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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "db717780",
+   "metadata": {},
+   "source": [
+    "# Game of Life"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07a5d936",
+   "metadata": {},
+   "source": [
+    "**A project by:**\n",
+    "- Bergamin Eleonora\n",
+    "- Boni Filippo\n",
+    "- Campagnola Stefano\n",
+    "- Santagata Luca\n",
+    "\n",
+    "**Abstract:**\n",
+    "In this project we have implemented an interactive version of the “Game of Life”. \n",
+    "First of all we have analysed  the behaviour of several  patterns, implementing  different types of graphs. \n",
+    "Then we have computed a 3D version of the Game of Life, and visualized its behaviour. \n",
+    "Finally we have introduced the Genetic Algorithms in order to explore new rules that characterize the evolution of patterns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0830308e",
+   "metadata": {},
+   "source": [
+    "In order to run the code is necessary to install the net libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67746f39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install pygame\n",
+    "#!pip install bar_chart_race"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c888d7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "import math\n",
+    "import pygame\n",
+    "import numpy as np\n",
+    "import itertools\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib\n",
+    "from matplotlib.ticker import AutoMinorLocator, MultipleLocator\n",
+    "\n",
+    "from IPython.display import Video\n",
+    "\n",
+    "from Game_of_Life_patterns import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7079a96",
+   "metadata": {},
+   "source": [
+    "## Introduction to the code\n",
+    "\n",
+    "Game of Life is a cellular automaton devised by the British mathematician John Conway in 1970. Once the initial conditions are set the evolution of the game is fully determined, meaning that the player does not need to intervene in the game.\n",
+    "The game consists in setting the initial conditions and observing how the pattern evolves.\n",
+    "\n",
+    "**Game of Life: Universe and Rules**\n",
+    "\n",
+    "The space where the Game of Life evolves is an infinite 2D grid of squares. Each cell of the grid is either alive or dead. The evolution of the pattern is based on the interaction of every cell with its neighbors, meaning the adjacent cells. Any cell can have from 0 to 8 living neighbors.\n",
+    "At each iteration, for each cell:\n",
+    "- if it is alive, it remains alive if it has between 2 and 3 alive neighbors.\n",
+    "- if it is dead, it becomes alive if it has between 3 and 3 alive neighbors.\n",
+    "\n",
+    "In all other cases if the cell is alive it dies, and dead cells stay dead.\n",
+    "\n",
+    "In the 3D game (see below) the rule is traditionally called 2333.\n",
+    "\n",
+    "At each time step, these rules are applied to obtain the new generation in the game."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7746aa53",
+   "metadata": {},
+   "source": [
+    "## Let's play"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1a0e6ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class game_of_life():\n",
+    "     \n",
+    "    def init(self, b, h, max_epoch, pixel_size): \n",
+    "        pygame.init()\n",
+    "        self.N = h \n",
+    "        self.M = b \n",
+    "        self.screen = pygame.display.set_mode((b*pixel_size, h*pixel_size)) \n",
+    "        self.data = np.zeros((self.N,self.M)) \n",
+    "        self.clock = pygame.time.Clock() \n",
+    "        self.start = False \n",
+    "        self.initial = True  \n",
+    "        self.epoch = 0 \n",
+    "        self.max_epoch = max_epoch\n",
+    "        self.pixel_size = pixel_size\n",
+    "          \n",
+    "     \n",
+    "    def game_rules(self,x,y): \n",
+    "         \n",
+    "        mask_1 = np.logical_and(x == 1, y < 2) \n",
+    "        mask_2 = np.logical_and(x == 1, np.logical_or( y == 2, y == 3)) \n",
+    "        mask_3 = np.logical_and(x == 1, y > 3) \n",
+    "        mask_4 = np.logical_and(x == 0, y == 3) \n",
+    " \n",
+    "        return mask_1,mask_2,mask_3,mask_4 \n",
+    "     \n",
+    "     \n",
+    "    def next_generation(self): \n",
+    "         \n",
+    "         \n",
+    "        n_alive = np.zeros((self.N,self.M)) \n",
+    "         \n",
+    "        grid_d_d = np.roll(np.roll(self.data, -1, axis=1), -1, axis=0) \n",
+    "        grid_d_d[:,self.M-1] = 0 \n",
+    "        grid_d_d[self.N-1,:] = 0 \n",
+    "         \n",
+    "        grid_u_u = np.roll(np.roll(self.data, 1, axis=1), 1, axis=0) \n",
+    "        grid_u_u[:,0] = 0  \n",
+    "        grid_u_u[0,:] = 0 \n",
+    "         \n",
+    "        grid_s_d = np.roll(self.data, -1, axis=1) \n",
+    "        grid_s_d[:,self.M-1] = 0  \n",
+    "         \n",
+    "        grid_u_d = np.roll(np.roll(self.data, -1, axis=1), 1, axis=0) \n",
+    "        grid_u_d[:,self.M-1] = 0 \n",
+    "        grid_u_d[0,:] = 0 \n",
+    "         \n",
+    "        grid_d_s = np.roll(self.data, -1, axis=0) \n",
+    "        grid_d_s[self.N-1,:] = 0 \n",
+    "         \n",
+    "        grid_u_s = np.roll(self.data, 1, axis=0) \n",
+    "        grid_u_s[0,:] = 0 \n",
+    "         \n",
+    "        grid_d_u = np.roll(np.roll(self.data, 1, axis=1), -1, axis=0) \n",
+    "        grid_d_u[:,0] = 0 \n",
+    "        grid_d_u[self.N-1,:] = 0 \n",
+    "         \n",
+    "        grid_s_u = np.roll(self.data, 1, axis=1) \n",
+    "        grid_s_u[:,0] = 0 \n",
+    "         \n",
+    "         \n",
+    "        n_alive = grid_d_d + grid_u_u + grid_s_d + grid_u_d + grid_d_s + grid_u_s + grid_d_u + grid_s_u \n",
+    "         \n",
+    "        m_1,m_2,m_3,m_4 = self.game_rules(self.data , n_alive) \n",
+    "         \n",
+    "        self.data[m_1] = 0 \n",
+    "        self.data[m_2] = 1 \n",
+    "        self.data[m_3] = 0 \n",
+    "        self.data[m_4] = 1 \n",
+    "         \n",
+    "        return self.data \n",
+    "         \n",
+    "    \n",
+    "    def update(self): \n",
+    "        \n",
+    "        self.screen.fill((0,0,0)) \n",
+    "         \n",
+    "        ind = np.where(self.data==1) \n",
+    "        for i,j in zip(ind[0],ind[1]): \n",
+    "            pygame.draw.rect(self.screen, (255, 255, 255), (j*self.pixel_size, i*self.pixel_size, self.pixel_size, self.pixel_size)) \n",
+    "    \n",
+    "        font = pygame.font.SysFont('cambria.ttf', int(0.05*self.M*self.pixel_size), bold=True)     \n",
+    "        punti_render = font.render(str(\"Epoch: \") + str(self.epoch), 1, (255, 140, 105)) \n",
+    "        self.screen.blit(punti_render, (0.7*self.pixel_size*self.M, 0.9*self.pixel_size*self.N))\n",
+    "        \n",
+    "        \n",
+    "     \n",
+    "    def user_initial(self): \n",
+    "        if self.initial:\n",
+    "            if pygame.mouse.get_pressed()[0]: #press the left button to add living cells\n",
+    "                x,y = pygame.mouse.get_pos()\n",
+    "                self.data[math.floor((y)/self.pixel_size)][math.floor((x)/self.pixel_size)] = 1\n",
+    "            if pygame.mouse.get_pressed()[1]:  #press the mouse wheel to delete cells\n",
+    "                x, y = pygame.mouse.get_pos()\n",
+    "                self.data[math.floor(y/self.pixel_size)][math.floor(x/self.pixel_size)] = 0\n",
+    "            if pygame.mouse.get_pressed()[2]: #press the right button to start the simulation\n",
+    "                self.initial = False\n",
+    "                self.start = True\n",
+    "            \n",
+    "     \n",
+    "    def start_game(self): \n",
+    "        self.epoch = 0 \n",
+    "        self.run = True \n",
+    "        while self.run == True: \n",
+    "            for event in pygame.event.get(): \n",
+    "                if event.type == pygame.QUIT: \n",
+    "                    self.run = False \n",
+    "            self.update()    \n",
+    "            if self.start: \n",
+    "                self.next_generation() \n",
+    "                self.clock.tick(10) \n",
+    "                self.epoch += 1 \n",
+    "            self.user_initial() \n",
+    "            pygame.display.update() \n",
+    "          \n",
+    "             \n",
+    "             \n",
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "pygame.init() \n",
+    "pygame.display.set_caption(\"Game of Life\") \n",
+    "pygame.font.init() \n",
+    "font = pygame.font.SysFont('cambria.ttf', int(0.5*b), bold=True) \n",
+    "font_small = pygame.font.SysFont('cambria.ttf', 30, bold=True)\n",
+    "\n",
+    "\n",
+    "game = game_of_life()\n",
+    "\n",
+    "game.init(int(b), int(h), 100, 15)\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "pygame.quit()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be6eb110",
+   "metadata": {},
+   "source": [
+    "## The code of  the game\n",
+    "\n",
+    "**Calculation of the new generation**\n",
+    "\n",
+    "The evolution of the game (so the definition of a new generation) is based on two matrixes defined through numpy: data and n_alive. The first one represents the grid of the game at a given generation. The second one contains the number of living neighbors around a given cell: the element (i,j) of the n_alive matrix is the number of living neighbors around the element (i,j) of the grid.\n",
+    "\n",
+    "The game rules are applied through some masks, bidimensional matrixes which are calculated using the data and n_alive matrixes. There is a mask for each rule, and each mask is applied to the data matrix. If the data matrix elements satisfy the mask condition, a new value is associated to them. In this way, we obtain the new generation.\n",
+    "\n",
+    "The n_alive matrix is calculated using a numpy function called numpy.roll. This function allows to roll a given matrix along one(or more) of its axis with a given step. The idea is to shift the data matrix in all 8 possible directions (up, down, left, right, up-left, up-right, down-left, down-right) creating 8 different matrixes. For each of these matrixes, each element (i,j) is different from the original one. For example in the matrix obtained by rolling the matrix on the right, the new element (i,j) was the element(i-1,j) in the original matrix. The sum of these 8 matrixes gives the matrix n_alive, since each element of them can be 1 or 0. Using this function, in any direction, elements that roll beyond the last position are re-introduced at the first at the opposite side. In this way, what we obtain is a game with periodic boundary conditions. In order to avoid the periodic conditions we set the \"re-introduced\" elements equal to zero; thus, they do not give any contribution to the sum of the matrixes.\n",
+    "(For technical details about numpy.roll() in the next link the official documentation is presented: https://numpy.org/doc/stable/reference/generated/numpy.roll.html)\n",
+    "\n",
+    "The process just explained is schematically shown in the next image.\n",
+    "\n",
+    "**Pygame**\n",
+    "\n",
+    "Pygame is a Python language module dedicated to graphics. The library contains several graphic functions, useful for creating games and animations.\n",
+    "\n",
+    "The Pygame package contains several methods and functions dedicated to specific aspects."
+   ]
+  },
+  {
+   "attachments": {
+    "Next%20generation%20computation%20%282%29.png": {
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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "6142bdcf",
+   "metadata": {},
+   "source": [
+    "![Next%20generation%20computation%20%282%29.png](attachment:Next%20generation%20computation%20%282%29.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "031bdc45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class game_of_life_np():\n",
+    "     \n",
+    "    def init(self, b, h, still_lifes, max_epoch, pixel_size): \n",
+    "        pygame.init() #Initializes the pygame library.\n",
+    "        self.N = h #setting the dimensions of the game\n",
+    "        self.M = b \n",
+    "        self.screen = pygame.display.set_mode((b*pixel_size, h*pixel_size)) #Initialize a window or screen for display\n",
+    "        self.data = np.zeros((self.N,self.M))  #initialize the grid of the game\n",
+    "        self.clock = pygame.time.Clock() #create an object to help track time\n",
+    "        self.start = False #intialize useful viariables\n",
+    "        self.initial = True \n",
+    "        self.count_alive = [] \n",
+    "        self.epoch = 0 \n",
+    "        self.last_generation = np.zeros((self.N,self.M))\n",
+    "        self.presentation = still_lifes\n",
+    "        self.max_epoch = max_epoch\n",
+    "        self.pixel_size = pixel_size\n",
+    " \n",
+    "        self.period = 0 \n",
+    "        self.pattern_name = 'Not computed' \n",
+    "        self.speed = 0 \n",
+    "        self.dir = 'None' \n",
+    "        self.heat = 0 \n",
+    " \n",
+    "     \n",
+    "    def initial_condition(self, pattern, x_pos, y_pos):  #function that paste the choosen pattern to the initialized grid\n",
+    "            self.x_pos = x_pos #setting pattern position\n",
+    "            self.y_pos = y_pos \n",
+    "            self.pattern = pattern \n",
+    "            self.data[self.y_pos:self.y_pos+pattern.shape[0], self.x_pos:self.x_pos+pattern.shape[1]] += pattern \n",
+    "            self.born = self.data.copy() \n",
+    "          \n",
+    "          \n",
+    "     \n",
+    "    def game_rules(self,x,y):  #function that sets the rules throught the masks\n",
+    "         \n",
+    "        mask_1 = np.logical_and(x == 1, y < 2) \n",
+    "        mask_2 = np.logical_and(x == 1, np.logical_or( y == 2, y == 3)) \n",
+    "        mask_3 = np.logical_and(x == 1, y > 3) \n",
+    "        mask_4 = np.logical_and(x == 0, y == 3) \n",
+    " \n",
+    "        return mask_1,mask_2,mask_3,mask_4 \n",
+    "     \n",
+    "     \n",
+    "    def next_generation(self): #function that calculates the new generation \n",
+    "         \n",
+    "        self.count_alive.append(self.data.sum()) #storing the number of alive cells\n",
+    "        self.last_generation = self.data.copy()  #storing the ccurrent generation\n",
+    "         \n",
+    "        n_alive = np.zeros((self.N,self.M)) #initialize the n_alive matrix\n",
+    "         \n",
+    "        grid_d_d = np.roll(np.roll(self.data, -1, axis=1), -1, axis=0) #calculate the shifted matrix with np.roll\n",
+    "        grid_d_d[:,self.M-1] = 0 # setting to zero the needed column and row of the shifted matrix \n",
+    "        grid_d_d[self.N-1,:] = 0 \n",
+    "         \n",
+    "        grid_u_u = np.roll(np.roll(self.data, 1, axis=1), 1, axis=0) \n",
+    "        grid_u_u[:,0] = 0  \n",
+    "        grid_u_u[0,:] = 0 \n",
+    "         \n",
+    "        grid_s_d = np.roll(self.data, -1, axis=1) \n",
+    "        grid_s_d[:,self.M-1] = 0  \n",
+    "         \n",
+    "        grid_u_d = np.roll(np.roll(self.data, -1, axis=1), 1, axis=0) \n",
+    "        grid_u_d[:,self.M-1] = 0 \n",
+    "        grid_u_d[0,:] = 0 \n",
+    "         \n",
+    "        grid_d_s = np.roll(self.data, -1, axis=0) \n",
+    "        grid_d_s[self.N-1,:] = 0 \n",
+    "         \n",
+    "        grid_u_s = np.roll(self.data, 1, axis=0) \n",
+    "        grid_u_s[0,:] = 0 \n",
+    "         \n",
+    "        grid_d_u = np.roll(np.roll(self.data, 1, axis=1), -1, axis=0) \n",
+    "        grid_d_u[:,0] = 0 \n",
+    "        grid_d_u[self.N-1,:] = 0 \n",
+    "         \n",
+    "        grid_s_u = np.roll(self.data, 1, axis=1) \n",
+    "        grid_s_u[:,0] = 0 \n",
+    "         \n",
+    "         \n",
+    "        n_alive = grid_d_d + grid_u_u + grid_s_d + grid_u_d + grid_d_s + grid_u_s + grid_d_u + grid_s_u \n",
+    "         \n",
+    "        m_1,m_2,m_3,m_4 = self.game_rules(self.data , n_alive) #calculate the masks\n",
+    "         \n",
+    "        self.data[m_1] = 0 #apply the masks to calculate the new generation\n",
+    "        self.data[m_2] = 1 \n",
+    "        self.data[m_3] = 0 \n",
+    "        self.data[m_4] = 1 \n",
+    "         \n",
+    "        return self.data \n",
+    "         \n",
+    "    \n",
+    "    def update(self): \n",
+    "        self.screen.fill((0,0,0)) #Fill the surface with a color given in a RGB format\n",
+    "        \n",
+    "        ind = np.where(self.data==1) \n",
+    "        for i,j in zip(ind[0],ind[1]): \n",
+    "            pygame.draw.rect(self.screen, (255, 255, 255), (j*self.pixel_size, i*self.pixel_size, self.pixel_size, self.pixel_size)) \n",
+    "    \n",
+    "        font = pygame.font.SysFont('cambria.ttf', int(0.05*self.M*self.pixel_size), bold=True)  #create a Font object from the system fonts  \n",
+    "        punti_render = font.render(str(\"Epoch: \") + str(self.epoch), 1, (255, 140, 105)) # draw text\n",
+    "        self.screen.blit(punti_render, (0.7*self.pixel_size*self.M, 0.9*self.pixel_size*self.N)) #draw the \"text image\" onto the screen dispaly\n",
+    "        \n",
+    "        \n",
+    "        if self.presentation == True:\n",
+    "            \n",
+    "            title_block = font_small.render(str(\"Block\"), 1, (255,0,0))                                                #**** \n",
+    "            self.screen.blit(title_block, (106,180)) \n",
+    "         \n",
+    "            title_tube = font_small.render(str(\"Tube\"), 1, (255,0,0))                                                   #**** \n",
+    "            self.screen.blit(title_tube, (280,180)) \n",
+    "         \n",
+    "         \n",
+    "            title_boat = font_small.render(str(\"Boat\"), 1, (255,0,0))                                                   #**** \n",
+    "            self.screen.blit(title_boat, (440,180)) \n",
+    "         \n",
+    "         \n",
+    "            title_ship = font_small.render(str(\"Ship\"), 1, (255,0,0))                                                   #****\n",
+    "            self.screen.blit(title_ship, (600,180)) \n",
+    "        \n",
+    "            title_bee_hive = font_small.render(str(\"Bee hive\"), 1, (255,0,0))                                                   #**** \n",
+    "            self.screen.blit(title_bee_hive, (106,380)) \n",
+    "         \n",
+    "         \n",
+    "            title_loaf = font_small.render(str(\"Loaf\"), 1, (255,0,0))                                                   #**** \n",
+    "            self.screen.blit(title_loaf, (285,380)) \n",
+    "         \n",
+    "         \n",
+    "            title_pond = font_small.render(str(\"Pond\"), 1, (255,0,0))                                                   #**** \n",
+    "            self.screen.blit(title_pond, (440,380)) \n",
+    "         \n",
+    "         \n",
+    "            title_snake= font_small.render(str(\"Snake\"), 1, (255,0,0))                                                   #**** \n",
+    "            self.screen.blit(title_snake, (600,380)) \n",
+    "         \n",
+    "         \n",
+    "            title_eater1= font_small.render(str(\"Eater1\"), 1, (255,0,0))                                                   #**** \n",
+    "            self.screen.blit(title_eater1, (280,580)) \n",
+    "         \n",
+    "         \n",
+    "            title_eater2= font_small.render(str(\"Eater2\"), 1, (255,0,0))                                                   #**** \n",
+    "            self.screen.blit(title_eater2, (455,620)) \n",
+    "         \n",
+    "        ind = np.where(self.data==1) \n",
+    "        for i,j in zip(ind[0],ind[1]): #drawing the living cell\n",
+    "            pygame.draw.rect(self.screen, (255, 255, 255), (j*self.pixel_size, i*self.pixel_size, self.pixel_size, self.pixel_size)) \n",
+    "     \n",
+    "    def user_initial(self): \n",
+    "            if pygame.mouse.get_pressed()[0]: #get the state of the mouse left button in order to start the game\n",
+    "                self.initial = False \n",
+    "                self.start = True \n",
+    "            \n",
+    "     \n",
+    "    def start_game(self): \n",
+    "        self.epoch = 0 \n",
+    "        self.run = True \n",
+    "        while self.run == True: #updating the game \n",
+    "            for event in pygame.event.get(): #we fetch all the events that pygame has detected\n",
+    "                if event.type == pygame.QUIT:  #if an event is \"closing of the window\" we deinit the pygame library\n",
+    "                    self.run = False \n",
+    "            self.update()    \n",
+    "            if self.start: \n",
+    "                self.next_generation() \n",
+    "                self.clock.tick(10) #set update frequency (frame per second)\n",
+    "                self.epoch += 1 \n",
+    "                if self.pattern_name == 'Not computed': \n",
+    "                    self.period_calc() \n",
+    "            self.user_initial() \n",
+    "            pygame.display.update() #the display is updated \n",
+    "            if self.epoch >= self.max_epoch: #ending the game till a certain number of generations is reached\n",
+    "                break \n",
+    "             \n",
+    "             \n",
+    " \n",
+    " \n",
+    "    def period_calc(self):\n",
+    "\n",
+    "        a = range(self.N)\n",
+    "        b = range(self.M)\n",
+    "        self.heat += np.sum(np.abs(self.last_generation - self.data))\n",
+    "\n",
+    "        if self.epoch == 1:\n",
+    "            if np.array_equal(self.born,\n",
+    "                              self.data):  # still lifes are constat patterns, so at the first iteration they remain the same. The number of cells changing is 0\n",
+    "                self.pattern_name = 'Still Life'\n",
+    "                self.period = self.epoch\n",
+    "                self.heat = 0\n",
+    "\n",
+    "        else:\n",
+    "            if np.array_equal(self.born,\n",
+    "                              self.data):  # if the grid returns to its initial condition this means the pattern did not move and it is an oscillator\n",
+    "                self.pattern_name = 'Oscillator'\n",
+    "                self.period = self.epoch\n",
+    "                self.heat = self.heat / self.period\n",
+    "\n",
+    "            else:\n",
+    "\n",
+    "                for i, j in itertools.product(a, b):  # we consider all points in the grid\n",
+    "\n",
+    "                    if np.array_equal(self.data[i: i + self.pattern.shape[0], j: j + self.pattern.shape[1]],\n",
+    "                                      self.pattern):\n",
+    "                        coord = np.array([np.abs(self.x_pos - j), np.abs(self.y_pos - i)])\n",
+    "                        self.period = self.epoch\n",
+    "                        self.speed = np.max(coord) / self.period\n",
+    "\n",
+    "                        if self.speed != 0:\n",
+    "                            if coord[0] == 0 or coord[1] == 0:  # the pattern moves only in one direction\n",
+    "                                self.dir = 'Orthogonal'\n",
+    "                            else:\n",
+    "                                if coord[0] == coord[1]:\n",
+    "                                    self.dir = 'Diagonal'\n",
+    "                                else:\n",
+    "                                    self.dir = 'Oblique'  # the direction is not orthogonal nor diagonal\n",
+    "\n",
+    "                        if self.born.sum() == self.data.sum():  # if the pattern is the same but shifted it is a spaceship\n",
+    "                            self.pattern_name = 'Spaceship'\n",
+    "                            self.heat = self.heat / self.period\n",
+    "\n",
+    "                        else:\n",
+    "                            self.pattern_name = 'Gun or puffer'\n",
+    "                            self.heat = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c57fa7f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "pygame.init() \n",
+    "pygame.display.set_caption(\"Game of Life\") \n",
+    "pygame.font.init() \n",
+    "font = pygame.font.SysFont('cambria.ttf', int(0.5*b), bold=True) \n",
+    "font_small = pygame.font.SysFont('cambria.ttf', 30, bold=True)\n",
+    " \n",
+    "game = game_of_life_np()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "419ad983",
+   "metadata": {},
+   "source": [
+    "### Animation Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44daeef5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "from matplotlib import animation\n",
+    "\n",
+    "def plot_animate(ptrname, g):\n",
+    "    fig, ax = plt.subplots(figsize=(8,6)) \n",
+    " \n",
+    " \n",
+    "    number_of_frames = g.epoch # x values in the plot\n",
+    "    data = np.array(game.count_alive) # y values in the plot\n",
+    " \n",
+    " \n",
+    "    def animate(num, data): \n",
+    "        plt.cla() #clears the current axis\n",
+    "        ax.plot(range(num), data[:num], color = 'orange', linewidth = 2)\n",
+    "        ax.set_title(ptrname, fontweight = 'bold')\n",
+    "        ax.set_xlabel('Iteration Number', fontweight = 'bold')\n",
+    "        ax.set_ylabel('Alive Cells', fontweight = 'bold')\n",
+    "        \n",
+    " \n",
+    "    ani = matplotlib.animation.FuncAnimation(fig, animate, frames=number_of_frames, fargs=(data,)) #creating animation, needs number of frames corresponding to the umber of iterations, and values to plot \n",
+    "    \n",
+    "    return ani\n",
+    "\n",
+    "\"\"\"\n",
+    "if we want to save the video:\n",
+    "\n",
+    "writervideo = matplotlib.animation.PillowWriter(fps = 10)\n",
+    "\n",
+    "and then:\n",
+    "\n",
+    "a = plot_animate('title', game)\n",
+    "\n",
+    "f = 'title.gif'\n",
+    "a.save(f, writer = writervideo)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40996c80",
+   "metadata": {},
+   "source": [
+    "### Bar Chart Race function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01160e26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import bar_chart_race as bcr \n",
+    " \n",
+    "def bar_chart_race(name, initial_configuration, gof):  \n",
+    "    grid_pixels = (gof.M *gof.N) \n",
+    "    height_figure = initial_configuration.shape[0] \n",
+    "    lenght_figure = initial_configuration.shape[1] \n",
+    "    figure_surface = height_figure*lenght_figure   #The oscilllators stay confined only in a part of the grid.  \n",
+    "                                                   #In order to avoid the plot of the cells that never evolve,  \n",
+    "                                                   #we take into consideration only the cells that evolve. \n",
+    "     \n",
+    "    count_dead = np.array(gof.count_dead) - (grid_pixels-figure_surface)  \n",
+    "    count_dead = list(count_dead)  \n",
+    "    df = pd.DataFrame(zip(gof.count_alive,count_dead), columns = ['live cells',\"dead cells\"]) #create a dictionary with the number of alive  \n",
+    "                                                                                              #and dead cells for each epoch \n",
+    " \n",
+    "    def summary(values, ranks): \n",
+    "        s = \"Number of iterations\" \n",
+    "        return {'x': .99, 'y': .05, 's': s, 'ha': 'right', 'size': 8, \"fontweight\":\"bold\"}  \n",
+    " \n",
+    "    BCR = bcr.bar_chart_race(df=df, fixed_max=True,  \n",
+    "                   fixed_order=True, # keep the order of the columns in the dataframe \n",
+    "                   title= name, \n",
+    "                   orientation='h', \n",
+    "                   figsize=(5, 3), \n",
+    "                   shared_fontdict={'family': 'Helvetica', \n",
+    "                                    'color': 'black'}, \n",
+    "                   period_summary_func =summary, #counts the iteration number \n",
+    "                   steps_per_period=1, #frammerate \n",
+    "                   cmap = \"t10\", #colormap for the columns \n",
+    "                   n_bars = 2, \n",
+    "                   bar_kwargs={'alpha': 0.6, 'ec': 'black', 'lw': 0.8}  #sharpness and contour of the columns \n",
+    "                      ) \n",
+    "    return BCR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "396761f4",
+   "metadata": {},
+   "source": [
+    "### Fourier Transform Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0388319c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy\n",
+    "from scipy.fftpack import fft, fftfreq\n",
+    "import operator\n",
+    "\n",
+    "def Fourier(titolo, game):\n",
+    "    \n",
+    "    fig,(ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(22, 8))\n",
+    "    ax1.plot(game.count_alive,linewidth=3.0, color = \"orange\")\n",
+    "    ax1.set_title(titolo,fontweight=\"bold\",fontsize=20)\n",
+    "    ax1.set_xlabel(\"Iteration Number\",fontweight=\"bold\",fontsize=20)\n",
+    "    ax1.set_ylabel(\"Alive Cells\",fontweight=\"bold\",fontsize=20)\n",
+    "        \n",
+    "    \n",
+    "    n = len(game.count_alive)\n",
+    "    dt = 1\n",
+    "    fhat = np.fft.fft(game.count_alive,n) ## Compute the FFT \n",
+    "    PSD = fhat * np.conj(fhat) / n #Power spectrum  density (power per freq) magnitude of each fourier coefficient (how much power is in each of this frequencies in the red data)\n",
+    "    PSD = np.real(PSD)\n",
+    "    PSD = PSD[1:]\n",
+    "    freq = (1/(dt*n)) * np.arange(n) #Create x-axis of frequencies in Hz\n",
+    "    freq = freq[1:]\n",
+    "    ax2.plot(freq, PSD,linewidth=3.0, color=\"black\")\n",
+    "    ax2.set_xlabel(\"Frequencies (Hz)\",fontweight=\"bold\",fontsize=20)\n",
+    "    ax2.set_ylabel(\"Amplitudes\",fontweight=\"bold\",fontsize=20)\n",
+    "    ax2.set_title(\"Fourier Transform of occupancy plot\",fontweight=\"bold\",fontsize=20)\n",
+    "    \n",
+    "    plt.show()\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85304371",
+   "metadata": {},
+   "source": [
+    "## Patterns and their characteristics\n",
+    "\n",
+    "**Pattern Categories**\n",
+    "\n",
+    "Analyzing their behavior, the patterns can be subdivided in three main categories: still lifes, oscillators, and spaceships.\n",
+    "\n",
+    "**Still lifes** are patterns that are constant in time, and are the same in different generations. This means their period (number of generations after the pattern repeats itself) is 1. Constellations can also be implemented: those are still lifes made of many non-interacting objects.\n",
+    "\n",
+    "**Oscillators** are patterns that after a certain number of iterations evolve into its original state without shifting.\n",
+    "\n",
+    "**Spaceships** in contrast return to their initial state with the same orientation but in a different position.\n",
+    "\n",
+    "There are then **guns** and **puffers**, that are patterns which create spaceships or leave “debris” behind. <br> <br>\n",
+    "\n",
+    "\n",
+    "**Pattern properties**\n",
+    "\n",
+    "A property of every pattern which is actually an initial condition and does not need to be computed is the **bounding box**, meaning the smallest grid of squares containing the pattern.\n",
+    "\n",
+    "The general features of the patterns that we considered are:\n",
+    "\n",
+    "- **occupancy**: number of living cells at each time step. For still lifes the occupancy is always the same since the pattern does not change. There are patterns such as guns and puffers that increase their dimension at every time step.\n",
+    "\n",
+    "- **period**: number of generations needed so that the pattern returns to its initial configuration (even if in a different position). When computing the period, we confront the initial pattern with its bounding box with the evolution.\n",
+    "\n",
+    "  For objects that create spaceships, i. e. guns, **true-period**, and **pseudo-period** are defined. A gun has a pseudo-period when the mechanism has a certain period but the spaceship has another period. If both the spaceship and the mechanism have the same period then the gun has a true-period.\n",
+    "\n",
+    "- **heat** (only for oscillators and spaceships): average number of cells changing at each generation in a period.\n",
+    "\n",
+    "- **speed** and its **direction** (only for spaceships): number of generations needed to travel some given distance. It is usually measured in terms of fractions of the speed of light c, and calling x and y the displacement along the axes it is measured as:\n",
+    "\n",
+    "$$ \\begin{aligned} v = \\frac{max (|x|, |y|)} {n}  \\end{aligned} $$ \n",
+    "\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;where n is the period.\n",
+    "\n",
+    "\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The direction can be orthogonal (if the displacement is 0 along one of the axes), diagonal (if we have the same displacement along the two axes),\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;oblique (if the displacement values along the axes are different)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2143581e",
+   "metadata": {},
+   "source": [
+    "## Still Lifes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a92f40a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 80\n",
+    "\n",
+    "game.init(int(b), int(h), True, 100, 10)\n",
+    "game.initial_condition(block(), int(0.15*b), int(0.15*h)) #initializing the grid with the needed patterns \n",
+    "game.initial_condition(tube(), int(0.35*b), int(0.15*h)) \n",
+    "game.initial_condition(boat(), int(0.55*b), int(0.15*h)) \n",
+    "game.initial_condition(ship(), int(0.75*b), int(0.15*h)) \n",
+    "game.initial_condition(bee_hive(), int(0.15*b), int(0.4*h)) \n",
+    "game.initial_condition(loaf(), int(0.35*b), int(0.4*h)) \n",
+    "game.initial_condition(pond(), int(0.55*b), int(0.4*h)) \n",
+    "game.initial_condition(snake(), int(0.75*b), int(0.4*h)) \n",
+    "game.initial_condition(eater1(), int(0.35*b), int(0.65*h)) \n",
+    "game.initial_condition(eater2(), int(0.55*b), int(0.65*h)) \n",
+    "game.start_game() \n",
+    "pygame.display.quit() #quitting the display \n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format(round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", game.heat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64257ed1",
+   "metadata": {},
+   "source": [
+    "## Oscillators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef59b1a2",
+   "metadata": {},
+   "source": [
+    "### Blinker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa50290e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15) #initializing the game\n",
+    "ptrn = blinker() #setting the pattern\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2)) #initializing the grid with the pattern\n",
+    "game.start_game() #starting the game\n",
+    "pygame.display.quit() #quitting the game display\n",
+    "print(\"Period: {}\".format(game.period)) #printing the interesting quantities\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43bc3f93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Blinker', game) #printing the plot of the alive cells in time\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd7a383f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Blinker.mp4\")  #printing the bar chart race of the alive cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a0636da",
+   "metadata": {},
+   "source": [
+    "### Toad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31eea10a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15)\n",
+    "ptrn = toad()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "290658f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Toad', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d66e840",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Toad.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88c1cf5a",
+   "metadata": {},
+   "source": [
+    "### Pulsar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b9df9f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15)\n",
+    "ptrn = pulsar()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13cfee65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Pulsar', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d74799bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Pulsar.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84a51ef2",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "Fourier(\"Pulsar\",game)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ad4fd3a",
+   "metadata": {},
+   "source": [
+    "### Pentadecathlon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e6e292a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15)\n",
+    "ptrn = penta_decathlon()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format(game.speed))\n",
+    "print(\"Direction: {} \".format( round(game.speed, 3)))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bfe2c1a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Pentadecathlon', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "feaec90d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Penta_decathlon.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2accc0da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Fourier(\"Pentadecathlon\",game)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "368a3df1",
+   "metadata": {},
+   "source": [
+    "### Bi-pentadecathlon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85713638",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15)\n",
+    "ptrn = bi_penta_decathlon()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d041993f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Bi-pentadecathlon', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b404b2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Bi_penta_decathlon.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a72e4a1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Fourier(\"Bi-pentadecathlon\", game)"
+   ]
+  },
+  {
+   "attachments": {
+    "penta%20vs%20bi%20penta.png": {
+     "image/png": 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TuYijSd9//z3S09MRGBiIPn36IDU1VXhs5cqVaNWqFU6fPo158+ZJvv+4cePwwgsvCPd/7bXX0KtXLyxdutSsM37vvffivffeAwBMmzYNJSUl6Nq1K0pLS5GXl4fff/8dqampWLlyJbp164bOnTvjyJEjKCsrww033IDnn38ecXFxyMnJwfXr1/HOO+/Ifh/iz2TDhg0YMmQIQkJC0KVLF1klBNa49957cejQIQC8U1pRUQGVSoUXX3xRuGbixIlOeS2C8FoUrDckCK/AmgyMOa5fv25VBgYwSJ6IF4EMHTrU6D7iQnQx48ePb3A/1q633nqrwWNNmjQxkj8RS4BY48knnzS6zx133NHgGn9/f4vvcdSoUTZfw9YiEADcO++8I1zv6Cpg8WIA9tO5c2cjeRVrMjCm/yfxIhD2P7XWzkOHDpldmJOfn8+Vl5dzSUlJDR4bOHCgrP4nXhzBfkJDQ7nmzZub7QPWZGBMXzMnJ8do9a256+S+jytXrphdKMI+T/EiEHG7LS2oYedMZWAGDx5s8T1ak4ERY+n/TRCNAUoBE4SDxMTEYPfu3XjttdfQrVs3hIaGIiwsDBkZGbjzzjvx1VdfoX///nbff+nSpZgwYYLZqNi0adMwb948pKamIiwsDMOGDcMff/yBxMRE2a9jmu41/RvgFw6MGjUKLVq0QHBwsCAi/N///hffffed7NcE+ChqfHw8srKy8O2339pc1CKH7Oxs3HfffYiOjkZkZCTuuecebN68GSEhIcI1jzzyCLZv34477rgDCQkJCAgIQEJCAvr27YuZM2fiww8/tPv1u3Tpgs8++wwdOnRAcHCw0WORkZHYtGkTRowYgYiICDRv3hxz587F3LlzZb3G+++/j5kzZyIpKUkQpM7OzkabNm3MXj937lysX79eiPoGBgaiefPmGDRoEBYuXGgkXt2zZ08cPHgQTz75JFq1aoWgoCDExMSgf//+GDNmjF3vo0mTJli3bh169OjRoKTCWQQHB2PTpk1CVDw0NFSIMi5YsAC///47goKCXPLaBOEtqDiOKlsJgmg8pKWlCbtFkHkjCIIwD0UACYIgCIIgfAxyAAmCIAiCIHwMcgAJgiAIgiB8DK90ALdv345x48YhOTkZKpUK69atM3qc4zjMmjULSUlJCA0NRVZWFk6dOmV0TUlJCSZNmoSoqCjExMTg4YcfRmVlpYLvgiAIV3D27FlB044gCIIwj1c6gFVVVejWrRs++OADs4+/+eabWLJkCZYvX469e/ciPDwco0aNMhImnTRpEv755x9s2rQJ69evx/bt2/HYY48p9RYIgiAIgiDchtevAlapVPjhhx9w2223AeCjf8nJyXj22Wfx3HPPAeBV8RMSErB69Wrcc889OHbsGDp27Ij9+/ejd+/eAHhB0ptuugkXLlxAcnKyu94OQRAEQRCEy/HKCKA18vPzUVhYiKysLOFcdHQ0+vXrh927dwMAdu/ejZiYGMH5A4CsrCz4+fkZbRVEEARBEATRGGl0W8EVFhYCABISEozOJyQkCI8VFhaiWbNmRo8HBAQgLi5OuMYcarXaaJN0nU6HkpISxMfHO7TRPEEQBEEQysFxHCoqKpCcnGy0P7Uv0egcQFeyYMECI5V8giAIgiC8l/Pnz6NFixbuboZbaHQOINsCq6ioCElJScL5oqIidO/eXbimuLjY6Hn19fUoKSmxuoXWjBkzjLapKisrQ8uWLXH+/HlERUU58V0QBEEQBOEqysvLkZKSgsjISHc3xW00OgcwPT0diYmJyM7OFhy+8vJy7N27F08++SQAIDMzE6WlpcjJyUGvXr0AAH/88Qd0Oh369etn8d5s71NToqKiyAEkCIIgCC/Dl8u3vNIBrKysxOnTp4W/8/PzkZubi7i4OLRs2RJTp07FvHnzkJGRgfT0dMycORPJycnCSuEOHTpg9OjRePTRR7F8+XLU1dXh6aefxj333EMrgAmCIAiCaPR4pQN44MABDB8+XPibpWUfeOABrF69Gs8//zyqqqrw2GOPobS0FIMGDcKGDRsQEhIiPOeLL77A008/jRtuuAF+fn4YP348lixZovh7IQiCIAiCUBqv1wF0J+Xl5YiOjkZZWRmlgAmCaBRotVrU1dW5uxkE4RCBgYHw9/e3+DiN314aASQIgiCcC8dxKCwsRGlpqbubQhBOISYmBomJiT5d52cNcgAJgiAIwflr1qwZwsLCaNAkvBaO41BdXS2ofYgVQQgD5AASBEH4OFqtVnD+4uPj3d0cgnCY0NBQAEBxcTGaNWtmNR3sq/im/DVBEAQhwGr+wsLC3NwSgnAerD9TTat5yAEkCIIgAPi2JhrR+KD+bB1yAAmCIAiCIHwMcgAJgiAIwk2cPXsWKpUKubm57m4KVCoV1q1bZ/HxrVu3QqVS0UrxRgI5gARBEITXMnnyZKhUKqhUKgQFBaFNmzaYO3cu6uvrnfoabCepxsDs2bOFrVIJ34VWARMEQRBezejRo7Fq1Sqo1Wr8+uuvmDJlCgIDAzFjxgx3N40gPBaKABIEQRBeTXBwMBITE5Gamoonn3wSWVlZ+OmnnwAAarUazz33HJo3b47w8HD069cPW7duFZ67evVqxMTEYOPGjejQoQMiIiIwevRoXL58GQAfLfv000/x448/CpFG9vwXXngBbdu2RVhYGFq1aoWZM2faXHG6b98+9OjRAyEhIejduzf+/vvvBtccOXIEY8aMQUREBBISEnDffffh6tWrwuM6nQ5vvvkm2rRpg+DgYLRs2RKvv/668Li1dq1evRpz5szBwYMHhfezevVq4blXr17F7bffjrCwMGRkZAifoyW+//57dOrUCcHBwUhLS8M777xj9HhaWhrmz5+Phx56CJGRkWjZsiU+/vhjq/cklIEcQIIgCKIBHMehWlOv+I8zdicNDQ2FRqMBADz99NPYvXs3vv76axw6dAh33XUXRo8ejVOnTgnXV1dX4+2338bnn3+O7du3o6CgAM899xwA4LnnnsOECRMEp/Dy5csYMGAAACAyMhKrV6/G0aNH8d577+GTTz7BokWLLLarsrISY8eORceOHZGTk4PZs2cLr8MoLS3FiBEj0KNHDxw4cAAbNmxAUVERJkyYIFwzY8YMLFy4EDNnzsTRo0fx5ZdfIiEhQXjcWrvuvvtuPPvss+jUqZPwfu6++27huXPmzMGECRNw6NAh3HTTTZg0aRJKSkrMvp+cnBxMmDAB99xzDw4fPozZs2dj5syZRg4lALzzzjuCs/vUU0/hySefxIkTJyx+ToQyUAqYIAiCaEBNnRYdZ21U/HWPzh2FsCD7hiaO45CdnY2NGzfimWeeQUFBAVatWoWCggIkJycD4B26DRs2YNWqVZg/fz4AXidu+fLlaN26NQDeaZw7dy4AICIiAqGhoVCr1UhMTDR6vVdeeUU4TktLw3PPPYevv/4azz//vNn2ffnll9DpdFixYgVCQkLQqVMnXLhwAU8++aRwzdKlS9GjRw+hbQCwcuVKpKSk4OTJk0hKSsJ7772HpUuX4oEHHgAAtG7dGoMGDZLUrtDQUERERCAgIKDB+wH4eseJEycCAObPn48lS5Zg3759GD16dINr3333Xdxwww2YOXMmAKBt27Y4evQo3nrrLUyePFm47qabbsJTTz0FgI9OLlq0CFu2bEG7du3Mfk6EMpADSBAEQXg169evR0REBOrq6qDT6XDvvfdi9uzZ2Lp1K7RaLdq2bWt0vVqtNtrxJCwsTHD+AH7rMLaNmDW++eYbLFmyBHl5eaisrER9fT2ioqIsXn/s2DF07doVISEhwrnMzEyjaw4ePIgtW7YgIiKiwfPz8vJQWloKtVqNG264wWntEtO1a1fhODw8HFFRURY/i2PHjuHWW281Ojdw4EAsXrwYWq1W2H1DfE+VSoXExERJny/hWsgBJAiCIBoQGuiPo3NHueV15TJ8+HAsW7YMQUFBSE5ORkAAP7RVVlbC398fOTk5DbYCEztYgYGBRo+pVCqbqejdu3dj0qRJmDNnDkaNGoXo6Gh8/fXXDWrg5FJZWYlx48bhjTfeaPBYUlISzpw549J2mfssdDqd9Deg0D0JxyEHkCAIgmiASqWyOxWrNOHh4WjTpk2D8z169IBWq0VxcTEGDx5s9/2DgoKg1WqNzu3atQupqal4+eWXhXPnzp2zep8OHTrg888/R21trRAF3LNnj9E1PXv2xPfff4+0tDTBkRWTkZGB0NBQZGdn45FHHmnwuJR2mXs/9tChQwfs3LnT6NzOnTvRtm1b2nvXC6BFIARBEESjpG3btpg0aRLuv/9+rF27Fvn5+di3bx8WLFiAX375RfJ90tLScOjQIZw4cQJXr15FXV0dMjIyUFBQgK+//hp5eXlYsmQJfvjhB6v3uffee6FSqfDoo4/i6NGj+PXXX/H2228bXTNlyhSUlJRg4sSJ2L9/P/Ly8rBx40Y8+OCD0Gq1CAkJwQsvvIDnn38en332GfLy8rBnzx6sWLECACS1Ky0tDfn5+cjNzcXVq1ehVqslfxZinn32WWRnZ+O1117DyZMn8emnn2Lp0qUNFrYQngk5gARBEESjZdWqVbj//vvx7LPPol27drjtttuwf/9+tGzZUvI9Hn30UbRr1w69e/dG06ZNsXPnTtxyyy2YNm0ann76aXTv3h27du0SFkNYIiIiAj///DMOHz6MHj164OWXX26Q6k1OTsbOnTuh1Wpx4403okuXLpg6dSpiYmLg58cP2TNnzsSzzz6LWbNmoUOHDrj77ruFmjop7Ro/fjxGjx6N4cOHo2nTpvjqq68kfxZievbsiW+//RZff/01OnfujFmzZmHu3LlGC0AIz0XFOWPNvY9SXl6O6OholJWVSS6wJQiC8DRqa2uRn5+P9PR0owUKBOHNWOvXNH5TBJAgCIIgCMLnIAeQIAiCIAjCxyAHkCAIgiAIwscgB5AgCIIgCMLHIAeQIAiCIAjCxyAHkCAIgiAIwscgB5AgCIIgCMLHIAeQIAiCIAjCxyAHkCAIgiAIwscgB5AgCIJo1Jw9exYqlQq5ubnuborLGTZsGKZOneruZmDy5Mm47bbbrF6TlpaGxYsXK9IeoiHkABIEQRBey+TJk6FSqYSf+Ph4jB49GocOHRKuSUlJweXLl9G5c2c3tpRn69atUKlUKC0tdXdTnIIvOdeNjUbtAFZUVGDq1KlITU1FaGgoBgwYgP379wuPcxyHWbNmISkpCaGhocjKysKpU6fc2GKCIAhCLqNHj8bly5dx+fJlZGdnIyAgAGPHjhUe9/f3R2JiIgICAtzYSoLwLBq1A/jII49g06ZN+Pzzz3H48GHceOONyMrKwsWLFwEAb775JpYsWYLly5dj7969CA8Px6hRo1BbW+vmlhMEQRBSCQ4ORmJiIhITE9G9e3e8+OKLOH/+PK5cuQJAWpRq9erViImJwbp165CRkYGQkBCMGjUK58+fN7ruxx9/RM+ePRESEoJWrVphzpw5qK+vFx5XqVT4v//7P9x+++0ICwtDRkYGfvrpJ6Edw4cPBwDExsZCpVJh8uTJAIANGzZg0KBBiImJQXx8PMaOHYu8vDyr77uqqgr3338/IiIikJSUhHfeeafBNWq1Gs899xyaN2+O8PBw9OvXD1u3bjW6ZufOnRg2bBjCwsIQGxuLUaNG4fr165LalZ6eDgDo0aMHVCoVhg0bZnTvt99+G0lJSYiPj8eUKVNQV1dn8f0UFBTg1ltvRUREBKKiojBhwgQUFRUJj8+ePRvdu3fH559/jrS0NERHR+Oee+5BRUWF1c+JME+jdQBramrw/fff480338SQIUPQpk0bzJ49G23atMGyZcvAcRwWL16MV155Bbfeeiu6du2Kzz77DJcuXcK6devc3XyCIAj3wnGApkr5H45zqNmVlZVYs2YN2rRpg/j4eFnPra6uxuuvv47PPvsMO3fuRGlpKe655x7h8R07duD+++/Hf/7zHxw9ehQfffQRVq9ejddff93oPnPmzMGECRNw6NAh3HTTTZg0aRJKSkqQkpKC77//HgBw4sQJXL58Ge+99x4A3pmbPn06Dhw4gOzsbPj5+eH222+HTqez2N7//ve/2LZtG3788Uf8/vvv2Lp1K/766y+ja55++mns3r0bX3/9NQ4dOoS77roLo0ePFrJdubm5uOGGG9CxY0fs3r0bf/75J8aNGwetViupXfv27QMAbN68GZcvX8batWuF196yZQvy8vKwZcsWfPrpp1i9ejVWr15t9r3odDrceuutKCkpwbZt27Bp0yacOXMGd999t9F1eXl5WLduHdavX4/169dj27ZtWLhwocXPiLBMo42H19fXQ6vVIiQkxOh8aGgo/vzzT+Tn56OwsBBZWVnCY9HR0ejXrx92795t9KUnCILwOeqqgfnJyr/uS5eAoHBZT1m/fj0iIiIA8A5LUlIS1q9fDz8/eTGOuro6LF26FP369QMAfPrpp+jQoQP27duHvn37Ys6cOXjxxRfxwAMPAABatWqF1157Dc8//zxeffVV4T6TJ0/GxIkTAQDz58/HkiVLsG/fPowePRpxcXEAgGbNmiEmJkZ4zvjx443asnLlSjRt2hRHjx41W7tYWVmJFStWYM2aNbjhhhuE9rZo0UK4pqCgAKtWrUJBQQGSk/n/5XPPPYcNGzZg1apVmD9/Pt5880307t0bH374ofC8Tp06SW5X06ZNAQDx8fFITEw0ujY2NhZLly6Fv78/2rdvj5tvvhnZ2dl49NFHG7yf7OxsHD58GPn5+UhJSQEAfPbZZ+jUqRP279+PPn36AOAdxdWrVyMyMhIAcN999yE7O7uBE07YptFGACMjI5GZmYnXXnsNly5dglarxZo1a7B7925cvnwZhYWFAICEhASj5yUkJAiPmaJWq1FeXm70QxAEQbiX4cOHIzc3F7m5udi3bx9GjRqFMWPG4Ny5c2av79SpEyIiIhAREYExY8YI5wMCAgRHAwDat2+PmJgYHDt2DABw8OBBzJ07V3huREQEHn30UVy+fBnV1dXC87p27Soch4eHIyoqCsXFxVbfw6lTpzBx4kS0atUKUVFRSEtLA8A7cebIy8uDRqMRnFUAiIuLQ7t27YS/Dx8+DK1Wi7Zt2xq1edu2bUIal0UAndUuMZ06dYK/v7/wd1JSksXP4dixY0hJSRGcPwDo2LGj0ecP8CuHmfNn656EdRptBBAAPv/8czz00ENo3rw5/P390bNnT0ycOBE5OTl23W/BggWYM2eOk1tJEAThgQSG8dE4d7yuTMLDw9GmTRvh7//7v/9DdHQ0PvnkE8ybN6/B9b/++qtQixYaGir5dSorKzFnzhzccccdDR4TZ5sCAwONHlOpVFZTuQAwbtw4pKam4pNPPkFycjJ0Oh06d+4MjUYjuX3m2uvv74+cnBwjRwyAEDG19f4daZc9n4M77umrNGoHsHXr1ti2bRuqqqpQXl6OpKQk3H333WjVqpUQqi4qKkJSUpLwnKKiInTv3t3s/WbMmIHp06cLf5eXlxvNVgiCIBoNKpXsVKynoFKp4Ofnh5qaGrOPp6ammj1fX1+PAwcOoG/fvgD4Or3S0lJ06NABANCzZ0+cOHHCyNmUS1BQEAAINXYAcO3aNZw4cQKffPIJBg8eDAD4888/rd6ndevWCAwMxN69e9GyZUsAwPXr13Hy5EkMHToUAL8wQ6vVori4WLivKV27dkV2drbZ4IaUdpl7P/bQoUMHnD9/HufPnxfG1aNHj6K0tBQdO3Z06N6EeRq1A8gIDw9HeHg4rl+/jo0bN+LNN99Eeno6EhMTkZ2dLTh85eXl2Lt3L5588kmz9wkODkZwcLCCLScIgiBsoVarhdKd69evY+nSpaisrMS4ceNk3ScwMBDPPPMMlixZgoCAADz99NPo37+/4BDOmjULY8eORcuWLXHnnXfCz88PBw8exJEjR8xGGs2RmpoKlUqF9evX46abbkJoaChiY2MRHx+Pjz/+GElJSSgoKMCLL75o9T4RERF4+OGH8d///hfx8fFo1qwZXn75ZaO6x7Zt22LSpEm4//778c4776BHjx64cuUKsrOz0bVrV9x8882YMWMGunTpgqeeegpPPPEEgoKCsGXLFtx1112Ii4uz2a5mzZohNDQUGzZsQIsWLRASEoLo6GhZnzsAZGVloUuXLpg0aRIWL16M+vp6PPXUUxg6dCh69+4t+36EbRptDSAAbNy4ERs2bEB+fj42bdqE4cOHo3379njwwQehUqkwdepUzJs3Dz/99BMOHz6M+++/H8nJyTbVywmCIAjPYcOGDUhKSkJSUhL69euH/fv347vvvmsgSWKLsLAwvPDCC7j33nsxcOBARERE4JtvvhEeHzVqFNavX4/ff/8dffr0Qf/+/bFo0SKLEUVzNG/eXFhMkpCQgKeffhp+fn74+uuvkZOTg86dO2PatGl46623bN7rrbfewuDBgzFu3DhkZWVh0KBB6NWrl9E1q1atwv33349nn30W7dq1w2233Yb9+/cLUcO2bdvi999/x8GDB9G3b19kZmbixx9/REBAgKR2BQQEYMmSJfjoo4+QnJyMW2+9VfJnIUalUuHHH39EbGwshgwZgqysLLRq1cro8yeci4rjHFxz78F8++23mDFjBi5cuIC4uDiMHz8er7/+ujA74TgOr776Kj7++GOUlpZi0KBB+PDDD9G2bVtJ9y8vL0d0dDTKysoQFRXlyrdCEAThMmpra5Gfn4/09PQGygm+wurVqzF16tRGs0MHYb1f0/jdyFPAEyZMwIQJEyw+rlKpMHfuXMydO1fBVhEEQRAEQbiXRp0CJgiCIAiCIBpCDiBBEATh80yePJnSv4RPQQ4gQRAEQRCEj0EOIEEQBEEQhI9BDiBBEAQBgFdGIIjGAvVn65ADSBAE4eOw7bXE+9kShLfD+rPp9nEET6OWgSEIgiBs4+/vj5iYGBQXFwPgBZFVKpWbW0UQ9sFxHKqrq1FcXIyYmJgG+yATPOQAEgRBEML+6MwJJAhvJyYmRujXREPIASQIgiCgUqmQlJSEZs2aoa6uzt3NIQiHCAwMpMifDcgBJAiCIAT8/f1p4CQIH4AWgRAEQRAEQfgY5AASBEEQBEH4GOQAEgRBEARB+BjkABIEQRAEQfgY5AASBEEQBEH4GOQAEgRBEARB+BjkABIEQRAEQfgY5AASBEEQBEH4GOQAEgRBEARB+BjkABIEQRAEQfgY5AASBEEQBEH4GOQAEgRBEARB+BjkABIEQRAEQfgY5AASBEEQBEH4GOQAEgRBEARB+BjkABIEQRAEQfgY5AASBEEQBEH4GOQAEgRBEARB+BjkABIEQRAEQfgY5AASBEEQBEH4GI3WAdRqtZg5cybS09MRGhqK1q1b47XXXgPHccI1HMdh1qxZSEpKQmhoKLKysnDq1Ck3tpogCIIgCML1NFoH8I033sCyZcuwdOlSHDt2DG+88QbefPNNvP/++8I1b775JpYsWYLly5dj7969CA8Px6hRo1BbW+vGlhMEQRAEQbgWFScOiTUixo4di4SEBKxYsUI4N378eISGhmLNmjXgOA7Jycl49tln8dxzzwEAysrKkJCQgNWrV+Oee+6x+Rrl5eWIjo5GWVkZoqKiXPZeCIIgCIJwHjR+N+II4IABA5CdnY2TJ08CAA4ePIg///wTY8aMAQDk5+ejsLAQWVlZwnOio6PRr18/7N692+w91Wo1ysvLjX4aBZvnACtuBOoo8klYYetC4JMRgKbK3S0hPJkd7wIfDQVqy9zdEsKT2f0BsHwQUHXN3S3xWRqtA/jiiy/innvuQfv27REYGIgePXpg6tSpmDRpEgCgsLAQAJCQkGD0vISEBOExUxYsWIDo6GjhJyUlxbVvQin+/hw4vxco+sfdLSE8mb/XABdzgEt/u7slhCeT+yVwORe4sN/dLSE8mdyvgMLDQIH5gAvhehqtA/jtt9/iiy++wJdffom//voLn376Kd5++218+umndt9zxowZKCsrE37Onz/vxBa7EXUF/1tT6d52EJ6NWh/xpgggYQ3BnlA/IaxA9sTtBLi7Aa7iv//9rxAFBIAuXbrg3LlzWLBgAR544AEkJiYCAIqKipCUlCQ8r6ioCN27dzd7z+DgYAQHB7u87YqirQPq9alf+iISluA4QK2fINBEgbAG6x9kTwhraMieuJtGGwGsrq6Gn5/x2/P394dOpwMApKenIzExEdnZ2cLj5eXl2Lt3LzIzMxVtq1ths3WADDZhmboagNPyx9RPCEvodOQAEtKgSLHbabQRwHHjxuH1119Hy5Yt0alTJ/z9999499138dBDDwEAVCoVpk6dinnz5iEjIwPp6emYOXMmkpOTcdttt7m38Uoinn1pKixfR/g24n6iphk7YQGjfkL2hLBAvQbQavhjigC6jUbrAL7//vuYOXMmnnrqKRQXFyM5ORmPP/44Zs2aJVzz/PPPo6qqCo899hhKS0sxaNAgbNiwASEhIW5sucKIB3OaiRGWoEgxIQUN2RNCAtRPPIJG6wBGRkZi8eLFWLx4scVrVCoV5s6di7lz5yrXME+DvoiEFIz6Cc3YCQvQhJKQgtGEkuyJu2i0NYCERNQiLUP6IhKWoAggIQUa2AkpkD3xCMgB9HVoxk5IgfoJIQUNDeyEBCjz5BGQA+jrUHE/IQVaLERIQU2lAoQE1LRYyBMgB9DXoVA8IQWjUgHqJ4QFyJ4QUiB74hGQA+jr0IydkAKlgAkp0GIhQgqUAvYIyAH0dahmh5ACGWxCChQBJKRAE0qPgBxAX4e+iIQUKFJMSIEmCoQUKFLsEZAD6OvQjJ2QAtXsEFIge0JIgeyJR0AOoK9DqzsJKdBqcUIKppFijnNfWwjPRdxPdHVAvdp9bfFhyAH0dWjGTkhBbLDrawCd1n1tITwX8SSS0wF1Ne5rC+G5mKZ9aexxC+QA+jpiB1Cr4TfpJghTTLW6yGAT5qB+QkihQT+hrII7IAfQ1zH94tWRwSbMQDN2Qgqm5QE0sBPmaNBPyJ64A3IAfR36IhJSoMgOIQWaKBBSMK03p37iFsgB9HVMDTYV+BPmaDCwUz8hzEARQEIK1E88AnIAfRmdzvDFCwzjf9NMjDAHiwAK/YQMNmEG6ieEFEz7CQUe3AI5gL6M2DhHJDQ8RxAAvzBIq18cJPQTmigQJnCcIbVH/YSwBhtnqJ+4FXIAfRn2JfQLAMLi9Ofoi0iYIJ4URCY2PEcQAC/5wun4Y6GfkD0hTNBpgbpq/pjsiVshB9CXYWH44EggKII/JoNNmMJU+wNCgZBo/pj6CWGKsFBIBYQ35Q+pnxCmmJ1QUj9xB+QA+jKs7iJI7ADSbiCECayfBEcAQeH8MRlswhQ2sAdH8j9Aw9XjBMH6hF8gEEqZJ3dCDqAvw5w9GtgJa7CBPUjUT6homzCFDexBZE8IK5idUJI9cQfkAPoyatHAHkwpYMICRgZbH9khg02YohH3E7InhAU0osxTMNkTd0IOoC9jVANIMzHCAqwGMDiKIjuEZcieEFIQ7Ekk2RM3Qw6gL0MzdkIK5lLA1E8IU8QZBbInhCWopthjIAfQlxFqdmgmRliBanYIKWjMRQDJnhAmaMxNFMieuANyAH0ZcykbKu4nTCG5IEIKZheBkD0hTFCLVovTRMGtkAPoy5hNAZPBJkzQ0MBOSMBoYCd7QlhAqAEkVQF3Qw6gL0M1O4QUxAM7rRYnLCGeUFI/ISwhXgVM445bIQfQlzE3E6MvImGKUWqPIjuEBZg9ocVChDXUlHnyFMgB9GWEGXsUzcQIy2ioZoeQgJrsCSEBs3JB1E/cQaN1ANPS0qBSqRr8TJkyBQBQW1uLKVOmID4+HhERERg/fjyKiorc3GqFUZuT96CtmwgTSLaBkILGwmpxjnNfmwjPw5yslK4OqNe4r00+SqN1APfv34/Lly8LP5s2bQIA3HXXXQCAadOm4eeff8Z3332Hbdu24dKlS7jjjjvc2WTlMWuwaWAnTNCI5YL0kZ26akCndV+bCM/D3ISS0wF1Ne5rE+F5mFMVACgN7AYC3N0AV9G0aVOjvxcuXIjWrVtj6NChKCsrw4oVK/Dll19ixIgRAIBVq1ahQ4cO2LNnD/r37++OJiuP+IvIira1Gn4mFhDkvnYRnoVFg10FhES5p02E56EW7S0eGG44r6kCgsLc0ybC8xCXlPgHAAEhQH0tfz4szr1t8zEabQRQjEajwZo1a/DQQw9BpVIhJycHdXV1yMrKEq5p3749WrZsid27d1u8j1qtRnl5udGPV6MWrcYSG+w6igISIsQp4IBgQOXP/03RYkKMIAQdBfj5GWwKRXYIMeJFZQBln9yITziA69atQ2lpKSZPngwAKCwsRFBQEGJiYoyuS0hIQGFhocX7LFiwANHR0cJPSkqKC1vtYjhOZLAj+Iifvz7qR19EQoy4ZkelogJ/wjziFDBAAzthHvGEEqB+4kZ8wgFcsWIFxowZg+TkZIfuM2PGDJSVlQk/58+fd1IL3UBdNV+fA5DBJiyjref7CsCnbAASgybMI04BA2RPCPOIdQABkoJxI422BpBx7tw5bN68GWvXrhXOJSYmQqPRoLS01CgKWFRUhMTERIv3Cg4ORnBwsCubqxyC8rrKYKiDIoCa66TKThgQG+UGDiAN7ISeejW/khMQ9RM2sJOyAKGH44xrigGyJ26k0UcAV61ahWbNmuHmm28WzvXq1QuBgYHIzs4Wzp04cQIFBQXIzMx0RzOVR1yIq1LxxxTZIUxhfcEvkK//A6ifEA0RTxopo0BYQlMFQC8LZBoppsCD4jTqCKBOp8OqVavwwAMPICDA8Fajo6Px8MMPY/r06YiLi0NUVBSeeeYZZGZm+t4KYPGqTqrtIkwxrdcBDDN3cgAJBovyBYYBfvpFQrQdHGEKsxkqP76vAJQCdiON2gHcvHkzCgoK8NBDDzV4bNGiRfDz88P48eOhVqsxatQofPjhh25opZswDcMDNGMnGkL9hJCC2Qkl9RPCBLHyhJB5oomCu2jUDuCNN94IzoIKfUhICD744AN88MEHCrfKQ9CYiezQTIwwRSwCzaCBnTBFLSopYZA9IUwR7z/PIHviNhp9DSBhAVPJBoBqu4iGmEsBUz8hTDE7oaTaLsIEDY07ngQ5gL6KMBOjyA5hBaoVJaTA7AlFiglrmJ1Qkj1xF+QA+ioaMymbYErZECaY6yfMYFNkh2BQCpiQgrmaYhp33AY5gL6K2RQwzcQIE0zFfQGK7BANsVpTTP2E0KOhxUKeBDmAvoq1mh36IhIMU9V+gGp2iIZYrSkme0LooUixR0EOoK9CNYCEFMzKwFBkhzCBVncSUjBbUkL9xF2QA+irqM1FdlhtF23dROixugqYDDahRxjYowznaCs4whTSi/QoyAH0VSgFTEiBZBsIKVAKmJACTSg9CnIAfRWS9yCkYK5UgLaCI0wxt1iItoIjTDErLK8/psyT4pAD6KtYLcYlg03oMdtPaMZOmGBNLoj6CcGgrSU9CnIAfRWNtS8iRXYIPVZTwGSwCT1qa1sGVgIWtuQkfAxrKWBdHVCvUb5NPgw5gL4KbQVHSMGacn9dNaDTKt8mwvOwVlPM6YC6GuXbRHge1iaU4scJRSAH0FexForXamgmRvAI/US8ulNksOuqlW0P4ZmYqykODDMcU7SYAEQTSpE98Q8E/IP5Y+onikIOoC9Sr+bD7YD5yA4A1NEX0efhOPMz9oAQQKU3HbQdHAGYrxX18zc4gRTZIQDzepHiv6mfKAo5gL6IeNA2GtiDAP8g/phmYoSmCoC+dktssFUqQ60X9RNCWw/U61O8YgcQoIUghAFLE0qA6ordhGIO4IYNGzB37lycPHkSFRUVGDduHKKjozFkyBBcuHBBqWYQgGEWFhjGz9LF0BeRYLC0nsrPOJ0HUL0oYUAs9EwDO2GJejWgq+ePTSOAtB2cW1DMAXzrrbcwd+5cREdH46OPPsIvv/yCiooK7Ny5Ey+++KJSzSAA85INDPoiEgzxPsAqlfFjNLATDJZR8A/iswhiyJ4QDI2FzBNA9sRNKOYAHj16FC1atEBCQgK2bduGqKgofPXVVwgJCcHWrVuVagYBmF8BzGBfRKrtIsyJ+zLIYBMMqxNKihQTeoTMUzhlnjwExRzA69evo2nTpgCA48ePo3fv3rj77rvRoUMHXL16ValmEIB5yQYGfREJhqV6HfE5GtgJKRNKsieEOUkpBtkTt6CYAxgXF4eTJ0/iq6++wtmzZ9G5c2cAQFlZGWJiYpRqBgGIVmJFNXyMirYJhjmpIAat2iMY5rYLZNB2cARDSukRZZ4URTEHcPjw4aioqMC//vUv6HQ6jBo1ClVVVTh//jwyMjKUagYB2Jix08BO6LE6Y6fIDqGHaooJKZjTimSQPXELAUq90LvvvouamhqcPn0a48aNw5gxY7Bz50706dMH99xzj1LNIABKARPS0Egx2DSw+zyUAiakYC2jQPbELSjmACYkJGDt2rVG5wYOHIgdO3Yo1QSCIWkmRl9En8eqwabUHqFHymIhSu0RkmqKyZ4oiUsdwO3bt0u+dsiQIS5sCWEEzcQIKZjb3YFBkR2CQZFiQgpkTzwOlzqAw4YNg8pUP8wMKpUK9fX1rmwKIUZSzQ59EX0eqzN2iuwQeszt78oge0IwrEWKabGQW3B5CpjjOFe/BCEXazU79EUkGCTbQEjBak0x2RNCj6QUcEXDxwiX4VIHMD8/35W3J+xFUgqYDLbPQ3JBhBRodSchBWHcMWdPqJ+4A5c6gKmpqa68PWEvkmbsFNnxeaSkgMlgE1Zru8ieEHpoZyGPw6UO4EMPPSTpOpVKhRUrVriyKYQYYcZuJQJItV0E6QASUtDQojJCAjSh9Dhc6gCuXr3a5iIQjuNc5gBevHgRL7zwAn777TdUV1ejTZs2WLVqFXr37i289quvvopPPvkEpaWlGDhwIJYtW9b4halpJkZIwWpqjyI7hB5KARNSoJpij8OlDuCQIUMkrQJ2BdevX8fAgQMxfPhw/Pbbb2jatClOnTqF2NhY4Zo333wTS5Yswaeffor09HTMnDkTo0aNwtGjRxESEuKWdiuC1VXA+nNksAmNldWdtBUcwbA2sAeTPSH0WI0U01Zw7sClDuDWrVtdeXurvPHGG0hJScGqVauEc+np6cIxx3FYvHgxXnnlFdx6660AgM8++wwJCQlYt25d496dRJJyP30RfR6KFBNSsDqhFNkTjgPcFBAgPAAppUe6OqBeAwQEKdcuH0axvYAZZ86cwVdffYUff/zRpa/z008/oXfv3rjrrrvQrFkz9OjRA5988onweH5+PgoLC5GVlSWci46ORr9+/bB7926Xts2taOuB+hr+mFYBE5bgOGmyDXXVgE6rXLsIz0PKhJLTAfW1yrWJ8Dyk1BQDFHxQEMUcQK1Wi0ceeQRt27bFv/71L7zxxhv4/PPP4e/vj/fff9/pr3fmzBmhnm/jxo148skn8e9//xuffvopAKCwsBAAv0WdmISEBOExU9RqNcrLy41+vA6xzpI1g61VA9o6ZdpEeB71tYBOL85ubaIA8E4g4ZvodNZTe4FhhmNK7/k21iLF/oGAf7D+Ogo+KIViDuCCBQuwcuVK6HQ6QRz69ttvR0BAAH766Senv55Op0PPnj0xf/589OjRA4899hgeffRRLF++3O57LliwANHR0cJPSkqKE1usEMwI+webD7OLnUKaifku4sHa3EQhIARQ6c0HGWzfpU70vzc3sPv5G5xAsie+i7bOEAE2Z08Ayj65AcUcwFWrViEwMBDr1q0TzkVERCAlJQXHjh1z+uslJSWhY8eORuc6dOiAgoICAEBiYiIAoKioyOiaoqIi4TFTZsyYgbKyMuHn/PnzTm+3y7GmAQjwTqFfoP5a+iL6LCyqExgO+JkxEyoVFW4Thv+9yp+fFJiDBnZCLco8mZsoALQLlRtQzAG8cOECOnbsiFtuucXofGRkJK5cueL01xs4cCBOnDhhdO7kyZOCOHV6ejoSExORnZ0tPF5eXo69e/ciMzPT7D2Dg4MRFRVl9ON1WNsFhEFfREJKPyHpBkK8UMjSAg/aNYZgNiIghE/3moO2g1Mcl+8FzGjSpAny8/Nx7do14VxBQQGOHTuGpk2bOv31pk2bhgEDBmD+/PmYMGEC9u3bh48//hgff/wxAF58eurUqZg3bx4yMjIEGZjk5GTcdtttTm+Px2BtJRYjKAKouU4Duy9jrWCbQZEdQiPRngBkT3wZawuFGGRPFEexCOCoUaNQXl6OLl26AACOHj2Knj17oq6uDqNHj3b66/Xp0wc//PADvvrqK3Tu3BmvvfYaFi9ejEmTJgnXPP/883jmmWfw2GOPoU+fPqisrMSGDRt8RANQwheRUnu+i7UVwAwy2IS1beAYJC1FWJOUYpA9URzFIoCvv/46Nm/ejAsXLgCAsIK2efPmmDt3rktec+zYsRg7dqzFx1UqFebOneuy1/dIaCZGSIFSwIQU5EwoyZ74LhQp9kgUcwCTkpKQm5uLpUuXYt++fQD4KN2UKVPQpEkTpZpBSBrYyWD7PNRPCClY2waOQf2EkBUppn6iFIo5gAAQFxeHWbNmKfmShCkaKaF4tn0TzcR8FikpYNoOjpC0qIzsic8jKQVMi4WUxuU1gOvWrcOIESOwZs2aBo+tXLkSI0aMMJKGIVyMkAKmmRhhBVmLQGhg91msifsyyJ4QcmqK1bQKWClc7gB+9NFH2L59u9GWa4xRo0Zh+/btDokzEzKh1B4hBVk1gNRPfBZKARNSkJQCJnuiNC53AA8fPowWLVqYFVdu3rw5UlJScOjQIVc3g2DIKtqmmZjPIqlomwZ2n0dOpJgiO76Lte0CGWRPFMflDuCVK1cQGhpq8fGQkBAjbUDCxUhaBUwzMZ+HdAAJKUhKAZM98XkoUuyRuNwBjI+PR15entlt0woKCpCXl4f4+HhXN4Ng0EyMkIKkmh1aBOLz0MBOSEHKhJIWCymOyx3AzMxM1NfX44477jBK9R4+fBh33nkntFqtxa3XCBdAW8ERUpBTA0iC4b4L1YoSUpC1WIjsiVK4XAZmypQp+OGHH/DXX3+hR48eiIyMhEqlQnl5OTiOg0qlwtNPP+3qZhAMWSlgN34Rq0sAP38gJNp9bfBlvCUFXHMdgAoIjXFfG3wZb4kU15YBOi0QFue+NvgyFCn2SFweARwxYgRmzpwJjuPAcRzKy8tRVlYGjuMAALNmzcLw4cNd3QyC4Q1bwdXVAEt7A8sHATqde9rg63jDIpB6DfBBf+DDTEBb5542+DresBWcTgssGwh80Beoq3VPG3wdUp/wSBQRgp4zZw4GDRqETz75BEePHgXHcejUqRMee+wxs/IwhAvxhi/i1ZNA9TX+p+IyEN3cPe3wZbxhK7jrZ4HKQv64tACIb+2edvgy3rDHa9kFoExfg16SByR0ck87fBlZi4UoBawUiu0EMnLkSIwcOVKplyPModOJUjYeXLNTcsb4mBxA5fGGFLBpPyEHUHlk7fHqIf2EHEDloT3oPRKXp4AJD6JO9MXy5B0eruUZjkvyLF9HuIZ6DaBV88eSDLab+om4b1yjfqI4HCc/Bawv/VEU6ifuR85WcFoNb4MIl0MOoC/BvoQqfyAgxPJ1bp+x54uOz1i+jnANYodOyh6vddV8nZXSmEZ2CGWpqwE4/f/dqryH/jFOB9S7oQaP7Il70ekMwQcpNcUApYEVghxAX0I8W1epLF/HvohatXuK62nG7l7YRCEgBPAPtHyd2GDXVbu2TeagSLF7EQ/SgeGWrwsMEz3HDZNKo35CDqDiSJ1Q+gcC/sH651AaWAnIAfQlpIhAA8ZpP3fMxIwiO/mWryNcgxRpD4B3EFV6E+IOg00RQPcilvbwszKU+PkbnEB3bAdH/cS9MHviFwAEBFu/luoAFYUcQF9CSiEuAAQEAX76yI/SX0R1BVBZZPi75AxJwSiNlAUgAB9Fdle5QL3GsLITAK6fIykYpZGyspPhroFdpwWuiyaR5RcBjRui1b6MeKJgLfPErgHIAVQIRR3Aq1ev4tVXX8WoUaPw2GOP4ciRI/jss89QUFCgZDN8FynSHgx3GWw2Qw+J4WeM9TW8FAyhHHb1E4UjxdfP8jVlgeFAQChfi1ZKdkRRpIj7MtxlT8ov8osK/IOAYL2o/HXKKiiKMKGMsn1tMEnBKIliDuDZs2fRrVs3zJs3D5s3b8aRI0dQWlqKyZMnY+nSpUo1w7eRIgLNcNe+jMwBbNIWiGlpfI5QBinSHgx3bQfH+kR8KyCulfE5QhmkRooBQ19S2p6w+r/YNINMEPUTZdFIWAHMcLeygI+hmAP4/PPP4/Lly2jevLmwC8igQYMQFRWFTZs2KdUM38YbZuzMYMeJB3Yq8FcUWQO7uyLF4n6Szh/TgiFl8YYUMHP2xPaE+omyeMO446MoJgS9efNmNGnSBMeOHUNkpMFgpKam4uzZs0o1w7exJ7WneGRHn56Jb63fB3gzzdiVRpbBdlPKRhjYWxukSKifKIu6nP8tKVLspsiOuJ+wCQ31E2WRNaGkFLCSKOYA1tTUICMjA+HhxnIBlZWVUKvVSjXDt/GKGbsossNqRmjGrize0E/EkWLBAaR+oijeECkW+km6oT+TA6gs3mBPfBTFHMDWrVvjn3/+wZo1awAAarUa77//PvLz89G1a1elmuHbSF0FLL7GbTP2VvoIIEgKRmm8abFQfGuDCDUN7MoiVS5IfI277El8a0OkkvqJsqjl1BSTA6gkitUAPvroo+A4Dg888ABUKhVyc3MxdepUqFQqPPTQQ0o1w7eRswjEHV9EsQRMnElxP0nBKIesgZ31EwX13cQSMOJ+QlIwyiJlGziGO+yJWAJG3E9ICkZZpGwDx6AUsKIo5gD++9//xhNPPAEA4DhOWAjy2GOP4d///rdSzfBtWM2OJIPtBj0mFukLiwdCY/hVwCp/XgqmslC5dvg6ciKAwmpxBftJ6TleAiYoAohIACKTSArGHQj2RIqqgBvsiVgCJjoFCIszZBWun1WuHb6OrBSwm1QFfBTFHECVSoUPP/wQeXl5+Pbbb/Htt9/i9OnTWLZsmVJNIIQUsIcWbQv1f3q5Bv9AIDaVP6Y6QOXw9NoucV2XSsXvQsFWAlN6Tzk0Hm5PxBIwfv58X2G2hepFlUNW6RGlgJVEsRrAe++9Fw8++CCysrKQlpam1MsSYmSlgN0QihfX/zHiWvHnS84A6YOVa4svY1cKWMlIsYV+UnyUHEAlkZUCdkNkx1I/ufQX9RMlkRMpJgdQURSLAH799dcYPXo0WrZsiZdeegnHjx9X6qUJhqfrMV0TFWwzaMauPHaVCrgxUgyQxps7kFXb5c6JgqifMNtC/UQ5ZEWKqQZQSRRzAAcOHAgAuHjxIt544w106tQJ/fv3x/Lly1FaWqpUM3wbTy/aFkvAMGiXB+Xx9H5yzUw/oV0elMdrIsXphnNkT5RHjj1xR62oD6OYA7hjxw4UFBTgnXfeQZ8+fcBxHPbt24cpU6YgOTlZqWb4Nhp7ivvdnLIRZuxksBXDHnkPd6T2KFLsXmTJBbnBnrCJgtl+QvZEMTxdfcKHUcwBBIDmzZtj2rRp2LNnD3766SckJiaC4ziXCEHPnj0bKpXK6Kd9+/bC47W1tZgyZQri4+MRERGB8ePHo6ioyOnt8Bg4zrOLcU0lYBjiGbt+5TjhYjx5EYipBAyDHZcWkBSMUniywK+pBAxDLAVTV6NMW3wdu0qPKAWsBIo6gKdOncK8efPQpUsX3HrrrYLDFREhoWPYQadOnXD58mXh588//xQemzZtGn7++Wd899132LZtGy5duoQ77rjDJe3wCOpqDDsmeOJWcKYSMAyxFEzFZWXa4svotECdfpBmO7FYQ+maHSYBExjOS8AwmBSMrp6kYJTCkwd2JgHjF8hLwDDEUjAkMK8MwoTSA+2Jj6PYKuBevXohNzcXAK8DqFKpMHz4cDzwwAMYP368S14zICAAiYmJDc6XlZVhxYoV+PLLLzFixAgAwKpVq9ChQwfs2bMH/fv3d0l73IrwhVIZjLE1lNYBNFf/B/BSMDEt+dn8tTwgisoFXIrY8HpipFhc/6dSGc4zKRi2Elic9iOcT72Gd7AAmaoCStkTfYqXScAwVCr9SuC/eZuT0FGZ9vgqHCcqPfJAe+LjKBYB/Pvvv8FxHDIyMjBv3jycPXsWmzdvxn333YewsDCXvOapU6eQnJyMVq1aYdKkSSgo4CMDOTk5qKurQ1ZWlnBt+/bt0bJlS+zevdvi/dRqNcrLy41+vAbxbF08cFpC6Rm7uRV7DCrwVw42W/cLAAKCbV+vtMEW6v9aNXyMCvyVw2iiIFMHUIlSDnP1fwyqA1SOumo+Yg/Im1BqNfwkg3ApikUAH3/8cTzwwAOKRdf69euH1atXo127drh8+TLmzJmDwYMH48iRIygsLERQUBBiYmKMnpOQkIDCQss7TixYsABz5sxxcctdhBzJBsANkR0zC0AYwsBOBf4uR/ZEQd+f6qr47fr8XDyntBQpFp8jiQ/Xw6SCAkIBfwnDCLMnnA6orwUCQ13XNsD8gjIG9RPlUNuZeQJ4mxIQ5JJmETyKOYBK7/gxZswY4bhr167o168fUlNT8e233yI01D7jM2PGDEyfPl34u7y8HCkpKVae4UHIKdgGDF9ErZovqvcPdE27GOZWdjJoxq4cGhn1OoDxhKKuSnr/sheKFHsGchYKAcaDv6bKvQ4g9RPlEI87UiaU/oGAfzA/7qgrgdBY17bPx3GpA9iqVSv07NkT//vf/9CqlZkvoh6VSoW8PNfOxmJiYtC2bVucPn0aI0eOhEajQWlpqVEUsKioyGzNICM4OBjBwRLSYp6InBXAptdpqowXZrgCa5EdkoJRDrmR4oAQQOXHR3Y0CjiAVlN7FClWDDlSQQBfhxcYxqcENZVAeBPXtQ2gCaWnIGehECMoHKhRUx2gArjUATx79qzgUJ09e9bidSopMwMHqaysRF5eHu677z706tULgYGByM7OFhagnDhxAgUFBcjMzHR5W9yCHC0mgA+9+wUCujr+ua50ANWV5iVgGKZSMAr0F59F7sCuUvHXqstdb7AtScAw2MDOpGBcHbX2ZeSI+zKCwnkH0NXKAjqdYYWvNXvCpGBcHY30ZeROKAHentSUkAOoAC51AF999VW0aNECADBr1ixFHD3Gc889h3HjxiE1NRWXLl3Cq6++Cn9/f0ycOBHR0dF4+OGHMX36dMTFxSEqKgrPPPMMMjMzG+cKYEC0vZfE1B7AG+zaUtd/EdlM3FQChmEqBUMrgV2HHHFfRlC43gF08cBuSQKGEZnERyTra3knkFYCuw452wUygsKBqiuutyflF/gUoqkEDINJwdSW8Y4irQR2HXJLjwDSAlQQlzuAjNmzZ7vypRpw4cIFTJw4EdeuXUPTpk0xaNAg7NmzB02bNgUALFq0CH5+fhg/fjzUajVGjRqFDz/8UNE2KorcFDC7trbU9V9Ea+lfwFgKpuQMOYCuRG5tF6DcgiFLEjAMPz/+seKj/MBODqDrkBspFl/rcntiQQKGYSQFc4YcQFdiz7hD28EphksdwO3bt0u+dsiQIU597a+//trq4yEhIfjggw/wwQcfOPV1PRa5KWDxtUpFAM0V9jPiWxu0ANMGubY9vgzT7JIi7cFQajs4axIwDMEBzAOQZfk6wjHsmigoZE+s1Yky4lobtAAJ12FvpBggB1ABXOoADhs2TFLaV6VSob6+3pVNIewtxgUUMNhWVuwxSONNGeyt2QHcHykWP0b9xLXYWyoAKDihpH7idhyKFFc4vz2EES6XgeEkiH5KuYZwEEcMtmKRHRszdoBm7K7G3uJ+wDMixaTxpgwaRyaUCtkTaw6goCxA/cSleLI9IVzrAObn016LHoNdxbhKR3bSLV8jzNipT7kUu2bsbqgBtARpvCmDXQO7wjWAkiKAZE9cij2lR+QAKoZLHcDU1FSb11y8eBGffvopXnrpJVc2hbBrEYgCX0RbEjAM8cBOUjCuw64UsAKRHbEEjNVIsb4PlZ4DtPXSdqkg5OPQ6k4X2hOxBIyUjEL5BZKCcSWsBtATI8WEcnsBi6mtrcWXX36JG2+8EWlpaZg1a5Y7muFbeGrNDputh8ZZV31nUjB11bwUDOEahH4iRy5IgeJ+WxIwjMhkXgpGV88/h3ANnlpTXH7RIAET1cLydWFxQHA0f0xRQNchRIrl2BP9GEURQJejqAO4a9cuPPbYY0hKSsJ9992H7OxsaLVaqgFUArtC8QqkbKTU/wEGKRjxcwjnY08KOFjBfmJJAobh5wfE6ksJaGB3HfasAlakn+jLBGLTrEd/VSrDanKyJ66DUsAejcsdwIsXL2L+/Plo164dBg8ejBUrVqCsrExw+hYvXoxz52im7nLUDsh7KGGwrRX2M6hw2/V4qg6gIO1hpUyAEU8LhlyOp8oFSZ1QArSwTAkciRS7evEh4fpVwKmpqeA4TnD4unbtivvuuw+zZ89GdXU1/v3vf7u6CQTgYG2XAilga/V/DJJucD12GWyFI4C2oH7iejy1pETKQiEG9RPX45BeJDmArsblEUCdTgcA6NOnD3Jzc5Gbm4tnn30WAQFUnK0onlq0fY1m7B6Fxp6aHSUmCjIixSQF43o8NVJsbQ9gUyij4Ho09tQUUwpYKRSrATxw4ADGjBmD559/HocOHVLqZQmAX0Gp1fDHHhfZkSABwyDpBtfCcZ5bsyMnskNSMK7HU7eCkyIWziB74npoKziPxuUO4MqVK4Vt3i5fvox33nkHPXr0QFlZGQDg+PHjrm4CITa4nrRqT6oEDMNUCoZwLnXV/EpbwLNqdqRKwDBMpWAI56LT8n0F8KzIjlQJGIapFAzhfDy19IgAoIADOHnyZGzZsgV5eXmYNWsW0tLSjFb9durUCR070mbcLoVpMQWGydNFc/VMTKoEDMNICqbQNW3yZZixhspghKUgyDa4yAGUKgHDEEvBlBW4pk2+jNBP4Fl7AUuVgGGIpWCun3VNm3yZejWgq+OP7RIMp63gXI1iKeC0tDTMnj0beXl52LJlC+6//36EhYWB4zicOHFCqWb4JvaE4cXXuyqyI2fFHmAiBUN1O05HvLuDHKFtV8/YpUrAMMRSMNcoDex0mKPvFwgEBEt/nqtTwKyf2JKAYYilYKgO0PmoPTTzRAi4RQh66NChWL16NQoLC41SxISLsKeuC3C9Iruceh0GrdxzHfbs7wq43mBfk1EnyqA6QNdhzwIQwNieuKKEg+yJZ8HsSWAY4Ocv/Xmsn2g1fPkH4TLc4gAywsPDhRQx4ULskfYAFIzsSIwAArRyz5XYU68DGPpVXRVfh+Vs5EaKAYOzSJFi52OPBAxgsCecDqivdW6bAJFWpJx+QsoCLsPucUd0fR1FAV2JWx1AQiHs2d4LMHwRtWpAW+fcNgGG9BzN2D0DtR1SQYBxvSBbHOBM7IrsUATQZdgjAg0Y9xNXTCrlSMAwyJ64DnvtiX8g4K8vLaA0sEshB9BbUVcAX9wF/PW57WvtTgGLrneJwWaRHTcP7JcPAitHA3kORqJ1OuDHKcDPUz1nlbKmGvhqIrB/hYRr7awVDQwFVH7G93Am9kSKXaEFWHQUWDkGOPm7Y/fhOGD9NGDdFH5FrSdQrwa++RewZ5nta+1NAfv58+lAwEX9xI6JgpBRcKI9uXoKWHUzcOxnx+/124vA2sdcMwG3B20d8N2DwJ+LbV9r77gDUB2gQpAD6K2c+p3/2TzbdtrN3kUgAUF8oTfgfIOtrgQq9St57THYzpKC0Wl5p61gN7DjHcfudflv4O81QM4q4PD/HG+bMzizFTjxK7B5jm1JFHtTeyqV61Z41muAUv1KXjmpPXats6RgdDrgp2eAgl3A9rccu1fxMeDASiB3DZD7heNtcwbndvIOy+Y5QJ2N9Kw9ovIMV0kGyZWAYThbCobj+AnguT+BbW84dq+SM8DeZcChb4ADqxxvmzO4sB/4Zy2w5XXb/0OmPiE3Ugwos20gQQ6g11J2gf9dfZV3PKxh78AOuG4mdl1vrKVKwDCcLQWT+wVQeJg/LthjLHEhl1ObDcebX+Wjb+6G9RN1GXBhn/VrndJPnGywSwvkScAwnC0Fc+R/wMUD/PHFA0B1if33Or3JcJz9GlBb7ljbnAHrJ/U1vDNoDXtruwDX2RO5EjAMZ0vBHPuZd/4A3q44YqPE9mTrfMf6nLNg/USrAfK3W7/W3hQw4PoFiAQAcgC9l7KLhmOxoTCHxs7ifsB10g32FGwDzpWCqS3nB2AAgIrXrDqzzf77CQO7ih+Qdr3vWPucQfkFw/GpTZavA+xPAQOuG9jFaT050jTOlILRVAGbXtX/oeId0rw/7L/fKVE/qSoG/nzXsfY5A7E9OW3Dnti7WAhwnT2RKwHDcKYUTL0a+P0VdmP+l63P0hpie1JzHdj2piOtcw5lIntyWqI9oRSwx0IOoLdSLjbYNr6IQgrYgyKA9tTrMJxVuP3nu/wAHNca6P0gf87WZ2mJ6hLggj5CdOM8/vfOxcYDqzsos6OfeJLBtkcChuGsfrLrfaDiEhDdEuj7GH/O3oFdXcFHmgFg1Ov8790fuF+I2K6JAtkTI/Ys40sOIpOAzCn8OVufpSXqaoD8Hfwx6yf7PwGunHSsjY4iHndObbJehmNv6RFA28EpBDmA3grbGgvgHY+qa5avdWjG7iqDbUdhP8MZUjDXz/IDL8Ab2HY38cenNttXW5j3BwAOaNaJN/4p/fk0dfZc+9voDMQz9sLDQPlly9c6lNpzcWRHbqQYMER2HIkUl100FLzfOBfoMJY/PrXJPsmbM9v4SHNcK6D/U0CrYXw6bdMs+9voDMT95Nop6/vjemJJiSP9xBlSMJXFwPa3+eMbXgU63sYfn9liXw3q2Z18Oj6qOd9P2o7myxmECKObEPeTsvPAFSubOLAaQE+KFBNGkAPorbDITmA4AM56SsqRom1XzcTskYBhOGPGvmkWP/C2GsYb17RBfM1Y+QXgih37U7OZfkYWn1YavYD/+9DXwIUc+9vpKOXifgLrkSuhVECmXBDguqJtd0d2sufwA3HLTH5QT+nPv9fqq8DlXPn3Y1HYNiP5fjJqPr+C+uiP/KDvLspk9BOHIsWuKilxsz35Yx7//UnuAXS9G2jek69vri3jF07IRegnenty4zzALwA4tdGxtLKjNOgnViKcwrhjjz2hFLASkAPojdTV8gMQAHQZz/+29kV0JBQvDOxO3pfRHgkYhqNSMGf/5AdclZ9+AFbxUiZpg/jHT8mU+dDpDEa5zUj+d/OeQLd7+eMNL7pHFkanBcov8cdy+kljiRTHORgpPr+fX4EJvUOvUvEr41sN4x+XOxBznGiioO8nCZ2AXpP54w0vukcWhuMMEwXWT6ylLh2qFXVxpNgeB9BRKZjLh4C/PuOPRy/k60/9/IHWI/hzcu0J0LCfNMkwlB9sfNk5K9vtgZUKSOknDo07tAhECcgB9EaYsQ4IBbpM4I9PZ1tOSWk8LGVjrwQMQzxjl+tY6bTAhhn8ca8H+QGYwZw3uXU7l//mHfKgSKBlf8P5G2bxM+UL+4Aj38u7pzOoLAI4LR856P4v/lzeVsuDh1MWgTjRYIslYBzpJ/ZIwXAc75ABQPd7+cgOo00W/1tuPyk+xn93A0IMkw0AGP4yvxK18BCQ+6W8ezqDmusGAe+eD/C/87dbloPxtBSwTmdQFXCkn9gjBcNxenvCAZ3HG3//mfMmt6645Awf+fYLANKHGs4PfZ6PKl45zktNKY2mmu8rgGHSUrDbctTfKaVH5AC6EnIAvRFWhxHdnDc4QZHW5WA8zWDbKwHDcEQKJvdLfqANjgaGv2T8GDPYcuVg2CrsVkP5VcqMqCRg8DT+eJMbZGFYuiYyCWjRm/+8rcnBONRPXFAqIEjAhAGRifKfH9XcfimYw3rZl8BwYMRM48dYP5ErB8McgbRBfMSZEd6EH9wBvmbU2dF2WzB7EtYEaN6L7y/W5GA8zZ5UXOK3lvMLBKJT5D8/LN5+KZjj63nZl4AQIGu28WOtb+B/y5WDYfakZSYQIkqfhsYabNaW+QZnTClY4CEoAkjuya+4tiYH45BeJC0CUQJyAL0R9kWMbsE7HK30s0RLcjDOSAE7cyZ2zYG6LoBPw8XoDb2cNLC6wrAoY+jz/MArJr41Lx0iVw7mtEm6Rkzm0/ygVH4B2L1U+j2dAVsoFN3CJCVlISLhFN0uJxpseyVgGGIpGDn9RFPN6zgCvAMflWT8eHQLoGkH+XIw7HNvY6af9H2Mf59VxcAOhWVhxPZEpQLa6B0XSyluT0sBM3sSmypPAoahUon2jpbRT8SyLwOeMchTMSKaGiLHcsoFxPV/pvR6EGjaHqgpAbY5KEguF7E9UakM/dhShFNYVEYOoKdCDqA3wiI7TPDUVqrBIT0mF3wRHVmxx7Bn5d4OJvvSylBPY4rctI1Y/sXcwB4YCoycwx//uchQk6cEbGCPas7/ttlPHFgFHOyCgd2Rui6GsCWcjIF91/v8ZxfdknfgzZGhH5ylDuxi+RdzE4WAIOBGN8nCCBkFvT2xVQrhSK2oKxaVOVInyrBHWWDvcv7/FJEIDJxq/hq5ZSV1tQb5F3P9xD+Ar1sGgH0fAVdPS2+vo5RZsCeWlBNIB9DjIQfQGykXpYABg5G5YCYlpa031Pc4pNvlzIGdRXacYLClztjFsi83vs4PuOZoY8OomSLIv3Q0/D9M6XQHkNJPeVkYZrBZu6ylpDjO8wR+7RULFxMvqheVQtlFXr8R4B13capWjBD92CxNDobJv8SmW34/7cbwNV9atUh4WgFMJwqthvElFtdOmXdEhX7iwOpOZ64WL3FCP5G7sKyy2BCBy3rV8neGOUlS5WDO/cmn3yOTeZtijjY3ABmj9LIwL0trrzMoN7EnaYMB/2C+vOKqGX1CZ8hKKV0O4WP4hAO4cOFCqFQqTJ06VThXW1uLKVOmID4+HhERERg/fjyKiorc10g5mM7EopvrjYUZORjxgOwpM7ESBwq2GUJkR+KMfdMsfmBNH8oPtJZIG8QbNalyMKespGsYYlmYg18BFxWShWETBRYptpaSqlfzAwrgQSlgJ0YApUaKs+fwjnpKf6DT7Zava5nJD1JVV4DCg7bva61MgMH6icoPOLoOOLdLWpsdpcxkQhkaw09YgIaRK47zvB1jnGlPpPYTI9mXeyxf17wXX7snVQ6GlfEwOSlLjHqdXyRycgO/AFAJykzsSVAYkDaQPzYX4fS0khKiAY3eAdy/fz8++ugjdO3a1ej8tGnT8PPPP+O7777Dtm3bcOnSJdxxxx1uaqVMTFM2gCgcb/JFZMbaPwgICJb/Wi6N7DhisGXM2M/uNMi+MDkPSwSFAemD+WNbaRux/Iu1gR3gBwJBFmaGMrIwpgM7AGTcyP+21E8ADxrYnRApliMFc+GAXvYFtvuJWA7G1laMHCca2G+0fm0DWRg7xKblYjqhBETlAibvTVMFQN93PS1S7IgDKEcKxpzsiyX8/A2RdyllJWKdSGu4QxbG3LgjZExMpG609XwkEyAH0INp1A5gZWUlJk2ahE8++QSxsYbVpmVlZVixYgXeffddjBgxAr169cKqVauwa9cu7Nmzx40tloi4aJthKSXlyAIQwPlfREclYBhSpWB0WoOcR6/JxrIvlrBV3My4nKuXf4ngI0a2uGEWv6L1/F5lZGHKrPQT05QUS7UEhvGDllycXSrgqAQMQ6oUjFj2pdu9vI6jLQQ5GBs6b1eO89FYU/kXSwx/mU+vXj4IHFRAFkYoKRGtoGUOoKkcDPv/qvz4viIXZ9sTRyVgGFKlYDgO2PgSAI4v7Wgp4XtvaXJuSskZ4NppPrLHJhfWGPo8H128cgz4a7Xt6x3FNAUMiJQTTORgNKLUrUOLhcgBdCV2LJnyHqZMmYKbb74ZWVlZmDdvnnA+JycHdXV1yMoypO3at2+Pli1bYvfu3ejf3/yXWq1WQ61WC3+Xl5e7rvGWqC03bLEjnrEbycHkGgYwRyQbAOcbbEclYBhiKZhVN1l0WioryxFx9RB0QZHwGy6xXiZjJLDhBeDcbv7zs/TZsehIq2GWawrFRCUBg6YDW+bxNV7tb7ZcY+Yo9Wp+wQtgSNkAhh0Kakr4lFRqJn/e4X7iZIPtqAQMI6o5n9LXqoHVNxvL9IiorKpExJW/oQsIhd8NErdlM5WDCYszfx0b+E3lXyzBZGF+f4WvGe14m33RNinodIbtAcUDe0JnXg6m4jJQsMuwgly8stOeldnOtieOSsAwmBSMugz49BaL2ZKq6mqEF+eA8w+Gii3usoVQe3sIqCgCIhPMX8eixCn9jeVfLBEay08Wfn0O+ON1oPOdfPreFXBcw8WHABDfBohJ5SdY+duB9votNZkz6B8szTaa4opFZUQDGm0E8Ouvv8Zff/2FBQsWNHissLAQQUFBiImJMTqfkJCAwkLLek0LFixAdHS08JOS4oDBsRc2CwuJNh4UxHIw4rSNIyLQ4uc564vIhG4tFThLJSAISOzMHxfsAs7uMPsTcZWvzzqQ9lhD2RdLiOVgLGlcAdLq/0wZ8DRf4F1+wbW1O2y1cUCIsWMiloMRRzgdqesSP89Zxf25X/C/m3W0z9Fg+PkByd354/N7LPeTK7yG5oGUBxvKvlhCqhyM1LSemL6P8wNrZRFf5+Uqqor5fq7y41ezMsRyMOIUtyMLhQDDQjRnOYDMnjRtZ58EDEOlMvSTC/ss9pPwYr5+d1/ypIayL5aQKgcj1InKsCe9HgTiM/gJ3fH10p8nl9pSoE7/P4tKNpxXqcyrCziyAhigFLBCNMoI4Pnz5/Gf//wHmzZtQkhIiNPuO2PGDEyfPl34u7y8XHkn0LQQV0zGSN4InNpkEJZ1VgrYGQP71VPAvo/548HTrV8rhXu/1RfKW04BL/jtOI5fV6F99G3oK+feGSP5tp7axEfqTKku4SM/7FqpBIYCHcbxEg6nNwEdxspplXSEftK8oQOVMRI48j/+vbFolyPSHoBzDbZ4xfbgZx2/391r+O3/rPSTt38/gSNXdUiPuVVmP8niU3CnNwNd7mz4uLqCjyQD8vpJQBDQ6TZg53v8/8ncvZ0B6yeRSQ0dqDYjgb/X6Ad2vfSIwxMF1k8q+KiSI859+SVeWglwjj25cyXv6HGW6y7fyz6Fg0V1SIgei35y7t1mJHDpb/6z7DGp4eNi+Rc5EwX/AKDzHcC2N/h+0uNfclolHUEsPJ6vkxbTZiSw//8MygkqlfPGHa0a0NZZjNwTjtEoHcCcnBwUFxejZ09DHY9Wq8X27duxdOlSbNy4ERqNBqWlpUZRwKKiIiQmWk43BQcHIzjYjoUUzsRcIS6jjZmUlMMzdicO7L+/wq80bTvaEF1whMhE3vhZ4X8/bMI1nQaxFWqr1zWgjd4BPL3Z/ECV9wc/UDTtYP5/YY2MkbwDeGqT44OgJczViTLMpaRYWYE9UkGAwdDXVfFpRWuF8bbY9Kq0FdtSiWhms5/8sP4PXNTVILRCI+/ebUbyuoGs9tb0fedvty3/Yu3eO9+zfG9nYM2eMDmYqyeB6+d4oWVnlZRwOj5160gJRPZc0YptJyzgC29ifeU3gB83bMUZXRWGV9TJu3fGSGD7m7zd0NY3dLbF8i9S6pTFtBnJO4B5W8zf2xmYWyjESDeRg2nazmBP7O0ngeGGY02lY+VChEUaZQr4hhtuwOHDh5Gbmyv89O7dG5MmTRKOAwMDkZ1tSMGdOHECBQUFyMzMdGPLJWCuEJfB5GDEKSlHtuMBDAM7m4nZy+lsPpXlFwDcOM/29U5AXa/FtSp+QC8st7CvqSWYHEzZeeDKiYaPS139a+neASH8/7L4mPznS8HawG4uJeVwPxEZbKY7aQ9nd/ISKFJWbDsJnY5Dkb5/yO4ntuRgWJmAPf1EyjaPjmKqAShGLAfD0nvOihQDjk0qL+TwkkqAYv2E4zihfxSWy5xQiuVgWOZAjFT5F7P37ml7m0dHKbdiT4LCG8rBOGpPAoJ45QqA0sAupFE6gJGRkejcubPRT3h4OOLj49G5c2dER0fj4YcfxvTp07Flyxbk5OTgwQcfRGZmpsUFIB6DtZkY0HCjekfEOAHnGGxtPS9VAPDSBU0y7LuPTK6Ion7Fcg12UJhhxabpamA58i/mCAy1fG9nYW1gBxrKNzg6sAeG8k4bYH8/sWfFthMoqdagXsenh2X3k4AgPlIJNJSD4ThDP5GT1mNI2ebRUUzFwk3JMLEnjuwWA/A1qAH6qJ+9dcX2rNh2ApXqelRrtACAYrkTBVtbMdpTJyr13s7A5rhjUgfoaApY/FxyAF1Go3QApbBo0SKMHTsW48ePx5AhQ5CYmIi1a9e6u1m2sTYTA4z1u3Q6x1M2AcH8CjvA/i9iziq+Tio01lCbqABFIiNdWF4LTq72niX5hsKDfMRHqvyLOeRuESUXmwO7iRyMoxMFlcpxjbeDX/Fp6eAofnWjQhSWGfpJUXktdDq5/YRtC2fyv7xygo8g+wdLk38xe2+ZWxPKhe3vaq6mGDD0UyYH46g9ARzfDu7I93ykKzDMUMOqAGJ7cq1KA3W9Vt4NLMlLleTLk38xh6v7ibXMk/j1z+3inT9HS48A12hGEkb4jAO4detWLF68WPg7JCQEH3zwAUpKSlBVVYW1a9darf/zGKyl9gDeIQmKMMjBOBqKBxzTeKu5DmzRF5APf1nRWo4iUTSnWqNFpVqmWGobE6PGYNEYqfIv5hD0s/a4Zrsja4uFgIY7FLi7n6grDNvkDX1e+optJ1BcYRjY63UcSqrtqAME+M9RvBUjG4zTBjUsnJd97wMNt3l0BrYG9sQu/Orgump+tb0juzswHFlYVldj2CZv0HTpK7adQJFJdPiK7Lpife3t5YN87S2DRYmlyr+Yw9o2j85AGHcsLHpkcjBaDT9ZcFR9AqCVwArgMw5go4DjDPIelkLx4h0KTm92cijeDoO97S1eoqBpe16yQEHEkR3AeAYvifjWQGyaXg5mm+E8S5vKkX8xe2+91MyZbbavl4utSLGpHIwzIjuOGOw/F/GSJ3GteAkUBSksU5v8LbOfxKTw/ZvT8RFVBusn9pQJMKxt8+gMzImFi1GpRGUlmx1fBSx+rj32ZNdSvm9Hp/CSSgrisD2JaAYkdeePxXIwQp2oA/ZEqtSMvYhVBcxhKgcjjDtumigQkiAH0JuovsavnAOMtZhMEacuHdVjAuwf2K+e5le7AsCo+a5ZnWYFUwNtOtDbRKVquHWavfIv5mD3dnbaRl3JR/YAy5Ed8euL+4lDA7ud/eT6OX5gB/gFQvZGVe3EdOGH7IEdEH3n9IOvkfyLje3fJN/byf2kXsM73YDlSLH49cUDuzvsSfll4M93+eORc1wnom4B034i254ADb/zdbUGrVF76v/M3dvZ/USnMwQerNmTNqLvgFNSwBQBdDXkAHoTbBYW3sz6vr5iOZjSc/yxM2Zicr+ITPYlY5RzZF9kYjqQ2zWwi7fY4zjH5F9METsNztwbmKX1gqOtR/TEcjDXTuuf44bIzqZZetmXIUC7m+x/fTsxLeg3TfVJQlzfpdM5Jv9i8d6bnbs3cMUlABxfo2gt5S6Wgyk6wp8LtjNVCdhvT7Ln6GVf+jlH9kUmDfuJAxMFJgfjiPyLKZa2eXSUqit8X4aK14u0RPpgfuVuWQFw6S/+nLsixYQkyAH0JmzV/zHEcjCFh/lzSqf28v4ATv6mqOyLKWzGHhLoZ/S3LEzlYITVvw6ka8T3DgjhU1pXjjt+PwYr7Lc2WweM00aX9RImSveTc7sMsi+jlJHzMMUp/cRUDsYR+ZcG9zbZ5tFZCCs7k61/7mI5GPb6TokUyxjYLyov+2KKaT+xywE0lYNhEeM2Nzj+npgcDKvrdRZGYuFWBJmDwoFUvRzMJb1skTsXCxE2IQfQm7BVsC3GtD7NkciO3O3gxLIvfR4Fmra1/7UdgEl6dEqO1v9th8EWy8Gc+t0xWQ9TxHIwzkzb2KrrEmP6PhyKFMvcDk6nM8h59LzfsLWfwhQ5o5+YysE4s59Y2ubRUayJhZtiOuFxKFIsczs4jgM2zOCPu03knSg3YNpP7HIATSVbTjtxomBpm0dHEeqJJYw7pu+DFoF4NOQAehO2VnaKMf0iOmPGLnVg/+tToPio4rIvYsSirV1b8AbbrsgOYPgs9ywzyL+0dJJguCVpCEewpQEopoHBVjCyc/ArPvIYHAUMf8X+13WQIqf1E72TdGCl4/IvDe7tgjpAqRkFwMxEQcF+8s9a4PxexWVfTHFaP2GfZe6Xjsu/mOKSfiLDnrikn7hAJYEAQA6gdyEnAsjkYBhKzcRqSoEtr/PHw17it6NzA2LRVmaw7artAgxGrUJfCJ0+1HkLFQT9rN3Ok4OxpQEohqWkGA71ExkpG3UlX9MFAEP+y6ej3YC6XosS/W4x3VrEAHBiP3FE/sXSvdk2j87A1spOMUwOhqFUDaCR7Ms064vfXIhOx6FYL/vC+ols0XAGq4dm/SSlHxAS7WAL9Rht8+gkORg5keImGUBMS8PfStkTwi7IAfQm5MzYxXIwgJO+iBJm7Nvf4lcrN20P9H7I/td0EDZbjwwOQHqTCKNzsmFyMAxn1P8Z3dvJcjDlMiLF4rQRoNwq4D/f5VegxqYD/ZSVfRHDBvGgAD+0TeC/I3b3EyYHw3BGWo9hbptHR5EzoRTLwQDKLRbatZSPpka1AAY8Y/9rOsjVKjW0Og4qFdC5Oe/82iUuDxjLwQCOyUk1uLcL5GDkjDsqlXEUkFYBezTkAHoTQihe4upTsWFRYmC/lgfsZbIvrysu+yKGSTQkRIcgMSoEAFBcwRtx2ZgaNWfUdYlxtoq/HIMNmBhsBSLFDWRfrKxodzHM2UuICkZSNN9PSuzZ5YEh/s45u5+wezttYGcOoAVxX1MyFLYnbpZ9EVOktydNIoLRPIaP6lZrtKiQKy7PEE8OnDlRAJy/y5CcSDFg/H5oKziPhhxAb0GnBSou88dSZuwArwul8ufTC44YT6kG+/dX+EhWm5HOndXaARvYE6NC0CQiCH4qQKvjcK3KzrRN+5v534ld+EiPMxHrZzkqB8Nx8lLAAP+/8gsEAsOViRRvfpWXfUkbbPhc3QRL9yZGhSAmLBBBAbxJtDu9x95Pk7aOy7+YYrrNo6OUyxzYWw3nV637Bzu2o49Ue5I9l5d9adEX6Dze/tdzAmJ7Ehrkj6gQfnJr14IhwCB3FNMSSHDy4ifTbR4dRU6kGODlnALD+dpGR3b0IRkYl+O+EA0hj4pCgNPyX6qIBGnPiW4O3PcD7/w5IjEg5YuYtwU48SvvcI6ab/9rOQlWoN0sKhgB/n5oEhGM4go1isrUaBYZIv+GrYcDE782TvE5CyY1w+RgmnWw/14113ldMUD6wB7RFLj/R76PWJN5sEWwhH5ybjfwzw8AVG6T8xBj6CchUKlUSIgKxvmSGhSV1yIlzo76vdQBwL3f8c6fs98bq+tlUjMs1WcPmiq+rwDSB/bQGL6faDWO1TayfmJtUdnFv4CDX/LHoxd6TD9JiArW/w5BeW0lCsvUaNPMjklT857Av74Hols6/72xut6a67wcTKoDC9a0dYZaQqmZp6Bw4IGf+JpmR2obKQXscigC6C2wWVhkEl+3JZVWQ4GUvo69ti0HUFsPbHyJP+7rPtkXMcWiGTsAJOrTe3bXdwFAuzFAXLrDbWuAkdSMg2kbQSy8qbzUatpA3nlxBFsGu4HsSxfHXs8JNOgnUayf2BkBBIC2Nzo/+gcY1/WecjANzKLEQZHyBumW/fkIjyPYsidi2Zeu9wAt3CP7IqZYcACdaE/aZLnGVjpTDqbiMnix8CDepkilRW9+0uwItBWcyyEH0FsQxH0d3H3CHmwN7H9/JpJ9eUG5dlmh0MRgs6if3dINrsZZdYBy63WciS15j0Nf80LCQZHACPfJvogxjew0i/LwfiLUATrYT+RouzkbW/bknx+A83t42ZesV5VrlxW8zp44qw5QsCfJgJ/C7gLVALoccgC9BTlaTM7GmsGuLQP+0O/04UbZF1NYBMcwY+cHeLtrdlwNM9iOysHIkWxwNtYMtroS2KyXfRn6X34lpAdQZBrZYQuGPLWfsInChf2OycHIEQt3NtbsiYfIvpgirhUFvMGeiOVgiuy/j9yFh87Enh1jCFmQA+gtyC3EdSbWUjbb3uRlX5q0A3o/qGy7rFBkWrPj6TN2sRwM2xzeHuSuAHYm1gb2PxcBlYW8nE6/JxRtljVMJwqsv3hsP4lu4Rw5GLdGiq1MFHYv5feSjWoBZD6tbLusUCSqKQYM/cVj+0lEM+fIwbgzUkxbwbkccgC9BTm7gDgbS19EI9mX+Y4tIHAiWpFoK6vVSYhmBtuB2i5XolI5R8Vfzi4gzsbSVnClBcCu9/ljN8u+iOE4DoVlxjWAwsBe5qEDO+AcOZhyd04URBNK8ar38svAjkX88cg5zhPRdgLM0RPsSZSH2xPAObsMeUKkWKvmF6MQToccQG/BEyI7pgP77zMNsi/OFEd2kGsi0damEcYzdo9N2QAig+2AHIxcCRhnwgb2uipjmZJNYtmXscq3ywIV6nrU1PF6fw1SwBUePLA7Qw7GE0pKOC1QL/o+/vEa33da9HG77IuY2jotSqt5B8R0ouDR9oT1k7w/7JeDceeEMjDccExRQJdADqC34AkpYPFM7MxW4MQvetmX15VvkxXEoq0B/nwXT/T0lA1gkIMpO8/LwdiDMFFwslahFIJEBruumv9dsIffyxUqPkrsZjkPMUX6KF9USABCg/iV9eIIoF27PCiBqRyMPbi1VtTMwH7pbyD3C/7YA2RfxIh3i4kO5bMcDovLKwGTg6kt42tG7cGdiw8DgvjVxwDVAboIcgC9gXo1b+wB9xbjArzB1mmBDXrZlz6PAE3bKd8mKxSZSHuIj0ur61BbZ+cuD67GUTkYndawv6hbZuyhAPQDt6bKRPblPiCpq/JtsoJQ2B9t6CfMAaypc2CXB1fjqByMkVi4G+yJnz8QoBemZ2lgQfblbl5CxIMoqjDYE5XeMXWKuLyrcYYcjDsjxQBpAboYcgC9ATZbDwhxzypb/yBegBrgv4h/fQYU/wOExADDXlS+PTYwlfYAgKjQAAQ7usuDEjgiB1NZDOjq+ahsZKJz2yUFlcq4vuvQN3xkJygSGDFT+fbYwFTaA4DRLg9FXlEHaEc/qbnOp1oB962yFQ/sR9cBBbt52ZcbPEP2RQyrBxXbEyYuDxgyDh6JI3IwmmqgRr/S3B0TBYC3HQBFAF0EOYDegLj+zx2pEZXKYLArLhtkX4Z7juyLGFPRVgBQqVQG8dYKTx7YHZCDsVcs3JmwflJZDGTrZV+GPOcxsi9iTCVgGAaRXw8e2B2Rg2H9JCzeffvrsn5SfQ34fRZ/PHCqe0pcbGC7n3iyPXFADqZcn00IinBsRw9HoAigSyEH0BtwdxgeMMzENs8Gqq/ye532fsh97bFCoZkUMCCSgvHkyE58a14qxR45GKFex439hK0Y3/I6P1mITQP6P+m+9ljBVCqI4fESHwA/GWzagZeDObNF3nM9wZ6wPae3vamXfWkODHjGfe2xgrmSEsALxKABfuKV1J0/lrtqnNmTqObuq8kkB9ClkAPoDbhTsoHBvohnd/C/PUj2xZRCE203RoI3zNhVKvvTNu6s62KY9pORr3mM7IspphIwDNZvPLqfAIaV93LrAMvduFCIYdpPsjxL9kWMJXvCxKA9v5/YWVbizoWHDNoOzqWQA+gNeMKMXbwQpE2Wwah4IEIKONo0AuhtBlumHIw7JRsYrAYQAFIHAR3Gua8tNijSS700Mx3YvcUBFMsGyZGDKXOjuC9DbE9a9AG63Om+ttigyKI98bJ+IlcOxiPGHRv7RhMOQQ6gN+BOyQYGM9gqf+BGz5J9McXcIhDAULPj0eKtAK+XJ8jBnJD+PHdqRTKEgV0FjPYs2RdTiixGAPW7gXhyqQAAtMzUy8EU8zVeUvGIgV3kAI5a4Nn9hNmTSJNSAW+xJy168wv2asuAiwekP8+TIsWUAnYJ5AB6A54wYw9vyv/u8zDQrL372mEDc6KtDK9J7QWFAWkD+WM5aRt3bu/FYP2kx7+ApG7ua4cNtDoOVyobysAAon7iyWLQAC8Hkz6UP5bTTzxhQsn6SZcJQEof97XDBhzHGWoAo81Hij1aDBowloORU1biCeMObQfnUsgB9AbcuSE3Y/jLfD1X1hz3tUEC5kRbGV7jAAL21QF6wsA+5L/AyLnA6AXua4MErlXyAr5+KiA+PMjoMaGfeHoEELCvDtATIsWDpvG25Oa33dcGCZTX1KO2jk+vN6gp9obFQgx76gA9IVKcNgjo86hhX2PCqZAD6OmoKwB1GX/szplYkzbAwH97bKE2w5xoK0Nc2+WxuzwwMm7kfxfsllYAXa/hpVcA9w7scenAwP8YVnl6KEzipWmkYbcYBov0XKn04F0eGGyicGEfr+9nC53OIO/hzoE9piUwaKr75EUkwuxJdGggQgKNpZW8QlyewXQjLx+UJgfDcZ4xoew8np8ktL3RfW1oxJAD6OmwWVhwtMcPqp6ApZWdANBMX9tVW6dDeY2H7vLAYHIwWo00OZiKSwA4vVh4vKtb5/WYE4FmNIkINuzyUOnhaeCYFKBpe14OJk+CHExVMS8xpPLj9SIJq1izJ14jLg8Yy8HkZdu+vrbMsPDCnRMFwqU0Wgdw2bJl6Nq1K6KiohAVFYXMzEz89ttvwuO1tbWYMmUK4uPjERERgfHjx6OoSKZQphJ4QrrGi2Dp3WZRDaVHQgL9ERPGp4U9Pm1jJAfzu+3rhXRNskcX1HsK1hxAfz8VmuoL/j2+nwCG9J6UcgHWTyISAf8A17WpkVBoxZ6IxeW9q59IsCcs+hca5/FZH8J+Gq0D2KJFCyxcuBA5OTk4cOAARowYgVtvvRX//PMPAGDatGn4+eef8d1332Hbtm24dOkS7rjjDje32gzlHlCI60VYEm1leI10AyBPDsYTFoB4EcUWVoozDOUCHh7ZAeTJwZA9kUVxY7IncuRgPGEBCOFyGq0DOG7cONx0003IyMhA27Zt8frrryMiIgJ79uxBWVkZVqxYgXfffRcjRoxAr169sGrVKuzatQt79uxxd9ON8YRCXC/CkmgrI8GbZuxy5GA8QbLBi7CW2gMM2oBe0U/kyMFQRkEW1iLFgJeIyzPkyMEIE0rqJ42ZRusAitFqtfj6669RVVWFzMxM5OTkoK6uDllZWcI17du3R8uWLbF79243ttQMnqDG7kVYEm1lJOojPh4v3QDIk4Mpo34iBybxYmlg9xqJD0CeHAxNKGXBIsC27IlXOIBy5GA8YQEI4XIatQN4+PBhREREIDg4GE888QR++OEHdOzYEYWFhQgKCkJMTIzR9QkJCSgsLLR4P7VajfLycqMflyPs70qRHSnYTAF7U2QHkC4H4wm7gHgRTOLFogPIIsXeIAUDSJeD8YRtJb0I6fbEC0oFAOlyMDSh9AkatQPYrl075ObmYu/evXjyySfxwAMP4OjRo3bfb8GCBYiOjhZ+UlIUcMpoxi4ZsWirpdouwWCXeZnBtiUHQ6k9WQhyQRYiO828aREIIF0OhuyJLKTaE6/QjASky8FQCtgnaNQOYFBQENq0aYNevXphwYIF6NatG9577z0kJiZCo9GgtLTU6PqioiIkJiZavN+MGTNQVlYm/Jw/f961b8BIi4kMti2sibYy2PniCi8x2PFtgJhU23Iw5ABKRrxbDCviN4U5hh4v78GQKgdD/UQy9VodruhLBWxFAIu8xZ5ENDPs0GNNDoYWC/kEjdoBNEWn00GtVqNXr14IDAxEdrbhC3DixAkUFBQgMzPT4vODg4MFWRn241KqS4B6vWGhGbtNWLTGnGgrIzHKy1J7KpXttI2mCqgt5Y+pn9iERXVCAv0QFWpeCsXrSgUAQ3TntIU0cL0GqNRHfcgBtMnVSg10HC8LFB9hfbV4YZkXiMszbJWViMXCqZ80ahqtAzhjxgxs374dZ8+exeHDhzFjxgxs3boVkyZNQnR0NB5++GFMnz4dW7ZsQU5ODh588EFkZmaif//+7m66ATYLC28GBJg3QIQBW/U6gCGVc7VSjXqtDckMT0Ew2BbkYASx8CggxMWTkkZAkWiluOluMQzmAJbVeMEuD4wMG3IwFZcBcIB/EBDWRNGmeSPMnjSNCIa/n/l+wvQB1fVeIC7PyLAhB1N9lc84QEVi4Y2cRusAFhcX4/7770e7du1www03YP/+/di4cSNGjuQ7/6JFizB27FiMHz8eQ4YMQWJiItauXevmVptAWkyysCbayojXG3Mdx8/wvYL0wfygXVYAXD3Z8PFy0gCUgy1pDwCICglASCBvHr1ihSfAy8EEhvNRvqLDDR8vF4mF+zVa0+80Cm3U/wFeJi7PaN6b34KvthS4mNPwcTbuRCYC/oENHycaDY3WCqxYsQJnz56FWq1GcXExNm/eLDh/ABASEoIPPvgAJSUlqKqqwtq1a63W/7kFKtiWhS3RVoBP57ACf68Z2IPCgVS9HIy5tA3VdclCSj9RqVTeJQYN8FmCVno5GLP9hNUTk6KAFIolTBQA4z3GvQL/AIMcjLmyEhKV9xkarQPYKCDJBlmwGbillZ0Md4v8Xiqtwd8FVlZqmiNDvxm6WYNNC4XkUFhmO7LDP+7eflJYVoucczL7ibU6QCYpRQO7JLzFnhRX1GL/2RJ5T2L2xNxEgTQAfQZyAD2ZMvoiyoFFaprZnLG7LwLIcRwmr9qH8ct24Z9LZdKfyOp2zu1qKAdTTpINcrAlAs1wt8THY58fwPhlu+RNFlg/Ob8PqCk1fowUBWRRZGNXIYZgT9zUT6Z88RfuWr4be89ck/4kQQ4mF6gsNn6MMgo+AzmAngyF4mUhZREIIBrY3eAAnrlahZNFldBxwKajVnS4TBHLwZzdYfwYRQBlYUsEmpHoxm2+LlyvxqEL/ARh4z8y+klMS6BJO4DTAmdM5GCopEQWRRJTwO6UguGjf/wEYcM/ljcxaIBYDua0iRwMicr7DOQAejIUipeF7NSeG8Sgtxw3zLa3nLgi/YliOZhTvxs/RhMFWUhO7blRDHqrqG9sPVFs5UozZFiQ+aCSEll4gz3ZZtRPZNgTQKQuYMGe0ISy0UMOoKei0xq0mGhgt0m9VoerldZFWxnuFIPedtJgpA9dKMW1ShmDhjk5GCOxcBrYbWG0W4wFEWiGO8WgxYP58cIKXC6rkf5kcR2gWA6GUnuykJtRcIc92SqyJ/lXq3DuWpX0J1uSgxEixdRPGjvkAHoqlUV8Gkflzy/HJ6wiRbSV4S4x6GpNPfae4Yu148ODwHHAjlNXpd/AnBxMzXWgrpo/pomCTcpq6qCu550ia3JBgPsWgajrtdiVx/eL+PAgAMaRHpukDmgoB6OpNmwRR/3EJjUaLcpreafIdk2xe+xJvVaHHXoHkPUTWVFAc3Iw2nqgUp9KpolCo4ccQE9FSOslA37md7UgDEgRbWUkRrtnEcjuvGvQaHVoHhOKCX14KQ5Z6T1zcjAs+hfWBAi0PlARhsL+2DDLu8UwxPIeSu7ycODsdVRrtGgaGYx/9U8FIHNgDwgG0ofwx6b9JCiCH/QJqzDbEBroj6gQ87vFMBKi3SMun3u+FOW19YgODcRDg9IByLQn5uRgKi7z2wn6BQLhTZ3cYsLTIAfQU6G6LlkIoq026roAw4y+vLYeNRrldnlgg/iwdk0xrC1vXLefugqdToZzYbotHC0AkYUUEWiGeJeHspo6l7ZLDBvEh7ZtiuHtmwEAdp6+ijo5zkWGiRyM2J5Y2P2EMCCuE7W0WwwjPtw94vLMngzOaIIbOvD9ZPeZa/J2rjHdFo7Ewn0K+g97KiTZIAtBtDXS9pZ5kcEBCAvioz9Kpfc4jsPWk/zAPqxdM/RMjUVkcABKqjQ4dFGGHEwbEzkYpu1G4r6SkLqyEwCCA/wR64ZdHsQTha7NoxEXHoQKdb08TcA2JnIwVP8nC9ZPmkmwJ2JxeUX7icietEuIRGJUCGrrdNibL0MT0FQOhvqJT0EOoKdCkg2ykLqyE+B3eVBaCubM1SqcL6lBkL8fBrSOR6C/HwZl8PuxykrbNMngpT6YHAxJNsiiSOLKTkaCwruBXCytwaniSvipgMFtmsLPT4UhQj+RkQaOTTWWg6EJpSyKZNgTwJBVUMqeFFfU4sjFcgB8pFilUmFYOz6rIMueRCYAiV3549PZ5AD6GOQAeiqCZANFdqTAJBikRHb465StA2SDd5/0WIQH8zVFBoMtUw5GnLahFLAsCiWu7GQoLQbNBu+eLWMRrY8+DmvXzOgxyWSIVo2XkVi4HOTaE6XF5bef5BcJdW4ehab66COzJ7IWDAHGZSU0ofQpyAH0VEiLSRZMgkG6A6jsjJ0N3sPaNhPODdUfH7xQipIqGbVDYoNNtaKykLpbDEPpfV7F6V/GkLZNoVLxcjCyVpqK5WDInsiiyAvtycA2TRDgp8KZq1UouFYt/WZtRHIwpQX8MfUTn4AcQE+FUsCyYAOj1MhOooLirTUarVCXIx7YE6ND0D4xUi8HI2PWnj6El4MpLQAu/cWfo5SNJKRquzHYoiIlars09TrsOs1HdljUDwDiwoPQtUUMAGDbSRlRQEEOphAo2M2fI3siiSKZ9kRJMeh6rU6QjxLbk8iQQPRKjQVgqA+URIs+/MrwmuvAma38OYoU+wTkAHoi9WqgSv8FpoFdEobifmm1XULNjgLirbvPXIWmnpd/adMswugxQ3pPhgMoloOp17ef+okk5CwC4a9jqT3XD+wHzpagSqNFk4hgdEyKMnpsuD3lAmI5GKGfUEmJFAwRQHm1okqIQR+8UIqymjpEhwaie0qM0WN22ROxHAzZE5+CHEBPhO0AEhAChMW7ty1egFi0VYoMDCBK7SlQ2yVO65lKSgh1Oyev2CcHAwAqPyCCxMJtId4thmm32ULJFDDb1WFoW37xhxg2sP95yk45GEZUskNt9AX43WLk1gAqJwYtln8J8Dcewpk92ZV31T45GAalgH0CcgA9EdLskoVYtDUy2LpoK4OJQbs6tcdxnMgBbNbg8V6OysEAQGQSP4snrCLeLaZJuLzIjhIpYKGuS5TWY4jlYP6yRw4GAELjgKAwR5vZ6CmtroNG4m4xDKXsCQCr9qR9ooNyMABfNhAS42ArCW+AHEBPhCQbZCFHtJXRLNKwz6srd3nIv1qFgpJqQf7FlEB/Pwxs44AcDEB1XRIpFGm7mUbYLMEcQFfv8nCxtAYni/TyL3rZFzFGcjAnZaT3YlOBJm35Y7InkmD9JC48CMEB0nZhYiUlFbX1qNbU27jafq5UqHFYP1Ec2rbhREGlUgnn7ZaDiabAg69ADqAnQpINspAj2spgM3uNVofr1a7b5WGLGfkXUxyWg6F6HUkUlsmr/wP4PVYD/FTgOOBKpevqANlg3aNlLGLCgsxeY1d9FyDqJ1T/J4VCO+yJWFzelfWi2/TOv1j+xRSH5WDInvgM5AB6IiHRQEJnPspD2ESuaCvA7/IQp99A3ZX1XebkGkwZqjfYsuVgBv4HaDsG6P+kQ230FYplFvYDfOSNOQKuHNiFtJ6ZqA6DycEcu1wur89mTgHa3cT/JmxSbIc9UUpcXoo9GZhhpxxM38eB9mOBAf92tJmEl0AOoCfS91HgyZ3A4OnubolXwKQXpEo2MFxd32VJ/sWUpOhQ++RgYlOBe78GUvo62lSfQK5UEEOQgnFRgb9Y/oXt/WsOIzkYOdGd6ObAxK+AtEGONNNnsN+euFYM2pL8iylR9srBRCYA93wBtB7uUDsJ74EcQMLrYZINUsV9GcxgF7vIYFuTfzHF7vQeIRm5ItCMBFYv6iKJD2vyL6awCOEWubuCEJKx3564NgLI5F+iQgIayL+YQvaEkAI5gITXI1e0leFqMWhmfIeakX8xhc3ot8uVgyEkI1cEmpHo4gigNfkXU1g/kS0HQ0jG0+3J4LZNG8i/mGK3HAzhU5ADSHg9ckVbGa5MARvJv1ip62IwOZhrVRphlR/hXOypFQUMC4ZcVSpgTf7FlK4tYhAbFihfDoaQjKP2xFURQDn2RCwHs0+OHAzhU5ADSHg19oi2MgT1fhcYbCb/EuivwoA2DWU9TDGWg6G0jSsolLlbDCNR6CfOj+xcsiH/Yoq/nwpDmMyHHDkYQjIsgmevPXGFA2gk/yJhomAsB0P9hDAPOYBeTEmVxinaZLV1WpTVuE4KxR7KaupQXmu7TdftEG1luFK8lRndvulxiJAoTs0iQM6u77pepXFKulBdr0VptYxVygpQUVsnqe9Wa+pRwXaLsTe158J+Yk3+xZThLqrvKq3WQF3veLqwTqvDdTmr2RWgUl2PMglyT3VaHa5V6ReByIwUu9KebBfJvzANU1sY5KWca0/Kauqcklau1+rkqR4QToccQC+E4zgs35aHXvM24eFPDzgkZFyn1eHO5bvQe94mfHfgvBNbaT/VmnpkvbsNYxbvsOkEFtkh2spghtQV8h4sOmNNrsEUu+VgrLDyz3z0mrcJ96/Y51BtoVbHYeLHe9Br3mZ8sfecU9rmKOp6LUYt2o4bF22z6XCw/3FYkL9kh5zRzIWRHYOsh+2oDsNuORgrfLH3HHrN24x7P9kLrQP9RKfjcN+Kveg1bxNW7cx3StscpV6rw5j3tuOGd7faXMhzpUINjgMC/VWIk+iQM1wpLm+PPbFbDsYK3x44j97zNuGu5bsdmlRyHIeHPz2AXvM24aNteS4V4ycsQw6gl1Gv1WHmj0ew8Lfj4DheGPS3I4V23+/z3edw5GI56rQc/vu/Q1i8+aTbv4x/F5TiSoUaF0tr8MEfp61ea0jryZutA4YZ/rUqtVML6ms0Wuw5cw2AtLouht1yMGbQ6TjM/fko5q4/Ch0H7D5zDetyL9p9v2/2n8dfBaXQ6ji8/MMRvLnhuNsXqxy5WIZLZbUoKldj8eaTVq8VS8BI3S2GwfqJs3d50NTrsPM0k/WQPrDbLQdjBp2OwxsbjuPlH45Aq+OQc+46vnVgIvjD3xex50wJdBww5+ejeG39Ubf3k+OFFThfUoOrlRq8+7uNfiKIQIdI3i2G4Spxea2OE+yBHHsSFRKInvbIwZiB4zgs2nQSz//vEOq0HA5fLMOaPfZPBH89XIhtJ6+A44AFvx3HrB//cWjiQdgHOYBeRLWmHo9/noM1ewqgUgF90+IAAPN/PWZXSP56lUYYOPul8/davPmU/kvuvhWGB84aittX7szH2atVFq8ttrOuCwDiwoIQ6K/f5aHCeVHAPWeuSZZ/MWWoPbuCmFBbp8WUL//CSn0Epq/+f/vGhuN2OTDltXV45/cTAAz95MOteZj2ba5TUob2Iu4na/YW4FRRhcVriwVpD/n9JCI4AOEu2OXhwDkm/xKETsnW5V9MGSbUAdo/sKvrtZj6TS6Wbc0DYPjfvr3xhKTyC1Oq1PV4c+NxAIY+t+LPfEz58i+3rkQ9cNawCOKbA+dxxMoiK2ZP7OknrhKXzz1fitJqafIvpti1y5AJdVod/vu/Q3gv+xQA47HCnlR/bZ0W8389BoAfw1Qq4PM95/D45wdcuo0e0RByAL2EKxVq3PPxHmQfL0ZwgB+WTeqJ1Q/1QWJUCC5crxEGezks3nwS5bX1aJ8YiS8f7Y/Xb+8MPxXwXc4FPLR6PyrsGAScQU4BP7CHBPqhTssJxsIc9oq2AmyXB+fXd7G0nhT5F1NYisdeOZiSKg3u/WQPfjtSiCB/PyyZ2AOfPdQXLWJDUVSuxvJtZ2Tf84M/TuNalQatmoZjzSP98NadXRHgp8KPuZdw/4p9kmqrXEHOOUM/0eo4zPvFWj+xT9qD4QoxaBa9GyJB/sUUNrDvOHXVrjrgsuo63L9iH346eAkBfiq8fVc3rHmkH1o1Dce1Kg0+2GI98m6Oj7bloahcjZS4UHz2UF+8d093BPn74bcjhZj0f3vdVu+VU1AKgO8nHAe8tv6oxSyHw/3EBfWi2/T2RIr8iynMntgrB1NRW4eHVu/H/3IuwN9Phfm3d8EXj/RD+8RIlNXUCU6hHFb8mY+LpTVIig7Bpw/1xYf39kRwgB82HyvGxI/3OHUyTlin0TqACxYsQJ8+fRAZGYlmzZrhtttuw4kTJ4yuqa2txZQpUxAfH4+IiAiMHz8eRUVFbmqxZU4XV+L2D3fi0IUyxIYF4stH+2N05ySEBQXghTHtAPCDtByh2lNFFViztwAAMGtsR/j7qTCpXyr+74HeCA30x45TV3HX8t0u0z6zhFbH4W/9wL7wjq7w91Ph96NFwk4Jptgr2soQ1Pud+D4N9TrS0zWM3mmxiLBTDubs1Src8eFO/FVQiqiQAHz+cF/c0i0ZIYH+eOmmDgD4QfpiaY2se7LJxcybOyLQ3w939U7Bqgf7ICI4AHvzS3Dn8l24cN05NUZS4ThOcABfv60LAv1V2HbyisUFNMJKcZmF/YwEoV7UmRMFltaTnv5lCHIwtfX4S+/gSOV8STXGL9+FvfkliAgOwOoH++LOXi0Q6O+HV27m+8mqP8/i3DXLkXdTLpbW4KPt/OTipTEdEBLoj1u7N8dnD/dFVEgAcs5dx/hlu2Td01nk6COAr93aGcEBftibX4KN/5gvmymqsG8FMCPRw+xJh6RIJEQF2yUHc7msBnct340dp64iLMgf/3d/b9zbryUC/P0wc2xHAHzk7nSx5ci7KcXltfhQP7l4YXR7hAb5Y0yXJHz5aD/EhgXi4IUy3LFsJ/KuVMpqK2EfjdYB3LZtG6ZMmYI9e/Zg06ZNqKurw4033oiqKoMBmjZtGn7++Wd899132LZtGy5duoQ77rjDja1uyL78EoxftgsXrtcgNT4Ma58aKGzzAwC3dmuObikxqNJo8c5G6/UtYub9cgxaHYeRHROMZEpGtE/AN4/3R5OIYBwvrMDtH+7E8cJyp74na5wqrkCFuh5hQf4Y2zUJ/+rXEgAwd/1RszUi9oq2Mpwt3ZB/tQrnrkmXfzGFl4OJByAvbfN3wXXcsWwXzl6rRvOYUKx9agD6tYoXHh/TORF90+KgrtfhzQ3HJd93wW/HUKflMKRtU6P6o8EZTfHdE5lIjArBqeJK3P7hLqupNWdz7lo1rlVpEBTgh7HdkvDgwHQAwLz1R82WL7D/b4LEFZSmsDpAZ/WTS6U1OFFUAT8VMESC/IspRnIwMlZ5Hr5QhjuW7cLp4kokRoXguycyMUj0+sPbNcPgjCbQaHVY8Kv0fvLGb8ehrtehb3ocRndOFM73bxWP758cgOYxoci/WoU7PtyFvwuU0y+8VFqDS2W18PdT4aYuSXh8SCsAwOu/HjNbvsDsib0OoMGeOCeKdaVCjUMXpMu/mGKvHMyxy+W4/YNdOF5YgaaRwfj28UyjbQoHtmmCrA4JNiPvprz9+wlUabTonhKDW7olC+d7pcZh7VMDkRofhvMlNRi/bBf2nyX9QlfTaB3ADRs2YPLkyejUqRO6deuG1atXo6CgADk5OQCAsrIyrFixAu+++y5GjBiBXr16YdWqVdi1axf27Nnj5tbz/HzwEv71f3tRVlOH7ikxWPvkAKQ3CTe6xs9PhVn62di3OdbrWxhbThRj28krCPRXCZEhMV1bxOCHpwagddNwXC6rxV3LdgvF6q6GRXV6tIxBgL8fpma1RVRIAI4XVpgtTi8UxH3l1+wA4pSNcwz2luP8YNwnTbr8iynCNk4S67s2/lOIiZ/sQUmVBp2bR+GHKQPQplmk0TUqlQozx3aESgX8mHtJ+JytsSvvKjb+UwR/PxVeublDg3R2hyT+tdonRuJKhRoTPtqt2BZlB/Tt79o8GsEB/nh6RBvEhwch70qV2eL0QjtFoBnOTu2xwbh7Soxk+RdT5NZ3bTlejLs/3o0rFWq0T4zED1MGoIPJ1nOsn/ipgA3/FGJ33jWb9805V4KfDl6CSsVnE0z7SUZCJH54agA6N4/CtSoNJn6yx2IEztn8pXc2OyRFIjw4AI8PbY2EqGCcL6nBqp1nG1zvPHvinH7C5F86JUuXfzFFrj3589RVTFi+G4XltWjTLAJrnxyAzs2jG1z38s0dEOivwtYTliPvYo5cLMN3ORcAALPGdWxQ9pDeJBzfPzkA3VNiUFpdh0n/txfrD12S1GbCPhqtA2hKWRnvGMXF8QWsOTk5qKurQ1ZWlnBN+/bt0bJlS+zevdstbWRwHIePtuXhma/+hkarw6hOCfjq0f6IjzBvlHqlxuKWbsk261sAvqB33vqjAIDJA9IaOJSMlLgwrH1yIPqmx6FCXY8HVu7D9/ovryvJ0Rf292rJRzljw4MwNastAPPF6cL+rnYaR2eLQQvpGjtm6wz23NzzpThysQxnrlRa/Pm/HWfwxJoc1NbpMKJ9M3zzWKbFz6JLi2jc2bMFANhcnanVcXhtPT+zn9SvJdomRJq9Lik6FN8+kYlBbZqgWqPFI58ewJf60gJXwhxYFg2PCgnE9Bv5fmKuOL3IgcVC4uc5SwzasPuH/PQvY0gGLwdz9HI5/i64brWffLb7LB757ACqNVoMzmiC757IRFJ0qNn7tk2IxKR+qQD4fmJtdaZOx2Guvp/c1auFWUcB4Es0vnksE8PbNUVtnQ5PrMnBagVkYg6Y2JPw4AA8P6o9AGDpH6cb1Js5Gin2RHsysE0T+PupcOZKFfaeuWa1n3y1rwCTV+1Dhboe/dLj8P0TA5ASF2b2vulNwvFAZhoA4PVfjlldOMhxHOauPwqOA27tnoyeLWPNXtckIhhfPdofN3ZMgKZeh6e//BsfbyeZGFdhX4jCy9DpdJg6dSoGDhyIzp07AwAKCwsRFBSEmJgYo2sTEhJQWGh+dqpWq6FWGwxGeblrUqPzfz2GT3bwxnHygDTM1NfoWePFMe3x+9FCob5ldOcks9d9secc8q5UIT48CM/ckGH1ntFhgfj84b547rtD+PngJTz73UFEhARgVKdEq89zBLYApJd+hTMA3JeZijV7z+HMlSp8sOU0Zozho5aOiLYynCneuv9siUj+xf6BPSk6FO0SInGiqAJj3/9T0nPu7dcSc2/pZLNI/L+j2uHXw5eRe74UPx28hNt6NDd73XcHzuPY5XJEhQRgmt4Bt0RUSCBWTu6DGWsP4/u/LuClHw4jPJivAXMVOef49FBPUTnE3b1T8PnuczheWIH3sk9h9i2dAPCDT7Gdu8UwnCkG/XfBdew4xeRf7B/Y4yOC0bV5NA5eKMPtH+6S9Jw7e7XAgju6INBGP5k2si1+zL2Io5fL8b+c87i7T0uz1/148CIOni9FeJA/nhvVzuo9w4MD8Mn9vTHzxyP4at95zP75KMKCAzChd4qkttvDX2bsye09muPT3Wdx6EIZ3t10Agvu6Co85mitqDPtyZGLZUJGwRF7Eh0aiF4tY7HvbAnu/lhaduuWbsl4666uNrVVn7khA2v/vojTxZX4cm8BHhiQZva6DUcKsS+/BCGBfnhhdHur9wwN8seyf/XCa+uPYvWus5j/63FcqVDj5Zs7Smo7IR2fiABOmTIFR44cwddff+3QfRYsWIDo6GjhJyXFNYarV2qskHabfUsnm84fACTHhOKxIa0B8PUt5lZ8Xa/SYNFmftXW9BvbIiok0OZ9gwP88d7d3TFJX4v32vqjLpN0uFKhxrlr1VCp+BQww1JxuiOirQxnFfevP3QJk/5vLzT1OgxoHY8MmfIvpjw2pBWaRAQhKiTA6k/TyGC8dFN7vH5bZ0krBJtFheCp4W0AAAt/My8LU1Fbh7f1si9Ts9oiNtz2ZxsU4Ie37+qKhwfxtXjzfz3mMkmHspo6nCzii8TF9bCWitOvV9dBo49O2BspdpYYNEvX19Rp0Ts1Fp2TzUfMpPLI4FZoEhFss580iQjGf0e1w1t3drXp/AG81uB/9I7/WxtPmFUEqNbU443f+H4yZUQbSZ9tgL8f5t/eBU8O423VG78dt0tyRgrVmnr8c4mfpIv7ibhs5uv95/HPJT47VKmuR6Xavt1iGM4Sl99yvBgTPtqNSnU9uraIRg+Z8i+mPDQoHU0jpfSTIPznhgwsvru7JGH96NBATB/J95NFm0+a3Smotk6L1/VKDo8PaY3kGPORZzH+fiq8Oq4jXrm5A/z9VOiVGmfzOYR8Gn0E8Omnn8b69euxfft2tGjRQjifmJgIjUaD0tJSoyhgUVEREhPNR7hmzJiB6dOnC3+Xl5e7xAkc3TkJfzwbhdR48+lZSzwxtBW+2V8g1LcwI8t4L/sUymrq0D4xEnfLmHX7+anw8s0dkH2sWJCceWpYG1ltkwJL67VLiGzgnLLi9B2nrmL+r8fw0X29HRJtZSREO2awOY7DJzvOYL6+YH5UpwQsvruHbPkXU8b3aoHxvVrYvtAOHh6Uji/3FvArN7edwbSRxhG+pVtO42olL/tyX2aq5PuqVCr8d1Q7/H60EOdLarB82xlhcHAmLKqTFh+GJiZlEQPbNMHIjgnYdLQI8345htUP9hVWsseHByEowL45L4sws10e7Pn/rt6Zjzn6NNiI9s3w/sQedvdbxrhuyRgnKqZ3Jvf1T8UXe87hzNUqfLAlDy+OMY7cLN92BoXltWgRG4qH9ItwpKBSqTAtqy02HinU39sQ1XcmB8+XQavjkBQdguYmTkfvtDiM7ZqE9Ycu47X1R/HVo/0F5z4iOMDu+l1TcXkpzrYpX+4twMwfeWHuQW2a4MN/9ZQt/2LK6M6JRotznMk9ffjI+4miCizebIi8M1buzMeF6zVIjArB40NbSb6vSqXCI4NbYWTHBNljISGNRhsB5DgOTz/9NH744Qf88ccfSE83NlC9evVCYGAgsrOzhXMnTpxAQUEBMjMzzd4zODgYUVFRRj+uwp4OHxYUIITXP9hiLAtzurgCn+uL42eO7SjboDgiOSMVc2k9hrg4feM/RdiVd9Uh0VYGm+mLZ/9S0eo4vPrTP4LzN3lAGj6c1AuhQfK2pFMaI1mY7Xm4JJKFOXetCqv+PAsAeOXmDrIHsJBAf7w0xj7JGan8JdT/mY8KvHSTcXE6kwqyN6oDAE0jDLs8yNWz0+k4vP7LUcz+mXf+7u3XEh/f1wvhdjoZShEU4IeX9ZH3lX/mG20nxk8eeAHpl27iZV/k3vuVsfZJzkjFmj0B+LKZoAA/7DlTgo3/FAkOoCP2RCwuXyxTz47jOLy18The+uEwtDoOd/ZqgVUP9pGUqXEnAf6G/6WpLExxRa2wm9MLY9ohLEh+nyfnz3U0WgdwypQpWLNmDb788ktERkaisLAQhYWFqKnhB6To6Gg8/PDDmD59OrZs2YKcnBw8+OCDyMzMRP/+/d3cevu5rXtzdGsRjUp1vdG2R0z2JatDAgbaIU8C2C85IxWhsN9CgbBxcfoxXCx1TAIGMJ7ty0nvsV1ZPtt9DioVZKXrPYGbuiSiT1osauuMZWEW/HocGq0OgzOaYLiddUejOyeib7p8yRmpmC4AMSW9STgm62uR5q0/iovX+e+8vQtAAN5haRLBdnmQPrDX1mnx9Fd/CTW9z49uJzld7wmMaC+ShfnNIPfx5ga97EtaHMbYGVmyV3JGKrbsSYvYMDw2mI9Izf/1GM6X8A6uI/ZELC4vx56o67WY9k0uPtjCO9X/uSFDcrreExic0RRZHZpBq+PwukgW5p2NJ1Gl0aJbSgxu7ea6mmDCPryjd9nBsmXLUFZWhmHDhiEpKUn4+eabb4RrFi1ahLFjx2L8+PEYMmQIEhMTsXbtWje22nH8/FSYNY6vb/nmAF/fsuVEMbae4GVf2Ize7nvLlJyRSm2dFkcu8vU6vdPMG2yAL06PCgnAscvlwmbzjkR2+OfLE2+9UqHGxI/3YPOxIgQF+OGDe3vikcHSUxuegEqlwqyxnaBSAetyL+GvguvYnXcNG/4phL8fk4yxz5nl7y1PckYq9Vodcs+XArDeT54ekYE4vSzMcn2kyt6FQgy5mpHXqzT41//txa+H+V1Z3runO54a1sbh8gAlUalUeOVmPvL+25FC7DlzDTnnruPHXL3syzjH+olcyRmp6HQGoXBr/eTJYa3RLDIYBSXVeF8fqXLEAQTk25Oymjo8sHIf1uXyu7K8eWdXTBvZ1qv6CWCIvG85cQVbTxTjyMUyfJvDS3fNGttQ9oVwP43WAeQ4zuzP5MmThWtCQkLwwQcfoKSkBFVVVVi7dq3F+j9voldqnCALM+fno8KMzJrsi/R7GyRn5tqQnJHDkYtl0Gh1aBIRhJYWZAcA4+L0C0Jkx0kDu4S0dt6VStyxbCcO6ndl+erRfripi/kV156OWBZm7s9H8ZpeHsia7ItUOjePxl36Gsa5NiRn5HC8sALVGi2iQgLQpqnlhTbi4nTWT+xdAMKQ4wCeu1aFO5btwoFz1xEVEoDPHu7r0lXRrqRdoiHyLu4n1mRfpCKO6lsSe7eHvCuVKK+tR2igfwOtQzHhwQF4Xl82I/QTZ9kTCf3kYmkN7ly2C3vO8LuyrJzcx6Wrol1Jq6YRgizMvF+OGcm+WIrWE+6l0TqAvs4LY9ojOMAP+/JLcLq4EnHhQXh6hHXZFzn3Dgnk773hiHMEXQ+I0nq2Zr739U9FK5Eja69oq/B8vcHOv1KFKxVqiz87T1/F+GW7cL6kBi3jwvD9kwO8fnXaf0e1Q1iQP3LPl+KoXvZlqg3ZF6k8d2M7hAf54+D5Uvx48KJT7nngrKGuy1ZE4Z4+KWifaHBknRUBzL9qvZ/sOXMNd3y4C/lXq9A8JhTfPzkA/UW7sngj00a2RWRIAI5eLkcuk3250brsi5x7s6j+d2bE3u2B2ZNuKdE206h39GiOLiJHNtGBUgHA0E/OXqu22k9yzpXg9g924lRxJRKi+N02htix3Zsn8cwNGYgNC8Tp4krJsi+E+/DsKmTCbprHhOLxIa2wRJ/WmD6yLaJDnVNM3FwvObMk+xTm/3YMw9s3k10Eboqtui4xrDj94U8PALBftJXBZvxL/jgtfF7W6J4Sg/97oHeDFajeSLOoEEwZ3gZvbeTlPP6T1RZxEmRfpN77Kf293/jtBEZ1SrSrCFxMjn7fW0t1XWKYLMyk/9sLwLEaQPHzP9p+Rtj31hqdm0dh5QN9HI4oeQJx4bw8CNv266nhbZz2vlhU/7X1R/H27ydwc9ckRDq48EGOPWFlM3ct5zcAcFZGYfWus1i966zN69snRmLl5D6S5FE8nejQQEy/sR1mrjsCAHhMouwL4R4oAtiIeXxoa3RPicGQtk1xTx/nphWeGNrK6pZKcuA4zubKTlNGtG+GCb1boH1iJLo5qJGV1aEZokJsOyZ+Kj6d8dWj/RuF88d4eFA6eqfGIrNVPO7rL132Req9W8SGorC8Fh9ts+002SJHHwHsZaWuS8zANk1wX/9UtGkW4XC0dni7ZogJs+2YqFTAzV2S+F1ZGoHzx7g/Mw2ZreLROzVW0Ht0Fiyqf7VSIyyEcARmT3pL/J/3SYvDo4PTkd4k3GgPbXsY2rYpYiX2k5EdE/DtE5mNykma2CcFQ9o2RfeUGDwhQ/aFUB4VR3us2E15eTmio6NRVlbmUkkYT2XtXxcw/duDiAgOwB/PDbW7xir/ahWGv70VQQF+ODz7RkkCpIT38Ovhy3jqi78QEuiHP54dZvdgd6m0BgMW/gF/PxUOvXqjx8uoEPLIPlaEhz89gCB/P2yePhQt4y3XAlvjWqUaveZtBgDkzhpp917LROPG18dvgCKAhANYkpyRC0vXdG0eTc5fI2RM50T0TYtrIDkjF9ZPOiRFkvPXCLEkOSOXv/RlAhnNIsj5IwgrkANI2I2p5Iy9sjBMsJVWijVOmNyHWHLGHnJkpvUI78Kc5Iw9HCB7QhCSIAeQcAix5MxrdsrCyCnYJrwTU8kZe2RhWD+xtLMD4f2YSs7YIwvzF9kTgpAEOYCEwzDJmb35Jdj4jzxZmLLqOpwsqgRAA3tjRyw589PBS7KeW62px9HLeqFw6ieNGrHkzP9y5MnCqOu1OHiBz0SQA0gQ1iEHkHAYJjkDAK//egzqeq3k5/51np+tpzcJb1Qra4mGMMkZAFj423FUa6TvvZx7vhRaHYek6JBGtWKSaAiTnAGAtzaeREVtneTn/nOpHJp6HeLCgxwWvSeIxg45gIRTeHxoa0EWZuWfZyU/L+esPq33/+3de1RTZ7oG8GcDSQi3cKkGELl5AVEBEUlpteJApQxSaGun2q4Z7HWdU5xKmVlWbRWdttKDy0vpaO1Zc6wzs8bW0XOwYzutWi/YiyBgmYIKgsWqRUDbcr8EyT5/RLbGIAITEiDPby3WIt+3E9+87hXe7G/vd/ejrxuNfM/ODsA414G3heGynnX5TbT+rkXXWjoH1Bbm1s+TkXYrNSJzYwFIJuGosMPyeH3H961Hq3C1ubNfz+vP/Tpp9LCX2WLlL/X7yXvHz+NKY3u/nlfEAtCqyO1s8Oov9fct3/FlNS791Nav5/HzhKj/WACSyTwy42ZbmI0HK+66/fVuHUouNQDgH3ZrkjjdC7P83W60hbn7fqLTiQNu7EsjX+yUm21h1v/z7m1hRFFE8UV+USDqLxaAZDK3t4U5XdN3W5izV5rR3tUNF3s7TBzjZI4QaRgQBAFrFkyFIAC53/yAb+7SFqbqaguaOq5DKbNFsJdzn9vS6DHQtjCXfmrH1eZOyGwFg3v7ElHvWACSSc30c0fSjbYwf9jfd1uYnv5/EX5usLHh+TrWZLqPCo/1tIW5S/ugnmW9sPEqyGz5kWVNgjyd8aTGF4C+zVRfbWGKL+o/T6aNU/3b9yYnsgb8NCWTe+WhoFvawtTdcbsiaVmPyzXWqKctzDcX+24LU3SBy7/W7OU4fVuY0zVN+N/iy3fc7uZ+ws8Tov5gAUgm5+PmgBdutIVZ30dbmFNs7GvV1C72eDFmAgB9W5h27R32E57XZdU8nBRSW5jsAxVo6ey9fRAbyhMNDAtAGhL/MXcCxjorcPGnNrz/1QWj+ZqGdtQ0dsDWRkD4eFezx0fDw3NzAjHOVYkrjR347+PGbWGutXSi+lorALYKsma/ifaHv4fDjbYwVUbzTR1dqKhrBsAvlET9xQKQhoSjwg7LH9K3+/jjEeO2MD3f1kO8XOAgtzN7fDQ82MtssSJBv59szzNuC9NzlHjSWCeoHGRmj4+GB7mdDV5N1F9g9j9fGLeFKbnYAFEEfN0dMNbZ3hIhEo04LABpyDw6YxxCb7SF2XTIsN0Hl2uox4JQL0T6uaG9qxsbbmsL09PWg33dKG7KWMyeqG8Lk/WpYVsY9okkGjgWgDRkbGwErFmg/9b+YaFhWxgWgNRDEG62D/q/b36QekMCvFMM3SQIAl5bMAU2AvDP0loU3NIWhneKIRo4FoA0pCL93bEg1AuiqG/jIIoi2rTXceZKEwB+YJNeqI/rzbYw+09DFEV0Xu/Gtz/ovzRE+vMKYAKCPV2wOErfFuYPN9rCdOtEqZckP0+I+o8FIA25FQnBkNvZIP+7n3DwTB1KLjWgWyfCW2UPb1elpcOjYWL5Q0FQymxx6kZbmLIfmqC9roO7oxz+Hg6WDo+GiYwHJ8NZcaMtzKnLKK9tQqu2G84KO0xWs1E4UX+xAKQh5+PmgBfm3GwLk39ev3TDq/XoVre2hfmvT8vxVdU1APrlX0Fgo3DS83BS4KUbbWE2HKjA8XP6/STc1xW2bChP1G8sAMks/jNmAsY4K/D9j23YfqPdBxu20u2efyAQ3ip71DR24I832n3wAhC6Xep9+rYwV5s7sfnzcwDYKJxooFgAklk4KuywPD4IAKC9rgOgv20c0a3sZbZY8cspAG7dT1gAkiG5nQ1WcT8h+rewACSzeSzCR7pJu1JmiylePF+HjCWFekl/zGW2grTPEN3qwRA17p/oAQCwEfRLwETUfywAyWxsbASsfXgqFHY2mD9VDTtb7n5kTBAErE2aCqXMFrHBatjLbC0dEg1DgiBg9YIQOCns8MDkMXBSsKE80UAIoiiKlg5ipGpqaoJKpUJjYyNcXFwsHc6I0djWBUeFLQtA6lNjexcc5dxPqG+N7V1wkNtCxv2EBoB/vwF+ZSKz4y29qD9USu4ndHfcT4gGh1+ZiIiIiKzMqC0Ajx8/jqSkJHh7e0MQBOzbt89gXhRFrFmzBl5eXlAqlYiLi0NlZaVlgiUiIiIyo1FbALa2tiIsLAxbt27tdT47Oxs5OTnYvn07CgoK4OjoiPj4eHR0dJg5UiIiIiLzGrXnACYkJCAhIaHXOVEUsWXLFrz22mtITk4GAPzlL3+BWq3Gvn37sGjRInOGSkRERGRWo7YA7Et1dTVqa2sRFxcnjalUKmg0Gpw4ceKOBWBnZyc6Ozulx42N+hvVNzU1DW3AREREZDI9f7etuRGKVRaAtbW1AAC1Wm0wrlarpbneZGVlYd26dUbj48ePN22ARERENOSam5uhUllns3mrLAAHa+XKlcjIyJAe63Q6/PTTT/Dw8DD5zeqbmpowfvx4XLp0yWp7FJkT821ezLd5Md/mxXyb12DyLYoimpub4e3tPcTRDV9WWQB6enoCAOrq6uDl5SWN19XVITw8/I7PUygUUCgUBmOurq5DEaLExcWFHyBmxHybF/NtXsy3eTHf5jXQfFvrkb8eo/Yq4L4EBATA09MThw8flsaamppQUFCA6OhoC0ZGRERENPRG7RHAlpYWVFVVSY+rq6tRUlICd3d3+Pr6Ij09HW+88QYmTZqEgIAArF69Gt7e3khJSbFc0ERERERmMGoLwKKiIsybN0963HPuXmpqKnbu3Inly5ejtbUVL7zwAhoaGjB79mx89tlnsLe3t1TIBhQKBTIzM42WnGloMN/mxXybF/NtXsy3eTHfgyOI1nwNNBEREZEVsspzAImIiIisGQtAIiIiIivDApCIiIjIyrAAJCIiIrIyLACHoa1bt8Lf3x/29vbQaDQ4efKkpUMaFY4fP46kpCR4e3tDEATs27fPYF4URaxZswZeXl5QKpWIi4tDZWWlZYIdBbKysjBr1iw4Oztj7NixSElJQUVFhcE2HR0dSEtLg4eHB5ycnPDYY4+hrq7OQhGPbO+++y5CQ0OlZrjR0dH49NNPpXnmemi99dZbEAQB6enp0hhzbjpr166FIAgGP8HBwdI8cz1wLACHmd27dyMjIwOZmZk4deoUwsLCEB8fj/r6ekuHNuK1trYiLCwMW7du7XU+OzsbOTk52L59OwoKCuDo6Ij4+Hh0dHSYOdLRIS8vD2lpacjPz8ehQ4fQ1dWF+fPno7W1Vdrm5Zdfxv79+7Fnzx7k5eWhpqYGjz76qAWjHrl8fHzw1ltvobi4GEVFRfjFL36B5ORknD59GgBzPZQKCwvx3nvvITQ01GCcOTetqVOn4sqVK9LPl19+Kc0x14Mg0rASFRUlpqWlSY+7u7tFb29vMSsry4JRjT4AxNzcXOmxTqcTPT09xQ0bNkhjDQ0NokKhED/44AMLRDj61NfXiwDEvLw8URT1+ZXJZOKePXukbc6ePSsCEE+cOGGpMEcVNzc38U9/+hNzPYSam5vFSZMmiYcOHRLnzp0rLlu2TBRF7t+mlpmZKYaFhfU6x1wPDo8ADiNarRbFxcWIi4uTxmxsbBAXF4cTJ05YMLLRr7q6GrW1tQa5V6lU0Gg0zL2JNDY2AgDc3d0BAMXFxejq6jLIeXBwMHx9fZnzf1N3dzc+/PBDtLa2Ijo6mrkeQmlpaUhMTDTILcD9eyhUVlbC29sbgYGBeOqpp3Dx4kUAzPVgjdo7gYxE165dQ3d3N9RqtcG4Wq1GeXm5haKyDrW1tQDQa+575mjwdDod0tPTcf/992PatGkA9DmXy+VwdXU12JY5H7zS0lJER0ejo6MDTk5OyM3NRUhICEpKSpjrIfDhhx/i1KlTKCwsNJrj/m1aGo0GO3fuRFBQEK5cuYJ169Zhzpw5KCsrY64HiQUgEQ25tLQ0lJWVGZyzQ6YXFBSEkpISNDY2Yu/evUhNTUVeXp6lwxqVLl26hGXLluHQoUPD5haio1lCQoL0e2hoKDQaDfz8/PD3v/8dSqXSgpGNXFwCHkbuuece2NraGl25VFdXB09PTwtFZR168svcm97SpUvx8ccf4+jRo/Dx8ZHGPT09odVq0dDQYLA9cz54crkcEydOxMyZM5GVlYWwsDC8/fbbzPUQKC4uRn19PSIiImBnZwc7Ozvk5eUhJycHdnZ2UKvVzPkQcnV1xeTJk1FVVcX9e5BYAA4jcrkcM2fOxOHDh6UxnU6Hw4cPIzo62oKRjX4BAQHw9PQ0yH1TUxMKCgqY+0ESRRFLly5Fbm4ujhw5goCAAIP5mTNnQiaTGeS8oqICFy9eZM5NRKfTobOzk7keArGxsSgtLUVJSYn0ExkZiaeeekr6nTkfOi0tLTh//jy8vLy4fw8Sl4CHmYyMDKSmpiIyMhJRUVHYsmULWltb8fTTT1s6tBGvpaUFVVVV0uPq6mqUlJTA3d0dvr6+SE9PxxtvvIFJkyYhICAAq1evhre3N1JSUiwX9AiWlpaGXbt24aOPPoKzs7N0Lo5KpYJSqYRKpcKzzz6LjIwMuLu7w8XFBb/97W8RHR2Ne++918LRjzwrV65EQkICfH190dzcjF27duHYsWM4cOAAcz0EnJ2dpfNZezg6OsLDw0MaZ85N5/e//z2SkpLg5+eHmpoaZGZmwtbWFosXL+b+PViWvgyZjL3zzjuir6+vKJfLxaioKDE/P9/SIY0KR48eFQEY/aSmpoqiqG8Fs3r1alGtVosKhUKMjY0VKyoqLBv0CNZbrgGI77//vrRNe3u7+OKLL4pubm6ig4OD+Mgjj4hXrlyxXNAj2DPPPCP6+fmJcrlcHDNmjBgbGysePHhQmmeuh96tbWBEkTk3pSeeeEL08vIS5XK5OG7cOPGJJ54Qq6qqpHnmeuAEURRFC9WeRERERGQBPAeQiIiIyMqwACQiIiKyMiwAiYiIiKwMC0AiIiIiK8MCkIiIiMjKsAAkIiIisjIsAImIiIisDAtAIhrRlixZAkEQEBMTY+lQLCYmJgaCIGDJkiWWDoWIRggWgERkoKeY8Pf3l8bWrl1rNGZugiBAEATs3LnTYHzChAnQaDQICQkxWyw9+RAEAQ8++KDBnKenJ4sxIhr2WAASkUVotVqTvM7q1auRn5+Pbdu2meT1Burzzz/HkSNHLPJvm4soiujq6rJ0GERkQiwAiahPMTExWLduHQDg+++/NzoS19jYiGXLlsHPzw9yuRw+Pj7IyMhAW1ub9Bq3LtNmZ2fDx8cH9vb2AIC//e1viIqKwj333AOZTAY3NzfEx8fj5MmTAIBjx45BEATptZ5++mmDo5G9LQG3t7fj1VdfxcSJEyGXy+Hu7o6UlBSUlpZK2+zcuVN6L0ePHkVERASUSiUiIiKQn58/oBytWrWqz/nejl7evmx74cIFabvs7Gw8/PDDcHBwQGhoKE6ePImvv/4a4eHhcHJywkMPPYSamppe/60333wTXl5ecHR0xKJFi9DQ0CDN6XQ6vP3225g2bRrs7e3h5uaGxx9/HNXV1b3m5bPPPsPUqVMhk8nw1VdfDSgnRDS8sQAkoj6FhIRg3LhxAAC5XA6NRgONRoMxY8ZAq9UiJiYGOTk5qK+vx5QpU/Djjz9i8+bNSEpKwu23Gj9x4gRWrlwJFxcXuLu7AwAKCwtRWloKDw8PTJ06Fe3t7Th48CDi4uJQW1sLFxcXaDQa6TUCAwOh0WgwY8aMO8b88MMPY/369fjuu+8wYcIEdHV14aOPPsJ9992H8vJyo+0TEhLQ1taG69ev45tvvsGiRYtw/fr1fuVnxowZKCgoQG5ubr+27481a9agrKwMNjY2KC0txaOPPoqEhAS0t7dDq9XiwIED+N3vfmf0vL179yI7OxsqlQptbW3YvXs3nnnmGWl+6dKlSE9Px+nTpzFx4kTY2tpi7969uO+++1BfX2/0esnJyWhra8P48eNN9t6IaHhgAUhEfdq2bRuee+45AICXlxfy8/ORn5+PxMREfPDBBygpKYFcLse3336Lf/3rX9LRsyNHjhgtjWq1Wnz88cc4c+YM6urqAABpaWn48ccfUVFRgZKSEpSVlQEAmpub8cknnxgdketZ8r1TwXX06FF8/vnnAIBNmzbh7NmzOHv2LJycnNDS0oKsrCyj52zYsAHl5eXYuHEjAP2Rzqqqqn7lZ/369QCA1157DTqdrl/PuZu5c+fi/Pnz2LJlCwDghx9+wK9+9StUVFTglVdeAaB/n7eTyWSoqKhAeXk5VqxYAQDIzc1FeXk5qqursX37dgDAn//8Z5SVleHChQvw8fFBbW0t3nnnHaPXe/nll1FdXY3q6mrMmTPHJO+NiIYHFoBENGg9y7RarRaTJ0+GIAgIDw+X5m9fSg0KCkJCQgIAwNbWFgDw888/Izk5Ge7u7rCxscGkSZOk7e+0zNmXwsJC6fcnn3wSAODj4yMVMEVFRUbP+fWvfw0ABheS9BSodxMTE4P58+fjzJkz+Otf/zrgeHuTmJhodNFNUlISAP0R0DvFN2/ePHh6egIAFi9eLI2XlpaiqKhIOiKbmpoKQRDg7OyMy5cvAzD+vwKA9PR06fee/y8iGh3sLB0AEY18crm81yVZNzc3g8dqtdrgcUtLC+Lj49HQ0AB7e3vMmDEDMpkMBQUFAIDu7u6hC/oWrq6uAAA7u5sfibcvX/dl/fr1OHToEDIzM/u8WOLW99PY2HjH7VxcXIzi6Rm79XzIwQoPD4dCoTAY8/PzM9ru9v8vIho9WAAS0V05ODgAANra2iCKolSEzJo1C4C+sNm2bRsiIiIAAB0dHfjkk08QGxtr8Dq3Fy8VFRXSRQo7duzA4sWLkZ+fj+joaKMYlEol2tvb0dra2mesPTEBwK5du5Ceno7Lly/jiy++AABERkb2923328yZM7Fw4ULs2bOn1/mxY8eivr4e586dAwCUl5cbXJBiKseOHUNdXR3UajV2794tjU+bNg0KhQKCIEAURSxZsgTLli0DoC90v/zyS6hUKqPXM0WxSUTDE5eAieiugoODAQBXr15FUFAQ7r33Xnz33XdYvHgxQkND0d3djVmzZmHatGkICgqCq6srFi5caHAFam8CAwPh6OgIAHj22WcRGhqKlJSUPmNYsWIFoqKi7njl7bx58xAXFwcAyMjIQEhICEJCQtDS0gInJyesXLlyEBm4u9dff/2Oy6Q9hfDGjRsxb948REdHD+gIY3/1LMUHBwdL5yYmJydjypQpCAwMxPPPPw9Av7QbGBiI0NBQuLq64oEHHsCpU6dMHg8RDV8sAInorhYsWIDnn38eHh4eqKysREFBAdra2qBQKJCXl4eXXnoJ48ePx7lz5/Dzzz8jMjISb7755l2XEN3c3LBnzx6EhIRAp9NBLpdj//79vW6bk5OD6dOnQ6vVorCwUDqa1pt//OMfWLVqFQICAlBZWQk7OzskJyfj66+/lgpJUwsKCrpj8+dNmzYhMTERSqUS58+fx6pVqzB79myTx7Bw4UIsX74cDQ0NUCqVePzxx7Fjxw5p/t1338XmzZsxffp01NTU4Pvvv4e/vz8yMjKs+k4qRNZIEIfiaygRERERDVs8AkhERERkZVgAEhEREVkZFoBEREREVoYFIBEREZGVYQFIREREZGVYABIRERFZGRaARERERFaGBSARERGRlWEBSERERGRlWAASERERWRkWgERERERW5v8BBeUSjnTkmIMAAAAASUVORK5CYII="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "a8b2e26e",
+   "metadata": {},
+   "source": [
+    "![penta%20vs%20bi%20penta.png](attachment:penta%20vs%20bi%20penta.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6fef43a",
+   "metadata": {},
+   "source": [
+    "### Octagon 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6a8004f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15)\n",
+    "ptrn = octagon()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6db3fccb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Octagon 2', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0478487f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Octagon.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ddf7531",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Fourier(\"Octagon 2\",game)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bec4fa53",
+   "metadata": {},
+   "source": [
+    "### Queen bee shuttle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b792e166",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15)\n",
+    "ptrn = queen_bee_shuttle()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78d5efe2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Queen bee shuttle', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a936df04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Queen bee shuttle.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "061d657d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Fourier(\"Queen bee shuttle\",game)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64857869",
+   "metadata": {},
+   "source": [
+    "## Spaceships, guns and puffers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa7b84ad",
+   "metadata": {},
+   "source": [
+    "### Glider"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9c13229",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15)\n",
+    "ptrn = glider()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d4873d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Glider', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e7713f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Glider.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fd5207e",
+   "metadata": {},
+   "source": [
+    "### Lightweight spaceship"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7ce19cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15)\n",
+    "ptrn = lw_spaceship()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9232d18e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Lightweight spaceship', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cea668e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Lightweight spaceship.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f1352cf",
+   "metadata": {},
+   "source": [
+    "### Weekender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67af54f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15)\n",
+    "ptrn = weekender()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4954f0b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Weekender', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c24d6af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Weekender.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05498c39",
+   "metadata": {},
+   "source": [
+    "### Spider"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1beefb43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15)\n",
+    "ptrn = spider()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "695cc768",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Spider', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5873b961",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Spider.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dbcee67",
+   "metadata": {},
+   "source": [
+    "### Glider Gun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4a2fb3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 150, 15)\n",
+    "ptrn = glider_gun()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b035a585",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Glider gun', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0957b94e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Glider gun.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04795263",
+   "metadata": {},
+   "source": [
+    "### Blinker Puffer 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bb90a1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15)\n",
+    "ptrn = blinker_puffer1()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652e0871",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Blinker Puffer 1', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf852268",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Blinker puffer 1.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f2b3cea",
+   "metadata": {},
+   "source": [
+    "### Pufferfish"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "800e229e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 80\n",
+    "h = 50\n",
+    "\n",
+    "game.init(int(b), int(h), False, 50, 15)\n",
+    "ptrn = pufferfish()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2 + 10))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()\n",
+    "print(\"Period: {}\".format(game.period))\n",
+    "print(\"Pattern: {}\".format(game.pattern_name))\n",
+    "print(\"Speed: {}c\".format( round(game.speed, 3)))\n",
+    "print(\"Direction: {} \".format(game.dir))\n",
+    "print(\"Heat (only for oscillators and spaceships): \", round(game.heat,3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b791dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = plot_animate('Pufferfish', game)\n",
+    "\n",
+    "html = HTML(a.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ca66a38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Video(\"bar_chart/Pufferfish.mp4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61ddf034",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Methusaleah"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "027406da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 160\n",
+    "h = 160\n",
+    "\n",
+    "game.init(int(b), int(h), False, 200, 5)\n",
+    "ptrn = Pi_heptomino()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "265504dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.arange(0,game.epoch,1)\n",
+    "fig, ax = plt.subplots(figsize=(15,8))\n",
+    "ax.plot(x, game.count_alive, color = \"orange\", linewidth=2)\n",
+    "ax.set_xlabel(\"Iteration Number\",fontweight=\"bold\",fontsize=15)\n",
+    "ax.set_ylabel(\"Cells Alive\",fontweight=\"bold\",fontsize=15)\n",
+    "ax.set_title(\"Pi heptomino\",fontweight=\"bold\",fontsize=15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04634efb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 160\n",
+    "h = 160\n",
+    "\n",
+    "game.init(int(b), int(h), False, 180, 5)\n",
+    "ptrn = B_heptomino()\n",
+    "game.initial_condition(ptrn, int(b/2- ptrn.shape[1]/2), int(h/2 - ptrn.shape[0]/2))\n",
+    "game.start_game()\n",
+    "pygame.display.quit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3bc5537",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.arange(0,game.epoch,1)\n",
+    "fig, ax = plt.subplots(figsize=(15,8))\n",
+    "ax.plot(x, game.count_alive, color = \"orange\", linewidth=2)\n",
+    "ax.set_xlabel(\"Iteration Number\",fontweight=\"bold\",fontsize=15)\n",
+    "ax.set_ylabel(\"Cells Alive\",fontweight=\"bold\",fontsize=15)\n",
+    "ax.set_title(\"B heptomino\",fontweight=\"bold\",fontsize=15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93a2633f",
+   "metadata": {},
+   "source": [
+    "# Game of Life 3D\n",
+    "\n",
+    "In the 80s the scientist Carter Bays published a paper in which different types of 3D Game of Life were studied and analyzed. The idea is to consider a third spatial axis in order to get a space where the Game evolves that is a series of parallel 2D grids. This set of grids gives depth to the problem. In this way, the number of neighbors for each cell is 26 and its evolution depends on how many of them are alive such as the simple 2D Game of Life.\n",
+    "\n",
+    "\n",
+    "Carter Bays found that a good set of evolving rules is the following:\n",
+    "- a living cell remains alive if it has between 5 and 7 alive neighbors\n",
+    "- a dead cell becomes alive if it has between 6 and 6 alive neighbors\n",
+    "\n",
+    "In all other cases if the cell is alive it dies, and dead cells stay dead.\n",
+    "\n",
+    "The rule is usually called (5766).\n",
+    "\n",
+    "With this project, we wanted to apply these rules to a three-dimensional glider. Its initial setting consists of two bidimensional equal gliders that are placed parallelly next to each other. Here we present an image of the 3D glider shown in the Bays' paper:"
+   ]
+  },
+  {
+   "attachments": {
+    "3dglider.png": {
+     "image/png": 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4ZWxYRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpVc2VyQ29tbWVudD5TY3JlZW5zaG90PC9leGlmOlVzZXJDb21tZW50PgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4Ku3UtrQAAABxpRE9UAAAAAgAAAAAAAADiAAAAKAAAAOIAAADiAAF8WfBzM3sAAEAASURBVHgB7N330yxHdT7wxQHnnBO+EkigABLKCXGVJctIyKIol/8p+weXq1w2ZSxTRkiAUL4KSFc5ogSSCAJdg3HOOX+/+2l45Paws/uG3ffd972nq+b2dPfpc04/Pe9sP/f0zLzp/03TpNJRicCiqX/Tm970Bi5kI6++b3tDqE4KgUKgECgECoFCoBAoBNYegVrXrecUvWk6MUXM1nNuyqtCoBAoBAqBQqAQKAQKgUKgEDhKEChidpRMdA2zECgECoFCoBAoBAqBQqAQKATWF4EiZus7N+VZIVAIFAKFQCFQCBQChUAhUAgcJQgUMTtKJrqGWQgUAoVAIVAIFAKFQCFQCBQC64tAEbP1nZvyrBAoBAqBQqAQKAQKgUKgECgEjhIEipgdJRNdwywECoFCoBAoBAqBQqAQKAQKgfVFoIjZ+s5NeVYIFAKFQCFQCBQChUAhUAgUAkcJAkXMjpKJrmEWAoVAIVAIFAKFQCFQCBQChcDuIzD8Wlm+D1zEbPfnpjwoBAqBQqAQKAQKgUKgECgECoE9hMCQXG3W9b5/EbPNolfyhUAhUAgUAoVAIVAIFAKFQCFQCEwR6InVVgDp+xcx2wqC1acQKAQKgUKgECgECoFCoBAoBI56BHpitRUw+v5FzLaCYPUpBAqBQqAQKAQKgUKgECgECoGjHoGeWG0FjL5/EbOtIFh9CoFCoBAoBAqBQqAQKAQKgULgqEegJ1ZbAaPvX8RsKwhWn0KgECgECoFCoBAoBAqBQqAQOOoR6InVVsDo+xcx2wqC1acQKAQKgUKgECgECoFCoBAoBI56BHpitRUw+v5FzLaCYPUpBAqBHUXATSs3qxhW9z//8z+t+O3f/u2prrwQKAQKgUKgECgECoG1QiDka5hzsl/f5Ly+Y7ZW01fOFAKFQBDITSw3q9T/93//9+S//uu/2g3tzW9+c6orLwQKgUKgECgECoFCYO0QsJ4ZHtY23/Zt3/aGr1nrFDF7A5I6KQQKgXVDwI0sN6v4JlqGnEnf+Z3fmerKC4FCoBAoBAqBQqAQWEsErF1CzuJgEbMgUXkhUAjsWQTc3IZbGYfkbc8OrhwvBAqBQqAQKAQKgX2HQEiZPKlfu+S8ImZBp/JCoBBYWwT6G1l/HodzQ1Puz9NeeSFQCBQChUAhUAgUAruFQNYuyYd+ZO1SxGyITJULgUJg7RDIFgCOCf27gbm5JXL2xg1tWp/ztRtEOVQIFAKFQCFQCBQCRzUCIWbJA0bWLkXMgkjlhUAhsJYIhIDlJuZNjCFmedZMuT/WciDlVCFQCBQChUAhUAgUAlMErGmyrgFIEbO6LAqBQmBPIODGhYDJ3bhEzByiZUNilmjanhhYOVkIFAKFQCFQCBQCRyUCRcyOymmfP+gw9bD0MenIae/PE6EY67fu9RmLcWSrXBb2WfDXd7J2fxbNU45cc3J15s25NMx33/PyoBAoBAqBQqAQKAQKgdkIZB2q9Y01zLTyf18PMrtf1e5TBP7zP/+zjSxkJBdFP1x1uUSSa3eefr18f97Lq5+lv5cfnus/1DGUmVdmb57N6Cbju1jKiJhx5TtZRczmIVxthUAhUAgUAoVAIVAIFALLQqCeMVsWkntQT7aHcR0ZkXoi05+3xh3+B1HKyx22Ypr/88aQNjZCUn0XK1hsxWb1KQQKgUKgECgECoFCoBAoBLaCQBGzraC2T/v0ESRDVB7WhcwM28cgiXz09HJ9XeTkOe9lt3Le+5/+vc1E/ETH/uM//qPZffOb39yiZtkityxfYr/yQqAQKAQKgUKgECgECoFCYBYCRcxmoXKU1IWk9AQGEUnEaCyiFrKCvETHLMgi17f1tvq+IUn6zOrX69joea+/P0//2DGOELFsXYx8sEifyguBQqAQKAQKgUKgECgECoFVIFDEbBWo7hGdyEhSiAiyEjKirq+PbAhNypvJoy99lKMved+Wc/lm2/WJveTqpJTp7MeLjCqn7hvS9W8hUAgUAoVAIVAIFAKFQCGwWgSKmK0W37XW3pMTjiJqnrX693//95YjLSFDZB1k0m8jg+v7k09fueiUdufDgyySlEhW70t0apvXbotion6z9NPDB8+VOTd2x/d+7/dOfvzHf3zy3d/93dyoVAgUAoVAIVAIFAKFQCFQCKwcgSJmK4d4fQ2ErCA3CMk//dM/Tf7mb/5m8pd/+ZeTv//7v2+OIywhQEgOspOyfF4SddI/doZ5nudST3d/qIt+OmYd8WVWmzrPjRmXFH3xQV1IGT/4+q//+q9tfD/7sz87eec73zl5y1veMvmu7/ouopUKgUKgECgECoFCoBAoBAqBlSJQxGyl8K6v8pAshESEDCk7cuTI5Pnnn5/89V//dYsiITHIT7b1ITnIUt5c+C//8i+Tf/iHf2ik5/u+7/sa0aFLQnZ+8Ad/8I0oFIKDCP3t3/7tRD/yP/RDP9QIlz6I1Hd8x3dM/u3f/q21s/E93/M9TZd2bSFJynSlnQ/qRLro5PM//uM/vtGHz3w3Hnok8vrzka5//ud/bn3+6q/+avIDP/ADk8suu2xy+umnNz9DMPlYqRAoBAqBQqAQKAQKgUKgEFgFAkXMVoHqGukMKeFSCJVzxExECVFBxP7sz/5s8txzz00efvjhyQ//8A9PTj31VGJN7kd/9Ecb0SInqmSb34/92I81kvX66683uQMHDjRdf/Inf9LIm6iTQxlx+rmf+7lGml588cXJ17/+9cmxxx7bIlLa/vzP/7wRoJ/+6Z9ukbo//dM/bYRJf/6TR8p+8id/so2BH/ynEwH80pe+1CJ9P/VTPzXhB1L22muvNYJFJ6KGECJmZODwta99rfkrKoagfeUrX5l89atfnXz2s59tY7z++usn559/ftvOiLg5QlDbgOufQqAQKAQKgUKgECgECoFCYIkIFDFbIpjrqEqkSBQKscgzUwgKkoH0iF598YtfnCBYzz777OTVV1+dnH322ZOrrrqqtZHJlj4yf/EXf9HIzTHHHNMIzJe//OWm+x3veEcb/uc///lGmk444YQmp2x75HHHHdeI1TPPPNMI0Mknnzz5xV/8xUaQ6ED03va2t03+7u/+rkXuRLKQN/aPTCN5COTP//zPNwInqiXCh7iJkiFhyBriRsY2TGTt+7//+ye/8Au/0IhZCCO/ReuME8lkE/Ek/8orrzRyKnp29dVXNxyQNlgVMVvHq7t8KgQKgUKgECgECoFCYP8gUMRs/8zlt4xEpEgKEZOrsyVP1Ahp+9znPjd54YUXGgFCvF566aUWKfrVX/3VRoZE1WwPFJny7BniZKvfj/zIjzTdiBriInpFr2gXOyJVSI0ym0gToiRShVT9zM/8TNOLRNGLYJFBFJX1RZjoFO1CjJA1tvL8m7JIGlImSoZwifZFhzLCxx86RNnoRFBFCBFWNsnRgfA98cQTjQgePHiwbWUkG2JWWxnblNc/hUAhUAgUAoVAIVAIFAIrQKCI2QpAXReViIdIk8O556oQLAeSIkL00EMPNZJyyimnNMJz+PDhyYknnjj5tV/7tYnoEmIm6YPgIHOISkgKkuUc6VLPhnKIn62KzhEvOQIlCoYMkXfOhj5IFhKHWMVPhCztxsFvES3tytr5xK6ESCmLhiF3saNdffppZ5vNHh942NJ57rnntiPj6sfcDNU/hUAhUAgUAoVAIVAIFAKFwBIRKGK2RDDXTRWSg1A4kBlEBFlBpjxThYR5fku065xzzmnRrLvvvnty/PHHTz74wQ+2CBVZpEokCkFSRn5EzZRFmpAjz50hTaJOSBZ5ROyP/uiPGkmy7VC0yrZJWxsPTJ8FE3VD1ESzEKaf+ImfaDq1I0za+Sy6JRf9EiUTtUP4kC7RPKQTmTNOviJdomrRwQ99kDM6+c9PSRkmbDoeeeSRts3xPe95z+S9731vG0cwXLf5LX8KgUKgECgECoFCoBAoBPYPAkXM9s9czh0JYoMwITS2Lj722GPtpRtvf/vbJ2eeeWZ7Xuupp56a3HXXXY2YXXvttY3IIDS2JTryog6kzLNcCJFnt5Aq5A7xsR2SLc+PkfOyDyTIM2e2L7LhJRsidCJy2mxvFJlKhM72R6QKEWPD81+iXXQieMggwuecH3xEGPVB3JCy6OA3Ypati545I8NP/iojkcbxx3/8x5Onn3665RdddNHkwgsvbLKIGewqFQKFQCFQCBQChUAhUAgUAqtCoIjZqpBdE72iWLYgIhfIi2fK8gIOxEZkCDlDap588snJoUOHGsny8g/RJ6RHZEo0CznK1kSRKgRMO+Ij8oTo9BEzdvOMmYgaQiRCRwfCpI9Il0gVckcmz4PRyX7KxoG8IXvGIdKGUNEhQobA6ZOIIJ3KonR0I2uJmNGL3In40acPnxwiZt7MeN5557WIGf1IWRGzNbmgy41CoBAoBAqBQqAQKAT2KQJFzNZ4YpESaYwULGrXN1v8kBDfKEM6kDSkxhsJRYyQMocokq2MSNfll1/e3qqIBNmqiMDIESCETNLHdkn1SBDiRp7+tCFN2m0fdCBYDmSJ/3QhTPoigOr5TC8dynRo9yyYw7ZMuXa5xK/YJW97JHvGCT992GUjOiLPB/hIDz74YCOoZ5xxRiNmiJ00nAO+D+uaYP1TCBQChUAhUAhMEfA7MfytqN+NujQKgUJgHgJFzOahs8ttbuhJs27mCIX6tIUw9XUiWoiKyJWIGAJjG6GIFVKGrIguITFkETMRpUsuuaSRGH1FuhwIFOIVUsSOyBM/bTtk37ZEpMrWRnpsIRTdEp0TEROVSrRLBIxNMkgSGbk3PbKb1+N7Hb5+tkKKYPnuGR3GgESS1c6edtEx2yX5TGfGj+TRKdm2aOx06KcPOVs8bZ308g/P3SF2Yym4j7VXfSFQCBQChcDRi4DfmJ6Y9b/N9ftx9F4XNfK9g0C/Dp/l9Sr+jouYzUJ6Ter6C2LR5CNFkSebHwMv+fDCDcRM3YHpSzcQJBEupEWkCNFRFlW655572ta+iy++uJE1daJlCA9iJpqVl24gVb5TRofvlNGBRCmLxunjWTDEz/NjiBVbygiRjz0jcl/4whcaibKlEnGkww+aMtJk+yVCSKc+dCJWb33rWxsh9Hp9pIpONpXpRRbZ5KctlMZh/LDy7TTkjl9IJIKJENrKSPcFF1zQiBl9Y2nRnIz1q/pCoBAoBAqB/Y/APGJm9PUbsv+vgRrh3kYg6+qxUazib7iI2RjaO1CfCR+b2LTHlTE57X4AJBEn/RAcJOrxxx9vxEy0yGvwfQgaiUGeEBc5ImO7oajRnXfe2QjZFVdc0aJLyJi+eaZMNMwzWQgTsiQyhegk2oV4sY8k6ZeIGULFnsiWgz6kJ6Qp0S32vIiDP2SQPaSJDd8cYzcRNQSTDTr4Rb922xjV6S/iZbzGaOujZ+X4R4ex08m2sSOM999/fyNtB6ffMfOcGTI3lubNx1ifqi8ECoFCoBA4OhDIf5j2vxVj50cHIjXKQmBvITBchw+97/+eh21bLRcx2ypyG+i3aEL79kxucur79jFzkfcD4NzheSwkw/e4vAURufEKfBEmhEkUDFkR+UKAlCUk6qabbmry1113XYuC0YuIIVHOET76kBxkEKFhE7lRRgZtdUSQlJEwBIgtWxf5hnyxi1RpQ/DYoEM7nbZWIpl080+OaOkXUsUGvfwyHvqUE9njI5sSogbPlOkkr6y/dnZt5Xz11VfbM3Zel8/mWOJTpUKgECgECoFCYBYCi37D6zdkFmpVVwisDwK78TdcxGyF879oQhGCJDfoHKkb69/Xp0/qlEWMbNVDMmzX8yKLA9/cwodgIS8IE+KEmESH7X833HBDIzC+YyZaJBL1lre8pUWaEBfRKyRKdEobMmccIk8IGXsIFZIlIYjIGoKjzjlShDTxg6+IlnZbCvW37VJbnu9SZsu2RITNVkdvf1T2nJyxs4MwJiKmnSw/ETV+84/OEEokT9SNL6J0DhEzWyVFDG3nzMs/Mid9DrdKhUAhUAgUAoXAIgTyG93L1W9Ij0adFwLrh8Csv9vey1X8DRcx6xFe8rkJnTeps4gZUpPU9511rg65cGEgGxICg7h40cfLL788eec739m+x4XoIC/IkW2FtvY5RJ1EjpAY5OUjH/lIiz69//3vfyO65VkvfWxTRJLoQIjY8gwb4uMbYyFa/EKI+IbsIHSIG1LIB9Gp+OEcIdRGp+iZZ7z4gxQhW+yKpCFdSBTihFjaPqlOFI/vxoKswQJh5A8/yLKrHRELUZOTV8dP2zI9Y2aMvmPmOTN+jaVV/EGO2ar6QqAQKAQKgb2PQP9bXr8he38+awT7G4H+73XWSFfxN1zEbBbSS6ozofMmFTHTbmKHR1xI/z7vz0WB9LUlECFBQnynzPfIyF199dVtSyJSguSQRW4QF6QIeUkkifwtt9zS6r2VETGiE7lBpESzkCy6EBbyyB2CRgbBs6VQPRm2yCNOInMibdpFzciSoR8Z45OomTK/+IqcyZXVs6kswibRqUw/HeSNBxbs0K+MgMGFrD5Inqgdv0UFyfDT+B566KG2ldFHtxEzxG8sGV+lQqAQKAQKgUJgowj4fUyq35AgUXkhsJ4I9H+vszxcxd9wEbNZSG+wLhM2nBj1SJf6YVuvOhGzyAxzZKTXgVA4JCRDQqoQDBEh2wIRLpEyz5YhS5deemmLLCFWymwiRuT4qY5OpAaR8VZGxMfzVSJlSA0Shbywg7ywSVZSlhAc0T7ybDhHtowhz38hZkiUMnvasrVRW7YNIknGxwabImZSyJ+xssEmG4gbP7Jd0jkdysgce8YES0RLX2X26YQNYkfPAw880L71dtZZZ7VPBogUZl74MHaurdLOI5C/wVjuy/3fTtorLwQKgUJgtxHIfar/Pdltn8r+/kYg15xR9uf9Ndif7wQa/LAeG/oU2/zpd5HttH9jfvX+5XyZeRGzbaAZ4tRfONSpdyAww7bNmBP5iQ4XcEiMLYJIC/LiXNTqpZdeas+ViQS50G3H44Pny971rnc1ckUeIUJUkBvntvEl2oY03XXXXa39sssua6QFgfFclu9/iTrZdsi2LYBI0ZEjR5q81+WLRtkOiBghWUiPPvxDkGxntOVQGeGhF+nSB2Hy6npEzKvt6SZDl1fy+4O01RFBNDbEy1ZGNnwYm598PDB9li7k1ItOvIWSD6+88kqbi5NPPrlh+sILLzQ8TzrppNaPT2QOHz7ctjVeeeWV7QUgfGR7eGxmHkt2NQj4m+h/YIZWMmfD+ioXAoVAIVAIFAJHEwL5vexzv5FZozqXku8ENlkrx1Z8S5lv1sBJ8S25+nlrAO29rPJeSEXMtjFLCJBJH068i02bC2rYthlzvX46kS7kKREs+pE1pMyzUZ6TOjAlJscee2x79uv555+fvPvd727RL7LpyweRLeSHLuROu++d3XzzzY1E2QLJd0QKwaETmUOCRJiQKCTJx5hFmxAgESwvHUGa+mfK6EDUEKm87INOMqJUyJ12xIpfyB9/jEVZBBAWiBnyZ5yIFHk6kbvYoBMxowNpowPx5Lfx+BaaMbMJD8/GIYWInbHAETFETD1nZkz6DY/NzGPJFgKFQCFQCBQChUAhsFsIhPT0OeJjbSMN853wM770toZEK36N+TiU73X1fYb1Gy0P9ff+bFTHZuWKmG0WsU4+E9ZPlDoHIoHw9G1d102d0odcICnOESqH6JKPL4vyIBq2A9qCZwuirYy2JSJmB6ff5JIQOzL+GJ0jJEhWtkgiJh//+MdbJOuaa65ppAfxQ5oQqWwJFDHTly+IlZTIEr1IpLEjQPxWJzpHhi1RMAQL6dGGWPFDVE0b4sVPpIseb1iUEEEJuZOMw3bF+CEiqEwnX9mgB3FEFvUXITQ36uDHB3LsIp5PP/10e8bslFNOad8xy7jMY380B+qftUfAtSEt4+9w7QdbDm4agbo+Ng1ZdSgECoE9ioD7XX9kTWM4+Y1MvptDzH05eXyJb8nVD2Uim7yXTd1Gc7qtF5Po6vX155FZRl7EbBkoTnX0F0fOh5O4VVOIDhJBLzKCgEiiQg8++GAjZwiEKJa3MCInt956a/u4NKL2nve8p/VFVpATehAVESdbEp0jWKJGt99+e9PtrYy2/SFSCBadIXRyRAvp0s84kSokCnlUDil1UbOrD0JnLEiRiBhyqQ3xQpDYQARFvMjayqif6Bg95OnmrzId7CJiyJw+B74ZIbM9UlkELTr57NX/dCKDMGWDXX6wk5d/nHPOOe1tlkd7xCzXcrsoZvxjPnYr8W3o39CftPf1/fki39N/TG4zusZ0VP3uIZAf3X4e+/Oa/92bm7JcCBQCy0XA/Sz3NHl/r8t58uVaHtfW+0OqL/Ol9yfnyXv5MQu97JjMWL3fB2vW6JiVp25Mx1bqi5htBbVBHxdSjkySPOcD8U0V6R0uHhAiROKJJ55oz0Vl259okK15okS2JIqanXfeee0lFsgcgkIWMaFTjnggX/QhN5/85Ceb39dee20jbWwhcwgQX5AipAuZocP2R3p9wJoMwkMfwoPQ2WKIvLFja6EyO8qibkgVEiVqZasi/zxDhjQhVXI2bDtURkARKOTNd88OTImY/p4Po//0009vxO+xxx5rZRFDY/jCF77QcEdcETbbI+m0tRE5NQbbHe+8885m75d+6Zfat8xE8jKXfb6pSdyjwuZ7UVrGNb7Ixli768+1Fh/mzc9QZkznsH4RBtE77FflvYGAH10p85hrKN7X/AeJyguBQmCvIzC8n/Xl/h64U+Nk3+94/Bjm1qiOpFk+pk9khnn6DOs3UuZb/xvR63KeYyO6NiNTxGwzaI3I9heXiXIh9RM40m1D1XQ7cnGGQCE3L774YiNJnv9CWlxEthwiSLYx+ljyqaee+sZLLPS1kEV+ECMRMnoRFXW+H/axj32s2UNMEDzytjIiRKJhSBbZ2PNMGVIjWkcn4uVCRsz0R9RExJAwfTy/5Ttkef5LX99C44NxIGhIl6haonuIF3JFh37IITuIKHJmHEgVeQQR2fPCEFi89a1vbbryQhERM8lYydHBLz7z895775185jOfmZx99tmTq666qrXlj6/PNzR5e1wo197YMILHWPuq681ZiFl8SR7bxiClPnna5+XrPv55vlfbxhDwHz9Srovk6e2eOi/lvjxPptoKgUKgEFhHBPrfOPc+KflO+Bv7+Z0e5nzp77HxLTkf02fM3152TGasnu7+NyC6hvlY/63WFzHbKnJdP5NnkSg3YdnG14ls+TQ6KXCOhHz2s59tER9kSZQJsUBoRIBs13OIltmWhzCJmiFKfEOuLEaQGKSJDpGtpNtuu62RJ6/ZR5RE2pAmOvVD7iT2JG88dOHSJ5FB3PRjj09sImlkkCHkTjsCqa8IGP1Ilz9Cz3oZqzplJA2+tjLyhc6U6dXuGTHyInlsWLCzp448MudcBIyP6pA9Y4/v6g5Pn9cTiRRZOzh9Ng9xM47h0Qa7z/8xB46xFEzG2lddzzfXgRRf5Cm7tobtrqfINME5/6z7+Oe4Xk0bRMB9Quqvn34h0P8oz1LZy85qr7pCoBAoBNYVgf43Lr+LyXfD51nrjb4uviXnY98+y+dedlb7vLrdwqeI2bxZ2WBbJi8XyGYWfzGRvso5t6i0MEBiJITGyz6OTF/0gRCde+65jdwgGkgHkqEvopLvcYkgiZqJZiFCZESkEmlA7Fy4SAqSc999901ee+21ycUXX9yeMaMP0UKIJARIHWLGLySIn/oipMgNf5AoZaSMLf7lmTJ92KSDLmRTf8SMLtEtKdshETV6yfADDiJtSFOidF4I4lxUTRsZY0U8YShCxpYXihgPm8ZMBxl9YProo4+2t1x6xsyzeXzMH7Y8R3PwKPgHZvNSsJkns8o2/o35oC0L68hsdv7WffyrxPZo0N0Td+MdXh81/+tzFWQukvOsiPH6zE95svcQ6P+W4n1+K1Pe7Xzo49C/YXvv71C2b9voefQvQ9dGbRYx2yhSK5azgHQB5GBOnQPBSKRMJMzWQlvtvLxDJEgUC2lBMpAhdSJftuSddtppTdYPGNKir62DCBNCgsy44JAppOvQoUON1CBmnr1CVkSZEBp9bCvkD0LHJpKIBHm2jW5lWwL5hggiWbYdKrNvCyZiyLZIH8KFCPLD8178fPXVVxvhE7FjW7sx8YcOOh0Hps+XZSsjGbJeWCJlm6fvlCGQooz8z2v9bXXkl62Otjfy2Thg5nk1xMxHtkXgpPxRJm+V9U8hUAgUAoXAjiAw6zfSf7LVPXlH4C8jhUAhsEMIFDHbIaAXmQkh63MkxYEwiYB5lTsC4aPRXmfvf3u9mdEPFlISEiFC9fjjj7fnpY6ZbmVEMLSLNtm2h2yJXiFOZEXY2BV18vIL9mxlRJy0y0Wv9EOqRL8QIr55xT5CIzKHmCFVtkh6ZT+yxT/6EDvEERkSAeMrYocQIp38Qe7knkFD2Mgri3bxMcSMPBz4JcrmnB062ZX4BRdkL+QOgUXmlL1gxBZKOjyLZgwiec8880wjdWeeeWaLmAXT/PgnXzSf1V4IFAKFQCGwPAT638acZzfJ8qyUpkKgECgEdheBIma7i/8b1vNDoyLnSJEfHqTDSzkQFARJFMzbBxEPkSOEA4ERhZJLDz/88OTTn/50IzO2PCIYiFRypA4p0TdEDcHxVkakyAemTzjhhBaBQ+CQGREn8nxCzsghMxISRj/iZYsgkiZ6R56f/HJo088Y/W+nOtEwOvURjUPc9EGClJEyffiAWJLnv7HQwX9+8MHBBl+18VMyNrbYkJBFZIy88SGltjOKSD733HMTxOzCCy9smJIPIUuurlIhUAgUAoXAziCQ30XWcu4eX6kQKAQKgf2EQBGzNZlNPzRSnyMOokqiVD78jOggZMgF4iKihLAgKggQkqEOebjjjjsmTz75ZHv5h1fGIzgICzKG4NBJT4gGUifS9ZGPfKQRlOuuu669AAMBEmnTzjd25EgO8sM+HWxLiJkomEiW57PIk3HuR1Q7EqQ/XxAyvmjTRxIRY8PYtetDj7HxQxmJ4j+7cBL9YoNO9hGxkDf+IbV02bYIR2QXoaMTMSNP57PPPvvGM2aXXHLJG0QuOCVvjtY/hUAhUAgUAjuCQP/byKCye3mlQqAQKAT2EwJFzNZoNvPDE5dEjkR8RHduuummRl4uuuiiRrJs97O1TwQJEUF2RKw8g6WPLYm+5eXtgiJmdJNBfkTdkDAJ+UHUkBME6UMf+lAjRr5jdmC67Q9Z0e4ZMeQIwWHH1kTEx/Nq6m0Z5Itnt/jtWS7bHdlBgMhrt8XQNkI+HHfccY1gqqPTVkZ+5rtlSJQ+XoUPA2Vj1t9BP72InO+UIWZ5xuyFF15offgBk3xvzVZHkTav+ReJs42RDcQMgfSMmb7eZHnFFVe0vnAKIUuurlIhUAgUAoXAziEw/I2s+/HOYV+WCoFCYGcQKGK2MzhvyQrikEjXhz/84RYZ8m2tEB7/WygKFBlRNERLhMszZr5lJlrmGTMyIkvkkSLbJBEopARR0Q/5ueGGGxp5uuyyy9pzY3SKUiE3iB1iRhaJQgjzHTMkTvQKiSLn2TZ6jQGBo4NtRI0dbUiVhNz5wVX2Q0un6B4ySE40zFZFETJ6lJE9pCoRNKTSWBA3fb1kRETPS0r4jrwpi5DBhw/k9IENn2GCzDpsF73yyisbVnzMAiC5ukqFQCFQCBQChUAhUAgUAoXAshAoYrYsJFegRyTKFj+RnN/+7d9ubyfMs1+IBFKhDakRLXIgS5LnpA5Pv8klYuRlIQgJIkeniBoyIkokGmZboaTOFkhvNPRWQpE2bfrJRc+QGXaQM4QLYWJTnf6R0c4vMuodSB5/yacOWUMY1SFhyvzS1/j4Gp0IlX7kJfLwyTNmfOAnG+xK/HCQEZWjkx9wQNTooxcpk/Js3oknnjhBTpE/KYQseausfwqBQqAQKAQKgUKgECgECoElIVDEbElArkoNEiHqJJIlsiT65RXvnrMSDUKURH9EjmxTRGiQpQcffLC9xdHr4m1nTHQJGdEHEXJuCyPi5RypEWmzle+UU05pr9nXJiE3+iA85BAaRKcnhuoQHnXk9UWy9GMHyRK54jtSJQJGh8gdvfzXR0TMOLTrI/qVbYf0ZHukSBg9SCoZBEvETF8RNLky8gY7KdslReWyxZIMn+GG0Hr7pRefeDMlG1IImTznraH+KQQKgUKgECgECoFCoBAoBJaAQBGzJYC4KhWIBcKCQHhbojcw2mKHmEmJgiEVSBECIhdh8h0zJMvLQnzzDMEQUZJEh0SdkCcRJMQJKXG89NJL7ZX3tkB6HgtpEsXyDBlCgtDob6uiet81Q+r4hEgd+eZ3zBAgZR/ERs68Tp8PnvXyHBqiiPwZHxm6PXNGp+fFjIFNOnxXjA5kiV0+eMaODc+dIXt0Go9nzOhCsCR4IXvK/PSxbTZEBY1XRBExM056+Pfyyy9PEFoRM2SRvuHRlNc/hUAhUAgUAoVAIVAIFAKFwJIQKGK2JCBXoUa0DCFAUv7gD/6gkQZkJhEwREQig2zkuSn9vMTi3nvvbVE1WxIRGDJIGPImaibCJGUroDYfYvYtMmSOLWQQoUFUkDgfcha9QopEs5T1R5oQRS/yEBGzFTDETBnJQoBEtkSvRL7UsYlUiZwhd3zTzr9ExPRh48D0OTbPofEJkdJuKyb9SBWdZPTlhxSyRydb/GbLy0Mkb3EUAUTS4IwkPvHEE41IesbMmIakTLlSIVAIFAKFQCFQCBQChUAhsEwEipgtE80l6xJNQiIQht///d9v0SkRMFEjW/i8bENULc9HIUwSciPydejQobbFUR+EBaGgD4FBRBAryfZDbQidV+wjdZ4xs5UPmWPDVkkROf34xYY+bLKPIGnPSzTYQORsO6QfSaNDXzJ05hkxWxf1JcMHpAlZ4p8DMRQVNF5l8uTYlPOBPv31Q+L0QTz5wCYZbcisNv2kbNXko3E8//zz7WPeMP7lX/7l9vIP4xwerXP9UwgUAoVAIVAIFAKFQCFQCCwJgSJmSwJyFWpCzETCbrzxxkYuzj///La9DlmzNRHBQHQQF+TEGxcRGG8WvP/++1sZMVNPBrERuRJlQlISRUNQkC4fpbbt7+KLL55cfvnljdhoQ3Do5wubysgKP/iZbZIiWfQgRbYBimTZNikypYwUkUHEsk0QEaNLmT8iZHSKViGAtjHyHfEyNhEzdcgfsod0IWt88MZKfelkQxkZRRBFzGyhVG/8vv+mj7HQzzeE1gtAELNrrrmm9R+SMuVKhUAhUAgUAoVAIVAIFAKFwDIRKGK2TDSXrMuWPKQC0UHMkAkv//DSD2QEwUCWEIxEjQ5MI2MIjefLvGHx2GOPnVx44YWN0CAv9HkNPZ0IF6IWooEw3Xfffe35K9/wEjUTfWIHUUF4bDvkl22HSJQtgqJNtgyKgNkGiTjxUR9bChEt5+yycWT6HBpZffjvGTI6ySBedJLTjjTRiQDa+oho6W/LJbJpCyUfRfmM54ILLniDmCJaZ511VisjXHTanmkLpGfM4AGfkFakk15bGeHoW262gBYxW/KFXeoKgUKgECgECoFCoBAoBL4FgSJm3wLJ+lQgIxLy8od/+IeNwCBmPn4skoSMifRkGx4igvDkJRuIGYLxnve8p0WjEDg6RZ6QGFEnpEMf0TN6Dk9fsW87oyibZ9PoR8DoQZ68HEPuWS06vPzDFkCkSfTKizlEyZAwfuT5MG22HurLLv9Fr2wv9G00viOUKbPreTLEUfRLlA4pI6Ps5R/O+cFvLwyREDU6lPXJc2v8REaRP76EUCKQyvwKwRNpZOv9739/G3cRswZt/VMIFAKFQCFQCBQChUAhsEIEipitENztqrb1ziHK9eHpB6YRBhEgH5lGPmwXRM5EekSWkBuH7XnIj+iXc29XREgQGfJICLKFQCF9tgQiTXSKJD3yyCMtUnTJJZc00hJbdLOFqOgvR8rk7IisaUeqlMkoI0jaHOq0q0O6EEKE0TiNQRIZJMuunL9y7fqTR7LYcPBLnTYRNz6xK8EIgSQj4qfMLhllemEAY+N46qmnJp/61KdaFO26666bHJgSUvXDoymvfwqBQqAQKAQKgUKgECgECoElIVDEbElArkoNEoMUiJghTF7/LmKGbHgmC6lAtpASRAMBkTxTZTujyJQtgYiZCBRdeSZNP0QF4VGPCImIiZjZwuc5M6QtJAl5ElViJ2RNHZKDYPHJ82SicvQie0gWAigqZcsgO7YUklXH/6997WtNp3a+8B0ZowPREhHrdSJdtiGyKfqnXRSNvEgX/45MtzvCLjrJ801ZpA6x4wfc9NPHOGx59DZLEb/rr7++4RDSxndJzu+UW2X9s2EEYOeQYLufU8Y5Nsa6hsaQqfpCoBAoBFaLwPB3fPjbNOv+Xffs1c5JaZ+uMacX3jdWSIXGWiKQSM9NN93Unu8644wz2nNRCItoEfKDmIgYITtIBlLjua0HHnigtR88eLBtK3RDQWSQEoQJEUFU9EHSkJO8Ll6U7cwzz2zEBblDArW//vrrrT/iRpcPOSNKiAyfECJ1onPqECZvOlT2Cn0+2IbIV9sfRc60GyebdLLBP/0diJrn1pBEWxMRS8+dGbsy0ma8dHqGjL/PPvtsG5NPC5Dz0Ww6tPt2GRt8RdIOTKNixqAdMX3mmWeaXsQsr9cfEoj82dRNenN/NnAz564lmCL9+zkZ67w0vK7myVZbIVAIFAKFwMYRyO/0WA/tfsPzO57fJvLZTaMu7b0smdQ7r1QILAuBImbLQnIFeixeJQTqd3/3d9uHj72QA9lAjBAhJAsRErlCsESR9PORZM+YSd7kmGfAkDl9EDE3HDok5A6xycsvkCjPmVk45nkwNyFkhizCIrqFeCmLVHlWzTNlIlGInIgYwogEibB55oyfiJabHlLFJpKlnk7+eR6MTm+PpMf46EQi2UHaPJdGPxmRMVE3OpEsiZ/IHmLHT2RQxEy7PtGJ7CGN7MMFbt5MqZ9nzDyDBoPhAjo3/LoxN7g3/E+IWX7s9jsx2zAwJVgIFAKFQCGwVATyOz2mNL9D+X23drIW8Lue36bI0KG+/83vz8dsVH0hsFkEiphtFrEdlM8NAvH4jd/4jUYavHXw5JNPblsTRYJEj5AWN5FEIRARxOSWW25pdT4WbTsjEoZkiajRjRQhTMpIjJuTSJO3EorMXXbZZY385NktN7lE2hAmNyUESkII+RBddHuWK320KWvnL3k62ETu6PJKff6FuPFNHxExRNK4jMF2Sv6KrtGL/MEIUUNOESzt2vguKbvBaqfX+OklgwzyEwEWMbzrrrsaCfQdMySOj7lxN2XTf3LDrxtzEPnWfAwj8+CQXCdS4dhgqH8KgUKgECgEloRAfoPG1GWNld8hZesov0fWBnI6+t+nsfMxG1VfCGwWgX1NzBb9UfZ/YJsFbifkc4NAKn7913+9vXr+4HRb4oFp1AeZQGSQC5EgY0V6kDWERxRKxAxZ8XbFRKsQDGTHjcj4kRR2ECzkxnNsiBk773vf+5ousgiTZMsfAoMk6YswKSNMykgTnxK947sXkSjzjQ3PnOnPT0mUTRK5cjNE1IxHH3J00uFc1IxNBNHYsjUTmTMu7fwJ2VPmFzKoH5v68JFeNuhB5hA8nwOwBdQWRxEzkclVErN51+i6X59t0ub8Y2wIGPz6saRe19Qn7+vmqN4TTcbZz28/xv00zj0xGeVkIVAIHHUI9PffWYO3VpDyH7jK+Q9ua5Hhf8gOdQzv6cP2KhcCW0GgiNlWUNuhPogWUiT/rd/6rcmRaRTsyiuvbNv5kAhkS3te5uEmZKsf8vH0009P7r777raF0evyRajIkUfoQqqQGTclxATBQ0q8mdAbGb2a3/8g0evFIfp4Dgs5U0bGbCnUL8+cKSNBtgDa2oiE2UaI/CBJ7NiqyK7tlXTbPsmOMsLpOS9ELM+YIW5eb0+fLZYIFuJJVpkOL+2g49RTT21ky3fN+OmZMnbJs3v88ce3w1ZGBwLm8HIQ2yFfe+21hp1I2Qc/+MEWaVwVMeP3orSXb/yJjI3hl6gZDDJOec4XYbPu7cbnhz7jydj68qIf/nUfY/lXCBQChcC6IrDoN3ZIzPqImXVRf3+epSv38nUdf/m1NxHY18SsX/jNmp7+j25W+zrUZQy/8zu/077N5aPHXl6BICFsyIokAiQ6hQB5bsr3xGxlFF2z/ZEcUmTMSAeChpggSA5tDtEyxMyzbLZAInP09i/mUOf5MDcukSpRMFEoES1kiDwf+IK0IWaiZQgSP5Xd0JAk4/OKfjc9JIse5EjEC2FC+ES29PE/WMdM3y5Jv/EjmwiimytSZRx0IpCea0PgEFV+0Id8IaX68IPv/EJwEUEyCKDxJ2LGHsyG10pu0tu5MdMRPbOuNbq3o3+Wzp2sM7d+6Fxr8BuORXuu74w1+U76uSpbxuZvVMq4ehzUDa+rVflSeguBQqAQONoQmPf7Cgu/T1K2LYqWJWLmd8s9eph6nbPah/JVLgQ2i0ARs80itsPybhLSb/7mb04effTRiW9rIVpIBXKBVCBI5CwE3SgQE4ToE5/4RCMenhc7MCVoiIsbEQKFzCAiyI3+yJWbkw8zI2eeSRNps4WRXnb0Q8pse0Sg3KCQJHkWnFlo06kvv0TQ2EaCtIuiyUX29DMWN0HETT9+6SciJyrGBrtkET6LXePguzr+OMjbmqjN+NgwJnjwUR18+KUeiVTWJ1s5EVov/+Cb6CQSx4ajT7k5b+fGTEf09LpzTvd29EfPbuXm0AG7WT9y5ke7lLGS3ctj7rE2t8PxZWzJe/k6LwQKgUKgEFgeAvN+X1lxf3Yvzu+O3ySHsmNWGuqse/kslKpuOwjsa2I2/AMaArVOf1C9rzmX8xHRsJXRNsMrrrhicuGFFzYigZggO4iKGwzyg9ggIKJhImbIlG+fHXvssY1MudmQ9YyWSJRokn76ICiiVQigrYjsZNsjIiMhSW5cyA49CI2cHXXIIpLnnC1RKySRn8gOeUSMn8gd2/ygk325dvaQMHK2bSJmbOQNirGhj/GLeFn8iwwaC3llOvjCLt/JI3swpZeMMn2OI9Ptog8++GDzFdaI2ZBUZH7gsZ1rqNdD16y0Hf2z9M2qm+XHPLu9/Dw5c+lwHZAbytLj2unb+vNZvq6qjp/SRu0Hg+GYhv6RWyQz7FPlQqAQKAQKge0hkHv0PC3u++7PIWH65J49774d3fNkhnbTR33O9d+MjqHOReXY6eVWaa+3s1PnGWPyZWMavcmH+lM/HG9w7ttTN5Qdlvc1MRsOdp3LbhAmMAdfnSMoyM5HP/rR9uHnd7/73ZOTTjqpESpbEk20LYiIhyRqhHR86Utfai//QEQ8K4ZgOBf5Ii8qJSFeCE9sPffcc42YeN7rXe96V+uD7ITweN4LEbJFkM+2GLKN+NFtuyCSRB4ZQ3RsM0T06ES6PEOW58MQM2U+0akfeVE2PiOOSKQom7HZ2mjM6mDDf4TL82NurgemkUHtIl8IGXljoxNJ9NyayBwyaDskTOhA5PjNX2+mJHf11Ve3/iGl8Mr8sOHYD8mYQkyMx7jyQ9WPj5zUYzBLru+zF86NB0GUG/uQiM8aQzDYD+OfNb6qKwQKgUJgLyDgXrzVtJO/4fzs13l89luzyt8Q9hwZpzzn7PfnyuuYFs1vfouTw9Ox0bFtR7+++c9l2MVmcnW9/tQn1z4rFTGbhcou1OWi6vNcXCI5tiU+9NBDjQD5jtkx02efRJwQFITDRCdyhUh4xur+++9vpM6WPFsT3QT0IY9ciUYhLyJM/nhF4Lw0Q2TOM2UnnHBCIy6iVKJdtv4hPOzQp6+tj0iNMiKFACFfntFCqpw76OAzAobMGVt0+HaYMSJ35JAsfZAqZAwp008bEsU+MmjMCB7/yRgfckg34sU//RE3hBHBFUFD/tSRsa0RgdQmguYlIXA+MCV43kqJoNFDp5T5YXvRH9cuXEZbNjnr5jFUFpnk2oPLUHYvlfsfS3NqTHLj1JbycEza99M1MBxflQuBQqAQWGcE+t+irfi5k/dvvg6PImaLZ20jc5zfcNr63/DF2v8vcRqTH9PPN21JuZ6Sq+/9T33y9BvmRcyGiCyxnAlZNAlMko18f64N8bj99tsbYUJWvGnQyzcQDJEvJAPBQLgQFeTjyDTyc+eddzbCcXD66nvRr+hHyJA3NwUXlX7aEDPPl913331N/uKLL27RJXa0IS8iWRKSZMFqmyL7Ik8O50gWG6JhCCD92viVbYfs08tvkSpjROTU24aJfCFZsEOq6FVGLOkgz35sIKjK2unQbnz0k+c3Mic6CCP6gxvf4qc3RBq/j13byogIGjs9UjDkV3/eGnfwn522HXtDDBZd2+k3JreofdWQsp+DLX7mcI3kOnMtOYZpt/0f+lPlQqBHoK7PHo06328I5Pre6rjGfpe2qm9ev/53JufWLI5VJXas8fpxjp2vyoft6t3IHAdPufHBtB/nPB+2o1/fELPY6/P4Fft9W+pm5UXMZqGypLpMGHWLJiQXR59bGOYP1zfJDh8+3D4ubTujSBBiY2sh4mPRiGjYMojA2I6HzJE766yz2hsPLTJFiWzlk2x5lPTTH5kSLTt06NDkzDPPnHgDpMhXvyilg49kkRUkTP+UtdOrT0iTaJeoE1KEHOVlIAiR8SF3xqpdMiY2kD82+CyaZSyIF/3RiczRaVsiXaJ2+oji0QkjviB3In7IYcidMh/I0EGnLaBe/gFDnwwwfn7RSX/mx3w678t8zzw730hK/1myQ12RTa7PUKbXM6+tlxs77+2MycyzoX909HI5X9Q+ZnOZ9fkbjU9059x15vp2/eQ/MlwDSevgf3ypvBCYhcC863uWfNUVAnsJgVn34Ny/jSO/P2Nj6mXHZJZVH1/63O/JKn2IrbExrNL2mM3N1m9kDGTc6+TGtBlct6Nf374/2z2ms9qNv5eZhUcRs1moLKkuFwp1mbB5E9JPsD4IF0JgUehFHl7K4U2J3rKIRCAqSI3Fo75Ike2Ath2KfImYIRbnn39+I2zkEBBb95CWECoXMWIlt/3xnnvuacTsoosuavb5ggBJthiKLtGL4CBESA9ChEiJdiFeIk1knNveiNyI9vHBVkX+6sOuF46EVBqT7ZLs2EbouTNtyhbIbMBVhAyp8pydMdEBW6/Lp5MO9Yiq8cYv8g745bk1fiJpDt9he/jhh5tuEUORM74jlvRnjpxvdn4bgN0/0dVVfctpf72Qz0FQW9/+LZ2/KTOrXl10zdKTsbkmxtIi2/TTI0W2t7WofczusupjPz7FR/q1IWauvXnEbN74luVn6SkEtoqA+7yUazvX+lb1Vb9CYJ0QyO9Uf13nWuen+/i81MvOk1tW2yx/dsqH3bS9Hfxm+d3rgx+ZHNpWQcxm6e/r+DGcy76dX2lPrm5WKmI2C5Ul1flRNDFSJm3eQndoFtGyKNT3k5/8ZHsphxd5nHfeeY1YRL+teciIKBEiJwKEyOiD6BycbmUUJeOLGxlylAsD6VOvjHz4MDVydvLJJ7fX8sd/5EXyog6Ex7fU2PW8mIgWAoU0sYuMiUyp0+bbYHQjjeyJiNkWySfbBBE1YyXP3yPTbZi2N7KJRFkgI1FSImZsGCsd+vIDVsd887tjIl/8RKz4or8+5OllH6FEukTHEDXyiJm3MsITMT0wfdaMj8owCh7OYdkvzNXlaM4u+Ieu6Jsl2uuKbHLyffui/rPaXT8Oc9Jfl8akXoJpEntS8tSP5YvwWdQ+pndZ9RmnMfbjpx/OMMjfIJkei17GeeYiubpKhcBuI+DeKeW6TL7bfpX9QmAZCLiH5zc017Y8KW0pD/Nedti2n8vBZS+MP76OzUfGQC7HsokZ29Etj37nfYovqUuflNOePPXDvIjZEJEllv0omhiTMDw2YsbCMBN44403Tm677bYWLUMYbBOU/I8+4iBKJpJly59+DlE2JM3Wx0TSkDdRNtEmZIl/yBM9dCBRIkaiW7by6c8HNtwEkRu6ESRlUT0JSSNjzIifnI8uYJEodkTd1GnnqzplpEgS2UISkChtynzkGxlEjN/KSBQ/ECsL5pBSNtQjXRbVSBff4OJwbkxkEpGkg7/qRN5sG+XX5Zdf3oid8yzK+eWAifE7hnObOWuDmvNPdI2JRK/2yPa59iGh6HX1/fv6nBszjJBO4yNPPxy0KZsPuTTMo2cshw1dkr6O3NDULWons8rEvnEae+Y39rQ5pPic8avLPMwbH7lKhcBuIuDvW8rfX/4Gd9Onsl0ILAsB91/34v76Ht6n59nqZefJ7bc2mCWtOwa9r/G5z+N/fpO1qUt9LzvrfLv603+Wvd6n+NXns/xp7dOO/ztDY1JVvyUEsmhrQHcXyqwJnGXAwpCs/GMf+9jkrrvumngj47nnntvIBZKBkCAcSBWyxCbiJfJDPq+997KQvBYeUbLgRqToR4T0s7UQMfOMlXNvc6RTsji1eI1PyshRFvZ0qbPQJUOfw6JfvYRYaUOsMi46+S8hTGRFy/RjmxwCpQ+S5kDqYptdOoyJ/hBGL0VhKy/vQD5hRZ4MfcgbfWTgQf6ll16a3HrrrS3K5nX5Im70zkrG6s+nX9TzN4c++fNSNzyHjzRsC178kejXVzk6QpyCKV369bL0qpP3KTrog6X2kDN15lQOk+jr9UfXUG/qk7OjH7nI9ueL2qNnVXnswyiYx5bxS72/aZPr6yDXy/TnvXydFwK7gUDuE2z316bzSoXAXkfA/dd9ONf2rOta+1iaJT8mu5/qe0zWHYPe1+EcDH3vZYdtw74p931Sl3yoo5dNm7qcp1+f933Uz5NNv4qYBYk1zP2oWjDKb7755vYdM8QM2UIwkCeT7vkpk42ciRiJSD3++OPt5R9kPStl+55tgolQIW8iRRbfiJE2xOixxx5rxAyREzGzsGbfFkCEBmmiXyRKPUJk8S66xVfkRzt9SI9FvsU/f9lDshAifRAiSZSKnK2M+iCVxuX5MGW+6sdXZJRPIVXGrG90ivQhGa+88krTgVjpQwcCCh9jMQ52jTl4spm3MvLt0ksvbT6xy6YfgSzgjV2ZLe2Scp/Iph8Z+s2Tej7T4Tx/qHK49Db0YUPe60csJfPJhjIb8Ipv+kj0OddOv3Zl53Qqa3M455s2ZYl+c6pOxFKf9G8C9U8hUAgUAoVAIVAIFAKFwLYRKGK2bQhXr8BCOc+YIR6+L4a0IDIWzKJDFs1IBxJC/sUXX2zfPrNw90yaZ8YQEgt3fRAdZMpCPERKFMo3vHybDFk555xzWpuFOIKDjCFN5NlGthAe9thGIDw/pg6Ji37bEPmHrHm2y4s7yHp+zALfc2t0sEGGDb5o5z/yRweioF0fOozV82P8ERFjg10pb3bkFx3GYMwhF+T50m/p5JNn1bxkBU7eTOkFJPrzj09sS3BTL2KFuIRgIaFkkSw62FVHd2SMA5EiR5+DLJkQI/2VtdEvqaMvR3zRrh8dbBiXcWqHjTrt5ssRH+nnS2zIldkhY3zm3Bj115YxkWWjUiFQCBQChUAhUAgUAoXAchAoYrYcHFeqxQL+U5/6VHtbIkIkauaZMVEwbaJFFucW3YiLhfjnP//59oyZhbnX5Z9++uktombRTRZJIWtxjfTog3g899xzjZghRb6Xhrg4kCCLchExCeHJNjo6QwTookeZjDZEzcKeHjlSpC+d7OaZMiTL4l80SwoJQzToRCSQPYlO46UDyWHXuEJOlNmS9GOPnDq+0elcvaSvenpfeOGF1n7gwIEWleQHnLXxg8/GxRYdSIxooHokkmwIX2wiOciMtpAfbeTpgBU7yjDmi2ieZK4k/fRnj98O80t/cxv4AABAAElEQVQeWcvnBfgZLPRzzhZ/+aGPI7hojx9s8t0cIaW+5YbshsTxgXxIHP2VCoFCoBAoBAqBQqAQKAS2j0ARs+1juGUNFrfzksW2xb6EmHlbomiZl3kgKBbzFtEW7hbSFsy29lngv/rqqxMvDLGQ94p9H5imi02LcwSDvP4W6RbuFvreZugbaBbg9JBBfrQhIyJVdLBjoY8k8BNJQib4Qrf+iII+3tSIKLFFRjt9FvtICZ3kjEmdrZXs8lM93xza+EQHIqGsD3+8lESdPuzor15/8nJ2+ZrxI7R0iAqphxV/RCBhYkwwNlb6ECCEztiMXTIuOrItE6lkD2nmM9LJHr/1pYN8SBe/yfEBAVKGFxnk2JzRKTcussbBN/L8FOXjv4gjPMibB0SX/+SV+c0XPmc+yBgzP+nKllefOODTBz7wgcmBKUFFPI2LH7Bgjz64VioECoFCoBAoBAqBQqAQ2D4CRcy2j+GWNSAO8xLSk8Xvxz/+8cnh6QemfZNM9MsiH7mxkJYsrC2+EQQL9hAzC3hvF/TMmMW4BTVSIEfmEAAyfNHfIv/I9HX1FvPkEYkQC/aQFgt5di3WkSpySJgyP5ALZM4zcJI+bGlnCwGyyFcOKVAOqSSLnCEibJOBBRkEBybGr5wom62LCIl2vkj8ZS/kDnHTB3lBcGyZZMNYQojYYV9fepATmNKhHnnhE99hpI4+0SVlfrB5zPS1/ex5/g85MiewRHjoJo/c8ZlOdoyfHzDUJjpqXpQzdtjBmw7yEiKHLMGbbXaMj014mFe+0Bm/1ZElI/HD2OmE95NPPtnGc9lll7WPkxtDSJhrx8Gn1DUl9U8hUAgUAoVAIVAIFAKFwJYRKGK2Zei239Giex45s0C2+HV4xuyBBx6YXHjhhe3ZL4TAYjzPjVl4q0M0LKIRLK/Ld+7bZ6IvFvRIgAW6I5EPi3cLfmTOAj3EjW+Im3aJH+QQI8RAmb6QFbJs8ENfhIYs4qCOXiSGDu0hnfrTxyc6kIoQMTb4qYwEIBZk9FGvLCF/SCIbiBYZOkV4kAhY8k3SRxn5I0MvvBAaekXSECXP5XmWD0HjBzLIJj+RM+TGtkFjImOMxoqUwjsRMnOAQLKHDBo3YoYY6W+8/IazPuaSX8rakDV9lNk3DvgZB19gY4zm3nj5JSdvbCFm/GKHPBJpTMZBhk66ycDSpxmMBam3pVU/id0c+lcqBAqBQqAQKAQKgUKgEFgOAkXMloPjlrRkgTvWOcTMIvsTn/hEe8viaaed1rYmWhRbYFvcW1xbyFtcW9grIwGInMX6qaee2ggGsmLRb7GPKFi404FQeEOhNxmSR0i0W7DrgwAgLPxgF0nQTzsfES0EweGcD+pDmhAdvtHpIKOdProQAj73ZTLqk/gML+PlE/vICB+QDORFzn+kCpGg23jpUk8eSaFHP7k+olhIXaKPCKrok5d/yOkK3nTAWhkmsFNGaII/ogYL7fTzVx2f+cm2SBUd+qgPfvQ79NFu3GSMJXjCl151xmUc5PjhcE4HrOgIadffWOChP5v6xwb8XRNf//rX2ycDROp8MsH1o54deiVjIF+pECgECoFCoBAoBAqBQmA5CBQxWw6OW9JioTsvISYWvw7ETBQDabKd0YLa4tji3OLbwhnZQTwkROO+++5r5/ocf/zxjXhYlIvSkLf4FyF7+eWXGynT5qUib3/729uWNmRDXRbjyBOy4WDLwt4C38Ie0UA+lJEVPvFP/5BBpICMcSEjdDh3hFTQqQ8yor+ynDw5ZWMmw0b080lSx4YIFp3GSkb0Tl8kkT9ICtkvf/nLbfx88/ZK+PmOm/IZZ5zRIl+IHqy08Rl2+tum6CBLxrhF3dhXx191/GcfjiJmdIjEsf/Vr361yfDXERLl3LZC+kSuzDfiCGvbJeGhPWNSL3oqOhc/nPMF0TJupNj2SPZ7nWwq84cNmN17772NRNrKmJfAuF6NyXhck/JKhUAhUAgUAoVAIVAIFALLQaCI2XJwXIkWi+0QL98xs1gWvfDyD4toi39kySJdGXlQtnhGtnxgGmE5++yzWz/b65AKC28LcaREpAw5QzSQsrwG3yLcwp59fig7l1vIi8hZnLMpR4KQBO1sIlaICF/k+mrnZ+rpcI500EM2BAyg6ugNMSNLh3pyyqJHOTf29OM7W9r4hxiJIBkrssMm+150oozsHjx4sG3nRILZPfHEExsxgwt8ECKkhy4ExmcFXn/99fY82YHpCzLog6e+XtJizLZI8p8O2PhOGp8ThTJPfPJMmk8hIFHseJGHPs69kMV4jjvuuPa8mbdGwsELXdgQ4WODvyFiCCOi5ho5Mn1mkB7kEekmK0oo2VKJuCKI8EQYzd3h6fOM5tlbGb0FFOYSGeOAqURXpUKgECgECoFCoBAoBAqB7SNQxGz7GK5MgwWwhbAjr8u3OPfyD4tsJIuMSE5IkgU7YmCh7YUhFuTeyigalAgKAuED1A8//HAjQ3SedNJJbSGPHCFi9CEykgiLMr3sWbAjd8oO/sU+eQt95IysCJqy6JuDbsTEgp4M3Rb9CIY69tXJQ9jI0RMySp6OEDl29OVv/GBLPRmEDbHiJz2IGFwQFnqybRFpeemll1pkUnTJZwYQl7yoQ7TLVkRtolVwV2YLCWJLZEoS8WKXLeQQmYOVSCY57RJSZrwIlfHCFjFSNr/By9iNQTvfjTfPqdEBP8TbmG1B1A8pY1Piqz70wojNkFZlfSSEE2lH6vmerYwhvTBkgz+VCoFCoBAoBAqBQqAQKASWh0ARs+VhuXRNFsEW8YiNbYy22CFRefbJwp0McoC8kEVOLMgRhFtvvbVFYMife+65jUCI+Hzuc59r3yuzGEdKRNQQM4THYpzOvJ0PcVHWZnFucY8ckEMULPyRADIW+A7nIVza+M8nBAbp4IMUOecW+iFn5JEG8uwiFfppR4qQTwQrutXrzz/2nZOhh5xzWBkLv5EjkS6YHZhGukSmQnS/8pWvtM8S2Jp5ySWXNHz4wYZxG5dxs0E3nTBHevkMd/MAP76TIUtGe8bBJ1jygZ/8IwMb41VmJ3jRxSf2ETGJz/wyHmQJ2YOpdnbJI3vq+EmWXvbYUa/MP33oQCC9/OOOO+5oY7n00ktbhJYv/NVH0meziX469lMyJmm/jWs/zVGNpRAoBAqBQqAQ2CsIFDHbxZnKom7MhSz2LPRFzES4spURCbFYzpY8i2mRMARB5ETUw+JaZEuETT+L6RdffHHy1FNPNfKAlIm6ICbOJTrIWaCHSFiMI2CIA+KDLNBrUY8wWPST5a/FvzyL8IwREaHDWPRHHDI+ZMO5drYRB4RFH+NUZiOkyLjpRczY5YOU3Dk/JXbI0o002cJnPGRh9ba3va2NlU8SkuN7cWTOOeectr0QyeIHMsMXJBPuopJwR5BEmrSrg4HPE+jDprGJqPHX9ke2bBkUvbLlEFm0hTSkyNxlbpzbymjMSDkdtkvCie8wOjKN/MHGVkg29XHwiW8IOPKOqLHLTx8gp8P2R1jxQ3LtIGl3331363fxxRdPvHAGOSWX+TTGjSZ9csAix0b7r6tcxsQ/Y0rqz1NXeSFQCBQChUAhUAgUAosQKGK2CKEVtmeRO2YCObAYRmY8Y+bbUhdccEF7tsji3uLYYt6iHCFAAhAAzyshDPogJgenz04hCrbAefOihbkFOFmEBclANizyEQ9+WeCHJKnjB2LGJtIkeqSfejYio10/iZwyEhS9dNCPKEix4ZwsfcaNcBk3eb7QZczakQR1Ehn9yNOrryRXr914ERXP1IlGwcLzWfQgPIkwGod2xEzuOTHPepE3VvghpDCHn6ibCBscESuYIH589rIVfZT5hvwaK2JsnmybZFd/OhGmEGpEij62EUXEzHgRL8QZuTNX73jHO5qtkE3ydMIW0RJdRMYQPzaUET5jo1NyrbgG9IEXMsc+Ygaziy666A1ihnDAWE52o0kf12j6wsE87vXUX28hY/Kc7/Xxlf+FQCFQCBQChUAhsLMIFDHbWbz/jzULO8dYQkSQGotY3yRDzHzHTAQM0bHgFnWxsLbwVrb4toB3fuONNzZSYkuebW5IAZ0W48iFxTXCIwrDTsiOhaWFP4LBjj4IDjvqnevHL/7rHyJERr2yg7yFOB3K/GULwaAnOrRJPSaIEn3IhoV9dPCL3ujmL7LBV+fRqQ9ShLh46QbCAQcvT7G9kx6RODYQEwSOzWeeeabpEWUMgWVLJIk8siNa6BwZg5voG0zYYF8/yTzAWR9jZMe4kTb1dPBRfzqNUZ0xK/MHVnTGJmLHHzqNV7vcnPIFFvRkvuBvjuSIaOaUv2TgYBz8ER1E6u+55572Bkn/EQAv/djIPJnDjSZ92JQbBzt82evJtWouJdj0x14fW/lfCBQChUAhUAgUAjuPQBGzncf8DYs9CXmjsjuxwLaQddjK6PX3Xn3vpRQW0CEuFuMWyhb0ForaLIS9/MMi2/NjFt9IgwW/LXEiI2TpyIIZkVG2wEQCkAV6kAv1FtRs8YesNgc96pAFfuivLgtwMogBGQt8cuoS5VKfxTu9yvoaT3yR66PNQT9SEYKAmGrXz1jJ0y/i5IUeXtyBLIlcITQiVLDQzjZ9IXLeesiGZ+/g1JMoc4ZcSvo4kAw22UO82EaQ6UN4jDeEKISWTf3I0qG/scAZVnBmlxzCKJkT8kiXRCZlciKAymyyoz8ZPtNrTPwgywa8YMgPpFNCXF0n3gAqt5URMaOrJyJwVqZzUTI+ttjln3Fm7Iv6rnO78Ztjybgc8JBXKgQKgUKgECgECoFCYLMIFDHbLGKdvIWmlEWZc4u1HIsWaeQWpSzybr/99vbMmC1uXl9u+5yFuoW2ZKFrAYwcWERbvCNzttuRQyZEVGxlQwKyQNePPDsIAIJjsZmIjDGIyDgsxhEOi3mLfzb0I2MscotuOWwcdFmU842sdroRBuRBvbL+0ckOUqhsPIkKRpfxkmFHexb+8YFudthHyLxOHhkTOUTORJyMmw1j4EfGyyYCLIdznr+Dme2B5OmCA8KK0NBDrzavx2f7wPSlIsZ7ZPr8Fx8RQX4pwxjh048vyuYzpAgh8mxYti7adkin7ZH8tm1VYhMOXrFvDImY6W/bJRueIaRfHfvq4Jg3O7oezA8biLBtm5LtnEjrVVdd1b5jlusl1zw/gnfr0P2j3pHknE05TDJ3zmelvr853kga2tOH/jEbQ52xudk+ua5jZzP9hz5UeT0R6K8tHmau19Pb/eXVEPv9hv9+H9/+uhprNHsRgfyNbeS+HVnjdL5bv+dFzLZxpVnQShapWUCGkFiwWUynfitmcpG4OJCFhx56qC20D0wX/ciRxbyFNYJggZ+Ft4Xv888/3xbXFvH0WLRb2Iumiep4tokOZaTEohwxy5gspC3GRWHUsWE8FvpyBEQf7fQgBqIyknJIEzz4H/KkHclBFslrZ4dMxqE/O4gOGZEtPiKHxsk+ogN3z0HRgaTol+2GzuGAXBi/TwYcPHiw9UVk4rvxZwsjEgavbAH1YgzPmXkeDDn0jBp7bHmeix7ENy9QQea8VINd/SRl/olSmp8846fd/CFEtlqy7WUeiCRskbJjpltO2fCtM3j50LOxePmHpI/kLZvwN78II9JFBokXLRV5RSr5hZiZT/MPczpgzC9yyCji+cADDzQS+d73vrc9j8c+2/o4JGUp5VaY/mOuXT+SOZIi0/dvDV1byunvGjLXixI/csA652wb86JEnk05//SLv4v6Vvv+R6C/plwXrsu6PnZm3nMfCd7y/nxnvFidlf6+w8p+G9/qkPuGZvfs7aRcS9vRsajvIh93wodFPu7Xdtj3+Afr5P24I9vn7vVZw/Syqz4vYrYEhN1chz/WJnfW5G/WHN30eFX+oUOH2qLZSx+QAYtwJAdZQVqUEQ0EAYnzrBRyIyImOoNc8FO7KIoFhzakx9Y2ZCV+a0PmEK8sTPjhIEuHRa+LVh96LfhDzuhFRLSJGNFB1kKbrHo/usYnIVLRrZ4cHWxEB//oUhe/2UPsEB82EFIEyrkIFJKRKCMyRTeyxq5zuPCLHtghRXfeeWfz0UenkR06gjO5lBEZJJfv6iR19PEPHiGA2pEnkSvtsOU3HBFDfjicm0tEiA761MGADra0000GlsiisdCpnU/00qcPLOmBrz7mD1Eji2TGB2UYwMf1BoszzjijHa4tiQ6+9Eldn2DPH4l/aR/m6ZP6lNNf/WaJGd9ysL0RYsaePnJ9ep/jU+VHLwK5NuSuybo+du5acJ8O7qzCv79f9Oc759XyLO338S0PqdmaXBvbSau+fhb5t2r728FmP/SFfz8HwTt5P8bI9rl7vfXaTqciZltAPBOdybUIdZ4f7EysushswUzrgojQIYJhO6PFtpdSiK6IliEK2ZZmYY2kiZaIxPDLwlxkxsLaItfikz4LVqRN2bkfCIc2F6LcIl0f40GyJIQOUZCQNm0hS84RAHaRDkRQQgToQ26QQHb4LemLsKg3Nva0IQvOkZPI008vf0M0YC6ihnzxDRm1dRE5ER3y9kXb85A09o0t+snz0diMyXgQJ58lYOPgNMKGBKvnC53Gwp4y3/nBh0SUtGtjJ1FE+vUhLypI3ryQIW9czh2ZB9jQoy0k2hyaYzbp4hfc+MVW325uYRVyDE9zAGNzHnKqT8bBnjIfRWgRXM+X+QYe0qe/Q3+HcTiGKe3q0ycyyn0alrWlvzaYbCTBqT/00Z9/s2xE56w+5Of1SV+5/huV7fvV+d5BwBy7JjPXi66pvTOy9fe0xz3e9n9v/Xna91K+38e36rnwN7mdtOrrZ5F/q7a/HWz2Q1/493MQvJMPxxj55GNrnGG/ZZeLmG0Q0X5yc57JVXaDVXakfRk/4CFL3pKHmNni5o2CSJNoh0W6wyLcNj6kQqQDKbEVj4zFNlKCMFmE89ECHymIfjDwFyFxWBDrY1z6KGsP2aDLol+dc3rIOpdHHhYOi34ki13tbEtyRIlNpME42EOUtLGhDdGgx1iUjZUtxCTtCCoS4/jiF7/YMLAVD2mji08IGj3Zzogg6Y+MsIXAPPHEE03G1kFb/TzvxW8vUoG1PogWQoQIO6dXGx/IimjC2TZFfvJBva2SxmIrJjz4wV/9HfwwNuNEvOGAIPENGTcG2x2j0xzZTskXBNx1YQx5Xb46ETZ+GSe/6FCGibGZG36J7ok80unlH7ZQegOoyCFfcj1nnnO9tImc/gNXSe7I30OrHPmHTNJ2+vd9o6/Pezv9ubEkpT556sfyjFN736c/H+tb9euBQK6bMW8yl64Th7LDtV9p9QhsdH7GPNlu/zG9y6pfd/+WNc5V6VmE3yK7+fteJLfVdv7Fx9hKvlWd1W/jCPT46xXskw819fLOye3Gvb6I2XBmRsrDCSNmwiyQLXDlFrwhCsueTNvrHKJlPnxsK5uFPnuiPspeh/+Zz3ym1XumCmFAmPjCfz6Sk1tkiPK48CJjgW7xnoN+fUPGjA9xkhI1Y59ui3q6lcnpgyCwRa96+pwHG3b1kdOhHSngE3KmTZk+5IXORLiQDXqQG774tpcokO2KZJ5++unWfnAa9UJGEBf6EFZ28ixa9NHBB/U+S8CmbYzkHewgZuQQKNjaQoh4IVGIFmLGD/h5VovvnuVyfSDUMDgyffkHOT7RgUAiVp5j89yZdkQMITxm+owZAoU00SXyB0/Em3/a4Sk6itDxU2TLXOhHP7/Y57s55zt8RQYlfvAr5M6YEWQRWm+zRE4RM7rh7aAn1ww/JHXwo9shJXeubVaKzKz+s+SHdemf+t6O85QjJ8+5NnZTl3q6hv2iv8/TX136RlcvV+fri0DmeczDzGuuT/L5OxjrU/XLR2BsnjI/YxbH+kV+Uf/I7UQ+y9d18m8nMNisjVmYbUbHqvHNfYOd2Eq+GT9LdmsIuD76ayTYJx9q7WXTNiab9lXkRcw2iGomOLluJkzExMIZacib9CzilzWZ7NF12223tbcyeuZHFMhi22GhLYLjJRPPPvtsk7WoRwwSeeKbBb0FBV0hRBb76kWstCEsFtpkkAskzFjcXEJckBBkAFHS16JeOzIgkddXGXniozHQS79DOxvaJGU6HHST5aMkUkSWffK276kTdTIuhzlAktQjEgjJ448/3siJ6KLIITKkDzLKBzrjM3ljoctcPvbYY82O1+WLOMLBOI2Hj+ThBTc48B9x4bNoFt1Ik3q+KcNNX+RRjhAaqzIC69pxOKcTOTI/xs0OTPjOT330RcL4pUxGHzLmna/68BUu0UMnP5X5R4ecDnjwAc4PPvhgI5muIziol+jM34BxOZSNKddqrrPWYcY/5JLm9Y+dyA7z2B/Wp6x/rqPIJifDZ+3GxOc+xfawfigzT38vW+friYBrYF7K/LseNnONz9NZbdtDwFwk+XuelzY6v/N07HTbZsa3076tm70eq634tuj62YrOvk/uGery27Nqm739o/3c9dFfI8E++briU8RsgzOTP7B+oi2An3vuubZ90MJfNEukyvYxadECdSOm2bBwFC27+eab21v2rrzyykYcQmwsqEWIREUQE5EVi3H99LdoFy3ij0W4ZEFpkY6MIAMuVIRFMkZyyghE5OhBCOgJWSDLD/2RBO3q4MUGMoQkIBRIG5JDll9sk1cv6UO3/sZEzjhSJzcmOkWERLdsBUQybBX0jTcRIPY8ayay5Bkz5Ey/YEA/gic3b2wYK/2IlWiRsXhVPCz1dZDlk778J29cDuMyXjKwI2M8cCKnTA7WkjHTxSd+sO+gg5w+2p3DiS461BmvckgmG+RCisnDiM+5DvQhg6ipjx/Kxgpv82gcoou2MsJPdNZ2RqSPTX350Cf+81uubZbMUD5l8mP91TvGkr5DX3pZ/vJLimxwVacdJvx19EmbRH4s8U1/aZb+sX5Vvz4IZJ7HPMr8m2uycnPdX0djfat+NQj094R5f/+sb3R+V+Pp1rRuZnxbs7B/evVYbWVUi66frejs+7j+HPl9SN7L1PnqEHB99NdI5jv56ixvT3MRsw3gl4nNJMstyLxiXGRBdEZZJOu6665r29YsCC3Q/YD3/Tfyhxl5siFfyMItt9zStupdccUVLVJmQZ7Xq9tSh0Tl49MW5hb6dGVhrxwSQjf/EAkRGnIIWIhVFrQW/xbvbi4W9+TTFv3Grp/+GS99Frt8pB+RQQDISCFmZPp+6c/n+EiG35LIjm13SIOE2PDntNNOa2Ony/ZA2xERVWQNsUA46MiY6XTOv2BsnHnxhTFdeOGFbYshDPiFCJJV5lv6Ghfd8EEKESRRTH3o42MiUyJ7dCS6lT4iavrozw5/9UGgvMwF1qJw7JprOkTz2PI8GTnRU8+Y0WELJSKHqLKBwBqzLZXmig1zpMxWdIj8mWvPNB6Zbqs8ON0K6gUgIZrGxAdHEj36yNVHRru6WSmy5LfSPzp7P1KXnF7XRi/Tn2t38NfRJ+OR+nqySTknF53yHJHbbs5O9G9UV3zr5cd0zJJNv7E+aR/Le52LdIyNLzrkkenngu3Uj/mxkfrYGZON//EjNtWnbazvMurj39DWWP3QpuszuA2v6b4t/aI3ZXnG3NcN/enb+vNZ+tIevWO60ncop5yxuKfNS9ExJjNme0x+p+rj97r6t1M4bMROsNqI7EZletxn6e/b5+l0neofeXnO5/Xr2+bZn9cWHZHZrN3038u5sWf8xhEMkvdji2yfu3cuusf0OpZ1XsRsAZImKT9gJtO5hbYIw4c//OFGypAIi1vP4vzKr/xKWzDnx5B6ffo/UBM978LQh10yDvpFxB555JG2JQ7RoM82Oc8KabfVTHTHwlwfxAL5sYDXTt6CHImQSyEpIiTsIQvIHVnETp2Fv7E5RzLI0pkxICQhVuwiEBLSAidy7OjPJwtldXygR52yREbSFh/J608v215IgZgYx4EDB9ozVLA2fod6xO2uu+5q2xtFMbO90TjoQRQdfIcXXxE+hMsYfWoAdvB0eP5LP89+ITT6IFKIn37IDFKEdCFRSIwtgOzpgxh5cQd8lc2bZ78QL4SJHhE/46HjyJQQIYm2pJJ99NFHm29IN7x8t4zPxobMecaMH3QiWsbgzZTss8tnBN542ZDYgKuIIvw8G2fOvfyDnzDwHw8XXHBBI7aJ1uU6zvVtzseS+cyczpLRd17/WX22Whdf+pxtGGwk9f1yvpn+G7ExSwbewam3G9khfmTG0lCWHP1jaZb8mGzq42PKuU4W+dXbig65v3+Jnn6u0tbXxeZ+yv29Gzt8hhiZu9w7h2MO3u7BuSe7p0l9OeeRT07Oecqx3/vgPO3kZ6V51xf5WXrVx7YcBuY5Y1WX35bU6VNpPRFYdI3M87q/3ubJbbUt15nr1Hmux/zNLWrfqt2N9ov95PrxMf7x2xGc4n+vX9/06/NeZr+e97j1Yw9e/bjh6PfG/UaubB2aYEIvu+rzImYLEDaxJikXvEmzgHVYvHrZhsW1Hz1b5kTMRDZMaP54okOuLvW96VxAw9wPDwKDmHmFOV8OTBfXyJbnoSyYEQcLciSKnxbgSIZ2BNKFFaJDPxl6Lf5FwCzk/UAjZZIfPT5a/Ls4tcmNWWSGP8rs0UOWDechg/Tol7H6YQ0m9IfcqedT9NDLZ7boR4AQDGUEBLHhPyLj2TE6RYiQI/b4jIgcPny4kQ/Y2F56YIoZm3TD0Lj5QT8yZ17ZQ2Duv//+ZsPLOJAjL8SAKTxhhQzpRxfiZAzsmxM+Go/X7CNw9MHMS0kQHmUkiv/IHFLmEP0S3ULMEv3Uzk99jNkc89X41NPH52AOJwcM+MZnc2rc/ISxbbZyPsABmTVH2uXGRreImecWETPPmBlv5sr4yCY317OSdsdYMibHTiXXVnyK77DYaNpu/43aGcrF59iHvTQLv3l4zpuLoc2tlKM//m5Eh7HE5+R9f+e9THTCwvWb+1rq91tunFLmOhhnnMEs5WEeUqPeueTvWFJedP2z19voz+mIf0O/tElD+W/U/t9/x2Ri232aTOZavSN/B/9XW5XWCYGx62KjPo5dGxvtvxG53FfjK5v9PWdR+0ZsbEdmnn1tjuAkz3ls9uNSN2yP3H7MjT3j78c+CwNywTOYuw5y3+n7rxqrImYLEB5OrMWAha7Dglr0AmmyaLdt7pprrmkLeQtoP4AugEx4JjY/KLlgYqPP45YfTotsr3C/4447WtTIgt1i3AWDeIh6IAF8Uo+gWLAnsmLxjYTxJ3/EzslYhCMO/HUgF34ILfCN1Y93/FKmn4ycjPHRSRedCBRiEP0ZvzKioKxdFEkdm/CQ85sNRAsBQjoRHqTPM2We5xMhNH7RIYe+2caHhPALcfGMmT4IkuekEBJjYZs8G8bJ74xDjhghJeSQEtjqyzdjM1ZyfDcvdJkjdfrwWxu8+UZGu7KcvDGSp9OckTF+41Q2J1Lwig2kif1sPUW+zY35cBi/uoxR/5QRMX6J0knKxkMnOWW6Ycsf15r/dBClg4N5IJeUa8KYxlJkxtpdC46dSvEnuflxbDSlX3K+zxv/RvXOk4stucRmfFbnmg6G6p2n3Ovt9USXdtfEKlJvI/pn+ZW2WTkd/kZh3OPsOpXGxjpL16rqMs7Njm2r/hh7b9P5EIfeF/eaXC/BbVjmS6+j79/7Gbt93aJz1+e8FF/GZMw7u47IDscx1rfqdx+BrVwzvddj12Ivs93zXF+5rth0rcX2ovbt2l/UP/aT9/6p43d8pas/VyYjpT55q9zn/wSzDDNjT556eXBK3tc5T5/kfZ3zZaYiZnPQNEH9JERUvT8Gua1rvi+GNIisvO9972uRnH4Rq1+vJ+fRo935sGzh7gaBxISYic5Z1JNFuERe5OxZ3FuIO0dQyPphzII9JCh2QrQsfoxH2cE/tulyrl2fLI6QB4t/unNTsMDja3QYU5/ocdBDnwMxUEaOkBL96eS7djr5zC7CYzxkRJfUI2YHppGw+CGqw8aR6TY9ZFkfz56JrOkXPBE2ZIeOECTjCVE6PI22sWXLqAPGZBEmfjnns62VCA27DuVssxT9gg1bxug7YPQjlvBDiES8YMBPfiSCSY+xh0x6Zs6cur7o8p8AMBJBg42tm8gaP2Hi3HVp/mxNVPafCDDWbo70gYFIHrvKxmfrJl+8bMYWSm8B9awd3erhyIccuSb6ue7PyW0nmc9lpfhMn3O6jWejabv9N2qnl8vfV3DggzqHFP+15+j79+fxX56U/in3bepiN+3z8lmyQ31jOnu5eXrIRZbvs2Tn+bjstsxD70d/vl177m305Yi+4JDrQz2ZzGd8yP1ce3BLm3Lao3/YP330l4blyH+j9Vv/HcqTiP1emlxk44t2/kl9XauY/kN+kf3IVr47COTvg/XMe3J1mXPns1IvO6t9WXX8zDXIpuuqt72ofVl+jOkZs78IP/oik/EkH7O1n+ozpxlTxp5c/RCfyMrhnnukcvr1ec61LysVMZuDpAkL6M4zSbqotyi18P7oRz/aXrF+0kknta2MImcW0iZUP3/ks35AtOWgs//jU7Y4Z8Pi3TelEAZb+UJs6CTDF+cW4iJnksW4NvX8oIdPomz6i7BoF5UiY5GOnCAd8V0f/iEBcsRCn/QXWVFWbwFBJjb0QSjTj4+RIY8kIDV0IQb0sI+08NFY+G0cdJBBfhAEUR26EZtjvvla+viCMCEYXnmPXHm+zHzQmT8wNuiM73zmi35kRNsQGSTGnNrSCBcvyBCN4ztSpI+XkIhCHZgSRL76lhzdCB05ZfV08NuzXKJy/LZNElHzog5kE0lyPR2ZEkuy2pG2559/vvmfl3CwibzxD07ZYimqhQCy5zoxt4iZ8fIb7uy4zmADM/L8ZNOcsQlD22a9QAV+iBm5EDPXlzE6XCPzEpntpPz9bUdH+s7yZTP6t9s/fmw0N0/+ZmAcnFPn70YyV7NS7+u8MUZOnoM+5/r5G5mXIjdLRhv/pfgg78/deyR/d9GVsapXJxl3309dys53K/Fbii/ynC/DJ/dE+mASrOiNXfe1pDG7sJPSvy/nPH2TR+cwNx+Zk8xXLzPsHz9TL8+5fjnnR3zhZ3x17yKjHLu5Plxbxh8dvR91vh4ImNNcJ5mn5DzMnI5528uOySyjnh852MwR3WmTp22nfOPDRuyTmZVSH3+Tz5Ldb3XBLePK2JMHG+2pi6zc9dvfw3oZ5zn6Pss4L2I2B0WTkh8Ik5MtfLr4cbDQtVj/vd/7vUYELMY/8IEPtBc/WOxn8TT8UdW/vyBSzkWUNpOurx8gC3YvY7CItyhH1vIDRj6ycj/mSAYfJAtu9cp9nQU5csOGrX3GZ6FvsZexsm0c+vsRdLCnDiYIizo2lPWFizKCAEM22dCPz+wiDXSod06OD8gCeQQTafGyD3oRJNsSEQvyZOmjiw/606cNiUMqvCxF1Om8885r5EkfftFtnM71QTj4ijDB9YUXXmgYInSibaJf/EYG6ZAQJ30RHv1FLS0i+GxMyha1yuYDoYSN58XoQeb4hhCzy2d9tNGZeYBHbGo3VnNLpz6wMlcOWDj0MQ4JHukDL/NtLOwaP9/V5xk0fqk/PP1PAFsZjT/4saWv5Hro81bY5D/sOsYSG7EzJrOf62Hj2oc5HFzj0hCXYBg8+7yX7ecuuMrTn+70dS65lucl8tE1lOOv61KKHxlL6vyN0OE+E13qep25B+nrWk6iP2NK3U7nfJMyvuTL9sN14ICRMZsXea4J9Q6pPyfjPiUF12CdsrZh35SDb8rsRb889jPuPqc3tof1ylLGFF1yNh1kYp8dutTlN0ZfY6i0vgjkeuHh8BpQZ77npVwn82SW1ZZrcJafbCxqX5YfY3rYD17xcZ5s2vo+6nYS0/iwW3mPWT/2HgMyfbn3VZv7TNrH8r7PMs6LmM1B0U3FRDhMjoW3Hwc/ivlhtIj+0Ic+1LZ9efnH9ddf30gEspIfEj8e+YGJuVkXQy6i5OymnwUO8iCCkkW1RXh+XJNbsPvh1Y+/xuBce/yOP9FPhqxk8Y1Y0ZGbanDQP3b4mHp6IuvcIVngq+9t88Whv0VWxgBfZEIf2CEUCNKnP/3pJusFFKI/fOCjvggqLJQletmmC6mwFQ+R84wUwmFM2ulGfOiCZ3yHnbJto/y79NJLW8SIPBl6zQM59o3Lefw13yHMMKQ/Ni0m9OevOmU6jBeRIg8LdugZ4qwvO8bIB/34pc7c0UkHLLSlTrsDmdMXmWMDASSnLPkPBjqQRenwlJiJ1Hnzo+fMEDp6Mrf8227KdT6mh63YG5PZz/XwcS2YS9eV6yT3FHPh+gqGyV0njlzTwdDck3cE0+gPhsp9UqZnoyl6e3n944OcH5Jz+l1zZOT81p6xpV/G7VpN5FYbLFzzu5n4IMVXeca4LL/8nfqd8Z88rgN4uXfAybWReern3rl6Mu5RkvsL/+CZcq4hOjMPOY8demKjz50baz/24fkYMScnZW7h6BqQ+MTv3GOU027XhDfNJtKvrdL6IpDrcHhdxGPX0LyU62SezLLa+BJ/4m+ve1F7L7uK883YzziS8ydYJl+Fj+ums8dsMxj0uOU8uCVf5ViLmM1B1w+TSchEuMmoy6JZVwv5G264oUVnLGKvvfba9iyQPibUD5cfj+iIubSlnDwXgby3nXb205YFQO+nH3Htftic0+GcbH64+ZMfNLL5QezHxV5sxXZ+hFOelafPPFk2+UaGb5K64Ms3bbYUeu29Nt+Is82OrxZjnpuzlVA0id/qPLelXfRHX8TOc1nmBWFBLkSILFTodG7+6OAHsuT80KFDTcYWPpE6ZEU7WbolETP+8gFBQgDptVXRYiSvnbeY4hN5OmwhRLBF7ZBF5+r4ZiuihRedFqHsWpDYukiHxYhx/H/27nXFmqP647iXsr0FwRfC/8UYTaLGqImHGIyCoFekCBKVaA4mT04mRCJ4ehAUBN95A3Mp//60+cZiuw9zzjMzVdDTXV1Va61aa1XVb3V177HDBqgp185rlwIteXIIwuzM0Yk8ebQB5rz+qO++QXMfyEHTLzDi68dkyOUfTAtu/aKl78zwGf0x0LQq4xJ/yHIobY+bQ3Vva1ljZhyT+s3m7OaXPu2Us7GkPl9TzqbVNaai5Zrv8Dl+oI4y1/xc4mt8GmDnA/IO40IdoNmDBDTw4e/4oqW+MaoOG8qjrb1gQb7dWHl13VOXbPwHTeONv+KRj8lL2pMND3y9PvzYY4+tbyTgpT/ofpKJDehDch6vt+Xa5evqu19faj+29baE+ezf//73Olewif7TF/vxC+3Miei4x1bmJfX4EBsY1/Rp7jD2zZfq0Hc2UM4+2bUHN+zKP9nMgSebmJPwcK2OMnajF/eUybOTcnLime+ok5+YC/msfvA/ffDASN6BJj/xoO1zn/vc+iaD/C6d7bo36nRe34wG+CP/zh67xuuucZF0tSt/nedRjl18x3Jy7KpzU/Id478t6yjXTcs98r7p6116ONb/2nSufueb6MMMzC6pZUD9/fffXwMzr30JzHwrVHBxyJi7JqlLinOnmtux8U+1Lchf+MIXPv7WSx6go2MLOVAhqLELBoC45x9yAzJ+VVGQBHBY3A02oMNh8QcKgAxtAB27cG+++eYKbgR0wKBXGtEXvAi8XAtgAJzT5dssAEeAA2T4pgzoEdCh58dAABJ05P2Qh3t9vyYY8rqmoAoNeYGYwM0PcZDNr2vyFa9yArSCKv3fbDYrb/WBGiAJXfpwDwBSBw2BGtD06eXbNkk/6HGkCdDRldcu7VT+85//XAGQHTOBJd2RY3vCulNOd8OdoUu+B4QWmCUCGwuO/VsONucjHjSYU3xDKOjnV3YR+B5/BISBaf4gYFemDlsD+IKj8nyXzfkE3/Gtoh+BkXegh6/gnk8ICPgv/pvFr/hX37xqb/wJIMmlDV/k+3wagDZOgH5jTFs0XeOpb8A2IOeHe/gsOdFUhx7I/oMf/GDdyTYObtP8yc6Nm87s7Fo/2MeZbvOD5jZzim8+2Yd96ZJuPDBxz/xAN94qoBe64k/mPnX9MBVb+kaVXTywEvAY+2zEZmxgjPsWli/Jk0meXOxsvmRn85/X6j1Q8r87zWvml+ZCPNgPD/0xr6HBd8jFh/UDTf0355Cfr/EX6ye/5f/q8MXN4m/mLA/NvFotQPeKeQkfad+5evM8NTA1MDXwqGtgBmaXtFCB2d/+9rcVjAvMLCQW1xaJfSwsVjPt1wBAYseMnk5OTlZQADQIqoBEi7nD4g700begxOLv+zILueDGP3tmE+UACvDnGjhw1AZYBCr9IiFQ4mf2gUc7db7vElABIAAMUAB8ACcCHsCSHJ5O4w+stOsAjGovCNSez8ijLYgjk7rACL7kkYBSsgK2aPb0GlDlX/KAGBoCUvI76IhsdIMGmnSmLhnUVUff5V0DQcrJrR9+PMUPztCdwEyg6D5bOEvH/HutNP8c1EC6ZCN2oFMHHwG4+b+Aip3sFPBJvsLP+AWf4dt8BjBm/80CYgVc/J+vemDEfwBffIBx/sOf+R7/5kt4oosGAK6ONvzUwUf843Iya8O38JTHRxAAjOsHuciMnwBDmYcj/Nk9ffCghNzGhDIAXjmefLoxQy7/woJsXi8WDODNf29TytbJLC8Q0RfJGJXohr3oxbeyzsaiex4WsScbC5rYWXBGv2PQrq4Ah17NUfTM7mzrAZM5hA3NW2ioY75Al//xDXr34z9okyHZ2FbeHKM+O5iPe9hlHtEv/WNrgZzEj93jW/mKvpo/ycvmysnhHl9AEz++aE5/44031v8Xyg8EgOqXmo86uz9eV2+epwamBqYGHmUNzMDsktaxeP3ud7/7VIHZN77xjTUYsDiNi8YuNjMw26WV/96zY+P/aVmo7ZhZ4AEAIMPCD2Qoo2cgjs4BDou8XSgABKC04+UJMzDAXnYTtKF/4FFCV3vJE3sg1KuMgIc6AhYBjUMecNDGQYZetQFW0AU4gBLAADhyBlAFafEmj7z+6Asa6gAj6qCBFh5ACzpkBHwBFXlnfBxkKtDUR3lyR4Ne8AF48HRfngx4kIcM0r/+9a/1GzNg2beTAjNJP8jW9Xox/1xIA+xQ0MIW9CrPR9O/wAlw5vuPP/74GpSwOz8zBjyQkGd/vsmG/BEdNnbNDyT0+Y5zQQ3agir8+Z1D4uPo8jv+gY466GrL7wLS+PJV99GXyKGuPvIZ9R180D0yySuT3CdXY1Ben9AxFr2V4JVOu0KCU4FkfVgJ3NI/9E23+kuf9EgvgtC//vWv686/nS969dDIQ5Ivf/nL67hNl3RUosPGKLvRNTuqm53ole/QdbbUbtQ/WfDUlnzkRMNhzpDnD8rJqy17o+m+9u478CUHXmRTTx1y146s+YS6yrXNh10/XL57ffDgwbqz+sQTT3z8MKy+50vb58rneWpgamBq4DZoYAZmB6xk0TiWLJZetyswe+aZZ9YnvxahY+1bQI7xuKvlx/Rj1+add95ZF3SgdLMESRZ1izWwJhgDDAQhnjpb6AVm6njqfro8zfdU2WuFwAXQA1h4wgzYqgeUCjoAYABCIGOnwauAAjo7bsAvIAjM4I2OAAaotWuhjae6ghtPhYEIT4UlckoBGDSAFnngV3tAXABpF07e02vgxz3yypPN021t9BEdMqmn/0AUOcnCJ72KRE6vCaHhCThA5qk58OT1JXLQKV8FBIEtPOlEUOy7FjtmAjO0JT6b3e67/64KucQfABQg5S9swh/Z2iu4XhVT5oGCXSq6tlPkNS6+x+bK26nSNlp8XZ5PoM2+eLG/vHEirw5bqo8/H+PffEob7eWV8TVt+Ij2fEtShy9qgw4aDiA74K0OGu7xOWNCmTGjX40RYxggx8N9PPSJb/pfkcbz5z//+fV70/jfBR+kE7agH3o3r9gp+8c//rHal83d4xd2wQRnfEBdh7lMok824hvGvDmGnumTjcwd6pg/tKFvNlNXuWvzB/uZL5ztwvERr8WiTUZ1+ubRgxtzAx58l1x8ks3xIUO24kto4csP9RsP5eyujXJy4sNfzcPo8y/6EKB75fXkozcotu0vv31vVc78MzUwNTA1cEs0MAOzA4aySBxKFiqv7gjMHi5P8wB5v8poMbOwSIdo3PcF5JBu6M5C7AkpPQvMvMID4FnABVJAgKCqwMTiDlxY6H2f5tsK31EAtAFPQAZAAHQEUhZ9ARGAgA6w4BUyoEJwBRijAZgCCYI6fAES7fuWy2s1wI2AUL+Ua3O6gEmgBqAS9GjvNSB0tXEtkBIwOfgTmsCPV430XV5SDtAATXjre4EauX3crz90IzADgARmyshAP2jwOzTJq+/qaYMmmcjtuyavTwFBvmVpx4wc2e2++y9dXDaxb4nPeRjhGyF29U0QH/S9Hx/xGpsdI/7NXvyY7dQR3PA97T69vNoo75setrTjwgcEeMr5s4DHAwhnfsl3BED8k68C1/zMgwYPBPgNGsqNoc0S0APdfWOGBrnyd35FDmPVwwZt+JA8AC9gUEdAYqwq98/g5e1083PzaOD8rbfeWuXxRsJXv/rVj4G7cf0op7OMFTqhD4lu7JSxlfTjH/94HW/u0RPbmBfY3HpD5/2KrJ1E/sSHBFt8hb29AmhekdfOvIoPv3CwIbvZlTVf8DWvkQvennzyyZUmecjoYZjgztxqjvGqo7kOPQ9y2Ni/jeGbaJgr8WUncrInOc3T6qtHBvUEe/L8ht/ycXKhaT7Ufz9K5E0GgRl5xznI9ZhfFTj/TA1MDUwN3DINzMDsgMEsqi2su6pZZABeuzoPl8DMzoz/Ywb4AC1zkdiltf/eO6ZfAOL1119fwZpXV+zeeHIKfAANAI1ABAix68QeFmugEggAaAUaX/nKV1YwATwACBZ9ABAIkFcfXTSBWT+zD4QAHYAMAMmeeDgE3fgAj8A0OwMP5ACA9AuYLeBRB6DBxzWg6hoYcS1Acq0+QK0OfmgIpgSRzvpKbmBZX7Uhv6DSff1CQ78EmWjUBg9y66e6nnCj4R5wB6CTXx4AF5gBY3bLBGbolBoT07/TyMXO/Jje2ZYtBEq+pfLKnp1eQTFbuAeksgWQC9TyEXYGvAVN8trLA9d82a4bn/Bggs396AN/4deSHRgPBsxXwDC/Ety5RlMeTcDbwTe9IszuHpLweUGkoEobY0a5gI4MfiCHf+fPgj/9MQ61EczpvzFHLm3oRP+de4igvnngdAHpzz333HrwY374qPugMSUlZ+f15vJHH9iffdjivffeW8ce/VhPnn/++XV8v/baa2uAZffsZAlK6N7cZFyyETrtbglsy+PHzuYtdjYf0rcg3MMhPNhZ3pxkXuNrgi7zmzcG+JD2aDrYmS08yGF3NAVz/Ms8x3fZ2fxhPpLXR/5IH3xH/8htbjOvm8vw5LeCT+3wVI6/uUwgZywIzPxKL17optPO6XaepwamBqYGbqMGZmB2wGotRPuqAMQWtbfffvtTDz/aMfv2t7+9LoDA7rho7KNxn+8f069vvQS9QBowArhZ0IE4QNbCDSTIAwIWfWBCHbteXsezeHv9yeIuWBF4WOTVAXDZUDvl7GVHwBNmi7xf/gI80JcnL77OEhqu8UQXLcGi+3iQW3300VbPWULPNUBGdm30BdgBuPkP8Kke8OpQR15QJalDNjTw1h98q6+ONvgKzMihDZnJ6ayNOsrkyaSuoBjAA+I9AZ+BGW1ebco/2McDHjpvZ6JdEf5jB4S9BFgCM37BxuzF/nyPTwicXPNlPiHPtgA3++YDQC878zOAHBgHjPkQIOyan2iDrrOATxt1+KmDbGjqhzZ8E03gmr+QRVlt5LURqKFlF8S5sRtd/NSTtMHDjz7YgTEmfWPFz9XR/0c5sRO9SM7jtXv6oN92i/70pz+tu1cCrnZL6UZA61VOD0v03a4hu9Z3fuCINrqSvPsSu9B1dqVTfievXnl0tTEP0bH7bEkOSZn7/AK9bMxmeLCpOvLO6qAp8WHJnIOnOujKS/L8lj60dY0Pf9VX48M3x17ldJCLX9bvziux+WdqYGpgauCWamAGZgcMZ3GQOle1BQDgAOQtmoIITyyfeuqp9QmhBcPCV93azvN/NTDqdbxOZ5700q0F2lNST3gBPeUOgRYw4J4ADCiw+FvsvarnCa57ngpb7AEAizzwqT0A5OwJsWShB5R83+FJvx0KNs2WQAKgok0BkLzreKOFFzBMLnny4IsO+RyArFeCtMdLmTquBZzK9QlYcQ9vgFsd13yPvPJ4AOFkQBOoohtyagMY2cXACwgHpuTR2CyvpOGNBl1qrz+CWvoXmAFB+oOeI1u5numwBviABKBKY941G7C310b5qznDAwi2pV8+64dA+ABb2L0UmPEpdlCPPdFpl5TN+TF78kXl8vwGzcC4oIEM/ANf4wydADf62qnDJ7TVBkhWP/CNL5p8kVz44KuOpI08P0NPfbT1g2x81315spBDnhx4Gi9+YIk/Csy81hyYV36dKV+Px1n50Zm2ztroj3PBlD7K0xH7+/ce5h1j/ktf+tK620iXXjNkYw+LlNsxMx7tpts1w8PYlcY8vtrhJxCmQ3k2Mva1wctul/nQbh3db5b5gF09cMTf/EdGgaM+mB+004avmHP0RTDOXuiztbkGHfcEluT08EGyU+q+XTZn9ekif1IHL35jtxQ/fO3OepXRgyL/X9EcyWe0lfDouvNaMP9MDUwN7NWAcSPtGzPHyvcSngUX1sAMzM6gOo45OmcO7N7p8kqHBcMiYwHymoZXcIAQi86jnOrTPhnr577yq7w/6ji9Aau+3wMA/CojkCABHQEdegdsBBz0DlwITLzK6NUYr8UAGxZxgFXyuiEgYcEHcIBeINY1AOPJNfBo1wJNr3EBHGwt4AMS3EPDa0HApdeEyO1VLqBEMMgHvL6Dvtd50LIjAuTwE4fA3s4UgPTpZXcOEEGTrF7x0Xffc0jRJAN5yacPXv2iB/TRAaocQJP+0xXZASht6AhPNHynop48Gl5dogcg0MMGr6PZqRHEBrbzm5v0j1UBt+wPPdG5M50DnmwB6ALMAW++yu5emRWU8TX1+JN2dooEJfxfYAKMAtdszF7a8VevIfIbNmNbr4oJmPgqXvJsJs832RtA1x5PvugA4N0jO18D+OXJBFyTnT+QjQxoGH+Cd/1Ag196kKK+g4/xd2NXEIAGgI4m33OfXPps3JLffIqGwO2ll15ag1c/ruQbs5uaX9mslL87d03u5qvqZXdljRlzArvTmfr6hIYAx04QnbAl22mDhmBYUKWvvq8yFxq/6gjSjXf1lKEtaKdP9Mxjvt1iOztt+JlDzQ8Pl7c72NU3i+h4RdAbBptlnkSDTPyNzE8//fQ6n5q3+BgZ2V6Zeda8qj/8gN3NHfiyH7/mB/Lk5Mf6jIc21k3BF776qK/0rQ09mTf5hjkMT/Ogvuj/s88+u7bhR5J29C3RxbZN1oL558Y1wO6HEn84S51DNGbZxTVA9+m/Oa0zqsfKL855tjykgRmYHdLOR2Xbzjk6LvAiMACggRsARLkF0gL7KC8QDch9Khj7ua/OVd0ni4UVzw6AwT9W9XTcYk63ASWgAOjUzjXdAwkSAOE7MeDVgi6gASjYAmBgM7wCSmwHGACCgiKvpkqezAIyAJBgDAAFHgKqwI4n2sAB4CrhCWwAquQWBOIp6AI4gE5AdbOAETK5BqjVV0d9NAFsQBUtebJqw6e04V9AkD7rL9lck1MeTzR6ik5ufW7nS32JTHQIpOMF9KMNBAFneM7AbFXVhf/Qr4PfSUAnXQti/JorgMq+XrkFliW+xa/YG5AW8Hu9jS3Yh5+Zd/gn4MrmAnRjxEMA9gdq+aCHBvgpN7b4FbqANLv7MQbt7NgJ4PkluwPGZBNQCQo9iADgApLl0QAAQABJREFU0QC2BXCCd2OSHMajPqlnLPE1foeP8YkG2T2Y0F9Bhj7anTU2jTflHsgIJAFwchjfr7zyynofIBeYtWO2Kusa/7DbdtL/kjG1Pcdrox/KXI+yCpgEXO4bg37Ugz6MVbtAxjQdspc5xRjUnk4EJXbLPLChO+Od7umYPukcT/ODOY1N1UGff/AL452N+Y650dzGzmQhl3t07xVXiQ3sgPYwiD3QVd9hzsKbjciBhj7ov37wM35CZ2SgK36RnNqaszzcwlc71+rpAznNYfzHwyI64Ed+lAQv/UM7XZNZ222buD/TzWtg1/g5jxTjWDtPu1n3bBpgn2yUrjujcKz8bFxmrfNqYAZmZ9BYzunMaZv05S2MFhP3LULOFhRPFC0m3TsDmxuvQv5DaRygh+pdRRlZLK4S/eLtya9/sCtoAkiBlwIrOgZwgRZ1LeZ07QBqAEZBVoGZwEcZcAwAOGunPX5ApUXek2s80fA9B5CqnXrasLW67gEsgQ3gQQJO1BHg6A9eZJPHh1+gg4Z+AWpoyAM8+gXkSPxHUg7AKieXJ9r6Th/6QCcADPmBIjzRIDMe8sAymbXBH/CX0ESbXOTWRn269yQeAJyB2aqqc/1pbLHPeI0IGwO6vh2zi8Bm9Gx3iJ9ow1fYpR0VQFng5gdBsjnfUR/Q5YuCLPZGj58I8tW1M8UX+RFZAGWJ36gH4PJR9ldHnp9oow45+IW28mTjm8rxNQfmq0A+ufgzms6jv6ornxzy2uChHxKZyMLH6+urr766Piz42te+toJytJV90kk/tuVwT78qq5y+jEs2omevJwrM9VugK+DSL2PZGGQHwY/2/ERw5vsy3xmaz/BwoEt/xrfUPMVO6KFlXmJT9iBXAaI6Je2iwUe1ZV9tlKGTf8o7+EU08gVyk5kd8RJ8qUNOZ3n3zTn4oaG+coeknjry6jh7k0Eg60GDH3Qil0NS1yFpm87XG/PPJ6aBbHJRAaYdL6q5s7Ubx0267ozCsfKzcZm1zquBGZidQWM5pzOn7bBYuFfadujyndUbr2v3SZ1H2XfJcEzWY+3PShOdDm3wtbh6Wm/3SpknpD3dFVTQPaAhILLQAxqAgbbAiqDC6zV2ICzkgB7AAEACKQ40PK11X1t0gIDaejrtiTEw4r7gpoDHPTzRAYo8iSazPF49JSYr+QVAQIQ8cCuIQkMeMHWtL8COe2jpH1qegJMV4AZWgTU08EDHk+nT5Sk4GeyGkNFTcvzspqgL2ANaAB2adjHoSeBJJ+MOjDoCM7uO+j8Ds12e/L/32Lk0XgOo7Ml2dC9gshts95TuC8r4H/9hfz6lvrydLL7nl+j6qXF0ANvANr+RB3IdbMvuePNxtLWR0OZPgWv38n3jQOKP6vNNCR9jADgnF19S7tCGr7kn4OB/6pCDf6qj/9rTS4BdHQGAPP7GoXruqUdG4xm9l19+eQ1Ovv71r6/fYKGtruNRSuTO9p31X1/Yw6E/XnH27aD8F7/4xTUoU0fiE3zATrprtrD7aUz+3//937pzymeatwS69GfHjL7YDG/zg/toCfboTB02xJdNe3DT3IkXObIhedjVXMdf2JP/oOHIbvyNHM5kJgefldcGTeXakoF85iT55vAeDvElvsIPPGwQuLqn/w+X3WNzkm+58Rntj2dpvO7ePN+8BhoD+ziP5dms87428/7VaYD+s0F674zLsfKrk2RSGjUwA7NRG3uuOaeFxZnTdox51xZgZQGGsQ3StdvD5sZvk/lQIu+hdKy9/o+p/m/TRSddqU9/6nh9y0f/AMDJycn62ozFOoADVHiFCjgAYIFCCS1tBSxegfSKVEBCgAQsACqAg8UdcEVTHeDF61oCF7ttm+XVHUAAiPFKITAN8Linve92AEwBjrOdEADGKz549eqib4fQkhc0CZi8quTagZegSj/Q0FYd/fUKGuCkPZAiUFNHoObe6RKUoUEugA0A8k0IQOQpvCQYoJf0JWilB3Lrs91FwRmeAllBca8NzcBsVeHRP/y4MeHMh50BTtd8DUgW+NM3YO0bP3ZhO7YNjGoHJAPIdtb4sgcFdkuAVPbkS+zFpoAzoGtMCKzl+Qg6HmgYU3zLWAGE+TqagiI+AiDL2zFWH3j2AAAP44N/omGM8XOy6hP/JzMZ+Cy/wodvuldQoA0afA7Yxk8dY4jv8kF+Z+zIS5vFt92nPz8Xb8fIjpl/nQG4S+g8ComeHSW6kdhBH+jBODZWBVnmHza3K58PCMQk8wA70b01RT1zkn+ubF4xl/g2zE4ousY629h1cxb0SejSOzvSOX9iS3nzCfuwY/OH+cL8oYwd9cFr4PqgDjuTzVzIV8xDfEmePxVoocuO2vBHMmtrTkbTwzLBHt9hWzvF8ujpr34ZG/xIMObVRfOtfpqTNotf+IEU40n/0NTvdL52fvnDHtv3Kpvnm9HAOCZ2cWzcsFO26ryr/rx3tRpI/6im987uHStXZ6ar18AMzM6gU84JPDk4bYdFxALqUCYPeFgkejqsbfU7n4HljVQh26FE3kNpHLS76tFJPOr7eK6NOtv6pUPfOXmtUBlQagG30ANjFm2AAWixqFvge/LODhZ9r/9Y5E+WoE47wCFgqi1QiA9g4b57AjQfrQtugBI8gV8gIDCivvtAKhCJP/DhrA7+yrUBNtACSgFmAAmoBmjITiYHUKscwAGc+A8Qpa/1LSCi72RXbpdMHg3lADUw76k5GvLukxVtfNIX8DTSJKe+kct3P16zErjNwCxPPXzOj0ef51/mhMoE917JAnz5BP0KhOic//EPIBZIBqTV+/DDD1e/4sdeZ+TLQDuwDkgDrvxWsMe+Ahw+wf/VVR5QBni14VOArrEl2MMfoOebyvmNhxOAsW+TAuiCC2BYMIe21/HkjRG09Y/voWEMCPYFHIITdIwNddQlp3GHJj9Hg1y+q1KPbgQO9OLn8o1p/yfSbkmB2WGLXF0p+2VXVLsmv9T85ZrNHeoIwIwxOmB3r1jTSfOSQFNiL3OFdsY0usa4IMe49auN/oeXsehBE93Qv7Eu0DOv+hbQmR+gI5jB27h28ClBvfmFv/A/dhbAtzsn8OIrBUmCPcn8wjbaoEM2vCW2Nffqg7mFzeRPl8BMXzyAkheYkUvAqI25k6/zf/bke3irzz/MWXaLN0sgRl9051cZ+Ra9mUP1F02H61L2cn+mT04DjZN9EmQn5ezXsa/+vH+1GtjWP+q7xtF4fyy/WmkmtTQwA7M0ceDMeceFd5w8theEQJg6OX31x/MBdjdWRL5DibyHUv3bVyddKE9Pu3Qw6rdy9QVmv//971c9eoULKAACLdraABvywIa8NngCCECAA/AQ1FnEgQMJKAAEgQhPh4EBwICNgSAfmQMefsUMAFK/YEb98q7JgBY+aARWgCayoKdP5FROTqBTmXby6JBdv9wjJzAqdY9s2qOlDA1lZAGY8MWTLoAu9MksoZ0cyYUH3mimBzRco9GH9vo/A7NVjQf/0OX2wVZ0L7GZ3ShBv2CG/QRMQKYdBP4DMAvs2RqgZlsA2Q/gsJfvi3xjls2VAcnasq+gjk8Aw/wXLcG7oIuPj7td6OFHPgGgA0jmS3jzA3mHIEE5uZTLkwFNvstnteFzdnDcw7c6gpPGCBo9NDAmtUFHuSMe9KO9ccOf2zHzKqNX/9Jr54PGuUQheSS27dy1vPEj6Ud16Ydc6rGD4FpQ4dVgNtosgUY7XoJVvsEe+q+dPB26Z3fMHCMgsXMqIPFKKxp4SupL9CVphw7dJRN6fIO82VkQrxxfNqJzQZU6/EkZOujWF/NHdqpcWYf67suzmwc9Yx5tchgb5MeHb8mrT3a+RIeSOYmPo/GHP/xhDU4FoP5fKL8mM55kGhP+2kRnLJvXN6cBdjiU8g32H49DbWbZ1WmgcYsi/Y9n18fK1wbzz5VrYAZmZ1Bpk7yqrptAXFsUcujKnd2z8Iz1uz/Wd++TSmTbldxvUTskq3r7aIz9j4d7jlFn8QpkVK6OJ/p+IhrwECR5sgoYShZ8i7aF3eJrsbegW6g93fUNjyfBgC8AbPFvtwhY0gYIAVLUB1wEJACpbz8Ed3YStAcM0Ac6gQfgxD28gUwJLeBAHX0qAMIL8JRHX1/0AUABXgERYA34AbDJDzCjgYckTz921NAhG5CtP/gCVeQAyOkAIMfXq2LqkAkNfcAjnnTs6TS57Y5oh4Y+e93OjhlgKCjGm8wOsjmy5yrkI/CHTNuJjKVj5dW7yJl9HHSZfuQDHnY17HC1O0H3Aox+Qp4/A9LsJuDi22xJfnbgr36NEJAv8RU+hQc/5mf8Fx2+6YhG9sI3sEo+fs+m/A8NbfiJe/wEbSlwro72yvFHQ8LXffXJgY/r6sk7yKGdfikzHtBwjYZEBvf0w32ByYMHD9b5wKt//qchXmioc9Up+4103ZM6d00+spJFf5WTTV/NFV4JtvtsR8o45B9+gl4wbt4xHrVvXqAbddBFU9JHcyE6Xs22E8YPmgPpO5togxY67mlLDnY23wiGzRfmD9fsys7mD/Ib59qorz3/IZ85y2F+cF99NMgvT4ba5EvmKeXpiF1dm8Pw4uNoqsP25h808SCr+miY48j0cPm+zBsUglo7p+Y2skn67aB3CX1lox7XgvnnxjUwjplt5soc+Xr2264389ejgWwz2uAYp2x1rN5FypNnbHsefpdtP/K9qettmfV3BmY3pf1HkE8OMTq+Bc2i7rCoWXTVG+uujvPRQliZdt3fnlyVOdRVB01nefctoJVrC5ioIzh4/fXX1wUaIANi1QUCyAYAAAMAjiDGAo9OT5iBIQBGO+XqAwCAC7AiWANOBEaAhmvAExAGkDfLU2mv0Hi9B1AoeFFPwALQ4EGmTy+vMpIZACcT8KSe17bQ95oW0KP+6fKKj7w6Xt8ButT1ig9gYqcPIJInj1e7JAFmPARa5BKseiXIU3kykcOrYfgKurwuhqbXm+gbDXro2yCvKtGDcv1Dj1x4Cm5dA5H6n86zHZm2be3eJ5Hyw/FMTr4kjfe7HssvIzN67MQv2U2eT/APfgaAApXOQKWdM8G/72R6JZZ/8l2+yffQUyZ98MEHq08/tvwPM+3Jzf78nn0AXfbnV8YAfwdq5QVu/I5sfE2eT5NPXrBgJ5rc6KGLBn/kN2gAytqgyUf4Af/lR/yEDxgr+oCe8Qc0O/QZXcEeOfTLa5jGoTbyfJWd+C9aeNCdBwt8U33/aJ5s5gEPCgB1Y8FxmUQP9OkoueeQChbzGfdGn6cTc6V62qinr+SnG69gqmOc0aHdUq8i+kfRghJzgwch+ikg10d6ZQ/+YCyjxxb0RO/4GadeJ6Vf8wUfELThz9fMZ3zDK6jGsQDnM5/5zCqDYJEPmgvUsZtnPjBX+pcM2rI7muYYPmyO0T/zCR/ykMG8pR/6xe7yZDVvkcsOIZvrL/2Sg3zeYJC8Juu1TjTY2ryrj/J8mN0Fo+TG1zeGL7744krvhRdeWOckdOnawS78wpmO3CPraK+V8fwzNXBFGuDPh9I4r+yqdxXtoxGvzrv4HbqHzvZh7ByaY6uPbnw7H+K1qyxa4/kY/5HO2K5rsozjP9k6j+3Pco2udNH22zzQa62JpvMMzLY1dY/yu5wMGLCocZYcJSenGvc4egNGXrl2q0Nxqo+OVJnzOddWHUm7DvmRrtfp3nvvvfWexRqAlNAR/Fh0AQHAB7ABaIAdQALYAH6BBq9/CVjUB3yBHAmY0gaIQM+rMUCxn2RWDxjAExgCMgEnIFOQ4h65PeWlL8DCBKZOeUAVAAKa1Rco2TFxuAa80ZMHhoEkwAwYk98swFVf0JR8b0JmYI6cnpiThfyAFxkA6vHJM70UHADUdACkkxEYpj86AJD1R959IMg3SgAgYISu+/hnczJlR9efZOIT20e+RK7tMvmx/KKyo0Nvzvk93QKF/BLwFPTapeADwDTgC4QCn0Aqm/NbNNjCud0V9vjtb3+7BlFeZfS/rvDjIwA5P+LbbO57HD4icOEfADi/Bc7RV5/PCO7I1o9EAOR8/3QB44IiYBpdAZZAAtBWx7jxsEQ/AXgBm2+f5AVV2vFV9Yw7eTQBdoGGvvNFryjzQ4CdDehCMk75l4CGT+sXGvpqHtD2+eefX1/lU58etL9MMhaymzPdm/taLOlfcr+zel2zXWOCzY0xY8m4pyvjyPjx+iGd+3VJAZE3ANjaq636SV9k6ZVC459+BS/0aSwKnujImKcXY9OYFPDQg1eOyS0ww4t+0eZ/bCnIMkcIfuTZGC024j/NOXzFnCTxJ3oml+TVZnOK/mljDtOOzwv06aY3EfDRR23Q4AcSfyKvvPvm2AJTfNDUdzKa68jJj+niV7/61Rq4ff/731/5mHPZJjvSBxnQdU+glr1W5vPP1MAVaqB5YR/JY753rH1099HRPhrV6Vzbs56jNZ6NU8c+mtXFozqdz8q3etEaz3gfCgxr6zy265osaJSSrXP3z3JG07wibdM9S/tdddAcsXZyzcBsl7bu6b0cj6NwwAZkTu7cvc5UVbnrHDYHq3x0aGXb5WjUvjLBVYEZ0GGxFjABPs7AHRBg4LpGw2JuQQZiAFVtAFWgxOIP8FnsyWMRB6SAGMACOEUb2ASe8QSiBFjaAiz4Apny9AQ0aQvgkBugltQhF3nwRAO4wAtoBuaigSYAoY5yNAEaNNEGSPFCUzt5sqDvqbZ+CCyd2YXszvjRSfrSlp7QTMfO7ruHB5r0B1TSH/D32c9+dtVj+lo7+Aj+yQ8704Gj1P3O+q6/l0n8iN74kTP74SnPz7x6JTgTdAPo7ssLmoBxu5Fsz6bkIk/BMlr8yRjgR16B83PpfINNBfyujQH1AGU23ywBPX+wy8FnAF+01Gd/NhVEeCgg8Xt0+KF72gSu9YFfOvgFQE5vwDi5BWLa4Svoc60faPAXfMnCx9zjZx4iKOPfygP90VSff9MVuvr11ltvrfL7/1V0QE/0TYbLJPbTH4eUb6DteqRf2VhPf8ipPZnJ6kc6BJfasjF5zT/u2fER5Nr5U6Y+3dAF/fEdtqVr1wIk+hKUnC4B1HPPPbe2F5ipT/7mh+ZAefLQd/Kbg9BkR3T1j+z8QB15fkev0dTeXMQG6LFV9NSX+Is5Vh/QUo8+JD6JBr0pa57DhwwSGfiCdmiyvXL3yOse2mj8+c9//tQvfvGL9RXz733vex8/LEJHuYROST/i0715nhq4Sg3kd/tojv64q86x9sodIx3X5StHu3udd/E7di96naM70nRdfrte9Y/x2Vcevc7G73nGcO06j7KOsiX/Pjm276NnPnFEh1zb9LfbHctHd5RnpbkU/GdGO0Zhlt9pDXCDDs7n2uI4Dgr3RgeqvnMO20BSr/IUV9vO3d939pTY/zGzePsex5NTQQeQgY9dHAAFvRZ1T7ABCK8UApGfXp4Y2zEAfIAKMgmM0ABCgBtnYAiwASqAIE97vX4EROEBYACQQJI2gCvg4B6awK1UXjlagC2AAhC5B6Dgr6w2wE9AVTkaZNI/tPVZ/9RXTx/RJId7rtUprxxN/QGq2AaARtOujfsAFQAHDAvu5PFFD9ASmHmNyGtWAtt2BPF/VNPob675xbb/ui/tKr9Iv+jWQbeBT/p0zw6VH61w7bsowRm/8iMO9GsHyeuMdM7/yMom/J2vAPqCOIAeOFWXLwui9C2gS27+4R6/YiM0jAN5dMmgDjkD1+rQg3P1oimvvrb6pR4aZNKeD2nnUId8jQc8+KL7aEjKkws9deQFnHjpk7w2aOqLvMQvvcpo58fOuV1D4wkNx1Unfe0gRwc+6YEuXOufQIKu2cmYscsowDB/+LESD2Do1Q6lHzOyE28+4w90Zj4whtHSL7rV3lxjzsMrn/HjH3Yn+Ud80SYLPZI1/SlnKz5lHqIrcwra5j9zmvpkV8dcYT5QTh5ysBsa7O4wj7KL+UYb8gv81ZUnA75ksIOm7/LakkFbfWJvPPAmk7xyB3r04Nq4UM4vvBrZq4wCM/NUY0H/x6Rf7mk309TAdWlg2++2+fDDQ+lYe2PfEZ3t80j7UNlY79A1eZKpa+dt2uVHWt3rPJad9Tqe6rs2fs9Db7v9dtvync8qFxuYn9GXtCfbeeXbxS+aY9ncMRu1cU+vOQbH23aQ0em2yzimex1NHqPD5sjROc9gQNdrQHYLLPKArVdcXOPlsMgDta4t8PgBFYCf12S8AiWwAICVAQdAgsUcoAAkAEhBh3YOIERAaLdB2WbZffDaFkACaAv4gCL3yAJoAzzABh7yaHt9R3vflAHXygWJQKY8UKGOAMruHvpAN5nkAR1BFNB1ujwpl/Rfn71uBtSgqf94CrzICvQBemiQyz26FKQ6A3UCB23og37wIhPZgCJ8gUhAyFN9QNgrTeg5HtWkfyXX/G30uWPltT3rmd/xNWd+I7nmB/RPfwJeOvZDMnTpuxk7KnbMvJbmuyC2B2gBZXblE+6RXZAN6PNv35h5/ZEPAKxoA87ANDnUZUt5ftOOmgcL2tud4fv8ho8qpxN57QQCAiXtyYImAM5ftCOX4Iis/Ijv4clnAXD1gGn+Z4wo52N8TTk9kFsePXnldoW0lac7/m7OsLPH3/D1P7zo9OTkZA1qyHcViR5GP6GPbOiczzuTSaqcrD2oMb4E2+aEZKMnu6RsaXz6H1wCrCeffHLdhWYjtkFboMT29ER3dGgOMk+YR+ziC06NRfOIcWoXTj0PktAwR5IRD7IJYs13dr75ildY0VIfLzt5bG+edfBPO7hkMXfSDZp4mA/kvYYoaDK/OviB+YIO9dE4QAd98yj+dtzZ3Svi0uOPP77amxz0TS40vZLJF/oejwyCO/Oe+Qe/l156aa0vMDNv07c02gZNeQe9zDQ1cF0a4GuH0rj+7Kp3rL25xiGN81T5teCjP/HqPJad9XqUx7Xjk+S/3edj/diWf7t+uum8Xb4vnx2ck8nZHHNeWvt4jLLPwGyflu7R/ZwuxxidrYGpTuU5pHpSdXJY9x0WTWUjqEmt0ZLfduzo+RbFTySjAzAAEUCjRRxYAIoCQYIqfKJlUff02qLetxcBV8AHAAJMgQ10AQflkg/bgQxtgQ4ACE+AUVAEaLlHTsABUAYcyAl4owk8ASXAMJACpDpcA7vk1hfAFKgDjAFTfcBbAsrIpZw+yQOI4wkMe8KtH3QBUJMLKALK8JAAMTrBVwrwA7vkl8dTOR2SwwGIAXieVp8sYJiOsutKaPkjf58Te7ODJNBwsJdg3AMF/mvHFRDlL+zCf/w/K7srdk3YnF3ZWiAkQGYLeucvfNR3Se57jQ9Q5gPygCofEtyxJ358AhDmE76x5B+CQv7BnvwBIFcuUMDTP2wWIADS/FdwbzeHD2ljDAiS0PBggl/7fsk48noZGl751YdoANfq+E6UnPjK83eBArl8X8cHPfAwhgSd8gIwNPHkn4IK/2je+HvmmWfWgw7MSZfxQbzYz/iIjnvospWzsUE2AYdAq6TMoa0gW/BMZg9sBBp8QeBl3jCu2JEu+YX5yDdneHk4U9BsPmMHuuRb+ssP6EC7V155Zf02zQMc45zvsDu6ZPM/7siTXdmZns2F6pmTzDvmNz4myEKL/gXceKnDNwRI+q0vdMRv+TZ50ZTng+QwN+iLeYkc7uk7O8rrjzmR3SXBKn0KBtUjB7n4L5p8hx7IjbfxgaZx9etf/3odU34u3z0ysZFDojd2Ibt72XUtnH+mBq5YA/z+UDIeD6Vj7ZU3z6ATvc7uRaN7nZVdNo3zHFrR7uzedfJH/zKJbMmHTnJ3Pg/tdBEdc8tF6JyF5wzMzqKlO16HwwVEONq4oHFq5aODq9PR/Zx/bG8xdn9cOFOl++iqv714ksUB5HlSDkQACMAtsAawWrDt8AB82gMe+FmYJaDGN2oCJmDWwm8RB5gAC0/ogQJgiAwSeSzoAjoAwdNpILVgBcACouSBUzK6pw2a+JMVPXIpV5986ugzGcgoD5yo6566aATo1KmcnGTTB/fQdGgL+KAF8KKvX/SNniPeeEjoS0CkFA95NNFw3auM9GenBjjCz6GOM773ObFJeqRzOgFo/c8pIJM/ALJ2SNiOr9ExYKxcAGXnA3ClZ8BWHUGH+kA9cCoo4Vd+IvxkCZLxwsfOE/80Jvghn+eXADvefJiNgG3lwLdy/PiUIItf2PXAy3gSBJGFXOp4KMCngHZ17YjppzzfasdMUOaePFmNUWNA3thEA136wocf2vGQJ5ukjiQwcJ8eBH/q+8YMkOeLAlRjxRjh/xdN+br2+uRgU/eNHdd0YIyQt7ETP3Xox04We7CFoEKwzEbakpMN2RNd30iRWZAq4KJ7dOhWOTtow08ExnRovvHrjubCH/3oR+u8hJ77aAmY2VlQLtE33cij3ZzBXnSqzHzhWnuy0ndzhXK7ZuSgE7S7Rz71yJt+8EEDTYmPqaP/dIaGJJ8M+srO5EMTj3YN+QW5+A659BUdAb7glJ59b8nf+Lm2+EvoOdzDa6apgevUAD8+lPL9fXWOtR/Lo9UZTeXV6X7nfTzPc/8Y/WPl5+F1HXVH+dBPN53Pw3Okpf1FaJyV3wzMzqqpG6jH8GO6TsOPfCywFrOSRW6b93ZeXe06kt1i2IGmcvTcG2mor0way7qvLeAIkAA/XsvpZ9sLJizcwIVF2KIPPGiHn9eA/OIZYOPJPOBYgEUOdZUBGMAoWQAldbxyA0wJzOx4ACjuAwkO/PF1DVzgB5wACUCS/myDJLy0A2wBJ7wACwBMXhkQmjzRdHYPbXIAr4JSIAZ4AVjIgC5AFNglhxRooiv99pSZ7PqMN72gob6DHPIFZkC+J/ECs+zERq7Jdp9SPr7dZ3plH7ujXseyg0KHXqGlb7oDWgVdfNVug9cc7RDxa3ZlE+AeDzZTX12+wR/Z2w6b3TH1+Tk/4DP8kP+qr72Db5DLoa6zpJ5yeTZUpr22ZNAPZWyrnnJtlEnqquO+Mz+Olnp8Cm+064OxqT3fRZM/q4OH+srQcKCrXPvqCNz8ywAPTIxlv2go8FPnKhPZpM76I8gkp7FlbCgjJ7kFjILmXs0UZAnK7PYIJsjtYZL+04V6vpn1oMj/pBN4ocOeaLOnMUgHdIInX0DrzTffXF+B/eEPf7juuLGDw/hFg6zmKLK5jwa53SczW6FDZ/TrUN+5cm3ZKNuZI5NDH/RHuXuu8SWje9qqgw/f50/quI+PxP8lPoOnOYhM+Z76eLpHdrogX/QEZn6hVGBGf3xAWwd6krND+5mmBm5CA/neLl78/1g61H5su4vWrra76kXnvPW1q80uupVF33lXvbH8Jq+vQr6Rxng99mNfn/fdH9vuup6B2S6tfAL3WkAZPuNbXK4afOzrWjw7q9c158rBOldObiDAIZE3YKZ9NNbCc/4RmL3zzjvrgg7M2DXDH0i1GLsGBAAC4MYhkQXwFZh5zcbrRZ7KAgiADpmBBAs+YAs0AUkBFzsewDPgbMfDjkRBnLrsIo+voJEsdiX0FXhQR0CHHjAGXCvHyyuCgkZ07VzYJdFPQZqn7draUcEDAKHPXu0C8tTTxuuOBXdeP/OaDznR9NoUHr0WJXATaKJJh3RBTqATTTpBDx3XACaZ6JDcXomygxIIOqcZ70T1fHn73PhkEwEUO9Ah+3sdENikf/WATGCUffiNH1VxALhsI3h2LfFroBtgf7j8/Dnb2GXxGhy7q8eH+SGgzBf5GR/nZ/J8WllgGvBHl/3xA3zJJphXh6x8Qhu+axwpVxdd4w0POtBHoBxN9bRRT7nAYCzHx5yAj4QHX8Kj8QyQk4ue8KQrMukfHuYBO+CCU7tmyujmKtK2TZuLyaZPBRvq0asxakx6ZZANBGPsLQDXT/2QjCm2oCdBpYcn3gJgR//6QJ4eBBh48iFzm/r0kD3MQwJ+/Fz3XaKgnzxeRSSjVwLZiX9pa4eSLHySXcwNdH667Kzyw81ms84n5goPFfiVBzH6wGbspA3baM/O7MrW+uZBgnlBv5WZ6yRziry5Tr/Ma3Rpl9h9rzKiQW5+3I6tOUewqx9k0Q+7vD3cMGa81ktuu6bGB12hjS5eEps1Ltcb88/UwNTAOm9vz3XGifEy024N0FfzStejDs09zTWuHdJ4rnw3h913Z2C2Wy83fnc0dtcW7Qx83QLhKXXe5pccnSvntB3uNdDVqx/VPe/ZNzKCJHR8JwM00AnQAawBEBJwYpEGGtwHIgAhQAAYFIwEUkxCAAfwB7QAANoHhIGQd999dw1MBEYAlyAqgIg2GgCXBFzJAyiS18fQAiaAR0CDTOQQOAJFAJggyqEMSFJXEAmoCArRRAPIBayceyIPcGsDhAF18kAMgIgGnniUxwOQojs8AVrlwK9+kItOBQb40onXxvq+SDBAFu3pPB/Z9oVVAXf4Dz/Pp9OB7rKNwNeuDvtvFuBotwxApWNlbCXINT4ExXbVAFaBGbr0jg6/5GOu2YYN/ZKfH0pAz8+s++aLj7ARexoXbA7Y8kP+zh989wS4Gjv4+66NnfH1UEJ78gLK/WKk74DIye/RICc57FZr63+OAdUelLgv0OBf/IMvAutkQcMhj68HCvyIPoxrfoQvH7I7awwC5OTCEy/l+oeeVwDx8m2R78z47EUWvF3uOdp0vFaXXRySMrbxwEcQTt9+dbEdZXbWX+PUQxH6o39j3/gTdHkd0Vzi1xWNQwGROsY/+sayevRAn/yB7tjS93psb4y7J0DDj/60tZto7Jpj+A/9y3vQY7yzqXmSnOzOVzy0IoOxTg67kYIc/ASdXsNlK31lIzzJbW419/lGTf/4gABP4MeX2FkgTS5t6NCcbH7lj3yI3OxsfjU/0xe/dl2wJ3ijL+OADu1Q4ikwM9+3BjQ22YlfXJVvoDfT1MBd0ICxuH0Yl3OsHLbuts7Ka2X9CgeN566rc5jD/5bOwOx/dfKJ3BmN3bVBMxr4OgXDU+q8zSs5Oo/lyeue8uqM98f6Z732lPjDDz9cJw67NoCp1NPc6AMSwIrFG0AQeAGdwIPFG6gEaIAUwADQ89QbwAAQLOqCDkAILf97ypNa7QAX4FAQB0QASiYydPUTUJNHW5Inl/p4AUhAljrACnCDB95AjPtkBjC0UT+aeAjy9JeM5AOwyeE+GtoJ5rRBjxz6Lw/QOCrXRzTJhwaawBj5ukc29egOOAOSCszwGoEQ2bP12vk7/id/66zvxiid2S3wAw18S+BjZwPwpSMBBRsAm+wj0PYDIACpb2VGm7I/uu65ZifBEIDqQYHXa4FrQTRQzq8FZsA8QM4H5NnfGOCDADkbA858UDnfUV87uyICCcE+2VwX4MuTGdjmc/qi//rHlwBpMgoUBBfyAL2+O4wXu318G6AmD5p8TAAjb/eJHAIJ/RY40Bee+sP//egDIC8QojOBGd1fVcqmnaObjcnp2tjyg0SCDn06Wb75E6DSEf3QBdnMK/ru0C90jfNf/vKX67j7zne+s85n+m4comWs0QtdOCR2ZisPqMxJfqjFt4n6ji//Ykf0+RM7sz07K+eb5jr00B/ni8q1ZXtlbK8ffAuPHijpNzry7guS6ESefNqQlQ/oP12hYc4qTxd46atycyE740tu9OXJRQ/8Xz/Q10/B4YMHD1Z/9q8j+IZ6Eh2W7tu8VL/neWrgmAaMv/EwVhwznU0Do+5qsa0/c9tl0wzMLqvBA+0Z8VAaDVjd8XyTC8zIN5lH+bo3no+VR3Nsc55rT9p9kwEUeJLryasE/FrULeiAQAEGMASAAEAAJLC8WXYvBFcWe/UBFUBPPcBCYGKB15eAnqAE2AFYgVqABmAEEoAHdfGNFjsBIJIn3u6XB9SAB/XVwxcN9wDb2pANuJUAW0k5WgAP3srJGoAhr3bK0AV4tNHe02x5wap+kovc9OAMvNGbwA0ook96JhcgBQQLzAD0Q4HZ9qS0Cn5H//DnfNqZHvXfNRBqp5WtvKom+OGP6ZpPshd/E7zYXQOCfSvDv9g02nyN7YBlvt7rsXZEBGdszk58Az32Yks2DMjGG0/11JeS27W6/CKAqwxtfSK3vDr1E0B2T1+AZj7Nd6LvzIfIog/qAuzOElnI4XCNjzYS/5Xnj+obN/jqn375gQ07RnbqHPFeG1/Bn2TsjGTX5NJnuiGvHyIRKHtYZMeT7ckvODP+9J/enB12E+UFnD/96U/XPn33u99ddwj5C1tJdEVvDvQEP/rpwZAdOjtOfuHzZAkG0aMX47MHN+YHOmMbBzrK0Cc3+dmLjeTJ61rCJ59CN58if/fXissffiCZQ1yjn83cZzPtmufIIQm61FNOTjamWzTU12c6MHeiaQ5TThay2/nrxz/sONbf7IRmabzu3jxPDdx3DRgrjRdn42uOlYt5Bf1dl+5mYHYxm5ypVQNgX+Vto471GzT72l71/Xh3Rn9bvl08D9UZae1qe+weQCow8xqO118cFmPAwSIu4LB7ADgAAcAKwGGRF5gBQoBsr98oBwiBFqACDaAUuDFBuQcYeIp9ugRmQKKn3X0DAUSRRV2gWv+8BoSm3RCpXYnNEhACaV7PESh5mo1P34MJmNwDTvACPOSdtdG3gipPysmGB5BGbt9bqEsfnkS7B9A4AHo0yC9AIKd7zkAiIBSPnqLrm0MbctKfb1rsyOwLzPT7kP2P2fe2lY/+3DVfc+3VMICdnv1/Mru7fAnwZRNglG8C+Hay6JZf8WnlALOgWRt1AFN+rr3XCwVoXu3re0m64/P8F6jmR+qwh10MvsEn8FSOHrr8Shs+zG/wYHPgGXB2kEV7deXZuQCsMcK30SFDQYB6eOqDsngad8YogI2vvEQn2pLBmRx8U5/dI5M25DYPeAWyb8zcV/eqU3aNLh3QBZn1x1lwJUj0Ot1TTz216ofMdKNPDroV9JqfjCH9t1P4m9/8Zh2zxpTAyDi0w6o/fEhSnx49HBFY8RFBiV1W499OvnFsbJPHThK7m+uczUFsoFyAb/5Bj83oTDm7mW/MHz3IIbc8nyY/2vg5CxLdJxt9KDcv8V195GfmRv0km7yAlV7s8GnDfuQyH6tn1zG52V4gZn40t5qX6JOsrtFE76Xl/5jpl11T9Ur8Cq0xseX2vbF8Xk8N3DcNjPNb42OOkUfPC2Zgdo024fjjQNhmZTF5VFJydiaXAdug7X7n5B7rdE8di/iuxbI6ZzkDsF4bAk7avRIoARDABFADGACBwJtF34IPfAiugCcLOhAEnAILwB65AFnAiawWfvfl8bJTJ6gTGPlWyCtYeAAnwAEACqA4u+eMD12cLkEWkCmIQhNYAYrQAnoEP8A7wIQG4IcmmeobgKIv+ua+4I9cgDvQDbSSlR6AO/QdZEQXGMJHea/Tqa//2tMRHmxELjzqO/CmjcDsj3/846qbAjPtACyJ3rS7z4mNAFx6ZEM7ZvzB/6gSQPEHtmBHwJv++aydD/5px4Xvuc+2fAZNtuOv7AmY+ofU2hgDAKmAThAG6LsnkAbqBXBs55cejQf+z0522vgiv+Y729+YeS0RTcG+XWY0N8uDBX7nBxz4lNcK+aCAUpDmuyqAHDjne/oBXAPW/JtvA9BoGBP8SkDB39E0ZgSv5LT7RAcCT8GPvgL+vjmjF/rwCiCffO6559ZvzMjU3HSdPkgusgpOCsx+9rOfrUGGoEygqI4+6qt+mguMPeODDZUbX/rpNWlzih8woTP2YWt+I8BiJwEWO+mvOg6vs/IDfiQvqLJ7Rn9+pAcddueLbGQOspvHD70BQDY2NN4FeWzE5mzNl3w3R58eKrAR3uRGQ5JnE3kyyrOL4Eh/jQP+yAd844g/udD0+q05GQ/+jQadKqdX/sdf+DB/N0ehqW+CN2XeXMDPrzLyGzvN+tI6Q9ejP9CH477PUdc5NibtqYGpgevRwAzMrkevK9UWh30sHqVFg6xSZ9cWuha77m+flW/3w4LuAOS3y9A9axKY+Qe2giigwtNbYBAwQB+YCcB6sm/RB2icLfAAHmAHGAAzgBVAoFx7QYZ2EkAFTAApnlQDWoChp73aAlrAB3BKBwCI9kApHcg7Ay/KA2SAB/CAdjx70g/g0pE2ZAI29U0b4Ii8yvUfSFEfMEPPoS/0i6428mhri54yMqKBvzZjuft4uK+NOu5pA+T/5S9/WcGPAAJQUuaY6T8aSG/sLdD1up1AxauMADF7CMwE5HyDHfkPgC4wA6Tplb75qbN66KHN54BVYL4fC/GNFTDNHwSDQKy2HgDweUAZ8OdTxg8fAGr5lXLjA6jnS4A0sAv48nF9EDiSCV00BWt8ebMEauQ3NiRjAzDm/2QhA5p9Y4YeWbQ3pvitBw/6JkggFz7y5DAOAW5y0mE7OvyVznxjJhjwa4aCU2MFjZtIZNRHejDO7HqR0Q9jmI/cE3ixXbbWRkDuME/xA/32sIO++Qd9CMbpiN4kOsWH/eiNXumAbwlsBNkCG7ozJ+LjIRLe9GccC7ycBV7mJUEYGdBmF7J4sEU2D3aMe35Gn+zMR8hjbuAHzj1gUs5P5fEgK5tpw07y6JKHv5mf6QhtfeLXbEduvievjYN/8Scy6bNrdczN2tC5Vxn1U1Dc7h7a2/MS+mgbgzNNDUwNTA1ctwbMOVLn+F1knbqXgdmoOAvImJrQ3atsVCwQ7H73TP4O9xzRHsvdk1euvfryFg33LpLQrG08y++id6xOfVCvuvVhpDf21/2xDv4OQYFF2wJuwdQGzXQw0us6vtHQBnj1lBiI8LQdEEGTDi3kAIbkHnAkARkWdGAFMLV4C64CGNoCWWT0RD/gBwTjiaYn+sAM8ORpLlCgXFt89I2c+gZQygOe+pccZJLHB2jRVhvJGd94AqFoADjuF0RpTy9kUgfIIgua6kj4SHiorz1e+pKM/Ewb99VRJkU/3Y/3AHA/vkJHAjM7MehcxmdXpnfoT3qjZwDSrycKHjYLeAUe2ZeOgWh6Y0NtAHG6VY9+gWVgVlDENtqpw2Z8zLd+gLBvq+zGqQsso6WOlH3Zv2CbjzT+3KsemcjDZ4DhaLmPt4S2hIb+8TO0+BCAzc/x4o/q8F2yG1vq9wDBWNRWWf0B1vFHi9/jpR3+6EnGlTqNB8GpX6a0o3NycrKO//q0NrimP+mBPHRDX6+99to6t7CFeUnf6KQxKE9249XDJHOJ1/LY384Xe5uTBGX6J2BzuKardCtAQUt9AZ23B+xe2ykTZKlPh/RPd80F6ZQdyOHMDvQvyEETD23YIt3rn7r6gaZDfXW1VaZ+fuA+uzvTU7zQJ5e8PrOTa/fUqz0+9dkZD/fQ1IbO8VRfmTnp1VdfXR8KCNAFrejph6OkjfvZofvzPDUwNTA1cF4NmEvGZH7Zlao3ntUd56Zd7XbduzeBGWWNCqMMC4XFjPJcSy0O7lOoNs4WFmcLs/rKHfIO1xYTi4GF0WLSAuSeNg7042sxkiqPX3K6H39tS+6hob771R+v1XW/uvFo8YyWNmgpJ4/6Flr3yS9fW23Ir3/phwzyFnHttWkhl1dPmXv0oxw97ciirXy0lWtjgQZOHz58uL6CZZfAa0NAKZBDPge6PQG2oANCZPT6jie2QA3wqx/oCtTw1UZwJy954h0wtnMgGBSMeE3MGUABMnpSDBgBMvLuk4vsnnLjD1QBMWgB7QJDT44BMP1CzxNzvuP1L7sNeOmT+u5nD7LQE55oKne4Jj9+wBUaPQHXRp+BQ0l9dYAZB6DmCTddSEAQnQPU5NCv02XXUHBqx7HvObJb+l8b39M//NbYYXe+4xdE7UzwQbsi7N3Y4Ct0x1/5mtcK7VzZXWMnNOic3bXhR/yTzQUkdsfYwSuE/Bgd9bRzkMM9PoOXMSdYYN+CHX4q8ZtkQaNxy4/QQI8vyfMT1/wVD/JJeOh/bXowYWyigad26Mura/zrK9n0AV9+xC+lgjljUyKng9xee7N7bjfQvwzg1+hdd9JnuiK3s/76Z89+jMOO57e+9a3VNmRU7mD35gv9di2o4COCbHOZwML4Qz9dshf68unKfCHIN0f4qfhe6+s7M/UFfnRqXOMvT+d0ZP4zF9CpOYhN+BIfNM7dYzN12Mx8ghZZ3fcat77zR33Rhk/xc75gXiAvO/NfCQ15ddjWzh1b6wMa+kNOD8/IIUClM3KjIU9uujEHmdMc6ts5NZ/7vg8fY0RSF23+xH+dZ5oamBqYGriNGrh3gZlFRnI2kZvEXQOm8hYjC2agweJjobJQ9lTYoie5Z+FxuLawWRC0sVBbwLSxaKCJjsXSPdcSvhYxixtZ0C6PjrboWMjQdrinjvsO9/TBIu2+a/XLq++e5L5DO/e11QZQIps2AEK8tKMXcqlPRnUl99RTrl06c1899fFBo6ef2rlPB+jgpYz+tEGPfO71ahWbqA/wCoDojj6BNrSBCsniTY9oAhb0xybq4QmUuEZbHfzQ0286ABIkZ23xJD/AqC2+ZNFfvIACwYy+Z1MAiEzqa4cuXwDOyEJu9MmJr/ZkSf/67h5eZEabvBJZ6QkNddAIEKuDpzrk45Pa6yN5yKCN9tpI6uGnHpvVDzs8BX92aXoVVBv1HfmHe/c90R+wa3dX0A1UApcONgI2s7dAx4/a2FnzIwh0yx/onq9JbMle7GlM2WXxoME3WF5l5HteNUMXcMYPcPb6Kdva5WV/bdCyy8IH8GQ335jxV6+v8T2g2MMHwPh0AcKbZScPAMcDGPYAAbjmI147REMd/i+PF4DPv+1y0wF6ALT+oosfmsYDvmjwM330LZxEH3Rgp5v/45lcdiO9yney7Jb5X2Z4p6+18TX9aYxEng4KzOwkP/vss+u4Us/cIBgxlvVNn81NrumEnf3sPXsJLOjNPWO0uYtNBWNswo70gKe+ayuoocf+JYP55uHy8IpejVW0vMLNn+hTsOZHN9hAG4GUhwJ4eDDgNVdzJRvw0ceWb9/w850a2f3POPLyLb7BX/mYb7/Q1F7/zEle0dVn35iZawSh9KKNeYdc5PQqJh154MDPBdvmdmOIP+mftyP0nRzklCfzz3/+89UnjAN8C8zw4eP8il84zzQ1MDUwNXAbNXAvAzMTuEMCMi1iFgEgAngBDCwsyiwkFqqAs0lffRO/Oq7Rcl8d18CQMnmBgAQAoQW8WADR1FYAYFFRz6JaMGbxU0eKLtqBYm3UlwDDFiZneUfltVNXOdkBPsAAPfTds6iqCxwAGQUT9RVNdYAp9cg/yoYm+uTWR4uuRA51Je3U0T86oxdl6qOlTH200FE/nZO1hRgQpTNl7EYuT1DVoX900EAzG6LrYJtkIwfwoq776AFX+g9sKkdDuf7XTlDFlviQsfZkoS90yJTs6MjjQe/qu6cuGurhQ3756DqnO/XVS2fADjrZR57NyImHunREPrp0qKteekFfvjr0o28Akqf8mwWAA3wAskQ+NDrWm/f8D/vYnQWcgceCMgCTTwK49M1fzDN+5AGwBTbtfNA/GmwpsZ/Ej+nbt35s4fU3gJ4fAPqAMR78FDC2I2F8CIj4GRuyK0AuyUuALp4AubGOBvvye3UEBewO9APxgLm8cYQvvwKk+XMPQNrNPV3Au6BeAIAmn9N//Mipb+TWL2302TdR/MmPXvA/P0BCH/jqq/Ze5QPs+aKfSjcHqXudqTGRXcjIJu+88866gyfgYEMPQ9jZ3CC4oCd9d1/AQxf6wTZ+xEQw5ufy7UbzBTrEg13oBQ287I5ZD1wLsgUp/EU9ARq6yqxX2Zlt2MC4FlBLfJN/kNXYZmc2YkN5uhaEa7tZ7KyP2kiCH3X4Cbv5tpDelZNNOb8276nDJ/gTHQjSyYevNu2Y4YFXNNIVO5MdPTQkuuA35jXfmP3kJz9Z5aY/8qOv7/oryaM909TA1MDUwG3VwL0KzEzeJvEOCxLAJA/sepLtJ4k90QWuLJKO2hU8qQ8YAD/Oggq0gGF1LQzaOVuQHNGycKgDoFgA5ZVL7ku1U27BdqiPh7rJ7Bw9MuEh71r9yt1zLQFKkr4Aie53rV4LqkXUgmrxR5cM9Ut79/TdPXKSHfiQABT3yaAeecgemHJdH5Vp654zXVqYLfDaq0cuNJUDN2QWOAEG0dcvtLQjBz1pDxCiATiorx/a6zu51QOqyIYGOewSAC/AAXBIHjTphp9Igjlt0CWXOs7kclaGF5oO4IbsZNAX/USTHPyIHNpomy7IqU94uFffAWZ00MQXPXJp7x56gJf2wC+afBMvtqYjfUcHX0/rtQHelLM5wGgsoHey7FIAYBJ9l8br7t21M/0fSsodxosfaAC6+2EHes6mdO9gR4FHP33uH+UC0PwGMFUO0AuO0GITr/GZkx5bdjMEA8YD33aW8GcL/uaQ0Mn/1Rvrus527M4XJX7SGDBu+I9xgD7f4yvKnZU3Pp3RQJcfqq/veDj0G23jsIQ2OvVjW0b1a6MvAhw7LILMk8Uf0SVD/YjuVZ71wzghK/nIahzZMSOLb8zsapkLjDOysKPxqr7+GpvasK35Q1tlXoEUaLtvrKItAEHLNT3ira2dQwG/wF//BUfq0gse6NJVdnFPHh0yKVfXXKHMffMHm7pWRib9VJ+No+tcG2fl2lQHT23xcJbUkdRLf9FQx7XEL1yj4UCDz7MrWbd90UNTO2aC0m9+85sfB2YrseUP2h3dm+epgamBqYHbpoF7E5i10FtsXUvds/hZ+HyQ//e//30FRS+88MK66FokLK4AjMUisG3R0T5AgoY66gNjLUoAijZoWHgAXwugPGAMFKtjMY8HGhYtNNFyyFuo0FJuAfIE09nTRIuh+uqQyaKbTBY/PPQdX2d90RZf8mjjbCeKDgAy378AiRZNQIDsaKJBJoGFRR2woAsBjrrAhgVZHl08yEUm7eTR0Bc8gU9P4skjINYffOgQD2dPldEQLJFd3/EQMOgbsAu8Akho0LW+KANyAR1PdMmjL3ihqR4a+qYf6HolRx/UE6R4Mgsw4a8O/QnY0MDTfU990cZTXkDDTujoC1posLl7ysvzPW3Jzs5e4dRnumc7eTSV6zvw56AnB5kAGrpSB7/T5ckzXW+Wp9PpwpmceOOpnfr40A0b0K/AQnAKCPLPkwUI93Ran/VfCoCtmTv6p77u6h4bOdiZHwDdfNk/Q7f7w2bydKaeMeJe/5PKt0Z+2c8OV3WMC/XYjo35uHmJvewWOZTF2/gxjviN+YO/8CV8jDN12VsCxvXHmMBPffTJj4axrI48Gnig0/wgr1xb5exPXvfxUU+eLMZ6eXzMC/hKeGqLhqSfEl9WT32+qi/RsFtkl9H/9HriiSdWGZSpc52JvvRXIrfx7cc/BGa+eX3++edXe9GdPiWveYT+6d74ohcPOwTvAi0/YqKv6Juv6M+8IG/elIx749AOGfsL5r266vVB9+iYTo13+jKH0Qde6JCFXdnMPfXZln7Nh/LGNz7mD7Kap9DCjzz4oWN+aP4gm7kCzeYkftJ65B6fML/SiflVX9EgVzt9ePAVeby1p19zELmsQe2YqePhx/iNGT76g4eDnbIVGWeaGpgamBq4jRq4d4EZ0OOwQJjUTf4WP4DZNdAE4Po427vvFjagyyJlAS0okAdW5S10FjaLg8XC4miRQROAt7B5DcNCDYRZqC1a2liE5C2O6gPrAIqFU330yWMRdFjALagWIK+KADdeecEbTXJZ1NAFvIEe/dRGv/GwaOJpsbTY0UWLoddLfM+Bl29TLJjkx6MgAA9y6Ku+04+kr/XdfTTxBU6BA9f0qd/uCRLQtPjSj74AtOqcLoFFfMmdjTwtFWhpkz71V3t9QVMdC76FH03AAH95cpNL3wVzAIUdC7KxPxp8gPz6rC27s4t7gAN9ATDkIgdeaBbcAXD6Re90hTeZAS7613f86Iue2Y2c6ZPv4CFPF/J4qI8GIMY/9MWBJjujiQ/++gsjsTsAAEAASURBVIo/+YFG/sXu5GRTry65p1zf+Ao7ag840oHXx/DzgxN0wMck/ZbKr5k7+IcNHPsSPTjYCFB98ODBen5s2dkS3GpLx+qwE/vwWd/eAPZ2v4wxtkWDz7Ad/+Cf/EmZunYL7Jb4mXD+zmfYmI+Yh/BhM7Y1b/ErryHijY+kXB6wR9/8wd/tjLO5az/Lz0fsypDVPXk85I0zc48xRj5ykdlYIQsfMr43ywMBY9E4NgbMY+Y+4N49vsifBRYPl2+k5OlCPTz4OL7yfPX9999fv3Py/6u8xmZuIMd1+6Cxg5ekn8Y/OwuWBdVeqzT2BB1k0Wf12dDcoo/k1B/98uuS9O17weZ1cx7/MPeymyCEP+CFP934TsyDEnMVHnzBbh1fYDP13CMDu+PNzuYAAaE5pLbmPb5jDuMb7IM+nxAwsrOHc2Q29q0Vdm3Nj/RvTpAnLxkEb+Ygfq0fHh7QFZvpo51F/eHH5PSjKXipT3eCbbb2QMw40g/zktdqzft8jz/xg5de+s8/mO4bM/5jzdHvbV8w/uh+pqmBqYGpgdukgXsVmFngABOHZPHy0+gm/AIV33IAOT5gtmgBHxYg9ywO6lmELbwWD4uUxQawtUBYVC0IwIf7FhULtUXGgldwZ2G0mFm4La4WKOWACACnrTygZXGzSDoAeHUsQnigDRBZgAAigF19cqAZgEeTXOTWfzwt2hZstPRLHf0AAPTXAmrxtqgC7HRBJoe+AvTkBCDKJwdeZMdDQEPXLaL6DYAAomQFDOhU3+iDndBUH380yakNOWqDDrnpQ7/0Vx+00Sd5bcmhz/i4r+/uawMoyuNFv+oAm/oPQAEzlesDnmihGQ2+pA5b0A2aAApe+i5PLjwAFvfoVB36c4+e9N81ObRVX8JTPfpiA+0D89qRR/AH7GhDf/LRUJcfSHSFd75BbvfQ5D9o4EMHvm3iR3aA+K3+SvixL5vf5aSPjn0pfdA30PrWW2+t34IBloKMgmg2Yyf64l92f4BUgNSumfuCFrZge/MRPzKu6drPpPvGyHzkHyzzPQ8EgFhzEH9nT+3YUtDEjwRexozvktjVHISeOYlvoCHAExjwc3nzH7mBbX7rRx+MOcEAH8GD/2yWuZC/As7GBTmMReOUv+KhDfp8j/+rk66c1acXgQVe5NQ3PHqYYY6Rf++999bgwTd2dJA+s8E+G13mPtuTw5jCh4zmDL+OaHx4DVVA2hyoT8aLce2afuiOndiTHrySya7shKZ1Rj316Y3d+AN7sZW5Dn3f1xmPAn7BkLmJjvFiV7Kxobany7ogT//aW2uMbXnymGvNb/yN7+DDfyU09RENdmYzNvCaPxp8Fg/BHV9WH02+QQ664n90x2fxsLunP/xLP+mM/1lX0eT7fI4M5n00tSMn36Ir9rZW+z9m+u9HSciln2ihv+0L+qVspqmBqYGpgdukgTsTmAWgnLs2KTdZW9wsCu4pB4AsrgUAFjUAtqd4nvp5agw8AD0WKwsBgAHsWBAkeQsVeujLo1+gAWgot8Art3Apx5dM6LtnQXZYaKtjMbcgWXQqB/Is5FL9afEhk3v46bdFDQ/1BQ0SOfHHQwrgu6eNs6elnl76VS96IQO+5MADXTzdx48+XJNdskgnE1kskNpZ6NHAU9KWXPjSjzJ01CWHto7quqcO2urQSW3QkpfIoR0+Iw99J4u+jAl/cqDp4BdALcAKONMV2emSTdAEFrTRVwlPcskrVz8boMkG6iR3PPXJPfJqQz79UJ4+ATo651v0A8DpWzpXlw2cyUpOfdWe/2nLh9EWdJGbP+OjH+q5do8c6gBZfhJce69s8YNsik72GfV4F6/181CiC7Yw3gUPfj0PsBREAaDsAhDzU74hD2B6LQ/A3CwBDrsCr/yGbdlbXfMNun6G384VMOoXCdmdvdThV2SQ2M4hzwf4VWX4K3MPnxKfcVTPfXXYml+QV55faa+v7sujQz/8Sx6dxpi8lP60zX/UK2kbfzLyTUk79LUz5vzipXnJP1j2c/nGsLojrWhe5TkZkoltBOB+cIMcPbDwQEtf2Kakv4IU/dIHwYygXNBrV5Ps+sFW+kvP7E+H7gua6NsaYbfTePRdml0t90r4sJ+69OVAm4+gS6bKxjmma3zTJVnJI6Xb7IaHuvrpGl1l5KUnNtceL235ibMy9VyTR1vXeMqTV9IObe2U8e/6o4wO6J7uBMWCTnTQrs/yErqOZF9vzj9TA1MDUwO3QAN3IjBrsrcAOEz8DqDFQqHcJG/Bcc/k78mwV0uAH6/xWFQstnaLLAp+BhmwavfFwoCWiR49fFqElUnKHRZECziaFj91W/y0Ue6JpcUECNbGgkw+ee3Iqw1eDmXkthgV/CmPL7ks/upZ2KQWZnn9VkYu9ABuOsKXTOqQR8DoW4bT5YmpHTOAUVlyWYQBeHJ4qolewa0nwWijSTZ16pv+kFvf9EM5meXTB733FBTQ0dbTWPfxpOcCWTzRlAdiPHlVR7/cwwMNPOT13dN78tmVyw/Yhpx0gKYDcAaE1QOsBCXkIqcdAW08GdemnTxPfNHEQxDUk2Tl5FBOP3TjHp2ONNHyOg9gAYyTFw95QSI981P6IBs+nmY71OdP9KuNIFcdevFwgdx2PshlN4dPewJODn3Dl1yni83pb7MEC15/+uCDD1aZAVC8+TJ56FAKBK2Ze/iHHppr7OrY2XIWiAnq+Q6d8UX1+Lwxat7hY3423C4R3zWW+IQ67GSc8Re289qcQFlQYveSr7AxumxunKBvfLAJn5cnX/fIwae04Rd8FQ38tFeujXLX5gL18UcHH3QFauoZt8YlWR3mB+PMmEQXD3LKGy/kUp888ujrb/MPmviSi47UiQeedGvXSGDiQYGxc1NJ/x0S2Xxj5tW/p59++lMny/eXxp6gi+xkbq405tjQ2FeeLe0QGVPsTqfasYN5Qh1j0Bg1L6lDj8aj9Qktr0HyMfeTKX3J8x361p7OldEhfbvHXna7yGo+YRN90EdznmQe5AvkYCvto8FX0WdnsitnZ3QldPXHfNwcrg6aeOHJP6LJlmzvNVi7e+Y0fFtrPDzif3Tw8ssvr3MR3WunT2RxSPvOa+H8MzUwNTA1cAs0cOcCMwt8h4XLRG0xsEBYBC1AgA6wa4EAOL2LbzEx8Xsv3uLrA3OvbWgHBFtE+ubBYun1FKDWgqG+RcgiZeG0SAG+FhSLDL4WJQufBYd8wBk6Ah9yoCdvwW1R8pqQhX2zAGWLtl0sffIai4XNQgY8WaDcxwNveSBJ3mJpMUZHGYBgoe67Bk8h6adXqewWvf322+s931DogwXYk2EHMO8VFDQ98bfAAyn4e30LEPCNA7k8GbYg6yv90AXZ9MVrLUAAmvKAJ116GmpRt6NgsQZc3Ve/V2cAHzLIC57pHCBmJ/pzKJMnJx50f7oEHvoqrw2Z2JVu9JNvAAaCdv5Bvj60F/AAKnQq4cGubMD+aOAljzce7gEXeLIX3SjDF6Agg77SqTL+haZAi420l+cb5ORv7IoHGuqroz25ADr32FlgJgkQ8edLbKI/6Q8I5H/0iz47kEu5wMFODd/0dNo4IaPxFEgNBK2M7ukfY9n44J98lg+xEbuwl7NXtOiKfQTYAgw+drKAesEZPzV3GBtoyatvTLpvPGrjWx2/RmdMGS98nv/zTX7Gv9mP77F1/m7nGz3yoS+44Y+APl/jI/zA/GN+kOdH/Mw4q5yPCDb4l4BfHXLwG9f48lW6MD74jP7iox/GtkDMfGGsmF/5Ern4qMBTPTtj6nltT9/QpwPzn3nZd058Vtvr9sHWEuNRMp8KDuyMeq1SgGUcGYNkUY9ujW26MH5d8wu0Hi7f0wls9ENeOXtqVyCCDn2yLdr8p2+63GcrawTfYmf6Q6t/Vi4vsTs7sJH5gX3MaeYGvsNPPTwgp/WK79C5szbsiIY25nTt7Nbhz7eyMznNR15J1daczUe8eslOXte0jqKhP/yAjtAjl7cS2BlP81TrgnmVnOYg/SDnG2+8sdY3J9Gjg/6kfKHzeG+tMP9MDUwNTA3cAg3cqcCsRdTZog30mqQtXhZAwMI/a7Xwu28RsqgARECnhd/rSECBBdfiKTiwIFg0LYotskCL4AbYUmbBFpxYRIAfiwyQYQG1cFiALL7KyQfwWKQ2S9BlcQGQ8LXYyqMpOMAvgCNwJKc88KwcDYuaBREPeTzxiSYQJA/8AH10AzRqA4jRk4VPXwRRvqEgy8kCHOlGOR7oAkn6rm8WVDoll77bjaEvfdNG3/Rdffohg75ZxAVA+gr06as6FmuLPj0BCmwEOJCfDbQjgyBKfbyBDXIDRmQjrwMgFAShSXY6B3DqCxr4Olzjoc/aAM1ABHsLggEFskrokk8/9FWefwEgbAPckosMgn06pwP6IQM9KKd7NnQPT3W0kfQDTbKjiZ88OelEOaDDN5z1iezK9ENdeTzQSCb1HQJMcqONJhr8hi6ME7blB34ERh+MA3bRXzz4j+T6vie6oG8A0qtmzh5AmBuMMbo1Lp2zr+AG0AZgBWbmCzZhO3ZgG9cO9P3fLA9MBGZ2CtgHKHbwQ/MBG6PLD81r6Aiu+RU+bAU4A8x2XNAQMBmrgLKHLOTUhl8KCtXlB3gYC2Q01p31zXg2jvm0coc5RxBizGyW8Q9co8mPPBTgl8aXPNDP38iNJjnoiW7UIzddun733XfXMWnHUGBGxutOdE+PZDRWnI3n119/fQ3MBAfsbFwYg8r1w7gzT2lLF/rkPttYX4z/xx9/fNUhesadecBYREtdYx5P+kNHMGvOExB74KUePmzAJuxLv5J1QTJ38SU2cTbXsov5g63p0Lzm+nQJtPgEG+iHOmQ3d7K/dYKt0dRGmbVEP+urOnyWb6BhDpXv4aT6+itPfn0zr6NnbPAjfTSf6xc9qIOGvnqI6FcZ9dMbLWQbU/PR9nmsM6+nBqYGpgYedQ3cqcDMQmqBc0gAE0BsoraQ+BjZYiEvqLBQWYwsFg5P8Dzlsyj6wNwiZVGSt3haHNCzCONhwUBLW3WAIIswvkCNuo4WeGd5dYFx4MRiLQFnaALS6FiQ6ge++KiDPr7qWMjwQsNCJ08WMmsDrCl3rRwNeW3dc12QQDa08aADevKkEwiwgJNVG+UWefTSDTr6hS46Etkd6rmnHaARX20CMdElHzra0TE9yaNBTgCFftSnH+XkQjd6+qC+RBdoyqOpjXI0yIQHGbTHx7U2gCQQAGgCqPrv0JYM6qmvHbqSMkkZusrxJjfZyOkev8iXRv9Rly/qU4EUu2qHp3aAm77zHTw88dY3PNkEDTTlBbHK1MGfHPJAjwTsoos+uq6BMTYUvAFRdszkA5H6C3BlY/zue6ILB9DqF/foTZADPNIrPbOVg4/QIQAr6DX/+PVX44hN2Yk/sUn32FXgAjzb6RLMsCG7sHM0+Y268viig5567IQv3+ZH6PMFNgeE2Zuvqac8X3HPmEFX+8YI2vyg8amN8cH3jHN8tMXbnOTsIJsycsvzZTTdpx+JLrUhGxrGqvpeq7UTJKjkj9ug/Lr8kFz0Ql5JX/2TaA/4vFLJHsatfqhDJxL9ui5I0kd2E2CiIbjU1ppDZ+5pb7zSpeS+vgt8vOVh19Sc7E0GNiQX22jHHh3uS3TJ/+iZzcjo2n2+QSbX6o/BoXv07746kmv32W/saz6Lvrr8ZOSvvrJo0Emy0Jv+yacvctATWuompz4KTl988cU1GPatpYeJ8VoZfCTn9r3K5nlqYGpgauA2aODWB2YWi30HUGGxssBZ2Pywh4Xek2dP/iwCFo4WIYuYj7MBE68MeToJbFgg1LEYWngsJBbsFkRlaMlXH8i18ABJ7hdIKEdDORoWYYuScrJUThYLjLIAu0VYHmDRZ3myyFvIgDsLPFCnny2C+DvwSw7l6AsCnC22+gcU+dDeU1hP3tsV1A+HhVJfyVFf6SsaeNK3eug5agMUaGMxJjtZ0aSP+qsvaMhrBzSqI49PAFA/HPFASzla7tVX9rfQ0xP90AOaeNhl066dPDLpv74DTMDfZnnq7ymzOtoIeMhFx2xAf2jL0wma5MJDX+3SsbU8gKUfnmDThSfF9I6etvpJd3iTQ2Cov4A82e0ekkd7Ox3oAfeCAff009NoerBjyy/ZD4BBk2x2vvQFYPS0Wn27juragSGHJ9766efyyQeA2lmmW/rUb4nN73OiB7anEzagL3YTPLAtnfIZia6MUf7ndS3fsrKdIINvsBlaDv5rB4Ef8S8PlNgfXbvSxgh6xpGUDGiXJ1u2Yjd2bIw0Dpv7lDu0UYcPkDXQrIxM+JDz/9m7u13JqmqB4+dR6rwHidugCYkRkBCMNEi48J0kIUpQ09oaY8INFxh2uCA8Rj3KWb/Z/tt5yqq9pZvuruqaM1l7rfk1vucYY66P2sbxSfDxBcaxXfP0W0dsER48mG+sOnkYjzbjk9EMszbjzYXDRtbGxGaGzNiwOc/bBg9xkI+NmTjhSfLN9laBMWjFo4MM3AgkL3LiG/gjT1P9rzu24WforT3yoGs+gA3NvsVmHGw3ET3d9KaHp6NeJyRvsOGFT+HTjSd/cPWRMZhkqs4O+Bj2RKfkyA7YGRrRYi5/gtfikz4w4KRbY+i2eKVPnV6LPdaC8fwUufA/aIEzuvkZMNibmxtg8FFkZj7/6RodfNqf/vSnIQP/NgKt+KuwhedtD+Fa5yWBJYElgeclgYvemAlCDuXwWptgICmWhEpuehXDtw2ChyBgniDThsGdWeP9LyKvqgggvcoogRUkBDZwXbdpkPhKIjxhAc/rOgKzDSBc+iUY+iVWc6Is8ReUBUdBSxASsNChr02kwCSYClwCHhgCpkAPtyAGh4AFjiAoaAtegimYngShX5IvYHo6ZLOALgm8TYn3+AVFSQfYZCSJkLBLqgRYAV6b4O0VUO2eEgiMYArUNgXkQ+54g88cSYEx+JR0mLvfXqUBE10SBfqiHzCTj0TFZgUcNCQvia1rcPEJLp6MBxNfEgZ1OsGTdvI1zhMOdIFJ12TUKzZkjSY04MkYBR3kQgfkaT68+40PSY16MOnFnXB0o5M86MTGj22gia26JovszXg2Zj7e4HRI+sDAK13TK7hsQh2d+tGHdrbgyQibhTOdgEN2Nl1sBV72bsOATxsNMK0FY9BIjpVrT4LIN92RJd9hjbqh4Umzwub4A7rlY6wXSbYbRTZanoIp5G5t66e/npyA61VGcP0Yz2uvvTb0R5fa2Bz/YI7NOjx0zg94HVjC6ykLe7fm6FVyz7bYiE2/dc3Os302po529swewGTnXqlko9YM3NYpmo03z6bTBoQ/QRfbdbA5Y8DweiT7V7f+8GHNB7N1Cae1y7b97zByY4uScrLk+/i9F1XoG71ep3OTT3ygE7TTn7WBLjzyC2RujVpzZOaJn2+Y6d0Gs42E9clG6AYcciRTugFLndz5WTIlN+vSmqRvT5LAAhctXgU13yYebPKlS/GLPNkJHbExr9LyDWCjl62ALQ7QvX5z2JJ57BUc4+kaDQ5yYQvoMQb+f/7zn0Pv/R8zTxm1+8VS9uDVWnKxVsgNH+Kgb+V22w0o+NkkOvk1N47EJnGlH/8g33zStfujF7UOFp4lgSWB5yuBi96YSYoqgmala8mKgCagC+ACgKREAOLEBStjJDHdEf7iiy/GeJsSTwo4fUHWJkEAEmQFIQFEwiJguEMpiEhcBRR0CVyCUBsxgdAmSRIkKLURE+QETwmYzZBEXHCTbINhE9Ev6wmw+JCwCEgSHoFNgqTdnOo2f4q65AzMkiB0od1mQ7DFv+CLN7z60N7mwr8MwLP5aDReAi8prE0iIGA644W88Aqv8QKuTQXebAK0kaWkAw/kIykhD/JDB51IDCQl5EUG5ENf6CBzyRp5usY/mPRAj3jVRx50bQ660EHvAr0znHCkRzTC4dUxySF+bBzJBQ3mwEG/PbVQV/AiqUJXeqRLsoWDHNKrMcaiW4IqwWSz2hS8kAl56Ec7OBIm/Lk2hw6U+sEwxlz6wDtb004eCnj6jGn9GIM3dMKJDvL3VAdf7u6TkT5HxZxrL2RLjnTqqY7kk81KRtkym6MzMiV3dmxd2/xLpG1GwLA2yJOeHHRrY8++Hj58ONYpPdiYGGt9gkE/vsVic24GsSlJPxgSZbZhowg/P8i+2TR7tk6tQzc/rEM+jL+0aZOgsxGbCWtst/k1NscHOfNB7FsyjS/zrW3+A21kYBNhsyG5tl7AYNv4Z0d8MbrQAAaY0clXmW+Oddx3dmTgRzfgbo08bxuknwo6fSdGtuhQ+DprjR1Y58bY9NK/TRXbsLbo3uuudIQvcr7Z4gx5khO5gUOOCl2xHePNY1t+IMXmnE/im/lyG13yFN/AavNMnuChhTzRAyc8DjLUBk8bSbEGzv1mC8awA/ht3uiRTtifuMHv85FgsDe2wcexabKw8UKXujOfQlZgkBMc2s3nl8AXW9gSu3aNPzCNY7+ff/75gPfee+8NObHvyvJHSWKdlwSWBC5ZAhe7MStYOjsEJI6ZAxcUBEJ3+bz+IiC4y+YHPQQdCbmgLjkxXvIjERI0brdfzRIkfQMgoEh09UuGBQsJhnEOCa2A0uYOfvDhl4AojUEPnObDaYOjXuItmJtng+UMJ7j6BVABSIDHJzoEXDSDoc/R9RygkpM2/QId3PCALaA2Hq9w+rl8mxMJnySPnOAqCOLdnI4ZP3yNA1uihV/8w6eezuBCB9ho0g4m+OquBWW8GgseGOjUT076wCcXbYo6WGSKlnCiAb74bA6Y8JhvjgTXEw0JQjYgOQGHDI1xhgPt5sOJXjC1wzvXoxMPDnYCp7HqeNEGDv61a3O41qYfHOPRih64XWtzzTYUvAZTXWKGtuyPPBVzHIo5Cp7o37eGNrWSYUlgek6/+Lu2QhdzIVNrynr1FIW/oQNPKyTl+iXD7E6iaZMhQbfxtzFxkL/Nj81Vm276No+eJOWSZDeKbrZEXmKsnz+gT/pgkw66YyfwGQMeO2PzbACdfBJ7pm80GQsG3tg1vYIreWZb6DJfG36MhQd8Sb054OFDnTzIAF4w2GFrEjwy0KYfTPjUFf2HMLT5ufyeMvoBED5JyRZH5cifQ301JNu9r994/CUX68xTGxvSfvyDTPWTJ54UurHhsAnxZAkPZOQpkj7xyKbHePKn5zYi5KgNTLqy9twk8c2ntz08MdRujDhDF66dlfRIV63tdIBv/c1jK+r5C/owls4V+su3aTffgTZ1ONXhCt9sS67xroBjHjtFVzYBf3P0kYk6maizPYVf/vTTT4dMfWrgxgUYChrm86isP0sCSwJnIQFrfC6t17ltXf9bAme9MaPMYwrUXpDgwDlvSQdnL5C4Sy2x9OqOBMhGy91pdxr1gylYSFoEIgmQAOMOtGRUv6ArqAqAgokiQAiAc6CBX7AWlPQLFGgxpiRZn3HgolFd0AFXYNQmyTEHHWDMcwpgxriWnOs339n8AjkYxlSHh7zQhgYBWeKkThYFYHWJvuTAN2buoEoubUw8YYITTDSYr54e0IFu8OiFXI0VYNUlotrAkZyg0QEG2slPsoYGm1B9kh2F/I1BJ7kaQ2dw4kddkcyVQKIDDMmGuWCig12gqydm7MIc/TYg5pCPO70SL3eu/QCMu8KSYXZWEoY3PKuTCRjJE16JNjzgw8sW8OIppyTbtadQZKiNztghOt3hhgtN+tXxK9GDc/+vu9n60UZ+9AamJxVg0J+zu+bm9HpYd7zdZUeH/t129x6NxpCpJyrukLMD8vOURiIJjkKGx9bl6HxF/9DD4YFV8rIGPUFwQ4MuydO6cS7hTizsy40hsmZbbnywIesCHHpgy2yRzOnUr6S6yeRHHzylgZN95A/U2Qd904t1pVin2uYNPdtUt9bRpk6v8MAPpjp7NB8dYLB3cK1jcmDrcFmT+s03Dzzz4GAn4MCDRvPBZ6/syhywzHc2Fg14UQfPOPB80+Uplad//i0H+MYZA/YpewRHqb/zaNz+wK3U3rm26nA50O47sdvtBh46fvWrX4111FsCvcKMZ3pGpzVGbp4w2WDi6aOPPho8GocHerfm8MIv8HFkyX/YmFmv/JLNvqdaNqZ8jvFoUugIv3wp+4CHjwAbHj6I3tFiDLnyB/DYLPKl+Wd2DFabH3TAAx498p1g8FnoCCY/iWdj+aR8OljZNb+IbvPRxWeB6YmZefwuXtgKHwUm2m1wfd9HHjancOOHjtLT4fUQzDP8yT5OgcDHKksCTysB68Jxqsz2fGrMy2yP9tbfIS3xN5+tGcepOYcw7qoHtzHB7Fz7yzof0ndIBzqP0XqRG7MCecFH8iAwCPScea+iEQJnz7l7XccdSgHAOIHPfILTJoDst4TXXUkbCe/WS4IEN0mXgFGCDodgJngIQnALQoKEwKZfm4ArCCkSZXPAME8Qhs/3AgKM13kEcoFXMIQPDHQJhnguQReUwTDfmF7nE4AFO0mTOWAIsBYBvOjxuol2eNFm82qOpH+3JZLGe20IfRIPtChwoNNc8kEXOiQr6ujTD4egLdnEi35JpboExVMoySn49AAG/BIbOpGMKoIwXOgW+MlG8gCH13jAgxddArekx0H+PakwR7IXD+THVswnI+PpTSIAjiRT8uAXKSVC4NikSBzIi62UgOGDvsFEJxrwAx446HRIaNCJB/34hjde0aFfgkIfyY9M8K6f/NgO/ZjHVtBHx+iTYDkkL2CwP/PxboyFz4a1e0JjHNthL2hEu75sWGKIRhsztmNjJoFDGxk4H3MmQ3Gv8J/8Ts6WbMiYTdlo+X6Ir+lJGDmSFVnzMeTMNtgXnVpfDnZtE8OXsAN2S390yTZ7dc7rkX7F0dqzTuFybYPHzvg9fsENFXjU+S/f7IANL513wwnN6PAUho3zlWwLXskvu/YvANihjSa+2Z3Nk3WKHxsmdesDrdaH9W/ttNb5GHTywdatV+3YtU0peuEFg13yyXgjU3TAbU36xgz9nhr6vqix5EUP6SK7pCvFevkhCvrwb534uXzfX6Lf91TWkHZrhj2QBX2jG59oJUt+z1NVtD148GCMwTvZgE9urUFrnU/2lIgs8e5/p/FTdMFv6qdfG1Z80zPYxpGjeGeuDR17Ypfmw8nGzPGNnJjgpiRZ2fjigZ75JTaHNzpDm7jI56BHrGS/4LI/9kRn9M6mwVS8YmkdsAPFmxh4ZntkAqd5fD4ZsCM+mkyMUWcHfKfv+1z74RU+1Tj0pnfnruGar9W/b8mOTs1jd6ssCTytBIolp+Yf2vOpcS+zHQ/H1pl2JR6rG2vdHJvzffkIdvOC2bn2l3U+pO+QDnQeo/UiN2aSfQwLPpyyM8fPkXuVSGASECUbgo4gI1gKCBIUybtgSSASWvC0C1aSK4nBzfbKkDvUHLP5Eh2JliBsjkAEjgApuEi+JFeCJdokQODaFDBCMARnNAmAxsMjqElwBCBJvoSopEgwlbjACZakR8ICB9wCmURHP34ETgGxDQ+5aMOnOWQEhs0cXoxrAwSGMZI9P+ksyfAKG/rwJQgKnjYIeBNY1Usk8YxX7QI62uEwF6/olCRoAwMeyQu6yU/dtaBO5uagH0ztkhww24iRpSCOB7SZ3wZIIAcD72RPR2SgH0zyJtdg4oNO6LSEQpKB3r6/kQwocNIFeUmK2AWY7Ac/6vhDszayZi/oVMcDvOTSBhEv5tjo0REY2vDFjtGFH3X8oIHM4ABX4sT28UD+YGkzFx76ajze9YFPJmiGEw/GuDbGujncmFlr6DCGvhUwrqXgeT7wTh/sgbysG3Zo08JubYrZCTuke7Zr/UpQJcuefEim6YD8yZV82Qw7YQfs9Ntvvx3fZRnbP31nE+yADUrAzfENDp1LhPV76os289iFRJmOJfbgosNr3hJjfgk8GzN2pc5uvULHH4GJd3yyHzdZ2IlNARy7bSPBnvGPbr6ktd73S9aQPjjxbG2RCz8IBnhkyl6zZ2uT/GzMbDS9xvjmm28O2ZIVuaUHunCdjtgl+/8hCr2B72xj5ntCcrexsdElM3zQAz6sVfTxr+RC/ta9p21k8Jvf/GbIjGzQSI/O+ME7uVjP5pOBDRpZ88c2hGxJDDBWbGE77A6N5KvdBkrRT5d8K9rYhgM+ehJ3jGETdIIWbXx2m0ybOb4xGHhhb3TMFsDHC7r5X3IQa/iMYiD7oh/2RP/8MTp2m+0kH3DxjU5zbcbIVht7/d3vfjf4evfddwce/NE3uJVT1/V/n/NsS8fmkfcqSwJPK4H77Istz/b8tHhexLx4cVacD32yNvzU/qx0hTM4yapz7S/rfEjfIR3oPEbrxW3MMCqIC3qCj+Di2qZKoBC8Jaacv4TEnT6bNUHKr0Fx8AIPwyhBFWxL1AVcgdP/SZE4aRdkwDRX4LEZcUiCBGB9gjK4Aib6JFaCj37tEmF0giHgCHrGSazRIujBo1+bsXAocOK1YAomvuGILrRIutAuyKmDDy/+wDDHeAdjAIcMtLcpEBi9biMhcCde0ia5RLM56JA84E0bWOjCgyAPDzrCCa5rbfCQJ94cCpiCOjhoNt+18eaShXlkoT1azcdfmwR8qpOfeewEb87moA8e+MhHMV4bGOaAr85mJEISawnpbkscJAdw4h8sc5y1wQGugk78gmcMHPqigezg0J4M1cEhG7DokGzV4YBTOztyJhvjyQoOMPEPHhmiAQzjJW/qbAUNEh6F/SlwaDef7l1LJCVZknLwJcOSTzTBH9/mu76WgneH0jU9kJEk1JMtMrRuJKdsgA013li6tLnxxMUNA4m9ZJjNsAmw2DC5gmlTst9uUkhQjePD0iF7deRD2IH58IJHx/rZgLbWpfngqxvDPo0xV5s621JaQ/q1gwe2NUQG7MrZ5oG96Qc7W8YvuOYaF5/q2Q65qBvHxqo3h/36zo4fd3Otn8tHX2NdGz8X+J61REN8ONuY+QXONmb5bLjIz1oiCzKg63yYTatvzKwxGwvrDL/0jkf6a92bjzfrGHwbeRsur+T7uXzrnE7FDXIzHlzz0WyuQ1tySD7qcCrmKugGgw9R6F0fmrQ1HjyFHMBBN1rgolsw6FU9ewZDHW3mmIsWsJqjDid48LEvc9gTWzTXN4affPLJ2IC+//77Y42ZB2a8DeKmP6faG2Lus5T74D8L7DX31ZfAffZ3SfaFF2t2PvgQ61ybfgee+IgfgrdgZinB7Fz7yzyj8a5yjNaL25hRMIfv7I4bB25D47UK/+fFBkwiKWhJFgRzd44l2F67EDgUwpCUCEiCgcQDTAmTTZ4fChEES4IEjYKf+QIKGAKGeZJrAYUh6hNQlILKnNAUkMBEv3mCFz7AFNDh0oZX412j11iBGh4bIjBsGvALh6TAfHAFQHUwmgMHWvU7JFZ4EFzBlghKgmzMJB42p2SgL5oKnvCAL+FQJBra0IEudJtHHmg3H63oQYN+dLumB7JCh7F0iy7Ja3jQjmbjCvauycJ8cIzVhnfj4WoMuGhBA1rQHkx4zUe7Jwte1YHLHWFPDyTYFpg5dESeaIfXHPPVyU8yZdOLdnTqNx5tEim84V0/eiTdHJUndM7J00YXb+5Wox0Nkjx3ns0B01MIOMA0d4ZJBjZU1oEnsuzaHXebBq82ggMn/aJZP1mZgwZrAZ1eH7MpQIuCfzZPHnR0LWV2sK6rkzt92JiRqcSZDMmIXK2BCnt059/3aJ5GkC2dWVPkyh7AYyt0TofWoifYXlG0MdNnnDUGhzpa2K+57FA7uzOfHWsznq24pkt96q61galO59rA0AY2HpzR2ZpCr/UDjjXFFqwPdTSBox8c8+Ew1xh0okuBAyzjzbWm4M9nGc+/e3LoZpvX2FqP6AELbmdw0NkBt6L98GzMXf3Gg6kYBwf5eHrnpoXXGN95551BM1jGOpM7vTvjG638jc2VG39ijW+Y+Qx+guzAZTvwaAOLfzePfXTTkc2wA3P4AvIiA/LKDshPO3nn58CFj4zZCT3oQx+76C0IMPFg/Turg60fjb0VwY9pg5P/gFMswSva2K6bFHwPGAr+0IU/+jBf22678YVWfo7M4CAjtsOvudkJjrcYPDHz1O6tt94afgz96XwgOfiT3g+an1Tx+CzlPvjPAnvNvQ4J3GWDl2Rf+OBn+Jau+Q4+R70DTx3PquFgBid5da79ZZ7ReKqcovOsN2bHmKF0BwMQvDhudyVtyiQwkiJBTxGwbMr8PLO72IK6JEARpAhFkBJEBRfBwDcTfiLaBs/PxQs0AqSgIzioCy4CjyIwwSMwgSmxFVDApBDj4UGjQCQpFmiMLyhJwAU6gUmyog4uHOZKUOCQ/OEbDG2eaghkxqMNDLQKfPgBH0wJhc2FBdIcr5+AIUlEryeM4Jjzt7/9bSTu+LcxUSQHNl6SJJsPdGqDj9wF7d0WYAVhOnHoJxMw8e7JGzxoRgf8cNKBRMBYGw30eGJlnETEPLzRARrID16BX93hiSVe4VcnL2PwThYCODqSD3mZP8OkM3WbGK90kRdZooG86AIONqRNArLfnmbgFR/wqNvg4AOv6NaPB/JAp4NdgEGeNoJs2qaJ/cChHQw2Se/kh2/09BSF/MBgj3hT4AHL0xb2x47JFT/mgQmGV5VKnNACH7rgl/jo9yojGUiEbdDJEExycHaUuA7kV/YnWViT5O3fTNC/G0C+xWm9CU7WC31ZJ3zSd999N8b4+XN2To5sVz8bNZfewPadFx/m6ZrvXiWxdEVHkmuvsLHbvt1yEwpMc/SzK7aKRjrnP9gqe2CrbESd3amzB2PYhDl8kTXFzqxbNPEnbJNfg6sNPv7ZIttnm/B5TQ7vNvfo9OQDXTYY1qdNJxmxTZsEONTNt2bQ6dsicnPTzXd2aCT/NqPgsEuHkn2iVal9PhtzV/+YuP1BSwWv/KNNIv/o5hV9kA1arB9640vILn9E/nTuphfZeiWVD8Iv2wBXTDDOxkTdzRPrnlzoUoyjJ/o1hm9mNzZ8ircb0NH/MUMbHHw82ujZ2qcT/lEfOfINNj30aQ7/wZbYl2/M8IAXNscP8g/0TI/Go0UfendbDHBTgh25KUo2fnQLnb6vY9/WB19ChmzON6z0zOejTazGG97Rxu/pB/+Pf/zj4MOvMrJJtB36IHqtpO/qh+f0f9he/RB27eu8JLAk8P8lMPvcrvllh1Kba+vyvrVp3H1lhmlsMDvfN/9c+y9qY0YJHKnA4ZCwctYCGkcvwAk0HLbALzj63y9ebxRAfORuHhiSDQFU0JUIlTjZmJkDhuApELbRcC0BF0wFEMmToKEuiAlCEmPB0RyJjYQFLZJ9CYaERZARdNQFOYFacuIQOAU39ODLXMEaLEFKQBUIS6SMcS3IGuMQrMDBlw2P4IIOMiIzQVrgA1vShH796BBgfQcBV6+w4RN8vJoruUKfQA4/ugVcsiFT/TaV6FZHB37RhHb4BGQwyMtc/caiwcaMvPAiKYGXLsF0DY5rMi65IEt1vBtDTvAkP2ebJPMkiWiHE1z6AAdetEsQJDPpQhKKDoV86Bev6JeEmmcs+tGAX3YIJnxwsDd4JVzogt+BTjDIAB1gokG7fjBdkzv9sTEwHPQiEVTQwAEaDxa9GIsu89HB1vWj3+bSWmAbxqnPMPDvdS39nh5bP8YpnF4O8doTl/wJHfrRHEmyxNnGLP2ySTIzlh5sMNz8kbDebN+y0qMxdMxW2D0dms9W/G9FibkbTr/4xS+GTkqU6V/CzR+BC44bUOZZQ/U7z4k+W+Sz0C3ZZnt8hTZ2yz+gh+9kQ+roBwNt1gO/Yw2xRf4SvdY1++VPHK75HPbPr6l7Gs1u0G1dsjWwJfTs0xpjj/lya9tP1KOFvPrnwsUDsE4FYnArx8aAUTnWjw7rTjGWfvxKJh68Ukl3ZIkPcjHGOnRW54vwDLYbWL4TIyubUuuJ/89P50f4QDxZw+aRnbhkw2W8m4/0QWd0YFNlXE9pyYn+24jRGZsCFy98FJ/AVvhjttQrl74/VLIVm2l48GCzxTbU2ZsNNDoc5AIOfvPh6EI7OvAj1vA96nyR+WDaiIkV6uyJ3dA9+cHDdsmXX/bEDI0ffPDBwAPuYblPp/P4tTGbpbGulwSeTgLzmgOhOr/kqO1Y++h8yj/gBROIcHV+SrAvfdpZb8w4zVmx6pQg2HLgvoUSBAWX3Rb0BTx1yY8gJ/C52yyouePvzr/AZL5x+h2CFtiC8O32M8heGxHkJAAScAESPmMlH8YJVJJoSQuaBCVBwnjBWvATWAQx42wKJDACFziuJT6COpokRcYIUOYIXNEJLpxtCgQtwQp8tOMFHLIS+IzPWAVgcARIwQ1OQdscdXTAj0bzk6tkScDsV8fQoz85meuAj7z1oQHu6EYzPOaiE03odkaDOXCrm2Ousfgic0XyAA9YDv1gwmucOer60AYe+YMHLhz6nY3XBl51bcabp4283Nl2N5cubeZtptENJzwKGGCZbx66FPKFmzzBRJN52pyNRY+jOWDojwZ1/erhIC/jtblGp2KcMdrQhE7X6FCX6KJB4qSwX3DMT57ROfPiKZ4fwgHbhqAfzlGf7Qs/11TwX2ktqJOvDZSnBBJTyaO1Tv7syFjrm19y08PGzIbIOMmvGxR8iLVEh+nGpsRrc/vtCYX/rcgnkT//YwxfYW1bxzZZfAv88LEBOuVT6AlsbehSR3M6ZxeKMezDHLzCo0jIZxjsm+0p6ECDfvw58wVsGg52iC5w0aU9O9Jm42a98998m3XCDxln/RvrtVqJuadCnlShhxyMgc/1sQKffkcFPO1KPNQ3n43DG/rhgQM/fuSFf/T0Es2tPX5Ev40W+dg0kUuxyMaDXxEz8GADkjzwzEeSP3jkRYZuCtGt12T9qJUf/rAhxD98aHLMPIKZfOPVmayd8eK6Yjy6+QH0KvxDsI0lB7aTnukrWPiBny0Z63rGCx4cyTo/aBxerRG4wHdkL2A49JnLL//2t78dT1L9m4LdFvPDB0fFHKW5tR87h+NYnza4V1kSWBI4LYHWm/V8V5nXmrH3jb8LVn2HMLX/EHCD/7LOZ70xExw4Xk6ZsAUHdQHA3XyJjKTZXUQBwzgBUeCRUDhLlLyS5a6e/30iWApoEhFjBYIClSc/Ps7W5hcJJQHGK4KJIAKHoCnQZFyct35G4ro6egU7c1zDo24eWIrrAmybLIGp4AWm+ea2iYK/xNpYB5xogod8HPhz1p58JAnVyRJMsI0FR1JBBpIgSaPXTiRL6Ij26EQ3PgRpsCRz6AJDcqmOduPhRYODfEteJF7okZDAYQ5eJKTaJSDo0o9WvKvDiTc0zLyBoU4O5hhLh9rZBHpKOiV/aMG75AJecN3VljibKxGyQZUkGasfXcbrtwHHGxzd7YdHcuwwHi/m4wVOc9Ql4+RnU84O1BUJPD4l3exA4moeHHTher8l6mTsyQU+ewLptTX0ublgrg0VGeuH1zqwUWDr7orD4Q42HtzRB9OGwZMTySB5+Q6qVxnRp00hUzK7hoLXwwPf+Kc78u1fbSRjSTt5thbNp2typh8y9aoYewWDfbMHerOOjGebbixJ6vkkB5z1sxNrwBrWprBLNgCvdekwR50No8latkbg04cGMPQbr18BEz3sFT361dFlnPngqIODB3jQpe6Q9OOP/aILDAUdChjmWD/GscXWlPn6/H9JTw09jfREEKzmDyBH/kQv+TgqyRe/1oYC71zQqcAPDh7EDLR4na4nnjZJ9MmfWRdocqMQL+aADzef4PVAr/OxDz8uRcZkAT4ZkjX4xpM3ncCv3dMmGzOwvfrHhyVj6x/95ij8A97yUWyOD7bu0Rcu+lMnD3pjN3jQzz848ycKP4ZHMjCHftCKDvPwQI/mq6MZHWDgV7t4jS4bUnyp80n8K17deARXP948LcMbvvgpr056cgrehx9+OPgZxN3xB55VlgSWBJYELkkCZ70xOxQkZy+YCBLuWjr7vsJdRUWAERAFhjYP7lDebk/BbOB8OC9QCizGuRYoHAKb1zl8AyCI3tzcjCRIwBAcBC7JhznwSLbRIkHg/CXPgpDACX+vj6ANDImVoKMu0Oy3xBpe8AQ7+AUpbRJ4sPEgQYAXTgm7oO2Ot6AuIVBsAOAQKMkIfnX0oAscSaJ2ARcd5CFww4k3gQ9edHtS4q6+b1pszgrgNg5goR29Enj4jFVskNElwAriu+2OJjlL8tEOPjwSFoksvskL3cYoYIIhcRWY6Rd/cEgwJAoSDHW82ajAEwzyMZ4cjVE3h47U2YzAbwwa6JEOHXiBA25PjMjZ0wyJAN7Jj22wB+MlJWjEK77UvZqDV/DxBiZ+JRp0Qn5gkJ856CRzesYHXaOphAb97s7D000I+P1CG73gBUw40dcTB09PJEvogJeezSM7vOATHjyaQ35wGGsO2XoCBMbamA3THDoijw5rMp/Dfn1jxofQk80ufdI7m7DOyZqebPzZmO/LrDFt9E/2YLMXCTt7sm5tSszpFwmtj3mjzT7ZoFfH2I+n/eyHXbEzeLMrcNmAdQmvte/a2mQD7EYSbQ5arJnWqcS7V5/xZg2wEzDRiV51tha/wYDDWoKPPaOHb2H/EnjrhB2yd3Rbp+jUZg6/bB55sUe+i/3fV8DHDzlUyJre9NFPpTFormhTRx+aycD3bjYJ9OeJun4ysKbAJn+yIQe46csYsvEGh7rv5Kx/86xL+sK7+a7xR6Z8Bd9F7t4QMQZe8M0HSx862Rw8XmHFH18KjlcgydiGlq2gE1z6Qis7QBt5RrdNsDneMgFL3OBn2DbdmqMfTvZBR+Dqc7OI/tzcI2Ov9aLLk182Rn/swRMw/AYTTeQBRrJm9/yoVz/5vIcPHw7/9etf/3rMI9+7ytqY3SWd1bcksCRwjhI4640ZZ16wJDxOmyPmwH2ALWnw7YuERVDh9AUbCY4AqtxumzJB1Ifk7szZlElCJLEKx+1agAMPXEHIe/zvvffe2EQJQAINuOYK0gKZ4CJ5gNcmSUAVDMGUeBUsBRlJkwAskAmWcKiXwAhgkiJzBEdneM2VJMGJPnwJZJJrgQpO1/gGAy14gUPQs0kSNMFAF7x4kXzDAwfY+HLst02XjRl6/SqlgIhvdBgvONtkwqWf7MgCLrKQOAjQNn8CqrlokPSjUaIFn8CuD10CvyQGz3iTLMEvoW1T5Jrebf4kLBJhcEsawSA/dgBHMNGHLvShG/3oIrM2VegCh2xtVnyj4RsSiQ+bMR/v6DIfb2i3wVZnl67JSIKsDUy2BiZa6MR8+jJfOx3RiSSbrcNjDJ2YQzbqZAUuezPHXDokazjonC2RH7rAoh9zwUefNvShwzj9ChhkhK5gwkNWnpiBvTZmQ1RDhuTYQYYOcrUGbMzIzY0A65wts0W2Su7WF9tgW24YsS2JL9mXwLc20gW78u2YJBfMNiXWA5xsxBpht5JpuvNUCV3WOntg7/RsDvjoYjfmm8em2gCxRTbDP+ATXuvADSUwbZq0o0U7mGwvfwImGNaLNnzwB9YDGMbaSKDTxgzv1jU7Mwed6LZGzCczc3y/1zdm3nwgF/Sw5VNF37H+9Gce/Ph1NFY/f+GsDU1w0RG6/vCHP4xvCflGuo5O65J8ybN1zJ/iHb823J6YWds2Zubye+ka//B2IwfvfJTC93tyKl54ysp/OYz35AmdNkT5OfS6aYN29iYG0Bk98yf5KH4Pfj4HbP1goQv/6s70DAYc7J0cohNv+KZHekG3OfRMFsUePOBdLCZLdPBN5GN9uHagOT/FlsDnG8U7PgnfXiNFB3zpbQjq4A+9rrIksCSwJHBJEjjrjdkceDlYgU/iLYh4pcFriu4eer1HgGlTwqkbKwi4QykwuVsrMAmYEgpwOHZOXTCAS1LhtUcBxg9/eI0NXH2SA0FPoqOgQdBSF5TU9QsyioRHoDNfkFJHk0Al0MAp2RaESjLgAM88/fhBrwMvEhyJhMCnDV44XeOFjLTBJ9lW5jo+4ATX+ORkPHlI1vDhzqbNLHl5VcdmiJyMMVZARQ86wEETOuCET9DWDh6aJAHg6icPbeA4BOjodDavBAgcY80Dv351vGgDgwwdyQNfaEIzWMbpo1fw0K09urXB62ycu7Tu5tKLu73ZDZ7DSxZw1qZuvn5t6vDiXRu9JittdKCN7iQ8CnnhRz/61BW2QBb68I5uxXWyoRP4yBf/cKYzY80xX18w4EUze9QvQYOX/dlg2KCz2bUxI8HHr26SYdfkS37kLjn2hJEe3CiyCSFn8iNn9kD25OsGgqdgfAX/JXHVTv7g8xH5BGtegutJHDv0gyH62YSDDwI3+4aHfaMNvGwCXGtfG/+HNuvBPHYIJpvsKY9NAt6sW2cwFXW2pR8u/EnQ2Qw62Ck87JBd8WmKvtYLnOZm32zPmrE+0A2mNaCOR3WviVqX+PedHZqNPSzpp3ZjaiMLBT8VvKjPbfrMcaADH+FCu7c1PFGnD0+cWqvWcrzglYxtMm0kzLepFY/g9L2gDYqYY1zy0kdH4GgnIzhtSmzQzbGZN5586MxYBU48klk+iJxcK3SAjsaDH9/m0AkYaFDPL2hjw3TLTtTj03zzKuAnK/JDXzjCa7wx8Ohn68awG3jxjLboNp5s3Xjw7wZs0sRn7dZQ8KNhPkfL3LaulwSWBJYEzlkCZ70xKzgSIOfLiXPGkgfv+e+3u5F+nOJme+1QgiKAcebGcsjqt9sTM6/AuDvtDi0HL6kQTEt2zRFsBGEJkzvc3uM3RyACr2An0JkrGVHAEwwFU/QJltrALBkRkODSVj/eJHGCkOAiSEmU1OFQwDOuAKqOFvgFSjy6JhPt8IABj0QJHZIaxbUx0SkYJgO40eWQePnQXhIkCZAIucOOBjKCUwBFV4GenMlAIIcDbWDD6Uyu5hiPV21wGot29KJLWzDcKUa78eCAiX/96ART3XwyCw+61MkCXer61NtQ0r8x+uonczzQqzvzkiB42Rc7gBfO6GQXYOBL8kmeZG4MXYNnPnjaJFJ4UZdsw8tmwdAGt+SXTOhQ3RzFXWO0my+JB69XziR9CpslR3e00QEG2vDqiYmNg82WpGa3vYZmvEQRTk95g0HPxngaaaOBjrUxG+IZOnt89Thx79qakGC7m+8GjO8yvYKrnR5KetXZrGTdd5x06UcgyJvO6I+dsulslq3YBLABm7ibzdexA3ox3jjj1c2Fgw6rszPr1hj9bILNOcw3TjFGMQZMh7no1cb+1a2p1ilcrsExv/Fs2FpXwDdGQYM52ox13REd9cOr4AV+31e5EecGm9cZrQ3w5gKmQ+mMlmgIhzYHXNGhrtQ2Ktuf2s118I82iXRCb36xVEGjuWjit6xpPFhf/Ce5u9mFD7K0pqx/c9gBX2Fdm0OWcKnrd81m+CUbfnPBoCPj8/FkhR/z6AQt/BSdoY9OmqMfbHZAP/yaPnSAx2b5WW3o4dP4YDSr6zcODgf42Xm+FEx48IcuT93wQm5g5I/JCC18mvH68QKeGxJ8q6eSrn1bzsf5ufz841DAiT/grrIksCSwJHBJEjjrjdmhIAsmHPxnn302ur224w61gCEQSIwEGgFHUBUIfXRtnMRGsClACZ6CiuAjgRUM+vUvr0j2D6bhFVj1GyfI7LdNoWAkYGiXmAlekmDB0HiBzDX4go5Ag040SNjAELR2W6IsyKmD5dUkgUsgwoM6muMNTONKxtX127CCYQ6Y2gp26mQiwNtIgIkOskQXPiT5PSmRtPt+wmbWOMmgfjzjVcCD11x0kLkna/jDm42VgCuBEIAlNJJP/IJB9ugxRuKAbkmGJIa8JCHk6XWhgjJabSKqw+vuP97AVCc3/XRmc4FOvJIX3tCFDwkOvHQk8KOTXCTCnrC6Q60d/+6Kw4NHONAFJl6UKfr7AABAAElEQVTJAl1er5RQ0BGZ41ObJAmvZChhIQf9EhR0SoTgRK86GZEXGcJJxvrBcE1ertFBTnAqaACTLOCBAy72ik4wJPfmw4NOtIPplSpFP3mRD9lJQNnS2pgN8fzHHzbHZsmbPnz/wo5vts0TX1PyaY2QMxujE3f+bfz5JGuMPSnWK9/CzsC05tik/1/lu6F+kRBc9sHHsB/6RQedsnO2x5/AZY1pA08bH6mNbZsDjqJeUg++8frZovlwoEvdWZ1toI8cjGc7aGKX7MwZTrZkLN/ojF/2DmfFOAU/aEQX39wGzKaMP7QWbXrxYOxhwYNDcbYmFLRVGuNs/aMJrc7G1W88WSh8BZrxa4Pl1XVr1cbMGrPOyRu92q03N0PIxUaCbvgVftUYukyuxoNvPZK5dU82yU4/2PC69iaHfjaVz0KjdYtfcNIRPdlEkiffCjYdqeMZbeSNXvzPMNU9FSQP/Wjj4+NPvGFreASP/0CXMeTqdX7+KfngHV1u+GXb+TV0R8Nu82H60YVnvpPfx6t14GbZu+++O/TPRmbdksMqSwJLAksClyyBs9+YFSQFCdecsED/+9///skTAMmNjZRAJ3Hg5AUkjr07rZJPT8EkPwIeeBy+sYKeIK/uGwCbAps9PxZSUCnJEWgEHcFOYJOgo0mgBlfiIPAKysZJRsCVuKlL1tEgkfbOPToFKn37LakzFkwBBw51MNVtLCR5YArCArB58KEf7zZeeLPxECTRTW7ojg4BDh3kJYEXcMETtNHpQ3v8SB4kj2CjWb+xaEcPmGAL0JIzdYFdsEWLgI0GSQtdSEzRCoY6/AKwOryCNt7AFoAlcBIDMsKrTZWgXR1eCSC8kia8SKT0q0uG6CjeBX5taMQ/vBKikjz44JBASZ7B9rSMLNkBPZOncep4UZeU4NN4+iFfssYbnujIXHzBqa5dgkMGYKC3JEfi5CC/ElzyNZ9uyQ4edWPANl6f8Qoa4KjfNRwKmSjslf7woYBrDHmwX68Nwbs2ZkM8//GHTB10SF6e4ks+ycsPLShsXz+bo3trmH25WWR9eVWY/oyhP+PZFx1ZL/r8bzTfpNGTJ0Z0Zj1YM2yMLZujjd1JtumRn1TMs37ZqXHsrE0i27O+0UfX1ge7YkdgoJcN8Q94VceHdQqGb0HRLukHRz88Nv/8hWt+jU1Zy2CSkTkSeDjZLlu0ptBpPFu0bvlCa5RPRqtNGfqtITJCk0ImHXx6ba6N4xeyezR0gE3u+pzJe55rHWmHB7/WOd9GTska33RB3mijD+PJCTx8gePAq758FBjG8Jn8GbrA0K+PfMJpc6qNDeCLDPEMpkLPYPNh4PAv9EBHxpAlvelTB7s62sDLf+OPzOgQfWijIzDYA19KDmSCJvoyBq38Jd7bSKOR3bFtxXzj6Bq9Sn6TrNkeuIo55pMHmtm4p8w2Zgq7TK+jYf1ZElgSWBK4cAmc9caM0xaAOObuXAoSgr0PsG0QBG8Jjp+eFiBtTAQVCYsA0E8US1a8m68tZy/oKIJigdkvWEkCPCkxvs0InCUsgohNlT5BVHBHi6ACjwDn7iGYAooABIdgJeAYJ8lAp+AZDm3qJXECERh4wXd1Y8iDfMBEl0MdnWTWHG36jAcD7+aQlQAtwUEPGiVP+tyhlwTYzN5sd//RJygnozYF+FToBx79rsFw7QyvQxIw02lec1yTobnoMi464Yg3/ehUR7uCJwce4FD0k4G6AwwHHtTBMN48cMBFozM63On1c9hk6MmHRANdxqMZDNfmqMOlqGtXd7hWjDcOXdFWPdkYVx/e9ZOhNmPAS/76HersGZ6SeTTDxw70syUwtOEVLLJwTY94LjmkL7DIxzry2pCEzT8at76MMx48Zwc5XmtJBmRGXp6Y8QN+nMEmhC1Z95JfyabEVQLPN9icaXcDyPriF6xFeikxlfx6tVqiLbkmf08OrFOw4DJPXWLMn7BTyTi90J05NgZswXh2YuNgHPjGSOiNQa82+pXwOuPLWH4MDDgUONiRTSMb5C/R7YaKjZd+6wY89KOTb7QGwUSXjRmbs6lVugHlpgh7tQHhX8mMvMjZN5/kxReSfz4xXbQW1RXnbN58dTIG3zU6FH34bcxo/NcfMK1HPOARza03vIODDu3GBJ+fJlP6UcjZWGsXXuOsNTLpQFc6M5/9oIvuyMDGyBMzcOHTRw+KevyjF0y4wWws+tGsDw/6jIl/uLWzRfSyo7kNTXCiy1z9DnypsxXwjdGW/0EPGrTlw9GKdn6ITPSDYa4xjmQHLltz05Bd+Ufr+vADfwUfqywJLAksCVyyBM5+Y8bp5mw5bkFWEuIDbAFEYiOp0CcgaHPnkiMXUHwr5W6zRMMTMHd2BU/BAFzwBCCBzmbLRk6yIvj5lgFMpQBkjoDqMLegJMg0pqAHv0OBT3AyR0EvGOChVdJjjH5zwFD0C17O+gtmxqGZLAQwPOkDF6zoBEPdeEERb/Aar6ibp1/QxYNNyVdfffVkYya4mlOwFbTRLDCiCQ36yMp8wVVJJubCjS944KQnNLpzChY45oGBX/1kMMsGTOP1S4T04x1c8LXBSZ/wqJORzQo46miFAxz40IJmAZ6ctPmxBT/+gT4JpwSyBKBNEd7R4a6xNomrJJJsJC3oIk+823ArYLCBXmVkk2jHK/rBzH6Nl7iDAYfDNVu36UIjHm2e0Sm5BVvSQhcSZLyjnzwkdujwS21uZkiY2TvZ7LankGQJBpj4tXmwMQPDurHOwKmQs0J211roltzYE9n5RVcbJhsKcu6GBluga2uTPPfbEyEbLRsV+slGW19sEzyvVTuzERsdcOmKzOmPnbEJumhTh5b0pI1dsWk0OLcpsP7Rbj3Ax/bAQSM6rPnWjDobi37Xxlsv6CAHOK0BMNm3MewdfrYLBhm0/uFCq6LfNXuHw1MRsG0gbQR9j+dmGTn4Nb5+fh1PxsNNhuiwJhV1OPGABnXyUkeDgkYw9Jtr/Sj6zdWm5F/IE93qdJAvgB9e/WQMhzp48Bqbf+4pZhtd9INpjnHsB130SgZoMMbNIjfMbMxubm6e4EAfHAqd4IUPgRtv9EgnySMfwxdmK+Cj3Rz+BQw+RjEfHXwH3tTRyW+iE5/kAT7e8Env6ObTyRFOc+KVfLNFOjEev+i0ieeT0Ea+7J98rSdP4HzHyS78eIoxYKHDoZw6j871Z0lgSWBJ4AIkcNYbMwGLQ69w3AKBZOIf//jHCFY2UL5D4qALhJw+p22unxiWZEuAJJiCBBjO4AlCgpGx7uQKfhIDiahXHwUN4wQu88AFX6Krrl/gEmDgF1TAlLAITL36sd+SMTDaGJYol9ALpl4DEkwlbIIdegQagQgOMARICZ9ETRATvNAveIEhYKIRHeQEBjlK2MnIt1OCsoTeGMEODHShVVLllyklQj2JTF5oxYMATH4SKbx67RFerz2ShzvmaNttSaSAS54SDsEVHfq8YopHd8nBL8EzR3C3aUCLOn7VJXCSU/Lwgy7kgSZjJAbkh0e8OLvTLkFIB+pgkhXa0YEX8iNzekUn2L4Bok9243VG4+mC/OHCC7rRQJ6STnSRA7jkgzYwJZfGGoMeNNCnfkUSRI6SHkW/JIYs6YgdGYMnumVreFFHN9hoUMCc7c9mkTzRwB7Igu3AYfMVnehhB2DqZ5++MdPmB2C8KoyWCptS5vVZ37WdyYLt9SuW1g3dsX22Qob0YBz7cudfwul1R2O10yFbMZ4u2Jm16sy+rRsbKPZjnDGSeYc5bCQ42tiqOlvQV+LPR5iLNkVdXxsUsM2jV+PU56Jdso1e13Cj0Rz2ARYaHcYpxrhmh9XRZSz7a0488LfWpbVFruTHj6HHLzLyXezXurU24bZxMc8Pr1g7fL71y26dydK6FCs8dWT71rlXI60h64teyK3X4vkx60EswI8bfNYQneGF70QfejxRplPfRLmxoY4HY6w5r6uTgxuF6OMr+TV1cqRf/tC/JwDHE1e8RTc8bsawI3P5Emeyh5M80YVOvhUOdXDRwO74RX4MfOub/PDvlXk+XB/eydscsPDB7vgbPpqPQzd44JjD57imA/1gmAsG3eajyFy7G0N0Fh36+VM6gIN/Zp9w8Pv66cD1o0ePhgy8ykgGZEZ+Ct3N58Pr0bn+LAksCSwJnLkEznpjJtjkbMlR4BUoBQ53zvxqnA+BfYDNmQtS5nDqEgdJsm/GPAESoHyLJqDqF2yMFywFKHWBwE/Fe2VEAPf/cjh/mxFBXfIiaTXHEwlJiyAvOAhCxgnSgpFgh14bD7AFPkFdwJWAC7QOgQ1tEmeBV+INJvolBoKOgCqxEZgEL8FPsEevOngOSZa6ZAdP+IdXkEYH2JIRY9QFWDgkRHAKgAKwJyUSBoFfcCdbPEgK8N6mFA4BVh0OvBsriSIvdQmqjZmAKyDjxQZLEkRu8NKp8WAI0HTUZs4ceARxdEt60E12+JcUkKG5aJe8mUNeEiO8GYN388mE/tHlWps6meINTPr3KidYJc8l1nTMxtBEN+hmj+QDDhjgmqtN0a/Aqd3GqkRWnV2ASXZg2mSxrZJWfcZYC2wLT+jWTu/a0KUfDYoxiraujXPAIVlln2DQAzrAgBcM+vLEBi+SXUlisME1zwHWtZZZBuxV0sh+yIqfYMvshI2wPzbJPvgGT8ysQeuLjshRP72A5XU+64S9sz1wrBN27jDeYS6/xD7YhOtslR2BB66Df6BjdtZ6gMN60oYftmqONcgH8Utsxnx2BAbbZZvsIVzsBl3mgmMM+sBBB7rwgP9uTJCRfvIA2yYBDE/rrT/XknzrmX/lA9wkINf8iTlwupHk7C0Hyb8bK2CKD+B08wg8MgWLv+Wj0EF+fLrCR+OXP7HZdHMGTHGEfOgN/2CQH5/Gh/M3YKAPDnyiC3z9eKdXdIKhna8hJzySEVngbbdtxtEGH5tiL/rIW2xyttmzdsFU3LQC2waJPsgGnTZEYp2YwVeCT6/8EVrJhs/mJ8FgI+p4BoOO8IkOc9gKW2PXYgY7AB+NYKALHWCgg28lT/JgF8Yo6NDPT9bG37A7tg4nOtkn2sH/85//PMb+8pe/HDJgaxVrQTk817/OSwJLAksClyCBi9qYCWyctoBrY+Z1RkmQ980FOY5egil4SDo5eRsRmwDO31gO3mZLMDReQBZYzBEEPS0S1CVMfpJXwIRXciG4GCdgCcpoETTgFKS0qxvX5kXAEWTMR7cgJ9FxDYbAKnBJHgQ7/dEiaIGFdmc40CJZME7wAxd+bWC0KRCQzbcJwKtAjt5gqOtXT2ZggO/VIXIQxP2/mBIVNMAZTAbeZlmih0/FGPzhEx3GgItXyYc+bYr+2oxBo3lkaZwgiz68CerGwgOHPjTpd5jfGDybL5E0Bh5BXDsc5qobAxac5qqzGRt6Mvahuc0jvEpJp2s4FbRGCxjk6qwfPnX8ggG3awec6uiOBnPQACZ5oT1ZuNYGHrt1LuHP/ugVr2Cg1bUCn3ryI094kw38kiBrQAJNx7fbv5pgX/7Zuk16G03wwFLwfa2FXsiRHiXl/sG0s+Tf2uGTrH8JLXsgY/pyU8Hmw3p75513hk7oAjznEmVwJbk23613+oGT3dAl+ZvDFtiaOhvS5yxhpkN1uuZjjKVL6wg8xVz42Jd2cLTBK1GHQx1eulfHT74UTeDqd8Dp4A+zY3OaF/zWCB7Z6n57SuYtB0+O2LC3HPw6LhrcRLIpYYvs33g04419w4/e+DIfXHV+wBgyb005o4PckxtZ0BUe0VapzTh95mgDwxFfaCIntDjIwHh6N1fdeJsbRbt+fYp1hx98oRffdGSD5/tnmzWv8dEf+sgdvo4BZPsDJvqyFWdyCB+4aDXOXPxo6+YLeSnRTdfxiy/w9JEx/aJf7Ml+wEI/OsFX4FKMdaibb6x56Jn5wTcc5hsvLv/1r38da8sTs25W6KsEu/o6LwksCSwJXJoEznpjdihMDlzAEgQ9LbvdEkd3It94442x4XKHTeHMJSQClzut7kpKELwOIXEVfEpES9wFSoHnm2++GRs5yYAnZpy/UvAuSAhO4JvjWruAq4CtaEev9oKZa4FXABGkjZVMaBekBE/94OoTvPSBL/CBg7+SBnOMd6BR4iUxEhDNxx9c5oQDDIHQeMEPTkHRGV6vc5Kvu6F+AMVdVTDhNCc5S3LMgce8xtAP3rQZA79Eyny8KuaAZQ669JuHTrQI6triDR/1G4MHtJMLHGCTD7j0Cyc6wZGcmTPDIB/2ZENDxvSMXjjd9fbakjHuuLv7DYcChoP8zCF/tmasMfroAN3oQKe6wsbQV6JuQ45/m3i8sE1jJGdoBw+OYLIlm3Y43X1Gt8TY2d1/46wHcMwH0918NxfcvfbE1WZBm7vonsyou1khIbJhcLYRpRubU7T0bxPIUIGPfK+9kAM5sUE69cME5G7zQPfs2kGGrS/6duffxsxa8LSfvI1jR57S0BM92JR5skKP8JA/uPCCya60sxnjzWcj9G1dspP8D12hjX2zK/aZ/0ETmOzHGqJbMNHHXku24bIGrRPw8Q2G9cH+bezV8coGwSUXcOEDD362i9+ekrBFY9kinjytB8u6ZZv8Dxi+/+WTvGJOFuhwoA//+NWOLnjQi05t+QvXcOFLWzoiH/MVMgNXf/yTD3/hjD/z9MPhGk6yUcCHF82KfjSCiS5jkyl5gxnd9Go8GsDQZw6ZWI/490ue7AAMNPNxYIJBd+iECwzt0U4HxuiHlz7wQnba8JofpEfz2Eq+1Th4wWaT6DMnv8b+golmMMgCnQo6FTjBJC944WA/aGIb9A4m3vSTB7rFJU+lyeDBgwfDfsDOFzl3PRCtP0sCSwJLAhcogYvYmAkQimDAEXPwXrez4RLMvb4iIS1IGVsy4GmZV0BsyowDSzAVsIwBS6ApmLor55BceVIgEdYvQDjDX1BRl1yAJbkWqCURxkiwPJGzIRRkJBzoN15dkFNXSoAkTgKLMYKqxBlMSY66wKeu3xx35wU/8AQz/ZIcwVPwIo9eVdztHv9vGDDNITfjJE5gCsjwSAy8znm7bXo9YfTkEC5j9Ztrs4tn/ebBgRY41N3xl1zaeGgjO2Pw4FUZgRYM8kOHgi4yoUf87Lc752Qq6ZNoSmaNkcSCiU8w4fNEj049gQTb0wpyNAdvXsdBv/HG4EPCSz82I+o2K2DYqHliJhFiE2BLkOEE23hJI7rIGB02PfjoNSG6pyNJJXmhW2JRnWwkJ16RgkOdfbAdhwQFneRgDp2wDYmQJIY8JTDmoptNkzlc5Ecm9Co5Ag8t9IMOdkIveMA/3uFIfuRFxtaFO/Rs1C9T9lpUa3ElQI+/naIX652cPMVns/yMJxv0RcZkyVYkn3TpGyA+iQ3RF1myo3RKX/Rq080WzLN26IjunfkgujDH2Xp0gMUu2Dubo084JeXoaTMHJhvUxm6MAddchwIeG+QnJdHa8WrdGo8WeLSFRx+fyQaNt6bhUcgo2iX0+tizs7XJnxtDLp6KWffggG1Txi+x35ubm4GTXK1jsKxL9PDb4IEFF1uHw5q0ttk2XZCbdWfNWtt49FRK4aPyH+Tv5gwa3LQDm375desJTDDQDAe98hcO8H0jBh8/gjc4wPLtFp/h7QQ60E/WbgqhDf9sA13weF3RU0S+2FqEn/+lV/EKvV67Zgu+r8O7m4vWN//EDvkCONHF3sgWHrLij/HhaS+cxqCTfOkRDHThBQx2bg4a2ao+/pbNkh87Iwv08i/ZOPqMRzcYZCY20zU6yAtM8gTDDSe80pt+rzKC9/HHHz+5KQW20nlU1p8lgSWBJYELlcBFbMwEe0VgKwnwP5YEMYHXD4Bw3Jw+Z+4skXGWBPk4WhLs+4TawRTIFUFMcBcsvJ8vsAkMElJJkmAssIEHn4RFQLHxEVDBNA88QUaAVRe0BSHBUeAS5AU1dWMldZKnXl0ULCX/gpRgKDiiU8CU3BgvWKqja79tXtAlORJk9Qugki7BG90CugRdoiipkQChS4BFmwRdIAYPXTZ3vi0iAwHWxkxig07z6QDvkgkyh8smAh3oBkfAxQtZCaL4xgs5tTFDB3kK5Io5kj/j4bORsLlQN8Z4eNEtwUC3hMVYugILDLqBV9FPXsajP5j0Q4aSS3DU6YUdsB8yYzOSXwkUWaJB4EcTGcBBXmSpDQx1MMGgE3qlZzjoU4IhmSQrsMmLPdMbHRkPBjmQGXvEHx70w1+yTWcKPMbRG7roCEzz8VNSzL7VwYDbGVzykjw5m4M+MNmrjZkx1hc7B4ucFfOvvVhf9NDa9C88JN78jF8OtBZseqxh9kIHbJRP4rskwZL+Em92wC6tEXqkJ/onc/K25pzpiK7ZhIPtZbv0CC8bMI6P4lPYiLqDndE52tFnDrjog4ut6jenoh0OPLNPcLThnU0qbJANWef5AnDRqmhXrAl2hS4H/n2/xY9Yb96AIC/ysJ7Qpd9BZjYg1hQa1PW7kQKm7yGdjcU7PcDLH+GVzyJjdZs5mwD+AU02Foo2vPGlitiBZxsgsPl4/tY6hsOa5T/4ATDbvLguRu22zR8cNhdg2PDwKdYYGegnczdYyJG/5nfYCzxuFokpfAibIV92Qv/6yJns2BocZGI+H48P8spn4Z0vZGN4tcHab7GE3YCJJ3UwrHsw8cKv2XCiGwzzbA7xq88ccgETDzZZbA1e9MGp8Olsi+/M5s3Tz8ejA5/0xe/rwwf+bczw/uGHHw64bLcCxypLAksCSwKXLoGz3pgJjpytwKAIbIKsgGFj5o4ch+3OoGBgrAAkgeDcFXdafbNgAyFhEpglE4KPoCbZAV9dEdQEdXAlCIKAoCx5EAQEcXQJoBJYwU29BEwQEXTUwRRgBFwJhkTIHHU8CEToknzBgXYJFZjG41dypi5ZEuwkI+p4wC886JIcwQeGsehwVtcueTBHMESHOtwCI9zwos1YyYLXZgTUH//4xyMYk00JvTF4lFCkGzLHU+3q5EK+EhG0o1MSCY6x9Wuni+CiUx+64MCHa4e5+uFpDt7ZBXnBWwJoLPocYFTiA44SzOagVaJTwiTRk0ThI5qzyeCYgwe0gmmceuMks/CQMTrUzVHMMV4/Ghx4cChwOPSDl2zMJy999OhaW3xIhpMF2l3rh98YsNCRbKqj3xhJsnVjjBsUZAB3xfhVHkuAzPgH/2CaX/BExQ0N64ndkycbtWmx9vZbAivJZA/WqU2whJQdeILRv/WgMzZOx0prjR7pl03kT/SB7UxPdAwe/wAO/NmwazYDDrz66RM95rFJOLMJ4/BofvbK3rJJfcY72L0zOA7X2Z4+69Z4fPFNnhjhH91szGuzu22TYq3b6PCViptFbpj4Ju9me2LGP6KXP2Gj4Cn8Gpz6ydsmAs1kgw58K+roIT/zjcGfgl9j1cFSx+vMJ9kkE/18Dx7AMy9d04V+Mjeev4UXX8broye8qOODvbSmxTG6tikkA/HrzTffHDEDzWgHr2Nel/BVB1sxTqE/OOGORzJPPugwF91kIVaZKx46449MzBWPjFXHe/1kLLZpQws4ChswPtrwZ4x29GgHA2+u4XDYfPuXFDbB7MCm7xDWQLD+LAksCSwJXLAEznpjxklz0Jy6MwfOQXPGbczcPXSXjwMXfDhzQdlcQU9AEfzNE9CMFQTmYK1PoHJ30qZMsmITJ0GysVLANq4gXIKrDS4HOhWw0SNYo7UAKjCpm2Ns88AyvkCnX+DTbo56QRR/2sFEExyuwTUWffhzgAcH2OrO+DRPwqCNTMFUBGG4PC3yj20ljeRgk2psfIEBlrH0AgeYxjjDqz/e0SRB1CbwOxvnHEyJALhokNC5FtjBxPvMm7q59Iz2YMKBFkmPftfkDAda8YoWyZwx5kqU9Esu8AKnu9yeaoAhUXQnWsKFJneGJTDmm2O8NtcSBTSAqZ/84HR3GSxJjbNE1Fk/vtios02/RMYdcom+a3Pw6VC3WYYfzObA5a44Ge62pJYMe7XR00T8SnLBlejRJ7txFxzv7orjzR1uMrNG9PtmSvLMBjwlSEZoX+XfEqCH/bbZ8u8F6E3i6IlHT6nJmFzpjT3QtxtAngawR21sx2aOrZGz9cE+zbMGWg9gZe/mGQcf+2Z7in5z2QNbsAYc7JvvcNYPJvx0bc2hQdHOP1hnaLWOHWAY79xaZ1PmskFnfWhGD1ytO+sELdaRzSp7RDs7VvckxpMwtJNncgknf+8bI9+8+vEL9MDpDB9+jEV3POBTIbNguma/5iQPuJKzNoc6+vTBoU3dkf3Dqa4Pb/we2ZEb+Viz+AcDrdrIwfh8Ozzm4Vt/MPBhLrqdvcbo3zFYz2RA1mQJFpjoCAca4k9btpDtsENz9JmvvY0YOhQ6AaMbnN3Q87QR7XCwP3SYg0c6JQd+Sj+7opM2q3DCDZ8DzXCAYV4wyAEMdLNpdXHYE00/+MVvvvXWW4M2cO4q+FtlSeBVlYD15VDyS51fFM/wH+Kc6Vpr8Ptr4uI2ZpQsUEoa/UCDV1m8asV5dxePkQhmAoNX6HwbwPH756ReSyn4CHqCp2CoGA+mp2y+VRAAJbECRkkOOIKOeQKw4MYIBRCBRBBCH5jm6Bd4ok1d0DUWPnSjB18FQzgELAk/OBJrY+AEEw5JFJgSI3RIzoy1qVSX0Ov3ugn6BXQw9lsCKUBKhARdCb2NgkCIV8Hy66+//p/b7Rszr5zYmAiKAqCAKCjbeKDffDIEU8D1SouxknkJKroFcnzCA8Zu2zjgVT9cYJCfOrnqx59Ng0Cv311wvOAdTIc6ul3jzVyJrtIGSJ1M8EHmcKDNXXR3nvGBdokwuslJG5l5GotX9uKVIPJFJ/nSCxg2SeChUx3ekk506Ydfcpu80MFW0JueJafqYNCRJAhv5qMdTHPQSZ7GeqKFHjjJE13oV0e3unF0Sm9ogAcObWRpjLHpkS2BWb8fqGBzNmbW2eHGzNhDhzwUcCV/8N/BtjzRoDtri03ShfVNhtYHG6BLPoltsh3t7ICu2Zhr68SZbtgW2OzJRo/ezKULOOgVTD6GHiXLbIefAc860QZW+jMeTdYfP+maXdK7M/tmb3RrLarjK1thZ9m0jSR/x9Yc+Sbz2Rec7E+fNvjQyt+gl69qnnFkhGY2jRayQP+XX345NrM2ZZ5Ikgn6bWbRpg4W2aObv4FPmzVDjtY6nZCbPnNcw9s6pS9rRN34fCV9kCta0I5nfcaQAV7c0HFDxNsbnv6BIZ6Qq7q5nvrwh3RJP54CooXvNM66pl/+yBz8o9O/DzDWGD4SfrwrXjsnC76ajnxXDbc1y67QhHa+RJ1/Ix8xxNNw9NCVQj58Ohr4QrKmb+ONg5+O+QowyQrt6t40IAd0szE3H+jCWwfmqLN3rz+iU53uzefj+ad8lDr4xqAZneo2ZmzbrzKS332FHa2yJPCqSqD4g79icecXwTP8M+4ZJ5+ElhdJz4z/kq8vemPmJ90FVj9pLgEQoBWBVuCS+LjTKDgKQj/60Y9G0JDkCFySYImOawHcZkgy6mmJoAeuoFHgMkbgkTgIoCVUkg1BAxz9Jd+SDLjamAlK88YMHHPRrqCXMRvjbHOCPvwYgw6BzLXAJTGQcIAvoOr31E/QE4T1u8uoXSKAFgFXIiaBEfwFfXgFTnMsNBvZ221jZgwZgA0mvM4SNcFagoIndMJBVvinB/KBD0xyJR86kSThTSLgbLyFi24w9KNFvzlwGoNviQ3ZkDu5SBzggJes8QKWgC0gC/J0ok4nJbFkZ46EyBj44MCTNh+ce9WMbmzMPEGSHICNN/IDA23G0yM66Ik80EYn7EuhB7yhW7tr9KIJneDhT79ETF0/+YIJL96NZYP61OkC7ehmB+SJLnSDYTwaFTgd5sCrGA+fQzKGD7DMIVvJMH7bmOERTPQobKXr0XBlfwqKZMJ+3SyiB4mpNcPn0J/1RSf0yTZs+q1L7bOtpDfri1zpiV5s5OjZa46K9aafTbJvcOiQftiR+XDpp1NtxrNH9mMOu9VuI6LO9lqLdG8emOhXxyPbYTPO1rQxfIgCFvsCLxtHh/Vq48KubCy0kYs1ZW1J9NGEV3ZnLtuNJmdz/EqszQkfLtFnl3iycSAf8sSDpF87GVvv3nqw1m0cPBG2PnbbxsZ8GxYyAQPtXt2F3+vb6n5shE7g1O5tCnx4dZ5s4LAO3eRBuw0P/4on/pbu0AwfmwDTpsm5jZl+MvXUmr74nv222UMzfdvg2BSZB7Z54JGXG5L04Eais40Y+ZMzuYFJtuRAV+iykWoDxEboBj3slj3rRzdfykbQQPf62Soa4WYT9MTm2Do62SV7EDfgIw+6E0uN0x+v5Gojq5jDTugEHHV6obPiCPmRw1/+8peB36uMNsDg3FXQvsqSwKsqgWIQ/vi9+Twqz/mP9YwG6yz8odSuHLbXfylnfLxoHs56YyZAcLyUT/ECCAFJ8CVBEkeBVvBtIyKAChTGSZIFPhszSYfXHgUnQUKyLHgJrOBLfCjA60jm+Enm119/fcCSCNskCHqCGToEEUFP4oI2QUzQBhsNgljJNXrV25iZZ6y6gAquoj4nNuZLeMwvATdPYAQDj/olCXDix6HeYtFPjgId2IJpdJCtawUsiY2kSxJgg4oXSYkAD34lnPCYByc44SVb+MEH0xkN+DDGfHy4BtfZfPNcxxuZgoN27fRUcW0O+ZEH2OAab77x+tBGP+qutcFJFsbSCx7gcAZTwtAPX7hT6+mpfnPpxJmcHPgAtzqYeHWGE0z2El704xN+stGvGI9O8OdNmH592vXjrTHBMF9Co73kFh/oMJ78yQYdaCKr6AQD7epsAz8SZ/ZtLbD7w40ZfIo50f+45br+ki95kacE0i/F2iT1pIJd8k+tffJ3w0FibB69sGM+ic/SJvmkB8mpuRJi9sDv0ItE1yaQ7ZijLkmmQ7qHgy3wFxJkbTYQbMNNDzqTdKtLsuFAExzWuwRZHxro1to3hz2gTz8c1qZrMBX9knE3QfhidmOTAcdu20iAz6/YHIFvU2aD1WYTD+RpbUrI0cg2bXqsPTz0I06eDJGbsfhEZ/TiQR/6rEtyMt+a0wamdYQHfPHfxju0k6Mx6uikW3zrc+08F+tav3nm0HX+CD/sQaE7c9GAXrIznq6c0WEeGYKJbgWN+t1g9N2vDYqNo/58HDzGgJ/PQTv+zScn/fhyaIu3gWT7Awa62KTCX6ibhy5z8MafgMG/6FfIRUE7utBhvIOcwSSfZNc8Z7waF1517ca6xo9+xQbUxow+/Y/R3WZX4N5VwnXXmNW3JHCpEmidoT9b7/wieOIf8j3wHuJu7Z6i5XD8sXH3wTg2Z277b3DM4+frY/LV/ywwZ/inri9qY1bCKhDYmPkOSiBwR04SwpkLIoK1wCEp8eqIbxMkCJJsiUPJhWRCYBSEJD7meVrk1RDBz6bEhg5c8ASkY8El4VJi/a7BFTgEF/MFwwKb/oJQQcl488MjWBb0wcC/BAP/+LQgtMFBDsaCAR44ztrgMQdcdXTUDx5a9JELPO4M25hJJjwx+9/tzmSBFU2SQfPJy3jw0QKf/mggF0mBsQK0sQ44BXk0SaTAxoe2eEGTQG+uQKygXT2c4MCrH/3mmxddkiBw0QAXGuiBrNTRDQe62YrFBoY7zp6agu3uuI1ZCR/ZldSQF/jwgAkvmkoAJVvGSsTR3Q0Dyba6fvy2YWQbaJKYogNOcNkomaMRDPLdb3fV0c3uwbAxwIt+tmy8ZFXi7S64ZN4YOH3LJmGU/JJdP2gDL/n1NNETA3TMG7PZIRk714eSruwPGSieSnjNysbKUwgypWM6pUPjbFbYAv3RkQ2MTRmds2n6ZKP0aB2xae1g0IN28+iUbumLDbKTbBcevkHdNX0by8bYvINdW0vGgFexNoxls2zZAaf1w/bYlCdT5vGjaLOhMt4YtgRua86cfBh7Y39ed2O77NY/r7cBBceasHbwgm+0s2ty5budJebm2dCRCV7gghcedJlLnubjUxs94M05f4RmY7Q5m0P+xjtrw4t28lTAUNBlHJzwowVcMs4X0LlCn+CgEdzoQCf6zKFbMOhS3UEmcIMLl42pjb+Nqh+lin99cBgLpzr7Sh7wgmcM/MboJ2u6Bwcd8UamSjYIvzHgmVOMwLc2sgATj3RY/IXPGDaAV/3mK8ajhQ7ggdM4dXYCJjtQpx9zzbGp9+MfcHiVUVzSf1chj1WWBF5VCfBTDiVb7/wieOZ3HNahNX0Md/SdoufYnHnsffPnsceu74N/bE5tcPOPSnCcu27cD30+640ZhyxAEQzFc/QEwpHbmAnUNg8SIa80ChqMw3jBR/Hz1e40Sk5vbm7GOZgCi2RDwiT47LY7cO5M2pS4o+tVEQmWImFBg6BBWWA4o6Ugow5O9QKZMQIQmtBojKAjqRH4JE/munONJptB47qzjnaBSSJmjiAmWXGtTYB159oCAQNtEnTyk5ALuhIg4yQBgqakS12yJBhK9swhOxteMiAPSRD85C7Iw2EOOBJLvAjIcKDbGMmJzQQ6o8MmAAywyEGCJ2ALrmDglYyNN49OwABTHUyHa0GbbOMdDMlHr3jRGVxepUEX20AXHOawGQkxeOQFHjzpxJ1+iRBd+74MfHRJCNiLwxy8kB0Y5Esn4MNDtpIosrbRQw9Y9CjJZMtg0jP5ob+NGlmRMRzGgIFW+I3BEzzsER9gooGNoQGvaCRnfIGj7kaEPro2Fh1gkhe9q7M/dbbFBuC2MWtzio8Km53rtV/LmcysMXqgQz/O4HWzNvNka51IKPfbZoS8bGhsfOnROmVz1gFbyx/RL7too6+dn3CAR3f66dU6UdfHrumL7qwptgMHvTvDySbyK8bXBocxfJc1rk637Fph52wUT2gGB04ycJijDp4x7BVe7WwVT+wUHGt/t/mWn//852MsPsBj99YFOMbym3A6k6vXi60pP4GPPrDhY9vs3zwbNwWPZEUGZNsagxdsdXP4E2sGHvjhoh/08h94dUOPbMQGdHoNEwz+hp/wBM+a8WokGvlFT03JEq/kjA5yMgft/DU5uWkIN7rR4Zsq9qIfDPpEi19ldMPQE0z4zOXX0OUmEll4IwROTxbNsWbpzxsA5AEm+3PNbvgvTyzJXFv2yabJm67gYgfpEAwyMp4M8cUHGctvqoudbJLfY6fkRW7eRMG7uEonYjPcvdLqpgYZ0ikZ4p0fZMtiPLv2c/no8+Mf4IKzypLAtUrAmnUoxeLOL0Im1rNDDOSLnOcy0ze3d43Wu+i9b35wTp3vg39qXu3FN/VgdW7M8zhf9MbMHUQBUSIkSHHoAilnz0gkPIKSX2UUHN1pFIgFQg6dw+fkBRgGIKh43cadb3B/8pOfjHf6jRcgKAQOhigZEAQFN3AkImBJesG2qaBUeCUMNh0CjUBlPDoFO2Mlz2BKFJwFaIFfgMYLuiQvEh144BC4zHfYBOy2hMNcyTW+BVD07LeE0CZGHS3olrC0wbFx0wYGntHlRww8LRH4ep2TPNEuAUAnugR9C1GQJyNBH1y8CtSu4QVTICcvc9QlQeSjX5KBbry1EUMTeZE3WQjK4JIDPsCAw0YD3mBaJHRCV2Cil6zQTlZkiFdz2txJgLMdeCVTEix6kzSQLZ2oSzjQiSZ0kKU6GGhTB0Pyghc8RgeceNUvcSKfYJIfutgDXo3Rhy6wyJt+yRCvDtfmGEc29I0O7XijG3QqORhy0c7eycZY8PWbgw4yRbNvOPFmcy5JhMv8ChhzvfZrO5MD23I3n/+Q7No8WKfab7fvNdkdGUp06YTPkawaw2bolh6tXfpxzRbolQ7oib3QE9/CfrSpw+8Ag07NZ2f0qh0+Y+m2fnozXr8+61tbdkKHruE2hs9yZofWEtuHxxwwzQ8n2tgvO+Y3zcGntYRnMjLW03h8ZrP8CH7ZPPjskZ9Q9psf48t9Y+UXQskVbWDosynAJ9mjyVgy16/dePTwBWCb0zdmXltHoxsR6BIn+B7xxdy33357yMI/ECdDT/rIll5tJmzc0GXTZTPCx/UKqDp/5OaO9cmnW7s2M/Rpc0KObtrgm9/Bi/HeBME/mZCZPq8ysiN0kSe9gEGXvq+jy35kwyYWDjTyleIIWvh88iAHG0L6Uqdv/cUN8rXZo3f+AO/oxju6wBEzyNRYeOAQi4317R/edpv/xLNNLFjpCJ3sxkaYzNosu2YvYKLLmiAfdv/w4cOhGz/k1Y0u+l5lSeAaJWA9OZTWQecXIQ++wlEMcZ7LTN/c3jVa76L3vvnBOXW+D/6pebXzieKbEqziXmOex/niNmaEwlH7+WR3qAUnyY6AIbAJCIJsSaS7nr6VEUwEXMFIIgSGBEMSao7gJEnwdM3GzIbB6xKcv6Ai6EiUBCHGIjBRmLog2eZEYDNOnSIl6+qSDDgEUngYsyQFP+oMQABGlzlwoNMcyQJa1cGBD92uwQBTcmMOmiwO8AQ9CYYx5EEGrh2ujUNHCyM63Jn1/R5ZSVps0OBQJADoBFsbGGjUbj76yQt+Be9oQoc+eEsI4TZHGx3gDw/4M5ZsHAqawQXPHCUcxuABDPPB0wYvHGh1uK6ObnPQpZCxOXjp/5gZI+mSZJGv8ebD4xqNzgq8xivowqM6vK7Th/FwNN94fKEVX9EAHl7BcA2nPmNc2yCqowsdYCp4B4ttONMR3sgPr+rkC2d0mAcW3sxpE2tjJqHrVcbWlPFoUuJ/VK7wD72SmwTy73//+3iqIdntqYaE2iaB3G0SPMUwh/6SN/uopHebemuc/K03OtFmwyPhNZfuwaIzNkGn5qdjejaPbrXTNztQtBunnp9hO3Cyp+C5bu1og4/O2aGx4LBRcPDRjQSbCE9I0AsPmXjqgR/+mI/2oxs/+9nPBn6wbZzYqEQdr/hGE7rB88SKL3LDDE78skn0mYdHPKNDHe14Ryd8+DTeupLkWyPw4Adf5sOHN3U0oT1ZJEfyM8ex3zZ4cNqQoMNmwly44VTQSi7m49G81hJak5E2cxVj8QeOwo5ut43gbtvktLnnnxVz2IACNj7YR/LRVqkPnejCazIGh01Fkzn0awx7pQebJvDI0qHfYQ59kZfxZKZNn+IaT2SkJFN1cLMz4/grtJFfuOnQzT1rDA/sxsYzeAPo+rMkcGUSsBYcSuu887OK4hA2uHyEdmtZsV75HnVrtvbDuermgjGPmevRa6yir3nVR8f2J/jGOA7LDAM+9WPjjs1DZyX80RKf+v8beMH5vuez3pgdMiPYctQCjruZNl0SZ8F6VoSgxNEzFBsqGy3BT0B3t5FwBRWC7alFxuWbNJszdwI5f4Go4IielFwghAMcymSkruFHqwBjbgqMRsFE2wwDbAEKfHMkOs5gKYIn+PFmnGuwHDNd8wLIyPTPBS0ZHTrwIWERMN2BlQQIqp6WuBsMvzHmCNJoo4t4hUcbGK7xYg4ZGEP++iUf8IGhCLjoL7EzHq0SB+OMh0fQN8Z4Y+iv5EubOcbArR4v5pjvEOiN0ScBwQ862JM56NXvrrYf/1CXSNt8S7zgxYekjt1IttCBVskZmOBJvuBw11dx516RBMKpn3z0g2kTDwZ43SmWhJCLOp7wAQd5wKkfT7stUUNniabx6p4GgOvutad97nib46mdNnzaMJCbu/Dge1KouPOOPjc/JNI2FO7e47mSLWfbtV/jmX7I2vepngqQPz2RqfUkgXajh+ytKXbNFslQnzHG5j9aR2SrT2E39M0mrCebCmd2Cz+YxrMnybFirnHmaGtN0K1rbfrDaz469LM1tLY20GS9sRuFLcANh7Ox5rJ/tuZ1OnbOtvhQtu/Mt3gNnTx++tOfjptQNrVuRrUZQxP68Ayvw6bMt6/Wo+9/0R5NrslAwauCrq7BwiP66IbNk50zHM50YYyzec7GuNafLtDWZo48FLJPr2CA6UCDuWiDF0yyVdTNU6xneqNf8gUj/5RerWdP7ODxb1zYErmjC50KXOCjC3xj4QTDEV3a6NWRHMHQbp6CDuPxr41e8Z5/IUvzFXpABxyujddnjjZ0wqMt+ZgHp3VAP+xJnR8jC/yTG3+LJ/6XXT169GjA8W8T3DSFb5XnJwE24ThV6N1xVzk1X/t/M/8u2M/ad4q24N7HW+Ne1hn98RCtndFUn+u5Xb3SGP3Bc209Wnv8Ct/EH/AB2vkWdeOdleBYt8bkg61n695c65U/aAw88xoGw1j9+WF1NMCTP9WWT+M/FH1KsI0xB/xoM6Zxxmp3BJePQjfYaGu+frJAE19mjn5HsMFTn8+jcsef8Dfkyfyt4/Sqa/SZnAushCZpdBfRaxGSxoIQQc7C8vTHXTbB1BMwT7QUAQY8Y1Oks4RCAuBJnE2JccakFHgoTzJBQYyMYTBEdGUkgiPRSnCUAozxDn2SXkGsu6leDQEHTgtB0BK0jYHLtcM1uM54AEsg+z/27m7XkRoJ4Pg+SngLkLiIEDcDEiOQBoQQPBoXfEkIjQbBA3AeJY+y/rX2L6xskmHZOUOSY0s93bbL9eWqctndOcN4JIgcQKJDFxJ+BooG3uFUtxDC67NEY9CUwOPbX7H0p6Mthk5n9THI7hZINGxeOZ2kwWcoNnD07PcJYOjam0ywkn78OTmnJwksnMbAQXaySTjIYhOBN5siJ+jwqcOJL4u+Nwdk6JMWm3ALOx7ggcOmSEJIH/rMGz30O5LmQIJsnsHD6zMbesW/JPKd8WkRvuiKrGjok3zG17wBQg+fdA0nXMaYE+PZE77MIx7MiznBG104dKBviS2boEPzXcKPD3U02C3d0Ss5BCI8mXt6QIfsxqBBh+re+poDfOMPX3CRl736rSF7WBuzzYX/6x86U+iMrdGXDQR7pkM2wG7MmXlVzDuboHeXseZXu8szP2HXYol+C4WYo5jb+sDpM/dwoadfG1vWzz7xCZc+eNCAgz2zR/VKfIg/eGZTCnxgXZ61G8duwaHvrRY/jz67cmjG7tklPYjHDsrYoQ0WH2KL+NOGP76NhrY2CPQq3vtsnT2SCU38eIaDvLtxUIF3fg4PH2L/fNAcsf3sW10s5kPiKJ+Dw8GFu34yGm9O4QTnDTpd8x/yoklWuhanyCve8iE46AcNfKq7w2kO1PkdvsQkfqsNvLhGBngcotCdeGG9Q0/sREesUqyF4sTDOFRjYw4txWi6Q4NceDVn6PBtuMSJw3jzR2bxWXE4RZb+j1Byklc8QleM90YUPH2YczyaY3Th9kk8/fkcnnzmT6yDAxya5o2PiFN4pGPP5k28wgf9+c/H8fnjjz9u/Pu81CErm17l8TTAVl3nCj91nSuXxhpzaew5nG+y/dr5e52s8/yky+6NTcbj9vrFXPFCPAKrLq6qF+fFBhe/Biv3EJ89gwGv/5hGa4tYbW1RxOfgjHfxY7Q96yuuu7tah3pWh9NljXTpw4NneODTD99cB6eAA1Ndm7ornPGir7VTG/p4pgfwFX1K99rP3aNXf+Ou+o0Z5VB0hWIKxE5dfRJj4fFJjMUWvH4GZfER/G20bMwsZIK5ZMQkWSgseo2hYErxezQLm89ubMyMMwkWELiNswj5HYBF3cKIXgZo4TTRFnBGbAGH16JmYbFoW3TAS8DRxbtiQSajRRtuiY6F1iKtDofFC02LuEmVlMGvjRFKDvAXXTTJq98ChycyS4Lc0bRYqsOpv42Zuo2ZpArukgkLtoSJLGQ/jAUWnd1Y9NGB06Jqbiy6cEp6FDjpk/7Irp9eJQptzMwbOclL3zZnEis48YIPeqE/Tu6TFnpXxwdZXMmGT3V98IDFG71oIwO+zRu5yOMPywgkkhkXO0ALj2DMRzbUnKADv+RCv3mOhrlqjiRa5tl8wMkWwOFLsGNrLrKyDfPJVumJbsCrmxMJo372ruiHQz+a6kpBEx3jzb2LzHhQyEZmOM1Hf5WxjRmbqsCtFEhqv6c7GZPzlFxzH31JRB3qmFcJK9sWR8w3m1HMjbkw/+5shA7NCf3zFcUcsAc+wl/MsTHmDA59ntk8enCYW+PyczYAzlj95houFzixULtYBbfLmHhFm2+YdzjJCxYdtoh/tg/Gsw0CP+drknUbC3QU8qEnobfBYNf+mxO2hx658QqeT/JvNPXr8xWDP4Dh92XemvFRNu8rCP18HV9iBV7QEQttLPiRGMcv+azLnNAdmfz1XTz4CoNc/igJHH5zZl79pozv4kEM2u/3Gx2HS3ggSzjRoC/zCqc1Sr/NCJz4pnObGzA2TfgU08DBH582rfyRLmx6vMWnI7/7snlhX3DYNIkF4iA92zAZ53ARTrEWDTHPRV/ilLuNM72RE3/is0J/YOCgE/Mq7qOBL7FZjBQb1dmYAy1zFk7zTM/vvffepj8yqMMhPtK/Ot3RL3251MnZescm2BL477//fpPRISs7wNsqj6cB/jHHuWNKbOZSuTTWOL7wT5Zr5+91upnnJ112b+ws43EfGL4rBojjYMUQ8QSs2GNN0G/9cLcG6LMOqRvPDlzaXfBYn6wpcBXHoxM8GLBwiVlo83fw0REL4LFugXUZz/fzfzQ9o6U/WDyDnevaFHDoGwumtu3hP/8YBwZP+DCWvPikLzThqcCldK/93N3Yk+NH459Yz43+h9pTWkLOBuP3LxJHC5PE0UKnn6JMqoBPgX58LmGi0GfPnm1wGZ3Ab/Ip2OLNOCzGNmeCvgXaQk9FjNPkSTpMlkUCHouwcfCAs+DgwaKnTTICzoJr0dFvDNks6mQz4YrEDKzFjQFbTMmEJhjJuqSFkWjjNHghLxkUdTjhwQ86SoZXHUz9aIHHm2JDSA9oSgLoAH58kY1ujUE3fNpzArRyJMmdQn9o4h1d/XDSnWdykRUNYzipyzM6nhuDljp6xqujWYDQ5pn+waFDNs90rN+8GIMvdPWjRa7DSJ78tTTjnShLCkpiyQsebTrDkwsP8OozToEPDbZA5tmx4TFevwtdhY48g68fLBzpDxwYdTT1sy3y4RMd8Piib3jYBd48u5IjHHCyR3jhkIQ5/GC35zZmeMT7vRby0dOlQn8KPYlJ3jQbw2fEFBtjOjU35sQcmDP2xmbYI/tEi/75NJz8wNyZN/3sjU3D4a7QPVrwgxFXzG127xl9OMGoo+OC24UmeHjgi0/44awNH+DxjRd8wGlDYTybI4vCX2xEbB6MB8c+wRyGb/nkE25vPFyKjRPdaC9WOuiw+TD+YRyWuWwU9mNjRBb8kI8eycje6Zx86KlbH/ALBz75pH60jHOpk4/sdI+HdCXmGl/MtmlSbB7Ihq5xxUZxDC51OqUn42084Nbvjm/94o85AC/OgVc84xENctrUvHz5ctO/T+wdFBkDH/wusHArZHKRRZ929bnQDx7QqjSejbFTd/1056IHdPCWjuCAe6ZPB9rRQJ+ewSvR8Ay/K51HFyy9Gudu3vwhlZ9++mnj58WLF9vmP5xwrfLmNXBsM8cU5rk87lM3PhzBdj8F/7bb4u0c3Wvi9RSPf0W/52Qkm7io5Efq4Pm3wxh/8dWBizZxCJw43zrVOqZeTMh3xWD+KwYbL9aFR2zg62KDOBZevl4uJraJOfKR4oC6uFSuhq64A4eCf/3iCVzq1h314paYRHZweBZz40GfcfqNQ0f8sw55a9+BPrzhJFMle+le+7m7sSfHj8Y/sZ4b/Q+1mzwCJmQTr+6PU9g8WOC8MXNCSxR9JtmkUp5NhETAs980OM3LcEyqy2SYGAbhlI8xSsh9gsGoTKgS/vhhWAxGv8m1GJlIxmZywbfgkMUCbKyJDie8Mz7tjXfHd2Pc6aAFL8OAg0zGgtevDl4dX9UZNhz41o/PjL1F1+9A2vTSrTd2GTueOCac6JMRPrSTlT7JijanQpuTgqkeX/AZLwHCi34BgD7VzYk28PjEdw6V3OnJXKLFocgGzURJOwAAO+lJREFU3gWfefEMBp/wkoF94JcdgSGbE2gbM3r0eysnvL0xIxeY7AU+vMKJDpsAo6ChHV0y6qeDkkY4tOlv46q/ZI0c+MKfTbqirqCDX8krmb0xcMKtLlkGr82c2iR4Poyk2Im4xFm/wMsWBRtFQkxvTsHxYGPmjYiNmTcVcFXoRsHvvRa2nZynZKSr5GdH3mj88ccfm02ZW+OLA57ZmzFs2NzBbZxnNmxOwXvOdtiSfnZkPFsr1kQfbj7BXtgCW2NT2o0zl2wonw+e7fEr+BQ40DAWb8aSg41r048vd+Pwihb+vBVxOMZGXPg2Bk/go8/mvFUUU7zNoj+bFvj4FV7c+fxut9v+0iGbZb/eBLFl4/COLlnIjG80Zr3Dmc/hE278ownWGLqGgy5dSnV3F70ZAweZ0Z1pGoOOfrzDQwfGoqm9uaETuOikuaYrfKGDJ/Dwq+sDa2PmN2bg/BErm16xADwaZDVfcGvDgzoa5hIvZHXpI390yKMODp9gxSiwaGdDeDFX4TUGPRf67NeY5oXdJDs8ntGgK7Kpw2m8NnLYoHsWxxQxSbER9ubPb8z0+w+mxSn0Vnk8DbCFSyWfOQeT7YELtvu5MW+z/f+V723yeooW/pMhvXYPvv7q7rWZHyU/4tNimJ+FWMvEajFLPOOD4g8/la+JM3IHbQ6stJfT8H0vL/TtRhyHU37C771UQIev832+jn55D/8WY6yN4pA4B0as0IaGNs/ogsebWAfe2uGAzrN+NNBEGx/aFW1ikL2C8eIWHjqMk0cZo40MftdqX0BuF7r6Z3333H0jdOGfef6ANe6qP2XEdIxiWuAX4LVLGv2hDhsnp66CdAuCfrCU7o2ZU2xK9NkLZVtE4GE8LkZisk2+T0sYpGRAsmEBBAufcRJZk2FiMkRGJYFlGAwYfBtDCxlDAZtB4AUeBk8+tOGTLHMUdbTAq+OLITIQbcYzKkbmExTy+pzEoqqOFofBp4SGjDYW+r0JMU6dLsDTET06FcCHBNPpvyTI7wXQIiM+yGYM+XyuRd4+G1IHa2PDscltXjjGYWwK3CVvnE4/R8MDnN5ekk3CRlZO7WojQSZ1eusEhmMr6vREVjr1+Y55A0+naODLM0elX7LRMeemJ7yq048EwOYUvxwSTzZn5lKi4ILfBacLfv1kmJMLsrId/MOHT7pjK/hCVyCgC7o0t3igU3yaV3wZo2hT2KzkjezGkJ1s6vDiiU1W1wcv3ZgnNmHe6V4bvsCwRzKTg48Jpqc2ZuxEKaBvlTv8h17OFboqPpkPfuE3MXSr6DO/xQ7xyVxpZ6fmXB8bMZdo0SudgtOmn91Ep5ioDZyxYI0V7/gHP2dv4oU6f2dD5to4NNwVtmwc3tivsfpc/MI4tNElY2PBk8F/EcA2vSFjq2yYXxiv3509wsOmfCbtk0RfJIjH+O7gRwwQO/ANlwOEeHBYZmPm01Bv8cEYCzf++DFa4oW7fnLjh8/wD7KKcRZhPue3sfSHX/Lpp1OxEW7xAx6HM/D4a63a8GWM+eanDgXJUF2cxD+9Poy3fLvhT37/ZS59jUCP9IUPn9rjBYwYwwfpQZ+6PvplV3Qg0ZEcoIcu/dAnW6JTxW+58A+O7NZJftzntcVSd7qku8OIz2QXnxUxkKx8Hw308UV2fPmvARxiiu90StdkY+8O88hqHRXn9uMNp/hIVrHRHNIRfOIUvtTRRIdu0WBrfrdpPqzF4pn/x8yc+ZTRODyvcr0aYIfFrGJY92vgGm+XyjXxeopP/CdDvHYPvn71nruDNUd8TRv/FBfFOrmwGOZlhrggpvFjsOIieGsGHxQ7+bzcTQzg9/IN64lcq3zEesTX9Yt1Ygt/h8N64fJcXYwS84zRJxbCqU3cEtc8i5f6rTHq4rVncd/aY6OGljhlnEIWMqBhvDWNXHhFH03xVwwy1osd8dIYesPHcUn33Y/7j+twuSqNu+qNWcx2p3hBmaL9HsBuXtC2QJl8iiQYY6JIhuI1rN8GSFD9NSuGo9/kMQxJFCNpgWaQv//++5bQ+PRR8FdMFoOUCMDNECUA8JpEuPCnboJNqMWS0TES8Iwe/YxZHQ5GwzksPGiAh9OnG9rJB6bk23iLIZw2OGRW5xDayEMfEjFj8EFPFmn91dGRTFggPZOVbm1kJeWSHL8DYbT4AYMWWRkzR4VTgs/Y0WSsTsW1GWchNxYNBmgMXXNaOMwZfcHJaei3jQb9wGmRNpecULKKD3qjY/qhGyXZ4eR0xsOpn27Ag4HfvMBFF+766d4cCEySGbqge8mHzQtZOa4LD/DCL1gYr409qZPRXJsDeNkGnhUwkjP9eKEHdTapLqDQh7FkMc4Y+tMPH31Upz/6xxc+XPgCYz7MGT0pZGW/cMJjbvCq4BEf5GQXbIAO2cD8xgzd8Bn/lAs90CO9em5ePNOnuTEH9K2NbxafjPGszxxV6LQ288EOXdrMnQu8y1ywYc8tFGAVcPrjCS+KfjzDp+ATTfNewasSDXxnO80/G/UbKjF2NzYW/IT/8hX4xUy41Y1l5zZmD2Ozwv8k8MaghRf2yz/xLV7hly/AY5y4YjNncfQGCayNA7ziHJriBVw2Dvz9g/HffcDDnuHBJ346/EAbDnzaSEgc0CCjBdnmxCft4onDKuOsIzZGNkT8DE7yhFNcEOfgdshlE2YzQj5v4t0lO2K2GK8u1qrzN3isI9rEdzRsWIw1Rw7L0AOPL3yYHxtIsvvtmzkXu/BmTaML+BzYkVUsddcvttAfG1A35/3GzObOfKjToVgMRjz3GzPPcJLBppoN+gMt7NpGWmzBr5gE3hy0nhXzzbXYCafLukE+cpNFDCSbOfX/mKHx5Zdfro1ZznrFdz7Jl9jUfF0Ly3i7VPB8zQX/yRCv3eO7fvUZvnZxyjyJEfxNnBVTxRR98kI+LW7KP4xrPRAvrSfWILBirTb+z3/FfXmOfjmKnEZ8NEZdzBHDwMGrHR5xjByewejHo/7qnvGp4AssumDFLDyKg2iKQ3BaT1sPtUUD3/rh1K+dHOJRB9TyIAdE6OuP5/SID+3zfatc+MfYk+NH42XLvID0bXVhkcAmgbIozsbMyaAF0AIlmFsgKA2cJFexQZAIUKI/5mEBMGHwWfxMJoM0YeoWIJ9Immg/+rYQmWwLlzHomUDGa5Fs02JBhYPRmWQLFcOz6FhILPBg1G2s4FJH36KlGIN/dTLDp994hmYhlah4xh85wKiTN9kZsH4y0wUYPIFTJ486w2SI8Cro4Ks/aQ3GD+M7uTYeLnwaS37weKUTd/jAtJHgdMZxUv1wgnWRDc+eyaIOLzibG3e6MOenin4lHtBWyI0H7dFCh93gSz/9aEOXvtDAJ91INH3+Sk5//MSmBHz8bkTGP/BrMz458KSuPdqeXehq0694nnn3rDQf+AkvPhX6M77gQV/w6sc7OQQi8ptrNNTR1w+fyzNe9YHRBkabuoRQIspGBSQJGt+JX7CewT7l0lzQo3moaDcH5ouulHRFx/QPpv70mQ00Bk7w6sbXr429RxMtdXywm3kMWoo+RV/+AZYtKfkZWtkb/GCa9/wbjFjkd08Sbkm/t0LuLuMl/8UIfkdeGwwHamzJYs+uxFzw6VB8ZNfisniKP29nJA1s0cYpXxd/4UWLXBJ6vIkf/FcyQW/qZIZPHT8KPpqrdEzmdKXNYQ3c1ofmo7nEm/HiAxj+4o4OnVkD8Co+k6mDIPD4UYcDvH64jEfHOJd2evv111+3JMdvzMiJR3LgEa1iGLm0weUyXr82Be9owI22OQoeXUU7fO7Gmx9xSZIFF5sCC5ex6i592qPnWWxRRw+sejygJU7pd8FBN2yuAy5j8MIGbMxs1L766qtt3YdrlevVgLlja4r5zW+uieP4O8UTnq+54D3+47U7vuvrPs+Hfv6tGNPhj5jkwExckLfKC/2+Sqzlz/y43EI8EB/4rVij3wWvmMGPrXHqckB1OR0+xBNjivvawgFOHa18PBp41e8ORqkORmFn2RqYYhD8rvrhrw6Hok1RtzETd+X/DvD24+0/ecJNr/GX3rtvSF7zj/HNzTzu6t+YUWqKdacQRuGk1lsNp3FO4EyuTQBjcCrHYBiIxdyO17MF3UmlIG9Bs/BbMCmacsD4y2IWgHfGZs83pRaBDMg4MCYOHxTpMrEp2D0jMCfxrJ3RGJcB6AserH740MjA6ocTH/g2Hi8ZlD54wVbggIuuXBY7ckoGjONM6LvAGQ8PWDpwOouOjZkT32iAg0NBQzt9lFiZB7zD4xkdTolv9Dk5+uDxqx8essGTjiVV6uDBeEY3/RlrjHtJkfkEJ9kyRoAxBg60wat7xpt+FxnmACNRdHou2XNSK4HsTSn8eKczOOAjH77RJLd+NAQ2z06GyWxTDk5Ci2806Zhu4MCnIhHURg4w4CV88HcQgG9zJ8jh30ZKIiMpdpovoAoqklxJn/HwesaHIIkv440hV8ktWcnhd5w2qW3M4DJX5oD8q9y/BtjpqbnWzi7ZCDtygMGOtHWYwsbYFFtmU2KpeNwf/+jH1ODYOt+wkVHYabGGbYrJDoycWPqNVbGHD+JFzOFj/Am//FK7fnj4C//vdBY8vGCNAcsHweJDO3zgogGnfvDu/I9/e+abcISTT5Jbe76NZ89wig9g1PFGZ/r1aQsfGL5I9odxwMh3fVJjkyjuoY0m2cDiHU/ip/ijLRj8tQnDG3r4IKfx6OhPF3hIf2iE0ziw4VPXL/6iR8dotjk27/C0OUajuEdeBR+eo2H+6cFbUXRsRMWiX375ZYPtN2bwXiqnbPcS/OpbGrglDfCNyilb55fleGDV8zm+ynfFXJ8Z+3RR7PB238+D+JsvFfimHNsBvQ2beC0eiSHyI4dfciY5hhcXfNXbbddut9tyaS9I5CRig3VCvPFyQwzzB53EVp8yixF9Fi+nMQ4+64d1Rk4jnsCLj8P4BBsO+bp4AV7x5YR8hWz2A3iUX+EBHnlVeRA5yYgOHep3xyd+bMysa/uxKfNyR6xOj/QnTr7pclMbMwuIgG9Sneb7TERC4DI52i0O4Cid8pzm+j/PjPMJiu/wJQEMwcQyWhcF26QxTp+wmEgnkyacIZsgk2dSwDMChq5NgQucRcdiwcDVTTDajBgO4xmZPosQuowELgakqCvqFm048YymC58ZGUOFfzcMVbvPYIzBP1poqoOnI3TpRR1NfOnHh8WQ7nwaI+FiqP4AijeRxpGLPBJ88tKNMXTOAdBz4Rtf+DYP8DtxUOfYcHAG82UTgS+fyTB+usFbGw2Ozsn0cSqJB5p4h1edo8IJB74kLfQFB170o41H8nJYfEka8Yme1/XRteEXeMhMRpt5b04FlebRHLnIJRjgiZ7wRD/42o05waegBZc6HPFhPPsxR+yF7iQsAhzbwDc5BAoJimf8KGiQGT56xAMdmbMCDrzkpl98041ndNi/eSMz/eIPHe30gx+ftGqzMfPWkIyCObquVe5fA+z31FyLgWzUX7z16e9u2DZ7Zjds0GLFvtxd+tmeAw9/xELMevfdd7dFmO0Zy5bRk/TDwxb5A7u2MRMzbMr8/2f8gT1LIoy3yIPjx+yXz0kMLPr5Oh9xsGHBxTsc0RX3xAr2ze/wxw/h4f/89DCSAPFXTOJD5PP2Dz508YsG3+/rDesPufxGQ/x0Ko1PPkdPfd4Hh9goholbeBBz0EDXYZnPN8kiGeKLaIoJxogpxtA1OLzTuWSq3/yi4a0mvcGpqIslYMwpHuic7sRH66UYgidrC11I0sQFvOMRXnOGLluhc7KyCzrt4FTiJVYZg0+x15ySgy7EUnSsXXiH00YeLDnI5GcG6j4vRdd6Ya7OlVO2ew52tS8N3KIGxEzlr9g6H+eDYiY/4lM2ZT475tPysffff3/zUXmQ37Vq8weX+Fv5Bj+VR2qTO/JlOMGKSXIP+Zr4KmaJMeKJvvINYxQxmQ+D4fNigRgudojT8jkxCE7xUOwV68RibWJNOY06faAjHwYjJ1MHBycYfcaIhejqE0voR5yDgyxitf2Du3XH5/fGzhsz8fJNl5vYmFGSBdPC507Z/qS9Bd6CR2GUatIoULJpYk247+vBUaRXkRYWxqLOkCWsJt9iyugYouBvrI2ZRcZkMTKGzMgUCyFDRIdhMjrJBCOCCy/4lIyYdEkAGAZl0WaAjMKk4kcxRlHHm2QDHQYEL55d+ARjoQODPj4t1owdXsav7pkjMiZ6CYdn/JOd4bYxI2sbMzpwOmuR1E6OdMG4LbDkwWN6bwPEyfHIydCnP7qAy3xKYOjLvNEXZ8CLOrg2ahZpDmKe6E+CRC444tsccGw6plf6wYegYxw+8E6/xhiPDp7RRZ++4EbXj9j74Sv7khTg2zwYj0/40eHUZOP4ZEUPXbjwrsBJ7/NmGZ/omn92wrbV2Tf86rMcAoo5DCdZyER245MVDF7QK/jmO+rmuXlkG/RGH+bCPJpf+vLMx8yjQO0EDd7jJOivLAabEtY/N6kBfnY8x9rYCRv058sl4GKrRUu7fvbLF9gcGxdLxByJtiRA3NiPE0h+JD7wK/GVj7FndeP4KR/zhYTY7PdlNnT5uk8h0bKRQM/Ggp0exsZDos9uxWz+ITZ65utg+a2xcKJpk8U34CeDOt8R57TzY/B8kCzihxjM78UHssLJj9GBw+EGf7I5MQZf/K7kw4ZTLJXg2KDgk2zkhhMNPmgdE5fg9QafnsRFuPXDIV7g0yYLHv1koxu8GyuekZVu+HLJh3hsnh38kFni1aYK3+QSf40vHttkmr8O7mxi4SAHHOzCXJADLf3iDBriIznFX3yBI4c2+qUf9oUPuGyG8ez/JGUn/k9SeDxfKse2ewl29S0N3KIGxBnldbYenLv8Q8wQi8W54po8Urzj89odUHuD5XCWn8of5BZyFPTkam30jNEPFxr6HXiJSdrFdO3g6oNLEevFBmPFlPCqey5PQovPix+tNWILfHCJbQoYNMVk7XgAhwd00ICTHqwF+sVS8NYbfFqjxL1Xr15tMds+w9qAH3K50ID3TZeb2JgRmvAUKkhT9MP4rMMrRgFdUsB4MhBtFK04xfOn343fj0SA4emnVJMgAbVQSmgtdt6W2chZbPzA2IkseibLZNsgmTRjjIfL5MBT3YQzejTgtVCrWyxNvkWXAbngymjIh0ZGaZFiNOrwgAfDsBmWhau6RdlYdQZrjIKuNrIZQxYXnrQHTwZtdCgB8LkRY2eM3pboAwM3WeEgJ5zgjFPHAz1wKHXjwB73k1vJqPXDncNwIPKiST9ouOBkA4ox6KAJjzoY+qH7CngXJyRvsoDFa7LAjQcJHPuiO0mQ5M684RleND2DN9aFBzTiSeAwB9HAC3gFvHbw7nC5PAevTvb68a3Aq+jHu4I2XbXZQ3eeA3TVwXie5wiN5CAXnuhHEuZtCF14fe/knA4q6CvNX+3rfl8ayC5nqfiIy6bBb31tLrxZFytsAlxinAMN9sVn2BQ/1ie2wGvT4KCM/RVP2aY4KUkH30GDJN8JrjjPH9k63BJ2+PGSH4uZ/FAcsLjCz5fFFHW8sHWXeGM8fti+4jm79qzADQ5NV230oA9OvhqMceIk2Hl90T7jJyu/5Ft4wZNCdrhc+LKO+YpBcvTRRx9t8RxtcAoc6KuTvYMbsaB4mE7A4RlveKRjdPBFDqX+7trovPijHV/iUm3wa4snGzv09aNpDvCJnjb8gzcv8KGPD/3mjG6sa/rYhU29/2Aa/i+++GKzBXCXCrqrLA3cswb4zaky2z6/4nv5p3zW32iwMePX4qp4LL6IQXzOoYiYKzb5hFxOzJ8VMZb/ii1gxQG4+bXYiZaCB3V38cGzOKfISfDOh/Xjwzg4xCHw5Wvq+sB4BpNMcIoP+NCvjk93NMAZgz/44ME3usYUt+BUJxt+5MwOk+wH4P1g/CEpOkKL7HiH8zHK1W/MCE9JCuWkCJ8x2kRRsN9B2UAJ8IxKMKd0i45TPX+VUTAH5zTPpIEzSS40jLEgPIyE3OmsU4Pnz59vp4Mmtg0SPOBNoDs6GYDJqg4vGAZhASQDHPiHQ52BuFc3Rj8c2qOjDQ08MyLwjIO8iufg1YP33NsXsoGDkzzwuTzDi0d09ftsxtsS/HtbQreMFl508GCcMXAqHAA+MPSgHy4wSnNHH/oybDQVyYt28wknePrSjzZc4WsMPtCdnQ59dbyT3Z2DNed0bjw5XMYnP17VnR77jR36NqXkb1OiHw6yGg9GHc34No94pnNy4gM8GyODpEm9AwTw5tIYQQs+ssEPJ71IPI2BUwGDNluQBNOfRAhOdfpj//giP/z4oAc0jC15049PNBWygnXa70S7jRk4PDUXYPG0yv1qwFyb87nkv2zSn8v3ux8L1jfffLO9fWVrcyzLXtwt9k5i+aDF3kaJzYJns+ydzypsV2Jg8+WgQGJus+fUkt2LFQrbZf/4zEb5dLyDZe/8Az3j+LEx/EE/WDywZ3QVMHAXL+qvro+s6M7JhhjkQhN+d3LB69k4PICBE6/64KVb8otFLjrBE9n9Vlri5DM+cuIZ/saQAyy++X4xG1544BWDgnHHP9r4gI8c2tMfHoI33+hpMw483OiIR2jqc3hJJ8UX8QQO+genHk16hgfveLB5gw+MeJQtqEuSvKFFwxszazk8x/YJZ+VSXzDrvjRwyxrgP8eltuxfjFDUxT1v1R3A8zG+JWcoN9YP3uGYHICvO5j1ttxbanFLjsSXvdnmj74c4N82eL5a8FLBoa5Y4Ksecd9mELyvB8Qoh21woSsmeqsvxvi6whg4jPHsqx45iZiCX3FcLDVGDLJpFD+MwQ95xBSyiCH4EHPFF+PCSf7ilHWI3GDgFCu9pRd34Xb46HfR8OOXjvH/GOWmNmYUShEup7R+r0B53piZbAsDJUteW7ic0EqyTZJEwGcsnltITIRJMPHGOUWwKdmNzy8kABZCsCW1FiYT3aJtETFRYEwW3CbMhONXPyNQnACCKWGXbFhYGJGFSxKMn4yIMXAKizq6jBYf1dEFA6cFldPpV2dk+KQPvDFmMOBdcODDgs2h6AAf+PHNsde34H2e5NMVuMmBD3xyAAkTJyInYy75wCecYI0lEwcyb/Tv3mdExtMZx4UDPN71u+jfZz4cTKCAk34UstIbvuAw3ryYezT8fsE4wQQesqpzUDDqHJ9sZKcDwcSfsLahh1vQMJ7j0wea5GN3+OS4ghT7AUMXcJp3NMxfNAQlOjLv7IOs8KibJzi0SYrIqqAJJ9smOz7pk37B4QEdONgX3eCj5IxewJAV7/hKDvpiA/iiL3W04FMEZQHXxqy3hnQ1l+P63Lee708DYhv7E2PYoL+SZ5H3SaFFWTuftnCCZaP8zZ0N8y2fSrNTf1zJIs6G2J24wi/YLBt1Ff98UuN01xcP4jg6fAluPs1OxTt8GMcXfP4HvziFV5sb/oNPPmyMeGezhz8+L6b63ZWiDre3emgdxmeIYqd4hAZ5bBzVHeShL87xU3EOrt788W16wwNZwdORA0Z+J8ZYw8QKOPgk3sGShfx+iE+3DsvEPLIoYjG+ilHersGDL3jpwTroE0EHTdYDuqZnY/BoDhU80Im4RmeSMTHDpkhsxLc2z2SnGzGLPiV6YiYa4oqYTif0S38SMXEIT2ItffoU32/myIoG/dCLOWIX1m78gKEnX8lo94m9mI5/setcWfHpnGZW+71oQJyt9NydP4qVFX4qBsuN+CS/419yA+u+uMw3xRNxupcaDqf5vXyoPEms5qvq4os4VLwVX8QgMap8Tl3c4v/iLf/Hg5iCRzGFj4tx4qW4JAbBgY5YLE+CD5/qxqAvxhfX1OEQf43HJ/h5Ywa/i5ziOD7ELPFKzK0Ovy+H8Gtj5gsqOhXLxZZLsSed/537VW/MMq6Cq4mgCApnNBRGmRaCJtfEGaedAk2+3yZQtAWNYdmkgLEAUjijsPiYPG+L7JAtvP1FQpNgk2AhYyTgGZCxxliITS7DkGijK1HmBPqNYfgWu5JrBgEGXQubwpgZBnhwxpdMMyoG4zJGv+QbHwrDxR/jzxDB0Bm+OB0cFkYXHTJwsIyXMVu44XCibWPGiSTl+KED8HCgYQx4fXCgA4fxnvGOTwmGQn/00iaA7HDSD17ohqwWW/ODhoSEgxkDpzGCCB2bP22KuvmlGzjxyGbo0xwZDyb96XfRNf0lG9sqSZHMwMGuBBt8kQePLrrAp2BENvjRkUjiGywY9Nkk2cmCBjm0CwzmBV9gyEDn+KI/9OFhK+r6jcEvvukbPNz60SYXvtiGeTEfxrFZNNGHF051+jSGPmtjN3B5Iy3g8hs+Rka0jF/laWqAnbA/l5j322+/bX7mx+EWXL7MD/Q7sGDLNilsin3Z7Dj4Yp8O1HbjAMwYdsoewbBXdOBX1y/pF5ttvJxaFj8kDOAkG/xIQs/++a+NABr8VyyAQ7yx+eBv+tm+PyiCjrjPH2wY8e8TeD5rzcCfRRqP4jV5+KADQnGQHHxM/NAHhp/QgbHiJF3YAOGTTuiJTHxXYoFPsogp6NInPeBVTPbfuIhDNiX6xXx6FIvxX4yy4cQH/PiQhNkkGWPTiQ8xCl82Nujhi2xo4l+/+Gq8GCQ+i3MdVNEnHdMlGLEGXfHBRsyYw1h76aQ6/eETvDInc3iFn77oij7IBqc4Jw5br72dRcN/ZYN3OlA/V1asOqeZ1X4vGhAjKp6ru7N/l3gmZjr4cHAjD+j3ZMaKIeKs2Cn3NdZBmIu/+uvn7vrFSPkB/+Tn8PN/eaY6f5Vj8kuxPP+UNynlG+INmuINmHJT+OERf7TFU+uDuAUGfXyQRb6PPnj9cJJZHZw6vsTe8mG8wRkfaHqGMxoOjqwLcPir7tYfRQzFM/4fo9zExozgJt9EmQTKsFA5TaNwC5oFw6JrIhgAeAuRYP8wPk+kSMZl12+iw6ndRFCyRFri0B8LMRESUn0WEaUTujZAjBUP6iYcTfxZyOC1aKNn8bOwWEgsQhkdeTiCwmgYBHhGBR9DYYSMmWwZDSMipzYGhnd0tVn89Ls4DBw2AODCga5+Cyd4MsaHZEFSztidTjt5BQc+HOqcV53B0yOdw1NgQLv5QkPRbyxeXfrJSnbt5AxfcMbAPRd1OOkIHnwoxuiDU8ELGi7w5Ie/C7z2xrlLOmz8wUpk2Ay+OCfY5MLXzAOczZl5VMcH/YOFg54kdPHkDrZ5A6e44z0+6cfY5MQLXhV3dbTIbhzewRuPBh3pA+cC03ySAY9g8YI2W5UUsatTf5VxI7z+eXIaYDfszMVmbV4cfNloiMFiyG5sUtiWTQZb08YW2am4aGNmrA2/yzO7yz7ZHFuUSLBdtPijP8jjL/D61l/hC2IlGjY3YB1coCW+wukSP/FiswJeLNSPrtjG9vM1eI0vpmjHNx608w04tRvn0k5e9LUXR/FvPRAv8Jpvw9czeDEDj9EmD/nhbpz17ocfftg2LV9//fW2rhiLFwV/6Cv4sbGiTzHHhQcxgezxvwGPf8BZV1xg9YcLL3hAS7+1TolHeMkOR3rzDF6cI6sDnUqy4xc+a4w7fK0H6RK/9EI/ePIlx7fffrvx9uLFi20TbjOaLqMx3/GyytLAPWuAr7kqfEyd7Ysh6nyJ/3jrru4NtEMssRYcPwbrzg/lqXJW8GKGHMAhsFjAF+UMxokX8FXnr9r5czzA5xluviqGGAPGXaxziR/qxoPXL07Ur00dPm1g1eE0plhcP3yNF8/AzTjpS7+x+sDon3NKOZDDR3DemLUfgBs8Wo9RrnpjRuCUTwEUT4GMwNsyO387WKemDISCLbZOGClN0LcA+9G0ZJtx+TzRwqkdTArO6Jyq+pxRu41cb3TUJcd4sHAwaJNo8dGGFoMDZ2Lx6dmdDIzSneGiC6aiD27tEgeyqsNvPNnwbLwEw0JGVoWBggcLT4aoHQwe3NEFB4b+GDk5OBpa9IaOcU5XnSZzSAmXsfBzSM9w4gUsemTRjw5+4QaDlmdyKcbTkTaJlLFowCWRUOhVHT/u+EPT2Ap8aJODLtDM2eExHwKLYp7JDQc+nQwZvxvJI5w21HAY78KXBE4gUzcWDxIAzzboNi10Rzd4dMqLF3MEli7wgKY2+kXXGKfBxrQ50wZWG77osLkkI3vQRg40zBG+8EyPElHj2AXeyQQGjpJVNkxHdKodzeiaI/AufNBHeNBxsMBv+Jm5qeB3ntva1/2+NGCe2Z/ruLBrmywbJp+0+dyVP/d2yCZMLGYn7IstOSjzOZr44LdSknYw4q9nyQA7FU/ZaL7kEM7mREz2xy/gY5982nM+Ywxe4WfzfNa9fs9gFPauP78jq3Fg+RG+xSj9/FqfuGC8cZ4V9Ph647WBN57PFku04RccHPDyP23iBH/n5+qtA+ij6+2UBIEPkt+Gj9zkQSOcaBuPtjLrBh7j4Sx+4AMOOlfMAXnMCZx4MqdoGINP8PiMJjk8w5+u6JB90Is2uk5fcKCLR3j04xMNcdKzuIaOMfilD6fXPp01N5999tl2EGAeVlkauFcN8BEXn3SdKvyuOFU84H/lSD4Rtilz4A5WDN3v95uvOzzid/ljOYLYapxPqPmrjZy34/IgPir35nveuukHKweRE/HVw3hbDrc3315mGKNfHxz8Xo6JjjfqZLQ2iBNedvBxdTGnWCc3kd8Zj474goZ4AS8a1hyyOHCDG00xRA5kTZHbuPS55GHq1iwxB1/oiFvafG3w8uXLTe105rd2+lo3xK7HKDe1MWNsGV4bM280KMtEUjIjMwEm0yRa0PzlMOM++eSTbRNngVSnYJNiMpo438374x8My4mChcqiw0DgLlluIWFIiolqkWBkisUNHZNnbAs+XsOpzzgOEy40tDEqY9BiRGDIqA3v4I0njzaGiU+L725sPPTBgTZ4jk1e/DFuOBUwLnTxFq/G0yV4OkoWNCQUFmzw+FL0o6EuOYAPXUUbfJwGDByCAB7AlRigRzct4vDThbHwezbGBQZf8KkbR+dotbDjEw54wdEfOBs3AYWs6dGzDTwYOuXcdG8c+4DHpszGjVxsTEAUHOgfPuMUNNTJ1rzQF3z4dqmnXzpGxx1O+OACo828kQVOl2eFfaKhnw7xpd/45gBeOnFHz0Wf5sC8u7voEg702ZCAygcEZM/Jhi6eFHOyytPTgPlnKzYLFmnf3luk2R374udsnC9V2I8TSJ+GWEj91Vv+wAbZrc0Z24OX/xQXPTss8/9meWO2Hwskn2XP/Bgv/JLvw8tP0dbu6wS2zK99DeHZQR6+JCs+3ePnaPFrPsPnyKHOz/gAOg5gbBwlG3iF03rBP+Dg55IJcYG/8CknzmKTMXQCB969XcSbTzDFD+OtZWT3yR8f1o8HNB0YejPJ3302ioYvRcjrc0r8Srj4o8+PxDLJlLf9kiYJi0QjPvFhPswZ/JIkdbqhP5824sMfdcHLYayHdIzv3Vhb1L3FhM+G3LzRhbv1WLzBl7lzyky/NvAKnHRibUYbD9YjdiQRJLfPVcE4CGMbNv705BN7MvqU0Ua/+JyNrfvSwD1poHWWTOLducLvwFrXwYkjfFyM41PioRxGXOI3YgGfFO8Uz/y+Ip6IXQ7DbIIczool4h7c4jicYp+Yqi4HER/ESrHDBscmi2+L8eIjf0VbXAWDptiIZzDihfirDr+6OCA2iafGiXlo6kdT/gOvmAeHsWC0kUO8PcYhpigdHvpUWkwVv8mCHlng838mw2ndEcvgoq/HzH2ufmPG4EwORVAywzMBvrf3Z5dNKoUxOEqUXFIqeIZosbDjNZH+khPlw6fOkF3q7jZyD+OzRz/Iths3AYwETXjRl3SYVAZtsixajMvk49WzyzMcFmMXeHwzInX8MVSTixdj0MjQbCwkGsZlVGiAMcYCDQdjBQOv0nMbD/i0oUEONPFGX9oYt1MBizejB0NmerXookPuWRZj1OkZXXpwJwM9wgNGwSN62tzpThue4DVHdKjfWDjBkdUznORFw6UNfy6ymDPtdGq8oAEv2ejNsyTBOLLB604uuOlY8gOXZM14sgheEjK84RU9OMAZ45nzo2kO4h1O8qUPY9u4mVN96JARbnyDJSM71A4XmbSlPzTRYX/okoM94QNN86GffuEltzqc8KNTwownQVPglmwZC66xdCOY0gF/MQ4OvCnNh2d8rvI0NMAu2YlLEVtszGwafIlgY6CP3/E/ReLNTtkMm2KTDtXc/X6VbbFl43prxh7hZtvaxHVv5mzOJOw2AhJ2MGiKNRZYtmij5W5jYePkU0mJPj6cGPMdiysf9BaOT/o0Ej8O5MQGb6T4i8QE7zZA4Cza/EbsxBe/tpnYjY1K649+fWQV0+AQOyz8fEis1a4u4bEx49vGizdkwje98D1FjKNj6xI+bE71ic/0bMMjxtlUkV0ihoZEShJEN+IWPfBp/k8f9AsP+oex0cKfMfEJRnIinsAhTtKvtUHc8HsV8dumiW2QxTzjQ91G2t0GES2bP/w5IRfX0CQbGuQwp/Rp7QIjrkmSwJoDeuk3Zs+ePdt0RrdwrrI0cK8a4EPKKTuf47Fn/med9nb5YeSyDmn4vnizH3myGCO2gRMjxDB3vsbP+T56YoTYJWbyX5/x8X2xHX5xTpzmq2KVeFrexV/FFHj0wS2uim1oedZHHn0zP7WRxbMLrDFinOdyHuuGuE0WuYsCPzhywGs8WcDAIZ7jEw7jrS3q+LQ2wKfNeLHF12PWBaX/OgpeMj5muamNGYVTCEXbmPk/TShUkLbIm2QwJpUxUq4Fzf/czYC8MbNQmRSLVPCU7zTV5kTwt9NmjHC3cbD4M0iGaUIt0mjpB5/RGaMOxkKGVokyHPhXZ0h4xKtFyThGpY3xwcu4yTobIhzGkgP/6KIlmWYwNhZg8KnO0OY6WHLkhBZfm9d+PM+A6criyonxhgc4GGr1knnwORc+8Q4WH/XRE3hOAda84IFDSDTg1adNwWOOyZlcHEyhP7K56MelD20FbWPNAR0qdIQHBQ18kR+PFn5BTIKgnR4lPAIZHeORDukSDXLYHJMDDbyhN/NLvuaMPowhE8c3d3iEFy/GaSNXOiEPHMkMll0Yx07MgTY6MxYv+uITX/oFS/TNJ7ngg4edu5xYqRvfRnQ3kkx6w2s6haMLDnpa5elpgK1ms9m1DZPLWw+boPyVPbPhNkppy4IOHi5/XAlcfgWGX7NJCYNnvginGOUNjbjUZowt2iTAJdbyMXaMLn/gp575uj542a6YY4wFmK/wodnWZ9/DE5nxQGYxGjw8cOCBr/GhYMGrg0cDbTThxQMcfBgePmu8GEgX4hC8+tXhQNs65oDRps86ZkOFZzjBVPATH+K2uKUNTnSSU0wSE4tr+tXx7Vm/PmPMgzb48AwfmtHybCydzLFBu9irDZ8usjRev74ZF3rgwMAJvjZv2L777rutv9+Y4WeVpYGnogG+Mhc+yVfya/7mgEM+55DDQZAxvmjw9wKs7WDlEWKkHMBdjLTm53f8Um5kY+YAyV/nds/f5B6KGAE/HGKEWKGIa+roiGN8WBufnvMm/l2cEyeNiYY4WlwRE9AspolT+tQ9u8CLrXDgC15t+PNMNjTwEg11ePTh3d14d7zRgcNHOKxX9gVoFcc2YR/hn5vamFFygdrGzI6egp0gdkpH0YyKEZhImy5v1hiek1GvVhmLhRKcxN6JI2NmMPqdXlrwLaImy1VybVLgtTFDS/LOCCzAjFMCDD+DkfijW2LAaBiYujF4hANObYxBMsFw9OMNTkYFhsHAS2YLNH1kmGjCJUExHl8W+9kh4DSWIXJg/KHhNFQSr4+scEmI4NCGT7DG4xNP9KGQBwzd6Vf0k4M88Q1eu/kzhix4w6Px+tRd9MsR8AGvQGKujHF5phPykh8dON21GWsMGPjAGKfPPEl+8EduiaJx+CCvk27z78I7PZk3zy7PzSPetBlPp2gp6Lq0FzCNoR/BD39kDDf5wZtPeBTyRBPv+DYPdIJXstAZ/C59dOwZHfhd6vB4FoQFanaNt3RGDgm0U2ryg8ejgja+lOC3yvrnyWkgW3BnH+zPWydvwBzieGvGv9ly/soWxRTt7Ey8Ae+Ni82cxd4mg90X29i2ix06MPNGhq94EyQueWtWzMEDvGyUD7B3dqrgEV0l3vGlbY6d2sBqA5ffauPTyYNHfKEdDn4NTlw0Vl1R1w5erOGz+EIDTnzq55fkBq/feP1kQgOMMf1RKuvWxx9/vOmMfsDSATh6hkObMQoc5IsvfOjrLb6YF1/kI5vxxRLj8YpPawC+qtMTHsCQAQ204KALfOlTp0N440k/nOA9g8GXOUTD1RqjHd+STb8xY1P+g2lv9+BfZWngqWiAn3eRmQ+6tPFBXwr4/FCctQnjT142yGf6fRQ/lIfwHXkfP+TTclf+zB9dNnY+n+ZvYu5uHNo65BXT5L/ihvhtfH/VFQw88iqXfodl+n2hI96I+3iWc+NbTHe3JsDdb8zwjX9yiG3wyV3ggFN8kBehJy56hoMM1hT8ib3iBxhykNc48UQuJQ56OQEfvODREX/wQU5/owIOB494pzf8K+Jaz1vDG/rnJjZmJo0y3AVyhuXU1QmqSfPNuiBtYaF0QZ7hSbwlo17nmixv1UwgOIZo4aBwk6GYHIbHKNFCBy6KtygxkhZpxtiCoU0/eAZgkdUvmUCHQehjdOolKmjiBb3wqsOLPhycTcEHHehzRwNf5AWTgQTP8FqM9c0yMTYnARxFn9NZcnMiz/AyTIaLLpxkYehkIQfemg+6xY+7sWRFm17V8UvXLkUdP3SBT8/kIJvL2HhAD1yywx3ddBpOMPqqo4d3fMcDm/BJjbsNGhmdgkgs2Ux6Qtc8KfEaDrKTFUwy00m6Z6P4IEswZErXcOKNPRmnPd6jT35X+NkXuvSML2PREUzA0J85goe86RMtsnor6C2ZORG0yOzwAW10zDtc5hvf+HBnj2QxB65Vnq4G2EH2SQtsyWcefkvkYKy/WMWn2Cj/lCD4VI1NssPefIm1Pi1nr2I4vBZWd3bGptmmceze/1n5MD7NYbcO2NipGAWeH1hwFYspe7bg8hl1OCzo/J2PO3zQb7MjYeEP/NGhhWIt4Q/4OYzP7bw9F9c9W7R9VueLC/j8hgN8B3liK3zkg9OJM37QgYM++JWDNbHBxhae3UhoyEankgtxEe9kkDh4Y2Yds375jRkZ6JXc2hUn4vTioJHuJWLmwRqIhviOd7JLiujFZ6FoWgvwqd9drKAz/eajL0rICQZOc2nTjG/01I11UAp3f0SLXZhn/eIdnOo22uIY/dFnb/LpyiERPdEfXOIzvp1e0xvbkdCZ+1WWBp6KBoq/7hW+Z+33EoLvy2/EL/muGKqNv/FLccfhvVgufrrzQbmL+KSfT4kJ/g8z8U0s85UCv9evTz4hN+CXYo7YiCd+LOaIc3gS2/HCt8U1OYYxipiDvrhkrDo+1OFUF0P5OV7FVvFX7NMmx0EHTy7xSlwSr/BBZnFP7qVf3ACPf7K64INXXBO3xVN8kmE3YjKcDhLFIhsz8Y5OK54fIwbd5MaMkmzMGByjsQA58TeZFMnQKN2zxdabNXXfiFIiwxXkJbl+vGgBlAiYUIbCGBSLTAkCg9GuH5yrDQR+PMNt4cdHSYk2BmJBMpZxGMsQtVt0PEs+wAQPp350PTMUF1h1cPjDkwusxRyMgr5Luwt+RngYDmRRtthzHE7A2BgyJ8Ibh8v4osmY8Rp+NNANllz6wKCLb/jxqY9s4PGvkEE7XZkvCQhYdMHiOV15VoylQzD4xJOxSnpLL7UZY244MRruzQUezJfEgLOD5Zj4VNAgjzvZ0IaDbOxCH3qKNrrQht/msg00nYQPfvV4TSfGmVN86U8PcJIBDXygjU/82FDrx5egAxcc6vRTcmkuJMwCGnldghV4dOjUXCjhgB9NfKKJP33ZX/OyDVr/PDkNsGP/Cbn4atH2xyvYp8WXDbI9ttMpKxuUPDiF5Yf+0qfYw34V9mocG7WYs2eLJlibD3Hb/0vm9NazfnULqc/cFJsEmxdtYrxNTJssizZ/h9/a4GBP/Hj+/Pk21skoXvw/ZvjwVYY6eOOMh9NaI16i7y2OujjKJ53yklc8oR8bSn4Fhv9JiviyfjglOPBIDGxG6EBsFsvB0JfkwgaF3sjmzaQ+fIkpNjx81edK/BQsn4YPjA0xnOK7cXTqYM4YyZu1QZtiPdVOVjGkt5P4xAs+dyNhIQedg7eGin3q9EXn+FKPD3EDX+A6ubfZIxse8SYRO4z1iW7RwRd9wokmHv3xD/r89NNPN5tbMWibtvXPE9EAP+rip+IYv7TxkdeJsTYx4o2DG7HT5gqsT/H4mjH8UrHu80lxR8yRJ4vZ4qKY602XuO6Pf/B1OZncRC7JPzvMBc/n0dYvxsEtHqEtjxAftaEhvhrvDicY6wVe5CX8Gjxc4iSfVxeTxHM4xFd8oynPiga5xFo48KUdDXHEeHTh1IZPeOA3Bv1k0W+d8ceq8GetcQBJznI398coV78xoyiFkimHIhimjZmFSkAXtCUG7ibSZKV4hmXHS+kWNAbl/+jymtaGjLE6DYAHDZNm8WXQjMqEm3h0TTB+5iRWkmoMQwePrjs+GJLC+IzFdwYCD3pwuzMY8J7BGJORwBUfcKDlzoDAk01C5Nk4/GUw6JKH80oiLIaMkfFbUOdNLf3CbQxDJRdcaNGHZ7yBQwce8keTLMbh21jzwBnwlp6MhU8/XIwcHNmDpTP8wwMWfjpQB4MOnMZxKrBgXHTgUtTx3YIvaNGjTYmkBZzx4BT4XfCRQx8do2UcXvBJbrzTB30ZQz74zD18+FUHA57c+AZrPuASYOrXbiz5jEUbDD60w6EdDmMEVzTZrTFkk+SAUegGjMt8ShwFV0mTuZ8L3hR8z8/a8KBkT/iED018rXLfGjD/7CIfyT5IzRd+/vnnfz2MN1nsi22Jw/yLf0sUFDarznaMsSFiqz4L2e/3m4/qY7Pw70YcN8ZmwgLMx3yeYwPi6whvzPDFD/gQP4OXP0gU4OD38IFh7/rQZMc2N3DyQ8kG2di0GIyP/J2Ng3N51scv8SbmGqOPz0lq8MEP8VaclGzgx2ksXtTxZnwxFg7jxUKbNPyQyx2/xovb/psBsvRbaf0u/Wjmo+p4Kl6AUZLDM93i33qFlmKO4CAjWH3hEH+045vfo6G4G6efjo2nq3hJD/DRHx2RMxx4QyMe8aRP3fyIb/Aa4yDW/+VGb59//vm2qQ92Y2b9szRwpxrgZ/kcX+czfOUwDjLksmKt3FZs80ZaPOZzDmDkfWKWN+reRvNN/iQ34Ld8Ei5+xXfREavgNHY34nFfmxkHBryiLp7BZbz4BJ+YwG+18VG+X3wRE8ign5+DV8iENpzFBW360cQrmYyHVx1tdfkZfNqMwb9xxSP4XMaDKy6hm16N0RdNY8kv7hpr3ekLArDa5vFb5eifYI6aX1u96o2ZyaNIxWS1+LjbmFmonZC6WuhMjAWZMVjUbczAHoYBW3CNtUDD61TOQr8bhqeYCAqHg+ExBgsmo3JyZyzDZgQMEbxJtWAZE01GY4ykPiPy3IkAOLjBuFt84HIiCA7fLounk0myqzNCvKsbY0GVCOAVn4yK8aOpDmdvyfDvsvAb5xSEs+HdW0X6Ixt84Iy3MNMTHO5OMrWT3aIJP0fEszGeO0nGO9nSBf14Fjjw6wSWHtDkQOAlRuSCJ52iR174yY4nODiPU3GG3+JdYiUImSPJGnnppYKWJEnyqM+JiH46MfdomBc2Acdh2A3e0HWR2zzCga4NL/2pO6GiCzj00QU+nT5rJ6u5EfDwKol1uq5u82iMwwJ80bkk0xi6cOrt3hzQBz0aTweezZ35py/2wt7Rg8PnWOydbs1D5e8Gjsbf671gvfTz5wzTCXvmH2Ihm3Ya682IeMKn+FA2KU6AF4/FAj7GnxyUafdpDZtku97kFM/4I99TPPNzb+ZszozxZ/bZMR7EJXbO7uHk+41TR9flGf/835zCi1cxjDz6tLtr8ww/nxOnXMaIY/zLpQ8+F1z66YR/qRsPN78kDz7VwcEXDfEFTfFFH3x4hl9cdreZ9QaP/3/44YdbDNSHDh0oxpETj57pIj600zM+8NV61Zqoz2UMHXhOb9rQwIfxeBW/8KpOFv3Wp2SLLn3gk6z04C5+GwMenDFowlmMl1iiIw7SGZtiO70x8zs7m2rzAKdLOX7eGtc/SwNXqgF2/7oChr+6iy1sXM7gr5668yW5g3brPt8pJvr7Cj41lwN42yOn4DdijXxTbmI8/+KLfJLf8XmbEp8jexbbXbuRF+FF/gZeXsF/vbEzrs/W9fPdd8abOzB8G275CN7kUHIp/it3Iht4ccuaQBabSnEIXTz05p8s8iR1MngLKC7KlfFhbUBTTNIPl7UFbusEWvigS7EQ/9XFqPiiI7mdN2boi7s+wxSPyIp3RV1B5/iuDd3/tfwbAAD//77kossAAEAASURBVOzd+Z9mR1X48Qd3RURQQVGwI4KKoqACooJtgMgeIEj8Xf4kXy9/xShLgklIAEFQGCABWV1YVBRoFMQF3AVx4fu97yKfWDx0T6Znumeembn1et2+91adOufUqTpb3Xufvt//W8pmh8t///d/b770pS9tvuEbvmHzv//7v5uv/dqv3fzP//zP5vd+7/c2H/7whzc/8iM/snnsYx+7+cZv/MbNv/3bv20+//nPb77u675u84AHPGDzoAc9aPPBD35wc/vtt2/++I//eNRr++IXv7j5+q//+s3P/MzPbL73e79384UvfGHzL//yL5tv//Zv33zbt33b5p/+6Z82f/u3f7v55m/+5oEDvb//+7/fEBX4b/3Wbx0w//zP/3wvX/rc73732zz0oQ/dfNM3fdNo//jHP7554AMfOHDg7d///d8397///cdhXO7x813f9V2DfzT+8z//c/Md3/Edo88nP/nJDRr4gucf/uEfNp/97GcHjR/6oR/a/Md//MfmT//0TwcedI3JOOD8zu/8ziErfJHJt3zLt4yDLLU/+MEPHudPfepTm09/+tNjTN/zPd8z+P6rv/qrwc+jH/3ozdd8zdds/vqv/3rI7OEPf/jg6+/+7u82+pGPPnjGF76/+7u/e8yDewVP5EfG5hCMeYSTPI1d/T/+4z8OPs2ZseL5X//1Xwc9vGpHFz7yUNRZD/qg8bnPfW7IwjwYo/5wk9WjHvWoMX7yAIcH9MH813/915h380Z+2h72sIcN3rSjY6xkqL95i088WSvkTT7GCT94Y8OLebXmrB315A3u+7//+weMef3MZz4zeIAHDw5r2liNBQza1h75mHtriDxcw6dY9+bAeiBrsPWxto2RzNZytARmk2j+rubCXsxllo01+upXv3rzJ3/yJ5sf+7EfG3pGl9g4a9aa1N+asyazx3fcccfQh5/92Z8d9dYwHadf9D4c+ln37MtHPvKRzR/+4R8Oe/+Lv/iLgyVwD3nIQ+61H/ix1s0ZHaXHaKJNd9hTvNExhf6gq46d/cQnPjHsCHj803t22316S9fYRXoF38HBwdD7RzziEQPHX/zFXwy9feQjHznwnjlzZugw+2M8aJDLD/zADwx78NGPfnTYA7ZCH2MwZuOC82/+5m82cL7//e8fvoxe83d0G0688XHsFHg02FbjN7Yf/uEfHrJD9/u+7/tGXzJmv42R/2Sf8KHAyWYaGxuOJzRcq3PNjrF7f/ZnfzZsyY//+I8PWb/73e8eMv2pn/qpIQPrwpyyv2wZWVk/cLJBaLJ7e3t7Y07w6DAOMjY/aJjTH/3RHx2wr3jFKwaO66+/fvODP/iDY375qPTUuWvjma/dr2WVwC5JYLanh/G1vX7ZNHr4nve8Z9gN/l6sQu+zm3RUYdP+6I/+aNgOccQTn/jEobt0EV12gL1h2/RBSx/6RG/ZnD/4gz8YccljHvOYYePpJltAd9kXOsg+02M6LR4Ui4l92SDwbFa2lN3EC7rFPOwJ+wyv8YiL6D57AZf27DH7yzaiLc5iK+FkD+Fky9hzMHyPWDZ7a5xogAODV/KEB4/iI7Ec+ZKReA6+N7/5zUMeT3va04ZdwpPCTuILneaps/b52v1xyv2WCdrpxMygHSadEJ0FuRKz3/md39lcc801m2uvvXYI1iSZOIuKszehnBo4Z5NuQgoYLByw7k0YQbuHowXAsaNv8ixYk5kjtyAtELT0x58Jdm/R/eVf/uVYIBY9WvgWFHA07gXoxG8RWTQWJ+WyyCxe44G3AF2bA0/6WLwUQJ3FjE8KAw8eKauFw+lzjuShGK9ijBwspcET3uPLWCkjmeGBEhsrXtw7LGRjs1AFBtqND373cKCPT3XkC0Y9eSrGqZ6yoO3aHOuvD3wObZREm3vja+kafwoFDxz41p8MjIMBaZ7gMRf4V4zNvMOJV3LElwNuclIoGpxg4NVHX7zBZV7IAg9waDd+7daGOcObsWsnb3ONBoOIP33QwB9cxkjm1ho4/dFnPBwCRzTQZ6CtA/OMF3OKTwWe5GUcF2I0BsIr+E9yMsQrXU7GetQYtVlrrZfO9Fex9t/1rndt7rrrrhHgP+5xjxvwtYPZW4JuNkZhh9i0t73tbUOnfuInfmLosnWPlrP1bf3TF2uaTkpO3vKWt2ze/va3j6D+53/+54cu0JEnPOEJgw9OHD/sEX1Bk27QBQkguq95zWuGE3/GM54x+Hzve9879A0++vfa17522KZnPvOZg9+3vvWtgz+JBD3l5NlsDpuu6WPDz7WkBz32mC3nl+geHMYhEcOXdvaEXPgSSRRbim+66xrv2sCANbYPfOADIzElQ3JmA9k08rE5hza+0CYvNocNxAc/JBHDJ1tIbvwTu4EOfyEhMr9wmgPw6vkNvLFZxs5mwcOGfuxjHxs0JE3sGVmYN/NqrPhSL9lzjy/3+lsjZKHOuOHFE3mg1yYXf2ZMcODp5S9/+Vgrz3ve8+4df+tyTNryx31lvq5uPa8S2BUJzL7mbDyBEw+Ic975zncOXWMvbaxIfsRF2tkDNoJ+0V+bIza0xF0SM7pVAc8u0ln18LE3cNFXyd/73ve+kYzYDGPj2DSxCHvATogz+Aj2xz07CR/74AweLw738Dr0qR1dsY2Yj76KgdgJ/GlzqGff9WHXipOMkR2TnIFn99yHQyxGduwlHthsMHiFBx1jZmPIzjUYfIv/2MU3vvGNg2cyYIfQmf1V8jzJ884nZgRAeBaLyXS2MGSxr3vd64bjffaznz0Wj0ng1E0k4R8sDo2QLSKTxemYQJNpAkomTFL99DX5FoIFZLLwALeJ5UBMYomD4BdcwYQFAR4NjoxTphRwmlDtaIDn6I3JooETrxaoBQHOYpc04Z0CwAseLJrqyMIiEvRTMkpIefEPFv+CAmO3EPHtqOCDrOCDAw18GCsFxzv5pxTo6Q+/seNVH+3ujZfjh1c9PMatztyRP1rwGKtr8q8PvsgKDfzDSRbgzRl4PCVndcYrSOC40RRsCEjw6J6cyCga8Y438wqHOUELj/qgo87Y8aGvs3tFXzDmxvgU8I0Xv4p7/eAkf7STn3mcjQzc8JGLa33iCw5rQdAmeBFs4Z3895YATgBlzNYSeSr6V1zPvONrLV8tAXJKbubKcSWX1sQ8zvna2JNHsumefeK0PCl5/OMfPw511ir9tL6tZ/pM7+mtdfuGN7xh6IyESdJjLbKXCsdO1+iI/tYy/fLETCJljT/1qU8dePEhyFckNOiyWfrQFTrIFhiPdnrOHqbj2VE6BoYsGjue4dPHWNgG7ZIH/eldfcDGJ96N1ZjYQH2MBQ6Ha/VshwKve3zBwfbChzdn9tT47XyTGxled911ww+xHWSgHS/GobhHF5+u0QLHRqOjwK3dfKljc8C61tc9m8k34JEs1GlHi2yNEU249XHWBq+zAq6xognGmtAOpzp4jF0hP/3BgK0ODhtRt95668D3nOc8Z/h+cEr0xs3056j6CWS9XCVwySTQGj+KgXSRPeP3D5aY1oYGXd5b/L43FSRVdIZ+0qfsANvFZtowsdFhQ4dtYofS+TZx2DO2BR48OfT70Ic+NGLrpzzlKfduLKPT5jJ7rQ+7RhfZKyU/wI5lX/UDQ5fZGke67lo8A4ZdYw/wQH/BwMFOsifZbdfoghXjGbv+7AkarvXRP7ulzqFPfAQDL5uUzdLOX/Fx+OZ3PMlHxwFvdis70/mo+TzX+ss2MfPEzC6iQPTJT37y2DXg3C0Yi1ig7mzxSkosXgvPAgBnwXMCnI861wXJ7i0Kk2RBgLXITbiFaDI4LHUmwn3KAI+J179Fp821ydQOv36US7Go4MCXRUJx9HFvkeALn8aj3ZgliJQMLgWcMUtSwBYcwENZ3KNp8VvMeLFYtRuHsYKxOPHfuMAYN7pgnI1dPwsXr9r1MUb1rvHlXHEPLqUiH0GIengsfDQdzU194FW0wYF3fNiVISMyNX798GtnZ2+Z9/CQB3rg9MVjdcbu0B5v6LjHk3mBBz00mlN1cBh/7eSjDn7XaIE3DjDqnckPftfwZ1Qar3MyxJN5MU5JN2NJbsYp4WaYBavtZpFTuOHHA5l1GFvF9Xxf/dV6JjfHXMjtSi2tE+NrHWyvCWtoLsnHmU54+nXTTTeN9SdY3lv0jk7QTcmQxMt6pUtsp80TT7/oklfe2GRBgXv6ydYp+GC3bF5pZ9e8WiPR2t/fHzYQTjyUNOlD3/BGf/AOxhzSR/V00L1r7fN4G6u6dFZ/ONXhj83Gl/roaKPHdE1AYvy1ZwfUKfiKZ7x0Xx80HPQe/vB6bV+AwBZ4Q4Q/wwf8eALLJmRv4MBXSVU2BQxYfeAGj1fwcGnHk3FoV+BQZ47iU3/j1dc4FDQU9jyZ62MscFsPaJtzuOFAF7xxaUMXPn3ICV0HG8m/3XbbbYMHrzJ6UhCP+h5WyGAtqwR2VQJHrdv4pT/0xuvK7B9bSn/4fk+/2QBrnF7CRafoGr2kg2JkG2c23b1dIHakX3ROgU/MDJZNYX/pHTx//ud/Pl4lposSO0+L9hb77gGAJ3FwiD/QtmFFl+FnUyR8cOPTWws2VfRjy8FoY9vRE7+IbySc+noKaFwSUbESm18MhFf2CB5ycdiMwx87oR2/4NkSNMiFH2Lb0BH/omu8+tjsZl/0ER/DaUza+R0PgchYcmoDHD62T8FvfsR99mY+gzlu2fnEjAAIwuBcO1sAv//7vz92A0z6k570pGGkCdkC8fqD1yjc+47MgjTRFrmJsXBNRAsaPpOhHi2LVDuBu0bXYkXbhDpbMAoYC92kK+o5KIuDcuRcXMOjHR0OBS598eZe4iVJUIc3faODD8qCtqRMH0mA8VACC9ATOnS9WuL1EAtYH4sNbTTCi472WS6NA6wDLXXkRkYWKz4sbLJ1TTnBkIOD/JwVZ+OObrIFr6/xwYtOhoTsyEdfcLUZl3vyME5jpujgKHrKC2986Wu8zuBaR9rR6DA+4wFLptrx4R5N/cCAbzzG53qedzAla+QHj7HrB5979a0JY4efkVHQAQ/WvJoj1+CtT2vbtfn37Uivp5oXvBgDnHCYF3TRS46DyD1/1Ctkc7UXcle2z+bfnF2pxXhbB8Y6H8ZMZ9ILcugAp3Cur3zlK0fQ8NM//dPjiZn1SgfYFf05PfrJedIPTs6r5XuLg/+VX/mVYWd7Uk+v6TQcdEIf65qDtSnhqZFgxM4lXGwBXTAGDtk65+TRtdPJRqDtqRp9oj9o8RlsmHt6wnYYp6QRPbw5c8r4F5Cw1ZIjAYZdU/pH17zuY1NEYEAeghn6JxCglzYQOXgBDF0T0BiP4AMdO9p0W38biD0Rl8Q2FnJBW5DFZpOBMXllkH1Xz8bjiczAw0MeP/dzPzf6kodxgjF24zCPXuMkO6/6kxuenH3bRX7Gjn/zok/yIxs+lvzNPXmZI3Pxkz/5k2Os5gsPEiiysvvOJhk7u8lnHSxPAPAkgJtp4NWYCsTA4OdVr3rVkO9LXvKSIePD7Fd6bI22Vl2vZZXArklgXquH8cbO2ZAQ70qw2CExradfbIZ4gJ6wuewOO0Z/2R66R4+9Tk0PvIrHxrAZ8LIlYPSnn2wuPOIOtlVfsbR6togeszvsFztH17ON7A2c7BabSpfhZTvYJLrLztF7sTUbpA8e2Bx8sy9sPx7RZJ/RYnMko+yrDT9j41/gY9fZCjYYfXViRfDGwKbjkx8gH/IiH3wUuxk7nuF1rR8caOhPfuZJYsZ2Fcc1d/Ar2ZrO6sCyjcctO5+YmTACIFQCYIhNnoXK0FucDjAm2wJg8E2iCZ8f9RIQoYG1AJsg+EywBWvitFtgaIFH16QTsglTTJ5JhNOkNllwulaHRn05JG3wweugFGAtAOPDM6WAUz+HhemsXdHHEa92Gews4JnSWqCUxUK0uPHNuTnrh3+w5OrQB19g8OKePPChHi8lFtrgKGFQr48FbTz41Lexwe86A1C7ccALF3hw5IIe3ObAPRkmLzTwjU+BB1lpx1PBFzr6Wgfw6oMGPtFwDQZOMHgDox1dZzCN2b2CL/jQj29n9+DNDfzWQ/fJAZx6ONBD2735Iw9rJJ60MxbomlNG0VrGKzjJtoCPgWTwrBt94hEP8aQefveuzdNc8IMXx9VcyKfSdWf1yTeYK+lsnNap0lpwbq3UXltwna1L32V5iusHIBx0gT5a9zaaBPB0lEO3XtnsW265Zdg1bzr4IQs2iS6zqfSCU7W+rVEJCWeLBlvn9UfJBr2gI76bQIdTh8OmFB2UAAlo6MrTn/704WDxKil77nOfO/r87u/+7ggSvArPPnpVEC8veMELht5IIPFkY4+PEajgvwSHXPDk3sGH0FkyQNf5zJkzYyz4IhP94eTg6TI+BRuCDwEJuyYgYRMkPWyBAEYS1DdxcLHz5I0HCRCcEkZ4Sii1eSopKTtYEiBtNnPYOgGHte0VVDRL1PBt3iVq6KIhsDIHZM4vRgOMuQbT2Kwn/KFtlx8su2UcZMNGGae5gg+v7tEVzJlTfcy/MbH1bB9Z4dFGgPmVmMGL7naZ9Rcfa1klsKsSmNfqYTzSJzbQZzv0mI6zgWwDPWCv+Hl201p3rdBtsRC74dtceCRmdJMusndiH7YWHnUKOHgcNlrQZqvYXAkVfdNWvMym44Guos3WZ8vhYgP5E/jxqD991mbs2hqHRI5tCif7X4wtLmI7tMMBr2tjBI8unO6148l9sVdjc0ZffzLAA37xjo9k2DjZ9ze96U3DF0nMbIqRrX7NHT4r+ivzuetgzuW804mZAZtQAiBI94RCiLJYHyZKPrQz6s4myKJlyHMAnIpJtLBMGEfIIQgWTIoFo3A6hCj4B8/pmET0TLA2k4cP7RajxQ1ngTl87vHSojMGsPpZRBaZdgtRm3v9JGEWljFaVNrR5KjAcKC9zggfRfXtBdqcowBib0lGySAe8a3dYRzwkSV68EcbPmPTF09oGSO4xuys4A0OvMJpvO6NyYF//cgdXY+I3etHGfQJBq1kq692dWipx4N6iqJ/BsF9yYlr8+CsD/zo6Uum+pEHGOPTbg04G78247cO8IW2Pg60tTubz9rjhzyMX4GPfDNErhuTM7z1w5siAMMrWO3Wh/H3C2VkbB0yqHbIBYjogT+q4IMc8Q0nfjr0O1vfo3BeqfXmRTnsnMyu5LFb462J1kX32+O2nqwtB3nRLcmN1xntqHpywqY62JaDJRlgc9FwFuCzWV5HYxvs/L7oRS8attuaBwc3e0cn6Cj9sJYFCXffffeg48c52BRrO/2hv3SFPiuSRrgkMPiWZKhj4+nPrB/u6TUewDrgyv7xAfgnl+xo409f9YeTTPDAHrJV+NNHAGRM7DcbhX949UPHWPSp6MMP6YeuHeo777xz9PHjJcaFJzYLXQVdOBX8O+Bl81yDA2NdkzVY9dkv49am4FHfeS2owyM4stSWvEen5Q+5aCd7Z2Nm88DiFV122H3y1RdedXhEl1zgcuinjyDTr4Diu1cZ0ddPAevaufvGMyrWP6sEdlACrdfDWNNmg8OmEtthI8rDBvqVPaNf2qx9ukEn6BD74ymbH2eihzbCJHbFY/RTn2IntoBOshcOG0FenxRHS8xsPtEntkl8iIb4W2Eb0nc8s4Ng2T+6DDc9xgf+wLAF6M8xEVi0Fe1sMjz61ce9o3b92Q/t4BW8KWiSFZz1wWf2Bt94Izc0suGu8V5iBrc3NSRm6B1VwFVcG19lvq5u+1z/nU7MMI1RgiRw1wRm0i0YO4USLAuU4C06h0DWBCsmxmFScsAWHxycuQnKuZtAE5PztYDgSaDaLQbnrrXjqYnWDkcLBE14FAsA7mg2NuODT1uH/vBYBBaNYhyUTdCjze6icQmEOOoSyeQFJzj3xoheSap7vGqjmJQ1hSgBbOx40hff+IHXgjY2bXiCR391aKrTP9ni05iSAXjjCh+c6sDp5wBrnswX+uDhxzdZKHjHl77oalMnaDEOfJi/Ga9rdXg3Dn2SxQyLJr6q06++1YHRN/7Vxz9ejQ8d/LlmMFyjG6y+h9GHp6SMQZCIJjN8XGmFLJXO5NO9Oscs/9G4/rkoEmh9I2Zdmgc6+I53vGM4fpsGdhRbn9Yz+2qtCxrYSBtm3mjwtoO+nubYjaWnEhwHfdeHDqNpzq0Dr+vpZ8PthhtuGHqBBpsSLN7wpb/DtUOhb/gB64C3xID9cJ9O4tU9v0KHwbHh4abHeELfOPQHh6axgIVDOzz0370+bDeeJZ/uXStwkysbpt6Bn3jl5zwtgs/4bdAUUOiXrCVy+HBPfuygsyQP3+4bq37ZI3zgjR8wHmNXh292XR/zyJ6bY/jJJXzZUjj1QRN/2WZ98AWvazi1k5ExG6d+cCqu4WwcnnR6yufn8uF58YtfPF6BNCYFPQe54aF5H43rn0sqAWvnsKLefB1Vaj8bzFF9L9f6ZNWZDfBU3b98on/7+/tjA8waF9vQEfaGHtMVOksn6Rfb5HteG2fqvP64t7c3DhtU8LHHcLHNCp0sVvTgwxN+fXvdGBxbYqOIjpYkeqPBPPWvqzwdh9+GnfjUWwAOvErw6PzBsnHnnk1n993TZ7EOXyC+ZS/oPh5s1MHhGk62gl1ke/GFd/Etvtgx956ykyE/I35Ch1wklPqxKQ7xs41DMiRX8iQXiZnkFp9kL79g07QprVHX7BIbxKZlj9gh8GSzfehT3Xb/nU/MDFbJyRkkwdpF8NO5JoZwCNpkWXCEYTKcLQDwYPRVTKgJ31sWqTaTaUGYXAva5PT+q0mu4GUWumsTBK+JRE8dZXHArZ7TUfCiHry6FEk/E6Sg0aEOLEfm2pjwDo+Jt8AtLsroMEbjiB9jdKi3sOClVClfiwt+7XhCJ/m41jfcLVZ4jAO8duNwxgtc4CkAPkrM0CIbvGtXKI8+xgInHGiCJRP0jMdhXsyxa/SMw9gUdOAwLjTw0E42mcEJX3iTd3OGtv5K9MGi5Vz/zvV3r+B75j2eyQcNstJePXpk4D7ek0E00NXP+vPKDuMEX/SiPSqukD9k1PicjdHR2tC+LfvRYf1z6hJIR1qf5oGO+wl631Fxpp6AWbfWLL13DYYO0HXr3S6sJ2bm8hd+4ReGDVPPKdJva57dhN9Bf+k1xy9IQONZz3rWSIC85sZR4onTZlskLGjzC2xEeiPpyKaDoX8csgSJz0CnIIWTRrtgwmt2bIpXIyVeaBoPp+11PPcO9khAYvwCGTZVcIMvCRP9xQfcfBUcniDiVdBAzz0p92SILOBkxwQX6iWn7IS3I/BkHGgcLAGNN0bwvbf4NP4M7+h5dbF7tlYf9h0+cpVQGxOaZC+AiS+8CeZ8/yXI8uoin4Mv9MwJPj3N11cAw/7qY724J2e7/HyrzVS8Ct7MC1nhDY9ev0dDwGdMZEFOZG5+wWj3xAxOT8wEhebFWkLPoVh3+q7l0ksgm36+nJj7q6kkr850lW56Q4zt8FYC/bO+6S+dkEhY+2IkG7nFVGwam+GtBsVTL7rJ1rK5+rNNYhB9ii210yGbbt7IYiPZFTrKxrCrbAG7yV6w8WwBfGDYOnaR3ZKY0WVjYK/ZD33YeAknOvq4Z1PoMxuFH3jZFfbcAUYdGGNFD8/sBTtJBmCsGTBsJZvPvrE3+oBnP9g4MmSP2Er2iQ0mC/XwORu/V0Hd942eevI7bG2at8MO8ge/fVTvrNR35xMzwiWIDC+BqLMw/EqVRcIZ1G4xmjyHBaaYXIXQCcZiNEEWjQIWDgGECUWDgOCcha8O7ejB16EvWO3omXxtFljJHd5mOhy5e/TAwjE7FfXqtMENFl7XZMI5g0k5jcu9heoAhya+jct4LFJ12hVtaDYOfdAwBnxrQ1eJN3jiW71r/eIfTjjcN359k50zePiNAzycinp1zl0LMOySOIzP3BmHBE2BW3/jT+76hDs8M86u9UdfAe+Az6HMbdt44jtc9cOj6+iTD9jtOnBkhHdHtKJT4i74aQ3NNOG/kkrj75xck416Y66++XG/ltOVQGs02ZsHTtCOLMcvwL92+bVA9WwHONf0kb6zP/RX8uMHQNg+Abskg37ART/orXnWnyNVxwH7R6cSQI7dj0vAI6HxS5D0SBDADkoiOFmJh8RCQC+QYS8kBhyy5A49/w8IPfjomld+6KOEkT0EL3kDD6c3NOCVWMApIIKDH/G6Dxz8krEKNvAvsTAGvsi4tKMtWIHTPd4FDhINQYJgQNCDjrFJVuBRbx4kffoXoPWaJhwCJ8kOvwiHYINcyIotBsN+Ggc/QF7mAn7zRl74lnSCQcP4BIX4lFAaG5yCLePSxxzaLFXMqfkjLzgFg3jxvYpd8Hav0dBnbwn60CE/NARBJWaSNwVN4/LjH/j2f8xsBhiTko1o3TlfjNJavRi0Lkca5HMh5WLN44XweJJ9k1dnMQ0dOLN8q0pPvWXA1ijsCL2mi+yVBIp+i+30o0++i2XX4GPH2Bh2OPsNp8IOsgsOuiresImGNhvhW1T94NZXPzTpKt137yxWoZPu4WILwYmx2RDwbDh+euKnnU7jV1+JHZup8B10Gz/OaKDvYFuME6x+zuDhdt0YwaCrDg148OlMfniV+KkrhjVOvosN4+PIhH9jl9AAxzYr6B21TuMZHJjtQ72y3X/nEzMTQaAYn685MJNp4A7CUQjLJDpbDMEQpEWinmBNqsm0ILVx/hYOGpy3nYcmOaGhgZb6aMLXAU49HGDwbdHN/KsHb9FwniUweG6yg9+ecH0VfePJPbhoaoN/5gUM3PqXhBo3eYDTP5wz3nB3rg28A+/O6uf+XaPn2nFU0X8bf3QolPlT8E9GZHNU2ZbBUXDV488R/8m99sapPZ5qc9ZuvpVwjJvlT3Lp/qhz49/Gr76g1rjJsLWnDa9nk8VR9Lbro79d3/02X9WfxjleOqPtcJ++uW99qZ9h4+li8hzNK/WcfDsbZ/PCyfkwmuP3y7heZWRDHWwrO8rBsYFskvUqMNeHXkuAJHTmtm8+JQUCDQmRIvDnFCVZr3/964cDfdrTnjYSDrjZMQde4ADLCbOtAnn42HLtaOsjsGHvBR10S9CPH4mDtSV4UW/XFw79HWynJEiAYldXgde4jJdPAYOGew5fYZf0ITeBAFqCKHwLXsCRlXuFPPCRzAVgEic/RqLON3bXLMkSHgU3Aor0Ql12gs1EGz2JqTkQ9LiHk5zAoot3YyEvcMbBNqKHd4XMjAst9Y7sElulHs253TXc1aPdWoCTjUcLD+Q222PteMIjvszFb/7mb44+nphJUMkeH3NB03ExCt6Umd58fTF42GUarZNZRrN87qt9l8d2Gry1ljujIbnyjRmb5ldUbXRY9+wGm6fQa3aWPjrTI/qiL9vBRniyzY7Re/p2WH92AQ5zxP7pK5Hy+wVwiBm1Z9vYLbzQazyzPwqdxwNY7Q66DgYOpXb2gP1gB1zjD326pV4xJtfwgumefQCrj6IdLe1oxRe7pRgfPOwJOPDuwbNR0XCPP08CPa1kr/kdG1/ZMDBoo+NccV8xBni0H3aAO6z/TidmGDYoAjSoJsVkmxDCdq0kUHAtHgtBf4XQHcGrn68Tjjo4unc9F/WE7Yymdvw59FUP9zwh2hR1DnDqTLDiGm/BjcqtP3CiB3+wrtHf5pEzBmvhzDjRJhOFAmkLh/NctnFqm2G0o6FQFPe11zdaLWCw0QtG3VEFrHGbd7zPctJfXfic8QNeG9hoOGvfLuCbD2NwNP9gZ/wzrm083aOhz9wvfPrP9d3Hb/hnXPp2wA0WDrDJPPjzPR8mlxnXNl9z20lfx0tntB3uO9BMpurIQ4nP+ozK9c8FSYB8W5/JfEZIfziuX//1Xx8O3yuGe3t7I8Au4RAAcIaSNU7c/HjFTUDhyYonJZykoJ/dcs0uurfGPVWxSylZ89TMUyY7lxxxPLEv+qKjv3o41KFvjbBFJRV4MC7FGPILrh1sB9j0Tf/0TR38Ch6zSdrBlWS4T3ZkgUd+CU/kOuNzn71i6/CHhvHYydV+sLxy4xsz5cYbbxy72PAbF9yujQNe9IyfjNTFVzzAJ7DCD1rxbjzoRRtu/AhKXEvutDv4X3QKCNFAM3z4wT9arskTjH7oNs9o62eeyAAP+uBVPXgFDwJT35ip98uZEmz0FDx3PSqWP/Bs19V2Umdjmumgd9o0T4r3i4Fnlk9y6Yz+fbVfDB53iYa1pMxnT8y9Mm5Tx1MvT/jpNtl5QEFHbWDRK/3oAr1y7YmP16npjyfw9Ex9doEO01P6SZfTR/aazXGo8x2YjRAbUjanPL3HQ2889Cq0DSO4PPkG560uB3vvLQdxp+QQXRtlaMLBRnjV0ZjYe2MBz57aGIITTzai2B/9jJtN0sYvdK+vJJKdwAObLrk0ZvJCGzy8cDrwxCaxi2wOGjaKbN75Ro98+R1PJNHJ7pN184X3DuOZ5wJMtmE+j87LH7D1dd7pxAzTHFlOi9EmiByRNiVFNyADdA/ORCQ4cNoqYJUEYvFV1BG+Ov0d0QjemfCjFYx7bQ7X9Qv3fI7/aMxt+iv1dw8+eRibBafe9Tat+ofTfYEHeu2sqtfXGW5n44YzeZK/or0xNyf6NvZodTZf4OMPPgd4czjjVzcXfCiNf25zrW+Lf4apH5jmz7X62rrWHx5wjhmPPtod1Tt3rd31YXzXL3k2/ur1ix5e4OiIt+SU7NFTGsPMx5dbjv83Wkf1ROMk6ByFf7u+sXWe6bdWtJFVfJGpEmznbdzr/fElQNYlKrPMw8RJ+ul7yZlXbOzk0nnOXyBg3Uus9pZkzToWANiB9cRM6R9Sc6Lo9FoJWE7ToZ7j9Mrgu971rkGnn7MXqJfsubZGBAHo+16Bg4WDk0Ubv5yt1/u02RE2Lq/WGavdZXZfEMK5CxTgBc8x6+9JnoAEXXwZnwRBHw4fDrbV633GL0AxHoEBWmjixT3eBD5ouhcYoIl3bWjCIfgQIHgFFJ/XXXfdeMpHboqgSOCDB3J3L6gRRAji8C1AMXavIvYaoiDEU0uyMA7F00J8GAeavTaJJ3V48mqTufJLmQK1gjPfsyi+gzFWASEZ2W0nQ+3kAR5vXuOEU3JOXsapjnyvWQI8OLxOxVYKKvHp+0TysnbMG5iObAIeyMmh7TQL+4NOdqfzadK8nHBnt2e5zPN0X+2X01hPgldrSeksuaATbCbb4PVduiKRoP90gm7SEXVsnU0UsQcb4k0Dr+LRfU/b6CE9o0P0rISH7QIDJ52RwPgfjfp7CwHd7At7wjaI4dTRAXbMXNJr9k7SBackSxIEH5uEHh7YQK9Ks6PemjBeffClDzi2wlg8pWKP2S20jdFY+QZ17C2cfI+nhPpK3NBgr8HxC3DgQ7KHJzDg1bknD236aXPgsR9P2d/fH/aObMnIOLMv+Df+YmTXDjD4U9KBzqPynj9zf/12PjEz6QZvMBahAVhA6ua2eZCuDU67kiFwn+DgMQEmiyMHYxE44CZM9QR7VAE7hLj0jcdo6aMOHYcyt8114ByV+uB3nnzt0YML/8kAnKOAP1yd9bNAncGVLNTeecbfAoy3ZIc2OtXXdz4bQ/JHT9E/+urwAwaumW8wDjjQApvswqtf7dqCi6d5HNt8aVOSB9poxCe8YBgJPFfioXN93KNbv8alHzkzYGDgcoBVD94xjy9a6rdLdLfrz/c++kf1R++kaR5FS31j7jzTbz1oIz9trltjwdZ2Njpr27lJgHzpwLzO554cvJ9wF1xLADhv88HBsZ+cHMfHWZsfdWyWb8U4Qz/f7DVCjlYygJY+4OgHe8+5m1PBie+RJAocuR1bCYiEkP5y+mC1S3I4eU6VQ1anTX+84VVS4hVM68r/OWPLvS4kEPLKCh8jsRAISCwkI5JKdDlxwRH5eM1FEiFAwXu/VmZccPjfaAIk8MYFhwBCO958AC95keAISNpZlgC5F3QZm0AIv4pXRiUzkiy44SBPOMkaD8ZPDuDgh5c/UyfZNGf4lSThR8KoSHbMB3pkLpgzPvOoTkDkXmJGngK5EluvgpoL8nWWsJtTCTuZgceXdYIPyaA+ZCu5Nd/mzByRjXVnx99ZH3Pmxz/w63VO80rG2tGbi7kxt9pOs7DnaGV/nK3XtXxZAof5aTKq3Fd7cFfL2VpSOpeY2ZSRZNBn+iKhoad0mk1li9kX+sDWSVjgYD9siNCD/SWx0F+9dQue/bBe6Z65UEen6ZqEji3xCqT/naaeXQbPdohzxDZ0D071+sOtXZv4uvlWp+AZvP7agmGn8IAOfvFAh40VDTaaPPCgD1h8owsm/6Gv5Mw9uaDBbuEHTvbDuPGKJhzk5t41OnhQx+aRA1mzu2xfY3Xu2ri676xOafzO8/Hl1v+b6/o573xiZmIIXjGxJoMTz+A2mGAaLDht4BJMbZ1NnAmvrz4FzurRVrTPOKKpzfV2u/q5BB+d7tFS8LiNA4wFpN6Cqu+MF3/b9N0rzi2C7tFT35gpBrzJEr6UBUxj7qwvGPfxo26WtXowsxwHQ8sfcNqiOfMTjWCdwcIPXrtrh2tH+FyDcZythC884Ur+c1+wytlwhi/+5v6ujQ9vyRc9PMerdvfaHWCV4OCPD23mJFwD8AL/JIfQJI/u0Yyn6k7zjL7SeaafrLVXD478uu8cjPNazl8C5MsRWnPZA3XkrHByvvuSnEgkJAYcpB1OdkSyILkQhJs/DpUj9NRDQO7Jiv9jBm62O4J4OqKeQ+XIJRLoSEb8OhaeOFA47eiClSxwnHvLEzrJjMRCIAKWrTcWDpv/ELxINiQBkhnBDZ7U689xq8Ova05asCPQ8VTKmOBjo8mjQICPcu0gHw4dDv3RAm/NsunpNj4EAvCSAx70Nza40TEWwZnyS7/0S2Nc5sS4teuDll1edNF0L+FDTx1484NHAQu5kgvabJFrPJK9dvdkZ261qzMOQRHZ1NcZDwp4PKNpfCXcgh71xm9cjgp+8KbOdWsBDf2sObKRXP7Gb/zGGIvXZiVrxoim/nPJXmg7zUIu6YTxzcdp0r1ccJsHxywX15X7ag/uajhbR0rnrnuVUWJmk4NNoyv0xPpnK+i/teiezrGNbIPNEJsbdNYGk80M9lUfSQj560N/2Aj6yebSG/830hMzfWzg0EH6qGT32BAFHvTTxWyC+3Qfv0o6Pc89+vqoYxsV1/NaGZX3/KmvWzTIg9xmfXSfXcInXGiwTe6dwaNdTIZHdXgAbyON3SU/iZmkWL0jG4d+dc5zwQNelWC6dq69tvrvfGKG+bWsElglcGESYAC2j4wBw8HIKAxUhurCKK69ryQJ5ISsFWuEM+L8/ZSwb78kYF6LE9hzYpybJyLWGCduTXFqHJ8nU9o87ZJkcX4cYYkJGoIFdDhSiYv/YWbnUnDx0pe+dLy6gic8gIdXsMHhCu7Rl4RY83hwDx7feFHUaUPHGWxO1nV4jUfJaesPTn99OX/3jRuserwJQowhRx8egUQ656kc3gVUcKGLRv1de6LllRp87S873+QN9mB5hUhiiTY560MG7uEBrz9enfElwZS4ScgkcuAaA74UQZtATX+vCZFPT93wiXdtyQt+4zRedUow6OpfMgcmmeIXHuOHA03t+uATf57wWRvwe2Lmla7+2at2Y1rLKoHLXQL0IJ3tTG88pbeZZWPEupcoKdmKNq9sTNERGyl0wuvLnlp7s4D+2AizocQushH60zfnkiG2SNFf34PFvkjk2G42xyYYO2ODCow6ugxOvXs+gL1ysE02y9hwtGxy4QVN+s1WZIPAg9HO/uOFzWV/4GQH4AFvjO4ln+5tQLFpklc2TB/2BwxZGjNZwknONvjIQH92zWaiPmwUGgo753VKvgc/+4vd9WaEAhd75ZwtGw0n9GdNzE5IkCuaVQK7LAHGaPtgVBwMl0NxXwC3y+NZebt4ErBuOLk5WOBQJUCve93rxrdGXkXz8/cF2p6s9PRDf2uKU+covd6nzQ4kxw23JIHzFXxo4xQ5Tt8xCRq84iZAkcz5qXTr1C6x3WP9OViOcm952lWbOkELp6wIHHL2+IdXEqDd2Dh2vKItwMErvjlt7XaPBSSCG8EAfPq4Fkygb3dbX4ECvIKlnjS55/jhkogoaPX6HlkICtwbu0CBXAQwdr79HzfBkFc57WDrK3BBkyyMXZ2ADF9wkI/+ZCGAwBdYAYfgyI8JCIbAaxc44U87PozLgW+yM1Y8mSN9mje4yIa8BHIKPsjIa4kCG/do6YNP8vIk1Bzhndwk6eZT4CXAElTC4XUqcyZAlZh53dQrkz1dHQTXP6sELmMJsD10r7OhWPP0+4477hj655VCbybQETqpnQ1zr9BhNoHOwyXZ8X0um8jeeu0YPBh99EdPe3jU0W9PiugfGyKpk5zRXf3YRnrJHqApuYEz+9Gr5XQ/m2Ozpw0ofkJ/Os5OsR9slDNbgSe2wpm95TvQYE/ZBrbRvfGxH3hjZ9ksvofNZk/YCnKQiKHl3tjghAdNtk5CidfsPp7YFq9e+3cwc2JGVo4Ss5LaMQEn9GdNzE5IkCua3ZJAxma3uLq03JDJfMRNdRkcwZ9jLasESIBj67BWFGuFY/XEzI9y+IZqf9lRLEDgODk5r9Fw+JwlRyh498RMAmL39xnPeMZwmPByypwrhyvA50z1VSe48GqNIAEdzlXw4Uch8MGBwuH1NsHBwbKDa/dUsoMXfMHLOeNDcsKJCyo4dXyhoR1fAhA44OzX/wQpkhF8tXMsWRR4wCFx8fRQACJ4AodPtAUwHLhAgFw4fYkavtF2LzCQvAgeBBuCBTwKUCQsvndQyBpPEtlrlt1ov3KJtp10fIXT2Lz6RNZkQZb4NCb3ZCDRxaMgT9AEJ/4EK4IeCZGgBExJFb7JEh0BjnEKUvRR8G19kJ+562e6BWsCOrJAR3BmfHAYBx7IGH04rRmvlyqSUTgFqGTkB1Dwbr7Ur2WVwOUuAXrJzjp3SJLovn+Vwd7Z/PLUhr6xM5Ih65+9pJf0ij2kd/Sdjp5Z/gcafP7BtB/3oVf6g2PP6DXbpC88dApur47TTxsg+tJrNgU9tpr9iGf3+rZZxB7ByRarjx7c7Ay+jdW9MeqPD9do4A0P+sIhUdLuwKcDDbaTfWLj2RP3xs3Gw8/2oWVzCW72F17tbLzxw0N27BLbw9ahp47NJfvDErN8HR5PuqyJ2UlLdMV3SSTAQFS6ZkBWp51Uvvw+c7Jx7gCRrObz//Vcr65WCcyBQtfJgsOTmAiWBde+feJoOSxOz1pyL0jgUDk3zlMix/kJMjh7jpoj1s81eO2cL+fOqfshDju4Ehg//CDhEmDgiVPnbN1z0AUtHLHgAU51+MUXPiQBEg+BhN1VtCUS+uBJ4aT14+htVIChMxyxsbmXRAkkOH5tdnS1udfHdUkPnMaGD0EAWnBEAyz5oIsvBU7Fq59eqTFer39KUBXt8TZvpsCLjgO/5KmvYAMd9WgIXPBhjOQUTufmW3/XeHcNToIlEZOANtfq8GNseJG8mXfyhd+4mg80jdXcWSPmxBogP7ASUzTxSX74xqtvGuH0gy0SM/woxlSZr6tbz6sEdl0C6RsdSu/xLDl64xvfOBIIT67oHFi6JKmgP/RFH3XaaveUX3IhubFJw27avKHDkhL2y7nEBA66SX89zT5YbCJ76B9M2yxhjxV6Sc/cu4ZDn5I7es0GoaPevQM8e9kYwShosoPKTMM4jA+t+sCnsCdshnZ0tMMBVp2iv6KPdvDO7A3a2sOrD5zsEh7xQQaemLGV+8uGYJt0cNYvftSdVFkTs5OS5IrnkkogJcGEa4XCrE56iGL8SS5uXDNKDnJKVqu8/k9eV/tVa2SWQ3XOHK0nYrfffvtIiCRmHKQiECio5uA4XnWCar/K6ImNJymcvqCCQ7UWJVeCC0E9xykZ8ISIQ7WDC6cgYW959U2AgQeBPGeKJ/StYQ52dvacrnZFm4KvxqM9fZAIuIdLHTwKHtHPEbtvdxe8A25nBc94Ezzpw7lrRxNt1xILQY0d2hJG49ZubBIq+CSRvvmAR4BFbvo74MVjQVmBj2QVvMQJLwVP5ANGEgvGmNByuA6va3ASrfiExxzh0Rzh0z062uIdDfiSsXbjcIbXfDlbD+ipB69e8i3RJ184BY/qrYWesHmdVZCqn5Ldcu56NKx/VglcBhJgEzqw2zUd8MSMzbQxYTPCk3F6RX88EQMr4XJPR9gcOunaL7ZKzugTu1HiRK/TQ3rnnp1g7xR67Om1/trYak/b2Sg2iU1jHz3ppm9et6Sr7BI8bIOnV+g6bNqUALLb9BkOZ7YPX20esUtshadbbBr7hhb8DmPDk/4OPPEtxtEGHZtlLGw52wjePXuDX/Bk6ymkM3niAz9ohtPmE7+DBxtic2JGTmR/GvZmTcxIdy2XvQQyZJ0NiNPOcV/2AzyFATBIjBUZMV6zgSFHZa47BRZWlDsuAevDGmgdWBfqOG5Ozm7imeVVGQ762uUfcHKAAgPBNcfP8dmN1U8fCdk73vGOgaNvzDi9kjFrUhKAnnXJIXOSAhA//oGmOsG6PmA4YYEAR+5JlUTPPVr46TUXr9Kh5VUgTrxXAwUc6HHCePaEh5MW2HiCIzFUvFZnPF5d9CoMJy5QkFgIFugQ/pz1RwO8IEPghG8JlmABTwIYPNuV1d9rmGSLdzLUjga5kR+8ZOMQSMCLXwEbOGMjK3UCFPSNBazX/9DSx2G8YAVNxulAG9/a8ShQIl/zhz84jdfY3etjTPqggUfzp4/DPXgywBP+wKrDr3Y00cOLOoGWwM2cwK2veSMDMtFfQub/mJkXdBTjmY9Ruf5ZJbDjEqADyva5OrZFYuVtAfrr21KvI9JJhX2ip3SLrYOHntAnOsO22DxjS3yfxlbSMXjpkoSIPrIV2TD92BR22hsRbITXGSVn8Ei42FY6L1FRfAPLZrhn+8Hg11M2NgifcOITDjyzc2wae6o+O6Y/m0Dv8cqml9yxY+yPOj7BoQ2NbIW2vWXjTiII3rj4BLTQMF48oAG/sfdqo/v6wGmcXmXEX28qsDMV8p7vq7/Q85qYXagE1/47IQEKwsk7OxSKmOPeCSZ3jAkGuECYnGYDkwznuh1jf2XnIkjAOtheA+qsHYmRf0J6ZknMOEf/80pAwOELEjhWh2+nOEL6KPmRzNlB9cMTfsTB2uMcOUW4OVZOk+N2SAAEGIIEztLRD3HYQeZ4JWqcv0CAk+WwPV3x2g/etNXfr0fCKdjhvJ///OePgMUPjbAhcNIL/9xYkOO1QTxK5CQnxoOGRAJOwY06sMZl/O7JwI+jGI+gxpj8gIcAwhgka8btNUWO324zvgQDkhIJinEIJgQ55KMIyiR22gUgZCHQQdNY4YRHMoSu+TtYXkmSFOIRbuMUWAnUzKOEUp05cobPAVZ/4xd4mXeBFB7IFDxezAE85GYsxhq8OjhLKJ2zzYIgNMiQXMwzeGMDY9zwahdcoSuxNifWmvpsFz7j9SKoxkpilcAFS4C9c1Tma3V0wRMzrzKyZzYjfFdLF+gYeHbIYe1LuOg9nXBmP+gUnewfTEtM4GUL6I8+dJ29gkMb+8G2s0V+MMQ3ZvSOrtJHtoge96QKHHrZBvYET/DDC59Df7ywDWyve7YULvfsC1+BD/Dg4EAnO0Xv2Ru2ls3QX7sx4Ct444STnVNHJnAYLx7cgyFDsgDHdsKDH33IzjfRrkvMsjfmR1+8nnRZE7OTluiK75JIgIJsHzn/S8LQZUCUEXSQk2MuAimFEVrLbkrgbE6hts7zCNQp5+tQ9LduOEE/X/6mN71pOEc/5OFJR0mLRIaz40StL05YH6+GCOr9oIMkiTPkGAUa1hun6NUYiZN/jip50J/jF1x4ZVKALgESwFurHLSAQsCiSGbAS4A4d06eU8Y7XMZuDPiT0CgcvWACTrxy2u7xA55DRyO5wS9R47QlJ4pASLsEyVjaBYaTHODQB8/hAyd5wwse8GXM6LpGR/L1/ve/f8iqHxbRZmySV7jwINnTT71+giKylbge3PMNnbGRtQCngIRM4CCj+hoPvnpS5qzsLckgHBI948CPQAcfaBo7vMYuMUcHT+aZbARRBUZg1Dkk7ZJhyRq8eBEMCtScrS3z7t64FPOhhGM+j4b1zyqBHZYAe5Q9xuZ83T29tcHDnnoi5YkZG0uH6ZEzWyWhoKPZWkkJ++lJP3hPvegm20In2TY66Uz34XHPrsBBd+mkZIvdtenFVml3uAaHZ/TVscnu6S6dd08ntbEN6tBpbM7waNMHPnayPnB17axdAZ9tZjfZC+3w1wde9gFt9dr1c9anNvdwsVn1R4csPG3kr9h4b4S0SQc3Oor+J13WxOykJbriWyWwSmCVwAlJ4DDjr46D4Yw4E06Vc+Dw1HFEDs7FPWeTwwHLIXEs6nKS58LujNu1QMC3D3ZyBQx2VTkwvOBRYADOay7q7LxKzPyAAz48LbP72z92vmZ5HVJ/3zacWZ7C+afKHDvcHKkgXjInARRY5KTJwHiNy5jgbnzOCn6065MjTbZgtAkunOGTeICDl4PWj6w4cPDak4d++IsP/RzkI3DK6Tcf6EbTWX/4tKNlbt0Hh3+JVU/0BGY9bSMfAZugpu87yN09HAI1fNvBhlvQg280XO8tSRYewAnEnPEseZL86Ie/kjU8GqfDmMlHf/XJGH7zoL1dbXKVNAsQwZOJOsmcJN0aOlgCUNf6CSKf9KQnjSd++DAGOBU08a9/QdxoWP+sEriMJUBvOuiSQ2EHJWZeD6QTXnlOZ9kZtoB+eLJGx22U2LygzzZzJGd0h92QZNkkauOHfrIV7Cmdch9tm0E2mmxc+T7NJg19Yw/YI3CSNXbGxhedtnHSPdvBlsDd0y+02QB88gXsiDo6jRZeJI/smg0attyrjeGExzWbwB/YkDMWfOCfjNgPNNgkNhjuNnPYRvzrg1f43etDlsavD57R4JtsEMJnQ1BiBg7u0yxrYnaa0l1xrxJYJbBK4AIkwFFzHpwBJ6ZwNJwJx8iBOHOKgmfBKofC6ehTAaOfc4F0bed61ncOHDgwr9h4yiHofvKTn3wvPxwXJ8v5coLa0fUxuR1I7V5l5PA4VYEFZy+YQEcAAKbvFCQMXgME69u0nroYpzFz6vjptbpegZTUwcOBC/oFKGDUCRzICg9oeh0HHfwKFhTBjqduAgXJAxpeJ5JAemKEZ3MAxvjwS+5owI0m3Pjm/I1PcCDYQAf8wZKQuOb00VcnoNEHTjQEVwI0cvF6IvzmU4CDDprWgODE2IxTnTZzJuAgJ3StI/3wAx5ObWSGN/XG56xYV/hSwDrg1lcfPII3x+ipR1N/+BqzuSJrfckRXnNq/PiDw5OxvSVZNHYy6Umi9YGOAjd+HYI0+NaySuByl8BsW9kMBz218eKtBElJP5dP1+keXaTT6YZ7Oshn6O81aa+O099+Lp99oHv5EW10yEHP2B60bLqxRTbDbKCxcRIcdp3tA2szLTrkL0k0DjrNR0kE2Wq80vl0Gg6JF9vJlvETYNxLBMPBlrhnG9gIiRc7gg/92Rb48YFeONHFlz7GiS57JJkzNjj0KzHLpuFBH/ZenSeGklu2zb/o4E/IKXmf1ppbE7PTkuyK94IkQKlWh3tBIlw7XwES4KA4J/pQkC0gVc/BctwFp+nLrDv6qlencKbBXYh44ONc3/a2t40nOZ7W+P9fEgLOTmKDP99gFXTbyeWABRmctEROoOHXBjk/QUDOXtLG2UqojEEA72f2BfbwgBX0e3qGFwkUp+p1N46eQxWUSHb29/cH/M033zxk+OIXv3jg7NsBH8XD4WN3OO0Oc8SSPa9Ueo3HLq4nVnDasZZACBI83cMTunBI3pzJQzDhf6OZK+1w4lOyJtjAm8BCkCNw4PQFSYILfY05P7B5AABAAElEQVRDgOB/o0l+BR4SFnDwCcjA9VRLwtWaIDPXeHGNV3yUIKIjuBCcwCtYEewJjARmAjd8wCEIssasNXNu/QhawIKzHsG1DtU50DNm+PGgoGMnnpzIWtJJfn7UwFOyvSUpg0dfdBxwW1dzEWw5Tmo9z7jX61UCl0IC1jx9dXRt3bNDnpg5S5LYDYUNTffA0dN54wMMW+N/TdJHG1r0TGEP2Fa6WRLHv5SwsNk23dgACR07zQbSeXqnL91k57Mv7An67ESvOUvm2BM2is3RHw380H/6iy67oB0+dk8720gWcILR7sj26M+fwGkDB114egqHH/YKv+woOwKe3OCAUxs4fCja0dAG3mYY+4svvobdV4wdb86nUdbE7DSkuuI8tgQs8krXFv1pLfxoredVApeDBOgEB1iAa/eP8+ZIOJ8CY2MByxmC4UA5GddgnIPh1HNIo/KYfzgwv1jF8XPAXvVAi1Ont5yfIIJzR5fzdH3nnXcOnjl7jtETNEGHJ0H7SxJl11WSgD8JDqcuabrrrrtGUuZdfwUMGgISNO2GCh44ds6X40ZTIABWMieAyYl7QsaJS3TwYZcXnKRJciB4ERxILhzwuZcMGa8xokmuEqvu8Yam+RH0kBPHbk5c4w3PZOVecAQnGQksjFsbniROikTmlltuGcHHs571rCEHiSjaxmxceCMHeIwLHkmQ3WOBicAFTkGPeSdnY0QLLDnjV3/Bimv18W6dWW/WF/nAAdYZbv3JwFgKiuAHa+xk62N6SXbBETl6xbX/k2R94w9ePKMF12GFfI9qOwx+rVslsMsSKBmz5h2K9U33b7vttqHLEiv6Qr/TFfonOaF76tkM/dgCG0meuNElP2rEFoChizZF6CxYdhSMa7rHJvm+io2UDPaWAhoKu6CwP+rit3a2w3jQqj19RaMxaqsdT+rbmHGvwKHv7PvYO/fgs1dg8OXQPvMWHXZKezTYQ/21K9rgUQe/TUP/P5IN9Op9G3DxrE99XZ9UWROzk5LkiueCJJCiQuJaocCnsegH8vXPKoEdlwA94Dg4mRyTOteSBImGJ1Ke6gjSc0x2GCVJnClHxOEqnCVcHHn4OL3zLfBx3n4AROD9ghe8YCQ1AngJBx7BcPqcJEeHZ30kNJIdyQFnWFDvNUHfQnCMdns9+ZJISTD188Rnf0nejBFe9RIBwTxa6rMZZJUDRcM1PipkwMbgy7k2Mk/u+pChIoGpTzTco6tNmYMJ/cg6568drGJ8rrV3xoOACI6CEXBgJF0SYN88mE8ysntLhnffffeQp4BNACEYE3TB4X8eka0Ay/gknfB7pVQy7B81C+C040OSR4bm7mBJVM0H2Umk9BMAmmvtJbb66Gt32VmyZZ3hGf/4KPmT2JELGEk7+pJUY1SSq+vmz/VaVglc6RKw3ufDeN2z7695zWuGPkmu+saMzaNLPSlzT7/YB3X01SaIb3wla55Isw/sEZ3O1rA7NpL0o78ONgNdOm5zTF+2RkKDJjsOD1tBv9kH/dAFwz7wA2wFvPC5xwce8AZGX0/+sxfqbHKFAy32HRyfAgca8Lh2aHdoZ8vYEveNw/j0mfmIr2TArqFBhurcw8O/nlm+d14Tsytd+9bxHSqB2SC5ViiUYy2rBK5GCQj6OVCFHpTccJaSFA6X8wTDqYDnMOmP1wqf/exnj28DODl1nA4YTudC9Qo+jtD3C/6/GMeHJofJyfZOP8cKVvLjWvBuB1LCJSm74YYbRoIB3tMiT9TwxhEKFgrovUaon6dhgnln4+CYjcmTGee95VU4suCUOVgOGEyJn0QBf2Bc40mCFw6y4tTdS1zA4gUfcEg28Cog0VcCxOHjRyF/yYUgRl+BEdlIKAUyxod2CSQe7YijKVkxR+jiScACl3mXgN50002bO+64Y4xHMoQvc1BiiQ9rQdILngzwLbFFEy73+jiMFW/4NBbjw6N6NPXHt34CL0GTYEUfgVVjlbzpc3DPdyVkoT9Z4cP862O33hiNHV5j8LQST3iIP/Ovj3kjB/VrWSVwpUvAmlecu6YLnnj99m//9tAviZknWOwFGPpOf+iXQiclZ2wnvffqtNfN2bp+Ll/yQv/pFrvAfmhnP+gam8SueYWP3tNvyZmD7dPG96DrtW+FncNPtoBNw4eNIHaqe3rPDsBrA4kNt6GEB0/36bx29ejgE122kD3h+7TblLIZCS97AoZt5VfYH0+2jJPsjAk8PsDY8CMv9gePcOKTzLQ70GfDvZLpbQ3X66uMY6rXP1eTBCi1YMjZoXDuFxpAXk0yXMd65UlAoCxAFhDTD86Lo/VKGGfGUXoqwiGC4Yg9VZGocDSenPkmC5zgm25xOuejV+loesqJ+u7rfe9736DlVUQBNmfPkZb4oEWX0fctmUROUODVEE7WNV7BCeTh5UQFFpIMCZt+kjM0JIASGIkEp6xw8pwsB8wxc+oHS6KwtyRq+sBzZtn5lED4fzSKwENw4TU6NDyRwruf8FeMTQIiGOK4OXkBibGROYePJzz4Hk4RGKgHLyn0XZux4Qstjt584gsefKmT0IIRsAgc9NUu8JIQCUoktQIR815wQmbgnQUj+CcLcHgXgJhvfcwfOtaUNrKAR5sACC10zQc+0CRnQQn87tUbDzjrwLXxGzN5O8xf65ZMBHx46xflBJUVtK1b/c2nNQK3cSjqK/mFua629bxK4HKXQOu7s/FY675j/a3f+q3x9EZS5h+ssw3sCT3JHrO39Jge0W02ny3gL+gYu7a32B2JF73WFxydY5PcwwEfXWf/6LLkS/JkU4VusrMHi57DKSGi62wYXtk9PsDmEhoSMX0kjOxYdh2faKBpgw59CRHa7D5/IfFik+BgZ7Tza8auD5udrdQHX/qgIekiB3zBgXe2EIwDT2whe8bnsHHsnmTOQbbslo1P8tPOl9pIUvB3mmV9lfE0pbviPicJZIic52NNzM5JfCvQFSwBjorj4wQ5Ij81b/eUI/ONluSGs+WMcm6epvn2i1PRziFz6BwPneJ06Bm8nOi5lnjRzzWn5gNxP+ZRwtPrdJy/nUiOnqMTIHCqggAO23gkM5IkDlAdx4s/ThZfHCte4ZIQGZfXeJ75zGfeO4YSDw5YUirREBxIzCRSElaJAPycNvwlqe4lQ5w+WniUKHDiYNCFl1NGR2AhkZL4OeD0qov+aApMBCCSGMGEcUnUjNm9BEnAIpgwRnQEOGjCD17AAo+5ImPBg7lCh5y1k0GBirHBXYJqTvBpbORu3vXHt3kzFrLHkzN4dMlbu4K+sQlYzF+JHZyKseOPfNCFH7yxk7nxwSk4simgPx7NDfkr+urnAIumYsyunSWPtbkHq2gn47WsEriSJND6NqaurXU65XVxmy02keg0nQLDPtJluq5IdOimM1th08ymFvvSDyqxHfRdOxwO+phe0S36bCOI/WND2Wk46C+7wddIePRR55w/ca2/M1hn+pvNwXP98eEABx9e3LOJ7tXXpn84wcxFv2wE/h3q2BC8sGN8VDDkAz+etYGFG10w8LONfnRJYsbOScy8fg0uOzbzcJLXa2J2ktJcca0SWCWwSuAEJcBhcB4cjYD31ltvHU+cBPYchdf6eq1lhvNNgp87tqPpxyI8reAQc0QcT87ofNnl9N797ncPOj3JEkg78MuRSkx8A4WenVdJg51YztCvMu4tSaWAnkPk7PQT+AsewNqx5QglmfrZKd1fvjEzZs5VoqHdWCroCjQEL85gFTACArQrnLCSowXDYeMXXtd4cmgTzHDwyS+czuDRNn744NZPHzTJ3zjB6m8u3BuHvmDdR0tAYWcXz3aXvTYqORIoSRwFYOSBxw40XcPjEExV0O1Q5xoOPKOBhwIpfc0v2bmGszHoZ0wCF/Mk4TY/Akh1e8uc7i9zZGcfP/A6yMS4k3V8redLJ4HWPw6ap0vHzVdTnteP1tZvvMZ/92HYhqt+1874VPB/WDE+b0DYALNR8oQnPGF878VGsI1sDZ1lH9hR+mrDh20Bzz77AQt1kjp+wyYeODrMxihwKJ4mKfTUkzrJoD6eeO8tes3eoGsTC0wbLzZ+1NtIYlMkig72R392AS02mc1gl9l3tg8M+2JzjByMiT2CTztejUc7HOxvdottghNcdkx/7dqMEW7tDv3BwQkPPtShW+KmjjzI2Le4ZG9cXmW08VgbObXOXCvNY+cv1x7/75qYHV9ma49VAqsEVglcFAlk+Bl6js7unf9pxbkKfP0IhCCd4+JgBL2cke+x/GNM99dff/14asaBcfQnFRhznJ7i3H777SMB9GMjnC78vaLHsXOyAgqvIErUPPHT1wflEkdjNAZ9jcHBiXKAnqJJEAQJDq8VGo+x6AdOwqMP58/JSyAEGBxwr9mBJSNnMuC0HfpKfsgXPCeuTtChHSw+tLf7ykm79wRT8KMd//oKkPRxTw4FHOZLnSdmkisJpSdKYLyWimevR+prl9vZL7AJMvAt8XnDG94w+Lp2+VVKQQTa+hcoCCgEL874U28srh0Cja4748+BHtmoT1bqrCWyNma4yISsyUI/r4NKzNwbT4GjQLAEE06l80VRnJXIOUnAXFd2cX6sQXzFG35bp+q6bgzz+WxtM9ylvj4bn3RfYuYNCLaQvffUhg7SO7bL02j2it47Zxvpp6dlfs2WbWVPwEqUwLAffAoZa2eD6Lekhe6eWV799sSI7tt487/MJGhsK57YF/yYBzbMOHpzgN1ni7yRwBbwB+ixW2j3VF8CpQ9e+Qa+oifrntjBjW99JJ42J9lS42B/2VLjcU8eXvVnM69Z3sYwHvD4wrezdrLDE9rGwnbxG8ZJhur4FffewpCYkY9NUDaazSUfBU5HpXXaufrjnq/axCxhXqgAjyvwGR4P50t/F/ifx7JerxJYJXCyEuCUJQscDIfLeXE+HBzn5F17MBwFe8CpguUQJXCesHBCL3zhCzeCec7tJItECA2JBMftFRuBPGfZK3scMkcvKZO4qfc0jwP0KibHjW9jwJ9xCBY4V85UMsAh+y7Nz+rvLbu2/u8YucDhng09WF4JNG47mhIEO7ocu9d3OG28qhMYFFwIWiSIfk2SHP2vIHw85znPGXxwyGQuIIFT4IAXNAsEjB9Nu9F4Igt8qbO77KmlAAYOyZh2T5YEG/vLUyWvZ77nPe8ZAZZ/YIoPSbX59kSULMELEJwFCBJygYTAQvBA5miio87ZfTvOxuSAs8O94EIg5EyGaJuLgmH18MFFfgIY1+RsHQpgfBgvkCJn/+9I4BI+fOl/vj7uJNfqiuurJXAh8cdXYzt+zX3R1x5Ma8j9vD6PT/Ure4T/K2svzR1e5hJvdJ8dYQskQr5zoo8SNfrKBkhO6HZ92C1yYvPYOfrOJtFVm1USD36EfQbL9oJxzbfA4/88emKEpjcuPKnnc+g1e8vOwIcHrzyaI3YRXfdsi6SL3cOvZEyffBQ7ok0Sha7Xz+FqU8e42Rz2lh2RZBlzm1oSM3bIvbcstOnDZ9jwIw9jRBNONNhwNoxtNmb98cGusqnosWtwqDNOspcA86GeEGbfyKi12Ly1TjtXf9zzVZOYEWJlvlY3C3G+Dv60zvhwzDTn65nuzPN8DWbuM1/P/dfrVQKrBC4vCTD6HBUnqgicJVrOHAcHp53DUcdhcIZ2LyUMXmHxPZJXMLw2qA/bAZbTOm6Z7Y5rzlbywvFzdJyfOgkBB4ee5At/HDYnLrHh8DlpT78kKIp+iuCA8+SkBQicoTF7JcfTOXUSM2MHK1Bw7UkVnAIBgQc6vnFzLfmDw3daEj4y4dQlSZwx3uES+JC5Pu4liPiQdMIrMHBvLMYqQPGapntBiyIwgEMwwZkbi4QNjEDAUz/OHk073+0sCwL0MTeSV3MugYTPkzYBxCte8YoRSPlm0LjhNQ/oCFysBUEGeRunesmcdWG+yQlegYpDPbm71o62oEV/4wdPpuiY054QCnbUg33+858/xi7oMwb8qOeHzD8aa9kNCWzrL64u5vwcRt86KWY5rF0dHoOhW9m6uf4oCc84D4OpPfxg5uvD+pxWnbEpeIovY+xpOftj84uu0XX66GyziR2jx3jXhx9gn9g0T4noM7sngWEb6Ds9dbDV+oCBTzv6bJsEi+/wi45sLzh8sgfo0XH2Qj9nvLAb8KoD415xn11xjUY8m1N2Bn44teMrGtqNBwz+HGAc4OFtLOg59I8n7Y1ZHXsJt2u+QcG7sekHVmE//SgUeHaXLyFjB/7Bz+ul684DyXn8uaoSsxZ75+SVEJ27ru00zyYVL9E8G31w8d053s6lf7DreZXAKoHLQwL0nEPiJFxzQhxJ9+o4F3ZEHWfMCXuFUbLELjz3uc8dyY9dVk6P09EvZ3YcSejnUJw5Sa+6vOxlLxsBgF3bvSW5kMi4zsFLBuxqSngOlqTEazkCe68y4kvi4V5SIXlROH40OE3tEi+BAhxeKZEo5UiNCyz5uOaE3c92cSDd+kO2+pChPoo6/eBRtDvm5JjTh19g1C6rPmSqf33xTgaSKvB4jye4yU+9ftWjZR7xE19kISiTBHuNFaxAQaJLNvggPyU+3Ts8rSwoKWBxhhufklfX1o+1ZX3gQR+40HLGgyeQkkZJoXXlR1gkqGCMo3EPRu75I1hStDngPQzuHvD1dIoSMEcOpbO5aO2dIul7aUa380xfXfXz2ZqJR/qVjlqz1Tcm/dRVH56jxqa9PmDmvkf1Oa364sHO+DJGmzneMqB7nrxLztSzH3TVNbvprC+ZOuiejSGJnXb21tM19sU46T0a7K7CRpO1drhtCrHXNpX0ZdPZKgkSmwYnfOiwNfqxcTZobPjAy3aw02DYQ5tFbCb88OATjDl1jz68cIHHI1tnXHCyT+6D0c+9sYOvDz7Bomus2tk9dOGKTzZPHRh8wqcfeLyxeRIzdlxyarNMwSfZwaVvpevO1R/3fNUlZoTpmAshzsfcdprXFgFeZtpHTWh8d575Opf+M/x6vUpglcDuS4CusxGcRNe4zilwKIJyTggMJyp4f+c73znuvfL2ohe9aDhUzpIT4ajg4gw5veOUbE9nwbrXPF71qleN10jsxvqBD6+0Sbg4YIG/nVZOD79ejfHLkgIKrzdes7zG4jVBTpwTBCPwV4c/8Prb+fWES5tfCHMu6SAjT3H0xQNZ4C0nbexsZIFAjh5NsiNPfcinoo7TFnDAL6Dg2AUM+sAnUCBPJfl6nUabhEkxBnDkDafXZOBzj18OHwz8AgH0jFWd4CFePQX9tV/7tRGkeZ0Gb4IjeMCAxws5OLuHm5zhRduh3lhd4xM97Yrxw4s/bQ5y9HSPXNUbi3m1thx2kNXrSy6Kfgpc5kCJPtqOtVx8CaS3nXFgXuaireLa2mg+qz/fc3Q7w9M6dF19Z3XKDGNNOfDUOgZjDTr0bb07b+MCO5fa4ZuPGeZiXdOh+cAbfZVY+f+FEjMJktcK2QvtbAU7xv5kU/ArcTEeff1LDzaUXWaXwJNhdoNNgoueo+f1RsfB4k8kdmy3NwI8qZOk2STzf8vYX3XweTsDDq89qtcXDhtobLUnb95AkPy5Z3fgNrfomjvjw5fXEPFhk1G9J31w2pwyXgmiPnwfe8c28RfaPeEih71lg9D64H/YWff482onGbOheMEHG8WmlWSSBxzu8e0VeraN/CRmrT1yhtNR0aZ0rv6456smMTMZCXEWZEIkSEdO6riCPB94ixAv0Z7P2/jAzWOY2+d+Xc/t6/UqgVUCl68E6D4HxcEItNkBZ7rOobjmCH3v45U/hRPxTZfAuUA4PPpziMcNumb7A5egndNCVxsnzIFzehwp58Zx4hH/HCBn7Acj8OSVxHZZwQj6wQsyON+SCM7TkzkJJ4fM+UuA1NvFlHDZVYbft174sNvLSYPtY3mvMgpG7Djr6wmc3VHfcim+7SIX/2JAsVOaU5dAgpUMcdiCC+P3K5mSUHS0e1IpMSIDwQLZcOrmQaIkUBJQuHfg27jhxLegQJ1gCJ/kSmbGI0mFg9zgFpzBha41gHftZOtefUmp8TTfs48Ab274IkWbMteZR3NRfUGLOctftj7hQR//cOFDiWY8jMr1z0WVwGExRPOCEe2Orp3NZXM8Gi7gz2H0s0PQHtaOPzDOivVkjbnHmzZFnTY48Gu91taYBuDWn2xacpjPW6CnetvY8TMfxuKHNjwxkxB5/VtiJvECx+axQ8ZLLxUbSGyOejZP0sS+sGtsDHtZH3i6Z0+U7LDNIMmVREkCxgZKZsBLWMgfPnaGrTUGdg8f2iVs7LWkkN1k2/gFvMAhkUIfXvZDYmZMcBqPJAqcp/LgJGXwlJix4fjDE3uoXTJn7HtLIoY/fsLa6Fs4dpRNZ3slqXgqsWXP2G421Rjcs702H92XmFlX2UNjntdX67TzEOh5/LkqErOEN5/Jyv2siPP1ecjy2F0smKN4mJHNfM/XYGae5+u5/3q9SmCVwOUnAbrOUSlzQJvNUM+hcUi+v/K0jFMRuPtBC6+9HFWyI+cadAU/nzl3yYtvxjg3TlngYAeTA80xc5Cct1JCZPfUExd8cp7GySnqxwly5njjcH2HJSnjuK9dvjmTcAoEHL5/QM/OKEcuEMAHh8upcsCcunuBDbwCATQlURIsMGwnHAonzvmCh1M7PiSNAgXtAgz84A19NMkGT2DQkZyYm3Z1BTjuff9m59nuqwAGbxIo7cYvkBJcSDLxoT+8fgBF8OQpKHrayNWZrMiQT3F2r57s8aXMc9c9WSsSZDIo4IBHAAMHXhR1eAVr/OSq6DcX9MFYmxK/7fYZdr2+OBKY536+tk621weOzKFiPYCpBOv+OPM609S3e+szPNXNZ23WoLND8lWyjzdt4K3N7cQM7mhFY1RMf4xT//BrOup66nbil3jAi4PexJcx0v35VUZ2HY+SMnYGDFtrntgHiQ98khS2xNMt9TagbJzRa/DoSGrIjj10r9B3+CV17BWb7mAryNTmVjyCR9e9ozlhl9ybH4d+6IB13fj0V9yz/9rYDLBsiL5waudv8I6+Ixh9HOAd+HRYD3iQqEZD0gkH+4UOvPAo4MDrB59ik8yGHVveq4zxREbk7KioUzpXf9zzVZGYHVcouwbf5G+fLZ7ZaO4a3ys/qwSuBgnQSwbeoTDc96WXGfOzGXD4cnbwuufwOJackR+j8M2Wb4/sAErKvO4ikeBMoqO/a07ImbO7Lx712S45LmcJiP8txnnZ1ZQkCQoUTtaOqARMwmOXU7vE8eUvf/nmYHnC55f8fKskcACvL2dpp1JwAD8eJQOemHmVUbLyy7/8ywMfOeeIyQYO/XLSOVhjdpA1meR08UkWzUGBAdlUjFO9wAYs/kuk1EWr5EXwYI60cfTo6i+JQQdP8WlsxijIgEc7GnAocJprMkHTz+WD9yqnJK6kCqx+hx3bYw1GH4XcGr971/O9/mDqpw3fwag/WwnubDBr28WRgLlqLs1Lc2lNqrfWC2htaqinX9kMZ+tRHd0Da43TF7jVw6MebrBwtnb0saYd2Z9wW1P6dg8WTPoHR/pmcwctwXV0SBDebALeXTfG+FSHVmu4sYNDA3116KpjL9iukhF1DjAK/YDrfEuy0R/e+eyarejn8tlST8u82qdIrvDVEyhPj8hAskWW7Ig3KPzyq80lfcGjCQ7vrsmGXNi2nt6rt+nWZpenWDaSrlk2tfRFGw3X7J3NI/Nlk8r8s1fos+XmDTzcrtVZG2wZWLaRvMHjw735zf43z9odaDq0wwNHcjSW7Kk5Am9+wtH8Jmv3rVnygNN44NCP7Dwx4089cdxbnsSpzy7qe7b5b72MCTvGnzUxO4awLiWohWcRtADxYnG0wC4lbyvtVQJXswTSTfpJHxnqsxlr8JVZf9PtjDl8HAdnEw2OSx0n7bUMrwXaUeXI7Gr6YQxPU+DIMcUXPBwau3G+BR+cqIOjtZvYkyc7ihwvXhycs2DBIXiwW+sp1J133jnGs7+/P/jkWD1BsvvL0doJVuD44Ac/OPoKxgQJfljEGHOc+nKkaElgBAZ49CRKoNH/BsODXWHy6zVJThs9955YkRfa+gsywHqChZZgx5yCIUcy9bTMOf7JHF04OHI4Ja1o4MvTOdfgBHzOZCioKQB0b6daEUyRibF7rejM8n+F3N9www3jLGjTr4IXvHdU716Zz10bYwU/ynzWDjbc2sghmPqu592XQHOO03n+rCPzWzzhSYtXlD0toVf0C7x2et8apm/a9LVOWkvo0BNHOPWDg17Rn9k+trbiS9u8xtgBONGja/CygXA5tLlHA67wweGazdPXNVyNJXrJQpsDHn3htsllA4k+Gx/Y8LquL1znU/DuUMLVWR1ZS8z8Kw82zK/ceq0QDFuBJ/bL+Mwj/pXG4fVnyZnvVb2hwM6CNS8lNu7ZKuOWQGkjK3RthlkDZMiGO8w5WwrOZiBYNso4tDvzT3gkNz4BDvxKyvAChz5kib45Za+NBw71xhMNdk67PvDh1701Cta6AO/QLhFDA1420z2+8KBeH/yRr0MyiEaJGXhjZpvR2FsSMj92dM2SmOrbujP2eb6G8Kc/2s7WPoF+xeWamH2FOHb3xqKaD5xmKHaX6yuXM3NxPgp35Urk6h1ZepmRzugfJRHwldaQvuFRB4ei3nWOlhMT+Pug2/8qE8RzZL5x8g48p80u6MfRciCuOSz4OTDtBTHuz1aivw2jnhOTLHhdxhMcO7ICJM6dA+ZU8QdOUuJbL6/xecIHzmuJkkjw+Mtpc6bGaNyuJUe+B/M6I8foyRucDoGKMfp2S9JHDpIxO/4CC//w2G6vNjjwJIFV8I0PDhctv2ZJLvtLwkjOZCzAEFx4RdIccOzG4hAkeH2UU+e4ydLTQXASMfUSV7iMUxADHxpeh5TsCbY4fkGBOsmiRNuYzCU5ePJoLAIs37P96q/+6giw0BHkVlo/ndW3jlyrn8/b163FztqtFf3Mt3Nrc4YBd1Spz1Hta/2llYD1rFijdMFGgn+1QVesSa/NCYLpTXbFWnAvcBUcu3etznxb39amgNc6LhFTjwY8YK1N9/q3rrSpd7TG4NJXH0VgDbd2OqVeHTj2Aqy2+KGXxqIeHHrwO0cbXrzA4aB37Au75i0ET6kE/PEG/iQKeTmUxttZnfGwoV4XZwckV+wCPtgK7WQg2VHYUWPUZhw2j9gx42cLbfawswq7QyZkrg8+4AVLvuiykdaIOYxPNCRa+GTj1KtTyIhM0XdtXZClAwx46wnfaFh3+HEtCcILu4geGGNo7ptjfcwpnHgzz/rBj64x2Eyz9vBoPPrAZb3Cow8aeIXfNXh8qGuuwaLFp1x//fXD/xhvuF0nlyGArT/oz/O51Xzk7ZqYHSma3WqYF0ALwQI8n0nfrZFdPtwk987nq3SXz4hXTu9LAq2FzuDPti7ABRuce87A4Tq97ly7ezCSjDNLQuQfE3McdnT9jxWvmoDl0ODmPGZ+tKk/1zLTPawfJ+i7J6+8SF7wwEFylpJFDtf9wfLaIodnt1awYxdWvYBHMmL31fdanKHEg4P1iib+JTSCAK9qChL83zOv8nGyEiM7m+jo7+kaPgST6Hl6hY46dD1xg5OcClokixyvsQgMXNuB5qgljBItwQXeOXN8KXC4F8iioQ/+0SErgYF5MDbzJrGDE894s4urHo+K4AEdbei6F2zBLQDxFMOvWbr3+mf04ayYL4fSGfzZimC00hx3Vo9HpfWpzVF9dAbQIX9qn3HO14d0WatOUAKHyX9Gbz0q5lMQTzfpmnUuKbnxxhvHU2prnR5aiwJreiegVSfYdtAhuisIBs8OuAfroA8CY/3qY6PC2nKvDxhrkp6WIMBjzdAvbTZ32D0BPDuDLr6i20YPeHVg6RX9ghMOdQr9ICM8KfRJm1+I9SYAvDZy2FgJ0FzI7ELXMtrbczTjJBs25vbbbx92Hy+SRHyTLf7YLPKddV0bWbOL3qzAuw2tfljDeMmNHXRmt4wdDuNnt2x2KWwu+2e8+HHWj0zJUx/91Vsb+EcbPu2KMepjXs0D+uCjaU70sQbwCocxgNNmXczw6sOhT3OId3Tg7S0HMmotWe9wuXeQXeMJh/Fo088r9A4bfv5no1f28YOmfs3dGOQRf+b5PALkq6rXxOyrRLKbFfMC6NqEn8+k7+YId58rcu/ALcVc5b/783ZaHFoLnIM1cC5rYV478aRfQa+zkl6HU7/gJA++6/L0xJMkiY3vrjhrzqjAX0DCAQpqPN3hpOCFi+NyzUk6n63k/A6D46AkDDfffPOg7RVDTpWT1oYOvjlwgYPkBM+eTDnbhZQYcYIFCeA5REGVp2MSI2M5WJI7r21KsvzDbGfJT8W45pJzVpdjJ4+5GJM2clBmHK7JkpN2LeBwNjbjwafAAe+ctPFp35aXORVwGI/5AeNA29E9+nC6RxdO4ysQsrvtdSaBx3XXXTcSWH3medG30vXcvg3vHr+VYDur7xof8YpPh0LOZyvkp4THueuz9VvbTkYC5u0omZtPbZ7EeOXN67LWnbWqzlr3FNy/wFDvCYpESsJG/yRl7JFgVR9Pqd0L4tkBsPSX7ZEQ0BubSs7qPFWWNODRNRp0hc2iVzYm4GMrJG7ogpU0OQuWjcGGC13UbpNGUulJtP5sH5rG4xof1izerWF8wOUeLn3YA8mpH9wA69si/9pDEshepAf4dVxICRcc6UXn8JoXv/5Kll6tNG4JBTmylUp2CD48waGNrCTc1yy2tDGYV2NmjySxbAt4/ciaTXfWn9zZaE/a2G/yZYN62mauzT14tsS84wGvcJMnGcPlAEuOkiN4wGZ/8cVeoKnNnOCTHcQr2aPBH7gHj4Zr9lkfaw4OdNWZPwcc+uqjoKEOTv3woJ92ckVD+f/s3c+OJEfV9/HnUuq9BDaIFWp5YfCMBtmyx5Y8YANLbogtYBgLGNsYGyN5gWaBXlbcQ1/Kk5+Qv1aonu7511U9Xd0RUnZWZkacOHHi/PmdiKxqSZnvmNml9OMpdAgdukNm+N+fr9Fw+vO851PV7z+uxOx7UZzWhxdRiNMa0c3nlpMgd4cyA5Sbz/3i8NASoA856Rz13Ec2mr7snzls7dxHK/2KlrOiD3UFNoHCq4B2hwQ3wUewE1SAEwc66rsnmHrdT1Deba/bvWxBKz7mtu5LILxi4xVDQA2AEbgVfGkHZOEDkFCADK9JeQb0AVMSJsHQSvj5loChC+xZqcW/wOpL2H563vXZ2dkIrICEhIhctMWTgIy2RE+/AjDQAqihjQ+7TYI5cKFOO2SAgEAvSGuj2NFSn4yBO6APiAQutHcAHsavT+A0ueMP33gAbAAB9MyRhBoNwNQ98rLTh0Y7ikCYa/OMroQWb3bMtFcAhcsKvdJemcHB/HlOVrvfeaabjjp7TsbO5sa9ywpZKOrOx2X11/3DSiBfQPYV8wWQOsyjHRm7Q5IgO9bsweu0Eh6vv/W6MJ9D3+kyGwDG2VWJGV323LXn7FhSAKxLiOh9dqoPdiNxwBu7Zi+AteSMXrJl1w52TufZgp0cY5Aw0j+8um933U60XSJ8+IwPiRm7lURIUNQ1VmPHA9/DBtHCu3aSUMkQ3wuQ+/4sHrSdZRmgT7YvezaO7Ce6naNVYmZcdsz4xTmpYGP8hzN/a149J08JNx9k3G9sr47zOebFOEp2zDOZ8m98oERFYsIH4sVc8G/6NQeSLq9W86W9cUDexsEf4QOv5o1/J3dt9IsOXdAHXcCj5+aCH3etbwkS/XLme7XDm3t0x2FeJZHmCx/6c03ffJ+Z7uMr+eiDDhpTcUEbdPBNZujTDX2iQ38thO622Cm+6RddNNEx5v35at6ucl6J2VWkt9reGQnkQOcz42Tsq9xNCXDQAhw9yFHPkggUdY/uKJ191k5xT31nOpWzL/FTR2DzOp9Acb4lGeoKtNoJaoKYe2gCG4Kc5MfOFNAvqHiOJr71U8KE/osU7QV+fQL9AJ3gL7ABSmh6XrIlmAqcgpxnQKDXHwVIP+QBCOEXQAJ8BGy8FTh99lqNhBRYMg79AGOSFcESgAhIAiCCMlkBB67tJqKhX3W1wQs5AjL3798foMw/cSUb14COPgE6gATAA14ASTTxTgb9OAkQqU8AT4AXyMn/k08+GcDD/yQD4shKPXMCSLoGUMgPAAYOvaKq2AnVh3kFnv79738PPn7xi18MwPMi80WeFTqVXnWe9bZ71Z/P5h0vzuqRn7N7zyrkqajboe0q1yMB85Pc65FN8Q90DcCWUCl0mJ3SRztGin/NYIcGiKe37NbiC/v2mW6yW36ELTnYrkMBfLNl9fkMbYFvegAEsws245rvoLN4rg98+uzAt2TQuPgM+iuRc80Xsm/X+PKZHfONDn046DA6zvj2GYB3ZofGx9YtOvFfXrn2GqC29Jks9Ouz81X0GQ8OxZjn87jY/kjMnjx5MvyZRJlfkDgo2urfOLQvMTN2/PnFW/7EHJxtC1p8jqKeRShj5ju9js5H0gF1yEEdc7PbfJnEmd8jY3Mq0TKH/KI5QwsffD2/Zw7cxyceJTzqaC8OmWPziL42eKcb2mrjOdmbe/pFL80pGuZUHfw66IGE0TO665lkrz70rz3axktXyMs84hNv6TQd1w8axkf2ZGOc5EcOeNX+mGUlZseU7qJ9ayTAuPcPjohjXuVuSoCDFzg4fHpQYE0anneP7ijz2bNZf9T3nF55JrABM4JEbV0LaAIKgCXQCbro4EVbQdlZe/RcAzXRELA9U7o/Ll7gj7b6dRa4/biFHwsQxKwsAw2CnGAnCcNTBY9WZe0IAUoSJt9LA4oEOkFacMWvAOieQCr4SgABCCvX9+7dG3TJwIqsvgE8AVUAJS/9kBP5COzJDU118K+O/gIT6gBfJbDGAByQL8BCZngx38aYHMkaXWMCLgK4ZI9HY8CHOvoAGPRjjiV66umzPgBlcnONV/UlgH7NEg1g2QoxPq5a9F1JV7uez+qln+o5jPt5ha4otZnPz2u7nr+6BMwXWXeOkmv6SFfplX/BYFfALobdGP7AzrRdbYsm/e8rNpW+00G2TJfpKfsr4UHXnAO9aHnuM17YGhpooeGaTXnuOn12z0Hf1OcXPMe7+/TO4Tk+3PdZH4r+0ztt0VdfPfSc++wZGmThGZ7xITFjb+zXjypZ4OKXtKuOz0r9jouX/BMv+3RmmnNiZvHGPElOFPwbu7nAv8/uaS9JsYPkxz889/8ffVeOL5Jc9ZqjBS4JCR9kEYpv0Z7/5v8sdFmIao7RJyNyS67mWd/mCR/6U4fM0DJOc+esDR10Nj+ea8N/u6YfaKHpmf6087mivudK/PisnWfqom9e6YIY4J7nDry7Rlf9Wd88wzd+LM5JzMQI8hNv9EcWxywrMTumdBftJYElgSWBa5SAQOOoCE6HKNGdzyVmVmWtqjsELEFMQBTw7ThZXRUYrfYqfoob4PELY3a/vGoj2FmRFTQFbYFRYJdwAXvqSOj0ITGRsCj6AQy167P7rgXegq/gHKgT/MlFQEbbtUNxT3FdW3SVgJn7irrodK1PYAE4ca/n+iiQ4yE+tQVglAAMMIBXY9LemTwArH7848GDB0NW8TwIrD93VgLZO52jg+ySHrIpiwr0jA4qkgoJkcQL6LT44PszwLfXxdigpMT3RgFUYJSN0kU7L+zRNRBvoQSg9/rjbttVsWjhnqQBHbrON9itsNOND88tXtiRwZsdO/yjwW4kifjzuiQQbJFCAiGZwKdn3hpgL/wJmhIX/kSffA9/4xpP/M759naB19WMD00JC9+EBzSNjZ9idxZDyMhOtlfG2Z5fbbVrZlxkrM9jFHPXkZ8tSfz000/HQpLv8eKF7Uuu8GJcfIo3CozBbryFI2Py+jO/yV/60SSLYfogE6+gG6cxSzwthpGza3Nk15T87T7Z8dePpIku0RuftSE/13ROezzRBTzpFy/0xpzwg+aEjlo488wCgLqek705NA7XxqkOP8k36oNeuq9fY6FHDrqID8/w4Zn5c42udsaFP3NJxuprZ9cNDYmoQ30HW7BQQW+8aWG82qHlrOT/D6kTKzE7pDQXrSWBJYElgdckgYJ5ZwFDADlUEeii7SzI+WL00+0XIq24C/rAlYAtILa7hA8BzdF3N9A6214NEaQFaMEYOAPmAIoSFiDKGOxueTXRK0V2zAAWwRtY0EY9Z0Fe4AXOABeJnL4EX4dgCxwAeNrgFe/6cG1cwIRrQVuQtoIMCEqOADqvzeAX2HMI6oAqwIJ3feBL0Se+gB00AiyAg2vjBDyBRN8pA2CAAWAEwMIHXsmNrLV77733Bo94PQYoOJS+LDrXI4FsMl2gU0ArQOoZHfKMHQC7fkTH4ggboaeBbtzSSWDdv4Ggs36Jzi6KQk+BY9+5pIfZJHtgd8C+A4jdbUmR5xIxdOy4RAMIZy/sUF9smZ3qW5KlXf6EDXo9GGjWB0AveWNj7EPhF9ieftRj967ZJf/ChvkYn9k2GmxdXbyriwY7lYS4jy87ZniyEGSnyX3yVJKnM39ziGJ+HProjD6ZfP7558PXSEbJ0n2+Rd/GqRi3uSYH7fkmbxpIft2z20bmXoumB+rSE3Ph9UWJPLqu+W9vQ0jMyIzsJde7bV7Jn3zcozuSJbqkDT8mBngLgq5oS+7kq89omQPzah7R0dY9C1Fo4N+88fF8tPt8o8O1xMp4+VO6aB59pn+uJenm1ViNyfjI1bXSr9oaH//q2gICfUs3yIw/90NbKzEbYlt/lgSWBJYElgSeJ4GAgvP+ISABZ4cq+/QFPq95COACn6QJeBGIAw4CGwAAAAI9AqFVXIHWSrRALWgLwoKttgI0kCT5AYwES/34v20CpNcZPRfMBWQBX5DXjxXwwIdADVRKCNGwei/hATDwhQ8r8/gABIwD31ZGjVXCCWBYocZnK8j60C8aVlOBE7sIElWABViSpOL/j3/84wBO2pgLIEs9yR7w6XtjXlNE72xLVD1zDWwBYWSMd+3sWOD3V7/61eAHz4cChYfSkUXn+iVAV+kC3aUPwLbis3uKewCnnWe7ZWwMwO/7ROwJ6NXGwgB7A5AfPXo07AXYnhMedikBlBwoALY+gHHP2Bn7RJNdsmuFPvMRbJTNs1HP2RefoA82AHw76gN9NNGXZEki2Jekzj3Jgfbua+9aH2gYGz7VRcO1vvXrWqLIzktS9GUh5Ntvvx3926Wyu48eOWd36JAXuoco6OZj+4xHyYbdcomWOeM3JCzGiQd84RkvZE++ZC/h8DqixKyx8yX8lrnlo8hHe8mY8ZsfCYl+/QqsZFxCxxfttqSMj0aXz6YT7vFr5kR7c4IP84g3825MeKdzJWb6kczRT23UNR5ziBdFP+bG2NBxbd7SH3NGv8QWyZ3P+EKPjOiORIwu8Jv4kFTiUxxJtvRHfQmdcZARmbgWr9aO2SG0e9FYElgSWBK4IxIogBtuAM1ZEfQAlUMVdKPtLNBaWbcq7nsYXoeSIAEFkjABU1AFLARiYEAQtAKJb9+VEEQLuFYstRMY7UwJ/FY+gRDgQmLi+mxLYIABNAKjAqo+BFPAwLWgjidJjn4BU8ACH0CARAsdiZUicVMkUegCqGgABegK9JJAPAMvaOAT2BDoyQTv+NYP/vBNBgK/uQBG0DROwR9IAGLU144s1NEGaCQToFLCJ5HEx7vvvjv6xI92qywJALbZgnOF7dBDYNyri75vxDbouH+YS5/YK9sAbOmbHSs7auzIL4CyU7rqOZunl67pKsDPhui2+nRb/66d2aQzfWYfnqvvKLnAnwTBPTrNJtR3rb4+AXaHa/x7hlf94N/42Q9+9Dknauixf3XR1V+8G0d8kVk0AHiv+rHFEhMJgboOY9KnMQP4hyj4ir6zgv755j99F9C8WGjiA/VNLuRFLuSHJ/UlZ+RALsbBj/EhkhrJDH9oB5B/JgfzQA/IgnyNk3wthPFfXmf1g0h8Xjxp41DPQcYKWeBd3/ghT2d8mhfy14/n7uG3OWmO+FuyMIeKeevaWRt9aI+2+vGQHbhW8NU8uYdn1/hCy3jRcp0uoetaWzHi6bZAt3bMhjjXnyWBJYElgSWB50lAcCmIO8/XgpZgc6hSP+j5LKhZfX/8+PFIFPyQh+AtsAuAngMBkhX1JTRWyCUYQJ6dq16vEQQFaABDHUASuMC/QA4gCZISHN+VACTUVyd+9Nl4fVYCKPuywI97CgCgXgEfgHAtOCsCuaCuTcCgtq49r191KsZdAqe+cSjGqh1gRVaugame4adr9/Bj5dp3XvDh9bLdtlINBB0KFI7O15+TlQAdonv0Id1kA4B3iT37Uc/uLHBvJ5n+0V8LDsAyG2V7ftAHQPVvNuyES4IsQtBpCxt02S4Iu2STgKvdivMtiUDXDgY6EgMJgEQQfbvB9Jn+2hmxqIMn13TbDoUEAk27eRIJfQDf+gDe2YJx2nW2MKGO+3wNvviP+RrP6qhrEcVY8W7seDX+fJTnaNih5qfw6v8mkgEe5jL7kPn+q35Gbz7QMYeSI69VSki9LdCrpcbhaJeJHC0W8VsOz8jTrhFfw6cZM7l5dRRtsjXGZOMe2Zl7C1XkQCZ2DPlr8kLXvPLVnqlrcYlP4+/MI//u2pyQmzp4wBffhk/94J1uGTc95YPzidXXj1iCz3yxsfhMp3xGk99EU8l3u1cbdPVDTuqzE+PNj/Lh6GnTZ99ntKCxErMh1vVnSWBJYElgSeB5EhBolDmg91ngESSPVQRB4MXqOiAGTLknkAJjzgKgwBhoEOgARN8ne/vtt8dzQEigBp4EX4EW+HO4JwEDLiSBrfaiW0AWRAV147YTJdAKsGgCpsACHgBFMsEb0ADAoaN/gEDwF6jRdWijfwASeAAY7Sz4jDe8Ai67DVQau7YAC5mTh7NVboEe3/pCw7VdB+MCYErg0EUTaARm8I1P9dHtxz/sdJDfKksCSSCbp8cK/adbgein26r/+ZY0eSXNq7tsky7TfbrGhuiaIhFg12zWa7xAOVDLBtFkY64lagA53ZfsSRzsmNNjgN0zNsd+6CvdRhtvbMZ9yRz99pndqq8e+wGGXavDXiST/AubY8e+Y4Z3NuY+m8QXmwXq0QTQPWfraLB9Nliy57tF6koQydAOOzp2qP0Ain74KXLQ1zGL/pXm0mdz4rXKL774YshXomwO+Q7+zRyoT/7mUALkzH8ZI5/J35KLMUiQJUN8U/5Oe23Ih+zJ3CEBJkt+iKwlZvrhn/DEp5EX2atrHr0yKLnn0+gDWTpc00UJIP0gc3Pi2rzigQ83Xn6SLzU+40APf+qjY1EOT/o1t2ShD74e36714Tle2QT9o/N0Ul90mG4Yn3bmllyM34EH+mi30Y+nrMSMNq6yJLAksCSwJPBSEhBw5gOoEFyOVQRO/xvMKz+Cq6AvKLYSDwAJ4LsNhAnYCsDjf/IAZ4CC1V8BUTuBVMAGIgTsEhLBGagEMARgK+kCroAKZKoHZDoDXuoL0PoHzqwOAwm+N4P22fYqpP59dwCAtGsHEFgZJT/fF3OWRKLhex3oAp2AUKARr/5PmfbAq7poABF+5Qxg/e1vfztAAmAj2AOAwJMxeO61TgAKTf2SgWtgBJgARtQ3fiDLeHzvh0xXWRKgtyVjszToIn2VxDjoJXv1/Uk2A4QDytpmfxIxixGSJ6+x8R8fffTRAOSAPBDLboBi9ipJA6AVCZ57QC0aQC/e0NTWtWf6BIIdnrMzNPkp9diwftXHGxsD2NktYK0um1CAbzbFZrRD07U2rtVni57jFU1t2BN/wE+wJ9eSP/ywK7KxW/S3v/1tyOzNN98cNo6HuaiPV8chClkonX1GW0Ls+158XjtXxuaZceHf2MmffIzdvPCJfB6fK7HwWqpEyjxIyhRjMv/qa+cZeZMHPVDPq6z8mXknQ31IcMivRS7+Fo18P/mah+YEn65LzPSjjvlCo7kmU/zQIX0bo7Gmb/QaDckZXcKHz+aRfkvc1HVPH/hqXo3V2PBJD/DvGm/0Fy/6ND40zIMdXn53JWZDXdafJYElgSWBJYGXlYBgMgf2i0Dby9K8rD7A4ztmQJ8kwoqq4CcYC7peowFyBEb3BUPgwq8LWukEEvwSoSAs2ArAgqX2AqZfQZQ4CZySJKuXXil66623RnDV/+67BEVABiwEaDRcAyz6QF+/gKVVXqvkgq8kCxi0CgwASIgASQDENRquAQc8AQXnW4KET/f0B/wK6oAAmsCCz/jWr2v3jV09ySRggB4+PdePhJMMzB3ZeK6+Yhx+tIHc8Pbw4cORuLkPTKxydyVAP9k4fa3QIaC8/2ElkQFg6QobtShAvwL3wChdZqcAKZ0GRunpBx98MOzFc/YmgWoHYwb4ALv22qDrWr/q02O6Djiza3rOL+CdjSgSKHXotL60b1FJHc/YKlvSrzF2v7F7rl7+z2eHoo36QD+67FA/eO05/vGqvQUUC07q2aXip8jPM/X0iR7Z18cgdIU/8d05UhIzrzGbF3NnkYsM+VS+Bj/8iPHwlfxHCYc2xsA/Wwjj//BNvoozWuTDP5o/9/jv/v8Zn+sVSnOmGLO+lOjgQekZeuSSf/JcG/e0wYP5b+7cN7cKHVQ/3faMTFw7mz/P5zlFE61kF139lOCZe0Udhzro4Ll2o8L2Rx8Syf/85z/jWIlZklnnJYElgSWBJYHXJgHBSjAVuDoKeJ4BWv2qoN0fO0eCPLAgwEt8AESBVlAU2CU/kjl0/Oy9hEVwBKrUU+oTWPPZqieQZPfNF9H9fLXAKTADiXjRn8AdDTQdArN7ATq86Vsb9wR09RRjVNBR9KEesBigcK2goU/3uw5IxI9n6gnwxkcGtXfGv3ue4wOf0UBTv7UBFIEzQMePf0jQjK3+BxPrz8Ek0DyZv9dd8DLzkX7S1+6757PkyQ6ZH8rxi3wWIegxO6Q7do/pFLtis4B+iRtbZE92ptmopIVN2wlXJHpspp1xuwlAPxoWKizEeKUNXd9jspChXzQtVLSwIaHQBj8WXNiBNnxHOysSJP2zAUkl+0DDNX/gerctymgrEUHLNZvwE/ESEz/qI4nhc7SxaIMGeq7Jy3My46v0b2xsyr8U8IMbElh+qp/z5yvITh02fciSz+ps3vVlxwsvxna27fZLEvge80+2ZFUiZZHMfJKxJExS4rBjb5HKmNXvWckzueiLLPWDvsUgcjKXds36SXl+30KXPrwSSAfssJkTb06YCzImV3OoT/OKDzFA/+YHHfLlk82jMahrHvCiDTnbhaOb6LnHT6JBn+hDr9Za4MKHeSEXNoEPNI1PO9fG5lrfElp8oE+exs8fe6ZvMhDjdptu+a6hRTzzY6z5CPUOXdb/MTu0RBe9JYElgSWBK0ggh4+EoPI6yz4odC1wC06CWj+pLPB7LbFkR3AVBAVtQQwYdM93pbzKKKj6sRCBXBs7RgCHAF2QFwT14QAWgTgr117JEXwFb4EfyBCgAUegQKAXwAV6rwcqwBl+AAO8CMbaGwsw5mxHDoj16qKg7lUeAAIwATyBzPNtxwzAwxugIVmUQAJuZALQoeFaoAeU8AgA4s3qN16NFaAABAR/iZbvjgBK2hiD15bwBZjph+wAWjuG5LXKcSRwk+zPCOmVQp8Uekcv6B07YDMlCpKxr7766vtXbr2Cxv7oDzBND+lQ9mis7IQ9AK7oSWy8ysiufJ+R/utPe2d6ixf6CMR6DZf+AvESHPYByOKLTdJlds6OJY14xQe7CEjb0QG27fAA2CVz7BhdY0UXTdf41C8abJsP4YOMgf2wVTaIVzLRj8QCX565Br7xxR7ZoUSDXQPuEluvaeNdYvaDH/xg8EdeJUWHBuTmuUM/Dr4E/75jRn58Ah7d50+N11jMGdkpeCYrB3/CbxqbJNt4ycz8Tk/D2AAAQABJREFU8W12yehTiYYxaUee/n2IBSG+io80Z9GUgOtHsodn/aDpOfnwk+iSeQty5GzO+C5xwTyjgR96YR75WX1py+8prukG3XMfj/QBPfpnbPSPflocaB7pCr7U1aezGMDvu1YPH/iX/JEBGmITnj1nN2yB3nilFf/mBQ/OyjFi9ErMhmjv3h9KdQyFunuSXCNeEnh1CeTcUehzZ/YZGHv1Hg7bUsIi8AqSAp/XXSQwQBHwAjyoYwVSHUGv1VFAz7UvU3tm9ws4At7QsopZ0oTrkihAyqqldv73jtdq0BRkBXIABbACMAEBwCWQKDEThIFIgV5dQEN7gAZwA+DwDBwI3l73Mgd2BABD4ANv6gOe2ugDz0CPgC6Z08Z3xgRt1+bOjyiQBfkAk5I/IBg4tksAjPg+C5p+whpYKVEDqvXhOXAmqQOOfEcIP/pZ5fASyP6cb4L90U12A4wrAClwyT8ArM52E+iWV6+AUzov0acvdIjtAJ39zz1gGPAE5oFXgNdY0aPj6qtjEYS9kAW9ZR/sku6xP9f02hl/QC3bZmfuuVYXXX3wD56xc+Af32jzA+xIffVcq2Oc7qHlnmvJBJrAOJ7xRT7GQxb8ALoWQvTruWvj4YNqo380+TP+QJJjbORlIch3UI3FAhJZqquYj2P4ZmMgi84+kym/JNnGF58pucIXPsjifEuC+A31+RF+xdjV5/e0JxuvMvKPxmns2phr8jWH5C8h3m1JCDn7ZU4+h1/S1rwpzYn+1Dc35IsG+aHNZ5Oz5854MS7P4w3/+PLcOOihOTIGc8LfekaXzbfP5J7stceDZ+7pt36MxeHa/Jt39eiGenQHH/px34GeRE3Bl6SPHTjIxP+zk8DpEy/HLCsxO6Z0bxBtylTpc0re/XVeElgSuF4JCFZKNjmf2aeAcZMKfgMFAhfw0oqi72LgX+AGqtQV0ErMBGnBzxfSBWEg0Q6Ye9ooQIN2ACMgKSkSPK3cSoKs3teP+1Yw8QMkKoK6AgACsIENAVcSJElyCNCSOfTxXHB2JveKZ0CHAiTpC9gTmAV819rgBd/6c3YNqDjUA+oCMPhCAz18AQqeAwo+o6EdmTjrQ/IKnGnzzjvvDLBMbmiucjgJmG9H5dgArH4uO+PFPNMpc58O0g0HAMm+JO4WSOiTXQs7UBYttAPMLRjQJf9qAnBP5yRBQDF7oGfa0Fe7Tp55fcsiAjkAuXTdLge9Y3N8AL0FqOk1sM+G8YB3oF9bz9FHM3CuLdsyPjSMTX28SQLcU/CDpnvqGq+xoOkZOeArW8FHtoGGftkju2J36nqOF230x194bmyeWRyxY4S+ZEhygm++YdaP2VdcNocvet9coT0fZCI58s+ujdOiFD8qCbdgIwEyZxaTjE1bctJO0mVxibw8t0hlLshCPfOnvc/mizzJgy8iE7uGkiMLRP5PJZ+uoEF25ERmznSDTtBHBa/oumdc5sQ8oJHM3Tc/ydBz9zxH04GG5+i4VsfY1Km+Z+opzu5row4+tNOPZ/hI943DvQ71jIt+4JMuWuhgCxYnJGbiDR7xcMyyErNjSvcG0U75sOSzQjEzinFj/VkSWBK4Vglw8ko2OZ/ZpiDxugpeBHF+oiNe8S0YC1x+Ll/geuONN77nV4D2XGAU5IwFDcmRH7EQIK3CWv0FNARLK/12qAAEyZP+JXB2hwRJq9hWOiVmzgERdNUVWMkrvgXsCn7xkL/rGn+C7Bxoq4NO442+oB9A8Tm5eI6m6wo6eALuWm0nT2NFo7ra1icarvGjrcM9K9/+l5F6fr6b3BTgY5XDSIDcHZXmoevXcW7+9U0nZp1hX+zIa2xeuWIzXvsCzNNFdgLY+zEPuiIxs3jBztCjh+i0y8T2PFNf4iQpsaPMloB4utwuCOBv8UQSiKYE8HzbvbGrTT+97qYO0C+5Uyy44A3AxSfejEl9Z8klmpINdCUPfABgzQ9oK5mTEGjPntR3v90gizjq4JvdSWIkn57b8eCDjMUuit0YvsU1mrttIUkSaJfkyy+/HPfsGvoBDEkb+5t1JLsdg7vin/SvM3LmxtzymeajBIzcHOZF4iRxNL9kpRg3/8Q/q+c1Rrt+7klC9UF3JPJ8JtmSsXvqkJ83DPgdcrIgRjbmnq6YZ/d7c8A80VX38OGan7MIIBZYWMK/OEFXyJwu4BNd/bqnLT02n/PiGr3Unm6aH+3wiBfPzKv25lYCjS9JlnvihDrGjC8+Xx/03+KcvvGApud03FyzDTIovlmkoLfGiU8Fn+gcuqzE7NASvaH0MvbO2KRQh3QsN3Toi60lgRsrAfZY6XNnthkQq851ngUdwZWfcOBl9heCnv/1AxgCXsCLAuAAhEBFwdg9QVVy9XT7n0pAFcCjCKSu0Rck9QlwtJoqkFod1o9XsyR0ABn6gILALyAL0p4LnlZ6AUNAoGs0gES7ZNoCLFaTgRbBFn18AjmurRjjx86efgAkYAJIAfKMXx+AAsCkWF0VtF0bj50Kbb3KaExAo76BTjICRNEAJOwenm+ARxvA1CtUxiFhdQA3AIjvOhiXfle5ugSyt/0zyscAXS/DMZ46Zvtzj+56lVjyA0wCxfQS6GQf7gHdwKdXge1m2XHxPSXt6SHboUfsGpDVjj7SffoqMfPdUeBW8sKe9UMu+tXONfsF8tk4UOseYM0u2TG9VRcvzsA5sEzf2Zrn7EYiwI7ZjKQJz5IN/bPd+Mw/GKM+yYbNOOvTONipsaGpjj7RxYNEDc/x2ffW+A988QWfffbZ4On+/fsjqeGn0McvPnw+ZDEnSvPtszm26+W1QvLmJ8hF0oAfnx3GQn78lXnFG5nxeX44iQ+UWJCzOXToRxJGVi1o8b3GR150gFzIRMIvedMvv8cvlVTh0zxpa+7NjTr4wC8ZmxOJlDnEt+fmGQ38q+ue+eIXxR7XaKrjwBNdwAMdT9/QQ5cu4CNdETfQMFfaGC8axqd9fJMBubAFOssOikfkxx/vtsQt+WlP1s0T2R26rMTs0BK9ofQoEWVPmbAZ2LqhLC+2lgTupATYaEVQeV0FH4KQII+PeOFH3BcIvcoIxAjAAB8QJugJpICEFUdJCWAg8AFFT7fETFsJkJVyAdaBLlCkrb4EawEdOLDya0VeG6+UACxWN10DI8CLQG+VHIDwKpJV774fghff9wICzs7OBjBBE09oCM4CMFAhwXTthzzIwCtAxqQPiRmQAgAL8iVm+sWzxAwN1/yr16GMCbgBDvCBX+MGFMlH0uVaYgZEogEo2N0wJgmk+xIz4BNAUF8SucrVJDDbWp/nszlM76/W09Va44l9ZItsA0j1v7bolZ0JRT3g2KE+cMp+vD4sGZFo0W866rl72tBRYBNIp+dAOcD7s5/9bOgywIyWNtrigw3TQeCZnNJH12i5x09op48+szl1u6e+65ID4yBzvGgLKOufb9Ev23R2sCX94EldtNzXPxrNn75cN5doqN+Y+RLtJG76Asi9Oqzvs81f+P4sGSnaontoQI5HpbPP+GX/fjDJvJhnCSc/UJJDTvynQg7kTA4K38Kv4Z3f4IvRN3dkJ4kxDp+1MTZ9Smj4P2d+SEKvHnkYO9nRBXNJhvRR2+hJwtAxZ8kJX2SsrefmnKwVfKNtLO43T+Phd8/xKKHUxnjQ0ac+jAlt7fDlwKcxOarTbiH7SA6zjugfL3gQq9iB14QthPkfgPyydto4K/u8jptX/LMSsysK8BSaU6CLDgZDmVdZElgSWBJ4EQkIroKhQ5D0mofDCqMERwLm8NwrRD4LjOoCkAKf76TZKZJcfPjhhwNgeC7xQB/gEIS9ogSICPzAiSDp2iuTAqvgCQgIktoL1AK2oBxQ0adDIoem1VvtAA7X2gv0/CNgoBTEXevHWPANzOBPcqe+a33jTx0loKFewdtOg2AvuXNfW0V7vOFXn4q6xuG+M3CMvhVqr1bhwy/l2aFEf5WrSaC5uOxMxscAXq/CNX3Ai8OiAJ2wY02HAWc6ASCnb8AkXQfo/cIgHZWYsSG0LIAodIqO0UGYwHc5LSho++DBg2Gn9BpWoKtkRY+10x9wzv7ZHOAruUG7ZAdNPLM3dueaHZKta/6B3WrjM97UQd/BDtmPPlyzGzw47K6g0z2f4w2v7Mi4HfpwjQ98G3P+gv9ADw/aWdjxLwOMmb+xa6itOnjAj/aHKuhW5s/kZv5+97vfjSTL/FlsImNjigcJtkUc428HjRy9oWAs7nmVUUJnHOiaH+MgA3InZ2eFPOiBHSgLU16XJH/zopCftvk79/BDXuTjuWfJjEwd+DU+vKnbNb7d84zeVWZZoGtu8dC4tUFXG3XVccYb+vlbbdQzRs/ZjGfak0X1jcF99OizmNN3zCSo5HcdZSVm1yHla+gjBe6sS4pIOR0+UzpnSsoQKKT67jm7dqh/iILmzM8+zfrbv7+ulwSWBG6WBLLl+QwMWYm3umyHyOt7gI0ERGD32p7AJ9AJfAI78CDQAUe+i+IABjwTyH0G9NS3I2CHabe9RmJnyqtFAMZHH300XgXECx7U58MEUiAjcFbiJVgraCr5QsE9cIOWoF3Qd413AdpZW/fwFxhBp+Cvz/3iOR9nHECvMSr61E+FrFw79KXuPl3Ay6o5eu+99973r4oCPqvcXgmwHbpg3hWJj2LevVr7ySefDH23C+b1WDpPh9hgSYcdba8y0kc7P73uaPWfbdhVYUd2vS0EWDjxbzAkPXbM7BCrZ9eW/kvs6KqFl3aQ7VLb0ZZoSWLQtpiS/eLNQgtfwQ68Tsxm7Gobo2vJBj3vlbdonm+vO1ogscMsIcUHPyNhwIcFHW0kou7r10KMXWoJID57JQ9N48WL+vwWv+Eef2CxiKzJy0/Uq//+++8PuWX32Tp/oDQ34+IV/6BFDvNhvsyn8Ums+U6vcUuS8CJpUsc88V3G7Xq3+Us+hN/ha80LWUjojAf/5pMuOfDPh+rbfXqjX8k5WVtcozN+aZY8zbm+zFf/IsXOP19pcQAv5IkHuuNsEUGyZg7RkBSaAzQsxKGnjnmmK4p+FG3wZjxo0z9tyIO+SZa083YFfTNG46UT4gbZ4t/YepVWfbJyjSaZGTea2rEfyRsb81YIGUjQ9aWdsb4oTta/NsbgM3nT22e1X4nZmPrT/2PClc4+UwST76AYrQZQCk6Igqjv8Ny141kKg+6LlmhfVr/+Lnu+7i8JLAncHAnwEdm0s6D+l7/8ZXzPRdAWhPkc4A74Ety9BuIeECQ4ugYmBWkrkO77LPAJpoCfAAsoAhD8FDAmQAq6gIlXcgRZdfXJrwnAAioa+kZDYBf4gUR1ATy+T1+CsTZ4w5dAC7zpV1AGfPCvb2DNPaAR4JMcKnjSD6DgFSFtrTLry+tGAIXkVMDv+y6+E2RHA/jAuxVtYFXAB7g86xVLIBp/AAtwpr/dBiD8+IcxGAvQs8rtlYD5F4/FSoX+KRIzevP5558PO/EPgNmSpEwbwFMdOskOJEBAp4UQCU2LBOyYXrMH99ibpMQuOLvxz8yBbTywL8/pP19AL/EjGaKHntN9+g7o4wWY9pkt4EUbNIBnNNiPazYGCLNBoB0vADye2aBn7unPc32iyXcA+PwBvtTr+074t6DCb/BVbEYSgyc02DT/wM5d48c1ftil10RdP3z4cMiMTzAP/JKz+soh8RJ65qRzfurp9vq3Mfi+K3+j6F/fZM8HlmSRAVnzJXbb+CC6cba9kulsHNoas7HHP70hL77IPPpem6ROG4tu9IDczQmdMc9kbE74JnyrQ+/0bf7JXD19mSPyR0MbCTFe9WkO0TTnfCNezBve6Ao+LWCRuzrGLCkzZvToO5rmXn90wRi0UZcOkhE6/CYekwFZoSkGoIdvMcx4vNnBZxuHxIzOaacuXtRxflZRRxtH9elQcr+o7UrMLpLKCd4z4Upnnym3QgEoxZyYMQb31Xd4TsEcz1KYQfAF/0T7sur1d9nzdX9JYEng5kgge+4soPm1MN9hkFRImgRCYEngEwx9loxpAxTyM0CfoApkSE4ES0WAVQ9wEhwFZAdg4bUir5Wo77UtdIEWfQIKVmzxI0G0mmrF1mqvPgV6fKkDdKHhGljlByV6grWdAsHcKz/qqa+eVWGB3so7upI/NPD4dANMEjuJGWCKJhr6tQNh50HQtzKPpqQLOMUngAx8ADUAjp+kNnbjJAtgGy3jAJCABH1ZwQdkV7n9EpjjstECs+ImYEg/fW8S0KZfQGO2CfDSabZlV0ASxz7olISf3gfmgWi6y2YBUgsW9BTY9UMz9Fh/wKS+nBU27D7giyd9o+lzyQt+Ffy4hwcFHfjEvc4+o6Ggoa5rMtAPnt0D8AP0+uML1MG/dgC6OhI5MsCna4mYOuyWXNBT4ps/wZe++CzfMVPXd1rZqmfoowkj6dv5KnhpHu9gZu+P52yf/zNH5o4vMh7+xLgdZOcwPvfVJXuJmUNi4TVIc5nsS3qNw5gUMuE/3eMPJcr8NJ+IBtp8Epmra+x8JHniNR/uHl7MExmSrefmRP/NY7riHhqutVFXu3RCf8ZsbtClM5IobSRi5pYcjFsbNBR0XGvrUNAwDs+0RxtNY9FeKXbxxeIbufUdM33PuoPn5xW86c+Rzjyr3UrMnifRE3luwpXOPlMghSJQDIrpHiOkWBRW0cZzhbJ0jBtX+DPzchGZZynmRfXXvSWBJYHXJwH2nE07C7YA3O9///uxinu2rcjyL+4DdYCigKkuHyT4WhUV6JwlI1ZYBXy+CZDglwRLq+S1k1hZ9QeUBHZgsXoSIrQlNMAAWoIsGgCZFVpBWPC1aqsd3gADq7T6QAMIAIAEbSDXNUCDJhpWqflIbdQxNmc0gZiAgD7xA6C4r41xkQPefQYofAaCWznmk/GpX/w78OjMT0reHj9+PHz3r3/96+9f93l92rB6vg4J0B/6VAEcXdMXuzrAMz2yEGCnQ0ynL/SaTdEvCwpW/emo19nYXbpF5yRmkjf6LPHX5um24ED32Rob9Yxuog20+qyd9nZU6Cmbxl87V+gA/3hic9rSfdd4NjY2wjY9Q8PYPDc+99kxfyL5Y1MzkHat6JPfwZf2864HWq71Y4FEH2gYm2foKvpAA8g3Vvb2zTffDNpnm1+TEPFfCt60VR/fPr9qMU8KOhcVz/khO6Pkaf74J/XNAb4V4/bZOOPNHNtpP9/eFLCY5HVXfodeqEdudpj0Yc6aY+0lX76bZv4sIkno+Tz9zIkZ+UVPOzSSb3PWHJMXHeSD8e4+eo7u0Qk0FHx55uxI3uSvLV9rHukS/+q5vrUxz9HoTJ/wjn66Fu/uJVM84B0tiZmFRDpeYuY5vceTz88rF82texfdj9ZKzJLEiZ8pidLZ5xSZovpMCZ0ZEENwphwpmLNr9Z+lNGi/SJl5uaj+Ifq4iO66tySwJHB4Ccz27LPADrxImPzkvNee3AfyBEBAhi+RjAANQIFrO2DqAEFWZ3e73QjUgIc6JTn8A1Di1x715XsW6vvxC6BMUNQG6OPXAJHAGj7iweeAgICLh/gH5rR1zR96VnFPyWfO/sozYwqYAAueu1eQ1w49SaMxGa8SOBgX2x9+WVt1gQe+2eFzAEPS+Ic//GHU++UvfznAcu3X+XZLgK6lewFP14FGu8p2dNgGvVGf7jg8s5jg/5JpY9dEgiKJAi4B2hIo+gegS7jYNdB+tiUl7RbRYzT1w1bs5ALHkjmv/Nn9Zeuu9aH++ZYU0Ht8sE+02Rygiwa9xpfEga6rb7FDH3Yp7Nh43RENiy7si+/gW3ab30DTDjRfIOHkP/AEuGvvvkWd+PbcuCQcaEhm0XTNbvVBDr6X51U+/fqe3bESs1lz+Qv8djaPPkuu/vGPfwzZeUOgBMvYLWDxPWQnSSF/vsbYjMmr1WTuVUTt1DXf6ZQ2+iBvNPgmspPM2v03H+auNw/QNq/No0UzPPPp5tG1PsicXM0BmZtTPphemH/08/d49SaCefbc9w3xR7+cvR2gDd03jz7TXfXpB59v3siDrvPJkkj6RR5sgn76jG/8ZSuN33PzX1KPZ31b0Pj/278boGt2TumY+2g7k93zCrnMx/Pqe74SsxeR0gnUoSRK5z5zMpSCAu0nZp5VTzsGlgL1bFRYf5YElgSWBPYkIND5grzXqaziesWQHxH4AD5BXLAUGAV9gdoXqX2pXIDz3Q2ATMAFIgRFPkqxSwW88UfAB5Bgh8ArhFaN+ScAQlAXqPECVAjICiAAwAFe+ADWAAoBV6DXJ1CIthVhPHqFy31BPmDjPprGBHwAdYCDHTR9AA/AhDZAA5qK75RJGL1uZgzGAuQBWe0WooGm16Y8l4CSl3Hi0yuUElJ1rF4Di/rx4x/ktnz0EPWt/8Om6JBCxwOFAOO33377P7sNNEoc6Bs9VZ+tOYBNANj3xtgDGwXSAVLPAVIgF02HfuisRRC0gHm06S7QS8fpIPBbYsbWs0HgG01gGa+SNc/YIT6Aac/or37pNn7pP17Pt0SAL2D/+mGzJWbsNpra2sljH/jgN9RHm42qx77Yr/Z8S3yizx/wC/giC2NzTZZwkKSXX8O7/7dYcko+5JL/cT6EHZIBjObg15zxYVz49yNLkh3+ryQcL3yJYu7VNW7yxzfZ87V+WMMcmntyM15n8lbMmWJc3Sd3r6lLesldv+aMjCQ3eNKHOcCrPrQlP/ybR/fNq3pkTt/4SrpkPvhevLrme/FrjtBUjBcNNI3PXNMhc4wPc+awY5Z+udZfCRQfra4EUXt9mC/JJn3Hg/FL1Ph8ekGG+lCPLdAFPHjlPbqemZ8w82D4GX/Uz4bnarNtz/dXYjZL44Q/m2Cls88UIWVwn+FSdArJiOZnnqdk7lOkVZYElgSWBC6TAJDmVSqvJgJ7XncBhARavkQwFuAlZQKhICwx8b0rbYEF9/gjiYi2EiNnAdxZomNVVFIieOvD7hxwAJS45q8EZAF1twVQgbgVWN9904e6XukS4H1nQoD3i2V8oh/TwMM///nPEaTPtl0C/tEuA3DgGjABACVRQIDES9un2ytfAAswIaj73z/u48vqMYAr4PtMDr7rAyADO+55FRRfaHhVRoJpl9D4jRPAMRbfL7P6DehIzLRHN3B12Ryt+6cvgWI6PadbdBX4lWxJzOiRJJ69KIFA14An26CH7Ai4lGzBAeqhhz6boUtsyCIIMGpBwg/0oA2osk390mMgHm08AcKuPXPoF4+KNmzJc0VS4Pmst/pUPxpo1l4bgFobR9fqoqEuwG88rt1v1wPPbATwxodnaBizIz58tiCiT3JAk51KTLSzEDQnZuqpw8cph8BK5iAMNp+Ni/1LEs2fRSS+iB8zNrJzKM7GagzGxnfxYfwWf2W3jZ81R2ShH0kNOvyh+2TIf/NlfDsf7v8r8tWSEu3MobnXBxknC/OsPR8mIUJHf3giX/Rdq2NO8SvBp0/Gad7IVDs0zYtrfapjns2HNnhWHx08oIse3tB3zzVeXBtn1+ZLH/rEFzmQmXlFT590Wl/swAKIXWC208JfY1Z31tUxEdOf5lKf+3rimYLWflmJ2b5ETvS6Se5sGCZ8nnSGQRkpCGXvmTYOSlabzicqjsX2ksCSwJElIKh53UeiJZBJLiRggpgAJ5ESFCUZAh//A1zYEZKwWcW0a+aZoG41UwLlPjqCf76qFWwg8dGjR+O+lXEgRR10BWXgQdAFRj0HZARxyZBVXQFbEiWo+x4JXq20AwFWgYGN3ZbcuQZogIECvc8AhyCvjroSJiAJv0AAQMfHAkJWYQEefNkBwJd+gR4rw8CEPjzHI8ChLVmqqz+FbICzv/71r+Ozfxegf3WfBQqOPP2L/DVJoJguJrMhc+5sF0ViRtfYgfjtPn0FZOmptnTSzjY9u3///qgLpFpgoFvua+cznWJrFlvY0o9//ONhLxY3YIaAqPbsEy+e6ZOuB5zpMhvyXF02iif3AHXXPiueGxsa6AfE8eKzeuqzN3yqb1zua6cPzx2eo6+g51C0wZv6/E196cO40dDOc8ViicTMAhIZSEzYqKKuMs/LuPGKf9Cp34tIWJD59NNPhz+TJPKB5Es2DvMngTJ21/wKP8cHW3yy0MM/Wejhi9VRtCML/sZn/oY8jJO86I0FMK8x+mEiNJL77JvID/+ekY3P5qZxoemeo7p49dycOeOpe+pcVNSLJlropgPaoFVRz3yqY67nZ9oq2tK3dMTn6NE1+mzxgy2UmLVjhob66Wv97p89x0M6Oj9vPDNvPV+JWZJY5/8jgRTn/zz47gblTMkvq3Mq9xvrfGYwOeFTGcfic0ngGBIowAgyHYCOoGVlFTgE+iQpgiS7EdgkIZIibezyCPi+vyJB8c6+xAlwEBy9egRMaAcsAB/6RQM48RqkACkxESD1ry8HuxXY9Rt/BVwgwjM0FTQdgYGCZjSAFG2BFvXwFBDyTD3gR0FbHaBT0Y+Cpmd8yAwajD++1ENbW/XQDRh0HYgiEz/f7fzG9rPNdkkCiujoFx/8ceNxf5XTkYD5VzrHeboQGHSm+0C37x3SH9+79Mt57IqOukfXJGe+J/N029mlh3Y/JGQS/3ZdLQ54ZY0+eWYRwIILnfrJT34ydsPZlIUNfQPqznaHLc748Rz9aMdWLdBYiGD3XlX02WKIuufbDjBbshhinHbE8cWu9a8NG7AzYfFCgmRMkgL2a0HH4ohFD/3oz86QgiZA7Zp80GSnFm0kkdpIWNCQyHhGBp6RgTF4JZv92Mn2S4jkaCfba8b8ATkYuzlRT2m+3Fc6j4sX/IMG+0Xf2dE9MrSbn+3jRTEOfZVYGRO+yMH8WpiyY6Y9/yy5JHvyNlb8Szz1yc/wRWjom+/il117dVvi7/uH5EFe9KVFLu30QT50wTziDb/mXl/mQD3zqJ15RsOc7LZFJgmgOvgz93TPPcVzccW8oyG51Idr/FsYIINooE+P6QD+ycJiH3tQh47gw5n+oNEOZAt68Sm+2a3kb3/6058OuvRT23zzq8z3GNgz/qzE7BnCueuPOIaczkWyoJDHUMqL+jr2vcY6nxfAObbUF/1TkQC7mEGD4G2HSNDyeiKAA3y5J/AL4IBBSYjAKdjZFfvTn/40AqYEQxBWFwgT7Fq1BjCAA/4FoNOH71kBhR988MEIxAJzO1FAgGAvCDuAU0EcKAAoBGnfF8Ab8CW4ei1R35I8Y9PetQTROOyo4b/vSQBvQAmQAwwAEtqhCSwoAjtZAX14BwTcAxQAHEBAO/WBBfQEf/1aDddWAoqmV87IwC6ixNT3RQCZDz/88H92G1hRVx/OgTk8LL9FCqdXzKPSuRE8KzHzwzvq+/6QnRQgkw2xC/ZEv+i5XTD24DVer+XRX0DaczpJj7NVrw3TQWDVD1+wOTzQQ3rGZl1L1NgZu6a/dJ0d0lF2x4boOrpskF7qy7U6CkAPjLMNzyV3aOoDXT4ETWCbD9EeXfQ9Z9f4ALolavgwXvUkE+TQ9zolavrlgyQO9UEu+mFz7NTYvM4pGWLHALlXk/kVcmJz6jiU5st9pfO4eME/aGTD89m4jIftm1fzvNtsv4UYspMg8BPq6pt8PScDCaYEi2wk7uaUjBx0RT3jVtBw0BvP+CWy49cl9Hx0fdEbfZMXGvl6c6J/c+jwGT2+Gn/0Tt/aO1znS+kPeRsfGXiDgVz452iSP10wTvTR1Ma88t+ScrqhDfrn20KA/iRWaJh38aS4gJ56aLIH46BfeFIsaog72vuF0sZLZs3/qHjgPysxO7BAT4UchX+eA1HHcVnR/nk0Lmt7E+9zBvOYZ+d7E/ldPC0JXJcEsovO+hXAvEplVRsIAgzdYzeCuAAo6GkjoLsn8AEZ6gB9VncFU6viQJTALtALmAKlgC4AAxde3QLwJHQSK6AP0BRkrb4KwsCWxEq/BWkrxQKyXQDBWXv07fQBW17TATK8lsgHSLzQ1ycaVswlZ17BBByN08ow4GPXwDOB2xgAYOAGmJV49QqZa7uDdgjQ1d6OoaBPHsZKHviyowjM2AXBN8DitSKAGY3f/OY3o8/mnnzzXfwx2d4Wv2xst2UszddlZ2NVOlfPfCYH80weEhI7ZnZE6Bu9cLAd1+0ASDroMP0B7NkLW2WPru0oaMMOAFif6bDv1bBFrxrTVUA0IK8entglviRMkip94cvzaNFnPLM7dTxXXCv8hfGggSabUh/YVnwuWbBL4jMwjj4afIx7aLNh/KNhbMB2dVyzMQfbdq2NPrXBl77IwT3ysmOmWDCRmKFnPPh1qKc0X+4pncfFS/zR/3ygizc+gk9wjRfJgbkwJ56Th7Hjj4yMj1zyYcZi4cyrjBIYMjD/5t648YuOsSlo8J++l8j38I++n4Z+Y9M/GuSFr+RpXvDlWp1omiNji0fzjj/zrr7kGz9448MVvl/h3/WBV33qQ1vjdh+/6tA/bciC/ugDTfXFDW35aDHIZ7LoHhp4mZM9nyW2FsX4d/4aHX3SnWOWlZgdU7o3iDZlqvSZkWVoPZvP1ZvvzZ+f1XaudyqfjbcDzxzvbRvjqczF4vNmSGD2Afu2INhaVfU/dgT+Bw8ejOBphygQJHBrJwBKvARLgMfqph/m8PPdVuIFS4FSgPXdNIkWYCipEpAlM8CJoCuxkrgIsgKmPiRpaEqGJHuACZr6lhAKsudb4gao7b5bkQVYjcFqMhr40M5n/QAX6gBCAjLAZxUX6EWDf5CUeqZPY8MnMALMtloviEsWnfEIhFjR1Q/go0/jwSfAAUyoqz2wgA+8SyzJ8d69e6M9DQFq5jlyz5j358r9UyzAnDKPZ/58imO6jOfmsXP16Jl7xk0ezmxCYuZ/LAHAFgsAbwCTrlfoDZ20S2QBwSID3QuQ0z2f6REw66DTT7dXH+maxQI25rO69JNt4wl4Zz/asCv2iBfX9FZdgB+/7AnvdF9bAN1zha7TfwW4dq0+O8KbPlzr12eHz/pUXz3ttdOf5+Tlnr5KOoxBG/xoo//aGFsyUM8ule+YuU+uXh80rubGGW2le+gqncfFS/whH7Q614fE4M9//vNIRLxaauGIXIzdWPHdTpSx8kOeGZ/dRHNvt9Cul3nBtzGqp73r5sB99CQ4FsLMscSMDPhmY0M3eaEnQcIPeZsndfBRsua+ftzzXB8+G5++FGMu0ZJIeWaOFPUV/bqP3mXFcwUPydH4XKM384EOPtID18aBD/ppjBYoHCVmYhS6eELzWGUlZseS7A2jS2FT2s4p7GWsVu+y58dUzMv6POb9xtvZ+G7bGI8pv0X7bklAAPPKD9AgMfGz0gKfJAlAFOj4GCCqVU1gwA6RBMUKv9VRSYx6gB3bEyxdC/peHxQkJSterwKO7CRJwARIfWgDEAKkkibgKxs2I2z4omv3sm9jEaDxGRCYZ1NfeJqL+oCFMQf4BHPgA9BR9GE8rmsfL8CTdmjER22c53sA7ZdffjnGKDH9f1sijNfAC9pz/ejs33P/lErjaxzOfT6lcbwIr+lF59qYW/eMmx46A5ASM//fin55PRF4pv902QEYsyk7JnZqvT7rhywkZiU0aKtLzuwUwGdzXlGm1xIBu736tOCCZrsGdo+1bZGh18TYNDothgDaFlnQY8d4ZL/4xhcb3n23WOLVObyhqY0FGgsi6EkM0ORD+AQLF/qXfGgDPFvcwL9xS0aM2bVFF4mpNhZb+B/08cG2XPMt+CCLkl6+wC6V1wD1r5iL5oFcmi+flc7j4iX+oKmgF03XkkS2L0nCi8UqRfLknnkrYSCbbIZP9KqoxR/JXK9w0x2+A5/kZh6M3WG8+iY/r/GRrfnU1rzzbcmLfzZP5oTPd02edApv5sa84c8imX7NhwUpfOrbPNIHftNY+ELXfCsaZDLTMCY09KVfOoUn7fCFb9fmihz0g5/6RNM4LZSphwd80VF6SIfRoSfaeUvBQbf8KiMZsAH98elk+KrzPSbxkj8rMbtEMLftdsbe2fhSrNs21kOMh5yOYXCH4G3RWBJ4XRLIfzgLel7V86qNRMGqskAq6ANIgq0CAAUC7IZ5hVBw830JyZlAWSAWKAVUgRUNgEmQFCy1k8hITARngEIQRxsvgrZg676++TdAQ8BV9Kmu+/pBEx301QdCBON20ARxwR8IcACIwIP6jU87PLmHb+BVn8asACT6Ai6AEMABr8aH5vm2owGMAgbANbkC0mha4cYvgOP7bn//+98H737eH9AyLnLDs0PdSvPk/ikXc6kY23yc8pgu492cKZ2rZw7dM/4SghIzO9Z0hT4AxXSCzOgFvdltiQYdtKhht9ciiH9tgY779JLuBcj145Vetqbf999/f7z+iBft0e1VM0lVCU67v3TdIg267EtSxL61cS1JYCv4Yo8SL/wD+IrdHffRUA/gZ4M+s3U02GGJGVtit+Qh8cpn4ANNdoYGW7bz7RoPxq6uNmxasoZPfKHlFVG7kfoByNsxGkxuf8hPMSfNl8/dGx9e8k90auZa4iEBxouklk/gM/lY8yuRUIyF3NSXCJlTspLUSUq1kWDzV2RWHbLlN1070HAPXd/JNb+SGHrDp/FZ5GXeyEvCQtbmzbVkWNEef+aNT0ZH3/gkUzw4POeDzQk+zY/+zEGJv2v6hYY2kiP1fKYLnqGBJ/omSY0v80oWJeD8LRvBJxp4wj8e2QD/zUfrQ/LFFhxiQomZcXnGPhziyaHLSswOLdEbSo+RcybOOQAKRbFWWRJYElgSeJ4E+A3JR35E8LSq6gB6rKoKboKvYChIOrxmJdBp59qOmeDnexsAD8Ah4ANSgB/Q5Mw/AZl8FPDlVT4B0euPgivgATAAdmgAFPrWDhgTtAVgvKEHWKorSLearC4+8AbkCvYADGAJKHiNSMIEHOAdWPDZjxWQBcALRPqCvIAtgQJuBHI0vLZJbp4DIYI8cOC57/sADuQHFNnNUNf31ozPtXEZp9fLgGVj+fjjj8f3hMhFX87kPhd0jMn4TrmYf2OZk7LbGrOMU+ncvBlvMjCnZMH27OrQN3q12xIKYJ0+KHSTjgdoJRqArd0W9sM+AWx6BygDptoCqhYG0Hbf/8xjQ/TIc3Tppmv2JhFQzz02rb3rQD6bYHMOz/SJJyCeTWqPJrs2RnXwok76jYZ7+tDG2NWPJr7IxD124HMy9JnM9IGeOgq90g5dPPAVLSihb5eEvaFjAemYiVlzOxjb+4M3c2wHk7y9/s33kIViXMaHhvE4Gwc5SY7MZYmZuVeXPM2/Ocw/5GfR0A9f6W0IdX/0ox8NX0XP0DZHfLQ5lqyRF/m5ljzrQx003TMn/Gr62Lzh3TybA32iIQ7wm8bN96Eh6TJeyTV+PMe/OvrxjD5pb8w+40t/aOABDXySBdmk8+i7xiPd0Id6JbrsgAzZgO+YkUH1yY5+OQ5dVmJ2aIneQHqM9aKDMt7WIHcDp2GxtCRw0hII4AioDoFR8H7y5MkIhlYUBUCBV8ADBAXAkjIrkxIm/xvGMz9CACTyQQK+4Ay4Ce7aSUgEYEHd92S8629nzg8StHpuxVtdAVlgdq0NYCEJ0rdETECXBAn8+jUWO1WAgYRQMLabxydKmgTb8y2Bwps6gjvegE8rssBBQFQdARvAwKtr9fUp0KunvjpoGBfwgG88uyYzdMnVWNDAFzqAgITQD60AD14ZlaCpi08AAd9zMT7H/v25zil8NnYlADSfT4H/l+FRjFY615YuuGfs5tSZvgKNvgdEvy2KsCE6QQ/VYW90ELCXaLhvt9lCiUK2wCv9oX+utbcAYUGC3vtlUPR9VtDDAzDs0BZ/Cp7Qwyu980wdbZpHbdkgXupfWzbLZ7AX9PiRkijgW9FGHX3g02d8oSmhYCvGgQ++KR7Vx4O6+tFW33jU1qGQmTae8wUWXdhrC0h2WCpzm+ZLe6VzdZ913m97UV0+9vHjx4Nn/5LEwhEeHUpyN3Z+Jl8qCekf2Nsxk9SRPfk2L86uyZg80MA//2yhin+SmDn4rAp5qmeOyRKN5tyYzE164JnrZK6d+dJWUQ/P6ijNi/vaqu8ZHl2bZ2fPFWfzoV73u65futOz2jQGY0YDP/giV/wYu5gj9kjM/LN1eom2uvo7VlmJ2bEku+guCSwJLAncIgkISIKrIAY0AT/AixVFCZDA344VsCA5EtwEf6BGUiX58r0YK5lWorWRiAARQMNuW/kXmIEpgVR9ANMuksNKqFf5gEtBVCIk4cGTgCpYFsgFzwCMe3hGs4BqPMYArCnq9hw9Y9VGcHdWPwAj0KujeK7tTN8zvKFtPEq8+KwfpWfjYvujT/0EkNDBLxlJaPUPKAJaq9x+CTT/M6g1avYBdNt99pog0Mg22BGdZof0kR6xz3abfUfJwkPglj6xU/QsDLAlr7BZBKC7JQJszWte6OkHP67pvkUCde3qWvzYbTZsV9lzr5cBsxZM9OWeBAnPgLHkEp8lltqrZ1FCPxYk+BD1XdupkTgCzmjiyzUaEha2A0iTgdc79eU5W7RrjQae8CGJwauFEzKwUEI2ZA6Q2+02Xjsl+ztmbFkpuRgXr/BHX4qzA/+OrslCgmSe9WUXnaw8l8Q2x+qRhfEal0JWfKYkk2z4Db5YW+NSXzv3tHGmB/onE23pE9mbT/Ljx8mKDFv0krySMZ3yBgN/5dr88vnkqr6+6Iq+LEbhj57SOwt2XofEg7nnH42PftFJ/HrdUVyxUIAPNMQG9BySSfOKHp59xqv27tEBfJAZPXDWB5r4IjtjwRd6xuMXSiXGxnV2djbokk/zj8YxykrMjiHVRXNJYElgSeAWSEAQL5ERjAAy9wQkn4FDr/wIapIsZ8FU8BL4BW/BVn1JCPBmhw0AsHovYHoueKuLJnAm8QGs1FcX2PJFdsDJjhFA5T66AIvgqmgHALQLha468WMs+hP49SXgK0AEeu4p6uDXc+NuRRdgcc843ScL/elXHxIo7awuo6mQB3oST/TR0BaYxbs2SgkmAKKQjTaAKQDrABYARd812U/qRqP151ZJIPujN+Y7W6SrbM+PQgCRQCPbcJ/d0Xt6StfYztOnT4eOlpjRX/QA4nSazmrr+0xsWuLz6NGj8Rqb/gFfes5O6TS91ZaO0ksgXh380F2LL2zDM0CafdBlfLlmH32/stfzAHqLMfpgD4A18Iyme+jpg03rAw1g21j4BPLRBr/a4xfQ5h+AdYkAenhjf+6RmWsyc62upITM8MpP8W126SvGorTA0v1XOZvjDnQdXRtfO6PG4nu8kgrzpI555FvUc49cjMv8mA87n+fbzr/vXEnMPMMzeUlqJCJ8ETp8nIKG+8avraRMYkK+5pL8PDeHnrmmY+TDL+Hfd9vwIJlDz7V5xQc62ptrMi9Zpov4oHdomEdjk9wZK90xv+qThXlSr/GiV8ItecvX4pmuKOICvswrWmiaf33iRXtjpicOr5n7NykSW34X3esoKzG7DimvPpYElgSWBE5QAgKfQC4wCmiCnc+CLSBgJdeqYq96qC/YCYQCndV6gVPQL/Bpo/jFN4FOwLbCDXwBh4CU1U2BXhAWxAFFK8cCtWCvAAR2CgRzyYx2+kVDULcC6/tfflENcAFkAZKurYrr2z+Q9fzrr78eY3vrrbcGv14TE5glgkCv/iWH/S8oYA54scKLJqDTd8q8ognkSUKNC7jApx0AySZQY+UbcAJwjRU4xY8fUyEzq/TAgXEAZwCp77v53g8elEMAw0Fo/bmREmBrACS7o7s+KxIU+uxfSLArrxzSH/bnmXqSfm0kLr5j5toraXYH2K9ndIrusxl0tJWYsWn06BowjR57A4glYvQOyFUAX7QV+gv0stOAM7+hL3X0pS1/4LOkwH103QeSXaPBJ7Aphz7YgvG5xj8bUUcCoi3b14/n+Sj10CQ/Y9RvdI0pGsatDzKQbNol4R/w5394ka/+K/jQ3nHVgtf5QLtr4/Iaqp/ux5/EjO8wRmMlZ3WdXavfPX6Tf5H88s8SMz4onvlYsiBvciLz5o4/9Q+2+SZ9+n4aX9q8kinZoWee+XzFHJhLfePRZ3yRb3Pgnmf0gi6hiRdtjHFO/PFmx5WuoKGteTAG7Z2N2Zk+omOO8YYv/RiTNu6ZT2MWu3xGg7y1SXfQoCueiTt2kL2h4F9NrMTsqtq+2i8JLAksCSwJvLIEBHxBS+BTBCoBTRHIBUtJltcZ7W5JYDz3Qx0CroRFQHTP4R7Q47UqgNDqqpVYARMAALAEZof6+u31JMnKZ599NgAjnrQRjJ0lZA6fJUECNPraC7ACvbEAG9oAD0CCOvgDLgRvYAN/AjpAIFBL8NCw+q5fzySLrtWXZLlv/ApAp19ACAAAOgAAY5J86R94JUsgQ/J2vq3Q6l/CqY1fuzMWSRhQDSg4jA048HoZ2aKBx1VurwToLZ3ZT8wATomZJIpdAc6+O8ku6Rc91ZbesCsLDJ5ZMLDLQd8c6Kqvj+xAXYkcHfe9UYsSdFxiRp8lBuwTbe30T5fRYnt0nf4C0tqxB8BXwTc+1Ke/eFTosXr033PXxuBwTx8OtsTv4F09z/DFXtmys37xpQ902Cl+8YR/n91TVx30AvBkYFzs2MIMHvcTM7SbF58PUdDrILPkgkc7Nl7/9tn8WeQhF/NmPOQ9j9UzY5To8E/8nCRbUm4ejFcf6jgU7cmXXMlMG74dDYkZX8SPGq95bp70H69o4tE8omuufNbGXFXc91w/2itokLVS3ejWZ3OGT0V9dNKv6numjfpkZEzmVT28699zZ23055mDbBz6oBN0gPzZlt1mixrazG31d+iydswOLdFFb0lgSWBJ4BZIQAByCFjOAqHA5rNgJhBbzZUwWdV89913B2gDBAU+wV7gFegEScEOaLASbbfJjpFgL9ih6xlwJRFyrR3QBSRI9gRZYFGCor1XHiU22qvnVRjJD14kWJI1QAQowbNXXYABOwDGArQI2HbdAApBWDCWDKpvtVk9q6X60V6fkiJ9Ai1efUHDDhYA4JUeY/dqjOBt50HddiMAGvWAqUAjOvjURr9WaB3GUAJmDozdT50DScDxKndHAuyBvtAthS2wI4kZe6F/Fgd8BlbpGV2yk0G/2Cld9s+CrfrTJ7ZJLyUiAVjJix/NQddzu8nsR1s2p13JjARIH2yWbbPBdq0tfri224smO1ff4gh7Sa/tQHuuD8WiBJ49d1iwwRubx2u7Imjo1zM2yB9JOPFpMUNhT2jzH9qxH/IxDjLBsz7YqT7RQJPfYbd2v7V/8803x6uM2hqvOZiP0dkB/qA9H0gaj0RZkmRevVIpCacLxsFnmifywCs/6x465pUPIx8+Q1LHzxk73228aPAz/Anf6Z625k5CQpbmjk+UoPNb2pCp+eDbyJZvJB++1Tx7M4B8JZF0Fx06aU7I3LyJE/rLN+qTb9QfPtFR8KYeXTA2umDM6rcQYPz4QtMc0nHjMDZ18W3cntNf+qm4p576aOgDr/rQL7vxtoIFRImZRTn16QwZon2MshKzY0h10VwSWBJYErgFEhCEAoOCkSDrnkAp0PoZZ697CGJe55NgCK52jATcfulQXXQEU9/fADL8+AfAIMA5Anxef5QAKQK+V2gEc4EefUG2gKgdsAhMoe+5gO6+BE09wdl9QVUdwVnBs0AMZKChvsDv2jNgUX3fjwCEdrvdqAdUxLP76pOJ8ZILWgI+UOoVKLwBDWQAPAIn5AAA6wcNBTiUgDq0J299AUQBssYggUQXz6vcfgmUmBkpnQS6JWZsiX0AzhIPNqTQNfpDv+i0RIsOej3WIoPndBaYpssSAGAYbYsLaGt77969AUrRopMKm6J7bJhtAbRskh2wJzrqkKTZcaajeASi2TW6ADuabAA9SaW+JWrGBihrw1YlG2gYG7vxHN8AuGf6ZXdsy1glBWjZgcYbHtgWMM5G8WgsPuvH+D03foAcX8C4XSrjkpjZNUJLXbQVdt7nceOKf8yHMp+N1yKNJMmc2bmRhDevEl3ykFThXVvjMDb1zbv5lMy9sX1XjqzpgTH6TF58iXHmf8lUHe0kdWRPv8iTDyZvi2D6MI98lLr8vsU2dM+3twDoLN/l2i+DtugleaM3/Jx5Qwc9YzGn+iFb187q4w0vxmoe9YUP86Y9vtBw4JfvVkd9ffisved0hw82d2iQkwQTDfKmH2i4tnNqbMZPfuLQdZSVmF2HlFcfSwJLAksCJy4BQX9OzIAzAEYiJfgJqoAL4CeQCYbnW4AGIoAFAVrge7p9L0vg9yqjFXw0BVCBGD07V/ryXIAFOtBGLx4AC5/1Z4W1FVgAQ+BFE9j0GYDUt0CrDYDgmXt4c18doNF9Qdl4gAfAReKkfyBBP9rpR0AHYtTVHxrq+BzwkRACAOpKGNHS3niBAn3rA3iUAAIPxmMXj2zwpz26DnTIXf9oHRIYnrh63mr26VSJkzmX5EiegHar+RKb7GHWRzpC3/3KItvzKqz67EZJh7ShX/SfPUsEJFe+Byrh0Tc9dVY3+8MXfXZfW3zZuaH3bMVBVyU12rN7Os+m9Yc3tABv/LMR9LWJpufxh4Z+1Me7goa6+sSPpMsztqMdG9UOTT4ET9rgG6jXL5vCDxrok6vvdblnp0kypD25qa945jh0iT665kxyYAcPXxIfO/jkSS75IGMhV+NQjMsY+VK7ZvTD3PNt5MFXoaeNtq6Tg+cSYu3Ikg/miyVh5GOO8UWW5pE+SpC0c+0gb/XIjD74rA/P3DPPeNe3wtfqy7gk5MYmQUKbHppHdfCMBrnXpzbGi6Z7aPLX2upDW4dnDvwUq9I/9OgQ/XHow3gktvyyeMYnz4kZeenjGGUlZseQ6qK5JLAksCRwyyQgEAmYzoCPQGjHzHfMgB2rkAKY1VnA6XxLygRBQVMwBgoEQr8k5/sxgp9X87w6IvgBFZI6gdkqKQAp+LsvwArK+u8smFolFlAFXHUkYq4BA2d9A4OCtQAPHEjABN/aFpQ7C7b6UQcAQQuAMSbjBM6M33M/249HSWX9xKuVXeAQoDBu/OhDUmbMEjOgC2jRJ/mhB3gBUuRzUeAnd+MBNla5GxKga/SHPjjojMSMHUka2Ao9pbd0np6xk/TN7g+w7dXE/o9ZdqwNnc62JGZ+6Ibu2jEDzOk7vabrDm3x4552bMM13axfwJb9x7M+0l08ouFQ0PeZndSH+9qibxw+k4OzvtT3WX/d4yvUYRvuRZfdK8lFPXTZkWRDveSLRzt55IuGXW82WWJWcqY+escqxke++DB/iiSRz9Sv+W78npkD4/KM7yDLp9simLnkVyVm3mTQxtzQIUkNGfBRCtmTneteg6Rb+kVDIbcSOfWbi/Fw++MZvtH1vGI8yR/vZO6aPPGOrjrum0OH5/hx39g8M19K9Zs3ddxL/7pvvA7P56KeA311FXyRDX9vDP5FiVdJycC/KrBoVj/O2h6jrMTsGFJdNJcElgSWBG6JBAQgAUzQE+AES4FMAPYqlR/zAFq86iGhAgrUAXAEXa+saG/FFVDzQx5WIs+3xM3KZYmSpK7XdIAK/XmFSHKkjkDvVR59A4+SG780pq6dJskZeoIn0GEXyqs02uAD0HBfgoY/NCVHXqcEWLx65dVJQRkd4EQd/RmTMVjFBSqBVaBG4Fa377d4FUtw77UyCRsg4dAeTSvDZAN4KPq2Im38kle8kS0gQq4KuT/rPB6uP7dOAmzP3NMHeuizo8SMHVnFPzs7G694Sb7olUUH9qIAlnZOgFr1siHXDvZDN+ko3WPT/R8zP+hDLxU2pm92DJD6Phjw6tUzSZxXCOm2BQb9q+8end5trwGzT78QSKcBXXbmOTtns2hrg5deO7TYwQ7QA97ZEztlj15D1B8/IAFg6+TFN2jDlxiTV4otqFjwYatsGA3940sfxqKusRibnXuLTuy45LQkQx/mwnHVgpZDuehMvubjq7fqTbAAAEAASURBVK++GvLxk+12zfRNnsZgMcr4yEJ9CRT5kZVfVmzHzI+Y8H0lY3SInNAyB3RMQc99/aJBznQMXXOpLvmZJ/LhW80b+aiHTtf4oI/qo6k+XZH8OPBiDMbi8MxrhWgaj7bmDI9eQ7SoRVe0a7z41bdrsQK9+NKnZ+YUv8asoKkP/pje0S96iW/64Fo7tmO3ks579Z4OsZEKutofuqzE7NASXfSWBJYElgRukQQABsFYIFMEJoegKXAJ/kCNlWUBU12gUH1nyRhAAAQCQL5ULzkr+RJQ1VVH0FUPwHBPsBTUJTz6s5MkqArAkhsg033BVNAWpNHRvkAuacKvoOu+PtD1WeDWznP9KgEXfQALQIE+jUVgd62OQK+tz8ALGeAD3+6RW2PTDz7JRj/6ByB+/vOfDxAVaCzIq+fAV/f2z3jtns+r3F4J0B16Z74d9M5OigQKCLWaLyEC0Om1xQP6Su+8luhXFumdnQ82Q1fbpaCjFhPUd7DNp9tOC/30/So2B4DSfTovKcKLJNA99qY/CY/+8aMtuhZH9KOOMVgIwQfwDfi69pyNKhZGAHbP0QGcjZUPwTNfUEIiSeAD2Cl+9MFeJGrGDUzjg+/Aq2u2L1FzLYlwTx/a4IMdszuJGRnow7/PkJziQT/s2hzo66qlZAydPnd2z1xKDCRm7tv1snunb3y7x5fgK//STj3Za2dX3uIP/2zMkh+yJDeyNg8OBQ3FXPrBEXWMnZz4KAkLHTC3eOBv8eG7xOQnOSZ7i3F0SaJLVnYg6QZdUsdn88BPSvjogT7Rowv48JwvpQsKXvSNf0W/nptTOuk5Gp4bp7G5RwfQ0Db9oiv4U5/u69Nc44Nu8PfGI8n3WqvxS8zQTQfI3udjlJWYHUOqi+aSwJLAksAtk4AgKBgFSgRPryAK4AKqd/AFM0G3xANwA54E0fNth8zqLRAkkArQQIT7nguGAp+ALzii47UdQElSJZFTB4gA1vSvveDoGRqeoa24BhLQwLsvcStetcSvxA3AEJQBSwAOwBDEtRG4gSJ9Aon6ADisCAdGgWP9AS3GbKyApyAP/Aj8wIGAjxfABlixYwCQ6Jc8O/AHQJCBw9gr6ij7556v8+2VwH6SThfpnkUOwBPotpNCN+ggm2AL7NViCD2mT35Ehu5JMugr8JqttWDADiRy6ErM2Cm7BmDpsCRNXXZQEpjuq6Ouvn3GC56AZfdcG4v6+GGj9F1fCrvGe+BbHf3or+RQHbS0MT5jUIB8bT3HJ9uqDXn5jDd+w4GGfvDJT5EJW8ajxMyPGun3bNtl9Ponn+GaLzGmfGH2OJg4wB90K/iUWEmw9OuXAdvxxLNxGRO/4qwO2fJdEhuvmfv1WQmnH2fiL8mJLEpyjcnYtGmxim/817/+NWSrnT75SfXMBxnzb+YAH/wcOpIkz/lr9PRnvj1Hmx9NxuRsjhz8MD1EUx00+Ezzz5eiYZ7RNE/a6tc41DePnosZeNKHa3XMs2tFH+65RkeSrw1/jg59dBinPslezJoTs+bmmOeVmB1Tuov2ksCSwJLALZUA8GM13quMAp2VZQFNUC+gGjoA6bUYQEAwFOS8uue1R6BMAW4EQkDEWZB3T4BvldYzoMIzfetDEcxdC+7OrtUBUiRZ6rkvAKMBwAjkAjhQALgCGoIz/tQHAPQviAMU+MSXA/jzDIAybmc09QvkSd4EdKuteHIfuLBq7RXIEkF03NceENFWXwp60dRXxef5uvvrfLslUDLQ3APkvl/G/uis1/bspNB7IJg+q0vHLYTQRcWiBD1Ub9Zx9NxT3+uGEjOg95133hmJGV1toYONA7t0lj347Dkbc4++a0uv8YJ3NuweG8RXiRl7ZFv6Vug/enhzH13t2aTDc6A8+0BLnwr6rj1Hl1zQwWN+Rj/ooOlAX1108a1v42C/XmX0zG4kf8XWtTcu/aDrjN6xij68lfD1118PXux48iH4Ns4SM37O3OEdf8Zmx8y8S0b43L4P6xm+6Qj/5rM5JdNkJWGhW84Sfq9768P49UFeZEte+qskW3Pic3oQ3a61R0uf+idn41GMQ32JlXGaRzx7jqbn6uNfoUvmbp4HNPHlUBe9eHWtbwe9MB58Nfeeo0nfxS47ZrvtlddeZURbQbvP48YB/6zE7IDCXKSWBJYElgTuigQkEsDhN998MxKchw8fjgDmewmCqkDpuy0SNQBC4gNUWL0FBAQ/q5Re/xMEBXoAToAUdIEC9+xY6UuSIxA6BGpBXR/oCJJoAH6CuUCtHgAAUOjfToDidRpt7GSp67Ngr616ABjaAA3eJVJWggVxySXe8GgMxinAa3e+7fxZnfa6DMDj/scffzyAhQQMH9rhK2Bh/MoMIoxJ2+6PD9ufxt71Ot8dCQSm6YBC39ieBIr+At3AM92le45AMICe7gPZ7I/+ORTAtCSI3qvru2v68r8J2Sw9p9cArh1f/djRYAt0m17rxzVbYcfZBP7sKLNhbdgaHhRttLV7zAb0oV2vp3nump1YlGF//IXP7Ak//AKbaZHDTpG+9IFP3xNVR0JqB6fvlqKJL+Pna4zdc0kFv/XFF18M2fqH7r7LihZ/UTEnir6PVfTh1VI//mG+/FJr34PDt2SFjzR2/Ls2l+aOnCRXFrck7XQEDX6Of0HbWNUlT/PknvbkJSEkf6/I+q4veSvmWD8SJnOHnoO+mTe01cEXGZtXflQbMQCv6vPX/HR+1BygQXfMq2tFfXIXV8yrOdK39voxbjFCP/y9awe9MQ5jdq0v40XLXBo3O/EcPc/JDG8W7twnA2+FSGznVxnxpb/8tOtDlpWYHVKai9aSwJLAksAtkoDgI4DtFwEcMJCI+AfTgqQvlwvEvtAviAqygp1ACyT1/Qb3C2iCr8+CoEDq7Lp7VvsBAuBHUBVgFX27B0QAemgCjwK3+3MgBggEeryoJzDbKQMIJF/6xSPe1RXsG59rvGivrYBd/2QjkGvvOxRef3INnFjZPttegZLU4du4yBGQAQJ81h6/+MFzQAl9fTnUS/6dBwPrz52SAP2jI+kAnbOab1EEyLQTBnwDvmwigMomJCrAJf3yiu5uW/1nPwCogm524bO6EhN9+eELiYC6EjY6iwYgjC67Z590mq26lmThCei1G83W2IFrddgQHvBjcYRNsBljlDSxCWBcsqAPY+E70JTY2YVDg08xDskavvGBP/4HDYkY2mjgX4KBLt+EDr7iA4CXSOADXxLeJ0+eDL/w9ttvj6SXv9CPPsim8zEUEW3FXHqVzvd4+RCvrJoP/o28jAu/kip+yrgV4+a76IgdUPohudSGTqhvzsmWDNHjR/WL5vm2yEQG5OQ7ZhIzCZG+PEc7387n2ZkjT689JnN+zRwZAx60oweSYbpgHswpnZU86ouMm1dJOn7MI17pBt+cLtA1c08XvG5rDiWS+NQH+vgyr30vD01ysjinGCcZ0B36Rj/xgYZr+usVeHrCp+vbuBS8oZVNjpsH+rMSswMJcpFZElgSWBK4TRIQeARiYGQOPu4J8EDAf//73/Hz917fE1AFZ4FNEBbcfKcFCHC/xEiQBeScBVoBVnBHE+ATELWXNLkHTOFFgNdOIAXOAAy8RAtwATLU1ZdDHef4FaSBAX3jXz94w2OJoQQNLUEZLc/VBUYEZW3RAED941SgQsDXzs+RG7NED9gJyKUX6OHFmUwdgntFv8akH/fVq8xz0L11vhsSoBd0Ip1hN0C3n0Jnd743RufoN12ly4FGiYmdE3YDYAOp9Irusx325qzQTYstFhkA9bNtcQEwdh/QddYfe2lRg32qyz70HehH34E+G6LXwDN7dI+tOtg1O0GbvRoDGj6j6Rlf4p6FHAd+jVcbslAAdGOW7KHbLkp+gvzQVjwnU3yhT16eu8YPvybp1afv2UlOfFbXOJqHQeyKf2Yb73NnPNox87o4vkrMyFshH/OKL23IXyFjn72SKVGlHxIzPsk4S8LIQXtt81fa0Rm/OCuBojPamgPjrg1Z84Pq83/6LImSMKkr0bIwxcfrx7zrJ1/refOqjWeSJG3om+Iaz567jw/JH97QwYOEiW5pQw8ke/TA2M25ZBYNO3fpMP7EKuOhw+aeTqNDd8iU7OfvmEn4kjWZoXmMshKzY0h10VwSWBJYEjhxCQg8AULBSCBTBDYgQUAG+Lxm4z18wVLSImAK2oKdOsCDNoKwoFod9bQRsIGrgJLAKgALugKk4CdxQwsYAwDQxANaDnW0B8Lc1796gr576Cme6c/ZmNQDFgIHAIQ+XQMCePBZ38aAN3IgG/V8GV9ChgZw4LUvQCDeBH386UvBh7bGrQB5PXPtmaN7PivJflysP3dGAubf3NMbOu6zg+1IzPxAhdes+tVA+kxP6R09pEd+0MbuB10G7H/4wx8O22AX6kly6Did1Y9FFgsO9Jp+77bdAnQ9U4dd4UVbNqIf4FgdRR02xmbyCc7ZaDrN1tiGZ2iwBeNSSpDYtPFIJNRDU7KnXckkPtRBA2/kgwf08OSMn/rxDF004lkdhbzUtQPuhzPw7Dtm2TXazUk2Ohq+4h+0ksdFZzKXHPj1TWM0f3Z7+Bv9e04mxuPI1+KbTzUOSZOEolddyZLskiMZaOdMfuQi+ZWQSFIk8nbc+PNKvJKbz8mCfHxOnq4VvDv04R6+52LuzGP6FQ1n9dHz3DUe0XLtmWSuOtrjx/X/sne3vZYc1dnH+SjHKFGCsEIiAYYQwA8YEJHgaybvkliRg7HNEEVyiBQQwsQYBWk+yr1/Hf/HRd97nzln5uyZ87BK6umurlXr4arae69rqrqPswMuSvOlOoyUYqB3neNImjng89BWRv8Z9yLKELMXgfLYGAQGgUHgHiDQj5gfPD+MfvQlZ37AFcmPH08/cH4Aq/sRdPQDqo2ufWK0/qjSI0Hrf8n1RYwkJXT5cSavONPnXj6SJ5tOdToV10r9Xa/93HeQp8OPP92SIHLqfLP1KZ3Ojr3O9LqfD2HB7pTPCWm4d5YkNlYPGSdzz9yCBWyQGkmjVR3Jor9vdXEgUJJtn0uJrrmKcFn59R8oElMvsfCfI4rPknloZYG8//RASqyWOch7oY/n0thsO5r/gNBma5m+bRH0PSDBt1oh+bcaYWUbaZPU+47wWUYS+KzNajNZdTHqI07ybFrh8F3hc+ZzqJ0d/8nDDzi0smIljy0EFOkkz08y6vqo84Gf4mdHezr5rg3h9cINsVkFt+Liu8z31TonfbYVPl+16CNWB/wdYlX2Z7FYvXv//fc30oIketGLPvTwhR6EFr78s8Lkvm19nhX0HW0MrXwZY3EgMOKGrTli/M0dBW7a2DVGtgki/rAip8246GfOKWxrg7EY6KLb2PKFf+rsu2cM3PM9qt7vhf6+V5FK/xnHhrlhTttm6GycjaMxg4/5bNzVWzEzN4wjv8TT74j5AzvycOMnv/0nGz/TyS58PWMGB/jBni0Yw/BY2Y/fXqbvss5ru74dQ8xWZOZ6EBgEBoFB4CQCfqz68SDk2o+oQ/G/jn74/Aj3Qyxp8WPWjxEd/c8m2QpdSmfX+vkB9wNM1g+sH02FDrrzJ986d5/dUwdZJZv7s36SAH6Q5Qe74nS/ODYln+lJh3t+9NWz4x6fxRIe7k35nBjDKwwlS1P+b8XBvDFnYCM5Rsy8OVCS6cUE3nLa587cJIeQeEbG6ofPjtUyJEebJLiVLZ8pSbJE1vNltsBJiL2V0fY0ibNEnz6ExudBEus+MkO3JBcZdO3z4TuBjCSWLW0+y5Jgq3E+SxJ8bVYkJMq20PlM8ZEOZFAy7tkkCTySIUGnA2Hgv2RcX35IwtmQlK9JPtv8phN2ETN2xIQo6It88NtbZOGLRL799tvbipm+8DcOFXZh6d56X3uf+WP39dPuewCWFbqUzsbDnzrgC1ysmFm9gztc4GDsGkf6+Cle2BpL2/msehl7GPWfTHBy6NOYik8dYX50+Dtuxpw9ZNB8MAbwgTm84GPOIejGmR5xkeGvuu9AhIi/dPBZXFakzFN1/hoHdf4hZfqIxdj7HlAXJ9JFrzHUTx9yYqFXf+0w4oc2dfPTfFQ3/2CMeNKDzIqNDjboNEY+N1YdvTglYmY8G7P9d3jj1rlx7Zx85+4769MxxGxFZq4HgReIgA+hL7E+pM5dv0A3xtQgcCUE+rF58uPxWZJiDvsRVPqfRDJ+2PyAPe+cpt+PP11+FI/pyyeyXfNHkqFc97NFhyK5kFgo7vkR54t7YhXjWurXmYzr/CIrBn7xSduxeFadD+UaFush7hKgh4LBqTjNs3XuR8ys7Eg0rYR5jkgCa85KTktWETPJJQInsZfYmo+STwk4efPUnHSNlFkxQ8gQM8m8z7fEWdHPuEhgJdvq7EUK6XHwmT7zmy8KMuazo48ioeeLpFhhgyyy4VrizTb5SIHkmT3EgGyEEBETh3b9EMR9Xb+1nQ5x+FzTxQZsbc22WuKelxr5O175bI4Wl2v+G5sOcfR5d1ZgsI6f+46w2oQ++4fOijj44nkveH7jG9/YxgXhEAdZY843h3gV8cDWM4jILf89J2bFik3jZizIsIGoIKX8ZAfxsn0SCdbPSz2sHiI4+iI4sEJo1BFbto2B/vxzhrf78KrORr7D33zgA59dOxAzxM08Q+bIrb7ygz56w7X5Ij424audD3wNEzp9FrSb/3xHMOkkIx7jzierZciZF954+QeMFDb1z/Z28/BPY9e5+53FrnTufmf9HEPMQmTOg8ALRsAXiw+34oO6Ht3bGuefQeAWINCPzZMfj8Oc7QdK8uTaj2rF3HY0r7vvnK71B6p7ncn5jDjck4Ck3w+uww/osULeQUZZfcimc5+/Uzr82LNZoimJozNfStb2/dcYXLPTPXbpW+3nU77u9T2EOnzCqDOcpvzpihk8JLZWUazsmIu2mlkVkYBKKBXXVhAiZhJPr1u3FQyJgq35DWvJqLPE0zM1VloQMtv4JN/mvLmvjzkvudUHKZIEu0cnOZ8XPtHHF2fJsc+xhF0b38iyX58+D3TRIUaFXvb0cV3izK4+vnvcz67kWj095LOR3+r688U1Gdd0ICze5oeg8hkZkpjTp4in77Wu+zyvn+PanGvfFFzjH34hiLYysvnGG29sYw0bxIWvsLXyx3dYOOCK3Hi+EOb895yY+I2ZdvrohxcSa7z5Dw8ySL8VTKQfoUeQ2NDPXCALk/CjG6Ywo1dR97tQ0ad29xtDOhx0wCq/1JF09/jJLpKmH3m+kKErHWTdU2DPZnbzG2b6wk8/hFI/1/yjnz2rlcgZYmvF0WdIgUO6txuf/eOe0vmz209O/FA6P2n47EK/Te/hn8/p+V5q6oPAIHA2BHwBOBQfVF8uzn1oO5/NgVE8CFwDgX4qOuvaj64fuXXu7tWufbSdmturHJ0+H+6xI3GquO9H1A/ouQq7Ehg/8g4+80nxI66cimNr/OwfevSrb59z5/Xzn76r6Fz135frdey7bn7dlxifNQ5zx7xobkjMraJIGiXHEkcrZsiLFQBJZWTIM2b+11/d6s/F4bkgyTud8DW3Jaju2VZm69sHH3ywJeu28b1y2GZI1iqJhFlyqh9iIOnXR2KfDuROXRKMIEmsHdoRRYm1PvraSuYzLOnnhxUMZM6KhlgeP368+YVMSp6t5OjDhi1u/BIvW+LzHcFH9/UXk3Z+aJdQ0+8+3PipjW/a1H3mkTIYkPH8nqQ8Yki3Qr5EHh4djbE53Odbm++Mxo+M9uZ59fXsWlzI9zvvvLPZ8jfV+CIGOCBQxgN+fFHve1K7F4fAXB8H+wib+I2Ds3H0H1zIneKesfHCGCTd81X6er7KWMELpvo46Iefa+PMZ3UxW6Hjj22IcO15MPqtTMIQrn2fw9tBhxj1VTem4oE9neKwgua+OMwNfdhofolDrPw1rmxop1OdDjLGRd2ZX3DMBy/BsX3WtlkvT+GrfuaS+Pal8ey8b9dX6bxvr9+smO2Rmfog8IIQ8EXRF7cPqg+6c8cLcmPMDAInEeiH4pSA9mSat87dq90PqkPbOs/Tu8q7p05WsnaqkPHDWslWdf0lC89a6PNjz19+RMZWfRIDha1K16v/xZ+sNnolAes99+ufvod6bg481PjXuPvsNDdKnBEz29CsliEvEl0JK+ImiZakWjWxNdH8tfLhRRb0SVgdPiP6+S2i37ZHpE+y+9Zbb23PrrmPmPm8SYx9HpAkCTr77LDp8yDBRxYk1u5JYrXzmW/aECt96fA5kLDzgQ3JM9v6aWcT8dAPiaADsUPMfD7p4btkOuIgPv2dkVSfM3FKyvlY3PzSRkZMbGiDl1fUS9gRMytO6eOvAybsmqfVG5/GTltl39b99bzKu89XJPHf/u3fNr+8ut/WQvfFHSGCR77ATpyw9AwiYu7FH55DRJyQN7ggv2KHKdzoEAdMESlv/USGEBLPmOmraEeq4AEf4+VQd7iOINIJVzbcN3f00Q5zOtX5IyZj5DB32OGXg6x4zBHzC0500kMHvfrDo/mlP//NHzb0cc/nQB/joY/75o7Y9VeP6Fk59ff7Imbmi37iPDae+iqdt8ryT306L01/cjnE7E/gmMog8OIQ8EXq8CH1pdCX+4vzYCwNApcjcOoHRi9tkhjztx8aP7D1cc+PamW9373rnPVfdWSze53Tqc6fy0p90kW2a2fxda/PqHNlJYbk175kJCVKvjgn53yMmK36t87zz4NFoPliHjdvgCEhtaKBQFwcVsAQM8kkeW0SW8mnfrYyfvTRR1ub5NyLDCSnfnuUVhAkveazVQKrNOYhEmArI3mfBbLsqJdcS1J9ziXe+ktyrSzwReJLloy5jpxJvCXPZPkgWVbcV3xm2JGM66ufe5GoNWEnzy++6q8feff0cc0vOIjPPW3sqvObn+6xpa7N33JDTBRbACXm8E8ve+SLwbXDfXJ0Ko1ZZ/f5woYze/xIfn+GsfHzrBh5viDgCARsxUsXv1zDyFk/xAUp85yY58M8JxaBgYlinMjzjx8wFIOVJnbJIWZWZN2HvYN98wvBZt89Y6y/cWOfvgiPuGDFjjExP7UhTPSKrbmin3YxuYaRPkg+jM0d8nygk/908UOdPF9c84V+vvKBXkUfhQxd+psb5Kubqz5fPg+2CtvS2rNp/FDEs5b9+K1trpPvvG+vPsQsJOY8CLxgBHwB+DLxIfXl4ZgyCNwmBMzRUz8ix+77YerHSRz96JrnzfUSGG2rbNed9e8H0LX7qw73XlbhC2xW/y7zJXlnuHVIPrreny/TN20PA4HmS59Dc0SRlFrV8gyMt+ZJHM1F80myqfS//razIXE+O/4umdUTiSud9NMlKXVPf6TEa9atmngBiKSVXIk7O3TpLwnuc0xGf8eqT7JbXwm8vhECbZJ/ibKkXgJOL3lydIpHGz8ky1Zr2FUvwWav5JoNCbi+7tPjoFOdf2zoK2GnS0ywdeaDtxFKyGHzymErpxU7fiAPdDQOdNGtX0dt7MMm3ers5o8zHMLXmJFZz+Rt7/QCDwUp47dVQ7r5byuhupUkb+Zkr9VM+pAkffz9M0RKDHRaTULkEB14iU8deXNNh7FBSq2y8td46U8eqSLfPfMHljBzuOYfPPRx8EOdXuNBRoyKdtiScc0eGfiIiU739HHmI3kHHfCg2/gZF7G7duYnHeSc+UpeXX/j4MyG+UMPfJBa/iDE3sqImCn6sUGOj5X9+HW/M3mlc/f35yFme0SmPggMAoPAIHDjCPjRetoP0o0bvaJCvu0PXf0oK/u26n7cleJyXq+3xvlnEHhOBCSCimTUtWQYKbOqI1m0RfHisHJmVUEiLtGMdNia6GUe5vKbb765vZVR8om4ITzum8cSfC+aoNOr1t2XrK5JtT4SZT64z59W0No25g13CIDkXXIvEaaLzxJq8uoIlkRY8sxfyTK/+GILHDsICTJmxcfWxV5oQU9b2ujhjz50pNc1HWT5wIZCB98k63zVV+zJaeu5KauF/EVS+coPdiTsyBtSox3u2tUd6siPONngS9v5+A13z2/BD+njmz4wop8O4ygW42urpzbbFxEB+tiDr+8iOtwTG9LDb2/fdG0LYGSEzur8btyNAR3usYno6QsbMZhLXg7CF23mBz/NOfqszIkBiYOjdrE1JsaQv+YTm8aZXuROPOaS+N0Tu778UIeX71R23NenMdPPOLJDN7IkVvLhyXcy2vjAXrEba/fNE/ixyTfbc9mgA/ZWjm1phdH6Hb9NqDP8M8TsDKCOykFgEBgEBoG7hYAf4giXsyIxqBxr35Ow/Y927emY8yBwHQTMQ3PI3HOWmLonabbN6t13392SZi/1sFoiUdUmGTV3JcGek0HMJKyeUbJyghhIZulFIJAESb7EFAFQJKqSewmvxF1/Cay+fOCLxNY9ReLMfi94kOAiOBJhvkiK6dZfn2LTt8RYH0UirY8/Yq2PhBjZ4B9CpEiukQBkQxz60E0H31yzDw+y/FIQSL7ACNEgj9BIwulyjQDRYYuol6rwVWzk4dFqEgLqnjZ9YUavawTGtSTfONBPZ6QAedEPEYERLLUjOPBwnc7ICh2KVSM+8dPYWOXiF5v6ITPuiV889PFFf+MEG0QLxsXKhvuwQUS99AI2cHPQiRTBUx94q9MPD33JGJP8Mm7ieXx4iQvbsOAXG3zT3zx07YClVUExGWfjyjb82EGi4Cd28mI1BuzqE17iTScbbPIbPtoiXuaC+0g/vT4HSDed8DD/fD6sMnve0hzki+OcZYjZOdF9iu71i+kpotM8CDw4BObz8eCG/KUGbL6tB2ckO5W1zbUS8eq83ttfq08ZBK6DQITMfIuguZbEeynEP/7jP26JJmLWK90l25JP8kgNYubV+pJJK2ZWNeiQfJKxZY8upEfCKjGOPEi2fQbIO5Az+vWTCEuMtfNT4i1xRgDY185PpMPBFzYiUSXYdCA0DkRAPzboIq9dkkyePnrYdJBT+ObaPb6JlU3EBwlQEAn3JOJs8NEhEfcSFa+GRyroYUdij+z6+1W2M8JCm750qtNXks4v+CBNbKiziZjQKza+aaeDfvLwUuAijn1cZBzawoddByIixsbbyhQ9CE52GiP1xkAcMGXLPTL0sUMfQmLl1JjaJmvOmBvs8INMcwMW6nTWzk9yxtR9eOQnG/yEgXaYGGcHkhYxQ5roiAzBq9hgvtZhaL6wKw6xuoYP+8iZGPmqblwUsRtrfosH+RMz/OjzufHZ8CcDfHbypTHflJzhnyFmZwD1qipNOoeJWVmvuzfnQeAhIOCzUOlzMZ+HEJnzuRHo+5id5qIkonKsvfnZOdnOp+7XPudB4DIEJPSSwL4PO0tykYlHjx5tCba35iEPElRzVgIrGUY6EC9/pFiy6XXrVsHcl6RKzCWvEmWJqRUOKxSSV4mzZNY1nRJV9/ThE9/M7+zxiV3yDr6SJ6OPNolwybNkmRw9EmlJuiS5pJ7/bNbOP3X6yHbNPhm2FDrpc1+SzqYY3WOT7+IKI4TMm/es8miHE2ysnqj7229eGe96jYM+Pins8z3bsOC/dr6y5R7bfFL4xWcyfNVOj3aH6/CsnV/iR/bIsLnqoFM/84AviliV6uuYwJCfCnIiPvceH1a4kBIrTFYMHUgJGf0bQ767djSGdPGhs3mgrbnLR/ONLnG431iwjzRlQxsbcNOPXGPIJjvhS1ZxdsAs3F07FH1gCD964Q4jtsMGflYWvZnTM5pegIKYWZnTJ12bwjP8M8TsDKBeVaVJYwKZJIpz11fVMXKDwH1BoC9U8fS58CU4ZRB4EQiYc5Wu1/nXPTJd1169/p3n+zwk5vwsCEgizTHzq7M5JZlEzLxt0TYtiTNCJdGVgEtaJfEOf5csYuYPRkt8rYZ4LkgS7hkob91r26IktcRasipJdUYCyNMvMS3h18Y3+Ywi4XVPPV3ak5P80qNI2BU2JNj8jSiUeJNnk1762CbrPht0u47YaC/phluJuAQfdlZabNWzQtPWNlvZ4KgfH/yJgV/96lcbVv6Gl2fLtMGODtvbrKxI1K3k0aXu2qqPGKy+KPrwiV5FH/GyjaBoFw8dZBAP91zzVZtxpQ9Z0JcOeCKc/LGaxT/kEg5iocN2QIc+VkgV7eoIKJvajUPb+fhkziAkfLTl0fbXi8PzZOITp/v5yS8Hncgbf/gFez7wUx9jzi+ETFxk9IGXwq7xFC9ZGNKhng66xaFOnhwd5oD56dCmD13IHB0RQO1kzQUFxu7RGR58FZs55fMFB28y9eKciBkb5yxDzM6J7lN0+8IwaUy29XhKt2keBO4lAj4L6+EL1DFlEHgZCPTdfMr2qXb3K77XpwwCz4qA5LI55Nyck0zaYuVtixJnb82T4EouJZWKpFUC+viw+uENjoiO//mnR8JplYi8bVpeBS7plMhKTBXJLXuSdt/DEmU5i0QY0ZBY84McUiCRVVp50RbJQqwiV/oiWJJieiW5En6+Srb1ySZ/EAk26NVHHNrp0x9G2WY/jNgpx3Kfbs9NeeskYsWHiwPZsBoGP6RBH2QF6X3vvfc2fGxlRDj4CWN+ej7MVjt9Ik36aYej2JE/9o2HvuwrCLB7yIz46ICpOh3GJLKHiJF1z8tD6FS3silO98TPP34hTPChkxyC5fkv5JtffOA3n62wsucFK/BV1889/T755JONCIrJmz9t68xvfvARIUSy2EDC2DG/6OCXvjBF/oyr2PUxx/ghbnU48N2Y2mJpzMWmaIdf81KdnDqcw5w8O7CCBx+sDPPZOJuvxi389DcfzAOx+xzBO4z5jZz7Tw3EzDzoeTf2z1mGmJ0T3afo9mXSl4jJ7JhE9CmgTfO9RMDnYH/4LPhynDII3CUEzOOK7/Qpg8CzIiC59T1YnhABkZAiZrYp9sZC35WSc0m3PuaeJN3zQoiZpFRCT07CKiH1couLQ9IqIZf8IhL17ZpO9iXP2iS+zhJpREkiHWninwSdDQm7dsm3e5EkJIAtMdCLZFmxcF8f99mQQEu2kQnJt3tsIlX6u4d8skGPuOkQF1v8pksCTq9+CISEXbvk2ivQkQ51/fgqFquJ//zP/7zJ2MJmRZKffNLOJ7rV2WGDXnHCg1/u8VO7Onm4IDR0ISoO+hBb8jBWd7h2sKEPm1bE6IvwIHP0skkH8sGWPmzT2QpaZA/x0IZkGC9kxHhGEMkjLL2dE2nxVkJntsW5+sGeOLSJX+FTY6seXsZI7LXzSaz6GyMxiI0P/DIuZIwVuwpiBkexmd/axW8emB/GkA7yyJ92RC0b7ImVvHa24KUPfepk6PK58R8YyLu/Acg2rPh5zjLE7JzoPkX3MWIWQXtK12keBO4VAr7E94fPgh+QKYPAXULAPFac/YhPGQSeFwFzyfehJNi1JBMx84egETMvTEDiJMeSVGcJsMQeuSFnBcR8REAk8IgOYkafZN09iTWSxI57ElT35Cp00amdnpLatlBKrPklEZY4I0KSX+366K8uceajgliIy2oOUqUuMbZ64Z7nv+h17eCTRFkfqy/OEnT+6MO25FqM/OaPPvQqiCm8iknCzj/xih8B4BsC9+jw/B5Cgbx5Kx9M2YMXeb9NEnT3EAjx6U+ODMzgEObGR3u/afoo2umgUx91B3lYkIcNebEpbLhPhi3yiv6KNvq0ZSf/3a+wW1/XDgXu77zzzhceH1ZbbeVESmCnsMGv/NRHXAp7fEpXZ33CDCbFuXU6/MNfOt3nJx3GkCyM2ULm3Dd/FPrUa2c7+/SIW7/8c73KuK8/veyb6/q4Jx7z5v33399WWCNmCF0+bU6c6Z8hZmcCdtQOAoPAIDAIDAKDwCDwrAhIJCWQ65kuSSxi5jX4Etmvfe1r24oGEoOIIV22lSEWtqp5TkabVQFJKDIjCUXo6EKiIjjIl+QUCZAERwqQKsmwdjokznQgOZJVKwx0tfJC3qoFHQ7ER10izT/3bD2TMCNHEUmJsiSZrwiSflYqWjGz8qePVQ59+EmGn0iV1Ryxtr3P6g+/rPq8/vrr2/NSETNkSqFPMi8usfDL34lzz1sJkT4yzuJry5trZJBNsfGJn/xBXPW5OKxIWimCMczoEB+yiDiKjT908huJLVZ9xIWUwp6MmOEFJ2MNT7GSUzeO7NHJB3Wkio78IktH4waHxoQPnrH77W9/u5FNRNaqojcz0suGcRE3HeJAYowXsiwmMualWGDIb3OKD+7rY9z0cZAxV8118cGz2NnVRxz8NMbmDh0w119df/1gKxb6zUl1nwV+8UMMMDc/m38+BzBxrz78sGr46EDQPYNp5VR8is+jYr6cowwxOweqo3MQGAQGgUFgEBgEBoHnQEAyK7lfiZnkVdKJmP3DP/zDlnh6Xb7kUZIu2ZboSpaRC8mrIin2EoeSWKRKAivhl/QiL/q2AqFd8t/KgsSWXgmrFQXt/JLQ6idp1i7xlUjz0T2ydCIl6SOjH6IgPoSGHfL8IO+e5FzCjQAgQdr5qi9ikN/siYtPno9CxsLNtk0++xtu/haVpJxvknAx8V2CzXdFTBJ2z5mxh5j17BRSwPdIFEz5iQAhNPBERvjFLj8vDsRMPBJ+utlnjww8wss48QmhEotrh9jEzk994MSOucEPMbMpRjro1J9NfkVe2NWG0BiTSBMywx826GDDqqTnENm3WuRPMSAv2smLNQJEnh9iRZKMn3YlcmwFzvgjNnzjFzyMKxk+0gsLfqojh/xEzBS+N7/4ywb/+M2uOr3GIxLKBp3a9adXDOpw4ztd5Nmiz5yEueJvBf7mN795QszMA2PoUPQ9Rxlidg5UR+cgMAgMAoPAIDAIDALPgcApYibJRMz+6Z/+aUtEvbxDAi0plRh71TtShqBIQr1xzzNCVk60l9AjGghMJCuS4h4CQNZZQWYkpBJwiSvf8k+/+iSvDfGRkLsnic2WukRYce1AEMXlrI0NftHDT7EhSnRK7iXc6vTzSUGGrBDSQ4b/nsNztiURTmQl+IiLJFxMrsl0eC7v17/+9abztdde2wgtn9hESCTwZNmQ6COPknx1vvIRAaIXcVD4qk6HuPjtHHbwdY8MW3zUh4zY6RZX5IQMm/rxSV27eMjqx0/tsETm9NXHOOhTvK7ZMA50mDePDitFiA5SZrXRSl/t4hO3PnTCURs9xcouPxBNOvkT9rBRkDAyCJMD2XaP38Yg0sRPdtTZNSfYdA/edOhDBxv0sA8/sesDW37Dmzyc3aODj4o+bGkj6+U6Xv7RilnEjH1liNkGw/wzCAwCg8AgMAgMAoPA/UdAAiiJlmxLfNVLjhEzWxQlnkiZ1TBJpS1oDtvNJK3eOIiYuZa8S2gdkRn6IwcQLensWvLJJt0KYqEvnyTc7uvvPv3qEl73JMTqknLJsMQd8ZIAZ1ebukOSzBabDjr4wx5dEQ117YiBWCT6Dn9MG+n0sg4kjG1/j8v2RwQDTvrRZaUm39yTxIcNYmvFDKl94403NgzFVpIvBkUMDoUu/pKDjbgiE2TEFybaw7n+6o50kkE09DN2YkYW3HdNV/4jEtXhp67w01gUc/fCmC4+Kvx2n7yVSsTeHPI2Rm/ztKqUDeMOQ36IUx9+u6fQ61DorLgmz2/y8FTI0qG4x2/jwZ55BRf9lPBtvmind7Xp2j39XIvfOR3q2tl0TxyKa3iwYa6ZO7Yzmk9WW6340bPq3Tre8D+zYnbDgI66QWAQGAQGgUFgEBgEnheBEvU10aRTYoyYOSS4VsKsNEjcbeVDTiSXtuEhZm19k/CSkYBaVbDS41oiL1lVSjxds0+PUuKMYElkySEOElg+0Ccxl1BLvOmUNEug1V07JL78159NdT7RL1lmL6IRseiefvxlRz/tth2Klw5b5pArz9z96Ec/2nx5dFj50e7PAsCjfjChh26+wKLDNrpf/vKX23Y/L75A4tiNXNp2R5YOh/jU6Wslih/wQ+7EY6sdO1bQxE2eL1ZrkBF12LWV0TW/9UWK9KWDDWMNL/jDDHGj02qY+42FZ73IaLd1kZ98p5Nf7FsVg6s+xpW8OYTcGztbCZF+K67GT3+HOBx8coi77ZFiUcjDAGk2VuJgFzb68JldsbHLn4iZ8TQ28OVvpFwsbNFJjzo5fcWHzNHLnrjgZcskP8iT1Z9+cZDVzgZ59+FA3nZOBBUxQ9B7xsxc4LO+5yhDzM6B6ugcBAaBQWAQGAQGgUHgORCQHCI9kkAJuIRQkUB6/uVf//Vft/sSYHKSWEm75DbC1hY0iT59Jaza6XRIMCWsklOJrcImWW2uJen6S1olu64RAXrpaHuY5FiSTEY/iS6yQL++inb92cpOdXIO/rEtZnJs0MUnshJ7K4bIC0LgGShy7CMAtp/xzxspJf2241k105ceuuEkZjol/vwUJ2JmG5sVOVsZJe58hnPkhE6JOhkrS55nQhIQD2224on3i1/84maHn2Kli20yETFEAUmIaCBnkRf2EEO+IVp0ik8MSBVc2KUTJuTMA+Oh3T346CM2fkbuzCNkVvxioUcfz1XZyslfxF4MDrGKAymiD+ZImD78EjsbZBTEjS2x8pdfrXLyS5xkjJPYxECHMdcuNniJhYwxIo/MeYaS3WLLD/rJwFM7m21BJMMGvJM3Z+BrLvCRDfbMP//x0VZGxIycYg7BRt9zlCFm50B1dA4Cg8AgMAgMAoPAIPAcCEj+JKedI2YSSMTMM2YSYTKOi8OLJiTQyAaCIrmUGJOXhEtSJfoIgcRfUktGYu4sudaXPffSwa4+7mlHnBAVh7aS1BJVusjlu34Odf5IjiW3+ivktSnODroky4p2NvggXmfEx7NQiKg/AHxxiF087NLLnuQeeUMkEDVyyADbDoUtZEasfBMbvd54SZcXq3gzIfsSevjBkn66Ih6IgIQeaZDcIwXkEWP+uycm/RX22IUFm3S6J2YH/ciIduPGvnjoUtcX4YE/ebrZEE9EzRjTy48IDxk6iwNR0wd2/KfTc3lWDM0bq2XIGXIjVnGSQX7EBmt22Ne/uljghyg5i0cM/OUTm+GnP2JGznhqE7tChr98EbO6ePnggIV46US2zGGxudbOJzJ06q/wlX/a+WvM2OAjGTrMMytmiJkVs9cPb/QcYrbBN/8MAoPAIDAIDAKDwCDw8BCQyEooO0siFUloK2ZWJySRkm+JIxnJtgQ+UmB1QaKq3QqEa4mwVQ+61fWTGCMJElQ2JMGSVMm05Jkd7RJbMhJkCa12h0JH5ItuskiQ/vpImvnqHrtk9BEnHWSKlyx72h181A+58ByYFRvP/thuJ6Emg4BoR9xg8+mnn24JOGL2N3/zNxsBKR4YOSTpir6SdC8R+cUvfrH5gph5XTy7/BIveX7xWazslvRrJ+dQyIiJTbLhq+2YbLHXj5w+Crvp4TfZMNGujU528sGYuTaO+pChuyIuRT/36fNMHmKGhPlbd7aAIj+KeUEnfY2j+OjhZ+PNV7rI0Z1dPmRzU3j4h3/GjIw5R497Fffpojss+OE+/WTp5T/dZJVssa+vok0/9/RzTad+2dQmdnPA6uEQsw26+WcQGAQGgUFgEBgEBoEXj0AJ2prAvngvPrfInxLOklSJrxcTIGHa+SoRVUoyyUpgI0UIhERWkaAm75qODv3oKMEvGe+ehBwpI68vUuLQTqdEV3KrHxlJsfuuydSHnEP7mlRrd+QfsoSkWYWybdAKiJXBr3zlK09WCK2KIXoSe/LseFYqguEZMyQLMeUHMuElF3xEyPghBqtxv//977dnrBBXNsQKb30dbCEvnu2z0oQAGge2rSSRtbpltQahsdqDCIvRi1jg59k4q0J0sN92SHU2yNtSacUJMeTb48MffIYrHYo+dNqGaKzU2RU/jJALvvHLSpTCF/b5ZV4g7Qp5/iJdcPPiC/4hJeyzSY92K00wETu86DA+8KLTOJlr2o0tPxAvNuEDc3jpQ4bP+pibdIgVvmKzUidmsbHlPxv4QSf/+Bzm/YcEPXSwyU8y+qqLE17GGxYO1/qQMe/4aS7BYF0xY1sxtxz8uk4x75TO4nPsy2xl3CMy9UFgEBgEBoFBYBB4kAiUNAn+WNL0MkDhU8lgpMm9p/mnj0Ra8inhlIA69EuneFzvD/cl13RIchEZRX8kwMGX2loVQqYkrBJyBKUEVl1Z+2hT9Mkn7XTxV1JNRps4rF54KQXbttd5W6BkWR8ERGItCZe46y9R/9WvfrWdPWPmpSBICf10Oosb+VFHKpAEyThbCJNVNsk8WQTAgZggZ0gUe1bnEClt7vED8eKHe3xHItiiS1wSf9ghTOT4T4Y8YoV40CFWK4MKG3BEXhR2YWwlVFx8N9ZsID18REYQPQSIfgQHCUF4+OXPC/AX8YIlHT1jJmZvZbQi+crh5R9wgikS5Vof9vkZETMWdMCfTbHwgQ0+8ouPsNaGBCHIkbv8enwgoeYFG40vnfBhi07+R7Too0ebcdMuVjZtRYU3ndrVYWAc+UXeGETu+c22N3OaC1YMXz9sZTS2ijlpLJvT280r/KOP0tmcc+zLELM9IlPfEDBxjk2YgWcQGAQGgUFgELivCJQ0OUvObkNZfXJdXWL4tN9pshJJh3jWmNJ1Sof72ers3iqffslzJIpfDom94jq75CTYjuRWfdrTJYkmoyAVXsghWZZs25ro+ScEoRUqKzFIDzJDP3LjBQ5Iz9e//vWNZPFRQo8gkFUk6XyVvCMVXnyhH8IjIUeMxKkPe5ECybwD0dCPr2TYZoMfdIuHj8WMuIqZTTGSo5P/5NXpdKTTPTrhyAc6ETX3kBv9kAzxITz6IVF0IjNk1G3TRK6RMGeEiG2rSAgfchIxo9tqmVVDhIlOccJV3PSKC25KeLIpNlggTuJ2T6xk6NXumg8RXePadlzkT6z662v84Vbs7GoXVzL8oo9d2IkFbnynA4HkC3n36TCuYlG3SqePuMRqrpkL5pots/T0OYAzmeuU+nbef5bSNcQsJB74uYkSDOqnJk0ycx4EBoFBYBAYBO4LAn731t/CyMTLii9fVuKy+mLFJRn393ISR+2SUYl8v+nicl1/191z7lofhZ7uqUtKHfrRT86RL8mTUepL3j3+OJeYb0LLP9ro0s+1s+TdGxZ/97vfbaTC3ylDQNKJlDjUJdlIwOPDCskHH3ywETPbGG1nRAocVpEiURJ+PknmrbxIxr0m3XbJH/7wh1+4OLxYJL106y9eNhABxT1+Vue/Po4VI9g0LsiC65Ws0E8HfdrpRFjYci8/6KyujRyiAS861I2vmLTToQ4jbVb62G4O1AcOVuK8/ASJsyr5zW9+cyMrdCNUbMPM4R4s6OQbHxxiEBcb+jjcc+ivdKajWPml0Bl+fGpskSjybLDJhjNd6dNPHzbZF5s2OpX01kedTqSTH4gZbD1jZiusFTPErJVLOvRl9zol/zrnx17HELM9Ig+03gQt/CadiTNlEBgEBoFBYBC4zwgc+w2U1L3MsiZwx/yo/Vibe/1+J1eMJZQl2eod6dJXuyIBT5e6JNahz3o/O+s98qdK/mivb9d0SKQl2MbBioln6rwx8OJAlLy+POIhOdcfqZJYIyG2CHpByLvvvrutLCFySIZ2ST1iRoYNfcXKloTcmxy9kc+q0ne+851tOx0ZpABx4AtbVmuQHqs7iKOE3qoTEmFbHRLinli0s6HOXyszdNhG19a7SAHCiSRY4YE9PxQrZHTRAX86YWPrHT/azolwshE+dFglIhtpghE/rXYZR/LkxMl3pMQ2R6uS3/rWt7aVLFghevSywQ/XfEV+Wr2kk67w4Sdb2verWzCFOUzpEAsf6IAXvOEotsaNnNU//egk45oNbVYPrQK2gkankj59XLNBJ0zZ0J9tY+g+DMyDlZhpV5zhdp2y9tWPXce+DDHbI/JA6ybMeoDBh/jYpHmgEE3Yg8AgMAgMAvcMgX2yVF2YfgNfZsmXU7/DkvPKsSQP0XBfHHSpS64llO6pu0/GPUd6nCW72te2SJn7km9tp0q69u3ZlPxKkNU79r7oi3RIsh8d/li0Z34uDsTs7bff3kiCpF8iTgaxQXaQDNvOtEmubVFDyl599dXNnriQHwk7DPKDLfggZl78gLwgdK2USOjd4wsigTAhFkgDouUaGdGGVPHLFkFYa0dieh5MO0KDBCCI/EZ6EEa+0Ynw0UkHvxAmhUx140FnZI8O/REn+mHAJ30iIxFXRMTfMeMXHyJajw8rjVYNbXPUzwtAPGumHx8QRNgZB7F6SQn8PbuF4LBLN3zIkXcvYqYP3Uga3z0Lpi5WWPOHD8bFOCJe2ukMP9jQAz8ybMCUv+rG3POI+nq+kE6xwsbzifBj1/jzg21kD1njs3lt1bStjOsfmG6uXjb3t4Ha/aOf0vnU52OI2Q64h1ptojn3Ze/L5LoT76HiN3EPAoPAIDAI3C0ESpB43fV69hsoebqtxW81f0vw9r5KxNffccmoRJQcUqUUbzrcS0/6k8nWKrPvR6ZD/qC9ev3S7z4bHdr1CXeJORlJvyTctkTPfnkhxPe///3tvoRffyRIX8REiWTYimZLnueE/IFpccPFuZUWhI6OVo0+/vjjjZjx0xZIxIS8dsQDiaJDki+hJ+cefF3zge9kkSL9tLND3qoOwoQE8IEe8gijuni1GyvyDrFZBYIH0sW+2MlYcaNTu/sIEH8RDXb1J0O/PvTDh65WopCR/LCVESFBJpEc8SNM4uAXvfzUh01kTdzInXFAoNilj11EVqyIHd+1ucdPftCHWGlHtMRAJzyRO/6ScZ9NsfENnmwiY+lQpwPp+sMf/rDZM+5k/X06MVwcCCU8+cBvYyQedX64hqtnzKzQiv+73/3uk5VLcwV25ul1ij5KZ7Yd+zLEbI/IA6ybJMcOk9/knTIIDAKDwCAwCNw3BEqQTp0jCLcl7n6n80fiqpTgOa+/2RLZlZhJKN0j4/f9WFJIX3is7SWj+nbfPUW9e+r52X31VXb1kbw2B/lVv1UO9/iKEPz0pz/dXgDi7Xjf+973tpUkCbk2ST0Cog8CEvlBzKykeImFNyxKwhU+kSGLFLHbyomE3otGJPO28VlxQTiKkTxcw9b91Ve6I8HIGf/dk+yLk60wKGbtDnV9kTt93dMHodBHnV32yLmm35lf+uunPUzdQ27UydBBl/vr4b5iNerRYXUSQfNcnlVDZEV/thSx0KHePEwnPemi35xjlw/i0Fcf+vThr3FQD2ft+mp3X7tr/bXBR924s5U8f/Qjzw55+rUXOz/Iwcg984CcusO1eWUOfPLJJxsx+/a3v70RQLHzxxEW7l2lhEnnsN/3HWK2R2TqGwImjkkzZRAYBAYBCPRj0nn9UXFvf2i/7g/XID0IDAJXR2D9LB7rpX3/O/60Psf0vIh7+dWZTb6rS7KRL1vLvP7edravfvWr22oIGYm37xqySICE3H3b36yyIRpWvrwuX9ItqZaoW+1RrLggdfpI1L1gxHZGqzvInFUb8laAHFZ8HEgeGX2sCPFBnSxShyyxp6jzD/FQrPio04dc5A//6YnEuO+eMzLCdzaQEriwoY5cWFV06ItIRTTERsZKllUhffhOlziUMIQB3xBa2wGtNiGm4uBD48EGve4ZG5irK0gN3+BqxQw+kST64c43cdPrmh984BdZMmK0quYMA/7qD2vtfNFfbDAjE1Zk3YMDXxV66XKfHjLkXZMxRvTCkk9eNuMPuXvOzoqZecemog8cr1r02x908GVfhpjtEZn6IDAIDAKDwP+HQD9InQn4kav4kVt/eE796CQ/50FgEBgEQqDvlc7u+w5RJPa24Hn5h8PKlufFXvnsb2uV3EZESuA9U/Tee+9tZERiHTGj14HEIBa2x0nG2UEykDLb2BAyr9lHFuhGMhxkkBf9kJFW7fhlGx1Z/rKhrwRen3TwF+lBtKxVVlA8AABAAElEQVT+kaEPkUQ06IUD/xA5dYTBChbyYisnHVYC6dCO5NiWiHghe0gEm/zQnt+2+NHBT/61nQ+JQk6Q2MeH57ts+9OXnG2gtgiywScyfIY/zGwV1Vcs/PHSFTFrzy9EjR9IlFhhxicYI1p8j5ghSLDw++L5OmOjHYnKbz7CBBa2WRpHvpE1JvzQzk/joo0O/tCh8IEMn/iCHMJMnW3/CWAerMRs63j4B3bPS8zYOKZjiFko37JzX04Gf8ogMAjcPwR8xu/S57vvpM5GZCVm7q/HdX+47t8IT0SDwCBwVQT6Xumsn+8Qh5UOBOI//uM/tte4S7y9Kc8h6dYH6ZHQS959L0nyEZef//znW7JtS+KXv/zlzR3y2kuMrcooknSkQTJudc4fVn7zzTc3ksMHCTvbknxJvGvESYLvnuelECKySA3ypA+iSN4ZaWKXHf4iPPQgTA7XiANZMSMSdPINWUKAvGQjYsauPu67jqjRwQfPTLGJeFgxY5csXxVEhC6kyn0k69NPP93IVcQMbtrZ4RMZmEXM+A0Hz6MhYAgxgqSPwgcEDvnjCx0wo0/8+qgba3hVz6+IGVyRMH7Cgl793QtjOsUKr/BjU9zIsBU0fpkD2o0B3xE41zASG1ueZ/SyGdtgX3/9//7A9Do/j5GqLeAT/+i7Hqd+I4eYnQDwRd9eB5vttW7wKut19+Y8CAwCtx+B9TPt2mf5Ln2e878zxI8RM/eTue4Pl75TBoFB4OEh0HdGZwhItvuORGxsr3NI8G2vswLjO0gSLamW0Otv1UmyjTA8OjwrhbB4TsqbBREEqyuSe0k8gkaeLUk5MoKYtYXtJz/5yUbQ+MEWspAdOrKvL12I1Oq79rbX6evgIyLpTIfvyUgBO3x3XyGjnX/IhnorOmKhRzvbdLClnQ5+Wo3SFkF0r5j1rc5vvtKJWHkrpRdweMbMqiECXDtCoyA5ij4KPPmNHPKTD4o6zNtWyKYxa2zro84PfcknU2zuGyt+06ld7Owib/zrgBe/yMBcH/go4UsfmXzVTp4+Mfpbbl4E4+UfCHqEjg592L5O0cehODen9jqGmO0ReUn1/YCtbjR5nbte2+d6EBgEbj8C+8/4qS/l2xrJ+oOSj34EK7Wrd722JzfnQWAQGAT2CPSd0Vm7JLk6YoYsWcGwkuVlDJJ398lJvJ0ly0iIVQ9b6t5///2N0Fgxs/KBuCgSfAm7BF4ffSX7SJytjK2Y/ehHP9pWhsghEA4ykngrMVZa+GHlKRl1fiMASAUiwk+FX3Qgcmzyg4y+fHP226C/78/i15dN9QgRf8nzA6FBwtjRDgPy6trIkEc42LRiBrOImz7sISpWo6w0WpHyx6VhJ05FH+SVXXr0cS0W5CgSBQM61cWqHz9gjmBq1yZW/fmWX+yQ0SYONorVWMHT4T592VXPH3HQEVElBzsHHXTTQS+/2IGFflbc3EdOkfT+jpltlQp/07PduOI/+lRc88GxL0PM9oi8pLpBWo/VjQav89o214PAIHA3EFg/330p+4G4K4XPSmfXfgRPlWI81T73B4FBYBAIgb5XOrsv2VeXxCMU3pKHNHnm56233npCzMiSkTBL9q14IECPD88h/exnP9tIU6+9J+OQqEvGK4iDRB2J+M1vfrMRMyskiIlEnS9WjfTx/JPVJM83SdYl9LbKsa2dHqSAjVb1+IIk2f5nxU9/BM81vexq1x9xs7XPM1HIDT8QDFvx6LRlUD74xz/+cbOFqPKRTquEYre9r2fMbIVEzLTxgw2rhwry5XfI1ke6bf/8/e9/v21nRE5g7QUgdCAwdNrKyK+Iilj9FrgnDnZgAR+xFCtyR4expEfs7PLBYdzohB9yKMaLi4tNt9iRJn7kp7p2+EVCxYYQw04fZI8f/OOnOjuu2eAH/PiijzGALx3IuReAIGa9BRRmz0rM9F3Lqd/IIWYrSi/x2gA12OsXE5ciZOv5Jbo6pgeBQeAZEFg/330h+4G5K6Xvpc78voyY3ZW4xs9BYBB4+Qj0vdKZR74zfccgXZLmDz/8cEuUPWP1+uGZHwm2NrmRfg4kQ1KNJCAIVswk/a+99tr2jBmdSBhZZAaBcL2SQK9I93wRPbZMRkAQHsSCL4iTZN7KkyRfPVJIv3tWcthg04s7kBa+ey6KDjKu6UUQkAIEiu/a+S9GxIJOpAnRQboUxAKJ4R85hFGsCJB77CF/fERYtPNDDAgNbNmBn1j8HiFd/mA0Akc3OSuNCFSkCuFhg1+wY4cufiOp7Iidn8aD3+4jUPzkU8QM0UJqvVAEaYIHQtiLThBIuumAAZ3GhZ908msltvTzgw/8hC0/jQGbbMAY1mJsTIwTLNhGzGDx0Ucfbau0XjRzDmK2DeKRf4aYHQHlRd8ysY8d/FjJ2Hr9on0ce4PAIPDsCBz7fPs8DzF7dkyn5yAwCNwfBHxHKp1dl/O4Z5XF6tcvfvGLbdXE9rpWeST/ZJA0BUFCDCTz/vaZpJ+856W0kUPEFMm+xJ8tfRAfWyCtmmnzbJpX5iMDyVmpUdj0Hc6+RJ9O+hX36KMXAbOiow25YaNCp4MtfpGh0wqZOhvkySBKCnLhfmSy3xF26EFM+IuM0KMvvfqTcU1GO2LinA394YWYIpt//dd/vR1spo8OsYUJfXwRa7HwT7uCQMGs1SzX2mFE3plvdLAjbn40JmTYVOe3Ii718OKbg6z78Eay+Fkf+sloZ58OZzr4gLgpiCyC642e/nQCYvb2228/Ieh05P/W4Yb/GWJ2w4BeR52BdZwqJo/S+ZTc3L/9CDTW67kvpNvv/Xh40wiYB/O5vmlUR98gMAjcRQTKg9bfR9d+I0u0EQivyreKYXXH3zGTdCM6rUpJ3iXbJfz/8z//8+StjLYyIlnIAt2S9Vat0mO1RUHMfv3rX2+2v/KVr2xbCZEFKy1WiiT9DiSCPqTCKlB6EBGkhqwVIO2IJQJAh6I/8lEf1+6JF9FCIuhEmMSr0Bt5oYtOJAdB5Ytnv2CAAKnDgm02rCqJG45+e+hlT50uOMKNfsTMFj5nr8qHQRiTd9BvNUpfvrNDpzpCxDaM1bXRyy+xIbHwZBN+7OpDhq8RM33pUI+YkQ8rmJBxTxGfuviQLH6yQQfd/NDGhnp+Nq78co+fsPafAAi6+P25BePQnGSLLsdNlyFmN43oNfQZWMepcq5BP2Vv7p8PgcZ6PfuA+0KaMggMAoPAIDAIPGQE/Da2ElFe5Ow3Ui4kafYyBgeSYCVHoiyJlowjGWT0UVdsZ0Pm2sqInEniJeVkJO6RAcl69zxjZcUIUbONz281/bbE2Upn693jwzY4z34hRQgUUoQE8Acps43Ota14YiCPUNjKyAe+0WnLoefQkEREiE0HfXQgdp6BQhj0QTLcQ3w8Y0bnK4fn1pALdhEKhMnh2S566ff3wpA4vovVPbiq00GnPgiNrYzeRghP/rF/cdjSKD4+OqwqaSPjLY4wIkM3P/kHG2QN/kgRmzDpGTPjSIc2vtPBBp18p6ttiLBA0NpmyCZMvvjFL252YGkcEFBY2AZqu2NbLosdfvwg+7//+79bvHwQPx/45tpc8DyjFbOImfswM57m2bly9CFmL/Gb0MA6TpVzDfope3P/fAg01p1Z8iXkmDIIDAKDwCAwCDxkBPpt7BwWfiPlQhJpKxiPDq+/l2z/1V/91fZSBiRGYt9qEVmrORJrJMMKm6Tes2KeM9MmiSeHCFrBQZRapUJUvPnRix+QGS//kOxL7NlCXiIW2iXrCAJbSAhdfI0AXRzICt2eB6NDHTlAPBAidURB3RGp0h+hQf4QL4QL2dCOkIoBOUEsEYtIFSxc88NzWnTwEUnSh14YtPqj3X118SEn/o7Zb3/72yf+ImYIJb/JI2ZIFd9gYYVR7H/+53++6dbOLzqRGH4jwcgcG/qwUyxwsCLWKhxiZhuqMWLDnFBHzGCBsLEJZ34ZB/rVYckOm48PZBhJQ47Z4FfELALKFrIHH3OIb3SYP/7AtLlgrlkxi5iZk3zin+OmyxCzm0b0GvoM7GXlHAN+mb1pOy8C/eA07sZ3iNl5MR/tg8AgMAgMArcfgX4fedpvZKTMPcTEH5i2AiaJRrSsXkmgER6JNhlky6oLAiAR18eqkefLPCvmd5c8IqEgTeQRiJJ3ybi/l4YE/PjHP95WjPJJH0RGPwSBfX6650yv1SK6yCAb6hJ+daSSD+TpZJsOhAq54Ae9+ji00yEuMoq6ghAiKxFL/dPJFz7oQz8ZOtxjn421zm99YIMEIbR8Rkq8lTG/tfOdfDr5gYghPfSS4YdrZ6SLH9r5ggyJDR7p0UfsdIpJO//IONPhGpnTLlY6xUIvGQe86NSOaLlG1BQ6+HPMbyt59IW5+fTo8J8AtjKuxIweftKjsH/TZYjZTSN6DX0N7Kku5xjwU7bm/vkRaLw7G98Z4/PjPhYGgUFgEBgEbjcC/S7ysut+IyXcVkOQJc9+IUx/93d/t5GMSIKEmozkWnJu1ePxYcXEK/Yl5J5J89pzBEJib8Wn319kwTXSsq6Y2Sb35uEPC1sp4YOVs1amJO4IElKgHxsSfis01REdvqgjOezQoURm6EBg+C0WMvrAgK+uHYgL4uk++XSIJWJiNQsO/OWHmPTjB+JBH8KiHrlzLzJINz8QMwQYobWND3b00S0WZ334qg8bDjojRfTykw9i1YcPxeo+vNTpQtbEIlYF3tlwH775zRadbCNr7JJX1z8fYK7wnT9ssItk8kUfmNMrluaPNuNr7tjWiZh95zvf2XCljzxbCn03XYaY3TSio28QuAIC5/xQX8H8iAwCg8AgMAgMArcWAb+Rkmlnibmkev0D0xJlyTqigZhI0JEKh2TfKolnxWx/lHx//etf315kkU7ETQJPN9LgkKiz46UhXn5Bxsoc/fp5tTvSgwDZRqguybcF0eqMNvf45fkmxTY5BMO2QjZsvUMerOaxZbsfkogEpcM9xMM9+vlh6x/SSJYdcdseyWdbGZEF2/sQCs9l2X7IDzoQE3rU28po1VERC6IDL37xwVZGq4ZIGDL75S9/edtSKA7kBUFEXpBMdX4orWDaOqqvOGDMhnFp6yd76kgu3+gjQ79YFb6qixdu8NMPvsiQWGEkVr7DUuyu6RCnZ/AQLn5pRzjZ9Nwg/9llhz448lsdnvyGQcTMHzSHu/kYMTsHKRP7EDMoTBkEBoFBYBAYBAaBQWAQuDUISMgViTAiYsUMOZOcf+1rX9u2K0rWJcvImGTatYTZKgrCgJhJ0q362M4o2adXuz4IhATdWV3xUgjPmLHLjuSfXsTFKo+VGIk+oiHB97ILNpAfZISvnnHiR2SFL2xYhdMHmWPXM1TIB3kkSn/x0Y+80YkQ0O9AqNjVjnggjGzwHfFAlMjQ6xrRomMlQOJgIzKCuLKLtCCACK2XfyCtKzFDfvfEjH5+wAoZNAYRM34iRmJlQ50dY6bOD4dY2BUD3+GGZCmtdiHC8BOrMUSY9LN6iowZE/ggZWwgesgbrMlEINX90Wx23UPu6FN6IQsb5sf6jBliFmk8FyHbnDj8M8QsJOY8CAwCg8AgMAgMAoPAIHArEECGFIkwEoEsIVqeH7OKgzBJ8CXlCAGCI6FGKBAWBMmWPKQIKbP90eqXVRHkDJmQmCMX+ugvcUcsvJXRPS8MQU6QEgeiwSbf2JTUu6ZTYZ+vDm1k+IIE6I/8uG/FyH1khE6+Ihr6i49OREQbHWzqgxg66EfsxMFnMtr5TAfd4ZcfCBE/+aEPXNlQ2IEHvQghAgwHscOO39rod9aXvDoMnfnND3bI8IMP/OQbmwiRdv3ZdHZoJ6ud7+ruw4cu2IiDH9rFoU6neOjUhzy78CRDHxk4GQPxInutgvJd0U9/tiKx//7v/76tmnnGzgotAqjw65xliNk50R3dg8AgMAgMAoPAIDAI3AEESuQ7lzSf2/XsOa+HBFyiH5HxqnzJshURWxNtpYvYIC6SbD5LuiXnCJnnhBA3K1/+jpkkPZJjVUYf8u5HLP7whz984b//+783OVsZrbhI/iXs9EruJfp8VZfMs4GokOFv5ANhYU8bG8mLjY6VeEU2yNDNjvjJIA0r8aCXzsgNWfHwhR39tTuzQ2eEB2Zi1sZPhR7y8KPHs3xWnGy9hDVSon84s88OPa7pooMfSJRrBFKbg171xpIcGbgq/HAdwVR3zVcxwcYZ0arU330x8i8d7Knzyz3txto9/RwKP8jwTZt+4kLWH3328g/EDKlvxSyf8+Omz0PMbhrR0TcIDAKDwCAwCAwCg8AgcGUEJMcSbGeH4iwJRhgk+0hZxMzWRFvSEA4rMraxSeatqCAREm1b8sgjGoiZV99rl6QjC3TqIxFHAtjR5lXxVozcszIXUepvZiFhnnnSjy36rTIhDbbr0a2OhCBqEQsxkUc2bN1DtGwpRDBtvXOQVxcTHciAlUE2bAlEHtmgs5U+5EidPOLEBt/UrRIhslYD2bRlEgZtO2RTzLZL6gPHTz75ZNvGx7+//Mu/3FbN2kJIJz/EcHFxsWHmeTrjZBsiTBE6WCJ1sONnNvgKP76IQ/x8UucjP7Q/Pry4RV9+mge2aRorz+zRwW/+iY3f8NEPPjAjy64xhIcVNLHxG778YRfho5NtK4RtJzUO/o6ZVdqIGT2Im1j55PocZYjZOVAdnYPAIDAIDAKDwCAwCNxiBCJA50owrxM6X9ZDX8m9A9GReFv9ckii/YHpV1999QnJQUAQIUl3RMVLPGx9lGzbjmfFTLsiZvIKEsc2UoMQSchtZWTHK/atrmhHEpAPCT/yIXlHJJCK/vizZ7usvCAv+mnXN3KCNCAciAXygLxI+JEEpIdNdfF6rkodIdQfAUKqvJyCTnHBhh/iodOqkP7k9OcbG/x2jeAgMv7mmLjpsGqkXR3hREY8z4fUWC1DzpBD8fATgUFC6UVwrExq69k4K478YQNR6tktWPArEsWme/npmg6xes4PTmyLlV+IGD/4D1+kijydSJlYtNFjLOFBhzGBtfjNDfio85svxtW8YJMd8vR6js1cGGK2fUzmn0FgEBgEBoFBYBAYBAaBcyEg4VWelZjV/5R/19FLV/o6I2V0IA4SaITBIbH2CnMrHciDdkk7kmA1A2mwMiLJtv1REm/ly6qZBJzeVj2sNEnekSmrKoiZFTMJuQT/rbfe+sLFYWUIiZPUZ68tcPWV9JPhG/1IA1mEBUngn3a+aUegkCqkgo6eiSJPhj9tCVSngwzZiCcZuvSBWdv9kJPq7CJcEVJ2+UGPvjBzhhd9CA5iZaWRzW9961vbmykRSjrdo0MfOtxDcowTP9mDBZ9g4X4rk0gaX+jQjw42HXTonw4ydIiVDsTbWIlN4bdiHLWzG8b6kQ1jxNVYuCd21/rAn23y4kcI2YWV8fV84hCzDeb5ZxAYBAaBQWAQGAQGgUHgnAhIjhVJ6rOU+p/qex29q66u6y8JR5g89+XZJystVswk+ZJqh+Tfao5k3BY5qzVWaj788MNtRca2R3+TCzmgV4IuUadbfwm6xF5/WyD9YWErJ3//93+/bZkk79BXQu+MTCAG9EjqEQ1JvXsSfHUHnWTZYd+ZLck/cuMgg3jpq06nI3JH1qHugBGdfFHnG50KXMhmgz0EkC/u850d7Q7t+ijqVub8YW5b/6wY9owZW/CiQ/8w5Is6PXAUuzqd+ohLHyQLAeJ32ISlfvpoZ8N9Mo2VPq61K2yqV2Clj6KfOkzZReb0I99RP7bYhqEzvfC32vbBBx9sfzpgVsxCa86DwCAwCAwCg8AgMAgMAmdFQDJacV29xLi2/XmV3bep75PgYzKn7tFd4i1hRrxsL0TMrJRZ/UIAyFjNIWOFzHZAKzVWyJCliNmf/dmfbVsfIwvsStYl8w4JvDM9iJlXpb9yeOnHm4c/MG3FRRtCKMm3eseO/m3NQwKt9tgCR4d2MdiKyEeJPhu24rln2x09npFC5tQdXm2vHRGl07U+VozUETs26LYtEZFCRJEefdjgL99sEYSBrY9W/6yGWT2EAZ3we3x4lss4q8ONTz1jhlAhuF/60pee/G0v+hzihgfygxDT6Tk09hE7JE07ggQLtujXTyx85ZPYbY3km1jd4wO/4Gas6UISFX7CmU624SdevtLBD3WrhzDXl07Ejl191Y0nO87k4c5HeBpj88022Fkx22CffwaBQWAQGAQGgUFgEBgEXgQCESxJqkRa3Vlyb1XkVKnfqXaJteNZC/0VibytZV7KITm3xU6yjQRJ+CXcknHkRj9JvtUQZA6BsWJm5cNqCPIgPkm75N4hIbdi40DM2LFi5u9XSdTZQYAQI8QEwXHNjq17SBNypA9Z7WJHThAfZIWdi8O2SESSPD1IVnVEQizqSINnnpAGz5ghHQiMOrIiRjrFqO4+MhExEydihtAgZggQMoO88hvh0tezcrDwvBjCwg5i5o8rw8TzV7Bjg076+E2fWNlkR5txEaNnt8wbRE3sxsRYIVGIGbzoURcrEqYOS3hpt53SWPDTOPEdrvxEvsTBVoTS2PPLWPGNX3QakwgnHdr5DT9+RWz5gbg5YMmWOQCHWTHrUzjnQWAQGAQGgUFgEBgEBoGzIiBJlZxLRpGzCJqkVnJ8qujnOFVuipjRg6hYwUDOJOf9XSmrHAgGEulwjZwgBJJz2x+RJH+Pq+2PEToJv4Qe6dNPvBJ3L334z//8z40AfO9739vIgRjJw0j/6gihvsgErJALpIRe8kgEee36Ig36sIlYsEfGNX9tqeMHWSSBTnVxkiGrj/HSTs5qj3uuFTIwYDcSChs62HWNIBk7OulyD2Z0ICxWJp29fMOfDEBcyGs3PxAx8SE24eEeGwgRv/lFJ3ycEUJ9xMkuHBx8tOIldn7AziGOdKizq5098myIQz++aXevcSoumJAXOz/0oZse94wXP/hOD5vmm+fsfve73w0xA+CUQWAQGAQGgUFgEBgEBoHzIyChlbAqJd+SZ0mwo1WzknLykl6HBFtCLtmlwz3n9F3He7Y76pceCbRE2VsZrewgC1ZXrG5IzCXefJGU81fdyg1ipq+3Mv7t3/7tRjDIIQ+KmMizKzEXi1Ujz5ixrY8VI3FJ+tmK+LmXPToRSHjBh1518ura2UEI9GOvQk4JN3357z5/2NWG4ChsaqNH0aaeTfJk2eUrW4hHMuTJKNrJhYFrK0sfffTRtqoHN9tGkSptdLHLVnrU6abTdfjwUyHbvdrFBat8QvDIR5jIK3SSNTaO5OmBLXkybHRPnT5Hc5jv+pOjTz/+q4tp1aGP1cl33313I+leNOM/Asy7Yg4zuFUa0858dVTc73CPbeSQDD0d87r8EJvzIDAIDAKDwCAwCAwCDwiBNYl0bRXGNjGHBFUpyZXslwBLgl1Ljm21syKFJCmS3DUh3W5e4R/26c0nSbBDEmybWsTM6g1iZnWq55asqFjlYNvqkWJLIoKBmJH3R4Jtn5MM04eA0N1WwOKzjc7LL5BNr9jXR6zqbRlEnJAdq1nwYV/C7+BH2+ZsI2TD1kW+qTv3zJkVtPrwq7p2WxXpt0JIh5U/eNCBxFjR4jMc6ECm+WnFB/FAMI0nHfTyk2/6khEDnQiBLZXk6BT/xx9/vOnyfBliqj2bZGAMFwSKTvbZaMslrGwzVMRuDPRhg12xIjr8sJqYn+GLOMPX9lExsWFuuMcmeXOMD+JXJ8MHc4KPMFQngxjzQwwRLHjRyS+4elYORmzC1Rs9PWPmpTHf/e53t36RKHPU9TrPm7edj7Vr6zCm7JBrrhuLIWbbtJl/BoFBYBC4XQj48l6/9G+Xd+PNIDAI3HUEShB9z0hQJbeea/JclsRcki2BlhhLaCXf+kTU9EESJK1vvPHGlrxLMMlok2SqX6Xoo+STa335JhGXaD969GjbyogkeFOgZ4Uk3sihxB/ZIi95d+/x4dklxEyC7mUg/ig1+YiUuMhK7CXJknekwXNFtvIhFohZ7fq2zY095MOhH2JGFzKLlHnuirxntGCBZMEDiYUl4isuq35IlD50tgooXsRSHF5cQlY86l5KgtxZ1XF/ffmHmBAPfiE3SI/VLr5YMVKnQ0yIjOfv+E0nP/jpGTPbOY23Z7yMMSzIkUfm2HSYI/AlGyHy3Bqbf/EXf7H52d8LQ6jZFad++htDsfILaUPMzEN4iIFfEWn4aRczeXOFDnrFIl4+qPMJxkhXz8Lx3fiYPwq75gJ5Y8JPZM0Y6WcO+BysxEy/U7/L6xxObpVd25vn5gaZjq3fofHz9VR3pgwCg8CGQB+c9YO1Xt80TOtH0fX6Qb1pW6Pv9iEw43/7xmQ8GgTuMwL9zohRgii5lZzaxmfVRNIqGZYgS/Yl5hJiiTdS4H/7kQbPYVmNkvwryAGiI9m9CWKGUEiirZh5IYMVLi//8HIJibZEXTKPHInJPeRD8k+er0iCBFvCTd6B3PAZIZCQI2Uw0Mcfp7Za9Prrr2+JPL3k6CUrPnZgog05o0/MkUREB24K0qjAUhGToj+M9UdA1OlVFxPd+jQ+8EQknNcVH37RKW9AXugUDxmy7in0asuGMYYJImU8xY94+Vtu8ISbA6EqTtjRh4yRoZNdOrQZK3V9nOGRHXb5xZa4YCpuesQAM/rEAkt2nPVpDGDBDt30OehgQ39+NQbFTl5sCrv6kNcP2YY7YmfuInvarJh5O6d5YyujlTc+0HmsaFM6889R6b6zY9+e3KyYhcScB4EdAj44/W+GplMfol23Z672YaXAtQ//+qF+ZsXT8U4gsB9/Y3/qB+BOBDRODgKDwJ1CANmQqEpgJba+gyTLEneJrOTZKoJtfogbUuYZJH/rynY7Mn4zHb67rvP95ftP6ey63z8+IYRe/CFRvji82dDbEtmTSJOTePPPWbKPEFkh8cIQW9S8WY+vyAMbyInkm48Sf33pcg8pQVCtsrFDl5gk/GwhWepIgEM/BIgeWCEdESAkgA2Y6sM3RUz66Y8U6MN3OiJV7kVWjA2ipg2RoFM7f/RRp1OBi/v6IDQKu+6xSZYe19ls7PSxWuXlJwitl3/AjZ+RST7BAhGlg106yShs0hPpFDfMybimxzUdClk66OWH+tqHnWKrD31kFO2wcIRf5I4P/KIz/9XFT6d76g5jLx5jaL797Gc/2/6TwjNm61ZG9tiGpzgqzd3O2h2V7nfet5PTNsQsxOY8COwQ8MHrw9cHaP2Q7cSfu+oDuR4+8OuH/rkNjIJbjcA69tuX8+EL3Y/HlEFgEBgEzolA3z39vqnvf/sksJ9++ulGymzvkvj+4Ac/2F4MYYVBQt3vlbPvrvSd8p2dy0rtkmWkBFlAzJBAK2YSacTCqgqbVmqsekisPReFmL3//vvbs0VWPRDIEn8JPP10SOjFoy6BRzoRM8/N6ScWMlaAJOX0Wo2zRQ7xQmTYRZhsRZTw8wUp4CscrDKKwzY5PtCP4Fn1s1WQPnUrePxHJq1cWR2znQ9R/uMf/7gRLLGJ1zNTdPODTtv3EEw6HDCjlx98EysbYlBHoMRCh62KVoT0t1r4y1/+cmtHaBFU+vhvHkR+xY6QihXxEwedjw9bLmFGJxl4wA/5gTHS4542RIuf+vFJvPxEZI0RIq1NHGS10wVvvujjIC82c0GdTw7bFmGs3dZGWFhxNc7swo0NpJBOtthwfnTYOrt/xgzu8NO/ed4cbr52dv9pn4Fj7UPMQnTOg8AOAR+8/Y+TD9GxD9Ku67WrPsj7wxebD/6Uh4GAubbOAfPMD8CUQWAQGATOiYDvHkm33zzF704JqO8kiTKSYIuftxy6hxx4W59VMwmzRNt3lkT3qr9d9FxWtNPJP8TEitl//dd/bUTEM2bssMu+QzItGXdfMi4m8oiTVQ8EQ4zIBxnJuD5i1d9ZG/KHBIpNP0W/CKgEH7m4OKzcIQKwUUfcEDM6rDYhK5Eoz4shFLYFsqWuDxt85SOiZXXOc1KuETHXdCAd+iAzr7766uZrf4OMHwiHPogSHUgWMsIG4oGsiBX5QHj4iuwgUXDwLBnfyVsxhIGY2bJqhvzppw+9/EDW+IVEilUcxgmBN2785j+fxK6P3zQEkh4+0MEmGdd8R7j4iciRQaCqiwsxhK+x5zMb/KCXDn3g6Xk5JAsZ1u75SVio62uumK90wgAW/DLO5oLVYX3WrYzmgrnT3BRnpfnc2f21PbmnnYeYPQ2haX+wCPhSepnEzBeDL4Ap9x8BX+T7wxf6ELP7P/YT4SDwshBYf9/6/uFLyaQzUiZx9vecrD5ZQZDIvvXWW9tzN5JxCXtb9yS4fruuUtYEdi/Pdv5pk/DbQul1+ZJt2+taKUE4JOMOfSTu/HCWXEvArZYhZu5J9H23IgqtkkTMkDmECAkVm7c5WsESI9IQsUA04NCKDv+QE3XEjE19rNj4HUdeYMlHdQQAORALMsEn5IEOZE8bmfwUI+KFgFmJUhBCfvALiUGY1BEVeuhAeGApVoUe9uFDXjuf+AE7flhF85whn9ny999goY/++uQnvPQx5uKQN/GLHL+MkbiNNWzYzi4d/OInMiaG/FZnz71s6Aub6uJSJ9fWReOozif40Wec+IOsNY78Na/ZQe7YQeSMozp/rRpaPVxfl8/mqdJ87kzuMnntMK8k+6CJGfBWAAOn81W/XJKf8/1DYJ0ffWiuE2VzLD10HJtXq1zX5HwRTXlYCDRXRP0sc+5hoTXRPg8Ca1JwTM+x76pjcnftXp+x+Xz96cjBRdLbf0paTZDwStK9eEOSinBIYr1t8LXXXtv+zldvudPfb9aKa1j/qaXPa8mucl1rq10S7Zmfn//851vCzzYyIQknL/E2nxEBBMHc5atVNispyIXVKgV5QdDIiI+8mLxJUeJOJ2KGUPg7ZrY0dt/ZfX6Jtf88o4f9VgxdJwNP5CNs2Hbt4IODjKM+62fTPUc+Ix/i5Qd76nQhGcZPLPVX1w9BEVd+wYw97Qof4EIHQubNnDC3Kho55YM+dOQ/PemABRk66ErGveLnr+va4akfH8npo40/2SPDBjnt+iuRaT7oQ4asvmTgow888nO1oY+6dtjo0xggl+aOVUnbOb38w3yjh35nfS8rZBynytr/T64PnU73OqXtntxv4p4KB/hTBoHnQaAPZh8zH75j82qV65qcL6Epg8AgMAicA4G+l07pXpOFUzJ37f4a832M73nHo2RdfuRa8molwZYuKza2kDkkvVYS3n777W27W8kv+zC+CrarTOPSOT1+B8lZpXl0eOYHQbQihDBYkbGa5XfSKgyfrKAgWgoiSR5RQC5sYaOLrw5kSjJuhURiLj6yVlr0dU9SbmubhB/pQywQFv3YQXiQJH3IWwFCEOjmu1UisnQrVqcUKzPai4H/Vtr0F5MVHzGzp24syCAa4l/rdCIp+vCfTf6yyzf4wCVSo42vxpYNuvRxj7xX5SMlrhFg46w/7PhNL3tW78TApjZ1bVb+jGOEVzviBXO2YaNOnk11h7pY+UWPWMWvqPOTDn1hFb50suGgg4y54NCfr9r0CU82jJmxgqc+ZPThE1krZrZ0tmK2J2b8EvepAoN1Pu/l9D3W/0GvmA0x20+TqZ8DAfOsD6cPoS+TfekD3Fk7uWOy+75Tv58ImAvHvrTvSrR33f+7gvP4eT0EzEvF+aF/v576jPrNckhOyUiIJcOS2ceH53BsKbRFUKLq75d5VT7yIvHXT7Lr+mn4rt9v67g0onTRQU7i7m+SsY0gIUwIFSLDVsRGEu6ePv4embcySrhtfXzls7//hWTQgcjoixyRJ2elREIuTtv49CEj+fdyC2TBM2YITc9Q2SKHjLQlUJvtgBJ8OuDmb4PBsGfyEF33ba8UB530eDGFOjLMB4TJPYTBih5iYbUSkfACEeTJNkMEEWFU54cYEQ/3kB1jBU++wYgMsoNk84sOdmECN8/YkUdoPX+GvIgdwaQXxhHEnlsLU34bN39ugB9smBP00yFO8cBdPLAyfuYQvLRZtSPbamx9+G2uaIefdvGJy8EnsZq7xkSbePXnOzz14Y+6GBF28Vhh5QefETw4IKgRM/6yLbbm6zqHm7edySTXvfWs77H+D5qYXQYY8I4BtoI614PAVRAwz3z4lT6Ix+ZWH+LmJZmn/bBdxf7I3A0EGnfeum6u3A3v/8/nfL2L/uf7nO8vAubl+jl76N+vfpcu+56RsJOBmSQZXlZMJKu2FEqOvRzC82aScORAsqwPUnIsgT3222fGNS6d3UuWTnaRBeRM4mx7oTceIgP6SMIRBn5KviX8/EPMkC0ECMGQcDvISd7pQhjYiPx8+OGHG+mRkNsCKS7ynlXiE9KEAEnokQDJPEJDl1UidWQEIeAjPJAoZ3W42CIXUaODj4gB0lDds24IGLJCFoHhB7sIBz8QVoRRTDAQA3LCF6SMTnjQiWBHzPgpZnbpYBcO+lsxs5XRfbghgggRgorgRF4iKuyKiU3jhEAqSCjbCBAiRN684BNSSKd6xAy+CKI2fWCtj4L80SE2fohNPOIyPuIyJuIiY2zNB/OADfr4TlacxkKdTjbhSSc9SJo+iJktreaB1+WzpRyb11vDkX/W+Xyk+ckcX9uGmK1o7K77UtjdnuoDQcAH6rIPlflxlTlChy+Big/1sX7Z6nxV/emd891GwLg39s7G31y5K+Wu+39XcB4/nw2BdX7SoC7Be8jF79JlvzMwkrhKlGElyVWs9iBntgkiRp6/sSIl0VaQB4mtQsdqw/WxQk7p7Lp+VrIk/4iZRFmyjjAhKBJ/PkrcHYrkHEnwbBmShcAgcla4EBhkx3crvfpL1MXIhmQdKbGV0TNp/iQAgiF+q2Z8kvCzSY+Eni6ExnndVqeOCOiDWMEFWSOLOGjXH6584YN22KlHHBANepE/+KiTQYKQE7GqIxXGlE51xEoffckobKuzQ5YfYuGne67hhQAjYF6agnTDSWGTn+riF5s6P8SB7CE4zmzSK3YHeTbElrz+dDqMjfnEB35qoxNOYlGno3xKjMYlH/Qj72CPXzDXH2kTK/l00BleMKmuv7ngjZ7ehLkSM7boa56qX1aSOyVzrP+DJmangJr7gwAEfKD6AjiGiA/nsQ/VXpaeDm19sPdya70P81X0r/3m+u4i0BzpbOz9iNyVkt+d75r/dwXnm/TTWF1W7sP3TzHuz+K+S5+vy8bpWdv8vhnj/Tj3GXaW4Eqy/d5JaGEmafXa/HfeeWdLfm1383fFeqW6xFaiSz8dq429rXw/Nj714wObthjaQmnV6Rvf+MaTLYvsIBGImWvJPfJi+6A3SSIsCAYiJ5lHGKyKIAgKQskGIofIeN27+Nj5yU9+spG6YhGXfuqS/mJFNMLNNbwQBXLi0C9MEAA4IinFWN/wcEYm2IK7wziQE4OCvLCljT3yCjKkjpi45xqRYiuyQoe+fOSLWLTTaXXPSqO+tqlaFaWTbe3ObDorYuSb+OiANzvq5NhS50eEUF91BfaRJuSMX/Skjww/K+Qjue5ln876kF/rrslVGhux84M995wdVhL9xwNi5nX5/YFp/fWhTyF702WI2U0jOvruDQI+xH34jgXVh/lY2/7eqssHuS+kvdzUHy4CzRFzzmGe+PG5K+Wu+39XcL5JPyVAlxUJyF0u6/d31+tZfOdIrO4yZj7H5oVzRzj1nSSJ9reu/uVf/mXbliZxf/PNN7ctb5JwybHfOHr6LtO34xg+67jUnrw2hMkqndWsVw7PfX3729/eEmnJtGRfso6cIQ6Se2RLUv3o8MIQpOCrX/3q1o9fVpQQFXoRCuTKNSKCAPqTAAiglTKEkzwyhZy4Fj/ChPxZncs2v+nikwNufHOtD1z0h4s+2tVhRj8/IzL066OQIUvGbwJiaWysMvFbu7hgRDdiyi6C5AiP/GCPDj6woQ4POmFoK6I3cdJn1dAWUHGyzaZ+Yuera37zAz7sWzFjC/mFB4wd/OBXhJJNthFquIujLZfGQt/8Kjb3+MkePPkkdjqzwS8+wYt+dsnzlbx+8KWzPu6TpzvsrJhZOV2JmTlpLOhzfY4yxOwcqI7Oe4GAD6jjVPFl4LhK6YuEbD825/pQX8WfkbldCPiS3x/mhx+Lu1D2vqsrfgCn3F4EJE+N1TEv7/r4FdupswRrvoc/H3k4mRMOCbbfNxiZB+qSX9eSXqTHiplnqSTvP/zhD7c/RCxxhikdFfX16P563o+RNkmz70A+IBhWcZAzz1QhTHxxIGHkJPPkyEvoXduShyjY+ohgiKk4EQF+ia04PYv0wQcffOHjjz/eXkaB5IgRYfESDoTEyprtjPxgx+qKfogJX+DkOSrXFxcXGwmAF0z4UB+kyPNNyMj+GTPPxyEsyA2b6eSzlTzFyhYyYWsnHPhFTp1t/fmp7vktZAQW/LS6SdbzXfBggw5xkqkvWXHa7sm2OBEa95A1RI2NxkFMdMKZjLMtmTCmA+mhG0kSO/2woZdP8DHu6XBPX+3ui8McYxeexodf7Bp/ccBTXR/j6545qw97SGl+us9PZA5+8CRPzhjqM8Rsm27zzyBwOxDox+KYNz7w1ynpum6/69gY2buNQHOkKO7aXLnr/of7Qzlf9h9PMJBU3cfSPL1rn6+bHguJLkJinJsLMIGPNkmrhFXSLel1T93qhteI+7ti+tnKaKsX4kKXhDm9+fw0rNncH3Q59JUw25b405/+dEvwrdCxV5HwS6bZbTWEz1bYEIP+jpkVFPqKzbXDqgrC4Rk2K2aIg5deiMVLIJAJL8hABCTsDsQEcUJMEBqES1LPvnYEAClCgJA7NtURC0RMHfGgAwFQ92ZC/dhDzpAOMmJ5/NnbDtVh1cs/IoyICD3a+SJu5JSf/EZwIq78MHbpIAND/dkSN3n4wwbZMfZIGxn63IOZWNhDcIpVf1iYO2IxfuJiQx91eLqXD/xGvNhAzMjCA5500KkdXuJwwId/fEK2+MSuOYpoaRMbe2QQVjrIwgeO5g1b4i1uZ3PJi1f8LTt/NoBeJTnt4rvpMitmN43o6BsEBoFBYBAYBO4AAiXjp1yVlE25vwhISpGSCFEkReIsMUYUJLoSWzIVW91s83JI0K1effOb39xk6Sihd33VQr/5mC/1MwclwMiCF3m89957W0Lv76dZcVH0K0lmk98ScWTICpuEnH/ImQRc0i0xJ4tsImESbIm+F5sgWRJ4Lwuh2z2YuIecRCwQBPHTQZ829+jjL51IGDyQC/fZFCMf+YyYkNNOTn/kkc8ICvlIFbKnrzq/+EEHkoVc8J8vkSx1eumnUx9+KenwdkhjzTaf6CRHH1KCbImBn1YlkVB6ER6+6uMeGfgo8GNHH4RNO8KGRJETm36RLCRKO79hiFBFbJE3MdEJP36TUY/sic01m/TzTd2YuDaH6UfuzCd+iVGdHfGImzwcwks/Lz6xVddcEEvzVH/Yn+M7cojZNo3mn0FgEBgEBoFB4GEhILm4rJwj6bjM3rS9HAQko8YaCVC8pt1Kk7Pk2zNdEnSJtSTWNj8vx5Bg28boeS9vrtNekcCmr3uXncmvB1kEig6+sYuYOayseEW/1RRtDnPZQYfEGgGzEuPvnulr1QMxQy4k4BJ78bVqItmX4OsvZqTE1jvYSOARHPFJxl23xVLizz4i4B49+YJ8RPrI0YWkuE8XH/Vlk5zCBzHTp9DpnoMM2cglGdcIA/ICL7Hr46Cnz3gYwcK4uS+Ox4dVODjR7aCTPthaMUR+2fh/7d3Xj2VHEQbw84QlEBkDJq6JBpNMWDIYMDkZEwQSFhJ/GOIBBJicMyzBgFmSAQNrMJicQQh44Yn5tfYzTfum2ZkbZqZaOlvndFdXVX995t7+tvqcm3GFEImHHiIUH/FrnCGA9OAtPrjS4Z9OiBhdvpPxREj10VedMcGMHz7FwwayFcLIvmsYwIikrz8/+stA5jfo6Bt/CFniNS8II7KLmJEyZQihOBT2lf3c363Div8UMVsRqFIrBAqBQqAQKASOEwJZYMwb07oWHvP8Vf12ELCANdeZ75tvvrm9mv7M3oszLJAtTBEVGRGLWQtcmQaLd6/J92INxC1kwCjcW7G3yqjo536MtHhng7RYt5VRTLIoXtEvJj7FT8fh2uLaAl6M119/fSMe3sh4au95LwTAot44ZGbILMYtxJ1b7BsrMmWBT99CXhuCo49FutiQBwVOfOtD12GxjywgNWzqi9jEZ/Bih55x8IHs6Ms3+2yqQy6UxKWPevbpak9Msa0+cYhJnMiZegeiCBPjZUsd7GDMDzvqEVLxiZ0f41evPeSSb4fCF1vmJKQHMaPLv5JxJJ4QNWRJH/cBvyGi+rCBbGVMYhUDXyFmmWNjE4f+4pbplf2UoRMHO/rD3PiRMKTMfaDeGDM++hmbONZZipitE92yXQgUAoVAIVAI7CgCWQDPC29TC5F5/qt+vQhYtFr49gtqHj2rJJvkzYskUmRhigAgChatFrDImJc1eBmFxa8FMT0LWnI/pb8X+3OxWXQjWV7KgWjxa/ukjI6Fs0V3fIsvhwU4fW8YDMFBAkKWxI10Go9FOfLEH+LBpzb283yTBTs7yBV/rvVBEpAJGLDlmg47cEBg9AsubIvZOENG+JCtkqVT59qBPKijCwPzBftcs+UauaCPeInLXPGBfJpD2zrZF4+YkWo6xqHvqT3S6to4YCAGpApWxuXgkw0+cy/wYZzGp06bPvywQx+xFTeMSfPCFnwV49LHOBEkc4RU6c+vAlOxImYIHR33b+bEuONHDF6M4tnAW/cygsbBn2I+Zb9kXeMPHvyS/Imx19cv12Jadylitm6Ey34hUAgUAoVAIVAIFAI7hoBFtQW1hahi8WnhaWFr4Wsx7PkjW93oarPgtmhHaixw88yPbIoFupJMSru4wH8Si+7OkQ7bEr3G3tbKPNMWv6RFt4N+FueImd8zU7QhTHQtzB3GRB+hQoKME3FAKGTk4OPlFhb3CCFSgEggDjBAipzro40d2CFECtLDB+LBP3ICQ0SJX2PhF8ZseJEHm3waM31+xSPjg9yd2iNRpOfD+GZDhksfNhAOJElm0/yJhV4IlHF7Zkoc9JEdWxbhgpTDzry6RnbEbVyksbGjHZFB5OBhnIiWONlEcIwLYULs9TU2cds+imTxyXe2F4qbDXMkDv1hATvxiytElk1zwiZfYnCYL75gddNNN7VY2IGhMdt6Cz929aPr/neYp5T+3Dy57uuitw5ZxGwdqJbNQqAQKAQKgUKgECgEdhgBC90sSJNVyLVFuQWpRbgjC1OL5BCZbHFznUM/NpIxOcjw2eSXTSTFq+/9rpQsnR+YRj7ij0SqEAmHc69htzhHMC3EkQcLepkWpEucCBIiYPGOaOiHBLCH7PCPSCiIBPKF6CAfsjUyLciHPkhByEiuEQ/Y8qEgH8gJIsb2qT2SYBwIFJKDQLDLhz4IkYMPscIcIdQnWTBjQVj0gRP75lYMSrJBxoQ4idPY+JNRYl9c5plNceX5PWSnJ0TswxeeDteIFVzYgJ8+YmDDHLKJANlOigy5Nh799XPNpjHAC7bmiT2k0/yJ1T0Fm+BnfMYGC32Mn/1k4swze4io7CpSljk09wqbYoSNw3XaUn8Y93JztuI/RcxWBKrUCoFCoBAoBAqBQqAQOC4IhPgYj0Wsa4vekKFI7VmcWrzSyzXSoa9+FrTaFH0PWthmBzGQQfGGRdkvP/x8+vTplk1ChCz6EQ7b1xAx2RILeVkZsSEAXvxhoc+ONsTEol5WyYGchGRZ5Bufa/4RJNcW+EgAMiJzhCRZ+MOETQXpQXAQWLoOMSARITiwYkM/REP82ukhM4kj2/fYMz7khU+xI5kIEIxCRMStDkFBbOjIEl122WWNkMADAWSLD/qwMgYYIVVIkrEiMPqLSxx8wFq72MWANMEBuTMmNoyBb2ODt/tBu3tDLGwgcuwau8wbfTHxZ3wh1uzJGLrOHIjbOMzzuXPn2viROxiZA/gjYd6mKEZkmw9+kTY+xJp7g77YjDlFzMZh7unmXlZvXErIW/ocpixidpholq1CoBAoBAqBQqAQKASOGAIjoXJtQRo5bzjao5MF7DzdZfXsKJGITBbNtun5gWlvg0RerrrqqpY5Q3yQINv6/LaalzuE8FiYI1wW5xb5FujsiRMBsDi36EcKUsc/e9os+C3YERbkKdvoEBXEQhud2GQDUWIvRCPEDYFhx0KfLT6UEAN9tRmPdmMQg4Ot9BUDgpG4ERbEBxmThUJQjA0pQmBl4IxfHOJkD+mAMUJkHPRDcMXEPx/qFbGKx3yIgw2xsktXfOypo2Os4uMToVMHK2NlR5z6GKv5Uac/HQcMHfQVhAvmxiZeZNKBUIsHeTPX7gvz7EDgxMe+cSt8wFnhUxG/utS3yr1/tM+qZ0MZ9VvlIf1TxOyQgCwzhUAhUAgUAoVAIVAIFAL7RyAL3kgWLMQtqi2eZboQM7+dhkR4K6NFvwU+gpFnqSzmERIZk2SSEBELfYv+kAHSYj9kxkKcH+TCuT4IRRbz6rUr2pAVpCwZGLb1Q1rETYodudGWOufq2XCune0QNG3qs/DXD8kRq3p6fNteiYTKPvGFhMhAIVTwCUlxzod6fozHuPhnky3jYpd09ITEmBzBwvgzBn3Y1OaInpjZ1QafjCnYBQv9QwjjNzbE59zYvbzE1k+kDBENETRez5ghgM6TgTMv7CVOfo17PPp2OrtSiphtaSbyx74l9+W2ECgECoFCoBAoBAqBnUDAmkiJdB5yY5GNmJ3Ze1U+cmZhb3sesoFYydDQRbZsN7TFjrTop4u4WdA7R1LyLBMCog6JCFFCFhAJbUiBeEIuZGeQAoQhZCrkhF5sORezmJzTVZwjA4o2xRgQCTra2eY/cfAndjEmXtsV4YGMGrvs2OWXX94yiGwpxhkbiZFd9o3VwV/GKi72EwM9ZJZUp4TIGJuj12XPmBC06JoPc8BH/MQ/veCeWPlnB84ORAzxdJhDhT0ETIbMM2O2LbINP4fzjKV16P7JWDIe/ujvWilitqEZyY0Qd67zB5q6koVAIVAIFAKFQCFQCJw0BLJGisz4LZ4dtup5K6PfMrOFzbZEpCbkAfmwaHeENJAIAPImO+RcHfKCNDgs1pEfRMC5drZIJIFEzMTAjkOMyEQW9ghB/CAb6aO/errq6OurHokj+bLdjg3XyA2yIDYyPumpYx8xQ9aMnY7+eZsjHyFxMMyY6GnThx+HmNhVEht9Og5rVIc6ceegq2hj16FkTNrZzRqX5CvXbMdPHx8b4hK/AxkzVuO2JVMm1LNviKjtigg2P4lXfH1MGTO7SuInFXEmplaxI/8UMdvQROSGiLvcQLnBU1+yECgECoFCoBAoBAqBk4RAFsuRGXsWzgiKFz6cPXv2thdUIEwW/Bb3IQ/WVFlwIzkW+lm4s8mePiEKiBNixj5iwT+ShEwhfhb+Fvjq2WKTPT7I2NMme8WGvrYW6hMyyKY++iOB9JyLI8RMnYNN5FF/cfGDiHpuSpHJQ1zoGTff7PBBN2Pgmw96fCva6WtTZ2xpd61vsGTT+F3rp4/DuTqxksn4qedPHbv6x35kfIiFniP2Ytu1OPhGvrx18oorrmhZQdtT2XWIW0lc+sRvxtQUzv+jXYkUi2PXShGzDc2IGyEHl87dQLt4U2wIknJTCBQChUAhUAgUAoXAbYvlLJpBgkBYYFuEq5clsoUPMbF2CnGKbogCYhRigyiEiCABsRebFvV0kjVzbW2GFMi+0dc/fV3TUZf4UodwqUvfxEXyp9BxhHwgUzJC2vUVi7EZAx26rj1HhaRETwxi0se4XQc7Ovor6rPO1K5P+rKviH8kTnTVOxQ29O37B29Ymws6bMe+viGkbLhOLGTWwMbs0I/ftMER+TZuGTOkjA1xR4ddfXKoT8zabXppAwAAIFRJREFU5hX6Cv1dK0XMNjQjuWl66Y9nF2+KDUFSbgqBQqAQKAQKgUKgELiNVGTBDJIQh5CGnhRod522rK3UKVn0O2enJwvWXv3iXV/9kIOQLaSAnnp18ZU69thwWMfFBj11/Mc3/4kzJCTx6Yu86BMbpHq2Yk/7qMe+dvb1cSjxn/VlZNoj9VW052gVe/+M9lJPpj8MHMaSQ3ticq4++q6V+BKn8+j38WhLOxsZu7kwXtfmIqWPN3Xxk+ujIouYbWCmcsOMcvxw2EAo5aIQKAQKgUKgECgECoGdQiCL90jBWVgr6izaXWex3uvlPAtx1w4L+CzwYyc6ri3yXVv4k+nX66SfttQ7T0k/MqW3o66Poycf0e/7pm60EWJp3SheBE8/BKWPJ/0TV9pm+aA7q16MDn219xjqo96Y0hbd4Bgdkk5icJ3S1/fnaY/U1hc4hJilHxm9+IoU+1ErRcw2OGO5UeIyN1KuS55cBNwbOaDgfPwwPLno1MgLgZOFQL4rsoibN3oLoUXFNig6s/4TkI/+Oyg+e3sWQP3/SmvLgix9Z/WLDTrRS11k+o3t6nPMGl/6kX0sPi9jKzq5js9R6q/0faPDRt9/vI5eyc0gsGhO57WN9bkepXnOXEcedFR8xFZ/fqF22Ygddp0r8XGhduf1i/15PtIe/4ltnr3Dqu/9Os/1LPtiS3zaoxvZ94leZN+26nnsRvb9YjeybxvPi5iNiNR1IbAFBPwhj4fFwqyFyRbCK5eFQCGwIQRmfanPc73sSz7Ejt4yXT7o54hPn0Fj39hbFuv4mcZm+sa+a591fYndEC9tiSH9o5N+ac+19ow/dfNk+vbx6pvP395XT+LGuOfZr/rtI5A5jBwjyj0QObbX9e4hkL/XeZGZy34+M/eRY7/oRo7ty65jN3LUj93IsT3XRcyCxA5Kk7tsAncw7ArpAhAw1/3BhC/9+uK/ADCrSyFwhBHIlzq57O+fzrLvCDo92aIfu4hPPmfUxzf4etupp7PMXw99+sUe2ff38gAlMZC9D2+eS7t6RCmxt4YZ//CZY5luurM9xprrWTbgqX0WaY1Nkk4/3r6tzjeLQOYzcvSeeYoc2+t69xAwl/PmU7Tmsp/P6EaOI4pu5Ni+7Dp2I0f92I0c23NdxCxIbFmOE9lf95PYn2855HJ/yAiY8xxMm+tZi4JDdlvmCoFCYEcQ6P/+hbTs7z8Zof57oT/PsNhFwuhrD8FJ/2V++i1/bOqnzgsSDlLiv4+5P88zQHyIUVvfHrzYcd4XeuM2zL59lXM2e3/pk+dcjH9We/QSX6/Tn0ev5PoRyP0ROXrMvESO7XW9mwjMm89E289ndCOjExndyNSvKmM3cuwXu5Fje66LmAWJLUsT2U9mf55JJHO+5XDL/RoQyJxH1nyvAeQyWQjsKAL+7vO3L8T+fFnI+V6Y95nBVl4eQCckJ3bTP9eRCE9sRoaUsec134sKndGfWNhweHFBCqKnLePWL1sJ6bgeS/qwpR8dZCy6sTX2y3XfXx1cHCF02hX2YpOvEDOvCKc/r/RxjXbm9an69SCQeyFy9JL5jRzb63o3EZg3n4m2n8/oRkYnMrqRqV9Vxm7k2C92I8f2XBcxCxJbliYyh1D6iTWJ/bHlUMv9BhDI/C/7A95AKOWiECgE1ohA/tZHyWWIwTz3IQX990P/mZEfoU2GbJ6dZfW9zT7Ovn6RjVEvNlJvnCE77BiXthC36EXGV+yQSJB+wYRO2qM/S9LJwX589/17v9HV3vuaZTvET//+mKVbdetFIPdC5Ogtcxw5ttf10Ucgcx85jihzHzm2L7uO3chRP3Yjx/ZcFzELEluWJnI8EpJJzLHsiyB9ShYChUAhUAjsNgL9F3jOR7loBNHN9wPZf0cgK0ra6eeHdL1u21Y8+g7kLQTOtT4hRmzo5wd+kQ0/+nrXu95V9ULywx7ChSDmeTI2ZdpItkZSJlulTQx+SFhxrt6ROFvD+X/4yG9NBQN9+vh7/ZzTTdE/eAYH7WN8bIqj7xsboxQXm3RziKvK5hHI3EaOEWQ+I8f2uj76CGTuI8cRZe4jx/Zl17EbOerHbuTYnusiZkFiyzJbHkxoDiGZwFnHlsMt94VAIVAIFAIHRCBf4PMkgrCo5BmsRd8RvluQqr/97W/Tb3/72+nWW2+dfvWrX01//etfJ1vx7nSnO033ute9pksuuWR64AMf2OQ97nGP9r3jdfv6I0i//OUvp1/84heNZF188cXT/e53v+kxj3lMI03zYvzXv/41/fOf/2y+2DBOpO6e97zndJe73KXFE0LFBsIjphA3/kOw1OfoCZf+9BzIn3j1WYUAiUNBHPv+8IzfEMtgLTaYieXOd77zQj/6KIvmpynUP2tHYPwbGx2aIyVybK/ro4/Auu+Bw7JfxGyN91omaZ6L8QNgv/rz7FZ9IVAInCwEfHbk86M/t9CtsvsIZM56KeqegMwaBX2L/+ghJghF/1IOBOyWW26ZbrrppuncuXPT73//+0aW9ENM6MsK6XPZZZdNz3rWsxrhQj4QFvfQH//4x+kjH/nI9IUvfKGRtIc+9KHTS1/60umFL3xh8y2OWeXf//739POf/3z61Kc+Nf3oRz9qZOaKK65o/R70oAdNv/nNb6YvfelLLTYZPMX3opgQIiRIDEjQIx7xiOlxj3vcdOrUqZatu+iiixoJM+abb755+v73v9/ilNX7xz/+0doQW32RNeNlkx9jY+cFL3hBs0v/s5/97PSDH/yg6YiBvv5wSWbPOb/3ve99p0c96lETHJBUdTAQq9hdszHiMn7nz8Ks6g4PgR7//nyZh36e+vNl/fbbPium/fpjw/3tUPR33+7Xzn5jPw76sNvvHIy4pn9s9dJnwYV8BxcxW+PdlQma58KkVSkECoFC4KAI5LOml75ALuRL4aCxVP8LQ8DCqp8/VkK4FllETBABJcQAgUBCZIG+853vNEL1ve99r5EqRCUZnzve8Y4tm/b3v/+9kTVE6NGPfnQjZ4997GMbAaGLmL3rXe+aPvnJT050H/awh02vf/3rp1e84hULiZks3U9+8pPpuuuum7773e82ovW0pz1tevWrXz2d2iNGv/vd76aPfexj0ze/+c1GmCwofS86jEt2TcYNLve///2nRz7ykc33pZde2jJ7d7vb3doCFOm7/vrrGwn8y1/+MiGE7n2ZwmCBkCFY6ozTGF73utdNT3ziE9uYPvjBD7Y4+NVHf2MVCyzFADsSbg95yEOm5z73uY3Mio2+vv7uQswWzVu1rR+B/D1dqCdzOS7EL9TWrH59fPETOUt/Vh0b7usQM/drEbNZSN2+rsf/9q23r8ncRI4asRdJz3zMK/PsFDGbgxhg54E2p8vtqjM5t2s4X8H+QX3Ms131hUAhcLIQyOdNpM8WX9BV5iNwGJ/z863vryXzFqn3KsS6J2ayPPqYe4QGMfrMZz4zffzjH5/+9Kc/tQzPk5/85AmxsQ3PVkMZJgTpZz/7WctcsSFr9rKXvWx6ylOe0rJTdD7wgQ80W8iKTNFrX/va6dnPfvbCRSBbsnTvfe97pxtvvLERoic96UnNNjJjeyNidsMNNzRi1m8TNK4HP/jBt40D6XQ/3+c+95nYcMhWIa/I36c//ekWP1JmmyT7MmH6sCUWBS6KrZhveMMbpssvv7xttZTV+9a3vtVIna2cD3jAA27LsLGD2PEFC7giX7ZywtMh+xhyzeeiBVkLoP5ZOwL939KFOFv3Gq2PL2vByFXjZcN91xMz995+7azq7zjp9fivMq5gGjmvD7vKMvvszPqcKGJ2HtkAef6yAToLsLSvIkebY5+095Pcn4/6dX24COSPpse8Pz9cb2WtEFgvArmfeXHuXj7oZ9h6I96+dYuZ8W9+vN5UlOP88buMWOuDMCRjZjzmXL1tgjJcX/va19oWPYTG9sPnP//5jfCwLRPksN3x7Nmz00c/+tG25fDhD3/49LznPW+65ppr2vNnskwI1JkzZxrZQcy0yaqx02PWn4eYvf/972+kSQYMobIF8t73vnd71g0hQtpk5vh9whOe0IgfgomMIUKeictWTGOTdbMN0dZL2S0ZMz5kBWW1bJc8ffr0JKMWO7IKYkWyEDVEynN1yJ3tnbYyImayjMaHlHrmTgwyhvqw9etf/3r64Q9/2LJp7D396U+fXvKSlzSy6zoLZDjU39+m/npm+3GvHKT09/JB7MzrK77EGF+R8/r09X3/3g4b+7HT2zxp58Ft3rj79mAaqU/fnvNe5nyWfZ8PPjPGUsTsPCLA6wF0DrR+Akbwll339mbpxmd8kDmfpV91h4sA/PuFWeF/uPiWtc0i0H/e5LwWhovnwGJdyefuNj8DMmfiyfkq84eYISd9QaR+/OMfT29/+9sbqfE5h2jIgiFTIXJIByKDQCEbn/vc5xqJkxlCSq699tpGoELMvvjFLzaiZBvfa17zmukZz3jG/xGzHj/nYpPNkm2TObv73e8+PfWpT52uuuqqRoi8jAThQx6RHhmoK6+8shErYxIX4viHP/xh+upXv9oydkgU8obc2YaIYPEhK+cZMUTLFsPnPOc5jVTCsCdLxoxkIYliNFbPwcm4ffvb327+8hzcqb3tlvrKLIoPbnD1rN1Pf/rTFhcMrr766rbNUsz0zZ/DoouPKttBIH9HF+p93XOX+0R88RW5Ssxj//30XcX+SdBZdo/07cE30t+6z6e+9PrqF32Gayti1qM3nOcGD6jkuj9ULQr4Mcn9MYRWl2tCwB+Vo8c+f3BrcllmC4GNIJDPsbqfF8OdL9Vd/AzId8PiEUxtYYA0KOlj26Lntt7xjne0jI9tezJlz3zmMxtx8bmXElKHfOmHBCU75IUbXosvy+QZLOTFNj5bIREz5IjvefixifAhTYiZLJltki960YtaHDJRH/rQhxrRQeIQM9knJBIJkr3yPWzL4ze+8Y2mK3OGMLEjayYW2cFPfOITjTTJkrHj8IIR2yN914pRrIgZnOjBweJIxs12T8/B0ZPVY9tWSnHQgZNYZNVg4WUj7CFmMpGwCjHLXOjHXpVCYBYC7kOHkvskcpb+WOf+1b/usxGZw7nu54fFzE2kz5VskZ7lkd4s4hXdee2VMTuPUCagl/nCCYiHLU3qSAz8gVXZDAKz8PeHkj+6zURRXgqBQmBbCORLNX/35FH7DA65yHeX+GWAZLeQDcWWP+QhhCffO4iTZ7JcyyDpq470ggsS2UCMkCvEDEnLizOQknxP9hhmPn3GIj3vfOc7m7SdUiYL+ZLp8vp9b3s8s7dFkq5M2Ctf+cqWMZPJQqL414YIffjDH26EE1n0hsYQIoTSs3QyZ+LQnsMWyRS2zLmxIXS2TqrzUwAyd3xYSHnjInIWYpf+4pFVkzHTB3HzfJksoGfSxAwzh/kgqxQC8xDI36x2920v28WSf/zdOtyz6Z8ubCtjfdpLLkegnx/awTJSu8+mlNTnmpxVl3Zts9qLmO0hFPBH6UN2FmgB9aDS/9bymcnp5UFtV//lCPiDygKlx36dc748qtIoBAqBTSEgSzP+7ed6UzEclh+fZ75PLNJst/Ps1le+8pVGDrzg4lWvelXb+ocsGLfPvs9//vPt2S1ZMC+8kB1CzLw8w3ZF5ARh8wya7YgIiSyWbNQb3/jG6fGPf/xti0K4jURETIjM2972tkZ6ZMwQM4TKb6d5g2Keg4ODrYkvf/nL2ws59GXP97DilfiegbOlkS/Pgb31rW9tJNEzaEiobBry5LtVf2NBzLJwZQtGfMNE5g5ZRBARM1sh6SJZxq+vGGxlNG62bz3/O3BiQHRtDUV84Yek8hF/LfD6pxCYg0DWnJrdT71sF0v+6f/m0z9d2FbG+rSXXI5APz+0g2WkuuDctzs/SCli1qHXA6y6B79TO7TTTfs7tMCPiaERf8Na95wfE+hqGIXAsUBg/Aw4in//FmfiRrSQCEeemZKtQn5khmw9lJGii7DIMp3Zy1TJgnluy9Y+hExGSRZJ1uhNb3pTe0EGYub5M0TP9kP2kBrbCW0VVNhFiJCTFNeyWDJmCBryYkslYsafNyTaFui3zBRkyfNnCA87tlSKBdGxXTFk03NxMmZseRZOTNr8NIA5RTD11S9zDJ+8+APp8tIO2xWRNMQOMbNN0TjgoMC27ycWdewjb2JFzIIbX2JLFrEZqX92BoHcC31A5vuoloznKI/hqGK/zriLmK0T3bJdCBQChUAhUAisEQHkBxmzSLNAc47EyIbJLv35z39uJCu/2UXH9kVkRBbsy1/+cssG2VroFfqyaYiF7NWLX/ziRkC8GfHMHonzW2HsIVCeL5NRQ8z4llFCYhATBMY5W17D/+53v7uRHtsLQ8yce/mHrYxiUNjl0xZJ2w3ZMB4xe6V/fuQaMUIykU2vxc9WRs/V0fcD0PrLcIlBncM4kEGkyvi87l8ct+5lwWTc9Fe8pASpQ+TEkLEZq2tZMYRQnLYyIpwIrThhx1eV3UIgJCZSdObLUaUQ2CUEipjt0mxULIVAIVAIFAKFwD4QCDHTxaIToZBBQqJkgWS7bLXzo87eNohcIBqyZLJYiBOigVB5lsy2Phk1pAvxQWL8dpnMmsyWbY/asuUQEVFCTPhHTMSCxNxyyy3Te97znkZ6EL8rr7yyZcyQHyTPVkbETIZLli6/n4Z8IVaxzw5dLwFBhGTV/MA1QmksnjH7+te/3nzLYvkx6lOnTrU42HCIETGzPVPGDilT73kxGTcZM3EbM8KXsSB3cNYf6YWpsfgxbi9U8eIPGCpZ6LOT89ZQ/2wVAfOhRDovYgaFKruGQBGzXZuRiqcQKAQKgUKgEFgRAWQh5MU2O+eIlBdZeOMh0uLZLgQCYfHCCuQM2UCyZIWQIITFs2kIHWKHfPkR6Uv3XpIhIyVbZTsjfaQIMbOdkL8QGFIRB/uKbNR1113XfkSaX8+Y6YtQITt+P0w2TjYLmUK2ZLMQHWTNQlps3u7ohSbsIUJeie8ZN4TSb4uxI0MoHpkw7QhWCCPfimyhBbnrZLa8+h7x9FZGMSGyxu9ZOnpsIImIJGLo99Loec7N770hvJ5HCxETsyP2m+P6Z6sIhJBFCqaI2VanpJzPQaCI2RxgqroQKAQKgUKgENh1BJCGZGuQIedIhOfMvKwD2UJGbO9DshAOhAoZQ6BComSkEJNsf0TkvDAECfKcmhdvIEZs5Xe+EKgsdC1yERGH89TLMPnx5xtuuKH59Fwau7JeCKRMlUycF2wgZkibGI2DDdk9sfmtMwc9xNDzYbY+IoOyfIiZdtsoZeVsmZT1UoyRPXHJ4rGrH3KFyMmY9cQM4bNVM6/LZ0OfZOZk+ODg7ZSeMxOvLBxbCn9KsGgX9c9WEcj9GCmYImZbnZJyPgeBImZzgKnqQqAQKAQKgUJg1xFAxpARBSELAZHdQVa8EANJQxaQIc9FIRNIC1JnW56MEwJHevsh0oHcID+yR3Rsi0SgZMy8PMOzVadPn27kBnkSB6LDv22CfMkiITNe8IGYiVM2y7bKSy65pGXs8qZF/RFHNhE0xEnMsnpikqVCwLxoQzbMQR/5yctObEVEOBE35MpzYMYoPnrJ7lmQK7ZsisNvt/kdNFjx6fk1Wyr1Nx6xOGQS4eD5PRh5CYpn4mCRtz/ywwZ951V2A4EQskhRFTHbjbmpKP4fgSJm/49HXRUChUAhUAgUAkcGgZ6YIRrIBwJkGyBydvbs2UY4kBvXiIhnozxfJXuE+KiXiVLnOTPbCpGNa665pmXMkLEQM+REdshvfPGF+CBk4uCfTa+g13711Vc3+7ZBerGGuGxlZNePXnv5hxeQ2B7IljckInTss6kgQDJr4qIjg4fcyfp5wYfFtefkZN5uvPHGVifThRwilGKCBT0kiw3nFugyfzKDtkqKEbETP9sIF+IVoqsPkml7qOfZkOBTe8+wecMksoswxjYbSJk+VXYDgRCySFGZn5qj3ZifiuJ/CBQx+x8WdVYIFAKFQCFQCBwpBHpihsQgB7JGFqBIk9/28vtcnjk7d+5cq0datMvs6O9cna2BSJjnuRC4t7zlLa0OccnLNbwdMfpID1+IlAyRvg4Ez/Nd1157bbtG6hDEEDNviJSNYut973tfy4YhZPohPOzKciFFKZ71EpMXhCBOiJ/4LayNy4tB/DyAWLyYQ3/ELASPHmwc4keekDLPtDlHEPNWRjjYCilzZnzalWzL9Kwd3MRj6+WVe9lFz8zBgB/2a8GfmdsNaU6USOfmqOYJElV2CYEiZrs0GxVLIVAIFAKFQCGwDwT6hWZPQpgIWZLlsk3Rs1qyPrJkyFYySEgUwoMcRU/fN7/5ze3lGjJbnj+zlRDhQVSyXQ/5kx1CusTiGony1kXEjj+/f4YgWgTbYoj0eD5LFs8WR4RK37xAgw0+1CFGF198cds+icyx66ArBtkwWxlluzwrpg8/4nTuoJcjsZOyXeKh7/k0z9DJFsqk2e6ZrBwbYoINPTEjaTJ8tl7KAnoRCDzZdcCktjLu40begqp5HUsRtRGRut40AkXMNo14+SsECoFCoBAoBA4ZAWQgRCQLTtkhhEQWylZAGSTbFknZNdvvLERlfmwRRK6QMMQNqfAWRxKJ0w8JQpTYRVQQQXac60tXnXNZK2QF0UHokEPFNkfPqCGBMmO37r1lEeERvzcsKmwYA3sIIiKGlGW7oPosoI2PbXbErSQOOuzQYTOHa/5s1/RSFPrIHfIp44c0ypZ5Ds94Ffrs80MX4VRO7W1nhJP4grf6ZM+cV9lNBPL3IrrcT5G7GXFFdRIQKGJ2Ema5xlgIFAKFQCFwrBHIIjPSYJ2HxPTkxDY8ZAoRQtpIpIeuNgTGAjUZNaREf/YQDpKe9pCc+FFPBzkLUUHo2FToqaejvzYEzbU2RZ2iXT/6ebmGuMUTG/TUIY+JSVtwiEz8keqNWXbPWPMsG1t82dqJlIkrRSwycXTFjOzStY2RFD+7/Gcs6Vty9xDIvSGyELLI3Yu2IjopCBQxOykzXeMsBAqBQqAQOLYI9ItM5woigVggCn1Ju0UoMoOsICGuEZMQi2x3RJB6okHH4eUbfOgfH0gVXWQPgWKz78u3PtrU6xuSJkb12hMD4khHDIprNhAqdVlI66eezdTRz1hnnUdPH3qxkXp9ck466EVXnCnqtAfv9Et7yd1DIHMpssxX5O5FWxGdFASKmJ2Uma5xFgKFQCFQCBxbBCwyU3KOdCEdOcZFp2u6IUKu9QkpSn9ko+8bAqMeEXMdYscWf9oQKkXfHK6zINY3pEuGjQ7CR/ZELzr6amcbKROfEn9kYmsN5//pfdNJSRx9XdqMIzhqd7CTOlIdf2IixRXsYqfk7iKQ+Rehue1lu6h/CoEtIFDEbAugl8tCoBAoBAqBQmCdCFh0IgxKSIXFp/Ox0M3C1LmSa+eIkXrkI2QIAaEjg0XKnrGN0LhGnGz3U4ewOEbfdEP+QuzEnD58a+c/z7C5DgGS0WNDLDJo6kPM4qsfR3/O9lgy9tSLRZ+MNfUk3R4Xev0YtSeGvl+d7w4C5ihznnsjcneirEhOGgJFzE7ajNd4C4FCoBAoBE4EAuPCc1x0IhbqcgBFn5AbRCPEKFsJQ1YQJbqIGQJi6yKZ58MQLb+NxnaIVIhK4lJP37X+dNl3jQyRsZf2bGXkP0RRP8SMfXX9eFadaL5S9FdCMl0n9uiQYtMWQuZ8lp2+T53vDgLmKvOVOY/cnSgrkpOGQBGzkzbjNd5CoBAoBAqBE4dAFqAGnsXnPBITXXqIkoIoKcickms2lBCXEDnEKYWd+EwdqS72kBuF7xAiNvsMWfTp9PpiSDz6KiF2zjOeUdKhH4I6PrPGnz7s9+PkW5u+YszYo+tae5XdRsB85Z7IfEXuduQV3XFGoIjZcZ7dGlshUAgUAoVAIXAABMaFK1Pq5i1gtSExByEnvU+2+Jrnbxxa3zdtqct1JJva0h6CpV1dfPY6fSy9TmzOqktbyYMhANtlJXM2S09/9xMinns092n6RYdMJnSWLXXpM6+96o8XArPuv/4eWNa+KhpFzFZFqvQKgUKgECgECoFCoBAoBDaOQBa9kWMAWSBHju25RspGYhZyRgdhy4GYJTOb/r1c5qvXrfOjj0DuvUjz398DqY8c21dFoIjZqkiVXiFQCBQChUAhUAgUAoXAxhHIYjdyDCAL5EjtCNasxbH62El7+qlPe0/YRn+u02dWW9UdPwRyz0Tm3slIUx85tkdvmSxitgyhai8ECoFCoBAoBAqBQqAQ2BoCWexGjoGEJEVqR7CUeQvk2Or7tA57//R9UzfKWf1Gnbo+Pgjkfokc76vUR47tqyJRxGxVpEqvECgECoFCoBAoBAqBQmDjCGSxGzkGEJIUqX1Z5outXn+0Oc9X9Bb1jU7J44NA7odI89/fA6mPHNtXRaKI2apIlV4hUAgUAoVAIVAIFAKFwMYRyGI3cgwgC+RI7cl69S91ST92HIsWz/N8xUbvK3Uljy8CuR8ix3sn9ZFj+6rIFDFbFanSKwQKgUKgECgECoFCoBDYOAJZ7EaOAYQkRWqP7liXl3+oR9pmPUuWvqOf/rq329fX+fFEIPdEpPnv74HUR47tq6JSxGxVpEqvECgECoFCoBAoBAqBQmDjCGSxGzkGkAVy5NjuWt+8lZFEyPJK/JGcJdu2yN6itln+q+5oI5B7L9L89/dA6iPH9lVHX8RsVaRKrxAoBAqBQqAQKAQKgR1GAKHIkYVhpLD78x0exsLQsvDtlYxrUdHHAZvIkDL95vWfV7/IV7WthsCFzONqlterlbj7eyN/cyH+rt1fF110UQsmupGLIixitgidaisECoFCoBAoBAqBQmAHEbD4U2R7UtT1W/UsBGcd0T8pMmSMzMIabj12IxarLKLHPnW9OgL9XOgVvCNXt7R9zXnE7A53uMPtxrVsfEXMtj+fFUEhUAgUAoVAIVAIFAL7QuA///lP0w/BsODLYjekTd147MvJMVEOLpGGBbdFi+RFbccElq0Oo58LgQTvyK0Gt0/nGYu/O+eKcfTEP+OKnOfivyqLAwj0WCnSAAAAAElFTkSuQmCC"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "2d642b7b",
+   "metadata": {},
+   "source": [
+    "![3dglider.png](attachment:3dglider.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e278acd",
+   "metadata": {},
+   "source": [
+    "The method applied to calculate the 3D Game of Life evolution is the same applied in the bidimensional case, so we applied the np.roll method, considering all the possible 26 movements, to calculate the n_alive matrix in a given generation and we used masks to determine the evolution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db3ab61d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from IPython.display import HTML\n",
+    "from matplotlib import animation\n",
+    "from mpl_toolkits.mplot3d import Axes3D #permits to use projection '3d'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01c9a243",
+   "metadata": {},
+   "source": [
+    "### The new algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d4cc41b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def glider3d(): #definition of the 3D glider\n",
+    "    glider = [[[0, 0, 0, 0, 0],\n",
+    "               [0, 0, 1, 0, 0],\n",
+    "               [0, 0, 0, 1, 0],\n",
+    "               [0, 1, 1, 1, 0],\n",
+    "               [0, 0, 0, 0, 0]],\n",
+    "              [[0, 0, 0, 0, 0],\n",
+    "               [0, 0, 1, 0, 0],\n",
+    "               [0, 0, 0, 1, 0],\n",
+    "               [0, 1, 1, 1, 0],\n",
+    "               [0, 0, 0, 0, 0]]]\n",
+    "    \n",
+    "\n",
+    "    return np.array(glider)\n",
+    "\n",
+    "\n",
+    "\n",
+    "class game_of_life_np_3d():\n",
+    "\n",
+    "    def init(self, b, h, z):\n",
+    "        self.N = h\n",
+    "        self.M = b\n",
+    "        self.Z = z\n",
+    "        self.data = np.zeros((self.Z, self.N, self.M))\n",
+    "\n",
+    "    def initial_condition(self, pattern, x_pos, y_pos, z_pos):\n",
+    "        self.x_pos = x_pos\n",
+    "        self.y_pos = y_pos\n",
+    "        self.z_pos = z_pos\n",
+    "        self.pattern = pattern\n",
+    "        self.data[self.y_pos:self.y_pos+pattern.shape[0], self.x_pos:self.x_pos+pattern.shape[1], self.z_pos:self.z_pos+pattern.shape[2]] += pattern\n",
+    "        self.born = self.data.copy()\n",
+    "   \n",
+    "\n",
+    "    def game_rules(self, x, y): #setting the new rules through masks \n",
+    "\n",
+    "        mask_1 = np.logical_and(x == 1, y < 5)\n",
+    "        mask_2 = np.logical_and(x == 1, np.logical_or(y == 5, y == 6, y == 7))\n",
+    "        mask_3 = np.logical_and(x == 1, y > 7)\n",
+    "        mask_4 = np.logical_and(x == 0, y == 6)\n",
+    "\n",
+    "        return mask_1, mask_2, mask_3, mask_4\n",
+    "\n",
+    "    def next_generation(self):\n",
+    "\n",
+    "        n_alive = np.zeros((self.Z, self.N, self.M))\n",
+    "\n",
+    "        x = [-1, 0, 1] #possible size of a step\n",
+    "\n",
+    "        for el in itertools.product(x, repeat=3): # itertools.producct calculates all the possible permutations of the steps sizes\n",
+    "                                                  # a given conbination of [-1, 0, 1] represent a movement along each axis\n",
+    "\n",
+    "            if np.any(el): n_alive += np.roll(self.data, el, axis = (0, 1, 2))  #summing al the possible np.roll configurations that has at least a movement along an axis different from zero\n",
+    "   \n",
+    "\n",
+    "        m_1, m_2, m_3, m_4 = self.game_rules(self.data, n_alive)\n",
+    "        \n",
+    "\n",
+    "\n",
+    "        self.data[m_1] = 0\n",
+    "        self.data[m_2] = 1\n",
+    "        self.data[m_3] = 0\n",
+    "        self.data[m_4] = 1\n",
+    "\n",
+    "        return self.data\n",
+    "\n",
+    "    def index_1(self,data): #function that finds the coordinates of the cells different from zero in a given generation\n",
+    "        indx = np.where(data==1) \n",
+    "\n",
+    "        return indx\n",
+    "\n",
+    "    def start_game(self):\n",
+    "        \n",
+    "        data_orig = self.born #saving the first generation\n",
+    "        ind = self.index_1(data_orig) # finding coordinates of the living cells\n",
+    "        ind = np.asarray(ind) #turning it to a numpy array \n",
+    "        \n",
+    "        t = np.array([np.ones(len(ind[1,:]))*0]).flatten() \n",
+    "        df_tot = pd.DataFrame({\"time\": t ,\"x\" : ind[2,:], \"y\" : ind[1,:], \"z\" : ind[0,:]}) #generating a dataframe where each row represents a living cell \n",
+    "                                                                                           # a living cell in this datframe is defined by its generation(t) and its spatial coordinates\n",
+    "            \n",
+    "        df_tot_2 = pd.DataFrame({\"time\": t ,\"x\" : ind[0,:], \"y\" : ind[1,:], \"z\" : ind[2,:]})\n",
+    "        \n",
+    "        for i in range(1,11): # repeating the process for 10 generations\n",
+    "            data = self.next_generation()\n",
+    "            ind = self.index_1(data)\n",
+    "            ind = np.asarray(ind)\n",
+    "            \n",
+    "            t = np.array([np.ones(len(ind[1,:]))*i]).flatten()\n",
+    "            df = pd.DataFrame({\"time\": t ,\"x\" : ind[2,:], \"y\" : ind[1,:], \"z\" : ind[0,:]})\n",
+    "            df_2 = pd.DataFrame({\"time\": t ,\"x\" : ind[0,:], \"y\" : ind[1,:], \"z\" : ind[2,:]})\n",
+    "            \n",
+    "            df_tot = pd.concat([df_tot,df], ignore_index=True) #concatenating the new generation dataframe to the previous ones\n",
+    "            df_tot_2 = pd.concat([df_tot_2,df_2], ignore_index=True)\n",
+    "            \n",
+    "\n",
+    "        return df_tot,df_tot_2  # the result is a dataframe containing all the coordinates of the living cells at each generation\n",
+    "\n",
+    "b = 8\n",
+    "h = 8\n",
+    "z = 8\n",
+    "\n",
+    "\n",
+    "\n",
+    "game_3 = game_of_life_np_3d()\n",
+    "\n",
+    "game_3.init(int(b), int(h), int(z))\n",
+    "game_3.initial_condition(glider3d(), int(0.0), int(0.0), int(0.0))\n",
+    "df,df_2 = game_3.start_game()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e07718f",
+   "metadata": {},
+   "source": [
+    "### Visualisation of the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03a863a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df\n",
+    "\n",
+    "def update_graph(num): #defininig the updating function\n",
+    "    data=df[df['time']==num] #choosing data of a given generation\n",
+    "    graph.set_data (data.x, data.y) #setting the x and y coordinates of data of a frame\n",
+    "    graph.set_3d_properties(data.z) #setting z coordinate of data of a frame\n",
+    "    title.set_text('3D Test, time={}'.format(num)) #setting the title\n",
+    "     \n",
+    "    return title, graph, \n",
+    "\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10)) \n",
+    "ax = fig.add_subplot(111, projection='3d') #defininig a 3D plot \n",
+    "title = ax.set_title('3D Test')\n",
+    "\n",
+    "data=df[df['time']==0]\n",
+    "graph, = ax.plot(data.x, data.y, data.z, linestyle=\"\", marker=\"s\", markersize=10, color='w', markeredgecolor='black') #plotting the points in a 3D space\n",
+    "\n",
+    "ax.set_xlim3d([-20, 20]) #setting axes limits and labels\n",
+    "ax.set_xlabel('X')\n",
+    "ax.set_xticklabels([])\n",
+    "\n",
+    "ax.set_ylim3d([-20,20])\n",
+    "ax.set_ylabel('Y')\n",
+    "ax.set_yticklabels([])\n",
+    "\n",
+    "ax.set_zlim3d([-8,8])\n",
+    "ax.set_zlabel('Z')\n",
+    "ax.set_zticklabels([])\n",
+    "\n",
+    "ax.view_init(70, 0) #setting viewing angle\n",
+    "\n",
+    "ani = matplotlib.animation.FuncAnimation(fig, update_graph, 10, \n",
+    "                               interval=500, blit=True)\n",
+    "\n",
+    "html = HTML(ani.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "088440eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df_2\n",
+    "\n",
+    "def update_graph(num): #defininig the updating function\n",
+    "    data=df[df['time']==num] #choosing data of a given generation\n",
+    "    graph.set_data (data.x, data.y) #setting the x and y coordinates of data of a frame\n",
+    "    graph.set_3d_properties(data.z) #setting z coordinate of data of a frame\n",
+    "    title.set_text('3D Test, time={}'.format(num)) #setting the title\n",
+    "     \n",
+    "    return title, graph, \n",
+    "\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10)) \n",
+    "ax = fig.add_subplot(111, projection='3d') #defininig a 3D plot \n",
+    "title = ax.set_title('3D Test')\n",
+    "\n",
+    "data=df[df['time']==0]\n",
+    "graph, = ax.plot(data.x, data.y, data.z, linestyle=\"\", marker=\"s\", markersize=10, color='w', markeredgecolor='black') #plotting the points in a 3D space\n",
+    "\n",
+    "ax.set_xlim3d([-30, 30]) #setting axes limits and labels\n",
+    "ax.set_xlabel('X')\n",
+    "ax.set_xticklabels([])\n",
+    "\n",
+    "ax.set_ylim3d([-20,20])\n",
+    "ax.set_ylabel('Y')\n",
+    "ax.set_yticklabels([])\n",
+    "\n",
+    "ax.set_zlim3d([-20,20])\n",
+    "ax.set_zlabel('Z')\n",
+    "ax.set_zticklabels([])\n",
+    "\n",
+    "ax.view_init(10, 20) #setting viewing angle\n",
+    "\n",
+    "ani = matplotlib.animation.FuncAnimation(fig, update_graph, 10, \n",
+    "                               interval=500, blit=True)\n",
+    "\n",
+    "html = HTML(ani.to_jshtml())\n",
+    "display(html)\n",
+    "plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f987c6da",
+   "metadata": {},
+   "source": [
+    "# Genetic Algorithm and the GoF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1096660f",
+   "metadata": {},
+   "source": [
+    "## Game(s) of Life:  a step forward"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad2749c9",
+   "metadata": {},
+   "source": [
+    "The typical Game of Life we introduced has some specific rules that characterize the evolution of patterns. For each cell, given its own state (alive or dead) and the number of alive nears neighbors, these rules are applied in order to generate the next iteration grid.\n",
+    "\n",
+    "What happens if one decides to use another set of laws, in order to play another type of game?\n",
+    "\n",
+    "First of all, rules characterizing the dynamic can be generalized to a new mathematical framework, as we can see in the next image."
+   ]
+  },
+  {
+   "attachments": {
+    "Regole%20generali.png": {
+     "image/png": 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+    }
+   },
+   "cell_type": "markdown",
+   "id": "ff38e926",
+   "metadata": {},
+   "source": [
+    "![Regole%20generali.png](attachment:Regole%20generali.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f020db21",
+   "metadata": {},
+   "source": [
+    "Every possible combination of cell state $i$ and alive neighbors $j$ should have its specific evolution called $P_{ij}$. This sort of matrix can be linearized in an 18 components vector. All the information on a generic \"Game of Life\" dynamic is so enclosed inside this vector of length 18 and made of 0 and 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4cc09f5",
+   "metadata": {},
+   "source": [
+    "This concept of multiple \"Game(s) of Life\" can be explored in many different ways. We can ask ourselves, for example, if there exists a better game with respect to the others on accomplishing a specific task. In our case, we decided to reach the following goal: __build an algorithm that, given__:\n",
+    "*  __an initial grid__\n",
+    "*  __a final grid__\n",
+    "*  $\\delta$ __iteration between the two grids__\n",
+    "\n",
+    "__finds the best game which, starting on the initial grid and evolving for $\\delta$ times, recovers completely the final grid__.\n",
+    "\n",
+    "One solution for this task could be found on running each of the $2^{18}$ possible games from the start to the end and taking the best among them. Another road, that allows us to deal with a smaller set of games, leads to the use of Genetic Algorithms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c268abbb",
+   "metadata": {},
+   "source": [
+    "## Some theory about Genetic Algorithms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3df65db0",
+   "metadata": {},
+   "source": [
+    "Genetic algorithms are a family of search algorithms inspired by the principles of evolution\n",
+    "in nature. \n",
+    "\n",
+    "By imitating the process of natural selection and reproduction, genetic algorithms\n",
+    "can produce high-quality solutions for various problems involving search, optimization,\n",
+    "and learning.\n",
+    "\n",
+    "\n",
+    "At the same time, their analogy to natural evolution allows genetic\n",
+    "algorithms to overcome some of the hurdles that are encountered by traditional search and\n",
+    "optimization algorithms, especially for problems with a large number of parameters and\n",
+    "complex mathematical representations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ce46fd2",
+   "metadata": {},
+   "source": [
+    "Genetic algorithms maintain a population of candidate solutions, called **individuals**, for that given problem.\n",
+    "\n",
+    "\n",
+    "These candidate solutions are iteratively evaluated and used to create a new generation of\n",
+    "solutions. Those who are better at solving this problem have a greater chance of being\n",
+    "selected and passing their qualities to the next generation of candidate solutions. \n",
+    "\n",
+    "\n",
+    "This way,\n",
+    "as generations go by, candidate solutions get better at solving the problem at hand."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "964d4ae3",
+   "metadata": {},
+   "source": [
+    "### Genotype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3a12a51",
+   "metadata": {},
+   "source": [
+    "In nature, breeding, reproduction, and mutation are facilitated via the **genotype** – a\n",
+    "collection of genes that are grouped into chromosomes. If two specimens breed to create\n",
+    "offspring, each chromosome of the offspring will carry a mix of genes from both parents.\n",
+    "\n",
+    "\n",
+    "Mimicking this concept, in the case of genetic algorithms, each individual is represented by\n",
+    "a chromosome representing a collection of genes. For example, a chromosome can be\n",
+    "expressed as a binary string, where each bit represents a single **gene**:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d38221e6",
+   "metadata": {},
+   "source": [
+    "<img src=\"notebook_images/cromosoma.jpg\" style=\"width:400px;height:100px\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38d3f524",
+   "metadata": {},
+   "source": [
+    "### Population"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d7f64a0",
+   "metadata": {},
+   "source": [
+    "At any point in time, genetic algorithms maintain a population of **individuals** – a collection\n",
+    "of candidate solutions for the problem at hand. Since each individual is represented by\n",
+    "some chromosome, this population of individuals can be seen as a collection of such\n",
+    "chromosomes:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd39e675",
+   "metadata": {},
+   "source": [
+    "<img src=\"notebook_images/population.jpg\" style=\"width:500px;height:350px\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e6a4f7f",
+   "metadata": {},
+   "source": [
+    "### Fitness function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e5668d4",
+   "metadata": {},
+   "source": [
+    "At each iteration of the algorithm, the individuals are evaluated using a **fitness function**.\n",
+    "This is the function we seek to optimize or the problem we attempt to solve.\n",
+    "\n",
+    "\n",
+    "Individuals who achieve a better fitness score represent better solutions and are more likely to be chosen to reproduce and be represented in the next generation.\n",
+    "\n",
+    "\n",
+    "Over time, the quality of the solutions improves, the fitness values increase, and the process can stop once a solution with a satisfactory fitness value is found."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "389dcfae",
+   "metadata": {},
+   "source": [
+    "### Selection "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dab9b0b",
+   "metadata": {},
+   "source": [
+    "After calculating the fitness of every individual in the population, a selection process is\n",
+    "used to determine which of the individuals in the population will get to reproduce and\n",
+    "create the offspring that will form the next generation.\n",
+    "\n",
+    "\n",
+    "This selection process is based on the fitness score of the individuals. Those with higher\n",
+    "score values are more likely to be chosen and pass their genetic material to the next\n",
+    "generation.\n",
+    "\n",
+    "\n",
+    "Individuals with low fitness values can still be chosen, but with lower probability. This\n",
+    "way, their genetic material is not completely excluded."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a8de33e",
+   "metadata": {},
+   "source": [
+    "### Crossover"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "456a0469",
+   "metadata": {},
+   "source": [
+    "To create a pair of new individuals, two parents are usually chosen from the current\n",
+    "generation, and parts of their chromosomes are interchanged (crossed over) to create two\n",
+    "new chromosomes representing the offspring. This operation is called crossover, or\n",
+    "recombination."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be71154a",
+   "metadata": {},
+   "source": [
+    "<img src=\"notebook_images/crossover.jpg\" style=\"width:600px;height:300px\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43b69b0b",
+   "metadata": {},
+   "source": [
+    "### Mutation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b467d7b",
+   "metadata": {},
+   "source": [
+    "The purpose of the mutation operator is to periodically and randomly refresh the population, introduce new patterns into the chromosomes and encourage search in uncharted areas of the solution space.\n",
+    "\n",
+    "\n",
+    "A mutation may manifest itself as a random change in a gene. Mutations are implemented as random changes to one or more of the chromosome values; for example, flipping a bit in a binary string:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a53e3f59",
+   "metadata": {},
+   "source": [
+    "<img src=\"notebook_images/mutation.jpg\" style=\"width:400px;height:200px\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2e8e293",
+   "metadata": {},
+   "source": [
+    "### Workflow of Genetic Algorithms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48eb2a2c",
+   "metadata": {},
+   "source": [
+    "The main stages of the basic genetic algorithm flow are shown in the following flowchart:"
+   ]
+  },
+  {
+   "attachments": {
+    "workflow.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "836dc7f6",
+   "metadata": {},
+   "source": [
+    "![workflow.jpg](attachment:workflow.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc688123",
+   "metadata": {},
+   "source": [
+    "## Algorithm implementation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bd4afb6",
+   "metadata": {},
+   "source": [
+    "From the practical point of view, it is not guaranteed that taking two random initial and final grids, there exists a game connecting them in a pre-fixed number $\\delta$ of iterations. In order to translate our goal into a different problem, we decided to generate the final configuration from the starting one using the standard GoF rules $\\delta$ times. In this way, we can recognize a successful Genetic Algorithm if it finds as best game just the standard GoF. A schematic related to the new goal is drawn below:"
+   ]
+  },
+  {
+   "attachments": {
+    "Goal%20of%20the%20GA%20%282%29.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "a4616e19",
+   "metadata": {},
+   "source": [
+    "![Goal%20of%20the%20GA%20%282%29.png](attachment:Goal%20of%20the%20GA%20%282%29.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d085f80",
+   "metadata": {},
+   "source": [
+    "Starting from a population of $N$ games (every game is characterized by a vector of 18 parameters) and letting it evolve through the flow-chart steps, at each epoch, the score of the new population, generated by the better individuals of the previous one, increase. In the end, we will take the best game among all generations as the output of the Genetic Algorithm."
+   ]
+  },
+  {
+   "attachments": {
+    "Population%20of%20the%20GA.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "0c964e81",
+   "metadata": {},
+   "source": [
+    "![Population%20of%20the%20GA.png](attachment:Population%20of%20the%20GA.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "088a6ab7",
+   "metadata": {},
+   "source": [
+    "### The needed GoF "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a168332d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(2058080)\n",
+    "\n",
+    "def visualization(ax, grid, title): # function for plotting the squared grids\n",
+    "    ax.imshow(list(grid),cmap='gray_r')\n",
+    "    ax.get_xaxis().set_ticks([])\n",
+    "    ax.get_yaxis().set_ticks([])\n",
+    "    ax.set_title(title,fontweight=\"bold\")\n",
+    "\n",
+    "class game_of_life_cl(): # class of the typical Game of Life\n",
+    "    \n",
+    "    def init(self, b, h):\n",
+    "        self.N = h\n",
+    "        self.M = b\n",
+    "        self.data = np.zeros((self.N,self.M))\n",
+    "        self.count_alive = []\n",
+    "        \n",
+    "    def clean_grid(self): \n",
+    "        self.data = np.zeros((self.N,self.M))\n",
+    "    \n",
+    "    def initial_condition(self, pattern, x_pos, y_pos):\n",
+    "        self.x_pos = x_pos\n",
+    "        self.y_pos = y_pos\n",
+    "        self.pattern = pattern\n",
+    "        self.data[self.y_pos:self.y_pos+pattern.shape[0], self.x_pos:self.x_pos+pattern.shape[1]] += pattern\n",
+    "            \n",
+    "    \n",
+    "    def game_rules(self,x,y):\n",
+    "        \n",
+    "        mask_1 = np.logical_and(x == 1, y < 2)\n",
+    "        mask_2 = np.logical_and(x == 1, np.logical_or( y == 2, y == 3))\n",
+    "        mask_3 = np.logical_and(x == 1, y > 3)\n",
+    "        mask_4 = np.logical_and(x == 0, y == 3)\n",
+    "\n",
+    "        return mask_1,mask_2,mask_3,mask_4\n",
+    "    \n",
+    "    \n",
+    "    def next_generation(self):\n",
+    "        \n",
+    "        self.count_alive.append(len(self.data[self.data == 1]))\n",
+    "        \n",
+    "        n_alive = np.zeros((self.N,self.M))\n",
+    "        \n",
+    "        grid_d_d = np.roll(np.roll(self.data, -1, axis=1), -1, axis=0)\n",
+    "        grid_d_d[:,self.M-1] = 0\n",
+    "        grid_d_d[self.N-1,:] = 0\n",
+    "        \n",
+    "        grid_u_u = np.roll(np.roll(self.data, 1, axis=1), 1, axis=0)\n",
+    "        grid_u_u[:,0] = 0 \n",
+    "        grid_u_u[0,:] = 0\n",
+    "        \n",
+    "        grid_s_d = np.roll(self.data, -1, axis=1)\n",
+    "        grid_s_d[:,self.M-1] = 0 \n",
+    "        \n",
+    "        grid_u_d = np.roll(np.roll(self.data, -1, axis=1), 1, axis=0)\n",
+    "        grid_u_d[:,self.M-1] = 0\n",
+    "        grid_u_d[0,:] = 0\n",
+    "        \n",
+    "        grid_d_s = np.roll(self.data, -1, axis=0)\n",
+    "        grid_d_s[self.N-1,:] = 0\n",
+    "        \n",
+    "        grid_u_s = np.roll(self.data, 1, axis=0)\n",
+    "        grid_u_s[0,:] = 0\n",
+    "        \n",
+    "        grid_d_u = np.roll(np.roll(self.data, 1, axis=1), -1, axis=0)\n",
+    "        grid_d_u[:,0] = 0\n",
+    "        grid_d_u[self.N-1,:] = 0\n",
+    "        \n",
+    "        grid_s_u = np.roll(self.data, 1, axis=1)\n",
+    "        grid_s_u[:,0] = 0\n",
+    "        \n",
+    "        \n",
+    "        n_alive = grid_d_d + grid_u_u + grid_s_d + grid_u_d + grid_d_s + grid_u_s + grid_d_u + grid_s_u\n",
+    "        \n",
+    "        m_1,m_2,m_3,m_4 = self.game_rules(self.data , n_alive)\n",
+    "        \n",
+    "        self.data[m_1] = 0\n",
+    "        self.data[m_2] = 1\n",
+    "        self.data[m_3] = 0\n",
+    "        self.data[m_4] = 1\n",
+    "        \n",
+    "        return self.data\n",
+    "    \n",
+    "    def start_game(self, delta):\n",
+    "        count = 0\n",
+    "        self.run = True\n",
+    "        while self.run == True:\n",
+    "            self.next_generation()\n",
+    "            count = count +1\n",
+    "            if count == delta:\n",
+    "                self.run = False\n",
+    "        return self.data\n",
+    "\n",
+    "\n",
+    "\n",
+    "class game_of_life_ga(): # class of the Game of Life with generic parameters\n",
+    "    \n",
+    "    def init(self, b, h, p):\n",
+    "        self.parameters = p # parameters that characterize the game\n",
+    "        self.N = h\n",
+    "        self.M = b\n",
+    "        self.data = np.zeros((self.N,self.M))\n",
+    "        self.count_alive = []\n",
+    "        self.score = 0 # attribute used to store the score of the game\n",
+    "        \n",
+    "    def clean_grid(self):\n",
+    "        self.data = np.zeros((self.N,self.M))\n",
+    "    \n",
+    "    def initial_condition(self, pattern, x_pos, y_pos):\n",
+    "        self.x_pos = x_pos\n",
+    "        self.y_pos = y_pos\n",
+    "        self.pattern = pattern\n",
+    "        self.data[self.y_pos:self.y_pos+pattern.shape[0], self.x_pos:self.x_pos+pattern.shape[1]] += pattern\n",
+    "            \n",
+    "    \n",
+    "    def game_rules(self,x,y): # 18 masks, one for each possible case\n",
+    "        \n",
+    "        mask_1 = np.logical_and(x == 1, y == 0)\n",
+    "        mask_2 = np.logical_and(x == 1, y == 1)\n",
+    "        mask_3 = np.logical_and(x == 1, y == 2)\n",
+    "        mask_4 = np.logical_and(x == 1, y == 3)\n",
+    "        mask_5 = np.logical_and(x == 1, y == 4)\n",
+    "        mask_6 = np.logical_and(x == 1, y == 5)\n",
+    "        mask_7 = np.logical_and(x == 1, y == 6)\n",
+    "        mask_8 = np.logical_and(x == 1, y == 7)\n",
+    "        mask_9 = np.logical_and(x == 1, y == 8)\n",
+    "        mask_10 = np.logical_and(x == 0, y == 0)\n",
+    "        mask_11 = np.logical_and(x == 0, y == 1)\n",
+    "        mask_12 = np.logical_and(x == 0, y == 2)\n",
+    "        mask_13 = np.logical_and(x == 0, y == 3)\n",
+    "        mask_14 = np.logical_and(x == 0, y == 4)\n",
+    "        mask_15 = np.logical_and(x == 0, y == 5)\n",
+    "        mask_16 = np.logical_and(x == 0, y == 6)\n",
+    "        mask_17 = np.logical_and(x == 0, y == 7)\n",
+    "        mask_18 = np.logical_and(x == 0, y == 8)\n",
+    "\n",
+    "        return mask_1,mask_2,mask_3,mask_4,mask_5,mask_6,mask_7,mask_8,mask_9,mask_10,mask_11,mask_12,mask_13,mask_14,mask_15,mask_16,mask_17,mask_18\n",
+    "    \n",
+    "    \n",
+    "    def next_generation(self):\n",
+    "        \n",
+    "        n_alive = np.zeros((self.N,self.N))\n",
+    "        \n",
+    "        grid_d_d = np.roll(np.roll(self.data, -1, axis=1), -1, axis=0)\n",
+    "        grid_d_d[:,self.M-1] = 0\n",
+    "        grid_d_d[self.N-1,:] = 0\n",
+    "        \n",
+    "        grid_u_u = np.roll(np.roll(self.data, 1, axis=1), 1, axis=0)\n",
+    "        grid_u_u[:,0] = 0 \n",
+    "        grid_u_u[0,:] = 0\n",
+    "        \n",
+    "        grid_s_d = np.roll(self.data, -1, axis=1)\n",
+    "        grid_s_d[:,self.M-1] = 0 \n",
+    "        \n",
+    "        grid_u_d = np.roll(np.roll(self.data, -1, axis=1), 1, axis=0)\n",
+    "        grid_u_d[:,self.M-1] = 0\n",
+    "        grid_u_d[0,:] = 0\n",
+    "        \n",
+    "        grid_d_s = np.roll(self.data, -1, axis=0)\n",
+    "        grid_d_s[self.N-1,:] = 0\n",
+    "        \n",
+    "        grid_u_s = np.roll(self.data, 1, axis=0)\n",
+    "        grid_u_s[0,:] = 0\n",
+    "        \n",
+    "        grid_d_u = np.roll(np.roll(self.data, 1, axis=1), -1, axis=0)\n",
+    "        grid_d_u[:,0] = 0\n",
+    "        grid_d_u[self.N-1,:] = 0\n",
+    "        \n",
+    "        grid_s_u = np.roll(self.data, 1, axis=1)\n",
+    "        grid_s_u[:,0] = 0\n",
+    "        \n",
+    "        n_alive = grid_d_d + grid_u_u + grid_s_d + grid_u_d + grid_d_s + grid_u_s + grid_d_u + grid_s_u\n",
+    "        \n",
+    "        m_1,m_2,m_3,m_4,m_5,m_6,m_7,m_8,m_9,m_10,m_11,m_12,m_13,m_14,m_15,m_16,m_17,m_18 = self.game_rules(self.data , n_alive)\n",
+    "        \n",
+    "        self.data[m_1] = self.parameters[0]\n",
+    "        self.data[m_2] = self.parameters[1]\n",
+    "        self.data[m_3] = self.parameters[2]\n",
+    "        self.data[m_4] = self.parameters[3]\n",
+    "        self.data[m_5] = self.parameters[4]\n",
+    "        self.data[m_6] = self.parameters[5]\n",
+    "        self.data[m_7] = self.parameters[6]\n",
+    "        self.data[m_8] = self.parameters[7]\n",
+    "        self.data[m_9] = self.parameters[8]\n",
+    "        self.data[m_10] = self.parameters[9]\n",
+    "        self.data[m_11] = self.parameters[10]\n",
+    "        self.data[m_12] = self.parameters[11]\n",
+    "        self.data[m_13] = self.parameters[12]\n",
+    "        self.data[m_14] = self.parameters[13]\n",
+    "        self.data[m_15] = self.parameters[14]\n",
+    "        self.data[m_16] = self.parameters[15]\n",
+    "        self.data[m_17] = self.parameters[16]\n",
+    "        self.data[m_18] = self.parameters[17]\n",
+    "        \n",
+    "        self.count_alive.append(np.sum(self.data))\n",
+    "        \n",
+    "        return self.data\n",
+    "    \n",
+    "    def start_game(self, delta):\n",
+    "        count = 0\n",
+    "        self.run = True\n",
+    "        while self.run == True:\n",
+    "            self.next_generation()\n",
+    "            count = count +1\n",
+    "            if count == delta:\n",
+    "                self.run = False\n",
+    "        \n",
+    "        return self.data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e614794",
+   "metadata": {},
+   "source": [
+    "### The genetic algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f6b9c1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def true_initial(b, h, dim, alive): # every game would start from this pattern\n",
+    "    start = np.random.binomial(1, alive, (dim, dim)).astype('uint8') # initialize a random QR code\n",
+    "    x_margin = int((b-dim)/2)\n",
+    "    y_margin = int((h-dim)/2)\n",
+    "    blank = np.zeros((b,h))\n",
+    "    blank[x_margin:(b-x_margin), y_margin:(h-y_margin)] += start # insert at centre of the blank grid\n",
+    "    return blank\n",
+    "\n",
+    "def true_end(b, h, n, ic): # the target, every game should recover it after delta iterations\n",
+    "    game = game_of_life_cl() # make use of the typical GoF\n",
+    "    game.init(int(b), int(h))\n",
+    "    game.initial_condition(ic, 0, 0) # ic would be indeed the true initial\n",
+    "    fc = game.start_game(n) # run the GoF for n = delta iterations, the output is the final configuration\n",
+    "    return fc\n",
+    "\n",
+    "def run_game(game, delta, generic_ic): # run a certain game for delta iterations starting from the true initial\n",
+    "    game.clean_grid()\n",
+    "    game.initial_condition(generic_ic, 0, 0)\n",
+    "    generic_fc = game.start_game(delta)\n",
+    "    return generic_fc\n",
+    "        \n",
+    "def fitness(candidate, target, b, h): # similarity score between two patterns\n",
+    "    \n",
+    "    return ((candidate == target).sum())/(b*h)\n",
+    "\n",
+    "def score_population(population, generic_ic, target, delta): # compute score for an entire population\n",
+    "    \n",
+    "    for i in range(len(population)):\n",
+    "        population[i].score = fitness(run_game(population[i], delta, generic_ic), target, b, h) # storing the score inside the game\n",
+    "    \n",
+    "def selection(population, retain_frac, retain_random): # selection among the population\n",
+    "    \n",
+    "    retain_len = int(len(population) * retain_frac) # how many on the population are preserved for the next generation\n",
+    "    population.sort(key=lambda x: x.score, reverse=True)\n",
+    "    selected = population[:retain_len] # preserved population\n",
+    "    leftovers = population[retain_len:] # discarded population\n",
+    "\n",
+    "    for par in leftovers: # from the discarded ones let's pick some at random\n",
+    "        if np.random.rand() < retain_random:\n",
+    "            selected.append(par)\n",
+    "    return selected\n",
+    "\n",
+    "def mutate(param, switch_frac=0.1): # given the parameters of a game, some numbers of the vector flip\n",
+    "    \n",
+    "    a = np.random.binomial(1, switch_frac, size=18).astype('bool') # mask of True and False\n",
+    "    \n",
+    "    param[a] += 1\n",
+    "    param[a] %= 2 \n",
+    "    \n",
+    "    return param\n",
+    "\n",
+    "def crossover(mom, dad): # mom and dad are indeed mom and dad parameters\n",
+    "    \n",
+    "    select_mask = np.random.binomial(1, 0.5, size=18).astype('bool') # mask of True and False (fifty-fifty)\n",
+    "    child1, child2 = np.copy(mom), np.copy(dad) # child1 is initially like the mom, child2 is like dad\n",
+    "    \n",
+    "    child1[select_mask] = dad[select_mask] # crossover between mom and dad \n",
+    "    child2[select_mask] = mom[select_mask] # crossover between dad and mom\n",
+    "    \n",
+    "    return child1, child2\n",
+    "\n",
+    "def evolve(b, h, population, ic, target, delta, retain_frac=0.5, retain_random=0.05, mutate_chance=0.15):\n",
+    "    \n",
+    "    score_population(population, ic, target, delta) # compute the score for every game in the population\n",
+    "    scores = [population[i].score for i in range(N)]\n",
+    "    next_population = selection(population, retain_frac=retain_frac, retain_random=retain_random) # select the fraction of population with best score\n",
+    "    \n",
+    "    for gene in next_population[1:]: \n",
+    "        if np.random.rand() < mutate_chance:\n",
+    "            mutate(gene.parameters) # mutate some individuals of this fraction of population\n",
+    "\n",
+    "    places_left = len(population) - len(next_population)\n",
+    "    children = []\n",
+    "    parent_max_idx = len(next_population) - 1\n",
+    "    while len(children) < places_left:\n",
+    "        mom_idx, dad_idx = np.random.randint(0, parent_max_idx, 2) # select randomly two games to let them couple\n",
+    "        if mom_idx != dad_idx:\n",
+    "            child1, child2 = crossover(next_population[mom_idx].parameters, next_population[dad_idx].parameters) # generate childs' parameters\n",
+    "            new_game = game_of_life_ga() \n",
+    "            new_game.init(int(b), int(h), child1) # insert child1 parameters in a new game (individual)\n",
+    "            children.append(new_game)\n",
+    "            if len(children) < places_left:\n",
+    "                new_game2 = game_of_life_ga()\n",
+    "                new_game2.init(int(b), int(h), child2) # insert child2 parameters in a new game (individual)\n",
+    "                children.append(new_game2)\n",
+    "    next_population.extend(children) # bring children inside the new population\n",
+    "    \n",
+    "    return next_population\n",
+    "\n",
+    "\n",
+    "b = 30 # grid width\n",
+    "h = 30 # grid height\n",
+    "N = 200 # number of individuals in the population\n",
+    "g = 50 # number of generations for evolving\n",
+    "\n",
+    "delta = 10 # number of iterations that games run\n",
+    "initial_config = true_initial(b, h, dim = 20, alive = 0.3) # initial configuration for all games\n",
+    "final_target = true_end(b, h, delta, initial_config) #final configuration for all games\n",
+    "\n",
+    "\n",
+    "population = [] # list with all the games of the population\n",
+    "\n",
+    "for i in range(0,N): # initialize population\n",
+    "    population.append(game_of_life_ga()) \n",
+    "    population[i].init(int(b), int(h), np.random.randint(2, size = 18)) # random rules\n",
+    "\n",
+    "average_scores = [] # average scores over the generations\n",
+    "best_scores = [] # best scores over the generations\n",
+    "best_games = [] # best games over the generations\n",
+    "\n",
+    "for i in range(g): \n",
+    "    population = evolve(b, h, population, initial_config, final_target, delta) # population will be itself, but evolved\n",
+    "    score_population(population, initial_config, final_target, delta) # compute score of the current generation\n",
+    "    scores_all_population = [population[i].score for i in range(N)]\n",
+    "    average_scores.append(np.mean(scores_all_population))\n",
+    "    best_scores.append(np.max(scores_all_population)) \n",
+    "    idx = np.argmax(scores_all_population) \n",
+    "    best_games.append(population[idx]) \n",
+    "\n",
+    "banal_score = 1 - (np.sum(final_target))/(b*h) # score of the blank grid (the one made of all zeros)\n",
+    "\n",
+    "best_rules = [best_games[i].parameters for i in range(g)] # rules concerning the best games at every generation\n",
+    "predictions = [run_game(best_games[i], delta, initial_config) for i in range(g)] # best_games final grids (to be compared with the target)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44e363aa",
+   "metadata": {},
+   "source": [
+    "### Visualisation of the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "931658d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axs = plt.subplots(1, 2, figsize=(10,5))\n",
+    "visualization(axs[0], initial_config, \"True initial\")\n",
+    "visualization(axs[1], final_target, \"Target\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43cd4a45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize = (15,8))\n",
+    "ax.plot(np.arange(g), best_scores, color = 'blue', label = \"Best individual of population\", linewidth = 2)\n",
+    "ax.plot(np.arange(g), average_scores, color = 'orange', label = \"Average of population\", linewidth = 2)\n",
+    "ax.axhline(banal_score, linestyle = '--', color = 'r', label = \"Blank grid score\")\n",
+    "ax.set_xlabel(\"Generation number\", fontweight = \"bold\")\n",
+    "ax.set_ylabel(\"Fitness value\", fontweight = \"bold\")\n",
+    "ax.set_title(\"Fitness over generations\", fontweight = \"bold\")\n",
+    "ax.legend(loc = \"best\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af72e5f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_scores = [population[i].score for i in range(N)] # scores of the games of the final generation\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize = (15,8))\n",
+    "ax.hist(final_scores, bins = 20, color = \"red\", edgecolor = \"black\", alpha = 0.7)\n",
+    "ax.set_xlabel(\"Fitness value\", fontweight = \"bold\")\n",
+    "ax.set_ylabel(\"Counts per bin\", fontweight = \"bold\")\n",
+    "ax.set_title(\"Fitness distribution of final generation\", fontweight = \"bold\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0468da3",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "fig, axs = plt.subplots(10, 5, figsize = (18,45)) # plot of best predictions over generations\n",
+    "m = 0\n",
+    "for i in range(10):\n",
+    "    for j in range(5):\n",
+    "        axs[i, j].imshow(list(predictions[m]),cmap='gray_r')\n",
+    "        axs[i, j].get_xaxis().set_ticks([])\n",
+    "        axs[i, j].get_yaxis().set_ticks([])\n",
+    "        axs[i, j].set_title(\"Best prediction of gen {}\".format(m+1),fontweight=\"bold\")\n",
+    "        axs[i, j].set_xlabel(str(best_rules[m]))\n",
+    "        m += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "966ce769",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_score_ever = np.max(best_scores)\n",
+    "idx = np.argmax(best_scores) \n",
+    "best_game_ever = best_games[idx] \n",
+    "best_rule_ever = best_game_ever.parameters \n",
+    "best_prediction_ever = run_game(best_game_ever, delta, initial_config) \n",
+    "\n",
+    "print(\"Best rule ever:\", best_rule_ever)\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 3, figsize=(15,5))\n",
+    "visualization(axs[0], initial_config, \"True initial\")\n",
+    "visualization(axs[1], final_target, \"Target\")\n",
+    "visualization(axs[2], best_prediction_ever, \"Best prediction ever\")\n",
+    "axs[2].set_xlabel(best_rule_ever)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5b4afc7",
+   "metadata": {},
+   "source": [
+    "## Conclusions for the GA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29a48fc7",
+   "metadata": {},
+   "source": [
+    "Our Genetic Algorithm finds as best game an individual that is very similar to the standard Game of Life (but non-equal), and despite that displays the maximum score possible that is $1.0$. The reason for this strange behavior in terms of the score is due to the initial configuration: in the evolution from the start to the end, probably, all games never experience an alive cell surrounded by 8 other alive cells, so the Genetic Algorithm cannot _train_ properly the population on it.\n",
+    "\n",
+    "A way to improve this Genetic Algorithm precision could be training games on multiple and random initial conditions.\n",
+    "\n",
+    "One other thing to take in mind is that the performance of the GA (how many generations are needed to find a perfect solution) depends strictly on the fixed random seed. There are cases in which only with 50 generations the algorithm can't improve enough, needing more training."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cba02747",
+   "metadata": {},
+   "source": [
+    "# References"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9856c58b",
+   "metadata": {},
+   "source": [
+    "Bays, Carter, “Candidates for the Game of Life in Three Dimensions”, Complex Systems, 1 (1987) 373–400.\n",
+    "\n",
+    "Wirsansky, Eyal, \" Hands-on genetic algorithms with Python: applying genetic algorithms to solve real-world deep learning and artificial intelligence problems\", Packt Publishing Ltd, 2020.\n",
+    "\n",
+    "https://www.conwaylife.com/wiki/Main_Page\n",
+    "\n",
+    "https://www.pygame.org/docs/genindex.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "001dd899",
+   "metadata": {},
+   "source": [
+    "# Backup files"
+   ]
+  },
+  {
+   "attachments": {
+    "Np%20roll%20condizioni%20al%20contorno.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "ed1255a6",
+   "metadata": {},
+   "source": [
+    "![Np%20roll%20condizioni%20al%20contorno.png](attachment:Np%20roll%20condizioni%20al%20contorno.png)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Game_of_Life_patterns.py b/Game_of_Life_patterns.py
new file mode 100644
index 0000000..7f18007
--- /dev/null
+++ b/Game_of_Life_patterns.py
@@ -0,0 +1,436 @@
+import numpy as np
+
+def block():
+    block = [ [0, 0, 0, 0],
+              [0, 1, 1, 0],
+              [0, 1, 1, 0],
+              [0, 0, 0, 0],
+            ]
+    return np.array(block)
+
+def tube():
+    tube =  [ [0, 0, 0, 0, 0],
+              [0, 0, 1, 0, 0],
+              [0, 1, 0, 1, 0],
+              [0, 0, 1, 0, 0],
+              [0, 0, 0, 0, 0],
+            ]
+    return np.array(tube)
+
+def boat():
+    boat =  [ 
+              [0, 0, 1, 0, 0],
+              [0, 1, 0, 1, 0],
+              [0, 1, 1, 0, 0],
+              [0, 0, 0, 0, 0],
+            ]
+    return np.array(boat)
+
+def ship():
+    ship =  [ [0, 0, 0, 0, 0],
+              [0, 0, 1, 1, 0],
+              [0, 1, 0, 1, 0],
+              [0, 1, 1, 0, 0],
+              [0, 0, 0, 0, 0],
+            ]
+    return np.array(ship)
+
+def bee_hive():
+    bee_hive = [ [0, 0, 0, 0, 0, 0],
+                 [0, 0, 1, 1, 0, 0],
+                 [0, 1, 0, 0, 1, 0],
+                 [0, 0, 1, 1, 0, 0],
+                 [0, 0, 0, 0, 0, 0],
+               ]
+    return np.array(bee_hive)
+
+def loaf():
+    loaf = [ [0, 0, 0, 0, 0, 0],
+             [0, 0, 1, 1, 0, 0],
+             [0, 1, 0, 0, 1, 0],
+             [0, 0, 1, 0, 1, 0],
+             [0, 0, 0, 1, 0, 0],
+             [0, 0, 0, 0, 0, 0],
+           ]
+    return np.array(loaf)
+
+def pond():
+    pond = [ [0, 0, 0, 0, 0, 0],
+             [0, 0, 1, 1, 0, 0],
+             [0, 1, 0, 0, 1, 0],
+             [0, 1, 0, 0, 1, 0],
+             [0, 0, 1, 1, 0, 0],
+             [0, 0, 0, 0, 0, 0],
+           ]
+    return np.array(pond)
+
+def snake():
+    snake = [ [0, 0, 0, 0, 0, 0],
+              [0, 1, 0, 1, 1, 0],
+              [0, 1, 1, 0, 1, 0],
+              [0, 0, 0, 0, 0, 0],
+            ]
+    return np.array(snake)
+
+def eater1():
+    eater1 = [ [0, 0, 0, 0, 0, 0],
+               [0, 1, 1, 0, 0, 0],
+               [0, 1, 0, 1, 0, 0],
+               [0, 0, 0, 1, 0, 0],
+               [0, 0, 0, 1, 1, 0],
+               [0, 0, 0, 0, 0, 0],
+               ]
+    return np.array(eater1)
+
+def eater2():
+    eater2 = [ [0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 1, 0, 1, 1, 0],
+               [0, 0, 1, 1, 1, 0, 1, 1, 0],
+               [0, 1, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 1, 1, 1, 0, 1, 1, 0],
+               [0, 0, 0, 0, 1, 0, 1, 0, 0],
+               [0, 0, 0, 0, 1, 0, 1, 0, 0],
+               [0, 0, 0, 0, 0, 1, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0],
+             ]
+    return np.array(eater2)
+
+def blinker():
+    blinker = [  [0, 0, 0, 0, 0],
+                 [0, 0, 1, 0, 0],
+                 [0, 0, 1, 0, 0],
+                 [0, 0, 1, 0, 0],
+                 [0, 0, 0, 0, 0],
+               ]
+    return np.array(blinker)
+
+def toad(): #oscillator
+    toad = [ [0, 0, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0, 0],
+             [0, 0, 1, 1, 1, 0],
+             [0, 1, 1, 1, 0, 0],
+             [0, 0, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0, 0],
+           ]
+    return np.array(toad)
+
+def clock(): #oscillator but still life
+    clock = [ [0, 0, 0, 0, 0, 0],
+              [0, 0, 1, 0, 0, 0],
+              [0, 0, 0, 1, 1, 0],
+              [0, 1, 1, 0, 0, 0],
+              [0, 0, 0, 1, 0, 0],
+              [0, 0, 0, 0, 0, 0],
+           ]
+    return np.array(clock)
+
+def beacon():
+    beacon = [ [0, 0, 0, 0, 0, 0],
+               [0, 1, 1, 0, 0, 0],
+               [0, 1, 0, 0, 0, 0],
+               [0, 0, 0, 0, 1, 0],
+               [0, 0, 0, 1, 1, 0],
+               [0, 0, 0, 0, 0, 0],
+           ]
+    return np.array(beacon)
+
+def pulsar(): #oscillator
+    pulsar = [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0],
+               [0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0],
+               [0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0],
+               [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+               [0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0],
+               [0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0],
+               [0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           ]
+    return np.array(pulsar)
+
+def penta_decathlon():
+    penta = [  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           ]
+    return np.array(penta)
+
+def bi_penta_decathlon():
+    bi_penta = [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                 [0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0],
+                 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                 [0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0],
+                 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               ]
+    return np.array(bi_penta)
+
+def box_plot():
+    box_plot = [  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+                  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+             ]
+    return np.array(box_plot)
+
+def blocker():
+    blocker = [ [0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
+                [1, 1, 0, 0, 0, 0, 1, 1, 1, 1],
+                [1, 1, 0, 0, 1, 1, 0, 1, 1, 1],
+                [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+              ]
+    
+def octagon(): 
+    oct = [  [0, 0, 0, 1, 1, 0, 0 ,0], 
+             [0, 0, 1, 0, 0, 1, 0 ,0], 
+             [0, 1, 0, 0, 0, 0, 1 ,0], 
+             [1, 0, 0, 0, 0, 0, 0 ,1], 
+             [1, 0, 0, 0, 0, 0, 0 ,1], 
+             [0, 1, 0, 0, 0, 0, 1 ,0], 
+             [0, 0, 1, 0, 0, 1, 0 ,0], 
+             [0, 0, 0, 1, 1, 0, 0 ,0], 
+          ] 
+    return np.array(oct) 
+ 
+def mazing(): 
+    maz = [  [0, 0, 0, 0, 0, 0, 0 ,0, 0], 
+             [0, 0, 0, 0, 1, 1, 0 ,0, 0], 
+             [0, 0, 1, 0, 1, 0, 0 ,0, 0], 
+             [0, 1, 0, 0, 0, 0, 0 ,1, 0], 
+             [0, 0, 1, 0, 0, 0, 1 ,1, 0], 
+             [0, 0, 0, 0, 0, 0, 0 ,0, 0], 
+             [0, 0, 0, 0, 1, 0, 1 ,0, 0], 
+             [0, 0, 0, 0, 0, 1, 0 ,0, 0], 
+             [0, 0, 0, 0, 0, 0, 0 ,0, 0], 
+              
+          ] 
+    return np.array(maz)
+
+def glider():
+    glider = [ [0, 1, 0],
+               [0, 0, 1],
+               [1, 1, 1],
+             ]
+    return np.array(glider)
+
+def lw_spaceship():
+    lw_spaceship = [ [1, 0, 0, 1, 0],
+                     [0, 0, 0, 0, 1],
+                     [1, 0, 0, 0, 1],
+                     [0, 1, 1, 1, 1],
+                   ]
+    return np.array(lw_spaceship)
+
+def mw_spaceship():
+    mw_spaceship = [ [0, 0, 1, 0, 0, 0],
+                     [1, 0, 0, 0, 1, 0],
+                     [0, 0, 0, 0, 0, 1],
+                     [1, 0, 0, 0, 0, 1],
+                     [0, 1, 1, 1, 1, 1],
+                   ]
+    return np.array(mw_spaceship)
+
+def hw_spaceship():
+    hw_spaceship = [ [0, 0, 1, 1, 0, 0, 0],
+                     [1, 0, 0, 0, 0, 1, 0],
+                     [0, 0, 0, 0, 0, 0, 1],
+                     [1, 0, 0, 0, 0, 0, 1],
+                     [0, 1, 1, 1, 1, 1, 1],
+                   ]
+    return np.array(hw_spaceship)
+
+def copperhead():
+    copperhead =   [ [0, 1, 1, 0, 0, 1, 1, 0],
+                     [0, 0, 0, 1, 1, 0, 0, 0],
+                     [0, 0, 0, 1, 1, 0, 0, 0],
+                     [1, 0, 1, 0, 0, 1, 0, 1],
+                     [1, 0, 0, 0, 0, 0, 0, 1],
+                     [0, 0, 0, 0, 0, 0, 0, 0],
+                     [1, 0, 0, 0, 0, 0, 0, 1],
+                     [0, 1, 1, 0, 0, 1, 1, 0],
+                     [0, 0, 1, 1, 1, 1, 0, 0],
+                     [0, 0, 0, 0, 0, 0, 0, 0],
+                     [0, 0, 0, 1, 1, 0, 0, 0],
+                     [0, 0, 0, 1, 1, 0, 0, 0],
+                   ]
+    return np.array(copperhead)
+
+def weekender():
+    weekender =    [ [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
+                     [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
+                     [1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1],
+                     [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
+                     [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
+                     [0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0],
+                     [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+                     [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],
+                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                     [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                     [0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0],
+                   ]
+    return np.array(weekender)
+
+def spider():
+    spider    = [  [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                   [0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0],
+                   [1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1],
+                   [1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1],
+                   [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
+                   [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
+                   [0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0],
+                   [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+                ] 
+    return np.array(spider)
+
+def queen_bee_shuttle():
+    qbs = [    [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
+               [1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
+               [1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],  
+           ]
+    return np.array(qbs)
+
+def worker_bee():
+    wb = [  [1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1],
+          [0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0],
+          [0,1,0,1,0,0,0,0,0,0,0,0,1,0,1,0],
+          [0,0,1,1,0,0,0,0,0,0,0,0,1,1,0,0],
+          [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
+          [0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0],
+          [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
+          [0,0,1,1,0,0,0,0,0,0,0,0,1,1,0,0],
+          [0,1,0,1,0,0,0,0,0,0,0,0,1,0,1,0],
+          [0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0],
+          [1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1],
+         ]
+    return np.array(wb)
+
+def glider_gun():
+    gun    = [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
+               [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+               [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+             ] 
+    return np.array(gun)
+
+def acorn(): #explode
+    acorn = [ [0, 1, 0, 0, 0, 0, 0],
+              [0, 0, 0, 1, 0, 0, 0],
+              [1, 1, 0, 0, 1, 1, 1],
+            ]
+    return np.array(acorn)
+
+def blinker_puffer1():
+    blinker_puffer1 = [  [0, 0, 0, 1, 0, 0, 0, 0, 0],
+                         [0, 1, 0, 0, 0, 1, 0, 0, 0],
+                         [1, 0, 0, 0, 0, 0, 0, 0, 0],
+                         [1, 0, 0, 0, 0, 1, 0, 0, 0],
+                         [1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 1, 1, 0, 0, 0, 0, 0, 0],
+                         [1, 1, 0, 1, 1, 1, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 0, 0, 0, 1],
+                         [0, 0, 1, 1, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 1, 1, 0, 0],
+                         [0, 0, 0, 1, 0, 0, 0, 0, 1],
+                         [0, 0, 1, 0, 0, 0, 0, 0, 0],
+                         [0, 0, 1, 0, 0, 0, 0, 0, 1],
+                         [0, 0, 1, 1, 1, 1, 1, 1, 0],
+                      ]
+    return np.array(blinker_puffer1)
+
+def pufferfish():
+    pufferfish = [ [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+                   [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+                   [0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0],
+                   [0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0],
+                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                   [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                   [0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0],
+                   [1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1],
+                   [1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1],
+                   [0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0],
+                   [0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0],
+                   [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+                 ]
+    return np.array(pufferfish)
+
+def B_heptomino():
+    B = [   [0, 0, 0, 0, 0, 0],
+            [0, 1, 0, 1, 1, 0],
+            [0, 1, 1, 1, 0, 0],
+            [0, 0, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 0],
+    ]
+    return np.array(B)
+
+def Pi_heptomino():
+    Pi = [  [0, 0, 0, 0, 0],
+            [0, 1, 1, 1, 0],
+            [0, 1, 0, 1, 0],
+            [0, 1, 0, 1, 0],
+            [0, 0, 0, 0, 0],
+    ]
+    return np.array(Pi)
+
+def random_config(b, h):
+    random_config = np.random.randint(2, size = (h, b))
+    return random_config
\ No newline at end of file
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