data/heart_failure.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About Dataset\n",
    "https://www.kaggle.com/andrewmvd/heart-failure-clinical-data\n",
    "\n",
    "Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide.\n",
    "Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.\n",
    "\n",
    "Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.\n",
    "\n",
    "People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.\n",
    "\n",
    "**Features**\n",
    "\n",
    "|Feature|Feature_Type|Description|\n",
    "|-----|-----|-----|\n",
    "|age|numeric|age of a person|  \n",
    "|anaemia |boolean|Decrease of red blood cells or hemoglobin |  \n",
    "|creatinine_phosphokinase|numeric|Level of the CPK enzyme in the blood (mcg/L)|  \n",
    "|diabetes|boolean| If the patient has diabetes | \n",
    "|ejection_fraction|numeric| Percentage of blood leaving the heart at each contraction(percentage) |  \n",
    "|high_blood_pressure|boolean| If the patient has hypertension |  \n",
    "|platelets|numeric| Platelets in the blood (kiloplatelets/mL)|  \n",
    "|serum_creatinine|numeric| Level of serum creatinine in the blood (mg/dL)|  \n",
    "|serum_sodium|numeric| Level of serum sodium in the blood (mEq/L)| \n",
    "|sex|boolean| Woman or man |  \n",
    "|smoking|boolean| If the patient smokes or not |\n",
    "|time|numeric|Follow-up period (days)|  \n",
    "\n",
    "\n",
    "**Target variable:**  \n",
    "\n",
    "|Feature|Feature_Type|Description|\n",
    "|-----|-----|-----|\n",
    "|DEATH_EVENT | boolean| If the patient deceased during the follow-up period |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from imblearn.combine import SMOTETomek\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "from mlxtend.plotting import plot_confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data (299, 13)\n"
     ]
    },
    {
     "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>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>265000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162000.00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>210000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327000.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  75.0        0                       582         0                 20   \n",
       "1  55.0        0                      7861         0                 38   \n",
       "2  65.0        0                       146         0                 20   \n",
       "3  50.0        1                       111         0                 20   \n",
       "4  65.0        1                       160         1                 20   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    1  265000.00               1.9           130    1   \n",
       "1                    0  263358.03               1.1           136    1   \n",
       "2                    0  162000.00               1.3           129    1   \n",
       "3                    0  210000.00               1.9           137    1   \n",
       "4                    0  327000.00               2.7           116    0   \n",
       "\n",
       "   smoking  time  DEATH_EVENT  \n",
       "0        0     4            1  \n",
       "1        0     6            1  \n",
       "2        1     7            1  \n",
       "3        0     7            1  \n",
       "4        0     8            1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# upload dataset\n",
    "data= pd.read_csv(\"heart_failure_clinical_records_dataset.csv\")\n",
    "\n",
    "# dataset shape\n",
    "print(\"shape of data\", data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   age                       299 non-null    float64\n",
      " 1   anaemia                   299 non-null    int64  \n",
      " 2   creatinine_phosphokinase  299 non-null    int64  \n",
      " 3   diabetes                  299 non-null    int64  \n",
      " 4   ejection_fraction         299 non-null    int64  \n",
      " 5   high_blood_pressure       299 non-null    int64  \n",
      " 6   platelets                 299 non-null    float64\n",
      " 7   serum_creatinine          299 non-null    float64\n",
      " 8   serum_sodium              299 non-null    int64  \n",
      " 9   sex                       299 non-null    int64  \n",
      " 10  smoking                   299 non-null    int64  \n",
      " 11  time                      299 non-null    int64  \n",
      " 12  DEATH_EVENT               299 non-null    int64  \n",
      "dtypes: float64(3), int64(10)\n",
      "memory usage: 30.5 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n",
       "       'ejection_fraction', 'high_blood_pressure', 'platelets',\n",
       "       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n",
       "       'DEATH_EVENT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### check missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                         0\n",
       "anaemia                     0\n",
       "creatinine_phosphokinase    0\n",
       "diabetes                    0\n",
       "ejection_fraction           0\n",
       "high_blood_pressure         0\n",
       "platelets                   0\n",
       "serum_creatinine            0\n",
       "serum_sodium                0\n",
       "sex                         0\n",
       "smoking                     0\n",
       "time                        0\n",
       "DEATH_EVENT                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*There are no missing values*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### check class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of NO and YES\n",
      " 0    67.892977\n",
      "1    32.107023\n",
      "Name: DEATH_EVENT, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.countplot(data[\"DEATH_EVENT\"], palette= \"RdBu_r\" )\n",
    "\n",
    "# percentage of class in target\n",
    "print(\"percentage of NO and YES\\n\", data[\"DEATH_EVENT\"].value_counts()/len(data)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The class distribution in the target variable is ~68:32 indicating an imbalance dataset*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc865e92220>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sG6ekvG4S2dtTAL4qopD8fyYyMpJ27drRunVr7t27R8+ePdFqtfz444+ULVuWefPmce/ePW7cuMHevXvZsMH2pt+7d28aN25MqVKl3vRTyBGVRuvwbd9isdg/ANVqLXp9qn2ZPi0FjdaFv3atp3LNBnT+bCgJcXeZO+FLJs7bzKYVs/l26gqKeJXmyL5NbF79Ez0GjH1pDCd2ziM6LJDY6BAKlcj40E03pDh8QAIoVc6kG1JQKFWk61NwUrtyPyqY8CtHGTDlEAonDXtWf0NI4D7K1/qQhwl3+H3pEGo29aVSnZwdkjq1ex4x4YHExoRQqPiL43tdVGpn9GkZc2G1ZponjTN6fcYc6vW2D3StszueRUoiVyjwLFIShdIJ3cMEXNyyvgfQ3n8O8qRGvfe5ExXBnB8GUKZCdYqXqmg/FzJfgcJMW/QHJ/7aweZVP/HFsMkv7P/IjnncvhHI/agQipTKNAd6x6IBwEntTLo+I0dUalesVit1W35ub1um6nvcvX2NstWb8SDhDlsXD8H7fV+q1Mt+jmjUWse5sVhfujfexdWVwkW9kCsUFC7qhUKh5OGDJNzc82S53yM7fub29QDuRYU+NSZOLxgTw6MxCzq3n9TkRDbOH4DuYRzGdD35C5VErXUnX8GSyORK8hcshVzhRGpyAlrXF+eNSq11yENrphxRqZ0xZBojQ1oqaq0LWhc3PIuUeJSjJVAobDlavV5zNFrbl8EadZuzecVMskql1mLIHIc1Yz6cNM4YMuWx4Rn5k1m3L6fyx9oZHNm1kmKlqiBXKJ/b9nla1pJR3FNKwbwSomIzCkAnBaSlP92+ZEEJHzWUs/WY7fxIsJ0v2aaOjPxuEjYcMmY7hsw0Gg2paRkFsS1fMwpCi8XC8pWriI6Owe/7sUgkEooUKULhQoWQSCQULVIEF1cX4hMSKODh8awuhFdEHNr+fyZ//vwcPHiQ0aNHs2TJEkwmE/fv36ds2bIAeD86FBAaGkpMTAy9e/fm888/JykpiVu3br3J0P+TMhVqcDnwFABhIZcoWryMfVnBoiW5f+cWKckPMBmNhF4LpFT5ami1rvY9UFpnN8wm22FurYsbao1tz4xbHg9SU5Kf7vAZmnw0kk9H+jN41ikSY2+RlpKE2ZRO1PVzFC5V06FtkVK1iLhyDICIq8cpWsYbJ7ULcoUKucIJqVSGxjkv+tSHpDyMY8uCL3iv4zdUbfjyiwWep1H7kXQZ5s+X006RFHcL/aP4om+co3DJmi/fwCtQqkINrgaeACAi9CKFvcralxUsUpLYTPN041oApcpVp3TFmgSdP4XVaiUp4T4GfRpaZ/cc9Z+TPLl54yplKtbkmynLqFmvOR6etj3rC6eP4F6M7TWjUmuytBeuWaeR9PrGn5FzT5FwPyNHboWeo8gTOVK0dC1uXLblyI0rx/Eq640hTcevE9uTrk/BarVyM/gfChWvgu5hHBvmfUFzn2+o0ThnOVK+UlXOnzsDQGjwFbxKvPxLZflK1bgY+A9Wq5WE+DgMBj0uLq4vXS+zZp1G0Otbf77+6SSJ92+RprONSWToWYqWdhyTYmVq2sck7PJxipWtTd2Wvej/w3Z6fetPow/7288rLVa2FmFXTmK1WklOuofRkIY6C3lTukJNrgaeBCAi9JJDjhZ6IkeuBwVQqlw1SleoybULp+05mm6w5ejCKYO4ef0yAMGX/6FY6UpZHpcS5WsSfOE4AJHXL1KwWEYcnoVLEXc3klRdEiZTOuFBAZQoW+O52wo6f4yuX06l37dLSNUlUa5KwyzH8djBQDMr9hmZsSGdvC6gVoJMCiUKSrl93/E8x5IFJbSvL2fNn0ai4zOKzo8byVHIJKw/aLIf4s6pSpUqcvas7XSQoOBgSpRwPCIwf+Ei0o1GJviNsx/i/vPAXyxdvgKA+Ph4UlNTyZc3738L5BWTyiSv7N/bQmK1nagl/D8xY8YMihcvjq+vL2fOnGHs2LHkzZuXWbNmUaZMGRYsWEB0dDS9e/dm9uzZLF++HIlEwurVq2ndujWFM1319qQ9ivL/KTZ18SLUXP8Tpxt3e3njF3C58PT5PY+vxo2OvI7VaqX3VxO5FR6MQZ9K09Y+9qtxrVYLjVt8TLMPu6FPS2X1okk8SIzFbDLRol136jX9kOtB59m29n/IZDJkcgW9BvuRv8DT4xJ6V/vUY489vmrbarVSpaEPtd7rQVpKEn+uG0/HLxeS8jCOvWu/I12fgsY5D+36zEXppOHC8Y1c/nsbMrkC9/xetOkxhaPbfyQ4cB/5PDM+2H2GLEOhtL05mkzZH8PHV21bLVYq1/ehRtMe6FOS+GvjeDr0W2hvd/Wf7STcC8/yVdtlChleuNx+1XZkKFitfDZkCrfDr2HQp9G41Sf2q7atFgsNmnfivQ8+BWCH/0+EXj2L1WLhI99hVKrx4nPxlLJnD0pO8iT5YSLLfhqLQZ+GRuvC50Mm4J7XgxvBF9m65mfkCgVKpYpeg/1wz+u4ZyMy/vk58viqbavVSo3GPtRuZsuR3WvG02XwQnQP49i5MiNHOva35cilv3/n7CF/ZAolJSs04L2Ph/Hnb1O5dnYf+Qpm5Ej34Rk5AlC1UOyzwnAYmxWL53LrZhhWq5VBI74nIiwEvT6Nlh98bG83acxX9Bvyjf2q7XUrF3P1ciBWi4VPe31JDe96L+znyt38zx+TR1dtWy0WajT2oU7zHqTpkti1xo+uQxagexDHzpVjMOhT0LjkoVP/OSidMg5vXjy1nbg7Efartg9umc3NkH+wWiw07zzSfn4lQCH3Z+dqxlXb1wErPYdM5nZ4EAZ9Ko1bfWK/attqtdCwWUfe+9CWo9v95xF65SxWq4WPfYdSqUYjboUHsWn5DORyBa7u+fAd+IPD6ROPpaY/vefXftX2LdtrpduXU4m6eY10fSr1W3S1X7VttVip+34nGrX2ta+bEBvNugWjGTbZdvHT1YAj/Ll1AQqlmjKV6vJht+FP9Qdw5mLWqrvHV21LJBBw3cw/QRbUSujUWM6Gwya+6qhALoXkRzsM4x5YORtiZtBHCiLvWu1Xav99zcy1yGdfbNO/aeQLY8i4avsmYOXrEcO5ERaGPk1P2bJlGDria6pUrmT/gtfxo4+oW6c2c+b9TGxsLEgk9O3dm8qVKr6wnxJlymVpTF6VUzW9X9m2Gp0PeHmjXCAKyf9nzpw5w8SJE8mTJw/u7u5cv36duXPnMm3aNDQaDQqFAk9PT6ZOncry5cs5ePAg6enpVKtWDT8/vxeeK/JfC8lX5VmF5JvwokIyN+WkkHxdXlZI5pbnFZK57UWFZG57WSGZW15USOam5xWSb8KzCsk3IauFZG54WSGZW0Qh+d+9HdktZFn9+vXZv3+/w2Pr16/nl19+IW/evMybNw+FQgFAv3796Nev35sIUxAEQRCEJ0hk794ZhaKQfAfky5ePL774Ao1Gg4uLCzNnZv2Eb0EQBEEQcsfbdG7jqyIKyXfABx98wAcfZO1uCoIgCIIgCK+KKCQFQRAEQRBygUQq9kgKgiAIgiAIOfAuHtp+9876FARBEARBEHKF2CMpCIIgCIKQC8QtEgVBEARBEIQckUjfvQPB794zEgRBEARBEHKF2CMpCIIgCIKQC8RV28I77W25NWFyjVpvOgQA7qy9+qZDAKBiqbfnwEG66e2IxWhWvukQAMjvYiQx5e14G41NdX3TIQC2MXkbGM1SIu+/HXliefbtpnNdvnzPv0VubgtL83rTIQBQIpf7E1dtC4IgCHZvSxEpPO1tKSIF4V0n3gUFQRAEQRBygTi0LQiCIAiCIOSIuGpbEARBEARBEB4ReyQFQRAEQRBygTi0LQiCIAiCIOSIuGpbEARBEARBEB4ReyQFQRAEQRBygTi0LQiCIAiCIOSIuGpbEARBEARBEB4ReySFF7JYLKxfOoOom6HIFUo+H+xHgUIZt7a6ePYYuzYvQyaT0ajFxzRt1RmTyciqBROIvx+DRCql1yA/ChUtya2IENb9Mg2ZTI5nYS96Df4B6Wv6duZetxoVpo/mTMter2X7mZUtLKFpFSkWK1wIt3A+zPrMdnXLS3BWSTh80Xa/tKolJDSoKMWQDhcjLFwIf/Z6LxJ0/ghHfl+MVCrDu2ln6jTr6rA8JTmRzUtGY0w34OruQef+01E6qQFIN6Sx6se+dO47FY/CpbBYzOxY8QNxdyOQSqV07jedfJ4vv42ZxWJh68opxNwKRS5X0G3AZDwKZqx3JeAoB7YvQSqTU++9TjRo8QkWi5lNSydw/85NpFIZ3QdOIb+nF9E3g9m2ejpSqRS5QkmPQdNxcc+f5fF4HEt0pC2WT798OpY/t9liqf9+Riy/LZ1AbMxNJFIZvgOnkL+gF1E3g9myfDJSmQyPQsX5dMDkbOVryIXDHN9lm5sajX3wfs9xblKTE9m2dBQmowEX9wJ83Gc6ikdzA7BrjR9qrTstPxllfyzlYTxLJ/vQc9RK8hcqlaXx2LR8GtGRIbbxHDjRYTwunzvKvm2/IpXKaNCsI41afgLAzG+7otI4A5CvQBF6Dp5iG4+VM5BIZcgVSnoNmYare74sj4fFYmHLiqnEPIrl0y8nPTU3+7f+gkwmo16zTjRs8Yl9WfKDeOaM7cbgcUvxLFKKqJvBbFs1w5YnciU9hkzDNRt5Enb5MGf2L0IqlVO5vg/VGjnOTZougb1rRmNK16N1K0Cbz2agUKq59u/vnDu0AieVC5Xqd6Jqgy4A+M/qiJPKBQC3fEVp89mMLMeSWfiVw/yzfxESmZzK9Xyo2rDrM9sFHl1N6sM4Gn802v7Yse3TyVOgJNUad89R35ndvHaYwIOLkUhlVKjjQ8V6z47j0ok1pCXHUa/tKIfHj231Q6Vxf+rxl7FYLPy2bNqj16+SHoMmOHzeXDp3lL1bliKT2fK1cSsfAPZvX8Hlc0cxmYw0bdOVRi06k/wgnvVLJpOa8hCLxcLnQ6fiUbBYNkfi1XsXD22LPZLvmKVLl3Lp0qVXtr0L/x7BaExn7Mw1dP5sKJtXz7MvM5mMbFo1l5ETFvPNlOUcP7CdB4lxXAk8hdlsZsyM1XToMoDfNywCYNempXToOoDvpq/EaDRyOeDEK4szs1Kj+lH116lIVU6vZfuZSSXQupaU9UfMrDlkplZpKVqVYxu5DDo2kFK7bMbLTa2E96tJWXvItl6VEhLctNnr22wysnf9TPp8u5x+49Zy9ugWkpNiHdoc+X0x1Ru0Z8D4dRQqXomzRzYBEBV+hWXTepJw/7a9bfD5IwB86beBFp2HsXfDrCzFceXcIUzGdEZMXk/77iPZuW62Q4x/+M9i4NilfPXDav4+vIWHSXFcDTgKwPBJ6/iwyxD+8Lets2PNTHx6f89XP6ymWp2WHNq1MltjcvncIYzp6Yycsp4OviPt230cy+9rZzHo+6UMnbCa04dssVx5HMtkWyy/P1rnz22LaeMzkOGT/DEbjVw7fzzLcZhNRv7cNJPPvl5B7+/8CTy+Gd0Dx7k5tmsRVeu1p8+Y9RT0qsi5Y5vsy84d/Y37UaFPbXP32gnIlVnP60tnD2MyGhg9bR0f+w5n+9o5DtvbtmY2X437lRGTVnHq0DYeJsVhTDcAMGLiSkZMXEnPwVMA2LpqFl2+GMuIiSupUbcFf/2Rzbl5FMvIqevp0H2EfZwfx7JjzY8MHvcrQyeu5vTBrTxMirMv27RsMgplxgtr++qZ+PQZy9AJq6hWtwWHdmY9FrPZyNHtM/AZspKuw/25fHoTKQ8d5+bv/Yup4N2ebiM3UKBoJS6d3ESaLoFTu+fTZZg/XYevI/jsLh7ER2Ey2sar63B/ug73z3ERaTYbObZjBp0Gr6TLUH+uPCMuU7qe/WtHc+nEBvtjqboEdvzSj/Arh3PU77Pi+HvXTNr1W8FHA/0J+mczqclPxGHUc2jjN1w9veGp9a+d+Y2Eu6FPPZ4VF/89jMmYzjfT/en42XC2r5mbEZfJyLbVcxjm9wsjJ63k5MFtPEiMI/TKWcJDLjBq6hpGTlpJYtw9AHb4/0ydpm35esoqOnQfwt3oiBzF9KpJpJJX9u9tIQrJd8yAAQOoVq3aK9ve9aALVKnZEIDS5asRGXbNvq1LHBwAACAASURBVOxuVAQFChZD6+yKXKGgbMUaXA86j2dhLyxmExaLhbS0FGQy245vr1LlSdE9wGq1Ysj0+KuWGn6LgC5DX8u2n5TfDRJ0VvRGsFjgVqwVLw/HF7hcCpcirJy8arE/lscZ7iVa0afb/r4TD0XzZe+NITYmnHyeXqi1bsjlSoqXq8XN0ACHNjdDAyhbtTEA5ao34cbVvwEwm9LpMXwBHoVK2ttW8m5Jxy8mAZAUF4OzW9b2NoWHnKdC9UYAlChbndvhV+3L7kWHk9/TC42zG3K5gpLlaxEeHEDVOi3o2n8iAAmxd+x99Rw2myIlKgBgsZhRKLJ3v+Tw4PNUrPGCWApmxFLqUSzV6rSg26NYEuPu4PIoliIlKpL6KF/1+uzla9ydcPIWsM2NTK6kWFlvIp+Ym1vXAylTtQkAZao2JeKabW5u3zhPdPhFvN/r5tD+wOYf8X6/Gy7uBbIcR1im8ShZrjq3Mr9+oyPwKFgMjbMrcrmC0uVrciMokOjIENINaSyc+iX/m9SXiNCLAPQZ8SNFH82N2ZyDuQkJpGJ1Wy6WKFed2w6xPDE3FWoSFmQbr9/XzaVRy6645fGwt/98+Gx7LBaLGbki68V1wt0w3D28UGlsc1OklDfRYecc2sSEBVCikm1uSlZqyq2Q0yTFRVGgaAXUWnckUimexaty5+ZFYqODMaWnsW3RF2z5Xy9iIi5ka1wc4sqfEVfhZ8RlMhmoWLcjdVoPtD9mNKRQ/4OhVKjzcY76fVLS/XBc83nh9CiOgiW8uRPhmLtmo4Fy3h9Tq8WXDo/fjTzPvVsXqVTfMXezKiz4PJVq2D5vSparRmSm1++dqEz5qlBQpkJNwoICuXbxNEW8yrL0x5EsmTmUqt5NH23rAknx95g/aQBnT+ylXOXaOYpJeDlxaPs10el0jBs3juTkZBITE+nSpQv79u2jQoUKXL9+HZ1Ox/z58ylSpAhz587lypUrpKSkULp0aWbMmEFycjLjxo0jMTERgPHjx1O+fHlatWpFzZo1iYyMpH79+iQnJ3Pp0iVKlizJ7NmzGTNmDG3btqVWrVpP9e/r65vt56FPTUH96BAXgFQqw2w2IZPJSUtzXKZSa0lL0eGk0hB//w5+QzujS05i6PfzAShQyIsNy2ayZ+sK1Bpnyld5PS/suzsOoC5e5LVs+0lOcjCkZ/ydbgInhWMbvRHC71qpVjKjUExIBg83CVoVGIxQwlNCfHL2Dm3r03So1Bnj76TSok9NdmhjSEtBpXGxLzek6gAoXq7WM7cpk8nZ+usYrgUcpPvQ+VmOQ/2oD7CdTP44R/RpKfZDpGDLkccxymRy1i/+nsvnDtF7xE8A9mIhIvQ8J/7cwNAJa7IUQ8bz1aFSvyCWzOOl1pL2RCyXzh6iz6NYPAoWZ9vKqRzYsRSV2pkylepkPQ69YxxOKi2GtOSn2jipM+ZGn5ZMctJ9ju1cSLchC7l6dp+97YWT29G65KVMlSac3Ls0y3HY5ibz6zfzeDjOm9OjuVE4laBFh89p2MKH+3ciWTJjMH4/77TPTXjIBY7/uZERk1ZlOQ6wvZdkzoUn5+bJ9xJ9qo5/jv6Os0seKtZoxME/ltuX2/Mk5AIn9m9k2KTVWY4jXa+zH4YGUKi0GNJ0Dm0Meh3KR20UKi0GfTJ5ChQn7s4NUh7GoVRpuR3yN3kKlECuVOHdvC9VG3YhKfYm25f0p8/4/Uiz+UU5Xa9DmSlnlCot6XrHuFQaN4pXaMzVf7bbH3PLVwy3fMW4GZT1PeYvjSPz+DhpSX8id500bhQr15iQcxlxpDy8T8BfC2ndayHhl/aRE7Y8yOg78+eNPk3nkD9Oag1pqTpSHiYRHxvD4LELib8fzZJZw5gw/w/iY2PQaF0ZPmEpe7f8woHfV9Hh0yE5iutVepv2JL4qopB8TSIjI2nXrh2tW7fm3r179OzZE09PT6pVq8a4ceOYN28ee/bswdfXF1dXV1atWoXFYqFdu3bcu3ePtWvXUr9+fXx9fbl58yZjx45l48aNREdHs2bNGjw8PKhbty5btmzBz8+PFi1a8PDhwxf2n5NCUqXRok9Lsf9tsVjse2bUai16fap9mT4tBY3Whb92radyzQZ0/mwoCXF3mTvhSybO28ymFbP5duoKiniV5si+TWxe/RM9Boz9D6P85rxfVUoxDwme7hAdn1EAKuW2wvBl9EY4EGjhk8YyklOt3E20kmrIWt9/bf2ZyNBA7t4OpWjpjL3PBn0Kao2rQ1sntRaDPgWFUoVBn1FUvsgnX84kOSmWXyZ1Y/jM3SidNC9sr1I7O+SI1Wq154hKrcXwRI6otBkx9hg8nYdJcfzs153vZv+Bk0rD+b/38deOpfT/djHOrnlfGq/j83XGoM9aLIY0x/HqMXg6HZLimDe+O2Pm/MGONTMZOnEthYqV4cSfG/lj3Ww++WL8C/s/vP1nbt0I4N7tUIqWcpybJ8feSeVM+hNzc+3cflJ1iayfPwDdgziM6XryFyzJ+ZPbkUggPOg0d28Fs2PFd3QfuhhnN48nQ3CgUjtjSMt4zlarJdN4OKPPNFaGtBTUWhcKFCqBR0EvJBIJnoVLoHV252FiHHnyFyTg9H7+3L6MQWMW4ZLNuVFptE/MjcVhbjLnkP5RLMf3rQeJhNArZ4i+GcK6RePo/+0CXN3zE3h6Pwd2LGXAmEVZypNTu+cRHRZIbEwIhYpnzI1Rn2Iv6B9zUjljNNjmxrbcFZXGjfc7j2XXiqG4uBekQLHKqLV5yONREvf8xZFIJOQpUBKVxp2Uh7G45CmUpXE5vWce0eGBxMWEUDBTXOnPiOt1+nf/z9y9GUDCnVAKeGUaH0PW4gi/tB99SiL7Vg4gLTkOk1GPe4GSlK/dOcsxqNRah5y0WhzzNXMuG9JSUWtd0Lq44VmkBHKFAs8iJVAonNA9TMDZxY2qdd4HoKr3e+zcuDDLcbxO7+JV26KQfE3y58/PmjVrOHDgAM7OzphMJgAqVaoEQMGCBYmLi8PJyYmEhAS+/vprNBoNqampGI1GQkNDOXPmDPv22b7ZPS4S3d3dKVy4MAAajYYyZcoA4OLigsFgeGn/2VWmQg0unjtOnUatCQu5RNHiZezLChYtyf07t0hJfoCTSkPotUBaf9yLO1ERyOS21NI6u2E22Q5za13cUGtsJwK65fHgRvDFHMX0Njh62XaYWiqBge1kqJS2vZHFC0g4E2x5ydogkUDR/BLWHDQjlcJnzWT2i3BeptUnIwDbOUPzx7YnVZeEUqXhZsg5Gn/4hUPb4mVrEXrxOLWadCL04glKlPd+7nbPn/qDhwn3eK/DABROaiQSKRKJ7KXxlCxXk6uBR6nZ4ANuXr9IoWJl7cs8i5Qi9m4kKTpbjoQHB9CsfW/OntjJg/h7tOzYH6VShUQiRSqVce7ELk4f2sKQH1ajdXbL0nhkVqp8Ta4EZC2WsOAAmnXozdnjO0lKuEerJ2LROLuhUmfka0TI+Zf237xzxtws9mtP2qO5uRV6loZtHOemWJmaXL90jBqNO3Pj8nG8ytamXste1Ht0gdiFk9uJuxtBjcadqdE448N49Y89ad9z0kuLSNt41OBKwDFqNWxDROhFCntljEfBIiWJvXPLPh43ggJo8dHnnDmyg5hb1+nWbzxJCffRp+lwzZOff4/v5tTBLQyfuDJHc1OyfE2uPp6bp2IpRezdjFjCggJo3qE3Neq3trdZMKkPXfv54eqen7MndnH64FaGTliV5VgatR8J2M4BXDOtHWkpSSidNESFncO7RV+HtoVL1SLi6jEq1+9MxLXjFCntjcVs4s7Ni3Qbvh6LxcTWhX1o3GEkV89sJS4mlBbdJqJ7cI90vQ6t68vn5rGG7TLi8p/eDn1KEgonDdFh5/Bu3vcla786dT8YYY9j85z26FOTUCg13Ik4S/X3vnjJ2lC1cS+qNrblbsi57STdj8hWEQlQukJNLp87hnfDNkSEXnLIkUJPfN5cDwqg5Ue9UCiUHNm7gRYdevEgMZZ0QxpaZ3dKV6jJ1cAT1HuvA9eDAilUrHS2YhGyThSSr8nKlSupUaMGvr6+nDlzhmPHjj2z3fHjx7lz5w4///wzCQkJ/PXXX1itVkqVKsVHH31Ehw4diI+PZ8uWLQBIJFnbLZ7V/l+mZr1mXLt4hplje2O1Wun91UT+Ob4Pgz6Vpq196Nr7a+ZNHoLVaqFxi4/Jk68ALTv0YPWiScwa9wVmk4lOPb7CSaWm12A/ls4di0wmQyZX0GuwX45ieptYrPBXoIUe78uQSGxXbSengUoJHepK2XLy2cWh1Qpmi5X+H8gwmeFMsIW09Gc2fS6ZXMGHvmNYPbs/VqsF76adccvrSaouiR0r/OgxfAHNPh7I1qVjOXt0CxrnPHQbPPu526tcuxXblo1j2bTPMJtMtO0xFkUWLuyoWqcFIZdPM/+HHliB7l9OIeDUHgz6VBq26MLHn33LrzMGYLVaqfd+J9zzelKtTks2/uLHgkmfYzab6NjrO2RyOTvWzMA9fyFW/TQcgNIVa/Nhl6+yPCaPY/nZzxaL78ApBJx8FEvLLnTs+S2/TH8ilrq2WP430RZLp8+/Q6F04tMBk1j7v2+QyuTI5HK69Z+U5ThkcgWtu33Hunn9sFot1Gjsg2seT9J0Sexc40e3IQto2n4Qv68cQ+AJ29x0HjDn5RvOpup1WxB86Qxzx/fEarXy2eApnD25B4M+jcYtP6Fzr9EsmjYQq8VC/Wa28WjQvDP+i8bzk9/nSCTQY9BkJBIJW1fNJE/+QiybYyt8ylbypl3XrB8qrFanBSGX/mae32dgteI7aArnTu4h/dHcdOr1DUumfYnVmhHLs1gsZrY/imXlXFvxU7pibdpmMRaZTMF7ncawfXFfrFYrVer74OLuSVpKEn9tGM9H/RdSr80g9q/7jsunN6N2zkPbz+fa8kCmYN3szsjlTng374PaOS9VGnzC/nVj+W1edyRIaNNjerYPaz+Oq2mnMexYYourcn0fnN090ack8ddv4+nQN3f2qMlkChp0+I69y225W76OD1o3T/SpSRzb6kebXgteW9/V6zYn6OLfzP6+F2Cl55DJnD2xF4M+lcatPsGn9ygWTB2E1WqhYbOOuOfzxD2fJ9eDApk1pgdWq4Vu/cYilcno/Pko1i+ZxPE/t6DWOPPFiJmvLe7seBdvkSixWq3Z/80R4aXOnDnDxIkTyZMnD+7u7ly/fp18+fIxffp0SpcuzcaNG4mLi+PTTz9l4MCByGQylEoler2esWPHUqpUKfs5jjqdjq+++ooWLVrQqFEjTp06BeDw/x9//DHLly9n7ty5tG3bFqVS+VT/e/fuRal8/gnyx6+mPHdZbkqu8ezz93Jb4NqrL2+UCyqWensOhWgU5jcdAmDbo/s2SEx5e76Le7hk85vIa2K2vh2TE3k/excDvU6WrB1seO3SDG/Px331Ulk8l+c1a1FV9fJGr1B47/avbFulVu9+4XKLxcLEiRMJCQlBqVQydepUihcvbl++c+dOVq1ahVQqxcfHJ0env4EoJIVMRCHpSBSSTxOFpCNRSD5NFJJPE4Xk00Qh+d+9rJA8cOAAhw8fZubMmVy4cIFff/2VJUuW2Jc3btyY3bt3o9FoaNeuHVu3bsXNLfunrbw974KCIAiCIAjvsNy82CYgIIAmTWw/Y1WjRg2uXLnisLx8+fIkJycjl8uxWq1ZPnXuSaKQFARBEARByAWv8ud/Nm3axKZNGTcy6NatG926ZfyGp06nw9k54yeTZDIZJpMJ+aOLYcuWLYuPjw9qtZpWrVrh6ur4qx9ZJQpJQRAEQRCE/2eeLByf5OzsTEqK48/3PS4ig4ODOXr0KIcOHUKj0fDNN9+wb98+Pvzww2zH8facfCUIgiAIgvAOy81bJNaqVYvjx20/VH/hwgXKlStnX+bi4oJKpcLJyQmZTEbevHkdfos6O8QeSUEQBEEQhFyQm+dItmrVilOnTvHpp59itVqZPn06u3btIjU11b4309fXF4VCgZeXF506dcpRP6KQFARBEARBeMdIpVImT57s8Fjp0hk/zN69e3e6d+/+n/sRhaQgCIIgCEIuEPfaFgRBEARBEHLkXbzX9rv3jARBEARBEIRcIfZICoIgCIIg5Ia35bZcr5AoJAW70LvaNx0CAHfeklsT1upV+U2HAEDayaA3HYJdwltyS0Cj6e14M/ZwM73pEOyS0hRvOgQA8miMbzoEAEoXNCCXvh33JkxMfTtu1/i23L4SwPoWxZKbxDmSgiAIgvD/wNtSRApCZuIcSUEQBEEQBEF4ROyRFARBEARByAXi0LYgCIIgCIKQI+LQtiAIgiAIgiA8IvZICoIgCIIg5AJxaFsQBEEQBEHIkXexkBSHtgVBEARBEIQcEXskBUEQBEEQcsM7eLGNKCSFl7px6TB/712ERCanagMfqjfu6rA8VZfA7pWjMRn1OLsV4MNeM1Ao1Vz7dydnD61CKpFSpaEPNZv6YjYb2e//PQ/iozGb0mnw4SDKVGuR7ZjKFpbQtIoUixUuhFs4H2Z9Zru65SU4qyQcvmj7ceKqJSQ0qCjFkA4XIyxcCH/2eq+Ke91qVJg+mjMte72ybQadP8KR3xcjlcrwbtqZOs0c5yMlOZHNS0ZjTDfg6u5B5/7TUTqpn7meyZjOtmXfkxh7Gye1Mx16+ZG/YAl0D+PZseIH9KkPsFgsfDJgJvk8vZ4bU+iFwxzfbdt2jcY+1Gr6RI4kJ7Jj2SiMRgMubgX4qM90FE5q+/Lda/1Qa9xp8cmojOfxMJ7lU3zo8fVK8hcqleXxuX7pMKf2LEIqlVOtoQ81mjydrztXZORru89nYEjT8cfyr+1t7kcF8X6nUdRs2h2AmIiLHNk+hx6j/LMUg8ViYfvKKcTcCkEuV9J1wCTyFyxuX3414Ah/bV+CVCan7nudqN+iC2aTkU2/+pEQG43JlE7Ljl9SpXZz+zqBp3Zz8s8NDJu8IctjARB8/ghH/rDNTa2mnanz/rPzxWQ04OLuQed+tnwBSDeksfrHvnTqOxWPwrY5OLZrKcHnD2M2Ganboju13/skW/FYLBY2LZ9G9M0Q5AolvgMnUqBQRm5dPneUvVt/RSaV0aB5Rxq1tG3/zx3LuXz2KCaTkaZtutGwReds9fu8WDYum87tm6EoFAp6DprgEMvFs8fYs+VXpDI5jZp/TJNWPphNRlYt8CM+NgapVEbPgT9QsGjJLPf3x5rJ3LkVgkyuxKffZPJ7ZuTFtcAjHP59CVKZjNpNO1O3WZfnrhMTGcTOtdOQSGXIFUq6fjkDF7f89n5Wzx1IpVrNqd/i0+fGkxvvJfejb/D7yglYsVKwWHk69BqPVCp77vi8qty4czuMDb9OBqwUKV6Orl+MRSp7dr+5SfIO3iIxV0vjs2fPEhwcDMBXX3313HaxsbFMnDjxtcYyZswYjh8//sq327x5cwwGQ5ba9uzZk7CwMIfHpk2bRkxMzCuPK6fMZiNHts2gy7CVdB/pz6WTm9A9iHVo8/fexVSq0x7fURvwLFaJiyc2AXB0+490HbYK39EbOXdoFfrUB1z7dydqrTu+ozbwyZBlHNw0JdsxSSXQupaU9UfMrDlkplZpKVqVYxu5DDo2kFK7bEaKq5XwfjUpaw/Z1qtSQoLba7wrZKlR/aj661SkKqdXtk2zycje9TPp8+1y+o1by9mjW0hOcpyPI78vpnqD9gwYv45CxStx9sim56539ugWlCoNAydson3P8exaOxWA/b/NoUbD9vQft45WPsOJvRP+wpgObJpJj5Er+PxbfwKPb34qR47vWkSVeu3p/d16CnpVJODYJvuygGO/cT8q9Klt7vGfgFyZvbEzm40c2jKDT4etpMcofy48I19P7bHl62ejbfl6/vgmnN086DHKnx6j/Hm/09d4elWyf2E68+cy9vmPx2zK2usa4Mq5QxiNBoZN3kC77iPZuW62w3P7w38WA8YuY/APqzlzeAsPk2IJOLkbjbMbX030p/93v7Jj9TT7OtE3g/jnyHas1ux98TGbjOzdMJPe3yyn7/drOfesfPnDli/9x2XkC0B0xBWWT+9JQuxte9vwoH+5deM8/cdvoO/3a3mQcDdb8QBcOnsYY7qB0dPX8XGP4WxfO8ch3q2rZzN0/K+MmLSKkwe38SAxjtCrZwkPucDXU9cyYtIqEuOz3++zXPj3CMZ0A2NmrKXTZ8PZuuYnh1i2rJ7D8B9+YfTkFZz4yxbL5cCTWMxmvpu+lnZdBvD7hoVZ7u9awCGM6ekMnrCRD7t9zZ4NPzr0t2f9TL74bhkDxq3h3yO2uXreOrv8Z/BRr3F8OW4NVWq35NjuFfZtHdg6nzTdgxfGklvvJQe2/EyrLiP40m8DxnQ9QYGHnxvTq8yNnRv/x0e+Qxk1dS3pBj2Xzh3N2iQJ2ZarheS2bdu4f/8+AAsXPv/F5+Hh8doLybfVuHHjKFy48JsOwy7+ThjuHl6oNG7I5EqKlPEmOuycQ5uosABKVGoCQMnKTYkMPg2AR5HyGNKSMRnTH30ASihf8wMadxhuX/d530xfJL8bJOis6I1gscCtWCteHo7f8uRSuBRh5eTVjNuk5XGGe4lW9Om2v+/EQ9F8r+/bYWr4LQK6DH2l24yNCSefpxdqrRtyuZLi5WpxMzTAoc3N0ADKVm0MQLnqTbhx9e/nrnc/+gblqtnmzqNQSWJjbAXjreuBPEi4x8qZfbjw9y5KVaz73Jji7oSTt4Bt2zK5Eq8y3ty67hjT7RuBlK5i66d01aZEBP0NQFTYeaLDLuL9XjeH9ge3/Ij3e91wcSuQrfGJvxNGHg8vVI9iKVram6gbT+TrjQBKVbbFUqpKRr4CWK1W/vptCm26T7TnpruHF52+XJCtOCJCAqlQ3TYHxctW53Z4xv3j70WHk9/TC42zbS5Klq9FRHAg1eu35oOuw+ztpDLbAaOU5CT2bJxHx15jshUDPCNfytYi8ol8icycL9WaEHbNNjcmYzq+wxaQv1DG3rYbl0/iWbQcG/43lHXzBlOhxvvZjiks6DyVajYCoGS56twKu2Zfdjc6Ao+CxdA4uyJXKChdoSZhwYEEXThNYa+yLJ09gl9mDaWK93vZ7vdZbgSdp/KjWEqVq0ZkWMY83YmyxaJ9FEuZijW5ERSIZ+HimC1mLBYL+tQUZPKsH9i7GRpI+Wq2sfYqU53oiIz+7seEk8+zOJpMr9GIkIDnrtP9q7kULl4RAIvZjFxhu5/35X//RCKRUq56kxfGklvvJb7D5lOyQh1MpnR0D+JwfrTX9FleZW70H/UTZSvVxmQ08jApDlf3fC8cj9wikUpf2b+3xUtfAXq9nrFjxxITE4PRaKRNmzYcP34ci8XCsGHDSEpKYvXq1UilUry9vRk9ejR3795l4sSJGAwGkpKSGDJkCAULFuTEiRNcvXqVMmXK0KVLF06dOkXPnj2pUKEC169fR6fTMX/+fKxWK19//TWbN2+mQ4cO1K1bl5CQECQSCYsXL8bFxYW5c+dy9uxZrFYrvXv35sMPP3xm/FFRUQwfPhwPDw/u3btH06ZNGTlyJACbNm1i+fLl6HQ6Jk6cSLVq1Vi5ciV79uxBLpdTu3ZtvvnmGwICApg1axZyuRxXV1fmzJnDgQMHOHToEDqdjsTERIYMGUKbNm0AmDhxIlFRUYCtYNZoNHz//ffcvn0bs9lMnz59aNu2rT3Gw4cPs2rVKhYtWsSQIUOYOHEie/fuJSoqivj4eGJiYhg7dixNmjRh//79rF+/3r7u/PnzARgxYgRWqxWj0cikSZMoX748/v7+7N69G4lEQtu2benVK/uHV9P1OpxULva/lU5aDGm6p9uobW2UKi0GfTIA+QuXxX+mDwqlmrI1WqHSuDqs88eyYTT+aES2Y3KSgyE9U/8mcFI4ttEbIfyulWolMwrFhGTwcJOgVYHBCCU8JcQnv75D23d3HEBdvMgr3aY+TYdK7Wz/20mlRZ+a7NDGkJaCSuNiX25I1T13vULFKxJy4SiVvFtyO+wiDxPvYbGYSYyLQa115Ysxqzj8+yKO715OS59hPIsh0/zDoxx4KqaMNk4qLYa0ZJKT7nNs50K6Dl7ItXP77G0vntqOxiUvpas04dTepdkan2fG8kS+Zm7j5GSL5bEblw6Tv3BZ8hXMOJReoVYbkuKishWHPtMcAEilUsxmEzKZ3DYXmZY5qbWkpSbjpNLa113z8wg+7DoUi8XMpqV+fNzrOxRK1VP9vDQOvQ6nTPOuVL88X/SptvEqXq7WU9tL0SWSFBdDz6+XkBgbzfqfBzN85t5sHa7Tp+lQazJiyjw2aak61JnGRqWyjY0uOZGE2DsMGrOQ+PtR/DJrGD/M3/mfDxPq01IcYpFIZZnmyXGZLRYdTioN8fdjmDCsI7rkJL4a+79sPXeVQ38Zz93wxDIntRZ9mu6567i6ewAQGXqe0wc38OW4tdy9fZ0Lf++hx9CfOfT74pfHkgvvJVKpjMS4aFbN+gIntQseBUu8MKZXlRtSmYz42BgWTB6AWuNMgcLP7zc3vYtXbb+0kPztt98oUqQI8+bNIzQ0lNOnT+Pq6sqSJUtISkrC19eXbdu2oVar+eabbzh16hQSiYQ+ffpQr149AgMDWbBgAatWraJJkya0bdv2qT1u1apVY9y4ccybN489e/Y4FFkpKSm0a9cOPz8/Ro0axfHjx3F2diYqKorffvsNg8FA165dadSoEa6urk+GD0B0dDQrVqzAxcUFX19frl61faOrXLkygwcPZvv27Wzfvh0nJyf27dvHb7/9hlwuZ+jQoRw5coR///2XVq1a0bdvXw4fPszDhw8B+D/27juuqet//PgLMkgCIiiKuFC0KHWA4KgL62qr1VZwULfWumpttVo31q2t9uOsq4K7iii0zg7rQK21DMUNylARURFUVkIgOGxSlQAAIABJREFU+f0RDUQcoBT5+jvPx8NHG+4997xz7rkn75x7b25mZibr168nJSWFnj170r694Vq/7t2707hxYyZNmsSJEydISUnB1taWhQsXkp6ejre3N++88w4Af/75J6GhoaxZswaVSmUSt1wuZ926dZw4cQJ/f39at25NfHw8a9euRalUMn36dI4fP461tbUxub569Srp6elcvXqV/fv38/PPP2NmZsagQYNo1aoVTk6Fu9bs2O7F3IyJ4O7NKBxqNDT+PVuTYfJBDSBXWJGtyUAmV5CtzsBCac2dhMvEnj/CsNl/IbNQsW/DN0RFHKCOeyceptzil7WjaOTZh7ebdC1UPADvNjCnWgUz7G3g5r28BFAuNSSGL6LWwh8ROnq0kpCWqScpVU9m4c9WvlZ/7lzCtegIkm5EU7VW3v7QqDNQqkz7vYXSEo3asD80asMHgUJphUadUaCci0d77ibG4Dd/INXfcqdyzXqYm0tQWdng0qgtAHXd2vLnziUFYjocvIQbV8K5nRBNFad8fUSdgYXKtI9YKK3IfiKmS2G/kZmWyralw0h/mIw2W42dQ03OHA8CM4i7+DdJNy7zq99EfEavxKpshWe2T8ivi7lx1dBfK9d8IpYn+quFIl8smgws8rXfhVO7adzu1a9nVSgt0WTltbder0fyaIaxwL7IykBpaYgx9d4tNvzwFS06foJ7yy5cv3qW5FvX2OU3G61Ww+2bMfyycT7dBk5+bv1/7lzCtSsR3L4RTdX8+yYrA4Xl8/uL8ol9l5/KyoYKDk5IpXIqONREKrMgIy0FK+vCz/YolFaoszLztY3O2DZKlRXqfG2jfhSPpZUN9lVqIpXJsK9SE5ncgvSHKZQp+2qzTAqlJer8+0mny7efTPehWm3YTwf3bKGeWwu8+n1JSnISi78dyvTFO5EV4jKMJ/e9XpfXLyyUVib1abIM7/15ZSL/OcDh3WsYNG4VVtblCNnnz8OU2/w0fzCpyTeRSmXYVqhCnYZ5s5MlPZYA2NpV4euFvxN6JJD9P39Hj+ELntk+xdk3yleozIzleznx1y6CNi5kwBdzC9QpvLoXJpKxsbF4enoC4OzszPnz56lZ03Cq4/r166SkpDBs2DDAkPTduHEDDw8PVq1axc6dOzEzMyMnJ+e5dbz99tsAVKpUieTk5Gcud3BwQKPRkJiYyIULF+jfvz8AOTk5JCYmPjORrFu3LjY2NoAhaY2LiwMMiSSAnZ0darWa2NhYXF1dkckM01uNGzfmypUrjBgxgtWrVzNw4EDs7e1p2NBw8DVp0gRzc3Ps7OywtrYmJSUFgPr165tsNyYmhhYtWgBgZWVFrVq1uHHDcN3RyZMnSU9PR/qU0yMuLi7GdsnONkzBlS9fnokTJ2JpaUlsbCxubm54enoSHx/P559/jlQqZeTIkURHR5OYmMigQYMAePDgAdevXy90Itn6I8OsbW6uFv9ZH5KVcR+5hYqEK2E06TDEZN0qTu7EnT9K/ebexF0IoWptDyyUZZDKFEhlFo8Sk3KoMx+S8TCZwOWf0sFnOo51mxcqlseOnDOcpjY3gxEfSlDIDbORjhXN+Oey7gWlwcwMqtqZsfFgLubm0K+txHgTTmnXsYdh5jY3R8vSyV3ITL+PXKEiPiqMVp0+NVnX8S13oiNDcG/tRXTkMWrU8aBCZSfu3b5WoNzN2HM4OnvwYd/JJMSeJ+XOdcM2nN2JOhtCo5YfEx8VRsUqbxWIqa1XXkyrp3ch69G2r0WH8s77pjFVq92Iq+eO4trSm5hzIVR7qzFNOwyg6aObkCJPBJF8Kw7Xlt64tsy7iWLT9/3p3H/mc5NIAM+P8/rruhl5/fXG1TCadXyiv9ZyJ+b8URq28Cb2fAjVansYlyVdv0CVWgVn4oqqpnMjLkQcwa35B1y7EolDtbz2s6/iRHJS3r6IvRzOu10Gk3Y/mbXzhuE1eCrO9Q1fNKvXbsiERbsBSLl7k83Lxr8wiQTT/rJsShH6y9ljONbxeNomDes6u3Pyj820/GAQaffvkq3JQmVlU6S2carrxrmwo3i0eJ+46EgqV89rm0pVanL31nUy0h5goVBx9WI4HboORCa34Mi+rbTvMoAHqXfRqLOwLGK9T1O7rhtnw47SuOX7xEafpYpjXiwOVWtyJ18sVy5G0PGjASQlxBmTG0ursuTm5qDTFW4ccXRuxKWIIzRs1onrVyOplK9fVKzsRPITx6hn58FgZvbUMqdP7ObUoR0Mm7LBuA869x5v3N6fQSsoU9bOJImEkh9LNi/+nE69J2BXqQYWCsvnnpItzr6xesFovAeOp6KDIwqFJWZmpeRUcCk6JV1cXphI1qpVi3PnztGhQwdu3LjB//73P7p16wZA1apVcXBwwN/fH5lMRlBQEC4uLixdupSePXvSpk0bdu3aRXBwMGC4W6moF4s/Lpefk5MTzZo1Y/bs2eh0OlauXEnVqlWfWT4mJoasrCzkcjlnz56le/fuHD9+/KnbXb9+PTk5OUgkEkJDQ+nWrRt79uzBy8uLiRMnsmbNGnbs2EHlypWNM5vJycmkp6dTvnz5p8Zbq1YtwsLC6NixI+np6URHRxvjnT59Ort372bZsmWMHz/epNyT20lLS2PZsmUcOXIEgMGDB6PX6zl16hQVK1bE39+f06dP87///Y+pU6dSu3Zt1q1bh5mZGRs2bMDZ2bmQLZ5HIpHRtvskdi4fgl6vp36L7pSxsScr4z6/b5lGt+EraN5pJPs3TSTyxA5UVrZ8OPgH5BYqXFv58PMPfZBIZdjYVaf+O14cCfoeddZDTh5YyckDhlMv3Uf9VKTTdjo9/Bmho++7EszMDHdtp2WBQg5dm5oTePzpg7peD7k6PUM/kJCTC/9c1pGV/dRVSy2JVEanPpPYsHAoer0OD09vypazJzP9PsF+vvT9ajltPx7BzrWTCT0SiMrKFp/PFz6znFQm5+CuZRzf74/C0hrvIYYL5Dv1nkCwny///rUdC1UZfEYufG5MHXtNZOuSz9DrdLi16o61rT1Z6ffZs9GXXqOW0+rDkez2n0RESCCqMrZ4DV30zO29UvtIZLTrOYmAZYb+2rBFd8rYGvrrgc3T8B6xgpadR7J3w0Qij+9AaWXLR0N+ACAzLQW5wrJY7qqs36QD0edOsmx6X0CPz/A5RJzYi0adSfP2vfio3wTWzh+GXq+nybtelC1nzy8b55OV8YCDQas5GLQagKGTVr/UKW1je0hldOo9iY2LhqLX6XD39Mb6UX/5xd+XPl8u592PRrDrp8mEHTHsm17P2dd13doSHxXG6pm90Ot0dB3gW+TrnF2btufy2X9YNLU/6PX0GzWb0GP70KizaNWxB90HjmfF3BHodTqat/PCprw9NuXtuXoxnO8n90Gv0+Hz2ZRiuQPXrVk7Lp39h++mDECvh0GjZvLvsf2oszLxfK8HPQaNZ+nskej1elq0+xjb8va079KPTT9+y8Jpg8nJ0dKt72gsFMoXVwbU8+jA1fN/s3JmH0BPj6FzOfO3oV80a9eLD/tMxP/7Yej1Oho/Okbr2RQso9PlsnvzPGzKO7B5qeGac6e6jenYvfDXZJfUWOLZZSi7fpqCRCpDJlfgNeTZN1gWZ994z2sIm3/0RSKVIZcr6DtyRqHb5r/0Jp7aNtO/ILPTaDRMmTKF27dvk5ubS4cOHUhNTTUmPb/++ivbtm0jNzeXKlWqMH/+fP766y+WLVuGnZ0dDg4OXL58mX379rF9+3a2bt3KkiVLGDBggPEayRkzZlCrVi22bdtGcnIyXl5exmsk27Vrx4EDB7CwsGDRokU4OTnh5eXFggULOHfuHJmZmXTo0OGZd4EnJCQwYMAAateuTXJyMh988AHDhg1j0qRJdO7cGU9PT0JCQti/fz8LFixg/fr17N+/H51Oh4eHB5MnT+bs2bPMnDkTlUqFTCZj1qxZhIaGEhAQgEKhIC0tjTFjxuDp6fnUeLt06YKvry/Xr19Ho9HQv39/vLy8jO+9WrVq9OzZE19fX5YuXWq8RtLOzo7evXsTExPDjBkz2LRpE2PGjCEuLg6VSoW1tTWNGjXik08+YezYsWRlZWFubs6oUaNo1aoV69at4+DBg2RnZ9OwYUN8fX2RPGfwXffXK/SkYnTrzvNnsEuK+4B6rzsEALKOX3rdIRips0vHIKjNKR1xVChbOvoqQFZ26ZjpsFUV4jqTEiA1Lz1nG1Iz5a87BABy9aXjuAGwUZaOftKhYfH9qkZhpMwZXmzbKjdtTbFt61W8MJH8vy4hIcGYlBanoKAgYmNjC8wi/l8mEklTIpEsSCSSpkQiWZBIJAsSiWRB/78mkqlzRxbbtmynriq2bb2KN+YHyQMCAti7d2+Bv3/99ddPWVsQBEEQBKGEvYGntt+YRNLHxwcfH5+nLivu2UgAb+9Xf6qCIAiCIAjC/2VvTCIpCIIgCIJQmpWmHxIvLiKRFARBEARBKAFv4l3bb15qLAiCIAiCIJQIMSMpCIIgCIJQEkrLD6MXI5FICoIgCIIglABxalsQBEEQBEEQHhEzkoIgCIIgCCVB3LUtCIIgCIIgvAwzszfv1LZIJAWjnFLytDcXp9Lxja20PJpQ2crldYdgZH6idLSJTl46nuxqbaF53SEY3c9Qve4QAIi7U7KPnHue1Ael4zGJEknpSB7ediwlgzyQphbpx5tC7ElBEAThjVNakkhBMCFObQuCIAiCIAgvQ9y1LQiCIAiCIAiPiBlJQRAEQRCEkiB+kFwQBEEQBEF4KeLUtiAIgiAIgiAYiBlJQRAEQRCEEmAmTm0LgiAIgiAIL+UNPLUtEklBEARBEIQ3jE6nY8aMGURFRSGXy5kzZw6Ojo7G5WfPnmXBggXo9XoqVKjAwoULsbAo+gMFRCIpCIIgCIJQAsxK8AfJDx48SHZ2NgEBAZw5c4YFCxawatUqAPR6Pb6+vixbtgxHR0cCAwO5efMmTk5ORa5HJJLCS4s5d4hTv/+IubmUeu90p0GLXk9dL+LwBjLSkmn90fhXqu/S6cMc/mUl5uYSPDy9adLWtL6MtFR2rBqPNluDtU0FvIfOQ26hBCBbk8X674fgPWQOFSo7odPlEuw3neSkOMzNzfH+bB7l7av/J/U/rVyONptdP00h9e4NLJRWdB3gi12lGqQ/vEew33TUmQ/Q6XT0GLaAqq/UagY2TRtSd954/ukwoBi2ludSxGH++mUl5hIJjT29afqUNtm+cjw52RrK2Fagx+M2eU659Af3WDG9B59O9KNiZSduxl/gl/UzkUrlODjWpUu/Kc/9CY3X3U/AMBOwde18EuKjkcrkDPzcl4oOeeUiQ4+yZ8dPSCQSWrb/GM+O3mi12WxYMYO7SQkoVVb0GToJ+8rVuR4XxbZ132FuLkEqkzHky9lY25Qv1P4BiD5ziJC9hvZwa9Udd0/T9shMSyX4p3FotRrKlK3IR4PnIXvUHgB7N/miVNnQvsc4Ik8EEXkiGICcnGySrl/i6/8dR6GyLnQ8j8WcO8Tf+3/EXCKlQfPuNGz5RFzpKexbP54crRqrshX5oP98ZHIlF079QuhBPyyUZaj/jhcNWvQsct35Xb90mNOHVmJmLsG5sTd1mzx9HDt/YiNZack0+WAcAHcTznFq33eAHqWVHW16fY9U9vKPh7x28RARfxniqNO4Oy7Nnh7HuWMbyUxPplknQxxnQ9YTFboLhZUtAK29Z2JToWjJgE6nY6f/bG5ei0YqlfHJ8FlUqJTXX8+HH+H3Xaswl0h5510vmrfvgU6Xy/a133I3MR4zcwl9RszGLl+Z8OP7CPn9Z8bO3vrC+i9GHObQL6vyjQem+9QwjnyDNluNtW1Fegydi9xC+dxy169GciDgfwyfuhGAm/EXCV4/E6lUhoNjXbr2m4L563jKTAk+azs8PJzWrVsD4Obmxvnz543L4uLisLGxYePGjURHR9OmTZuXSiJB3LVdIjQaDe3atWPu3LkkJiY+c7127dqh0RTu2b2hoaFcvny5uEIsstxcLUeD5+P9uT89v9zMub8DyHh412SdnGw1BzaNJ/L4z69eX46W/VsXMHjCOj6buonQI4Gk3Tet7/AvK3Ft3oVh07bg4Pg2oYcDAEiIPc9Pc/uTcueGcd3Lpw8DMNz3Z9p7f8n+n7/7T+p/VrnQI4HIFSpGfBtAl/7T2LNpDgC/bV+EW4suDJ26hY7dv+LurdhXbjuncZ/RYM0czBXF+wzk3Bwte7cuYMjEdQybuol/Dxdsk7+CV+LWvAvDfbdQ2fFt/j0U8NxyuTlagtd/i1SeF2uw/7d06TuZ4b5bUCitiDy597kxvc5+8tiZfw+j1WYzecFGvPuNZseGxcZlOTlaAtb/wNhvV/LN7HWE/BHEg9Rkjv0ZhIVCyZTvNtH7swn8vG4BAAF+C+n92US+mf0T7s3acSB4Q6FieNwefwQsoO9YPwZO2ExEyA7SH5i2R8ieH6nfrAuDJm6lUnUXwo8GGJeFH93OnYRo42vXlt4MmLCZARM24+BYjw96T32pJDI3V8vhnfPpOdqfT8ZsJvJ4ABlPxHVy/0pcGneh99c/U7Ha20QeDyAzPYXje5biM2Yzn4zZwsXQPTy4l1Dk+h/T5Wr5Z98CPvh0HR8O3UTUv4Fkpj0xjmnVHNkxgUv/5I1jer2e48HT8ewxly7Dt1LVuRXp9589thcmjpN7F9B5iB9dh2/m8r87nhrHoe3fcOGk6XianHiRd30W0HX4ZroO31zkJBLgXNhfaLOzGTt7K137jOXXzQuNy3JztPyy6TtGTlnL6G838PdfgTy8n8z58CMAfDVrC516juKXfGUS4i/zz+Eg0OtfWHdujpZ9Wxfw6cSfGDZ14zPHEdfmHzLCdwuVHV04dWjHc8sd3evHLr/p5GjzPk+D/L+la99JjPDdgkJZ5rnjyP8VAQEBeHt7G/8FBASYLE9PT8fKysr4WiKRkJNjeN56amoqp0+fpk+fPqxfv55//vmHkydPvlQcIpEsQVOnTqVy5crFsq1du3Zx586dYtnWy0hJisHGrjoKVVkkUjmVnTy4GRNmsk5Ojoa3m3Sj6XsjXrm+u4mxlLevjtKyLFKpHEdnd+Kjw03WiY8O560GrQBwdm3N1QuGgyI3J5u+Xy2ngkNN47pve3Sg26czAbifnIhV2efP8Lxs/c8qd+fmVZwbGr4pVnCoyd1EQ8J4/UoED1Ju479gMGdO7sHJpenLNplRZux1wnuOfuXtPOnOE++thrM78VGmbXItOhznhoY2qdPQ0CbPK7d/20KatfsEa5uKxm08SLmNo3MjgEftF/HMmF53P3nsyqUz1G/UAoBadRpyLeaicVlSQhwVK1XD0soaqUzGWy5uXLl0mls34qjfqCUAlarUICkhHoChX8+nes06AOh0uchk8kLFAJB8K5ZyFQ3tIZHKqV7bg+tXTNvjxtUIatU39MVaDTyJu2Roj4SY09yMicSjjU+B7SbGn+PuzSu4P2VZYaQkxWBTIW/8qFrLg4Qnxo+bseHUfNsQV823Pbl2+W8eJCdQsWpdlJY2mJmbU8mxAbfiIl8qBoD7d2KxLl8dC6UhDvsa7tyON22f3BwNtRt9jOu7eePYg+R4LJQ2nD+xiX1r+6PJeoBNhZpPbr7QUh/HoXochwdJcU/EodXg7P4xjdoNN/l7csIFzhxey6+r+nD68JqXqj/28mlc3Ax9r8ZbrtyIvWBcdvtmLHaVqqOyKotUKsOpjjuxl8Np2KQ9PkNnGOJPvkWZR8dGRtp99m5bjNfAiYWq2zAeOKLKd8zGRT15zEZQ59E44mwyjjy9XDn7avT/aqnJNh6kJBnHkRrOjZ47jvynzM2L7Z+Pjw9BQUHGfz4+psejlZUVGRkZxtc6nQ6p1HAi2sbGBkdHR2rXro1MJqN169YmM5ZFeksv3xrC82RkZDBy5Ej69u3LjBkzAOjfvz8xMTEkJSUxYsQIBg8ejJeXFwcPHjSWmz59Ov369eOrr75CrVaj1WqZMmUKffv2pXfv3pw6dYrz589z7NgxFi5cSGJiIgcOHMDHx4fevXuzaNEiwDCl3atXL/r06cOIESNIT08v1veXrU7HQlnG+FpuYYkmy7QOhaosji6tiqU+dVY6CmXeNysLhSXqzDSTdTRZGShUZYzLNZmGeByd3bEp71BgmxKJlJ1rJrF38xzqN3n/P6n/WeUcHF2IOnMEvV7P9atneJh6G50ul9TkRJSW1nw6aT025R0I2buuMM3zXEnBf6DX5rzydp6kyUpHocr33pSWqLNM20Sdv02Ulqiz0p9ZLjwkGMsytsbE87FyFaoSe+lfwHDaOluT+cyYXnc/McaRmYEy33s0N5eQm2vYB1lZpssUSkuyMtKpVtOZs+HH0Ov1xESdJTXlDrrcXGzKVQDg6uVIDu0PoGPXfoWKAUDz5HGqsERToD3y1rFQWKLJSiPt/h2O7l5Bp77Tn7rd4/vW4vnRqELHUSCuLNO4ZIqC40d2VjryR+vIFZZkZ6VhW9GRe7eukvEwGW12FtejTqLNfnZ/eJFsTTpyRd6+kMktyVabto+FsixV32ppGn9mKneun8alWW86DfEnMeYfEq++3GwOgFadjlxhOp4WiENVlqrOBcfTWq6dae09ky5DN5AUH8G1S4eLXL8mKx1Fvv1hZm5u7K/qrAzTY0ppSdajPiSRSNm6cgq7NszDtVlHdLpctq2ZTrcBE7BQWBa+7gLjQfpT1inMOGIo16DJe5hLZCbbKFehGrGXQgG4dPoI2ZqsQsVX7MzMiu/fC7i7uxMSEgLAmTNncHZ2Ni6rVq0aGRkZXLt2DYCwsDDeeuutl3pL4hrJ/0hwcDDOzs6MHTuWyMhITp06ZVwWGxvL4MGDadasGRERESxfvpwOHToA0Lt3b9zc3Pj+++/ZsWMHUqkUW1tb5s2bR2pqKv369WPfvn20bt2azp07o1KpWL58Obt27UKpVPLNN99w4sQJjh8/TseOHRkyZAiHDh3i4cOHJlPcL+vE3sUkxkZwNzEKB8eGxr9nazJMPhiKy587l3AtOoKkG9FUrZVXn0adgfKJU2oWSks06gxkcgUadV6y8Dw9hi8g7f5dVs/04asFe5FbqIq1foXSCo06o0A5F4/23E2MwW/+QKq/5U7lmvUwN5egsrLBpVFbAOq6teXPnUsK31gl5I/AJcQ/apNq+dskK6PAaU6F0hJN1qM2eZTAWSit0GRlFCj39x+bMcOMqxdOcuv6ZQLXTGLA2B/pMWweezbPI2SfH1WdGiCVFpyRe9395EkKlSXqLNOZAInEMNwqlZao1XnJjzorA5VlGdyavcuthDgWTR9G7bquODq5YC6RABB6/Hf27fLjy6nLKFPW9oXxHg5ewo0r4dxOiKaKU77jVJ2BxRPv10JpRfYT7XEp7Dcy01LZtnQY6Q+T0WarsXOoiWtLb9SZD7mXFEuNuu+8MI4nHd+zmISYCJJvRlGpRl5cWnXB8UOutCJbY4jLELc1ClVZ2vaYzO6fRmNlWwn7avVQWr64PZ4U9scSbl+LICUpmorV8sWRnYFc8eJT9RZKG6zLV8fWvjYAVd9qRXLiBSrXbl6kOEJ/X0JSfDgpt0zjyNZkmCSWz6LX62nQaqAx4a5etw33bl7C0aVtkeKweGKc0uv1xv6qUFqiyddfNVmmx1Tfz+fR9X4yi6f1pu/n80hOukbgutnkaLNJuhlD0MYFeA+cVKDO3wOXPhpHogqMI8qn9NH844jyGePIk+Xy6zlsLns2z+foPj+qOtVH8pRx5E3TsWNHTpw4wSeffIJer2fevHns2bOHzMxMfHx8mDt3LuPGjUOv19OoUSPefffdl6pHJJL/kStXrhgvcnV1dTVOJwNUqFCBVatWsXPnTszMzIzXLMhkMtzc3ADDN4kTJ06g1+sJDw/n7NmzAOTk5JCammrc1vXr10lJSWHYsGGAYSb0xo0bjBgxgtWrVzNw4EDs7e1p2DDvQH0VLbuMBQzXOG2a9yHqjPvILFTcvBpG43ZDiqWO/Dr2GGOoL0fL0sldyEy/j1yhIj4qjFadPjVZ1/Etd6IjQ3Bv7UV05DFq1PF45nZPn/iVhym3adN1GDILJWZm5piZSYq9/gqVnbh3+1qBcjdjz+Ho7MGHfSeTEHuelDvXDdtwdifqbAiNWn5MfFQYFau8BRx7lSYsdu/1zGuTxZPy2iQuKozWnZ9oE2d3oiJD8PD0IursMWrW8aDiE23yuFyDpnmzfWvnDqDb4BmUsanAmZP76DF0Lta2Fdm9aY7xkoD8Xnc/eVLtum5EhoXQpOV7xESdpapjbeOySlVrcufWdTLSHmChUBF9MYL3Ph5A/NUL1HZphM+n44m/epG7SYZr//45uo+jf+zim1k/YVmm7AvrBmjrldceq6d3IetRe1yLDuWd903bo1rtRlw9dxTXlt7EnAuh2luNadphAE0f3ZgVeSKI5FtxuLb0BuBadBg1XYqWMD3Wqmve+LF+9odkZdxHbqEi4WoYjTuYjh9VnNyJO3+U+s29ibsYQpVaHuhyc0iMi+STsVvR6XIIXDaYVh+NLXIcjd8ztI8uV8uuJV3QZN5HKleRFBdGg1afvqA0lClXFW12Jg/vXcO6vCNJ8eE4N+5e5DiavJ8Xx44fuqDOvI9MriIpLhRXzxfHoVWnE7i4K73G7UMqV5F49R/qNCl6HE51GnE+/AiNmn9A/JVIHKrlzUzZV3HibtI1MtIN/TXmcjhtuw4iNGQ391Nu07HbUORyBWZm5jjWbsikRb8CcO/OTTYt++apSSTA+z2/Agx99H+Tupocs56dB5us6+jciMuRITT29CL6rOGYrVjZieQnxtYny+V3+cxRegydg7VtRX7dNIc6TxlHSkJJ3rVtbm7OrFmzTP5Wq1Yt4/83b96cnTt3vnI9IpH8jzg5OXHmzBk6dOjAxYvpa6rhAAAgAElEQVQXjckiwNKlS+nZsydt2rRh165dBAcb7oLUarVcunQJFxcX4zRzTk4OlSpVYsSIEajValatWkXZsmUxMzNDr9dTtWpVHBwc8Pf3RyaTERQUhIuLC3v27MHLy4uJEyeyZs0aduzYwRdffFFs708ikeHZbRJBq4ag1+mp9053rGzsUWfc589t0+j62YpiqwtAIpXRqc8kNiwcil6vw8PTm7Ll7MlMv0+wny99v1pO249HsHPtZEKPBKKyssXn84XP3F69xh3Z9dNUfprbj9ycHDr3nYxM/uybUV62/meVk8rkHNy1jOP7/VFYWuM9xHCzTafeEwj28+Xfv7ZjoSqDz8iF8KN/sbZlcZFIZXzYZxL+3xveW+N8bRLk50u/R20SuOZRm5Sx5ZORC59Z7lns7B3ZsGg4MrkCp7ebUdetDbpnXMP/uvvJY42ateVi5D8smDwIvV7PoC9mcCrkABp1Jp7vdafXoK9ZPGsUer2OVu0/xrZ8RaQyGb9uW8Ufv25CZVmGgaO+RZebyza/hZSzq8TK7w2/euBcz52PPxn5whget0fHXhPZuuQz9Dodbq26Y21rT1b6ffZs9KXXqOW0+nAku/0nERFi2EdeQxc9d5v3kuKwrVCtUPU/My6JjLbdJ7FzxRDQ66nfvDtlbOzJyrjPH1un8fGwFbzzwUgObJrI2b93oLS05cPBP2AukSKRyNj8nTcSmQWN2w1GZVXupeMwl8ho1nkSv6039BdnD28sy9qjybzPsSBfOvRb/vT4pXJae8/hcMA3oNdTsXojqtd995XiaN5lIgf8PkOv11GncXcsy9qjzrxPyE5f3hvw9DjkyjI0+WAse9cOxFwqp0qtd6het02R62/QpD1R5/5miW9f9ECfEbMJP74PjTqTFh160q3/BFbPG4Zer6fZu17YlLOnYdMObFvty7IZA8nNzcFr4MRCHRtPMowHE/H/fliBcWSX33T6f7WMdh+PIHDNFEKP7ERVxobexnGkYLlnKW/vyPpFw5HJldR6uyl13YreTsXiDXyyjZleX4jbqoQiy8nJYfLkySQkJODk5ERYWBgVK1ZkxowZXLp0iWXLlmFnZ4eDgwOXL19m3759vP/++3h4eHDt2jUqV67MvHnz0Ov1TJs2jcTERNLT0+nTpw+9evVi+/btbN26lSVLlnD+/Hm2bdtGbm4uVapUYf78+URHRzNz5kxUKhUymYxZs2ZRrdrzB//Vv5dQ47yAnY3udYdQqihbubzuEIw0Jy697hAAnplIlrSKVq/pOqunuJ7y/NPtJSVLUzqe3JH6oPSMIxJJ6WiTtx2L/1rpl6XWlo6Eyqvpi88wFKesLfOKbVvKflOKbVuvQiSSgpFIJEsnkUgWJBLJgkQiaUokkgWJRLKgEk8kf55fbNtS9plcbNt6FeLUtiAIgiAIQgkwewNPbb9570gQBEEQBEEoEWJGUhAEQRAEoSSYl47LHIqTSCQFQRAEQRBKgji1LQiCIAiCIAgGYkZSEARBEAShJBTi0Yb/14hEUhAEQRAEoSSU4JNtSopIJAVBEARBEEqCuEZSEARBEARBEAzEjKRgVNtB87pDACA7p3R8v0nJKB2Hh3kpeZoMgEXL0vGUHf2pC687BABydaWjrwIo5KXjcT+uDndedwhGf5yv+LpDAKC8Tem4Ls7MrHT0EYCQf0vHU6G8mlqVbIXi538EQRAEofQrLUmkIJgQp7YFQRAEQRAEwUDMSAqCIAiCIJQE8fM/giAIgiAIwkt5A3/+5817R4IgCIIgCEKJEDOSgiAIgiAIJUGc2hYEQRAEQRBeirhrWxAEQRAEQRAMxIykIAiCIAhCSXgDb7YRiaQgCIIgCEJJENdICv8/0+l0BKyby834KKQyOX1GzKCiQ3Xj8nNhR9i/cw0ScwnN23WjZYceAPwevI5zoUfIydHi+b4PLdp7v1TdO/1nk3g9GqlUhs+wWVSolFf3+fAj/BG0CnOJlGZtvGjevgc6XS4Ba7/lzq14zM0l9B4xGzv76tyMv8yuDfMwNzdHKpPTd+Q8ytjYFSme6DOHCNm7EnNzCW6tuuPu2ctkeWZaKsE/jUOr1VCmbEU+GjwPmYXSuHzvJl+UKhva9xhn/FvGw3usm92dvl/7Y+fg9My6L0Uc5q9fVmIukdDY05umbU3rzkhLZfvK8eRkayhjW4EeQ+cht1A+t1z6g3usmN6DTyf6UbGyEzfjL/DL+plIpXIcHOvSpd8UzIvpm7RN04bUnTeefzoMKJbt5afT6Qjyn03i9SikUjm9hs3ErpKjcfmF8MP8+aifNG3jxTvte5KboyVgjS8pd2+Sk5NNh27Dqd+4nbFMxIm9HP/9Z76c9XOR4tj20zwSrhn6a/+R35ocK2fDjrIvcA3mEikt2n5M647d+fvwr5w8vBuAHG02N+Kj+H7dQe7dSSTA7zvMzM2RyeQMGj0Ha5vyRWqXS6cPc/gXQ3/18PSmyVP6zI5V49Fma7C2qYD3oz4DkK3JYv33Q/AeMocKlQ39MuNhCmtm92b03N3I5BZFikWn0/HTyv9xLS4GqUzGyC8n4FC5qsk6GrWaWdO+5vOvJlKlmiM5OTms+N887ty+hblEwsjR31ClmuMzaiiaaxcPEfHXSszMJdRp3B2XZr2eut65YxvJTE+mWSfDMXs2ZD1RobtQWNkC0Np7JjYVnn3cPsuVyEMc3/cj5uZSXFt2x631E2NJegq/rhtPjlZNmbIV+XDQfGRyJYnxZ/krcAHo9ViWrcBHny7kYug+zp0MBiBHq+H2jUt8ufAECpX1c2PQ6XQE+s0h8ZphbP9k+MwC4+tvO1cjkUho1taLFu17GJelPbjHosk+fD51LfZVnEiIv8yu9fMN46tUTt9Rc7Eu4vj6WL0aEt5rIkenh1MXtfxzMcdkuY2VGb3bW2D+6LGDOw6rUWfDgPcVxnWq2Jmz928Nf18wLSsUnzdvjvUJY8eOJTs7u0hlAgIC0Gq1XLp0iRUrVhRbLIsXL8bb25tTp0699Da2bNkCQEhICAEBAcUVWqGcDT2ENlvD+Hlb+LjvVwRtWmRclpujZeeGhYyetoYxM9dz/OAuHqQmE30hlNioM3w9ZxNjZq4n9V7SS9V9PuwvcrTZjJm1lS69x7J7y0KTun/d/B0jJq/li+kbOHkokIf3k7kQfgSAr2ZuoVPPUfy62VAmeOMCug+awhfTN9CwSQf+2uNfpFhyc7T8EbCAvmP9GDhhMxEhO0h/cNdknZA9P1K/WRcGTdxKpeouhB/N21fhR7dzJyG6wDb3bf4W6Qs+lHNztOzduoAhE9cxbOom/j0cSNp907r/Cl6JW/MuDPfdQmXHt/n3UMBzy+XmaAleb1p3sP+3dOk7meG+W1AorYg8ubdIbfQsTuM+o8GaOZgripZ8FNb5sL/QajV8OetnPnxGPxk2+Sc+n76Bfw4F8vD+XcKP70VlVZYvZmxm6MQ1BG+YayxzM/4Spw4HodcX7RnFkf8eRqvVMHHeJrz6fcXOjf8ziSNwwyK+9F3NuJl+xmOlRduPGTfLj3Gz/Kju5ILPpxNQWVqzY/1CfIZMZNwsP9yatef3X9YXKZbcHC37ty5g8IR1fDZ1E6FHCvaZw7+sxLV5F4ZN24KD49uEHjb014TY8/w0tz8pd24Y171y9jjrv/+M9Af3ihTHY/+ePIY2O5t5P6yi36DhbFz3o8nyq1cu4ztxNLdvJRr/FhF6ktzcXOb9sIqevQfy86afXqruJ+lytZzcu4DOQ/zoOnwzl//dQWaaadvkaNUc2v4NF06afpFITrzIuz4L6Dp8M12Hb36pJDI3V8vBwPl88pU//cZv5vSxgAJjyfG9K6nXtAv9v/kZ++pvczokAL1ez4HNvnw4cD79J2zDqV5rHty7ScMW3vQdt5m+4zZTqXo9OvpMe2ESCXAu9BA5Wg1j52yla+8x/LLZ9LgJ3vg9n09dw+gZG/j74E4e3k82Lgv4aRYyeV7iFrRhAd0HT2b0t+tp2LQ9f+0u2vj6mLk5fNzKgtW7s1gRlEXzejLKqExn8zq/I+fYWS0/BmdxMDybLs0tSMvU82NwFj8GZ7H3pIaEu7mcvFiKkkgz8+L7V0qUnkj+I4sXL0YulxepzJo1a9DpdLi4uPDFF18UWyz79+9n06ZNNGvW7KW3sWrVKgA8PT3x8fEprtAKJebSad5u1BKAms6uXI+5aFyWdDOOCpWqobKyRiqTUatuI2IuR3DpzN9Urv4WaxeOYfV3o6nv0eal6o6NOk1dV0PdNd5y5UbsBeOy2zdjsbOvjsqqLFKpjJp13Im9HE6DJu3pNXQGACl3b2FV1jCL0//LhVSpURcAnS4Xmaxo/SP5VizlKlZHaVkWiVRO9doeXL8SbrLOjasR1KrfGoBaDTyJu3QSgISY09yMicSjjem+Oxj4PR5tfChT9vnPB76TGEt5e0PdUqmcGs7uxEeZ1n0tOhznhq0AqNOwNVcvnHxuuf3bFtKs3SdY2+TV/SDlNo7OjQBwdHYnPjqiSG30LJmx1wnvObpYtvU0cVER1HU1vHfH5/YTOTXruBN3OQLXd97jg15fGtczlxhO1GSk3WfftsV0GzCpyHFcvXyaem6G/urk3JBr+eK4lWA4VizzHStXL+W177WrF0i8EUPrjoZZn8/GLqBazUf9NTenyP317hP73rA/TftMfHQ4bzUwtJuzq6HPAOTmZNP3q+VUcKhpXNfM3IxPJ/qhtCpbpDgeu3zxHG4ehjHQuW49Yq9GmSzP0WqZMG0OlavlzYhVrlKN3NwcdDodWZkZSKTFczIt9U4s1uWrY6EyHMv2NTxIijNtm1ytBmf3j2nUbrjJ35MTLnDm8Fp+XdWH04fXvFT9927FYFshbyypVtuDG1fDTNZJuBqOUz3DWOJUz5P4S3+TcjsOpaUNoX9tZMuifqgz7lO+Ul4ieyv+HMm3rtLIs3CfEbFREbg8Om5qOLtyw2Rsj8WuUt746lS3ETGXDG30y5YfaNmhF2VtKxjXH/jVQqrmG1+lspf70mhva07yAx1ZGsjVQdytXJwcTFOWX49ruHgtFwCJGWhzTb/wdfe0IPCIhiJ+D/xvmZkV379S4o06ta3Vavn222+5du0aOp2OMWPGMHnyZA4cOEBKSgq+vr5oNBosLCyYPXs2Dg4OrFy5koMHD5Kbm0vv3r2RSCTcvXuXsWPHMnDgQLZv387ixYvZvXs3GzduRC6XU6NGDWbNmsWePXs4evQoarWa69evM3ToULy9n37adsWKFSQlJTF8+HCGDRvGihUrkMlk9OrVC4VCwdatW43rLl26FBsbG+bMmcPZs2fRarWMHj2aK1eu8ODBA2bMmEHDhg2JjY1l/Pjx+Pv7s2/fPqRSKY0bN+abb75h+fLlJCQkcO/ePRITE5k8eTKtW7d+pfZVZ6WjVFkZX5ubm5Obm4NEIiUrMx2lqoxxmUJhSVZmGulpqaTcvcXISSu4dyeB1d99yfSluzEr4kFgqDtv+2b56lZnZaDIF5dCaYk6Mw0AiUTK1pVTOBf2F4PGGGaFHg96cdGnOfb7z4z+dmORYtGo07FQ5sUiV1iieVSfcZ2svHUsFJZostJIu3+Ho7tX0OvzFVwMO2BcN/JEEKoy5ahVvzUn9q99ft1Z6Sbv1UJpiTrLtG5De5TJtzz9meXCQ4KxLGOLc8NWHNmTV3e5ClWJvfQvTi5NuXT6MNmazMI2z3MlBf+B0rFKsWzrafK/dzDto+qsdJNlFkpDH7VQWBrLblwyhk69Rj+6LMKXjwdMNJltKUocpseKxKS/Kp/or1mZ6cbXB4L86NJrhPH14/4ac/kMR34LYNwsvyLGko5CmW/fK/KOj8c0+fuMwhLNo3gcnd0LbK92/ZZFqv9JWZkZqCwtja/z7yOAum83KFBGoVRy904SXw3vR9rDB0z6dsErxfCYVp2OXJHvWLawJFtt2jYWqrJUdW5FVFiQyd9ruXamXou+yCws+WPzaK5dOoyjS9si1a9Rp6N4cizJSi+wzuOxRP5oLMlKT+Vm7Gne+8QXW3tHAleMoFL1+tRwaQ7A3wfW0KrLqELHoc40HUOfHF+f7K/qzHROHfkFqzK2uLi15OCv64zLjeNr1BmO/baNL2duKHyD5KOQgzo7LwNUZ4PCwvRzI0Nt+G8FGzM+ammB3/4s47J6NSQkpei4e780ZZFvpjcqkQwMDMTW1pZ58+aRmppKv379jMu+++47+vfvT5s2bTh58iSLFi1iyJAhhISEEBgYSHZ2Nj/88ANTp05l1apVLF68mDNnzgCQmprK8uXLCQ4OxsrKinnz5hEQEIBKpSI9PR0/Pz/i4+MZMWLEMxPJL774gqCgIPz9/Tlz5gwajYbAwEAAVq9ezdq1a1EqlUyfPp3jx4+jVCpJTU1l586d3L17ly1btjB27Fi2bNnCjBkzCAoyDGpRUVEcOHCA7du3I5VKGT16NIcPHwZALpezbt06Tpw4gb+//ysnkgqlFeqsvIRCr9cZB3+lygq1OsO4TK3OQKkqg6WVDfZVaiKVybCvUhOZ3IL0hymUKVu0a7wMdedtX6/XG+tWKC3RqPPiUmdloLDMO53T9/N5PLyfzBLf3kxc+CsWChWnTx7gz+C1DJ2wEivrcoWK4XDwEm5cCed2QjRVnBoa/56tzsAiX4ICYKG0IludgUyuQKM2fEhfCvuNzLRUti0dRvrDZLTZauwcanLmeBCYQdzFv0m6cZlf/SbiM3olVmXzvuX/EbiE+OgIkm5EU61WXt2GBMD01JVCaYkm61HdjxIEC6UVmnzt97jc339sxgwzrl44ya3rlwlcM4kBY3+kx7B57Nk8j5B9flR1aoBUWrRZsNfl8Xt/zLSfWKFRm7aB0tKw31Lv3WLDD1/RouMnuLfswvWrZ0m+dY1dfrPRajXcvhnDLxvn023g5ELHkf940Ot0Jv01f19W54sjM+MhSYnx1KnfxGR7YSd+58CudYyaspwyZQvXX//cuYRrj/pM1fx9Rp2B8ok+Y6G0RPNEf/2vKFWWJuOITpe3j55l7y+BuLk3pe+g4STfvc2MKWP4348bkBfx+szHQn9fQlJ8OCm3oqlYLd+xrMkwSSyfRa/X06DVQOSPErzqddtw7+alQieSR39ZTEJMBHcSoqhc03QsyZ9YAlgo8sYSw1hjjdLKBtsKjthVrg2AU73WJF0/Tw2X5qgzH3LvdiyOdd4pVCwACpWlybGRf2x/Vn8NObAVzMyIPv8PN+Oj2PLjVIZOWI61jR0Rf//GH8FrGTbpx0KPr491aibHqbIEh/LmXL+dmxejHNSagklh7SoSerSxYOtBtUnS2LiOlJBIbZHqLhHiru3SLTo6mvDwcM6ePQtATk4OqampxmVr1qxh3bp16PV6ZDIZcXFxNGzYEIlEglKpZNq0aU/d7o0bN6hduzZWVoZvZU2aNOH48eO4urpSt65hCt/BwaFI12LWrJl3qqh8+fJMnDgRS0tLYmNjcXNzIy4uDjc3NwAqVKjA2LFjn7qd2NhYXF1dkclkADRu3JgrV64A4OLiAkClSpWKfJ3o0zjVdeNc2FE8WrxPXHQklau/ZVxWqUpN7t66TkbaAywUKq5eDKdD14HI5BYc2beV9l0G8CD1Lhp1FpZWNkWuu6ZzIy5EHKFR8w+IvxKJQ7W8uu2rOHE36RoZ6Ya6Yy+H07bLIEKP7ebBvdt06DYUuVyBmZk55uYSwo7t4e+/Ahk1fQOWRTg919ZrDGC4Lmj19C5kpd9HrlBxLTqUd97/1GTdarUbcfXcUVxbehNzLoRqbzWmaYcBNH10g0nkiSCSb8Xh2tIb15Z5Xz42fd+fzv1nmiSRAO/1zKt78aQuZD6qOy4qjNadTet2dHYnKjIED08vos4eo2YdDypWduLe7WsFyjVo+r6x3Nq5A+g2eAZlbCpw5uQ+egydi7VtRXZvmoNzw1f7ElJSHvcTt+YfcO0p/SQ5Ka8NYi+H826XwaTdT2btvGF4DZ6Kc33Dh2/12g2ZsMhw40vK3ZtsXja+0EkkQK26bpwNO0rjFu8TG32WKvmOFYeqNbmT/1i5FEHHjwz94srFCFwamF76cipkH8f+2MnXM9dhWabw/bVjj7w+s3RyXp+JjwqjVacn+sxb7kRHhuDe2ovoyGPUqONR6HqKqu7b9Qk79TctWrcj+vIFqtd48bWFllZlkEokAFiVsSY3JxedTvfSMTR539A2ulwtO37ogjrzPjK5iqS4UFw9P31BacNMZuDirvQatw+pXEXi1X+o06R7oetv080wnufmavlpxodkZdxHbqHixpUwmnUcYrJu1VruxJw/SsMW3sReCKFabQ9s7KqRrckg5c41ylV05MaVMFxbGS6FuHEllBp1WxQ6FoCadRpxIfzR+FpgbHfibtJ14/gacymcdl0H4fbOe8Z1ls8cTK/PfLG2sSP02B7+PriT0d+uL9L4+tiBU4bPKnNzmNRHhcoCNFpwqizh8GnTxLB2FQlereWs2ZNFapppklm1ooS4JE2R6/+v6UvRKeni8kYlkk5OTlSqVIkRI0agVqtZtWoVv/76q3HZp59+iru7OzExMYSGhuLk5MS2bdvQ6XTk5uYybNgw1qxZg5mZmckgVbVqVWJiYsjMzESlUvHvv/8aE8GinqJ97PEdsGlpaSxbtowjR44AMHjwYPR6PU5OTvz222/GdcaMGYOfn1+Bi/6dnJxYv349OTk5SCQSQkND6datG5cvX37p2J7FtWl7Lp/9h0VT+4NeT79Rswk9tg+NOotWHXvQfeB4VswdgV6no3k7L2zK22NT3p6rF8P5fnIf9DodPp9NwfzRB0JRNGjSnqhzf7N0el/0QO/hswk/sQ+NOpMW7Xvycb8JrJk/DL1eT7N3vbApZ0/DJh3YttqX5TMHkpubQ7cBE5FIpQRvnI+NnQPr//cVALVcGtOpZ+GvhZVIZXTsNZGtSz5Dr9Ph1qo71rb2ZKXfZ89GX3qNWk6rD0ey238SESGBqMrY4jV00Ys3XMi6P+wzCf/vh6LX62js6U3ZcvZkpt8nyM+Xfl8tp+3HIwhcM5nQI4a6Pxm58JnlnsXO3pENi4YjkytwersZdd1e7trWkla/SQeiz51k2fS+gB6f4XOIOLEXjTqT5u178VG/Cax91E+avOtF2XL2/LJxPlkZDzgYtJqDQasBGDpp9Uud0n7MrWk7LkX+w/dTBqAHBo6ayb/H9qNRZ9K6Yw96DhrPsjkj0ev1tGj7MbblDfvidmI8dvZ5p/51ubkE+H9HObtKrF74NQDO9Tzo6vN5oWORSGV06jOJDQsN+94jX58J9vOl76M+s3Ptoz5jZYvP5wtfvOGX1LS5J5Gnw5gybiQAo8ZM4tiRP1FnZdGx00dPLdOlW09WLvmOaRO+IEerpc/AoSgUyqeuWxTmEhnNu0zkgN9n6PU66jTujmVZe9SZ9wnZ6ct7A5Y/tZxcWYYmH4xl79qBmEvlVKn1DtXrFv0YkUhktO8xie1Lh4BeT8MW3Slja09Wxn32b5pG95EraPnhSPasn8iZ4ztQWdny0ZAfkEjldB4wl91+49Dr9VSt1YjaDd4F4F5SHDZ2VZ9f8RMaNmlP1NmTLPbtB3o9fUbOJuz4PrLVmbTo0BOvAd+wau5w9Hod77Q1jK9Po9PlErR+AbZ2Dvj/YEjWa7k0pnOvwp9mz9uW4RrI4R8pMTODU5dyeJChR2UBPu0UrD+gpltrORKJGX06GI7VO6k6Ao9osFSAJluc0i4pZvqi3o5YimVnZzNt2jQSExNJT0+nT58+rF69mgMHDnDnzh1mzJiBRqNBrVYzdepUGjVqxJo1azh06BA6nY7evXvj7e3NxIkTSUxMZNSoUQQEBLB48WL27NnDxo0bMTc3p3r16sydO5d9+/YZr1PUaDR06tSJQ4cOPTO+du3aceDAAc6cOWO89lKv1zNmzBji4uJQqVRYW1vTqFEjRowYwZw5c7h48SK5ubmMGjWKNm3a0L9/f+zt7WnRooWx7vXr17N//350Oh0eHh5MnjyZFStWYGdnR+/evYmJiWHGjBls3rz5ue138Gzp+PaWnVM6pv5TMkrH9yyVRek5RC1aurzuEADQn7rw4pVKgKWs9Jw6u5f539wJX1R1yt998Uol4I/zz79xrSSVtykds1CVbF79zFRx+f146Yhl8RdWL16pGGUd3vrilQpJ2bZvsW3rVbxRieTTeHp6cvjwYSQvMQv2/xuRSJoSiWRBIpE0JRLJgkQiWZBIJAv6/zaRPLKt2LalfLd3sW3rVZSOT8r/yOjRo3F1dS3RJDIgIIC9ewv+5t7XX39No0aNSiwOQRAEQRCE/9obnUguX/7061v+Sz4+PiX++46CIAiCIJR+4mYbQRAEQRAE4eWUoifSFJc37x0JgiAIgiAIJULMSAqCIAiCIJQEcWpbEARBEARBeClv4JNt3rx3JAiCIAiCIJQIMSMpCIIgCIJQAsRd24IgCIIgCMLLeQPv2haJpGAkl+S87hAA0ObKX3cIAGhzSsc3R5289DzZprQ8UcasWb3XHQIAmYAs7NzrDgMA89LRXUlIK/e6QwDgbccc4m/LXncYAEhKSe7w74VSEgjw8bviaXNvCpFICoIgvKTSkkQKBZWWJFIQ8tOX4IykTqdjxowZREVFIZfLmTNnDo6OjgXW8/X1pWzZsowfP/6l6ik9X08EQRAEQRDeZGZmxffvBQ4ePEh2djYBAQGMGzeOBQsWFFhn+/btREdHv9JbEomkIAiCIAjCGyY8PJzWrVsD4Obmxvnz502Wnz59msjIyFd+rLM4tS0IgiAIglACivPUdkBAAAEBAcbXPj4+Jklheno6VlZWxtcSiYScnBykUil37txhxYoVrFixggMHDrxSHCKRFARBEARBKAnF+PM/TyaOT7KysiIjI8P4WqfTIZUa0r7fftQRZW4AACAASURBVPuN1NRUhg0bxt27d1Gr1Tg5OeHt7V3kOEQiKQiCIAiC8IZxd3fn8OHDdO7cmTNnzuDs7GxcNmDAAAYMGABAUFAQsf+PvfOOiup4//DDNnYpggXBhoqIJRYUuwa7SYyJir0bjT1GjRoxloi9JSbGXrBgw4K9J2KNMQqKighSBMWKYqHssuzu74/FhbUCwfL1N885niM7c+f97Mw7c9+dmXsnOjpXQSSIQFIgEAgEAoHg3fAOn9pu3rw5p06donPnzhgMBqZPn87u3btJSUn5z/sisyICSYFAIBAIBIJ3wLs82UYikTB58mSzz8qUKfNCvtzORJrs/KerBQKBQCAQCAT/bxEzkgKBQCAQCATvAnFEouD/G3q9nvXLZnDzegQyuYJegydQuIizKT3k7DF2b16OVCqlftPWeDb3QqtNY/WCSdy/cxOVlQ1d+3njWNSZWzei8Vs8FYPBQIlSbnT59kck0uwdk6XX69nqO4X42AhkMjmdB0zGwSlTx+WgoxzcthiJVEadRm2p27Q9er2OTct+5v6t61hIpHQdOIVCTs7cvH6VLSsmI5FKcShSks79JyOR5KxzX7t4hFN7FyKRyKhSrx3un3Y0S09JesiulaNI16qxsSvMl71moElNYueKH0x57t0Mo1HbkVTz7ALArZgQAgPm0m2kX7Z1hJ0PJHDHIiQSKR6eXtRsbK4j+WkimxePQpumIZ+9A179pqOwVAGQpkll1ey+ePWdikNRF/R6HdtXTiThTgwSiQSvb6dT0NH5ZWbN0Ov1BPhO4VZcODKZgo79fSjklHl6QmhQIIcDjG1Tq2Fb6jTtgC5di//SCTy8H096ehrN2gygUo0mpmuCT+3h5MENfD95Q7brIqfY16pC+emj+KdZz7dmA4z1s3nFVOJjw5HJFXQd6GPmu5fOHeXAtiVIJFLqNG5L/WbtAZj1YweUVsZXdxQsXIzug6fmyG5YcCB/7ViERCqlhqcXtV7iG5sWjSI9TYNtfgfaZ/jG665LevyABRPb02fMSgoXdSH+eig7VvkgkykoUrI8rbr/9Np6yKs+vOb3UTx5lADAw/u3KFW2Cr2Gzc1R/byM6MtHOHNgIRZSGZ/Ubkfleh1fmi/46GpSniTQ4OvcnQSSlYiQI5zYvRCJVEbV+u2o7vncWPL0IdtXjCI9TY2tfWG+6j0DuaWKfw6t4sLJrVjZGo+k/LKHDwWdXDi1bykRIUfQpWvxaNSFap92yLU2t2IWfFpZgkEP56P0nI8yP641nxV8XUeCRGKBBbDnjI4HT3NtDr1ez8bl07lxPQK5XE6PQT+/cL/Zu2UpEqmM+k1a82nzdvx9ZCenj+4CQJuWxo3r4cxZ+ScJd+NZv3QqMrmC4qXK0anPjzke598GBj6Qs0zzkPdaqwEBAcyda975R4wYQVpa2iuvqV+/frbLb9KkCRqN5o02c8rcuXMJCAj4T2X8r3Dh30C02jTGzlyDV/ehbF49z5SWnq7Ff9UvjPh5EaOnrOD4oQAeJyZw4nAAlkoVP81aS5dvf2TDCuPb9LevX0DbbkPwnrEKTZqaC2ePZVvHpXN/oU1LY8SU9XzVdQQ7/eaY0nTpWnasncWgn5Yx9OfV/P3XFp48SuBy0FEAhk1exxcdhrAj45qD2xbxWbuBDPPxQ6fVcuX88RzViU6n5a8tM+j8vS/dRvpx4aQ/SY/vm+U5tXcRFWu2ovuoDTiWqMj54/7Y2DnQbaQf3Ub60ajtDzg6V6RqA+NN45+Dy9nvNx5duuZlJl+uI13LvvUz+ebHFXw7bi1nj27h6SNzHYE7FlG1biv6j19HkZIVORtofOfYzejLLJ/Wg4f3bpjyXj0fCMCACRto6vU9+zbMypaOy+f+QqvV8P3kDXzZZQS71pm3zU6/WfQfu5zBE1fzz5EtPHl0n6CTe7CyseO7SX70G7OU7aunma6Jvx7GmcAADIa3d8a4y8hvqbx0KhKl5Vuz8YyLZ4+g1WoYOW09X3cdzva15vUTsGY2Q8YtZZjPav7+aytPHiWgTTP6wbBJqxg2aVWOg0hdupY962fSd8wK+o9by7+BL/rGX9sX4V63FQMmrKNoyYr8e8T/tdfp0rVsX/UzMkVmnW33/ZlW3cYyYMI6lCobQk7veaWmvOzDvYbNZejPq+k78ndU1ra06TkmR/Xz0jrTaTm2fQZtB/vSYagfl//2J/mJeZ2lp6k5sHYUF0/kzQ8cXbqWw/4z6DrCl56j/Th//MWx5MSeRVSq1YpeYzbg6FyR4OPGPnwnLpTWfWfRc7QfPUf7UdDJhevhZ7gZdZ7eYzbSc7QfTxLv5FqbxAJaeEhYf0TH6j91VC8rwVppnqdxVQlnIwys/VPHyVA9Tdz/W0hx4d9AtGkavGespW33YWxd86spTZeuZcvquQybuIRRk1dy4vA2HicmUK9Ja0ZOXsnIyStxLlOBTn1+xMo6H35LptDxm9GMnroKlZUN/574b+9KFLya9x+eP8e8efNQKBTvW4Ygg2thF6hUrR4AZcpVITbqiintzs0YCjuVwNomHzK5nLIV3LkWdp7bN2KoVM0Y8DsVK8Wdm9cBGDR6Dm6feJCu1fIkMYF89gWzrSP66nkquBvLLFW2KjeiQ01pd+OjKeTkjJWNHTKZHJdy1Ym+GkSVmk3p1G8SAIkJt7G1M9orVqoCKUmPMRgMqNXJSKU5m5h/cDuK/A7OKK3tkMoUFC/jwc3Ic2Z5bkYG4fKJ8UQBl0qexF7925RmMBg4vGkKn3WZhERinJG1d3Cm7YA/cqTj/q1oCjo6o7K2QyZTUNKtOtcjgszyXI8IomzlBgC4Vf2UyNDTAOjS0+g27A8cipQ25a3o0Yw2fXwAeJRwCxu77LVPTHgw5asabZR8Wds4PmsbBaXLVSfmajBV67Tg847fm/JJMtog+ekj9m6cR5ue3jmqi5ySEh1HUIehb9XGM6KvBlPR3Vg/pd2qEpe1D8VH42Dmu9WICgsiPjacNI2ahVP7M9+nLzERITmyee853yjlVp3r4ea+ERsRhFsVo65yVYy+8brr9m2cQ+0mnclnX9hUxuOHdynpVg0gw/+CX1MPedeHn7F/60I8P+uKXX6HHNXPy3h4Jwr7Qs4orYz9uqiLB/FR5v06PV1DhVptqNli4H+2B5BwJ4r8hY31LZUpKFHWg7hr5jZvRAZRppJxLHGt5ElMmHEsuR0byql9y1g9qwun9i0FIDr0JA7F3NiyaAj+CwZStkqjXGsrZAcPnxpQp4FeDzfuGXAubD6bdihYz7V44w8+iQWk63NtDoDIsPN8knHvcHGrQmxUpo/cvhmDQ5b7jWuFakSGZfrb9chQbt+IwrOFcUb/0YO7lCnvDoBreXeirp7/b+LyCIOFJM/+fSi896XtkJAQ+vTpw8OHD+nSpQtLly5l//793LlzB29vb2QyGcWKFSM+Ph4/Pz/S0tIYOXIkt27dwt7envnz5yOXy19Z/sSJE4mPj6dgwYLMmmU+w+Lr68vevXuRyWTUqFGD0aNH8+TJE0aPHk1SUhI6nY5hw4ZRt25dDh48yOLFiylQoABarRYXF5dX2vT29sZgMHD79m1SUlKYNWsWlpaWDBo0CHt7ezw9PfH09GTqVOMsg729PdOnT0er1TJ8+HAMBgNarRYfHx9KlSrFsGHDSEpKQq1WM3r0aGrXrk39+vU5deoUYJzF7dy5M/Hx8Wzbtg29Xs/333/Po0ePWL16NRKJBA8Pj1wdyK5OSUZllflmfIlEik6XjlQqIzXVPE2psiY1OYkSpd24GHSCarUbEx1xicSH99DrdEikUh7cu8WvPoNQWdngVOzFw+NfhSY1CaXK1vS3hURi0qFOTUapytRhqbImNcW4viKVyli/6Ccunv2Lb4Ybf906OJVkm+9UDm1fhlJlg2vFmjmqE406CcssWhRKazSpSa/MY2lpjSY1c70n8uIRChUtS0GnTB8qX/0zHiXczJEOdWqS+fdWWqNOMV9X0qQmo7SyNaVrUow6S7pVf2mZUqmMrUu9uRL0J12G/p5NHZk2wPikYGbbJJmlPWsbS6W16do1vw3ni45D0et1+C+bQOueY5ArlC/YyUvubD+EqmSxt2rjGcb6ydqHnvPd5/tQShKOlkqafNWLek3bcf92LItnDGL8b7uz/aNHk5pkVq6lyhp1qrlvZG03Y3rSK68LOr4da9v8uFVpwNHdy0zpBRyKEx32Ly4VahF2PpA0TcrrNeVRHwZ4+vgB1y6foW0ezEYCpKmTUDzXr9PU5v1aaWVHyfINCD2TNytSz9fJS8eS1MyxRKHMbMdPan5JjcZdsVTZsGXRd1wLCSQlKZHHD27ReegSHiXcxH/BIAZNOYBFLp4UtpSDRpv5d1o6KJ+71aZmLKAUtIVm1SVsPqbLsZ2sqJ+7p1hkud88n6ZUGvvKM/YHrKRVh8wAv5BjMSJCz+H2SQ0unjuGRp36n7TlGR9QAJhXvPdAUiaTsXLlSuLj4+nfv7/p89mzZzNw4EAaNmzI5s2biY+PByAlJYURI0ZQvHhxevToQVhYGFWqVHll+V26dMHd3Z3Zs2ezefNm03FB4eHh7N+/n02bNiGTyRg6dCiBgYH8+++/1KtXj169enH37l26dOnCn3/+yZw5c9iyZQv29vZmOl9FiRIlmDVrFseOHWPOnDmMHz+e+/fvs23bNhQKBR07dmT69Om4urqyZcsWVqxYQbVq1bC1teWXX34hMjKSpKQk4uLiSEhIYPXq1Tx48IDr16+/1m6+fPlYvHgxjx49omvXrmzbtg2VSsXo0aM5depUjrYGACitrFGnmr8Z/9nNTKWyRq3OvHGoU5OxsrbFvXYjbt+MYe7E/riWr0pJlwqmvZAFCxdl2sKdnDi8nc2rfqXP9+avJngVliobNOpMHQaDwaRDqbJGk0WHJjUZlVU+09/dBk/nq0cJzBvfBe+5O9m+ZiZDJ62lSAlXThzcyM51c2jfZ/wbNRzfOY8bkcHcjw+naOlMn0tTJ5sFlgCWShvS1MnIFUo0mmQss+gJPbOLGk1yvy/v8NbfiI0I5s6NCIqXydShUZt/bzDekDXPdKjNA75X0X7ATJ4+us8Sn04Mm7kHhaXVa/MrVdZoUl/VNubtpklNRmVt1JD44DarfxlGveadqV6/FXGRF0m4Hcu2lVPQajXcjY9ix5oZtOk19s2V8gHzYv3on/PdzDR1Rv04FClFISdnLCwsKFy0FFY29jxJTCB/IafX2jq05TeuZ/hGiay+kZqM8jnfeKZLrlCafnBYqmzMtD677u9DflhgQWToaW7HXWXLUm96jlhI+/7T2e03neN7V1LcpTIy2atXk/KyD1sqrQg5c5jq9VuaZvVzy9975xEfHUzCrXCcSr6+X+cVgduNY8m9m+EUczG3+XwftVRljiVp6mSUqnwYDAZqNetlyutauSF3blzBytqeQk4uSGUKCjq5IJNbkvL0Idb5sr/607iqhBIOFjjaQ/yDzO0lChmoX7LrrJSjBV/UlLDj7/+2PxKMfpD1fmPQP9dXsqSp1ZljSUryE+7GX6dc5cxJgV5DJuPvO5uDO1ZTyvUTZHKx0vm2eO+hccWKFbGwsMDBwQG1Wm36PCoqimrVjEsmHh4eps/t7OwoXrw4AIUKFSI19dW/MuRyOe7uxqnt6tWrExMTY0qLjo6matWqyOVyLCwsqFGjBteuXSMqKoqaNY3O6OjoiI2NDQ8fPsTGxob8+fNjYWFh0vU66tSpA0C1atVMdosXL25ato+KisLHx4cePXqwbds27t27h6enJzVr1mTw4MHMnz8fiURC2bJl6datGz/88AM+Pj7o9S+uHWTdS1a6tHGpMi4ujocPH9K/f3969OhBVFQUN27ceOHaN+Fa3p1LwcaZz6jwixQv6WpKcypemnu340h++ph0rZaIK8G4lKvC9chQXCtUY/SU5VSr3QQHR+PMz4Lpw7l7Kw4ApcoqR7+SXcpV48r5EwBcvxZCkRJlTWmOxVy4fyeW5KTHpKdriboaRCm3qpw9vovDO5YDoFAosbCQIJFIsbKxQ6kyzojZ5XcgJelJtjR4th5Bt5F+DJ1zisR7caQmP0KXnsaNyHMUczH3iWJlqhN12bgHNPrycUq4ZvrwnbhQipV5+Yxgdmjefjjf/rSWsX+c4OHdWFKSHpGensb18HOUcHU3y1uybHUiQox7QCNCTlCqnMfLigTg/KmdHMuYbZJbqrCwkGBh8eabdGm3aoRdMNqIfUnbJNzJ1Bh9NYhSZd15+iiBZdP782XXH6jd2PgOM2fXKvw4dxeDJ66mx/dzcSxW5n8+iASj74Zm+G5MRAhFnDPrx6mYC/dvx2X6blgQpd2q8k/gdravNe7lfvzwHurUJPLlL/RGWy06DKf/uLWMW3CCB1l8Iyb8HM7P+4ZbdcIzfCP84glKl/OgcFGXl143YPw6+o/3o/+4tRRxLk+HATOxtXfg6oVjtO83jd6jlpKS9AjXSvVeWw951YcBIi6dpoL7p2+skzdR78sRdBjqR/+pp3h8Pw51Rr+OjzpHkVJvHutzQ+O2I+g52o8Rv5ziYZaxJC7ixbGkeJnqRF4yjiWRl4/jXNYDTWoSSye1Ik2djMFg4PrVMxQpWYkSrh5EXT6BwWDg6aO7aDWpqGzsc6QtMETP2j91/LJNR34bC5QKkEjAubAFNxPM9y2XcrTgMw8JGwJ13H743+oEjPeby8EnAYiOuEixkpk+UuS5+821K8G4uBmD8GtXgilfpbZZWZeCTtBryCSGjltA0tPHVKhS578LzAMMFhZ59u9D4b3PSL4qmHBzc+P8+fM0bNiQkJCQN+Z/GVqtlrCwMCpUqMC5c+coWzbTKV1cXFi1ahXp6elIpVLOnj1LmzZtSExM5Ny5c1SsWJG7d+/y5MkT7O3tefr0KQ8fPqRAgQJcunQJJ6fXzwyEhoZSo0YNgoODTXazPjFWunRpZs2aRdGiRQkKCuL+/fucOXOGwoUL4+vry/nz5/n1118ZP348ycnJLFu2jHv37tG5c2caN25Meno6ycnJyOVyIiMjTeU+s1G8eHGKFCmCr68vcrmcgIAAKlSokO26e0a12o25EvIPM8f2xmAw0Pu7SZw5vh+NOgXPFu3o2PsH5k0egsGgp0HT1uQvWBiZXM7OjYs5tHMtVta29BryMwCfe33Dqj9+RiaXo1Ao6Tl4QrZ1VK7ZlPBLf/PbhG4YgK4DpxB0ci8adQr1mnWgTY8fWTK9PwaDgdqN2mJfwJEqtZqxcckE5k/qhU6XTtteY5ArLOnc34e180cjkcqQymR06ueTozqRSuU06eCN//y+GAwGqtRrh21+R1KTH7HfbzxeAxdQv+Ug9qweQ8jJzahs8vN1318A4xOYCqV1rpaaXtAhk/NFV29Wz+mHwaDHw9MLuwKOpCQ9YvvKCXQb9geNWw9k67KxnD26BSub/HQaPOeV5X1Soznblo9j+bTu6NLTadltLHLFmx9GqVSzGRGXTjN/YjfAQKcBUwk+tQeNOoW6TTvydfcfWTbD2DY1G7XFroAjO9bMIDX5MX8GLOHPgCUA9PNe8taXtN8HVWo15erF0/w6vjsGg4Fug6dwLsN36zfrQNueo1k0bQAGvZ46jY2+W7eJF+sWjmPehJ5gYUG3QVNytJdXKpPzZVdvfGcbfaNGFt8IWDmB7hm+sWVphm/Y5qfzoDmvvO5VFHIsyeq5A5ArlLhUrE1594bAy5c387IPA9y7fZ2ChYvnoCXeUGdSOZ5tvdm+2NivP6nTDht7R9TJjzi8aTxf9V2QZ7ZMNmVymnf0ZsM8o033Bu3IlzGW7Fkzng6DF9Cg1SB2+Y7h/InNWNnkp02/X1BYWtG47Qj85vZEKldQunxdXCs3BCDu2ll8p7XHYDDwedeJuZ6x1RvgcLCebk2kWAAXovU8TQWlAr6qLWHLCT0tPCRIJdC6rtHGgycG9v6b+42S7rWbEHbxH2b91BODAXoP8eHfE/tQp6bg2aI97XuP4vcpgzAYDNRr0pr8BY2+eSf+OoUczbeqFC7izB/TvkNhqaTcJzWp7PHff3TkBR/S3sa8wsLwNh+NfAPPznccNWoUGo2GL774AoD9+/dz9+5dfvrpJ6RSKba2tiQnJ7Nq1aqX7g2sXbv2S8v/7LPP8PDwIDY2lqJFi5qOB3pmc9WqVezbtw+9Xo+Hhwdjx47l8ePH/PTTTzx+/Bi1Ws2wYcPw9PTk33//ZcaMGdjZ2SGTyWjZsuUr3wbv7e3N/fv30Wq16PV6ZsyYgYWFBT/88AObN28G4PLly8yaNQudzjjoTps2DXt7e0aMGEFqaioSiYQhQ4ZQs2ZNRo8eza1bt5DL5XTq1Ik2bdqwcOFC9u/fT/HixdHr9fTt25f4+HjTdwPYuXMnGzduRKfTUaxYMWbMmIFKpXplexwPTX5l2rskOe3DWIK48+jVe2/fJbZW/3EHex6ilH8YWixqf/K+JQAgP3fpfUswkaR57/MCAKjk/22fXF5x/e6H0X8BbF6/O+SdEXMj/X1LMOFZVfvmTO+ARpVefU98Gzy8eCLPyipQ5cMIjt9rIPk6du3aRdWqVSlZsiRbtmwhODiYGTNmvG9Z2cLb25uWLVvi6en5vqXkCBFImiMCyRcRgaQ5IpB8ERFIvogIJF/k/20geelknpVVIOONHO+bD2PkeQlFihRhxIgRqFQqJBIJ06dPf2m+ixcvMmfOi8t1X3zxBV27dn1r+tLS0ujbt+8Lnz/boygQCAQCgUCQFbG0LfioETOS5ogZyRcRM5LmiBnJFxEzki8iZiRf5P/rjOSDy3+/OVM2Kfiah9veJR/GyCMQCAQCgUDwkfMxHpEoAkmBQCAQCASCd8DHuLQtAkmBQCAQCASCd8EH9P7HvOLjC40FAoFAIBAIBO8EMSMpEAgEAoFA8A4wfITzdyKQFAgEAoFAIHgHfEhHG+YVH19oLBAIBAKBQCB4J4gZSYFAIBAIBIJ3gHhqW/BRE/vA+n1LAEAq+TDeke9g92G8vDefpeZ9SzCh038Yg6D2A3kRuLZG5fctwYQ68Or7lgBAPuWH8dL6skU1hN34MA43KGjzYYwlduU+jP4LcDNR+b4lvBc+xvdIfjheJRAIBAJBHvGhBJECwceOmJEUCAQCgUAgeAeIpW2BQCAQCAQCQa4QT20LBAKBQCAQCAQZiBlJgUAgEAgEgnfAx/iwjQgkBQKBQCAQCN4BH+MeyY/vGwkEAoFAIBAI3gliRlIgEAgEAoHgHSCWtgUCgUAgEAgEueJjXNoWgaTgjUSEHOHE7oVIpDKq1m9Hdc+OZukpTx+yfcUo0tPU2NoX5qveM5Bbqvjn0CounNyKlW0BAL7s4YN9oRLsXvMTjxPi0aWn0eDLQbi5N82WjvALRzi+exESiRT3Bu3waPi8jkS2LRtJulaDrX1hWn8zHbmlypS+e80EVNb2NGs/0vRZ8pMHLJvcjh4jfSlUxCVbOvR6PQG+U7gVF45MpqBjfx8KOZU0pYcGBXI4YDESqYxaDdtSp2kHdOla/JdO4OH9eNLT02jWZgCVajQxXRN8ag8nD27g+8kbsqXhmY71y2Zw83oEMrmCXoMnULiIsyk95Owxdm9ejlQqpX7T1ng290KrTWP1gkncv3MTlZUNXft541jUmbiYcDaumIVEIkUml9P3+ynksy+YbR0bl0/nZmwEMpmcHoN+NtNx8dwx9m5ZikQqo17j1nzavB1/B+7kdOAuANK1ady4Hs7sFX/y4N4t/FfOwkIiQS5X0Hvo1GzreF7T5hVTiY8NRyZX0HWgDw5OmZounTvKgW1LkEik1GnclvrN2gMw68cOKK1sAChYuBjdB0/Nse2cYl+rCuWnj+KfZj3fSvlvo9/cjA7hz61z6f2jX7Z16PV6/FdMI/76szaZZOYnl84dZd/WpUglUuo2aWNqk4PbV3Dp7FHS07V4ftaJek29uBFzlS2+MzL8VUHP76blyk8AYkKPcPbQIiwkUirWascndTu+NN+FY2tIeZpAvVbGeogMOUjwkeWABZ/U7cgndTrkyK5er2er7xRuxRn7Taf+k8189HLQUQ5ljCO1G7albtP26NK1bFw6gYf3b6FLT6N5mwFUqtGY+OtX2bJyMhKpFAenknTqPxmJJPsBi16vZ/uqycYxTa6gw7eTzca0K8GBHN6+GIlESq2GXtRukvld4yJD2LvpVwaNX2P67NLZP7l45iDdvpuTozoBiLhwhON7Mv31xftNItuXj0Sr1WBrV5ivn/PXPWsnoLKyp2n7kaRr09i1aiyPEm5iqbTm824TKehYKseaBK/n4wuNP3CaNGmCRvPqI+8OHz7M3bt3X5nu7e3N8ePHX5l+9uxZrl7Nu6PSdOlaDvvPoOsIX3qO9uP8cX+SHt83y3NizyIq1WpFrzEbcHSuSPBxfwDuxIXSuu8seo72o+doPwo6uXDpzC6srO3pNWYDnYct58DGKdnWcdB/Jt1/WEnvMX4EH9/8go5juxdSuXYrvvFej5NzBc4d8zelnTu6iXs3I14oc8/an5EpLHNUJ5fP/YVWq+H7yRv4sssIdq3LHCx16Vp2+s2i/9jlDJ64mn+ObOHJo/sEndyDlY0d303yo9+YpWxfPc10Tfz1MM4EBmAw5OxoyAv/BqLVpjF25hq8ug9l8+p5prT0dC3+q35hxM+LGD1lBccPBfA4MYEThwOwVKr4adZaunz7IxtWzATAf+Ucunw7htFTllO9dhP2b1+dbR0h/wai1WoYM30tbbsPY+uaX83qY8vquXw/YQkjfVZy8s9tPE5MoF7j1oycvJKRk1fi7FKBTn1+xMo6H5tXzaFT3zGMnLwS99pNObhjVY7q5BkXzx5Bq9Uwctp6vu46nO1rzdsoYM1shoxbyjCf1fz911aePEpAm2bsl8MmrWLYpFXvJIh0GfktlZdOobSnewAAIABJREFURaLMmQ9ml7fRb07tX8Hu1eNJ16blSMvFs0fQpmkYNX0drbsNI2DtXDOdW1fPYej4pQz3WWXyk4jQs0SHX+CHqWsZ7rOKxAd3ANi6ahYd+oxluI8vVWs35fAO35xWjdGuTsvJHTP5esBKvIb4EfrPZpKfmNdPepqaQ+tGc+lU5o88vV7H6b2/0nrgKtoP28T5wJWkJiXmyPblc3+Rrk1j+OT1tHrFODJw7DK+m7ia00e28ORRAudO7sHaxp7vJ62l/5glbMsYRw5uW0QLr4F8P8mP9HQtV86/+h7xMkKD/kKrTWOoz0ZadvqB3etnm2nZtW4m/byXM2jCGv4JNI5pAIG7V7Jl+UTS0zLvaTvXTme//zwMhpwflalL13LIfybdRqyk148v99fjuxdSqXYreo8x+mtQFn8NOmbur+dPbEahtKLPT/581nU8BzZk737zNjFgkWf/PhREIPmBsXbtWpKSknJ9/bZt27h3716e6Um4E0X+ws6orO2QyhSUKOtB3LVzZnluRAZRptKnALhW8iQm7G8AbseGcmrfMlbP6sKpfUsBqOjxOQ3bDDNdK5FIs6fjdjQFntMRGxFklifuWjCulTN0VPYk5srpDH3niY8OwaNhJ7P8hzbPxqNRJ2ztC2e3OgCICQ+mfNUGAJQsW5Ub0aGmtLvx0RRydMbKxg6ZTEHpctWJuRpM1Tot+Lzj95nfW2pcDEh++oi9G+fRpqd3jjQAXAu7QKVq9QAoU64KsVFXTGl3bsZQ2KkE1jb5kMnllK3gzrWw89y+EUOlavUBcCpWijs3rwPQ74cZOJcuBxhvknJ59o+Xi7x6nk/cjWW6uFUhNkt93L4Zg0MWHWXKVyMyLNiUHhsZyq0bUXza3Dj79O2ImZQoXd6oQ5eeIx1Zib4aTEV3YxuVdqtKXNa6iY/GwelZG8lxKVeNqLAg4mPDSdOoWTi1P/N9+hITEZIr2zkhJTqOoA5D31r5b6Pf5HcoQcchf+RYS1TYeSpm+N6LbWL0E6ssfhJ1NZiwC39T1Lksy+YMZ8msoVTyaAhAnxGzs/iJDpkid36SeDcau0LOKK2M9VOktAe3o83rJz1dQ/maranRbIDpM4lESrcxe7FU2aJOfoQBA3JLqxzZjg4/T/mqxvoo9dpxRE7pctWJvhqEe53P+KJjpr88G0eKlapAStJjDAYDmtRkpNKcLTY+P6bdjMmi5VY0BR1LYmWdMaa5VScm3FhHBR1L0HPE72ZllSxbDa9vJubI/jOe91dnVw/irpm3x43IYNP9pkxlT2LCjP56M+o88VHm/nr/VhSulTwBKOTkQsLt6FzpyksMFpI8+/eh8OEo+QgJCAhgyJAh9OrVi6+//pqDBw+a0iIiIujTpw+9e/fGy8uL4OBgjh49SlhYGGPGjCEtLQ0/Pz86depE586dWbt2rVnZWq2Wn376iW7dutGlSxfOnDnD5cuXOXHiBHPmzOHWrVt4e3vTtWtX2rVrx759+3L1HTSpSShVtqa/FUprNKlJL+SxzMijUFqjTn0KwCc1v6Rl90n0GLmGG5FBXAsJRKG0xlJpg0adxLYl39OozfDs6VCb67BUWqPJsJM1zzMdlhk6nj66x7FdC2jZzXxgu3AyAGvbArhmDEg5QZ2ajNIqU4tEIkGnS89ISzJLs1RZk5ryFEulNUqVNerUZNb8NpwvOg5Fr9fhv2wCrXuOwVJlnXMdKcmoMpZhjTqkJh2pqeZpSpU1qclJlCjtxsWgExgMBqLCL5L48B56nQ77Ag4ARF4N4cg+f5p/1T1H9fEqHc+nKVXWpKZk+s/+gJW06jjQ9LddfqOOqKsXOHrAn6atsq/jeU1KM00SM03Kl2hSWCpp8lUvBo9bSud+E1j7h7fpmrfFne2HMGjfno287jcAFWt8luNABYx9Q/WKNklNSUKVpd8olcZ+k/Q0kbioK3z7wy906Tee1b97YzAYTH4SHX6BYwc20uTLHjnWA5CmTkKRdXyztEajNq8fpZUdzuUavHCtRCoj6uIhNs1tQzGXmqagLrsY6yPTtsUbfFSd8hRLpZVpHFn92whaZgSVDk4l2b5mBjNHfc3Txw9wrVgzR1qM4/zL20aTmoQqS5qlyhp1Rh+uUqsFUqncrCz3ul9gkcvTW7L6ImTcb1Ke89dUc3/VZPHXL57zV6cS5bl28SgGg4GbURd4mngXvV6XK22CVyP2SL5lUlJSWLVqFQ8fPqRDhw7odEYnjoyMZMyYMZQrV47du3cTEBDA1KlTqVChApMmTSIuLo59+/axYcMGLCws6N27Nw0aZA5mW7ZsIX/+/EyfPp3ExES6d+/O3r17+fTTT2nZsiX58uXjzJkzbNu2DYBTp07lSHfg9nnciAzm3s1wirlUMX2epjYPogAsVTakqZORK5TGdFU+DAYDtZr1MuV1rdyQOzeuULZqYx4/vM3WRUPwaNSVSrW/eq2OIwG/ERcZxN0bERTPokPzMh3KTB3P0q+cO0BKUiLrf+9P0uMEtGlqCjmV5vzJACwsIDrsb+7EXWX7yjF0GboIGzuHN9aNUmWNJjXZ9LfBYDDdWJUqGzTqzDRNajIqa6POxAe3Wf3LMOo170z1+q2Ii7xIwu1Ytq2cglar4W58FDvWzKBNr7Fv1ACgtDLeUJ6h1+tNOlQqa9TqFFOaOjUZK2tb3Gs34vbNGOZO7I9r+aqUdKmARGqcFT578iB7t63k+3HzsbXLny0Nz+pDneU7G7LoeHbTy6rjWX2kJD/hzq3rlKtkftM7d+og+7etYMhPf2BrVyDbOp7XZN5G5pqyttEzTQ5FSlHIyRkLCwsKFy2FlY09TxITyF/IKVca3idvq9+4N/DKtSalygZ1aqZPZm0TlZWNmQ+p1cmorGyxtrHHsVhpZHI5jsVKI1dYkvTkIbZ2BQk6dYADAcsZPHZhjv3kn32/cSsmiAe3InAsmWV80ySbBTJvokyVFrhUasafm8Zy9dwOKtZql+1rjfXxqnHEGs1z/VdpnQ8wjiO+vwyjQfPOeNT/EoDta2fy3c9rKVLClZOHNrJz3Rza9xmfbS2Wz41bBn2mlufTNKnJZgFwXhC4/TduXAvi7s2IF+43lq+53zzz17BzB0h5msjG3/uT9CTDX4uUxr1BOxJuR+M3pyfFXatTpOQn2V4Fe1t8SEvSeYUIJN8yNWvWRCKRUKhQIfLly0dUVBQAhQsXZtGiRSiVSpKTk7GxsTG7LiIiglu3btG7d28AHj9+TFxcnFl6UFAQFy9eBCA9PZ3ExMw9OjY2NkyYMIEJEyaQlJTE119/nSPdjduOAIx7Vpb8/CWpyY9QWFoRF3GOOi36muUtXqY6kZeOUbW+F5GXj+Nc1gNNahJLJ7Vi0OR9yC2tuH71DO4N2pH0JIEN8/rwedeJlK5Q9406mngNN+lYNKEVqUmPUCitiIs4S73P+pjlLeFajWsXj+HewIvIS8dxLluD2s16UjvjIYYLJwNIuBODewMvsxvi6tk9aNXDJ1tBJEBpt2qEBh/Fve7nxF4LoUiJsqY0x2IuJNyJJSVDZ/TVIBq1+oanjxJYNr0/bb8Zh1ulOgA4u1bhx7nGB04e3o/Hb/6obAeRAK7l3Qk5d5ya9VsQFX6R4iVdTWlOxUtz73YcyU8fY6m0IuJKMC1a9+R6ZCiuFarRqc8orkde4f6dmwD8c2wvxw5tY/Tk5Vjb2mVbA0CZ8u5cPHeMGvU+IzriIsWcM+ujyHM6IsOCaf61sT2uXQmmQuXaZmWdOb6XE4e28oPPihzryIpLuWpcCjpK9XqfExMRQpEsmpyKuXD/dhzJSUZNUWFBNP26N/8EbudW3DU6fTuexw/voU5NIl/+QrnW8D55W/3mv+BS3p1L547hUe8zYiJCKGrWJqWNbfLMT64E0eyrXsgVlhzdu56mrXryOPE+GnUq1jb2/Ht8DycPb2H4JN9c+Umdlhn1o9OyYVYr1MmPkFtacSv6LNUa9XnD1caZzD0rBtJ6oC9SmQK5QoVFDpcan40j1ep+zvWXjCP378SafDT6ahCNW/Xm6aMElkzvT7ss4wiAlY0dSivjqka+/A7EhJ/PkZZSbtW4EnyUqnW+IPZaCE5ZtRR9fkw7R8Mvv8lR+W+icdtMf10yMdNfYyPOUucl/vrsfhN16TglytagVrOe1Mrw15BTASTcjqFqfS9uRp2nRNnqtOg8llvXL5F4/0ae6s4NH+NZ2yKQfMuEhhr3miQkJJCUlETBgsYnC6dNm8bcuXMpU6YM8+fPJz4+HgALCwsMBgMuLi64urqyYsUKLCwsWL16NW5ubhw4cAAAFxcXnJycGDhwIGq1msWLF2NnZ2e6/t69e4SGhrJw4UI0Gg0NGzakdevWyGQ5a3KpTE7zjt5smNcXg8GAe4N25MvvSGryI/asGU+HwQto0GoQu3zHcP7EZqxs8tOm3y8oLK1o3HYEfnN7IpUrKF2+Lq6VG3Jw01TUKU84sWcRJ/YsAqDLsOXIFco36mjRaQzr5n2LwaDP1JH0iF1rJtBpyB94thrEDl9vgk9swcomP1795762zNxSqWYzIi6dZv7EboCBTgOmEnxqDxp1CnWbduTr7j+ybEZ/DAYDNRu1xa6AIzvWzCA1+TF/Bizhz4AlAPTzXvLG7/06qtVuzJWQf5g5tjcGg4He303izPH9aNQpeLZoR8fePzBv8hAMBj0NmrYmf8HCyORydm5czKGda7GytqXXkJ/R63RsXDmHAoWcWDR7FABun1SndedB2dLhXqsJYSH/MPunnhiAXkN8+PfEPjTqFD5t3p4OvUcxf+ogDAYD9Rq3Jn9BRwDu3rpOIcdipnL0Oh3+vrMoUMiJJXN+yNDhwVedBue4bqrUasrVi6f5dXx3DAYD3QZP4dzJvWjUKdRv1oG2PUezaNoADHo9dRq3xb6AI3WbeLFu4TjmTegJFhZ0GzQlV0u4HxIfUr+pWqspVy/+w9xxPcBgoPuQKZw9sReNOpUGzdvTrtcoFkwbiEGvp26TttgXdMS+oCORV4KYPbYrBr2eTt/+BMAW35nkL1SE5XONP3hdK3rQqtOQHGuSSuU0aD2GXcuM9VOhVjts7B1RJz/iyOYJtPzm5XtBFUob3Dy+ImBBdyRSGQWLlKOcR85+rFeu2ZTwS3/z+8RuGIAuA6YQdMroo/WadqB19x9ZmjGO1G5k9NGANTNITX7CoYAlHMoYR/p7L6FTPx/Wzh+NVCJDKpPRqb9PjrRUqtGMa5f+ZsGkrhgMBjoNmMb5U3vQaFKo06QjX3Ufw/JZ/THo9dRs6IVdAccclZ9djPebMaz/7VsMenN/3b1mAh2H/EGDLwexy9eb4ONbsLLNT9t+r/bXAoVLcXTHfE4f9EVplY+ver/9B+j+P2JhyOmjooJsExAQgL+/P0qlkqdPnzJ8+HAmTZrE/v372bBhA5s2baJgwYI4OTmRmJjIqlWrmDdvHidOnMDX15etW7fy559/kpaWRpUqVZgwYQLjxo2jZcuW1KlTh/Hjx3Pr1i2SkpLo2rUrHTt2ZNOmTaxfv57ffvuNNWvWcPHiRaysrGjUqBH9+/d/rV6/nD3o99aQSj4Ml8xn9WHspcln+eqn/N81Ov2Hsa1aq3+/y1PP0Nao/L4lmHgcmHdva/gvFM6Xs6e53xZhN3L3AM7boEyRt7vXNrukfyD9F+BJ6oehpfun73aGMDIqJs/Kci1TOs/K+i+IQPItEhAQQHR0NKNGjXrfUrKFCCTNEYHki4hA0hwRSL6ICCRfRASSL/L/NZC8FhWbZ2WVLVPyzZneAR9GSwoEAoFAIBAI/uf4394A9IHj5fXfNqcLBAKBQCD4eBBPbQsEAoFAIBAIcsW7DCT1ej2TJk0iPDwchULB1KlTKVkyczl8z549rFmzBqlUipubG5MmTcrR0ZrPEEvbAoFAIBAIBB8Zzx7W9ff3Z+TIkcycOdOUplar+e2331i7di2bNm0iKSmJwMDAXNkRM5ICgUAgEAgE74B3OSMZFBTEp58aT29zd3fn8uXLpjSFQsGmTZtQqVSA8V3UlpaWubIjAkmBQCAQCASCd0BeBpL+/v74+/ub/u7UqROdOmWeNZ6UlGR22IlUKiU9PR2ZTGY6KAXAz8+PlJQU6tevnysdIpAUCAQCgUAg+B/j+cDxeWxsbEhONj9GN+uhJHq9njlz5hATE8Mff/yR6zPSxR5JgUAgEAgEgneAwWCRZ//eRPXq1Tl+3PiC6AsXLuDm5maWPnHiRDQaDYsWLTItcecGMSMpEAgEAoFA8A54l3skmzdvzqlTp+jcuTMGg4Hp06eze/duUlJSqFSpElu3bqVGjRr06tULgJ49e9K8efMc2xGBpEAgEAgEAsFHhkQiYfLkyWaflSlTxvT/q1fz5jQsEUgKTFQucv99SwDgfkq+9y0BgEep8vctAYBHyVbvW4IJpeLDOL5S8oG801f9gRxLCGDXuPz7lgDA6bWh71tCBjoqu34YjvJE/WHcauPvvW8FmXQtH/K+JWTg/k6tiReSCwQCgUDwP8CHEkQKBFn5GANJ8bCNQCAQCAQCgSBXiBlJgUAgEAgEgndAdp62/l9DBJICgUAgEAgE7wC9WNoWCAQCgUAgEAiMiBlJgUAgEAgEgnfAx/iwjQgkBQKBQCAQCN4BH+MeSbG0LRAIBAKBQCDIFWJGUiAQCAQCgeAdIJa2BQKBQCAQCAS54mNc2haBpCDb6PV6Vi76hdiYSORyOQO+98apaHGzPBq1mqkTRjDwe2+KlSgJwPbNfgSdOUl6upYWX3rRpEWrXNn2XzGN+NhwZHIF3QZOwsHJ2ZR+6dxR9m9bikQipW7jNtRv1h6AmT92RGllA0DBwsXoMXgKN69fZYvvDCwkUmRyBT2HTCOffcEc6bl6PpDAnYuQSKRU9/SiZqOOZunJTxPZvHgU6VoNtvYOeH07HYWlCoA0TSqrZ/elbd+pOBR1AeDY7mVcPX8EXbqWWk27UKNh+2zpiLhwhON7jDrcG7Sjuqe5jpSniWxfPhKtVoOtXWG+/mY68gwdAHvWTkBlZU/T9iMJORVAyKntAKSnp3EnLowffj2J0ip7R1aGnQ8kcIdRi4enFzUbv7xOtGka8tk74NXPvE5Wze6LV5Y6SX7ykKVTujB02i7kCsvX2w4O5K8di5BIpdTw9KLWS2xvWjSK9DQNtvkdaJ9h+3XXJT1+wIKJ7ekzZiWFi7oQfz2UHat8kMkUFClZnlbdfwKkr9QUfuEIx3dnto1HwxfbZtuykRk+UpjWz7XN7jUTUFnb06z9SNNnN6ND+HPrXHr/6Pfa+sgt9rWqUH76KP5p1vOtlJ+VskUt8KwkQW+AC9F6zke9/PjNWuUssFFacCRED0DlUhbUrSBBkwYhMXouROfs2E69Xs+O1ZO5HReOTKag3beTKeRU0pR+JTiQv7YvNvpEQy9qN+7wymvWLxhJ0qMEABIT4inhWpVu3/2SIz3hF45wdKfRB6t92o4aDV/03W1LR6JNM/pJm75G3730zx5OH16LxEKCY4lyfNnjZ0L+3sGFkxl9WGvsw6N+P4kqm334GbFXjhD81yIsJFLK1WhHhdodX5rv0ok1pCQlUPsLo49ePL6K8LPbUNrkB+BTLx/sHVxyZPsZer2eeUtWEnU9FrlczujvBlC8iJMp/a/jp9i6ax8SiQSXUs6MGNiXdJ2OWfMXc+vOXaytrBg+oA/FixbJlX1B9hGBpCDbnP3nBFptGlN/WUrE1cv4rVzA6AkzTelR166yYuEcHiRkntkdejGYiLBLTJ6zmDSNmt0BG3Nl++LZI6RrNYyato6YiBAC1s5lwI/zAdCla9m2Zg4/ztiIQqni1wk9qVyjESorWwCGT/I1K2vrqll06DOW4qXKc/LwFg7v9KVdr9HZ1qJL17Jvw0wGTdqM3FLF8qndKO/eGFt7B1OewJ2LqFq3FdU/bcuxPcs5G+hP/c97Ex9zmZ2rJ/Ek8a4pb3TYv8RFnqff+A1o01I5uX9VtnUc8p9J3/FbUFiqWDWzK25VG2Njl6nj+O6FVKrdiqr1vTi1bxlBx/yp06I3AEHHNnHvZgQl3WoBULW+F1XrewGwf/1k3Ot7ZTuI1KVr2bd+JoN9jHWybEo3yld7rk52ZKmT3Zl1cjP6xTq5dvEkBzf/StLjB9myvWf9TL6bbLS9ZHI3Kjxn+6/ti3Cv2woPz7Yc3b2cf4/4U7d5t1dep0vXsn3Vz8iyBLDbfX/mq+7jKOlWjUNbfiPk9B4q1Gz9Sk0H/WfSL6NtfGd0pZy7edsc272QyrVb4d7Ai5P7lnHumD91M9rm3NGMtilXy5T/1P4VXDy9E7nl2zl73WXktxTr/jW65NS3Un5WJBbQorqElQd1pOngm2ZSIuJ1JKsz88ik0KqWhKIFLbh6wxgsqhTQqIqE5Qd0qNOgexMJMXcNPE7Ovu0rQX+Rrk1jyKSNxEaGsHfDbHr9sBDI8KV1M/luymYUlioW+3SnYrVGxF678NJrngWNKcmPWTatN191885RPejStRzYOJP+E7cgt1SxcprRT2yz+smuhVSu04pqDbw4sXcZ5476U7NxZ/4K+J3BU3ahsFSxZckPRIQcpVoDL6o1MPbhPX6TqfapV46DSL1Oy+k9M2n73RZkChW7FnelZMXGWNlmakrXqjm+bQL34i5SunIL0+cJt67QqNNMHIpXypHNl3HyzFnStFoWzZ5KaHgEi339mDbOOE5rNGmsXO+P7/w5KC0tmTz3d06fDeZuQgIqpZLFc6YRd/MWvy/1ZY7PuP+sJS/5GJe2xcM2Hxjr1q0D4Pjx4/j7+78yX0BAAH/99de7kgVAeOhFqlavDYBb+UpEXbtqlq7VpjFy3HSKFc+cKQwJ/hfnUmX4ZdpPzJo8huq16ufKdtTV81RwN15b2q0qcVFXTGl34mNwcCqBlU0+ZDI5ZcpVIzIsmPjYcNI0qSyYOoD5Pn2JiQgB4JvhsyleqjwAOp0OuVyRIy33b0VT0NEZlbUdMpmCkmWrExsRZJYnNiKIspUbAOBW5VOirpwGjLMEXb//g0JFSpvyRl46iWNxNzbMH8q6eYMp794oWzoSbkdToLBRh1SmwNnVg7hr5jpuRAZTptKnAJSp7ElMmFHHzajzxEeF4NGw0wvl3rp+ifvx16j+krRX8UKduFXn+nN1cj1rnVT9lMhQoxZdehrdhv2BQ5Y6sZBY0GfMSlQ2dm+0fe8526XcqnM9/MX2cKtitF2uitH2667bt3EOtZt0Jp99YVMZjx/epaRbNYCM7xf8Sk3Pt02Jsh4v+EjctWBcKxvbxrWyJzEZPnIj8jzx0S+2TX6HEnQc8scb6yO3pETHEdRh6FsrPyuF7OBhkgG1FvR6iLtvwNnB/AYrk8DFGAMnQ/Wmz/LbwN1EA+o049+3H0Dxgjm7MceEB5t8oaRrVW7GhJrSjD5REqtnPlGuOjHhQa+9BuDwtgXUa9GdfPkdyAn3s/iJTKbA2c2DuOf9JCLTT8pW9iT6ymmkMgXfjttomtHX63TIsoxj8THGPlyjUfb78DMS70WTr6AzllZG33Us5cGdGHNNOq0Gt+qtqdZkgNnnCTdDuRC4jJ2Lu3I+cGmObWfl0pVwalWrCsAn5dwIj4wypcnlMhbMmozS0vhDT6fToVDIib1xk9rV3QFwLl6U2Jvx/0nD28BgsMizfx8KIpD8wFi8eDEAnp6edOr06kHAy8uLpk2bvitZAKSkJmNlbW36WyKVoNOlm/4uX7EKhRwcza55+uQx0ZFXGeE9hX5DRvPHXB8MhpwtRQGoU5NQZSxRA0gkmbaNabamNEuVNeqUp8gtlTT9qhdDxi2hU78JrPljLDpdOnYZg310+AWOH9xI41Y9cqZFnYSlKlOLIsNeVjSpySgzNFkqrVGnJAHGAMS+oPlSS3JSIvExl+n83Ty+7j2JLUtGZ6uONOokLFWZ31uhtEbzgo7MPJZKazSpT3n66B7Hdi3gi24TX1ruyb3L8Px6yBvtZ0WdmoQyS50Yv/Pr60TzmjpxrVQfK9v82bKtSU0ybV+AjPZPNbetzmpbZY06NemV1wUd3461bX5T4PCMAg7FiQ77FzAu46dpUl6tSZ2EMkvbPKv75/NkbRt1lrZp+ZK2qVjjM6TSt7eIdGf7IQza9DdnzAMsZaBJy/w7LR0s5eZ51FqIvmPeDx4+BQc7C6yVxhnLUo4WyHNYJc+3u8VzY4mZT2T03dddk/T4AZGh/1DDs03OhGDePzPtvegnz3xJkeFHEokEG7tCAPzzpx9pmhTKfJL5I/3EnmU0ap2zPvwMrToJhTLLuGJpTZraXJOllR3F3Ro8fyllqrbkUy8fWvVbzZ3rwcSGBeZKA0BySgo21pmz7xKJhHSdzvT/Avb2AATs2U+qWkMN9yq4li7F6XPBGAwGQsMjSHj4EJ1O/7LiBXnI//TSdkxMDGPHjkUmkyGVSpk9ezbr1q3j7NmzGAwGevfuzRdffEGPHj3Inz8/T5484csvvyQ2NpZRo0ah0Wj44osvOHLkCD169KBcuXJcu3YNKysratSowcmTJ3ny5Am+vr7Y2b18ZiQkJIRp06ZhMBhwdHRk7ty59OvXz2Rv2bJlTJo0idjYWPR6PcOHD6d27docOHCA9evXm8r5/fff8ff35/Hjx0yaNIkqVaoQHR1N586dGTlyJE5OTty4cYPKlSvj4+PDH3/8QaFChXBxcWH58uXI5XJu3rxJy5YtGTRoELdv32bChAloNBosLS2ZMmUKRYr8t70iVipr1KmZN0+D3vDGG5ttvnwULe6MTC6naHFn5HIFTx4/ws4+e0HCM5QqGzRZbRv0JttKlQ1qdebaliY1GZW1LYWLlMLByRkLCwsci5bC2saeJ4kJ5C/kRNDfBzgYsJxB3guxzVcgWxoOb/2N2GvB3L0RQXGXKqbP01KTUVqbLx9ZqqzOhXX6AAAgAElEQVTRqJORK5Ro1Mlmge7zWNnY41DEBZlMgUOR0sjkliQ/fYhNvpfv2wzc/hs3rgVx92YExbLqUCdj+ZwdS5UNaVl0KK1sCTt3gJSniWz8vT9JTxLQpqkpVKQ0Vet7oU55woM70ZQqXyf7dRIRzJ0bERQvk6nF+J1fXyfK19RJdji05TeuZ9gukdV2avILS/JKlTWa1AzbGUGlpcoGTWryC9f9fcgPCyyIDD3N7birbFnqTc8RC2nffzq7/aZzfO9KirtURiZ7cSb7SMBvxEUGveAjL/u+lsoX2+bKuQOkJCWy/vf+JD3OaBun0rhnLFf+r9OosoQSDhY4/h979x0VxfU2cPy7DXYpAhawYkERO2LvscQUNUZs2PVniSaaaKIBe4s1tsSSxIqiUcQWS5LXWNGoCWJvoIAQxYaKStlly7x/rC6sgAIhSsz9nMM5sDN37sOdZ2bv3jsz6ww376d3Em2UoNO/vLxWD3tPmejSVMGTFInbDyVSdLmL4fn9nvE8ptY4oMt4LtEmo7bPnCsZy5z/cy+1G7dDLs/+etnn7d+6iLgsjuHs8uTZcZOWYbnJZOK3zV9z/851un/yLTKZeYQqNeUxCbejKV8lZ8fwM2H/t4jb18N5cCsS1zIZziu6ZKuOZXYkSaJG037YPO30unu14P7Ny5St0jJXcTxjb2dHSmr6tQ4mSUKpSG9jk8nE92s3cOPmLaYFfI5MJuO9Ni2J/esmIydMo4ZXZTw9KqBQFKzxsjexW1uwWjiXjh07RrVq1VizZg1Dhw5l79693Lhxg02bNrFu3Tq+//57Hj9+DECHDh0IDAxEocj+YK9ZsyZr164lLS0NtVrNmjVrqFixImFhYdmWmThxIrNmzSIkJIRGjRoRFRVlVd+WLVtwcXFhw4YNLFu2jGnTpgFw/fp1li9fTlBQEOXLl+fo0aMMGzYMJycnpkyZYlXH9evXmTFjBiEhIYSGhnLv3j2r5fHx8SxevJjg4GBWrlwJwJw5c+jTpw9BQUEMHDiQefPm5bp9n1e5ag1OnzwBQOSVC7iXe/lF1JWr1uTsqT+QJIkH9xPQ6bQ4Oubumh2ACpW9uXj6CAAxkWcp6V7Jsqx4qfLcuxVHctIjDAY91y6HU96zFicObmf7OvP/nfjgLtrUJAq5FOXP0N2E/rqRz6aspqhb6Szry8rbXUYyaOw6Ar49woO7saQkJWIwpHE94iTuHt5W65at5EPk2VAAIs8doWzlOtlut6ynD1fPH0GSJB4/vEuaLhU7B+ds12/ZaSR9vwzi8wVHeXg3jtSkRIyGNGIjwyjtUdtq3TIVa3Pt/GEAos6HUqZSXeq36cvgSdvo+2UQTd4bTPX67S3XRsZGnqR8lUa5a5Nx6xi7+AgP7li3SZmKL2iTs0co94I2yYm2XUcyZPw6xi85wv0MdcdEnMT9+bo9fYh4WnfEuSOUr1wH15IVsiz30YT1DJkQxJDx6yjh7kXXj2bj6FyMK2cO02XwDPqP/oGUpEQqVm+cKaZWviPp/2UQoxce5UGGfROXzb65es68b66dD8W9Ul0atOnLkEnb6P9lEE3fG2y5hvJNcei8iaADRhZsN+LiKENtA3I5lHWVcSPh5aPwMhmULipj7T4jO06YKFJIxl/3cjfDUc6zNhFnzeeS2GtnKV4m/VziWrICCbcz5MSVk5St6P3CMlcvHqdyzea5iqF155EMCAhizKKjPLgTZ6kvNiKMMhWfy5NK6Xly9WmeAOxaOxmDXoffiKWWKW6A2IiTVKia82P4mXrvjKTDR0H0mXiUR/fj0KaYc/d2TBhuZWu/tLxem0TIwg7odclIkkT8tRMULV0t13E8U71KZU6EnwbgYkQkFcq6Wy2fv2wFaWl6vho32jLFHXE1ihpVvfhmxmSaNqxHCTfXTNt93d7Eqe1/9Yhkly5dWLFiBYMGDcLR0REvLy8uXrxInz7mqUqDwUB8fDwA5cuXz1T++enDatXMSV+oUCEqVqxo+V2ny/4j7/379/Hw8ACgV69eltef1RcZGUl4eDjnzp2zxPTw4UOKFCmCv78/9vb2REdH4+3tnXnjT7m7u+PgYJ5WKVasWKZ4PD09USqVKJVK1Gq1pd4ffviBlStXIkkSKpUq03Zzq16j5pw7HcbE0UORJIlhI8dx9NBetNpU2ryb9U0Hdeo34fKFs4z7fDCSycT/hn6O/AWd+ezUqt+aK+dOMH9CHyRJovfH0wk7ugedNpWmbbrg23c0S2cMRTKZaNiyE86F3WjUypegpRNYMLEfMhn0GjYNmUzGljWzcSlaghXzRgFQqWod2nXL+TSQQqnivR4BrJ1n/p98mvtSqLAbKUmJ7Fg9kZ6fLuatD4aydcVYTh4Kwc7RhW7Dvs52e17eLbkecZLvp3ZDMpno0HdijkY3FEoVb3fzZ8OiQUgmE95NO1PIxY3UpER2rZ1It08W07TdMHauDuBUqDmOToNf/IHi/u0YXIqVyXFbWLVJzwACvx6MJJmo09wXp6dtsn3VRHp9tpiWHYeyZflYwg6FYOfgQvePs2+T3NbdrmcAq+ea666boe5tqybS+2ndIT88rdvRBb9hX2dbLjtF3coSOO8jVDZqKlRtgJd3C8u1elnF1La7P+sXDkKSrPfNzrUT6f7JYpq3H8aO1QGcOmJuD98hf//D3r+FSYLfTpno9ZYCmcx81/aTVFDbQIf6ckKOZj1uI0lgNEkMfleBwQgnrphIzWYfZKda3TZcvXCMpVN7giTRdcgMTh/bTZo2hQatutG+lz+r5gwx50QLc05kVeaZe7diKOya8w+kGSmUKt7t4U/QfHOe1G5mzpOUpER2rpmI34jFtOgwjO0rAwg/bM6TLkPnEX/9IqePbMHdsw5r5/YDoOHbfalS520S8ngMPyNXqGjU3p9fVpljqly3M/ZObmhTEgndMpG2fbO+TtdG40i9d0exe3k/5EobSnk0xN2rRZ7jaNawHifPnOOTLyciIeH/6TD2HT5KqlZL5YoV+HnfQWpW9WLUxOkAdGn/HjWqebFqQzDBO3bhYG/Pl8M/ekktQn6QSXm5YK2A+Pnnn3FxcaFRo0bs3r2bBQsW0KRJE6ZPn47JZGLZsmX079+fYcOGMWXKFDw8PNizZw+hoaHMmTOH8PBwxowZY5nafrbOqFGj8PPzo0GDBsyYMQNvb2/atWuXZQydOnVi4cKFlCtXjuXLl1O+fHnWrVtn2da6detISUlh6NChaLVavvvuOwYNGsT777/PoUOHABgwYAAdO3akc+fONG7cmGPHjrFt2zbL1Pbnn3/O5s2bAejWrRsLFixg+/btlqntTZs2sXDhQgCaNGnC77//zvDhw/nf//6Hj48PUVFRhIWF4efn98L2PHP13guXvyr3UnI/YvlPSEz9+53v/KDTF5xPnmqbgnG6kBeQJtGmFZBAAKeWXq87BABOrbv48pVegRoVC86+0eoLxuTfzbuvO4J0Pb3Ovu4QACjhlf0gzj/h2OUnL18phxpX+XuXB+WXgpHdeVS9enUWLVpEz5492bRpE99++y12dnb07NkTX1/zdNCzkbxnmjVrxs2bN+nRowe//PIL9hluHsmLqVOnMm7cOHr37s3ly5dp0cL6E5ifnx/R0dH07t0bPz8/SpUqhYODAz4+PnTq1IlevXqhVqu5e9d8hHt4eDB69Oi/FROAv78/S5cupXfv3vj7+1O5cuW/vU1BEARBEPLuTZza/lePSAr5S4xIWhMjkpmJEUlrYkQyMzEimZkYkczsvzoiefRSLh56+hJNq/69gbD88q++RvJViY+Px9/fP9Pr9erV49NPP30NEQmCIAiCILx+oiOZAyVLliQo6J/5SjJBEARBEP4bTAVjUidfiY6kIAiCIAjCKyC+IlEQBEEQBEEQnhIjkoIgCIIgCK9AQbrbOr+IjqQgCIIgCMIr8CY+J0dMbQuCIAiCIAh5IkYkBUEQBEEQXgHTG3izjehIChYXbhd93SEAUNRR/7pDAMDFrmDEEXPX9nWHYFGrRMF4ovGNJ4VfdwgAFFJn/Z3Qr8PxAvIgcJ++1V53CBZPDl953SEAoFYVjDypV0n7ukOwWBFe/XWHAMCkV/wc/zfxGkkxtS0IgiC8cQpKJ1IQ3nRiRFIQBEEQBOEVeBNvthEdSUEQBEEQhFdAPJBcEARBEARBEJ4SI5KCIAiCIAivgPiubUEQBEEQBCFPxF3bgiAIgiAIgvCUGJEUBEEQBEF4BcRd24IgCIIgCEKevInfbCOmtgVBEARBEIQ8ESOSwktFnjlA6O5lyOUKvJt2xqd5N6vlKU8esn3FF+j1OhydXPlgwExUthrL8t3rJqKxc6Z1ly8AOPrzD0SeOYDRoKduy57UbtblpTGYTCZCVn1FfGwESpUNfh9NpVhxd8vyC+GH+HXL9ygUChq07ETj1unbfPLoPvPGdufj8ctxK1WBG9evsHXNLORyOUqlDb0+mUEh59x/PaTJZCJ45QxuXjfH1HPoFFxLpMd0/uQhft7yAwq5gkatPqRJG3NM/7d9JefDDmEw6Gn+Tncat/bNdd1ZiTp/gGM/L0WuUFKjUWdqNnluPyU9YM+a0Rj0WhycXHm3zyxUNhou/rGDsH2rsNU4Ur1hJ2o07prnGEwmEyuWLSA2JgqlSsWwT7+kRMnSVuvotFqmTficjz/zp1SZshgMBpYsmMndO7eQKxQMGzGGUmXK5rn+LaunczM2EqVShd9H0zLlyf9t/Q65QknDtzrRqHUXTCYjm5ZP5l78dWRyBT2HTqdocXfWfjOax4kJADy4F0+5SjXp99m8HMeRX7nxV8wVQlbPQi5XoFTZ0Hf4DAo5F8lT+1QqKaN5dTkmCc5EmzgdlfU8W/3KMhzUMg6cNX+1X41yMhpVkaNLg7MxJs5E/7Pzc871a+I1czQn2vT9R7YfceYAh35ahlyhoHazztRtYX2sJD95yNYfvkCfpsPR2ZUPB87ExlbD+RO7Of7bOuQyOW5lKtOuz2Qkk5HtKwNITLiJTK7ggwHTKVaiQpb1mkwmdgRO41ZcBEqlDZ0HTaNo8fRcv3TqIPu3f4dcoaBuC18atOz60jKnj+3m2N4NfDJlIwCH96zm7PGfkcnktPxgCNXrtXlpe5hMJjYsn8WN65EoVTb0+3iiVb6eDTvMrs0rUCgUNGndkeZv+2Iw6FmzeDL378Yjk8vpO2wiJUqXJy4mgvXfz0ChUOJW0p2+H09CLv/7Y1aepWQ0qyFHMsHpqMy5W8gOPmgoRy6XIQN2/2Hk/pO/XW2+exOntsWIpPBCRoOevcGz6TVqFf2+DOJU6GaSHt2zWid011KqN2hPf/8NFHevQvjhYMuy8MObuHsj0vL39St/cOPaaQYEbKTfl0E8fnArR3GcDzuAQa9j1Fcb6NBjJDuCvraKcfvauXw8/gdGTAnk2L4tlg6A0aAneMU0VDZqy/rbAmfTecBYRkxeQ836rdm/c3We2uZc2AH0aTpGz1xPx16fsW1deifDaNCzJfBrRkz4gZFT13B031YePUwg8mIY0RFn+PyrdYycuoaH92/nqe7nGY16Dm6ZRdcRq/EbGcTZo8EkP7efjv+8jCp129Pj8x9xLVOVs0eDSUl6wNFd39B9ZBB+I9dzKWwXj+7fyHMcfx4/gj4tjZnzv6N3/49Yu3Kp1fJrV68w0X8Ed27FW147FXYco9HIzPnf0bVHP35ctyLP9Z8/uR99Whqjpm+gQ89R/PRcnuxYN4dh45YzYnIgx/aH8DgxgQvhhwD4bNp63uv6iSW3+n02jxGTAxn4xTdo7B35sK9/juPIz9zYsmYOXf83lpFTV1OrQWt+25G3fJXLoK2PnA0Hjazdb8THQ4692nodpQI+bCSnbqX0twaNDbxVU866/eZy1cvJcLLPUwg5UuGLQdT44Svk6n/mO+aNBj2/bpxN39GrGBAQRPihzTx57lg5vHMpNRq2Z+C4DZQoW4WTh4LRp2nZv+0b+n+5lkETNqFNfULk2UNcPReKyWRk0IRNvNXxY/ZvXZht3ZfC92PQp/HJlI286/c5e36caxXX7vWzGRiwgo8mrOXPAyE8Sbz3wjLxsZcJO7QV6WnvJDX5Mb/vXc/HU35kYMAKdq2flaM2OfPnQfT6NMbOXotv7xFsDkz/HwwGPcFr5jNq8jLGTF9J6N5tPHqYwIVTv2M0GgmYFUiHrkPY8aP5WN8VvJwO3YbgP3M1er2e8+FHchTDi8hl0LaOnA0HjATuM+JTKXPutqwlJyxSYt0+I0cvmmjlXTC7N5Iky7eflzGZTEyaNInu3bvTp08fYmNjrZYfOHCAzp070717dzZv3pzn/6lgtrRgZdSoUfzxxx+EhoYSHBz88gL5KOFWNIVd3dHYO6FQ2uBesQ5xV8Ot1vnr2ik8qjcDwKNGc2IuHwfgRtRpbkadpU6L7pZ1oy4exbW0J5uXDWfT4mFUqvVWjuKIjjhFlVpNASjnWYu/oi5Zlt2+GU3R4u7YOTihVKqo4FWbqMvmGHesn0+TNt1wcilmWb/fZ19TupwXACaTEaUqb29YUZdPU7V2EwDKe9YiziqmGIoVL4OdQyGUKhUeXrWJunKKy2eOUdK9Esu/Hsn3c0ZQvU6LPNX9vAe3o3Au5o7azryfSnvU4UbUSat1bkaHU76qeT+Vr9qc2CvHeJRwA9fSXmjsnZHJ5RQvW4NbMWfzHMeVS+fxrtMAAE+vakRfi7BabtDr+XLCV5Qskz7aUbJUGYxGAyaTidSUZBTKvE+URF85TRVv8z4pV6kWf0VftCy783yeVPYh+ko4Neu1pvvgKQA8TLiFo5P1aN8vW5bS/J2eVjn0MvmZG/8bNZcy5Z/mq9GI0sYm9w0DFHWCB0kSWj2YTBB3T8K9mPWbkVIO52Ikjl40WV5zcYA7DyW0aea/b92H0kX+ueu8UqLjCO864h/b/r0M5zSl0gZ3zzrERVqf0+IiT1GxhvlYqVSjOdGXjqNQ2jBo/EZsns62mIxGlCobihQvh8loxGQyoUtNQqFQZVt3TMQpPGuaz2NlK9biRkx6ft6Nj6aIW1nsnsZVrrIPMRHh2ZZJfpLIL5sW0qHPWMs2bGw1uBQpSZoulTRtKrIcjgRevXyG6rUbA+BRuSaxGfP1Rgyuxctg/zRfK1Xx5url07iVdMf07LhNTUahMB+37hUqk5z0CEmS0GV4/e8o6gQPnphz0GSCv+5KuLta5+DeUyau3jR3qOUyMJiy2tJ/y759+0hLSyM4OJgvvviC2bNnW5bp9XpmzZrF6tWrCQoKIjg4mHv37r1ga9kTHcl/kebNm9O9e/eXr5iPdNokbDWOlr9t1PboUqznC3Sp6evYqu3RpT7hSeJdDu9cwnu9Jlmtm5qUyK3rF+gydBHv95nC9hVjLJ+mX0SbkozazsHyt0wux2g0mJelJqPJsEytsUebksQfh3bg4Ohi6Vg886xDEBNxhiO/bqRluz45aYrMMaUmWdUrzxBTakoSGrv0dlOr7UlNeULSk4fERV1i0Ofz6TF4AoHfBOTo/3+ZjPsAQKW2R5eaZLVOWmoSNk/XsVHbk5b6BBfXsty/dY3kxwno01KJiziOPi0lz3GkpiRjZ58+XJWxTQC8qtagaDE3qzJqjYZ7d2/z2Ue9+f7br3m/Q+c8169LTUKdoR2ezxO1Jn1/2WrM+wRAoVCyYdk4tgbOpFaDty3rPHl0n6sX/qD+Wx/mKo78zI1n+RodcYbDv26kVR7z1VYJurT0v9MMYPtcn0erh+jb1vn44AkUc5JhrzaPWJZzk6H6By+Kur19L5Le8PIV8+j5Y8VWbY/2+XOaNj2PbJ6e0+RyOQ5O5ktgTuwLIk2Xgke1JtjY2pGYcJMl495jZ+AkGrTp/cK6sz+PWS8zx5WUZRmDPo0tKyfQvrc/tmrr4WHnIiVY8GUHvp3YhSZts48lI21K8nP5qkjP1yzOr6nJSdiq7bh/9xYTR/iy7rvptGrXAwDXEu5sXPU1kz7tzONHD6hcvW6OYngRWxXo9Ol/pxlA/VzupurMD/su4ghtfOSEniuYPUmTlH8/LxMeHk6zZuYPRN7e3ly4cMGyLCoqCnd3d5ycnLCxsaFOnTqcPHkyu0290H/mGsmYmBjGjh2LUqlEoVAwd+5c1q9fT1hYGJIk0b9/f9577z369OmDi4sLjx8/pl27dsTGxjJ69Gh0Oh3vvfceBw4coE+fPlSuXJmrV69iZ2dH3bp1OXr0KI8fP2b16tU4OTllGcOGDRvYsWMHcrkcHx8f/P39uXHjBuPHj8dgMCCTyZgwYQJeXl5s2LCBkJAQihUrxv379wHYtm0b0dHR+Pn58fnnn1uGort168aCBQvYvn07sbGxPHz4kEePHtGzZ0/27t1LTEwMc+bMwdvbO8ftdXD7Iv66Gs6dG5GUqlDT8nqaNhnbDG+CALYaB9K0yahs1Oi0yajtHLl88ldSnjxk4zdDSHqcgD5NS9ES5dHYO1OkeHkUShuKFq+AUmVLypMH2Bd68TVfajt7dNpky9+SZLJ80lVr7NGmpi/TpiajsXck9JcNIJMReeEEN69HsH7peAZ/uZhCzkU5dexX9m5fzpCApTgUKpzjdrGKSeOANjW905UxJo2dA9oM8Wq1yWjsHLF3cMatVHmUKhVupcqjsrEl6fGDTKNgOXV010JuRJ0i4WYExcul7ye9NtnqzRLARuNAms68n8z7sRBqOydadhnLzhUjcHApjluZamjsXfIUC4DGzt6qTUwm6aUjErt3hODtU59e/T8i4d4dpowbyYKlgdjY5H6k2Fbj8FyeSFZ5otOmx6ZLTUZjV8jyd6+PZ9IhMYGFE3oQMO8nbNV2nP3jN3yavI9crshVHPmdG+G//8qv21bw8dilODrlLl/fqiGnTDEZbs5w8376u4+N0vrNOTtavXm0p0tTBU9SJG4/lEjR5SqEAmH/1kXEZXFOe3bOyshWbc6jZ8fKs+Umk4nfNn/N/TvX6f7Jt8hkMo7vDcSjehPe7voFj+7fInBufz7+aieqLGY6bDUO6DKcqyRTxvy0zl2dNhm1vWOWZW7FXSHhdiw71kxDr9dx92YUO4NmUbFqAx4n3sN/4W8ArJo7mHKetSnjkf7/ZkVtZ30ONZky5KvGHm2G40abmoydvSO/7dpAtdqN8O09ggcJt5k/+SOmLNxM8Kqv+fKrVZRy9+DgL8FsDlxAryFjM9WZEy1rZZ+72rTM65dzk/FePTk7jhXM6yPh1V4jmZSUhIND+ocAhUKBwWBAqVSSlJSEo2N63tvb25OUlJTVZl7qPzMieezYMapVq8aaNWsYOnQoe/fu5caNG2zatIl169bx/fff8/jxYwA6dOhAYGAgCkX2bx41a9Zk7dq1pKWloVarWbNmDRUrViQsLCzbMtu2bWP8+PEEBwdTpkwZDAYDc+fOpU+fPmzYsIHx48czbtw4njx5wrp169i8eTPLli1Dr8/B2f4ptVrNqlWraNu2LYcPH+b7779nyJAh7NmzJ+eNBbTsNJK+Xwbx+YKjPLwbR2pSIkZDGrGRYZT2qG21bpmKtbl2/jAAUedDKVOpLvXb9GXwpG30/TKIJu8Npnr99tRq4kuZSj5EXTiKJEk8SbyDXpeKxsH5pfGUr1ybS6fN19pcjzxLSfdKlmXFS1Xg3u04kpMeYTDoibocTjnPWnw6dS2fTglkxOQ1lCpXmd5Pb6oJO7KLI/+3kRGT11DUrUyu2iWjCl7eXDxljikmU0zluXcrjuQnjzDo9Vy7FE4Fz1p4VKnN5dO/I0kSiQ/uotOmYp+D/z87TTuMwm9kEMNm/07ivThSk8376ca1k5SsYL2fSlXwIeaCeT/FXAqllEcdTEYD8TFn8Ru1gff7zuHB7WhKevjkOR6vqtU5FXYCgMgrF3Evl/VNBxnZOzhiZ2ceVXFwLITRYJ4mzIsKGfPk6llKlEnfJ26lKnDvdmx6nlwx50lY6E5+22G+LtPGRo1MJrd0HCPPH6eKd7Pcx5GPufFn6G4O/7qRkVNWU9StdHZVZuvQeRNBB4ws2G7ExVGG2gbkcijrKuNGwsvf1WQyKF1Uxtp9RnacMFGkkIy/7v377hho3XkkAwKCGLPoKA/uxJGSlIjBkEZsRBhlKj53TqtUm6vnzMfK1fOhuFcyj6rtWjsZg16H34illilutb2TpaOpcXDCZNQjmYxZxlDOszYRZ815EXvtLMUz5KdryQok3I61xBVz5SRlK3pnWaaMR02+mLOLjyaspefw+biW8uCDPmPR2BdCZWOLUmWDysYWjZ2jZdT9RSp6eXP+1O8AREWco3TZipZlxUuX526GfI28dIoKlWtib1/IMlJp7+CE0WCe5rZ3dELz9Hh2cilGSnLee3QHz5pYt8/I/K1GXBzSc9c9i9wt5ybjnTpyfjxo5NaDPFf5rxIcHIyvr6/l5/lL3xwcHEhOtv6AoHx66dDzy5KTk606lrnxnxmR7NKlCytWrGDQoEE4Ojri5eXFxYsX6dPHPE1kMBiIjzffAFC+fPlM5Z+ffqxWrRoAhQoVomLFipbfdbrsP6o/ux5h3rx5eHt7I0kSUVFR1KtXD4AqVapw+/ZtoqOjqVixIjZPr4WqWfPFnyYzxla1alUAHB0dLXE5OTm9MK4XUShVvN3Nnw2LBiGZTHg37UwhFzdSkxLZtXYi3T5ZTNN2w9i5OoBToSHYObrQaXD2d7Z61mpJXORJVs3oimQy8V6viTka7alZrzUR546zcGJvkCR6DpvOyaN7SNOm0LhNVzr1HcN3Mz5Ckkw0bNkJ58JuWW7HZDKybc1sXIqWYPX8kQB4VKnL+90+yXXb1KrfmivnTjBvfB+QJHp/Mp2wI3vQaVNp+nYXOvcbzZIZQ5FMJhq16oRzETeci7hx7VI4c8f2RDKZ6D5oHPIXfGDJKYVCRcvOAWxZMhAkieqNOuPo7FxrIgoAACAASURBVEZqciJ7N0yg45AlNHx3GL+s8+fcsc1o7F1oN2A+coUShUJF0BxfFCpb6rYagJ1D3kZoAeo3as7Z0ycZ98UwAD4ZGcCRQ7+hTU3l7fc+yLJM+w+7smzRHCZ8ORyDXk/PfoNRqzVZrvsyNeq1JuL8MRZN7IUE9Bw6nfCje9A9zZMP+3zJ9zOHIEkSDd4y50nN+m3Y+P1Evp3SD6PRQKd+/qiejobevXWdIq6577zlV24AhKw25+uKeaMAqFi1Du275z5fTRL8dspEr7cUyGTmu7afpILaBjrUlxNyNOvOuySB0SQx+F0FBiOcuGIiNYvRoH8LhVLFuz38CZo/CEkyUbuZ+ZyWkpTIzjUT8RuxmBYdhrF9ZQDhh0Owc3Chy9B5xF+/yOkjW3D3rMPauf0AaPh2Xxq17cdPq8ezamYvjEY9rTuPwsbWLsu6q9Vtw9ULx1g6tSdIEl2HzOD0sd2kaVNo0Kob7Xv5s2rOECTJRN0WvjgVdsuyTHbKe9Xl6sXjLJ3ih0wmp5ynD5WqN35pm9Ru0JJLZ08we2x/8wzd8Cn8EfoLOm0Kzdt2plv/z1k47RMkyUTT1h1xKeJKmw69CFw6lTnj/4fRYKBTr+HYqjX0/Xgiy+ePRaFQoFCq6PvxxLztqAwsudtKgYzncreBnJAjJtrWkaOQQ8dG5vPp/ccSe/4seNPbUj4+R7J79+4vvNzNx8eHgwcP8v7773PmzBk8PT0tyzw8PIiNjSUxMRE7OztOnjzJwIED8xSHTMqPC7T+BX7++WdcXFxo1KgRu3fvZsGCBTRp0oTp06djMplYtmwZ/fv3Z9iwYUyZMgUPDw/27NlDaGgoc+bMITw8nDFjxlimtp+tM2rUKPz8/GjQoAEzZszA29ubdu3aZRnDV199xZgxY7C1tWXgwIF89NFHrF+/no4dO9K6dWsuX76Mv78/gYGB9OjRg59++gmVSkXXrl3x9/fn5s2bREdH069fP/z8/Ni7dy/Jycm88847hISEsH37dooWLUqPHj3YuHEjCQkJjBgxgn379hEaGsq0adNe2EbrjxSMVCjqmPMR2H+SUl4w2iPm7j9z92peNCx353WHAMCNJ3nv7OYnlaLgvFEdv/j3P5DkB5++1V53CAA8OXzldYdgoVYVjDwpbKd93SFYHDpTMM5rk3q92vG0LX/kXy50afDiSWWTycSUKVOIjIxEkiRmzpzJpUuXSElJoXv37hw4cIClS5ciSRKdO3emV69eeYrjPzMiWb16dcaMGcPixYuRy+V8++237Nq1i549e5KSkkKbNm2sriUAaNasGRs3bqRHjx5Uq1YNe/u/98yLypUr06VLF1xcXHBzc6NWrVqULFmSiRMnsnr1agwGAzNmzKBw4cJ89tln+Pn5UbhwYTQa69GZYsWK0aRJE7p06YK7uztly+btmXuCIAiCILyZ5HJ5pgEkDw8Py++tWrWiVatWf7ue/8yIpPByYkTSmhiRzEyMSFoTI5KZiRHJzMSIZGb/1RHJkBP5lwtdGxaM21z+MyOSr0p8fDz+/pkfXFyvXj0+/fTT1xCRIAiCIAgFwZs4dCc6kvmsZMmSBAUFve4wBEEQBEEQ/nGiIykIgiAIgvAKmHLw1Yb/NqIjKQiCIAiC8Aq8iVPbBeNKTUEQBEEQBOFfR4xICoIgCIIgvAJv4oik6EgKgiAIgiC8AqY3sCMpprYFQRAEQRCEPBEjkoJFCee8fR93frt2S/26QwCgcsnU1x0CAJWKp/JnRMF4eO/eC66vOwQA7NQF585Hg7FgDDHUqFgwHnpdkB4E7tjC63WHAEDLfVNfdwgA7DF2e90hWFSv9LojeD0kcde2IAivWkHpRAqZFZROpJBZQelECkJGb+I1kmJqWxAEQRAEQcgTMSIpCIIgCILwCryJN9uIjqQgCIIgCMIrIKa2BUEQBEEQBOEpMSIpCIIgCILwCryJI5KiIykIgiAIgvAKvInXSIqpbUEQBEEQBCFPxIikIAiCIAjCKyCmtgVBEARBEIQ8MRWML6DKV6IjKbyQyWRi04oZ3IyNRKm0odewybiWcLcsP3fyED+HLEehUNCo5Yc0fbszAL9uW8X5k4cwGPQ0f6cbTVr7Ehd9me9mj8C1eFkAmr3TlbpN3s1xLFHnD3Di16XI5UqqNexMzSbWX/eVmvSAn9eOxpCmxd7JlXd6z0Jlo+HSnzs4uX8VtmpHqjbsRI1GXQEImvMhtmpHAJyKlOad3rNy3TYbV8zkr+uRqFQq+jzXNmfDDrMn5AfkCiVNWnWk2dudMRr0rFk8kfv34pHLFfQZOonipcvnqt7sxF0+yOkDy5DJFXjW9cWrXtZfh3bh97WkPkmg3rtfAHDvxnn+2DMHkNA4FKVFt7koVXn/Np3YSwc4td8cR+W6nanSIOs4zh9ZS0pSAg3eM8dxLnQNEWFbUTu4ANDMdyrOxSrkOY6Moi8c4I9flyJTKKnWoDM1Gmcd06lDgaQ8TqDpB6Pzpd5nYi4eIGyvuU2q1u9MtUZZ13/m8FpSniTQuL25Ta6d/T9OHVgByKjWqBvVGnbNVb0mk4kdgdO4FReBUmlD50HTKPr0+AO4dOog+7d/h1yhoG4LXxq07JptmQ1LviApMQGAhwk3KVOxFr2Gz89VPBFnDnDop2XIFQpqN+tM3RbW7ZD85CFbf/gCfZoOR2dXPhw4ExtbDedP7Ob4b+uQy+S4lalMuz6TkUxGtq8MIDHhJjK5gg8GTKdYifzJl2ec69fEa+ZoTrTpm6/bfZ7JZGLmj3uIvHEbG6WSSX0/wN21SKb1pgftpJC9hs983wbAb/p3OGjMXylbqqgzU/t3ynMMV04f5OBPy5DLFfg096XeW5n3zebvRmPQ63B0LobvIPO+AUjTpRI4dyCdBn5FsZLmfXB413KunD6A0aCnfuse1G3RJdu6L586yP4d5ryo29yX+i0z171p2WgMaTocXYrRZbC57heVS3p0nyWTuvA//1W4lqzAnZvX2L56MkgSxd0r80HfCcjlijy3l5BOdCSFFzr75wEM+jTGzAwiJvIc29bOZ2jANwAYDXq2Bs7Df/aP2NhqmDehHzXqtuDOzRiiI87wxVdrSdNp2bdzLQB/RV+mdfs+tPmgX67jMBr1HNo2i15jtqCy0bBpYQ88arTEvlAxyzrHf12GV532VGvoy597l3PuaDBV63/A77u/obf/dtSaQmxZ0h93z0aWct0+C8pz25z58yD6NB0Bs9YRHXmOLWsX8HHAIkvbhATOY+ycDdjaapg7vh8167Yg5up5TEYj/jPXcenscXb8uIShX+buzTgrJqOeE3tm0/GTzShVGnb/0At3r5bYOaa3j0Gv5ej2Sdz76yzlqrUFQJIkjm6fROueiyhUpCwRYSEkJcbjXCxvnVuTUc/x3bPpNDwEpY2Gnd/1pGzVzHGEbp3I3bhzlK/R1vJ6Qvwl3uo+m2Klq+exFbJmNOo5vH0WPb4w587mRT2oUN06dwxpWvZtmsDt2HNUrNX2BVvLW/1Hd8ym66gQVDYati7uSblqmes/sHkid+LO4VHTXL/JZOT4ngV0G7UFla0dP85pR4XqbdA87WjnxKXw/Rj0aXwyZSOx186y58e59Pt8qTkug57d62czfPpmbGw1fDe1N1Vrv0Xs1TNZlnnWaUxJfsTyGf3p0Csgd+1g0PPrxtkMmRSCylbDqhk9qezdEken9HY4vHMpNRq2p3ZTX47sWc7JQ8HUa+nH/m3f8PH0ndjYagj5/nMizx4CScJkMjJowiaiLv7O/q0L8Ru+OFcxvUiFLwZRqvcHGJNT822b2Tl45gppegPrAgZzLvovFoT8H4s+6Wm1zpbDYVy9eYc6nuUA0On1AKwcPeBv12806Pn5x9kMm7IZla2GFV/1wsu7JY7O6fvm4E/LqNWoPT7NOnF49wrCDgbT5N3+3Iy5wE+BU3j88I5l3ejLfxJ37TSDJ/yIPi2Vo7+seWHduzfMZvg0c93fT+tFldrWde/fvgzvRu2p07wTh3at4M8DwTR6u1e25YwGPdvXTEZpk/6BeO/mRbzTdSTlveoR8sNYLp86QLW6b//ttsutN3FqW9xsI7xQ1JXTVPVuDEB5z5rERl+0LLt1I4Zixctg51AIpUpFRa/aRF0+xaWzxyjlXonlc0fx3ewR1KjTHIC46EtcOHWEBRMHELRsMtrU5BzH8eB2FM7F3FHbOaFQ2lCqQh1uRp20Wic+KpxyVZuZY63anLiIYyQm3MC1tBcae2dkcjluZWtw6/pZ7t28giEtla1L/0fIt32JjzmT67a5dvk01Wo3AaCCZ01iozK3jf2ztqlSm2uXT+FWsixGkxGTyYQ2JRmFMn8+yyXejaZQEXdsNeb2cSvnw53r4VbrGA06KtbuSK23hlpee5RwHVuNMxd+X8ee5X3QpT7KcycS4OGzOOyexVGH2zHPxaHX4enTkdqtPrJ6PeHGRc4cXM5P3/Xk9MEf8hzD8x7cjsK5aHrulMwidwwGHVXqf0i9tkOz2UrePbwTjVOG+kuUr8OtaOs2MRh0eNXrSN026W0ilyvo5b8HW40j2uREJCRUtna5qjsm4hSeNZsCULZiLW7EpOfo3fhoiriVxc7eCaXShnKVfYiJCH9hGYDfti6hcdveFHIpRm7cuxVNYVd3NE/rc/esQ1ykdTvERZ6iYg3zMVypRnOiLx1HobRh0PiNltEvk9GIUmVDkeLlMBnNx5IuNQmFQpWreF4mJTqO8K4j8nWb2Tl9LY7G1SoCULNCGS7FxlstPxv1F+djbtCleV3La5F/3UGbpmfYwnUMmR/Iuei/8lz/vfhoiril75uylXyIfW7fxEaGU6mGOS88azYj6tJxAAz6NHp+upiiJdLPG9fOH8WttCc/fjuC9Qs/xsv7rWzrvvtc3eU8fbgekbnuZzlZuWYzrl08/sJyP2/8mgat/Cjk7GrZRq/PvqG8Vz0MhjSePErAoVDRPLfX3yFJ+fdTUIiO5L9QTEwMfn5+9O7dm379+nHnzh3mz5+Pn58f3bt355dffsFgMODn58eRI0dISEigXbt23Lp1K9d1aVOT0dg5Wv6WyxUYjYany5JQ2zlYltlq7EhNSSL5cSKxURcZ9MU8eg6ZyJpvxyJJEuUqVse3z+d8Pn0NRd1Ks2fz9zmOI02bZJmGBlCp7dGlJlmto9MmYfN0HZXaHp32CS6uZUm4dY3kxwno01L5K+I4+rQUlDZq6rQaiO/Hq2jjN5Vf1o3G9PT/yl3bpP//Mqu2sV6mVtuTmpKErdqO+3fjmfzphwR9P41W7/fIVZ3ZSdMlYaNOr09lY0+a9onVOrYaJ0pXamL1mi7lIXfjTlOlQQ/eG7ia+KgTxF87nuc49Bn2AYCNbRZx2DlR2rNpprIetd6nme9U2g8O5Pb1U8RePpjnODJK0yZho8kQk9qeNK117qjtnCjrlTmmf6R+W3NuPl+/e+XM9csVSqLO7WXTvA8pVaEeckXuPnjonjtGZXJ59sev2h5tStILyyQ9us+1iyeo2/zDXMXxLBbbDO1grs+6HXTaJNRP17FR26NLfYJcLsfByfymf2JfEGm6FDyqNcHG1o7EhJssGfceOwMn0aBN71zH9CK3t+9F0ufunJBXyVqdZYoaQCGTYzAaAbiX+IQfdh0koEc7qzJqGxV92zZm2cg+jO/dnvErt1rK5JZWm4StJn2f22iy2Depyaifvhc8yxWAsp4+OBcpYf3/JD3kZswF/IYv5IP+Uwj5fgxSNj2f5/PNVmOPNtW6bm3GujX2aFMz5+mzcuGh27F3dLF0PJ+RyxU8TLjJooAOpCQ9pGiJcjlpmnxnkvLvp6AQU9v/QseOHaNatWoEBARw8uRJ9u7dy40bN9i0aRM6nY5u3brRpEkT5s2bx9ChQylWrBhffvklJUqUePnGn6PW2KPVpo8cSiYTiqdvZmqNA7rUFMsyXWoKGntH7B2dcCtVDqVKhVupcqhUtiQ9fkCtBq2wsy8EgHf9VmxeNful9f++eyE3o05xLz6CEmVrWl7Xa5Ot3pQAbNUO6HXJqGzUT5cXQm3nxFu+Y9m1agSOzsVxLVMNjb0LLsXK41y0LDKZDBfX8qjtnEl+fA9Hl5y3kVpjbzWqat029ugyLNNqk9HYO7Jv13qqeTemU+9PeZBwm4WTBzNp4RZUNnm7JvHk3kXciT3Fg9uRuJbJ0D5pydioC720vK3GmUJF3HFxM4+GlK7UlIT4i5Ss2ChXcYT93yJuXw/nwS3rONJ0yVYdy+xIkkSNpv0sHS53rxbcv3mZslVa5iqOjI7tWcjN6FMkxEdQPEPupGWRO/+EEz8vIj4mnPvxkbiVtW6T3NTvUbMtFaq3Yd+msVw5uYOq9TvnuKytxsEqDyWTZH38Zji2ddpk1PaOLyxz/s+91G7cLlfXlu3fuoi4q+HcuRFJqQrp7aDTpncOLPGqzTGpbNSkZVhuMpn4bfPX3L9zne6ffItMJuP43kA8qjfh7a5f8Oj+LQLn9ufjr3ai+hvX974u9mpbUnQ6y98mSUKpMLfxb+EXSUxKYcTiDdx/lIQ2TU/54kV5t14NyrgWRiaTUdatKE4OdiQ8SqJ4Yacc1/vblkXEXj3Fnb8iKZ1h36SlJqO2tz5/2GrsLftGp7UeYHienYMzxUpUQKm0oViJ8ihVtiQ/eYBDofTrPveGLOJ65Clu/xVJGY8MeZGajNrOuu5n51OVjdrSoX0+T5+VO7Y3CBkyrl08zq24K4T8EEDfUUtxdC6GS9FSjJ73f4QdCmHPj3Po9tHL34OElxMjkv9CXbp0wcXFhUGDBrFhwwYePXrExYsX6dOnD4MGDcJgMBAfH0/p0qXx8fHh/v37NG/ePE91eXjV5uKpowDERJ6jpHsly7ISpctz91YcyU8eYdDruXo5nAqeNfHwqs2lM8eQJInEB3dJ06Vi7+DMkunDuH71PABXzv9BGY+qL62/SftRdPssiKEzfycxIY7U5ESMhjRuRJ2kRPnaVuuWrOBDzMXD5lgvhVLKow4mo4Fb18/S/bMNvNtnDg/uRFOqgg8XT2whdLv5JJL06A5p2iSra9ZyoqKXNxeetk105DlKlX1B21w6RQXPmtg7FLKMVNo7OGE0GjD9jdv46rYdSbvB6+g17giP78eiSzG3z+2Yk7i6e7+0vGPh0ujTUnh8PxaA29fDcXatmOs46r0zkg4fBdFn4lEe3Y9Da4kjDLeytV9aXq9NImRhB/S6ZCRJIv7aCYqWrpbrODJq3G4UXUcEMeSr33l0Lw7t09y5GXWSEuVeHtPf1fD9kfh+EsT/ph3lUUJ6/fHRYRTPQZukaZPYtqQ3RkMaMrkclY0GmSx3p+xynrWJOHsEgNhrZyleJj1HXUtWIOF2LClJiRgMacRcOUnZit4vLHP14nEq18zduaR155EMCAhizKKjPLgTZ6kvNiKMMhWt26FMpdpcPWc+hq+eD8W9knkqd9fayRj0OvxGLLVMcavtnSwdTY2DEyajHsmUtxG5183bw52j568CcC76LyqWSp+S7dm6IT9OGMrK0QMY8F5T3m1Qgw8a12bH76dYEPJ/ANxNfExyqo6iTg5Zbj87b3cZyaCx6wj49ggP7qbnwvWIk7h7WJ8/ylbyIfJsKACR545QtnKdbLdb1tOHq+ePIEkSjx+a3wPsHJyt1mnbdSRDxq9j/JIj3L+TIQ8jTuJe0TvT9iKe1h1x7gjlK9fBtWSFLMt9NGE9QyYEMWT8Okq4e9H1o9k4Ohdj3YKPSbh9HTCPqMpzeSzlF0mS8u2noBAjkv9C+/fvp06dOgwfPpzdu3ezYMECmjRpwvTp0zGZTCxbtozSpUtz5swZrl69Sr169Vi9ejUDBw7MdV216rfi8tnjfD2uLyDR55NphB35GZ02haZvd6Fz/y9Y/NUwJMlE45Yf4lzEDecibly9fIo5Ab2QJBPdB41FrlDgN2QCwStnoVSqKORchJ5DJ+U4DoVCRYtOAWxbNhBJkqjesDOOzm6kJify248T+GDwEhq8M4xf1/tz/thmNA4uvN9vPnKFEoVCxfqvfVEqbanTagAah8JUb9SFX9ePZdPCHsiQ8U6vmbmeNvRu0IrL504wZ1xfJAn6fzKVP4/8jDY1heZtu9Cl/2i+mT4MSZJo3KojLkXcaN2+N+uWTubrCQMwGPR82GsEtmpNLvdKZnKFigbvB/DrmsFIkgnPOr7YO7mhS0nkyLaJtOmd9U0ICqUNzXy/4mDwGJAkXN1r4+711t+Ko1F7f35ZNQhJMlG5bmfsndzQpiQSumUibftmHYeNxpF6745i9/J+yJU2lPJoiLtXizzHkZFCoaJ5pwC2f2fOnWoNO+Pg7IY2OZHfNk2gw8Al+VLPi+pv2tGfncvNbVKlfnr9BzZP5P0B2bSJ2gHPOh3YtqQ3coWSIiUqU7nOB7mqu1rdNly9cIylU3uCJNF1yAxOH9tNmjaFBq260b6XP6vmDEGSTNRt4YtTYbcsyzxz71YMhV1L560dlCre7eFP0HxzO9Ru1plCLm6kJCWyc81E/EYspkWHYWxfGUD44RDsHFzoMnQe8dcvcvrIFtw967B2rvlGvYZv96VR2378tHo8q2b2wmjU07rzKGxyeQ1pQdGqthcnLkfRb/ZKJCSm9vuQX/44R4oujc4ZrovMqFNTHyYF7mDAnFXIZDC5f0fLKGZuKZQq3usRwNp5g5FMJnya+1KosHnf7Fg9kZ6fLuatD4aydcVYTh4Kwc7RhW7Dvs52e17eLbkecZLvp3ZDMpno0HditqPYCqWKdj0DWD3XfO6q29ychylJiWxbNZHeny2mZcehhPwwlrCndfsN+zrbctlp0X4wW5aPQ6FUobJR4ztoep7a6u8qQP2/fCOTClK3VsiRuLg4xowZg0KhQC6XExAQwK5duzh//jwpKSm0adOGfv364efnx5IlSyhZsiRdu3ZlxowZ1KhRI9vt7j+vfYX/Rfau3VK/fKVXoHLJf/5uzZz4M6LgTNUpFLLXHQIAduqCEYfBWHBOn2VcC8YD6rT6gjHR5djC63WHYNFy39TXHQIAe2yzfuzU66AoGGmCb/1XG8jiPfl3zhjRrmCcB8WI5L+Qu7s7wcHBVq9Vr575kSl79uyx/L5z585/PC5BEARBELInHkguCIIgCIIg5MmbOAdcQAaXBUEQBEEQhH8bMSIpCIIgCILwChSk5z/mF9GRFARBEARBeAXE1LYgCIIgCIIgPCVGJAVBEARBEF4BKV/ntsXjfwRBEARBEP4z3sRrJMXUtiAIgiAIgpAnYkRSEARBEAThFXgTb7YRHUlBEARBEIRXwPQGzm2LjqRgkZJWMNKhoHyF1MMUm9cdAlBwvt8aoIhzwYiloHxPbxEHw+sOweKxtmAcv2pVwTiA9ccu0TYl5HWHAcDBNpNfdwgASKEF57u238D+1H9WwTjzCIIgCEI+KiidSEHISExtC4IgCIIgCHkiOpKCIAiCIAjCv5JWq2XMmDHcv38fe3t75syZQ+HCha3WCQwMZM+ePQC0aNGC4cOHv3CbBeRKI0EQBEEQhDebSZLy7ScvNm7ciKenJz/++CMffvghy5Yts1r+119/sXPnTjZt2kRwcDBHjx7lypUrL9ym6EgKgiAIgiC8ApIp/37yIjw8nGbNmgHQvHlzjh8/brW8ePHirFy5EoVCgVwux2AwYGtr+8JtiqltQRAEQRCEN0xISAhr1661eq1IkSI4OjoCYG9vz5MnT6yWq1QqChcujCRJzJ07l6pVq1K+fPkX1iM6koIgCIIgCK+AlI932wQHBxMcHGz5u3v37nTv3t3yd9euXenatatVmeHDh5OcnAxAcnIyhQoVyrRdnU7HuHHjsLe3Z/Lklz+6SnQkBUEQBEEQXoH8fE7y8x3HnPDx8eHw4cPUrFmT0NBQ6tSpY7VckiQ+/vhjGjRowJAhQ3K0TdGRFARBEARB+A/o0aMH/v7+9OjRA5VKxfz58wFYs2YN7u7umEwm/vzzT9LS0jhy5AgAn3/+ObVr1852m6IjKQiCIAiC8Ark59R2Xmg0Gr799ttMrw8YMMDy+/nz53O1TdGRFF7IZDKxbc10bsVGoFDZ0G3wVIoWL2tZfjH8IL9t/w6FXEm9tzrRsFX69Rix186xZ+MCPp4YCMCNmEtsXTUVpcqGkmW96Nh3LHJ53h8cEH3hAH/8uhSZQkm1Bp2p0Tjrr/86dSiQlMcJNP1gtOW1w9tm4uJanppNe+SoLpPJxE9rp3ErLgKF0obOg6ZR1C29HS6dOsiBHd8hVyio29yX+i27ZlsmPvYyO9fNQCZXoFTZ0O2jWTg6FbXUEzh/KFV9WtGwtV+u2iP20gFO7V+GTK6gct3OVGmQdXucP7KWlKQEGrz3BQDnQtcQEbYVtYMLAM18p+JcrEKu6r569gBH9yxFLldSq0lnvJtZ152S9ICfVo7GoNfi6ORKu/6zUNloiL9+jv0hs0GSsHcqxgf/+5pLYXs4f3w7AAa9jjt/XebTr39HbZf5Wp6sRJ49wJFdS5ErzLH4NH8ulicP2L5yNIY0LY7OrnToPwuVrYYTe9dw5ugW7BzNz1Rr12cqRYpX4PeffyDy7AGMBj113upB7WZds6rWislkYsvq6cTHRaJUqug+ZBrFirtbll8IP8Tebd8hVyhp0KITjVp3wWjQs/GHiTy4F4/RkMbbH35E9botuXn9CiGrpiFXKChWvCzdh0zL1XETceYAh35ahlyhoHazztRtYd0eyU8esvWHL9Cn6XB0duXDgTOxsdVw/sRujv+2DrlMjluZyrTrM5mzx3Zw5uizfZPG7bjLjP7mKJps9o3JZGJHoPkYUD47BopbHzf7tz89blr40uDpcfOiMqeP7ebY3g18MmUjAIf3rObs8Z+RyeS0/GAI1eu1yXHbmEwmZv64h8gbt7FRKpnU9wPcFTIglAAAIABJREFUXYtkWm960E4K2Wv4zPdtAPymf4eDRg1AqaLOTO3fKcd15pVz/Zp4zRzNiTZ9/5HtXzl9kEM7lyGXK/Bp5kvdtzLnScj3ozHodTg6F6PT0zw5d2IPx/euQyaXU7x0Zdr3nQRI/LRmEgm3YpDJ5fgOmklhV/esK37O5dMHObjDHEed5r7Ua5k5js3fjUafpqOQczF8B5vjAEjTpbJm7kB8B35FsZIVMJmMbF81iYTbMcifxlHELWdx/FPexK+GFI//+Rf4448/GDVqlNVrly9fZsmSJf943RdP7seg1zFi2o+08xvFrg1fW5YZDXp2rp/DkIAVDJsUyIkDITxOvAfAwV2rCFkxCYNeZ1l/y8opdOwbwCeTg1DbOXD62J48x2U06jm8fdb/s3fe8TWe7x9/n5WcLAlBBBmC2CuhqFWlqi1Ve6+abaWoImZpzWqL2nvEqMamtEbtGUmMGklkIQQRIeOck7N+f5w4yYkkEpR8+7vfr1denOden3M91/U817nv+5yH9l+uprNfAP+c3kLq04cWdXTpav5c/y2XT2wyH0tLSWTH0oFE/fN3gca7FnwYbXo6X363mY+6fsMfm37M1KLT8sfGWXw+dgWDJ6zj/JFAkpMe5tpmT8BMPu0zgSET1lG9bkuO7V1l7uvA1vmoUp4U2B4GvZYze2fx8YBVtB0SwI3zv5OWnM0eWjV//zaaq2c2WRxPuHuN97rOou2QANoOCShwEqnXazkUOJNuw1fT69sAQk9sIeWJ5dgn9y6m2jtt6D16Ey7uVQk9vgWj0cj+gEl80ncmvcdsxqtaE548iqPmux3oOSqAnqMCKOVejQ+6Tsx3EqnXaTm4ZSY9Rq6mz+gAQo8/r+XE3sVUf6cNfceatIQcN21Wj791lXYDZtNndAB9RgfgXMqLmLBz3IkMpd/YzfQZHcDTx/H50vHPhcPotOmM+H4jbbqPZPcGy7jZFTCboeOWM2zyWs78HcjTpAQunNyLnb0TX09Zz+CxS9m2djoAf21bTKsOQ/l6SgA6nZZrocfzpeHZWH9unkWfb1fR3z+A4KO/k5zNHsd2L6JGgzYMGL8RV48qXDi6BW26msPb59NvzDoGTvwNtSqZ8EtHqdO4A/39A+jvH4CrZzU+6jkh1yQSTHGj06bz1ZTNtO72fNzs3TCLAf4rGDJxHef/zoyb3Nrcjb1O0NFt5lkdVepTTh3YwJdTNjHAfwV7NszMt20Ajly8QbpWx3r/QXzdoSW/BP71XJ2tx4KIiLtvfq3RagFY+W1/Vn7b/40kkV6jBlJj2TSkyrx/huVl0eu07N88i77fruTzceu5cMx0LrJydNdiajZow8DxG3B1r2r2k0Pb5tN/7FoGT9yMWpVM2KWj3Ag9AsCgiZto0f5r9m+enW8d+zbOov+YlQycsJ6go8/rOLJzMbUatmHwxA24elQl6Igpfu9E/cOK6b1JfHDbXPeZjiGTNtGiw9fs25Q/HYKCIRLJ/1GqVKnywl+bfx1Eh4VQqWZjADwq1uJ21FVz2f27URR3ccfW3hG53IpylXyIvhECgLOLG31HzLfo60liPJ7epn0Wnt4+RIeFvLSuxPhInIq7o7R1RCa3orSXL3GRFyzq6HQaqrzzGfVaDTUf02pSadDaj8r12hVovJjwTDu4V6hFXHSmHR7cjcLZxQNbO5MdPLx9iA4LzrVN92E/U9qjCgAGvR65wgqAK+f/QiKR4l2rSQGtAY8fRFHE2R3rDHu4ePoSHx1sUUev1eDt04467w+xOJ5w5yoXjyxn15IehB5ZVuCxH92LpGgJd2zsTGO7VfDl9k3Lc3HnZjBe1Uzvy6taU2KunybxfjQ2dk4EHV7Hhp96oU5NwrlUZhJ7L+YKCfduUqdp/jeTJ8RHUrRkFi0VfbkVYanl9s1gylc3aalQvSnR10+bxou9yql9y1k7uzun9pnsEHX1JCXKeBO4+Cu2LBxKxZrv5UtHVFgolWs1AsAze9zEZY0bBeUq+RB1I5jaDT7koy5+5npSmWnBqIxnFdJSnmA0GtGoUpHJ8r+Q9PBeFMUy7CGXW+Hu7cutcEu/uBUeQoUaJntUrNGUqGtnkMmtGDhhs3mmJ6ufAsRFX+FhXAR138v73ESHheD97PpRoRZ38ogbz0qmuMmtTWpyEvt/m0vb3uPMfVhZ21DUuTTpGhXpahWSAq5whN68xbvVKgBQ08uNa7F3LcovRd7mSvQdOjWtaz4Wfvs+6nQtX8xdz+Cf13I56jb/NmlRtwju7Pfiii/Jc35S0YfYbH4SGxFMxRqm81KxZhMir5r8ZPDETRZ+olBYU9W3JZ/2mwpA0qO72Bd5fpY3Rx13o3B2ydTh4e1DTDYdMeGZOrxrNeHmVdPvIOp16fQcvoASrpk/VVPVtyWffZ6hI+Eu9o750/FvYjQYX9tfYUEsbf+LREdHM27cOORyOTKZjI4dO7Jr1y4UCgXx8fF069aNs2fPcuPGDfr06UOPHj04deoU8+bNw9raGicnJ2bMmGHuT6VSMWzYMNq1a4eLiwu//fYbc+fOpVWrVvj4+BAdHY2zszMLFixAq9UyZswYHjx4gKurK0FBQZw8ebLA70GtSkVp62B+LZVK0et1yGRyNGkpFmXWSjvUKtNvUtV8pxWJD+Ms+ipW0o3I60GUr1KPayFHSNeoCqznGenqFKxsMse2UtqRrk6xqKO0dcSjcmOunttuPubo7Iajsxsx1/M/qwOgVqWgtLU3v5ZktUO2MmsbO9SqlFzbFHEqAUBseCinD21iyIT1xN+O4OKZP+jpN4/DOy2fNJAftOoUrJRZ7GFtR7ra8vfBrG0dKevdmLAL2y2Ol6/1MdXe7YnC2o4DAX7EXj+CR5Xm+R5bo05Bme1caFQpz9WxzqhjKk9GlfKYuKhQWnWbRFEXDwIXDqWUe3U8qzQE4PT+ZTRu81W+dQCmc/EiLSpLLc98tlq9T6jbvAfWNvYELh5GxKUjpKU85smju3TzW0pSwh22LPyCL374E4lEkqcOtSoFmyyxkdVfTDGV6RdKGzvUaclYK20z2qaydt5IPs5IKkuU8mDbmmkc3LkcpY09FarWK5A9rG2yxWiapV9kPX/Pzo1UKsU+Y7vF2UMBpGvSKF+tkbnNib3Lea/di89N9tiwtEO2uFHaoU5LybGNTpvO1pUTadNrLAorpcUYTs6u/DKmLQajgeZtB+XHLGZS1RrzEjWATCJFp9cjl8l4mJTMsj1H+PmLbhwMzkyAlVYK+rR6l/aNfbn14BHD5m9gxw9+yGWyAo1dEOJ3HMDGo8y/1v9z17AscZFZJ/NeYCpPsfSTgxsy/ORdAGQyOdtW+HM9+BDdhllOKuSGWpWC0ia7T+StQ5Nmim8Pb58c+5TJ5Gxd5s+14EN098ufjn8T8axtQYE4ffo01apVw9/fnwsXLhAZGUl8fDw7d+7k6tWrDB8+nIMHD3L//n2GDRtG9+7dmTRpEps3b8bFxYV169axZMkS3nvvPdLS0hg6dCh9+vShRYsWnDt3zjzO7du3WbduHa6urnTr1o0rV65w6dIlypYty6+//kpkZCRt2rR5qfegtLFDo041vzYajeYZEWtbezSqzDKN2jLpzE7XIdPYtX4mR/asxs2rusUMR345/cdc4qJCSLgbRimPmubj6epUixvm60ZpY29pB0MWO9hks4MqFRtbhzzbXDq7nyO7l9Fv1BLsixTj+B+reZp4nxUz+/M4IQ65XEHREmWoVDPv2cmgv+YRHxNM4r1wSrplsYcm1SKxzA2j0UiNxn3NSbl75WY8iruer0Ty2M653IkM4cGdMEqXszwXymznwlppT7o6FYWV0nSubItgY+9E0RIeFC9tmhHyqtaE+Fv/4FmlIeq0pzy6H4VHpQYv1AFwZMdcbt80aSnjlU1LNp+0trHUorQpgtFo5J2Wfc11K9RoRvzta9jaOVG8lBcyuRXOpbyQK6xJS07E7gUzLEobe9SqnOPGFFNp5jK1KhWlnWl5+PGje6z+eTiNP+iGb6NPANixfhbDvluPq1sFTh7YzK4Nc+j0+cQ8xz+8bR63IoK5fyfcwh45xai10uSnZntklBsMBg7+PodH92Po+tWv5uRZlfaUhPgoylV58bnJHhtZYyB7fGjUqSjtHHJsc+/WDRLiY9m55nu0Wg0P4iLZHTCTClXr8zTpIWPnHgRg1Y+D8PSug1v5zPecF3ZKa9I0mdtvDEajOSE8GHyVpJQ0/BZs5NGTFNTpWsqVKk7rejVwK1kMiUSCh0txHO1tSXiSQqlijvkaszBxaNs8YsNDuH8nnLLP+YnllgXrjHuBwkpp4UcGg4EDv/9EQnwM3YbNt/iQ1XHQLJI7P2TZD135esZerKxtc9RxcKtJR/ztcMqWt9SRfetEbjryotOQWSQnPWTp1K4Mn5W7DsHLIZa2/0U6depE0aJFGThwIBs3bkQmk1GxYkUUCgUODg64u7tjZWWFo6MjGo2Gx48fY29vj4uLCwD16tUjIiICgPPnz6PRaEhPT39unKJFi+Lq6gqAq6srGo2GyMhIfHxMn9DKly//3EPZ84tnpTrcuGiavYuNuEQpt4rmMpfSXiTEx5KWkoROl07U9WA8K9bOta/rocfoMmQaA8csIS0lCe/q7xZYz7ufjKSzXwCDp53iycNbqFOT0OvSiYu8gKtn7j9P8Kp4eNfhxkXTTyHcumlph5KlvUi4n2mHmLALuFeonWub0FO7OXNwI4PHr8W5pBsAH3f/lq+mbmHIhHX4NvmMxq37vjCJBKj34QjaDgmg96STPHl0C3WayR7x0UG4eLzYHlp1CoFz26LVpGI0Grl78yzFy1bLl02afTaSnqMC+PqnUzx+eAtVxrm4HXGBMl6WY5ct70PkP8cAiLp6HLcKvjgVdyNdk0rig1gAbkdcoHjpihn/D8Kzcv79o3n7kfQZHcDIn0+R+CBTy63wnLXcvGLScvOf47hX9EWjSmHZlDakq012iLlxDleP6rhV8CXynxMYjUaSk+6j1aiwsXd6oZ5y3nW4nnHuYyIu4Zo1bsp48TA+ltSUJ+h0WqJuBONZsRbJSQksnTGYtj2+oX7zDub6tvaOKG3tAChStASq1KcvHL9FxxH09w9g9LyTJN6/ZfbN2LAg3CpY2sOtYh0iLpvsEXHlOO4VTcu4e9Z9h06roZvfIvPSJUBs2AW8qjZ8oQYAT+86hF0y2SE2p7jJcv2IvnEBjwq1c2zjVr4mo2bvYcjEdfQY9jMly5Tn097jsLErgsLKGrnCCoWVNTa2DqiyzWDlRe3y7py8YrrGXo66TYUyJc1lPVo0YNPEoaz8tj/9P2pM6/o1+PTdOuw8FWLeS/kg6SmpKg3FHe1z7L+w07LjCAaMW8/Y+Sd49CA2i5+YrmFZca/gQ/gl070g4vIJPLxNvz+4e63JT3p8vdDsJxdP7eLY3uUAKKxtkEikSCS5z9h+0GkEA8evZ9yCEyRmu5a6ZdPhUTFTR/ilE3hW8s2pSwBCT+3i2J7863gTGAzG1/ZXWBAzkv8ihw8fxtfXl2HDhrF3715++eUXatWqlWv9okWLkpKSwoMHDyhZsiTnz5/H09MTgPfee48JEybQs2dPc4L4jJyW2by9vQkNDaVly5bcunWLx48fv9R7qF63JeFXzrDgu55gNNJ1yDRCTu0lXZ1GgxZdaNtrDMtnDcZoMPLOe+1xLOaSa1/FS3mw6sehKKxsqFD1HarUafpSmgBkMgVN2/uzY8kAjEYj1Rp0xN7JBXVqEgd/m0jbAa/3i0jVfFty85/TLJ7aAzDSadB0Lp7ei0adRv33u/BJj7Gs/nEwRqOBuk074FjMhWpOz7cxGPTsDpiBk7MrAfOHA+BVuS4fdHy1/U9SmYKGbcayf9VAjEYDlep2xM7RBXVaEse3TqJVnwU5trOycaBe65HsXd4XqdyKMuUb4F65WYHGlskUtOjkz2/zB4DRSM13O+JQ1AVVahL71k+k4xcLafTJF+xZM5aLJ3/H1r4onw74GZncio/7TGf3qlEYjUbKlq9DhRrvAfAoPhqn4mULbAeZXMEHXfzZNNfkF7Ubd6RIhpa96ybS+cuFNG7zBbtXjyX0hEnLZ4N+xsralubtRxLwUx9kCivKVW5IhRomO9yKCGL19E4YjUZa95iMVPriG1GNei0Iu3Ka+ZN7YgS6D/mB4FN/oFGn8W6LzrTrNYZlMwdjNBqp/157nIq5sH3dTFSpTzmwfSkHti8FYLD/UroOmsr6X0cjk8qRyeV0HTy1QPZo3X0sAT+b/KJOE5M90lKS2L1mEt38FtCs7RfsWOlP8LFAbO2L0mnoT9yNuUroia24e/uy7se+ADT4oA9VfD8gIT6aoiXc8jV+tbotifjnNIum9gCjkc6DpxN62nT9qP9+F9r0HMuq2Rlx0ywjbnJokxvlKtcl4uoZFk3phkQixdPbh4oF+ID6fp3KnL0eSd9ZKzFiZGrfz9h/7jJpmnQ6ZtkXmZX2jX2YvHYn/WevQiKB7/q1+1eXtd8EMrmCj7r5s/7nQRgNBnyadDD7yc41k+jht4D3Ph3KthXjTH7iUJTOQ+dwN+YqISe24eHty5rZ/QBo2Ko3Vet+wPaVE1g5oxcGvY6Pe4xDYfXiLwrJ5Ao+6uHP2jmDMBoN+GZcS9NSktixahI9hy+gebuhbF0+jqCjJn/t+uWcXPurVvcDtq2YwIrpvdDrdHzcM386/k3e9s///BtIjP/Fd1VIuHXrFqNHjzY//LxFixZcvnyZuXPnEhkZyZQpUwgICODp06d06dKFP//8k9OnTzN/vml5wNHRkZkzZxIREWHeD7l371527NjBoEGD2LJlC3PnzqVRo0acOnUKgJEjR9KtWzdq1aqFv78/Dx8+pHTp0hw6dIjQ0NA89e4J1r0Js7yQOw8Lx+ebUsX0b1sCAFF3C8/CgbNT3nsD3xSyQmISZ3vt25Zg5qm6cMSNUvEaH93xCrRKC3zbEswcafnix8y9CVKPX3/bEsy8wi+/vVY61X+zQsYuf/nvBmRn9mCbF1d6A4hE8j9KSEgIaWlpNG7cmJiYGAYOHMihQ4fybCMSSUtEIvk8IpG0RCSSzyMSyecRieTz/H9NJMcsfX2J5I9DC0ciWTiuPILXjpubG9988w0LFy5Ep9MxefLkty1JIBAIBIL/1xj+g3N3IpH8j1KiRAkCAgLetgyBQCAQCAT/YUQiKRAIBAKBQPAG+C/uJhSJpEAgEAgEAsEboDD9bM/ropBsdxUIBAKBQCAQ/K8hZiQFAoFAIBAI3gD/wZVtkUgKBAKBQCAQvAmMYmlbIBAIBAKBQCAwIWYkBQKBQCAQCN4A4nckBf9pzl4qHE9ycXYuHM+t1RsLx1NcqnoUjicOAUgkheMieP5q4VhMcaxUOHQAxD142wpM1KuoftsSALhg25b7yYXjyR/G413etgQA7JpWedsSzMxsvfxtSwCg055mb3Q8sbQtEAgEAsH/AIUliRQI/uuIGUmBQCAQCASCN8B/cUZSJJICgUAgEAgEb4D/YB4plrYFAoFAIBAIBC+HmJEUCAQCgUAgeAOIpW2BQCAQCAQCwUth/A/+/I9Y2hYIBAKBQCAQvBRiRlIgEAgEAoHgDWAQS9sCgUAgEAgEgpfhv7i0LRJJgUAgEAgEgjeA+LKN4P89ld2kNK8tw2CE4HA9F8INFuWOdtChiQKpBCTAzlM6Ep4aqekl5d1qMgwGuP/YyO7TOl4lnGKu/U3IocVIpDIq1+tIlfo5P4Ls8ol1qJITqP/xKIvjx7ZOQmnr9NzxvLgeeoQjOxcjlcrwbdqBes0tx0xNfszvS75Fm66hiFMJOgyagZW1TY7tdNp0tq0Yz+OHt7G2sadtn0kUL+XJg7ib7Fz9HUaMlHKrRNs+E3PVYzAY2Lr6B+Jiw5HLFXQb8j0lSrmby/8JPspf25Yglclp8F57GrbohMGg57fl3/HwbgwSqYweQ3+geJY2wSf/4Phfmxj5w8Z828VgMBC4ahp3Y8OQK6zoNmTqczr+3LoUmUxG/ebtebdFJ3NZ8pNH/DSuK19OWI5LGS/uxNxg25qZSKVS5HIren41nSJOxfOtJTe8y0hoUkOK0QChkQZCIy29r4gtfNpAilQqQQLsPafnUfLLj2cwGNix5nvu3jLZpPPA7yleysNcfi3kCAd3LEEqlfFOsw7Uf7+zuezWzUv88dsvfDFxnfnYlaBDXD73Fz2HzXl5UUDstb8JOWyKm0p1c4+bKyfWkZaSQP2PTPFx+fgawoK2obQvCkCTDlNxKuFVoLENBgMbl8/kTkw4coUVfb+cREnXTD+5FHSMPb+vQCaT0ahFO5p+0AGdTsuaBd/x6MFdJFIpfb6YhGvZctyKDmPD0unIZHJcSrvT58vJSKX53/J/I/QIR3aZYtKnaQfqvZdzLOu0GhycStBhoCmWAdI1Ktb+OID2A6ZRorTJBsf2LOdG6N/odVreadGdus06PTdmXlqO7s7Q0qQDdXPQErg0U0v7ASYtl8/+wZkD65FIpZQqW4k2fSYDRnatmUzCvWgkUikdBs6gWEn3nAd+SZzeqUnlGd9ytmWf19pvTjSq50y/7u7o9fDHwXvsORBvUe5gL2fzsneIjk0F4PiZBAL3xNHs3eL06uSG0Qi7/7rH3mztBK8X8WWb/wE0Gg2BgYFs376dw4cPvzUdUgl8XF/Omr+0rNynpV4lGfbZnkLW0kfO2Wt6Vu3XcvSyjlZ1ZchlpuOr9mlZ/ocWayuo5PbyrqfXazmzZxafDFzFp0MDuH7ud9KSH1rU0WnVHN48mqunNz3X/trZ30iMDy/YmDot+zbOov+YlQycsJ6go4EkJ1mOeWTnYmo1bMPgiRtw9ahK0JEtubYLOhqIldKWod9toU3viexZPw2AA4Hz+KDzCIZM2oQ2Xc31kL9z1XTlwmG06emM/GEjbXuMZFdAZpKh12nZuX42X4xfjt93azl9OJCnSQn8E3wUgOHfb+Cjzl+xM0ubOzE3OHtkOxRw6eVK0N/otBpGTttI2+4jLPrU67TsWPcjX05Yht+UtZw+tJWnSQnmsi0rvkdhpTTX3752Fh37j8PvuzXUfKcFh3evLpCWnJBKoJWvlI1/61l7SI9PRSl2Sss6zWtJCQo3sv6QnpNXDbxf+9UujVeDD6PVpuM3dTMfd/2GPRt/NJfpdVp2b5jFIP8VfDFpHWePBPI0w5eO7FlF4IrJ6NI15vq71s9g/5a5GI2G58YpCAa9ljN7Z/HxgFW0HRLAjfM5x83fv43m6hnLuEm4e433us6i7ZAA2g4JKHASCXDx/BG02nTGzVpHh15+/L52bua4Oi1b1vzMyO8WM/qHlRw/sJ0njxP4J+QUer0e/5lradt5MDs3LQJgz5bltO0ymLEzVqPVarkSfCLfOvQ6Lfs2zaLf6JUMGL+eCznF8i5TLA+akBnLAHHR/7ByRm8SH9421426fp5bN0MZNHETA8av50li/pMWvU7L/s2z6PvtSj4ft54Lx57XcnTXYmo2aMPA8Rtwda/KhaNb0KarObRtPv3HrmXwxM2oVcmEXTrKjdAjAAyauIkW7b9m/+bZ+daSH7xGDaTGsmlIldavtd+ckMkk+A0szzeTrjBs3EU+be1KMSeFRZ1K5e05dOwBfuMv4Tf+EoF74pBKYWjfcoyYeJmho0Pp0d4NxyKFZ87MaDC+tr/Cgkgk/wd4+PAhgYGBdOjQgRYtWrw1HSWcJDx6akSdDnoDxN434Oli6UL7z+sIu2264ckkEnR60Oth2d50tHpTHakEdPqXD4KkB1EUcXbH2tYRmdyKUp6+3IsOtqij12rw9m2HT4shFsfjY0O5f+sSVRt0LdCYD+9G4ezijo2dI3K5FR7ePsSEW44ZEx5MxRqNAfCu1YSbV8/k2u5B3E28azYBoIRrOR7ejQKgx9fzKVe5HjpdOilPErB3zH02LupGKFVqNwLAs2ItbkddNZfdj4uieCl3bO0dkcsVeFXyIepGMDXrtaDroCkAPE64h4OjMwCpyUns3TyX9n3HFsguAFFhIVSpZXrfnt61uB15zVwWn11H5TpEXjfZbeeGn2nUsguORUuY6/cdPoeynpUBMBj0yBWvfsMq7giJySa/NRjg9gMj7iUlFnUOhBiIiDP5pFQCulfL2YgOC6Fyhk08KtbiTnSWc3M3CmcXD2wzfKKctw/RYSabOLu40WfkfIu+PCrWoUP/ya8mCHicLW5cPH2JzylufNpR533LuEm4c5WLR5aza0kPQo8se6nxI65fpHqddwEoX6kmsVn95E40JUu5YWdfBLlCQcUqtYm4HopLaXcMeh0GgwGVKhWZzJQQuHtVIjXlCUajEU2W4/nhuZis6ENstliOzRrLNZsQee0MADptOj2+XkBx13LmujevnMSlrDebfvVjw9wvqVz7vfxruRdFsZKZWtxz0hKRqaVizSZEXj2DTG7F4ImbzLOkBr0ehcKaqr4t+bTfVACSHt3FvohzvrXkh7SoWwR39nutfeaGp5stcfdUJKfq0OmMXL72lFrVHC3qVKrggHd5exbMrMUPY6viXNQKgwF6fRFEapqeIg4KJBJQqfRvRHN+MBiNr+2vsCASyf8Bli5dys2bN6lcuTKbN2/m3LlzDBgwgKFDh/LZZ5/x22+/MWLECFq3bs2mTaaZhPPnz9O9e3d69erFuHHj0Gq1r6xDaQXq9Ezn1WhNx7KSpjE9Aqp4EQmt35Hxd6geI5CqNpU3qCLFWiHh5t2XD4J0dQpWSgfza4W1Hekqy3VIa1tH3LwbWxxLffqA4IMLafxZwW/KalUKShv7zP6VdqjTLMfUqFJR2jqYyzVpKbm2c/WoQtjFoxiNRm7dvMjTx/cxGPRIpTIeJ8Tx67i2pCY/pkQpz1w1aVQpKG0y7SCRStHrdRl6Uy3HtbFDlaFXJpOzcfH8CM2DAAAgAElEQVR4tq2dQa36H2Aw6Nm8bDKf9RmDtdKu4LZJS0VpmzlWdh02WcqUNnao01I4d3Qn9g5FzYnwM54lldFhFznx52aaf9K7wHqyY60w+eoz0nWgtJzYQJXht84O0NJHyvHLr5ZJarKdd2kWm2hUKdhkOzfqtBQAar7TCpnMUlzthh8hkVgmvi+DNlvcWFnbka5+Pm7KZosbgPK1PqZJh6m0GbSW+JgQYq8fKfD46jRLX5BKZWabqHLwE1VqCtZKWx49uMckvw6sX/ID73/SHYCSru5sXjWHyV935OmTRCpVr5t/HeoUrLPY38rmxbH87Px4ePvg5OxqUTc15TFx0f/QbdhcPu03hcClo/P9hQqNKsUidqyVdqhVL9CiSkEqlZo/ZJ49uIF0TRrlq5mSdJlMzrYV/vyxYRrV6n2YLx35JX7HAYxa3WvtMzfsbGWkpGWOlabSY2dn+YEh9k4aqzfF4DfuEsfPJjBiSAXANNHRtGFx1v7qy8WrT15p4kLwYgrPfK8gV4YOHUp4eDhNmjQxH4uPj2fnzp1cvXqV4cOHc/DgQe7fv8+wYcPo3r07kyZNYtOmTTg7OzNv3jx27NhBly4574d6ES19ZHi4SClVTMKdh5kBaa0AVfrz9cuVkvDpu3K2HjPtjwTTfskP68ko7ihh0+GXS2rP/zmP+JhgEu+FU9K9pvm4VpOKdZaEKjeiLv+JOvUx+1cPRpWcgE6rxqlkOSrV7ZBrm4Nb5xEbHkL87XDKls8cU6NOxca2iEVdaxs7NOpUFFZKNGrTxV9pY49Gnfpcuyq+LXh4N5JVM/viXtGH0uWqIZXKAChavAzfzPmLoKOB7Ns0m75+03PUZp2tb6PRaJ6ZUdrYoVGnZY6rstTb88sZtE1KYO7E7vT8cgYJ8bEErvwBnTad+LhItq+bRYe+/i+0KYDS1i6bDoOFDrUqs0ytSsXGzoHj+zeCREL4P2eJiwljw6IJDBqzgCJOxQk5/ScHdixnsP8i7IsUy5eGnGheS4pbCQkuThD3KNNvreSgzsFvPV0kfFRPys7Tr7Y/EnI4N4bMc5O9zHRuXuy/L0vQX1nixi3Th9M1qRaJZW4YjUZqNO6LVUaMuVduxqO463hUaV4gHUpbS18wGDL9xMbGDnUWf1WrUrG1c+Dgno1Uq9OQDr38SEyI5+fvhjBl7u9sWTWHMdNWUca9PEf2b+H3tb/Qc/C4PMc/uHUesREh3L8dTlmvLHZQpaK0yzuW8zo/tvZOlHD1Qi63ooRrOeQKa1KTE/OcDTy0zXRduX/HUovpuvHi68oz+x34/ScS4mPoNmy+xYeNjoNmkdz5Ict+6MrXM/ZiZW2bp20KE4N6eVKzqiPlPe24Fp4ZiLY2MlJSLJPY4MtJaDSm2cbjZxMY2NPTXHb8TAInziYwYUQlWjd3Yd/h+29E/4soTEvSrwuRSP6PUrFiRRQKBQ4ODri7u2NlZYWjoyMajYbExEQePHjAiBEjAFCr1TRq1OgFPebOoRA9oEcqgeEdFNhYmWZ1PEtJOfmPZVJYrpSENg3krPtLS1LmPYN2jeTo9bDx0Mt/yead1qb3o9dr+f2nNqjTklBY2XIvOohazT5/YfsajftQo7Fpg3jYhe0kPYjOM4kE+KBTxpg6LfPHtSEtJQkrpS0xYRdo/JHlmB4VfQi/dByfJu0Jv3QCz0q+lCjtxaP7sc+1i4u6goe3L5/0HMedqH9IfHALgIC5X/JR9zEUL+WJtdIOSR5fIPCqVId/go9Sp2FrYiIu4epW0VzmUsaLh/GxpKY8wVppS+SNYJq37UfQ8d0kJd7ng88GYWWlRCKR4lGhJv4/7QLg0YM41v86Ot9JJEC5SnW4+kxH+CVKu2fqKFXGi4fxtzJ1XA/m/bb9qN2glbnOgqn96TJwEkWcihN0Yg+nD23F77s12Nk75jRcvjlyyTSrKJXAF21kKDP81r2khDPXLWccPV0kfOgrZdMRPU9Sc+qtYHh61+FayFFqNfiI2IhLlMp6bkp7kRCf6RNRNy7Q7JP+rz5oLtT70OTDBr2W33/OjJv46CBqNX1x3GjVKQTObUuXUX8gt7Ll7s2zVKrXscA6KlSuzaULx6nXqBWRYZcp61HBXFaqbDke3LtFarLJT8KvhdCqXR/u3YlGJjfdpuzsHdHrTMvcdg6O2NiaZs8di5bg5o1LLxw/ayz/Or4AsXz5BB6VfHPt18PbhzMHAmjUuh/JSQ9J16iwtXfKU0vLjlm0TMjUEpuDFvcKmVoiLp/Aw9ukZffa75ArrOjx9ULzF40untrFk8f3adZmMAprGyQSKRKJ7IW2KUys2BADmPZIblhcFwd7OSq1ntrVHNm8/bZFXX8/b46dTuDvkw+pW7MoYZHJ2NrImD25Ot9MuoxWZ0SlNlCYcjfx8z+Ct4JUKsVgsLzx5bXUVbRoUUqVKsXixYtxcHDg8OHD2Nq++idSgxH2n9fT70PTvpPgCD1P08DGCto3lrPpbx2fNJAjk0LHpqbluYQnRoLC9Ph6S4mNN/L5R6bjZ67puRb7csuHMpmChm3Hsm/lQIxGA5XqdcTO0QV1WhLHtk7iwz4LXvm9PjemXMFHPfxZO2cQRqMB36YdcCzmQlpKEjtWTaLn8AU0bzeUrcvHEXQ0EFv7onT9ck6u7eQKKw5t+5WT+1ajtCtChwGmL9s0bTOIbSvGI5MrUFgpaT/gh1w11ajXgrArp5k3qSdGoMfQHwg++QcadRrvtuzMZ73HsHTGYIxGI/Xfa49TMRdqvtOSzUsn8euUvuj1Otr3HYvC6tX2Idas14Kwy2eYO6kXGI30+OIHLpz8g/QMHe37jGbJ9CEYjQYaNDfpyAmDQc/2NbMoWtyV1T+bbrTlq9Tl4y5fvZI+gxEOhhjo+b4MCXAxykCyyrQto219KYEnDLTylSKTQruGppvuo6dG/jj/8svb1eu2JOLKaRZO6YHRaKTrkOmEntqLRpNGg/e70LbXWFbMHozRYKBeM5NP/NtIZQoathnL/lUZcVM3M26Ob51Eq1zixsrGgXqtR7J3eV+kcivKlG+Ae+VmBR6/Tv3mXLt0llnj+mE0Guk3bArnju9Ho06jaauOdOn3DXO//wqj0UDjFu0o6lySlm17snbRVGZP+By9Tkf7nsOwVtrQ58tJLP95HDKZDJlcQZ8vJ+Vbh0yu4KPu/qz7aRBGgwGfph0okhHLO1dPosfXC3jv06FsWzGOC0cDsXUoSpcvcv+2fOXazYkJu8DSqV0wGgy07TPJvLqQLy3d/Fn/c4aWJh0oUjRDy5pJ9PDL1BJ8zKSl89A53I25SsiJbXh4+7Jmdj8AGrbqTdW6H7B95QRWzuiFQa/j4x7jXjm+3xZ6vZGFK6P45fsaSCUS/jgYT0JiOg72cvz9vJkw8xpL10Uz7utKtP+4NCq1ntkLwklT6Tl49AGLZtVGpzcSGZPCgaOFYzbyv4rE+F9Mj/9jaDQaunTpQuPGjSlbtixeXl789ttvzJ07l8jISKZMmUJAQABPnz6lS5cu/Pnnn5w8eZJFixZhNBqxs7Pjxx9/xNk5743XE1Zr8ix/Uzg7W7240hvAvVThCA07q8KzUVwiKRw2OX+1cGzvrl3p1fcuvi4i7hQOm9SrmPbiSm+A+8k2L670higsd1m7plXetgQzM1svf9sSADi5p+Afil6FXhPuvra+Nkwv/dr6ehXEjOT/ANbW1uzatcviWP369QEoX748AQEBABQpUoQ///wTgMaNG9O48fOb5gUCgUAgELwd/ot7JAvHR1iBQCAQCAQCwf8cYkZSIBAIBAKB4A3wX9xNKBJJgUAgEAgEgjeA0fCKTzsohIilbYFAIBAIBALBSyESSYFAIBAIBII3gMFgfG1/L4NarcbPz48ePXowaNAgEhMTc9FpYODAgWzevPmFfYpEUiAQCAQCgeANYDQaX9vfy7B582a8vb3ZtGkTn332GYsXL86x3rx583jy5Em++hSJpEAgEAgEAsH/A4KDg82PW27atClnzpx5rs6ff/6JRCKhadOm+epTfNlGIBAIBAKB4A3wOn9HcsuWLWzZssX8umvXrnTt2tX8OjAwkHXr1lm0cXZ2xsHB9Lx2Ozs7kpOTLcrDw8PZu3cvv/76K4sWLcqXDpFICgQCgUAgELwBXmcimT1xzE7nzp3p3LmzxbFhw4aRmpoKQGpqKkWKFLEo37lzJ/fv36dv377ExcWhUCgoU6ZMnrOTIpEUmBnUNPZtSwAgUuX+tiUAYDQWjsffJasLT5geP6962xIAaPde/p5l/G9z57HybUsw06PypbctAYAVwdXftoQMDFSvWDh2bxWWh5kUlscSAoz7c/DblpBB2NsW8Ebx8fHh2LFj1KxZk+PHj+Pr62tRPmbMGPP/FyxYQPHixV+4xF04okwgEAgEgtdIYUkiBYKsGIyG1/b3MnTv3p2IiAi6d+/Oli1bGDZsGABr1qzh8OHDL9Vn4ZnqEAgEAoFAIPgP87aftW1jY8Ovv/763PH+/fs/d8zPzy9ffYqPbAKBQCAQCASCl0LMSAoEAoFAIBC8Ad72jOS/gUgkBQKBQCAQCN4AL/tD4oUZsbQtEAgEAoFAIHgpxIykQCAQCAQCwRvAYHi5b1sXZkQiKRAIBAKBQPAG+C/ukRRL2wKBQCAQCASCl0LMSAoEAoFAIBC8AYwv+UPihRmRSAryjcFgYMHiJURHR6NQKBjxtR9lSpc2lx85eowdu3YjlUopV84Tvy+/QCqV8qXfcOzsbAFwcXHh25EjXmrs31ZMJy42HLncip5ffEdJ18xHKV6+cJR9gcuRyWQ0bP4ZjT/oCMCf21dx5cJRdDotTT/sQqMWHUh+8oiNS74nLfUpBoOBvn7TKFHKrUBatqycTlxMGHKFFT2GTrHQcuXCUfZtXYZMKqPh+5/RqGUnAP7asZIrQc+0dOXdFh24dzuSTcu+B4yU8fCmy+fjkMryfvzftZAj/L1zCVKZjLpNO/BOc8tnqaYmP+a3xaPRpqspUrQknQZNx8raJs92t25eYv+WXxgyYR0AcTHX2LFmKnK5AlePyrTtNR6pNH8LGNU8ZbSqZ4XBCOeuaTl7TWdR7mQvoXsLa6RS0yMofz+iRp0OfT7MfNxgmeJS9p7WcPqqZdsXYTAY2LxiBrdjwlEoFPTO5ieXgo7xR+AypDI5jd5vR5MPOnL6712cObobAG16Ordjwpiz6hAJ9+PYuGwacoUVZT0r0fXzMfm2AUD4xb85vncxUqmM2o074tO0i0V5WvJjdqwYhVarwcGxJJ/2n4HC2sZcvnf9JGxsnWjRaRQ6bTq714wjKeEO1ko7WvecjLOLZ4FtM3fpKiJjYlEoFIweNoSyrqXM5YePn2Lr7n1IpVK8PN0ZOXQAOr2e2b8u4W78fexsbRkx5HPKlnYt0LgvwruMhCY1pBgNEBppIDTScumviC182kCKVCpBAuw9p+dRcsHHuR5yhMM7F2fxf8vzYYqbb9Gla3AoWoJOg2ZgZW2TZ7uUJ49YOLkTn49dRcnSXtyPu8mO1d+B0Ugp90p82mciUmne8Xw99AhHdpr8xLdpB+rloOv3Jd+iTddQxKkEHTJ0AaRrVKz5cQAdBkyjRGkvDAY9O1ZNJiE+GqlUSoeBM3B2KfgjZxvVc6Zfd3f0evjj4D32HIi3KHewl7N52TtEx5qe2Xz8TAKBe+Jo9m5xenVyw2iE3X/dY2+2dq8Tp3dqUnnGt5xt2edfG+PfQCxtC/5fc/rMWbTp6cz7+Sc+79eX5StXm8s0Gg3rAjbw48zpzPt5DmmpaZw7H0R6ejoAc2bNZM6smS+VRAJcOv83Om06o2cE8Fmv4Wxf97O5TK/Tsm3tT3w9aSkjp67m5KFtPHmcQPg/QUSFXWTUtHWMnLqaxwn3AdgRMI96TT/mmx/W0Lb7V8THRRdIy+Wgv9Gma/h2xgba9RzO9vU/WWjZunYOfhOXMWLqmkwtV01avpm2nhFT1/D4kekCu3vzr3zaw49R09aTrlFz+cLRPMfW67T8sXEWn49dweAJ6zh/JJDkpIcWdQ7vWEythp8wdNIGSntU4dzfv+fZ7tjeVWxbNRmdVmPuY/vq72jb05+hkzagtHHg0pm9+bKNVArtGluzdLeKhdtVNKymwMHW8pnlHzew4sRlLYt2qDgUnE6bhtYkpxlZtEPFoh0q9p7RcOehnjPXCpZEAlw8fwRtugb/metp32s4W9f9YmG7wLU/MXzyUr79fhUnDprOzbvvt2PU96sY9f0q3MtXoevnY7C1K0LA0h/o0n80o6etwcbWnvMn9udbh16n5cCWWfQcuYq+YwIIOf47KU8sz9PxPYuoXr8N/cZupJR7FYKPbTGXBR/7jQd3ws2vQ0/8jpXSls/Hb+HDHhP5c9MPBbbNyXNBpGu1LP5xGoP7dGfJ6gBzmUaTzqqNW5g7fTKLfvyB1LQ0zgSFsPfAYWyUSpbMmc7Xg/ozf9nqPEYoOFIJtPKVsvFvPWsP6fGpKMUu2+PLm9eSEhRuZP0hPSevGni/dsFvW3qdlr0bZzFg7EoGT1ifa9zUbtiGIZM2UNqjKuf/3pJnO71Oy4413yG3sjb3ceD3eXzYeQRDJ29Cq1FzPeTvF+rat3EW/cesZOCE9QQdfV7XkZ2LqdWwDYMnbsDVoypBR0x+cifqH1ZM703ig9vmujdCjwAwZNImWnT4mn2bZhfYVjKZBL+B5flm0hWGjbvIp61dKeaksKhTqbw9h449wG/8JfzGXyJwTxxSKQztW44REy8zdHQoPdq74Vjk35mr8ho1kBrLpiFVWr+4suBf5z+ZSJ47d46GDRvSu3dvevXqRbdu3di3bx937tzBx8eH3r17W/zp9Xpz26FDhzJ06FAAEhMTzXXq1q1Lp06d6N27N4GBgSxYsIDNmzdbjNulSxfu3LmTq67evXub+3j2d/ToUcaMGcPWrVst6q5du5a5c+eyYMECPvzwQ4s2S5YsAeD9999n/fr15jaRkZH07t07T92vwtVr16ib8YD3KpUrE3EzwlymUCiY+9OPKJWmu4Ber8fKSkFUVDQajYZxEycxZtwErt+48VJjR94IpWrtdwEo512T2Kir5rJ7d6IpUcoNW/siyBUKKlSuQ+T1EK5dOk0Z94os/3EkS2b5UcO3aUZfF0l6dJ/5UwcTdGIf3tXqFkzL9VCq1mmUoaUWtyKvmcvi4yy1lK9ch8gbIVy/eJrS7hVZPmcES2f7Ud23GQCDRv1Cxap10Wm1PE1KoIiTc55jP7gbhbOLB7Z2jsjlVnh4+xAdFmxRJyY8hEo1GwPgXbMJN6+eybNdMRc3eg+fb9HHk8R4PLzrAODpXYeY8JB82calqJSEJwZUGtAbIPqeHi9Xy8vMrpMarsWaYk4mAa3e8hN6x6bWBB7V8DI/t3bzeijVMs6Nl3dNYiOf9xO7Z35SpQ43r2e+r5ibV7l3O5KmrUwzyEmP7lO+cm0AKlSuTeSN0HzrSLgXRbGS7tjYOSKTW+FewZdbEZbn6fbNEMpXbwJA+RpNib5+BoA7kaHERV7Ct1lXc92HdyOpUN3kv8VLeZFwLyrfWp5x5VoY79SpBUC1St6E3Yw0lykUchbO/h6ltenG/Cx+Y2/fob6PyQbuZUsTeyeuwOPmRXFHSEw2ok4HgwFuPzDiXtLyg8eBEAMRcSZnkEpA9xIrgyb/N50PudwKT28fYrLFTWx4MN4ZcVPJIm5ybrdv8xzqv9+NIk4lzX30HD6fcpXrodOlk/wkAfsixfPU9TBb/x7ePsSEZ4/nYCrWyIjnWiZdAHpdOj2HL6CEazlz3aq+Lfns86kAJCXcxd4x7+tJTni62RJ3T0Vyqg6dzsjla0+pVc3Rok6lCg54l7dnwcxa/DC2Ks5FrTAYoNcXQaSm6SnioEAiAZVKn8sor0Za1C2CO+fv8X2FDaPB+Nr+Cgv/2aXtBg0aMHfuXABSU1Pp3bs306dPp0KFCgQEBOTY5t69e6SlpaHVarl9+zZubm7mur1792bKlCmUL18egAULFryUrtmzZ5v7eIa9vT3z58+nU6dO5mM7duxg0aJF7Nixg379+tG9e/cc+1u7di2NGzfGy8vLfKxYsWK56n4V0tLSzEvUAFKpFL1ej0wmQyqVUrRoUQB27d6DSq3Cp04dYmJj6dihPR992Iq4u3eZOHkKq5YvRfaC5dvsqFWp2Ng6ZBlbhl6vQyaTo1aloLS1N5dZ29iiSksh9WkSjx7e5ctxC3n0II4ls7/mu/m7ePTwLrZ2RRj+3XL2BS7lwM41tO32VQG0pGCTZTyTHUxaVGkpFjqVSjtUacmkJD8m8eE9vvBfyKMHd1g6+2smz9+NVCbj0cO7LPh+MDa29pQs7Znn2Jrn3qsdalVKDnUcLMrzalejXisSH1omCMVKuBF1PQivKvW4HnqUdI0qX7ZRWoE6PfMCp04HpbVlYpCqNv1bwknCp42sWbUvs+9qnjLiEw08THq5i6TJTzLfp8TCTyzLTOcm03b7t6+iTeeh5tfFXcoQfvUC3tXqcvnCMTTq/NkAQKNOwdom0w+slHZo0izXYzWqzDrWSjs0qmSSkx5wbPdCuny5kGsXMmdAS7lVJuLyUSrVaUlc1CWSH9/HYNC/cNk0K6lpadhni1+dXo88I36LOTkBsH3vflRqDXVr1yT+wUPOXAihcYN6XAuPICExEb3egEz2euYgrBWg0Wa+TteB0nLyC1XGRLmzA7T0kfL7sYInJzn7v+X5UKtS8xk3yQQf34GdQ1G8azbm6J7l5nKpVMbjhDhWzfocpa0DxV0989SlVqWgtMnSv9IO9XN+kkWX0g5Nhs96ePvk2KdMJmfrMn+uBR+iu9/8HOvkhZ2tjJS0zNWANJUeOzvLVCH2ThphN5O5cCmJD5qVZMSQCkyadQ29AZo2LM43Qytw5kIiOv2/k+zE7ziAjUeZf6XvfxuD2CP5v4mdnR1du3Zl1apVedbbunUrLVq0QKlUsmnTJsaOHftG9NWtW5fExETi4uIoU6YMly9fpnjx4pQtW/aFbf39/fH3939udvTfwNbWljRV5s3UaDBaJIQGg4GVq9cQF3eXSePHIZFIKFOmDKVdXZFIJJQtUwaHIg48SkykZIkSBRpbaWOHWp2aZWwDMpk8o8wejSrNXKZRpWFj54CdgyMuZTyRKxS4lPFEobAm5Wki9g6O1Kj3HgA1fJuxe/PCAmqxR51lPKMxU4uNrb2FTrXalADb2TvhUqZchpZyKKxMWhwcnXEuUZopC/Zy6vA2tq+bQ59h058b86/A+cSEhxB/Owy38jWzvFfLBBvA2sYejSoVhZXSXP7sWF7tstJ58HT2BMzk2B+rKOtVHZncKk+bfFTfCq/SMlydpdy6n3mjV1qBWvP8zaRCGRmdmlmz8ZDaImmsW0nO8Uva5+rnF6WNHWpVbn5iZ2EDtToVGzuTDdJSn3I/LoZKNeqZy/t+9T1bVv/IXzvX4lmhGnJF3jYAOLJjHrcjgrl/J5wyXpnnKV2dinUO5yldnXGe1KZk4fqFP0lLfszm+YNJeZqANl1Ncddy1G7ckYR7UQTM6UPZCj64elQrUBIJYGdrS5pKbX5tMBqRZ4vfpes2cifuHt/7f4NEIuGjls2JvR3HiInfU6NyJbzLe72WJLJ5LSluJSS4OEHco8zzbyU3ffjIjqeLhI/qSdl5umD7Iw8EzsuIm/Dn4kZpW8Si7jP/eBY3ylziRmlbhNMHApAg4ebVM9y7dYPAZf70GbkIB6cSFC1ehm9/+ougo4H8sWk2XYbMek7Xwa3ziM3QVTarLnUqNtl0WdvYocnmJy+i05BZJCc9ZOnUrgyftRcra9sXthnUy5OaVR0p72nHtfBMI9vayEhJsdxmEnw5CY3GFOfHzyYwsKenuez4mQROnE1gwohKtG7uwr7D9184tuB/m//k0nZOODs78/jxY27evGmxTDxrlinIDQYDe/fupV27dnzyySfs27cPtVqdZ59r16616OvmzZsv1DF27FiLNomJiQB06tSJ3btNG/63b99Ot27dch3n1KlT5rJmzZrh7e3NihUrCmyTglK1ahWCgi4AcP3GDTw9PSzK5y9cRLpWy3eTJpiXuP86cJDlK00J/KNHj0hLS8O5WLECj12+ch2uhpwEIDr8MqXdK5rLXMuW48G9W6QmP0Gn1RJxPRgv75qUr1yHaxdPYzQaSUp8QLpGhZ29U0ZfJwCIuB6Cq1vBZmu9Ktc2t48Ov2ShpVSZcjzMouXmtWC8vGtRvkodroeeMmvRqE1als7y48G9WMA0QyaR5BySH3YezpAJ65i48ASP7t8iLSUJnS6dmLALuFeobVHXw7sONy4dByD88gk8K/lSsrQXCfdj82yXlRsXj9Fp0DT6f7uUtJQkKlZvmKdN9p9LZ9EOFZNXp1LcUYqtNcik4FVaRky85SfwCmVktG9ixbI9Km4/sCwrW1JGdPzLf2KvULk2/2T4SVT4Zcp45OEn10Lw8jbdxCOuhVC5Zn2Lvq4En6DvV1Pwm7CQlOQnVKnZ4IXjN28/gj5jAvjml5M8fnALVUoSel06seFBlC1fx6KuW4U63LxyDIDIK8dxq1iXd1r2YdDk7fQZE0CjjwZR/Z021GrUgbsxV3Cr6EOfMQFU9mmJU4n8fznsGdWrVOJssGl5/mpYOF4ell/C+HnxCtLTtUwb/615iTssIpIaVSszf/p3NG5QD1eXks/1+zIcuWRg/SE9P2/TU9RegtLKtL/WvaSEOwmWHzw8XSR86Ctl0xE99xILNk6rziMYPGE9Exae4FEW/4/OMW58CMuIm7DLJyiXETc5tRsycQODJwYweMJ6XN0r03nILBycSrD+ly9JiI8BTLOH0lzi+YNOIxg4fj3jFpwgMVtcumXXVdGH8GfxfMkUz7kRemoXxzJmSBXWNkgkUiSS/H3gWLEhBkPe0yMAACAASURBVL/xl2jb+wxlXJU42MuRyyXUrubIPzeeWtT19/PmvXdNkwF1axYlLDIZWxsZC2bWQiGXYDSCSm2gEK2+FhrE0vb/MHfv3sXX15fk5OQcl7ZPnDhBamoqo0aNAkyJ5Z49e+jcufNzdZ+Rfcm5S5cuudZ9Rk5L2wDt2rWjX79+fP7555w/f56JEyfmOk52/P396dixI+7uBf92XkFo1LAhIaEXGTFqNGDkmxHD+fvoUdQqNRUrVuCvAwepXq0qY8dPAOCzTz+ldasP+GnuPL4ZPQYkEr4ZPrzAy9oAtd55n+uXzjBnfB/ASO+vvifoxD406jQaf9CJjv1GsWDaFxiNBt5t/hlOzi44ObsQcT2E2f49MRoNdB1o+kZ0h76j2LhkKsf/CsTG1p7PRzw/Y5C3lhbcuHyWnyb0BqORXl/9QNCJP9CoVSYtfb9l4fShGA0GGr7f3qzl5rVgfhzXA6PBQNeB45HKZLRqP4CARZOQyRVYWSnp+cWUPMeWyRV80uP/2rv3uJzv/4/jjysdJLEIq0RJGL9yKpsxG7OvvswUouQ0hzkfin0Lk+PkVGPmTCynHOZ82CE7xeYwGxnzRXIaNVNJpOP1+6Nv11eL775f6/q8L3rdbze3W9cVPs9buvK63p/3+/UKJXruO+j1BXi16UrlKjW4n5nOJ6vD6TPmQ9p1GcrW5RM5/vU2Ktg+R+CweY/9c49TtUZt1swfgoWlNW4NW9Cgyav/1demoKBwD+SQt6zR6eDoL3ncuaenghX0bFeeNQce4PuKJeXK6ejVvvDNxm9pBWz9Ohub8pCd89d+ODZ5sR2/JBxhzsS+6PXQf8Q0jsXv50HWfdr8rTvd+49n4Yxh6PV6Xm7XBbuqhV+D5F8vY1+j+K2y6g61WPT+SCytylO/kTcezV/5r3OUM7fgjR6hbFgwCH1BAU1ad6OSXQ2yMtPZ8/FkeoxYROtOw9gdHcaP326lgq0dfoPnP/bvq1Ldha93fsj3n0VTvkIlOvef+T9/bV55yZsfTiYw4h+T0aMndPQw4r45RNaDB9SvW4f9cV/h2bABwZMLD/J0f/PveDRqwOoNm9m8cw8VbWz4x8gh//N1/5MCPXzxYwFB7cqhA05eKuBuVuFKducXzdgaX8DfmptRzgy6tCz8uXE7Q8++Y//bm43C7/8woucOLvG62b56Mr3HLKJtl6FsXT6B418X/nsEGF43Jf/c47z65mC2rZhIOXMLLCzL03XQfz4UVc7cgr/3CmPtvMK/v/lDuXasnkzQv3JtW/GvXBXt6Dl83mP/vkZeb/DJykmsfL83+Xl5dAyagIXl/3YgJT9fz0erLhE13QMznY59XyTze2oOthXNCRtVj0kRZ1n2cRITRtfHr6MjWQ/ymbPoPPez8vni699YPLsJefl6Ei9n8vnXshr5R/pncLKNTv8MThA/evQosbGxhj2SmZmZBAYGsnDhQsLCwtiyZUuJPzNq1Ci6devGa6+9BsCJEyeYOXMmO3bsAB69R9Le3r5EIRkVFfXYW9J/tl8xODjYUAwGBwc/9jpF2rVrx4EDB7CysuLo0aOEhIRQp06dYoXy/7JH8vLF83/6e7SQmGXcgvi/pdfr/vw3aeDuA9N5v/ftsf9+n6AxdXntf38zYgzX08r/+W/SyOvVTqmOAMDKE/+nOgIA/+duOjfcTGXxaMHMeNURDCZ8+o7qCAB0yv2nptd7I+jEn/+m/9IXGx6/Oq0l0/kfqpQdOXKEPn36GA6EjBo1CktLS8Ot7YeNHz+eU6dOGQpPgObNm5Odnc2PP/5Is2aP3tT8JEJDQ7G2/nevuL///e/06tULKCxEBw8ezKefflrsz6xdu5b9+/cbHru6ujJ9+vRiv+fFF1+kU6dO/PLLL6WWVQghhBClx5RuSZeWZ3JFUjwZWZEsTlYkS5IVyeJkRbIkWZEsyVRqB1mRLEnrFcnXA46V2t91MLZFqf1df4Xp/A/1jEhISGDevJJ7WB5eeRRCCCGEeBZIIVnKPD09H9unUgghhBBlV4GpLE+XIikkhRBCCCE08Cye2jadTSRCCCGEEOKpIiuSQgghhBAaeBZPbUshKYQQQgihAf0zOGtbbm0LIYQQQognIiuSQgghhBAakFvbQgghhBDiiTyLp7Zlso0QQgghhHgiskdSCCGEEEI8ESkkhRBCCCHEE5FCUgghhBBCPBEpJIUQQgghxBORQlIIIYQQQjwRKSSFEEIIIcQTkUJSCCGEEEI8ESkkhRBCCCHEE5HJNqJUXL58mStXrlC/fn1q1KiBTqdTHYnc3FwsLCyUXd8Uvib3798nIyMDc3NzNm/ejK+vL05OTprnMDXff/89165dw9PTE1dXV6ysrJTmKSgowMxM3fv6lJQU5s2bR1paGh06dKB+/fo0btxYSZaEhAT27dtHdna24bmpU6dqmiElJYUaNWoYHp85c4ZGjRppmsHU5Ofns337dm7evMmLL76Iu7s7VapU0TTDJ598Qrdu3TS9pvhzUkiKv2z9+vV88cUX3LlzB19fX65evUp4eLjmOTZt2sTatWvJy8tDr9djbm7O559/rnkOMJ2vyfjx4+natSuff/45devWJTw8nNWrV2ue4/Dhw6xZs4acnBzDczExMZrnAIiKiiI5OZnExEQsLCxYsWIFUVFRmuc4cOAABQUF5OTkMHfuXAYNGsTAgQM1zwEwefJk3n77bZYsWYKXlxdhYWFs2bJFSZbQ0FAGDx5MpUqVlFwfYODAgYSFhdG6dWuio6PZvXs3O3fu1DzHK6+8QmpqKnZ2dqSnp2NpaYm9vT1TpkyhVatWmmYJDw+nevXqfPfdd/zf//0foaGhrFy5UtMMu3btkkLSBMmtbfGX7du3j7Vr12Jra0v//v05deqUkhxbt25l3bp1tGnThoiICOrWraskB5jO1yQjI4PXX3+dlJQU3nnnnWKFnJYiIiIYMGAAU6dONfxS5cSJE8ydO5cKFSrg5+fH9evXleSIjo7m5ZdfZvfu3XzzzTd89dVXSnIAZGdn07JlS3Q6HXXq1FG6Qlu7dm26du1K+/btDb+09vHHHxMdHY2vry83btxQVlR7e3uzZ88eDh06xP79+2nfvj0rV65k4cKFmme5evUqY8aMwcrKinbt2nH37l3NMzx48IDLly+TlJRU4pdQR1YkxV9WNK696NatpaWlkhx2dnZUr16de/fu8eKLL/Lhhx8qyQGm8zXJzc0lOjqahg0bcvHiRe7du6ckh4ODAy+//LKSa/9Rfn4+2dnZ6HQ68vPzld1SLvqesLGxwdLSUtm/TVGW+Ph4CgoKOHnypLLvV4AOHToQHByMm5ub4bmRI0dqmuHcuXPcunWLZs2a8csvv5CcnEytWrU0zQCQnJxMnTp1AKhVqxY3b96kdu3alCtXTvMs+fn5pKamApCZmankdZOUlER4eLjh52sRnU6n7A6HkEJSlIJOnToRFBTEjRs3GDx4sJIVBABbW1vi4uLQ6XTExsYafuip8Oabb5rE1yQ0NJS4uDiGDRvGnj17lK0EVq1alfDwcBo2bGgornv27KkkS//+/enatSupqan4+/vz9ttvK8nh7OxMt27dmDx5Mh999BGenp5KcgDMmDGDOXPmkJaWRnR0tNIV440bN/LGG28ovbW9aNEili9fjqOjIydPnmTEiBHs2bNH8xzVqlVj/vz5NG3alJ9++gl7e3sOHz6sZO/32LFjCQwM5NatW/Ts2ZOJEydqnqFBgwZSMJognf6Ppb0QTyAxMZHz589Tp04d6tevryRDZmYmV69exd7enujoaNq2bcuLL76oJAuYxtcE4LvvvuP69etKD5Z89NFHJZ7TepWpyJEjR3jhhRe4cuUKNWvW1PzAQJHk5GRsbW2xsbHh1q1b3L1717D6VJYNGjSIVatWKc2Qn59PVlYWv/76K87OzhQUFFCxYkXNc2RnZ7N582YSExOpV68e3bt35+zZszg7O2Nvb695HoDU1FRlr5k+ffqwbt06JdcWjyeFpPjLJkyYUOyxhYUFzz//PEFBQVSuXNno1z99+jQeHh4cOnSoxOdat25t9Os/yoULF8jMzESn0/HBBx8wdOhQWrZsqXmOhw+W9O7dm/j4eCUHSwC+/vprLly4gKurq7IVWoCgoCA2bNig7Prnz58nJSWF+fPn8+677wKFhUtUVBS7du1SkmnZsmWsWrWK8uXLG5571OtJC++++y7W1tZKV68/++wzli5dSn5+Pj4+Puh0OoYPH65pBijcInP69OliJ9i9vb01zwEQGxvL5s2bi2XZv3+/phnS0tKws7PT9Jriz8mtbfGXZWdn4+zsjJeXF6dOneL06dNUqVKF0NBQli1bZvTrf//993h4eLBv374Sn1NVSE6ZMoVJkyaxaNEigoODmTdvnpJC8sSJE2zYsIE+ffrg5+fHpk2bNM8AEBkZyZUrV2jWrBk7d+7kxIkThIaGKsmi0+kYMWIErq6uhn1eISEhml0/IyOD/fv3c/v2bcP3rE6no1evXppl+KMDBw4QHx+PtbW1sgxFateuDcDvv/+uLMOaNWvYsmULAwcOZPjw4XTr1k1JITly5EjS0tJwcHBAr9ej0+mUFZIxMTGsWLFCk8WBxxk9evRj26jJLW91pJAUf1lqaqphleuVV15hwIABjB07lqCgIE2u/8477wCFJ4MzMzOLvWNWxdzcHHd3d3Jzc2nSpAn5+flKcpjKwZLjx48TGxsLQL9+/ejRo4eSHIDy9iFeXl54eXkZehPeuXOHSpUqKe296uTkVGw1UqWuXbuqjoCZmRmWlpbodDp0Op2yAvv27duG141q9evXx8HBQclBnyLTpk0r9vjcuXPMmjWLN998U1EiAVJIilKQmZlJYmIibm5uJCYmcu/ePdLS0rh//76mOUJDQzlx4gS2traGd+87duzQNEMRnU7HuHHjaNOmDfv371f2H1G/fv1M4mBJXl6eoel20b+NKjVr1lR27Yfdu3ePN99803D71NHREX9/fyVZcnNz6dy5M/Xq1QMKv38jIyOVZAkODkan01FQUMD169epXbu25ivpXl5ejBs3jpSUFMLDw/Hw8ND0+kVcXV1LNEdX5aWXXqJ9+/Y4OzsbXsNarwIW7SHW6/WsWLGCnTt3EhUVRYsWLTTNIYqTPZLiL0tISGDq1Kn89ttvODg4MHnyZE6fPo29vT0dOnTQLIe/vz9bt27V7Hr/SWpqKqdPn6ZNmzYcPXqUBg0a8Nxzz2meIzk5GWtra8PBkvT0dCUHOqKjo/nss89o3LgxCQkJ+Pj40L9/f81zwL9vY+v1ei5evIiTk5MmWzD+KCgoiMWLFzNq1ChWrVpFYGAg27dv1zwHwLFjx0o8Zwr/OWdkZBAeHs6CBQs0vW5KSophoMD27dtZtGgRDRs21DQDwN/+9jeuX79e7HCLqr2rXbt2ZcqUKdja2hqeU/Gz5PLly4SGhlK/fn1CQ0OxsbHRPIMoTlYkxV925swZ7t27h6WlJbdv32b8+PFKJsp4enpy6dIlkzj5amZmxt27d9m1axd6vZ5Tp04xZMgQza7/qAMdP//8M5GRkUoOdAwYMIDWrVtz6dIlunfvblj5UuHhw0Y5OTmMHTtWSQ4zMzOee+45dDodVlZWSv5D/Oqrr2jbtu0jGzqbQiFpa2vL1atXNb9uWFgYQ4YMYePGjYSEhBAREaHktLCqyVyPUqNGDTw8PJSO8ly/fj1r1qxhwoQJtGnTBsAwZEFl79OyTgpJ8ZcVTZRZunQpPj4+fPzxx0pyVKxYke7du1OhQgXDc6revY8ePRoXFxfOnz+PlZWV5re2TeVAx9atW/H39ycyMtJwO/vs2bOAtgdcHic/P59r164puXatWrWIjIwkPT2dFStW4OjoqHmG9PR0AG7duqX5tR+nZ8+e6HQ69Ho9qampSg6p5eXl4e3tzfLly+nUqRMbN27U9PpLlixh+PDhhISElNgGomrLQU5ODl26dMHd3d2QSess0dHRAMyaNYuIiAgAw232gwcPappF/JsUkuIvM5WJMkePHuXYsWOYm5vGt/X06dOZMGEC77//vmYHj4qYyoGO559/Hih5C0zlHsmHT/Ln5eXRt29fJTmmTZvG1q1bad68ORUqVGDGjBmaZ/Dz8wMKTwffvn3bJA6qPbxibGVlpaRfYm5uLhERETRv3pwjR45ofliuXbt2AAQEBGh63f9Eyzsqj/Pll1+qjiAewTT+xxVPNVOZKOPi4sLt27dNYmM6FLZFysrKQqfTaX7wqIjqAx2vvPIKUNjrMzw83PD8P/7xD3x9fTXL8TBVq9SPur6zszPOzs5A4T5FVe2qpk2bxjfffEP16tUNKzxanxYuWr2OjY0t8UZD69Xr2bNnc/jwYfz9/YmLi2PevHmaXr9BgwYA/Pbbb4YTyb/99hsTJ07UfMuBKW1/WL58uaGg/f777w2r1VOmTClxoltoRwpJ8ZfNnDmTq1evMm7cOKXj1X788UfatWtn2HcG6oqGoKAg1q5dS6tWrXj11Vdp3ry5khwLFy5k/fr1jBo1iqFDhxIYGKhpIblhwwaWLl1Kenq6Yb+XXq+nbt26mmX4o+PHj5OVlYVer2fGjBmMGTOGzp07a3b9R/U7LaKqkDx16hRxcXFK9789bvVaBRcXF1xcXADo2LGjshw7d+7ExsaGnJwcoqKiGD16tOYZ7ty5AxS+2Xi4l6aK1evDhw8bCsmlS5caCslLly5pnkX8mxSS4i+rWLGi4URjWFiYshymtDH94dPqf//735WMVwP1BzqCgoIICgpi2bJlDB06VNNrP868efOYP38+06ZNY9OmTYwdO1bTQrJobxdAUlISV69epX79+lSvXl2zDH9Uu3ZtsrOzlTYkL1++PMePHzeZ9kym4KOPPmLo0KFkZ2ezadMmJaMJc3Nz6dmzJ9bW1sTHxwNQUFBAXl4e48aN0zTLw01mHv5Y5VYZIYWkeIZcuHCBKVOmcPfuXTp37oy7uztt27ZVkuXw4cOsXbu22Lt2FZMXig50pKWlKTvQAeDu7s7ChQsZM2YMAwcO5O2331a2+mZlZUXVqlUxNzenWrVqhlOfWlu/fr2hxYyfnx9XrlwpdvtfSzdv3qRt27aGqTIqbm0X9Yq8evUqubm5eHh4cPbsWWxsbMrcfOWHD9mUL1+ehIQE3n//fUD7Ay5dunShZcuWLF++3PBm0MzMjKpVq2qaA4oXjFI8mg7pIymeGf369WP69Om89957LFy4kEGDBinry/fmm28yceJEw+06UHPLLi8vj61bt3L+/Hnc3Nzo0aOHkjYZfn5+rFq1iqpVq3L37l0GDx6sbGLHsGHDuH37Nr169eLevXscPXpUyQGxwMBANm7cSN++fVm3bh3dunXjk08+0TwHwK+//lriOScnJwVJCidVLVmyBHNzc/Lz83nnnXdYvXq1kiyqPKqvZxFTaMukSocOHRgwYAB6vZ41a9YYPl67di2ffvqp6nhllqxIimdK7dq10el0VKlSRWmjWgcHB15++WVl1y9SdAtKp9Nhbm6ubLyZubm5YQXD1tZW6V68hQsXcvXqVerWrcuFCxeUTZMpeg9ftLKiosA3pQMuRR5uRZSfn6/s8J5KRcViZmYmixcvJjExERcXFyXzvk1J586dDd8fD38sIxLVkkJSPDMqV65MbGwsWVlZ7Nu3j0qVKinLUrVqVcLDw2nYsKHhP+iePXtqniM0NBQnJydefvllTpw4wcSJE5kzZ47mOTw9PRk3bhxNmjQhISFByZSQIleuXCEzM5NTp04RFRXF0KFDlfQq7NSpE0FBQdy4cYPBgwfTvn17zTOYYnum7t2706lTJ+rVq8fFixcZNWqUsiyqTZw4EW9vb9566y2OHTtGWFiYkilMpiIvL48ePXoo26IjHk1ubYtnRmZmJsuWLTPcxh0yZIiSsYRQuEn+j0aOHKl5jj59+hTbX9a7d2/Wr1+veQ69Xs/Bgwe5dOkSbm5uvP7665pnKNKrVy8mTZrEokWLGDp0KPPmzWPDhg2a58jLy+PKlSucP38eV1dXHB0dlb35mT59eon2THPnzlWSBQpfy5cuXcLZ2Rk7OztlOVT74+u3V69emjdHNyVr165l586dVKtWjZ49e9KuXTuldzdEIVmRFM+MihUr0rdvX/Ly8tDr9SQlJdG0aVNNMyQnJ/P888/TqVMnTa/7R0UHSGrWrElCQgKenp6cO3fO0NJEa3fu3OHBgwdUr16djIyMYv3gtGZubo67uzu5ubk0adJE82bTt27dIjMzk9DQUObOnUuDBg0oKChgwIABbNu2TdMsRe2Z7ty5U6zrgZubm6Y5HvbPf/6TiRMnkpycTLVq1Zg1a5bSFWyVsrOzuXXrFtWqVeP333+noKBAdSSl+vfvT//+/Tl9+jTbt2/ngw8+4I033pBVSsWkkBTPjAkTJnDq1CmysrJ48OABzs7ObNmyRdMMRXNgw8PDDbcHixo8a3lq28fHxzBm7ujRo1hYWJCbm4uVlZVmGR6memTkw3Q6HePGjaNNmzbs379f8yynTp3i448/JikpicmTJwOFp2BVnGI3xfZMM2fO5P3336dBgwb88ssvTJs2TdnBLNXGjBlDQEAAFStW5N69e0qmH5kiDw8PPDw8yMnJYfHixfj4+JCQkKA6Vpklt7bFMyMgIIBNmzYRHh5OcHAwY8aMUdY25O7du9ja2hoenzhxQllTclPQt29fYmJiio2MLGr3orXU1FROnz7Nq6++ypEjR2jQoIGSLRDffPMNr776qubXfZT09HQOHTpkWM3/7bfflK0Y/3H7hartGKYkNTVVSQ9JU3Xz5k12797NgQMHcHNzo3v37kr2OYtCsiIpnhk2NjaGcYRVqlQhNzdXWZaRI0eyYsUKypUrx8KFCzl06BA7duzQPMfBgwfZuHEjubm56PV60tPT2bNnj+Y5wDRGRkLh6egff/yRzz77jNdee407d+4oKSQrV65MeHi44fv0t99+U9bmxpRWjM3Nzfnqq6/w8vLi+PHjSk6zm4rY2Fg2b95crB/t/v37FSZS65NPPmHnzp2kp6fTrVs31qxZU6b30JoK2aUqnhmNGjVi9erVVK9eneDgYPLy8pRl6devH8OHDycwMBBzc3PNb7EXWbx4MSNHjsTBwQE/Pz/q1aunJEdQUBAff/yxYWSkyjF4EydOxNnZmcuXL2Nvb8+kSZOU5Jg5cyYtWrQgMzMTR0dHZQfDikyfPh1XV1fWrFljGIunwvvvv8+OHTsIDAxk165dZfp2bkxMDIsWLWLz5s2GX2XZsWPHGDt2LHv27KF///5SRJoIWZEUz4yQkBAyMzMpX7483377LY0bN9Y8Q1JSEgCurq60aNGCI0eO8NZbb3H9+nVcXV01z2NnZ0fTpk2JjY2la9euyhq0Z2dn88477wBqR0ZC4W3c7t27s3v3bpo1a4aq3T2VKlXizTff5PDhw4waNYrevXsryVHEVFaMnZyc+OCDD9Dr9Zw8eZJq1aopy6Ja/fr1cXBwUNb/1dR07NjRsEUoLS3NUEjGxsYSEBCgMlqZJoWkeGbcvHmTvXv3Gm4DnT17VvOWO48acVd08EbFiEQLCwuOHz9OXl4e8fHxxZo9a2nLli289dZbAEqLyCKJiYlA4Sl7Ve1DdDodFy5cICsri0uXLin7t4HCFeO1a9caVoxV7uedN28ezs7O3LhxgzNnzmBvb6+k96kpeOmll2jfvj3Ozs5KDu2ZmtWrVxv2FY8ZM8bwtdi/f78UkgpJISmeGWPGjKFly5Y4ODgoy/Dw4Z60tDSuXbtGzZo1lW2UnzZtGpcuXWLYsGEsXLiQ0aNHK8mRk5ODr68vrq6uhsJN65nBRd577z0mTpxIYmIio0ePZsqUKUpyhIWFceHCBfr06cP48eMJDAxUkgMKR89BYZsm1SvGJ06c4N133zX0UOzXr5+yLKpt3ryZBQsWFDu4V5Y9fPfgcR8L7UkhKZ4ZNjY2BAcHq44BwIEDB1iwYAFubm5cuHCBkSNH0qVLF82uX9TP8v79+4bpJcHBwZpPLDl+/Dje3t6MHDmS8uXLa3rtx4mPj1e616yox2ft2rWpXbs2gPL2NsePH2fatGnk5+fj4+ODo6OjstGRBQUFJCQkULNmTXJycsrkiMQiNWrUwMPDQ5pu/8vDP78e97HQnhSS4pnh7u7Ovn37eOGFFww/WFTsS4TCCQzbt2/HxsaGzMxM+vXrp2kh+ah+lkW0vDU2Z84cYmJiWLlyJdHR0SaxcvDNN9/Qv39/ZfvOinp8wr9nSVetWhUzMzMOHjyoJNOCBQtYv349o0aNYujQoQQGBiorJLt06cKMGTOYNWsW8+bNo2/fvkpymIKcnBy6dOmCu7u74XtG1Uq+KcjKyuLy5csUFBTw4MGDYh8LdaSQFM+MX375hXPnzhke5+TkKFvp0el02NjYAIV7ArVuBP7FF18QFxdnKNxUNSRv1aoVvr6+JCcn4+PjA/y7QbuqoiktLY1XXnmFmjVrotPp0Ol0mn6ffPnllwB8/vnnzJ49mzp16pCZmcnUqVM1y/BHZmZmPPfcc+h0OqysrAzfuyoUNUkHip2o/+ijj5SMGVXJ19eXjIwMypUrx8qVK+nTp4/qSEqVL1/e0MTfysqq2MdCHSkkxTOjY8eOrF271tCXz9xc3bd3rVq1mD17Nl5eXvzwww/UqlVL0+t/+umn6PV6pk2bRkBAAJ6enpw9e1bzJuDBwcEEBwezePFiRowYUeLzp06d0vx0/bJlyzS93uMsWbKErVu3UrVqVX7//XeGDh2qZLoNFH6/RkZGkp6ezooVK0xy3NyxY8dUR9Dc9u3bGTJkCBs3biQkJITY2Fj69++vOpYyfzZgQk5vqyEbL8QzY+vWraxbt45XX32ViIgI3N3dlWWZNWsWzs7OfPfddzg7d8OvzgAAFGxJREFUOzNz5kxNr29paYmVlRXXrl3D09MTgIYNG3Lp0iVNcxR5VBEJam7T5eTkMHPmTAYNGqT0NPBzzz1H1apVAbC3t1d6wGXKlCk4OjrSvHlzrK2tTbJ3oylsi9BaXl4e3t7eZGRk0KlTpzI/a/vPlOVm7SrJiqR4ZtjZ2VG9enXu3bvHiy++yIcffqgsS1ZWFlWrVjXcIty7dy++vr6a57C1tWXBggV4enpy8uRJnJycNM/wn6goDkJDQxkxYgTNmjXjxIkThIWFKRmlWbFiRQYOHIi3tzdnzpzhwYMHREVFAYU9UbU0dOhQoqOjNb3m/6osHqjIzc0lIiICLy8vjhw5Qn5+vupIJq0svtkwBVJIimeGra0tcXFxhj1vKk97jhgxAicnJ+zt7QF1/wnOnz+fHTt28O2331KnTh3GjBmjJMfjqPi6WFtbG3rRvfbaa6xZs0bzDACvv/664eMaNWooyVCk6LXzcHsmVQfVxL/Nnj2bw4cP4+/vT1xcHPPmzVMdyaSVxTcbpkAKSfHMmDlzJlevXmXcuHFER0crPbyg1+uJiIhQdv0iFSpUMBxcEIUcHBxYsmQJL730EmfOnMHS0pJDhw4BaLpH0c/PT7Nr/ZnU1FQ+/vhjw2NTbHxdFlebXFxccHFxAQr3gAthiqSQFM+MihUr0rBhQ6Cw2bMKRT0CnZ2d+emnn2jUqJHhc5aWlkoymTIVxYFOp+PatWtcu3YNKNyfuG/fPkDbQtKUrFq1isTERBo2bEhcXJxhxVaFu3fvcuzYMcOEKigsoubOnassk3g6lMU3G6ZACkkhSlFRj0C9Xs+RI0cMz6tsd2PKOnfurPk1H7dSrGrCjSl49913admyJQ0bNiQpKYkDBw4o61c4YMAA3NzcqFSpElD42unYsaPSiVXCNNy4ceOxn3N0dOTdd9/VMI0oIoWkEKWoqEcglGw2XZbt3LmT5cuXk5OTU6yPZI8ePVRHM0hKSlIdQZmUlBTDiMbBgwcr7Vdoa2vL7NmzlV1fmK6i6Vx6vZ7ExETq1q1r+HkSGxtr6FAhtCWFpBBG8MUXXxAREUHlypUNzaZbtWqlOpYyK1euZNmyZSa9qlTWb4slJSXh6urK1atXlbaZad26NZs2baJu3bqG57y9vZXlEabj4dGmRbPYhXpSSAphBIsXLy7RbLosF5LOzs6GudKmqiyf+Jw0aRJjx47l9u3bVK9enenTpyvL8sMPP5CTk8Px48eBwn8XKSTFH5Xl16upkUJSCCMwpWbTpqB8+fIMGjSo2Bx0rXslisfz9PRk165dJZ5XMZbw/v37rF27VtNrCiGenBSSQhjBw82mf/75Z6XNpk2BylPA/62yfmv7UVSMJXR3d2ffvn3F3nRIT0sBxW9tp6SkFHvcs2dPFZEEUkgKYRSm1GzaFHTu3JnNmzdz8eJFXFxcDAc7tLRz587Hfs7X19fkJ7uooKK4PnfuHOfOnTM8NsWelkKNW7duGT7u3LlzscdCHSkkhTCCDh06kJGRQbly5diyZQu+vr4mN55QS+Hh4VSqVIlWrVpx7Ngx3nvvPc37AiYmJgJw8uRJrK2tadq0KadPnyYvLw9fX18sLCw0zfM0ULEPTQ5QiMdxcHCgW7duqmOIP5BCUggjGD9+PF27duXzzz+nbt26hIeHs3r1atWxlLly5QobNmwAoH379gQEBGieYdy4cQAMHDiQFStWGJ4fMGCA5lnE47Vr165YAWtra/sfV5NF2bFr1y4pJE2QFJJCGEFGRgavv/46MTExzJ07l/j4eNWRlMrOziYrKwtra2sePHhAfn6+siypqalkZGRQqVIl0tLSSE9PV5bF1Km4tf3pp58arv3zzz8bHguRlZXF5cuXH/l9Kfto1ZFCUggjyM3NJTo6mkaNGnHx4kXu3bunOpJSffv2pUuXLri7u3Px4kVGjRqlLMvQoUPp1q0bFStWJDMzk1mzZinLYipMaSzhw6NEmzdvbjikJsTly5cJDw8vUUjKPlq1dHo5qihEqfvxxx+Ji4tj2LBh7N69Gw8PjzI/dSE9PZ1r165Rs2ZN7OzslGbR6/WkpqZiZ2dX5qcOAfj7+5cYSzhhwgQlWSIjIw23tm/dusX169dl36QApAm5qZIVSSGMoFmzZmRkZLB582ZcXFzKbBG5ZMkShg8fTkhISImDG6pmOR85coRJkyZha2tLRkYGM2bMKNPN4sG0xhLWrl2bcuXKAdCgQQNeeeUVxYmEqTt58iRNmjRRHaPMkhVJIYwgMjKSK1eu0KxZM3744Qdq1qxJWFiY6liaO3fuHA0aNHhkP8IWLVooSASBgYEsWLCAGjVqkJKSwsiRI9m6dauSLKYiOjoaa2trkxhLOGDAAGnFJB4pNTWVKlWqAJCTk8OePXvYsGEDOTk57N27V3G6sktWJIUwguPHjxMbGwtAv3796NGjh+JEari7u5OTk0NMTAwffPABer2egoIC3nnnHWV7msqVK2fo7VmjRg2srKyU5DAlpjSW0NbWloMHD+Li4mLYdiAHKQRAlSpVuH79Ohs2bODAgQPo9Xo++OADmjVrpjpamSaFpBBGkJeXR0FBAWZmZuj1+jI7F/aTTz5h2bJl/P777/j4+ABgZmZG8+bNlWWqWLEi69atw9vbm+PHj1O5cmVlWUyFKY0lTE1NLZZFDlKIIsOGDSMjIwNfX1/27t3L2LFjpYg0AVJICmEEHTt2JDAwkMaNG5OQkEDHjh1VR1KiR48e9OjRg23bttG9e3eg8JbUwydztTZv3jyWLFnCggULqFOnjpzaxrTGEq5bt467d+/y66+/4uzsjI2NjZIcwvTo9XrMzc158OABBQUFZfYNuqmRPZJCGMn58+e5dOkSbm5uuLu7q46j1ObNm0lMTGTixIkMGDCAt956C19fX2V5vv76ay5cuICrqyvt27dXlsNU9OnTp9hjlauAn332GUuXLiU/Px8fHx90Oh3Dhw9XkkWYnuTkZLZt28aePXu4f/8+77//Pq1bt5buCwpJISmEEZw+fZodO3aQlZVleC4iIkJhIrX8/PyIjY3FysqK3NxcevfuzebNm5VkkYNQpi0gIICYmBgGDhxITEwM3bp1Y/v27apjCROj1+uJj49n27ZtJCQk8PXXX6uOVGbJrW0hjGDq1Kn07t0be3t71VFMgpmZmeFQi4WFhdJbUnIQqiRTGktoZmaGpaUlOp0OnU6HtbW1khzC9Dy831yn09GmTRvatGlDQkKC4mRlmxSSQhhBxYoV8fPzUx3DZLz++uv06tULT09Pzpw5Q7t27ZRlkYNQJZnSWEIvLy/GjRtHSkoK4eHheHh4KMsiTEu/fv0MWy7mzJlDaGgoAPPnz5cDWQpJISlEKTp06BBQuKKzbNkyGjVqZChUWrdurTKaUsOHD6dt27YkJSXh6+tLgwYNlGWRg1AlmdJYwqCgIL744gvq1KnD9u3bWbRokbIswrQ8vBPvzJkzj3xeaE8KSSFK0b59+4DCQvLKlStcuXLF8LmyXEimpKSwevVq0tLS6NChA9nZ2TRu3FhJlgEDBtC6dWsuXbpE9+7dqVevnpIcpuSPYwlVHlwICwtjyJAhbNy4kZCQECIiImQsnijh4eJR7iqoJYWkEKXo4QM1Z8+eJSkpibp161K/fn2FqdSbPHkyb7/9NkuWLMHLy4uwsDC2bNmiaYaHi6UiZ8+eBSAkJETTLKbGlMYS5uXl4e3tzfLly+nUqRMbN25UlkWYlodfv1I8mg4pJIUwggULFnDkyBE8PT1Zt24d7du3Z9CgQapjKZOdnU3Lli1ZunQpderUUTJNpk6dOsUe63Q6uSX2L/v37zeZsYS5ublERETQvHlzjhw5Qn5+vupIwkScOXOGgIAAAC5evEhAQAB6vZ7ExETFyco2KSSFMIJvv/2Wbdu2YWZmRn5+Pj179izThaSlpSXx8fEUFBRw8uRJJQ3Jiw4/ZWdns2XLFi5fvoy7uzv+/v6aZzE1pjSWcPbs2Rw+fBh/f3/i4uKYN2+ekhzC9OzevZu7d++yevVqKlasiJeXFz4+PlhYWKiOVqZJISmEETz//PPcu3cPW1tb8vLyynwboBkzZjBnzhzS0tKIjo5m6tSpyrKEhYXh5OREy5YtOXHiBBMnTmTOnDnK8pgCUxpL6OLigouLC4AchBLFJCQksGrVKgICAqhSpQo3btxg9OjRjB49GicnJ9XxyixpSC6EEXTv3p0bN27QoEEDLl68iIWFBdWqVQMw9DAsC/Ly8jA3NycnJ6fE51T1k+zTp0+xwxu9e/dm/fr1mucwNTKWUJi6wMBAVq9eTYUKFQzPZWZmMmzYMDmQpZCsSAphBAsXLnzk87du3dI4iVqhoaFERkYaRt2lp6dTuXJldDod+fn5NG7c+LFfq9JWVMzWrFmThIQEPD09OXfunGH1qyyTsYTiaWBubl6siITCnr1FB8WEGlJICmEEj7vNMmHChDLVODcyMhIo3Pc2bdo0qlWrho+PD05OTvj7+zN69GjNshQVSHq9nqNHj2JpaUlOTo6Sgz+mZs2aNWzZsoWBAwcyfPhwunXrJoWkMDmPu4NRUFCgcRLxMCkkhdBQWd1JsnDhQjZs2MDo0aMZNmwYgYGB+Pv78+GHH2qW4csvv/yPn4+NjTWcCC1rZCyheBpcvHiRcePGFXtOTm2rJ4WkEBoqq73PzMzMeO655wCwsrIyyT14+/fvL7OFpIwlFE+DBQsWPPL5svq6NRVSSAohjK5WrVpERkaSnp7OihUrcHR0VB2phLK6WgwyllA8HVq0aKE6gngEdXOwhCiDymqxMm3aNBwdHWnevDnW1tbMmDFDdaQSyupqMRS2RKpbty7//Oc/DWMJhRDivyErkkIYSWZmpqGdStFJw86dOytOpYa5uTmBgYGqY4jHkLGEQognJYWkEEbw6aefsmzZshLtVHr06KE6mniMsrpaDDKWUAjx5KQhuRBGEBAQQExMDAMHDiQmJoZu3bqxfft21bEEkJ+fz9mzZ3nw4IHhOW9vb0NvybLo8uXLxcYSenh44OzsrDqWEOIpICuSQhiBtFMxXaNHjyYjI8MwaUin0+Ht7V1mi0iQsYRCiCcnhaQQRuDl5UVISIi0UzFBaWlpsgdQCCFKiRSSQhhBSEgI3377LQ0bNsTNzY22bduqjiT+xdHRkZs3b+Lg4KA6ihBCPPVkj6QQRpCZmcm3335rmO8M4OvrqzCRaN26NVA4c/v+/fuGmd8Ahw4dUhlNCCGeWrIiKYQRDB8+nOrVqxtWvcpyj0JTIcWiEEKUPikkhTACvV7P/PnzVccQj9C3b99ijy0sLHj++ecZNmwYNWvWVJRKCCGeTjLZRggjqF+/PqdOnSInJ8fwS5gGJycnOnfuzNSpU/H19aVChQo0adKESZMmqY4mhBBPHVmRFMIIjh07xpdffml4rNPpOHjwoMJEosiNGzcMIwDr1KnDnj178Pf3Z9euXYqTCSHE00cKSSGMYPfu3aojiMfIzc0lPj6epk2b8uOPP5KXl8e1a9fIyspSHU0IIZ46cmpbiFI0ffp0wsPD6dmzZ4kDNrGxsYpSiYddvXqVuXPnkpiYSL169Rg/fjwnT57EwcEBLy8v1fGEEOKpIoWkEKXo999/x97enl9//bXE55ycnBQkEkXy8vIwNzd/5H5VS0tLBYmEEOLpJ4WkEEZw8+ZN9u7dS3Z2tuG5kSNHKkwkxo0bR2RkJO3atSu2WqzT6YiLi1OYTAghnl5yalsIIxgzZgyZmZnY29sbfgm1IiMjARg6dCjly5dHr9ej1+spKChQnEwIIZ5ecthGCCOwsbEhODhYdQzxCLGxsaxYsYJq1aqpjiKEEE89KSSFMAJ3d3f27dvHCy+8YLiN6urqqjiVALCzs5P9qkIIUUpkj6QQRtCnT59ij3U6HTExMYrSCICoqCgAfvrpJywtLWnYsKGhyA8JCVEZTQghnlqyIimEEaxbt051BPEHRSvCsjIshBClR1YkhShFo0eP5sMPP6R169YlPnfo0CEFiYQQQgjjkUJSCCO4efMmDg4OhseJiYm4ubkpTCSEEEKUPrm1LUQpOn/+PCkpKcyfP59//OMfhvYykZGRMstZCCHEM0cKSSFKUUZGBvv37+f27dvs3bsXKDxo06tXL8XJhBBCiNInt7aFMIIzZ87QqFEj1TGEEEIIo5IVSSGMIDk5maioKHJzc9Hr9aSnp7Nnzx7VsYQQQohSJSMShTCCxYsXM3LkSBwcHPDz86N+/fqqIwkhhBClTgpJIYzAzs6Opk2bAtC1a1eSk5MVJxJCCCFKnxSSQhiBhYUFx48fJy8vj/j4eG7duqU6khBCCFHq5LCNEEaQkpLCpUuXqFatGgsXLsTHx4dOnTqpjiWEEEKUKikkhTCS77//nmvXruHp6YmrqytWVlaqIwkhhBClSk5tC2EEUVFRJCcnk5iYiIWFBStWrCAqKkp1LCGEEKJUyR5JIYzgxIkTzJ07lwoVKuDn58f169dVRxJCCCFKnRSSQhhBfn4+2dnZ6HQ68vPzMTOTl5oQQohnj9zaFsII+vfvT9euXUlNTcXf35+3335bdSQhhBCi1MlhGyGM4MiRI7zwwgtcuXKFmjVrUqVKFdWRhBBCiFInhaQQRhAUFMSGDRtUxxBCCCGMSm5tC2EEOp2OESNG4OrqatgfGRISojiVEEIIUbrkBIAQRtCmTRtatGiBm5sbO3bswM7OTnUkIYQQotRJISmEEXz++ee0atUKPz8/Nm7cSFxcnOpIQgghRKmTQlIIIzA3N6du3boAODs7S/sfIYQQzyTZIymEETg6OhIVFUWTJk1ISEigevXqqiMJIYQQpU5ObQthBNnZ2WzatImkpCTc3NwICAjA0tJSdSwhhBCiVEkhKYQQQgghnohs3BJCCCGEEE9ECkkhhBBCCPFEpJAUQgghhBBPRApJIYQQQgjxRKSQFEIIIYQQT+T/AXmz6DENwc07AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(data.corr(), annot=True, cmap= \"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### separating dependent and independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# features\n",
    "x= data.iloc[:, :-1]\n",
    "\n",
    "# target\n",
    "y= data.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### performing oversampling to handle imbalanced target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialising oversampling\n",
    "smote= SMOTETomek()\n",
    "\n",
    "# implementing oversampling\n",
    "x_sm, y_sm= smote.fit_sample(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>210000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90.0</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>204000.00</td>\n",
       "      <td>2.1</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  55.0        0                      7861         0                 38   \n",
       "1  50.0        1                       111         0                 20   \n",
       "2  90.0        1                        47         0                 40   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    0  263358.03               1.1           136    1   \n",
       "1                    0  210000.00               1.9           137    1   \n",
       "2                    1  204000.00               2.1           132    1   \n",
       "\n",
       "   smoking  time  \n",
       "0        0     6  \n",
       "1        0     7  \n",
       "2        1     8  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_sm.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaling= StandardScaler()\n",
    "\n",
    "columns_to_scale= [\"age\", \"creatinine_phosphokinase\", \"ejection_fraction\", \"platelets\", \"serum_creatinine\",\n",
    "                       \"serum_sodium\", \"time\"]\n",
    "x_sm[columns_to_scale]=scaling.fit_transform(x_sm[columns_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>age</th>\n",
       "      <th>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.572926</td>\n",
       "      <td>0</td>\n",
       "      <td>7.156587</td>\n",
       "      <td>0</td>\n",
       "      <td>0.100510</td>\n",
       "      <td>0</td>\n",
       "      <td>0.017351</td>\n",
       "      <td>-0.390878</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.457337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.972779</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.507269</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.440078</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.535271</td>\n",
       "      <td>0.308287</td>\n",
       "      <td>0.242771</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.443988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.226042</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.570557</td>\n",
       "      <td>0</td>\n",
       "      <td>0.271686</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.597412</td>\n",
       "      <td>0.483079</td>\n",
       "      <td>-0.971082</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.430639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.173074</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.305536</td>\n",
       "      <td>1</td>\n",
       "      <td>1.983451</td>\n",
       "      <td>0</td>\n",
       "      <td>1.991802</td>\n",
       "      <td>-0.390878</td>\n",
       "      <td>-1.213853</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.403941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.426336</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.495402</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.156255</td>\n",
       "      <td>1</td>\n",
       "      <td>1.308250</td>\n",
       "      <td>6.862966</td>\n",
       "      <td>-0.728312</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.403941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0 -0.572926        0                  7.156587         0           0.100510   \n",
       "1 -0.972779        1                 -0.507269         0          -1.440078   \n",
       "2  2.226042        1                 -0.570557         0           0.271686   \n",
       "3 -0.173074        1                 -0.305536         1           1.983451   \n",
       "4  1.426336        1                 -0.495402         0          -0.156255   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    0   0.017351         -0.390878      0.000000    1   \n",
       "1                    0  -0.535271          0.308287      0.242771    1   \n",
       "2                    1  -0.597412          0.483079     -0.971082    1   \n",
       "3                    0   1.991802         -0.390878     -1.213853    1   \n",
       "4                    1   1.308250          6.862966     -0.728312    1   \n",
       "\n",
       "   smoking      time  \n",
       "0        0 -1.457337  \n",
       "1        0 -1.443988  \n",
       "2        1 -1.430639  \n",
       "3        1 -1.403941  \n",
       "4        1 -1.403941  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scaled dataset\n",
    "x_scaled= x_sm.copy()\n",
    "x_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### splitting into train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test= train_test_split(x_scaled, y_sm, test_size=0.2, random_state= 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score : 0.8185997703296982\n",
      "best parameters : {'C': 1.0, 'max_iter': 100, 'penalty': 'l2'}\n",
      "best estimaotrs : LogisticRegression()\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 136 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed:    0.5s finished\n"
     ]
    }
   ],
   "source": [
    "# initialising logistic regression\n",
    "lr= LogisticRegression()\n",
    "\n",
    "# hyperparameter tuning\n",
    "lr_params= {\"C\": 10.0**np.arange(-2,3),\n",
    "            \"penalty\": [\"l1\", \"l2\"],\n",
    "            \"max_iter\": [100, 1000, 2500, 5000]}\n",
    "\n",
    "# defining stratified K fold cross validation\n",
    "lr_cv= KFold(n_splits=5)\n",
    "\n",
    "# using gridsearch for respective parameter\n",
    "lr_gridsearch= GridSearchCV(estimator=lr, param_grid= lr_params, cv= lr_cv, scoring=\"f1_macro\", n_jobs=-1, verbose=2)\n",
    "\n",
    "# model fitting\n",
    "lr_gridsearch.fit(x_train, y_train)\n",
    "\n",
    "print(\"best score :\", lr_gridsearch.best_score_)\n",
    "print(\"best parameters :\", lr_gridsearch.best_params_)\n",
    "print(\"best estimaotrs :\", lr_gridsearch.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8852459016393442\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.90      0.89        30\n",
      "           1       0.90      0.87      0.89        31\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.89      0.89        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#best model\n",
    "lr_grid= lr_gridsearch.best_estimator_\n",
    "\n",
    "# checking model performance\n",
    "lr_grid_pred= lr_grid.predict(x_test)\n",
    "\n",
    "cm= confusion_matrix(y_test, lr_grid_pred)\n",
    "\n",
    "plot_confusion_matrix(cm, figsize=(6,6))a\n",
    "print(accuracy_score(y_test, lr_grid_pred))\n",
    "print(classification_report(y_test, lr_grid_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest\n",
    "*Since ensemble techniques does not require dataset to be scaled, we are using just oversampled data to split into train and test data.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test= train_test_split(x_sm,y_sm, test_size=0.2, random_state=10 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    4.4s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   33.6s\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:  1.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8496909331071434\n",
      "{'n_estimators': 300, 'min_samples_split': 10, 'min_samples_leaf': 5, 'max_features': 'sqrt', 'max_depth': 400, 'criterion': 'gini'}\n",
      "RandomForestClassifier(max_depth=400, max_features='sqrt', min_samples_leaf=5,\n",
      "                       min_samples_split=10, n_estimators=300)\n"
     ]
    }
   ],
   "source": [
    "#. initialising random forest classifier\n",
    "classifier= RandomForestClassifier()\n",
    "\n",
    "# hyperparameter tuning\n",
    "params= {\"n_estimators\" : [int(x) for x in np.linspace(start= 100, stop= 1000, num=10)],\n",
    "         \"criterion\" : [\"gini\", \"entrpoy\"],\n",
    "         \"max_depth\" : [int(x) for x in np.linspace(start=100, stop=1000, num=10)],\n",
    "         \"min_samples_split\" : [1,5,10,15],\n",
    "         \"min_samples_leaf\" : [2,3,5,6,7,],\n",
    "         \"max_features\" : [\"auto\", \"sqrt\", \"log2\"]\n",
    "         \n",
    "        }\n",
    "\n",
    "# defining KFold cross validation\n",
    "cv= KFold(n_splits=5)\n",
    "\n",
    "# using randomized gridsearch on respective hyperparameters\n",
    "RandomSearch= RandomizedSearchCV(estimator=classifier, param_distributions= params, cv=cv,scoring=\"f1_macro\",\n",
    "                                 n_iter=100, n_jobs= -1,verbose=2,random_state=10)\n",
    "\n",
    "# model fitting\n",
    "RandomSearch.fit(X_train, Y_train)\n",
    "\n",
    "print(RandomSearch.best_score_)\n",
    "print(RandomSearch.best_params_)\n",
    "print(RandomSearch.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9508196721311475\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.93      0.95        30\n",
      "           1       0.94      0.97      0.95        31\n",
      "\n",
      "    accuracy                           0.95        61\n",
      "   macro avg       0.95      0.95      0.95        61\n",
      "weighted avg       0.95      0.95      0.95        61\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# best model\n",
    "rf_randomSearch= RandomSearch.best_estimator_\n",
    "\n",
    "# checking model performance\n",
    "rf_random_pred= rf_randomSearch.predict(X_test)\n",
    "\n",
    "cm= confusion_matrix(Y_test, rf_random_pred)\n",
    "\n",
    "plot_confusion_matrix(cm, figsize=(6,6))\n",
    "print(accuracy_score(Y_test, rf_random_pred))\n",
    "print(classification_report(Y_test, rf_random_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating pickle file for *Random Forest model*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# create pickle file with name classifier.pkl\n",
    "pickle_out= open(\"classifier_heart.pkl\", \"wb\")\n",
    "\n",
    "# dumping rf_gridsearch classifier into above created pickle file\n",
    "pickle.dump(rf_randomSearch, pickle_out)\n",
    "\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_in= open(\"classifier_heart.pkl\", \"rb\")\n",
    "classifier= pickle.load(pickle_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict([[57,1,115,0,25,1,181000,1.1,144,1,0,79]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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