{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Mad Max's Journey to the Title. Max Verstappen vs Lewis Hamilton\n", "\n", "![](https://lh3.googleusercontent.com/THbrMwV_5p_WTumGzkCJpYz8UlElGpXk8HTPmCL3jdAsRMrO9MacvMbuevysfIlE_NSFiaQ5tUWuF5uYiYQcdo99OLNy7HA=s1349)\n", "\n", "`\"This Race started with Controversy has ended with Controversy\"`\n", "\n", "Following the controversial win by Max Verstappen in the final moments of the Abu Dhabi Grand Prix. This project explores different statistics of both the drivers Max & Lewis Hamilton.\n", "\n", "The data is extracted from the F1 2021 championship wikipedia page. First we will extract the data-tables from the wikipedia web pages and after that we will extract the required data tables in data-frames format. After some data cleaning and data preparation, we will try to analyze the tables keeping in eye the two key drivers Max and Lewis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's begin by importing libraries " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import math\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import requests\n", "%matplotlib inline\n", "\n", "sns.set_style('darkgrid')\n", "matplotlib.rcParams['font.size'] = 12\n", "matplotlib.rcParams['figure.figsize'] = (9, 5)\n", "matplotlib.rcParams['figure.facecolor'] = '#00000000'" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Downloading the Dataset\n", "We will download/extract our dataset from `Wikipedia`. For this we will use `requests` library.\n", "For extracting data, first we load the data-frames of each url in a list and after that we assign required tables from each data-frame to different list `season`.\n", "\n", "Urls of the web pages from which the data will be acquired" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hidden": true }, "outputs": [], "source": [ "final_season_result_url = 'https://en.wikipedia.org/wiki/2021_Formula_One_World_Championship'\n", "urls=['https://en.wikipedia.org/wiki/2021_Bahrain_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Emilia_Romagna_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Portuguese_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Spanish_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Monaco_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Azerbaijan_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_French_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Styrian_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Austrian_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_British_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Hungarian_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Belgian_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Dutch_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Italian_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Russian_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Turkish_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_United_States_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Mexico_City_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_S%C3%A3o_Paulo_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Qatar_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Saudi_Arabian_Grand_Prix',\n", " 'https://en.wikipedia.org/wiki/2021_Abu_Dhabi_Grand_Prix']" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hidden": true }, "outputs": [], "source": [ "#Data reading from the web pages\n", "df=[pd.read_html(requests.get(final_season_result_url).content)]\n", "for url in urls:\n", " df.append(pd.read_html(requests.get(url).content))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "hidden": true }, "outputs": [], "source": [ "#Creating a array of required data frames\n", "season=[]\n", "for dataframes in df:\n", " race=dict(qual=dataframes[2],race=dataframes[3])\n", " season.append(race)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Correction for some races as there were `sprint races` held in some GPs (GP 10, 14 ,19)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hidden": true }, "outputs": [], "source": [ "season[10]['qual']=df[10][3];season[10]['race']=df[10][5];season[10]['sprint']=df[10][4]\n", "season[12]['qual']=df[12][3];season[12]['race']=df[12][4]\n", "season[14]['race']=df[14][4];season[14]['sprint']=df[14][3]\n", "season[19]['race']=df[19][4];season[19]['sprint']=df[19][3]" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Data Preparation & Cleaning \n", "Our data contains 4 types of data-frames: Season Overview, Qualifying, Final Race and Sprint Race for some races. We removed some columns and row entries which were of no use, using `drop` command. Also we changed the column names. After that we handled some exception cases in data entries. We concluded this process with changing the data types of columns to suitable data types using `astype`." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "In the season overview data-frame, we removed the column of report and also the last row entry." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "hidden": true }, "outputs": [], "source": [ "season[0]['race'].drop(columns=['.mw-parser-output .tooltip-dotted{border-bottom:1px dotted;cursor:help}Report'],inplace=True)\n", "season[0]['race'].drop([22],axis=0,inplace=True)\n", "season[0]['race'].columns=['Round','Grand_Prix','Pole_Position','Fastest_Lap','Winning_Driver','Winning_Constructor']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hidden": true }, "outputs": [], "source": [ "season[0]['race'].at[4,'Pole_Position']='Charles Leclerc';\n", "season[0]['race'].at[11,'Fastest_Lap']='None Recognised';\n", "season[0]['race'].at[13,'Pole_Position']='Max Verstappen';\n", "season[0]['race'].at[15,'Pole_Position']='Valtteri Bottas'" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "### Qualifying Data Cleaning\n", "We removed the first column and also changed the name of columns along with correction in data entries also." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Removing the first column and also 2 rows which are of no use to us." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "hidden": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.9/site-packages/pandas/core/generic.py:4150: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n", " obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n" ] } ], "source": [ "temp=[2,5,11]\n", "for i in temp:\n", " season[i]['qual'].drop([19,21],axis=0,inplace=True); season[i]['qual']=season[i]['qual'].reset_index()\n", " season[i]['qual'].drop(columns=['.mw-parser-output .tooltip-dotted{border-bottom:1px dotted;cursor:help}Pos.','index'],inplace=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "hidden": true }, "outputs": [], "source": [ "temp=[6,7]\n", "for i in temp:\n", " season[i]['qual'].drop([18,21],axis=0,inplace=True); season[i]['qual']=season[i]['qual'].reset_index()\n", " season[i]['qual'].drop(columns=['.mw-parser-output .tooltip-dotted{border-bottom:1px dotted;cursor:help}Pos.','index'],inplace=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "hidden": true }, "outputs": [], "source": [ "temp=[1,3,4,8,9,10,12,13,14,15,16,17,18,19,20,21,22]\n", "for i in temp:\n", " season[i]['qual'].drop(columns=['.mw-parser-output .tooltip-dotted{border-bottom:1px dotted;cursor:help}Pos.'],inplace=True)\n", " season[i]['qual'].drop([20,21],axis=0,inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Changing the Column names of the qualifying data-frame" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "hidden": true }, "outputs": [], "source": [ "temp=[1,2,3,4,5,6,7,8,9,11,12,13,15,16,17,18,19,20,21,22]\n", "for i in temp:\n", " season[i]['qual'].columns=['No','Driver','Constructor','Q1','Q2','Q3','Finalgrid']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "hidden": true }, "outputs": [], "source": [ "#Sprint Race GP\n", "temp=[10,14,19]\n", "for i in temp:\n", " season[i]['qual'].columns=['No','Driver','Constructor','Q1','Q2','Q3','SQgrid']" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Editing Some data in specific qualifying data-frames, such as correcting name of `Nikita Mazepin` and modifying some data in `Finalgrid`" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "hidden": true }, "outputs": [], "source": [ "season[1]['qual'].at[17,'Finalgrid']=20;season[1]['qual'].at[19,'Driver']='Nikita Mazepin'\n", "season[2]['qual'].at[19,'Finalgrid']=20;season[2]['qual'].at[18,'Driver']='Nikita Mazepin'\n", "season[3]['qual'].at[19,'Driver']='Nikita Mazepin'\n", "season[4]['qual'].at[19,'Finalgrid']=20;season[4]['qual'].at[19,'Driver']='Nikita Mazepin'\n", "season[5]['qual'].at[19,'Finalgrid']=20;season[5]['qual'].at[18,'Driver']='Nikita Mazepin'\n", "season[6]['qual'].at[5,'Finalgrid']=9;season[6]['qual'].at[18,'Finalgrid']=19;season[6]['qual'].at[19,'Finalgrid']=20;season[6]['qual'].at[17,'Driver']='Nikita Mazepin'\n", "season[7]['qual'].at[17,'Driver']='Nikita Mazepin';season[7]['qual'].at[18,'Finalgrid']=19;season[7]['qual'].at[19,'Finalgrid']='PL'\n", "season[8]['qual'].at[1,'Finalgrid']=5;season[8]['qual'].at[7,'Finalgrid']=11;season[8]['qual'].at[19,'Driver']='Nikita Mazepin'\n", "season[9]['qual'].at[19,'Driver']='Nikita Mazepin';season[9]['qual'].at[7,'Finalgrid']=11\n", "season[10]['qual'].at[19,'Driver']='Nikita Mazepin'\n", "season[11]['qual'].at[18,'Driver']='Nikita Mazepin';season[11]['qual'].at[19,'Finalgrid']=20\n", "season[12]['qual'].at[7,'Finalgrid']=13;season[12]['qual'].at[9,'Finalgrid']=15;season[12]['qual'].at[14,'Finalgrid']=19;season[12]['qual'].at[18,'Finalgrid']='PL';season[12]['qual'].at[19,'Driver']='Nikita Mazepin'\n", "season[13]['qual'].at[19,'Driver']='Nikita Mazepin';season[13]['qual'].at[13,'Finalgrid']='PL';season[13]['qual'].at[15,'Finalgrid']='PL'\n", "season[14]['qual'].at[19,'Driver']='Nikita Mazepin'\n", "season[15]['qual'].at[18,'Driver']='Nikita Mazepin';season[15]['qual'].at[6,'Finalgrid']=16;season[15]['qual'].at[13,'Finalgrid']=18;season[15]['qual'].at[14,'Finalgrid']=19;season[15]['qual'].at[17,'Finalgrid']=17;season[15]['qual'].at[19,'Finalgrid']=20\n", "season[16]['qual'].at[19,'Driver']='Nikita Mazepin';season[16]['qual'].at[0,'Finalgrid']=11;season[16]['qual'].at[14,'Finalgrid']=19;season[16]['qual'].at[15,'Finalgrid']=20;\n", "season[17]['qual'].at[19,'Driver']='Nikita Mazepin';season[17]['qual'].at[3,'Finalgrid']=9;season[17]['qual'].at[11,'Finalgrid']=18;season[17]['qual'].at[13,'Finalgrid']=19;season[17]['qual'].at[14,'Finalgrid']=20;\n", "season[18]['qual'].at[18,'Driver']='Nikita Mazepin';season[18]['qual'].at[8,'Finalgrid']=17;season[18]['qual'].at[9,'Finalgrid']=18;season[18]['qual'].at[12,'Finalgrid']=16;season[18]['qual'].at[14,'Finalgrid']=19;season[18]['qual'].at[19,'Finalgrid']=20;\n", "season[19]['qual'].at[18,'Driver']='Nikita Mazepin';season[19]['qual'].at[19,'SQgrid']=20\n", "season[20]['qual'].at[19,'Driver']='Nikita Mazepin';season[20]['qual'].at[1,'Finalgrid']=7;season[20]['qual'].at[2,'Finalgrid']=6;\n", "season[21]['qual'].at[19,'Driver']='Nikita Mazepin';\n", "season[22]['qual'].at[19,'Driver']='Nikita Mazepin';season[22]['qual'].at[19,'Finalgrid']=-1" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "### Sprint Race Data Correction\n", "Removed the last two row entries and some minor correction in data entries" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "hidden": true }, "outputs": [], "source": [ "temp=[10,14,19]\n", "for i in temp:\n", " season[i]['sprint'].drop([20,21],axis=0,inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Correction in the name of `Nikita Mazepin` and some other data in `Finalgrid` column" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "hidden": true }, "outputs": [], "source": [ "season[10]['sprint'].at[8,'Finalgrid']=12;season[10]['sprint'].at[18,'Driver']='Nikita Mazepin';season[10]['sprint'].at[19,'Finalgrid']='PL'\n", "season[14]['sprint'].at[0,'Finalgrid']=19;season[14]['sprint'].at[16,'Driver']='Nikita Mazepin';season[14]['sprint'].at[19,'Finalgrid']='PL'\n", "season[19]['sprint'].at[4,'Finalgrid']=10;season[19]['sprint'].at[17,'Finalgrid']='PL';season[19]['sprint'].at[19,'Driver']='Nikita Mazepin';" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "### Final Race Data Preparation and Cleaning\n", "Removed the last two rows and some other minor data entry corrections followed by changing the column names." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "hidden": true }, "outputs": [], "source": [ "for i in range(1,23):\n", " season[i]['race'].drop([20,21],axis=0,inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Changing the columns name" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "hidden": true }, "outputs": [], "source": [ "for i in range(1,23):\n", " season[i]['race'].columns=['Pos','No','Driver','Constructor','Laps','Time/Retired','Grid','Points']" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Correcting the name of `Nikita Mazepin` and some other data entries" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "hidden": true }, "outputs": [], "source": [ "season[1]['race'].at[2,'Points']=16;season[1]['race'].at[4,'Grid']='PL';season[1]['race'].at[19,'Driver']='Nikita Mazepin';\n", "season[1]['race'].at[16,'Pos']=17;season[1]['race'].at[17,'Pos']=18;\n", "season[2]['race'].at[1,'Points']=19;season[2]['race'].at[14,'Grid']='PL';season[2]['race'].at[16,'Driver']='Nikita Mazepin';\n", "season[2]['race'].at[14,'Pos']=15\n", "season[3]['race'].at[2,'Points']=16;season[3]['race'].at[18,'Driver']='Nikita Mazepin';\n", "season[4]['race'].at[1,'Points']=19;season[4]['race'].at[18,'Driver']='Nikita Mazepin';\n", "season[5]['race'].at[6,'Points']=7;season[5]['race'].at[16,'Driver']='Nikita Mazepin';\n", "season[6]['race'].at[16,'Pos']=17;season[6]['race'].at[17,'Pos']=18;season[6]['race'].at[13,'Driver']='Nikita Mazepin';\n", "season[7]['race'].at[0,'Points']=26;season[7]['race'].at[19,'Driver']='Nikita Mazepin';\n", "season[8]['race'].at[1,'Points']=19;season[8]['race'].at[17,'Driver']='Nikita Mazepin';\n", "season[9]['race'].at[0,'Points']=26;season[9]['race'].at[16,'Pos']=17;season[9]['race'].at[18,'Driver']='Nikita Mazepin';\n", "season[10]['race'].at[16,'Driver']='Nikita Mazepin';\n", "season[11]['race'].at[4,'Points']=11;season[11]['race'].at[12,'Grid']='PL';season[11]['race'].at[13,'Driver']='Nikita Mazepin';\n", "season[12]['race'].at[16,'Driver']='Nikita Mazepin';season[12]['race'].at[18,'Grid']='PL';\n", "season[13]['race'].at[1,'Points']=19;season[13]['race'].at[16,'Pos']=17;season[13]['race'].at[19,'Driver']='Nikita Mazepin';\n", "season[14]['race'].at[0,'Points']=26;season[14]['race'].at[15,'Driver']='Nikita Mazepin';\n", "season[15]['race'].at[6,'Points']=7;season[15]['race'].at[18,'Pos']=19;season[15]['race'].at[17,'Driver']='Nikita Mazepin';\n", "season[16]['race'].at[0,'Points']=26;season[16]['race'].at[19,'Driver']='Nikita Mazepin';\n", "season[17]['race'].at[1,'Points']=19;season[17]['race'].at[16,'Driver']='Nikita Mazepin';\n", "season[18]['race'].at[17,'Driver']='Nikita Mazepin';\n", "season[19]['race'].at[3,'Points']=13;season[19]['race'].at[16,'Driver']='Nikita Mazepin';\n", "season[20]['race'].at[1,'Points']=19;season[20]['race'].at[17,'Driver']='Nikita Mazepin';\n", "season[21]['race'].at[0,'Points']=26;season[21]['race'].at[17,'Driver']='Nikita Mazepin';\n", "season[22]['race'].at[0,'Points']=26;season[22]['race'].at[14,'Pos']=15;season[22]['race'].at[19,'Driver']='Nikita Mazepin';" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The final race data has been corrected and ready to use" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "### Data Conversion" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "hidden": true }, "outputs": [], "source": [ "# overall season data-frame\n", "season[0]['race']=season[0]['race'].astype({\"Round\":\"int64\",\"Grand_Prix\":\"string\",\"Pole_Position\":\"string\",\"Fastest_Lap\":\"string\",\"Winning_Driver\":\"string\",\"Winning_Constructor\":\"string\"},errors='ignore')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "hidden": true }, "outputs": [], "source": [ "temp=[1,2,3,4,5,6,7,8,9,11,12,13,15,16,17,18,20,21,22]\n", "for i in temp:\n", " season[i]['qual']=season[i]['qual'].astype({\"No\":\"int64\",\"Driver\":\"string\",\"Constructor\":\"string\",\"Finalgrid\":\"float64\"},errors='ignore')\n", " season[i]['race']=season[i]['race'].astype({\"Pos\":\"int64\",\"No\":\"int64\",\"Driver\":\"string\",\"Constructor\":\"string\",\"Laps\":\"float64\",\"Grid\":\"int64\",\"Points\":\"float64\"},errors='ignore')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "hidden": true }, "outputs": [], "source": [ "temp=[10,14,19]\n", "for i in temp:\n", " season[i]['qual']=season[i]['qual'].astype({\"No\":\"int64\",\"Driver\":\"string\",\"Constructor\":\"string\",\"SQgrid\":\"float64\"},errors='ignore')\n", " season[i]['sprint']=season[i]['sprint'].astype({\"Pos.\":\"int64\",\"No.\":\"int64\",\"Driver\":\"string\",\"Constructor\":\"string\",\"Laps\":\"float64\",\"Grid\":\"int64\",\"Points\":\"float64\",\"Finalgrid\":\"int64\"},errors='ignore')\n", " season[i]['race']=season[i]['race'].astype({\"Pos\":\"int64\",\"No\":\"int64\",\"Driver\":\"string\",\"Constructor\":\"string\",\"Laps\":\"float64\",\"Grid\":\"int64\",\"Points\":\"float64\"},errors='ignore')" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "### Creating some other datasets for further use" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "hidden": true }, "outputs": [], "source": [ "maxvslewis_pts_df=season[0]['race'][['Round','Grand_Prix']].copy()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "hidden": true }, "outputs": [], "source": [ "#Qualifying Position Data\n", "temp=[1,2,3,4,5,6,7,8,9,11,12,13,15,16,17,18,20,21,22]\n", "for r in temp:\n", " for i in range(0,20):\n", " count=0\n", " if season[r]['qual'].Driver[i]=='Max Verstappen':\n", " if season[r]['qual'].Finalgrid[i]=='PL':\n", " maxvslewis_pts_df.at[r-1,\"Max_Qual\"]=20\n", " else:\n", " maxvslewis_pts_df.at[r-1,\"Max_Qual\"]=season[r]['qual'].Finalgrid[i]\n", " count=count+1\n", " if season[r]['qual'].Driver[i]=='Lewis Hamilton':\n", " if season[r]['qual'].Finalgrid[i]=='PL':\n", " maxvslewis_pts_df.at[r-1,\"Lewis_Qual\"]=20\n", " else:\n", " maxvslewis_pts_df.at[r-1,\"Lewis_Qual\"]=season[r]['qual'].Finalgrid[i]\n", " count=count+1\n", " if count>=2:\n", " break" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "hidden": true }, "outputs": [], "source": [ "temp=[10,14,19]\n", "for r in temp:\n", " for i in range(0,20):\n", " count=0\n", " if season[r]['qual'].Driver[i]=='Max Verstappen':\n", " if season[r]['qual'].SQgrid[i]=='PL':\n", " maxvslewis_pts_df.at[r-1,\"Max_Qual\"]=20\n", " else:\n", " maxvslewis_pts_df.at[r-1,\"Max_Qual\"]=season[r]['qual'].SQgrid[i]\n", " count=count+1\n", " if season[r]['qual'].Driver[i]=='Lewis Hamilton':\n", " if season[r]['qual'].SQgrid[i]=='PL':\n", " maxvslewis_pts_df.at[r-1,\"Lewis_Qual\"]=20\n", " else:\n", " maxvslewis_pts_df.at[r-1,\"Lewis_Qual\"]=season[r]['qual'].SQgrid[i]\n", " count=count+1\n", " if count>=2:\n", " break" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "hidden": true }, "outputs": [], "source": [ "# final race data\n", "for r in range(1,23):\n", " for i in range(0,20):\n", " count=0;\n", " if season[r]['race'].Driver[i]=='Max Verstappen':\n", " if season[r]['race'].Pos[i]=='Ret':\n", " maxvslewis_pts_df.at[r-1,\"Max_Pos\"]=20\n", " else:\n", " maxvslewis_pts_df.at[r-1,\"Max_Pos\"]=season[r]['race'].Pos[i]\n", " \n", " if season[r]['race'].Points[i]=='Nan':\n", " maxvslewis_pts_df.at[r-1,\"Max_Pts\"]=0\n", " else:\n", " maxvslewis_pts_df.at[r-1,\"Max_Pts\"]=season[r]['race'].Points[i]\n", " \n", " count=count+1;\n", " \n", " if season[r]['race'].Driver[i]=='Lewis Hamilton':\n", " if season[r]['race'].Pos[i]=='Ret':\n", " maxvslewis_pts_df.at[r-1,\"Lewis_Pos\"]=20\n", " else:\n", " maxvslewis_pts_df.at[r-1,\"Lewis_Pos\"]=season[r]['race'].Pos[i]\n", " \n", " if season[r]['race'].Points[i]=='Nan':\n", " maxvslewis_pts_df.at[r-1,\"Lewis_Pts\"]=0\n", " else:\n", " maxvslewis_pts_df.at[r-1,\"Lewis_Pts\"]=season[r]['race'].Points[i]\n", " \n", " count=count+1;\n", " \n", " if count>=2:\n", " break" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "hidden": true }, "outputs": [], "source": [ "# sprint race data updation\n", "temp=[10,14,19]\n", "for i in temp:\n", " for j in range(0,20):\n", " count=0\n", " if season[i]['sprint'].Driver[j]=='Max Verstappen':\n", " if math.isnan(season[i]['sprint'].Points[j]):\n", " maxvslewis_pts_df.at[i-1,\"Max_Pts\"]=maxvslewis_pts_df.Max_Pts[i-1]\n", " elif math.isnan(maxvslewis_pts_df.Max_Pts[i-1]):\n", " maxvslewis_pts_df.at[i-1,\"Max_Pts\"]=season[i]['sprint'].Points[j]\n", " else:\n", " maxvslewis_pts_df.at[i-1,\"Max_Pts\"]=maxvslewis_pts_df.Max_Pts[i-1]+season[i]['sprint'].Points[j]\n", " count=count+1\n", " if season[i]['sprint'].Driver[j]=='Lewis Hamilton':\n", " if math.isnan(season[i]['sprint'].Points[j]):\n", " maxvslewis_pts_df.at[i-1,\"Lewis_Pts\"]=maxvslewis_pts_df.Lewis_Pts[i-1]\n", " elif math.isnan(maxvslewis_pts_df.Lewis_Pts[i-1]):\n", " maxvslewis_pts_df.at[i-1,\"Lewis_Pts\"]=season[i]['sprint'].Points[j]\n", " else:\n", " maxvslewis_pts_df.at[i-1,\"Lewis_Pts\"]=maxvslewis_pts_df.Lewis_Pts[i-1]+season[i]['sprint'].Points[j]\n", " count=count+1\n", " if count>=2:\n", " break" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "hidden": true }, "outputs": [], "source": [ "maxvslewis_pts_df['Lewis_Pts']=maxvslewis_pts_df.Lewis_Pts.fillna(0)\n", "maxvslewis_pts_df['Max_Pts']=maxvslewis_pts_df.Max_Pts.fillna(0)\n", "maxvslewis_pts_df[\"Lewis_Pts_cum\"]=maxvslewis_pts_df.Lewis_Pts.cumsum()\n", "maxvslewis_pts_df[\"Max_Pts_cum\"]=maxvslewis_pts_df.Max_Pts.cumsum()\n", "maxvslewis_pts_df=maxvslewis_pts_df.astype({\"Lewis_Pos\":\"int64\",\"Max_Pos\":\"int64\"},errors='ignore')" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### Data Summary\n", "\n", "There were total `22 GPs` held in `F1 2021` season. Out of which there were only `3 GPs` in which `sprint races` were held. `(GP 10, 14 ,19)`. We have a total of `22 qualifying` data-frames, `22 final race` data-frames and `3 sprint race` data-frames. Also we have a season overview data-frame. So. in total we have `46 data-frames`. Any other data-frame is derived from these data-frames\n", "\n", "Further we have created some more data-frames derived from these above stated data-frames." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploratory Analysis and Visualization\n", "![](https://lh3.googleusercontent.com/cVM95w1ZMI3wJov2-gjgdCPuoHvr_XBFHiJDeV80AxNJtu2v2kPTKVs655b7IsyW00oAbs_2cWAosulaJLMv=w1366-h657-rw)" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### Overview of the Dataset\n", "These are the 4 type of data-frames which will be used in getting insights and visualizing the data." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/html": [ "
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RoundGrand_PrixPole_PositionFastest_LapWinning_DriverWinning_Constructor
01Bahrain Grand PrixMax VerstappenValtteri BottasLewis HamiltonMercedes
12Emilia Romagna Grand PrixLewis HamiltonLewis HamiltonMax VerstappenRed Bull Racing-Honda
23Portuguese Grand PrixValtteri BottasValtteri BottasLewis HamiltonMercedes
34Spanish Grand PrixLewis HamiltonMax VerstappenLewis HamiltonMercedes
45Monaco Grand PrixCharles LeclercLewis HamiltonMax VerstappenRed Bull Racing-Honda
\n", "
" ], "text/plain": [ " Round Grand_Prix Pole_Position Fastest_Lap \\\n", "0 1 Bahrain Grand Prix Max Verstappen Valtteri Bottas \n", "1 2 Emilia Romagna Grand Prix Lewis Hamilton Lewis Hamilton \n", "2 3 Portuguese Grand Prix Valtteri Bottas Valtteri Bottas \n", "3 4 Spanish Grand Prix Lewis Hamilton Max Verstappen \n", "4 5 Monaco Grand Prix Charles Leclerc Lewis Hamilton \n", "\n", " Winning_Driver Winning_Constructor \n", "0 Lewis Hamilton Mercedes \n", "1 Max Verstappen Red Bull Racing-Honda \n", "2 Lewis Hamilton Mercedes \n", "3 Lewis Hamilton Mercedes \n", "4 Max Verstappen Red Bull Racing-Honda " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "season[0]['race'].head()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/html": [ "
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NoDriverConstructorQ1Q2Q3Finalgrid
033Max VerstappenRed Bull Racing-Honda1:30.4991:30.3181:28.9971.0
144Lewis HamiltonMercedes1:30.6171:30.0851:29.3852.0
277Valtteri BottasMercedes1:31.2001:30.1861:29.5863.0
316Charles LeclercFerrari1:30.6911:30.0101:29.6784.0
410Pierre GaslyAlphaTauri-Honda1:30.8481:30.5131:29.8095.0
\n", "
" ], "text/plain": [ " No Driver Constructor Q1 Q2 Q3 \\\n", "0 33 Max Verstappen Red Bull Racing-Honda 1:30.499 1:30.318 1:28.997 \n", "1 44 Lewis Hamilton Mercedes 1:30.617 1:30.085 1:29.385 \n", "2 77 Valtteri Bottas Mercedes 1:31.200 1:30.186 1:29.586 \n", "3 16 Charles Leclerc Ferrari 1:30.691 1:30.010 1:29.678 \n", "4 10 Pierre Gasly AlphaTauri-Honda 1:30.848 1:30.513 1:29.809 \n", "\n", " Finalgrid \n", "0 1.0 \n", "1 2.0 \n", "2 3.0 \n", "3 4.0 \n", "4 5.0 " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "season[1]['qual'].head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Pos.No.DriverConstructorLapsTime/RetiredGridPointsFinalgrid
0133Max VerstappenRed Bull Racing-Honda17.025:38.42623.01
1244Lewis HamiltonMercedes17.0+1.43012.02
2377Valtteri BottasMercedes17.0+7.50231.03
3416Charles LeclercFerrari17.0+11.2784NaN4
454Lando NorrisMcLaren-Mercedes17.0+24.1116NaN5
\n", "
" ], "text/plain": [ " Pos. No. Driver Constructor Laps Time/Retired Grid \\\n", "0 1 33 Max Verstappen Red Bull Racing-Honda 17.0 25:38.426 2 \n", "1 2 44 Lewis Hamilton Mercedes 17.0 +1.430 1 \n", "2 3 77 Valtteri Bottas Mercedes 17.0 +7.502 3 \n", "3 4 16 Charles Leclerc Ferrari 17.0 +11.278 4 \n", "4 5 4 Lando Norris McLaren-Mercedes 17.0 +24.111 6 \n", "\n", " Points Finalgrid \n", "0 3.0 1 \n", "1 2.0 2 \n", "2 1.0 3 \n", "3 NaN 4 \n", "4 NaN 5 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "season[10]['sprint'].head()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PosNoDriverConstructorLapsTime/RetiredGridPoints
0144Lewis HamiltonMercedes56.01:32:03.897225.0
1233Max VerstappenRed Bull Racing-Honda56.0+0.745118.0
2377Valtteri BottasMercedes56.0+37.383316.0
344Lando NorrisMcLaren-Mercedes56.0+46.466712.0
4511Sergio PérezRed Bull Racing-Honda56.0+52.047PL10.0
\n", "
" ], "text/plain": [ " Pos No Driver Constructor Laps Time/Retired Grid \\\n", "0 1 44 Lewis Hamilton Mercedes 56.0 1:32:03.897 2 \n", "1 2 33 Max Verstappen Red Bull Racing-Honda 56.0 +0.745 1 \n", "2 3 77 Valtteri Bottas Mercedes 56.0 +37.383 3 \n", "3 4 4 Lando Norris McLaren-Mercedes 56.0 +46.466 7 \n", "4 5 11 Sergio Pérez Red Bull Racing-Honda 56.0 +52.047 PL \n", "\n", " Points \n", "0 25.0 \n", "1 18.0 \n", "2 16.0 \n", "3 12.0 \n", "4 10.0 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "season[1]['race'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analyzing the Overall Season Statistics" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
RoundGrand_PrixPole_PositionFastest_LapWinning_DriverWinning_Constructor
01Bahrain Grand PrixMax VerstappenValtteri BottasLewis HamiltonMercedes
12Emilia Romagna Grand PrixLewis HamiltonLewis HamiltonMax VerstappenRed Bull Racing-Honda
23Portuguese Grand PrixValtteri BottasValtteri BottasLewis HamiltonMercedes
34Spanish Grand PrixLewis HamiltonMax VerstappenLewis HamiltonMercedes
45Monaco Grand PrixCharles LeclercLewis HamiltonMax VerstappenRed Bull Racing-Honda
\n", "
" ], "text/plain": [ " Round Grand_Prix Pole_Position Fastest_Lap \\\n", "0 1 Bahrain Grand Prix Max Verstappen Valtteri Bottas \n", "1 2 Emilia Romagna Grand Prix Lewis Hamilton Lewis Hamilton \n", "2 3 Portuguese Grand Prix Valtteri Bottas Valtteri Bottas \n", "3 4 Spanish Grand Prix Lewis Hamilton Max Verstappen \n", "4 5 Monaco Grand Prix Charles Leclerc Lewis Hamilton \n", "\n", " Winning_Driver Winning_Constructor \n", "0 Lewis Hamilton Mercedes \n", "1 Max Verstappen Red Bull Racing-Honda \n", "2 Lewis Hamilton Mercedes \n", "3 Lewis Hamilton Mercedes \n", "4 Max Verstappen Red Bull Racing-Honda " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "season[0]['race'].head()" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "##### Objective:\n", ">Total Number of Races in 2021 Season" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A total of 22 Grand Prixes were held in 2021 season\n" ] } ], "source": [ "print(\"A total of {} Grand Prixes were held in 2021 season\".format(season[0]['race'].shape[0]))\n", "# total_gps,c=season[0]['race'].shape" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "##### Objective: \n", ">The driver with most number of wins and also the constructor with most wins." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "hidden": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Winning_Driver\n", "Max Verstappen 10\n", "Lewis Hamilton 8\n", "Daniel Ricciardo 1\n", "Esteban Ocon 1\n", "Sergio Pérez 1\n", "Valtteri Bottas 1\n", "dtype: int64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "season[0]['race'].groupby('Winning_Driver').size().sort_values(ascending=False)\n", "#Total_race_wins_driver_df=season[0]['race'].groupby('Winning_Driver')[['Round']].count().sort_values('Round',ascending=False)\n", "#Total_race_wins_driver_df.columns=['Count']\n", "#Total_race_wins_driver_df" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "Winning_Constructor\n", "Red Bull Racing-Honda 11\n", "Mercedes 9\n", "Alpine-Renault 1\n", "McLaren-Mercedes 1\n", "dtype: int64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "construct=season[0]['race'].groupby('Winning_Constructor').size().sort_values(ascending=False)\n", "construct" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "temp=list(construct.index)\n", "x = np.arange(0,13)\n", "plt.bar_label(plt.bar(temp,construct,width,label='Pole Position'),padding=3)\n", "plt.yticks(x);" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "**Conclusion:** \n", "> We can see that **`Max Verstappen`** won the most races (10 Race Wins) followed by **`Lewis Hamilton`** (8 Race Wins)\n", "\n", "> **`Red Bull Racing-Honda`** won the most races with 11 race wins followed by **`Mercedes`** with 9 race wins" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "##### Objective: \n", ">The driver with most number of fastest laps" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "Fastest_Lap\n", "Lewis Hamilton 6\n", "Max Verstappen 6\n", "Valtteri Bottas 4\n", "Sergio Pérez 2\n", "Daniel Ricciardo 1\n", "Lando Norris 1\n", "None Recognised 1\n", "Pierre Gasly 1\n", "dtype: int64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "season[0]['race'].groupby('Fastest_Lap').size().sort_values(ascending=False)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "**Conclusion:**\n", "> `Lewis Hamilton` and `Max Verstappen` both claims the most number of fastest lap in the final race of the GP.\n" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "##### Objective: \n", ">How many drivers claims the pole position in penultimate stage of Qualifying session? Whether a pole position holder wins the race or not?" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Out of 22 races, the total races in which a pole position holder wins the race are 11, implying that only 50% races were won by the pole position holders\n" ] } ], "source": [ "count=0\n", "for i in range(0,22):\n", " if(season[0]['race'].Pole_Position[i]==season[0]['race'].Winning_Driver[i]):\n", " count=count+1;\n", " \n", "print(\"Out of 22 races, the total races in which a pole position holder wins the race are {}, implying that only {:.0f}% races were won by the pole position holders\".format(count,(count/22)*100))" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "hidden": true }, "outputs": [], "source": [ "overall_final_df=season[0]['race'].copy()\n", "overall_final_df['pole_won']=(overall_final_df.Pole_Position==overall_final_df.Winning_Driver)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/plain": [ "Pole_Position\n", "Max Verstappen 10\n", "Lewis Hamilton 5\n", "Valtteri Bottas 4\n", "Charles Leclerc 2\n", "Lando Norris 1\n", "dtype: int64" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp1_df=season[0]['race'].groupby('Pole_Position').size().sort_values(ascending=False)\n", "temp1_df" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "hidden": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " pole_won\n", "Pole_Position \n", "Max Verstappen 7\n", "Lewis Hamilton 3\n", "Valtteri Bottas 1\n", "Charles Leclerc 0\n", "Lando Norris 0" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp2_df=overall_final_df.groupby('Pole_Position')[['pole_won']].sum().sort_values('pole_won',ascending=False)\n", "temp2_df" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "**Conclusion:**\n", "> We can see that `Max Verstappen` is the lead pole position holder with 10 pole positions followed by `Lewis Hamilton` with 5 pole positions. However, we can see that out of 10 pole positions for `Max`, he won in only 7 races. Similarly, out of 5 pole positions for `Lewis`, he won in only 3 races." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "**Visualization**" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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ERCRDXX319f/K4Zs5cyatW69Z/rhNmzXLe7pWZEX7bbFFB6ZO/YXZs2fx/fffMnz4DYwceRdz587lq6++pGPH5Qc5a6yxRvnPDRs2pKhoYfR+M2jd+p+esPiflyf2+37yyUdcdtnFzJs3l6ZNmwIwceIEHnnkIaZN+x2AoqIi5s6dC4QewbZt1/7X8aZN+4NZs2bSs+fu5dtKSkrp2LFTpe+/+eZbUlS0kB9//IHJkz9m//0PonHjxrRq1bp820EHHbbc9rdo8c+/RYMGDSkqKlrp71xVCvhERESySMuWLZk+/Q/WX38DAKZPn1almbsr2q9hw4aYbcKjjz7MeuttQL169dhiiw488siDtG3blubNm1e7nWus0ZI///yz/PH06dOrvG/nzlvRq1cfbrvtJq65ZgTTpv3BddddxU033cEWW2xJXl5elFcY8gVbtWpdab5c69atWXPNtXj44Ser9L4NGjRgk00245133mTWrFmsu257ADp27MQ777zJDz98v8Lh7dqkIV0REZEs0r37XowdO4o5c+Ywd+5cRo++p0qlUla2X6dOW/H44+PLhyw7dw6PO3XaapXa2bXrnjz66MPMmPEnf//9Nw8+OLZa+x9yyBF88MF7fPedU1RURE5ODgUFzQF47rln+OmnH8pf26fPfjz88AN8883XlJWV8euvU5k27Q823XRzGjdejQceGMPixYsoKSnhxx+/5+uvv1zu+3bq1IXx48exxRYdyrd16NCJ8ePH0aLFGpX2JNYF9fCJiIhkkf79B7Jw4QKOOSYMLe6xR3f69x+4kr1Wvl+nTl24//7RdOwYZqB27tyFhQsXlM9Ira599tmPqVN/pn//w1lttdU46KBD+eSTj8rr2a1MQUEBPXv2ZsyYe7nqqv9w6KFHMmjQseTm5tCzZ2+23LJj+Wu7du3OX3/N5bLLLmLmzBm0abMmQ4deTps2a3LddTdyyy03cvDBfVmyZAnt2q3L8ceftNz3jf07dOjwz/E7dOjEnDmz6datxyr9WyRCTmz6c11aurSkbO7chXX2foWFTWl//nO1+h5Tru3NjBlVr40k6at588bU5d+vJJfOd3apzvmeNu1n2rRZd5ltqVR4OdO8++7bXH/9NTz++ISEHTMvL5eSktKEHa+uVPa3B1BY2PQjYOvK9lEPn4iISIKEYEw3/4mwePEiPv74Q7bZZnvmzJnN6NH3sOuueyS7WWlLAZ+IiIiknLIyGDnybi655EIaNGjAjjvuzHHHDVr5jlIpBXwiIiKScho2bMi9996X7GZkDM3SFREREclwCvhERERWSQ5lZemX8C/pbVUn2yrgExERWQX16zdk7tyZFBcvXeWLsEh1lJWVsWDBX+Tn16/2vsrhExERWQUFBYXMnz+P2bOnU1pakuzmyCrIyclJu2A9P78+BQUrXxnlX/vVQltEREQyXk5ODk2bNqdp0+bJboqsomyqs6khXREREZEMp4BPREREJMMp4BMRERHJcAr4RERERDKcAj4RERGRDKeAT0RERCTDKeATERERyXAK+EREREQyXEIKL5tZe+B2YAdgMfAYcIa7Fyfi+CIiIiKy6hLVw3c78CewJtAJ2A04OUHHFhEREZEaSFTAtx4w3t0Xufs04AVg8wQdW0RERERqIFFr6d4MHGZmrwMFQC9gaIKOLSIiIiI1kKiA7w3geOAvIA8YCzy1vBfn5eXQvHnjBL116sjE30n+LS8vV+c6i+h8Zxed7+ySTee7xgGfmeUCk4C7gB2BJsAoYDgwpLJ9SkrKmDt3YU3fusoKC5vWyfvU5e8kydO8eWOd6yyi851ddL6zS6ad7xXFO4nI4WsBrAPc6u6L3X0WMBrYOwHHFhEREZEaqnHA5+4zgZ+Ak8ws38yaA/2ByTU9toiIiIjUXKJm6R4A9ARmAN8DxcCZCTq2iIiIiNRAQiZtuPunwO6JOJaIiIiIJJaWVhMRERHJcAr4RERERDKcAj4RERGRDKeAT0RERCTDJWqlDamCPffcZZnHixcvZv/9D+LMMyutTy0iIiKSEAr46tBLL71Z/nNRURH77tuDPfbonsQWiYiISDbQkG6SvP76KzRv3oKOHTsnuykiIiKS4RTwJcnEiRPo2XNvcnJykt0UERERyXAK+JJg2rRpfPrpx/Tq1SfZTREREZEsoIAvCV54YQIdOnRirbXaJrspIiIikgUU8CXBCy88T8+evZPdDBEREckSCvjq2OefT2bmzD/p2lWzc0VERKRuKOCrYxMnTmC33fagcePVkt0UERERyRKqw1fHhgy5KNlNEBERkSyjHj4RERGRDKeAT0RERCTDKeATERERyXAK+EREREQynAI+ERERkQynWbqJsnQRhYVNa+3wpUuKmDWvuNaOLyIiIplLAV+i1GsIw1avtcPnDpsH/F1rxxcREZHMpSFdERERkQyngE9EREQkwyngExEREclwCvhEREREMpwCPhEREZEMp4BPREREJMMp4BMRERHJcAr4RERERDKcAj4RERGRDKeAT0RERCTDKeATERERyXAK+EREREQynAI+ERERkQyngE9EREQkwyngExEREclwCvhEREREMpwCPhEREZEMp4BPREREJMPlJ+pAZnYYcCnQDpgGHOPubybq+CIiIiKyahIS8JnZnsBw4FDgfWDNRBxXRERERGouUT18lwGXu/v/ose/Jei4IiIiIlJDNQ74zCwP2Bp4xsy+BxoCTwHnuntRTY8vIiIiIjWTiB6+1kA94CBgF2Ap8DRwMXBRZTvk5eXQvHnjBLx1dtG/WWrIy8vVucgiOt/ZRec7u2TT+U5EwBfrxbvF3f8AMLMbWEHAV1JSxty5CxPw1lVTWNi0zt6rNtXlv5ksX/PmjXUusojOd3bR+c4umXa+VxTv1Lgsi7vPAX4Fymp6LBERERFJvERN2hgNnGpmLxCGdM8AJiTo2CIiIiJSA4kK+K4AWgLfAouA8cBVCTq2iIiIiNRAQgI+d18KnBz9JyIiIiIpREuriYiIiGQ4BXwiIiIiGU4Bn4iIiEiGU8AnIiIikuESNUtXJO0MHnwCX331BXl5eQC0bFnIuHFPJLlVIiIiiaeAT7LamWcOYZ999kt2M0RERGqVhnRFREREMpwCPslqd911K717d+Okk47l448/THZzREREaoWGdCVrnXTSaay33nrk59fjlVde5LzzzmLMmIdo23btZDdNREQkodTDJ1lr8823oHHj1ahfvz69evVhyy078u67byW7WSIiIgmngE8kkpOTQ1lZslshIiKSeAr4JCv9/fffvPfeuyxevJji4mJefHEikyd/zHbbbZ/spomIiCSccvgkKxUXF3PPPXfw889TyMvLpV279lxzzfW0a9c+2U0TERFJOAV8kpUKCgq49977kt0MERGROqEhXREREZEMp4BPREREJMMp4BMRERHJcAr4RERERDKcAj4RERGRDKdZupJxmjRrRKMGtfenvWhpSa0dW0REpDYo4JOM06hBPu3Pf67Wjj/l2t78XWtHFxERSTwN6YqIiIhkOAV8IiIiIhlOAZ+IiIhIhlPAJyIiIpLhFPCJiIiIZDgFfCIiIiIZTgGfiIiISIZTwCciWWXq1F/o2nVHLr98aLKbIiJSZxTwiUhWueGG4WyyyWbJboaISJ1SwCciWePllyfRpElTttpqm2Q3RUSkTingE5GssGDBfO699y4GDz4j2U0REalzCvhEJCvcc8+d9OmzL61bt0l2U0RE6pwCPhHJeN9953z44fsceuiRyW6KiEhS5Ce7ASIite2TTz5i2rTfOfDAPgAUFS2kpKSUKVN+ZNSoB5PcOhGR2qeAT0Qy3r77HkC3bj3KH48b9wDTpv3O2WdfkMRWiYjUHQV8IpLxGjZsSMOGDcsfN2rUiPr1G1BQUJDEVomI1B0FfCKSdQYOHJTsJoiI1ClN2hARERHJcAkL+MxsIzNbZGYPJOqYIiIiIlJziezhuw34IIHHExEREZEESEjAZ2aHAXOBVxJxPBERERFJnBoHfGbWDLgcOLvmzRERERGRREvELN0rgJHuPtXMqrRDXl4OzZs3TsBbZxf9m6UOnYvUUQI0rJdXa8dftLRE5zuL5OXl6nxnkWw63zUK+MysE9Ad6Fyd/UpKypg7d2FN3rpaCgub1tl71aa6/DdLZ3VxvnUuUkdhYVPan/9crR1/yrW9mTHj71o7vqSW5s0b6/OdRTLtfK/o+lfTHr7dgfbAL1HvXhMgz8w2c/cuNTy2iIiIiCRATQO+u4GH4x6fQwgAT6rhcUVEREQkQWoU8Ln7QqC8L9TM5gOL3H1GTRsmIiIiIomR0KXV3H1YIo8nIiIiIjWnpdVEREREMpwCPhEREZEMp4BPREREJMMp4BMRERHJcAmdtCEiy7r88qF89NH7FBUtokWLNTjyyKPZZ5/9kt0sERHJMgr4RGpRv37HcP75Q6lfvz4//zyFU08dxEYbGZtssmmymyYiIllEQ7oitWj99Tegfv36AOTkhP9+++3XJLdKRESyjXr4RGrZ9ddfy8SJz7J48WI23tjYYYedkt0kERHJMgr4RGrZOeecz5lnnssXX3zOJ598WN7jJyIiUlc0pCtSB/Ly8ujYsRMzZvzJk08+luzmiIhIllHAJ1KHSkpKlMMnIiJ1TgGfSC2ZM2c2L788iYULF1JSUsJ7773Lyy9PYquttk5200REJMsoh0+k1uTw1FOPc/3111BaWkabNm047bSz2WWX3ZPdMBERyTIK+ERqSUFBAbfeeneymyEiIqIhXREREZFMp4BPREREJMMp4BMRERHJcAr4RERERDKcAj4RERGRDKdZuiLVtXQRhYVNa/UtSpcUMWteca2+h4iIZA8FfCLVVa8hDFu9Vt8id9g84O9afQ8REckeGtIVERERyXAK+EREREQynAI+ERERkQyngE9EREQkwyngExEREclwCvhEREREMpwCPhEREZEMpzp8IiKSUZYsWcKIEdfy4Yfv89dff7H22mtzwgmnsMMOOyW7aSJJox4+ERHJKCUlJbRq1Zpbb72bSZNe57jjTuKSSy7gjz9+T3bTRJJGPXwiIpJRGjVqxMCBg8of77TTLqy11lq4f82aa66VxJaJJI96+EREJKPNnj2LqVN/Yb31Nkh2U0SSRgGfiIhkrOLiYi67bCg9e/Zm3XXbJ7s5IkmjgE9ERDJSaWkpV1wxlHr18jnrrPOS3RyRpFIOn4iIZJyysjKuvfYKZs+ezfXX30x+vi53kt30CRARkYxz/fXXMGXKT9x00+00aNAw2c0RSToFfCIiklGmTfuDp59+gvr169O3717l288990J69OiVxJaJJI8CPhERySht2qzJW299mOxmiKQUTdoQERERyXA17uEzswbA7UB3oAXwPXChu0+s6bFFREREpOYS0cOXD0wFdgNWB4YC482sfQKOLSIiIiI1VOMePndfAAyL2zTBzH4CtgKm1PT4IiIiIlIzCc/hM7PWwMbAl4k+toiIiIhUX0Jn6ZpZPeBBYKy7f7O81+Xl5dC8eeNEvnVW0L9ZdtH5Th06F6mjBGhYL6/Wjr9oaYnOdxbJy8vNmvOdsIDPzHKB+4ElwOAVvbakpIy5cxcm6q1XqrCwaZ29V22qy3+zdKbznV3q4nzrXKSOwsKmtD//uVo7/pRrezNjxt+1dnxJLc2bN86oz/eKvg8TEvCZWQ4wEmgN7O3uSxNxXBERERGpuUT18N0BbAp0d/eiBB1TRERERBIgEXX41gUGAYuBaWYWe2qQuz9Y0+OLiIiISM0koizLz0BOAtoiIiIiIrVAS6uJiIiIZDgFfCIiIiIZTgGfiIiISIZTwCciIiKS4RTwiYgkwOOPP8LAgUexxx47cNVVw5LdHJGsoc9e1SR0aTURkWzVsmUh/fsP5P3332Xx4sXJbo5I1tBnr2oU8ImIJMBuu3UF4JtvvmLGjD+T3BqR7KHPXtVoSFdEREQkwyngExEREclwCvhEREREMpwCPhEREZEMp0kbIiIJUFxcTElJCaWlpZSWlrB48WLy8vLIz9fXrEht0mevavSvISKSAGPHjmT06HvKH0+aNJEBA45n4MBBSWyVSObTZ69qFPCJiCTAwIGDdIERSQJ99qpGOXwiIiIiGU4Bn4iIiEiGU8AnIiIikuEU8ImIiIhkOAV8IiIiIhlOs3RFRFZk6SIKC5vW6luULili1rziWn0PkXTTpFkjGjWo3TBl0dKSWj1+KlHAJyKyIvUawrDVa/UtcofNA/6u1fcQSTeNGuTT/vznavU9plzbO2s+eRrSFREREclwCvhEREREMpwCPhEREZEMp4BPREREJMMp4BMRERHJcAr4RERERDKcAj4RERGRDKeAT0REZBX89dc8LrjgHLp335kDD+zDiy++kOwmSS1K9/OtwssiIiKrYMSI4dSrV49nnnmR7777liFDTmfDDTdi/fU3SHbTpBak+/lWD5+IiEg1FRUV8cYbr3LccSfSuHFjOnbsxM4778qkSc8nu2lSCzLhfCvgExERqaapU38mNzePdu3WLd+2wQYb89NPPyaxVVJbMuF8K+ATERGppqKiIpo0abLMtiZNmrBw4YIktUhqUyacbwV8IiIi1dSoUSMWLJi/zLYFCxbQuPFqSWqR1KZMON8K+ERERKppnXXWpaSkhKlTfynf9v3337LeeusnsVVSWzLhfCvgExERqaZGjRqx2257cO+9d1JUVMRnn33KW2+9wV577Z3spkktyITzrYBPRERkFZx99vksWbKYffbZk2HDLuLssy9ImxIdUn3pfr5Vh09ERGQVNGu2OtdcMyLZzZA6ku7nWz18IiIiIhkuIT18ZtYCGAn0AGYCF7j7Q4k4toiIiIjUTKJ6+G4DlgCtgSOBO8xs8wQdW0RERERqoMYBn5mtBhwIDHX3+e7+FvAMcFRNjy0iIiIiNZeIHr6NgRJ3/zZu22RAPXwiIiIiKSCnrKysRgcws12AR929Tdy244Ej3X335ew2A/i5Rm8sIiIiIvHWBQoreyIRkzbmA80qbGsG/L2CfSptjIiIiIgkXiKGdL8F8s1so7htHYEvE3BsEREREamhGg/pApjZw0AZcBzQCXge2NHdFfSJiIiIJFmiyrKcDDQC/gTGAScp2BMRERFJDQnp4RMRERGR1KWl1UREREQynAI+ERERkQyngE9EREQkwyngE0kzZpaX7DZI7dN5zk4Vz7uZ5SSrLZIYqfJZVsCXIcwsEUW0JcWZWZ67l8R+TnZ7JPHMLMfMcty9xMzyzWztZLdJ6oaZ5cZ9vgeY2YburpmVaazCd3Z3M0vawhOapZvmogtDWdzjo4Ei4Cl3X5q8lkkixZ9nM1sLGA1MAz5x95uS2TapHWbWG7gNuAO4y93nJrdFUheigOAZwopV5wCT3L00ua2Sqoq+n1dz9+/ittUHHgU6E+oVv5aM67MCvgxhZk2A/wI5wBrAW8Dl7v5NUhsmCWVmHYCbgcnAX8DewAR3H5bMdklimdmJwGXAOe5+f7LbI7UnvgcoejwK+NvdT09is2QVmFlrwuf2WXd/zsxygebAeOA3YEAyg3cN6WYAMxsIXAg85O6dgd2ANsBhZtYmqY2ThInO87XAV+5+BnAlMAw4zsx2TWLTpAaii0LFXK2OwEXufr+ZNTGzQ8xsSzNbIzmtlESLne9o6L6BmR0b9Q6tA7wUvUZpG2nE3acDF7j7c9HjUqAYmA/c5u6lZnakmQ0xs65mtnpdtk8BX5qJXRwqaAWcDzQAcPefgFuBbkBXM6tXdy2URFhOTubPQAGwiZk1cvclhF7dB4CbdHFIP1HvTmV3/L8B55vZFcB3wInAPYRVjSQDxKVorAdMBDoQVqxaH5gTvSw3yulsZGYNktNSqYrYd7a7zzGzQjMbYWbdgBJgNvAfM5sEDAU2A+4Edq7LNirgSyPxFwczax99EeS6+zXAc0DX2Gvd/QnCsO5h1PEfldRMdE6Lo58PMbMeZtbU3V8G7ibkaO4H4O5/EXK8lhICP0kjUe/OmtEw3t1mdpiZNQZuAoYDS4Cj3L0r8B7QOHmtlUSL8jTHA++7+xnu/gOhd+8uAHdfGgWG/QAN8aaoit/ZhGtxIXA4UEbokLkWGO3um7j7McCPQJ1OttTMzjQSN9NnHOEO4XvgF+BMwp3/F2Y22N1vjXa5mpD8u1oSmiurKOr23xh4irA+dQEw28xuA8YCnYC9zOw7d/8Q+BU4GwUDaSG6OMRu3A4i3Ok/TLgBPwzYyt3PBe6JTdaJcnS3AV5IVrulZirm6kVyCDlem8RtOxX4POoNegdoSfi7GFgX7ZSqi30+o+/sJsClQA9gd8Iw7rHAGe5+NaEXN7bfEGBj4Pe6bK8mbaSJKN+jAXALYfbWWUBT4H3gFne/yMyOAO4FNo+GdTGz1d19XpKaLVVgZg3cfXGFbdcCzdz95OiL5FxCXtdVhOGeK4GpwH/c/c+6brNUX1Ri4/u4x00JN2XvxyZmmNmjwPbAUHcfY2ZbAgcQbuoecPfBSWi61FCFIH8PwhCfu/siMzuZcH6Pdvd3o9e0Ao4C2gP1CfmcM5PSeKlUhXO6GaEHdh3gMHf/KxriHQzsAdzr7s+aWV/gDKA1cKi7f16XbVbAl8IqKbnSDHge6OfuU6Ku4/sJM/luiV7zBKGHYN0VHUtSQ/Tlv5e7nx8l8PZy94fN7F3gbncfHb2uPXAJ8Lm73xjN4twLuNDdv05W+6VqzGxr4FWgC7AucCihJ2cLd//IzDYgDO2VRa/bETiCEBicCPzP3d9KRtul+sxsPXf/qUI5pVi5lfqE3txPCb27nwA3EIK7Ayve/Enqiet5zyfky88ENiD07m3p7r9Hr2tHCPBaAUMIPbrbRSlXdU45fCkoNjEj+oNqGPdUIfA30NTMngauA3q6+y1mtm30mqOpJNdDwV7yxWblmdnFZrZTtLlN2GT3EyZlrBNt/4LQswOAu08h5O7tFT2+EzhOwV7qiZ9tGzcT80NC/uXrhMDutejC/mmUs3cH8Ia7bw18Q0jgv83d57v79Qr20kdUmuMHMzsw+g6PTZq7lHDDthUhxysHGElI6n+IMJvz8grH0iobKSg6r1sApxBq7l1MmFT1AXFD7+7+CyHILwSOcPffkhXsgQK+lFNhYsYFwKNmNtrMto0SeusTarDNcPf27v6amXUBzjGzTaMLxFPJ+w1kBWJf3lsCD5hZvruPA9Yk9OZc4e7/iV5zP1BgZvFDeLMJAQMA7j6r9pss1RXXo7N/3M8tCDl4rYDH3X1cdGNXCqxFuNg/FB2iI3AFYdhe0kxUmuMi4M5oNv3SKC1jHUINTYALgD7A1VFe33uEwGCvKH83dizdqKeQuJv2doSJkscT6u5BmCT5ArBVNBkHAHd/nfBZHlGnja2EhnRTUHTHvwfwH0I38AlAHqFS9wuE3p8h0f93Itw5DnOtuJCyzGxfoLu7nxY9/gZ4jVA/8WDCeZwGjHJ3N7PmhAvCHYTJG60IE3X2ruu8D6k+C/UvfycE8psTzt01hKB/PHCWuz8ZXUBaAG8DnwM7AF8Rhvb+TkbbZdXZsstovQYscvdeUcmkb4DbCT33DYGD3P1nM+sOfAYsJuTtTk1S86USlU22iUrknEgYZdskLmd+XeA0wk38xe7+Y123d0UU8KWYKJHbCbNvh7j7f6NtRxLyfo4izOjqA7QlTOC4KBoyUq5eCoou6t2Bn93922hbB0Luzv7u/kxUr+l0woX/VndfEL1uB8JsrrWA4cup2SYpJHaBMLNzCHf2U4B93f3bKEVjCOHzvL27z4n22ZLQ89vQ3UclqemyiqLPeE785zO6+H9ByLO9Jeqt/z9CT/6l0WsOAE4iFOv9MAlNlxWokIN5LGEVq88IObXzzGwCUM/d94rbpxuhasJ4dx+ThGYvlwK+JKrsSyLafhZwPbC7u/832rYBoUv4vlgOgJk1cff50c+5QJmCvdRmZusDuPuPZjaMkAOymbvPMLNzgW2BB939KTM7hTD8Ny15LZaqigv0Yv+/k5DE/ZO7d4vbvhYhWR9Czu3TwLnu/n6y2i6JYWadgBuBa919koW1ze8F1nf3X83sKUIZlscJvb6HAKe4+8PJabGsTDQc/yghD+8LYCPC0nc9zcyAJ4Axcek4ROlVKZdfrRy+JKlQv2dbMxtkZtuYWX13v4GQq3Vx3Gt/IAz9rBN3mFgvUK67lyrYSz0VEvjXIAy/3wbgYf3bnwiJ2xCS+qcBg83sN6AvYZKOpLjoM1oSzdrbOHp8oruvT8jpuTh6PjeawXcOYcLOB8AUBXvpz8z6AS8TUjV+iM71fcCThKAed98PmEAYmVlEGA5UsJcCLCxkkG9m15lZx7indgYaufvWHgomHw60N7P/uLsTSiudHTdxklQM9kA9fElRoZt4CHAe4Yu/NWHYb78oKfQLwsyf+wiTNR4k3A2+lJyWS1XZsjWa8v2fKuw9CTUUX3H34dGMvp8JxTnvjB5vBrSJJnRImojO7a2E3L0mhN74m8xsb8IFf4e41IvVCatotHL3n5PVZkmMKNB/BHjY3R+t8FwBYaLdA+5+YTLaJ5WrLAXKzD4lnK+z3X2mmQ0gBHkHAvOjGbpdCZ/1vQglWW4GPnL3u+r0F6gm9fAlQfQH09rMTiPM3NvC3XsC+wM7mNmV0XTuWEHOiwjJ/Xco2EsPccHeMcBYMzsxmozzMmESxv5mtnM0o+8k4BYz28zdp7v7awr2Uo9Vso51bFs0VH8lIT/vAMKw3hUWSnM8T1j2boyZnWxmvwB7unuRgr30Y5Wvc92WMPrye9RTVN6zH+VpnkBYG3mzOmqmrESFjpf6cU/tD+xGmEwHoTpCO2D1uOBwQfQf7l4EnJrqwR4o4KszlVws1iUUZNyJMGMrVmvtUEL38Fru/gAwilDIcX8PBXdzKrvwSPKZWW6FIdwjCHW1viT04p5HGJYfD3xIuAA08FBc+XXC34KkoAo9toeY2d5mVhCXf7shYYblE+4+08PKGTcQ1s/E3QcQZtjvCVzm7o8l4deQGoqChGIzq2dmB5rZ7tFTvxCWQOsQBQX50evbWijC/ALQx92/Sk7LpaKo42VjM3sTGGFmBdH38U+EdazPMbNt3P1pYDpwQ1zA3gqYByyMjpUWxbI1pFvLKlwo6rv7ktg2MzuOMLX7and/wv4p0TARON/dX42GAz4jlOu4NH54UFJHhbvFLoQh+J2A5939azPbD+gPvELI4duKUGvtZ3c/MTmtluqIevEejx4uJix12NfdfzGz7Qjn8wJ3/yhun9+AY6ME/nwAfX7Tm5ntRqiZ+BGhZuJzhHIr6wOPAdsB37h7kZk9B3zv7v8qhi/JZ2YHEnKnCwjnbi5wmocl70YRevb6ENajf5gwS3ca4Rwf5+5PJqPdq0o9RbUsLtg7HHjdwooKF0fP3UuozXSgme0eBQyrEXr8pkavmUNYgHmomXXRxSL1VAj2riMUUR1BqKPYGsBDMewPgV2BblEu1+OE/D1V1E9xZnYY8C3wkLt3dvftCUH90dFLZhKKKPewsA4qZtYS+JrQw4u7F+vzm/qq8Fk8kTALd1+gc/TfJe7+DGH29Rhggpl9R5iYcWYtNldq5nVCKsabhHSbFsCLZnYZobRKO+B0D0XujyQMzT8EbJRuwR6oh69WVEwENbP/I+T1XEroFTiLkI83wsw2IUzM2IgwOeMQYIJXWCQ96g18zN3n1s1vIdVhZhsBhxEK695CGNJ5izCdf7i7/x311l4PNCJU2v/VKxT0lNRjoQj2jcB6hKUMF0Xbryac55vd/Tcz25+wrFIDwnDuuYQL/iHAAs2iTz+VfJe3B1529w2jnNyHCZOs9o0N15rZpoSUnYUeldWS1GWhBuYZhLJmx1lY3/wm4H+ENa3rE67ZEz3N66Aq4EswM+vg7p9FP8d660YA17i7W1hA+23CQstbufunUbfypcA44C13fzPaX0WUU1QlF4I+hKWRnnT3A6NtewBjCbX2XvCwxFJXwmoKI2KBg6S+aBjvWOA3d7/QQsHcxwgTcDYi9A6cAxhhslVz4A93PzcpDZZVFn1v/wcY6+7vWYWVFszsRUIx7V7Aq+7eP9reG3jP3WcmodlSA9EM+wuAR9391ugmbxdCoLcbodf2hHTvoVfAl0BmNpDQW9ccuIqw2P2lhBIbP5nZUYTp27cRZnR1cPcu0b53ELqTr3D3L+Jz/yR1VMjJrOfuS+Oeuw44zN3bxW27hrBM3kB3/7LOGyzVEvW4f1/ZF3vUy74PsDbhs3qKuz8ffe4HE5ZSei56bUMF9OkpytW8HGhMWP6sNC7vuhEhCDiLMIx7W7TP5YR6bcdEFRYkRdgKFiWI3bhbWM2qP2HY9nSP6mJGozKd3P21Om10LVEOXwLEZs26+0hCIu9PhK7gG6LSCz9ZWBD7VOBQdx9KSPzsZGa3RIcZCjQFjjeztRXsJV/FXJ7oyyEW7PUHHjCzm81sm+glFwGLzez22D7ufgEhONhXeXqpzcxOIqyD2aDC9th5e5KQj1cAHBKVW4GQi1mP8JkGQMFe+vKw/uldhNSLS6JtpdHnvwh4FhgNDDSzs83sDULKzskK9lJL1DtbGgV165pZc4tKsERBfBmAh3WrnyfUwx0aN8FqTqYEe6CAr8Zif1DRz/mEnJ5mhLyeafZPzaYCoBiYEiVztyMMCVwFEA0D3ENYdDktpnhnuop3hNGXRp6ZPUBI9H2dMJx3lpn1jXr7jiYE7T3idt3J3a/R8HzKG+3uJ7v7grjPLXEXhVmEYdzXCWtax1xOKM8wow7bKrXrA0Ih5b3MbM9oW14U9H3m7ucQ8jSLgKfdfQt3/yZZjZXAzBpEuZUAxIbioxG0Z6P/bjezdaIgPv5z/iNhmbQCoHfdtrxuaEg3AaJu3wsJeVnTolyOR4Ft3f2L6DVdCBeGNYDNCQHh0Og5lVpJUWZ2CbCmu59kZnmEYZujCStj/G1mOxIStz8hlNL5OprhNZSwqHaJcjFTX/zwfDQjtxlhTeMFleRrHgbsB/wGdCcsf7e/uyvgSzMrSp0xs7aEHt8uhJGZ2dH2poRg7yR9b6eGqBe+GeFm7GbCqibF0ejbUEJ1hAOA3QkrZnRw906xfeOqLDQAWmdqT616+GrIzA4BfiCsi9k2+gJ5DpgE3GNm9QDc/WPCrL3LCEssxYK9HH1ppIblDLm+DQwys12ju8UpwI1RsHcaYaLGK4TZuf2jHt9LCcO7pfDvnkJJHbFzHk2oWcvMtibckB1KqLVF3MUg9vfxDPA9oZdvvLvvrGAvvVgoYB8/OtOw4mvc/TfCjfvfhM8zZrYP4TtgC6BEaRqpwcO69PMIw7InE2ZOx8qiGaEe6jzCZ/cMoL6ZnR/bN+44izM12AP18FVbXPJuDqHG2vOEyvlPR883ivI8MLPYMO0vQD/CLJ8vY8dhOYmkUvcq64WLS+i9mlBks1Xcc10JU/dPcPf/WajWviahPte9ddl2qTkz60yYaXs9YYbmg4QirCPc/du418X+JtoCxR6WxpM0FZ33Cwglr8ZX8nweoTf3dEIPkhEm64yqy3bKilm0qEH088uETphLCKthPAJMcvfb415/MaFSxomeJqtkJIJ6+KoobmJGqZntSrgwdCEUY/3LzDYzs0eBO83sgmi3vsAmwHGECRzlszRjiaR1+kvIckUX8eZmdoGZ7RJtjiXuXgjMik3GiIL9bYBPomBvU0LC/jBCaR1JA7HeGTMbQhi6uyzKtSwm5NZuBvQxs9Vjr4/L5/tNwV56M7OzCEOAnwH/jY3GxIt69d8A3gH+ANZVsJd64oK9nQjn9CDCDOtF0ePzLCxzF+uRbQL8mU3BHijgq7K4rv+9CBf2dwiFdfOBQYS7iN8JFbuvsrAG39vAAEIu3+Pq/k95bQk5HntbWMs4ltM1mFCK40Qz6xdd9H8CDjGzR4BPCcVYH3D3BclpulRF1GMDLDOU04ywYHq76DU5HmppPgD0JNTh0tB8Gqtkxn0BoZLC7u5+pbtPA1aP8vNiE/CA8gl117p7L3f/sy7bLVUX3ZA/CcwBvgCuNLNt3f1GQsfMPYT1y/sRRtw+TFpjk0RDuisQN3yTA+QRgrfLCbP5Loxe04IQ9K3mYdFlLKyfeJW7vxN3LNXVSwNRQv5hhJ66iYQcnnaEYZ3OwCignbvPjCbntAPecC2KnvJs2RqKOwKz3f2b6PN9P2EW/TXu7nH7PEhY7vBUd5+ajHZLzVQ2Kc7M1iSU2LkQmE1I7P+BUB919yhHV5OtUlQlE6naEDpdjnT3X6NtTxBu5voQSi2dCHQilMka4e4v1nW7k00B33JYherq0bYdCHlbc919r2hbLKevkPBlMYqwrmZf9fakjwozta4Gtick7d/rcQufm9kzhGGdjslpqdSEmW1GKK0yk7BU2kOEGZfNCLXVngBGRgnesZma63u0+o2kp2i4dhghp+sDd3/NzE4AuhLqn04EXiPkbt7v7iOS1VZZseVcm1sQ1q0+KhbIWSjPMp2QfnVVbNZuNne8aEi3ErE/qCin6xwzO9LMNnD3dwkJ3WuZ2UHRy2M9gIcTll951t27x8o5JOlXkJWI5WTGRD25sW23EXLyXiLMrI5N1wc4CSitkA8iKaiSYbxcojxLd9+VENT3IqyY8B3/DOF2je0T5eop2EsTZraPme0c/ZwX/b8DIRgwQmrGJRZWxhjr7ocBB7r7rYSZ178TSixJiombUV9iZh3M7HYzO8zMzEPJnOeBXaJAD3dfCEwGzgN2i/bP6h4u9fBFoovBGYTaW9PNrBthWO8lQsmVeYR19saZ2TDCcllHRFP3MbPWhLprse7kf92FSPJFH/pc/6cgZ/nsrtjzUfC3NzAQeBe4xd0XZ/vdYToxs32BX939YzNrRiimugh4x903iF5zK+FGbZ9Y+oWZjQeWAqd5KLQsacJCQfurCcXQe0cXfMzsZEKv/HnR44eArQm1E7+MZupuTVgG803C97oujCnKzPoSVkJ5jZBSM42QbtWL8Hn+yN2vMLMtCN/hs4GbPKymkdXUw/ePPQh/NAXR436EbuAjgT2BhsBFUTLvE4RSKzfEdnb36e7+q4X6TjkK9lJPLJiL7hA7Rhf3XazyVRWeB/5HGNbdP9qmYC99nE1YAeVsQq7WxoRk7q/N7Hwz+wbYkBAIvGNmu0f7nQqcp2Av/USTK0YSyulcHvfUoYRJVpjZRMJn+pAo2GtA+G4fAFzg7ocr2EtNZtbQQiH8A4F+7n44MIRwI3eluz9CyOMbaGG5u0+Ab939CgV7gQK+f7xKCPZi66JuBNwXJYP+l1BjbV93L45m8D0J1Ks4tBcFFPrCSBFmdotFSyPF5eidSbiT/57Q5V/Z0B+Ei0cZUFhxCFhSj5mtFvfwaGBvQjL+IHd/CWgM/AycT6jE39Pd55vZKcCFZrZu7MatzhsvNWL/rH36HmGi1TYWiuIDPAUcZ2Z/Aj+7+wbu/mn0fJcoVaeru9+fjLZL5eJn1EP5+tQdCBPoWkeb/0cI8rqY2VHuPo4w+/o8YGN3v6PuWpz6snZIt0KSfn13X2Jm1wFru/sRZvY5oaL6VsAT7j44eu3RhADwN6BUPXmpK7p7PwB43P+p09QOuJswZPdttK0VMC8ato0N6cYm47SJSjZICjOz44E13P1aM9ueMNO6JbAtoYf2u+gzvhvQn7DE4X3AEYSbvH7u/t/ktF5WVYXv8daENa5fIuTrdSfUY1sLuIVQd+2g6LXnA8cDx7r7G8lou1QuLlcvlle9BrDA3Rea2XqEXPmXgLvd/c9owkZ/wvDtEVGHjFQiKwM+MxsA7ATc5e4fxG0/A9jL3XuZ2QGEO4djY3d+ZnYj4eIwIEryVrmVNGFmO7n722bWHPiYUJPpC8IF4hdgPmEI/4vktVKqIrog1ANuBy6Kcm4vJJTNaULoBTjK3V81s1GE+oon+j9lk1oSJt/UJ8zQPFs3bunNzLYh9OoudfezzGxLwnJoc939xCgndyhhuLchIYg4Qp/31GVmPQmTJOcCfwIXurub2TGEIfhbCZ0xJVG+Xj9gjLt/k6Qmp7xsDfh2BC4mDNMOJ6yHWRrdPXwJ7OTun0Q9fr0J3cabEWrxHeiqx5XyKtz5rw98RPjCuMPM+hN6/vKA54DvCBeHse4+JklNlmoysynAx+5+gJltTFj3uAw4wN3fil7ThFAk/UlgWNRrsLW7Z13R1UxlZtsRbuD+Arq7+6KoZ6g3YWjvHncfG6XnrAms4+7PJK/FsjJmdjoh9eJc4EfCutU7elQOy8xuIwTt/+dx9W5lxbIuLynqkXuH8Ac0ErgRuNbMtoh6AJ4llGbA3YcAgwk5IDe7+7buPrViboGknujCvq2ZneDuPxJm4J1jZu3dfSzhbvDgKMfjFcJnQYF8GoibZLMdYVWUAYSZtVcS1sPdKRrmwd3nEyZwHAlcY2Y/A/3MLN9UViftxE+wivMb4cZtc0I9xdgEqzcJNRdPNLMu7j7N3T9RsJd8VcyJPtndHyAsfbc2sKWZ3RU9N4ww6aqHmTWqnVZmnqzs4YtnZgcC+xJqb+1HuKP4wd0vWs7rVW4lRVU8N9G5vRrYmTBkew/Qxt27R8+vD3QjfHm8RhgGzO4PRIqLy7Gs5+5Lo9y9EUBnd//BzE4klGd4LD4J38KqKLsB011FddOSLbtSymGEUllvRhNvjDD8N8PdB8btswlwSfQ6JfCnkCh3usD9n5Vt4p5bndBjuxuhOPoEYDwwCdjb3SeZ2baAe1QkXVYu4wM+W87yOBW3R7k+qxHqMa0ObOjuc+usoZIQ0Rf8lsALQHNCwPeIu08ws80JXx7jouT+HsAFhKXy7ktWm6VqrJIlsqLtzxEuHDuaWX3gOsKM+xsIKRqnRz9rPdw0F+VqPQoUAY2APwgTsL6IgvrzCKkZI+P2aemhZIukEAtlsQoIN9r/mhgX9eaOB1539/8zsz0IxdEbAK2UO199GT+kG/UGbGFmwytuh2VmBB0LjCUsxbKQEPxJiqtkaOA6wmSbS6Ncy8X8U2rne8Kw3zlmtr2HJXh6KdhLfVGPXnE0FHuSmfWN8vYgzMLd1MwuiWZj30MotXM7MAto7SqXlPbMbANC4H6/u3dx900JN+dnmtnahNSMJ4GTzax86UMFeynrVMIM6n7LGZZtRphY1cRC8fTzCJOtOivYWzUZGfBVkmO3NbBu9NwyeTtRQBgL+p4nDPFt5NEKGpLaosk2TaI7fwglVx4EulpYPul5wgWgqbsvJuR4PUhY9zhW20lSlJldaWaF0fBtd0IdvYOAa4CrzKx7FOQdRVgya1t3/xK4kBD894pycSUNxHIvl5OrN4swwe7q6DV3EPK4dgYOBoqBp4FPCasjSQpz9+nAZcCJwA6V5NTOJfTQdyV87n9192c0aXLVZeyQbnTHt8Dd50QzfvZ09z5V2C+WI1Tp8JEkn/2z1nEuoTzHjUAr4A5CqZWrCRf7SwlraJ4EXOPut0T769ymAQt11SYSyi+8BtxPWPrwjui5kYSZ1v3cfZaZ3U3I3zOPltWS9GFhtZOzgctis6ijbXOA39x9ZpTbVUyYXFfs7j2iclkHAae7+xNm1lA3cukj+tyuB5wQK50U91w3QtDXUiV0ai5jevji7w7MrJBwkXjbzMYCWwA/xD2/3N/b/1laSwFBirG4Zeuic9gs6rW7ilCS40HCEM/qQB9CIc65hLv97tGwgM5tClrOjNl5hBSLFtH/r4mCvdUIxVc7EoZ8LgZw9xOifTau5FiSgiqc9yWE83e0mTUys5GEwO4WQqUEogT9LoSi9z2i/f4i1NJcGr1GwV56OYVQJP1EM2sIYGb7mNlfwHbR7GoFewmQ9j18Vknh4+hLJA/YgVCL6RighLDg8jPA11GgIGnIQkHOa4HfCQtjXxrN0LyGMHz/X6AvsI+7/2Fm/YCf3P3tpDVaVsqiFW+in/OjnL0zCHf+m0XDfK0JF//PgOMIn+m+wFB3v1uz6NNHhVm3zdz9LzPrSyiZNIMQAJ4NbECYbPWNu/ezUFR5MiFQ6EFI4j+lYu+QpA8LK+DcC5xFqJZxBHCWZlYnVloHfBW+MPoQLvZfAG+7+x9xr7uM0OX/ArAXodL6kR7WXZQ0En0x3E0otfAZoeL68UA7d//bzM4l5PTsA+zv7k8nrbFSZWa2MyGXZ4K7Pxy3fVtCcfTz3P19M9sFOA04JEq9uJ0wY/Ntd38iGW2X6rFli6JvA1xOKK4bWxf1XOAEwuz6IdHrtiAUTz/S3R8zs9MI+dYLgKPVa5/+zOwqQtWE9wnf3X+sZBepprQe0o0L9sYS7g7WJnx5DLGwZmrMLOBVdz+bkAB6voK91LSi4fZIJ+Atd3+EMIzTnjAMFJtVfTMhQPia0Esg6WEyYdj2DjM7zczWjbZPI8zkax49bk+YdX22mb1NSNq/WsFe+ogL9oYSJlFNAO4k1MpbAjxMKJq8WVwaxhfAEGCkmTV39/8j5G4eoWAvM3iofdvb3bdXsFc70q6HL7oLeMvdJ0azcfsBBwKHu/sCCwU5hxHW2Lsw2ucOoEFUeiX+WFoHNwXZcgpyRkP1lxGGcX8iDOe95O5HWVgjt7G7/x69VhMz0pCZHUnosS0mDOl8FvXire3u+0avuYCQpzfP3c9IWmNllZnZDoQbszPc/eO47bFJc70JvfefuPtVcc9/ALzi7ufXeaNF0lxa9fCZ2eGELt8bzKxVlKvzFXBuFOwNAG4jTOHeKno9hCGf76NjlCcJK9hLWbcCt1pY+xJYZhhoCiF/7w5gkLsfFb3kfMKwPaCJGenK3R8kLGf4I/C0mQ0ifManm1mslM41wPEK9tLanoSJFx8vp1d/EvAWsI2Z7RO3fXcFeyKrJi0CvrgvhJcIddXaEgI7CIunu5kdA1wE7E1I4t4COC0q1nltrHaTiq+mheUW5HT3UYRSHW8Cc8xsQzN7E+hOqMElac7dv4hm3N4L7EEosbM3oYh27DUK6NNU9H3ejpCDC3HXoQpVEh4grG892MzWirYvqNvWimSOlA74zKy5mTWI64lrSKjJczDQ08xOjivR0RG4NcrNW4+QAPwgMNPd/4yOl9K/rwSVFeSMhnnqRS/pBzhhyaw7CEH/1u7+c3JaLIkU1ws/HDiDMOt6daDtcsq3SBqJvs+dMBOTaDZ2HpSXXmpgZkMIw/qPASNjqRoisupSNofPzLYH3iHMrB3t7o9G258BXiQM0T4FbOnu35nZQ0BnwlDA0YRh3pHRPpWupyupbSUFObd39/+Z2RruPis5LZTaFBfoNwFw9/nJbpMkRjQp53nCZLpTKzzXnrBSyiVeyRqrIrJqUrnHayahaG4z4LiomjqEVRR2d/cXCEU5H4q2Hw3cBzQG9lawlxFWVJCzK4CCvcwVN7w3X8FexvmFkIt7gpmdbWYbmFmemfUg3NDPUbAnklgp28MHYGbHE/K5hhKG7z4kLKVVRpiiXwr8Rlhu6ZwK9Z1yAS2YnuaWU5DzTHe/M5ntEpGaiYbn+xMmXDUnfL9vBVwU5eqKSAKldMAHYGbPEu4G7ycssXQXYdLG3u7+gpntDdwDbBYtu6NevQyjgpwimSuafb0m0Aj4zN3nJLlJIhkpHQK+VoTp+fe4+3/MbDvCXeBjhAkZpWZWz92XJrWhUqvMrJe7T0x2O0RERNJRygd8AGZ2CHANcJy7v7ac12gNTREREZFKpEXAB+WrZWxKCPq+T3Z7RERERNJFKs/Sreg0wozNtZLdEBEREZF0kjY9fABmtpoqrYuIiIhUT7oFfPHr4KZPw0VERESSKK0CPhERERGpvnTK4RMRERGRVaCAT0RERCTDKeATERERyXAK+EREREQynAI+ERERkQyngE9EREQkwyngExEREclw/w9yGCgqBSaZHQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "temp3=list(temp1_df.index)\n", "width=0.20;\n", "x = np.arange(len(temp3))\n", "plt.bar_label(plt.bar(x-width/2,temp1_df,width,label='Pole Position'),padding=3)\n", "plt.bar_label(plt.bar(x+width/2,temp2_df.pole_won,width,label='Following Race Win'),padding=3);\n", "plt.legend()\n", "plt.xticks(x,temp3,rotation=35)\n", "plt.tight_layout()\n", "#plt.xlabel('Drivers');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analyzing the whole season in detail for Mad Max & Billion Dollar Man" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "##### Objective:\n", "> Total points scored by both drivers (Max & Lewis)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "hidden": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total points for Max Verstappen after final GP: 395.5\n", "Total points for Lewis Hamilton after final GP: 387.5\n" ] } ], "source": [ "print(\"Total points for Max Verstappen after final GP: {}\".format(maxvslewis_pts_df.Max_Pts.sum()))\n", "print(\"Total points for Lewis Hamilton after final GP: {}\".format(maxvslewis_pts_df.Lewis_Pts.sum()))" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "**Conclusion:**\n", "> Max Verstappen: `395.5` Points\n", "\n", "> Lewis Hamilton: `387.5` Points" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "##### Objective: \n", "> Comparing total number of pole positions, podiums, wins for Max & Lewis" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "hidden": true }, "outputs": [], "source": [ "Max_temp=[maxvslewis_pts_df.Max_Qual[maxvslewis_pts_df.Max_Qual==1].count(),maxvslewis_pts_df.Max_Pos[maxvslewis_pts_df.Max_Pos<=3].count(),maxvslewis_pts_df.Max_Pos[maxvslewis_pts_df.Max_Pos==1].count()]\n", "Lewis_temp=[maxvslewis_pts_df.Lewis_Qual[maxvslewis_pts_df.Lewis_Qual==1].count(),maxvslewis_pts_df.Lewis_Pos[maxvslewis_pts_df.Lewis_Pos<=3].count(),maxvslewis_pts_df.Lewis_Pos[maxvslewis_pts_df.Lewis_Pos==1].count()]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "temp1=[\"Pole\",\"Podium\",\"Win\"]\n", "width=0.20;\n", "temp2=np.arange(0,21)\n", "x = np.arange(len(temp1))\n", "plt.bar_label(plt.bar(x-width/2,Max_temp,width,label='Max Verstappen'),padding=3)\n", "plt.bar_label(plt.bar(x+width/2,Lewis_temp,width,label='Lewis Hamilton'),padding=3);\n", "plt.legend()\n", "plt.xticks(x,temp1)\n", "plt.yticks(temp2);\n", "#plt.xlabel('Drivers');" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "hidden": true, "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.pie([10,8,4],labels=[\"Max Verstappen\",\"Lewis Hamilton\",\"Others\"],startangle=270,autopct='%1.1f%%',shadow=True);" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "**Conclusion:**\n", "\n", "`Max` holds a total of `9 poles` in the whole season while `Lewis` holds `6 poles`. In view of podium, `Max` bags a total of `18 podiums`. `Hamilton` is just behind him with `17 podiums`. We can see that out of 22 races, 18 races were won by both Max and Lewis alone in which 10 won by `Max` and 8 won by `Lewis`." ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "##### Objective:\n", ">Comparison of respective positions after each GP" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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yuAsd9hj+33yFI/kIKalZasdxG/2ff1CuXQtS336X7D591Y7jNlp53AF06NCaatWq8sYb82nevAE1atRkxYo1Xr10zLXw9mukO3iA4DmvE/jZJ+B0kh3bj4zhT+Kw3HDZ+3h7n4ricn1SzqQS+P67BM2fS8qKNTirVc//LBYWDiW0FmpGRgaffvohb7wxC5stkZtuqsuIEU/Ro0dv9Hp9ofcpTdfIl12uT2ev+Zw5M7HbbdStW5/hw0de8Zq7XU4Ofvv2kNeoCbhchD7yADm3dM4vtvn7F3oXLV6j83l899XrUVqLcme5XC6+/XYtM2fG8/PP2yhfvgKPPfY4Awc+RFhYuGdC5eURsHwJwbNn4KxYmdNLvsi/3eGAqzwxr+nJkZEBQUGgKISMeQq/HT+T8cRT5HTr4bE/OCdPnmDevLksWPA2KSkptG3bgSeeeJK2bdtf9Q2YFp/wQUF6TCYjXbvezpw5b6sdJ5/TiZJ2BldYOPq9ewi/ux+Zjw0l896BEBJyxbsGv/QCwbNmcDzxGPhpY/Ctlh53gQvewbBjO/pPP9FMn87S0nUCKN+xNTnRVUj94FO1o1y39PR0OnduS1ZWFt999yMREWWBa7tGe/b8yqxZM1i5cjkBAQHcffd9DBkynOho7xw9o4nHnctFuUY3kdekGbqlS3y/P+dRUk9TvmY0aRMnk/n4cLXjuI0mHncFateO4Y47BvDii6/w7rvzGDPmKRYtWsott/xP7WjF4u3XKOzeO/DfuIGsu+4l4/EncMZUuep9vL1PRXHVPuXlnXs/G3ZPf/SH/iVj+JNk947z2Pvc1NTTvPfefN5++w2OHz9O8+YtGTHiKTp1urVUflYqjX3Kyclh2bLFzJoVz19/7ad69RoMGzaSfv0G4H+ZwlixpacT9NH7BL01B+X0aU7+8juu8IhruqsWr9H5ZE05L6IoCrfeehurV3/DF198Rb169Zk8+XkaNarDlCmTOHbsmPtOlpVF4HvzKdeyMWGPP5J/04C74Gwx1l2V8uDgcyPjchs3RTmTSvhD91K2TTMCFn0Mbpyqm5ycxIQJY2ncuC7Tp0+jVas2rF27gaVLV3r9ZhqeFBAQQI8evfjyy1VkZmZe/Q6elJtLwKKPKdu2OSHPPAWAo249Tu7cS+bgoVctyAE4o6ugOBzokpM8nVYUgWHrFvx271Q7hrgGLrPpsrsae7vnnx/PwYMHmD177rmC3LWqV68B8+a9z5YtO4iL688HH7xHixYNGTr0URISrB5KXLrpD/6FPslOTtv2akdxO1dYOK7QUHTJvvlc0rr09HRSUlIwm80A3HPP/cTEVGXKlBe8bi1nX+e39SfC7umP7uABANJeeIkTO/aSNi3+mgpypdZ5hbfs3nEAhA15mHItGxP4/gLIct/I4qNHj17w2bJ+/YasXLmW1au/oXPnLqX2s1Jp5O/vz4ABd7N583YWLPiQ0NAwRo4cSrNm9Zk7dw7p6eluO5eScorg6dOIbFKHkAljcVStRuq7H+aPChVXJEU5FSmKQqtWN/PZZ8v59ttNdOhwC7NmxdOkSR3Gjh1FYuKhYp8j6P35hD7zJM4K5Tn9wSJObfyJ7L53eHRqaXb/Ozm1ZSep77wHAYGEDR9MmRcmFLvdgwf/4sknh9G0aT3mz59Lt249+P77bSxc+AmNG19ScC6VYmP7kZ6exrffrlUnQEYGgfPnUq5FQ8KGDwb/ALJv6/bf8ev4JtBRsE6e3g3PA+F+etshnLJej28wR6P3waLcN998xcKFCxgyZDht2rQrcjs1atRixow5/Pzzbzz00COsXv0Fbds2Z+DAu9kthWX3ys4hu2s3ctp3VDuJZ5jM6JPkiyJvdPhw/nUxmfKLcv7+/jz99Fj27PmVVatWqJhMI1wuDBu+JbxnV8r27IJh58/4HfwLAGe16rgqVVI5oG/J7nsHpzb+xOmFn+KsUJ7Qp0cS9PYbxW43MfEQY8eOomnTusyePYOOHTuxfv1mFi1aRsuWrd2QXPgqvV5Pjx69+OabjXz22XKqV6/Bc889S5MmdXjttZc5depk0RsvGOyjTzxEmWlTyG3anFOrv+X0ijXkduxUYkta+TKZvuoBxRl2+ddf+5k9ewaLFy8C8hdyHDZsJLWvcTtt5cQJgua9RV6deuT06IVyJhW/X38h9+a2RX5CFGsYacEuK3nVa+KsXgO/337Bf8M6Mh8YdM3DWPfu3cOsWdNZuXIFBoOBu+66l8cff4KYYnwbp8WhsRERwZw4cYaGDW+kceOmLFz4SYlnKDNlEsEzp5PbvCUZI54ip9OtRX7cKamniUg8QEqVWrhCQt2cVB1aetxF1qlJdpfb8Ht3gWb6dJaWrhNAxOljpJ48g7NadbWjXLOjR4/SoUNLKlWKYu3aDQQEBFxwvDjX6MSJE8yb9xYLFrzD6dMptGvXkREjnuLmm9uqOnpAc487jfUHIOLQX6TqA3EWFH60QCvX6fvvN9K3b0++/XYdDRo0B8DhcNCxY2tyc3PZvHk7fj66FIbq18jhIKL7rRh2/ozDaCLz8eFk3nUflClT5CZV75MHFLlPLheGHzeTd1MdXOUi8V/3NX47fibz4cG4Igtfd/liCQlWZs2KZ9myxSiKQv/+dzJ06BPUqFHr+vMUkGvkG4rTp59/3sasWfF8/fVXlCkTwv33P8jgwUOpVKnyNd1f98/fBM+ZCU4HafGzz93mrFqtSHlAm9fofDJ91UfUrFmLmTPfZPv2X3nggUGsXLmctm2b88AD9/DLFbbT1iXZKTNhDJFN6lAm/hUMu3YA4AoNI7dNO/Uq1IpCTucuOKvXAMB/wzrKTH2Bco3rUmby8yhHj172rlu3/sRdd/XllltuZt26b3n88SfYsWMv06bFF6sgp2V6vZ7eveNYv/4bTp9O8fj5lGPHKDNlEoYfNwOQ+dAjpKxcS8rqb8jp3KVYjztXWDium9topiCnKZmZ6I4dxemla3OJi1Sp4lMFOZfLxZNPDuXMmTO89db8SwpyxRUZGcmYMePZtWsvzz33In/8sY/Y2O7cfnsn1q5dI1PdisrhQHfksNopPKt+fU0V5LQkqWA0sOm866PX6xk79jkOHPiLRYs+Viuab8rJwf/rr/L/W68n539dSJ35Jie3/0rmw4OLVZATF1EUctu0O7fxmWHbVsrEv5L/mW7CmCsuP7F7904GDrybtm2bs2rVCh588GG2b/+VGTPmFKsgJ0qHZs1a8OGHn7Fx40906XIbc+fOoUmTuowaNYJ//vn7svfT/76P0MceolzLRgQu+ggMhnOj5YpTkCvNpCjnpczmaKZMeYWdO/cxYsRTbN68iVtv7UC/fr348cfNF2xrHBz/CuWa1Sdo/ttkd+/Fyc3bSZ/4oorpLy9jxChOrd9Mzi2dCZo9g8imdSkzZdK54y6Xiw0bvqVnz6707NmF3bt3MnbsBHbv3seECZOoJMPjryourh85OTl8+eUqj51Dl3iIkLGjiGxSh6BZ8Rh+3gaAs3IUuW4cHq+sWoX/N1+5rT3hHrrTKeTWb0hezdpqRxHX4sgRgt6cfW79H2/3wQfv8c03a5kwYRI33HCjx84TGhrG0KFPsHPnXl55ZQbHjh3jvvsG0KFDK5Ys+Yy8vDyPnVuL/Pb8SmS92vivXaN2FM/Zu5egN2e7dZ1c4R7JyWenr5ouuL1r19tp0qQZr732MlluXLNLs9LTCXrnTco1b0D4vXfg99svAGQ8+TTZd95z2R0Thfukj5vIyc3bye7ei6D5b1OuWX2CX5l67rjL5eKHH76nb99edOnSkR9/3MzIkaPYuXMfkydPu6AwLcS1uOmmOsydu4CfftrFgAH3sGjRR7Rs2YjHHnuI33/fd8HPBn60kHIdWhGwdg2Zjz7OyR17SJsWL1NUi0mKcl6ufPnyjB37HLt372P8+En8/vs++vTpxsgOrVi/YhlOpxNHtepk3TuQk9t+4cyct6+4Bbk3yKvXgDPz3ufUlh1kxfXHZTDgcDj4YvkSHmrbggED4khMPMSUKdPYuXMfI0eOJvwap7oKaNCgEdWr12Dp0sUeab/MpAmUa9GQwIXvkhXbj1M/7iBjxCiPnEv32isEzS3+GhvCvZyVo0hZ9z05PXqpHUVci1OnCHl+HAYfWD/twIH9TJz4LO3bd2TQoMdK5JyBgYEMHPgQW7fu5s035wEwZMjDtGzZmPffXyAf5K+R4ftNQP5mT1qlbN9GyPPjtD8i0AfZ7XYiIyMJCgq64HZFURg3biJJSXbef3++Sul8QFoawQWjs0LGj8FRpSopi5aRV6+B2slKJYflBs7MeZuT234h696BOKJjcDqdfLtyBSM6tCI2tjt//LGP5557kV279jJmzATKly+vdmzh46pVq85rr73Ojh17eOyxoaxdu4YOHVoRf1snEj75EICcWzqTPnosJ3bvI33SFJyVo1ROrQ1SlPMRoaFhDB8+kj1vzuNA7Rv4+I/f2fHIQDp2bM0nTicpk6f53I5Hjhq1ODktnnkmMzff3JQvHn2QLxL+ZH/9Bux6+10efngwwcHBasf0OYqiEBvbjx9+2MQRN31w8Pt197mRAQ6TicwHH+bkz7+R9vobOGp6cHh8TBXZ6EGI4ioYOaLz8gXqc3NzGTLkYQICApg9ey46Xcm+RfHz86Nv3zvYuPEnPvhgERUqlOfpp0fStGk95syZSVramRLN42v8v99I3o034apYUe0onnP2uWT3vY1TtC452U5UlKnQY23atKN9+47MnDldnscXczgAUHAR9PYb5DZpxqlV33D6i6/IvaWzjH5RmTOmCimTp/GxwUCHDq34YdB9fPLH7/xluYE9b81n6NAnCA0NUzum0JjKlaN4/rkX2D9tOocqR/HSzp9JHPE4ffp0Y0OClfRRY3CVLad2TE2RopyP8F/3NRHdb6VSv15UPXmc1DHjsbz6Ok6nk8GDB9GqVWMWLnzXZ77RT09P55133qR58waMHDmUkJBQBrz+BhlPPk31f/+lYvdbCe/bC8MP35+boy6uXWxsP1wuFytWLC16Iy4Xhs2bCO/bi7L/a09AQVtZgx4jffK0EllXx1WlCjq77dybRuEdgmbNIKJHF3lu+orQUJxh4eiTbGonuaLp06exe/cuXnttJpVV/OZVp9PRtevtrFmznmXLVnPDDTfxwgsTaNSoDi+/PJkTJ06ols1rZWVh2P4TOe06qJ3Eo1xndwVPlqKct7Hb7ZdMXT3fuHETOXHiBG+9NacEU3kv3T9/EzJ6JBG3dgCnE1dIKCd/2kXqx4vJa9FS7XgCyMrK4v33F9CyZWOGDHkYgCavzSR1zHiqnThOpb49iejRBf/138j7MeFW/qu+oGy7FhiHPYYxMJDjU1/l4HMvcPDgAfr3782tt3Zg9eqVsgavG0lRzpud9wIb9OZsdEl2zrz0Kid27CX7yafpff+DbNq0lYULPyUyMpLRo0fQtGk93nhjltd+E5iScorp06fRpEkdxo8fQ9Wq1Vi0aBnffruJW++6l8wx4zm5ex9pz72I/s/fCR0+GGRdn+tWs2Yt6tdvyLJlRZjC6nLhv3YNEbd3IiKuB35/5F+PnK63uz/o1VSpgpKXh+5wcsmfW1yW3+978q+JfIPuM5wmk1ePlNu+fRuvv/4aAwbcTY8evdWOA+SPOm7Tph1LlnzB119/x803tyU+/hWaNKnDhAljzi0sL8Dw8zaUrCxy27ZXO4pnmfO/jJKRct4nf6Sc8bLHGzZsTLduPXnrrTmlurCu/+N3QgcPolyrxgR++iF5DRuhZKQDnNtoQKgrLe0Mc+bMpGnTejz99EgqVCjPwoWfsnHjT/S87wGyn3yaEzv2cmbqK+hsiQS/Nu2/O0txThRVRsa5z9z6vw+Cn4HUt9/l5JaduAY9ysNDR7B9+6/Ex88mNfU0Dz54D23bNmfRoo/JlXVWi02Kct4oO5vAjxZStn1LdAUL1555c17+ugIPPQrnTenU6XTcdls3vvpqA0uWrMRiuZFJk8bTuHEdpk2bwsmT3vHG48iRI7zwwnM0apSfq2nT5qxe/S0rVqzhlls6o5z34d4VEkrm0Cc4uWMPpz9enL+jS3Y24X17EbB4kRTprlFsbD92797FwYN/Xd8dXS7KTJ2E7thxzrwygxM795I59AlcKgyPd1XJn5KtS0ws8XOLy9MfOoTDx6bLl3bOKOMVd3BTU1raGYYMebhgg6NpV7+DCho1asL773/M5s3b6d69F/Pnv02zZvUZOXIoBw7sVzue6vJurMOZ198gt3UbtaN4VlgYzjIh6GSknFfJyMjg1KlTGI2XHykHMHbsBDIy0pk5c3oJJfMuhh83U659SwK++pLMR4bkL9A+fZbscu8lTpw4wcsvT6ZRozq88MIEbrjhJpYtW82aNeu57bZuFy7pEBxM1qDHOLntF1LnLwRFQTl2jLK3tCHw4w8gJ0e9jgifoqSeJmjmdCKb1j03Kypz8FBOffcj2X36gp/fuZ8NCAjgnnvuZ8uWnbzzznsYDP4MHz6YFi0asmDB22RkZKjVDZ8nRTlvkpZG0Nw5lGvegNAnh+EKCEQ5fhzIX1gdg+Gyd1UUhXbtOrB06UrWrt1Aq1ZtmD59Go0b12HChLHndqUqaf/88zejR4+kadO6vPnmLLp06cp3323ho48+p3nzFle+c2AgjhtvAkBnt6E7doSwxx+hXMvGBL43H3xkqq5aeveORVEUli1bcuUfzMoi8P0FRHRuh3ImFXQ6Tn/0OSe37iJr4EMQGFgygQvhurkNJ3buJa9pM9UyiEvpEg/hiI5RO4a4DqlvzCNl1ddqxyjUuHHPYLMdYs6cd7x+bRyL5QbmzHmbbdt+4d57B7J06ee0bt2Uhx8eyJ49v6kdTzWu8uXJuute7X+4VxRO/bCd9PGTrv6zosQkFxRJr1aUq13bQv/+d/Lee/Ow2717Or9buFwYNm4490E7t0Ur0iZO5sSuvbJAuxdJSrIzYcIYmjSpQ3z8K9x8c1u+/vo7liz5gjZt2l0wcOES/v7nlpPRHTuKS68ndORQyjWrT9DcOZCeXkK9EL5GOXaMMlMmUa5RHUKmTCKvfkMc1WvkHzQYrjgbRq/X07t3HN999yOffLIYo9HE2LGjadq0LjNnTic19XQJ9UI7pCjnLdLTiWzRkJDnnsVRvQYpny0n5ZuNOOrVv+6mGjduysKFn/D999vo1q0n8+fPpWnTejz55LDrHzVVRH/88fu5te4+/fRD+ve/iy1bdjJ37rvUqVP3uttzVq/Bqe+2cPqDRTgrVCD0mSeJbFIXnU1GUF2O0Wiides2LFu2GFchw9mVtDMEzZlJuab1CH16JPjp0R09AuQvLHv+NyOqKVMGZ3SMd2QR+bKz0R85jLNgbSXhG1yRkaoW2C9n9eqVfPrpRzzxxJO0bNlK7TjXLCamCi+/PJ0dO/YybNhINmxYR6dObbjzzji2bt2idrwSpaSeJvCD91COHVM7Solwmsxe+VwqzZIKpuZfrSgHMHr0WFwuF/Hxr3g6lnqcTvy/XEVElw5E9O9N0KwZ+dMa/fzIfHy4TFP1EgcO7GfkyKE0a1af+fPfpnv3XmzevJ333/+YRo2aXHd7jpvqkPLtJlI+W46jWnVCnnuWyKZ1UU6nuD+88Hnhd/claFY8OR07cWr9Zk4vWkbede6erigKnTt3YfXqb1i5ci316zdkypRJNGpUh8mTn+fo0aMeSq89UpRTke5wMoEfLcz/nzJlyBg+klNffsvp5V+S27FTsddruuGGG3njjXfYtu0X7rnnfhYvXkTr1k155BHPfaO/Y8d27rtvAO3bt+Srr77kkUeGsGPHHqZPn0n1s9X3otLpyOl6Oylr1pGy/Euye/Y+9+2QYcM6lFK8RsjlxMb246+/9rNnz68X3K4cPUq5xnUIeWECjhtuImXpKlK+2oCjhgd3Ui2iwPfm509bFl5BSU8ju2s38uo3UDuKuA76P36nzPhn0LlpR2Z3OHw4maeeGkbDho0YNWqs2nGKpGLFiowf/zy7du3l2Wef49dfd9OzZ1d69OjCunVfF/qFiNYYfthM6Kgn8NtvVTtKiTBsWEfwSy+oHUOc5+yot2spykVHx3D//Q/yyScfanLquWHTd5Rt14LwB+5Gl5LCmemzSPlqvawB60X27PmNhx8eSOvWTVm69HPuvXcg27b9wpw5b2Ox3FC8xhWF3I6dOL1iDae+/JaMwcNxhUcAEPDZJ171HkCULH2ClZBRI1AK1p1Pe3Eap7bs4Mz8heTVK/57+pYtW7No0TLWr99Mx46dmD17Bk2b1mXs2FEkJh4qdvtaJ0U5Fej+PkjIU09Qrmk9QkaPyN9dEsh89HHyml1lSmcRxMRUYdq0eHbs2Mvjjz/BunXf0qlTG+66qy9bt/5U7PZdLhebNn1HbGx3br+9M1u3bmH06LHs2rWXSZOmuH8XPUUh9+a2pL30Wv6bjPR0wgbdT2STOvkfOkvDlIRr1L17TwwGA0uXLkZntxGwPH8qq6tiRTIHPcapr7/j9JIv8hfn9tI3bIGff0LgZ5+qHUMUcJWLJPWDT8m59Ta1o4jroDtymOB33kJ/8IDaUQBwOp0MHz6YrKws3nxzPoYrLM/gC8LDIxgxYhQ7duxl6tRXsNkSueuuftxySxtWrFiKQ8M7SPtv3ogrOJjcJqVjmQHDz9sIfn06yMLWXuPsEi1X2ujhfCNGjCYgIJBp06Z4MlbJycxESTmV/996Pej9SJ27gJNbdpJ170AICFA1nsi3desW7rwzjk6d2rBhwzqGDRvJjh17efnl6cR4YJ3evGYtyBw+Esj/Mj70yWGUa1KXkFEj0P3zt9vPJ7yT3y+7CHvgHsq2bU7g4k/x270LgLwWLT0yGKNevQbMn7+QLVt2EBfXnw8+eI8WLRoydOijJCSUji/vikKKciVIl5xE6GMP5u949NnHZN15Lyd/2nVutJenVapUiQkTJrF79z7Gjp3A7t076dmzCz17dmX9+m+u+xt9p9PJl1+uokuXDvTr14v9+xOYNGkqu3b9zujRYylXUsPjy5QhZe0Gsnv0JujdeZRr3oCQEY+jk6o8ZcuW476WrWj7/oL8tQpHDkUpmOef8fSz5BVheHxJc0THoEv8V+0Y4qxSMPJHi5wFI0i8ZbOHd999h40bN/D881OoWdP7RugWVXBwMIMGPca2bb8wa9ZbZGdn8cgjD9C6dRM++mgh2dnZakd0O8PmTeS2aFVqPvg7jSYUl0tGnHiRpKQkypUrR1BQ0DX9fIUKFXjssSGsWLHskpkEvkRJPU3QrHgim9Ql+NWXAMi9uS2nNm4hO7afLP3hBVwuF+vWfU2PHvmft379dTfPPvscu3btZfz456lYsWLJ5KhYkZM//EzWgHsIXPQR5Vo2InTwIBnIoGUZGYT360XZWztg2LyJjJGjOLFzX4ntkl6jRi1mzJjD9u2/8uCDD7Nq1Qratm3OwIF3s3v3zhLJ4EuKXZSzWCwBFotlgcVi+ddisZyxWCy7LRaLDKE4j3ImFQBXYCCGH38gc/AwTu7cS9qrM3BWrVbiecLDIxg5cjQ7d+5jypRpJCYe4s47+9KpU1u++GLZVb/Rz83N5bPPPqFduxY88MDdpKSkMH36LHbs2MPgwUMJCQkpoZ78x1HbwpnZc/N3qL3vAQKXL0FJTT0buMTzeAPdP38TOuh+5v2wmd6ZGfz9v66c3LwdV1i42tGuizO6CnpbIjidakcRQPC0yZRrUleuh49xGvNHkOiS1Nn053x//vkHL7zwHJ0738rAgQ+pHccj/P39GTDgbjZv3s6CBR8SFhbOk08Oo3nzBsydO4e0tDS1I7qFLjkJvwQrOe06qh2lxDhMZwvc6j+XRL6kJBtG4/V9wT148DAiIiKYOtX3piJfsED75OfJq1efnB69Cw4qXjvzoTRxOBysWLGUW25pw1139cNmS2Tq1FfYsWMvI0aMIrxgSmlJclarTtprr3Nyxx4yHxuK/6YNuAz++Qc18jep1HM60e/dk//fwcE4IyNJm/ACJ3fvI2PMBFzly5d4JJPJzOTJ09i5cx8jR47ixx8306VLR/r27cUPP3xfKpb5uBbuGCnnByQC7YFwYALwucViqeqGtn1KwNLPKde4Dn4BBso1rkPwC88R3qcb4X26g8uFq2w5Tu7aR/rEF3FWqqx2XIKDg3n44cFs2/YLM2e+SVZWJg8/PJCbb27Kxx9/wGeffULjxnUICDDQuHEdPv30IxYseIeWLRsxbNhj+PkZePvtd9myZSf33juQAC/4ltwZHUPaS69xYk8CjoINJUKHDyb8jj4YfvqRgCWfXXCNApZ+rnLi4rv4cRfw8Yf5BxQF/80bSR08jJuCgnk+snz+pgk+xhEdg5KbK6MSvIT+339BUVi6fMkFrw9LNfBcWrr0c8316SxXSCjOsHB0yeqOlMvJyWHIkIcJCQnh9dffvPKuchqg1+vp0aMX33yzkc8+W0716jV47rlnadKkDq+99jKnTp306cfd2Wkwue1K5pt3tZx/jfo+MQQAfZKMMPEWSUlJ3A3X9f4uPDyCYcOeZP36b712c5ZL3t8V9KnMlOf/W6B93fec/mw5uS1bq5z22vjy693lnN+nRo1uYuTIobRu3YRHHnmA7OwsZs16i23bfmHQoMcIDg5WOy7OylGkPz+ZE7v/wFUwUi9iQCzhfbrl79ZbGj4raa1PjW6izNMjKduhFWW7dDg3K+LM3HfJHDYClxfsbF++fHnGjJnArl17ee65F/njj30FS191Yu3aNSxe/JnmXhuuh+KJ6qTFYvkNmGS1WpcWdjw31+FKSclw+3nVFLD0c0KfHIaSmXnB7c6wcDJGPUPmoMe8fhi5w+FgzZpVzJwZz2+//QIowKWPj2bNWjBixFN07tzFJz5QBc2ZSfCbM9EdP45Lp0M5b4SPKyiIM/GzyY7rr2LCoivscefS6Tjzxjv5fcrOhoAAhgx5mHXrvmbv3r/w9/dXMfH1iYgIJn3ZF4Tf1Y+UNeuue1cgbxMREYyvv/ZFdL+VoymnqJV4iMzzHndBQUHEx88mzkefS0uXfs6TTw7TVJ/OOvu4K9u+JXn1GnBmztuqZXnxxYnMnj2DDz5YRNeutxe5HV9+Lv388zZmzYrn66+/wt8/AKfTQV5e3rnjvva4U44dy9/dV3fh97y+fI3Od/FrQxhwGth8x13cMHuuqtncQQvX6dmqlZmTnU3AeTM9ruX9XUZGBi1aNKRKlaqsWvW1V72nLfT9XUAAZ15/g9xmLVBycnD42NR/Lf6dLaxPANHRVXj++cncfnt39Hq9SumukcNB0Ly3CHpzNvrDybgUBeW8+oAmPytpsE8Ajigj6eOfJ7tPX6+vO2RlZfHppx/xxhszOXToXxRFuWDUnK+/NlxOhQqhO4FLPtC6vShnsVgqAf8CDa1W65+F/YwWi3LlGtfJn2J3EYfJzMndv6uQqOhcLhd16tTk+PFjlxyrUKEie/fu96o3LtckI4PIhjegS0m55JBLp8NZ4cI1Hc7MfIPcW/6H4fuNhD7+yCX3OTPvfXJbtsZ/7RpCRo+45HjqJ4vJq9eAgGWLKTNx3CXHT6/4EkeNWgR++D7Br0y95HjKNxtxRhkJmjuHoDdmXXL81I8/4woLJ7JmNLqCNeLO5zBHc3LXvnP/v27d19x1Vz8+/PAzunTxndnlERHBpBwr6J+PLwQP2vjwU67BDSw7nUL/jEv7odPpqFChZNZHcbdjx47iLGRKrtkcza7znku+6NzjLi9P1TdpW7b8QJ8+3bjnnoFMnz6zWG1p4bn0++/7uO22TmRmXtoPTT3ufFzjxnWwXfT+LgCooIFrBL5/nTIyMnBVrUzVQo5d/P7OaTSS8vVGAEIfGYjhpy1kZmSQeiaViIiy+NWtx+llqwEIu6c/fr/+ckF7eQ0bkfrhZwCE9+mG/q8Ld2/NbX0zZ95+D4CILh0umeKc878upMXPBqBs2+YoF70nze7Vh/TJ0wAoH1UWpZDlZC5+f+dLCnsugbx38BrZ2UTWr43u1KlLDhX2WencZ52ln1Pm+fGX3Of0F2twVK9J4AfvnVvz8Hwp327CWTnq8p91tuzAFRpGcPwrBL43/5LjJ3/5A/R6yrw4kYDPL9oQLiCQkzt+AyCymhFd+qXTc88+l0KHPYZh44YLjjmjY0hZsw6A0EH3Y9h24caIjhtu5PTiLwAIuzMOv7NTRgvkNWpC6gf5mcJ7dkX/98ELjue2aceZt/L7FNG53SWzgXK63E7aa68DULZVY5SLphcr6enoCnZRLaxPviQvL4+6dWty8uTJS4755PPoKi5XlHPru3OLxWIAPgYWXq4gB6DXK0REqD98150ut1CmLsnuk309ceJ4obcfP36MsmXLlHAaN4gIRjl9afEKAKcTpXv3C24qUy0GIoKhWvQlxwDKVDHlt1mraqHHQ8yV849bahR6PDSqQv7xm2oXejysUrn84/VuKvR4ePlwCA7+b928i+jstgsed716dScyMpJVq5Zxxx1xhd7HG+n1OiIq+NYaeFei1+t88vXgnJwcdIeT+f0yX+Y4nU66F/J49QULFlz6hg/AftFzyRd5w+MuJSWF4cMfo0aNmsya9TplyhQvjzf0qbhat25GVlZmocd84nGXkIB+9FM4Jk+FevUuOezr18jlcvH112sLLSJk4yPX6Br4+nU6dszODZc7eNH7O125cuf6qmtzM0pkOQKdTlYvWYyfTqF3h/b/HW/XFiX6wnXq/KpV++94xw4oN1guOG648cb/jnfujHLRh0z/Ro3OHVe63IqSfmEx1L9ZEwxnr8Vl1ne++P2dL7Ff5rOSvHfwFsGXFIrPKeSz0jV/1qljKdpnnciw/M86DeoVejyibBnQ6VCaNELJuLBA5fL3/++5VkhBDv57Lulat0QJufBa6cqXP++1ojVKhQs3L9Sbzf8db98OpeqFu+f61ajx3/FbOqIcuemC44Y6dc57reh0yWdUQ5Mm/+W/7bZLRsQpl3nc+errw6lCCsHgq8+jonHbSDmLxaIDPiF/ZH8vq9V62dX1S9VIOR+sWMPlv83y5Yq11q4RXF+fnn56JJ999gn79h1QZTOOojj7DX7wSy/grFiZrIcuHbXoS3x9RIJyJpUyUybx4MoVLCpkJK0vvz5o8TXvrLOPO8OGdQR9+D6pb78LJTyNffDgQaxYsZQvv/yWxm6Yhu7rz6WzLve4A+jS5TaGD3+SZs1alHCqaxO44B1Cx47ixPZfC920ylevkcPhYPXqL5g5M569e39Dr9dfsgHWo0Dd0FDuOOAduxkXh69ep7M2b95E47gehY6Uu9b3d0uXfs7gwYN4++136dOnr9szFoUW37PWqVOTY8eOXnK7L/+d1dp7By0+7qRP3k9rz6MrudxIOXds9IDFYlGABUAlIO5KBTmtSh83EddFW7G7goJIHzdRpUTFM27cxEu2lg8KCmKcj/YHtHeN4Pr6FBvbn8zMTNau/bKk4rmN/8YNBHy9Ru0YpZ4rNIy0l6fT/sWX8Pe/cGMXX399KOw1D6BBg0aa2RlKfziZgC9Xoksu2V0jly1bzNKln/PUU8+4pSCnJYU97gIDg+jevSfbt2+lW7f/0adPN777br3XPQ79N2/CEVNFlV3kPSEnJ4ePP/6Am29uysMPDyQrK5OZM9/k9dffuOQatdbreUDn5etElRJJSXaeBRyBgRfcfj3v7/r06cuNN9bh5Zcnk5vrHR9htPae9fTpFPLy8i5Z/kaL7x18uU9ae9yB9MkXaO15VBRuKcoBbwE3Aj2sVmvhczE0LjuuP2fiZ+MwR+NSFBzmaJ9eQDIurj/x8bMxm6NRFAWzOdrnF1vU2jWC6+tT8+YtMJujWbZssQpJi8cRXQVd4iG1Y4j0dMjNJS6uP7169QbQzOvDxa95JpOZli1b8+WXK3n++fFeVxApCofRBIC+BItydruNp59+kiZNmjFixKgSO6+vKOxv7YwZs3n33Y/YuXMfL7wwlYMHD3DHHX249dYOrFr1RaHrF5U4hwPDj5vJaev7u66mp6fz9ttv0KxZfUaOHEpISCgLFnzA5s3bufPOe7jjjruIj59N+fIVAKhQoQI3dbmN4NTT4CUFnNIsKcnOp8CpafG4AgJwwXW/v9PpdDz77HP8/fdBPv30I4/mvVZae886ZswoUlNPM3r0WE19ttDa5yWtPe5A+uQLtPY8KopiT1+1WCxVgH/IX2Ij77xDj1qt1o8Lu48Wp6+ez9enAlxMa/2B0tunF1+cyJtvzmLPnv2UL1++hJIV3dk+lXl+PEEL3ub4v0cu2eHPl/j64y745ckEz3md4/8cZtSYUaxcuYwjR45x+rS2vos5e52cTifjxj3NggXvcO+9D/DKK/Hev4taIc72R59gpVybZqS+Nb9E3rg5nU769u3Jrl07+e67H6lWrbrb2vb151JhLten7OxsFi9exOzZM/j774PUqlWbYcNGEhfXH4NKG+D47d5J2S4dSX373fxd3grh7dcoJeUUCxa8w7x5b3Hy5Elat27DE088RYcOtxS6mdXBgwdo2bIRs2fP5f6cHEKfGs6JXftwmqNVSO8+3n6drmb06JGsWrWcP//8h7Ktm6Br0IATb7173e24XC66dfsfNlsi27b9UujIabX4+jVatmwxjz32EE8//SyjRo0BfL9PhdFan7TWH5A++QKt9ediHpu+arVa/7VarYrVag20Wq0h5/1TaEFOCKGe2Nh+OBwOVq1aoXaU6+KIjkHJzkY5duk6ZqLk6BMP4axYCfz82L/fSu3aN/jeTszXQafTMXXqqzzxxFN8+OF7DB36KHl5eVe/o5dyGo0Al+wI6Clz577BDz98z9Spr7i1IFfaBAQEcM8997Nly07eeec9/P0DGD58MM2bN2D+/LlkFLITsqcpWVnkNm1OThvfGyl35MhhJk2aQKNGdZg2bQpNmzZn9epvWbFiDR07drrsa5qxYKSp3W4r8eeSuLzkZDtRUfnXBoMBV/WivdYoisL48c9z+HAy7747z40JSzcZLS2EEFfnu0NOhBDX7aab6nDDDTf63BRWZ9WqOCpHoTt5Qu0opZou8RCO6BgAEhL+pHZty1Xu4fsURWHcuImMGzeRpUs/56GH7iM7O1vtWEXiCgnFUbUalMBU3L179zB16iRuu607d955j8fPVxro9Xp6945jw4Yf+OSTxZhMZp599mmaNq3L66+/xunTKSWWJbfVzaSsWYerQoUSO2dx/fPP34wePZKmTevx1luz6dKlK999t4WPPvqc5s2vvplGYGAglSpVwm634TBF4yxfASXtzFXvJzzLbrdjMuUX5U5t2opzytQit9W6dRs6duzErFnTSU09ffU7iCtyOp0MG/YYeXl5vPnmPPz8/NSOJIQQXkmKckKUIoqiEBvbj23bfrrsjn/eKKfTrZz8zYrjxpuu/sPCY/SJh3Caozl+/DgnTpwoFUW5s5544ileeulVvvpqNffc05/09HS1IxXJye2/kjl8pEfPkZWVxZAhg4iIKEt8/GxNj6ZUg6IodO7chdWrv2HlyrU0aNCIqVNfoHHjukye/DxHj166u6Fb5eaCCqPziuqPP35n8OBBtGrVmE8//ZD+/e9iy5adzJ37LnXq1L2utqKjo7HZEnHccCMnfj9A7i2dPZRaXKsLRsq5wbhxEzl16hRvvTXHbW2WVjJaWgghro0U5YQoZfoUrP+zfPlSlZMIn5Kbiy45CUd0DPv3WwFKVVEO4KGHHmXmzDfZvHkTAwbEykiKy5gy5Xn+/PMPZs16k8jISLXjaFrLlq359NOlrF+/mVtu6czs2TNo2rQuY8Y8xaFD/3rknIatWyhfOwa/7ds80r677NixnfvuG0D79i356qsveeSRIezYsYfp02dSvXqNIrUZHR2D3W5zc1JRVBkZGZw8eRKj0Yhh8ybC+/eGQ8XbFKp+/Yb07NmHuXPf4Pjx4+4JWgqdHS19++09ZLS0EEJchRTlhChlqlSpStOmzX1uCmvosMcIfv01tWOUXrm5pI97npxb/kdCwtmi3A0qhyp5d955D++88x47d/5MXFxPTvrYlOrA9+YTPiDWY+1v3LiBt99+k4ceeoRbbvmfx84jLlSvXgPmzXufLVt2EBfXnw8/fJ8WLRoydOijWK1/uvVc/t9vBKcTx03eN3LZ5XKxceMGYmO7c/vtndm6dQujR49l1669TJo0hcqVo4rVfkxMNDabDZfLRZnxz1BmyiQ3JRdFcfhw/pp+RqMJvz9/x3/jBnDDBg1jxownMzODmTPlPUdRnD9aevr0WTJaWgghrkKKckKUQrGxfdm3bw9//vmH2lGumf7PPzBs3aJ2jNIrOJjMYSPIa96ChIQ/CQ4ug8lkVjuVKnr27MMHH3yK1foHvXvfzpEjh9WOdM10x4/hv2Ed5OS4ve1Tp04yfPhgate2MGHCC25vX1xdjRq1mDFjDj///BuDBj3K6tVf0LZtcwYOvJvdu3e65RyGzRvJa9wUV0ioW9pzB6fTyerVK7n11g7079+bv/7az6RJU9m163dGjx5LuXLuGbEZHR1DRkY6KSmn8PvzTww/bHJLu6JokpL+K8rpbDZcgYHghp3la9asxYABd/Pee/N9aqkPbyGjpYUQ4vpIUU6IUqhnz1h0Oh3Ll/vOaDlndAy6xOJNSxFFpxw9mv/7dzpJSLBSu3btUv3td+fOXfjkkyUcOnSIHj26kOgjj01nQSFVdzjZre26XC5GjRrBiRPHefPNeQQHB7u1fXF9jEYTL774Mjt37uPJJ5/mxx8306VLR/r27cXmzZtwFXGzD+V0Cn6/7CanrXfsupqbm8uiRR/Trl0LHnzwHk6fTmH69Fn8/PNvDB48lJCQELeeL7pgoxubzYbDZJLdV1V2diqxyWRCZ7fhMJnBTX+XRo0aA8Brr73slvZKCxktLYQQ10+KckKUQhUrVqRduw4sXbqkyB/OSprDHI3ellgiO0eKSwW9P59yTetBXh779ydQq1bpWk+uMG3atGPJki84deoUPXp04cCB/WpHuipHlBEAfZLdre1+/vmnrFq1gmeeGUf9+g3d2rYousjISMaMGc+uXXt57rkX+eOPfcTF9eD22zvx1Vdf4nQ6r6s9w48/oDid5Lbv6KHE1yYzM5MFC96mRYuGDB8+GD8/A2+//S5btuzk3nsHEhAQ4JHzxsREA/nFIGeUEd2Rw5CX55FziatLTs4vilaubERvT8RpinZb22ZzNA88MIhFiz5m//4Et7WrZTJaWgghikaKckKUUrGx/Th06B927vxZ7SjXxBETg5KZiSILL6tCn3gIZ5SRM9lZJCXZsVhK33pyhWnatDnLl39JTk42PXvexr59e9WOdEXnRsq5sSj377//MHbsaFq2bM3jjz/htnaF+4SGhjF06BPs3LmXV16ZwbFjx7n//jvp0KEVixcvIu8aC0t5N9Uhbfwkchs39XDiwqWmnmbmzOk0aVKXsWNHExVl5OOPP+e7736kT5+++Pn5efT8Z0fK2e2JOI0mFKczvzAnVJGUZKds2bIEBwfjNJrJa9jIre0/8cQogoKCefnlyW5tV4tktLQQQhSdFOWEKKW6detBQECAz2z44KhlIbdJU5S0M2pHKZV0tkSc5uhzIwZkpNx/6tatx8qVX2MwGOjT53Z27dqhdqTLchqN5DZohMvfPSOJHA4Hjz/+CIqiMGfO2+j1ere0KzwjMDCQgQMfYuvWXbz55jwAHn/8EVq2bMx7780nKyvrivd3Vq1G5vCR4O9fEnHPOXbsGFOmTKJRozpMmTKJ+vUb8MUXX7F69Tf8739dS2wqfYUKFQgICMifvlq9Brn1G6JkZJTIucWlkpLsGI35XzSkLviA9PHPu7X98uXL89hjj7Nq1Qp+/XW3W9vWmv9GS4+X0dJCCHGdpCgnRCkVGhrGrbfexooVy655lISactt3JOWrDTirVVc7SqmkTzyEIzrm3M6rFosU5c5Xs2YtVq5cS3h4BHFxPdmy5Qe1IxXKFRJKyrebyOne0y3tzZ49g+3bt/Lyy68RE1PFLW0Kz/Pz86Nv3zvYuPEnPvhgERUqlOeZZ56kadN6zJ79OmfOpF5yH+X4cfzXrIa0tBLLmZh4iLFjR9GkSR1mzYqnY8dOrFv3PYsWLaNVq5tLfF1LRVEwmczY7YnktmlHyrrvcdSqXaIZxH+SkpIwGo0ePceQIcMoV64cU6fKdMzLOTtaulWrm3n88eFqxxFCCJ8jRTkhSrHY2H4cP36MzZtlBzlxBXl5+Ytox+QX5fz9/YmJqap2Kq8TE1OFVau+xmw2M2BALOvXf6N2JI/69dfdvPLKVHr3jqVv3zvUjiOKQKfT0bXr7axZs55ly1Zz44038eKLz9G4cV1efvlFTpw4ce5n/dd/Q/jAu9D/87fHcyUkWBk27DFatGjIwoXvEhvbjx9/3MH8+QtVH4VjMkVjs9lUzSDyJSXZMBrN+G3bStm2zdHv+c3t5wgNDWP48Kf47rv1Xvtli5pktLQQQhSfFOWEKMU6dfofYWHhPjOFNTyuB2VenKh2jNLH6eTMG++Q3b03CQl/UqNGLY+v3eSrKleOYsWKr6hd+wbuu+9OVq36Qu1Ilygz7mnC7upbrDYyMjIYPHgQFStW4pVXZpTqnXi1QFEU2rRpx+LFX/D1199x881tiY9/lSZN6jB+/DPY7Tb8v9+IMzISx011PJbjl1928cAD99C2bXNWrlzOgw8+zM8//8brr79BzZq1PHbe62E2m8/t+hke253gV19SOVHplJmZycmTJzEajej/OYif9U9cZcp45FwPPDCIqCgjkyc/7zObY5WU80dLn11zUQghxPWRopwQpVhgYCDdu/fkyy9XkZmZqXacq1JSUtD/sU/tGKWPvz/Zsf1w1KtPQoKV2rVl6uqVREZGsmzZKho1asLDD9/PokUfqx3pAkpGBn6//VqsNiZNGs9ff+1n1qy3iIgo66Zkwhs0atSE99//mM2bt9O9ey8WLHiH5s3qk7H6C041bAw69751dLlc/PDD9/Tr14tbb+3A5s2bGDlyFDt37mPy5GmYCjYn8RYmk5nDh5PJzc1FdzgZvz//UDtSqXR259WoKCP6giKp02jyyLmCgoJ46qln2LFjO998s9Yj5/BFMlpaCCHcQ4pyQpRysbH9SEs7w7p13j/Vzhkdgz7xkNoxSh3dP3/jt/UnMs+c4d9//5Gi3DUICwvns8+W06ZNe4YPH8y7785TO9I5TqMJ3bGjkJNTpPuvW/c17703n8ceG0q7dh3cG054DYvlBubMeZvt23/lmV59KJeZydj13zJo0P3s2VO8oi6A0+lk7do13H57Z2Jju/P77/uYMOEFdu/ex5gxEyhfvrwbeuF+ZnM0LpeL5OQknEazW3cyFtcuqeD3bjKZ0dltOMtXgMBAj53vzjvvoVq16kyd+gJOp9Nj5/EVMlpaCCHcR4pyQpRyN9/clooVK/nEFFZHdHR+UU6mj5SowCWfUbZnFw7uT8DlcklR7hqVKVOGjz76jK5db2fMmKeYPft1tSMB+UU5xeVCdzj5uu977Ngxhg8fwo031uHZZ5/zQDrhbaKjY3i6VZv8/x74EN99t55OndoyYEAsW7duue728vLyWLr0czp2bM199w3g2LGjTJsWz44dexg2bAShoWHu7oJbnR25Z7fbcBqNUpRTydminNFoRG9LxGH27IhKg8HAmDHj+eOPfT7xfsnTzo6Wnj17royWFkKIYnJLUc5isXxksViSLRZLqsViSbBYLIPc0a4QwvP0ej29e8eybt3XnD6donacK3JGx6BkZKCcPKl2lFJFl3gIR+UorP8cBKB27RtUTuQ7AgMDWbDgQ2Jj+/Lii8/x0ksvqL4mkSMqf7dC/XUWE1wuF089NYzU1NO89dZ8Aj04KkV4l6x7B3Lyxx089soMdu/ex7hxE/ntt1/o2bMr3bvfyrffrr3q4zorK4v3319Aq1aNGTx4EC6XizfeeIetW3fzwAODCAoKKqHeFM/ZopzNlojDaEJ35DD4wA7mWnO2KBcVZSKvXgNybuns8XP26hVL3br1mTZtCjlFHGmsBeePlm7btr3acYQQwue5a6TcS0BVq9UaBvQEJlssliZualsI4WGxsf3Izs5mzZrVake5orx6Dcju0RslJ1vtKKWKPvEQzuj8nVd1Oh3Vq9dQO5JPMRgMvPHGPO69dyAzZrzG+PHPqDr9yVG9Btldu+G6zqLaxx9/wNq1axg//nlu8uBi/8ILKQqOWrWB/KnZTzzxFDt27OWll14lKcnO3Xf355Zb2rB8+RIWL15E48Z1CAgw0LhxHT7+eCFz5sykadN6PP30SCIjI1m48FM2bvyJfv0G+NymMcaCdcvsdlt+MejW21Ay0lVOVfokJdkpW7YswcHBpI9/nowxEzx+Tp1Ox7PPTuDff//hk08+9Pj5vJGMlhZCCPdzyzshq9V6/srrroJ/agA73dG+EMKzGjVqQtWq1Vi6dDF33nmP2nEuK7fVzeS2ulntGKWO/tAhcps2IyHBStWq1QgICFA7ks/R6/W89tpMgoPL8Pbbb5Cens706bPQ6/UlnsVZtRqpH3x6Xfc5ePAvxo9/hrZtO/DII0M8lEx4I/3ePQS/8ybpo8fiPG93xeDgYB566FHuu+9Bli79nNmzZ/Doow+iKMq5UXM2WyIjRw4DoF27jrz11nzatGnn0+tPBQcHExkZic1mI2fEKHK69VA7UqmUnJxEVJTpv+UsSugx1anTrbRo0Yrp06fRv/+dBAcHl8h5vcH5o6WXLFkpo6WFEMJN3LamnMViedNisWQAfwLJwBp3tS2E8CxFUYiN7ccPP2ziyJHDase5OllkueQ4HOiSbDijY9i/3ypTV4tBURReeGEqTz31DJ988iGDBz9Ebm6ueoGucRptbm4uQ4Y8jMHgz+zZb6Fz8+6bwrv5r/+GwEUf4woqvPhgMBgYMOBuNm/eTmRkZKHTWCtWrMSSJV/Qtm17ny7InWUyRWO3J6odo1Sz2+2YTCb0e/cQWd2EYcO6EjmvoiiMGzeRI0cOs2DBOyVyTm8ho6WFEMIzFHeubWOxWPRAK6ADMM1qtRb6acPpdLocDu0u1K7X63A4tFM00Fp/QPpUmD/++IMGDeoxfXo8w4YNd2OyoiusT3433YDz1i44X5+pUqqi88nHncOB8tMWciPLE9akESNHPsmUKVPPHfbJPl1FSfQpPn46Y8Y8w+23d2PRos88OuKgsP7o/9cJykXi+Ozzq97/hRcmMXnyi3z88af069fPUzGvizzuSo6+y/9QTpwkb8fVJz8EBBgKLcopikJ2tooFaDc5e4369o3jr7/+4pfNP+BX50aco0bjHP6E2vGKxFsfd1djNFamT59Y3ux6G35xfcjb8hOups1KrD89e3Zn27ZtWK37iYiI8Oi5vOEa7d+/n2bNmtCyZUvWrFlb7C9nvKFP7qa1PmmtPyB98gVa68/FDAb9TqDpxbe7dSEPq9XqAH6wWCz3AIOBWYX9nMPhIiUlw52n9ioREcGa6p/W+gPSp8JERVWhXr0GfPzxx9x7r3fs1VJYn8r6B+L46wCpPnj9fPZxV7cJ+/cnkJeXR5UqNS7og8/26QpKok8PPjgYvT6Ap58eSbdu3Vi48FNCQkI8cq7C+hOu80P555+r9vPnn7fx0ktT6ddvAP/7XzevudbyuCshmZmU37KFzAceJv0asplMZmy2S0eQmUxm7+tbEZy9RpUqRbF+/XpO5SpUSE0le//Ba/r9eCOvfNxdRWZmJsePH6d8+UpkJvxFKJASVgFXSkaJ9Wf06HF06tSWl156mbFjPbu2mtrXKDc3l3vvvQd/f3/i498gNTWr2G2q3SdP0FqftNYfkD75Aq3152IVKoQWerun5qD4kb+mnBDCh8TG9mPXrp0cPHhA7SiX5YiJQZ8o04ZKiv6P3wlYvoS/9v4GQO3aFpUTacf99z/InDlvs2XLD/Tv37tEdz92GE3o7FfefTUt7QyPP/4IRqOJl156tYSSCW9i2L4VJTub3HbXtsPiuHETL9lFNSgoiHHjJnoinmpMpmjS09NIPZOKw2i67p2MRfEkJycBEBVlRG+z4QoIwFW+fIlmqFevAb17x/L2229x9OjREj13SZsx41V27drJq6++fm6jEyGEEO5T7KKcxWKpaLFYBlgslhCLxaK3WCxdgDuBDcWPJ4QoSX36xKEoCsuXL1E7ymU5omPQJR665vWwRPEEfLWasEcf5K/9CQDUrFlb5UTa0q/fAObP/4Bff91Nnz7dOX78eImc1xllRHfsKOTkXPZnJkwYy7///sMbb7xDWFh4ieQS3kVJTyevVm1yWl7bBjtxcf2Jj5+N2RyNoiiYzdHEx88mLq6/h5OWLLPZDIDNZsMZZUInRbkSdbYoZzSa0NkTcRhNoMJal2PGjCc7O4vXX9fulxY//7yNGTNepV+/AfTqFat2HCGE0CR3/AVzkT9V1QacAl4DRlit1i/c0LYQogQZjSZatbqZZcsWF7oukDdwRsegSzuDknJK7Silgi7xEM4KFdl38ABmc7THpliWZt269eDDDz/jwIH99OrV9dwHTk9ymswoLhe6y2zssmbNaj7++AOGDRtJy5atPZ5HeKec27tz6scdcB3P+7i4/uzatY/s7Fx27dqnuYIc5E/HBbDbE3GapChX0ux2GwAmk4ncm9uRrdKu8dWr1+Suu+5l4cJ3SUw8pEoGT0pLS5PR0kIIUQKKXZSzWq3HrFZre6vVGmG1WsOsVms9q9U6zx3hhBAlLza2H/v3J7B37x61oxQqt1kLMh59HDS8CKg30Scm4oiJYf/+BJm66kG33NKZzz5bTnJyMj16dOXff//x6Pny6jcg86FHwO/SpWWPHDnMk08OpV69Bjz99LMezSG8WF6e7HR9GWZzNJA/Ui6nwy1k9+glo7dL0H/TV01k3f8gGSNGqZblqaeeQafT8eqrL6mWwVOee05GSwshREko+bHeQgiv1r17TwwGA8uWLVY7SqHymjQj/cWXSnz9mNJKl/gvDnMMf/2VQK1aUpTzpJYtW7Ns2SrOnDlNjx5dSEiweuxcefUakPbSazijjBfc7nK5eOKJIWRkZPDWW/Px9/f3WAbh3fzXriGyTg30BVPXxX8qVKiIwWDAbreRHduP9MnTQFHUjlVq2O02ypYtS3BgIErqaVWzGI0mHnzwET7//FOs1j9VzeJOa9as5qOPFjJ8+JMyWloIITxMinJCiAuUKxdJx46dWL58CU5vHSWRmYlSgovil1pOJ3pbIqfCI8jMzMRiuUHtRJrXsGFjVqz4CofDQa9eXdmz51fPnSw3FyXtzAU3vfvuPDZsWMfEiZNlZGQp5//9dyiZWTiqVFU7itfR6XQYjSbs9oJNh3Jzr7g+o3Cv5OQkoqJM6Ow2yteMJuDTj1TNM3z4kwQHl+HllyermsNdzo6Wrl+/IaNHj1U7jhBCaJ4U5YQQl4iN7UdSkp1t235SO0qhIuvWIviVqWrH0D5F4eRPu9javDmAjJQrITfeeBOrVq0lKCiYPn268/PP2zxynsibahD80ovn/j8hwcqkSeO55ZbOPPjgwx45p/Adhs2byGl9M8hoyUKZzdHYbDb01j8pby6P/9ov1Y5UaiQlJWE0GtEXrC138YjfkhYZGcngwUP58suV7N69U9UsxXX+aOk335wno6WFEKIESFFOCHGJLl1uJzg4mKVLvXMKq9McjV6Diyp7HUXBGVOFX4/l7whau7bsvFpSqlevycqVaylfvjz9+vVm8+ZNbj+Hs3Jl9En5azPl5OQwePAggoODmTnzTRSZileq6ew2/A78RW7bDmpH8Vomkxm73YazUiUUlwu9bPZQYpKSbBiNZnS2/JGKzoI1/tQ0ePBQIiMjmTLlBbWjFIuMlhZCiJInRTkhxCXKlClD167dWLVqOTleOCXHEROD/pAU5TzNb/dOgubM5NC+PVSoUJGyZcupHalUMZuj+eKLtVSpUoW77urLN9985db2nVFGdEn5I01effUl9uz5lenTZ1OpUmW3nkf4HkNBETinXQd1g3gxs9lMcnISuWVCcAUHo7NLUa4kZGVlceLEifyRcgVFOYfRpHIqCAkJ5YknnuL777/zyJcoJUFGSwshhDqkKCeEKFRcXD9OnTrFxo3r1Y5yCac5Gp2MlPM4/03fEfLCBBL+2i/ryamkUqVKrFixhptuqsPAgXezYsVSt7XtMJnRJSWxdesWZs2K5+6776Nbtx5ua1/4LkedumQ88RSOG29SO4rXMpmicTqdHD5yGEeUEV3BjqDCs5IKRiQajSZ0NhvOyEgIDlY5Vb6BAwdhMpmZOnUSLh/bjTcnJ4chQx6mTJkyzJz5loyWFkKIEiRFOSFEoTp06ES5cuW8chdWR3QVdGdSZbMHD9MlHsIZWZ7fDvxFrVoydVUtZcuWY8mSlTRr1oJHH32QTz750C3tOqOM6I4e4YkhD1OlSlVefPFlt7QrfF9evQakj5sIOnmbeDkmkxkAm82G02iW6aslJLmg+Gk0msjp0pWMkaNVTvSfwMBARo0aw86dO1i7do3aca7Lq6++xG+//VIwWrqS2nGEEKJUkXdbQohCGQwGevTow9q1a0hLS1M7zgVy2rYn7YWpIN/kepQ+8RBZUVGcOZNK7doyUk5NoaFhfPrpUjp27MSIEY8zb95bxW4zp0MnPqlbn6NJdt58cx4hISFuSCp8nXLkCH7btubvKCouy1ywjllSko2sAXeRdcddKicqHc4fKZfzv65kPjJE5UQXuuOOu6hRoyYvvfQCDodD7TjX5PzR0rff3l3tOEIIUepIUU4IcVlxcf3IyMjg66+96xtfR736ZD42FFdYuNpRNE2XeIgToWEAsuCzFwgODmbhwk/p1q0n48Y9w4wZrxZritSSJBv37PmVwU8+TdOmzd2YVPiygFXLKdvjVpmOeRXGgnXMbDYb2f0GkHX/gyonKh3OFuWioozo//gd0tNVTnQhPz8/xowZz59//sHSpZ+rHeeqUlNP8/jjj8hoaSGEUJEU5YQQl9W8eUtMJjPLly9RO8qFXC50Bw+gs9vUTqJdLhf65CTsfnoAGSnnJQICApg373369RvASy+9yOTJzxepMJeUZOfpUSPoVqcuT0kxQZzH//uNOKpUxRlTRe0oXi0kJISyZctitydCbm7+OqdZWWrH0rykJDsRERGEOPIo174lQQvfVTvSJXr06E29eg145ZWXvHKzrPONHTuaJBktLYQQqpKinBDisnQ6HX369GXDhnWcPHlC7TgXKHdLG4LmzlE7hnYpCscTDvF+dBXCwyOoWLGi2olEAT8/P2bPnsvAgQ8xe/YMxox5CqfTec33dzqdDBs2mMDcHFbv20vIEu8fzSFKSF4ehh9/IKddR7WT+ASTKRq73Yb/xvVENqmL397f1I6kecnJSURF5W/yAOAwm1VOdCmdTse4cc9x6NA/fPjh+2rHuawvvljG4sWLGDlytIyWFkIIFUlRTghxRbGx/cjLy2PVqi/UjvIfRcERHY0+MVHtJNrm78+ef/6mdm2L7MTmZXQ6HdOmxTN06Ajee28+TzwxhLy8vGu67zvvvMnmzRsZPXkazpBQdEky4lTk8/tlF7ozqeS2a692FJ9gNpux2Ww4jPmFIZny63l2ux2TyYTenv/332nyvqIcQMeOnWnV6mbi418h3cum2EL+iMPRo0fQpElTRnrRZhlCCFEaSVFOCHFFderUpXZti9ftwuqIjsmfLiQ8wrB1CyFjR3H0z99lPTkvpSgKEyZMYuzYCXz22Sc88sgDV50q9fvv+5g8+Xm6dr2de+65H6fRiD5JCgkin/8P3wOQ00aKctfCZDJjt9twGo0A6GVJBY9LTrZfMFLOWbDhhrdRFIVx457n2LGjzJ8/V+04Fzg7WjonJ5c33ngHg8GgdiQhhCjVpCgnhLgiRVGIje3HTz/9iN2LPnA4o2PQJ/6rdgzN8tu+laAF75B88qSsJ+fFFEVh5MjRvPjiS6xe/QX3338nmZmZhf5sVlYWgwcPIjw8gvj4OSiKgjPKKCPlxDkZg4dxau0GXJGRakfxCSZTNKdPp5Cq1+MKDkYnBW6PysrK4vjx4wUj5Wy4DAacFbx3aYXmzVtw661dmTNnJikpp9SOc87Z0dIvvvgS1avXVDuOEEKUelKUE0JcVZ8+fQFYvnypykn+44iugi4lBSX1tNpRNEl/6BDZYeGkA7Vr11Y7jriKRx99nBkz5rBhwzruvDOOtLQzl/zM1Kkv8Mcf+5g58w3Kly8PgMNklkKC+E9AAHmNm6qdwmeYC9Yzsycl4YgyyvRVD0su+P0ajSaye/bmzPRZoPPujzJjxz5Haupp3nhjltpRgEtHSwshhFCf2/6SWSyWWhaLJctisXzkrjaFEN6hWrXqNGnS1KumsOZ0vZ3TCz7AZfBXO4om6W2HOBUWBsjOq77i7rvv4+2332X79q307duTU6dOnjv2/fcbmTt3DgMHPkTnzl3O3Z414B7SX5iqRlzhZfx2bKfMpAkoJ7xrUx9vZipYz8xuTyTj6WfJuvs+lRNp29miXFSUkbz6DckecLfKia6uTp269OnTl3nz3uLIkSOqZilstLQQQgj1ufPrpTeAn93YnhDCi8TG9mPv3t+wWv9UOwoAjpq1yOnRG4KC1I6iSbrEQ9gNBoKDg8998BTer3fvON5772P27dtLx44306DBDQQEGOjfvw8VK1bi+eenXPDzeS1bkV0wElaUbgFrVhP0zpu4AgLUjuIzzAXrmdlsNrL79CW3wy0qJ9K2s0tomExmDJs3ofOiJTWu5OmnnyUnJ4cZM15RNUdho6WFEEKozy1FOYvFMgBIAda7oz0hhPfp2TMWnU7H8uVeMlrO6cTww/fovaRIqCkuF0puLvvz8qhVy4LOy6cHiQt16XIbjz76OElJdpKTk3C5XDidDk6fTuGrr1Zf+MMZGfht/Qnl+HF1wgqvYfh+I7lNmkFIiNpRfEbFipXw8/PDbrehnDyBYesWcDjUjqVZ50bKVahIeP/eBH7wrsqJrk316jW46677+PDD9/n3339UyXB2tPQDDwy6YLS0EEII9RX7k5bFYgkDXgCeKn4cIYS3qlSpEm3btmfp0sW4XC6144CiEH5XXwI//kDtJNqjKJzc/itP5eVRq5asJ+eLCptqnp2dzZQpky64TX/oX8r27IL/D5tKKprwQsrJE/jt+ZXcdh3UjuJT9Ho9RqMJmy2RgJUriOjZFd1RdacoapndbiMiIoLQtDMoDgdOk3fuvFqYp556Gr1ezyuvlPxyASkppxg27DFq1qzFxImTS/z8QgghrszPDW28CCywWq2JFovlmu6g1ytERAS74dTeSa/Xaap/WusPSJ+K6t5772HQoIfYv38vzZu38Oi54Br6VKUqgYftGHzkWvrS4y41NZWk5CQaNKh3xcy+1KdrpYU+XW6n5PwPtef17cb8nffKnDpGkA/1WQvX6GJq9klZ/xWKy0XA7V3wd1OG0nKNqlSJ4ciRJIJqVwcgPPU4rht9Z0dLX7pOx44dwWyOJvz0MQCCLDUIvCi7t/YnIqImQ4Y8zowZ8YwdO4a6dete832L0yeXy8WQIQ9x7NhRli//EaPRO6ateut1Kg6t9Ulr/QHpky/QWn+uVbGKchaLpSHQGWh0PfdzOFykpGQU59ReLSIiWFP901p/QPpUVB063EpAQAAffPARtWvX8+i54Op9CjeZUf7+x2eupa887gybviMv/hUqA9HR1a+Y2Vf6dD200CeTyYzNlljo7Rf0zeVHZJkQsg/8TboP9VkL1+hiavYp0JZMsDmaU7XqgpsylJZrVKmSke3bt5IaVp5yQHrCQXIs9dUJWAS+dJ0OHUqkUqXKZPz5F2FAatlKOC7K7s39eeSRocybN49x48azcOEn13y/4vRp8eJFLFmymGeffY7q1W/0mt+NN1+notJan7TWH5A++QKt9ediFSqEFnp7caevdgCqAocsFsthYBQQZ7FYdhWzXSGEFwoLC+d//+vK8uVLcXjBujkOcwz6xH/VjqE5fnt+o9JPP5IBXOsIaOFdxo2bSNBFm6AEBQUxbtzEC39QUXCaTOiTkkownfA2Wfc/yMmde8FgUDuKzzGbo0lKspNbqTIAeh/ZfMAXJSXZMBrN6Gz5v2OH0aRyoutTrlwkjz8+nK++Ws3OnZ7fG+/QoX8ZM2YUzZu3ZNiwkR4/nxBCiKIpblHuHaAG0LDgn7nAl4CsICqERsXG9uPYsaP88MP3akfBEROD7uRJSEtTO4qm6BP/JT0ggCx/f6pUqaZ2HFEEcXH9iY+fjdkcjaIomM3RxMfPJi6u/yU/64wyokuSQkKpdXaNUEVRN4ePMpnM5OXlcSQ3B1dQEDopcHtEVlYWx48fx2g0kh3bl9Mff+6Tm5I88sgQypcvz9SpL3j0PA6Hg6FDH8XlcvHGG++g1+s9ej4hhBBFV6zpq1arNQM4N77QYrGkAVlWq/VYcYMJIbxT5863EhoaxrJli2nfvqOqWbJj+5F7c1sICFA1h9boEg9h9w+ghtmMn587lh4VaoiL609cXP+rTgVIHzNeCjKlWMCijwmePYOU5WtwVaqkdhyfYzabAbDZbVR5+z0c1WuonEibzu68ajKZcZqjyTH7ziYP5wsJCWHEiFGMHz+GTZu+89j7qDfemMXWrVuYPXsuVapU9cg5hBBCuEexd189n9Vqfd5qtd7jzjaFEN4lMDCQbt16sHr1SrKyslTN4jRHk9ekmUy5cjN94iH+cuRRq5ZMXS0N8po0I69xU7VjCJX4f78RXUoKrooV1Y7ik0wFO4Da7TZyut6Oo7a8bnrC2aJcVJQR/5XL8ft1t8qJiu7++x/CbI5m6tRJHtnN/rfffmHatMn06NGb/v3vdHv7Qggh3MutRTkhROkQG9uPM2dSWbfuG3WD5OURsOhj/HbvVDeHxuRFlmdrZia15cNlqaA7cpiAZYtRTqeoHUWUNJcLw+ZN5LRrL6Mli8hkyl/XzGazof9rP/5frlI5kTYlJdkBMBpNhD71BIGffqRyoqILCAhg9Oix7N69iy/d/HjJyMhg8OBBREaW59VXZ6DI81oIIbyeFOWEENetTZt2VKhQkWXLFqsbRK8n9OmRBHyxXN0cGvPT5GlMcrmkKFdK6PftIeyxh9BbrWpHESVMb/0T/dEj5LZTdykCXxYaGkZYWDh2eyIBiz8lbNB94AUbIWnNuaJcSCi60yk4TL45ffWsfv0GUKtWbV5++UW3bpz14ovPsX9/ArNmvUW5cpFua1cIIYTnSFFOCHHd/Pz86N07lm+/XUtq6mn1gigKDpMZfeIh9TJo0P79+cWZ2rVvUDmJKAnOqPyRPnrZ7KHU8f/+OwBy2rZXOYlvM5nM2O02nFEmFIcD3dEjakfSnKQkO+HhEYQVjOh1Fqzl56v8/PwYM2YCCQlWFi9e5JY216//hgUL3uHRR4fQocMtbmlTCCGE50lRTghRJLGx/cjOzmbNmtWq5nBGx6CzSVHOXfy/+YrbnhtLVUWhRo2aascRJcBZMP1Odo0sffJurEPGo4/jjI5RO4pPM5vN2Gw2nEYjALqCUV3CfZKS7BiNJvT2RACfHykH0L17Txo2bMSrr75EdnZ2sdo6ceIETzzxODfccCPjxj3vnoBCCCFKhBTlhBBF0rhxU6pUqcrSpZ+rmsMRXUVGyrmRfv9+oo8cITymCgGyq22p4AoNw1kmBF2yFBJKm9y27Ul/8SW1Y/i8/JFyiTiM+aO3pMDtfklJSRiNRnS2/BG9vj5SDkBRFJ59diKJiYf48MP3ityOy+XiySeHkZJyijffnE9gYKAbUwohhPA0KcoJIYpEURTi4vqxefMmjhxRb6qOIyYG3fHjkJ6uWgYt0dsOkarTEXXjTWpHESVFUXAajejtUpQrTXRHDqM/sB88sPtjaWM2R3Pq1CnOhIcDMhXcE86OlMuO68ep9ZtxVqqsdiS3aN++Izff3Jb4+FdJS0srUhuffvoRX321mrFjn6Nu3XpuTiiEEMLTpCgnhCiy2Nj+OJ1OVq1Sb6OFrHsHcuI3KwQHq5ZBS5R//+Vvp1PWkytlUud/QNqUaWrHECUo8OMPKNu6Kcqpk2pH8XkmU/6oLVtGBikr15LVd4DKibQlOzub48ePYTSacIWEklevAej1asdyC0VRGDduIsePH2PevLeu+/5//32QZ599mjZt2jF48FAPJBRCCOFpUpQTQhRZ7doW6tatz9Kl6u3C6ioXibNyFCiKahm0xPH3Af4BatWqrXYUUYIcN96EM8qodgxRggybN5FXtz4u2aGx2EwF65vZ7DZyW7bGFSm/U3dKTs6fDmw0mgj8aCH+33ylciL3atq0OV273s4bb8zi1HUUyfPy8hgy5GH8/PyYPXsuOp18rBNCCF8kr95CiGKJje3Hzp0/888/f6sTICuL4BmvYvhxszrn15jkylF8D1gsMlKuNNHv20vQ7NchN1ftKKIkZGRg+HkbubLrqluYC9Y3s9ttGDZvIsBNu2mKfEkFG2cYjSaCX3+NgOVLVU7kfmPHPseZM6nMnv36Nd/n9ddfY+fOn3n11RnnRmsKIYTwPVKUE0IUS+/esQAsX75EnQD+/gS/9jL+G9apc36NebddB+KBmjVlpFxpYtj5MyEvPofuyGG1o4gSYNj2E0pODjntOqgdRRMqV45Cp9NhtycS+NknlJn6gtqRNOVcUa5SZXRJdpxm39959WI33ngTcXH9mT9/LocPJ1/153fu/Jnp06cRF9efPn36lkBCIYQQniJFOSFEsZjN0bRs2ZqlSz/HpcaC4TodDnM0usR/S/7cWuNykZBgxWyOJiQkRO00ogQ5TSZAdo0sLfy/34jLYCC3RSu1o2iCn58fUVFGbDYbDqMJ3eFkcDjUjqUZSQWvS2aDASUvD4dGR4U9/fSz5OXlER//yhV/Li0tjSFDHiYqysjLL79WQumEEEJ4ihTlhBDFFhvbj4QEK/v27VXl/M7oKugTD6lybi3xX7Oat79Yxi1Gk9pRRAlzROVfc9k1snTIGDaC058thzJl1I6iGSaTGbvdhtNoQnE40B07qnYkzUhKshEeHkFowXprTrM2i3JVq1bj3nsH8tFHC/n774OX/bmJE8fxzz9/M2fO24SHR5RcQCGEEB4hRTkhRLH16NEbPz8/li1TZ8MHR0wM+kNSlCsu3aF/iMzLI/LGOmpHESVMRsqVLq5ykeS2aad2DE0xm83YbIk4jfkbpugKplyK4ktKSsJoNKK3539p4DBpb/rqWU8++TQGg4FXXpla6PG1a9fw4Yfv8fjjT9C6dZsSTieEEMITpCgnhCi2yMhIOnbsxPLlS3A6nSV+fqc5GiXlFGRnl/i5tSTj932kAuZ69dWOIkqYKywcV3AZdMlSSNA6w9YtBM2ZCRkZakfRFJMpmqQkO3kFuxjr7PJccpfk5CSiooxk9+zD8T/+xqHh3cErVarMoEGPsWzZYn7/fd8Fx44cOcLIkY9Tt259nnlmnEoJhRBCuJsU5YQQbhEb2w+73cb27VtL/NwZg4dx/NBRCAgo8XNrSe5fCfwD1LbcqHYUUdIUhZNbdpA+QRao17qAJZ8THP8K+PurHUVTTCYzubm5HC5blhM79pDT9Xa1I2mG3W7L311UUXBFRoKfn9qRPGrYsBGEhobx0kv/vR67XC5Gjnyc9PR03nprPgHyfkcIITTDLUU5i8Wy0WKxZFkslrSCf6zuaFcI4Tu6dLmd4OBgli5VYQprUBDo9SV/Xo3xs9nyi3K1tTsKQVye02iSQk0pYNi8kdyb22i+sFHSzAXrnCUeOYIzpgoYDCon0obs7GyOHz9GVJSRoHfeJPDdeWpH8riIiLIMHfoEX3/9Fdu3bwPg/fcXsG7dNzz33AtYLDeonFAIIYQ7uXOk3FCr1RpS8I/Fje0KIXxASEgIXbvezqpVy8nNzS3Zk6enEzJqBP7rvi7Z82rMT+Ur8F1IKGXLllM7ilCB//pvCJ4qI+W0TJd4CL+/D5Lbtr3aUTTHVLDOmd1uI/DjDwj49COVE2lDcnL+Opcmk5mARZ+Umr/zDz88mNDQUOLiuhMQYGDMmKe48cabePDBR9SOJoQQws1k+qoQwm1iY/tx8uRJNm3aULInDgwk8JMPMGz9qWTPqzFTAgP5uUFDtWMIlfj9vJ3gWfGQl6d2FOEh/ps3AZDTtoO6QTTo7Eg5m81GwNLPCfpoocqJtOFsUS4qyojenohTw5s8nG/t2i/JysoiOzsbl8uFy+Xi778Psnz5ErWjCSGEcDN3FuVeslgsxy0Wy48Wi6WDG9sVQviIDh06UbZs2ZKfwqrX4zSZ0dlkB9aicmVlcSjBSu3aMtC5tHIaTShOJ7ojh9WOIjxEZ7fhMJlx3CDrRrpbWFg4ISGh2O2JOKOM6JJlJ2N3SCrYxdZcthy6U6dwFBQ/tW7KlEmXzDrIyspiypRJKiUSQgjhKe5aUOQZ4HcgBxgArLJYLA2tVuuBwn5Yr1eIiAh206m9j16v01T/tNYfkD55TjB9+/bjk08+xmBwUaZMmWK1dj19UqpXwz/J5gW/g8vzjmtUuFPvf87RM6l8Eln2ujJ6c5+KSmt9utb+KLWrAxB+5gSuCO9eV1Br1whKqE9TXsT5/HNElMB6Z6XxGsXExHD06GEMFgu6ZYuJCA3w+vVOvf06nTx5FIAbQ/I3NgisVYOAK+T19v5cK7vddtnbtdA/rVyn82mtT1rrD0iffIHW+nOt3FKUs1qt287734UWi+VO4HZgdmE/73C4SEnJcMepvVJERLCm+qe1/oD0yZO6devNvHnv8PnnS+nTp2+x2rqePoVUNuH/3Xqv+B1cjrdco8Ik/7SVikBYnQbXldGb+1RUWuvTtfZHH1aeckCG9QDZNzTwfLBi0No1gpLuk+fX/SyN1ygqysg///xLRut2hDocpO7/B2flqBJMeP28/TodPPgPYWHhcOwUrsBA0spVJPcKeb29P9fKZDJjsyUWersW+qeV63Q+rfVJa/0B6ZMv0Fp/LlahQmiht3tqTTkXoHiobSGEF2vZsjVRUUaWLSvZKazOqtXyd46U9bCKJDvhT9KAqk2aqR1FqMRpNOJSFJQTJ9SOIjwg8N15RPToAunpakfRLJMpOn/6qtGES1FkKrgb2O12TCYTuS1bc/zfI+Q2b6l2pBIxbtxEgoKCLrgtKCiIceMmqpRICCGEpxR7pJzFYokAWgCbgDzgDqAdMKK4bQshfI9Op6NPn768886bnDx5gnLlIkvkvBkjR5MxcnSJnEuL9ImJJOp0VKxUWe0oQiWu8AiOJx7LL24LzfHf8C3KsaNQzGUFxOWZzWaOHz/O6VY3kyPPJbdITk4iKsqY/z+Kkv9PKRAX1x/IX1vObrdhMpkZN27iuduFEEJohztGyhmAycAx4DgwDOhttVqtbmhbCOGD4uL6kZeXx+rVK9WOIq5RyInjnAgNRSklH3hEIRRFighalZuL4ccfyJVdVz3KZMrfhCDp2BF5LrlJUpIdo9FE0JyZlJk0Qe04JSourj+7du0jOzuXXbv2SUFOCCE0qtgj5axW6zFA5jsJIc6pW7c+tWrVZtmyxdx33wMlck7ldAqhgweRdee95PToVSLn1JJ3UKhSrwGy92rpFrjgHfS2RNInvqh2FOFGfrt3oUtPI6ddB7WjaJrZHA2AzWaj/ocLybvhRrIH3K1yKt+VnZ3NsWNHMRpN+SM9c3LUjiSEEEK4nafWlBNClGKKohAb24+ffvrxsjuIuZurTAj+363Hb++vJXI+LTl58gTx6Wmc6txF7ShCZYZfdxNQwutBCs/z37wRl6KQ26at2lE07exIObvdhv9Xq/H/bp3KiXzb4cPJABiNJvS2RBwmk8qJhBBCCPeTopwQwiP69OmLy+VixYplJXNCPz+cJjP6Q4dK5nwacuCXX6gO3FCzptpRhMocRlP+4vSyYYqmOGrWIuvBh3GVLad2FE2LijKiKAo2W/5mD3q7Xe1IPi0pKf/3Z6wchS7JjtMUrXIiIYQQwv2kKCeE8Ijq1WvQuHGTEt2F1REdgz5RinLXK+frLzkA1M9zqB1FqMxpNKE4neiOHlE7inCj7F6xpL30mtoxNM9gMFC5chR2uw2n0YQuOUntSD7tbFGuSlAQSk4OjoKRiEIIIYSWSFFOCOExsbH92LPnV/bvTyiR8znN0eikKHfdshL+BCCyiSwPWto5jfm7HOpKaNq58DzlyBGUEyfUjlFqmExmbLbzinIO+bKjqJKS8ouaxuBg8qrXwFm1qrqBhBBCCA+QopwQwmN69YpFp9OV2Gi5vHr1cVStBk5niZxPK3T//kumoqBUrKh2FKEyhykaR8VKKOnpakcRbhL85iwiG90IWVlqRykVzGYzdnsiDpMZZ2R5lJQUtSP5rKQkG2Fh4QQ0aMSprbvJ6XSr2pGEEEIIt5OinBDCYypVqkybNu1ZtmwxLpfL4+fLfGQIp7/4CnTy0nY9Qo4f50RICCiK2lGEyhw33sTJvfvJ7XCL2lGEmxg2byK3STMIDFQ7SqlgMkVjt9vIvP9BTu5JwBUZqXYkn5WUlISxYPSuEEIIoVXyyVUI4VGxsX35+++D/PLLLrWjiEKkpZ2hUlYmGRVklJwQWqMcP45h72/ktm2vdpRSw2w2k52dzfHjx9WO4vOSkuwYjSaCZsUTdv9dascRQgghPEKKckIIj+rWrQf+/v4lMoVVOXaMsu1bEbDkM4+fSyv270/geeDf2H5qRxFeImTsKMpMfl7tGMIN/H/8HoCcdh3UDVKKmAp2CE0+sJ+w++4kYPkSlRP5rrNFOcOunej/PqB2HCGEEMIjpCgnhPCo8PAIOnfuwvLlS3F4eMFrV0QEeusf6P/a79HzaInV+iergRApyokC+oQEDD9uVjuGcAPD9xtxhoaR17Cx2lFKDVPBDqGHjh3Df+N6/H79Rd1APionJ4djx44SFWVEZ7fJzqtCCCE0S4pyQgiPi4vrx9GjR/jR0x/0DQacRhN62YH1mtn3/EYnvZ5q5curHUV4CafRmL9rpPB5GUNHcGbufPDzUztKqWE25xeP7Ek2HFFGdMl2lRP5puSC1yCTyYzenoizYASiEEIIoTVSlBNCeFznzl0ICQktkSmsDnM0Oluix8+jFf47t7PO4SBARheKAg6TCd3hZMjLUzuKKCZnterk/K+r2jFKlYiIsgQHB2Oz2fK/JLJLUa4ozhblzJGR6I4fx2kyqZxICCGE8AwpygkhPC4oKIhu3XqwevVKsrKyPHouZ3SMjJS7Dsq//wDgiK6ibhDhNZxRJhSnE93RI2pHEcVg2PIDAYs+luJqCVMUBZPJjN1uwxklo06LKikpv5gZHVGWnLbtybvhJpUTCSGEEJ4hRTkhRImIje1Haupp1q//1qPnyW11M7ktW4PL5dHzaEFWVhahJ06Q6+eHq6LsviryOWrUJLdRY5TMDLWjiGIIXLiAMlMmgV6vdpRSJ78ol0jejXVwRhnl71ER2AtGGFaoU5fTS1eRc1s3lRMJIYQQniFFOSFEiWjbtj3ly1fw+BTWrHvu58yb80BRPHoeLThw4C9igPTI8vL7EufktmlHytcbcdSopXYUUVROJ/6bN5Hbtr08t1VgNkdjs9nIHDaClNXfyDUoguRkO6GhYYSEhKodRQghhPAoKcoJIUqEn58fvXvH8u23azlzJtXzJ3Q6PX8OH5eQ8CdVyZ/yK4TQDv0fv6M7fpycdh3UjlIqmUxmjh076vHlGrTMbrdjMpkImhVP2TbNwMO7twshhBBqcVtRzmKxDLBYLH9YLJZ0i8VywGKxtHVX20IIbYiN7UdWVhZr1qz22Dl0SXYia8UQ8PmnHjuHViQkWHlcUcid+KLaUYSXCe91G8Gvvax2DFFE/ps3AuSPlBMlzmTK34H1xI7tRHRuh//6b1RO5HuSk+1ERRnRH/gLJTVVpmELIYTQLLcU5SwWy/+AacADQCjQDjjojraFENrRpEkzYmKqenQKq7N8BZTU0+gP/euxc2hFQoKVE1WroWvRSu0owsvojh5B/+cfascQRaTfv5+8GjVxFhSHRMkym6MBsJ1OwfDbL+gPHlA5ke9JSkrCZDKjt9nkcSyEEELT/NzUziTgBavVurXg/2X/dyHEJRRFITa2L7Nnz+Do0aNU9MTmAv7+OCtHyQ6s1+DEH/sYHBKKcvSobPQgLuA0mtEnyZ9yX5U2fSZK2hm1Y5RaZ0fK/Z2aiisgAJ1dnkvXIycnh2PHjhIVZUS35Qfy6jVQO5IQQgjhMcUeKWexWPRAU6CCxWL5y2Kx2CwWyxyLxRJU/HhCCK2Jje2Hw+Fg1arlHjuHMzoGnRTlrigvL49yfx9k9J5f0f8tA5vFhZxGIzopyvk0lyyQrxqj0QSAzW7DGWVElyzPpetx+HAyLpcLY5QRvV1GygkhhNA2d4yUqwQYgL5AWyAX+AIYD4wr7A56vUJERLAbTu2d9Hqdpvqntf6A9ElNLVs2oV69+nzxxVKeemrkFX+2qH3S16iOsuVHr/t9eNM1slqtmAoWzg6pa4Ei5vKmPrmL1vpUlP7oqlVBt+QwESH+4OeuQfXuo7VrBO7rk+71GShbt+L4+BNV1+Eq3dcomMqVK3Ps2GGUKjH4Hz3stb8Lb7xOe/ceB+CGqmZc/e/Av0M7DNeY0Rv7U1zSJ9+gtT5prT8gffIFWuvPtXLHO+3Mgn/PtlqtyQAWiyWeKxTlHA4XKSkZbji1d4qICNZU/7TWH5A+qa1XrzgmT57IL7/so2rVapf9uaL2KaDj//CrGEW6l/0+vOka7djxS/7Oq35+pASFQxFzeVOf3EVrfSpKf/xvqEdg126cST6OKzTMQ8mKTmvXCNzXp4ilyyA7i5Qz2W5IVYwcpfwaGY0mDh78m8xmrdAdP0aal/4uvPE6JSTkj94OqWDixPQ5+TdeY0Zv7E9xSZ98g9b6pLX+gPTJF2itPxerUKHwWQzFnr5qtVpPATbAVdy2hBClQ58+cQCsWLHUI+1n944jfdxEj7StFfv3W6kKOExm0LltI26hETm3dyf1vY+8siAnriAtDb+dP5PbtoPaSUo9kykau91GxtPPkvbKDLXj+JSkpCQAjBUrgks+XgghhNA2d30Sew8YZrFYKloslrLACGC1m9oWQmhMdHQMLVq0YunSz3F56g13WhpkaPebluKyWv+ktsEfV5XLj1QUQj4Q+xb/bVtQ8vLIadte7Silnslkxm63ee5vnIYlJdkIDQ2j4uJFlK8WhXI6Re1IQgghhMe4qyj3IvAzkAD8AewGpripbSGEBsXG9sNq/ZPff9/n9rZ1Bw9QobqRgFUr3N62Vuzfn8DUZs0580q82lGEF1LOpBJ5Uw2C3nlT7SjiOhi+34QrIIDcFq3UjlLqmc1mMjMzyVy7hsibauC3e6fakXxGUlISRqMRnc0GgCssXOVEQgghhOe4pShntVpzrVbrEKvVGmG1WitbrdbhVqs1yx1tCyG0qWfPPvj5+bFs2WK3t312pza97MBaKKfTyf79VsrXq4+zeg214wgv5AoJRUlPQ2eXXSN9iSMmhqw774GgILWjlHomUzQAhzPS0R0/Js+l65CUZMNoNKG3JeYvsaAoakcSQgghPEYWEhJCqCIyMpIOHW5hxYqlOJ1O9zYeEICjUmV0UpQrlM2WSERmJvccOoTu33/UjiO8kaLgMJrQJUkhwZdkPfSorF/mJczm/C+H/s7NA0CfLM+la5U/Us6Ezp547ks2IYQQQqukKCeEUE1sbD8SEw/x88/b3d62MzoGvS3R7e1qQULCn9QD2ny1Gl1ystpxhJdyGk3opSjnM5QTJyBb3R1XxX/OjpQ7mJqCy98fXcHmBeLKcnJyOHbsKFFRRvQ2Gw5ztNqRhBBCCI+SopwQQjVdu3YjKCiIZcs+d3vbjpgY9If+dXu7WpCQkEDVgv92xsSoGUV4MafRhC5ZCgm+oszUSZRrVl825/ASkZGRBAYGYrPbcUYZ0clIuWty+HAyLpcLk9FE5r33k/O/rmpHEkIIITzKT+0AQojSKyQkhC5dbmPlyuVMnjwNg8Hgtraz+95Bbsub3daeliQk/EnzoCBceXk4K1VWO47wUjkdO+EsW07tGOIa+W/aSF6jJrL+lpdQFOXcDqxZcf1xli+vdiSfkFQwojDKaCLjnvtVTiOEEEJ4nhTlhBCqio3tz4oVy/j+++/o1OlWt7Wb07mL29rSmoQEKwPLhOAsUwb0erXjCC+VHduP7Nh+ascQ10D3z9/oD/1DxuDH1Y4izmMyRWO3J5Ixf6HaUXxGUlL+jqvRZcuhnDqJK6KsFJqFEEJomkxfFUKo6pZbOhMREcHSpW7ehTU3F32CFeXUSfe26+NcLhcJCVaq6HQ4YqqoHUd4u+xsWafMB/hv3gRAbtsO6gYRFzCbzdhs+UUmsrNlavE1ODtSruavuylvqYru74MqJxJCCCE8S4pyQghV+fv706NHb9asWU1GRobb2tX/8zfl2jTDf903bmtTC44ePUJq6mmWDx9J6oIP1I4jvJj+j9+pEF0B/2++UjuKuArD5o04KkfhqFVb7SjiPCaTmaNHj+C38F0qRFdAOXpU7UheLznZTkhIKGVOHAeQ3VeFEEJonhTlhBCqi43tR0ZGOt+48cP/2R3bZAfWC1mtfwJQ84ab8qcFCXEZzkqVAGQHVh+QOXgYaS+9JtP8vIzZHI3L5eJ4wXXRy2YPV2W32zGZTOjsNhwVK0FAgNqRhBBCCI+SopwQQnUtW7YmKsrIsmVunMIaFISzQkV0iYfc16YG7N9vxQR0+PxT9H/8rnYc4cVcZcvhCgpCZ5dCgrfLa9SEnG491I4hLmIqGOVlLyjKyXPp6pKT7URFGdHbEnGaZZScEEII7ZOinBBCdXq9nt6941i//ltOuXENOEdMDPpDUpQ7n9X6J02Dg4n8/FN0KafUjiO8maLgiDKiS05SO4m4AsOWH/Bf97WsV+aFzAVFpYM5+esy6mSk3FXlj5Qzo7PbcJqi1Y4jhBBCeJwU5YQQXiEurh+5ubmsXr3SbW06zDHoEv91W3tasH9/Ak0rVAT+m+IrxOU4TWb0dpvaMcQVBM15nTLPPStTV71QVJQJgL9On8bl748+SQrcV5KTk8OxY0eJijKSMWwkWXferXYkIYQQwuP81A4ghBAA9eo1oGbNWixfvoR77x3oljYzH3oU3ZnTbmlLK6zWPxlVqRIuvR5nlFHtOMLLZd15D0pmptoxxOXk5OC/5UeyBtyldhJRiKCgIMqXr4DNbidjxCjyGjRUO5JXO3LkMC6XC6PRRPYAKcgJIYQoHaQoJ4TwCoqiEBvbj1dffYnk5CSi3FAwymvZyg3JtOPkyRMcP36M6lHG/B3t/ORPgLiy7L53qB1BXIHfrp0oGenktO2gdhRxGWazGbs9kYzpM9WO4vXsBWvuVQmPQL93D46atSAwUOVUQgghhGfJ9FUhhNeIje2Ly+VixYpl7mkwLQ3Dd+vRHTnsnvZ8XEJCAgBlAwNxVKmmchrhE3Jy0P19ELKy1E4iCuH//Xe4dDpyb26jdhRxGSZTNHa7DbKz0clu4FeUXLDmnuVwMuVuuRm/P2UzIiGEENonRTkhhNeoXr0mDRs2ctsurPokOxF39MHww/duac/X7d9vBeDoW/M5veQLldMIX+C/aQORLRrit2+P2lFEIfx+2UVeg4a4IsqqHUVchtlsxmazEfzKVMq1aAhOp9qRvFZSwZp7lbLzvwRwyEYPQgghSoFiz12yWCxpF90UBLxptVqHFbdtIUTpExvbj+eee5a//tpP06YNitXW2Y0M9ImyAytAQsKfBAcHYzZHy6Lw4po4Chaq1yUlQROVw4hLpH70Ocrx42rHEFdgMplJT08jvWxZyuTmohw/jqtiRbVjeaWkJBshIaEEHz+OKyAAV/nyakcSQgghPK7YI+WsVmvI2X+ASkAm4J5hLkKIUqd37zgAunbtSECAgcaN67B06edFayw4GGf5CuikKAdAQoKVNjFVibj3Dvx2bFc7jvABTmP+2o76JNmB1SvpdFLg8XKmgtFeRwz+gDyXriQpKQmj0YjObsNhMsuXR0IIIUoFd09f7QscBTa7uV0hRCnx44+b0el0pKam4nK5sNkSefLJYUUuzDmio2WkXIGEBCutK1Qg4NuvUbKz1Y4jfICrbDlcgYH5I+WEVwmaM5MyE8aoHUNchdlsBiARF4A8l64gKcmG0WhCb0vEKVNXhRBClBLuLsrdD3xgtVpdbm5XCFFKTJkyCedFa+5kZmYyZcqkIrXniK4iI+WAtLQz2O026oWGAf9N7RXiihQFh9GELsmudhJxkcAln+H3+z61Y4irODtS7q+CzVJ0MlLusvJHyplIHzeRjJGj1I4jhBBClIhiryl3lsViiQHaAw9d7Wf1eoWIiGB3ndrr6PU6TfVPa/0B6ZM3s9sL/8Bit9uK1r/nJ+LKy/OK342a1+ivv/I/vN8QFIBLpyPsplpgMBS7Xa087s6ntT4Vuz8vvIhf+Uiv+p1o7RrBdfbp6FH8ft+L48XJXvt7KPXXqEBYWAwBAQEkZqbhmPYKgR1vIdCLfi/ecp1yc3M5evQI1atXpUz3rkVux1v6407SJ9+gtT5prT8gffIFWuvPtXJbUQ64D/jBarX+fbUfdDhcpKRkuPHU3iUiIlhT/dNaf0D65M1MJjM2W2Khtxepf+bq+f/2gt+Nmtdo585fAaiQnokzykhKei6QW+x2tfK4O5/W+lTs/tzaPf/fXvQ70do1guvrU8CXazEAqc1ak+elv4fSfo3OFxVl5MDf/3ByzMT8G7zo9+It1ykx8RAul4vKQaFkfL6U3KbNcZWLvO52vKU/7iR98g1a65PW+gPSJ1+gtf5crEKF0EJvd+f01fuAhW5sTwhRCo0bN5GgoKALbgsKCmLcuIlFak85cYLAjz9AV0ihrzRJSLBiMBgIrhxFXiPZRlNcO+XECQxbfgCHQ+0oooDh+404w8LJa9BI7SjiGpjN0dhsNnRJdvz2/Kp2HK+UVLDW3o05WYTfcwd+e35TOZEQQghRMtxSlLNYLK0BE7LrqhCimOLi+hMfP5vy5csDUL58BeLjZxMX179I7emOHiF05FAMP29zZ0yfs3+/lRo1apLxSjyp736odhzhQwJWLiei9+3ojh1VO4oo4CpfgezYvqDXqx1FXAOTyYzdbqPMixMJe+AeteN4paSCtfaMjvw1ZZ0FG2QIIYQQWueu6av3A8usVusZN7UnhCjF4uL607VrN2rXjmHAgLuLXJADcEbnL7KtSyzdI+Ws1j+pW7e+2jGED3KaTADo7DaclaNUTiMA0os4cliow2Qyc/hwMrmVKxOQnAROJ+jcvdeabzs7Uq5CRv60JYdRinJCCCFKB7e8I7BarY9ardZ73dGWEEIAlClThpYtW7J586ZiteMKCcVZrhz6UrwDa1ZWFv/++w8tKlembIfWGDZuUDuS8CGOqIKiXMGHZqEuJe0MuGSTe19iNkfjdDo5FRKKkpuLcvy42pG8TnKynTJlQgg+fgxn+fJw0TIWQgghhFbJ13RCCK/VseMt/PbbL5w6dbJY7Tiiq6BP/NdNqXzPgQN/4XQ6qR8ait/ve2WEhrguZ0fK6ZPtKicRACEjhxHRtaPaMcR1MJn+z96dx8dV1/sff51zJvvStE3aZmaSQpecAi1l30rSIrK5X1G8Kldx1yv6U9yvqIC4K+534bqCgqIgrih6gbYg+9IF2pN0TTIz3Zu02TNzzu+PSQtt03aSzMyZmbyfj0cfpGd9fzoTknzyPd9vctTXNiv5gIo+l44UiUQIhUKYkU4SoQa/44iIiGSNfjITkZx18cUX43keDz+8ckLXcRsaMSfxSLm2NgeA+UXFACQaGv2MI3nGmzoNr7RUI+VygetS/PByEvNtv5PIGITDySbTVje5WIoZUVPucLFYhPr6IL03f5Web37H7zgiIiJZk6455URE0u6ss86moqKSlSsf4tWvfu24r9Nz81fxikvSlivfOM56TNOkfnAQzzBwQ5qrR8bAMNj3o5+TmDPP7ySTnvX8WszduxlqWeZ3FBmDYDA52nRdPM6+W39K/AytgH24SCTCxRefTGLufL+jiIiIZJVGyolIzioqKmLJkgtZseKhCV3HDYbwRlZznYza2lqZPfsESmJR3PogFBf7HUnyzNClV5CYpx+W/VY8MsfmsJpyeaWiooJp06axaedOBl93pRZMOczw8DA7dmxn9vRaSn/2Y8wtm/2OJCIikjVqyolITmtuXsqmTRvp7Bz/6qlmpJPyr38Zc/OmNCbLH62t62lqskmccCJDL7/M7ziSh6xWh+I//cHvGJNe8YoHic9vUlMnD4VCDUQiHQSefpLAY4/6HSenbN++Dc/zWFBcTNUnP0pgzSq/I4mIiGSNHl8VkZzW3LwMgJUrl/PmN189rmsY3d1UfPOrJOwFDJ44J43pcl88Hmfjxg1ccsnl9H3sU37HkTxV8ttfU/6D77CrYydYlt9xJq3+d78Po7fX7xgyDqFQmK1bN1Nx0+fB8+j+w1/9jpQzIiNz7J1oGgCaYkFERCYVjZQTkZx20kknU1tbN6FHWN2G5CTbZsf4R9vlqy1bNjM8PMz8+U1+R5E85tYHMeJxzJ07/I4yqQ29/DIGX/t6v2PIOITDYTo7O3Hrg1haNOUQsZHVaGcNxwG0+qqIiEwqasqJSE4zDIOWlqWsXLkcz/PGdQ2vqhq3pgarY2ua0+W+1tbkyquLp05lelMjxX/8vc+JJB+5oeRE9WZUq0b6peixfxJY9azfMWScQqEG9u/fR39tLWYsAq7rd6SccWCkXG1fH15xMV5dnc+JREREskdNORHJeS0tF7Fjx3YcZ/24r5EIN2J2tKcxVX5obU3+m80vKsLs6sKrqvI5keSjRP1IUy6ippxfKr74BSo/dZ3fMWScwuHkI5m7Ssswhocxdu3yOVHuiMUiVFRUUrZzB24wBKZ+PBERkclDX/VEJOc1Ny8FYMWKB8d9DbehEXPbtnRFyhutrQ6hUJiKnTsBcBsbfU4k+cgNJptyVkxNOT8Y+/cReOYphkfm2JT8ExqZJy1mJb/11ufSi6LRKMFgkJ5vfJuuX//O7zgiIiJZpaaciOS8hoZGTjxxDitXLh/3Nfb98Fa6/rEijanyQ2urw/z5TVgjowQ1V4+MhzdtGnv/eD8Db3iT31EmpaJHH8FIJBhqWeZ3FBmncDj5/97nqqew909/Jz5P83weEI12EgyG8Kqn4E6yxZhERETUlBORvNDcvIxHHnmYeDw+vgtUVk66R2Jc12XDhlZsewFmZweJmbOgpMTvWJKPDIP4uefhTZvud5JJqWjlcrzSUobPPtfvKDJOM2bMpKioiI179xI/51yoqPA7Us6IRqM0zKqn4ks3Enj2ab/jiIiIZNXk+glVRPJWS8tSenr28+w4v2G3NrZR+ZEPYrW1pjlZ7urs7KCvr4/5823iZ5zFwJuv9juS5LGi5Q9S8us7/I4xKRX98xGGzzkfSkv9jiLjZJom9fUhOjs7KLn3booeWel3pJwwPDzM9u3bWFBdTfl3v0Vg7Rq/I4mIiGRVwO8AIiKpWLKkBcMwWLlyOWePZ7RIXz9ld9zO0MWXkJg/OR4bamtLrrza1LSAgfPO9zmN5LvSu+6k6NFHGHzTW/yOMul0/fFvmLt2+h1DJigcDhOJdFJx840Mn30Ow0ua/Y7ku+3bt+F5HvOKk6O4EyNz74mIiEwWGiknInlh+vTpLFx4KitWPDSu8w8scGB1dKQxVW5znJGm3Lx50NvrcxrJd24whLktBomE31Emn/Jy3MbZfqeQCQqFkk25RCiEGdVCD5B8dBWgEQ8AN6x5T0VEZHJJS1POtu0TbNv+i23be23b3mbb9g9s29YoPBFJq5aWZTz11BP0jqPB5E2pwa2egtWxNQPJclNbm0NtbR21g4PUnVhPyZ2/8DuS5LFEMIQRj2vEVpaVfe/blP3gu37HkDQIh8PEYlESs+qx1JQDkos8AMwaGgaS/58RERGZTNI1Uu4/gR1APXAasBT49zRdW0QEgObmpQwNDfH444+O63y3oRFzZBXSycBx1tPUZGOOjA50Z87yOZHkM3fkh2Uz0ulzkknE8yj72Y8oevpJv5NIGoRCDSQSCfZNmYIZi4Lr+h3JdwdGyk0d6MedOjW5KJOIiMgkkq6m3InAXY7jDDiOsw34K3BKmq4tIgLAueeeT3FxMStXLh/X+fG58ybND0Ge59HW1kpTk31wdKAef5OJODCCxRz5IVoyz9y8Cauzg6HmpX5HkTQIh5Pzpe0sLsEYGsLYvdvnRP6LxSJUVFQy9I3vsOfptX7HERERybp0PWL6XeBfbdt+CJgKXAF8Lk3XFhEBoKKigrPOOmfcTbn9P/p5mhPlrh07ttPd3TXSlEuODtQE2jIRCXsBu599QSMus6h45P91w0uX+RtE0iIUSs6X9vSCBdQ/+wLe9Ok+J/JfJBIhGAximCZeZZXfcURERLIuXU255cB7gH2ABfwcuPdoB1uWQU1NeZpunXssyyyo+gqtHlBN+WK0mi655OXcdNONJBL9TM+zH2iy+Ro9++wWAM44YzFld92FN3MmNfXp//eaLO+7fJa+esphRk0arjNxhfYaweg1WY+uxAuHqTrjVDAMn5KNz2R5jcbi5JPnAxAd6KX6lNxYBdzv12nHjhiNjQ1M/8x1uK96Fd4rXzWh6/ldTyaopvxQaDUVWj2gmvJBodWTqgk35WzbNoG/Af8DXABUAj8BvgZ8crRzEgmPrq6+id46Z9XUlBdUfYVWD6imfDFaTeecswTP8/jLX/7Gq1/9ujFdL7BmFRU3fZ6em79Gwl6QxqSpyeZr9MwzqwAIBk9gf8vFWCfOoz8D954s77t8ls56Sm/7KQQCDLzl39JyvfEqtNcIRq+pyirCu+wV9HT3+5Rq/CbLazQ2FjU1NWxx2hi88YvEzz6X4QsuTFu+8fD7dWpv7+AVS5oxf/wj+sIn0L/kZRO6nt/1ZIJqyg+FVlOh1QOqKR8UWj2Hq6sbfUR4OuaUmwY0AD9wHGfQcZzdwE+BV6Th2iIihzj99DOprKxi+fKHxn6y51G8/EGsDW1pz5VrWlsdqqunMHPmLIZe8Sr63/dBvyNJASj53W8pveN2v2NMGvt/eCs9X/mm3zEkjUKhBtpjUSq+/mWK/+/vfsfx1fDwMNu3b2NBRQUAblhTLIiIyOQz4aac4zi7gM3AB2zbDti2XQO8HVg10WuLiBwuEAiwZMmFrFz50JjPTTQ0Ahxc+KCQtbY6zJ/fhOF5WG2tMDDgdyQpAG59MLlqpGTe8LDfCSQDwuEwHZEIbn0IMxrxO46vduzYjud5zCsuBl5cTEZERGQySdfqq68HLgd2AhuAOPDRNF1bROQQzc1L2bx5Ex0jCxikyquZiltZhTnG8/JRa6uDbS/A3LGdaUvOovTOX/gdSQqAGwonm3KJhN9RCt6Uq6+i+h1X+x1D0iwUChOJdJAIqsEdiSSbkuGRVdHdcIOfcURERHyRloUeHMd5DliWjmuJiBxPS8tFAKxcuZy3jGVuK8PAbWjA6ujIULLcsHfvHnbu3MH8+TZme7IB6TY2+pxKCkGiPogRj2Pu2qlVWDNpcJCix/5J/1vf5ncSSbNQqIGuri6G6mZQtvo5v+P4KhZLNuWmlZbhTqnBnTHT50QiIiLZl66RciIiWWPbC5gxYyYrVjw45nOHzzoHt7Y2A6lyR2trKwC2bR98VDfRMNvPSFIg3FAYzzQxt2/zO0pBK3r6SYz+foabl/kdRdIsPDJvWldVFebOHeB5Pifyz4GRct5HP87u1q1gWT4nEhERyb60jJQTEckmwzBobl7KihUP4XkehmGkfG7Pt76XwWS5obV1PQDz59tYv/stAAk9FiRpMPSyl7OrcxcE9O1DJhWteBDPNBle4u/KnJJ+oVDy/8WPXXYFS7/5XRjD169CE4tFKC+voLp6yqT+dxARkclNI+VEJC+1tCxj584drF+/zu8oOae11aGsrIyGhkbMjvbkyMDycr9jSSEoKlJDLguKVywnfvoZeNVT/I4iaXZgpNzWnTsn/edSNBolFApR/b53UPrzn/gdR0RExBdqyolIXmpuXgow5kdYA489ytQlZ2G98HwmYuWE1tb1zJvXhGmaDLzprfTc9BW/I0kBqfjcZyj51S/9jlHQBq5+O/3v+6DfMSQDZs6chWVZ7GtdT+WnriPw9JN+R/JNNNpJaFaQkj/+HjPS6XccERERX6gpJyJ5KRxuYM6cuaxcuXxsJ5aWEGhrxdq6JSO5ckFbWytNTTYA8XPOZfANb/I5kRSS4vvvo/jBf/gdo6ANvOXfGHzdlX7HkAywLItgMEQ0FqXspz8i8NyzfkfyTTQa5eSpUzESCdxQ2O84IiIivlBTTkTyVnPzMh555GGGh4dTPufAggcHFkAoND09PXR2diSbcp5H0UMPYOzY4XcsKSBuMIQVjfodo2AFnnoCs6Pd7xiSQcFgiHW7duEVFWHFJufnUjweZ/v2bSwYmVrBDaspJyIik5OaciKSt1paltHb28Ozzz6T8jnetGl45eUF+0Pvhg3JlVebmhZg7NhBzVWvo+SP9/obSgqKWx/EjEb8jlGwqq77EFUf+7DfMSSDQqEwHZFI8nNpkj62uX37NlzX5UQrOa9eIqTFiEREZHJSU05E8taFFzZjGMbY5pUzDBINjVgdHZkL5iPHSa682tRkHxwN6DY2+hlJCowbDGHGouC6fkcpOMb27QTWr2OoeZnfUSSDwuEGYrEIiVn1yc+lSSg60tivmT6N+Pwm3FDI50QiIiL+UFNORPLW1KnTOPXU08Y8r9zQJZcTP+nkDKXyV1tbK0VFRZxwwolYncnGYyKsppykT6KhEbduBkbXXr+jFJzilQ8BMNyy1NccklmhUJjh4WF6Z8zAGMP0C4XkQFMu8fqr2PvIU3hV1T4nEhER8cfkXotdRPJec/NS/ud/fkhvby8VFRUpndP7+ZsynMo/ra3rmTNnLkVFRZjtyUd03QY9FiTpM/D2dzLw9nf6HaMgFa1cjjt1KvGFp/odRTIoPDJ/2mMf+BBnnX2uz2n8ER2ZlzIYDPqcRERExF8aKSciea25eSnDw8M8/vg/x3ai5yX/FJjWVoempgUAWB3tuNOm4VVW+ZxKRFJRvHI5w0tawLL8jiIZFBqZPy0yiedmjEY7KS+voPHa91PxpRv9jiMiIuIbNeVEJK+de+75FBcXs2JF6o+wFj3wd6bPCWGteyGDybJvYGCALVs2M39+EwD97/939v3PT31OJQWnr4/qf3sTJb/7rd9JCk7Xff9H7/Vf8DuGZNiBkXLDTz1J9dVXYbU6PifKvmg0SjAYpOiJRzH2dfsdR0RExDdqyolIXisvL+fss89lxYqHUj7Hq5mK2duDVWArsG7atBHXdbHt5Ei5xNz5DC+9yOdUUnDKyih+6AECq1f5naTguDNnkZgzz+8YkmHV1VOoqqpm97YYJff/FWvTRr8jZV00GmFO3UzMri6tvCoiIpOamnIikvdaWpaxdu1qdu/endLxiYbZAJgjq5MWitbW5Mqr8+fb4HmU3PkLrI1tPqeSgmMYuPVBzNjkffQuE8xvfoOSu+70O4ZkSTgc5oWREWLmJHyMNRqNsKimBgB3ZOSgiIjIZKSmnIjkvebm5EqFDz+c2iOsXm0tXlkZVnthjZRrbXUwTZO5c+dh7NpF9f/7d4oe/D+/Y0kBSgRDWJHJ10jIGNfF/OY3KHpkpd9JJEtCoTDP79yJFwhgTbKmXDweZ/v2bTSVlgJopJyIiExqaWnK2bZ9km3bD9i23W3b9gbbtv8lHdcVEUnFaaedQVVVderzyhkGiXADVmdHZoNlWWurQ2PjbMrKyrBGRgG6I6MCRdLJDYYwY1G/YxSMwNrVGHv2MDzyCwYpfKFQA53RzuSo00nWlNu+fRuu6zJlVj1DLRfhztbXKRERmbwm3JSzbTsA/B74EzANeC/wC9u2myZ6bRGRVAQCAZYsuZAVKx5M+ZyBf72aoQL7AbitzTk4n9yB+fISDY1+RpICFT/pFBKhcEGuYJxtJXffxZQrXw1AxRe/QMndd/mcSLIhHA6zZ88eBk5ZhFc1uVbIjo40Id3mFrp/+3vcWfU+JxIREfFPOkbKLQCCwLcdx0k4jvMA8Ajwb2m4tohISpqbl7J16xba21ObJ67/Qx9h4B3vznCq7InH42zY0JacTw4wRx7NdRv0WJCkX/+HPkL37+8Dw/A7Sl4rufsuqq77EGZ3cm4xKxal6roPqTE3CYRCyXnUVn3uRnq+dovPabIrNjLKtr4+5HMSERER/6WjKTfad+QGsDAN1xYRSUlLS3KV0ZUrU3yEFTC69kI8nqlIWbV162aGh4dpako25azOdtyaGryqap+TicjRVHzpRoz+/kO2Gf39VHzpRp8SSbaEw8lfmHQW2DQKqYiMzEd59uc/Q9V7r/E3jIiIiM8CabjGemAH8Anbtr8NXAQsBY76HJllGdTUlKfh1rnJssyCqq/Q6gHVlC/GUtM555xOfX09jz66kg9+8P3HPd74zW8IvPXNDD+7Ck45ZaJRU5LJ16izcwsAZ565OHmPL3+JxLUfzPh7YrK/7/JBRurZuJHAv76JxBe/iHf5Fem9dgoK5TUyI51H3Z7v9RXKa/RS6azppJPmA1C9/O/UfvlG4vf9FWpr03LtsfDjddqzZzvl5eWUd3bghYJpvb/ed/lBNeW+QqsHVFM+KLR6UjXhppzjOMO2bb8O+D7wKeAp4C5g8GjnJBIeXV19E711zqqpKS+o+gqtHlBN+WKsNS1Z0sIDD/wfe/b0YJrHHggcqKllKtD3gsNQ6MQJJk1NJl+j555bDUB9/ezkPawyaJgLGX5P6H2X+zJRj+EGqF31HP1r1zFwXvbnZiyU12haKDzqgjNuKJz39RXKa/RS6aypvLwG0zSJdUQxVj1Hz7o24ouy/4OIH6/T5s3thOqD0L6VgVe/jt403l/vu/ygmnJfodUDqikfFFo9h6urG30O2bSsvuo4zmrHcZY6jjPdcZzLgDnAE+m4tohIqlpalrFr1y7WrXvhuMe6jcnV3syOwnh0yHHWEwyGqKysAs+j/JavE3jmKb9jSYHyamvxiouxIpNr1ch06/3sF/ACh/5+1Csro/ezX/ApkWRLUVERs2bV0zby+LI5iT6XIpFOTqmtwxgeTi4YIyIiMomlpSln2/aptm2X2rZdbtv2x4F64GfpuLaISKqaR1ZTXbnyoeMe69bNwCspObhKab5ra2s9OJ+csWcPFV+9maKn9LsRyRDDwK0PYsYmTyMhEwavvIpEw2y84mI8wyARbmD/Ld9n8Mqr/I4mWRAKhXlhXxcAZnTyfC7FYlFOGVlx1g2rKSciIpNbWppyJFdajZGcW+5i4BLHcY76+KqISCaEQmHmzp3HihUPHf9g0yQRbsAsgKac67q0tTkvLvLQkVyBNtEw289YUuASobBGyk1UIoG1fRv9b3sH8cFh9jzzvBpyk0g4HGb19u14gQDWyIqkhS4ej7NtW4zqUIj+N19NfF6T35FERER8lY6FHnAc5xPAJ9JxLRGRiWhpWcZdd/2K4eFhioqKjnls/7Ufwa2ZmqVkmROJdNLX10dT0wLgxUdyEyOr+4lkwvCFLZi7d/kdI69Zmzdh9PUSP/U0jv1/KylEoVADf/7zHxm85DISs2b5HScrduzYjuu6mAsX0/P2d/odR0RExHdpacqJiOSK5uZl/PSnP+KZZ57m3HPPO+axA299W5ZSZVZr63qAl4yUS47+cxsbfcskha/v45/2O0LeC6x+DoD4wlP9DSK+CIXCDA0NseHr32HmzJl+x8mK6Mhjuo21teB5YBg+JxIREfFXuh5fFRHJCUuWXIhhGKxY8eDxD+7rw3p+LQwNZT5YBrW2tgIvNuXMznbc6il4U2p8TCUix+NOncbgZVeQsBf4HUV8EB4ZzRyJFMaCQ6k40JS76Me3UvPyFp/TiIiI+E9NOREpKFOnTmPx4tNYuXL5cY8tue9PTLvoAqwtm7OQLHNaW9dTW1vLtGnTAei9+WvseexZn1NJoSt6ZCXTF5xA4Lln/I6St4Yvuph9t/8ajvOovRSm0MjKozU/+V+mLV6QHDlW4A405ar27sGdMcPnNCIiIv5TU05ECk5z8zKeeuoJenp6jnncgYUQDiyMkK9aW52D88kBYJp4tbX+BZJJwauqwtyzBzM6OSaoTzvPw+jZ73cK8VF4ZOXRvfv2YcWiGHv2+Jwo8yKRCOXl5RRvi+GGNO+piIiImnIiUnBaWpYRj8d5/PF/HvO4A3Oume35uwKr53m0tjrMn28f2EDlJz9K0QP/8DeYFLxEfQgAM9rpc5L8ZMai1M4JUfKrX/odRXwyZUoNFRWVbEnEAbAmwedSLBblxJmzMHfvxh1pSoqIiExmasqJSME555zzKCkpYfnyh455nDtjJl5x8cGFEfLRjh076O7uwraTTTmjay9lP/sxAWe9z8mk0HnTpyc/fzRSblwCa1YDkJgzz+ck4hfDMAiHw7T29QFMilGn0WiExdOSI7m1QriIiIiaciJSgMrKyjjnnPOOP6+caZIIhTE787cpd2Dl1QMj5Q40GBMNWnlVMsw0ceuDGik3ToHVz+EZBvGTT/E7ivgoFAqzpmsvAGak8D+XotEI1aEgvR/7FPHFp/sdR0RExHdqyolIQWpuXsrzz69h586dxzyu94Yv0f+u92cpVfq1tjoA2COrNx54FPfAo7kimTTwhjcxfPZ5fsfIS4E1q0nMnQeVlX5HER+FQg2s3raNgde9HrehsEeOxeNxtm/fRtncefR96rMk5jf5HUlERMR3Ab8DiIhkQnPzUgAeeWQFr3vdlUc9buiKV2YrUka0tq6nqqqamTNnAWB1aqScZE/fpz7rd4S8FVi7muGzz/E7hvgsHA6zY/cudnz3vygrK/M7Tkbt2LGdRCLBvMpqjF27tCCRiIgIGiknIgVq8eLTqa6ectxHWI0dOyj+x99gcDBLydKrra2VpiYbwzAAMPbvx506Fa9mqs/JZNLo7wfP8ztFfvE8+q79CANveovfScRnoVBysYNYLALDwz6nyaxoNALAZcsfYOrFF/qcRkREJDeoKSciBSkQCHDBBRced7GH4uUPMOUtb8zbxR4cZz1NTfbBv/d94jPsXrcZRpp0IplUevvPqJs9E+M4j4nLYQyDgXe+h+GXXeJ3EvFZeGSxgxmf/RRTmwt75GQsllzIYtr+/bghrbwqIiICasqJSAFbunQZ7e1b2LJl81GPSTTMBsBs35qtWGmzd+8edu7cQVPTgkN3mPpfu2SHW1sHgKXFHsbE2tiGtWmD3zEkBxwYKbfH87CikYIedRoZWciiYs9uEmE15UREREBNOREpYM3NywCO+QjrgYm1rc6ObERKq9bWVgCaml6cLLv67W+h5He/9SuSTDJuMAiAGY36nCS/lH/jq0x5/av9jiE5oL4+iGEYdALGwADGnj1+R8qYaDRKeWkpRdtiuKHCXtRCREQkVWrKiUjBmj+/iVmz6lm58qGjHuPOqscLBPLy8dW2tuTKqwdGyhndXZTc9yfMWMzPWDKJJILJ0S5mLOJzkvwSWLua+KJT/Y4hOaC4uJiZM2exeWgIADNauJ9L0WiEhTNnYQwOaqSciIjICDXlRKRgGYZBc/NSVq5cjuu6ox9kWbihMGZH/j2+6jjrKSsro2FkpVWzXSuvSnZ506fjFRdjaaRc6vr6sDa0EV+oppwkhUJh1vX0AiQfYS1Q0WiEqcEQ+2/5PsMjI9lFREQmu0AqB9m2fS1wDbAIuNNxnGtesu9i4IdAI/A4cI3jOPn3062IFKTm5qX85je/4oUXnmfhwkWjHrPvh/+LVzs9y8kmrq3NYd68JsyROeQOPIJ74JFckYwzTfqu+yTxxaf5nSRvBF5Yi+G6xE89ze8okiNCoTDPrH6OvvdfS6KAF0CIxaLMWdLMwNVv9zuKiIhIzkh1pFwUuBn4yUs32rZdC9wDfA6YBjwF/DqdAUVEJqKlZRlw7Hnl4uecS2LOvCwlSp/WVof581+cT84aGe13YPEKkWzou+6TDF18qd8x8kZgzWoAPb4qB4VCYdZui9Fz45dIHOWXR/kukUiwbVuMUysqCKx+rqAXtBARERmLlJpyjuPc4zjOvcDuw3a9HnjecZzfOI4zANwALLZt+7ClAEVE/BEMhpg3bz4rVjx41GPMTRsp/flPYHAwi8kmpqenh87ODl76v1vPsojbC/CmTfMxmUw6/f2YW7f4nSJvDL7yNXT//E7cAh4RJWMTDocZGBhgT2cnxo4dfsfJiB07tpNIJLisrZWa11zhdxwREZGcMdE55U4BVh34i+M4vcDGke0iIjmhpWUZjz76T4ZGJtI+XNETj1H1iY9gRfJnBdYNG5Irr86fbx/cNvCu97F35RNgGH7Fkkmo4htfYdqSs+Bo8zbKIbwZMxi64pX6PJWDQiMrkc76tzdR/f53+pwmMyKRTgBmDA4kF3nQ+19ERARIcU65Y6gEdh62rRuoOtZJlmVQU1M+wVvnLssyC6q+QqsHVFO+SFdNl19+GT/5yf/S2rqGCy9sPmK/cXLyEdDqPdvxzsjcI2XpfI06O7cAcNZZp/n6uut9l/syXY8570SMoSFqhnth5syM3eel8vY1Gh7G/J//xr38Cph36CPzeVvTURRaPZC5mk46Kfle2FdTzZRt0az+u2XrderuTj5sM723F/OE2Rm7p953+UE15b5CqwdUUz4otHpSNdGmXA9Qfdi2amD/sU5KJDy6uvomeOvcVVNTXlD1FVo9oJryRbpqOu20szFNk7/85W8sXHjmEfvNqTOZDvSv38DAWUsmfL+jSedr9NxzqwkEAkyfXn/wmjVXXMzAG97EwLvem5Z7pELvu9yX6XqKa+qYAvSs30C85Ji/k0ubfH2NrBeeZ9p1H6W3tJLB2uAh+/K1pqMptHogczVVV9cBEDUsGjo76drbm7WRZNl6nTZs2ARAyY7tDJ55Nj0Zuqfed/lBNeW+QqsHVFM+KLR6DldXN/r3yRN9fPV5YPGBv9i2XQHMHdkuIpITamqmsnjxaaxY8dCo+936IJ5lYXbkz8LRra0Oc+fOo6ioCABj/z6Knn4So69wv5BJbnJDIQDMSMTnJLkvsCY540d80eLjHCmTybRp0ygrK6PdA2NgAGPvHr8jpV0kEqGmtJTArl2aT1FEROQlUmrK2bYdsG27FLAAy7btUtu2A8DvgIW2bV85sv/zwGrHcdZnLrKIyNi1tFzEM888RU/PKAN5AwHcYAirvT37wcaptXX9IfPJmR3J+fDcxka/IskklagfacrF1JQ7nsDa1XhlZSTmzfc7iuQQwzAIhcJsHBwAwIxGfU6UfrFYlJn1Qbp+dQ+Dr/0Xv+OIiIjkjFRHyl0P9AOfBq4e+fh6x3F2AlcCXwL2AucC/5qBnCIiE9LcvJR4PM6jjz4y6v7u39xLz1e/meVU4zM4OMiWLZtpanqxKWd1JBuKiQY15SS7vNpaer74FYbPy9yj34UisHoV8ZNPAcvyO4rkmFAozMMD/fTc9GW8ujq/46RdNBphRriB4Ze9nMRcNaVFREQOSGlOOcdxbgBuOMq+fwAL0hdJRCT9zj77XEpKSlixYjmXXHL5EfsTc+aNclZu2rhxA67rHtKUO/DobaJhtl+xZLIyTfrf90G/U+Q+zyOw7nkGX3ul30kkB4XDDfx93Qv0v/9av6NkRDQa4apFp1J8358ZuvgSKC72O5KIiEhOmOicciIieaGsrIxzzjmflSuXj7o/sOpZyr98EwwNZTnZ2LW1OQA0Nb34+xBv6jSGmpfi1db6FUsmMbOzg8Dq5/yOkdsMg93PrqP3U5/1O4nkoFAozI4d24mvX4e5ZbPfcdIqkUiwbVuMS7u6qL7mLeB5fkcSERHJGWrKicik0dKylBdeWMvOnTuP2Gete4GK73wTM9LpQ7KxcZz1GIbB3Lkvju4bvPIquu/+Y9ZW7BN5qYqbb6D6XW/zO0buq6hQ41xGFQ43ADDtja+l4ptf9TlNeu3YsZ1EIkEwkcCdMRNKSvyOJCIikjPUlBORSaOlZRkADz985Gg5d2QutgNzs+WytrZWZs8+gbKyMr+jiADgBkOYsSi4rt9RclbJr++g/Ktf9DuG5KjQyIqkPTVTMaOFtWhKdKSeuv4+3LBWXhUREXkpNeVEZNI49dTTmDKlhhUrHjpiX2JklILV2ZHlVGPX2rr+kPnkAKaddSrlBTa6QvJHIhjEGBrC2L3b7yg5q+Teuym57y9+x5AcdaApt6e8vACbcsnVZKu7u0mEGnxOIyIiklvUlBORScOyLJYsaWbFiofwDpvTxg2G8EwTs32rT+lSE4/H2bhxwyHzydHTg9W+Ba9YjwSJP9xgsqFgxQqrmZBOgTWriS861e8YkqOCwRAAsUAAKxopqHnXotHktBBlO3fghjRSTkRE5KXUlBORSaW5eSkdHe1sOXwi7aKi5CN422L+BEvR1q2bGRoaOmSk3IHRfW6DRiCIP9xgEAAzoqbcaIzt27F2bFdTTo6qtLSUuroZtLsuxsAAxt49fkdKm2g0SmlJCXvvX07/u9/ndxwREZGcEvA7gIhINh2YV27lyuWceOKcQ/btXf4oXmWVD6lS19raCnBoU64jObovMTIvnki2JebNp/vHtxE/40y/o+SkorWrAIgvWuxzEsll4XCYv1oWr/7xbXilhTNnaDQaIRgK4y44ye8oIiIiOUcj5URkUpk3bz719UFWrjxysQevqjrnVy9tbV0PwPz5TQe3mR0HRsqpKSf+8CqrGHr163BnzvI7Sk4y9uzBnTqV+MJFfkeRHBYKNfDE3j0Mvfp1UF7ud5y0iUYjLJlSQ+mPb8XY1+13HBERkZyippyITCqGYdDcvJSVKx/CPWylyKKVy6m69n0wPOxLtlS0tjoEgyGqqqoPbnNnz2bgyqtw62b4mEwmu8CTj1P02D/9jpGTBt/4r+xevwWveorfUSSHhUJhYp0dBJY/iLWhze84aROLRbnI86j6zMdhKHe/voqIiPhBTTkRmXRaWpaxZ88enn9+7SHbrfatlN51J2Ys6lOy42ttdQ4ZJQcwdPGl7P+vH4Gp/6WLfyq++AXKv/JFv2PkrhwfhSv+C4fD9PX3U/Ovr6fkN3f6HSctEokEsViUE0wTr7QUb/p0vyOJiIjkFP0EJyKTzoF55VaseOiQ7QfmZLM62rOcKDWu69LW1optLzh0R2+vP4FEXsINBpOrRsohjP37mLrsAor/dp/fUSTHhUINuMDg9OlYBbJoys6dO0gkEtQPD5MIhdWcFhEROYyaciIy6cyaVU9Tk83KlQ8dsv1AU87M0aZcJNJJX18v8+fbh2yffvYiKv7jEz6lEklyg+HkKFPP8ztKTgmsXUPghbVg6VsuObZwOAzAvuqanB6xPRaRSCcAtX29uCGtEC4iInI4fYcoIpNSc/NSHnvsnwwNDR3c5obCeIaB1b7Vx2RH19bmABw6Uq6vD3PXLrwZM31KJZKUCAYxhoYwdu/2O0pOCazRyquSmtBI02pXWSnmSDMr30WjyeZiVddeEiNNRxEREXmRmnIiMim1tFxEX18fTz/95Isbi4tJnDgHY3DQv2DH4DjJptxLR8pZncmVVxNaeVV85gaTP3Bb0cJoJqRLYM1q3LoZWplWjqu2tpaSkhJipoVVIKNOY7HkY7jtf3uQ3s/d5HMaERGR3BPwO4CIiB8uuGAJpmmyfPmDnH/+koPb9z76TM7OedPW5lBbW8v0l0yUbXUkR/UlGmb7FUsEgOHzL2Dvff9HfF7T8Q+eRAJrVhNfdKrfMSQPGIZBKBTmt7V1nPHVbyabcjn69ShVkUiE0tJSpjTMxsvzWkRERDIhpZFytm1fa9v2U7ZtD9q2/bOXbC+2bfu3tm1vsW3bs217WaaCioik05QpNZx22umsXLn80B05/EOD46w/Yj45sz05/53bqJFy4i9v6jTiZ54N5eV+R8kdnkf81MUMvvxSv5NIngiFGniya2/yc6kAVtSOxSK0TK+l8uYbCmaePBERkXRK9at9FLgZ+Mko+x4Grga2pSuUiEg2tLRcxDPPPMX+/fsObiv+0x+YcuWrIR73MdmRPM+jrc2hqenQlVfji06l70MfxdWccpIDSu6+i6LDG92TmWGw/3v/xcC73+93EskT4XCYfZ0dlPzql1ibNvgdZ8IikQjN5RWUf//b0N/vdxwREZGck1JTznGcexzHuRfYfdj2IcdxvuM4zsNAIgP5REQyprl5KYlEgkcffeTgNnPvHopXLs+53+jv2LGDrq4umpoOfTQwftY59H7uxoIYUSH5r+IrX6T0jtv9jpE7BgcLYl4wyZ5QKEzf9m1Uf/gDFK3I/wZ3LBZlXnExAG4w5HMaERGR3KOf4kRk0jr77HMpLS095BHWAwsmHFhAIVccWHn18JFy5uZNGD37/YgkcgS3PphzDW0/VX7+M0w79zQ15iRl4XADMcCzLMyRRRLyVSKRYNu2GI0GuHUzoLTU70giIiI5x5eFHizLoKamcOecsSyzoOortHpANeWLzNdUzpIlS3j44RUv3ueU5Jxtlbu24aX53hOpp6NjEwBnnXXaIdcIvPZyvCteQeJ/bk1LxrHS+y73ZbMea3YjxlNPZvx++fIaWevWQjhEzdSK4x+bJzWlqtDqgezUZNtzcYHB6dMp27WD4jz+XIpGo8TjcUKJODQ2ZuX9oPddflBNua/Q6gHVlA8KrZ5U+dKUSyQ8urr6/Lh1VtTUlBdUfYVWD6imfJGNms4/v4Wbb/4CjrOZmTNnQtV06oDB1g30pfneE6ln1ao1VFVVU15e8+I1+vup27aNvpnBtGdNld53uS+b9VTUzaKss5Ouvb0ZXTQlL16jRILa1avpf+vb6E0ha17UNAaFVg9kp6aamjoAuioqmbZlK90Zv1/malq/PjknXsXgEMNz5rMvC+8Hve/yg2rKfYVWD6imfFBo9Ryurq5q1O16fFVEJrWWlqUAPPzwyCOspaUMn3MeXllu/ZamtdWhqakJ4yWNDivSCbz4yK2I39xgEGNoCGPXLr+j+M7atBGjr4/4osV+R5E8EgyGAdhRWooZze/HVyORZP7V//tz9v3PaGvFiYiISEpNOdu2A7ZtlwIWYNm2XWrbdmBkX8nIPoDikX2Z+/W4iEgaLVq0mClTag6ZV67rT/fT/+8f8jHVkZJNucPmk2vfCoCrppzkiIE3/iu7V63Hmz7d7yi+C6xZBUB84ak+J5F8UlZWRm1tLT89eSHd9/7F7zgTEhuZEy8YDMHIYg8iIiJyqFRHyl0P9AOfBq4e+fj6kX3OyN9DwN9GPp6d3pgiIplhWRYXXtjCihUP4eXoZOxdXXvZsWM78+fbh2w/sBiFRspJrvBqpuLWB7UaMJCYN5++f/8wCXvB8Q8WeYlQqIE1XXtxZ87yO8qERKNRFhYXc8JnP4m17gW/44iIiOSklOaUcxznBuCGo+w7IX1xRESyr7l5KX/+8x/YvHkTc+bMpfSXt1H2n99j74rHwbL8jkdraysAtn1oU274vAvY/5Vv4M6q9yOWyJEGByn/4XcZPu8Chi+40O80voqfehrxU0/zO4bkoVAozICzjvJbvs7Am96CGwr7HWlcotFOzquZSuk9v6H/ff/udxwREZGcpF9li8ikt3TpMoAXH2GNxwm0tWJu3+ZfqJdobV0PcMRIuUSTzcC73pcTjUMRAIqKKP/mVyl+4B9+J/GX5xFY9SwMDPidRPJQKBQiEYlQ8dWbCbyw1u844xaNRjmpshKARKjB5zQiIiK5SU05EZn05syZRzAYYsWKh4AXHwc129t9TPWi1laHsrIyGg57TDXw5OMH55UTyQmmiVsfzPsJ6ifKjHQy9ZKllN5xu99RJA+FQg20DfQDYEajPqcZv1gsytyiIrySErzaWr/jiIiI5CQ15URk0jMMg5aWZTz88HJc18VtTE6LaXXkRsOrtXU9c+fOxzpsRFz1O/+N8m99zadUIqNzgyHMWP42EtIhsGY1APFFWuRBxi4cDrMN8EwLM9rpd5xxSSQSxGJRGjyPRDCkeSZFRESOQl8hRURIziu3d+9e1q5dTWJk/h6rIzdGyrW1tdLUdOijqwwOYm3fppVXJeckgkGsSH42EtIlsGYVnmEQP3mh31EkD4VCYVygr6YGK09Hyu3atZN4PE5JeYUWOxERETkGNeVEREg25QBWrFgOZWUMvuq1yd/u+6ynp4eOjvYjmnJWRCuvSm5y60MYO3dCjq5mnA2BtatJzJsPFRV+R5E8FA4n51/rqqzEjMV8TjM+kZHG/LMf+xT7bvuVz2lERERyl5pyIiLArFn12PYCVqx4EIB9P7mdwX99q8+pYOPGNgCamg4daXBgvjuNlJNc0/vJ/2D3hg4wDL+j+CawZrUeXZVxq6ubQVFREd+7/BV0//oev+OMS3RkhF8wGPQ5iYiISG5TU05EZERz81Ief/xRBgcHkxtyYKSP4yRXXj1ipNzIo7UaKSc5p7x8cq8I7Hns/95/0f++D/qdRPKUaZoEgyE27NqZt59LsViEEHDeJ6+j6JGVfscRERHJWWrKiYiMaGm5iP7+fp5++knKvv8dps9vBNf1NVNbWyuBQIATT5xzyPahSy6j++d34tZrFILkFnP7Nio//hECTz/pdxR/GAbDzUuJn36m30kkj4XDDUxZv47Kj/0/jO4uv+OMWSQSwS4qovzpJ2F42O84IiIiOUtNORGRERdcsATTNFmx4kG8ykrMfd2YO7b7mslx1jNnzlyKiooO2e7OqmfoildCIOBTMpGjK7vtJwRWPed3DF8Ennyc4r/dlxMjbSV/hUJhirbFKLv9p5id+bdwSiwW4dQpNQC4I3PkiYiIyJHUlBMRGVFdPYXTTz+TFSuW4zYmHws9MHebX9ranCPmkwMovu/PBJ592odEIsfm1s3ACwSwYvm5auRElf34f6j89Mcm9Zx6MnHhcJg1e/cCYEXzrykXiURYUF4OkBOLJomIiOQqNeVERF6ipWUpzz77NPumTgPA6vSvKTc4OMjmzZtoamo6Yl/lZz5O2U/+14dUIsdhmrj1QcxI/jUS0kGLPEg6hEINtI+MtjSj+dfgjsWinBgI4E6fnpxnUkREREalppyIyEu0tFxEIpHg4Y4OAMwO/5pymzZtxHXdI0fKDQ1hxqJa5EFylhsMYU7GkXK9vVgb2ogvVFNOJiYUCrMN8EwTMxbxO86YuK5LLBbFq61j+IJmv+OIiIjkNDXlRERe4qyzzqGsrIwHn3iU/ne+h8SCk33L0tqaXHl1/vxDV141I50YnkeicbYfsUSOK9E42/dFUvwQeGEthucRX7TY7yiS58LhBhLA/qnTMHr7/I4zJjt37iAej/Pc69/Ivh/f5nccERGRnKYZwkVEXqKkpIRzzjmPlSuX07PicV+ztLY6GIbBvHnzD9ludSZH8WnybMlV+394q98RfBFYuwZAj6/KhAVH5mH7xvs/yIf/38d8TjM20WhyZF9Qc8mJiIgcl0bKiYgcpqXlItavX8f2bdsw9u7xLUdrq0Nj42zKysoO2W6NPFKrx1dFcsvA297BnoefxA2F/Y4iea6yspKpU6fSmYdzM0YiEaqBN3zyo5T87rd+xxEREclpKTXlbNu+1rbtp2zbHrRt+2cv2X6ebdt/t217j23bO23b/o1t2/UZSysikgUtLUsBGPr4h5l+qu3bY3itrQ62feTKq4OveR17/7FCP/hLzgqsWUX1W96A1er4HSW7LItEk62VVyUtQqEG5jzzFNVvezOMLPqQD2KxCI1A2bYYnmX5HUdERCSnpTpSLgrcDPzksO1TgVuBE4DZwH7gp+kKJyLih4ULT2Xq1Kms2r0bY3AQY+fOrGeIx+Ns2rThiPnkALzKKuKnngYBzUAgOWp4mJJ/3I+1eZPfSbJneJjKT11H4Okn/U4iBSIcDlO8bRslf/0zRneX33FSFo1GmTvy9Um/PBIRETm2lJpyjuPc4zjOvcDuw7bf5zjObxzH2ec4Th/wA2BJ+mOKiGSPZVksWdLCg1uSDQWrY2vWM7S3b2FwcHDUkXKlt/+M4r//NeuZRFLljswlZebho3fjZbU6lP30R1hbt/gdRQpEKBTmhf37ATAj+bMCazTaycLqKYDmPhURETmedM8p1wI8n+ZriohkXUvLMp7Znfw9xIE53LKptbUVgPnzm47YV37L1ym5955sRxJJmVs3Ay8QwIpF/Y6SNYE1qwC08qqkTSjUQGt/cuVVK5ZPTbkodlkZXlERbt0Mv+OIiIjktLQ9+2Tb9qnA54HXHu9YyzKoqSlP161zjmWZBVVfodUDqilf+FnTK195OTd98qMAVOyMUZaGHGOpp719IwBnn3061dUvOWd4GDMWpWjenJx4vfW+y32+1RMMUrprO0UZuHcuvkZm2zq88nKqzlgE45hHKxdrmohCqweyX1NT01x+PPJx5d6duHnyubRtW5SBUAjvoouomVaZ1msfj953+UE15b5CqwdUUz4otHpSlZamnG3b84D7gP/nOM7K4x2fSHh0dfWl49Y5qaamvKDqK7R6QDXlCz9rqq0NMiUU5vbKSl61+GziacgxlnpWr15LfX0Q1w0cco65dQvTXZe+GUEGcuD11vsu9/lVT/XiM3BLy+nJwL1z8TWa8tTTuCcvpGv/4LjOz8WaJqLQ6oHs1zR1ah3bgJ66GXj9wxn5f366a3Jdl0gkwtrXXsnu62+ALL8H9L7LD6op9xVaPaCa8kGh1XO4urqqUbdP+PFV27ZnA/8Avug4zu0TvZ6ISC4wDIOWlmV8ePs2Bs86O+v3b21dT1PTkYs8WJ0dACQaGrMdSWRM9v34Nnq+8k2/Y2SNuW8f8VP16KqkTzjcQAL4r098hoG3vcPvOCnZuXMn8Xic+pmz/I4iIiKSF1IaKWfbdmDkWAuwbNsuBeLATOAB4IeO4/x3xlKKiPiguXkpf77zF2z+0++Z99rXZ+2+nufR2trKW9/6b0fsM0fmt1NTTiS37H3wEUgk/I4hBWTGjJkEAgEiebRgSjTaiQV8/MbrGRgaov+DH/Y7koiISE5LdaTc9UA/8Gng6pGPrwfeDcwBvmDbds+BPxlJKiKSZc3Ny/g8cOYH3gOel7X7RiKd9PX10tR05Mqrg296C7te2ITbODtreUTGo/i+PzN16fkYe3Yf/+BCMY655ESOxrIsgsEQZ//jfqrf9q9+x0lJNBqlHggMDeFVjf6YjoiIiLwopZFyjuPcANxwlN03piuMiEgumTlzJv0zZlK8YzvGrl14dXVZuW9rqwMw6uOrGAZebW1WcohMSCJBYN3zmJEIiWnT/U6TUWX/9QOKnnqCfT/6ORiG33GkgIRCYczNmyjevCn5y6Ecf39Fo50cGMedCId9zSIiIpIPJjynnIhIIZt6+hkAJDa2Ze2era3rAUYdKVf23W9Reoem75Tc5waDAFixiM9JMq94+QNYGzfkfMNE8k8oFKZtoB+jrxeju8vvOMcVjUaZMzJi1A01+JxGREQk96kpJyJyDLNbLgJgy4qHsnbPtrZWpk+fzvTpR44uKrv9ZxStXJ61LCLj5QZDAJiRwm/KBdasJr7oVL9jSAEKhxt4obsbADMa9TnN8cViEU6pqgYgEdJIORERkeNRU05E5BhOeeWrANjx+D+zdk/HWT/qKDniccxIJ4lGLfIguc+tm4EXCGDGcr+RMBHm9m2YO3eoKScZEQqF2Toyp6kVzf0FHyKRCDvr6+l/13uhstLvOCIiIjlPTTkRkWOoDIa56YQTuW13diarT668up7584+cT86MRTESCdwGLfIgecCyGLrsFbgzZ/mdJKMCa1YBEF+02OckUojC4TBbgT0nn4JXVOx3nOOKxaJsPXkhPV/5pt9RRERE8oKaciIix9H9+jdwz/p17NvXnfF77dy5k66uLmz7yKac1dEOQCKseXokP+z76S8YeNd7/Y6RUZ5pMnzm2cRPWeh3FClAoVADMeDuj3yc4aUX+R3nmFzXJRaL0jRtOriu33FERETygppyIiLHcXnTAi5yXf75z0cyfq+2tuTKq6ONlDP27sUrLcXV46siOWP4ZZfQdd//4VVP8TuKFKBQKDk3Y2dn7j+6unPnToaHh/n87T+j8rOf9DuOiIhIXlBTTkTkOM578nF+D6xY/kDG7+U4yZVXbfvIOeWGXvlqdm3dTuLEuRnPIZIOZT/8HtMWNcHInFgFKZHwO4EUsKqqaqZMqeGKX/6c6nf+m99xjikWi1AFlA70kwhqkQcREZFUqCknInIcxgknUgGseSjzTbm2NofKyipmzao/Shgj+UckHxQFsLZvw9i7x+8kGWHs66Z2boiSO3/hdxQpYKFQmOGeHqz1L/gd5ZgikQgHJldww2rKiYiIpEJNORGR40iMLKwQ37iBbdtiGb1Xa6uDbdsYozTeKm64nrLvfiuj9xdJp0R98tE7MxLxOUlmBNauwejrw5sxw+8oUsDC4TBb4nGsaDSnR53GYhEOTK6QCGnuUxERkVSoKScichyJhuSPGbOBlSuXZ/Rera3OqPPJAZT8+Q8E1j2f0fuLpJMbDAJgxQq0KTey8urwQq28KpkTCoVx+nox+noxsrDg0HhFo1HmWBagkXIiIiKpUlNOROQ4DiyscHJZGStWPJSx+3R3d7F9+zaamo6cT45EAjMawQ1rkQfJH24o+YO5GY36nCQzAmtWk5gxE2/mTL+jSAELhcK09vcDuf25FI12srVuBr2f+AzuzFl+xxEREckLasqJiByHN6WGrl/dTceFLaxcuRwvQ48PtbYmV15tamo6Yp+5fRvG8PDBUXsi+cCtm8HAv1yJ21CYj7IF1qwmvuhUv2NIgQuFwrwA7Lz4Uigq8jvOUUWjUfacOIe+T3wGRkbMiYiIyLGpKScikoLhl13CwkuvIBqNsHHjhozc48Wm3JEj5cz2dgA15SS/WBb7/+enDF18qd9JMmLgyjcy+IY3+R1DClwo1MDzwAPveT+JefP9jnNU0WiEsyqrMHbv9juKiIhI3lBTTkQkBYFnn+Z1vb0AGXuEtbXVobS0lIZRGm/G4ADxE+fgzp6dkXuLZNTgoN8JMqL/w9cxeOVVfseQAhcemZ8tEumERMLnNKNzXZdYLMoXHv8nldd/yu84IiIieUNNORGRFJT89tfM+eZXaQg3ZGyxh9bW9cyb14Q1ymM/w0svYu/jz5GYm7ujJERGU/XhDzB16Xl+x0g7c1sMY49GBEnmzZpVj2mavOXLN1J53Yf8jjOqXbt2kRgepqanBzdcmI+ri4iIZEIglYNs274WuAZYBNzpOM41I9tPBm4D5o4c+jTwYcdxXkh7UhERH7kNjZg9+7n8siu46x/3k0gkRm2eTURbWytnnXV2Wq8p4jd36jSsaAQ8DwzD7zhpU/6tr1Pyu9+yu629oOqS3BMIBKivD7K/v59pkdxcyTga7WQWYLkuiZBWXhUREUlVqiPlosDNwE9G2f4GYBpQC/wB+FXa0omI5IhEQ/Kx0cvsBXR3d7Fmzaq0Xr+3t5f29q2jr7wKVP6/f6fis59M6z1FssENBjEGBjD27vE7SloF1q4ivnCRGnKSFaFQmE4DzFiuNuWiHBgf54bVlBMREUlVSk05x3HucRznXmD3Ydu7HMfZ4jiOBxhAApiX9pQiIj47sHrk2TNmAemfV27jxjYA5s+3R91f9Ng/MXfuSOs9RbIhEUz+gG5Goz4nSaNEgsALz2vlVcmacDjMpqFhrMjIqNMcE412cmA21ERIj6+KiIikKi1zytm23QUMAN8HvpyOa4qI5JIDq55O29fFSSedzIoV6Z1XznHWA2Dbo4yUc12sSCduWCuvSv5xg0EArGinz0nSx9rQhtHfT3yhmnKSHaFQA05vD0ZfL8a+br/jHCEajfJcURHdt3yfRKMWJBIREUlVSnPKHY/jODW2bVcAbwe2Hu94yzKoqSlPx61zkmWZBVVfodUDqilf5FRNU8oYfuY5Sk48kUt2bePWW2+ltNSktLQ05Uscq5729k0EAgFOP30hRUVFh+6MRjGGhiix51GUK/8eI3LqNUqTQqvJ93oWn0LiuusoP6mJ8jTl8LsmY2OyiV52wbmUFUhN6VZo9YC/Nc2fP4c7XJd973s/U6pKIcfed7t2bSfR0ED5tR/Az1dd77v8oJpyX6HVA6opHxRaPalKS1MOwHGcXtu2/xvYadv2SY7jHPU5q0TCo6urL123zjk1NeUFVV+h1QOqKV/kXE3hOTAM55yzhO9973v8/e8P0ty8NOXTj1XP6tVrmTNnLr29w8DwIfsCax2mAj21sxjKpX8PcvA1SoNCq8n3egLl8Okbkh+nKYffNZmnnknxLd9nYFZjwdSUboVWD/hb07RpM3gM+Ofr3siZZmnOve+2bNnKxeUV7H/saRILTkpDsvHR+y4/qKbcV2j1gGrKB4VWz+Hq6qpG3Z6Wx1cPu145EErzdUVEfFf8f/dT9t8/4IILLsSyLFauTN8jrG1tzlHnk8MyGbqwhcSJc9J2P5Gs6unB2L7d7xRp44YbGLj67XD4qFaRDAmNzNO2feOGnH189dMdHVTc9Dm/o4iIiOSVlJpytm0HbNsuBSzAsm27dGTbJbZtn27btmXbdjVwC7AXWJfBzCIivij+x/2Uf/NrVFZWccYZZ7FixYNpue7Q0BCbN2/CtkdvysXPPJvue/5EYo7W0ZH8VPOmf6H6A+/yO0Z6eB4ld9+FGSmcOfIk94XDYSzg3z70fsr+6wd+xzmE67rEYhFmDg7gapEHERGRMUl1pNz1QD/waeDqkY+vB2qAO4FuYCPJlVcvdxxnIO1JRUR8lgg3Yu7rxujuoqVlGc899yzd3V0Tvu6mTRtJJBJHHyknkucSwRBmNOJ3jLQwOzuo/sC7Kb7/r35HkUmkunoKZZVVdJeV5dzn0q5duygeHqZiYIBEOOx3HBERkbyS0pxyjuPcANxwlN2/SVcYEZFclmhMrn5qdnTQ0rKMb33razzyyMO84hWvmtB1W1uTk8Y3NY2y8ipQ/bY34xUXs/9HP5/QfUT84gZDWPffB54HhuF3nAkJrFkNQHyRVl6V7DEMg3A4zPZYjDk51pSLxSIcGB/nhtSUExERGYt0zyknIlKw3IZkU87qaOfMM8+mvLyclSsfmvB1W1sdDMNg3rz5o+632pwJ30PET24wiNHfj9G11+8oExZYswrPNImfvNDvKDLJhEJhOvBybqRcJBKhceRjN6zHV0VERMZCTTkRkRQlGmYDYEYjFBcXc955F7BixUMTvm5r63oaG2dTVlZ25E7Xxers0A86ktcSweToGTOSW82E8QisXU1i3nwoL/c7ikwyoVADGwcHsSKR5KjTHBGLRXgC2PK/Pyd+iprVIiIiY6GmnIhIirxp09jV1s7Au94LQHPzMtraWonFohO6bmtrK01No88nZ+zciTE4SKKhcdT9Ivkgftrp9Hzpa7h1M/yOMmGBNauJL9Sjq5J94XCYOwcG2PuJz4Dr+h3noEgkQl9xMWWvfi1eVbXfcURERPKKmnIiIqkyDLwpNQf/2tKyDGBCo+USiQQbN7YddT45q2MrAG6jmnKSv9yGRvrf8wG8mTP9jjJhXX99gN7PfM7vGDIJhUJhVgDOZVeAZfkd56BoNMIbp0yl5KEH/I4iIiKSd9SUExEZg5Jf30HFzTcAcMopC5k+fTorVy4f9/W2bt3C4ODgUUfKeRWVDFz1ZuJamVXynLWhDXPzJr9jTJg7qx539gl+x5BJKBxuIAB0P/4oxu7dfsc5KBaL8pHBfipu+brfUURERPKOmnIiImNQ9MxTlN72EwBM0+TCC5eycuVyvHHO79PamlzE4WhNucRJJ7P/B/+De8KJ4wsskiOmvPG1VHzra37HmJDi+++j7AffzalHB2XyCIXC1AOXf/RaSv7yR7/jHBSJRAglEiTCWnlVRERkrNSUExEZg0S4EbOrC2P/PgCam5cSi0XZsKFtXNc70JSbP79p9AP6+nJqQm+R8XLrg5gTnH/RbyW//TVlP7kVTH37JNlXXx9kG+BCzqzA6rou22MRavsHcINqyomIiIyVvqsUERmDA3O7mR0dwMTnlWttXU99fZDq6imj7q9+179R86pLx3VtkVySCIYwI51+x5gQLfIgfioqKqK2PkhXWVnONLh3795NzfAwRW6CREhNORERkbFSU05EZAwOrIJqdbQDcMIJJ9LYOHvcTbm2Nof5x5gvzupox52R/5Pji7jBEFYsmr8jP3t6sDZtJL5ITTnxTygUZlsggJUjDe5otJPZIx+74QZfs4iIiOQjNeVERMYgEW7Erag8+PgqJB9hfeSRlSQSiTFdy/M8Wltbse2jNOU8D6uj/WAjUCSfucEgRn8/Rtdev6OMS+CF5zE8j/iixX5HkUksHA6zNeHmzEi5aDTKs8AjP/o5wxcs8TuOiIhI3lFTTkRkDLy6OnZvijD4hjcd3NbSsox9+7pZterZMV0rGo3Q29tz1JFyxq5dGP39JBrVlJP8N3Tp5XT/9Jd4JaV+RxkXq7MdLxAgfqqacuKfUKiBbw0Psf9zN/kdBUiOlBsGas5bgldV7XccERGRvKOmnIjIWBhG8s9LXHjhUgBWrlw+pks5znoAbHvBqPutjq0AuA2zR90vkk8Sc+Yx9MpXQ3m531HGZfD1b2TX5hhufdDvKDKJhcNh/m94mOjpZ/odBUiOlHu9ZdHw1z/7HUVERCQvqSknIjJGZT/8HpUfvfbg3+vq6jj55IWsWDG2plxb24GVV0cfKedOr6X3ox8nfvIp4w8rkivicYoeegBrnCsV54SSkiOa8iLZFAo1UA30/+F3GHv3+B2HaDTCe4pLKP/Rf/sdRUREJC+pKSciMkbWls2U3PenQ7Y1Ny/liScepb+/P+XrtLY6TJ8+ndra2lH3u7NPoO8zn8fVnHJSCAyDKW++kpLf/srvJGM3PMyUN7yW4r/d53cSmeRCoTALgbP+4xMEnn3a7zhEoxFmm4ZWXhURERknNeVERMYo0dCAuWcP9PQc3LZ06TIGBwd58snHU75Oa+uxV141Y9G8nRRf5AiWhTurHisS8TvJmFnOeopXPIjR23P8g0UyKBwOc2DdVSvq/2IP0WiEYDyOG9LKqyIiIuORUlPOtu1rbdt+yrbtQdu2f3aUY75g27Zn2/bL05pQRCTHHBi5ZnV2HNx23nlLCAQCrFjxUErXSK68up6mptHnkwOo/NiHmXLlayaUVSSXuPVBzBxoJIxVYO1qAOKnnuZvEJn0amqm0lVWjguYkc7jHp9JruuyNxphyuAgbijkaxYREZF8lepIuShwM/CT0Xbatj0XeAMQS1MuEZGclTjQlBtZiAGgsrKSM844i5UrH0rpGrt27WLv3r00NTUd9Rirox03rNEHUjgSwRBm1N9GwngE1qzCK68gMWeu31FkkjMMg1kNDewtKcGM+dvg3r17NzOGhwH0+KqIiMg4pdSUcxznHsdx7gV2H+WQHwCfAobSlEtEJGclGk8gbi8A1ztke0vLMp577lm6UnjktLU1ufLqUUfKeR5WRweJRs0nJ4XDrQ9ixaLgecc/OIcUrV5F/JSFYGrWD/FfKBQmZllYPo+Ui8UibAB++d8/YfBVr/U1i4iISL6a8HeXtm2/ERhyHOcvacgjIpLzvBkz2LvyCYYuu+KQ7S0ty/A8j0ceefi412htTa682tQ0+pxyxp49GH29GiknBaX/He+m63d/9jvGmCVmzmKoeanfMUQACIcb+HhxCT03ftnXHJGR+SFnzJkD5eW+ZhEREclXgYmcbNt2JfBl4NKxnGdZBjU1hfvF27LMgqqv0OoB1ZQv8q2ml72shYqKCh5//GHe+tY3HbH/pfVs3bqRqqoqTjppHoZhHHGssfEFAEoXNFGSw/8G+fYapaLQasqpes5YlJbLZL2m3/4GCyjO4C1y6nVKg0KrB3KnprlzT+T2rr0UnbGIqtLSCV1rIjV1de3kSuDce35N2dIlMMrXsmzLldconVRTfii0mgqtHlBN+aDQ6knVhJpywI3A7Y7jbB7LSYmER1dX3wRvnbtqasoLqr5CqwdUU77I5ZoqvvBZrC2b2ffzOw7Zft55F/CPf/xj1NwvrWfNmueZP7+J7u7+Ua9vVE6n5BvfYajpFNwc/TeA3H6NxqvQasqleox93ZT86Q8Mn3seibnzx32drNbkeVlpNuTS65QOhVYP5E5N06bN4ARg59e+QdV7PjChUWoTqWnDhs28yjCouPde9tz0tXFnSKdceY3SSTXlh0KrqdDqAdWUDwqtnsPV1VWNun2ij69eDHzYtu1ttm1vAxqAu2zb/tQErysiktOMnv0UPfn4EdtbWi5iw4Y2otHIMc8/3sqr3syZDLz9nbj1wQlnFckVRk8PVR/5IEUPr/Q7SsrKv3YzU5eeD67rdxQRIPn46rnAnC/diNW+9bjHZ0o0GmFecQmuFnkQEREZt5SacrZtB2zbLgUswLJtu9S27QDJptxC4LSRP1HgfcAPM5JWRCRHuA2NmLt2Qt+hv81pHpl3asWKh456bnd3F9u3b2P+/NHnkwOw1r2AtX5dWrKK5Ap35iw8y8KMHbtpnUuKnns2ucCDFnmQHBEKhekY+dg8zi+AMikWi9JoQEJzn4qIiIxbqt9hXg/0A58Grh75+HrHcXY7jrPtwB8gAex1HKcnM3FFRHJDoiG5KqrV2XHI9pNPPoXp06ezcuXyo557YJEH2z56U67iyzdS/f53pSGpSA6xLNyZs7AiedKU8zwCq1cRX3Sq30lEDgoGQxxYd9WKRX3LEensYNbQkEbKiYiITEBKc8o5jnMDcEMKx50wsTgiIvkhEU425czOdhIvWUHVNE2am5eyYsVDeJ436iIObW2tAMceKdfeTmL27DSnFvGfWx/EjPrXSBgLc/s2zF071ZSTnFJSUkKibgbuzh2Ykc7jn5ABnufRH4syXFSMG1ZTTkREZLz0LIaIyDgkTjiRoYsuhtKyI/Y1Ny9j+/ZtB5tvh3Oc9ZSWltLYeJSmm+dhdrQfHI0nUkgSoXDePL4aWLMKgPiixT4nETnUzIYG9hYXY/o0Um7Xrl3sGB7me1+4iYF3vteXDCIiIoVgoquviohMSt7MmXT/+nej7mtpWQbAihUP0tR05Gi4tjaHuXPnY1nWqOcbXXsxe/bjqiknBaj3pi/jWfnx7YdbW0f/m68mfspCv6OIHCIUauDqXTu5/fobfbl/bKSxXl8f0nyLIiIiE6CvoiIiE+F5R2yaPfsEGhtPYMWK0eeVa211jjmfnNXRDrz4iKxIIXGDIbyZM/2OkZL46WfS893/xKuq9juKyCFCoTDLd+7EnTbNl/tHo1HeAFz+sx/BwIAvGURERAqBmnIiIuNU9cH3UvOay0fdt3TpMv75z4eJx+OHbO/t7aWjo/2Y88klTjiR7jt+w/C556c1r0guMLduofwbX8E8bJGUXGR2dozaeBfxWzgc5tT+foyvfNGX+0cinZwHzHjiMSgp8SWDiIhIIVBTTkRknLziYqxNG0fd19y8lH37ulm16tlDtm/c2IbneTQ1LTj6daunMPTyy/Dq6tKaVyQXmDu2U/GNrxBY/4LfUY7J6O5i+hmnUPbD7/kdReQIoVADzUDdd76JsX9f1u8fi0U5wTBwww0wyoJGIiIikho15URExsltaMTcuQP6+4/Yd+GFSwFYufLQR1hbWx2AUeeaO6DosX9S9NADaUwqkjvcYAgAM5Lbiz0E1q4BIH7yyT4nETlSOBzmwFhTP1YzjkQ6mVNUhBvSyqsiIiIToaaciMg4JcINAFiRziP21dbWcsopi1ix4qFDtre2OgQCAU48cc5Rr1v2n9+n8gv/kdasIrnCnTkLzzRzfgXWgyuvLtTKq5J7QqEGDnzlMUf5GpRpsViUBs87+HVQRERExkdNORGRcXIbZwNgtm8ddX9z81KeeOIx+vr6Dm5rbXU48cQ5FBcXH/W6Vkc7Ca28KoUqEMCdOQvLh9E9YxFYs5rErHq8GTP8jiJyhOnTp7NzZC43K5b9z6VtkU72lZeTmNeU9XuLiIgUEjXlRETGKT5nHv1Xvx2vtnbU/UuXLmNoaIgnnnjs4LbW1vXHnE8OwOxoT87TI1Kg3GAIc1vM7xjHFFi7mviiU/2OITIqwzAwQ2FcwIxmd9Sp53lEtsW45epr6P/3D2X13iIiIoUm4HcAEZF85c2YQc8t3z/q/nPPvYBAIMDKlctZtuxlDA0NsXnzJl796tce9RyjuwtzXzeJhtmZiCySE7p/fQ9eZZXfMY6p9xP/gVdZ6XcMkaOaEW7ksspK7vjYp7J63927dzM4OEgwGMzqfUVERAqRRsqJiEyE62Ls6x51V2VlJWeddc7BeeXa2tpIJBLHHClndiSn7k406vFVKVxe9RQwc/tbkKFXvYbhZS/zO4bIUYXDYdZs3571z6VYLMLrgXf99EcYO3dm9d4iIiKFJre/IxYRyXFT3vQvTHnzG466v7l5KatXP8fevXtYv349cOyVVxPzm9jz0KMMNy9Ne1aRXBF46gkqr/sQxv59fkcZlbV+HYEnHgfX9TuKyFGFQmFatm+j5OYbsnrfSCTCycDMDW141dVZvbeIiEihUVNORGQCEsEQZkf7Ufe3tFyE53k88sjDrFv3AoZhMHfu/KNfsKSExMmn4NVMzUBakdxgRiOU/eLnB0eG5pqyH9/KlLe8AQzD7ygiRxUON3AhUP7jW7N632g0QiMwXFsHI4tNiIiIyPioKSciMgFuQyPWthgMDo66/4wzzqSiopIVKx5k/fr1NDTMpry8/KjXK77/Pkru/EWm4orkBLc+OReVFcvuBPWpCqxdRXzhIjXlJKeFQmE6gUBvT1ZHnUajEWYbBmiVcBERkQlTU05EZAISIz+UWJHRR/wUFRVx/vkXsHLlctatW4dtH/3RVYDSX95O+X9+L+05RXKJGwwBYEZysCkXjxN4fq1WXpWcFw6HOfCVx4xGs3bfaDTCiZalVcJFRETSIKXVV23bvha4BlgE3Ok4zjUj208ANgO9Lzn8a47jfDGtKUVEcpQ70pQzOzpIzJk36jEtLcv4xz/ux7IsmpuXHfN6ZmfHwUafSKFyZ87CM03MaO415awNbRgDA8QXqiknua2+PkTnyMdmpJOEffRFhNIpFouytXoK9WeenZX7iYiIFLKUmnJAFLgZuAwoG2V/jeM48bSlEhHJE/H5Nr2f/A/ccPioxwyOPNqaSCS4885fsGjRqVx55VWjHmt1bCV+ln7QkQIXCOCGGzH6+/1OcoTAmlUAxBct9jmJyLGVlZXRO3Ua8e4uzK69WbtvJNLJfy5dxuJ//1DW7ikiIlKoUmrKOY5zD4Bt22cBR//JU0RkkvHq6uj7+KePuv/uu+/illu+fvDve/fu4brrkj/IHN6YM/Z1Y3Z1kWiYnZmwIjlkz5OrcnLOtsErXsXeP/yNxDFWSRbJFV5jI5dNPY1fvf6N2bmf5xGLRXnFK16dlfuJiIgUunTNKbfVtu1O27Z/att2bZquKSKSF4xduzA3bxp135e+dCP9h40G6u/v50tfuvGIY83O5INIiUY9viqTQA425ACorCR+3vkQSPVhAhH/hMKNdGbxMfDdu3dzyeAgX/7lz7HaWrN2XxERkUI10e84dwFnA88B04EfAr8k+ZjrUVmWQU3N0VcfzHeWZRZUfYVWD6imfJEvNVlvuAYSCRIPLj9iXyTSeeQJI9uPqO2CsxnesYvy4uJjrtCaS/LlNRqLQqspV+sx7roL81d3krj7njE36DJWk+dh3vxF3Fe+Es44M/3XP4ZcfZ3Gq9Dqgdysae7cEzjr/vuYdstXcG8a+5TOY61p8+bdnABM6e5meHYQcuzfIxdfo4lSTfmh0GoqtHpANeWDQqsnVRNqyjmO0wM8NfLX7SMLQsRs2652HOeoa7MnEh5dXX0TuXVOq6kpL6j6Cq0eUE35Il9qqqoPU7Ry+ahZQ6EwnZ1HrswaCoWPUlsxDAFDuV835M9rNBaFVlOu1lO2cQuVf/oje9pjeFNqxnRupmoy27cy/Ys30Vc1lYE5J6X9+seSq6/TeBVaPZCbNdXWzuKU4WHid9/Nvus+M+bzx1pTa+smGoFEURFdRRWQY/8eufgaTZRqyg+FVlOh1QOqKR8UWj2Hq6urGnV7uh5fPcAb+W+OPpMiIpJ+iXAD5rYYDA0dse+zn/0CZWWHro9TVlbGZz/7hSOOLbn7Lsq/9bWM5RTJJW4wCIAZyZ0VWANrVgMQX6SVVyU/hEINdAJmNJqV+0UinTQC8WAodx9BFxERySMpjZSzbTswcqwFWLZtlwJx4EygC2gDpgLfAx5yHKc7I2lFRHJQonE2hutiRiO4J5x4yL4Dizl86Us3Eol0EgqF+exnvzDq6qslf/4jlrOOvo99Kiu5RfyUqA8BYMUiJE4+xec0SYE1q/BMk/hJuZFH5HjC4TD/AIp6ezB69uNVjv5b+HSJxaIsBWjUgkQiIiLpkOpIueuBfuDTwNUjH18PzAH+CuwH1gKDwJvTH1NEJHe5DcmFGayO9lH3X3nlVTzzzPMMDg7zzDPPj9qQAzA72g9eS6TQuaFkUy5bI3xSEVi7msT8JsiTOR1FDoyUg+x8LkUinTxVVc3wpZdn/F4iIiKTQUoj5RzHuQG44Si770xXGBGRfBQ/+RT2ffc/kz/MT4DVsZXBxaenKZVIbnNnziJxwok59QictWUz8UWL/Y4hkrLa2lqiRUXsKivD6u3J+P1isSi3n3Qyb3rfBzN+LxERkclgoquviohMet606Qy++eqJXaSnB3PPHhKNGiknk0QgwJ4nVvmd4hB7VzyO0dfrdwyRlJmmSXu4gfcsPo1bT8/8isHbIp2cdpp+eSQiIpIu6V7oQURkUrKeX0vg2afHfb65YzteeYUeXxXxk2lmfE4ukXQLhxvo7Ow8/oET5HkeJ0Yj/Pr3v6Po0Ucyfj8REZHJQE05EZE0qPzMx6m44fpxn+/OmcuuzVEGX/MvaUwlktvKb/k61f/2Jr9jAFDy6zuo/MgHIR73O4rImIRCYT7z/BrKv/7ljN5nz549zBoawvI83BkzMnovERGRyUJNORGRNHAbGo+60EPKDAMsKz2BRPKAsWc3xStXgOf5HYXi/7uf4pXLIaCZPSS/hEJhGvr7sZ5+MqP3iUY7aRj5OBEMZ/ReIiIik4WaciIiaZBoaMSMRmB4eFznl/7sx1Re96E0pxLJbW4wjNHXi7Gv2+8oBNasJr7wVL9jiIxZOJxcgdVr35rR+0SjURqBwSk1UFaW0XuJiIhMFmrKiYikgds4G8N1k425cSh6eAVF/3w4zalEcpsbDAJgRqP+Bunpwdq0kfgiNeUk/wSDITqAwLZYRu8TjUZoBNyQRsmJiIiki5pyIiJpkAgnH+oZ7yOsVsdWLfIgk86BR+CsaOYnqT+WwPNrMTyP+KLFvuYQGY8DI+WKensxevZn7D7RaITfmyZDb7smY/cQERGZbNSUExFJg/ji0+j6ze/HPdLG6ugg0Tg7zalEcpvb0MDQ+Uvwikt8zWH09hCfO08j5SQvBYMhXgA2N87G6O3N2H2i0Qh/CYYYeud7M3YPERGRyUazGYuIpIE3pYbhpReN7+S+PsxdOzVSTiYdtz5I9+/v8zsGwy97OXsffcbvGCLjUlFRwaPTpnHjRS/nGzNnZew+2zs7WFxbB4mEFiUSERFJE42UExFJk6IH/kHRA/8Y83lm117iTTbxOXMzkEpERApdKNRAJNKR0XtUbN3Cn557huI//yGj9xEREZlM1JQTEUmTilu+Tvn3vz3m89xgiL0PP8nQa/4lA6lEclvV+99J9Tuu9i/A0BDTzlxI6R23+5dBZIIagiH+c+Vyyn74vYxc3/M8ynbuAMAdmUNVREREJk5NORGRNEk0NI57oQeRycoYGsZqc3y7v+Wsx+poxysv9y2DyEQFGxqYMjSEtbEtI9ffs2cPs4aHAUiE1JQTERFJFzXlRETSJNHQiBmNQDw+pvPKfvBdplz1OvC8zAQTyWGJYBAzEvHt/R9YuxpAizxIXguFGmj3PLz2rRm5fjQaoRFIBAJ4dXUZuYeIiMhkpKaciEiauA2NGPE45rbYmM4LrH4Wc+sWMIzMBBPJYW59CLO3B2P/Pl/uH1izCq+8gsSJmtNR8lc4HKYTcDM0WjsWSzblButmgKkfH0RERNJFX1VFRNIkMbJ66lgfYbU62nEbZmcikkjOc0MhgORoOR8UrVlN/JSFajRIXguFwnQAxdu3ZeT6kUiEnwG7r/1/Gbm+iIjIZBVI5SDbtq8FrgEWAXc6jnPNS/aVA98ErgKKgFWO47SkPamISI4bPusc9vzzaRKNY2uwWe3tDF56eYZSieS2eNMCBl7/Rigu8uX+Q0suxJte68u9RdIlHG7gNuBl9gJmxOMQSOlb/JTFYlEeCAQoeud703pdERGRyS7Vr9hR4GbgMqDssH23jlznJGAPcFq6womI5JXKShLz5o/tnP5+zJ07cEdG2YlMNomTT2H/f//Yt/v3ffpzvt1bJF1mzJjJ3UVFzGhexvVpbsgBxDrauWLqNAI9+/Gm1KT9+iIiIpNVSl+1Hce5B8C27bOA8IHttm3bwGuAsOM4ByaDeTrdIUVE8kXJr34JpaUMvu7KlI43enoYWvYy4qcsynAykRw3PAxF2R0tZ+zrxistg+LirN5XJN1M06S+PkRnZwe4btofx45v3cIfdu5g/x9/z8DVb0/rtUVERCaziX7FPhfYCtxo2/Yu27bX2Lad2k+iIiIFqOznP6H09p+lfLxXV0f3XfcydNkVmQslkuOmnn8GlZ/4SNbvW/7dW6htmj3mFZNFctHpdXXcdu89lPz6jrRf24p0ApAIhY9zpIiIiIzFRMe3h4GFwN1AEDgf+LNt2y84jrPuaCdZlkFNTfkEb527LMssqPoKrR5QTfkiH2uy5s7BeOrJUXPnYz3Ho5pyXz7UY06bSsmOGIEUc6arJmv9Wpg/n5ra6glfa8JZ8uB1GotCqwdyv6aa+fOoePpJEnt2UJbGzyXP8yjftROAigXzIIf/DXL9NRoP1ZQfCq2mQqsHVFM+KLR6UjXRplw/MAzc7DhOHFhu2/aDwKXAUZtyiYRHV1ffBG+du2pqyguqvkKrB1RTvsjHmipmhSjruIeu3fvBsg7ZN1o95V//MiW/v4e9Dz8JhpHNqGmRj6/R8RRaTflQT/WMeqw2J+WcaanJ85j+7LMMXHI5PTnw75MPr9NYFFo9kPs1TasPsR2o2rCJ3jR+Lu3Zs5tZw8MAdFVNhxz+N8j112g8VFN+KLSaCq0eUE35oNDqOVxdXdWo2yf6+OrqCZ4vIlJQEg2NGMPDmNu3pXS8tWkDxuBQXjbkRNIlEQphRqNZvae5LYa5axfxRadm9b4imRIKNdABxLdsTut1I5EIjcBARSVUVqb12iIiIpNdSiPlbNsOjBxrAZZt26VAHFgBtAOfsW37KyTnmFsGfCIjaUVEclyioQEAs7MTNxg67vFWRweJRq28KpObWx/C7NmfXHihekpW7hlYswqA+MLFWbmfSKaFw2E6gabOjrReNxaL8F/AOR//FPpqJSIikl6pjpS7nuSjqp8Grh75+HrHcYaB1wKvALqB/wXe5jjO+gxkFRHJecNLWti5OUb8nHNTOt7saCfRoB9zZHIbPvc8+j700eSqkVkSn2/T87mbSCxcmLV7imRSKNTA7wDn1NPSet1oNMoaoPjKq9J6XREREUlxpJzjODcANxxl3/MkF3gQEZGSkuSfVAwOYm2L4aopJ5Nc/KxziJ91Tlbv6Z44h/4PfSSr9xTJpFAoxG1A4+LT+XAarxuNRniDaTKrvz+NVxURERGY+JxyIiJymPJvf4PSn/34uMcZA/30v/VtDGe5GSGSi4z9+zD2dWftfkUrHsLYvTtr9xPJtMrKKmpqatizeSMMDqbtunu3bOY3rkvZX/6UtmuKiIhIkppyIiJpVnz/fZT86Q/HPc6bUkPPt3/A8LKXZSGVSA4bHmb6vAbK/vuHWbmd0bWXmje8htJf/jwr9xPJllfXTOOHv7yNokcfSds13a1bkv8Nh9N2TREREUlSU05EJM0SDY2YHVuPf+DgYFbn0BLJWUVFuDNmYkYjWbldYO0aAOILtfKqFBbzwGJDsfStZlwUS35eJkJqyomIiKSbmnIiImnmhhuxIp3HbbiV3/I1ps8NQyKRpWQiucsNBrGy1ZRbsxqA+CKtvCqFpWTOXIC0fS55nkfFyGPerppyIiIiaaemnIhImiUaGjGGhjB3bD/mcVZ7O9706WBZWUomkrvcYDh7I+XWrCIxqx6vri4r9xPJlpmNJ7AdSIw8cjpRe/fuYdbwMAnTxJ0xMy3XFBERkRepKScikmZuYyPu1KkYO3ce8zirs4NEuCFLqURyWyIYxIym75G7YwmsXU18kR5dlcITDofpBOKbN6XletFolO8D933yP/QLJBERkQwI+B1ARKTQDL3sEnY7x59TzuxoZ7h5aRYSieS+oVe9lsScecnHvs3M/s5w360/g3g8o/cQ8UMo1MB3gfedex5NabheNNrJNiCw9KI0XE1EREQOp6aciEi6GcbxjxkawoxFNVJOZMTw+UsYPn9JVu6VWHBSVu4jkm3hcJjbgVMbZqepKRflPcDckXnlREREJL30+KqISAZUfuKjlH3/O0c/YHiYvo9/mmGNPhBJGh7GWr8OI8M//Acee5TSX96mkXJSkGbOnEWlaZJY/RwMD0/4etsiHfwnUP/EYxO+loiIiBxJTTkRkQwIrHqG4oeXH/2Aigr6PvEZhs+7IHuhRHKYuS3GtJZzKfnrnzN6n9Lf/IqKG6/X/FhSkCzL4h01U/nU7T/D2rJ5wtfr27iRAOA1NE48nIiIiBxBTTkRkQxwG2ZjdrQfdb+xZ3dyIQjPy2IqkdzlzqrHMwzMSGdG7xNYu4r4wlNTe8xcJA8Nz5oFkJbVjI2O5Pyobjg84WuJiIjIkdSUExHJgERDI1Znx1GbbmW3/hfTF83XI3QiBxQV4c6YiRnL4Aqs8TiBdS8km3IiBcpomA2Qls+lopFrJEKa/1RERCQT1JQTEcmAREMjxsAAxo4do+63Ojtw64NQVJTlZCK5yw0GsTI4Us5qa8UYGCC+SE05KVzl8+YDYHR0TOg6nudRuWcPAG4oNOFcIiIiciQ15UREMiAxdx7xUxZh7t836n6zo52E5ugROYRbH8roSDmrzQEgvmhxxu4h4reZs09gBzC0eeOErtPVtZdvxeP88CMfw6uqTk84EREROUTA7wAiIoVoeNnL2LvsZUfdb3W0M3z+kiwmEsl9/e//IPT1Zuz6Q6/5F3ata8GrqcnYPUT8Fg6H+RDwkQuWMG8C14lEIgwCZWpii4iIZExKTTnbtq8FrgEWAXc6jnPNyPa3Av/zkkNNoAw4y3Gcp9OaVESkUAwPY0YjGikncphsrEbsTZ+e8XuI+CkYDHMXcElV9YSacrFYhI8Di9OwiquIiIiMLtXHV6PAzcBPXrrRcZxfOo5TeeAP8O/AJuCZ9MYUEck/1VdfRfmXbzpyh+vS863vMXTFK7MfSiSHGd1dFD3wD4y9e9J/cc+j6gPvpvj/7k//tUVySDgcJgiULH9wQteJRqN8Gpjd6qQll4iIiBwppaac4zj3OI5zL7D7OIe+HbjNcZzRlxsUEZlEzO3bKVr93JE7SkoYeOvbiC8+PeuZRHKZtW4dNf/6egLPpv93e2b7VkrvvguzM3MLSYjkgurqKby7uIR/+8XPoadn3NfZtWUT04GSkYUjREREJP3SttCDbduzgRbgtnRdU0Qkn7nhBszOI1e/MzvaCax6FhIJH1KJ5C43GATAysBiD4E1qwG08qpMCgO1yce0rW2xcV9jeGNyoQhPUy2IiIhkTDoXengbsNJxnONOPGFZBjU15Wm8dW6xLLOg6iu0ekA15Yt8r8mcPwfzgb9TM6UMDONgPeb378K8+YvE9/VASYnfMSck31+j0RRaTXlVT/lcPMOgfM8OSo+ReTw1mW0v4FkWleefDWVlE02adnn1OqWg0OqB/KrJC4chGqW6eydezdEXajhWTYFo8pdK5QvmUZYndefTa5Qq1ZQfCq2mQqsHVFM+KLR6UpXuptyXUzkwkfDo6upL461zS01NeUHVV2j1gGrKF/leU1ldPZX9/XRvaMerqztYT1XbRopm1dPVn4D+/K0P8v81Gk2h1ZRv9Uyrm8HQpi30HCPzeGqqfupprPlNdA16MJh7/x759jodT6HVA/lVkxeaDTxBX9tmBs8c3+eSF9sGQPeUOtw8qTufXqNUqab8UGg1FVo9oJryQaHVc7i6uqpRt6fl8VXbtpcAQeC36bieiEghiJ+8kMFLLsMYHDhku9nRjqvHgURG5YZCWNFI+i9smgyffW76ryuSgyqamgBIjHPlVM/z+GFvDx9+1/tw64PpjCYiIiIvkdJIOdu2AyPHWoBl23YpEHccJz5yyNuBux3H2Z+ZmCIi+Wd4STPDS5qP2G51tKs5IHIUPV/5Jl5FZdqvu+/2X6f9miK5aubsE7gCuHlJM+P5FVBX1176+vqomz0bzLRNQS0iIiKHSfWr7PVAP/Bp4OqRj68HGGnQXQX8PBMBRUTynveSBanjccxohIRGyomMKn7GWSTsBX7HEMlr4XADfwU2xuPHPXY00WiUG4CL169LZywRERE5TEoj5RzHuQG44Sj7BoCatCUSESkgUy9awtAFS+j90tcPbuu+47e4obCPqURyl7llM8UrlzNw5VVQnp7Jfsv+978oufceun73ZyguTss1RXJZKBTmdKD8L3+Eiy4e8/nRaCdXA2WZeJRcREREDtJ4dBGRDPIMA+ulc/oEAgwvexmJ+U3+hRLJYUXPPEXVxz6M1dGetmsGnngcc/t2NeRk0pg1q553Ay/71R3jOj/a2UkDYM2Zm9ZcIiIicig15UREMshtaDykuWC1OhT/5U8wOOhjKpHc5QZDAJhpHKETWLOK+KJT03Y9kVxXVFREd1U15YMD0Ns75vN7NrZRDJQ22ekPJyIiIgepKScikkGJxkas9vaD88qV/OF3TLnmLYfOMyciByVGmnLpWoHV6NlPYNNGNeVk0umvrQPAikXHfG5808bkB5r/VEREJKPUlBMRySC3oRGjrxdjzx4AzI52EjNnQWmpz8lEcpM7qx5I30g5a+1aADXlZNLxQuMfddq3bRt7AwESoYZ0xxIREZGXUFNORCSDhk8/k/63vwsSCQCszg5cjTwQObriYty6Gel7fLUowODlryC+aHF6rieSJwInzgHA6OwY87l/6O/jmle8msTJp6Q7loiIiLxESquviojI+MTPPpees889+HerfSvDZ5zpYyKR3Nd1719wa2vTcq34mWez77ZfpeVaIvmkzD6J04FfnncBdWM4z/M8YrEoL3/5ZZmKJiIiIiM0Uk5EJNPicejpgUQCM9KJ2zDb70QiOS0xvwlv6rT0XKynJz3XEckzwdkn8BzQ0bV3TOd1d3fxub4+3r5mVUZyiYiIyIvUlBMRybDpC+dR8eUbwTDYu/wx+t/xbr8jieS0wBOPU37L1yd+ocFBau3ZlH33WxO/lkieCYXCvBYo/e2vx3ReJBLhEiC8b19GcomIiMiL1JQTEckwd1YQq6MdTJPE/CbcUNjvSCI5rejxR6n46s0YPfsndJ2Asw5jeBh39gnpCSaSR8LhMO8A7D/+fkznxWIRGgGvQYs8iIiIZJqaciIiGZZobMRqb4dnnqb0R/8Nvb1+RxLJae7BVSOjE7pOYM1qQCuvyuQ0ZUoN2wNFVI7x8dXtmzdTBxTNa8pMMBERETlITTkRkQxLhBswO9ox//pXqv7jk2Dqf70ix+IGR5pykc4JXSewZhVuRSWJE+emI5ZIXjEMg56aGioHB6GvL+XzBtocAErtBZmKJiIiIiP0k6GISIa5DbMxe/ZjrFqFWzcDysr8jiSS0xL1QQCs2MRHyiUWLlIjXCatwRkzALBikZTP6YpFWVNUBHPUzBYREck0fZcqIpJhw0supPc/Pg/t7SQaG/2OI5Lz3Fn1AJjR1BsJo+l/2zvof9d70xFJJC95oeS8cGYk9c+lf/b1845TTyN+1jmZiiUiIiIjAn4HEBEpdPFTTyN+6mmU33UHiYWa20rkuEpK2LVuM960aRO6zOCb3pKmQCL5qf+006n7+195+qxzSHWMdiwW4aSTTsloLhEREUnSSDkRkUzzPMxIJ8aGDbhhjZQTSYU3fToYxrjPNzs7sNpawXXTmEokv8yafQK7gGiKj696nse1WzZz05pVmQ0mIiIiQIpNOdu2r7Vt+ynbtgdt2/7ZYfuusm17nW3b+23bfsG27ddlIqiISD6beuE5uNe8g74Pf9TvKCJ5oeR3v6X8KzeN+/yyn/wvU5edD/F4GlOJ5JdwuIGPAeYvb0vp+O7uLhbG40xPJDIbTERERIDUR8pFgZuBn7x0o23bIeAXwHVANfAJ4A7btmekM6SISD4ruec3GIMDGD/7KVNfdiEld9/ldySRnFf0xGOU/fh/x31+YM0q4gtOhuLiNKYSyS+hUJi3AHUP/F9Kx0ejURqB4ZF5HUVERCSzUmrKOY5zj+M49wK7D9sVBrocx7nPcRzPcZw/A72AlmsSEQFK7r6Lqus+hBGPYwBWZwdV131IjTmR40gEw5j7ujF69o/9ZM8jsHY18UWaw1Emt/r6IJ1Ayc7tKR0f7WynAfAaZ2c0l4iIiCRNdE65p4B1tm2/xrZta+TR1UFg9YSTiYgUgIov3YjR33/INqO/n4ov3ehTIpH84AaDAJjR6JjPNWNRzN271ZSTSa+4uJg9ZeVUdXendHxXWyulQMm8+ZkNJiIiIsAEV191HCdh2/ZtwB1AKTAEvNFxnN5jnWdZBjU15RO5dU6zLLOg6iu0ekA15YtCqMmMdB51e77XBoXxGh2u0GrK13oMOznovnrfLrya0w/Zd7yajIfXA1B2/jmU5knt+fo6HU2h1QP5W1Pf9GlUdXZSWgyUH5r/8Jr279rOH4DLXrYUMw9rzdfX6FhUU34otJoKrR5QTfmg0OpJ1YSacrZtvxz4OrAMeAY4E/iDbdtXOI7z3NHOSyQ8urr6JnLrnFZTU15Q9RVaPaCa8kUh1DQtFMbq7DhiuxsK531tUBiv0eEKraZ8rcesms600lL6ItsZPCz/8WoyTjmdott+xdAJNuRJ7fn6Oh1NodUD+VvT0IxZDEYi9LRtwZ19wiH7Dq/p2d17+U59kFWnn5c3nzsvla+v0bGopvxQaDUVWj2gmvJBodVzuLq6qlG3T/Tx1dOAFY7jPOU4jus4zpPA48DLJ3hdEZGC0PvZL+CVlR2yzSsro/ezX/ApkUh+cBtns2vrdgZf+/oxn+tNncbQ5a84YlSQyGS08ZzzmFJURCKFeeKikQjBYCgLqURERARSbMrZth2wbbsUsADLtu1S27YDwJNAs23bp40cdzrQjOaUExEBYPDKq9h/y/dJhBvwDINEuIH9t3yfwSuv8juaSG4zjOSfcSj9+U+w1r2Q5kAi+SnY2Mjg0BC7du067rFvW/Mc965fl4VUIiIiAqmPlLse6Ac+DVw98vH1juMsB24Afmvb9n7gbuDLjuPcn4GsIiJ5afDKq9jzzPPEB4fZ88zzasiJpKj8lq9T/uWbxnSOsXcPVZ/4CMV//1uGUonkl1B9iB8DiV/87JjHeZ7H9P37sYqKspJLREREUpxTznGcG0g230bb9wPgB+mLJCIiIgKBVc9hbdlE3398PvVz1q4B0MqrIiPCjY2cCux69BH46CeOety+fd2EEgl6a2uZkr14IiIik9pE55QTERERyQg3GMSMRMZ0TmBNcgaN+KLFmYgkkneCwTAdgBWLHvO4SCRCAxCvD2Yll4iIiKgpJyIiIjkqEQxj7uvG6Nmf8jmBNatIBEN4tbUZTCaSP6ZNm0bMNCk9zpxy27duZhZgzD4xO8FERERETTkRERHJTW4wOWLHjB57hM9LBV54Xo+uiryEYRjsraxiyr7uYx63vb2dHwJWc0t2gomIiEhqc8qJiIiIZFuiYTbxE+dg9PWmfM7evz6AeZzmg8hk011bS1ekk7KhISguHvWYLXv38B3TpPPVr8tuOBERkUlMI+VEREQkJ8XPOZe9jz9H/LQzUj+prAx35qzMhRLJQ/88fwlnT6k5akMOYHdHO6EZMwkE9Dt7ERGRbFFTTkRERApC8d//SsUN18PAgN9RRHJKKBRmx47tDA4OHvWYliceY9O2bTA0lMVkIiIik5uaciIiIpKzqt57DeVf/3JKxxb/9T5K77gNSkoy251OvQAAdvVJREFUnEokv8ybUsP9QN+vfnnUY6r27qG7pPiYo+lEREQkvdSUExERkZxlbd5M0TNPpXRsYO0q4osWg2FkOJVIfqk7cQ6XAMPPPjPqfs/zmN7TQ/eUmqzmEhERmezUlBMREZGc5dYHMWMprL46PJxceXWhVl4VOVz9nLnsAtytm0fdv29fNyHXpb+2LrvBREREJjk15URERCRnuaEQZvT4TTmrrRVjcJD4IjXlRA4XDIboAALbYqPuj0YiNAKJcDiruURERCY7NeVEREQkZyXqQ5jdXdDTc8zjzO3bcKdNSz6+KiKHKC0tZUdxMeV7do+6P9axlRuBvmUXZzeYiIjIJKemnIiIiOSsxIIFDC1pxujtPeZxwxddzO51m0k02VlKJpJfNk+bzlYrMOq+yI4dfA0oufwV2Q0lIiIyyakpJyIiIjlr6NIr6P7dn/Fmzjz+wYahRR5EjuLPZ53Du6dOHXXfng1tzDYMZtbNyHIqERGRyU1NOREREclvnkfNFS+j5M5f+J1EJGeFQmEikU48zzti3/zHH2WL51F8nMfERUREJL3UlBMREZHcFY8zdclZlP3gu0c9xNy6haKnn8IYHs5iMJH8cqZl8nRfHwN//+sR+0p3bGfAMPGmTfMhmYiIyOQ1+sQSh7Ft+1rgGmARcKfjONe8ZN+7gU8Ds4CHgXc6jnP8ZdJEREREjicQwNy7F2vzxqMfsmY1gFZeFTmGaQ2zWQC8sGY1ZZdecci+qq697Covo0SPf4uIiGRVqiPlosDNwE9eutG27aXAl4HXAtOAzcCd6QwoIiIik1siGMKMRo66P7B2FZ5lET/plCymEskvNacsBGBg44ZDtnueR21fH/tqRp9vTkRERDInpaac4zj3OI5zL3D4OuqvBn7jOM7zjuMMAV8EWmzbnpvemCIiIjJZucEQ1rGacmtWJ1ddLS3NYiqR/FI/dz67ADraD9m+f/8+GlyXwRkpLKYiIiIiaTXROeWMkT8v/TvAwgleV0RERAQANxjEjB59ZozE3PkMXvHKLCYSyT+1tbVEDIOibbFDtkciEa4DOl5+qT/BREREJrGU5pQ7hr8Av7Zt+7+BNuDzgAeUH+skyzKoqTnmIXnNssyCqq/Q6gHVlC8KraZCqwdUUz4ohHqMi5bBYD81lcUQCBxZ0/eTi0AU+xEuTQrhdXqpQqsHCqOm39bUMKO8jAUjdViWyb59u/gV8IFXXZH39RXCa3Q41ZQfCq2mQqsHVFM+KLR6UjWhppzjOP9n2/YXgLuBKcC3gf1A57HOSyQ8urr6JnLrnFZTU15Q9RVaPaCa8kWh1VRo9YBqygcFUc9lr07+6RkChg6taWgIioogzyeoL4jX6SUKrR4ojJp+tXAxfX29LB2po6amnM6nn+MCoLqoIu/rK4TX6HCqKT8UWk2FVg+opnxQaPUcrq6uatTtEx0ph+M4PwR+CGDbdhNwPbB2vNfr7++lp6eLRCI+0Wi+2b7dwPM8v2OkTW7WY1BcXMrUqXUYef6DmIiIpMDzwHXBsg7ZXPZf36f8v77P7mdegPLJ99tVkbEIh8M8+MA/kp9PI98/TXnsUR4Btruuv+FEREQmoZSacrZtB0aOtQDLtu1SID6ybR7wPNAA3Ap813GcveMJ09/fy/79e6mpqaOoqDhvmy2WZZJIFM43NrlYj+e5dHXtoqenm6qqGr/jiIhIBpmRTqYtOZv9X/0mg//61kP2BdasxqusVkNOJAWX9fVy6/Zt9LQ6WPYCAALR5AMuZuNsP6OJiIhMSqku9HA90A98Grh65OPrgVLgDqAHeAJ4FPjceMP09HRRU1NHcXFJ3jbkJDsMw6Sqair9/T1+RxERkQxza+sw+nqxIkfOjhFYs4r4olN9SCWSfypDDVQC3WtXH9xWunMHuwIBrV4sIiLig5RGyjmOcwNww1F2p+074UQiTlFRPk/TLNlkWQFcN+F3DBERybSSEtzauiNWYDX27yOweRODb3qLT8FE8kvFyOi4nvXrmDayraa7mz0VlUz1L5aIiMiklepIuazRCDlJld4rIiKTRyIYwoweOlIu8HxyCluNlBNJzbRTk58rw5s2AOB5HnX9/eyfNu1Yp4mIiEiG5FxTbrJatepZ3vzm1/sd47juv/8+PvrRDx78+4UXnkVnZ4ePiUREZDJwg0Gsw0bKubV19F37EYYXn+FTKpH8MmvOfHYDdCYb3Pv27eOdnsdTL7/M11wiIiKTlZpyY/CGN7yaJ598PCPXXrz4dO68854xn/elL93Arbf+5yHbYrEoF154FvF4+lewvfTSK/j2t3+YchYREZF0GHzlaxj8lysP2ZaYN5/ez9+EN2OGT6lE8ktZWRk/LyvjuZGFUTo7O3kUMM8+x99gIiIik1RKc8qJiIiI+Gm0eeOs59eSOHGOVl4VGYPbmhYwvaSEK4Gda1bzJqCxvNLvWCIiIpNSwYyUu/vuuzjjjFOYOXMKZ5xxCnfffVfW7u26Lrff/jOuuuq1vOIVF/PZz36Kffu6Abj55i9w552/AGDnzh1ceOFZ3HPPbwDo7Ozgiitehud5PPPMU/zLv7zi4DV/8Yuf8brXXcEll7Tw5je/nqeeemLc+f75z4d5xzvewqWXLuX1r38lP/7x/xzcd2BU3Z///Ade//pXcvnlF3Hvvb9l3brnefvb/5XLL1/GN7/51YPH/+Uvf+QDH3jXEff4/e/v4f777+OOO27jkkua+eQnPwrAli2bufba93L55cu4+uqrePjh5QfP+dKXbuBb3/oan/jE/+OSS1p4z3veTmSUlfVERETwPIyuvTA4mPz74CBTL2mh4pav+5tLJM+EgiH2t28FIPHoo/wKmO2m/+kKEREROb6CaMrdffddXHfdh+js7MDzPDo7O7juug9lrTH329/+ipUrH+IHP7iVe++9j6qqKr71ra8BcNppZ/Dss08D8OyzzxAMhnjuuWcAeO65Z1i8+LQjFixob9/CPff8hh/96Db+/vcV3HLLD6ivD447X2lpKddffxN//euDfP3r3+Hee+9mxYqHDjnmhRfWcued93DTTV/he9+7hdtu+wnf+c5/cvvtd/HAA38/WMPRvPa1r+fSS6/gLW95G3//+0q+/vVvE4/H+dSnPso555zHH//4dz760U9w442fo719y8Hz/vGPv/GOd7yH++57gHC4gVtvHf3RWBERmdyKHn2E2qbZFD32TwACzjqMeFyLPIiM0bt27+SJDW14/f0Mb9wIQM2pp/kbSkREZJLK6cdXf/3rOw6OMjuWp59+ksEDvzkf0d/fz0c+8kFuv/1nxzz3zW++mjeN8kjMWPz+9/fw0Y9+khkzZgLw7ne/j9e97hXE43FOP/1MfvCDb+O6LqtWPcNb3vI2fv7zHwPJptxppx05ObVpWgwNDbF58yZqaqYetyH3q1/9gnvuebEB6breIfvPOOOsgx/Pmzefl7/8Mp577mlaWpYd3H7NNe+mpKSEc845j9LSMl7+8suYOjW5EtfixafT1uZw+ulnjunf5fnn19Df38/VV1+DaZqceebZXHDBhfz973/jXe96HwBLl17EyScvBODSSy/n+9//9pjuISIik0Ni5GuhGUsu9hBYvQrQyqsiY2WGGoDH6WtdjxWNMABYs+r9jiUiIjIp/f/27jvOiur84/hn6aAoIigCYo3f2EuiMYm9gS0aa+xiLNEk9i5iCxr9WbD3Hks0tlgwGo29RY0aNT52RQQVBRRQ6v7+OHN3797dpdx72bnn8Lxfr30xM/eu+xxn5szMmXOeU9ONcnOqtEFudturbcyY0Zx44jG0a9fY4619+/aMG/cN/fr1p2vXbrz33ru8/vpr7Lvv/jz44H18+unHvPbaq+y002+a/ff691+SQw89iuuuu4qPPvqQn/1sXf74xyPp1at3i3//N7/ZkwMPPKRhffToz9l55181rL/11ptcccXFfPTRB0ybNo1p06ax8cabNvlvFBrgADp37txsffLkyXP9/2Xs2K9YbLHFadeusUNmnz5LMHbsVw3rPXsuWvR3uvD999/P9d9xzjmXvplZo1z7z0cB0OG/rzNzwe7MWHrZPMNyLjqdl1segHH/fYNuX33Fl50707VdEoNnnHPOuejUdKPcrrvuPke92NZaa2U++2xks+39+y/Jvfc+NC9Ca2KxxRbnhBOGslrW9b99+3bMmDGz4fM11liLJ554jOnTp9G792KsscZPGDHiQb777lt+9KMVWvxvbrHFILbYYhCTJk3knHPO5PLLL+Lkk88oK77TTjuJHXfchXPPvYjOnTtz4YXnMWHC+LL+W7NSOgy3V6/efPnlF8ycObOhYe6LL8aw5JIDqv63nXPOJa5LF2b26kW7UYVGuTeYsfIq4I0Jzs2VBX+8IgCT7R0W/vZbxi3Yna45x+Scc87Nr5K4kz3ppFPo2rXp7UTXrl056aRTqv63pk+fzpQpUxp+pk+fzvbb78hVV13GmDGjARg3bhxPP/1Ew++sscZa3HXXHay++poArLnmT7jrrjtYbbU1aN++fbO/8emnH/PKK/9m6tSpdOrUmc6dO9OuXfPvzanJkyez0EIL07lzZ95++00effThsv9bs7LIIj35POvBALDSSqvQpUsXbrnlJqZPn86rr77Ms88+zWabDZwnf98551zaZvTtT7vR4Toz8ZQ/Men4ITlH5Fx8eq66BgDTPv6QPdq142+bbJZvQM4559x8rKZ7ys2pHXfcBYBhw05j1KjP6NevPyeddErD9mo65pjDmqzvvfd+7L//76ivr+eII37P2LFj6dmzJ5tssjnrr78RAGuuuRaTJ09qyB+32mprMGXKDw2NdKWmTp3GFVdczMcff0yHDh1YddXVOPbYk8qO+aijjuOSS4Zz/vnnsOaaa7HJJpsxceLEsv97rdlmm+04+eTjGTRoI9Zc8yecddZ5/PnP53P++Wfzl79cT+/eizFkyGkstdTSVf/bzjnn0vf9QYdQ37kz3YDpP1s373Cci1KvpZbitHbt6Egd/5s0ifZZbl/nnHPOtb26+vr62X+ryqZNm1E/fnzzHGVjxnxCnz5LtXk81VY6fDV2tVyeco+ZHj260dIxGDMvU+1LrTzgZYpBauUB6DHyAya/8TZTNx8InTrlHU5VpLafUisPpFWmtddejb6dOvPL94y1/vRnNivKTRyzlPZRgZcpDqmVKbXygJcpBqmVp1Tv3t1fAX5auj2J4avOOeecmw9MmkT7t96k3Y3Xs9BBg6Ekl6lzbs6ozxKs9J7xf8AyM9v+Bb1zzjnngiSGrzrnnHMufZ0fup+Ffn8g9QMGMP3HK0HHjnmH5FyUjv7qKzbPlruv7MNXnXPOubx4TznnnHPORWFm334A1H36KdNXXS3naJyL1/Q+fRqWe66+Rn6BOOecc/O52faUk9QZuAzYDOgJvA+caGYjss83BS4FBgAvAvua2SfzLGLnnHPOzZdmZI1yANNX8UY558pVN2ApeO4ZADostHDO0TjnnHPzrznpKdcBGAlsCCwMnAzcIWlpSb2Au7NtPYGXgb/Oo1idc845Nx/r+MLzDcvdzj+HznfdkWM0zkXsXWtYnLLEIrx+3JE5BuOcc87Nv2bbU87MJgGnFm16QNJHwE+ARYG3zOxOAEmnAmMl/djM3ql+uM4555ybH3W+6w66H9/YcND+qy/pfuQfAZiy4y55heVcdF4/7kg2fPXlhvX+M2awyPXX8Dyw+tnn5xeYc845Nx+a65xykhYHVgDeAlYGXi98ljXgfZBtd84555yrigWGnUbd99832Vb3/fcsMOy0nCJyLk4/vul6upZsWyDb7pxzzrm2NVezr0rqCNwC3Ghm70haEPiq5GsTgO6z+u+0b19Hjx7dmm3/4os62rdPY+6JVMpRUKvlqatr+Vianfbt25X1e7XMy1T7UisPeJlikEp52o36rNXtKZQvlf1UkFp5IJ0ytZ8xo8XtfWfMYEbk5UtlHxXzMsUhtTKlVh7wMsUgtfLMqTlulJPUDrgZmAr8Ids8EVio5KsLAd/N6r81Y0Y948dPbra9vr6eGTNmzmlINat9+3ZJlKOglstTX9/ysTQ7PXp0K+v3apmXqfalVh7wMsUglfL07Nef9p+NbLZ9Zr/+SZQvlf1UkFp5IJ0yTWnfnv4tNMx93r49nSMvXyr7qJiXKQ6plSm18oCXKQapladU794t912bo+5PkuqAa4HFgR3NbFr20VvA6kXfWwBYLtuenJ122paNNlqX8ePHN9m+7767s956P2X06M+r9remTJnCoEEb8cor/2722UUXnceQIcdW7W8BrLfeT/mshYcd55xzrhZMOukU6rs2HXRX37Urk046JaeInIvTO3sPZlLJtknZduecc861rTkdk3g5sCKwrZkVJ3S5B1hF0o6SugBDgTdSnuRhiSX68s9/Ptyw/sEH7zNlyg9V/zudO3dmk0025+GHH2yyfcaMGfzzn48waNA2c/Xfmz59ejXDc84559rUlB134bvzL2ZG/yWpr6tjRv8l+e78i32SB+fm0upnn8/zg/fns/btmQl81r49zw/e3yd5cM4553Iw20Y5SUsBBwFrAGMkTcx+9jCzr4AdgWHAOOBnwG/mYbyt6nzXHfRca2V6Lb4wPddamc533TFP/s7AgVvx8MMPNayPGPEAgwZt3eQ7zz77NIMH784WW2zIDjtszbXXXtnw2WOPPcLOO2/HpEkTAXj++Wf51a8GMm7cuGZ/a8stt+GJJx7nhx8aG/1eeul5Zs6cybrr/oKJEydy1lmns912A9l++y256qrLmJENR3joofs5+OD9uOii89hyy0247rqr+OyzkfzhDwcycOCGbL31pgwdegIAv//9AQDsu+9ubL75+jz22CN8++23HHvs4WyzzWZsscWGHHvs4Xz55RcNcfzhDwdyxRWXcMABezNw4IYcf/yRfPvtBABGj/6c9db7KffddzfbbTeI7bYbyG23/aXhd2fOnMnNN9/ALrtsx1ZbbcrJJx/f7HdHjHiAHXbYmq233pQbb7y2jD3lnHMuNVN23IVvXn2L6VOm8c2rb3mDnHNlWv3s8+k8ehwzpk6n8+hx3iDnnHPO5WS2jXJm9omZ1ZlZFzNbsOjnluzzf5rZj82sq5ltZGYfz/OoS3S+6w66H/lH2n82krr6etp/NpLuR/5xnjTMrbzyqkyaNJGPP/6IGTNm8PjjjzJw4FZNvtO1a1eGDDmdhx/+F+ecM5x7772Lp556AoBNN92CVVZZleHDz2XChPH8+c9ncNxxQ1hkkUWa/a1VV12dRRftxZNPPt6w7eGHH2LzzQfRoUMHhg07hfbt23P77fdy/fW38O9/v8D999/b8N23336Lvn37cf/9j7DPPvtx9dWXs/baP2PEiH9x990PseOOuwJw6aVXA3DDDbfx6KNPs+mmW1BfP5OtttqWv/3tAe655yE6derMBRec0yS+hx9+kBNOGMq99z5M+/btGT783Cafv/rqy9x++z2cf/6l3HLLDfz73y8C8Le/3c7TTz/BJZdcxb33jqB79+6cd97ZTX73jTde47bb7mL48Mu54YZr+Pjjj+Z4HznnnHPOOeecc87VutqcUrPIwttv1eyny3WhEYnJk1l4+63ofvjvqfv++ya/V/f99yxwxikt/n7ne+8CWp/JbXZCb7kH+fe/X2TAgKXo1at3k8/XWuunLLfc8rRr147ll/8Rm202kNdee6Xh8yOPPI5XXvk3f/zjQfzyl+vzy1+u3+rfGjRoq4YhrJMmTeSZZ55kyy235ptvvuaFF57jsMOOomvXriyySE922WV3HnvskYbfXXTRXuy002/o0KEDnTt3oUOHDowZM4axY7+ic+fOrL76Gq3+3YUX7sFGG21Kly5dWGCBBdhnn/34z39ebfb/Ydlll6dr167sv//BPP74ow099QD22+9AunbtynLLLc9WW23LP//5DwDuu+9uDjzwEBZbbHE6derEfvsdxBNPPNZkiO3gwQfQuXMXfvSjFVh++R/x/vvvzmKPOOecc84555xzzsVljmdfrWlTprS4ud3oz5mx9DJV/3ODBm3N739/AKNHj2o2dBXgrbf+y6WXXsRHH33AtGnTmDZtGhtvvGnD5927d2fjjTfjr3+9hT/96Zxmv9/0b23D9ddfzVdffcmLLz5P//5LssIKP+btt99k+vTpbLfdoIbvzpxZz2KLLd6wvvjiizf5bx1yyKFcc80VHHDAPnTvvhC/+c0ebLPNdi3+3R9++IGLLjqPF198nu++C5PpTp48iRkzZtC+fXuAJn+rT58lmD59OhMmjG/Y1jSWJfjggw8AGDNmNCeeeAzt2tU1fN6+fXvGjfumYb1nz0Ubljt37sL3JY2uzjnnnHPOOeecczGr+Ua5Cfc+1PqH3box4d6H6LnWyrRvYebQmf36z/L3Z/brX1ZMffoswRJL9OX555/j+OOHNvv8lFNOYocddubccy+ic+fOXHjheU0aq957z3jwwb+z2WYDGT78XM4//+JZ/K0+rL76mjzyyAheeOE5Bg4MjYCLLdaHjh078cAD/6RDh9Z2Y12TtUUX7cVxxw0B4PXXX+OIIw5hjTXWon//JZv95u23/4VPP/2Eq666gcUWW4x33vkfgwfvQX19fcN3inPMffHFGDp06MDCC/do2P7ll1+w1FJLN3zeq1evLPbFOeGEoay22hrN/m41Z7B1zjnnnHPOOeecq1U1P3x1Tkw66RTqu3Ztsq2+a1cmnXTKPPubJ5wwlIsuupyuJX8XQo+yhRZamM6dO/P222/y6KONs7VOmTKF008/mYMO+j0nnngKY8d+yd133znLv7Xllttw11138N//vs4WW2wJQK9evVhnnZ9xySXDmTRpIjNnzmTUqM/4z39eafW/8/jj/2xoMOvevTtQR7t24RDo2XNRPv98VJMydO7chQUX7M6ECRO4rjBkuMgjj4zgo48+5IcffuCaa65go402behFB3DDDdfwww8/8OGHH/DQQ/ez6aZbALD99jty1VWXMWbMaADGjRvH008/Mcv/B84555xzzjnnnHMpqfmecnOiMPvaAsNOo92oz5jZrz+TTjplns7K1m8WveyOPvoELrrofM4//xzWXHMtNtlkMyZODLOtXnnlJfTuvTi//vVOAJx88hkceujvWHvtn7HkkgNa/O9ttNGmnH/+OfzkJ+s09DYDGDLkdK644mL23HMXJk+eRN++/dhjj31ajeudd97ioovOY9KkiSyySE8OO+wo+vbtB8B++x3AsGGnMGXKFI455iR22WV3Tj31JLbZZjN69erNrrvu0azhbODArTjzzFP55JNPWGONtTj66BOafL7GGmux667bU19fz2677ck666wLwM4770Z9fT1HHPF7xo4dyyKLLMKmm27B+utv1GrszjnnnHPOOeeccympKx6O2FamTZtRP3785Gbbx4z5hD59lmrzeKqtfft2zJgxM+8wqqal8vzhDwcycOBWbLvt9s2+P3r05+y886944okXZjG0tjrKPWZ69OhGS8dgzLxMtS+18oCXKQaplQe8TDFIrTzgZYpBauUBL1MsUitTauUBL1MMUitPqd69u78C/LR0exLDV51zzjnnnHPOOeeci4k3yjnnnHPOOeecc84518aSyCnn2t4ll1zV6mdLLNGXZ555uQ2jcc4555xzzjnnnIuL95RzzjnnnHPOOeecc66NeaOcc84555xzzjnnnHNtrMYa5eqor09n1lI3b+Uxc7BzzjnnnHPOOedcNdRUo1ynTl0YP34s06dP8wYXN0v19fVMmvQtHTp0yjsU55xzzjnnnHPOublWUxM9LLJIbyZOnMA333zBzJkz8g6nbHV1dUk1KtZqeTp06MQii/TOOwznnHPOOeecc865uVZTjXJ1dXV0796D7t175B1KRXr06Mb48ZPzDqNqUiuPc84555xzzjnnXN5m2ygnqTNwGbAZ0BN4HzjRzEZI6gTcCvwUWArY2MyemHfhOuecc84555xzzjkXvznJKdcBGAlsCCwMnAzcIWnp7PNngD2BMfMiQOecc84555xzzjnnUjPbnnJmNgk4tWjTA5I+An5iZh8DwwEkxZsEzjnnnHPOOeecc865NjTXs69KWhxYAXir+uE455xzzjnnnHPOOZe+urmZVVNSR2AE8IGZHVTy2WfAnnOYU+4r4JO5iNM555xzzjnnnHPOuRgtBfQu3TjHs69KagfcDEwF/lBhMM0Ccc4555xzzjnnnHNufjFHjXKS6oBrgcWBrcxs2jyNyjnnnHPOOeecc865hM1pT7nLgRWBzczs++IPJHUG6rLVTpK6AFPMbM7HxTrnnHPOOeecc845Nx+ZbU45SUsBHwNTgOlFHx1kZrdI+pgwNrbYMtnMrM4555xzzjnnnHPOuRJzNdGDc84555xzzjnnnHOucu3yDsA555xzzjnnnHPOufmNN8o555xzzjnnnHPOOdfGvFGuQpJWaGX7L9s6lmqRtGgr25dr61iqJbUySerUyvZF2joW17rU6ofUziNIbx/NiqSOecdQDj/uXF5SO/ZSvHdIsUytibUOb01q5UmV7yc3L6V2nS2XN8pV7kVJBxdWJHWUdDZwd44xVepNSVsWb8jK+GJO8VRDamV6RdJqxRuy8v03p3gqJukaSd1Kti0h6eG8YqqC1OqH1M4jSG8fIelRSUuUbFsNeDmnkCrlx10EJO3ZwrY6SSfkEU+VpHbsJXfvQIJlSq0OT608kGZ95/up9iX6rJTadbYsHfIOIAEbATdJ2gY4P/v5HFgjx5gqtR9wjaT7COW5GOgLbJJrVJVJrUyXAU9I+nO2fC6wDbB/rlFVpjvwhqS9zOx5Sb8h7Kdrco6rEhuRVv2Q2nkE6e0jgFeB1yX9AbgTOA44Bjgp16jK58ddHE6RtC3wOzMbJ2lZ4GZgJnBWvqGVLbVjL8V7hxTLlFodnlp5IM36zvdT7UvxWSm162xZfPbVKpDUBXgJWBm43sxivhEAQFJP4AlCmW4D9jOzqbkGVaHUyiRpeeDvwFLAY8C+ZvZNvlFVRtIewAWAAUsA+5jZs/lGVZnU6ofUziNIbx8BSFofuAmoIzT27G1m7+cbVfn8uKt9khYAhgNbAtcDBwPnAWeb2cwcQ6tIasdeovcOKZYptTo8tfKkWt/5fqpxiT4rJXWdLYcPX62QpH7AA8BU4DBgO0lnSoq2F6KkBQlvGhcmnPRbAfvmGVOlUiuTpHbA7sDiwAjgJ8DauQZVHaOAH4BlgY+AD/INpzKp1Q+pnUeQ3j4qsgywEPAVsADQJd9wyufHXRzMbBJwIvANoWfF/cCfY33wgfSOvRTvHVIsUyaZOjyTVHlSrO8yvp9qX2rPSkldZ8vljXKVew14HljXzC4hDD35KRGPvwdeBzoCq5nZ0YTuo3+U9GC+YVUktTI9SyjDWma2E3AAcL2kS/MNq3ySzgVuBw4FliacW29I2jnHsCr1GmnVD6mdR5DePkLSnYSb0IFmtjZwFfCUpGPyjaxsftxFQNLWhH31L2A1YAXgaUnL5BpYZVI79pK7dyDBMqVWh6dWHkizvvP9VPsSfVZK7TpbFm+Uq9yvzOxkM5sOYGajzGwL4Lqc46rECWa2l5lNADCz1whvHS3XqCqTWpnuATY2s08AzOwhwsWmT65RVWZFYHUzu9fMppnZMcBOwDk5x1WJ1OqH1M4jSG8fQXjDvaaZvQxgZpcC6xLOpxj5cReHKwjDaA4zszeB9YF/EHFDI+kdeyneO6RYptTq8NTKA2nWd76fal+Kz0qpXWfL4jnlnHM1TVJ3M/su7zici52k9mY2I+84XJokLWJm41rYvpaZvZpHTM6lJLU6PObyzE/1ne+n2ufPSvGLNndJniQ9bGaDsuWngRZbNs1sgzYNrAKSrjKzA7Plm2m9THu3aWAVSK1Mkk4ys2HZ8umtfc/MhrZdVJWRtIGZPZUtz2qWncfbKKSKpVY/pHYeQXr7CCCbievmbHm/WXw1ip5YftzFQVKdmRXKMSHL71XqtTYMqWKpHXuJ3jukWKbU6vCkygPJ1ne+n2pcos9KSV1nq8Eb5cpzU9FyzFMQF/uoaDnaWXZKpFam/kXLS+YWRXVdBqySLV/bynfqCclMY5Fa/ZDaeQTp7SOA3YCbs+W9WvlOPfHcWPtxF4cJhKTgANNpfmNdl21r35ZBVSi1Yy/Fe4cUy5RaHZ5aeSDN+s73U+1L8VkptetsxXz4agUktQdOAYaZ2ZS846mGrEz7ALea2Q95x1MNqZUpe+OzEfBsKscdxN09viWp1Q+pnUeQ3j6C8IaYMHvap4V8ZTHz4662SVrSzEZmy0u19r1Cvq+YpHbspXjvkGiZUqvDkylP4vWd76cIJPqslMx1thLeKFchSV8DvS3uqZWbkDTezHrkHUc1pVYmSd+ZWfe846iWrFKeCPRI5cYa0qsfUjuPIL19BCBpEtA9lTL5cVf7sjr8McKsfSnV4Ukde6ndO0CyZUqtDk+tPKnWd76faljCz0pJXWfL5bOvVu5G4Hd5B1Fl90vaNu8gqiy1Mj0lad28g6iW7K3Pu8CiecdSZanVD6mdR5DePgL4D7BC3kFUkR93NS6rw5chvfvK1I69pO4dMimWKbU6PKnyJFzf+X6qYQk/K6V2nS2L55Sr3DrAHyUdC4ykaNx6TMmaS3QB/ibpeZqXKdaEi6mV6RNghKT7aF6eaBIbl7gFeEDShcBnNC1TNMlLS6RWP6R2HkF6+wjgCeBhSTfQvEwx5YUp8OMuDqcBl0s6heZ1eKw9L1I79lK8d0ixTE+QVh3+BGmVB9Ks757A91OtS/FZKbXrbFm8Ua5yV2c/KXkz+0lJamXqCtybLRcnPI55PPrB2b+nlmyPLXlpsdTqh9TOI0hvHwH8kpBEd8OS7bElay7w4y4OhckripOFx5ZQu1Rqx16K9w4plim1Ojy18kCa9Z3vp9qX4rNSatfZsnhOuQpI+jGwEvBfM3sv73iqQVIPwkn9npl9l3M4VZFomXqa2Td5x+Fal1r9kOh5lNQ+SpEfd/FIMKF2D9I79pK7d0ixTK72pVbfpcr3U21L8TpbLm+UK5OkfQlvuccBCwN7mtmduQZVIUlbA3cQ3jp+B2xvZv/KN6rKpFamLG/K3cDihC6+25vZa7kGVSFJiwMXEKb7fhU4ysy+zjeqyqRWP6R2HkF6+whAkoDraTyXBpvZR7P+rdrlx10csln7DiA77szshnwjqlxqx16i9w4plim1Ojyp8kCy9Z3vpxqX6LNSUtfZSiWR+DAnxwE7mdliwG+AE3KOpxrOIJRrQWAoMCzfcKoitTKdC9wMrArcma3H7jLCTfUVwJLA8FyjqY7U6ofUziNIbx8BXAx8SCjPKMINXMz8uIvDuYS8PX2AsySdlnM81ZDasZfivUOKZUqtDk+tPJBmfef7qfal+KyU2nW2It4oV76+ZnZftnwvMCDHWKplWTO7xMwmA5cCy+cdUBWkVqaVgBPN7G1gCOGNSezWB3Y0s8uAXYGN8g2nKlKrH1I7jyC9fQSwFrC/mT0EHESYTCBmftzFYRdgQzPbBdgU2D3neKohtWMvxXuHFMuUWh2eWnkgzfrO91PtS/FZKbXrbEW8Ua58dYUFM6snjf+XDWUws+mkMRFIamXqkE2JjZlNATrlHE81dDGz8QBmNhZYIN9wqiK1+iG18wjS20cAnczsBwAzm0iY0SpmftzFYWEzexcgayDpmXM81ZDasZfivUOKZUqtDk+tPJBmfef7qfal+KyU2nW2Ip5TrkySZhC6+Bb0K1nHzKJ6Ay5pOvBc0aafA88Xf8fMNmjToCqUWpkkTQVuL9q0K/DX4u/ENn20pO+BQ2h8WL0Y+GPxd2Kbij21+iG18wjS20cAkqYAZxdtOgb4v+LvmNnQNg2qAn7cxUHSd8BqNNbhrwJr0rQB8sMcQitbasdeovcOKZYptTo8qfJAsvWd76cal+izUlLX2UrN1y2SFdok7wDmgd+WrF+bSxTVlVqZSsfbn5lLFNX1IlB84/wSTacuj3Eq9tTqh9TOI0hvHwHcSsg1UnB7yXpsb+H8uIvDAsD7FD3sAB8ULdcD7ds0osqlduyleO+QYplSq8NTKw+kWd/5fqp9KT4rpXadrYj3lHPOOeecc84555xzro2lkMvEOeecc84555xzzrmoeKOcc84555xzzjnnnHNtzBvlnHPOOeecc84555xrY94o55xzzjnnnHPOOedcG/PZV8sg6WbmYCaamKZil3T6nHwvpimxUyuTpP3m5HsxTYktadk5+V5k05YnVT+kdh5BevsIQNIczeppZo/P61iqwY87l5fUjr1E7x1SLFNqdXhS5UmV76fal+izUlLX2WrwRrnyvF+03AvYB7gf+AQYAGwL3JhDXJUonvq6C7Aj8G8ay7QOcFcOcVUitTIVT31dB/wSGAOMJJR1ceBZ4poS+33CQ2odTR9WS9djmrY8tfohtfMI0ttH0Hwq+X6Ec+hrYFHCOfUZMEc3dzXAj7sISBrJnDU0DmiDcKoltWMvxXuHFMuUWh2eWnlSre98P9W+FJ+VUrvOVswb5cpgZqcVliX9A9jazJ4u2rYecHIesZXLzAYXliXdDuxmZncVbdsB2DmP2MqVWpnMbOPCsqSLgXvNbHjRtsOA5XIIrWxm1jCEXtJgYDPgVEKlvBQwFHgsl+DKlFr9kNp5BOntIwAzW6awLOlEws30yWY2WVI34HTCTXYU/LiLxp5Fy2sTGhovorEO/wNwUw5xlS21Yy/Re4cUy5RaHZ5UeTIp1ne+n2pcos9KSV1nq8Eb5Sq3LvBCybYXgZ/nEEu1bAnsUbLtPuD6HGKpltTKtCehp0WxS4CxwKFtH05VnAH8yMy+z9bfk3QQ8C5wQ25RVSa1+iG18wjS20cARwB9zWwaQHZzfQLwOXBWrpGVx4+7GmVmTxaWJV0KDDSzUUXbRgAPA+flEF41pHbspXjvkGKZUqvDkyjPfFDf+X6qfSk+K6V2nS2LT/RQuf8AZ0rqCpD9Owx4Lc+gKvQ+8PuSbYcAH+QQS7WkVqYxwK9Ktm0LfJlDLNXSDli6ZNtSxNUdu1Rq9UNq5xGkt48AJhG6/hdbG5icQyzV4MddHPoCE0u2TSQMh4pVasdeivcOKZYptTo8tfJAmvWd76fal+KzUmrX2bJ4T7nK7QvcCkyQNA5YBHiZ5i2+MdkfuEfSscAoQsU1Hdgh16gqk1qZDgX+JukYQg6VAcBKxN3V9wLgcUnX05gXZt9se6z2Ja36IbXzCNLbRxCGQD4s6X4az6VtaH7TEws/7uLwd+Dvkv5EyEG0JHBCtj1WqR17Kd47pFim1Orw1MoDadZ3vp9qX4rPSqldZ8tSV18/2zyIbg5IWpLQGj/azD7NO55KSepIGEazBDAaeL7QnTlWqZVJ0qLAVmTHHfCgmcWW96EJSYMIN9KFMt1hZg/nG1XlUqofUjuPClLaRwCSViIkzi2cS38zs7fzjap8ftzVPkldCHlumtThwGlFQ22ik9qxl+i9Q4plSq0OT608qdZ3vp9qXIrPSqldZ8vhjXJVImkxYMHibTFNTdwaSU2GOJvZzLxiqZYUy+RqW4r1Q2rnUYr7KEV+3Lm8pHbsOeecc7Vkfr7O+vDVCmWt1dcSWnaL1RPp+G5JawGXAqsRpimGxmmXvUw1QNIyhPxDa9D8gS6WKb6bkNSJ0AV7DZqXae8cQqpYavVDaucRpLePACT1BI6m5XNpgzxiqoQfd/GQJGB1mh931+UTUWVSO/YSvXdIsUyp1eFJlacgwfrO91ONS/RZKanrbLm8Ua5ylxJmQrkx1m6wLbgRuB/Yj7iTexZLrUy3EhJgHkUa5YGwj1Yn7Kcvco6lWlKrH1I7jyC9fQShfuhMGKKRwn7y4y4Ckk4EhgKv03Q/1QPRPfxkUjv2Urx3SLVMKdXhqZUn1frO91PtS/FZKbXrbFm8Ua5yiwBXmllK44CXAk7yMtW0lYFfJtatdxCwjJmNzzuQKkqtfkjtPIL09hHAL4DeZjYl70CqxI+7OBwOrGNmb+QdSBWlduyleO+QYplSq8NTKw+kWd/5fqp9KT4rpXadLUu72X/Fzca1wOC8g6iye4At8g6iylIr01PAmnkHUWWfEt7QpSS1+iG18wjS20cAbwD98w6iivy4i8P3wDt5B1FlqR17Kd47pFim1Orw1MoDadZ3vp9qX4rPSqldZ8viPeUqty5wqKTjgTHFH0Q8/r4LYWriZ2hepijHq5NemT4G/iHpbpqXZ2guEVXuJuA+SRdS0iXbzB7PJ6SKpVY/pHYeQXr7COBx4GFJ19O8TDEO1/DjLg4nAxdLOpXmdXisvZhSO/Y+Jr17h49Jr0yp1eGplQfSrO98P9W+FJ+VUrvOlsUb5Sp3TfaTkrezn5SkVqYFCOPvOwJL5hxLtfwh+/fMku31wLJtHEu1pFY/pHYeQXr7CGB94DNg85LtseZQ8eMuDjdk/+5ftC32ZM2pHXsp3jukWKbU6vDUygNp1ne+n2pfis9KqV1ny1JXXz9fD991zjnnnHMVkrRUa5+Z2SdtGYtzzs1LXt/FwfeTi4U3ylWBpMWBdYBehNZ3IOquvoUpl0XzMsXaNTbVMnWneXk+zC8iVyq1+iHR8yipfVRMUh1NyxTjcA0/7lxuEj32krt3SLFMkE4dXpBaeVLl+8m1pRSvs3PLh69WSNL2wF+A9wgzQL0FrAI8Q6RdfSWtB9xJSCS5EPAt0B0YSaRdY1Mrk6SVgFsI02LX09gVG+Lsjo2khYBTgQ1pXikPyCmsiqRWP6R2HkF6+whAUj/gEmADoEfJx9HVD37cxUPSr2i5Do8yL0xqx16i9w4plim1Ojyp8hQkWN/5fqpxiT4rJXWdLZfPvlq5PwGDzWxNYFL274HAK/mGVZELgHPMrCfwXfbvGcBl+YZVkdTKdBnwL6AnofJaBLgS2CfPoCp0GbAWcDqhXH8kzDJ0QZ5BVSi1+iG18wjS20cAVwBTgU2BiYTz6u/A7/IMqgJ+3EVA0imE61A7YGfga2AgMD7HsCqV2rGX6r1DamVKrQ5PrTyp1ne+n2pfis9KqV1ny+KNcpUbYGZ3lmy7EYiu9b3ICsCFJdv+DByRQyzVklqZVgeOM7PxQJ2ZTQCOIVRisdoC2NHM7gNmZP/uCuyVb1gVSa1+SO08gvT2EcAvgP3M7DWg3sxeB34LHJVrVOXz4y4O+wGbm9kRwNTs322BpXONqjKpHXsp3jukWKbU6vDUygNp1ne+n2pfis9KqV1ny+KNcpX7MssLA/CxpJ8DyxFxN19gAqH7KMDobGjAIsCC+YVUsdTK9ANhpjGAsZIGEM7nRfMLqWLtCPsJYKKkHsBoYPncIqpcavVDaucRpLePAGYA07Pl8ZJ6A5OAfvmFVBE/7uLQw8zezJanSupoZi8RhtnEKrVjL8V7hxTLlFodnlp5IM36zvdT7UvxWSm162xZvFGuclcD62XLFxC60L9O3F0u7wa2ypavJZTpFcJ471ilVqangV2y5b8BI4AngZgTYr5O40XyaeBS4HLg3dwiqlxq9UNq5xGkt48AXqRxP/0D+Cth372cW0SV8eMuDh9IWjlbfhM4WNJewLgcY6pUasdeivcOKZYptTo8tfJAmvWd76fal+KzUmrX2bL4RA+V+7/CjDRmdpOkJ4AFzOx/+YZVPjM7vGj5PEkvEhIu/iO3oCqUWpnMbJei1RMJF5ruwE35RFQVB9CYsPRQ4CxCotmYh3MlVT+kdh5lktpHmb1ofOl2OGHoSXdgeE7xVMSPu2gMobF30vHArYQ33YfkFlGFUjv2Urx3SLFMJFaHk155IMH6Dt9PMUjuWSm162y56urr62f/LdciSe0JiTB7mNmUvOOphqxM7wIreZlqU1aex4CBKZQHGsp0CjAssTIlUz+kdh5BevsIGsp0HXBgCmXy4y4OktoBGwHPmNnUnMOpitSOvYTvHVIsU2p1eDLlgaTrO99PNSzhZ6VkrrOV8OGrFTCzGYQDKea8FU1kZZoBdMk7lmpJrUxZeZYhofM3K9PvgWl5x1ItqdUPqZ1HkN4+goYybQHMzDuWavDjLg5Zr7/7UnjwKUjt2Ev43iHFMqVWhydTHki6vvP9VMMSflZK5jpbCR++WrlbgAckXQh8BjR0PTSzWPNZDAfukHQmzcv0YV5BVWg4aZXpNODybKrv0vLEekG9kTDtesw5lUqlVj8MJ63zCNLbRxBylJ0m6RQzS+HmbTh+3MXgKUnrmtkLeQdSRcNJ69hL8d4hxTKlVoenVh5Is77z/VT7UnxWGk5a19my+PDVCkn6qJWP6s1s2TYNpkoktXYTU29mUc4Ml1qZispTfALXEWl5ACQ9A/wMGAWMpGmlvEFecVUitfohtfMI0ttHAJJGAn0Ibx+/oum5NCCvuMrlx10cJF0G7AbcR/M6fGhecVUitWMv0XuHFMuUWh2eVHkg2frO91ONS/RZKanrbLm8p1yFzGyZvGOoNjNLZhhAQYJlSu64I8xGeHXeQVRTavVDgudRcvsos2feAVSTH3fR6Arcmy33zzGOqknw2EvxuEuxTEnV4aRXHkiwvsP3UwxSfFZK7TpbFu8p55xzzjnnnHPOOedcG/OecmWStDmwlZkdka1/D3Qi6zJPmAnqnzmGONckrQ1saWanZ+v/AzoXfWUXM3s5l+DKlFqZJK0AbG5ml2brDxOOu4KDzcxyCa5MkhYGVjezp7L1E2laN11sZuNyCa5MqdUPqZ1HkN4+ApDUF1jPzO7I1q8EOhZ95WQzG5VLcGXw4y4ekjoWchBJWo+mifefM7Pp+URWntSOvUTvHVIsU2p1eFLlKUiwvvP9VOMSfVZK6jpbKe8uWL7DgRFF61OAFYAfAfsAR+QQU6WOAd4sWu8LHJD9XAUcl0dQFUqtTMcDE4vWf0FIGH4L8Fb2eWyOBjYuWj+RcB79iDAT1JF5BFWhw0mrfkjtPIL09hGE/dCnaH13Qt6RUcBCwLF5BFUBP+4iIOlg4LqiTY/QeF26h1Cu2KR27KV475BimVKrw1MrT6r1ne+n2pfis1Jq19mKeE+58q0GFM+QVm9mHwBI+gQ4M5eoKrMOTSupmWb2GDQklnwnl6gqk1qZNiA81BXMMLNrASR1B17NI6gK/RoYVLQ+zcz2ApDUH3gIODmPwCqQWv2Q2nkE6e0jCOfRL4vWp5vZyQCSegFP5xJV+fy4i8PehNngCqaY2ZIAktYALgeuzSGuSqR27KV475BimVKrw1MrD6RZ3/l+qn0pPiuldp2tiPeUK1+PkvXi2dLqgJ5tF0rVLAr8ULS+UdHyNKBXm0ZTHamVaTEz+7Zofe/Cgpl9Byze9iFVrK+ZfVa0flVhIdseY2LWHiXrsdcPqZ1HkN4+AljczMYWrQ8pLGTb+7Z9SBXx4y4Oy5jZ60Xrbxctv07TMsYitWMvxXuHFMuUWh2eWnkgzfrO91PtS/FZKbXrbEW8Ua58o4A1Cisl47jXBD5v64CqYCzw48JKSWW2IvB1m0dUudTK9J2kpQsrZnZ/YVnSsjQdyhENSb0Ly2Z2XEvbI5Na/ZDaeQTp7SOAqVluGAAKuZagIWfM1FyiKp8fd3FYUNIChRUzK+5x0Q1YoPmv1LzUjr0U7x1SLFNqdXhq5YE06zvfTxFI8FkptetsRbxRrny3ApdLWqJ4Y1Z5XQL8JZeoKnMPMFxSl+KNkroC5wF35xJVZVIr04PAGa18dlr2eWyeBQa38tlg4Pk2jKVaUqsfUjuPIL19BPAYreckO5KmwyZj4MddHN4k5LRpySBCfq/YpHbspXjvkGKZUqvDUysPpFnf+X6qfSk+K6V2na2I55Qr31nAT4H3Jb0IjAGWANYG/pV9HpuhhNg/kPQPGsu0BTAaOCXH2MqVWpmGAs9J+g+hMiuUZ3tgEWDd/EIr22nAv7KH0rtpLNMOwH7AJjnGVq7U6ofUziNIbx9ByCfyYjYr4V00PZfWJ776wY+7OAwHLpNUD/zdzGZKagdsR2hojDEBdWrHXor3DimWKbU6PLXyQJr1ne+n2pfis1Jq19mK1NXX1+cdQ9QkbQZsShj3/DXwmJk9mm9U5ZPUiZCXo0mZgJvMbEqesZUrtTJJ6km4mJSW53wzi7Krr6RfAGcDPyf04J0JvAgca2bP5hlbJVKqH1I7jwpS2kcAkpYn3MhsRtP9dKqZvZdnbOXw4y4Oko4iPDR0IgxJ6UWYWfZ0M/u/PGMrV2rHXqL3DimWKbU6PKnyQLL1ne+nGpfis1Jq19lKeKOcc65mSOpGeMM9zswm5x2Pc865OSNpIcLDQuHG+nkzm5BvVM45V31e38Uhxf3kz0pp8kY555xzzjnnnHPOOefamE/04JxzzjnnnHPOOedcG/NGOeecc84555xzzjnn2pjPvurmK9nMO4ub2ei8Y3HOOeecc845V12S6oBlgE/MbEbe8Tg3K94oVyFJywDDgDWABYs/M7MBecRUDZJ+BWxISIxZV9huZnvnFlQFJPUALgN2AqYBC2RlXMfMhuQZWzmy2Wr2peXjLsp9BCBpY2AvoB8wCviLmT2eb1TlS61+yGa6O5qWy7NBHjFVKrV9VCBpC1ou09BcAqqAH3dxkbQYzcv0YU7hVCS1Yy/Fe4cUywTJ1eG7Aa+Z2f8kCbgamA4cYmbv5BtdeSSdT5gh8rW8Y6kmSYNpeh9+s5ldn29U5TGzekn/BbrnHUs1ZefQ6jSvG67LJ6K5J+kqMzswW74ZaHGSg5jr8LnljXKVuxX4ADgKSGIGFEmnAL8Dbgd2Bq4Edgf+mmdcFboCGAcsBbydbXseOA+IrlEOuJFQId8PfJFzLFUhaX/gTOAawhTfA4BbJZ1sZlfnGlz5UqsfbgU6A3eQRnkgvX2EpEuAXYB/0bRMsc7s5MddBCQNAq4F+lD0Mo9w3LXPJajKpXbsJXfvQIJlSrAO/xPwi2z5XOAlYCLhZfkmeQVVoY7APyR9BdwM3GJmn+UcU0UknQTsTXg2+oTwzHSspL5mNizX4Mr3H2AFIMrG31KSTgSGAq/TvG6IplEO+Kho+f3coqgh3ihXuZWBX5rZzLwDqaL9gM3N7E1Jg83sCEm3EWfjVcGmQF8zmyapHsDMvsre6MdoELCMmY3PO5AqOpZw3L1e2CDpr8BdhLeqMUqtfvgF0NvMpuQdSBWlto8AdgPWMLOReQdSJX7cxeFS4AzgRjP7Pu9gqiS1Yy/Fe4cUy5RaHd7bzL6Q1AVYj8ZRK2PzDat8ZvZHSYcDWwJ7AEMkvQjcBNxtZhPzjK9M+wMbmdknhQ2S/gE8RejZHaMngIcl3QCMpKhhO6aeZUUOJ4zyeiPvQCphZmcVLZ+WZyy1whvlKvcUsCbwSt6BVFEPM3szW54qqaOZvSRpw1yjqswEwlDchlxykgYUr0fmU8Lb+5QsSmMvxgIDeuYQS7WkVj+8AfQn9PBJRWr7COBrYHzeQVSRH3dxWAS40sxi7c3TktSOvRTvHVIsU2p1+FeSlgdWBf5tZlMkdaNpj9roZHnKHgAekLQyoWftDcBlkm4HTjGzUTmGOLcWAL4q2fY10DWHWKrll4ReWaXPsLH1LCv4nkR6/RWTtAnhZURf4HPgdjN7LN+o2pY3ylXuY0L35buBMcUfxJj3IfOBpJXN7C3gTeBgSeMIwz9jdQ1wV9Y1u52knxOGSl6Rb1hzLquwCm4C7pN0ISXDNSLOwfYMcL6k48xssqQFgLOA53KOqxIfE3n9IGm/otXHCW8cr6d5eWK8uYEE9lELzgNukXQWzeuHKHJ7+XEXpWuBwcT5oNMg8WMvxXuHJMokadmi1ejr8BJnEF5AzAB2zbZtShiCFy1JCxHS/OwJrEYY2XEIoaH4KGBEtj0WDxOOu+MJZViK0EPuH7lGVQEz2zjvGKrsZOBiSafSvG6Isue9pCOB44HrCcONC+mLzjGz83INrg15o1zlFiDksegILJlzLNUyhNBrCeAE4BZCMslDcouocmcDPxCG13QkPDRcCVyYZ1Bz6doWtp1Zsl4PLNvC92JQyGM4QdI3hB5yzxHyGcYqhfphr5L1z4DNS7bF+sYR0thHpS7P/t2mZHtMub38uIvPusCh2QNdaQNWTJMipHzs/SH7N6V7h1TK9D4h5uLeYzHX4Q3M7AZJd2TLhTxYLwK/yS+qykj6GzCQ0Ov5CuDe4mHuWUPDhJzCK9cfgEsIjaUdCUOM7wAOzTOoaslmYy2evDDGRqwbsn/3L9pWR6R1Q+YoYJOiUXqFyR8eJbygmC/U1denNMrAORc7Sf3Jui/HnjTXOefmF5L2ae0zM7uxLWNxztUWSYsCWwFLmNk5kvoC7WK9z5N0NPAXMxszi+90K2qEjIakdoSUP2MjbbhqIKkfoaFxA6BH8WdmFl0jlqSlWvusOBdgTCSNApYzsx+KtnUF3jezfvlF1ra8p1wZJC1tZh9ny62+hYu0izkAkhYGRPPplqMZClBM0sbAx2b2kaQ+hJ5zM4ATZ3VBjUVWvulm9nTesZRL0haEffQuoWdCYdrvAWb2aK7BVUhSd8INTvEbuujqh5J9VNi2ArCU7yM3r0jqDXxvZhMltSfMDjedMNtd7A8MyRx3KTa8pVznuXhkzxozIn7o3pAwtPNlQo6vc4AfAUcD2+YYWiXWN7NzSzdKutvMdoAmvQJr1qyeY4EFw214vNclQi/GyYTh0k8SGudOBR7KMaayxVoHlMoafgtOBa7NhuR+Rhg9cDJwSttHlh9vlCvPf4Hu2XJL3c0h4m6kkvYlDPOcSPPplmMaClDsMkI3c4Dzs3+nA1cBv8ologpIepLQoPispOOAI4Hpki41s9IhHLG4lHCxLPZdtn2Ftg+ncpJWIgz/Xp3GeqLQPTnG+qGlfTQR30c1RVIHQrqBDWne4BPTMMKCBwjD2/9DGKK2DWFYzVrAETnGVbYUjzsASYsD69D8uItxqCckVudlObBOpeW6YUBOYVUk0TLdBlxsZs9JGky4h50p6VAzaymVSa0bDuxqZo9lOaohDF9dJ7+QKtZarrKN2jKIKmjtObZYtM+0hBm0B5jZJEn1Zva6pN8S0uNcnXNsZZH0K1qu7/bOLai5N53Ge55CGXYr2bY7ISf8fMEb5cpgZt2LltvN6ruRGgbsZGYj8g6kivqZ2afZw+pAQvLSqYQZXmK0CvBCtnwA4SZgIvAszfOqxGIxMyudDXc00CePYKrkMuBfhJu3j4CliXvyCt9HcbgA2ITw0mEYcBJwMCFnY4xWAF7Llvcg3GRPBN4i0kY5EjzuJG0P/AV4D1iZsH9WIUziE2ujXGp13mWE2WRPJ+yrPYFjCL2YYpVimTYFCsPBjwQ2I8zGei8t5xeudUsXzaRYeOieSoTPoZJOzxY7FS0XLAtE1ZMp0efYYjMIDUAA47Oe998CUQ6LlHQKjTm4dybkR98d+GuecZVhmbwDqDWpn4iuPB2AR/IOosq+zd7gbwi8bWYTs+0dc4ypEu2AeknLAXVm9j8zGwksknNclfiwZIZZCI2NH+UQS7WsDhxnZuMJ+2kC4WHhjFyjKp/vozjsAGxpZhcShrVfCGxP62/2a90MwgPQqsAEM/uU8IC64Cx/q7aleNz9CRhsZmsCk7J/DyTMuhir1Oq8LYAdzew+wnDI+wizYZZObhGTFMvUycymZvmweprZs2b2FrB43oGV6W1JA0u2bUYYeRSbJbOfdkXLSxIahkcSGkpc7XiRkMsQwiyyfwXuJgyljtF+wOZmdgQwNft3W8KLvWiY2Sdz8pN3nG0pujcUtSbBYUIQ8q0NkXRG7Pl6ilwM/BvoBByebfsl8E5eAVXoGULi0iWAewCyBrqxeQZVoVOBuyVdC3wALAcMzn5i9QONM1iNlTQAGEfj7MaxORXfRzHoRng4APg+Szb9jqQ18wyqAiMIM8AtSmNvv5WAUblFVLkUj7sBZnZnybYbCTOxHp1DPNVwKmnVee1onBFyoqQehJ5/y+cWUeVSLNNrkk4gjOp4EBoS1n+ba1TlOwp4QNKDQFdJVxIaErbLN6y5Z2aDASQ9Z2ZRDn9sjaSnaezJ2ETEz7R70dgJ6XDCsdidMKIgRj2KZimdKqmjmb2U5W2MViJDciviPeUqdwFwEGFK7J8QussvBkQ5IULmCGAI8J2kT4t/8g6sXGZ2NuGt3C/NrPBQN4qmU0rHZF9CT5E3CA8NAD8GLswnnMplb7e3ABYAts7+HZhtj9XTwC7Z8t8IjQtPEmn94PsoGv8D1s6WXwZOlTSEeBux9ic8mF5LGOIJ4cbt1LwCqoIUj7svsx7pAB9L+jmhESvWXEQp1nmvEx58IByDlwKXA++2+hu1L8Uy/RZYFehKuB8H+DkhD2V0zOwFYDXCkPbrCD1N1zGzf+ca2FyStHTR6mOSlm3pJ6/4quAawnW28PMgYaj+P/MMqkKbm9k3AGb2vZn9ycyOA9bLOa5yfSBp5Wz5TeBgSXsRXupFKRuSeyWhXWpn4GtCqqnxOYbV5rynXOV2AH6e5Ss7zcwulPQPwsF1ar6hlW3PvAOYRz4C1pX0UzP7K/E+oGJmXwMnlmx7MKdwqsbMXgJeyjuOajGzXYpWTyTckC4I3JRPRJXzfRSFwwhDPiHkI7qc8Gb4wNwiqoCZTSHkxyve9kQ+0VRHosfd1YQHnbsILyz/BcwEzsszqEpI2jnr/fdSyfadzOxvOYVViQNo7IVwKKGRuwdhRuNYJVcmM/uAkCeqeNvfCA340ZF0dDZT6Tkl2480s/Nb+bValPREfy3NoC3pLuB6Qs7GGF0LlPbghnBPEeP5NITGHvUnEBrqFySM2otVYUjum5IGm9kR2WQ3Q2b3iympq69vsZeqm0PZLEI9zaxe0mhgOTObLOlbM1so7/hckOUi+jswBehvZgtK2grYx8x2zTe6OSPpJDMbli23enE0s6FtF1V1SVoDWJ/m3ZejLVNKJHUi9NJcg5J8XvNTF3M370m6yswOzJZvpvUhNX7c1ahsSO4CZva/vGMpV2v3cpK+MbOeecTk0iRpLzO7OVver7XvxTiTsZ9H8ZLUFRhjZgvnHcvcKOqx+Aah12lx4+mywE1m1rfNA3PNSJpQOL4kfUmYnHFa8fb5gfeUq1xhmNBLNA4T+paIe2HNosFnCvAZ8LCZfdGGIVXD5cBQM7u5aDr2J4lrOuz+RctL5hbFPCLpQELvikeALQnDubYAohomNKeNCYSZxz4CrjazL9sqvgrdSEhQfz8QWx3QIMUGH0kbmNlT2XJpYvoGZhbL8MjiRPrv5xZFFaV43M1KNiFHlIoe6NpJWobmD3Q/tH1U5UmxsSfFMgG7ATdny61NUlFPRDMZF12L2kvamObn0XdtH1Vlskaq5YryehV/tgrwvplFUz8Ua+Fc6kYYEfZCDuFUqrgn4wcln40hotFskpY2s4+z5VaHR5vZh20WVHV9IGnlbDKbwpDccUQ8JLcc3ihXucNonGo5+mFCmRWAXxMaGkcSGoDWITyIbwtcJmlHM3s4vxDn2srAX7LlegAzm5RdXKNgZgcXLceaZHpWjgUGmdnTksaZ2a8lbQn8Ju/A5tKcNia0I/Q4uxf4xTyMp5oGActkM0bGLLkGH+AyYJVs+dpWvlNPeBCqeWZ2VtHqlWY2pvQ7kvq0YUjVkNxxJ+l/ZrZitjyS1hsaB7RpYJVL5oGOBBt7SLBMZrZV0XKLM2VLKh0qWesK16IuNN0X9YTz6I9tHlHljiUMjz6ihc8GE/JgxTqLdum5NAl4jggnRTCzdgCSnjSzqCdBIPFh06Q5JHeueaNcBSS1J3SJvQXAzN4jTCYQu3bAb8zsnsIGSdsBu5vZupL2Af4MxNQo9zFhIo6GKbAlrUPED0aSFgZE82GEsfSEKbWYmT2dLc+U1M7MRkiKKrFxcWOCmZ02q+9mN9ivzPOgqudToHPeQVSqsI+yOnwkcGusb7YLzGyVotXlzWxGq1+Oz7tAS+kg3gaiGfqU4nFHyOdVkEw+2pQe6OaksSc2KZap2CxyrV1LyL8UBTNbBkDSTan0AAZ2BTZv5bPzgUeJtFEuxXMp9vobwMy6Fy0nN0mnmT1UtPwicc+cXTbPKVchSePNrEfecVSTpAmEPHkzira1B8aZ2ULZ8vjiSqLWSdqGcDNzBWE67GHA74ADzOyRPGMrh6R9CTOMTQQmF31Ub2ZR9IQpJeltYCsz+1jS84SEwGOBO80smh4xCQ4jbCDpKMLMSBdSMnw1xvJAenV4Vj9PBHpkEyRET9J3pdcbSQsBH5pZr5zCqkhqx52rXZLm6CHOzGbO61jmBUm9ge/NbGJW/+1NGMFyS8Rleh24yMyuzdbrCKM9epvZFrkGN5+bXZ6rFPJgSepO89zOUQ6NlNSB0ONqQ5qXaYO84qqUpH5AX2CUmX2edzyVSrCjyVzznnKVu1/StmZ2f96BVNEHwMHAJUXbfkfjEI5ehC7N0TCzB7KhkPsTcsktBexgZjH1Uio2DNjJzEbkHUgVnQOsSOjVeDphVqROhNnUYpLUMMISf8j+PbNke6zlgcTqcDObIeldwlCAqG/UioZDdpVUmp9sUeC2to+qapI67iDNfLSSnqb1IbmxPNBNp/XcphAeUmMe+vQA4R71P4Rr0zbANGAtWh5iGINBwBOSviPMZnwbsAAhhUx0spcop9Jyw0hsQ9snSVrSzEaWfpBNbjO5hd+JgqSVCKO/VqdxiGSh7oi1frgA2IQw2+ow4CTCM+7teQZVruwYuwX4OfAN0FPSC8AeZvZJrsGVaVYdTYj32WKueaNc5boAf8t69jTJpxJxV+39gbslHUeYsKIfMIOQ7BNCS/bJOcVWNjN7lXTGp3cgTIiQDDO7oWh5hKRFgE5mNjG/qOZe8TDCwtCNVKRWnkyKdfgtwAOSLiQ0hhSXKaa3jnsSHgoeommum3rgCzOzXKKqjhSPuxTz0V5Tst4H+C2NOWpjkGK9XWwF4LVseQ9CjtaJwFtE2ihnZqMlbQE8Qci7NhbYzsymz/IXa9dlhMnKTiecO3sCxxAaHGPzEKHxt6VchmcAD7ZtOFV1GfAvYGNCDtSlgbMIeeVitQPwczP7VNJpZnahpH8AVxJXbtCCGwlpbwZludEXJBx3NwIb5RlYBVLsaDLXvFGucm9mP8kws1cl/YjQCr8EMBp43symZZ8/BTyVY4hzbRZv8DGzoW0ZS5WcDQyRdEaswzNaI2kxirovS1os1m7zKZLUEVgX6Gtmf5W0AISJU/KNrGzJ1eGEt8DQ/IYzqreOZvYkgKReZhZt74NWpHjcJZeP1sxuLN0m6S7gekIDQ82LtffEXJgBdJK0AjAhe/huR8kwqFrXyiyydwO7Exqy9pYU24yyBVsAK5rZ15JmmNl9kl4mNNjHNonAEOD5bIjx3YRnpCUILyQWIp6Ju1qyOrC5mU2TVGdmEyQdQ7hWxfQiolg3wksigO8ldTOzdyStmWdQFfgJsEXRM/nErBPN1/mGVZHkOpqUwxvlKjS7RO6xyk72qBreZmPJkvU+hG7097Tw3RgcQSjDsZKaVMQRDgUAQNIgwnDPPjSdVSjaYTWJDdlA0qrA3wnD0foDfyWUbR9C8uPopFiHp9CjUdJJZjYsWz1eUovfi/SlSpLHHTCQMCtmsQdonCXzLzRNixGrUcBqeQdRLkm/ouVrUqw9NEcAdxCGtBeGpK1E2E8xaW0W2XdonIU+qhlli7QDJmTLEyX1IDRmRZfQ3czGSFqLkJ96EOG4+5rQwHi+mY3LM74K/QB0JAz/HpsNlRxH48yYMfofsDahB/fLwKmSviW++qHgBUIP9GeLtv0UeD6fcKoi2Y4mc8Mb5apA4WlhdZonJ4zxwplcQwKAmQ0u3ZY1ApU+QMQimVnuilxK1gXbzL7PO5gqSWnIBsDlwFAzu1lS4cbzSeDqHGOqiKRXgZsIM2F+mXc81SDpPjPbroXtd5vZDi39Tg3qX7Rc+lKlIOqZqiR1IqSDKL3OxjTEuFhy+Whb6L3UjTAc6oUcwqmYpFMI++R2wqQ9VxJ6Yv01z7gqtD/hxdA0GhuAexHZ0LTCzJfZpA7LAJ9GPFy11OuEZ4rHgKdpzB/1bp5BlStreBuS/aTkaWAX4AZCXucRhJewsV6TAA4j9KYFOJJwH9sdODC3iOZSyWivD4CHJD1IY5qIrYBb84itXEU5gyHc/yTV0aQc3ihXIUknAkMJF5zS5IRRNsqRXkNCax4h0hvRwrCuxCwCXGlmUT9ol0hpyAbAyjQOYaiHMGxVUtf8QqrYMEIeomGSniI81N0TecPwxq1s36gtg6iEmR0MDTNH3gw8m8pssgCS1gPuBDoThjx9S3hQGElEQ4xLpJiPtrT30iRCfqUY62+A/QjD096UNNjMjpB0GxE3LmT1wlWF9ex69JyZTc0vqvKZWb2k/xLqg1QcQOOLh0MJecp6EGbKdTXCzHYpWj2RMGy1O+HFZZTM7N9Fy+8Bm+UYTrlKX0zenf27GKHR9B5CntqYpNi5pCLeKFe5w4F1zOyNvAOpotQaEpBU+pDTjfB2uNnsSbGQtAawPs17WUQ5nIswdHUw8TZmtySZIRuZjwn5LF4ubJC0DvB+XgFVyszuAu6S1JPwhvgQQkL6u4G/xNRrqehtaqcW8mguC0SXW8rMZmY9/1J6QIVwLT3HzC6QNM7MekoaSsQz96WYj7bQeykhPcyskMtwqqSOZvaSpA1zjaoCks4F7sjKsTWhh0+9pF0jnt34P4QJLN7JO5Aq6W1mLwKY2VeEBvzC/YPLmaTFCdekVYBXgaPM7GvizSMHgKT2hGe9LYDewFeEyVNujenFa0ujvWKXaOeSinijXOW+J52LZkFqDQkQGg0K03tDePD5D2HIQ3QkHUi4gD4CbEnoYr4FcF+ecVVoXeBQSccDY4o/MLMN8gmpYkkN2SD0cnlQ0hWEhp8TCEOhDsg3rMqZ2TeSbiLsn2OBHYENJM0EDjGzf+Ya4JwpvE1tR9M3q/WEFxCntnVAVfKUpHXNLMohg61YAbiwZNufCTPendv24VRHavloJa1EePnVE/gGeNrM3s43qop8IGllM3uL0Avm4CwVQcx5sPYgjFgh+3dPwj3sBYSXyTF6AnhY0g00n505xheXjxJ6BJd6mHBuuXxdRui5eAXh3ucCIu/FmKVieoQwFHwE4ZlvCcLMuYdL2pzQDvILM7sjt0DLJKk7zTtlRDcpXtYgfBRNr7NPAReY2ZhZ/W5qvFGuDNlwmoKTgYslnQp8Ufy9iJMVptaQgJm1m/23onIsYTrsp7NeFr+WtCWNyYBjdE32k5LSIRt/JuIhG2b2QHac7U/IJbcUsIOZvZJvZOXL6vPNCcPUtiEky/0z2RBWSTsS3hb3yS/KOVN4myrpOTOLNs9fCz4BRki6j+YPqLH2DJ5AeEgdD4zOGn++Jr4ZI5vsj9bElhcmy+t1LeHF3WfA54ThuH0l3QzsF2mqhSE0Jm0/npCHaEHg97lFVLluZjZZ0qLAslnvZyQtlXNclfgloYG+tAdjVKlxsutrHVCXnVPFk3gtB6SSMy926wMrmNl4SXcQGrBidxbhmrqpmTXkMpW0AGFimFsJx+CJ+YRXnuxe4RZCLvtCZ5PCtSiqSfEk9QFeIfRgvI/G6+y2wF6SfmJmo3MMsU15o1x5ptM0OSFkXbGLtkU7YySe+yEGi5nZ09nyTEntzGyEpFtyjaoCZnZj3jFUW/Fbq2zIxm9zDKci2TCAd4GVzOyQvOOpos+BsYScKcea2efFH5rZXZL+kEtk5XtW0uJm9oWkBQk5QWcA55pZjMMjuwL3ZsvFE0DE2ChScDeNyZmvBf5FSFR/Z55BlSHVvDAHEnIwrluck0jS2sBtwEGEXiVRMbOHipZfIu4REAXvStqDUJZHAST1IoxkiVJCw6aLn5dKG+BmEnK6RklSR0Ij996EHlifE/KfDoswn2EXMxsPYGZjs4ar2P2aUH83mVwoy4P8e+BDwsuV2J6bLiPcL2xMaLhfmvCc/lyOMZXrJELcuxZ3ZMomJLo9+zy2+++yeaNceZbJO4B5qYWGhP1n8fUozOJt/hTCW/C7gcsjmunqM0lLm9nHhIaS7SSNBWK7EWhC0mBCj6V+hEThN5vZ9flGVZls9r7dgL6Em7bbgeti62VhZjMkzSAkk00m4T6wjZm9PKsvRPiAdCuwK6H39rmEBPs/EGZaLE1cX/MSzadyeNHyeZJeIvRY+kduQZUh4bwwewGHFjfIQUgaLulw4AQibJST9I2ZNRsuKOlLM1ssj5iq4BDCUPCpNL74GkgYuhalkhE5TUQ2CmcZwkv+J4HiNCT1wFcx5fVqwTnAOoQG+k8IIwdOJvSAPiLHuMrRMbv/LnTI6Fw683SEw6YXJjxHtOQzYIqZ3dB24VTN6oTJeqZJqjOzCZKOIaQjiC0H4ObAr0vrtGyym1NpfBk7X/BGuTKYWXTJsueWpPWBNSkZSmNmZ+YTUcUuIrzRv4gw/GkAYbjGnYTx60cRcjAdm1eAc+kcYEVC4v3TCYmNOxF6NkZJ0kmEN47n0XiDc6ykvmYW5dtUSecA2wHDaSzT0YRGkliOtWLDgTsknUm4qSkeRhhdLovMI7SQ0ybyh9Slzcyy4UK/Jsya+z3hrWrUJC0GrAe8bWbJ5HMt6vkcLUmdCTm9dgMWNbOFJW1BGBZ1Sb7RzbWVCA0JLXmS0CMmRh1LN2Q9fmId2VGYXfEXJdtuIQzxilVxD7NS0eyrouelJkOJJS0SeYMcwM7A6tmECAAm6VVCCqDYGuVepOloqJdo+gIvqmHTmQ+ATch6z5bYlHgnKPuBUI9PA8ZKGkDICbroLH+rNi1B66mx3iN0ZphveKNcFUj6FSHvQ2nCxSiHe0q6mDAL4dM07f4fVc+eEvsS3iw0DE2TNAJ4xMxWlvQv4J9E0lBS/HYnG7a6CNDJzCbmF1XF9gc2Km70lvQPQsLPKBvlCMfdWmb2WWGDpAcIs1tFcayVKDxYb16yPebh+sk9pAJTsiTAKwEjs+EoHQi9HKMhqR9wMaEczxN6/T1FGIrbQ9LeZnZ7jiGWTdLTzKb3doQzR15A6OW8ByGxNsBb2fbYGuXam9l3LX1gZt/NqidTLSo63rpIKp2Ioz9xDn0CQNImrX0W0+zZJUpH5CxByAEYVZ0gaW/gCzP7R7b+U+AeQm7G94FfmZnlGWMF6uZye80ys43yjmEeOB+4KUs/ck82k3s7YAfCfUVUueSKPE14Rr+B0CFjBOG+Icq6zsxmtLJ9uqSY2x3mmjfKVSgb9/w7wpC0nQnDg3YH/ppnXBXaA1ilNLdS5JYgTFZRbBKNrfDvEvLmRSObFXdrGodFPphrQJVbgJDss9jXhHxSsfou+ynd9m0OsZRNUh8zG5PShCkpP6QShq8+DnSnsTFkLeLrKXcF8CWh18GuhKGd+5vZPZK2A84gXHtj9ARhEoEbCb23lyT0VLiV8FB3naT/M7Nzcotw7v0aWD7L2TMTwMxGZY2rsekoaWNaf8CO7f75GkJZ1ibkMCyoJwxzj/KBLnNtyXpvwsiBz4Bl2z6cyrUwIucTSfsA/6Z5eWvZUTTtgXUV4QX4uYRhx/8H/CqHuKrhTuB+SacBnxJ6Aw4hTCLgcmZmN2STv9wA3Jal+OlFaMA6PdbUOGa2S9HqiYRhq90JeZFj01VSa3HXAZ3bMpi8xXZTUYv2I/TAelPSYDM7QtJthIo5ViNJK2cUhLeL90kaRrhR60/ICVN46/hzwlDQKGRvhu8GjDAscgBwqaQdzeyxXIMr38PALZKOp/EGZxiR5ViSVPwQMBy4W9KfCcfdkoSk+xfkEFol3iXkSQFA0t1mtkOO8VRDsg+p2XVoC2Camf0r2zyT+IbU/AJYwsymSnqSMGPpvQBmdt8sbuZisAUw0Mz+V9iQTdRzo5n9TNLdhAbHmBrlplJyXympN+HlSmy+ZNbDtb5sq0CqoTCRkqQXUhr2DWBmTXqVZZMSDaH5C7HYLURocIzJAOC/AJKWBFYFNjOzb7J7vViHEEIY7TAEuJTwcnwUoc7+U55BuUZZvtarCPcSvQiTej1vZlG9GC/I6rbHCPcOU7JcbLHlkSs2u1FQsabMKos3ylWuh5m9mS1PldTRzF6SVDqNeUx+C1ydNS5+UfyBmZX2KInFQcCphJ6MfYHRhLdZp2eff0jodRaLS4ADzazhjZyknQk3Bz/OLarK/IFQrtdpzJdwB/HlyXufxmnKC0onCtiEuIZzlfYW2SiPIKop5YdUADN7pGR9lpNZ1KiOhVnszGyypO9KJkiJbphQkR8TrjvFPiHkmyS7j4gtp+GdwI2SjgCQtAThxUR0vRnNbOm8Y6im4iGeklrM0xPxUM8mskmJCi9gz887nrkhaTczu03SzTQd3t6NMFFCbA/g0wm9Fn8gNIy8Y2bfZJ9NJuKRENm1aWj242pUloYgqpf7rcnqtmWAJEatmNlpecdQS7xRrnIfSFrZzN4idCE9WNI4QtLFWP0E2JJwA1CaU25ALhFVyMx+IOTjOL6Vz8e0bUQV6wvcVbLtHuDqHGKpiuzN1d6S9iV7oxXZLGMApDTEs0hyeR0k/YQw+9ab2XpvQgPCKoT8ZUfHmqNxFvnKMLMNWtpeozqUDCEsXY85799TwPWShtLYe/tU4BkASasSXh7F5ERCz77/EhoR3iNck/zGO3+zG/JYT6RDPVuxOaF3cGyuBG6jeQ+yScAVZvbPtg+pIk8CwyTdCPyRpjnxfgzEdu/dIOvp91jxDM2S1iHkRo6ph7OLy2nA5Vn6rNIJ12Ks81zGG+UqN4TGGU9OIMz2tCAhV0KszgS2jfDi36oEEwHfRJg99qKibQcTYU6BbOaglvSXBICZfdp2EVVH1s38XWAlM4t9OPjsGkdiPI+GE25uCj2dryE0dl9FmD3yHOKtx68pWe9D6AEdWy+L0iGEX5esRzWEsMQ+wGXA24TGxemElAT7Zp9PJRyH0ch6jhwOHJ41co8t6dnoclI6xDMlkkbSvFdZF8I9UmzqIKkeJIcRZio+kPCy6+yiz/YipC2J1WGECQOKvU1IsRBto5ykHxGuPf3IhuSaWWszZLq2V7i/K54dt464J1xzQF19vd8vuaYkfUpI1jw171iqRVJpgvOGRMBmFt3bYUnPAD8jDC8eRbh4LkaY1rz4rUnN94rJEoIXYm5pOFq9mUV5oZH0LrCOmY3PO5ZKSPqYWfeWq4/tPMqS/vYzsynZpClfEia4eTfLffOcmS2Za5BVJGl54HozWz/vWFyjbDa43sBXKb3lzoberge8neLwcFc7WkgXMwl4N8a8UZImE1KptDo0P8IXYEmS9DVZztOibZ2AMWbWM7/IyidpW0LnkgdozFe9DbCXmf09z9hcIGmp1j5rYYIYFxHvKVdFCd2EDgWGSzqdkp4IsT40JJgI+GoiHqpa4g3CW+0bCT15Upr1dzjwV0ln0rybeWk+qZqVWn6lTAdCbySAdQk30u8CmNnIrKEuJaOA1fIOwjWStDAhh9yC2ToQ30N3NrvqxcBKhN4w5xKG584Aekja28yiyyvn4mBmT5Zuk7SapJPNbOc8YqpAZ8JQ49Ya5VIbZhyzVwi96YcXbfsd8Gou0VTHmcB2RRNEIWkjQg5kb5SrAS01vElahNC78bK2j8hVizfKlSnxm9DC8KCDirYl1TU25kTA0JikPgVmtoakVQjDuZ4B3iEMw73bzL6f5S/XvsJkDpuXbE/mXIrYW8DOhMlEfgM0DNfP6vcJOcVVMUn7lWzqBuwAvJBDOK4FWe7MS4GJhITnBTE+dF9BeIF3BLArIan2/mZ2j6TtgDOIcLIHV9skdSOkjVmDkL/wVEI+2vMI19wY75MmxdbrfD52BPCopL2AD4DlgcVpfr8Xk/7A0yXbnsm2uxqSdS7ZGtib0JvxPSJtlGvhnrWBmc1qFvSkeKNc+VK+CU0290iJ6BIBp5qcPivPMZKOI+yXfYFLJW1iZtG+dUx00odUHAfcL+kKwsuU9Yo+2xV4NpeoqmOvkvVJwHPABTnE4lo2DNjJzEbkHUgV/IJsGJekJwkN2vcCmNl9kqLLdeqicCmwJuH+e0tgVcLkATcCB5jZ2Bxjc4kzs7ckrUBoEFmSkBP0gRjvwYu8BhxF09x/R2bbXQ2QtBahIW43wuzFnQn3EvfP8hdrW+k9ax9gOcJ9uDfKudlK9ia00DU2y3WzOPBFrMNWCxJKBDycdJPTA/wI2BD4OfAf4p7FuEGWo6yfmXlPpRphZs9kk4ysQMg/VDyU/UHifamCmW3c0vasTne1oQPwSN5BVEnHQl4lM5ss6buSCR5azY/lXAUGAmuY2ZeSLgY+BTY0s9KePjHxcyUiWQNctPcKLTiY8LLyMGAkobFxEvCrXKNySDqaMKLoR4R7h8MIQ4o/IOQTj1ZL96xZ77kVcwgnN94oV75kb0IlLUQYdvcbwjEyTdLtwKFmFuuQrj1L1mNNBLwiWdfyLOfVljQmp/87oTdMVI1yknoSGhT3AboTZuraIMYZV0tljT63EYbX1AMLStoJGGRm++cZm4OsIe6VFrZbDuHMM5JWJbxZ3YPQiO/ydzYwRNIZsb/0YvazM/tQfTcvLGhmXwKY2WeSJkbeIIeZdc87hmqRtMmcfC+mHJqSHjazQdny07QyAVYMk6y1xMzekbQi4cX4EoQczy+a2bR8I3OEThdfE+7l7iy0ORRy0SboBmAscEzOcbQZb5QrX8o3oRcBCxCGRH4CLEUYanMRoeEkRmub2bmlGyUdaWYx5ZRLMTn958BHhMa4Qk+y5bPZIoG4btpKXEnodbU+4WIK8Cgh541z80w2tH13Qp29OiEvzGG5BuWKHUEYonFsNotfAzMbkE9IZfuSpkNMvi5Z/xLnqq/0vpvS9YjvHVJwbcl6P0Ij1tfAooT99Blx5dAsHgV1TW5RzENmNp3meeVc/jYhNMhdA1yQdZa5lVYahmPSwiiOboTONOPbPpr8eKNc+VK+CR0ELGtmheTT70oaTOgiG6uhhMk4Sg0hrokeUkxOP4YwlPiA7KdUjInPC9YBtjazmZLqAcxsQjbronNVJakjYZjJvoShXe8TemouBexc6FXiakJp7+1oJTo7s6t9s7sPj/neIXpm1pCfWtKJhIa4k7PRRd2A02l8WRkFM7u1aDnGiURmKRspdSohjUwvmjZwx/ayKClm9gTwhKTfAzsRGugOI+yjgyRdZmZRnU9FptO8cXEULT8TJssb5cqU+E3oD0BvQi+5gl7AlHzCKV9R9/n2pW9QCTdr3zX/rZqWXHL6xM+lLwgzcr1b2CBpJULuG+eq7QvC5DU3AKcUJkmRFNWQ9vmBmT2ZdwzOxSzxe4fUHAH0LQyDzBrmTiCMlDgr18jKJOkaQlqfyUXblgCuLwxxjYGkx4Enzew0wuyd/QkNpn8hvDw6BrgrvwhdMTP7njCy6GZJ/QmNc3sRZqLulmdsFSidYHLS/DhRjzfKuZZcQ5jm+3wah68eQZhMIDaF7vNdaP4GdQzwxzaPqAIpJ6dP1LnAA5LOIgy12Q04EfhzvmG5RL1BaKj/GfCepI/MLInJUlIj6fTWPjOzoW0Zi3POzWOTCCMHil8crw1MbvnrUegOvCFpLzN7XtJvgIuJb1jroTSOGBoI/NjMvpY0I5u48GXgfnz29ppjZp8BZwJnSvpZ3vGUq2iCyX6EvMej8o0oH3X19dEPRXZVJqkOGEzIR9SX8CbrNjMrzQ8RDUm3mNkeecfh5j+StgcOJDRufwpcaWb35hmTS5ekpQhvTvcGBhBm6doQWNHM5ssbnVok6fqSTX0I++kev1Y551IiaS9CL6z7aZzVcxvg92Z2c56xVULSHoTGKiNMjLCPmUU1YkXSx8AmZvahpLFAHzObLukzQm7xb4HxZrZQnnG6dGWdTW4hTDDyDdCTkGN8j0KD3fzAG+XcHJHUCTjAzC7NO5a5Jak9MBHoYWbRDcF1zrlySFqP0Di3CyFnx3Vmdmy+UbnWSBoE7GZmsU6o5JxzLcpSd+xIeNk/Gvibmb2db1SVkbQRYfKH9sDbwF5mNibXoOaSpKHAKma2i6THgDPN7DFJtxHSYUwEfmJmP801UJcsSf8CXgdOMrNJkhYEzgDWNLONcg2uDXmjnGtC0qbAGsB7ZvZ3SR2AQwi5zL4xs1XzjK9ckl4HtjSzz/OOxc0/JM0gJNrfz8ymFm3/1t86urYiqQvwa2BvM9sy73hcy7IZyMaZmU8E45xLTlbHLW5mo/OOpVKSziXkXPsdIX3MmYTZzn9vZnfmGVu5JC0L1JnZB9kM7n8GFgROi70B1dUuSd8CixZyTmbbOgFfm1n3/CJrW55TzjWQdBxwMmGGz5UlXQZsRJjg4UAzezDH8Cp1CyG314WEKdgbWqPN7PHconKp+yH791lJ2xU1Cte19gvOVZuZ/UBoHL4t71hckD38FOtGSBkxModwnHNunpHUgzB8dSdgGrCApF8B65jZkDxjq8CKwOpm9kW2foyk+4EbgSgb5czsw6Llr4Df5hiOm3+8QPOckz8Fns8nnHx4o5wrdhCwoZm9ImldwslxtJmlkNzz4OzfU0u21xNmYXVuXphhZntmDd4vSdrZzJ6n+dTfzrn5y/uEeqDQQD8Z+A+hp4VzzqXkCmAcIbduocfV88B5QJSNcma2dQvbnpK0Wh7xVELST4ApZvZmtt4bGE7IKfc84VlwYn4Rzt8knWRmw7LlJCaJKinHB8BDkh6kMefkVsCtecSWF2+Uc8V6mdkrAGb2gqQphEo5emZWOt2yc23GzM6W9AZwj6Qob0Cdc9VjZu3yjsE559rIpkBfM5smqR5CTyxJi+UcV9kkdQaGArsRht4tLGkLYAXgklyDm3vDgdOAN7P1awi5/64ilO8cQiojl4/+RctL5hZFdZWW4+7s38UII/TuAbq2aUQ580Y510Q282rh54dsW8PDg5nNzCm0imX58X4B9CMMYX3ezKbnG5VLXMMwVTMbIWlD4F5ggdwics4555xrOxOAXoQJHoCGGRdjzi03nNBwtQcwItv2FmE21tga5VYEnoaGocZbEiZ/eFfS34Hn8Ea53JjZwUXLg/OMpVrmpBzF7Q/zA2+Uc8UWJMzQV1BXtF5HGGrTvq2DqgZJPyZMxd6Vxq6xP0ja1sz+l2twLmUrFK+YmUlaB1g7p3icczmS9DSzGb5uZhu0UTjOOdcWrgHuknQS0E7SzwkTI1yRb1gV2R5YPpstciaAmY2S1C/fsMrSAShMRrYuMMbM3gUws5FZQ53LSQs5aFtUnBMwZpJWBfYmNHj3zTmcNuONcq5YykM8LyN0wz7XzOoBJB2dbd84z8BcukpnGJtfLzTOuQbXFC3XAZfiPRCcc2k7mzD65lKgI3AdcCVwYZ5BVWgqJc/RWS62r/MJpyJvATsDdwC/Af5Z+CBrZJyQU1wuKM5BW/xSr3Q9yo4z0HDu7E7Iq7s68AxwWK5BtTFvlHMNzOyTvGOYh9YANi80yGWGAyflEo2bb/iFxjlXYGY3Fq9LOr90m3POJWZxMxtOSZ5qSX2AMXkEVAV3AjdKOgJA0hKE8t2eZ1BlOg64X9IVwAxgvaLPdqXprJiujRXnoJU0GNiMMHHhJ4TJU4YCj+USXAUkdQR+BewLDCQ0Pt5GKNPOZvZlftG1PW+Uc/OLz4ENgceLtq2fbXeuqvxC45xzzjkHwLvAQi1sfxvo2caxVMuJhAkQ/gt0A94DriZMmBAVM3smy/G3AvCumX1X9PGDxNnQmKozgB+Z2ffZ+nuSDiKcYzfkFlV5vgBmEuI+xcxeBZA0X44e8EY5N784Efi7pAdofLOwNbBnrlG5VPmFxjnnnHOuaNKrAkkLEe6TomRmU4HDgcOzERFjS0bjRCVriHulhe2WQziude2ApYHifOhLEefQ1TcIvTJ/Rmhc/MjMxuUcU268Uc7NF8zs75LWJHTD7kuY9ntoIZGpc1XmFxrnXDOSNinZ1EHSxjSdqflxnHMucpJGEnJedZX0acnHixJGEEQl61HWkiUlAWBmpWV1rlouAB6XdD2NExfum22PipltJGkpQq7to4GLJD0CLEDIPTlfqauvj7ZR37nZktQNGAKsArwKnGVmU/KNys0Pii40ewMDgEcIQ6hXNLNRecbmnMuHpI9m85V6M5ujmdacc66WSdqQ8MLhIWDLoo/qgS9i7IWVzbRaeHhu1gOQUIfH2GvJRULSIMLEHH2B0cAdZvZwvlFVTtJ6hGemXYDpwHVmdmy+UbUdb5RzzUjqQJgNbkOgF03f4G+QV1zlkHQdsDYwAtgK+JeZ/THfqNz8Zn6/0DjnnHNu/iSpm5lNzjuOapD0GtAFuBH4Cy3kpjazGW0clnPJkNQF+DWwt5ltObvvp8Ib5Vwzki4GNgGuAoYRZig9GLjdzE7NMbS5Jmk0sJaZjZa0JPCUmS2Td1xu/jS/Xmicc845N/+QdJKZDcuWT2/te2Y2tO2iqg5JqwD7EF60vgPcBNxdlHzfuXkitXPJNWo3+6+4+dAOwJZmdiEwPft3e2DjXKMqzwJmNhrAzEYCC+ccj5uPmdkPZnabN8g555xzLmH9i5aXnMVPdMzsTTM7BlgGOB/YBhgtaa18I3PzgdLzZ21CPrbl8gzKVc4nenAt6UZIHgnwfdbt/J1sooTYlCbR9qTazjnnnHPOzSNmdnDR8uA8Y5mHfkRI9fNz4D+AT+jl5qmWzqUsx9xuOYTjqsgb5VxL/kdoeX8JeBk4VdK3QIzJ6b8Erita/7pkvR7wpNrOOeecc87NA5IWBgQsWLw9thfjknoSGkD2AboDNwMb+IyrLkePAH/NOwhXGW+Ucy05DCgkKT0SuJxw4Tkwt4jKZGZL5x2Dc84555xz8yNJ+wKXAhOB4gkfYnwx/jnwEaEx7oVs2/KSli98IbaGRhcPSaXnSzdgdxpHuLlI+UQPzjnnnHPOOeeqTtIoYH8zG5F3LJWS9DGhMbE19WYWW0Oji4SkmYTjr5CGaTJh6PThZvZKboG5inmjnANA0gZm9lS2vElr3/O3P84555xzzrk5IekLoK+ZzZjtl51zbj7kw1ddwWXAKtnyta18J8Zu5s4555xzzrl8nA0MkXSGmc3MOxjnnKs13lPOOeecc84551zVSRoJ9AGmEiZca2BmA3IJyrkISVoIOJUw628vGoex+rkUOe8p55xzzjnnnHNuXtgz7wCcS8RlQH/gdOAvhHPrGOCuPINylfOecg5oeIs124PBW+Gdc84555xzzrm2I+lLYEUz+1rSeDPrIakfcL+ZrZV3fK583lPOFfhbLOecc84551zFJN1M8xf+04BPgL+Z2dttH5VzUWsHTMiWJ0rqAYwGls8tIlcV3ijnADCzJ/OOwTnnnHPOOZeE91vY1hEQ8Lyk3c3swTaOybmYvU7IJ/cY8DRwKTAReDfPoFzlvFHOASDpJDMbli2f3tr3zGxo20XlnHPOOeeci42ZndbaZ5I2A/4MeKOcc3PuABondziUcA71APbOKyBXHd4o5wr6Fy0vmVsUzjnnnHPOuZQ9BiyXdxDOxcTMPixa/gr4bY7huCryiR6cc84555xzzrWJLDn9M2a2TN6xOFfrJP0EmGJmb2brvYHhwCrA88DRZjYxvwhdpbynnGuRpG6EpJELFm83s+fyicg555xzzjkXA0nLtrC5I7A0MAS4o00Dci5ew4HTgDez9WuAvsBVwG7AOcAhuUTmqsIb5VwzkvYGLgGmAt8XfVQPDMglKOecc84551ws3ic8O9QVbZsBfAr8FWg1h7VzrokVCRM7kM24uiWwipm9K+nvwHN4o1zUvFHOteQcYEczezTvQJxzzjnnnHNxMbN2ecfgXCI6EDrLAKwLjDGzdwHMbGTWUOci5pWla8lU4Im8g3DOOeecc8455+ZjbwE7Z8u/Af5Z+CDLzzghj6Bc9XijnGvJycD5knrlHYhzzjnnnHPOOTefOg64UtI3wNbA2UWf7Qo8m0tUrmp89lXXjKSfA7cD/Ys21wH1ZtY+n6icc84555xzzrn5i6TuwArAu2b2XdF2Ad+Z2ee5Becq5o1yrhlJ7wO3EZKwFk/0gJl9kEtQzjnnnHPOOeeccwnxiR5cSxYFhpqZt9g655xzzjnnnHPOzQOeU8615Hpgr7yDcM4555xzzjnnnEuVD191zUh6BlgH+Aj4ovgzM9sgl6Ccc84555xzzjnnEuLDV11Lrs5+nHPOOeecc84559w84D3lnHPOOeecc84555xrY55TzjWQdFHJ+m9L1u9q24icc84555xzzjnn0uSNcq7YviXr/1eyvnkbxeGcc84555xzzjmXNG+Uc8XqZrPunHPOOeecc84556rAG+VcsdIEg55w0DnnnHPOOeecc24e8NlXXbEOkjamsYdc6Xr7fMJyzjnnnHPOOeecS4s3yrliXwLXFa1/XbL+ZduG45xzzjnnnHPOOZemuvp6H6HonHPOOeecc84551xb8pxyzjnnnHPOOeecc861MW+Uc84555xzzjnnnHOujXmjnHPOOeecc84555xzbcwb5ZxzzjnnnHPOOeeca2PeKOecc84555xzzjnnXBvzRjnnnHPOOeecc84559rY/wOJmRMXCwXaxgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(18,8))\n", "temp=maxvslewis_pts_df.Grand_Prix.to_list()\n", "plt.ylim(21,0)\n", "temp2=np.arange(0,22)\n", "plt.plot(temp,maxvslewis_pts_df.Lewis_Pos,'k-o')\n", "plt.plot(temp,maxvslewis_pts_df.Max_Pos,'r--o')\n", "plt.legend([\"Lewis Hamilton\",\"Max Verstappen\"]);\n", "plt.tight_layout()\n", "plt.yticks(temp2)\n", "plt.xticks(temp,rotation=90);" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "##### Objective:\n", ">Comparison of total points after each race" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "hidden": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(18,8))\n", "temp=maxvslewis_pts_df.Grand_Prix.to_list()\n", "plt.plot(temp,maxvslewis_pts_df.Lewis_Pts_cum,'k-o')\n", "plt.plot(temp,maxvslewis_pts_df.Max_Pts_cum,'r--o')\n", "plt.legend([\"Lewis Hamilton\",\"Max Verstappen\"])\n", "plt.xticks(temp,rotation=90);" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "**Conclusion:**\n", "> We can see that after the Saudi Arabian GP, the score was tie with 369.5 total championship points. The ultimate decider for the championship was the Abu Dhabi GP, which ends in a controversial manner with `Max Verstappen` emerging as winner of the `F1 2021 Championship` with just a margin of 8 points." ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Conclusion and Future Work\n", "\n", "In the F1 2021 season, a total of 22 GPs were organized. The Drivers Championship goes to `Red Bull Racing-Honda` driver `Max Verstappen` and the Constructors Championship goes to `Mercedes`. While our main focus was on Max and Lewis journey throughout the season, we can extend the analysis to other drivers journey also leading to interesting insights" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## References\n", "* Dataset: `wikipedia`\n", " * https://en.wikipedia.org/wiki/2021_Formula_One_World_Championship\n", " * https://en.wikipedia.org/wiki/2021_Bahrain_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Emilia_Romagna_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Portuguese_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Spanish_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Monaco_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Azerbaijan_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_French_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Styrian_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Austrian_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_British_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Hungarian_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Belgian_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Dutch_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Italian_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Russian_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Turkish_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_United_States_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Mexico_City_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_S%C3%A3o_Paulo_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Qatar_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Saudi_Arabian_Grand_Prix\n", " * https://en.wikipedia.org/wiki/2021_Abu_Dhabi_Grand_Prix\n", "* Images:\n", " * https://racingbabiesconnect.com/" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "hidden": true }, "outputs": [], "source": [ "project_name = \"Zero2Pandas-Project-Mad-Max's-Journey-to-the-Title\"" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "hidden": true }, "outputs": [], "source": [ "!pip install jovian --upgrade -q" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "hidden": true }, "outputs": [], "source": [ "import jovian" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "hidden": true }, "outputs": [ { "data": { "application/javascript": [ "window.require && require([\"base/js/namespace\"],function(Jupyter){Jupyter.notebook.save_checkpoint()})" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[jovian] Creating a new project \"dudidharam12i/Zero2Pandas-Project-Mad-Max's-Journey-to-the-Title\"\u001b[0m\n", "[jovian] Committed successfully! https://jovian.ai/dudidharam12i/zero2pandas-project-mad-maxs-journey-to-the-title\u001b[0m\n" ] }, { "data": { "text/plain": [ "'https://jovian.ai/dudidharam12i/zero2pandas-project-mad-maxs-journey-to-the-title'" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jovian.commit(project=project_name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "hidden": true }, "outputs": [], "source": [] } ], "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": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 4 }