/JQVa5B5ZW4Fv2cYX1OXJecc6udc6Odc+c653bC/4p+BX4c1y3x4/4BbBbuR1rJCXGcmTng07A+XWJWmet6tki0Nmhfjm0TX2gcsBDoFJanV3dQ2cg59yt+3N5m+C95mST+3zMyHGcRvitjM3zlXEIiAf+9RWho7XoiftKetBP25YjE+2ZBpGyzcP9xhvfcmJR92+K/+Mx1zi2t8Yhj4pxb4pwb6ZxbHnKcG4CtKXkd+dw5l6j0fBLfDXbrGMKNm/Lx8lFOHg/l5PlBOXk5KB8vF+Xk5aScvEqUk5dTNubk9WvqwDUgkXAeYmbvp6xrGFnXIbyhy6O0XxRXVSi67PAisAjfuqQj8I1z7vMy9vkr0AffXety4HNgoXNurZltDUwh/Xm6H/gLPil/0sz2Af4AvJPhF8VcV9oPTK6Udfkql36QK6/qvgZV5jVVnlYQWSe0/LnVzFbix7g7KqyqF+5fofRukXWia2PEf4F98S0xnixj213D/dfhupwoz6frzs34MYIvMrPbyti2POcl2u34KzP7EtjFzLYJ3UaPAlrix0TNNGFfLugW7idHyhLvudFknowQ/MRnUXX69RZa6N6OnxCsNb41HvgvKLPC379P1OWcc+F6lnctzVE+Xl7KyWuOcvKKUU5etrzIyZWPp6WcvGKUk1eOcvJyysacPCcqzcN4UV3D4las3zUxoQDffeP2lPLO+MktUm0a7n+paozZwDm32szGkBzX8K5y7JboutbHOfdVyrotSnmsKWY2Fj/uVFf8ZCzgu3/lokQrhUzj33XKUJ6v8up8VeEatJaKnaNEYr9pmnWZ9sklY8N94hfzmeH+fudcRbvgdy6jPJuv66/gWxD2MrPLy+g6n+iS+Gq4T7R42rKmgss2zrnZZnYfvkLpCnxrk1SJ//fm6Y5hZi3xSfdy/ORUUcPww0L0Ba6nDnQDDc/3iLD4TmTVTPy17Cbn3JflONQ8/MRPG5pZ8zrcsuUy/Jfh/ZxzM8NYrwvJwUqRmqR8vPyUk1dJXuWY1SCvzpdy8moxNtznez4OyskrRDl5xSknr7Csy8lz5ZfnRPeqW51zlu6Gn40e0k9Qsd7A8Ga2Df4Xn6X4Mb7qiieA+fg3VXkuLonJCNJ1PTuljH0Tkw/9NWy7AHimHI+ZjRKJ0XrdOsxPprRL7YaT9fLtfFX2GvQr0MbMWqc55uFpyj4I90eaWZM063ulKcsaKWONpdMl3CeSqTfD/fGVeLhuoeVdqj7hfnwljllbXsa3GNwYuDLTRmZ2AP5/vgY/oRIkz1m/DK+RVIkv0znxI3kpbsa3wrgQP75dqs/wieQeZpbuC3Tiffl+GDcxaji+1UZfM2uBb9Uyl+S5zkW34yft+sQ592GkvELvudDtcWxYHFDKprmuBb5F80Lzk8cNKmP7fKV8vGKUk1dOvuWYVZVv50s5eRmUj1eIcvKKU05eMcrJKybrcvKsrzQ3s/okk+wRpWz6Nv4N1c3Mtk9Zd7GZ7Rw5ZlN8lyQDHnXOra7GkGPlnHvPOdfWOdfOOZdpjLGoxMQ450cLzawXvktpaZ7BzyJ9Ln7ClSdy9Vw6537Aj023g5kdlygPr5WHKDneVt7Lp/NVxWvQuHB/XeR4ZmZXk5xg6HehG/U7+K5Ig6JJr5n1Aw6s7POoJTea2S1mtl7LgpA0JVr7JL7Ij8F39exvZleaWYOUfQrN7EQz2yHNYxUAd0WTVDPbFbgI343rgTT7ZAXnXDF+nNq1wD/Cc68X3cbMeuInhjHgKufcjLDvBPxrZCPggdQk3cy2DJVQCYkvRF3JYc65ufhZ5Jvgx/dNXb8ceBT/urg3XIsACF/mrg2Ld6fZdybwLv5L5M1AI2Ckc25dNT+NGmdmW5jZSOBsfAues1M2eQD/uX2NmZ2Z+sXazJqa2Rlmtkmk+Gb8F5jrzGz/lO0bmFmPan8itcsB/wcUAXPwQ2N8WOoeeUj5eMUpJ6+cfMoxq0M+nS/l5OWmfLyclJNXnHLy8lFOXinZmZM757L6BhyDP3nflmPbe8O2g8PyjLB8D/6XvVeBkfhfmh3+n9Ay5RhDwrrucT/3cjxfB6wq57Ybhe1npJQfAKwL6z7F/7r3SVi+NdyPLeW4iTeuA7aL+5xU8NwVp5SdGcrX4ZOt5/HjJU3FTzyx3usi3TmNrOsf1g+M+/lm0fmaQZggO1duVbwGbYf/Jd7hJ/Yag2/NsCKy7cCUY3TFt0xzwDfhPfkBPvFM7PNg3Oclw/O/M/FaCbE/E665H+I//BLXmZaRfbbBTyLk8Mnkq8CosM/CUH58ZPvuoeyFsN+v4TFexV/nHXBj3OeinOfrCPyYtw6fGDwbnsvkUFYEXJtmv43D68jhWzA+ix8P74tw7vtHtu2Ib+2xDt8F9RHgYaBr3M8/wznJ+LmGn4BpGcnPnKtS1jcPr6/E+RwFvBSevwP+VcrjDogc1wF7xn0uyjhHDp+vDMG3Zn02vG6Kw7qpwG4Z9t83co2ZgR97+Wn8Z3/i/HZL2edvkcf9OFyX3sQn+zPiPidVOJefA/3ijiMXbigfL+s5Kyev2rlTTl7752sGysnrZE6O8vHKnDPl5Os/N+Xk5TtHysmr51xmbU4eewDlOHmjKGeSA+wXtv0Z/8vWjLBs+LFxvsE39f8Fn7hvkOYYQ8iRJL20C1mabdMm6GHdXsBb+K6cS/DdqE7Ez+hbVoJ+eNhmfNznowLnrVGIeWmadf3x422uxiebDwFtMr0uMp3TyLFyPkGv5vM1g9xL0Ct9DQple+FbISzHzxT+MrBTaa8P/Nh4o/FJ6nJ8gn4kfjZxB/xv3Oclw/Nvi+82OzS8LubjW278hv8SdyFQmGa/DfAzY0/EJwjLgWn4L3r9gWaRbbuTTE464iftmYu/tk8kkpzmwg2fdN4UYl+CTya/D++lHUvZr0U4Z5PwX/iWAF/jf53fNGXbw4H38cMfJJKs7nE/9wzPq9TPNUpWCl2VZn1T/BiIX4fXxBLgPfwYwaU9bquwvQOmxX0eynGOore14b02KbwvTgTql3GMjsBt+LxoRXhtTAGewg/tkO59eiD+i8Bc/Bfimfgvx33jPieVPI874j+7svLLarbdUD5e1nNWTl6586acPL7zNQPl5HUyJ0f5eGXPm3LykrEqJy/fOVJOXvXzmNU5uYUg6yQzmwF0dn5sM6kBZvYg/tfAM51zQ2IOp1zM7A/4i/dk59x2cceT7XS+sof5iVfOB051zo2MO544mFl3/Beex51z/eONRkRykZk9gB8n8zbn3J1xx1PXKR+vHcrJ6z6dr+yR7zm58nERqQ65kJPn+iQEEiMz64yfyGEevvtS1gtjRV0cFsfGGEpO0PmqfWbWCNjcOfdNSvlJwDn4ljEvxRGbiEhd4Jw7L+4YRKqTcvK6T+er9iknFxGpWbmQk6vSXCrMzC7Hd6E4DGiM746zMt6oSmdmbYF/ATuE21KSk6BICp2vWLUCJpvZt8B3+G5e24ZbMXChc25ZjPGJiIhIFlBOXvfpfMVKObmISJ4riDsAyUlH4VuzrMOP37XezMdZqBnQB9gEP2HJ/s657+MNKavpfMVnMf7L0Br8eIzH4sekfA440Dk3PMbYREREJHsoJ6/7dL7io5xcRCTP1ekxzUVEREQkf5lZQ/xkk4fiJ0ebBdzinHs0rG8B3A8cjZ/06x7n3I1hXXvgDvyESy2A6cANzrnnI8fvCDwctpkP3Oice6h2np2IiIiISPbL1Zxcw7OIiIiISF1VH/gVn6B/D+wJvGJmM51zr+Nb5rYGNgXaA2+a2Y/OuSfwLTy/AK4EfsG36n3KzHZ3zk0Oxx+BT9zbA9sDr5nZVOfcuFp7hiIiIiIi2S0nc3K1NBcRERGRvGFmzwBfAYOBhcC+zrlPw7rLgaOdcwdm2PdzfMuXR82sCzAV6OicmxPW3ws0c879sRaeioiIiIhITsqFnFxjmouIiIhIXjCzRsAewJdAV6AQmBjZZCJ+YsV0+7bHTwD3ZSjaEfg1kZyXtb+IiIiIiOROTq5KcxERERGp88zM8GMdfgc8g+/qudw5ty6y2SKgeZp9GwJPAaMSLWDC/otSNk27v4iIiIiI5FZOrjHNRURERKROC8n5ffiWLIc654rNbBnQxMzqR5L0lsDSlH0LgdHACmBAZNWysH3UevuLiIiIiEju5eRqaS4iIiIidVZIzu/FdwE93Dm3OKyaAqwFdops3g2YFNk3kZwXAic559ZEtv0S6Bi6iKbdX0REREREcjMnV6W5iIiIiNRl9wD7Aoc55xYmCp1zK4CRwI1m1tLMtgL+hO8uipk1AEYBTYHjnXOrowd1zk0HxgODzKyJme0B9AMeqYXnJCIiIiKSS3IuJ1eluYiIiIjUSWbWGbgQ3wX0RzNbFm73h00uBhYDM/HJ9iPOuSfCun2A4/DJ/bzIvtdEHqIPsDHwG/A0cIVzblyNPzERERERkRyRqzm5Oeeq8iEkrwAAIABJREFUegwRERERERERERERkTpBLc1FRERERERERERERAJVmouIiIiIiIiIiIiIBKo0FxEREREREREREREJVGkuIiIiIiIiIiIiIhKo0lxEREREREREREREJFCluYiIiIiIiIiIiIhIoEpzEREREREREREREZFAleYiIiIiIiIiIiIiIoEqzUVEREREREREREREAlWai4iIiIiIiIiIiIgEqjQXEckCZuYit71L2e6UyHYzajHEcjOz7iG+IXHHIiIiIiJSXsrJRUQkQZXmIiLZp18p606rtSgyMLP+IQEfGHcsIiIiIiI1RDm5iEgeU6W5iEj2WA1MBnqbWf3UlWbWBjgC+Ly2AxMRERERyRPKyUVERJXmIiJZZhjQFuiRZl1voAEwtFYjEhERERHJL8rJRUTynCrNRUSyyzDAkb7L52nAMuC50g5gZkea2RtmttDMVpnZFDMbbGat0mw7MHTr7G9mO5jZ82G/5WY2zsz2Sdl+LPBYWLwhZdzH/mmO39rM7jOzX81stZl9ZWZnletMiIiIiIjEQzm5iEieW6+rkYiIxMc596OZjQeONbNmzrllAGa2ObA38ASwItP+ZnY1MAhYB4wD5gH7AlcCJ5jZAc65OWl23Q24F5gJvAVsCRwAvGVmuzvnvgrbvYr/7NgX+C8wMXKMaSnHbAV8CLQEJgDNwjEfMbMC59zD5TglIiIiIiK1Sjm5iIiopbmISPYZCjQBToyUJVq5DMu0k5ntDtwELAX2dc4d6pw7FZ9sjwa2Bu7OsPtFwN+dc12dc72cc92AO4FGwBWJjZxzg4FEYv2sc65/5PZ+yjGPAyYBXZxzxzrnDgZOCuuuK+X5i4iIiIjETTm5iEgeU6W5iEj2GQWsAfpFyvoBs/EtTjK5GH9dv9M5NyFR6JxbHdatBE4ys43T7Pu+c+6WlLKbwv0BFQv/d0uAc51zyyOxPIdP2jc1s80qeVwRERERkZqmnFxEJI+p0lxEJMs45xYCLwOHmNlGobVKV2CEc66olF33D/frtXxxzs0FXsdf9/dJXR/Wpe4zH5gPdKjYM/jdp865BWnKp4b7yh5XRERERKRGKScXEclvqjQXEclOQ4F6wKkku4EOLWOfjvgJi37MsH5GZLtUMzPsswwoLONxMyntmAANK3lcEREREZHaoJxcRCRPaSJQEZHs9CKwCDgDn1B/45z7vJqO7cpZVhOPIyIiIiKSK5STi4jkKbU0FxHJQmHMwzHAzsCGlN2iBeAXwIDOGdYnyn+tcoAiIiIiInWccnIRkfylSnMRkez1BH78wnmkGRMxjffCfb/UFWbWDjgcKAY+qGJca8K9eiuJiIiISF2nnFxEJA+p0lxEJEs5595zzrV1zrVzzmUaEzHqXnwC/mcz2y1RaGaFwN1AE+AZ59ysKob2S7jvWsXjiIiIiIhkNeXkIiL5Sb9IiojUEc65CWZ2HfBP4EMzG4tvEbMv0An4Dri4Gh7qI2Au0Cs8xvf4LwaPOueq2mJGRERERCRnKScXEakb1NJcRKQOcc4NAo4GxgG7AycCq4FbgD2dc3Oq4TFWAUcBbwDdgP7A2cDWVT22iIiIiEiuU04uIpL7zDlNpCwiIiIiIiIiIiIiAmppLiIiIiIiIiIiIiLyO1Wai4iIiIiIiIiIiIgEqjQXEREREREREREREQlUaS4iIiIiIiIiIiIiEqjSXEREREREREREREQkUKW5iIiIiIiIiIiIiEigSnMRERERERERERERkUCV5iIiIiIiIiIiIiIigSrNRUREREREREREREQCVZqLiIiIiIiIiIiIiASqNBcRERERERERERERCVRpLiIiIiIiIiIiIiISqNJcRERERERERERERCRQpbmIiIiIiIiIiIiISKBKcxERERERERERERGRQJXmIiIiIiIiIiIiIiKBKs1FRERERERERERERAJVmouIiIiIiIiIiIiIBKo0FxEREREREREREREJVGkuIiIiIiIiIiIiIhKo0lxEREREREREREREJFCluYiIiIiIiIiIiIhIoEpzEREREREREREREZFAleYiIiIiIiIiIiIiIoEqzUVEREREREREREREAlWai4iIiIiIiIiIiIgEqjQXkViZ2Vgzc2Y2MO5YRCS3hWuJM7PuccciIiK5w8wGhs+PsXHsHxczmxHi7h93LCKSu8xss0gevlnc8YhUF1Wai4hUs0jCUNZtYNyx1lVm1j18ge0fdyw1ycy2jbyelptZixp8rIHhtllNPYaIiIhIZaVU3JV16x93vHWVmR0fcsbj446lJpnZkZHX009mViP1a2bWKpKHt6qJxxCR9OrHHYCI5L2fgCnAvLgDqQHLgWWlrC9tnVRNd+AGYBwwJNZIatbZkb+bAH2AB2rosW4I92OBGTX0GCIiIlJ7pgOrgMVxB1IDlgArS1lf2jqpmuOBPwKPA8/GHEtNOivydyfgMOC1GnicViTz8CHAohp4DBFJQ5XmIhIr59wZccdQg25zzg2MOwipm8ysAXB6WLwb+BO+Er2mKs1FRESkDnHOHRJ3DDXoz865IXEHIXWTmbUDjgWKgAeBC/B5eE1UmotITDQ8i4iISG46BmgPfAtcje+5sLuZbR9rVCIiIiIiddvpQAPgDeAWwAHHmVmbWKMSkWqlSnPJS9HJJ80bYGYfm9kSM1tqZh+a2WllHKOBmV1qZhPDWMILwnF7pT5Gmn03NbOLzOwlM5sa9l9mZpPN7E4z27SMxz7FzF4xszlmttbMFpnZd2b2fDhuoyqenxITeZjZVmY2xMxmmtnqMGbb/Wa2cYb9uyf2D8s7m9mwsP/a6CRJpZ2nyDbbmtm94fwsDedqipk9ZWYnZRo/LsQxIsS7yswWm9kEM7vCzJpW5RzVpBD3aDObFc73PDN7y8zONLN6GfYpMQFVOC+vm9lcMyvO8Dqs9PkxszZmdn143ywI+88ws9fM7Hwza5myfXszO8vMnjGzb8JjrTSzaWb2sJltV8bj9Qj7zjSzNeG9+n14jn8zs9Zhu83C6y7RhfFAq8QYliHetWH7Y8vY9saw3bSU8sYhtg/NbGE43m/hdfy4mZ1UVhxlSAzN8rhzbjnwdEp5qcysk5ndYv4alvh/TDez58zsjMR1JLz3XWTXd1LO54zIMfunlqV53IwTBZlZgZnta2aDzeyjyP97vpmNC6+tBuV5fmkedwMz+4eZfR5eP2vMbLaZfWn+elaXW9uJiOQE884Mn51Lw+fTx2Z2blg3JHx+DPl/9u48Pooi///4q3JwhUvuSwi6gD9kFQX8qisYRFRUFEVFRAVcV3bX1RW8r5Vdv+KBJ3uwKiqogCKHiqsrooAoi6t8Ax4RVpZDUUG5YwiQo35/VHemM5mZTCYJk8D7+Xj0I9PdVd3V3ZOZ6s9UV8XYxoXGmDeMqyfv9/6+YYy5oJJlG2SMece4evdPxphVXp0poe+lGPsJr0f3NsbMNsZ879W31hpjJpoofRuHfxcbY/obY1718hcFz52JYyBQY8z/GGOe8/ab532H5hhjnjXGnBEj3xBvv99512GHMeb9ynyXHwiJvH+C70vvfXq1MeYDr/4S8fxW5vyYOOtwgfQH5N7Tf+/iumYBGGnK1sOzYu3L287xgfTHlJP2BS/dwrDl1V3v87tmmWat3QC8D9QBYsYQAuWL6/7WuHu79YGs68PO5+LANssdkNiEfb6ErUs3xgw0xkwyxnzifWbsN+5+8m1jzHBjjInn+CJsu4Mx5jFjzBfe+2+f995f4S3vk8h2RaqdtVaTpkNuwvXJa4F7cf2sWaAA15+fDUx/jJI/A9dXsp+uENgOFHvz9wf2MT7G/v1pJ+7RruD8KVH2/UxY3lxc39nBZZmVPD+ZgW0Nw/UJ6O9rT2DdNuD4CPmzAmmGAvu917tw/QcujnAuypwnb/2tYecmP1Aef2oalicNeDrCeSoMzK8GOkXZp59maoLnz8Y6pnLyPhrIXwzsCCv3u0CjCPnGe+sXA48E8m/38o8PpK3s+TnD266ftsArZ3B7Q8LyTA1bv8vL58/vBYZG2d8fwvLmeeUNLsvy0h4ObMa1urbee29z2DQszmvxhreNV2KkMcA6L909geWNgJURrmXwmDdU4n+0vXe9ioAO3rL+3nZ/BOqUk/8K3P+SX5Z9uM+d4Dnt6aV9wjtv/vLtYefz48B2R5V3bJT+fMmMsS7a5/L7QP1y/veywpZ3ADYG1hcR+t/wly2Odc40adKkSVP1TkAq8FLYd+d2QvXAGYTqE1Mj5K8Tlr8oLL+/jfQIecfH+i4IrPen4Hf6EmBCVX2XULoefb73He3XnfYF1m0I/x718o8KrL+e0P3JTly9aGog7QZv3ago1+OJsOP+idL3HTsj5GsIzA/LtytQDgssAw6LkDdYDxifwLkL5i9zTOXkrcz7x39fTgNeiZB/VCBtwufHyx93HS6QZ3HY+mq59wROxtUN/fLlU7YefnKc1+MzbxsTY6TJIFTnHxlYXq31PuDEwHWr7y0b7S37NI78cd/fAnNxdXt/+Y9h53NuvJ9j4Z8v5Xz2WNz9Wfg91ywgpZz/vcywdcdS+t4xPHYS8TNdk6aaMCW9AJo0JWMKVBy2e5WEkYEvvA7A64Ev2C4R8v89sP4WoKG3vAWhyqUfRBwfIf9fvC/L/xfYbxpwAvCWl+9bwgJDwClh+20WWNccF8ycCrSr5PkJfuntBFYBJ3jrjLcfvyKykbAgbtgXbi7wD+CowPougdf+tYh0nn4T2M5rBCqAQDPcYCsvAY3D8j3u5dnsbaOZtzzdK9v/eetXRPnSr9SXdyB/mWMqJ9/vAnmfBNp4yzOAGwjdnL0UIe/4wPm2wINAS29dXQIB8MqcH+A4QhXhz4FBeDcPuIEoewMPAwPC8t2D+5GqJ5DhLUsBjgZeJHQj1i4sXydClcpHguuBJrj/ib8CvaKcj8WV+D+4hFCFsWmUNP7/pAWOCCy/y1u2DbgQqBs45na4G56nKlG2O73tLwwsM4Rufi+KkfdsQpXUD7xjSPHWNQb64vpm7B7lfZ0VY9ujvDQbYqTJDGwrM2xdB9wPmZd458kvV0Nv2996+R4t538vK2z5FG/5emAAkOotT/XeY78GHkj0emjSpEmTpspPwG2Bz/FHgObe8sa4bsj8IHrEOhqu/mG9dH8iFHQ6DLgvsO0yn/ex6g24fouDAaPDveX1gd/igpY7ouVP4DxkBfa3E1gE/D9vXZr3Hemfh3/732mB/P53cT4uOPVcoMypwJGBtBuIEmDG1SX9cjwDdA2sa4UL6Eeqk87z8nyFG6C8kbe8nncu/+utnxchb2Zgn+MTOHfB/GWOqZy8lXn/TCVUDy8AbsS7P8HVYdpW0flJtA53QO89ifHjVgWuxy2BcpW5X/PSXE7oHqJhYHm11vsINT6aEljWiNCPCb1j5K3w/S0x6s5h2x5P5YLm/wNM995nrQETKNf1hBqyXF/O/15m2LqFhO4tTwxstw7QBff/cnOi10OTpuqckl4ATZqSMVH61/b+EdbXJRScuTNsXUdCQby7omx/amD74ytYtlRckNoCl4et8ysPb1fz+Ql+6W0FWkVI8/8ItXa5OWxdViD/R4RV5qNci/Fhyw8j9Iv7TP/LNY6y98BVJvOAn0dJ0wj4xtv2kAjr/bJPTfD8+fl/omzrCn96LixPfVyA1QIzomz3usC2e4etGx9Y90h1nR9gqbf8P0CTKnzP+a267wpb7geu11Rwe/75WFyJMtUj1HLnmihpnvTWLw1b/qa3/PaqOkeBbRtgrbf9K8LW/a+3/K0oedMItYxfSjkt0qO8r7NipBlFJYLmcZShd+B/q168ZQRyvOXDq/p6aNKkSZOmyk+4H979gMyUKGmCdZ2pYevaE2pcMCFKfv9JvP0Egphh214cId8X/joiN7YYEyhXmfwJnIuswPbWEOHpKuD0QJqLw9aNCqybU86+NhAhwAx0JXS/82AFyn6Ol+d7oH2UNB0ItQ4ObxEdrCOMT+DcBfPvIno9/E9V/P6ZGtjvddVxfqhEHa6cc1bl955UTdC8XeA9eEaUNG97618IW15t9T5cYyb/HrVv2Dq/IdDkKHkTvb8Nvq8zY6QbTyWC5nGU4yIv79qKlJHQk+onVfX10KSpuif1aS6Hug+ttYvCF1pr9xEa+Tq8H7WhuNaie4DHomz33kQLZK0tAv7pzZ4Stnqn97elidK3dTX4u7X2h/CF1tovgdne7KUx8k/0jqmiLsIFbwuAcdZaG2e+X+KCiv+w1n4WKYG1NhfXmhXgzAjrjTeNqnCpS8vA/UofaTosLO1A3K/44Co8kfwNV8kG1zIlkmJcy6BoEj4/xpguhN6Td1hrd8XYT0X9w/sb7T3fyBzgfuittXtxj9iCaxleijGmLi6oD/BC2Gq/3G2roWhZwJG4Hz7mhq2b5v09wxjTIULe/kBn7/VYa+3+aihftbHWfgL8gPvf6lmBrNV5PUREpPLOxLWUBdeqN5JHcPXvSIbigop7gQeipPlfXIOPdFw9s1xef8rd/fzW2uIIyZ7GNbapDhOttfnhC621C3FdeEDsevj9Ce53JO5+ZxuhcWLicbX39wVrbcRzYq3dhGs9D2H1cGvthkA9fHzFilxGY6LXwxuHpa2q988OXIOKaCpzfqqlDlcD7z0BsNZ+B7znzUaqh7fFtSKHA1sPvwR3j7oe19o/yK+HDzfG1I+QN9H725rCv1870jv/8VI9XGotBc3lUPdRjHXfeX+bhS0/3vv7iXWD75Vhrf0vrqVuVMaYvt5gMau9gT9KBvTA/aoPrqVB0EJcZe44YKkx5pfGmM5Ur/fiWHdMjAFrPkxwvyd7f1dYa7+PmbI0v7I3yBvsJeKE63cO3CN61eWPgYp/+DQkLG1v7+831tr/RNqYV6l9Lyx9uLWRfuQIqMz58a9JEe5RzgoxxhxrjPmbNwDPbuMGKPXf83/zkoW/5/+Ne9qhLfCRMeZ3xpijEh2EJgHPe39/EeF/7VygKe4GalbYuje8v78zbrDVIcaYFlVUJn+gzznhn0HW2q+Af+G+30dFyOtfw81eALrGMcbUMW4QrAXeAEF7wz4fW3lJI/0oEI1/PR4wxjxljDnLGBN+wywiIsnj16+/ttauj5TA+1F/RZT8fr3oY2vt7ij5dwCfhKUvj5+uENe6N9J2i3Gt0KtDPPXwaMeSj+tyLxF+feEdrxFBvPx65jXl1DNP99JVZz18dIx6+A1haavq/fNxOcHsypyfStXhatG9Z5BfD78gQuOZy3Ct5L/DlTOoOut9fj38hQhB73eBTbguJIdGyJvo/e0BY4xpZIy52RizxBsAdH/gfRL80bJ9BTbrX49pxphHjDGnGmMaVF2pRaqPguZyqMuNsa7Q+xseDG7p/f2O2KK2ODHGPIgbzG4k0A3XDcQOYIs3+YGwUpUDa+06XAuFn4CTcP21rfO+0F42xpxfDcHEWC1n/HVplP1xwRcrgBtLG+/vxgrma+f9bUj01iWtCZ3bmvKF7QcCy2uptCksfbjyzndlzo9/TbZG+8EoGmPM73A3br8Bfu7tfxeh97x/gxL+nt+Ja1X/I67/8z8DXwI7jDGvG2Muj/GDTVX4ANeSxOD6TQzyW7287pUzWO4ZhMY3uBTXf+WPxpivjDF/Ncb0SqQwxpgmuD7SIXQjEc5v5XJVhM+DRP+vDghjTCvcDelk3NMXbQl1E+W/V/xWfhV58mAi7oeNdOBXuB99dhpjPjPGTDTGdK2aIxARkQRVtn5dVfWoaNvd6j2JWt52q1o89fBox7ItSsv4eFS4vuDVx/wGAk2IXc+s56U7ZOrhVXB+Eq7D1bJ7z6C53r4zCNV/fX49fHqE93m11PuMMd2AX3izZerhXjle9GZ/Gb6eml8P74rr2uYhoB/uc7kAdx/mv1d8FamH34J7eqIhMA73I+NuY8wnxpg/GmMqEoAXOaAUNBepOL9iUN7jVBErEMaYgYR+zf8bLoBY11rbzFrbxlrbhlC3L2W2Ya2dTmgAk5dxLdpb4h4VexVYUsUtKCv12FiCXbNUZv/+o4O3xWhdEpyyKlm+qhbv8UZLV975rorzU6FrYoz5f7jBR1Nw3Z2cgOuP+rDAe36cn7zMztwjyJ2BK3EB4a9wNxuDcY9jZldXZctrQeJXfkseDTXGNMcNkgNRgtdeK6ZuwB14lXXgZ7hBwz4xxjyeQJEuw/V/D7Aw2Eoo0Ark7976zrhHeSMWL4F9HwiP4T4TtwFX4foMrW+tbRl4r/gBlbhv0qy1BdbaYbguXf6Ea523B9fH/01AjjHmxio8DhERqZhK1a8DKluPqqr0NUFl6+BQseMOdt9xaZz1zFFVUMaqVJ318Ko6PxWth9e2e8/gvvNwDU/A3Qe4Qhrzc+BYbzZS8Lq66n1XBV6vjVIPv81bf6ox5shoh5bAvg+E53BPG2wALsYNxpxhrW3lvU+C91sVqYfvtNaehhuo9iHck+iFQC/gD8BXxphoXY+KJJWC5iIV57cgaBczVfT1fr+Db1trr7XWfh4hsNwmPFOQtXa7tfZJa+2l1tqOuEDcA3gDkhC9T+xExOoCwf/iLAS2V+E+IdR3d2YF8232/v686opyQPjvq8PLSedfjx8T3E9lzo9/TVpGeEQylotwNwpf4m4SIj26Wt57Ps9a+4K1dpS1tivuPNyKe2TUb4FeXfzKeBdjzIne62G41is/EuoHsgxr7Vpr7f3W2rOB5rhWOn5/8b83xpxXwbJEarUSy1Vh8/41rK5Ha/0ndOrFSNMk0kKvBZbfiuh31trnrLWbw9KkEmqlVWHW2lXW2nustQNwXeucjmt5lQpMNMYcG3MDIiJSXSpbv66uepS/3ZbGjWUSTXW1lIy1XX9dok91xlLherjXjYs/3o3q4WGq4PwkWoerbfee4fx6+GmBRjJ+Q5aV1trPo2WsynqfMSaNQOA+niyEurv0JXp/G6/K1MMPJ9R9zHBr7Wxrbfj9fcz3SXmstR9Ya2+11p6Cux7nA5/hGgQ9a4xpXZnti1QHBc1FKs7vG7B3tMChMeYIole6/OXZUfIa4LSKFMha+19r7e3ADG/RwIrkL0e0lqrBdZ9aawuqcJ8QGtyot6nYQCN+H+rnGGMaVnGZqpPfN2GHaI8MegFD/5x/nOB+KnN+/GuSCgyqQD7/Pb8qxmPCp0dZHpG19ltr7UO4QcGg7Hve30+lHxm11q7F9RMOoUq6/3emtbawbK6I2ym21i7H/Yjwtbc47v9Vr2Lvd+vSBzeQULTJH6BqqDGmaWAz/jVsbYyJtz/XkkPwixIjzQ7vb6sYwYX/ibK8JaFKfsTPR1xfoLFuBOJmrS201r4LnIPrl95QwfehiIhUGb9+3ckYkxkpgVdvida9WUlf015XZpHyNyXQd3Wc5fK3m0bZQRL97abgBumuDvHUw6tjjBK/vjDQGFOR712/nnmxd15qi+p6/4SrzPlJtA6XjHvPKquH41qJb8LFri7zzttl3rpoXRWWUQX1vnNwQeMC3I8nserhfiv2Uab0AKqJ3t8G75/iqYfH+vEnWj08mCdaPbzK6snW2r3W2tcJNZipR5TPWJFkqk1fZCI1xVzcF1cG8Psoae6Mkd9vYRDtl+1fA0dEWlFOCxdwA/5A1TyOWVIeE2EAQ69PNz8w93IV7s/3Cq6f6zTgsQr0l/c0LrjXFNefXVTGmPQaFFh/B9clBURvrTGGUAurmQnuJ+Hz4wWP3/dmJ1TgUcySVjWRrqMxZhBRbjYr8Z73+0hvStXwK+XDjDHdgRPDlpcSq9xe6x6/pX1F/lf9VuarrbWfWGt/ijbhBtzZhauAXhbYxiJgnff6MWNMnQrsP55zusr7a4ALwlcaY+oDY2Ns3w/Ml/l89Fr43BdXScvmjfU+2kfoOlTlZ6eIiMRvAaHvmTuipBlL9D6w5+BaWdbDPYkWyR1AXVzQa048hbLWfop7Ug7gziiBzquo2ODUFXFTpKC1MaY/ob6Vq6MePhX3ndgc+GMF8j3l/e0K3BwroTEmo4L1kOpULe+fCCpzfhKtwyXj3rPK6uFeg5vp3uwVuAB/e2+fMyLlqaZ6n18Pf9drvBOrHv4SLl7QHjgzsI1E72+Dg9PGUw9vF3g6toQ3dtCvouTdFXgdqR7eCLirnHKWYYxJK+cHovzAa9XDpcZR0Fykgqy1G4FnvNk/GWNu8gOLxpjmxphHcZXnnVE24XflMMgYc7ffWt0Y09QYcweum4ltUfL+xRgzyxgz1PvSw8vb0Bjza0KPjL2Z8AGWlQ68Y4zp4+3LGGNOB97GVRy/IdSHcpWx1u4i1P/eMGCeMaanv94Yc5gx5hxjzGvB4K21diWu/2xwAf9XjDE9/UqJMSbVGHOsMeZu4L+4vu5KCfRLN7Wqjysaa20+oWD5cGPM3/1H1IwxDYwx1xE6rpettSsS3E9lz8/vcV2idAE+NG40+vRAOf/HK3uwJYL/nj8a+KsxppmXPsMYMwaYTfT3/K3GmLeMMVcYY0puSI0xdY0xlxC64Qh/z/uPah5tjDmZynsZF+huTmigzS9jXIePjDGTjDFZwSdSjDHtjDF/xj3WGqncEXmV/xHe7Kzy0ls3WNnr3uxVgeVFwO9wwelTgHeNMaf4lVljTGOvzC96Pw4E+ed0hIky4r21dhNu8FSAR40xp/stbIwb/HQhUQbP8m4yPgzkPS1Qrh64c9Wb0GBVFbHRGHO/MebE4I2UMeZnuBuxBribm7cT2LaIiFSS13fxg97sr4wxDwXqC42MMbfi6kk7ouT/FjcAN8Btxg0u19TL39QYcy+hOsOj1trvI20nCr8xTH9ghl8fMcbU8+rffyF6vb+y2gL/MK6xih+AughXdwLXQn9uVe/UayjhN664xRgzxRjTxV9vjGlpjBlmjJkXlu81Qn1QP2CMmWwCT1AaY+p4dcUHcYMhlqrK8bfUAAAgAElEQVQTGGMyA/Xw8VV9XNFU8/snuJ+Ez08l6nDJuPf064x9jTFHxXFqyuM3Uvk5cL/3eoG1dkuU9FVa7zOuVbj/lG089fDvCNVpg/XwRO9vdxIapHa0cQ1JIllGaJDRqcaY3sZJMcZk4QbgjBYDzCH0JOyzXr3dL9dJXt7Doh91VB1wfZbfZYw5Llh2Y8wxhMaOyiPUOEuk5rDWatJ0yE24D30LjI+RZryXZnGEdQ2Bpd56S6hP72Jv/l5giff6trC86bgvBD9vsZe3yJt/w8tfZt+4Vh82MOXibh6Cy5YCGZU8P5mB7Q0j1AI0F/eF5q/bAfSOkD/LT1PZawHcHjg3FjeIy25KH3PTsDypuAFtgmnyga241iHB5b+IsE9/3dQEz5+NdUzl5H00wnsjWOb3gEYVeb9GSFvZ83MG7ubQT7PfK2cw35CwPDPD1u/A/d9Y3COx/k3AhijHFbz+2wj9r1lcJa9NWL40YHUgzXbcoDYbgIsSvK5zwspyW4y0G8Ku4w7gp7D8j1Zg35cG8vWIM8/gQJ5jw9Zdifvxw1+/l7KfJT3D8lweds03ecf5QVi6npT+H80PHPtm3ACq/rrMsLy9ws7T3sC2CnAtjPxzOyrG/15WlOUW93my3StX8BrdkMj7QpMmTZo0Vc3kfXe/EuHz2q8vPI/74doCf4+Qvw7uR+7w/MF65AwgPULe8cSoRwH/G/ZdEqyfvQ9MiJW/guchK7Cf873vXIurewW/uzcCnSPkH0WEOlWUfcX6Tk3F/SAQPO7we4GdEfI1oGy976cI18IC7cPyZgbWjU/g3AXzlzmmcvJW5v0zlTjvHSpzfrz8FarDkYR7T1xw9YdAmh8J1cNPTPD/YkXYfi+NkbZK6324wT39+u9hcea5LpCnZdi6RO5v7wq75l975/OlsHRnEvrMsLj/V//Y/0PgniJCmc+l9L1gHqH/9zxgQGBdVoz/vcwoyy3u83wbrsW/v2wfCd6fadJU3ZNamoskwLoWkQNwLQ4+xX0xGVyg/EJr7d2EHp3aGZa3ABd0/CPui6vAy/tv4DfAeUR/NOle4HpcC4XVuC+dhrhKyTu4X7KzrGutU1U+wrXufB732FYa7pfup4GfW2urox/FEtba+3GPiD0NrPUWG2ANrsJ5IaUfWcNaW2StHQscj3sMcg3unDbBVfQ+xN0c9bTWfkgNYq0dh3vscA6wBXd9c3GPZF4FDLTW5lZyH5U6P9baBbiW5vfh+rzLxw3gsgHXYmMMLrgfNAK4Aff/sg93I/YZrtL4C9zNQiRPAdfgrvXnuEplY6+cS71tHm/DBoy0rp/xAcAUr1wZQCdvSrRLnmBXLMWEWkZEcilwD/AusB53I5aOu8F9GRjgXet4+Y+EfmljDHgU5m1Cj1qWGkDUWvs8cBTuqYMc3GdJHdzTBa/igtNfhuV50Vv+Ae46tMWdzw5h6VYCJ+AeTf0B16JlK/BXXEA9J1qBrWu5fwKuFc9WL2+uN3+ytfaFOI893Bm4lklLcU/H1PeWrwWeA/pYax+PkldERA4A77v7EuBqXL04H1fv/AS42lp7JVHq117+/dbaYcBQ4C1cYKaR9/ctXB39MpvAODzW2rtwAaX3cPXOurjvydtw9Y3wAc6rhHUtk0/G1Qv34urA63FjuvS01q6vjv16+y6y1v4O16p5Oi5Il4471i9wT94OjZBvj7V2OK5l/gu4LkVSCN2zvIdrbdvFuhbeNUJ1vn/C9lOp81PROlwy7j2ttTuAfri64Le4ewy/Hp7o2DTBevhu4LUYaau63ue3Fl/oHVs8ZuPuF9IJjYUEJHZ/i/th7ve4z0O/X/VOhA3Oaa19Gzc46xu4+6VU3Dl4ANc4pdQ9U1jeN3DX7R+4z9g0XH38Odz91rtxHnvQt7j32GPActxgqA1x76Uc3P1BD2vt7KhbEEkiY61NdhlEDjrGddeyDVeB6WetXZrkIlWIcQMw+ZXwztbaDUkrjIiIiIgc8ryu5L7GBYuurMQPqTWa143CIgBrbVUMpCgiIiIJUEtzkeoxDhcw307io6uLiIiIiIhzBS5gXoh7kktERESk2ihoLpIAb0Cil7xBEJsGlncyxkwkNKDj49bavUkppIiIiIhILWKMmWmMucgY0yKwrLUx5jZcVwYAz1s30J6IiIhItVH3LCIJ8ALlwf7M/D6mGwWWzcENUFJ4wApWRdQ9i4iIiIgcaMaYnbj+j8GNn1EQmAfXR/G51trw/n4PGuqeRUREpGZIS3YBRGqpn4DfAQOBHkBL3AAj3+MG53gemGOT+KuUMeZwKt41zDfW2j7VUR4RERERkXJcDwwCjgNa4QaM+xFYiRtU8IXKDsR4IBhjTgbmVjDbMmvthdVRHhEREak4tTQXOUiFtRaP10ZrbWaVF0ZERERE5BARbC1eAUustVlVXxoRERFJhILmIiIiIiIiIiIiIiIeDQQqIiIiIiIiIiIiIuJR0FxERERERERERERExKOguYiIiIiIiIiIiIiIR0FzERERERERERERERHPQR80N8a8box5PdnlEBEREZHaQfXHqqdzKiIiIiIVkez6Y1qydnwAHdm9e/fugE12QURERESk5uvevTs5OTk5yS7HQUZ1chERERGJW7Lr5Ad9S3MRERERERERERERkXgpaC4iIiIiIiIiIiIi4lHQXERERERERERERETEo6C5iIiIiIiIiIiIiIhHQXMREREREREREREREY+C5iIiIiIiIiIiIiIiHgXNRUREREREREREREQ8ackugIiIiIiISDTWWqy1yS6GiNQixhiMMckuhoiI1GIKmouIiIiISI1SXFzMrl272LFjB/v27Ut2cUSkFqpbty6HHXYYTZo0ISVFD9mLiEjFKGguIiIiIiI1hrWWzZs3s2vXrmQXRURqsX379rF582b27t1LmzZt1PJcREQqREFzERERERGpMXJzc0sC5q1ataJx48akpqYmuVQiUpsUFRWxe/dufvjhB3bu3ElGRgaNGzdOdrFERKQWUdBcRERERERqjN27dwPQrFkzmjdvnuTSiEhtlJKSQvPmzSksLGT79u3k5uYqaC4iIhWijr1ERERERKTG2LNnDwCNGjVKcklEpLbzP0fy8vKSXBIREaltFDQXEREREZEawVpLUVER4AbxExGpDP9zpKioCGttkksjIiIVUpTcweDVPYuIiIiIHDDFxZaC4mIKiyyFRe51QZGbLygqprDYsr/Q/S0sKqagyFLopSnw8rh5L32pNDa0reLQNl0+t00376fxlhdZ9hcVU+jl2bhNLRKTJRjU0qB9IlJZwc8Ra60+V0REaov8nbBzU1KLoKC5iIiISC0QKdgcDDSHB4+L/GVh64J5YgaUIwWnw4PMMYLVhUXF7I+wz6Limt/Sb39hzS+jiIiIiMhByVqYfz0UFyS1GAqai4iIyEHJWusFk0sHcssLIgcDwaVfu7xFJWlLby9y/tKB5tKvQ/suKrbl5q8FsWYREREREZHK+eRZyHkt2aVQ0FxERETKVxRohey3Ki73daHr7mJ/lNcFRX43HGF5C/0uOxLYRmFxSd7a0KJZSktLMaSlGtJTU0hPTSEtxb32l4XPp6ca0lICf9NSSPe2kZbqXrv0wTSG9JSUCGlC+7hmdn02bkv22RAREREROcRs/gz+eXuySwEoaC4iIpJU1roWy/uLitlXUOT9LWZfYTH7C4vZV1jk/XWv9xVGDlT7XWEE+3jeH+V12SB36W459kd4rbGzarYUQ0kAOBggDgaCQ8tLpykJPqeGAs7BoHVaagp1vPVpqaGAc3rYfup4weloaUr2EVamOl6etBRTY/qazaibmuwiiBxQGzZsoHPnzpx66qksXrw47nyZmZls3Lixxg2wmJWVxZIlS1i/fj2ZmZnJLk5EeXl53H777cybN4/vv/+eoqIi7rnnHsaPH5/soomIiCTHvp/gldGhAUBTUoHipBVHQXMRETlkFRXb0kHpgmL2FxWxt6A4ELwOrQ8Gr0svKyq1PrhsX/iykm17AfJCBaSTwRhCrY0DLY2DwV2/1XOwJXOkFs+hPGHB4ZSygWY/UBxtn5HyxA6Au/mUlJoRbBYRkfjcfvvt/PnPf+ZnP/sZl1xyCXXq1KFnz57JLlaNVVN/oBERkSr05k2w7StvxkCjdrBlY9KKo6C5iIhUu/C+pYsC/Tn73X746/cXRQgwlwlsu6DzvgitsiO30I68TN13VF5qiilpOZweaEUcfF3SUtnrPiP4Ot0LDqenxbeNSK/TA62c02MGtUPLUxVkFhE5aD3//PPs2bOH9u3bJ7soUb366qvUr1+flStXkpGRkeziiIiIJNfKGbBqZmi+7zh45eXklQcFzQ9ZxcWWTTvyWbMll/9syeXbnfkYXPAjxfgTpKSEXqd6j02nhq1LTaFUnpJ0KW6+JE8gnb8uOG9M+fsvWZcCqab0ftzysuUrVVYTvh9qzKPgIn5guajUYIChgQoLA4MXFhUHBjYs8zcwsKAfpA4MWugHpwuLi8tsszDQF3TkMoQGMywKG8TQH1SxzL6K1bd0RRkD9dJSqZOWQt20lJK/db1ldVJDQea0lBTqRHmdnuYFk0u9Nl6wOhhgjr6NkteRgtMpKWrhLCIiNU7Hjh2TXYRybdq0iY4dOypgLiIi8uN/4B83huYPPxGy7gAUNJdqZK1l60/7WbM51wXIN+eyeksuX23JZc/+omQXr0YwBi/QHgrshwfXTUmA3cuDKclbsp2S7Zky2w9/HZ7flEpvSi+LsY/QfDBN2bJF23bJ/mPkD5XDBNKEH09ZwRBp8CnKUqHTwIrwkGrpPDby8ijbjfXYZlzbjbEtG2UmWp7w81BswwPWgQC3Ass1QihA7YLUwaB1nbiWhYLdddNdgLtueqr3t/S2w4Ph/jZqUt/OIiJSM8yaNYuJEyfy+eef07hxY84++2weeOABbr31VqZNm8aiRYvIysoqSZ+Tk8N9993He++9x7Zt22jZsiUDBgzgzjvvpFu3bnHvt7CwkIkTJ/Lss8/yzTff0K5dO0aMGMHdd99d6WMK9j3+wQcf8Nhjj/Hll1/SsGFDBg0axIQJE8q0Fh8/fjx//OMfee655+jevTt/+tOf+Ne//sX27dvJzs6mZ8+eMfs037p1K4888gjz589n/fr1pKenk5mZyTnnnMO4ceNo3rx5SVprLc8//zzPPPMMn376Kfv27aNLly5ceeWV/P73vyc9PT3hYwbYuHFjqe/7YB2yItdv8eLF9O/fn5EjR/LAAw9w11138c9//pPNmzfz8MMPc8MNNyR8PHl5eUyaNIlXXnmFr776CmstHTt25IwzzmDs2LF06tQJgJ07d/LCCy/wxhtvsHr1ajZv3kzDhg3p06cPN954IwMHDoy47b/85S/MnDmTDRs2UFhYSOvWrTn++OO5+uqrOfPMM0uOzRc8X506dWLDhg0VvgYiIlKDFOTDK6OgYI+br38YXPQMpCY/ZJ38EkiVyd1bwH+2/MSaza71uB8o3563P9lFq9GshUJrAQv6HUEkKYJ9R4e3ro4cqE4tCT6XCVRHCkqnp1AnNTUQxC4bqK7rteBWsFpERGqaxx9/nLFjx5KamkpWVhYtWrTgnXfeYfHixRxzzDFl0r/77rsMHjyY/Px8jj/+eLKysli9ejUvvPAC8+bN480336Rv375x7Xv48OHMnj2bhg0bctZZZ2Gt5dFHHyU7O7vK+pd++OGH+dvf/kbfvn05//zzWb58Oc8//zzvvfce//rXv+jQoUOZPO+//z7XXHMNXbt25YwzzuC7774jJSUl5n5ycnI444wz+Pbbb2nbti1nnXUWRUVFrFmzhgkTJjBw4MCSHx6Ki4u59NJLeeWVV2jcuDF9+vShYcOGfPTRR9x8880sWrSI+fPnl7vPcGeddRaZmZlMmzaNjIwMLrroojJpEr1+P/74I3369KGwsJBTTjmFvXv30qBBg4SP5/vvv+f0008nJyeHZs2acdppp5Gens7atWuZNGkSPXv2ZNSoUQAsX76c66+/nsMPP5yuXbty0kkn8fXXX7NgwQIWLFjAlClTuOqqq0q2XVRUxBlnnMGyZcvo0KEDWVlZ1KlTh02bNvHGG2+QkZHBmWeeSZs2bRg5ciSzZ88mLy+PkSNHlmyjRYsWFTr3IiJSA/3zdvjhi9D8kMnQpOz3fjIoaF4L7Sss4r8/5PGfLbmsDgTIv92ZX+FtHd6sPt1aN6JziwxSUgzFxZZi6wbHs9ZSZN28W24pKqb0cmujrytZbrEWb7kttY9if1mpbcXaf/i2XL4iazWQnhz00lKM1390Skk/0mkpgddhAxf6r/30fr/O/jZKpQ0MUJieYkgNDECYmlJ6wMG04L4C5QnuK9Kgh8G+pYNlSFWrahERiZO1lt17C5NdjAprXC8t4e+6devWceutt1KvXj0WLFhQEizdu3cvF110Ea+//nqp9Hl5eYwYMYL8/HwmT57Mr3/965J1jz32GOPGjeOyyy5j7dq11K1bN+a+Z86cyezZszniiCN4//33S1p9r1+/nn79+rFp06aEjinck08+yRtvvMHZZ58NQEFBAaNHj2b69Olcf/31zJ07t0ye5557jgcffJBbbrklrn0UFhYydOhQvv32W2688Ubuv//+Ui2rs7OzadmyZcn8ww8/zCuvvMLAgQOZPn16ybq8vDyGDx/O/PnzmTx5Mtdee22FjvW2224DYNq0abRo0YKpU6eWWl+Z6/fmm29ywQUXMGPGDOrVq1dqXSLHc8UVV5CTk8Pw4cN5+umnS3Ul89VXX1FUFGpx1K1bNz788ENOPvnkUvvNzs7mtNNOY+zYsVxyySU0bNgQgKVLl7Js2TLOP/985s6dWypYv2vXLtauXQvAUUcdxdSpU1m8eDF5eXllzpeIiNRin8+FFc+F5k+8FroNSl55wihoXoMVFVu+3r6nTMvx9VvzKtw/cIuGdenWpiHdWjemW5uGdG3diK6tG5FR9+B5C9hg8D08oF4cIWgfCPiXCdQXh7ZT5KX3W9LYkv2V3nekdTZC/x2x0tgyaSJ08xGWL1J3JbHy27ANReqWJLxskdOUfQ9aG7lbGCj9KGXp7mgCr8M6eonU/U348uCaaOnj3n+0soRtLFJ3OjG3G1iTkkJJkDq9QgFvBZZFRCTEGPM7YBTwc+Ata+2QwLrFwElAQSBLV2vtd3Gubwz8HTgXyAf+Yq29t7qOpSJ27y3k2D8uSHYxKmzVPWfQpH7Fu/IAePbZZ9m/fz9jxowp1bq4Xr16PPHEE7z11lsUFxeXLJ81axZbtmyhb9++pQKuAGPHjmX69OmsWLGCefPmcemll8bc9+TJkwG49957S3WT0rlzZ+6++27GjBmT0DGFu+SSS0oC5gDp6ek88cQTvPrqq7z22mt8++23Zbpp6dGjBzfffHPc+5g7dy6rV6/mmGOO4aGHHirTQvy4444ree13SdOoUSNmzJhRqkVzRkYGTz/9NJ06deLJJ5+scNC8PJW5fnXr1uXPf/5zmYB5Isfz73//m3fffZc2bdqUCZgDdOnSpdR8586d6dy5c5njOe6447j22mu57777WLRoEYMHDwbghx9+AFx3NeHXokmTJvTq1avccyUiIrXY9nUw//eh+XbHwenjk1WaiA6eiGktZq3lh9x9rtW4FxhfszmXr37IZW9BcfkbCGhYN42urRvSrU0jurVuRFfvb/OGsVuRHAzcgKOQGrGHbREREZGDynfA/wKnA5GeYb3VWvt4jPyx1v8ZaAZ0BFoBC40xG621z1emwJKYZcuWAXDxxReXWXfkkUdy3HHHsWLFipJlS5cuBWDEiBERt3f55ZezYsUKli5dGjNoXlBQwEcffURKSkrELkSGDx9eZUHzSOVo3rw5AwcO5NVXX2XZsmVljn/w4MEValCwcOFCAH71q1+V26VKdnY2W7duZdCgQRG7AGndujVdunTh888/Jz8/n/r168ddjvJU5vodf/zxZX5cgMSOxz9fI0aMiHuw0qKiIt59912WLVvG5s2b2bt3L+BapQf/AvTs2ZOUlBQmTpxImzZtOOecc2jUqFFc+xERkVqucD/Mvgr27XbzdRvDRc9CWp3kliuMguYH2K78glC3KoEA+a78gvIzB6SnGo5s2ZCj2rjA+FFtXMvx9k3rqzWqiIiIyEHOWjsXwBjTk8hB84QYYxoAlwK/sNbuBHYaY/4M/BJQ0DwJvvvuOwAOP/zwiOs7duxYKmjupw8fANPnL/fTRbNt2zb2799P27ZtqVOn7E1so0aNaNq0KTt37izvEMrlDyZZkbJ27NixQvv45ptvAPdDQ3n8wSXfeuutcu+ttm/fHjFQnajKXL9o5ySR46nI+QLYtGkT5557LqtWrYqaJjc3t+R1165dmThxIrfddhvDhw8nNTWVHj16cPrppzN69GiOPvrouPYrIiK10MLx8F12aH7wE9DsiKQVJxoFzavJ3oIi1v7wU0mXKn4XK9/v2luh7RgDnZo1oGvrRiUB8m6tG5HZIoP01IoNOiMiIiIih4y7jDF/ADYCj0VoJR5tfTegDrAykHYlcEd1FzgejeulseqeM5JdjAprXK/yt13Rgp3RBuMsLzha3np/u8lskBNroNHwLkjiFc/x+H11d+nSpUwf3eHK6xc+UYlcv2jnpDLHE+/1v/rqq1m1ahUXXnght956K926daNRo0akpKTw1FNPMWbMmDLXc9y4cVx88cW8+uqrvPPOOyxdupRHHnmExx57jEmTJlV51zciIlIDrH4Tlv81NN9rNPS4MHnliUFB80oqLCpmw7Y9oT7HveD4hm15VLDbcVo1qlvSrUq3Nm76WauGNKijyyQiIiIicbsdyAH2AKcBs4wxudbaeXGsbwjkWWuDo23uBGpEvwnGmIT7Bq+t2rZty5o1a/j666/L9CMNoRbUvnbt2gFusM5INm7cWLLdWFq0aEGdOnXYvHkz+/fvL9PaPDc3t0pamftlOuaYY8os//rrr4HQMVWG31LfH2Aylg4d3MMbPXr0OOADT1bV9QtK5Hgqcr7y8vJ45513aN26NbNmzSI1NbXU+nXr1sXcz3XXXcd1111HYWEhL730EqNHj2bcuHGMGDGCpk2bxlVeERGpBXZtgtd+G5pvdTScdX/yylMONVWOk7WW73bms2j1D/x9yX8Z9/JKzn5iKd3veZvTH13Cb6f/H0+8+xX//GIz67bGDpg3qpdG706HMeJ/OvKn84/m5WtOJPvugfz7ztN54Zf/w13ndufi3odzTIemCpiLiIiISIVYa/9lrd1lrS2w1r4NPAkMi3P9T0ADY0ywEtoEyEWSwm8ZPHv27DLr1q1bR3Z2dqll/mCh06dPj7g9f3lwUNFI0tPTOeGEEyguLmbOnDll1r/00kvlFz5OL7/8cpll27dvZ8GCBRhjOOmkkyq9j9NPPx2AKVOmxGzBDtCnTx+aNGnCokWL2L17d6X3XRFVdf2CEjke/3xNnz6dPXv2xEy7a9cuiouLadu2bZmAeWFhIfPmzYuSs7S0tDQuv/xy+vTpw/79+/nPf/5Tss7/0aawsDBadhERqcmKCmH2LyF/h5tPbwAXT4X0qhsXpKopaB7Bjrz9LF+3jWnLNnDHvM+4aPIyjvnjAk5+4D1GT/2YB95azdzsb8n5fjf7C6MP1FknLYWj2zXmwuPac/ugo3hudB/+dftpfHrPGcz+zcncd8HPufKkTP7niOYcllGzOrsXERERkYNGeSPLB9evAQqAYwPLegKfVXWhJD6jR48mPT2dqVOnlgwKCrB3715uuOEGiotLX95LLrmE1q1bs3TpUp566qlS6yZNmsTHH39Mhw4duOCCC8rdtz/Q5x/+8Ae+//77kuUbN27k3nvvrcxhlTJr1izefvvtkvnCwkLGjh1LXl4e5513XklL6cq48MIL6dq1K6tWreK2224rE3xduXIlmzZtAlwXJTfddBM7d+5k6NChJa27gz799NOIwf7Kqsrr50vkeE444QT69+/P5s2bGTNmTJnA+dq1a1m9ejUArVq1okmTJnz++ed8+OGHJWmKioq45ZZbSgW/fYsWLWLhwoVl3r8bN27kyy+/xBhT6rr7LfDXrFkT93GLiEgNsngCfLM8NH/OI9Cya/LKE4dDuhnznv2FfLXlp1J9jq/ZnMsPufsqtJ0UA5ktMujWulGpvsc7NWtAmvodFxEREZEq5rUE96cUY0w9XPC7AXAysBjYB2QBY4BrvHxNY6231u4xxrwM3GuMGQ60Aq4D7j4gByZl/OxnP2PChAncfPPN9OvXj/79+9O8eXM+/PBDUlJSGDx4MPPnzy9piZuRkcH06dMZPHgwY8aM4amnnqJr166sXr2a7OxsMjIymDFjRlx9cY8YMYK5c+cyb948unXrxoABA7DWsnDhQk499VSMMSVdqFTGNddcw6BBg+jXrx/t2rVj+fLlrF+/nnbt2jFp0qRKbx9cK+Y5c+YwcOBAHnroIV588UVOPvlkCgsLWbNmDV9++SWLFi0qCdTecccd5OTkMHPmTLp168bxxx9Px44d2bp1K+vWrWP9+vWcf/75DBs2rJw9V0xVXr+gRI7nhRde4LTTTuPFF1/krbfe4pRTTiE9PZ21a9eyatUqnn32WY466ijS0tK45ZZbuPPOOzn11FM57bTTaNasGR999BFbtmzh2muv5a9//Wup8qxatYqxY8fSsmVLevXqRfPmzfnxxx95//33S34QCnbLc95557FkyRIGDBhA//79ycjIoEWLFjzwwAOVO+EiIlL9/vseLH00NH/scOh5WfLKE6dDImhuga+25LLaC4z7f7/evodynswro22TeiX9jndtHep3vF56avmZRURERESqxl3APYH5fGAJcLG33O87YwNwo7X2FW8+vZz1AL/DddmyydvuXyIMJCoH0E033aIpl9UAACAASURBVESHDh2YOHEiS5cupXHjxpx11lk8+OCDXHnllQA0b968JP2AAQP4+OOPue+++3jvvff49NNPadGiBZdffjl33XUX3bp1i2u/xhhefvllJk6cyDPPPMObb75J27Ztue666xg/fnzc24nn+Pr06cPjjz/ORx99REZGBldccQUTJkyoklbmvh49erBy5UomTpzI66+/zvz582nQoAGdOnXirrvuKtWvekpKCjNmzGDo0KFMmTKFTz75hE8++YQWLVrQqVMnRo4cyaWXXlplZQuqqusXlMjxtG/fno8//pjHHnuM2bNns2DBAtLS0jj88MO54YYbOO2000rS3nHHHXTo0IHHH3+cDz/8kPr163PKKafwpz/9if/7v/8rU55zzz2Xbdu2sWjRIlatWsW2bdto2bIlffv25be//S1Dhgwplf76669nx44dzJw5kzlz5lBQUECnTp0UNBcRqelyt8Dca3DRWaB5Fzj74aQWKV6mvP7cajtjzBd1WnTs3vaXf6tQvib10+nWxms17gXHu7ZudMgNPCQiIiJyqDn66KPJycnJsdYeneyyHCyMMV907969+xdffBEzXXFxcUn3C926dSMlRU9txpKXl0dmZib5+fns2rWrTH/SNV1WVhZLlixh/fr1ZGZmJrs4chDSZ4qISBIVF8ELF8D6JW4+tS786l1o8/O4sie7Tn5otDSP8btAvfQUuga7VfEC5K0a1cUYc+AKKSIiIiIiEsG6deto3rw5TZo0KVn2008/8Zvf/IatW7dy1VVX1bqAuYiIiBzkPng0FDAHOOv+uAPmNcEhETQHSE0xdG6RUdK1iv/38GYNSE1RcFxERERERGqmWbNmMX78eHr16kWHDh3YsWMH2dnZbN26lczMTCZMmJDsIoqIiIiEbFwGiwL1k+7nQ++rkleeBBwSQfOOzRvwxZ/OpG6aWl+IiIiIiEjtMmDAAFauXMny5cvJzs7GWkvHjh0ZOXIkt956Ky1btkx2Efnggw+YMmVKXGmHDBlSps/q2mzUqFFxpWvRogUPP1w7+nEVERFJWN42mP1LsMVuvmknGDwJalmPHodE0LxuWooC5iIiIiIiUiv16dOHl156qfyESbR27VqmTZsWV9rMzEyGDBnC4sWLq7dQB0i8x92pUycFzUVE5OBmLbz6G8j9zs2npMPFz0H9psktVwIOiaC5iIiIiIiIVJ9Ro0bF3eL6YGNjDaIlIiJyKPnXX+Grt0Pzp4+H9r2SVZpK0dDRIiIiIiIiIiIiIpK4TStg4T2h+a5nwUnXJq88laSguYiIiIiIiIiIiIgkJn8nzB4NxYVuvnF7GDK51vVjHqSguYiIiIiIiIiIiIhUnLUw/3rYudHNmxQYOgUaNEtuuSpJQXMRERERERERERERqbhPnoGc10Lz/e+ATicnrzxVREFzEREREREREREREamYzZ/BP+8IzR+RBaeMS1ZpqpSC5iIiIiIiIiIiIiISv30/wSujoGifm89oCRc8BSmpSS1WVVHQXERERERERERERETi948bYdtab8bAhU9Do9ZJLVJVUtBcREREREREREREROKzcgZ8+lJovu+NcGT/5JWnGihoLiIiIiIiIiIiIiLl+3GNa2Xu63gSZN2evPJUk2oPmhtjfmeM+cQYs88Y82o5ae81xnxmjCk0xjxe3WUTERERERE51G3YsAFjDFlZWRXKl5mZiTGmegpVCVlZWRhj2LBhQ7KLIiIicnApyHf9mBfscfP1D4OhUyA1LanFqg4HoqX5d8D/Ak/HkXYtcAvwerWWSERERERERERERETi98/b4Yec0PyQydCkQ/LKU42q/WcAa+1cAGNMTyDmWbTWTvPSDqvucomIiIiIiMjB5/nnn2fPnj20b98+2UURERE5eHw+F1Y8F5o/8VroNih55almB1/beRERERERETlkdezYMdlFEBERObhsXwfzfx+ab3ccnD4+WaU5IDQQqIiIiIiISA03a9Ys+vTpQ/369WndujWjR49my5YtjBo1CmMMixcvLpU+JyeHESNG0LZtW+rUqUP79u258sorWbNmTYX2W1hYyP3330+XLl2oV68eRxxxBHfffTf79++v9DEF+x5/8cUX6dWrFw0aNKBVq1aMHDmSb7/9tkye8ePHY4xh6tSp/Pvf/+bcc8+lefPmGGNYuXJlme2G27p1K7fffjs9evQgIyODpk2b0rNnT+688062bdtWKq21lmnTptGvXz+aNm1K/fr1OeaYY3j44YcpKCio9PGLiIjUCoX74JXRsG+3m6/bGC56DtLqJLdc1UxBcxERERERkRrs8ccfZ9iwYWRnZ/OLX/yC/v37884773DiiSeyY8eOMunfffddevfuzYwZM2jXrh1Dhw6lVatWvPDCC/Tu3ZulS5fGve/hw4dzxx13sHnzZs466yx+/vOf8+ijj3LhhRdira2S43v44Ye58soradiwIeeffz4ZGRk8//zznHjiiWzatClinvfff59TTjmFDRs2cMYZZ9CvXz9SUmLf3ubk5NCzZ08eeOABtm/fzllnnUVWVhb79u1jwoQJfPbZZyVpi4uLGTZsGKNGjWLVqlX07t2bM888kx9//JGbb76ZIUOGUFxcXCXHLyIiUqMtHA/frwzNnzcJmnVOWnEOFHXPIiIiIiIiUkOtW7eOW2+9lXr16rFgwQL69u0LwN69e7nooot4/fXXS6XPy8tjxIgR5OfnM3nyZH7961+XrHvssccYN24cl112GWvXrqVu3box9z1z5kxmz57NEUccwfvvv1/SR/j69evp169f1IB2RT355JO88cYbnH322QAUFBQwevRopk+fzvXXX8/cuXPL5Hnuued48MEHueWWW+LaR2FhIUOHDuXbb7/lxhtv5P777yc9Pb1kfXZ2Ni1btiyZf/jhh3nllVcYOHAg06dPL1mXl5fH8OHDmT9/PpMnT+baa6+tzKGLiIjUbKvfhOV/C833vgqOviB55TmAqr2luTEmzRhTDxegTzHG1DPGRGy/b4xJ99KmAqle2vRIaUVERERE5BBjLeTvrH1TJVpkP/vss+zfv5+RI0eWBMwB6tWrxxNPPFGmdfWsWbPYsmULffv2LRUwBxg7diy9evVi06ZNzJs3r9x9T548GYB777231KCanTt35u677074mMJdcsklJQFzgPT0dJ544gkyMjJ47bXXInbT0qNHD26++ea49zF37lxWr17NMcccw0MPPVQqYA5w3HHH0aFDB8AF2CdOnEijRo2YMWNGqWB6RkYGTz/9NHXr1uXJJ5+s6KGKiIjUHju/gVd/E5pv3QPOnJC88hxgB6Kl+V3APYH5fGAJkGWMeQtYaq31z/jTwMhA2t8B04BRB6CcIiIiIiJSk+3dBQ92SnYpKu7WjVC/aUJZly1bBsDFF19cZt2RRx7Jcccdx4oVK0qW+V2vjBgxIuL2Lr/8clasWMHSpUu59NJLo+63oKCAjz76iJSUFC666KIy64cPH86YMWMqdCzRRCpH8+bNGThwIK+++irLli0rc/yDBw/GGBP3PhYuXAjAr371q3K7ccnOzmbr1q0MGjSIFi1alFnfunVrunTpwueff05+fj7169ePuxwiIiK1QlEhzLka9u508+kNXD/m6YfOd161tzS31o631pqwKctbNygQMMdaOypC2lHVXUYREREREZGa6LvvvgPg8MMPj7i+Y8eOEdNnZmZGTO8v99NFs23bNvbv30/r1q2pU6fsg8KNGjWiadPEfggI16lT5B9CYpU1/LjL88033wDuh4by+AOIvvXWWxhjIk6ff/451lq2b99eoXKIiIjUCosnwDfLQ/PnPAotuyavPEmgPs1FRERERERquGitqqMNxlleK+zy1vvbrUhr7qoWa6DRevXqJbTNeI6nqKgIgC5dunDyySfHTFtev/AiIiK1ztp3Yemjofljh0PP4ckrT5IoaC4iIiIiIrVDvSauq5Papl6ThLO2bduWNWvW8PXXX9OlS5cy6/0W1L527doBbrDOSDZu3Fiy3VhatGhBnTp12Lx5M/v37y/T2jw3N5edO3fGfRyxbNy4kWOOOabM8q+//hoIHVNl+C31165dW25av2/zHj16MHXq1ErvW0REpNbI3QLzxgDeD9fNu8DZDye1SMlS7d2ziIiIiIiIVAljXN/gtW2qRGttv6Xz7Nmzy6xbt24d2dnZpZb5g4VOnz494vb85cFBRSNJT0/nhBNOoLi4mDlz5pRZ/9JLL5Vf+Di9/PLLZZZt376dBQsWYIzhpJNOqvQ+Tj/9dACmTJkSswU7QJ8+fWjSpAmLFi1i9+7dld63iIhIrVBcBHOvhrwf3XxqXbh4KtRtmNRiJYuC5iIiIiIiIjXU6NGjSU9PZ+rUqSWDggLs3buXG264geLi4lLpL7nkElq3bs3SpUt56qmnSq2bNGkSH3/8MR06dOCCCy4od9/+QJ9/+MMf+P7770uWb9y4kXvvvbcyh1XKrFmzePvtt0vmCwsLGTt2LHl5eZx33nklLb8r48ILL6Rr166sWrWK2267jcLCwlLrV65cyaZNmwDX5cpNN93Ezp07GTp0aEnr/KBPP/00YrBfRESk1lr6KKx/PzR/1v3QpkfyypNk6p5FRERERESkhvrZz37GhAkTuPnmm+nXrx/9+/enefPmfPjhh6SkpDB48GDmz59f0n1KRkYG06dPZ/DgwYwZM4annnqKrl27snr1arKzs8nIyGDGjBlx9cU9YsQI5s6dy7x58+jWrRsDBgzAWsvChQs59dRTMcaUdKFSGddccw2DBg2iX79+tGvXjuXLl7N+/XratWvHpEmTKr19gLS0NObMmcPAgQN56KGHePHFFzn55JMpLCxkzZo1fPnllyxatKgkQH/HHXeQk5PDzJkz6datG8cffzwdO3Zk69atrFu3jvXr13P++eczbNiwKimfiIhIUm340A3+6es+BHpflbzy1ABqaS4iIiIiIlKD3XTTTcycOZNjjz2WpUuX8t5779G/f3+WL19Ofn4+AM2bNy9JP2DAAD7++GOGDx/Opk2bmD17Nps3b+byyy9nxYoV5XbN4jPG8PLLL3PffffRsmVL3nzzTVauXMl1113H3Llzq2yQ0JtuuonnnnuOXbt2MW/ePHbv3s0VV1zBRx99RMeOHatkH+D6KF+5ciU33ngjGRkZzJ8/nyVLllC3bl3uuuuuUv2qp6SkMGPGDGbPnk3//v356quvmDt3Ljk5ObRu3Zrx48fz4IMPVlnZREREkiZvG8y5Gqz39FrTTnDepEp1L3cwMOX151bbGWO+6N69e/cvvvgi2UURERERkVrg6KOPJicnJ8dae3Syy3KwiLdOXlxczJo1awDo1q0bKSlq4xNLXl4emZmZ5Ofns2vXLlJTU5NdpArJyspiyZIlrF+/nszMzGQXRw5C+kwRESmHtTBjGHzldZOWkg6/fBva90puuUh+nVzfGCIiIiIiIjXYunXr2LVrV6llP/30E7/+9a/ZunUrw4YNq3UBcxEREakB/vXXUMAcYOAfa0TAvCZQn+YiIiIiIiI12KxZsxg/fjy9evWiQ4cO7Nixg+zsbLZu3UpmZiYTJkwofyMiIiIiQZtWwMJ7QvNdz4ITf5u88tQwCpqLiIiIiIjUYAMGDGDlypUsX76c7OxsrLV07NiRkSNHcuutt9KyZctkF5EPPviAKVOmxJV2yJAhDBkypJpLJCIiIlHl74TZo6C40M03bg9DJh/y/ZgHKWguIiIiIiJSg/Xp04eXXnop2cWIae3atUybNi2utJmZmQwZMoTFixdXb6FERESkLGth/vWw82s3b1Jh6DPQoFlyy1XDKGguIiIiIiIilTJq1ChGjRqV7GKIiIhIeT55BnJeC833vwM6nZS88tRQGghURERERERERERE5GD3/afwzztC80dkwSnjklWaGk1BcxEREREREREREZGD2b6fYPZoKNrn5jNawQVPQYrCw5HorIiIiIiIiIiIiIgcrKyFf4yDbWu9BQYufAoatU5qsWoy9WkuIiIiIiI1gjGm5LW1NoklEZGDQfBzJPj5IhFYC3k/wg9fusEBmx0BHXpDWt1kl0xEqsLKGfDpy6H5vjfCkf2TV55aQEFzERERERGpEYwxpKamUlRUxL59+2jQoEGyiyQitdi+fa4LgtTUVAXNg/K2uuD4j6tL/83fXjpdegPoeCJ0PhWOOBXaHAMpqckps4gk7sc18OZNofmOJ0HW7ckrTy2hoLmIiIiIiNQYDRo0IDc3l9zcXAXNRaRScnNzAcjIyEhySZJkz/ZQQDwYHN+zNb78BXvgv++5CaBeU+jc1wXRO58KLbqAfowQqdkK8uGVUe7/GaB+Mxj6/9m77/CoqrWNw7+VSgIkJFQBqdIRUWzYQAUFpCrYsIG9HcvxHHvvn713RKwoTaQXFQUVEURAikgH6SGBhISU2d8fa3L2BAIkkMmemTz3de2LeffsmTzRQJJ31rzrA4hWS/hg9F9IRERERERCRlJSErt27SItLY2YmBiSkpKIjtbKRhEpuYKCAnbu3Elaml05XbVqVY8TBVl2+r6rxrcuhczNpX+uxBpQ7Uj7HPk5Re/LSYcl39gDoGpdaHyGXYXeuBMk1zv8z0VEytake2DLYrfu+5b+rpaQmuYiIiIiIhIyqlatSnJyMhkZGWzZsoUtW7Z4HUlEwli1atUip2mes9OOWdi6BLYs9f+5BHZtLP1zJaRCrVZQs2XRPyvXsPfn5cD6ObBqBqycARvmglNQ9Dl2/QMLvrAHQPWjbBO9cSf7Z2Lq4X2+InJ4Fo2EuUPduuMt0KKbZ3HCjZrmIiIiIiISMowx1KlTh4SEBHbs2PG/mcQiIqURHx9PSkoKycnJ4TfPfE8mbFu211iVpbBzfemfq1Iy1GwFtVpCrdYBzfGaBx6tElvJP4rldDjrAduwX/OTbaKv+gE2L9r3Mdv/tsdvQwADdY52V6E36AjxVUqfX0QOTdpKGHubW9c9Ds5+2Ls8YUhNcxERERERCSlRUVGkpKSQkpKC4zg4juN1JBEJI8aY8GiU5+72N8eXFl09nr629M8Vn+RviLd0m+Q1W0HVOmUzd7xSkl2hWrhKNXMrrP7BrkJfNQN2rN7rAQ5sWmCPn16DqFiof7y7qWi94yEm7vBzici+8vfAV4Mg1+7rQHwS9B+iv3OlpKa5iIiIiIiErLBpfomI7E9eDmz7y79qPKA5vmMNUMoXBeOqQM0WRRvjtVpBUt3y3ZSzSk1oe4E9wH4uq35wx7lk7TVay5cHa3+2x4xnIDbRrj4vXIlepx1ERZVffpFINu0R2DjfrXu/CqmNPYsTrtQ0FxERERERERE5XPl7YNvyfTfl3LEKHF/pnis2sZjmeEtIqh+azeWUhpByORx3OTiO/dwLV6Gvngl7dha9Pm83rJhuD4CEFGh0ur+J3hmqNy3fFwFEIsXSCfDLm259/NXQpp93ecKYmuYiIiIiIiIiIiWVnwtpK4o2xrcssTOE994s82BiKkGN5vtuylmtYWg2x0vCGPt51GoFJ98ABfmw8Q9Y9b1tpK+bDfk5RR+TvQOWjLUHQFI9d1PRJp3sSnoRObD0dTDmRreu3RbOfcq7PGFOTXMRERERERERkb0V5O/bHN+61G526csv3XNFx9nmeGFjvLA5ntIIoqKDEj9kRMdA/Q72OP3fdlzNutnuOJcN8/Z9sWHnBvjjc3sAVG/mX4V+hl2Rnpha/p+HSCgryIORV0NOuq1jK0P/D+2mvnJI1DQXEREREQkzxphbgKuAo4GJjuP0Dbjve6AjkBfwkOaO4/zjvz8JeBvoCWQDrzuO83jA4w94v4hIxPEVQNqqovPGtyyF7cuhILd0zxUVCzWaFV01XqsVpDS2zWOxTbwm/hXkPAg5GbDmJ/84lx9gy5/7Pmb7cnvMeR8wcEQ7dxV6g44QV7m8PwuR0PLdU/bFqEI9X4Sazb3LEwH0L7aIiIiISPj5B3gC6ALUL+b+ux3HeXk/j30NSAUaALWAacaYNY7jDCvh/SIi4St9HWxaCFsW+1ePL7WbdBbsKd3zRMVAatOi88ZrtrKzuKNjg5M9UlVKhhbd7QGQuaXopqLpa/Z6gGPHvWz8A3561b5QceSJ7jiX+sfr/4FULH9Ph5kvufUxl8IxF3uXJ0KoaS4iIiIiEmYcxxkFYIxpT/FN82IZYxKBi4FTHcdJB9KNMa8BVwPDDnZ/GX8aIiLl6/tn4PunS/cYEwWpTfyrxgOb40dBTFxwclZ0VWrB0f3tAbBjtW2iF24smrW16PW+PFgzyx7fP23HUjQ8xT/OpZOd6xyu8+FFDmbXJhh9PeDYukZz6PGcp5EihZrmIiIiIiKR5wFjzEPAGuClgFXiLYA4YH7AtfOB+0p4v4hIeFo90zbN98tAauOijfFaLe0sbc0E9lZKI3scdwU4jp0tX7gKfc0s2LOz6PV5WfD3VHsAJKRC49P941w62xdBjCnfz0EkGHwFMOpa94Wk6Hg7xzy+ire5IoSa5iIiIiIikeVeYDGwGzgL+NIYs8txnNFAFSDLcZzAHezSgar+2we7X0Qk/OTshNE38r+VmAkpcORJ/nnjrW1zvEZziE3wNKaUgDFQu7U9Tr7Rbta6cT6s/N420tfO3nfUTnYaLP7aHgBJ9d1NRRt3gqQjyv3TECkTP75o34VRqPszUKetd3kijJrmIiIiIiIRxHGcnwPKycaYd4CLgNFAJpBojIkJaIwnA7v8tw92v4hI+Jl0L2SstbejYuCyUVDvOG8zSdmIjrEzzOsfD2fcBXnZdjPEwk1F/5kHjq/oY3auh/mf2gPsCyaFm4o2Os2+qCIS6lbPgu+fcus2/aDDIO/yRCA1zUVEREREIltgt2AZkAccA8z1n2sPLCzh/SIi4WXpeJj/iVuf8V81zCNZbIIdwdKks61zMmxzsXCcy9Yl+z5m21/2mPMeYOCIY9x56A06QlxiucUXKZGs7TDyavcFoZRG0OsVjR0qY2qai4iIiIiEGWNMDPZn+RggyhhTCdscTwROAb4H9gCdgeuB6wAcx9ltjBkOPG6MuQSoBdwKPFiS+0VEwkrmVhj7L7euexycfqd3eaT8VUqGlj3sAZC5xb+p6Pe2kZ6+dq8HOHbcy8b5MOsViI6D+ie641zqdYDo2PL+LERcPh+MuRF2bbR1VCz0H2K/1qVMqWkuIiIiIhJ+HgAeDqizgRnAAP/5L/znVwP/dhznq4BrbwHeAdb7H/d6wEahJblfRCT0OQ58cxvs3mbrmAQ4/101PCu6KrXg6P72AEhbZZvnq36wR+GGioUKcmHNTHt89yTEVYGGp9hV6K17Q7UG5f85SMX2yxuwfLJbd33MvpgjZU5NcxERERGRMOM4ziPAI/u5+6SDPHYncMmh3i8iEhbmfwrLxrt118egRjPv8khoSm1sjw5X2Rdatiz2z0OfYce65O61pUduJiyfYo8pD8BRXexjm5+rF2Qk+Nb/BtMecevm3e2GuBIUapqLiIiIiIiISOTYsQYm3uPWTc6EE67xLo+EB2Ogdht7dLwJCvLtRqKF89DXzbYrz//Hgb+n2qNKHTh2IBx3hZ0vLVLWstNhxCDw+fdpT6oHfd/UHPMgUtNcRERERERERCKDzwdjbnJXCFdKhj5vQFSUt7kk/ETHwJEn2uOM/0BeNqz9xTbRF38NaSvdazM3wY8vwI8vQtMz7erzFj20+lzKhuPA2FvdGfwmGi74ABJTvc0V4dQ0FxEREREREZHI8Msbdv50oR4vQHI97/JI5IhNsA3xpmfC2Q/D6pkwdygsGRuwAt2BFd/ao3JNaO9ffV69qZfJJdzNed9+nRU68z5o2NG7PBWEmuYiIiIiIiIiEv42L4bpj7l1m37uho8iZckYaHy6PbK2wx+f2wb69uXuNVlbYdbL9mjcya4+b9kTYuK8Si3haOMCmHy/Wzc5E06707s8FYia5iIiIiIiIiIS3vJzYfR17orfKnXgvBc171eCr3J1OOUW6HgzrPkJ5n0Ef46Bgj3uNav8m4sm1oD2l8BxV0GNozyLLGFiT6adY174tVS5Fpz/rsZNlRP9VxYRERERERGR8DbjGdi00K37vK55v1K+jIFGp9qm5r+XQrdnoGbLotfs3gY/vQavd4ChPWHhCMjL8SavhDbHgfF3wva//ScMXPAeVKnlaayKRCvNRURERERERCR8rfsVZr7k1scPhmZdvcsjkpgKJ98IJ91gvz7nDoU/R0F+QIN89Y/2SEiFYy6BDldCzRaeRZYQM/8zWDDcrc+4C5p09ipNhaSV5iIiIiIiIiISnnKzYPT14PhsndIYuj7ubSaRQsZAg5Og31t29Xn356BWm6LXZKfZDWzfOBGGdIc/hkNetjd5JTRsXQYT7nLrBqdAp3u8y1NBqWkuIiIiIiIiIuFpygOQttLeNlF2NEZ8FW8ziRQnIQVOug5unAVXT4NjL4PYxKLXrP3JzuZ/oQVMvBu2LPEmq3gnLxu+ugrydts6IRUueB+iNSykvKlpLiIiIiIiIiLhZ/lU+G2IW592Bxx5ond5RErCGDjyBOjzhl19ft4LUOfootfkZMDst+HNk+GDc+yojtzd3uSV8jXpHtiy2K37vQ3J9bzLU4GpaS4iIiIiIiIi4WV3Gnx9i1vXOVrjCyT8VEqGE66B63+Ea7+D466E2MpFr1k3G8bcCC+0hPF3waZF3mSV4Fs00s6/L9TxFmh+rmdxKjo1zUVEREREREQkfDgOjL8TMjfZOjoO+r0LMXHe5hI5VMZAveOg96tw1zLo+TIc0b7oNXsyYM578Pap8N7ZMO9jO9NfIkPaShh7m1vX6wBnP+xdHlHTXERERERERETCyKKR8Odotz77Iajd2rs8ImUpviocPwiunwHXzYDjB0Nc1aLXbPgNxt4Cz7eAcXfAROtwAgAAIABJREFUxj+8ySplI38PfDUIcnfZOj4Z+g/RC4EeU9NcRERERERERMJDxga7yrxQw1Ph5Ju8yyMSTHXbQ8+X7Ozz3q/Z1ceBcnfZuf7vnAHvdrajPfbs8iKpHI6pD8PG+W7d+1VIaeRZHLHUNBcRERERERGR0Ofzwdc3200Swa6+7fsWREV7m0sk2OKrwHFXwLXfwg0z4YRrIT6p6DX//A7f3GZnn4/9F2yYZ0cZSWhbOgFmv+XWx18Nbfp6l0f+R01zEREREREREQl9v30AK79z6+7PQEpD7/KIeKHO0XDe83b1eZ83of6JRe/PzYR5H8F7Z9oV6HM+gJyd3mSVA0tfZzd5LVS7LZz7lHd5pAg1zUVEREREREQktG1bDlMedOsW50H7gd7lEfFaXGU4diBcMxVu/BlOugEqJRe9ZtMCO87ohRb2XRrrf9Pq81BRkAcjr4acdFvHVoYBQyG2kqexxKWmuYiIiIiIiIiEroJ8GH095GfbOrEG9HoFjPE2l0ioqN0auj8L/14G/d6BBh2L3p+3G37/BN4/G94+DX59D7LTvclakfl8kLEeVv1gX8xYN9u9r+eLUKOZd9lkHzFeBxARERERERER2a8fX4ANc92696tQpaZ3eURCVWwCHHOxPbYshXnD4I/PIHuHe83mRTDhLvvOjTb9oMNVcOSJehGqrOTnQvpa2LEK0lZC2ir39o41ULBn38e0H2j/n0lIUdNcRERERERERELThnnww/+5dfvLoOV53uURCRe1WkK3p+Dsh2DJNzB3KKyZ6d6fn20b6n98BjVb2eZ5uwshMdWrxOFjTybsWO1vhK+yjfHC2xnrwfGV/LlqNIcezwUtqhw6Nc1FREREREREJPTkZduxLL58Wyc3gG5Pe5tJJNzEVoJ2A+yxbbndJHT+Z7B7u3vN1iUw6W6Y+hC06Wsb6A06VtzV544Du9P2bYgX3s7acnjPX7kWpDaGmi3hjLvsfHoJOWqai4iIiIiIiEjomfYobPvLXxjo9xZUSvI0kkhYq9EMznkCznoQlo6zq89X/eDeX7AHFgy3R43mcNyVcMwlULm6Z5GDxueDXRuLXy2ethr2ZBz6c5soSK4PKY1tczy1iXs7pRHEVy2rz0KCSE1zEREREREREQktK2fA7LfcuuPN0Og07/KIRJKYeGh7gT22r7Czz+d/Cllb3Wu2/QVT7ofpj0Kr3nb1eaPTwmv1eUGenS8eOFe88PaO1ZCfc+jPHR1nG+CBDfHC29UaQExcWX0W4hE1zUVEREREREQkdGSnw5ib3LpmK7syVkTKXvWm0PVROPN+WDbBrj5f+Z17f0EuLBphj+pH2dXn7S+FyjU8i1xEbpY7X3zv5njGenAKDv2545P8jfHG+zbHq9aFqKiy+iwkBKlpLiIiIiIiIiKhY+LdsHO9vR0VC+e/Y+cyi0jwxMTZeeZt+tqG8+8fw++fQOZm95rtf8PUB2H6Y9Cqp3/1+RnBbx7vTit+tXjayqL5DkXlmsWvFk9tDInVw2tlvZQpNc1FREREREREJDQs/hoWfOHWne+BI47xLo9IRZTaGM5+CDrfC39NsqvP/54OOPZ+Xx78OdoeKY2hw5XQfiBUqXVoH8/ng8xNxW+6uWMV5BzmfPGk+pDaqJjmeCPNF5f9UtNcRERERERERLy3azN8c7tb1z8BTr19/9eLSHBFx0KrXvbYscauPP/9Y7uBZqEdq2DaI/DtE9Cih1193uTMfVefF84X/19DfJV7e8eqspkvXtxq8WoN7Ax3kVJS01xEREREREREvOU4MPZWyE6zdWwi9HsHotW2EAkJKQ3hrPuh092wfIp/9flUcHz2fl8+LBlrj2oNoU0/2LPLHaOSvu7w5ovHVd3PavHGkFQXoqLL4rMU+R999xERERERERERb80bBssnu/U5T9gNCkUktETHQMse9shYb1efzxsGOze416SvgVkvl/65K9csfrV4ahPNF5dyF/SmuTHmFuAq4GhgouM4fQ9wbRLwNtATyAZedxzn8WBnFBERERERERGPpK2Cyfe59VFd4PjB3uURkZJJrm/3HTjjP/D3NLv6/K9J7urzfRj7mNTGxTfHNV9cQkh5rDT/B3gC6ALUP8i1rwGpQAOgFjDNGLPGcZxhwY0oIiIiIiIiIuXOVwCjb4DcTFsnpEDv17WiVCScREVD83PtsfMfmP8pbFoEVesUbY5rvriEkaA3zR3HGQVgjGnPAZrmxphE4GLgVMdx0oF0Y8xrwNWAmuYiIiIiIiIikean12DdL2593ouQdIR3eUTk8CTVtSvPRcJc1MEvKTctgDhgfsC5+UA7b+KIiIiIiIiISNBsWgTfPenWbftD2/O9yyMiIuIXSk3zKkCW4zj5AefSAQ00EhEREREREYkk+Xtg1HVQkGvrqnXhvOe9zSQiIuIXSk3zTCDRGBM4MiYZ2OVRHhEREREREREJhu+ehC1/unXfN+w8cxERkRAQSk3zZUAecEzAufbAQm/iiIiIiIiIiEiZW/MzzHrVrU+8Dpqe5V0eERGRvQS9aW6MiTHGVMJuOhpljKlkjInb+zrHcXYDw4HHjTHJxphmwK3A+8HOKCIiIiIiIiLlYM8uGH094Ni6+lHQ5VFPI4mIiOytPFaaPwBkA/cDvfy3pwAYYyYaY+4LuPYWIANYD8wCPnAcZ1g5ZBQRERERERGRYJt8H6SvsbdNNPR7F+ISvc0kIiKyl5iDX3J4HMd5BHhkP/d136veCVwS7EwiIiIiIiIiUs6WTYJ5AevizrgL6nfwLo+IiMh+hNJMcxERERERERGJRFnbYOytbn1EezjjP97lEREROQA1zUVEREREREQkeBwHxt0OWVtsHVMJzn8XomO9zSUiIrIfapqLiIiIiIiISPAsGA5LvnHrLo9AzRZepRERETkoNc1FREREREREJDjS18GEgDEsjc+AE6/3Lo+IiEgJqGkuIiIiIiIiImXP54MxN8KenbaOT4I+b0KUWhEiIhLa9J1KRERERERERMrer+/A6h/dusdzUO1I7/KIiIiUkJrmIiIiIiIiIlK2ti6DaY+4date0O4iz+KIiIiUhprmIiIiIiIiIlJ2CvJg1HWQn2PryrWg5ytgjLe5RERESkhNcxEREREREREpOz88Bxvnu3Xv16Byde/yiIiIlJKa5iIiIiIiIiJSNtbPhR+ed+vjroAW3bzLIyIicgjUNBcRERERERGRw5e7G0ZfB06Bras1hHOf8jaTiIjIIagYTfPd2+G3D2HxWFg9C7YshcytUJDvdTIREREpSzkZMG8YbPvb6yQiQWWMucUY85sxZo8xZsx+rqltjEkzxszf6/xqY0y2MSbTf6TvdX9dY8wEY0yWMWatMebaYH4uIhJBpj0M2wu/Bxvo9w7EV/U0koiIyKGI8TpAucjaCuNuL/6+StUgMRUSqwcce9UJAXVCNYiKLt/8IiIicnA5O+H9LrDtL4hPhhtnQrUGXqcSCZZ/gCeALkD9/VzzOrAAqFbMfZc4jlNssx34HFgB1ALaApONMX85jjPj8CKLSERb8S38+q5bn3obNOzoXR4REZHDUDGa5geSk26PtJUlfICBhJS9mux713s14OOTIapiLOoXERHxhOPAN7fZhjnAngyY+hAMGOppLJFgcRxnFIAxpj3FNM2NMb2BGsBQYD+rR/ZljGkKnAZc6DhOFjDbGPMpMBhQ01xEipe9A8bc7Na128KZ93mXR0RE5DBVjKZ5fFVofAbsTrOjWnZvg4LcQ3wyB7LT7LF9eckeYqKKrlY/4Mp2/+34JDDmEDOKiIhUMHPehz9HFT3352g44VpodKo3mUQ8YoxJAl4CegD7W+b5jjHmfWA58LjjOBP859sBGx3H2Rxw7XzgpmDlFZEIMP4u2PWPvR0dZ8eyxMR7m0lEROQwVIymeVI9uHKsWzsO5Gb5G+jb3WZ6dlrAuYDzhYfvEGegOz7bqN+9reSPiYrZazzM3o32wCa7/764Kmq0i4hIxbNhHkzez2q2iXfD9TM0Wk0qmmeBYY7jLDPGFNc0vxyYCxQAFwAjjTFnOI4zB6gCpO91fTqgocQiUrxFI2HRCLc+836o09a7PCIiImWgYjTN92YMxFexR0rDkj3GcWDPruKb6YFH9o6iTffCXcNLy5cPmZvtUVLRccWvXj/QKve4xEPLJyIiEgqyd8BXV7rvIKtSG857EYYPtPXmhTDvIzh+sHcZRcqRMeZUoBPQfn/XOI7zY0D5mTGmL7Z5PgfIBJL3ekgysKuMo4pIJNi5Ecbd6dYNOsIpt3qXR0REpIxUzKb5oTAGKiXZI7VxyR7j89mZqkWa7AdZzZ69A3AOLWNBLuzaaI+SikmwjfSURnD6nXBUl0P72CIiIuXNcez81PS1tjZR0H8INDoN2l0EC4bb89Mfhzb97J4kIpGvK9AAWGvsOxATgERjzCbgWMdxivtB0RdwewFQ1xhTy3GcLf5z7YGFQcwsIuHIceDrm+0eYWDf+dz3Lb27S0REIoKa5sEUFWV/QU9IgepNS/YYXwHkZOx/Jfv/muwBzfacvd9BWwr52bBzgz3W/gx93oD2lx7684mIiJSXn9+AZePd+qwHbMMcoMsjsGQc5GXZ8WvfPwvdn/EipUhQGGNisD/LxwBRxphK2Ob388DbAZdeBFwLdAG2GGMaAI2A2f7r+wF9gDMBHMdZYYyZBTxljPkX0BYYCPQth09LRMLJb0NgxXS3Pvepki8wExERCXFqmoeaqGh3TjnNSvaYgvy9xsIUrlpP288omTTYs3Pf53F8MOZGyNkJJ99Qpp+WiIhImVo7G6Y97NZHdYVT73DrpLr2HVTfPm7rX9+FDldBrZblGlMkiB4AAv4SkA3McBynM3bECgDGmAwg33GcTf66CvAqcBSQD/wFXOg4zi8Bz3UJ8D6wFUgD/us4zozgfSoiEna2r4ApD7h1825w3BXe5RERESljappHgugYqFLTHiWVn+s22tPXwNh/QZb/HbiT7rZN9TP+o41FRUQk9GRthxGD3A26k+rD+e/ad3gF6ngLzBtmv885BTD5XrhslL63SURwHOcR4JESXDcUGBpQL+YA887912wAuh9OPhGJYAX5MPp6yNtt64RU6PWqvr+KiEhEiTr4JRKRYuKgam2o3RpadIfBkyD5SPf+7560KwecQ5yvLiIiEgw+H4y+zo4VA4iKgQEf+t+htZfYSnDuk2694ltYNrF8coqIiESqWS/D+jlu3etl+7uliIhIBFHTXKzqTW3jvHrASJifX4ext9o56yIiIqFg5gvw9zS37voYHHni/q9v2RMad3LryfdB/p7g5RMREYlkG/+A759263YXQ+s+3uUREREJEjXNxZVc3zbO67Rzz/3+MYwYbMe5iIiIeGnVD/DdU27dsiecfNOBH2MMdHsGTLStd6yCX94MXkYREZFIlZcDo64rOh6tx/95m0lERCRI1DSXoirXgKvGQYOO7rnFY+CLSyB3t3e5RESkYtu1GUZcbTetBqjWEPq8UbL5qbVbwwlXu/UPz8OuTcHJKSIiEqm+fRy2LnXrvm9CpWTv8oiIiASRmuayr0rJdqO0o7q45/6eBp+cDzkZ3uUSEZGKyVcAI692N6yOjoMLP4KEaiV/js73QkKKvZ2bCdMeLfucIiIikWr1TPj5Dbc+6UZo0mn/14uIiIQ5Nc2leHGJcPHnRefTrf0ZhvaErG3e5RIRkYrn+6dh9Y9u3e1pqHts6Z4jMRXOvN+t//gM1v9WNvlEREQiWc5OGH0j4Ni6Rgvo8rCnkURERIJNTXPZv5g46P8hHHuZe27TAviwO2Rs8C6XiIhUHMunwQ/PuXXb/nD81fu//kA6DIJabdx64t3g8x1ePhERkUg36V7IWGtvR8XA+e9AbIK3mURERIJMTXM5sKho6P06nHyze27bXzCkG2xf4V0uERGJfBnrYdS1bl29GfR6uWRzzIsTHQPdn3HrDb/BguGHl1FERCSSLR0P8z9x6zP+W/p3e4mIiIQhNc3l4IyBc58s+rb2jLW2cb5pkXe5REQkchXkwYjBkJ1m65gEO8c8vurhPW/jM6BVb7ee9gjs2XV4zykiIhKJMrfC2H+5db0OcPq/vcsjIiJSjtQ0l5IxBjr9F7o9657L2gJDe8C6Od7lEhGRyDT9UVg3263PewFqt9n/9aVxzhMQHW9vZ26CH18om+cVERGJFI4D3/wLdvv3s4pJgH7v2ndtiYiIVABqmkvpnHwD9H0LjP9LJycDhvWBFd95m0tEwl9BPiz4Elb94HUS8drS8fDTa27d/jI4dmDZPX9KQzg1YOXcz29o5JiIiEig+Z/Csglufc7jUOMo7/KIiIiUMzXNpfTaXwoDPoLoOFvnZcFnF8KScd7mEpHw9s1tdn71R71g1itepxGv7FgNY25061qtocdz+738kJ12ByTVs7cLcmHKg2X/MURERMLRjjUw8R63bnLmoW/CLSIiEqbUNJdD07o3XDocYhNtXZALX14Bf3zhbS4RCU9LJxTdZGrqQ/Dre97lEW/k74GvrrLvYgKIqwIXDoO4xLL/WHGVoetjbr1sPKz4tuw/joiISDjxFdgXr3P9+31USoa+b0KUWgciIlKx6DufHLqmZ8HlY+wPUgBOAYy+Hma/620uEQkvu9Ng3O37np9wF/z+yb7nJXJNvh/++d2te70CNZoF7+O1vQCOPNmtJ91rNyAVERGpqH55E9bMcuvzXoSkut7lERER8Yia5nJ4GpwEV42HyjXdcxP/Az88ZzePERE5mIl3Q+Zmezs63h2ZATD2Vlg00ptcUr4WjYQ5Ae8uOP5qOLp/cD+mMdD9GcDYeutSmPNBcD+miIhIqNq8GKYHvAurTT/7ArOIiEgFpKa5HL46R8OgSZBU3z337RMw9UE1zkXkwJaMg4VfuvVZD8CV30CV2rZ2fDDqOju+RSLXtr9hbMDGnEccA+c+VT4fu+6xcOxlbv39U5C1vXw+toiISKjIz7U/cxXk2rpKHbvK3Bhvc4mIiHhETXMpGzWOgsGToHrAjuo/vWY39vMVeJdLRELX7jQYd4db1z8ROt4M1ZvCFV9DQqo978uHr66Ev6d7k1OCKy/b7omRm2nr+GS72XRspfLLcPbDEJ9kb+dkwHdPlN/HFhERCQUznoHNC926zxuQmOpdHhEREY+paS5lp9qRMGgi1D7aPTfvIxh5jV25ICISaMJ/IGuLvR1Tyb/JVLSta7WCK8bYBirYVU9fDITVs4p/LglfE/4DW/50675vQGrj8s1QpSZ0utut5w6FTQv3e7mIiEhEWTsbZr7k1scPhmZdvMsjIiISAtQ0l7JVpRZcNQ6OPMk99+coGD4Qcnd7l0tEQsvisbBohFuf9eC+Gz4ecQxcNgJiK9s6Pxs+uxDWzy2/nBJc8z+D3z9265Nvhla9vMly4nVQ3f816Phg4j0aMSYiIpFvTyaMvt5+7wNIbQLn6B1XIiIiappL2UuoBpePhqZnueeWT4FP+0POTu9yiUhoyNoO4+906yNPhpNvLP7aI0+ES4fblehgR3h80k+rgCPB5sUwLuDroP6J0PVR7/LExEG3p916zUxYPMa7PCIiIuVh6oOwY5W9baKg3zsQV9nbTCIiIiFATXMJjrjKcMkX0Kq3e27NLPiolzZYE6noJtwFWVvt7ZhKdmZm4ViW4jQ+HS76FKJibZ2TAcP6wtZlwc8qwbEn086pz8+2dUIqDPgQomO9zdWsKzQ7x62nPGhnrouIiESi5VPhtyFufdoddsGCiIiIqGkuQRQTD/0/hPYD3XMb58OH3WHnP97lEhHv/DnGjmwqdPbDdiPhg2nWxTZVjb+5vnsbDOsDaSuDk1OCx3Fg3O2w7S/33PnvQnJ97zIFOvdp9wWajHUw61Vv84iIiATD7jT4+ma3rtMOOt3jXR4REZEQo6a5BFd0DPR+HU4KGL2wbRkMORe2r/Aul4iUv6xtMP7fbt2gI5x0Q8kf36qXfcswxta7NsJHfSBjfZnGlCCbOxQWfuXWp//brvAOFTWOgpMDvi5nvgTp67zLIyIiUtYcB8bdAZmbbR0db1/AjonzNpeIiEgIUdNcgi8qys6J7Xyvey59rV1xvvlP73KJSPka/2+7QhwgJsE/lqWU34baDYBer7h1xlr4qDfs2lx2OSV4Nv4BE+9264anQef7vMuzP2f8ByrXtLfzs2Haw97mERERKUsLRxTdt+PsB6FWK+/yiIiIhCA1zaV8GAOd77Fvey+UuRk+7AHrf/Mul4iUj0Wjiv5y1uURqN700J6rw5XQ7Vm3TlsBH/e1bzOW0JWTAV9eCQV7bF25JvT/wL4jKdRUSrajgwotGglrfvIuj4iISFnJ2AATAt751/A0OPnm/V8vIiJSQalpLuWr4012danxf+nlpNtVoitneJtLRIInc6vd/LNQw1PhxOsO7zlPvgHOfsittyyGj/vZxqyEHsexc1N3rPKfMHDBB1C1jqexDqj9QDiivVtPvBt8Bd7lEREROVw+H3x9k/vzUlxV6PdW6d/5JyIiUgHou6OUv2MvgwFD3Y3W8rLg0wGwdLynsUQkCBwHxt8Ju7fbOjYR+rxeNr+cnf5vOD2gGb9xvv23ZE/m4T+3lK3Zb8OSb9z6zPugSSfv8pREVBR0/z+33rQAfv/YuzwiIiKHa877sPJ7t+7+LFRr4FkcERGRUKamuXijdR+49As71xjs2/WHXw4LvvQ2l4iUrT9HwZKxbt3lUUhtUnbPf9YDcPJNbr1uNnxxCeRll93HkMOzbg5MecCtm55V9MWOUNbgJDj6Qree/hhkp3uXR0RE5FBtWw5TA96l1+I8aH+pd3lERERCnJrm4p2jusAVYyA+ydZOAYy6Dn59z9tcIlI2MrfA+IDmaKPT4YRryvZjGAPnPgUdrnLPrfoBvrwC8nPL9mNJ6e1OgxGDwJdv66p14fz3wutt4F0ese+QAPuOiRn/d6CrRUREQk9Bnv09K9+/qKByTbuxujHe5hIREQlhYfRbq0SkBifDVeMgsYb/hGNnH//4gqexROQwOQ6MuwOy/ZtzxlaG3q8Fp1lqDJz3ErS7yD23fAqMvBoK8sv+40nJ+Hww+nrIWGdrEw39h0DlGgd+XKhJrgen3+nWv74DW5d5l0dERKS0fnwR/pnn1r1egSo1vcsjIiISBtQ0F+8dcQwMngRJ9dxz0x+DqQ/bxpuIhJ9FI2HpOLfu+iikNg7ex4uKgj5vQqve7rklY+1mVz5f8D6u7N9Pr9gXLwp1eRgadvQuz+HoeIs789WXD5Pu1fcnEREJDxvmwYxn3br9ZdDyPO/yiIiIhAk1zSU01GhmG+epTd1zs162K1V9Bd7lEpHS27XZvmOkUKPT4firg/9xo2Pggg+g2TnuuQXDYfwdanCWt9WzYPrjbt28O3S81bs8hys2Ac550q1XTIe/JnuXR0REpCTysu27vhz/71PVGkC3p73NJCIiEibUNJfQUa2BbZzXbuuem/uhnb9XkOddLhEpuf+NZdlh69jK0Of18pthHRMHF34Mjc9wz80dCpPvU+O8vGRuhRGDi/6C3u+t8JpjXpxWvYp+XU2+F/L3eJdHRETkYKY9Ctv+8hcG+r4FlZI8jSQiIhIuwvw3WIk4VWrZGef1T3TPLRoBXwy0KyVEJLQt/AqWjXfrcx6DlEblmyG2Elz8ORx5knvulzfh2yfKN0dF5CuAUddA5iZbR8XCgKGQkOJprDJhDHR7Boz/R6e0lTD7bW8ziYiI7M/K72H2W27d8WZodJpncURERMKNmuYSehJS4PLR0KSze275ZPikP+Ts9CqViBzMrk0w4T9u3bgTdBjsTZb4KjDwKziivXvux+e1yXCw/fCc/SW90LlPQb0OnsUpc7XbFB01NOM5O45IREQklGSnw5ib3LpWazjrQe/yiIiIhCE1zSU0xVeBS7+Elj3dc2tmwrDekLXdu1wiUjzHgW9uh5x0W8dVKd+xLMWplGxfgKvV2j03/TH45a39P0YO3Yrv4Ptn3Lp1XzjxWu/yBMuZ90GlavZ27i77NSUiIhJKJt4NOzfY21Gx0O8d+048ERERKTE1zSV0xcTDgI/gmEvdc//8DkN7wM5/vMslIvtaMBz+mujW5zxuZ1l7LTEVLh8D1Y9yz026B+Z+5F2mSLRzI4y8BvDPjU9tAr1fsyNNIk1iKpz1gFvP/wQ2zPUuj4iISKDFX8OCL9y68z1wRDvv8oiIiIQpNc0ltEXHQJ834MTr3XNbl8KQbpC2yrtcIuLauREm/tetm3SGDoO8SrOvqrXhiq8hOaCJ/81tsOBL7zJFkoJ8u/Hn7m22jo6HC4dF9kZjHQYVfQfDxLvB5/Muj4iICNiRYd/c7tb1T4RTb9//9SIiIrJfappL6IuKgu7PQqe73XPpa2zjfPNi73KJiH8sy22Qk2HruKrQ+/XQW2GcXB+uHAtVj/CfcGD0DbDkG09jRYRvH4e1P7l1j+egztHe5SkP0TF2U9BC6+fYTXBFRES8krEehvWB7DRbxyZCv7ft9ywREREpNTXNJTwYY+fInvOkey5zkx3VorfFi3jnj8/tRr2Fzn0Cqh3pXZ4DSW0MV4yFxBq2dgrgq0GwfKq3ucLZskkw62W3bncxHHeFd3nKU5NO0KqXW097GPZkepdHREQqrs2L4f2usHWJe+6cJ6B6U+8yiYiIhDk1zSW8nHKLnZOLfxVr9g74qDes+tHTWCIV0s5/YOI9bt30LDjuSu/ylETN5nDFGHcjR18eDL9M/4YcivS1MDpgdFbNltDzxdB7l0EwnfOEHUcDsGsjzHzR2zwiIlLxrJ5p34G7K2DPp9PvguMHe5dJREQkAqhpLuHnuCug/xC7EzxAbiZ8coFd8Sgi5cNxYOy/YI9/LEt8Uvhs/FjnaLhslB0lA5CfA59dBOt+9TZXOMmoskk6AAAgAElEQVTPha+ugpx0W8cm2o2b4yp7GqvcpTSCU251659e134bIiJSfv4cDR/3c38eM1Fw3gtw9oPh8TOZiIhICFPTXMJT2/Phks8hJsHWBXtg+EBYOMLbXCIVxfxP4e+AsSbnPmnnhoeL+h1g4JfuvyF5WfBJf/hnvre5wsXUh4qOxur5MtRq6V0eL51+J1Sta28X7IEpD3ibR0REKoZf3rZj5gpybR1TCS78GE64xttcIiIiEUJNcwlfzbrC5aPsClcAXz6MvAbmfOBtLpFIl7EBJt3r1kd1gWMv9y7PoWp4ClzyGUTH2XpPhl2ttWXJgR9X0S3+Gma/5dbHXQnHXORdHq/FVYauj7r10nGw4jvv8oiISGTz+WDKgzDpbsCx5xJS7L4trXp6Gk1ERCSSlEvT3BgTa4x53RiT5j9eM8YUu423MaapMWaiMWaHMWaDMea/5ZFRwlTDU+DKbyCxuv+EA+PvhJkveRpLJGI5Doy9FfbstHV8MvR6NXzfAtz0LDtWJMr/LSk7DYb1ge0rvM0VqravgK9vces6R0P3Z73LEyqOHgBHnuTWk+6Fgnzv8oiISGTKz4XR18FPr7rnkhvA4CnQ4KT9P05ERERKrbxWmj8AnAa08R+nA/ftfZExJhoYC8wDagFnAbcYYy4tp5wSjuq2h0GT3LfHA0x7xB6O41Uqkcj0+8ewYrpbd3sKkut5l6cstOwB579r54ACZG62Gwynr/U2V6jJy4GvrnRfMImral9wiE3wNlcoMAa6PcP/NqneugR+G+JpJBERiTA5O+HT/rDwK/dc7aPh6il2o3MREREpU+XVNB8MPOE4zkbHcTYCTwJXF3NdC//xqOM4eY7jLAM+AK4rp5wSrmo2h8GTILWJe27mS3bVuc/nXS6RSJK+Dibf79bNzoH2A73LU5baXgC9X3frnett43znRu8yhZpJ98CmhW7d53Wo3tS7PKGm3nFwbMDfh++ehN1p3uUREZHIsXMjfNgDVs1wzzXuBIMmQNIR3uUSERGJYEFvmhtjUoD6QODuavOBBsaY5P3kMXudaxe8hBIxUhraFee12rjnfhti38JYkOddLpFIUOxYllfCdyxLcY4dCD2ed+sdq+yolqxt3mUKFQu+hLkfuvVJN0Cbvt7lCVVnP2xX4APkpNvGuYiIyOHY+hd8cA5sDnjh+ugBMHAEVEryLpeIiEiEK4+V5lX8f6YHnCu8XXWva5cBq4DHjDHxxpg22FXq+mlASqZqbbhqHNQ/wT238CsYfrkdLSAih2beR7AyYHPD7s9AUt39Xx+uTrwWuj7m1tuWwbC+kL3Du0xe27oMvrndret1gK6Pe5cnlFWpBZ0CtmL5bQhsWuRdHhERCW9rZ8OQcyAjYGTcKf+Cfu9CTJx3uURERCqA8miaZ/r/DFxVXnh7V+CFjuPkAb2B9sB64FPgQ2B7kDNKJElMhcvH2LcsFvprop0BuGfX/h8nIsVLXweTH3DrZufCMZd4lyfYTr0NOt3j1psXwicV9N+P3Cz48krIy7J1pWrQ/0P9on4gJ90Aqf6xNY7PjrXR/hoiIlJaS8bBsN4BL9wb6PYsnPM4RJXXlFUREZGKK+jfbR3H2YFtgLcPON0eWOc4TkYx1y9xHOdcx3FqOo7THogHZux9ncgBxVeBS7+EFue551b/aGcUa8asSMk5Doy9BXL9DeNKETiWpTid74FTbnXrDb/BZxdB7m7vMpU3x4Hx/7abWhbq944dhSX7FxMH3Z5269U/wpKx3uUREZHwM+d9+PJyyPe/UzY6DgZ8CCff4G0uERGRCqS8XqL+ELjfGFPHGFMHuA94v7gLjTHtjDGVjTFxxpjz8W8iWk45JZLEVoILh0G7i91z/8yzm+js2uRdLpFwMvdDWPm9W3f/v4qx4ZQxdgTJCde459bMguEDIX+Pd7nK0+8fwx+fu/Wpt0GLbt7lCSfNz4Wjurr15AcgL9u7PBKRjDG3GGN+M8bsMcaM2c81tY0xacaY+Xudr2uMmWCMyTLGrDXGXFua+0UkSBwHpj9mX7R2fPZcfDJcPhra9PM2m4iISAVTXk3zx4GfgSX+4yfgKQBjzNvGmLcDrr0QWAfsAO4C+jqOs6CcckqkiY6Bvm/BCQG/621dAkPOhR2rPYslEhZ2rIEpD7p18+7Q7iLv8pQ3Y6D7c9B+oHtuxbfw1aDI31x400KY8B+3btARznpw/9fLvro9DVEx9nbGWvjpdW/zSCT6B7uw5L0DXPM6UNzP0Z8Dm4BawADgOWNMp1LcLyJlrSAPxtwEP77gnkuqB4MnQaPTvMslIiJSQZVL09xxnDzHcW52HCfFf9ziOE6+/74bHMe5IeDaBxzHSXUcp7LjOKc4jjOrPDJKBIuKgh7Pwel3ued2rIYh3WDLUs9iiYQ0n88/lsW/LUWlatDr5cgfy7K3qCjo/VrR1V3LxsPo68FX4F2uYMrZaeeYF74lPLE69B8C0bHe5go3NZrZ+eaFZr4IGRu8yyMRx3GcUY7jjAG2FXe/MaY3UAMYutf5psBpwL2O42Q5jjMbu4/Q4JLcLyJBsCfTjoH74zP3XM1WcPUUqN3au1wiIiIVmHYQkYrBGDj7Qej6mHtu10b4sDtsmOddLpFQNXcIrPrBrXs8B1XreJfHS1HRcP57dqV9oUUj4Zt/2RcXIonjwNhbIW2F/4Sxn3tSXU9jha1O/4XEGvZ23m6Y+pC3eaTCMMYkAS8BxQ1AbgdsdBxnc8C5+f7zJblfRMpS5hYYeh6smO6ea3iaXWGeXN+7XCIiIhWcmuZSsZx6m93EEP9q2ew0uzno6pmexhIJKTtWw5SA5l6L8+DoAZ7FCQnRsTBgKDQ50z33+ycw6W7baI4Uc96HxQGjkTv9F44627s84a5SMpwd8Hdp0QhY87N3eaQieRYY5jjOsmLuqwKk73UuHahawvtFpKxsXwEfdIWNAdsOtO4Ll42EhGre5RIRERE1zaUC6nAV9P/AnTWbuws+uQD+muxpLJGQ4PPB17dAXpatE1Kg50sVbyxLcWIrwcWfQoNT3HO/vgvTHomMxvmGuTDpXrdufAZ0utu7PJHi2MvgiGPcetLdkTvaR0KCMeZUoBPw9H4uyQSS9zqXDOwq4f0iUhbWz7UN88B9lk66Efp/aH/mEBEREU+paS4VU9sL4OLPIcb/A2l+DnxxKSwc4W0uEa/99gGs/tGtezwPVWt7lyfUxFWGS4dDvQ7uuVkvww/PeZepLGTvgK+uAp9/g9MqdeCCD+xoGjk8UdHQ/f/ceuMfMP9T7/JIRdAVaACsNcZsAl4B2hhjNhljjsBuDFrXGFMr4DHtgYX+2we7X0QO11+T4aOesHu7e67r4/5NpPUruoiISCjQd2SpuJqfA5eNgjj/u419+TDyGvjtQ29ziXglbVXRmcste9oXmKSoSkkwcATUbuue++5J+Ol17zIdDseBMTdB+lpbmyi78WeVWgd+nJRcg5OhbX+3nv4Y5GR4l0cigjEmxhhTCYgBoowxlYwxccDzwFHYRnd74CFgmf/2ZsdxVgCzgKeMMYnGmBOBgcAHAAe7X0QO09yP4PNL7F4XAFGxcP77cOq/9M4+ERGREKKmuVRsjU6Fq76BhFT/CQfG3Q6zXvE0lki5+99YFv8vcAmpGstyIImpcPkYqNHcPTflfpgThj2ln1+HZRPc+qwH7b+NUra6PgaxifZ21laY8X8Hvl7k4B4AsoH7gV7+21Mcx8l0HGdT4QFkAPn+unD34kuAesBWYCTwX8dxZgQ898HuF5HSchz4/lm7kbjjH9MVVxUuGwHtKvjeMSIiIiFITXORusfCoIlQ9Qj33NSH7ErASJhTLFISc96DNQEb4p73vFYaH0yVmnDF15DSyD03/k6Y/7lnkUpt7S8w9WG3bnYOnHq7d3kiWXI9OO0Ot579Nmxb7l0eCXuO4zziOI7Z6+hczHVDHcdpv9e5DY7jdHccp7LjOEc6jvNeae4XkVIqyIdvboPvn3LPVakDgyZAk85epRIREZEDUNNcBKBWSxg8CVIau+d+fAEm3GVX4IpEsrSVdjPLQq16Q5vzPYsTVpLqwhVjIamee+7rm+DP0d5lKqmsbfDVIHe1W1J96PeOZqkG0ym3QrUG9rYvv+jGqyIiEplys2D4QJj3kXuuRnO4Zioc0c67XCIiInJA+s1YpFBKI9s4r9XaPTfnfRhzAxTkeRZLJKh8PhhzszuWJbE6nPeixrKURkpD2ziv7F+Z7/js/gjLJnmb60B8Phh1Hez6x9ZRMTBgqB07I8ETmwDnPOHWf0+Fv6Z4l0dERIIraxt81Bv+CviZ4MiTYPBk90VUERERCUlqmosEqloHrhoP9Tq45xYMhy+vhLwc73KJBMuv78Dan9z6vBfs2BEpnRpH2VEtCSm29uXDl1fAiu+8zbU/P74AK6a7ddfH4cgTvMtTkbTqDY1Od+vJ90J+rnd5REQkONJWwQfnwIbf3HMte9qfF/QitYiISMhT01xkb4mp9ofZwKbGsvHw2QBY9YNdMSISCbavgGmPunXrvtCmn3d5wl3t1nD5aIhPsnXBHvjiUljzs7e59rZyRtGZqi17wsk3epenojEGuj0Dxv8j2Pa/7XxzERGJHP/Mtw3ztBXuueOvhguH2XcdiYiISMhT01ykOPFVYeAIaN7dPbfqB/ioFzzXFJ47yt6eeDfM/QjWzYGcnd7lFSktXwGMuQnys22dWMOuMpfDU/dYGPgVxCbaOm83fDoANszzNlehXZvs6BjHv1dDSiPo84bG8ZS3Om2hwyC3nvF/kLnFuzwiIlJ2/p4OQ8+DrIB/18960P6cFRXtXS4REREplRivA4iErNhKcNHHtrG48Mui92VthVVbbSM9UHIDqNXKf7S2f9Zobp9LJJTMfhvW/eLWPV+EyjW8yxNJGpwMl3wOn15oV5vn7oKP+9nRT3XaeperIB9GXO3+Eh8dBwM+goRq3mWqyM56ABaNhJx0+zUy/VH7AoaIiISv+Z/D2FvsmDawe4b0ehWOHehtLhERESk1Nc1FDiQ6Fvq9A/WPhyXfwOY/ITtt/9dnrLXH8snuORMFqU3t6IbCRnqt1pDSGKL1V1A8sO1vmP6YW7c5H1r38S5PJGrS2b7o9sVA8OXZxujHfeGqCVCzuTeZvn8a1sx0627PQN323mQROwrszPth4n9s/fun9q379Y7zNpeIiJSe48DMF4v+fBVb2Y5jadbFu1wiIiJyyNSxEzmYqCg46Xp7OI5dZb5lMWxZEvDnEsjNLP7xjg+2L7fH4q/d89HxtnkW2Eiv1QqSj9SoBAkeXwF8fRPk+ze2rVwTejzvbaZI1fxcuOB9GDHI/juQtRWG9YHBE+1YlPK0fCr8GPD/uW1/OH5w+WaQfR0/GH4bAluXAA5MugcGT9b3ABGRcOIrgIn/hTnvu+cq17Tj2uoe610uEREROSxqmouUhjFQpZY9mnR2z/t8kLFu30b6tmVQkFv8cxXsgU0L7REoruq+I15qtYYqNYP1WUlF8subsG62W/d8CSpX9y5PpGvT175AMfoGwIFd/9j9EAZNguR65ZMhYz2Mus6tqzeDXi+rMRsKomOg+zP2xRSwfzcXfgXtLvQ2l4iIlExett0rZOk491xqE7hsFKQ29i6XiIiIHDY1zUXKQlQUpDS0R4tu7vmCfEhbGdBI/9P+mbbS3Yhvb7m7YP2v9giUWKNoI712G6jZEiolBe/zksiybTl8+4Rbt+0PrXp5l6eiOOZiuyHouDtsnb4WhvWGQRPtC3DBVJAHXw1yx0rFJNi3isdXDe7HlZJr0hla9nQbLlMfghY9IL6Kl6lERORgdqfB55cU3SOmXge49EvtEyMiIhIB1DQXCaboGDuCpWZzu+K0UF42bPtr35XpGev2/1z/z959h0dRdn0c/06yaSQhCIReVYrKi0hTBCSggCgIgiC9WLBgLwiCjw0RH1AUbBTpAaUG4RFFqUEQRYoISFGK0jtJIHXn/WOy2d0kYIAkk/L7XNdeu/c9s7NnEkg2Z+855/wJ2Bdt3TyFVcyk+WgNNR8Vb84UiHrCoyxLKbhnpL0xFSb1H7L+33/3qjU+uQemdYC+i63a1jnlhze8P4Br+4HVX0HyllbDrBI6KQkQcxjWjIY7X7M7KhERuZgzf8OMTtZVpS7VWkPnyeAfbF9cIiIikm2UNBexg18QlL3ZunmKPwvHd1oNR9MS6tvh/MmLH+vs39Zt91L3nKv5aPoSL8WvVfPRwmrdx/DPL+5xuw9zNlkrGTUaAInnYUXqav9j22BGR+i9EALDsv/1/vif9X13uaUn1Ome/a8jV694Vbj9KYh+3xqvHQt1e+V+7XsREfl3R36HyAesDzld6vaGe0frfbaIiEgBot/qInlJYBhUbGjdPMVerPloTObH8Ww+uuNr97yvv7UK3bUyvfRNaj5aGBzfCcvfcY//rwvUvNe+eAqzO16CpDhrJTHAoU0Q2QV6zc/elWmn9sKCJ9zjUjdBG11ZkKc1eQE2z7SSMCkJsHQoPDjD7qhERMTTX6vgq56QcM49FzEYmr2i99IiIiIFjJLmIvlBSDiENINrm7nnTDOT5qPb4fguK+GSmZREOLrVunnyD4VSNTOuTA8O1x8A+Z0zBaKedP+bCCkNbd6zN6bCzDDgztetUi3rP7fm/v4JvuwO3b7KnrJKyQkwpy8knLXG/iHQZSr4F7n6Y0vOCQiBu96EBalNW3csspIznj/3RUTEPlvnWo29nUnW2PCxGqrX62trWCIiIpIzlDQXya8MA4pVsm7VW7vnU5Lh9F7vRPrR7XDqz39pPvqLd/kOgCIlUpPoHon0UjVzppSE5Iy1Y+HgBve4rcqy2M4woPW7kBgHm6Zbc3+thDl9oMt0cPhf3fG/exUOb3aP7xsDJatd3TEld/xfZ/hlgvtn8beD4LFoXe4vImK3tWOtK4BcHEHQeQrUuNu2kERERCRn6a8wkYLG12ElyEpWgxvbu+eT4i/SfPTAxY91/mTmzUeLVsjYfDS8hlWrXfKOY3/ACo+yLLW7Qs177ItH3Hx8oN1HVmPWrXOsuV3fwvxHodMXV54k3ToXfpnoHjd4BGp1uvp4JXf4+FhXgkxoYY2PbYdfJ0PDR+2NS0SksHI6rWT5T5+454KKQ485UKG+fXGJiIhIjlPSXKSw8AuEsrWtm6f4c1bN62PbvBPqcccvfqxz/1i3Pd+75wwfq0Z605esZL3KutgrJRminrBK8gCElIE2I+yNSbz5+EKHz6xSLX8stua2R1kfPrX/1EqgXo4Tu2HRs+5x2TrQenj2xSu5o3w9qNMTNqfWM18+zPrgQ1eIiIjkruQEWPAYbFvgnitWGXrOh5LX2xeXiIiI5AolzUUKu8CiULGBdfMUexyO78i4Mt2z8ZEn0wlHtlolJqo0hbtHQJlaOR+/ZG7tGDi00T1u9xEEXWNfPJI5Xz94YJJV03zPD9bclllW4vzeD7L+4VPieZjdBxJjrXFAmHXZuCMgR8KWHHbnf2D7Qqt0VvwZWDEc7h1ld1QiIoXHhTNWw0/Pqy3L3gzd50BoafviEhERkVyjpLmIZC4k3LpVvcM9Z5pw9p9Mmo/u9G4+ui8axjWFev2gxVCtkMxtR7fDynfd45u7q+ZmXuYIsGqZR3aG/WusuQ2TwK8ItBqWtcT5kpetq0VcOnwKxavmTLyS80JLQ7OX4fv/WOMNX0D9ftbVPCIikrPOHoTIB6z3uC7X3Wk11Q4ItS8uERERyVWXee23iBRqhgHFKkL1VtDkOeg4Dh6PhiGH4bHVcH1L976m00r0jLkF1o+3yoVIzktJhoVPusuyhJaFu9+99HPEfv5FoPuXUMHjio91H3t/+HExmyJh0wz3uNFTcEPb7I9Rctetj0Pxa63HptNqCmqa9sYkIlLQHdsBX7T0Tpjf3A26f6WEuYiISCGjpLmIXD0fX+uS1Z5zrctWS3jUeYw/Y62C/bwJ/LXSthALjR8/hEOb3ON2YyComH3xSNYFhEKPuVDGo+/AqvdgzYcXf87R7fC/F93jCg3hrjdyKkLJTY4AaO3xocne1bBjkX3xiIgUdPvXwqTWcO6ge67pi1b/EV8/++ISERERWyhpLiLZq3oreGIdtHwb/D1W5BzfAdPaw5c94NRe++IryI5ug5UezT7r9LS+H5J/BBWDXlEQXtM998Pr1tUa6SXEwuzekHwh9bnFofNk/WFfkFRvDdff5R4vHQJJ8fbFIyJSUG2LgmkdIP5s6oQB94yyekyoub2IiEihpKS5iGQ/hz80fgae/hVu6Ql4/LHxx2L45FZY9paV9JPskZIEUU+AM8kah5aD1u/YG5NcmeAS0HuhuzQHWFdrbJzuHpsmLH4OTu52z3UcD2EVci9OyXmGYa0290ltQXPmAKwba29MIiIFzfpxMKevuz+PbwA8OB0aPmprWCIiImIvJc1FJOeElob2n8Cjy62yES4pCRD9PnxcH36brTq92WHNh3B4i3t8n8qy5GuhZaD31xBW0T339dOwda71+NfJsHWOe1vTl6BaS6QACq8ODR9zj6M/gHOH7ItHRKSgcDrh+9dhyUAg9b1oYDHrg+sb2tkamoiIiNhPSXMRyXnl68LDS6HjBKsxpUvMYZj/KHzRCg5utC++/O7I71bta5dbeimBWhAUq2j94R5SJnXChPn9YfUoWPKKe78qTSFisC0hSi5pNhCKlLQeJ523kjwiInLlkhMh6nGrF4xLWEXr/WrlRvbFJSIiInmGkuYikjsMA2p3gac2WKtifQPc2/75GSa0gKgBEHPUvhjzo/RlWYqWV1mWgqTEdVbivEgJa2ymwPK3ISXRGgeXgk4TwddhX4yS84KKwZ2vucdbZ8OB9fbFIyKSn8Wfg5md4bev3HOla8HD30N4DfviEhERkTxFSXMRyV0BIVbyZ8B6qNnWY4MJm2fA2Hrw4xhrBZD8u+gP4Mhv7vF9YyAwzL54JPuVqgm9FmT8vho+8MAXVikXKfhu6QVlarvHSwZapQVERCTrYo7AlHvgr5XuuSpNod83ULTsRZ8mIiIihY+S5iJij+JVoWuktYo2/Ab3fGIMfP8afHob7PrOvvjyg8O/wer/usd1+8D1d9kXj+ScsjdDj3ngH+Kei3gVqt5hX0ySu3x8oY1HGabDm2FzpH3xiIjkNyd2wxct4chW91ytB6DnPC04EBERkQyUNBcRe10bAY+vgTYjreZLLqf+hJldYMYD1h854i05EaKeBGeyNQ6rCK2G2RuT5KyKDazmoNXbQLNB0PRFuyOS3Fb5dqjVyT1e9qZVZkBERC7t75+thPmZA+65Rk9Z/XYcARd/noiIiBRaSpqLiP18HXBrf3hmEzR4xCo74bLne2vV+XdDIP6sfTHmNdHvw1GPlVL3jYHAovbFI7mjQj3o/iU0Hww++hVeKLV8CxxB1uO4495Xm4iISEZ/fANT28GF06kTBrR+1+oBo9+lIiIichF6lyAieUeR4nDv+/BYtFVf0sWZDOs+tuqdb5wGzhT7YswLDm+B6FHucb1+cF0L++IRkdwTVgGaPO8e//Q5nNhjXzwiInnZhknwVQ9IjrfGvv7wwCRo9KS9cYmIiEiep6S5iOQ9ZWpBn0XQZRqEVXLPxx2Hr5+GCc3hwE/2xWenDGVZKkGrt+2NSURyV+NnrJJMAM4k+O5Ve+MREclrTBOWD4PFz4OZ2jQ5IAx6zodaHe2NTURERPIFJc1FJG8yDLixPTz1MzQf4i5HANZK60mtYe7DcPagfTHaYfVIOPq7e9x+LASE2hePiOQ+vyDvD8t2fwe7v7cvHhGRvCQlCRY+Zb1ncgktBw8tgapNL/48EREREQ9KmotI3uYXBM0GwtMboNYD3tt+nwsf14dVIyHpgj3x5aZDm6xa5i71H7IaqYpI4XNjB6jcxD3+drB1JYqISGGWEAuzusHmGe658JrwyPdQ+ib74hIREZF8R0lzEckfwirAA19Av2+h7M3u+aTzsGIYfNIQti+0LsctiJITrLIsZmo992KVrIaAIlI4GQa0GeFunHxyN/w83t6YRETsFHscpra1msi7VLodHvrWeh8pIiIichmUNBeR/KVyI3h0BbQbA0VKuufPHIDZvWFqOzi6zb74csqq/8Kx7e5x+09UlkWksCvzf1Cvr3u86j2IPWZbOCIitjn5J3zR0roqz+WG+6DXAgi6xr64REREJN9S0lxE8h8fX6jXB57ZCI2eAh+He9u+aPi8CfzvRTh/yr4Ys9PBjbBmtHvc4BGoeod98YhI3tF8KASGWY8TzsFyNQYWkULm4K/wRSs4vdc91/Ax6DwF/AJtC0tERETyNyXNRST/CgyD1u/AE+vg+rvc86YTfpkIY+vCzxMgJdm+GK9WhrIsleGuN+2NSUTyjuASEPGqe7xxuvdKSxGRgmzXUpjSFs6fcM+1fAvavGctshARERG5Qkqai0j+F14desyF7rOh+HXu+Qun4ZuXYFxT+GuVffFdjZUj4PgO97j9JxAQYl88IpL3NHjYanQHgAlLBhXc/g4iIi4bp8OsrlZ/G7CuPLx/PDR+1ur7ICIiInIVlDQXkYLBMKB6a3jyJ2j5Nvh71Ps+th2m3Qdf9YTT+2wL8bId/BV+/NA9btgfqja1Lx4RyZt8/eDud93jv3+C3+fZF4+ISE4yTavXy9dPua/E8w+BHnPg5gftjU1EREQKDCXNRaRgcfhD42fg6V+hTk/vbTsWwccNYdnbkBhnT3xZlRQPC56wSs0AXFMF7nrDxoBEJE+7rgXUuNc9Xvpa3v85JyJyuVKSYfHzsOId91xIaej3jfVzUERERCSbKGkuIgVTaGno8Ak8uhwqNHTPpyRA9CgYWx9+m513SxisfBdO7HSP238K/sH2xSMieV/rYeDrbz2OOQRrPrz0/iIi+UnSBZjdC36d7J4rcQDPs+EAACAASURBVD08vBTK3mxfXCIiIlIgKWkuIgVb+XrWH1P3j4fQsu75mEMw/1GY1DrvNc37ZwOsHeMe3/o4VGlsXzwikj8UvxYaDXCP146B0/vti0dEJLuYJiwcADu/cc9VaAgPLbWuxhMRERHJZkqai0jBZxhWjcunNkDTF90rMQH+Xg/jm8PCpyD2mH0xuiTFQ5RHWZbi18Kd/7E3JhHJP5q+CCFlrMfJ8fD9a/bGIyKSHdaO8e7VUOMe6L0QgkvYF5OIiIgUaEqai0jhERBiJaAH/Aw123psMGHTdBhbD9aOheRE20JkxTtwYlfqwFBZFhG5PAGh3v0Pti+EvavtikZE5Or9uRx+eMM9rtoMukwH/yK2hSQiIiIFn5LmIlL4FK8KXSOhVxSE3+CeTzgHS4fCZ41g19Lcj+vvn2Hdx+7xbU9A5Ua5H4eI5G+1H4Ty9d3jJYOs5nkiIvnNqb0wp5/7CrxilaDzFPB12BqWiIiIFHxKmotI4XVdc3h8DbQZCYHF3PMn98DMzhDZGU7syZ1Yki6kK8tyHbRQWQURuQI+PtDmPff42DbYOMW2cERErkhiHHzZA+LPWGNHEHSdCUWK2xuXiIiIFApKmotI4ebrgFv7w9Mbof7DYHj8WNy9FD69Db4bAvFnczaO5cOsZD0ABnT4VJcdi8iVq1Afbu7uHi8fBudP2RePiMjlcDX+PLbNPdf+Yyjzf/bFJCIiIoWKkuYiImA1kmr7ATy2Gio3cc87k6ySKWPrwcbp4HRm/2sfWA/rPnGPGw2ASrdl/+uISOFy1+vgH2I9vnAaVo6wNx4Rkaz68UPYtsA9vv0Z+L8H7ItHRERECh0lzUVEPJX5P+i7GDpPhbBK7vm44/D1UzChuZXkzi6J562yLJjWuMT10GJo9h1fRAqv0DJwx0vu8S8T4eh2++IREcmK3T/AD2+6x9c2925wLCIiIpILlDQXEUnPMOCmDvDUz9B8iFVD0+XwZpjUCuY9CucOXf1rLR8Gp/5MfV0f6PAZ+AVd+jkiIll125NwTVXrsZkC3w6yyh5IvmcYxlOGYWwwDCPBMIyodNvmGoZx2DCMc4Zh7DUMY2i67fsMw7hgGEZs6u1Muu3lDMP4xjCMOMMwDhiG8WhunJMIJ/+EeQ+RtpigWGV4YBL4+NoaloiIiBQ+SpqLiFyMXxA0GwhPb4Ba6S4J3jrbKtmyeiQkxV/Z8fevg58+dY8bDYCKDa88XhGR9BwB0Hq4e7x3FfzxP/vikex0CBgGTMhk25tAFdM0iwLNgO6GYfRMt0830zRDUm/F0m2bBRwBSgGdgZGGYTTL3vBF0kmITW38mdpHxq+IGn+KiIiIbZQ0FxH5N2EV4IEvoN+3UKa2ez7pvLVS/JOGsGPR5a3eTDwPC5/EXZalmrWqXUQku9VoA9e1cI+/e/XKP+yTPMM0zfmmaUYBJzLZttU0zQTXEHAC1bJyXMMwrgOaAINN04wzTXM9EAk8lD2Ri2TCNK33Rcd3uOfafwJlatkXk4iIiBRqSpqLiGRV5UbQfyW0+wiKlHDPn9kPX/WEafdlvV7wsrfg1F/WY5VlEZGcZBhw9wgwUssbnNkPP31y6edIvmcYxqeGYZwHDgAhwJR0u4wzDOOEYRjrDMO4x2O+NnDYNM2jHnObU+dFcsaaD2D7Qve48XNQq6N98YiIiEihp6S5iMjl8PGFen3h6Y1w2wDwcbi37V0NnzeG/70E509d/Bj7foT1n7vHtz8NFRvkWMgiIoTXgIb93ePV72dPXwbJs0zTfBIrWd4AmA6c9tjcC6gKlAfGAvMMw3D9IgoBvGqcp45DczRgKbx2LYVlb7vH190Jd/7HvnhEREREUNJcROTKBBWDu4fDE2utP+5cTCf8MgHG1oWfJ0BKsvfzEuO8y7KUrAERr+Za2CJSiEW84r5KJikOlg5VU9ACzjRNp2maG4BzwCiP+WjTNM+bpplgmuZMYBHQKXVzLBCW7lBhQExuxCyFzMk/Yd4jpL0vuqYKdJqoxp8iIiJiOyXNRUSuRngN6DkPun0Fxa91z184Dd+8BOPusFagu/zwJpzeZz1OK8sSmKshi0ghFXQNtBjqHv8+D1aOsC8eyU1+XLqmudPj8W9AOcMwSnnM1QG25kRgUoglxMCX3SHB1fgzWI0/RUREJM/IlaS5YRh+hmF8bBjGqdTbWMMwHBfZt7xhGFGGYZxMrbM4xzCM0rkRp4jIFTEMqHE3PPkTtHwL/D2uYD+2Daa2g696wZav4Odx7m2Nn4UK9XI/XhEpvOr2gQoN3eNVI2DtWPvikStmGIbDMIxAwAH4GIYRaBiGv2EYlQ3D6GQYRohhGD6GYdwOPAN8l/q8SoZh3GEYRkDqe/QuQHsgCsA0zT+BH4HhhmEUMQyjIdAD+MKO85QCyumEBY/D8T/ccx0+hdI32ReTiIiIiIfcWmk+FGgC3JR6awpcrB7Bp6n3lbFqLQYAH+V0gCIiV80RYCXCn/4V6vTw3rbja1jgUU84vCZEDM7d+EREfHyh25fWzyCXpUPh1ym2hSRXbChwARgCtEt9vDR123PAP1i1yCdh1S13XVYQAowBTgLHgZeALqZp/uRx7G5Y9c6PA/OAgaZprsrJk5FCJvp9+GOxe9zkBbipg33xiIiIiKSTW0nzh4BhpmkeNk3zMPAO8PBF9q0KzDZNM9Y0zRjgK6BWLsUpInL1Qktbq6UeXQ4VMmnwafha2x0BuR+biEhwCegVZdUOdln0HGyda1tIcvlM03zDNE0j3S3CNM39pmk2NU2zmGmaRU3TrGma5jumaTpTn7fdNM06pmmGpO7T0DTNRemOfdA0zTamaQabplnRNM0J9pylFEi7voMV77jH17f0Lh0lIiIikgfkeNLcMIxrgArAZo/pzUAlwzDSNxkC+ADobBhGmGEYxbBWuvwvp+MUEcl25evBQ0vh/nEQUsY93/QFa5uIiF2KloXeCyG0bOqECQseg53f2hqWiBRwJ/Z4N/4sfi10mqDGnyIiIpLn5MZK85DU+zMec67HoWT0I1AKOA2cAooDw3IsOhGRnOTjAzd3tUq23PcxdJwIzYfYHZWIiLXSvFcUFClhjZ3JMLu3d/NiEZHsEn8utfHnOWvsH2I1/gy6xt64RERERDKRG0nz2NR7z1XlrscxnjsahuEDfI+VOA9Jva0htXGRiEi+FRACdXtB7c5W41ARkbygVE3oOR8CilrjlASY2RX+/sXeuESkYHE1/jyx0z3X4TModYN9MYmIiIhcQo4nzU3TPI3ViKiOx3Qd4G/TNM+m2704VgPQMaZpnjdN8zxW46JGhmGUzOlYRURERAqdcnWg+2xwBFnjpDiI7ARHfrc3LhEpOFaPhJ0eFTebvgQ33mdfPCIiIiL/IrcagU4GhhiGUcYwjDLAq8DE9DuZpnkC2AMMMAwj0DCMQGAA8E/qNhERERHJbpUbQddI8PW3xvFnYfr9Vv1hEZGrsXMJrBzuHldrDc1ftS8eERERkSzIraT528A6YEfqbS0wHMAwjM8Nw/jcY9/2QF3gIHAYaAhoGYKIiIhITrr+Tuj0BRipbw/jjsG09nDmb3vjEpH86/gumN/fPS5+HXQcr8afIiIiclGmaRKXkEyy07Q1DkduvIhpmklYK8YHZLLt8XTj7UDr3IhLRERERDzceB+0/xSiUt+enfvHSpw/9C2ElLI3NhHJX+LPXqTxZzF74xIREZEckZTiJC4hmZj4ZGITUm/xycQkJBPn8Tg2PpnYhCRiU/eNy2RfpwmHjsfZej65kjQXERERkXyiTjdIjIVvXrLGp/6EaR2g72IoUtze2EQkf3A6Yf5jcHK3e+7+cVbzYREREckzTNPkQlJKaiLbO3ntOeeZ3I7xSHp77hOf5LT7dLKVkuYiIiIi4q3ho9bq0GVvWeNj2yCyM/SOgoBQe2MTkbxv1Xuwa4l73OwVuKGtffGIiIgUMMkpTuISUojxSF57ruiO9Vzx7Rpn2G491+YqKHmWkuaXYJompql/OSJydQzDwDAMu8MQEbk8TV+EhBhYM9oaH9wAs7pBj7ngF2hvbCKSd+1YDKtGuMfV20CzQfbFIyIikkeYpklCstMrmR2TkHTRFd2XWvF9ISnF7tO5pGB/X0ICHQQHOAgNcBAS6CAkwEFIgB+hrsee2z32cW1v+nUIO07adw5KmqeTkpLCyZMniYmJITEx0e5wRKSA8Pf3JzQ0lBIlSuDrq+ZXIpJP3Pm6lTj/ZaI13hcNc/rCg9PB18/W0EQkDzq+ExY85h6XqAYdx4GPj30xiYiIXCWn0yQu8SKJ7IvU6Y7NZMV3XmhueSkOH8Mjue1OXgd7PA4J8CMk0J0ID063b0igg2B/B74+V79w0O61h0qae0hJSeHAgQPEx8fbHYqIFDCJiYmcPHmSuLg4KlWqpMS5iOQPhgFtRkJCLPz2pTW3awkseBw6jgcf/SwTkVSuxp+JsdbYP9Rq/BkYZm9cIiJSaCWlODOWKklIumhS26uud7pxXhbk5+uVyA7xWLkdmpr09t7ul2lSPMDho6vkPShp7uHkyZPEx8fj6+tL6dKlCQ4OxkerIkTkKjmdTuLi4jh69Cjx8fGcPHmSUqVK2R2WiEjW+PhA+0+sRNgfi6253+eCfzC0+8j+JSAiYj+nE+Y9Cif3uOc6jofw6vbFJCIi+ZJpmsQnOb3LlmRSpiRj0jspw1xCct5tTOnrYxDs70tooFWuJDhdojt9uRLvFd1+aduC/X1x+Cp3mROUNPcQExMDQOnSpQkL04oIEckePj4+aT9TDh06RExMjJLmIpK/+DrggUkw80H4a4U1t3Gq1RS01TAlzkUKu5XDYfd37nHEYKh5j33xiIhIrktxlTDJpCTJ5ZQwiU1IJiUPlzAJcPh4lSJJW9XtsYo7NJPV3iGBnmM/Av20qjuvU9I8lWmaaTXMg4ODbY5GRAoi18+WxMRETNPUL0gRyV8cAdA1EqbfD3+vt+bWfWyVXmg20N7YRMQ+27+G1SPd4xr3wh36mSAikl8kpTiJS5e0zpDk9hi79rPG7tXdcYl5uzGlK4kdHOBLSKDfRZtPeo8zljHxd2hVd2GhpHkq03R/iqWSLCKSEzx/tihpLiL5kn8wdJ8NU9vCka3W3Ip3rBXntz1hb2wikvuO/QFRHv/3S1aH+z9X408RkRxmmiYJyc6LrNRO+pdmld4lTeKT8nYJk/RNKTMkudM1psxsdXewvwOfbGhMKYWLkuYiIiIiknVBxaBXFExuAyd2WXPfDgL/EKjby97YRCT3XDgDX3ZzN/4MKJra+LOovXGJiORhpmlyPjElk/IkmTSnzCzp7VHnOyklb5cwSUtseyS3L574dpct8dymxpRiJyXNRUREROTyBJe0EueT7oazB6y5Rc9YK9FrdbQ3NhHJec4UmPcInPordcKAjhOgZDVbwxIRySnJKU7iElKISUgiLiEl8yR3pknvJK+kd1xCMnm4XDfB/r4eq7VVwkQKNyXNRUREROTyhZWHPgthUhuIPQKmE+Y/aq04r97K7uhEJCeteAf2fO8eN38VatxtXzwiIpnwbEwZ57Fy2/XY1aAy1qN5pau2t2dDy9iE5DxdwsTHIDVp7efdnNJVoiSzJHcmSfBgfwe+KmEikkZJc5FcEBERwapVq9i7dy9VqlSxO5xMxcXFMXjwYBYsWMDhw4dJSUnh9ddf54033rA7NBERyauKXwu9U0u1XDgNzmSY3Qt6zoMqTeyOTkRywvaFEP2+e1yzLTR9yb54RKRAcTpNzieleCWsPZtUxiW45z33ic0k0X0+jzem9PM13InuTOpweye9/TIkuV37Bvn5qoSJSA5Q0lxEABg8eDBjx47l+uuvp0uXLvj7+1OnTh27w8qzqlSpwv79+72aCIuIFEqlboCe82HqfZAYA8nxMPNB6PM1lK9nd3Qikp2ObocFHo0/w2uq8aeIYJomF9Iluj2T2hlWdqeWN7H2SyE23lXyxEp85/U/sYL8fAkOcFDUq153+qS33yVWelvjAIev3aciIpegpLlILpg2bRrnz5+nfPnydodyUVFRUQQFBbF582aCg4PtDkdERPKT8nWh+1cwo6OVNE+MhRmdoO83UPpGu6MTkexw4TR82R2S4qxxQJjV+DMg1N64ROSKmKZJfJIzrUZ3WlmSBO/E98Xm05c5yct1usHdmNJVhsSV0A4OSJf49khyB6cbhwQ4CPb3xeGrDwpFCgMlzUVyQaVKlewO4V/9888/VKpUSQlzERG5MlUaw4MzYFY3cCZZCbbpHaDfEihxnd3RicjVcKbA3Ifh9N7UCQM6TdT/bZFc5ipdEneRxLX345S0ZHdcojvxnZ8S3X6+hlei27PRZKhH8jt9ojstGe7aL8CBnxLdInKZlDSXS9q3bx9Vq1alWbNmLFmyhDfffJNZs2Zx5MgRKlasyKOPPsrAgQMz1M9atmwZb775Jr/++iuBgYFEREQwYsQIIiMjefPNN5k8eTJ9+/ZN23/z5s3MnDmTZcuWceDAAc6dO0f58uW5++67GTp0KOXKlcsQ244dOxg+fDhr167l4MGDhIaGUr58eSIiInjllVcoW7bsZZ+vZ+3xNWvWMHr0aHbs2EFISAht2rRh+PDhGVaLv/HGG2nndOONN/LWW2+xbt06Tp06xaZNm6hTp84la5qfOHGC999/n0WLFrF37178/PyoUqUK9957Ly+88AIlSpRI29c0TaZNm8YXX3zBb7/9RkJCAtWqVaN37948++yz+Pn5XfE5A+zfv9/re+lZemT79u288847LF++nJMnTxIeHs6dd97JkCFDqFGjhtcxV65cSfPmzenTpw8jRoxg6NChfPvttxw5coRRo0bx3HPPXfH5xMXFMWbMGObMmcPu3bsxTZNKlSrRqlUrnn/+eSpXrgzAmTNnmD59OosXL+aPP/7gyJEjhISE0KBBA1588UVatmyZ6bE//vhjZs2axb59+0hOTqZ06dLUrVuXRx55hNatW6edm4vn16ty5crs27fvsr8HIiIFRrWW0GkCzH3IagwaexSmdYCHlkBYBbujE5Ertfxt+HOZe9xiiBr+imSRqxmluzSJZ23u1NIkiSleSXDvfVyPU/JF6RJfHyPTFduuMiXuld2+Hg0prcfBAb6Ept6rfImI2E1Jc8mSxMREWrVqxbZt22jYsCE33HADq1atYtCgQcTExDBs2LC0fefNm0eXLl1wOp00btyYihUrsmHDBho2bMh9992X6fFHjBjB3LlzqVWrFo0bN8YwDDZv3sxnn31GVFQUGzZs8Eqcb9y4kSZNmhAfH0/Dhg1p2LAhMTEx/PXXX3z00Ud06NDhipLmLqNGjeLTTz+ladOmtG/fnp9++olp06axfPly1q1bR4UKGf/wX716Nf3796d69eq0atWKQ4cO4fMv9R23b99Oq1atOHjwIGXLluXuu+8mJSWFnTt3Mnz4cFq2bElERAQATqeTrl27MmfOHIoWLUqDBg0ICQlh/fr1vPzyy6xYsYJFixb962umd/fdd1OlShWmTp1KcHAwDzzwQIZ9li1bRrt27bhw4QJ169YlIiKCP/74g+nTp7NgwQK++eYbmjZtmuF5x48fp0GDBiQnJ6d9v4oUKXLF53P48GHuuusutm/fTvHixWnRogV+fn7s2bOHMWPGUKdOnbQPY3766SeeeeYZKlasSPXq1WnUqBEHDhxg6dKlLF26lIkTJ/LQQw+lHTslJYVWrVqxdu1aKlSoQEREBP7+/vzzzz8sXryY4OBgWrduTZkyZejTpw9z584lLi6OPn36pB2jZMmSl/W1FxEpkG66HxLjYOEAa3z2gJU477cEQsLtjU1ELt/v82HNaPf4hnZq/CkFXnKK01qpneCqtZ1EbGoJk9hMGlKmT257zuf1ZpQAPgZpSe5gj5XcnnOh6cuVpCtr4tonwOGjppQiUiAoaS5Zsm7dOpo2bcquXbvSEoMbNmygUaNGjB49mkGDBhESEsLZs2fp378/TqeT2bNn07lzZ8BKSD7xxBNMmDAh0+P379+f0aNHeyW6nU4nw4YN4/XXX2fo0KFMmjQpbduYMWO4cOEC8+bNo2PHjl7H2rFjB8WKFbuq8x03bhyLFy/mnnvuASApKYl+/foRGRnJM888w/z58zM8Z/Lkybz33nsMHDgwS6+RnJxMp06dOHjwIC+++CLvvvuu18rqTZs2ER7uTi6MGjWKOXPm0LJlSyIjI9O2xcXF0a1bNxYtWsRnn33GgAEDLutcBw0aBMDUqVMpWbIkU6ZM8doeFxdHjx49uHDhAp999hmPP/542rbRo0fzwgsv0L17d/bs2UNAQIDXc7/55hvuv/9+Zs6cSWBgoNe2KzmfXr16sX37drp168aECRO8Ssns3r2blBT3G9IaNWrw448/cvvtt3u97qZNm2jRogXPP/88Xbp0ISQkBIDo6GjWrl1L+/btmT9/vley/uzZs+zZsweAmjVrMmXKFFauXElcXFyGr5eIiAC39ISEWPj2FWt8cjfMuB/6LIagq/sdLSK56Mjv7g/AAMJvgA6fgRJikkclpTiJiU8mJj6JmPhkzqXee87FxCelNqJM9i5zkuiu352Q7LT7VP6Vw8fwKluSluwOcBAc4OtVpiSzciaeCfBAPyW6RUTSU9I8i0zT5Fx8st1hXLaigY5s+eXn4+PDxIkTvVbS1q9fnzZt2rBo0SI2bNhAREQEc+bM4dSpU7Ru3TotYQ7g6+vLqFGj+PLLL4mJiclw/BYtWmT6mv/5z38YP348Cxcu9Np27Nixiz7vhhtuuOLzdOnSpUtawhzAz8+Pjz76iKioKBYuXMjBgwczlGmpVasWL7/8cpZfY/78+fzxxx/Url2b//73vxlWiN9yyy1pj5OTkxk5ciShoaHMnDnT6/sQHBzMhAkTqFy5MuPGjbvspPm/mT17NkePHqVp06ZeCXOA559/nsjISH799VcWLFhA165dvbYHBAQwduzYDAnzKzmfn3/+mWXLllGmTJkMCXOAatWqeY2rVq1K1apVM5zPLbfcwoABA3jnnXdYsWIF7dq1A9z/piIiIjJ8L8LCwqhXr96/fq1ERMTDbY9DQgysSL0a7chWiOwMvRZAQIi9sYnIvzt/KrXx53lrHBgGXSPV+FNyTFYS3ucupM4luPbxTobHJ+XtZLe/qxllWtLaN0MCOzjAu6xJZqVLggO0oltEJKcpaZ5F5+KTufnNpXaHcdm2vN6KsKDLr3OdXpUqVahevXqGedfc4cOHAVi7di2AV8LcpWjRorRq1Yp58+Zl+honT57k66+/5vfff+fMmTNpq4aTkpI4deoUp06donjx4gDUq1ePJUuW0Lt3b4YOHUr9+vUvuyzJpaRP/gKUKFGCli1bEhUVxdq1azOcY7t27S7rTcsPP/wAwKOPPvqvsW/atIkTJ07Qpk2bTEuAlC5dmmrVqvH7779z4cIFgoKCshzHv4mOjgagR48emW7v2bMnv/76K9HR0Rm+bnXr1s3w4QJc2fm4vl49evTIcrPSlJQUli1bxtq1azly5Ajx8fGAtSrd8x6gTp06+Pj4MHLkSMqUKcO9995LaKj+KBQRuSp3vAQJZ2HtWGv8z89WEq77bPALvPRzRcQ+zhSY9zCc2Z86YUCnSWr8KReVmOz0SF5bCWzvhLbH43yW8A7087Fqb6cmq72S265V2/7uOt1eJUxcq7tT9/d3qBmliEh+oaS5ZElmNbyBtNIWCQkJABw6dAiAihUrZrp/pUqVMp2fNWsW/fv3JzY29qIxxMTEpCXNX375ZdasWcOiRYtYtGgRYWFh3HrrrbRt25a+fftedbLT1UwyPVcTT9d5errYuV3M33//DcB11/37Hx+u5pJLliz518T8qVOnMk1UXynXuaZvYOpyJV+TKzmfy/l6Afzzzz+0bduWLVu2XHQfz6seqlevzsiRIxk0aBDdunXD19eXWrVqcdddd9GvXz9uuummLL2uiIh4MAxo+ba14vzXKdbc3lVWo9AuU8H36j/YF5EcsOxN+HO5e3zna1DtLvvikRyVkJySaQmTc5nMZVj5nfo4r5UzMQwISS1bEhrol3rvfhwSmK50SfoyJq7EuL8vDl8lukVECiMlzSVLLveyr4vtb2bS6nv//v307dsX0zT58MMPuffeeylfvnzaaunbb7+ddevWeT23aNGiLF++nB9//JFFixaxcuVKli1bxtKlS3n33XeJjo7OcnL1cmQWv0v6EiRZlZWvrWvVfbVq1TLU6E4vfV3x7PJvcWa2/WJfk6s5n6z+W3zkkUfYsmULHTt25JVXXqFGjRqEhobi4+PD+PHjeeyxxzJ8P1944QU6d+5MVFQU33//PdHR0bz//vuMHj2aMWPGZHvpGxGRQsEw4N4PrOagW+dYczv/B1FPwv3jIBuvFBORbLB1Lvz4kXt8Y3to8oJ98cglmabJ+cQUzlxI4sz5RM6eT+LcvyS8z6VLjCfmxYR3gIOimSS7vR/7UTSTudBAByH+Dnx8VLpERESunJLmWVQ00MGW11vZHcZlKxqYu99iVyPPAwcOZLrdtVrY0zfffENiYiIvvvgizz77bIbtf/31V6bHMgyDJk2a0KRJEwCOHz/Os88+y6xZs3j11Vf56quvrvQ02L9/P7Vr184w7zqvcuXKXfGxXVyr8V0NJi/FtdK/Vq1aud540nWue/fuzXT7/v3WZbueTVz/zZWcz+V8veLi4vj+++8pXbo0s2fPxtfX12v7xf5NuV7n6aef5umnnyY5OZkvv/ySfv368cILL9CjR4+rbjIrIlIo+fhazQMT42DnN9bc1tlWbfN7P1BTQZG84shWWPiUe1zqJmj/qf6P5gLTNIlJSObs+STOnE/izIXE1Pskzp5P5HTq/FmPedc4KeXiC3ty2+UmvIumS3a7mlUq4S0iInZT0jyLDMPIdUD4jAAAIABJREFUltrgBd3tt9/OlClTmDt3Lg8//LDXtnPnzvH9999neM7p06eBzEu6rF69mqNHj2bptcPDw3njjTeYNWsWW7duvYLo3b766qu0BpEup06dYunSpRiGQaNGja7q+AB33XUXEyZMYOLEiQwYMOCSK6gbNGhAWFgYK1as4Ny5cxQtWvSqXz+rmjZtyuTJk4mMjOSxxx7LsD0yMjJtv6y6kvO56667GDJkCJGRkbz11lsUKVLkovuePXsWp9NJ2bJlMyTMk5OTWbBgQZbidDgc9OzZk08//ZR169axa9cuGjZsCIC/v3/a8RwO/SgVEflXvn7wwGSY2Rn2rrbmNkyymgre9aaSciJ2czX+TL5gjQOLQdcZatx7mVKcJjHxSWmJ7dOpq7/PnE/0SHS7x2ddifELSaQ47U1++6QmvF0J7MwT3+45JbxFRKQgU6ZHslXnzp155ZVX+Pbbb5k3bx6dOnUCwOl08sorr3Du3LkMz3E1E50xYwaPPPJIWpPHgwcP8vjjj2f6Op9//jmtW7ematWqXvNLliwBLr++eHqzZ8+mV69etG7dGrASo88//zxxcXG0b9/+ojXeL0fHjh2pXr06W7ZsYdCgQbzzzjteydfNmzdTsmRJKlSoQEBAAC+99BKvvfYanTp1YuLEiRnqrv/222/s2LGDBx988Kpj89SlSxcGDx5MdHQ048ePp3///mnbxowZwy+//EKFChW4//77s3zMKzmfhg0b0rx5c1asWMFjjz3GuHHjvBLne/bsITk5mZo1a1KqVCnCwsL4/fff+fHHH2ncuDFglYUZOHAgu3btyhDTihUrSElJoUWLFl6NWffv38+OHTswDMPr+16uXDl2797Nzp07Ve9cRCSr/AKh6yyY3gH++cWa+/EjCChqNQ0VEXukJMOcvnAm9WpRwwce+AKKX2trWHZKTnF6reY+cz7Ja+W3a1v68bn4JC5R0TFHBfv7EhbkR9Egv4smvIsGeSbFvbcF+/tedllOERGRgkpJc8lWxYoV4/PPP6dbt2488MADNGnShIoVK7JhwwaOHTtGz549mTFjRtoqXYD77ruPm266iQ0bNnD99dfTuHFj4uPjWbFiBXXq1OH2229n7dq1Xq/z+eef88QTT3DjjTdyww034HA42LlzJ5s3byYoKIjXX3/9qs6jf//+tGnThjvuuINy5crx008/sXfvXsqVK8eYMWOu6tguDoeDefPm0bJlS/773/8yY8YMbr/9dpKTk9m5cyc7duxgxYoVaYnaV199le3btzNr1ixq1KhB3bp1qVSpEidOnOCvv/5i7969tG/fPtuT5sHBwURGRtKuXTsee+wxxo8fT/Xq1fnjjz/YtGkTwcHBzJw587JrqV/J+UyfPp0WLVowY8YMlixZQpMmTfDz82PPnj1s2bKFSZMmUbNmTRwOBwMHDmTIkCE0a9aMFi1aULx4cdavX8/Ro0cZMGAAn3zyiVc8W7Zs4fnnnyc8PJx69epRokQJjh8/zurVq4mPj+e5557zKstz3333sWrVKu68806aN29OcHAwJUuWZMSIEVf3BRcRKegCQqDHHJjSFo7+bs0tf9tacX5rxiuaRCQX/PC61aTX5c7X4fqC0fgzITklbTX3mfPpV3h7lkBxj8+eTyImIdm2mIsGOihWxJ9iRfwIC/KzHgf5ZTq25vwJC/LD36EeESIiItlFSXPJdl26dOGaa67hrbfe4tdff+X333+nWbNmLFq0iFGjRgFQokSJtP39/f2Jjo5myJAhLFmyhMWLF1O+fHmefvpp/vOf/3DPPfdkeI23336bqKgo1q9fz7Jly0hMTKRChQr079+fl19+meuvv/6qzuGll16iQYMGfPjhh6xfv57g4GB69erF8OHDs2WVuUutWrXYvHkzI0eO5Ouvv2bRokUUKVKEypUrM3ToUK+66j4+PsycOTNtZfaGDRvYsGEDJUuWpHLlyvTp04euXbtmW2ye7rzzTn755Rfeeecdli9fzm+//UbJkiXp2bMnQ4cOpUaNGpd9zCs5n/Lly/PLL78wevRo5s6dy9KlS3E4HFSsWJHnnnuOFi1apO376quvUqFCBT788EN+/PFHgoKCaNKkCW+99RYbN27MEE/btm05efIkK1asYMuWLZw8eZLw8HCaNm3Kk08+SYcOHbz2f+aZZzh9+jSzZs1i3rx5JCUlUblyZSXNRUSyIuga6LUAJreBk6m9KpYMtBLndbrbG5tIYfPbHFj3sXt8U0donLHPkJ2cTpO4RKuJ5dn0q78vshr8zHlrfCEpxZaYfQzSEtxhrgR3+nERzwS4dV80yA9flTcRERGxnWHade1YLjEMY9uNN95447Zt2y65n9PpZOfOnQDUqFHDqzyDZA+n00nt2rXZtm0bhw8fpkyZMnaHlEFERASrVq1i7969VKlSxe5wpIDRzxkRkXTO/gOT7oazqY3CDR/oPAVubG9rWDfddBPbt2/fbpqm6m9lk6y+J5dcdngLfNHaXce8dC14eCn4B2fryyQkp3DuQjIx8Umci0+9Tx3HxCdzznV/wdruGlv7JRGbkIxd5b4dPkbaqm/3au90Y8+V30H+hBXxIzRAtb1FRESuht3vybXSXLLdwYMH8fPzo1SpUmlzSUlJDB06lG3bttGiRYs8mTAXERGRXBZWAXovtBLnccfAdMLch6F7cIEpDSGSZ8WdhC97ejf+fHBGhoR5itMkNsFKaKdPcGdIenttS05LgCcmO204QW/+Dh+u8Uhqu8ubeK4E9yyJYm1TnW8REZHCSUlzyXbR0dH07NmTunXrUrlyZeLi4tiyZQuHDh2iePHijB071u4QRUREJK8ocR30joLJ90D8GXAmWYm8XvOh8u12RyeSr5mmSXySMzWZnbqK+0ISsefjqb/mYcqctRp/OvHh8/ChbPj6BDHxh71WhcfaWNs7M0F+vlyTfnV3kfR1v/3d86mPA/187Q5dRERE8hElzSXb1atXj169ehEdHc2OHTtITEykXLly9O/fn8GDB+da2ZM1a9YwceLELO3boUOHDDWr87O+fftmab+SJUum1ZkXERGxTemboOc8mNYeEmOtla+RXaDvIih3i93RidgmOcWZYSV3lsqceKz4Ts6krslQx3TKOH5OG7+b1JUJu8sBx3L0fHx9DIoGOggN9KNokIPQgNT7QD9CAx0Udd0H+VE0bey9j5+vytuJiIhIzlPSXLJdtWrVmDx5st1hsGfPHqZOnZqlfatUqUKHDh1YuXJlzgaVS7J63pUrV1bSXERE8oYK9aHblxD5ACTHQ2IMTO8I/ZZAqZp2RyeSLeKTUjgek8Dx2ASOnbPuj5+Lt+5jEjgVl+iV9D6fmP1NLDv4rOERx5K08cKU25mQcm+Wnhvs70vRoIwJ7lBXIjzdXNHA1OR36jjIT6VOREREJH9Q0lwKrL59+2Z5xXVBU9Ab/IqISAFVtSl0mQZfdgdnMlw4Za0+f+hbKF7V7uhEMmWaJqfPJ3E8JoFjMfFWUjwmgWOp957z5+LtK3Xi52vQMOBv3nO6r8T82/86frn+TR4pEuK1ottzNbgrER4S4MChVd4iIiJSSChpLiIiIiJ5R/XW0HG81RAUE2KPuBPnRcvZHZ0UIv+2KtyVFD8Rm0BSSs4uWDAMCAnwWN0dePGyJhcbByScwpgwEM4mWgcNKk7F/gsYdk3lHI1dREREJD9S0lxERERE8pZanSAhFhY9Y43P7IdpHaDfNxBc0t7YJF/LK6vCryniR3hoAKVCA1PvAwgPDaB4sH9qQtxV8sRKfIf4O/DxuYqyJilJMLcfnP3bGhs+0HkyKGEuIiIikiklzUVEREQk76nXx2oK+t2r1vjETph+P/RdDIFh9sYmeU5eWBXu7/AhPCSAUkUDCA8JyDQpHh4aQMmQAPwduVzmZOlrsC/aPW75NlwbkbsxiIiIiOQjSpqLiIiISN7UaADEn4NVI6zxkd8gsgv0mg/+wfbGJjkur68Kd92scSBFAx15s8nl5lmw/jP3+P+6WP+3REREROSilDQXERERkbwrYhAkxMBPn1jjv3+Cr3pCty/BEWBvbHJFtCo8Fx3cCIuedY/L1IZ2H1lF0kVERETkopQ0FxEREZG8yzCg9TuQGAMbp1lzfy6HeQ/DA1PAV29n84v9J+Oo/cZ3WhWeW2KPWx8wpSRY4yIloGsk+BexNy4RERGRfEB/ZYiIiIhI3mYY0PZDqznotvnW3I5F8PVT0P5T8MnHK4ELkcRk84oS5oVmVXh2SkmCOX3g3EFrbPhC5ylQrJKtYYmIiIjkF0qai4iIiEje5+ML94+DxDjY/Z01t2UW+IfAPSNVbiIf0qrwHPTdq7D/R/e41TCoeod98YiIiIjkM0qai4iIiEj+4PCHLlMhsjPsi7bmfpkAgUXhzv/YG5v8q3LFAlk4oLFWhee0TZHw83j3uHZXuO0J++IRERERyYf0TlVERERE8g+/IOg2C8rXc89Fvw9rRtsXkw0Mw3jKMIwNhmEkGIYRlW7bXMMwDhuGcc4wjL2GYQxNt72cYRjfGIYRZxjGAcMwHr2c7VcqOMDBzRWLUa5YkBLmOeXgr7D4efe4bB1o96GuxBARERG5THq3KpILIiIiMAyDffv22R2KiIhI/hcQCj3mQqkb3XM/vAE/T7AtJBscAoYBmZ30m0AV0zSLAs2A7oZh9PTYPgs4ApQCOgMjDcNodhnbJS+KPQZfejb+LAkPzrA+aBIRERGRy6KkuYiIiIjkP0WKQ68oKH6te+6bl2DLl/bFlItM05xvmmYUcCKTbVtN00xwDQEnUA3AMIzrgCbAYNM040zTXA9EAg9lZbvkUcmJMLs3xByyxmmNPyvaGpaIiIhIfqWkuUgumDZtGjt27KB8+fJ2hyIiIlJwhJaG3guhqMfv16gnYcci+2LKIwzD+NQwjPPAASAEmJK6qTZw2DTNox67b06dz8p2yYu+GwwH1rnHd78LVZvaF4+IiIhIPqekuUguqFSpEjVr1sTPz8/uUERERAqWYpWsxHmRktbYTIG5D8Gfy+2Ny2amaT6JlSxvAEwHTqduCgHOpNv9DBCaxe2S12ycBr9MdI9v7g4N+9sXj4iIiEgBoKS5XNK+ffswDIOIiAguXLjAoEGDqFy5MgEBAVx//fW89957mKaZ4XnLli3jjjvuIDg4mBIlStCpUyd2797NG2+8gWEYTJkyxWv/zZs3M3DgQOrVq0d4eDgBAQFce+21PPnkkxw6dCjT2Hbs2EGvXr247rrrCAwMJDw8nDp16vDcc89x+PDhKzpfz9rjM2bMoF69ehQpUoRSpUrRp08fDh48mOE5nuf0888/07ZtW0qUKIFhGGzevDnDcdM7ceIEgwcPplatWgQHB1OsWDHq1KnDkCFDOHnypNe+pmkydepU7rjjDooVK0ZQUBC1a9dm1KhRJCUlXdE5i4iI5Hslq0HvKAgMs8YpifBlDzjwk71x2cw0TadpmhuAc8Co1OlYICzdrmFATBa3S17y9y/wvxfd43K3QNvRavwpIiIicpWUNJcsSUxMpFWrVowfP54bbriB5s2bc/DgQQYNGsRrr73mte+8efNo1aoV0dHR3HLLLbRq1YrffvuNhg0bsnfv3kyPP2LECD744ANSUlJo3Lgx99xzD6Zp8tlnn1G/fv0MifONGzdSr149IiMjCQ8P5/777+fWW28lMTGRjz76iJ07d17V+Y4aNYrevXsTEhJC+/btCQ4OZtq0adx22238888/mT5n9erVNGnShH379tGqVSvuuOMOfHwu/V9s+/bt1KlThxEjRnDq1CnuvvtuIiIiSEhIYPjw4WzdujVtX6fTyYMPPkjfvn3ZsmUL9evXp3Xr1hw/fpyXX36ZDh064HQ6r+q8RURE8q0y/2c1B/ULtsZJ5yGyCxzeYm9ceYMfqTXNgd+AcoZhlPLYXgfYmsXtklfEHIXZvawPiQCCw1MbfwbaG5eIiIhIAeCwOwDJH9atW0fTpk3ZtWsXJUtalz9v2LCBRo0aMXr0aAYNGkRISAhnz56lf//+OJ1OZs+eTefOnQFISUnhiSeeYMKECZkev3///owePZqyZcumzTmdToYNG8brr7/O0KFDmTRpUtq2MWPGcOHCBebNm0fHjh29jrVjxw6KFSt2Vec7btw4Fi9ezD333ANAUlIS/fr1IzIykmeeeYb58+dneM7kyZN57733GDhwYJZeIzk5mU6dOnHw4EFefPFF3n33Xa/yLZs2bSI8PDxtPGrUKObMmUPLli3TPiwAiIuLo1u3bixatIjPPvuMAQMGXM2pi4iI5F8VG0K3mRDZ2UokJpyF6fdDvyUQXsPu6LKVYRgOrPfyDsDHMIxArIafZYH6wHfAeeA24BlgDIBpmn8ahvEjMNwwjGeAWkAPoENWtksekdb4M/XqSh8HdJ4KYRXsjUtERESkgNBK86wyTbhwJv/dMimdciV8fHyYOHFiWsIcoH79+rT5f/buO16Oqv7/+OuTDmmQUBMIV+nli3SkiBEpBgRpUqQYaWIXaaIosfyQol8R9CsqSoDQIYIgoUrogpFOQqgJhNCTkN4/vz/Omexk7uze3bt77+699/18PPaxd+ecmTlz7uzMZ86eOTNiBPPnz2fChAkA3HTTTcyYMYN99913RYM5QPfu3fn1r39N//75w2HuueeeKzWYJ+v86U9/ytChQ7nttttWSnv//fdXzJe1+eabN1tWpQ4//PAVDeYAPXv25He/+x19+/bltttuyx2mZauttuKMM84oex1jx47lpZdeYuutt+bCCy9sNt75tttuy3rrhQufpUuXctFFF9G/f3+uvfbalRrT+/bty1/+8hd69+7Nn/70p0o3VUREpHP55PDQeGjdw+f5H8FVB8HMqfUsVVs4B1gA/Bg4IP59T0z7PjCNMBb534BLgfNT8x4FDAU+AG4BznT3BytIl3q76yx4KzX80BfOh6bd6lceERERkU5GPc3LtfBjuGCDepeicmdNhVWq63UN0NTUxCabbNJsejItGUP8scceA1ipwTwxYMAA9tlnH2655ZbcdXz00Uf84x//4IUXXmDWrFksW7YMCL28Z8yYwYwZMxg0aBAA22+/PePGjeO4447jnHPOYYcddmhxKJRKHHnkkc2mDR48mL333ptbb72Vxx57rNk2HnDAAVgF40fed999AJx00kktlv3pp5/mww8/ZMSIESv9cJFYe+212XjjjXnhhRdYsGABq6yyStnlEBER6XQ22w8O/hOMPQlwmDMdrjoQvnYXDKjuh/VG4e6jgFFFkj/TwrxvAyNamy519t/RMKFwBybbHAM7nli34oiIiIh0Rmo0l7IkPZ6z+vXrB8CiRYsAVow9vv766+fmHzZsWO706667jpNPPpm5c+cWLcOcOXNWNJqfccYZPPLII9x+++3cfvvtDBw4kJ133pkvfvGLjBw5smiP9nJtsEH+DyRNTU0AuQ8nLbZtxbz11lsAbLjhhi3mTR4gOm7cuBYb5mfMmMHQoUMrKouIiEins/WXYfEcuOPU8HnmlDhUy52w6qC6Fk2k1d56Ev55euHz0O1h/9/owZ8iIiIiNaZGcylLJT2oS+X3nOFipk6dysiRI3F3Lr74Yvbff3+GDh26orf0rrvuyuOPP77SvAMGDOBf//oXjz76KLfffjvjx4/n/vvv55577uFXv/oVDz/8cFmN0ZXKK3+iT5/WPXSpnLpNet1vvPHG7LrrriXz9u7du1XlEBER6XR2OB4WzYV740PLP5gEYw6B4/4BfQbUt2wilZr9DtxwLCxfEj73XQsOv1oP/hQRERFpA2o0L1efgWGok46mz8B2XV0ylvibb76Zm570rk678847Wbx4Maeddhrf+973mqW//vrrucsyM3bffXd23313AD744AO+973vcd111/GjH/2IG264obWbwdSpU9l6662bTU+2a8iQIa1ediLpjf/qq6+2mDfp6b/VVlsxevToqtctIiLSZez2XVg0Gx66KHye/jRcdyQcfTP0WrW+ZRMp19JF4cGfc98Nn7v1gMOvgoG6u1BERESkLehBoOUyC2ODd7RXO9+qmfSCvvnmm5ulzZ49m3vvvbfZ9JkzZwL5Q7o89NBDvPfee2Wte80112TUqFEAPP/88+UWOVdeg/uMGTO45557MDN22WWXqpYPsNdeewFw+eWXl+zBDrDjjjsycOBAHnjgAWbPnl31ukVERLqUz/0Ydj6l8Hnqo3DjsbB0cf3KJFKJO8+AaU8WPo+4ADaoPh4VERERkXxqNJea+vKXv8zqq6/OXXfdtdIDP5cvX85ZZ52V2+CbPEx0zJgxzJs3b8X0t99+m1NOOaVZfoDLLruMN954o9n0cePGAZWPL5514403cvfdd6/4vHTpUk499VTmzZvHgQceWHSM90occsghbLLJJjz77LP88Ic/ZOnSpSulP/PMM0ybNg0IQ66cfvrpzJo1i0MPPZSpU5vf9fDcc89V1bteRESk0zKDfX8F2xxdmPbqfTD2RFi2tPh8Io1gwt/gqSsLn7c9FnY4oX7lEREREekCNDyL1NRqq63GZZddxlFHHcVhhx3G7rvvzvrrr8+ECRN4//33OeaYYxgzZgy9evVaMc+BBx7IlltuyYQJE9hoo43YbbfdWLhwIQ888ADbbLMNu+66K4899thK67nsssv4xje+wRZbbMHmm29Ojx49mDx5Ms888wyrrLIK5557blXbcfLJJzNixAj22GMPhgwZwr///W/eeOMNhgwZwiWXXFLVshM9evTglltuYe+99+bCCy9kzJgx7LrrrixdupTJkyczadIkHnjggRUN9D/60Y+YOHEi1113HZtuuinbbbcdw4YN48MPP+T111/njTfe4Etf+hJHHHFETconIiLSqXTrBgdcAovnwsTbwrSJt0Gv78GBl4Z0kUbz5r/hzjMLn9fbUQ/+FBEREWkHujqQmjv88MO566672H333fnvf//LuHHj2GKLLXjiiSdWPCxz8ODBK/L36tWLhx9+mG984xv06dOHO+64g0mTJvGd73yHe++9l549ezZbxy9+8QuOP/54zIz777+f22+/nfnz53PyySfz3HPPVT18yumnn84VV1zBxx9/zN///ndmz57NscceyxNPPFF1L/a0rbbaimeeeYbTTjuNvn37cvvtt/Pggw/Su3dvzjnnnJXGVe/WrRvXXnstN998M5/73Od45ZVXGDt2LBMnTmTttddm1KhRXHDBBTUrm4iISKfTvQcccjlstFdh2jNj4O6zoYWh0kTa3ezpYRzz5MGf/dYOD/7soYe+i4iIiLQ1a2ks5Y7OzF7cYosttnjxxRdL5lu+fDmTJ08GYNNNN6WbehvV3PLly9l666158cUXeeedd1hnnXXqXaRmhg8fzoMPPsgbb7xBU1NTvYsjnYyOMyIiDWLxfBhzKLyZupNtjzNhzx8DsOWWWzJx4sSJ7r5lnUrY6ZQbk0u0dBFcsR+8PSF87tYTRv4Thu1c33KJiIiItJN6x+RqsZGae/vtt3n//fdXmrZkyRLOPvtsXnzxRfbcc8+GbDAXERGRLqLXqvCVG2DItoVpD10Ij9ZmCDaRqrjDP08rNJgD7HehGsxFRERE2pHGNJeae/jhhznmmGPYbrvt2GCDDZg3bx7PPvss06dPZ9CgQVx66aX1LqKIiIh0dX0GwDFj4YoR8MFLYdq9P4He/etbrs5s0WyYdAf0XCW8evSBnqtCzz7QY5XC9O7Nh+brUib8FZ6+uvB5+5Gww/F1K46IiIhIV6RGc6m57bffnmOPPZaHH36YSZMmsXjxYoYMGcLJJ5/M2Wef3W7DnjzyyCNcfvnlZeU96KCDOOigg9q4RCIiItJQVh0Ex94KV3wBZk4J0+44FRap4bxNzJ4ONxzdcr5uPWIjep/YuJ78vWqRhvZMo3vuPOmG+lSeHr0b66GaUx+DcWcVPq+3E4y4sH7lEREREemi1GguNbfxxhtzxRVX1LsYvPrqq1x55ZVl5W1qauKggw5i/PjxbVsoERERaSwD1oXjboO/jYA50wEPjbtSP8uXwuI54dXmrESv97xG90oa83OWW+p5Jh+/HR/8uTR87rcOHKEHf4qIiIjUQ7s0mptZT+C3wFfipGuAU919aU7euZlJvYFJ7r5125ZSOpuRI0cycuTIehdDREREGt3qTaHh/IovwPyP6l2azqtHH1hrS1i6AJbE19KF4VU3Dkvmh9eCGW2/uu69izfGf/wmzPsg5OvWMzSY99dzgERERETqob16mp8D7A4kTzsdB/wI+Hk2o7v3S382s+eA69u6gCIiIiLSha25CRz7dxh9AJDtwyE1sXoTfPOx5tOXLy80ni+ZD0vi+9KFqcb1BUWmp+bJNsavmJ5ZLt7eW16wbFF4Lfy4dL79fwPr79Q+ZRIRERGRZtqr0fx4Qs/ydwDM7P8Bvyan0TzNzHYCtgBGt3UBRURERKSLW/dTcPSNcPFngOX1Lk3X0a0b9Fo1vBjUtutyh2WLcxrjF5RodC8yvcXG/AWwfEnlZdzhBNj+q7XfdhEREREpW5s3mpvZ6sB6wDOpyc8Aw8xsoLuX6mZxAjDO3TWwpIiIiIi0vWGfhgFD4f2p9S6JtAWzMEZ4j96wymptv75lSytojF8A/daCzQ5o+3KJiIiISEnt0dM8GW5lVmpa8nd/ILfR3MxWBY4Ejmu7oq20vhV/L1++nG6lHtIjItIKy5cXei2mjzkiItJgevWtdwmks+jeA7r3h979610SEREREalAe7QMJ4NCDkxNS/6eU2K+w4H5wD/bolBZZkavXr0AmDdvXnusUkS6mOTY0qtXLzWai4iIiIiIiIg0qDbvae7uM81sGrAN8FqcvA3wVgtDs5wIXOnuS9u6jIn+/fvz0Ucf8d577wHQt29f9TgXkaotX76cefPmrTi29O9o3CBtAAAgAElEQVSv3mYiIiIiIiIiIo2qvR4EegXwYzN7NH7+EXB5scxmtimwK+EBou1m8ODBzJs3j4ULFzJ9uoZRF5Ha69OnD4MHD653MUREREREREREpIj2ajT/BTAYmBQ/XwOcB2BmlwG4+ymp/CcAD7v7y+1UPgC6d+/OsGHD+Oijj5gzZw6LFy9uz9WLSCfWq1cv+vfvz+DBg+nevXu9iyMiIiIiIiIiIkW0S6O5uy8BvhVf2bRTcqad2R7lytO9e3fWWmst1lprLdwdd69XUUSkkzAzjWEuIiIiIiIiItJBtFdP8w5JDV0iIiIiIiIiIiIiXYuecikiIiIiIiIiIiIiEqnRXEREREREREREREQkUqO5iIiIiIiIiIiIiEikRnMRERERERERERERkUiN5iIiIiIiIiIiIiIikRrNRUREREREREREREQiNZqLiIiIiIiIiIiIiETm7vUuQ5sys9m9e/fuv+GGG9a7KCIiIiLSAbz22mssWrRojrsPqHdZOgvF5CIiIiJSiXrH5F2h0fxdYFXgrXqXRUREREQ6hPWB+e6+Tr0L0lkoJhcRERGRCtU1Ju/0jeYiIiIiIiIiIiIiIuXSmOYiIiIiIiIiIiIiIpEazUVEREREREREREREIjWai4iIiIiIiIiIiIhEajQXEREREREREREREYnUaC4iIiIiIiIiIiIiEqnRXEREREREREREREQkUqO5iIiIiIiIiIiIiEikRnMRERERERERERERkUiN5iIiIiIiIiIiIiIiUYdsNDeznc3M4+vsepenXlJ14Ga2S4l8h6fyTWnHIjaspD7qXY6Oolb1ZWZTOkO96xhUmpntbWa3mtm7ZrbYzD4ys4lmdo2ZnWRmvepdxkZhZmuY2S/M7Gkzm2Vm883sVTP7s5ltVe/ytbfU92qmma1WJM+omOeH7V2+RpA597uZLTGzD83seTMbbWaHmlmPepdTOj+dCwsUk7eeYvLKKCZfmY5DxSker4xi8pUpJm+ZYvKuoUM2mgPHFvm7Kzu6RNox7VYKka5Bx6AizOxnwD3Al4APgNuB+4ElwFHAn4FBdStgAzGzvYBXgHOAocCDwB2EujoJeKbaIDQVzI6ssrjtbTXg1HoXosFdGV/XAY8CPYDjgJuBSWa2Ux3LJl2DzoX5FJOLtB8dh3IoHq+MYvKSFJO3TDF5J9bhfvUws57AEYAD7wGbm9l27v5UfUtWN4uA14AjzOz77r40nWhmg4EvAE8B29WhfCKdio5BxZnZDsBPgcXAwe5+ZyZ9KCHwXFSH4jUUM9sR+CfQEzgb+HX6+G1m+wFjgF+Z2Xx3v6Q+Ja2L5cBS4PtmdrG7z6x3gRqRu4/MTjOzDYHzgMOBB8xsN3d/pr3LJp2fzoW5FJOLtCMdh/IpHq+MYvKSFJOXQTF559YRe5qPANYAHiL8Qgr6VfkaQp3sm5N2BOEEMKZdSyTSeekYVNzB8f3GbIAO4O5vu/uorh5wmZkReiP0An7q7udnG1di/R1EuBC8wMw2aP+S1s0S4HJgAPCDOpelQ3H319z9COCvwKrA3+pcJOm8dC7Mp5hcpP3oOJRP8XiZFJO3SDF5Kykm7zw6YqN5ciIcQyHoPMrMumczJmO1WfC9OIbXQjN728wuyRubKY495GY23Mz2NbMH4phWXmwspwZwDeEgnnfL5zHAXOC2vBlj3RxlZteb2ctmNs/M5pjZk2b2TTPrlsn/h1gXJ5VY3mtmtqwjnVDi/9vNbHSR9BX7RWa6x/2su5mdGetwkZm9ZWYXmFnv9ih/e2ttfXUSlRyDio5ZamYjY/qonLRPmtmNZjbDzOaa2SPxeFSy3hvAmvH9g0pmMrM1zezXZjY5HqNnmtk4M9sjJ++KOjCzdeP7e2a2wMyeMrPjarIlbWsEsDnwNnBBsUzu/hBwE9AH+FY6zcz6mtnZcZvnxP1kopldnBx74753bpzlClt5zL3htd+smjqP0APqe2ZW9u3DZraqmf3EzF6I+8THZvaQmR2ZydfTwtieC4ud281sp1hXj1a3KXVxGjAP2NbMds8mmlmTmf0pnr8WmdkHZnazmW1dbIFmtks8Lk2P87xtZnebWYcdbsLMxpvZ9+tdjg5K8Xg+xeRVam2MaYrJRxdJV0xOl4vJFY+XTzF5yxSTV0cxeRmsgWPyDtVobmYDgS8SvrQ3u/srwJPA2sDeJWa9FLgImEYIVLsD3wEeNLP+Reb5CjAO6Bvf/0MIghuOu08ljJ10oJn1S6ab2SeAXYCxwPwis/cGrgX2Ad4njHf2BLAl8Aea/yJ2WXzPDdCBPYFPAvfEcnUV1xBug5tGGD+uP3Am4ZdF6SSqOAZVso6NCd/BL1P4TnYH7gQOrcU62tC0+H6oma1ZMmdkZpsBTxMCimQ7nyMcSx4ws68UmXUQ8G/Cre7jgYeB/wGuNLNzi8zTKPaL7ze5+5IW8l4b30ckE8xsXcJ+dx6wAfAv4C7CbbjfBT4Xs94MPBv/fpTCeHtXAu9Wtwlty93fBv5COJaeVs488Xz+EPBzYC3CWJSPAjsB15nZxanlLyFc/PSm+Pcq2feuacUm1JW7f0yIXaCwPwAQA/ZngZMJDXj/IIzjeQjwbzNbKX+c5/uEuvwy4Xs+FniJMMTEL9tmK6RRKR4vTjF5Q1BM3gUoJi9J8Xj5FJO3QDF5dRSTdwLu3mFehKDQCSfGZNp34rQxOfmnxLSPge1T0/sRHoThwP9m5hkdpztwRL23uYX6cGBh/Pvr8fNxqfSfxGn7AOvEv6dkltGD8KXslZm+JoULkz0yaY/G6Z/KKdP1Me2QetdPGXXnqc/D47TRRfIn+8XwvOUAE4Gm1PRPADNi2ob13t4Gqq8p6eV0tFcrjkHNvnOptJExfVRm+n1x+iVAt9T0Y1P7W2691/sFbAgsSB13rwROJFzwW07+7sDzMf9303mAbYEPCQHEWjn7nhMuhvum0nYE5gDLgG3qXR8l6umRWP5jysi7Xsy7DOiZ2UeuTW9/TNsY2Cz1eVTMO7Le211m3aTPa0Pi/jQbGJyzTT/MzHtpnH4v0C81fTPCWKcO7Jea/pk47f6ccnQDphNuS12j3vWSU0deRr4fJ/tJatoA4B3Cxdxhmfx7ERofppGKCYA9CGNafkzzY3ovYN9610kVdTke+D4hLryN0CjyMeFC71OpfKMIjSW/B2YBb9LgMWIb15vi8ebbqJi8urrz1OfhKCZvj/qaQhnnkkZ9teI41GVichSPV1JXismLb69i8vLqyMvIp5i85ToaT4PG5B2qpzkr34KVuJ7wcIKD0z06Mn7v7v9NPrj7XODbhB33hCK36/3T3W+oQZnby42EL9zRqWlHE365vL/YTO6+1N3HuvvizPQPCA/CgPDU7bQ/xfcT0xMtPODoIMKB8PZKN6CD+467T0k+uPsbFPbTz9SlRNIWWnsMKouZbQR8nnBxd7a7L0/S3P1qwkmjYbn7a4TjxXRCIHAcoWfCC8C7ZnZh5ra7A4CtgOvc/RKPZ8K4rKeBXxB6F+bdauaE79281Dz/IfTG6wZ8o5bbVmOD4/v7ZeRNbq3tBgyy8PT1zxOO7Seltx/A3V9x95dqVtI6cvfphDFK+wOnl8prZn2BEwiB5DfjeT5ZzksUel58NzXbI8BUYLiZDcksck9gXeBud/+wmu2oo6Tcq6emHU9osPu1u9+czuzu9wH/Bwwl9N5L/BAw4OfuPj4zz2J3v7vG5a6HboQL3k8Qeik+DdxoZpbKsy+hgXIwcA5weYne0Z2d4vHSFJPXl2LyrkExeRGKxyuimLwMismrppi8fA0Xk3eYRnMzawJ2J5y4VjzQIgaSdxMG1z84b17CCXQl7j6JcCvEACBvvKB/VFXgdubhQR53Ap83s3UsPAV6U8LJb1lL85vZNhbG//uDmV1hYXy25CS3cSb7jYT/wzFmtkpq+nGE22pGe8u3N3UmSwi/jGW9HN/Xbb+iSFup8hhUrl3j+53ZwCu6qcrltzl3v4dwO/jhhAD9OULQtBZwBvCf1K2iye2ztxZZ3CPxfcectKfdfXLO9Ovie7Mx4xqIZd7LyQvhwmSv+Pc1RfaRzuZ8YCHwbTNbo0S+7YFVgCc93KKddXV83y0JuuJF4XWEWOjITP4OextoSrLveGpaRd85C+PCDo/T/pw3Q2fg7rPd/QZ3n+fuCwnjjm5C6FmVeMrdk5jqakKPnk3qUNy6UjzeMsXkdaWYvAtQTN4yxeNlU0xePsXkraeYvEyNGJN3mEZzwi+bRngK9OJMWvILc7GnZRcbx29KfM/+mgWhm39HM4Zwe9WRFH4JHlM8O5hZLzO7lvALzgXANwm3qH2VcIsohF8UV4g771XAasBhqaQTCQeCrjZm4DtFLoKSX1U75YOHuqBqjkHlSo5FbxVJ7xDHJXdf5O43ufvJ7v4pwq/oZxLGcd2IMO4fQFN8v8FWfiCOm5kDE2J6XmDWmuN6o0h6G6xVRt7kgsaBmcD68fNrtS5UI3L3dwjj9vYjXOQVk/y/pxRZzizCLX79CI1ziSQAX9EjNPZ2PYTw0J7cB/Z1EMn3ZkZqWlN8f6LId+7mzLxrEC583nf3OW1e4joxs1XM7P/iQ5hmU9iP0seeFWOOxou7BWTioy5C8Xh5FJPXh2LyrkExeRkUj5dFMXmZFJNXRTF5mRoxJu/RVgtuA0nA+XkzeyST1juVtm78Qpej1C+KCysqXWO4gzCuz3GEg9Ukd3+qhXl+ABxFuF3rDOApYKa7LzGzTYDJ5NfTZYQxh04ErjazXYEtgAeK/KLY0ZX6gclLpHVVHekHuXLV+hjUmn2qnF4QDSf2/LnIzBYQxrjbPyZ1j+/jKH1bZKe4tTHlWWA3Qk+Mq1vIu318fzEel5PpXem4cwFhjOBvmdmvW8hbTr2kbzt+wcyeA7Yzs83ibaP7AwMJY6IWe2BfR7BNfJ+YmpZ8526i+MMIITz4LK2z72+nEb5ru7v7tHjb+kw66DG3jSkeL49i8rajmLwyislb1iVicsXjuRSTV0YxeesoJi9fw8XkHaLRPI4XtWn8uDHNb01MdCPcvvGbzPQNCA+3yBoW36dXW8ZG4O6LzOxmCuMaXlLGbMmta0e5+wuZtE+WWNdkMxtPGHdqU8LDWCDc/tURJb0Uio1/t36R6V1Vl6qvKo5BS6isjpLAflhOWrF5OpLx8T35pXhafL/M3Su9BX+DFqY38nF9HKEH4WFmdkYLt84ntyTeFd+THk8btVXhGo27v2tmfyQ0KJ1J6G2Slfy/P5G3DDMbSAi65xEeTpV2DWFYiK8AP6UT3AYat/cL8eMDqaRphGPZL939uTIW9SGh98baZta/E/dsGUBonJ0Zx8E9r4X8XZLi8fIpJq9Kl4oxa6BL1Zdi8poYH9+7ejwOiskropi8corJK9ZwMXlH+eU5ub3qIne3vBfhafSQ/4CKI7ITzGwzwi8+cwhjfHUWVwEfEb5U5RxckocR5N16dngL8yYPH/pBzDsDGFvGOhtREhg1GwspPkxpu/YtTsPravXV2mPQO8BgMxuUs8x9cqY9Ft/3M7NVc9IPy5nWMDIP6MizYXxPgqn74vtBrVjdNrHnXdZR8f3RViyzvdxJ6DE4FDirWCYz24PwP19MeKASFOrs6CL7SFZyMd0hfiQv4QJCL4xvEh4Kk/VfQiC5k5nlXUAn38tH4m18adcSem18xcwGEHq1vE+hrjui3xAe2vUfd388Nb2i71wc5mB8/HhSiawdmQP/CywjPDTxBeDxknN0XYrHK6OYvHW6WoxZra5WX4rJW6B4vCKKySunmLwyisnL15gxubs39ItwUHmfUIHblsjXnVCxDmwVp02Jn2el5yXstPfGtIszyxkdpw+v97aXUTcOLCwz7zox/5TM9Dvi9LMy0w8jPH3cgfFFltkr9b9x4Lf1rpMK684z06bG6V/K7CtjU9s4PGc5U4qsY2RMH1Xv7W2g+pqSXU6jv6o8Bl2V/W4Qbi06O1VHozLL+VdybAIsNf3o1Dyj610vRergl8CFwCdy0jYGXo3l/99U3U4iPJjoLKBnZp5ehHHs/ic1bXiqHu4CVk2lbQ/MJpxot693fbRQV58mBM/JtnfPpI8gNHo4cGqRfeTq9PbHtI2AzVKfk+PQRfXe5jLrpeh5Dbgops+P7z/MpP8+Tr8H6JuavgnhYtmB/Ysse3xM/2N8v6TeddFCHXmRtE8CN8Q8c9PfnZi+ejyeLQS+lj7GxPS+hOEk1ktN+2zcT2cCn8nk7wnsW+86qaIunwKOrnc5OsILxeMt1Y9i8urqzjPTFJO3fX1NKXYuadRXlcehLhOTo3i80vpSTJ5fL4rJy6sjL5KmmLyyumzYmLzuBSij8g6IO9pLZeT9Q8x7fvw8JX7+fTwQ3hV33OSL+gIwMLOM0XSQIL3UgSwnb7EAfQ8KgfgEwq97/0kO6JQI0OP8F1A4YW5Z7zqpsO6WZ6Z9LU5fGk+A/yA8ZOBlwoMnunqAXov6mlLsxNKoryqPQVtSCCaeJjzQY3KcluQdlVnGpoSeaU4IYK8l9HZZnprnz/WulyLbf3Gyr8Syj43H3McJgXNynBmYmmczChd70+Nx+sY4z8w4/aBU/uFx2u1xvnfiOu4iHOcd+EW966LM+voCoRHJCRd3t8ZtmRinLQPOyZlvaNyPnNCD8VbCeHhPx7ofmco7hNDbYynhFtS/ApcDm9Z7+4vUSakAfU1C0Jmcc7IBev+4fyX1eSPwz7j9DvyuxHpPSi3XgZ3rXRct1JET4pXRhIaAW+N+szymvQzsUGT+3VLHmCmEhrpbCOf+pH63ycxzemq9T8Tj0n3ABxQ5Bzb6i3D776JG/S402gvF4y1ts2Ly6upOMXn719cUFJN3ypgcxeOtqTPF5M23TTF5eXWkmLz6emzomLzuBSijAm+kzCAH2D3mfYsw9MyU+NkIA8pPIvySM50QuK+es4zRdJAgvdSBLCdvboAe0z4N3E/4BXU24TaqQwhP9G0pQN8n5nm03vVRQb31iWWek5M2kjDe5iJCsPkXYHCx/aJYnaaW1eED9BrX1xQ6XoDe6mNQnPZpwvhl8whPCr8T+FSp/YPQM+EmQpA6jxCg70ehZ8uv6l0vRbZ/DcJts2PifvERYQzJDwgXcd8EeuXMtzpwLvAMIUCYR+gFc1usp36pvMMpBCdDCD07kl/pnyEVnHaEFyHo/GUs+2xCMPl6/C5tXWK+AbHOnidc8M0GXiTc0jYsk3cf4BHC8AdJkDW83tteZLtKntdYuVHohznpfQljIL4Y94nZwMOEMYJLrXe1mN+BV+tdD2XUUfq1JH7Xno/fi0OAHi0sYwjwa0JcND/uG5OB6wlDO+R9Tz9LuBB4n3BBPI1wcfyVetdJK+rwT7H83693WTrKC8XjLW2zYvLW1Zti8vrV1xQUk3fKmBzF462tN8XkK5dVMXl5daSYvLo6bPiY3GJBOyUzmwJs4GFsM2kDZvZnwq+BX3P30XUuTlnMbAvCwXuiu29Z7/I0OtVX44gPXjkFONLdb6h3eerBzIYTLniudPeR9S2NiIi0RPF4+1BM3vmpvhpHV4/JFY+LSFfRUR4EKg3IzDYgPMjhQ8LtSw0vPhjl2/Hj+DoWpUNQfbU/M+tjZpvnTD8UOJHQM+af7V4wERERaUiKyTs/1Vf7U0wuIiId/cm9UgdmdgZh3KG9gVUIt+MsqG+pSjOzNYDfAf8TX3MITzKWHKqvuloNmGhmLwGvEG7z2jy+lgPfdPe5dSyfiIiINADF5J2f6quuFJOLiHRx6mkurbE/oTfLUsL4XZfWtzhl6QccBaxHeGDJZ9z99foWqaGpvurnY8LF0GLCeIwHEsakvA34rLtfW8eyiYh0KGbW28z+YmZvmNkcM3vJzI5PpQ8ws2vNbLaZvWdmP0mlrWVm15jZtJj+tJkdmFn+EDO708zmmdmbZnZSe26fdHmKyTs/1Vf9KCYXEamRjhqTd+qe5u7eVO8ydEbuPrzeZaiUu09BPxKVTfVVP7GH2On1LkejcvfxhIfJiYiUowfwDrAX4YFeOwPjzGyau99DaGQcBAwD1gLuM7Op7n4VobHqaeAswkMr9weuN7Md3X1iXP51wGtx3q2Au83sZXd/sN22sMEpHm87isk7P9VX/SgmL07xuIi0QoeMyTv1g0BFRERERNLMbCzwAnA+MBPYzd0nxLQzgC+6+2eLzPsU8Ht3/5uZbQi8DAxx9/di+h+Afu7+1XbYFBERERGRDqkjxOT61VpEREREugQz6wPsBDwHbAr0Ap5JZXmGMEZ03rxrEcayfS5O2hp4JwnOW5pfREREREQ6TkyuRnMRERER6fTMzIDLCQ90G0u41XOeuy9NZZsF9M+ZtzdwPXBj0gMmzj8rkzV3fhERERER6Vgxeace01xEREREJAbnfyT0ZNnL3Zeb2VxgVTPrkQrSBwJzMvP2Am4C5gPphwrNjfnTms0vIiIiIiIdLyZXT3MRERER6bRicP4Hwi2g+7j7xzFpMrAE+FQq+zbA86l5k+C8F3Couy9O5X0OGBJvEc2dX0REREREOmZMrkZzEREREenMfg/sBuzt7jOTie4+H7gB+IWZDTSzjYHvEG4Xxcx6AjcCfYGD3H1ReqHu/hrwKHCema1qZjsBRwN/bYdtEhERERHpSDpcTK5GcxERERHplMxsA+CbhFtAp5rZ3Pi6LGb5NvAxMI0QbP/V3a+KabsCXyIE9x+m5v1RahVHAUOBD4BbgDPd/cE23zARERERkQ6io8bk5u7VLkNEREREREREREREpFNQT3MRERERERERERERkUiN5iIiIiIiIiIiIiIikRrNRUREREREREREREQiNZqLiIiIiIiIiIiIiERqNBcRERERERERERERidRoLiIiIiIiIiIiIiISqdFcRERERERERERERCRSo7mIiIiIiIiIiIiISKRGcxERERERERERERGRSI3mIiIiIiIiIiIiIiKRGs1FRERERERERERERCI1mouINAAz89RrlxL5Dk/lm9KORSybmQ2P5Rtd77KIiIiIiJRLMbmIiCTUaC4i0niOLpF2TLuVoggzGxkD8FH1LouIiIiISBtRTC4i0oWp0VxEpHEsAiYCR5hZj2yimQ0GvgA81d4FExERERHpIhSTi4iIGs1FRBrMNcAawL45aUcAPYEx7VoiEREREZGuRTG5iEgXp0ZzEZHGcg3g5N/yeQwwF7it1ALMbD8zu9fMZprZQjObbGbnm9lqOXlHxds6R5rZ/5jZP+J888zsQTPbNZN/PHBF/HhuZtzHkTnLH2RmfzSzd8xskZm9YGbHl1UTIiIiIiL1oZhcRKSLa3arkYiI1I+7TzWzR4EDzayfu88FMLNPALsAVwHzi81vZmcD5wFLgQeBD4HdgLOAg81sD3d/L2fWHYA/ANOA+4GNgD2A+81sR3d/Iea7i3Du2A14FngmtYxXM8tcDXgcGAg8CfSLy/yrmXVz98vLqBIRERERkXalmFxERNTTXESk8YwBVgUOSU1LerlcU2wmM9sR+CUwB9jN3fdy9yMJwfZNwCbApUVm/xbwM3ff1N0Pc/dtgIuBPsCZSSZ3Px9IAutb3X1k6vVIZplfAp4HNnT3A919T+DQmPaTEtsvIiIiIlJvislFRLowNZqLiDSeG4HFwNGpaUcD7xJ6nBTzbcJx/WJ3fzKZ6O6LYtoC4FAzG5oz7yPufmFm2i/j+x6VFX+F2cDJ7j4vVZbbCEH7MDNrauVyRURERETammJyEZEuTI3mIiINxt1nAncCnzezdWJvlU2B69x9WYlZPxPfm/V8cff3gXsIx/1ds+kxLTvPR8BHwLqVbcEKE9x9Rs70l+N7a5crIiIiItKmFJOLiHRtajQXEWlMY4DuwJEUbgMd08I8QwgPLJpaJH1KKl/WtCLzzAV6tbDeYkotE6B3K5crIiIiItIeFJOLiHRRehCoiEhjugOYBRxHCKgnuftTNVq2lzmtLdYjIiIiItJRKCYXEemi1NNcRKQBxTEPbwa2Bdam5R4tANMBAzYokp5Mf6fqAoqIiIiIdHKKyUVEui41mouINK6rCOMXfkjOmIg5Ho7vR2cTzGxNYB9gOfBYleVaHN91t5KIiIiIdHaKyUVEuiA1mouINCh3f9jd13D3Nd292JiIaX8gBODfM7Mdkolm1gu4FFgVGOvub1dZtOnxfdMqlyMiIiIi0tAUk4uIdE36RVJEpJNw9yfN7CfA/wMeN7PxhB4xuwHrA68A367Bqv4NvA8cFtfxOuHC4G/uXm2PGRERERGRDksxuYhI56Ce5iIinYi7nwd8EXgQ2BE4BFgEXAjs7O7v1WAdC4H9gXuBbYCRwAnAJtUuW0RERESko1NMLiLS8Zm7HqQsIiIiIiIiIiIiIgLqaS4iIiIiIiIiIiIisoIazUVEREREREREREREIjWai4iIiIiIiIiIiIhEajQXEREREREREREREYnUaC4iIiIiIiIiIiIiEqnRXEREREREREREREQkUqO5iIiIiIiIiIiIiEikRnMRERERERERERERkUiN5iIiIiIiIiIiIiIikRrNRUREREREREREREQiNZqLiIiIiIiIiIiIiERqNBcRERERERERERERidRoLiIiIiIiIiIiIiISqdFcRERERERERERERCRSo7mIiIiIiIiIiIiISKRGcxERERERERERERGRSI3mIiIiIiIiIiIiIiKRGs1FRERERERERERERCI1mouIiIiIiIiIiIiIRGo0FxERERERERERERGJ1GguIiIiIiIiIiIiIhKp0VxEREREREREREREJFKjuYiIiIiIiIiIiIhIpEZzEREREREREREREZFIjeYiIiIiIiIiIiIiIpEazUVSzGy0mbmZja53WTobMxsZ63ZKvcsiIh2bjtUiIl2HmY2Kx/zx9S5LWzGzpriNbmZNdSxHUv/JEmcAACAASURBVIbh9SpDe2mUOheR2jKz8fF7PareZZGOT43mIiKtlGq4a+k1pd5l7azMbLV4MT3KzFard3nakpk9mdqnTmzD9RwU6/OgtlqHiIiISGeWarhr6TW+3mXtrOIPI6M6e+OpmXU3s+mpfWqvNlzXyFinw9tqHSKNpEe9CyDSYN4BJsd3qa2PCXX7dr0L0gaWAx+USC+VJtVZDTg3/j0amFW/orQdM9sK2DE16QTg8jZa3UHAV4ErgVvbaB0iIiIiXcESYEaJ9FJpUp0mCtcJo+pXjDY3Alg39fkE4L42WtdI4LPx7/FttA6RhqFGc5EUdz8bOLve5eiM3P3vwN/rXY428pa7N9W7ENKpnRDfRwOHAp82sy3cfWL9iiQiIiIiLXjM3YfXuxDSqSXXCX8AvgkcbGaru/vMOpZJpFPQ8CwiIiINzMx6AcfEj5cBt8S/j69PiUREREREpN7MbG3gi8Ay4DzgQaA3cHQ9yyXSWajRXCSl1MPl0g+UMLOeZnaamU0ws1l5D8wxs3XM7CIze9HM5prZvPj3hfHklrf+lR5IY2Zrm9nvzOwNM1toZu+Z2fVmtlkNtzm9Xb3M7Idm9lws70wzu9fMRpSYf0qcf6SZ9TOzn5vZ82Y2J/1gnXIeBGpmfc3sB2b2oJl9aGaLzGxa/HxaiXpbx8zON7NnzezjWFevm9nlZrZFlVXUZmq0j2xoZn+O+8iivPqtpn7MrJuZHW5mt5rZ23EdH5jZf+Myt8rk72lme5vZJfH78Y6ZLTaz983sbjM7ysysxPrWM7PfxnqYF9c3Pa7vt2a2YyrveOCN1OxvWCvGiDSzf8T8Y1vIt2Fq2btn0vY1s7Fxf11sZrNjHd9jZqeb2aByylLEl4A1gMnu/gRh2BSA48ysZ0szx+/1iWZ2VzyGLIr/l8fN7Kdm9omYb7iZOWFoFoCvWvNxN4enltviw8KsxIN4zGyYmX3LzP5pZi/H//dcM5toZheb2bAy6ydvvYeb2bi4vUssHKdfif/rb5lZn9YuW0SkszGzgWZ2pJldYyGGmxFjhalmdq2ZfbrEvC0+mNnKiwFHWIg5Z8VzwbNmdmY557lKlFmWkg+INLPNLMReL5vZfDNbYGZvmdm/zew8KxKnm9lQM/tTzJvEuFeY2UY128Dm69zczP4Qz61zYt1OtnA9caiZVdQeYGZ9zOz7ZvaYheuEZD+5ysy2KTFfDzM7OcYFH8Zz80exLDeYWdGOAFZdHNvudV4rFuLOP8b4ZYGF2PKpGLsNKDLP8GTfjZ+3jd/rabHOxxdZz6VmNinuH/Pj3y3GYlZmjJnKv4qZHWhmfzGzZyxcUySx/q1W4pozzl/2dy9+xx9Ifc7GtKNLrSs137Mx//+2kO/zMd/ybL1Z28alxxFGkLjP3adTuE44ofgsK5WtrOtvi8dOCkOznJtTp00xb1kP2bVUO0JO2qZmdoaZ3Wdmr6W+A0+b2S/NbI2yaqf5clt9LJIuyt310kuv+CIMfeDA6Jy08THtfODR+HcyRp0Dw1N5PwvMjNMdmAfMTX2eAeyes46mVJ79gfdS8y9MpX0MfKpG25xs13nAQ6ntSpffgVFF5p8S008jjFnuwKLU/E0x38j4eUqR5WwHvJla37JYT8tT076fM98XgTmpPIszdb0IOK6F/7dXub/kblML89ZiH/lKatuT+adk8ldTP2sQeiuk94NZcRnJ51sz8wzP5F+YWb8DNwLdctb3KQrfJweW5uwDo1P5xxLGi0/SPgDeTb3Glvm/OCxVF4NK5Ds35nsdsNT0n2a2b17ONg8vpyxF1ntXXMaP4mcj/FjgwCEtzPsJ4PlUOZbH/W5patrFMe+usd4WxOkLMvX5LrBratktbhuF40uz40cqLb1vLct8bvYdyHz3Ruek/TWz3Dnxf5Ke1tTa/4deeumlV2d7Ecb6zR4303HncuC7ReYtejxO5RlJ6Rgwu/6ZhFjUCXHIefHv8TXY1pJliXmaip0vgL0zdbOYMmJmQpybjnHmU4gVPgYOr/U5Cjgrc15dAMzOlHW1zDxFz+3AUFaOKRbHc3XyeRnwnZz5ugP35Jzz0/XoRbahmji2qjqnEKcU3VdaqP9k/or321i2dP3Mznx+E9g8Z77hqTyHUojZP47///GZ/Cexcly/MNaTp+bbu0gZy44xc75/6f9JNkb7dZH1VfTdA/6T+f9nY9rflfm/OD3O/w7QvUS+K2K+BzLT2zQuBSbFZXwlfu5H4TuyXQvzln39DRwR6y3ZX+bm1On6MW9TOdtGoR1hZIm09L6VLtc0YNMWvnujMtNbfSzSq+u+6l4AvfRqpBflNZrPia+RwCoxbTCxsQ1YP3UCfxHYLbWMzwAvxbSPgKGZdaRPMDOAR4AdYloPYC9gekx/qEbbnGxXcsL4OtAntS03pcp0YM78yQltDiGYOBjoGdPWA1aNf4+kSOAZ15M0fr4ZT8rJfL2BrQgNlkdn5tuJECw7YdiKzYjBDDCMMK6bEy68dijx//Yq95dm29TCfLXaR+YA/05vG7BJLeon7m+PUAigzwTWjGndYzlOBs7LzLczcA2wH7A2sXEZGAR8lxB8OzkX34QH1jjwX+DTqXl7ARsTfpg5o0R9NLXy/9ibQlB9Sol8r8Q8P0tN24DCBelvgCGptIHA7rGet29l2daPy18ODEtN/3lc5x0l5h0AvEzheHISMDCm9QQ2AX4AnFpkv252HMzkS+p9eIk848kJWmPa7wkX9JtTOJb2iPvtuDjf20laOWWM9Z0E/WeS+hGEcJzeJ847pNS26aWXXnp1pRdwCvC/8Ry+WpxmhEaxi+M5aCmwbc68LZ4zKB0DHpg6n9xIodFlFcLYvOmOGONrsK1Fy5LK00SR2CIVC9wNbJWa3odCvPq1zDz9galxvqmExr8kxvk08AIrN/411WA7v5Fa3m3ANqm0QbEM1wMDMvPlntsJsd+/KVwzHA30immfBG5PzTsiM+8xcfoCQu/Xfql9bC3CtcPNOdtQTRxbdZ1Tp0ZzQkNm0jD5CLB1nN4NOIDCteCrSV2m5h2e2qY5wD+BzVLpG6f+PohC4/OvCDGtxdemhO+jE2L3YZn1tDbGPAj4Uyzn4NT0dQmdUJLtzrvmbM13b0V9VPFdWpfCDwFfKJJnFQo/SH0tNb1N41Jgt7j82aTiZeCqOP33JeZt7fV3sl+PKrHsJkp8t1L5plC80fx64NvAhhSONb2AzwNPxPn+28J3b1RmequORXp17VfdC6CXXo30orxGcwcOKLGMP1IIINbJSV+PQsPh7zNp6RPMJPIbiw5I5VmvBtuc3q7jc9K7Ueht/GJOenKyy72YSuUbSfELpqtj2ofEi6Uyy/5knO/nJfL8Lua5NSct+X97lfvLMpr/0p5+ZS9IarWPTCETLNeqfmIg4YQL5f2q3c9Sy016db+ak5b0bNmlguWl66OpinJdFpfxWJH0XVLr2Sg1PemlNLlWdZRZb9KL/f7M9I1S37uhReb9BYUfPYp+N0vs16NbyJfUx/ASecbTQmBdZL7uwLNx3mPKLSPhgsSBu9vi/6GXXnrp1RVfhB85Hbg8J63FcwalY8AXY9p48u9C+3rqfDO+BttStCypPLmxBaFRJZm+bgXrTM5Ni8jvHbwOK/eIbSp32UXWtzqFBrzrSN0dV8a8ued2QoNakrZvznw9KDSqP59J+784/U8Vbkc1cWzVdU7tGs0XU/waoVn8SKHjwCvERsxM+rYU7sQ4PZM2PLVNT1CkZzSh8XEaRa7/Uvlui3myPcZbFWOWUWdJr+77MtNb+91bUR9Vliu56/PaIulHxfT5QP+c/bBN4lLgb3H5f81M3ytOn0nsDJczb2uvv5P9elSJPE2lvlupfFMo0mjeQhn6xe+Pk39ndm4ZaeWxSK+u/dKY5iKVe9Hdb89LMDMjNKIBXObu72bzuPs0QgMdwJEl1vMbd1+QM30cIfgC+J/yilyWtwi3la3E3ZcDv4wftzCzYuu8y92frnSlZtaXEIgDnO/ub5U536eAHQlB429KZL0qvu9lZt3TCe4+0t3N3YuOsV2mboRe1cVeK461Nd5Hfu/uc/MSalA/yXhud7r7nSXmr9Q/4/uGZrZuJm1WfM9Obw9Xx/ddioxzeWx8f9zdX01NT8rcP+7LNRP3la/Fj1el02IZHiM0Ln+1yCKS/+Hlrflu1pO7LyNcoEDopVOu5P+xZvb7LiIirZacuys5HrfIzLYGkjGpfxljzqy/EO46agRzCJ0JoLJYJYnlbnL3SdnEGAtelp1ehcMIPa2XAD9wd6/BMpNY/XF3vzub6O5LgZ/Fj1tlrheSc/M65a6sBnFs1XXu7sPjdUJTueUuoielrxNWMLPVgH3jx4vcfX5OuZ4mDFEIobG2mItiPJVnBGG4nffIuf5LSep338z0tooxk2PNLpn/Z2u/e7WSXCccZGb9c9KT64Rb3X1OanqbxaVm1o/CNeVVmeR/Ea7tVwMOyZm3VdffjSJe/z4YP7bmOqHsY5GIGs1FKvdoibRPEG55hDDURDH3xvfB2QekpDyRNzEGpR/Ej9U8XDBrfImg+iFCj1aAHYrkKVUvpexACCYh3NpZruQE2Q2YbGbv5r0oNLz1JdwG1xamJo3vRV6zUnlruY+UqvNW14+Z9SBcqEBl/xPi/P3jg1setPAA0MVWeChROvgfmpn1jvh+pZn9xsw+a2arVrr+1nD3R4HX4sdj0mlm1otCYJkNSp8k9NBYF3jCzL5t4SFF1f4QA+H2wybCuIe35KQnD/r5WnZ9ZrYBMCR+rPh/2F7M7DMWHiL3koWHT3lqXzkzZluvgkXeR+z1BDxsZieU+P6IiEhkZp80s19bePD2LDNbljoeJz+eV3I8LkcSUy4FHs7LEBvSx9d4va0SO7PcHz/eZWY/N7OdY5yQK6YlDcj/KrH4UmmV2jW+/9fd36nRMpP/VanY9QHC3Zfp/BD2HwcOtPAwxKPMbEizuVdWTRxbjzov5cES1wirZfJuRxgqAsq7Ttjaij8st5zrhNWBd0rU719ivg2SGauNMc1sbTP7mYWHhX5kZktTx5qJMduqsWxA6757NfZ3QsP9KoSx4lew8KDMvePH7HVCW8alRxL2+6mEa/UV4nFzTPyY92DL1l5/tysz+2J8OOfrZjYvc52Q/GBQyXmpNcci6eLUaC5SufdLpK2V+rtUr5hpReZJm1NkOhQasIsFSa1RtLzuvogwvjYUL2+peikl/Uvv1ArmS05w3SndeyP9ZO12aYBtQS33kVJ1Xk39DKawb1XyP8HMNiEEvBcCewBrEnoJfUDozfJeKnu2Z/aZhAuufoRxEMcDs81sQgyus43stZb0Ijk2M30/wg8di4Ab0gnxB5GjCNu3JXApYWilmWb2DzM7psTFTEuSIPfvRe4ouIEQiG9EqOu01n6v2o2ZXUAI8r9KGDuzD+E20mQ/mRezlt2D391fB04kPJxoF+By4PX4480NZvalGv2gISLSaZjZwYRz92mERruBhOPo+4Tj8cyYtaZ3VFGIbz6MsWYx00qktbcTCcOHrQn8hDAkyRwzeyR2GMh2aBlEGLoEyo/7qpXEALU8/yf/q1LXCwsJHQnS+XH3RwjPMFkMfAG4FnjbzN4ysyvM7HM5i6smjq1HnddKpdcJPSjeiaqc64RelK7fpOF6ldS8rY4xzWwXwrObfkoYW34QYXzp5FjzYSp79nhT6XevZmKP/6QDS/Y64SjC/+FdCj9mJPO1ZVyaXCdcXaTjW9K5Zs+chvqGvk4ws25mdi2hQf9wQqezXqx8nbAwZq/kOqE1xyLp4tRoLlK5Yre5ZZV7K2QtbpmshWrLUW691Epyi9tLLfTyTr+mtHMZW1LtPlKqzmtVP5XuF1cQfvGfAnyZ8JCfvu6+lruvw8q9y1cKEt19lrvvSXgY6oWEHjJLge0JwfUrZlbqNtRqJY3mnzSz3VLTk+D4DnefmZkHd7+PEMwdRwhQXyE0OBwQl/l0pQ3+ZrY64WE0AMeke1akeljMIjQ0QxiDvphGOcasYGZ7U+hJ/n+EHmG93X2Qu68T95XfJtkrWba7X0PoEXUK4YeFtwgXWIcDtwIPmtmA6rdCRKTjM7PBhHHJexN63g4njKM80N3XjsfjL7dxMRruPFWMu79J+GHhC8AlhIeXdyM8kO9C4FUz27PY7O1SyLZdX6tiV3e/iBArnUo4F79PiBdHAv8ys5synQzqFcd2RLnbWGJoFijU713l1m8l685j4U7W6whDhjxD6JQywN37p441n07Pktmear57tZBcJww3s/VT05PrhGvy6rwt4lIz25zQCA9wTpHrhJeS7BSGe+woTiD8GLEM+DmwMc2vE26OeSu9Tqj0WCRdnBrNRWor/Yv++kVzrXwb0QdFc7Wvorc2mVlvCkObtLZHeTHp20Y3KJqruWQs8E9ajceSbmPttY9UUz8fEXqHQxgepCwxgExuCT7K3W929xmZbC2OIefuj7j7We6+OyGw/hLwPKGXy9/ibZA1F3uDJLeyHgsrGq/3j9OuzpsvzjvP3a/2ME7+JoT/31mEXhBJD/RKHE2hQbwch2YC7vT3qqnCdZcruTAoVc6BRaYnY43e7e7fcvcXci40Wj3eoLvPcPc/ufuR7j6M0Bv/fMLF3WeAUa1dtohIJ7MfMIDQg+8Ad3/Qmz9Tp9TxOLn7sTXngiQmWjPGmsXU8k6zasoLhKEP3P1ud/+eu+9A6C17NPAmoWfutalhI2ZQOF+WGkagltuYxABNNVxm8r8qGruaWR8K1wvNYld3n+7uF7v7we6+NrA1oecthHHYv5HKXk0cW486r5X0dUKpsidpSyncCVKJpH5b83ys1saYuxCu9ZYBX3T3cb7y+N/QQuxX4Xev1h6I6+kW14mZbUFoyIfS1wm1jktLdZbJM9LM0m1/rb3+LtfS1N/VXCdc7u7nuvur3vyZF9VcJ1RyLJIuTo3mIrX1BiFQgzAecTF7xfeP3P2Nti1S2T5b4vawz1C4zXFCjdc7gcKDTQ+oYL6kcbMXhR65HUF77SOtrh8P4+Y/GT9W8j9JX0gVeyjQXkWmFyvLQnf/B4WH2PRh5Qe+pAOoWgy7kYxFeHi8gD+c0PvuQwpjurbI3d929wspPLxq71L5cyTB8O8ID/Mq9hpIuDBdldTDoGJvnOTW3Ur+h1Co05bqM7lIy72AtvCgpM2LzJvMk7ufxGNRzXoLuftr7n424TZMqPz/ISLSWSXH48me89DBqNS5u+S5INq5yPQkpuxBkYe5xYae4SWWXamkvGuVaKgvVt5c7j7H3a+lcO5em9gY6e6Lgefi9FK3/teyh+xj8X0Ha/7Q9dZK/lelYtfhFK4X/tPSAt39eXc/iULMmj43VxPH1qPOa+UpCnFYOdcJz7r7khL5iknqd6iZVfSA3ypizOQY8YG7Fxt6ptLrhKLfvWjFdUK1w/O5uwPXxI/HZt6fc/dnK1hWq+PS2As6We8PKH2dsB6hAXv9zDpae/0N5V0npH/IKXadsAmhc1Selq4T+lHhcbqUFo5F0sWp0VykhuLJNBnz+Otm1uwX0Piwia/Hj9e1V9nKMIwwtvBK4sXKj+LHSe7+fC1XGi/Qro8ff5i53a2UCRROpP/PzNYslbktx7mrRDvuI9XWz1/j+35mtl+Z6/w49fenctbRHzinyPp7ZHpAZKV7vaV7JM9O/V0s8KrEjYSxy1cHvkghKL0+76KkhZ5xUCh32cMXmdl2wDbx43XuPrfEazYwNubN9jr5W3w/0cy2LXf9FOq0pfpMLg4OLZJ+OuEHhzzJvtJsP4lOAT7ZwvqbaYv/h4hIJ5ccjzeJPYVXYmbbAF8pMX9yLtgxL4aLwwgckp0O4O7PEZ4DAvDjInHA8dT2AaRJeY2cxlgzW4Vw234zZfRgLRarJHHfl81s05zlrkU479XKTYRzeQ/gtzV6lkcSq+9iZvtkE+PQGz+NH19w9xdSaa05N1cbx7Z3ndeEh2fl3B0/nmFmzZ7HZGafohB7tfY64XYKvY1/l7eezDqz9duaGDM51qydd9eoma0HfLfI+lv73av1dULSuWYLM9uB2OOc5g8ABdosLj2AMPb9csL1SanrhLcpPEB1xXVCFdffUMZ1grvPA16LH4tdJ/y4xDpauk74CeFHgYroOkFaxd310kuv+CKM6ejA6Jy08TFtVAvLWI/w66oDLwC7ptJ2IzxoyQlDYAzNzNsU0xxoKrGOKTHPyBpsc7JdswgnipOAPjFtfULQmZTp4NaWhTBOmANTitTZBzH9TULv3lViWm/CLVMXAcdm5tuJMPyFA68TbqdaNZU+FDiG8FCWv5T4f3uV+0uzbar3PlJt/RAutB6O8y4AzgDWiGndYzlOBS5IzWOEh8kk27V9Km0XwtiDH6bKPzyzXa8RGtW3BXqk0rYm3BLphAfpDMqUdVpMuyQ9XxXfiZvi8iakyrpTkbw/BcYRGtfXS01PeqnPivNfW8H6/xDnmVpm/j1T5dwqNb0/8HKcPoPw3R4Q03oCm8Tyn55Z3ompeTYrsd4TUuv9WWrZawDnEQLOZD8fVWLenwB94/TVCD/SLU3tK+NLfPdGZ6b/hfDDx6HAWqnp/QgXx4vifOdVu5/opZdeenWGF2Gs2GXx2HgLMe4g9PA9nDBcxIpzd878AwkPr3fgcWDTOL0nYXi1twnxTLEY8ODU+eD65FxKuLPsFEIck5xLxtdom5P4ZjqhZ2v3OH17Qk/Dj1JlakrNN5zQg/lUwp1U3eJ0IwxP91yc561kmTF9QJzmhDsOPw9YTNspzjczb51VbOPXU8u7FdgmlZYMPXdbcu5OpTWL0eL07oQHLybXDF8Besa0T8RlJfOOyMw7jtDIOgJYLTV9ECHuWx7nO/n/s3fn8VFV5x/HP2cmJIEQAoatFIMVFUQKKuICKqtWBRG3ulRF0EqrLdXiXqoi/kBbqyBa6wqliFIpUGRRsawuCEURKxBQ2QTCTjbIOuf3x53JTJJJMkkm3Czf9+s1r8y599x7nzuZJDfPnPucEttV5zq22q85wf+RSr1vI/weBLav1PsW5zo4z7/tSuCn/uUenHJKu/zrvgWalti2L2X8rIY5ztCQ1/5L4GdAbMj6n/jfR6uBMSW2rfQ1JsEJhi2wHDgt5L31M//5hP6fEI2fvSYEr/0eCLwHqvmztca/v8DXAuBHZfSN+nUpsCDwGkbYf4S/fy7OXFOB5VX9//sp/zZbKPF/aol+4/z98oC7Q/Z9Ik4plBwgmzB5hJBt84G7Au9LnJIsz/vXBd4rU8v52XuixPIq/S7So2E/XA9ADz1q04MoJM39ffsQTJZZnAuErJD2YeCiMNudRJgLhTD9toX7A1PFcw6c13iC/0Tk4VwA2ZDHuOrEQjlJc//6swkmPwMXIIdC/nhZ4N4w211C8QusQLItm+Lx15qk+fF4j0Tp9WkJrAjp4/PHlReybG6JbQbjXOAE1meHHCsb55+WwLq+ZZxXIM6DBC8mAxd714WJc0xInxycC79tOKMvqvJ9vbJELJvK6ftEib5H/XGHvm83AG0jPHY8wX/ino1wGy/OLPIWeL7EupOBb0JiKcT5uSoIWTaxxDYtcJIkgfX7/a/nNuD8EsddUuL9EfiZ9eGMNF9G+IvWRmHeW4EapBaYT/CCeVk5P3tTy/qZ9j8yKf5PscX5PZdQlfeGHnrooUd9fBCsrRt4HCH4t/57nARpmddLFP8g1OKMRAz8/f4MuIfyrwGfKrH9IYLXEitwrlHD/j2o4vme6Y8xcLxjBK/B0nASk6WutQhJSBK8Xj5A8euedMJfv51T4u9RNsEPGzJwElYRX99FeJ6PhPxdDVyjZJQ4h+Yltil1jRay7sc4gyJCr8tCz6kQGBVmu2Uljpnuf4Quexd/IrTEttW5jq3Wa45LSXP/tjdQ/Bo43f8+DbR3AKeH2a7oPRrhcX5R4rXM97++ORR/ff8QZtuqXGP+qsR+M0POaz/Fr8Gj9bP3eon3wHaca9qIrrPD7O+3JWJ5v5y+U8Ocb5WvS3F+BgOv728i3OYEgr/Pf1diXaX//8b5oDXwPSvE+Z25zf8IHUDUNMz7I3DueTh1y7f527eXOEZznLuQSm4biOtvVCFvQzV+F+nRcB8qzyJSA6y1y4HOOPWMN+KMDDD+58/iXOSsdC/CsPJwEpqPAqk4nzCn49zSNcha+8eaPLi19guckQMP44xkyQQScP6QL8Op2TYjzHaLcSZTeQT42B9zc5w/qhtwyowMwbnAqTWO13ukOq+PtfYAzkXqLTifzO/H+Z4cxhk1/jTB0j2BbeYDF+OMgjiCM2L9ADAFONta+x/C2+WP43mc7/8enIutAn+cL+GMop4VZtvxwO9wRobn44yc6EDVJ4gJnGtA2Fsu/V7FGQHxNs4/kkcJTqi2ErgX57zTytxDcdcSvN3xn5FsYJ0JNAMlWm4JvYXVOpObnoUzwmOZP66mOEn2z3BGeT9fYn+Hcb6H7+B8X5JwXs8OhEzm4z/uIOBxYBPBC/IPgUustc+WE3M+cCnOCPXNON83gzOa6dc474Wq3Bo5DufW3jn+mAr857sPZyTaCJxEQHYV9i0iUi9Zax8GbsP5HXwM54PNb3H+vp6FMyK7vO3fwEk0LyFYFmQzzjVdH5xEVXnbj8H50D2wfRzO9dDDONemeWVvXXnW2nU4o43fwfn74MG5VnkJJ6G+oYxN1+AkWl8mePdcEk6CcR3wJ8q4frPW/pfgZHO7cF6jdODvOImr1SW3qS5r7QSc8gav4Xw/wflbm4pz3XINxctXVLS/XTiJ6N/jXKsdwxnJuxNnEsQe1toXwmz6W5zJ0RfijE41OJO77wbmAddaa6+3pSf6q+517HF/zaPFWjsTZyL5V3DuxIzDuaZZh3Pd1dVau7HsPUR8nLdwXt+ncK6js3Be38B7+kWcuzGeugtG1wAAIABJREFUCbNtVa4x/4Zz7bjMf6wYnO/NZJz3alllQKv8s4fzod0TONfp4JQk7YAzOKgq3sa5bg0o7/+EaF+X3o4zaMWHc2dQhay1hwhTosW/rtL/f1trt+DMFTAP5/+lZIL/J8SE9MvCmaviOZy7PQpwXrd/ARdYa9+hDNYpU9QLmIiTWC/0b78MuMlaW9XSSlX+XSQNV+AWJRFpoIwxy3D+oRlrrX3C3WhERERERERERETcpZHmIiIiIiIiIiIiIiJ+SpqLiIiIiIiIiIiIiPgpaS4iIiIiIiIiIiIi4hdTcRcRqe2MMbNxJsuojGustZ/WRDwiIiIiIjXBGNOL4ATYkfrUWntNTcRTU4wxa4ATK7lZT2vtzpqIR0REpKFR0lykfjgBaFPJbWIBrLV9ox6NiIiIiEjNiKXy170n1EQgNawVlT9Pb00EIiIi0hAZa63bMYiIiIiIiIiIiIiI1AqqaS4iIiIiIiIiIiIi4qekuYiIiIiIiIiIiIiIn5LmIiIiIiIiIiIiIiJ+SpqLiIiIiIiIiIiIiPgpaS4iIiIiIiIiIiIi4lfvk+bGmHnGmHluxyEiIiIidYOuH6NPr6mIiIiIVIbb148xbh34OOrYpUuXLoB1OxARERERqf26dOnChg0bNrgdRz2ja3IRERERiZjb1+T1fqS5iIiIiEhDYIxpZIx50RhzyP+YbIwJO0jGGJNV4pFvjFnvXxdnjHnNGLPVGJNpjNlkjBlxfM9GRERERMQ9SpqLiIiIiNQPY4ALgTP8j4uAR8N1tNY2DX0AG4F3/KtjgD3AQKAZcDvwF2PMpTUbvoiIiIhI7RDVpLkxprEx5ltjzJFy+nQxxvzHGHPYGLPXGPOGMaZJyPpmxpgZxpgM//o/RjNGEREREZF6agTwlLV2j7V2D/B/wB0VbWSMORfoAkwFsNZmW2sfs9Z+Zx2rgKU4CXkRERERkXov2iPNnwR+qKDP20Aq0Abo6n88FrJ+MnACkIIzOuaXxpjbohyniIiIiEi9YYxpAbQH1oUsXgekGGOSKtj8DmCRtXZ3GfuOB84F1kcjVhERERGR2i5qSXNjzNnAFcCECrr+BJhurc2z1u4H5uEkzvGPOL8RGGOtPWKt3YyTRK9whIyIiIiISAPW1P819I7PwPPEsjYKuf5+vYz1xr9uCzC7+mGKiIiIiNR+UUma+ycYeg24B8itoPuzwG3+Ui5tgauBBf51nYBYSo+Q6RaNOEVERERE6qks/9fQUeWB55nlbPdz4CjB6/Ei/oT5yzjX6EOttb4oxCkiIiIiUutFa6T5aGC9tXZZBH3fx6mHmIkzwdBOnIQ7OCNksq21BSH9j1DO6BgRERERkYbOWnsYp0zimSGLzwR2WmvTy9n0TuDvJa6/Awnzl3DKslxawT5EREREROqVmOruwBjTEWeE+VkR9G0BLMapYf4ykIBTfuUfwE04I2SaGGNiQi7ckyh/dIyIiDRQ1lqstW6HISK1mDEGJ//bIEwB/mCM+cTffpQyyq4AGGM6Ab1wJhAt6UWgN9Dfn5AXEREREWkwqp00x5mssxXwjf8fkligmTEmDRhirV0d0rcj0AR4wTpZjjxjzCvAIv/6VCAf6A6s9S87E/g6CnGKiEg9UFhYyMGDB8nMzCQvL8/tcESkDoiNjSUxMZHk5GS8Xq/b4dSkcUAysNHffgsYD2CM+RuAtfZXIf3vAFb65xEqYozpANyNU3Zxe8iHDtNLbC8iIiIiUi9FI2k+E6fkSkAvnFEuZwIHS/TdhDNq/G5/srwx8EvgSwBr7VFjzExgnDHmJqA18Fvgj1GIU0RE6rjCwkJ27NhBTk6O26GISB2Sl5fHwYMHyc7OJiUlpd4mzq21+Th3gN4TZl2pZLe19sEy9rMdaDDD80VERERESqp20txaeww4FmgbYw45i22av70IZwTLeGttljHmSuAZ4P+AQuATYFjILn8DvIJTk/EY8KK1dlp14xQRkbrv4MGD5OTk4PV6adOmDQkJCXg80ZqeQ0TqI5/PR3Z2Nnv37iUnJ4eDBw/SunVrt8MSEREREZFaLBojzYvxTwbaPKR9eYn1n+BMBFrW9hk49c1FRESKycx0prho06YNSUlJLkcjInWBx+Mp+n2xe/duMjMzlTQXEREREZFyaXieiIjUCdbaohrmCQkJLkcjInVN4PdGXl6eJhAWEREREZFyRX2kuYg0UAe+hdwMaHcWGJVBlegLTXKpJIuIVFbo7w1rLUZ/q0TEBT6fpdBaCn2WAp/z1Xnuw+eDAp+vaFnpPrbEOh8+aykoDK73WUus10NifCMS42NIjI+haXwMzeIbERfj0e8+ERGRCClpLiLV979/wey7wFcAQ16Es291OyIRERGpZQ4fzeff63bRplk8bZrF07ZZPI1j6+ekrBI91loyjhWwLzOHvRm5ZOcVlEoq+4oSyr5SSWZfaLI5kKwudPoWa9vw2wTbZSezA/sumbwuKPQVP67P4uaNLo28pngyPS6mqN3M/zV0mfMo/jwh1qvEu4iINAhKmotI9exZD3PvcRLmAKv+qqS5iIiIlHIgM5ffvbOu2LLE+JiiBHrrZnFOQj0xjrZJ8bT2J9dbNY0jNkZ3GNU31loycgrY70+G783IYV+m/2uJdm6Bz+1w64X8Qsuh7DwOZedVeR8eQ5mJ9eJJeOe5s6x4cj4hzkuMVz/TIiJSuylpLiJVl30Q3vkFFBwLLtu3AQ5thRN+4l5cIiIiUidk5hSQmZPFt/uyyu3XsmksrRPjadPMn1BPjPePWI8rGrmenBCLx6MRsG6z1pKZW8C+jFz2ZeSw158U35eRy97MHPaFJMNz8pUML0sjr8FjDDEeg7fo4SnWjvEYPJ5gn0A7J99HZk4+mTkFZOU6I/OjxWchI6eAjJyCau2nSay3VMK9WYlEfGjCvSgJX7Q+hrgY3akiIiI1R0lzEamawgJ4dxik7yi9LnUhXHDP8Y9JpIEyxtChQwe2bdvmdii10rJly+jXrx/Dhg1j6tSpbodTpsWLFzN27Fi++uorsrKcBKImrJT6JCHOS7f2SezNyGF/Zi6VyeMdyMrjQFYeG/aU3SfGY2idGOcfoR5MpgeS685o9niaxceovEQVWGvJyi1wEuCZpUeD7/Mv35uRy7H8wqgfv5HX0DoxnsT4GBp5PcWSxV5jiPEGn3s9TjuYdPaUSjB7PSUT0iWT0B68Brxef6I6ZL/hj+Mpta+yktvB5Z7S/Y2J6oc/1lqO5Rf6P6DKJyOngKycgqJ20dfcksuchHtgm7woj/Y/mlfI0bxC9mbkVnkfsTEempVRUsZJwocuDybcm8XHEFPB/DgV/YowVPw9qu6vmQpjqKBDRYevzDkG+prgylL9TLFlpWMMLiu9/8DT6uwj3PmU1y+S/ZdcLyINi5LmIlI1i/8I21YG2yd0hEPfOc83LVDSXESkEnbs2MHVV19NXl4eAwcOpHXr1m6HVKtNnTqV4cOH8/jjj/PEE0+4HY5EqF3zxsz7zYUAFBT6OJidx96MHNLSc9ib6R+VnJFDWkbw+eGj+RHvv8Bn2Z2ew+70nHL7xTfyFE+oh5aDSQwm2xtSvXUnGR6a+PYnxYsS4k5y/GhezSXDWzeLo3XI61/sA5DEeJo3aaTkVRUYY2gSG0OTWKcUUlXlFjiJ99CEe4b/a1aYhHtmbvGkfFZOAdlRfv/kFfiKPlATOZ6McRLsXk/JD9E8pe4QKf/DueJ3kRRt6/V/GFjRB3sewt+BUu4HicW3C/+BXnAfwbg8RXGVdR4xHqPf01KvKGkuIpX31TtO7fKArtfB+b+G1wc47R2fOaVbEpLdiU+kgdm4cSONGjVyO4xa69xzz2Xjxo0kJSW5HUqZPvroI7Kzs/njH//Ik08+6XY4IjUuxhtMXHdrX3a/nPxC9vsTt3szcknzJ3Cd5LqT2E3LyKlUMjcn38f2g0fZfvBouf2aBeqtF5WDiSs2cr1Ns3haJcbRqBbXZs4OJMPLqBW+z/9hRbSTmVB65H/gNWztT4gHXsvmjRuprE4dEBfjJa6pl5ZN46q8j0KfJSungIyQ0jGhifWMUstKP8/KLajUXSoiNcFasICv0JJfqDdkKI+BknfThJaQ8nrLWB7a31vGco8psb7kck+YfZrgHUNl7dNb/rFC71gqtQ9v8WPpQ4P6RUlzEamcXV/AvFHBdtufwpDJEBMPTdtCVhpYH2z5AM682b04RRqQzp07ux1CrdakSZNa/xr98MMPAJx88skuRyJSu8Q38nLiCU048YQm5fbLyi0gLT2nqIZ2WnogKexPtqc7zyuT3HDqNmexpZx668ZAckJc2IR66OSmJzSJbr31o3kFxSbPDHyQULxUSi5ZudWrOx2ONzQZnhjnnKO/xnyroudxtIjyOUvd5/UYkpo0IqlJ1T/ot9aSnVfoH/EeHO1eOglfIumem19spHxhOeXPKiqNVtFvEVVWk4bMZ0M/TGh481Z4DBEn5IMfADjlwEqW+Qp7h0GYEmTh7j4IV7asvLsdQu8uCFdyLNxdE8XKlpU6hgePxzknj6m7ZY6UNBeRyGXtg5m3QKG/9mCTZLhxBsT6/5HtdDmsneI837RASXOR4yRcTfPQOt5PP/00Y8aM4f333yctLY1nn32We++9F4CjR4/yl7/8hZkzZ/Ldd98RGxtL9+7dufvuu7nxxhtLHeukk05i+/btWGt5/fXXeeGFF9iyZQtJSUlcddVVPPPMMzRv3rxK5xEa84QJE3jkkUdYtGgRGRkZnH766dx7773cdtttZZ7/5s2befrpp5kxYwZbt27l8ssvZ+7cuRXWNF+0aBEvvfQSq1evJj09nTZt2nD22Wfzy1/+kkGDBhXru23bNiZMmMAHH3zAnj17aNasGX369OGxxx6jW7duVT7ngOHDhzN8+HCAYqVHCgoKePnll5k6dSqpqakAdOnSheHDh3PXXXfh9RYvI9G3b1+WL1/O1q1b+fTTT3nhhRf45ptv8Hq9HDlypFrn89lnn/H888/z8ccfc/DgQVq2bEnXrl259dZbueWWW4r6rVy5kpkzZ7JixQp27txJTk4OHTp0YOjQoTz88MNh3yeff/45Tz/9NF988QVpaWm0aNGClJQUBgwYwB/+8AeaNm1adG4AY8eOZezYsUXbT5kyhdtvv70S3wGpL5rGxXBK66ac0rppmX2stRw+mu8vBxMYsR46et15fiArN+Kkl7VwICuXA1m5fLM7o8x+oSVI2iT6R6+HJJvbJjlJ6BiPKXM0+N6iiTRrLhneqmlcmNHgccHYa+ADAJHKMMbQNM6pY942qerlZmq7ChP3FfyOqjixH/kHA4Gu1r803Kahy8L1s0X9bJn7Jwr7sJTeINJzKbn/kutLLvNZi89aCn2WAp/zNfDcZy0Fhf5l1lLo81FQ6F9eom9hyW2Llvso9OFsG9hPYcj+fU670Jbc1lf+/kvF5mzj81HhtlIxn4W8Qh9E/wauOi000V5WWaBwCfydh8q/K7CmKWkuIpEpzId/DoOMXU7beOH6qdA8Jdin86Bg0vy7JZB/DBo1Pu6hikjQ/v376dmzJwUFBVx44YXk5OTQpInzQVdmZib9+vVj7dq1tGrVisGDB5Odnc2SJUtYuXIlq1atYuLEiWH3++CDDzJp0iR69uzJZZddxqeffsqrr77Kxo0bWb58ebVGExw6dIjzzz+f3Nxc+vbty+HDh1m6dCnDhg1j69atPP7446W28fl8DB06lBUrVtCnTx+6detGcnLFJaJGjx7Nc889h9fr5YILLqB9+/bs3r2bpUuXcuTIkWJJ848//phBgwaRkZHBGWecwZAhQ9i1axezZ89m4cKFLFiwoFgCPBJt27Zl2LBhrFu3jq+++orevXtzyimnAHDmmWcCUFhYyFVXXcXChQtp1qwZAwcOBGDJkiXcfffdLF68mFmzZuEJM6nZhAkTeP311+nduzeDBw9m586d1TqfiRMn8vvf/x5rLT179qRPnz7s27ePL774gtTU1GJJ8wceeIB169bRtWtX+vfvT25uLl988QXPPPMM8+fPZ9WqVTRtGkxwLliwgCFDhmCMoXfv3vTq1YvDhw8XfRgycuRImjZtymWXXUZBQQGffPIJ3bt3L3qdgKLXTiQcYwwnJMRyQkIsXWhWZr+CQqdOcrAETDChHqz1ncORStRbzy+07DpyjF1HjkXjVCrF6zG0bBrrrxMemqyP89cRdxL3JyTE4lUyXKRWqHCSz2r/qOpnXSrPF5pML0ri+4LJ9xIfDBRv+4qS9QXFEvO+Yv3DLc8vLNGvWP8wy8s8ni/M9mGWF60vvVyqJvDhS2Xl5Lt7t4KS5iISmfcfgR2fBts/Gw8/ubh4n59cDLFNIS8L8o/C98uc0eci4pqFCxdy9dVXM2PGDOLji4/IevTRR1m7di0DBw5kzpw5RQnMTZs20adPHyZNmsSll17KFVdcUWq/06dP5/PPPy9KWB44cIALLriAlStXsnTpUvr371/lmN977z0uueQS5syZQ0JCAgBr1qyhf//+PPnkk1x11VXFEqUAO3fuJC4ujtTUVH784x9HdJzp06fz3HPP0b59exYsWFBsZHV2djaff/55UTsjI4Prr7+eY8eO8e6773LdddcVrfvoo48YNGgQt956K99//z2xsbERn2vnzp2ZOnUqTzzxBF999RV33nlnqZHSEydOZOHChfz0pz/lo48+KpokdM+ePfTr1485c+bwt7/9jbvvvrvU/qdNm8aSJUvo06dPseVVOZ8VK1bw+9//nsTERP7973/Tt2/fom3y8vJYunRpsWM89thjXHDBBbRo0aJoWW5uLqNGjeLVV1/lueee47HHHita9+c//xlrLatXr6ZHjx7F9rV69eqiD0Eefvhh2rZtyyeffMLQoUM1EahEXYzXQ9skZzR493L65eQXFiXQ09KLl0dxysE4ZWGO5dfMcDOPgZZNg6PBWxWrux4cHZ6cEKdkuIiIVJvHY4htwH9PrLX4LMWT7IWRJeULyknSl3wE7yzwUWiDdxsEPrTwldi2rLsPCovFFbIfW/oDjrAxhH5A4v8QIXD+gTsT6vvnCEqai0jFvpgGa14LtrvfBOeNLN0vJg5OGQgb5jrtTfOVNJfjxlpLRk70b1evac3iY2q0xltcXByTJ08ulTDPzs7mjTfewOPx8Ne//rXYiN/OnTszZswYRo0axQsvvBA2aT5u3LhiieuWLVvy61//mtGjR7NixYpqJc2NMUyePLkoYQ7Qs2dP7rnnHp555hlefvllXnnllVLbTZgwIeKEOcD48eMBJyldshRJQkJCsXN48803SUtL45FHHimWYAYYOHAgd999NxMnTmT+/Plcc801EccQiRdeeKEozkDCHOBHP/oRf/7znxkyZAgvvPBC2KT5HXfcUSphXtXzefrpp7HW8thjjxVLmAPExsbys5/9rNiycO+buLg4Jk6cyJtvvsm///3vYknzffv2kZSUVCphDs5kriK1TXwjLynJTUhJLrveurWWzNyCYHmVkAlMQ5+H1lv3GEj2l0lpkxgftlRKm2ZxJDdVMlxEROR4McbgNeD1eCvu3EBYG2niPlxi3hcsF1SqzJDzdeS78ew86N75KWkuIuXbuQYWjA62250Fg58v+57AzoOCSfPU98FXCPqjIsdBRk4B3cd+6HYYlfbV45eS1LjqE2JV5Oyzzw6bSF67di3Hjh3j/PPP59RTTy21/tZbb2XUqFF88sknWGtLJfYvvfTSUtucdtppgDMCujrOOussOnXqVGr5TTfdxDPPPMPHH39cap0xhiuvvDLiY+zevZuNGzeSnJzMtddeW2H/xYsXAzB06NCw6y+88EImTpzImjVropo037FjBzt27KBt27ZhP4gYPHgwzZs3JzU1lf3799OqVati64cMGRJ2v5U9n8LCQpYtWwbAXXfdFXH8u3bt4r333mPTpk1kZGTg8zm3WMbGxrJly5ZifXv06MH06dO54447uO++++jatWvExxGprYwxNItvRLP4RpzSOrHMfj6f5dDRPAp9luSEWGK8pcstiYiIiNQmxl97PKaGUj6J8e6mrZU0F5GyZab5J/7Mc9oJreCG6eXXKT/1EvDEgK8Ajh6AH9ZAyvnHJ14RKSUlJSXs8t27dwPOxJ7hNG/enKSkJNLT08nIyCApKanY+vbt25faJjBaPTc3txoRQ4cOHcIuD8QaiD1U69atiYuLi/gYgdreHTt2jKh/YJLV8847r9x+Bw4ciDiGSFT0fQpMgnrkyBF2795dKmle1ve/sudz4MABjh07RuvWrUlMLDvxF+q5557jkUceIS8vL6L+48eP5+uvv+bNN9/kzTffpGXLlvTq1YuhQ4dy8803V+r7K1LXeDyGlk31HhcRERGpLZQ0F5HwCnJh5q2Qlea0PTHw82mQVDpRVkzjFtChN2xd7rQ3zVfSXMRFJcuylBRJaZhwfWqypExVVHSeZYn0PAoLnZrE119/fdFEquFUlISuqqp+n8p6Xap6PpG+XqtWrWL06NEkJSXx6quv0rdvX9q2bVuU+G7Xrl2pOxJOPPFE/vvf/7JkyRLmz5/P8uXLee+995g3bx5/+tOf+PTTT4vVRxcREREREakpSpqLSHiLHoQfVgfblz8DHXpFtm3nQSFJ84VwybhoTPEuUq5m8TF89XjpkiG1XTOXbjlr164dAFu3bg27Pj09nfT0dBISEiIeWRwt27dvL3d5IPbqOPHEEwH49ttvI+rfvn17UlNTGTNmTKn65zWpou8TOCVcwKlxHqnKnk/Lli1p3Lgxe/fuJTMzs8L3xJw5cwB46qmnGDZsWLF1x44dIy0tLex2MTExXHrppUXlf3bs2MHw4cNZsmQJTz/9NM8880wkpyciIiIiIlItKpYnIqX9901YOzXYPvs2OOeOyLfvFDL526Hv4MDmqIUmUhZjDEmNG9W5h1sjtnv06EHjxo1ZvXp1qdrSANOnTwec2tbHO8Z169axeXPp3xtvv/02AL179672Mdq1a8fpp5/OwYMHmT17doX9Bw4cCMDcuXOrfezKSElJISUlhbS0NJYsWVJq/YIFCzh8+DCdOnUqVZqlPJU9H6/XWzT552uvvVZ+Z+Dw4cNA8MOJUO+++y7W2oiOm5KSwkMPPQTA119/XbQ8NjYWgIKCujf5r4iIiIiI1H5KmotIcds/g4UPBtvte8IVz1ZupHjzE6FtyMjFTfOjF5+IREVCQgIjRozA5/Nxzz33kJ2dXbRu8+bNPPXUUwD89re/Pe6x+Xw+Ro0axdGjR4uWrV27lpdeegmPx8PIkSOjcpyHH34YgHvvvZdvvvmm2Lrs7OxiSeqRI0fSqlUrxo8fz5QpU0olfbOzs5k2bRo//PBDVGILFfge3Hfffezfv79oeVpaGg888ECxPpGqyvk89NBDGGMYN24cK1euLNY/Pz+fDz74oKgdmBT2jTfeID8/v2j5hg0bipLgJT3//PPs3bu31PL3338fKF6fPTACPzU1NaLzFRERERERqQyVZxGRoPRd8M/bwOdPcDRtCz//B8RUYWKqzoMgbb3zfNNCuGh09OIUkaiYMGECq1atYvHixZx88sn06dOnKFmck5PDqFGjGDRo0HGPa/Dgwaxfv56OHTty8cUXk56ezpIlS8jPz2fMmDH06NEjKse57bbbWLNmDS+++CLdu3enV69etG/fnt27d/Pll19y1lln0b9/fwBatGjBnDlzGDJkCCNGjGDs2LF07dqVuLg4duzYwcaNG8nOzubLL78MO0lqddx3330sWbKERYsWceqpp9K/f3+stfznP/8hMzOToUOH8utf/7pS+6zK+fTp04c//elPPPDAA1x88cWce+65dOzYkX379vHVV1+RkJBQNMHo8OHD+ctf/sJ7771Hp06d6NmzJ4cOHWL58uUMHTqU1atXlyrDM3bsWO6//366d+/OqaeeirWW9evXk5qaSsuWLYs+IAA4//zzad26NbNmzaJv376cfPLJeDweRowYQa9eEZYSExERERERKYNGmouIIz8HZt4C2fuctqcR3PAPaBZ5jdxiOock2nb9FzLD168VEfckJiayfPlyxo4dS8uWLZk3bx4rV67knHPOYcaMGUyaNMmVuJKTk/nss88YOHAgS5cuZdmyZXTp0oUpU6Ywbty4qB5r8uTJzJkzhwEDBvC///2Pf/3rX2zdupUBAwaUGhHdu3dvvv76a0aPHk3jxo1ZsmQJH374IRkZGQwePJiZM2fSpUuXqMYHTmmUefPmMWnSJE4++WQ++OADPvzwQzp16sRLL73ErFmz8Hgqf0lXlfO5//77WbZsGVdddRVbt25l1qxZbNq0iR49ejB+/PiifsnJyaxZs4abb76ZvLw85s2bx65du3jyySeLyuyUNHnyZG688UaOHj3KokWLeP/99/F6vdx///1FH6IExMfHs2DBAi655BLWrVvH1KlTeeONN8KW9REREREREaksE2lNybrKGPNNly5dupS87VpEQlgLc++Gr2YEl135AvQYVvY2kexzYjdIdyaoY/DzcM6I6sUpDZrP5ysqxdCpU6cqJQmldlu2bBn9+vVj2LBhTJ061e1wpJ6pzO+QM844gw0bNmyw1p5xvOKr73RNLiIiIiKV4fY1uTIOIgKrXy2eMD/njuolzMGpgd45ZELQTQurtz8REREREREREZHjQElzkYZu60p4/5FgO+UCuOzp6Ow7tETL1uWQmxmd/YqIiIiIiIiIiNQQTQQq0pAd2QnvDgNb6LQT28HPp0FMbHT2n9IL4ptDzhEozINvP4Izro7OvkWk1rv//vs5cOBARH3rUzmWuXPnMnfu3Ij63nnnnVx44YU1HJGIiIiIiIhUhpLmIg1V3lF452Y4etBpe+PgxunQtHX0juGNgdN+ButnOu1NC5U0F2lAZs2axfbt2yMghUIfAAAgAElEQVTqO3XqVPr27Ut9mGtl3bp1/P3vf4+ob9++fZU0FxERERERqWVUnkWkIbIW3vsdpK0PLrtyEvy4R/SPFVqiZcsHUJgf/WOISK20bds2rLURPeqTJ554IuLzvv32290OV0REREREREpQ0lykIfrsJfj6n8H2eb+CM2+qmWN1HOCMYgfISYftn9bMcURERERERERERKJASXORhua7pbD4j8H2SRfBpU/V3PHimsLJfYLtTQtq7lgiIiIiIiIiIiLVpKS5SENyaCvMGg7W57STToTrp4K3Uc0eN7RES+pCpzyMiIiIiIiIiIhILaSkuUhDkZcN7/wCjh122jGN4ca3IKFlzR/7tMsB4zxP3wlpX9f8MUVERERERERERKpASXORhsBa+Pc9sO+b4LIhk+FH3Y/P8RPbQPtzgm2VaBERERERERERkVpKSXORhuCTifDNnGC712+h2/XHN4ZiJVqUNBcRERERERERkdpJSXOR+m7LR/DR2GD75H4w4InjH0enkKR52tdwZMfxj0FERERERERERKQCSpqL1GcHv4N/jQD8E2+2OAmuexO8Mcc/llanQfIpwfamhcc/BhERERERERERkQpENWlujGlsjPnWGHOkjPUpxpisEo8CY8y8kD7LjDG5Jfq0i2acIg1Cbia8czPkpDvtRk3gxhnQ5AT3YlKJFhERERERERERqeWiPdL8SeCHslZaa3dYa5sGHsAJwGHgnRJdHwrtZ63dHeU4Reo3nw/m/Ar2bwouG/pXaHOGezFB8RIt2z6BY4fdi0VERERERERERCSMqCXNjTFnA1cAEyqx2VDAC8yOVhwiAqx8FjbND7Yv/D2ccbV78QS0PwcSWjnPbSFs/tDdeETqCWMMJ510ktth1FrLli3DGMPtt9/udigiIiIiIiJSB0QlaW6MiQFeA+4Bciux6R3AW9banBLLxxhjDhljvjTG3BaNGEUajNRFsPT/gu1TLoH+Y9yLJ5THC50uD7ZVokVERERERERERGqZaM0GOBpYb61dZozpG8kGxpgOwEDgwRKrHgE2AEeB/sA/jTGZ1to5UYpVpP7avxlm3xVsn9ARrn3dSVbXFp0GwRfTnOff/gfyc6BRvLsxidRxGzdupFGjRm6HUWude+65bNy4kaSkJLdDERERERERkTqg2klzY0xHnBHmZ1Vy0+HAl9bar0IXWms/C2l+YIx5BbgBUNJcpDw56c7En7kZTju2qTPxZ+Pm7sZV0sl9nElJ849CXhZsXQGnXep2VCJ1WufOnd0OoVZr0qSJXiMRERERERGJWDTKs1wEtAK+Mcak4dQnb2aMSTPGnBtuA2OMBydp/noE+/dFIUaR+s3nc0aYH9wSXHb1K9C6FiaJGjWGjv2DbZVoEam2cDXNQ+t4p6Wlceedd9K+fXtiYmKYOHFiUb+jR48ybtw4unbtSuPGjUlKSuLiiy/mnXdKztHtOOmkkzDGAPD666/TrVs3GjduTNu2bRk5ciRHjhyp8nmExrxnzx5uv/122rRpQ+PGjTn77LOZNm1aueefl5fHk08+SefOnYmLi2Po0KGl9hvOokWLGDx4MK1btyYuLo6UlBSGDh3KggWlfz9t27aNkSNHctJJJxEXF0erVq247rrrWL9+fZXPW0RERERERGqXaJRnmQm8H9LuBUwBzgQOlrHNJUBL4O3QhcaY5v7tl+HURu8LjATuQkTKtmwCbA75MezzEJw+2L14KtJ5cHCi0tRFMOh58ERtXmIRCbF//3569uxJQUEBF154ITk5OTRp0gSAzMxM+vXrx9q1a2nVqhWDBw8mOzubJUuWsHLlSlatWlUswR7qwQcfZNKkSfTs2ZPLLruMTz/9lFdffZWNGzeyfPnyosR6VRw6dIjzzz+f3Nxc+vbty+HDh1m6dCnDhg1j69atPP7446W28fl8DB06lBUrVtCnTx+6detGcnJyhccaPXo0zz33HF6vlwsuuID27duze/duli5dypEjRxg0aFBR348//phBgwaRkZHBGWecwZAhQ9i1axezZ89m4cKFLFiwgH79+lX5vEVERERERKR2qHbS3Fp7DDgWaBtjDjmLbZq/vQhYaa0dH7LZHcAsa216id01Ah4HAsPbtgGjrbXvVjdOkXprwzxY8adg+7TLoc/D7sUTidN+BsYLthCy9sKutXBiT7ejkrrOWqdMUV0TnwTVSDBXZOHChVx99dXMmDGD+Pji8wc8+uijrF27loEDBzJnzhyaNm0KwKZNm+jTpw+TJk3i0ksv5Yorrii13+nTp/P5559z5plnAnDgwAEuuOACVq5cydKlS+nfv3+pbSL13nvvcckllzBnzhwSEhIAWLNmDf379+fJJ5/kqquuKjpuwM6dO4mLiyM1NZUf//jHER1n+vTpPPfcc7Rv354FCxbQrVu3onXZ2dl8/vnnRe2MjAyuv/56jh07xrvvvst1111XtO6jjz5i0KBB3HrrrXz//ffExsZW+dxFRERERETEfdGaCLSItXYZ0DykfXmYPj8vY9v9wHnRjkmk3tq7Aeb8KthueRpc82rtH7Xd5ARIuQC2f+y0UxcoaS7Vl5MOz3RwO4rKe2h7jc49EBcXx+TJk0slzLOzs3njjTfweDz89a9/LUqYg1MjfcyYMYwaNYoXXnghbNJ83LhxxRLXLVu25Ne//jWjR49mxYoV1UqaG2OYPHlyUcIcoGfPntxzzz0888wzvPzyy7zyyiultpswYULECXOA8eOdz/MnTpxYLGEOkJCQUOwc3nzzTdLS0njkkUeKJcwBBg4cyN13383EiROZP38+11xzTcQxiIiIiIiISO1TyzNrIlKmY4ediT/zs512XDNn4s/4Zu7GFanOwZIHbFroXhwi9dzZZ58dNpG8du1ajh07xrnnnsupp55aav2tt94KwCeffIK1ttT6Sy8tPYHvaaedBsCePXuqFfNZZ51Fp06dSi2/6aabAKdMSknGGK688sqIj7F79242btxIcnIy1157bYX9Fy9eDFBUJ72kCy+8EHBGxIuIiIiIiEjdpqS5SF3kK4RZd8Dhrf4FBq59HVqWTnzVWp1DRq4eSIUD37oXi0g9lpKSEnb57t27AUpNIBrQvHlzkpKSyMrKIiMjo9T69u3bl1oWGK2em5tbxWgdHTqEv2MgEGsg9lCBSTwjtXPnTgA6duwYUf9t27YBcN5552GMKfUIjD4/cOBAxDGIiIiIiIhI7RT18iwichwsGQff/SfY7v8Hp054XdLiJGh9Buz7xmmnLoCWv3M1JKnj4pOcUid1TXxSze6+RFmWkiKZsDNcn+pM9FkTKjrPskR6HoWFhQBcf/31RROphnPeeaoyJyIiIiIiUtcpaS5S1/xvNnz8fLB9+hC46H734qmOzoOCSfNNC6G3kuZSDcbUaG3w+qZdu3YAbN26Nez69PR00tPTSUhIIDEx8XiGxvbt4T/8CCwPxF4dJ554IgDffhvZXS7t27cnNTWVMWPGlKp/LiIiIiIiIvWLyrOI1CVpX8O/7wm2W50OQ192koV1UWiJlp2fQ9Y+92IRaWB69OhB48aNWb16NVu2bCm1fvr06YBTq/t4jypft24dmzdvLrX87bffBqB3797VPka7du04/fTTOXjwILNnz66w/8CBAwGYO3dutY8tIiIiIiIitZuS5iJ1xdFD/ok/jzrt+CS48S2Ia+puXNXxozOhWWCCQgub33c1HJGGJCEhgREjRuDz+bjnnnvIzs4uWrd582aeeuopAH77298e99h8Ph+jRo3i6NGjRcvWrl3LSy+9hMfjYeTIkVE5zsMPPwzAvffeyzfffFNsXXZ2NkuWLClqjxw5klatWjF+/HimTJlSanLU7Oxspk2bxg8//BCV2ERERERERMQ9Ks8iUhcUFsC7t8ORHU7beOC6NyE5sgnsai1joNMVsOY1p71pIZx9m7sxiTQgEyZMYNWqVSxevJiTTz6ZPn36FCWLc3JyGDVqFIMGDTrucQ0ePJj169fTsWNHLr74YtLT01myZAn5+fmMGTOGHj16ROU4t912G2vWrOHFF1+ke/fu9OrVi/bt27N7926+/PJLzjrrLPr37w9AixYtmDNnDkOGDGHEiBGMHTuWrl27EhcXx44dO9i4cSPZ2dl8+eWXYSdJFRERERERkbpDI81F6oKPHoety4PtAY/DKQPdiyeaQku0fL8U8rLL7isiUZWYmMjy5csZO3YsLVu2ZN68eaxcuZJzzjmHGTNmMGnSJFfiSk5O5rPPPmPgwIEsXbqUZcuW0aVLF6ZMmcK4ceOieqzJkyczZ84cBgwYwP/+9z/+9a9/sXXrVgYMGMBDDz1UrG/v3r35+uuvGT16NI0bN2bJkiV8+OGHZGRkMHjwYGbOnEmXLl2iGp+IiIiIiIgcf6bk7cX1jTHmmy5dunQpedu1SJ3x1UyYc1ewfcY1zijzulrHvKSCPPhzR8jNcNo3TIfTr3Q3JqmVfD4fqampAHTq1AmPR5/71jfLli2jX79+DBs2jKlTp7odjtQzlfkdcsYZZ7Bhw4YN1tozjld89Z2uyUVERESkMty+JlfGQaQ2270O3hsVbLf5KVz1Yv1JmAPExMKplwbbmxa6F4uIiIiIiIiIiDR4SpqL1FZZ++GdX0BBjtNufIIz8Wdsgrtx1YTQEi2bFzk13EVERERERERERFygiUBFaqPCfGfiz4wfnLbxwvVToEUHV8OqMadcAp5G4MuHY4dh5yo46UK3oxKRarr//vs5cOBARH1VjkWk+owxjYDngZv9i94C7rPWlvo02hiTVWJRHLDRWtvNv/43wO3AT4FF1tqhNRW3iIiIiEhto6S5SG30wR9g+8fB9qXj4OS+bkVT8+KbwU8uhu/+47Q3LVTSXKQemDVrFtu3b4+o79SpU+nbty/1fa4VkRo2BrgQCNR9XAQ8CjxZsqO1tmlo2xizHngnZNFu4ClgINC+JoIVEREREamtVJ5FpLb5cjqsfiXY7nYDnH+3e/EcL6ElWjbNByXOROq8bdu2Ya2N6CEiUTECeMpau8dauwf4P+COijYyxpwLdAGmBpZZa2dba+cCkd0uIiIiIiJSjyhpLlKb/PBfmH9fsP2j7nDlpPo18WdZOoUkzY9sh30b3ItFRESkjjHGtMAZEb4uZPE6IMUYk1TB5nfglGDZXVPxiYiIiIjUJUqai9QWmXth5i1QmOe0m7SEG96CRo3djet4adYO2p0dbG9a6F4sIiIidU+g3MqRkGWB54llbWSMaQLcCLxeQ3GJiIiIiNQ5SpqL1AYFefDPWyFzj9P2xMDPp0HzE92N63grWaJFREREIhWY2DN0VHngeWY52/0cOAosqImgRERERETqIiXNRWqDRQ/Czs+D7cuehpN6uxePWzoNCj7fsw7Sd7kXi9Q6JqRMkc/nczESEamLQn9vmHpY9sxaexj4ATgzZPGZwE5rbXo5m94J/N1aW1CT8YmIiIiI1CVKmou47b9TYO2UYPusW6Dnne7F46bWp0OLnwTbqSrRIkHGGGJjYwHIzs52ORoRqWsCvzdiY2PrZdLcbwrwB2NMW2NMW+BRyim7YozpBPQC3gyzLsYYEw/EAB5jTLwxJraG4hYRERERqVVi3A5ApEHb8TksfCDY/nEPuOIvDWPiz3CMgc6D4LMXnfamBXDuL92NSWqVxMREDh48yN69ewFISEjA49HnvyJSNp/PR3Z2dtHvjcTEMst71wfjgGRgo7/9FjAewBjzNwBr7a9C+t8BrLTWbg6zrzHA4yHtY8ByoG90QxYRERERqX2UNBdxS8Zup465L99pJ7SGG6ZDo3h343JbpyuCSfNtH0NOOsQnlb+NNBjJyclkZ2eTk5PD7t273Q5HROqY+Ph4kpOT3Q6jxlhr84F7/I+S634VZtmD5ezrCeCJKIYnIiIiIlJnaHieiBvyc2DmLZDljHrD0whu+Ac0a+duXLXBiedBE39Cw5cPWxa7G4/UKl6vl5SUFJKTk4tKtYiIVCQ2Npbk5GRSUlLwer1uhyMiIiIiIrWcRpqLHG/WwsLRsGttcNkVf4aU892LqTbxxsBpl8G6t5z2pgXw0+vcjUlqFa/XS+vWrWndujXWWqy1bockIrWYMaY+1zAXEREREZEaoKS5yPG25nX4cnqw3WM4nDPcvXhqo05XBJPm334EBXkQo1HFUpqSYSIiIiIiIiISbSrPInI8bfsY3n842D7xfLj8T+7FU1t17A8xjZ3nuRmwbaW78YiIiIiIiIiISIOhpLnI8XJkJ/xzGPgKnHbij+Dn0zSCOpzYJtCxX7C9aYF7sYiIiIiIiIiISIOipLnI8ZB/zJn48+gBp+2NhRvegsQ27sZVm3W6Ivg8dZFTC15ERERERERERKSGKWkuUtOshfd+B3vWBZcNfh7a93Avprqg0+Vg/L+iMnfD7i/djUdERERERERERBoEJc1Fatqql2H9zGD73LvgrFvci6euSGgJJ54XbKtEi4iIiIiIiIiIHAdKmovUpO+XwYdjgu0OveFn410Lp84pVqJloXtxiIiIiIiIiIhIg6GkuUhNObwN3h0OttBpN2sP1/8dvI1cDatO6Two+HzfBjj0vXuxiIiIiIiIiIhIg6CkuUhNyMuGd26BY4ecdkw83DgdmrZyN666JrkjtOocbG/SaHMREREREREREalZSpqLRJu18O/fwN6vg8uufAHaneVeTHWZSrSIiIiIiIiIiMhxpKS5SLR9Mgm+mR1sX/Ab6H6De/HUdZ0HB5/v+AyyD7oXi4iIiIiIiIiI1HtKmotE07cfwX/GBts/6QMDx5bdXyrW7ixo2tZ5bn2w+X134xERERERERERkXpNSXORaDn4Hcwa4SR2AZqnwHVTwBvjblx1nccDnS4PtlWiRUREREREREREalBUk+bGmMbGmG+NMUfKWJ9ijMkq8SgwxswL6dPMGDPDGJNhjNlrjPljNGMUqRG5WfDOLyAn3WnHNIYbZ0BCsrtx1RehJVq+WwL5x9yLRURERERERERE6rVojzR/EvihrJXW2h3W2qaBB3ACcBh4J6TbZP/yFOAi4JfGmNuiHKdI9FgLc38F+zcGlw19Cdr+1L2Y6pufXASxic7z/KPw/TJXwxERERERERERkforaklzY8zZwBXAhEpsNhTwArP9+2gC3AiMsdYesdZuxkmi3xGtOEWibuWzsPG9YLv3vdD1WvfiqY9i4uCUAcH2pvnuxSIiIiIiIiIiIvVaVJLmxpgY4DXgHiC3EpveAbxlrc3xtzsBscC6kD7rgG7RiFMk6lLfhyX/F2x3HAADHnMvnvostERL6vvgK3QvFhERERERERERqbeiNdJ8NLDeWrss0g2MMR2AgcDrIYubAtnW2oKQZUeAxGgEKRJVB7bA7F8C1mm3+Alc9wZ4vK6GVW+degl4/JOqHj0AP6xxNx4REREREREREamXqp00N8Z0xBlhfn8lNx0OfGmt/SpkWRbQxD9yPSAJyKxelCJRlpMB79wMuRlOu1EC3PQ2NG7hblz1WePm0KF3sK0SLSIiIiIiIiIiUgOiMdL8IqAV8I0xJg2nPnkzY0yaMebccBsYYzw4SfPXS6xKBfKB7iHLzgS+jkKcItHh88GckXBgc3DZ1X+D1qe7F1NDEVqiZdNCZxJWERERERERERGRKIpG0nwm8BOc5PaZwJ04I8PPBL4sY5tLgJbA26ELrbVH/fsbZ4xJMsacCvyW0sl1EfcsfwZSFwbbFz8AXYa4F09D0uny4PND3xX/4EJERERERERERCQKqp00t9Yes9amBR7AIWexTbPW5htjFhljHi2x2R3ALGttephd/gZIB34APgHesNZOq26cIlGxcT4sfzrYPu0y6Fvy7S01pvmJ0DZkXmCVaBERERERERERkSiLqbhL5fgnA20e0r48TJ+fl7N9BnBTtOMSqbZ9m5yyLAHJp8I1r4InWvPpSkQ6D4a09c7zTQvhotHuxiMiIiIiIiIiIvWKsn0ikTh2BN65CfKynHZsItw4A+KT3I2rIep8RfD5rv9CZpp7sYiIiIiIiIiISL2jpLlIRXyF8K874dD3wWXXvgatTnMvpoasTVdISgm2Q+vLi4iISO1VmOd2BCIiIiIiEVHSXKQ8Ph/MGwXfLg4u6/eH4hNSyvFlDHQeFGxvUtJcRESkTji8HdK+djsKEREREZEKKWkuUhZr4YNHYN304LLOg+Gi+92LSRyhJVq2LofcTPdiERERkcjYQph2Fezb6HYkIiIiIiLlUtJcpCxLx8Pnfwu2U3rBNa9p4s/aIKUXxPvnGy7Mg28/cjceERERiczRg/D3IXBgi9uRiIiIiIiUSdk/kXA+eQFW/CnYbncW3DwTYpu4F5MEeWPgtMuCbZVoERERqTuy98Hfr4SD37kdiYiIiIhIWEqai5T03ymw+I/BdqvOcMtsiG/mXkxSWmiJli0fQGG+e7GIiIhIxRJ/BBjneeYeZ8T54e2uhiQiIiIiEo6S5iKhvp4F8+8LtlucBLfOhSYnuBaSlKHjAPDGOc9z0mH7J+7GIyIiIuWLT4IrJwXbGT/A3wdD+g/uxSQiIiIiEoaS5iIBqYtg9l2AddqJ7eC2f0OzH7kalpQhrimc3CfYVokWERGR2q/HMLji2WD7yA6YOhgy9rgXk4iIiIhICUqaiwB8vxz+OQxsodNukgy3zXVGmkvt1XlQ8HnqQrDWvVhEREQkMuf+En42Idg+vNWpcZ61z72YRERERERCKGkusnMNvH0TFOY67bhmTg3zVp3cjUsqdtrlFNVGTd8JaetdDUdEREQidMHdcMmTwfbBLU6N8+wD7sUkIiIiIuKnpLk0bGn/g7euhfxsp92oCfziXWh3prtxSWQS20D7c4JtlWgRERGpO3r/DvqNCbb3b4RpQ+HoIfdiEhERERFBSXNpyA58C/8Y6kwiCeCNhRumQ8r57sYllVOsRMsC9+IQERGRyuvzAFz8YLC992v4x9Vw7Ih7MYmIiIhIg6ekuTRMR3bCtKsge7/TNl647k04ZYC7cUnldQpJmqd9DYe3uxeLiIiIVF6/R6H3vcH2nnUw/VrIyXAvJhERERFp0JQ0l4Yna5+TMM/4Ibjsqpfg9Cvdi0mqrtVpkHxKsJ26yL1YREREpPKMgYFPwPl3B5ft+i+8dT3kZrkVlYiIiIg0YEqaS8Ny9JBTK/PQd8FlVzwLZ97kXkxSfSrRIiIiUrcZAz8bDz3vDC7buQrevhHyjroXl4iIiIg0SEqaS8ORm+mMWNr3TXDZgMfg3F+6F5NER2iJlm2faAIxERGRusgYuPzPcPZtwWXbVsI7N0F+jntxiYiIiEiDo6S5NAz5OfD2Tc6tvgEX3gcXjXYvJome9udAQivnuS2ELYvdjUdERESqxuOBwZOg+83BZd8vg5m3QEGua2GJiIiISMOipLnUf4X58O7tzkilgJ53woDHXQtJoszjhU6XB9sq0SIiIlJ3eTxw1Yvw0+uDy75d7FzPFeS5FpaIiIiINBxKmkv95iuEOb+CzSGTQ3a70bn11xj34pLoCy3RsuUj3cYtIiJSl3m8MPRv0OWq4LLUhfCvO6CwwL24RERERKRBUNJc6i9rYf598L9ZwWWdB8NVLzkjmKR+ObkPNGriPM/Phq0r3I1HREREqscbA9e+UfyD8Y3zYM7/s3ffUVJX9//Hn3cLS2fpRYrSQUFAsYINUZqCCioqWAD1GzUxllii8ReNphg1URMLViyIDUEpKigqioJIU5pI772Xbff3x93NnUUWFnZ278zs63HOHu/7zmdmXiYqs3fu532vdxsjREREROLVxp9h/li1n4thWjmUxGQtfHIf/PCqn2t8NvR9yf0CJokntRw07eJrtWgRERGJf8mp0O9laHaen/vxPRh1E+TkhMslYeTkuM/5IiIi8Wzjz/DcGe6w8/H3hE4jBdCiuSSmLx+FKU/7usHJcPkbkJIWLpMUv8idaAvG6ZdpERGRRJCSBpe+5jZA5Jk1HD76nf6sL01+GgmPt4J/t4UNC0KnEREROXJT/gOZu9141nDI2B02jxyQFs0l8Xz7DHz+sK/rtIEr3oYyFcJlkpLR/HwwyW68cx2smh42j4iIiERHalm4/E04urOf+2EYjLtTO48TXdY+GHOHOwh251rYuhw++I2+MBERkfi0dxvMftvXmbth0YRweaRAWjSXxPLDazD+bl9XbwZXjYRy6eEySckpXw0aneZrtWgRERFJHGXKQ/+3oMEpfm7aC+62Zi2cJ6bNS+DF82Da0Pzzq76HH14JEklERKRIZr3lzmGLNHdUmCxyUFo0l8Tx00j48Le+rtIQBo6CijXDZZKS16KHH8/XormIiEhCSasIV74DR53o5757BiY8oIXzRDPvQ3juTFgz08+llPXjCf8Pdm4o8VgiIiJHzFr3hf/+Fn6sA0FjkBbNJTH8/Cm8NwRs7m2aFevA1aOgylFhc0nJaxmxaL5xIWxcFC6LiIiIRF/ZynDVe1C3nZ/7+t/w+SPhMkn0ZGW4uwdGXAX7tvn5joPhpu+gTCVX790Gn94fJqOIiMiRWPKFW6cAwPgvgzN2wC+fB4slB6ZFc4l/Sye7D9U5ma4uVxUGfgDVGofNJWFUPRpqH+drtWgRERFJPOXSYcBIqN3Gz335D/jiH+EySdFtXQ4vd4dv/+vnylSEvi9Bz8fc57xz7vOPzRrufhcQERGJB1Mj2o01Pdedy5Zn3uiSzyMHpUVziW+rfoA3L4esva4uU8ntPKrVKmwuCUstWkRERBJf+Wpuo0TNiM99nz8Mk/8VLpMcuQXj4dnOrl95ntrHwfVfwHGX+LmOg6FOW19/dJvbnS4iIhLLtq2EBWN9fdIQaN3b1/PHQHZmyeeSAmnRXOLXurnw+sXuNhZwt7VcMQKOOiFsLgkvskXLiqmwc324LCIiIlJ8KtSAq0dDjatHHrkAACAASURBVOZ+bsIDMOW/BT9HYkt2JnxyPwy/DPZu9fMdBsLgCVCjaf7rk1Og178A4+qNC+Db/5RYXBERkSMy/RXfUji9kdtp3uw8SE5zc3u3wpIvg8WTX9OiucSnzYvhtT6wZ4urk1Lhstfh6NPD5pLYULcdVM7rZ29h4figcURERKQYVawFA0fnb8338T35b4GW2LRtFbzSC7550s+lloeLnocLn4LUcgd+Xv0T4MRrff3FP1xrFxERkViUlQHTX/V1x0GQlAxplaBpFz+vFi0xRYvmEn+2rYJhvWHnOlebJLhkKDTrGjaXxA5j1KJFRESkNKlcF67+0O3cyjP2DrerS2LTzxPguc6w4ls/V7MlDPkcjr/s0M/v8ieoUNONM3fDuLuKJ6eIiEhRzRsNu3LvgE9Og/YD/GOtLvTj+WMgJ7tks0mBtGgu8WXXRrfDPHInyQVPwrEXhcsksSmyRcviSZCxK1gUERERKQFV6ruF88r1/dyHt8LMN8Nlkl/LzoKJD8Ebl8DuTX7++CtgyGdQq2XhXqdcVTjvL75eMBbmjy34ehERkVAi73477hJ3LkueFt0gKcWNd22A5VNKNpsUSIvmEj/2bIXXLoKNC/1ct79BhwEFP0dKr0adIK2KG2fthV8+C5tHRESkGBljUo0xTxtjNuf+PGWMSSng2p37/WQaY2YfyWvFnKqN4JoPoVLd3AkLH/wGZr8TNJbk2rHW3TH61T/9XEpZ6P0fuOgZKFPh8F6v7WXuM1+ecX/QRgkREYkta+fkv6vqpMH5Hy9XFRqf5eu5atESK7RoLvEhYxe8eSmsne3nzv4jnPJ/4TJJbEspk79lj1q0iIhIYrsP6AQcm/vTGbj3QBdaaytG/gDzgLeO5LViUrXGbsd5hVq5ExZG3gA/fRA0Vqm3eBI82wmWTfZz1Zu53eXtrzqy1zQGej7md+htW+H6m4uIiMSKyF3m9TrAUSf8+prIFi3zRkNOTvHnkkPSornEvqx98NaVsOI7P3fqzXDGneEySXyIbNGycLy7HVhERCQxXQf8xVq7xlq7BngYGHSoJxljTgJaA68U9bViSo1mcPVoKF/d1TYb3hukL9FDyMmGSX+DYX3cbed52vSD6z+H2scW7fVrtYTTbvH1lKdh/byivaaIiEg07NkKcyLudjtpyIGva9nTndcHsGMNrPq++LPJIUV10dwYU84Ys8gYs/UQ1w02xiwwxuwyxiw1xvSOeGypMWZPxO2iB30tSXDZWfDudbD4cz/X4WrXv9CYcLkkPjTtCkmpbrxnS/5bokRERBKEMaYqUB+YGTE9E2hojKlyiKcPAsZZa1dH4bViS61WMHCUu+0ZICcL3r4aFn4SNldpsnM9vH4xTPorYN1cchr0egIuHgpplaLzPmf8Aao0dOOcLBhzO1gbndcWERE5UrOGu8OqAcpVg2MvPvB1FWpAo9N9PXdU8WeTQ4r2TvMHgZUHu8AYcz1wG3A5UBE4GZiz32X9I24ZTY9yRokXOTkw6iaY/5GfO+4S9yFbC+ZSGGUrwzFn+Fq7y0REJDFVzP1r5GaTvHGBq5LGmPK4z+QvFPW1YladNjDgAyibu96fkwkjrtJZJyVh6WR4trNry5KnWmMY/CmceF10P8+XKQ89ItqyLPvaLVSIiIiEkpMD0yI+YnUYAKllC76+dW8/njdaX/7GgKgtmhtjOgA9gL8e5Jpk3ML6rdbaGdZZZ61dHK0ckiCshXF3wuyI9prNu8NFz0FScrhcEn8iW7TMH6M/eEREJBHtzP1r5E7wvPGOgzzvUmA3EPmt8pG+Vuyq1w6uGgllctf8s/fB8P6w5MuwuRJVTg58+U949QLYudbPt+4N10+CuscXz/u26A4tevr6k/tg9+bieS8REZFDWTIJNi3KLYz7wvhgWvZy1wFsXQ5rZh70cil+UVk0N8akAEOBm4B9B7m0BVAbaGaMWWKMWWmMec4Ys/+uleeMMRuNMVOMMT0O8DqS6CY+mP8buaM7Q79XIDk1WCSJUy0i/hOydRmsnxsui4iISDGw1m7B3e3ZLmK6HbDCWrvtIE8dDLxqrf3foR9FeK3YVv8EuOpdSK3g6qy98OZlsGxK2FyJZtcmeLMffPYQ2NxDzJJSofuj0O9Vv+O/uHT/O6SWd+Pdm2Din4v3/URERAoyNWJNq9l5UPXog19fuS40ONnXc0cXSywpvGjtNL8dmG2tnXSI66rl/rUP0BH3AbwJ8HjENQOAY4CjgKeA94wxHaOUU+LBV4/D5Ih/JI46EfoPP/htLCIFqVzPnVCdRy1aREQkMb0M/NEYU8cYUwe4l/xtV/IxxrQATgNeKuprxY2Gp8CVb0NKOVdn7oY3+sGKaWFzJYrl38JznWHRBD+X3hAGfQInX18y7RXTG8CZd/l6+iv6/1dEREre1hWwcJyvCzoAdH+tL/RjtWgJrsiL5saYJrgd5ncU4vK82z3/Zq3daK3diGvn8r/GPdbar6y1u621+6y1bwIfApcUNafEialD8+8IqXUsXPlO9A4JktJp/xYtIiIiiechYAowL/fnG+ARAGPMs8aYZ/e7fhDwlbV24eG8Vtw7upPbjJGc5uqMHfD6JbDqh7C54pm18PWT8HIP2L7Kz7foCTd8CUd1KPi5xeHUm6BmK1+P+T1kZxV8vYiISLR9/5K/46rqMdCkS+Ge1+oCP960CNbPi342KbRo7DTvDNQEfjLGrAXeByobY9YaY07a79oFwF7+d3R6oeREIaPEg1lvwdiI716qNYEBI6F8tYKfI1IYLXv58ZqZsO2g5xWLiIjEHWttprX2Jmtt1dyfm/Parlhrb7TW3rjf9X+w1p55uK+VEJqcDZe/CcllXL1vG7x2EayZHTZXPNq92fWH//R+sNluLikFzn8ELn8DylUt+UzJqdAr4q7VtXNg2tCSzyEiIqVT1j74YZivOw6CpEIuv6Y3hHrtfT1PLVpCisai+QhcO5V2uT+DcYcEtQNmRF5ord0DvA7cbYypaoxJB+4CRgEYYxoaY84wxqQZY1KNMZfidqF/EIWcEsvmfQgf/MbXlevDwFFQqXa4TJI4arZ03+7mWTCu4GtFREQk8TU7Fy4d5hZ4AfZuhdf6wDqdfVJoK7+H587Mf/t55fpw7Xi327sk2rEUpNFp0O5KX3/2MGxfEy6PiIiUHnNHwe6NbpxSNv+fR4XRurcfzx0VvVxy2Iq8aG6t3WOtXZv3A2x203attTbTGDPOGHNvxFNuBVYBS3A7z5cBt+U+VhF4EtgEbMC1fLnUWvttUXNKDPvlM3j3Or87pUJNt2Ce3iBsLkkcxkDLnr5WixYRERFp0R36vgQm2dW7N8GwC2HDgTrWyP9YC98+Ay91g23L/Xyz8+HGr6BBjBxH1fVBKJvuxhk74ON7wuYREZHSYWrE3U1t+h5+94RWEX3N18+FjYuik0sOW7QOAv0fa+0ka216RN3dWvtIRL3LWnuttTbdWlvbWjvEWrsj97G51tp21tqKuY+fZK39MNoZJYYs/xbeuhKyM1xdtgoM+ABqNA2bSxJP5KL50smwd1u4LCIiIhIbWveGi58Hk/tr0a4N8OoFsOmXsLli1Z6t8PYAGH835GS6OZMM5/4Z+r8VW20VK9SArhFnJf00EhZNDJdHREQS35pZsHKqrzsOPvzXqN4Eah/n63nabR5K1BfNRQptzSx4ox9k7nZ1agW48j2oc9zBnydyJBqcDOWru3FOJvz8adg8IiIiEhva9IU+zwC57UR2rnUL51uWhkwVe1bPhOfPdG0V81SqC9eMgU63Fr5fa0lqPxDqR+x8H3sHZO4Nl0dERBJb5C7zo07M35/8cETuNp+rvuahxOAnGykVNixwBy7t2+7q5DToPzx2bueUxJOUDM27+VotWkRERCTP8ZfDhU/6evsqeOUC2LoiXKZYYa1bBHixa/4vEpqcAzdOhkanBot2SElJ0PNxfyfB5sUw+YmwmUREJDHt2QJz3vX1SUOO/LUi+5qvmQlblh35a8kR06K5lLwty2BYH9c3EtwtnZe+Co3PDJtLEl9ki5ZFEyArI1wWERERiS0dBroF1jzblsOrvWD76nCZQtu73Z09NPYO307RJMHZ97k7RCvUCJuvMOq2hZNv9PXkx9V+R0REom/GG5C1x43LV4fWfY78tWq1hBrNfT1Pu81D0KK5lKzta9wBSzvyfvkwro9ki+5BY0kp0fhsSCnnxvu2w9KvwuYRERGR2NJxEHT7u6+3LHWtWnasCxYpmLVz4Pmz4Kf3/VzF2jBwFJx5Z2y2YynI2fe6VjLgFv/H3O520IuIiERDTg58/6KvOwyE1LJFe021aAkujj7pSNzbtQle65P/ts5eT7g+kiIloUx5aHK2r9WiRURERPZ3yo3Q9SFfb1rkNn3s2hguU0myFqa/Ai+cC5sjdmQf3Rlu+AqOOSNYtCOWVgm6/dXXiz/P/2WAiIhIUSz+zLUAA3dH1onXFf01W0csmq+cWrrvfAtEi+ZSMvZuh9cvhg3z/dx5f4ETrw2XSUqnyBYtC8Zpl5GIiIj82um/hXPu9/WG+TCsN+zeHC5TSdi3E0beAB/+DrLyDsw0cOZdbod5pdpB4xVJ6z7QpIuvx9/rfkcREREpqqkv+HHzbpDesOivWactpDfy9byPiv6acli0aC7FL2M3DL/cHV6Q54w/wGm3hMskpVfzbv4wqB2rYfWMsHlEREQkNp1xh1sszrPuR7dwvmdLuEzFaf08GHo2zB7h58rXgAHvu/YmScnhskWDMdDjUUhOc/XOtfD5w2EziYhI/NuyDBaO93XHQdF5XWPyHwg6d1R0XlcKTYvmUryyMuDtAbDsaz938v+5D94iIVSoAQ1O9rVatIiIiEhBzroHOv3e12tnw+uXJN4O5ZlvwvNnw8aFfq7haXDjZGhyTrhc0Va9CXS+3ddTn4c1s8LlERGR+Pf9S0DuHezVGkPjKP65Gblovvwb2Lkheq8th6RFcyk+2Vnw/mBYNMHPtbsKzn/EfWMmEkq+Fi1jw+UQERGR2GYMdHkATrnJz62aDm/0da1M4l3GbvjgJvjg/yBrj5/vdBtc/SFUrhsuW3HpdCtUa+LGNgc+us0d4CYiInK4MvfCD8N83XFwdA/KrtcBKh/lxjYH5qtFS0nSorkUj5wc1wsx8vaR1n3gwiej+x8QkSPRoocfr5/rD+wQERER2Z8xcP7D0HGIn1vxHbx5KWTsCperqDYshBe6wMzX/Vy5qnDFO3DuA5CcEi5bcUpJg56P+XrV9/DDK8HiiIhIHPtpJOzJPe8kpRy0uyK6r5+UBK0u8PW80dF9fTkorV5K9FkLH9+T/wN4065w8dD474UoiaF6E6jZ0tfztdtcREREDsIY6P4POOEaP7fsaxjeHzL3FPi0mDX7bXj+LLd5IE/9k1w7lubnBYtVYpqcDcdd4usJ/0+3vIuIyOGbFnEAaNt+7svnaGt1oR8v+TLxDyWPIVo0l+j7/BH47llfNzodLh0GKWXCZRLZn1q0iIiIyOFISoKeT0C7K/3cki9gxFWQtS9crsORucfdDfr+EMiM2CV/6s1w7VioUj9ctpJ23sNQppIb790Gn94fNo+IiMSX1TPc3Up5Iu9Ii6aGp0CFWm6ckwULxhXP+8ivaNFcouvrJ+HLf/i6Xnvo/xaUKR8uk8iBtIhYNF8+BXZtCpdFRERE4kNSElz4FLTp5+cWTYC3r4asjHC5CmPTL/BCV5j+ip8rWwUuH+7azySnBosWROW6cM59vp41HJZODpdHRETiy9SIXeb1T4K6bYvnfZKSoVUvX6tFS4nRorlEz/cv59+hUbMVXPU+lK0cLpNIQeq1h4p13NjmwMLxYfOIiIhIfEhKhj7PuvN68iwcB+9dB9mZ4XIdzI/vw3Nnwro5fq5eB7jhK2jZo+DnJbqOg6FOxCLHR7fF/pcfIiIS3u7N8OO7vj6pmHaZ54ls0fLLZ7B3e/G+nwBaNJdomfMufPR7X1c9GgZ+AOWrBYskclBJSfl/SVSLFhERESms5BS45AVoGbnz60MYeQNkZ4XLtb+sfTDmDnj3WsjY4edPvhGu+xiqNgqXLRYkp0CvfwHG1RsXwJSng0YSEZE4MON1yNrrxuVrQOvexft+R3fy/dKzM+DnT4r3/QTQorlEw4Jx8P71gHV1pXowcBRUqhM0lsghRbZoWTQRMnaHyyIiIiLxJTkV+r4Ezc73cz++B6NugpzscLnybF4CL54H04b6ubTK7qyh7n/XeUN56p8AJ17r6y/+AVuWhcsjIiKxLScHvn/R1ydcDSlpxfueyan5z2WbO6p4308ALZpLUS3+wvVwtLm/GJSv7hbMqx4dNJZIoRzT2R8AlbUHFk8KGkdERETiTEqaW4Ruco6fm/0WfPhb90t1KPM+dO1Y1sz0c3Xawg1fFP9uuHjU5U9QoaYbZ+2BcXeFzSMiIrHrl4mwZakbmyQ44dqDXh41rSL+/P75U8jYVfC1EhVaNJcjt2IaDO8P2ftcnVYFBoyEms3D5hIprJQ0aHaurxeMCZdFRERE4lNqWbjsDTi6s5+b8TqMvR2sLdksWRkw/h4YcRXs2+bnTxwEgz6Fao1LNk+8KFcVzvuLrxeOg/n6XCgiIgcwNeIOrhY9IL1Bybxv4zPdHWPgvuBdNKFk3rcU06K5HJm1P8Ibl0Bm7jdbqeXhyreh7vFhc4kcrsgWLQvGx8bt1CIiIhJfypSHK0ZAw9P83Pcvwfi7S27hfOtyeLk7fPvfiFwV4ZIXodfjbnFfCtb2svxffIy7S7v4REQkvy1L8/cT7zi45N47JQ2ad/P13NEl996llBbN5fBtXASv9YG9ubtXksvA5W9Aw1PC5hI5Es26QlKKG+/eCCumhs0jIiIi8alMBbeJpH5HP/fds/Dp/cW/cL5gPDzbGVZ97+dqHQvXT4I2fYv3vROFMdDzMUhKdfW2Fa6/uYiISJ5pL/K/8/yqN4VjzizZ9299oR8v/Bgy95bs+5cyWjSXw7N1BQzrDbs2uNokuwOQIvs4isSTcunQ6HRfq0WLiIiIHKm0SnDlu1CvvZ/75in47KHiWTjPzoRP7ofhl8HerX6+w0AYMhFqNIv+eyaymi3gtFt8PeVpWD8vXB4REYkdmXtgxmu+7jgYkkp4WbXpuZBawY0zdsDiz0v2/UsZLZpL4e1c7xbMt6/0c33+C60uCJdJJBpa9vLj+WNKvv+oiIiIJI5y6XDV+1CnjZ/76rHo71retgpe6QXfPOnnUsvDRc/BhU9Barnovl9pccadkN7QjXOyYEyA3vQiIhJ7fnwf9mxx49TycHz/ks+QWs7dLZ9HLVqKlRbNpXB2b4ZhfWDzL36uxz/h+MvDZRKJlhbd/XjzYtiwIFwWERERiX/lq8GAUVCrtZ+b9Ah89Xh0Xv/nCfBcZ1jxrZ+r2RKGfK7P50VVpjx0f9TXy76GWcPD5RERkdgwLeIA0LaXui/JQ4hs0bJgrLvrTIqFFs3l0PbtgDf6wfqf/FyXB+CkIeEyiURTegOo09bXatEiIiIiRVWhOgwcBTWa+7mJf4Zvnj7y18zOgokPwRuXwO5Nfv74/jDkM6jV8shfW7wW3fIfFv/JfW4TkYiIlE6rpsPqGb7uGHA9rNl5kJzmxnu3wpIvw2VJcFo0l4PL3AvD++c/VKjTbdD5tnCZRIpDvhYtY8PlEBERkcRRsRZc/SFUa+LnPvkjfPf84b/WjrWuVeJX//RzKWXhwqehzzPuIFKJnu5/d7ffg/uCYuKfw+YREZFwpr7gxw1PhTrHhcuSVgmadvH1PLVoKS5aNJeCZWfCO9fA0q/8XMch0OVPwSKJFJuWPfx41fewfU24LCIiIpI4KtVxC+fpjfzcuDvh+5cL/xqLJ8GznWDZZD9XvSkMnggdBoAxUYsrudIbwJl3+Xr6K7BiWrA4IiISyK5N8ON7vu44OFyWPK17+/G8jyAnO1yWBKZFczmwnGwYeSMsHOfn2l4O3f+hD+WSmGofB1Ua+jryn30RERGRoqhylFs4r9LAz310K8x4/eDPy8mGSX9zZwvt2uDnj+sL108Ku9OtNDj1JqjZytcf/d61yBERkdJjxmuQvc+NK9SCVhce/PqS0LwbJKW68e6NsOybsHkSlBbN5desdR8If3zXz7XsBb3/A0n6R0YSlDHQMqJ3pVq0iIiISDRVbQRXj4ZK9fzcqJth9tsHvn7nenj9Ypj0V8C6ueQ06PUEXPKCuz1bildyKvSKOLx13RyYegStdUREJD7lZMP3L/r6hKshpUy4PHnKpUPjM32tFi3FQiugkp+17qCbH171c43Phr4vQXJKuFwiJSGyRcuSL9whuCIiIiLRUq2xWzivWDt3wsLIG+CnkfmvWzoZnu3s2rLkqXoMDP4UTrxOd36WpEanQbsrff35w7B9dbg8IiJScn7+FLYud2OTDCdcGzZPpMgd7/M+hJyccFkSlBbNJb8vH4UpT/u6wSlw+RuQkhYuk0hJaXgalE134+wMWDQhbB4RERFJPDWawcDRUL6Gq20OvDvI/8L75T/h1Qtg51r/nNa94YYvoO7xYTKXdl0f9J8RM3bC+HvC5hERkZIxLeIA0JY9XLu1WNGyJ5jcZd0da9zZbBJVWjQX79tn3M6JPHXawhUjoEyFcJlESlJyiusNlkctWkRERKQ41GoJA0dBuaquttnwzrXwcnf47CG3kA6uX2n3f0C/V6FslXB5S7sKNaDrn3099wNtrhARSXSbF+f/b33HIeGyHEiFGnB0J1/PHRUuS4LSork4P7wG4+/2dY3mMGCk65MkUppEtmj5+WPIzgyXRURERBJXneNgwAd+MTwnE1Z86x9PbwiDPoaTb1A7lljQfiDUP8nXY++EzL3h8oiISPGa9iL/O1OkRnM45oygcQ4oskXL3NGu5bJEjRbNxfVQ/PC3vk5v6D7AV6gRLpNIKE26uEO2APZug2Vfh80jIiIiiateO7dRJa1y/vkWPeGGL+GoE8Lkkl9LSnKHgppkV29eDJOfCJtJRESKR8ZumPG6rzsOjs0vsFtdAOTm2rYc1swMGifRaNG8tJs61PVQzLsFtGIdd6toLPVpEilJaRWh8Vm+VosWERERKU5HnQBXvgtVGrjF8/MedmcK5bVukdhRpw2cfKOvJz8Om34Jl0dERIrHj+/B3q1unFoBjr88bJ6CVKoDDU729dzR4bIkIC2al1bZWTDmDhh7h+uhCO6D+cAPoFrjsNlEQots0TJ/jG5xEhERkeLV8GT47Qz4w2I47ebY3M0mztn3QKW6bpydAWNu12dFEZFEYi1MG+rr4y+L7XNFWvf247mj9GdSFGnRvDTasxXe6Jv/PwKV6sHA0VCrVbhcIrGieXf+d4vT9pWwdnbQOCIiIlIKJKe6H4ltaZWg2199vfhz+On9cHlERCS6Vn4Pa2b5OtYOAN1fqwv8ePMvsH5uuCwJJqqL5saYcsaYRcaYrYe4brAxZoExZpcxZqkxpnfEY/WMMWNzH1tujInxfzrjzKZf4MWu7sNdnnrtYchnULdtuFwisaRSbajf0ddq0SIiIiIieVr3cefg5Bl/L+zdHi6PiIhEz7QX/LjR6VC7dbgshZHeAOp18LVatERNtHeaPwisPNgFxpjrgduAy4GKwMnAnIhLhgNrgVpAP+BRY8yZUc5ZOi2dDC90gY0L/dyxF8E1Y6Fy3XC5RGLR/i1aRERERETAtc/p8ag/PH7nWvj84bCZRESk6HZtzH/3UMfB4bIcjtYX+vE8LZpHS9QWzY0xHYAewF8Pck0ybmH9VmvtDOuss9Yuzn28CdAJuMdau8ta+x3wBnBdtHKWWj8Mg2F9YM8WP3fm3XDJS1CmfLhcIrGqRU8/XjcHtiwLl0VEREREYkv1JtD5dl9PfR5WzwyXR0REiu6HYe68CoCKtaFlr7B5CqtVxKL5+rmw8edwWRJIVBbNjTEpwFDgJmDfQS5tAdQGmhljlhhjVhpjnjPGVMp9vC2wxlq7LuI5M3Pn5UjkZMPHf4TRt0BOpptLToNLXnSH2CSprb3IAdVsDtWb+XrBuHBZRERERCT2dLoVqjVxY5sDY25zv3+JiEj8ycmG71/29QnXQEqZYHEOS/UmULuNr+eOCpclgURrxfR2YLa1dtIhrquW+9c+QEegHdAEeDx3viKwfz/0rUAl5PDt2wFvXQFTnvZzFWrBtWOhTd9wuUTiRb4WLR+FyyEiIiIisSclDXo+5utV02H6K8HiiIhIESz8GLYtd2OT7BbN44latERdkRfNc1uq3ATcUYjLd+b+9W/W2o3W2o24di69Ix6vst9zqgA7ipqz1Nm6HF48HxaO93O127gDP+ufGC6XSDyJbNGy7BvYvTlcFhERERGJPU3OhuMu8fXEP8PO9eHyiIjIkZk21I9b9YLK9cJlORKRLVrWzIItS4NFSRTR2GneGagJ/GSMWQu8D1Q2xqw1xpy037ULgL2ALeC1ZgP1jDG1Iubakf+gUDmUFVNh6Dmw/ic/16IHXDfenaorIoVT/0R3dwaAzYafPw2bR0RERERiz/mPQFplN967DT79U9g8IiJyeDYugl8+83XHIeGyHKlaLaFGc1/P+zBclgQRjUXzEcAxuMXtdsBg3M7wdsCMyAuttXuA14G7jTFVjTHpwF3AqNzHfwG+Bh4xxpTPXXS/EngxCjlLh9lvwys9YdcGP3f67+CyNyCtYrhcIvEoKRladPO1WrSIiIiIyP4q1YFz7vP1rOGwdHK4PCIicni+f8mPa7aCozuFy1IUkbvN56pFS1EVedHcWrvHWrs27wfY7KbtWmttpjFmnDHm3oin3AqsApbgdp4vA26LeLw/cBSwAXgP+IO19oui5kx4OTkw8SF4f4g/6TcpFXr/F7o+qAM/RY5UZIuWRRMhc2+4LCIiIiISmzoOhrrH+/qj2yArI1wetYhu+gAAIABJREFUEREpnIzdMPN1X3ccBMaEy1MUrXv78cqpsG1VuCwJIOorqdbaSdba9Ii6u7X2kYh6l7X2WmtturW2trV2iLV2R8Tjq3KfU8Fa28BaO3T/95D9ZOyGd6+Br/7p58pVg6tHQ/srg8USSQiNz4TUCm6cuQuWfBk2j4iIiIjEnqRk6PkEkLvQsnEBTHk6aCQRESmEOe+41loAZSpC28vC5imKOm2g6tG+1t3yRaLtx/Fu+2p4uTvMHeXnarRwB342Oi1cLpFEkVoOmp7ja/2hIyIiIiIHUv8EOPFaX3/xD9iyLFweERE5OGvzHwB6/OVQtnK4PEVljFq0RJEWzePZ6hnuwM81M/1cky4w+FOodky4XCKJJrJFy8Lxrh2SiIiIiMj+uvwJKtR046w9MO6usHlERKRgK6bC2jm+7jg4XJZoiWzRsvwb2Lmh4GvloLRoHq9++gBe6g471vi5k2+EK96GslXC5RJJRM3PB5PsxjvXwarpYfOIiIiISGwqVxXO+4uvF46D+WPC5RERkYJF7jI/ujPUahUuS7QcdQJUru/GNkd3yxeBFs3jjbXw5aPwztVu5wK4xbyej0P3v0NySth8IomofLX87Y70h46IiIiIFKTtZW7xJc+4uyBjV7g8IiLyazs3uA2peRJhlznktmi5wNeR7ZzlsGjRPJ5k7oX3r4fPInYulK0CV73nTvcVkeLToocfLxgbLoeIiIiIxDZjoOdjkJTq6m0r4Iu/h80kIiL5/fAq5GS6caW60LLnwa+PJ60j+pov/Qp2bw6XJY5p0Txe7FwPr14Ac972c9WawOCJ0OTscLlESouWEYvmGxfCxkXhsoiIiIhIbKvZAk67xddT/gPr5obLIyIiXnYWfP+yr0+4BpJTg8WJugYnQ4VabpyTBQvGhc0Tp7RoHg/W/ugO/Fw51c8d3RkGT4AazcLlEilNqh4NtY/z9QL1phQRERGRgzjjTkhv6MY5WTDmdtduU0REwlo4HravdOOkFLdonkiSkqFVL1/PGx0uSxzTonmsWzAOXjrf3dKXp8PVMGCk67MsIiUnskWLDnQSERERkYMpUx66P+rr5d/AzDfD5RERESfyANBWF0ClOuGyFJfWvf34l89g7/ZwWeKUFs1jlbXwzVMwvD9k7HRzJgnO/ytc8O/Eum1EJF5E9jhbMdW1TRIRERERKUiLbtAyYrffp/ert6yISEgbf4bFk3zdcUiwKMWqUScol7vZNjsDFn4cNk8c0qJ5LMrKgNG3wCf3Abm375WpBP3fglN/4w6WEZGSV/d4qFw/t7Duli4REZEYYYxJNcY8bYzZnPvzlDEm5SDXX2iMmWmM2WWMWW2MuTHisROMMZONMduNMYuNMQNL5u9CJAF1+xuklnfj3Ztg4p/D5hERKc2mveDHtVpDo9PCZSlOySn5z2abNypcljilRfNYs3szvHYRzHjNz6U3hEGfQPPzw+USEfeFVYvuvlaLFhERiS33AZ2AY3N/OgP3HuhCY0w34L/ArUDl3Osn5T6WDowFXgeqAv2Bp4wxnYo3vkiCSm8AZ93t6+mvwIppweKIiJRaGbvyt8nqODixN6a2imjR8vME9/cvhaZF81iyYaE78HPZZD/X4BQY/BnUbh0ul4h4kS1aFk/SHzoiIhJLrgP+Yq1dY61dAzwMDCrg2oeAB621k6y12dbaLdba+bmPnQbss9Y+m/vYd8D7wOBi/zsQSVSn/AZqtvL1R7+H7KxweURESqPZb8O+3N7eZSpB28vC5ilujc+EtMpunLUHFk0ImyfOaNE8ViyaCC+cC1uW+Lm2l8PVo6FizXC5RCS/oztBWhU3ztrrDtQQEREJzBhTFagPzIyYngk0NMZU2e/aCsAJQGVjzHxjzFpjzAhjTN4pWEnA/tuukoC2xZNepBRIToVej/t63RyY+ny4PCIipY21+VuztOsPaRXD5SkJKWn575afqxYth0OL5rFg6lB4ox/s2+bnujwAFz3r/gEXkdiRnArNuvpaLVpERCQ25P3WtzViLm9cab9rq+IWxQcA5wNNgUwgrz/gN0B5Y8zNuX3STwcuwrVxEZEj1eg0aHelrz9/GLavDpdHRKQ0Wf4trPvR1x1LyQ10rS7044UfQ+becFnijBbNQ8rOgjF3wNg7wGa7udTycNnr0Pm2xO6rJBLPIlu0LByvW2tFRCQW7Mz9a+Su8rzxjgKufdJau8xauxN4AOhijKlgrd0M9AIuB9YCfwNeBjYVS3KR0qTrg1A23Y0zdsL4e8LmEREpLaYN9eNjzoCaLcJlKUlNu0BqBTfO2AmLPw+bJ45o0TyUPVvhjb75/6WtVA+uGw+tLgiXS0QOrem5kJTqxnu2wIpvw+YREZFSz1q7BVgJtIuYbgessNZu2+/arcBywB7gpUzuNVOstZ2stdWttZ2BOsAXxRJepDSpUAO6/tnXcz9Qj1mReLNjHcwcDrs2hk4ihbVjHcwd7euOQ8JlKWmp5fLfLR/5v4MclBbNQ9j0C7zYNf+3O/Xaw5DPoO7x4XKJSOGUrey+mc6jFi0iIhIbXgb+aIypk9uf/F7ghQKufR74rTHmKGNMOeBPwMTcXecYY9obY9KMMeWMMUOAs4B/Ff/fgkgp0H4g1D/J12PugMw94fKISOHtWAfPnwkf3AjPdlaLpXjxwzDIyXTjykdBix5h85S01hEtWhaMgayMcFniiBbNS9rSyfBCF9i40M8dexFcMxYq1w2XS0QOT2SLlvlj3KEiIiIiYT0ETAHm5f58AzwCYIx51hjzbMS1fwMmArOAFUB5XI/zPL8F1gEbgH7AOdZarQyIRENSkjsU1CS7essSmPxE2Ewicmg52fD+ENixxtU7VsNbV0DG7rC55OCys2D6y74+4VpITgmXJ4Rm50FKWTfeuw2Wfhk2T5zQonlJ+mEYDOvj2jnkOfNu6PsylCkfLpeIHL7Ib6a3LoP1c8NlERERAay1mdbam6y1VXN/brbWZuU+dqO19saIa7Ottbdba2vk/vSz1q6NePxaa226tbaitfY8a+1PIf6eRBJWnTZw8o2+nvwEbFwULo+IHNpXj8OS/TqVrZ4Bo27SJqpYtmAsbF/lxkmp0GFg2DwhpFWCJl18rRYthaJF85KQkw0f/xFG3+JvB0lOg0tehLPv0YGfIvGocl2o18HXatEiIiIiIofj7HvcuVYA2Rkw9nYtvInEqqWTYdIjvq5Qy49/eh+++EfJZ5LCiTxLsPWFUKl2uCwhRbZomT/GrVXKQWnRvLjt2+Fu15nytJ+rUAuuHQtt+obLJSJFt3+LFhERERGRwkqrBN3+6uvFk9zim4jEll0b4b3BYHNcnd4IbvoOGp/lr5n0CPz0QYh0cjAbFsCSiFYkpekA0P017+Z22gPs3gjLvgmbJw5o0bw4bV0OL54PC8f7udpt3IGf9U8Ml0tEoiNy0XzNTNi2MlwWEREREYk/rXtD03N9Pf5e2Ls9XB4RyS8nB0be4PuYJ6VCv1egfDX31+pN/bUjb4TVM0OklIJMizgPvfZx0PCUcFlCK5ee/4ueeWrRcihaNC8uK6bC0HNgfUT7xxY94LrxkN4gXC4RiZ6aLaHqMb5eMC5cFhERERGJP8ZAj0f9AW0718LnD4fNJCLeN/+GRRN8fd5DcFRum85yVaH/CChbxdVZe2B4f9ix9tevIyVv3w6YOdzXHQerPXJki5a5o92XQlIgLZoXh9lvwys9YdcGP3f67+CyNyCtYrhcIhJdxqhFi4iIiIgUTbXG0Pl2X099XrtVRWLB8m9h4kO+btkr/wG+ADWaQr9XwSS7esdq16I3c0/J5ZQDm/02ZOxw47Qq0PbSsHliQYue/p/VnWth5bSweWKcFs2jKSfH/Qf1/SHuIBdwt+70/i90fRCS9D+3SMKJXDRf+hXs2Roui4iIiIjEp9N/B9WauLHNgTG36ZA2kZB2b4Z3rwOb++9hlYbQ++kD71RucjZ0/7uvV02HUTfrYN+QrM3fmqXdFVCmQrg8saJCdTj6dF+rRctBaRU3WjJ2w7vXwFf/9HPlqsHVo6H9lcFiiUgxa3AylK/uxjlZ+W/dExEREREpjJQ06PmYr1dNh+mvBIsjUqpZCx/8H2xf5eqkFOj7kmvHUpCThsCJg3z947v514ekZC37BtbP9XXHQQVfW9q02q9Fi77cKZAWzaNh+2p4uTvMHeXnarZ0B342Oi1cLhEpfknJ0Ly7r9WiRURERESORJOz4bhLfD3xz7Bzfbg8IqXVlP/AwvG+7vIANOh46Od1/zscc6avP/uLW5SUkjdtqB83PgtqNAuVJPa0ugDIvWNi23JYPSNonFimRfOiWj3DHfi5JqLnXNNzYdAnUO2Ygp8nIomjZQ8//vlTyNoXLouIiBRdTlboBCJSWp3/CKRVduO92+CT+8PmESltVn4PEx7wdbPz4dSbC/fc5FTo94o7pyDPyBtgzeyoRpRD2LEW5n3o645DwmWJRZXqQMNTfK0WLQXSonlR/PQBvNQddqzxcyffmP/0ZBFJfI3PhpRybpyxw/U2FxGR+GItLP4CRgyATYtCpxGR0qpSHTjnPl/PfguW6LOlSInYswXeudZ/eV6pHvR55vDOpytfza0JpeWuCWXuhuH9Yce66OeVA5v+iv//sHJ9aN4taJyYpBYthaJF8yNhLXz5KLxzNWTlnohskqHn4+52nOSUsPlEpGSVKQ9NzvH1/LHhsoiIyOHZuw2+ew7+cxIMu1C7bUQkvI6Doe7xvh5zO2RlhMsjUhpY6w7v3Lbc1SbZ9TGvUP3wX6tmc+j3MpjcJbftK2HElZC5N3p55cCyM+H7l3194rVaozuQVhf48eZf8vd/l//RovnhytwL71/velPlKVsFrnpPBwuIlGaRLVoWjIOcnHBZRETk0Nb9BB/eCo+1gnF/gI0LQycSEXGSkqHXE/yv5+zGBTDlqaCRRBLe1Odh/ke+PueP0OjUI3+9pl3g/L/6euU0+PC32tFb3OaPgZ1r3TgpFTpcHTZPrEpvAPU6+Fq99w9Ii+aHY+d6ePUCmPO2n6vWBAZPdIe2iEjp1byb30mwYzWs0WEaIiIxJysD5rwLL3WDZ06D6S9D5q6ICww07QpV6geLKCICwFEnwInX+fqLR2HLsnB5RBLZ6hnwSURbpCbnwOm/L/rrnnwDnHCNr2ePgMlPFP11pWDTXvDjY/tAxZrhssS61r39eO6ocDlimBbNC2vtj+7Az5VT/dzRnWHwBJ3CKyJQoQY0ONnXatEiIhI7tq10dwk+cSy8NwiWT8n/eLmqcNot8Nsf4Kp3oUzFMDlFRCJ1+RNUyF3wydoD4+4Km0ckEe3d7vqYZ+e2QKpYBy56/vD6mBfEGOjxT7d2lGfig243tETf+nn5zxfTAaAH1zqir/mGebDx53BZYpQWzQtjwTh46XzYtsLPdbgaBox0hzyIiAC07OnHC7RoLiISlLXwy+fw1pXwrzbuPJpd6/NfU6899P4v3DYPzvsLVGscJquIyIGUS4fzHvb1wnFabBOJJmtdy5QtS1xtkuCSF6K7Ozk5FS4dBlWPyXtTeG8IrJ0TvfcQJ3KXeZ020OCkcFniQbXGULuNr7Xb/Fe0aH4w1sI3T7mTjjN2ujmT5PpSXfBv9x8/EZE8LSL6mq+fC5sXh8siIlJa7dkK3z4DT3eE1/q4/qQ24pyJ5DQ4/goY8hlcPwnaXwmp5UKlFRE5uLaX5t+lOu4uyNhV8PUiUnjfvwQ/jfT1mXfBMZ0Lvv5Ila8GV4yAtMquztzl1pl2rj/486Tw9m6HWW/5uuMQt9NfDi5yt/k89TXfnxbNC5KVAaNvye1rlXtQQ5lK0H8EnPob/csnIr9WvQnUbOlrtWgRESk5a+fA6N/C461g/N2wab9bTNMbQdcH4fb5cNEzrl+wiEisMwZ6PuYOtAN39/MXfw+bSSQRrJ0D4+/x9TFnwBl3Ft/71WwBfV/y52BtWwEjroKsfcX3nqXJ7BF+s2vZKtCmX9g88aJVxKL5mlmwZWmwKLEoqovmxphyxphFxpitB7lmkjFmnzFmZ8RPvcI+XiJ2b4bXLoIZr/m59IYw6BNofl6JRhGROKMWLSIiJSdrH8x+B148D57tBD+8Cpm7Iy4w0Ox8uOId+O1MOP13aq0nIvGnZgt37kKeKf+BdXPD5RGJd/t2wDvXQHbugnWFmnDxC5CUXLzv26yraweXZ8V38OGtrsuBHDlr87dmaXcVlCkfLk88qdUSarTw9VztNo8U7Z3mDwIrC3HdXdbaihE/qw/z8eKzYaE78HPZZD/X4BQY/BnUbl1iMUQkTrWIWDRfPgV2bQqXRUQkUW1dARP+DI+3hvcHu186I5Wr5hbIfzsDrnzbbXqIxoFeIiKhnHGn28gFkJMFY27XQpvIkbAWProNNi3KnTBw8VCoVLtk3v+U30D7Ab6e9SZ882TJvHeiWjoZNsz3dcdB4bLEI7VoKVDUfnswxnQAegB/jdZrlrhFE+GFc/0hEABtL4erR0f3IAgRSVz12rsT18H10F04PmweEZFEkZPjPqsNvwL+3RYmPw67N+a/5qgToc+z7mDPrg9CtWMO/FoiIvGmTHno/qivl38DM98Ml0ckXs14Hea87evOt0OTs0vu/Y2Bno9Do9P93KcPwIJxJZch0Uwb6sdNznFtU6XwIlu0rJwG21aFyxJjorJoboxJAYYCNwGFach0nzFmszFmhjFm4BE8Hn1Th8Ib/WDfNj/X5QG46FlISSuRCCKSAJKSoGXEgaBq0SIiUjR7trhWBE+fCK9fDAvG5D/YM6Wsuw33+kkwZCK06w+pZUOlFREpPi26Qctevv70ftdaVEQKZ/08GBvRt7zhaXDWPQVfX1xSysClr7nzVgCw8N5gWPdTyWeJd9tXw7yPfN1xSLgs8apOG6h6tK/nf1TgpaVNtHaa3w7MttZOKsS19wBNgNrA3cBTxpiLDuPx6MrOvbVt7B1gs91canm47HXofJsO/BSRwxfZomXRRMjYXfC1IiJyYGtmwaib4bFW8PG9sPmX/I9XPQbOe9jtKu/zH3enj4hIouv2N/f7KsDuTTDh/wWNIxI3MnbB21dD1h5Xl6sGfV+E5JQweSpUhytGQJlKufl2wvDLYdfGgz9P8pv+il/Lq9IQmp8fNE5cMgZa9/b13FHhssSYIi+aG2Oa4HaY31GY6621U6y126y1mdbaj4HngMsK+3hU7dkKb/TNf2BApXpw3XhodUGxvKWIlALHdPYffrL2wOJJQeOIiMSNzL0w6y3XLu+5M9yh7Hm/3AJgoHl3uOo9uOUHOO1mHewpIqVLegM4625f//AqrJgaLo9IvBj7B9i4wNcXPw+V64XLA1CrlVu4J3ez5tblMOIqd9C5HFpWhls0z3PitcV/mGuiahWxaL7sG9i5PlyWGBKNneadgZrAT8aYtcD7QGVjzFpjzEmFeH5OER8/Mpt+gRe7wuLP/Vy99jDkM6h7fLG8pYiUEilp0OxcXy8YEy6LiEg82LLM9fN8ojWMvMH1U4xUvjp0+j38bhZc8RY0PVcHe4pI6XXKb6BWa19/dJu7g1pEDmzWWzDzdV+f/jto1jVcnkjNz3fnsORZPsX9O62Dfg9t/oewc50bJ5eBDiXT3TkhHdUBKtfPLaxatOSKxm8bI4BjgHa5P4OBHbnjGZEXGmPSjTE9jDHljTHJxpguwA3Ae4V5PGqWToYXusDGhX7u2IvgmrFQuW5U30pESqnIFi0LxkNOdrgsIiKxKCcHfp4Ab14G/z4evv6XazUQqf5JcNHz8Pu5cO7/g6qNDvRKIiKlS3KqO0gwz7o5MPX5cHlEYtmGhW4ROk/9k+Cc+8PlOZDTbnHns+SZ+bo7z0UObtqLfnzsxVChRrgs8c6Y/B035o4OlyWGFHnR3Fq7x1q7Nu8H2Oym7VprbaYxZpwx5t7cy1OBB4C1wBbgCeB2a+07hXy86H4YBsP6uEOl8px5N/R92Z1ILiISDc26QlJuf7zdG3XbrIhInt2b4Zun4KkO8MYlsHA8ELGbKqWc2yl0/Rcw+FM4/jId7Ckisr9Gp+ZfZPv8YXcgnoh4mXvgnWsgc5ery6ZD35fcF0+xxBjo9Tg0PNXPfXo/LPwkXKZYt+4nWPa1rzsODpclUbS+0I+XfKmDpoGon3iQexhoekTdPWK8ATj5IM896ONFkpMNn/4Jpjzt55LToM9/oU3fYnlLESnFyqXD0Z18P/MFY9wvNyIipdWqH9yOoB/fhay9v368WhP3C0+7/lCuasnnExGJN10fdJ8x92xxhwiOvwcufTV0KpHYMf5uWP+Tr/s8484FiEUpaXDpazD0HNi2HGwOvHud20BQq1XodLEn8mzCusdD/RPDZUkUDU6GirVdyxubDQvGQvurDv28BFY6mkHaHHjrivwL5hVqwbVjtWAuIsUnskXL/DHqSycipU/mXpj5pvsFcOjZ7nbjyAVzk+T+WzlgJNz8PZz6Gy2Yi4gUVoXqcO6ffT33A1g0IVwekVgy5938h0SechO07BEsTqFUrOnObilT0dUZO1wbu12bDv680mbvNpg1wtcdh7jd+lI0ScnQspev1aKllCyab1mWe+tvrtpt3IGf+iZKRIpTi+5+vHkxbFhQ8LUiIolky1L45H54vBV88H+wanr+x8vXgM63w+9mQ/83ock5OthTRORItB/gejTnGXOHa0khUppt+gU+/J2v63VwZ6PEg9rHwiUvALmLwFuXwdsDICsjaKyYMuut/C13jrskbJ5EEtmiZfHnsHd7uCwxoHT8dpK9z49b9ITrxsfuLTkikjjSG7hbxfIsGBMui4hIccvJdr033+gH/24H3zwJe/brhdjgFLj4BbhtLnT5kz6PiYgUVVKS64Vskl29ZQlMfiJsJpGQMvfCO1e7lkUAaVWg38uQUiZsrsPRonv+Rf5lX8PY23XnMrj/DSJbs7S/SucTRlOjTlCumhtnZ8DCj8PmCax0LJrnOf13cNnrkFYxdBIRKS32b9EiIpJodm+Gr/8NT7aHN/vBz5+Q72DP1PJwwjVww1cw6GNo28/17RQRkeio0wZOvtHXk5/49R0+IqXFJ/fB2jm+7v00VD06WJwjdvrv4Pj+vv5hGHz7TLg8sWLJl7Bxoa87DgqXJRElp+RvYzRvVLgsMaCULJob6P1fd1CKbv0VkZIU+QfOqumwfU24LCIi0bRyOoz8P3ispTtsfeuy/I9Xbwbd/g63zYML/g1124bJKSJSGpx9D1Sq58bZGfDaRbB6RthMIiXtpw9g2lBfn3R9/nYT8cQY6PWv/O2XPvkj/FzKzy2I/P+36blQrXG4LImqdR8//nkCZOwKlyWw0rGCXO1oaH9l6BQiUhrVPg7SG/p64bhwWUREiipzD8x4HZ4/C144B2a9mb8NnklyBwgNHAU3T4NTboRy6cHiioiUGmmVoPdTkJTi6r3bYFgfWD0zbC6RkrJ5CYy+xdd12kLXh8LliYbUsnD5G1Alt52dzYF3ry29Z2VtWwXzx/q645BwWRLZMWe6tkYAWXvg50/D5gmodCyaJ+sWYBEJxBi1aBGR+Ld5MXz8R7erfNRNv969WKEWnHEn3DrH/XLX+Cz33z8RESk5Tc+Ffq9ELJxvhWG9Yc2soLFEil1WBrx7HezLPbSwTCX370Jq2aCxoqJiLeg/HFIruHrfdnjzMtcer7SZ/jLYbDdObwjNuobNk6hSykCLbr6eNzpclsBSQgcQEUl4LXvAd7n955Z86X5x+d+XeRF9f/Md7FJC8/nOkgmcJU+t1lChBiISWE6221kybSgsKuBW4IanuV6SrS6MrwO2REQSVasLoO/L8M41bnEpb+F84Gi1yZLENeEBWP2Dry/4F1RvEi5PtNVpAxc/DyNyOyhsWQJvD4QBIyE5NWy2kpKVAdNf9fWJgyApOVyeRNfqQpg9wo0XfuwO2E2EL6EOkxbNRUSKW8PToGy6+6UlOwOeOyN0otiWlAqdfg+dby+VfzCLBLdrI8x4Db5/CbYu//XjqRXg+MvcLyt1jiv5fCIicnCtL4S+L7mdtzYb9myBYRfC1R+6xTeRRDJ/LHz7X1+fcA206RssTrFp1Qu6/AkmPujqpV/B2Duh1xOl4+6+eaNh13o3Tk6D9gPC5kl0Tbu4z/yZuyBjJ/zyWf7z2kqJ0tGeRUQkpOQUaNE9dIr4kZMJX/4Dnj0d/j979x3fVNn+cfxzku7FFpE9ZG8QUcoUEWSICCooiIqCIo8oDnBP8KcPiDyKyFAQFBDZG1lCQaaAIHtDtQotpaW7yfn9cSc5CW2h0HHS9nq/Xn2R+z4nydWykm/uc92nNptdjRBFg67DuZ2wYDCMqwNr38sYmJeuBV0+gxGH1Rs0CcyFEMJ71esJvaeB5liJmXQJZvSAqAPm1iVEboo9C4ueM8Zl60PnT8yrJ6+FvwwNHzHGu7+DHZPNqyc/7XDbALT+QxBcyrxaigLfQKjZyRgX0RYtstJcCCHyQ7tR6k1K9DG3SbcVAR6rA3IyT8b5XHvsG5i/2bqTLkPKZXU7+jjM6AZNHleb+ASVRAiRy1IT4cDP6o1I1B8Zj2tWqN0VWjwDVVoXjZVMQghRWNR7UH0oOn+QY8V5jGPF+TIoW9fs6oTIGVuaupoiOVaNfYNVayLfQHPrykuaBt0nQPQJiNyl5laNhFI11MrgwipqP5zbZoxbDDKvlqKkTg/4c6G6fWSFapFTxNoxSmguhBD5oURleC7C7Cq8X0o8rP8Ytk/C1et8zyzVR63zJ2pVgYR2QuRc9AnYOQ32zoLkyxmPh5RVlzc3Gwhht+V3dUIIIXJL/V6g22HBM+rXxGiY0V21apHgXBRk6z6A8zuNcbdxUKamefXkF98AePRHmNIe4iLV3+t5T8Iz66D07WZXlzd2TjVu39YEyjczr5ai5PZO4BMA6cnq/cLpTWpGmStgAAAgAElEQVTD6SJE2rMIIYTwHv6h0OUT9aKvrFvPzYQLMP9pmPUQXDptWnlCFHh2Oyx7Gf7XFLZ9lTEwrxyuVmm99Ce0f0MCcyGEKAwa9IYHJ4PmePufeFEF5/8eMrcuIW7W0TWwdYIxbvw4NHrUvHryW2hZ6DsbfIPUOOUy/PgIJMaYW1deSIqFP34yxnc8Y14tRY1/CFR3u4LhYNFr0SKhuRBCCO9Tvhk8uwHu/QB83C6xPLEOvmoJWyaALd28+oQoiHQdVr4Gu6Z5zvuFwB2D4Lnf4MnlalWi1decGoUQQuSNhn3gwW8yCc4Pm1uXEDfqciQsHGyMy9SG+z81rx6zlGuk/k47xZyAeQNV25rCZN9sSEtUtwNLqNepIv/UfcC4fXhZkXsPLqG5EEII72T1hVYvwtBtnp9wpyfBL2/DlHYQudu08oQocH79P9jptolSqdvh/v+qjT27jpXL9IUQorBr+DD0nIRrM5mECyo4v3DU1LKEyDZburr6NMmxotonEPpMB79gU8syTd0e0P4tY3zqV9XjvLCw2z1bszTpX7h71nujmveBxbGYJjEazm41t558JqG5EEII71aiCjw+Hx6aBkGljfmo/TC1I6waBSlXTCtPiAJhxxTYOMYYl28Gz25UG3z6h5pVlRBCiPzW6BHo+TVGcP6v2nhdgnNREGwcA2d/M8b3fwa31DGvHm/Q5hWo39sY75yqXvcVBqc2QvRxx0CDO542s5qiKbA4VGtnjItYixYJzYUQQng/TVP9OF/YCU0eN+Z1O2ybCF/dCUdWmVefEN5s/8+w4lVjXLom9Jun+hQKIYQoehr3hZ4TcQXnV/5RwfnFY6aWJcQ1nVgPm8ca44aPeL4vKKo0DR74Em5rasytfB1ObDCvptyyw22V+e2d1GIqkf/q9jBuH1qqrgAoIiQ0F0IIUXAElYQHvoInlkGpGsZ83HmY/Qj89ATER5lXnxDe5vhaR99PXY3DKkD/hRBcytSyhBBCmKxxPxW0uQfn07vBxePXvJsQpoiPgvnP4Ho9U6oGdB2nAmOhWpb0nQ2hjg3cdRvMe6Jg/32OPQdHVxrjOwaZV0tRV6sraFZ1+0oUnN9pbj35SEJzIYQQBU/V1jBkC7R5zeixBnBwEXzZAnZ9W6Q+ARciU+d2wtz+YHds2BNYUgXmxSqYW5cQQgjv0ORx6PE/Y3wlSq04jz5hXk1CXM1ug/mD1Oa1AFZ/1cdcrpjzFHor9P1R9XkHSL6sFhUlXTK3rpu1+zt1VTGoFeY1OppaTpEWXAqqhBvjg4vNqyWfSWguhBCiYPINgA5vwpAIqNjSmE+5DMtegu+6wL+HzKtPCDP9exh+7ANpiWrsGwyP/QxlappblxBCCO/StD90n2CM4/9WK84lOBfeYtNncHqzMe7yCdzawLx6vNltTeDBr41x9HGY96TaQLUgSU+B3TOMcfOnwSLxpamubtGi6+bVko/kT50QQoiC7Zba8ORK6PY5+Bcz5s9tg0mtYf3HkJZsXn1C5LfYszDzQWNlkcUXHv0BKjQzty4hhBDeqdkT0P0LYxz/F8zoDjEnzatJCIBTm2DjJ8a4Xi9o9qR59RQE9R6EdqOM8ckNsPoN8+q5GQcXG1cW+ARI73pvULs7rnZel8/CX3tMLSe/SGguhBCi4LNYoPlT8MIOqNvTmLenwaZPYVIrOLU56/sLUVgkXFSBefxfjgkNHpoC1dubWpYQQggv12wgdBtvjOMiYXp3iDllWkmiiLvyr2rL4uxjXqKq+nBH+phfX9vXVXjutOMb1b6yoNgxxbhdv7fa10qYK7QsVHK7uvvQEvNqyUcSmgshhCg8Qm+Fh2dA37lqw0On6OOqR+fioZAYY159QuSllHj4obf68+7UbZznmyYhhBAiK82fVJsrOsWdVyvOL502rSRRRNntsOBZtUEtgNVP9TEPCDO1rAJD0+CBiapdi9OKV9XKfW/39z44v8MYt5ANQL1GHbcWLQcXF4kWLRKaCyGEKHxqdYah26Hl86C5/Ve3ZxZ8eQf8Ma9I/CcvipD0FJjTz/NSyfZvqSswhBBCiOy642m4/7/G+PI5teL80hnzahJFT8Q41VbEqdPHcFtj8+opiPyC4NEfIeRWNbanqw3ivX2/gp1Tjdvlm3sG/8Jcdbobt2NOwj9/mldLPpHQXAghROHkHwKdx8CgdZ6bBSVehAWDYNZDsnJKFA52m7p82X310J1DoM0r5tUkhBCi4GrxzFXB+Vm1OWjsWfNqEkXHma2w4WNjXKe7+jMpblzYbdD3R9UXHCA5FmY/Ckmx5taVlaRLanGT0x2yytyrFK8I5d32SCoCLVokNBdCCFG4lW8Kz2yEez8En0Bj/sQ6+KolbPmi4O0oL4STrsPylz1ftDZ4GO4bIz0/hRBC3LwWz0CXT43x5bMwvSvEnjOvJlH4JUTDz0+Dblfj4pWgx5fymiYnyjeDnhON8cWj8PNT3vn+Z++PkJ6kbgeVkhaD3sijRYuE5kIIIUTBZ/WBVv+BodugRkdjPj0JfnkHprSDyN2mlSfETVv/Ieyeboxr3KveGFnkJZ4QQogcunMwdP7EGMc6gvPL582rSRRedjssGmJsZm7xgd7TIbC4qWUVCvUfgjavGeMT6+CXt82rJzN2u2drlib9wTfAvHpE5uq6heYXDsGFo+bVkg/kHZUQQoiio0QVeOxneGgaBJU25qP2w9SOsHKk2kxRiILgt4mweawxrtBCbYRr9TWvJiGEEIVLy+fgvtHGOPaMBOcib/z2Pzi2xhh3fB8qNMv6fHFj2o3yXCW8bSLsnmFePVc7uV71yQZAk315vFXJap6tTw8tNq+WfCChuRBCiKJF06BBb3hhp1rB4KTbYfvXqmXLkZXm1SdEduybA6tHGeNb6kK/ueAXbF5NQgghCqe7hqqNGJ0unVY9zi9HmlaSKGTO7YB1Hxjjml3UnzuReywWeHAS3NrQmFv+MpyOMK8mdzvcVpnX7AwlKptXi7i2Og8Ytwt5ixYJzYUQQhRNQSXhgS9h4HIoVcOYjzuvNsj5aQDER5lXnxBZOboaFj1vjItXgscXqD/TQgghRF64+wXo9JExvnQKZnSDuL/Mq0kUDokxqse23dFjO6yCajUnfcxzn18w9J0DIWXV2J4Oc/tDzClz67p0Bo6uMsYtZANQr+beoiXqD/P//OQhCc2FEEIUbVXCYcgWaPs6WNzaWhxcDF+2gJ3TVI89IbzB2W3w0xOg29Q4uAz0XwRh5cytSwghROF39zC41201cMxJteI87m/zahIFm67D4qFw2bHBrGaF3t/KQoC8VKw8PPojWP3VOClGLRhKjjOvpt3fAbq6XbIaVOtgXi3i+srUgtK1jPGhpebVksckNBdCCCF8A6D9GzAkAiq2NOZTLqvLFr/rDP8eMq8+IQD++RN+fFhtYAvgFwqPz4dS1c2tS3gNTdN8NU37UtO0GMfX/zRN87nG+T00TduraVqCpml/aZo2xO1YXU3T1mmadknTtH80TZumaVpQ/nwnQgiv1epF6PieMY454VhxLsG5uAnbJ8GRFcb4nreh0p3m1VNUVGgOD3xljC8chvlPg92W/7WkJcPv3xvj5k/LhvYFgftq80OFt0WL/EkUQgghnG6pDU+uhG7jwb+YMX9uO0xqDes/Ui/shMhvl07DzF6QfFmNrf7QdzaUa2RqWcLrvAWEA/UcX62BNzI7UdO0zsBEYDgQ5jh/o9sps4EjQFmgvuPrnTyqWwhRkIS/BPe4/XMQfRxmdJe2duLGRO6GNW8b4xr3wt0vmldPUdOwD7R+xRgfWwO/mPDf/MFFkBitbvsEQpPH8r8GcePquvU1P7+z0O5xIaG5EEII4c5igeZPwgs7oN6Dxrw9DTZ9Bl/fDac2m1efKHqu/Avf94QrjjBCs6hLl6u2Nrcu4Y2eAj7Sdf1vXdf/Bj4Gns7i3A+BD3Rd36jruk3X9Uu6rh92O14VmKXreqqu6xeAJajgXAghoPUI6OAWeEYfcwTn/5hXkyg4kmJh3pPq9TVAaDm1SaWsMM5f7d+E2t2M8W9feq76zg87phi3G/SGwBL5+/zi5pStDyWqGuNC2qJF/kUSQgghMhN6K/SZDn3nqg2JnJyXIS8aqjYuEiIvJV+GWb3UhmtO3b+AOt2yvo8okjRNKwFUAPa6Te8FKmmaVuyqc4OBZkCYpmmHNU2L0jRtrqZpt7qd9l9ggKZpgY75B4HleftdCCEKlDavQPu3jPHFoyo4v/KveTUJ76frsGQYxJ5RY80CD02D4NLm1lUUWSzw4DdQtoExt+xlOLM1f57/rz0QucsYt3gmf55X5JymFYkWLRKaCyGEENdSqzMM3Q4th6oX9U57Z8GXd8Af89SLfyFyW1oSzO4LUfuNuY7vQdMBZlUkvFuI49dYtznn7dCrzi0BaEB/4D6gBpAGzHQ7ZxWq1Us88DdwDpiCEEK4a/sqtHPrAnXxiATn4tp2TvUM2Nq9AVVamVdPUecfolr+Bd+ixvY0mPu4ag2Y13ZMNW5XaCFtBwuaOm4tWs5sLZT/7udqaO5YiXJc07TYa5yzUdO0FE3Trrh93eZ2PEzTtB81TYtzbDr0dlaPJYQQQuQL/xDoPBqeWQ+3NjTmEy/CgkFqJXDMqazvL8SNsqXDz0/BmS3G3N3DoNVw82oS3u6K41f3VeXO2/FZnDtB1/Uzuq5fAd4F7tE0Ldixav0XVEgeBJQEEvAM1YUQQmn3OrQdaYwvHIYZPeDKBfNqEt7p732w2u1DlmrtoPXLZlUjnIpXhEd/AKufGidGq4UbKVe/fMhFiTFw4GdjLKvMC57yTd2uyNbh8DJTy8kLub3S/APgfDbOe13X9RC3r7/cjv0P9cK8Emrzomc0TZMlVUIIIcx3WxN4ZgN0+gh8g4z5E+th4l2w5QuwpZlXnygcdB2WvghHVhhzjR+Dez9Ul0IKkQld1y+hXoc3dptuDJzTdf3yVefGAmeBzC6T0YDqqLB8gqOn+SXgG6B7XtQuhCgE2o+Ctq8b4wuH4PsekHDRvJqEd0mOg3kDwZaqxsG3QK8pYLGaWpZwqNgCenxpjP89CPMHgd2WN8+39wdIT1a3g0p7biwpCoarW7QcXGxeLXkk10JzTdOaAvcDY3LwGEHAo8Bbuq7H6rp+FBWiZ7WBkRBCCJG/rD5qxe/z26BGR2M+PUntOD+5PUTuNq8+UfD98o5q/+NU637oPkECc5Ed3wFvapp2q6MP+RvA1CzOnQz8R9O08pqmBQLvAOscq84Po1anP69pmo+maaHAM8CevP8WhBAFVrtR0OZVY/zvQbXiPCHavJqEd9B1WDYcYk46JjR4aAqE3GJqWeIqjR6B8JeM8dFVsPa93H8eux12TjPGTQeAj3/uP4/Ie3XcQvNTmwvdnl+5EpprmuaDunxzKJCSjbu8pWlajKZpe65aRV4L8CPjBkYNEUIIIbxJicrw2M+OjYvKGPP/7IepHWHl63l7SaMonLZ8AVsnGOPKraD3t+rDGiGu70PgN+CQ42srMBpA07RJmqZNcjv3E2AdsA/VrzwI1eMcR3DeHegLXAROA8WBJ/LjmxBCFFCaBu3fhNYjjLl//3SsOJfgvEj7fQYcmG+M276mWrMI79PhHajV1RhvnQB7fsjd5zixztjkXrNA86dy9/FF/ql4J4SUVbd1m+eVsoVAbq00HwH8oev6xmycOwp1yWdZYCTwP03THnQcCwESdF1Pdzs/loybFwkhhBDm0zRo0BuG7oAm/Y153Q7bJ8FXd8KRlebVJwqWPbPUKnOnsg3Uxky+gebVJAoUXdfTdF0fqut6CcfXC87X1bquD9F1fYjbuTZd10foul7a8dVH1/Uot+NbdF0P13W9uK7rpXRd76Hr+snMnlcIIVw0DTq87bla9Z8D8P0DhW4FosimqANqMYlT5XDPVj7Cu1gs0OsbuKWeMbdsOJzdlnvPscNtX/GaXVRPdVEwWSxQu5sxPrgk63MLoByH5pqmVUetMH8lO+fruv6bruuXHS/qV6P6Iz7iOHwFCHKsXHcqRsbNi4QQQgjvEVQSHvgSBi6HUrcb83GRMPtR+GkAxEdlfX8hDi+HJcOMcYmq8Ph8CCiW9X2EEEIIb6RpcM+7nptX/7NfrTiX4LxoSbkCPz/p2bv6oanSx9zb+YdCvznq9wtUH/o5j0Hs2Zw/9qXTcGyNMW4xKOePKczl3tf8xHpIvpz1uQVMbqw0bw2UAf7UNC0KWACEaZoWpWlai2zc3+52+wiQBjRym2sM7M+FOoUQQoi8VSUcntsCbUeCxdeYP7gYvrxD9e6z27O+vyiaTkfAvCfVFQqgLnHsvxBCy5pblxBCCHGzNA06vgd3/8eYi9ovK86LmhWvwMWjxrjXNxBWzrx6RPYVrwSP/gBWPzVOvAiz+6oPQnJi5zRc+5CXqgFV2+Xs8YT5KodDYEl1254GR1ebW08uyo3QfC5QFRVuNwYGoVaGN+aqzYI0TSuuadr9mqYFaZpm1TTtHmAwMB9A1/VEx+N9qGlaMU3TbgeGkfUGRkIIIYR38fGH9qNUeF7pLmM+JQ6WvwzfdYZ/D5lXn/Auf+9Tb0Bsji1h/IvB4wugZFVz6xJCCCFyStPg3g/grheMuag/YGZPSLpkXl0if+z5AfbNNsbhL0GNjubVI25cpZbQ/Qtj/M8BWPDszS8CSkuCPTONcfOnVXsPUbBZfaC2Wx/8g4vNqyWX5XhXKV3Xk4Ak51jTtBg1rXoiapq2Etis6/powBd4F5jjOP00MELX9XluD/kCqmXLecfjfqnr+vc5rVMIIYTIV2VqwcAVsOd7WPMOpDguUzu3HSa1hvDh0PoV8A0wt05hnugTMOsh9YEKgE8A9JsLt9Y3ty4hvIyu6+i6bnYZQoib1fED0DXY8Y0a/3MIZvaBx36CwOLm1lZAaZqGpmlml5G1fw+rVeZOFVtC+7fMq0fcvMb91IIf50b1R5bD+g/UlSQ36s+FxgdmvkHqsUXhUPcB4wOR4+sgNQH8gs2tKRfkODS/mmMz0OJu4y5uty8Ad17n/nFA39yuSwghhMh3Fgs0G6g2uFk1Ev5coObtabDpMziwALqPh6ptTC1TmCDub7XSLuGCGmtWePh7qHzXte8nRBFhs9mIjo4mPj6e1NRUs8sRQuRU5cegeFvj/z2A3ZtUewbpb31TrFYrQUFBhIWFERoa6j0hemoizBsIaYlqHFgCek9Tq1FFwdTxPdVm5+gqNY74HMrUhkaP3tjjuG8A2qCPfGhWmFRtq66YTbkM6Ulw7Beo19PsqnJMroMQQggh8lpoWejzHfT7CYq57Q4fcwJmdIdFQ6W/Z1GSdAlm9fLcTKnnRKh5n3k1CeFFbDYbZ8+eJTo6WgJzIQqTYhWgbD11NV6ZWlC8olpIIFeS3BSbzUZ8fDyRkZFERUVh95Z9c1a+BhfcWhE++I36vRcFl8WqNnC9pa4xt2QYnNuR/ceI3A1//W6MWzyTe/UJ8/n4Qa0uxriQtGiRj/qEEEKI/FLzPqjcCjaMhu1fGxs/7p2lVm50HqNWXXjLSiGR+1IT4cdH4d+Dxtx9o298pY4QhVh0dDTJyclYrVbKli1LcHAwFul5KkThoOsQF6k2FVQT4GOBUtXAIvFEdum6TkpKCvHx8cTExBAbG0tAQAAlSpQwt7A/fvLsWX3XC7IooLDwD4W+s2FKB0iMBlsqzOkHz2xQH4Bdzw63rQortoRbG+RdrcIcdXvAH45u3MfWQFpygW9FKv8rCSGEEPnJPwQ6j4aGfWDJf9SGWKDePC54Rm2Y1HWcbARZGNnSYN4TcG6bMRf+Mtw11LyahPBC8fHxAJQtW5ZixYqZXI0QItcVr6gWCDiDc1sSXDolrVpuUFBQEEFBQfj4+PDvv/9y6dIlc0Pzi8dh6XBjXL75zfW9Ft6rRBV4ZBbM6KGuEkm4oDa0f2qVeo+TlYRoODDfGMsq88KpegfwDYa0BEi9AifWQ+37za4qR2TJhhBCCGGG25qolRmdPlIb4TidWA8T74KI8SpkFYWD3Q6Lh6pVF05Nn4B73jGvJiG8kK7rrpYswcEFfwMpIUQmNE216wgqbcylJaoNsu028+oqoMLCwgBISUkxb9PktGRHH/MENQ4oBr2/BauvOfWIvFP5buj2uTH+Zz8sHKxe62Zlz0ywpajbwWWgTo+8rVGYwzcQanYyxoeWmFdLLpHQXAghhDCL1QfuHgbPb4Ma9xrz6Umw9l2Y3F71/xMFm67Dmjfhj7nGXJ3u6g2HtOIRwoN74CMtWYQoxDINzhMkOL8JVquxOt+00Hz1KBWeOj0wEUpUNqcWkfea9letd5wOL4MNH2d+rt0Gu751u+8Tqv+1KJzcPxA5sgLSC/beNPJKVAghhDBbicrw2Dy1Iie4jDH/z36Ycg+sfB1S4s2rT+TM5rGwbaIxrtIaek2VS9CFEEIUba7gvJQxl5agNkqX4LzgOLDAMxS9cwjU6WZePSJ/3PsB3O62qnjzf+GPeRnPO74WYs+o25oFmj+ZP/UJc9zeCXwcfcyTL8OpTebWk0MSmgshhBDeQNOg/kPwwk61AsNFh+2T4Ks74fAK08oTN2nXd7D+Q2NcrjE8+mOB3xRHCCGEyBWaBsUqegbnqRKcFxgxJ9UePU7lGqswVRR+Fis8NA3K1DbmFg+F87s8z9sxxbhd6371QZkovPxDoEZHY3xosXm15AIJzYUQQghvElgCekyAgSug1O3GfFwkzOkLc/tD3N/m1Sey789FsOwlY1yqBjw+HwLCzKtJCCGE8DZZBucnJTj3Zukpqo95quNqSP8w6PMd+PibWpbIRwFh0HcOBJZUY1sKzOkHl8+rccxJtdLcSTYALRrcW7QcXg62dPNqySEJzYUQQghvVKUVPLcF2o4Eq1vfv0NL4H/NYO37kBhjXn3i2k5uhAXPAI7eoqG3Qf+FEFz6WvcSQgiv1a5dOzRN4/Tp02aXcsNOnz6Npmm0a9cuX57vvffeQ9M0pk+fni/Pl1eqVKmClsO9N3bv3k2nTp0oXrw4mqZl/WfIGZw7wzeA1CsSnHuzNW/D3/uMcY8JULKaefUIc5SsCo/MBIuPGl/5B2b3VR987ZyG67Vw6ZpQta1pZYp8VPM+sDg2AU6MhrNbza0nByQ0F0IIIbyVjz+0HwVDIqDS3cZ8WgJEjIMvGsHG/4PkOPNqFBlF/g5zHgObY+ObgOLQfwEUr2RuXUIIIUQ+io+Pp0ePHqxdu5amTZsyYMAAnnjiCUJCQjK/g6ap/yuLaHC+ceNGNE1j4MCBZpdyfYeWwo5vjHHzp6Heg+bVI8xVJRy6jjXGUX/Agmdhzyxj7o5B6u+4KPwCi0O1dsb44BKzKskxCc2FEEIIb1emFgxcDt2/8Lx0OSUONo6GLxpCxHi1okOY68JR+KG3epMP4BsEj/0Mt9Qxty4hhBAin+3cuZO//vqLxx9/nPXr1zNjxgymT59O6dLXuOqqiAfnBcKlM6p3tVPZBnDfaPPqEd6h2UC48zljfHgZJMeq277B0OhRU8oSJqn7gHH70FKw282rJQckNBdCCCEKAotFvRh98Q/o8DYEFDOOJV2Cte/CF41h2yRISzatzCLtciTMfFBdhgjqMtWHZ0LFO8ytS2RK13WS02xEX0nhbHQih/6OY/eZGDYdvcCVlILbe1EIIbzF+fOqr3G1ajfYsuOawXnBDF4KjfRU+PlJSL6sxn4h0Ge6bHAulE4fQfV7Ms43fNjzvYso/Gp3Bc2qbl+JgvM7zK3nJkloLoQQQhQk/iHQ5hUVnrd5Tb1ZcUr4F1a9Dv9rCru+A1uaeXUWNYkxKjCPc2x8hAYPfgO3d7zm3UT2OAPumIRUzsUkcjgqjt1nLrH52AVWHYhi/u7zzPztNJN+PcG4NUf4YOlBRs7/g2Gz9/D09J088s1vdP9fBB3GbqTl6HU0eHc11d9YQe23V9Hso7W0+WwDXb7YzENf/8aAb3fwd6x88CS8z/Lly3nqqaeoU6cOYWFhBAcH06hRI0aPHk1KSorHudfrqX2tXtWTJ0+mQYMGBAQEUL58eYYNG8bly5dzXP+1eqJn1XNc13XmzJlDmzZtuPXWWwkICKBixYp07NiRr776KsPjXLx4kcGDB3PrrbcSFBREkyZN+P7773Ncu/vjjxo1ivr16xMcHEzx4sVp3Lgxb775JtHR0dl6jOjoaF599VVuv/12AgICKFmyJJ07d2bNmjWZnn/u3DmGDh1KrVq1CAoKomTJktSrV4/Bgwdz5MiRDOefPn2awYMHU6VKFfz9/SlTpgy9e/fmjz/+yPTx09PTGTNmjKueatWq8fbbb5Oampr9H0wmNWiaxhNPPAHA+++/7+pnfnXrkZkzZxIeHk5YWBhBQUE0bNiQMWPGkJyS4gjOS7jOHfj8CDSrlY3r17N69Wrat2/v6pUeGxvrOu/ChQu88sor1KpVi4CAAEqUKEGXLl3YtGlTljUfPHiQJ598ksqVK+Pv70/ZsmVp06YNX3zxhcd5e/fu5bXXXqNZs2aUKVMGf39/qlWrxvPPP89ff/2V6WMfOnSI/v37U716dQICAihTpgyNGzdm+PDh/P232tx94MCBtG/fHoAZM2a4fl6apvHee+9l+2ef59a9D5G7jXG38VC6hnn1CO9i9VGbwZau6TkvG4AWPUElVdsepwLaosXH7AKEEEIIcRMCi0OHN+HOIbBlPOyYAulJ6lhcJCwbrubbjlSrOyxWc+stzFKuwA994KJbeNHlU2jQ27yaTJaSbiMxxUZCajqJqTaupKS7jdO5kmIjMSWdhFTnr+kkpNhIdPyqxuq+zl/T7brZ35YQpnr66adJSEigXr16NGjQgLi4OHbs2MGbb77JunXrWLNmDVZrzv6tf+WVVxg7diz+/v506NCBoKAgfvjhB7Zs2YK/v38ufSfZNzyW+fsAACAASURBVGrUKP7v//6P0NBQwsPDKV68OH///Tf79u3j+PHjDB1qtIiIjo6mVatWHD16lAoVKtCjRw+ioqJ48sknGTJkSI5rOXjwIJ06dSIyMpJy5crRuXNnbDYbR44cYfTo0dx7773X3Wg0MjKSNm3acPLkSSpVqkTPnj25cOECa9euZfXq1YwbN46XXnrJdf758+dp2rQpFy9epGHDhnTv3p3k5GTOnDnDlClTuOuuu6hVq5br/IiICLp27UpcXBz16tWjR48eREZGsmDBAlasWMHy5ctdwaxT3759+fnnnwkJCaFz587ous64cePYs2cPun5z/+6GhITwxBNPcPz4cbZs2UKjRo1o3LgxAOHhRogyePBgJk+eTEBAgOvP28aNG3njjTdYunQp69atI7B4ZXVy0iXX/X6cPpmps36iefPmdOnShRMnTrg+BDp8+DAdO3YkMjKS6tWrc//99xMdHc369etZs2YNM2fOpF+/fh71zps3j/79+5OSkkK9evW4++67iYmJ4cCBAwwfPpwXX3zRde4nn3zCzz//TP369WnVqhWaprF3716+/vprFi1axK5du7jttttc5//++++Eh4eTnJxMixYtaNGiBfHx8Zw8eZIvvviCnj17Uq5cOcLDw4mKimL16tVUr17d4+fk/NmZ7sgq+O1LY9x0ADTsY149wjsFFIO+c+DbzmpBT92eULae2VUJM9TtAad+VbcPLYH7Pi5wfe0lNBdCCCEKsuBS0OlDuGsobB4Hu78zNqC8dBoWDVGbhrYbpV60WuQis1yVngo/9YfIXcZc25Fw57Pm1XSD0mx2EjwCbBVUu0JrR4DtCrVdxz3HrhA8NZ00W8ELuIP9rAT5+xDsZyXa14JcpyG8zaRJk7j33nsJDg52zcXHx9OvXz+WLVvGDz/8wIABA2768bdu3crYsWMpWbIkmzZtol49FXJER0fToUMHtm3bluPv4UYkJyczfvx4qlSpwu7duylZ0mjVkZ6eztatWz3Of+ONNzh69CgPPPAAc+fOdYX8K1eupEePHjmqJT09nYceeojIyEhGjBjBmDFj8PX1dR3fs2cPZcqUue7jDBkyhJMnT9K/f3+mTZvmeoyIiAjuu+8+Xn31Ve655x4aNmwIwNSpU7l48SJjx47l5Zdf9nisM2fOkJ5utJKKi4ujT58+JCUlMW/ePHr3Nj64Xbt2LV27dqV///6cPHkSPz8/AGbPns3PP/9MtWrV2LRpE+XLlwfg1KlTtGnTxtVe5UaVLl2a6dOnM336dLZs2ULPnj0zrJaeP38+kydPpnz58mzcuJEaNWq4vo+uXbsSERHBu+++y6effgrFK4PbfytTZs5lzpTxPPLUMI/XNTabjT59+hAZGckXX3zBsGHDXGH6nj17uPfee3n22Wfp2LEjt9xyCwDHjh1jwIAB2O125s6dy8MPP+x6PLvdzooVKzzqfvbZZ/n8888pV66cx3kfffQR7777Lm+99Rbffvut69iECRNISkpi/vz59OrVy+OxDh06RPHixQEYNGgQNWrUYPXq1YSHh2d5lYhpLp9XrymdbqkLnf/PvHqEdytVHV7YCf/8CRWam12NMEvt7rD8FUCHy+fgr9+hfDOzq7ohEpoLIYQQhUHorXD/p3D3MNj0mdqtXndsmHXxqOo/WXYstH8TanUpcJ/yeyW7DRYOhhPrjbk7noF2I/P0aZ0h9xVHcH0lJd0VcrtuuwXfV1Ict1PdjjtXfafYSLUVvP6wgb5Wgv19CPa3EuSngu7Mxz4E+VkJ9nPc9le3g/yshLiNA32tWCzG34l604M4GGXiNyiypOs6cckFr+d8WIBPlu1Qsqtnz54Z5kJDQ/n8889ZtmwZixcvzlFoPmnSJABGjBjhCswBSpUqxWeffcZ999130499M+Li4khJSaFRo0YegTmAj48Pbdq0cY2vXLnCzJkz8fHxYcKECR6r4rt06UKfPn2YPXv2TdeyYMECDh8+TMOGDfn000+xXPUBdJMmTa77GCdPnmTZsmWEhYUxYcIEj9A9PDycIUOGMG7cOCZOnOj6vfj3338B6NChQ4bHq1y5ssf422+/JSoqilGjRnkE5gAdO3bk+eefZ/z48SxbtswV3n799dcAfPjhh67AHKBq1aq8/fbbDB48+Lrf182aMGECAB988IErMAcICwtj4sSJNGrUiEmTJvHRRx+pkL9EZbCqsL/rPeE8cn8buHQSSlRzBedLly7lwIED9O3bl//85z8ez9ekSRPefvtthg8fzqxZs1wfQnz++eckJyfzwgsveATmABaLhW7dunnMZfZ7YbFYeOedd5g8eTKLFy/2OHat38M6dQrIJuG2NPj5KWO1v2+Q6mPuF2RqWcLLBRaHKq3MrkKYKbQsVLoLzjo+5D64REJzIYQQQpioeEXoMQFavQi/fgp/zMW1POufAzCnL9zWFDq8BdU7SHh+s3QdVr4Gfy4w5ur1Um1ZrvqZ2uy622ptI8T2DLuvmnO2KXHOpRoBeWp6wQq5A3wtRmjtCLNdobWfEXSHOANv1ziT8/1VwG21yJ/boiouOZ1G72fe+9mb7Xu3E8UCfa9/4nUcO3aMFStWcPz4cRISErDb7a4WGseOHcvRY0dERABkCA4BOnXqRMmSJYmJicnRc9yIW265hQoVKrB8+XI+++wzHnvsMY+2F+5+//13kpKSaNWqFZUqVcpwvG/fvjkKzdeuXQvAM888kyEwzy7nz/f+++93rS52179/f8aNG8fmzZtdc82aqXBh6NChfPTRR7Ru3Rofn8zfwv/yyy9A5h+ugArmx48fz86dO+nVqxdpaWls374di8WSIWQH9TPLq9A8LS2Nbdu2oWlahlYpAA0aNKBhw4bs27ePffv2cccdd6j/W/3VPi49OrVVJ6bEw6VTULIqaJZs/QwAdu7c6Zpz/t7eyPcaHR3NkiVLOHDgALGxsdhsNtf3FRMTQ0xMjOuDnmbNmrFy5UoGDBjAW2+9RfPmzW/6z5BpNnwM57Yb465joUytrM8XQginuj2M0PzQEuj4XoF6/ymhuRBCCFEYlaoOvb6B8Jdg4xg4uMg49tfvMKsXVLpbheeyCsSD3a6TlGbLfDW3Y7V27UNf0uz0VNd9DgU1Z2z8IOImb/cIuRNS0klKs5n43dwYfx+La8W2c0V2sL+Puu2c8zdWbru3NLn6PGf4LQG3EDmn6zqvvPIKn3/+eZZ9puPj43P0HH/99ReaplGxYsVMj1eqVClfQ3NQGyI++uijvPbaa7z22mtUrVqVNm3a0K9fPzp16uQ6z7kBY2aB+bXms+vcuXMAVK9e/aYfw1ljlSpVMj3unHffTHLgwIGsWbOGn376ydXz29nH+6mnnnK1GAFcG6zeeeed16zj4sWLgAp+U1NTKVeunKtdi7vQ0FCKFy/uscFmbnE+t3Nz18xUqVKFffv2Zbq5ZqWqbhtPpsRBjArOnT+DRx55hEceeSTL53f+DMD4va1WrVq2ap89ezbPPvssV65cyfKc+Ph4V2j+6quvEhERwdKlS1m6dCnFihXjzjvvpFu3bgwcOJDQ0NBsPa9pjq2FiM+NcaO+0DjjBx1CCJGpOt1hleMq3JiTqmXPrfXNrekGSGguhBBCFGa31IaHZ8Dff8CG0XB0pXHs7FaYfj9Ua6/C8wLcczAl3caVZBVUx6ekea7Sdgu/PVuUuM27nZuYZuNae58NsK7mMd8ZrvEeew0ei3mexJjcDxauJ8DXQoi/jyuwDnEG3v4+rpXcIf5GqxLXuf6eK72D/X0I8rXiYy1gq9+EKCLmzp3LuHHjqFChAuPHj+euu+6iTJky+Pr6kpqair+//w1t2mi3e9cVK1nV06FDB44fP86yZctYtWoVv/76KzNmzGDGjBk8/PDDzJ07F8D1vee0Bc715MbjZ/UYznn341arlblz5zJy5EgWL17Mhg0b2LZtG5s2bWLMmDGsXr2ali1bArhWO/fp04egoKzbZjhD9fz6mV1Ldp47s3MCSldSmw0mX1YTjuDcZlOtm7p06eLxgcLVateuneE5slPLmTNnGDhwILquM378eLp27Ur58uUJDAwE4O677+a3337z+LsYFhbG+vXr2bJlC0uXLmXjxo2ujXvHjBnD5s2bc/RhTJ6K+wsWuu3RUrom3P9f8+oRQhQ8xSqoliyRu9X40BIJzYUQQgjhZco1hH5z4PwuWP8RnNxgHDu5QX3V7ALt31Dn5gNn25IrySqwjk82guwryenEuwXb8cluAbjj2BVHOH4lOT3f+nL3sGzlPZ/vXeNj9vI8mfoqiWS+Uu5qflaLR6hthNnG6u0Qt7kgjzmrx32C/STkFkVPWIAP+97tdP0TvUxYQM7edi1cuBBQPaiv7rF88uTJDOc7Vw5nthrWZrMRFZWxaX+5cuU4ffo0586d8+gx7XT27Nmbqj07NTlX+2YmLCyMfv36udp4bNu2jT59+vDTTz8xcOBAunTp4mrbcubMmUwfI6e1O1ffHz9+/KYfw1njqVOnMj3uXCXtvsGkU5MmTWjSpAnvvfcecXFxvP/++4wbN44XX3yR7dtV24wKFSpw5MgR3nrrLddGotdSunRp/Pz8iIqKIjU1NcNq8/j4+DxZZQ6qT77zuZOSklyhszvn72VmPw80C5SoojY8dwvOK5RWbW+GDBmS7c1fK1asyLFjxzhx4gT16187yFmxYgWpqamMGDGCF198McPxzP4uggrlw8PDXe1hLly4wIsvvsjs2bN54403XB/+eBVbOswfBInRauwToPqYO1rkCCFEttV9wAjNDy5W7zcLCAnNhRBCiKKkQnMYsAhOR6jw/OxvxrGjK9VX3Z7qxUwm/Sp1XSc5zU58SprHyu4rbqF2fIoRhF+5+rbbODHV+9uWWC0awY5+2m2tf/Bh4tdYHD3iL/ncwuJa/6NvaHkVZvtdHYZbr1oB7oOfj4TcQuSEpmm50hu8oLl0SW3Al1nrlJ9++inDnDNoPHr0aIZj69evJy0tLcN8eHg4p0+fZt68eYwaNcrj2C+//JLj1izuNV0dTq5Zk/0+9S1btqR///6MGTOG/fv306VLF5o1a0ZAQADbt2/n3LlzGX5Oc+bMyVHtHTt2ZMqUKUydOpWhQ4fe1OpsZ2C6fPlyYmNjM/Q1nzVrFgCtW7e+5uOEhYUxevRoPv/8c/bv3+9R47p161i0aFG2QnNfX19atGhBREQE8+fPp2/fvh7Hc/ozu95zt2zZkk2bNjF79myeeuopj+MHDhxg3759hIaG0qhRo8wfJJPgvGOrJkz74ScWLVyY7dC8Y8eOHDt2jMmTJ7s2J83Ktf4ebtq0iX/++Sdbz1mmTBnee+89Zs+e7fF76PzgIj3dCzY7/vX/4MwWY9zl/6BsvazPF0KIrNTpAb+8o25fOAwXjkKZmubWlE0SmgshhBBFQJrN7hleU5crd80gsMImbv9zPKUuHzBOPrgI+8ElbA3qwI+BfTmRfotjtXcaCak2bPbstwDIb5qGq5+2K8D2c1vNndkKb/fWJc4NKB2ruv19LCocObcTvv8UcAT9gSUp8dRyXikgL/iEEAVbzZo1+eWXX5g8eTJffvmlK7TdvHkzn332WYbz27ZVGyXOmjWLl19+2dUv++TJkwwbNizT5xg8eDCzZs1i3Lhx9OzZkzp16gAQExPDa6+9luPvoW3btnz//feMHTuWzp07u1qIrF27lvHjx2c4/+zZs6xfv56HH37Yo91ISkoKGzaoq6WcvcpDQkJ47LHHmDZtmmsFr7+/P4CrJ3hO9OrVi5o1a7Jv3z5GjhzJxx9/7LEh5969eyldujQVKlTI8jGqVatG165dWb58OS+++CJTp07F11d9APTbb7/x9ddfY7Vaef755133mTlzJk2aNMnwIcOqVavQdd2jV/vgwYMZN24co0ePpmLFigwcONAj3E9ISGD+/Pl06NDBVefgwYOJiIjgnXfeoV27dq4PNs6cOcOHH36Yg5/Y9Q0bNoxNmzbx7rvv0q5dO1dP8fj4eF544QV0XWfw4MGZ9lt3cQbnMach5TK9u97D+zWqMH3GDGrVqsXLI0a4fsYAqampLFu2jNtvv50GDRoAMHz4cL777jsmTZpE27Zteeihh1zn2+12Vq1axf333w+ov4eg/l4NGjSI4OBgACIjIxkyZEimJU6aNIn77ruPqlWresyvXKna5bn/HjqvRjhy5Mj1fny5y26D+L8h9hxcPgcXj8ImtzYs9XtD0yfytyYhROFRsirc2gCiHB8SHloMZV41t6ZsktBcCCGE8FJpNrurPUliqs2jF3d8Fiu73Y+5j1PSs2pfEgSMoqPld0b4zKOORV3CbsFOeOJa7kzYwDxbG75Mf5A4SufZ9+rszR3i70NIgAqvQwPcxv4+hLqO+xLibyXE35cQ5zmO84J8rVhye+PJfw/Dj30gLVGN/ULg8Z8LzAoJIUTB95///Ifp06czceJENm7cSMOGDYmMjCQiIoIRI0bw3/969hmuVq0aAwYM4Pvvv6dx48a0adOGhIQEtm3bRteuXUlOTs7QyiQ8PJzhw4czfvx4mjRpQseOHQkMDGT9+vVUqlSJli1bsm3btpv+Hvr27cunn37K1q1bqVOnDnfccQfnzp1j165dvPzyyxm+h5iYGJ588kmGDh1K8+bNqVChAgkJCWzdupULFy7QokULevXq5Tr/k08+4ddff2XhwoXcfvvttGrVin/++Ydff/2VwYMH8/XXX9907T4+PsyfP597772XTz/9lFmzZnH33XeTnp7OkSNHOHToEBs2bLhmaA7wzTff0Lp1a77//nt+/fVX7rrrLi5cuMDGjRux2WyMHTvWY5X4/PnzGTBgANWrV6dBgwYEBgZy+vRptm3bhtVqZfTo0a5zS5QowULHCuunnnqK999/n/r16+Pv78/Zs2c5dOgQCQkJ7Nmzx1XnY489xoIFC1i4cCG1atXinnvuQdd11q5dS9u2bdE0LcetbbLSu3dvnn32WSZPnkz9+vVdG51u3LiRCxcu0LJlS95///3rP5BmgZJVIOYUPsSxcNpY7us3lJGjRvHFhAk0bNiQsLAwzp07x+HDh4mNjWXhwoWu0LxmzZp8++23PPHEE/Tu3Zv69etTv359Ll26xP79+/nrr79cPcp79OhBvXr12LVrFzVq1KBVq1YkJyezYcMGGjduzN13383WrVs9yps0aRLPPfccdevWpU6dOvj4+HDkyBH27t1LYGAg7777ruvcKlWq0LBhQ3bt2kWLFi2oV68eVquVHj16ZHvlfKbSU+DyeYg9q0JxZzgeew4un1W9y+1ZrG4vWQ26j1erEoQQ4mbVecAIzQ8ugTYSmgshhBBFhq7rpKTbSUy1uQXd6VxxbDLp+kq1uW1C6bid6jxu87idX326QWOtvRnrUptwv2UHL/vMo7rlbwB8NRv9fDbwkHUzP9ruYWL6A1xAXVJutWhGYO0IrV2/+hljZ/gd7DgWelUwHuzvg6+39uaOPQszH4QkdUk2Vj949Ae1oY0QQuSTmjVrsnPnTl5//XW2b9/OkiVLqFWrFt988w3PPPNMhsAZYMqUKdx222388MMPrF69mooVK/LGG28wcuTILDceHDduHLVq1eLLL7/kl19+oVSpUjz88MOMGTOGnj175uh7CAwMZN26dbz66qusWrWKFStWUK9ePebOnUvz5s0zfA/Vq1fnv//9L+vXr+fgwYPs2LGDkJAQqlatyttvv82gQYM8ViGXLl2aLVu28Oabb7JkyRIWLVpEzZo1mTJlCh06dMhRaA5Qv3599u7dy2effcaSJUtYunQpQUFBVK5cOdt9xMuXL8/OnTsZM2YMixYtYsGCBQQFBXHPPfcwYsQIOnXy7Nf/8ssvU6FCBbZs2cLmzZtJSEigfPny9O3bl1deeYUmTZp4nN+qVSv279/PuHHjWL58OevXr8dqtXLbbbfRrVs3evXqRd26dV3na5rG3Llz+eyzz5g2bRorVqygXLlyDBs2jPfee49atTK2actN33zzDeHh4UyaNIlff/2V9PR0qlevzvDhw3nppZcy7XWeKc2iVjLGnKJ2jarsXTOHCd/OZuHqTURERKDrOuXKlaNNmzY8+OCDdOzY0ePuffv2pW7dunz66ads2LCB+fPnU7JkSWrXrs3IkSNd5/n5+bF582befPNNVq5cybJlyyhfvjzDhg3jnXfeca1Id/fhhx+yaNEitm/fzrp160hNTaVChQo8++yzvPrqqxn2D5g/fz6vvvoqmzdvZvfu3djtdipUqHDt0NxuA90OKfGw61uIPeMZjl/JXtuYDKz+jj7moTd3fyGEcKrbAzZ8pG5H/QExp9S/215Ou5Fd1gsiTdP+rFu3bt0///zT7FKEEEJ4EWdvbmdIffVq7sQU4/aVVDXO9Dy32+le3LbEXbCf1XP1tluoHervQ6g/NI1dyx1nJhOaFOlxX7tPAMmNn0YLH05AsTI31de1wEi4CN/eB9HOjd809eaxXs6CI+H96tWrx8GDBw/qui4NXHNJdl+T2+12V2uCWrVqYbF46QdqQgjvo9tVEJMSZ8wFFIcSlVWwXhDpuloFbkt1+0qDdGNst6dzJFqHC0eoteVFLPbUm3uuoFJQrCIUrwjFK0O9XlBBFgkIIXLJV3eqnuYA934ArTJuqHw1s1+Ty0pzIYQQBYKu62oVd6pjRXaWq7ndV2urY4luwbjzeKKX9+a+WoCvxa03t0+GtiSh/p7HQjNb2e3o2W3NVvuS+pD+POydpfpaxqnw3JKeTNCur+CPGdDyebhrKAQWv85jFUAp8TDrIbfAHOj2uQTmQgghhLfSLFCiKlxyC86TY+ESqve5N37Qr+sqBPcIxd2+0tOA3LjyUIOw24xQ3PVrJcevFcAvOBeeRwghslCnhxGaH1ySrdDcbBKaCyGEyLF0m53kdDvJaTbHVya30z3nUzI7P91OottqbveV3Qmp6RSki6OC/BwbTPoZG006b4dcNVZfVrdNKI0NKoP81Hk+ZrQv8fGD5k9Bo36w+zvYPBYSLqhjqVdg06ewYzLcPQzuHAL+IflfY15IS4Y5/eDvvcZch7eg+ZPm1SSEEEKI67M4g/OT6gNwcATnZxwrzvM5ONftjvDbbZX41avGyaUXuFZ/qNIaipVzC8Md4XhYebD6Xv8xhBAir9Ttod4/AkTugsuRUKy8uTVdh4TmQghRyOi6TppNd4TUNlJcQbWd5HQbSak2V0CtjmcVbKvzU9JsJGUShKe4nVtQ2pJkRdNwrOK2ulZjB/tbVYDtWt1tJcjPLfB2hNzOEDzIcX6wfx5tRmkm3wBo+Rw0HaBC8i1fGD2+k2Nh/Yew7WsIfwnueBp8s9mD1BvZbbBgEJzaZMzd+Ry0fsW8moQQwsscPnyYTz75JFvnhoeHM2jQoDyu6MZMnTqViIiIbJ07cuRIateunccVeb+IiAimTp2arXN79uyZ4x74OWKxQIlqVwXnlxwrznM5OLfbrloZftVK8aw22LxRmkXtq+Lx5Qs+/qBZIe4UlK0LbX5W378QQnibsvWNq4EADi2FlkPMrek6JDQXQog8lm6zk2qzk5pud1tdbQTQSR4hdMbQ2hVSXxVqJ7nPu4XgyWk2CniGfV0WjSxXbKug2y3AdrvtHnQ7xyH+PgT6Wgt3b+7c4hesgvHmT8O2ifDbV8blz4kXYc2b8NuX0HoENH1CrVQvSHQdlr2kXsA5NXwE7hvtnZd0CyGESaKiopgxY0a2z/e20DwiIiLb9Q8cOFBCc+D48ePZ/plVqVLF3NAcjOA85iSkugXnsah+3dn5fz0b/cTRbblUr48KwTME444vizXrmu35tXG8EELkgKap1eZbvlDjg4u9PjSXjUCFEIWO3a6TalMBdWq6EVg7v1LSbepXm/uc87bN4/wU57HMHuPq+9vspKRdda7NXqD6ZucWP6sFf18LAb5WAnwtBPpa1W0fq9u8lQAf4xznXKDvVSG3Y+W3+2pufx+LhNzeIDEGtk6A7d9AWqLnsWKVoO1r0KgvWAvIZ/TrPlAtaJxu7wSP/iiXMxdBZm86VBjJRqBCCFPY7Z7BOUBgCRWcQ9b9xNOdrVNyKZC2OAJxn8xCcV8Vit8k+XdTCFFgRO6GKR0cAw1GHIHQslmebvZr8gLyLlYI4e1sdp0kR6uP7IXJNo+w2j3gVufaPIJnjwA7w7znuWm2ohdSX4+/I5wOdAuo/TMLrX2uOu5rccxZCfQzbvtncr7zcfx9rNncaFIUeEEloeN7akPQiM9h5zSwpahjl8/CkhfUfLtRUL9Xjt4Q5rnfJnoG5hVbQp8ZEpgLIYQQBZnFAiWrOoLzK2ou6ZJq22K3kTv9xDW3dilZhOKaBNlCCMFtTdVeC5fPATocXqbae3opCc2FEBmk2+zEJqVxKSGVS4lpxCSkEpuYSkxiKrFu40uJznNSiU1KK1CbNJpJ0yDAx0qgnxFaewbUGUNoz2Dbou7ra3Uc8zw/4KrzZVW2yHMht0DnMXDXC7D5v/D790YPz5gTqkf45rHQ/g2o0937Wp3smwOrRxnjW+pBvzngF2ReTUIIIYTIHRYrlKzmGZzfSK/xzPqJu4fjFh/ve20jhBDeSNPU+8FtE9X40BIJzYUQ5klJtxGbmMalxFRH2K1uOwNxZ+gdk5imgvGEVOKTc2nDGi9itWj4+1jw87HgZ3X86rjt72vFP8PcVef5WPB3nJvx/p7n+vtY8LNaM38Mx30kxBaFUrHy0O1zaPUi/Pop7JsNuuOy5guH4Kf+UK4RtH8Lbr/XO95gHl0Ni543xsUrwePz1aXbQgghhCgcMgvOXcdy0E9cCCHEjanTwwjNT21WLT+DSppbUxYkNBeiAElOsxHjCLkvJTjC76tvuwXhlxJSSUjNpc1pbpCm4QqM/X3UaumrA+vMQuzsnuvvms/6XH+r1TUn8KpTBgAAIABJREFU7UKEyEclqkDPiWrT0I1j4MB849jf++DHPlChBXR4C6q1Na1MzvwGPw0wNvEKLgP9F0FYOfNqEkIIIUTesFihVHXVmsW9pYo3t48TQojCpuKdEHIrXIlS78MOL4em/c2uKlMSmgthAl3XSUi1ccmx8lu1PUl1BOJuobcjEHe2RklOy9+d0f2sFkoE+1IiyI/iQb6UDPajeJAfJd3GJYL8KBHsR4kgX0IDfF2BtY9Fk9XUQhR1pW+H3t9C+MsqPD+8zDh2fgd83wOqtlErzyvdmb+1RR2AHx+B9GQ19g9TK8xLVc/fOoQQQgiRfzQLBBQzuwohhCi6LBao0w12TlXjQ0skNBeisNJ1nbjkdFfo7ez5fSkxYyBu9ANPI9WWvwF4gK/FEXb7OcJvX4/AO7NAPMjPKsG3ECLnbq0Pj/4Akb/Dho/h+Frj2KlNcKoT3N5J9Ty/rUne1xNzCmb1gpTLamz1h76zVesYIYQQQgghhBB5p04PIzQ/sQGSL3vlB5q5GpprmhYI7AdK67pe/DrnlgUOAWd1XW/sNn8aKAs4e0qkX++xhMgrqel2jv4Tz4HIy5yJSXRre2K0Q4lNTCPdnr87YAb7WR1htxF6lwhS45LBvhQPch4z5gP95LJDIYTJyjdVq7nP/AbrP4IzEcaxY2vUV53u0O4NKFs3b2qI/wdmPghX/lFjzaJWw1cJz5vnE0IIIYQQQghhqNwKAktCUgzY09Q+Uw0fNruqDHJ7pfkHwHmgdDbO/RL4A8gsEO+r6/qi3CxMiOtJTrNxOEoF5H/+dZn9kZc5EhVPmi1vA/GwAB9KuFZ5e67+dgXjbgF48SBf/H0kABdCFGCV74KBy+DUryo8P7/TOHZoKRxaBg16Q9uRULpG7j1v8mWY9RBcOvX/7d15eFRF2vfx750AgQBhZ9hkVVAWBbcRQWQQUcRBB5cRGH1wG3zF7XEb1NHBcR5QUWQcHXdFhFFQcQNURAEFxWVERUFcMMgqqywBAgn1/lGnQyfpJJ21u5Pf57r66nRVndN1Kt2n766uU3Uw7fcP+ssDRUREREREpPwlV4PDB8GS5/zjZa9V7k5zMzsaOAO4HphWRNnB+I71ScB1ZVUHkWjt3pfF8vU7+HrtDpau3c7Xa7fz/cZdZJdixLgZ1K+Vu9M7fCqU8KlRQh3i9WtVp1pyUhkemYhIgjCD9n2h3cl+hPl7d8GGpUGmg6UvwtczoPtQ6HMzNGhTuufbvweeHwq/LD2Ydurf43b+PBERERERkUqr89kHO81/mAuZuyClTmzrlEeZdJqbWTXgCWBUFGXTgAfwHew9Cyj2mJk9CXwP3OWcm10W9ZSqaefe/Sxb5zvHv1m3g6/XbufHTbuItn+8RnIShzevS8ff1KVxnZRc05+E/m6YWoO0WtVJTtL83yIixWIGHU+DQ0+Fb9+AeWNh07c+z2XDkinw5TQ45n/gpBsgrUXxnyM7C166BFYtOph24jXQ69qyOQYRkSqgb9++LFiwgJ9++om2bdvGujrFkp6eTrt27Tj55JOZP39+uT/fmDFjuPPOO3nmmWcYMWJEuT9feWnbti2rVq3CuYqdilJERKqAdn0gpZ5fZyprL/zwDnT5Q6xrlUtZjTS/AfjKOTffzPoWUfYeYLJzboWZReo0vxD4L35O83OAl82sj3Pu0whlRXLZvns/X6/zI8e/DjrIf9qcEfX2NasncUTzNLq2qEe3lvXo0jKNw5rWpUY1jQYXESlXSUnQ+Sw4/Ez4+mXfeR6aRuXAfr9QzJIpcNxl0Os6qNMkuv06B29cCyvCfn/v/ic/ylxEREREREQqXrUa0GkgfPWCf7zs9crXaW5mHfAjzHtEUbYXcDLQvaAyzrkPwh7+x8zOxneeq9NcctmyKzOnYzw0B/nqrXui3j61RjJdWqTRJegg79qyHh2a1NZ0KSIisZSU7Oez6/IH+PJ5WHAvbF/t87L2wkcPwWfPwAlXQM+rILVh4ft75w74YsrBx50Gwe//6Ue4i4iIiIiISGx0Hnyw0/z7OX5Kzeq1YlunMGUx0vwkoAnwjfkvoDWANDPbAAx2zn0SVvZUoDXwc1C2FpAalO3hnFsfYf8HyqCOkuA27tgbjCAPpllZu5112/dGvX3dlGp0aZmW0znepUU92jWurelURETiVXJ1OPoiOPKP8PlkeH887PrF5+3PgA/uh0+egBOvht9eATXT8u9j4UT48MGDj9v0gnOf8gvPiIiIiIiISOx06AfVa/vvd/t2wY/v+QVC40RZDKmdBrTDjx7vDlwG7Az+XpKn7H3AoWFl7wBWBH//YmatzayPmaWYWXUzOx84C3i1DOopCcA5x7pf9zDnmw1MmLOCSyZ9yvH/N5fjx77LJZM+Y8I73/HOsl8K7TCvn1qdkw5rzBUnd+ChYT1YcFNfvvzbAF74c09uG9SZs7q35NCmddRhLiKSCKqlwPGXwzVfwIB/QGqjg3mZO2De/8E/j4JF/4R9uw/mff4czP3bwcfNusHQ5+Nq5IKISHHMmjWLSy65hCOOOIK0tDRq167NUUcdxdixY8nMzMxVdsyYMZgZkyZNirivtm3bYgVccfP444/TrVs3atasScuWLbn66qvZvn17qevft29fzIz09PR8eenp6ZgZffv2zZXunOOFF16gT58+NGvWjJo1a3LIIYfQv39/Hn744Xz72bx5MyNHjqRZs2akpqbSo0cPJk+eXOq6h+//lltuoWvXrtSuXZv69evTvXt3brvtNrZs2RLVPrZs2cJNN93EYYcdRs2aNWnYsCGnn346c+bMiVh+9erVjBo1ik6dOpGamkrDhg3p0qULI0eOZMWKFfnKp6enM3LkSNq2bUtKSgpNmjTh3HPP5auvvoq4/6ysLMaNG5dTn/bt23P77bezb9++6BtGRESkJKrX8utbhSx7PXZ1iaDUQ62cc3uAnDkxzGyrT3YbgsdvAh8458Y653YBu8LKbgeywsrWAR7Ed6xnAd8B5zvnFpe2nhJ/nHOs2baHpWsPzkH+zdrtbMmIPkBrXKcGXVvWo2sLP4K8a8s0WtavVeCXABERSVA1Uv2o8mNGwMePwqJ/+UVjAPZs9dOwfPiQXyy0ThN445qD2zZoB3+aATXrxaTqIiJl4dJLLyUjI4MuXbrQrVs3duzYwSeffMJtt93Gu+++y5w5c0hOTi7Vc9x4443cf//9pKSk0K9fP1JTU5k6dSqLFi0iJSWljI4kerfccgv33HMPdevWpXfv3tSvX5/169fz5Zdf8sMPPzBq1Kicslu2bKFXr1589913tGrVisGDB7NhwwYuvvhirrjiilLXZdmyZQwYMIC1a9fSvHlzTj/9dLKzs1mxYgVjx47l1FNPzdfpn9fatWvp06cPK1eupHXr1px99tls2rSJuXPn8vbbbzNhwgT+93//N6f8mjVrOProo9m8eTNHHnkkv//979m7dy+rVq3iiSeeoGfPnnTq1Cmn/MKFCxk0aBA7duygS5cuDB48mLVr1zJjxgxmz57NrFmz+N3vfperTkOHDuWll16iTp06nH766TjnmDBhAkuWLNECoCIiUv46D4ZvZvi/V7wJWfv8fOdxoMyvT3bOzQfqhz0eWEjZScCksMfLKGS+c0lcBw44Vm3dnTO1Smiqle179ke9j2ZpNenaMi1XJ/lv0lLUQS4iUpWk1IU+N/kFQT96GBY/4i/lA8jYCG/9JXf5Os3golehTtOKr6uISBl69NFHOfXUU6ldu3ZO2s6dOxk2bBgzZ85k6tSpXHTRRSXe/4cffsj9999Pw4YNef/99+nSpQvgO6P79evH4sUVO45p7969TJw4kbZt2/Lf//6Xhg0PrmGRlZXFhx9+mKv8rbfeynfffcdZZ53FtGnTcjr533zzTQYPHlyqumRlZXHOOeewdu1abrjhBsaNG0f16tVz8pcsWUKTJkUvUH3FFVewcuVKLrzwQp566qmcfSxcuJDTTjuNm266iVNOOYUjjzwSgCeffJLNmzdz//33c/311+fa16pVq8jKysp5vGPHDs477zz27NnDiy++yLnnnpuTN3fuXAYNGsSFF17IypUrqVHDd0Y8//zzvPTSS7Rv357333+fli1bAvDTTz/Rp08f1qxZU8IWExERidKhp0K1mn79qszt8NP7cFj/WNcKKIdOc5HsA46Vm3blmoN82bod7MrMKnrjQMv6tYL5x9PoEnSSN6lb8aNbREQkTtVqAP3+6uczXzTRz2+elWfqrpr14MIZ0KBtTKooIuXAOdhb+qlCKlzNeqVegPjss8/Ol1a3bl0eeOABZs6cyWuvvVaqTvNHH30UgBtuuCGnwxygUaNGjB8/ntNOO62gTcvFjh07yMzM5KijjsrVYQ5QrVo1+vTpk/N4165dPPfcc1SrVo0HH3ww16j4gQMHct555/H888+XuC4zZszg22+/5cgjj+Tee+8lKSn3LKc9evQoch8rV65k5syZpKWl8eCDD+bqdO/duzdXXHEFEyZM4N///nfO/2Ljxo0A9OvXL9/+2rRpk+vx008/zYYNG7jllltydZgD9O/fnyuvvJKJEycyc+ZMhgwZAsAjjzwCwF133ZXTYQ7Qrl07br/9dkaOHFnkcYmIiJRKSh04tD98O9M/Xv6aOs2lctiffYAfNu7y06sEU6wsW7eDPfuzo95H20apOR3j3VrWo0uLNBrUjo9LMUREJM7VbuznOu95lV8c9LNn4MB+qFYLhk2H33Qpeh8ikjj2bod72hRdLt78ZRXUql90uSJ8//33zJ49mx9++IGMjAwOHDiQM4XG999/X6p9L1y4EIDzzz8/X96AAQNo2LAhW7duLdVzFEfTpk1p1aoVs2bNYvz48QwfPpwWLVpELPv555+zZ88eevXqRevWrfPlDx06tFSd5nPnzgXg8ssvz9dhHq1Q+55xxhnUr5//tXDhhRcyYcIEPvjgg5y0Y445BoBRo0bxj3/8g5NOOolq1SJ/hX/nnXeAyD+ugO+YnzhxIp9++ilDhgxh//79fPzxxyQlJeXrZAffZuo0FxGRCtH5rLBO85kw6AFIjn2XdexrIAkjMyub73/ZlWsO8uXrd7Av60BU25tB+8a1c02v0rlFGvVqVS96YxERkcLUbQZnjIcTr4GV86FtL2jYPta1EhEpE845brzxRh544IEC55neuXNnqZ5j3bp1mBmHHHJIxPzWrVtXaKc5wLPPPssFF1zAzTffzM0330y7du3o06cPw4YNY8CAATnl1q1bl1PHSApKj9bq1asB6NChQ4n3Eapj27ZtI+aH0kPlAEaMGMGcOXOYPn16zhzzxx57LAMHDuSSSy6hadODU4+FFlj97W9/W2g9Nm/eDPhpd/bt20fz5s1zpmsJV7duXerXr8+vv/4a7SGKiIiUTMfTIKm6H/y0ZyusWgTtT451rdRpLpHt3Z/N8vU7chbnXLp2O9/9spP92dEtBpNkcFjTunRpmRZMs1KPI5qnUSdFLzkRESlH9Q+Boy+MdS1ERMrUtGnTmDBhAq1atWLixIn07NmTJk2aUL16dfbt20dKSkqxFm08cCC6QS8VpaD69OvXjx9++IGZM2fy1ltvsWDBAp599lmeffZZzj//fKZNmwaQc+zlvdZRWey/oH2E0sPzk5OTmTZtGqNHj+a1115j3rx5LF68mPfff59x48bx9ttvc8IJJwCQne2v9D3vvPNITU0t8PlDneoV1WYiIiJFqlkPOvwOvp/jHy9/XZ3mEh9+3b3v4BQr63bw9drtfL9xF9kHogu8qyUZHX9TN9cc5Ec0S6NWjeRyrrmIiIiIVCk16/mpThJNzXql2vyVV14B/BzUZ555Zq68lStX5isfGjm8a9eufHnZ2dls2LAhX3rz5s1JT09n9erVHHroofnyf/755xLVPZo6hUZyR5KWlsawYcMYNmwYAIsXL+a8885j+vTpjBgxgoEDB+ZM27JqVeTXRmnrHhp9/8MPP5R4H6E6/vTTTxHzQyPFmzdvni+vR48e9OjRgzFjxrBjxw7uvPNOJkyYwLXXXsvHH38MQKtWrVixYgV//etfcxYSLUzjxo2pUaMGGzZsYN++fflGm+/cuVOjzEVEpOIcMTis03wmDBwf2/qgTvMq4cABxy8797Jqy25+3rKbVVszSA/9vSWDHXujX6CzRnIShzevmzPFSreW9ejYrA4p1dRBLiIiIiLlzKxM5gZPNNu2bQOIOHXK9OnT86WFOl6/++67fHnvvfce+/fvz5feu3dv0tPTefHFF7nlllty5b3zzjulnpolvE5du3bNlTdnzpyo93PCCSdw4YUXMm7cOJYuXcrAgQM55phjqFmzJh9//DGrV6/O104vvPBCqerev39/nnjiCZ588klGjRpVotHZvXv3BmDWrFn8+uuv+eY1nzJlCgAnnXRSoftJS0tj7NixPPDAAyxdujRXHd99911effXVqDrNq1evzvHHH8/ChQt5+eWXGTp0aK780raZiIhIsRw+CN64Flw27NoAaz6JdY0o2SomEncys7L5cdMu5n27kUmLfuLON77h0kmf0n/CAg6/4y16jnuPCx5fzM0vf8XD835k1lfrWbp2e6Ed5jWrJ3F06/pc1LMN955zJLOvOYlv/n4ar1/Vm7F/6Maw37amW6t66jAXERERESlHHTt2BODxxx/PNQ3LBx98wPjx+UdinXyyv6R5ypQpOSOYwY9Kv/rqqyM+R2jRxwkTJrB8+fKc9K1bt3LzzTeX+hhCdbr//vvZvXt3TvrcuXOZOHFivvI///wzkyZNylUWIDMzk3nz5gEH5yqvU6cOw4cPJysri2uvvZbMzMyc8qE5wUtjyJAhdOzYkS+//JLRo0eTlZX7O9QXX3zBmjVrCt1H+/btGTRoEDt37uTaa6/N9cPFRx99xCOPPEJycjJXXnllTvpzzz3H119/nW9fb731Fs65XHO1jxw5kiZNmjB27FieeeaZfNP1ZGRkMHny5Fz1DP3P77jjDtavX5+TvmrVKu66665Cj0dERKRMpTaEdmE/HC97LXZ1CWikeQLZsXd/MDrcjxYP/f3z1t2s276HYkxjmE+dlGp0bp6Waw7y9o1rUy1Zv6uIiIiIJAIzqw48AAwLkqYC/+ucizhKwswGA38HDgO2A393zj1qZq2BZXmK1wRmO+cGl0vlpVDXXHMNkyZN4t///jfz58/nyCOPZO3atSxcuJAbbriB++67L1f59u3bc9FFFzF58mS6d+9Onz59yMjIYPHixQwaNIi9e/fmm8qkd+/eXHfddUycOJEePXrQv39/atWqxXvvvUfr1q054YQTWLx4cYmPYejQodx77718+OGHHHHEERx33HGsXr2azz77jOuvvz7fMWzdupWLL76YUaNGceyxx9KqVSsyMjL48MMP2bRpE8cffzxDhgzJKX/33XezYMECXnnlFQ477DB69erFL7/8woIFCxg5ciSPPPJIieterVo1Xn75ZU499VTuvfdepkyZwoknnkhWVhYrVqxg+fLlzJs3j1atWhW6n8cee4yTTjqJyZMns2DBAnr27MmmTZuYP38+2dnZ3H///blGib/88stcdNFFdOjQgW7dulGrVi3S09NZvHgxycnJjB07NqdsgwYNeOWVVxg8eDCXXHIJd955J127diUlJYWff/6Z5cuXk5GRwZIlS3LqOXz4cGbMmMErr7xCp06dOOWUU3DOMXfuXE4++WTMrNRT24iIiETtiMGwcr7/e/kbMa0KqNM8rjjn2LQzk1Vbg87wLX4alVVb/d/bdue/jLI4GtepQeuGqbRtVJvWjVJp0yiV1g1r06ZRKo1q19AiMCIiIiKJ7a9Ab6BL8PhN4FZ8x3guZnY68G/gT8AHQBrwGwDn3M9AnbCyNYC1gOZriJGOHTvy6aef8pe//IWPP/6Y119/nU6dOvHYY49x+eWX5+twBnjiiSdo0aIFU6dO5e233+aQQw7h1ltvZfTo0XTo0CHi80yYMIFOnTrx0EMP8c4779CoUSPOP/98xo0bx9lnn12qY6hVqxbvvvsuN910E2+99RazZ8+mS5cuTJs2jWOPPTbfMXTo0IH77ruP9957j2XLlvHJJ59Qp04d2rVrx+23385ll12Wax7uxo0bs2jRIm677TZef/11Xn31VTp27MgTTzxBv379StVpDtC1a1e++OILxo8fz+uvv84bb7xBamoqbdq0iXoe8ZYtW/Lpp58ybtw4Xn31VWbMmEFqaiqnnHIKN9xwAwMGDMhV/vrrr6dVq1YsWrSIDz74gIyMDFq2bMnQoUO58cYb6dGjR67yvXr1YunSpUyYMIFZs2bx3nvvkZycTIsWLTjzzDMZMmQInTt3zilvZkybNo3x48fz1FNPMXv2bJo3b87VV1/NmDFj6NSpU6naTEREpFgOPxNm3QA42L4ashrHtDpWnFXWE5GZfdO5c+fO33zzTayrAsD+7AOs3bYnpyN8VU6nuB8xvmd/don3nWTQskEt2jQMOsUbHuwYb90olTop+o1EREREpChdunRh2bJly5xzXYouHT/MbDV+ZPlLwePzgPucc20ilP0UeMI593gU+z0feBRo4ZzbW8K6RRWTHzhwgBUrVgDQqVMnkpJ01aOISGF03hSRSuXpgfDzhwB0mZTCslWbYhaTqxe1HGRkZgXTpuTuFF+1NYN1v+4l+0DJf6ioWT0pf6d4o9q0aZhKywa1qK7pVERERESqHDNrALQCvghL/gJobWb1nHPbw8rWBo4BppnZt0B9YAFwrXNuQ4TdXwpMLWmHuYiIiIhIVDoPzuk0J3NnTKuiTvMScM6xJWMfq0IjxYNR4qu2ZPDz1t1s3rWvVPtvkFo9pyPcjxRPpU2j2rRtlEqTuimaRkVERERE8gpNp/JrWFro77r4OctDGgAGXAicBmzBjyR/Djg1fKdm1gboD5R+JUgRERERkcIc8Xt4a7T/O7t0/aulpU7zAmRlH2D99r35Ft0MTauSsa/k06iYQYt6tYLO8NRg1HjtnL/TalYvwyMRERERkSpgV3BfD9gc9jdA3mE6obIPOudWAZjZ34Dvzay2cy4jrOzFwBLn3JflUGdJUN9++y133313VGV79+7NZZddVs41Kp4nn3yShQsXRlV29OjRHH744eVcIxEREQGgXitoeSys/SzWNananeZ79mXnGiEe3im+ZtseskoxjUqNakkc0qAWbRrVDhbf9KPFWzdKpVWDWqRUSy7DIxERERGRqsw5t83M1gDdgR+D5O7A6vCpWYKyv5rZz0CkYDfnkkYzS8J3mo8rn1pLotqwYQPPPvts1OXjrdN84cKFUdd/xIgR6jQXERGpSJ0Hq9O8ouzdf4DXvlgbNpWKn1Zl487MUu03rWa1nI7w8EU32zRKpVlaTZKSNI2KiIiIiFSYZ4DbzGxR8PhW4MkCyj4OXGNmbwNbgTuAd51zu8LKnAo0Bp4vp/pKgurbty/OlXyAUaxNmjSJSZMmxboaIiIiEskRg+GdO2Jdi6rRab56626ufeGLogtG8Ju0lJypU8IX3WzTKJX6qTXKuKYiIiIiIiV2F9AIWB48ngqMBTCzRwGcc1cEeXcDDYHQtCvz8HOch7sUeCnvSHURERERkXLTsB006wZ8FNNqVIlO88JUTzZaNUg9OL94sOhm6O+a1TWNioiIiIjEP+fcfmBUcMubd0Wex9nADcGtoP2dX9Z1FBEREREpUuezUKd5BUhKgs7N0/IvutkwlRb1a5GsaVREREREREREREREYu+Is4DRMa1Cleg079CkDrOvPSnW1RARERERERERERGRwjTpCA3awqaVMatCUsyeWUREREREJIzZwStADxw4EMOaiIgkhuzs7Jy/w8+hIiIJr1rNmD69Os1FRERERCQumBk1atQAICMjI8a1ERGJfzt27AAgJSVFneYiImWoSkzPIiIiIiIiiaFu3bps2bKFX375BYDatWuTlKSxPiIiIc45MjMz2blzJ1u3bgWgQYMGMa6ViEjlok5zERERERGJG40aNSIjI4O9e/eybt26WFdHRCTu1a9fn3r16sW6GiIilYo6zUVEREREJG4kJyfTunVrtmzZws6dO9m3b1+sqyQiEneSk5OpXbs2devWpW7dupqaRUSkjKnTXERERERE4kpycjJNmzaladOmOOdwzsW6SiIiccPM1EkuIlLO1GkuIiIiIiJxS51DIiIiIlLRtKKOiIiIiIiIiIiIiEhAneYiIiIiIiIiIiIiIgF1mouIiIiIiIiIiIiIBNRpLiIiIiIiIiIiIiISUKe5iIiIiIiIiIiIiEhAneYiIiIiIiIiIiIiIgFzzsW6DuXKzHakpKTU7dChQ6yrIiIiIiIJ4McffyQzM3Oncy4t1nWpLBSTi4iIiEhxxDomrwqd5huAVGB1rOsiIiIiIgnhEGC3c65ZrCtSWSgmFxEREZFiimlMXuk7zUVEREREREREREREoqU5zUVEREREREREREREAuo0FxEREREREREREREJqNNcRERERERERERERCSgTnMRERERERERERERkYA6zUVEREREREREREREAuo0FxEREREREREREREJqNNcRERERERERERERCSgTnMRERERERERERERkYA6zUVEREREREREREREAgnZaW5mvzUzF9xuiXV9YiWsDZyZ9Syk3Plh5dIrsIpxK9Qesa5Hoiir9jKz9MrQ7joHFc7MTjWzV81sg5ntM7MtZrbMzKaa2eVmViPWdYwXZtbYzO4ysyVm9quZ7TazH8zscTPrGuv6VbSw99U2M6tfQJkxQZnRFV2/eJDns9+Z2X4z22xmS81skpmdY2bVYl1Pqfz0WXiQYvKSU0xePIrJc9N5qGCKx4tHMXluismLppi8akjITnPgwgL+rsqGF5L3pwqrhUjVoHNQAczsTmAOcBawCXgDeBfYDwwFHgelO+TcAAAcn0lEQVQaxqyCccTM+gPfA38FWgILgJn4troc+KK0QWhYMDuilNWtaPWB/411JeLcs8HteWARUA24CHgJWG5mx8ewblI16LMwMsXkIhVH56EIFI8Xj2LyQikmL5pi8kos4X71MLPqwB8BB/wCHGFmRzvnPo9tzWImE/gR+KOZXeecywrPNLNGwOnA58DRMaifSKWic1DBzOxY4A5gH/AH59zsPPkt8YFnZgyqF1fM7DhgFlAduAW4L/z8bWZnAFOAcWa22zn3YGxqGhMHgCzgOjOb6JzbFusKxSPn3Ii8aWbWARgLnA/MM7NezrkvKrpuUvnpszAixeQiFUjnocgUjxePYvJCKSaPgmLyyi0RR5oPBBoD7+N/IQX9qjwV3yanRcj7I/4DYEqF1kik8tI5qGB/CO6n5w3QAZxza51zY6p6wGVmhh+NUAO4wzl3d97OlaD9zsZ/EbzHzNpUfE1jZj/wJJAGXB/juiQU59yPzrk/Ak8BqcDTMa6SVF76LIxMMblIxdF5KDLF41FSTF4kxeQlpJi88kjETvPQB+EUDgadQ80sOW/B0Fxt5l0bzOG118zWmtmDkeZmCuYecmbW18xOM7N5wZxWrqC5nOLAVPxJPNIln38CdgGvRdowaJuhZvaCmX1nZhlmttPMPjGzK80sKU/5h4O2uLyQ/f1oZtmJ9IES/L+dmU0qID/ndZEn3QWvs2Qzuzlow0wzW21m95hZSkXUv6KVtL0qieKcgwqcs9TMRgT5YyLktTez6Wa21cx2mdnC4HxUaLvHgSbB/abibGRmTczsPjNbEZyjt5nZm2bWJ0LZnDYws+bB/S9mtsfMPjezi8rkSMrXQOAIYC1wT0GFnHPvAy8CNYFR4XlmVtvMbgmOeWfwOllmZhND597gtfe3YJNnLPece33L/rDK1Fj8CKhrzSzqy4fNLNXMbjezr4PXxHYze9/MLshTrrr5uT33FvTZbmbHB221qHSHEhM3ABlADzPrnTfTzNqa2WPB51emmW0ys5fM7MiCdmhmPYPz0rpgm7Vm9raZabqJqknxeGSKyUuppDGmKSafVEC+YnKqXEyueDx6ismLppi8dBSTJ7iE6jQ3s3rAmfg37UvOue+BT4DfAKcWsum/gPHAGnygmgxcDSwws7oFbDMMeBOoHdx/ig+C445zbhV+7qTBZlYnlG5m7YCewAxgdwGbpwD/AQYAG/HznX0MdAEeJv8vYo8G9xEDdKAf0B6YE9SrqpiKvwxuDX7+uLrAzfhfFqWSKMU5qDjPcRj+PXgeB9+TycBs4JyyeI5ytCa4P8fMmhRaMmBmhwNL8AFF6Di/wp9L5pnZsAI2bQgsxl/qPh/4AOgGPGtmfytgm3hxRnD/onNufxFl/xPcDwwlmFlz/OtuLNAGeA94C38Z7jXA74KiLwFfBn8v4uB8e88CG0p3COXLObcWeAJ/Lr0hmm2Cz/P3gb8DTfFzUS4CjgeeN7OJYfvfj//yk0LB76vQa29qCQ4hppxz2/GxCxx8PQAQBOxfAn/Gd+C9jp/Hcwiw2MxylQ+2uQ7flufh3+czgG/xU0z8o3yOovyZ2fzg2KQYFI8XTDF5XFBMXgUoJi+U4vHoKSYvgmLy0lFMHp24jsmdcwlzwweFDv/BGEq7OkibEqF8epC3HTgmLL0OfiEMB0zIs82kIN0Bf4z1MRfRHg7YG/w9Mnh8UVj+7UHaAKBZ8Hd6nn1Uw78pa+RJb8LBLyZ98uQtCtKPilCnF4K8IbFunyjazoU97hukTSqgfOh10TfSfoBlQNuw9HbA1iCvQ6yPN47aKz18P4l2K8E5KN97LixvRJA/Jk/63CD9QSApLP3CsNdbxHaP9Q3oAOwJO+8+C1yG/8JvEconA0uD8teElwF6AJvxAUTTCK89h/8yXDss7zhgJ5ANdI91exTSTguD+v8pirKtgrLZQPU8r5H/hB9/kHcYcHjY4zFB2RGxPu4o2yb8c61F8HraATSKcEyj82z7ryD9HaBOWPrh+LlOHXBGWPpJQdq7EeqRBKzDX5baONbtEqGNXBTlbgu9TsLS0oD1+C9z5+Yp3x/f+bCGsJgA6IOf03I7+c/pNYDTYt0mpWjL+cB1sa5Hot1QPB6pTRSTl67tXNjjvigmr4j2So/msyRebyU4D1WZmBzF48VpK8XkBR+vYvLo2shFUU4xedFtNJ84jckTaqQ5uS/BCnkBvzjBH8JHdOTxkHPuv6EHzrldwFX4F+6lBVyuN8s5N60M6lxRpuPfcMPD0objf7l8t6CNnHNZzrkZzrl9edI34RfCAL/qdrjHgvvLwhPNL3B0Nv5E+EZxDyDBXe2cSw89cM79xMHX6UkxqZGUh5Keg6JiZocCp+C/3N3inDsQynPOPYf/xT5uOed+xJ8v1uEDgYvwIxO+BjaY2b15Lrv7PdAVeN4596ALPjGDfS0B7sKPLox0qZnDv+8ywrb5FD8aLwn4f2V5bGWsUXC/MYqyoUtrk4CG5ldfPwV/br88/PgBnHPfO+e+LbOaxpBzbh1+jtK6wI2FlTWz2sCl+EDyyuBzPrSfbzk48uKasM0WAquAvmbWIs8u+wHNgbedc5tLcxwxFKp3g7C0S/Addvc5514KL+ycmwv8G2iJH70XMhow4O/Oufl5ttnnnHu7jOtd4cysjpm9ZmYbwy4fPiosf4yZvWFmD5mfIuRnM/tjLOscY4rHC6eYPLYUk1cNiskLoHi8WBSTR0ExeakpJo9SPMbkCdNpbmZtgd74D66cBS2CQPJt/OT6f4i0Lf4DNBfn3HL8pRBpQKT5gl4vVYUrmPMLecwGTjGzZuZXge6E//DLLmp7M+tufv6/h83sGfPzs4U+5A7LU3w6/v/wJzOrFZZ+Ef6ymkmu6MubKpP9+F/G8vouuG9ecVWR8lLKc1C0TgzuZ+cNvAIvlnL/5c45Nwd/Ofj5+AD9K3zQ1BS4Cfg07FLR0OWzrxawu4XB/XER8pY451ZESH8+uM83Z1wcsTz30ZQF/8Wkf/D31AJeI5XN3cBe4Coza1xIuWOAWsAnzl+inddzwX0vMzMIhoX410sScEGe8gl7GWiY0GvHhaUV6z1nfl7YvkHa45E2qCSS8KPE2uEv7V8CTA+9VgKn4Uf1NgL+CjxZyJQilZbi8aIpJo8pxeRVgGLyoikej5pi8ugpJi85xeTRi7uYPGE6zfG/bBp+Feh9efJCvzAXtFp2QfP4pQf3eX/NAvi5WLWLD1Pwl1ddwMFfgqcUXBzMrIaZ/Qf/YrwHuBJ/idr/4C8RBf+LYg7n3F5gMlAfODcs6zL8iaCqzRm4voAvQaFfVSvlwkNVUGnOQdEKnYtWF5CfEOcl51ymc+5F59yfnXNH4X9Fvxk/j+uh+Hn/ANoG99Ms94I4zswc8FmQHykwK8l5PV6ERhs0jaJs6AuNA7YBhwSPfyzrSsUj59x6/Ly9dfBf8goS+n+nF7CfX/GXMtbBd86FhALwnBGhwWjXIfhFeyIu2JcgQu+brWFpbYP7jwt4z72UZ9vG+C8+G51zO8u9xjHinNvhnJvmnMsIYpy/AR3JfR753DkX6vR8Dn8ZbMcYVDfWFI9HRzF5bCgmrxoUk0dB8XhUFJNHSTF5qSgmj1I8xuTVymvH5SAUcJ5iZgvz5KWE5TUP3tDRKOwXxb3Fql18mAn8ih9d0gJY7pz7vIhtrgeG4i/Xugn4HNjmnNtvZh2BFURup0eB6/BB+XNmdiLQGZhXwC+Kia6wH5hcIXlVVSL9IBetsj4HleQ1Fc0oiLgTjPwZb2Z78HPcDQqykoP7Nyn8sshKcWljmC+BXviRGM8VUfaY4P6b4LwcSq9K55178HMEjzKz+4ooG027hF92/LWZfQUcbWaHB5eNDgLq4edELWjBvkTQPbhfFpYWes+9SMGLEYJf+CxcpX69BSN078cvCNYQPxoP/BeUtcHfOQt1OedccD6rciPNUTweLcXk5UcxefEoJi9alYjJFY9HpJi8eBSTl4xi8ijFY0yeEJ3mwXxRnYKHh5H/0sSQJPzlG/fnSW+DX9wir9bB/brS1jEeOOcyzewlDs5r+GAUm4UuXRvqnPs6T177Qp5rhZnNx8871Qm/GAv4y78SUWiUQkHz3x1SQHpVVaXaqxTnoP0Ur41CgX3rCHkFbZNI5gf3oV/M1wT3jzrninsJfpsi0uP5vP4mfgThuWZ2UxGXzocuSXwruA+NeDq0vCoXb5xzG8zsEXyH0s340SZ5hf7f7SLtw8zq4YPuDPziVOGm4qeFGAbcQSW4DDQ43tODh/PCstbgz2X/cM59FcWuNuMXfvqNmdWtxCNbbsB/Ge7tnFsTzPW6jQTsFClPisejp5i8VKpUjFkGqlR7KSYvE/OD+6oej4Ni8mJRTF58ismLLe5i8kT55Tl0edV455xFuuFXo4fIC1TkmxjezA7H/+KzEz/HV2UxGdiCf1NFc3IJLUYQ6dKz84vYNrT40PVB2a3AjCieMx6FAqN8l3WYX0zp6IqtTtyrau1V0nPQeqCRmTWMsM8BEdI+DO7PMLPUCPnnRkiLG3nmGoukQ3AfCqbmBvdnl+Dpugcj7/IaGtwvKsE+K8ps/IjBlsBfCipkZn3w//N9+AWV4GCbDS/gNZJX6Mt0QvxIXoh78KMwrsTPb5fXf/GB5PFmFukLdOh9uTCYNzHcf/CjNoaZWRp+VMtGDrZ1Irofv2jXp865j8LSi/WeCy57nB88vLyQookuDT+ieZv5xePGFlG+qlI8XjyKyUumqsWYpVXV2ksxeREUjxeLYvLiU0xePIrJiyfuYvK47zQ3s2ocDLKfL6Toe/g3VHcz65on7yoz6xG2z9r4S5IMeNo5l1mGVY4p59wHzrnGzrkmzrmC5hgLF1oY54rwRDM7F39JaWFm4FeR/jN+wZXJidqWzrmf8HPTdTOzs0LpwWvlCXLPt1XlVaX2KuU5aEFwf3vY/szMbuHgAkM5gsuo5+EvRRobHvSa2XDg5JIeRwW5y8zuNbN8IwuCoCk02if0Rf4l/KWeI8zsL2ZWPc82NcxsiJl1i/BcScCD4UGqmR0DjMJfxvVYhG3ignPuAH6e2v3A34NjTw4vY2YD8QvDGDDaOZcebPsJ/jXSDHgsb5BuZocGnVAhoS9EnUhgzrmN+FXkU/Hz++bNzwCexr8uHg7ORQAEX+b+Gjz8V4Rt1wDv479E3gPUBKY557LK+DDKnZm1N7NpwKX4ETyX5inyGP5z+1YzuzjvF2szq21mF5lZq7Dke/BfYG43s5PylK9uZqeV+YFULAdMALKBX/BTY3xU6BZVkOLx4lNMXjJVKcYsC1WpvRSTR03xeJQUkxefYvLoKCYvkfiMyZ1zcX0Dfo9vvG+jKPtwUPbu4HF68Pgh/C97bwHT8L80O/w/oV6efUwK8vrG+tijOF4H7I2ybLOgfHqe9D5AVpD3Gf7XvU+Dx+OD+/mF7Df0xnVAl1i3STHb7kCetIuD9Cx8sPU6fr6k7/ALT+R7XURq07C8EUH+mFgfbxy1VzrBAtmJcivlOagL/pd4h1/Y6yX8aIbdYWXH5NlHJ/zINAcsD96TH+IDz9A2j8e6XQo4/omh10pQ9xnBOfcj/Idf6DxTL2ybw/GLCDl8MPkWMD3YZluQfnZY+b5B2hvBduuD53gLf553wF2xboso2+t0/Jy3Dh8YvBocy7IgLRv4a4TtWgavI4cfwfgqfj68JUHbjwgr2wI/2iMLfwnqU8CTQKdYH38BbVLg5xp+AaZdHPzMGZ0nv27w+gq153RgVnD8DvhnIc97edh+HfDbWLdFEW3k8PHKJPxo1leD182BIO874NgCtu8Vdo5Jx8+9/DL+sz/Uvt3zbHNj2PN+HJyX5uKD/fRYt0kp2vJzYHis65EINxSPF3XMislL13aKySu+vdJRTF4pY3IUj5ekzRST5z82xeTRtZFi8rJpy7iNyWNegSgabzpRBjlA76DsavwvW+nBY8PPjbMcP9R/HT5wbxBhH5NIkCC9sBNZhLIRA/Qg7wTgXfylnDvwl1ENwa/oW1SAPiAosyjW7VGMdqsZ1HlnhLwR+Pk2M/HB5hNAo4JeFwW1adi+Ej5AL+P2SifxAvQSn4OCtBPwoxAy8CuFzwaOKuz1gZ8b70V8kJqBD9DPwK8m7oBxsW6XAo6/Mf6y2SnB62ILfuTGJvyXuCuBGhG2a4BfGfsLfICQAfyA/6I3AqgTVrYvB4OTFvhFezbiz+1fEBacJsINH3T+I6j7DnwwuTJ4Lx1ZyHZpQZstxX/h2wF8g/91vnWesgOAhfjpD0JBVt9YH3sBx1Xo5xq5O4VGR8ivjZ8D8ZvgNbED+AA/R3Bhz1s/KO+AH2LdDlG0Ufhtf/BeWxq8L4YA1YrYRwvgPnxctDt4bawAXsBP7RDpfXoy/ovARvwX4jX4L8fDYt0mJWzHI/GfXXH5ZTXebigeL+qYFZOXrN0Uk8euvdJRTF4pY3IUj5e03RST566rYvLo2kgxeenbMa5jcgsqWSmZWTrQxvm5zaQcmNnj+F8DL3bOTYpxdaJiZp3xJ+9lzrkusa5PvFN7xQ/zC69cAVzgnJsW6/rEgpn1xX/hedY5NyK2tRGRRGRmj+HnybzPOTcx1vWp7BSPVwzF5JWf2it+VPWYXPG4iJSFRIjJE30RAokhM2uDX8hhM/7ypbgXzBV1VfBwfgyrkhDUXhXPzGoC7Zxzy/OknwNchh8ZMysWdRMRqQyccyNjXQeRsqSYvPJTe1U8xeQiIuUrEWJydZpLsZnZTfhLKE4FauEvx9kT21oVzswaA/8EugW3nRxcBEXyUHvFVH1gmZl9C3yPv8zriOB2ALjSObcrhvUTERGROKCYvPJTe8WUYnIRkSouKdYVkIQ0CD+aJQs/f1e+lY/jUB1gKNAKv2DJSc65lbGtUlxTe8XOdvyXoX34+RgH4+ekfA042Tn3nxjWTUREROKHYvLKT+0VO4rJRUSquEo9p7mIiIiIVF1mloJfbLI/fnG0tcC9zrmng/w04FHgTPyiXw855+4K8poCD+AXXEoDfgT+5px7PWz/LYAngzJbgLucc09UzNGJiIiIiMS/RI3JNT2LiIiIiFRW1YD1+AB9JfBb4E0zW+Ocm4MfmdsQaA00Beaa2Srn3GT8CM8lwF+AdfhRvS+Y2XHOuWXB/p/HB+5Nga7A22b2nXNuQYUdoYiIiIhIfEvImFwjzUVERESkyjCzGcDXwN3ANqCXc+6zIO8m4Ezn3MkFbPs5fuTL02bWAfgOaOGc+yXIfxio45z7nwo4FBERERGRhJQIMbnmNBcRERGRKsHMagLHA18BnYAawBdhRb7AL6wYadum+AXgvgqSjgTWh4LzorYXEREREZHEicnVaS4iIiIilZ6ZGX6uw++BGfhLPTOcc1lhxX4F6kbYNgV4AZgeGgETbP9rnqIRtxcRERERkcSKyTWnuYiIiIhUakFw/gh+JEt/59wBM9sFpJpZtbAgvR6wM8+2NYAXgd3A5WFZu4Ly4fJtLyIiIiIiiReTa6S5iIiIiFRaQXD+MP4S0AHOue1B1gpgP3BUWPHuwNKwbUPBeQ3gHOfcvrCyXwEtgktEI24vIiIiIiKJGZOr01xEREREKrOHgF7Aqc65baFE59xuYBpwl5nVM7PDgKvxl4tiZtWB6UBt4GznXGb4Tp1zPwKLgLFmlmpmxwPDgacq4JhERERERBJJwsXk6jQXERERkUrJzNoAV+IvAV1lZruC26NBkauA7cAafLD9lHNucpB3InAWPrjfHLbtrWFPMRRoCWwCXgZuds4tKPcDExERERFJEIkak5tzrrT7EBERERERERERERGpFDTSXEREREREREREREQkoE5zEREREREREREREZGAOs1FRERERERERERERALqNBcRERERERERERERCajTXEREREREREREREQkoE5zEREREREREREREZGAOs1FRERERERERERERALqNBcRERERERERERERCajTXEREREREREREREQkoE5zEREREREREREREZGAOs1FROKAmbmwW89Cyp0fVi69AqsYNTPrG9RvUqzrIiIiIiISLcXkIiISok5zEZH4M7yQvD9VWC0KYGYjggB8TKzrIiIiIiJSThSTi4hUYeo0FxGJH5nAMuCPZlYtb6aZNQJOBz6v6IqJiIiIiFQRislFRESd5iIicWYq0Bg4LULeH4HqwJQKrZGIiIiISNWimFxEpIpTp7mISHyZCjgiX/L5J2AX8FphOzCzM8zsHTPbZmZ7zWyFmd1tZvUjlB0TXNY5wsy6mdnrwXYZZrbAzE7MU34+8Ezw8G955n0cEWH/Dc3sETNbb2aZZva1mV0SVUuIiIiIiMSGYnIRkSou36VGIiISO865VWa2CBhsZnWcc7sAzKwd0BOYDOwuaHszuwUYC2QBC4DNQC/gL8AfzKyPc+6XCJseCzwMrAHeBQ4F+gDvmtlxzrmvg3Jv4T87egFfAl+E7eOHPPusD3wE1AM+AeoE+3zKzJKcc09G0SQiIiIiIhVKMbmIiGikuYhI/JkCpAJDwtJCo1ymFrSRmR0H/APYCfRyzvV3zl2AD7ZfBDoC/ypg81HAnc65Ts65c51z3YGJQE3g5lAh59zdQCiwftU5NyLstjDPPs8ClgIdnHODnXP9gHOCvNsLOX4RERERkVhTTC4iUoWp01xEJP5MB/YBw8PShgMb8CNOCnIV/rw+0Tn3SSjROZcZ5O0BzjGzlhG2XeicuzdP2j+C+z7Fq36OHcCfnXMZYXV5DR+0tzaztiXcr4iIiIhIeVNMLiJShanTXEQkzjjntgGzgVPMrFkwWqUT8LxzLruQTU8K7vONfHHObQTm4M/7J+bND/LybrMF2AI0L94R5PjMObc1Qvp3wX1J9ysiIiIiUq4Uk4uIVG3qNBcRiU9TgGTgAg5eBjqliG1a4BcsWlVAfnpYubzWFLDNLqBGEc9bkML2CZBSwv2KiIiIiFQExeQiIlWUFgIVEYlPM4FfgYvwAfVy59znZbRvF2VaeTyPiIiIiEiiUEwuIlJFaaS5iEgcCuY8fAnoAfyGoke0AKwDDGhTQH4ofX2pKygiIiIiUskpJhcRqbrUaS4iEr8m4+cv3EyEOREj+CC4H543w8yaAAOAA8CHpazXvuBeVyuJiIiISGWnmFxEpApSp7mISJxyzn3gnGvsnGvinCtoTsRwD+MD8GvN7NhQopnVAP4FpAIznHNrS1m1dcF9p1LuR0REREQkrikmFxGpmvSLpIhIJeGc+8TMbgf+D/jIzObjR8T0Ag4BvgeuKoOnWgxsBM4NnmMl/ovB08650o6YERERERFJWIrJRUQqB400FxGpRJxzY4EzgQXAccAQIBO4F/itc+6XMniOvcAg4B2gOzACuBToWNp9i4iIiIgkOsXkIiKJz5zTQsoiIiIiIiIiIiIiIqCR5iIiIiIiIiIiIiIiOdRpLiIiIiIiIiIiIiISUKe5iIiIiIiIiIiIiEhAneYiIiIiIiIiIiIiIgF1mouIiIiIiIiIiIiIBNRpLiIiIiIiIiIiIiISUKe5iIiIiIiIiIiIiEhAneYiIiIiIiIiIiIiIgF1mouIiIiIiIiIiIiIBNRpLiIiIiIiIiIiIiISUKe5iIiIiIiIiIiIiEhAneYiIiIiIiIiIiIiIgF1mouIiIiIiIiIiIiIBNRpLiIiIiIiIiIiIiISUKe5iIiIiIiIiIiIiEhAneYiIiIiIiIiIiIiIgF1mouIiIiIiIiIiIiIBNRpLiIiIiIiIiIiIiIS+P+V46P31vTrGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1500x1500 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=int(len(df.columns)/2), ncols=2, dpi=150, figsize=(10,10))\n",
    "for i, (col,ax) in enumerate(zip(df.columns, axes.flatten())):\n",
    "    df_results[col+'_forecast'].plot(legend=True, ax=ax).autoscale(axis='x',tight=True)\n",
    "    df_test[col][-nobs:].plot(legend=True, ax=ax);\n",
    "    ax.set_title(col + \": Forecast vs Actuals\")\n",
    "    ax.xaxis.set_ticks_position('none')\n",
    "    ax.yaxis.set_ticks_position('none')\n",
    "    ax.spines[\"top\"].set_alpha(0)\n",
    "    ax.tick_params(labelsize=6)\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Square Error value of the VAR Model is: 90.61682802068084\n",
      "Mean Absolute Percentage Error value of the VAR Model is: 13.474870996473\n",
      "Mean percentage Error value of the VAR Model is: -3.062496537218274\n"
     ]
    }
   ],
   "source": [
    "#evaluation metrics\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error    \n",
    "\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(df_test['aud_usd_closed'],df_results['aud_usd_closed_forecast'].values))\n",
    "print('Root Mean Square Error value of the VAR Model is:', (1-rmse)*100)\n",
    "\n",
    "mape = np.mean(np.abs((df_test['aud_usd_closed']-df_results['aud_usd_closed_forecast'].values)/df_test['aud_usd_closed']))*100\n",
    "print('Mean Absolute Percentage Error value of the VAR Model is:', (mape))\n",
    "\n",
    "mpe = np.mean(df_test['aud_usd_closed']-df_results['aud_usd_closed_forecast'].values)\n",
    "print('Mean percentage Error value of the VAR Model is:', (mpe))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}