{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Processing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Data Visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Training / Evaluation\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading Data\n",
    "dataset_path = 'datasets/UCI HAR Dataset/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load feature names\n",
    "features = pd.read_csv(f'{dataset_path}features.txt', sep='\\s+', names=['feature_id', 'feature_name'])\n",
    "feature_names = features['feature_name']\n",
    "\n",
    "# Creating a unique name for \"duplicate\" feature names (example: fBodyAcc-bandsEnergy()-1,8 )\n",
    "name_count = {}\n",
    "unique_feature_names = []\n",
    "\n",
    "for name in feature_names:\n",
    "    if name in name_count:\n",
    "        name_count[name] += 1\n",
    "        unique_feature_names.append(f\"{name}_{name_count[name]}\")\n",
    "    else:\n",
    "        name_count[name] = 0\n",
    "        unique_feature_names.append(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading training data\n",
    "X_train = pd.read_csv(f'{dataset_path}train/X_train.txt', sep='\\s+', names=unique_feature_names)\n",
    "y_train = pd.read_csv(f'{dataset_path}train/y_train.txt', sep='\\s+', names=['Activity'])\n",
    "\n",
    "# Loading testing data\n",
    "X_test = pd.read_csv(f'{dataset_path}test/X_test.txt', sep='\\s+', names=unique_feature_names)\n",
    "y_test = pd.read_csv(f'{dataset_path}test/y_test.txt', sep='\\s+', names=['activity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Activity\n",
       "0         5\n",
       "1         5\n",
       "2         5\n",
       "3         5\n",
       "4         5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-meanFreq()</th>\n",
       "      <th>fBodyBodyGyroJerkMag-skewness()</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.288585</td>\n",
       "      <td>-0.020294</td>\n",
       "      <td>-0.132905</td>\n",
       "      <td>-0.995279</td>\n",
       "      <td>-0.983111</td>\n",
       "      <td>-0.913526</td>\n",
       "      <td>-0.995112</td>\n",
       "      <td>-0.983185</td>\n",
       "      <td>-0.923527</td>\n",
       "      <td>-0.934724</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.074323</td>\n",
       "      <td>-0.298676</td>\n",
       "      <td>-0.710304</td>\n",
       "      <td>-0.112754</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>-0.464761</td>\n",
       "      <td>-0.018446</td>\n",
       "      <td>-0.841247</td>\n",
       "      <td>0.179941</td>\n",
       "      <td>-0.058627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.278419</td>\n",
       "      <td>-0.016411</td>\n",
       "      <td>-0.123520</td>\n",
       "      <td>-0.998245</td>\n",
       "      <td>-0.975300</td>\n",
       "      <td>-0.960322</td>\n",
       "      <td>-0.998807</td>\n",
       "      <td>-0.974914</td>\n",
       "      <td>-0.957686</td>\n",
       "      <td>-0.943068</td>\n",
       "      <td>...</td>\n",
       "      <td>0.158075</td>\n",
       "      <td>-0.595051</td>\n",
       "      <td>-0.861499</td>\n",
       "      <td>0.053477</td>\n",
       "      <td>-0.007435</td>\n",
       "      <td>-0.732626</td>\n",
       "      <td>0.703511</td>\n",
       "      <td>-0.844788</td>\n",
       "      <td>0.180289</td>\n",
       "      <td>-0.054317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.279653</td>\n",
       "      <td>-0.019467</td>\n",
       "      <td>-0.113462</td>\n",
       "      <td>-0.995380</td>\n",
       "      <td>-0.967187</td>\n",
       "      <td>-0.978944</td>\n",
       "      <td>-0.996520</td>\n",
       "      <td>-0.963668</td>\n",
       "      <td>-0.977469</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>0.414503</td>\n",
       "      <td>-0.390748</td>\n",
       "      <td>-0.760104</td>\n",
       "      <td>-0.118559</td>\n",
       "      <td>0.177899</td>\n",
       "      <td>0.100699</td>\n",
       "      <td>0.808529</td>\n",
       "      <td>-0.848933</td>\n",
       "      <td>0.180637</td>\n",
       "      <td>-0.049118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.279174</td>\n",
       "      <td>-0.026201</td>\n",
       "      <td>-0.123283</td>\n",
       "      <td>-0.996091</td>\n",
       "      <td>-0.983403</td>\n",
       "      <td>-0.990675</td>\n",
       "      <td>-0.997099</td>\n",
       "      <td>-0.982750</td>\n",
       "      <td>-0.989302</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>0.404573</td>\n",
       "      <td>-0.117290</td>\n",
       "      <td>-0.482845</td>\n",
       "      <td>-0.036788</td>\n",
       "      <td>-0.012892</td>\n",
       "      <td>0.640011</td>\n",
       "      <td>-0.485366</td>\n",
       "      <td>-0.848649</td>\n",
       "      <td>0.181935</td>\n",
       "      <td>-0.047663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.276629</td>\n",
       "      <td>-0.016570</td>\n",
       "      <td>-0.115362</td>\n",
       "      <td>-0.998139</td>\n",
       "      <td>-0.980817</td>\n",
       "      <td>-0.990482</td>\n",
       "      <td>-0.998321</td>\n",
       "      <td>-0.979672</td>\n",
       "      <td>-0.990441</td>\n",
       "      <td>-0.942469</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087753</td>\n",
       "      <td>-0.351471</td>\n",
       "      <td>-0.699205</td>\n",
       "      <td>0.123320</td>\n",
       "      <td>0.122542</td>\n",
       "      <td>0.693578</td>\n",
       "      <td>-0.615971</td>\n",
       "      <td>-0.847865</td>\n",
       "      <td>0.185151</td>\n",
       "      <td>-0.043892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 561 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
       "0           0.288585          -0.020294          -0.132905         -0.995279   \n",
       "1           0.278419          -0.016411          -0.123520         -0.998245   \n",
       "2           0.279653          -0.019467          -0.113462         -0.995380   \n",
       "3           0.279174          -0.026201          -0.123283         -0.996091   \n",
       "4           0.276629          -0.016570          -0.115362         -0.998139   \n",
       "\n",
       "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
       "0         -0.983111         -0.913526         -0.995112         -0.983185   \n",
       "1         -0.975300         -0.960322         -0.998807         -0.974914   \n",
       "2         -0.967187         -0.978944         -0.996520         -0.963668   \n",
       "3         -0.983403         -0.990675         -0.997099         -0.982750   \n",
       "4         -0.980817         -0.990482         -0.998321         -0.979672   \n",
       "\n",
       "   tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  fBodyBodyGyroJerkMag-meanFreq()  \\\n",
       "0         -0.923527         -0.934724  ...                        -0.074323   \n",
       "1         -0.957686         -0.943068  ...                         0.158075   \n",
       "2         -0.977469         -0.938692  ...                         0.414503   \n",
       "3         -0.989302         -0.938692  ...                         0.404573   \n",
       "4         -0.990441         -0.942469  ...                         0.087753   \n",
       "\n",
       "   fBodyBodyGyroJerkMag-skewness()  fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "0                        -0.298676                        -0.710304   \n",
       "1                        -0.595051                        -0.861499   \n",
       "2                        -0.390748                        -0.760104   \n",
       "3                        -0.117290                        -0.482845   \n",
       "4                        -0.351471                        -0.699205   \n",
       "\n",
       "   angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "0                    -0.112754                              0.030400   \n",
       "1                     0.053477                             -0.007435   \n",
       "2                    -0.118559                              0.177899   \n",
       "3                    -0.036788                             -0.012892   \n",
       "4                     0.123320                              0.122542   \n",
       "\n",
       "   angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \\\n",
       "0                         -0.464761                             -0.018446   \n",
       "1                         -0.732626                              0.703511   \n",
       "2                          0.100699                              0.808529   \n",
       "3                          0.640011                             -0.485366   \n",
       "4                          0.693578                             -0.615971   \n",
       "\n",
       "   angle(X,gravityMean)  angle(Y,gravityMean)  angle(Z,gravityMean)  \n",
       "0             -0.841247              0.179941             -0.058627  \n",
       "1             -0.844788              0.180289             -0.054317  \n",
       "2             -0.848933              0.180637             -0.049118  \n",
       "3             -0.848649              0.181935             -0.047663  \n",
       "4             -0.847865              0.185151             -0.043892  \n",
       "\n",
       "[5 rows x 561 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9etToNYELa1R3vl7gVgWBehUd6Ry/tm3bVnmfzf379wMwY8aMI8aVn59/xAHqXXfdxYsvvkhSUhIXXXQR7du3d86rnTZtGsXFxUds/2h+yXKp6lywwBfIioOUo6nuvQPlubm5QPky/d///sf//ve/atvLz8+v0fsGLrrz3nvvkZ6ezv/+9z9s22bEiBH07duX8PBw5s+fz7nnnsuCBQvo2rWrM2ioGE9N+7iu60R9LeejOXDgALZts3v37koXFzs8voBnn32W3/3ud7Rp04bRo0fTsWNHIiIiAHjuued+8fpZU1WtQ4Hl/dZbbx3xtYF8hgwZwoIFC3jyySf517/+xeuvvw7A4MGD+fOf/xz040R16rq91qSPA9tgxXPgK6rtucgfffQR+/btY/z48U6fQflA8U9/+hP/+te/uOOOO4Dy7bA2A/bauu6663j66ad58803nQHqP//5T+e5+nDrrbcyfvx43nzzTSZNmsSSJUtYvXo1119/va4KLCINRgNUkWPcaaedxoIFC5g3bx4jRoyo0WsCXwAzMzOrfD4jIyOoXkVVDUCP9lygndWrV1d538ma2Lt3LzNmzKBfv34sXrw4aIYzIyPjiIOImvoly6W+VPfegfLAl8ZADPfdd1/QhXp+icCFlebPn8+iRYuIiYlh4MCBuFwuhg4dyvz589m0aRO7d++udPGU2vZxfawTDSkQ36BBg/B4PEet7/V6efzxx2nfvj0rVqwIGjjZtl3t1VKrE5gl9nq9lZ4LDI6qU9V2GMjn008/rdGMOvhnIf/73/9SWFjIkiVL+PTTT3nppZc4//zzWbNmjXNBpao09PYayOfwqyMHVLcdVSdw79PXX3/dGYxXVScwQA3MNu7evbtW71Mbffr0YcCAAXz22Wfk5uYSEhLChx9+SI8ePRg8eHC9vMfYsWO59957efXVV5k0aRKvvvoqoIsjiUjD0m1mRI5xN9xwAy6Xi3/84x9kZWUdsW5gxqJnz56Eh4fz448/UlBQUKle4PYHR7vibU0Frjy7ePHiOrexdetWbNtm1KhRlQ6//e6776p8jcvlAmo+s/ZrL5eqpKWlVXkLjkCOAwcOBPwzWYZh1GqZHm15BGbFvvnmG+bPn89ZZ53lvGbEiBGkpaU5t/A4/BYnte3j+lgnGlLLli3p1asX69atq9Ghw9nZ2eTm5jJ06NBKs3oej4fCwsJKrzlSfxxpAJSWllaDDIL9kuUdERHBsGHDePbZZ3nooYcoLCzkq6++OuJr6rK91kZ0dDQpKSls3rzZ+eGoru+xY8cO5s2bR0JCAjfddFOVj5SUFNLS0pxlHziM+ssvvzxq+7XdD1V03XXXUVRUxPvvv8+HH37IoUOHuPbaa2v8etM0j/i+ERERjBs3jpUrVzJ//nzeffddevXqxWmnnVbrWEVEakoDVJFjXNeuXbn//vvJzs7m3HPPZdu2bZXqFBUVMX36dOfcs9DQUK666iqys7N56qmngurOnTuXL774gq5du9bbl5Tx48fTsmVL/vCHP7B27dpKzxcUFDjnyFUncDuTRYsWBZ3HtmvXLh588MEqX9OqVSsAdu7cWaM4f+3lUpWcnJxKt6X54osvmDdvHn369HHOz2vXrh1XXHEFixYt4i9/+YtzO5iKlixZEjTQPtry6NmzJ+3atePTTz9l3bp1QTPyw4cPx+fzObO1hx/iWds+ro91oqHdddddFBQUMGHChCoPld62bRvbt28H/IeaRkREsHz58qBlfuDAAe68884q2z9Sf0RHR9OjRw++//57Nm/e7JQfPHiw2vX9SC6++GJOOOEEpk+fzrffflvp+dLS0qB75y5evJiioqJK9QIzk+Hh4Ud8v7psr7V13XXXUVJSwpQpU4LKv/zyy1qdf/r6669jWRa33norr776apWPBx54ACifaR08eDCDBw/m22+/dW7JUlHFHxbi4uIwDKPG+6GKrr76alwuF//85z/55z//iWEYtRqgtmrVil27dh2xTuBep9deey0HDx7U7KmINDgd4ityHHjiiScoKirir3/9Kz169GDEiBH06dOHkJAQtm3bxtdff82+fft44oknnNcE7kX4xBNPsGjRIoYMGcL27dt57733iIyM5PXXX690MZq6atOmDW+//TaXX345/fv355xzzqFnz54UFxezfft2Fi5cyKmnnsrcuXOrbaN9+/Zcdtll/Oc//yE1NZWRI0eSmZnJZ599xsiRI6u80M6IESN4//33ueyyyzj33HMJDw+nf//+XHjhhdW+z6+5XKpyxhln8Pe//50lS5ZwyimnOO8dERHhHH4X8NJLL7Fhwwbuv/9+/vnPfzJ06FBiY2PZuXMnHo+HTZs2sWfPHmcGqybLY/jw4bz99tvO/wNOPvlkoqKiyMrKokePHs75uAG17eP6WCca2q233soPP/zA7Nmz+d///seoUaNITEwkMzOT9evXs2TJEv71r3+RnJyMaZrcfvvtPPvss84yzcvL47///S+dOnUiMTGxUvsjRozgmWee4ZZbbuGyyy4jKiqKTp06OecX3nfffdxyyy0MHTqUyy+/HMuy+O9//1unwzvDwsJ4//33OffccznrrLOcc4sNw2DHjh189913tG7d2rno05///Gfmz5/PmWeeSUpKCuHh4Sxfvpx58+bRuXPnoAsJVaUu22tt3X///XzwwQe88sorrF27ljPPPJOdO3fy73//m/PPP585c+YctQ3Lsnj99dcxDCPoXqGHGzt2LPfccw9vvfUWzzzzDOHh4bz11lsMGzaMW265xdn+ioqKWLt2LWlpaezbtw+AFi1aOIPZ6667jm7dumGaJtddd13QfYSr0q5dO0aNGsWXX36JaZqcfvrptbpX7ogRI/j3v//NJZdc4hyuf9FFF9GvXz+nTu/evTnjjDP47rvvCAsLY9y4cTVuX0SkThrvAsIi8mv78ccf7RtvvNHu2rWrHRERYYeFhdnJycn21VdfbX/11VeV6mdlZdl33XWX3alTJzskJMSOj4+3f/vb39qrV6+uVPdIt8Swbf9tZjp16nTE+NavX2/fdNNNdqdOnezQ0FA7Li7O7tu3r33XXXfZS5cuDapLFbdHOHjwoH3ffffZycnJdlhYmN2tWzf78ccft0tKSqqsX1paat9///32CSecYLvd7kq3eqjqNfW5XAK3M5k/f/4Rl4ttB9+KYs2aNfZ5551nR0dH21FRUfaoUaOC7rdZUUFBgf3000/bgwYNsqOiouyIiAg7JSXFvuSSS+w33ngj6FYUR1setl1+78/4+Hjbsqyg50aPHl3pVjSHq00f16Z+XW67Up1Av7z99ts1qv/uu+/ao0aNsuPi4uyQkBC7Q4cO9rBhw+xnn33WzsrKcuqVlJTYf/zjH+1u3brZYWFh9gknnGDfd9999sGDB6vdPp5++mm7W7dudkhISJU5zJgxw3n+hBNOsKdMmVLt+n602yrZtv/epHfffbcTY3R0tN2rVy/75ptvtufNm+fUmzt3rj1u3Di7R48edsuWLe0WLVrYvXv3th966KGgnI+kttvrkeKvbjvbt2+ffcstt9ht2rSxw8PD7UGDBtkffPBBtbddOdwXX3xR43XnmmuuqXQbmoyMDPvuu++2O3fubIeGhtqtWrWyhwwZYk+fPj3otRs2bLDPO+88OzY21jYMI2i/cLRY33zzTecWSP/3f/9XZZ3q2tizZ499xRVX2PHx8bZpmtW+T+DWUVdeeeVRl4OIyC9l2HYVx32JiIiIiACTJk1ixowZtbrYnohIXWmAKiIiIiJVysrKonPnznTo0IF169Yd8UrtIiL1QeegioiIiEiQOXPmsHz5ct5//30OHTrEo48+qsGpiPwqNEAVERERkSDvvfces2fPJjExkSeffJIrr7yysUMSkeNEs7rNzLfffsuFF15IYmIihmHw0UcfHfU1CxYs4KSTTiIsLIyuXbsya9asSnVmzJhBcnIy4eHhDBkyhKVLl9Z/8CIiIiLNxKxZs7Btm927d9fbrX9EpPlpqPHXkTSrAWp+fj79+/dnxowZNaq/bds2zj//fIYPH86KFSu45557uPnmm/niiy+cOu+++y6TJ09m6tSpLF++nP79+zNmzBj27t3bUGmIiIiIiIg0eQ0x/jqaZnuRJMMw+PDDD7nkkkuqrfP//t//Y86cOaxZs8Ypu/LKK8nJyXHunTdkyBAGDx7Miy++CPjveZaUlMSdd97p3Hj7cMXFxRQXFzt/W5bF/v37ad26tc7PEBERERFpZLZtc/DgQRITExv0/uQNoaioiJKSkgZp27btSuOVsLAwwsLCjvra+hp/Hc0xfQ7q4sWLGTVqVFDZmDFjuOeeewAoKSlh2bJlQYeumKbJqFGjWLx4cbXtPvXUU0ybNq1BYhYRERERkfqxc+dOOnbs2Nhh1FhRURGJES04gK9B2m/RogWHDh0KKps6dSqPPvpovbR/tPFXTRzTA9SMjAwSEhKCyhISEsjLy6OwsJADBw7g8/mqrLN+/fpq233wwQeZPHmy83dubi4nnHAC27ZtIzo6GvAPdE3TxLIsLMty6gbKfT4fFSevqyt3uVwYhoHX6w2KweVyAeDz+WpU7na7sW07qNwwDFwuV6UYqytXTspJOSkn5aSclJNyUk7KqTnklJOTQ0pKCi1btqQ5KSkp4QA+ZrlSiKznszELsLjh0DZ27tzpjFmAGs2e1tTRxl8RERFHbeOYHqA2lOqmwVu1ahXU2SIiIiIi8usLHNbbXE+/iwpxEWm46rVNw/aBD6Kjo5v0mOWYHqC2a9eOzMzMoLLMzEyio6OJiIjA5XLhcrmqrNOuXbtfM1QREREREZFm7Wjjr5poXmcM19LQoUOZN29eUNlXX33F0KFDAQgNDWXQoEFBdSzLYt68eU4dERERERGRX5PhNjDr+WG4G342+Wjjr5poVgPUQ4cOsWLFClasWAH4L2O8YsUKfv75Z8B/bui4ceOc+rfddhtbt27l/vvvZ/369bz00kv8+9//5t5773XqTJ48mVdeeYXZs2ezbt06Jk6cSH5+PuPHj/9VcxMREREREWlKGmL8dTTN6hBfj8fD8OHDnb8DFyq6/vrrmTVrFnv27HEWFkBKSgpz5szh3nvv5fnnn6djx468+uqrjBkzxqkzduxYsrKymDJlChkZGQwYMIC5c+dWOrlXRERERETk12CEmBhG/c4lGnW4u2hDjL9qEGfzvA9qU5KXl0dMTAy5ublN+oRjEREREZHjQXP9fh6I+z9xPYiq54sk5ds+Ljuwockvk2Y1gyoiIiIiInKsM10Gplm/54yaVvO4orEGqCIiIiIiIk2IEWJg1PMA1WgmA9RmdZEkEREREREROXZpBlVERERERKQJMd3H7yG+mkEVERERERGRJkEzqCIiIiIiIk2IzkEVERERERERaWSaQRUREREREWlCTJeB6arnc1B9mkEVERERERERqTHNoIqIiIiIiDQhhsvAqOcZVIPmMYOqAaqIiIiIiEgT0iCH+DaTAaoO8RUREREREZEmQTOoIiIiIiIiTYhhNsBtZmzNoIqIiIiIiIjUmGZQRUREREREmhDDZWK46ncu0cCu1/YaimZQRUREREREpEnQDKqIiIiIiEgToqv4ioiIiIiIiDQyzaCKiIiIiIg0IYbRAFfxtTSDKiIiIiIiIlJjmkEVERERERFpQgwX9X4OqtE8LuKrAaqIiIiIiEhTYrgMjHofoOoQXxEREREREZEa0wyqiIiIiIhIE2KYJoZZv3OJ9d1eQ2keUYqIiIiIiMgxTzOoIiIiIiIiTYhhNsBtZuq5vYaiGVQRERERERFpEjSDKiIiIiIi0oSYLqPebzNj6iq+IiIiIiIiIjWnGVQREREREZEm5Hg+B1UDVBERERERkSbEMBrgNjNG8zh4tnlEKSIiIiIiIsc8zaCKiIiIiIg0IcfzIb6aQRUREREREZEmQTOoIiIiIiIiTUiD3GbG0gyqiIiIiIiISI1pBlVERERERKQJ0TmoIiIiIiIiIo1MM6giIiIiIiJNiGE2wH1Q67m9hqIBqoiIiIiISBOiQ3xFREREREREGplmUEVqYU5IDwDOL93QyJGIiIiIyLFKM6giclSBwamIiIiIiDQMzaCKiIiIiIg0IZpBFREREREREWlkGqA2EXOjezV2CCIiIiIi0gT4Z1DNen5oBlVERERERESkxprdAHXGjBkkJycTHh7OkCFDWLp0abV1hw0bhmEYlR7nn3++U+eGG26o9Pw555zza6QiIiIiIiJSiWEamK76fTSXGdRmdZGkd999l8mTJ/Pyyy8zZMgQnnvuOcaMGcOGDRto27ZtpfoffPABJSUlzt/79u2jf//+XH755UH1zjnnHF5//XXn77CwsIZLQqSJ+iqhD2dnrmnsMERERESOe8fzRZKa1QB1+vTpTJgwgfHjxwPw8ssvM2fOHGbOnMkDDzxQqX6rVq2C/n7nnXeIjIysNEANCwujXbt2NY6juLiY4uJi5++8vDwAvF4vXq8XANM0MU0Ty7KwLMupGyj3+XzYtu2U26Z/MvvwcpfLhWEYTrsVywP1a1LudruxbTuo3DAMXC5XpRirK69tTtWVN9ec7NAQ8PowLOuYyaliue12B8V/LOR0LPaTclJOykk5KSflpJyOntPh7yvNR7MZoJaUlLBs2TIefPBBp8w0TUaNGsXixYtr1MZrr73GlVdeSVRUVFD5ggULaNu2LXFxcYwYMYInnniC1q1bV9vOU089xbRp0yqVp6WlOW23adOGLl26sG3bNrKyspw6HTt2pGPHjmzcuJHc3FwAsr9eiNm7CwBr1qyhsLDQqd+zZ09iY2NJS0sL2jD79etHaGgoHo8nKIbU1FRKSkpYtWqVU+ZyuRg8eDC5ubmsX7/eKY+IiKB///5kZ2ezdetWpzwmJoZevXqRnp7Orl27nPLa5ATQuXNn2rZte8zkZE2+BuO//8NYuemYyQnK+6n0ivOC4jwWcjoW+0k5KSflpJyUk3JSTkfPacOGDTRngQsb1XebzYFhV/ypowlLT0+nQ4cOLFq0iKFDhzrl999/PwsXLmTJkiVHfP3SpUsZMmQIS5Ys4eSTT3bKA7OqKSkpbNmyhYceeogWLVqwePFi55ecw1U1g5qUlMS+ffuIjo4Gavfr0/xuJ+M9UMy5OWub3K9Px+IvanXNaW7MAGcG9Zyin46JnCqWf9lhACN2lH9YHAs5HSvrnnJSTspJOSkn5aScapdTTk4OrVu3Jjc31/l+3hzk5eURExPDymvOoWVoSL22fbCklP5vzW3yy6TZzKD+Uq+99hp9+/YNGpwCXHnllc7/+/btS79+/ejSpQsLFixg5MiRVbYVFhZW5Xmqbrcbtzt4kQY22sNVHPwapaUYZRtXdYPiw9utS7lhGFWWVxdjbcuri/1YyckoKa2yvK6xV1feWP1keL1Vvm9zzulY7CflpJyUk3IC5VRdjLUtV07Hbk7Vtd9cHM/noDaPeV4gPj4el8tFZmZmUHlmZuZRzx/Nz8/nnXfe4aabbjrq+3Tu3Jn4+Hg2b978i+IVERERERGR2mk2A9TQ0FAGDRrEvHnznDLLspg3b17QIb9Vee+99yguLubaa6896vvs2rWLffv20b59+18cs4j8+hb2GtDYIYiIiIj8IoEZ1Pp+NAfNZoAKMHnyZF555RVmz57NunXrmDhxIvn5+c5VfceNGxd0EaWA1157jUsuuaTShY8OHTrE73//e3744Qe2b9/OvHnzuPjii+natStjxoz5VXKS5unbPgMbOwQRERERkWNOszo4e+zYsWRlZTFlyhQyMjIYMGAAc+fOJSEhAYCff/650nHoGzZs4Pvvv+fLL7+s1J7L5WLVqlXMnj2bnJwcEhMTGT16NI8//rjuhSoiIiIiIo3ieL6Kb7MaoAJMmjSJSZMmVfncggULKpX16NEj6IpiFUVERPDFF1/UZ3giIiIiIiK/iC6SJCIiIiIiItLImt0MqoiIiIiIyLHseD7Et3lEKSIiIiIiIsc8DVCbmB+GnNzYIYiIiIiISGMyjIZ5NAMaoIqIfhgRERERkSZB56CKiIiIiIg0IYbRAFfx1QyqiIiIiIiISM1pBlVERERERKQJOZ6v4qsBqoiIiIiISBNimA1wiG89t9dQmscwWkSkDpYNP62xQxARERGRWtAMqoiIiIiISBNyPB/i2zyiFBERERERkWOeBqgiIr/AitFnNHYIIiIicowxzPLzUOvv0dhZ1UwzCVNEGsLKc85s7BBERERERBw6B1VERERERKQJ0VV8RURERI4Dq84bxqrzhjV2GCIiUg3NoIqIiIiIiDQlpul/1HebzUDziFIaxeoLhrP6guG1qn+kv0XLRERERESOzjCMBnk0BxqgioiIiIiISJOgQ3xF5Ji09uIRjR2CiIiISJ0YpolRz4fk1nd7DaV5RCkix4SfLh3Z2CGIiIiISBOmGVQREREREZEmRLeZEREREREREWlkGqAeI9ZfPrqxQxARERERkfpgmOW3mqmvh1G3od+MGTNITk4mPDycIUOGsHTp0iPWf+655+jRowcREREkJSVx7733UlRUVOP30wBVRERERESatQ1jxzR2CMekd999l8mTJzN16lSWL19O//79GTNmDHv37q2y/r/+9S8eeOABpk6dyrp163jttdd49913eeihh2r8nhqgioiIiIiINCVl56DW54M6nIM6ffp0JkyYwPjx4+nduzcvv/wykZGRzJw5s8r6ixYt4rTTTuPqq68mOTmZ0aNHc9VVVx111jUo9VpHKSIiIiIiIg3GMMwGeQDk5eUFPYqLi6uMoaSkhGXLljFq1CinzDRNRo0axeLFi6t8zamnnsqyZcucAenWrVv5/PPPOe+882qcuwaoIiIiIiIix4mkpCRiYmKcx1NPPVVlvezsbHw+HwkJCUHlCQkJZGRkVPmaq6++mscee4zTTz+dkJAQunTpwrBhw2p1iK9uMyMiIiIiItKU1PGQ3KO2CezcuZPo6GinOCwsrN7eYsGCBTz55JO89NJLDBkyhM2bN3P33Xfz+OOP88gjj9SoDQ1QRUREREREjhPR0dFBA9TqxMfH43K5yMzMDCrPzMykXbt2Vb7mkUce4brrruPmm28GoG/fvuTn53PLLbfwhz/8AdM8+gG8OsRXRERERESkCTFMs0EetREaGsqgQYOYN2+eU2ZZFvPmzWPo0KFVvqagoKDSINTlcgFg23aN3lczqCIiIiIiIlLJ5MmTuf7660lNTeXkk0/mueeeIz8/n/HjxwMwbtw4OnTo4JzHeuGFFzJ9+nQGDhzoHOL7yCOPcOGFFzoD1aPRAFWkidp240WkzPykscMQERERkV+Zc2uYem6ztsaOHUtWVhZTpkwhIyODAQMGMHfuXOfCST///HPQjOnDDz+MYRg8/PDD7N69mzZt2nDhhRfyxz/+scbvqQGqiIiIiIiIVGnSpElMmjSpyucWLFgQ9Lfb7Wbq1KlMnTq1zu+nc1CPQztuuaSxQxARERERkeoYBhhmPT/q+arADUQDVBEREREREWkSdIiviIiIiIhIE9JUzkFtDJpBFWkCfr7tN40dgojIcWfn7Zc1dggiIlUzzYZ5NAPNI0oROebsmnR5Y4cgIiIiIk2MBqhyVL90IKGBiIiINDX6bBKRpswwjAZ5NAcaoB7jdt15xXHxniIiIiIi0vzpIkkiIiIiIiJNidEA54wazWNusnlEKSIiIiIiIsc8DVDlV5d+71WNHYKIiIiISJMVuM1MfT+aAw1QRUREREREpEnQOagiIiIiIiJNiWHW/zmjOge1YcyYMYPk5GTCw8MZMmQIS5curbburFmzKl1aOTw8PKiObdtMmTKF9u3bExERwahRo9i0aVNDpyEiIiIiIlI102iYRzPQrAao7777LpMnT2bq1KksX76c/v37M2bMGPbu3Vvta6Kjo9mzZ4/z2LFjR9DzTz/9NC+88AIvv/wyS5YsISoqijFjxlBUVNTQ6RzXMn5/bWOHICIiIiIiTUyzGqBOnz6dCRMmMH78eHr37s3LL79MZGQkM2fOrPY1hmHQrl0755GQkOA8Z9s2zz33HA8//DAXX3wx/fr144033iA9PZ2PPvroV8hIREREREQkmGGYDfJoDprNOaglJSUsW7aMBx980CkzTZNRo0axePHial936NAhOnXqhGVZnHTSSTz55JOceOKJAGzbto2MjAxGjRrl1I+JiWHIkCEsXryYK6+8sso2i4uLKS4udv7Oy8sDwOv14vV6ndhM08SyLCzLCorZNE18Ph+2bQNgh4Rgm/46tsvltAHgcrkwDCOoLFAO4PP5ALBcbrxeb+Vy09+e2+3Gtm18Pp9TZhgGLperUoyBctswsU3TqX+0nKyy2AP1bcMoi8EMit/GcJbXkXI6WnnFnA6Pvbqcqo29Jv0UGgJeH4ZlYbvdDd9Prpr10y/JyXK7nTp1zelo5cE5uZ2cgIbpp5CQ8pzquD3VKie3u8H76UjlDZHT0WJXTsqpOedkud0Yzr7XbPB9xK+R05HKlZNyOl5yslxufD6fU374+0rz0WwGqNnZ2fh8vqAZUICEhATWr19f5Wt69OjBzJkz6devH7m5uTzzzDOceuqprF27lo4dO5KRkeG0cXibgeeq8tRTTzFt2rRK5WlpaURFRQHQpk0bunTpwrZt28jKynLqdOzYkY4dO7Jx40Zyc3MBKL5pLObn/wPg4Kiz8Xg8Tv2ePXsSGxtLWlpa0IbZr18/QkNDnbqHzhhNnsdDamoqJSUlrFq1CoCivqeTmZbG4MGDyc3NZf369RSeNIxMj4eIiAj69+9PdnY2W7duddqOiYmhV69e5HfuxqEuPTgQGUq6x3PUnHIGDMbj8VDU93TSPR6MxCQAdnUfRHqFnFq3iKVlUd5Rcwo4PCfw78Qq5hRwtJzS09PZtWuXU16bfrImX4Px3/9hrNxE4Xlj6tRPB0eeR2EV/VQ48CyyDuungtQRZNWgn35JTnlnX0B2djYApVecV6ecatNPh4ady6GynFpDg+SUf/XlTk45Z45o8JzyRl+Ip4H7CaBz5860bduWNWvWUFhY2KA5BSgn5XQs5pQ36nyi16QBhezqMZg9DbyP+DVygmOvn5STcqptTodOHw0bNzo5bdiwgWatIc4ZbSbnoBp2xZ86mrD09HQ6dOjAokWLGDp0qFN+//33s3DhQpYsWXLUNkpLS+nVqxdXXXUVjz/+OIsWLeK0004jPT2d9u3bO/WuuOIKDMPg3XffrbKdqmZQk5KS2LdvH9HR0UDtfn2a3+1kvAeKOTdnLYtPHcrgb79z6tf016eN115E9zc/qVSeft91JD77z6Bfn3ZOGkvSi+8e9Re1VReOxDZN4jq1JnH6m0fNaeXFozjxP3Od9/zp0tH0+/Qbfr5rLInT33LqZ/5+HC63SfxTs46Y09HKf+1fCefGDHBmUFucGM1pnvKZ+5r209rLzuXE//y3Uvmuu66i4wtvB+W0Y+LldPr7ew2a0+pLRtP/k69ZOvQU8naVMGJH+YdFQ/zyuf7K8+n5zhwMw2DP3VeS+MK79Z7T9ycN5cwVS1h36SgK870MmPtNg+a06qKz6ffJV8fsL9T7ptxE/OMzj6mcjsV+Uk41z2nNb87B8PlonRKPD+jw3NvNPqcjlSsn5XS85LRp3MV0/+cnTnlOTg6tW7cmNzfX+X7eHOTl5RETE0P6878jOiKsftsuLCbx7mea/DJpNjOo8fHxuFwuMjMzg8ozMzNp165djdoICQlh4MCBbN68GcB5XWZmZtAANTMzkwEDBlTbTlhYGGFhlVcYt9uN2x28SAMb7eECGyGAUVqKUbZxGT5fpTYCbVclUG76vEF1nHKrvD3DMHC73UFlR4rRsC0Mn1Xj+mZZ7IH6RtlOyrSsoNcb2DXKqSblgZwqxVJdjLUsD+qnktLy/3u9Dd9Pvhou91+Qk1l26PYvyakm5eU5eRs8J6O0tDynOm5PNSl3cvI2fE41KW9u21NNypWTcmqo/Z7z/8M+n+oSe3Xl6iflBMqpuhhrW16TnExf+SlUpmlW235zYZgmRhXL/5e22Rw0jyiB0NBQBg0axLx585wyy7KYN29e0Izqkfh8PlavXu0MRlNSUmjXrl1Qm3l5eSxZsqTGbYqIiIiIiEj9aFY/LUyePJnrr7+e1NRUTj75ZJ577jny8/MZP348AOPGjaNDhw489dRTADz22GOccsopdO3alZycHP7yl7+wY8cObr75ZsD/a8w999zDE088Qbdu3UhJSeGRRx4hMTGRSy65pLHSFBERERGR45lh+B/13WYz0KwGqGPHjiUrK4spU6aQkZHBgAEDmDt3rnORo59//jnoUIQDBw4wYcIEMjIyiIuLY9CgQSxatIjevXs7de6//37y8/O55ZZbyMnJ4fTTT2fu3LmEh4f/6vmJiIiIiIj4L5JUzwe7NpOLJDWrASrApEmTmDRpUpXPLViwIOjvv/71r/z1r389YnuGYfDYY4/x2GOP1VeIIiIiIiIiUgfNboAqIiIiIiJyTDuOD/FtNhdJEhERERERkWObZlBFRERERESaEN1mRkRERERERKSRaQZVRERERESkKTFM/6O+22wGmkeUIiIiIiIicszTDKqIiIiIiEhTYhj1f9/SZnIVXw1QRUREREREmhDDMDHq+ZDc+m6voTSPKEWkycr9y52NHYKIiIiIHCM0QBURqaHcZ+5u7BBERETkeGAaDfNoBjRAFRERERERkSZB56CKiIiIiIg0JbrNjIiIiMixJ++5yeQ9N7mxwxARkRrSDKqIiIiIiEhTYhj1f1uYZnKbGc2gioiIiIiISJOgGVQRkcMc/NvvAWh5518aORIRERE5Lpmm/1HfbTYDGqCKiIiIiIg0JbpIkoiIiIiIiEjj0gyqiIiIiIhIU2Ia/kd9t9kMaAZVREREREREmgTNoIqIiIiIiDQlhtEA56BqBlVE5LiS/39/aOwQRERERJo1zaCKiIiIiIg0JYZR/zOemkEVERERERERqTnNoIqIiIiIiDQlpul/1HebzUDziFJEmp2CmVMbOwQRERGpI32ON7LAIb71/WgGNEAVERERERGRJkEDVBGRJqZg1rQm1Y6IiIj8ygyzYR7NQPOIUhpcwezHGjsEERERERE5zukiSSIiIiIiIk2J0QAXSdIMqoiIiIiIiEjNaQZVRERERESkKWmIq+7qKr4iIiLHn8J/PdXYIYiIiDRbGqBKk6MvdyIiIiJyXDuOr+KrQ3xFRERERESaEh3iKyIiIiIiItK4NIMqIiIiIiLSlJgNcJuZ+m6vgTSPKEVEREREROSYpxlUERERERGRJsQ2DOx6Pme0vttrKJpBFWkkRe8929ghSDNQH+uJ1jURERFpLjSDKiIiIiIi0pQYRv3fFkYzqCIiIiIiIiI1pwGqSCMq+uiFxg5BRESkySr64PnGDkGkcRhmwzyaAR3iKyIiIiIi0oToIkkiIiIiIiIijUwD1GNQ0ccvNnYI0gCKPpnR2CGIiDRr+nwUkWbjOD7Et3lEKSLNhgbSIiIiIlJXzW6AOmPGDJKTkwkPD2fIkCEsXbq02rqvvPIKZ5xxBnFxccTFxTFq1KhK9W+44QYMwwh6nHPOOQ2dhoiIHOP0Y42IiNSZYTTMoxloVgPUd999l8mTJzN16lSWL19O//79GTNmDHv37q2y/oIFC7jqqquYP38+ixcvJikpidGjR7N79+6geueccw579uxxHm+//favkY6IiIiIiIhU0KwGqNOnT2fChAmMHz+e3r178/LLLxMZGcnMmTOrrP/WW29x++23M2DAAHr27Mmrr76KZVnMmzcvqF5YWBjt2rVzHnFxcb9GOiIiIiIiIpWZZsM8moFmc5uZkpISli1bxoMPPuiUmabJqFGjWLx4cY3aKCgooLS0lFatWgWVL1iwgLZt2xIXF8eIESN44oknaN26dbXtFBcXU1xc7Pydl5cHgNfrxev1OrGZpollWViWFRSzaZr4fD5s2wbADgnBNv11bJfLaQPA5XJhGEZQWaAcwOfzAWC53Hi9XlwuF3ZZLACW6W/P7XZj2zY+n88pMwwDl8uFZVn4KH+PQLltmNim6dQ/Wk5WWeyB+oFLWVumGRS/jeEsrypzwqi0DCrmGlAxp4CKOVWMsbryWvVTaAh4fRiWhe1210s/WWW5VtlPrsP6yQ5eZvWRk+V2O3XqmpPPMJ2cAHy2/zWBfgzOye3kBDRMP4WElOdUx+3JV3YRgUCbh+calJO7/nMKvKdt285yrCrXo+UU2BdU3KZquz3VV05VxV6XnKqKvansIwLlgVgC28KxkFNz7icAn1G+X7LcbozAPvmwz6fmktOx2E9H256OpZyaTT8d9n2sqedkudz4fD6n/PD3leaj2QxQs7Oz8fl8JCQkBJUnJCSwfv36GrXx//7f/yMxMZFRo0Y5Zeeccw6/+c1vSElJYcuWLTz00EOce+65LF682NlQDvfUU08xbdq0SuVpaWlERUUB0KZNG7p06cK2bdvIyspy6nTs2JGOHTuyceNGcnNzASi+aSzm5/8D4OCos/F4PE79nj17EhsbS1paWtCG2a9fP0JDQ526h84YTZ7HQ2pqKkW4WFlWXtT3dDLT0hg8eDC5ubmsX7+ewpOGkenxEBERQY/dS8hLvYRNUZ1wlb0mJiaGXr16kd+5G4e69OBAZCjpHs9Rc8oZMBiPx0NR39NJ93gwEpMA2NV9EOkVcmrdIpaWRXnV5rQyrKMTC0BqaiolJSWsWrXKKXO5XEE5BURERNC/f3+ys7PZunWrUx7IKT09nV27djnltekna/I1GP/9H8bKTRSeN6ZO/XRw5HkUOv3kZh3xmB4PhQPPIuuwfipIHUFWWT/179+f/YSxq8J71kdOeWdfQHZ2NgClV5xXp5y87fviLsuppKSElbTC9HjwtexMaFoafbPSKDr1CtavX8+hYedyqCyn1tAg/ZR/9eVOTjlnjqhbTvE9ATjF5wta97zt+hB2WD/ljb4QT1lOXYH0z99gV9nr65pTK3BystztMD0eOnfuTNu2bVmzZg2FhYU1ysnCwOPxOG1A7ben9g3UT0CdcqrYTwFNZR8RyKklsGbNGvLLtoVjIafm3E/RwI6YThR5PFi05tCo84lekwYUsqvHYPbUYR/R2Dkdi/1UXU7RcMzl9Gv3U2jWNk668Kpa57QrPJ6cCu/blHKqqp8OnT4aNm50ctqwYQPN2fF8H1TDrvhTRxOWnp5Ohw4dWLRoEUOHDnXK77//fhYuXMiSJUuO+Po//elPPP300yxYsIB+/fpVW2/r1q106dKFr7/+mpEjR1ZZp6oZ1KSkJPbt20d0dDRQu1/U5nc7Ge+BYs7NWcviU4cy+NvvnPo1/fVp47UX0f3NT3C5XBR9MoOQ82/zL7f7riPx2X8G/fq0c9JYkl58F8MwKP3iNULPuZn8f/6RiGv8s9OBX6VWXTgS2zSJ69SaxOlvHjWnlReP4sT/zHXe86dLR9Pv02/4+a6xJE5/y6mf+ftxuNwm8U/NqjKn/Lf/TPgVv6s214Bf+5fPuTEDnBnUFidGc5qnfOa+pv209rJzOfE///X30/vTsUJCCbtgIrvuuoqOL7wdlNOOiZfT6e/vObEXfDyD0PNvrdecVl8ymv6ffM3SoaeQt6uEETvKPyxqmlPec/cRfc+zTnnBJ38n7PxbKHzjCSLGPYz3y5mEnTsBn8/H+ivPp+c7czAMgz13X0niC+/Wez99f9JQzlyxhHWXjqIw38uAud/UOqdDL/8BgJg7/hSc6/O/J/ruvwT106qLzqbfJ19hGAZFr07B6NiJ0DE3/qKcit54nLDrHsa2bYo/fIGwS++q0y/UBbMfI+zaPzhtVJVrQHXb074pNxH/+EzNJNQip+J3/kzo2Psp+uz/CDv/lmMip+bcT4de+B0+w6DlHX+meM7/sem1DzF8PlqnxOMDOjxXft2J5pLTsdhP1eVU8tHfCLl40jGV06/dT8VfzCTq/FtqndOhmY8SPu7hJplTVbFvGncx3f/5iVOek5ND69atyc3Ndb6fNwd5eXnExMSQ/tVbREdF1m/b+QUknn1Nk18mzWYGNT4+HpfLRWZmZlB5ZmYm7dq1O+Jrn3nmGf70pz/x9ddfH3FwCv5fgeLj49m8eXO1A9SwsDDCwsIqlbvdbtzu4EUa2GgPV3F21igtxSjbuAyfr1IbgbarEig3fV7n/0bFcqu8PcMwcLvdQWWlZTG6sCu9h2FbGD4rqP6RcjLLYg/UN8p2UqZlBb3ewD5iTlXFUl39QE6VYqkuxlqWB/VTSWn5/73eeumnQK5V9tNh64JpVN3+L8nJLDt09Jfk5LKD+9dl+HMK5OYNyslbs3Xpl/RTaWl5TnXcnlx22fZY9ktjVbk6OR223Oq7n7yHbQ/VHdlR5fZRVn54G9XW/xW3p5qUH62falLemDkFtoW6xl5dufqpDjnZtrMtmBUPWTzs86kusVdXrn5STtA0cvIaR46x2vJqvo81hZycGCvEbvrKTzcyTbPa9qXpax5nygKhoaEMGjQo6AJHluW/4FHFGdXDPf300zz++OPMnTuX1NTUo77Prl272LdvH+3bt6+XuEVERERERGrDNswGeTQHzSPKMpMnT+aVV15h9uzZrFu3jokTJ5Kfn8/48eMBGDduXNBFlP785z/zyCOPMHPmTJKTk8nIyCAjI4NDhw4BcOjQIX7/+9/zww8/sH37dubNm8fFF19M165dGTNmTKPkKCIix7aiL19v7BBERESarGY19z127FiysrKYMmUKGRkZDBgwgLlz5zoXTvr555+DDlH4+9//TklJCb/97W+D2pk6dSqPPvqo/zzLVauYPXs2OTk5JCYmMnr0aB5//PEqD+EVERERERFpcIbhf9R3m81AsxqgAkyaNIlJkyZV+dyCBQuC/t6+ffsR24qIiOCLL76op8hERKShFM17g/CR4xo7DBEREWlgzW6AKiIiIiIiciyzqf9zRu1mcnZn84hSRERERESarcJv/tnYIUgdzZgxg+TkZMLDwxkyZAhLly49Yv2cnBzuuOMO2rdvT1hYGN27d+fzzz+v8ftpBlVERERERKQpaSLnoL777rtMnjyZl19+mSFDhvDcc88xZswYNmzYQNu2bSvVLykp4eyzz6Zt27a8//77dOjQgR07dhAbG1vj99QAVURERETkGFM4/y3Af0/sxhaIRZqGvLy8oL/DwsKqvUDs9OnTmTBhgnPXlJdffpk5c+Ywc+ZMHnjggUr1Z86cyf79+1m0aBEhISEAJCcn1yo+HeIr0gwULni7sUMQERERkV+LYYBh1vPD/3NFUlISMTExzuOpp56qMoSSkhKWLVvGqFGjnDLTNBk1ahSLFy+u8jWffPIJQ4cO5Y477iAhIYE+ffrw5JNP4vP5apy6ZlBFRERERESaENswsOv5EN9Aezt37iQ6Otopr272NDs7G5/P59zSMyAhIYH169dX+ZqtW7fyzTffcM011/D555+zefNmbr/9dkpLS5k6dWqN4tQAVURERERE5DgRHR0dNECtT5Zl0bZtW/7xj3/gcrkYNGgQu3fv5i9/+YsGqCIiIiIBBd/+W+c1iUjzETgst77brIX4+HhcLheZmZlB5ZmZmbRr167K17Rv356QkBBcLpdT1qtXLzIyMigpKSE0NPSo76t9tUgzUvDde40dgojUQMG3/27sEERERH6R0NBQBg0axLx585wyy7KYN28eQ4cOrfI1p512Gps3b8ayLKds48aNtG/fvkaDU9AAVUREREREpEmxMRrkUVuTJ0/mlVdeYfbs2axbt46JEyeSn5/vXNV33LhxPPjgg079iRMnsn//fu6++242btzInDlzePLJJ7njjjtq/J46xFdEREREREQqGTt2LFlZWUyZMoWMjAwGDBjA3LlznQsn/fzzz5hm+ZxnUlISX3zxBffeey/9+vWjQ4cO3H333fy///f/avyeGqBKs5C/6AOiTv1NY4fxqyn433+O+nzkaZf9StH8Oo63Ppbjz7G43YqISMOwDRO7ns9BrWt7kyZNYtKkSVU+t2DBgkplQ4cO5YcffqjTe4EO8RURqbX8RR80dggiIiIixyTNoIqIiIiIiDQlTeAqvo2leUQpzV7+4o8aOwQRERERkWbBNowGeTQHGqCKiIiIiIhIk1CnQ3x9Ph+zZs1i3rx57N27N+g+NwDffPNNvQQnIiIiIiJyvGlKF0n6tdVpgHr33Xcza9Yszj//fPr06YPRTKaLRaRhHVryqU5sFxEREZE6q9N3yXfeeYd///vfnHfeefUdjzSQg0vn0PLk8xs7DBERERERORrD8D/qu81moE7zvKGhoXTt2rW+YxEREREREZHjWJ0GqPfddx/PP/88tm3XdzwiIiIiIiLHt7JzUOvz0VxuM1OnQ3y///575s+fz3//+19OPPFEQkJCgp7/4APdxF5ERERERERqp04D1NjYWC699NL6jkVEREREROS4Z2NgU7/njNZ3ew2lTgPU119/vb7jEBEREREREY7v28z8oiizsrL4/vvv+f7778nKyqqvmESOKm/5V40dgog0Em3/IiIix646DVDz8/O58cYbad++PWeeeSZnnnkmiYmJ3HTTTRQUFNR3jCIiIiIiIscPg/JbzdTbo7GTqpk6DVAnT57MwoUL+fTTT8nJySEnJ4ePP/6YhQsXct9999V3jCJSAzlp3zR2CHIMy13+dWOHICIiIseBOp2D+p///If333+fYcOGOWXnnXceERERXHHFFfz973+vr/hERERERESOKzYm9i87G7PKNpuDOkVZUFBAQkJCpfK2bdvqEF8RERERERGpkzoNUIcOHcrUqVMpKipyygoLC5k2bRpDhw6tt+Dk+HVg5cLGDkFEREREpFHYhtEgj+agTof4Pv/884wZM4aOHTvSv39/AFauXEl4eDhffPFFvQYocjw5sHIhcf3PauwwREREREQaRZ0GqH369GHTpk289dZbrF+/HoCrrrqKa665hoiIiHoNUOrX/lXfNXYIIiIiIiJyBMfzfVDrNEAFiIyMZMKECfUZi4iIiIgc5/av/p5WfU+v8/MixwIbA7ue7wtT3+01lBoPUD/55BPOPfdcQkJC+OSTT45Y96KLLvrFgYmIiIiIiMjxpcYD1EsuuYSMjAzatm3LJZdcUm09wzDw+Xz1EZuIiBxj9q1ZROs+pzZ2GCIiIk2aDvGtAcuyqvy/iMjxat+aRY0dgojIcWXfmkXN5CBFEamrOg2j33jjDYqLiyuVl5SU8MYbb/zioERERERERI5Xx/NtZuo0QB0/fjy5ubmVyg8ePMj48eN/cVAiv0TW2iWNHYKIiIiI1BN9tzu+1OkqvrZtY1QxAt+1axcxMTG/OCgREREREZHjla7iW0MDBw7EMAwMw2DkyJG43eUv9/l8bNu2jXPOOafegxQREREREZFjX60GqIGr965YsYIxY8bQokUL57nQ0FCSk5O57LLL6jVAERERERGR44mu4ltDU6dOBSA5OZmxY8cSHh7eIEGJiBwLMtctI6HXoMYOQ0RERJqZ4/kQ3zoNo6+//noNTquQuXElmeuWNXYYIiLSDOjzQkSaKu2fpDHV6SJJPp+Pv/71r/z73//m559/pqSkJOj5/fv310twIiIiIiIixxubBjjEt25zk7+6OkU5bdo0pk+fztixY8nNzWXy5Mn85je/wTRNHn300XoOUUSkcWWsTyNjfVpjhyEiIiIV7Fm/gj3rVzR2GFLP6jRAfeutt3jllVe47777cLvdXHXVVbz66qtMmTKFH374ob5jlCbg8I2/PnYG6RtW/eI2REREROT4c6wPTAPnoNb3ozmo0wA1IyODvn37AtCiRQtyc3MBuOCCC5gzZ079RSci0sj0Q4qIiDQ3+uyS5qxOA9SOHTuyZ88eALp06cKXX34JwI8//khYWFj9RVeFGTNmkJycTHh4OEOGDGHp0qVHrP/ee+/Rs2dPwsPD6du3L59//nnQ87ZtM2XKFNq3b09ERASjRo1i06ZNDZmCiNTA7o2rGzsEERERkUZhG4Zzq5n6exzDM6iXXnop8+bNA+DOO+/kkUceoVu3bowbN44bb7yxXgOs6N1332Xy5MlMnTqV5cuX079/f8aMGcPevXurrL9o0SKuuuoqbrrpJtLS0rjkkku45JJLWLNmjVPn6aef5oUXXuDll19myZIlREVFMWbMGIqKihosDxERERERqR/6UfvYUqer+P7pT39y/j927FhOOOEEFi9eTLdu3bjwwgvrLbjDTZ8+nQkTJjB+/HgAXn75ZebMmcPMmTN54IEHKtV//vnnOeecc/j9738PwOOPP85XX33Fiy++yMsvv4xt2zz33HM8/PDDXHzxxQC88cYbJCQk8NFHH3HllVdWGUdxcTHFxcXO33l5eQD4LAufZeH1ejFNE9M0sSwLy7KcuoFyn8+HbdsA2CEh2Ka/ju1y4fV6nfoulwvDMILKAuXgv6IygOVy4/V6cblc2ODUt8zy9mzbxmfZTplR9iuKZVn4KH8PwzD87RgmtmlimS4oyyOQUyDPijlZZbEH2g/8SmOZJj67PKbA8e8V2wjKCQOfZTvPuVwu7Aqv97/WLs+pbBkEYg/kVHG5B3Kqrj9q1E+hIeD1YVgWtttdL/1klS33Sv1k4yxPJycbZzn6bH+bFXMKLLPa5GS53U6duubkM8zynGwbn+1/TcV1KtBPgfyr6qfDc6pyHavh9uTkVMftyVd21Tzbtsu2GysoVycny8Zyuyv1U6COz7Iq9dPhsVdVHojFtm1nOVaV69FyCuwLKvbF4bkGuN3uI25PFbfJX7I9VWynNjn5bJw6FduoS05VxV4f+4hAeXm8lZd7YPv1er34LKs8p8Nib2o51Xbda0o5AfgMA8P2f/5YbjdGYJ9smnXaRzR2TsdaP+3Zso6kHn3L93sVPs8CsVTc/v17N5p0To3eT5aFr6x5o+zf2uQE5d9R6iMnnw2U7fvqmlPFWAKfT7ZtY9k2FgaWyx30mXv4smxujuf7oBp2xTWpCSspKSEyMpL333+fSy65xCm//vrrycnJ4eOPP670mhNOOIHJkydzzz33OGVTp07lo48+YuXKlWzdupUuXbqQlpbGgAEDnDpnnXUWAwYM4Pnnn68ylkcffZRp06ZVKv/666+Jiopi2doCtuwwmP7YcO7/4/d0al9KaVExp5wUy7/m2KTnJdC74y7OOyOUH1cfYnDfFnTu3Jm2bduycuVKFv6wl+hWUfRMMnl7YVsIb0OXqOWc3CeMH9JyMU2TpZs6MOtP3Xnptfm4Q9yc1DsCgM/W9uOhq2DhouXEtfD//rBup8H4y4eQk5PD+vXrAVi/02Jg9yj69+/PshU/UVrkH2QX5ueTS3vmLElg6rhCdu3aVZ6g6aZV67bs25eNYZWQX1AIQMuYVrzyRUcmX7rXOR951eZSLhndw8mpsLCQVZtL6dc1hJ49exIbG8uPP/6Iz+cjv6AQG3C16scZfWLweDwAFBTkExkZRXTrRJI7xPP1dytpG2Ow/5CPNjGhtGrdmtatWrFs+XJ/eLZFq/g29O/fnx/SNlJwYBeRkZHk5pcQHhFJ5zaRGBHRbNqwnoioFvy4+hDnjUjh1ocPcPJAH+3jDjK4bwt+WJ7DzgPx7CtNonPsFlq1KCQkLIT+3UKD+mnB4kxaxEZxYrKLf37VivhOKcQVfscp/SP5YXku7lA3c742+fTNM3j+718AcNrg1gBOTqtWlZ8jkn3Q4IKzg/tp/yEfHdq0oH///uzdu5etW7c69ffkRzPoBAMzoqWTk2dNPucOT+aep0vo2SGbE9qXMLhvFEtXHWLz7iiyCjswZXw+83/IwOe1GNw3qlJO0a2iANie14VDJS1Jci3BZdpg2ximgd3mFCxCiS39kZJiH327hAAwf8dJ3H2hNyinLZkurrl4MDk5OSzyrKVVCxdp64s4dWAc/fv35/2FGcT5fiIqMtJff18MZ3a3MSKi2bVrF4X5+aza5OOCs7swbVYkHaJ20C42jyEDoklbX8SGHRHsyGrFk3d4+daTha/Ux0m9I4K3pyVZnNyvBWu3efFF92FXdiQX9l7JomUHCAkLxVfq5ZsfW1JUbHLOqQfweb2cmtoKgNUHU7nmtJKgnPbkmlw65uSgflr3s4+TepT305IVm2kT7f+AL/SF8szrofzfo7F8+vVmACyfxTnDOvH/XjDo3Dadrp1s+nUNIW19EWs3hfDiH09j3bp1rNmaS1JryMixOfWkLox/aC8jBucRGVbCwJ7hLFtbwMqtCezOtHn01kLSNhTh8/ooKSphwrjTCQ0N5cMvPCT5Vzsycmxe+XcYLVu6OfVE/5EnBmCbbuZ8F0t8bDFnDS6hqKAYb3EJB/NNpj5wDn//OJOBCdsAOHDIy6JlPtbsTORvD0Tw4X/XY5omIWFuRg7twKTH8zntZIhx72Vwvxas32kxIMVFdFw8xSUl5ObmUlBQwNrNFmMv6cc900sY2Gk7LSN9DOgRxvJ1hSz9KZaU/n04r8cKlqzMw1vi5ZSTYujXrx/fb4Do4lXkFxQSFRmBZ00+ny1oSWzrcO65qoBlPxVSWlSCbbi5d+JIbnpwFX2T0jFdLk7u35JFq22+XtyCLp0shvQtAMAGfK5Y3v2wlK4dDzGoP+TnFREaHsKIUxL5/fM2XRMySG5fykm9I1i8LIf1W0PZuDOKl6ZFk5GeTpEdhonNwH7B+73AMktISOCkvt2c/V5g/9avXz+e/dBFp/DleEsteie72JRus6t0EBEhJZySuIbteyE81ObAQZPxlw/hkf9Lp3ubHYSFGpwQD6t2hLK7uC+H9mzm8rNyWbPNi89rUUwsd13Xj7+8sZXWoXswXQbRUSa+0Da8P9dN3865tG91EICwUJMV68MojuxK59itRIfnExpqYgCjT+/Cf5a3Jda3EryFpCTAtkyIiO/OvB/djDttAxt2enG7/V98LhrVj9+/VMgZ3TeRlZnP4L5R/JCWy+KNXTCsAu65qsDZR5Z64d7bRnL1HYs57ST/8hp0YiRfL8pn5c7ODBkcxumdf2bdDh8ut8nmLSU8dv9ZPDVzM7HmLqJahNG9o8En3xqk/RRBar8Szj/VyyLPfmzbZuOOSP7vmeGsW7fO+XwCgvYRKzcXlC0Dg225XYltE8+gVj+ycZeP0FAoKYFLx/TjibdsukavICTEpFuiwaZ0mz3WIMJDSjk5YTXZeRbZueD1mdx85Sn85e19dIjcRGGhjxOTXaz5OYwlm08gsfVBzu2fwcpNJYSEuij0RXPH1X350+tbSIjMIC7GpF2swbwVUWzJTKDXCfs4o1cu63b4KCosZV9pBx6e0I2X3l5Dy7CDGEDXRIOf9neG8LaEHFyJy/YvyxYRNqv2dCGkRTyJLg8hLgvLgpJSi+2HetM2oQW+XQswTIPUPlEsXXmQ/61Pcfpp6cqDGKaBz2dyz20juO7uHxk6IB/bthnUO4KFy0pwdzgDsziDoZ12sGUPZGfm4Y5uzx1X9+XPs7YQH7aHwvxiUvtE8cWPYazdHscJ0T/z2zGh/JCWi23ZXH5xHzp27MhH836C0jzaxfr7qUWYmz4DU/n+24W4Q8MozD9ERFQL53vEN/MXgu0lPSeSrokGn6zshtcOZUD7tZhl38MtCwpapNKmRRHkriYnz8aybELDQ7nm4sG89U0Opft+IiTEpEs7WLktlMgOJ+Eq3UvnqK2kHwDTgGJiGHtOL976chdR1k4O5Fr06uRiW24bjJadsQ9uJSUmi5+2+4iNcbElqy2/H9eZt+asw/Tm4nZDUmvYlNeZPF9b4rwr6dymkIwc2J/joyiiN7dfHMc7n/2Iy/CRl+flxBQ3a/P68fVXGZzVZ2fZPrKIkFAXJbGnMH54Ka+/s4SWMeHkHywm/2AJcxe14trxXekZvd5Z3yMiInj4pVA6xOdzYqcc/77fNCh1t+beGwZwz7Ql9O1e6vTTO5/l89wTI9myZQtZWVls2QOmCT+udjP94VQef24xcS0KMF0mJ/WKYHl6Ml99c5DRp+YxsHMpnjX5eEu9tEgews3nxvL5vMW0iiofGL36eVtKfG6GdtvKwJ7hpK0vIjwihG9WJvF/D8fz8VeryDlQRHFBMacNbkVkXEc6xEfw3dKfaBNtsu+gj9iwUk47c1il70bfrwllU3Yy3TvmMqD9VkaNGkVubi7R0dE0F3l5ecTExJC2YgUtW7as17YPHjzIwAEDmvwyqfEM6ieffFLjRi+66KI6BXMk2dnZ+Hw+EhISgsoTEhKcL4mHy8jIqLJ+RkaG83ygrLo6VXnwwQeZPHmy83deXh5JSUkMHDiQ6OhonntvB/v2ZAOwbnsMO3Oi2J+exaRbBvHEKz8R3gJWbIplyr29efqfm5g4vpvza1WfPn145MU19B/ajWtTQ3l5bjYx4fDDxhO4/YYkHnt5Je4QN64wNy6Xi6+XJ9AipiW3jEsC4JO1XiIiwohu34fUHi0A+O+GUgBiYmJITU0F4LO1JVzXx3++cGSLlnTr0x2An7duZuWBzsBBEhMTadeunZPnlm3+wWpsXDzdUhLZXPY3ZbNN3bt3d345m73wADfHt3Zysm2b2QsPcOOVcc4vZAMHDixrdycWJpnF4bhcLifGHdu20imlM5u27yEiIgJX64GkDnCzeH0BA3tGsuPnn4mJiSEpqRMAoVYRSV17AhARFUPrliYpycms2LyfENNHUrf2WJbl5PT0Pzdxx00pwAE27o5j/Y5oJo7vxtS/LaNFfAwt42DV9ngMIC4+mpuuig/qp4dfsOl+UgrX/zaKFz/OIB74bnUik27qyqMvLqdlfCwH89NxuVx8Os8/6Lt7oj+3QE6BXAG+WV1SqZ9+WJ9Pn17+foyPj6dVq1ZO/U+W2ZzQzT9bWFpwkKTO3XjmX1u4/cYUYAM/bYtm694wJo7vylOvr8dn2bSM8/fTH2dHUHioiInjuwbl9NDzPvqd4l8XSn3+D5PvVicC/l803SFuTh/p/zFka/5AsrMKGD82xh//DirltHiO7eTUsl1fUntG8vc5e7jlOn+bdmg8SfGd6Jzi78OV8wySurmcfvp562am/6uIO29JATLZsDOOVRtDuePmfrz0aTo5+w7hcvtzeu7DVhzMKeCWcUlBOU15aR2339iTd5ceomtMDFDKwIEDeezlNcQmtOLggYPOjy1fLo2nMC+fu27z57B6vqtSTh95rEr99PGqIsb1iXD6KbRNS1L7+gfuKzcfADJJTExk8Sb/a4sLSpg4PgXYzrrtMRx0t+PGK+P4+5w97N3t33d0796dJRk2qanw2TKL+Hg3sJcVO5IwDJsJ1yby13e3Y1kGUMjAgQOZ+c0BCg4Vkb07i7tu9ceTH5lKIPzPlll4fWvJL3Izb5l/OzBMg4gWEUAe+3JCSdvdmfStmeRl7ccGpgKFxJOa6t+el244SNqGHYREQmJiIvNXHCQkNITYttHcfHU8sIbt+xPI3u1i4o09+GxtCcOjt+OjfB+xfds2XnivhDtujgfSWbYlkcioMG6+JoEXP9pFXl4BKfj3EU+/tZWD2TlMuuUk/77DKCY1NZUt23bSJSWJZ/61Ba8vj/wiN6mpqTz/n585sCebsKhI7gUOHAznv/+LI6xFBLff1JuXP88E9pNxoAU7S3v510MboiJM4Ce27o4itnM3Nq5JJ75DHDelxAFbWbc9hl25LbhlXBKPPO/BW+IDlz+nMJfBntJ2hJhe4uNbOeteYH/448Y8wkL861hgvxfYv/lnur3sKDqJvLxixv02kq/f82GaJl78696yL2w6xNlsz/TPKhwqbcnOkkG0jTb968dPJbjcsDcvmtTUbry95CBF+SW0jPWvA/uKEtibH094mJve7UPwn92zi+37E9hv+be9Nm0i2LF3J+2SYXtuJ9z5JnGtwnG7jLJ1Dw6F9qHAa3F5qsGSOTadQgyggIEDBzJvm0XLMAPDMIiIcGHZxexzDWLByi1MHN+VaS+lERVnUFzWT4F9ZGlxKfcC2bkhfLumDQC3Xp/MtL+vokUrOOhtTWpqWz5aUURUyxDSNvo/77MK2rJuTyide7Tl6lQ3z7y9FShi857WpKYm8dAzi7G8PsyQUGd7qmqmu0+fPny82v8ZmdDCTaFlEVvWT/N3WLSNNNlbaBEREYJFEesP9CM2NoyrUk2+ed/C5XbhxZ/T/DUlbN4JJaX+fiq0oslkELv2FXD9b6OYu9EL5HOgMJbU1I7M/Dqb2PgoXC5/LNmFbckpTmBIciipg0xmzj8A2KQf6kBqajc+WlHEnt0HiGnt/8KaXtiZcFy4XQZXppqs+NokFDhgnIi3LNee8T4KdkEMsMc3gBZhJkUlNvvzSrEwsXExL6097hA3t93QhSdf/4nQ8PJ+evK1tbhD/cvwHmBfbiiLNyXgLfVy67gTeOadrQztAIes1qSmtuG7jy2+/fEn+pzSuSynBDLyWrNzcya33dCFv3+eCZSyZksL/pTaj2kvpeEt9XH3xLLPhBZdsYHUQf5lsnvrRgAS2yVwQueuTv8FvkckduxECCUszU7mylST95YXYRgGmQwi1F+FYguicYErHOIGsWWPj5IiHwnt/d+BLFcMm/MGEBfnZmyqyceri+kN+NzxpKa24oOlFm4XFBf6GywNScSObMuG7SVcd1k4678zCQeKQ1NITe3Ef9IKGJQUwb6d/s/0Q+7uFPssWkYYpKbCTwtN8EGuuw+pqTafLLNYt62IDhH+z/rckIGEhsCaLYe4/vIWrJ7vwmcZLN3alZuvSWDGJ+lEt4qiV6yLiAg336/rRK/+iWzetJe9O/zfXb1mTNDnln+2cSPp+yLJ2O/fL4SEhZCY4t/mtu5piRGX6PTTQ9P913dJSUmhU6dOfPuRRbjLYHum/4fK1TvaYPl8hEeGcct1Hfn+E4CDbM3tzoTUCJ59Zyv5OQe5KMX//SCq7Ymk9op04nnhoz0YhsHiTZ2ZcG0iL32aTmJSK3zWASIiIsgyBuHZtIe9OzKYdMtANmzPIiYmhrA2A0jtG8q3a4voEL4DqPzdaPbCHAD2F7dj4MAYpHmq8QC14qzlkRiGUWlq/lgTFhZW5cWg3G43brcbn2WWfWkEyzbwWSZen4Hb7cayA4e3mk5dt7u8G1wuF16fiYWrrDy4vtdngmniwihb1uZhbfgwDAPDdDllNv4vxYZhOGUWPmcHX7HcNE0wXM7/Ax/iAEbZ/w3T/36B5wKHCwTa8+ftcp4PlFu2KyjXoPfE9Md9WCxut9spD+RUMTfDMJz3Se52YnmsZeX+OF2Ypl0pJ59V/n/LNvFZdtkyNrDtwKGNgcP1XFX3U1m5XUU/+SwTyvrJWzbYqxh3xVz9y9VXqT8M0+Usv8P7A8MKKg+sT+U5GUHrmW1bTuw+6/D1Jnjdo6xnwS7Lw39YlWGZEDisBtdhfeqrlJNddhhWxf7zWeU5YZTH7v+boJxM08TrC87J6zOddgL95HK58NmuKnMKlFm2y4m9Yh8F+gn8Mx+BbTUQj2EQ1GZguQdvT8H9FLSOlsVumiY+q+zwpcP6KbAcfZbL2Uf4B2PgdgOmL2hdxcbp14BAjj7L34+Bw58w3Djhm76yXA3ntUaF/9sYZW2Yzjpb9kJndsww/c+HODmZmJYZtM3bdvn6ZeGjU/febN2yJWgZVdr+nGVgOttT4O9An1dc7hXX+UBOQfUr5OT1Gbh9gfUgEKOBXbau2+DcDN2yDSz8OVbMqeL25PUZWLaBWdZPgT43TbvSfi+wzAwjeB8QtN5jBG1PZVtNWU4ubMMG06J8eFUWu+HC7QYbn5Orsx7Yrgr5mVi2fz3FqLB92v4c/XXKtyfLdmHZ/sGLTfk+FsN/6ofbbfhjKtsW/DH7/MvQMMo2MwPbcBP02VWhn4K3P39/BP7v78fA+hdYj/z5lK+r/u3JLvusDPSrZVfoI5/hrLcV+6Mi/w8EZYc2Gm6gtEJOFhgmNlZZ//m3D/97usqWe3lOhunDwn+Yf9X9ajuxu91ufLb/OZPynCzMsn51Ydsm4HPq+9dLF7ZT3+X0kdvtcvaftuFy1hX/al12ygNu/6k72FhOzv7lbgQ+K6roJ6PCfsYu218427fznOksM6/PPGzdK98P23b59hRo3+uzgtYx/3v7/+7Uvbe/dTN43x5gmiZm2UDb7XY5sftzDcRM2XppgOHy91HZ8ggsA//65cbtNrHxOgvP7Tb967nhLEz/v4ar/HuaUfYuZfX9nzXuCu24nG2jwtc6Z/vFsII+nzD8bTqfr2XbU8V9ZKC+UdZ/NuX7/kBOVS0v2zbwlS2YwH47UF6xnwLfYQOfwzaW/4I9Fb4b+Sy7wmduYB0rj9HrK/++UPEzsSxAf05Whc8+/P1nGIF9h6vSvr/i98DkHicGxRjg7OOpep1pTmy7fJnXZ5vNQY0vkhQ4Pv1oj4YanMbHx+NyucjMzAwqz8zMDJrlq6hdu3ZHrB/4tzZtioiIiIiISMOo01V8K/q1rnYbGhrKoEGDnKsHg3/QPG/ePIYOHVrla4YOHRpUH+Crr75y6qekpNCuXbugOnl5eSxZsqTaNkWaok5dezR2CCIiIg0iuWv3xg5BpBGY2PX8qIeh36+iTlH6fD4ef/xxOnToQIsWLZyTkx955BFee+21eg2wosmTJ/PKK68we/Zs1q1bx8SJE8nPz3eu6jtu3DgefPBBp/7dd9/N3LlzefbZZ1m/fj2PPvooHo+HSZMmAf7DBe655x6eeOIJPvnkE1avXs24ceNITEys8SHNxxp9CIiI1I72myIiIvWnTgPUP/7xj8yaNYunn36a0LKT58F/sYFXX3213oI73NixY3nmmWeYMmUKAwYMYMWKFcydO9e5yNHPP//Mnj17nPqnnnoq//rXv/jHP/7hv2Lo++/z0Ucf0adPH6fO/fffz5133sktt9zC4MGDOXToEHPnziU8PLzB8hCRmtEXfxEROZKULl2PXkmkGQrcZqa+H81Bnc4efuONN/jHP/7ByJEjue2225zy/v37V3tF3foyadIkZwb0cAsWLKhUdvnll3P55ZdX255hGDz22GM89thj9RWi1BN96IjIsUr7NxERkarVaQZ19+7ddO1a+cPVsixKS0t/cVAiInJs6dylS5X/FxERkcqO5xnUOg1Qe/fuzXfffVep/P3333fu8SbS0Lp07vyLnhcRERERaYqO5wFqnQ7xnTJlCtdffz27d+/Gsiw++OADNmzYwBtvvMFnn31W3zGKiIiIiIjIcaBOM6gXX3wxn376KV9//TVRUVFMmTKFdevW8emnn3L22WfXd4wiR9S1Swpdu6Q0dhhSR+o7ERERkWCaQa0Fr9fLk08+yY033shXX33VEDGJiIgcUbcunRo7BBEREWkAtZ5BdbvdPP3003i93oaIR5qw7l1OoHuXE6r9u670RVNEREREpJxtGw3yaA7qdIjvyJEjWbhwYX3HIiIiIiJSI/XxI7mIND11ukjSueeeywMPPMDq1asZNGgQUVFRQc9fdNFF9RKcHD96dEkiY21BY4chIiIiItLoGuKc0WP2HFSA22+/HYDp06dXes4wDHw+3y+LSpq0nl06Bv3do0sSsK9xghERERERkWNGnQaolmXVdxzSiA4fcDY1TT2+ujgWcxIRERGR+nE8z6DW+hzU0tJS3G43a9asaYh4REREREREjmvH821maj1ADQkJ4YQTTtBhvCIiIiIiIlKv6nQV3z/84Q889NBD7N+/v77jEREROeb07prY2CGIiEgzYtMAt5lpJjOodToH9cUXX2Tz5s0kJibSqVOnSlfxXb58eb0EJyIiIiIix68Tu7Zv7BDkV1anAeoll1xSz2GIiIiIiIgIgIWBVc8znvXdXkOp0wB16tSp9R2HiIiIiIiIHOfqNEANWLZsGevWrQPgxBNPZODAgfUSlEh969s1obFDEBERERGpkeP5NjN1GqDu3buXK6+8kgULFhAbGwtATk4Ow4cP55133qFNmzb1GaNIrfXr1raxQxARERERkVqq01V877zzTg4ePMjatWvZv38/+/fvZ82aNeTl5XHXXXfVd4wiIiIiIiLHjXq/gm/Zozmo0wzq3Llz+frrr+nVq5dT1rt3b2bMmMHo0aPrLTgRERERERE5ftRpgGpZFiEhIZXKQ0JCsCzrFwclIiIiIiJyvLKp/3NG7XptreHU6RDfESNGcPfdd5Oenu6U7d69m3vvvZeRI0fWW3AiIiIiIiLHm+P5EN86DVBffPFF8vLySE5OpkuXLnTp0oWUlBTy8vL429/+Vt8xiog0Wyd1b93YIYiIiIg0G3U6xDcpKYnly5fz9ddfs379egB69erFqFGj6jU4kfqgAYKIiIiINCfH821majWD+s0339C7d2/y8vIwDIOzzz6bO++8kzvvvJPBgwdz4okn8t133zVUrCIiIiIiInIMq9UA9bnnnmPChAlER0dXei4mJoZbb72V6dOn11twIiIizUVqj7jGDkFERI4ROge1hlauXMk555xT7fOjR49m2bJlvzgoEREREREROf7U6hzUzMzMKm8v4zTmdpOVlfWLgxIRERERETle2UB937zzmLzNTIcOHVizZk21z69atYr27dv/4qBERERERETk+FOrAep5553HI488QlFRUaXnCgsLmTp1KhdccEG9BSciIiIiInK8OZ7PQa3VIb4PP/wwH3zwAd27d2fSpEn06NEDgPXr1zNjxgx8Ph9/+MMfGiRQERGR5uiUnjGNHYKIiDQzx/NtZmo1QE1ISGDRokVMnDiRBx98ENv2H8lsGAZjxoxhxowZJCQkNEigIiIiIiIicmyr1QAVoFOnTnz++eccOHCAzZs3Y9s23bp1Iy5Ol9dvSk7t1bKxQxCRY9DQXpVvMyba54qISP1qiENyj8lDfCuKi4tj8ODB9RmLiIiIiIiIHMfqPEAVkYZ3Wu8WjR2CiIiIiPzKjudzUGt1FV+RpuD03lGNHYLUgfpNREREpPmZMWMGycnJhIeHM2TIEJYuXVqj173zzjsYhsEll1xSq/fTAFVERERERKQJseyGedTWu+++y+TJk5k6dSrLly+nf//+jBkzhr179x7xddu3b+d3v/sdZ5xxRq3fUwNUERERERFpEGeeqCOomrPp06czYcIExo8fT+/evXn55ZeJjIxk5syZ1b7G5/NxzTXXMG3aNDp37lzr99QAVZqMs06MbOwQREREREQaXeAc1Pp+AOTl5QU9iouLq4yhpKSEZcuWMWrUKKfMNE1GjRrF4sWLq439scceo23bttx00011yl0DVBERERERkSYkcJuZ+n4AJCUlERMT4zyeeuqpKmPIzs7G5/ORkJAQVJ6QkEBGRkaVr/n+++957bXXeOWVV+qcu67iKyIiIiIicpzYuXMn0dHl9zUPCwurl3YPHjzIddddxyuvvEJ8fHyd29EAVUREREREpAmxbf+jvtsEiI6ODhqgVic+Ph6Xy0VmZmZQeWZmJu3atatUf8uWLWzfvp0LL7zQKbMsCwC3282GDRvo0qXLUd9Xh/iKiIiIiEizMLxvRGOHcNwIDQ1l0KBBzJs3zymzLIt58+YxdOjQSvV79uzJ6tWrWbFihfO46KKLGD58OCtWrCApKalG76sZVBERERERkSbEwsAqu6hRfbZZW5MnT+b6668nNTWVk08+meeee478/HzGjx8PwLhx4+jQoQNPPfUU4eHh9OnTJ+j1sbGxAJXKj0QDVBEREREREalk7NixZGVlMWXKFDIyMhgwYABz5851Lpz0888/Y5r1e1CuBqgiIiIiv9CofmF882PVt2oQEamtilfdrc8262LSpElMmjSpyucWLFhwxNfOmjWr1u+nc1BFmoiz+9fPFdREjmXaTkREjk/a/x8/ms0Adf/+/VxzzTVER0cTGxvLTTfdxKFDh45Y/84776RHjx5ERERwwgkncNddd5GbmxtUzzCMSo933nmnodMRERERERGpUuAqvvX9aA6azSG+11xzDXv27OGrr76itLSU8ePHc8stt/Cvf/2ryvrp6emkp6fzzDPP0Lt3b3bs2MFtt91Geno677//flDd119/nXPOOcf5O3Ayr4hIXYzuH9rYIYiIiEgzZmNg1/NFkuq7vYbSLAao69atY+7cufz444+kpqYC8Le//Y3zzjuPZ555hsTExEqv6dOnD//5z3+cv7t06cIf//hHrr32WrxeL253eeqxsbFV3sunOsXFxRQXl59nkpeXB4DX68Xr9eIyLUzT/xOFadi4TAu3y8br9WIa/nKXaTl1vV4vpmlimiY+nw+3y8LEh9frBYLru10WbpcF2Ni2jctlOc/5+ct9Pp8Tn4H//xXLTXz4fD5cLheWZTn3KPJX9P//8PJAjFWVA/h8Pmw7kLcPy7KcnGzbxjT8OblcLgzDqBCzn8vlctqpqty2fHi95RuW2+2unKthVJlTVeUus/z/pmGBs4xtjLJ+Mk3/9c5cZbFX6qeycqOKfnKZ5f3kdtnOOnKkXGuTk/9nMPMIOdlB65nPsp33dJnWEdc9AF/ZTsyfB2CX5VTWxwY+J//ykA5f92wgJKjcZZave9gWXm/FnAwgOFe3KzgntyuQk8/pJ5/Ph8vwVZmTU2b4nNgr9lGgnwLvFdhW/fG4sO3D+sm2gNAab0+G4V+OlmXhMgPLIDgnZ9swfc4+wufzx+v1ApaFZRll9S0Mw3b6NVAeyNFl+vsxsC1i+3C6yArkajv9aphG+f+xy9rwL4fyH1otvN7yvwL1/Tn5H4Ft3p9zhfWLyvuCysvAcrYxl2k521Pgb6fPq9huyvvPDqpfMSe3y7+v9C+jQIy2s28EMOzyfbaJr1JOFbcnt8vG6yvvJ7vCT9IV172K5Ufe79kYVNyf+ACXswwM2warYrxl/y9bP/zlJgZ2+Xpg+Jz6gbZNDLC9BA6eMgx/ub9O+fZkGv710MCHgVG2jplg+zBsC6/X8Mdkl697BhaGbWFgYNtlOdleKu4TK/ZToI8ss7w/An3m78fAsitfjwzMCv3q354M/J8LgX41jfI+smw7aHuqrp+c5WobUGHdM7DANjGwsO0QKNs+/O/p77PyfirfFwSWqb+fvIf1qz92r9eLy/CVtWc75SZ22TZrYxhWUH3/eukre9+yPgMMDP/2aZv+9cAub9O2Ku6PvRi2fz0x8WFhgm0HfR64qumn8jb89W1nXxt4znKWmdtlVVr3Au0bRvn25KwXlu3sI7B9ZcvTrNRPR9qeDGcfFViWXsq63v9JZvs/W7H9n3H+9ckLhIBtl/1t4PWaFdaHss8n2ypbJC5/f9sW2L7y72m26X+Xsvr+zxqv00+B7Qbb8O+Ly/op0M/YVoXPp7Jt1Kb887Vse6q4jwzUt23K9pnl+36vzyzbNwR/j/D/W75NOO045RU/Q/11Ap9n/u3bCPpuFPhOUHGfZlAeo9tlOTkdvt87/Luty/SVfffwf2fy7zt8zr6/Nt+NTMPCKttuK7+vNBfNYoC6ePFiYmNjncEpwKhRozBNkyVLlnDppZfWqJ3c3Fyio6ODBqcAd9xxBzfffDOdO3fmtttuY/z48c7GXJWnnnqKadOmVSpPS0sjKiqKM3oXsCXK//peybl0ap9NaUoxHo+Hzok26XktGNAtB4/Hw/ABh/B4cuncuTNt27ZlzZo1jErdS3TLfDwek5iItkAUp3T/GY8nk9FDcjFNk6WbOuDz+Rh1UibukH14PP4b6Jr0o7CwkFWrVjlxJUcawBByc3NZv349AF2jLdasiaJ///5kZ2ezdetWp76RHw0kkJ6ezq5du5zyNm3a0KVLF7Zt20ZWVpZT3rFjRyCCjRs3OodQD+xQSnZ2DyenwsJCBnYoxeMJoWfPnsTGxpKWlha0s+nXrx+hoaF4PJ6g5ZqamkpJSQm+fSvxeMoGTS4XgwcPDsoJICIiosqcYmJi6NWrV1BOwwccYts2/0xX9w4HaB93EI8nl3NPy2HnARf7SqPpl5xNqxaFhISF4PFsD+6nwXtpEZuPx+MiJrIVAGf0TcfjyeGcU3Nxhx5kTob/g/XCkfkATm6BnCr2U21z8vdT76CchvXLZ9s2//rdOyWPE9qX4PHkMHLQITbvjiKrMIaNGzdyao8MfF4LjycnKKfRJ2cTHVsIwPa8LhwqackZfdP9XxZtG8M0sOmIRSido9Lo6Pbh8YT44+EkCguDc2qLCwjO6ZQuRaxZs5/+/ftjlGTj8Wx36rfwxgDB/TRqcB7btm0DIumRdIB2sXl4PB6GditiQ2gEO7JasXHjRk5KysLXzofHkxmU08hBWXg8h+jTxovP7gNEkpaWxqjBBwgJy8FX6uWbH1tSVGwy+uRsfF5vhXUwtVJOrlwTODkop+6xPtasaVHtugf+ozqGdvOXWz6Lbdv8Xy17JefStUMOHk8Ip3QpYq3lX54bN24kqiAXjwfIscnO9t/YekCnnUSGleDxpHNmnwJWbk2gIN+//xnYoQif10dJhxIKCwsJDQ0lqsCDk06OjdsVRlS4l1NP3FvWb2CbbubsjaV1bAkDO6ynV1wx3uISDub7vyRGkI3Hs83JaWCPYtbsjCI9PZ3hA3ZjmiYhYW62besAQHKrTPq33YvHc5Cu0Rbp6SfQsWPHoH3EyIGHyM5uA8CgLum0jPTh8ezk1B6FLP0pFvDndFafPLwl/j6pah8xrF8+ny1oSVS4v87pvQopTSnBNg4CfYhrWcSZvQ9guvLweArol2Tz9a4WtIs7RFLIMsD/VcmH/z07d8inS8sVtOtTRGj4HrZt8/8A2is5l+T22Xg8mZx3eg7rt4aycac7KCcgaN0rLCx0yo+03zNw0Sl8OV6XhcfjolOYza7SQbgpweNZQ2sfcMAmOdIEhtAi5CBJoWsIKzLweOCEqFB2F/elbXQeHs9O+rb14vNaFBML9KN1eCatQ/dgugzC8kx8oW0AN8mtMmnf6iAAYSEm+9qGUUwbkmN2EB2eT2ioiQFl615bWpSsoQWFeDzQFqC0O+AmLS2Njm4vbp9//1xY2A/TsGjtW8aw/vl4PDmMGZrL4o1xTj8F9pGlXoATiY8p5bQ+PwPg8WRzWv98Vu6Mo6V7Hx7Pz/SI8+Fym5R2LwGgTeReunXbRVTkbjweg+4dDNJyI+ja3v95eMGwg9i2zcYdtrM9VddPyREF/mVQYnDA7ArEk5aWRgeXj9AC6OAqywmb7nGrCAkx8XgMkkJt9liDcFOKx7Ma8H+2en0mcAoRZh4JbCK6tQ+Px0XH8DB2cwJxETl4POsZ1KmEkFAXhb5ooC/xEXtJiMzAOOBvv1NcFFsyE0hssRuPJ5cecT46hZeyr7QDEEtixFZahh3EADweg1CrM9CWOHstLvzrnjfbJtLVBYinvWsFIV4LC2gVbbH9UG8M3IwcuAfDNPB4DjDqpIP8b32K00+jUg9imAY+nwn0onVMCUO7bcW2bTyevQzuVgJ0o4W5D49nB4mmf5/tjnKV5ZRJfOweukQW4/EcoHObMNbmx9GnyyE8Hg9jhuZiWzbp6el07NgR49BmjNI8Z59V0+2po9vG4zEIc3XDa4eSwDLKfg/EAgpIBV8R5K6mS5SNFWETSigwGNOXS9fonwgxTTweSG4RCpyEy5uNx7MVdy6YBoTh/3wKKU3HOLCTHnH+7TWspA2EdSasZBseTxa94nyE5rloHd4W6EwL70ai7VzcheDxQIivM4W0Jca7Bo+nEDMHesX5KKI3EEdMaRour48+bbx4PG5Mux8u0+bkzpvxeHYytFsRIaEuSjiFwsJSTu+1g5YRmbTpVkx+uxLmLmqF28rF4wn+HgGhJLYu4MROOYD/x8lSdwEQR+f2B+nTZo3TT/26+5d14PteBxeYNiQn+L9f9O2URVyLAkyXiceTQSTJ/v6K2YjHU8pZffLxlnpx27lA5f1eZGhbSnxuhnbbiseTztBuRYRH7GT3z0kUFhbSxl7F0G5FFHcoJi0trVbfjbq0DmVTdjKtwjJISysvb44s/2+T9d5mc9AsBqgZGRm0bds2qMztdtOqVSsyMjJq1EZ2djaPP/44t9xyS1D5Y489xogRI4iMjOTLL7/k9ttv59ChQ9x1113VtvXggw8yefJk5++8vDySkpIYOHAg0dHRPPfeDvbtyQZg3fYYduZEsT89i0m3DOKJV34ivAWs2BTLlHt78/Q/NzFxfDdnFrJPnz488uIa+g/txrWpobw8N5uYcPhh4wncfkMSj728EneIG1eYG5fLxdfLE2gR05JbxvlvfPvJWi8REWFBg/n/bigF/F+UA+WfrS3huj7+k83j4+Np1aqVU//TZRZwkMTExKCZ5UCMKSkpdOrU6bDyA3Tv3t35hXP2wgPcHN/aycm2bWYvPMCNV8Y5syADBw4MWq6B8oqxB8ojIiJwtR5I6oDgVbZiTlD+K+HhOQXKK+b09D83ccdNKcABNu6OY/2OaCaO78bUvy2jRXwMLeNg1fZ4DCAuPpqbrooP6qeHX7DpflIK1/82ihc/ziAe+G51IpNu6sqjLy6nZXwsB/PTcblcfDovCoC7J6YG5XR4rrXJ6ZNldqWcnvnXFm6/MQXYwE/botm6N4yJ47vy1Ovr8Vk2LeOge/fu/HF2BIWHipg4vmtQTg8976PfKd0BKC37svndav8XdJ/PhzvEzekj/TfI3po/kOysAsaP9Q/AvtlBpZwWz7Er5fT3OXu45Tp/m3ZoPKmp8U79lfMq99MfX1nDnbekAJls2BnHqo2h3HFzP176NJ2cfYdwuf05PfdhKw7mFHDLuKSgnKa8tI7bb+zJu0sP0TUmBihl4MCBPPbyGmITWnHwwEHyC/wfxl8ujacwL5+7bvPHunp+5X76yGNVyunjVUWM6xNRZT/5+28diYmJLN7kf21xQQkTx6cA21m3PYaD7nbceGUcf5+zh727/fuO7t27syTDJjUVPltmER/vBvayYkcShmEz4dpE/vru9rLZrUIGDhzIzG8OUHCoiOzdWdx1qz+e/MhUAuF/tszC61tLfpGbecv8y9cwDSJaRAB57MsJJW13Z9K3ZpKXtR8bmAoUEk9qamsnpyf+sYaQSH8/zV9xkJDQEGLbRnPz1fHAGrbvTyB7t4uJN/bgs7UljEsMd3IK7CP+NHsDd9wcD6SzbEsikVFh3HxNAi9+tIu8vAJS8O8jnn5rKwezc5h0y0lV7iOe+dcWvL488ovcpKam8vx/fubAnmzCoiK5FzhwMJz//i+OsBYR3H5Tb17+PBPYT8aBFuws7eVfD22IijCBn9i6O4rYzt3YuCad+A5x3JQSB2xl3fYYduW24JZxSTzyvAdviQ9cwTkBQeve4TM+UPV+z8bLjqKTyMsrZtxvI/n6PR+maeLFv+4t+8KmQ5zN9kz/F7xDpS3ZWTKIttGmf/34qQSXG/bmRZOa2o23lxykKL+ElrH+dWBfUQJ78+MJD3PTu30I/hnUXWzfn8B+y78vb9Mmgh17d9IuGbbndsKdbxLXKhy3yyhb9+BQaB8KvBaXpxosmWPTKcQAChg4cCDztlm0DPNfxyEiwoVlF7PPNYgFK7cwcXxXpr2URlScQXFZPwX2kaXFpdwLZOeG8O0a/w8Wt16fzLS/r6JFKzjobU1qals+WlFEVMsQ0jb6v6BmFbRl3Z5QOvdoy9Wpbp55eytQxOY9rUlNTeKhZxZjeX2YIaGV1r3D++nj1f7PyIQWbgoti9iyfpq/w6JtpMneQouIiBAsilh/oB+xsWFclWryzfsWLrcLL+6gz9aSUn8/FVrRZDKIXfsKuP63Uczd6AXyOVAYS2pqR2Z+nU1sfBQulz+W7MK25BQnMCQ5lNRBJjPnHwBs0g91IDW1Gx+tKGLP7gPEtG4JQHphZ8Jx4XYZXJlqsuJrk1DggHEi3rJc7xpp8MNsLzHAHt8AWoSZFJXY7M8rLZuvdTEvrT3uEDe33dCFJ1//idDw8n568rW1uEP9y/AeYF9uKIs3JeAt9XLruBN45p2tDO0Ah6zWpKa24buPLb798Sf6nNK5LKcEMvJas3NzJrfd0IW/f54JlLJmSwv+lNqPaS+l4S31cffEss+EFl2xgdRB5TOoNdmeFnxgcWWqyXvLizAMg0wGEeqvQrEF0bjAFQ5xg9iyx0dJkY+E9v7vQJYrhs15A4iLczM21eTj1cX0BnzueFJTW/HBUgu3C4oL/Q2WhiRiR7Zlw/YSrrssnPXfmYQDxaEppKZ24j9pBQxKimDfTv+PKYfc3Sn2WbSMMEhNhZ8WmuCDXHcfUlNtPllmsW5bER0iWgCQGzKQ0BBYs+UQ11/egtXzXfgsg6Vbu3LzNQnM+CSd6FZR9Ip1ERHh5vt1nejVP5HNm/ayd4f/O7HXrOp7xEbS90WSsd+/XwgJCyExxb/Nbd3TEiMu0emnh6YvBcq/7337kUW4y2B7pv+HytU72mD5fIRHhnHLdR35/hOAg2zN7c6E1AiefWcr+TkHuSglJqifAl74aA+GYbB4U2cmXJvIS5+mk5jUCp91gIiICLKMQXg27WHvjgwm3eJ/bU2/G81emAPA/uJ2DBwYgzRPjTpAfeCBB/jzn/98xDrr1q37xe+Tl5fH+eefT+/evXn00UeDnnvkkUec/w8cOJD8/Hz+8pe/HHGAGhYWRlhY5SuJud1u3G43Pst0DruzbAOfZeL1GbjdbqyyQ6J8lunUrTij63K58PpMLFxl5cH1vT4TTBMX/i8CPp95WBs+DMMIatMOHA5UodzC5+zgA4fQOMoO+ahUXqa68kB7/rxdTp1AuWW7guI6fCb7aOXnp4ZXKjs816PFWLHcZ5X/37JNfJZdtozLL+ttWWV1D4vd6aeycruKfvJZ/sN+DMPAWzbYOzzWqmKvcU6BQ6WqzckIWs/sskO3XS4XPuvw9SZ43fPzHy7lCywDy8awTCj7ULBxHdanVa17dqWcfJarfF0xTNzuijlVztXrC87J6wvk5HL6yeVy4bNdVeYUKLNslxN7xT4K9FPgvQLbaiAewzisn4yqtidX9dsT5Tn5LFeV/RRYjj7L5ewjXC4XGOB2A6YvaF3FxunXgECOPsvfj85RIIYbJ3zTV5ar4bzWqPB/G6OsDdNZZ8teiNtd/rfPMglxcjIxLTNom7ft8vXLwldpX1B5GZjONuazTGd7Cvwd6POKuVZsJ5BTUP0KOXl9Bm5fYD0IxGhgl63rNmAbFfoD/zKomFPF7cnrM7BsA/OwnCqqrrzq/ZsRtD0FDlL05+TCNmwwrQqHXJfFbrhwu8EOnMaBUb4e2K4K+Zn+X+JxgVFh+7T9OfrrlG9Plu3Csv2DFxujfH02XNiGf12wDdvZFvwx+/zL0DDKNjMD23AT9NlVoZ+Ctz9/fwT+7+/HwPoWWI/8+ZSvq/7tyS77rAz0q2VX6COf4ay3R+qnwGekf9mUVsjJAsPExirbnvzbh/89XWXLvTwn8H+2ls9QHN6vthO72+3GZ/ufMynPycIs61cXtm0CPqe+f710YTv1XU4fud0uZ/9pGy5nXfHnH1g/3NiGiY2N5eTsX+5G4LOiin4yKuxn7LL9hbN9O8+ZzjLz+szD1r3y/bBtl29Pgfa9PitoHfO/d/A+9Gjbk43PvwzKYvfnGoiZsvXSAMOFhX9W1Q58BTaMsvXLjdttYlN2WGjg88mwypo1nXJ/O2Wff0bZu5TV93/WuCu043K2jQpf65ztF8MK+nzC8LfpfL6WbU8V95GB+kZZ/9mU7/sDOVW1r7FtA1/ZggnstwPlFfvJOaWk7PPMxsI2gr8b+Sy7wmeuVbasy2P0+sq/L1SOJfA5VOGzD3//GUZg3+EK2vfX9LuRs4/HrLJ+s9IAt5mhvttrII3ac/fddx833HDDEet07tyZdu3asXfv3qByr9fL/v37j3ru6MGDBznnnHNo2bIlH374ISEhIUesP2TIEB5//HGKi4urHISKiIiIiIhIw2jUAWqbNm1o06bNUesNHTqUnJwcli1bxqBBgwD45ptvsCyLIUOGVPu6vLw8xowZQ1hYGJ988gnh4ZVn4A63YsUK4uLiNDgVEREREZFG0RC3hdFtZupRr169OOecc5gwYQIvv/wypaWlTJo0iSuvvNK5gu/u3bsZOXIkb7zxBieffDJ5eXmMHj2agoIC3nzzTfLy8pyr7bZp08Z/XuCnn5KZmckpp5xCeHg4X331FU8++SS/+93vGjNdERERERGR41KzGKACvPXWW0yaNImRI0dimiaXXXYZL7zwgvN8aWkpGzZsoKDAfzW+5cuXs2TJEgC6du0a1Na2bdtITk4mJCSEGTNmcO+992LbNl27dmX69OlMmDDh10tMRP5/e3ceHkWV74//XVWdPWSDkBCIhLCYIAQCQQzjOmQgLjiojKhoAFkERMcBr6J3BHFD+Yk6KIqDCtw7MG4XFBlkBNxmHH5iYRCDLApBkJBAhOwhSVfV949KVbqTTmehm+5K3q/nqSfp09Wnz6fOUnW6qquJiIiIyEH9D/x4PE8rsMwENSYmBuvXr2/2+aSkJKc7vF199dVOj13Jzs5Gdna2x8pIRETUWYwfIWHHV74uBVHr3Jih3xBoV57SwppE/qEzX+Lb9FaTRETkV8aPcH0HS1/lQ0REROQtnKBSEzddyoNYIiJ/wg8XiPwHj5PoQtDqf2bG04sVcIJKRERE5Cc68+SnM8dORA04QSWyqJsvZfclIqKOxR/3bd4skz/GS/5B1byzWAF7BRFRG0wYyWGTiIiIyFsscxdfIiIiIiKizoB38SUi8oE/XMYhiIiIiIga8AwqEREREZELt2byg1TyDQ0CNHj2rruezs9bOEElIiIiIiLyIyo8f1Mj1bPZeQ0/FiIiIiIiaqfbRgluHxNR2/AMKhERUSdx+2/0A+cPNvq4IERE5BZvkkRERERERETkYzyDSkRERERE5Ed4BpWIiIiIiIjIxzhBJSIiIqJO6a4rfV0CItdUTfDKYgW8xJeIiIiIqB4nrUS+xQkqERERERGRH+nM30HlBJWIiIiIyI2cq3xdAupsOvMEld9BJSLyU1Ou9o88iDor9h9qrHGbMB6zrRB5Ds+gEhERERER+RFNA1SeQSUiIiIiq7r7t74uATVn6jX639bUkbEuUWfFM6hERERERER+RNMEaB7+WRhP5+ctPINKRERERB3W9NG+LsGF1dnipY6HZ1CJiIgsiAehREQdF+/iS0RETczI8nUJiPwH+wPRhcd+R50RJ6hE5DfuGePrEhBRZ8Xxh7yB7YraS9W8s1gBL/ElIiIiqjdrrK9L0D5WLTc544SWDLzEl4iIiIiIyEP4oQm1F8+gEhERkVuzs31dAqKOhX2KWsIzqEREncC91/q6BEREvsMxkIisgBNUIiIi8ghOgKyF9eUb3O7UGp35JkmcoBIREfmxudcJvi5Cq1ilnNQ5zL1O6NBtsiPHRsQJKhEREXkcD6CJiNrP+A6qpxcr4ASViIiIiIiI/AInqEREREREXvLHcda/muBCxtARtpcnqKp3FivgBJWIiIiog+DBvTU9cCPrjcjACSoRtckDNwrckVKHwzbdOsZ24vYiIvIufgeViMgL5v2eB7EdAeuRqHnu+gf7jm+0ZruzbsjfcYJKRERERB3C/PEC5o93PQFrLv183suf+Xv5iKgpm68LQERERERERA1UAKqHz3ha5B5JPINKRETkjx68mbtooguF/a39uO3I09iiiIiIiOi8PXQLDyuJPEXTNK8sVsCRhIiIiIiIiPwCv4NKRERE1MjDE0Snv+7W8XdWKSf5J7Yf3/DGXXctcgKVZ1CJiIjItUdulXxdBGoB64iIOhqeQSUiIqLzwkkS+RLbH3VEmgqoHr7trmaR2/ha5gzqmTNnMGnSJERERCAqKgrTpk1DRUWF29dcffXVEATBaZk1a5bTOseOHcP111+P0NBQdO/eHf/1X/8Fu93uzVCIOqRHJ/IAgazl0YkS2209bgciIv9iXOLr6cUKLHMGddKkSTh58iS2bduGuro6TJ06FTNnzsT69evdvm7GjBl44oknzMehoaHm/4qi4Prrr0d8fDz+85//4OTJk8jJyUFAQACeeeYZr8VCRET+479v87/JGSeM/stoL/7YboiIOgJLTFD379+PrVu34ptvvkFGRgYA4OWXX8Z1112H559/HgkJCc2+NjQ0FPHx8S6f++STT/DDDz9g+/btiIuLw9ChQ/Hkk0/i4YcfxuOPP47AwECvxENERHQ+ODkiIurYVE1fPJ2nFVhigrpz505ERUWZk1MAyMrKgiiK+Prrr3HTTTc1+9p169bhb3/7G+Lj4zFu3Dg89thj5lnUnTt3YvDgwYiLizPXHzt2LGbPno19+/YhPT3dZZ41NTWoqakxH5eVlQEA7HY77HY7JFGFKOotQBQ0SKIKm6TBbrdDFPR0SVTNde12O0RRhCiKUBQFNkmFCKX+UmPn9W2SCpukAtB/y0iSVPM5nZ6uKIpZPgH6/47pIhQoigJJkqCqKlSHi9wFQQCAJulGGV2lA/oZaeP3lURBgaqqZkyapkEU9JgkSYIgCE0upZYkycynNek2m61prILQbEyN0yWx4X9RUAFzG2sQ6utJFFUIAKT6sjepp/p0wUU9SWJDPdkk/Xkj5gsTk+bUzpT6UUlRFEii6rbtAYACwcwTAKDprzHqWIBixt+WmCTRfdtrnG6TnGOySUZMillPiqJAEhSXMZlpQkP7dKwjo56M9zL6altiam1/kkRjGzjHZPYNUTHHCMf+BDT0M1FQIQiaWa+qKpgxiYICSdTr0Xht47LrsWpmvQqi0PA/tPo89O1gvHvjmIz19Zj0xejzeswO7QtNx4Km20A1+5gkqmZ/Mh6bde6iPhrqT3Na3zEmm6SPlfo2MsqoOY1BRj2JggYRSpOYHPuTTdJgV5qvp8axAmhh3NMgoGE8eeQPcFl/xlhuxOqcLkKA1tAOBMVc38hbhGD2D6OeREExt7U57gl6O3zkD2jSnxrHZLzWVUyO+zhjv2WkG3Wkig31YdSZ/jqz9ZntSIDoUK96fxKgwG4XzHoVhYY6UjXNbX8yYjK2k90uAHAeqxvH5DjmOW4Dx7HA2KZwqA99P9FQH3a7HZKgr2u0dwEqRIe6FQTVaX29XSoQYKQreOQPkrl+c/XkKiYRiv5umua0P5CaqSeDUD92aOZYazzXcBxik9Qmbc/IXxAa+pPZLlTN5RjhLqbm+pP7+tPrSYRiHmMZY3nDMVdDP2tp/2S8pvGxUUN9q+Z7thSTsX9yjMnxmAnQnMZIx/2ZPmY2jP12RXR5HKH/begTRj4N6Y77UK2ZbdBwbGQcEziNKWgoo7EfcldPDccoSv2xR8MxrBmPQz215thIFFSommj2G7ImS0xQCwsL0b17d6c0m82GmJgYFBYWNvu6O+64A71790ZCQgL27t2Lhx9+GAcPHsSGDRvMfB0npwDMx+7yXbJkCRYvXtwkPTc3F2FhYbhiYBUOh+mDQWpSKXr3KEZdnxrIsozkBA0FZeEY2r8EsizjmqEVkOVSJCcno3v37sjLy0NWxilEdKmELIuIDOkOIAyXDTgGWS7CmJGlEEURu37sCUVRkDWsCLaAXyHLRQAAEWmorq7G3r17zXIlhQoARqK0tBQHDhwAAPSLUJGXF4YhQ4aguLgYR44cMdePjIwE0B0FBQX45ZdfzPTY2Fj07dsX+fn5OH36tJneq1cvACE4dOgQSktLAQDpPetQXHyxGVN1dTXSe9ZBlgOQkpKCqKgo5ObmOg02aWlpCAwMhCzLTts1IyMDtbW1TjFJkoQRI0Y4xQQAISEhzcaUmprqFNM1QyuQn6+fJR/Q8yx6RJdDlktx7W9KcPyshF/rIpCWVIyY8GoEBAVAlo8619OIUwiPqoQsS4gMjQEAXDG4ALJcguxRpbAFluMfhfpOaNzoSgAwY/NWTFenVSI/X+/WA/uU4aIetZDlEoweXoGfToThdHUkDh06hFEXF0Kxq5DlEqeYxlxajIioagDA0bK+qKjtgisGF+gHi5oGQRRQXZ2IwMBAJIflopdNgSwHtCmmy/qeQ17emVbHlDWiDPn5+QBCcXHiWcRHlUGWZWT2P4eDgSH4+XQMDh06hGGJp6HEK5DlIqeYRg8/DVmuwKBYO0pLBwEIRW5uLrJGnEVAUAmUOjs+/aYLztWIGHNpMRS7vc31NCBKQV5euJv+BBQUFCCzv56uKiry8wUAAlKTStGvZwlkOQCX9T2Hfaq+PR37EwAkJycDAIb2Po7QoFrIcgGuHFSF747EoapSH3/Se56DYldQ27MW1dXVLvuTTQpEWLAdoy45BQAQAGiiDf84FYWuUbVI73kAqdE1sNfUorxSP+hoHFP6xTXIOx6GgoICXDP0BERRRECQDfn5PQEASTFFGNL9FGS5HP0iVBQUXIRevXo5xTQ6vQLFxbEAgOF9C9AlVIEsH8eoi6ux64coAHpMVw0qg71WrxNXY8TVaZXY/HkXhAXr61yeWo26PrXQhHIAgxDd5RyuHHgWolQGWa5CWqKG7b+EIz66wikfo56Se1aib5c9iB90DoHBJ5Gfr1+hk5pUiqQexZDlIlx3eQkOHAnEoeM2l/XkOO4Z3I17AiT0Dv4WdkmFLEvNtj1jLA8PKIcs7zPTLwoLxImawegeUQZZPo7B3e1Q7CpqEAUgDV2Di9A18CRESYAsi4iNjQVgQ1JMEXrElAMAZFlC7+6BqEEskiJ/RkRwpVkWdzEBosuYREGFLMu4ekglZLkEYzNLsfNQtFlPxhhZZweAS9Atsg6/GXSsvizF+M2QSnx3PBpdbL9Clo/h4mgFkk1E3YBaAEBs6Cn07/8LwkJPQJYFDOgpILc0BP166PvDG64uh6ZpOPSzfiDsrp6SQqrq31dAiNgPQDfXMUHDgOi95nZxVU/9IvQJAnAZ7r++ErJ8AAO7KpBlCb2Cg3ACFyE6pASyfADDe9ciIFBCtRIBYDC6hZxCXGihmX/v6DAcLopDQvgJyHIpLo5W0Du4Dr/W9QQQhYSQI5DlhntxNFdPoVIygGCnmPpFqDhaMRCKYsPo9JMQRAGyfBZZw8rx1YE+Zj1lZZRDEAUoigggFV0ja5HZ/wg0TYMsn8KI/rUABuCe0SWQZX2MyBpRBluYVB9TEbpFnUTf0BrI8lkkxwZhX2U0BvXV+9/YzFJoqoaCgoImY0RLbc9VfwqS+sOuuT+O6BumQg3RkJsbaI7l/SJ+gCzr411SeCCAYS3uny6O1uu18bFRan161+DuAJJbFVNqtILS0kucYhoUa4cs25CWlgZJ1HBp8k+Q5ePI7H8OAYESFCUTtbW1uDz1Z3QJKUJs/xpUxtdi639iXB5HAIFI6FqFS3qXANA/nKyzVQGIRnKPcgyKzTPrKW2AXq7Gx3tJcfrxxeDepxEdXgVREiHLheb+KTnyEGS5DlcNqoS9zo7S0lKX9RQa2B21ig2Z/Y9AlguQ2f8cgkOO48SxRPMYNrP/OdT0rEFubm6bjo36dg3Ej8VJiAkqRG5uQ7oVdeafmfHpBHXBggV47rnn3K6zf//+duc/c+ZM8//BgwejR48eGD16NA4fPoy+ffu2O99HHnkE8+bNMx+XlZUhMTER6enpiIiIwEvv/YxfTxbr5T8aieMlYThTcBpzZw7HU6t+QHA4sOfHKCz800As/d8fMXtqf/PTp0GDBuGxV/IwJLM/7swIxMqtxYgMBv7/QxdhzpREPLHyO9gCbJCCbJAkCdu/jUN4ZBfMzEkEAGzaZ0dISJDT2eaPD9YB0AdXI33zvlrcNSgIANCtWzfExMSY6+uftJUgISHB6fJoo4x9+vRB7969G6WfxYABA8xP9NZ+cRbTu3U1Y9I0DWu/OIu7b4s2P8lsfIbaSHcsu5EeEhLSJL1xTA1lby4mOMW09H9/xL3T+gA4i0MnonHg5wjMntofi17ejfBukegSDew92g0CgOhuEZh2ezenevrzcg0DhvXB5AlheOXDQnQD8K/vEzB3Wj88/sq36NItCuWVBZAkCR/tCAMA/HF2hldjen79Ycy5uw+Ag/ghPwJHTgVh9tR+WLL6ABRVQ5doYMCAAXh6bQiqK85h9tR+TjE9+hcFaZcNAADUKXr+//peP0BXFAW2ABvunRYCADhSmY7i01WYOjGyTTG99o+TmHlXQqtjenpVHu6b2QdAEQ4ej8beQ4G4d3oaXv2oACW/VkCy6TG9tDEG5SVVmJmT6BTTwlf3Y87dKXhnVwXujowEUIf09HQ8sTIPUXExKD9bjsoqfWf8ya5uqC6rxP2z2lZPH+49h5xBIW5i2o+EhATs/FH/lLemqhazp/YBcBT7j0ai3BaPu2+Lxmv/OIlTJ/Sxw7E/AUY/O4U9PydCEDTMuDMBL75ztP4MajXS09Px1qdnUVVxDsUnTuP+e/TyNC67/bXvUXnOhh279e0riAJCwkMAlOHXkkDknkhGwZEilJ0+Aw3AIhcxPfXXPASE6vX02Z5yBAQGIKp7BKbf0Q1AHo6eiUPxCQmz774Ym/fVIichuElMz649iHundwNQgN2HExAaFoTpk+Lwyge/oKysCmvuDIDdno6l646gvLgEc2cOczlGPL/+MOxKGSrP2ZCRkYG//N8xnD1ZjKCwUPwJwNnyYHz8VTSCwkMwZ9pArNxSBOAMCs+GIyPjkkb1tA9HToQhKrk/DuUVoFvPaEzrEw3gCPYfjcQvpeGYmZOIx/4iw16rAFJz9dQw7hncjXt/vgNQlBF4ZFUVciaEmumN254xllfUdXFK3/xDLSQbcKosAhkZ/fH3r8txrrIWXaL0NvDruTicquyG4CAb7swIrC/jcRw9E4czqj6WT54QhmVv/4z4JOBoaW/YKkVMnhDWipjKXcakajXIyMjA//e3nzB7aj8sfjUXYdECaurryRgj62rq8CcAxaUB+DJP/8DinslJWPzaXoTHAOX2rsjI6I4P9pzDU9ODcdMsfWJ+uqo79p8MRPLF3XFHhg3P//0IgHP46WRXZGQk4tHnd0K1KxADApu0vcYxffi9vl3vyLDh40Nqs/Wk4hwOnE1Dzi0hTumO9bR5Xy1q6/QDcWOMeG93JSZPCMPWQ3YAlThbHYWMjF54a3sxorqFQZLqPwiq7o6SmjjcdYveX9767CwADQUVPZGR0R8f7DmHkyfOIrJrFwBAQXUyZmY0fBWpuXratM/eJKbN+2qhQoQkSdiR2wO2ABtmTemLZ1b/gMDghnp65s19sNV/3ekBAL+WBmLnj3Gw19lxT85FeP5tfQLgOEY8vSoPgy5Lro8pDoVlXXH8pyLMmtIXr20pAlCHvMPheDYjDYtfzYW9TsEfZye0WE+t6U/vfXsOgiC4PY7YcqAOtecU3JUebNbTT2VDcWf9tvzw+5omMQFN908f7DmHu24JbnJs9H+5ej/+YG9Nq2P6v9wqREZ2cYrpnV0VmPyHcEiSBEUVsOtIP0yfFIcVmwoQERNmxvTv/b2ROiQBP/14Cqd+LjRjanoccQgFv4ai8IzefgOCApDQR+9zR052gRCdYNbToy/scorJsHSdXt/f/xwLVVEQHBqEmXf1qo+pFEdKB2BGRgiWvX0ElSXl+OOsSJf1tPyDkxAEATt/TMaMOxPw6kcFSEiMgaKeNevp1Y8KcOrnQsydme4mpqb1tPaLEgDAmZp4pKdHgqzJpxPU+fPnY8qUKW7XSU5ORnx8PE6dOuWUbrfbcebMmWa/X+rKyJEjAQA//fQT+vbti/j4eOzatctpnaIi/Uyku3yDgoIQFBTUJN1ms8Fms0FRRfOyO1UToKgi7IoAm80GVdPTFVU017XZGqpBkiTYFREqpPp05/XtigiIIiTodyVWFLFRHgoEQXDKUzMuB3JIV6GYA7xxuUljbU038tPjlsx1jHRVk5zK5fh/4+3Y2vTGsbal7Ira8L+qiVBUrX4bC9A047LM+nUbld2sp/p0zUU9KaoI1NeTvX6y17is3o1JcGpnWv29xfWdXeN249z2dPplXoqxDVQNgiqaOwUNUpM6bU1Miiq1qe3ZFeeY7IoRk2TWkyRJUDTJZUxGmqpJZtkd68ioJ+O9jL7alphUtC4mRa2/zKxRPRnbUVElc4xw7E+OVE0ENJj16lhGVZOgqHo9OsbaqOQABPO1gsP/GoT6PESzzbqKSVFFBJgxiRBV0anPa1pD+1KhNBkLmm4D0exjiiqa/cl4bNS5Y6yO+RgxOa3vEJNdEWBTjHZglNF1P1M1ASr0beAYk2N/sisCVE2A6Kaemkt3N7611J+MsdyItSFdMWM124EmQavvyxpE/btMcM5f0/QYjfcxxz1NgqqJTcrStpga6sPcdznUk3P/0+vD+F9/ndH+jHYkObV5DXp/0urTjXo1ym1XBKiKAJvNfX+SJAkL73R8rs5tTK7qyHF9FYr5HS9jjGjYT2hm2W02GxRNH0NFNMSkomG7a5oIQDHX19ulBM1c33VZmsaqNIlJNS7BFfTtLhj7Chf1JDiMM1r9eGH27/rnGo/Zzm2vYRzWtIb+ZORvV1SXY4T7mJy3e4OGsje3vgoVqsNrBUFw6hsanC+Zbsy8pLfR9jfTzfqua3VMjfdPRlpD/oLTGOm4vqKKWHhnMCY9ItX3s+aPIzRNgFLfz41x20h3rCfjGLbxNnA8NlJUrck+V3Pop63bDzns+yDBOGYy0hzH/tYeG5ljPJqOYVajqRo0D39p1NP5eYtPay42Nrb+UiP3MjMzUVJSgt27d2P48OEAgE8//RSqqpqTztbYs2cPAKBHjx5mvk8//TROnTplXkK8bds2REREYODAgW2MhoiIyDcW5wTgzv/2dSmsb3FOgFfznsEfCCAiapElfgc1NTUV2dnZmDFjBnbt2oWvvvoKc+fOxW233WbewffEiRNISUkxz4gePnwYTz75JHbv3o2jR49i06ZNyMnJwZVXXom0tDQAwJgxYzBw4EDcdddd+O677/DPf/4Tf/7zn3Hvvfe6PENK5I43D2zIc56YzLtzkzV1xrbb0rjKcZeIOirjLr6eXqzAEhNUQL8bb0pKCkaPHo3rrrsOl19+Of7617+az9fV1eHgwYOoqtJvdhAYGIjt27djzJgxSElJwfz583HLLbfgo48+Ml8jSRI2b94MSZKQmZmJO++8Ezk5OU6/m0pErnXGg2UiIiKiC8G4SZKnFyuwzMXZMTExWL9+fbPPJyUlOX0JPTExEV988UWL+fbu3RtbtmzxSBn9FScSRERERERkBZY5g9rZPH03LzEmIqKWtXZ/wf0KEfDUFH5oT9agqppXFivgBJWIiMgBJ3JkBWynRNRRcYLaST0zjTs2IvI+jjXUmfhje3dVpmemBfllWYk8pSO07878HVROUDuYjtAhiYiI/M2zM4Itla8vWS0mHjsR+RfL3CSJiIiIiMibrDa5po7LG2c8eQaViCzpuZkheG5miK+LYSncXuTP2D6JLjz2O6L24wSViIiIyA8sndX8pMbdc0RWx/bdlKppXlmsgJf4EhERkV/jwSv5G7ZJ8jZN1RdP52kFPINKRERE5APPzw71dRE6nNZsU04uvYPtmTyFE1Qicok7GiJrWzYnzOX/1H6tGRc5djbojNuCfY08RYMGTfPwAmtc4ssJKhE1iztaoguH/a1zY/2fP6tuQ6uWm8hbOEElOg8vzg33dRGIqJO5EOOOv49t/l4+IqLzpamA6uGF30ElIiIiv/bSfeF46T5O9rzpfCfTrB8i8rUVK1YgKSkJwcHBGDlyJHbt2tXsuqtWrcIVV1yB6OhoREdHIysry+36rnCCSkRERERE5Ec8/v3T+qWt3nnnHcybNw+LFi3Ct99+iyFDhmDs2LE4deqUy/U///xz3H777fjss8+wc+dOJCYmYsyYMThx4kSr35MTVAv4yx+7+LoIRJbHfkRERFbBfRb5ixdeeAEzZszA1KlTMXDgQKxcuRKhoaF46623XK6/bt06zJkzB0OHDkVKSgreeOMNqKqKHTt2tPo9OUElIiLqQF5+IOK8Xns+r6eWne/2tVr9cKLlvxzbEuvJ/6iadxYAKCsrc1pqampclqG2tha7d+9GVlaWmSaKIrKysrBz585WxVFVVYW6ujrExMS0OnZOUImIiKhdrDZZIiKyCk3VvLIAQGJiIiIjI81lyZIlLstQXFwMRVEQFxfnlB4XF4fCwsJWxfHwww8jISHBaZLbElur1yQiIiIiIiJLO378OCIiGj5gDAoK8sr7PPvss3j77bfx+eefIzg4uNWv4wSViIiIiIjIj2iavng6TwCIiIhwmqA2p1u3bpAkCUVFRU7pRUVFiI+Pd/va559/Hs8++yy2b9+OtLS0NpWTl/gSERERERGRk8DAQAwfPtzpBkfGDY8yMzObfd3SpUvx5JNPYuvWrcjIyGjz+/IMKhG122v/FeXrIhB5Ddt3+3HbERGdH1XVoKqePYXanvzmzZuHyZMnIyMjA5deeileeuklVFZWYurUqQCAnJwc9OzZ0/we63PPPYeFCxdi/fr1SEpKMr+rGh4ejvDw1v2uMyeoREREREQWwQ+A6EKaOHEiTp8+jYULF6KwsBBDhw7F1q1bzRsnHTt2DKLYcFHua6+9htraWkyYMMEpn0WLFuHxxx9v1XtygkpERERERORHNE2D5uEvobY3v7lz52Lu3Lkun/v888+dHh89erRd7+GI30GlC+b1Ba3//SOizsRTfcMT+bCfEhERkS/xDCp5BQ9yiTqXVY929XURiIiok+tIx5+aqi+eztMKeAaVXGrrwSYPTomIiNxry76S+1Wizk3VNK8sVsAJKhF1CG/8dzdfF4HIb7A/EBGRVXGCStQKbz4W6+siEFkG+wtRy9hPiMgd4yZJnl6sgBNUIiKiTogTJCIi8ke8SRIRERGRH3prUXdfF4GIfERVNaiqZ894ejo/b+EZVCIiIiIiIvILnKD6uTWPx/m6CJ0Wtz0RERER+YKmeWexAk5QiYjI59Y+Ee/rIhAR+S2OkdSZcIJKRERERF73P0/28HURiCxD0zRoqocXi5xC5QSViC6ovz2d0K7X8cCGPIHtiIiIrEDTNKgeXjhBJSKiNmnv5N3TeZA1se47PtYxEXUGnKASecC6JT19XYROx6rb3BPltmrsRJ7EfkBkXeuW9HTbh9m/4fnLe+sXK+AElYjoAuHklIiIzsf6Z3u5TLfyvsHKZSfv4ASViDqt5nb0dOGwDoishX2WzldzbYhtyxnPoBIREREREbXS35de5OsiUAfFCSpRJ2LsTLhTIX/FtklERASomncWK+AE1SLeeb53u54jIiLP4Fjr/1hHRETWxwkqEXUYPDi1pneXJfllXh0Z+wqRdbgb1zjmdVz8Dip5FQcPIvJnHKPIH733Yh9fF4GacT5jBscbotbRNM0rixVwguoF3KkSERERERG1HSeoREQ+8v5fkv0iD2qqM25Xb8fcGbepFbGeqCVsIxeGqgKqqnl48XVUrcMJqg+wY/ufDcv7+boI1Il4agzwl7HEX8rRFlYsc0f3/l+SWS9ERGSdCeqZM2cwadIkREREICoqCtOmTUNFRUWz6x89ehSCILhc3nvvPXM9V8+//fbbFyIksoANy/u1e/LKSS/R+WM/6rg8WbdsJ0T+g/3RM/gdVAuYNGkS9u3bh23btmHz5s348ssvMXPmzGbXT0xMxMmTJ52WxYsXIzw8HNdee63TuqtXr3Zab/z48V6O5vxtfKW/r4vQoXFwJXLPX8aglsrhiXJyPKCOrr39hH3DutpTd+7aib/sE6hjsPm6AK2xf/9+bN26Fd988w0yMjIAAC+//DKuu+46PP/880hISGjyGkmSEB8f75S2ceNG3HrrrQgPD3dKj4qKarIukRV5ewfhjzsgfywTERE14DhtDW2pJ9ap93njZ2Gs8jMzlpig7ty5E1FRUebkFACysrIgiiK+/vpr3HTTTS3msXv3buzZswcrVqxo8ty9996L6dOnIzk5GbNmzcLUqVMhCEKzedXU1KCmpsZ8XFZWBgCw2+2w2+0AAFHUG4AoaLDb7bBJ+l9R0NMlUYXdbjf/iqIIURShKApskmrmI0kSBEEwH9skFTZJNU/RSw7rGusDgKIoTmW22WzQNM0pXRAESJIEVVWhOnxrurl0o4zNpSuK4nTpQHPpjWNqqezeikkSG/5XVdWsC5ukQVXVFmNyVU8NeRjP6ZdT2CQ9D8f1vRGTUUZAb3vvL08225lSPygZMRnPtRTT+8v174RNuO+gntcFriebpJr1seGVfhg3fY9ZRkFwjslVPRl14hiTYx1JogpAM9/L6KuejAlAs/3GGCPMdDcxuUo3yuiYh+MY0bjseqxafdyAIDb0RQGauU1skgbjXRqX3XitY79xLKOr9Mb9yeh/rvqZAOf+YtZVM/VhxOS4vlFGo/8ZY2Vz9WGkN6kPF/VkkzTYlZbbnj+Oe45lvBBjuTHONLRJzUX/0/uy076sfh/qru25GveMdJukQdU0t/2J9SSZ27RhzGtaT8Y2BxrGCHdlt0mqPva6ianh2EgF1Nbtczt7PbUmJpukwq6ILmPS/zb0CWM81N+38bjnuv+1JSajz59PTMY40d56avy+ZB2WmKAWFhaie/fuTmk2mw0xMTEoLCxsVR5vvvkmUlNTMWrUKKf0J554Ar/97W8RGhqKTz75BHPmzEFFRQXuv//+ZvNasmQJFi9e3CQ9NzcXYWFhAIDB/aoBAKlJpZBlGdf+pgSyLCM5Qe+4Q/vrjx+6C3p6cjK6d++OvLw8ZGWcgizrr09JSUFUVBRyc3OhKArGjCyFKIqorq5GYGAgsoYVQZarzDJkZGSgtrYWe/fuNdMkScKIESNQWlqKAwcOmOkhISEYMmQIiouLceTIETM9MjISqampKCgowC+//GKmx8bGom/fvsjPz8fp06fN9F69eqFXr144dOgQSktLzXTHmKqrq830xjEZ0tLSEBgYCFmWnbart2K6ZmgF8vMDAQD5+fm4ZugJyHIprv1NCQoKClqMKWvEKbOsRkwLJpdDlmVkj9LrvUuYPqCOG10JAOb63q4nABjYp8x8v9HDK/DTCb1tuotpzKXFTWIy6ml0egVkufyC11PWiDLk5+ebbc+xjMk9hBZjGj38NGS5oklMWSPOQpZljE6vwKffdMG5GhFjLi2GYrd7vJ4ANNufjDHC0Len0mxMAFz2JwBO/WnMpWXmGNG4nmxSIMKC7Rh1ySkAgAAgN7cKQAC6RtXq22R4Bew1tSiv1A/6G8eUfnGNGdNDdzWU32h7+fn5GJ1+ErJcDsD1GDE6vQLFxbEux4iYiCCnmIz+1Fzbs0kiwoIb6i17VCk0oRzAIJSWluLa35yFKJVBlqsQEhICQGy2npJ7Vjrl76qerru8BAeOBOLQcZvlxj3HmC7kWD42sxSKopj1ZNTpVUMqAVyCbpF1Tuv/Zog+ZjZpewMa2p6rcc+I6Yary6FpGg79rO9zWU/Nx+TYV7OGlUNRBpr1lJVRDkEUcNlQvX90jax12gauYsoaUYZDhw65jWlQ3wrIsoyxmaXQVK1V+9zOXk+tiSlrZBm2/ifGZUxAIBK6VuGS3iUAAFkuN2NK7lHulH/aAL1c5xPTmJGlKC0tPa+YskeVIjc3t931dPDgQVhZZz6DKmg+/LbsggUL8Nxzz7ldZ//+/diwYQPWrl3bpKF1794dixcvxuzZs93mUV1djR49euCxxx7D/Pnz3a67cOFCrF69GsePH292HVdnUBMTE/Hrr78iIiICAHBtzm5sWz8S42Z8j42vpeL6ybvxj7XDMf6eH7D5zTTcOHMvNrw60MzD8dOnm2blYcNrlwBo+onajdO/gy3Ahg//mgYA+P2s77FhRUM+neVTQk/GNGbSt/hk3TAz/frJu/Hx/45oMabxM7/HxtcHuYzphinfYvOaYbj6pn/jX5uuwjU3fQEA2P7+5Rckpt/PPgi1thYfrtLb0S33HoCiati8arDbmG6c8Z3Ztvylnm66Jw8frkoz29646XvMMram7d08Zz/+b0VKk5huuicPG18fhAn3HURlmb5DDQwSUV1WiU/ezvRoTDfesx8f/XWgy35jjBGGcdO/x8dr0l3GNG7mD9j0emqT/nTDjH34cGWKmfb7mXux+c10l2W/ftr3CAwOgoiGM6jvv3wxrp+WBwEaNr85GLfcewBlp89AA/D5hiubxDR+Zh42vzXUZdu7/u48/OOtQbj53v14/+WLm62nCfcdxIYVqS7r7+Z7D2HT6wOb9Kfm6iP7rlyERIRh44oB5vpBYaH48PVB0DQN2XfuQlB4CDasGAhBEDBuxg/Y/MYlTerphmn7IAqa2W+aq6dr75Jhr1UAyYbtf8+w5Lh3ocfycXfnYsvaDDPdqNMJ9x3EB69dgjF3ytiyZmjD+tP24uO1w5qU8aZZ+/DRG0Pctj1VVTHmtp1Q7QrEgEB8+t4oy+6fLkQ93TTnB7Ov3nzvD/hw5WDcOGs/Nq4YgJtn7zOPR2w2G66b8h02vdHQP5obsz/462C3MWXnfIvNb6Vh3N25sNcp+Oe6Sy19HOEvx0Y3TPkWdkXEtvXDm8Q0fs4hpzOo7798sRnTjbN+wIZXBpjrj7ljFz59d9R5xXTj9O/wj9XDzismY5xobz2VlJSga9euKC0tNY/PraCsrAyRkZG4fcGPCAzq4tG8a2vK8fdn+/v9NvHpGdT58+djypQpbtdJTk5GfHw8Tp065ZRut9tx5syZVn139P3330dVVRVycnJaXHfkyJF48sknUVNTg6CgIJfrBAUFuXzOZrPBZtM3qarWX0amCbDZbLAr+l9V09MVVTTXdSRJEuxK0+eMx3ZFBETRvFxDcbGu4/qOBEFwmW4MLOebbgwsrU13VZa2pnsjJrsimP+7i6mletL/1+8MbVcElzF4K6aPXk/FtVP2mvkoqghNU88rpsYuVD3ZlYb/RVF0WUZ3Mbnqa3qf1NM/eO0S/G7SbvO9jL7qzZgcGWOE4+OWYnLFMQ+70jBGNC2jAECAoupl2fx6wwdcGvSyKKpotllXZTde6y7WD167pEm6Y9kdn28c06b6MjXtT01jNWL6yCEOuyLCVl9Go//ZGuXR2vpwlW5XBKiaANFF2ZuLqfmytz3dimN54zbp2P8AQGu03ZX6fWh72p4+TghQFQE22/n3p/amW6WeHPuiUl9PRn9q3PeMMcJdGe2KaJahpX6mj7lqq/a5bYmpI9ZTa/sZ4Dqmj15PRXM2rRzo9Fhtpv8ZWhOT+/1Q07K7Sndsf+2pp+byJ//n05qLjY01L8txJzMzEyUlJdi9ezeGDx8OAPj000+hqipGjhzZ4uvffPNN3Hjjja16rz179iA6OrrZySmRFW1eNRDXT8/zdTF8YvOqgS2v1Mm4O1AB9G12xe//dYFKQ0RERI115kt8LfHRQmpqKrKzszFjxgysXLkSdXV1mDt3Lm677TbzDr4nTpzA6NGj8T//8z+49NJLzdf+9NNP+PLLL7Fly5Ym+X700UcoKirCZZddhuDgYGzbtg3PPPMMHnzwwQsWGxEReR4/mCAiIrImS0xQAWDdunWYO3cuRo8eDVEUccstt2D58uXm83V1dTh48CCqqqqcXvfWW2+hV69eGDNmTJM8AwICsGLFCvzpT3+Cpmno168fXnjhBcyYMcNj5f7HG4PalO6p/IkaY1uhzoztn6hl7CdE/kPTNKfv+noqTyuwzAQ1JiYG69evb/b5pKQklxv9mWeewTPPPOPyNdnZ2cjOzvZYGVtjy1uD/SIPIrpw3B30teWA0NsfeHUW7RlDuY39A/d/Fxa3NxH5gmUmqJ0JdwjkCR+vSfN1ETyio8RBRGR1rRmPOWYTeYamalD5HVQios6DB1Ht527bWXm7WrnsxPojIuooOEElaqeOeDD0z7VDfV2EFlmhjOfDE/G5y6O55zr6diUiIrKSznwX36Y/HERE1ElxkkZERETkWzyDajGf/G+6r4tARETkVdzXdWys346PdXz+eBdfIqJObNu64b4uAhEREZFJU1VoqurxPK2AE1QiIiLyqpY+BOKHRJ3TtnXDcc2tX/u6GB3G9r9n+LoIRB7BCSp1et4Y0LmTIPKclvpT4+fZ//wf64iIWtLZxwnVCz8z4+n8vIU3SfKSHW9f6vYx+S/WFVHHwj5NRERkHTyDSkREREQ+8dm7I31dBCK/1JlvksQzqERERG1gHFDzwJqIiMjzeAaViIiIiIgAAJ+/n+nrIhAATdWgefg7o57Oz1t4BpWIiIg6JB5oExFZD8+gEhERtdEXG0b5ughERNSBdeYzqJygEhE5+HLj5b4uAhEREXVyKlSomurxPK2AE1QiImriXx9e4esiELXblxsvx+XjvvB1MYiIqB04QSUiIiIiIvIjmur5S3I9fELWa3iTJKIL4N8fXeXrIhARdSocd4mIrIlnUIm8yPEAiQdLRERERNQanfkmSTyDSkRERERERH6BZ1CJiIiIiIj8iKZp0DQPn0H1cH7ewjOoRERERERE5Bd4BpWIiIiIiMiPqKoKVfXw76B6OD9v4QSViIiIiIjIj/AmSUREREREREQ+xjOoREREREREfkTTVGiaZy/J9XR+3sIzqEREREREROQXeAaViIiIiIjIj/A7qEREREREREQ+xjOoRERERERE/sQLZ1DBM6hERERERERErcczqERERERERH5E1VSoHr7rrqfz8xZOUImIiIiIiPwIb5JERERERERE5GM8g0pERERERORHNE2Fpnr2klzNIpf48gwqERERERER+QWeQSUiIiIiIvIj/A4qERERERERkY/xDCoREREREZEf0TTV498Z5XdQiYiIiIiIiNqAZ1CJiIiIiIj8iKoCqoe/M+rhmwJ7DSeoREREREREfkRTvfAzMxaZofISXyIiIiIiIvILPINKRERERETkR/gzM0REREREREQ+xjOoREREREREfoQ/M0NERERERETkY5aZoD799NMYNWoUQkNDERUV1arXaJqGhQsXokePHggJCUFWVhZ+/PFHp3XOnDmDSZMmISIiAlFRUZg2bRoqKiq8EAEREREREVHLjO+genppjxUrViApKQnBwcEYOXIkdu3a5Xb99957DykpKQgODsbgwYOxZcuWNr2fZSaotbW1+MMf/oDZs2e3+jVLly7F8uXLsXLlSnz99dcICwvD2LFjce7cOXOdSZMmYd++fdi2bRs2b96ML7/8EjNnzvRGCERERERERJbxzjvvYN68eVi0aBG+/fZbDBkyBGPHjsWpU6dcrv+f//wHt99+O6ZNm4bc3FyMHz8e48ePR15eXqvfU9A0zRq3c6q3Zs0aPPDAAygpKXG7nqZpSEhIwPz58/Hggw8CAEpLSxEXF4c1a9bgtttuw/79+zFw4EB88803yMjIAABs3boV1113HX755RckJCS4zLumpgY1NTXm49LSUlx00UXIz89HREQEAEAURYiiCFVVoTr85pCRrigKHDd9c+mSJEEQBNjtdqcySJIEAFAUpVXpNpsNmqY5pQuCAEmSmpSxuXTGxJgYE2NiTIyJMTEmxsSYrBBTSUkJ+vTpg5KSEkRGRsIqysrKEBkZiYzR70KyhXk0b8VeCXnHrTh+/Lg5ZwGAoKAgBAUFuXzNyJEjMWLECLzyyisAAFVVkZiYiPvuuw8LFixosv7EiRNRWVmJzZs3m2mXXXYZhg4dipUrV7auoJrFrF69WouMjGxxvcOHD2sAtNzcXKf0K6+8Urv//vs1TdO0N998U4uKinJ6vq6uTpMkSduwYUOzeS9atEgDwIULFy5cuHDhwoULFz9ejh8/3ub5hi9VV1dr8fHxXtse4eHhTdIWLVrksiw1NTWaJEnaxo0bndJzcnK0G2+80eVrEhMTtRdffNEpbeHChVpaWlqrt0GHvYtvYWEhACAuLs4pPS4uznyusLAQ3bt3d3reZrMhJibGXMeVRx55BPPmzTMfq6qKM2fOoGvXrhAEwVMhEBERERFRO2iahvLy8maviPRXwcHByM/PR21trVfy1zStyXylubOnxcXFUBTF5XzqwIEDLl9TWFjodv7VGj6doC5YsADPPfec23X279+PlJSUC1Si1nF1Gry1N24iIiIiIiLvs9KlvY6Cg4MRHBzs62L4jE8nqPPnz8eUKVPcrpOcnNyuvOPj4wEARUVF6NGjh5leVFSEoUOHmus0/oKv3W7HmTNnzNcTERERERF1Nt26dYMkSSgqKnJKLyoqanauFB8f36b1XfHpXXxjY2ORkpLidgkMDGxX3n369EF8fDx27NhhppWVleHrr79GZmYmACAzMxMlJSXYvXu3uc6nn34KVVUxcuTI8wuOiIiIiIjIogIDAzF8+HCn+ZSqqtixY4c5n2osMzPTaX0A2LZtW7Pru2KZn5k5duwY9uzZg2PHjkFRFOzZswd79uxx+s3SlJQUbNy4EYB+F68HHngATz31FDZt2oTvv/8eOTk5SEhIwPjx4wEAqampyM7OxowZM7Br1y589dVXmDt3Lm677TbLXa9ORERERETkSfPmzcOqVauwdu1a7N+/H7Nnz0ZlZSWmTp0KAMjJycEjjzxirv/HP/4RW7duxbJly3DgwAE8/vjjkGUZc+fObfV7WuYmSQsXLsTatWvNx+np6QCAzz77DFdffTUA4ODBgygtLTXXeeihh1BZWYmZM2eipKQEl19+ObZu3ep0Tfe6deswd+5cjB49GqIo4pZbbsHy5csvTFBERERERER+auLEiTh9+jQWLlyIwsJCDB06FFu3bjVvhHTs2DGIYsM5z1GjRmH9+vX485//jEcffRT9+/fHBx98gEGDBrX6PS33O6hERERERETUMVnmEl8iIup8pkyZAkEQmiw//fTTeee9Zs0a3oGdiIjIz1jmEl8iIuqcsrOzsXr1aqe02NhYH5XGtbq6OgQEBPi6GERERJbHM6hEROTXgoKCEB8f77RIkoQPP/wQw4YNQ3BwMJKTk7F48WLY7XbzdS+88AIGDx6MsLAwJCYmYs6cOeaN9T7//HNMnToVpaWl5lnZxx9/HIB+k70PPvjAqQxRUVFYs2YNAODo0aMQBAHvvPMOrrrqKgQHB2PdunUAgDfeeAOpqakIDg5GSkoKXn31VTOP2tpazJ07Fz169EBwcDB69+6NJUuWeG/DERERWRDPoBIRkeX861//Qk5ODpYvX44rrrgChw8fxsyZMwEAixYtAgCIoojly5ejT58+OHLkCObMmYOHHnoIr776KkaNGoWXXnoJCxcuxMGDBwEA4eHhbSrDggULsGzZMqSnp5uT1IULF+KVV15Beno6cnNzMWPGDISFhWHy5MlYvnw5Nm3ahHfffRcXXXQRjh8/juPHj3t2wxAREVkcJ6hEROTXNm/e7DR5vPbaa3H27FksWLAAkydPBgAkJyfjySefxEMPPWROUB944AHzNUlJSXjqqacwa9YsvPrqqwgMDERkZCQEQWjTj4c7euCBB3DzzTebjxctWoRly5aZaX369MEPP/yA119/HZMnT8axY8fQv39/XH755RAEAb17927X+xIREXVknKASEZFfu+aaa/Daa6+Zj8PCwpCWloavvvoKTz/9tJmuKArOnTuHqqoqhIaGYvv27ViyZAkOHDiAsrIy2O12p+fPV0ZGhvl/ZWUlDh8+jGnTpmHGjBlmut1uR2RkJAD9hk+/+93vcPHFFyM7Oxs33HADxowZc97lICIi6kg4QSUiIr8WFhaGfv36OaVVVFRg8eLFTmcwDcHBwTh69ChuuOEGzJ49G08//TRiYmLw73//G9OmTUNtba3bCaogCGj8C2x1dXUuy+VYHgBYtWoVRo4c6bSeJEkAgGHDhiE/Px8ff/wxtm/fjltvvRVZWVl4//33W9gCREREnQcnqEREZDnDhg3DwYMHm0xcDbt374aqqli2bJn5A+Lvvvuu0zqBgYFQFKXJa2NjY3Hy5Enz8Y8//oiqqiq35YmLi0NCQgKOHDmCSZMmNbteREQEJk6ciIkTJ2LChAnIzs7GmTNnEBMT4zZ/IiKizoITVCIispyFCxfihhtuwEUXXYQJEyZAFEV89913yMvLw1NPPYV+/fqhrq4OL7/8MsaNG4evvvoKK1eudMojKSkJFRUV2LFjB4YMGYLQ0FCEhobit7/9LV555RVkZmZCURQ8/PDDrfoJmcWLF+P+++9HZGQksrOzUVNTA1mWcfbsWcybNw8vvPACevTogfT0dIiiiPfeew/x8fH8LVYiIiIH/JkZIiKynLFjx2Lz5s345JNPMGLECFx22WV48cUXzRsPDRkyBC+88AKee+45DBo0COvWrWvyky6jRo3CrFmzMHHiRMTGxmLp0qUAgGXLliExMRFXXHEF7rjjDjz44IOt+s7q9OnT8cYbb2D16tUYPHgwrrrqKqxZswZ9+vQBAHTp0gVLly5FRkYGRowYgaNHj2LLli3mGV4iIiICBK3xF22IiIiIiIiIfIAf2xIREREREZFf4ASViIiIiIiI/AInqEREREREROQXOEElIiIiIiIiv8AJKhEREREREfkFTlCJiIiIiIjIL3CCSkRERERERH6BE1QiIiIiIiLyC5ygEhERERERkV/gBJWIiIiIiIj8AieoRERERERE5Bf+H4xQFF+CYV+qAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Selection\n",
    "feature_correlations = X_train.corrwith(y_train['Activity'])\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "bars = plt.bar(range(len(feature_correlations)), feature_correlations,\n",
    "               color=plt.cm.coolwarm((feature_correlations - feature_correlations.min()) /\n",
    "                                     (feature_correlations.max() - feature_correlations.min())))\n",
    "\n",
    "plt.title('Correlation between Features and Activity', fontsize=14)\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Correlation')\n",
    "\n",
    "plt.xlim([0, len(feature_correlations)])\n",
    "plt.ylim([-1, 1])\n",
    "plt.xticks([])\n",
    "\n",
    "plt.colorbar(\n",
    "ax=plt.gca(),\n",
    "mappable=plt.cm.ScalarMappable(cmap='coolwarm'),\n",
    "orientation='vertical',\n",
    ")\n",
    "\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting features with correlation > 0.7 or < -0.75\n",
    "positive_correlations = feature_correlations[feature_correlations > 0.7]\n",
    "negative_correlations = feature_correlations[feature_correlations < -0.75]\n",
    "\n",
    "# We only work with the top 20 features\n",
    "selected_features = pd.concat([positive_correlations, negative_correlations]).sort_values(ascending=False)\n",
    "selected_features = selected_features.head(20)\n",
    "\n",
    "feature_indices = selected_features.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Updating data\n",
    "X_train = X_train[feature_indices]\n",
    "X_test = X_test[feature_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the models\n",
    "models = {\n",
    "    'Random Forest': RandomForestClassifier(n_estimators=100, random_state=0),\n",
    "    'K-Nearest Neighbors': KNeighborsClassifier(n_neighbors=5),\n",
    "    'Decision Tree': DecisionTreeClassifier(random_state=0)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training the models\n",
    "for model_name, model in models.items():\n",
    "    model.fit(X_train, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluating the models\n",
    "model_scores = {}\n",
    "for model_name, model in models.items():\n",
    "    y_pred = model.predict(X_test)\n",
    "    model_scores[model_name] = f1_score(y_test, y_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of results\n",
    "model_scores = pd.Series(model_scores\n",
    "                            ).sort_values(ascending=False)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "bars = plt.bar(model_scores.index, model_scores,\n",
    "               color=plt.cm.coolwarm((model_scores - model_scores.min()) /\n",
    "                                     (model_scores.max() - model_scores.min())))\n",
    "plt.title('Model F1 Scores', fontsize=14)\n",
    "plt.ylabel('F1 Score')\n",
    "plt.ylim([0, 1])\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best model is \"Random Forest\" with an F1 score of 0.6806382183305906\n"
     ]
    }
   ],
   "source": [
    "print(f'The best model is \"{model_scores.idxmax()}\" with an F1 score of {model_scores.max()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Debugging: Export data to csv\n",
    "data = pd.concat([y_train, X_train], axis=1)\n",
    "data.to_csv('datasets/data.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}