{ "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4-final" }, "orig_nbformat": 2, "kernelspec": { "name": "Python 3.7.4 64-bit ('venv')", "display_name": "Python 3.7.4 64-bit ('venv')", "metadata": { "interpreter": { "hash": "e284c72d79b42194b3fe2a0767ff9cca6d233ae03063bab113c99e4bc6bd25a8" } } } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "source": [ "# Titanic Model with 90% accuracy\n", "https://www.kaggle.com/vinothan/titanic-model-with-90-accuracy" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "train_df=pd.read_csv(\"./datasets/titanic/train.csv\")\n", "test_df=pd.read_csv(\"./datasets/titanic/test.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ], "text/html": "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" }, "metadata": {}, "execution_count": 3 } ], "source": [ "train_df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "__Test_DataSet_\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " PassengerId Pclass Name Sex \\\n", "0 892 3 Kelly, Mr. James male \n", "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n", "2 894 2 Myles, Mr. Thomas Francis male \n", "3 895 3 Wirz, Mr. Albert male \n", "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", "\n", " Age SibSp Parch Ticket Fare Cabin Embarked \n", "0 34.5 0 0 330911 7.8292 NaN Q \n", "1 47.0 1 0 363272 7.0000 NaN S \n", "2 62.0 0 0 240276 9.6875 NaN Q \n", "3 27.0 0 0 315154 8.6625 NaN S \n", "4 22.0 1 1 3101298 12.2875 NaN S " ], "text/html": "
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
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" }, "metadata": {}, "execution_count": 4 } ], "source": [ "print('__Test_DataSet_')\n", "test_df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def missingdata(data):\n", " total = data.isnull().sum().sort_values(ascending = False)\n", " percent = (data.isnull().sum()/data.isnull().count()*100).sort_values(ascending = False)\n", " ms=pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n", " ms= ms[ms[\"Percent\"] > 0]\n", " f,ax =plt.subplots(figsize=(8,6))\n", " plt.xticks(rotation='90')\n", " fig=sns.barplot(ms.index, ms[\"Percent\"],color=\"green\",alpha=0.8)\n", " plt.xlabel('Features', fontsize=15)\n", " plt.ylabel('Percent of missing values', fontsize=15)\n", " plt.title('Percent missing data by feature', fontsize=15)\n", " return ms" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Total Percent\n", "Cabin 687 77.104377\n", "Age 177 19.865320\n", "Embarked 2 0.224467" ], "text/html": "
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TotalPercent
Cabin68777.104377
Age17719.865320
Embarked20.224467
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" }, "metadata": {}, "execution_count": 6 }, { "output_type": "display_data", "data": { "text/plain": "
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TotalPercent
Cabin32778.229665
Age8620.574163
Fare10.239234
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AdvpA4IQ5j0iSpJ6a82SeZCPgUcDnB4rfCjwqyfnAI9t5SZLUwXpzvcOqugHYYqjsdzS92yVJ0jQ5ApwkST1nMpckqedM5pIk9ZzJXJKknjOZS5LUcyZzSZJ6zmQuSVLPmcwlSeo5k7kkST1nMpckqedM5pIk9ZzJXJKknjOZS5LUcyZzSZJ6zmQuSVLPmcwlSeq5Tsk8yd8lOWBgfsskn0xyRpKjktxl9kKUJEmT6XpmfiSwy8D8e4B9gR8AzwZeP7NhSZKkrrom8/sDpwMk2RB4IvCSqvpX4FXA02YnPEmSNJWuyXx94KZ2ek9gPeCkdv48YJsZjkuSJHXUNZmfA+zXTj8T+H5VXd/O3xO4eqYDkyRJ3azXcbkjgM8kOQjYBDhgoG4/4KczHZgkSeqmUzKvqhOT/AWwG/DzqjpvoPr7wJmzEZwkSZpa1zNzquoi4KJxypfMaESSJGlaOg8ak+Svk3w6yYVJViZ5UFv+piT7z16IkiRpMl0Hjdmf5ta0ewDHAoODxKwEDpn50CRJUhddz8zfAhxdVQ8H3jRUdwaw6wzGJEmSpqFrMn8A8Ol2uobqrgM2n7GIJEnStHRN5suB+0xQ95fApTMTjiRJmq6uyfw44IgkDxsoqyQ7Aa8GPjHjkUmSpE663pr2WmBn4JvAlW3ZCTQd4r4OvHnmQ5MkSV10HTRmJfC4JPvSPC1tS5ohXJdW1cnT2WGSTYGP0DyFrYDnAOfSXJNfBFwMPLWqfj+d7UqStLbqPGgMQFUtBZau5j7fA3y1qp6cZH1gQ+DfaX4YvDXJYcBhNM33kiRpCp2SeZKdp1qmqs7usJ1NgL1onoFOVd0M3JzkAGDvdrFjgNMwmUuS1EnXM/OzuPMtacPW7bCdHYAVwMeTPJBmIJqXAFtX1RXtMlcCW3eMS5KktV7XZL7POGWbAX/fvl48jf09CDikqn6Y5D00Tep/VlWVZNwfDkkOBg4G2G677TruUpKk+a1rB7hvTlD1xSRvBJ4KfKnDpi4DLquqH7bzn6VJ5lcl2aaqrkiyDc197ePFsQRYArB48eKpWgokSVordH7QyiRO5Y7PN59QVV0J/DrJ/duifYGzgROBA9uyA2lue5MkSR1Mqzf7BB4LXDON5Q8BPtH2ZL8I+BeaHxXHJzkIuITmTF+SJHXQtTf78eMUr08zZvuONLeWdVJVZwCLx6nat+s2JEnS7bqemS8cp+wm4NvAy6rqyzMXkiRJmo6uHeDG680uSZLWADPRAU6SJI3QhGfmSY6cxnaqqhyxTZKkEZismf0p09hO4fCrkiSNxITJvKp2mMtAJEnSqvGauSRJPTetQWOSPAzYCVgwXFdV75+poCRJUnddB43ZmuY55jvTXB9PWzU4PrrJXJKkEejazH4UcC1wb5pE/mBgEfBa4Hyas3VJkjQCXZvZH07z3PGxZ46nqi4F3pxkHZqz8r+fhfgkSdIUup6ZbwqsqKrbgOuArQbqvgc8dIbjkiRJHXVN5r8CtmmnfwE8c6DuH4CrZzIoSZLUXddm9pOARwPHA28ETkhyGXALsB0OGCNJ0sh0fdDKawamv5LkocATgQ2Ak6vqK7MUnyRJmsK07jMfU1XLgGUzHIskSVoFna6ZJ7k4yduS7DbbAUmSpOnp2gHuM8DTgGVJzktyRJJdZjEuSZLUUadkXlWvrKpFwMOALwPPAX6W5Kwkr02y4yzGKEmSJjGtB61U1fer6lCakeD2Bk4DDgF+OdOBSZKkblb1qWkb0dyStj2wCbByxiKSJEnT0jmZJ9kgyVOTfA5YDnyU5kErzwG2nqX4JEnSFLo+Ne14YH+aR5+eArwQ+EJVXTN7oUmSpC663me+EHgl8Nmq+u0sxiNJkqap6whw+8x2IJIkadWsagc4SZK0hjCZS5LUcyZzSZJ6zmQuSVLPmcwlSeq5rveZ/9ck1bcB1wE/q6pvzkhUkiSps673mR9CM2DMRu38H4C7tdM3tNu5a5IzgP2r6qqJNpTkYuB64E/ArVW1OMnmwKeBRcDFwFOr6vfT+SCSJK2tujazPwa4guYxqBtU1cbABsDT2/JHAnvRDC5zVIft7VNVu1bV4nb+MGBpVe0ILG3nJUlSB12T+f8Ab62qz1TVSoCqWllVxwNvA95bVd8B3gj8/SrEcQBwTDt9DPCEVdiGJElrpa7J/K+BKyeouwL4i3b6HODuU2yrgK8nOT3JwW3Z1lV1RTt9JT64RZKkzrpeMz8PeEmSb1TVzWOFSe4KvBQ4ty26BzDh9fLWw6rq8iRbAScnOWewsqoqSY23Ypv8DwbYbrvtOoYuSdL81jWZvwQ4CbgsycnACprr44+i6RT3mHa53YDPT7ahqrq8fV+e5AvA7sBVSbapqiuSbEPziNXx1l0CLAFYvHjxuAlfkqS1Tadm9qo6DdiR5nr2PWmui98TOBrYceyWtKo6rKpeOtF2kmyU5O5j08CjgbOAE4ED28UOBE5Yhc8iSdJaqeuZOVX1G5rHoK6OrYEvJBnb9yer6qtJfgwcn+Qg4BLgqau5H0mS1hqdk/lMqKqLgAeOU/47YN+5jEWSpPmi6whwd6G5bv4kYFuaAWTuoKq2mtnQJElSF13PzN8FPB/4EnAqcPPki0uSpLnSNZk/BTisqrqM7iZJkuZQ10FjApw5m4FIkqRV0zWZfxh4xmwGIkmSVk3XZvargGcmORU4GbhmqL6q6gMzGZgkSeqmazJ/d/u+HfDwceoLMJlLkjQCnZJ5VXVtjpckSXPMJC1JUs9NeGaeZGfgwqpa2U5PqqrOntHIJElSJ5M1s58F7AH8qJ2e6CllaevWndnQJElSF5Ml832AswemJUnSGmjCZD72WNPhaUmStGbp1AEuyVZJdhiYT5KDk7w7yT/MXniSJGkqXXuzHw28dGD+COD9wH40zyd/9syGJUmSuuqazB8EnAKQZB3gX4F/r6oHAG8CDp2V6CRJ0pS6JvNNgN+1038DbA58op0/BbjfDMclSZI66prMLwPG7jV/LHBOVV3ezm8C3DTTgUmSpG66js3+MeDIJI+kSeavGajbA/jlTAcmSZK66To2+1uSXA78LXAITXIfsznwkVmITZIkddD1zJyqOhY4dpzyf53RiCRJ0rR0vc/8L5LsMTC/YZI3J/likkNmLzxJkjSVrh3g3g8MDg5zJPASYAHwtiSvnOnAJElSN12T+S7A9wGS3AX4Z+DQqtoP+HfgObMTniRJmkrXZL4RcF07vUc7//l2/ifA9jMclyRJ6qhrMv8VTRIHeCLw06oaG0RmS+D6mQ5MkiR107U3+zuBDyR5CrAb8C8DdXsDZ85wXJIkqaOu95l/NMn5NPeZH1ZVSweqrwbePQuxSZKkDqZzn/m3gG+NU374TAYkSZKmZ8JknmRn4MKqWtlOT6qqzp7RyCRJUieTnZmfRdPp7UftdE2wXNq6dWc2NEmS1MVkyXwf4OyB6RmTZF1gGXB5VT0uyQ7AccAWwOnAP1fVzTO5T0mS5qsJk3lVfXO86RnyEponrW3czr8NeFdVHZfkg8BBwAdmeJ+SJM1LXe8z/7Mk67Vjs9/hNY31t6V5jOpH2vkAjwA+2y5yDPCE6cYlSdLaquuDVjZJ8v4kVwA30QwSM/zq6t3Aq4Db2vktgGuq6tZ2/jLgXhPEcXCSZUmWrVixYhq7lCRp/up6a9rRwMOBDwMXAKt0PTvJ44DlVXV6kr2nu35VLQGWACxevHiiDnmSJK1VuibzfYHnV9WnVnN/ewKPT/IYmieubQy8B9g0yXrt2fm2wOWruR9JktYaXa+ZXwrcuLo7q6rXVNW2VbUIeDpwSlU9EzgVeHK72IHACau7L0mS1hZdk/mrgP9Mst0sxfFq4GVJLqC5hv7RWdqPJEnzTtex2b+c5JHABUkuBq4ZZ5ndp7PjqjoNOK2dvgiY1vqSJKnRKZkneQdwKPBjVqMDnCRJmnldO8A9F/iPqnrLbAYjSZKmr+s18xtphlmVJElrmK7J/D3Awe1obZIkaQ3StZl9S+DBwLlJTuPOHeCqql49g3FJkqSOuibzJwO3AncBHjVOfdHcXiZJkuZY11vTdpjtQCRJ0qqZ9lPTJEnSmsVkLklSz5nMJUnqOZO5JEk9N2EyT7JXkrvNZTCSJGn6JjszPxXYGSDJRUkeODchSZKk6ZgsmV8PbNZOLwLWn/VoJEnStE12n/n3gI8k+WE7/5YkV0+wbFXV02Y2NEmS1MVkyfw5wH8AD6AZ4W0zYN25CEqSJHU3YTKvqiuBQwCS3Aa8oKp+NFeBSZKkbroO5+otbJIkraG6PmiFJJsCzwceBmwOXA18G1hSVdfMRnCSJGlqnc64k9wX+DlwBLARcGn7fgRwZlsvSZJGoOuZ+btonmG+R1VdPlaY5F7Al4F3AgfMeHSSJGlKXa+F7w3812AiB2jnjwD2meG4JElSR12TeTHxbWnrtPWSJGkEuibzU4E3JNl+sLCdPwJYOtOBSZKkbrpeMz8UOAU4P8lPgKuArYC/AX4NvGxWopMkSVPqdGZeVRfTjAT3YuAXwF2As4EXAX/R1kuSpBHofJ95Vd0MfLB9SZKkNYQju0mS1HMmc0mSes5kLklSz81pMk+yIMmPkvwsyS+SvL4t3yHJD5NckOTTSdafy7gkSeqzrmOz75XkbhPU3S3JXh33txJ4RFU9ENgV2C/JHsDbgHdV1f2A3wMHddyeJElrvekMGrPzBHX3b+unVI0/tLN3aV8FPAL4bFt+DPCEjnFJkrTW65rMM0nd3YAbu+4wybpJzgCWAycDFwLXVNWt7SKXAffquj1JktZ2E95n3jad7z1Q9Nwk+w0ttgB4LM3jUTupqj8Bu7bPR/8CzWA0nSQ5GDgYYLvttuu6miRJ89pkg8Y8GDiknS7gKcCtQ8vcDJwDvHK6O66qa5KcCjwE2DTJeu3Z+bbA5ROsswRYArB48WIf7iJJEpM0s1fV26tqYVUtBC4F9h6bH3jdq6r2raqfdNlZkoXtGTlJNgA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}, "metadata": { "needs_background": "light" } } ], "source": [ "missingdata(test_df)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "30.272590361445783" ] }, "metadata": {}, "execution_count": 8 } ], "source": [ "test_df['Age'].mean()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "train_df['Embarked'].fillna(train_df['Embarked'].mode()[0], inplace = True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "test_df['Fare'].fillna(test_df['Fare'].median(), inplace = True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "drop_column = ['Cabin']\n", "train_df.drop(drop_column, axis=1, inplace = True)\n", "test_df.drop(drop_column,axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "test_df['Age'].fillna(test_df['Age'].median(), inplace = True)\n", "train_df['Age'].fillna(train_df['Age'].median(), inplace = True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "check the nan value in train data\nPassengerId 0\nSurvived 0\nPclass 0\nName 0\nSex 0\nAge 0\nSibSp 0\nParch 0\nTicket 0\nFare 0\nEmbarked 0\ndtype: int64\n__________________________________________________________________________________________\ncheck the nan value in test data\nPassengerId 0\nPclass 0\nName 0\nSex 0\nAge 0\nSibSp 0\nParch 0\nTicket 0\nFare 0\nEmbarked 0\ndtype: int64\n" ] } ], "source": [ "print('check the nan value in train data')\n", "print(train_df.isnull().sum())\n", "print('___'*30)\n", "print('check the nan value in test data')\n", "print(test_df.isnull().sum())" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "all_data=[train_df,test_df]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "for dataset in all_data:\n", " dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "import re\n", "# Define function to extract titles from passenger names\n", "def get_title(name):\n", " title_search = re.search(' ([A-Za-z]+)\\.', name)\n", " # If the title exists, extract and return it.\n", " if title_search:\n", " return title_search.group(1)\n", " return \"\"\n", "# Create a new feature Title, containing the titles of passenger names\n", "for dataset in all_data:\n", " dataset['Title'] = dataset['Name'].apply(get_title)\n", "# Group all non-common titles into one single grouping \"Rare\"\n", "for dataset in all_data:\n", " dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', \n", " 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n", "\n", " dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n", " dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n", " dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "## create bin for age features\n", "for dataset in all_data:\n", " dataset['Age_bin'] = pd.cut(dataset['Age'], bins=[0,12,20,40,120], labels=['Children','Teenage','Adult','Elder'])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "## create bin for fare features\n", "for dataset in all_data:\n", " dataset['Fare_bin'] = pd.cut(dataset['Fare'], bins=[0,7.91,14.45,31,120], labels=['Low_fare','median_fare',\n", " 'Average_fare','high_fare'])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "### for our reference making a copy of both DataSet start working for copy of dataset\n", "traindf=train_df\n", "testdf=test_df" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "all_dat=[traindf,testdf]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "for dataset in all_dat:\n", " drop_column = ['Age','Fare','Name','Ticket']\n", " dataset.drop(drop_column, axis=1, inplace = True)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "drop_column = ['PassengerId']\n", "traindf.drop(drop_column, axis=1, inplace = True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " PassengerId Pclass Sex SibSp Parch Embarked FamilySize Title \\\n", "0 892 3 male 0 0 Q 1 Mr \n", "1 893 3 female 1 0 S 2 Mrs \n", "\n", " Age_bin Fare_bin \n", "0 Adult Low_fare \n", "1 Elder Low_fare " ], "text/html": "
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18933female10S2MrsElderLow_fare
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" }, "metadata": {}, "execution_count": 23 } ], "source": [ "testdf.head(2)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "traindf = pd.get_dummies(traindf, columns = [\"Sex\",\"Title\",\"Age_bin\",\"Embarked\",\"Fare_bin\"],\n", " prefix=[\"Sex\",\"Title\",\"Age_type\",\"Em_type\",\"Fare_type\"])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "testdf = pd.get_dummies(testdf, columns = [\"Sex\",\"Title\",\"Age_bin\",\"Embarked\",\"Fare_bin\"],\n", " prefix=[\"Sex\",\"Title\",\"Age_type\",\"Em_type\",\"Fare_type\"])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " PassengerId Pclass SibSp Parch FamilySize Sex_female Sex_male \\\n", "0 892 3 0 0 1 0 1 \n", "1 893 3 1 0 2 1 0 \n", "2 894 2 0 0 1 0 1 \n", "3 895 3 0 0 1 0 1 \n", "4 896 3 1 1 3 1 0 \n", "\n", " Title_Master Title_Miss Title_Mr ... Age_type_Teenage Age_type_Adult \\\n", "0 0 0 1 ... 0 1 \n", "1 0 0 0 ... 0 0 \n", "2 0 0 1 ... 0 0 \n", "3 0 0 1 ... 0 1 \n", "4 0 0 0 ... 0 1 \n", "\n", " Age_type_Elder Em_type_C Em_type_Q Em_type_S Fare_type_Low_fare \\\n", "0 0 0 1 0 1 \n", "1 1 0 0 1 1 \n", "2 1 0 1 0 0 \n", "3 0 0 0 1 0 \n", "4 0 0 0 1 0 \n", "\n", " Fare_type_median_fare Fare_type_Average_fare Fare_type_high_fare \n", "0 0 0 0 \n", "1 0 0 0 \n", "2 1 0 0 \n", "3 1 0 0 \n", "4 1 0 0 \n", "\n", "[5 rows x 23 columns]" ], "text/html": "
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" }, "metadata": {}, "execution_count": 26 } ], "source": [ "testdf.head()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
", "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-10-19T20:09:19.335574\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "image/png": 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Emxd1MSjXv0dV6lV3JiVVS//vduN77iEedQwbLnOV4dQtAh9EcmD711z2e8TkWX+x5bfBBuUmz/qLaeM6U61KMQZ+vR7fU7fwbOjKnEUH+GpAEzwbunLs5E3mLDzA+hWfsmHLWcqWcWD5/I+IehJPmx4L6di2KibGr3fZqtXqmD19J4tXforG0Yp+vZfi0bQiZcpmHqe9t1/AysqcHXtGcWDvZRbN38fMuR/QtkN12naoDigNQaO+/l2vIejwIT8Kmef+ZnHWjEtm+zJjcSfsNBZ83W8LdT1K6+2fDo4WjJzUnG2/X9J7b7VaxViysTcAsU+T+LTb79Solz+/6btO9AzKOTFnUB5IklQf6ADUkGW5KtACePia731hjSbL8s78aAj6tx2/fYmo+JgC+e7Dl8PoXM8ZSZKoXsaGmIRUwp4mG5SzMFdWe5pOJlUrI2WpM2ZtvcGobq7kRzXic+QqXTrVVvJUK0VMbCJh4U/1yoSFPyUuPonq1UopPWo61cbn8FUlp0XmCX5iYopezrwI8HtEsRK2uBQrirGxES3buON75JpeGd+j12jf6T0AmrV04/zZu8iyDBIkJaSQlqYlOTkNI2M1hS1MsbO3pGJl5UBZuLAppUrbEx6W8+3g6pVAipewo3hxO0xMjGjXrgZHDl/RK3P48BU6d64LQKvW73HmzA1kWaZQIVNq1iyLyUvungbeCyUqKpaatXJ2Z/N5UXdjKOxgTmEHc1RGKorVceDxP5Gv/f4ngbEkx6SgqVIkTzmyo65QF+3lwwDIj26AWWGwyNn3GDf7iLQTW5HTUvOU5U1sa2/C/RtPsHcujJ1TYYyMVdTwdOHqKf0LRLPCmdtVSpJWr464cvIxto6F9RpB8pO6Uj20l7L8puY5/03fhAc3nmCXZb2918QFv9MvW29pZK3Irp56jK1joXxbbz6Hr9Clc12lzq1empiYBMLCnqtzw54SF5dE9eqllTq3c118fC4DULasE2VKGzboVa5cHI2DDQDlyzuRnJxKSkru9o1rfsG4FC+Kc7EiGBurad66MieO3tQr4+RiQzlXDdJzlX6JkrYUL6lcJNg5WFKkaGGinyTkKoePzxW6dK6Xvq7KEBOT+JJ1VSZ9XdXD59DlzPd3URr1unSpz6H0121trajqXsqg8SI2NpHzF27Ro0dDAExMjLCyKvTqnEf86dKxlpKzakliYpMIC9c/toSFxyjH0aollZwda+Fz2P+ln1uokCm1apTOdW8beLa91Uk/xpd+8TE+Lonq1Z5tb3Xw8VGOaWXLOma7vZ08eZ0Kri5UrKj0gCpiY4FanbtT9Rv+oTgXt8GpmDXGxmqatHLl9LG7emUcna0oU94O1UsuoE743KZ2g5KYmeVsfV25EkiJkg4UL26vHNPb187473/G5/AVunRVtqXWrWtw+vR1ZFnGx+cK7drXxsTEmGLF7ShR0oErVwKRJInChZXzo7Q0LWlpWoN9JS4ukbNnbtCiRe4ag3xO3KFz60rKb+vmRExcMmGRcQblqrs54WD7r9xrzZbPqft0blleyVlJQ0x8CmGR+nWCuZkR9aor52cmxmoql7MjJCI+/zIcu06X9tWVDO7FlX00IlavTFhELHHxyVR3L67sB+2r43NMudkkSRAfr5yrx8Yl4WCvHA8kJOLjk5FlmfiEFKytzDHKwX7gf/URxUvYUqx4+vlH26oce/7848g12qf3NmrWsgrnz95Rzj+y2L/3Mq3aVs34OyEhmY3rTvLp4Jf3anwdN/3DcC5unbF/erYsz5lj9/TKaJytKF3ezmAbz+q4zx1q1S+R4/1TEJ4RjUF54wREyLKcDCDLcoQsy8GSJAVKkmQHIElSLUmSjqb//8mSJK2XJOkksF6SpDOSJLk9+zBJko6ml/9EkqTFkiRZS5J0X5IkVfrywpIkPZQkyViSpLKSJO2TJOlvSZKOS5JUMb1MaUmSTkuSdFWSpB/+5fVRYEKjk3EsktmA4ljEjLDopGzLDlh4gUbfHqGwqZrWNRwB8LkchsbGlIrFDIeW5SpP6FMcHTMv2Bw1NoSGPjUso7F5YZn5P+/Gs/lk/tr9N18PaZfx+qXLgXTqNpsBny/n1u3H5ERYaAwajXXG3w4aK4OGm/DQGBzSyxgZqbGwMOVpdALNW1bBrJAJ7Zv/SKdWc+jTrxHW1von9MFBT7h5/TFu7oZd+V8lNOwpTlnWmUZTxGCdhYU+xdGpSEY2S0tzoqNf78Rmz56LtGlb46UH1deR9CQF86KZDRPmRU1JfGLY8Bj0dwSHJlzgzBJ/EiKVbVHWyVzddIcqvfLWIPUikpUtckxExt9yTCSSlW22ZU06D8f084UYefTOfL9TWSQrO3S3LuQ5y5vc1oYN/o2+vZayY+v5POd8GpGEjb15xt829uY8jTSsO47vvMvUfgfZucqfbl8pXf6TE9Pw2XyLNh/nftjQq0hWtshPwzP+lp9GvPg37fYNpl8uwqhJb73X1W4NMf1qMSa9xyBZ2WX73pyKjtRfb9Z2ZjyNSDQod2LnPX745BB//RJAty+rAM/W221af5R/6y00NFq/znUsQmhYtH6ZsOjn6twihIbql3mZ/Qf+oXKl4piY5O6kOzwsFgfHzMYve40VEWGxL3lH9gKuBpGWqsWleO4aBUNDozPqUQBHRxuD9WC4PjPLREbG4OCg7Lf29lZERr688f/RowiKFrVgzJi1dOk6nXHj15OQYFhnGuQMe/4YaU3oc41WryqzYdNJOvb4iTET/+RpTO4az7LPFp3NMT5av0zo89ubjcE2+bx798OQJPhs4GK6dp/FqtUHc50xMiwee01mY4WdgwURYYaNGq9y9MAtmrR2fXXB54SGPtE7pivr6IlembDQaJyeP6Y/iX/pe7VaHV06/0DDBt/SoEElqlUrrfeZhw5dpl79ClhYmJMboRFxODlk7qeO9haEhudsvR08dotO/dczbOJfPM7FPv46QiPjcbLP/H0d7QoTGvni86GYuGSOnHlA/fcMh3HlOkN4DI5ZjvOODlaEPnecDw2LwdHBSr9MeqPu2BHtmL3wAJ7t5/Ljz/sZ8VVLAPq8X5c7geE0bjuHTh8sYdzItqhUr3/JGh72FI1jZi6Nxprw0OcakrOUUc4/zHgarV9HHNx3Va8xaPmiQ/Tp1wgzs7z3DIoIj9PfPzUWRIbnvKHO92Du9k9BeEY0BuXNAaC4JEk3JUlaKkmS5yvfAZWBFrIsfwD8CbwPIEmSE+Aky3LGVZgsy0+BS8Czz+0A7JdlORVYCQyVZbkmMApYml7mZ2CZLMvuQM5aCv4jfhlWC98fm5CSpuPMjUgSU7Ss3HeXoR3zf66PvPjm6/Yc85lMx/Y1+X2jMn7ZrXJxDh+cxM7to/n4Qw++Grb6X8vj7/cItUpi96Hv2LF3JBvXniToUVTG8oSEZL4f8QffjG6n17PpbbF379+0a1/r1QXzgWN1W9rMqUuLabVwqFyEv3+5AcDdw8E4Vi1KoaJvppfL60rZNpfkZUNIXvMdqpKVUVdrBpKEcesBpB7497apF3nZtrZy7SDWbf6KBUv7snXTWf65cO8Vn5Y/Gncqw8S1Lek4wI0DG5TeHHvXX6dJt7KYmhf8iOuULXNJXvwVyb+MRlXSDXV1ZYiC9vpZkn7qT/KSIWhv/4Nx9+yHmL0pjTqVZvxvLejwWWUObFTW2771N/DsWuatWG+v69atYOb+5MXUKR8WaI6I8Fh+GL+TMVM6vrQ3x79FkqRXNrCnpekICHjIBx944rVjHObmJqxctf+NZ/vg/QYc3DUG783f4GBvxay5f73x78wrbZqWvy/eZc7sT9j4+wgOHbrM6dM3CixPZEQ8gbcjqFU/Z0PE3iS1WoWX93iOHpvJlSuB3LwZpLd8967ztG9fu4DSKcPHfP78jJ2/fkyDWiX5fsab39ZfJU2rY+SMw3zcxY3iTvlz0zM//LHtHGNGtOHY7lGM+aYt46Z5AXDizG0quTpxfO+3eG34gqlzdhMXl/0N3jfF78pDzMyMKVdeuWl843owjx5F0rS52yve+e+Jiojn3u1IatYXQ8SeeXZMKsh/75p350zsLSTLcpwkSTWBxkBT4E9JkgwH3uvbKcvys9unm1EalCahNAptzab8n0Av4AjQG1gqSZIF0ADYkmWje3Z12RDonv7/1wM/viiIJEmDAGXyGY/SUDl/5m74t2w4+oCtJx8BUKWkFSFPMg8UIU+ScLB5cYOEqbGaZtUcOHw5DHsrUx5FJNLlh1OA0suo+4zT/PldPeytX/+ifcMfx9m89TQA7lVKEBKSeQcsJDRar5cEKHcqQrLcScyuDEDHDrUY9MUKhg1pq9fI4ulRmSk/bCHqSRxFi7xeV2UHjZVeb5uw0BjsHfRPDOw1VoSFKndM0tK0xMUlY21TiP17rlCvYXmMjNUUtbWg6nsluOYfhEuxoqSlavl+xB+0aV+Npi1yd6DUOFjzOMs6Cw19YrA+HDTWhDx+gqNjEdLStMTGJmJj8+q5Y65ff4Q2TZvtJKk5ZVbEhMSozLvaiVHJmBfR305MLTJ7DpT2dMJvi9I1P+pODBE3n3L3cDBpyVp0aTJGZmqq9Mz9pH/q2u0xqtkaAF3QLb2eH0pPoWyGsMWmv5aSiPbqMVQurmivn0HlUAKTT5T5UCSLIph+MIHkP6blahLpN7WtOWiUzyhqa0GTZpXw9wvivVr6d4ZzwtrOjOjwzB4t0eGJWNu+uO6o0cSFLQuVYTH3rz/h8vFgdv7iT2JcKpJKwshEhUfnvE3iqK7THqNaykSruqCbSNaZc8pI1nav/k2vpP+mlw5DYuZdae3fBzBu/Wmesj1jY6u/3p5GJGFt9+I78e81cWHrImWIyP3rT7h8Ipi/VgeQGJeKSpIwMlHTuHPOfscNG46xeetJANyrlNSvc0OeZAzvekbjYPNcnfsEjUa/THZCQp4wZOhKfpzVjxIl7F9Z/kXsHSwJC8n8PcJDY7BzeP1hcvFxyYwe+icDhzTBrapLjr57w4ajbN6iTDjq7l6SkMdZ11W0wXrQaGyeW5+ZZWxtrQgLe4qDgzVhYU8pWvTl/w2OjjY4amwyenC0aV3jhY1BGzadZPN2Zb4kd7fiz/1eT9E4PHccdXj+OJpZxs42M1fPbnX5fGjeGro3bDzG5i3KeYK7e8lsjvE2+tk0z29v0Qbb5PMcHW2oXatsxjHdw8MN/4CH1K+f8150tg6FCQ/N7NESERaHnUPOhjX5HrxFg6ZlczVvkUZTRO+Yrqwj/d5sDhobHj9/TC9S+LXea2VViLp1K3D8uD+ursr+8CQqjitXA1m85PMcZd2w4xJbdvkB4F5Bo9ebJyQ8LmMy6NdRxDqzHuzZvgpzlx9/Semc2bDTny17rqfntOdxlh5LIRHxaGyzPx+auOA4JV2s6dct7xNZb9h8ls1efysZKrsQkuU4HxIWg+a547zGwYqQLL2FQsJi0NgrZXbsusS4kUrP97Yt3Bg/3RuA7X9dZFC/xkiSRMnithRzLsLd+xFUdXu9Xuf2DtaEhmTmCg19ir1GP5dDepnM848krG0yeyAf2HuF1u0yhxpevfyAa/5BdGo9G22ajqioeAb3X8WKXwe+Vqbn2dlb6O+foXHY2udsLkTfg7dp0KRMrucVEwQQPYPyTJZlrSzLR2VZngQMQWmISSNz3T5/VRGf5b1BQKQkSVVRGnz+zOYrdgJtJEkqCtQEDqd/drQsy9Wz/KuUNdZrZl8py3ItWZZrvWsNQQB9mpRgx7gG7BjXgObVNHifCUaWZS7djcbS3AiH5xpy4pPSMuYRStPqOOYXQRnHwri6WHJyTlN8pnviM90TjY0p28bWz1FDEECfDxrjvW003ttG06KZO147zyt5LgdiaWGOg/1zDRv21lgUNuPS5UBkWcZr53maN1UO1IH3M4eF+By+mjG3QHhETMaY5itX76PTyRR5jcaQZyq5ufDwfiTBj6JITU3j4L6reDSpqFemcZOK7N75DwCHD/pTq44yZ4SjkzUXzimNGokJKfhdeUjJ0vbIsswPk3ZQqrQ9H/ZtmKN1llUV95I8uB/Oo0cRpKSksWfPRZo2rapXpmlTd7y9lQuFA/v/oW4919dqhd+zO/96BRUpbUVcWCLx4Yno0nQ8OheG03v6w3YSozMbi4L/icTSSTnBqD24Em1/qkebufVw71WWEg00eWoIAtCe303y8mEkLx+G9vpppZcPIBWrAMkJEKffLR+VCgqlnxSp1Khd66ALuw/JCSTN7kPygs9IXvAZukc3ct0QBG9mW0tMSMmYXyAxIYWzp29Ttlze6q4SFWwID4on8nE8aak6Lh4Lokp9R70yYUGZJ2wBZ0OxT58s/Ot5jZm0vhWT1rfCs2tZWvZ2zXNDEID23G6Slw4leelQtNfOZPTykYpVgKT4V/+mFWorvynozS+kqlgXOfy1prV7peLP1luIst7+ORqEWz39OVDCs663c6HYpa+3YfMaMXFdSyaua4ln1zK06F0+xw1BAH36eOK9YyzeO8bSonlVvLzPKnXupXtYWppnDGV6xsHBGgsLMy5duqfUud5nad6s6gs+XRETk8Cgz5cyckRnatbI2/DOim7OPHoQRXBQNKmpWnz2B9DI8/W696emahk7YittOlTNeMJYTvTp0wRvr/F4e42nRfPqeHmfSV9Xd7G0NHvJurqbvq7O0Ly5sq6aNauKl5dy48PL63TG6y9ib2+No1NR7t5V5pQ6ffo6Zcs6ZVu2T++GeG8egffmEbRo6obXXxeUnFfuY2lhhoP9cxd09lbKcfTKfSXnXxdo3lS5IZF1fqFDh/0oXy7773xdfT70xHvHGLx3jEnf3s6lH+PTt7fsjvEWZly6/Gx7O/fK7a1Rw8rcvBlMYqIyZ9r587cpV87xpe95kQqVNQQ9jCYk6CmpqVqOHrhJPY+c7WdH99/M9RAUd/eS3A8M49HD9GP67vM0e+6/v1mzqnjtULal/fsvUq9eBSRJolmzquzZfZ6UlFQePYzgfmAYVauWIioqlpj04X5JSSmcOnWNMmUy18/+/Rdp0sQ9x/NB9elaHa/VH+G1+iOaNy6L9/5rym/r/xjLwiY5mhso6/xCh0/epWzJoi8pnTN9Ornhtbw7Xsu707xBKbwP3lJyXgtNz2k4F9eCX88TG5/C2C/qZ/OJucjwfl28N36J98YvadGkIl67LykZrj5U9lE7/cZhBztLLAqbcunqQ2U/2H2J5p7KuYCDvSXnLgYCcOb8XUoVV9aVk6MNp88r5wARkXHcux9BMZfXHxZbuYoLD+5HEPTs/GPvFTya6NebyvmHMnH64YN+1E4//wDQ6XQcOnCVlm0yt9ceveqx9/AYdu4fzap1gylRyjbXDUEArpUdCH7wlJCgGFJTtRw7eIt6HqVy9BnKEM78fSLtu05SSQX+710jegblgSRJFQCdLMu30l+qDtwHzFEabvaS2UvnRf4ERgPWsixfeX5heu+j8yjDv3bJsqwFYiRJuidJUk9ZlrdISu1VVZbly8BJlB5EvwP/6vPdN346lSauNbCzsOHhjJ1M2rWKNaf+nW7ZnlXs8PULp/XE4+mPlq+Ssazr9FPsGNeAxBQtXy27SEqaDp0O6lYoSq/Gb6ZrpadHZY4dv0bLtj9gbm7CjGmZT6Lo3H023tuUp9xMGt8j49HyHo0r4dFYOVj9NP8v7gWGIUkSLs5FmTKxJwD7D1zmjz9PolarMDMzZt6cfjnqkmhkpGbU2A4M+2ItOq2Ojl1qUqachhVLDlGpsgseTSvRqWtNJo/dSvf287CyNueH2b0A6NG7LtMmbKd314XIskyHzjUo7+rIpYuB7N11iXLlNXzUczEAXwxrScPGObuTaWSkZtz49xk0YAk6nUzXbvUoV96JRQt34ValBM2aVaV7jwZ8/9062rSejLV1Yeb+1D/j/S2bTyQuPonU1DQO+1xh5S9fZTyJbP++iyxbkfNHzGZHpZao3qccJ3+6iqyTKdnYESuXwgTsuIdNKUuc37PjzsEgHl+KRKWWMC5sRK0BFV/9wflAd+sCcvlamA5bpTyG3HtBxjLTzxeSvHwYqI0x/WgqqNUgqdDdvYz27/zvxv4mtrWgR1GMHq487Umr1dG6bVXqN8rbWHm1WkX3IVVZNvY0Op1MvdYlcCplxZ611yjuaoN7fSeOe9/j5j/hqNUS5pYm9Pk2Z4+4zQvdzfPIrrUw/eaXjEfLP2P65SKSlw5VftO+05TfVKVCd+cS2gvKb2pUvxPqinVBp0VOiNN7f16o1Sq6f+XOirFn0Olk6rZS1tvetdcp7mpDlfqOHN95j5sXI1AbSRSyMObDUe/ly3dnx9OzCsd8/WnZehLmZibMmPFxxrLOXWfgvWMsAJMm9s54tLxHYzc8PJSGg4MHLzFt+maiouIY/PlSKlUsxupfhvL7hmM8eBDOkmV7WbJsLwBrfhmKrW3OJ742MlLxzfetGfnFH+h0Otp3rkbpcvb8svQYFSs70aiJK9f8ghk3YiuxMUmc8r3FmmW+rN8+mMMHArh88QEx0Qns3an0TBs7tSPlK+a8oUBZV360bDUhfV31y1xXXX7A22t8+rr6kDFj15KUlJK+rpTj6qCBrRn+zSq2bjuJs7PyaHmA8PCndO8xM+OR6GvXHWbP7klYWJgzYXwvRn27htRULcWL2zFzRt9X52xciWMnrtOywyzMzYyZMbVXZs735+G9WRnyOGlct/RHy6fh0bACHo2U+nbO/F1cvxEMkoSLcxGmTuiR8f5mbacTF5dEaqqWQ0f8WbN8IOXKvv669PRwU7a3NlOUbNM/yszWdSbe6U+xnDThfcaM/T19e6uMh0dlAA4eusy06VuU7e2L5VSq6MLqVUOwti7EJ/2a0eP92UiShIeHG008q2Sb4VXURiq++taTsUN3otPqaNWpMqXK2rJ2+RlcKzlQ37MMN/xDmfrtbmJjkjlzPJB1K8+yarNy6hgSHEN4aBxVa+SsF9ozRkZqJkzsxWcDFqLT6ujevQHlyzuz8OedVKlSkmbNq9GjR0NGf/srrVpOwNq6EPPmDwCgfHln2ratSft2U1Cr1Uyc2Bu1WkV42FO+/34tWq0OWZZp06am3k2j3XvOM2hgmxdFei2e9UrjeyaQVh/+ipmpETO+b5WxrMtnv+O1Wvmt5yzzZZfPDRKTUvHssYoe7aswtH991m+7xJGTd1CrVVhbmjHz+9Z5yvPCnHWK43vuIa0++VPJOSpzpooun2/Da3l3QsLjWP7HJcoUt6Hbl9sB6NPZjZ5t8+ecRHkK2C1adl2g7AcTu2Ys6/zhUrw3fgnApO86MGbKDmU/aFAejwZKA8a0cZ2Z8dMe0rQ6TE2MmDq2MwBffubJmCk76Nh7MbIMo4a0omgObnwaGakZPbYTwz7/Fa1WplPXmpQtp2H54oNUciuGZ9NKdO5Wi0ljttC13VysrAsxfXbmXHv//B2IxtGaYsXzryHveWojFV+Mbsz4YTvRamVadapEybK2rFt+FtdKDtTzLM0N/1Cmjd5LXEwyZ0/c4/cV51ixWRmqHBocQ0RoHO653D8F4Rnp+ZnThdeXPkRsEWCD0hvoNsqwq0rAaiAGOArUkmW5iSRJk4E4WZbnZvkMDRAETJNleUr6a5+kv2dI+t89gC1AE1mWj6W/VhpYhjKJtTGwSZblqemvbwQsAG9g+Os8Wl76ot5buSHIy86gOzysoGNkS9VsIaTuLegYhozbAhCdvKWAgxiyMe1Jmi73E2K+SUaqlow5NaigY2RrZoOVJE7uUNAxsmU+eddbua2Bsr3tu2/4ePGC1qbkbAASJ7Qv4CSGzKftZk+g4SOy3wbtSs0BnU9Bx8ieqjlhiesKOkW2HMz7gnykoGMYktKfyJP0Fs7lY9YRtG/nsQp1SwJjFxd0imyVshyCzFu4rQESTZFDlhd0jGxJjp8j35/76oL/MqnkKOX/xGQ3cKGAWfUiJmVbQafIlpVJd+7GLCzoGNkqYzXs3eu6kgOmozwK/Ho2ea7vO7WORc+gPJBl+W+UuXuedxwwuF0ty/LkbF4L5bnfQZbl34Dfsvy9FfSfeC7L8j3A4PZH+utZ+4KOf/F/gSAIgiAIgiAIgiC8297FYVoFTcwZJAiCIAiCIAiCIAiC8B8iegYJgiAIgiAIgiAIgvDOEj2Dck70DBIEQRAEQRAEQRAEQfgPEY1BgiAIgiAIgiAIgiAI/yFimJggCIIgCIIgCIIgCO8sMUws50TPIEEQBEEQBEEQBEEQhP8Q0RgkCIIgCIIgCIIgCILwHyKGiQmCIAiCIAiCIAiC8M4Sw8RyTpJluaAzCG8HsSEIgiAIgiAIgiD8f/q/bi0pPL5ZgV/Pxv9w+J1ax6JnkCAIgiAIgiAIgiAI7yzRMyjnRGOQAIDu8LCCjpAtVbOFSF/UK+gY2ZKXnSE6eUtBxzBgY9oTgDTdwQJOYshI1ZLY1B0FHSNblsZdkSPWFHSMbEl2n/I44e3M5lTo07dyP4D0fUHnU9AxDKmaA/AobmUBBzFUzGIQcsjygo6RLcnxc+SHCwo6Rrak4sMJS1xX0DGy5WDel8ikPwo6hgFbsw8A3tps8tnvCzpGtqS6s5D9pxZ0jGxJbhPxi5xV0DGyVcX2e2JSthV0jGxZmXRn2dUvCjqGgS/clwFwM3puAScx5GozCpJ3F3SM7Jm2J/WXDwo6RbaMB7x99a1QsMQE0oIgCIIgCIIgCIIgCP8homeQIAiCIAiCIAiCIAjvLDFMLOdEzyBBEARBEARBEARBEIT/ENEzSBAEQRAEQRAEQRCEd5YkiZ5BOSV6BgmCIAiCIAiCIAiCIPyHiMYgQRAEQRAEQRAEQRCE/xAxTEwQBEEQBEEQBEEQhHeWmEA650TPIEEQBEEQBEEQBEEQhP8Q0TNIyBFZlpmx+Tq+/uGYmaiZ0dcdtxJWBuUGLrpA+NNk0nQytcoVYULvyqiztNb+eiiQ2dtucGpOU4pYmLzx3Ks/HkcH94aExT7BfVqfN/59p0/cZN6Pe9DpdHTqVpN+n3nqLU9JSWPKuK1cDwjG2roQP8zphbNLEdJStUyfvIMb1x6j1epo27E6nwzwJDk5lc/7/0JKihatVkezFm4M+qp5rrIdPx7ArBlb0ep0dO/RgIEDWz2XLZUx363HP+ABNjaF+Wnep7i42BL9JI7hw1fj53efLl3qMX7C+xnv2b37AqtW7EeSJOwdrPlxdj+KFLHIcbZTJ24wd9Zf6LQyXbrX5pMBTQzW26Qxm7kWEIS1TSFmzv0AZ5eiANy68ZgZU3cQH5eEpJJYt2kIpqbGpKamMXv6Tv4+fxdJJfHlsFY0b+me8xWXhSzLTF/gg+/pO5iZGTNzXDvcKjgalJu/whfvfX7ExCZx8dCIjNc37fiHDdsvolapKFTImKmj21CutF2eMgGcPXmXxXN80Op0tO9SjT6f1tNbfvnvhyye68OdW2FMnNmJJi0rAhAS/JQJI3eg08lo07R07V2Tzj3fy3Oe/N4PQkOimTxuG1GRcUiSRJfutej9UYM855RlmekztnDM1x8zM2NmzeiLm1sJg3J+/g8YM2YdScmpeHq4MW5sTyRJYu++iyxevJs7d0PYsnk07lVK5jnTM+dO3WPJ3CPotDLtulThg/519ZZfufiIJXOPcPd2OONndMCzhave8vi4ZD7t+RsNm5Rj2He5qzNeRJZlpi88iu/Ze5iZGjNzTCvcXDUG5eavOon3/gBi4pK5uG9Ixuvb9/ozZ9lxNPZKXdGnazV6dsjbvqmXbclJfM/dx8zUiJmjm+FW3t4w25qzeB+8QUxsMhd3Dcx4PSg0lnFzjxAVnYi1pRlzxjTH0T7nddrzzp68w8+zD6DTyXToWp2PPtXffi/9/YCFcw5w91YYk2Z1pWnLShnLRn75BwFXgnB/rzizF/XKcxaAMydvseDHfWh1Ojp2rUHfzxrrLU9JSWPauB1cv6bso9Nm98DJpQipqWn8OHUX1wOCUakkho9uQ43apQE4sPcq6345jiSBnb0lk2Z0w6ZI4bci26F9fqz9xRedVqaBhytffdMyl2sue7IsM/33K/heDsXMVM3MgTVxK2VjUG7AnJOERyeh1cnUdLVlYr/qeudI+Zpn9d/4XgxS9oMh9XErW1SvTGJyGsPnHOdBaBxqlUTTWi6M/Fi//t9/+gFfzznOltltcC9nm+dc/5x5xJoFZ9FpZZp3dKVb36p6y3f+4YfPXzdRqVVY25jx5dhGODhZcO9mJCvnnCYhIRWVSqJHv6o0bFEmz3lOnbjJTz/uQqfV0blbbT4ZYHismjR2C9fTzz1mzPkAZ5ci7N11ifW/Hc8od/tmCOs3f0WFis4Zr40Yuo6gR1H8uWN4nnMG/hPJsV9vodPJVGnuRO2upbItd+tMGLvn+vHBrFpoylnxNCyRdcPPUsS5EABO5a1oPrhinvO8yN+nH7Jq3ml0OpmWnSrQs191veVeG69wwPsGaiMVVjZmfD3eAwcny3zNIMsy03/cwbHj1zAzM2HWtA9wq1zMoJxfwEPGjP9DOa43rsS477rqTUK8Zu1RfvxpJ6ePTaVoEQt27v6bVWsOgyxTuLAZk8d3p2IFlzzlnHn4EcfvxmBmJDG9XSkqawq9sPyQ7Xd49DQZr/6VAVhyMphtVyIpYq5c0n/t4YxHGetc53nXiZ5BOSd6Bgk54usfwf2wBPZNacyUD92Y+kdAtuXmD6iO1/iG/DWhIVGxKez7OyRj2eOoRE4GROBU1Ozfis1vp3fTZtE3/8p3abU65sz4iwXL+rLJaxgH9l7l7p0wvTI7t/+NpZU523aPoPfHDViyYD8APgf8SEnVsnH7UNZu+gKvrecJDnqCiYkRS375lA1bh/D75q84c/IWVy8/zFW26dM2s3zll+z8azx7dv/N7duP9cps23oaK2tz9u2fTN++TZk31xsAE1Njhg7rwLffdtUrn5amZdaMrfy69mt2eI/F1dWFjRuO5Srbjz94s3BZf7bs/Ib9ey5x906oXhnv7eextDLHa++3fPhxIxbN25eRYcL3fzJmQhc2e49gxa+DMDJSA7BmxRGKFC3M9t2j2OL9DTVr5f3E0ff0Xe4/imL/n4OYOro1U+YeyLZc04Zl2byqr8HrHVpV5q/1n+G1tj8DPqzLrEWH85xJq9Xx86yD/Li4J2u3DeDwvgAC70TolXFwsuL7Ke1o0aay3uu29hYsWfsRq//sz9L1fdn46xkiwmLznCe/9wO1Ws3XI9vyp9fXrP59MFv/PGvwmbnh6+tP4P0wDuybzLQpfZg8dVO25SZP+YNpU/twYN9kAu+H4Xtcqf9cyzuxaNEgatcql+csWWm1OhbO8mHmwm6s2foJh/ffIPBupF4ZB0dLRk9pQ/M2lbL9jF+XnaTqe4YnwPnB92wg9x9Fs39Df6aOasGUedlvx00blGHzig+yXda2mSteqz/Ca/VH+dYQBOB77gH3g6LZv/ZDpn7jyZSffbPPVq8kmxd3N3h99opTdG7pys5Vvfjq45rMW302z5m0Wh3zZu5j7pLerN8+mEP7/Ll3J1yvjMbRirFTO9KibRWD93/Qrx7jp3fKc46seebO2MNPS/uwccdXHNrnx73n9qe/dlzE0sqMLbu+ptdH9Vi64BAAO7ddBOD3bV+yYPnHLPrpADqdjrQ0LQt+3MviX/qxfuuXlHPVsHXTubci29PoBJbMP8DClf3YsOMroiLjuHD2bo6zvYzvlVDuh8azf05LpvZ/jym/Xcq23IIhdfCe3py/ZjRXzpHOBeVrjow8F4O5/ziG/Us6MfXzukxZmf1v0b9zJfYu6sj2uW25eD0c34uZeeISU1m/+zrVyue9EQiU33bV3DOM+6kVCzZ25cShuzy8F61XprSrLbPXdGL++i7Ua1qK9UvPA2BqZsTQiY35eUNXJsxrxZqfzxEfm5znPLOn7+TnpZ+w2Xs4B/Zezubc4wJWVubs2DOKDz9uyKL5yrlH2w7V2bh1KBu3DmXqjJ44uxTRawg6fMiPQub5c8NTp5U58ssNuoyrRt/5dblxIozIh/EG5VIS07i0+yGO5fVv1NpozPlobh0+mlvnjTYEabU6ls85yeQFbViyqQe+B+7w4O4TvTJlXO2Yt7YrizZ0p2Gz0vy6OOd1xKv4nrhG4P0IDuway7SJPZn8w9Zsy03+YSvTJr3PgV1jCbwfge+J6xnLHoc84eTpGzg7Fcl4rZhLUX7/9Sv+2j6aLwa1ZMKULXnKefxeDA+eJLNnQGUmty7JtIMPXlj24M0nFDIxvHT/uKYD2z6pxLZPKv2nG4KE3BGNQUKOHL4cRud6zkiSRPUyNsQkpBL21PBAbJHeQp2mk0nVymR90t+srTcY1c2Vf7Pt9vjtS0TFx/wr3xXg94hiJWxxKVYUY2MjWrZxx/fINb0yvkev0b6TcuetWUs3zp+9iyzLIEFSQgppaVqSk9MwMlZT2MIUSZIoVMgUUBo+0tK05ObpiVevBFK8hB3Fi9thYmJEu3Y1OHL4il6Zw4ev0Lmz0vugVev3OHPmBrIsU6iQKTVrlsXE1FivvCwr/xITUpBlmfj4ROwdcn4w8r/6kOIlbClW3BZjYyNata3GscP6jY3HDgfQoXMNAJq3qsK5s7eRZZkzp25R3tUR1/STMBubwqjVSvW2c8cF+g9oCoBKpcrVHern+Zy4Rec2VZT9oIoLMbHJhEXEGZSrXsUFBzvD3gQWhU0z/n9CUmqufsvnXfd7jEtxG5yL2WBsrKZZ60qcPHpLr4yTszVlXR0M7pwYG6sxMVH22dQUrbIt5tGb2A/s7C2pWFn5jQsXNqVUaXvCw/K+X/scvkKXznWV37N6aWJiEggLe6pXJizsKXFxSVSvXlrpldS5Lj4+lwEoW9aJMqUNe8Tk1XX/EL3ftGmrCpw6eluvjKOzNWXL22f7ONWb10J5EpVAzXr511MpK58Td+jcupKy3tyciIlLJiwym/3AzQkH27z3qslRtlOBdG5ZQclW2TE9m+GFU/XKjjjYGtYJd+4/oV51pRGtbnUXfE7dy3Oma37BuBQvinOxIhgbq2neujInjt7UK+PkYkM5V022v2etuqUzjgP5IcAviGLFi2bsoy3aVOH40Rt6ZY4fuUHbTtUBaNqyMhfOKfvovbvh1Kyj9LYpamuBhaUZ1/2DIb3qSExMVY4HccnY2ef8jv+byBb06AnFSthSpKjye9eqW4Yjh7K/oZVbPhcf07lhcWW7K1dUOUeKTjIoZ2GuHEfTtDKpabo3dj7kc+4RnZuUUfJUsCMmPoWwqES9MuamRtRzV3q2mhirqVymKCGRmWUWbrzMgC5umJio8yXT7YAIHItZ4uhiibGxmkYtynD+uP4FsHtNJ0zNlGOSq5s9kWEJADiXsMa5uHJ+UdS+ENZFzHiazfrNCf+rj9LPPdKPVW2rcuz5Y9WRa7TvpJx7NGtZhfNn7xgcJ/fvvUyrtpk9nBISktm47iSfDm6ap3zPhNyOwdqxENYac9TGKlwbOnDnfLhBuVOb7lKrS0nUxgVziXcrIBynYlY4ulhhbKzGo2VZzvre1ytTtZYzZum/b4UqDkSGGdbNeeVzxI8uHWsp2361UsTEJhIWrn++EBYeQ1xcMtWrlVKO6x1r4XPkasbymbO9+fabDnrnaDWql8baSum5U71aSULCovOU88itp3RyK4okSVRzLkxskpbwuFSDcgkpWtZdCGNwPcNe6IKQF6IxSMiR0OhkHItk9uhxLGKW7YkOwICFF2j07REKm6ppXUOpvHwuh6GxMaViMcOhZf8vwkJj0GgyG0McNFYGF6zhoTE4pJcxMlJjYWHK0+gEmresglkhE9o3/5FOrebQp18jrK2Vg45Wq+Ojnotp02QWdeqXo0rV4jnOFhr2FCfHzDscGk0RQkOfu+gNfYpj+l0QIyM1lpbmREe/+EBtbKxmwqRedOk8gyYe47hzO4Tu3XM+dCcsLAaNY9b1Zk3Yc+tNKWOTkc3Cwoyn0Qk8uB8BksSQQavp03Mha9coPZNiY5QT2mWLD9Cn50K+G7GByIi89XgBCA2Pw8khcxt2dLAkNDxnn7th20Va9lzB3KVHGTe8RZ4zhYfFYq/JzGSvsSQ83PDC/EXCQmL49P01vN92KR98Ug87h7x12X5T+8EzwUFPuHn9MW7uee/1EhoajWOW/cLRsQihz53ghYZF46ixySyjKUJoqH6Z/BYRFoe9JvN3sNdYEvGav6lOJ7N8/lE+H+756sK5FBoRh1OW7cTR3oLQHGxzAAeP3aJT//UMm/gXj/PYG00/WzxOWYZ1OdpbEBrx+hccFcrYcvCE0mvk4Il7xCek8uRp3i46w8NicXDM+nta5bkHXt7yxKBxzFJnOFgRHhrzwjJGRmoKp9e55Vw1nDh2g7Q0LcGPnnDjWjChoTEYGasZNa49H/dYSqcWP3Hvbjgdu9Z4K7IVK1GUB4ERPA56QlqaluNHrhMWkr83iUKjEnEqap7xt2NRc0Kfa3x55rPZJ2k4ZA+FzYxoXSf3w0xenicBJ7vMutPRthChUQkvLB8Tn8KRC0HUd1cat/3vRPE4MoEmtfIvX1R4AnaazAbYovaFiAx/8b7ps+smNeoZfv+tgHDSUnU4uuTtfDI87KneuYdGY22wrYVlKZP13COrg/uu6jUGLV90iD79GmFmlj89g+KjkrG0y2wMtrQ1JT5K/2Zs2N1Y4iKSKV3TcNj507BENow6x5aJFwkKiM6XTNmJDIvHTpNZ99o6FH7p73tw5w1q1s//3quhYTE4pp8vAjhqbAh97iZPaNhTHLOcpyhllN/+0BE/HBysXzoEbOv2s3g0zL5X7mvnjEvB0TJzG9FYmhAal2JQbtGJx/SrpcEsm0a+P/4Jp+uvAYzfe5+nSWl5yvOuk1RSgf9714jGIOGN+WVYLXx/bEJKmo4zNyJJTNGyct9dhnbM36EU/0/8/R6hVknsPvQdO/aOZOPakwQ9igJArVbx+5Yh/HXwW/z9HnHnVugrPu3fkZqq5c9Nx9m6/TuO+k7HtYILq1ZmP2zqTdGm6bj8TyA//Nib1es+56iPP+fO3Ear1REa+pSq1UuyYcsw3KuVYMHcPf9qthfp070GB7cMZuQXTVj22+mCjoODoxVrNn/KBu9B7P/Lj6hselH8W162H4Byx/X7EX/wzeh2WFj8e8NN3yU7t1yiTsPSeo1Jb5umDcrg8+dn7Pz1YxrUKsn3M/YXdKQMowc34PyVYLoO3sL5K8Fo7AqjVr97J3lvSocu7+GgseKzD1eyYM4+3KsVR6WSSEvVsmPzBX7783N2HhpJufIa1q0+/uoP/BeyWVmZ8+24DkwYvZUv+v+Ko7MNqgL8TVePbsjxhW2Vc6QAwx4e/7Y0rY6R807wcbsKFHe0RKeTmfXb33z3Sc4b8/LLsX13uHM9ks599IeQPolIYOFUX4aMa4TqLbj48rvyEDMzY8qVV2583rgezKNHkTRt7vavZZB1Msd+u0Xjfobn2IWLmPLZ8ob0mVsHj37l2PuzP8kJBd9ocGTvLW5fi6DbR9UKOoqexMQUVqw6xNdftXlhmTPnbrF1x1lGfdPhjee5HprAw+hkWrjaGCzrVd2evQPd2PZJJewtjJhz5M0MORX+f4kJpIVX2nD0AVtPPgKgSkkrQp5k3h0NeZKEg82LL8ZMjdU0q+bA4cth2FuZ8igikS4/nAKUXkbdZ5zmz+/qYW+df13fC5qDxkqvt01YaAz2Dvp3ruw1VoSFKnea0tK0xMUlY21TiP17rlCvYXmMjNUUtbWg6nsluOYfhEuxzAkfLa3MqVm7NKdP3qJs+ZwNTdE4WPM4JHPsdmjoE73eG0p+a0IeP8HRsQhpaVpiYxOxsXnx0Krr15Vto0QJZXLWNm1q8MuqnDcGOThYERqSdb09xeG59aaUic6y3pKwtimEg8aa92qWzhgC1rBxBa4HBFG7blnMzI1p1kI5IWvRyp2d28/nOBsoPXm27FSGBblXcuRxll4uIWGxaHIxFAKgfYtKTJm7H2ifq/c/Y+9gqXc3Mzw0FvtcTHhr52BJ6XJ2XLn4MGOC6dx4U/tBWqqW70f8QZv21WjaIvcn2hs2HGPz1pMAuFcpSUiW/SIk5AkaBxu98hoHG0Ky9AQKCX2CRqNfJr/ZOVgQHprZcyQ8NBa71/xNA64Ec/WfIHZuuUxiQgppaTrMzY0ZOMwjT5k27LjEll1+ALhX0Oj15gkJj8uYDPp1FLHO7EHRs30V5i7PW6PBBm8/tuxRhv24uzrwOEsvpZDwODR2rz9EVGNXmEWTlQuB+MRUDhy/i5VF3o5T9g6WhIVk/T1j8twDL295rAjN0jMmPCxGr3dh1jIOGmUfjU+vcyVJ4utvMy+UBvX9hRIlbbl5Q5kfsFhx5ZjVrLUbv6858VZkA2jUpAKNmlQAwGvrhXxp4Ntw6C5bjgYC4F7ahsdZegKFRCWiydJT6HmmJmqa13DC5+JjGlZxyHMWgA17b7Dl4B0lT7miPI7I7MESEpmApmj2k9NOXHaWkk5W9Ouo1PvxiancevCUvhOUuZgiohP5cuYxlo7xzNMk0kXtCxERmnmzISo8AVt7w33z8vlgtq29zLQlbTHOMkQtIT6F6aMO8uGgmrjmwzqzd7DWO/cIDX1qsK05pJd5/tzjmQN7r9C6XWaDxtXLD7jmH0Sn1rPRpumIiopncP9VrPh1ILlVuKgpsRGZPYFiI5MpXDSzTkpJ1BL5MJ6tk/4BICE6hZ0/XqHTd1XRlLPCKL1HiaasFdYac6KDE9CUy/9e+rYOhYkIzax7I8Pis/19L50LYvNvl5i5rIPe75sXGzadYPO2MwC4uxUnJCQ6Y1lIaDSa56Yw0DhYE5LlPEUpY8WDhxE8Coqic8+56a8/pVuveWzZOBx7Oyuu3wxm/OTNrFo6kCIvOT9+kT8uhrP1ijKnYxWnQoTEZvYECo1NQfPcg3UuBcfjH5JAqxV+aHUykQlpfLLpJr/1dsWucObUDT2q2vHV9js5zvP/5F3smVPQRM8g4ZX6NCnBjnEN2DGuAc2rafA+E4wsy1y6G42luREOzzXkxCelZcwjlKbVccwvgjKOhXF1seTknKb4TPfEZ7onGhtTto2t/3/VEARQyc2Fh/cjCX4URWpqGgf3XcWjif5FdeMmFdm9UzlgHz7oT606yph+RydrLpxThiYkJqTgd+UhJUvb8yQqPmPIU1JSKudO36FULp4+VcW9JA/uh/PoUQQpKWns2XORpk31n+LRtKk73t7KZKkH9v9D3Xqu2c5f8YxGY82d2yFERSkXOadOXadM2ZyPaa5cpRgPH0QSlL7eDuy9jEdT/YmOPZpWZpe3MjmozwE/atctiyRJ1G9Yntu3QkhKVOaZuXjhHmXKKvNuNPasxN/nlXV6/uxtSpfN3dwufbrXwGttf7zW9qe5hyve+/yU/cAvCEsL02znBnqRwIeZvVyOnrpDySyNfblVwc2JRw+e8DgomtRULYf3X6NBk9frhRcWGkNykjJGPTYmiav/PKJEqbxNFvom9gNZlvlh0g5Klbbnw74N85SvTx9PvHeMxXvHWFo0r4qX91nl97x0D0tLcxyeO2l0cLDGwsKMS5fuIcsyXt5nad6s6gs+PX9UrOxI0MNoHgc9JTVVy5EDN2jgWfa13jt2env+2DOIjbsGMni4Jy3bV85zQxBAn67VMyZ8bt64LN77rynrzf8xloVNcjQ3UNb5hQ6fvEvZknnbD/p0roLXivfxWvE+zRuWxvugMt/ZpYAQLAubZjs30Is8eZqITqfMCbLyj4t0b5P3CVcrujnz6EEUwen7qM/+ABp5ur76jW9IJTdnHj2IJPjRE1JT0zi0z49GnhX0yjRuUoG9Oy8BcORgADXrKHNmJSWmkJigXMCcO30HtVpF6bIO2DtYEng3nCdRysX++dN3KVna8CluBZENICp9m4uJSWTH5vO5GsL2vD4tyuD1QzO8fmhG85rOeJ98qGx3t6OwLGRscMMsPiktY3h9mlbHsUshlHHKvzm1+rStgNe8dnjNa0fzOsXxPqrMpXTpRgSWhUxwyKZxasHGS8QmpDL205oZr1kWNuHM2h4cXtGFwyu6UM3VLs8NQQDlKtnx+FEMocGxpKZqOXHoLrUa6Q97v3sjkhU/nuL72c2xzpI3NVXL7O8P06RtOeo3K5WnHM9UruLCg/sRGeceB/dewaOJ/tAf5VilnHscPuhH7fRjFYBOp+PQgau0bJN5POjRqx57D49h5/7RrFo3mBKlbPPUEATgWM6S6McJPA1NRJuq4+bJMMrWzjwPNC1sxOe/NuazZQ34bFkDHMtbZTQEJTxNQadV6rOnoYlEhyRgrXlxI2VelK9kT/DDGEKCY0hN1eJ78A51PPSfznnnRgRLZh1nwpxW2LyksTSn+vRuhPeWUXhvGUWLZu54/XVB2fYvB2JpaYaD/XONfPZWWFiYculyoHJc/+sCzZtWoYKrM6ePTeXwvgkc3jcBR4012/8cgb2dFcGPnzD0m1+ZPeNDSpfKXWPkBzXsMyZ8blbOhp3+UciyzOXgeCxM1dhb6M/N2fs9e4586c6BwVVY96ErpYqa8ltv5diRdX4hn1vRlLN7M7+r8P9L9AwScsSzih2+fuG0nng8/dHymU886Tr9FDvGNSAxRctXyy6SkqZDp4O6FYrSq3HO57fJTxs/nUoT1xrYWdjwcMZOJu1axZpTf72R7zIyUjNqbAeGfbEWnVZHxy41KVNOw4olh6hU2QWPppXo1LUmk8dupXv7eVhZm/PDbOUxwT1612XahO307roQWZbp0LkG5V0duXUzhKnjt6HT6tDpZJq3rkIjz5xfnBgZqRk3/n0GDViCTifTtVs9ypV3YtHCXbhVKUGzZlXp3qMB33+3jjatJ2NtXZi5P/XPeH/L5hOJi08iNTWNwz5XWPnLV5Qr58SXX7Wl38cLMDJS4+RclBkzPspVtm/HdmLo4DVotTo6da1F2XIali8+QCW3Yng2rUznbrWYOGYzXdrOwcranBlzlCcUWVkXok/fxvTtvRgkiYaNK2Ssn2Ej2jJxzJ/8NGsXRYoWZtIPPXOc7Xme9cvge/oOrd5fiZmZETPGtstY1qXfr3itVdbZnCVH2HUwgMSkVDy7LKFHx2oM/awRG7Zd5PT5QIyM1FhZmjFrfLsXfdVrMzJS8fV3Lfn2y83odDJtO7tTuqw9a5Yep0JlRxo2Kc91/8eMH7GduJhkTvve5rflJ/ht2wAe3Itk6bwjSCjzv/bqW4cy2TyGO2d58n8/uHQxkL27LlGuvIaPei4G4IthLWnYuMLLorySp2cVjvn607L1JMzNTJgx4+OMZZ27zsB7x1gAJk3snfFoeY/Gbnh4KD2TDh68xLTpm4mKimPw50upVLEYq38ZmqdMAGojFUNHN+O7Icq+37ZzFUqVtePXZSepUFlDA89yXPcPYdIob+Jikjh9/A5rV5xizZZP8vzdr8OzXml8zwTS6sNfMTM1Ysb3rTKWdfnsd7xWK/XAnGW+7PK5oewHPVbRo30Vhvavz/ptlzhyUrlYt7Y0Y+b3rfMvW90S+J67T6u+G5Vs32ZO4tpl8Ga8VryvZFt5ml2Hb5GYnIZn73X0aFuJof1qc/ZyMPPTnyBWu6oTE4fmvSHNyEjFN9+3ZuQXf6DT6WjfuRqly9nzy9JjVKzsRKMmrlzzC2bciK3ExiRxyvcWa5b5sn77YAC+6r+O+4GRJCak0K3VQr6b3J66DV6vcTD7PGpGjGnHN1+sR6uT6dDlPcqUc2DVksNUdHOmcZOKdOj6HlPH7aBnh5+xsjJn6uweADyJiuebL35HUknYO1gycXo3QOmt8+lgT7789FeMjFQ4OtkwflqXtyIbwILZ+7h9U+m91H+QJyVK5fymyst4VtPgezmEVt8eVM6RBmQ2NnUZfxivH5qRmJzGl/NPk5KmQ9bJ1KlkT+9mpfM1R0aems74Xgyi1Zc7MTNVM2NI/cw8I/bgNa8dIREJLN/qTxkXK7qN2gtAn7au9Gz5Zob0q41UDBhRj2nfHECnlWnWoTwlyhThj1UXKVfRjtqNS7BuyXmSElP5afxRAOw0hRkzuwWnfAIJuBRCbEwyR/Yok+kPGdeI0q65b6AyMlIzemwnhn3+K1qtTKeuNdPPPQ6mn3tUonO3Wkwas4Wu7eZiZV2I6bN7Z7z/n78D0ThaZ/SGe1NUahVNB7iy44dLyDoZt2bO2Ba34PSmuziUtaRs7Rcfs4OuRXN60z1URhKSBM0HVcTM0viF5fNCbaTi81ENmDRsLzqdTIuOFShZpii/r7hA+Ur21PUoya+LzpKUkMassUqvM3tHCybMzb/6H8CzcSWOHb9Gy/YzMDczZsa0zCdadu45F+8towCYNK5HxqPlPRpVxKPRy+cAWrL8ANHRCUyZvk3571Wr2L5pRK5zepSx4vjdp7Rd5Y+5sYppbTMf+ND9t2ts++TleX46FsSN9AnWXaxNmdSqxEvLC8LzpPx4aozw7tMdHvZWbgiqZguRvqhX0DGyJS87Q3Ry3h4p+SbYmCqNHWm6gwWcxJCRqiWxqTsKOka2LI27IkesKegY2ZLsPuVxwtuZzanQp2/lfgDp+4LOp6BjGFI1B+BR3MoCDmKomMUg5JDlBR0jW5Lj58gPFxR0jGxJxYcTlriuoGNky8G8L5FJfxR0DAO2ZsrF2duaTT77fUHHyJZUdxay/9SCjpEtyW0ifpGzCjpGtqrYfk9MyraCjpEtK5PuLLv6RUHHMPCF+zIAbkbPLeAkhlxtRkHy7oKOkT3T9qT+8sGryxUA4wF//F+Po7Kd067Ar2cjv93zTq1jMUxMEARBEARBEARBEAThP0Q0BgmCIAiCIAiCIAiCIPyHiDmDBEEQBEEQBEEQBEF4Z4mnieWc6BkkCIIgCIIgCIIgCILwHyJ6BgmCIAiCIAiCIAiC8M4SPYNyTvQMEgRBEARBEARBEARB+A8RjUGCIAiCIAiCIAiCIAj/IWKYmCAIgiAIgiAIgiAI7ywxTCznRM8gQRAEQRAEQRAEQRCE/xBJluWCziC8HcSGIAiCIAiCIAiC8P/p/7rrjGZhpwK/ng0dtvOdWsdimJigSN1b0AmyZ9yW6OQtBZ0iWzamPZG+qFfQMQzIy84AsPbalwWcxFC/SkuxmtKyoGNkK2bSQcIS1xV0jGw5mPdFjno7s0lF+7IrcGRBx8hWh1I/vZX1h41pTwDkq5MLNkg2JPfJxKd6F3SMbBU27gxp+ws6RvaMWrPz3oiCTpGtTqXngXykoGMYkpoCIEesKeAghiS7TyH27as7ALDsCYlv5z6K+du9j8am7ijoFNmyNO6KfGN6QccwIFUYB4B8f24BJzEklRxFaMJvBR0jW5pCn7An8NuCjpGtdqXmFHQE4S0jhokJgiAIgiAIgiAIgiD8h4ieQYIgCIIgCIIgCIIgvLPEBNI5J3oGCYIgCIIgCIIgCIIg/IeInkGCIAiCIAiCIAiCILyzRM+gnBM9gwRBEARBEARBEARBEP5DRGOQIAiCIAiCIAiCIAjCf4gYJiYIgiAIgiAIgiAIwjtLDBPLOdEzSBAEQRAEQRAEQRAE4T9E9AwSXkqWZabP3M6x49cwMzNm1vQPcatc3KCcn/9DxozfSFJSKp6NKzFuTDckSWLBoj34HL6KSiVhW9SSmdM/RONgzdlzt/hy2GqKuRQFoGWLqgz5ok2Osp0+cZN5P+5Bp9PRqVtN+n3mqbc8JSWNKeO2cj0gGGvrQvwwpxfOLkVIS9UyffIOblx7jFaro23H6nwywJPk5FQ+7/8LKSlatFodzVq4Meir5rlfea9p9cfj6ODekLDYJ7hP6/PGvy+rOxcjOfjLTWSdTLWWzjToXirbctdPhbF99lX6z62NUzkrEmJS2T77Co9vx1K1mROtB1XI92wtytbixzZfolapWHtxL/NP/qm3vJiVPcu7jMbazAK1SsXkQ6s5cPscxiojfu4wnPecXdHJOr7bt5QT96/ka7azJ+/w8+wD6HQyHbpW56NPG+gtv/T3AxbOOcDdW2FMmtWVpi0rZSwb+eUfBFwJwv294sxe1Ctfc8myzPT5B/A9dQczM2NmTuiAWwUng3Lzlx/Be+9VYmKTuHh4dMbr5/95wMwFB7hxJ4yfpnalTbNKBu/Nrevnw/Ba7o9OK1O3bQma9yqnt/zUrvuc/CsQlUrCxFxNz6+r4ljSkqiQBH4ceBSHYhYAlKxoQ4+vq+Y5z7tSf8iyzPQ1F/H9JxgzEzUzh9TDrUxRvTKJyWkM/+kkD0JiUaskmtZyYeRH1QHYfuQuc9ZfQlPUHIA+bVzp2aJsrvOcPHGDubO80WplunavQ/8BTfWWp6SkMWHMJq4FBGFjU4hZc/vgnF7P37zxmOlTtxEfl4xKJbF+01B0OpnvRvzOo0eRqFQqPJpUYtg37XKcSzlWbeOYbwBm5ibMmt7nBceqB4wZt0E5VnlUZtyY7kiSxI9zvThy1A9jYyNKFLdj5g8fYmVVCIDrN4KYNOVP4uKSUKkktv45ClNT41ysPbh+IYydywLQ6WTqtClOs+f2g9O773Pqr/tIKglTMzU9vnZHk74fzBl0DPss+0H3Ye65yiDLMtOnb+aYrx9mZibMmtkPN7cSBuX8/O4zZsxakpJT8fSowrhx7yNJEtHR8XwzYhVBQZG4uNiyYP5ArK0Lc+duCGPHrMU/4CHfDO/EZ5+1yvismJgExo9fz81bwUiSxIzpfXmvRlOD73xp5gU++J5Or9vGtcOtgqNBufkrfPHe56fUbYdGZLy+acc/bNh+EbVKRaFCxkwd3YZype1yuOaeyzN3N8dO3lTOjSZ3x62is0E5v2tBjJm8XVmHDV0ZN6o9kiRx7cZjJs30JjklDbVaxeTvOlG1SjEOHb3Gz8sPoVJJqNUqxo5sR63qpfKWc/ZOjp24ruSc+j5ulYoZ5gx4xJiJm5WcjSoybnQnJEm5w77+j5Ns+PMUapUKz8YVGf1N+9xneUv30VMnbjB31l/otDJdutfmkwFN9JanpKQxacxmrgUEYW1TiJlzP8io127deMyMqTuIj0tCUkms2zQEU1NjDuy9zJqVR9DpdDTyrMSwEW1ztd6ykmWZ6avO43shCDNTNTOHN8StrK1emcTkNIb/eIwHj9OPB3WKMbJfTQA27b3Bhj03UKskCpkZMfWr+pQrYZPnXNnmXHoa3/MPMTM1YuYoT9zK6+9viUlpDP/hEA+CY1CrJZrWK8nIz+rkexZQztkWzjmETqejfZfqfPRpfb3ll/5+wKK5h5RztpldaNKyIgC3boQyb/o+4uNTUKklPv6sAc1bV87XbNfOh7Fj+VVkrUzdtiVp0au83vKTuwI5+dc95ZhgbsT7X1fDsaRlxvInYQnMGniENh9VoGnPcs9//H+OSnRzyTHRGFTAJEkaB3wIaAEdMBgYCMyTZTlAkqQ4WZYtsnlfPeBnwDT935+yLE/O73y+x68R+CCcA3vGcfnKfSZP28KWP0YYlJs8bQvTJveiWtWSDPxiBb4nruHZuDID+jdj+FDlxH7d78dYsmw/Uye9D0CtGmVYsXRQrnJptTrmzPiLRSv746Cx4pMPltO4SSXKlHXIKLNz+99YWpmzbfcIDuy9wpIF+5k+pzc+B/xISdWycftQkhJT6N11Ia3aVsXJ2YYlv3xKoUKmpKVqGdRvFfUbueJezfBkJT/9dno3i49uZd0nE9/o9zxPp5XZv+IGH0x5DytbU3799jzl69hhX1x/c0tOTOP8roc4u1plvGZkosLzw7KEP4gj/EF8vmdTSSp+ajeUzuu/IygmgqMDF7PnxmluRDzIKPOtRx92BBxj9YVdVLArwdY+03H/+WM+qalsb/WXD8KukA3b+kynyaohyMj5kk2r1TFv5j7mL/8Qe40VA/usoaFneUqXtc8oo3G0YuzUjmxad9bg/R/0q0dyUireW//JlzxZ+Z6+w/2HUezf8gWX/YOZMnsfm1f3NyjXtJErfXrUos37y/Red3K0YuaEjqzZYJg7L3Rame1L/Bg8sy7WduYsGHoct3oavROaGk2dadChJAB+p0PYuSKAQTPqAmDnVJiRyzzyLc+7VH/4/vOY+49j2b+oA5dvRTJl5QU2z2plUK5/p4rUq6IhJVVL/ylH8L0YjEcN5eK0bYMSTBxQK085QFlvP/6wg6WrBqJxtOajXovwbFqZMmU1GWW8tp/DysqcnXu/Y/+eS/w8bw8//vQRaWlaxn//Bz/M7I1rRWeio+MxMlKTkpLGx/09qF2nHKmpaQz+bCUnj1+nYeOKOcrmezyAwPvhHNg7gctXApk8dTNbNo00KDd56mamTelNtaqlGPj58oxjVcP6FRg5vCNGRmrm/OTNilUH+XZkZ9LStHz7/XrmzPyYihVdeJKeOzd0WpkdS/wZNKMu1nZmLBx2Ard6GjRZ9oP3mjhTv72yH/ifDmXnymsMnK5cINk6FWLE0sa5+u6sfH39CLwfxoH9U7l8+R6Tp2xky+bvDcpNnrKRadM+olq10gwctBjf4/54elRh5ap91K9XkUGD2rBy5T5WrtrPt6O6YWNdiHHje+Fz6JLBZ02fvpnGjd1YuHAwKSlpJCWl5Czz6bvcfxTF/j8HKXXb3ANsXtXXoFzThmXp070GbXqv1Hu9Q6vK9O76HgCHj99i1qLD/DLv/Rxl0Mtz8iaBDyM5sOMbLvs9YvLMnWxZ+7lBuckzdzJtfBeqVSnGwK/X4XvqFp4NXZmzcB9fDWyGZ0NXjp24wZyF+1i/cgD165ShuecQJEni+q0Qhn+/iX3bhuc+54nrBD6I4MDO0Vy++oDJ03ew5fehhjmn72DaxO5Ucy/BwCFr8D15A89GFTlz/jY+R/3ZufkbTEyMiIyKy32Wt3QfVeo1b5as+gyNozV9ey3Go2klvXrNe/t5LK3M8dr7Lfv3XGbRvH3M/OlD0tK0TPj+T6bOfF+vXouOjufnn/bw++ahFClqwaSxmzl35jZ16uXtYt337yDuB8ewf0UXLt+IYMqys2yea9h43r+LG/WqOirHgwkH8f07CI+aLnTwLE3vtsrNu8NnHzJr9QV+mdIiT5myzXn+IfeDnrL/1/e5fD2MKQtPsHlRF8OcPapSr7qzkvO73fiee4hHnfw959ZqdcyfdYB5y3pjr7FiUJ/faORZnlJlMxunNE5WjJ3SweCczczMiLHTOlK8ZFEiwmIZ0OdX6jQog6WlWb5k02llti25wucz62NjZ878ob5Uqeeod25Us6kLDTuUApRzI+8VfgyekdmY5bXCn0q1HZ7/aEF4baL9rABJklQf6ADUkGW5KtACeCjL8gBZlgNe8fa1wCBZlqsDVYDNbyKjz5GrdOlUG0mSqF6tFDGxiYSFP9UrExb+lLj4JKpXK4UkSXTpVBufw1cBsLDIrDATE1OQ8mkoZ4DfI4qVsMWlWFGMjY1o2cYd3yPX9Mr4Hr1G+07KyV+zlm6cP3sXWZZBgqSEFNLStCQnp2FkrKawhSmSJFGokCkAaWla0tK0+Zb3ZY7fvkRUfMyb/6LnBN+KoYiTOUUczVEbq6jcSMOtsxEG5Xw33KV+t5IYGWdWFyZmaopXttF7LT/VcqnA3ahgAqNDSNWlsc3/KO0r6ve+kZGxNC0MgLVZYUJiIwGoaF8S38BLAEQkRPM0KZ4azq75lu2aXzAuxYviXKwIxsZqmreuzImjN/XKOLnYUM5Vk3FnVe+/rW7pjO0sv/n43qRz26rK/lrFhZi4JMIiYg3KVa/igoOdpcHrxZxsqFBOk+9jrh/ciMbWuTC2ToUxMlbxXhMX/E+H6pUxK5x5Fzcl6c3ue+9S/eFz/hGdmyh1a3VXO2ISUgh7kqhXxtzUiHpVlAsXE2M1lcsUISQyIe9f/hy/qw8pVsKOYsVtMTY2onXbahw97K9X5ujhADp0Vhqemrdy5/zZ28iyzJlTNynv6oRreu8JG5vCqNUqzM1NqF1HuUAyNjaiUiUXQkP1jzGvw+fwVbp0qpN+rCr9imNV6fRjVR18fJReg40aVsq4gKxerRQhodEAnDx1nQquzlSs6AJAkfTcufHgRjR2ToWwdSqEkbGK6p7Or9gP0t7IfuDjc4Uunesp66p6GWJiEgkLe25dhT0lLi6J6tXLKOuqcz18Dl3OfH8X5WKkS5f6HEp/3dbWiqrupQwuxGNjEzl/4RY9ejQEwMTEKKNHx2tnPnGLzm2qZNZtscmERRg2TCh1m8H9MywKZ9a5CUmpeV6vPseu0aVddSWPe3FiYg3r2rCIWOLik6nuXlxZh+2q43NUObWTJIn4+GQAYuOScLBXbrYULmSacdxQzpnyFtTnaABdOtRQclYtmb5f6J9vhIXHKPtF1ZJKzg418Dmi7Nd/bD7DoP5NMTFR7h3bFjVct6+d5S3dR/2vPqR4CduMeq1V22ocO6x/Cn7scAAdOtcAoHmrKpzLqNduUd7V0aBeC3oYRYmSdhRJX1916pXj8EG/HK4xQz5nH9K5aVllHVa0JyY+hbAo/bre3NSIelWVXnMmxmoqly1KSIRy086ikElGuYQ3VL8A+Jy6T+eW5ZWclTRKzueOSeZmRtSr7pyZs5xdRs78pJyzFclyzlbJ8JzN2Yayrg4G5z7FS9pSvKTSA8zOwZIiRQoTHZV/x9YHN55g51wYuyznRn6nQ/TKPH9MyPqjXT31GFvHQnqNR4KQU6JnUMFyAiJkWU4GkGU5AkCSpKPAKFmWL6T/PR9oBYQAvWVZDgccgMfp79MCAellJwNlgXKAHTBbluVVuQ0YGvoUR8ciGX87amwIDX2Kg721fhmNjUGZZ+b/vBuvneextDRj3ZohGa9fuhxIp26zcXCw4rtRnSlfznA4y4uEhcag0WRmcNBY4X/1kV6Z8NAYHNLLGBmpsbAw5Wl0As1bVsH36HXaN/+RpMRUho9uh7W1cmKq1ero13spjx5E0aN3XapUfbO9ggpSbFQSVnaZjXWWtqYE39I/SQy5E0NMRBLlatlxZsf9fy2bk6Udj2LCM/4Ojomglot+T4GZR9fj9dEsBtfpTCFjMzqv/w6AqyF3aOtany1XD1PM2oHqzuVxsbbn7+Ab+ZItPCwWB8fMA6+9xoprV4Py5bPzKjQ8FidNZg8uR3srQsNjs234+Tc9jUzExj5zW7O2M+PB9ScG5U7sDMR3+13SUnV8MbtexutRIQn89KUvZoWMaNuvAmXcbQ3emxPvUv0RGpmIk23hjL8dixYiNDIBhyLm2ZaPiU/hyIUg+rbPHLp58MxDLgSEUcrZijGfvIeTXeFs3/sq4WFPcXTMut6s8bv68IVllPVmRnR0AvfvRyBJEl8O+oXoJ3G0aludTz5tovfe2JhEfI9d44OPGuU4W2jYUxwdbTL+fq1jlaMNoWGGDU/btp+hbVvlwu9eYBiSBJ8NXErUkzjata3BwM9ydyc9JjIJG/vM383azowHN6INyp3cGYjvjntoU3UM/jHrfpDI/K+OY1bIiNb9KlCmSlGD976O0NBoHJ2yHNcdbQgNjcbBwVq/jKNhGYDIyJiMsvb2VkRGvvxmxqNHERQtasGYMWu5fiMIN7cSjBv7PoVysBmGhsfh5JClbnOwTK/bXr9xYsO2i/y26TypaVp+W9j79b882zyxevuCo8aK0LAYvbo2NCwGx6z1scaa0HClwWjsyHZ8NmQtP/68F51OZtOazF7SB48E8NPiA0Q9iWfFgo/zljO7/SLsaUbjU0YZjbVBGYDA++FcuHiP+Yv3YWpqxOhvOlC1Su7qtbd1Hw0Li0HzinpNKaPkelavPY1O4MH9CJAkhgxazZMn8bRqW41+n3pSvIQt9wPDCQ6KwkFjzdHD/qSlal8704uERibgZJ/ZkOpom348KJp942pMXApHzj2ib8fMId8bdl/nN+8AUtN0/PaDYS/T/BAaGY+Tfea+6WhXmNDIeBxsX5QzmSNnHtC3a5V8zxIRFodDlv3QXmNJgF9wjj8nwC+Y1DQtLsWLvLrwa4rO7piQ7bnRPY5uv4M2VceXs5Ubo8mJafhsvs0XM+tzZOvtfMv0rlP/G3fx/8+InkEF6wBQXJKkm5IkLZUkyTObMoWBC7IsuwHHgEnpr88HbkiStEOSpMGSJGXts1gVaAbUByZKkmQ4kP1f9M3X7TnmM5mO7Wvy+8bjALhVLs7hg5PYuX00H3/owVfDVv9refz9HqFWSew+9B079o5k49qTBD2KAkCtVvH7liH8dfBb/P0ecedW6Cs+7f+XrJM5tOYWzfuXf3XhAtCjSlM2XD5Apfkf0nPjOFZ2/Q4JifX/7CM4Npxjg5Yyq/UXnHsYgFanK+i4wmto1KkUY39rRofPKnFoo3JyY1XUlPG/N2fkUg86Da7M77P+ISk+tcAyvs31R5pWx8j5p/i4nSvFNcqJeNNaLvgs68TOee1oUNWR7xef+VczPaNN03Hpn3tM//EDVq/7kiM+fpw9cysze5qWMaM30rtPQ4oVz1tjX14sW7EftZGaTh2U3k1arY6/L95lzuy+bFw/nEM+Vzh9Jn8all+kYadSjPm1Ke0/q4jPH8o6sipqyrj1zfhmSWM6DqrMxgLeD56RJOmVvVfS0nQEBDzkgw888doxDnNzE1au2v8vJczUp3sNDm4ZzMgvmrDst9P/+vdn9cfWc4wZ0Y5ju0czZkQ7xk3bkbGsZdPK7Ns2nCVzP+Tn5YcKMKWy/T+NSWDz+iGMHt6e4aN/V3pIFqC3YR99Rpum4/I/gfzwY29Wr/ucoz7+nDtzGyvrQnw/oQtjRv3BwH4rcHYpgiqXPQpzK02rY+RcXz7uUJHiWW5g9WlfkYMruzGyXw2W/Zm/8ynmRppWx8gZh/m4ixvFnaxe/YYCEBEex/TxfzFmcntUBfC0qkadSjP+txZ0+KwyBzYqvZr2rb+BZ9cymJqLfh1C3ogtqADJshwnSVJNoDHQFPhTkqTnB+7rgGcz5/4ObE9/71RJkjag9Bj6EPgAaJJezluW5UQgUZKkI0AdwOv575ckaRAwCGDF0qEMGqBMbrfhj+Ns3qqcKLlXKUFISGYrdUhotN4ddQCNxjqju+6LygB07FCLQV+sYNiQtnrDxzw9KjPlhy1EPYmjaJHXu8vnoLHS630UFhqDvYP+QcReY0VY6FM0jtakpWmJi0vG2qYQ+/dcoV7D8hgZqylqa0HV90pwzT8Il2KZd1ktrcypWbs0p0/eomx5Df+PLIuaERORlPF3bGQylkUzu9InJ2oJfxDPhvEXAYiLTmHL9Mv0HFcNp3Jv9oD9ODaCYlaZc/A4W9kRHKs/hK3ve23otmEsAOceXcPUyATbQtZEJEQzZv/yjHIHP13A7Uj9Xh95Ye9gSVhI5nCA8NAY7BwKrufNhq0X2LJTmX/IvZIzj0Mz79KHhMegsS/47sPWtuZEh2dua08jkrC2y75nC0D1Js5sW6QMNTUyUWNkogwNKF7eBjvnQoQHxVPc1SbXed72+mPD3pts8bkDgHtZWx5HxgPK/hASlYDmBXdXJy4/R0knS/p1yOxFV8Qyc5/u2bwMc3+/lOM8z9g7WBMSknW9PcXh+fWWXkbjaJO+3pKwsSmERmNNjZplKFJE6Q7SqHFFrgcEUbee0tj8w+RtlChhR5+PX39OnA0bfZ87VkVnLHutY1VINJosvWG27zjL0WP+/LZ6SEYDh6PGhto1y2UcmzwaV8Y/4BH16+V80nwrWzOiwzOH+D2NSMLa9sVzT1TzdGb7ImVoSdb9oFh5a2ydcrYfbNhwlM1bTgDg7l6SkMdZjush0Wiy9MYA0Ghs9I/9WcrY2loRFvYUBwdrwsKeUrToy+sYR0cbHDU2VKtWGoA2rWu8VmPQhm0X2bJTGYLmXsmRx2FZ6raw2FzXbe1bVGLK3P1AziZC3rD5DJu9Lih5Krvo7QshoTFontsXNA5WhGStj0OfZmTesesfxo1Svr9tiyqM/8HL4Ptq1yjNw6DtREXHU9Tm9btRbdh0is3blblP3N2KG+4XDs/tFw7WhIQ+zbaMRmNNy+bK8Lyq7iVQqSSePImn6GsOF3sX9lEHBytCX1GvKWWisxwPkrC2KYSDxpr3apbGJr1ea9i4AtcDgqhTrxweTSrj0USZbHj7lrOocjmz7Ybd19lyQGkUdi9vy+PwzGFKIZEvOR4sPk1JZyv6dc5+wuP2jUszZVn+zQ+4Yac/W/ZcV3JWsOdxeOYwzpCIeDS22W/DExccp6SLNf265W5C/Fexc7AgLMt+GB4ai30O6o74uGS+G7aZgV954lbVJV+z2WR3THjJudF7TVzYukhpwLt//QmXTwTz1+oAEuNSUUkSRiZqGncuna8Z3zVq8Wj5HBM9gwqYLMtaWZaPyrI8CRgCdH/VW7K8944sy8uA5kA1SZJsny/zgr+fvX+lLMu1ZFmu9awhCKDPB43x3jYa722jadHMHa+d55FlmUuXA7G0MNfr0gvgYG+NRWEzLl0ORJZlvHaep3lTpVIPvJ851Mfn8FXKlFYujMIjYjLuLl25eh+dTqZIDk52Krm58PB+JMGPokhNTePgvqt4NNEfRtS4SUV2p18kHz7oT606ytwHjk7WXDh3F4DEhBT8rjykZGl7nkTFExujVMpJSamcO32HUnl42sjbzrm8JU8eJxAdmog2VUfAiVDK18n87zUrbMQ36z34alVDvlrVEBdXq3+lIQjg76AblLF1oaSNI8YqI7q7NWHPDf07uY+ehuFZWpnTxdWuBGZGJkQkRGNuZEohY+UCq2mZGqTptHoTT+dVRTdnHj2IIjgomtRULT77A2jkmX9zEuVUnx618Fo3EK91A2nu4Yr33ivK/uoXhGVh0wIfIgZQvII1EUHxRIYkkJaq45+jQbjV028kCQ/KPHG8di4MOxelPoiLTkanVeqKyMfxhAfFY+uYs/lGnve21x992rriNbctXnPb0ryOC95Hlbr10s0ILAsZZztEbMEfV4hNSGVs/xp6r2edX+jwhSDKuuR+/3WrUoyHDyIISl9v+/dexrOp/oWGZ9PK7PJWLph9Dlyldt1ySJJE/Yau3L71mMREZb6lvy/czZigdcnCfcTFJTHq+445ytPnQw+8t3+H9/bvaNG8Kl47z6Ufq+5haWH2kmPVvfRj1TmaN1OOVb7HA/hlzSGWLR6IuXnmvBqNGlbi5q3gjNznL9ymXFnDp1i9juIVrIkIjicqfT+4dCyYygb7QeacGddfuB8kEBEcj63T6+8Hffo0wdtrPN5e42nRvDpe3meUdXXpLpaWZnpDxAAcHKyxsDDj0iVlriwv7zM0b648xa9Zs6p4eSn1sZfX6YzXX8Te3hpHp6LcvavMhXH69HXKln31sPA+3WvgtbY/Xmv7K3XbPr/Mus3CNEdDxAIfRmX8/6On7lCyWM6H2PV5vx7eG4fgvXEILZpUxmvPJSXP1YfpefTrWgc7SywKm3Lp6kNlHe65RHNPZbiOg70V5/6+B8CZ83cpld4b7v7DyIxzI//rwaSkpFHEOmf1XZ/eDfDe/A3em7+hRVM3vHZdVHJeuZ9+DvdcQ4e9lbJfXLmv5Nx1kebpjRgtmrpx9rzSMH3vfjipqdqMBt3XyvIO7KOVqxTj4YPIjHrtwN7LeDxXr3k0rcwub+XGmM8BP2rXLZter5Xn9q0QktK/++KFexn1WlSkckyLeZrA1k1n6NK99mtnyqpP+4p4/dwRr5870rxuCbyP3FHW4fVw5XiQzRCxBb//oxwPBuh/Z2BwZqPI0QuPKOmcf+dzfTq54bW8O17Lu9O8QSm8D95Scl4LxbKwSbZDxBb8ep7Y+BTGflE/m0/MH8o525Ms52zXaNjk9Xq8p6ZqGTdyG607VMl4wlh+Kl7BhvCgeCJD4l/r3CjgXGjGMWHYvEZMXNeSieta4tm1DC16l//PNwQJuSN6BhUgSZIqADpZlp/1la8O3EeZEPoZFdAD2ITSA+hE+nvbA3tk5ayhPMrTyKLT39NZkqSZKEPMmgCGjwl5TZ4elTl2/Bot2/6AubkJM6Z9kLGsc/fZeG9THks9aXyPjEfLezSuhEdj5YTnp/l/pY/nlnBxLsqUiT0B2H/gMn/8eRK1WoWZmTHz5vTL0USJRkZqRo3twLAv1qLT6ujYpSZlymlYseQQlSq74NG0Ep261mTy2K10bz8PK2tzfpitPMa7R++6TJuwnd5dFyLLMh0616C8qyO3boYwdfw2dFodOp1M89ZVaOSZ/5X/8zZ+OpUmrjWws7Dh4YydTNq1ijWn/nrj36tSq2g1sAKbpvyDTgvVWjhhX8KCYxvv4FTOCtc69i99/5KBJ0lOTEObJnPzbDi9J1c3eBJZbmllHd/uWcyOj2aillSsv7Sf6+H3GdekHxeDb7L35mnGHljBoo4j+KpeN2TgC685ANgXtmHHRzPRyTLBsREM2vFjvmR6xshIxTfft2bkF38ojyntXI3S5ez5ZekxKlZ2olETV675BTNuxFZiY5I45XuLNct8Wb99MABf9V/H/cBIEhNS6NZqId9Nbk/dBrl/1HdWng3K4XvqDq16LsXM1JgZ4ztkLOvSdxVe6wYCMGexD7sO+JOYlIpnp4X06FSdoQM8uBoQzJDvtxITm8SRE7dY/IsvuzYOznMutVpFt6/cWDn2LLJOpk6r4jiWsmTf2hsUc7WmSn1HTu4M5ObFCNRGKswtjPlgVHUA7l6NYt+6G6iNVEgq6DGsKoWsTF7+ha/wLtUfnjWc8b34mFZDdmFmqmbGl3UzlnUZtRevuW0JiUxg+TZ/yrhY0W30PiDzEfLr99zgyPkg1GoV1hYmzBxS70Vf9UpGRmq+G9uZrwb/gk6ro1PX2pQt58iyxfup7FYMz6ZudOlWmwljNtGp7Y9YWxdi5pwPAbCyLkSfvh583HsRkgQNG1eksWclQkOiWb3yMKVKO/Bhz58B6PVBA7r2qPuyKIbryaMyx3z9adl2KuZmJsz4oU/Gss7dfsR7uzKn2KQJ7yuPrU5OwaNRZTwaKxd906ZvJSU1jf4DlgJQrVoppk7qhbV1IT7p15QeveYiSRIejSvTxNMtV+tPrVbR5csqrBp3Tnm0fKtiOJayZP+6GxQrb4NbfQ2ndgZy658IVEYqClkY0WtkNQDu+kVxYN1NVEYqVBJ0H+pOIcvc7QeenlU45utHy1YTlHU1o1/muuryA95e45V1NfFDxoxdS1JSCh6N3fDwUE5NBg1szfBvVrF120mcnZVHywOEhz+le4+ZGY/3XrvuMHt2T8LCwpwJ43sx6ts1pKZqKV7cjpkzDJ8E9tLM9cvge/oOrd5fiZmZETPGZj5BqUu/X/Faqzw1cc6SI+w6GKDUbV2W0KNjNYZ+1ogN2y5y+nwgRkZqrCzNmDXe8AlMOcrT0JVjJ2/Ssss8ZR1O6pa5Dj9cjPdGZX7ESd93YszkbSQlp+LRwBWPhsqNg2njOzNj7h7StDpMTYyYOq4zAPt9/PHecwkjIxVmpsbMn9krT5NIezauyLET12nZ8Ucl55SemTnfn4/35m+UnGO7ZDxa3qNhRTwaKXVX9y61GTtpCx26/4SxsZpZ03Kf523dR42M1Hw7thNDB69Bq9XRqWstypbTsHzxASq5FcOzaWU6d6vFxDGb6dJ2DlbW5syYo5wLK/VaY/r2XgySRMPGFTLq/bmz/uLWjccADPi8OSVLvfyc6rXWYS0XfP8OotXgHZiZGjFjWOaDNbp8/RdeP3ckJCKe5ZuvUqaYNd2+2QUoDUo9W5Vnw+7rnL70GCMjFVYWJswa3jDPmbLNWac4vuce0uqTP5WcozJnwejy+Ta8lncnJDyO5X9cokxxG7p9uV3J2dmNnm3z97zbyEjF8O9aMurLTeh0Mu06V6V0WXtWL/WlQmUnGjUpzzX/YMaP2J55zrb8OOu2DeTIgWtcvviQmOhE9u1UeiuPmdqB8hXyZ8SAWq2i+1furBh7Bp1Opm6rEjiVsmLv2usUd7WhSn1Hju+8l35uJFHIwpgPR72XL98tCM9IBT32978sfYjYIsAGSANuowzb2kr6BNKSJMUBK1GGg4UBvWRZDpckaRNQA0hIf+84WZb3p08gXQalgej1J5BO3ft2bgjGbYlO3lLQKbJlY9oT6YvcX1i9KfIyZU6Qtde+LOAkhvpVWorVlJYFHSNbMZMOEpa4rqBjZMvBvC9y1NuZTSral12Bho8Hfht0KPXTW1l/2JgqF2Ty1ckFGyQbkvtk4lO9CzpGtgobd4a0f3+umddi1Jqd90YUdIpsdSo9D+QjBR3DkNQUADliTQEHMSTZfQqxb1/dAYBlT0h8O/dRzN/ufTQ2dceryxUAS+OuyDemF3QMA1KFcQDI9+cWcBJDUslRhCb8VtAxsqUp9Al7Ar8t6BjZaldqzv/1OKrya3oU+PXsrU+3vlPrWPQMKkCyLP8NNMhmUZMsZbLtaiHL8ssehXFFluWc3XYTBEEQBEEQBEEQBOE/QcwZJAiCIAiCIAiCIAiC8B8iegb9n5FleXJBZxAEQRAEQRAEQRCEf4tadHPJMbHKBEEQBEEQBEEQBEEQ3iBJktpIknRDkqTbkiQZPORJkqT5kiRdSv93U5Kk6CzLtFmW7cyPPKJnkCAIgiAIgiAIgiAI7yx1Hp6++G+QJEkNLAFaAo+A85Ik7ZRlOeBZGVmWv8lSfiiQ9RFyibIsV8/PTKJnkCAIgiAIgiAIgiAIwptTB7gty/JdWZZTgE1A55eU/wD4400GEo1BgiAIgiAIgiAIgiAIeSBJ0iBJki5k+Tcoy2IX4GGWvx+lv5bd55QESgOHs7xslv6ZZyRJ6pIfecUwMUEQBEEQBEEQBEEQ3llvwzAxWZZXAivz4aN6A1tlWdZmea2kLMtBkiSVAQ5LknRVluU7efkS0TNIEARBEARBEARBEAThzQkCimf5u1j6a9npzXNDxGRZDkr/37vAUfTnE8oVSZblvH6G8P9BbAiCIAiCIAiCIAj/nwq+68wb9N7vvQr8evafj/584TqWJMkIuAk0R2kEOg98KMuy/3PlKgL7gNJyemONJElFgARZlpMlSbIDTgOds04+nRtimJgAQHTyloKOkC0b056k6Q4WdIxsGalasvbalwUdw0C/SksBkL6oV8BJDMnLzlByVbeCjpGt+wO3czH8h4KOka0a9uOR/acWdIxsSW4TORMyuaBjZKue42Qik97ovHu5Ymv2AQC6o8MLNkg2VE0W4Bc5q6BjZKuK7feQ6F3QMbJn3pntd4YXdIpsdSu7AHQ+BR3DkKo5APKDeQUcxJBUYgTIRwo6Rvakpm/n7wnKbxr7dp5PYtmT+NS3s/4obNz5rTzGS24TAZDvzS7gJIak0qO5H7u0oGNkq6Tll1wIm1bQMbJVy2FCQUf4T5NlOU2SpCHAfkANrJFl2V+SpKnABVmWnz0uvjewSdbvtVMJWCFJkg5ldNesvDYEgWgMEgRBEARBEARBEARBeKNkWd4D7HnutYnP/T05m/edAtzzO49oDBIEQRAEQRAEQRAE4Z2l/r8eBPdmiAmkBUEQBEEQBEEQBEEQ/kNEzyBBEARBEARBEARBEN5ZapXoGpRTomeQIAiCIAiCIAiCIAjCf4hoDBIEQRAEQRAEQRAEQfgPEcPEBEEQBEEQBEEQBEF4Z6klMUwsp0TPIEEQBEEQBEEQBEEQhP8Q0TNIeKnTJ24y78c96HQ6OnWrSb/PPPWWp6SkMWXcVq4HBGNtXYgf5vTC2aUIaalapk/ewY1rj9FqdbTtWJ1PBngSGhLN5HHbiIqMQ5IkunSvRe+PGuQq2/HjAcyasRWtTkf3Hg0YOLDVc9lSGfPdevwDHmBjU5if5n2Ki4st0U/iGD58NX5+9+nSpR7jJ7yf8Z7duy+wasV+JEnC3sGaH2f3o0gRi1zle+bOxUgO/nITWSdTraUzDbqXyrbc9VNhbJ99lf5za+NUzoqEmFS2z77C49uxVG3mROtBFfKUI6dWfzyODu4NCYt9gvu0Pv/qdwN4FnuPSfU/RS2p2HTjEMsu79BbPqFef+o7VwHA3MgUWzNrqq77GIAxdT6mWYmaqFBxPOgyk0+vztdsl84Ese7nC+h0Mk07lKPzx1X0lu/eFMCRXbdRqSWsbMwYPKY+9o7KdvShx++UKGMDgK2mMN/+2DTfcsmyzPTVf+N7MQgzUyNmDqmPW9miemUSk9MYPuc4D0LjUKskmtZyYeTH7wGw/fAd5qz7B03RQgD0aetKz5bl8iXblbPBbFh0EZ1OxrN9WTr0qay3fN+f1zm2+07GOvvsu7rYORbm2sVQNi65mFHu8YMYvpjYkJqNi+VLruedOXmLBT/uQ6vT0bFrDfp+1lhv+T9/B/Lz7H3cuRXKlB970Kyl2xvJ8Ywsy8z48xq+fuGYmaiZ8Yk7biWsDcoN/Pk84THJpGllapUvwoQP3FCrJH72vsnhy2GoJChqacLMT6riYGOWL9n+OfOINQvOotPKNO/oSre+VfWW7/zDD5+/bqJSq7C2MePLsY1wcLLg3s1IVs45TUJCKiqVRI9+VWnYoky+ZHpGlmWmz97JsRPXMTMzZtbU93GrZLjN+AU8YszEzSQlp+LZqCLjRndCSr+zuP6Pk2z48xRqlQrPxhUZ/U37POe6cSGcXSuuodPJ1G5djCbvl9Vbfnb3A07vuo9KLWFiZkTXYW5oSljyz5Egjm+7l1Eu5F4sQxY2xLmsVZ7yyLLM9BlbOObrr6ynGX1xcythUM7P/wFjxqxT1pOHG+PG9kSSJPbuu8jixbu5czeELZtH416lJABXrgQyYdLGjO8Y+lV7WrasnrecS0/he+6BUrd92wS38vYG5eavOYf3oZvExCZz8a/PMl4PCo1l3NyjRD1NwtrSlDnfN8PRPmfHdlmWmT59M8d8/TAzM2HWzH7Zryu/+4wZszZ9XVVh3Lj3kSSJ6Oh4vhmxiqCgSFxcbFkwfyDW1oUz3nflaiC9e89m3k+f0aZNTQCCg6MYP349j0OeIEmwcsUQihV/RcY38Hs+eRLHsOGr8PN7QNcu9Zg4oVeO1l1Gtrm7OXbyppJtcnfcKjobZrsWxJjJ25VsDV0ZN6o9kiRx7cZjJs30JjklDbVaxeTvOlG1SjHuBIYzdsp2/K8H882XLfns40Y5znbyxA3mzvJGq5Xp2r0O/QfoH5dTUtKYMGYT1wKCsLEpxKy5fXB2UY6tN288ZvrUbcTHJaNSSazfNBRTU2MW/7yP3Tv/JiYmkZPnf8hxpuzk9Rj/zP7TD/h6znG2zG6DeznbfMlmkHPZGXzPP1RyjvTArbydfs6kNIZP9+HB41glZ70SjPy0dr5nATh/KpBlc4+h08m06eJG70/0v+fKxSCW/3SMu7cjGDu9LR4tymcsCwuJYd40H8JDY5EkiR9+7oyjc97q3Wcunw1m/c/n0elkmnQoR6eP9M8l92wK4MiuO6jTz4sGjqmXcS75kecGiqefS9ppCjFyVv6dS77LxATSOScag4QX0mp1zJnxF4tW9sdBY8UnHyyncZNKlCnrkFFm5/a/sbQyZ9vuERzYe4UlC/YzfU5vfA74kZKqZeP2oSQlptC760Jata2KiYkRX49sS8XKzsTHJ9Ov91Lq1C+n95mvm236tM2sWj0EjcaGXu/PoWlTd8qVc8oos23raayszdm3fzJ7dl9g3lxvfpr/KSamxgwd1oHbt4K5detxRvm0NC2zZmxl567xFCliwdw5XmzccIyvhuT+AkCnldm/4gYfTHkPK1tTfv32POXr2GFfXP8kNDkxjfO7HuLsmnmAMTJR4flhWcIfxBH+ID7XGXLrt9O7WXx0K+s+mfivf7dKUjGt4UD6/I+98w6Ponjj+GdzCemFtEslkEAooYQeBBJC7wQQRVHBAjZURBRpAiK9qoA0UUBQ6aG3BAi9E3qH0NJDer/b3x8bkhwXlBQJ/pzP8+R5uN13777M7sy88847s9vGE5UWz6agaeyJOMH1xPv5NhOO/pL/7wE+nfGxqwJAQ8fqNFLXpMO6oQCs6zYRP2cfjkZeLBNtWo2WX2YdZ+Tsttg5mjHqve00bOGGWxWbfJvK3rZMXNIZYxNDdm+4yqr5p/nsW38AKhirmPJr1zLR8iRhpx8SEZnMznndCb8Wz/hFx1k9taOe3ds9auJXx4nsHA1vjwsh7PQD/Bu4AtCpuQffDCxbh0yr0bJ8zim+mhmIrYMp497fRf3mrrhWLghqeFSryLhFHTA2MSRk43X+XHCWj8c1p2YDNRN+7gRAanIWX72+hdqNncpU32M0Gi0zJm3j+4Vv4qi24t3XF9OyVXWqFGqfnJysGT0hiFXLDv8jGp4k7EIsETFp7JjgT/jtRL5deZE/R+gH0GcP8sXC1AhZlvls4Rl2nIqkS2MX3m1fhc96eAOwIvQO87feYFy/2nrXFxeNRsviGUf55vsO2DmaMfzdzTRuWQn3QvWgircd05Z2x9jEkB3rr7Bi/gm+mBCIsYkhn3zTEhd3axJi0/nynU34NnXF3NK41LoeE3bwCnfuxrFr01eEn7/LuIkbWPPbJ3p24yZuYMI3valXpxIDBy8l7NBVAlrU4OiJG4Tsu8im1Z9ToYIh8Qmppdak1chsmn+Rdyc2wcrehHlDDlPTzxF1Jct8m3qBzjTtogzeLx2NZuviK7wzoTH1A12pH6jU0ajbKayYcKrUgSCAsLCL3ImIYdeOcYSH32Hct3+w5s+v9OzGjf+dCd/2o169ygx8fx5hBy4R4O+DdzVnfvxxEGPzAj+PqVbNhXVrhmNoqCImJokePScSGFgHQ0NVyXQev0fEgyR2/tqX8MsxjP/hIKt/7KlnF+jnQb8ePnQc8IfO8WkLj9KjnTc921fn6JkHzPr5ONO+bl08DWEXlLLa+S3h4bcZN34Va1Z/rWc3bvwqJkx4g3r1qjBw0FzCDlwkwL82ixbvoJlfDQYN6siiRTtYtHgnXw7rBeS1PTM20Lx5TZ3vGj78Fz74oBPNm9ciLS0TA4O/Tuj/p+6nsbERn33ajetP+E3FIezQNe7ci2fXhs8Jv3CfcZM3sWbZB/raJm9iwugg6tV2Y+Bnywk7fJ2A5t5M/2EHHw9sTUBzb/YfvMr0H3awYtF72FiZMmpYF0L2XS6RLo1Gy9TvNjB/8UDUTta88eqPBATWwtNLnW+zcf1xrKxM2bR9ODu3neX7WduYOvMNcnM1jP76d76b3BfvGi4kJqblP+P+rWry6usvEdR5Wol0FUVZ9PGpGTms2HqFetXKPgiUr/PEfSIeJrNzaR/Cr8Qyfu5hVn/fXV/ny3Xwq+ei6Px6O2En7uHf+C+inSVAo9Eyd+o+pszrib3agk/e+oNm/p54eBb8/x2dLBk2rh1rV5zWu37aN7t47Z3GNPTzICM9G6mMgg1ajZZfZx1nxOw22DqYMWbgdho01/UlPbxt+W6JN8YmhuzZcI3ffzrDp+OVCaoKxiom/1L6CQqBQCwTEzyVSxfu41bJDlc3W4yMDGnXsQ5he3U727B9l+nSXZlxaN3OhxPHbiHLMkiQmZ5Nbq6GrKxcDI1UmFsYY+9gSY1aykyQubkxlas4EBuTXGxt58/dwb2SPe7u9lSoYEjnzg3YG3pOxyY09Bw9ejQFoH2H+hw9ehVZljEzM6ZhQy8qGBvp2Muy8peRno0sy6SlZeDgqD8DXxweXk+morMpFZ1MURkZUKuFmuvH4vTswlbeolkvDwyNCqpkBRMV7rVsdI49Tw7cOEtCWvHvTVng61CVO8mR3EuJJkeby+abB2nn0eSp9t29WhB88yAAMjLGKiOMDAypYGCIoYGKuIzEMtN243I8Tm6WqF0tMTRS0aytBycP3tOx8WnghLGJEmuv6uNAQmx6mf3+XxFy/D49WnkiSRK+1e1JTssmJiFDx8bU2BC/OkowpYKRilqetkTFZxT1dWXGrcsJqF0tcHSxwNBIRdPWlTh98L6OTc0G6oIyq2VXZJmd2HePuk2d8+3KmksXHuDmbpvf5rXtWJsD+67q2Di7VqSqtxMGz2n2KTQ8hh5+rso99axIckYuMUmZenYWpkp7lquVycnVIiHpHAfIyNKUma4bl+JwcrPEydUSIyMVLdp6cuLAXR2bOg0L7pW3jwPxMco9dalkjYu70rbaOphhXdGEpET9/1NpCNl3iaCuDZRyq+tBckoGMbG67VlMbDKpaZn41vVQMlW7NiBkrxI0/n31UQa9HUiFCop+O9vSZYgC3LuWiJ2LObbOZhgaGVDP35nLR2J0bEzMCu5XdqaGop6y8P0PqRugn1FREkJCzxHUo6lSTr5VSE5OJyYmSccmJiaJ1NRMfH2rKOXUoykhIeEAeHk541lFrfe9pqYV8gfFWdk5+dlWJdZ55A492norOmupSU7NIiZef5LEt5YaRztzveM37z7Cz1cZDDf1dSHkyJ3iawg5R1APv7yy8iQ5OeMvysozr6z8CNkTXnB9UDMAgoKasSfvOMCK3/bSoX197GwLAoM3bjwkV6OleXMli9Lc3ART0wp/rfEfup9mZsY0algV4yf8puIQsv8yQZ19FW113ElOySQmLkVXW1wKqWlZ+NZxV7R19iVk3yUAJEkiLS0LgJTUTBwdlGCona0FdX3cMDQsma904fw93CrZ4+Zuh5GRIR061WNfqO7k0b7QS3Tt0QiANu3rcOLYDWRZ5ujha1TzdsY7L8PJxsYclUrRUbeeBw4OZZNB8piy6ON/WBXOe0E+VKhQssDsM+k8EkGPNlUVnTUdSU7NJiZet083NTHEr55Lgc6qdkTFlf3E59WL0bi4W+PsZo2RkYqA9t4c3n9Lx8bJxQrPag56gZ6IW/FoNFoa+ikZcqZmFTAxKXkdKMzNy/GoXS1xdFF8Sb82lTn1hF+k60vakxDzfHxJwX8LEQwSPJWY6GTU6oJgiKPaSi9wExudjGOejaGhCgsLY5IS02nTrjYmZhXo0mYq3dtPp1//Flhbm+lc+/DBI65dicSnTvGXe0THJOHsVDH/s1pdkejoJxye6CScnCvma7O0NCUx8ekdjZGRijFjXyWoxyRa+Y/i5o0oevcu2RK2x6QkZGJlX7Akw9LOmJSELB2bqJvJJMdlUrWR/ZOX/2dxMrcjMjU+/3NkWjxO5rZF2rpaOOBuqebww/MAnI65xpHIC5zo9zMn3viZsPtnuZH4oMy0PYpNx86xYLBh52DOo9inB1P2bblBvaYFA7ecbA0j393KmEHbORF296nXlYTohHSc7QvqmZOdGdEJT3cektOy2XvyAc3qFDj/u4/cpfvnW/l0WhiRZeSYPYpLx9axQJetgxmP4p5eZvu33aJuU2e948dCI/Br41EmmooiNiYZtVOB8+7gaEVsdPkERB8TnZiJk21BG+JkY0LMo6wibd/7/gQthoVgbmJIh4YF2VNzNl4j8Ou9bD7+kE+7Vyvy2uKSEJuOvbqgHtg6mBEf+/TnJWTLNRr4ueodv34pltwcLU6uZTtoio5JwsnJJv+zk9qG6CcGxdExSTgV6uMK29yJiOXk6dv0eeNH3nj3J85d0A34loTk+EysC/UHVvYmJMXrB8GObI5g+jv72LH0Kt0+qKV3/lxYJPUC9OtHSYiOTsSpUF/q5FSR6JhEXZuYRJzUNgU26opER+vaFEV4+G26dJ1A9x4TGT/2tRJnBQFEx6XhXKjddbI3Jzru2QdG1T3t2H1QWWa3++Bt0tJzeJRcvABkdHRivk8B4ORko1cO+uVZYBMfn4xj3gSTg4MV8fHJedc8Ys/us7z2mr/Od925E4OVpRmDP1lAUM+JTJ22Do1G+/ca/6H7WVqiY1Nwcipc36yIfsKnjI5JxkltVcjGmuhYJWA08ovOTPt+BwFdpjH1+x0MHdyuTHTFxiTp6HJUWxPzpK9byEbxdU1ITEwnIiIOSZL4aNASXu8zh1+X7isTTU+jtH38xZsJRMan06qRfltcpjrj03F2KFRfHcyILiJ4m68zNYu9x+7RzLdsgtyFiYtJxUFdEGR1cLQgPubZMj3v303EwtKY8V9u4cPXV7Ho+wN/WweflYTYdOz0/KKn38t9W29Qz0/Xlxz93ja+eX8HJ8NK3z/9v6CSyv/v34YIBgn+ES5euI/KQGLrnuFs2P4Fq5Yd4sH9hPzz6elZfD30dz7/qjMWFmWzf0VpycnR8OcfB1i7fjj7wibiXd2VxYt2/aO/KWtl9iy9Tpu3y2aA9l+km1cLtt0+glZWOmgPKyeq2rjht2ogTVcO5CWXOjR2qvk33/LPcGDnLW5diafb6wX7yvy4theTfu7C4LEtWP7DSaIfpPzFN/xz5Gq0fDHrIG92ro67k+IoBTZ2I2RhEJtmd+Gles58/cOR567r0K7b3LmaQOe+uvcsMT6D+7eSqN2kbAbB/48s+awxYdNak52r5eiVgmDqkCBv9k4JpFsTF1buLdsA5LOwf8dNbl6Jp0e/OjrHH8Wl88O3YQwe1eK5ZVo9KxqNlqTkdFavGMxXQ7ow5KvflKzX50Czbh58ubQVHd+uTugfN3XO3b2SiJGxCqfKlk+5+sWhXr0qbN0yhrWrv2Lh4p1kZeWUm5avBvlx4txDen6wlhPnIlHbm5fr3hKSJOVnS02ctIZhw3rqLQHLzdVw8tR1hn/Vm7Vrvub+vTjWb3j+bfKLwu9rjzNiaGf2b/2KEUM7M2rChr+/6B9Gk6vl7JnbTJz6Gj8v/4i9IRc4dvR6ecsC9Pt4rVZmyq+nGD6gQXlL0yFXo+WLKft4s0ct3J3LdlKgtGhytZw/85BBn7Vk7vK+RN1PYtfmS89dx8Gdt7h1JYGurxVMDny/piffLenM4LHNWfFj+fmSgn8/Ys8gwVNxVFvpZNvERCfj4KjbUDuorYiJTkLtZE1urobU1CysbczYue0cfs2rYWikwtbOgrr1K3H54gNc3WzJzdHw9dDf6dilHoFtS7b5qtrRmsioR/mfo6Mf6WQxKfqtiYp8hJNTRXJzNaSkZGBjo58+/pgrV5T0zEqVlE0pO3ZswJLFpQsGWdqakBxXMPuYEp+FpW3BvhhZGRpi76axcrSyTjk1MZs1E8PpM6oezlVfrE7xeRKVFo+zRcF6bmdzO6LSEoq07e7ZnDGHFud/7li5KWdirpGeq5T73nunaeBYnRNRJdtP4EkqOpgRH1MwwxUfm0ZFB1M9u/MnItm4/DzfzG2PUaF0bFsHZSZI7WpJrfpq7lxLQO1a8oHdyu1XWbNbGTDWqWpLZKGZpaj49PzNoJ/km5+O4eFsRf9uNQr+b4X2bOnT1osZK86UWFdhKtqb6aQ3J8SmU9Fev8wunoxi84pLjPyhjU6ZARzfe5cGLUu+FOBZcHC0IjqqYEY4NiYZB/Xzr4cr90awNm/pYe3K1kQlFLQhUYmZOFZ8+t46xkYqWtdTExoeTfNautmGXZu68P6PJ/mkDLKDbB3MiIsuqAcJsenYOei3r+EnHrJuWTgT5nXSuafpadlMHLab1wc1xLt28faMexor/zjM6vXHAKjj405UVGL+uajoRNRPLPtVO1oTVaiPK2yjVlvTrk1tJEmibp1KGBhIPHqUhm0plotZ2ZmQVKg/SI7LxNru6ZMhdQOc2ThPd7nKubBI6rUq3ez5ypX7Wb32EAB1ansQVagvjYp6hNrRRsde7WhDVKHMkajoR6jVujZ/hZeXM2Zmxly7/jB/Q+Jn0hl8gTXbrig6qzsQWajdjYpLQ21fdNtWFGp7c34c1wGAtIwcdh28jZXF3+9RtXLlPlavUZYg16njQVRk4bJK1CsHtdrmifIssLGzsyImJglHR2tiYpKwzVsSduFCBEOHLgHgUWIa+8MuYmiowsmpIjVruOPurvgkbdrWIzz8Nk/yvO9ncVi5+iirN55UtNVyJSqqcH1LRv2ET6l2tCKqUDZmVHQSagelnDZsOcOoYcoeKZ3a1mb0dxvLRKODo7WOrpjoJByf9HXzbNRONnm+biY2Nmao1dY0aOhJxYpK29eiZQ2uXHpAU7+ym+Arqz4+LSOH63eTeGvMHgDiEjP4aPJ+5o8IKJNNpFduusSaHcqy6jre9kQWyhSNik1HXcTyTYBvvj+Ih4sV/XuWfi+7orB3tCA2uiBQEhuTip3js7XjDmoLvKo74Oym9AsvtfLi8oWoMtFl62CWv3QaHvtF+vfywslIgldcYPSPRfuSji6W1PQtvS/5/4LYQLr4iMwgwVOp6ePKvYh4Ht5PICcnl907zuPfqoaOTctWNdi6SRkwhu6+SKMmylpmJ2drTh5X1uRmpGdz4dw9PKo4IMsy343dQOUqDrz+VvMSa6tdx4O7EbHcvx9HdnYu27adJjBQ9002gYF1CA5WBga7dp6hqZ/3X+5boFZbc/NGFAkJSqdx+PAVPL1Kt1GtSzVLHkWmkxidgSZHy6WD0VRrUjBAMzE35PMV/ny8uDkfL26Oq7fVfz4QBBAee4MqVs64WzpiZGBIN68W7L57Qs/Oy9oVK2MLTsUU7OvyIDWOps61UEkGGEoq/Jx9uJF4X+/akuJVw46oeynEPEwhN0fDkT0RNGyuu+Hh7WsJLJl+lGFTArGuWBD0SE3OIidb2bclOTGTa+djdTZRLgn9OlVn46zObJzVmTZN3Anep+zbdfZqHJZmFXC01Q+6zFl1lpT0HEa+01DneOG9B0JPPMCrjJbuVKlhS/T9FGIjU8nN0XAs9C71m+suD424lsAvM08wZLI/VhX1B8hHQyJo9g8uEQOo6ePC/bvxPLz/iJycXPbsuECLgOf7Fj+AfoEebBjTgg1jWtDGV03w0QfKPb31CEtTQxytdcsnLbNgH6FcjZb952PwzHvjyJ1CAZvQs9F4Oj09IF4cqta0J/J+MtEPU8jJ0XBwzy0atdCtB7euxrNw6mG+ntYG60LPYU6Ohmlfh9KqU1Wata5cJnoA+vV9ieDVnxO8+nPaBvqwcctppdzORWBpYZq/x8hjHB2ssDA34ey5CGRZZuOW07Rppcy8tg304dgJZQB2OyKWnBxN/oCvpLh5WxP3MI2EqHRyc7SEh0VS0083EBb3oOB+XT0Rg71LweBAq5U5fyCSev6ly47r1y+A4A0jCd4wkrZt6rIx+JhSTmdvY2lpmr+U6TGOjtZYWJhw9uxtpZyCj9Gmdd2nfLvCvftx5OYqbd2DB/HcuhWNq2vxBpz9etRm48KX2bjwZdo0r0zwnmuKzkvRWJpXKHJvoKfxKCkDrVbJ7Fr0+xl6d3i2et2vXyuCN44meONo2rbxZWPw0byyuoWlpclflNWtvLI6Sps2Slm1bl2XjRuVzJ6NG4/kHw8NmUho6CRCQyfRoX19xn7Tl7ZtfalTpzLJKen5Psmxo1ep6qV/75/H/Swp/V7xI3jVYIJXDaZtq1ps3HZW0Xb+HpYWxjja6w5eHe0tsTA35uz5e4q2bWdpE6BkiTo6WHH8lBIMO3riFpXdy2YDZJ/abty7G8eDPF935/ZwAgJ1l2cGBNZiS7AS1ArZdZ7GTZX9cJo19+bG9UgyMpQ9Mk+dvKWz8XRZUFZ9vKV5BY4ue5nQhUGELgyinrd9mQWCAPp1r8XG+T3ZOL8nbZp5EByi7Kt09nIMluZGONrpBzrm/HqSlLQcRn7gVyYaiqJ6LTUP7iUS+SCJnBwN+3ddo5n/s7290ruWmrSULBIfKUGbsyfv4VGl6C0LiotnDTui7qcQ81Dxi46G3KFhC12/6M61BH6efowvJrfCupBflJZS4EumJGZy7ULpfUnBfxeRGSR4KoaGKoaN7MqnHy5Dq9HSLaghnlXVLJy3h5q1XPEPrEn3ng0ZN3ItvbvMwsralO+mKa8bfblvUyaMWU/fnj8gyzJdezSgmrcTZ0/fYfuWs1StpuaNPnMB+PDTdjRvWbwBl6GhilGjX2HQe/PQamV69vKjajVnfvxhCz61K9G6dV16v/wSXw9fTscO47C2NmfGzLfzr2/X5htS0zLJycklNOQci5Z8TNWqznz0cSf6vzkHQ0MVzi62TJr0RqnK0EBlQPuB1flj/Bm0GqjX1hmHShbsX3UT56pWeDfRfzVuYeYNPERWRi6aXJlrx2LpO85X701k/xSr3vmWVt4NsLew4d6kTYzdspilhzc/l9/WyFq+ObyE5Z2+QSUZsPpqCNcf3WNow76ci73JnrzAUDevFmzO2zj6MdtuH+Ellzrs6j0HGZn9988QcvdkmWlTGRowYGgTJg8NUV4H2qUq7p42rFlylio17GjUwp1V806RmZHL92PCgIJXyD+MSGLJ9GNIkoQsy3R/w0fnzRGlJaChC2GnH9D+o02YGKuYNLhZ/rmgodvYOKszUXHpLFh7EU9XK3oN2w4UvEJ+xbYr7D3xAJWBhLWlMZM/afa0nyoWKkMD3hzSiOnD9qHVyvh39sStijXrfz5H5Rq2NGjuxh8LzpKVkcO8scr9tHU05/PJyh4asZGpxMekU923bDJInoahoYqhIzrz+Ycr0GhlugbVx7OqI4vnhVLDx4WWrWpw6cIDRnz+BynJmRzcf42f5+9j5YaP/zFNAbUdCDsfS4fR+5VXy/cvGLT1nHCQDWNakJGt4eN5p8nO1aKVZZp62/GqvxKYmbXhKrej0zCQJFxsTcrkTWKg3NP3hvox4fNdaDUyrbtWo5JnRX5ffJqqNexp3LISy+edIDMjh5mj9wFgrzZnxLS2HA65w6WzUaQkZ7F32w0ABo9qQRXvsnu7TUDLGuw/eIV23aZialKBSeP75J/r8cpsgld/DsDYkUH5r5b3b14D/xbKhEfvoMaMHLuGrr1nYmSkYsqEV0u9CbJKZUD3D2uxdPQJZK1Mo/ZuqD0s2b3iGq7VrKnlp+bI5ghunI1HZShhamFEny8K7vedCwlY25tg6/zsGTF/R0BAbfaHXaRdh7FKOU16M/9cj56TCN4wEoCx3/TNfxW5f0sf/P2VrN7du88yYeJqEhJSef+D+dSs4cbPSz7h1KmbLF68C0MjFQaSxLhvXsW2Ysn7roAmlQg7dpf2/f/AxNiQScNa5Z8Len8tGxe+DMD0xUfZEnqDjKxcAl77jZc71eCTtxpxLDyS2T8fA0micR1nvvmk+K8fV8rqAu3aj8krq/4FZRX0HcEbR+eV1euMGLmMzMzsvLJS6tyggR0Y8vli1q47hIuL8mr5v0KlMmD4V73pP2AOyDI+PpXo0+evdf9T9xOgdZvReX6Thj0h4Sxd8onOG1z/tvyae7P/0DXaBc1StI3tVaDt9bkErxqsaPu6OyPGrVO0veSNf3PlbYgTRvdg0oxt5Gq0GFcw5NtRPQCIjUuh91s/kZqWhYEksez3w2xb/ekzb0FgaKhi+MgefPz+ErQaLd17NsarqhM/zd1JLR83AgJ9COrVmDEj/qB7p6lYW5sxefrrAFhZm9HvLX/e7PsjkgTNW9agZV7was7MrezYdpbMzBw6tplIUK/GfPBx+2curyLLsJR9/PMioIk7YSfu0/6dNUp9HdqyQOdHG9g4vydRsWks+CMcT3dreg3eqOjsVos+ncp2AkZlaMDgL1sx8pONaDUyHbrXorKXHcsWHMG7pppmAZ5cvRjF+C+3kpKcydEDt1mx6CiLV7+JSmXAwM9aMPzD9cgyVKvpSKcyymBSGRow4PPGTP1C8SUDunjhVsWGtUvCqVLDloYt3Fk1/7TiS35zACh4hfyDO8n8POMYBhJoZejer2x9ScF/C+l5rYEXvNgkZq15IR8EG+M+5Gp3l7eMIjE0aMeyyx+Vtww9+tecD4D04T8301JS5J+O4rG4198blgMRA9dzOva78pZRJA0cRiNf/La8ZRSJ5PMNR6PGlbeMIvFzGkd85u/lLUMPO5PXANDuG1K+QorAoNUcLsRPKW8ZRVLb7mvICC5vGUVj2oP1N4eUt4oi6eU1B7Qh5S1DH4M2AMh3Z5WzEH2kSkNB3lveMopGCnwx7yco9zRlTXmrKBrLPqTlvJjth7lRjxeyj5d8vgFAvj2tnJXoI1X5ioiU+eUto0g8LD/iZMyE8pZRJI0cx/xfr6Nqv/6Nch/P7ur127+qjMUyMYFAIBAIBAKBQCAQCASC/xAiGCQQCAQCgUAgEAgEAoFA8B9C7BkkEAgEAoFAIBAIBAKB4F+LqpR7+/0XEZlBAoFAIBAIBAKBQCAQCAT/IURmkEAgEAgEAoFAIBAIBIJ/LSqR5lJsRJEJBAKBQCAQCAQCgUAgEPyHEMEggUAgEAgEAoFAIBAIBIL/EGKZmEAgEAgEAoFAIBAIBIJ/LWID6eIjMoMEAoFAIBAIBAKBQCAQCP5DSLIsl7cGwYuBeBAEAoFAIBAIBAKB4P+T/+vUmV5b+pf7eHZ912X/qjIWmUECgUAgEAgEAoFAIBAIBP8hxJ5BAgBytbvLW0KRGBq0IyVnQ3nLKBJLo55YjW9X3jL0SB6r3EuPxb3KWYk+EQPXI33oV94yikT+6SjXEmeUt4wi8bYZhhzxYmqTPIax/uaQ8pZRJL285hCX+Vt5y9DD3uQNALShn5azEn0MWv/AvdQF5S2jSNwtPoDMzeUto2hMurH00kflraJI3qk1H+S95S1DHykQAPnenPLVUQSS+xDICC5vGUVj2gOytpa3iqIx7vJCl1ty9rryVlEkVhV6I58YWd4y9JAaTwJAvvxdOSvRR6o5moiU+eUto0g8LD9i0+2h5S2jSLpXmVXeEgQvGCIYJBAIBAKBQCAQCAQCgeBfi9hAuviIZWICgUAgEAgEAoFAIBAIBP8hRGaQQCAQCAQCgUAgEAgEgn8tKpHmUmxEkQkEAoFAIBAIBAKBQCAQ/IcQwSCBQCAQCAQCgUAgEAgEgv8QYpmYQCAQCAQCgUAgEAgEgn8tYgPp4iMygwQCgUAgEAgEAoFAIBAI/kOIzCCBQCAQCAQCgUAgEAgE/1pUBiIzqLiIYJDgLzlw4BJTJq1Fo9XS++WXGDiwvc757OwcRgxfwcVLd7GxMWfmrHdwdbUj8VEqQ4b8zIULEQQF+TF6zCsApKVl8uYbs/Ovj45KpGu3xowY+XKxtR0+eJUZUzaj1cgE9W7MgPdaPaEtl7EjVnP50gOsbcyYPOM1XFxtAbh+NZJJ324gLTUTyUBi+R+DMTY2Iicnl2kTN3HqxC0kA4mPPm1Pm3Z1iq2tMG29GjG140eoDAxYdno7sw/9qXPezcqBBUFfYW1igcrAgHF7fmbXjeMYGRjyfdch1HfxRitrGb5jPgcjzpVKy5MEuNVnbLN3UEkG/HF1Dz+Fb9A5P8bvbZq51AbA1NAYOxNr6i5/E4ARTd6kdaWGGGDAgQfhjDvyc5lq+yt+fnMUXes0JyblEXUm9Htuv1sUp47cY/GsI2i1Mu26V6dPf1+d8xtXnWNX8FVUhgZY2Zjw2Wh/HJ0tn4s2WZaZOP8IYSfuYWJsyORhAfhUs9exycjMZch3e7j7MBmVSiLQz4Mv3m1S5lqunoxly8LLaLUyjTu40eoVL53zx7be5ciWCAxUEhVMDOn5qQ/qSpac2fuAA+tu59tF3U5h8A/NcfGyKjNtRw/dYM7UnWi1Mt161ufNd5vrnD97KoLvp+3i5vVoxk/tRWC7Wvnnhn64iovn71PXtxLT5/YtM02PkWWZSauvEHYxFpMKKia9VQefSvr/94E/niQ2KYtcrUyjqhUZ07eWjlP0y547TFt3lcPTA6loUaFMtB0/fIf5M/ah1WjpFFSb197WfW7Onb7P/Bn7uXUjltGTOuPf1jv/XPvGc6hSVXkWHZ0smTC7R6m0yLLMxKnB7D94GROTCkyZ8Co+Nd307C5cus+IMX+QmZVDQIuajBreA6lQWvnSZfuYOmsLR/aNx7aiOUt+3cvmbWcA0ORquHk7hiP7xmNjbVYinbdOxxPy8zW0Wpl6bV3w6125SLurR2LYOO08b01vjHNVKx5eS2LnT1eU/yvQ4tUqePs5lkiDLMtMnLia/WEXlLKa3B8fn0p6dhcuRDBixDKlrPxrM2rUK0iSRGJiGp8PXcyDB/G4utoxZ/ZArK3NWfLzLjZvPg6ARqPl5s1IjhyegY2Nef6x3i9PRu1ow8KFHxdf87xDhB2PUNqyr1rjU81Bz2720mME775KckoWp7cMzD/+IDqFUTP2kpCYgbWlCdNHtMHJwaJYGvT0TNvE/oNXMDExYsq3rzz9eftmdd7zVoNRX3VHkiR+/GkXq9cfx7aiUjZDP+lIQMuaZOfkMnbCei5cuo9kIDHqy+40beyl971/q23qBvYfeFwXXsOnVlHa7jFi9O+KtpY1GTW8p35dmLmJI/u/xbaiUlbHTtxg0rSN5OZqqGhjzm+/DP6/KLPDB68xc+oWtBotPXo1ZsB7ATrns7NzGTtyDVfy/MlJ01/DxbUi27ecZcWvB/LtblyLYsXqj3Fzt2Ng/0X5x2Oik+jU1Zcvhnctlq4nkWWZiSvCCTsbhYmxismDGuFTpaKOTUZWLkN+OMbdmFRUBhKB9Z35oq/ixz6MS+frhSdJSc9Go5X54tXaBPg6l0qTjrYlJwg79VDR9ulL+HjZ6WubFsbdqBRFW2M3vnirgY7NzsMRfDYtjDUzOlOnqu71JeXE4Tv8NGM/Wq1MxyAf+g5orHP+3OkHLJi5n1s34hg5sRP+bavln4uJSmbWhBBio1OQJInvvu+Bk0vZ+R9XTsaw6adLaLUyTTq60/rVqjrnj2yN4PDmCCQDCWMTFS9/Vge1h+JDPryVzLofzpOVnotkIPHpD80xqqAqM22C/wYiGPQXSJKkAc4XOhQky/KdUnxfd6CWLMtTJEkaB6TKsjzjL+y7AhNQlvMZAd/LsrxQkqQPgHRZlpeXVMuzoNFomThhNYt/HoxabcOrr0wnMLAOVasWdBzr1h7BytqUHTvHsW3rSWbNCGbm7HeoYGzEJ5925cb1h1y/Hplvb25uwvoNI/I/9+k9lXbtfEukbep3wcxb/C5qJ2veenUu/oE18fRS59sErz+BpZUpG7d/yc5t4fw4aweTZ75Obq6GMV//ybeTX8G7hguJiWkYGiqN59KFe6loa876rcPQarUkJ2WUoOQKMJAMmNn5E3qsGM6D5Dj2DZzLtqtHuBp3N9/mS/9+bLi0n59PbqG6fSXW9ptIne/fZEDDzgA0WzAIezMb1vWbSKvFg5GRS6WpsLYJzQfSb9t4otLi2RQ0jT0RJ7ieeD/fZsLRX/L/PcCnMz52VQBo6FidRuqadFg3FIB13Sbi5+zD0ciLZaLt7/j1yFbm7lvL8gHfPJffexoajZYF0w8x4cfO2DmaM3TARpq29KCSZ4Fz5ultz6xltTAxMWTbukv8Mvc4wye2eS76wk7cI+JBEjt/eYXwKzGM/+Egq38M0rN7++W6+Pm6kJ2j4e3hWwk7fg//Ju5lpkOrkdk0/yLvTmyClb0J84YcpqafI+pKBUGxeoHONO2iDEgvHY1m6+IrvDOhMfUDXakf6AoogaAVE06VaSBIo9Eyc9IO5izsh6PaivdeX0KLVt5U8SoYaKqdrBk1oTu/Lzuid/3rA5qRmZFD8NrTZaapMGEX44iISWfH+JaE307i298v8edwPz272e/5YmFqiCzLfLboLDtORdGlsdJWRyZkcOhSHM62JmWmS6PR8uOUUKbO74WD2pKP31zFSwFeeHgWOO+OTpZ8Nb49q1ec0ru+grEhC39/o8z0hB28wp27seza/DXh5+8y7rt1rFn5mZ7duO/WMWFsH+rVqcTAj5cQdugKAS1qAhAZlcihI9dwcbbJt39vQCDvDQgEIHTfRX79LazEgSCtRmb3oqu8Oq4+lnbGLPvqBFWb2GPvrhuUyMrI5eSWezh7FzznDh4W9J/RGAOVAakJWfzy+TGqNrbHoATv0Q0Lu8CdiBh27fyW8PDbjBu/ijWrv9azGzd+FRMmvEG9elUYOGguYQcuEuBfm0WLd9DMrwaDBnVk0aIdLFq8ky+H9eK9d9vz3rvKhFFo6Dl+XRaSHwgCWL48FC9PJ1JTM4uv+fhdIh4ksnPZ64Rfjmb892Gsnttbzy7Qz4N+PWrTsf8qnePTFh6mRztveravwdEz95n18zGmfV3ydlh53uLYtekr5XmbuIE1v32iZzdu4gYmfNNbed4GLyXs0FUCWtQAYMAbLXm3v27QYc06JZi2ee1Q4hNSGfjxz6xd+QkGBs9+n8MOXuZORBy7towk/FwE475by5pVQ/S1fbeWCWNfoV5dDwZ+tJiwg1cIaPm4Ljzi0JGruDgX9GXJyRmMn7iOJT8NwsW5IvHxKc+sSdH1YpaZRqNl2sRNzF30DmonK/r3nY9/YI0n/MmTWFmZsmHbMHZtD+fH2TuYPOM1OnX1pVNXX0AJBA377Deq13ABYNXagv/bm6/MJbCNz7MX1lMIC48iIiqVnTM7EH4zgfG/nmH1+NZ6dm93qYZfLUeyc7W8PSmMsPAo/Os58VPwZTo1deW1tl7ceJDMoOmHCJ1TNsGgsFMPiYhMYedPPQi/Fsf4BcdYPb2zvragWvjVcVL8jW/2EHbqAf4NlT4+NSOHFVuuUM/bXu+6kqLRaJk7dR9T5vXEXm3BJ2/9QTN/T72+ati4dqxdod+PT/tmF6+905iGfh5kpGcjlWHmiVYjs2HeRQZNaoq1vQk/fHoQHz91frAHoH4rF5p18QDg4pFoNi26zMCJTdBotPw+7SyvfeWLi6cVacnZqMR71QUlQDw1f02GLMu+hf7ulObLZFneJMvylGexlSTJCFgEdJNluR5QH9iX9z0L/ulAEMD5c3dwr2SPu7s9FSoY0rlzA/aG6mamhIaeo0ePpgC071Cfo0evIssyZmbGNGzoRQVjo6d+/53b0SQkpNCwUfFmcAAunr+HeyU73NztMDIypH2neuwPvaRjsz/0El17KDMObdrX5vixG8iyzNHD16nm7YR3XodtY2Oe34Bu2nCSt99THH8DAwNsKppTGhq5VudWwkPuJEaRo81l3cV9dKnxko6NjIylsfI71ibmRKXEA1DDwYOwO2cBiEtPJCkzjQYu3pQVvg5VuZMcyb2UaHK0uWy+eZB2Hk/PCOnu1YLgmwfzNRurjDAyMKSCgSGGBiriMhLLTNvfceDGWRLSkp/b7z2N65dicXazwsnVCiMjFf7tvDgWFqFjU7eRCyYmSty9em1H4mPSnpu+kMMR9GhXDUmS8K2pJjktm5j4dB0bUxND/HyVulDBSEWtqvZExZWtxnvXErFzMcfW2QxDIwPq+Ttz+UiMjo2JWUFbkZ2poSh3K3z/Q+oGuJSptssXHuLmXhFXt4oYGalo09GHA/uu6tg4u9pQ1VtdpBPYqGkVzMzLJtOmKELDY+jh56LcQ08bktNziEnK0rOzMFWesVytTI5GpvAeilPWXmVYL+8iy7SkXL0YhYu7DS5uNhgZqWjVvjqH9t3UsXFyscazmgMGz2FDx5C9Fwnq1kgpp7oeJKdkEhOr20bExCaTmpaJb10PJEkiqFsjQkILAtiTpwfz5edddbIjCrN1x1m6dqpfYo2R15OxcTbFxskUlZEBNVuouX48Ts/uwKpb+PX0wNCowEUzMlblB35yc7SU5maGhJwjqIefUla+niQnZxATk6RjExOTRGpqJr6+nkpZ9fAjZE94wfVBzQAICmrGnrzjhdm69QRduzTK/xwV9Yh9+8/zcp/merbPpPnwHXq0q65oruVEcmoWMfH67ZRvLScc7fT77ZsRj/DzVbJQmvq6EnL4tp5NsfTsu0RQ1waFnreMv3/eujYgZO9fT5jcuBVN0yaKT2Rna4GlpSkXLt7/y2v0tO29UFAX6lV+urbULHzrVS6oC3sL5j4nT3tcFwqu2bztNO3a1MkPENnZFS/D9UUts4vn7+f5k7YYGRnSrlNd9u+9rGMTtvcyXbor/mTrdrU5cewmsqw7MbdzezjtO9XV+/6IO3EkJKRRv2HlZ9b0NEJORdKjhVI2vlXtSE7LIeaR7qSlqbEhfrWUrMEKhgbUqmxDVIJiIyGRmpELQEp6Do4Vy26CIOT4PXq0UtoL3+oOiraEJ/wNY0P86jgp2oxU1PKyJaqQT/LDyrO818uHCkZll91y9WI0Lu7WOLtZY2SkIqC9N4f339KxcXKxwrOag14fH3ErHo1GS0M/JRhjalYBE5Onj2uKy92ridg7m2GX5xv5Brhw8Ui0jo2JeWHfKDe/Tl47FYdzFUtcPJVJA3OrChioxBIplSSV+9+/DREMKgaSJFlIkhQiSdJpSZLOS5LUI+94ZUmSrkiS9KskSdckSVopSVJbSZIOSZJ0XZKkJnl2AyRJmvvEd3pJknS60OdqeZ8tUTK34gFkWc6SZflqns04SZKGSZLkIknS2UJ/GkmSPCRJcpAkaZ0kSSfy/krkfUXHJOHsVDArpFZXJDr6CYcxOgmnPMfA0FCFpaUpiYnPNpDctu00HTs1eKrj/VfExCSjdrLO/+yotiYmJrkIG5t8bRYWJiQlpnM3Ig4kicGDfqZfnx9YtnQ/ACnJSmf509xd9OvzA8OHriQ+rngzX0/ibGnP/eTY/M8Pk+NwsdSd8Zi8bwWv1mnD5c9Xseb1iXy5fR4A56Nu0sm7GSrJAA8bJ3xdquFqrZ8WX1KczO2ITI3P/xyZFo+TuW2Rtq4WDrhbqjn8UHEWT8dc40jkBU70+5kTb/xM2P2z3Eh8UGba/i3Ex6Rhry6Y1bdzNCc+9unP/+5NV2nYTD8t/p8iOj4N50JLIZzszYkuYgD1mOTULPYevUuz+mUbcEmOz8TavsDptLI3ISlePzvgyOYIpr+zjx1Lr9Ltg1p658+FRVIvoGxmMR8TG5OMo1NBBoajoxWx0aWr92VJdGIWToUcdqeKJsQkFp1Z8d4PJ2nx5V7MjVV0aKA43CHhMahtjKnhVnbZVABxMak4qgsGgw5qC+JjU5/5+uzsXD56YyWD+//Oob03Sq0nOiYJJ7VN/mcntTXRTwQ4/spmz94LODpaU6N60c9+RkY2Bw5doX1b/cHes5KSkIlVoXpgaWdMarxuYC/qZjIpcZl4NdKfGX94LYklnx5l6ZBjdPigRomyggCioxPz+20AJycboqMT9W2ciraJj0/G0VHpfx0crIiP1+17MzKyOXDwIu3bFyz/mDRpNV8O61XiwGB03BNtmYMF0cUIWlf3tGP3QWUAuPvgbdLSc3iUVPwMpXw9MUk45fkXAE5qm6c8b9ZPtVn5x2G69ZnFiLGrSUpWBsQ1vJ0J3XeJ3FwN9x4kcPHSfSKf8Lv+XltyCbUp97GgLrjqXHMnIobk5AzefGcevV6dxcZNJ4qp68Uss9iYJB1/Uq22Jjb6SX+ywKawP1mY3TvOFxkM2rU9nHYd65TI132S6EcZONuZ5n92sjUl+tHTn+PktGz2nomkmY/iOw7uVZNNh+4S8Mk23p9+iNFv+ZZaU762hHSc7QsCsU52ZkQnPD27Pjk1m70n7tOsrtJXXbwZT2RcOq0ala2PFBeTikPhvsrRgviYZ+ur7t9NxMLSmPFfbuHD11ex6PsDaDTaMtOWHJ+JjUPB/bR+im90aNMdJr+9l60/X6HHh0qGWdyDNCRJYvHIY8z5+AB719zUu04geBZEMOivMS0UaNkAZAI9ZVluAAQCM6WC1r0qMBOokff3OtACGAaMfNoPyLJ8E0iSJMk379DbwC+yLCcAm4AISZJ+lySpnyRJBk9c+/Bx1hKwGFgny3IE8D0wW5blxkBvYElRvy1J0iBJkk5KknRy8aKtxSya0rN9+yk6F5o5fF5ocrWEn7nDd1P78vPyD9gXcpHjR2+g0WiJjk6irq8HK9d8Sp16lZgzY9s/rufl2oGsDN9Fzdmv02fVKBb1HI6ExIozO3iYEsv+QfOZ0uFDjt+7hEZbdp1Qcejm1YJtt4+glZXf97ByoqqNG36rBtJ05UBecqlDY6ea5aLt38Le7de5cTmOXm/UK28pRZKr0fLFpFDeDPLB3blsAwfPSrNuHny5tBUd365O6B+6js3dK4kYGatwqvx89lv6N7Lk00aETW1Fdq6Wo1fjycjWsGjHLT7pVvXvL37OrNryHvN/68fIiZ2YP3M/D+8llpuWjIxsFi4J4bOPOjzVZu/+SzTwrVziJWLPgqyVCf3lOq3frlbkeRdva977wY+3pjXm6LoIcrM1/5iWZ0WSJL1B7t6952hQ3yt/idjeveewtbOkdm2P8pAIwFfvv8SJcw/p+f4aTpx7iNreHFU5zqK/9kozdm8ZTvCfQ3C0t2LKzC0A9A5qjJPamt6v/8Ck6ZuoX8/juW6ImpGRzcLFe/js44565zQaLRcv3WPh3PdYsmAQ8xft5vadmCK+5Z/hRS0zgAvn7mFiYkTVak5653bvOEeHTs+/38/VaPli3nHe7FAVd0clkLr1yD16+nuw/8fOLPyyOcN/OoFWWzZbDxRb26wDvNmlBu5Olmi1MlOWnmL42w2fu5a/QpOr5fyZhwz6rCVzl/cl6n4SuzZf+vsLy5jm3Ssz4pdAurxbg5DfryvaNFpuX0zg9eH1+WjmS1w4FMX1M/qZpgLB3yH2DPprMvICLUD+0q1JkiT5A1rAFXi8qPi2LMvn8+wuAiGyLMuSJJ0HKv/N7ywB3pYkaSjwKtAEQJbl9yRJqgO0RQkqtQMGPHlxXubPQJTgE3n2tQo5aFaSJFnIsqwTCpdleRHKUjRytbv1egO1ozWRUY/yP0dHP0JdaMYGlIycqMhHODlVJDdXQ0pKhs4eAU/jypX7aHI1RW5a+Sw4OloRHVUw8xMTnYSjo1URNomonazJzdWQmpqJtY0Zjmpr6jeskr8ErHnL6ly59IDGTb0wMTWidVsl6t62fR02rS/ezNeTRKbE4WZVkM3jYmXPwxTdxvqt+h3ptVKJFx6/fxljwwrYmVkTl57IiJ0L8u12vzOHG/HFSxf/K6LS4nG2KFgz7WxuR1RaQpG23T2bM+bQ4vzPHSs35UzMNdJzlRmMvfdO08CxOieiLhd5/f8rdo7mxEUXVKv4mDTsHPSf/7PHH7D617NM/qnrP76538pNF1mzTdlotk51ByILZWtExaWhLmIJBcA3cw7g4WpN/16l2zC9KKzsTEiKK5jtSo7LxNru6enpdQOc2ThPd2nAubBI6rUq24wlAAdHK2KiCmaBY2KSdWYRy4OV++6y9pBS12t7WBFVaOY36lEmjjZPLztjIxWt6zkSGh6Dg5Ux9+MyCPruMKBkGfWedIQ/h/vhYG1cKo32jhbEFMqgio1Oxa4YG/La5w1OXNxsqNfQjRtXY3BxtymWhpV/HGL1+mMA1PFxJ6pQdktUdBJqR93+Su1oXaTN3fvx3H+QQI9XZuUf79V3NmtWfoqDvdKvbN1xli6lWCIGYGlrQnKhepASn4WFXcF9yM7QEHc3jVWjlWThtMRs1k8Kp9fIejhXLejf7N3NMTJREXs3Tef4X7Fy5T5Wr1GW+dap40FUZEHfHhWViLpQxhSAWm1DVFTRNnZ2VsTEJOHoaE1MTBK2trr1Zeu2E3TpUrBB6+nTNwkNPUfY/gtkZeeSmprBsC+XMmNG4F9rDr7Amm3KwKuOt6NuWxabitr+2Zdxq+3N+XGcEuBIy8hh14FbWFkUrw6s/OOw7vMWlVigJzrxKc9bUpE29oWWWPXp1YQPPlX25zM0VDHyy+755/q+NY/KHn+fEbzyj4OsXne0lNqsuHsvTqkLfWbkHU+i16uzWLNqCE5qG2yszTEzM8bMzJhGDT25cu0hVSo/fSPzF7nMHuPgaK3jT0ZHJ+GgftKfVGye9Ccfs2v7OTp01g/4XLsaiUajpaaPq965Z2Xl7pus2assa6zjWZHI+IJsm6iEDNRPWer1zc+n8XCyoH/HguDyuv13WPyVMlSoX82OrBwNj1KysLMu2XKxlduusmaXEpyoU82OyELZelHx6ahtTYu87pv5R/FwtqR/d2USMS0jh+t3E3lr9C4A4hIz+GjiXuaPCiz1JtL2jhY62b6xManYOT5bX+WgtsCrugPObsoz+FIrLy5fiCqVnsJY2ZmQGFtwP5P+xjeqF+DC+h8vAGBjb4pnHVvMrZVl6jUaO/LgRhLV6pfdfkv/RsS2ScVHFFnx6Ac4AA3zgkTRwONaWzjXW1vos5a/D7qtAzoBXYFTsiznr92RZfm8LMuzUQJBerslSpLkDPwMvFIo2GMA+BXa68j1yUDQs1C7jgd3I2K5fz+O7Oxctm07TWCgbgpsYGAdgoOVjn7XzjM09fN+plTYbVtLlxVUq7Yb9+7G8+B+Ajk5uezaHo5/oO6yEv/AWmwJVpzqkF0XaNzUC0mSaNa8GjeuR5GZkU1urobTJ2/j6aVGkiRaBtTk1AkllfzEsRtUKbSBYEk49eAqnnaueNg4YWRgSG+fVmy7qrsJ7f2kGAKqKIMMb/tKmBhWIC49EVNDY8yMlMcr0LMBuVqNzsbTpSU89gZVrJxxt3TEyMCQbl4t2H1XP/jlZe2KlbEFp2IK9lF5kBpHU+daqCQDDCUVfs4+3Egsu0DVv4VqNR14eC+ZqIfJ5ORoCNt9kyb+ugHOm1fjmDflAGOmt8fmKY5RWdKvuw8bF/Rm44LetHmpMsG7ryPLMmcvR2NpXgFHO/3Mhjm/nCAlLZuRHzb7RzS5eVsT9zCNhKh0cnO0hIdFUvOJNyHFPShwIq+eiMHepUCnVitz/kAk9fzLdokYQA0fF+7fTeDh/Ufk5GgI2XGRFgFltzdXSejXqhIbRr3EhlEv0aaemuCjD5V7eCsRS1NDHJ8I5KRl5ubvI5Sr0bL/QhyeTuZ4u1pyaHogIRMDCJkYgNrGmHUjm5U6EARQvZYTD+49IvJBEjk5GvbtuspLAZ7PdG1KcibZ2cq+FUmPMrgY/lBnM89npV/f5gSvHkrw6qG0DfRh4+aTSjmdi8DSwgRHhycGdA5WWJibcPZcBLIss3HzSdoE+lC9mjNH9o0ndPsoQrePwkltzfo/Ps8PBKWkZHDi1E3atCrdBrDO1Sx5FJlOYnQGmhwtlw9GU7VxgeNubG7Ip8v9+XBRcz5c1BwXb6v8QFBidAbavOUJSTEZJDxIw9rx2Qdw/fq1InjjaII3jqZtG182Bh9VyursLSwtTfKXfeWXlaM1FhYmnD17Symr4KO0aaP0/61b12XjRqUf27jxSP5xyCurE9dp06ZgYPzFFz0J2z+F0NBJzJr5Ln5NazBj+jt/r7lHbTYufIWNC1+hTfMqBO9W9iQ8eykKS3PjIvcGehqPkjLyMyAW/X6a3h1rPPO1+Xr6vkTw6s8JXv258rxtOV3oeTP9++dty2natFL8lMJ75ewJvUC1qkpGSUZGNukZ2QAcOnINlaEBVZ/BD+nXtwXBa4YRvGYYbVvXKagL4XeU+1uUNgtjzobfKVQXalPd24Uj+78ldMcYQneMUerCn0NxsLeiTWBtTp25TW6uhoyMbM6du4tXlb/W9iKX2WNq1XblbkRcvj+5e/s5/FvpZjq3bFWDrZsUfzJ09wUaN/HM93W1Wi17dp2nXUf9JWI7t4XTvpRZQf3aebFxUls2TmpLm4YuBB9UyubsjXgszYxwrKjvV8xZc5GUjBxGPpGJ7GxnxpGLSjbXzQfJZOVosbUqeX/Qr3N1Ns7pysY5XWnT1J3gfUp7cfZqLJbmRjjaFuFvrDxDSloOI98tCBhbmlfg6IpXCF3ci9DFvajn7VAmgSCA6rXUPLiXmN9X7d91jWb+z9ZXeddSk5aSReIjZUng2ZP38KhS9HYKJcG9uq5vdHb/Q2r56T67sYV8oyvHY7B3Vdo974YORN1OITtTg0aj5db5eNSVSv6GRMF/F5EZVDysgRhZlnMkSQoEyiTnWZblTEmSdgI/Ae+Csj8R0EiW5X15Zr6Azs60eZlKa4DhsixfK3RqF/AJMD3PzleW5bPF1WVoqGLU6FcY9N48tFqZnr38qFrNmR9/2IJP7Uq0bl2X3i+/xNfDl9Oxwzisrc2ZMfPt/OvbtfmG1LRMcnJyCQ05x6IlH+e/iWznjtP8tPDD4krS0fblyO588v5SNBot3Xs2wquqmgVzd1HTx42AwFr06NWIb0asJqjTdKysTZk0/TUArKzN6PdWS97qOxckieYtq9MiQHEMPx3aiW9G/MnMKVuoaGvO2O/6lFgjgEbW8uW2uWx4YzIqyYAVZ3dyJTaCUa36c/rhNbZfO8LIXQv5sdtQPvbrhQx8uHE6AA7mNmx4YzJaWeZhShyDNkwtlZaitH1zeAnLO32DSjJg9dUQrj+6x9CGfTkXe5M9eYGhbl4t2Jy3cfRjtt0+wksuddjVew4yMvvvnyHk7sky1fdXrHrnW1p5N8DewoZ7kzYxdstilh7e/Nx+/zEqQwM+GPYSYz/djlYr07ZbdTw8bflt4Umq1XSgqb8Hv/x4jMz0XKaM3AOAg5MFY2Y8fTlKWRLQxJ2w4/doP+BPTIwNmTSs4C0sQR+sY+OC3kTFprLg97N4utvQ66P1APTr4UOfTsUfLD0NlcqA7h/WYunoE8hamUbt3VB7WLJ7xTVcq1lTy0/Nkc0R3Dgbj8pQwtTCiD5fFDjWdy4kYG1vgq1z2S/RMTQ04PMRHRn64So0WpmuQfXwrOrI4nn7qOHjTMtW1bl84SEjPl9NSnImh/ZfZ8n8/azcoLRfHw74lbt34klPzyao3RxGjOtG0+bF3xT/aQTUtifsQiwdvjmQ92r52vnnek48zIZRL5GRreHjn06TnatFq4Wm1W15tWXZvQ2uKFSGBnzyVWu+HrwerUamYw8fKnvZ8+tPh/GupealAC+uXIxi3LDNpCZncuTALZYtPMLPa/pz93YCsyfuwcBAQquV6TugcYmCQYUJaFmT/Qev0K7rFExNjJj07av553q8Movg1cqbD8eO6pX3avlc/JtXx7/F3z/nu0Mv0LxZdczMShdEM1AZ0G5gdVaPP4OshTptnHGoZMGBVTdxqmpFtSZPz2a4fzmRo+sjUKkkJAOJdu/XwMyqZBuXBwTUZn/YBdq1H4OpSQUmTeqff65H0HcEbxwNwNhvXmfEyGVkZmbj39IHf3/l2Rs0sANDPl/M2nWHcHFRXi3/mN27z9C8ea1Sl5We5qaVCDseQfu3Vilt2ZcFWUVB769m48JXAJi+6AhbQq+TkZVLQN/lvNypJp/0b8yx8IfM/lmZuGpc15lvPvEvnZ6WNZTnrdtUpQzHF/gKPV6ZTfDqzwEYOzIo/zXp/s1r5D9v0+ds48rVhyCBq0tFvh2tzPXFJ6Ty7kdLMDAwQO1oxbTv+pZAW032H7hMuy6TlLow4bUCbX1mELxmmKJt1Mv5r5b3b1ED/xZ/vdTby1NNy+bV6f7yDAwkiZd7NcW72rMH6F/UMjM0VPHVyO58+sEvaDQy3Xs2zPMnd+f5kzXp0asRY0esoWfnGVhZmzFxWsFvnDl1B7WTNW7u+kGCPTvP8/38/nrHS0qArxNh4VG0/2Kn0h8MKphUDRq5h42T2hIVn86C4Ct4uljSa3QIoASU+gRWYXi/uoxZcpplO24gAZPfb1QmexkBBDR0JezUA9p/sFGpo58WvCwlaMgWNs7pSlRcGgvWXMDTzYpeQ5XtKfp1qU6fdkUvjS0LVIYGDP6yFSM/2YhWI9Ohey0qe9mxbMERvGuqaRbgydWLUYz/cispyZkcPXCbFYuOsnj1m6hUBgz8rAXDP1yPLEO1mo506ln773/0WbWpDAj6qDaLRx1XXi3f3g2nypbsXH4Vt2o2+DRTc3jTHa6ficPA0AAzC0Ne/UIJ8JlZGtGyVxV++PQgSEpmUM2mpZvA/n/g37iBc3kjPbkbvqAASZJSZVm2KPTZHtgMWAAnAT+UjB6ALbIs186z+zXv81pJkio/PidJ0gCUAM/gJ18tL0mSH7AW8JBlWSNJkiXwJ+AFZABpwGeyLJ98fC1wAtgJXCkkuzOQDcwDaqIE/MJkWf7gr/6vRS0TexEwNGhHSs6G8pZRJJZGPbEa3668ZeiRPHY3AB6Le5WzEn0iBq5H+lD/tdgvAvJPR7mWOKO8ZRSJt80w5IgXU5vkMYz1N4eUt4wi6eU1h7jM38pbhh72Jsor1bWhn5azEn0MWv/AvdQFf29YDrhbfACZzz/o+0yYdGPppY/KW0WRvFNrPsh7y1uGPpIS0JHvzSlfHUUguQ+BjODyllE0pj0g6/nv8/hMGHd5ocstOXtdeasoEqsKvZFPPHV70XJDajwJAPnyd+WsRB+p5mgiUuaXt4wi8bD8iE23h5a3jCLpXmXW/3W05P3Q98p9PLuw9ZJ/VRmLzKC/oHAgKO9zHPC0dRS1C9kNKPTvO4/PybL8K/Br3r/HPXF9C5SNozV551NQAjtF6Sp87dPyxF99ynGBQCAQCAQCgUAgEAgE/2FEMOgFIO9NZV5A6/LWIhAIBAKBQCAQCAQCwb+JcnxJ5L8WEQx6AZBluWd5axAIBAKBQCAQCAQCgUDw30AEgwQCgUAgEAgEAoFAIBD8azEQG0gXG/FqeYFAIBAIBAKBQCAQCASC/xAiGCQQCAQCgUAgEAgEAoFA8B9CLBMTCAQCgUAgEAgEAoFA8K9FbCBdfERmkEAgEAgEAoFAIBAIBALBfwiRGSQQCAQCgUAgEAgEAoHgX4uByAwqNpIsy+WtQfBiIB4EgUAgEAgEAoFAIPj/5P86XPJ52MByH8/O9l/8rypjkRkkAGDE4UHlLaFIJr+0CDluaXnLKBLJ/h1iMpaXtww9HE3fAuB07HflrESfBg6juZY4o7xlFIm3zTCkD/3KW0aRyD8dZZVUvbxlFMnr8lXkyPnlLaNIJOePaLOuX3nL0COk90oADnnUKGcl+jSPuMJXh17M/mBa80WQuq68ZRSNRW9ScjaUt4oisTTqSaZma3nL0MNE1UX5R9aLpw3jLpC7s7xVFI1hBxKz1pS3iiKxMe5DcvaLWUetKvRGjv+1vGUUiWQ3APnh3PKWoYfkMlj5R8oL+LxZ9uFR1p/lraJIKhq/+kKOD6BgjCAQPEYEgwQCgUAgEAgEAoFAIBD8axEbSBcfsYG0QCAQCAQCgUAgEAgEAsF/CJEZJBAIBAKBQCAQCAQCgeBfi4HYQbrYiMwggUAgEAgEAoFAIBAIBIL/ECIYJBAIBAKBQCAQCAQCgUDwH0IsExMIBAKBQCAQCAQCgUDwr0VsIF18RGaQQCAQCAQCgUAgEAgEAsF/CJEZJBAIBAKBQCAQCAQCgeBfi9g/uviIYJCgWESdT+DcqhvIWpnK/s5U71JJ53zEwSjO/3kL04oVAPBs40qVAOf88zkZuewedQKX+vb4vlmtzHTJsszEOSGEHbmJiYkRk0d1xqe6k57d7IVhBO+4QHJKJqf3DM0//seGM6xcfxqVgQFmZkZ8+1VHqlaxLxNtxw7d5Ptpu9BqZbr29OWNd17SOX/21F1+mL6LW9djGDulJ4Htauaf++Kj37l07gF16rsz7cdXy0SPzm8ffcDy70+i1coEdq1Kjzdr65zf+scl9m65gYFKwsrGhPdHNMPByQKA1/1/o5KnDQB2anO+nBpY5voec+rIPRbPOoJWK9Oue3X69PfVOb9x1Tl2BV9FZWiAlY0Jn432x9HZ8h/T81f8/OYoutZpTkzKI+pM6FcuGp6GVXVP/H6ZRMUGPoSPms2VmUuf22/LsszEH/cTdvQOJiaGTP66PT7ejnp2s5ccJnjnZZJTsji94yOdc9v3XmPur8eQJKjuZc/MMZ3KRFtjdV0+rvcmBpIB227v449rm/VsAlyb0r9Wb2RkbibeZdKJeQAMrN2Xpk6+APx2ZSP77h8tE02FqTJuFBUD/dFmZHJ92AjSLlzSs7Hv3gW3j98HWSY7OoZrQ74k91Ei1efOwsSzCgCGVlbkJicT3rlnmeiKPp/A+VU3QJap1NIZ7yf6g7sHo7i4+hYmhfoDD3+lPwh+dz9WbuYAmNmZ0PRT3banuMiyzMTpW9h/6ComJhWYMq43PjVd9ewuXH7AiLFryczKIaB5dUZ92RVJkhjy9e/cjogDICUlA0tLU4J//wSAK9cjGTtxI6lpWRhIEmtXfISxsdEzazt88CozpmxGq5EJ6t2YAe+10jmfnZ3L2BGruXzpAdY2Zkye8RourrYAXL8ayaRvN5CWmolkILH8j8EYGxuxY9tZflm8FwkJB0crJkx5FZuK5sUut0MHLjN18ka0Gi09X/bj3YFt9LSN+noVly/ew9rGnGmz3sI1TxtA5MNH9Ow2lQ8/7kD/d5Q+oFPbCZiZG6MyMEBlaMDva4ZSEmRZZuLUDew/cFm5pxNew6eWm57dhUv3GDH6d+WetqzJqOE9kaSCEcHSZfuYOnMTR/Z/i21Fi/zj5y7cpe+bPzBr6pt0bF+v+Nomr2N/2CVMTCswZWI/fGq562u7eJcRo1aSmZlDgH8tRo3ojSRJzPlhKyF7z2MgSdjZWTB54huoHa25eSuakaNXcvHSPT7/rCvvvt2miF//a44cvMasqdvQarV079WQ/u8G6JzPzs5l/Ki1XLn0EGtrM76b/iourhXJzdEwcdwGrl6ORKPR0qmbLwPeCyArK4cP3l5CdrYGjUZL67Y+DPq4+LoOH7zGzKlb0Gq09OjVmAHv6esaO3INV/LqwaTpr+HiWpHtW86y4tcD+XY3rkWxYvXHVK/hkn9s6CfLeXA/gT83DCm2rieRZZmJs3cX+JOjuxbtTy7YT/CO84o/GTIs//gvvx9n7eazqFQG2NqYMXFkF1ydrUutK1/bj2GEHYtQ+tHhbZ/Sjx4heNcVpR/d/kH+8cnzDnDszH0AMrJySXiUzokt75dOz4yt7D90DRMTI6XdLXRfHnPh8gNGjFuf1+56M2pYF6XdHfFHoXY3E0tLE4JXDSY7J5exk4K5cOkhkoHEqC8607SRZ7G0HTl4ndlTt6HVynTv1YC33vXXOa/Ug/VcvfQQK2tTvpv+Ci6uFcnJyWXKt5u5cvEBkoHE58M707Cx0ocO+WA5cXEpaDRafBt4MGxkV1Sqki+yKen44PqVKGZO2kFaahYGKgPeeq85bTrUKrEOwX8bEQwSPDOyViZ8xXVaDKuLqa0xe789jbOvHVauug6oWxOHpwZ6Lq2/g723TZlrCztyi4j7Cez8cxDhFx8yfsYuVi9+S88usLkX/Xo3oGPfRTrHu7avRd+e9QEIPXCdKT+GsmTWK6XWpdFomTV5B7MXvI6D2oqB/ZbSPKAaVbwc8m3UTlaM/LYbfyw/pnf9a/39yMrMIXjtmVJreRKtRssvs44zcnZb7BzNGPXedhq2cMOtik2+TWVvWyYu6YyxiSG7N1xl1fzTfPat0qFWMFYx5deuZa7rSTQaLQumH2LCj52xczRn6ICNNG3pQSXPivk2nt72zFpWCxMTQ7atu8Qvc48zfGLxndWy4NcjW5m7by3LB3xTLr//V2QlJHLy04m4BT3/sgk7doeI+4nsXNmf8EtRjJ8dyuqf+urZBTarQr+e9ejYb5nO8Tv3H7Fo5UlWze2DtaUJ8Y/Sy0SXARKf+g7gq4OTiU1PYH7rCRyJPE1EyoN8G1cLNa/V6M6n+8aRmpOOjbEVAE2dfKlmU5lBISOpYGDETP9RHI8KJz03o0y0AVQM9Me0igenAzpgUb8eXt+N5VzQE4FhlYoqY0dypm0Xch8l4jFiGM793+DenLlcHVwwEK88ejia5JQy0SVrZc79dp2XvlD6g/3fnsapiP7AtYkDdd/Q7w9UFQwIHN+oTLQAhB26xp178eza+AXhF+4xbnIwa5Z/pGc3bnIwE8b0pF5tdwZ+uoyww9cIaF6dOVNey7eZMmsbFhbGAOTmavhy9BqmT+hDDW9nHiWmY2ioemZdGo2Wqd8FM2/xu6idrHnr1bn4B9bE00udbxO8/gSWVqZs3P4lO7eF8+OsHUye+Tq5uRrGfP0n305+Be8aLiQmpmFoqCI3V8PMKZtZEzwUm4rmfD9zG3+uOsz7H7crVplpNFomfbeehUs+QK225vVXZ9Mq0AevqgUD3w3rjmFlZcqWnaPYvu0Mc2ZuYfqsgr51xrRgWrSsqffdS379iIqFAi8lIezgZe5ExLFry0jCz0Uw7ru1rFk1RM9u3HdrmTD2FerV9WDgR4sJO3iFgDxNkVGPOHTkKi7OFXWu0Wi0zJi9hebNvEum7cAl7kTEsmv7GMLP3WHct6tZ88cX+tq+Xc2E8X2pV7cyAz9YQNjBywS0rMV777RmyKddAFj+237m/bSDb8e+io21GaNG9CYk9HyJdGk0WqZP2syPi97GUW3FgNcW0LJVTTy9CgIGm9afwtLKlHVbh7Jr+znmzdnJxOl9Cdl1gewcDavWf0JmRjZ9e/5A+051cXaxYd6SdzAzMyY3R8Og/otp1sKbOvX0g19/pWvaxE3MXfQOaicr+vedj39gjSfqwUmsrEzZsG0Yu7aH8+PsHUye8RqduvrSqasvoASChn32m04gKHTPBcxMK5SovIoi7MhNIu4/YufqDxR/cvoOVi8ZoGcX2KIq/V5uSMdXF+gcr+mtZu3StzE1MeL39aeZMX8vsycElY22YxFEPEhk529vEn45mvGz97H6J31fNfClKvTrWZeOb6zQOT7i45b5/16xPpzL12NLp+dxu7vhc8Iv3Gfc5E2sWfaBnt24yZuYMDqIerXdGPjZcsIOXyeguTdzJhf4AFNmb89vd9dsOAnA5j8/IT4hlYGfLmft8g8wMHi2wItGo2XGpC38sKg/jmor3n5tIS1b1aCKTj04jZWVCWu3DmH39vPMm7ObidNfIXjdKQBWrh9MQnwqn3+0gl9+fx8DAwMmzngFcwsTZFlmxNA/Cd11kXad6pSo7EozPjA2NWLUhO64e9gSF5PCu6//TJNmnlhamZRIi+C/jdgzSPDMJNxKxtzRFHNHUwwMDXBr4kjkmfhnvv7RnRSykrNR167498bFJOTgdXp0rI0kSfjWdiU5JYuYuFQ9O9/arjja6zuoFubG+f9Oz8xBKqM0w8sXHuLqbouLW0WMjFS06VCLg/uu6dg4u9pQ1VutM5P5mEZNq2BmZqx3vCy4cTkeJzdL1K6WGBqpaNbWg5MH7+nY+DRwwthEiRlX9XEgIbZsBuDF4fqlWJzdrHBytcLISIV/Oy+OhUXo2NRt5IJJns7qtR2Jj0l77jofc+DGWRLSksvt9/+KrNgEEk6eR87Jfe6/HXLoFj061FTqqI8zyalZxMTr3ydfH2cc7fQzHNZsucjrQXWxtlScHbuKZmWiq4atFw/SoolMiyVX1rD3/lFecmmoY9Olcms23dxNao7y/CdmKffXw8qVc3FX0MpaMjVZ3E66R2N13TLR9Rjbdm2IWRcMQOqZcAytrDBydNCxkSQJSZJQmSllYmhhQXZ0jN532XfpSOymrWWi69ET/YFrU0eizj57f1DWhOy/RFCX+srzVacSyamZxMTq1sOY2GRSUzPxrVMJSZII6lKfkH26WVayLLN9z3m6dlQyRQ4dvUH1ak7U8FYymiramBVrJvji+Xu4V7LDzd0OIyND2neqx/5Q3d/cH3qJrj0aANCmfW2OH7uBLMscPXydat5OeOcNfG1szJXflkGWISMjG1mWSUvNwsHRqngFBlw4fxf3SvaKtgqGdOxUn32hF3Rs9oZeoHtQYwData/L8aPXkWUZgNA953F1tcWrqlrvu8uCkL0XCOrWSLmn9SqTnJLxlHuahW+9yso97daIkL0FgZTJ04L58vOuen36ilUH6NCuLna2JcsgDQk9T1D3JnnaquRpS3pCWxKpaZn41quiaOvehJCQcwBYWJjm22VkZOXrs7OzpG4dDwwNS+aeX7pwH7dKdri62WJkZEi7jnUI23tZxyZs32W6dFcmv1q38+HEsVvKPZUgMz2b3FwNWVm5GBqpMLcwRpKkfD8kN1dDbq6m2D7SxfP38+pBnq5Oddn/pK69l+nSvUGertqcOHYz/1l7zM7t4bTvVNDGpqdnsWr5Id55v+wyk0MOPOFPphbPn/Rr6IGpiZI5WM/HhaiYsvMHQg7dokf7vH60lhPJaU/pR2s5FdmPFmZr6DW6tClZMDRfz/7LBHX2zWt33UlOySQmTnfCISYuhdS0LHzruCv1oLPv09vdDsq9vXE7Nj8TyM7WAktLEy5cevjMupR6YPtEPbiiY3Ng32U6d/cFILBdLU7m1YPbN2Np1ETJBLK1U3778kXlt80tFP9Dk6slJycXSjFWKM34oJKHHe4eSoamvaMlFW3NSSyjCbJ/Oyqp/P/+bYhgkOCZyXyUjaltQWDC1NaYjEdZenYPTsWxZ8xJjs67SHp8JqDMIp//4ya1X/X6R7RFx6biXMgZdnK0JDq2eDPgK9edpl2fhcyYv49RQ9qWia7YmBQcnQqcTQe1FXExZTMzX1oexaZj51jgLNg5mPMo9ukZDfu23KBe04LZuJxsDSPf3cqYQds5EXb3H9MZH5OGvbrA4bJzNCc+9unBnt2brtKwmf5SAkH5Eh2birNDwX10crAgOlbfwX4ad+494s79RF4bvJpXP/yTA8fulIkue1NbYtMLghixGQnYm+oGrN0snXCzcOb7gLH82Gp8fsDnZuJdGqvrYayqgFUFC+o51sLRzK5MdD2mgpOarIeR+Z+zoqIwVusOvuXcXG6OHo/vzk00PhGGaTUvov9cq2Nj1aQROXHxZN7RDaSWlMzEJ/qDisZkFtEfPDwVx95vTnJ83kUyEjLzj2tztOwbf4qw704TeTqu1HqiY5JxUhcsxXBytCL6icBBdOwTNmorop8YqJ08cwc7WwsqV1KWCd++G4ckwbsf/0LP1+eyeFlYsXTFxCSjdir4TUe1NTFP/KZiYwOAoaEKCwsTkhLTuRsRB5LE4EE/06/PDyxbul+xMVLx9Zgg+vacQ8fASdy+FU2PXo2LpQsgJjoJp7zfBXB0siE6JumpNoaGKiwsTUhMTCM9LYtffg7lg4866H+xJPHBewvp+/Is1q4+Umxdj4mOSdbR56TW1xcdk/TEPbXJv6d79l7A0dGaGtV1lwtGRyeyJ/Q8r72iuySjeNqS9LVFP6EtOgkndSGbJ8p39vdbCGjzDZu3nOKzwZ1LrKUwMdHJqNWFnzcrYp943mKjk3HMs1GeN2OSEtNp0642JmYV6NJmKt3bT6df/xZYWysBZo1Gyxt95tKx1RSaNKtK7brPnhUEEBuTpFMP1GprYqOfrAcFNoXrQWF27zivEwxa8OMe+vVvgYlJ2WUGRcem4Kwu5E86FN+ffMzaLeH4+5Wd3xsdl4azY6F+1N6C6CICVX/Hg6hkHkQm41e/dL5SdGwKTk5/3aYqbXOh8lRb65Xnk+1ujWpOhIZdITdXw70HCVy8/JDIJ+rXXxEbnZL/jMPT6kFKfl0pXA+qVXfiwL6r5OZqeHj/EVcuRxIdVfDbn32wjE6tpmJubkzrdj7PrElPYxmNDy6df0BujgZX97KfaBf8NxDLxARlipOvHW5NHVEZGXBr70NOLblKy+H1uBX6EKe6tpjZ/jNZLmVBv94N6Ne7AZt3XeKnX48wdUyX8pb0wnBg5y1uXYnnm7nt84/9uLYXtg5mRD9I4bvPdlPJqyJq1/LZp+cxe7df58blOCYv+OeXrwmeL7kaLRH3E1k+pzfRsam88elaNi19AyvLf75NUUkqXC3UDA37DgdTW2YHjOG9PV9zKuY81St68kOrcSRlJXMp/joaWfuP63kSydAQpzf6Et65J5l37+H57RjcPh7E/R8Lli/Yd+9SZllBz4qTrx2uef3BnX0POb3kKs2/UjJu2k33w7SiMWkxGRyaHo6VmznmjqZ/843/PFt2hOfPToMyA3zqbARrl3+EqYkRAz78mdo1XWjWpOo/rkWTqyX8zB2W/zEYExMjPnxvCTVrudKgYRXW/XmUlWs+xdXdlmmTNvHLkr289/7zW/7507ydvPFWAGbm+vXv198Go1bbEB+fwgfvLaCKpyMNG/0zE0FPIyMjm4WL97B0of5+KBOnBTNsSNdnXnLyT/H5Z135/LOuLFy8i99WHeDTMgoIlZSLF+6jMpDYumc4yckZvD9gCU38vHB1s0WlMuC3NYNJSc7gq89XcfN6NF7V/pmMsKdx4dw9TEyMqFpNWcZ49cpD7t+PZ+jwLjx88Oi5ankWNu24wMUrUayY92LtHQiwbe912gdULdV+N2XJlp3nddrd3t0bcPN2LL3f+gkXJxvq162E6jmlXHQNqs+dW7G8/dpCnJxtqFPPXaecvl/Qn6ysHMZ+vY6Tx2/RtNk/3xc8jbjYFL4bvYlRE7pjIHZOFpQQEQwSPDMmFSuQkVAw85uRkIVpRV1H0NiiYFPNKgHOXFhzC4CEm8nEXUviVuhDcrM0aHNlDE1U1O5TvA3hCrNy3WnWbAoHoE5NJyILRf2jYlJQO5QsMNGlbU3Gz9gJlD4Y5OBoSUxUQaQ/NjoZe8fyDZg8pqKDmc5yqvjYNCo66A/Gzp+IZOPy83wztz1GFQr2yrB1UGYM1a6W1Kqv5s61hH8kGGTnaE5cdMHMV3xMGnYO+unPZ48/YPWvZ5n8U1cdnf91qn30OlUHKnsK7Os8iIxI/eVD/xQrN4SzZouy7KRODTWRhTKBomJTUTs8+54iTg4W1K3lhJGhCjdnayq72xDx4BF1auhv7Fkc4jIScCiUzeNgaktchu7AIjYjgcsJN9DIGqLSY7mfGombhRNXH91i1dVgVl1VlnGNbPwx91MjKS1Ob72Oum8fAFLPncfYxZnHrYixkxNZ0dE69ua1agCQeVdZ5hm3ZTuuHw0sMFCpsOvYjvCuvUut7TEmNk/0B4+yMHmiP6hQqD/w8HfmYl5/AOT3HeaOptjXsCHpbmqxg0ErVx9hdd7eEnVquRJVaOY4KiYZtYPu0im1g5WuTXQy6kIZpbm5Gnbvvcj63wbnH3NSW9G4fmVs8zZn9m9enYtXHj5zMMjR0UpnVjkmOgnHJ5Z0KTaJqJ2syc3VkJqaibWNGY5qa+o3rJK/MXTzltW5cukB5nkBGLdKynPbrkMdfv15/zPp0fldtTVRUYkF2qISUTtaF2mjdrJRtKVkYmNjzvlzEezZFc6cmZtJSclAkiQqGBvyWr+WqPOyYezsLGndpg4Xzt195mDQyj8Osnqdsgl7HR93HX1R0fr61I7WT9zTRNSOVty9F8f9Bwn06DMj73gSvV6dxZpVQ7hw8R5Dhyt7qTx6lMb+A5cxNDSgbeu/3vtj5aowVq9VMp3q1K6kr039hDa1NVHRhWyKKF+Abl0aMejDhWUSDHJUW+lkKMVEJ+stIXRQWxETnVToecvC2saMndvO4de8GoZGKmztLKhbvxKXLz7A1a1gw3BLK1MaNq7CkUPXixUMcnC01qkH0dFJOKifrAeKzZP14DG7tp+jQ+eCjb7Ph9/l8sUHdO8wDU2uloSENN5/ezELfxlIcVm57hRrNp0FoE4NZyILZS1FxRbfnzx84jYLlh1mxbx+VKhQuqHWyg3nWLP1Yp42RyJjCvWjcamoi1iq9ndsC73GmM9alUzP6qOs3lio3Y16epsKoHa0IqpweUYn6ZRnfru7omCPN0NDFSO/KKgPfd9ZmJ819Cw4qC2J+dt6YEl0dBKOT9QDSZIY8lXByykGvrmYSh66Gb/Gxkb4B9bgwN4rJQ4GlXZ8kJaaxVef/MnAwa3wqav/soT/KgZltc/Hf4gXIyQs+FdQsYoVqTEZpMVmoM3Vcv94DM71dRvIjMSCwcHDM/FYOisdeeP3a9Jpph8dZ/hR51UvKr2kLlUgCJRMno3L3mbjsrdp4+9N8I4LyLLM2QsPsLQwLnIt99O4cy8h/9/7Dt/Eo5DzUxpq+Lhw/24CDx8kkpOjIWTnJVoElG6NdlnhVcOOqHspxDxMITdHw5E9ETRsrpv6fftaAkumH2XYlECsKxYM1FKTs8jJ1gCQnJjJtfOxuFYum7dlPEm1mg48vJdM1MNkcnI0hO2+SRN/3bcW3bwax7wpBxgzvT02tuWfXfAicX3+KrbXD2J7/aDnGggC6NezHht/7sfGn/vRpoUXwTsvK3X0YiSW5sZ/u6dBYdq28OL4WWVT50eJGdy5l4hbGbyh5cqjW7haOOFk5oChpCLQzY/DD0/p2Bx6eBJfB2VDWqsKFrhZOBOZFoMBElYVlHbG08odT2t3TkaXbOPXwkQtX0V4556Ed+5Jwq4QHHv3AMCifj1yU1LIidHd9DM7Kgazal4Y2ipp4jYtXyLjRkHgxaZFMzJu3iY7SjeIVBpsqliRFl3QHzw4FoOTr25/kFmoP4gs1B9kp+WgyVEyqLJScki4noyFc/H3gOr3SjOCf/+E4N8/oW2rWmzcekZ5vs7fxdLCBMcngkGODlZYWJhw9vxdZFlm49YztAkoeAPL4eM38azsoLPsqEUzb67diCYjQ9lL5cTp21Stov/2nqdRq7Yb9+7G8+B+Ajk5uezaHo5/oO5bX/wDa7El+DQAIbsu0LipF5Ik0ax5NW5cjyIz77dPn7yNp5caR7U1t27G8ChBGRQeO3KDKp4Oer/9d/jUduduRCz378eTk53Lju1nCAjUfatbq0AfNm08AcDuXedo0rQqkiTx62+fsH3PGLbvGUO/N/15b1BbXuvXkvT0LNLSlOWA6elZHDl8LT+T41no17cFwWuGEbxmGG1b12Hj5pPKPQ2/g6Xl0+6pMWfD7yj3dPNJ2gTWprq3C0f2f0vojjGE7hiDk9qa9X8OxcHeitAdo/OPd2hXj7Gjev9tIAig3+v+BK8fTvD64bRtU5eNm47nabud97w9EUhzsMbC3ISz4bcVbZuO0ybvd+5EFLTFIXvP41mMZ+qvqOnjyr2IeB7mPW+7d5zHv1UNHZuWrWqwdZPyUorQ3Rdp1MQTSZJwcrbm5HGl3chIz+bCuXt4VHHgUUIaKcnKEvLMzByOH7lJ5WK+bbVWbVfuRsTl14Pd28/h30p343FF1+k8XRdonKcLQKvVsmfXedp1LMgeeflVP7aHjmDTzq9YvPx9KlW2K1EgCKBf74ZsXPYuG5e9q+9PmhfPn7x0NYqxU3cwf9rL2NkW/w1/etp61mXjktfYuOQ12jT3JHhXXj96KQpL8wrF6kcBbt1NICkli/o+JZtI6feKH8GrBhO8arDS7m47m9fu3svzvXUDGo72lliYG3P2/D2lHmw7S5uAgntfVLubkZlNekY2oOzbplIZUNXz2euIUg8SeHj/UX49aFlEPdiWFwDcu/sSjZooe3tlZmSTka789rEjym9X8XIkPT2LuLzlbbm5Gg4duIZHleK3u48pzfggJ0fDyKFr6di1rs4biAWCkiAygwTPjIFKwrdfVQ7NPI+slfFo6YSVqzmXNtzGprIlLvXtubn7AZFn4zFQSRiZG9LovRp//8VlQEAzT8KO3KT9K4swMTFk0siCGYWg/r+wcdnbAEyft5ctuy+RkZlDQNA8Xu5Wj0/ebcHKdac5cuIOhoYqrCxNmDK6bNK1DQ0N+PzrDnzx4e9otVq69KhHlaoOLJm/nxq1nGnRypvLFx4yauhaUpIzORx2naU/hbFivZLa/vHby4m4E09Geja92v/A8HFdaPpS2aTbqwwNGDC0CZOHhqDVyrTqUhV3TxvWLDlLlRp2NGrhzqp5p8jMyOX7Mco+GY9fIf8wIokl048hSRKyLNP9DR+dt5CVJSpDAz4Y9hJjP92OVivTtlt1PDxt+W3hSarVdKCpvwe//HiMzPRcpozcA4CDkwVjZhSxl8VzYNU739LKuwH2Fjbcm7SJsVsWs/Sw/qvKywMTtT0dT67DyMoCWaulxpD+bKnVmdyUf37D7QC/yoQdu0P7fsswMTZk0vCCNx8FvbuSjT8rqfTTFxxky56rZGTlEPDyz7zcxYdP3vajRRMPDp68S5f+KzAwkPjygxZUtC594E8ra/nx7K9MbTEcA8mA7Xf2E5HygAG1enP10W2ORJ7mRPQ5GqnrsLTdNDSylkXnV5GcnYqRgRFzApS3xqXlZDD5xE9oy3iZ2KPQ/VQM9KdB2C60GZncGDYy/1y9bRsI79yT7JgY7s2ZR501vyHn5JL14CHXvxiRb2ffrQtxm7aUqS4DlUTdN6pyZJbSH1RqofQHl/P6A+f69tza84Cos/FIBhIVLAyp/67SH6RGpnN22XUkSdkIuVpnd723kBWXgBbV2X/oKu16zMTUxIhJ4wqyoHq89mP+a+LHft2dEePWkpmZi39zb/ybFzjf23aeo0sH3VeMW1uZMuCN5rz81nwkSckMatXy2fs1Q0MVX47szifvL0Wj0dK9ZyO8qqpZMHcXNX3cCAisRY9ejfhmxGqCOk3HytqUSdOVN5tZWZvR762WvNV3LkgSzVtWp0WA8tsDP2zDwP4LMTRU4exiw9iJfYpdZoaGKkaM6sWHAxeh1WoJ6tmEqtWcmPfjdnx83GnVujY9ezdl1PBVdO0wESsbM6bN0H9LZ2ES4lP5/NOlAOTmauncpQHNi3jb2LMQ0LIm+w9cpl2XSco9nVDwxrcefWYQvEZ5nffYUS/nv1rev0UN/Fv884OjAP9a7A+7SLtO32JqUoFJ3xUsBerRayrB64cr2sa8orxaPisb/xa18G+pBAJnztrM7TsxSAYSrs4VGT9WeUNgbGwyvV+dTmpqJgYGBixbsY9tm0bqbDj9Vxgaqhg2siuffrgMrUZLt6CGeFZVs3DeHmrWcsU/sCbdezZk3Mi19O4yS3ml9jTlt1/u25QJY9bTt+cPyLJM1x4NqObtxPVrUXw7eh1ajRatVqZNh9r5z+GzYmio4quR3fn0g1/QaGS692yYVw9259WDmvTo1YixI9bQs/MMrKzNmDit4E1TZ07dQe1kjZt72UzU/RUBL3kp/mSfBZiYGDFpVEGWeFD/n9m47F0Aps8LZcuuPH+yx1zFn3yvJdPn7SU9I5shozcA4Ky24qdpxa+fRWrzq0zYsQjav7EcE2MjJg0vWBoa9N7vbFyi1JHpCw6xJSSvH+2zVOlHBzQFYGvodbq0rlbkS0uKrae5N/sPXaNd0CylHoztlX+ux+tzCV6lZFkq7e46pY6+9ES7u+s8XdrrvnghPiGNdwcvw8BAQu1oybRvXy6WLqUedOGzD5ej1WjpGtQAz6qOLJoXQo1arvgH1qBbzwaMH7mel7vMwcralAl59yghIY0hHyxHMpBwcLRi7CSlL8nIyOHLT1eSna1B1so0aFKFnn1K/kbM0owPQnddIvz0XZIT09met0Ji5LfdqFbKTOn/B/6NGziXN9KTO/UL/puMODzohXwQJr+0CDluaXnLKBLJ/h1iMpaXtww9HE0VZ/107HflrESfBg6juZY4o7xlFIm3zTCkD/3KW0aRyD8dZZVUvbxlFMnr8lXkyPnlLaNIJOePaLPuxduvIaT3SgAOeTyfYHlxaB5xha8ODSpvGUUyrfkiSF1X3jKKxqI3KTkbyltFkVga9SRT83z3i3oWTFR5g+ysF08bxl0gd2d5qygaww4kZq0pbxVFYmPch+TsF7OOWlXojRz/a3nLKBLJbgDyw7nlLUMPySVvyWzKC/i8WfbhUdaf5a2iSCoav/pCjg8AHE3f+r8Ol0w4/n65j2fHNFn4l2UsSVJH4HtABSyRZXnKE+cHANOBB3mH5sqyvCTvXH9gdN7x72RZXlZavSIzSCAQCAQCgUAgEAgEAoHgH0KSJBUwD2gH3AdOSJK0SZblS0+Y/inL8uAnrrUFxgKNABk4lXdtqXbQF3sGCQQCgUAgEAgEAoFAIPjXYiCV/9/f0AS4IcvyLVmWs4E/gB7P+N/rAOyWZTkhLwC0G+hY0rJ6jAgGCQQCgUAgEAgEAoFAIBD8c7gC9wp9vp937El6S5J0TpKktZIkPX67z7NeWyxEMEggEAgEAoFAIBAIBALBvxaVJJX7nyRJgyRJOlnor7gbMW4GKsuyXBcl+6fU+wL9FWLPIIFAIBAIBAKBQCAQCASCUiDL8iJg0VNOPwDcC312o2Cj6MfXxxf6uASYVujaVk9cu68UUgGRGSQQCAQCgUAgEAgEAoFA8E9yAqgmSVIVSZIqAH2BTYUNJElyLvSxO3A57987gfaSJFWUJKki0D7vWKkQmUECgUAgEAgEAoFAIBAI/rU8wwbO5Yosy7mSJA1GCeKogKWyLF+UJOlb4KQsy5uATyVJ6g7kAgnAgLxrEyRJmoASUAL4VpblhNJqEsEggUAgEAgEAoFAIBAIBIJ/EFmWtwHbnjj2TaF/jwBGPOXapcDSstQjgkECgUAgEAgEAoFAIBAI/rWoXvDMoBcRSZbl8tYgeDEQD4JAIBAIBAKBQCAQ/H/yfx0umXn6g3Ifz37RYMG/qoxFZpAAgIxxXctbQpGYjttCZHqZZsOVGc5m7yAnLC9vGXpItm8BIF/8tpyV6CP5fIMcMaO8ZRSJ5DGMVVL18pZRJK/LV5E+9CtvGUUi/3SUiJT55S2jSDwsPyKyZ9PylqGH84ZjAFSc3LGclejzaMQOMsZ0KW8ZRWI6YSvJ2evKW0aRWFXoDekbyltG0Zj1hKyt5a1CH2PlOcvUvHjaTFRdyNGWel/OfwQjgw6Qsqa8ZRSNZR9IfTHrKBa9iU7/tbxVFInabAB3UuaWtww9KlsOBiAx68V73myM+0Dyn+Uto2isXn0hxwdQMEYQCB4jgkECgUAgEAgEAoFAIBAI/rUYiPekFxtRZAKBQCAQCAQCgUAgEAgE/yFEZpBAIBAIBAKBQCAQCASCfy0q6V+1Xc8LgcgMEggEAoFAIBAIBAKBQCD4DyGCQQKBQCAQCAQCgUAgEAgE/yHEMjGBQCAQCAQCgUAgEAgE/1oMxCqxYiMygwQCgUAgEAgEAoFAIBAI/kOIzCCBQCAQCAQCgUAgEAgE/1pUIjOo2IjMIIFAIBAIBAKBQCAQCASC/xAiM0hQbIw6DcKgWiPIySJ74xzkyJt6NhUGTEayqAi52QBkrRgDaUn55w1qvoTxqyPJXDQE+eGNUms6dugWc6eHoNFq6RJUj37v+OmcDz91j7kzQrh5PYZvJnenVbsaAEQ9TGLMFxvQamU0uRp69m1Ijz71S62nMLIsM3H2LsIO38TExIjJY7riU91Zz272gr0Ebz9Pckomp0O/yj9+4sxdJs/ZxdWbMcz8ticdW9csW20/nyLs9ANMjA2ZPLgZPl62OjYZWbkMmX6Au9GpqAwkAhu58sWbShmtD73J9OVnUNuaAdCvkzd92lUtM306OucfIezEPUXnsAB8qtnr6szMZch3e7j7MBmVSiLQz4Mv3m1S5lqeBavqnvj9MomKDXwIHzWbKzOXlouOJ/n5zVF0rdOcmJRH1JnQ77n+9onDd/hpxn60WpmOQT70HdBY5/y50w9YMHM/t27EMXJiJ/zbVss/FxOVzKwJIcRGpyBJEt993wMnF6sy1Wf17lCMG76EnJVJ4o8TyL11Vd/I0BDrgV9SoXYD0GpJWbmAzKN7UTk4YT14NAZWNmhTk0mcMw5tfEyZ6Grj2ZDJbT9EZWDAirM7mHN0tc55NysH5ncdhrWxOSoDFeP3LWX3zRP08Qnkk6Yv59v5OFYhYOlgLsTcKhNdjzHq/D4G3nn9wfrZRfcH70xGsrSFnLz+YNloSEtCVb8tRh3eQU6OByD32GY0p3aVSMfhg9eYOXULWo2WHr0aM+C9AJ3z2dm5jB25hiuXHmBtY8ak6a/h4lqR7VvOsuLXA/l2N65FsWL1x1Sv4UJOTi7TJm7m9MlbSJLER5+2p3W72sXWJssyE6dtZv+hq5iYGDFlfB98arrq2V24dJ8RY9eQmZVLQPPqjPqqG1Lea3JX/H6IlauPojKQCGhZg6+GdCYnR8Pob9dx6coDcjVagro04P13A4uvbeoG9h+4jIlJBaZMeA2fWm5FaLvHiNG/k5mVQ0DLmowa3jNfG8DSZfuYOnMTR/Z/i21FC46duMFHny3FzVXpT9q1qcPgDzoUS9uhA5eZOnkjWo2Wni/78e7ANjrns7NzGfX1Ki5fvIe1jTnTZr2Fq2tB/xX58BE9u03lw4870P+dgnLRaLS81mc2jmpr5v70XrE0PebggUtMmbQejVZL75eb8d7Adk9oy2HE8N+4dOkeNjbmzJg1AFdXO86fi2Dc2D8Apew/+rgTbdvVA2D0qJWE7buIra0lGzePKJGux987ccZW9h+6pjxv43rjU8NFz+7C5QeMGLdeuafNvRk1rAuSJHH5aiRjJweTlZ2LSmXAuOHdqVvbjT37LvP9gj0YGEioVAaM/KIzjXwrF0/X9C159aCCoquoenD5ASPGrs3TVZ1RX3ZFkiSGfP07tyPiAEhJycDS0pTg3z/Jv+5hZCJd+sxh8KA2vPtWy+IXXB7HDt3kh+l70Gq1dAny5Y13mumcP3vqLj/O2MOt6zGMnRyU709evxrNrIk7SEvLxkAl8ea7L9GmQ60S6yiKE4cjWDAjDI1WplNQLV4d0Ejn/PnTD1gw80BeP9qRlm0Vf+zsyfssnFXQzt2784iRkzrwUiuvUuk5cvAas6ZuQ6vV0r1XQ/q/q9/ujh+1liuXHmJtbcZ301/FxbUiO7ae5bdfD+bb3bgWzfI/P8K7hjO7d5zn18X70GhlWvhXZ/DnxWs3HiPLMhNnbmP/oetKPRjb8yn14CEjxq/Pa3erMeqLzkiSxJVrUYydson09GxcnW2YMeFlLCxMOHfxPmMmblJ+A5lPBgbSLrDk97m0Y4Rffj/G2k1nUakMsLUxY+Korrg6W5dYj+C/hwgGFRNJkkYBrwMaQAu8L8vysXLWVBnYIsty8b3UYmJQrRGSrQtZPwxCcqtOhS4fkbXkiyJts9fPKDrQU8EUQ7/uaO9fKRNNGo2W76fsZsZPr+KgtuSDfstoHlCVyl4FwQJHZyu+Ht+ZP5cf17nWzsGCecveoEIFQ9LTs3n75Z9pHlAVe0fLMtEGEHbkJhH3Eti55kPCLz5k/LQdrP75bT27wBbe9Hu5ER1f+UnnuLOTFZPHdGPpyrJ/zMJOPyQiMpmd87oTfi2e8YuOs3pqRz27t3vUxK+OE9k5Gt4eF0LY6Qf4N1CcuE7NPfhmYGO9a8pU54l7RDxIYucvrxB+JYbxPxxk9Y9B+jpfroufr4uic/hWwo7fw7+J+z+qrSiyEhI5+elE3ILa/L3xc+TXI1uZu28tywd881x/V6PRMnfqPqbM64m92oJP3vqDZv6eeHja5ds4OlkybFw71q44rXf9tG928do7jWno50FGejZSGe8QaNzgJVQu7sR+9DJG3rWxfv8r4oe/q2dn8fLbaJISiP24D0gSkoUSkLIc8CkZ+7aRsXcbFeo0xPKNj0j6flypdRlIBkxv/zE9/xjJw+Q4Qgf8wPbrR7kafzff5ouXXmPj5TCWntlKdbtKrH5lAvV+6s+ai3tZc3EvALUcKvNb72/KPBBkUK0Rkp0LWXMGKv1Bt4/JWjS0SNvsNdOL7A8058PI2bqgVDo0Gi3TJm5i7qJ3UDtZ0b/vfPwDa+Dppc63CV5/EisrUzZsG8au7eH8OHsHk2e8RqeuvnTq6gsogaBhn/1G9bwBw9JF+7C1NWfdli/QarUkJ2WUSF/YwavcuRvHruBhhJ+/x7hJG1mz4mM9u3GTNjJhTG/q1XFn4OBfCDt0jYAW1Tl64iYh+y6z6c/PqFDBkPiEVAB27DlPdnYum9d8TkZGNl16z6JLp3q4udjqfffTtV3mTkQcu7aMJPxcBOO+W8uaVUP0tX23lgljX6FeXQ8GfrSYsINXCGipTExERj3i0JGruDhX1LmmUQNPFs4tWbBFo9Ey6bv1LFzyAWq1Na+/OptWgT54VXXKt9mw7hhWVqZs2TmK7dvOMGfmFqbPeiv//IxpwbRoqT95snJFGJ5ejqSmZpVY23cT1rD4549xUtvw6iszCAysjVfVggHc+rVHsbI2Y/vOb9i29RSzZmxi5uy3qVrNmT/XDMPQUEVsTBK9e06lVWBtDA1VBAU15fXX/Rn59W8l0vWYsEPXuHMvnl0bPif8wn3GTd7EmmUf6NmNm7yJCaODqFfbjYGfLSfs8HUCmnsz/YcdfDywNQHNvdl/8CrTf9jBikXv0ayJJ20CBisD5etRDPn6D3asG1J8XRu/IPzCPcZNDmbN8o+K0BXMhDE9qVfbnYGfLiPs8DUCmldnzpTX8m2mzNqGhYWxznVTZm+l5Uvez15QRaDRaJk9ZRezfuqLg9qKQf1+pUVANR1/Uu1sxcjxXfljua5PZmJiyMgJ3XD3sCUuJoX3+v1Ck5c8sbQ0KZWmwtrmTd3H5HlBef3on/j5e+LhWVDfHZws+WJcW71+1LeRGz+tUsovOSmTt3sup4FfpVLrmT5pMz8uehtHtRUDXltAy1Y18fRyzLfZtP4UllamrNs6lF3bzzFvzk4mTu9Lxy6+dOziCyjt7ldDVuJdw5mkxHR+nLWDZX98REVbc8aPWsuJozdp7Ff8oFXY4evcuRvPrvWfKfVgymbW/Pq+nt24KZuZMKpHXj1YkV8PRn23keGfdaBJwyqs3XSaJSsOMeTDNlTzcmTd8vcxNFQRE5dCj9fnE9iyOoaGqhKVY2nHCDW91az95R1MTYz4ff0pZswLYfZ3vUqk5f8BsYF08RHLxIqBJEnNgK5AA1mW6wJtgXvlq+r5oqreFE14KADy/atgYg4WFf/mKl2MWr9B7sG1Zp3qvQABAABJREFUyLk5ZaLpyoVIXN1tcHGzwchIResONTm077qOjbOLNV7ejnqDSCMjFRUqKDHRnGwNsiyXiabChIRdo0enukiShG9tV5JTM4mJS9Gz863tiqO9fhDKzdmG6lXVZT4ABgg5fp8erTwVbdXtSU7LJiZBd8BjamyIXx3FAa9gpKKWpy1R8SUbFJVY5+EIerSrpuisqVZ0xqfr6jQxxM/XpUBnVXui4tKeq87HZMUmkHDyPHJObrn8/tM4cOMsCWnJz/13r16MxsXdGmc3a4yMVAS09+bwft3AhJOLFZ7VHPSe84hb8Wg0Whr6eQBgalYBExOjMtVn3MSfjL3bAci5dgEDc0sMKtrp2Zm16UbaumXKB1lGTlGyHQ3dqpB17iQA2edPYdLEv0x0NXSpzq1HkUQkRpGjzWX95f109m6mZ2dprGTmWZmYE5Uar3e+d61WrL+0v0w0FUZV0w/N2UL9gWnx+4Oy4OL5+7hXssPN3RYjI0PadarL/r2XdWzC9l6mS/cGALRuV5sTx27qtfc7t4fTvlPd/M+bNpxiwHutADAwMMCmonmJ9IXsv0RQ1wZK+1W3EskpGcTE6tbDmNhkUtOy8K1bCUmSCOragJB9FwH4fc1RBr0dkN9X2dlaACABGZnZ5OZqyMzKwcjIEAvz4g08Q/ZeIKhbI0VbvcpP15aahW+9yoq2bo0I2Xs+//zkacF8+XlXpDLsoi6cv4t7JXvc3O0wqmBIx0712Rd6Qcdmb+gFugcpExHt2tfl+NHr+fc0dM95XF1t8aqq1rkmOiqRA/sv07O3bvZwcTh/LoJKlRxwd7fHqIIhnTo3IDT0vI5NaOh5evRQMlPbd/Dl2NFryLKMqWmF/EFjVnYuhQutUeOqWNuYlVjXY0L2Xyaos69yT+u4k5yi73PExKUoz1sdd+WedvYlZN8lACRJIi1NCZSlpGbi6KAEvc3NjPOzwTIysnUyw55N1yWCutTP01VJ8YWKfNYy8a2TVw+61M/X9RhZltm+5zxdO9bLP7Zn7yVcXWypVigQURIuX3iIq3tFXNwqYmSkok2Hmhzcd03HxtnFpkh/0t3DDncPJTBj72hJxYrmJCbo+imlQelHbfL70VbtvTlSZD9qj8Ff+IsHQ27Q+CWPUvejly7cx62SHa5uee1uxzqEPdnu7rtMl+5KJnnrdj6cOHZLr93dtf0c7Toq7e6D+wm4V7Kjoq3S1jb282Lvnosl0hey/wpBXYpZD7r4ErJfmai+czeexg0qA9C8iRe79irPoalJoTqclVvqdq+0YwS/hpUxzbuX9XxciYrRv1Yg+CtEMKh4OANxsixnAciyHCfL8kNJkhpKkrRfkqRTkiTtlCTJWZIka0mSrkqSVB1AkqTfJUka+LQvliQpVZKk6ZIkXZQkaY8kSU0kSdonSdItSZK659lUliTpgCRJp/P+Xirie1R533NCkqRzkiTph8FLgWRlh5wcl/9ZTo5HstIfNAFU6DEE4w9+wNC/b8H1zl5IVvZor58sM02xMSk4qAuWjDioLYmNTX3m62OiknnnlaW80mk+rw3wK9OsIIDo2BScC+lzcrAiOvbFaKyjE9Jxti9wPp3szIj+C+clOS2bvScf0KxOgYO9+8hdun++lU+nhRH5DwVfouPTcHawKNBpb050/NN/Kzk1i71H79Ksvn5KsOD5ExeTioO6oF45OFoQH/NsdfT+3UQsLI0Z/+UWPnx9FYu+P4BGoy1TfSo7BzTx0fmfNfExqGwddGwkM+X5s3j9fexnLMPmy0kYWCuOf+6d65g0U5ahmPi1wsDMHMmy9MvYnC3seJAcm//5YUoczpa67e2UA7/xik9rLny8gtV9vuWr3fP1vqdnTX/WXdpXaj1PIlnZIScV6JOT4p7eH/T6HOOPfsSwVV+d4yqf5hh/PJcKfUcgWdkXee3fERuThNqpIC1erbYmNvqJQWYhG0NDFRYWJiQl6rZ1u3eczw8GpSQrAe8Fc3fzxitz+XroKuKLcNCfheiYZJycbPI/O6mtiY5J1rdxtC7S5k5EHCfP3KHPm/N4492FnLuozEF1aFsHU5MKtGg3icBOU3jnrZbYWBcvmKCvzYbomKQnbJJwUls/YaNo27P3Ao6O1tSorr/c52z4Hbq/PJ33PlzE9RtRxdIVE52ko8vRSV9XYRtDQxUW/2PvvOOiuL4+/MwuXaqUBcHewRY1dkHsHWyJiVHTi1FjNNHYWyyxxRijRpP8Eo2aWMHesGDvBbEXEJCuSG+78/4xCKyLUYpB39wnHz+BnTM7X2bmtnPPPdfKjISEFFJTMvjfr/v5dIjh8pI5s/348qvu/zhYfqa2mAQ9bRqNLTHRBWhzMdQGcOliCD7dZ9LLZxaTJr9R5IiCpxEdm4Szc/7nZV3w+5a/X6Kxye2XjBvVlTk/7MKr2xy++2EXI4fmLYHbe+AKnfss5JMRq5g5qVfhdMUk6r9HTtZEP+EMio59wqYA7WfOh2Bf1pJKFZT6IiU1gxV/HGLox20Lpacg4mKScTLoTxa+3F+5fJ+sbC2u5UvOOR4fk4KjJq8f5OBkSdxztqP5ObjnJm06FS+CCiAmOhFNvmflpLEm9olnFRudiJMmf71ralDv7tudV++6VbAnNCSO+xEPyc7Wcmj/VaKj9MvW82LwLjk9pRw4Wevb5LyT1as45TqGdgVcJjJfGb94OYxub/xIz7d+Yuo3PYpVhktyjLBh6wU8mxdv6Z/gv4dwBhWOPUB5SZJuSJK0RJIkL0mSjIEfgb6yLDcCfgNmyLL8CBgK/C5JUn/ATpblFf/w3WWA/bIsewBJwLdAB6AXMC3HJgboIMtyQ+BNYFEB3/MB8EiW5deB14GPJEmqXNAFJUn6WJKkM5Iknfn17L2CTIpM5sZ5ZCwdSsZvY1BVdEddvy1IEsadPiRrz68leq3i4uRszW/r3me1/8fs3nqZB//gZPgvk63VMWrBEQZ2rUl5Z2Vg7/26GwE/+7Ll+260qO/CN4uOl7LKHJ0z9zPQ14PyLiWbV0bw76PN1hF0/j4ff9GaxSv7ExX+iD1brzz7xJJGrUbtoCHrWhBxXw0m63oQVu8OByDx90WYeryGw/yVmHg0RBsXAyXssHoafdzbsCZoL3V+Gsgb6yexrMfXSOQNdBuVq0laVgZX40L/FT0Fkbl+HhmLPyfjl9GoKnqgbqAM2rTXTpI+/z0yfhqK9tZ5jPsUvMTs3+DypTDMzIypVl2JgtRqdcREP6Jegwr8uW4odetX4If5O0tFm1ar49GjVNatHMLoL7syYvQaZFnmUnAYKrWKw3vGEbB9DL+tOkxYuGFk2IsiLS2Tn1fs44vPDZcWe9R2Y//uiWzZ8DUD327F5yP+vbxpS3/azTuDvLAoo7+M6NDBYMqWtcTd499fOpyfevUr4b9tHH+t+4pfVuwlI6NkoqRLirUbTjF2ZFcObR/N2JFdGT99c+6xDt7u7No4gp/mvc0Py/aVir5tuy7SvVNeBN/inwMY/HZLyliY/sNZ/x5xscnMmLCVsVO6Fcvp+CKIj0sh5FYcjZsXb4lYSaHUuyZUra5MMFpbmzNmQk8mfP03n7z7C+Vc7VCpS2eoOmOSL2s2nKL3wKWkpGZiYpzn8Klfpzzb1w1jwx+f8PPvh1+KMrxlVxDB1yL5YEDRox7/P6CWpFL/96ohcgYVAlmWkyVJagS0BryBv1GcNnWAvTkhs2ogMsd+ryRJ/YCfgPoFfmkemcCunJ+DgAxZlrMkSQoCKuV8bgwsliSpAUrOooJc+x2BepIkPc4cagNUB+4W8PcsB5YDpE3p/tT1UerXu2HUSJlh00Xc1Ju9VSKFCuh8JuV8lpmGNugQKtcaaK+dQOVUAZN3ZynnWtph+tZEMtZOL1YSaUcnK71Z4NjoJBzzRZE8Lw5OVlSu5sClc2G5CQGLyuoNZ1i/5TwAdWuXIzKfvqjYRDSOJRt9VBhW77zO+r1Kkte61coSGZc3SxMVn5qbDPpJJi09SUUXawb3yLs3dlZ5na9+7asyb9X5ktO5JZj1O5RZmbo1HYnMF+0VFZeCxr7gJRuTFh6moqsNg3vXLTEtz0P1IW9T7aM3ADjY9WPSIksmgfD/BxycLImNzpvpio1Jxt7p+cqoo8aSqjUdcXFTZvhatKnK1cuFizIoCIsufbHo4ANA1q0rqO01PO7Oqe2d0D6I1bOXkx6hS08j/YSShyftaABl2/UEQPcwjofffQOAZGaOWTNv5NTCz9g+SWRyPK7WeRFK5awciEzSr2/fqd+Jfn+PB+B0xFXM1CbYW1gTl6rMYvau7VWiUUHqJt0waqwM/nURN5Bs8vRJNg7Pbg8u5bQHF/ZDWt47oT27B+NO7xdJk6OTjd7scXT0I71oUQCnHBuNsw3Z2VqSk9P1luTs2XmJTl3zmmkbWwvMzI3xbu8BQLtOdfDf/PwRrav/Ps66TUqOuroebkRFJeQei4p+hMZJX5/GyZqofJEv+W00Ghs6tKuDJEnUq1MelUri4cMUtu28QOsWNTA2VmNf1pKGDSoSdCWC8m4FR2flavvrCOs2nsjRVv4JbQlonPSTj2qcbIiKfvSEjTX3wuIIj3iAT795uZp7v7mA9WtG4OiQ9/d5tXZn6oyNPHiYTFm75yv3ThobPV0xUYa6HttonG2VZ5qUjq1tGYIuhbJvz0UWzt9KUlIakiRhYmpETPQjDh4I5kjgVTIysklJSWfs6D+ZNeed59KUe10nWz1t0dEJudEPetoiE3B2ttPTlp+qVZ2xsDDl5s1I6tQp3uB89boTrPNT3s+67q5EReV/XokFv2/5+yXRj3L7JZu3nWf8V90A6NK+DhO+9TO43usNKxMWsYkHCSmUtX368snV646zbnM+Xfnfo5hENI5P6HK0fuJd09eena1l74FgNv05NPezi5fD2B1wmXmLdpGYlI5KJWFqasQ7bxouqX0WDk6WxBj0J5+/v5aSnMGY4ev46HMvPOoZRssVB3unMsRG57UrcTHJODxnO/qYwL03aeFdtUSi0Zw01kTne1Yx0Yk4PvGeOWqsiYnOX+9m6NW7SjSmfl+tdZtatG6j9DM3bzhdKIfa6nUnWed3FnjK+1ZgvZuob5PzTlat5MhviwcDcDc0joNH9JcLAlSt7IiFhQk3bsdQ1/35n3dJjxGOnbrLst+PsmrJwNzlxALB8yIigwqJLMtaWZYPyrI8GSXypw8QLMtyg5x/dWVZ7gggSZIKqA2kAs+KFc2S8xbS6oDHS9F05DntvgSiURxLjQGTAr5HAobl01NZluWibc+Sg/b0djKWDSdj2XC0144rUT6A5FYTMlIh+aH+CSoVWORUuCo16hpN0MWEQkYq6XMGkLHwAzIWfoAu/HqxHUEANT1cCL/3kMiIBLKytOzffZUWbZ5vR6uY6EQy0pUhYFJiOkHnw6lQ6Z870s/DgL6N8Vv5EX4rP6KdZw38d15ClmUuXI7Aqoxpget+/y0GdKmJ34Ku+C3oSrsm5fE/qKzhvnA9DisLE5zKmhucs3DNBZJSsxj3fiO9z/PnF9p/OoKqriUXiTOgpwd+y/rgt6wP7VpUwn+vkg/iwtVorMqY4GRv6LRa+L/TJKVkMu6zwncCi8vNJWvY+ZovO1/zFY6gJ6jpriEiLIHIiEdkZWk5tOcGzT2rPNe5Ndw1pCRlkPBQcVpeOBNGxcrPnyD3aaTu3EDcyIHEjRxI+slAzL27AGBcow661GR0Dw2dGhmnjyg7iQGm9V4nO1zxsUtWNrm5Pyz7DCZ1/9Zi6wM4d/86Ve3KUcFGg7HKiN61vdh584SeTURiDJ6VlJwMNezLY2pkkusIkpDwre3Jxqslly9Ie2o7GUuGkbFkGNqrJ3KjfCS3mpCe8uz2oObrSnsAevmFVLWaIscWLQWfex1X7oXGERH+gKysbPbuvIRnG/3Ewa3b1GL7FiWp6v69l3m9SZXcnCc6nY59e4Jy81aAkjeltVctzp5WnvHpE7epUuX585EMeLM5/n9/gf/fX9De2wO/beeU+uvSPawszXLzsDzGydEayzKmXLh0D1mW8dt2jnZeyg417du4c/K04sC/GxpLVpYWO7syuDjb5n6empbJxUthVKmkv7yxQG39W+G//iv8139F+7Z18dt6RtF2MQQrq6doszTlwsUQRdvWM7TzrkPNGuU4fmga+3dNZP+uiThrbNj090gcHayJjUvMzQ1yKSgUnU7G7h+cBk/iUac890JjCQ+PJyszm107z+Plrb9HRhtvD7b4nQZg755LNGlaDUmS+P3PYezcN5Gd+yYyYKAnH37cnrcGtOaLkd3Ze2AyO/dN5Lv5A3m9afVCO4IA6tStoKdt545zeHvrD2i9vevg7684A/fsvkDTZkrOu/DweLKztQDcj3jA3TvRejugFZUBbzTDf81Q/NcMpX0bd/x2XFCeaVAYVpaGfQ4nByvlfQsKU57pjgu081LKjJOjNafOKu/9idN3qFRe6ROFhsXnPtPga/fJzMzG7hnLEge80Rz/tcPwXztM0bX9fI6ufygHlmZcCMopB9vP55YDgGOnblOlkqPe8p81v37C/m2j2b9tNIPfbsEn77UpkiMIoJZHOcLvPeR+Tn8yYPdVWrap/uwTgawsLeNHbaRT9zrFnlAsiMftaFROO3pwzw2aeRYY+P9UDu6+USJLxABqe7gSFhrP/cf17q4gPNvo/91Kvas4PfbvDabxE/VuwJ4gOuTL0wbwIF5xeCUmprHx75P49NbfMe2fGPBGU/zXDMF/zRDat6mF3/b85cDs2eVg+wXaeSl/w+NE/TqdjqW/HaJ/HyU/WVjOEjaAiMgE7oTE4VrO9rk1QsmOEa5cj2LynB0smfsG9mWLltfu/xMqqfT/vWoI92EhyMn/o5Nl+XF24gbAVaCjJEnNZVk+nrNsrIYsy8EozpurwDjgfzk2xYkltAHCZVnWSZI0GCUK6Ul2A59JkrQ/J7KoBhAhy3KJrH3S3TyDXL0xpsNXKFsJ+y/MPWb66SIylg0HtTGm70wDtRokFbo7F9Ge3V0Sly8QIyMVX4zpwNdD1qHTyXTxqUvlqo78tuQwNd2dadmmOteCI5kwchPJiRkcD7zF78uO8PvGD7l3N54lCw4gATLw5qAmVKn+7M50YfBqUY3AY7fp2G8JZqbGzJzQPfeY76AV+K1UUknNXRzAtj3BpKVn4dVzEX17NmDYh54EXbnP0G82kJiUzoEjN1n8SyDb1pRMKiivRuUIPBdBxyFbMDNVM3NoXgfKd+QO/BZ0JSoulWUbgqniak3vr5RlEo+3kF+14xoHTkegVknYWJkya9iLccJ4NSlP4KkwOr77N2amRsz8Km/7Ut9PN+K3rA9RscksW3uBKuVt6T1kk6LTx4N+XUq+U/YszDQOdD6zEWNrS2SdjlojBrPNvSvZSaW7BHHN+9NoU6MhDpa2hM3cwuRtK/jtWMk4Lv4JtZGKoV+3YdwwP3RamU493alU1Z4/lh2nRm0Nzb2qcD04iqlfbycpMZ0Th++yavkJVqwbiFqt4qMvWjHms03IMlSv7USXXiW7cWLG2aOYNmqB49KNyBnpPPpxeu4xhwWriBs5EICkVYux+WIKqve/RJeYQEKOnWkdZQcxkMkMPs+j5XNLRJdW1jF67xI29p+BWlKx+tIersWFMrb1QC5E3mTnrRNMCFjBD12/YMjrvZCR+Xz7/NzzW1SoS0RiLKEJxY+kKgjdjdPINRpj+uUvuVvLP8Z0yI9kLBmmtAeDpivtgUqF7vYFtGeU9sCoeU/UtZqCToucmqx3fmEwMlIzelxPhn/6P7RamZ69GlG1moZli/dS28MNL+/a+PRuzOSx6+nVdR7WNhbMmJOXu+j82RA0zja4ldcflA/7sjOTx65nwXfbsS1rweTpfZ+89HPh1aomh45co0PPuZibGTNzSr/cYz5v/oD/318AMHmsb87W8ll4tqyJZ6uaAPTxbcy4KRvo3vd7jI3VzJ7WD0mSGPBmc8ZO3kC3PguQZejt04haNQy3JP5Hba1rc+jwVTp0m6lom563Y5NPv3n4r/9K0Ta+b+7W8p6tauHZynCXrvzs3nuRteuOoVarMDM1ZsGcgYVKOGxkpGbs+N589tFydDodvr2aUK26Mz/9uBMPj/K0aVuHXn2aMn7MGrp3moG1rQVz5g169heXAEZGasZN6MsnHy5Bq9PRq3czqlV3YfGi7XjUqYB327r07tucsWNW0aXTNGxsLJg7/10Azp29za8r9mFkrEYlSUyY9AZ2OdFSX4/6ndOnbpGQkEy7NhMZMrQrffoWvk31almDQ0dv0MF3AeZmJsycnLezkM/bi/Ffo0TVTP6mJ2OnbFSeaYsaeLZUnATTJ/gwc94OsrU6TE2MmDZeiaDcHRCM/44LGBkpz/T7WW8W6pl6tarJoaPX6eAzP6cc9MnT9daPudvEK7o2kJ6ejWfLPF0AO3ZfolunZwXaFx0jIxUjxnTgqyF/odPJdPWpR+Wqjvy6JJCa7i60alOdq8H3mTByE0mJ6RwLvMlvyw6zcuNHHNhzlYvnwkhMSGPXFiWh+Nhp3aleU/OMqz4faiMVn3/txbhhW9BpdXTMbUdPUKO2U047Gs20r7eTlJjBicMhrFx+khXrBgAQdT+R2Ohk6jUsmYglIyM1X43rzvDP/kCn1dHDtxFVqmn4+ad91HZ3xdO7Nj17NWLKuA306bYAaxtzvp3zZu7558+G4KSxwdVNv95d8N12bt5Q2qwPPvGmQqWi5ZJTysFNOvRaqLxv+XJc+by9BP81yk52k8d0Z+zUzTnloDqeLRTn37bdQazZoDh0O7SpTZ8eyqTL2YuhrPj9MEZGalQqiSljuv9jdNwzdRZzjDB3cQCpqVmMGL8RABeNDUvnvlFkPYL/HtKL2D3p/ys5S8R+BGyBbOAW8DHghpK/xwbFwbYQCAT8gCayLCdJkrQASMqJKCrou5NlWbbM+XkKkCzL8rz8xyRJqg5sRPFb7AI+z/m8Ejlby+dEI30L9ECJEooFfHNyGD2Vf1omVpqYT9lGZOq/l2ugMLhYvI/8YGVpyzBAKqt0iOXgac+w/PeRPCYhh84rbRkFIlX8ijVKvveXjrfl60ifvZzrwOWlJwhNMkxa/DJQ0WoIkb2alrYMA1w2K1sS280yzLVS2jwcu4u0id1KW0aBmE/fTmLmxtKWUSDWJn0gdfOzDUsDi16Qsb20VRhiqrxn6dqXT5uZuhtZuhc3iVUcjFWdIGl9acsoGKt+kPxyllEs+xCd+ntpqygQjcW7hCQtLm0ZBlSyUhyHCRkv3/tma9oPEv8ubRkFY/3mSzk+AJDKDnoFY1een9+uDCn18ez77kteqXssIoMKgSzLZwGDHbyAOKCgvYRzp85kWf7HzJiPHUE5P08p6FhORFL+eMoxOZ+HoOQterysbFzOP4FAIBAIBAKBQCAQCP5fo36l3DAvByJnkEAgEAgEAoFAIBAIBALBfwgRGfQvI0nSSeDJ/S8HyrIcVBp6BAKBQCAQCAQCgUAgeJVRvYJbu5c2whn0LyPL8suXwEIgEAgEAoFAIBAIBALBfwaxTEwgEAgEAoFAIBAIBAKB4D+EiAwSCAQCgUAgEAgEAoFA8MoiEkgXHhEZJBAIBAKBQCAQCAQCgUDwH0JEBgkEAoFAIBAIBAKBQCB4ZREJpAuPiAwSCAQCgUAgEAgEAoFAIPgPIcmyXNoaBC8H4kUQCAQCgUAgEAgEgv+f/L8OnfnrxtBSH8/2r7H4lbrHYpmYAICEjPWlLaFAbE37vdTatoWMKm0ZBnSvNB+AE1FTSldIATRznsKm2yNKW0aB9K66EDlySWnLKBDJZQihSS+ntopWQ5A+a1baMgpEXnoC+cHK0pZhgFR2EABy0JTSFVIAUt0pkPh3acsoGOs3IWtnaasoGOMudPcfWNoqCmSbzypIegnbUat+AMhXvy1lIYZItSfAo7WlLaNgbN56qcuBTg4obRUFopLakaXbXdoyCsRY1Qk5/vfSlmGAZP+u8kPyxlLVUSCWfYhO/b20VRSIxuJdHma8nO2onembpS3hhSKWiRUesUxMIBAIBAKBQCAQCAQCgeA/hIgMEggEAoFAIBAIBAKBQPDKIiKDCo+IDBIIBAKBQCAQCAQCgUAg+A8hnEECgUAgEAgEAoFAIBAIBP8hxDIxgUAgEAgEAoFAIBAIBK8sKknEuRQWcccEAoFAIBAIBAKBQCAQCP5DCGeQQCAQCAQCgUAgEAgEAsF/CLFMTCAQCAQCgUAgEAgEAsEri9hNrPAIZ5DgHzl+5AYLvtuBTqejZ+9GDP7AS+94ZmY2U8dv4NqV+9jYWPDt3Dcp52pHdpaWGVM2c/1qJFqtji49GvDuh8q5vp3nYWFhikotoVar+OOvIS+FtuioBKaM38iD+GQkScK3T2P6v9OiaDcuH9dOx+C3LBidVqZplwq0e7Oa3vFj20I5ujUElUrCxFxNvy/q4VzRigdRqXz30UGc3CwBqFjLlr5f1Cu2nvxcOnmf1T+eQ6eT8epWle4D3PWO7/r7Goe230allrC2NeODMU1xcC7D1XPRrPnpXK5d5L1EPpvUkkat3UpM2/UzsWz7+So6nczrndxo80ZVveMnt9/j+LZQVGoJEzMjeg33QFPBivMHIji88W6uXdTdJIYuakm5qtYlpk2WZWb8eIjAEyGYmRkx65uOeNRwMrD7/pdj+O++SmJSBud26b/nOw/cYPHvJ5EkqFnVgfkTuxRb1+ljISyddwidTqazrwf9331d7/ilcxEsm3+IO7fiGDejC57tq+cei4lKZMH0AGKjk5AkiW9/8MG5XMnds2fx68DxdK/bkpikh9SdPuBfuy7kPM/v9xB47DZmZsbMmtgdj5ouBnbfLzuA/84gEpPSObd/dO7np8/fY9bCPVy/HcP8ab3o3LZ2yWr77RyB5+9jZqJm1tBmeFQpq2eTlpHNiPlHuReVhFol4d3YlVHvNABg04E7zF11AU1ZcwAGdK5Bv/ZVn7xM4fTM38GhozcxMzNm9uReeNQqZ2B3+ep9xk7dRHpGNl4tqzN+VFckSeLq9Ugmz95KRkY2aiMVU8Z0p56HG0nJ6Xw9cQP3ox+hzdbx/jst6dOzYeF0zdrEocNXFV0z3sbDvbyhruAwxk5YQ3p6Fl6tazN+bG8kSWLhjzsI2B+ESiVhX9aKWTPeRuNkw8lTNxky/FfcXJV73qF9PYZ+1rnI96+hU10+rjsQFSr23DvIhpvbDGxalWvC27V6I8sydxPvMe/sUuo61OajOnnlws3ShTlnlnAi6myRtUDOfZu3nUNHbyj3bUqfpzzPCMZO2UR6RhZeLWsw/qtuec9zlj8Zmdmo1SqmjOlJvTpu3A6JZdzUTQRfu8+XQzrwwcBWxdf5y2kCz97HzFTNrOEt8Khqr2eTlpHNiDmBeeXgdTdGDdJ/h3YfC+WLOYGsn9eVutX0zy+0nvk7OXQspxxM8n16OZjmp9y3FtUZP6oLkiRx7UYUk2dvIzUtE1cXW+ZN642lpRlZ2VomfLuFK9cjydbq8O1an0/ebV14bS95WTh8OJiZM9aj08n07duCjz7upHc8MzOLMWP+4EpwGLa2ZViw4ANc3ex5+DCZEV+s4PLle/j6NmPipDeLdP0nOXL4CrNnbkKr09Gnb3M+/KiDgZ6xY/7kyhVFz7wF7+Lqas+xo9dYuGALWVlajI3VjPral6bNauidO3TIcsLD4vHbOrbYOpW2ai+Bx3Paqgnd8ajpbGD3/bJD+O/KaasCvsr9/H9rT7Fh6wXUahVlbS2YMa4bri42xdMzdxuHjl7HzMxEqT9quxrYXb4awdjJG3Lqj5qM/7o7kiQx4pu13A2NAyApKQ0rK3P81w7j0uUwJs7wy73GsI/b0aGtR5F1njx6m0Vz96HT6ejm24B33m+ud/zC2Xv8OG8fd27GMHmWL2061ALg5vVoFszYRUpKJiq1xMAPWtCuk3tBlygyx4/c5PvvdqDTyfTs3ZBBH3jqHVfGMpu4fuU+1jbmfDv3Dcq52pGVlc3saVu5FhyBpJL4ckxXGr1euUS1Cf4bCGeQ4KlotTrmztzKj8vfw0ljzbtvLaN1m9pUqZo36N2y6SxW1uZs3D6SPTsv8dPC3cyY25+APZfJzNKyZtMw0tMy6d9rER271KOcqx0AS359H1u7Mi+VNhMTI74Y1YVa7uVISclgcP8lNGleTe87C4tOK7Ppp8t8MqspNg7mLBx2GI9mGpwrWuXaNPQuR4vuFQG4fDyKLT9f4eOZTQFwcCnDqKWeBX53cdFpdaxceJbR870p62jOlE/28FpLV1wr5XUMKla3Y8ryTpiaGRHgd5O/l13g8yktqd1Qw/RfFedFcmIGo9/eRp3XDTskRdcms2VJMB/MaIK1gxk/jThG7WZOaCrk3bf63i407VYBgCsnotm+4hrvT3+d17xdec1b6YxE3U1i1fSzJeoIAgg8GUJoeAK7Vw/m4pUopn6/n3VL+xvYeTevzIBe9ek84A+9z0PCH7J89RnWLO6HjZUZ8Q9Ti61Jq9Wx+LuDzP6pFw4aS4YN+ovmnlWoWCVvwOPkbMVXUzqwYdU5g/PnTNrDW++/TqNmFUlLzURS/buzK78f387igxtY+e6kf/W6AIHHbxMa9oDd6z/jYvB9ps7Zxbpf3zOw825VgwF9G9P5jaV6n7s4WzNrYg9+W32y5LWdjyQ0MondP3bn4s14pi4/w7rZHQ3s3utZi2Z1NGRmaXlv6gECz93Hs6EyOO3SogKTPmxcMnqO3STkXjx7Nn3BxcvhTJm9lfW/f2JgN2X2VqaP96F+HTc++mIVgcdu4tWyBnN/3MPnH7bBq2UNDh29wdxFe1j18/usXn+SqlWcWPb9Ozx4mELnvovo0aUeJsbP100JPHyVkHux7NkxnouXQpkyfT3r14401DV9PdOnvEn9ehX56LOfCTxyFa/W7nz4XltGDOsKwMo/D/HT0t1Mm/wGAI0bVuHnJR8X464pqJD4rN5gJhz7jvi0B3zvNY2TUecIS7qfa1OujIZ+1Xvw9eFppGSlYmOi1F1BcVcZfnACAJbGZVjRfh7nY4OKrSnw6A1CwuLZs/lL5XnO2sL6Pz41sJsyawvTJ/jmPM+Vec9z0S4+/6it8jyPXGfuol2sWv4httbmjP+qGwEHrxZbI0Dg2ftKOVjqw8UbcUxddpJ1c7sa2L3n606zus5KOZi0j8CzEXg2UtqD5LQsVm27Rv0aDsXXc+wmIWEP2LNxuHLfvtvO+v99ZGA35bttTB/XQ7lvI1YTePyW4hSasYUxX3SkScNKbNhyjl/+PMaIT9uya18wmVnZbF07hLT0TLq9+RPdOtbBrZzd82t7ycuCVqtj+rS/+fW34Wg0trzR7zu829ajWrU8B/yGDcewsbZg956pbN9+hnnzN/P99x9iamrM8C96cPPmfW7eiCyWjvx6vp2+nhW/fo6zxpY335iHt3cdqubTs2nDCaxtLNi5exI7tp9lwbwtzP/+PezsyrB46Sc4Odlw88Z9PvloKfsPTc89b++ei1hYmJaITshpq8Ifsnvdp0pbNXcX635518DOu1U1BvRtROc3l+l9XruGhg2/vYe5mTFrN51j3pIDfD/dt+h6HtcffqO4eDmMKbP8Wb/ScIJ3yix/pk/sRf065flo+B8EHruBV8uaLJz9Vq7N7AU7sLRU7lX1qho2rhqCkZGamNhEfN76EW/PWhgZqQutUavV8f3sPSxY2h9HjTUfD/idVl7VqVQ1rx7QuFgzbmp3/lqp34abmRkxbnoPylcsS1xMEh8O+B9NWlTBysqs0Dqepm3ezG0sWj4YJ4017731M63b1KKy3ljmHNbWZmzYPoK9O4P4aeFeZsx9A/+NykTA6k1DeRCfzJdDVvG/tZ+gUv23M8CIyKDC899+YwT/yJXL4bhVsMfVrSzGxkZ06FyXwAP6HbvAg1fp1vM1ANp28OD0yTvIsgwSpKdmkp2tJSMjGyNjNWUsS65BfBHaHBytqOWuDJ7KlDGlUmVHYmMSi6Xz3vUE7MuVwd6lDEbGKl5r40rw8Wg9G7Myxrk/Z6Zr+bfqsTtXH6BxtcSpnCVGxmqatq3AuSPheja1G2owNVMGY9Xc7XkQa+i0OH0wjHpNXXLtSoKwG8p9K+tigZGxivqeLlw9HqNnY2bxxH0r4HsuHrpPPS/D2driEnD0Dj6daiNJEg08XEhMziAmPsXAroGHC072hk7P9duCedu3HjY5HQp7O4tia7oeHE258ja4uNlgbKzGq2MNjh26o2fjXM6aKtUdDRw9oXfi0Wp1NGqmOCXNLUwwMzPm3+TwrQs8SCleeSsqAYE38OlST3medVxJTE4nJi7JwK5BHVecHKwMPndzsaVmNc0LcaAFnA7Hp00lRVsNBxJTM4l5mKZnY25qRLM6GgBMjNW4V7EjKr74DsYC9Ry6hm+3BoqeuuVJTDK8VzFxSSSnZNCgbnklyrJbAwIOXQNAkiAlJQOApOR0nByV+ykhkZKSgSzLpKRmYmNtjpH6+bsoAQeC8O35uqKrfiUSk9KIiX2kryv2Eckp6TSor9xP356vE7BfcahYWuZ17tPSMl9IPVzDriqRKdFEp8aSLWsJjDhBM+dGejadKnqz/e4+UrKU5/co07BMtCzXhLPRl8jQZhZbU8Chq/h2LeTz7NqAgINXAJAk6YnnqTiv7MtaUs/DDSOjkulmBpwKw6dNFUVnTUcSU7KIeaD/jpubGtGsrjIpYWKsxr1qWb1ysGj1BT7s7YGJceEHlAZ6Aq/j27V+Ie9b/dxyEHIvntdfU+rblk2rsudA3v1MS8siO1tLeno2xkZqLMsUru/0speFS5dCqFDBkfLlHTAxMaJr10bsD7ioZ7M/4BI+vs0A6NTpNU4cv44sy1hYmNKoUTVMTUqufQq6FJqrx9jEiC5dG7J/v76jdf/+IHx8mgDQsVMDTp64gSzL1HYvj5OTMoFWrboL6RlZZGZmAZCaksHKPw7wyaeGzvuiEnD4Jj6d6+RrqzKIiUs2sFPaKkuDz5s1qoh5Ttte36McUcXs4wYcuoJvt9dyykEFpe2M1f/OmNhEkpPTaVC3Qk578Fpu/fEYWZbZuS+I7p3rA2BubpLr+MnIzC7WO3j18n1cy9tRzs0OY2M17TrV5sjBG3o2LuVsqVrDyaANL1/RnvIVlUg4Bycr7OzKkPCg5NpWZSxT9omxzDU9m8MHr9K1ZwMAvDu4cyZnLHP3diyNmyiRQGXtLbGyMuNq8P0nLyEQPBPhDBI8lZjoRDSavCgRJ421gXMkNjoRpxwbIyM1lpamPEpIpV2HOphZmNCt3Xf07DiXAYNbYWOTN+Ad/snvDHpzCZs3nH7ptAHcj3jIjWuReNQt3rKnR/Fp2DrmdaxsHMx4FJdmYHdkSwgz393Ptl+u4jskLxT2QVQq84cE8tNXx7gTFF8sLU/yMC6Vsk55f3dZRwseFqDtMYd23KFeU8OlMyf3h9KsXcUS1ZYYn46NQ959s3Yw41F8uoHd8a2hzH3/ILt+u06PTw1Ddy8FRlLfy1BzcYmOTcbFMa+j5exoSXSsYYfsaYSEPSQkPIG3hq7jzc/+5vDJkGJriotJxlGT56hwdLIkPub5NIXfS8DSypSpX2/js7fXsPyHw2i1umJrelWIjk3CRZMXPebsaE10rKEzqDSIjk/DJZ9D0bmsBdH/4OhJTMnkwJkImtfLi9TbeyKMniN3MHzeESLjDJ2WhdITm4hzvrrX2cma6Cfq3uiYRJydrPVtcgYI40Z2Zc6iPXh1m8d3P+xm5OfKcowBbzTldkgsrbvMpedbPzF+VJdCzXBGRz/C2TkvesJZY0t09CNDG43tU22+/2E7Xu2msHX7Wb4Ymhd1cuFiCD17z+HDT5dx81bRoxHszeyITXuQ+3tc2gPszfQjPspZOuNq6cKcVhOZ13oyDZ3qGnyPp2szDkUcL7KO/ETHJuHsnO95ap7yPPOXD41NbvkYN6orc37YhVe3OXz3wy5GDtVfXlNSRD9IxcUhXzmwtyD6wdPbq8TkTA6cDs8tB8G344mMS6VN45JZymxwT563HOTYVK/imOsY2rUvmMho5fNO7dwxNzemVdf5ePf8nvffaYGtTeEmC172shATnYCzS54+jbOdob6YBFxybIyM1FhZmZOQULy666l6YhJwdrbN06OxJeYJPTHRj3B2sc3VY2llZqBn754LuNd2wyTHUfXjou0MftcbM3OTEtNq2FZZFbmt2rDtIp7Nir5kGB6XgyfagyecQQZtRgF1zJnzIdiXtaRShbxonYtBYXTrt5Ceby5i6ljfIkUFgdI3csp3zxw1VsQW4Z5duXyfrGwtruWfP0rvWcRGJ+WOU+BpY5mk3PFO/rFM9ZrOHD54nexsLffDH3LtaiTRUfrvrUDwPAhnkOCFEHw5HLVKYvu+MWzeOYo1fxwlIlzpBC//42NWrvuchUsGseGvk5w/c/cZ3/bvaQNITc3gm5Fr+XJ0V70ZshdJq56VGPd7W7p/UJt9a24BYF3WlAl/tmPUEk96fuLOn7PPk56S9a/oeZKje+4Scv0BXfvr50JJiE8j/M4j6jQpeYfL89C8R0W+/q0Nnd+ryf6/busdu3ctAWNTNc6VDCM5SptsrY7Q8ARWLuzD/EmdmTgvgMSkjFLTo83WEXT+Ph9/0ZrFK/sTFf6IPVuvPPtEwUtFtlbHqO+PMbBrDcprFGeld2NXApb2ZMuCrrSo58w3i0+Uqsa1G08xdmRnDm3/irFfdmH8dD8Ajpy4Re0aLhze+TV+qz9j2tztJCcbOoBfJF9+0Y1DAVPo0a0Rf645DICHe3n2753Mlk2jGfi2J58P//WFalBLKspZahh7dCZzzy5hWIMPKGOU5wywM7WhkrUb52KKv0SsJFi74RRjR3bl0PbRjB3ZlfHTN5e2JKUcLDjMwG61KO9shU4nM/u3s4x5r9GzT/6XmDHRhzUbT9N70M+kpGZikjPQvRQcgUql4vCOUQT4fcFvq48TFvHgGd9W8rwMZeFV4tbNSBbM38KkqUoOo2tXwwkLi6N9h/qlrKxgtuy6TPC1KD4Y0LS0pQCwbddFunfSz4lZv255tq8fwYZVQ/j590NkZJRO/xcgLjaZGRO2MnZKN1T/8hL6p9Hd97XcpWXfz9lJ3frlURcimvb/KypJKvV/rxrirRE8FSeNtd5sTUx0Io5O+rlXHDXWuTMo2dlakpMzsLG1YPeOSzRrWR0jYzVl7S2p91oFrgZH5H4vKGGNbdrWJvhyxEujLTtLyzcj19K5W3282xc9Wd1jbOzNSYjNG9A8ikvHxsH8qfYN2pTj8rEoAIxM1JSxVmaUyle3xaGcBbERJTczZudgwYOYvAiDB7Gp2BWgLfhMFFtXXWHETE+MTfRnZk4duEfD1iW3FOAx1vZmPIrLu2+JcenY2D/dMVfPy4UrTyy/uxQYSf02JbdEbPXmi/h+sBrfD1bjZF+GyHyRQFGxyWgcDUOyn4azoyXeLStjbKTGzcWGSuVtCY14WCx9Dk6WxEbnzXbFxiRj7/R8mhw1llSt6YiLmw1qIxUt2lTl1vXYYul52Vm94Qy+g1bgO2gFTg6WuTPzAFGxiWgcS8+JuHrnDXy/2onvVztxsjMnMt8SxKgHqWjsC44UmLTsFBVdrBjcvVbuZ3ZWprnLYvq1q0LwncK/Z6vXncTn7SX4vL0ER3srovLVvVExiWieqHs1TtZ6yw+iYhLR5Cwf2rztAh29lSi+Lu09uHRFqXs3bT1HR29l6WXF8va4lbPjTk5i0afqWnsYnz5z8OkzB0dHa6Ki8v62qOgEvehRAI3GhqjohH+0AejRvTF79inLViwtzSiTk/PDy9Od7GwtDx4+fxRgfuLTH+Jonpf828G8LPHp+s8jPu0BJ6POoZW1RKfGcj85inKWmtzjrV2bcjzyLFpZWyQNAKvXncDn7cX4vL0YRwdLovLNJkdFP+V55i8f0Y9yy8fmbefp2Pbx86zDpeDCt+dP1bnjOr4jtuE7YptSDvJFtUXFp+YmRX+SSUtOKOWgpzJ5kZKWxc17CQyasIe2H23i4o1Yhsw4QNCtwkXbrl5/Cp8BS/EZsBRHByv9e/K85SDHpmolR377cRCbVn5Ct451KO+mRBts2x1E6+bVMDZSY1/Wkob1yxN05dlLP16lsuCksSUqMk9fdNRDQ31OtkTm2GRna0lKSsPWtuh5Jv9Rj5MtUVEJeXqiE/SiNRTNNkRFJuTqSU5Kz9UTFfWQL4b9wszZA6lQwRGACxfuEnz5Hh3bTWHQgIWEhMbw7qBFRdK3euNZfAf/iu/gX3Gyf7KtSip0W3Xs9F2W/XGMJd/1xcSk8Mv7V687js9bP+Lz1o855eCJ9sDxiXLgaK1v80Qdk52tZe+BYLp2LHiDlKqVnbAwN+HG7egCjz8LBydLYvLds9joJBwLcc9SkjMYM3wdH33uhUc9w+TYxcFRY6UXhVbwWMYqd7yTfyxjZKRmxOgurFo/hLmL3iY5KZ0KFYueFF/w30U4gwRPpbaHK2Gh8dwPf0BWVjZ7dwXh2aaWnk3rNrXYvuU8APv3BtO4ibKm39nFhjOnlHwlaamZXL4URsXKjqSlZubmF0hLzeTk8VtUrVb4BM0vQpssy3w7eTOVKjvy9qCWhdZUEOVr2hAXkUJ8VCrZWTrOH4zAo5lGzyY2Iq8zdfVUDA6uSgcjOSEDnVYGID4yhdiIFOydi59b5jGVa5UlOjyJ2MhksrO0nNx/j9da6ofQh954wP/mn2bELE+s7QydMScCQmlewkvEANxq2BB3P4UHOfftYmAktZvpvydx+Rxj10/H4FAu797odDJBhyOp71lyEUsDetXH79cB+P06gHatquK/+yqyLHMhOBKrMqYF5gZ6Gu1bVeXUBWXQ9DAhjZCwBNyKsaMHQE13DRFhCURGPCIrS8uhPTdo7lnluc6t4a4hJSmDhJxE1hfOhFGxctlnnPVqM6BvY/xWfoTfyo9o51kD/52XlOd5OUJ5ngXkBvrXtHWpgd+8LvjN60K7Jq74HwxRtN2Iw8rCGCc7w0HwwrWXSErNYtx7+rsn5c8vtP9MBFVdC59MfcAbTfFfMwT/NUNo36YWftsvKHqCwrCyNDO4V04OVliWMeVCUBiyLOO3/QLtvJT62cnRilPnQgA4cfoOlcor75mLsy3HTyv1clx8MndD43Bz/edw/AFvtcZ/42j8N46mfdu6+G05rei6GIKVpTlOjk8M6BxtsCxjxoWLyv3023Kadt7KMqyQ0DznZ8D+IKpUVurp2LhEJdcccCkoFJ1Oxq6Ig9IbCXcoV8YZjYUjRpIaT9dmnIzST+Z+POosde0VJ4a1iSXlLJ2JSsnT5unavNhLxAa80Qz/NUPxXzOU9m3c8duR/3kavvsGz3PHBdp5KRqdHK05dVaJ7lWeZ8kNRgZ0rYnfwu74LexOu6bl8T+o5Mq4cD0WqzLGOJU1bA8Xrj5PUkoW4z7I20nRqowJJ1a9wf4Vvdm/ojf1aziyZLx3oXcTG9CvCf6rP8N/9We096qF346LhbxvF2nnWROA+AdKu6/T6Vj6WyD9eysJ3l00NpzMiZZOTcvk4uVwqlR6dsLrV6ks1K1bkdDQGMLD48jMzGbHjrN4t9V3BHi3rYe/nxLFuHv3eZo1q4n0gmbc69StwL3QWMLD48nKzGbnjnN4e+svz/T2roO//ykA9uy+QNNm1ZEkicTEVIZ8+jMjRvakYcO89rb/W605EPgtewKmsHL1CCpVdOL3lcOLpG9An0b4/fEBfn98oLRVuy4/0VY9/0TUletRTP5uF0vm9MW+bNHqsQFvNMd/7TD81w5T6o/t53PKwT2lPXjCGeTkaI2lpRkXgu7ltAfnaeeVt6z/2KnbVKnkqLeULCziAdnZisM7IvIhd0JicXUp2vKsWh7lCL/3kPsRCWRlaQnYfZWWbao/+0QgK0vL+FEb6dS9Tu4OYyWJMpZ5wP3wh7ljmdYFjGV2bLkAwIG9V2jcpDKSJJGelklaqpI37uTxW6jVKr3E0/9VVC/Bf68aYjcxwVMxMlLz1bjuDP/sD3RaHT18G1Glmoaff9pHbXdXPL1r07NXI6aM20CfbguULQ/nKCGyffs3ZfrETfTvtQhZlunu05DqNZyJCH/A6BFrACWLfqcu9WjeqsY/yfjXtF04F8LObReoVl3DO/0WA/DZ8A60bF2zyPdQrVbR+3MPlo87iayTadKxPM6VrNj1x3XcathQp7kzR7eEcONcHGojFeaWxrz1VQMA7gQ9YNfK66iNVEgq6Du8HhbWJbf2XG2kYuCIxsz96iA6nYxn1yq4VbZh06+XqFSrLA1buvHXsgtkpGXx0+QjAJR1KsOXs5TdzWIjk4mPSaVmg5JvfNRqFT0/c+e3CaeRdTKNO7qhqWjF3lU3cK1ug3szDce3hnLrQjxqIwlzS2P6jcrrTIZcfoCNgxllXUrOeZYfr2aVCDwZQscBf2BmasTMMXl5Mnw/WI3fr8oW0HOXHWHbvuukZWTh1fdX+nbzYNh7zWjVpCJHztyj2+BVqFQSX3/aCjubp0eMPQ9qIxVDv27DuGF+6LQynXq6U6mqPX8sO06N2hqae1XhenAUU7/eTlJiOicO32XV8hOsWDcQtVrFR1+0Ysxnm5BlqF7biS696hRLT2FZ8/402tRoiIOlLWEztzB52wp+O7b1X7m2V4tqBB67Tcd+SzAzNWbmhO65x3wHrcBvpbJL0NzFAWzbE0xaehZePRfRt2cDhn3oSdCV+wz9ZgOJSekcOHKTxb8Esm2N4Q5bRdLWsByB5yLpOHQbZqZqZg7JC+v3/WonfvO6EBWfyrKNwVRxtab36F1A3hbyq3Zc58DpCNRqFTaWJswa2qx4elrW4NDRm3TotRBzM2NmTuqVe8zn7SX4r1F2kpk8pjtjp24mPSMLzxbV8WyhdL6nj/dh5vwdZGt1mJoYMW2cDwBDPvBi7NTN9Oi/GFmGr4Z2pGwhBppenu4cOnyVDl2+xdzchJnT83ap8ekzB/+NoxVdE/rmbqft2bo2nq0Vp8b877dyNyQGSZJwLVeWqZP6AbB7z0XW/n0UtVqFmZkxC+YOLvKgVCfrWHZpJdOaf41KUrH3XiD3kiIYUKs3NxPucirqPOdigmjoWJclbWejk3X8L/gvkrIUx4GTuQOO5mW5HHftGVd6fh7v6tbBdwHmZibMnNw795jP24vxXzMUgMnf9GTslI05z7MGni2Vdnv6BB9mzsv3PMcrzzM2Lok+g5aSnJKBSpL4Y+0xdqwbXuSl116NXAk8G0HHT/2UOnd4i9xjviO24bewO1FxKSxbf5kqbtb0HrkdgAHdatKvw/MN/Aqlp2V1Dh27SYfei5RyMNEn95jPgKX4r/4MgMmju+VsLZ+NZ4tqueVg257LrFmvOBc6eNemTw9ls4sB/V5n7DR/ur35EzIyvbu/Rq3qhdup82UvC0ZGaiZMfJMPP1iMTqejd5/mVK9ejkWLtlKnTkXatq1H374tGDP6dzp1nIyNjQXzF3yQe367thNISUlXBvYBF/nl12F6O5EVRc+4CX355MMlaHU6evVuRrXqLixetB2POhXwbluX3n2bM3bMKrp0moaNjQVz578LwNrVhwm7F8eypbtYtlSpe5f/MgR7+xczmeDVoiqBx2/Tsd8yzMyMmTm+W+4x38G/4veHcp/m/rSfbXuuKG2Vz2L69qjPsA9bM/enA6SmZTJigrKc00VjzdI5/Yqup1VNDh29Tgef+Uo5mNIn95jPWz/iv3YY8Lj+2EB6ejaeLfPqD4Aduy/RrZP+crqzF0JZ8fshjIzUqCSJKd/4ULaIOxAbGakYMaYDXw35C51OpqtPPSpXdeTXJYHUdHehVZvqXA2+z4SRm0hKTOdY4E1+W3aYlRs/4sCeq1w8F0ZiQhq7tihLc8dO6071mppnXPV5tan5alw3vvhsJTqtju6+DalSzYnlPwVQy90VT+9a9OjVkKnjNtG320KsbcyZnvO8HjxIYcSnK5FUEo5O1kye2ecZVxMICkZ67OUX/LdJyFj/Ur4Itqb9SMhYX9oyCsTWtB/bQkaVtgwDuleaD8CJqCmlK6QAmjlPYdPtEaUto0B6V12IHLmktGUUiOQyhNCkl1NbRashSJ8Vz8HwopCXnkB+sLK0ZRgglR0EgBw0pXSFFIBUdwok/l3aMgrG+k3I2lnaKgrGuAvd/QeWtooC2eazCpJewnbUShnUyFe/LWUhhki1J8CjtaUto2Bs3nqpy4FODihtFQWiktqRpdtd2jIKxFjVCTn+99KWYYBk/67yQ/LGUtVRIJZ9iE79vbRVFIjG4l0eZryc7aid6ZuvXlKbQrAj5OtSH892rTT3lbrHIjJIIBAIBAKBQCAQCAQCwSvLq5jAubR59Ra2CQQCgUAgEAgEAoFAIBAIioyIDBIIBAKBQCAQCAQCgUDwyiIigwqPiAwSCAQCgUAgEAgEAoFAIPgPIZxBAoFAIBAIBAKBQCAQCAT/IcQyMYFAIBAIBAKBQCAQCASvLCpJxLkUFnHHBAKBQCAQCAQCgUAgEAj+Q4jIIIFAIBAIBAKBQCAQCASvLCKBdOERkUECgUAgEAgEAoFAIBAIBP8hJFmWS1uD4OVAvAgCgUAgEAgEAoFA8P+T/9ehM/vDx5X6eLat28xX6h6LZWICAHaFji5tCQXSueIc0AWUtoyCUbUjIWN9aaswwNa0HwDx6WtLWYkh9mZvEZf+Z2nLKBAHs3dot3FAacsokIA+q4ns1bS0ZRSIy+aTyA9WlraMApHKDkL6rFlpyzBAXnoCgOSh7UpZiSGWiwOQ784pbRkFIlUeDUkvX50LgFU/bj9aWNoqCqSqzQhi0l6+MupkPkj5Ic2/dIUUhLkPcuSS0lZRIJLLEDK0O0tbRoGYqrsQkrS4tGUUSCWrocixv5S2jAKRHD98Ofu6qpw2Sj5QujoKQvKGjO2lraJgTLuRpdtd2ioKxFjVqbQlvFDEMrHCI5aJCQQCgUAgEAgEAoFAIBD8hxCRQQKBQCAQCAQCgUAgEAheWURkUOERkUECgUAgEAgEAoFAIBAIBP8hhDNIIBAIBAKBQCAQCAQCgeA/hFgmJhAIBAKBQCAQCAQCgeCVRSWJOJfCIu6YQCAQCAQCgUAgEAgEAsF/COEMEggEAoFAIBAIBAKBQCD4DyGWiQkEAoFAIBAIBAKBQCB4ZVEhdhMrLMIZJCgUV09Hs2lpEDodNOtcgQ79a+gdP7LtLke23EWlkjAxN6L/iPo4V7TOPf4gJpVZH+6ny8BatO1XrcR0ybLMjJnrORQYjJmZMbNnDsLDo4KB3eXge4wdu5L0jCy8PD0YP64fkiSxc9c5Fi/ezu07UaxfN5q6dSoWS8/xIzdY8N0OdDodPXs3YvAHXnrHMzOzmTp+A9eu3MfGxoJv575JOVc7srO0zJiymetXI9FqdXTp0YB3P/QiIyOLT9/7hcxMLVqtjrbtPfj483bF0vgkJ47eZOF3u9DqdPTo1ZBBH7TWO37+bAg/zNnF7ZvRTP2uL207eJTo9Q313GLhd7vR6WR69HqNgR+01Dt+4WwoP8zZk6OnN94d3HOPjfxsDcFB4dRrUIG5i/uXuLbXNfX4vP5AVJKKHXcP8teNrQY2Xq5NGezeBxmZ2wn3mHn6JwA+qtOfps4NAPjzmh8Hw0+UqDbrD0Zi2qgFckY6CT9OJ/vOdUMjIyNsPvoakzoNQacjafUy0k8cQO3ojM3QCaisbdElJ5KwcAq6+JgS0SXLMjO+30PgsduYmRkza2J3PGq6GNh9v+wA/juDSExK59z+0bmfnz5/j1kL93D9dgzzp/Wic9vaJaLrWfw6cDzd67YkJukhdacP+Feu+U+Y9P0cI4+myJkZZKyagy78pqGR2gjTN4ahrt4AdDoytv2G9sLhEtciyzIzlp4g8HQYZqZGzBrliUd1Bz2btPRsRswI4F5kEmqVhHezCox6/3UATgdFMmvZSa7ffcD8sd50bl25+HrmbefQ0RtKOzClDx61yhnYXb4awdgpm5R2oGUNxn/VDUmSuHo9ksmz/MnIzEatVjFlTE/q1XFj38Gr/LBsHyqVhFqtYtyorjRuUKlIGs8cv8fP84+g08l08qnNG4Mb6h0POnef5d8f5e6teL75tgOt2lXNPfbrouOcPhqKLMu81qQ8n4xqiVSC2+iePHqbH+bsQaeT6d6rAe+830Lv+IWz91g0dw93bsYweXYvvDvklcFRQ9Zy5VIEdV8rz5wf3ywRPbIsM2POFg4duaY8z2lv4FHbzcDu8pVwxk5apzzPVrUYP7onkiTx49I9rNt0irJ2ZQAYOawzXq0VzdduRDL5240kJ2egUklsWD0MU1Pjouv88RCBJ0IwMzNi1jcd8ajhZGD3/S/H8N99lcSkDM7tGqJ3bOeBGyz+/SSSBDWrOjB/YpciaTly+CrfzdqETivTu28zPviovd7xzMxsxn/zJ1eCw7GxtWDugsG4utoTdCmUaZP/Vv4e4LPPO9OufT0AEhNTmTLpb27djESSYNq3b1G/QfHK6uljoSybF4hWJ9PF1503322sdzzoXATL5h/mzq04xs3oTOv2Sn/xwplwfl6QV5eFhTxk3MxOtGhTlZJClmVm/LCfwON3lOc5riseNTUGdt//fBj/3cFKW7V3hMHx3Qev88WELaz/ZSB1azkXT88L7N/ev/+Abj2mM/Tzrnzwfofn0zNjHYcCL2NmZsLsWYML1nM5lLFj/8jRU4fx499AkiQSElL4cuQKIiLicXW1Z+H3H2Fjo5TRkyevM3PWerKztdjZWvLnn6MA+P33fazfcBRJkqhRvRyzZg3G1Ow5dH63mUOHryo6p7+Fh3tB9UcYYyesVXS2rs34Mb306tXf/jjId/O3cPzQNMraWXL7bjTjJv5F8NVwvhzWlQ/e9X7mPcvPkcNXmD1zE1qdjj59m/PhR/r3PDMzi7Fj/uTKlTBsbcswb8G7uLrac+zoNRYu2EJWlhZjYzWjvvalaTNlDJaVmc2Mbzdw+tRNVCqJ4SO606Fjg0LpEvx3EcvEBM+NTiuzfvElPpnRnLEr2nLuYARRoYl6No293fhmeVtGL/Om3RvV2PxzsN5xv2WXcX/dsFEtLoGBwYSExrBn1xSmTx3AlGl/FWg3Zepapk8bwJ5dUwgJjSHw8BUAalR34ccfP+b1xsV3UGm1OubO3MrCpYP4y284e3YGcee2/oB6y6azWFmbs3H7SPoPbMFPC3cDELDnMplZWtZsGsYff32G34bT3I94iImJET/98j6rNwzlz3Wfc+LoTYIuhhVba37N82buYP6SAazZ/Dn7dl3m7hOanZ1tmDDdlw5d6pbYdf9Jz/yZu5i/5G1Wb/4sR0+sno3G2Ybx03vSoUsdg/Pffrc5E7/1fSHaVEgMb/AuY4/O4f09o2lbvjkVrVz1bFwtNbxVqyfDD07hg71jWHJpFQBNnRtQ3bYSHweMY+iByfSr3hULI/MS02basAXqcuWJHdKXR0tnY/PJ6ALtLPu+h/bRA2I/70fs8P5kBJ8DwOrd4aQd3EHcl++QvO5XrN4ZUuD5RSHw+G1Cwx6we/1nTPumK1Pn7CrQzrtVDdb9+p7B5y7O1sya2IPuHQyf94vk9+Pb6fzjl//qNZ+G2r0JKkc3UqcOImPtAkz7f1GgnUmnAchJCaROG0zqjPfR3rz4QvQEng4n9H4iu3/rx7QvWjF18bEC7d7rW5edv/Rl00++nAuOJvC0Une5OFoya5Qn3b1LZiAXePQGIWHx7Nn8JdPH+zJl1pYC7abM2sL0Cb7s2fwlIWHxBB5THGpzF+3i84/a4r9mKF980o65i5R3tHmTKmxZOxT/NUOZOak3E6b7FUmfVqtjyZzDTPuhO8v+7s+h3be4d+eBno2TsyUjJ7WlTcfqep9fuRTFlUtR/LTmDZasfZMbV2IIOne/SDqepm3BrF3M+6k/qzZ9wr5dwQXUudaMm9aD9gXUuW8NbsaEGT1LTA9A4JFrhNyLY8+W0Uyf2IcpMzYXaDdlxmamT+rDni2jCbkXR+DRPAf4u++0xn/dl/iv+zLXEZSdreXr8WuZOr432zeNYuUvn2BkpC66zpMhhIYnsHv1YKaNasfU7/cXaOfdvDLrlhlOToSEP2T56jOsWdyPbb8PZNxQrwLOfjZarY6Z325g6c+f4Lf1G3buOMftW1F6Nps2nsDa2oLtuycwcHAbFs5XJjKqVXdh7fpRrN88mqXLP2HalHVkZ2sB+G7WZlq2qsWW7ePYsGk0lasUrw+n1er46buDfLuoJyvWD+DA7huEPlEOHJ2tGDWlPd6d9CccGzR2Y+mat1i65i2+W9oLUzMjGjYzdEQUh8ATdwkNe8juvz5k2tedmDpvb4F23i2rsm75OwUeS07NZNX6c9R3N5zwKLSeF9y/nf3dRlq3di/wWMF6Lit6dk9j+rQBTJm65il61jB9+jvs2T0tR48yHli+YhfNm9Viz+7pNG9Wi+UrlP5vYmIqU6etZemSIWzfNpkffvgIgOjoh6xcdYCNG8aybesktDod27effrbOI1cJCY1jz7ZxTJ/UjynfbihY57cbmD75DfZsG0dIaByBR67lHouMesjR49cp52KX+5mttQXjv+nFB4ML5wQC5d3/dvp6li7/lC1bx7Fj+1lu34rUs9m04QTWNhbs3D2JgYPasGCe0o7Z2ZVh8dJP2LxlLDNmvcPYMatyz/n55z2ULWvJ9l0T8d82jsavl9xk+6uGSpJK/d+rhnAGPSeSJNlLknQh51+UJEkROT8nS5K0JMemjSRJLfKdM0WSpK+KcK0pkiTJkiRVy/fZiJzPGuf8vkOSJNsS+NOem9DrD3EsVwYHlzIYGato6OVK0DH9joZZmbyZtcx0rV6w3qWjkdg7l8G5olWJawvYfwlfn6ZIkkSDBpVJTEwlJuaRnk1MzCOSk9Np0KAykiTh69OUgABlkFS1qgtVKpeMk+rK5XDcKtjj6lYWY2MjOnSuS+CBq3o2gQev0q3nawC07eDB6ZN3kGUZJEhPzSQ7W0tGRjZGxmrKWJoiSRIWFqaA0pHNztZSkvXNlcsRuJUvm6u5fec6HD6oH1Hi4mpHtRrOqFQvvqK7evk+buXtcHWzw9hYTbvOHgXosaVaDQ1SAXoaN62MRRmTF6KtVtmqRKREE5kSS7as5UD4CVqUa6Rn061SW7bc3ktyVioACRmK07SitSuX4q6hk3WkazO4+yiM1zX1SkybaRNP0g7sBCDrxmVUZaxQ2dkb2Fm060HKxj+UX2QZOUkpK0Zulcm4dAaAzKCzmDXxLDFtAYE38OlSTymjdVxJTE4nJi7JwK5BHVecHAzrCDcXW2pWK/h5v0gO37rAg5TEZxv+CxjVa0n2qT0A6EKuIplbIlmXNbRr3pnMPWuVX2QZXpD+gOOh+LSrpjzT2k4kJmcSE5+qZ2NuZkSz+kp0jomxGvdq9kTFpQDg5mxFzSplSyy6JeDQVXy7NlD01C1PYpLhOxYTl0RySgYN6pZX2oGuDQg4qAyaJEkiJSUDgKTkdJwclajWMhamuRrT0jKLrPdGcAzl3GxwcbXG2FiNZ8dqHA8M0bPRlLOmcnV7g3pWQpn9zc7SkZWlJTtbh21ZiyLpKIirl+/jWr4s5R7XuZ3cOXLwhp5Nbp1bwN/fuGnl3DaqpAg4eAXf7g2V51mvIolJacTE6r/LMbGJJKek06BeReV5dm9IwIHgp3yjwtHjN6hZ3YVaNZX30s62DGp10bvDAUfv4NOptqLTw4XE5Axi4lMM7Bp4uOBkX8bg8/Xbgnnbtx42Vkqog71d0Z7r5aBQKlRwwK28A8YmRnTu8hoH9gfp2RzcH0RPXyUyr0PH+pw8cRNZljE3N8l1iGVkZOf2L5KS0jh75ja9+zQDwNjECGvr4r1314OjKVfeFhc3G4yN1bTpWIPjh+7o2TiXs6ZKdYd/7G8cCbjF6y0qYmZWtIiupxFw+CY+nT1y2qpyOW1VsoFdgzrlcHKwLPA7Fq04wocDmmBiUvwFGC+yf7tv3wVc3eypXu35nVYBAZfw9WmWo6cKiYlp/6CnSo6eZgTsu5h3vm9zAHx9m7Mv5/Ot207RocNrlCuntGn29nmrCrRaHenpWWRna0lPy8LJyfbZOg9cxrdHY0Vn/UpPrz+SM2hQv5Kis0djAg7klZlZc/z5+svuev1te3sr6tWpgJFR4euMoEuhVKjgSPmcMtqla0P2P1FG9+8PwsenCQAdOzXg5IkbyLJMbffyODnZAIrzNj0ji8zMLAA2bzrBhx8rEUYqlQo7u4LfS4GgIIQz6DmRZTleluUGsiw3AJYB3+f8binL8uPp8zZAi6d9RyEJAvJPIfUDcns4six3lWU5oYSu9Vw8ikvH1jEvisHW0ZxH8ekGdoe33GHa4L1sWRFM78+VKJKMtGwC1t2k88CaL0RbdHQCzs55nntnZzuiYxL0bWIScNbY5tlo7IiO1rcpCWKiE9FobHJ/d9JYExuj3wDFRifilGNjZKTG0tKURwmptOtQBzMLE7q1+46eHecyYHArbGyUjpdWq+Odfovp3GY2TZpXo0698iWmOTYmEY1zXsPr6GRNbHTpDYBjYxJxyqfHycma2GhDx0Fp4GBeltjU+NzfY9Me4GBup2fjZuWMm6ULP3hN5sc2U3MdPrcT7vG6pj6mahOsTSyp7+SOk4Whs6aoqO0d0cZH5/6ujY9BXdZRz0ayUDoJlm9/gsO8P7D9eiYqG6XzlR1yE7PmymyXWbM2qCzKIFlZUxJExybhosn7LmdHa6JjX45n+qog2Tqge5gXraFLiEWy1V+Whbky2DTp/h7mY5Zh9v4kJCv997OkiI5PxcUxb3Dr7GhBdAGD4MckJmdw4GQYzRsYLt0qET2xSTg759W9zhprop+oe6NjEnHO/x5qbHLfw3GjujLnh114dZvDdz/sYuTQvPD9vQeu0LnPQj4ZsYqZk3oVSV98bAoOmrz75eBUhvjYp9+v/NSu50y9Rq680/UP3umykkbNylOhcsk919iYJJyc85ywjhpr4mJKt3xGxzzC2dk293dnjS3RTww6o2Me4ayxearN6r+O0aPfAsZOXsejRMVReTc0DkmS+OCzX+jVfyEr/neweDpjk3FxzBt8OTtaEh1r6Dx4GiFhDwkJT+Ctoet487O/OXwypGg6oh+hydcP0jjbGgzS89sYGamxtDIjIUF5By9dDKFXj9n08fmOiZPfwMhITUR4PGXLWjJx/Bre6D2XyRP/IjU1o0j6HhMfk4KjJu9+OThZEhfz/PfrMQf33KTNE5FDJUF0XDIuTnllwdnJiugCnEFPI/h6NJExibRpUTIRjy+qf5uSks6KX/YydEjXwutxya/H1uBahprzbOLjE3OdGo6O1sTHK3V0SEgMiYmpDBw4n969Z+Lnpyyh12jseP/99ni3HUer1mOwtDKjVatnRzJFxyQWsf5Q9Ow7cBknJxtq1dSP/C4OMTEJepo0Gltiop9wpEU/wtlFsXmyjD5m754LuNd2w8TEmMScem3xou306z2HkSN+Iy7u5ZjAErwaCGdQMcmJBtomSVIl4FPgy5yIodZP2FWVJGmXJElnJUk6LElSrWd8tR/g8/hc4BEQl+/7QiRJcpAkqYwkSdslSbooSdJlSZLezDk+W5KkK5IkXZIkaV7J/cXPpnXPKkz6owM9PvRgz2plZnHnqmu06V0VU3ORpuqfCL4cjlolsX3fGDbvHMWaP44SEa6ET6vVKv5cP5Ste78m+HI4t29GP+PbBKWFWlLjaqlhZOC3zDi1mJENP6SMsQVnY4I4GXWBRW2mMKHJUK7E30Qr6/5lcWrUDhqyrgUR99Vgsq4HYfXucAASf1+EqcdrOMxfiYlHQ7RxMaD9l/UJiodKjcrOCe2dYNK++xRtyBVMen1S2qrI1uoYNfsgA33cKe9SMg7GkmbthlOMHdmVQ9tHM3ZkV8ZPz1uW1MHbnV0bR/DTvLf5Ydm+f13b/bBHhIU8ZOW2QazaPoiLZyK4fL7klon9f+StN5qzd9sY/P8egZODNbPnbwOUiZWz5+8yd+ZbrPnfEPYduMzxkwXk3vqXyNbqCA1PYOXCPsyf1JmJ8wJITCqew6Uo1Ktfic1bv2HtupH8umIfGRlZaLU6rl4J5403W7Ju09eYm5vw2y8B/7q2J4mPSyHkVhyNm5fsErHiotPJzP7xAGOGFn4J0b/N4p+2M3hwW8qUeUbynReIJEm5kYbabC3Bwff4+eeh/PLrcJYs3c7du9E8epRCQMAlAvZ9y+HA70hLy8R/y8kXqistLZOfV+zji887v9DrFIVbNyNZMH8Lk6Yqudm0Wh3RUQk0eK0y6zeNpn6DSsyb41e6IksRlaQq9X+vGmJkXkLIshwiSdIyIFmW5XkAkiTlz/C7HPhUluWbkiQ1BZYAbf/hKxOBMEmS6qA4hf4GDBNpQGfgvizL3XKuaSNJkj3QC6gly7L8tOVkkiR9DHwMMGxmR7q+Xf8f/0YbBzMSYtNyf0+ITcPG/umNSMM2rqxfpIR/hl57yMXD99nySzBpyVlIKgkjExWePlX+8Zr/xOrVh1i34SgAdetUJCrqYe6xqKiHaJ4II9U42RKVb/YiKvohGo2+TUngpLEmOp+nPyY6EUcn/cGPo8aamOhHaJxtyM7WkpycgY2tBbt3XKJZy+oYGaspa29JvdcqcDU4Ale3vKUgVtbmNHq9MseP3qRq9ZJZ2uboZE10VN5MQmxMIo6a0huwOTpZE5NPT0xMIo6akl9eWBTi0h7gmC+ax9G8LHFpD/VsYtMecPXBLbSylqjUWMKTI3GzdOb6wzusue7Pmuv+AIx7/XPCk/XXixcWiy59sejgA0DWrSuo7TVk5RxT2zuhfaCf90NOeoQuPY30EwcASDsaQNl2Sq4P3cM4Hn73DQCSmTlmzbyRUws/Y/uY1RvOsH7LeQDq1i5HZL5os6jYRDSOL8czfZkx9vTBqIUyc6sLvY7KzpHH7jmVrSNyQpz+CSmJyBlpaC8qSVazzx3CrHnRktEWxOotV1i/S1myWbeGA5H5IluiYlPRFLAMBmDSD0eoWM6awb1KNufT6nUnWOenLG2s6+5KVFRe3RsVnYjmibpX42RNVP73MPpR7nu4edt5xn/VDYAu7esw4Vs/g+u93rAyYRGbeJCQQlnbgv/Wp2HvWIa46Lz7FReTgr3j833HsYN3qFlHg7mFsiSmcYsKXA2Kps5rJRNl5ehkRUxUXiRQbHQiDk7/fvlc/dcx1m1SBnp1PcoTFZWQeywqOgGNk42evcbJhqjoRwXaONjn6e/XuwmfDv8foESDvd6wSm5iac9WtQi+GkHzpvp5mv5R5+aLrN92WdFZS0NkvkigqNhkNI7Pv0zD2dGSeu7OGBupcXOxoVJ5W0IjHhY66bBGY0N0vn5QdFRCbgTGkzbOzrZK3yMpHdsn3uMqVZ0xtzDl1s1INBpbNBob6tWvBChLy4rrDLJ3KkNsdN79iotJxsGpcMtaAvfepIV31WLlesrP6o3nWL/1EgB1a7sQmS8qLiomCc1TloM9SUpqJjfvxjFomJLXJ+5BCkPGbGLJd70L9Tz/jf7txUsh7N59nnnzNpOYlIZKJWFqasw7A9oUoOcg69YfUfTUrUhUZH49CQbX0mhsn9CcZ2Nvb01MzCOcnGyIiXlE2bJKOXV2tsPW1hILC1MsLExp3Lg6166HA+DmZp9r17HDa5w/fxsfH8O/afVfR1i3UYkoKnr9Yc29sDjCIx7g029ezueP6P3mAtavGYGjQ9H7xk5OtnqaoqMTclcJ5NpobIiKVCKrniyjUVEP+WLYL8ycPZAKFZSob1vbMpibm9C+gzKG69jpNTZtKNmNSQQliyRJnYEfADXwiyzLs584PhL4EMgGYoH3ZVkOzTmmRVk9BHBPluViJ+t79dxXryCSJFmiLB9bL0nSBeBn4HkW6P6FslTMFyg4c6LyQnSQJOk7SZJay7L8CCWKKB34VZKk3kBqQSfKsrxcluXGsiw3fpYjCKBCTVtiI1KIj0whO0vHuUMR1Gmu37jFROQ18FdORuPoqlRgXyxozeRVHZm8qiNevarSoX+NYjmCAAYM8MJ/8zj8N4+jfbt6+PmfRJZlLly4i5WVuUEnyMnJBktLMy5cuIssy/j5n6Rd25LL1/KY2h6uhIXGcz/8AVlZ2ezdFYRnG/1AsNZtarE9Z5C8f28wjZso66qdXWw4c0pZO5+WmsnlS2FUrOzIwwcpJCUqjrj09CxOHb9NpcpPLA8pluZyhN+L5374Q7Kystm36zKtvF7Mkr7noZZHOcLvPcjRoyVgVzCtvEo+HLwoXHt4B1dLZ5wtHDGS1Hi7NePY/bN6Nkfvn6GBo5Ks1NrEEjdLFyJTYlAhYW2idCqrWJenik15zkQHGVyjMKTu3EDcyIHEjRxI+slAzL2Vgb9xjTroUpPRPYw3OCfj9BFlJzHAtN7rZIffBUCysuHx4njLPoNJ3W+4S1phGNC3MX4rP8Jv5Ue086yB/85LShm9HIFVGdMCcwMJ9MkK9Cdt9iekzf6E7EtHMWrSEQBVpdrIaSnIiQ8Mzsm+fAJ1daVOV9dsiBwZWmJ6BvR0x29JL/yW9KJd84r4B9xSnunVGKzKGONkb5hPZOHvZ0hKyWLcp81KTEeunjea4b9GSe7cvo07fjsuKHqCwrCyNHzHnByssCxjyoWgMKUd2HGBdl5KWXVytObUWaUsnDh9h0rlFadvaFi8ktMNCL52n8zMbOxsCp83pYa7E/fDEoiKSCQrS0vgnls0a13puc51dLbi8rn7aLN1ZGdrCTp3v0SXieXWuREJSp27+0qp1LkD+rfITfjc3tsDv23nlOd5KRQrS/PcPE6PcXK0xrKMGRcuKbus+W07R7s2yhKS/PlB9u2/TPVqSn+lVYsa3LgVSVqakp/v9Nk7VCtkUuQBverj9+sA/H4dQLtWVfHffVXRGRyp1G1PcYoWRPtWVTl1IQKAhwlphIQl4OZi84yzDPGoU4HQ0DjCw+PJysxm187ztPHWd7628a7DFj8l+e7ePRdp0rQ6kiQRHh6fmzD6fsQDQu5EU861LA6O1mic7bh7V4lEPnniBlWqFm8Sqqa7hoiwBKIiHpGVpeXgnhs08yzc7mQHd98o0SViA/o0xO/3d/H7/V3ata6G/67gnLbqfk498nzOICtLU05sH8r+DZ+wf8Mn1HcvV2hHEPw7/ds1f45if8C37A/4lsGDvPnk404FOoIUPW3w95uAv98E2rdrgJ//iRw9d7CyMvsHPXdy9JygXTtFT9u29fDzOw6An9/x3M/btavP2XO3yM7WkpaWyaVLIVSt4kw5l7JcvHiXtLRMZFnm+PFrVK1S8BBqQP9W+K//Cv/1X9G+bV38tp5RdF4MUXQWVH9YmnLhYoiic+sZ2nnXoWaNchw/NI39uyayf9dEnDU2bPp7ZLEcQQB16lbgXmhsbhndueMc3t76m7J4e9fB3/8UAHt2X6BpM6WMJiamMuTTnxkxsicNG+aNnyRJwqtNHU6fugUoZbRqtaLvXveqU9rJo5+VQFqSJDXwE9AFcAfekiTpyXWP54HGsizXAzYAc/IdS3uctqYkHEEgIoP+LVRAQk6+ocKwDZgLnJFlObGgpI2yLN+QJKkh0BX4VpKkAFmWp0mS1ARoB/QFhvLPUUjPhVqtos/QeiwddxydTqZZpwq4VLJmxx9XKV/DlrrNXTjsf5cb52NRqyXMrUwY8HXDZ39xCeDlVYdDgcF06DQZczMTZs4cmHvMp9dM/DePA2DypP65W296tvbA01PZHn3v3gtMn7GOBw+S+eTTJdSu5cavvwwrkhYjIzVfjevO8M/+QKfV0cO3EVWqafj5p33UdnfF07s2PXs1Ysq4DfTptgBrG3O+naOEe/bt35TpEzfRv9ciZFmmu09Dqtdw5uaNKKZN2IhOq0Onk2nXqQ6tvJ610rBwmkeO7cqXn61Cq5Pp7vsaVao5seKn/dTyKEfrNrW4cjmCsV/+RVJiOkcO3eDXJQdZvfnzEtOgr0fFl2M7M/KzNTl66ufoOUgtDxdat6nJ1cv3GfvlOpIS0zl66Ca/LDnE6s2fAfDZu79zLySe1NRMfDssZOyUHjRtWTLr93Wyjh8v/M53rcagklTsDDlEaFIE77r34frDuxyPPMfp6Es01tTltw5z0Mo6lgetITEzGWOVMQu9JgGQkpXGrNNL0ZXgMrGMs0cxbdQCx6UbkTPSefTj9NxjDgtWETdSKRdJqxZj88UUVO9/iS4xgYQcO9M6jXJ2EJPJDD7Po+VzS0ybV4tqBB67Tcd+SzAzNWbmhO65x3wHrcBvpbJzyNzFAWzbE0xaehZePRfRt2cDhn3oSdCV+wz9ZgOJSekcOHKTxb8Esm3Ni1/+tOb9abSp0RAHS1vCZm5h8rYV/HaseE6yoqINPonaoykWk1chZ6WT8Wfe8zH/5mfSZiv3I9NvOWaDx0Kfz5GTE/TsShKvJuUJPB1Ox/fXY2ZqxMyReaujfYdsxm9JL6JiU1j210WqlLeh91A/AAb0cKdfl5oEXY9l6PR9JCZlcuDkPRavOse25X2KrqdlDQ4dvUEH3wVKOzC5d+4xn7cX479mKACTv+nJ2CkblXagRQ08WyqDyukTfJg5bwfZWh2mJkZMG69MPe8OCMZ/xwWMjFSYmRrz/aw3i5REWm2k4rOvWzNh+DZ0OpmOPWpRsWpZVv18iuq1HWnmWZkbV2KYPnoXyYkZnDwcwp/LT7Ps7/60aluFS2ciGPL23yBJNGpWnqbP6Uh6HoyMVHz5TSdGfbYWnU5HN5/6VK7myC9LDlHL3YVWbWpw9fJ9xo/cQFJiOscCb/Lb0kBWbVLeuc/fW0loSDxpqZn07riIMVO60bSYOVO8Wtfi0JFrdOjxnfI8p/bLPebzxvf4r1N2+Zs8zjd3a3nPlrXwbKW0jXMX7uDa9fsggWs5O6ZNUN4tG2sL3h3oSd8BPyJJSmRQG8/aRdfZrBKBJ0PoOOAPpRyMycs15fvBavx+HaDoWXaEbfuuk5aRhVffX+nbzYNh7zWjVZOKHDlzj26DV6FSSXz9aSvsbAq/y6SRkZpx4/vw2UfL0Op0+PZqSrXqLvz04w7cPSrg3bYOvfo0Y9yYP+nW6VtsbC2YM28QAOfP3eG3FQEYGamQVCrGT+ybm4R27PjejB39J1lZ2bi52TN9xttFvleglIPPv/Zi3LAt6LQ6OvZ0p1JVe/5YdoIatZ1o7lWF68HRTPt6O0mJGZw4HMLK5SdZsU65j1H3E4mNTqZew5LL5ZIfr+ZVCDx+h45vrsDMzJiZ4/IiK33f/R2/398FYO6Sg2zbe1Vpq3otpW/3egz7oGXJ63mJ+rd5ei7ToePEHD2D8/T4fou/34QcPW8zdtwfpKdn5uhRHJMff9SJEV+uYMPGo5Qrp2wtD0qi69atPejpMx2VSkXfvi2pUUN5xp06NqRX7xkYGampXbs8b77Z6tk6W9fm0OGrdOg2E3MzY2ZOfytPZ795+K9X9veZPL5v7tbynq1q4dnqn+uC2LhE+vT/nuSUdFQqiT/+DGSH3xgsLZ+93M7ISM24CX355MMlaHU6evVuRrXqLixetB2POhXwbluX3n2bM3bMKrp0moaNjQVz578LwNrVhwm7F8eypbtYtlTZ6XL5L0Owt7di5KiejB2zitmzNlG2rCXfFrOMCl4oTYBbsizfAZAk6S+UFUBXHhvIsnwgn/0JoOBtC0sI6fFsl+D5kSRpCjnLwSRJagN8Jctyd0mSRgHWsixPLsDuGErS6fWS0ousJ8tygfv9PnFef+CGLMvnJEk6mHOtM5IkhQCNARPggSzL6ZIkdUcJK3sHsJBlOUaSJBvgjizL/5ildlfo6JfyRehccQ7oSn99eoGo2pGQsb60VRhga6p0mOPT15ayEkPszd4iLv3P0pZRIA5m79Bu44DSllEgAX1WE9mraWnLKBCXzSeRH6wsbRkFIpUdhPRZyUejFBd5qRLCnTy03TMs/30sFwcg353zbMNSQKo8GpJevjoXAKt+3H60sLRVFEhVmxHEpL18ZdTJXHFEkOZfukIKwtwHOXJJaasoEMllCBnanaUto0BM1V0ISVpc2jIKpJLVUOTYX0pbRoFIjh++nH1dVU4bpTc2fUmQvCFje2mrKBjTbmTpdpe2igIxVnV69fY+LwRnYqaX+nj2dc2kT8hJw5LDclmWlwNIktQX6CzL8oc5vw8EmsqyPLSg75IkaTEQJcvytzm/ZwMXUJaQzZZl2a+4ekVkUMmyFdggSZIP8KTbfQCwVJKkCYAxyhKwAp1B+ZFl+a9nmNQF5kqSpAOygM8AK8BfkiQzlB1pRxbqrxAIBAKBQCAQCAQCgeAV4VnLtP4Nchw/y4v7PZIkvYMS+OGV7+OKsixHSJJUBdgvSVKQLMu3i3Md4QwqArIsT8n380HgYM7PN4D8i3QP57O7i5LsuVDf/8TnbfL9XCnnx905/56kyfNcSyAQCAQCgUAgEAgEAsELJQIon+93t5zP9JAkqT0wHvCSZTl3e0lZliNy/n8nZ8XQa4BwBgkEAoFAIBAIBAKBQCD4b/IKbO1+GqguSVJlFCdQf0AvyZMkSa+hbDbVWZblmHyf2wGpsixnSJLkALREP7l0kRDOoFJEkqTxQL8nPl4vy/KM0tAjEAgEAoFAIBAIBAKBoGSRZTlbkqShKKt61MBvsiwHS5I0DWXDqC0om0dZouxCDnlbyNcGfs5JDaNCyRl0pcALFQLhDCpFcpw+wvEjEAgEAoFAIBAIBALB/2NkWd4B7Hjis0n5fm7/lPOOoeQKLlGEM0ggEAgEAoFAIBAIBALBK8vLkED6VeOlX1gnEAgEAoFAIBAIBAKBQCAoOURkkEAgEAgEAoFAIBAIBIJXFhUiMqiwiMgggUAgEAgEAoFAIBAIBIL/EJIsy6WtQfByIF4EgUAgEAgEAoFAIPj/yf/r0JnL8bNLfTxbx/6bV+oei2ViAgDSJnYrbQkFYj59O+HJy0tbRoG4WX6MHDSltGUYINWdAoDu4IhS1VEQqjYL0e0fXtoyCkTVdhFHK9YqbRkF0jL0GnazOpe2jAJ5OHbXS1kOQCkLyUPblbYMAywXBwAgfdaslJUYIi89QebiN0pbRoGYDF2HfG9BacsoEKnCyJeyzoWcelcOKG0ZBqgkpWyGJC0uZSWGVLIa+lLeM8i5b8kbS1tGwVj2gaT1pa2iYKz6EZO2srRVFIiT+SAuxM0sbRkGNHAYB0B8+tpSVmKIvdlbyDEv5/hAcvoYOXJJacsoEMllSGlLeKGIBNKFRywTEwgEAoFAIBAIBAKBQCD4DyEigwQCgUAgEAgEAoFAIBC8sqgkEedSWMQdEwgEAoFAIBAIBAKBQCD4DyGcQQKBQCAQCAQCgUAgEAgE/yHEMjGBQCAQCAQCgUAgEAgErywigXThEZFBAoFAIBAIBAKBQCAQCAT/IYQzSCAQCAQCgUAgEAgEAoHgP4RYJiYQCAQCgUAgEAgEAoHglUUSu4kVGnHHBAKBQCAQCAQCgUAgEAj+Q4jIIEGhMe76CaoajSErg8xN3yNH3jawMXl/FpJVWcjKBCDjjwmQ8gj1a+0x7vQ+cmI8ANknt6I9u6fYmk4du8tP8w6g08p09a3DW+811Tt+6Vw4P807wJ1bsUyY2R2v9jX0jqckZ/B+v99p2aYaw8e0K7ae/MiyzIzfzhF4/j5mJmpmDW2GR5WyejZpGdmMmH+Ue1FJqFUS3o1dGfVOAwA2HbjD3FUX0JQ1B2BA5xr0a1+1xLTN/PsqgZdjMTNRM/PdunhUsDGw++iH08QmZpCtlWlc3Y6Jb3mgVkn84H+D/RdjUElQ1sqEWe/Ww8nWrOS0rbtGYHCOtkF18ahgbajtxzPEPsogWyfTuJodE/u7o1blJZD7374Q5my8zrG53thZmpSINoDKU8Zj5+2JLi2dm1+NJeXyFQMbh57dcPv8E5BlMqNjuDHia7IfJlBz8QLMqlQGwMjamuzERC527VVsTe2qNGJW+89Qq1SsurCLhSfW6R13s3ZkSfevsDEtg1qlZurB39h7+zT9PLwZ1rRvrp2HU2W8fhvK5Zg7xdb0mJe5HDyJSd/PMfJoipyZQcaqOejCbxoaqY0wfWMY6uoNQKcjY9tvaC8cfiF6nsavA8fTvW5LYpIeUnf6gH/12qA809mB9zkcmoiZkYpv25fH3cniqfbDtt0l/FEmmwfUBOBabBrTD4STodWhVklM8HKjrvPTzy+sthlLjhF46h5mpkbM+roNHtUdDey+/+0U/vtukJiUwbmtH+R+HhGdxPh5B3nwKB0bK1PmftMWZ0fLEtFV2nXu4cPBzJyxHp1Opm/fFnz0cSe945mZWYwZ8wdXgsOwtS3DggUf4OpmD8Dyn3exceNxVCqJ8ePfoFVrdwB+/z2ADRuOIUlQo7orM2cNxNTUmNV/HmTlygPcuxfLseNzsLMr2j08fSyUZfMC0epkuvi68+a7jfWOB52LYNn8w9y5Fce4GZ1p3b4aABfOhPPzgrxyGRbykHEzO9GiTfHqjqLew4cPkxnxxQouX76Hr28zJk56E4C0tExGjFhB2L04VGoV3t51GTXKt0jaZFlmxtxtHDp6HTMzE2ZP6YNHbVcDu8tXIxg7eQPpGVl4tazJ+K+7I0kSI75Zy93QOACSktKwsjLHf+0wLl0OY+IMv9xrDPu4HR3aehRO17ztHDp6AzMzY0VXrXIF65qyKUdXDcZ/1U3RNfavfLrSsbIyw3/NULKytUyYvpkr1yLJ1urw7daAT97zKsKdUzh59DY/zNmDTifTvVcD3nm/hd7xC2fvsWjuHu7cjGHy7F54d6ide2zUkLVcuRRB3dfKM+fHN4us4WlcOBHB7wtPodPJtO1RHd+BdfWOb/srmP1bb6JWq7C2NeXTcS1xdM4rc6kpmYwa4M/rrcvz/qhmJa7vMSeO3mThd7vQ6nT06NWQQR+01jt+/mwIP8zZxe2b0Uz9ri9tOzz/e1QUZFlmxg8HCDxxV2kPxnXGo6bGwO775Ufw3x2stAd7hud+vmnHZeYuCUST0wYM6N2Afj3qlZy2Hw8ReCIEMzMjZn3TEY8aTobafjmG/+6rirZdQ/SO7Txwg8W/n0SSoGZVB+ZP7FIi2l5FVCLOpdAIZ5CgUKiqN0ayL0fGwo+Q3Gpi0uNzMpaPLNA2c/1c5Pu3DD7XBgWStX1ZiWnSanUsmh3AnCV9cdRYMWTgapp7VaNSFftcGydnK0ZP7cz6VWcK/I7/LT1KvdfcSkxTfgLPRxIamcTuH7tz8WY8U5efYd3sjgZ27/WsRbM6GjKztLw39QCB5+7j2VDpKHVpUYFJHzY2OKfY2i7HEhqTwq7pnly8m8C01cH8PbaFgd33HzfA0twYWZb54ufz7DobSbfXy/FBx8p84aM41lbtD2HJ9ltMGVCnZLQFxxEak8quqa25ePcR09Ze4e8xhp2X7z9sgKW5kaJt+QV2nY2i2+suAEQ+SOPolThcypaMg+oxdt6emFeuyDmvTli+Vp+q307mku8THT+1msqTx3G+fTeyHyZQcexXuAx+h7CFi7k+NK/MVJowBm1iUrE1qSQVczt+Tq+/xnE/MY797y5i580TXI+/l2szqsVb+F0N5Lfz26lpX4F1b0yn/tLBrA8+wPrgAwC4O1bizz6TStQRBC93OciP2r0JKkc3UqcOQlWpNqb9vyBt3lADO5NOA5CTEkidNhgkCSysXqiugvj9+HYWH9zAyncn/evXBjgcmkRoQgbbB9biUnQq3x6MYM0b1Qu03XcrAXNj/U7agqP3+bSJhtaVrAkMSWTBsfv8r3e1EtEWeCqM0IhH7P69PxevxjB10RHW/WjocPVuVpEBPh50fvcvvc/n/HwCnw416NWxJifOR7Dg11PM+aZt8XWVcp2r1eqYPu1vfv1tOBqNLW/0+w7vtvWoVs0l12bDhmPYWFuwe89Utm8/w7z5m/n++w+5dSuSHTvOsnXbBGJiHvH+e4vYuWsKcXGJ/LnqINu2T8TMzIQvR/zCju1n6NW7Oa81rEqbNnUZNOj7It8zrVbHT98dZNZPvjhoLBk26G+aeVahYj5nsqOzFaOmtGfDqnN65zZo7MbSNW8BkPgonfd6raRhswpF1vJYT1HvoampMcO/6MHNm/e5eSNS73vff689TZvVJDMzm/ff+4HAwGA8PQs/SA48eoOQsHj2+I3i4uUwpszyZ/3KIQZ2U2b5M31iL+rXKc9Hw/8g8NgNvFrWZOHst3JtZi/YgaWlKQDVq2rYuGoIRkZqYmIT8XnrR7w9a2FkpC6crs1fcvFyOFNmbWH9H58WoGsL0yf4Ur+OGx99sZLAYzfxalmDhbP65+n6fmeurl37LpOZqWXr38NIS8+kW79FdOtUD7dydoW6b6A82wWzdvH9srdx1Fjz0YDfaOlVncpV8xzJGmdrxk3rwV8rTxqc/9bgZmSkZ+G/4Xyhr/0sdFodv80/wfiFHbF3smDsh9tp3Ko8bpVtc20qVS/LrF+7Y2pmxJ7N11j901lGTM9zjK1bcYHaDQydICWJVqtj3swd/PDzQJw01nzw9gpat6lJ5ap5Dg5nZxsmTPdlzR/HXqiWxwSeuEto+EN2r32fi1cimTp/H+uWG06geLeswoDeDej89m8Gx7q0q8mkL0t2shgg8GQIoeEJ7F49mItXopj6/X7WLe1vYOfdvDIDetWn84A/9D4PCX/I8tVnWLO4HzZWZsQ/TC1xjYL/3wj3maBQqGs3Q3thPwBy+HUwLwOWhW9wS5JrwVG4lrelnJstxsZqvDvW5NhBfSeUczkbqlZ3RCpgy8EbV6N5+CCVRs0qvhB9AafD8WlTCUmSaFDDgcTUTGIepunZmJsa0ayO0kCbGKtxr2JHVPyLr9D3X4zBp5mroq2KHYlp2cQ8SjewszQ3BiBbJ5OVrUNC0vscIC1D+wK0lcvRZktiahYxjzIK0GaUp00rk/8Rz95wna9616CkN5os26EdMRv9AUg+fxEja2uMnfSjDiRJQpIk1BZKpIORpSWZ0TEG3+XQrTOxW7YXW1OjcjW58zCS0IQosnTZbLp6iK41mhvYWZkqeqzNyhCVHG9wvI97GzZdOVRsPU/yMpeD/BjVa0n2KSVaURdyFcncEsm6rKFd885k7lmr/CLLkJL4b8oE4PCtCzwohes+5sCdR/SsbYckSdR3LkNShpbYlCwDu9RMLSsvxPLJ6/qDEEmClEyl3kjO1OJYxtjg3KIScDwEn/Y1lPfNXUNicgYx8SkGdg3cNTjZlzH4/Pa9hzRroERTNG1QjoDjISWiq7Tr3EuXQqhQwZHy5R0wMTGia9dG7A+4qK8x4BI+vorjvVOn1zhx/DqyLLM/4CJduzbCxMQYNzcHKlRw5NKlEAC0Wi3p6VlkZ2tJS8vEyUmJdnJ3L58bVVRUrgdHU668LS5uNhgbq2nTsQbHD+k7q53LWVOlugMq1dNr+yMBt3i9RUXMzIr3nhXnHlpYmNKoUTVMTfQ1mJub0LSZEjFnYmKEu3t5oqIeFklfwKEr+HZ7TXnH6lYgMTmdmFj9eiImNpHk5HQa1K2AJEn4dnuNgIP60a2yLLNzXxDdO9fP1fjY8ZORmU1hd3AOOHQV364NcnSVJzEpnZg4/YmQmLgkklMyaFC3vKKra4On6+qkRGZIQFp6JtnZWtLTszE2VmNZxrRw4nK4evk+ruXLUs7NDmNjNe06uXPk4A09GxdXW6rV0BTYn2zctDIWFkW79rO4dTUOjZs1GlcrjIzVtGhXmdOHw/Rs6jRywdRM6RNV93AkPjavzrtzLZ6EB2nUe90wGqskuXI5ArfyZXF1K4uxsRHtO9fh8MHrejYurnZUq+H8j+W1JAk4chufzu7Ku+dRTmkP4pIN7Bp4lMPJofgRoIXSdvQOPp1q52hzeXpb5eFSYFu1flswb/vWw8ZKmfS0tyuZ6FrBfwfhDBIUCsnaHvlRbO7v8qM4JOuCO3omvb/EdMiPGLXR93CrPVpi+vliTPqPRbJ2KLamuJhkHDV5s/KOGiviYg0r+YLQ6WSWfX+QT0cUPaT4WUTHp+GSrwJ3LmtB9D8McBNTMjlwJoLm9ZxzP9t7IoyeI3cwfN4RIuMMG4kia0tIxzlf1IyzrRkxDw0dLgAf/nCaVl8FUMbMiE6N8rQt9LuB9zcH2HrqPsN7FhwVUDRtGTjb5dNmZ0ZMguGgCeDDRWdo9fUBypiq6dRQ0RZwMQaNrSm13AyXlhUXE2cNGffzZnUzoqIw1egPdOXsbG5PmEqD3Vt4/XQg5tWrEv33Bj0b6yaNyYqLJz0ktNiaXCztiUjMK5v3k+JwsdIvm7MP/8kbHm25/Pkq1vWbxui9Swy+p1dtTzZeOVhsPU/yMpeD/Ei2Duge5t1HXUIsku0T9ZS58neYdH8P8zHLMHt/EpJV6TrFS4OYlCycLfMGtRpLY2KSDZ1BP56MYvBrTpgZ6Xc5xrR2Zf7RSNr/7wrzj9xnRHMXg3OLSnRcCi5O+d43hzJExz2/Y7FmFXv2HrkLwN4jd0lJzeJhYsH1T6F0lXKdGxOdgLNL3ruqcbYjOvqRvsaYBFxybIyM1FhZmZOQkEJ09KMnzrUlJjoBjcaW995vT7u2E/BsPRYrK3NatnIvlK5/Ij4mBUdN3gDNwcmSuJjna+Pzc3DPTdp0qvFsw2dQnHv4PCQmpnLgQBDNm9cqkr7omEScNXlLD52drIl+whkUHfuEjcaa6Bh9mzPnQ7Ava0mlCnn138WgMLr1W0jPNxcxdazvc0cFKddMwtn5n6+paLfOZ2NDdKy+w+hJXZ3a18HczIRWnb/Du/tc3n+nFbY2RRsQx8Yk4eScvz9pTVxM8SN3S4IHsanY56vT7J0seBj79HfqwNabNGimOLR1OplVi08zcOiLjawFiI1JROOc9wwdnayJjS69SQuA6NhkXJzynquzoxXRBTiD/om9B2/Sc/AfDJ+whcgS/HuiY5NxybcE2dnRkujnHMMAhIQ9JCQ8gbeGruPNz/7m8MmQEtP2KiJJqlL/96rx6ikWvBJkrp9HxuLPyfhlNKqKHqgbKOH12msnSZ//Hhk/DUV76zzGfQpeYvZvsWX9BZq0rKznTCpNsrU6Rn1/jIFda1A+p/Pr3diVgKU92bKgKy3qOfPN4hOlou2XL14ncE5bMrN1nLiWF1EywrcGB2Z706NJOVYfuPcP3/ACtQ1vTOB3bRRt1+NJy9SyfNcdhvUomSUnRUEyMsL5nf5c7NqL0697knrtBm6ff6xn49CzW4lEBT0vfdzbsCZoL3V+Gsgb6yexrMfXuREHoEQXpWVlcDWu+M6p4vAylwMAVGpUdk5o7wST9t2naEOuYNLrk9LT8xJzLTaN8EeZtKtqmBfn76B4Rrcux7733Pm6tSuTAsIK+IbSYfTHzTh96T69Pt3A6UuRaBzK6OUi+zd4mevc/Dx6lMr+gEvs3TeNQ4GzSEvLYMsWwyU0pUl8XAoht+Jo3Lx4S8ReNNnZWr4a9RvvDPSmfPniT5YVh227LuZG3zymft3ybF8/gg2rhvDz74fIyDB0/r5wXbuD9HRduhyOSi1xeNcYAraM4rc/jxIW/uBf1/UycXj3bW5fi6fn28oS0j2brtGguZueM0nw/Hi3rErA+g/Z8sdgWrxekW9m7iptSblka3WEhiewcmEf5k/qzMR5ASQmFTzBIBAUhMgZJHgm6ibdMGrcGQBdxA0km7zlMJKNQ24yaD2Scj7LTEN76RAq1xrK8rK0vBkW7dk9GHd6v9j6HJwsiY3O+97Y6CQcnjPR55VL9wk6H8GW9RdJS80kO1uHubkxHw33LJam1TtvsD5ASaxdt6o9kfEpgHLfoh6korEveNZq0rJTVHSxYnD3vBlBO6u8kON+7aow788LxdN2IJQNR5RBV51KNkQ9yJvtjkpIx8nu6SHOpsZq2tbXsP9iNC3d9Tuq3ZuW45MfzzCsGNFBqw/eY8PRcEVbRWuiHubT9jD9HxOlKtqc2H8xBkdrU8Lj0vD9VlmPHp2QQZ+Zx/l7TDMcbYoWwu086G00/fsBkHwpCNNyLjx+60ydncmIjtazL+OuPMP0e8q9jtu2E9chH+UZqNXYd+7Axe59iqTnSSKT43G1ziub5awciEzSL5vv1O9Ev7/HA3A64ipmahPsLayJS1VmtXvX9irRqKCXuRzkx9jTB6MWXQHQhV5HZeeILueYytYROSFO/4SUROSMNLQXlcS02ecOYdb8v5Gwce2lODYGK+9VHScLovJFAkUnZ+Fkqb/85WJUCsExqXT6/QrZOniQls17m27xv97V2HLtAd94KksWOlWzYUoxnUGr/S+zfsc1AOrWdCQyJm/WPCouBY3D80cLaBzK8OMUJSlwSloWe47cxdqyaHXHy1TnOmlsiYrMW34UHfUQjUbfUadxsiUy8iHOznZkZ2tJSkrD1rYMGo3NE+cm4KSx5fjxa7i62VO2rDKp0r5DA86fv0PPnvobORQVe6cyxEbnzZTHxSTj4FS4pRyBe2/SwrtqoSJZnkZx7uGzmDxpDRUrOjF4cOHyU61ed5x1m5WciHXdXYnKF6kUFZOIxlE/QlbjaK1vE52IxinPJjtby94DwWz60zBfGkDVyk5YmJtw43Y0dd2fnm9x9boTrPPLpyvq6dcE0DhZE5Uv6iIq+hEax7zJulxdq/JyIG3bfYnWzatjbKTGvqwlDetXIOhqBOXdDJf3PgtHJytiovL3JxNxcHo5JgvLOloQn69Oi49Jxc7R8J26dPo+m/4IYspPnTA2Ud73G5djuXYphr2brpGelk12lg4zC2Pe/qxRiet0dLImOirvGcbGJOKoKfkI7WexetN51m8NAqBuLWci80V4RcUmoSnEcjA7G/Pcn/t1r8u8pYHF07b5Iuu3Xc7RpiEyXyRQVGxybqLq58HZ0ZJ67s4YG6lxc7GhUnlbQiMeUreW87NP/n+I6hWMzCltxB0TPBPtqe1kLBlGxpJhaK+eyI3ykdxqQnoKJD+xrl2lAoucil+lRl3zdXQxOZEG+fILqWo1RY4t/kxwLXdnIsISiIx4RFaWlgN7rtPC6/l2Chk3oxtrd3zMmm0f8ckILzp0cy+2IwhgQJca+M3rgt+8LrRr4or/wRBkWebCjTisLIxxsjM3OGfh2kskpWYx7r2Gep/nz6uy/0wEVV2L16gO8K7I5omt2DyxFe0aaPA/EaFou/MQK3MjnGz0HS4p6Xk5LbK1Og4FxVAlZ3eKkOi8jsn+C9FUcS7erNOANhXYPL4Fm8e3oF19Df4n7udoS8jRpj9oUrRl5Gm7HEcV5zLUcLXi6FxvAmZ4ETDDC42tKRvHNS+yIwggauUaLnbtxcWuvXiwJwCnPj4AWL5Wn+ykJLJiYvXsM6NisKheFaOyyjtv27oFabfy8lzYtmpO2u27ZEbpO5GKyrn716lqV44KNhqMVUb0ru3Fzpv60TMRiTF4VnoNgBr25TE1Msl1BElI+Nb2ZOPVkssX9DKXg/xkBfqTNvsT0mZ/Qvaloxg1URJbqyrVRk5LQU40nGXOvnwCdXUll4a6ZkPkyNKNpvq3eKueAxveqsmGt2rStooNW64+RJZlLkalYGmiMsj782ZdB/a/78Hud91Z2bcalWxNc5NEO5Yx5kyEUoecDE+mgm3xcm0M8KmD38998fu5L+1aVsJ/3w3lfbsSjVUZkwLzLTyNh4/S0OlkAJavPU+fTjWLruslqnPr1q1IaGgM4eFxZGZms2PHWbzb6kd/eLeth7+fUnfs3n2eZs1qIkkS3m3rsWPHWTIzswgPjyM0NIZ69Srh4mLHxYshpKVlIssyJ45fp2qVkhuI1HTXEBGWQFROG39wzw2aeVYu1Hcc3H2jRJaIQfHu4T+xcOEWkpLSGDuu7z/aFcSAN5rjv3YY/muH0b6NO37bzyvvWNA9rCzNcHrCGeTkaI2lpRkXgu4hyzJ+28/Tzitvad+xU7epUslRbylZWMQDsrOVPFURkQ+5ExKLq8s/L48d8EYz/NcMxX/NUEXXjgs5usKwsjTFyUHf0eLkYIVlGVMuBIUpunZcoJ1X3m5dBely0dhw8ozStqamZXLxchhVKhnuHPg81PIoR/i9B9yPSCArS0vA7iu08iqZ96a4VK3lQFR4IjH3k8jO0nIs4C6NW+k74u7eiOeXOccZ/V1bbPK1r8OneLJkU18Wb+zLO583xrNzlRfiCAKo7VGO8Hvx3A9/SFZWNvt2XaaVV9Hrz6IyoPdr+P1vEH7/G0S71tXw33VFefeC7+e8e8/vcMmfX2j/0dtUrVi8PGgDetXH79cB+P06gHatquK/+2qOtkisypgWqq1q36oqpy5EAPAwIY2QsATcXAwjcQWCpyEigwSFQnfjNHKNxph++Uvu1vKPMR3yIxlLhoHaGNNB00GtBpUK3e0LaM/sBsCoeU/UtZqCToucmqx3flFRG6kYNrotY4ZuRKfV0cWnDpWqOvC/pUep6a6hhVc1rgVHMfkrf5IT0zl++DZ//HyM39a/W+xrPw9eDcsReC6SjkO3YWaqZuaQvNlS36924jevC1HxqSzbGEwVV2t6j1bCTx9vnb1qx3UOnI5ArVZhY2nCrKEltx2oVx1HAoNi6TThkLLN8eC8Dm2v6UfYPLEVaZlaPv/pHJnZOnSyTNMa9rzpWR6ABZuvczc6BZUkUa6sWYntJKZocyDwciydJh3O2Vo+77t7zTjG5vEtFG1Lc7TpoGnNsrzZunyJaXgaD/cfws7bk4aBe9ClpXPrq3G5x+rv2MzFrr3IjIkhbOFP1F3/J3JWNhkR97k5amyunUOPbsRt2VZimrSyjtF7l7Cx/wzUkorVl/ZwLS6Usa0HciHyJjtvnWBCwAp+6PoFQ17vhYzM59vn557fokJdIhJjCU2IKjFN+XmZy0F+tMEnUXs0xWLyKuSsdDL+nJt7zPybn0mbrSwHy/RbjtngsdDnc+TkBD27f4s170+jTY2GOFjaEjZzC5O3reC3Y1v/teu3rmRFYGgiXVdew8xYxbft8spe37XX2fDWPw8AprR1Y3bgff6PvfsOi+J4/Dj+3qMISlPKgYK9gi32CmLvYNdoTDGapomJsWAvsbd8jd1ojEaN2MBeggr2GhvYFawUUaRIvdvfH3sBDlABMae/zOt58kRv5+4+7t7M7s7Ozmq0MoWMVUxoXnBPdPSoV5KgU/do/fGfmBUyZtqPzdKXeX+xGb9lygn37BUn2XnwFonJaXj0+YPu7SozpH8dTl18zPyVp0CSqFvNifFDmhRMLgO3ucbGRowd14vPByxEq9XStVtDKlQozoIFO6hatRTNm1ene/dGjByxmjatJ2BtXZi58wYAUKFCcdq2q0XHDlMwMlIxbnxvjIxU1KhRhjatP6Bb1+kYGauoUsWFnr2U9bV2zSFWrjzAkyexeHWeiruHGz/91C9PmY2MVXwz3IPRQ7aj1Whp3dmV0uVs+X3pSSpWcaChR1muB0cwefgu4mKTOXkklDXLT7HCV3laUPijWKIi4qleK/vj1fPjTdYhQIvmY0lISFI6GwIu8uvKIVhYmLFs6V7KllXTresMAD7s60GPHo3znM+jSSUCj12nlddczM1MmDYxY/SpV59f8N8wBIAJozrjM3EzSUlpuDeuiHvjjE6P3fsu0aFNDb3PPXchjBWrAzE2NkIlSUwc5UWxork/afVoXJHAYzdo5T0PczNTpk3ompHrw4X4rx+cKdcWkpJTcW+UJdf+y3Rord/x1rdnfXwmbaVDzwXIskzXTrWoXCF/nZHGxiq+H9WGYV9tQKvV0sGrBmXK2/Pr4kAquzrRpFlFrl55xJgfNhMXm8TxoJusWhLE2q3KfuGbT9cQFhpN4osUurZewMiJHajfKHcXJ1/HyFjFZ9/XZ9oPf6HVaGnWsQIuZYviu+Jvyla2pU7Tkvyx6BxJiWnMH3sYADt1EUbMKvgnYL2KsbERP/i05/uv1qLRynT0/oCy5R1Yseggld2K07RZZUKuPMTn+z+Ji03iaOANVi4+zLpt37y1TB4NyxB08g6te6/EzMyEaT5t0pd5f7oGv9/6AzB7cSA7/7pGYlIqHl2X0b1jNYZ81oi1m//m0LHbyvGHlRnTR7d52VflPVuD0gSdCqV139+VfdXIVhnZBqzDb6XSjs1eepSdf10nMTkVj+4r6d7BjSGfNqBJvVIcPXuPDh+vRaWSGP5lE72RTILwOpIsy4bOILwDEsd1eCd/COZTdvEgfrmhY+TI2WIQ8uWJho6RjVRtIgDaw0MNmiMnqmY/oz34raFj5EjVfAHHSuVvws63rXHYNYpOb2voGDl65rP3nawHoNSF+MH/7oFwblgsDABA+urtdGi9CXnJSVIW9jR0jByZDvZFvjfP0DFyJJX84Z1sc0HX7soBho6RjUpS6mZo3EIDJ8mutOXgd3KdgW69xW8xdIycWXSDuE2GTpEzyx5EJq4xdIocOZj358KTaYaOkU1NO+VCV3TSBgMnyc7WrA9y5Lt5fiA5DEJ+nP0BHe8Cyenrf3fyu39ZWNxig5/PlrJ8v9axuE1MEARBEARBEARBEAThP0TcJiYIgiAIgiAIgiAIwntLTCCdd2KNCYIgCIIgCIIgCIIg/IeIziBBEARBEARBEARBEIT/EHGbmCAIgiAIgiAIgiAI7y1JjHPJM7HGBEEQBEEQBEEQBEEQ/kPEyCBBEARBEARBEARBEN5bYgLpvBNrTBAEQRAEQRAEQRAE4T9EdAYJgiAIgiAIgiAIgiD8h0iyLBs6g/BuED8EQRAEQRAEQRCE/58kQwd4mx6/WGXw81mnwp+9V+tYzBkkALA7dLihI+SofenZyOFLDR0jR5LjlySk+hs6RjZFTLwAuBI9w8BJsqtqO4r78e/m9nSx+JIRxwYZOkaOZjVeTuK4DoaOkSPzKbsgdqOhY+TMqhfy3VmGTpGNVGYEACkLexo4SXamg32Rvmpg6Bg5kpechBfbDB0jZ4W7IEevNnSKHEm2n7yb661wFwC0coCBg2Snklrw+MUqQ8fIkVPhz4hLfQe3J2Bp0uWd3h8gHzJ0ipxJnu/0vornGwwbJCfWfbgTu8DQKXJU1upbIl6sNnSMHKkLf2LoCMI7RnQGCYIgCIIgCIIgCILw3lKJGXDyTKwxQRAEQRAEQRAEQRCE/xDRGSQIgiAIgiAIgiAIgvAfIm4TEwRBEARBEARBEAThvSVJYpxLXok1JgiCIAiCIAiCIAiC8B8iOoMEQRAEQRAEQRAEQRD+Q8RtYoIgCIIgCIIgCIIgvLdU4jaxPBNrTBAEQRAEQRAEQRAE4T9EjAwS8uTqmUi2Lb2MrJGp364ULXtV0Ft+bGcox3bcRVJJFDI3pud3NXAsZZm+/FnkC2YMPETbfpXw7FG+wHLJsszUBYcJOnUXs0ImTPdpjVtFdbZy81ccw39fCLHxyZzfOzj99a17gpm95AhqewsA+napQY+O1fKd59jR68yZ4Y9GI9OlWz0+/dxTb3lKShrjfP7kashDbGwKM2NOX4qXKAbAjeuPmTp5CwnxyahUEmv/HIJWKzPyhz948CAalUqFe7MqfPt9+3zn+8ffJx+w6udTaDUyLTpVpGv/6nrLt2+4QsCOG6iMVFjbmPH16CY4OFlw90Y0y2ef4MWLVFQqie4fV6dxy7JvnCez08dDWTznMFqNlnbeVenzaT295ZfOP2DxnEDu3Ipi7LT2uLesmL6sdd2fKVPeDgAHR0umzPcq0GwRl59yef0tkGVKNnWiYoeSesvvHQ0n2PcOZkVNASjbogSl3J0A8B8QiJVzEQAK25pR/9uqBZrNpP0XqCrWgdRkUrbOR358O1sZ08+mI1kWg9QUAJJ/HwsJzzH6oCUmbT5Djo0GIO3UDjTn9uc7iyzLTJ27m8BjNzEzM2HGhC64VS6erdyVq4/wmbSVpOQ0PBpXYMyw9kiSxNXrj5kwYwfJyWkYGauYOLIj1d2ciYtPYvi4zTyKeI4mTctn/RrTrXOtN8u55CRBZ+5jVsiY6cPccatgp1cmMSmNoVMDuPc4DiOVhGeDkgz7rC4AZy4/ZvrSU1y/+5S5Pp60bVom31lyyjYj6BFHwmIxM1bxU0sXXB0Kv7T8kJ13efA8hW19KwFwLSqRKYcekKzRYqSSGOvhTDXHl7+/oKz8aAwdqzUmMu4Z1ab0fevfJ8syU2ftIPDYdeW3NqkHblVKZCt3JeQBPhM26X5rlRgzohOSJAGwdsMx1vmexEgl4dG0MiOGKm3stRuPmfDTNuITklCpJDb/MZhChUzyn3P+AYJO3MbMzITpYzviVskxW7n5SwPx33uZ2Lgkzgf8mP76bxtOs3nHBYyMVBSzKczU0R0o4WSdryzped7SegN49DiGDt3mMfjLlgzo757vnEeOBDNt6ia0Wpnu3RsxcFAbveUpKamMHPk7IcH3sbEpwrx5AyjhbMuzZ/EM/W4FV67cw9u7AePG98p3hpc5dewOC2cHoNFq6eBdg76fNdBbfvHcfRbOCeD2zUjGT+9Ms1aVAQh/9Jxxw7ah1cpo0jR06V0brx4fvFGW40evM2fGDrQaGe9udfnk82Z6y1NS0pjg48vVkIdY2xRm+pw+6cceN68/ZtrkbSTEJyGpJNb8qfzO9+6+wG8rDiEhYe9gxZQZvbApWuSNcr7pvuHajXAmzNjOixcplHCyYc6U7lhYmOU9w1RfAoOuYGZmyozpH+PmVjJbuStXwvDx+Z2k5FQ83KsyZkxPJEkiJiaB739YwcOH0ZQoYcvP8wdibV2E7TtOsWLFfpBlihQxY+LED6lc2RmA2NgXjB27lhs3HyFJEtOm9ueDD3J/7PSu7auU7biHwOO67Tje++XbcbKfsg4bVWDMsHaZtuNOXiTqtuPkrlhYmPHg0TPa91pEmZK2ANSo6sxkn075znn2eBhL5x5Fq9XS1suVnp/U1lt++fwjls07wt1b0Yya2pqmLZTzk4tnH7B83tH0cvfDYhg1tTWNmhXM8e6pY7dZMPsvtFotHbxr0u+zhnrLL5y7xy9z/uLOzUgmTPdObztuXo9g3tS9JCSkoDKS+GhAI1q0cS2QTO87CSNDR3jviM4gIde0Gpktiy7x5fSG2NiZM39IEFUbOOp19tT2LEHjjqUBuHIiHP9lV/hiWkbj5rcsmCp1HQo8W9CpUMIexLBv3adcDAln0ryD+C7tk62cZ6Oy9O1ag7Z9V2db1q55RcYPbf7GWTQaLTN/2sbiFQNRO1rTr9cveHi6UrZcRueU39bTWFmZs33PSPbtvsD/5u1m5tx+pKVpGDtqAz9N703FysWJiUnA2NiIlJQ0PvrUnbr1ypOamsYXA5Zz7Mg1Gjet/EY5V8w5yfj/tcHWoTAjB+ygbtOSuJSxSS9TpqIts1Z1ppCZMXu3XmPt4jMMm+JJITNjhoxvSnEXa55GvWD4Z9upWb8ERSwLvcmq08v2y4yDzFzcFXu1Jd98tJ5GHuUoVdY2vYyDoyUjJrXGd+25bO83LWTMsg39CiRLVrJW5tIfN2k0rDrmxQoROPk8jjVtsSqhf3Bcop491ftVyPZ+I1MVnpPqvJVsqgp1kGyLk/zzQCTnSph2+obk5T/kWDZl02zkR7eyva65HETqrqUFkifo+E1C70Wzf+t3XLzygIkzdrBp9RfZyk2csYMpY7yoUdWZgd+tJej4TTwaV2T2L/v55vNmeDSuSOCxG8xesJ+1yz5j3aZTlCvrwNL5/Xj6LIG23RfQqV11TE3yt0sLOvOAsEex7FvVg4vXopi08Di+/+ucrdyn3avRoEZxUlI1fDpqD0Fn7uNe1wUnewumD3Nn1ZbL+fr+VzkSFkdYTDK7PqrMpYgX/HT4Iet7Zv9dAfx1KwZzE/0Bv/OOPeLLemqalrYiKDSWeccf8VvXguuIf5nVJ3ax8PBm1nwy/q1/F0DQ0euE3nvCfv8fuXj5PhOn+bFp7TfZyk2c5seUcd2oUc2FgYN/I+jYDTyaVOLkmdsEHL7K9o3fYWpqTPTTeADS0jQMH7uR2VN6UrlScZ7p2uR85zxxm7AHz9jn+yUXgx8xafZefH/9JFs5zybl6du9Nm176dfFKhXVbF71KeZmJmzYep45iw8xf4p3/vO8pfX2jxlzd9K0caV85wNlfzBl8kZWrvoWtdqGnj1m4tm8OuXLO6WX2bz5ONZWhdm3fxK7dp1lztxtzJ//OYUKmfDtd524efMRN288fqMcL8v2vxkHmLOkF/ZqS77s+zuNPcpTulzGCbqDkxWjJrVn45rTeu+1tbdg0e/9MDU15sWLFD7tvpLGHuWxc7DM+jW5zjLzJ38WrRiA2tGa/r0W4u5ZRe/Yw3/rGSytzPHbM5x9uy/yy7y9TJ/7IWlpGsaN2sjk6T31jj3S0jTMnbGDTf4/YFO0CP+bu5uN64/zxTet8rfCdN503zDmJz9GfteGerXLsHn7eX5de4yhX7XIW4agK4SGRbJ/32QuXrzLxEnr2eQ7KnuGSeuZMqUfNWqUYeCghQQdCcbDvSrLV+ylYYPKDBrUluXL97J8xT6G/9gV5xJ2/LH2B6ytixAYdIVx4/9I/9ypU31p2tSNBQu+ICUljaSklLxlfsf2VUHHbxJ6/yn7t3yrbMeZu9j028Bs5SbO3MmU0Z2U7Th0HUEnbimdQlO3M/K71tSrVVrZjn8cZ+iXynF4yRJF8V/31Rtn1Gi0LJoVxLSFnbFTW/Ddx5uo716GUmWLpZdxcLRg2IQWbPnjgt57a9RxZtH63gDEPU/is65/UKuByxtn+ifX/Bn7mbekN/ZqKwb1XU0Tjwp6bYfayYrRkzry55pTeu81MzNm9JROuJQqxpPIOD7v+xv1GpXF0jJvHaKCAOI2sXyTJMlWkqQLuv/CJUl6qPtzvCRJi3VlmkmS1CjTeyZKkvTjyz/1pd81UZIkWZKk8pleG6p77e2cVebg3vVn2BUvgp1TEYxNVHzQrARXToTrlTErknG1NCUpDXRXDgEuH3+MrWNhvc6jghJw9DZebaogSRI13ZyIjU8mMjo+W7mabk442FoU+PdnduXyfZxL2uHsYouJiTFt2tXg8MFgvTKHD4bQ0UvZdC1aV+PMqVvIsszJ4zeoUNGJirorKzY2RTAyUmFubkrdesrmNzExpkqVEkREPH+jnLdCnuDobIljCUtMTIxo0rIsZ47c0ytTrbYThcyUE+yKbvZER74AoHhJa4q7KFeji9kXxrqoGc9jkt4oT2bXg8Mp7mJDcWcbTEyMaNa6EscO649wcSxuTdkK9qgy/cb+Dc/uxFLEwZwiDuaojFWUqO9A+IXofzXDyxhVaYDmwkEA5AfXwbwIWBQ1WJ6AwGt4d6ip1MtqLsTGJRH5JE6vTOSTOOITkqlZzQVJkvDuUJOAwGuA0nwkJCQDEBefhIO90nZISCQkJCPLMgkvUrC2MsfYKP+7s4ATYXi1KK/krOJAbHwKkdEv9MqYmxnToIZSL01NjHAtb0v4kwQAnB0tqVS2WPpIiYJ06M5zOlcpiiRJ1HAsQlyyhqiE1GzlXqRoWHMhii/q6o+IlCRISNEAEJ+iwb5I/ka05NWRWxd4mhD7r3wXQEBgCN4daynbsHpJYuMSiYzS//7IqFjlt1a9pPJb61iLgMNK27xh00kGfeqBqanS3tkWU/YTx07cpFIFRypXUrZ9UV2bnO+cR27i1baqkrNqCWVf9SSHfVXVEjjYZd9XNahdCnMzZRvWcCtOeOSbreO3td4A/joUTIkSxahQ7s0uAF26FErJkva4uNhhampM+/a1ORhwUa/MwYBLeHkrI3LatPmAkyeuI8syhQsXonbt8hQyfTu/+2tXHlMi076qeZsqHDt8U6+MU3FrylV0QFLptw8mJkbp6y01RYMsy2+UJfjyfVxK2qYfe7RuV4PAgyF6ZQIPhtDRSxlF2aJ1VU6nH3vcpEJFx2zHHsggy5CYmKK0t/HJ2DtYvVFOePN9Q+i9aOrWKg1A43rl2H8oJOtXvD5DwCW8vRooGWqWJTY2kchI/eOqyMjnxMcnUbNmWSWDVwMC/rqY8X5v5WKnt3dD/tK9XqtWOaytlYtDNWuUITz8GQBxcYmcOXuT7t0bA2BqaoyVVd5Gab5r+6qAoOt4t6+Rt+3Yvob+dvygFACN6+dvO77OjeBIirtY4+RsjYmJER6tKnAy8K5eGXVxK8pUsHvlejkScJs6DUtiZlYwbcnVK48o4VKU4s5FMTExokWbKhw9fEOvjFNxmxzbDpdStriUUjqz7BwsKVq0CDFP9X8HgpBbojMon2RZjpZluaYsyzWBpcB83d8tZFn+WlesGdDoZZ+RR5eB3pn+3gMIzqmgJElvZYxcTHQSNvbm6X+3tjPj+ZPEbOWObr/LT5/8xY5fQ+j6tXILTHJiGgG+t2jT782uEL5MxJN4nDJdTXO0tyAiKvsB9qscCLxJ50/X8u34HTyOjHv9G14iKvI5jo4Zw/Yd1NZEZjlgz1zG2NgICwszYmJeEBb2BEmS+HrQr3zY42dWrzqc7fPjYhMJCrxKvfpvdnX/adQL7NQZo1mK2RcmOirhpeUDdt6gVoPstw/cDIkiLVWLY4k3P0D8x5PIeBzUGdvTXm1BdB62Z0pKGl/3W8fgjzdw7FD20S9vIikmBfNiGSOgzIsWIulZcrZyj8494dD4s5xeFEzi04yOMm2qlsOTzhH003ken39SoNkkK1vk51Hpf5efP0Gyss2xrGnX7yn09S8YN+ut97qRW2MKfbMQ094+SFZ2Ob43tyKiYnFUZ9QFRwcrIrLUhYjIWBwznVw4OlgRoTsZHf1De2Yt2I9HhznM/N8+ftBdje7bsz63Q6No2m42nfssYsywdqhU+d+dRUS/wMk+oy442hcmIvrldSE2PplDp+7TsGb24fAFLTIhFUeLjINPtYUJkfHZO4N+ORXOxx84YGasvx5GNi3B3GOPaflbCHOPPmJoQ6ds7/3/ICIyFkdHm/S/O6qtX/Jbs86xTGjYE87+HUqPjxbRb8AyLgXfB+DuPaVNHvD1Srr0WcCK1YFvljMqDid1pt+7vSURUfnb32zeeRH3BuXeLM9bWm8JL5JZ8Vsgg7/I22iNnERGxODolNGprXYsmu1iSERkDE66MsbGRlhamhMT8/I6XFCiIuOwz7Q97dWWROVhXxUZHstnPVfRs91i+nzSIN+jggAiI2NRv+bYQyljA2QcezyPecG9sCcgSQwetJK+PRbw+yrld25sYsSocd707vIzbT2ncfdOBF5d6+Y74z/edN9QoaxDeofC3oArPM7HxbGILL8rR0cbIiJispdxzLlMdHQsDrp6YW9vRXR09o7ZzZuP4e6uHAc/ePCEYsUs8PH5He8uUxkzdi0vXmQ/dnhl5ndsXxURGYujOss2ys12jPxnO9pnbMe/gnkckfHeB49i8O63lH5f/MbZv8PynfFJVDz26oxOaju1xSuPdV8m6MBNmrWp+PqCuc0VGY9DtrYj7/uCkCuPSE3TUMLFcBf+3iUqSWXw/94371/id5xuNNBOSZJKA18C3+tGDDXNUq6cJEl7JUk6J0nSEUmSXne/jx/g9c97gedA+pmkbkTSXEmSLgINJUmaIUlSiCRJlyRJmvOSrIMkSTorSdLZPesv5lQkX5p0LsPY1S3pOMCV/euVXu69a6/j0aUshczfzTsTPRuVJWDjALb/9hGN6pRi1LR9BsmhSdNy4e+7TJ3Zh5VrvuZQwBVOncy4ypiWpsFnxHp6922Ms0vOJ/lvQ+De29y+Fo1XX/15lJ49ecGCyUEMHtMElerfHaHzKut3fs7iP/oyemo7Fs8N5NH9mH/1+x1r2tJqVn08J9fBwa0o53+9nr6s1ewGNJtQm9qDqnB5wy0SIrN3qL5tKZvmkLzwG5J/HYGqlBtGNZVh2Zprp0ia+ynJiwajufU3Jt1yvsXs37Jhy2l8fmhL4K4f8fm+HWOm+AFw9OQtqlR04sie4fit+4rJs3cRH19wI9NeJU2jZdiMw3zk5YqLU8F1gL6Ja1GJPHieQoty2eeO2Xg5mhFNi/PXp64Mb1qC8QH3DZDw3afRaHn+/AW+a75mxPftGTpiPbIso9FoOfd3KLOn9mb9qi/562AwJ04VbAdzfmzfe4Xga+EM6FvfoDlett4WLv2Lj/s1oUjhgrl1+P8rB0crVvl+xjr/QezbcYWnrzixf5s0aVou/h3KTzN7s3LNlxwOCOb0yVukpWrYsvEk6zZ9y95Doylf0Ynffj1kkIyZTR3vzfrNp+n60RISXqRgamLYeUIkSco2quTkyets3nKcH4d1ASAtTUtIyH369PHAb9sYzM1NWb7i7R1rvov7qqymjvNi/ZYzdO2/TNmOultwHewsObT9e/z++JJRQ9swbNyWf20fn5OnTxK4eyua2g0L5haxgvIkKp6pY3fgM7HDO3UMLrxf3s0z8/8HZFkOlSRpKRAvy/IcAEmSMl8iWw58KcvyTUmS6gOLgVdNWBML3JckqSpKp9BG4NNMy4sAp2RZHiZJki2wEqgsy7IsSZLNSzIu1+Vgd+jw145PtrE1IyYq48T1+ZMkrO3MX1r+g2Yl2PzLJQDCrj3j4tFH7FgZQmJ8KipJwtjUiKZe+Z+8bt22C2zaeQWAapXUeqN5wqPi0yeDzo2i1hn/jh4dqjJn6ZF857J3sCY8POMqVWTEcxyyDKv+p4za0Ya0NA3x8UnY2BRGrbamVu2yFNVNztikaWWuhTykfgNljpCfJm6hZEk7+n6k17eYL8XsC/MkIuPA82nUC2zts08KefHMI7b8fpEpi9phYppxwPUiIYWpPx7gw0G1qVi1YOeBsnOwIDIiY3tGRcRjm4ftaeeglC3ubEON2s7cuh5JcRebAslmZmNK4tOMq3mJz5IxK6p/wmOaaSRHKXcngjfdSf+7ua5sEQdz7Crb8PxePEUcXl6PXseoXgeM67QFQPvwBpK1ffoyydoufTJoPXG611IS0VwKRFWionJ7WWLGOtec249Jm8/ynGed7yl8/ZR5nKq5liA80xXb8MhY1FnqgtrBSu9Wl/DIWNT2SpltOy8wZpgyGW27lm6MneoPwNYd5xn0cVMkSaKUiy3OxYtyJ+wJ1d2cc59zewib9iqddNUq2vE405XC8KgXqG1zniB1/P+OUqq4FR93KdiJvzPbcOkJW4KVbVTVoTDhmUYCRcSn4mChP0z9YngCwZEvaLM6hDQtPE1M49Ott/ita3m2X3vKKHflqnCb8tZM/H/UGbRu4wl8tyrzsFRzcyY8PCZ9WXjE85f81p7nWEattqZVC+X2repVXVCpJJ49S8DRwZq6tcpQTNcmuzepRPC1hzTMw8jMdVvOsWn7BSVnZSe9q9/hUXGo7fM2GuT4mbss/f04axf1Tb/NKC/+jfV28cp99v11mTk/7yY2Tpl4u5CpMf16532wtIPahvDHz9L/HhH+DLVav+NT7WDD48fPcHQsSlqahri4RGxs3myS49ywd7AkKtP2jIqIwz4P+6p/2DlYUqa8HZfO30+fJDavHBysiHjNsYdSJga1o3X6sYe1TWEc1NZ8ULtM+sTQjZtW4lrIQ4oUUfZXzrqJfFu1qcbqlfkbHVeQ+4Zype1ZtfBjAO6GPeHwUf3ba16aYd1hfDcpEwJXq1ZK73cVHh6DWm2jn0Ftk36bV9YytrZWREY+x8HBmsjI5xQrllGPr11/wNhxa1mxfAhFiyq/B0dHGxzVNtSooRz3tm1TK1edQe/avmrdptNZtmOWbZSb7eiQaTv+0h/QbcdjynY0NTVOb9uqVilOSeei3L0XTTXX7KPTX8fO3oKoiIzRek8i4nM81n2VoAO3aNSs7BvNF5ctl4MFkdnajtzvCxLikxn5rS8Dv/HArXre18v/V5IY55JnYo0ZgCRJFii3j22SJOkCsAzIzdj9P1FuFfMGtmVZpgG26P78HEgCVkqS1BUokBtJXSrZEPUwgejwBNJStfx9+CFuDfTnp4h6mNHghpyOwE43qe6385owfk0rxq9phUeXsrTsXeGNOoIA+napid/Kfvit7EeLpuXw33cVWZa5EPwYyyKmeZobKPP8QgeP3aFcqWKvKP1qblWduX/vCQ8fPCU1NY19ey7i4ak/y7+Hpys7/c8CELD/MnXrK/eAN2xckVs3H5OYmEJamoZzZ++kT/64aMFe4uOT+HFU/p+okFn5KnY8fhBLxKM4UlM1HP3rDnWa6F/1uHM9mmUzjzNqVgusi2V0WKSmapg16iDN2pWnYfPSBZIns0qujjy8/4zHD5+Tmqrh8P7rNPLI3dMb4mKTSElJA+D5s0SCLz7Sm3j6TdmUsSIhIpGEqES0aVoenorEsab+5yfFZHQWPf47GksnZV6AlIRUNKlaAJLjUnl6MxYLpzd7spPm9C6SFw8hefEQNFdPpo/ykZwrQVICxD/Tf4NKBYV1B2sqI4wq1UUbqRuCnWl+IVXl+shRee846NuzPv7rv8Z//de0bFYZv10XlHp5+T6WFmY42Okf7DjYWWJRpBAXLt9HlmX8dl2ghYdyMuRgb8np86EAnDxzh9IuSr10crThxBmlg+1JdDx3w57gXCJvQ6T7dnbFb3EX/BZ3oUXDUvgHKHNnXLgaiWURExxss2+Xn1efJS4hldFfNsjhEwtOn+p2bO5Tic19KtG8rDXbrz5DlmUuhidgYarKNu9Pr2p2HPzMjX2fuLKme3lK2xRKnyTavogJZx8qJw+nHsRT0ub/z0iNvr0a4r/xO/w3fkdLTzf8dp5XtuGle8pvzT7LibC9lfJbu3RP+a3tPE8LD6VtbtnMlVNnlHnJ7oZFkZqqoWjRIjRpVIEbt8LT2+Qz5+5Svmz2J1W+Mme32vj9PgC/3wfQwr0i/nuvKDmvPMSySKEc5wZ6mZDr4UyYuZfFs7pjWyx/nR3/xnpbv+pLDu4excHdo/i4b2O+GOCZr44gUE7aw8IiefDgCSkpaezefQ7P5vpPvvRsXh1/v5MA7Nv3Nw0aVHorc3hlVcnNiQf3nvH4YQypqRoO7rtKo2a56yiMjIglOUnp6I2LTeLy3w8oWTr/+yrXqs7cvxedfuyxf89F3LMce7h7urLT/zwAAfuvULd+Od2xRwVu3QwnSfc7P3/2LmXLqXFQW3PndiTPdBODnzpxizJl7bN9d24U5L7hn4nKtVotS1YF0rtb7m5d69u3Gf5+Y/H3G0vLFjXx8z+pZLhwB0tLs/TbvtIzOFhjYWHGhQt3lAz+J2nRQvntNW9eHT+/EwD4+Z1If/3Ro6cMGbKMWTM/pUyZjLbC3t4aR6di3LmjzLV54sQ1ypV7/aH/u7av6tujHv7rvsJ/3Ve09KiM3+6LmbZjoddvx90XaeGuTBmhvx2D6N1VmUvz6bMENBrlWOn+w6eE3n+KSx738f+o6OrAo3vPCX8YS2qqhsADN2ngXjpPn3F4/02atcn5wQ35VdmtOA/uPeORru0I2HeVxs1y9x2pqRrGDNtCm45V8915LAj/ECODDEMFxOjmG8qLncBs4Kwsy7FZDnSSZFnWAMiynCZJUj2gBdAdGMyrRx3lipGRim7fVGPZ6JNotTL1W5fEqbQVe36/hktFG6o2dOTI9rvcOP8EI2OJwhYmfPjjmz0mNbc8GpQh6GQorT/8DbNCxkwb1Tp9mfeAP/BbqTxZavaSIHYGXCcxKRWP7ivo3qEqQz5tyNotFzh07DZGRiqsLc2YPqrNy77qtYyNjRg52otvvvgVrUZL5y51KVfekSUL9+Hq5oyHpxveXesyzudPOrebibV1YabP/hAAK+vC9O3vzke9f0GSoHHTyjT1qEJEeAwrlx+kdBkHPuzxPwB69WlEl+75v0XAyFjF5z80YMr3+9FqZJp3rEDJskXZsOI85SvbUbdpSdYsOkNSYipzxx4GwE5dBJ9ZLTkeEErIhXDiYpM5tFu5ZWLwmCaUqVgwnS5GxiqGjGjOqMFb0Wpk2nq5UbqcHauXHKeiq5pGHuW4FhzOxB93EB+bxIkjd/h92QlWbvqYe3efMn/qX6hUElqtTO9P6hZoZ5DKSKJ6v/KcmHcZWStTsokjViWKcHXbXWxKW+L0gR13/npI+IVoJJWEqYUxHwxQdtbxj19w4febSJIyKWeF9i7ZnkL2JrQ3ziBXrEOh739Nf7T8Pwp9/QvJi4eAkQmF+k8BIyNQqdDevoDmrHJ10rhhZ4wq1wetBvlFvN7780N5CthNWnX5GXMzE6aN75K+zOvDxfivV6ZXmzCyIz6TtpGUnIp7owq4N1IOiKaM8WLa3N2kabQUMjVm8mgvAL4e4IHPpG106r0QWYYfB7em2BuMAvCo50LQmQe0/myT0n78kDHyzvvrbfgt7kJ4VAJL/7xIWRdrug72A6BvJ1d6tKvE5etRDJ7yF7FxKRw6dY+Fa8+zc3m3fOfJrGlpS4LCYmm/5hpmJip+apHRYdt9w3U293n1PGwTmzszI+gRGq1MIWMVE5rnfvTUm1j/2WSaVayFnYUN96dtZ8LOFaw6vuOtfZ9Hk0oEHr1Gq86zld/axB7py7x6/Q//jd8BMMHHW/eI9FTcG1fCvYmy/rp512H0xM107D4fExMjZkzugSRJWFsV5pN+TenebyGSJOHepBLN3uApjh6NyhF04jateyzFzMyEaWM6pC/z/nglfr8PAGD2ooPs3B+i7Ku8FtK9Uw2GfN6U2YsO8SIxhaFjletBTmorlszqkeN35SrPW1pvBcnY2Iix43rx+YCFaLVaunZrSIUKxVmwYAdVq5aiefPqdO/eiJEjVtOm9QSsrQszd96A9Pe3aD6WhIQk5YQr4CK/rhyi9ySyN8um4ruRrRj+tS9arUw7r2qUKWfPqsVHqOTqSONmFbgW/JixP2wlPjaZE0G3WL30KKu3fM69u9EsnncICZCBXv3rUbZC/jpalCxGDB/dmSFfrEKj0dK5Sx3KlVezdOF+qrg54+HpilfXOoz38cW73WysrM2ZNlt56qpy7NGU/r0XgiTRuGklmug6XgZ+1YKBHy/D2NgIp+I2TJia/9/bP95037Bz32XWb1ZGt7VqVoVunfJ+rOnhUZXAoCu0aj0OczNTpk37OCOD90/4+41VMoz/EJ/Rv5OUlIJ7U7f0OYAGDWzD0O9XsHnLMYoXVx4tD7Bo8S5iYhKYNHkDoBw7b90yGoBxY3vx4/BVpKZqcHGxY/q0/nnL/I7tqzwaVyDw+E1adV2gbMdxXunLvPouSX8a2IQRHXSPlk/DvVH5jO24/wrrN+m2o2fGdjzzdxgLlh3C2FiFSiUxaVRHbKzzd+HMyFjFVyOaMvbb7Wg0Mq07V6FUOVvWLD1FxSoONPAow/XgCKaM2EN8bDKnjt7lj2WnWearHJdHPIrlSUQ81WoV7OgbY2MVQ0e24sev/0SrlWnvVZ0y5exZuTiISq5ONGlWgavBjxj7w1biYpM4HnSTVUuPsGbLQA7tv8rF8/eJjUlk73blyXA+kztSoVLeLlQIAoD0pk8vEJSnfaG7HUySpGbAj7Isd5QkaRhgJcvyhBzKHUeZdHqTpBw5VZdlOceJe7K8rzdwQ5bl85IkHdZ911lJkuJlWbbQlbcACsuyHClJkjVwR5blV54N5+Y2MUNoX3o2cnjBPOa6oEmOX5KQ6m/oGNkUMVF2xleiZxg4SXZVbUdxP/7d3J4uFl8y4tggQ8fI0azGy0kc1+H1BQ3AfMouiN1o6Bg5s+qFfHeWoVNkI5UZAUDKwp4GTpKd6WBfpK/e7sin/JKXnIQXWQfFviMKd0GOXm3oFDmSbD95N9dbYaUDQCsHGDhIdiqpBY9frDJ0jBw5Ff6MuNR3cHsCliZd3un9AbLh5zvKkeT5Tu+reL7BsEFyYt2HO7ELDJ0iR2WtviXixWpDx8iRuvAn/68nF4pN2WLw81kr027v1ToWt4m9XTuALjlNIA30BQboJnwORjc59OvIsvynLMvnX1PMEtgpSdIl4Chg2FlgBUEQBEEQBEEQBEF4Z4jbxAqALMsTM/35MHBY9+cbQOYb249kKncXaJvXz8/yerNMf7bI9OfHQL3cfLYgCIIgCIIgCIIgvM+k9/DR7oYm1pggCIIgCIIgCIIgCMJ/iBgZ9A6RJGkMkHVmvk2yLE81RB5BEARBEARBEARBEP7/EZ1B7xBdp4/o+BEEQRAEQRAEQRCEXFKJm57yTKwxQRAEQRAEQRAEQRCE/xAxMkgQBEEQBEEQBEEQhPeWmEA678QaEwRBEARBEARBEARB+A8RnUGCIAiCIAiCIAiCIAj/IZIsy4bOILwbxA9BEARBEARBEATh/yfJ0AHepiTNLoOfz5oZdXiv1rGYM0hQaAMMnSBnqhbI9382dIocSS5DIW2foWNkZ9xG+X+iv2Fz5MTcC5J2GDpFzsw6QfwWQ6fImUU3YlPezWxWpt0gdY+hY+TMpB3EbTJ0iuwsewAg35tn4CDZSSV/gBfbDB0jZ4W7IH3VwNApciQvOflut23v6v4A4PkGw+bIiXUfrj2bbegUOapcdDip2nfw2AMwUbVB5pChY+RIwvPdPGYD5bgtdqOhU2Rn1QvgndymEp6kaQ8YOkaOjFWtSEh9B9tcoIiJl6EjCO8YcZuYIAiCIAiCIAiCIAjCf4gYGSQIgiAIgiAIgiAIwntLEuNc8kysMUEQBEEQBEEQBEEQhP8QMTJIEARBEARBEARBEIT3lkoS41zySqwxQRAEQRAEQRAEQRCE/xDRGSQIgiAIgiAIgiAIgvAfIm4TEwRBEARBEARBEAThvSUmkM47scYEQRAEQRAEQRAEQRD+Q8TIIOGVZFlm6rRNBAYFY2Zmwoxp/XFzK5mt3JXge/j4rCEpORUPdzfGjO6BJEns2XuehQt3cftOOJt8R1CtaikAjh27ytx5fqSmajAxMWL48K40bFDpzXIuOkbQ6TDMChkzfURz3CrYZys3f9Up/A9cJzYumfM7B6a//jAijjFzDvE0JhFrSzNm+7TA0d7izfJM30JgUAhm5qbMmNoXN1eXbOWuBN/DZ8w6kpJS8XB3ZYxPNyRJYuYcPw4dvoKJiTElXeyY/tOHWFkVBuDa9YdMmLSR+PgkVCqJzRt/pFAhk/znnLWdwKPXlO07uSduVZyz5wx5gM94X2X7NqnMmBGdkSQJgLUbjrFu43GMVCo8mlZmxPcd8pUlPc9MfwKPXsXMzJQZU3q9PM+4P3V5qjBmpFd6HoBVvx9m5rydnDg8iWJFi/Dr6kPs2P03AJo0DbfvRnLi8CRsrAvnPtfsnQQeu67kmtgNtyolsue6+hCfCZuVXI0rMWZ4RyRJYuioDdwNewJAXFwilpbm+G8YAsC1m4+ZMNWP+IRkVJLE5rVf52l7Hj96g7kzd6LVaPHqWpdPPvfQW56SksaE0Zu4FvIQa5vCTJvdh+IlirJn5wXWrj6SXu7WjXDW+n5DpcrFSU1NY9bUHZw/ewdJkvj629Y0b1U115n01tv0rQQeuar8vqZ++JJ6cB+fseuVetC0CmN8uiJJEj//spuAg5dRqSRsi1kyfeqHqB2sOXX6Jl9/uxLnEsUAaNWyOoO/apv3bHN2EXjshpJtYjfcKhfPnu3qQ3wmbtVt04qM+bEDkiRx9fpjJkz3JzklDSMjFRNHdqZ6VWf+OnyV/y39C5VKwshIxehh7alTs3Se151ezsXHCTp9T2nbhjd7Sdt2Gv+/biht244B6a8rbdthnj5PwtqyELNHNX/ztm3WDl1dMGHGpB4514WQB/hM2ERScppSF0Z00m8zfE9ipJKUNmNoewCu3XjMhJ+2EZ+ga9v+GJzvtu1VVn40ho7VGhMZ94xqU/oW+Odn9a62a+nZ3sI+4FlMAt/+uJYrwQ/o0rkO432887jWdNnm7iHw+E0l23jvl9TRR/hM9lOyNarAmGHtkCSJazfCmTBjJy8SUyjhZMOcyV2xsDDjwaNntO+1iDIlbQGoUdWZyT6d8pzvH+dP3GfF/JNotTKtOleie/8aesv9119m//brGBlJWBc1Z8iYpjg4WQIwceheblyJpEoNNePmtsl3hpc5eiSEGdO2otFq6da9IZ8PbKW3PCUlFZ+RfxASch8bmyLMmfcJJUrYcvlSGBMn/Ako2+Hrb9rRslWNnL4imyNBwUyd6otWq6V7j8YMGqTfNqekpDJyxGqCg+9hY1OEefM/x9nZDoBly/ayZfMxVCoVY8b2pGlTt1d+5pjRa7hyJQxZhtJlHJg+/WOKFDHjzJmbTJ/my/XrD5k7bwBt29Z+Zea3dcy2fecZVq46mP7+6zcesW3TcKrkUMdyQ6kTuwk8pqsTE7q8vE5M2qprfyswZlj7THViOy9e6OrElO5YWJjlKcPb2L6xsS8YO3YtN288QpIkpk7rzwcflAVg7dpDrF93GCMjFR4eVRk+olvuch4JYca0zbrffiMGDmydLafPyLUEhyg55877jBIlbIl5Fs/QoSu5ciUMb+8GjB3XM/09u3adZcWyfUiShL2DNTNnfUzRonnbnx47ep05M/zRaGS6dKvHp597ZsmVxjifP7ka8hAbm8LMmNOX4iWK8ejhU7p1nkOp0sr+v1r1koyZ0I2EhCQG9F+S/v7IiOe061iL4aM65ynX/xdiAum8E2tMeKWgoGBCwyLZv3ciUyb1ZeLkP3MsN3HSBqZM7sv+vRMJDYsk6EgIABUrOPHLL4OoW6e8XvmiRS1YsuQrdmwfy4zpHzNi5Oo3y3n6HmEPY9j3+4dM/t6DSf8LyrGcZ4NS+C7MviOZtew4Xq0qsn1FL775qDbzVp56szxHQggNi2L/nnFMmdiLiZN9cyw3cbIvUyb1Zv+ecYSGRRF09CoAjRtWYqefDzu2jaJ0KXuWrTgAQFqahuGj1jJpfC92bR/NmtXfYmxslP+cR68Reu8J+7ePYMq4bkycui3nnFO3MWV8N/ZvH0HovScEHbsOwMkztwg4HMx23+/ZtXUYAz72yPH9ecsTxf4do5gyvjsTf9qSc56ftjBlQg/27xhF6L0ogo5dS1/2ODyGYyduUNzJJv21zz/xxN/3B/x9f+CHb9tTt3bZPJ0wBR27Qej9aPb7DWPKWG8mTvfPOdd0f6aM68J+v2GE3o8m6PgNAH6e0Qf/DUPw3zCE1s2r0srTFdBtz7GbmDTam12bhrJm+cA8bU+NRsusqdv53+JP8PUfyv49F7lzO0KvjP/Ws1hZmbNt9498+FFjfpm/F4B2HWuyfvMQ1m8ewuRpPSheoiiVdAeVq5YfplixImzZOQxf/6HUqlMm15kyCzpyVdmeu8co9WDKphzLTZyyiSkTe7F/9xhle+rqweefNmfHtpH4bxlBMw9XFi3Zl/6eOrXK4r9lBP5bRuS5IwgybdNt3zNljDcTp2/POdv07UwZ683+bd/rtulNAGYv2Ms3A5vjv34w333RgtkLlPXasF5Ztm8YjP/6wUwb35WxU/zynE0v5+n7hD18zr7VvZk81J1JC47mWM6zQSl8f+mS7fVZy04qbdvyHnzTrzbzVp5+szxHrytthv+PTBnblYnT/HIsN3GaH1PGdWO//4+6NkOpCyfP3Cbg8FW2b/yOXVt+YEB/d+CfurCRSWO82bXlB9asGPRGbdurrD6xi7a/fP9WPjsn72q7lpGt4PcBhQqZ8N03bRjxQ/4vDgQdv0no/afs3/ItU3w6MXHmrpyzzdzJlNGd2L/lW0LvPyXoxC0AxkzdzrDBLdmx4WtaNqvMr38cT39PyRJF8V/3Ff7rvnqjjiCNRsuyOceZML8NCzd048j+29y7+0yvTJlKtsxb7c2Cdd1o5Fma1Qsz6mCXvtUYOuHN9puvyvbTlE0sWf4l23eMZveuc9y+9VivzNbNJ7GyLsyefeP5qH8z5s1R2sHyFZzYuOlHtmwbybLlXzF54kbS0jS5+E4NkydvYMWvg9m5awK7dp7h1q1HemU2bzqGlVVh9h+YwseftGDuHOU3d+vWI3bvOsPOXeP59dchTJ60AY1Gi0ajfeln+ozugf/2cWzfMQ4np2KsW3cYACenokyf/jEdO9bN1bp6W8dsnTvWxX/rSPy3jmTWjI9wdi6W744g0NWJe9Hs3/odU0Z3ZuKMHTnnnLGDKWO82L/1O0LvZey3xvzkx7BvWrHjz8G09HTl17XH8vT9r9oW/8jr9gWYOtWXpk3d2LN3En7+YylXzhGAkyevczDgIv7bx7Jz1wQ+G6DfmfmqnFOn+LJ0+dds3zGW3bvOcSvLb3/L5hNYWZuzd99E+vf3ZN4c5bjOtJAJQ77tyPDh+vvTtDQNM6Zt5rffv2Ob/2gqVizB+nWBeV5/M3/axi9LBrBl+zD27r6Q7ZjNb+tprKzM2b5nJH0/asr/5u1OX+bsYsufW77nzy3fM2aCci5TpIhZ+mt/bvkex+JFad4y7xfvhH+PJEltJUm6LknSLUmSRuWwvJAkSRt1y09JklQ60zIf3evXJUkqkCsIojNIeKWAg5fw9qqPJEnUrFmG2NgXREY+1ysTGfmc+PgkatYsgyRJeHvVJyDgIgDlyjlRtow62+e6urqgdrABoEIFJ5KTU0lJSc1/zuOheLWqpOR0dSQ2PpnI6IRs5Wq6OuJgWyTb67fDntGgprKDrl+zBAHH7+Y7C0DAwct4d66n5KlRhti4RCKjsqy3qOfEJyRRs4ZuvXWuR0DAJQCaNK6SfiJUs0ZpwiNiADh2/BqVKhancmXlSnxRmyIYGeW/GgccDsG7Yy0lZ/VSupyxWXLGKjmrl1JydqxFwKFgADb4nmTQp56YmiqDDG2L5X/EAUDAoWC8O9XJlCfp9Xk61SHgYHD68umz/Rn+fUe9K+qZ7dp7gY7tPshbrsAQvDt8oOSqVpLY+Jfkik+iZrWSSq4OHxBwOESvjCzL7PnrMh3bKldYj528RaUKjlSu6ARAUZvCedqewZcf4FLSFmeXYpiYGNOqXXUCD13VKxN06CodOtcCoHmrqpw5dRtZlvXK7Ntzkdbtqqf/ffu2c3zyeTMAVCoVNkWz15ncCDh0Ge/OdXX1oPRr6kFpXT2oS8DBywB6VywTE1N4ySbNX7bAq3i3r6nbpi7Kb+1JnH62J3HEJyRTs5qLkq19zfRtKkkSCQnJAMTFJ+FgbwVAkcKF0n97SuY3Cx1wIhSvlhV1bZv6FW2bOue27d4zGtRU2ov6NYsTcCL0zfIEZm4zSr6izUimZvWSGW3GYV2bsekkgz71yNZmHDtxU6kLlZQOyTdt217lyK0LPE2IfX3BAvKutmvw9vYBhc1NqfNBGQqZ5n9kV0DQdbzb18hjHa1BQKDSiRZ6L5q6HyijkRvXL8f+QyHZvuNN3QyJwtHZCscSVpiYGNG0VVlOB4XplaleuziFzJT1U6mqA9GRGfW3Rt0SmBcu+NFvAJcvhVGypD0uLnaYmBrTrn0tDura1n8cPHgZL696ALRuU5NTJ28gyzLm5qbpxyDJKWnktvG9dOkSJUs54OJij6mpMe071E0/rvlHwMFLeHdpCECbNrU4ceIasiwTEHCJ9h3qYmpqgrOLHSVLOXDpUiiXLoW+9DMtLMwBZd+anJSKhJLT2dmOSpWdkVS5y/22jtky27X7HB3avXqE0mtzBl7Du0Me91sdaurXiVqlAWhcL+914lXbIj1jHrdvXFwiZ8/cpHv3xgCYmhqnj4T/c0MgAwe1wVTXjtjaWuUq5+VLobiUtMPFxU7J2b4Whw7q5zx48BJeXvUBaN3mA06evI4syxQuXIjatcthmmVUqiwr/yW+SEGWZRISErF3sM7T+rty+T7OJe1wdrHFxMSYNu1qcDhTOw9w+GAIHb3qANCidTXOnLqV7ZjtZcJCo3gWHU+t2vm7gCe8fZIkGQGLgHaAK9BHkiTXLMUGAM9kWS4PzAdm6t7rCvQG3IC2wGLd570R0RkkvFJERAyOjkXT/+7oWJSIyBj9MpExOKptMsqoixKRw47wZfbt/xvXKi7pjX2+cj5JwCnTrQ+O9hZEPMl+wvQylcracuDoHQAOHL1LwotUnj1Pyn+eyOc4Otpk5FHbEBGhf2AREfFcf7052hCRpaMNYMvWk7g3VdqJu6GRSBIMGLiYLt1nsWLlX/nO+NKcWTJERD7HUW2dY5nQsCjOnr9Lj36/0G/AEi5duf/mefR+S9YvyZNzmb8OXcHBwTr9hDKrxMQUjhy7RuuW1XNc/vJcsfrrwMGKiCwnTBFRWcqorYiI1C9z9u9QbItZULqkMmT67r0nyvb85je6fLiQFb/nPKLtZaIin6N2zPhOtdqaqIgsJ3KZyhgbG2FhYcbzmBd6ZQ7svZzeGRQXmwjA0oUH6NdzIaN+WE90loPN3IqIeK7ffuSmHmQpM/9/u/BoMZEdu87x3eD26a9fuBhK566z+PzLpdzMcsUvV9mi4nB0fPX2Ura7VaYy1kREKeti9LD2zPrfXjw6zGLm//byw+CMK5YHDoXQttvPfDF0LdPGZx+tk6ecTxJwcsjo5HG0K0LEkxeveIc+pW1TOrfT27bYN2nbYrO0GdY5rzcH6xzLhIY94ezfofT4aBH9BizjUrDSZih1QWLA1yvp0mcBK1bn7Yrru+xdbdfSv/cd2gfof2+W+ufwkjrqkHOZCmXt00+C9/4VzONMbeODRzF491tKvy9+4+zf+p03eREd9QK7TPXT1qEI0VEvr58HdtygdsPstx+9DZGRMXrbVq22ITJL+xsZ8RxH3WgzY2MjLCzNiIlRjp0uXQzFq+M0unhNZ/yEnrkaqRcREYFTtjZff6RUZEQMTk5F07/T0tKcmGcJREQ8y/G9L3v9Hz4+v9Ok8Qju3Amn30f6t9zk1ts6Zsts997zdGhfK1/50jNkPc7IbZ2I+qdOOGTUiYArPI7Inv+V3/+abQF5374PHjyhWDELfHx+p4v3VMaOWcuLF8qFltDQSM6evUXPHjPo128uly+F5i5n5HO971Kri2bbnspvP0vOmJefN5iYGDFuQi+8vabRzH0Mt2+F061bo1zl+UdU5HO94w4HtTWRWbZf5jL/HLPF6I7ZHj58Sp/uP/P5J0s4fy77Ret9ey7Qum2NN74I9T6TJJXB/3uNesAtWZbvyLKcAvwJeGUp4wX8rvvzZqCFpGxUL+BPWZaTZVm+C9zSfd4bEZ1BgkHdvPmIOXP9mDzpQ4PmGPFFI85cekSXLzZx5tIj1HZFMDIyfGO6ZNk+jIyN6NxRuUqg0Wg5d/4Os2f1Z/3aofwVcIkTJ68bLJ9Go+V57At81w5mxNAODB3xR66vYBS0xMQUlv0awHdfv3zU5KHAEGrVLJ3nWykKys69F+nYJuOETZOm5dyFMGb/1JP1Kwfx16FgTpy+9a9munLpPmZmJpSvoAzL1mi0REY8p3rNkvzhO5hqNUryv7l7/tVMmX3/XQcCAybSqUNt/livzHHk5urCwQMT2L51BB996M43367813Nt2Hwanx/aE7hrBD4/tGfMlIzba1p5urJ3y1AWzfmQ/y19sw7bNzViUAOlbftyM2cuPVbatlxeLX8bNBotz5+/wHfN14z4vj1DR6xHlmWlbfs7lNlTe7N+1Zf8dTCYE6f+3brwLnrX27V3aR+Q1dRxXqzfcoau/ZeR8CIFU11nhoOdJYe2f4/fH18yamgbho3bQnx8/jtIc+vwnpvcuhpFl35577QzhOo1SuO/czR/+v7IrysOkJyc/9Hbb9P06R8TdGQm5co5snv3WYNmyXrM9o+Ll0IxNzOlYoWcO3T/LVPHe7N+82m6frREqRMmb+dW3LxIS9MSEnKfPn082OY3BnNzU1YsV24JV/YXCWz0HcmIEV0ZOnSFwdqX1FQNG/88wuatIzkcNJWKlUqwYvn+f+377eyt2H1gNBs2D+WH4Z0YM2J9tnZr356LtGlf81/LJORLCSDzVZMHutdyLCPLchrwHLDN5XvzTEwgLWSzbl0gvpuV+4irVS1FeHhGr394+LP027v+oXaw0RsSGx7xDLVav0xOwsOfMXjIcmbO+JiSJbNPiPranP5X2LRbGeJaraIDj6PiMz47Kh61Xe5vbVHbFeGXicq8IwmJqew/cgcri0J5y7M+CN/NJ5Q8VUsSHh6TkSciBrVafzipWm2tv97CY1Bnupq+ddspDgcGs3rl4PRefke1DXVrl6eYbsI696auBIc8yNPk2+v+PI7vVmVOpGpuLtlzZhn2qnawJjzTFZXMZdRqa1q1qIokSVSvVhKVSuLZswSK5eF2sXV/HtPPo/dbev6SPNnL3HsQzYOHT/HqOS/99a6957Np3bfY2ylXyXbtvUCHXN5Ksc73BL7blAPLaq4l9NdBZCxqe/3hymp7qyzrKRZ1pqtzaWkaDhwKZusfg9Nfc1RbUfeD0hTT3Ybl3rgSwdce0bCe/hxbL2PvYE1EeMZ3RkQ8x16tn8tBV0btaE1amob4+CSsbTJOGvfvuUSb9hkTg1rbFMbM3ATPlsrEji3aVMV/W+4PsNdtOJKlHmRqP3JTD3IoA9CpYx0GfbWMbwe307t9zMPdlUk/beLps/j0evHSbL4n8fXLtE3DX769ANQOVoRnGk0QHvEctb0y8eu2nX8z5kdlPpR2Lasy9ie/bN9Xt1YZ7j/cytOYBIrZ5L49Uto25epttUr2PM50W0n4kwTUdrk/6VfaNqUjISExlf1H7+a9bdt4At+tyjwn1dycs7QZz3Neb5HPcyyj12ZUdUlvMxwdrKlbq0xGXWhSieBrD2lYP3d14V3zrrZrSrZ3ax+gl23TaXz9zinZXEvo17/Il9TRyJzLlCttz6pf+gNwN+wJh3XzVpmaGqff1la1SnFKOhfl7r1oqrnm/Zja1r4wTzLVz+jIBGzts9fPC6cfsmn1BaYu6YiJ6b9zAu7gYKO3bSMiYnDI0rY6qK0Jf6yMAE9L0xAfl4RNlraqXDlHChcuxM2bj6laNfsDRDJTq9U8ztbmF9Ur46C24fHjZ+nfGReXiE3RIqjVRV/63td9ppGRivYd6vLrr/tzPVrj3zhm+8eu3efp0D5/t4it8z2VpU5kORbJTZ2wz1QnFn4M6OrE0Rt5yvKqbfSPvG5fR0cb1I421Kih3NrUpm2t9M4gtdqGVq2UW/SrVy+ja1/iKVbM8tU5Haz1visi4lm27an89rPkfMV++tq1BwDp5ytt29bi1xV56wyyd7DWO+6IjHiOQ5bt908ZtaNN+jGbjU1hJElKb7dc3ZxxdrHlXmgUrlWVkYY3rj1Co9Hi6pb/Oan+P5DegWsRkkoaBAzK9NJyWZaXGyrP64iRQUI2fft64L9tNP7bRtOyRXX8/E8hyzIXLtzF0tIchywHig4O1lhYmHHhwl1kWcbP/xQtmr/6ylds7AsGfbmYYT94UbtWufzl9KqK37Ke+C3rSYvGZfA/oNzveyEkHMsihXKcP+Nlnj1PRKtVWpDlG87TrW3lvOf50D19osCWLarjt/20kufiXSwtzHCwz7Le7K2xKGLGhYu69bb9NC2aVwOUyQx/XfUXSxYOxNzcNP09TRpX4cbNRyQmppCWpuHM2VuU1020l+ucvRvh7/s9/r7f09LTDb+d55Wcl8KwtDBPn/skI6eVkvNSmJJz53laNFOGQLf0dOPUmdsA3A2LIjVVQ9E8zi/Tt3fj9ElQW3q64bfjbKY8Zq/Ps+MsLTzdqFTBiROHJ3FwzxgO7hmDo9qarX9+n37CFBeXyJlzt2nRzC13uXo2TJ/0uWUzV/x2/a3kunzv5bkszLhw+Z6Sa9fftPDIGCp+/PRtypa21xvi3aRhRW7cisjYnufvUr6MQ67XnWvVEtwLe8LDB09JTU3jwJ5LuDerolemabPK7Np+HoCDB65Qt17Z9ANVrVbLX/sv06ptRn2VJImmHpU5d0YZgnzm5G3Kls19pr59mqZP7NyyeTX8tp/R1YNQ3e/rZfUgVFcPztDCU6kHoWFR6eUCDl5On38s6kls+tXBS5fD0Gpliuais6Vvzwb4r1cmd27ZzBW/3Rd02/Q+lhaFcLDTP8B0sLPEokghLly+r2TbfYEWHlV0ua04rRumffLMHUq7KE8mCrsfnZ4t+NojUlLSKJrHERtK29Ydv2XdadG4NP5/3dC1bRFYFjF9g7btb7q1yftTG/v2aoj/xu/w3/hdljbjFXWhSCEuXLqX0Wbo6kLLZq45thlNGlXgxq3wjLpw7i7ly2afb+598a62a0q2d2sfoJetR730iZ1belTGb/fFPNbRi7RwV37j0U+VC0RarZYlq4Lo3VUZrfH0WUL6xLX3Hz4l9P5TXEron9DmVoUq9jy+H0vEozhSUzUcOXCHek1L6ZW5c/0JS2YeZczs1tgUM8/X9+RH1WoluRcWxYMH0aSmpLFn93k8dW3rPzw9q+Lvr3T07t93gfoNKiBJEg8eRKdPGP3o4VPu3omghO7pja9SrVo1wkIjeXD/CSkpaezedYbmWY4Hmzevjt82pRNm377zNGigzPfYvHl1du86Q0pKKg/uPyEsNJLq1UtTrVqpHD9TlmXCwiIBZc6ggwcvUjYPbca/ccwGyu9vz76/6dAuf7eI9e1ZH//1X+O/XpkI3W9X5v2W2evrxK4LtPBQjmn160QgvbvlboLtf7xsW2SW1+1rb2+Nk2Mx7twJB+DEiWuUK6fMo9iyZU1On1JGv9+9G6FrX17f0Vy1Windb1+Xc/d5PD31c3p6VsPfX+kU37/vb+o3qPjK26vUamtu3wrn6VPlVvHjx69RNo/H325Vnbl/L+OYbd+ei3h46t9S6OHpyk5/5aJVwP7L1K1fHkmSePY0Pr3denA/mnv3nlBCd9wBsHfPBdq0q5mnPMLbIcvyclmW62T6L3NH0EMg873CzrrXyKmMJEnGgDUQncv35pkYGSS8kodHVQKDgmnVZgLmZqZMm/ZR+jKvLtPw3zYagAnje6c/Wt69qRvu7sqB6YEDF5gy1ZenT+P54svFVKnszMpfh/DHukDu3Yti0ZI9LFqi3IKy6tch2Nq+urf/pTnrlyTodBit+6/HrJAx04Zn3Dfu/YUvfsuUR0POXn6CnQdvkpichkfvNXRvV4UhH9fl1MVHzNc9QaxudSfGD3HPV470PO6uynprN1lZbz9lPL7Yq+tM/LeOBGDCuJ7KY0qTU3Bv4pp+n/mUqZtJSU3j088XA1CjRmkmT+iFtXVhPvnYk+695iBJEu5NXWnmkfuTgGw5m1Ym8Og1WnWaqeSc1CMjZ8/5+PsqT9yZMNo7/bHC7o0r495EObDo5l2X0RM20bHbXExMjJgxpdcb3avs0bSKkqfjDMzNTJg2uVemPPPw9/1ByTOmq+4RzGm4N66UnudVDhy8QuOGlShcOG+jIgA8mlQi8Nh1WnnNVXJNzHginVefX9IfEz9hVGd8Jm4mKSkN98YVcW9cMb3c7n2X6NBG/9G81lbmfNKvMd37L0aSlJFBzZrmviPS2NiIEaM78+2Xv6HRyHTuUpty5dUsXXiAKm7OeHhWwatrHSb4bKJL+zlYWRdm6qze6e//+1woakdrnF30D/KHfN+WCT6bmDdzFzbFCjNhSvc8ra9/eLi7EnjkKq3a/YS5uSnTpvRJX+bVbRb+W0YAMGFs9/RHy7s3rYJ7U6XDZe78Hbp5siRKFC/GpPHK73Pf/ots2HgMIyMVZmYmzJv9cZ5/dx6NKxJ47AatvOcpv/0JXTOyfbgQ//XKCC5lm25RfvuNMrbplLFeTJuzmzSNlkKmxkweo9zyvS8gGP/dFzA2VmFWyIT509+wTtQrSdCpe7T++E+lbfuxWfoy7y8247dM2TazV5xk58FbStvW5w+6t6vMkP51OHXxsdK2SRJ1qzkxfkiTfGcBXV04eo1WnWfr6kKmNqPX//Df+B0AE3y8dY+WT9XVUeUEvZt3HUZP3EzH7vOVNmNyDyRJwtqqMJ/0a0r3fguVtq1J3upCXqz/bDLNKtbCzsKG+9O2M2HnClYdz/mpPAXhXW3XlGxvbx/QvN104hOSSE3V8NehYFYt+Zzy5XJ/su7RuAKBx2/SqusCZb2Ny5hWwavvEvzXfaVkG9FB92j5NNwblce9UQUAdu6/wvpNSkdHK88qdOukjJ4683cYC5YdwthYhUolMWlUx3zfYmdkrGLQj42Y+N0etFqZFh0rUrJsUdYtP0f5ynbUdy/Fb7+cJvFFKrPGBABgp7Zg7BzlMdc+X+zgQdhzkhJT+azTegaPcadWg4K5sm9sbMTosd354vPFaLRaunRtQPkKTixcsAu3qiXxbF6Nrt0b4jNyLe3aTMbaujCz534CwPlzt1m54i+MTYxQSRJjx/fM1Um4sbEx48b3YsDnC9BqtHTr1ogKFYqz4H/bqVq1FM1b1KB798aMGP4brVuNw9q6MPPmfw5AhQrFadeuNh3aT8LIyIjx43unTyKf02dqtVpGjVxNfEISyFCpUgkm6qYcuHwplMGDlxIb+4JDhy6z8Jed7Nr18vmE3tYxG8CZs7dxcrTBxcUuL5sv55yNKxJ47Catuvys1IlMc9J5fbgY//VfKzlHdsRn0jbdfqtCRp3Yd5n1m3V1ollGncgtY2Ojt7J9x47rxfAfV5GaqsHFxY5p05URfV27NWLM6DV06jhZaV9m5G5fb2xsxJixPRn0+SK0Wjn9t//Lgp24VS1J8+bV6da9EaNGrqFtm4lYWxdhztxP09/fqsV4XduVxsGASyz/9RvKl3fi62/a8fFHP2NsbIRT8WJMm9Yvz+tv5GgvvvniV7QaLZ271KVceUeWLNyHq5szHp5ueHetyzifP+ncbibW1oWZPlv5TZ8/d5clC/ent1ujx3fFOlO7dWDfJRYs/ixPeQSDOANUkCSpDEpHTm8g61wp24GPgRNAd+CgLMuyJEnbgfWSJM0DigMVgDd7RCwgvSv3dgsGpg14N38IqhbI9382dIocSS5DIW3fa8v964x1c0sk5vz4c4My94Kkt3fS9UbMOkF8zo98NjiLbsSmvJvZrEy7Qarh5hR6JZN2EJfz4+wNylI54ZbvzTNwkOykkj/Ai5wfL25whbsgfdXA0ClyJC85+W63be/q/gDg+QbD5siJdR+uPZtt6BQ5qlx0OKnad/DYAzBRtUHmkKFj5EjC8908ZgPluC12o6FTZGeldGi9i9tUwpM07QFDx8iRsaoVCanvYJsLFDHxMvyEqG/Tu3A+q2rxynUsSVJ74GfACFgly/JUSZImA2dlWd4uSZIZsBb4AHgK9JZl+Y7uvWOAz4A0YKgsy298AC5GBgmCIAiCIAiCIAiCILxFsizvBnZneW18pj8nAT2yvk+3bCowtSDziM4gQRAEQRAEQRAEQRDeX7LW0AneO2ICaUEQBEEQBEEQBEEQhP8Q0RkkCIIgCIIgCIIgCILwHyJuExMEQRAEQRAEQRAE4f0lbhPLMzEySBAEQRAEQRAEQRAE4T9EjAwSBEEQBEEQBEEQBOH9JUYG5ZkYGSQIgiAIgiAIgiAIgvAfIjqDBEEQBEEQBEEQBEEQ/kMkWZYNnUF4N4gfgiAIgiAIgiAIwv9PkqEDvFXJuwx/Pluow3u1jsXIIEEQBEEQBEEQBEEQhP8QMYG0AEBk4hpDR8iRg3n/dzrb9rs/GDpGNp3LzANg6+2hhg2Sg67lfmZVyNeGjpGjz1wXE5e6zdAxcmRp0gVevJvZKNyFjv4fGTpFjnZ6reX2858NHSObctZDAdAeHmrQHDlRNfsZOXq1oWPkSLL9BJJ2GDpGzsw6IX3VwNApciQvOYkc84ehY2Qj2fQDQLtzkIGTZKfquBz5ySpDx8iRZPcZaA4YOkbOjFpB2j5Dp8iZcRuSNXsMnSJHhYzaIT9ebOgY2UhOuuO1+C2GDZITi26Exi00dIoclbYczLPkjYaOkaOihXoZOoLwjhGdQYIgCIIgCIIgCIIgvL/E08TyTNwmJgiCIAiCIAiCIAiC8B8iRgYJgiAIgiAIgiAIgvD+EiOD8kyMDBIEQRAEQRAEQRAEQfgPEZ1BgiAIgiAIgiAIgiAI/yHiNjFBEARBEARBEARBEN5f4jaxPBMjgwRBEARBEARBEARBEP5DxMggQRAEQRAEQRAEQRDeX1oxMiivRGeQkGunjt3mf7P2o9XKdOxSk36fNdJbfuHcPRbM3s+dm5FMmNEFz1ZVALh5LZy50/aSEJ+MykhF/88b06KN6zuRDWDY1xsIufSQah+4MOuXXgWaC+Da2Ui2LwlBq5Wp19aF5r3K6y0/sSuM4zvCkFQShcyM6P5dNdSlLHka/oLZgwKxd7YAoFRlG7p9W61As10/G8XOZVfRamXqtnGmWc9yestP7brHiZ1hqIwkTM2M6fKtG+qSlvx96CFHttxNLxd+N47BCxpTvJxVgWW7cz6agJU30GplarQsToNupXP+N5yIxG/WZfrProtTeSse3XjOviXXAJCBJr3KULGBwxvnOX70OnNm7ECrkfHuVpdPPm+mtzwlJY0JPr5cDXmItU1hps/pQ/ESxQC4ef0x0yZvIyE+CUklsebPwRQqZMLe3Rf4bcUhJCTsHayYMqMXNkWL5CmXLMtMnbWDwGPXMTMzYcakHrhVKZGt3JWQB/hM2ERSchoejSsxZkQnJEkCYO2GY6zzPYmRSsKjaWVGDG1PaqqGsZO3EHLtIWkaLd4davHFAM/8rTyglkM1BlX7CBUq9t87zOabO7OVaVK8Hh9W7oosy9yNvcecc0uoZleFgVX7ppdxtnBi1tnFnAw/l+8sWZ09cY9lc4+i1cq08apCz49r6S2/fP4Ry+cf4+6taEb91IomLTLqycoFJzhzLAxZlvmgngtfDGucvl4LgizLTNt4laArUZiZGjHtk2q4lbTOVm7g/84QFZtMmkamToWijOvjhpFK4n/+Nzh4MRKVBMUsTZn+SXUcbMwKLNvU+QcIOnEbMzMTpo/tiFslx2zl5i8NxH/vZWLjkjgf8GP6679tOM3mHRcwMlJRzKYwU0d3oIRT9n9brrPM9Cfw6FXMzEyZMaUXblWcs5W7EvIAn3F/kpScikeTKowZ6aW3vVb9fpiZ83Zy4vAkihUtwq+rD7Fj998AaNI03L4byYnDk7CxLpyvnK+z8qMxdKzWmMi4Z1Sb0vf1byhAsiwzdd4+go7fUrbnuM64VXbKVm7+koP4775MbFwi5w+PSn/9zN9hTJ+/n+u3Ipg7pSttWxTcfl6WZab53SboarRSD3pXws3ZMlu5gcsvERWbQppWpk5Za8Z1rYCRSuLqw3gmbr5BSpoWI5XE+G4VqF6yYPZXsiwz9eeAjHowpn3O9WBZEP57ryj14K8f0l//c9vfrNt6HiOVisKFTZg8oi3ly9jlP8u0zQQGBWNmbsqMaR/h5uqSrdyV4Hv4jF5LUlIqHu5ujBndHUmSmDl7G4cOX8HExIiSLnZMn9oPK6vCHDt+lbnztpOamoaJiTHDf/SmYYNKec82fQuBQSFKtql9X55tzDpdNlfG+HRTss3x02UzVrL99CFWVoWVfe+kjVwJvockSYzx6Ub9ehXylO3okavMnL4VrUama/cGDBjYUm95SkoaY0b9QUjwA6xtCjN73seUKGHL5UthTJ6wUfn3AV9905YWLatz924EI374Pf39Dx5E8/WQdnzUv1mecmUlyzJTfwkk6GQoZmbGTB/VGreK2Y9v5v96HP99V4mNS+b83q/1lu05dIOFq08hSVCpnB1zx7V7szyzd+qOP0yZMbFbzscfVx/iM2Gz0u42rsSY4R2RJImhozZwN+wJAHFxiVhamuO/YQgA124+ZsJUP+ITklFJEpvXfk2hQib5ynnmeBhL5wSh0cq083al1yd19JZfPv+QpXOPcOfWE0ZPbUvTlspx+oWzD1g270h6ufuhzxg9rQ2NmukfK+fFiaM3mT9zN1qtTOeuteg/wF1veUpKGpPGbOV6yCOsrM35aXZPipcoSmpqGjMm7+Ba8EMklcT3I9tTu24ZAJYs+Is9Oy4QF5vEoVNj851N+G8SnUFCrmg0WuZN38v8pR9ir7ZiYN9VNPaoQJly9ull1I5WjJ7ciT/XnNJ7byFzE8ZM6YxLqWI8iYxjwIcrqdewLJZWBXNC8ibZAPp83IDkpFT8N/9dIHky02pkti0KZtC0+ljbmbHg26O4NVCjLpVxEPtBs+I07FAKgOATEWxffpWBU+sBYOtUmB8WNy3wXP9k2744mAFT62FlZ8aiocep0sABdcmMbDU8najfoSQAIScj2LXiGp9NqcsHniX4wFPZ4YffjWPtlHMF2hGk1cgcWH6dXhM/wNK2EL+POEP5enbYuVjolUtOTOPszvs4Vcz4bvtSFnw8py4qIxXxT5P57ftTlK9rh8oo/3fFajRaZv7kz6IVA1A7WtO/10LcPatQtpw6vYz/1jNYWpnjt2c4+3Zf5Jd5e5k+90PS0jSMG7WRydN7UrFycWJiEjA2NiItTcPcGTvY5P8DNkWL8L+5u9m4/jhffNMqT9mCjl4n9N4T9vv/yMXL95k4zY9Na7/JVm7iND+mjOtGjWouDBz8G0HHbuDRpBInz9wm4PBVtm/8DlNTY6KfxgOw96/LpKSksWPT9yQmptCh2zw6tKuBc/FieV5/KiS+qv4xY4/PJDrxKfM9JnMq/Dz34x6llyleRE2PCp0YfmQyCakvsDZVtunlJ1f59rBycGNhUoQVLefwd9TlPGd4GY1Gy+JZR5i6sBN2DkUY+vEWGjQtTcmyGf9OB0cLfhjfnC1/XNB7b8ilcEIuhbNofU8Ahg/04/L5R1Svnf1gOL+CrkQRFpnA3inuXLwbw+R1wWz0aZSt3PxBNbEwN0GWZb5b9jd7zz2mQ93iDGhdhu+8KgKw9mAoi3fdYmLfqgWT7cRtwh48Y5/vl1wMfsSk2Xvx/fWTbOU8m5Snb/fatO21VO/1KhXVbF71KeZmJmzYep45iw8xf4p3/rIcvUbovSj27xjFxcv3mPjTFjat+y5buYk/bWHKhB7UqFaSgd/8StCxa3g0US4OPA6P4diJGxR3skkv//knnnz+idIJevBwMKv/CHprHUEAq0/sYuHhzaz5ZPxb+46XCTp+i7D7T9m3+RsuXnnIpFm78V01IFs5zyYV6dujLm27L9J73UltzfRxnVm17kTBZ7v2lLAnL9jrU4+L9+KYvOUmG7+rla3c/P6uWJgZK/Xg9xD2XoyiwwcOzNl5h29al8K9ii2BV6OZs/MOa76uWTDZTtwh7MFT9m0cpNSDOfvxXdE/WznPxuXo260WbXsv13u9Y2tXenf5AICDR24y45eD/DqvZ/6yBIUQGhbF/r0TuHgplImT/mTTxuHZyk2cvJEpkz+kRvXSDPxiCUFHQvBwd6Nxo8oM+74zxsZGzJ7rx7IV+xk+zJuiNhYsWfwFagcbbtx8xICBizhyeGresh3RZdszTsk22ZdNfw7LIZsvUyb1VrJ9uZSgo1fxaOpK44aVGDa0ky6bP8tWHGD4MC82bT4OwA4/H6Kj4xj45RI2b/wRlSp3+3yNRsu0nzaz/NevUKtt6NNrHs08q1KufEaH3tYtJ7GyKsyufWPZs/s8P8/dwex5n1C+ghMbNg3D2NiIqKjndO8yG49mbpQpo2bTthHpn9+y2QRatKiep/WV4zo8FUrYgxj2rfuYiyHhTJp/EN8lvbOV82xYhr5datC27+96r4c+eMbydWdZv7AH1pZmRD978WZ5jt0g9H40+/2GcfHKfSZO92fTmq+zlZs43Z8p47pQo6oLA7/9naDjN/BoXImfZ/RJLzNj3m4sLAoBkJamYfjYTcye0oPKFZ14FvMCY2OjfGXUaLQsmnmY6Yu8sVNbMKT/Rhq4l6VUpn28vaMlwya2ZPPa83rvrVnHmSXrlYyxz5P4tMsaajUoma8c/2SZM20nC5Z/jIPaik/7LKNps8qUKZfRobd963msrMzYvGsoB/ZcZtHPB5g6uyf+W5QLYOu2DuZpdDzff72W3zZ8gUqloqlHJXr0qU+Pjv/Ldzbhv8ugcwZJkuQtSZIsSVLlAv7cTyRJKl6Qn/ma7+svSdIVSZIuS5L0tyRJP+pePyxJUp0cyteRJGlBpqwLX/K58W83ee5dvfKIEi7FKO5cFBMTI1q0ceXo4Rt6ZZxK2FC+ojrbVfGSpWxxKaU0unYOlhQtVoSYN9wBFVQ2gDr1y1C4cKECy5PZvesx2DkVxtapMMYmKmp6FCf4RIReGbMiGVc6UpLSKMBBBa90/0YMtsWLUEyXrYa7E1dPROpnK5w5m4acol0MfER1j4Ktbo9vxmLjZI6NozlGJiqqNFFz8/STbOWOrL9Dgy6lMDbJaMpMChmld/ykpWrJMXQeBV++j0tJW5xdbDExMaZ1uxoEHgzRKxN4MISOXsrJSYvWVTl96hayLHPy+E0qVHSkYmVlHdnYFMHISAUyyDIkJqYgyzIJ8cnYO+S9Qy0gMATvjrWQJIma1UsSG5dIZFSsXpnIqFjiE5KpWb0kkiTh3bEWAYeDAdiw6SSDPvXA1FS5NmBbTOlwk4DEpBTS0jQkJadiYmKMRZH8deBWLFqOxwkRRLyIIk3WEPTwJA0ca+uVaVPKk113/yIhVWkbnqfEZvucxsXrcS7iEsmalHzlyMmN4EiKO1vjVMIKExMj3FuX50RQqF4ZdXErylSwRaXS/zFJQGpKGmmpWlJTNaSlabEpVrAdBQcvRuLVoISyfcsWJTYxjcjnSdnKWZgrdTVNK5OapkXS/fD/eR0gMVlToNkCjtzEq21VJVvVEsTGJxP5JPtuq2bVEjjYWWR7vUHtUpibKflquBUnPDL7Ns91lkPBeHeqo6sHpYiNS3pJPUiiZvVSSj3oVIeAg8Hpy6fP9mf49x1fOrJr194LdGz3Qb4z5saRWxd4mpD/9fAmAoJu4NWuurIOqzkr6/BJXLZyNas542CXfVSOc3EbKlVQI6kKfid28Eo0XrUdlWylrJR6EJucrZyFmdKOpWllUjXa9OZfAuKTlN9/fKIGB6uC2+cHHM1SD+LyVg8simRkeZGU+kbHAAEHL+HtVU/JUqOMbn/wXK9MZNRz4uOTqFmjjFIPvOoREHAJgCaNq6SfdNesUYbw8BgAXF1dUDvYAFChvBPJSamkpKTmMdtlvDvnIltCpmydX5atNOERSrZbt8OpX18ZCWRra4mlZWGuXLmf61xXLodRsqQdzi52mJga07bdBxw6qH/B4fDBy3T2rgtAq9Y1OHXyJrIsY25ump4pOTnn47dTJ2/gUtIufaTwmwg4dgevNlWUdejmpLS50QnZytV0c8LBNvso4007g/nQuzrWlsq+3Lbom+2vAgJD8O7wga7NKEls/Eva3fgkalbTHX90+ICAw/rHT7Iss+evy3RsWwOAYydvUamCI5UrKiMTi9oUVo6b8uF6cATFXWxwcrbGxMSIZq0rciLwjl4Zx+JWlK1gl20fn9nRgFvUbVQKM7P8jU4CCLnyAOeSxSjhXAwTE2Nata1G0KFremWOHL5K+841AfBs5crZU3eU0dK3o6hTTxkJVMzWAktLM64GKxfUqtZwwc4+e5v8nyRrDf/fe8bQE0j3AY7q/l+QPgH+lc4gSZLaAUOB1rIsVwMaAM9f9R5Zls/KsvxtPr/PIKO5oiLjcHDMaGjs1VY8icx+kPg6IZcfkpaqoYRL0Xcu29sQG52Ejb15+t+t7cx4Hp39RO7Y9lCmf3qIXSuv4fWVW/rrT8MTmf/NEZYMP8GdK08LPJu1XcbJvdVLsp3YEcbszw6zd9V1On2Zfdj/paDH1PDIfivBm4h7moRVpmyWtoWIj9Y/8A+/HUvckyTK1ck+nP7Rjef8+u1JVg09RZsvK7/RqCCAyMhY1I4Zt684qK2JzHLiqpSxAcDY2AgLCzOex7zgXtgTkCQGD1pJ3x4L+H1VoFLGxIhR47zp3eVn2npO4+6dCLy61s1ztojIWBx13wvgqLYmIku2iMhYHB2scywTGvaEs3+H0uOjRfQbsIxLwcpBdJuW1TA3M6VJq2l4tpvBZ/2b5ntEhK1ZUaISM36/TxKfYmum3wYUt3CkhIUTs5qMY07TCdRyyH5LpHuJBgQ+LNhRB9FRCdipMw6a7RyKEB2V/eA6J1WqO1K9dgn6tf+dfu3WULuBCyXLFFzbBhARk4RjsYy64GhjRuSz7CfBAJ//7wxNfgygiJkxbWpnXNX+2e8GnqMOseP0I77tnLfbJ16ZLSoOJ3VGB6ajvSURUflrezfvvIh7g/wPvY+IfI6j2iYji9qaiMjnuS7z16ErODhYU7lSzocOiYkpHDl2jdYt3/zq/rsq2/Z0sMr39ixoEc+TcbTJ6DRxtC5E5POcO4U/X3aJJhNOUKSQMW1qKCOEfbzLMWfnHTwnn2TWjtt8375MwWWLisfJIfN6y3s9WLflPK16LGPO4sOMGdry9W94WZbIGBwdM9ogR7UNEbpOk/QyETFZ6oENEZH6ZQC2bD2Be9Ps+/x9+y/g6uqCqWneToojIp9n2VfZEBGRpY5GZKmjjjbZ6rGS7WR6tsqVSnDw0BXS0jTcfxBNcMh9Hoc/y32uiOeoM60ztaMNkVnbjkxljI2NsLA0IyZG2U9cuhhKl04z6OY1k3ETemYbwbJ393natc8+ii0/IqLicbLP6FB0tLcgIir3141D7z8j9EEMfQb70uurjRw5FfpmeSJjcVRnOrZwsCIiS2dQRFSWMmqrbMcoZ/8OxbaYBaVLKsdzd+89QZJgwDe/0eXDhaz4PSjfGaMjE7BXZ6wzOwcLnkTm/Vr74f03adamYr5zAERFxOGgznwsaUVUlnURFRGHWldGOZYsxPOYF1So5MiRw9dJS9Pw6MEzrl19TET4K083BSFXDNYZJEmSBdAEGAD01r2mkiRpsSRJ1yRJOiBJ0m5JkrrrltWWJClQkqRzkiTtkyQpx7NPXfk6wDpJki5IktRBkiS/TMtbSZK0TffneEmS5kuSFCxJUoAkSfa618tJkrRX911HXjNyyQf4UZblRwCyLCfLsrwi0/IekiSdliTphiRJTXWf30ySpGwTZkiSVEaSpBO6EUY/ZXq9mS7HdiBEkiQjSZJmS5J0RpKkS5IkfZGp3GFJkjbr1uE66RWTV0iSNEiSpLOSJJ1ds/LQK/6JBeNJVBw/jd2Oz6ROr+x9/y9q3Lk0Pr950mFAZQI23ATAqlghxqxtzveLmtJpkCvrZ/xNUkLersQVhIadSjF8VTPaflqJg3/e1lt271oMJoWMcCz9716RkLUyB3+7SfNPcz6xLV7Rms8XNKD/rLqc3BJGWkrBjojIC02alot/h/LTzN6sXPMlhwOCOX3yFmmpGrZsPMm6Td+y99Boyld04rdf3349zJZPo+X58xf4rvmaEd+3Z+iI9ciyzKXg+6iMVBzZP5qAXSNZtfYI9x9Ev7UcRpKK4hZqfI5NY/a5xQypOYAixhmdT0ULWVPaypnzkQV3i9ibenT/OfdDn7FmZ3/W7urPxbMPufL3o9e/8S359bu6BM1qTkqalpPXMrbVUO+KHJrhSad6xVl36J7B8r3M9r1XCL4WzoC+9Q3y/YmJKSz7NYDvvm7z0jKHAkOoVbP0W71FTCgYv35RnaAJDZV6cFPpFPjz+GNGeZXj0PgGjPIqx1jf6wZOqa9vt1oc2PQFw75qxpLVBX+bXV4tWboXIyMVnTvpX6C4efMxc+b5M3li9luT/i1Llu3DyNiIzh2VgffdujbAUW1Dt55zmDZjCx/ULJPvUST5Ub1GabbtGMUG3x9YueIvkpMzjtNSU9I4fCiY1m1q/mt5XiVNoyXsQQxrfu7G3PFtGTcngNi4nC8u/Jt27r1IxzYZHe2aNC3nLoQx+6eerF85iL8OBXPi9C2D5Yt+kkDorSfUaZj/W8TeVEfvD9JvLZs/aw/Varj8q7/z94ahRwW9hyODDDlnkBewV5blG5IkRUuSVBsoA5QGXAEH4CqwSpIkE+AXwEuW5ShJknoBU4HPsn6oLMubJUkajNJBc1bXGTJXkiR7WZajgE+BVbriRYCzsix/L0nSeGACMBhYDnwpy/JNSZLqA4uB5i/5d1QFXjWTqbEsy/UkSWqv+/xXXfL5H7BEluU1kiRlnfSjFlBVluW7kiQNAp7LslxXkqRCwDFJkvbryn0AuAGPgGNAY5TRV9nIsrxc928lMnGN/Ipc2DtYEhmecaUrKiIWO4fcdwAkxCczYshGBg5uhlv1gptPoyCyvU1WtmbERCWm//35kySsbV9+q00Nj+Js/eUKAMamRhibKleYnCtYY+tUmKiHCbhUtCmwbM+fZIwEin1NtuoeTvgtCtZ77VLQY2o0K/hBeJbFzIjNlC0uOhkL24yrwimJGp7cS2D9WOX+7oSYFLZOu0jX0TVwKp9xhdbOpQgmZkZE3UvQez2vHBys9K7AREY8xyHLLV1KmRjUjtakpWmIj0/C2qYwDmprPqhdJn1i6MZNK3Et5CFFdLcGOJe0BaBVm2qsXhmYqzzrNp7Ad+tpAKq5OacP5QcIj3iOOks2tYMV4ZmudGYuo1Zb06qFcotD9aouqFQSz54lsHPPBZo2qoiJiRG2xSyoVbMUl0Me4uJsm6uMmUUnPcPePGOIvJ15MaKT9K/cRic+5XrMbTSyhogXUTyKD6e4hZqbMcpE5U1L1OfE43No5ILt2LO1L8KTiIyRQE8iE7C1z90k3scP36FSVTXmutsp6zQqydXLEVT94M3qxLpDYWw+qozQqlramvCnGXUhPCYJh6Ivv8WlkIkRzWuoOXgxgsau+qPmOtYvzhe/nGXIG4wOWrflHJu2XwCgWmUnHkdkXNUMj4pDnceh6sfP3GXp78dZu6hv+q2Kuc7y5zF8tyrzwFVzc0m/bQT++Y3rT0atdrDOscy9B9E8ePgUr57z0l/v2ns+m9Z9i72dUk927b1Ah7d8i5ghrNt0hk3+ynx51VyL62/PyNg8b8+CtO7oQzafegxAVRdLwmMyTlrDnyfjYG360vcWMlHRvKotB4OjaVypGH5nwxntrYw8a1vDnnG+N1763lxl23KeTdsvAlCtiiOPIzOvt7zXg390aFmFSXP2AR1yn2V9IL6blHlzqlUrRXimUTHhETGoM420AVCrbbLUg5j0W8AAtm47yeHAK6xe9a3eLZPh4c8Y/O1yZk7/iJIl7cmNdeuD8N2sdG5Vq1oyy74qJn0EREa2LHU0PEavHm/ddorDgcGsXjk4PZuxsRGjR3VNL9O77zxKl8pdvn++MyLTOosIj8Eha9uhK+PoaKPs3+OSsLHR30+ULeeIeeFC3Lr5GLeqSqfB0SNXqeLqjG0Ot1Xm1rptF9m0UzkurFZZzeNMI4HCo+JR22e/9fBlHO0tqO7qiImxEc5O1pR2sSHs4TOqVc4+4flL8/iewHfbWSWPawnCM43uUtqMLMcf9lb6ZSJi9Y5R0tI0HDgUzNY/BmfkVFtR94PSFNMdN7k3rkTwtUc0rKf/AJbcsHUoQlRExjp7EhmPnUPu1xlA0IGbNPIsl+95i/5hr7YkMiLzsWRstukB7NWWREQ8xyH9WDIZa5vCyoTbIzIm+x740QpKlsr78ZggZGXILsU+wJ+6P/+p+3sTYJMsy1pZlsOBfy6TV0LpdDkgSdIFYCyQ/TEhOZBlWQbWAv0kSbIBGgJ7dIu1wEbdn/8AmuhGLDUCNum+axnwJvfAbNX9/xxKR9erNAY26P68Nsuy07Is//P4ptZAf12+U4AtUCFTuQeyLGuBC7n4zlyp7FacB/ee8uhhDKmpGgL2hdDEI3fDJVNTNYz+YTNtO1bXe4pXQXmTbG+bSyVrnjxK4Gn4C9JStVwIfIRrA7VemaiHGSei105HYldC2fnFxySj1Sh9dNGPX/DkUQK2TgV3Vdq5on62i0GPqZLlqVtPMmW7fiYSu+IZ36/Vylw+8pga7gV7ixiAUwVLnj1+QUxEIppULVePRlC+bsaJbaEixny7xp2vljfmq+WNKV7RKr0jKCYiEa1G6Zl/HpnI04cJWDu82WTlrlWduX8vmocPnpKamsb+PRdx99QfPu/u6cpOf6VzKmD/FerWL4ckSTRsXIFbN8NJSlTm3zl/9i5ly6lxUFtz53Ykz3QTNp86cYsyZXN3ANu3V0P8N36H/8bvaOnpht/O88iyzIVL97C0MMMhy8GYg70VFkUKceHSPWRZxm/neVp4KPlbNnPl1BllxNfdsChSUzUULVoEJ0eb9NdfJKZw8dJ9ypbO/QF2Zjdi7lC8iCPqwvYYS0a4l2jAqXD9iRpPhJ+jmq3SPliZWlDcwpHwhKj05e4lGhb4LWIAFV0deHQ/hvCHsaSmagjaf4sGTUvn6r32jpZcOf8ITZqWtDQNl88/KpDbxPp6lmLbuCZsG9eEFjXV+J98qGzfO8+wNDfGwVr/95yQlDGPUJpGS+DlSMo6Kge7oZk6ug5eiKCsY96eVpctW7fa+P0+AL/fB9DCvSL+e68o2a48xLJIoRznRHmZkOvhTJi5l8WzumNbLO+5+vZujL/vD/j7/qDUgx1ndfUg7BX1wIwLl5Snv/ntOEsLTzcqVXDixOFJHNwzhoN7xuCotmbrn9+ndwTFxSVy5txtWjRzyynGe61vj7r4/TEIvz8G0cK9Ev57Linr8PIDZR2+wUnsG2drUoJtw+qwbVgdWlS1w/9cuJItLBZLM+Ns8/4kJGvS5xFK08gEhjylrIOyz3KwKsSZ28pJ2MmbMZTKdPt2vrJ1q4Xf75/i9/un2euBRd7qQej9jFtoDx+/TSnnvM0t0/dDD/y3+eC/zYeWLarj539ayXLxLpaW5jjY63dsONhbY2FhxoWLd5V64H+aFs2VURlBR0L4deVfLFn0BebmGZ1tsbEvGPTVUob94EXtWrm/nbPvh+74bx2J/9aRSrbtmbJZmOWcrUimbNtP06J5tYxsq/5iycKBetkSE1N48ULZ7seOX8PIyIjy5XN/XOJWtSRhYU948CCa1JQ09u75m2ae+pPsN/Osyna/MwAc2H+RevUrIEkSDx5Ek5amXKB49PApoXci9OYG2lMAt4j17VIDv5V98VvZlxZNyuG/76qyDoMfK21uDnMDvUzLJuU4feEhAM9iEgm9H4NzHp/g2LdnQ/w3DMF/wxBaNnPFb9ffujbjFccfFmZcuKw7/tj1d/rxB8Dx07cpW9pe71ayJg0rcuNWBIm646Yz5+9Svkz+ngpbyVXNw/sxhD98TmqqhsP7b9DAPW+3iR7ed+ONbxEDqOJWgvthT3n04BmpqWkc2HuZps30bz5p2qwyu3UXXA4dCKFOPWX+rKTEFBJfKLfGnjpxCyMjld7E04KQXwYZGSRJUjGUkTbVJEmSASOUpzJue9lbgGBZlhvm8yt/A3YASSidTWkvKSejdJDFyLJcM5efHQzUBg6+ZPk/l7I05OUoB38AAQAASURBVG59v2yETuZJLCRgiCzL+zIXkCSpWabvy8t3vpaxsYrvR7Vh2Fcb0Gq1dPCqQZny9vy6OJDKrk40aVaRq1ceMeaHzcTFJnE86CarlgSxdusXHNwfwsXz94iNecEe3ZW00ZM7USEPVyLeVjaAbz5dQ1hoNIkvUujaegEjJ3agfqP8z12RmZGRCu+vq7JizGnl0fKtnXEsbcm+NddxrmCDW0M1x7eHcvPvJ6iMVRS2MKbXMGUCvTtXnrJ/zQ1UxipUEnQbUo3Cli+/EpqfbJ2/cmXV2DPIWpk6rZ1Rl7LkwNoblKhgjWsDNSd2hHHrQjRGxhLmFib0GJYxjDf0ylOs7cwoVoAdVP9QGaloNbASvpP+RtZCtRZO2Je04Mj62ziWt6JCvZd3Sjy4GsPJrWEYGUlIKolWX1SmsNWbrTdjYyOGj+7MkC9WodFo6dylDuXKq1m6cD9V3Jzx8HTFq2sdxvv44t1uNlbW5kybrUyFZmVdmL79m9K/90KQJBo3rUQTD2XnP/CrFgz8eBnGxkY4FbdhwtQeec7m0aQSgUev0arzbMzNTJg2MeMzvHr9D/+NyhOVJvh46x4tn4p740q4N1EeC9zNuw6jJ26mY/f5mJgYMWNyDyRJom+vhvhM2EyHbvOQZejqVTt9Mse80spall5aw+SGw1FJKg7cC+Je3EP6Vu7KzZi7nA7/m/ORl6llX43FzWeglbX8FvwncalKR5mDuR325sW48uTaa74p74yMVXw1vCljv92JVivTulNlSpUrxtplp6lQxZ4G7mW4ERLJlBF7iY9N5tSRUP5YfoalG3vTpHlZLp19yNcfbgRJonYDF+rnsiMptzyq2hN0OYo2YwOVR2p/nFEHu0w5yrZxTUhM0fDNovOkpGnRyjL1K9rSy115ZPO8bde5G5GASpIoXsyswJ4kBuDRqBxBJ27TusdSzMxMmDYmYzSD98cr8ftdeRLV7EUH2bk/hMSkVDy8FtK9Uw2GfN6U2YsO8SIxhaFjlV2/k9qKJbPyXgcAPJpWUepBxxlKPZjcK32ZV895+Psqj/KeMKar7tHyabp68PrnVxw4eIXGDSu9tQcNZLb+s8k0q1gLOwsb7k/bzoSdK1h1fMdb/14Aj8blCTp+i9bdFmFmZsy0cZ3Tl3n3W47fH4MAmP3LX+zcd0XZnh1/prvXBwwZ6MHlkEcMHuFLbFwSh47cZOGKQHb++VXBZKtSjKCrT2kz/TRmJsqj5f/RZe5Ztg2ro9SDVcEZ9aCcDb0aKqP0JveoyDT/W2g0MoVMVEzuXnAXjDwallXqQc/lynob3T59mffHv+H3+6cAzF50iJ0HdPXAe5FSDwY0Yd2W85w4E4qxsRFWlmbMGNv+ZV/1+izubgQGBdOq7SSlHkztl77Mq8t0/Lf5ADBhXE98Rv+h7A+auuLurpycT/nJl5TUND4doDzXpEaN0kye2Ic/1gdx714UixbvYdFi5Xrqql8HY2ub+85CD3dXJVu7yZibmTLtp74Z2brOxH/ryIxsY9aRlJyCexPX9LmBpkzdrGT7fHFGtgm9iH4ax4BBS1CpJNQO1sya8VGe1pmxsRGjx3Tjq4FL0Wi1eHepT/kKTiz6ZTeubiXxbF6VLt0aMHrkH3Ro8xPWNoWZNUd5Wtzf5++wakUAxsYqJJWKMeO6U7So0hH44kUyJ45fZ9zE/D0ZLiceDUoTdCqU1n1/x6yQMdNGZjx91HvAOvxWKut09tKj7PzrOonJqXh0X0n3Dm4M+bQBTeqV4ujZe3T4eC0qlcTwL5tQ1Dr/HaMeTSoReOw6rbzm6o4/uqUv8+rzS/pj4ieM6ozPxM0kJaXh3rgi7o0z6t/ufZfo0KaG3udaW5nzSb/GdO+/GElSRgY1a5q/Zw0ZGav4ZrgHo4dsR6vR0rqzK6XL2fL70pNUrOJAQ4+yXA+OYPLwXcTFJnPySChrlp9iha+yLsMfxRIVEU/1Wm9+V4OxsRE/ju7Ad1+tQavR0tG7FmXLO7B8UQCVXUvg7lmZTl1qMWn0Vrp3+Bkra3Om6PaJT58mMPTLNUgqCXsHKyZMy1jXv8zbx/7dl0lKSqVTyzl07lqLgV+/7IaW/+few9u0DE1SBs78y1+q3OZUW5blLzK9FojSoVIX6AzYo9wmNgjYDoQAH8myfEJ321hFWZaDs3248lk7gHmyLB/K8lotoKUsy1d1r8lAH1mW/5QkaSyglmV5iCRJx4H5sixv0t1mVl2W5Ysv+a72wBSggyzL4ZIkmQL9ZVn+VZKkw2TcrmaHcktaaV2nzY+yLHeUJOkToI4sy4N1cwL5yrL8hyRJXwGzZVm2yFw+0/prD/SQZTlVkqSKwEPdustcbqHuO1e/bpu87jYxQ3Ew709k4hpDx8iRg3l/tt/9wdAxsulcRrnVYevtoYYNkoOu5X5mVUj2x46+Cz5zXUxc6sv6ow3L0qQLvHg3s1G4Cx3983bw/W/Z6bWW289/NnSMbMpZDwVAe3ioQXPkRNXsZ+To1YaOkSPJ9hNI+nc6RvLMrBPSVw0MnSJH8pKTyDF/GDpGNpKN0lGh3TnIwEmyU3Vcjvxk1esLGoBk9xloDhg6Rs6MWkHavteXMwTjNiRr9ry+nAEUMmqH/HixoWNkIznpjtfitxg2SE4suhEal+PDmA2utOVgniVvfH1BAyhaqNf/70lbn28w/PmsdZ/3ah0bas6gPsDMLK9tAaoAD1A6fu4D51HmxknRTQy9QJIka5TcP6OMysnJamCpJEmJQENZlhOBdYD9Px1BOglAPV1HUCTwz6XEvsAS3esmKLex5dgZJMvybkmS1MBfuo4jmYw5ifLqO2C9JEkjAf9XlPsV5fav87rvjAK88/mdgiAIgiAIgiAIgvDekgt4Xsn8eK96gjBQZ5Asy545vLYAlKeMybIcL0mSLXAauKxbfgFwz+Xnb0HpXMqsCbAih7LZhnbo5uZpm5vv0pX/DeVWtKyvN8v05yfo5u+RZfkwcFj359UonVf/fG/mW+HGZi2v+7sWGK37L7Os5QYjCIIgCIIgCIIgCIKQiSGfJvYyO3UTPZsCU3QTSb8RSZLOoYwCGvamnyUIgiAIgiAIgiAIgvA+e+c6gzKPpnkdSZIWoTyBK7P/6UbqZP7M2i/5rlw/7kGSpDFA1pktN8myPDW3nyEIgiAIgiAIgiAIQgHTigmk8+qd6wzKC1mWv/kXv2sqIDp+BEEQBEEQBEEQBEF4r73XnUGCIAiCIAiCIAiCIPzHiUfL55nK0AEEQRAEQRAEQRAEQRCEf4/oDBIEQRAEQRAEQRAEQfgPEbeJCYIgCIIgCIIgCILw/hK3ieWZJMuyoTMI7wbxQxAEQRAEQRAEQfj/STJ0gLdJfrLK4Oezkt1n79U6FiODBIV8yNAJciZ5Ep20wdApcmRr1ufdXG+Sp/J/bYBhc+RE1eLdXGcAkidJml2GTpEjM6MOkPxuZqNQB4jbZOgUObPsQWTiGkOnyMbBvD8AWvndq6MqqQW82GboGDkr3AUS/Q2dImfmXsgxfxg6RY4km35IXzUwdIxs5CUnAUhZ0N3ASbIz/XYz8qOFho6RI6n4YGTezf2ohOc7va9K1e4zdIocmajavJPHH2ZGHQCISX739vE2hXpA/BZDx8iZRbd3uh4IQmaiM0gQBEEQBEEQBEEQhPeXuE0sz8QE0oIgCIIgCIIgCIIgCP8hYmSQIAiCIAiCIAiCIAjvLzEyKM/EyCBBEARBEARBEARBEIT/ENEZJAiCIAiCIAiCIAiC8B8ibhMTBEEQBEEQBEEQBOH9pRW3ieWVGBkkCIIgCIIgCIIgCILwHyJGBgmCIAiCIAiCIAiC8P4SE0jnmegMErKRZZmpU30JDLqCmZkpM6Z/jJtbyWzlrlwJw8fnd5KSU/Fwr8qYMT2RJImYmAS+/2EFDx9GU6KELT/PH4i1dRFu3wlntM/vBIfc5/uhnRkwoHX6Z8XGvmDs2LXcuPkISZKYNrU/H3xQ9pU5Tx67yc8z96LRaunUpRb9BzTVW56SksaUMdu4dvUR1taFmTKrO04lipKamsbMyTu5FvIIlUpi6Ii21KpbBoD9ey6z5tcjSBLY2VsyYVpXbIoWeb/WWy3P1+ectonAoGDMzEyYMa1/zjmD7+Hjs0aX040xo3sgSRJ79p5n4cJd3L4TzibfEVSrWgqAS5dCGTdhffp3DPmmA61a1TTYevt15X527DgNgEaj5fbtx5w4PgcbmyLpr3XrPh21gw3Lln3z2pzHjlxl5nQ/tBotXbo3YMDAFnrLU1LSGDNqPVeD72NtU4RZ8/pTokSx9OWPHz2jS6eZfPVNGz7+TNlG7VpOoXCRQhipVBgZq9iw6YfX5shx/c3cRuCRq8r6m9IHN1fn7Osv5D4+Yzco669pFcaM7IIkSenLV/1+mJlzt3MicDLFilpw6swtvv5uFc66f0OrFtUY/GWbvGebs4vAYzeU39rEbrhVLp4929WH+EzcqmRrXJExP3ZAkiSuXn/MhOn+JKekYWSkYuLIzlSv6szt0ChGT9pK8LVHfP91KwZ81CSPa03fqWO3+d+s/Wi1Mh271KTfZ430ll84d48Fs/dz52YkE2Z0wbNVlfRlw77eQMilh1T7wIVZv/TKd4YjR4KZNnUTWq1M9+6NGDhIf12npKQycuTvhATfx8amCPPmDaCEsy0Ay5ftZcuWE6hUEmPG9KRJU1cAVq8OYPPm40gSVKxQgmnTP6JQIRPW/XGYNWsOce9eFMdPzKJoUYtc55RlmamzdhB47LqyTSf1wK1KiWzlroQ8wGfCJpKS0/BoXIkxIzql/97WbjjGOt+TGKkkPJpWZsTQ9unve/Q4hg7d5jH4y5YM6O+ex1zbCTx6Tck1uSduVXKqBw/wGe+r/NaaVGbMiM76uTYex0ilUnJ934FnMQl8++NargQ/oEvnOoz38c51ppfmnLePoOO3MDMzYfq4zrhVdspWbv7/sXfeYVFcbR++h67SVNjFgr0DtmjsomJvYI1GozGWNBM1GnsFa6xvojGWmMRYsYG9gYq9RhF7AytNpUrdne+PwYV1VwXUoF/OfV1ewswzMz9Oe86cec45iwPx33mRuPgkzh0crTt++p8wZszfy7WbEcz16UxrjypvpCcn/P7ZONq7NSAy/iluPr3+teeCkm4zDz/icFg8VuYmTPUoThXHfC+1/25HKPfjUtnSswIA16KT8D74gGdpWorZWjCzhTPWFqZvTdu0X4IIOhmGlZUZM0Y1x6WCysBu/vLj+O+9Slx8Cud2faU7PmPRYU7+cx+ApJR0njx9xuntX2b7+YeDLjFtmi9arZau3RowaFBrvfOpqWmMGvknly7dVdqO+QMoXtwBgCVLdrNp41FMTEwYN747jRq58OjRE0aN/JPHj+OQJInu3RvSp6/i7/63YCsBARcwMZEoVNiGGTP6olbbZz+d3oGv2n8ghP8t3IWJiYSpqQljR3pRq+ar+48vcuTwZWZO34xGq6VL13oMGNjCIA3HjFrF5ctK+ztn3ucUK1aYY0evsmDeVtLSNJibmzL8Ry/q1FXK3Od9fiY6Kg5LK3MAli7/hsKFbXKkC95+3yMlJY1+fRaSlppOerqWFi2r8c13rV98bLY4fuQ682btRKvV0rHzR/Tt726gbcq4jVy9rPTDp87+hKLFCpKepmHa5C1cu/IIjUZLmw7V+XyAcq3PxM0cPXSNgoUKsHbL97nSBRnlbfb2DF9lofQ/jPmqKw8YM2ljRv+jIuN+bI8kSQwdvZY7YdEAxMcnYWOTD/+133H0xA3m/rJHl+c/DmlDvY/LvpnOd1AvBILsIqaJCQwICgohNCySvXu88fHuxeQpa4zaTZ6yBh+f3uzd401oWCRBhy8BsHTZburVrcTePT7Uq1uJpcv2AGBvl59x4z+h/xfNDe41bZovjRq5sHvXFPz9xlO2rNMrNWo0WuZM38ncX3uxZsu37N8dwp1bkXo227acw8bWig3bh/BJ77r8umA/AFs3nQNg1aZvWPDbZ/wydy9arZb0dA0LZu1i4fK+/L3xG8pVULNx3an/V+mm6Lyk6Nw9GZ8pvZjsve4lOtfi492LvbsnZ+i8DECF8kX45ZdB1K5VTs++fPmibNowCv8tY1m+dDATJ68hPV2TDT3vJt0G9G+Jv994/P3G88MwL2rXrqAbCAJYuTKQsmVen16glLfpUzfz65JBbNk2it07z3HrZriezZZNJ7G1zcf2PePo3dedBXO3652f85M/DRtV5kWW//kNvltG5GogCCDoyBVCw6LZu30sPhO7MXnqRqN2k6duxGdSd/ZuH0toWDRBR67qzj0Kf8rR49coWqSg3jW1apbBf8MI/DeMyPFAEEDQ0euE3nvM3i3D8BnnxeQZW41rm7EVn/Fe7N0yjNB7jwk6dgOA2T/v5tuBzfBfM5ghX3ow++fdANjb5mPciHb07/1mg0Cg5O28GbuZs6gHf2/+kv27L3HnVpSejdrJlrHeHWjextXg+p596zJ+Wsc31uDjvZ6lywazbfsEduw4w82bj/RsNm48hp1tfvbsnUKfvs2YM3cLADdvPmLnzrNs2z6eZcsH4+29Do1GS0REDKv+PsjGjaPYtm0CWq2WnTvOAFCjZllWrPieokULvSjltQQduUbo3Wj2+o/AZ3xnJk/3M2o3ebofPhO6sNd/BKF3owk6eh2AE6dvEXDwClvXD2HHph8MBnxmzt1OowYVc6HrqqJr60h8JnRh8rQtxnVN24LPxC7s3ToyQ9e1DF03CTh4ia2+w9ixeTj9+yovJpaW5gz5thUjf2iXY01GdR67Sdi9J+zZ+C3eo9sx5aedRu2aNqyA7x9fGBwvorZjxoSOtG9pWBbfNX8e30HrX4b9688FOBwWT1hsCjt6V2BSk2JMPfjgpbb7b8WSz1y/ezvpwAOG1nNiS88KeJS25Y9/ol5ydc4JOhlG2IMY9qz6DO/hzZgy/6BRu6b1S+O7uLvB8THfNsJveU/8lvekd6eqtGiU/RdLjUaLt/dali0fzPYdk9ix/TQ3bz7Us9m44Si2tvnZu8+Hvp97MHfO87bjITt3nGb7joksX/4d3lPWotFoMTU1ZdToruzYOZl160exes0h3T37D2jB1m0T8PMfT5Mmbvy6aEe2tb4rX1WvTnm2blT81HTvHoyf7JttTaCk4VSfDSxe+hVbt41l546z3Hqh/d288QS2dvnZtWcin/Vpwrw5ii8rWLAACxd/yZatY5g2ozdjRv2td93M2X3YtGUUm7aMytVA0Lvoe1hYmLF8xTds2PIjvptHcPTIVYIvhOZK2+zp21iwuA/r/L5n766L3H6hH75181lsbPOxaccP9PisPosWKP2zgL0hpKZpWLP5O/5a9zV+G0/z8MFTANp3rMGCxX1zrOdFdP0Pv+H4jPdi8gx/o3aTZ/jjM6ETe/2GZ/Q/FF+1YGZP/Nd+h//a72jZzJUWTZWB94L2BVi8oA/bfIcwc0pXRk7c8GY632EfTiDIDmIwSGBAQEAwXp51kSSJ6tXLEBeXRGRkrJ5NZGQsCQnJVK9eBkmS8PKsS8D+C5nXe9UDwMurHvszjhcubEtVt1KYmel/jYuPT+L0mRt07doAUByVrW3+V2q8HPKA4s6FKFa8EObmZjRv7crhg9f0bA4fuEabjtUBaNqiCmdO3UaWZe7cjuKjj5VIoEKFrbG2seLqpYcgK9clJaUhyzKJCSk4OGbfeX8I6QYQEBiMl2edDJ2liYt79gqdpTN01iEgQNFTtmwRypRWG9w3Xz4LncaU1DS9Lxav1POO0i0rO3acpn27Wrrfw8OfcvDQRbp2a5AtjSEX7+JcwoHizoUxtzCjdZsaHAwM0bM5EBhCR6/aALRoWZVTJ24gy0qhCtx/kWLFClG2nGG6vSkBB0Lw6lBLSb9qpYiLTyIyKk7PJjIqjoSEFKpXK6WkX4daBBy4qDs/4yd/fhzWnmxmWfa1HbqCV9vqijY3Z+Lik4mMjtfXFh1PQmIK1d2cFW1tqxNwUBl4lCSJxMQUAOITklE52gJQuJA1VV2KY2b25i7sSshDijkXomjxgpibm+LRqgpHDl7XsylSzJ5yFdRGy3StOqXJn9/yjTQEB4dSooQjzs4OWFiY0bbtRwQG6JfjwIBgPL3qAtCqVQ1OHL+GLMsEBlygbduPsLAwp3hxB0qUcCQ4OBQAjUZDcnIa6ekakpJSUansAKhSxVkXVZRTAg5dxqt9TSVPq5Z4eXlLTKF61RJKnravScBBZfB27YYTDOrnjoWFEphcuFDmF8z9By5RrFghypc1jKp4ra6DWXWVfIWuZKpXLZmp60CGLt8TDOrX1EBX/nwW1KpRGksL8xxrMqoz6Dqebapm1IniRusEQHW34qgcDP1P8aL2VCyvRjJ5y5U1Gxy+eZ4niXGvN3wHHLgTT8eKBZEkiWpO+YlP1RCVmGZg9yxVw8oL0XxZS78MhcWkUKuo8jGgnrM1+2+9vb8j4OhtPFtWVvK0ihNxiSlEPk40sKtexQlV4VdHGu8IvE47jwrZfnZwcCglSqpwdnZU2o52tQkICNbXFxiMVyfFR7ZqVZPjx68iyzIBAcG0bVdbaTucHShRUkVwcCgqlZ0uOtfa2oqyZZyIiIjJ+D0zGispKTXbfh7ena8qkN9Sp0PRlG1JAFwMDtO1v+YWZrRpW5PAwIt6NoGBF/H0/BiAlq2qc/LEdWRZpnIVZ127Wq58EZJT0khNNSyXueVd9D0kSSJ/AcVnpadrMj7a5bw9uRxyn+IlCuv64S1auxF04IqeTdDBK7TrWAOAZi1cOH1S6YcjQfKzVNLTNaSkpGNmbkoBa0VTjVqlsbV7edRfdgk4dBmvdjUy2toSxCUkv6S8JVPdLcNXtauh6388R5Zldu2/SPvW1QCoUqko6oy+SPmyalJS0khNTc+9zve4D/dBImvz/t8HhhgMEhgQERGDU5bRZScne11HQM/GybjN48dxOufo6GjL48ev7nTdvx9NoULWjBnzF16dpjFu/N88e5byymuiIuNQO9nqfndU2RIVEfdSGzMzUwpYWxEb84xyFdQcOXSN9HQND+8/5dqVh0RExGFmbsqIce34rOuvdGw+lzu3o+jQqeYrdRikyXuebsY1FCQi8gWdkTE4ZQn9dlIXNPhbjHHhwh3atfeho+c0pkzqaTCA9VI97zDdkpJSOXzkEi1bZubl9Om+/DiiMybZ9JyREbE4Odnrflc52RPx4oBVFhszM1OsbayIiUnkWWIKf/weyFffGImskSS+GrCEHl3nsdH3eLa0vEhEZJyeNie1obaIyFic1HYv2CjptP9ACCqVHZUqGoZPn78QSseusxnw9VJuvPA1MlvaouJxcsr6XFvdc/X0q22z2NgREaW8HI8d3paf/rcb93Y/Met/u/lhsH7o/tsgKjIelVPmS7ej2pboSMOX83dJ5At1QO1UkIiIF/MwhiIZNmZmptjY5CMmJpGIiNgXrrUnMiIGtdqefl80x6PZeBo3GoONTT4aNHzzKUWG5c3OeJ6q7IzahIZFc+afULp9toje/ZcQfOkeAInPUlj2xyEGf6k/BSL7umJzWQ9iM3RFcebcHbr1/oXe/RcTHHIvVzpeqzMqniJZy7vKVlfeBS8nMjENJ+vMATl1AXMijQwG/XIqgr7VHbB6YaC4bCErAu8oZXDPrVjCE97eC3tEdCJFVJmDmk4O1kREJ+T4Pg/C43jwKI66NQyniLz02RFPKZLVN6rtiYh4qmcTGWGk7XiamK1r79+P5sqVe1SrVlp3bP58P5q4j2H7tlN8P6RD9rW+Q1+1LyCY1h1n8uW3y5ju3SPbmgAiI2P0dKnV9kRGGPHvRRSbrP5dT8Pe81SpXByLLAPHE8aupkunWfz2627dAE2OtL2jvodGo6V7pzk0bTiRuvUrULVayVxoi0OdJa9UaluiXvAFURFxqDJszMxMsba2JDbmGR4tXLHKb0E7j1l0bDmbXn0bYmf3+o+ZOUHpW2QpSypbIl4YZImIesHGSB/lzD+hFC5kTakSDgbP2BMQQpVKRXUfEXKt8x3VC4EgO4jBIME7RZKk1345Sk/XcvnyPXr2dMdvyzjy5bPQTfV5F7T3qoFKbUv/T5eyYPZu3Ko5Y2IikZ6mYYvvGf5c/xVb9w+nXHk1K38//M50vIr3Md2yQ7VqpdmxfQIbfUeyZNkeUlLeXoc7OxhLtwMHgqlZo6xuitiBA8EUKmyDq2vOOz+5YfGiPfTu4677EpeVP1cNZv2m4SxaMpD1a49w9sytf0XTc5KSUlmybD9DvjVcL8ClcnEC90xg68Yf+ezThnw7dMW/qg1g7cZTjPmhLYd2jGTMD20Z52N86o/AkNjYZwQGBLNvvzeHgmaQlJTC1q0n81oWGo2W2Nhn+K78hpHD2jJ05BpkWWbhb/vp27shBd4wyuqNdMU9w/fvwYwc2o6hI1fl6uVNkHdcjUrifmwqHmXsDM55NyvG+pAndPe9wbNULeZ5EFn1OnYeuEFL93KYmr4fXfPExGS+/34pY8Z214sIGjbMi4OHZtC+w8esWnXwX9HyKl8F0MKjKru3jmbRgi/438Jd/4qmrNy88Yh5c7cycUrmunGzZvdhy9YxrFw1hLNnb7HV//S/qulVfQ9TUxN8t4xg74FJhFy8y40bj4zc4d1xKeQ+piYSO/aPYsuu4az56ygP7j/5VzVkl+27L9C+VVWD4zduRTDn5z14j/X690Vl8Lp68Z8kr6OCPsDIILGAtACA1asP4rvhCABubiUJf5T5dSg8PMZggUC12p7wcOM2hQvbEhkZi0plR2RkLIUKvXqqlZOTPU5qe92Xp9atar52UMNRZUtEeObofVRkHI5ZvrZmtVGp7UhP15CYkIydfX4kSWLIj5kN56A+yylRsjDXrymRD8WdlXU0mrVyYdWKI6/U8aGk2+rVh/DdeFTR6VryBQ1PUate0KmyJzxLdE54xNNsLxIJylSy/PktuX7joW6BaX09/1667dh5mnbtaut+P3fuFoGBwQQdCiElNZ2EhCRG/LiCOXNevvC2Sm1HeHiM7vfI8BjUKjujNmone9LTNSTEJ2NvX4CLwWHs33uBBXO3ER+fhCRJWFia0bNXoyx/gw3NPNwICb7LR7Vev17E6nVH8N10Qkk/F2c9beERhtrUKjvCs3zpVGxsuXsvmvsPnuDZbU7G8Vg6fzKPDWuG4uiQWZ/cG1VhyrRNPHma8NqFCVf7nsDXT1mbxq1KMcLDsz43DrVKv56qVbaEZ4nqC4+IRZ0xPXPL9n8YN0JZq6VNc1fGT/V75bNzg6PKhsjwzMiMqIg4HFQ5X9vhTVCp7fXqQET4U70vrqDUyUePnuLkVJD0dA3x8UnY2xdArbZ74doYVGp7jh+/SrHihXX1oXmL6vzzz206dqyTY32r1x/Hd7OyfpqbS/EXylus8TyNjDVqo1bb0cLDFUmSqOqqDMQ/fZrIhZB77Nl/kTkLdhIXn4yJiYSlhRm9e+gv5q2na90xfDefzNCV23pgZ6jLrYROV6FCb74Q5+oNp9ng/4+is0pRHmUt75FxuvIu0GftxcdsuqS8ILqq8+lF80QkpqEqoD9170LEMy5FJtFq5VXStTJPkjT023KbPzqVoUxBK5Z2VHxlaEwKQWFvFo21ekswG3YoUwzdKql4FJkZCRQenYDaIeflZmfgdSYMaZKja9TqgjzK6hsjYlCr9dcNUamNtB0FC7zy2rQ0Dd9/v5QOHT6mZcsaRp/docPHfDloId9///LooH/bV9WuVZZ7Ex5ny1fp0kdlr6crIiJGF82is1HbEf4oRpeGz/07KH2oId8tZ/rMzyhRwjHzb8nw7wUKWNGufS1CLobh6fVxtjTpPfcd9D2eY2ubj9ofl+PY4auUL2+4kP2rtdnqRbBGRsTh+IIvcFTbEhkRi9pJ6YcnJKRgZ5+fPTuDqdugPGbmphQqbE3VGiW4cukBxYrnfB27rKz2PY7vliz9j6xlKTJON73rOWpH2xfKm34fJT1dw74Dl9i8arDedeERsQwesYpZ3t0o4ZzzKdf/dr0QCF7F+/H5QZDn9OrVRLfYbnOP6vj5n0CWZc6fv42NjZVuGs5zVCo7rK2tOH9emf/r538CDw9l5LxZs6r4+SlTXvz8juuOvwxHRzucihTi9m1lMOb48auULftqp1TZpSj37z7m4f2npKWls393CA3d9RcdbdSkIru2ngfgwL7LfPSxsv5NclIqSc9SATh1/BampiaULqvCUWVD6O0onj5RQn9PH79NydKOvIoPJd169XLHf8tY/LeMpblHVfz8T2bovIONTb5X6LyTofMkHs1erefe/WjdgtEPHjzm9u0IihUz7iT/rXSLj0/i9OkbeHhU0x0bPrwTQYdmEhg4nXlz+1O3TiXmzDZcqDUrLq7O3A2L4v79x6SlprN71z+4N9VfwLVJUxe2+ilf/vbtDebjOuWQJIk/V33Hrv0T2LV/Ar0+a8yAQc3p2asRz56lkJiYDMCzZykcP3adcuWzt6B1rx4NdQs7N2/mht+2M0r6XQhV0u+FDo/K0RZra0vOXwhV0m/bGTyaulKxQlGOH/ImcPcEAndPwEltx+b1P+DoYEtUdJwuMiL4YhharUxB+9fvrNere1381wzGf81gmjepgt/O84q2i/ewsbY0WAdF5WCDdQFLzl+8p2jbeR4P98o63afO3gHgxOnblMpFp+t1VHIpyv27T3j4IIa0NA0Bey7T0D37a3a8DdzcShIWFsn9+9Gkpqazc+dZmr5Q35o2q4q/n9J53LPnH+rWrYgkSTRtVpWdO8+SmprG/fvRhIVFUrVqKYoUKciFC6EkJaUiyzInjl/L9oLpL9Lrk3r4rx+C//ohNG/qgt/2c0qeBt/Fxvol5a2AJeeD7yp5uv0cHu7KFLXmTapw8rQSAXcnLIq0NA0FCxZgzYqvCNw5msCdo+nbqwFf9m/6yoEggF496uPvOwx/32Ev6ArDxjrfS3RZcT44LFNXkwxdTV2M6nob9OpWG79Vg/BbNQiPxhXx3xWcUSfuK+lnZG0gAfR0K8zGHuXZ2KM8zUrbsvXaU2RZ5kL4M6wtTHF8YTDoE9fCBParzJ4+lVjZuSyl7C34o5Oyq9TjZ8qaHlpZZumZSLq7vNlLZ69OVXWLPns0KIP/3itKnl4Ox6aAxWvXBnqR23efEBufQg2XnNVRN7eShIVGcv9eRtux4zTNXmg7mjWrit8WxUfu2XNO13Y0a1aVnTtOK23HvWjCQpW2Q5Zlxo9bSdkyTvTrp79xRWhohO7ngIALlC7z6nXw/g1fFXY3SuerLl2+T2paerZ81XNc3Uro+fddO8/RtKmbnk3Tpq74+ysD4nv3nKdO3fJIkkRc3DO++WoJQ3/oSM0sO5ilp2t4+lQZIExL03DoYAjlcjjYAu+m7/HkSQJxcUkAJCencuLYdUqVyfk6bZVdinEv7DEP7z8hLS2dfbsv0rhJJT2bRk0qsWOrMhAeuO8StT5W1n10KmLHmVO3AUh6lkpI8L3X9rezQ6/u9XSLPjdvUgW/Hf9ktLWv8FXWVpy/mOGrdvyj81UAx07dokwpR70pWnHxSQwa8hfDv2vFR9VzF2H+b9QLgSC7iMgggQHu7q4cCgqhRcsJ5LOyYPr0zFX9Pb2m4u83HoBJEz9lzNi/SE5OpXEjFxo3VhzUoIGtGDpsGRs3HaVoUWWrb4CoqFi6dJ1BQoLy1fevlYHs3DEJa+t8TBj/CSN+XEFamgZnZwdmTO/zSo1mZqb8MKYtw77+G41Wpr1XDcqUU7FsUSCVXIrSqEkl2neqgfe4LXRr/z9sbfPh/VNXAJ4+SWTY16uQTCQcVTZMnNYZUCKJvvjSnW+++AMzMxOcitgz3sfr/1W6Zeq8RItWkzJ0fpaps9N0/LeMzdDZQ7e1vKLTBYB9+87jM82XJ08S+PKrX6lcqTi/L/+Os2dvsWzZXszMTTGRJCZP/CRbX+beVbopWv+hQYMqb7zAr5mZKWPGdebrgUvRarV4dfqYcuWdWPTLLlxcnGnSzJVOXeowbtQa2reahq19fn6a8+q8ePI4gWHfK1Ov0tO1tG1XkwZGdht7He6NKnPo8BVatJtOPitzpvv01J3z7DYH/w0jAJg0rqtuW9LGDSvRuOGrn7Vn3wXW+h7D1NQEK0tz5v30WY4WCwVwb1CBQ0ev08JrnpK3kzpnavt0If5rlK9tk0Z3ZMzkTYq2+hVo3EAZjPEZ78n0OTtJ12ixtDDDe5wnAFHR8XTps5iExBRMJIm/1h5jp+/3WFtb5UgfgJmZCcNGt2L412vRarW086xG6XKOLP/1EJWqFKFhkwpcCXnIuB82Eh+XzLGgG6xYHMTfm5Wtn7/tt5Kw0MckPUulc8ufGTW5HXXq52ybWTMzU8ZP+IQB/Rei1Wrp3KUe5csX5eeft+HqWpJmzarStWt9Ro38k1YtJ2Fnl5+58/oDyi5+rdvUpH07H0xNTZgwsQempiZUq1aaVi1r0KXzDEzNTKhc2Znunyi7r/298gC//76P6Og4PDtOo7G7C1On9s6WVveGFTl05CotOs5Wytvkbrpznp/8D//1QwCYNMYrY2v5NBo3qEjjhspgfRevWoydvJH2Xedjbm7KTO9uOS5XRnU1qqTo6jBLKWtTsujqPh9/X2UXrEljvXRbyzduUInGDStl6KrN2EkbaN9lrqLL5xOdrmZtZpCQmExamob9By6xYvEAypXN3WLw7g3KEXTsJi27LMLKyozpEzJ3ovPqvRS/VYMAmP3LfrbvCSEpOQ339gvo6lmD7wa6c/HyQwaP9CUuPpkDh2+wcNkhtq/7OldacsqaL7xpUqEmDtb23Ju+lUnbl7Hi2LZ/5dmNStoQFBZP21XXsTKTmOqRua5O13U32Nij/Cuv33UjhnUXHwPgUdYOr8pvb9cd97qlCDoZRsveK7GyNGf6qMx1r7wGrMVvudImz/7tKNsDrpGUkoZ7txV0befCd58rkXo7Am/Qrln5HNcFMzNTJkz8hP4Dfkar0dKlS32l7fjfVqXt8KhG164NGPnjH7RsMQE7u/zMmz8AUNqONm0+ol3bKZiamjIxo+04e+Ym/v4nqVChGF6eUwEY9oMn7u5uzJ3rR+idCCRJomixQkyZ8mn20+ld+ar9wfhvO4OZmSlWlubM/6lPjtLRzMyUseO78uWAX9FotXTqXJdy5Yuw8OcduLiWoGkzNzp3rceYUX/TppU3dnb5mT33cwDWrj7MvbvR/LZ4N78tVna7XLr8G/Lls+DLAb+Slq5Fq9FSt35FunZ79eD2y7S97b5HdFQc48co/k6rlWnZuhruTVxypW3E2PZ8//VfaDVaOnh9RJlyapYs2k/lKsVo3LQyHTt9xOSxG+nSbh62dvmY+pMyja5rjzr4TNhMj04/I8sy7T1rUr6CMhA6fuR6zp25Q0zMM9o3/4lB3zSjY+dar5JiFPeGFTl09BotPOdm+KouunOePX/Bf+13wPP+x0aSk9Np3CCz/wGwc08w7VpV07vvqvXHuXvvMYuWHWDRsgMArFjUT28zhBzpfEf14j+L9sObppXXSGJOvAAA+cD7WRCkpjxOXpvXKoxS2KonyAfyWoYhUsZ0J21A3uowhonH+5lmAFJTkjXZ3yb338TKtB2kvJ/asGwH8W+2teo7w6YbkUkr81qFAap8SmddK79/ddRE8oBn7+naTPk7QZLx7YHznHyeyDGr8lqFUST73khf181rGQbIi5VIt9Sfu+axEkMsvt+I/HBhXsswilR0MDLvpx+VaPpe+6o0bd6uq/gyzE1avZf9DytTZZp2TMr75+PtLbtBwqa8lmEc6y7vcz14/xZMe4vI9xbk+fus5Dz0g0pjERkkEAgEAoFAIBAIBAKB4MPlA1zAOa8RawYJBAKBQCAQCAQCgUAgEPyHEINBAoFAIBAIBAKBQCAQCAT/IcQ0MYFAIBAIBAKBQCAQCAQfLmKaWI4RkUECgUAgEAgEAoFAIBAIBP8hxGCQQCAQCAQCgUAgEAgEAsF/CDFNTCAQCAQCgUAgEAgEAsGHi1ZME8spIjJIIBAIBAKBQCAQCAQCgeA/hIgMEggEAoFAIBAIBAKBQPDhopXzWsEHhyTLItEEAIiCIBAIBAKBQCAQCAT/P5HyWsC7RL41K8/fZ6Wyoz6oNBaRQQKF5G15rcA4Vh14nLw2r1UYpbBVT+ToFXktwwDJ4QsA5Lvz8liJIVKJH5DvLchrGUaRnIdCyo68lmEcy3Yka95PbVam7ZCvTM1rGUaRKo+HJP+8lmFIPk8AQuMX5rEQQ0rZDEYrB+S1DKOYSB4Q+376A+x6ot0+KK9VGMWk/VJSf+6a1zIMsPh+IwDS13XzWIkh8uITJA5rkdcyjFJg/j6QD+S1DONITYlMWpnXKoyiytcHnm3JaxnGyd+JsPhf81qFASVtvgEgXbsvj5UYYmbS4r3Oz/fx/QAy3xEEgueIwSCBQCAQCAQCgUAgEAgEHy5iAekcIxaQFggEAoFAIBAIBAKBQCD4DyEigwQCgUAgEAgEAoFAIBB8uIjIoBwjIoMEAoFAIBAIBAKBQCAQCP5DiMEggUAgEAgEAoFAIBAIBIL/EGKamEAgEAgEAoFAIBAIBIIPF22e7yz/wSEigwQCgUAgEAgEAoFAIBAI8ghJkgpJkrRPkqQbGf8XNGJTXZKk45IkXZIkKViSpE+ynPtTkqQ7kiSdz/hX/XXPFINBAoFAIBAIBAKBQCAQCD5ctNq8//dmjAYCZFkuDwRk/P4iz4A+siy7AK2BBZIk2Wc5/6Msy9Uz/p1/3QPFNDHBK5FlmWmz/Dl05ApWVhbM9PkEl8rFDexCLt9nzIR1JKek4d6wMuNGeSJJEr8s3oPvppMUKmQNwA/ftcG9UWWexiTy/fCVhFy6R6eOtZg4tnOOtZ04eoMFs3aj0Wrp0Kkmffo30jufmpqOz7gtXL3yEDu7/Pj81JUixQqSlpbOLO/tXL38EBMTiaEjW1OzdmkA9u8O4a/lQWg1MvUbV+DbYS1ykWr6yLLMtAUBBB2/hZWVOTPGtcWlopOB3fwlQfjvDiEuPplz+3/QHV+35R9Wbz6HqYkJ+fOb4z2yNeVKO7yxLp22X48RdOouVpZmzPixCS7lHQ21rTiF//7rxMWncG5bf93xBxHxjJtzkCexydjZWDJ7dDOcHK3fnrZFRwk6FaZoG9nsJdpO4r/vmqJt+8AXtB3gSUwSdjZWzB7j8UbalLqwhUOHn9eFnrhUMVYX7jFm/FqlLjSqzLhRnZAkSXd+xV8HmTV3K8cPeVOoYKae4JC79PjsZ+bN+ozWLatlW9fRw1eYNcMPrUZLp6516T/QQ+98amo640av4cqle9jZF+CneX0oVqyQ7vyjh0/p1GEWX3/bir5fNNUd12i09Ow2H5XajoWLB2Rbz8uQZZlpy08TdPYhVpamzPi+Pi5lC+vZJKWkM/SnIO6Gx2NqItG0dnGG96mpZ7PnWBhDfgpiw5y2uJXTvz7Hen7ayqEjV7GyMmemd/eXt20TfTPatkqMG9kxo23bi+/mUxQqWACAH75rjXujygBcvf6ISVM3kZCQgomJxMbV32FpaZ4rnaePhfHbnCA0Wpk2XlX45PNaeucvnnvAb3MPc/tmNGOntaZR83IAnD9znyXzDuvs7oU+Zez0VtRvUjZXOoxx+PAlpk/bgFYr07VrfQYOaqV3PjU1jVGj/uLypXvY2xdg3rz+FCtemKdPExg6ZBkhIXfx8qrLhImfvOQJ2UeWZabN3cWhYzeU/JzohUulogZ2IVceMsbbT8nP+uUZN7wNkiRx9Xo4k2Zu51lSKsWK2DPHuzPW1lbcf/iUtp8sonQJpaxVcy2O95gOb6Rzut8tgq48xsrClOk9KuJS3MbAbuDSYKLiUknXytQqY8eEzuUxNZG48iCByRuvk5quxdREYmKX8lQtYZtrPS9qm3n4EYfD4rEyN2GqR3GqOOZ7qf13O0K5H5fKlp4VALgWnYT3wQc8S9NSzNaCmS2csbYwfSvaXsXvn42jvVsDIuOf4ubT650/zxgWnb7BtPLHkJZCytrZaO/fNLCx+nYOkm0h5LRUAJJ/Gw0JMUgFVVj2GIFkbYf8LJ6UVTORY6Oz/WxZlpk2zZdDQSGKb5rRFxeXEgZ2ISFhjBnzl1L2G7syblx3JEkiJiaRYT8s48GDxxQrVpgF8wdiZ1eAkyev8c23iyleXOlvtGhRg8HftuP27XCG/bBcd99796L5/vsOfN7Xw+CZL+Pk0Vv876e9aLUy7TtVp/cX9fXOnz97l59n7+X2jUgmzexE0xaVdeeGf7OWy8EPcKvhzE+/vHnbAc/9wTYOHb2mtB9TuuFSuZiBXcjl+4yZtIHklHTcG1Rk3MgOOv/+99qjrPY9gamJhHujSowc2pb7D5/QtvM8SpdU+i7V3ErgPb5TrnWePhbK4jmH0GplWnu50OPz2nrng8894Le5hzL8QRsaNy+vOxcZHsc8nwCiIuKRJImp//PEqeibtR2HD19m5vSNaLRaunStz8CBLfXOp6amMWbU31y6fBd7+wLMnfcFxYoVJuZpAkOH/k5ISBheXnUZP6E7AImJyXzWe77u+ojwGNp3qM2YsV1zrO1d5Sk89/FbSEhMVnz8qsG59vHv8zuCIE/wBJpk/PwXcBAYldVAluXrWX5+KElSJOAIxOTmgWIwSPBKgo5cJfRuFHu3jebCxbtMnrqJDauHGNhNnroJn0ndqOZWgoHfLifo6FXcGyrO+/PPGtO/bxM9e0sLM4Z825obNx9x42Z4jnVpNFrmTN/J/5Z8hkptS/9Pl9GoSUVKl1XpbLZtOYeNrRUbtg9h366L/LpgPz6zu7F10zkAVm36hiePExj+7Wp+XzOQ+LhkFs3fy4q1X1KwUAF8xm/hzMnb1KpTJsf6shJ0/DZh95+wZ/0gLlx6yJQ5e/Fd1sfArmmDsvTqUpPWPZbqHW/fsgo9OtUAIPDwDWb+Esjyed3fSJNO26l7hD2IZc+fPbhwJZIpPx/B9xfDjkrTuiXp5elC68/X6R3/ackJPFtUoFPLipz45wHzfj/FT6ObvSVtdwl7EMOevz7lwpUIpvwvCN+FXV6izZXWfde8oO1YhrZKnPjnPvN+P8lPo7PfWTXQc+QKoWHR7N0+lgvBYUyeupENa4Ya2E2euhGfSd2pVrUkA79ZRtCRq7pBgkfhTzl6/BpFi+hHfWo0WubM306DehVypEmj0TJ96maWLP8KtdqOTz+ZT5OmLpQtl9mR2LLpJLa2+di+Zxy7dv7DgrnbmT0vs/zN+cmfho0qG9x79d9BlCmrIiEhJUeaXkbQ2YeEPYpnz2JPLlyPZspvJ/Gd3dbArp9XFeq6OZGapqHfxP0EnX1A44+UDlxCUhp/b79KtQpv3tFR2rZo9m4dqbRt07awYdV3BnaTp23BZ2IXpW0bvIKgo9dwb1gJgM97N6J/X3c9+/R0DT+OW8vsqT2oVLEoT2MSMTPL3QuxRqNl0ayDzFjkhYPamu/6rKdu4zKULJM5mOfoZMPwyc3Z+Pc5vWur1yrO4jU9AYiLTaZfp5XUrGv4gphbNBotPt7r+X3F96jV9nTvNoumzapSrlwRnc3Gjcews83Pnr1T2LHjDHPmbmH+/AFYWprz/ZAO3LjxkBvXH70VPUHHbhB67wl7N33PhZD7TJ61gw1/DDSwmzxrOz5jO1DNtTgDh64m6PhNZVBo2lZGDWnJxzVLsXHrOZavOsbQr5S2rESxgviv/vrt6Lz6hLDoZ+we8zEX7sbjvekG64fUNLCb36cK1lZmyLLMkL8us/tCFO1qqJiz/TbftixJ48qFOXTlMXO232blN9XfirbDYfGExaawo3cFgiOSmHrwAWu6lTNqu/9WLPnM9YPLJx14wPD6TtQuZs2Wy0/4458ovqtj+FLztvnz+A4WHtzIys8nvvNnGcO08sdIjsVImv45JiUrY9H1e5IXfG/UNmXVTLT3rusds+j4Jeln9pF+eh8m5apj0b4/KatnZfv5QUEhhIZFsnePNxcu3GHylDVs8DX8mDx5yhp8fHpTrVppBg5aSNDhS7g3dmXpst3Uq1uJQYNas3TpbpYu28OPI5QPdLU+Ks+SJd/q3adMGSf8/cYDSjvQ2H00LZpXz7ZejUbLvBm7mf/bpziqbRnYawUN3MtTumzmxx61ky1jvTuwbuVJg+t79q1LSnIa/hv/yfYzX0fQkWuKP/AfwYWL95g83Y8Nf39rYDd5uh8+E7pQzc2ZgYP/IOjoddwbVuTE6VsEHLzC1vVDsLAw4/GTBN01JYoXxn+9Yb85p2g0WhbOOsjMRZ0y/ME66jUuQ8kymR9FVE42jJjcwsAfAPw0cS89v6jNR3VLkvQsFclEMrDJqZ5pPr4s+30warU9n3SfTdOmbno+YNPG49ja5WP3nsns3HGGeXP8mTv/Cywszfnu+/bcvPGQGzcyfUCBAlZs3jJG93u3LrNo0aJ6rvS9qzxNT9fw4/j1zPbp/sY+Ht7vdwRB7pAkaRAwKMuhpbIsL32Z/QuoZVl+XinCAfVrnvUxYAHcynJ4miRJE8mILJJl+ZUd+Q96mpgkSV6SJMmSJFV6y/f9XJIkw0+K75CMeX3rXnG+iSRJ27Nxn4SM/0tJkvTpm+oKOHAJrw61kCSJ6lVLEhefTGRUnJ5NZFQcCYnJVK9aEkmS8OpQi4DAS6+8b/78ltSqWTrXI+mXQx5Q3LkQxYoXwtzcjOatXTl88JqezeED12jTsToATVtU4cyp28iyzJ3bUXz0sRIJVKiwNdY2Vly99JAH959SvERhChZSvvTXqlOGA/sv50pfVgKO3MCztauShq7FiItPITI6wcCuumsxVA6GkSvWBSx1Pz9LTkN6M/+tr+14KJ7NKyjaqqiJS0gh8nGiobYqalSFCxgcv3X3KXWrKy/qdaoXJeB46NvTdiwUzxYVM7Q5vUKbk3FtYU+pW714hrZiBBy782Z6DoRk1oVqpYiLTzJeFxJSqF6tVGZdOHBRd37GT/78OKy9QR7+veYwrVpUpXAhwwiBVxFy8S7OJRwo7lwYcwszWrepwcHAED2bA4EhdPRSvh62aFmVUyduIMvKAnuB+y9SrFghypbT9zUR4TEcPnSFTl3q5kjPqwg4dQ/PJmWU9KvoSFxiGpFPnunZ5LM0o66b8vJoYW5KlbKFCH+cafPz6vMM6OyChfmbRxsEHLyMV/uaWdq2l+Rn1ratfU0CDry6bTt6/DoVyxehUkXFhRS0L4Cpae5c7bVLERR1tqdIcTvMzU1p0rICxw/d1rNxKmpLmfIOmLyiY38k4Ca165fEyip37a0xgoNDKVHCEWdnBywszGjb9iMCAy7o2QQGBOPppZShVq1qcOL4NWRZJn9+Sz76qByWFm9PT0DQNbzaVlPy081Z8VXR8Xo2kdHxJCSmUN3NWcnPttUIOHQVgNC7j6ldoyQADeqUZe+BN2/7jREY8hjPj5wUnSVtiUtKJzLOsJ9mbaV8q0vXyqRptDzPXQlISNYAkJCkQWVraXBtbjlwJ56OFQsiSRLVnPITn6ohKjHNwO5ZqoaVF6L5spZK73hYTAq1iiptcT1na/bfijO49l1w+OZ5niT+O88yhqlrPdJP7wdAG3YFKZ81km2h11yViYlTCTQ3zivX3zyPqWu9HD0/ICAYL8+6SpmqXoa4uCQiI2P1bCIjY0lISKZ6daUN9vKsS8D+C5nXeynP9PKqx/79Fwye8TKOH7+Ks7MDxYplP0rzSshDijkXomjxgpibm+LRqgpHDuoPkBUpZk+5Cmq9qNrn1KpTmvz53165Bwg4lNUflHiFP0ihetUSmf7goOIP1m44waB+7lhYKPW2cKG3EyGdFcUf2On8gXvLChwz6g8cDQZ6wm4/RqPR8lFdpY3Ll9/ijf3BxeBQnEs4ZPEBNTkQGKxnExgYjKdnHQBatqrBiRNZfUBZLF7xDhB6J4InT+L5qFbuolnfVZ4ePX6DiuWd3oqPh/f7HeGDJK+niGm1yLK8VJblWln+6Q0ESZK0X5KkECP/PLPayUpn/aUrYkuSVAT4G+gny/Lz+WljgEpAbaAQL0QVGeODHgwCegJHMv5/m3wO/GuDQZIkVQZMgUaSJBm+1eaOUsAbDwZFRMbipLbX/e6ktiPihU7G62xWrztKh65zGTNxPbFx+i9/uSUqMg61U2Z4q6PKlqiIuJfamJmZUsDaitiYZ5SroObIoWukp2t4eP8p1648JCIijuIlCnE3NJpHD56Snq7h8IGrRIa/eQczIiqBIqpMrU4qGyKi4l9xhSGrN52jRbclzPn1IOOGNn9jTTpt0YkUUWUWOSeHAkREZz+PKpYpzL4jyiDLviN3SHyWxtO45LenLcu0LidHayKiDQeDXq3ttr622Nxri4iMw8nJPlOP2v4ldcHuBRulDO0/EIJKZUelivphyhERMewPvEjP7vph8tkhMiJWT5PKyVBTVhszM1OsbayIiUnkWWIKf/weyFff6E/tAfhpph/DRrR/5QBDTol48owiDlnKWuH8RDxJeql9XEIqB07fp15VZXDo0q3HPIp+RpNahlO5cqUnMjaX+Zm1bTtGh27zGDPJV9e23QmLRpIk+n+9nE49FrDsj4O51vg4MhFHdWYdcFBZEx1p2El8HQf33qBJq5xFnb2OyIgYnLJEuKmdChIR8WL6xVAkw8bMzBQbm3zExGS/DueEiMg4nNRZ21lbXd3Ts1EZtylfxlE3MLR7/yUeZfEn9x/G4NX7N3p/+Qdn/gl7M52xKTjZZ3benewsiYxNNWo7YEkwDScdp4ClGa2qKVETY7zKMmf7bZp6n+CnbbcY1rb0G+nJSmRiGk7WmS9n6gLmRBoZDPrlVAR9qztgZabfhSxbyIrAO0q67bkVS3iC4bX/H5HsHJBjInW/yzHRSHbGoxcte4zAasRvmLfInM6mfXAb06oNATB1a4hkVQDyZ//DQMQLddHJyZ6IiBhDGyfjNo8fx6FSKe2co6Mtjx9nlv3z52/T0dOHAQN/4caNhwbP3rHzDO3b1TY4/iqiIuNROWX+fY5qW6Ijc9YnetsY+ne7l7QfdkZtQsOiOfNPKN0+W0Tv/ksIvnRPZ3f/wRO8evyP3v2XcOZc7j9KRUcm4KjOkm4qax5n0x/cvxuDtY0lU37cztefrmHp/w6j0bzZ2iYRkbEUyVKm1GpDHxAZEasrmzn1ATt3nqN1m5pGBwSzp+/d5Omduxk+/pvf6dTzZ5b9eShX+nQa3uN3BMG7QZbl5rIsuxr55w9EZAzyPB/siTR2D0mSbIEdwDhZlk9kufcjWSEF+AP4+HV6PtjBIEmSrIGGQH+gR8YxE0mSfpUk6WrGCtw7JUnqmnHuI0mSDkmSdFaSpD3PE9rIfbsCtYDVGdE67SRJ8styvoUkSVsyfk6QJGl+xmreAZIkOWYcLytJ0u6MZx3ORuRST5SRvb0ocwWfP6t1xt9yDuic5fhkSZJGZPk9RJKkUi/ccybK4NJ5SZKGveb574ye3euzb/sY/H2HoXK0ZeacbXklRUd7rxoZU8uWsmD2btyqOWNiImFrm48fx7VnwsiNfN3vD5yK2mNi+n4MsffqUpN9G75k+NdNWPzn8byWo2PkoLqcDn5Ip682cjr4EWqHApi+xQGEN2Hkl/UVbV9u4HTwQ0VbHuVnUlIqS5btZ8i3rQ3OTfvJnxFD22Ni8u82x4sX7aF3H3fyF9D/wnro4CUKFbKmiovzv6onK+kaLcPnHeazdpVwdrJBq5WZueIso/p9lGeaXqRn93rs2z4K//VDUTnYMnOuErip0Wg5+88dZk/vyZo/vmH/gRCOn7yRZzofRycSejOaWvXe3hSx/49Mm+DJmk2n6dxnCYnPUrHICPtXOdhwYOsw/FZ9xeihrRg+YRMJCW9nwPt1LP+yKkGT6pGaruXEjacArDv2iNGeZTkwsS6jPcsy3vfaa+7ydrkalcT92FQ8ytgZnPNuVoz1IU/o7nuDZ6lazN8TX/C+kLJqBkmzB5H8yzBMy7phVkt5aUvduhTTslWxGr4Y03JV0cZEvY1FSHOFJEm6l28XlxIEBk5jq/8EPuvdhG8HL9azTU1NJzDwAq1bvz/tcl6h0WiJjX2G78pvGDmsLUNHrkGWZVQOthzYNRq/dUMYPbwdw8eu+9faDz196Vou/vOQQUMasXBlD8Lvx7J327uJfnxb7Np1lrbtar3e8B3xsjxVfHwos6f1YM2Kr9gfeInjJw3XCPs3eV/fEfIErZz3/96MrUDfjJ/7Av4vGkiSZAFsAVbKsrzxhXPPB5IkwAsIefH6F/mQ1wzyBHbLsnxdkqTHkiR9BJRGiYipAqiAK8AKSZLMgV8AT1mWozK2YJsGfPHiTWVZ3ihJ0mBghCzLZzISc64kSY6yLEcB/YAVGeYFgDOyLA/LmJs3CRgMLAW+kmX5hiRJdYBfgVctpPIJ0AIlrOs7YI0kSVbAsozrbgLrc5g+ozP+hvYvM8g6p3HJwm8Y1F95UV297ii+m5V52m4uzoRn+cIUHhGLOssoOoBaZfdSG4fCmV8xunWuw1ff/Z7DP8M4jipbIrJE7URFxuGY5atwVhuV2o70dA2JCcnY2edHkiSG/Jj5Uj6oz3JKlFRCnBs2qUjDJhUB8Nt4JteDB6s3nWPDViXU2q2yE4+yfI0Ij4xH7Ziz6UDPade8MlPm7AHa5ep6gNX+IWzYqXwBd6voyKPIzK804dGJqB3yZ/teaocC/DJZiSxJTEpj75E72FrnPnxb0aZ0UNwqqHgUlfnVKzwqAbVD9gPnFG2tM7Udvp1jbavXHcF3kzLg7ubiTHh4TKaeiJiX1IXYF2xsuXsvmvsPnuDZbU7G8Vg6fzKPDWuGEnLpHj+M+huAp08TOXT4CmZmJjRv5vZafSq1nZ6myHBDTc9t1E72pKdrSIhPxt6+ABeDw9i/9wIL5m4jPj4JSZKwsDQjMiKWgwcucSToCikp6SQmJjNm5Cpm/NQ7R2kHsHrnNTbsVQZC3MoX5lGWyK7wx89QFzK+OO3EX09QsogNfTsqaxklJqVx424MfcbvBSA6Jolvph3g13FNc7SI9Op1x/Tbtlzlp7G27WO++v4PQPmyWLtmGd3C0o0bVuLSlQfUq1OenFJYVYCoiMw6EB2ZgIMqZ9MPgvbdoH7Tsm+0poExVGp7wh891f0eEf4UtfrF9LPn0aOnODkVJD1dQ3x8Evb2byv4FVZvOIWv31kA3KoUIzwiazsbh1ql7xPUKlvCI43blC3lyIpflHUa7oRFc/CoMm3FwsJMN03AtXJRShQvyJ27j3GrYrgQ6Ut1HnnAxpPKEgCuzjaEx2ROCwuPTUFlZ/HSay3NTWjmWpjAS49pULEQfmfCGeulTJtoXc2RCb7XX3ptdlh78TGbLj1RtKnz6UXzRCSmoSqgP43jQsQzLkUm0WrlVdK1Mk+SNPTbcps/OpWhTEErlnZUIpVCY1IICsvbaI93iVmDjpjVU9Y80969hmSvApTpJZK9g9EFoOXYx8oPKUmknw3EpEQlOLMfOe4xKX9MUc5ZWJGvakNIfnX0xOrVB/HdcAQAN7eSenUxPDwGdZZobQC12p7wcOM2hQvbEhkZi0plR2RkLIUypitbW2e2z+7ubkyZspYnTxN0Gx8EHQ7BpUoJHBxytgixo8qGyPDMshEVEYeDKnd9ojdh9frj+G4+BYCbS/EX/EHsS9qPWKM2arUdLTyUqT5VXZUPjE+fJlKokHVm+1GlOCWKF+JOWDRuLjmPcHVQWRMVkSXdIhMonE1/4Ki2pmxFR4oUV9ro+k3KciUk5+t1ZkWtsuNRljIVEWHoA1RqO8Jz4QOuXr2PJl1jdCH0V/Fv5KmTyo7aNUtn8fEVuXT1AfXqGF9fzajO9/gdQZDnzAR8JUnqD4QB3QEkSaqFMrYwIONYY6CwJEmfZ1z3ecbOYaszglMk4Dzw1ese+MFGBqFE0zxfY2ddxu8NgQ2yLGtlWQ4HDmScrwi4AvskSToPjAey1RJnzNf7G+gtKdu21QN2ZZzWkjlIswpomBGxVB/YkPGsJYDRKCTQZW60LMt3URZ6qiFJUiGUgaE7sizfyNCwKjt6c0LWOY3PB4IAevVogL/vD/j7/kDzpi74bTuDLMucDw7DxtoKlaN+Y6pytMW6gBXng8OQZRm/bWfwaOoCoDc/d39gCOXLvTQpckRll6Lcv/uYh/efkpaWzv7dITR0r6hn06hJRXZtPQ/AgX2X+ejj0kiSRHJSKknPlLD8U8dvYWpqolt4+slj5cUrLi6JLb6n6dDJcGHP7NCrS038/uqH31/98GhcAf/dIUoahjzAxtrS6LzflxF674nu54PHblGyePbXIjCqzdMVvyVd8VvSFY8GpfDff13RdjkCmwIWRtffeRlPY5PQZoyCL137D11aVXzNFdnR1h2/Jd3xaFAa/33XMrSFY1PA8g20naNL65wvLdarR0P8N4zAf8MImjdzy6wLF0KxsXlJXbC25PyF0Cx1wZWKFYpy/JA3gbsnELh7Ak5qOzav/wFHB1sCd4/XHW/VohqTxnXJ1kAQgIurM3fDorh//zFpqens3vUP7k1d9WyaNHVhq99pAPbtDebjOuWQJIk/V33Hrv0T2LV/Ar0+a8yAQc3p2asRQ35oz74Dk9i1fwKz5n5G7TrlczUQBNCrbUX8FrTHb0F7POo4439QWbfr/LUobAqYoypkOPC4YPU/xCemMbZ/5tQDmwIWnPi7O4HLOhO4rDPVKjjmeCAIoFeP+vj7DsPfd5jStm0/l6Vty/f6tm37OTyaVAGMtW3KdLaG9Stw/eYjkpJSSU/XcPrsbcqVeeX6fy+lYhU1D+7FEP4glrQ0DQf3Xqdu45xNCzq45/pbnyIGygtoWFgk9+9Hk5qazs6dZ2narKqeTdNmVfH3UwZT9+z5h7p1K+Y65N8Yvbp9jP/qr/Ff/TXN3Svht/OCkp8X72W0s/odapWDDdYFLDl/8Z6Snzsv4NFYabOeLw6q1WpZvCKIHp2VL9JPnibqplPce/CE0HtPcC6mvwD8a3U2LMaW4bXYMrwWHq4O+J8NV3SGxWFjZWaw7k9iika3jlC6RubQ5SeUUSl1RWVryelbysvLiRsxlHzFbl/ZoadbYTb2KM/GHuVpVtqWrdeeIssyF8KfYW1hiuMLg0GfuBYmsF9l9vSpxMrOZSllb8EfnZRNFh4/SwdAK8ssPRNJd5c381XvM+lHt5I85yuS53yFJuQoZrWVKB+TkpWRkxKR457oX2BiAgUy2hcTU0xd6qAND1V+L2DL84U+zJv3JP3kntc+v1evJvj7jcffbzzNParj539CKVPnbyu+6cWPAio7rK2tOH9eaYP9/E/g4aHU12bNquLnp0QT+Pkd1x2PiorVrS8XHHwHrSxTMMuL/I4dZ2iXwyliAJVcinL/7hMePoghLU1DwJ7LNHR/+23U6+j1ST381w/Bf/2QF/zB3Vf0dS05H3w30x+4K/6geZMqnDytrN96JyyKtDQNBQsW4MmThMz24/5jQu8+xjmXfbjn/uBRhj84tPc69Rpnb4OTClXUJManEPNUmc58/sw9SpZ+s/rp6lYyo//x3Aeco2nTF3xAUzf8/ZUPMHv3/EOduhWy5QN27shdVNC/kacN65fn+s3wLD7+To59/Pv8jiDIW2RZfizLsocsy+UzppM9yTh+JmMgCFmWV8mybJ5l+3jdFvKyLDeTZdktY9pZb1mWXzuX9IOMDMoYLGkGuEmSJKOstyOjhEwZvQS4JMtyzlbly+QPYBuQjDLYlP4SOxllgC1GluXq2bx3T6CSJEmhGb/bAl2A06+4Jh39gTyrbD4rx7g3qsyhI1dp0X4m+azMme6duY2nZ/d5+Psq2xtOGtc5Y2v5dBo3qEjjjN12Zs/fztVrD0GSKFa0IN4TMreHbNZmGgkJyaSladh/4BIrfhtIubLZ23nEzMyUH8a0ZdjXf6PRyrT3qkGZciqWLQqkkktRGjWpRPtONfAet4Vu7f+HrW0+vH9Snv30SSLDvl6FZCLhqLJh4rTMbe0X/LSbm9eVryX9BrlTotSb71rkXq8MQcdv0bL7UqyszJg+NnMHJa++f+D3Vz8lrRYdYPu+yyQlp+HutYiuHarxXf+GrN50juOnQzEzM8XWxoqZ4w13YMq1to9LEHTyLi37rsPK0ozpI5pkavtyI35LlDSbvewE2wNvkpSSjnvPVXRtU4nv+tTi5IVHzP/9JEgStd2KMPG7hm9PW50SBJ0Ko2WfNYq2HzO3Pff60he/JcpuCbOXHmd74A1FW4+VdG1Tme/61ubkhYeKNqB21SJM/K7xm+lpVJlDh6/Qot10pS74ZC5V5tltDv4blJmbk8Z11W0t37hhJRo3NNyp621hZmbKmHGd+XrgUrRaLV6dPqZceScW/bILFxdnmjRzpVOXOowbtYb2raZha5+fn+YY7lLxb+D+UTGCzj6g5Vd+Sn5+n7lGktfQ7fgtaE94dCK/bQihTHFbOv+wA4Be7SrSrUXOo2peq6dRJaVt6zCLfFYWTJ/STXfOs/t8/H2V2bWTxnrptpZv3KBSZtu2YGdG24bSto1Xdrqzs83P5581pmuvX5AkJTKoSePclQFTMxO+/dGdsd9tRavR0rJjFUqVLcxfv52gQmUV9dzLcO1SBN4/7iA+LoUTh0NZufQky3yV9UjCH8YRFZFA1ZrZj2LJLmZmpoyf8AkD+i9Eq9XSuUs9ypcvys8/b8PVtSTNmlWla9f6jBr5J61aTsLOLj9z5/XXXe/RbDyJiUr7HxBwgeW/f6e3C01OcW9QnkPHbtCi889K/ZyQuQ6jZ6/Fut3AJo1sl7G1fDqN65ejcX2lbG3fG8KaDcoX5RZNK9Olg7I7y+l/wvh5yQHMzEwwMZGYMro99nbZj5400Fm5EEFXntBqximszJWt5Z/Tae4ZtgyvRVKqhm9XXCI1XYtWlqlT1p5P6inLGHp3q8B0/5toNDKW5iZ4d317L9GNStoQFBZP21XXsTKTmOqR+c2s67obbOzx6nq460YM6y4q0S8eZe3wqpyzQbPcsuYLb5pUqImDtT33pm9l0vZlrDj2701J11w+hWnlOuQb9xekppCybo7unNWI30ie8xWYWWD15QwkUzMwMUFz/R/Sj+8EwLRcNSza9QdZRnP7Iqkbf8nR893dXTkUFEKLlhOUtmx6X905T6+pup2/Jk38lDFj/yI5OZXGjVxo3Fj5cDBoYCuGDlvGxk1HKVpU2VoeYM+ec6xdF4SpqQlWVhbMmztA9yL/7FkKx45ewXtKL3KKmZkJw0a3YvjXa9FqtbTzrEbpco4s//UQlaoUoWGTClwJeci4HzYSH5fMsaAbrFgcxN+bvwTg234rCQt9TNKzVDq3/JlRk9tRp37uFhnWpWHDioo/6DhbaT8mZ/EHn/xPtxvYpDFeGduQp2X0dZX628WrFmMnb6R91/mYm5sy07sbkiRx+twdfl68DzMzU6X9GOeV6/bD1MyEwT82Yex3fmg1Mq10/uA4FSqrM/xBOFN+3EF8XDInDt/h76UnWOb7GaamJgwc0pBRX29GlqF8ZRVtOrm+/qGvwMzMlHHjuzNowCK0WplOnetSrnwRfvl5Oy6uJWjWrCpdutZn9KiVtG41GTu7AsyZ2093fQuPiSQkJpOWlk5gQDBLl3+r8wF7dp9j8ZI328HxXeWpnW1+Pu/diK69FyJJEo0bVqRJo9zvY/Q+vyN8kOTRFNsPGen5qP+HRMb0po9kWf4yy7FDQCDK6tkdAUeUaWKDUObfXQY+k2X5eMa0sQqyLBvdFkaSpG3APFmWD7xwrCbQXJblKxnHZKCnLMvrJEkaj7Id3HeSJB0D5suyvCFjmllVWZYNtmeQJMkEJQSsjizLDzOONQUmAG2B60BTWZZvSZK0FrCRZbm9JEm9gfayLPeQJKkmysBRWVmWQyVJSpBl2Tpj2tw8WZbdX3yuUZK3vZ8FwaoDj5PX5rUKoxS26okcveL1hv8ykoMy+1G+Oy+PlRgilfgB+d6CvJZhFMl5KKTsyGsZxrFsR7Lm/dRmZdoO+crUvJZhFKnyeEgymG6d9+RTBipC4xfmsRBDStkMRisH5LUMo5hIHhD7fvoD7Hqi3T7o9XZ5gEn7paT+3PX1hv8yFt8rSx1IX7+9XQvfFvLiEyQOa5HXMoxSYP4+yOyevl9ITYlMWpnXKoyiytcHnr3sm3Eek78TYfG/5rUKA0rafANAunZfHisxxMykxXudn+/j+wGA5PDF/+vF3OR/JuT5+6xUw+eDSuMPdZpYTwyjgDYBTsB9lIGfVcA5IFaW5VSgKzBLkqQLKHPoXrV1z5/AbxmLLz+PwV4N3Hs+EJRBIvCxJEkhKJFK3hnHewH9M551iSyLQr9AI+DB84GgDIJQ1jwqiDKQtSNjAemsq4lvAgpJknQJZY0iY4sGBAMaSZIu5OUC0gKBQCAQCAQCgUAgELxT8nrx6DdfQPpf54OcJibLclMjx34GZZcxWZYTJEkqDJwCLmacP4+y2FJ27r8JZcAlKw1RFnR+0fYHI8fuAIbbBhnaHQLqvnBMgzKoBfAIZe2gF69LAlq+5J7WGf+n8epFqwUCgUAgEAgEAoFAIBD8B/kgB4New/aMhZ4tAJ+MhaTfCEmSzqJEAQ1/03sJBAKBQCAQCAQCgUAgEOQl/+8Gg2RZbpJdW0mSFgENXjj8P1mW/3jhnh+95FnZXu5dkqRxQLcXDm+QZXladu8hEAgEAoFAIBAIBAKB4AXEAtI55v/dYFBOkGX523/xWdMAMfAjEAgEAoFAIBAIBAKBIE/5UBeQFggEAoFAIBAIBAKBQCAQ5IL/dGSQQCAQCAQCgUAgEAgEgg8cMU0sx4jIIIFAIBAIBAKBQCAQCASC/xAiMkggEAgEAoFAIBAIBALBB4ssy3ktASmvBeQQ6X1INMF7gSgIAoFAIBAIBAKBQPD/kw9trCJHaE+MyvP3WZO6sz6oNBbTxAQCgUAgEAgEAoFAIBAI/kOIaWICBc2+vFZgHNMWyCdH57UKo0h1ZkL8hryWYYhNN+V/+UDe6jCG1BSS/PNahXHyeUL6nrxWYRyzVqRp309t5iatIHZtXsswjl1P5Ee/5rUKA6Qi3wCglQPyWIkhJpIHj56tyGsZRimS/wuuPp2d1zKMUqngj8jR72e6SQ5fID9cmNcyDJCKDgYgcViLPFZiSIH5+5C+rpvXMowiLz5Buvb97LOZmbR4P/tFADbdSNHsymsVRrE0bfNeth+SwxfKD2nvYbqZt3mv+0UPE5fntQyjFC0wIK8lvFvEAtI5RkQGCQQCgUAgEAgEAoFAIBD8hxCRQQKBQCAQCAQCgUAgEAg+XERkUI4RkUECgUAgEAgEAoFAIBAIBP8hxGCQQCAQCAQCgUAgEAgEAsF/CDFNTCAQCAQCgUAgEAgEAsGHizbPd5b/4BCRQQKBQCAQCAQCgUAgEAgE/yFEZJBAIBAIBAKBQCAQCASCDxexgHSOEZFBAoFAIBAIBAKBQCAQCAT/IURkkOCVyLLMtOkbORR0Cat8Fsyc/hkuVZwN7EIu3WXM2L9JTk7DvbEL48Z2RZIkdu0+x8JFO7l1O4IN60fg5lpSd83Vaw+YNHktCQnJmJhIbPQdiaWl+dvTvSqYoAsRWFmaMmPgR7iUsjewGzD7KFExyWi0Mh9VKMzEvtUxNZHezvPn7ODQ0etYWZkzc3IXXCoVNbALufKAMZM3k5yShnuDCowb0Q5Jkrhy7RGTZviTkpqOqakJk0d1pKprcfYfvML/ftuPiYmEqakJY4e3pVb1UtnTM82XQ0EhWFlZMHNGX1xcShjqCQljzJi/FD2NXRk3rjuSJBETk8iwH5bx4MFjihUrzIL5A7GzK6C7LvhiKD16/MS8uf1p3fojAB4+fML48X/zKPwpkgRLlwymuGHRMdT501YOHbmqpJt3d1wqFzfUefk+Yyb6KjobVmLcyI5IksQvi/fiu/kUhQoq2n74rjXujSqTmpbOJJ/NhFy+j2QiMe7HjtSpXfa16WagbcYmDgVdVurCtF4vrwvjVmfUhSqMG9MFSZJY8PMOAg5cxESSKFzYmhnTeqNW2XHrdgRjx6/m0uV7DBvSnv79PHKk68jhy8ycvhmNVkuXrvUYMLCF3vnU1DTGjFrF5cv3sLcvwJx5n1OsWGEuBocxedI63d/2zbdtaN6iGgDjx60m6OAlChWywW/bmBzpyYosy0ybu4tDx24o+TnR6yX14CFjvP2U/KxfnnHD2yBJElevhzNp5naeJaVSrIg9c7w7Y21tRVq6hvFTt3L52iPSNVq82lbjy88bvZnOXw4RdCIUKyszZoxuiUsFlYHd/OXH8N9zhbj4FM7t/kbv3K4D11n450kkCSqWdWDuhDa51nP48CWmT9uAVivTtWt9Bg5qpXc+NTWNUaP+4vIlJU/nzetPseKFefo0gaFDlhESchcvr7pMmPgJAElJqQwduox7d6MxMTWhaVM3hg/3yrW+55w8epuFswPQaLW086pGry/q6p2/cPYeC+cEcOtGJBNndKRJi0oAhD+MZcLwLWi1Mpp0DZ16fIRntxpvrOc5547fY9n8E2i1Mi06VqRrn2p65/3XXGTv1muYmkrYFczHd+MaoSpiA8Dkobu5HhJJ5WpqJsxtZez2b4Qsy0xbEEDQ8VtYWZkzY1xbXCo6GdjNXxKE/+4Q4uKTObf/B93xdVv+YfXmc5iamJA/vzneI1tTrrTD29P2SxBBJ8OUejCq+UvqwXH8915V6sGur3THZyw6zMl/7gOQlJLOk6fPOL39y7eiDcCi0zeYVv4Y0lJIWTsb7f2bBjZW385Bsi2EnJYKQPJvoyEhBqmgCsseI5Cs7ZCfxZOyaiZybPRb0/Yyfv9sHO3dGhAZ/xQ3n17v/HmHD19m5vSNGf6gPgMHttQ7r/iDv7l0+S729gWYO+8LihUrzLGjV5g/bytpaemYm5sx/Ecv6tatCMD/Fmxlq/8pYuOecebsvFxre1f9opNnbvPN8NUUL1YQgBZNqzB4YLMcaTty+AqzZmxGq5Hp3LUu/Qc21zufmprOuNGruHzpPnb2+Zk9r6/Oj3pPWq/8fcDX37bGo3lVAFo3n0L+AlaYmkiYmpmybsPwXKSaPu9b+6H0izZz6PAVJU+nffqSftE9xoxfo/SLGlVm3JjOSr/ol50EBF7ExESicCEbZkz7FLXKjtjYZ4ydsJa796KxtDRnuk9PKpQvkiNt73PfKCunjt5h4ZwANBqZdp2q8mm/OnrnL5y9x6K5gdy6EcXEGR1wb67Uy/CHsUwc4YdWK5OerqVzj5p07Fr9rWgS/LcQkUGCVxIUdJnQsCj27p6Ez5SeTJ6yzqjdZO/1+Hh/yt7dkwgNiyLo8GUAKpQvyi8/D6R2Lf0X7/R0DT+O+ospk3qwY9t4Vv41BDMz07enOziCsIhE9sxugXe/Gkz587xRuwWDP8Z/mgfbpnvwJD6V3acevJ3nH71O6L3H7N0yDJ9xXkyesdWo3eQZW/EZ78XeLcMIvfeYoGM3AJj9826+HdgM/zWDGfKlB7N/3g1AvY/LsHXtYPzXDGb6xM6M9/HLnp6gEELDItm7xxsf715MnrLGuJ4pa/Dx6c3ePd6EhkUSdPgSAEuX7aZe3Urs3eNDvbqVWLpsj+4ajUbLnDlbaNCgst69Ro36g/79W7Br52Q2+I6mcGHb1+s8cpXQu9Hs3ToSnwldmDxti3Gd07bgM7ELe7eOJPRuNEFHr+nOfd67Ef6+w/D3HYZ7I0XThk2nANi28Qf++G0gs+ZtR5vDUNKgwxl1YdcEfCZ/wmRvX+PavH3xmdKDvbsmKHXhyBUABnzRjG1bRuO/eRRN3F1ZtFjJU3u7/Iwb0yXHg0CgpP1Unw0sXvoVW7eNZeeOs9y6+UjPZvPGE9ja5WfXnol81qcJ8+YoZbFc+SKs3zCCTVtGsWTp13hPXk96ugYAL686/Lb06xzreZGgYzcIvfeEvZu+x2dMBybP2mHUbvKs7fiM7cDeTd8Teu8JQceVl7xx07YyfHBztq39huZNKrF81TEAdu+/RGpaOtvWfsPmlYNYv+UM9x8+zb3Ok6GE3Y9hz+q+eA/3YMr8QKN2TeuVxve3HgbHQ+8/ZenqM6xZ2I3tf37G2MHuudai0Wjx8V7P0mWD2bZ9Ajt2nOHmC3m6ceMx7Gzzs2fvFPr0bcacuUo9sbQ05/shHfhxZCeD+37Rrzk7d01i8+Yx/HPuFkFBl3Kt8bnO/83cx6yF3fhr0wACd18m9Jb+y7WqiC2jp7SleesqescLO1qz6K/e/L6+H7/+3Yc1f5wgOjL+jfRk1bVkzjEmzW/FwrVdOLz3Fnfv6JeN0hULM+9PL35e3YX6TUvx58JTunOderkxdFLu8+91BB2/Tdj9J+xZPwjvka2YMmevUbumDcriu6yPwfH2Lauw7e/++P3VjwGf1mHmL8bLaq60nQwj7EEMe1Z9hvfwZkyZf9C4tvql8V3c3eD4mG8b4be8J37Le9K7U1VaNMrZgPurMK38MZJjMZKmf06K7wIsun7/UtuUVTNJnvMVyXO+goQYACw6fkn6mX0kzf6S1D2rsGjf/61pexV/Ht9B61+G/SvP0mi0TPPx5bel37B123h27jhr0HZs2ngcW7t87N4zmT59mjJvjj8ABQtas2jxl/htHcf0GZ8xZtRK3TVNmrixbv2Pb6zvXfWLAGrVKIX/GqVvlNOBII1Gy/SpG1m85Ev8to1m185z3LoZrmezedMJbG3zs2PPeD7r24QFc7cBih9du2E4G7aMZPHSL/Ge7KvzowC///ktG7aMfCsDQfD+tR9Bh68QejeKvTvHKf0inw1G7Sb7bMBn8ifs3TmO0LtZ+kX9mrFtyyj8N42kiXsVFi1W+pa/LdtH5UrF2LZlFLOm92LazM050vW+942y6vzfrH3M/KUrf276goDdVwi9re9H1UVsGTW5DR6t9fvYhR2tWfhnL5av+5zFK3uz5o+TREclvDVtHyxabd7/+8AQg0GCVxIQGIyX58dIkkT1aqWJi08iMipWzyYyKpaEhGSqVyuNJEl4eX5MQEAwAGXLOlGmtNrgvkePXqVihWJUqqREfRS0t8bU9O0Vx4Bzj/Bs4KzoLleIuGdpRMYkG9hZ51MikdI1MmnpWt48Jijj+Yeu4NW2uvJ8N2fi4pOJjNZ/2YmMjichMYXqbopOr7bVCTioDKJJkkRiYgoA8QnJqByVgZQC+S2RJEVlUlKq7ufX6gkIxsuzrqKnehni4pKIjHwhHyMz8rF6mYx8rEvA/guZ13vVA8DLqx77M44D/L3qAK1a1qBwIRvdsZs3H5Ku0dKggfISWKCAFfnyWbxe58HLeLWvqeisWjKjvMXp64yKIyExmepVSyo629ck4MCrX2xv3o6gzsfKi0nhQtbY2OQj5NL91+rR0xZ4Ea+O2agLiVnqQsfMumBtnU9nl5SUwvOsK1zYhqpuJTEzy3n5vxgcRokSjjg7O2BuYUabtjUJDLyoZxMYeBFPz48BaNmqOidPXEeWZfLls9ANwKakpkOWslSrdjns7PPnWM+LBARdw6tttRzWg2oEHLoKQOjdx9SuoUQTNqhTlr0HMutHUlIa6ekakpPTMTczxbqAZe51Hr2NZ6vKik6XIsQlpBD5ONHArrpLEVSFCxgc37D9Ep96VcXOxgqAwgVzn3bBwaG6PLWwMKNt248IDLigZxMYEIynlxKF06pVDU4cv4Ysy+TPb8lHH5XD0kI/wjJfPgvqZHzlt7Awo0oVZ8LDcz94BnA15BHFnO0pWtwec3NTmrWqzNGDN/RsihS1o2wFFdIL0Zbm5qZYWCiByWmpGmT57e3+ceNyFE7FbXEqZou5uSmNWpThVFCYnk3Vj4piaaU8v6KriseRmXldrXYx8uV/OxGqxgg4cgPP1q5KWXMtRlx8CpHRhh346q7FUDlYGxzPWs6fJaeRTReQPW1Hb+PZMqMeVHEiLvEl9aCKk9F6kJUdgddp51HhrWkzda1H+un9AGjDriDls0ayLZTt602cSqC5cV65/uZ5TF3rvTVtr+LwzfM8SYx7veFb4GJwKM4lHLK0HTU5EBisZxMYGIynpxJ50LJVDU6cUNqOylWcUansAeVlODkljdTUNACqVS+No8rujfW9q37RmxJyMYwSJRwonuFHW7epwYEX/OjBwIt09KoNQIuW1Th54oahH01Jf6v10RjvW/sRcOAiXh1rZ/SLSr2mX1Qqo19Um4CM9LW2ttLZKX1a5edbtyKoW6c8AGXLqHnw4AnR0dn/YPC+942eczXkEUWLF8ziRytx9KB+xKNThh81eYUfTX3LflTw30JMExO8kojIGJycCup+d1LbExERg8oxs2MQERGDk9pe3yYy5pX3vRMWiSRB/4ELefIkgbZtP2Jg/xavvCZHup8kUaRQ5su3U6F8RDxJQmVvZWDb/6ejXLz9lEZV1bT6uNjbeX5UPE5OmWnkpLYlIjIOlUPmgElEZBxOatssNnZERCnObuzwtvQf/Bez/rcLrVZm3YpBOrt9By4zd+FenjxNZMmCz7KnJyIGpyJZ8tEpIx9VL+Sjk6ENwOPHcTpbR0dbHj+Oy7jmKfv3nWflymFcvBiquzY0NBJbm/wM/u437t9/TL16lRgxvBOmr2lxIiJjcXKyz5Im9kRExup1+iIiY3FS2xnYPGf1umP4bT+La5XijB7eHjvb/FSqUITAg5dp37o6jyJiuXT5Po8iYqnq9vq0e6W2iNgX6kKsfl1w0tc2/3/b8dt6ChvrfKz8Y3D2H/4SIiNj9DSp1fZcDNZ/8Y2MiMWpiGJjZmaKtY0VMTGJFCxoTfCFUCaMW8PDR0+YMfOztxqdB0bKuOol9UBlaANQvowjAYeu0rxJZXbvv8SjCOV4K48qBARdpWHbuSQnpzFmWCvs7XLfQYuISqCIY2bH2cnRmoiohNe+8D4n9J4ysNJzsC9ajczgz+vQqE6pXGmJfKGuqp0KEnwhVF9vZAxFMmzMzEyxscmny9PXERf3jAMHLvJZn5x9PX+RqMh4HLPkraPahsshj15xhT6R4XGM/n4jD+495auhTXFQ2bz+omzwOOoZDqrMfCusKsD1S1Evtd+37Tof1XvN/NW3SERUAkX0yrsNEVHxRl/cXsbqTef4c91p0tI1/PmzYaRarrVFJ1JElaUeOFgTEZ39evCcB+FxPHgUR90ahlN8c4tk54AcE6n7XY6JVo7FPTGwtewxAlnWorlwmLR9qwHQPriNadWGpAdtwdStIZJVAchvA8/eTkTa+0BEZCxFsvhwtbogwcGhejaKP3h127F373mqVHbGwuLtDoq+y37R+Yt36dhzISpHG0YNaU35soYfIV+qKyIWddZ0czL0o1ltjPnRSePX8fDhE6bP6p3pRyWJLwf8hiRBt+716dq9frY1vVTre9Z+RETEGnlHeE2/KMPmOfP/twO/raexsbFi5QqlX1SpYlH27g+m1kdlCb4YxsNHTwmPiMHBIXt+4n3vGz0nOioBlVPm3+SosuFKDv3omO838eB+DF8OccfBMfvl4P8tYmv5HCMigwR5giZdw9lzt5n90+esWfUD+/df4Pjxa6+/8B3w+8gGHP65DanpWk5cfvlLw7/J2o2nGPNDWw7tGMmYH9oyzidzulSLplXYvWkoi+Z8yv9+2/+va5MkSReRNG36BkaM6ISJiX5Tkp6u4czZG4wa2YWNG0Zz/140m7ccf+faenavx77to/BfPxSVgy0z524HoItXbZzUdnT59Gemz95KjWol38raUDll2JD2HArwpkP7j1i15vC//vwXqVqtFP7bx7LOdwTLl+0jJSUtryXpMW2CJ2s2naZznyUkPkvFIqNDFnzpASYmJhzeOZwAvyGsWH2cew8MXwr/LdI1WsLux7ByQRfmTmzNhDkBxMWn5Jmel5GermHE8BX0/qwpzs5vZ52Z3KJysmWF7xes9h/Enm0hPDESgfKuObjrBjevRNGpd9V//dlvQq8uNdm34UuGf92ExX+++3Y1p+w8cIOW7uXearRvdklZNYOk2YNI/mUYpmXdMKulrP2SunUppmWrYjV8MablqqKNifogw/nfNTdvPGL+XH8mTXl7g4xvi5f1i1wqFSVw2wi2rh3MZ93r8u0I49Pg3xVVq5Viy7bRrPX9gd+X7df50b9WfY/vphH8uuRL1q09wpkzt/5VXS/jfWs/hg1px6GAyXRol9kvGjSgOfHxSXh2+Ym/Vx+mcqVimJr+e322971v9ByVky2/+/Zjlf9A9m6/lCd+VPDhIyKDBAasXnMI3w3K2hxubiX1phOER8SgzjLCD8qIe3hGBInORqVv8yJOTvbUrlWWQhlfoxo3duHS5XvUq1cx97r332bDwVBFd2l7Hj1JytT0JAl1lkihF7G0MMWjZhECzj2igavhgpnZer7vCXz9zijPr1KM8PDMLx/hEXGoVfohzWqVLeERcVlsYlE7Kl8Itmz/h3Ej2gHQprkr46f6GTyvds3S3HuwmScxiRSyN/xyu3r1QXw3HFH0uJUk/FGWfAx/ST6GG7cpXNiWyMhYVCo7IiNjKZQxJSwkJIwfflgOwNOYRA4FXcLMzBQnp4JUruSMs7MjAB7Nq3Hhwh3j6bbuGL6bTyo6XZwJD4/JkiYxqF8IT1er7AiPiDVq41A48wtLt84f89X3fwDKV5+xP3bUnevRZxGlSjoa1aOnbU0QvhuVzpKbawlDbeoXtKnt9OtCuKF+gA7tajHo6yV8P7jtazW8CpXKXk9TREQMqhc0qdR2hD9Sor7S0zUkxCdj/0J5KVvWifz5Lblx4xGuroYLi+eE1RtO4et3FsioB1nLeORL6kGkcZuypRxZ8Yuy7sGdsGgOHr0OwPY9F2lUrxzmZqYULmRNzWrOXLz8EOdi2Z82snrLBTZsD1F0VlLzKMtc+/CoBNQ5+MLm5GhN1SpOmJuZUryIHaWc7Ql78BS3SoYLe74Oldper65GhD81LGcqex49eqrL0/j4JIM8NcakiWsoWVJF375vFhUEyhfMqCx5GxURj2Muvko6qGwoXc6B4HP3dAtMvwmFHfMTnWXa1+PIRAo7GkaNnT/1gA1/nmfa4vaYW7ybr77PWb3pHBu2KlP93Co78UivvMfr2v2c0q55ZabM2QO0y722LcFs2KFMs3WrpOJRZJZ6EJ2AOgcRB8/ZGXidCUOa5FrTc8wadMSsntJGau9eQ7JXAYpWyd7B6ALQcuxj5YeUJNLPBmJSohKc2Y8c95iUP6Yo5yysyFe1IST//3pxUqvseJTFh0dEGLYdij8w3naEhz/l+++WMn3mZ5Qo8Xr/mB3+jX5R1qlG7g0rMmXWtpf2i4yhVtsRkTXdwvWjprPaODnZv9SPlinrRL78lty88QgX1xJZ+k82NPNwIyQ4jFq1cr6O1vvWfqxee/iFftGL7wiv6RcZsQHo0P55v6gN1tZWzJj6KaAs4uzRyhvn4tn/gPE+9o2M4eBoTWR4ZnRiVGQ8Dqpc+FFHa0qVdeDiP/d1C0wLBNlFRAYJDOj1qTv+W8bgv2UMzT2q4ud/ClmWOX/hDjY2+fTCPwFUjnZYW1tx/sIdZFnGz/8UHs1e/aW1YYMqXL/+kKSkVNLTNZw+fZNy5XL+4qSnu3kZ/KY2w29qMzw+Kor/0XuK7ptPsMlvbjBFLDE5XbeOULpGy6Hz4ZQpkvsQy17d6+oWMGzepAp+O88rz794DxtrS71QaACVgw3WBSw5f1HR6bfzPB7uygJxKkdbTp1VBk9OnL5NKefCAITde6ybF3zp6kNSU9Mp+JLpMb16NcHfbzz+fuNp7lEdP/8Tip7zt7GxsTLo7KhUGfl4/nZGPp7Aw0PJx2bNquLnpzh/P7/juuOBAdMIDJxOYOB0WrWswaSJPWjevDpubqWIi3/GkyeKkzt54hrlyhrfCaJXj/q6BZ+bN3XBb/s5RWdwGDbW+QzWBVA52mJdwIrzwWGKzu3n8GiirE2UdX2h/YEhlM8oU0lJqTxLUnaXOXr8OqZmJpTLRhh5r08b4795FP6bRyl1YWuWumBtZbwuFMhSF7aewqOZMhctNCxzikPAgYuUKZ27QcesuLqV4G5YFPfvPyYtNZ1dO8/RtKn+3LemTV3x91cWyN275zx16pZHkiTu33+sWxTx4YMn3LkdQbEcDKa8jF7dPsZ/9df4r/6a5u6V8Nt5IYf14AIejZXOzOMnyoupVqtl8YogenSuBUARtR0nzyj141lSKhdC7lOmVM4iXXp1qobf773w+70XHg3L4r/niqLz0iNsCljmaGpM84ZlOXVeWXz+aUwSofdiKF4kd2tsuLmVJCwskvv3o0lNTWfnzrM0faE9bdqsKv5+JwDYs+cf6tat+Nr1wxYs2Ep8fBJjxnbNla4XqehShPt3n/LoQQxpaRoC91yhfpNy2bo2MiKOlGTlS2t8XDIX/7lPiVKF34qu8pUdeXQvjoiH8aSlaTi87zYfNyqpZ3P7WjSLZx1h3OyW2L/iI8HboleXmvj91Q+/v/rh0bgC/rtDlLIW8iCjTmTf74Tey4yAO3jsFiWLv1md7dWpqm7RZ48GZfDfm1EPLodjU8Aix1PEbt99Qmx8CjVc3syfA6Qf3apbCFoTchSz2kqUj0nJyshJiYZTxExMoECGvzAxxdSlDtrwUOX3Ara6tT/Mm/ck/eQe/r/h6lYywx88bzvO0bTpC21HUzf8/ZWPL3v3/EOduhWQJIm4uGd8/dVvDPvBk5o1397C3/9GvygqOl7XLwoOuY9WK7+0X2QMF9cShIVF6/zo7l3/0KSpq55Nk6aubPU7DcC+vRf4uI5xPxp6O4KixQrx7FkKiYlK//LZsxSOH7tGuRzuhvWc96396NWzEf6bRuK/aSTNm7nht/V0Rr8oNKPP9rJ+UWhGv+g0Hk2f94syo/EDAi/q1hiNi3tGalo6ABs2naDWR2X1Bv1ex/vYNzJGJZciPLiX1Y9epb579vxoVES8nh8NOf8A55LvRucHRV4vHv0BRpyKyCDBK3Fv7MKhoEu0aD2FfFbmTJ/WW3fOs9MM/LcoWytOmtCdMWNXkZySRuNGVWjcWHk537f/Aj7TNvDkSQJffv0blSsV4/dlg7Gzy8/nfZvRtftPSJJE48YuNHF3NaohV7qrqQm6EE7LH/dhZWHK9AE1dee8xgfiN7UZSSnpfDP/OKnpWmStzMeVHenRrPTbeX6DChw6ep0WXvPIZ2XB9Emddec8P12I/xplXvSk0R0ZM3mTkm71K9C4gbLgps94T6bP2Um6RoulhRne4zwB2BNwCf+d5zEzM8HK0pz5Mz7J1iLS7u6uHAoKoUXLCYqe6X0z9XhNxd9vvKJn4qeMGfsXycmpNG7kQuPGSp4MGtiKocOWsXHTUYoWVbaWfxWmpiaMGtmFvp8vAFnGxaUE3bo1fL3ORpU4dOQqLTrMUnRO6Zaps/t8/H2VXVkmjfXSbS3fuEElGjdUIgpmL9jJ1WsPQYJiRQviPb4LoAwq9P9mOSYmJqhVtvw0Nech8O6Nqyh1oY23om1q5jbBnp1n4b95lKJtQndla/mUVBo3rELjRkpdmDtvG3dCI5FMJIoVKciUScq231FRcXT5ZDYJCcmYmJjw198H2bl1rN6C0y/DzMyUseO78uWAX9FotXTqXJdy5Yuw8OcduLiWoGkzNzp3rceYUX/TppU3dnb5mT33cwDOnb3F78v2Y2ZuiokkMX5id926ET8O/5PTp24SE5OAR5MJfDO4LV265nzBVfcG5Tl07AYtOv+stB8TPDPTrNdi/Fcru3JMGtkuY2v5dBrXL0fj+srCkdv3hrBmg9JZa9G0Ml06KNuP9+pWmzHe/rT7ZBEyMp3b16BS+dy/fLrXLUXQyVBa9voLK0szpo/KXL/Mq/9q/H5X8nr2b0fYvv8aSSlpuHf9na7tXPiuX10aflySI2fu0q7v35iYSPz4VUMK2uVukMHMzJTxEz5hQP+FaLVaOnepR/nyRfn55224upakWbOqdO1an1Ej/6RVy0nY2eVn7rzMnZE8mo0nMTGZtDQNAQEXWP77d1hbW7Hkt92UKaOmS+eZAHzay51u3RrkOs3MzEwYMqoFP37ji1Yr08bTjdJlHVnx62EqVnGiQZPyXL30iPE/bCYhLoXjQTf587cj/LlpAHfvPObXeQeQULZj/qTPx5Qp/3YiEUzNTBg0oj6Thyjrini0r0CJMgVZvfQs5So5UKdxSf745RRJz9L4aVwAAA5qa8bPUbbgHvPlNu6HxZKclMYXHdYweFxjatZ9e2vfuNcrQ9DxW7TsvhQrKzOmj82MDvTq+wd+f/UDYPaiA2zfd5mk5DTcvRbRtUM1vuvfkNWbznH8dChmZqbY2lgxc/ybRRfqaatbiqCTYbTsvRIrS3Omj8rc4dBrwFr8lvdUtP12lO0BGfWg2wqlHnyuLEq8I/AG7ZqVz/bmBtlFc/kUppXrkG/cX5CaQsq6ObpzViN+U3YOM7PA6ssZSKZmYGKC5vo/pB/fCYBpuWpYtOsPsozm9kVSN/7yVvW9jDVfeNOkQk0crO25N30rk7YvY8Wxbe/kWWZmpowb351BAxah1co6f/DLz9txcS1Bs2ZV6dK1PqNHraR1q8nY2RVgzlylvK1ZHcS9u1EsXryLxYt3AbBs+WAKF7Zhzmw/du44Q3JSGs2ajKdL13p8Ozjn0Wjvsl+0dtMpTE1NsLI0Y9707PWLsqbb2HFd+Hrgb2i0Wrw61aFc+SIs+mUnVVxK0LSZK5261GXsqFW0azUVO/v8/DRHiVj959xtViwLwMzMBMnEhHETulKwoDX370Uz9PsVAGjStbRpV5OGjSq/Skb20vA9az/cG1fh0OErtGgzlXz5LJju01N3zrPLT/hvGgnApPFddVvLN25UmcYZaTF3fka/SJIoVrQQUyYqfb5btyMYPW4NSFC+bBGmeeesz/a+942eY2pmwvejmjPy241otVradHSjdFkHViw+ovhR93JcvfSICcP9MvzoLf747Sh/bvyCsDuPWTzvgDLILct0/6z2W/Ojgv8Wklh9XACAZt/7WRBMWyCfHJ3XKowi1ZkJ8ca30cxTbDIGUOQDeavDGFJTSPLPaxXGyecJ6e/p12KzVqRp309t5iatIHZtXsswjl1P5Ee/5rUKA6Qi3wCglQPyWIkhJpIHj56tyGsZRimS/wuuPp2d1zKMUqngj8jR72e6SQ5fID9cmNcyDJCKKi//icPe3uYRb4sC8/chfV03r2UYRV58gnTtvryWYRQzkxbvZ78IwKYbKZpdea3CKJambd7L9kNy+EL5Ie09TDfzNu91v+hh4vK8lmGUogUG/PsLZv6LaHd/nefvsyatF39QaSymiQkEAoFAIBAIBAKBQCAQ/IcQg0ECgUAgEAgEAoFAIBAIBP8hxJpBAoFAIBAIBAKBQCAQCD5ctHk+S+yDQ0QGCQQCgUAgEAgEAoFAIBD8hxCDQQKBQCAQCAQCgUAgEAgE/yHENDGBQCAQCAQCgUAgEAgEHy5abV4r+OAQkUECgUAgEAgEAoFAIBAIBP8hRGSQQCAQCAQCgUAgEAgEgg8XERmUY0RkkEAgEAgEAoFAIBAIBALBfwhJlsUWbAIAREEQCAQCgUAgEAgEgv+fSHkt4F2i3Togz99nTTou/6DSWEwTEwAQGr8wryUYpZTNYORL3nktwyiSy0RI8s9rGYbk81T+1wbkrQ5jmHhAyo68VmEcy3bEpGzIaxVGsbfsBvHvpzZsukHarrxWYRzzNqRo3j9tlqZtlB8SNuWtEGNYdyE+bUteqzCKjXkn0rR78lqGUcxNWoFmX17LMI5pC2QO5LUKAySaKj/I7582pKaka9/P/DQzaYH0dd28lmEUefEJ5MileS3DKJJqENdrV8lrGUapcPry++lHzRVfJYfNyWMhhkglR7yX/h0UH38v4be8lmEUZ+uv8lrCu0Wb52NBHxximphAIBAIBAKBQCAQCAQCwX8IERkkEAgEAoFAIBAIBAKB4MNFLCCdY0RkkEAgEAgEAoFAIBAIBALBfwgxGCQQCAQCgUAgEAgEAoFA8B9CTBMTCAQCgUAgEAgEAoFA8MEia8QC0jlFRAYJBAKBQCAQCAQCgUAgEPyHEJFBAoFAIBAIBAKBQCAQCD5cxNbyOUZEBgkEAoFAIBAIBAKBQCAQ/IcQkUGCbHP6WBi/zQlCo5Vp41WFTz6vpXf+4rkH/Db3MLdvRjN2WmsaNS8HwPkz91ky77DO7l7oU8ZOb0X9JmXfmjZZlpn2+1mCzj3AytKMGYPr4VK2kJ5NUko6Q2cf5m5EAqYmEk1rFWP4ZzX0bPYcv8uQ2YfZ8FNr3MoVfnvaftrKoSNXsbIyZ6Z3d1wqFzewC7l8nzETfUlOScO9YSXGjeyIJEkA/L32KKvXH8PUxAT3RpUYOazdm+mZvoFDQZcUPdP74OJSwlDPpbuMGbNS0dPYhXFjuyFJErt2n2Phwh3cuh3OBt+RuLmWBODp0wS+H7qMkJC7dPKqy8QJn+RO26wtHDp8BSsrC2b69MSlirG0useY8WsVbY0qM25UJ11aAaz46yCz5m7l+CFvChW0BuDk6ZtM/8mP9HQNBe0LsOqPwTnSdvzIdebN2olWq6Vj54/o299d73xqajpTxm3k6uWH2NnlZ+rsTyharCDpaRqmTd7CtSuP0Gi0tOlQnc8HuJOSksZX/ZaTmqpBo9HSrLkLg771yF2azdnBoaPXlfyc3AWXSkUN0+zKA8ZM3qykWYMKjBvRDkmSuHLtEZNm+JOSmo6pqQmTR3Wkqmtx9h+8wv9+24+JiYSpqQljh7elVvVSOdc2Y3NGfpozc9qnuFRxNtR26R5jxq8hOTkjP8d0RpIkFvyyk4DAi5iYSBQuZMOMaZ+iVtlx8tQNvvn+d4oXU+p4i+ZVGfx16xxpO3L4CrNmbEarkenctS79BzbXO5+ams640au4fOk+dvb5mT2vL8WKFeZicBjek9Yrfx/w9bet8WheFYC4uGdMnriemzceIUngPbUn1aqXzpEuXbrN3s6ho9eUejC5Cy6Vixmm25UHjJm0MSNPKzLux/ZIksTQ0Wu5ExYNQHx8EjY2+fBf+x3BIfeYMM1P94zvBnnQoplLjrQdO3KNOTO3odXIeHWpzecDmuidT01NZ9IYX65cfoCdfX5mzOlJ0Yx8unHtEdO9t5CYkIxkIrFy3WAsLc3ZvfM8fyw7gISEo8oWn5mfYF+wQI7TLStHDl9m5vTNaLRaunStx4CBLV7QmcaYUau4fPke9vYFmDPvc13+Tp60DlDS6Jtv29C8RbU30qK0uRuVNjefBTOnf/aSenCXMWP/VupBYxfGje2KJEnMmr2FAwdDMDc3pYSzAzOm9cbWNj9Hj11h7rytpKWlY25uxo8jvKhXt+Jr9RwOusS0ab5otVq6dmvAoEH6dSc1NY1RI//k0qW72NsXYN78ARQv7gDAkiW72bTxKCYmJowb351GjVx49OgJo0b+yePHcUiSRPfuDenTV2nL/rdgKwEBFzAxkShU2IYZM/qiVtu/Oq2m+XIoKEQp+zP6GvdPIWGMGfNXhn9yZdy47kiSRExMIsN+WMaDB48pVqwwC+YPxM6uACdPXuObbxfr/o4WLWow+Nt23L4dzrAfluvue+9eNN9/34HPP2/66jQ8fJmZ0zdmlK/6DBzY0iANx4z6m0uXlTScO+8LihUrzLGjV5ifJc+G/+hF3Yw8+9+CrWz1P0Vs3DPOnJ33yue/DX7/bBzt3RoQGf8UN59e7/x5WZFlmWn/O0DQiTtKn21sa1wqqg3s5i89gv+eS8TFp3Bu7/e645t3hjD71yDUjop/79W5Ot06VH1r+hyHj6VAg8bIyUmETxlLyrUreuel/PlxXrZK97u5Sk3crm1EzZuJmboITpOnY2Jji2RiQvTC+SQeC8q1lvfZj75S86/HCTp9T8nfEe64lHfQs0lKTmfo1P3cfRiHqalE07olGd7/47fyfHg3Pv7vvw6yeeMJkKB8hSL4TPsUS0vzXGs8dSyUX+ccRKvR0sbLlZ799P/+4HP3+XXOIW7fjGL89LY0bl5Bd65l7QWULqekqcrJBp/5nrnWIfhvIwaDBNlCo9GyaNZBZizywkFtzXd91lO3cRlKlskccHF0smH45OZs/Puc3rXVaxVn8ZqeAMTFJtOv00pq1jXs3L0JQeceEvYojj2LOnLh+mOmLD2F7yxDp9bPszJ13ZxITdPQb3IAQece0Lim8pKVkJTG3zuuUq382xkE0mk7cpXQu9Hs3TqSCxfvMnnaFjas+s7AbvK0LfhM7EI1txIMHLyCoKPXcG9YiROnbxJw8BJbfYdhYWHG4ycJb6Yn6BKhYZHs3T2ZCxdCmey9jg3rRxrqmbIWH+9eVKtWioFfLiLo8GXcG7tQoXwRfvllEJMmrdGzt7Q0Z8j3Hbhx4yE3bjzKnbYjVwgNi2bv9rFcCA5j8tSNbFgz1FDb1I34TOpOtaolGfjNMoKOXMW9UWUAHoU/5ejxaxQtUlBnHxeXxJRpm1i+eBBFixTk8eP4HOnSaLTMnr6NX5b2Q6W25fOev9GoSWXKlFXpbLZuPouNbT427fiBvbuCWbRgD9Nm9yBgbwipaRrWbP6O5KRUenT6mZZtqlKkqD2Lln9B/vyWpKdpGNR3GfUaVsCtmmEn75VpdvQ6ofces3fLMC6E3GfyjK1s+OsrwzSbsRWf8V5Ucy3OwCErCTp2A/cGFZj9826+HdgM9wYVOHTkGrN/3s3fSwdQ7+MyeLgPRpIkrt4IZ+jodezeNDRn2g5fIfRuFHt3jlPy02cDG9b+YKjNZwM+kz9R8vPrJQQduYJ7oyoM6NeMod+1BWDlqkMsWrwH70ndAahVswxLfh2UIz3P0Wi0TJ+6kaXLv0attqfnJ/No0tSVsuWcdDabN53A1jY/O/aMZ9fOcyyYu43Z8z6nXPkirN0wHDMzU6KiYunaaTbuTVwwMzNl1owtNGhYiXkL+pGWmk5Scmqu9Ony1G84F0LuMXmGPxtWfmNgN3mGPz4TOlHN1ZmB3/9F0LHruDeoyIKZPXU2M+ftxNraEoDyZdVs+vsbzMxMiYyKw7PnLzRtXAkzM9Nsp9usqf4sWtYftZMdfT5ZSOOmlSlTNvMlzn/zaWxs8+G360f27LzAL/N2M2Pup6Sna5gwej3eM7pToVJRYmISMTMzJT1dw9yZ29jg/wP2BQvwv7k7Wb/mGF9+2+IVSl6vc6rPBpb9/i1Oans+6T6Hpk1dKVuuiM5m88YT2NrlZ9eeiezccZZ5c7Yyd34/ypUvwvoNI5T8jYylS6dZNGnqmu00MkZQ0GVCw6LYu3sSF4JDmTxlHRvW/2hgN9l7PT7en1KtaikGfrlY1+Y2qF+J4cM6YmZmyuy5fixZtpcfh3tR0N6axb9+iVplz/UbD+k/cBGHD057bdp4e69lxR9DUKsL0q3rDJo1q0q5cpkDyBs3HMXWNj979/mwY8dp5s7ZwvwFA7l58yE7d5xm+46JREbE0q/fAnbv8cbU1JRRo7vi4lKChIRkunSZTv0GlSlXrij9B7RgyNCOAKxcGcivi3YwxfvlAw9BQSGKf9rjzYULd5g8ZQ0bfEcbptWUNfj49KZatdIMHLSQoMOXcG/sytJlu6lXtxKDBrVm6dLdLF22hx9HdAag1kflWbLkW737lCnjhL/feF3aNHYfTYvm1V+bhtN8fFn2+2DUans+6T6bpk3dKJelfG3aeBxbu3zs3jOZnTvOMG+OP3Pnf0HBgtYsWvwlKpU9N64/ZNDARRw4pORZkyZufPqpO23aTHnl898Wfx7fwcKDG1n5+cR/5XlZCTpxh7D7T9mz9gsuXH7ElLn78V1qWC6aNihDr87Vaf3pCoNzbTwqMnFYzj+gvI4C9RtjUaIkoZ1bY+VaFdXoSdzr10PPRn72jLu9Out+L7FyAwkH9gFQqP+XxO/fTeym9ViULkuxBb9xxzP37dn76kdfqfn0PcIexLLnj+5cuBrJlJ+P4PuLl4Fdv65VqVu9qNInH7WDoFP3aPxxzvpAxngXPv7x43hWrwrCb9torKwsGDHsT3bvPIdnpzq51vjLzEBm/doZR7UN3362hvruZSlZJvMdROVkw8gpLfH9+6zB9RaWZixZ2ztXz/5/jVhAOseIaWJGkCRJI0nS+Sz/DHsiObtfKUmSPn1b+rLxPCdJktZJknRLkqSzkiTtlCSpwuuvfDnXLkVQ1NmeIsXtMDc3pUnLChw/dFvPxqmoLWXKO2BiIr3kLnAk4Ca165fEyir3I+nGCDh1H88mZZAkieoVHYhLTCXySZKeTT5LM+q6KY7AwtyUKmUKEf440+bnNRcY4OWChUXuO/1GtR28jFf7moq2qiWJi08iMipOzyYyKo6ExGSqVy2JJEl4ta9JwIFLAKz1PcGgfk2xsFDGbgsXsn4zPYHBeHnWUfRUL01c3DMiI2P19UTGkpCQTPXqpRU9nnUICLgAQNmyRShT2vALXv78ltT6qNwbfSUJOBCCV4dairZqpV6eVgkpVK9WStHWoRYBBy7qzs/4yZ8fh7UnS6AQ23aeo4WHm26AqHBhmxzpuhxyn+IlClOseCHMzc1o0dqNoAP6XwqDDl6hXUcl0qxZCxdOn7yNLMsgQfKzVNLTNaSkpGNmbkoBa0skSSJ/fuUlPT1dQ3q6Rk9zdgk4dAWvttWVNHNzJi4+mcho/cGuyOh4EhJTqO7mrKRZ2+oEHLwMgCRJJCamABCfkIzK0RaAAvktddFWSUmpepFX2dZ24CJeHWu/kJ8vlLWoWKXsP8/PjrUJCFTy09raSmenaMixBKOEXAyjRAkHijs7YG5hRus2NTgQeFHP5mDgRTp61QagRctqnDxxA1mWyZfPQjcwkJKSrtMUH5/E2TO36NylLgDmFmbY2ubPlb6AQ5fxalcjI09LEJeQ/JJ6kEx1txJKurWrocvT58iyzK79F2nfWols0dOemp7j9Lx08R7OJQpT3Lkw5uZmtGxTjUOB+s88FHiZ9p41AfBo6cqpkzeRZZkTx25QvoITFTKi1uztC2BqagIyyLKSv7Isk5iQgqPKNmfCXuBicBglSjjinJG/bdrWJPCF/A0MvIinp/IVtmWr6pw8cd0wf1PTeRuFTmlzP86oB6VfXg8Skqle7Xmb+zEBAcEANGxQWaeperXShIfHAFClijNqlT0A5csVISU5jdTUtFdqCQ4OpURJFc7OjlhYmNG2XW3dc/T0dqoHQKtWNTl+/CqyLBMQEEzbdrWxsDCnuLMDJUqqCA4ORaWy00XvWFtbUbaMExERMRm/59PdNzvtSEBAMF6edTP8Uxni4pJe4Z/KZKRVXQL2X8i83kvR7uVVj/0Zx7PD8eNXcXZ2oFixV38QuhgcinMJB5ydHZQ0bFuTA4H6aRgYGIynp/KS2LJVDU6cuIYsy1Su4owqI8/KlS9CckpmnlWrXhpHlV229b4ph2+e50li3OsN3wEBR27h2bqKks8uRYlLSCEy2vBDV3WXoqgc3qzPk1MKuDcjboc/AMkhwZja2GBa2OGl9uYlSmJaqBBJ/2S8sMtgUkDRbGJtTXp05BvpeV/96Cs1HwvDs0V5RXNltdInf/xMzyaflRl1qyv+wMLclCrlHAiPTnwrz38XPh6UAZyU5DTS0zUkJ6e+UX29dimcos72FC1un/FeVZGjB2/p2TgVtaNMeUdM/o1ME/xnEZFBxkmSZbn6W7xfKeBTYM1r7N4YSelpbQH+kmW5R8axaoAauJ7b+z6OTMRRnemQHVTWXA0Jz/F9Du69Qede1XMr46VEPHlGEYfMFy+nwvmJePIMVaF8Ru3jElM5cOYBfdop4dmXbj3h0eNnNKlVjN/9Lxu9JtfaImNxcrLP1Ka2JyIyVvfSrbNR2xnYAISGRXHm3B3mL9yNpaUZI4e1p6pr7r+cRETE4OSUGTXj5FSQiMgYVFmcWkRkDE5ZQvmd1AV1nft3SURkXC7TSunQ7j8QgkplR6WK+lNqQsMiSU/X8tkXi0hMTKFPr0Z4daydbV2REXGoszxTpbbl0sX7ejZREXGoMmzMzEyxtrYkNuYZHi1cCTp4lXYes0hOSmPoyLbY2SllVaPR0rfHr9y/+4SuPergWjXn+RoRFY+TU9b0sCUiMg6VQ+aAV0RkHE5q2yw2dkREKQNGY4e3pf/gv5j1v11otTLrVmR+Jdx34DJzF+7lydNEliz4LOfaImL1y5ranoiIWFSOdvo2emVNsXnO/P/twG/raWxsrFi5InNq3/kLoXTs/BMqlS2jRnhSPstX+ezoUmfRpXay52Jw2EttzMxMsbaxIiYmkYIFrQm+EMqk8et4+PAJ02f1xszMlP9j77zDori6OPwORQFpFliw9wKixhI7iF1RwRZjiCWJ6ZqmibH3bjTFFktiiZpYwS6KCvYSAyr2AopKVaS33fn+GFxYdlWaYr7c93l43J05s/Pz3rn3zpw599z74bGUKWPJhHEbuH71AfWcKzF6TG+twy8/KPWVo07trYmMjtdtB9G5bLLqPSfn/gmlbBlLqlbOfpAJvniPsVO38uBhHHOn9s9XxEtUVDwqh5ztwIZLF+8ZsLEFnrYDM57EJXM3LAYkieEfreLx4yQ6d2vIkPfdMDE15vsJXrzd+0fMzEtQuUpZRo8vXLh7VFScTj+iUunXb1TkExwcc+jMVb8Txm3gwcNHzJo9qFBRQZDVn+q1g7hc7SB3n2tLZFSc3m9t3XaSbl0b623f7xeEk1MlSpR4vjM+MvIxjrm0BF+4o2MTFRmHo2P2tW9lZU7c4yQiIx/TqGH1XP+PxzrHhofHcOXKPRo2zJ4euXChD74+p7GyMmfN2q9foC8OB8ec41NWWdnnKisHfRuA2Nh4ra2dnTWxsdltIijoNr08p2Fvb8vo7/pSq5budNrde87Rw+PF40Jk1BOdMlSpSnPhQqiOjXJ95SrDrOvrKX5+QTjVe3Gd/T8SGZ2Io332GOVgZ0VkTGK+HD8HjtzgXFA4VSuVZsyIdjiqCudEfoqJnT0Zkdn3t5lRkZjYq1DHxhi0t+rcnYQD+7TfY5cvouKildi+5Y2RuTnhn39QKD2v6zj6XM2xSTjaZdelQ7lSRMYmYV/W8AuS+MQ0Dp+6y+De9Yvm/C9hjFepbBnynjudO0zBzMyUlq3q0qp13QJrjIlKxF6V3QbsVPl7rkpPz+Szd9djZGzEwKHNaO1es8Ba/p+QRQLpfCMig/KBJEmhkiTNyooWOidJUmNJkvZnReDoz8vIZjbQNuu4ryVJCpQkqVGO3z0mSVJDSZImS5K0TpKkk5Ik3ZAk6cMcNt9KknRWkqQLkiQ9L4bYHciQZXnZ0w2yLAfLsnz0Oce8EmJjkgi9GUPTlkU7RSy/ZKo1jFxwjEHd61DJwQqNRmb26r8ZPVT/Bvt1QK3W8CQ+mU3rhvPdVx589d0fSsSJQIeUlHR+XXGQLz/Xnx6oVmsIuXyPXxcNY+Wyj1iy/AB3Qgv3ti6vhFwKx9hIYvfB0WzfO5INa45zP/wRAMbGRvyxeTg7D3xLyKVwbt2IfCWacrJxyxnGfNOdgN3fMeab7oybtl27r5O7E/u2fsXi+e/w07KDr1wbwNdfehDgP5meHk34Y4PSjTk7VeLQgUns2PYdg95x5fMvVr1STQ0aVmX7zu/ZuOkbVq04SFpaBmq1hiuXw3lrQGs2bfsWc/MS/LbS/5Xqys2ufcH06KKbR6OhSyV2b/6KLes+49fVAaSlPT+SpKhQZ2oI/ieU6XPeZtXaTzjiH8KZUzfJzFCz9a9TrN/8BfsOj6VmbUd+X3n4lWh6Fg0aVsV311j+3DSKlSsOvLIyehFLl+3D2NiIXj11HRY3bjxk/gJfpk5++xlHvhqSklL54ovljBn7lk5E0Ndfe3EkYBY9er7JH38ceWV6JEnSRiI5O1fm0KEZ7PCdwKB32/H58KU6tunpmRw6FEzXrk1eibabNx6y8AdfJk0p3jr7t+Leugb+m4exY80QWjWrwvcz9734oJeEVafuJOzfnf29iwfxu3y406M997/6BIcpc4okwrAwvI7j6FMy1RpGzjzEIC9nKjkWjUOvsBga4+OfJHP40CX2HpjIwSNTSUlJY9eOc8WmccOuYSz5w5uxM7qx5IcAHtyLKzYtgn83whlkGPNc08RyZsK9mxU1dBRYDfQDWgDPc9B8DxyVZbmRLMsLgVXAUICs6Vtmsiw/jWVuALQHWgITJUkqL0lSZ6AW8CbQCGgiSZLrM85VH9CfXGoASZI+ynJqndvw+/Hn2pa1L0V0ZHYIb0xUIuXs8xe6G3jgBq3caxT6LetT1u+9htc3e/D6Zg/2pc15GJMdghoRm4yqjOE3EBOXnqaKozVDeioe/aSUDG7cfcLgCQdp/7EPwddj+GxWABdvxhZc258n8HxrIZ5vLcSunLU2rB8gIjIOVa7QUpW9DRE53uLktFGpbOjUoT6SJNHApTJGRhKPH+cvlHb9+gA8e8/Es/dM7OxsiIjIfpsbEfFYO9UgW48tETkigSIiHz836WdhWP/nMTz7z8ez/3zsylkVsKysuXsvhvD7j/DsP5/2XacREfmEPgMWEB0Tj4PKljat6mJhUZIypS1p2qQ6V68/yLNGe5W1zlu2qMh4vaksdiprorJsMjPVJCamYWNrwf49F2jRuhYmpsaUKWtJgzcqcyXkvs6xVtbmNGlWjZPHb+RJz/pNp/B8ZxGe7yzCrpwlERE5yyMeVS5tKntrIiLjc9g8QWWnvJHavusfOrd3AqBbx/pcyKUNoFnjaty7/5hHcS++7tZvPIpn37l49p2LnZ217rUWGacTYQXK9a17renbAPTs0RS/rCkflpZmlMqKuHFzdSIzU82jx3nPpaVS2RCZQ1dkhG7kQW6bzEw1iQmp2NrqJjWuXsMBc4uS3LzxEJXKFpXKhgYNqwJK2PmVy7rRY89j/aaTeA78Bc+BvyjtIOc1HhWPyi5XndpZ52oHuvWemanmwOEQunc2nFS1RjV7LMxLcP1W3h2Q9vbWREbkbAdPsM91rSk2cVoNiYmp2NhaYK+y4Y0m1bAtXQoz8xK0bluHq5fvc+2q0g4rVi6LJEl06uLChaC7edZkWKetTj8SGRmnjdrT2qhsiHiYQ6eB+q1RwwELi5IFyoG2fkMAnr1n4dl7ln6fGxmn15+qVLn73Didfnnb9lMcCbjE/LlDdaZaRUQ8ZvgXy5kzaxCVK9u9UJdKVZqHelpK69jYq2x5+DD72k9ISMG2dKnnHpuRoeaLL5bTs+ebdO6suzDDU3r2fJMDfv/obV+//gieXtPx9JqOnb01EQ9zjk/PKKsIwzZly1prp5VFRT2hTBmln7O0NKdUKWW6jJubC5kZun1G4NFLODtVply5Fz+MquxtdMohMvKxXp+lXF+5yjDr+oqIeMwXI5Yzc3be6uz/hfXb/sHrvbV4vbcW+7KleBiVPZ05IjoBVT6igkrbmGunzvfv4ULItcK9SLHpP5DK67dRef021LHRmKqyc8uY2KvIjDL8+yVq1UEyNibtanZEuY1nXxIOKs6p1IvBSCVLYGxb2uDxz+LfMI7qad4RgtcnW/H6ZCv2ZSx4GJ39WxExSajKGl4UYOKPR6lSwYYhfVwKfO7cvIwx/tTJ61SsUIYyZSwxNTWmQ6cGBAXpRlXmh3L2lkRFZreB6MhEytrlvQ08fQYrX9GWhk0qcvPaq3nBKfj/QziDDJOS5bh5+vdXjn07sv69CJyWZTlBluVoIE2SJNs8/v5moIckSabA+yhOpaf4yrKcIstyDHAYxQHUOevvH+A8UBfFOVQoZFleLstyU1mWm77zXuvn2tZxUnH/XhwR95+QkaHmiN91Wrjmb5WcI/uv065LoVIX6eDdrQ4+C7rjs6A7Hd6shO8RJUdL0LUYrCxKGJwi9uOGIBKSMxj7fvbbP6tSJTi1ph+HfvXi0K9eNKxdjiVj3Aq1mpj3263w3fQ1vpu+pqO7Mz67zivaLoRhZWmuM90DwN7OGstSZgRdCEOWZXx2nadDO+UBvaO7M6fPKvOI74RFk5GhpnQ+V9rx9nbDd/tYfLePpWOHBvj4nlb0BN3Byspcb5C0t7fB0tKMoKA7ih7f03RoX3Qrdehoe7sNvptH4bt5FB3bu+Cz85yiLTgUKyszw2VlWZKg4FBF285zdHCvT53a5TkZMJVD+yZwaN8EHFQ2bPvrG+zKWdPBvT5//3OHzEw1KSnpXLhwlxoG8h49i3rOFbgXFsuD8EdkZGRyYN9FXNvphge3bVeX3TuUB51DB0Jo+qaSz8LB0YZzZ5T8WinJ6Vy6cI8q1ex4/CiJhHglZ1VqagZnTt6iarVn5yXQKbO3WuC7YTi+G4bTsZ0TPnuClDK7eA8ry5I6U8QA7MtZYVmqJEEX7ylltieIDm71tOV55m/lhubU2dtUraRc92H3YrURaCFXH5CenklpmxfnwPEe2Bbfrd/hu/U7pT53nM2uT0tzndB25fw2yrX/tD53nKWDu3JTGBoWrbXzP3RRm6sqOiZeq+3CxTA0GpnStnlvE871KxMWFkN4eCwZ6Zns2/sP7dx1w9Pbuddnh89ZAA74BfNmcyX/QXh4LJmZagAe3H9E6O1IylcoQzk7a1QOpblzR3loOH3quk5i5ReW21st8d04At+NI5Q63f1PVp3excryWe3AjKCLd5Vy2/0PHdyctPtPnLlF9ap2OlPJ7t1/pNV+/+FjbodGU8Ex7w8pTvUrcu9uLPez2oHf3mBc3Z10bFzdndjlqywi4O93iWbNayBJEi1b1+LmjQhSU5T8WefP3aF6DRX2Khtu34ricVZi/NMnb1KteuEekOu7VOZuWLS2fvfuOY+7u+6Dhrt7fXx9zwDgtz+I5i0M1++d25FUqFBG7xwvwvsdN3y3j8F3+5isPvdMVjvI6nMNtQNLM4KCn/a5Z7R9buDRy6xcdZCliz/G3LyE9pj4+GQ++nQZI7/xpEnjvK3O6eJShbDQKMLvxZCensme3Wdpn6tvb9++AT7bTwKwf/95WrSogyRJtG/fgD27z5KenkH4vRjCQqNo0KAqsiwzftxaalR34L33dFfsCQ3Nfoj29w+mWnX9NuHt3Q5fn/H4+oynY4dG+PieyhqfbitjwDPHp9tZZXWKDh0aZGv3UbT7+JzUbo+OfpLdZ1y4g0bW7TN27z6HRx6miAHUd6mSdX1lleGe87i765ahu7sLvr6nAfDb/w/NW9RGkiTi45P59JNlfP2NJ43zWGf/L3j3eQOf3wfj8/tgOrStie++y0o9hzzIGrfy/iCcM7/QoeO3qFGlcAt/PNm8kbvefbjr3YfEI/5YeyhTVc3qN0CTmPDMKWLWXbqT4LdHZ1tmxEMsmim540pUrY5RiZKoHz/Kl55/wziqp7mXMz7L+uKzrC8dWlXF94CSgyfoSiRWpUoYnCL24+9nSUhKZ+ynLQt8XkO8jDHewdGWC8Fh2vx2p0/doLqB/iyv1HFy4P69xzzUPlddo5Vb9RcfCCTEp5KengnAk8cphAQ/0Ek8/Z9GLRf/378MkTMo/6Rl/avJ8fnp9zyVpyzLyZIkHQA8gbeAnHHJua8iGZCAWbIs/5qHnw9BiVYqUoxNjPj8WzfGjtiBRq2hcy8nqtYoy5plp6hdz56WbtW5FhLJ1G93kxCfxqmjoaxdfpoVm5TVISIexBMdmUiDxvrLIxcFbk3KE3j+Pp0/24FZSWNmDs8eWLy+2YPPgu5ExCSzbEsI1StY02fUXgC8u9Wmf6eXO8/WrW1dAo5dpVPPOZiblWDmlP7afZ5vLcR3k5JDYdJYL+3S8q6t6+LaRnE29PVqxthJm+nR9wdMTY2ZPW1AgZL5avW41ScgMIROXSYpemZm54Lx7D0T3+1jFT0T39YuLe/a1hlXV2UJ6gMHgpg2YxOPHiXy8SdLqFe3IqtWKqujte8wnsSkVDIy1Bz0D+a3lSN0Vlh5cVnVI+DoFTp5zMTczJSZ07JXRfLsPx/fzaMUbeP6aZeWd21TF9c29Z77uzWqq2jbug69+s3HSJLo16c5tWvlXZeJiTGjxvbgi0/XoFFr6OnVhOo1Vfy6+CD1nCrg6l6PXr2bMHnsFvp6LMDaxpzpc5WAwn5vN2fahG283ftnZFmmh2djatV24Mb1CKaO34pGrUGjkenQpT5t3PI//9ytdW0Cjl+nk9cCpT4nZa9w4vnOInw3KPkBJn3fizGTtypl1qo2rq0Vx+y08Z7MnL+HTLWGkiVMmDpOuQne7x+C754gTEyMMCtpysJZ+b/u3FydlPrsNh1z8xK69dl3Lr5blVXsJo3vp10S17VtPVyzVob7YeFO7oRGIUkSFcqXYcpEpe3s9wtm41/HMTY2wszMlAXzhuRLm4mJMWPH9eXTD5eh1mjw6t2cmrUcWfzLHpycK+Pevj69+7Zg7Og/8OgyHRtbC+bOHwzAP+dv89sKf0xMjJCMjBg3oZ82D8iYcX0Y890fZGRkUrFiWabNKNi6AW5t6hBw/BqdPH9Q2sHkvtnlNvAXfDcq7U2p0y2kpmbi2jq7TgH27L+ARxfdJdH/DgpjxeoATEyMMZIkJn/vSZl8OJZNTIz5dmwvRnz8G2q1hl69m1Kjpopli/yo51wRN3cnPPs0ZeKYTXh1m4e1jTkz5yl1bm1jgffgtgx+exFIEq3b1tFe7x9+2oEPh/yKiYkxjuVtmTSj//Nk5Enn2PH9+HjYEtQaDb37tKBmLUcW/bwb5/qVcW/vQp9+LRkzeh3dukzFxsaCeT8MBeD837dYteIgJqZKGY2f+JZOnpeC4ObqrPS5Xaco9TkjewUYz96z8N0+BoBJE95izNg/svpcJ1xdFUfbtOmbSM/I5L0PFgHQsGFVpk4eyB8bArl7N5rFS/ayeIkypv22cvhzE+SbmBgzYeIAPhj2Mxq1hr59W1GrVnl+/mkH9etXoX2HhvTr15rvvv2dzp0mYGNjwYKFwwCoVas83bo1waP7FIyNjZk48W2MjY34+9xNfH1PU7t2Bbw8pwPw9TeeuLm58MMPPoTeiUSSJMpXKMOUKc9vE8r4dIlOnSdkjU9DssvKa7p25a9JE99hzNg1pKamZ41PyoPeRx924auvV7Bl63HKl1eWlgfFqbXxz8CsPqMEC34Ypu0zkpPTOHH8ClOn5G15dRMTY8aNf4uPhi1Go5G119cvP+/CuX5l2rdvQN9+rfh+9Fq6dpmMjU0p5v/wHgAb1gdy7240S5fuZelSpc5WZNXZ/Hk+7Nl9jtSUDNq3G0/ffi35fLhHnjQVhA3vT6Vd7caUs7Tl3swdTNq1gt9O7Hxp58uJW8tqBJ66Tee3V2FmZsrMMV20+7zeW4vP70p/O29JALsOXiUlNQO3Pr/Sr4cLI95vxbot/3D4+C2MjY2wsTZj1tguzzpVvkk6Hkip1q5U3b4POTWViKnjtPsqr9+ms4qYZceu3P9SN0tE9I9zUY2bQumBg5GBiCljC6XndR1Hn6v5zUoEnrlH56F/YVbShJmj3LT7vD7Zis+yvkREJ7JsYxDVK9nS57NtAHh7OtO/W8Hz8DzlZYzxpUtb0rFzQwb0m4+xsRH16lWk31utCqzR2MSIEd+15/vh29CoZbp6OlO1RjlWLz1BbScVrdxqcDUkgsmjdpIYn8rJo7dZ8+tJVm0ewt07j1g44yBGRhIajczbQ5sJZ5CgwEgi94g+kiQlyrKsd/cnSVIo0FSW5RhJkoZmfR6ee5+B45oAC2RZdsu1bSfK9LEBWdsmA14o085KoUQCtUCZ+jUN6CDLcqIkSRVQ8gLpxQRmJZA+BaySZXl51rYGgM3z8gaFJix6LS+EqlbDkUOmFrcMg0jOEyHFt7hl6GOelXxVU7z5Sgxi1AHSdr/Yrjgo6UFc2ubiVmEQ25L9IeH11IZVf8jYW9wqDGPajTT166etpHE35UPi1uIVYgjLviRkbH+xXTFgZdqbDM3+4pZhEFOjLqA+UNwyDGPcCZnizcNkCAl35YP8+mlDcidT83rWp4lRJ6RPWxS3DIPIS08hRy0vbhkGkew/4nozpxcbFgO1z15+PcdRU2WsksPmF7MQfaQqo17L8R2UMf5e4rIXGxYDlSw/+b9emixzzbvF/jxrMuSPf1UZi8ggw5hLkhSU4/s+WZYLs7z8BUAtSVIwsFqW5YWyLP8tSVI88LsB28NAOWCaLMsPgAeSJNUDTmZ57ROBdwE9Z5Asy7IkSb2BHyVJGg2kAqHAV4XQLxAIBAKBQCAQCAQCweuJWlPcCv51CGeQAWRZNpjhWJblqjk+ryZHrp+c+wwcl4GSFFqLJEnlUXI2+eUyvyDL8mADv/ET8NOLtGfZPkCZfiYQCAQCgUAgEAgEAoFAoINwBhUDkiQNBmYA38iyLFyYAoFAIBAIBAKBQCAQFBBZU+yzxP51CGdQESJJkguwLtfmNFmWm+fcIMvyWmBt7uNlWZ6cj3OVBQwlhekgy3LB10QXCAQCgUAgEAgEAoFA8H+NcAYVIbIsXwQavaJzxb6qcwkEAoFAIBAIBAKBQCD4/0E4gwQCgUAgEAgEAoFAIBD8e1GLaWL5xai4BQgEAoFAIBAIBAKBQCAQCF4dIjJIIBAIBAKBQCAQCAQCwb8XkUA634jIIIFAIBAIBAKBQCAQCASC/xCSLAsPmgAAcSEIBAKBQCAQCAQCwf8nUnELeJlkLB1Q7M+zpp/+9a8qYzFNTACAzOHilmAQCXcuxc4ubhkGqV/2e8jcX9wy9DHpovybsLl4dRjCqj+k+Ba3CsOYexKfvrW4VRjEukRfSHw9tWHZF43sX9wqDGIkdSA0YVFxy9CjqtVw5cPr2kbj/ypuFYaxHvBaj1Wv5XgAypiQtru4VehT0gOAqJS1xSxEH3vzwa9n+wSw6o8ctby4VRhEsv8I6dMWxS3DIPLSU6Qv7FvcMgxS4uutHAofW9wy9GhfcSYAcuzq4hViAKnsUFAfKG4ZhjHuhBy9srhVGESyG1bcEl4qskggnW/ENDGBQCAQCAQCgUAgEAgEgv8QIjJIIBAIBAKBQCAQCAQCwb8Xjaa4FfzrEJFBAoFAIBAIBAKBQCAQCATFhCRJZSRJOiBJ0o2sf0s/w04tSVJQ1t+OHNurSZJ0WpKkm5Ik/SVJUokXnVM4gwQCgUAgEAgEAoFAIBAIio/vAX9ZlmsB/lnfDZEiy3KjrL9eObbPARbKslwTeAx88KITCmeQQCAQCAQCgUAgEAgEgn8varn4/wqHJ7Am6/MawCuvB0qSJAHtgS35OV44gwQCgUAgEAgEAoFAIBAIig+VLMsPsz5HAKpn2JlJknROkqRTkiR5ZW0rC8TJspyZ9T0cqPCiE4oE0gKBQCAQCAQCgUAgEAj+tcia4l9aXpKkj4CPcmxaLsvy8hz7DwIOBg4dl/OLLMuyJEnP+g9VkWX5viRJ1YFDkiRdBJ4URK9wBgkEAoFAIBAIBAKBQCAQFIIsx8/y5+zv+Kx9kiRFSpLkKMvyQ0mSHIGoZ/zG/ax/b0uSdAR4A9gK2EqSZJIVHVQRuP8ivWKamEAgEAgEAoFAIBAIBAJB8bEDGJL1eQjgm9tAkqTSkiSVzPpcDmgNXJZlWQYOA/2ed3xuRGSQQI+jgSHMmLEJjUZDv/6t+eijrjr709MzGP3dakJC7mJrW4oFC4dRsWI5AH79dR9btxzHyMiIcePfom1bZ9LSMnjXez7p6Zmo1Ro6d2nMF1/0BMD7nfkkJaUCEBubQIMGVVm85NN8a/7nVDi//XgajVqmQ8/a9BncQGf/jo2X8N95HSNjI2xszfhsbBvsHS25cz2W5fNOkpycgZGRRL8hDWjdsXpBik0HWZaZMWsrAYGXMTMvwewZ3jg7VdKzuxRylzHj1pOamoGbqxPjxvRFkiTmzPfh8JFLmJqaULlSOWZNfwdrawsArl67z6Qpf5GYmIqRkcSWv0ZRsqRp/rTN303A8euYmZkye3JfnOuW19d25T5jJm8jNS0Dt9a1GTfKA0mSuHLtIZNm+ZKWnomxsRGTR/eiQf2K3AqNZuyUbYRcfcDXn3Xig0FtClZuc3cQcOyqom3qWzjXq6iv7XI4YyZuUrS1qcu473ohSRK/LPVj07YzlCldCoBvRnTFrW090jMymTRtG5cuhyMZSYz7thfNm9XIl7YTx67zw5xdaNQaPPs0Y+gwN5396emZTBq7mauX72Nja8HMeQMpX6E0e3cFsW71Ua3dzesRrNv0OXVylPk3I9ZyP/wRf23/Kl+aIKvM5u0i4Pg1zMxKKPVZT3+K8KUr9xkzaUtWfdZh3Lc9kCSJr77fyJ2wGAASElKwsjLHd+MI7XEPHsbh0f9Hhn/UgQ8Gt823vqccPRrCzBmb0Whk+vVrxYcfddHZn56ewejRa7gcck/pVxZ8QIWKZXn8OJGvvlzBpUt38fJqwYSJAwqs4VmcPRHGsvmBqDUy3bycGDC0qc7+i+fvs+yHo9y+GcPYGV1p27EmAEHnwvl1QXbd3gt9zNiZXWjVLn/XVm4K20a/GvNnjjpNxcrKDN8Nw8nIVDN+2nYuX31IplqDl0cjPn7PTe9386Xzhz0EHL+h6JzU+xk6HzBmyjZS0zJxa12LcSO7I0kSV69HMGn2DpKT06ngaMv8af2wtDR77jmLenx63m+OG7uWS5fCkGWoWs2eWbOGUKqUGWfP3mDWzE1cu3afHxZ8QNeuTV5cTi9hPEhPz2TSlL+4FHIXSZIYN6Yvzd+s9VwtBrXN2U7A0StK/zFtIM5Ohvrce4wZv1G51trWY9zo3ii5KhV+W3OEOT/s4GTAVMqUtuTg4Uv8tGgvRkYSxsZGjP3Oi6aNCz62nj5+i5/m+qHRyPTo3Yh332+lsz/o77v8PM+P2zeimDS7N+6d6mn3jfxsI5cv3MfljUrM/aVo+o+XNY6ePnebz0aup2IFZTXhTu5ODP+wfeF0/nSYwFN3MCtpwqyxXXGuo5+CYuHyY/juDyE+IY3zfl9ot2/bc4l5SwJR2VkC4N2nEf17NtA7vqhZNWgcPVxaE5XwGJdp3i/9fM/DuN37GFVrjJyRjtrvF+SoO3o2Jv2mIJUqjZyZDkDmtqmQEl/kWkLORLBpcTCyRqZ192p0GVhHZ3/gztsE+N7CyEiipLkJ3l83xrGqNYlP0lgx5TRh1x7RoksV3v7ijSLXJssyMxYeIPDkLczMTJk1vgfOdfRnwixcFoDvvovEJ6Ry3n+UdvvvG8+wZWcQxsZGlLG1YMZYDyo42hROz8wtBASGKP3uzEHP7nfHrsvqd50ZN7YfkiSxd995Fi3ew63bkWz+axQu9atoj7l67T6TJm/Mvg/f9F2+7sP1dP50iMCTtzEzM2HW2O6G2+ivR7PaaCrnD3ylt3//kWt8OX4Hm1cOwqWuoRlI/xEKn8C5uJkNbJIk6QMgDHgLQJKkpsAnsiwPA+oBv0qSpEEJ7Jkty/LlrONHA39KkjQd+AdY9aITisgggQ5qtYapUzeyYuVwdu2exO5dZ7l584GOzZbNx7G2tsDvwDSGDO3AD/O3A3Dz5gP27D7Lrt0TWblyBFOnbESt1lCihAmr13yN744JbPcZz7GjIQQF3QZg/YZR+PiOx8d3PI3eqE6nzvkfoNRqDSvmn2LcD535cUNvjh28zb07cTo21WqXZe5vvVi4zosW7lVZt+QsACXNTBgxsS0/re/NhAWd+e2nMyQlpBWg5HQJPHqZ0LBo/PZOYNrkAUyeusmg3eSpm5g25W389k4gNCyawGNXAGjdsg67fMawc/v3VK1ix68rDgCQmanm2+/XMWXiAHbvGMva1V9gYmKcP23HrxN6Lxa/7V8zbZwXk2ftMKxt1g6mjffCb/vXhN6LJfDEDQDm/byPzz9sj++G4Xz5cQfm/bwPAFtrc8aN8uCDd/PvBNJqO3aV0Lsx+O34jmkT+jJ5xnbD2mZsZ9rEvvjt+I7QuzEEHr+m3Tf03bb4bvoa301f49ZWeSDYvPUMADu3fMPvyz5kzoJdaDSaPOtSqzXMnbGDn5YMZZPvV/jtDeb2rUgdG99t57C2Nmf7nlG8M6g1vyxUyqVbj0Zs2DKCDVtGMHVmf8pXKK3jCDp08BIW5iXyrCU32vr0Gcm08V5MnmX4JcDkWb5Mm9AbP5+RWfV5HYAfZw/Ed+MIfDeOoHP7+nRyd9I5bvbC3bRtVbvA+kApv2lT/2L5iuHs3DWB3bvPcfPmQx2bLVtOYGNtwX6/KQwe0p75Pyh1X7KkKV982ZNvv+tdKA3P07Z4zhGm/9yLFZu9Obz/OmG3H+nY2DlYMXJyR9y76JZDo6YVWbphIEs3DGTO0t6UNDOhcYvKhdZU2Db646y38d0wHN8Nw+nc3llbp/sOXiI9Xc3Ov0aw7Y9P+WvbWcIfPC64zhM3CL0bi9+2L5k2theTZ+80rHP2TqaN88Rv25eE3s3WOW66DyM/78TOP4fT0d2JleuOP/d8arW6yMen5415Y8b2x3fHBHbsnICjYxnWrz8CgKNjaWbNGkKPHs3yVk4vaTzYvOUEADt9xvD7ys+ZM297vvo1gMBjVwgNi8Fv11imTezP5OlbDNpNnr6FaZPewm/XWELDYgg8dlW772HEY46fvEZ5x9LabS2b12LHllH4bh7FzKlvM36y4f9zXlCrNSyYtY/5i99m3baPObgvhDu3onVsVA7WjJ3ak47d6usdP3BIC8bP6KW3vTC8rHEUoOkbVbXttzCOIIDAU3cIC3/M/o3vM/W7Tkz54aBBO/fW1dn0q2GnS7cOdfD5fTA+vw9+JY4ggNUnd9P1l69fybmeh1S1MZKtIxm/D0d9cCnG7T96pm3mvp/IXD+KzPWjXoojSKOW+fPnIIbPas3E3zpz9tA9HobqnqdZ+0pMWNmJccs70mlAbbYsuwCAaQljer7nRJ9PXl79BZ68pVxrmz5h6uhuTJm3z6Cde5uabFo5VG97vdoqtvz2HjvWDaOLe13mLzlcOD2BWf3uvklMmzKQyVP+NGg3eepfTJv6Dn77Jin97lHlmbp2rfL88vOHNGuq+3InM1PNt6PXMGXS2+zeOZ61a77M9324js5Tdwi795j9fw5j6rddmDL/gEE799Y12LT8XYP7EpPTWbf5PA2dHAusQ/B6IMtyrCzLHWRZriXLckdZlh9lbT+X5QhCluUTsiy7yLLcMOvfVTmOvy3L8puyLNeUZbm/LMsvfKgVziCBDhcuhFK5ij2VKtlRooQJ3T2a4e9/QcfG/9AFvHq3BKBLl8acPHkVWZbx979Ad49mlChhSsVK5ahcxZ4LF0KRJIlSpZS3vZmZajIz1TpvFAESE1M4feoaHTs2zLfmm5djcKhohUMFK0xNjWnTsTpnj97VsXFp4khJMyUQrrazHbFRyQCUr2xD+UrKm4cydhbYlDbjSVxqvjXkxv/QRbx6vYkkSTRqWI34hBSionXzekVFPyExKZVGDashSRJevd7UlnWb1vW0g0ujhlWJiIwD4PiJq9SpXZ66dZXIj9K2pTA2zl8z9g+4glf3Roo2l0rEJ6QSFZOgqy0mgcSkNBq5VFK0dW+E/xFlgJQkiaQkpW9JSEzF3s4agLJlLGngXBETk4J3K/5HLuPVo7GirUGVrHLTvdmJio5Xyq1BFUVbj8b4Hw557u/evB1J8zdraHVaWZlzKSQ8z7pCLoZTqXJZKlYqg6mpCZ26NSDg8BUdm8DDV/Do1RiA9p3qc/b0LZSIzWz27w2mc7fsm7Hk5DQ2rD3O+x+751lLbvwDLuPl8UZWfVYmPjHVcJklptLIpbJSZh5vaOvzKbIss/fgRXp0zW6DBw9fpkL5MtSqYV9gfZDVr1S2o1Klckq/0r0Jh/yDdWwO+V/A06sFAF26vMGpk9eQZRkLi5I0aVKTkiUK9tbtRVwLiaR8JVscK9pgampMu861ORlwW8fGobw11WuVw8hIesavwDH/mzRrVQUzs8LrLGwbfYq2Trso15wEpKSmk5mpJjU1E1NTYyxLlSyEzqt4eeRTp0cj/AMUR0Lo3ViaNa4KQOs3a+B3+HLuU+hw4cKFIh+fnjfmWVqaa8sxLTUDCaX+K1YsR526FZGecz3oano548HNWxE0b65EApUta4WVlQWXLt3LkyattsOX8OrZNEtb1Wf3uYlpNGpYVdHWsyn+hy9q98+a68u3X/cg57BeyqKkdpxPSUlHyltRGeTKpQdUqFSG8hVLY2pqTIcuThw7cl3HxrGCLTVrq/TuLQCaNq+GhUXBr3NDvKxxtKjxP3YLz65Oik7n8sQnphEVk6hn18i5PPblLF+KhoJw9GYQj5KK3qGSX4xqNENzJQAAOeIGUslSUMq2WLSEXn2EXYVS2JW3xMTUiKbuFQk+oesMNy+VPf6kp6q1n0uam1DTpRympi/vsc//6A08u9ZXrrX6FZ59rdWvYPBaa9GkCuZZ42dD5/JERBWu/v0PXcDLMw/9bmKOftczu9+tUcOB6tX0I3SOH79KndoVqFtXiaAsbWuZ7/twHZ1Hb+DZ1Tmr3Mor93AGy+3ZbfTnFccY5v0mJUqICT/Fvqz8vzAySTiDBDpERj7G0SH77Z6DypbISN03x1GRcThmvQE0MTHGysqcuMdJzz1Wrdbg5Tmd1q2+pVWrejRsWE3nNw8eDKZFyzram+/88Cg6mXKqUtrvZewsiI1Oeqa9/67rNG6hP43mxuVoMjM0OFQo/E1ZZNQTHBxstd+VstAdhCIjn+CgymHjYEtklH4i+K3bTuHaVnmzfyc0CkmCDz5cQu9+c1mxyvBbvudqi07AwSE79NZBZU1krkE3MioeB5V1DhsbIqOVG92xI7sz96d9uHnMZc5P+/hmeKd8a3imNkPllqtMIqOe4KCyeabN+j9P0LP/AsZM2sSTeMXpV7e2I4eOXCYzU829+48IuRzOw0j9sn4W0VFPUOUoM5XKhujIXA9MOWxMTIyxtDTjSVyyjs2BfRd1nEHLfjmI95A2mJkVPDJIqasc5WFvTWSuh7nI6Fw2Bur83D+hlC1jSdXKypSapOQ0VqwJYPhHhXszDUqf4ZAjakDlUFq/PUQZ6Ffint2Oi4rYqCTsVNk3WOXsLYmJ0r8RexFH/G7QrkvhIqieUtg2+pTcddqlY33MzUrQpusc3HvM4/1322BrY1EInQauPUM67a11bbKuz1rV7bWOoX3+l17YJiMjI4t8fHrRmDdmzBratP6O27cjeHdQwZy2L2s8qFunAocOX1L6tfBYQi7f42FE/iK9IqPiC9jnKnV48PAl7O1tqFtHf0w94H+Brr1m8/HnK5g59e186cpJdFQC9g5W2u92KmtiohKec8TL52WOo0EX79Jr4CKGfbGGG7kiUPOvMxFH++yyc7CzItLAg+bzOHDkBr2GrOGL8Tt4GFn8DppXiWRZBjkhRvtdToxFsixr0Na48+eYeM/HqHk/g/sLS1xMCqXtsvvr0nbmxMWk6Nkd8bnFhHf3sX35RQYMz/8L1oISGZ2AY87r3c5Kb0zKK1t2BePaonDTrSOj4nDQ69vjdG0i43T7XZUtkVG6Nrm5E/b0PnwRvfvOZsUqw5E8edYZk6uN2uevjYZci+RhVDztWhWuvAT/XYQzSPBKMDY2wsd3PEcCZnHhQijXr+smN9+96yweHnkLuS8MAftucetqLJ7eLjrbH8ck8/PUQIaPa/Pct/+vmqW/7sfYxJhePZQcJmq1hr/P32be3MFsWPcVB/0vcPLUtRf8StGyccsZxnzTnYDd3zHmm+6Mm2Z4KldxMPCtlhzYNRrfv77Cvpw1s3/YBUBfr2Y4qGzo+87PzJy3gzcaVsH4FdfzpQv3MDMzpWYtZS73tasPCA+Pxb2D8yvV8Sx27QvWRpAALPrVnyHvtKZUEb9R/38kNiaJ0JsxNG1Z+CliRcmu/Rd16vTCpXCMjCWO7huN/46R/PbHce6FP3rOL7xcZkz0YsOWM/QZtJSk5HRKmBY81P5lMWvWEAKPzqFGDQf27DlXrFpyjwd9+7TAQWVL37fmM3P2Vt5oVK1Qb6jzS0pKOr+uOMiXn3c1uL9Thwbs2/E9i398n58W7X1luv4NPGscda5bnkM7R7Fj43AGvdWCz0dtKFad7q1r4L95GDvWDKFVsyp8P9Pw1J//Opl7fyJz3TdkbhqPUYV6GNUreC62wtLOqwbT/uiK14f12fPH1Rcf8JqxY98lQq5G8IF38+KWYhB1pjrrPnwoG/74hoMHgzl58tXehz9Fo5GZ/cthRg8veHS5QCDiyQQ6qFSldd4sRkTGoVKV1rGxV9ny8OFjHBxKk5mpJiEhBdvSpfJ0rLW1Bc2b1+Ho0RBq11beJD5+lMiFi6EsWvxJgTSXsbMgJjI7guBRdDJl7Urp2QWffcDWNcFMW9wN0xLZDx3JSenMGHWAdz5qQu36BZ8Os35DIJu2nATApX5lIiLitPuUstBNhKdS2WjD/QEiIuJQ2WfbbNt+miMBIaxeNVwb+u6gsqVZk5qUKa1EMri2dSLkcjgtW+gmEdTTtukUm3yUBxkXpwpERGS/+Y2IjEdlrxsNpbK3JiLHG8CIyCeo7JQ3F9t3/cO4UR4AdOtYn/HTfZ577hex/s8TbNp2WtHmXEm/3OxzlZu9DRGRTwzalCub/Xalf583+eSL3wElQmDst9k5I94evJiqVezyrNHO3obIHGUWGfkEO5Vumdln2agcbMjMVJOYmIqNbfZbPL+9F+jSPfst3cXgu1wJuU+vLnNRZ2p49CiJj99bwa+/f/hCPes3nWTT9hz1mbM8ouJR5ZpyoLKzzlVmunWemanmwOEQtv0xXLst+NI99vtfYv7P+4hPUJIklixpwrsDWr5QX27sVbZEPMzuGyIjHuu3B3sD/YqtfjsuasralyI6MvstXExUIuXs8zddIvDADVq51yhU3oCibKOQo07Xfabdtmv/Bdq2rIWpiTFly1jSuGFlLl65T6WKZfKh8zSbfP7O1pn72jOkM0fERM7rs0ZVO35bpCyacScshiPHdKf+5EalUr2U8elFv2lsbER3j2asXOlH3766iYufxasYD0xMjBn7fR+tzdveC/LUr63/8xibtp5StBW4z7Xm7r0Ywu8/wrP//KztT+gzYAGbN3yFXbns66BZ0xrcmxDLo8eJ2rErP9jZWxEVkR1hEB0ZT7kcb9JfFa9iHM2ZQN2tTR2mzNnJo7gkyuSjL1y/7R8271Sm8bnUdeBhjiiqiOgEVPmYDlbaJjtau38PF+YvDczzsf9WjBp2xai+svKzHHkTyaocTyd+SJZlkRNj9Q9KynKqZ6SiuXoMyaEWZE0vKypsy5nzODo72vhxdAq25Z4dTd/UvRIbf/qnSDXkZv3Wv9m8IwgAl7qOOpFjEdEJOmNSXjhx9g7L1pxg3WLvAk15Wr8hgE2blVxqLi5ViNDr22117FUqW91+NzIOlb2uTW4cHGxp1rRG9n24qzMhl+/RsuXz78N1dG49z+adynQ0l3qOum00Ku9tNCk5nRt3Yhg8QsmHFPMoic9Gb2PJnD7/2STSsubfN02ruBGRQQIdXFyqEBYaRfi9GNLTM9mz+yzt2+smnGvfvgE+25Wb3P37z9OiRR0kSaJ9+wbs2X2W9PQMwu/FEBYaRYMGVXn0KIH4rOk6qanpnDhxherVszup/fvP066dS4Ez8desV46H4fFEPkggI0PNsYO3adpGd8WA29di+XXOCb6f2wGbMtmDZ0aGmrnfH6Jdt5q0bF+1QOd/ivc7rvhuG43vttF07NAAnx1nkGWZoOA7WFmaYW+ne4Ntb2eDZSkzgoLvIMsyPjvO0KG9ErEUePQyK387yNJFH2KeI7lwm9b1uH7jASkpSt6Ps+duUrPGizt877daaBNSdmznhM+eIEXbxXtYWZbEvpzugG1fzgrLUiUJunhP0bYniA5u9bJ0W3Pmb2U1jVNnb1O1kuGQ6TyX29uttAmfO7o747PrvKLtQhhWluZ6uRTs7ayVcrsQpmjbdZ4O7ZRpEzlzXRw8dIlaNZWySUlJJzlFWeXj+MnrGJsYUbOG/lzwZ+FUvwJ3w2K4H/6IjIxMDuy9gGu7ejo2bdvVZfeO8wAcOnCJZm9W1z60aTQaDvpdpFPX7LbUb0AL9h4aw47937Fi7cdUrlo2T44gAO+3WmqTPnds54TP7n+y6vNu1rVmoMwszQi6eFcps93/0MEtO1H0iTO3qF7VTmcqyIZVH3No13cc2vUdQ95pxcfvtSuQIwiy+pWwKMLDs/qVPX/jnqtfcW/fAF8f5QF1//5/tP3Ky6aOk4r79+KIuP+EjAw1R/yu08K12osPzMGR/dcLPUWsKNsoGK5TR5UNp88p+ZCSU9IJvnSP6lXz7hRVdDbHd8Nn+G74jI7t6uKzO6dOsxfr3B1EB7e6AMQ+UpxwGo2Gpb8F8Hbf50eHuri4FPn49KwxT5ZlwsKiACVn0KFDwVSvnvc+41WMBykp6SQnK3lnjp+4irGxMTVrvjiBqPfbbfDdrCR37tjeBZ+d57K0hWJl9az+oyRBwaGKtp3n6OBenzq1y3MyYCqH9k3g0L4JOKhs2PbXN9iVsybsbrQ2Z1rI5XDSMzIpXUDnbl3n8oTffcSD+3FkZKjx33+ZNm5FMyUzP7yKcTQ6JkFbbhcuhaPRyJTO51RO7z5vaBM+d2hbE999lxWdIQ+ydObdGZQzd8mh47eoUaVw4/2/AU3wPm0iaM2tM9ooH8mhFnJ6MiTF6R4gGYFZVt0bGWNUvQlyrG7eyqKgSt3SRN1PJOZhEpkZGs4dDqdBK90V7KLCs50Kl049xL7Cy80D5d23CT5rPsBnzQd0cK2N775LyrV26T5WpfJ3rV2+FsGkOftYMrcfZcsUrK/wfscN3+1j8N0+Rul3fXP0u1bmhvtdyxz9ru8ZOuQaU3LTprUT16/nuA8/e5OaNfPnePHu2xif1UPxWT00q42GZJVb/tqolWVJTu0ezqEtH3Noy8c0dCr/n3YECQqGiAwS6GBiYsyEiQP4YNjPaNQa+vZtRa1a5fn5px3Ur1+F9h0a0q9fa7779nc6d5qAjY0FCxYOA6BWrfJ069YEj+5TMDY2ZuLEtzE2NiI66gnff78GtVqDLMt07doEd/fsznb3nrN89KHhMPO8YGxixLBvWjDtaz80apn2PWpRuXppNq44T8265WjWtjJrF58lNSWDH8YfAaCcqhRj5nbkhH8ol4MiSIhP4/CemwAMH9eGarULd8Pj5upEQGAInbpNxdysBDOnZ6/S4dlnDr7bRgMwacJbylLCaem4tnHS5oKYNmML6RmZvDdsCQANG1Zl6qQB2NhYMHSIO/0GzEeSJFzbOtHOLX/TjNxa1ybg+HU6eS1QtE3KfrPs+c4ifDco0SGTvu/FmMlbSU3LwLVVbVxbKzff08Z7MnP+HjLVGkqWMGHqOE9AuYntO3gpiUlpGEkSazaeYM+mL164VLSOtrZ1CTh2lU495yjapvTP1vbWQnw3KauLTBrrpV1a3rV1XVzbKA+X837cw9VrD0CCCuVLM3V8X0B56Pzgs5UYGRmhsrdm7vT85a8wMTHmu7G9+OKT31GrZXr1bkKNmiqWLTpAPeeKuLnXw7NPUyaN2Uzv7vOxtrFgxtzsc/zzdygqBxsqVsp7BEZecWtTh4Dj1+jk+QPmZqbMnNxXu89z4C/aZeKV+txCamomrq2z6xNgz/4LeHR5ebkFTEyMGT9hAMM+WIRGo6FP35ZKv/LzTqVfad+Afv1aMfq71XTpPAkbGwt+WPCB9vgO7ceTlJSqPAj6B7Ny1Yg8PfTmBWMTIz7/1o2xI3agUWvo3MuJqjXKsmbZKWrXs6elW3WuhUQy9dvdJMSncepoKGuXn2bFJqVNRzyIJzoykQaN9XOmFJTCtlGAPX4X8eise1Pr/VZzxkzZhsdbPyPLMn16NqZurYLfNCo6b9Cp94/KtTcxe8U3z3eW4LtBiUqaNLoHY6Zsz9JZC9dWStLjXfsvsmGLstJfp3b16Nvz+atJmpiYFPn4BBj8TY1Gw/ejV5OYlAoy1KlTgclT3gHg4oVQhg9fRnx8MocPX2TRL7vYvfvZYfovazyIfZTABx8txchIQmVvw9zZg/JYczm0ta1HwNErdPKYqdThtIHZ2vrPx3ezsvTzpHH9tEvLu7api2ubes/6SQD2H7yA785zmJgYY1bSlIVzBxfYuWtiYsTX33dh5Kcb0Wg0eHg2pFpNO1YuCaCukyNt2tXmyqUHjPtmCwnxqZwIvMFvSwNZt+1jAD5/by1hobGkJKfTp/PPjJ7sQfNC5td4WePofv8QNm49g7GxEWYlTVgwc0ChnOJuLasReOo2nd9ehZmZKTPHdNHu83pvLT6/DwZg3pIAdh28SkpqBm59fqVfDxdGvN+KdVv+4fDxWxgbG2FjbcassV2edaoiZcP7U2lXuzHlLG25N3MHk3at4LcThlcrfJnId84jV22M6XuLkTPTUPst1u4z8Z6vrBxmbIpJnwlgZAJGRsh3L6C5mP98ji/C2NiIt0c04pfRx9BoZFp1q0r5qtbs/D2EynVK07BVeY743OLq+SiMTYywsCzBkNHZDvZx7+wlNTkDdYaG4OMP+WJOGxyrFl3icrdWNQg8eYvO/Zcp19o4D+0+ryGr8FmjjOnzFh9il99l5VrzXES/ng0ZMawt8xYfJjklna/GK1MmHVXWLJ3b3+C58qTH1Vnpd7tOUfq2GdkrcXn2noXv9jFAVr879g+ljbZ1wtVV6XcPHAxm2ozNPHqUyMefLqNe3QqsWjE86z68Pf3emqvch7s6085NfxXDPOtsWZ3Ak7fpPGCFUm5ju2n3eQ1djc/qoQDMW3KEXQeuKOXWeyn9ejRgxAetC3xegeApUu7VbgT/TWQOv5YXgoQ7l2JnF7cMg9Qv+z1k7i9uGfqYZN2sJWwuXh2GsOoPKYaXPi92zD2JT99a3CoMYl2iLyS+ntqw7ItG9i9uFQYxkjoQmrCouGXoUdUqazre69pG4/8qbhWGsR6ATOGWG35ZSLi/nuMBKGNC2u7iVqFPSeVhMSplbTEL0cfefPDr2T4BrPojRy0vbhUGkew/Qvq0RXHLMIi89BTpC/u+2LAYKPH1Vg6Fjy1uGXq0rzgTADl2dfEKMYBUdiioC5e8+aVh3Ak5emVxqzCIZDfs9UmM+hJIndqz2J9nzSbu/FeVsZgmJhAIBAKBQCAQCAQCgUDwH0JMExMIBAKBQCAQCAQCgUDwr0UkkM4/IjJIIBAIBAKBQCAQCAQCgeA/hHAGCQQCgUAgEAgEAoFAIBD8hxDTxAQCgUAgEAgEAoFAIBD8e1GLaWL5RUQGCQQCgUAgEAgEAoFAIBD8hxCRQQKBQCAQCAQCgUAgEAj+vYgE0vlGRAYJBAKBQCAQCAQCgUAgEPyHEM4ggUAgEAgEAoFAIBAIBIL/EJIsi3AqAQDiQhAIBAKBQCAQCASC/0+k4hbwMkn+vluxP89azN77rypjkTNIAIAcsay4JRhEcviE+PStxS3DINYl+pKQsb24ZehhZdobgKQM32JWok8pU8/Xuj7l2NXFLcMgUtmhRCavLm4ZBlFZDCVDs7+4ZRjE1KgLcvTK4pahh2Q3DIColLXFrEQfe/PBIB8ubhmGkdwh8/W81jDpQpp6b3GrMEhJ426vZRs1NeqifEh+/cZRLHq/1vV5vZlTccswSO2zl0lf2Le4ZRikxNdbkT5tUdwyDCIvPYX8YFFxy9BDKj8cgPPR04tZiT6N7caD+kBxyzCMcSdI213cKgxT0qO4FQheM4QzSCAQCAQCgUAgEAgEAsG/F5FAOt+InEECgUAgEAgEAoFAIBAIBP8hhDNIIBAIBAKBQCAQCAQCgeA/hJgmJhAIBAKBQCAQCAQCgeDfi1pT3Ar+dYjIIIFAIBAIBAKBQCAQCASC/xAiMkggEAgEAoFAIBAIBALBvxZZJJDONyIySCAQCAQCgUAgEAgEAoHgP4RwBgkEAoFAIBAIBAKBQCAQ/IcQ08QE+UKWZWb8fITA03cwK2nKrDGdca6t0rNbuOI4vvsvE5+Yxvl9w7Xbt+0NYd7So6jsLAHw7t2Q/j1cCqTlxLHr/DBnFxq1Bs8+zRg6zE1nf3p6JpPGbubq5fvY2Fowc95Aylcozd5dQaxbfVRrd/N6BOs2fU6duuW1274ZsZb74Y/4a/tXBdR2jfmzd6JRy3j1bcbQYe30tY3ZxJUsbbPmD6R8hTIA3Lj2kJlTt5OUmIpkJLH2z+GULGmK395gflt+GI1GQxu3enzxTbcCaTt+7BrzZ/uiVsv07vsm7w1z19M2YcyfXLl8H1tbC2bP99Zqu37tITOmbiUpMQ0jI4l1f46gZElTFv20j907/iY+PoXjZ6cXSBcUfZ1WrFSWD4cs126PinxCtx6NGDm6R4E1QlY7WHiAwJO3MDMzZdb4HjjXcdCzW7gsAN99F4lPSOW8/yjt9t83nmHLziCMjY0oY2vBjLEeVHC0KZQmgNPHb/HzvINoNBo8vBrx7vstdfYH/X2XX+Yf5PaNKCbN8qJdp7oA3LgWyYIZ+0hKSsfIWGLQB63o0MWp0HqOHb3M7JnbUGs09O3XkmEfdtLZn56ewZjRf3D58j1sbUsxf8FQKlQoy4njV/lxwQ4yMtSYmhoz8lsvmreorXPs8M+WE34vFp+dYwqtU5ZlZvx0iMCTtzEzM2HW2O441zHQr/16FN/9IUp9HvhKb//+I9f4cvwONq8chEtd/euhIJw+fouf5vqh0cj06N2Id99vpbM/6O+7/DzPT6nT2b1x71RPu2/kZxu5fOE+Lm9UYu4vAwp0flmWmTFjEwGBlzAzK8HsWUNwdq6sZ3fpUhhjxqwhNS0DN9f6jBv3FpIkEReXxNffrOD+/VgqVCjLjws/xMamFDt2nmbFCj+QZUqVMmPy5HeoW7ciAPHxyYwfv47rNx4gSRIzZwzmjTeqv1jnrK0EBF7GzLwEs2d44+xUSV9nyF3GjFtPamoGbq5OjBvTF0mSmDPfh8NHLmFqakLlSuWYNf0drK0t2LHrLKt+O6Q9/tr1B2zf/C316lXMcxkeO3qFObO2oVHL9OnXgg8+7KizPz09k3Hf/8HlkHBsbC2Yt2AIFSqU5eKFMKZO+kv5/wGfft6VDh0bcOdOJN99s0Z7fHh4LJ+N6Magwe3yrClbW9G30aGDfyYmOp6SZqYALF/5GWXLWuVbmyzLzJi7k4Dj1zAzM2X2lP4416ugZ3fpcjhjJm0mNS0Tt9Z1GPddTyRJAmDdxuOs33QKYyMJt7Z1+e6r7oQ/eET3PguoVsUOgIYulZk6vnc+y61o6xSga8cpWJQyw9hIwtjEmD83j8xvkRnEbuRYSrV2RU5NIWLKWNKuXdHZL1lYUGnFH9rvpvYq4vfuJHrBbExUjjhMnomRlTWSkRExixaSdCKwSHTlxrjd+xhVa4yckY7a7xfkqDt6Nib9piCVKo2cmQ5A5rapkBL/UvQ8i1WDxtHDpTVRCY9xmeb9Ss8tyzIzfgkk8HSYMlaN7ohzbXs9u4UrT+Lrd5X4hDTO7/1Eu33W4qOc/iccgJS0TB49Tubsro+LRFvQqfus/ekcGo2Me4+aeA6qr7N/95+XObzrJkbGEta2Znw8piV2DsqzwDuuf1C5ui0AZVWl+HaOe+6fzzeyLDNj5hYCAkOUMWHmoGePCWPXZY0Jzowb208ZE+ZtzxoTjJUxYca7WFtbcOFCKBMmbVTOAYz4vDudOjbMn6452wk4ekUZU6cNxNlJfzy5dPkeY8ZvVMbUtvUYN7q3tl8D+G3NEeb8sIOTAVMpU1opx9NnbzJzrg+ZmWpK25bij9+H6/3u/z1qMU0svwhnkCBfBJ4OJSw8jv3r3yP4cgRTFhxi07KBenburarj3achXb1X6+3r1r42E79qXygdarWGuTN2sGj5+6gcrBny9hJc3etSvUb2A5zvtnNYW5uzfc8o/PYG88vCfcyaP5BuPRrRrUcjQHEajPryDx1H0KGDl7AwL1EobXOm+7J4xQeoHGwYPGARru71cmk7i5W1OT57v2X/nmB+WbCPWT+8Q2ammgnf/8XUWW9Ru2554uKSMDExJi4uiZ9+2MMfm0ZQuowlk8Zu4sypm7zZomYBtG1nyYoPUTnY8O6AX3Bzd9LR5rPtDNbW5uzYO5r9e4L4acEe5vzwLpmZasZ/v5Hps97W0Qbg2q4eA95phVf3uYUqt5dRpxu2jNAeP+itRbh3cC6wxqcEnrxFWPhj9m/6hOCQB0yZt49NK4fq2bm3qYl3vyZ0HbBMZ3u92iq2/PYe5mambNx2nvlLDrNwmlehNKnVGhbO9mPB0rexU1nzkfdq2rjVomqNcloblaM1Y6f04M+1p3WONTMzYey0nlSqUoaYqASGef/Om62qY2VlVig906dtZsWqz3FQ2TLgrfm4u9enRk1Hrc22LaewtrFg7/6J7Nn9Nwvm7+CHhe9RunQpFi39GHt7G25cf8DHHy7lUMA07XEH/IKxsChZYG25CTx1h7B7j9n/5zCCQx4yZf4BNq14V8/OvXUNvPu+QdeBK/X2JSans27zeRo6OertKyhqtYYFs/axcNk72Kms+dD7N1q71aJaDTutjcrBmrFTe+rVKcDAIS1IS83Ad8s/BdYQGHiJ0LAo/PZPJTj4DpOnbGDzpu/17CZP2cC0ae/SsGE1PvxoEYFHQ3Bzrc/yFfto2aIuH33UleXL97F8xX6+HdWHihXK8ce6b7CxKUVA4CUmTPxD+7szZmyibVtnfv75Y9LTM0lNTX+xzqOXCQ2Lxm/vBIIvhDJ56iY2/6n/MD156iamTXmbhg2q8uEnywg8dgW3tk60blmHkV/1xMTEmHk/+PLrigN8O9KTXj2a0atHM0BxBH3+xYp8OYLUag0zp29h+cpPUalsGThgAe3c61OjZrazcNvWU1hbW7B7/3j27jnPjz/sZN6CodSs5cjGzSMxMTEmOvoJ/XrPw62dM9Wqqdi8/Tvt73dsN4kOHRrkWVNObS+rjc6eN5j69fWdhvkh8Ng1Qu/G4Oc7iuCL95g804fN6z7Xs5s804dpE/rS0KUSHw7/ncDj13FrU4dTZ2/hf+QKO/76khIlTIh9lKg9pnLFsvj+9WWBdL2MOn06lq5a/Tmlsx7sioJSrVwpUbkKoX26Yla/AfbfT+Lee2/r2MjJydz17qP9XnntZhIPHwCgzAcfk3BwH0+2/kWJajWo8OMy7njqOgyLAqlqYyRbRzJ+H47kUAvj9h+R+adhR3/mvp+QI28VuYa8svrkbhYd2cLaoRNf+bkDT4cRdj+O/X8MIvhKJFMWHmHT0rf07NxbVcO7dwO6vrtOZ/uYz9tqP6/bFsyVG9FFokuj1vD7gjOMXdiRsvYWjBu2lyZtKlKxmq3WpmrtMsxY2Z2SZiYc2H6NDUvO8+VUVwBKlDRm9urCvaTLTWBg1piwb5IyJkz5k81/fatnN3nqX0yb+o4yJny8lMCjl3FzdaZ1q7qM/LpX1pjgw68r/Ph2pBe1apVn6+bvMDExJir6CZ69Z+Herr62Db9Q17ErhIbF4LdrLMEXwpg8fQubN3ylr2v6FqZNeouGDarw4WcrCDx2Fbe2ysuehxGPOX7yGuUdS2vt4+NTmDJjKyuXfkR5x9LExiYUrOAE/znENDEDSJKkliQpKMef/p1v/n6vqiRJ7xSVvjycb5wkSSGSJF3I0t+8qH7b/9gtPLvUQ5IkGjk7Ep+YRlRsop5dI2dH7MsW3Q1NbkIuhlOpclkqViqDqakJnbo1IOCw7tuuwMNX8OjVGID2nepz9vQtZFnXY7x/bzCdu2XfRCcnp7Fh7XHe/7jgbyVCLt7L0lYWU1MTOndrSMChyzo2AYcu08NT0dahc33OnL6JLMucOnGDWrUdqJ3lyLC1LYWxsRH37z2icpVylC6jlOmbLWpy6MClfGu7dPEeFSuX02rr0q0hRw6F6NgcOXSZHp5Ns7S5cFar7Tq1ajvqaQNo0LAKdnbW+daTk5dVp08JC43h0aMk3mhStVA6AfyP3sCza32lHdSvoLSDGAPtoH4F7Mvpt4MWTapgnvXWvKFzeSKiCv9m88qlB1SoVJryFUtjampMhy71OHbkuo6NY3lbatS2RzKSdLZXqlKWSlWU6K9y9laULl2KuEfJhdJz8UIYlSvbUalSOUxLmNCte2MOHbqoY3Po0EU8Pd8EoHOXRpw+dR1ZlqnnVAl7eyVSqmYtR1LTMkhPzwAgOSmNtWsO8/EnnQulLydKfTpn1Wd54hNTn1Gf5Q3WJ8DPK44xzPtNSpQouncsSp2WyVGnTvp1WsGWmrVVOm8Mn9K0ebVCO838/S/g5dlCKZtG1YmPTyEq6omOTVTUExITU2nUqDqSJOHl2QL/g8HZx3spEWpeXi05mLW9ceMa2NiUAqBRw2pERDwGICEhhbPnbtCvX2sASpQwwdra4sU6D13Eq9ebis6G1YhPSCEqOpfO6CckJqXSqGE1RWevN/H3vwBAm9b1tDfzjRpWJSIyTu8cu/f8jUe3Jnkqt6dcuhhG5crlqJjVDrp2e4PDudrBkUMX6eWlOJw6dW7I6VM3kGUZc/MSWk1paZkYqGJOn7pOpcrltNGb+eFltdGiwj/gMl49Git12qByVp3q9pVR0fEkJqXRqEFlpU57NMb/iDKmbdx8io/ec9O2ybJliuae5GXXaVFSyq098bt9AUi9dAFjKyuMy5Z7pr1p5SoYlylDyj9/KxtkMCqllJuRpSWZMVEvRadRjWZorgQop4y4gVSyFJSyfSnnKixHbwbxKOnVRiM9xf/4bTw7Z92DOzkQn5RGVGySnl0jJwfsy5Z67m/tPnQdjw61n2uTV25eicWhohWqClaYmBrTsmMVzh27p2Pj3NiBkmZKW6zpbMej6MLdY7wI/0MX8PLMw5iQmGNM8HzWmFCNiIg4gFxtOMPg2PtcXYcv4dWzaZauqs/u1xLTaNSwqqKrZ1P8D2f3MbPm+vLt1z10+o+de87TqYOL1kFUkGjM/wdkjVzsf/82hDPIMCmyLDfK8Te7kL9XFXglziBJkloCPYDGsiw3ADoC955/VN6JjEnE0T67g3GwsyQyWv+h6XkcCLhBr/fW8cXEnTyMKpjnOjrqCSqH7Gk1KpUN0ZG5OtMcNiYmxlhamvEkTnfwObDvoo7jYNkvB/Ee0gYzs4JHBkVFxetos1fZEBWVW1s8KgdbPW13w2JAkhj+0Sq8+//Mmt+Um6NKlcsSFhrNg/uPyMxUc+RQCJFZA1N+iI56gsMLtOW0eaotLi6ZsLAYJEnis49W8k7/H1n925F8n/9F2l5GnT7Fb28wnbq65HvgNkRkdAKOqmznl4OdFZHRBbuWt+wKxrVFjUJriolKxD6HJjuVFdEF0HT50gMyMtVUqFT6xcbPISoqDoesaxxApbIlKjLXjVjkExwcFRsTE2MsrcyIi9O9sT3gF4RTvYqUKKE4z375eTdDhrpjVojovdzo9Wv2VkQacAY9i5BrkTyMiqddq8LXY06ioxKwd8jWZaeyJqaAfWZBiYyMwyHH20cHB1siczlKIiPjcHAwbBMbG691GtjZWRMbq/8QtWXLcVxdlSkF4eExlCljyZgxa/DqPYNx49eRnJz2Yp1RT3SuNweVLZG5rrfIyCc4qHLYONgSmcuxBbB12ylc2+pPk9yz7zwe3Ru/UEvuc6pylI3KwVbPmZbTJnc7uBAcSu+es+nrOYcJk97Se/u8b895uuVT01NeVhsFmDB2PX17z2HZkn16Dvu8EhkVn6tObYjMNV5FRsXjYG9j0CY0LIZz/4TSf9Bi3v3gVy6EZN8Khd9/hNfbP/HuB79y7rz+dKTn6npZdSpJfDxsGQP6zWfLphP50vQsTOzsyYiM0H7PjIrExF5/CuxTrDp3J+HAPu332OWLsO7Wk2q7DlHhx2VEzZtRJLpyI1mWQU6I0X6XE2ORLMsatDXu/Dkm3vMxat7vpWh5nYmMScLRPtup6VDOMl9j1VPuR8Rz/2E8Ld7Ie5Tj83gcnUxZ+2znU1m7UjyOTnmm/ZFdN2nYPDsiPyNdzdgPdjPho72cDbxbJJoio3KNS6pnjF05xwSVLZFRujYAW7ed1BkTgoND8eg5nV6eM5ky6e08RwUpunL3a/rjUGTUExxUNrlslH7t4OFL2NvbULeO7pTZ0LAo4uNTGPT+YvoMWIDPjrN51iT4byOcQflAkqRQSZJmZUXbnJMkqbEkSfslSbolSdInzzl0NtA267ivJUkKlCSpUY7fPSZJUkNJkiZLkrROkqSTkiTdkCTpwxw230qSdDYr2mfKc87lCMTIspwGIMtyjCzLD57x//ko6/9xbvm6o4ZMihz3VtXx/+sDdvw+iFZNq/D9zP2v5LyGuHThHmZmptSspYR2X7v6gPDw2CKZRlRQ1Jkagv8JZfqct1m19hOO+Idw5tRNrG0s+H6CF2NGbeTDIb9SvkJpjIxfbfNVZ2oI+ucOM+YMZNXazzjsf4nTp268Ug0vIned5uTAvgt06Zb3ed2vgh37LhFyNYIPvIsseK9QxEQnMmP8TsZM9sDI6CW/ss4DN288ZMEPO5g4Rcl3c/VKOPfuxdCx0+tTjxqNzOxfDjN6eOFzHPy/I0mSnjP21KlrbNl6glEjlZwtmZkaLl++x8CBbvhsH4e5eQmWr3h148TSX/djbGJMrx5NdbYHXwjF3KwEtWuVf8aRL4cGDauyfef3bNz0DatWHCQtLTv6JiM9kyOHQ+jcpdEr1ZST3G0UYM68wWzfMYa1f3zJ33/fYodv8TyUqNUanjxJZtPaz/ju6+589d0GZFnGvpw1h/d+j8+fX/L9SA9Gjv2TxMTUV6brWXW65o8v2LR1FEt+/Zg/Nx7j3LlXPxXKqlN3Evbvzv7exYP4XT7c6dGe+199gsOUObz0cKbnkLn3JzLXfUPmpvEYVaiHUT23Fx8k0GPP4Rt0dqupje5+lRzdf5vbV2Pp+U72vfYvW/owc5UHwye1Ye3P54i8//pMcVq6bB/Gxkb06tlMu61hw6rs3jmeLZu+49cVfjr98sskJSWdX1cc5MvPu+rtU6s1hFy+x6+LhrFy2UcsWX6AO6EvJ5JP8P+FyBlkGHNJkoJyfJ8ly/JfWZ/vyrLcSJKkhcBqoDVgBlwCdBODZPM9MEqW5R4AkiQ9AoYCX0mSVBswk2U5WJKk3kADoAVQCvhHkqTdQH2gFvAmIAE7JElylWXZUBY/P2CiJEnXgYPAX7IsBxgSJcvycmA5gByx7Jmv7tZvD2LzLmVKkksdlU40T0R0ojYZdF4obWOu/dzfoz7zlxXMCWVnb0NkRLYnPTLyCXYq3WlK9lk2KgcbMjPVJCamYmObPd3Ab+8FunTPfqi8GHyXKyH36dVlLupMDY8eJfHxeyv49fcPyQ/29tY62qIin2Bvn1ubNZERcXra7FU2vNGkGrallTcsrdvW4erl+7zZoiau7Zxwbae8mdi2+TRGRvkfxO3sbYh4gbanNioHW602W1sLVCobGjepTuksbW3a1uXq5fs0b1Er3zqepa2o6/Qp1689RK3WUM9ZP/loXlm/9W827wgCwKWuIw9zRC1FRCegsstfSO6Js3dYtuYE6xZ7F8nUonL2lkTl0BQdmYBdPjQlJaYx+otNfPi5G84NCl5OT7G3t9WGVYPyBs4+x5suUCLTIh4qb+8yM9UkJqRia6tcXxERj/lyxEpmzh5E5cpKjpygoDuEXLpL5w6TUavVxD5KZOjgn1m99ot861u/9Tybdyrh4C71HHX7tagEVM+YDpabpOR0btyJYfCIPwGIeZTEZ6O3sWROn0InkbaztyIqIltXdGQ85exffuj3+vVH2LT5GAAuLlWIePhYuy8iIg5VjjepoESUPJ3mldumbFlroqKeYG9vQ1TUE8qUydZ/9Vo44yesY8XyEdo8KQ4OtjiobGnYsBoAXbs0fqYzaP2GQDZtOanorF9Z53qLiIxDlet6U6lsdKZ/RUTEocoRVbJt+2mOBISwetVwPafV7j3n8eievyliT88ZmaNsIiPitJFSuW0cnva5OdrBU6rXcMDcoiQ3bzzEOSsXz7GjV6jnVJGy5Qp2TbyMNqr8f2wBKFXKDI8eTbl0MQxPrzfzpGn9XyfZtO0MAC7OFXPV6RNUucYrlb01ETneque0Uals6NRBmc7boH4ljIwkHj9OokwZS22fW9+pIpUrluFOWAwuznmLknhZdZrdZqxo38GFSxfCaNo0/9GGNv0HYuPVH4C0yxcxVTnw1NVlYq8iMyrS4HElatVBMjYm7Wr2tHYbz77c/+IjAFIvBiOVLIGxbWnUjx/lW1dujBp2xai+knhbjryJZFWOpzeikmVZ5MRY/YOSss6bkYrm6jEkh1pwxeAt7v8N67dfYPNuZeqjS117HkZlRwJFxCTmeazKyZ5D15nwZbuikkhpOwtio7IjBmOjkyhtZ65nd/HsQ3zWXmTios6YlsiOpiljp9zHqSpY4fSGitDrj1BVyH+/tn5DAJs2K1F1Li5VdMelyGeMXTnHhMg4VPbZNtu2n+JIwCVW//aFwajyGjUcsLAoyfUbD3CpX+XZuv48xqatpxRdzpX0x6rc/Ye9DRGRT3LZWHP3Xgzh9x/h2X9+1vYn9BmwgM0bvsJBZYutTSksLEpiYVGSpk2qc/X6A6pV1U8w/v+MLBJI5xsRGWSY3NPE/sqxb0fWvxeB07IsJ8iyHA2kSZJkm8ff3wz0kCTJFHgfxan0FF9ZllNkWY4BDqM4gDpn/f0DnAfqojiH9JBlORFoAnwERAN/SZI0NI+6DOLduxE+q97FZ9W7dGhbA9/9V5BlmaCQh1iVKpGv3EA58wsdOn6bGlXyn+cAwKl+Be6GxXA//BEZGZkc2HsB13b1dGzatqvL7h3nlXMduESzN6trO3ONRsNBv4t06po9najfgBbsPTSGHfu/Y8Xaj6lctWy+HUGKtorcuxur1ea3NxhXd90pB67uTuzyVbT5+12iWfMaSJJEy9a1uHkjgtSUdDIz1Zw/d0ebQPlRVtnFP0lmy5+n8OrbjPziXL8i9+5ml9v+vcG45dLm5u7ELt9zWdou0qx5zSxttbl54yEpWdr+PndbJ7lzYXkZdfqU/XuC6VzIqCDvvk3wWfMBPms+oINrbXz3XVLawaX7WJUq+cxcMoa4fC2CSXP2sWRuP8qWef68/rxS17k84Xcf8+B+HBkZavz3X6F1u7w56jIy1IwbuZUuPeprVxgrLPVdKnM3LJrw8Fgy0jPZu+c87u66Kwe6u9fH11d58PPbH0TzFrWQJIn4+GQ+++RXvvqmF40bZ68i9fbAthwOnI6f/2TWrv+KqlXsC+QIAvDu2xif1UPxWT2UDm1r4rsvJKs+H2Blmff6tLIsyandwzm05WMObfmYhk7li8QRBE/r9FGOOr1MG7eiyfHwPLy92+HrMx5fn/F07NAIH99TStkE3cbKykzvwdfe3gZLSzOCgm4jyzI+vqe0CY3bt2+Aj4/isPHxOand/uDBI0aM+JW5c96jWrXsfsTOzgYHxzLcvq1MbTl58io1ahhOyu39jiu+20bju200HTs0wGfHGUVn8B2sLM2wt8ul084Gy1JmBAXfUXTuOEOH9so1GXj0Mit/O8jSRR9inmsKokajYe/+f/Dolv/pWM71KxMWFqNtB/v2/kM7d91Vdtq512eHjxI9c8AvmDebK+0gPDyWzEy1Ul73HxF6O1InN9DeQkwRg5fTRjMz1Tx+rIxVGRlqAo5comatvCdV9x7QEt+/vsT3ry/p6O6Mz67zSp1euJtVp7leENhZY1mqJEEX7ip1uus8HdyUMa1jOydOn1Wia+6ERZORoaZ06VI8epSIWq0B4F54LKF3Y6lUMe/3Ii+jTpOT00hKUlw2yclpnDxxLV/llpMnmzdy17sPd737kHjEH2sPTwDM6jdAk5iAOjbG4HHWXbqT4LdHZ1tmxEMsmrUAoETV6hiVKFkkjiAATfA+MtePInP9KDS3zmijfCSHWsjpyZAUp3uAZARmWQ4CI2OMqjdBji2aKUWvM969G+CzciA+KwfSoXV1fP2y7sEvR2Tdg+fvHuL23Uc8SUjjDeeiWfESoEbdskTcSyDqQQKZGWpOHgyjSWvdlbvuXH/EynmnGDXbHZvS2Y6ixPg0MtKVNhEfl8r1i9FUqFqw1VW933HDd/sYfLePUcYE3xxjgpW54THBMseY4HuGDu2VMSrw6GVWrjrI0sUf64wJ98JjtG34/v1H3L4dQYUKhqc0anW93QbfzaPw3TyKju1d8Nl5LktXqDKmGurXLEsSFByq6Np5jg7u9alTuzwnA6ZyaN8EDu2bgIPKhm1/fYNdOWs6uNfn73/ukJmpJiUlnQsX7lKjWtHdowv+fxGRQfnnafICTY7PT7/nqTxlWU6WJOkA4Am8heK80e7ObY4SDTRLluVf8/j7auAIcESSpIvAEHQdTgXGrUU1Ak+F0vmd3zEracLM77OTuHp98Ac+q5QVeOYtDWSX/zVSUjNw67eCfh71GfFeS9ZtDeLw8VsYGxthY2XGrO+7FEiHiYkx343txRef/I5aLdOrdxNq1FSxbNEB6jlXxM29Hp59mjJpzGZ6d5+PtY0FM+Zmr6Dxz9+hqBxsqFipYM6oF2n7dmwvRnz8G2q1hl69m2Zp88vS5oRnn6ZMHLMJr27zsLYxZ+Y8ZUU2axsLvAe3ZfDbi0CSaN22Dm3clIfz+bN3cuPaQwCGfdKBKlXtnqnhedpGj/Xk849XolFr6NW7GTVqOrB00X6cnCvi5u6MV59mTBjzJ726zcHGxoJZ897Joc2VQW//giRB67Z1aeumOGt+/GE3+/YEkZqaQdcOM/Dq04xPPs9fgt+XWacH91/kpyVD8l1ez8KtVQ0CT96ic/9lmJmZMnOch3af15BV+Kz5AIB5iw+xy++y0g48F9GvZ0NGDGvLvMWHSU5J56vx2wFwVFmzdG7/QmkyMTHiq9GdGPXZn2g0Mt09G1Cthh2rlgRSx8mRNu1qcSXkAeO/2UZCfConAm/w27KjrN36IYf9rhB8/h7xcSns26EkKRwztQe1DCyvnnc9xowd34+Phy1BrdHQu08LatZyZNHPu3GuXxn39i706deSMaPX0a3LVGxsLJj3w1AANq4/yr27MSxbuo9lS5X8FQVdnjovuLWsTuDJ23QesEKpz7HdtPu8hq7GZ7Wia96SI+w6cEWpz95L6dejASM+aP1SNIFSp19/34WRn25Eo9Hg4dmQajXtWLkkgLpOjrRpV5srlx4w7pst2XW6NJB125Slgj9/by1hobGkJKfTp/PPjJ7sQfN85jVyc6tPQOAlOnWegLlZCWbOzG5Hnl7T8fUZD8Ckie8wZuwaUlPTcW3rrM0B9NGHXfjq6xVs2Xqc8uWVpeUBFi/ZTVxcElOmKkv0GhsbsW3rWAAmjB/AqG9/IyNDTaVK5Zg1c/CLdbo6ERAYQqduUxWd07OXfPbsMwffbaMVnRPeUpaWT0vHtY2TNg/EtBlbSM/I5L1hSwBlGsDUScrUp7PnbuHoYEulSs9OvPssTEyMGTuuL59+uAy1RoNX7+bUrOXI4l/24ORcGff29endtwVjR/+BR5fp2NhaMHe+8v/95/xtflvhj4mJEZKREeMm9NNGUD11GEyYrL+SUL60FXEbNTcvwcfDlpCRqUGj1tCiVR369W9VIH1ubeoQcOwqnXrNw9zMlJmTs/tIzwE/aVcDmzTGK2tp+QxcW9fBtU0dAPp6NWXs5C306LcQU1NjZk/tjyRJnD1/h5+XHsDExBgjI4kp47ywtXlxknKdciviOg2/F8NXX/wGKFOyu3k0pk3bes+TkSeSjgdSqrUrVbfvQ05NJWLqOO2+yuu36awiZtmxK/e/1M14EP3jXFTjplB64GBkIGLK2EJrMoR85zxy1caYvrcYOTMNtd9i7T4T7/lkrh8FxqaY9JkARiZgZIR89wKaiwdfip7nseH9qbSr3Zhylrbcm7mDSbtW8NuJna/k3G4tqhJ4OozO767FrKQpM0d30O7zGrYRn5XKveS8ZceVe/C0DNz6/0Y/D2dGDFWmo+8+dAOP9rWKJH/iU4xNjBj6zZvM+sYfjUamnUdNKlW3ZfPKIKrVLUvTNpXYsPhvUlMy+WmCMqnh6RLyD8KesHLeaSRJQpZler3rrLMKWUFxc3VWxoSuU5T+Y0b2CqGevWfhu11ZrW7ShLcYM/YPpf9o64Sra9aYMH2TMiZ8sAjIGhMmD+Tv87dZscJP239MnjBAu7R7nnS1rUfA0St08pip6JqWvSKzZ//5+G4epega10+7tLxrm7q4tnl+f1Cjuoq2revQq998jCSJfn2aU7uADmXBfwupoIn9/p+RJClRlmW9li1JUijQVJblmKxom6ayLA/Pvc/AcU2ABbIsu+XathM4KsvygKxtkwEvckwTy/pcH5gGdJBlOVGSpApAhizLepNBJUmqA2hkWb6R9X06YPtU57N43jSx4kRy+IT49K3FLcMg1iX6kpCxvbhl6GFlquTeSMrwLWYl+pQy9Xyt61OOXV3cMgwilR1KZPLq4pZhEJXFUDI0xZf763mYGnVBjtZfBr64keyGARCVsraYlehjbz4Y5MPFLcMwkjtkvp7XGiZdSFPvLW4VBilp3O21bKOmRlkvg5Jfv3EUi96vdX1eb6af4Px1oPbZy6Qv7FvcMgxS4uutSJ+2KG4ZBpGXnkJ+sKi4ZeghlVceHc5HTy9mJfo0thsP6gPFLcMwxp0gbfeL7YqDkh7FnxDyJRL/sXuxP89a/3r4X1XGIjLIMLlzBu2TZbkwy8tfANSSJAUDq2VZXijL8t+SJMUDvxuwPQyUA6ZlJX9+IElSPeBklic/EXgXMJQZzBL4JWvKWiZwE2XKmEAgEAgEAoFAIBAIBAKBcAYZQpZlg2sEyrJcNcfn1eSYepVzn4HjMoD2ObdJklQeJWeTXy7zC7Is68XEy7L8E/BTHrT/DRQsJlsgEAgEAoFAIBAIBIJ/GRqRQDrfiATSxYAkSYOB08A4WZY1xa1HIBAIBAKBQCAQCAQCwX8HERlUhEiS5AKsy7U5TZbl5jk3yLK8FtBLFiHL8uR8nKss4G9gVwdZlg2sxykQCAQCgUAgEAgEAoFAIJxBRYosyxeBRq/oXLGv6lwCgUAgEAgEAoFAIBC8rsgaMU0sv4hpYgKBQCAQCAQCgUAgEAgE/yFEZJBAIBAIBAKBQCAQCASCfy2yRqTizS8iMkggEAgEAoFAIBAIBAKB4D+EcAYJBAKBQCAQCAQCgUAgEPyHkGRZJFoSACAuBIFAIBAIBAKBQCD4/0QqbgEvk9hBbYr9ebbsumP/qjIWOYMEAMhh84tbgkGkKqNYevHT4pZhkE9dliJfm1HcMvSQ6owDQA6ZWsxK9JGcJyKfHVvcMgwiNZuJ/GBRccswiFR+OKEJr6e2qlbDkWNXF7cMg0hlh4LGv7hl6GPUAYCgmJnFLESfRuXGIt+ZW9wyDCJV+w7i/ypuGYaxHoD8cElxqzCI5PgZqerdxS1DDzNjDwDCEl6/cqti9RlyzG/FLcMgUrn3IWNvccswjGk3DoW/nmN8+4qv9xgvfdqiuGXoIS89BYDm2MhiVqKPUZsf0AR+U9wyDGLkuuC1vtcVCHIinEECgUAgEAgEAoFAIBAI/rWIpeXzj8gZJBAIBAKBQCAQCAQCgUDwH0I4gwQCgUAgEAgEAoFAIBAI/kOIaWICgUAgEAgEAoFAIBAI/rXIajFNLL+IyCCBQCAQCAQCgUAgEAgEgv8QIjJIIBAIBAKBQCAQCAQCwb8WkUA6/4jIIIFAIBAIBAKBQCAQCASC/xDCGSQQCAQCgUAgEAgEAoFA8B9CTBMTFBhZlpmx5CSBZ+9hVtKEWaPccK5VTscmJTWTr6Yf5O6DeIyNJdxbVGHkB28WuZbQf2IJ+P0GGo1M/Q6ONOtd1aDdjVNR7J5/iYGzm6Kqac2TqBTWfnWa0uUtAHCsZU2Hj+sWqTZZlpmx4iyB5+5jVtKYWV+1xrlGWR2blLRMvpoTwN2HCRgbSbi/WZGRQ5oA8Ofea6zfcw1jIwkLMxOmft6SmpVti07bqr8JPH9fqcPhLXGuUUZf27yj3I1MVLQ1rcDIQW/o2Ow/eZcv5x1l89yuuNTU/b8VStu6YAKDIpRy+6gpztVK62v7+TR3o7K0veHIyLddAHgQk8z3v54jITkdtUZm5ID6uDVyLDptvwQSeDoMMzMTZo3uiHNtez27hStP4ut3lfiENM7v/US7fdbio5z+J1z7f3j0OJmzuz4utK6zJ8JYNj8QtUamm5cTA4Y21dl/8fx9lv1wlNs3Yxg7oyttO9YEIOhcOL8uOKq1uxf6mLEzu9CqXY1Ca3qKLMvMWHiAwJO3MDMzZdb4HjjXcdCzW7gsAN99F4lPSOW8/yjt9t83nmHLziCMjY0oY2vBjLEeVHC0KZyemZsJCAzBzMyU2TMH4+xcWc/uUshdxoxZS2paBm6uzowb2x9Jkti77zyLFu3m1u0INm/6Dpf6VXSOe/DgER49pzH88+588H6nAusMOnWf1T+eQaORad+zFl6DXHT27/ozhEM7b2BsbIS1bUk+GdsaOwdL7f7kpHRGevvSrG0l3h/ZosA6DCHLMjOWnsoeA0a6Gh4DZvhn920tKjPy/WYAnL34kFnLTnPtziN+GONO17bVilbbD3sIOH5Dqd9JvXGuW17P7tKVB4yZso3UtEzcWtdi3MjuSJLE1esRTJq9g+TkdCo42jJ/Wj8sLc2KRtcvAQSeClX6ju87P6PvOIHv/itK37HvM519ew9fZ9Hq00gS1KlRjh8mdCuwnuNHrzBnlg8atYbe/VrwwYcddPanp2cy7vsNXAm5h41tKeYuGEyFCtljxMMHj+ndcw6fft6FIe+7k5aWwXuDF5GRnklmpoZOnRvy2YiuBdb3lLMnQlk6PwCNRqarlzNvD22ms//C+fss+yEgq2/rhmvHWtp9URHxLJjmT3RkApIkMf0nTxzKWxda01NkWWbGj/7Zfdu47ob7tl8D8d13SenbDn6j3f7n9n9Yv+08xkZGWFiYMvW7rtSsVk7v+HzpmbWNgKNXlGt/xjs4O1XSs7sUco8x4zeQmpqBW9t6jBvTB0mS+PGXPfgfuoiRkUTZMlbMmvEOKnsbTp+5wWdfrKJiVv136tiA4Z8WrG5DzkSwaXEwskamdfdqdBlYR2d/4M7bBPjewshIoqS5Cd5fN8axqjWJT9JYMeU0Ydce0aJLFd7+4o1nnKHgvK7j+4tYNWgcPVxaE5XwGJdp3i/9fLmRZZmZG0MIvBiFWQljZr7fCOcq+mP0hwtPEx2XSqbPqn6sAADW4klEQVRGpmmtMkx41wVjI4l9Zx+waMd1bj9MZNP4NtSvalu02v68nK3tvYaGtf14hugnqWSqs7R518fYSGLe5iscvhCJqbERlewsmPleQ6wtTItM2+t6r/tvRCOmieUbERkkKDCBZ+8Rdv8J+39/i6lftWHKz8cM2r3XrwF7f3uLbUv6cD4kgsAz94pUh0Ytc3jlNbzGNWTwwuZcOxZF7L0kPbv0lEyCdt/DoZbuTaCtypx357/Ju/PfLHJHEEDg3/cJexDP/l+9mPp5S6YsPW3Q7j0vZ/Yu9WLbjz04fyWawL/vA9DDrRo7f+mFz089GdanPrNXnSs6becfEPYwnv2LezH1k+ZMWX7GsDbPeuz9pSfb5nfj/NVoAs/f1+5LTMlg3e6rNKxVNE4grbbgCMIiEtn/QxemftCYKav/MazNoxZ753Vh24yOnL8eS2BwBABLfa/QrXkFts/oyILhzZmyOqjotJ0OI+x+HPv/GMTUke2ZsvCIQTv3VtXYtPQtve1jPm+Lz8qB+KwcyLu9G9CpbeGdLmq1hsVzjjD9516s2OzN4f3XCbv9SMfGzsGKkZM74t6lts72Rk0rsnTDQJZuGMicpb0paWZC4xb6jpHCEHjyFmHhj9m/6ROmju7GlHn7DNq5t6nJppVD9bbXq61iy2/vsWPdMLq412X+ksOF0xMYQmhYFH77JjNtijeTp/5p0G7ylI1Mm+qN377JhIZFEXj0MgC1aznyyy8f0axpTYPHzZ6zlbZtnQqlUaPW8NsPpxjzQ0cWrPfk+ME7hN+J07GpWqsMs1b1YN7aXjR3r8L6xX/r7N+0Ioh6jVSF0vEsAs+GK33bb/2Z+mUbpiw6YdDuvX4u7F3Zj22LvTgfEkngWWUMcLSzZNZIV3q4F53TUavtxA1C78bit+1Lpo3txeTZOw3aTZ69k2njPPHb9iWhd2MJPHEDgHHTfRj5eSd2/jmcju5OrFx3vGh0nQ4lLDyO/euHMHVkB6YsPGTQzr1lNTYte1tve2j4Y5avP8eGRf3ZtXoQY4e7FViLWq1h5vRtLPn1I7bvHM2+Pee5dTNCx2b71tNYW5uza/843h3ixo8/7NLZP3+uL23a1tN+L1HChJW/fcbm7d+yadsojh+7yoXg0AJrfKpz0ZwjzPjZixWbB3Fk/3XCbsfq2Ng7WDFqcifad6mjd/zciX70H9SYVVsG88uaAdiWMS+UntwEnrxNWPgj9v/1EVO/68KU+X4G7dxb12DTisF623t0dmLnug/wWfMew95pzuxfDF8TedZz9Aqhd6Px2zOOaZMHMHnaZoN2k6dtZtrkAfjtGUfo3WgCj10BYNh77dm5fTS+W7+jnZsTi5fu1x7TtHF1fLd+h+/W7wrsCNKoZf78OYjhs1oz8bfOnD10j4eh8To2zdpXYsLKToxb3pFOA2qzZdkFAExLGNPzPSf6fNKgQOfOC6/j+J4XVp/cTddfvn4l5zJE4MUowiKT2DfTnSmDGzB13UWDdgs/aYzPFDd2TnXjUUI6+84+AKBWBSt++bwpTWuXMXhcobRdiiYsKol9M9oxZZALU9dfMqzt4zfwmeTKzimuPEpMZ9+5hwC0cirHjsmu+E52paqqFMv33Cw6ba/xva7gv4FwBgkKjP+JMDw71UKSJBrVUxGflE5UbLKOjbmZCS0aKW9jS5ga41SzHBEx+o6awhBxMx4bBwtsVOYYmxpRu7U9t85G69md+PM2Tb2qYGz6ai97/9P38HSvoZRTXTulnB7lKqeSJrRooLxJLGFqjFONMtpysrQoobVLTs1EkopQ25lwPNtVV7TVKZelLUVfm0sObdXLEBGbbfPzhmCGeTlTooRx0QkD/P9+iGebKoq2mmWJT8og6rEBbU7KG7sSJkY4VbUlIku/hERiSiYACckZ2Jcu/Ft9rbbjt/HsXE/R5uRAfFIaUbH613UjJwfsy5Z67m/tPnQdjw61n2uTF66FRFK+ki2OFW0wNTWmXefanAy4rWPjUN6a6rXKYWT07IvomP9NmrWqgplZ0bz1eor/0Rt4dq2vlFn9CsQnphEVk6hn16h+BezLWeptb9GkCuZZmho6lyciKl7PJl96Dl3Ay7O5oqdRNeLjk4mKeqJjExX1hMTEVBo1qoYkSXh5NsffPxiAGjUcqV7NsJPl4MEgKlQsS62ahXs7d/NKDKqK1qgqWGFiakyrDtU4e1TXmV6/iSMlzZQg31rOdsRGZ1+Ht6/GEvcohQbN9CNiigL/k2F4dqiZNQbYE5/4jDGgYc4xoKy2b6voYEWd6mWQirJTe6ot4CpeHo0UbS6ViE9IJSomQccmKiaBxKQ0GrlUUurXoxH+AVcBCL0bS7PGVQFo/WYN/A5fLhpdx2/j2SWr73B2VNqBob7D2dFg37F5VwjveDXAxkrpz8qWtiiwlksX71KpcjkqViqLaQkTunZ7gyOHdB+SDh+6RC8vJQqnU+cGnDl1A1lW3rweOniRChXKUKNmdjuQJAmLUiUByMxUk5mpBgpXv0rfZqPt29w61+aEwb7NDilX3xZ2Oxa1WkOTFkrknrlFiaLv247l6tsS8te3WWaVF0Byakahx3j/wxfx6tVM0dOwKvEJKURF5+rbop+QmJRKo4ZVlWu/VzP8DykP7zkj4FJS0ov0ngMg9Ooj7CqUwq68JSamRjR1r0jwiQc6NualsusoPVWt/VzS3ISaLuUwfYn3ca/j+J4Xjt4M4lFS4cbFwnAoKBLPVhWVcqtRmvjkDKLiUvXsLM2Vus1Uy2SoNdr+v0Z5K6o56LePItPWokL+tGVqtNd+a2c7TIyVa65h9dJEPtY/tqC8zve6/0ZktVzsf/82hDNIUGAiY5NwtMvuuB3KlSLSwID5lPjENA6fukvLN4r2wSTpURpW5bJvpqzKliTpUZqOTdTtBBJj0qjWRD/0+klUCutHnWHzxPPcvxxXpNoAImOTcbTLvmF3KGtBZK4HppzEJ6Zz+Ew4LRtmP0iu332VTh9tY/6avxn3UdFNs4t8lIxjuVzaHj1HW1I6h8/dp6WLcvMfcusRD2OTade0QpFp0mp7nIJj2ew3uA5lzJ87AMcnpXP4n4e0dLYDYHifeuw4fhe3EXv4eN5xxg9uVHTaYpJwtM957VsSaeDm/0Xcj4jn/sN4WrxRsdCaYqOSsFNlaypnb0lMVP41HfG7QbsuRX/zGhmdgKMqOyrPwc6KyOiE5xzxbLbsCsa1ReHetkZGxuHgkB2K7eBQmsioOF2bqDgcVLbZNqrSREbq2uQmKSmVFSsPMPyz7oXSB/AoOpmy9tkPG2XtLXgc/ew+9vDOGzRqobRFjUZm3aKzDBre9Jn2hUXp27L1OdhZvHgMOH2Plo1ejnNKR1t0PA6q7GkADvbWROZyIEZGxeNgb61rE63Y1Kpur3UM7fO/xMNI3YfpgutK1B037SyJjM57Ow2995jQ8DgGDt/EgE//4ujp0AJriYp8goODrfa7vYMtkbkdojlsTEyMsbQyIy4uieSkNH5fdYhPPuui97tqtYa3es/Hvc1EWrSqTYOGVfRs8kNMVCJ2Kivtdzt7S2Lz2LeF343D0qokU77dxafvbGD5T0dRqzWF0pObyOhEHHWuo/z3beu3nqdT/1+Zv+QI477qWDg9kU90+zaVLZG5rt/IyCe5+jZdm4U/7catw2R27v6bL4dn92VBwaH06jOXYZ8s48bNhwXSFxeTQukc90Sl7cyJi0nRszvic4sJ7+5j+/KLDBjesEDnKgiv4/j+byDycSoOOaLuHEqbGXS4AAxbcJo2Xx+glJkJXZq+/ClN+dK28DRtRmZpa6Kvbdvxe7R1sStCba/vva7gv4FwBgleCZlqDSNnHmKQlzOVHIturn5ekDUyAatv0HaI/nSOUqVL8sGy1njPfxPXITXZ+1MIacmZr1RfTjLVGkbOD2RQj7pUcsi++fX2qMuB5X0YOaQxS/+6UHzaFhxjUPc6VHKwQqORmb36b0YPbVwsevS0LT7DoC41qZR1E7f75D16u1Yh4Jfu/Ppta0YvPfvazSXec/gGnd1qYmz8enTFsTFJhN6MoWnLop0iVpTs2HeJkKsRfODdvLilGGTR4t0MGdKeUqVe7du5o/tvcetqLL3eqQ+A37arNGpZUceZVJxkqjWMnH2EQZ5Or3wMKAgzJnqxYcsZ+gxaSlJyOiVMizbysaBkqjWEhcex9se+/DCxKxPm+xOfkPbiA4uYpYv38+5gN20UUE6MjY3YtH0UfocnceniXW7cKJjToChQZ2q4+M8DPvqyLYvWvk1E+BP8dhZNlFdR4t23MQc2f8zIT9uxdPXJ4pbD1196EOA/mZ4eTfhjg5JPztmpEocOTGLHtu8Y9I4rn3+x6qVqaOdVg2l/dMXrw/rs+ePqSz3Xy+B1G99fJ1Z+05zABR1Jz9Bw6kpMccvRYeXXzQmc35H0TA2nrupqW7b7BsZGEj2bF/0L0Lzwb73XFbzeiATSgnyxfkcIm/cog7JLHTse5nijGRGThOoZIbMTfzxKlQo2DOnjYnB/YShVpiQJMdk3wwmxaZQqk32Dmp6iJvZeElsmKfNwk+PS2THnAr1GN0BV0xqTrHBjVQ1rbFTmxD1IRlWzcA8r63dfZbOfknvCpVZZHkZnR9tExCajKms4tH/iopNUKW/NEE/DuUY82lZ7Zs6hPGvbe43NB24p2mqW4WFMLm1lnqFt6WmqOFozpKeSVykpJYMbd58weMJBAGLiUvhsVgBLxrgVOIn0+gO32Hz4jqKtemke5piOFvEoBdUzwl8nrjpPFQdLhnTNThq6NSCUFd+1AeCNWmVJy1DzOCGNsjYFe0hfv/0Cm3eHKNrq2vMwKue1n4jKQPj/i9hz6DoTvmxXID25KWtfiujIbE0xUYmUs8+fpsADN2jlXgMTk6J58F2/9W827wgCwKWuIw8jsyMzIqITUNlZPeNIw5w4e4dla06wbrE3JUrkf/havz6ATVuU3C8u9asQEfE4W0/EY1T2tjr2KntbInJEAkVEPkal0rXJTfCFUPbv/4f587cTn5CiJEAtacq73u3yrbeMnQWxUdmRNrFRyZS20+9jL5x9wLY1F5m8uAumWdM1r1+K5uqFKA5su0pqSiaZGRrMLEx559Mm+daRk/U7LrN53zUAXGqX42GOSKWI6ORnjwE/HVP6tt71C3X+52rbdJpNPkrOJBenCkTkiHSIiIpHZa/br6vsrXWmG0ZExaOyU2xqVLXjt0VDALgTFsORY9cLrmt7MJt3KdOvXOqqdMfN6ERUdnlvpw52ljRwcsDUxJiKjjZUrWRL2P3HuNTVT1j8IuxVNkRExGm/R0XEobK3MWijcrAlM1NNYkIqtraluHghjIN+wfz4w04SElKQJIkSJU0Y6N1We6y1tTnN3qzJiaNXqVWr4G//y9lbEh2ZHWkTHZVI2Tz2bXYqS2rUscOxovL/atWuBlcuRbzgqBezfut5Nu9Qpoy61HPgoc51lP++7SkeHesxZf5+wCN/ejYeZdMWxYnkUr+ybt8WGYdKpVuvKpVNrr5N3wagZ4+mfPTpr3wxvJvO9DE3VyemTN/Mo8eJlCmdv3HGtpw5j3PcEz2OTsG23LPzODV1r8TGnwznUSkqXvfx/XVl/aFQtgTeBaB+VRvt1CWAiMep2Ns++36rpKkx7d9w4FBQBK2diy7SRqvtcChbApVp1fWrFUBbQxWHgiJp7aRo2378HkcuRPH7Ny0KPbX5db7X/bcjC0dYvhHOIEG+8O7ljHcvZwCOnL7Let8QPNrVIPhqFFalSmBvwMnx4+9nSUhKZ/rXri9Fk0NNK+IeJvMkMgXLMiW5fjyKbl9lO1NKljLhk9+zb1A3TzyP6+CaqGpak/wkHTNLU4yMJZ5EphAXkYyNqvDJJb096uLtoThNjpwNZ/3uq3i4ViX4WgxWFqbYG3C4/PjHPyQkZzB9RCud7aEP4qmatfLJkXPhVCnkKije3erg3a1O1u/dZ/3ea3i0qULw9VisLEpgbyC55o8bghRtn2WvRmRVqgSn1vTTfh804QDfDWlcqNXEvDvVwLuTMv3nyD8PWX/gFh4tKxJ865FSbqUNaNscQkJKBtOH6T7kOpa14GRIFH1cq3LrfjxpGRrKWOu/xc6ztt4N8O6tJK08cvIO630u4NG+FsFXIrOu/fxFYNy++4gnCWm84Zz/hzhD1HFScf9eHBH3n1DW3pIjftf5frr+FI7ncWT/dd4b3urFhnnEu28TvPsq9XLk+E3Wb/0bj05OBIc8wKpUSYP5M57F5WsRTJqzjxULB1C2TMGiXby93fD2VhLuHjlykT82BODRvSnBwaFYWZljn/tB2N4GS0szgoLu0LBhVXx8TzPoBU6dDX+M1H7+ZdEuLCxKFsgRBFCjbjkiwuOJepBAGTsLTvjf4YtJbXVs7lyPZeXck4xZ0AmbHO3ji8nZ/e2R3Te5fTWm0I4gAO9eTnj3UvrXI6fvsn7nFTzaVSf4ajRWpUwNjwGrz5GQlMH0r9rq7StKvN9qjvdbSsTYkWPX+GPTaTw6uxB8KRwrSzPsy+k+oNuXs8KyVEmCLt6jYf2K+OwOYtAApY+LfZRI2TKWaDQalv4WwNt9m+mdL8+6ejfEu7cyzeXIyTus3x6MR/vaBF+OUNpBPvqOjm1qsOvQdfp2c+ZxXAqh9+KoWMBV9ZzrV+JuWDTh4bGo7G3Yt/cfZs0dpGPTzt2ZHT5nadioKgf8LvBmcyVH1Oo/Rmhtli7ah4VFSQZ6t+XRo0RMTIyxtjYnNTWdUyeu896w9gXS95SnfdvD+08oZ29JgN91vp+et+TFtZ1UJCWkEfc4GdvSFgSdu0fteoVPqO7dtzHefZWo2CMnbil9W8d6St9mmb++LfTeI6pWKqP9rSoV859A13tgW7wHKu3rSEAIf2w8ike3xgRfCMPK0hx7u1x9m50NlqXMCAoOpWGDKvjsOMugd5Q+IzQsmqpVlAdg/0MXtbnRomPiKVfWCkmSuHAxDI1GprRt/vviKnVLE3U/kZiHSdiWM+fc4XDeH6c7/T0qPAH7ikp7vXTqIfYVXk4umae87uP764p3+6p4t68KwJHgSDYcCqX7m+UJvh2HlYWJnsMlKTWTpNRM7G3NyFRrCLgQSdNaRZ8wGsDbvSre7lnaLkSy4XBYtjbzPGi7GKXVdvRSFKv232btty0wL1n4l2Wv872u4L+HcAYJCozbm5UIPHOPzkP/wqykCTNHZa9q4vXJVnyW9SUiOpFlG4OoXsmWPp9tA8Db05n+3Ypu1S4jYyPch9Vm+/QgZI2Mc/vylK1kyck/b2Nfw4oazZ79xuH+lThO/nkHIxMJSYIOH9XFzKpok0u6Na1A4N/36fzxdqWcvsh+2Pb6cic+P/UkIiaJZZsuUr2iDX2+VlZr8faoS//OtVi/+yongx5iYmKEtWUJZn/Vuui0NSlP4Pn7dP5sB2YljZk5vGW2tm/24LOgOxExySzbEkL1Ctb0GbVX0datNv07GV5Fqci0NXIgMDiCziP3K0uBfpSd+8Rr7EF8ZnYkIjaZZb5XqV7eij7j/RVtnWrQ370ao70bMGHledbsu4kEzPq4aZElqnVrUZXA02F0fnctZiVNmTk6ezlmr2Eb8Vk5EIB5y46zy/8aKWkZuPX/jX4ezowYqjys7j50A4/2tYpMk7GJEZ9/68bYETvQqDV07uVE1RplWbPsFLXr2dPSrTrXQiKZ+u1uEuLTOHU0lLXLT7Nik7IEbcSDeKIjE2nQ+OWEP7u1qkHgyVt07r8MMzNTZo7LfvPtNWQVPms+AGDe4kPs8rtMSmoGbp6L6NezISOGtWXe4sMkp6Tz1fjtADiqrFk6t3/B9bjVJyAwhE5dJmFuVoKZM7Mfgj17z8R3+1gAJk18W7u0vGtbZ1xdFWf4gQNBTJuxiUePEvn4kyXUq1uRVStHGDxXQTE2MeL9r5sz85uDaNQa2vWoRaXqpdm04h+q1y1L07aV+WPx36SmZLJw/BEAyqlK8d3cDs//4SLC7c1KBJ4Np/P7m5W+7ZtsZ4/XZ9vxWdKbiOgklv0ZTPVKNvQZ7gOAd08n+nerw8Vr0QyfdpD4hHQOn77LonXn2bW8b9Foa12bgOM36NT7R8zNTJk5sbd2n+c7S/DdoCzXPml0D8ZM2a7Ub6tauLZS3rju2n+RDVuU1RU7tatH355Fs4S10neE0tl7jVJmoztp93l9sB6fVUp7nLfsGLsOZvUd/VYpfcd7LWjzZhWOnbuLx5B1GBlJfPtJG0rbFOwFhomJMWPG9eHTD5ej0Wjw6v0mNWs5sPiXvTg7V6Jd+/r07tuccaM30KPLDKxtLZg7X381rJzERMczfsxGNBoNGo1M564NcWvnXCB9TzE2MWL4t+0YO8IHjVqmi7ZvO0nteqqsvi2CKd/uJiE+lVNH77Bu+SlWbBqEsbERH37ZhtGfbkOWoVY9e7oVcXSaW8vqSt/21nLMzEyYOTY7x47XkN/xWfMeAPMWH2bXgay+zWux0rd90Ib1W89z8myo4kSzMmP2+MLlG3NzdSLg6BU6dZuOuXkJZk4bqN3n2Xcuvlu/A2DS+H7apeVd29bDNWtVuB8W7uROaBSSJFGhfBmmTFT62f1+wWz86zjGxkaYmZmyYN6QAo1fxsZGvD2iEb+MPoZGI9OqW1XKV7Vm5+8hVK5TmoatynPE5xZXz0dhbGKEhWUJhozOdsaOe2cvqckZqDM0BB9/yBdz2uBYteimnr6O43te2PD+VNrVbkw5S1vuzfwfe3cdHsXRwHH8OxESIEpILkhwDy7FCe4lOKW0hbZQoUZLW4pbkQKlVLBCWwoFWjTBXYJrcZfgMUKIEL2b9489klxyQAwuvJ3P8+SB3M7d/rJ2u7Mzs2sYvW4ev+83/xTF58GnqgcBp0NoM3Sn8dHyKeM8dRkTwOoxTYiN1/PRz0dISDJgMEDdCm70aqqNKbb1+D0mLDlLeFQCH/x4mApezsz/Ime6g/tU8SDgdChthu/SsvVLeRpdl7F7WD26MbEJej765aiWTUrqlnejl4/WZf7bJWdJSDLw7nTtO6FaKRfGvJkzPR1y87mu8t8gHj8VQvlvkzem5coNQRT/ktmnP7R0DLM+rDIbeXGCpWOkI8oPB0CeHWfhJOkJ71HII8MsHcMsUWci8u4vlo5hlij8MYFRuTNbCcePkfcXWDqGWcKtHxi2WzpGelbaxcWJsIkWDpJe9YLDkNenWDqGWaLk1xD5j6VjmOfUC3lvlqVTmCUKDSROv97SMdKxt9YqhG9E5b7lVtxxIDLsd0vHMEsUfAcSN1o6hnm27dhxO3d+xzcvmru/48WH9Z5d8AWTsw8CYNg7+BklXzyrRt9jCPjC0jHMsmoyPTef6/5f1xTdebWOxa9ni6w98lItYzWqmaIoiqIoiqIoiqIoyn+I6iamKIqiKIqiKIqiKMpLS+ot3jDopaNaBimKoiiKoiiKoiiKovyHqMogRVEURVEURVEURVGU/xDVTUxRFEVRFEVRFEVRlJeWNBgsHeGlo1oGKYqiKIqiKIqiKIqi/IeolkGKoiiKoiiKoiiKory01ADSmadaBimKoiiKoiiKoiiKovyHqMogRVEURVEURVEURVGU/xAhpWpOpQCgNgRFURRFURRFUZT/T8LSAZ6nGy2qW/x6tvj2Ey/VMlZjBimayH8sncA8p15ciphm6RRmlXP5Enkj92UTxb8EQF6fYuEk6YmSXyPPf2vpGGaJiiMgarmlY5jn2IOI+NyZzcWuB0SvtHQM8xy6gdxp6RTpiWYA3I9bauEg6bnZ94aHuS8XAM69keTC9QkImuXq/SA3Hj9c7HoAkGTYauEk6dlYtYLEjZaOYZ5tu1x57gHa+Ye8v8DSMcwSbv04Hpo7zz9quo/AsHewpWOkY9XoewDEh/UsnCQ9Ofsghl2DLB3DLKumM5CHvrF0DLNE3cmWjqA8hRCiAPAPUAIIBHpKKR+kKdMM+CHVSxWA16SUfkKIBYAP8NA4rZ+U8sTT5qkqgxRFURRFURRFURRFeWkZDBZvGJRd3wDbpZSThRDfGH8fkrqAlHInUB2SK4+uAFtSFflKSrkiozNUYwYpiqIoiqIoiqIoiqJYji/wp/H/fwKdn1G+O7BRSvkoqzNUlUGKoiiKoiiKoiiKoijZIIR4TwhxNNXPe5l4u05Kec/4/yBA94zyrwFp+/ZPEEKcEkL8IISwe9YMVTcxRVEURVEURVEURVFeWlJv+W5iUspfgV+fNF0IsQ3wNDNpeJrPkUKIJ/5BQohCQBVgc6qXh6JVIuUxZhgCjHtaXlUZpCiKoiiKoiiKoiiK8hxJKVs+aZoQIlgIUUhKec9Y2RPylI/qCayWUiam+uzHrYrihRB/AF8+K4/qJqYoiqIoiqIoiqIoyktLGqTFf7JpDdDX+P++gP9TyvYmTRcxYwUSQgiBNt7QmWfNUFUGKYqiKIqiKIqiKIqiWM5koJUQ4jLQ0vg7QojaQoj5jwsJIUoAXsDuNO9fLIQ4DZwGCgLfPmuGqpuYoiiKoiiKoiiKoiiKhUgp7wMtzLx+FOif6vdAoIiZcs0zO09VGaQoiqIoiqIoiqIoyksrNwwg/bJRlUHKM0kpmfD9Bnbvu4y9vS2TR3fBu0LhdOXOnL/L0LGriItPwqdhWYYPbo8QgvMX7zF68lri45OwtrFizJCOVPUuSlR0HF+NXMHd4Ifokwy880ZDunWqme28xw7cYt70AxgMkladytOjb3WT6X5LTrHF/yLWNlY4udjz2YgmeBRyzPZ8M0JKyYRZBwg4cgt7OxsmfemDd9mCJmVi45IY9O02bt6NxNpa0KxecQa/+8qLyTb7YEq2wU3MZ5uwnZv3orC2EjSrV4zB79R5fnnmHyHg2F3s7ayZ9GkDvEu7meaJT2LQlABuBhnz1CnK4LdMt6HN+2/w2ZQAlk9rT5Uypu/PVJZp69m975K2D4zp9oR94A5Dx6wiLj4Rn4blGP5lB4QQDBr6N9dvhAEQFRWHo6M9/ks+JiExidET/Tlz7i7CSjB8cHvq1i6VqWwH9l5i+ncbMBgMdOpai77v+phMT0hIYuzwFVw4dxdn53x8O7UXhYu4smn9Cf5asDe53JVLwSz8ZyDlKhRi66bTLJi3C71B0qhJeT7+vE0WlppxuU1dx+59F7G3z6Mtt4rpbmRoy230CuNyK8/wrzpqy+2bpamWWyyOjnnxX/oJp87cYuQEv+R5fPJeC1o1985YngnL2B1wRsszqS/e3sXS5zlzg6FD/9TyNKnM8OE9EUIQERHD51/M486d+xQp4saMHwbg7JwfgEOHLjJx0nKSkvS4ujjw11+DAViwYBvLV+xDCEG5soWZNKkvdvZZWpwc3HeZGd9tQm8w8GqXmrz1bmOT6f8eC+THKZu4ejmYsd91p3mrZy+TzNK+Dzaye7/x+2BU5yd/H4zz05Zhg7IMH9wOIQQXLgUxevI6HsUmUKSQC9PGdcXBwZ7bdx/QvtdMShbT9tFqlYsybuirT82yJ+AsEyYsw2Aw0L1HQ957r63J9ISERIZ8vYCzZ2/i4pKf6T/0p2hR7Zg2d+4mVq7Yh5WVFcNH9KRxY21ZRUY+YsSIRVy+dBchBBMmvkWNGto+uWjRTpYs3oW1tRU+PpX56utuGV9mz2E/ALhw+R6jJ/gRHROPlRCsWDQQOzvbDOWCrB8/khL1TBizmovn76HXG2j3anX69dfeO37UKvbtvohrgfwsXf1phrOktWfPOSZPXIHeYKBb9wYMGNA6TbZEhg5ZxNlz2vr9fvo7FCniRsSDaAYN+o0zZ27QuXM9RozsCUBMTBxvvvFD8vuDgyLo+Godhg7rnulsUkomTFrF7j3ntf1gwut4V/JKV+7M2VsMHbGEuLhEfBpXZPjQrgghmPHzBrbvOI2VlcCtgCOTJryOzsOZhw8fMWzkUm7eCsPOzpaJ43tTrmyhTOd7YuZccv4hpWTCD1sJOHAVe3tbJo3oiHf59A/X+WHObvw3nSYyKo7j21PGQf1j6WFWrD2BtbUVBVzyMWFYB4oUcs6RbCcO3mHhj0cxGCTNOpbB983KJtPX/32OneuuYGUtcHKx5/2h9XH3dADg9SZ/UayUCwBuuvx89V2zHMn0mJSSiUvPEnA6BPs81kx8pzrexdP/3QN+OERoRBxJBkntsgUY+UYVrK0Em47c5Zc1l7h2L5plIxpRuYRLjuZ7kt/eHE7HKg0JiXpAlfF9Xsg8U5NSMvGf8wScCdWWW78qeBczs9x+PEJoZDxJekntsq6M7O2NtZXgR/9L7DgZgpWAAo55mNSvKh4uWfwiN5Ntwl+nCDgZrJ3rDqiFd5r1EhufxKBfDnMzJEY7163uyeBe2nZ5J+wRw+cfJzwqHuf8eZj6QW08C+TNkWzKf4MaM0h5poD9lwm8eZ8tqz5j/LBOjJm81my5MZPXMn64L1tWfUbgzfsE7L8MwNSft/BR/6b4LxnIZ+83Z+pPWwBYvPwQpUt5sGbJRyya+w7f/biZhMSkbGXV6w3MmbqPMTPaMvPv7gRsucrNaw9MypQqV5Dpf3bh58XdaNi8JH/8cjhb88yMgCO3uHHnIZv/6Mm4QY0Y+9Nes+Xe7l6Vjb/3ZNWsrhw/G0TA4VsvINttbtyNZPPvPRj3WSPG/rL/CdmqsHF+d1bN7Mzxs8EEHHk+2QKO3eXGvSg2z/Zl3MB6jJ1zyHyezpXYONOXVdM7cPx8KAHH7iRPi45NZNG6C1QrV9DsezOcZd8lAm/dZ8vqzxk/vDNjJq0xW27MpDWMH9GZLas/J/BWyj4wY9Jr+C/5GP8lH9O6uTetmlUCYPnqowCs/ecT/pjZj+9mbMJgMGQ4l15vYOrEtcyY/RZ/+33Klo2nuXbV9MEDa1Ydw9EpLyvXf8FrbzZg5gztCZRtO1Tnr+Uf89fyjxkzoTuFi7hQrkIhHkY84ufpm/hl3jv8vfpT7odFceTg1UwvM0i13PwGM35EZ8ZMMj8O3phJ/owf2YUtfoONy+0SADMm98Z/6Sf4L/2E1s0rJy+3sqV1rFw0EP+lnzD/536MmuhHUpL+2XkCzhB4I4Qtm8cxflwfxoxdYj7P2CWMH/8GWzaPI/BGCAF7zgLw67xN1K9XgS2bx1O/XgV+nacty8jIR4wdt5TZswayft1ofvxxAADBwQ9YuGgnK1cMZd3aUegNBtavP5K5hWik1xuYNnED38/qw5LVH7Ft0xmup1nXnp7OjBjfmVbtqmRpHhkRsP8ygbfC2bLyU8YPfZUx3603W27Md+sYP+xVtqz8lMBb4QQcuALA8AlrGPxxS9YuHUjLphWY/1fKcaZYEVf8F3+I/+IPn1kRpNcbGDduKfPmf8y69aNZv+4IV67cNSmzYvk+nJzysWXrePr2a8H301YDcOXKXTasP8K69aOYP/8Txo1dil6v7XcTJiyjcWNvNm4ai5//CEqX1i5QDx68yI7tJ/FfM4J160fzzrutMr7MntN+kJSk56sRyxk7rDPrlw9i4a8DsLGxznCu7Bw/tm85Q0KiniWrPuHPvz/Eb8UR7t7Rvms7dqrBjNl9080vM/R6AxPGL2POrwNZs3YEG9Yf48qVeyZlVq44gJNzXjZtHsNbbzVj+jRtueaxs+WTTzvy1VddTMrnz2/PqtVDk38KFy5Aq1bVs5QvYM95Am+GsmXDcMaP6cWY8cvNlhszfjnjx/Riy4bhBN4MJWDveQD6v92ctauH4L/ya5r6VGLmbG25zpm3lYoVirB29RC+m9iHCZNXZSmf2cy56Pwj4MBVbtx+wOZlHzBuSDvGTt1ktlyzRmVYNr9futcrltOx4ve3WbOoP22aVWDarJ05ksugN/DH9MMMmdacaX+9yv5tgdy+HmFSpkS5AkyY354pf75K3abFWDLrePK0PHbWTF7QkckLOuZ4RRBAwOkQbgTHsGliM8a+VZVxi06bLffDBzXxG+vD2nE+hEclsOmIdmwsW8SRnz+qTe1yBXI829MsOLCetj9//kLnmVrAmVBuhMSwaXwTxr7hzbjFZ82W++G96viNbMTa0Y205XZMO+a827ok/qMasXpkI5pW9WDW+is5l+1UMDeCY9g8tRXj3q7B2AUnzJZ7u11ZNn7XilXjm3P8cjgBJ4MAmLL0NL4NvVgzoQUfda7A9GXm/zZFeZJnVgYJIfRCiBOpfkrkdAghhIsQYmBOf+6LIIRYIITobvz/fCFEpRz8bDshxDbjcu+VU5+bWdt3X6Bzh+oIIahexYvIqDhCwqJMyoSERREdE0/1Kl4IIejcoTrbd18AQAiIiYkHICo6Dg93rRWOQBATE4+UkphHCTg75cXGOnv1k5fPhVKoqBOeRZywtbWmSavSHAq4YVKmau3C2NtrjeLKV/bgfkhMtuaZGdv338C3VVltWVbUERmTQMj9RyZl8trbUK+6dqc9j601lcoUJCjs+WfcfuAGvi3KGLN5EBn9hGzVUmdze27Zth++hW/TUlqe8u5ExiQSEp4mj50N9ap4puQpXYCgVJl/WnyC/l29yWOb8Qsks1l2n6dz+0zuA+2rs33XOZMyUko2bjtNxzZVAbhyPTS5JZBbAQccHe05c870gvZpzp25TdFibhQpWgBbWxtata1CwM7zJmUCdp2nQ6caADRv5c2RQ9eQ0rQZ7ZaNp2jVVst053Y4XsXccC2gtXipU680O7dl7eRi++5zdO5Qw7jcihEZHUdIaKRJmZDQSKKj46hepZjx2FHjycutbTUA8ubNk3zRG5+QhBAZzLP9FJ1962l5qpciMjKWkJCHpnlCHmp5qmvbXmffemzfdjLl/Z3rA9C5c322GV9fu+4wrVrVoHBh7QTbzc0p+fP0egNxcYkkJemJi03Ew8MlY2HTOHfmDkW9CiSv65ZtK7Nn10WTMoWKuFKmnCdWVhlcIFmwPeAindtXy+S+UC35+yDw5n3q1CgOQMO6pdmy81y6eWTEqVOBFCvugZeXO3ny2NC+Qx22bz9lmnXHKTp30dZXmzY1OXDgAlJKtm8/RfsOdciTx5aiXgUpVtyDU6cCiYqK5eiRy3Tv3hCAPHlscHLKB8DfS3cz4L025MmjtbpJvY6f5XntB/sOXqF8WU8qlNNajri65MM6E9+h2Tp+CIh7lEBSkp74+CRsbK3J72AHQI3aJXFyzt6d6dOnAvEqVhAvr4La+m1fk507TNfvjh2n8PWtC0DrNjU4ePAiUkry5bOjVq3S5HlKC6nA68GEh0dRq3bpLOXbvvM0nTvV0dZptRJERsUSEprmWBL6kOiYOKpXK6Gt00512L5Du3h3cEhpVRAbm5B8DLt6NZh6dcsCULqUjjt3wglLs39lVW46/9i+5zK+bStrWSoXITI6npCw6HTlqlcugkdBh3Sv16tVnLz22vqt5l2YoJDIdGWy4sr5+3gWdURXxBEbW2vqtyzO0b2mlWHeNT2xM55DlvF2Jzz0kbmPei52nAjGt0FRbbmVdiXyUSIhEXHpyjnk1ZZNkl6SqDcgjBtY6cKOlPRMvzyftz1XThAekzPrKCt2nAzBt14RbbmVciUyNomQh09ZbgZJYpIBgTB5HSA2/tk3njJj+/F7+DbUviurlylgdp3mtbOhXiV3APLYWFGphAtB4bEAXL0blTytbsWCbD9uWmn+X2PpJ4nlwNPEXriMnDXESimrp/oJfNYbhCYzV/UuwEtZGZSalLK/lDJrZ7bm1TB+bnUp5T8ZeYMQIse7/gWHRuKpS2lO6enhRHCaL97gkEg8PZxMyxhPdod90Z4pP23Bp8M0vvtxM198pN1R7dOzLlcDQ2ncbiqdes9k+OB2WFllrzLofkgMBXUpX3RuHvm5H/rkE5mtay5Sq37RbM0zM4Lvx1DIPSWfZ8H8BN9/cr7I6Hh2HrxJ/Rrpu2HkfLZHFHLPn5LNPd+zsx26Rf3qzydbcPgjChVMlcctH8HGLz/zeRLYeeQ29atqlUNnr97nXtgjmtbO/voNDo3C0zPVPqB7wj6gS7UP6JwJDjU9iT/6byBuBRwoUUxrqVShrCc7Ai6QlKTn1p1wzp6/y71g0wuKpwkJjkSXat/00DkRmiZXaHAkHsYyNjbWODjY8TDC9OR12+bTtG6nVQYVLebGjcAw7t55QFKSnt07zhMclPFMqWnLJM2xI81FcLrji5llm3a5AZw8fYsOPWbQqddPjB3aOUMtIoKDI/As5JoyL08XgoMj0pfxNF/m/v1IPDy0rO7uTty/r+UMDAwhMvIRb775PV27TsTP7yAAOp0r77zTkmbNh9Go8RAcHO1p1Chr9wtCQyLReaZsX+4eToQGv/iT63TbeUa/D4xlypZyT64Y2rTtLPdS/Q2370bQ+Y05vPH+Hxz917QSP12O4AcUSr2edC4EB5u2Ag0JjqCQcX3b2Fjj6JiXiAcxT3zv7dthFCjgwNChf9Kl8wRGDF/Eo0fajYzAwBCOHr1Czx6TeeON7zl9KvCZy8pkeTyH/eD6zTCEgHc/+oMur//CvD8DMpwJsnf8aNGqMvb58tChxXd0aj2VPn0b4eycL1Pzf5rgkIcm60incyU4zbExJPhh8v6cvH4jMlZxsWHDcdq2q5l8kZzpfMEPTY8TOpd0+YKDH+Kpc3limR9+XI9PizGsXX+Mzz5uD0CF8oXZsk2r9Dp1+gZ37z0gKM0xKqty0/lHcGgUhVIfR9wd031fZtSKdSdpUi9rlXppPQh9hJtHynmHm3t+HoQ++bxj17orVKubsnwSE/QMe3c9I9/byJGAmzmSKbXgB3EmXYA8Xe3NVgYB9J9+iEafbyW/vQ1taudMV8OXVXBEHJ4FUipgPV3sCXkQb7Zs/x+P0OjL7dpyq5XSdXGG3yWafbOTtYfv8mmnsjmXLTyWQqnXaYG8Tz/XjUlg57/3qO/tAUB5L2e2HtVuIm49epeYuCQeRJn/2xTFnExfeQshHIQQ24UQx4UQp4UQvsbXSwghLgohFqI9095LCPGVEOKIEOKUEGLsUz52MlDa2AJmqhBioRCic6p5LhZC+Aoh+gkh/IUQu4QQl4UQo1OVeUMIcdj4GXOFEE+8MhBCRBvnc9bY8uYV42deE0J0MpaxNpZ5nP994+tCCPGL8W/dBnik+txdQojaxv/PFkIcNc5jbKoygUKIsamWX4UnZPQA/gLqGP+m0kKIUcY8Z4QQvwrjWYxxvjOEEEeBz4QQtYQQu4UQx4QQm4UQZr8FhBDvGTMe/fWPbU9ZPdmzdOVhhn7Rlt3rv2To5+0YPt4PgL0Hr1CxXCH2bPwKv8UfMm7qeqKjzX+pPQ87N17myvkwur5R7YXNMzOS9AYGT9zBm5298SqU8bvQL0KS3sDgybt407dSrsiWpDcwePoe3uxQAS9PRwwGyeTfjzHk7VqWjmZi3eaUVkEA3TrVxNPDmW5vzWbi9xuoUbUY1tbPr1WHOWdO3cLePg+ly+oAcHLKy5ARnRjx1T+8328+hYu4YpXNFnvZtW7TSZPlBlCtihfrlw9ixaKBzF2wm/j4xBeaSQiRfCGpT9Jz9uxN5s79mPm/fcqs2eu5fj2Yhw9j2L79FNu3fcuegO+IjU3Af4357o7/FRNG+rJk5RG6vjWXmEcJ5DFW4nkUdGTnms/x++sDvhnUhsEjV77Q7wOApCQD587dondvH1b7DSdv3jzM+1XrvqPXG3j4MIZ/lg3h66+7MmjQvHQt7J63tPuBPsnAsRM3mPptT5b89h7bdp7lwOGc677wNGfP3MbaSrB+2xBWbxzMkj/3ced2+AuZd07YuPEY7TvUtmiGzz/rwO7tY3i1Qy3+WrIHgPf6tyQqKhbfblNYtHgPFSsUeeHfCZC7zz9SW7PpDGcvBPFun7ovfN57Nl/j2oX7vPp6ythsP6/oysTfOvDx6EYs/OkowXdyplVXVsz/oi4B01uSkGjg4Pkwi+V42cz/rA4BU5qTkGTg4IX7ya8P6lyOnZOb8eorhVm8M+cr+jIiSW9g8OyjvNmqNF7GSsuve1fmyIUwuozYwZGL99G52mP9HFsH53aWbhX0MrYMykgrkrxCiBPG/18HegBdpJSRQoiCwEEhxOMBNMoCfaWUB4UQrY2/vwIIYI0QoomU0tytq2+AylLK6gBCCB/gc8BPCOEMNAD6Am8YP68y8Ag4IoRYD8QAvYCGUspEIcQsoA+w8Al/U35gh5TyKyHEauBboBVQCfgTWAO8CzyUUtYRQtgB+4QQW9Ba65Q3ltUB54DfzcxjuJQy3FgptV0IUVVK+biNc5iUsqbQusZ9SapHxT0mpQwRQvQHvpRSdjQul1+klOOM/18EdAQeD+CTR0pZWwhhC+wGfKWUoULrXjYBeMfMPH4FfgUg8h+TrXfxskMs8zsGQJVKRQhKdTcrKCQSnYfpyYHOw8mkmW5QSCQ6d63M6nUnGD5Yu+vVrqU3IyZo/fpXrT3Oe30bI4SguJcbRQu7cu1GGFW9s96Sw80jP2HBKU2N74fE4JaqtctjJw7fYdmCE0ya3RHbPNnrQvQsi9ecZfkG7U54lfLu3AtNyRcUFoPOLX0+gFEz9lC8iDN9uz6/8T8WrznH8k1aV5Mq5QpyL1UrqqDQR0/O9uNeihd2om+XymanZznPhoss36KNs1OlrBv3UjVPD7r/CN0TBsUbNesgxQs50rdTRQBiYhO5fDOCt0Zo41OFRcQycMJOZg1vluFBpBcvO8gyP21MnyqVihCUqnVMUPAT9oFUrRyCgh+ic08ZmDwpSc/WnWdZtSilEaSNjTXDjPsGwGvvzDVp/fIsHjonkzvNIcGRuKfJ5a5zIiT4ITpPZ5KS9ERHx+PsknIHf+um07ROM8ZM46YVaNxUq6deveJIprodLV52gGWrUy23tMcO9zTLzd3JtEyaZZu83P762Oz8Spf0IF/ePFy6GkyVSumPHYsX72LZcm1sjCpVihN0L6X1SFBQBLpUd+4BdDoXgoLMl3FzcyIk5CEeHs6EhDykQAFt/Xp6uuLi4kC+fHbky2dH7dpluXDxNgBFi7oll2vdqgb//nsVX1+zf8pTuXs4ERyUsn2FhkTirnsxF2mLlx9O832Q5lifke8DY5nSJdz5/ee3ALh+I4xd+7RxcfLksSFPHu2UpHLFwhQr6sr1m/epUin9QMugtRS5l3o9BUeg07malPHQuXDv3gM8PV1JStITFRWLi2v+J77X09MFnacL1aqVBKBN25rJlUE6nQutWmldvapWLYmVleDBg+jkdZtumb2A/cBT50SdGiUo4Kodp5s0LM/ZC3ep/0oZs5nSys7xY/OGU9RrWBYbW2sKuDlQtUYxzp+9Q5GiOTMWic7D2WQdBQc/MGnFpOV3Jijt+nUx/52V2oULt9En6c0OHv80i5fuYdmKAwBUqVzM9DgRHJEun07nbNKqx1wZgFc71ua9D+fy6cftcHCwZ9K3rwNat8AWbcbhVTTrY97lpvOPxSuPsXzNCS1LhUImrQKDQqNMvi8zYv+R68z5cz+LZvZJPnZkl6t7PpOhA+6HxuDqnv684/SRe/gtPM2oX1qbnEMWcNe+W3VFHKlUQ0fgpXB0RbL3gJLFOwJZYWxlVLmEc3L3IICgB3FPHcjYztaa5jU82XEiiIbe7tnK8bJZvPMGK4xd/LTllnJzISgiDg9Xuye+187WmubVdOw4GUzDSqb7X8e6hXn/56N8ko3WQYu3XWP5rkAAqpR04V7qdRoe++Rz3d//pbguP33bphzjda55+fmzegDExCWx5cgdnPLnyXI25b8ns93EuqBV7EwUQpwCtqE9415nLHtDSnnQ+P/Wxp9/geNABbTKoWeSUu4Gygoh3IHewEop5eORhbdKKe9LKWOBVUAjoAVQC61y6ITx96c9kicBeDxa3Wlgt5Qy0fj/Eqnyv2X8vEOAmzF/E2CplFIvpbwL7HjCPHoKIY4b/35vtMqjxx6PCHgs1fwyopkQ4pAQ4jTQ3Pi5jz3uRlYerbJsqzH7CCDTtSt9etbFf8lA/Jdog3z6rT+BlJITp2/h6GCPR0HTLzePgo445LfjxOlbSCnxW3+CFj7axaSHuyOHjwcCcPDINUp4aSeLhTxdOHDkGgBh96O5fiOMokVMT+Yzq2xFd+7eiiTobiSJiXoCtl7llSamJ3xXL4Yxc/IeRk5tjcsLGHG/Tydv/OZ0w29ON1o0KIH/1svasjwfjGP+PHi4pW9aP+OPI0TFJDDsw/rPOVsl/GZ1wW9WF1rUL47/9ivGbCE45rc1n23BUaJiEhn2Qb2cz9O+PH4zOuI3oyMt6nrhv0sbn+LExVAtTwEzeRb/q+V5N+WpZo7583BwUU92zOvKjnldqVbOPVMVQQB9etZLHvS5ZdNK+G1IvQ/YPXsf2HCCFj4Vk6fvP3yVUiXcTbqCxMYl8Cg2AdDG/7C2tqJMKQ8yqqJ3EW7duM/d2+EkJiaxddNpmjQ1bWzYuGkF1q/5F4AdW89S+5VSyS1aDAYD27ecplU701Y34fe1C4bIyFhW/nMI364Zv4Pep2f95MFuWzathN/6f43L7aZ27EhzEezh7oSDgz0nTt80Hjv+pYVPyuHS3HK7dSc8ecDoO/cecC0wlCKFzB87+vRpir/fCPz9RtCyRXX8/A9qeU5cw9HRPrnbV3IeD2ctzwlt2/PzP0iLFtryad68Kn5+2sWgn9+B5NdbtKjGseNXSErSExubwKlTgZQu5UnhQgU4efI6sbEJSCk5cOACpUtlrbl+Re/C3L55n7u3H5CYmMS2TWdo5FM+S5+VWX16vJI8sHNLnwr4bTiZyX3hJC2aaFnvh2vblsFgYPbvAbxm3LbCH8QkD+J86044gbfC8XrK90GVKsW5ERjC7VthJCQksWH9EZo3N92Omzevit9qbX1t3nycevXKI4SgefOqbFh/hISERG7fCuNGYAhVq5bA3d2ZQp4FuHZNG5jzwIELlC6tra+WLatz+JBWcX79ejCJiXpcXZ889saL2A8a1S/HpSvBxMZqY/ccOX6dMiVfzPHDs5AzRw9r39+xjxI4c+oWxUvm3MVm5SrFuXkjlNu3jet3w3GaNTNdv82aVcHfX2tpt2Xzv9StVy5D3b42rM9aq6A+vRvjv/Jr/Fd+TcvmVfBbc0RbpycDcXTIi4d7mmOJuzMO+e05cTJQW6drjtCimVa5EngjNLnc9h2nKVVSO42OjHyU/CCN5SsPUrtWaZPxhTKdORedf/TpVgu/P9/F7893adGkHP6bzmhZztzBMb+d2bGBnuTcxSBGf7eJWVO641bg2RWAGVW6ghtBt6IIuRtFUqKeA9tuUKuh6VPirl8KZ/7Ug3w5uRnOrinnkNGR8SQmaN9LkRFxXDodSpES2X/CWZ/mJVg9pgmrxzShRQ1P/Pff1pbb1Qc45rNJVxkUE5eU3HUsSW9g96lgSllgnCBL69OsOKtHaoM+t6iuw//gHW25XXuAY14bPJzNLLeHqZbb6ZDk5RYYnFJBuONEMKU8s7fN9WlZCr9vm+P3bXNa1CqM/z7tu/LElXAc89mareCbseIcUbFJDOtjehx8EBWPwdga5de1F+nWpHi2sin/PVmpSu8DuAO1jK1wAoHHW23qzscCmCSlnJvFbAvRWgK9Bryd6vW07a+kcV5/SimHZvCzE2VK+24DEA8gpTSkGnNHAJ9IKTenfqMQoj3PIIQoidbip46U8oEQYgEpy4jH8wP0ZHAdCCHsgVlAbSnlLSHEmDSf+XjZC+CslDLHvsV9GpZj977LtOoyg7z2tkwclfKEDt/XZ+G/RGvpMHpIR4aOXU1cfCJNGpSlSQOt7m/8cF8mfr+BJL0Buzw2jBum3RYf+K4PQ8eu5tXXfkFK+PLj1hTIwF29p7G2seKDLxsw+tONGAySlq+Wp3ipAvw19yhlK7pTt0lx/vj5EHGPkpg8TOsa5+7pwMhpWXt0dmb5vOJFwOFbtO73D/Z2Nkz8MuUxvp0/WInfnG4EhUYzZ+kJSnm50HWgVm/Yx9ebHu3M9ijM2WxHbtP6neVati9SHlvdeeBq/GZ1ISg0hjl/n6SUlzNdP/bTsr1aiR7tcv6i1KdWEQKO3aH1B35ank8bpOQZtA6/GR0JCothzvIzlCrqRNcvtKca9elQnh6tcq4/NzzeBy7RqvN08trnYeLorsnTfF//Bf8l2t360d90YuiYlcZ9oBxNGpZLLrdhy2k6tDb9Er8fHsO7H/+JlZVA5+HIlHGZe8SxjY01Xw7ryKcf/olBb+DVzrUoVUbH3JnbqFipCE2aVaRTl1qMGbaCbh2m4+Scl2+npIxF/++xQDx0zunu5k//bj2XL2kXxO++34xiJbJ2Z9qnUXl277tIK9/vtWPHmJRHcfv2/jn58djacltBXFwSTRqmWW6bT9GhjWlXzmMnbjBvwW5sbKyxEoIx3/gmt454ah6fyuwOOEOr1iO19Tgx5YlHvp2/xd9vhJZn1OsMHfYncXEJNGnsTZMmWgu49wa0YdDn81ixch+FC2uPlgcoXboQjRt708l3PFZWVnTv3pBy5bQWLW1a16RL1wnY2FhTsaIXvXo1ysqixMbGmi+GtufzDxehN0g6dq5BqTIezJu5gwrehWnctALnztxh6Od/ExUZx97dl/ht1i4Wr/4oS/N7Ep+GZdm9/zKtuv6krdORKc2cfPvMxn/xhwCM/rqD8dHySTRpUCb5+2DdljMsWa49wbFVs4p0e1UbnPjIvzf4ae5ObGyssLISjP2mIy5PGYPGxsaakaN68W7/nzDoDXTr1oCyZQvz049rqFy5OM1bVKN794Z8/dUftG41EmfnfEz/QWuEW7ZsYdq1q0WH9mOxtrZm1KjXkgdeHjGyF199+TuJiXq8vAoycZLWiqlrtwYMH7aQVzuOw9bWmsmT+2Z4vJnntR84O+Wl3xsN6f7WLITQWgY1bZzx74nsHD+6v1aX8SNX8VqXn5BS0tG3JmXLaeNrjPj6H44fvU5ExCM6tpzCewOb0ykTFcqPsw0f0ZP3+s/EYJB06VqPMmUL8fNP6/CuXIzmzavSrXsDvhmykLZtxuDsnJ9p36ecKrZqMYromDgSE5PYsf0Uv87/iDJltIq9zZuOM3vuh5nKk5ZPk0rs3nOeVu2+JW/ePEwc3zt5mm+3Kfiv/BqA0SO6Jz9avknjijRprN0g+P6HtVwPDEEIQZHCBRg7qgcAV68F883wJSCgbOlCTBj3WrZymmTORecfPg1KE3DgKq17zMHe3paJwzukZOn7G35/vgvA1Jk7WLflHLFxifj4/kL3V6vxSf/GTJ25k0exCQwaoT0hsJDOidlTemQ7l7WNFf2+eIVJX2zHYJA07VAGr1IuLJ9/gpIV3KjdyIslM48RF5vEjyO1jg6PHyF/98ZD5k89hBACKSWd3vCmaEmXbGdKzaeqBwGnQ2gzdKfx0fIpx4QuYwJYPaYJsfF6Pvr5CAlJBgwGqFvBjV5NtQqCrcfvMWHJWcKjEvjgx8NU8HJm/hfPv4vdknfG0bRcTQo6uHBr4hpGr5vH7/vNP5n4efCp7E7A6VDajNitLbe+KediXcbvZfXIRsQm6Plo5nFtuUlJ3XJu9GqiVQROX32R68ExWAlB4QL2jOmTc63ifarpCDgZROuvtmrZ+tdMntZ5xA78vm1OUHgsc9ZcpFQhB7qO0p6c16dlKXo0LcGh82H8sFx7yEedCgUZ9VbuHPriRZH6l6+blqWJZ/V5F0JESykdUv3+GVBGSvmJEKIZWsuYksbJ66SUlY3lWgPjgRZSymghRBG0SpiQNLNACOEGHJdSFk/1mg44DARJKesaX+sHTERr+RKL1mLnHbQuY/5o3cRChBAFAEcppdkRKFP/TcZKlWgp5bTU04QQ7wHtgR7GSq9ywB2gDfC+cZoHWjexAVLKFUKIXWiVQIlolVk10CrOTgFDpJQLjJVntaWUYcbxhaZJKZs+IWdTjN3EhBAuwEW0lkTWwEFghZRyzOP5SimPCiHyGDO9KaU8YOw2Vk5K+fTHAaXpJpZrOPXiUsQ0S6cwq5zLl8gbuS+bKP4lAPL6FAsnSU+U/Bp5/ltLxzBLVBwBUeYfD2xxjj2IiM+d2VzsekD0SkvHMM+hG8iceeRwjhLaI4fvxy21cJD03Ox7w8PclwsA595IcuH6BATNcvV+kBuPHy522sV7kmGrhZOkZ2PVChI3WjqGebbtcuW5B2jnH/L+AkvHMEu49eN4aO48/6jpPgLD3sGWjpGOVaPvARAf5nxL8OySsw9i2DXI0jHMsmo6A3noG0vHMEvUnfx/PaDQxZoVLX49W/74+ZdqGWelZdBiYK2xq9JR4IK5QlLKLUKIisAB492zaLSWPukqg6SU94UQ+4QQZ4CNUsqvpJTBQojzgF+a4oeBlWhdn/6SUh4FEEKMALYI7SlmicBHwNMfR/J089EqXo4bB2oOBToDq9G6aJ0DbgIHzPw9J4UQ/6Itm1vAvmzkePyZEUKIeWiDcwcBR55QLkFoj7r/yTjekg0wA8jas6EVRVEURVEURVEUJRczvIQDOFvaMyuDUrcKMv4eBjypC1LlNGV/BH7MSBAp5eupfxdC5EMboyftLcrbUsrOZt7/Dynj5jxrXg6p/j/G3DQppQEYZvxJy+xIpqlb+Egp+z2hTIlU/z8KNDVXzjh9F7Ar1e8j0MYAeuJ8jb+fQBvbSFEURVEURVEURVEUxYRlnxf8BEKIlsB54Gcp5cNnlVcURVEURVEURVEURVEyJmeexZhBxrGBtpuZ1EJKef/xL1LKbUC64dCllAuABZmY3yEg7bMD35RSns7oZ7wIQoi3gc/SvLxPSpmzI38qiqIoiqIoiqIoyv8Zg8HSCV4+L7QyyFjhU/0Fzu/5D5GfA6SUfwB/WDqHoiiKoiiKoiiKoij//15oZZCiKIqiKIqiKIqiKEpOUi2DMi9XjhmkKIqiKIqiKIqiKIqiPB+qMkhRFEVRFEVRFEVRFOU/RHUTUxRFURRFURRFURTlpaW6iWWekFJaOoOSO6gNQVEURVEURVEU5f+TsHSA5+lkpQoWv56tdu7CS7WMVcsgBYDIhJWWjmCWU55uEL/e0jHMs+tA8KMFlk6Rji5fPwBuRM2ybBAzijsOzJW5QMv2IP4fS8cwy9WuF0Tmzmw49cqV+wEY94XcePyw6wCADPnVwkHSEx7vcS3yJ0vHMKuU06ckGbZaOoZZNlatCIz6xdIxzCrh+DFE58LveIdu2r+PVls2hzn5upBo2GzpFGbZWrUhXr/R0jHMsrNuB/rcuY9i3SpXZzMEfGHpFOlYNZkOgGHXIMsGMcOq6QzEh/UsHcMsOfsghoNDLB3DLKt631k6wnNlsHhV0MtHjRmkKIqiKIqiKIqiKIryH6IqgxRFURRFURRFURRFUf5DVDcxRVEURVEURVEURVFeWmoA6cxTLYMURVEURVEURVEURVH+Q1TLIEVRFEVRFEVRFEVRXlqqZVDmqZZBiqIoiqIoiqIoiqIo/yGqMkhRFEVRFEVRFEVRFOU/RHUTUxRFURRFURRFURTlpaW6iWWeqgxSnmr/3kt8/906DHoDvl3r0K+/j8n0hIQkRg9bzoVzd3B2ycfEqb0pXMSVjetOsGjBnuRyVy4FsWjZR5SvUDj5tS8+Wcid2+H8s3pQlrJJKZnw3Wp27zmPvX0eJo/vjXelounKnTl3i6EjlhIXn4hP44oMH9IFIUTy9N//3MV336/hwO5xFHB1YM36Y8z7fQdISf789owZ0Y0K5YtkKSPAoX1X+WnqNgwGAx06V+eNd+qbTD9x7CY/T9vGtcshjJ7UmaatKgBw+WIw0ydsIiYmAStrwZvvNqBFm0pZzmHOkf2BzJ62G4NB0razN6/1q2My/dTxO8z5fjfXroQxbEI7mrQsmzwtJCiS6eO3ExochRCCb3/0xbOw0/9ttgN7L/PDdxswGCSdutbkrXebmExPSEhi7PBVXDx3FyfnvHw7tSeFi7iSmJjE5HFruXD2DsJK8PmQ9tSqUxKAQR8sJCwsCr3eQPWaxflyWEesrTPXYFNKyYTvN7B732Xs7W2ZPLoL3qn2s8fOnL/L0LGriItPwqdhWYYPbo8QgguXghg9eQ2PHiVQpJAL08Z3x8HBnlNnbzNywhptHkg+GdCMVs2yvv3ltv3geR0/rl4PZtjIvzl7/jaff9Ked/s1y37OH3cScPA69nY2TBrWFu/yunTlfvh1L/6bzxIZFc/xLZ8mv75qwxmmzgpA5+4AQJ+u1enxatVsZXrs6P4bzPl+LwaDgba+lejZr5bJ9NPH7zJ3+h6uX7nPNxNa07hFGQBOHr3Nr9P3Jpe7dSOCbya0pkHTUlnOsmfPOSZPXIHeYKBb9wYMGNDaZHpCQiJDhyzi7LmbuLjk5/vp71CkiBsRD6IZNOg3zpy5QefO9Rgxsmfye9avP8q8uZsRQuDu4cx3U/ri6uqQ5YwAR/bfYM60APQGSbvOlejVr7bJ9NPH7zDn+z3G41pbGrfUltmJo7eZOz3lO/VW4AOGTWxDg6als5VHSsmEqevYve+ith+M6YZ3xfTfd2fO32Ho6BXaftCwPMO/6ogQgkHfLOX6jTAAoqJicXTMi//ST9h38DLf/7yZxEQ9trbWfPVZO+q/krmsUkomTFlrzGbL5LE9zGc7d5uho5cbj23lGf71q8n76KKl+1i87CDWVgKfxhX4elB7AC5cusfob1cTHROHlZVgxV8fY2dnm+Fse/ecY/LEVcbtrT79B7Qyma5tb39x7twtXFzyM216P4oUceP0qRuMGf138t838KN2tGxVDYARwxcTsOssBQo44rd2aKaWlWm283w3aRUGvaRr93q8O6BlmmxJDP/mL86dvY2zSz6mTu+bnG3c6H+0bMCHH7WlRUvtWLHoz12sWnEQBJQtV4jxE17P1PJ6/PdOmLiC3QFnsc+bh8kT38S7kle6cmfO3mTosEXExSXi08Sb4cO6I4Rg46bj/DJzA1evBbP8ny+pUrl48nsuXLzD6DFLiY42rs9lX2cqX3azfTd1NTt3ncHW1ppiXgWZNOENnJzycepUICNHL01epp981J5WLatlarmlzTnx73MEnA7BPo81E9+uhndx53TlBsw4TOjDOJL0ktplCzCyT2WsrQRTl59n56lgbK2t8HLPx8S3q+GUL3Pr8anZ/jlPwJlQLVu/KngXM5PtxyOERsYbs7kysrc31laCH/0vseNkCFYCCjjmYVK/qni42OdItqf57c3hdKzSkJCoB1QZ3+e5z+9ppJRMXHyagJPB2jIcUBPvEi7pyg2Ytp/QCOP6Le/GyLeqYW0l0n+gomSAqgxSnkivNzBlwhp++fUddJ5O9H1tFk2aVaBU6ZQLEf9VR3FyysvqDV+yZeNJfv5hE5Om9aZdx+q061gd0CqCvvzsL5OKoB3bzpAvb55s5QvYe57AG2FsWTeMk6duMObbFSxfMihduTHfrmD86J5Uq1qcAQPnEbD3Aj6NKwJwL+gB+w5cpHAh1+TyRYsU4K8/PsLZKR+795xn5NjlZj83I/R6Az9M3sL02a/hrnPivT4LaORTlhKlCyaX0RVyYtjYjvy98JDJe+3tbRg2/lW8ihcgLCSK/n3+4JUGpXB0zJkvR73ewC/f7WLyzC4U1DnwyVt/U79JKYqXcksu4+HpyJdjWrFi0fF0758yagu936lDrXrFiX2UgMjBL6Lclk2vNzBt4jp++rUvHjon3u49l8ZNK1CytEdymTWrjuPkZM+K9YPYuvE0M2dsZcLUnvivPAbA4lUfE34/ms8HLuKPpe9jZWXFhGk9ye9gj5SSoV/8w44tZ2nVrkqmsgXsv0zgzftsWfUZJ8/cZszktSxf8H66cmMmr2X8cF+qVS7KgM8WEbD/Mj4NyzH8Wz+GfNaGV2qVZMWa48xftI9BH7agbGkPVi58Hxsba0LCovB9fRbNGpfHxsY6S8svt+0Hz+v44eKUj+HfdGH7jjPZypec8+B1btx+wOal73Dy3D3Gfr+NZb+mP2Ft1rAUfbpWp+3rv6eb1q5FeUZ93iJH8jym1xuYOSWAib90oqDOgc/6Lqduk5IUL1UguYyHpwODR7dg5V8nTN5brXZRZi55DYCoh3G80/UvatZLf+GVmSwTxi9j3m8fo9O50KvnVJo1q0KZMoWSy6xccQAn57xs2jyGDeuPMn2aP9//8A557Gz55NOOXLl8l8uX7yWXT0rSM3niCtasG4GrqwPTpvqxZPFuPvq4Q7ZyzvxuF5NmdjYe1/6hXpNSJsvM3dORwWNapjuuVa9dlNlLegMQ+TCOt7sspGa9YlnO8ljAvksE3rrPFr/BnDxzizGT/Fm+cGC6cmMm+TN+ZBeqVfZiwKd/ErD/Ej4NyzNjcu/kMpOnb8DBwQ4AV5f8zJ7xFjp3Jy5dCeLdjxewZ9M3mcu29yKBN8PY4v8lJ0/fYsxEP5Yv+ih9tol+jB/ZjWpVvBjw8R8E7LuET6PyHDxyle27zrPmn8/Ik8eG++HRgLZuvxrxD1PH96RC+cI8iIjJ1HFNrzfw7fjlzPvtIzx1LvTqOY1mzSpTOtX2tmrFQZyc87Fx8yg2rD/G9Glr+P6HtylTthD/LP8SGxtrQkMe0q3LdzRtVhkbG2s6d67L6683Ydg3f2VqOaXNNvHbFfw6/0N0Ohd695pO02aVKV3GMyXbyoM4OeVj/eYRbNxwnBnfr2Xq9H6UKVuIpcsHa9lCH9K9y1R8mnpz/34Ui/8KwG/tN9jb5+HLzxewacNxfLvUzVS2gIBzBN4IZcum0Zw8FciYsX+z/J+v0pUbM+4fxo97nWpVSzDg/dkE7DmHTxNvypUtzM8/DWD0mKUm5ZOS9Hw15E+mTn6LChWK8iAiOtPfU9nN1rBBBQZ/3gkbG2umfu/H3Hlb+GpwZ8qWLczK5V9r36OhD/HtMolmTStn6XsUIOBMKDdCYtg0oSknr0UwbvEZ/hnWMF25H96vgUNeW6SUfDbnOJuO3qPDK4VpUKkgn3ctj421FdNWnOfXDVf4snvFLGV5YrbxTTh5PYJxi8/yz9AG6bO9Vz0l29x/2XTsHh3qFObd1iX5zLccAIt2BDJr/RXG9KmcI9meZsGB9fyyawUL+4167vN6loBTwdwIimbTlJacvPqAcX+e5J/RPunK/fBRnZRl+MthNh2+Q4d66W9mKUpGqDGDlCc6e/o2XsXcKOpVAFtbG1q1q8runedNygTsPE+HTjUBaN6qMkcOXUVKaVJm88aTtG6Xcif60aN4lizcxzvvZ++O+fadZ+j8am2EEFSvVoLIqFhCQiNNyoSERhIdHU/1aiUQQtD51dps33k6efqkKf589XlHUt3op2b1kjg75QOgerXiBIVEZDnj+TN3KeLlSuGirtjaWtOiTUX27rpkUqZQYRdKl/NIV2HhVdwNr+LaRUJBD0dcXfMTEf4oy1nSung2mMJezhQq6oytrTU+rcuxf/c1kzKehZ0oVdY9XbYb1+6j1xuoVU+7M5c3Xx7s7XPm7lJuzHbuzG2KFitAkaLGfaFtFQJ2XjAps2fXedp3qg5As1aVOHroGlJKrl8NpfYrWkugAm4OODrac/7sXQDyO2gVGvokA4mJSZCFOqvtuy/QuUN1bT+o4kVkVBwhYVEmZULCooiOiad6FS9tP+hQne27tfyBN+9Tp2YJABq+UpotO88BkNc+T/IJa3x8ksk+klm5cT94XscPNzdHqlYuho1Nzny9bt97Fd+2lbSc3oWJjI4nJCw6Xbnq3oXxKJi9ViuZcelsiOk+2qosB3dfNymjK+xEybIFTVpSpbVn+1Vq1y+WrX309KlAvIoVxMurIHny2NC+fU127jhlUmbHjlP4+moXr63b1ODgwYtIKcmXz45atUqTJ00rAim1n9hHCUgpiYmJxd0j/V3uzNCOay7Jy6xp63IcMHtcK4jVUyqw926/Qp0GxXPkmLt99zk6d6hhPH4UIzI67gn7QRzVqxQzHj9qsH3XOZMyUko2bjtNx7Zaq4dKFQqjc9daY5YtrSM+PpGEhKTMZ+tYU8tWtdiT99GYeKpXNWbrWJPtu84CsHT5Qd5724c8ebT7nm4FtP1j34HLlC/rSYXy2g0qV5f8mWqRefrUDYoVc8fLqyC2eWxo174mO3acNimzY8dpfH1fAaB1m+ocOngJKSV586Y6riYkkfrgUbtOGZxd8mVmEaVz5vQNihUrSFFjtrbtarAzTbZdO07TqbPW0rZV62ocOng5fbY0x3y93kB8XCJJSXri4hKytC9s33GKzr6vGI+5JY3r86FJmZDQh9q2Vq2ktj59X2H7dm1fLl3ak1Il07eK3LfvAuXLFaFCBe1i2NXFIdMtbLObrVHDisnLrnq1kgQFRQCkWaaJTz0WZsSOE8H41iui5SztSuSjREIi4tKVc8irHRuS9JLEJEPyumzo7Y6NcdlUK+VK8IP0781ytpMhKdlKuRIZm0TIw6dkMxizGU98Hr8OEBuvz7Fcz7LnygnCYyKfXfAF2HE8CN+G2rGsepkCmV6/itZNzNI/LxtVGaQ8UWjIQ3SeKV/4Op0zocFpTsRSlbGxscbBwZ6HEaYXals3nTapDJrz8zb69G2EvX32WgYFh0Ti6emS/LunzoXgkIdpyjzEU+ecpoz2N2zbeQYPD+endgFbseoQTRpm/a5JWEg0HrqU7knuOkdCQ6Oe8g7zzp25S2KSniJers8unIls7jrHlGweDtwPSX+Rac7tmxE4ONox9qt1fPj6En79cQ96fc4dAXNbttDgKDxSbUceOidCQyLTldHpUu8LdjyMeETZ8p7s2XWRpCQ9d28/4ML5ewQHpWynn33wJ+2afkf+/HY0b+Wd6WzBoZGm27iHU/I2nlwmJBJPDyfTMsaLqrKlPJIrhjZtP8O94JRsJ8/cokPPn+nUeyZjv3k1y3czc+N+8CKOHzkhODSaQh4p+4KnuyPBZiqDnmbrrst06vsnn45Yw73gnDnpDQuNxl2XUvlUUOfA/dCYTH9OwNbLNG1TLltZgkMeUsgzZZvQ6VwJDk5zIRf8EE9jCy4bG2scHfMSEfHkvLa21owc3YvOvhNp2mQ4V68E0a1b+rvcmXE/JMZ0mXk4EJbB41pqu7Zkf5k9Fhxi5viRpsIl3TFGl/4Yc/TfQNwKOFCiWEHS2rz9DJUqFE6ulMlUNpN91PkJxzZns2UCb4Rx9N9Aerw5kzfencups7cAuH4zDCEE7w78jS69f2Legt2ZyhUSEmGSS6dzIcTs9qaVsbGxxsHRPnl7O3UyEN+OE+niO4lRo3tm+bhqTnDwQ3Sp9wVPF0LSHtdSlTGXrcurk+nm+x0jjdl0Ohf6vt2M1i3G0sJnFA4OeWnQsELms4VE4Jkqm6fOheDgiDTZIvDUuZiWecYNues3QhAC3h3wC126TWbeb1stmm3lqgM0aZzSlfnkyUA6vPotnXwnMnb0a9la38EP4vAskDclg6u92coCgP4/HKLR4K3kt7ehTa1C6aav2neLxlXcs5wlXbaIODwLpLTY9XSxJ+RBvPlsPx6h0ZfbjdlSWq3N8LtEs292svbwXT7tVNbse/+fBT+IxdMt1fotYE/Ig1izZftP3U+jTzaS396WNnWe73mI8v9NVQYpz9WZU7ewt7elTFntYH/xwl1u375PsxaZv+jNSbGxCcydt43PPmr7xDIHD19mxepDfPl5xxeYLL2w0GgmjFjL0DEdnnq3+EXSJxk4/e9d3vusMb8sfI2g2w/Zsvbcs9/4AuS2bB0710juWvbDlI1UqeZlctfyxzl9WbfjKxIS9Bw9fO0pn/R8TBjVmSUrDtP1zdnEPEogj23KiWq1yl6sX/YJK/58n7kL9hAfn/jC8z2Wm/aDjBw/cotmDUuzfXl/1vzZlwZ1ivPNxE2WjpQsPCyG61fuU6t+1ruIPS+JiXr++XsPK1YNYVfABMqVL8K8X7dYOhb3w2IIvBJG7frZ7yKWk9ZtOknHNunHorp8NZhpP21m3LDOLzyTXm/g4cNHLFs4kK8/b8+gr5cgpUSvN3Ds30CmTniNJb9/wLYdZzlw6MoLy1W1Wgn81w3j72VfMn/eVoseV9OqWq0Eq9d+w9JlX/DbvG3ExycS+fARO3ecYePWUWzbNY7Y2HjWrTlq6ajJ9El6jh2/xtQp/Vjy1xds23aSAwcuWiTL7DmbsLa2otOrKWMcVqtWgvVrR7Bi2dfMnbflha3v+Z/XJWBaSxKSDBy8EGYybc76y1hbCV6ta5lKhPmf1SFgSnNjtvvJrw/qXI6dk5vx6iuFWbzzpkWyvSzmf9WAgB/bkpCk5+C5UEvHyTUs3SroZWwZpMYMUp7I3cPZpAVDcPBD3FPd3QfwMJbReTqTlKQnOjrOpJnzlo2naNM+ZbC80ydvcv7sHTq1mYI+yUB4eAzvvz2PuX8MyFCmxX/vZdnKgwBU8fZKbooLEBQcgS5N02WdhzNBqe7YaWWcuHkrjNt3wvHtMc34+kO69prO8iWDcC/oxIVLdxkxZhnzZg3A1SV/hrKZU9DDgZBUd+JDg6Nwd3d8yjtMxUTHM+TTZQz4yAfvqjn7pV3Qw4HQ4JTWGaEh0bh5ZKybibvOgdLl3SlUVFveDZqW5vyZoP/bbO46R5M7vyHBkbh7OKUrExz8EI/kfSEeZ5d82iCrX7dLLjfgzXkUK+5m8l47O1uaNKvAnp0XqFu/zDPzLF52iGV+2lhEVSoVMd3GQyLRpcmm83AiKNUd9aCQyOQuHKVLuPP7L30BuH4jjF17TbtvAZQu6U6+fHm4dDWEKpUyvx3mlv3gRR0/smvxqn9Zvlbr2lGlgif3QlL2haDQKHSZ6A7m6pxyl7FHxypMmx2Q7XwABd0dCA1OadUSFhyNm3vmjpUBW6/QoGmpbLeM0Hk4cy/oQfLvwcEPklvpPeahcybo3gM8PV1JStITFRWLy1OO7Rcu3AagWDHtznnbtjWZPy97lUFuHvlNl1lINAUzeFx7LGDrZRo0K52tZbZ42QGWrdYu5s0eP9zTHD/cndLsB6bHmKQkPVt3nmXVXx+bvC8o+CEff/kX343rQTEv02PeE7P9c4Blqw5r2byLptlHHz7h2PbQbBmdzplWLSojhKBqZS+srAQPHsTg6eFMnZolKeCqrf8mjcpz9sId6td99rEXwMPDxSRXcHCESctReLy9RSRvb9FRcem2t9KlPcmXz47Ll+9RuXLOVO7pdM4Ep94XgiLwSHtcM5bx9HR5YrZSpT3Jm8+OK5fvced2OEWLFKCAsZtdi1ZVOXHiOh07mQ5+bs7iJbtZtnw/AFWqFCcoVbag4Ah0qVraaNlcCErVIkc75pqWScvT04U6tUtTwDi4e5Mm3pw9d4v69cu/0GyrVh9k1+4zLPj9U7PdwR6v70uX75oMfv0si3cGsiJAa9VWuaQzQeEpLUWCHsQ9dZBlO1trmlfTseNEMA0racey1ftusetUCH98US/b3dYW77zBir3GbCWcCQpPaaUUFBGHh6vds7OdDKZhJdMWhR3rFub9n4/yyX+gddDibddYsTsQgMolXQm6n2r9hsfh4Zr3Ce8EuzzWNK9RiB3H79GwsscTyynK06iWQcoTVapchJs3wrhzO5zExCS2bjxFk6amXaYaN63A+jXaQJc7tp6hziulkr9cDAYD27acplXblLuF3XvVY+OOoazZ/DXzFr5PsRJuGa4IAujzWiP8l3+J//Ivadm8Cn5rjyKl5MTJQBwd7fFIcxLr4e6Eg4MdJ04GIqXEb+1RWjSrTPlyhTmwexw7No1kx6aReOqcWfXPF7gXdOLuvQd88vkfTJn4OiVLZO/gWsG7MLdvPuDunQgSE/Vs33yehk0z9uWWmKhn+OCVtOlYOfnJSjmpfCUdd25FcO/OQxIT9ezecon6TTL2NJ9ylXTERMUT8UDrEnji6C2KlyzwjHe9vNkqehfh1o1w7t5+oO0Lm07TuKnpOmnctAIb1pwAYOfWc9R+RRtXIC42gdhHCQAcOnAFa2srSpb24NGjeMKMXaWSkvTs23OJ4iUz1mS7T8+6+C8ZiP+SgbRsWgG/9Se0/eD0LRwd7PEoaFrR4lHQEYf8dpw4fUvbD9afoIWPlv/xoKoGg4HZv+/mtW7aHc1bdx6QlKT1279zL4JrgWEUKeyS+YVH7tkPXsTxIyf06VoDvz/ewu+Pt2jRuAz+m85pOc/exdHBLlNjA6UeX2jHvquULp6xi/JnKVfJg7s3HxJ0J1LbR7depl6TEpn6DK27U/ZP9itXKc7NG6Hcvh1GQkISGzYcp1kz01YqzZpVwd9fG5x8y+Z/qVuv3FMvhHQ6Z65eCSI8XNtH9++/QKnSnk8snxGPj2tBxuPari2XqNekZKY+Y9fmS9nuItanZ338l36C/9JPaNm0En7r/zUeP25qxw+z+4E9J07fNB4//qWFT0o3mP2Hr1KqhLtJV7LIqFje++xPBn/ShlrVM37h26dXffz/+Qz/fz6jZTNv/NYd17Kdekq2/HacOGXMtu54craWTStx6MhVAK7fCCUxUY+ra34aNSjLpStBxMYmkJSk58ix65QplX4smiepXKWYcXu7T2JCEhs3HKdZM9OB/5s1q4y/v1aptWXzCerWK4sQgtu37ycfV+/eCef6tWCKFMm5707vysW4cSMsOdumjf/StJnpILxNm1Vmjd8RALZuOckrdc1nC7wWTOEiBfAs5MKpkzeIjdXGzzp08DKlMri8+rzug//qofivHkrLFlXx8z9sPOZex9ExLx7uaSrR3J21be3kdW19+h+mRfOnP/2wUcNKXLp0N2V9HrlCmTLP3ldzMlvAnnPM/20bs2e+T95UD0e5dTss5Xv0TjjXrgVRpEjmjsF9mpVg9ejGrB7dmBbVdfgfvKPlvPoAx7w26SqDYuKSkruOJekN7D4dQilP7Ttjz5kQftt8jVkf1yavXfa7J/ZpVpzVIxuxemQj02zXjNmczWR7aD5bYHBKt90dJ4Ip5Zn1G7Evkz4tS7F6fHNWj29Oi5qF8N+nHctOXAnP2Po9GUypQhm/uaYoaamWQcoT2dhY8/WwTnz6wR/o9ZJOXWpRuoyOOb9spaJ3UXyaVcS3a21GD11Ol/bTcHLOx4QpryW//99jgeg8nSnqlXMnOqn5NK7I7j3nadVhInntbZk4PuWpJr49puG//EsARg/vnvxo6CaNKtCk0dPHAJo5ZwsREY8YO2ElANbWVqz6+4ssZbSxsWLQkFZ8OfBvDAZJe9+qlCztzm+zAihfqRCNmpbl/Nm7jPhiFVGRcewPuMzvc/awcOUAdm45z8njt4iMiGXTGq2VwNBxHSlr5rHSWWFtY8XHXzVl2Cd+GPSSNp0qUaK0G3/OOUC5ijrq+5Ti4tkgxn61nqjIOA7uuc6iXw8yb9mbWFtbMeCzRgz5cBVSQtmKHrTrknNPfcht2WxsrPlyWAc++3AhBr2Bjp1rUqqMB7/O3E6FSkVo0qwCr3apydhhq+jeYQZOznkZP6UHAOHhMQz6YCHCSuDu4cToid0AiI1N5KtPF5OQoEcaJDVfKUmXHs++05qWT8Ny7N53mVZdZmj7waguydN8X5+F/xLtyUCjh3Rk6NjV2n7QoCxNGmgX4es2n2bJCu2ipVXTinR7tQYAx07eYN6CPdjYWGNlJRgzpCMFsthKLjfuB8/r+BEaFkm3135IfmT1n38FsMFvCA4OWXv6mU/9kgQcvEbr137D3t6WiUPbJE/r/PZC/P54C4Cps3azbtsFYuMS8ek6l+4dq/DJOw1YtOJfdu67irW1Fc5O9kwa1uZJs8oUaxsrPvy6MSM+XYNeL2ndqSLFS7uxcM4hylX0oJ5PSS6eDWb81xuJjozn0N7r/DX3MHOXvQ5A8N1IwoKjqVIz+y0ebWysGT6iJ+/1n4nBIOnStR5lyhbi55/W4V25GM2bV6Vb9wZ8M2QhbduMwdk5P9O+fzv5/a1ajCI6Jo7ExCR2bD/Fr/M/okyZQgz8qB1935yBjY01hQoXYOLEN7KV09rGio++8mHYJ2sw6A20Tj6uHaRcRQ/jcS2YcV+tJyoynoN7Aln46yHmLdOeHhd0N5LQ4Giq5sAye8ynUXl277tIK9/vtf1gTLfkab69f8Z/6ScAjP6mE0PHrCAuLokmDcvRpGFKhdSGzafo0Mb0cdl//XOAm7fuM3PeTmbO2wnA7zPfTh7EOcPZ9l6gVaepxmw9UrL1+hH/fz7Tsg3tbHy0fCJNGpanSSOtRUi3zrUZNmYFHbv/gK2tNZPH9UAIgbNTPvq90Zjub/yCEIImjcrTtHHGK5ptbKwZNqI77/efhd5gSN7efvlpPd6Vi9GseRW6dq/P0CGLaNdmHM7O+Zj6fT8Ajh+7ym/ztmFja42VEIwY1RNXY4uWrwYv4MjhK0RERNOi6UgGftyebt3rZzhXcrbh3fhwwBz0BgOdu9SlTNlCzPx5A5W8i9GseWW6dKvHsCF/0aHNtzi75GPKNO0Y8u/xa/w+bzs2NlYIKyuGj+yOq6sDrq4OtGxdjV7dp2FtbUXFikXp3jPz42f5NPFmd8BZWrUdq63PCSn7k2+XSfivHgrA6JE9GTrsL219Nq5EkyZa5d7WbScZP2E54eHRvP/hHCpWKMJv8z7G2Tkf/fo2p3vPKdr6bOJNU5/MfednN9v4b5eRkJjE2+/+Amhdw8aN6c2x49eYN29LyvfoyF7JLZiywqeKBwGnQ2kzfJfx8e0pFWVdxu5h9ejGxCbo+eiXoyQkGTBISd3ybvTy0VqefbvkLAlJBt6drn3nVyvlwpg3M/cE0ydmq+yuZRuxW8vWN1W28XtZPbKRlm3m8ZRs5dzo1UTrJjx99UWuB8dgJQSFC9i/kCeJASx5ZxxNy9WkoIMLtyauYfS6efy+f+0LmXdaPtV0BJwKps1XW7G3s2Fi/xrJ07qM3MHq8c2JjU/ioxkHSUg0LsOKBenVvIRF8uZGL2M3LUsTaZ/8pPw3RSaszJUbglOebhC/3tIxzLPrQPCjBZZOkY4uXz8AbkTNsmwQM4o7DsyVuUDL9iD+H0vHMMvVrhdE5s5sOPXKlfsBGPeF3Hj8sNMeTy5DfrVwkPSEx3tci/zJ0jHMKuX0KUmGzA8O+yLYWLUiMOoXS8cwq4TjxxC90tIx0nMwVjw9Wm3ZHObk60KiYbOlU5hla9WGeP1GS8cwy866Hehz5z6Kdatcnc0QkLWbjs+TVZPpABh2DbJsEDOsms5AfFjP0jHMkrMPYjg4xNIxzLKq913uGHz0OdnjVcHi17ONb114qZaxahmkKIqiKIqiKIqiKMpLS7UMyjw1ZpCiKIqiKIqiKIqiKMp/iKoMUhRFURRFURRFURRF+Q9R3cQURVEURVEURVEURXlpqW5imadaBimKoiiKoiiKoiiKovyHqJZBiqIoiqIoiqIoiqK8tNRT0jNPtQxSFEVRFEVRFEVRFEX5D1GVQYqiKIqiKIqiKIqiKP8hqpuYoiiKoiiKoiiKoigvLTWAdOYJ1bdOMVIbgqIoiqIoiqIoyv8nYekAz9M2j/IWv55tGXLxpVrGqmWQAsC1yJ8sHcGsUk6fkji/t6VjmGXbfykbAr+ydIx02peYCsDRkPEWTpJebY+RrLn+haVjmNWp5HRCYhdaOoZZHnnfQobnzmyiwFs8iP/H0jHMcrXrRaJhs6VjpGNr1QYAeW+WhZOkJwoNJPjRAkvHMEuXrx8xif6WjmFWflvfXL0fEL/e0jHSs+sAgAz73cJB0hMF3+FuzHxLxzCrcP7+3IqeY+kYZnk5fIAMzZ3LTbj3z537AYBdB+SRYZZOkY6oMxEAeegbCydJT9SdjOHgEEvHMMuq3neID+tZOoZZcvZBS0d4rlTLoMxTYwYpiqIoiqIoiqIoiqL8h6jKIEVRFEVRFEVRFEVRlP8Q1U1MURRFURRFURRFUZSXluomlnmqZZCiKIqiKIqiKIqiKMp/iGoZpCiKoiiKoiiKoijKS0u1DMo81TJIURRFURRFURRFURTlP0RVBimKoiiKoiiKoiiKovyHqG5iiqIoiqIoiqIoiqK8tFQ3scxTlUFKhh3df4M53+/FYDDQ1rcSPfvVMpl++vhd5k7fw/Ur9/lmQmsatygDwMmjt/l1+t7kcrduRPDNhNY0aFoqx7JJKZm04zZ7rkVibyOY0L4ElXT5nlj+41VXuf0wHr+3KwEwc99dVp66j2tebZf4rElhmpRyzpFs54+EsHrOaaReUrddcVr2Kmsyfd+6QPatvY6wEtjltaHnZ9XwLO6YPP1ByCMmD9hJ2zfK06xHmRzJ9NjJQ3dZ9OMRDAZJ045l6PRGZZPpG/4+x851V7G2Fji52DNgaD3cPR0AeMNnMV6lXAAoqMvH4MnNcjTbhaMhrJl9DoNB8kpbL5r3Mv3bD6y/wf61N7TlZm9N98+qoDMut7vXIln502niHyUhrASf/tQQ2zzWOZbt0L6r/DhlCwaDpGOX6rzxTgOT6SeO3eSnqVu4djmE0ZO70KxVRQAuXwji+4mbiImOx8rairf6N6RFm0o5lktKyYQfthCw/yr29rZMGtkR7/KF0pX7Yc5O/DeeJjIqjuM7vk5+/Y+lh1ix5gTW1lYUcMnHhOEdKVIoZ/aDA3sv88N3GzAYJJ261uStd5uYTE9ISGLs8FVcPHcXJ+e8fDu1J4WLuJKYmMTkcWu5cPYOwkrw+ZD21KpTMtt59u45x+SJq9AbDHTrXp/+A1qlyZPI0CF/ce7cLVxc8jNtej+KFHFj/74LzJi+hsREPba21gz+qjN165UDIDEhiQnfruDI4ctYWQk+HdSRVq2rZyunlJIJP+8m4GAg9vY2TPqmNd7lPNKV+2H+fvw3nycyKp7jmwaaTNu48xK/LDiEEFC+dEG+H9kuW5keO7TvKj9N3YbBYKBD5+q88U59k+knjt3k52nbtP1gUmeatqoAwOWLwUyfsImYmASsrAVvvtsg2/vBvr0XmTbZH71e0qXbK7zd3/R4lJCQxMihf3P+3B1cXPIxeVofChcpwN074XTrNI3iJdwBqFK1GMNHdyMmJo5335qd/P6Q4Ie061iTr77plOlsz2Pbn/3TNjauPUFUZBw7D43IdCZzpJRM+G41u/ecx94+D5PH98a7UtF05c6cu8XQEUuJi0/Ep3FFhg/pghAiefrvf+7iu+/XcGD3OAq4OuRcthnbCThgPLYNb493ec905X6YG4D/pjPasW3bF8mv/736XxavOo61lRX58tky7uu2lClZMEeyHd53nV+mbUevl3ToUpXX365rMv3ksVvM/H4HVy+HMmrSq/i0LA9A0N2HjPrSD4NBkpRkoOtrNenUvXqOZErOtj+QWdN2YdAbaNe5Mr3ffsVk+qnjt5k1bTfXroQyYmJ7mrQslzytdZ0ZlCyjLSMPT0fG/+CbY7mklEz4cQcBB65px7Vh7fEur0tX7oe5e/DffFZbn1sHpZu+eddFPhuxhuXz36RKhfTbQ6byPKdt/9CRK0yc4kdSkh5Xl/z89cfH2cu56CQBJ4Kwt7Nm0nu18S7palImNj6JQT8d4mZINNZWgmY1CjH4tSoA3A17xDdzjxL1KAG9QTK4V2V8qqc/R8hytr9OEXAyWMs2oBbeJVzSZ/vlMDdDYrRs1T0Z3Es777wT9ojh848THhWPc/48TP2gNp4F8uZItrQ5Jy4+reXMY83EATXT5QQYMG0/oRFxJOkltcu7MfKtalhbifQf+Bz99uZwOlZpSEjUA6qM7/NC5638d6jKICVD9HoDM6cEMPGXThTUOfBZ3+XUbVKS4qUKJJfx8HRg8OgWrPzrhMl7q9UuyswlrwEQ9TCOd7r+Rc16Xjmab8/1SG4+iGdD/0qcuveI8VtvsvSNCmbLbr30gHx50veQfLOWB2+/kv5kJDsMesnKmaf4YFJ9XArm5YdPAqhcz9OksqdWsyI07FgCgDMHgvCfe4b3J6ZcVPnNPUvFOukv/rKfzcCC6YcZ+kMLCrjnY+SAjdRsWJSiJV2SyxQvV4Bv55fDzt6GbasvsXT2v3w6tjEAeeysmfRHhxzPpWWTrJ55lvcm1sW5oD0/fboX73q65MoegBpNC1O/Q3EAzh4IZs2v5xkw4RX0egNLp5yg99fVKVzKiZjIBKytc65HrF5vYPqkTfww53XcdU4M6PM7DX3KUrK0e3IZnacTw8a9yt8LD5m81y6vLcPHd8KreAHCQqJ49/XfeKV+KRyd7HMkW8CBq9y4Fc7m5R9y8uxdxk7ZxLLf3k5XrlmjcvTpXpu2PWebvF6xnI4Vf7xDXntblq46xrSZ2/nh267ZzqXXG5g2cR0//doXD50Tb/eeS+OmFShZOmW7XrPqOE5O9qxYP4itG08zc8ZWJkztif/KYwAsXvUx4fej+XzgIv5Y+j5WVllfp3q9gW/HL2febx/hqXOhV89pNGtWmdJlUk6KV604iJNzPjZuHsWG9ceYPm0N3//wNq6u+fll9vt4eDhz+dJd3h8wmx27xwMwd+4WChRwYP2mkRgMBh4+fJTljI8FHArkxu0INi/uy8lzQYz9YQfLZr+Wrlyz+iXp06Uabfv8afJ64O0H/Lr4KEt+6YGzoz33H2Q/E2jL8IfJW5g++zXcdU6812cBjXzKUqJ0ygW2rpATw8Z2TLcf2NvbMGz8q8n7Qf8+f/BKg1I4OmZtP9DrDXz37WpmzRuAztOZN3r9jE+zSpQqnXI891t1GCenvKzZOITNG07w4/QNfPf9GwAU9XLj75Wfm3xm/vz2Jq+93vNHmrc0rSzPaLbnse039ilPj9516dHxx0xnepKAvecJvBHGlnXDOHnqBmO+XcHyJYPSlRvz7QrGj+5JtarFGTBwHgF7L+DTWKvwvhf0gH0HLlK4kGu692Ur24Fr3LgdzuZ/3tOObdO2sGzeW+nKNWtYmj7datL2tV9NXu/YuhKvdakBwI49l5n88w7mT++Z7Vx6vYEfv9vK1Fk9cdc58sEbi2jgU5oSpUz3gyFj2vHPoiMm73Vzd+CXBX3Ik8eG2EcJvN3jDxr4lKGge85UoOn1Bn6evIPvZnXFXefIR28uoYFPaYqXcksu4+HpyNdjW7Ns0bF0789jZ8PcpW/kSJa0Ag5e58atB2z+uz8nz95j7LStLJuXfl7a+qxB297z002LfpTAouXHqVYp+5UZz2vbj4yMZeyElcyf/R6FC7ly/35U9nKeDOJGUDSbv2/DyavhjF3wL8vGNk9X7u0OZalXyYOEJANvTwwg4GQQTap5Mtv/PO3qFqF3y9JcuRPJe1P3sWNGzlQGBZwK5kZwDJuntuLk1QeMXXCCZWOaps/Wriz1Krlr2SbvTc42ZelpfBt60aVxcQ6eC2X6srNM+aB2jmRLlzMomk1TWnLy6gPG/XmSf0b7pCv3w0d1cMhri5SSz345zKbDd+hQL30F4fO04MB6ftm1goX9Rr3Q+Sr/Lc99zCAhhF4IcSLVT4nnMA8XIcTAZ5fMsflFP+H1D4QQ6c9OTMv0E0L8kol59RBCnBdC7Mxszpx06WwIhb2cKVTUGVtba3xaleXg7usmZXSFnShZtqDJXZK09my/Su36xbC3t83RfDsvP6STdwGEEFQrnJ+oOD2h0Ynpyj1K0LPwaAjv18v6HaTMuHnxAQUL56dgofzY2FpRo2kRzhwIMiljnz9lWSTEJUGq5Xd6/z3cPPOZVB7llKvn76Mr4ohHYUdsbK2p16IEx/beNinjXdMTO3utzriMd0HCQ3LmQvJZbl6MoGChfLgVyoeNrRXVfQpz9kCwSZm0y+3xYrt0LIxCJR0pXMoJgPxOebCyzrm7OefP3KWIVwEKF3XF1taaFm0qsXfXJZMyhYq4UKacLt2+UKy4G17FtQrUgh6OuBbIT0QOXZwDbA+4hG+7qgghqF65CJHRcYSEpT/5rF65CB4F029T9WqVIK9x36zmXYSgkOyduD527sxtihYrQJGiBbC1taFV2yoE7LxgUmbPrvO071QdgGatKnH00DWklFy/GkrtV7TWEAXcHHB0tOf82bvZynP61A2KFXPHy6sgtnlsaNe+Jjt2nDYps2PHaXx9tbvordtU59DBS0gpqVjJCw8PrbVUmbKFiItPJCFBO9asXnWQ/u9pLYysrKxwzYFWEdv3XcO3TUVtnXoXIjI6npD7MenKVfcuhIdb/nSvL193ltc7V8XZWNHi5vrkFpOZoe0Hrqn2g4rp94PCLpQu54FIczfVK+1+4JqfiPCs7wdnTt+iaLGCFPVyw9bWhjbtqrFrx1mTMrt2nKOjr3Zh0aJ1FY4cuoKUMkOffyMwlAf3o6lZK/Mt0p7Xtl+5mhcF3XP2e2H7zjN0frW2tq1VK0FkVCwhoZEmZUJCI4mOjqd6tRIIIej8am2270zZdyZN8eerzzvylNOArGXbexnftpVTjm1R8YSEpT8V045t6fc7h/x2yf9/FJeYY/kunLlH4aKuFC7qgq2tNc3bVGDfrismZTwLO1O6nAdWafYDW1tr8uTRvl8TEvQZ3h4z6uLZIAp7uSRna9q6PPt2XU2XrVRZd6xyeoU9w/Y9l/Ft621cn4WN31Xm1mdhs+sT4Kd5e+nf55XkZZitPM9p21+74TitWlRJriByc8vePrv92D18GxXXcpZxIzImkZAHsSZl8trZUK+SVtmcx8aKSiVcCArXyggE0bFJAEQ9SsTDNWduRAFsP34P34ZexmwFiHyUSEhEnJls7mazXb0blTytbsWCbD9+L8eypbbjeBC+DYs9NSeAQ17tXChJL0lMMuT4MS0j9lw5QXhM5LMLKskMBsv/vGxexADSsVLK6ql+Ap/1BqHJTDYX4IVVBj2JlHKOlHJhDn/su8AAKWWG+uAIIZ5La6+w0GjcdSlfyAV1DtwPTX9B8iwBWy/TtE25ZxfMpODoBDwd8yT/rnPMQ3B0QrpyP++9R9/aOuxt029eS/8Npcsf5xix8QYP45JyJFfE/Thc3FOauToXtOdhWGy6cnvXXOfbfttYO/8cXQdqd5/jY5PYvuwKbd4onyNZ0goPfYSbR8qFYQH3fDwIe/IF2a71V6hWr3Dy74kJekb038Co9zdxNOBWjmaLNLfc7qf/st63JpBJb+9k/W8X8P3QG4CwOzEIIZg37BAzPtrDzuVX070vO0JDovDwTDmhc9c5EZaFSpNzp++QlKiniFfO3UEPDo2ikM4p+XdPdyeCQ7NWobNi7Qma1C+dI7lCg6Pw0KV0N/PQOREaEpmujM5YxsbGGgcHOx5GPKJseU/27LpIUpKeu7cfcOH8PYKDHmYrT0hIBJ6eLsm/63QuhASbfmZI8EM8C7mk5HG0JyLC9Ji3dcsJKlUsSp48tkRGavvOLz+tp0fXKXwx6HfCwrJ/EhccGk2hVC0FPN0dCA41ez/CrMBbDwi8HUHvj5fR68N/2HMoMNuZAMJCovFIta256xwJzcK2du7MXRKTsrcfhIY8xNMz9fblTEja7StVGW37siciQltnd+6E07v7DPr3m83xY6Y3OQA2bzxB67bVnnqj44nZctm2/zTBIZEm+4WnzoXgkIdpyjzEM9Xfo5XR/p5tO8/g4eFMhfJFcj5baDSFPFId2zwcM31sW7zyOK16zGXarF0MH9QyR3KFhUabfh94OBIWkvH9MyQoknd7/kGv9nN4re8rOdYqCB7vo6m/qxy4n4ljR0JCEgPfWMzHfZeyb+eVZ78hE4LDoinkkZLN08ORYDOVQU9y9mIw90IiadogZ76jnte2H3gjhMjIWN58ZyZde03Hb41p67BM53wQSyG3lHMjzwJ5CX6Q/tzosciYBHb+e4/63loly8ddK7Jm3018PtnA+1P3MeKt6tnKY5ItPJZCBdJkC09/vps+m1ZxVd7Lma1HtcrurUfvEhOXxIOo+BzLl5zzQSyeJsvQPl2F2mP9p+6n0ScbyW9vS5s6OX9cU5Tc4IU/TUwI4SCE2C6EOC6EOC2E8DW+XkIIcVEIsRA4A3gJIb4SQhwRQpwSQox9ysdOBkobWx5NFUIsFEJ0TjXPxUIIX2OrHH8hxC4hxGUhxOhUZd4QQhw2fsZcIcRTBxgRQkwQQpwUQhwUQuiMr40RQnxp/H8dY+7Hmc6kenthIcQmY4YpT5nHKKAR8JvxM0oIIfYYl91xIUQDY7mmxtfXAOeEENbG8o+X3ftP+1telPCwGK5fuU+t+jnbRSyjLgQ/4lZEPC3LuaSb1qu6OxsHeLOyX0XcHWyYuvPOC83WqFNJRixoScd3K7FliXZ3fdOii/h0KYVdXsv35ty7+RrXLoTTsXfKuB4/Lu/Ct/Pb8/Hohiz6+SjBd3KmFUlmNOxUgqF/NKPDuxXYvvQyoDWNv342nNeH1GDg9w04sy+Iy/+GvfBsTxMWGsW3I9YwdOyr6e4W5wZrNp3m7IV7vNunnqWj0LFzjeTuNT9M2UiVal452u0vq65cvsf079cwamwvQNvugoMiqF6jJMtXfU216iWYNsXPsiGBJL2BG7cjWDijG9+PasvIaduJfA4n2FkRFhrNhBFrGTqmg8X2g4LuTmzYOoylKwbxxVevMvzrJURHm15cbd54kjbtq7/wbLl12zcnNjaBufO28dlHbS0d5Yn6dKvJ1uXvM/jDpsxecMDScQDw8HTit2Vv85f/ALasO0u4mVZ/lrJkXX9m/dWHYRPaMev73dy9FWHpSAAYDJLJP+9kyMc5O05hVj1t29frDZw9d4u5v/Rn/pz3mPXrVq4HhryQXEl6A4NnHubNNmXw8tAqGdcfuEWXJsXZ/XN75n7VkCGztTEjX7QkvYHBs4/yZqvSeHloLVq/7l2ZIxfC6DJiB0cu3kfnav/Cx+hJa/5XDQj4sS0JSXoOngu1aBYlYyzdKuhlbBn0Iq4y8wohThj/fx3oAXSRUkYKIQoCB42VGABlgb5SyoNCiNbG318BBLBGCNFEShlgZh7fAJWllNUBhBA+wOeAnxDCGWgA9AXeMH5eZeARcEQIsR6IAXoBDaWUiUKIWUAf4EmtfPIDB6WUw42VOQOAb9OU+QOtRc8BIcTkNNOqAzWAeOCiEOJnKWW6phVSynFCiObAl1LKo0KIfEArKWWcEKIssBR43KG2pnEZXBdCvAc8lFLWEULYAfuEEFuklCa3PI3l3gP4dkYver9tOghuagXdHQgNTrlrExYcjZt7+i4JTxOw9QoNmpbCxiZnBvJdejyUFae0i/zKhfIRFJXSEig4KgGdQx6T8ifuxnA26BGt555Bb5Dcf5REv78vseC1chRM1eWoe9WCfLQqZ1qTuLjZExGacsfhYVgczgWfPCBejaZFWPHzKQBuXHjAyb13WfvbOWKjE7ESAps81jT2zf7guaC1BLqfqttXeOgjXAum70Jy5ug9/BedYcTPrU0GYS7grpX1KOxIxeo6Ai+FoyuSM90WnMwtN7cnN2eu5lOYVT9r9a0uBfNSqkoB8jtr679CHQ/uXHlI2Ro5M1iou4cjIUEpFV+hwZEU9Mj43x0THc/Xn/zDgI+b4l01+3eaFq84yvI1/wJQpWJh7gWntDoICo1El8muJPsPX2fOgn0smvVmjjS/B63VSOqWNyHBkbinusv/uExw8EM8PJ1JStITHR2Ps0s+hBAM+jplwOMBb86jWHE3ssPDw4WgoIjk34ODI0xab4DWuiToXgSenq5anqg4XFy0Y15Q0AM++2Q+Eye/SbFi2h1XF5f85M2bh5atqgHQuk0NVq04mKV8i1efZPk6bXuuUkHHvVR384NCo9FlovWAp7sDVSt5YmtjTdFCzpTwcuHGnQfZGmwVoKCHAyGptrXQ4CjcM7GtxUTHM+TTZQz4yCfb+4G7hzNBQam3r4d4pN2+jGV0ni7G7SsOF+P29Xg7r+RdlKJebtwMDKVSZe2mxaULd9HrDVTyztpYEblt209r8d97WbZS206reHuZ7BdBwRHoPEz3C52HM0Gp/h6tjBM3b4Vx+044vj2mGV9/SNde01m+ZBDuBU3/3gxnW3mc5WtOatkqenIvVYuqoJCoTB/bHuvQsiJjp20Gsj/mXUF3B9Pvg5AoCnpkvnVPQXcHSpQuyOl/bycPMJ3tbB4OhASn/q6Kxi0Tx47Hf0fhoi5Uq1WUKxdDKOzlkuU8i1ceZ/la7fymSsVC3EvVojYoJArdE7qDpRXzKIHL18N465O/AQgLj2HgkFXM+q5rpo5rL2Lb99S54OKcn3z57MiXz47atUpx4dJdSpbI+DiQi7deZflO7fS9SilX7t1POTcKCo9F94SuXqN+O05xTwf6tk15aMnK3YHM+7oRADXKuhGfqOdBVDxuzlnrLrZ42zWW7wrUspV04V54mmxPGAB61O//UlyXn75tUx4MonPNy8+faTegYuKS2HLkDk7585h9f1Zyrtit5axc0pUgk2UYh4frk8/L7fJY07xGIXYcv0fDyjk/fqeiWNqL7ibWBa1iZ6IQ4hSwDSgCPB7l8YaU8vHZc2vjz7/AcaACWuXQM0kpdwNlhRDuQG9gpZTycb+frVLK+1LKWGAVWsubFkAttMqhE8bfn/aoqwRgnfH/x4ASqScKIVwARynl41tPS9K8f7uU8qGUMg44BxTPyN8F2ALzhBCngeVA6sevHE5V2dMaeMv4txwC3DCz7KSUv0opa0spaz+tIgigXCUP7t58SNCdSBIT9ezeepl6TUpkMLZm15bLNG2ToVWYIb1rurOyX0VW9qtI8zIurDkbjpSSk3djcLCzxt3BdFyi12q4s3NgFba8X5mFr5ejRAE7FrymdVlLPb7Q9ssRlHlKhU1meJV3IfRODPeDYkhKNPDvrjt41zMdpDr0TsqF3rnDwRQsol1wfjq9EaMWtmLUwlb4dClFy9fK5lhFEECpCm4E3Y4i5G40SYl6Dm4PpFYj0wuewEvh/Db1EIMnNcU51QlHTFQ8iQl6AKIi4rh0JpQiJXLmqVMAXuWdCbsbQ3jQI5ISDZzYfZdK6ZZbyh3UC4dDkpdbuVruBF2PIiFOj15v4Nrp++iK5VzT+wrehbl9M5y7dyJITNSzffM5GvlkrOtjYqKeYV+soG3HqslPGMuuPt1r47dwAH4LB9CiSTn8N55CSsmJM3dwzG9ndmygJzl3MYjRUzYwa2pP3ApkrrL3aSp6F+HWjXDu3n5AYmISWzedpnFT0wHeGzetwIY1JwDYufUctV8piRCCuNgEYh9pFb2HDlzB2trKZPDdrKhcpRg3b4Ry+/Z9EhOS2LjhOM2aVTEp06xZZfz9DwOwZfMJ6tYrixCCyMhHDPxgLoO+6ETNmilfE0IIfJpW5shhrTvFoYOXKF0maxUufbpUw++3Pvj91ocWjUrjv/m8tk7P3tPWqZmxgZ6kZaPSHD6htXR8EBFL4K0IiubAE+K0/eBBqv3gPA2bZuz4npioZ/jglbTpWDn5CWPZ4V25KLduhnHndjiJiUls3ngSn2amTyfzaVaJdf5HAdi+5TR16pZBCMGD8Gj0eu1W3u1b97l5M4wiXikVLps2nqBNu+pZzpbbtv20+rzWCP/lX+K//EtaNq+C39qj2rZ2MhBHR3s83E0rcjzcnXBwsOPEyUCklPitPUqLZpUpX64wB3aPY8emkezYNBJPnTOr/vkiyxVBoLXk8fvzbfz+fFs7tm06k3Jsc7B74lgy5gTeCk/+/679VyletMBTSmdcBe9C3Ln1gHvG/WDH5gs08MnYUz9Dg6OIj9POPaIi4zhz4k7yWFo5oXwlT2O2hyQm6tm15SINfDL2FNeoyDgSErTT5ocPYjl78q7JwNNZ0adbTfwW9MNvQT9aNC6D/6azxvV5N1Pr09HBjoPrP2bHivfZseJ9qlUqnOmKIHgx236LZpU59u91kpL0xMYmcOrUTUqXzNyDSvq0Ko3fxJb4TWxJi1qF8d97Q8t55T6O+WzNVmTMWH6WqNhEhr1RzeT1Qm75OHBWa5l09U4k8YkGCjjZpXt/hrO1LIXft83x+7a5lm3fLWO2cC2bS/pKphkrzhEVm8SwPlVNXn8QFZ/cSunXtRfp1iSjl0YZy7l6fHNWj29Oi5qF8N93MyVnXpt0OWPikpLHEUrSG9h9MphShXJ+7E5FyQ0s0f+kD+AO1DK2wgkEHu+FqdvHCmCSlHJuFuezEK0l0GtA6sfppG0PKY3z+lNKOTSDn50oU0b605P55Zi6jX5m3v85EAxUQ6vIS92WPe2y+0RKuTmTuZ7I2saKD79uzIhP16DXS1p3qkjx0m4snHOIchU9qOdTkotngxn/9UaiI+M5tPc6f809zNxlrwMQfDeSsOBoqtR8Pn1um5RyYs+1h7Sbd5a8tlaMb5fyJdJtwXlW9nv6hff3u+9w0dhKpoizHaNbF8uRXNbWVnT7qApzhx3EYJDUbV2MQiWc2PjnBbzKuVC5vid71lzn0vEwrG0E+Rxsef3LGjky72dms7Gi3+d1+G7wdgwGiU+H0hQt6cKK+ScpWaEAtRp5sWTWceJik/hx1B4g5RHydwIj+W3aIawEGCR06uNt8hSybGeztqLzwMrMG35Ye7R866J4lnBk88KLFC3rgnd9HfvXBHL53zCsbKzI52BDr8HaSU8+R1sady3JT5/uBaG1DKpYN+eeEmdjY8Xn37Rh8IdLtUdq+1ajZBl35s/aTYVKhWjUtBznz9xl+BcriIqMY3/AZX6fHcCiVe+zY8s5Th6/SWTEIzYa73gPG/cqZbPZSuMxnwZlCNh/ldY9ZmFvZ8vEER2Tp3V+ax5+CwcAMPWX7azbcpbYuER8Ov1E907V+aR/E6b+sp1HjxIZNHwlAIV0zsyemv0n7tjYWPPlsA589uFCDHoDHTvXpFQZD36duZ0KlYrQpFkFXu1Sk7HDVtG9wwycnPMyfkoPAMLDYxj0wUKElcDdw4nRE7vlSJ5hI7rzfv9Z6A0GunStR5myhfjlp/V4Vy5Gs+ZV6Nq9PkOHLKJdm3E4O+dj6vf9AFi6eA+3boYxZ/Ym5szeBMCv8wfi5ubIF4M7MXTIIiZPWkWBAg58O+H1bGf1qVeCgEOBtO7zJ/Z2Nkwc0ip5Wud3F+P3m/a42alz9rJu20Vi4xPx6f4b3Tt488nb9Wj0SnH2Hr1Jh76LsLISfPVBI1yds1/ZbWNjxaAhrfhy4N8YDJL2vlUpWdqd32YFUL5SIRo1Lcv5s3cZ8cWqlP1gzh4WrhzAzi3nOXn8FpERsWxaow3AOnRcR8qaebR0xrJYM2SYLx+9Px+D3kCnLnUoXcaT2b9sppJ3UXyaedO5ax1GDv2bTu2+w9k5H5Omauvm+LHrzP5lCzY2VlhZCYaN6oqzc0oLya2bT/HTrHeysZyez7b/8/TNbNlwmri4RF5tOY1OXWsyYGD6pwplhk/jiuzec55WHSaS196WieN7J0/z7TEN/+VfAjB6ePfkx2s3aVSBJo1ypmL7qdnqlyLgwFVa9/wVe3sbJg5rnzytc98/8PtTO82bOnMn67ae045tnWfS/dVqfPJuIxavPM6BI4HY2Fjj5GjP5BHtnzSrTLG2seLTIS35+qMVGAwG2nWqQsnSBfl99l7KV/KkoU8ZLpy9x8jBfkRHxnMg4Cp/zNnHghXvcOP6fWZP36k9NEJKer5Zh1Jl3Z8900xk++Tr5nzz8SoMeklbX29KlC7Igtn7KVdJRwOf0lw4G8SYL9cSHRnHgT3X+HPuAX5b3peb18P5YcI2rKwEBoPktX51sl0ZlJq2Pq/Rutc87O1tmTgspQVc534L8FvQD4Cps3axbut5bX12mU33jlX55N2GOZYjOc9z2vZLl9LRuGF5OnWfhpUQdO9al3Jls/70Lp/qngScDKL14M3aY9HfS3naVudh2/Cb2JKg+4+Y43+BUoUd6TpiO6BVKPVoVpIhfaoycv5x/tx0RbvIer92lsZCM5utmk7L9tVWLVv/minZRuzA79vmBIXHMmfNRUoVcqDrKO25OH1alqJH0xIcOh/GD8u1gf/rVCjIqLeqmZ1PjuQ8FUybr7Zq36n9U865u4zcwerxzYmNT+KjGQdJSDRgkJK6FQvSq3mJ55LnaZa8M46m5WpS0MGFWxPXMHrdPH7fv/aF53iZWKDX40tP5PTTC9LNQIhoKaVDqt8/A8pIKT8RQjQDdgCPmzusk1JWNpZrDYwHWkgpo4UQRdAqYdJ1thVCuAHHpZTFU72mAw4DQVLKusbX+gET0bqJxaK1mnkHrcuYP1o3sRAhRAG0lj03nvU3CSG6Ax2llP2EEGOAaCnlNOMYQe9KKQ8JISYCnaSUlY0ZakspPza+fx0wTUq56wnz2kVKN7EfgNtSyu+FEG8Dv0sphRCiqbFMR+N73gPaAz2MFW7lgDtSyid2Rr8W+VOu3H1KOX1K4vzezy5oAbb9l7Ih8CtLx0infYmpABwNGW/hJOnV9hjJmutfWDqGWZ1KTickNqfHf88ZHnnfQobnzmyiwFs8iP/H0jHMcrXrRaIhx+rEc4ytVRsA5L1ZFk6Snig0kOBHCywdwyxdvn7EJPpbOoZZ+W19c/V+QPx6S8dIz07rpiXDfrdwkPREwXe4G5P+cea5QeH8/bkVPcfSMczycvgAGZo7l5tw75879wMAuw7II8MsnSIdUWciAPLQNxZOkp6oOxnDwSGWjmGWVb3vEB9afsxFc+Tsg7lvoMoctCpveYtfz3aNvfhSLWNLtAxaDKw1dnU6ClwwV0hKuUUIURE4YKy1jkZr6ZOuMkhKeV8Isc9YAbNRSvmVlDJYCHEe8EtT/DCwEigK/CWlPAoghBgBbDE+xSwR+AgwWxmUQe+idekyALuBnHgEyCxgpdAeX78J09ZAqc1H67p2XGgLLxTonAPzVxRFURRFURRFUZRc5WUcwNnSnntlUOpWQcbfw4D6TyheOU3ZH4EfMzgfk/b4xsGWHw+ynNptKWVnM+//B8jQbb3Uf5OUcgWwwvj/MamKnZVSVjVm+Qat4gsp5QJgQar3d+QppJRNU/3/MpC6k+0Q4+u7gF2pyhmAYcYfRVEURVEURVEURVGUZLnzGaXZJIRoCZwHfpZS5kSLnKzoYHys/BmgMemfNqYoiqIoiqIoiqIoivLCWaKbWJYZxwbabmZSCynl/ce/SCm3YeYJXWlb5WRgfoeAtMPsvymlPP2s92ampVF256UoiqIoiqIoiqIo/1Wqm1jmvVSVQcYKn+ovcH51/x/npSiKoiiKoiiKoijKf9dLVRmkKIqiKIqiKIqiKIqSmmoZlHn/l2MGKYqiKIqiKIqiKIqiKOapyiBFURRFURRFURRFURQLEUL0EEKcFUIYhBC1n1KurRDiohDiivGp5Y9fLymEOGR8/R8hRJ5nzVNVBimKoiiKoiiKoiiK8tIyGCz/k01ngK5AwJMKCCGsgZlAO6AS0FsIUck4+TvgByllGeAB8O6zZiiklNkNrfx/UBuCoiiKoiiKoijK/ydh6QDP0xJR3uLXs6/Li9lexkKIXcCXUsqjZqbVB8ZIKdsYfx9qnDQZCAU8pZRJacs9iRpAWnksxw4OQoj3pJS/5tTn5SSVLfNyay5Q2bJKZcua3Jott+YClS2rVLbMy625QGXLKpUta3JrttyaC1S2/xc5URGTXUKI94D3Ur30aw6vvyLArVS/3wbqAm5AhJQyKdXrRZ71YaqbmPI8vPfsIhajsmVebs0FKltWqWxZk1uz5dZcoLJllcqWebk1F6hsWaWyZU1uzZZbc4HKpuQQKeWvUsraqX5MKoKEENuEEGfM/PhaIq9qGaQoiqIoiqIoiqIoivIcSSlbZvMj7gBeqX4vanztPuAihLAxtg56/PpTqZZBiqIoiqIoiqIoiqIoudsRoKzxyWF5gNeANVIbCHon0N1Yri/g/6wPU5VByvOQm/u1qmyZl1tzgcqWVSpb1uTWbLk1F6hsWaWyZV5uzQUqW1apbFmTW7Pl1lygsim5gBCiixDiNlAfWC+E2Gx8vbAQYgOAsdXPx8Bm4DywTEp51vgRQ4AvhBBX0MYQ+u2Z81RPE1MURVEURVEURVEURfnvUC2DFEVRFEVRFEVRFEVR/kNUZZCiKIqiKIqiKIqiKMp/iKoMUhRFUSxCCGEthJhm6RwvGyGElRCigaVzKIqiKIqiKC8vVRmkKBYkhPAUQnQSQrwqhPC0dJ6XiRAin6UzPEluzSaEKC6EaGn8f14hhKMl80gp9UAjS2Z4GiHEFCGEkxDCVgixXQgRKoR4w9K5pJQGYKalczxJbl1uQoh8QoiRQoh5xt/LCiE6WjqXknVCiM8y8tqLlpsrbIUQZYQQDc283lAIUdoSmdLkyCeEqGr8sbN0HuX/X247N3ost+ZS/r+oyiAlW4QQUUKIyCf9WDofgBCi9OMTCiFEUyHEp0IIFwvHQgjRHzgMdEV7DOBBIcQ7lk2lEULYCSFeF0IME0KMevxj6VwAQogGQohzwAXj79WEELMsHAvI9dkGACuAucaXigJ+FguU4l8hxBohxJtCiK6Pfywdyqi1lDIS6AgEAmWAryyaKMV2IUQ3IYSwdBAzcuty+wOIR3tKB8Ad4FvLxTFlPH68LoR46/GPpTM9JoRoJIR42/h/dyFESUtnMupr5rV+LzpEWrm8wnYGYO78LNI4zSKMlcczgNto++oC4JoQ4hvj9OoWzFYn9Q074/7pL4T4SQhRwFK5UhNC6IQQvwkhNhp/rySEeNfSuSDXZ8uV50a5NZfy/0dVBinZIqV0lFI6AT8C3wBF0A5YQ7DgSUUaKwG9EKIM2uMZvYAllo0EaBdHNaSU/aSUfYFaaMstN/AHfIEkICbVT27wA9AGuA8gpTwJNLFoohS5OdtHQEOMFwFSysuAh0UTaezRlldz4FXjT25prWFj/LcDsFxK+dCSYdJ4H1gOxBsr36NySwU8YGv8N7ctt9JSyilAIoCU8hGQKyrThBCLgGloLeXqGH9qWzSUkRBiNNp301DjS7bAX5ZLBEKI3kKItUBJY2Xy45+dQLgls6WSWytsdVLK02lfNL5W4sXHSfY94AAUl1LWklLWBCoCpYQQs4HVFsw2F0gAEEI0ASYDC4GH5J7Hfi9Ae9R0YePvl4BBlgqTxgJyb7bcem6UW3Mp/2dsnl1EUTKkk5SyWqrfZwshTgK5oTWJQUqZJIToAvwspfxZCPGvpUOhXQBHpfo9yvhablBUStnW0iGeREp5K835td5SWdLKxdnipZQJj7MJIWwAadlIIKV829IZnmKdEOICEAt8KIRwB+IsnAnQKuItneEp1uTS5ZYghMiLcbs3domJt2ykZLWBSlJKi++TZnQBagDHAaSUd3NBd4X9wD2gIFolwmNRwCmLJErvfeALtJtRsWgVj9J4A82SXJ4yLe+LCmFGe6Bs6n1AShkphPgQCAPaWSwZWEspH1cy9gJ+lVKuBFYKIU5YLpaJglLKZUKIoQDG897ccv6Rm7PlynMjcm8u5f+MqgxSckqMEKIP8Dfawao3uaclSaIQojdac/JXja/ZPqX8i3IFOCSE8EdbZr7AKSHEFwBSyukWzLZfCFHF3N3DXOCW0MZikEIIW+Az4LyFMz2Wm7PtFkIMA/IKIVoBA4G1Fs6EEKIcMBvtbnVlIURVtMpli3ffkVJ+I4SYAjyUUuqFEDFo+6nFCCFqPm26lPL4i8pijhDCCm27mkrKcnuEhZeb0WhgE+AlhFiMdte1n0UTpTgDeKJVcOQ2CVJKKYR4XImW39KBpJQ3gBukdPnLdXJxhe1RIcQAKeW81C8au64fs1Am0G7cpbvYNR5DQqWUBy0RyshaCGEjpUwCWgDvpZqWW66lYoQQbqRUdtdDa7mUG+TmbLny3Ijcm0v5PyNy500o5WUjhCiB1lWsIdrBfh8wSEoZaMFYgNY3GfgAOCClXGoc66CnlPI7C+ca/bTpUsqxLyrLY0KI02jrzwYoC1xDu3P++I5m1RedKS0hREG0ba0lWq4twGdSSou3qsrl2ayAd4HWaNk2A/Mt3RJBCLEbrcvkXCllDeNrZ6SUlS2Zy5ijB7BJShklhBgB1AS+tWSFixDCgFZxEPb4pVSTpZTyf+3de7zlc93//8dzRuXUhJqUwoXKITmFVK6rJEpnjBBdLnT4FlfppNJB0eGqK7+KrpA0jhWSSqlEolTOhyF0MHSgJIcRiuH5++P9XrM/s2bvPVu29XrvNa/77bZv1vqsPfbztmb2Wuvz+rzfr9eLBp9qYZIu6/1dtqL++58FnA1sQXnefmn71nH/4IDU7U0bUfrILVitZPtVUZl6JL2b8n6wDfBJYC/gq7YPC8x0F6NfJW9l9Q11e9huwBq2D5a0KvBk2xcG51qZsuXqPkaKP5sCjwa2t/3noFzfAr5p+7i+47sDO9kOKyhL+gBl5dKtwGrAJrVA+jTgWNuLNOQetHqh4DBgfcp7xExglu3wlXKNZxPwBtr7bNRkrjR8shiUliiSVgRWbeENqKvmuiP6RV7S6uM9Xq/GhpK0qu0/9B17UtQH2PTwSLrI9mbdAoKky21vFBwNSVfa3kDSlpRGw/8LfNj2cwIz7UcpatxJWYl5mu2/R+UZjaTPAL+gnNg18yFD0sW2m+jD00/SC0Y7bvvcQWcZTb0yveCkxPaPgiM1r/a5eRB4ke116/v8mbY3C44GgKStKCfnAFfb/nHf4yvavn2AeZ4CfJOyvbRbpFqGUqT606CyjKauZnky5e/w7nrsGcDyvQsEg37OOtmmA2+jFFzWpvyeXmf7/kFn6TcFsl1te53oLF2t5krDKYtBaVK0vNVD0k+AV1FWu1wC3AKcb/udQXk+DJxs+1qVKWffp1wRng+8zvZZEbm66oeeq23fVe/PANa1fUFsMpA0n9I4dy/b99Zjl9Zmk1GZDmOcvdy23zbAOAvprPYaVfRqL5XpIvtSGg1vImkWsLftyP4QwMgKF0mfBObY/morq14krQnsQtl+dSPwCduXh4aq6qqN5Sivaf+gkdUakv6HcmX/JDrbmDu9QELVYvzTbZ8laVlKn5K7FvfnlmSSVhvtuO3fDzpLv977Ul+h+wov3F+xWVHvq5JeBDyz3v2V7bP7Hg8puExE5GcRSRfa3jziZy9O49m+Dfx3C68ZXa3mSsOnlX2uaeo7irrVA8D2lZK+Shtjex/n0oTwDcBxtg+UFLkyaGfg4Hp7D8pUv5nAM4BjgfBiEKWw1/1A8/dRjkWZA/wUOF/STrZ/R/xEoIuDf/54WpnMNZZ9KNNY1pH0J2AuZWtFC/4k6UjK9phP1eJtE1M4bV9fPywuA7ye8vpxeWioquFeKTvX/+7TOWZgzYAsC1EZI/wmYCVgLcpkziMo/UlCjbEl607K6967bF8/+FQLfK9ze2lgDeA6RooJke6vV/h7fVJmUlYKTRUh76t1hdKPx/mWs2njs8hoIj+LnC/pCyxa7A7tI1e1nG1F4GpJF7Jwtugtuq3mSkMmi0Fpsixr+0ItPEVpflSYPktJejLwWuAD0WGozTjr7ZcAX7P9AHCNyrSAFqi7vcP2gw1ls+0vqkyrO13SewmesGD72MifP54WtvYthm2/WKUp7bTan2eN6FDVa4GXAp+xfUd9HXlPZKC+FUF/oGwV+0RvlVwkNd7c2nYr/65Gsw+wOXABlDHCkloZI/w54I/AVyknu7tQClaXAl8BXhgVzPazuvfrv8G3BsXpdyilN88TJX2csr3zg7GRHpJWtw5EX/wZT+RztlH970GdYwbC+8jRdrYPRQcYQ6u50pBp5eQuTX23qozp7V0Bm0U7U1EOojRe+5nti+rJ1G8C8/xT0vrAX4CtgHd3Hls2JtIirpf0NspqICgfriOv/nYJwPb5krYGTgaa2Fddr/y+F1iPcpUagEaa+m5B2bO/LqVR6HTg7uitO8CplGac3emD3wCeHZQHSTNsz6P8Hf6kHluJ0tg3ehXYbymjs78NzKM0M31LrxDv2CmEFzNOc2sa+OBfX3v7fz+PG/tPDEzLY4Rf1be16Uu1r9d7VabdNMP2pZLCenp12T5R0iWU1V0CXmO7lemSU1krvxdNsb1VdIaxNJ6tib5s/VrNlYZPFoPSZGl2q4ftUyg9Znr3rwd2jEvE2yknuzOBz9qeCyDpZcBlgbm6/h/lquYHKR+8zmbhUaqRXta7Yfvm2gTzeYF5uk6kLIN+OeU53AP4a2iiEV+gXNE/hdKU8z8pW4tCSFqHspXjcZJ26Dw0g86JepCvUrbXXUL5999f1IjcVnQQIydDywfmGM07Kasf7qXB5tYqExxfSCkGnQFsB/wMaKEYdK7aHSN8j6TXUt63oPwd/6Pejh560O39N42yfeimoDjAgsJxzy3A17qPtdKjagJaXoHTqtDnTNLLKe+r3WL3QWP/icFpNVurF8pazZWGTzaQTpNC0nTbD3S3ekRn6pG0NGWkdv+b0F5hoRpWexwcZ7uJYl6PpN1tn9D34X+B4BURAEi6xPaze1Oo6rGLWpge05uk1JctrBmypFcDr6E0d/9O56G7gK/b/nlErmEh6f22Pxn0s5tsbl2bqW8IXGZ7Q5UR2yfY3iY4WtNjhOvf5+eB51KKP78E3gH8CXi27Z8FZjuwc3c+cANwqu1/jP4nHnmS5jJSRF4NuL3eXgH4fUvbFVUmJT7d9uy6snX5zgWqJgtX0U38W33OJB1BWV2+FfBlStH2Qtt7R+TpajzbxYxyocz2+zNXWhLkyqA0WeZK+gFlVcR4jf8iHA9cS+nPcxBlxVL4Um1JjwcOBLakfHD8GXCQ7b9F5qpFvdUlPdr2fZFZ+ixX/9tqc1qA3qjUm+tVsJsoDWFbcI+kRwOXS/o0ZRtnWDNk298Gvi3pubZ/EZVjNK33vpmgnYCQYlDDza3vrf3P5qtMSLwFWDU6lBYeI3xUdJ5+dTXtK8d4OKwQBGD7o5E/fzS9Yo+koyir486o97ejFMCbUAtpm1LGfc8GHgWcADwfYqfsjVdwIbCpesvPGfA82xvUCz4flXQIZVptC1rOhu3f9i5qA7MlXQaEF11azZWGSxaD0mRZh7KtYh/gaEnfpVzdD/2gWD3N9k6SXm37WJUpZz+NDkXZRnEeI1vWdqMU014clmjE9ZTpD99h4SkGYatvbPcm1TX34b/jY5IeB7yLsrx3BuUKegteT1lmvC8l06rEbpfs2V7S1ZStRT8ANgDeYfuEwEzN976ZgIFvV2i5uXV1saQVKAWXSyhTEsMLkbUAf52k1dzgGOEWV9dKOp1xtqg1MnFnC9tv7N2x/f1aiG/F9sDGlEbg2L5JUvjFlsYLLk0+Z1VvNdw9klYB/gY8OTBPV8vZmrpQ1tFqrjRkshiUJoXteyiNfE+WtCJlSfm5lJPPaL3VGnfU5qF/BlqY0vJk2wd37n9M0s5jfvdg/a5+TaORlTgqo5d/UqfsCDiaUsy4EdjDdni/JdvfrTfvpCyHbkZnqti9QEsFtW1t7y9pe8oWjx0oRdLIYlDTvW8mKGJ7UcvNrbHdmzJ1RF3JOsP2lZGZOloeI9zi6trP1P/uADyJkdeLXSnDGVpwk6QPMpJtN4L7GfW5z7Yl9QZ/LLe4PzAgLRdcWn3OoExXXQH4X8pzZ9pZadhyttdTPuu2dqGs1VxpyGQxKE0aSS8AdqaMYr6YMpa5BV+qBaoPUXqTLA98ODYSAGdK2oVSRINy8vnDwDwLNLr65u3AMfX2rpTeH2tSPjQeCvx7TKwRKiPR/xv4Nzqvry2c0El6BXAwsDolmyhj3aObET6q/vflwCm27+wVD6LY/hzwuc5Kl7MlNdP7ZoIinsRmm1vXrVgr2u6t9roJ2FbSSbbXDYzW0/IY4eZW1/Ym7Ug6xPamnYdOr702WrArZSv4afX+efVYK06WdCSwQr3YshdtnKC3XHBp7jmTtFMdlHKC7TuAU+vq/KVt35nZxsx2tu2tgbfafi9l9VL4Z99Wc6XhlQ2k06SQdANlEtbJwHe88Jjo1CHpLkaaSy4HPFAfmg78vYGT896I9P1ZdFtA2PYYlVHGG9XbXwUusP35ev9S2+P2eRkESVdQVizNAR7sHXcDI0Il/ZZyFX1OC01peyT9D6WPxr3A5pQmq9+13cR4aEnPpBSEXg/sb/vkxfyRJkg6wPYnonOMRgNubl2L7kdSVtz8Bvg48BXgIuDgKdIDKoykC21vLuk8ypSzP1Oav0ZO1QNA0jXAy2tfo15B/oxGCnzNU5lct229e6btH0XmAZD0buDpwDaUvmd7AV+1fVhosKq156z3+aeVz0FdjWf7FaVp/9HA6+i7gBL1vtBqrjS8shiUJoWkGbbnRefo0hhTp3qityy0TNKZlP5F76YzIr1epYjKdCll9cjtlK1hL7J9dX3smhY+/Eu6oJUiRj9J5wBb235wsd88YCqjmO+svVOWpWzf+XNgntF633yvod43SHoGcDiwsu31JW0AvMr2x4KjLdagTwwkXQW8pjbj3ITSJ2iW7fDR7Z2LA6Nq5OLAG4BTKf28ZlNX19o+IjQYIOmlwJcofe5EWfn4Ztthq2ynSD8jACQ9iVKEN3BR5OtuV2sFl67WnjNJP6pZNmOUFXuR/94azzaL0gttS8qFgYV6A0Zd/Gw1VxpeWQxKD4uk/W1/WtJhjPLhx/bbAmIBi4ycXUTUVihJ69i+VmNMLGqh6q8GR6TXbU5HUlZQnd5rzFm3J+5v++VR2XokvY5yRfNM4J+94438nW5G2SZ2LgtnCy+K1l5e67HwKrTjAvM8yMK9bxZ6bWvkOTsXeA9wpOuYZUlX2V4/NtniacCjofuLTy0+T5IOpjQIPZ7y4X83Sl+5FrY0N03SYyhDLKD0NlrBdljfoPqeNKYWVorCgiLfhykTYAW8gDLR9CuhwWiv4NLT4nNWmwxvQnnteEP/45H/3lrO1iPpQ164f2f/48/sXXgcpFZzpeGTxaD0sEh6pe3TJe0x2uO2jx10ptZJ+pLtN9WVGj0LfhFbqPpL+qXtLST9kNKP5ybgG7bXCs61FPBY27d3ji1HeS0Lb/Ar6ZOU7US/Y2SbWBNXcupqr7+z6Ba20L3otWj7Qkox6AxgO+BntmcFZvoI41/ZD9+/3yvOdgsr3a2ULQtYGfRHoFvAe2f3fiPFvStsb7i4YxFqsWVHFu2FdlBUpn4qzWl3pGyrWNf2KoFZxlwprYYmxkm6jjLy+2/1/uOBn9teOzhXcwWXnlafs5plpu2/jvP4Ybb/e5CZOj+72WyL0+IWN2g3V5p6soF0elg6y+zntLD6YTSSjgXe7tK8DpVm0oc4bizulyU9yfZWNc8elA+xNwAfCcrUr8kR6bbnSzpL0tHA12zf7rb6U+0ErGn7vuggo1iltdUQ1SxKM/DLbO8paWViJ4lh+yMT+b5B977pc6uktahFq7q0/OagLA/VoJtbH8XCUxH777fgbkm7UbYkmtJouJXXtm9TJiReQmdVYTRJy1C2cr6OMkjgsZT+Y+cFxgL4CWU1RLcZbM+3eo814G/AXZ37d9Vj0d4DbNxfcKH0+YrW6nPGeMWW6vkDCTKKlrNNQOxEi7G1mitNMVkMSpPlkLqs9xvASbavig7UsUGvEARg+3ZJA9uiMIojgBcDSPoPSoPE/wY2ovQ+iFwRsTSlR9DTgKcAR/eKVg3ZGdgTuEhlasxsSk+BFpY5XkVpgHxLcI7RnCFpW9tnRgfpc6/tByXNlzSD8tytGh1qgnai/P5G2IfyerGOpD8Bc4Hdg7I8VKcM8odNdCVXcHHvdcDn65eB8+uxFjzV9kujQ3SpDBH4d8qW3MMoq0h+a/snkbmq7knaSuM8Fu23wAWSvk35N/dq4Mpev8XAFXPNFlxo9zlLj5wWPluOptVcaYrJYlCaFLa3qsWg1wJH1pO6kxppZjpN0oq9rUW1WW3kv/3ptm+rt3cGvmT7VMrIzcvjYgFwLHA/pdHfdpStO28PTdTH9m+BD0j6EPAKytXCByTNBj7feW4jrABcK+kiFu7L00LD0LcA75b0T8rfcSuj5S+uWzyOoqw8+Dulwe9UEHZi5zI96cV1m+Q023ct7s8MyuKaW7vRKWcEFvds30A5sWzRzyU9y/ac6CAd61GGCVwDXOPSfL6VkyOPcXu0+5F+V796vl3/G71qruWCS6vPWUop/UuyGJQmTW3wd2jthbM/Zc93C8WgQ4BfSuqNhN6JMlo4ynRJS9meD2wNvKnzWPTv5Hq2nwVQt2JdGJxnVPXkck/gZZQpNydSJi/8mLLCKsq4Tcsj2R73w2pUM0Lbb603j5D0A8oksSsHneNfNPATO40xJVEqdalGrkwfRW1uDWD7yrqSo4X3g/EMvLg31vCFHgcOYejYEvgvSXMpRe5eIXmDqEC2N5K0DmU73VmSbgUeK2nlyObR1RPr76k6t6n3Z8bFWsQ3Gyvw9bRccGn1OZuIllal9Ws5W4vb/qHdXGmKiT7xTENC0rqUVS47UpbznkTpNxPO9nF1O1Gvie8Otn8VGOlrwLn1w+u91HGbkp5G6csQ6f7ejdqfJzLLqCRdAtwBHA28z3ZvBc4FkkL3nds+V9LqwNNtn6UyJn16ZKaH4HgG2MtCY0zT6z3Wag+yPhG/IOOdELWy6mBZ2xf2vX7MjwrzEEQ8fxcH/MyHarvoAKOxfS2lAH+gpGdTCkMXSfqj7ecFRuv2pOrvT/XlwccZ0xdrc/BjgBNtR3/+6Gm54NLqc4aktWz/bpxv+fzAwjx0YdlU3qh2o/R7PEjSasCTbF8IYHuLwGxPAVZn4cb950XnSsMlp4mlSSHpF5TGl6fYvik6DyzS/2YOpf9NEyckkrYAnkzpdXN3PfYMYPnIk2BJDzDStFTAMsA9tLOlCElr1i0yzZH0RspKr5VsryXp6cARfQ1Em6TBj/t+kNJj6dbeoc7DTUxgWxxJB0RteZL0fNvnL+5YBEnfB/alvB9sUptb7227yaJCz6B/B6YSSVtSityzJc2kvFfNjc7Vr57Y/XvvhCm4D9S4WshWP3fsSVkxfSFwTHRfOUk/BZosuECbzxmApHOBpwIXUS4yntdKUa0+Z+9h0cJG+Pu8pMMpE1ZfZHvdOmTmTNubBef6FOUi+6+AB+phN9J2IA2RLAalh03SdOB42600uwRA0kks3P/mBtv7hYZK/7Kxtsf0tLA9pvZ82hy4wCPjvuf0tt61bNBjSiXtR2mWfielkHya7b8P6udPxOJ630Qa7e9r0H+HY5G0JqW59fMofV3mArvXvjjNiijuSfqc7f0knc4oK5Na+OAv6UBgU2Bt28+QtAql0NfyBCCgnd+J0bSSrX6Gew1wKDCPUpg/wPY3AzM1WXDpafE5q7keDWwGvBB4M6Vo29/EfOAkXUEZnnIJI4UNbF8SFqrq/R52LwZIusL2hsG5rqMMwGlmgmMaTrlNLD1stXHjqpIe7bZGak+J/jdpwlroF7A4/7R9X297jKSlaGfrTlNsfw74XC0c7AKcLelG4BO2L4/M1tFc7xtJz6UUWWb2FUhn0MiWxFabWzfa2Pr4+t/PBPzsidqeMrr9UgDbN0maCq/H0HYvktBsnd57Lwd+BLzS9qW12PcLIKywYfvXkj5I2UZ5KLBxXfUVXaRq9jmrq/f+vX6tAHyX2oagAfNtHx4dYgz31+KeAerKxwdjIwFwPfAoOsNIUnokZDEoTZa5wPmSvsPINqPo1RrN979JE+cJjocOdq6kA4BlJG0DvBU4PTjTRIUUcm1frzI1Zhng9cAzgMsjsoyixd43jwaWp7x/d0/I51FWWoWZAs2tmyvu9a6M2z43KsME3GfbqtO6apFvqmi5GB+d7TBKD6MDbN/bO1iLfR+MCtVywYVGn7PqJ5SVN58Ezmjs4uzpkt4KnMbCk1Yjp7/2HErJ9URJH6e8j0b+++8NFbgHuFzS2Sz8nLUwVCANkSwGpcnSm/4wjXZWcGwoaV69LcoJem85bxP9b9LESdrf9qc1xvSdRt4g3wfsTelR9WbgDBppGCppb9tHd+5PBz7YK7INuhlhZ0XQq4E/ULaKfaL7AbsBt0pai5ErhrOAmyMD1aLBuZKOsX1jZJZRtN7cusXiHlD6PQEfYaSnRu99as3IXNXJko4EVqh90faiFNamgpavAkVnO8328d0Dkt5u+/P9xwes5YJLq88ZwBOA5wP/Abyt9uX7he0PxcYCYI/63/d0jhkIf32zfaLKYJKtKb+Tr7F9TWCk3lCBS4DvBOZIS4jsGZRSmhIkvdL26ZL2GO1x28cOOlOPpLNtby3pU7bfG5VjPHUFxAqUYtVKlOac59p+d1CeB4ErKWOD59FXLGhgFUmTvW8kfcH2vo33mGmyuXXLja0lXQu8g0V7avwtLFRHXem4LeVk6Ye2fxQcaUIim7wvTnS2MfqOLeibEkXSfnUbcffY222HT8Nq9TnrUZns+wLKVrHnAb+3/YLYVG2TNFpPpbts3z/K8ZSGThaD0qSQdA6jn5iETwpI6ZEm6VfAGyjj7l9H3xVfNzImXdLOwP9RtnK+LvLkXNJHGGe1SEvbAlvqfSNpnu0Zkkb9gN/CdqNWm1u3WNzrkXSB7edE5xiLpNUp08TOkrQsML2R34eWm7w3mU3SrpT3qS1ZuKfMY4EHHTz9ssWCS+vPGYCk64FrgZ8B5wEXtrRVTNL6wHrA0r1jto+LS1RIugFYlfKeIMpFsz8DfwHeGNXkWtIcFv2MdCdl5dDHWrlQkKa+LAalSSHp2Z27SwM7UhrG7R8UKQ0pSZsCH2DREaUbBGaaRVlxsyUjS3x73EJRVGXM/bGULWzrUsaVvtP2PaHBFkMB45fH6n3TE7lqKfqkaDyd5tb7AZ/tPDQD2N7B01l6Givu9U56X0tpAP5NFu4PEV5IrlvD3gSsZHut+lpyRCMnwOdS+0B5ZBLQVbbXj03WbrZa2FuD0lvmfZ2H7gKutB2ydbLlgkurz1mXpGm2W2h8vAiViYQvpBSDzqBM+P2Z7dA+dwCSjgK+YfuH9f62lHOY2cDno4r0kj5NWSX61XpoF2BZSqFqS9uvjMiVhk/2DEqTYpTK+fmScnpXeiScSPmAPYc2Jj5g+xvANyR9yPbBY32fpGfavnqA0bpOB/atV/YFvBO4CHhmUJ6J2onyAXyQWu590z9FbCHB2+uabG7deGPrQ/rub9q5bSC8kAzsA2wOXABg+zeSnhgbaYFm+0DRaLbaa+xG4LnjfZ+kX9ge93sm2c8pPdmewMK/F3dRthSHafg561ql9lR8fr3/U+Dttv8YlKdrFrAhcJntPSWtDJwQnKlnC9tv7N2xfaakz9h+s6THBOZ6cd8KuTm9VXOSdg9LlYZOFoPSpOjbczuN8oH2cUFx0nD7q+0mm+qNVwiqjgeitspsbnselKVKwCG170zrBt5ktbdFbazeN4PO02c6peAS3Xx2EW63uXWzxT3bW0X+/An6p+37ekUNSUsRXxTtaa7Je0fL2SZi6cV/y+SZIgWXxRnoc9ZnNmUVyU71/u712DZhiUbca/tBSfMlzQBuoWzNasHNkt5LGWIBsDPwlzpkI/KC43RJm9u+EEDSZpT3f2igqJyGRxaD0mS5hJEPh/OBGyjbZlKabAdK+jLQP24zctzsREWewC8j6bPAU2y/VNJ6lA/dvw7MNBGRJ52HsWjxbrRjg3Sz7YMCf/6Yes2tgS+ojiHvimpu3XJxr65autOdSX/1+N7AY/sb6Q6SpH1tf4FS4DuA8hqyDfBWykrDFuxD6QO1jqQ/UfpA7RYbaYGWs01EKwW/fpEFl8WJfM5m2p7duX+MpP2iwvS5WNIKlCmElwB/B34RmmjE64ADgW/V++fXY9Mp23ejvAH4iqTexZ95wBvqNudBr5ZOQyx7BqWHpVaq/2D7z/X+HpS9tjcAH7F9W2C8NIQknQCsA1zNyFUb294rLtXERDbRrZOUZgMfsL1hvbp/me1nReSZqIgeOS33vmm8Z1DTza1bbGytMtJ4i/7JNZIeDVwc3AuttyVhGuXizoJpYsCX3dAHyJb6QPVrOdt4on83xtJqLgh/jz+b8h7/tXpoV2DPFnp7dUn6N2CG7dCtf1OFpMcB2L4zOksaTrkyKD1cRwIvBpD0H5Rq9X8DG1GuiIU3h0tDZzPba0eHmIKeYPtkSe8HsD1f0gOL+0MNOCXgZzbZ+6Zq6oN9n99BfNGnX6e4199vaQYjy+6jLNVfCAKo27Ka2ApYm9IeVb+aIunxlKv6WwKW9DPgoBYm7bScbYKa+Pc3xUQ+Z3tRVq5+lrJC6efAfwXmQdI6tq/tNMrvPrZJIw3yZwL7U/ondiedhfRrk7S77RP6e9010uMuDaEsBqWHa3pn9c/OwJdsnwqcKunyuFhpiP1c0nq2fxUd5F8QOeb17npy0utfsQVlTGkoLWb8su1PDDpTw71vaHy1ZavNrVsu7k2TtLLtv3QP1gar0TaQNG+U46Ksxpwx6ECj+DpljPaO9f5uwEnUi1TBWs62QO3h0p3M2XuNeX1MosUKL1K1+JzV96qFtuJK+gzw7phEALwLeCOLNsqHdhrkn0j5vXwF8P+APYC/BuZZrv53vF53KU2a3CaWHhZJVwEb1VUG1wJvsn1e77HoEapp+Ei6BliL0n/hn4ycmIRtp+iRtHe390dtQPjBXs+SSPXK3GHA+sBVwExgVvRSbTU4frnX+6Y22G6m903rJN1MKeyNerIW/XsgafXWinuS/hN4G+WkqXeV/NnA/wJfsH1sYLZmtyT2jPZaIWlOC9tfW84GIOnNwEeBfzDyOmfba8alGjFWwUXS+ravCsrU9HPWT9Lvba8WnaNlki6x/WxJV/Y+R0q6yPZm0dlSGoRcGZQerq9RrqDfCtxLGWWJpKfRwKqDNJReGh1gHFtL2pHSX2Ml4Bgguk9Kr6/XpbWXy5spV6rPBFoYOdvi+OX/BPYFPhOcY6ppsrl1q42t688+TtJfgYMohVpT+qF92Pb3o3JNIWdK2gU4ud6fRelp1IKWs0FZMbK+7Vujg3SNVXAB1gSIKgRVTT5n4whdRSVph/Eeb2TwR2+b7s2SXg7cRPn8FqpuX3sj8G8sXBRtvj9mmlpyZVB62Op2kycDZ9q+ux57BrB8C/uB03CS9EQW3t/9+8A4C0jaGfg/4G7gdf3TiwLyXAq82PZtta/X1xnp67Wu7dBtMrWx9b7AKbVZ7Sxgb9vbBWZqfkVEi1p93lpvbD0Rkt5ve6ATZCQdMJFtmhHZOj/7Lsq2il7/s+mU114I3srWcjYAST8AdrB9T2SOfpJ+Azy3xYJLi8+ZpLEKFwKusP3UQeZZKIDUm272RErfth/X+1sBP7f9ipBgHZJeQbmQvSpl9fQM4KO2vxOc6+c11yWMvIZQW3GkNGmyGJRSmlIkvYqy/3wV4BZgdeAa288MDQZIejpwLDAHWBf4FfDOyA+Okq7oTb+S9H/AX21/pN6/3PZGUdlqhjUpzeafB9xO2f63u+0bAjP9ERizv002cBydpJVa7GnUapHqocgJSmmySdqYMn3qAsqWawBsvy0sFG0WXHpafM4kzaWsnBptFVATW9gknQnsYfvmev/JwDG2XxKcazrwNtufXew3D1gLn8/SkiG3iaWUppqDgS2As2xvLGkrYPfgTD2nA/vaPqtOAnoncBFlSkWU6ZKWsj2fMonqTZ3Hwt8DbF8PvFhtjV+eTmk4HN6odCppsRBUtdrY+qFo+d9iWDZJpwJHAz+oU8+a0XK26kjKSo05QEv53k8ZFNFMwaWjuefM9hoT+T5Jz7R99SOdZwyr9gpB1V+A8F5Gth+QtCtlAltrvivpZbbPiA6Shlv4iUBKKT1E99v+m6RpkqbZPkfS56JDVZvbngflchxwSG1CHKnJvl5jnZw3Mj61yd436V82DMW9lpdxR2Y7HNgTOEzSKcBs29cF5ulqORvAo2yPWSQN1FzBpaPV52wijgeiVvCdLemHlM8jUKYPnxWUpd/5kr5AmSjW28ZJVJuLur20t9LrAEn/pPQ1ammKYxoiWQxKKU01d0hanjKy90RJt9B5Aw+2jKTPAk+x/VJJ6wHPBX4dFcj2xyWdzUhfr96J2zRK76Ao441NjT7xncpFg7SoYSjutfxvMiyb7bOAsyQ9Dti13v4DcBRwgu37x/0fLKHZqu9LehNlRWt3BU70Cr+WCy6tPmcTEfl7uq+k7YH/qIe+ZPu0qDx9Nqr/7b5HhI29tz2hkfLBK73SEMmeQSmlKUHSarZ/X7cT3UspZuwGPA440fbfQgOyoBnybOADtjeUtBRwWSujhFsk6fn9TbZHOzbgTE32vkn/miHpGTShZs4RorNJejxlq/DrKZOATgS2BJ5l+4VRuaD5bHNHORzeY0bSJ4AbaLDg0upzNhHRvb0krQ48vW6jXxaY3si28Ckp+u8zDY8sBqWUpoTuG5+kU23vGJ2pn6SLbG/WPfnMJoDjG+0DTX7ISZNpKhT36gTOw4GVba8vaQPgVbY/Fhyt9WynAWtTtsAc0+1LIuli25tmtqllKhdcWhb5virpjZR+hSvZXqsO2zjC9tYRebokrQx8AljF9na9Fd22jw6ONq5huMiR2jAtOkBKKU1Qd4lzqx8K765Xgg0gaQsC+/K0TNJzJb2L2ty38/URSo+XlCZF64Wg6ihK49z7AWxfCewSmmhE09lsr2f7k51JRY8BaKDY0mQ2Sft3bu/U91j46jPba4zyFb1aqennbILuC/zZ+wDPB3o9FX9DGTffgmOAH1Im1ELZ1r9fVJiHIFdzpEmRxaCU0lThMW635J3Ad4C1JJ0PHEdsX56WPZrS1HcpSv+g3tc8YFZgrpQiLGv7wr5j80OSLKrlbKOtTvrFwFOMrtVs3ULe+/see+kgg3Q1XnBp8jnrUrG7pA/X+6tJ2rz3uO0t4tLxT9sLilF1C30rn+OeYPtkarPyOnn1gdhIKQ1ONpBOKU0VG0qaR1khtEy9DQ1MWJC0GfAH25dKegHwZmBH4Ezgj1G5Wmb7XMqUs2Ns3xidJ6Vgt0pai5FVhbOAm8f/IwPTXDZJTwKeQnkv2JiRlaMzgGXDgtF2tkpj3B7t/iDtAny63n4/cErnsZcCBww80YhWn7OuL1IKGi+iNEO+CzgV2CwyVHWupAMovxPbAG+l9IRqwVRd0R250isNkSwGpZSmBNstbx06Enhxvf084AOUFUEbAV8iV7osQtIXbO8LfEHSIlcIbb8qIFZKUfahvFasI+lPwFxKg/wWtJjtJcB/AU8FDmHkhHwesUUDaDsbjL/KNnK1RssFl1afs67n2N5E0mUAtm+X9OjoUNX7gL2BOZSLZWcAXw5NNOJdLLyieyYNfGaTJMrr7Jq2D5K0GvCk3irN4JVeaYhkA+mUUnqYJF1he8N6+/+Av9r+SL2fDaRHIWme7Rl1JdUi6sqhlJYodVritBan7LSYTdKOtk8d5/E9bB87yEydn91kNkkPAHdTV9kC9/QeApa2/ahBZ6q5ukMiFmp2HD1UoNXnrEvSBZSLURfVotBM4MxsMrx4ddva2pS/z+ts3x8cCUmHU1d62V5X0oqUv88WVnqlIZLFoJRSepgkXQVsZHu+pGuBN9k+r/eY7fVjE7YnJ2GkNKJuUziQMnbcwM+Ag2z/LTQYbWdbnOgiwnhazhZhKhRcWiZpN2Bn4NmUpsizgA/aPmW8PzcIkl4BHAysTtmVEr69v0fSlcDXgZNs/y46T0/v9aFvOu2CC48pTZbcJpZSSg/f1yh74m8F7gV+CiDpaUyNvecRZkp651gP2v7/BhkmpWBfB86j9BqDsj3gJEa2n0ZqOdviRG8vGk/L2Qau8a3gzbN9oqRLgN649tfYviYyU8fngB2AOW5vFcIrKUW0kyU9SHltO9n272Njcb+k6Yz0MppJbXKd0mTKaWIppfQw2f44Zd/5McCWnQ8708hpYmOZTpkm9tgxvlJakjzZ9sG259avjwErR4eqWs62OK2deHa1nC1NTctS3lunUVZXteIPwFUNFoKwfaPtT9t+NvA6YANKX7RohwKnAStL+jhlRWb0VL00hHJlUEopTQLbvxzl2K8jskwRN9s+KDpESo04U9IuwMn1/izgh4F5ulrOtjgtr75pOVuaYupI+Z0oE8QEzJZ0Si3eRtsfOEPSucA/ewdbWQEsaXXK6qCdKWPl949N1PxKrzREsmdQSimlgcueQSmNkHQXsBzlRATK1f276+3Q3hotZ1ucztTC5rScLU09kq4DNrT9j3p/GeBy22vHJgNJZwJ/p0wTW7DVyfZHw0JVtfH2o4BTKH2Drg+OtICkTRjp1Xa+7UuDI6UhlMWglFJKAydpJdu3RedIKU1dklambJ1YxfZ2ktYDnmv76OBoTWdLw0fSOcD2tu+o91cAvmn7RZG5apZmB2lIWtv2dfX2csD2wK62Xx6cq3+l12uAVlZ6pSGSPYNSSikNXBaCUhoh6VRJL5PU3OeylrNR+rT9EFil3v81sF9UmD7H0G62NHzuBK6WdIyk2cBVwB2SDpV0aHC2MyRtG5xhLHMlbS/pFOBmyrasI4IzQWnUv5ntj9g+ENgCeH1wpjSEWnxjTymllFJakhxO+fD/G0n/Iyl8a0dHy9meYPtk6tYT2/MZ2c4WreVsaficBhwAnAP8BPgA8G3gkvoV6S3ADyTdK2mepLskzYsMJGnbWjSbS5mUeBxwm+09bZ8ema26CVi6c/8xwJ+CsqQhlg2kU0oppZQC2T4LOEvS44Bd6+0/AEcBJ9i+P7ON6m5Jj2dk/PIWlBUSLWg5Wxo+twHfs93c+HHb404IlfRM21cPKk/1A+CnlAmwc2uOzw84w3h6K71+RHkN2Qa4sLfKy/bbIsOl4ZE9g1JKKaWUgtXCwe6UrQA3ASdSmoc+y/YLA6M1m602WD0MWJ+yLWYmMMv2lVGZelrOloaPpBOA51J6zHzF9rXBkSZM0qW2Nxnwz9wI2IXSl+d64OvAh22vPsgcY5G0x3iP2z52UFnScMtiUEoppZRSIEmnAWsDxwPH2L6589jFtjfNbKOTtBQln4DrglcqLaTlbGn4SJpBWb23J2U1yWzga7bvCg22GNHTRSU9j/K87QhcAZxm+0tReWqmV9LoSq80XLIYlFJKKaUUSNLLbJ/Rd+wxtv8ZlamTo+VsO4xy+E5gju1bBp2nq+VsaXjVVXyvpzQrvwZ4GnCo7cMic40nYmXQGDmmAS8GdrG9Vz0WsYVtSq/0SlNLFoNSSimllAKNdjLU0AlSy9m+RzlhOqceeiGlWe4awEG2jw+K1nS2NHwkvRr4L0rx5zjgWNu3SFoW+JXtfwuMN65WXk9GE5ltqq70SlNLNpBOKaWUUgog6UnAU4BlJG1M2U4EMANYNiwYbWfrWApY1/ZfACStTDkRfg5wHmVrW2ZLS4IdgM/aPq970PY9kvYOyjRR90UHGIcW/y2PDNvzJH0DWIay0mt74D2Sml7plaaWLAallFJKKcV4CeVq/lOBQxg58ZhHGRMdqeVsPav2ii3VLfXYbZKi+/O0nC0Nnz/3F4Ikfcr2e22fHRWq5hCwG7Cm7YMkrQY8yfaFALa3iMy3GCFbaEZZ6bV5d6UXpTl9Sg9bbhNLKaWUUgokaUfbp47z+B5R02Maz/ZFYDXglHpoR+CPwHuA79reKiJX69nS8BljO+eVtjeIytTJcTjwIPAi2+tKWhE40/ZmwdEWK2qbmKRjgaP7C3z1sa2jC3xpeEyLDpBSSimltCQbr9hSvX0gQUbRcjZgH0ofjY3q18WAbd/dQLGl5WxpSEh6i6Q5wDqSrux8zQWujM5XPcf2PsA/AGzfDjw6NtKERW1hG3WlF0AWgtJkymJQSimllFLbwvpWTEBkTw0D1wPzKf00tqJMUArXcrY0VL4KvBL4dv1v7+vZtnfvfVNdjRPlfknTqVuuJM2krBQKp2J3SR+u91eTtHnv8cAtbNuMcmy7gadIQy97BqWUUkopta3lPf0DzybpGZQpO7sCtwInUVofhK+4aTlbGj627wTupPx7G8/ZQNTErkOB04CVJX0cmAV8MChLvy9St7ABBwF3Uca5h2xhk/QW4K3AWpK6K7seC5wfkSkNtywGpZRSSim1LVcGLexa4KfAK2z/FkDSOwJyjKblbGnJFbmC70RJlwBb10Ovsd3KKrnn2N5E0mVQtrBJitzC9lXg+8Angfd1jt9l+7beHUkr1u12KT0suU0spZRSSqltLV8Rjsi2A3AzcI6koyRtTTsFs5azpSVX9OrCZYHplHPPZYKzdDW1hc32nbZvsL2r7Rs7X7f1fWv2DUqTIotBKaWUUkqBJK0s6WhJ36/315O0d+9x2/tmthG2v2V7F2Ad4BxgP+CJkg6XtO2g80yVbClFqP14jgVWAp4AzJbUyjax/i1sPwM+ERtpQrLAnCZFjpZPKaWUUgpUCy2zgQ/Y3lDSUsBltp8VHK3pbF21Qe5OwM62t17c9w9Sy9nSkkHSZbY3DvrZ1wEb2v5Hvb8McLnttSPy9JO0DiNb2H7c0Ba2MUWNvE/DJ1cGpZRSSinFeoLtk6nbE2zPBx6IjbRAy9kWsH277S+1WGxpOVsaHpK2lLRnvT1T0hqdhyP/7d0ELN25/xjgT0FZRtPqFraUHnFZDEoppZRSinW3pMcz0rdiC8qEoBa0nC2lBEg6EHgv8P566FHACb3HR+k5M0h3AldLOkbSbOAq4A5Jh0o6NDBX61vYxpPbxNKkyG1iKaWUUkqBJG0CHAasTzlRmgnMsn3luH9wAFrOllIqJF0ObAxc2tsOJulK2xuEBis59hjvcdvHDipLv5a3sEnaEni67dm1sfXytufWx1YKLvClIZGj5VNKKaWUAtm+VNILgLUpV3yvs31/cCyg7WwppQXus21JvRV8y0UH6rgN+J7tsCld4+htYftHvd/EFra60mtTyuvubEZWej0fwld6pSGSxaCUUkoppUCSdug79AxJdwJzbN8Skamn5WwppQVOlnQksIKkNwJ7AUcFZ+rZGficpFOBr9i+NjpQR28L248oW2G3AS7sbV+z/bagXNtTV3rVHDdJemxQljTEshiUUkoppRRrb+C5lFHkAC8ELgHWkHSQ7eOjgtF2tpSWeJIEnASsA8yjrCb5sO0fhQarbO8uaQawK3BMXb00G/ia7bti03Fa/er5SVCOfi2v9EpDJItBKaWUUkqxlgLWtf0XAEkrA8cBzwHOAyILLi1nS2mJV4sGZ9h+FtBEAaif7XmSvkGZ1rUfZeXLeyQdavuwwGitbmFreaVXGiI5TSyllFJKKdaqvWJLdUs9dhsQ3Z+n5WwppeJSSZtFhxiNpFdLOo2y6uZRwOa2twM2BN4VmY2yhe03kj4taZ3gLMBCK72+AZzKyEqvyKJZGlK5MiillFJKKdZPJH0XOKXe37EeWw64IyxV0XK2lFLxHGA3STcCd1OavbuFaWLADsBnbZ/XPWj7Hkl7B2XqZWhuC9tUWOmVhkeOlk8ppZRSClSvBO8AbFkP3Q6sbHufuFRFy9lSSoWk1Uc7bvvGQWfpJ+lTtt+7uGORJD0eeD1lC9s1wNOAsC1sko4FvmD7ooifn5YcuU0spZRSSimQy5W564H5lF4aW1FOSMK1nC2lVNi+sRZ+7qVMxep9tWCbUY5tN/AUo2h4C9tzgF9I+p2kKyXNkXRlYJ40pHKbWEoppZRSAEnPoGxP2BW4ldInQra3Cg1G29lSSguT9CrgEGAVSl+v1SlF22cGZnoL8FZgrb5CxmOB82NSLaLVLWwvCfzZaQmS28RSSimllAJIehD4KbC37d/WY9fbXjM2WdvZUkoLk3QF8CLgLNsbS9oK2N12WEFD0uOAFYFPAu/rPHRXbUDf+74Vbd8+6Hz1Zze9hU3SE4Gle/dt/z4wThpCuU0spZRSSinGDsDNwDmSjpK0NaXxawtazpZSWtj9tv8GTJM0zfY5wKaRgWzfafsG27v2trHVr9v6vvXskIBFk1vYJL1K0m+AucC5wA3A90NDpaGUxaCUUkoppQC2v2V7F2Ad4BxK89InSjpc0raZLaU0QXdIWh44DzhR0ucpU8WmgoEXmSW9RdIcYJ3ak6f3NRdooTfPwcAWwK9trwFsDfwyNlIaRrlNLKWUUkqpEZJWBHYCdra9dXSerpazpbQkkrSa7d9LWo7SPHoasBvwOODEulqoaZIutb3JgH9m01vYJF1se9O6/W9j2w9KusL2hoPOkoZbFoNSSimllFJKaYrpFlIknWp7x+hMD1VEMWiiorJJOgt4DaVY9QRKU/DNbD9v0FnScMttYimllFJKKaU09XS3WE3V5u4t9yIbaDZJq9WbrwbuAd4B/AD4HfDKQWZJS4YsBqWUUkoppZTS1OMxbjdF0paS9qy3Z0pao/Nwy1tOB/2cfgvA9t3AKbbn2z7W9qFTYctfmnqWig6QUkoppZRSSukh21DSPMoKlmXqbep9254RF60GkQ6kTDZbG5gNPAo4AXg+wCjTxZZkw7DSK00hWQxKKaWUUkoppSnG9vToDBOwPbAxcCmA7ZskPTY20oQNegvblFjplYZHFoNSSimllFJKKT0S7rNtSQaok8+aIWlL4Om2Z0uaCSxve259eNBb2Jpf6ZWGSxaDUkoppZRSSik9Ek6WdCSwgqQ3AnsBRwVnAtrbwjZFVnqlIZKj5VNKKaWUUkopTSpJAp4KrANsS1nh8kPbPwoNVkm6nLqFzfbG9diVtjcIDZbSgOTKoJRSSimllFJKk6puDzvD9rOAJgpAfZrewpbSIy1Hy6eUUkoppZRSeiRcKmmz6BBj6N/CdhaNbGFLaRBym1hKKaWUUkoppUkn6VrgacCNwN2MNEMO3YrV+ha2lAYhi0EppZRSSimllCadpNVHO277xkFn6SdpTt3CltISKbeJpZRSSimllFKadLZvrIWfewF3vlrQ8ha2lB5xuTIopZRSSimllNKkk/Qq4BBgFeAWYHXgGtvPDA1Gu1vYUhqUnCaWUkoppZRSSumRcDCwBXCW7Y0lbQXsHpyp5yXRAVKKlNvEUkoppZRSSik9Eu63/TdgmqRpts8BNo0OBc1vYUvpEZcrg1JKKaWUUkopPRLukLQ8cB5woqRbKFuywo21hQ0I38KW0iDkyqCUUkoppZRSSpNG0mr15quBe4B3AD8Afge8MipXn94Wtl/bXgPYGvhlbKSUBieLQSmllFJKKaWUJtO3AGzfDZxie77tY20fWreNtaDZLWwpDUJuE0sppZRSSimlNJnUub1mWIrxNbuFLaVByJVBKaWUUkoppZQmk8e4HW6KbGFL6REnu6nfzZRSSimllFJKU5ikByirbAQsQym6UO/b9ozAbJfa3qTePtX2jlFZUoqU28RSSimllFJKKU0a29OjM4xjKmxhS+kRl9vEUkoppZRSSiktKZrdwpbSIOU2sZRSSimllFJKS4SWt7ClNEhZDEoppZRSSimllFJaguQ2sZRSSimllFJKKaUlSBaDUkoppZRSSimllJYgWQxKKaWUUkoppZRSWoJkMSillFJKKaWUUkppCZLFoJRSSimllFJKKaUlyP8P+1/6jBKSudYAAAAASUVORK5CYII=\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "sns.heatmap(traindf.corr(),annot=True,cmap='RdYlGn',linewidths=0.2) #data.corr()-->correlation matrix\n", "fig=plt.gcf()\n", "fig.set_size_inches(20,12)\n", "plt.show()" ] }, { "source": [ "g = sns.pairplot(data=train_df, hue='Survived', palette = 'seismic',\n", " size=1.2,diag_kind = 'kde',diag_kws=dict(shade=True),plot_kws=dict(s=10) )\n", "g.set(xticklabels=[])" ], "cell_type": "code", "metadata": {}, "execution_count": 28, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 28 }, { "output_type": "display_data", "data": { "text/plain": "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "image/png": 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A+10MacrLQ1sIN0rDYDDEO9FWGpZg8iUAgnk1ou0LMuCoqOh5pNFsPWUwGAzxTLQf6P8JfCgiTwe/3w78NMq/MeCoqICUlO6PMUrDYDAMBKLtp/FnEdkFNAfXuXWwBR4Ml0Cg+1SvzbjdOiqCzwe2IT82MxgM8Uq0/DRcwP3AZCAP+F+llC8abQ90amp0mJCegr+KtKxrZIcUeMVgMBj6n2itaTwK5KIVxrXAL6LU7oCnrAzS0kI7NiXFLIYbDIb4JloTITOVUnMAROQh4KMotTvgKSvreT2jmZQUfbzBYDDEK9EaaTRHtyXup6VKS+HkSR2Gup9+ridz22aSk/XxBoPBEK9Ea6QxT0Saw5kLkBD8LoBSSoX4rt3HfPABPPywjj8+aRJ885s6G1gfUlwc+kgjNVUfbzAYDPFKVJ6YSimrUiol+ElWStlabceHwigogD/9CT77WfjKV/Qr/fPPh91McTH87Gfw0EPa0imU40Nd00hLM0rDYDDEN337mh1PPPqozr+cnQ1WK3ziE/DSS2GtPAcC8B//oR/uJ0/Cs8/2XKe4GNLTWxU0NMDTT8PPfgpPPqkzLwVJS4MBmpjQYDAMEYaG0jh1Ck6fhsWLW8rS0mD+/LBGG1u3atPYK67QOmfTpu7TtCrVTmkEAjq/sc8Ht9yqD3jqSV1Oi9Lop+UWg8FgCJuhoTQ2boSlS/H6Hfz6uVF8/6EJnLrggmXL4P33tTNFCLz8sh6siOh1ihkz4M03uz6+udmEhGDBxx/rnNrLlkGCC5YuBY8Xdu8GwOnUHxPt1mAwxCuDX2lUVsLevbBgAf/9wijq6q3MGFfHvz0xjmpLGkyfDm+80WMz587ph/nkyS1lCxbA5s1djwwuXIBhw7SSob5eH7xsKViC6UMsAkuXwLvv6mkr9PGFhb04X4PBYOhDBr/SePttmDWLA4UZnLyQwPXLypg/uZYZY+t46t0sWLIEXn+9x1Xt996D2bPBcvK4XpN4+WVGJpQjAkePdl6noEArAQC2b4eRIyEjo+1BGRmQkwM7d176WlDQy3M2GAyGPmJwK41AAN56CxYu5Nn3slk5txKbVQ8LVsypZGteGpVJo/Wiw44d3TazdSvM9u7Ui+dpaeBrQv7wB2aNrmTLls7rnT0LmZlAvRc+2gFz53Z+4Jw5Wqk0NZGZCefP9+60DQaDoa8Y3Epj715ISOCsZTz5pU5mj6+9tMudEGDWhFo2fZSuRxsbN3Y5z3T8ONiaPGQf2wLXXgtTp+hF9BWXM/vQM2zfGsDv71jv9Gk9iGD7dhg9BlK68PJLT9PaZe9esrP1VJjBYDDEI4Nbabz2GixcyKs7Mlg0tQarte3uxdNqeGt3Br7J03WI2f37O21my6vVzPTuRtau1gvYzYwaRcacUaQ1FpG3t63WCAT0SGNkah189JEeTXTHzJmwdSvDswOcPWssqAwGQ3wyeJVGYSGcOIFn8lw+PJTKwikdLaSy0poYltrEjiMpsHIlPP74JfPXZnw1XrZ9aGH2fJt22W7PjBnMTjrDu39uOzw4f16HBUn86F0YP6HrUUYzOTngcpJ07iB2u3HyMxgM8cngVRovvgiLFvHOwSwmj/SSlNDJ/BGwaFo1r3yYCbNm6df7115r2en3s+9nL5Pu8pA5a3jnvyMw+8pM9hQOp+adXZeKDxyAcZm1emPevJ7lFWD2HHjvPUaPVl0urhsMBkMsibnSEJGHRaRYRA5ErdGyMti+nUDuEl79MJPF07v2wJs+xkN5tZ1jBYlw442wYYNePM/Ph1/8gtcL5/b4zE9MtTN9TC1v/PEs7NKK48P3G5h05m24bBm4nKHJPWoUWKyMsV4gLy/UkzUYDIb+Ix5yxD0C/Bb4c9RafPJJWLiQbWdGkOjyMya7octDLRZYNrOKZ9/L4rv3eOGee7TfxrPPUjBhBScCk7huUn6PP7l8UQOPvvYJ1v3+u9Q8/AYFpV/ntmVuGDs2dLkFWLCAyVte5zHrFwgEpE/jKda99CbOcyeoHz+NpOvXXCr3b9yIJT8f/+TJ2K65pqX8hReRCwU0zc/FubzFu97zwutIcRGB1WtwTxnddwLHiNLXd5J4Io/G0eNJu2lNh/0N73yA48gB1LBhWG6/vcP+6g8/xp23h0BiIk0rV5M4totRaxcEAnpZrKZGO5e63RGfSr9QvHkv7qN78GePIuWTVwMQ2LABKSzEN2069nVrqdyZR/K+j8DpoDZ3BakzJlD9yns4zhyjafwUkq9fpc3gX3kF6urgmmsgM5OC3YXs+aiRkSOFhTeFcW8ZokbMlYZS6n0RGR+1Bg8dgj17aLr3qzz5UA7XLO45QcWCKTV8eCiV/SfdzJ2UA5/5DABPPDWGpTOrsdt6XpUeltrEwmke/rXoX/A3+lmxsA7btEnhyz88h8zhJ0gorObw4VRmzQq/iVCoPnWO5E0vICUluE+fxLt8AQnp6fj27sX6wgtIXR3WsjJ9swKN23fieOYpaGrC4fVAUGnUvbcT95N/gkCApvpamPLVvhE4hqR/vBnrnt0kJCRQOXwEaUtntNnv2P8xsnUrYrXiT0nB2krRArj37sS6+W2sgHK6YOwtYf3+wYPwy1/q7YYGuOGG3pxN35Ox931sH24Bm42KjEyS7fVYN2xAGhuxeTywbi0pe7ZjeestANx2J40jskh+eyOSn4/zaB7e+bNJOLxXrzMCNDXB5z7HG+86eG3HCOx2+NmIi4xbEp4CNvSemE9PhYKI3Cciu0RkV0l3Ef3Ky+HXv4brr+fZHWPISGli4sj6ro8PYrPCtUvL+O8XRlFapfOyvrMnjdOFLpbO6Ca4VDtWza9gwZRaFs/ysGS2J+R6HVi8mAX+3bz8aPjJNULtKyW05KC12VBBm2EVCLQkKW9lbiYWC6q5vPXwx2679F1ZY/4OEhYhX1fN/WCz6X7rar/FAp31QXO/iYC1h7y/nWCzBaMKELv88SH3FaCC/aGsVpRYdL+0v6ZanYiy2bHYrS1lNhtitejrs92J24N+VjZroK+zGhi6YEDc5UqpB4EHAXJzczt/7T9/XoegXbSI97yL2bwnnS9ddyHk35g8ysuyWdV8+/cTGZPdwIVSB/esKwpplNGMCMybVNvzgT3hdLBgXSa/e9XG7v/9iEX35Yac9yOkvgJSJ4ylZv0tOM+fpGHCNJKDruv2hQvx3Xwz1gsX8E2eTPMjzr50Eb6yu5EL+TTlXkaz4bF7+QLqar6CFBcha1ZFesYxIdS+Kl+yDlf6KJpGjSVjyYwO+72Ll+JKSEBlZWFdt7bD/pqll+G2u8DtxnFL+MOEGTPge9/T01OLFoVdPSqE2lcAFYtWkeDOxDd8BBmrFgDgLy/DUlhI44yZOIGqpZeTYneinE7UiiuxuxOovvpWbKeP4xs/mZThGTD8Sj3CqK2Ftbpfr74qwIjk0wwfbWVMrpmeigWi4sAhIDg99bJSanZPx+bm5qpdwcVmKit1DI+PPsKz+zDH5t3GO1WLOHI+kTtXF5Gd3tRtW51RXm2jtNrOuJx6nPbY9s35MwGefH8E3x72MDOvHQ/TpsGECc1vXZ2987ahTV8NbUxfhY7pq9Dpsa8GIwNOaYhICXAWYM9VV02fn5bmBvgJP+AALQ50dtXYN8L2M03i4D5+zzreBuBb+/ad/eWxY6VAqVJqfXd1W/dVNwwDYpFktj9/d6D3VahEQ75I+yre+6YreiN3j301GIm50hCRvwKr0P+8IuBHSqmH+vg3dymlcmNVP1ptRINYyREv5x8O8S5zLOWL977pioEqdyyJ+ZqGUuquWMtgMBgMhtAw9gcGg8FgCJmhqjQejHH9aLURDWIlR7ycfzjEu8yxlC/e+6YrBqrcMSPmaxoGg8FgGDgM1ZGGwWAwGCLAKA2DwWAwhIxRGgaDwWAImQGnNNavX68A8zF9ZfrK9FWsP0OSmPtphEtp6UB0Om3hvfdg61Yd7vqyyy6F1OkTBnpf9Sd92Vevvgo7d0JiIqxZE7v4UdFisF5XGzbodDhjx8LNN8NwE0C3Uwac0hjoHDkCe/fq7fT0vlUaoVBZCUppWQx9w7FjOrw5QFbWwFcag5WjR+HECf2ZNMkoja4YcNNTA53Ro3XucLcbxoyJtTTw4x/DD34QaykGN6NHg8MBmZk6OaMhPhk1SkeqnjQJRoyItTTxixlp9DPXX68fIhBa6vC+RCkoKNDbgUDI0dcNYXLbbTBxIjid9FlSLUPv+exndRj69HSYPDnW0sQvRmnEgFgri2ZqavQ8u9Wqp6kyMmIt0eBl4cJYS2AIhcWLez5mqGPeLYcwxcX6rSojA4qKYi2NwWAYCJiRxhCmogJSUvRIo6Ii1tIYDIaBgFEaQ5iqKj09ZbPp6SmDwWDoCaM0hjCVlVpp2O1QXh5raQwGw0DAKI0hTPNIw+HQ2waDwdATRmkMYWpqIKf2BPYEGwVN42MtjsFgGAAY66khTE0NJOzfQeJH71JdHWtpDAbDQMCMNIYwtbWQgBen1U9tbaylMRgMAwGjNIYwdXXgwouLAHV1sZbGYDAMBIzSGMJ4PIoE6nEFmqir02FFRGItlcFgiGfMmsYQRSnweMDpACs+bDZFfX2spTIYDPGOURpDlMZGsFrA5rSCw0FigjJTVAaDoUeM0hii1NVBgjOgnTTsDhIcAbMYbjAYesSsaQxRPB5w2f06hohSJDj81Naay8FgMHSPeUoMUS4pDYcd0NseT4yFMhgMcY9RGkMUjwecVh9YbWAHp7XJTE8ZDIYeiarSEJHhwBJAATuVUhej2b4hemil0aSnp1A4LT6zEG4wGHokagvhIvI3wEfArcBtwIci8sUQ654RkTwR2Ssiu6Ilk6FrPB5wiE8rDasNl7XRjDQMBkOPRHOk8U/AAqVUGYCIZALbgIdDrL9aKVUaRXnikr174Z13tJ/EihWwZEls5PB6wSFNYLOCUrgsRmn0Fe+9Bx99BAkJsH69yT/d17z/vu5vpxOuuQamTo21RIOLaCqNMqCm1feaYJmhFTt3wvbtejsxMXZKw+MBpzTqtH0KEmigyExP9Ql79+r/O0BqqlEafc3evVppAKSlGaURbaKpNE4AO0RkI3pN4yZgv4h8E0Ap9ctu6irgDRFRwO+VUg+23iki9wH3AYwdOzaKIvc/I0boN6BAAIYPj377ofaVVhr1YLODAlegfsiNNPrruho+HCwWPdLoi/95fzCQ7sHhw/W7kMs1cPs7nomm0jgZ/DSzMfg3OYS6K5RSBSKSDbwpIkeUUu837wwqkQcBcnNzVbQEjgU33AAZGVpprFgR/fZD7SuPB1JUQ9BPI0BCo3fILYT313X1qU9BTk5sR5a9ZSDdg3fcoZWF0wlLl8ZamsFH1JSGUurHzdsikg5UKqVCuriUUgXBv8Ui8jzaAuv97msNXJYvj7UEWmlkqXq9phGw4VL1Q05p9CerVsVagqHFFVfEWoLBS6+tp0TkhyIyPbjtFJHN6BFHkYisC6G+W0SSm7eBq4EDvZXL0D0eD7hUvfbTsNlICNQZpWEwGHokGia3nwKOBrc/F2wzC7gS+FkI9XOALSKyD22y+4pSalMU5DJ0g9cLDuXV01M2GwnKcyk8usFgMHRFNKanGltNQ10D/FUp5QcOi0iP7SulTgHzoiDHgOFAXhNKwZy59pjJ4PWCS3nBlgYqgNXXgM2myxMTYybWgGXfPkhKgkmTOt9/JK8eh83PxBnu/hVsELJvH6SkwIQJ+nvh6Voqqq3MnJcQW8GGCNFQGg0iMhsoAlYD/9hqn3n8tOOVV3y8+KINvx9uvNHPjTdaYyKH1wtOu+fSmga+JhITdQpYozTC48kn4cUXYcwYuOUWWLas7f6NG5rYsNFJSgrccZOHletMB0fK44/Dyy9r5XzbbeCWGv78bBInTgif+ESAu+82gbv7mmgojW8Az6KnpH6llDoNICLXAXui0P6gIj/fSkWFTo93/nzsLnCvF5yWuuCahoLGRtxurTSys2Mm1oDk3Dnw+eD0aSgs7Lj/1FkrXq/g9cLpfAcr+1/EQcO5c+D3w7FjcPEi2HFw9Ki+n86cMWkn+4NeKw2l1A5geiflrwKv9rb9wcbEiT4uXrQRCMCkSX5iETMyENBJmBxO76XQ6DT5SEiF6up+F2fAM2UKlJZqM8/pHe4EmDGlidJSB4mJMGeKl9Cs0A2dMW0alJXB2LG6r53Kx8qVDs6ehZnTY3M/DTWi1sPBsCE/AlagnfW2AD9pDiti0Fx1lZ2rrmr+FpsL3OvVNuzibwp6hAcuTU/V1PRc39CWW27Rn65Yf4OT9Tc0fzMKozfceqv+tODm619v3jYKoz+I5vzIk0AJ8El0wMIS4Kkotm+IEh6P9palyQd2HbCQJh8JCUZpGAyG7omm0hihlPoXpdTp4Odf0ea0hjjjktLwNV3y02geaVRVBQ/asgVOnIilmAaDIQ6JptJ4Q0TuFBFL8HMH8HoU2zdECY8HXE6lJxEtFq00mny43VBRETzoN7+BP/0plmIaDIY4pNeTgCJSg378CPD3wGPBXVaglrYmuDHFc64Yte1D1IQJJC2dE1kj+flQUACzZmnD/Ah4/vkAfj9ceQVkZfe/BZXHA05HQJvbCnpdw+/HnRjQFl3NeV+Ni7imqAh27ICJE2H27A67T5zQuzMy4NprO1YvL67n3c2Q7FZc9QnjS9Alp07BgQMwY4a2LgA2btRrcGvWaKu+0wdq2LHHwcgcH1dcbXxeYkE0rKcGzMqe4/mnsW19H5WaiifjhyROGRNeA5WV8G//BiUl8IlPwGc+E7YMzzwT4NlnLSgFjY0B7r477CZ6jccDTntAR7gFrThsNpIT/FRUWLQpUEqKNlNRCmSImzI++SRs3Qrp6fDAAzpUcSvefFPnSElI0J/2caZeetnGK5tsWK0AXqM4OsPng4cf1ra0EybAj3/MS2+6ePJJbWLr9cIXvgCvvOPm/Q8sJCc7SEmuY/5lRnH0N9EYaUxXSh0RkYWd7VdKfdzb34gW4tVvzlJXB/UN4TfQ2KgVB7Sa/A+P1qE6PJ7YPIzr6sBp8wdTvQaxWUlxNVJRYYfycv1ad+4c1NfrJ+FQpnnEVVur+6MdXq/+W1/fst1mf73+P/v9UFM3xBVwV/j9LfdUdTX4/dTV6WJo3ce6/zweubRt6F+iYaP2TXSc/f9sVdY6gtGaKPxGVPCuug5HYgq+8ZNInBNBJpzsbPjWt/Qwur3bb4gsWdxIY6MTpSB3QQPgiqid3qDzg7dXGnbc9ka8XjeNpdU43G5ITtaLHENdaVx3nR5lTJ7cEruiFQsX6q4cNqzz6akrFtcBbhJcilVxczfEGU4n3HWXjhEyZw643axZo6/V+vqWkPLL59ficCQyakSAZauNZ30siIbS+KOIDFdKrQYQkc+hzW7PAA9Eof2okbRsDiybg6M3jSxcqD8RMnOWi5mzmr/1v8IA/cLssjW1VRp2GxZfI2lpUFrYxEi3G9xu/dY3cmRM5Iwb5s/Xny5Ytar70OczF6cwc3HzN+NL0CXLl7fJG5CdDV/8YrtDrkpm+VWgl0wNsSAaq7D/CzQCiMgVwL8BjwJVBJO2GOKL2lpwWYM+Gs3Y7ODzkZ6uwzOQkNCiNAwGgyFINJSGVSlVHtz+FPCgUuo5pdQPAJMNOQ6pqwOXpVH7aDRjs0FjI5mZcL7UpaMWGm8/g8HQjqgojVYh0NcCm1vtM2PxOKS2FhIs9e3WNLTSyMmBkxUZWmm4XEZpGAyGNkTjof5X4D0RKQW8wAcAIjIZPUUVNzRtehPbgX0ERo3GetedEbWx8claTuc7WDq9istuyAq7/qmPCtm900JAwcIFTUy5fHREcvSG2lpIyOpcaYwbB+/UjCGQcAaLGWkA8N5LVew5msiE0T5uurOjUUD9G+/iOLgHlZGJ3HIrlpS2/jvFe87D+++jEtwk33oVicPamYnW1sJbb2mzunXrtAHCIGfvB6Vs25OC065YdbmHSXPTadzwErZTx/BPnIz91pvwlNZR89ybSIMHufIKsuaN1hZ9H3ygIxauNPGCY0E0/DR+KiJvAyOAN1olZLIAX++6Zv9j+3gnsncvVssumiZOxr40N6z6h7eV89cXMggEwNuQyWU39FynPccO+HnmPW3nn2gvYMrl4bfRW2prIQFvh4VwGhtJTwc3deSVjmReYqlZ0wDe35tMXp6FHbvtTBhXzdzLUtrsd+Z9jOz4EIDGYTk4rr+mzf7A1u0M37oRgIKUTBLvbPew27kTnnhCb6emak+2Qc6uvCTe3aJNUtKSmhg3uhL725uQkhLk+DGaliymYssJRr2t++WixQ7zbodNm7SCFYHx43USE0O/EhV3ZKXUh0qp55VSda3KjsWTjwagHdYAlZlJICn8t7mEBD/DhmmdmJ7ij0iEtDQduUME0tIjaqLXeDyQoLwdTG5pbARguWUHf/5wCj5nMCvTECcjNQBAZqbC6Qp02B9ovq6Skwkkp3bYr1KD/2i7HcnouJ+MjJaLIiMjeoLHMRlpuh9tNr1tdSdqm2WArCwsbjeWzDSwawdU1XyztDoGt3HsiwVDas2h4cZbsWePwDdqDM5Z08KuP35BFl+5vYj8i3bmzldAZthtLLt1NN9x5RMIwMIb+n9qqrFRO0w5fO2Vhg0aGiAQYFbTXvY61vH2+alcU2PyaF29spbxOcLwbD/TFnR8qAeuuwF/RhYqPRPXFR39d7JuX82FjAwsiYmMXD2j4w/Mmwf/+q96e/LQsB25+joHae5qHE7FivVakXpvuBXrzBMEJkzElZ7KiKvmctH2fQIeD8OvWaArXn+9DjGSlTVkFGy8MaSUhmvsSBj7yV5ZeM+6ModZPR/WLfOv639l0Ux1tX5Bk8aGS29xgN5uqIeGBsRu48p5lby0ZTJXJ9Uy1P1upyxIY8qCrvfbRw2HT97U5X6by8bI6xd1/yNDRFk0k5RmY81Nbaf5EhbPg8Xz2pQNb69knU7t/GeIGSah7hCjWWnQ0InSqG8Izl25GJ3VgBLhZPWwmMlqMBjiD6M0hhjV1dqatoPScNh1vAaPB5wuRGDqaC+762a0BMsyGAxDniGnNBoLCnrdRk1Baa/qF12opeRibMKOV1UFI7rX14OjVUAVhwPqva0yNMHEUfXsk3mdR+EbYvT0P68rvEhVN0EsqwvKqC3p2hKt0eun0du1cUVjY0vE+s4I+BXe2qZuZQzaOcSM9n1YW1KKp7RtWfWF8jbffY0BGjy+tvU6sc1of+4+HwQ62iwYosCQWtMIPPUUjs2bUVOnUrf+WpJmzQyrftHB02Ru2kjyscPUrb4O951dz2N3xb5N+Tz7dia+gIU71pxnwfX9azJYWRkcaVxoP9Jwgrc+GALXCcCY7AaeUmOoL6nENW7oBofzPLmR5M2v4Jsxm5Krb2TErPFt9jc9t5HEtzbBqFFUrr2JtMvazrnnbTzBn94ey7DkRu5ec4zxa6e22X/+w3yGPfu/CIrzt97PmOVtr4nDh+Hxx7XCv/FGWuWY15QVeHjiER+H85P49LUlLL+xrf+Qzwd//at2b/jsZ2HFit71R7hczDtN1qbnST5xlLq1N+C+4xPUv7gJ9+ZNkJBAzdrrSV63gpcfvsjG7cO5YUkRN92bQ+GBUpKf+D2O2jLO3ng/49ZN5fnntdXtxIk6LpXTUs8Tf6xn76kU7l5fwhW3ZHH8OPzud3qd/Mtf1mvmhugxpEYacuwYVFQgO3aQUFwUdn17aRW2HdugooKEo/sjkuH8WTh6NoGT552cOdP/QdfKy4NrGu1HGk6nHlHU1l4aadhtipH2Eo4d8nXe2BDBdehjqKzEtn0LrvKOownbyaNIWRmyfz8JF8922H/4tIv8iw72Hk/ifH5HswJ/3mESzh3Dde44/oOHO+zfv1+nmSgqgoMHO8p37lAdH+xLobTMwv4j9g77y8rgpZf0C8N774V2ztHEcbEE684P9X1zPA8A+5njyIULyMmTuM6dpPpCORu3D6eqCl7YlkPZqSK8eSdIOrEPx8V8rIf0/XbkiA68vHu33s4/Usfm3WmUV1jYe1hft/v361xp+/ebjMV9wZBSGkyZAklJqEWL8AwLf4G3MSMNf+5SSEqiflpkFhyjx8DEUQ2MHdHIuHGR+Xr0hub8StTXXxpRAOAKKo2aGkhoib47xlXKwcND236qYcZ8cLvxLV5GfUZah/2+CVNQqamoWbPwjuhoGTd9QgM5mU3MGl/H6FEd14csM6fRMHICDSPHY5nR0RR85kz9Zp2ZqZPatWfM9ESWzaomNSXAnGkdp6gyMnR0d7e7/0cZAL5hWfgX5kJyMvWTtO1h0/jJqKws1NixNIyeQMrIDG5YUoTbDZ9YVkTmxBwSZk3CM34mTcOG45+h77epU7XD/Lx5MG0ajJzi5or5VSQnw9xpOtfJ7NkwfLjut0mT+v98BzuiBtgiZ25urtq1a1fE9ZtOnsTjcJAaoSdp1bl8aLKQOinycOEX86tRPhgxPqXng7umxyd5Z331ne/AqisCjHnkX3TmQUuwGYWeAxkzBsaNg/HjADj55ml2+Bbx09924pQ2cIior1pTdfICDHOTmtp5P9SePguJLpJycjrdX3G6GHHaSRvZuUdnfU0TAaVITOk8cH9dnV6TSO/CIdTXGMBT00RKprPT/UqFnE+r133VGZXn85EGIXXyqEtlNSfPo6xWUsa33Etlp7TCaKa+zkegKUBiWku/lJfrdbnmgbKvMYCntomUjJZzb2zU/pK2vp2AH5JvU0NqTQPAPmkSvXn8pY7tvY/F8NG9Uha9orQUUp1ePbKwtLrmBf0qmp+v858HGZNew9NH3Hi9QzsXU08vCUkTxnW7P31Cdrf7Xckdp5Va05zepCtsDkuXCgO0s3ks/39pYzreN8mTOr64tVYYAC53x0dUe58+m8PSRmFA25lXQ3QZWtNTQ5zmdKTJUgeuTp4gSUnga7oUbgXA4XYw1l1OXl4/CmowGOKWuFAaIrJeRI6KyAkR+U6s5RmsFBbqeXGprYHETpRGdja4k7TPRjNuN1PsZ9i+vf/kNBgM8UvMlYaIWIH/Bq4FZgJ3iUh4trAhUnbwON4NL1P17s6I29j7bilvPlvO8T2R+2q8/rqfTZv6fxH83DmtF/RidydKY95cuPXWtmVJSczy7ePjj4duwNtj+0rZ+ewZDr9/odP9NcdO4HvhRRo3d26aVF9YwrmX95L/ViemT82cPq0/XZCfD8ePd+1neeyYNqmtr+/6J/qLUwdL2P3CGQ690+ITVb/5fXwbnqc678ilsnOv7CP/zQMtFWtrtXlYqwut/XnX1cH77w9MqygR+Z6IHBSR/SKyV0SWRqHNG6P1oi0iIUUnjYc1jSXACaXUKQAReRK4CTgU7R9Kf+8tLO++gysnhyp3AqmLZ4dV/+jOch7ekMnFi8LaK5u6jUfUFRs2BHjmGStKQU1NgNtv7z+9feJEUGlUVEBiFxPklnZreykpuGuLmLMgwGOPWfjqneVIkrut5dUgZ/f78PwH45kw3Mu97hImL2pr+O9+/z0sb7yOSk2lQSw4V7cNfV6y+RBjN/43AaeLcw3/yNjr57b9gbw8+NnP9PY//zPMbbv/9Gn40Y+0Qvi7v+toAXXyJPz859qP44YbtC9GLMnb4uPxt8eTk97IV22FTHDk49zwNFJcTPLq1TBnOuf++gFjX/gt2Gyca/gnxl4/Dx56CLZuhSVL4Jvf5NQZCw88oIMXfOMbOn34U09pP41hw3RXDZTI6CJyGXADsFAp1SAiw4CQVl5ExKaU6tTuXSn1IvBi9CTtmZiPNIBRwPlW3/ODZZcQkftEZJeI7CopKYn4h6RCe5tKcTG2+vC9nBvqhaIi/VAtrYzMx6K8XPD7tbdqeXn0jS+666uDB3XuGm13G2JoeJsVkpNZM6eEUwc8PPDVYnZ/41ECtd24Jwepr4ezZ7v3ZI4loV5XBeXaBPlscQKqoeMIUSor9N+qKqx1HV/WpEyPSi0N9fgqOxmulZTo0MN+v95uR0VFywiiqBP3otJSrTBA+2T0BeHcg8VV+oWiqMKBz+PD6vEizXUqK3V7ZWV6+NDUhL+8SnsgHj+ujzl2DJqaKCvT560UFBfrXeVBh/GyspbtAcIIoFQp1QCglCpVSl0QkTNBBYKI5IrIu8HtB0TkMRHZCjwmIh+KyCULFRF5N3j850XktyKSKiJnRcQS3O8WkfMiYheRSSKySUR2i8gHIjI9eMwEEdkuInki8q+hnkg8jDR6RCn1IPAgaHO/SNvxzF1KgsNBYNQY3CsXh11/7sp07jzv4XSBndzpNUD4oZlnz2qgvt6JUjBrZiMQ3Tf2rvrqwgV9v44aBRReCM+APScH576P+FzdUfLm3Mpfjt3KI3/bxBU3aIueM2fg4kXtaT5xol5HP3QI9u7VOYWqqnSStdtu69pkNBaEel2tXVBGZqKXSaPrmbK846tt48y5OERQmZnUzJpJ+1OUmVMpCtyM3+Umdf7wjj+weHHLE3DJkg67Z83So4fqarjiio7VFy3Ss4oXL/Zd/qZw7sEls2qwW3yMzm4ie74De84VBC7kIxcK8E2fhR2wzZ7MRd/NBJwJJM8YoaMT3HcffPwxzJ8PTidz5mir8NraltHV6tVgteoRxrx53UkRd7wB/FBEjgFvAU8ppXpytZwJrFBKeUXkH4A7gB+JyAhghFJql4jMBlBKVYnIXuBK4B30qOZ1pVSTiDwI3K+UOh6cEvsfYA3wa+B3Sqk/i8jfhnoiMffTCA7bHlBKXRP8/s8ASql/6+z43vppDCLCsqf/j//QxlFXziiGRx7RT/D2U1FdUVkFmzdrb6np01D1jZzf+DHHxq2jyZ3GsLpzZJYfw5uQwcWMWTTYk8geFmBm4mkSy/KpS8hka9l09ubZLjmpuVx6qmzCBB0VPFRz0EBAv5Du3KnDa3g82pFr4UL98OwixUKf+B4MUkxfhU5YUwXB9duVwGrgy8B3gAeAXKVUqYjkAr9QSq0SkQcApZT6cbDuKHRm1Fki8g0gWyn1PRH5fLD+10TkbuAKpdT9IvI8WjlsB0qAo61EcSqlZohIGTA8qFhSgAtKqba5ijs7jzhQGjbgGLAWKAB2AncrpTpdNRSREqBjrIbwGAb0Jupgb+tHo41SpdT67g5o3Ve3364WVVXlebLtyjo+0e6s8QX0PItSgkjYF0GCxWKxWVpuGqW0L0B31OPiLBPC/aku8fsblM9X63c6MzsdMb/22tS82trjjYTZV90Qjf97XxIN+SLtq3jvm67ojdw99lVXiMhtwOeAGcBypVSxiKwA/rWV0qhVSv2iVZ33ga8B/4seOexvpzSSgAPAQmAvMAFwA0eVUiM6kaEMyFFK+cJRGjGfngoK/DXgdcAKPNyVwgge3+vwYyKySykVXoLwKNaPVhs90bGvOoY+6Q85OiM6v+uk++m9YyG3FMp1Fau+CpX+kq+zvor3vumK/pJbRKYBAaVUcOGG+WjFmwAsAl4DPtlDM08B3wZSlVIdgt8ppWpFZCd62ullpZQfqBaR0yJyu1LqGRERYK5Sah+wFbgT+AtwT6jnEg8L4SilXlVKTVVKTVJK/TTW8hgMBkOUSQIeFZFDIrIfvV7xAPBj4NcisgvoyQ7/WfRD/ulujnkK+HTwbzP3AF8SkX3AQbR1KsA3gL8VkTzaGR91R8xHGgaDwTDYUUrtBpZ3susDYGr7QqXUA52UFdHuma2UegR4pNX3Z2m31qKUOg10mEYLll/Wquj7XZ9BC3Ex0ogBD8a4frTaiAaxkiNezj8c4l3mWMoX733TFQNV7pgR84Vwg8FgMAwchupIw2AwGAwRYJSGwWAwGELGKA2DwWAwhMyAUxrr169X6DxzQ/1j+sr0lemrOO+rwciAUxqlpQPR6bSF+nodpfOVV/o+kF+kfbVvHzz9tI4pNVQY6NdVfxLvfVVdDRs3wltv6TiIBk208hYZP41+ZtcuePhhve1ywdq1sZWnPRUV8Itf6HDUp0/D//k/sZbIYAiP99/X6e5BB8hctCi28sQDrfIWXYWOJL5TRF5USoWdgsIojX4mKUnHaFIKkkOMTt6fOBw6V0FBQZfB/4Ykmzbph1Fz2gtD/NJ8X1mt3edVj2fuuIO7gZ8BY4FzwHeffponetFk1PIWGaXRz8yfDw88oFMnzJrV09H9j9sN3/62zpg2bVqspYkftm/XSawCAbAMuEndocWKFZCWpiMnT+3gax3/BBXGH4DEYNE44A933AG9UByd5S2KKHOgURoxYMaMWEvQPSNG6I+hheYMpJWVZgQW71itAy7XRnt+RovCaCYxWN6b0UZUMO9MBkMIlJXpPCB9lRnPYGjF2DDLQ6EAaJ1BbHSwLGyM0jAYeqCxEZqadNKoAZZi1DAwORdmeSjsBKYEU7w60NFyI8otbpSGwdAD1dXagCEhAWpqYi2NYQjwXaC9Qb4nWB4RSikfOoHT68Bh4Onu8hZ1h1EaBkMPVFdrAwGXS+erNhj6kuBi973oJE0q+PfeXlpPRS1vUZ8uhIvIGaAGnVzE1z5DVjCL1K+B69Ca9PNKqY/7UiaDIVxqa/UoIzERqqpiLY1hKBBUEDFf9O6M/rCeWq2U6sqF9FpgSvCzFPgdEZqBDSRee6kRFYCrr7Njs4eVmz6qnDoFhw5pS5MxrZbITn5cSf6JemYsTSZ73AA1dI8iHo8eZSQktFhRGWJDZSVs26av1znB7MXHPqqg6Fwjc1amkpbjiql8Q4FYm9zeBPxZ6aQeH4pImoiMUEoVxliuPuOF55r469MOlAKPt4nb7rTHRA6vF37/e+31PWMG/OhH2v+grMDDfz+cQH5xGuvOVnLfP8VEvLiithacTv2pq4u1NEObp56Ct9+GlBT43vfAXl/NLx9Morzazk0Xy7nna0Zp9DV9vaahgDdEZLeI3NfJ/s4cTjrkqhWR+0Rkl4jsKikp6SNR+4fqau0NDlBdE/1RRqh91dio39pAT7k0x+hp9AYornQCUF4bG4XWX4TaV80jDZer7+OFxSvxcg82K+3aWr3trfNTGbxOa7zWmMk1lOjrkcYKpVSBiGQDb4rIEaXU++E2opR6kGBaxtzc3AEdXXLtaj/1jRZUAFZd4SPa/4JQ+yo1Fe6+G/bvh4ULdfgQgBGTk/jqHcWcOinMX2gBBu/0VKh9VVen+8flGrojjXi5B6++Wv8fxo6F2bMB0vnKbcUUFAiLlhml0R/0qdJQShUE/xaLyPPo+CetlUbUHE4GCqPGu7jvy83fYnuRX3ml/rRn+Y3ZLO9/ceKWujr9oHI69bSeIXbMnt2sLFq48rbs2AgzROmz6SkRcYtIcvM2cDVwoN1hLwKfFc0yoGowr2cYBiZer1YYLpdRGoaBi4g8LCLFItL+ORwWfbmmkQNsEZF9wEfAK0qpTSJyv4jcHzzmVeAUcAIdoOurfSiPwRARHk/LQnh9faylMRgi5hFgfW8b6bPpqWAI3g5hw5RS/9tqWwF/21cyGAzRoHmkYbPp6MR+vw6KZzD0GXfc0SE0Ok8/3VvnvvdFZHxvRTMe4QZDD3i9eiFcxKxrGPoBrTD+gA6JLsG/fwiWxxyjNAyGHmgeaYBRGoZ+obvQ6DFnyCmN8nLtoxBLzp+Hc72JVxklfD7dH4FA2/Lqajh2TE/DGPQ6RrPSMIvh/YvH09EL//jxQR+ivi9Co0eNIaU03noLvvIV+O1vY7eg+f778JOfwI9/rD1bY0UgAE88AfffD88+21JeVQW/+Q384Afw2GOxky+eqK9v8WNxOMxieH9x4YL2+v761yEvT5f99a/62vzP/9QpiQcpfREaPWqEpDRExCkid4vId0Xkh82fvhYu2uzbp72xP/wwdnkRTpzQD+aaGr0dK+rrW5TW5s0tHuEFBdrhTyk4ciR28sULSkFDQ1ulYUYa/cP58/p69Hrh6FFdduaMfuE5cULHThukRD00OoCI/BXYDkwTkXwR+VIk7YRqPbURqAJ2Aw2R/FA8sHo1VFTAggU6oU4syM3VN0IgoD2xY0ViInzmM/DOO9rL1ha8EiZPhmuu0Qpj6aAPHdkzDQ26b5rzghuz2/5j+nRYtUqHu8kNxseeM0ffw2PHwty5sZSuD3n66Se44w6IvvXUXVGQLmSlMVop1Wv73lizcGFsH9SgL/R4udjXrdOf1jgc8KWI3j8GJ63XMwDsdjPS6C9SU+Gr7Ty3brhBfwY9WkHEZWj0UNc0tonInD6VxGCIQ1qvZ4BZ0zAYuh1piEgeOlKtDfiCiJxCT08J2jcvTt6ZDYa+obORhlEahqFMT9NTQ2EgaDB0SWsfDTAL4QZDt9NTSqmzSqmzwAigvNX3CmB4fwhoMMSSzqanjNIwDGVCXdP4HVDb6nttsMxgGNTU1+spqWaM0jAMdUJVGhIMLgiAUipA7FPFGgx9TnPcqWaM0jAMdUJ98J8Skb+jZXTxVXRI86GFzwdPP60Tay9frh0/wuTikXIcr7yABAJ4r76RkfNimEBmyxbYuVOfSyunjMK/vEXS6TwqF6xmzA3zdaHfD6+9BidPwlVXwcyZsZG5n2k/PWViT/Uh27drz9slS+Dyyyk/V0tgw/PYG2qpXn0jY5aMgsOH4Y03YOJEuO46E244BoSqNO4HfgN8H21N9TbQWc7vwc3x4/DCC3o7EIhIaXi37WX4jk0AVKeOhnkxcn/xeOCPf9R/jx+HRYvAZqPkcCkjXvoDKIXNUwXNSiM/H/78Z71tsw0ppdF+espYT/UBjY3wyCPacy8vDxYupHLbQSZuewmAmoRsWPJJ7Y26dav+zJ0L48bFVu4hSI/TUyJiBX6llLpTKZWtlMpRSt2tlCruB/nii+xsmDRJb48fH1ETlnFj8CcmEXC6kFhe8C4XLFumt5cuveQS7spOomrKIgBqJrRKh5KRATNm6O0pU/pT0pji8QRHGo2NUFtrlEZf4XC0jHaXLQOnE/v4kTSlZoLNRmD8BL2v+dqbPh3S02Mj6xCnx5GGUsovIuNExKGUinF82BiTmQn/+I9QWKgv2ggYt3YKxSN/DkoxduawKAsYBhYLfP7z2r22VUyV5EwXVV//OgXnysmckdNyfHIy/NM/6cBZI0f2v7wx4tKaxksvwYE8HF/9IV7vkIrz2X98+tM6pk1WFlgsjFk2hpKMn+Gvb2Ts3OC1uG6dHuWmpkJSUmzlHaKEvKYBbBWRF4G65kKl1C+7qiAiY4A/o9O+KuBBpdSv2x2zCh3X6nSwaINS6iehCh8TMjP1pxdkz+hd/ajhcsHo0R2KU3MSSM0Z1fH4pKQhd6N6vZCZoXR0PIsVZ00pDQ0xXIcazDgcHa7HrKntRhMiMKqTa9PQb4SqNE4GPxYgOcQ6PuBbSqmPRSQZ2C0ibyqlDrU77gOllHEiNMQlXi84VD0E/DBhPM6yC9TXG6VhGLqEpDSUUj8Ot2GlVCFQGNyuEZHDwCigvdIwGOKW+npweqsgNQ1SUrFVlJg84YYhTaj5NLJE5Oci8qqIbG7+hPojwWTmC4Adney+TET2ichrIjKri/r3icguEdlVUlIS6s8OSUxfhU4ofeX1grO+Uq/pJCcj5WVD0uzWXFeGZkJd0XscOAJMAH4MnAF2hlJRRJKA54C/V0q1S9zIx8A4pdQ84L+AFzprQyn1oFIqVymVm5WVFaLIQxPTV6ETSl95veCoqwS3G5LcUFk5JJWGua4MzYSqNDKVUg8BTUqp95RSXwTW9FRJROxohfG4UmpD+/1KqWqlVG1w+1XALiIxNCkyGNri9YKzrjyoNJKgutrkCTcMaUJdCG8K/i0UkeuBC0BGdxVERICHgMNdWVmJyHCgSCmlRGQJWon1Wcp4z+vv4jx2EP+oMThuvTGyNja8hqXgHI1TZ5Jyzcqw6x88WMOOHUkEArBoQR0LFsXOGunsix8jZ86iJk1k3PUtPhl7XzjJmXM2Zk5tYOr6qZfKLzy/HblwAcldwPClEy+VP/dkIyXlFi6b72He8hQAassb2fiqlZpaYeWiWmYt1uXFxfDqq9q5ft26iN1d2LYN9u7V7iN33hlZG6FQXw9OTzm4J4IrARoacDoCxuy2l5R8eBjvvjPgcpK8dBLp08dx8MVjHD/rZMq4JmbdOBlfvY+SZ98FjwfXVctJnzCMne9Ws+ugm5E5Pm66TYcffv5ZHxeKLMyb5mHFuiSOHoV334WEBLjtNp2lMhQ2b4aDB2HECF3P0DmhKo1/FZFU4FvoaaQU4B96qHM58BkgT0T2Bsu+i05fiFLqf4HbgK+IiA/wAne2jnEVbRL27kB278bicOAdPoKE5YvDql/34V4SNzyONDZir6uECJTGrl1uNm0SACwWNwsWhd1EVKg6ls/ITY9iLy2k4cxEPMvHkZieRuHH5/l/z0/A47WwtKKGbwUd1ot3nGLkM/8FPh+V3nIIKo3Nr3p4+vlEnUu7MYl5y/Xxb24WNr6oV4pVIIlZwa5+4w2tNEBbT0aaJXDnTu0ULKJN9q+9NtKe6JpAIJgfvLYcEmfrLDKJiTitPjweR4/1DV1Tv+8UY99+FIAzjr/HkpHCo++M50yBg5FHG3lg/kU8u04x8sUHAbgY8MP9t/D+7mR27BBsNisjsjz4lIVnNrjw+cDrdbNinXYa3xxccXW5CGZO7Zndu/V1ZbdDTg6sDP/2HhL0lITJhQ4hMhlt+fSQUiqk2BlKqS3o26y7Y34L/DY0UaNAczwIpxOs4cdbVBKs29gIdmePx3dG24Q+ih66qM8Qm+BzubEDPpcbq0P3jcUiuJ0+PF4HCQ5/y/F2G36HC6uvlkCrc3c6FDYbNDWBw96i7x0O7T8YCIC9XeymZlqH5wiX5ro2W0t+82ijQ4goLHU1La+r7kSc0oTXa5RGb1DNF4UI4rRjs9pwO3yAgySXD5RVX0QioNSl4+02fc/Y7QqbLYAFcDgUPp9cuiaa/4q0jRvWE831HI7eXZuDnZ5ut0fRU1MfANcCM4Fv9LVQfUXt4hW4UjMIDB9FwtIFYddPWjqfmrJPYyksxD91GikRyJA7rwyLJZNAAObOqQFSI2il96RMHMX5a+6i6XwR9nE5jHG7AciZP5qv33qCC/kwcWIASAMga+FYLtzzDwQKCklc1jKVdfk6Nx6vl+JSK5df1kCzG8/1N9rx+5qorrWw+nIvoKfhbr5Z38xNTXDNNZHLv2aNnn4YNkzHT+wLPB5IcAEeWp4iCQk4VD0ej7tvfnSIkLx0ImccX0dcTsbdqoehn1l7nDPnLIwdEyBt3BTSxmVxwf9NVJ2XtGv0MVdfUUd6kpORw30svFxfU3dXNnDhoo1Fs+uAFG68UY8wEhP19RYqK1dqI7nhw1si7Bg6It3NBolInlJqTnDbBnyklFrYX8J1Rm5urtq1a1csRYgXehyimL66RER9de4c/PzffNzf9F9wy8268KOPeNO7gokrRnFjZMti8Y65rkInNtMEMaan1bzmBXCUUr4+lsVgiCs8HnDZmtqupLoScPo91NV1Xc9gGMz0ND01T0SafSsESAh+F0AppSKZoTEYBgQeDzgtTdpqqpnEBFy+WmpqYieXwRBLulUaSikTKMEwZKmrAxcNevGkmYREXI01VJqRhmGIYozNDYYuqK0FV8CjV1WbSUzE1VBlRhqGIYtRGgZDF9TVgctf13ZNIzGRxPpys6ZhGLL0kYV7fFKz7xDuYwfxD8vBvvqKiNoo3JyHv6wK59RssuZN7blCJ5S/uh1EkXHt8ojqRwvve9txFOXTMHosictbcoRXv/chrqIL1E2aTvqima2O34G15CIsycUxtiWnwebNOjfT5Ze3yefEG2/ocBtXX912hmegUF0NLl91W6XhcJAYqKWmJoB55wqd+s3vYS8roW7mDFJm6bikDS9vQtmduK7Rrl/e3XlYT57AP2EiCYu1Wfebb+pr6JprtI/PyZOwZ49OaRNBtmVDFBhSSiNpyzvIe+8h2dnUuxNwLQnPI7x051HSNzyCq/g8ZVfcDBEojcoNb5PxzB8hEKCqporUO/rAlTkEPPn5JLzxMnL8OAmzZ0NQadTkHSP5haeQggLsl10GQaVR/9FeXE8+gpSVESgphK/cD+hwDY8/DjU1UFYGf/M3uv1Nm3R5Q4NWKJ/9bCzOsnfU1kJmQzUkttKEAgluqDXTUyFTv30nzueeRkpKSF61CmbNoumJp3Bu3AA2Gw1NDTiuWoPr5Q3IwYOoadPwTx7P69tTeeIJ7UtbVQWf+Yx+EXnnHR0+xuWCyy6L9dkNPYbWq1JlJQBSWoq9oSHs6oGGAK7SAgCcVUURiWAtL9bJGJTCVlEaURvRwFJfDxcv6i8lJTSUalls9V6kubysJQyY1NYg5eV6u7rqUnlVFZfm96taiikv1wqjfflAoqpKkdhQDgltgxe53HYamwSfMUIPCUtDPRK8vpovBktlOSgFTU1IeTm+qhooCt5TRUUE6uqoqNAKo1W1S9daRQVmijBGDKmRRmD+fCwOB2r0aKwrV4RdP3vFDC6euxdbwVkap88lklCD1bMW42ioAaWonJVLrPyKXZMn419/PZbTJwhMnopzmA4unLB4Hr6bbsVy7gyN02bRvATsXLMS/4XzyMULNM5ccKl89WqtWyorYc6clvaXLtVv6nV1MKvTLCnxT3VlALd4wdE2poS4E0lyNlFV5eht5t8hgWPVSgIXziMFBfhmzsYO1M1YSFJjE8rpoHbeQjKyM2laux7rqWP4J07GPnokl1+upwgbGmBeMAjBrFk6PM3w4TrgpaH/6dYjPB4x3qiXMJ67oRNRX91/r4/P8BfSbr6y7cEf7+GPZ9bxte+mMGlSVOWMB8x1FTrGI9xgMGiUgupaC+7OhpNJSSRRS0VFv4tlMMQcozQMhk6orQW7xY89pROzr+RkkgNVBJd4NAcOwF//2rKQYzAMUozSMBg6oaICUu0eOh1qpKSQ0lByad2Wykr8v/gV1buOasVhMAxijNIwGDqhtBRSVLWOld2exETSVTkXz+t4nv5XNvGA5cfcX/BDdr9VroNWGQyDlD5VGiKyXkSOisgJEflOJ/udIvJUcP8OERnfl/IYDKFSVARpvlKdFrA9AllpTeSf84PPx9ubGmlKSOHudUX8MfAlfFu2d6jS2KA4eaiBQKAfhDcY+pA+UxoiYgX+m5bkTXeJyMx2h30JqFBKTQZ+Bfx7X8nTTPXRs9QUFkZc/2JhNflHK3slw4kTjRw71tirNqJBWVkZ+YfLKWzXH5Xn86k8dLbD8UXnazl7pLZjQ/X1DLZV4Qvnmkj1l3U+0gCG5Vgpr7JR/f5eNvhuZPXiWiaMqCclOcBHr5S0Obahzsf37i3mZw808PvvnO4P8WNO/pFKzp+pvvS96EQhlUfPtTnm/Mlqzp9q5SUZCOghntGscU1fjjSWACeUUqeUUo3Ak8BN7Y65CZ0dEOBZYK2I9JkZm/eJDaT87IckPvJnSvacDLv+yYOl/PkRN9/7WRobnvBGJMMrr/j4zW/s/PrXdl5+2d9zhT6ipKSEt58M8M8/y2DHyy2XwcWD50h68mnS/u8PqH/s6Uvlx3ZX8MdHEvjhvyXx/JOtzr2wEH76U/jWt7S77iDh3PFGslPqwdL55WgdP5rxcoZ/fWg4w9L8jM7SC+C5sxt4tSi3xXESePb/HidZavjaLQXknU1h78aOCnkwseWp83z//6bx7KMBjuwspmjfSTKfeoS0n/0Az19fAGDzxip++vNkfvVfSXz0VtCJ9JFH4B/+AR56KGayG3qmL5XGKOB8q+/5wbJOjwkmeaoCOrhLich9IrJLRHaVlJS03x0yzpMHwevFumc39qB3eDjUlsCuPVa8Xjh4PLIc4fn5Vi5eFEpKhLNno9/9ofZVQ5mdVz7KoqEBNu3OobJcu9c6a2qx7fwQ6utxHvz40vElJRb25elzP3Kq1bkfPao/tbWQlxf18+lLuuqrQADOXHAwYng3PkzDslg95iQpiT6uW9XimjxtvJdi63BOPvkRAMV5Rbx5dCxXrfHjSHGxdlYhjz7lJNAUuxeGSAjnHtx/KgmPB7YfTKP+ohd7cRm2fXvA68V16iAAx846KS8X8guE0+ec2gt0+3ZtfbZt26XoDYb4Y0AshCulHlRK5SqlcrOysiJuxzttAWrYMHyXraBxePiuvCk5sHK5j2HDFPOnRzbSGD/ez8SJivHjFePHR//BEWpfjZ6exidXFpGZCTctKyQtQ/ume9NSaLpiDSozk/r5LUEMR44JcNkSP1lZijlT6lsamjULFiyAnBxYGNNMwGHTVV+dOqlIVlW4xw3rurJAzhVTueVmRWpSy//RaoHLZtfw2IdT8e3N48GfV7F0VD4pwxwATFvgxm7x89avD/bZefUF4dyDC6fUkJPlZ92ichKGu6nPzsG39HJUVhbeqdq1e+aEesaMUUydEmDS2AZwu3V4gcxMnQA+La0fzsoQCX3mES4ilwEPKKWuCX7/ZwCl1L+1Oub14DHbgznILwJZqhuheuuN6imrJDEzLeL6AFVldaRmRh4ApK6ujvp6ITMzseeDuyYqnruVZR7SOpGjtqyapMyOiRmryhpIzexklOXzgS1uo9KE1Vf/8y9lcOwoV94xvMvpqe4IBOCJl1MprrST467lzpvrsbZKZ1ac38hjb4/ie18sZNL6KSG1qZTuYqtVh9HoDJ9P7+tqf4j02XXV/pqqqahH7DaSklpdN42N4HCEKXLMGJIe4X15l+8EpojIBKAAuBO4u90xLwKfA7YDtwGbu1MY0aC3CgPolcIAcLvduGMVdKodnSkMoFOFAXSuMCCeFUZYNBaW8m7eMFYkJbBtc33PFbpgfGI9yZYkMl217Hin4yWd4ajlnx+eAg/3RtqesdNIQGz4VVtNYrfrZ7PFAldcoaMQR3M1sbPrqv01lZzu6nDMAFIYQ5Y+jT0lItcB/w+wAg8rpX4qIj8BdimlXhQRF/AYsAAoB+5USp3qoc0SoLcricOA3oSY7W39aLRRqpRa390BIfZVNM4lEvrzd0Puq0yH0/qFK/57csCS1OF9PaD8NotYoxbbNmBNsdQlzHP6xIkt4FGiGpWyOKXBkmrxirbaSqSOERQyhvO4qaURJwWMopAR1JCMg0ayKWaUOo9bVeHHRrFlJAWMprzV8qAK+PAHGgMiFqxWV5tzu3DhpfKtW28+DQGI/LqK1XXUW3ojd499NRgZcAELo4GI7FJK5caqfrTaiAaxkiNezj8c4l3mWMoX733TFQNV7lgyIBbCDQaDwRAfGKVhMBgMhpAZqkrjwRjXj1Yb0SBWcsTL+YdDvMscS/nivW+6YqDKHTOG5JqGwWAwGCJjqI40DAaDwRABRmkYDAaDIWQGnNJYv369AszH9JXpK9NXsf4MSQacG29p6UD0H2rLCy/oUBPXXw/OyOIehsRg6KuuOHNGx7XLyID1UXCv6su+Ki+Ht9+GxET9Px/oDObrytAzA05pDHSeeQaefRaU0gE977or1hINTDZtgs2btdK12WDdulhL1DUbN8Jrr+m4UX4/3HhjrCUyGCJnwE1PDXTq6rTCAB1N3BAZ9cGwUA0NLdvxijcYENnvHzqZYMvK4OGH9TkbBhdmpNHPrFihA3kGArB8eaylGbjMn6/f3NPT4aqrYi1N9+Tm6heFhARYtSrW0vQPb72lR4NLlsDs2bGWxhBNjNLoZyZP1h9D71i1auA8gJcs0Z+hxIkTer3p2DGjNAYb/TY9JSIPi0ixiBxoVZYhIm+KyPHg3/T+ksdgMPQdhYUwfToUFMRaEkO06c81jUeA9nYu3wHeVkpNAd4OfjcYDAOYQEBbjE2cqJWHYXDRb0pDKfU+OmdGa24CHg1uPwrc3F/yGAyGvqGyUq/fZGbqBXHD4CLW1lM5Sqnmd5GLQE5nB4WT1H6oY/oqdExfhU44fVVZCcnJ+lNdrUcehsFDr5SGiPQqyXVrgmleO/WyDCep/VDH9FXomL4KnXD6qqJCKwyrVTs0VlX1k5CGfiEipSEiy0XkEHAk+H2eiPxPBE0ViciIYBsjgOJI5DEYDPFDZSW43Xo7OVl/NwweIh1p/Aq4BigDUErtA66IoJ0Xgc8Ftz8HbIxQHoPBECfU1Og1DYCkJD3yMAweIp6eUkqdb1fUre+niPwV2A5ME5F8EfkS8H+Bq0TkOLAu+N1gMAxgqqpalIbbbUYag41InfvOi8hyQImIHfgGcLi7CkqprqIsrY1QBoPBEIdUV2tPfdDKo7o6tvIYokukI437gb8FRgEFwPzgd4PBMMSpqdEL4KBHGmYhfHAR6UgjQSl1T+sCERkeBXkMBsMAp7a27fSUsWYeXEQ60jgtIn8VkYRWZa9GQyCDwTCwaa00EhPN9NRgI1KlkQd8AGwVkUnBMomOSAaDYSBTV2eUxmAm0ukppZT6HxHZB7wkIv+HIZz+0GAwaJTSSsPl0t8TE03emMFGpEpDAJRSW0VkLfA0MD1qUg1i3n8f3ntP31yXXw5r48x2LBCAV1+FHTt0hrnFi6Pb/ocf6tSnPh9ccQWsXh3d9uOR11/XqWkTE3Vq2nnz+vf3AwF4+WXYuRNuuknn9+grGhp0JkVb8MniduuFccPgIdLpqeuaN4Kxo1bTMYKtoRMOH4a8PDhwAI4cibU0HSkrgz//GY4ehVdeiX77eXmwbx8cPAiHDkW//Xjk8GH92b1bn3t/U1ICf/mL/p++/nrf/lbr9QwAu10rrYaGvv1dQ/8R1khDRD6tlPoLcJdIp0sY70dFqkHMmDGQmqrTYI4dG2tpOpKaCitXwgcf9E3ioPHjIS0Nmppg3Ljotx+PjBunFUZKSmzOOTVVj2q3bYNFi/r2tzyetkpDpGW04XT27W8b+odwp6eCEWVIjrYgQ4XrrtMPToCZM2MqSqc4HPDlL8Ndd+nQ1tHmqqv0g9Pvhxkzot9+PHLLLTBtmu7bWGRtdLngK1+Be+7pm/9pa2prW9YzmmlWGsOG9e1vG/qHsJSGUur3wb8/7htxhgbxqCxa43D07Q0+dWrftR2vxPp/3tf/02Y8no5Kw1hQDS7CWtMQkXtFZEpwW4IpXKtEZL+ILOgbEQ0Gw0ChteVUM4mJZjF8MBHu9NQ30GlbAe4C5gETgQXAb4CVUZNskLLnw1o++MiNUnDZAg9LrnD3XKmf2fx0CcdP21g030fuNZHlmTh3rJpXNyfh8QhL53m4fG38nWd/se3tWj7c58blUly3uobxM1Kj2r6vMcCmv5ZxsdjG8uUBZl7ex3NQ3VBX13HtwsSfGlyEqzR8Sqmm4PYNwJ+VUmXAWyLyH9EVbXCy+0AiW7ZoIwKnM5ElkQSU70Pyj9bwh+ez8PuhpMZL7jWRtbNtVyKbN+uBrNWawOVxZlrcn+w6mMiHHwogpKe6GR/ltZyDW8v58ytauSupYObl0W0/HDweozQGO+Ga3AZEZISIuNDRad9qtS+hizqGVmRlKux2ndUsa1j85cFMSrMzeUQdAGOzvBG3k5XhJyFBIQLDhg1tv8/sYbofEhIUWendZhCIiLRsO9npjQDkZPqi3n441Na2UhpK/99N9r7BRbgjjR8CuwAr8KJS6iCAiFwJnIqybIOSm26xkpLciD8grLvaHmtxOpCW4+KrX62l8GQJkxZGbiS3dr0Tl8NLXb2Vq69zRFHCgcedd1vJTG8kwelnxZrov1uNm5XKP32tioriamYuz4h6++FQW6tNfDl6FJ5+Gr77XdxuK8UmJ+egIVzrqZdFZByQrJRqnY9rF/CpqEo2iFm9Lr4foiMmJzFiclKv27m8Dx6QA5Wrru3b//m4OanEg9tLXR3k5KA9NwN+uHCBxMQxZnpqEBG2R7hSyodew/iqiKQHy+qUUibCjMEwxLlkPVVcDKlpUFxscmoMMiKNPfUp4AvAThHZBfwJeEMpFdeT16e3FrBvT4CxYwIsvCnC97K8PDh1SgdlGjkyouo7dujtRYtgQQwNlbdt0+E85s9vG2Nq0yY4eRIWLoTLLmtVYc8eOH8eli4Nvk5qnn2yiZIyC5ctqGX+cm0ZVFGh4x3V1elzXLq0f84pFrz3nv6/DhsGd97Zcf/xPTW8uyMBd6Li5puFxJR2t11DA2zZotcAVqzoYLPa2AjPPaenfq6+unOv8tdegwsXdCyviROjeHJhcslPo7ICJkyA0hLcM4zJ7WAiIqWhlDoBfE9EfoC2onoY8IvIn4BfK6XKoyhj1Ni0JYl3dqdit8O/jy1i9IKcniu1prQU/uM/9E1eUABf/WrYMuzeDW+8obdFYqc0amrgscd0rKnDh7UcNptWFn/9K3i9+nQvKY3CQvj5z3WkwdJS+OIXAdj8mpdnnk9AKWhsSmb+cn3422/DSy/p7UBgcCuNPXu0AhbRIVLWt4vC9vb2RDa/awUgMaGem29vd9t99BH8/vd62+HQkRxb8frr8Pzzetvvh/vv7/j7jz6q+9njga9/PUonFgEeD7isjdDYBFnZcLGQhASor9eXji3S11RD3BBpwEJEZC7wn8DPgeeA24FqYHN0RIs+iQ5tueJ2+bDbIzh1m03HRICWfJZh0volsr0TVH9isbScQkKC/g46wFyzXG1MJ222lqBCrQR32tWlB4HD0TLQdDpb2nTE9xJOr2k5f91/7XEG+0UEXJ31ResLoZMATS5X933pdLb8bqz72uMBl69O3ydJbqiqRgSSkswU1WAhIr0vIruBSuAh4DtKqeYYljtEJIZW4t1z7TV+xqWfYuQ4Ozmzx4TfQFoa/PM/6ymaOXMikmHlSm1uGwh0eKHsV9xuuPdeHW133ryWh9LYsfD5z8OJE+1GB1lZ8P3v6zmQVrG9L1+XiKfeS0mZlcuXNdAcluwTn9BvlnV1esZlMLNypX4oDhvWeaj769c34E7wk5SoWH9TJ28KixbBd7+rp6c6iZt+1VV6hFFTo6en2jNzJnzta3owGMu+DgT0iMLVVKNfMNxJl+alkpKgsrLvY18Z+p5IB4u3K6U6NbFVSt3aC3n6lOyZWWTPjMzD+RLjxvUqVOmoUXD77b0TIVpMn64/7bnssnZrGc1MmKA/7bjqhmYrqbavubfc0nsZBwLz5nWfIyN7tJs77ummAYtFLyx1Q/spr/bEw/Rffb0e8Vi8wdR9LhfUe8HvJznZSmVlrCU0RINwQ6N/s9V2h/1KqV9GIoSInAFqAD/a67wP08QYDIa+oK4uOOVZVwdOB1gEEnS0Qrc7nYqKHpswDADCHWn0ZUj01Uqp0j5sX4/ft2/Xb8uRrkCfPQv5+TB7dtCLKTxqa7WlSyCgpzJiGS764kU9DTVtmp59aub89nyaTpwhackssqelXyovfPsg/vxCEpfPJWNK9qXyl1+G8nL9tjttWn+eQXxw5Ii2iBs2DK6/vuN+T7mHijd2Im43Iz/R8X3I1xigYNN+UDDq2rnYHB3X2w4d0s/iefNiv27RFZfMbT114AiuzSS5obqapKR0yuPSPMYQLuE69w3skOhPPw1bt0JyMjzwgM6IFA6VlfBv/6afkNdeC1/4QtgivPwybNigt5ua4NOfDruJqNDYCL/9LRw7ph9E3/2uXqgtP1dDziP/jqOiiIqCNfAdbapT/PFZsv7yK2x11ZRXfgK+8RkA3nwTnnxSt1dbOzSVxltv6TS+TqeelVmzpu3+quffYdTrj4IIFyzfZuT1bTMhFby0m3FP/RyAs03/yLjb2ma/OnoUfvIT/aJx7716jSMeuZS1r7ZVqNuERKipJjlZW+oZBj7hTk99Wyn1HyLyX0AHnwyl1N9FKIcC3hARBfxeKfVgu9+9D7gPYGxv0t0155ysr9dPuXDx+fSdAdomNQLq6zuKE01C7SulWoLIVVfr7yKgmvzYvHqHvaGVv2aTD1u9jkllaWw5CZ9PKz+IrEtjSbSuK18w3FNjY+d9IA3B/lIKVd/xn648Lf2pOrmuGhq0woC2109/EkpftYw0PNpoBIKBp6pJydL2I4aBT7jTU4eDf3dFWY4VSqkCEckG3hSRI0qpS6ljg0rkQYDc3NzIHQhvuAHS07X306RJ4dcfNgy+/W3t3BdhLtRly/RDRintPBdtQu0rp1Nb3Bw82NZ6KnNSGmfv+icsJ0/Aolyag4lkL51E4e1f1/4pixeSFiy/9lptSlla2jfn05dE67patEib3Q4b1vmCtf2qlRRarJDoJuf6jtfNsOuXcM53HwCZ13Vc0Z49W/te1NRoS61YEEpfXVIaFZ4W5093IlRWkjLJjDQGC+FOT70U/PtoNIVQShUE/xaLyPPAEvoi3/jMmb1PoTZ3rv5ESFcWS7Fg6tTOs+iNu3Y2MLtD+Yhbl3faTmde0EOJlSu7f5hnTsmGKTd3uT8pw0nSF9Z1ud9iiZ2yCIdLEW69XnA1r2kkw7mzpKZqpdE8ojUMXCJy7hORXBF5XkQ+Dmbt2y8i+yNsyy0iyc3bwNXAgUjaMhgMsaOmptX0VPNCeHIylJfjcmmFUVcXUxENUSBSP43HgX8C8oDeJoXIAZ4PmvDagCeUUpt62WanePJL8O/eh8oZTsqyjm/SoVC9ZTdSUgazppM8NbJ58BNvnUQhTFkXwyBBoF8NCwpg9OgWT3fg/JaTVJQossfbGb6gxSelfvtupKQI69LF2HJamVsdP65fI3NzB2eciLo62LtXez52YjwRqK6l6b0tqPQMXCs6n7Y8vOkUDhdMWtXF/7x5wj9c44w4oqYmaHJbX9+yEJ6SDNU1iN9HZqaNoiLt6GcYuER6h5copV6MhgBBJ8FuXKOih/3lF0jc/CZqxEhqEv6W5HlTwqpfvfsgyc/+BblQQNOaq2DqvWHLsH/jKf796UkEAvCtytPk3tbRWa5f8Pt1vKMdO2DVqktxtIr2nuMPz+dw5HwSN60o5Z6gZXL9rn04H/8TUlxM4GIB3Bc892PHdEyqqiq49dbBOVf1+OPaRGrUKPje9zrYSatnn8W56VVUejr1AYXrirbrEh88cZbfbpxIgivA//GeYMa1k9u2f+AA/Oxn+lX8u9+NONpArKmpgYx0v7aMaLYLtlr1oviZM6T7Uik6lcikSUM39e9gINLYUz8SkT+KyF0icmvzJ6qS9QHWMp0JRi4WIhEE+Ld4vEjhBd1WSVFEMpSU6HvK74eSvvVK6Z7GRm38DzpiYdAEqMln4fgF/Sp4urjl5paqKiSYSUfKW61oFhe3BBUqiqxP4p7CQv33wgU681CzlOt/pFRUIFUd9xeXWlEKPF4LFZ35KhQV6f73+wd0H9bUQIKlUSuM1usWo0bB44+TUXGCgjfMzPNAJ9KRxheA6YCdlukpBWyIhlB9Rd3cZSTanfhGjiVp5aKeK7QjaWUuDeduw1ZwFu+0+UQyyp41Bz7pKcKvYO6cGKbmTEiAr3xFT7u0mlYanTuar9x4nqPnE1k8tRLQVmaOFcvwF5zDUlRI44x5XAqrl5urA02Vlem43IORq67ScyqTJsGUjqPThlkLcSIE0jOwLe9o/bRgpgePrxh3gp+Jszu55RYvblklbh2jfoBRUwOJ4u0YiXP2bEhPJ9MzhvMfX9DK0WqNjZCGXiORpMAQkaNKqZi4ceXm5qpdu6Jt8Tsg6dEGxfTVJUxfhU7EfXXvvfDFawpIefdFuLaj7XFhmYOXX7Py/37qiW3Sj+gxJO3AIp2e2iYivbRdNRgMgwWltF1FoqrrNLw7QFZaIyWBTBoPnehn6QzRJFKlsQzYKyJHg+a2eZGa3PY39R/vp+rg8Yjrlx8+Te3Hh3olw+l9ZZzZF8sFDU3FifOU7DpF+cEzbcrLDp7Cu3MfJSUlbcpPH6jkyO6hl+y5ttLH4Y+rOXu464QQ3j15VOV1fV2V7j1Ded7pvhAvLvB69QynraFrpWGzQra7jtP7TGKNgUykaxo9BGqOTwKPPYbr5ZdxTp1K1a13kbogvMFSxe5jpL74BNYjh2m4/hacnw3fUujwK8eZ/MIvkYCfIzd/k+mfiI2nX+XpC/hf3EzWh69QcuUnYdZ4AErzTpC58Ulk/36c66+DL35ey72jjMdfTOf4CQuf/EQDd3y68wfDYOTFF/1sfCWFWTP8fPqTlUyck9Zmv++pZ0h4YQOu0aOpuekukle0dY0/9/xORm/4LxpThpF/0xcZfXVk5t7xTGVl0JTW4+lSaQCMzmrgyEk7QzBE2aAhopGGUuqsUuos4EUvgDd/4ho5cwaUQo4exdmJlUtP2OqqsR4+BEphP30sIhls+eewV5Vhq6nEfi52b56q3s+wfTrJYlbeO9SVVwLgrqtG9utBoxw7cun48gobx45bUApOnhuEvhjdcPSEA6XgwCErVZUdbxnr2dPg9yNnz+IoKexk/yksDfW4SvJpuDA4Y2lUVWk/vkth0btg7BjYXzfZJA0fwESaue9GdKrXkUAxMA4dl2pW9ESLPoFZs7HU1MCkSTSMHEu42VabsnNoWnsNtpPHaJi9iISeq3SgccpUqouXIwE/9ZNj113pM8ZQuPYeMg68T9mCtYzMSAPAm5WD8+prkGNH8ecuvXSBjJ4Ia65s4sw5GwumeyEi27GByZJ5Xrz1CUyf7GP46I5Ko3HqTBwVFTB8OJ5Jk2n/nu2bPI26sgU0JqbhnpTdof5goLIy6B9aWxvUHp0zcZSXl5iCd/ceEhqqdPCzv/3bbkcnhvgiUuupfcAa4C2l1AIRWQ18Win1pWgL2B5j5XIJYxEUOqavQieivnr1Ve32c23ZYzB2nB5SdMGTL7m5qvFVrrBuhYwMnfe4s0Qk8Y+xngqDJqVUGWAREYtS6h3AZNszGIYoZWXBNY3aYKrXbpg/J8CrlutRd98Dl18OW7b0j5CGqBCp0qgUkSR0JNrHReTXgAlFZjAMUcrKICUFvaaR0P3E79SxXuqsKewsnwjjxun4Z815agxxT7hJmMYqpc4BN6EXwf8BuAdIBX4SffGiS+DZZ5EPPoDJk/GsWYN7VnhrCkUHz5O1eSNy4jhNK1bhuP2WsGWoff0D3FvegEAA78q1JK5f03OlPiLwpz8hH3+MWrIEy2d0Jr7Kc/m4X3kN2+E8vMvXknjnTTGTL16oe+ZlEj94A9+0WZRd9QmGTx3ZZr//+RewvP8ejBhB7dqrSF4UYSrhAUxZGUyeGNCpXnsYaVgscM3ich56dQQzv+IhaexYPbcVYY4aQ/8S7kjjBQClVB3wjFLKp5R6VCn1m+B0VVwjhw8jhYXIBx/gCsZRCofEkkIsH7yPFBZiP5QXkQwJZw4hR48ix4/jOnmk5wp9RMOR41jefRcpKsKydSuNwQTO6kI19nfeRC5eJGHv9pjJF08k5O1ELl7E/t7bOItKOuy3HD+GFBQgu3aRcO5cDCSMPaWlkOaqB7sjpBAhE0bUM32Mh//ZOBI1ZizsHxBuXgbCVxqtF34GXhyAiRPBZkPNmYM3IyPs6p70bNTcuWCzEZgcXoTcZhpHT0RlZqLS02kaE6MIt0BjVgZq4UKwWlHz5+MI9kdjejL+hblgtdI4s1+CD8c9jVNngc2Gb94CGjNSO+wPjB8PTidq8mSasob3v4AxxufTJrcpqjKsuOdrFpZTUOLkfd9yozQGEOGa3KoutgcEcs89+ObNo8FqJWnGjLDr58wbT1navdhr6kiZHZnOTLj+KqomTkaAlBmxUxrJmZlU3XIrCavXUeNykxksz5k2hqLbPoPrhttIjfAcBxuuT99B1fxcGpPc5IzP6bDfescdNM6eS5PVhnva5E5aGNwUF0NqKlhrqiAxdEN0mxU+sbyUP2+ezkKLkHzhAowc2XNFQ0wJV2nME5Fq9IgjIbhN8LtSSqVEVbo+wDZ7dsRu8ACZ4zo+NMIlNYbKojWpY8fAWC4pjGZyJo+IiTzxTE8K1DFzOl27tA1uCgqCKUYuuYWHzojMRmaMrePJii9x76uvwt/8TZ/IaIgeYU1PKaWsSqkUpVSyUsoW3G7+HvcKw2AwRJ8zZyAri6DdbdeOfV1x5bxKtlfN4Mz2C/DTn5qpqjhnSMWDqHxhM8mHd9E4ahyVV13BiBHhvVEXFhby3psZnCuwMW9GPdfcHH4Gsi1bGtm1y04gAAvn+1i1xh52G9Fiy1P57D+VxMIp1Sy7LbLUtUOB3c+f5aMjKUwZ7WXdZ8z0SXuOH4fJk4EdRTAt/Fhqia4AqxZU8btT3+Kn41/C9utfw9///YDNYDjYGVJKI+XAdiz795GwZxcN46ZCmEoj/0QKz7/iRClo8CVwzc3hy3DokJ1t27Q9QWKijVUxsrgtOF7FQ5tGU1cHB8+nMH1NHWkZJg1nZ7y9bxi7Drl5d2864yZdZMryobfY3RV+v874u2ZVAF4qgcuWR9TOwik1HM9P4A/nrubLNyRg+d3v4Fe/0gYGSq+b7N8Pu3frkY3NBnPnwg03mGWQ/mZIKQ1f9kgc7EONHkNTQvgPSJdbMWa04tx5YWSOn0h8I4cPD+B0WggEhOxsRawiEfisMH9CJVsPpDFvXBVpGekxkWMgMGl4HbsOuZk+thblGlK3TI8cOgTp6ZDsLdaBCntw7OsKEbhlRQlPv5vDP1+8mmW2ZHw/PsSp1AUcP673jx+vDSBXrNApkw8dgu9/H6ZP10kj58zpmDTQEH2G1B1QueYa7KOn0pSWRvaSqWHXn7MwiXsaa6gsE4aPD6Cz3YbHjTdaSU31EQgIq1fHLuXluImpXLO+hFWLzuHKToyZHAOBBSuFKWPOYU9PYOrCrFiLEzcoBRs3wrx5wIE8GN11vKlQcDoU96y7yNHziZy9OA/ryeOMm3yIFZ8aR+oIN9Lu/So7G5Yvh/17/Dz3pJ9f/z87WdnCyJF638iROkPv+PHaobAniovh9GlobIThw2HChEtZkA2tGFJdkj15JEzu3Vh2wbLwF/rac+WV8dHt05eYB2AoTJyVFefxm2PD9u2Qlwe5E8u5sPUULFwEJ729bjcVL6nDALcdDu3Gc/gDPEDQSLPD8SODHz82SgqyKPNM4fSZTHY3JHGxWr8QZWfD1Kn6r9utlUhDA5SXa0VxPJg/KyUF7Ha9pg96gX/aNK1E3G49mhk7xJf/IopyG0tEpAQ428tmhgG9SZ3X2/rRaKNUKdVtMqwQ+yoa5xIJ/fm7A72vQiUa8oXcVyNH3pyyYMGvxzpVPaMT7M6A6jvfLZsgNoseavgCigAoq+ixhy/Q8hRzWduOR055Ghv8tmyrw5HW45taTc0xb+vnodOZaXc6h7Wpd+HCi+Vbt97UnAinx74ajAw4pRENRGSXUiriqLy9rR+tNqJBrOSIl/MPh3iXOZbyxXvfdMVAlTuWRBrl1mAwGAxDEKM0DAaDwRAyQ1VpPBjj+tFqIxrESo54Of9wiHeZYylfvPdNVwxUuWPGkFzTMBgMBkNkDNWRhsFgMBgiwCgNg8FgMISMURoGg8FgCBmjNAwGg8EQMkZpGAwGgyFkjNIwGAwGQ8gMaaUhIn4R2SsiB0TkGRHpMtyriDwgIv/Yn/LFIyLyPRE5KCL7g323VET+KCIzg/tru6i3TER2BOscFpEH+lXwfiacayvE9saLyIFoyddXtDrv5s/4XrZ3o4h8J7jd4z0oIjeIyB4R2Scih0Tky8Hy+0Xks72RxaCJj3CrscOrlJoPICKPA/cDv4ypRHGMiFwG3AAsVEo1iMgwwKGUCiWx86PAHUqpfSJiBab1paxxQETXlojYlFK+PpatL7l03tFAKfUi8GIox4qIHe2st0QplS8iTmB8sJ3/jZZMQ50hPdJoxwfAZAAR+WzwTXqfiDzW/kARuVdEdgb3P9f8FikitwffLPeJyPvBslki8lHwrWu/iEzp17OKLiPQkT0bAJRSpUqpCyLyrohcCvomIr8KjkbeFpHm+OvZQGGwnl8pdSh47AMi8piIbBeR4yJybz+fU3/wATBZRD4RHG3tEZG3RCQH2vTBVuAxEckRkeeD19E+EWlOh2cVkT8E+/YNEUmI2RmFiIgkBa+Dj0UkT0RuCpaPF5EjIvKIiBwTkcdFZJ2IbA1eB0uCx31eRH7brs1JIvJxq+9Tgt+T0S/CZQBKqQal1NHgMQ+IyD+KyMh2IyG/iIwTkazgvbwz+Lm8n7powGGUBvrtDrgWyBORWcD3gTVKqXnANzqpskEptTi4/zDwpWD5D4FrguU3BsvuB34dfPvKBfL77kz6nDeAMcGb/H9E5MpOjnEDu5RSs4D3gB8Fy38FHA0+DL8sIq1zrM0F1gCXAT8UkUGTwLP1tQVsAZYppRYATwLfbnXoTGCdUuou4DfAe8HraCFwMHjMFOC/g31bCXyyX04iPBJaPZCfB+qBW5RSC4HVwH+KXApfPhn4T2B68HM3sAL4R+C7Xf2AUuokUCUi84NFXwD+pJQqR49KzorIX0XkHhGxtKt7QSk1P3g//gF4Til1Fvg18Cul1GJ0v/6x1z0xSBnq01MJIrI3uP0B8BDwZeAZpVQpQPBCbM9sEflXIA1IAl4Plm8FHhGRp4ENwbLtwPdEZDRa2RzvixPpD5RStSKyCFiJfgA8JcH55lYEgKeC238h2A9KqZ8Ep2muRj8c7gJWBY/bqJTyAl4ReQdYArzQh6fSH3R2bU1D99kIwAGcbnX8i8E+AK1APwt6VIZ+QKYDp5VSzW3uJjj1Eme0mZ4KThn9TESuQF8bo4Cc4O7TSqm84HEHgbeVUkpE8uj53P4IfEFEvgl8Cn3NoJT6GxGZA6xDK5+rgM+3rxwcSdyLVlIEj5/Zos9IEZEkpVSna3RDmaGuNDrMv0r7nJKd8whwc3B+/vMEH35KqftFZClwPbBbRBYppZ4QkR3BsldF5MtKqc3RO4X+JfgQexd4N3hzf66nKq3qngR+JyJ/AEpEJLP9MV18H4h0dm39F/BLpdSLIrIKeKDV7roQ2mxote0H4n56CrgHyAIWKaWaROQM0DzKbH0+gVbfA/T8bHoOPYrdDOxWSpU17wgqojzRU8unaac0gkr7IeDGVkrBgh4F1od1dkMQMz3Vkc3A7c0PNBHJ6OSYZKAw+BZ1T3OhiExSSu1QSv0QKEFP5UwETimlfgNsRE/FDEhEZFq7NZn5dMx2ZwFuC27fjZ6SQUSubzUtMQX90KsMfr9JRFzBPl8F7Iy68PFBKlAQ3O5O2b4NfAVARKwiktrXgvUhqUBxUGGsBsZFo9Hgw/114HfAn+DS+smqVofNp931GbxnnwH+j1LqWKtdbwBfb3Xc/GjIORgxSqMdSqmDwE+B90RkH51bvPwA2IGejjrSqvznwcW+A8A2YB9wB3AgOFUxG/hzH4rf1yQBj4o2ZdyPnod/oN0xdcCSYB+sAX4SLP8Mek1jL/AYcE9w1AKwH3gH+BD4F6XUhT49i9jxAPCMiOym+7Ss3wBWB0dyu9H9PFB5HMgNnstnaXu/RKPtAPqBDzqJ+LdFpPk6+zEdp6aWo9cWf9xq7WUk8HdBOfeLyCH0WqShE0xodENMEe2vUauU+kWsZTEMLET7bKQqpX4Qa1mGEkN9TcNgMAxAgpZZk9CjWUM/YkYaBoPBYAgZs6ZhMBgMhpAxSsNgMBgMIWOUhsFgMBhCxigNg8FgMISMURoGg8FgCJn/DyfndEmNxtKbAAAAAElFTkSuQmCC\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "((623, 22), (268, 22), (623,), (268,))" ] }, "metadata": {}, "execution_count": 29 } ], "source": [ "from sklearn.model_selection import train_test_split #for split the data\n", "from sklearn.metrics import accuracy_score #for accuracy_score\n", "from sklearn.model_selection import KFold #for K-fold cross validation\n", "from sklearn.model_selection import cross_val_score #score evaluation\n", "from sklearn.model_selection import cross_val_predict #prediction\n", "from sklearn.metrics import confusion_matrix #for confusion matrix\n", "all_features = traindf.drop(\"Survived\",axis=1)\n", "Targeted_feature = traindf[\"Survived\"]\n", "X_train,X_test,y_train,y_test = train_test_split(all_features,Targeted_feature,test_size=0.3,random_state=42)\n", "X_train.shape,X_test.shape,y_train.shape,y_test.shape" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--------------The Accuracy of the model----------------------------\n", "The accuracy of the Logistic Regression is 82.46\n", "The cross validated score for Logistic REgression is: 81.93\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.05, 'Confusion_matrix')" ] }, "metadata": {}, "execution_count": 30 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "# machine learning\n", "from sklearn.linear_model import LogisticRegression # Logistic Regression\n", "\n", "model = LogisticRegression()\n", "model.fit(X_train,y_train)\n", "prediction_lr=model.predict(X_test)\n", "print('--------------The Accuracy of the model----------------------------')\n", "print('The accuracy of the Logistic Regression is',round(accuracy_score(prediction_lr,y_test)*100,2))\n", "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n", "result_lr=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n", "print('The cross validated score for Logistic REgression is:',round(result_lr.mean()*100,2))\n", "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n", "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n", "plt.title('Confusion_matrix', y=1.05, size=15)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--------------The Accuracy of the model----------------------------\n", "The accuracy of the Random Forest Classifier is 82.46\n", "The cross validated score for Random Forest Classifier is: 83.73\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.05, 'Confusion_matrix')" ] }, "metadata": {}, "execution_count": 31 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "# Random Forests\n", "from sklearn.ensemble import RandomForestClassifier\n", "model = RandomForestClassifier(criterion='gini', n_estimators=700,\n", " min_samples_split=10,min_samples_leaf=1,\n", " max_features='auto',oob_score=True,\n", " random_state=1,n_jobs=-1)\n", "model.fit(X_train,y_train)\n", "prediction_rm=model.predict(X_test)\n", "print('--------------The Accuracy of the model----------------------------')\n", "print('The accuracy of the Random Forest Classifier is',round(accuracy_score(prediction_rm,y_test)*100,2))\n", "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n", "result_rm=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n", "print('The cross validated score for Random Forest Classifier is:',round(result_rm.mean()*100,2))\n", "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n", "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n", "plt.title('Confusion_matrix', y=1.05, size=15)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--------------The Accuracy of the model----------------------------\n", "The accuracy of the Support Vector Machines Classifier is 83.58\n", "The cross validated score for Support Vector Machines Classifier is: 83.16\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.05, 'Confusion_matrix')" ] }, "metadata": {}, "execution_count": 32 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "# Support Vector Machines\n", "from sklearn.svm import SVC, LinearSVC\n", "\n", "model = SVC()\n", "model.fit(X_train,y_train)\n", "prediction_svm=model.predict(X_test)\n", "print('--------------The Accuracy of the model----------------------------')\n", "print('The accuracy of the Support Vector Machines Classifier is',round(accuracy_score(prediction_svm,y_test)*100,2))\n", "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n", "result_svm=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n", "print('The cross validated score for Support Vector Machines Classifier is:',round(result_svm.mean()*100,2))\n", "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n", "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n", "plt.title('Confusion_matrix', y=1.05, size=15)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--------------The Accuracy of the model----------------------------\nThe accuracy of the K Nearst Neighbors Classifier is 81.34\nThe cross validated score for K Nearest Neighbors Classifier is: 81.26\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.05, 'Confusion_matrix')" ] }, "metadata": {}, "execution_count": 33 }, { "output_type": "display_data", "data": { "text/plain": "
", "image/svg+xml": "\n\n\n\n \n \n \n \n 2020-10-19T20:11:50.479823\n image/svg+xml\n \n \n Matplotlib v3.3.2, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "##knn\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "\n", "model = KNeighborsClassifier(n_neighbors = 4)\n", "model.fit(X_train,y_train)\n", "prediction_knn=model.predict(X_test)\n", "print('--------------The Accuracy of the model----------------------------')\n", "print('The accuracy of the K Nearst Neighbors Classifier is',round(accuracy_score(prediction_knn,y_test)*100,2))\n", "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n", "result_knn=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n", "print('The cross validated score for K Nearest Neighbors Classifier is:',round(result_knn.mean()*100,2))\n", "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n", "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n", "plt.title('Confusion_matrix', y=1.05, size=15)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--------------The Accuracy of the model----------------------------\nThe accuracy of the Gaussian Naive Bayes Classifier is 79.48\nThe cross validated score for Gaussian Naive Bayes classifier is: 79.8\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.05, 'Confusion_matrix')" ] }, "metadata": {}, "execution_count": 34 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "# Gaussian Naive Bayes\n", "from sklearn.naive_bayes import GaussianNB\n", "model= GaussianNB()\n", "model.fit(X_train,y_train)\n", "prediction_gnb=model.predict(X_test)\n", "print('--------------The Accuracy of the model----------------------------')\n", "print('The accuracy of the Gaussian Naive Bayes Classifier is',round(accuracy_score(prediction_gnb,y_test)*100,2))\n", "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n", "result_gnb=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n", "print('The cross validated score for Gaussian Naive Bayes classifier is:',round(result_gnb.mean()*100,2))\n", "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n", "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n", "plt.title('Confusion_matrix', y=1.05, size=15)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--------------The Accuracy of the model----------------------------\nThe accuracy of the DecisionTree Classifier is 79.1\nThe cross validated score for Decision Tree classifier is: 81.15\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.05, 'Confusion_matrix')" ] }, "metadata": {}, "execution_count": 35 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "# Decision Tree\n", "from sklearn.tree import DecisionTreeClassifier\n", "model= DecisionTreeClassifier(criterion='gini', \n", " min_samples_split=10,min_samples_leaf=1,\n", " max_features='auto')\n", "model.fit(X_train,y_train)\n", "prediction_tree=model.predict(X_test)\n", "print('--------------The Accuracy of the model----------------------------')\n", "print('The accuracy of the DecisionTree Classifier is',round(accuracy_score(prediction_tree,y_test)*100,2))\n", "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n", "result_tree=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n", "print('The cross validated score for Decision Tree classifier is:',round(result_tree.mean()*100,2))\n", "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n", "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n", "plt.title('Confusion_matrix', y=1.05, size=15)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--------------The Accuracy of the model----------------------------\n", "The accuracy of the AdaBoostClassifier is 80.22\n", "The cross validated score for AdaBoostClassifier is: 81.03\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.05, 'Confusion_matrix')" ] }, "metadata": {}, "execution_count": 36 }, { "output_type": "display_data", "data": { "text/plain": "
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lWnNBSOoq6XlJL0maKmX37ki6S9J0SZPSMjiVK03XUCdpsqSDWmure8BmVipVvAuiARgaEcskbQs8J+l3advlEfFAo/rHAQPT8lHg9vS1We4Bm1mpVGsynsgsS6vbpqWl3YYD96T9xgE9JL1/asQKDmAzK5X2DEFIqpX0QsVSW3ksSTWSJgHzgCciYnzadF0aZrhF0ro5BPrw3hPDAPW89wBbkzwEYWal0p4RiA3nrWly+xpgsKQewEOSDgCuAuYAndO+VwD/e2Pa6h6wmZVKR0zIHhGLgKeBYyNidhpmaADuJJuqAbJpGvpV7NY3lTXLAWxmpVKtMWBJu6WeL5K6AZ8C/rpuXFeSgJOAKWmXR4Gz0t0QhwOLI2J2S+fwEISZlUoV74LoDdwtqYass3p/RDwmaYyk3cheTjEJuDDVHw0cD9QBK4BzWzuBA9jMSqVaT8JFxGRgSBPlQ5upH8BF7TmHA9jMSsWPIpuZ5aRA+esANrNycQ/YzCwnRXqPtAPYzErFPWAzs5wUKH8dwGZWLu4Bm5nlpED56wA2s3JxD9jMLCd+Lb2ZWU4KlL8OYDMrFw9BmJnlpED56wA2s3JxD9jMLCe+CGdmlpMC5a8D2MzKxUMQZmY5KVD+OoDNrFyK1AP2W5HNrFSq+FbkrpKel/SSpKmSrknlAySNl1Qn6ZeSOqfyLmm9Lm3v31pbHcBmVipro+1LKxqAoRHxEWAwcGx63fz3gVsi4oPAO8B5qf55wDup/JZUr0UOYDMrlYi2Ly0fJyIilqXVbdMSwFDggVR+N3BS+jw8rZO2D5Okls7hADazUmnPEISkWkkvVCy1lceSVCNpEjAPeAJ4A1gUEatTlXqgT/rcB5gJkLYvBnZpqa2+CGdmpdKei3ARMRIY2cL2NcBgST2Ah4B9N7F5G3AP2MxKpVoX4TY4ZsQi4GngCKCHpHWd177ArPR5FtAPIG3fCVjQ0nEdwGZWKtUaA5a0W+r5Iqkb8ClgGlkQn5qqnQ08kj4/mtZJ28dEtHwWD0GYWalUcS6I3sDdkmrIOqv3R8Rjkl4B7pN0LTARGJXqjwL+S1IdsBA4o7UTOIDNrFSq9SBGREwGhjRR/iZwWBPl7wKnteccDmAzK5UCPQjnADazcinSo8gOYDMrlQLlrwPYzMrFE7KbmeWkQPnrADazcvEYsJlZTgqUvw5gMysX94ArdL22o89gRXTp4Xm3wMqqQPnrHrCZlYvvgjAzy4mHIMzMclKg/HUAm1m5uAdsZpaTAuWvA9jMysU9YDOznPguCDOznBQofx3AZlYuRRqC8Es5zaxUqvVWZEn9JD0t6RVJUyVdksqvljRL0qS0HF+xz1WS6iS9KumY1trqHrCZlUoVe8CrgcsiYoKk7sCLkp5I226JiBsrK0saRPYizv2BPYEnJe0TEWuaO4F7wGZWKmuj7UtLImJ2RExIn5eSvZK+Twu7DAfui4iGiJgO1NHEyzsrOYDNrFSqNQRRSVJ/sjckj09FF0uaLOkOSTunsj7AzIrd6mk5sB3AZlYuEW1fJNVKeqFiqW18PEk7AL8GvhYRS4Dbgb2BwcBs4KaNbavHgM2sVNrTs42IkcDI5rZL2pYsfH8WEQ+mfeZWbP8J8FhanQX0q9i9byprlnvAZlYq7ekBt0SSgFHAtIi4uaK8d0W1k4Ep6fOjwBmSukgaAAwEnm/pHO4Bm1mpVPE24COBM4GXJU1KZd8EPi9pcDrVDOACgIiYKul+4BWyOyguaukOCHAAm1nJrF1bneNExHOAmtg0uoV9rgOua+s5HMBmVioFehDOAWxm5VKkR5EdwGZWKgXKXwewmZWLA9jMLCcegjAzy4knZDczy0mB8tcBbGbl4iEIM7OcFCh/HcBmVi7uAZuZ5cQX4czMclKg/HUAm1m5eAjCzCwnBcpfB7CZlYt7wGZmOSlQ/jqAzaxcfBeEmVlOPARhZpaTAuWv34psZuVSxbci95P0tKRXJE2VdEkq7ynpCUmvp687p3JJ+qGkOkmTJR3UWlsdwGZWKtGOpRWrgcsiYhBwOHCRpEHAlcBTETEQeCqtAxxH9ir6gUAtcHtrJ3AAm1mprI22Ly2JiNkRMSF9XgpMA/oAw4G7U7W7gZPS5+HAPZEZB/SQ1LulcziAzaxUqjUEUUlSf2AIMB7oFRGz06Y5QK/0uQ8ws2K3+lTWLAewmZVKe4YgJNVKeqFiqW18PEk7AL8GvhYRSzY4V0QbRzOa5rsgzKxU2tOzjYiRwMjmtkvalix8fxYRD6biuZJ6R8TsNMQwL5XPAvpV7N43lTXLPWAzK5VqXYSTJGAUMC0ibq7Y9Chwdvp8NvBIRflZ6W6Iw4HFFUMVTXIP2MxKpYoPYhwJnAm8LGlSKvsmcANwv6TzgLeA09O20cDxQB2wAji3tRM4gM2sVKr1KHJEPAeomc3DmqgfwEXtOYcD2MxKxY8im5nlpED56wA2s3JxABsApw46hCuOPI6aTp0Y/frLfHtMdhdL55ptGHXiuRzUey8WrFzOmQ/+hLcWL8i5tdYRenTdmS98+Fx26NwdgD/P/AN/eHsMx+z9GQ7v+zGWrVoGwOjXH2ba/Cnss8t+nDDwZLbptA2r167mN6/9mrqFr+b5LRSOhyCMnt2253vDTuGIUdcxf8UyfnriOXyi/748PeOvnDP4SBa9u5z9f/zvnDboEK4d+jnOfOgneTfZOsCatWt45K+/YtbSmXSp6cKlR3yL1xZMA+CZt55i7IwnNqi/fNUyRk28jSUNi9ljhz254OCvcs0zVzZ1aGtGgfLX9wF3lAE9dqVu4Tzmr8h6OGOmT+OkfYcA8Nl9PsK9k8cB8OC0CXxiwL65tdM61tJVS5i1NHs6tWFNA/OWz2anrj2arT9r6UyWNCwGYM6yv7FtTWdq5H5Se1RrLojNwf9nO8gb7/ydgbv04p922oX6Je/w2X0G07km+3Hv2b0H9UsWArAm1rKkYSW7dNueBSuX59lk62A7d92FPt334q1F0xnQY28+ttfRHLLn4cxc/BaPvvoAK1ev2KD+gb0Oon7J26yJ1Tm1uJiKNASx0T1gSc3eZFz5fPWav0zb2FMU2qJ3V/DV3/2c//rc+Tx19uW8tXgBa2Jt3s2ynHSu6cI5gy/g4b/eT8Oad/njzGe47tlvc9OfrmVJw2JO/NCpG9TvtX1vPrPP5/jV1HtzanFxVXE6yg63KUMQ1zS3ISJGRsQhEXFIzaH7bcIpim3065P5+J03cPRd3+f1BXN5fcFcAP62dBF9d+wJQI06sWOXbu79llgndeKcwRcwYfbzvDxvIgDLVi0l0n/j6p9jr536r6+/U5cenDvkX/n5y3eyYOX8nFpdXB0xG1pHaXEIQtLk5jbx3hRs1ozdtuvO31cspUfX7ag9+Ci++GA258djr03mSwcezvhZb/K5/Q5i7Iy/5txS60j/sv9ZzFs+h2feenJ9WffOO7J0VTax1od7DWbOsr8B0HWbbpx/8MX89rWHmLHojVzaW3RbQK62WWtjwL2AY4B3GpUL+FOHtKhEbjrmdD68e18Arv/Db6lbmE2adNek57hj+P9k6pf/DwtXLuesh36aZzOtAw3osTeH9jmCvy2t57Ijvg1kt5wN6X0ofbr3IwgWrlywfqjhY3t9gl267c6n9z6BT+99AgAjXvwBy1Ytze17KJot4eJaW7UWwI8BO0TEpMYbJI3tiAaVyVkPjWqyvGHN6vW9YSu36Yve4OuPX/C+8mnzpzRZ/8k3R/Pkm6M7ulmltiUMLbRViwEcEee1sO0L1W+OmdmmKVD++jY0MyuX0vSAzcyKpkD56wA2s3JxD9jMLCdlugvCzKxQCpS/DmAzK5ciDUF4NjQzK5VqzgUh6Q5J8yRNqSi7WtIsSZPScnzFtqsk1Ul6VdIxrR3fAWxmpVLluSDuAo5tovyWiBicltEAkgYBZwD7p31+LKmmpYM7gM2sVKrZA46IZ4GFbTz1cOC+iGiIiOlkr6c/rKUdHMBmVirtmZC9curctNS28TQXS5qchih2TmV9gJkVdepTWbMcwGZWKu0ZgqicOjctbZmk5XZgb2AwMBu4aWPb6gA2s1Lp6AnZI2JuRKyJiLXAT3hvmGEW0K+iat9U1iwHsJmVSkdPyC6pd8XqycC6OyQeBc6Q1EXSAGAg8HxLx/J9wGZWKtW8DVjSL4CjgV0l1QPfBY6WNDidagZwAUBETJV0P/AKsBq4KCLWtHR8B7CZlcraKr56MSI+30Rx0xN9Z/WvA65r6/EdwGZWKgV6EM4BbGbl4gA2M8tJkeaCcACbWakUKH8dwGZWLu4Bm5nlxBOym5nlpED56wA2s3LxEISZWU4KlL8OYDMrF/eAzcxyUqD8dQCbWbn4Lggzs5x4CMLMLCcFyl8HsJmVi3vAZmY5KVD+OoDNrFx8Ec7MLCcegjAzy0mB8tdvRTazcqnmW5El3SFpnqQpFWU9JT0h6fX0dedULkk/lFQnabKkg1o7vgPYzEol2rG0wV3AsY3KrgSeioiBwFNpHeA4slfRDwRqgdtbO7gD2MxKpZo94Ih4FljYqHg4cHf6fDdwUkX5PZEZB/SQ1Lul4zuAzaxU1kbbF0m1kl6oWGrbcIpeETE7fZ4D9Eqf+wAzK+rVp7Jm+SKcmZVKey7CRcRIYORGnysiJG30dT/3gM2sVKo5BNGMueuGFtLXeal8FtCvol7fVNYsB7CZlUqVL8I15VHg7PT5bOCRivKz0t0QhwOLK4YqmuQhCDMrlWo+iCHpF8DRwK6S6oHvAjcA90s6D3gLOD1VHw0cD9QBK4BzWzu+A9jMSqWaD2JExOeb2TSsiboBXNSe4zuAzaxUPBeEmVlOPBeEmVlOCpS/DmAzKxf3gM3MclKg/HUAm1m5FOkinKJI/fWCk1SbHn00W8//LrZefhJu82rLRB+29fG/i62UA9jMLCcOYDOznDiANy+P81lT/O9iK+WLcGZmOXEP2MwsJw5gM7OcOIA3E0nHSno1vbL6ytb3sLJr6pXntnVxAG8GkmqA28heWz0I+LykQfm2yrYAd/H+V57bVsQBvHkcBtRFxJsRsQq4j+wV1rYVa+aV57YVcQBvHu1+XbWZlZ8D2MwsJw7gzaPdr6s2s/JzAG8efwEGShogqTNwBtkrrM1sK+YA3gwiYjVwMfA4MA24PyKm5tsqy1t65fmfgQ9Jqk+vObetiB9FNjPLiXvAZmY5cQCbmeXEAWxmlhMHsJlZThzAZmY5cQCbmeXEAWxmlpP/BtF5+9vIUnfTAAAAAElFTkSuQmCC\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "from sklearn.ensemble import AdaBoostClassifier\n", "model= AdaBoostClassifier()\n", "model.fit(X_train,y_train)\n", "prediction_adb=model.predict(X_test)\n", "print('--------------The Accuracy of the model----------------------------')\n", "print('The accuracy of the AdaBoostClassifier is',round(accuracy_score(prediction_adb,y_test)*100,2))\n", "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n", "result_adb=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n", "print('The cross validated score for AdaBoostClassifier is:',round(result_adb.mean()*100,2))\n", "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n", "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n", "plt.title('Confusion_matrix', y=1.05, size=15)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--------------The Accuracy of the model----------------------------\nThe accuracy of the LinearDiscriminantAnalysis is 82.84\nThe cross validated score for AdaBoostClassifier is: 82.38\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.05, 'Confusion_matrix')" ] }, "metadata": {}, "execution_count": 37 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", "model= LinearDiscriminantAnalysis()\n", "model.fit(X_train,y_train)\n", "prediction_lda=model.predict(X_test)\n", "print('--------------The Accuracy of the model----------------------------')\n", "print('The accuracy of the LinearDiscriminantAnalysis is',round(accuracy_score(prediction_lda,y_test)*100,2))\n", "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n", "result_lda=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n", "print('The cross validated score for AdaBoostClassifier is:',round(result_lda.mean()*100,2))\n", "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n", "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n", "plt.title('Confusion_matrix', y=1.05, size=15)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--------------The Accuracy of the model----------------------------\n", "The accuracy of the Gradient Boosting Classifier is 82.84\n", "The cross validated score for AdaBoostClassifier is: 82.05\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.05, 'Confusion_matrix')" ] }, "metadata": {}, "execution_count": 38 }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "from sklearn.ensemble import GradientBoostingClassifier\n", "model= GradientBoostingClassifier()\n", "model.fit(X_train,y_train)\n", "prediction_gbc=model.predict(X_test)\n", "print('--------------The Accuracy of the model----------------------------')\n", "print('The accuracy of the Gradient Boosting Classifier is',round(accuracy_score(prediction_gbc,y_test)*100,2))\n", "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n", "result_gbc=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n", "print('The cross validated score for AdaBoostClassifier is:',round(result_gbc.mean()*100,2))\n", "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n", "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n", "plt.title('Confusion_matrix', y=1.05, size=15)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Model Score\n", "3 Random Forest 0.837303\n", "0 Support Vector Machines 0.831648\n", "7 Linear Discriminant Analysis 0.823820\n", "6 Gradient Decent 0.820474\n", "2 Logistic Regression 0.819301\n", "1 KNN 0.812597\n", "8 Decision Tree 0.811498\n", "5 AdaBoostClassifier 0.810325\n", "4 Naive Bayes 0.798002" ], "text/html": "
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ModelScore
3Random Forest0.837303
0Support Vector Machines0.831648
7Linear Discriminant Analysis0.823820
6Gradient Decent0.820474
2Logistic Regression0.819301
1KNN0.812597
8Decision Tree0.811498
5AdaBoostClassifier0.810325
4Naive Bayes0.798002
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" }, "metadata": {}, "execution_count": 39 } ], "source": [ "models = pd.DataFrame({\n", " 'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n", " 'Random Forest', 'Naive Bayes', 'AdaBoostClassifier', \n", " 'Gradient Decent', 'Linear Discriminant Analysis', \n", " 'Decision Tree'],\n", " 'Score': [result_svm.mean(), result_knn.mean(), result_lr.mean(), \n", " result_rm.mean(), result_gnb.mean(), result_adb.mean(), \n", " result_gbc.mean(), result_lda.mean(), result_tree.mean()]})\n", "models.sort_values(by='Score',ascending=False)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "((891, 22), (891,), (418, 22))" ] }, "metadata": {}, "execution_count": 40 } ], "source": [ "train_X = traindf.drop(\"Survived\", axis=1)\n", "train_Y=traindf[\"Survived\"]\n", "test_X = testdf.drop(\"PassengerId\", axis=1).copy()\n", "train_X.shape, train_Y.shape, test_X.shape" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fitting 10 folds for each of 192 candidates, totalling 1920 fits\n", "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 2.7s\n", "[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 11.2s\n", "[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 27.8s\n", "[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 47.9s\n", "[Parallel(n_jobs=-1)]: Done 1234 tasks | elapsed: 1.3min\n", "[Parallel(n_jobs=-1)]: Done 1784 tasks | elapsed: 1.9min\n", "[Parallel(n_jobs=-1)]: Done 1920 out of 1920 | elapsed: 2.0min finished\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "GradientBoostingClassifier(max_depth=8, max_features=0.2, min_samples_leaf=100,\n", " n_estimators=300)" ] }, "metadata": {}, "execution_count": 42 } ], "source": [ "# Gradient boosting tunning\n", "import xgboost as xgb\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "model = GradientBoostingClassifier()\n", "param_grid = {'loss' : [\"deviance\"],\n", " 'n_estimators' : [100,200,300,400],\n", " 'learning_rate': [0.1, 0.05, 0.01,0.001],\n", " 'max_depth': [4, 8],\n", " 'min_samples_leaf': [100,150],\n", " 'max_features': [0.3, 0.2,0.1] \n", " }\n", "\n", "modelf = GridSearchCV(model,param_grid = param_grid, cv=kfold, scoring=\"accuracy\", n_jobs= -1, verbose = 1)\n", "\n", "modelf.fit(train_X,train_Y)\n", "\n", "# Best score\n", "modelf.best_score_\n", "\n", "# Best Estimator\n", "modelf.best_estimator_" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.8227215980024969" ] }, "metadata": {}, "execution_count": 43 } ], "source": [ "modelf.best_score_" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n", "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n", "[Parallel(n_jobs=4)]: Done 45 out of 45 | elapsed: 28.6s finished\n", "0.8215868432615656\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "RandomForestClassifier(n_estimators=600)" ] }, "metadata": {}, "execution_count": 44 } ], "source": [ "# Random Forest Classifier Parameters tunning \n", "model = RandomForestClassifier()\n", "n_estim=range(100,1000,100)\n", "\n", "## Search grid for optimal parameters\n", "param_grid = {\"n_estimators\" :n_estim}\n", "\n", "\n", "model_rf = GridSearchCV(model,param_grid = param_grid, cv=5, scoring=\"accuracy\", n_jobs= 4, verbose = 1)\n", "\n", "model_rf.fit(train_X,train_Y)\n", "\n", "\n", "\n", "# Best score\n", "print(model_rf.best_score_)\n", "\n", "#best estimator\n", "model_rf.best_estimator_" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n", "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n", "0.8215491808423827\n", "[Parallel(n_jobs=4)]: Done 20 out of 20 | elapsed: 0.2s finished\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "LinearDiscriminantAnalysis(tol=0.001)" ] }, "metadata": {}, "execution_count": 45 } ], "source": [ "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", "model =LinearDiscriminantAnalysis()\n", "param_grid = {'tol':[0.001,0.01,.1,.2]}\n", "\n", "modell = GridSearchCV(model,param_grid = param_grid, cv=5, scoring=\"accuracy\", n_jobs= 4, verbose = 1)\n", "\n", "modell.fit(train_X,train_Y)\n", "\n", "# Best score\n", "print(modell.best_score_)\n", "\n", "# Best Estimator\n", "modell.best_estimator_" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n", "Fitting 5 folds for each of 56 candidates, totalling 280 fits\n", "[Parallel(n_jobs=4)]: Done 128 tasks | elapsed: 3.5s\n", "SVC(C=50, gamma=0.01)\n", "0.8338710689849979\n", "[Parallel(n_jobs=4)]: Done 280 out of 280 | elapsed: 46.5s finished\n" ] } ], "source": [ "model= SVC()\n", "param_grid = {'kernel': ['rbf','linear'], \n", " 'gamma': [ 0.001, 0.01, 0.1, 1],\n", " 'C': [1, 10, 50, 100,200,300, 1000]}\n", "\n", "modelsvm = GridSearchCV(model,param_grid = param_grid, cv=5, scoring=\"accuracy\", n_jobs= 4, verbose = 1)\n", "\n", "modelsvm.fit(train_X,train_Y)\n", "\n", "print(modelsvm.best_estimator_)\n", "\n", "# Best score\n", "print(modelsvm.best_score_)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Important features\n____________________________________________________________\n89.11\n" ] }, { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "# Random Forests\n", "from sklearn.ensemble import RandomForestClassifier\n", "random_forest = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=400, n_jobs=1,\n", " oob_score=False, random_state=None, verbose=0,\n", " warm_start=False)\n", "random_forest.fit(train_X, train_Y)\n", "Y_pred_rf = random_forest.predict(test_X)\n", "random_forest.score(train_X,train_Y)\n", "acc_random_forest = round(random_forest.score(train_X, train_Y) * 100, 2)\n", "\n", "print(\"Important features\")\n", "pd.Series(random_forest.feature_importances_,train_X.columns).sort_values(ascending=True).plot.barh(width=0.8)\n", "print('__'*30)\n", "print(acc_random_forest)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "submission = pd.DataFrame({\n", " \"PassengerId\": test_df[\"PassengerId\"],\n", " \"Survived\": Y_pred_rf})" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "submission.to_csv('submission_1019.csv', index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ] }