{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LaTeX macros (hidden cell)\n",
    "$\n",
    "\\newcommand{\\Q}{\\mathcal{Q}}\n",
    "\\newcommand{\\ECov}{\\boldsymbol{\\Sigma}}\n",
    "\\newcommand{\\EMean}{\\boldsymbol{\\mu}}\n",
    "\\newcommand{\\EAlpha}{\\boldsymbol{\\alpha}}\n",
    "\\newcommand{\\EBeta}{\\boldsymbol{\\beta}}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports and configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import re\n",
    "import datetime as dt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "from mosek.fusion import *\n",
    "\n",
    "from notebook.services.config import ConfigManager\n",
    "\n",
    "from portfolio_tools import data_download, DataReader, compute_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6.9 (default, Jan 26 2021, 15:33:00) \n",
      "[GCC 8.4.0]\n",
      "matplotlib: 3.3.4\n"
     ]
    }
   ],
   "source": [
    "# Version checks\n",
    "print(sys.version)\n",
    "print('matplotlib: {}'.format(matplotlib.__version__))\n",
    "\n",
    "# Jupyter configuration\n",
    "c = ConfigManager()\n",
    "c.update('notebook', {\"CodeCell\": {\"cm_config\": {\"autoCloseBrackets\": False}}})  \n",
    "\n",
    "# Numpy options\n",
    "np.set_printoptions(precision=5, linewidth=120, suppress=True)\n",
    "\n",
    "# Pandas options\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "# Matplotlib options\n",
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "plt.rcParams['figure.dpi'] = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare input data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we load the raw data that will be used to compute the optimization input variables, the vector $\\EMean$ of expected returns and the covariance matrix $\\ECov$. The data consists of daily stock prices of $8$ stocks from the US market. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data downloading:\n",
    "# If the user has an API key for alphavantage.co, then this code part will download the data. \n",
    "# The code can be modified to download from other sources. To be able to run the examples, \n",
    "# and reproduce results in the cookbook, the files have to have the following format and content:\n",
    "# - File name pattern: \"daily_adjusted_[TICKER].csv\", where TICKER is the symbol of a stock. \n",
    "# - The file contains at least columns \"timestamp\", \"adjusted_close\", and \"volume\".\n",
    "# - The data is daily price/volume, covering at least the period from 2016-03-18 until 2021-03-18, \n",
    "# - Files are for the stocks PM, LMT, MCD, MMM, AAPL, MSFT, TXN, CSCO.\n",
    "list_stocks = [\"PM\", \"LMT\", \"MCD\", \"MMM\", \"AAPL\", \"MSFT\", \"TXN\", \"CSCO\"]\n",
    "list_factors = []\n",
    "alphaToken = None\n",
    " \n",
    "list_tickers = list_stocks + list_factors\n",
    "if alphaToken is not None:\n",
    "    data_download(list_tickers, alphaToken)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the daily stock price data from the downloaded CSV files. The data is adjusted for splits and dividends. Then a selected time period is taken from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "investment_start = \"2016-03-18\"\n",
    "investment_end = \"2021-03-18\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found data files: \n",
      "stock_data/daily_adjusted_AAPL.csv\n",
      "stock_data/daily_adjusted_PM.csv\n",
      "stock_data/daily_adjusted_CSCO.csv\n",
      "stock_data/daily_adjusted_TXN.csv\n",
      "stock_data/daily_adjusted_MMM.csv\n",
      "stock_data/daily_adjusted_IWM.csv\n",
      "stock_data/daily_adjusted_MCD.csv\n",
      "stock_data/daily_adjusted_SPY.csv\n",
      "stock_data/daily_adjusted_MSFT.csv\n",
      "stock_data/daily_adjusted_LMT.csv\n",
      "\n",
      "Using data files: \n",
      "stock_data/daily_adjusted_PM.csv\n",
      "stock_data/daily_adjusted_LMT.csv\n",
      "stock_data/daily_adjusted_MCD.csv\n",
      "stock_data/daily_adjusted_MMM.csv\n",
      "stock_data/daily_adjusted_AAPL.csv\n",
      "stock_data/daily_adjusted_MSFT.csv\n",
      "stock_data/daily_adjusted_TXN.csv\n",
      "stock_data/daily_adjusted_CSCO.csv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# The files are in \"stock_data\" folder, named as \"daily_adjusted_[TICKER].csv\"\n",
    "dr = DataReader(folder_path=\"stock_data\", symbol_list=list_tickers)\n",
    "dr.read_data(read_volume=True)\n",
    "df_prices, df_volumes = dr.get_period(start_date=investment_start, end_date=investment_end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run the optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the optimization model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we implement the optimization model in Fusion API. We create it inside a function so we can call it later.\n",
    "\n",
    "The parameter `a` is the coefficient vector in the market impact cost term, the parameter `beta` is used in the exponent in the market impact formula, and `rf` is the risk free interest rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def EfficientFrontier(N, m, G, deltas, a, beta, rf):\n",
    "\n",
    "    with Model(\"Case study\") as M:\n",
    "        # Settings\n",
    "        #M.setLogHandler(sys.stdout)\n",
    "        \n",
    "        # Variables \n",
    "        # The variable x is the fraction of holdings in each security. \n",
    "        # It is restricted to be positive, which imposes the constraint of no short-selling.   \n",
    "        x = M.variable(\"x\", N, Domain.greaterThan(0.0))\n",
    "        \n",
    "        # Variable for risk-free asset (cash account)\n",
    "        xf = M.variable(\"xf\", 1, Domain.greaterThan(0.0))        \n",
    "        \n",
    "        # The variable s models the portfolio variance term in the objective.\n",
    "        s = M.variable(\"s\", 1, Domain.unbounded())\n",
    "        \n",
    "        # Auxiliary variable to model market impact \n",
    "        t = M.variable(\"t\", N, Domain.unbounded())\n",
    "\n",
    "        # Budget constraint with transaction cost terms\n",
    "        M.constraint('budget', Expr.sum(Expr.hstack(Expr.sum(x), xf, Expr.dot(a, t))), Domain.equalsTo(1))\n",
    "        \n",
    "        # Power cone to model market impact \n",
    "        M.constraint('market_impact', Expr.hstack(t, Expr.constTerm(N, 1.0), x), Domain.inPPowerCone(1.0 / beta))\n",
    "        \n",
    "        # Objective (quadratic utility version)\n",
    "        delta = M.parameter()\n",
    "        M.objective('obj', ObjectiveSense.Maximize, \n",
    "                    Expr.sub(\n",
    "                        Expr.add(Expr.dot(m, x), Expr.mul(rf, xf)), \n",
    "                        Expr.mul(delta, s)\n",
    "                    )\n",
    "        )\n",
    "        \n",
    "        # Conic constraint for the portfolio variance\n",
    "        M.constraint('risk', Expr.vstack(s, 1, Expr.mul(G.transpose(), x)), Domain.inRotatedQCone())\n",
    "    \n",
    "        columns = [\"delta\", \"obj\", \"return\", \"risk\", \"t_resid\", \"x_sum\", \"xf\", \"tcost\"] + df_prices.columns.tolist()\n",
    "        \n",
    "        df_result = pd.DataFrame(columns=columns)\n",
    "        for d in deltas:\n",
    "            # Update parameter\n",
    "            delta.setValue(d);\n",
    "            \n",
    "            # Solve optimization\n",
    "            M.solve()\n",
    "            \n",
    "            # Check if the solution is an optimal point\n",
    "            solsta = M.getPrimalSolutionStatus()\n",
    "            if (solsta != SolutionStatus.Optimal):\n",
    "                # See https://docs.mosek.com/latest/pythonfusion/accessing-solution.html about handling solution statuses.\n",
    "                raise Exception(\"Unexpected solution status!\") \n",
    "            \n",
    "            # Save results\n",
    "            portfolio_return = m @ x.level() + np.array([rf]) @ xf.level()\n",
    "            portfolio_risk = np.sqrt(2 * s.level()[0]) \n",
    "            risky_return = m @ x.level()\n",
    "            t_resid = t.level() - np.abs(x.level())**beta\n",
    "            row = pd.Series([d, M.primalObjValue(), portfolio_return, portfolio_risk, \n",
    "                             sum(t_resid), sum(x.level()), sum(xf.level()), t.level() @ a] + list(x.level()), index=columns)\n",
    "     \n",
    "            df_result = pd.concat([df_result, pd.DataFrame([row])], ignore_index=True)\n",
    "\n",
    "        return df_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute optimization input variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use the loaded daily price data to compute the corresponding yearly mean return and covariance matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Number of securities\n",
    "N = df_prices.shape[1]\n",
    "\n",
    "# Get optimization parameters\n",
    "m, S = compute_inputs(df_prices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we compute the matrix $G$ such that $\\ECov=GG^\\mathsf{T}$, this is the input of the conic form of the optimization problem. Here we use Cholesky factorization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = np.linalg.cholesky(S)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also compute the average daily volume and daily volatility (std. dev.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lin_returns = df_prices.pct_change()\n",
    "volatility = df_lin_returns.std()\n",
    "volume = (df_volumes * df_prices).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we specify the parameters of market impact, risk free rate, and portfolio size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Market impact coefficient\n",
    "beta = 3 / 2\n",
    "c = 1\n",
    "rf = 0.01\n",
    "portfolio_value = 10**10\n",
    "\n",
    "# Compute portfolio relative volume , because the variable x is also portfolio relative.\n",
    "rel_volume = volume / portfolio_value\n",
    "\n",
    "# a1 means no impact, a2 means impact\n",
    "a1 = np.zeros(N)\n",
    "a2 = (c * volatility / rel_volume**(beta - 1)).to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Call the optimizer function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We run the optimization for a range of risk aversion parameter values: $\\delta = 10^{-1},\\dots,10^{2}$. We compute and plot the efficient frontier this way both with and without market impact cost. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd81ef61320>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB+QAAAU2CAYAAACsog+NAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAB7CAAAewgFu0HU+AAEAAElEQVR4nOzdd3hUVf7H8c+dSZn0UAIhlCQgIE1RQEBpVlQsoMKigqLiIuq6iG5R176r/nQta0FRXFGwYVdEQVfpQZAiVUAhKCUhlPQ+c39/TGbIJJMyyaS/X88zDzdzzz33O+UmIZ855ximaQoAAAAAAAAAAAAAAPiXpaELAAAAAAAAAAAAAACgOSKQBwAAAAAAAAAAAACgDhDIAwAAAAAAAAAAAABQBwjkAQAAAAAAAAAAAACoAwTyAAAAAAAAAAAAAADUAQJ5AAAAAAAAAAAAAADqAIE8AAAAAAAAAAAAAAB1gEAeAAAAAAAAAAAAAIA6QCAPAAAAAAAAAAAAAEAdIJAHAAAAAAAAAAAAAKAOEMgDAAAAAAAAAAAAAFAHCOQBAAAAAAAAAAAAAKgDBPIAAAAAAAAAAAAAANQBAnkAAAAAAAAAAAAAAOoAgTwAAAAAAAAAAAAAAHWAQB4AAAAAAAAAAAAAgDoQ0NAFoOkwDCNYUr+SL9Mk2RuwHAAAAAAAAAAAAADwJ6ukmJLtLaZpFtS2QwJ5+KKfpHUNXQQAAAAAAAAAAAAA1LFBkn6sbSdMWQ8AAAAAAAAAAAAAQB1ghDx8kebaWLt2rTp06FCnJ8vLy9Py5cslSSNGjFBISEidng9orriWgNrjOgJqj+sI8A+uJaD2uI6A2uM6AvyDawmoPa4j+NuhQ4d0xhlnuL5Mq6xtdRHIwxfuNeM7dOigTp061enJ8vLy1LZtW0lSp06d+CYK1BDXElB7XEdA7XEdAf7BtQTUHtcRUHtcR4B/cC0Btcd1hDpmr7pJ1ZiyHgAAAAAAAAAAAACAOkAgDwAAAAAAAAAAAABAHSCQBwAAAAAAAAAAAACgDhDIAwAAAAAAAAAAAABQBwjkAQAAAAAAAAAAAACoAwTyAAAAAAAAAAAAAADUAQJ5AAAAAAAAAAAAAADqAIE8AAAAAAAAAAAAAAB1gEAeAAAAAAAAAAAAAIA6QCAPAAAAAAAAAAAAAEAdCGjoAtDy5OfnKz09Xbm5ubLb7RW2czgcatOmjSTpt99+k8XC50eAmuBaAmqvKV1HVqtVoaGhio6Ols1ma+hyAAAAAAAAAKBFI5BHvTFNU4cOHVJGRka124eEhEiS7Ha7HA5HXZYHNFtcS0DtNaXrqLi4WAUFBTp+/LiioqLUoUMHGYbR0GUBAAAAAAAAQItEII96c/To0XJhfEBA5W9BV4BQVTsAleNaAmqvqVxHxcXF7u2MjAwFBQWpbdu2DVgRAAAAAAAAALRcjfsvymg2CgsLlZaW5v66Xbt2io6OltVqrfAYh8OhzMxMSVJkZGSjnh4YaMy4loDaa0rXkd1uV3p6ug4fPixJSktLU2RkpIKCghq4MgAAAAAAAABoeRrvX5PRrGRnZ7u327RpozZt2lQaxgMAgJqxWq3un7UupX8OAwAAAAAAAADqD4E86kVOTo57OzIysgErAQCgZSj987b0z2EAAAAAAAAAQP0hkEe9KCwslORcfzc4OLiBqwEAoPkLDg52r3vv+jkMAAAAAAAAAKhfBPKoFw6HQ5JzGl1XOAAAAOqOYRju5WFcP4cBAAAAAAAAAPWLQB4AAAAAAAAAAAAAgDpAIA8AAAAAAAAAAAAAQB0gkAcAAAAAAAAAAAAAoA4QyAMAAAAAAAAAAAAAUAcI5AEAAAAAAAAAAAAAqAME8gDQyI0aNUqGYWjUqFG16mflypWyWq0yDENLly71S22oneTkZBmGIcMwNHfu3IYup0aWLl3qfgy8rwAAAAAAAAAA8EQgDwAAAAAAAAAAAABAHSCQR4uVlV+k3alZ2vR7unanZikrv6ihS2qSHnroIffoWDQM1/P/0EMPNXQp9a4lP3Y0X3xfBQAAAAAAAIDmI6ChCwDqk2maStpzVPOS9mnJ9lTZHaZ7n9ViaHSf9po0JF5Du7YhCEGjwTTgzVdCQoJM06y6YSM2atSoJv8YAAAAAAAAAACoKwTyaDG2HsjQzAWbtCs12+t+u8PUoi0pWrQlRT3ah+uZCf3Vt2NUPVcJAAAAAAAAAAAAoLlgynq0CCt2p2nC7KQKw/iydqVma8LsJK3YnVbHlQEAAAAAAAAAAABorgjk0extPZChafPWK7fQ7tNxuYV2TZu3XlsPZNRRZXWj7NrDGRkZevTRR3XaaacpOjpahmFo7ty55Y779NNPNX78eHXp0kU2m03R0dEaOHCgHn74YR0/frxc+7lz58owDD388MPu+1znLX1LTk52709ISJBhGJoyZUqlj2HKlCkyDEMJCQnl9iUnJ7v7dj2Ojz/+WBdffLHi4uIUEBCgUaNGVdj2m2++0aWXXqrY2FgFBwcrMTFR06dP1/79+yutqTJ9+/aVYRiaOHGi1/2u58owDPXv399rmzVr1rjbfP311x77Ro0aJcMw3I/LxfV8ujz88MPlnv+qnusFCxbo3HPPVUxMjEJCQtSzZ0/99a9/1bFjx6p83NnZ2XriiSc0dOhQtW7dWsHBwerUqZOuuuoqLVy4sNJja/te8MdjL6v065ScnKyCggL9+9//1umnn66oqChFRkZq8ODBmjVrluz2ir+flH29du/erdtvv13du3dXaGiox3Xh7T1akVWrVmnq1Knq2bOnIiMjFRQUpE6dOumSSy7RSy+9pPT09AqP/eWXX3TnnXeqX79+ioqKUkhIiLp27aopU6boxx9/9Ol5Kmvp0qXux+BteYWyr2FKSoruvvtu9ejRQ6GhoerYsaMmTJigbdu2eRyXnJysO+64Qz169FBISIjat2+va6+9Vr/++muFtZT9/peenq4HH3xQffr0UXh4uFq3bq2zzz5b7777bqWPqbCwUF988YVuv/12DRo0SK1atVJwcLC6du2q8847Tw8//LCOHDlSreenoKBAr776qsaMGaOOHTsqODhYYWFh6tOnj6ZOnarFixe7p/yvyfdVAAAAAAAAAEDjxpT1aNZM09TMBZt8DuNdcgvtumvBT/p6xvAmuab87t27dcEFF1Qa3hw/flxXXXWVvvvuO4/7CwoKtH79eq1fv16zZs3SZ599piFDhtRxxb4xTVPXXXed5s2bV63299xzj5544gmP+5KTk/XKK6/oo48+0rJly9SrVy+f6xg5cqS2bdumZcuWed1f+v7Nmzfr2LFjat26tdc2AQEBGjZsmM81+MrhcGjy5MmaP3++x/27du3SU089pU8++UQrVqxQbGys1+M3btyoSy65RAcPHvS4/8CBA/roo4/00Ucf6YorrtDbb78tm81WZ4+jrriui/Xr13vcv3btWq1du1bvv/++vvzyS4WHh1faz2effaZrr71WOTk5Na4lLy9PN910k9cQ+cCBAzpw4IC+/PJLpaWl6aGHHirX5t///rfuvfdeFRUVedy/d+9e7d27V2+99Zb+8Y9/6JFHHqlxjdX1008/6cILL1RKSor7vry8PH3wwQdatGiRvv76aw0bNkzfffedrrjiCmVknPhAVH5+vt555x199dVXWrFihfr06VPpufbu3avzzz/fI8DPycnR0qVLtXTpUn366ad6++23FRBQ/lehP/7xj3rzzTfL3X/8+HH398WXXnpJn332mc4666wKa9i0aZOuuOIK7d271+P+wsJCbd++Xdu3b9frr7+uvXv3ev0AEgAAAAAAAACg6SOQR7OWtOdotaepr8jO1Cyt2XNMQ7u18VNV9eeqq67SgQMH9Kc//UmXXXaZWrVqpd27dys+Pl6SM3Q/77zztGHDBlmtVl1zzTW6+OKLlZiYqKKiIi1fvlzPPPOMDh8+rIsvvlgbN250Hzt27FgNHDhQs2bN0ssvvyxJ2rJlS7kaOnbsWGeP77nnntPmzZs1fPhwTZ8+XT169FB6errXDyC89tprWr16tUaOHKlp06a527711lt66623lJaWphtvvFFJSUk+1zFq1CjNmjVLKSkp+vnnn3XyySd77C89atg0TS1fvlxjx4712ub000+vMuR1WbJkiQoLC9WvXz9J0vTp03Xrrbd6tGnVqpXXY++//36tXr1aY8eO1XXXXaf4+HilpqbqpZde0pdffukeUV1RCHzuuefq+PHj7pHoEydOVJs2bbR9+3Y9/fTT+umnn/Txxx9rypQpeu+996r1eHxRm8deHdOmTdP69ev1hz/8Qddff73atWunXbt26dlnn9W6deu0fPlyTZ48WZ988kmFffz222+aNGmSQkNDdf/992v48OGyWq1at25dtV9jh8Ohyy+/XN98840kqXv37rr11ls1cOBAhYaG6tChQ1q9erUWLFjg9finnnpKf/3rXyVJp5xyiqZPn67u3bsrOjpaO3fu1IsvvqikpCQ9+uijatu2re644w4fn6nqy83N1bhx41RYWKjHHntMI0eOlNVq1ddff63HHntMOTk5mjx5sr755huNHTtWUVFReuSRRzR48GAVFxfro48+0nPPPafjx4/rpptu0po1ayo93x/+8Aft3btXt9xyi6666ipFRUVp8+bN+r//+z/t2rVLCxYsUFxcnJ599tlyxxYXF6tr164aN26czjjjDHXp0kUWi0U///yzli5dqrfffltHjx7VuHHjtHXrVrVr165cHzt27NDw4cOVne38GTRu3DhNnDhRXbt2ld1u165du7RkyRKP91Bj+L4KAAAAAAAAAPAvAnk0Gg6HqeO5haW+digr1zmis8hSIIvF9xUWXl+5t+pG1epnj3q0r16AVl2tQoNksdTtqPutW7fqq6++0gUXXOC+b8CAAe7tRx55RBs2bFB0dLS+/fZbj32SNGzYMF177bUaOnSoDh06pHvvvVdvv/22JCk6OlrR0dEeQVTfvn3r9PGUtXnzZl133XXuaZ4rs3r1at18882aPXu2R9tzzz1XQUFBmjNnjtasWaONGzfqtNNO86mOkSNHureXLl3qEcj/9ttv7mnJx4wZo4ULF2rp0qUegbzdbteqVaskqdy09JXp0aOHx9ft2rXz+ho4HI5y961evVr//Oc/dd9993ncf+GFF+rCCy/UkiVL9OGHH+r5559XTEyMR5sZM2a4lzF47bXXdNNNN7n3DRgwQBMmTNBFF12k77//Xu+//76uv/56XXTRRdV+XNVR3cdeU+vWrdNjjz2me+65x33fgAEDNH78eF1yySVavHixPv30Uy1atEgXX3yx1z727t2ruLg4JSUlqUuXLu77Bw8eXO06XnzxRXcYP27cOL377rsKDg72aDNmzBg9+uijOnTokMf927dvd7++Dz74oB588EGP9/6AAQM0ceJEXX/99Zo/f77uu+8+TZ48uVYfZKhMWlqaTNPU2rVr1a1bN/f9gwcPVtu2bXX77bcrOTlZZ555pmJjY7Vq1SqP995ZZ52lgIAAPfXUU/rhhx+qvFbXrVund955R1dffbX7voEDB2r8+PEaPny4fvrpJz3//PO66aabyr13Hn74YXXt2tXj+XI4HOrRo4cuu+wy/fnPf9awYcOUlpamF154QY8++mi580+aNEnZ2dmyWCx6++23yy1pMXjwYE2ePFlHjx5VaGiopMbxfRUAAAAAAAAA4F8E8mg0jucWasA/v23oMrz6dsdhv9e2/h/nqU14cNUNa2HKlCkeYXxp2dnZeumllyRJjz76aLkw3iU+Pl7333+/br31Vn3wwQd69dVXFRYWVmc1+yI6OlovvvhitZYT6NChg1544QWvbe+++27NmTNHkrRixQqfA/l27dqpV69e2rFjh5YuXapbbrnFvc818r13794aP368O5Avbf369crKypLkGe7XpQEDBujee+8td79hGJo5c6aWLFmi4uJiJSUl6bLLLnPvP3jwoHtE74UXXugRxrsEBwfrv//9r7p3767i4mK9+OKLfg/k69opp5yiv//97+XuDwgI0Jw5c9S1a1cVFRVp1qxZFQbykvTEE094hPG+cDgceuqppyRJnTp10ltvvVUujHexWCzlRk0//fTTKioq0sCBA8uF8aWPe+GFF/TBBx8oOztbH374oW6++eYa1Vsdjz76qEcY73LjjTfq7rvvVn5+vtLS0vTWW2+V+yCI5JwJwfWcVHWtXnLJJR5hvEtERIReffVVDR48WA6HQ6+88opefPFFjzbeaiytX79+mjp1qp577jl9+umn5QL5JUuWaMOGDZKkO+64o1wYX1qbNk1v9hUAAAAAAAAAQPX5PuQYQJNx7bXXVrhv2bJl7vWZr7rqqkr7GTFihCSpqKio3JraDenSSy9VREREtdpeddVVFYaZPXv2dE8hvmfPnhrV4grSy64j7/p61KhR7tHvrnXky7axWq31sn68JF1zzTUVfpCh9Iczyj4fS5culd1ulySvYbxLQkKCzj///HLHNBXXX399hc9Pp06d3B90qeyxBQUFafz48TWuYdOmTdq/f78k6eabb672NPcuX3zxhSTpyiuvrPRDK9HR0e6p/2uyZEN1GYahCRMmeN0XEhKi7t27S3IuNTB69Giv7RITE93XfFXX6g033FDhvjPOOMO9Bv2331b9Yavjx4/r119/1Y4dO7R9+3Zt3bpV0dHRkpwzERQVFXm0X7hwoXt7xowZVfYPAAAAAAAAAGi+COSBZuyUU06pcN+PP/7o3u7QoYMMw6jwVnrK5JSUlDqt2ReVPb6yyq7rXpZrmm7XSHVfucJ21zryLq7R8KNGjVKXLl2UmJjoXke+bJvTTjtNkZGRNTq/ryp7Plq3bu3eLvt8bN261b1d1dTrrv25ubk1/qBDQxk0aFCl+8844wxJUk5OToWPrXv37rLZbDWuYePGje7t4cOH+3Tsvn37lJaWJkm65557Kr2+DcNwfz+oy+u7bdu2Hu+tslwB90knnVTlBwikqq/V6r6Gu3btUmFhYbn9W7Zs0Y033qgOHTqodevW6tGjh84880ydddZZOvXUU/XQQw9Jcs5k4FrCwcX12nXp0kXx8fGV1gEAAAAAAAAAaN4I5IFmrLK1oA8fPlyjPnNzc2tajt/5sta1a43milgszm+HNR3JXXYdeUnav3+/9uzZI8Mw3Ptdwb2rjd1u18qVKz321YfKng/XcyGVfz5Kj+wvvc61N7GxsV6Pawqqemzt27d3b1f02Gq7FvuRI0fc2x06dPDp2MZ4fVf3GvTXtVrd19A0zXKB+uuvv67TTz9db7zxRrU+pJCXl+fxteu18/V1AwAAAAAAAAA0P6whj0ajVWiQ1v/jPPfXDodDWdnZkqSI8HCPkLA6sguKdc6/l8lumrWuzWox9N1dIxUe7L9LplVokN/6qojVaq1wX+kwa8OGDQoMDKxWn506dap1Xf5S2eOrb7GxserZs6d27tzpXkfeNRV979693ethjxw5Um+88YY7kN+0aZMyMzPd+5qSykYxN3X+eGwN+f4sfX0/8MAD1Z46PywsrK5Kqnc1fQ1//vln3XLLLSouLla7du30l7/8Reecc466dOki0zQVGBioyMhIzZ07171sg+mHnzMAAAAAAAAAgOaJQB6NhsViqE34iTW+HQ6HAh0FkqTI8GCfA/k24cEa3be9Fm2p/RTMF/aJVXyb5hNUSVKbNm3c2zExMfUStLteQ4fDUWm7nJycOq+lLowcOVI7d+50B/Glp6t3KbuOvKuNxWLxeVryhlB6yvHU1FR17ty5wralRxaXnaq8sb8XUlNT1aNHj0r3u1Q2DXtttG3b1r196NChKpddKK309R0YGOix7ERLUdX70/UaGobhMZvB3LlzVVxcLKvVqmXLlrmfd4fD4f7wjFT5rA+u1+7QoUO1egwAAAAAAAAAgKaPKevRrE0a4p+1e/3VT2Ny2mmnubdXrVpV4358GYUaEREhSeWmhy5r165dNa6nIZVdR94VzJcO5OPj45WQkOBeR97Vpn///oqKiqrvkn1WOtj94YcfKm27du1aSc4pyLt27eqxr7G/F9atW1et/d4em7+cfvrp7u3ly5f7dGzXrl3d76faXN9NWXVfw+7duyso6MSMJdu2bZMknXrqqZV+COLHH3+scJ/rtfvtt9+0b9++atfs0pxnnwAAAAAAAACAloZAHs3a0K5t1KN9eK366Nk+QkO61s0I2IZ03nnnuddqfv7552s85bLNZnNvFxQUVNo2MTFRknOK/IrOt23bNm3evLlGtTS00lPOv/POO9q9e7fH+vEuroD+u+++04oVKzzuqwnXa1DV8+8Po0aNck/F/t///rfCdr/99pu++eabcse4+Ou9UFePfd68eRXWdeDAAS1ZskSS98fmL6eeeqp7hPecOXOUXbKER3VYrVZdfPHFkqQlS5Zox44ddVJjY/bmm29WuG/dunXaunWrJOf3wtKKi4slVT47w6FDh/T5559XuP/SSy91bz/77LPVqrc0X76vAgAAAAAAAAAaNwJ5NGuGYeiZCf0VGlSzwCw0yKqnJ5zaLEcrRkdH6/bbb5ckrV69WnfeeWel04enpqZqzpw55e7v0KGDe/vXX3+t9JyuYPrgwYN69913y+3Pyspyr8ncFMXFxal79+6SnB9ykDzXj3dxPQ9vvfWW0tPTPe6rCddrUNXz7w9xcXEaN26cJOmrr77yGnoWFhbqxhtvVFFRkSS532el+eu9UFePfdOmTXrqqafK3V9cXKybb75ZhYWFkqTp06f79bylWSwW/eUvf5Ek7d+/X9ddd537vGU5HA4dPHjQ47577rlHVqtVDodDV111lfbv31/huex2u95+++1K2zQ1n3/+uRYsWFDu/uzsbE2bNk2S8zl2bbu4ruHdu3dr9erV5Y7Pzc3VpEmTlJeXV+G5zzvvPA0YMECS9MILL+i9996rsO3Ro0fL9eXL91UAAAAAAAAAQONGII9mr2/HKM2ePMDnUD40yKrZkweob8fGP414TT3yyCMaPHiwJOk///mPTj/9dL300ktatWqVNm3apO+//14vvviixo4dqy5duuiVV14p18eZZ57p3r7zzju1fPly7d69W7/88ot++eUX92hTSZo0aZIiIyMlSTfddJMeeeQR/fDDD1q7dq1efvllnX766frpp588ptNvalxBc0ZGhiTvI99d97naWCwWjRgxosbndL0Gn3/+uWbPnq2tW7e6n//Dhw/XuN+KPPvss+41t2+88UbdfPPN+vbbb7V+/Xq9/fbbGjx4sP73v/9JkiZMmKCLLrqoXB/+ei/U1WMfOHCg/va3v+maa67R119/rQ0bNuj999/XWWedpa+++kqScxT0JZdcUuNzVMdtt92m888/X5L0ySefqF+/fvrPf/6jVatWaePGjfrqq6/04IMP6uSTT9arr77qcWy/fv3073//W5K0fft29e3bV3/961/19ddfa+PGjUpKStK7776rO+64Q507d9akSZPcHxBpDgYOHKhrrrlGt912m77//nutX79eb7zxhgYOHKiNGzdKcj6/p5xyisdxkydPluT8kMOYMWP02GOPafny5Vq7dq1ef/11jRgxQkuXLtVZZ51V6fnnzZun8PBwORwOXX311bryyiv1wQcfaP369Vq7dq3eeecdTZkyRfHx8e717F18+b4KAAAAAAAAoOXKyi/S7tQsbfo9XbtTs5SVX9TQJcGLgIYuAKgPw7vHaMG0oZq5YJN2pVY97XPP9hF6esKpzTqMl6Tg4GB98803mjJlij7++GP99NNPXkczu7gC1NJOOukkTZgwQQsWLNCSJUvcU3m77N27VwkJCZKkmJgYzZkzR1dffbXy8/P14IMP6sEHH3S3DQkJ0bx587Rw4UJ3YNbUjBo1ymMmAW+BfEJCguLj491rS59yyimKjo6u8TnvvvtuffjhhyooKNAtt9zise/666+vdGr5mujUqZP+97//6ZJLLtHBgwc1Z84cr7MnXHHFFRVOG+6v90JVj33u3Lk1eoyvvvqqbrrpJr377rteR/CfddZZevvtt2vUty8sFos+/fRTXX/99frwww+1a9cuzZgxo9rHz5gxQ2FhYZoxY4YyMjL01FNPeR35L0lBQUEeU6U3dQsWLNC5556rWbNmadasWeX2X3nllXrmmWfK3T9o0CA9/PDDevDBB5Wenq777ruvXJuZM2eqX79+WrVqVYXn79Wrl5YuXapx48bp999/18cff6yPP/64WrX78n0VAAAAAAAAQMtimqaS9hzVvKR9WrI9VXbHieVXrRZDo/u016Qh8RratU2znAG6KWKEPFqMvh2jtHjGCL178xBd3C9WVovnN6EAi6Ex/Tro3ZuH6OsZw5t9GO8SERGhjz76SCtWrNDUqVPVs2dPRUREKCAgQK1bt9agQYN02223adGiRe41wcuaP3++nnzySZ1xxhmKioqSxVLxt5bx48dr9erVGjdunGJiYhQUFKTOnTvr+uuv17p163TVVVfV1UOtF6Wnnve2frxL6aC+NuvHS1L//v2VlJSkq6++Wl26dFFwcHCt+quO0047TTt37tTjjz+uwYMHKzo6WkFBQYqLi9MVV1yhzz//XB999FGlAa8/3gt19dhbtWql1atX6/HHH1f//v0VERGh8PBwDRo0SC+88IKWLVumiIgIv5yrKqGhofrggw/03XffafLkyUpMTFRISIj7+br00ks1e/Zs3XXXXV6Pv/nmm7Vnzx49/PDDOuuss9S2bVsFBAQoLCxMPXr00JVXXqlXXnlFBw4c0EknnVQvj6k+JCYmav369br33nvVq1cvhYaGKioqSiNGjND8+fP14YcfKiDA++cSH3jgAX355Ze64IIL1KpVKwUFBalTp0669NJL9fHHH1f4oYayBgwYoJ07d+r555/XOeeco3bt2ikgIEDh4eHq16+f/vjHP+p///uf13Ddl++rAAAAAAAAAFqGrQcyNPq55brmtR/01dYUjzBekuwOU4u2pOia137Q6OeWa+uBjAaqFKUZpmlW3QqQZBhGJ0m/S9Lvv/+uTp06VfvY3bt3q7i4WAEBAe71eavicDiUmZkpyTky299hRFZ+kVIz85VdYFd4sFXtI22KsAX69RxAY1DX11JzMXfuXN1www2SGIHcVD300EN6+OGHJTk/JepPTfU6qsnPX6Cu5OXluWd8uOCCCxQSEtLAFQFNE9cSUHtcR0DtcR0B/sG1BNReS7qOVuxO07R565VbaK/2Ma7lmYd3j6nDypqX/fv3q3Pnzq4vO5umub+2fTaNvyYDdSDCFqiT2kWof+dondQugjAeAAAAAAAAAAAAjc7WAxk+h/GSlFto17R56xkp38AI5AEAAAAAAAAAAACgETJNUzMXbPI5jHfJLbTrrgU/+X1WUVQfgTwAAAAAAAAAAAAANEJJe45qV2p2rfrYmZqlNXuO+aki+IpAHgAAAAAAAAAAAAAaoflr9jWqfuA7AnkAAAAAAAAAAAAAaGSy8ou0eFuqX/r6eluKsvKL/NIXfBPQ0AUAANAYTJkyRVOmTGnoMlALDz30kB566KGGLgMAAAAAAABAFbLyi5SSka+cQrvCgqyKjbIpwhbY0GU1CNM0lZ5bpJTMfKVk5CslM1+HMvKVmpGvX9OyZXf4Z+13u8NUamZ+i32eGxKBPAAAAAAAAAAAAIA6ZZqmkvYc1bykfVqyPdUjaLZaDI3u016ThsRraNc2MgyjASv1n2K7Q4ezCpSS6QzYD2XkKzWzVOheEsIXFDvqpZ7sAnu9nAeeCOQBAAAAAAAAAAAA1JmtBzI0c8Em7UrN9rrf7jC1aEuKFm1JUY/24XpmQn/17RhVz1X6Jrew2D2i3ePfkqD9UEa+jmQXyE8D3P0iPNja0CW0SATyAAAAAAAAAAAAAOrEit1pmjZvvXILqzc6e1dqtibMTtLsyQM0vHtMHVdXnmmaOp5bpEMZeSUj2AuUkpHnDNwzS7Yz8pWZX1zvtdVGgMVQ+0hbQ5fRIhHIAwAAAAAAAAAAAPC7rQcyfArjXXIL7Zo2b70WTBvq15HyRa4p5EuNYk8ttWZ7Ssl08oX1NIV8aYYhtQkLVmxUsGIjQxQbFawOUSFavC1Fm/dn1Lr/0X1iWT++gRDIAwAAAAAAAAAAAPAr0zQ1c8Emn8N4l9xCu+5a8JO+njG8WmvK5xQW62B2tnut9tJTyZeeQt5sgCnkg6wWtY8KVmykTe0jbeoQ5frXGby3j7SpXYRNQQGWcsee1iVa17z2Q61rmDQkvtZ9oGYI5AEAAAAAAAAAAAD4VdKeoxWuGV9dO1OztGbPUfVoH+EezV56rfYDx3O055BVGYVSXtJyP1XumwhbgGIjbYqNsp34t2TbFb63Dguq1ocKvBnatY16tA+v1XPZs32EhnRtXePjUTsE8gAAAAAAAAAAAAD8av6afX7p59o5P8hR6aj2mgXdVTEMqW14sHs0e+nQvUOUTe1LtsOC6zZuNQxDz0zorwmzk2o020BokFVPTzi1xh8IQO0RyAMAAAAAAAAAAADwm6z8Ii3eluqXvioP42smyGopN6K99FTysVE2tYsIVqC1/BTyDaFvxyjNnjxA0+at9ymUDw2yavbkAerbMaoOq0NVCOQBAAAAAAAAAAAA+MThMHUku0D70/N0MD1PB47n6UDJv3uOZMteF0l6NUTaAkpC9hDFRgaXhO7OtdpjI0MUG2VTq9DAJjdifHj3GC2YNlQzF2yq1vT1PdtH6OkJpxLGNwIE8gAAAAAAAAAAAAA8FBY7lJKRr/3puR5h+8GMkn/T81Vod9RbPYYhxZSaQr5DlE1tQq06vG+3ooOki885SwntohQa1Hzjz74do7R4xgit2XNM89Yka/G2VI8PPgRYDI3uE6tJQ+I1pGvrJvehg+aq+b4jAQAAAAAAAAAAAHiVXVBcErS7Avf8ktA9VwfS83Q4q0BmwwxyL+fdmwdrUEJrBZSZQj4vL09LcndJkhLbhCqkGYfxLoZhaGi3NhrarY2y8ouUmpmv7AK7woOtah9pU4QtsKFLRBnN/10JAAAAAAAAAAAAtCCmaepIduGJUe3pzhHu+0tGuh9Mz1NGXlFDl1ktARZDfTtGlQvjIUXYAgngmwACeQAAAAAAAAAAAKAJKbI7p5M/UGbtdlfYfiA9TwXF9TOdfKDVUIeoEHWMDlFcdIg6tgpRp+gQfbhhv9buPVbr/kf3iSV0RpNGIA8AjdyoUaO0bNkyjRw5UkuXLq1xPytXrtSll14qSfr+++81atQo/xTYSDz00EN6+OGHJTk//dkUTZkyRW+++abi4+OVnJzc0OUAAAAAAAAAaCC5hc7p5PeXDdpLtlMz8+Wopz+DhgVZ1bGVM3B3/hta8q9NHaNDFRMRLKul/FrlnVqH6JrXfqj1+ScNia91H0BDIpAHAAAAAAAAAAAA6olpmjqWU+g5ur3Mdnpu/U0n3zY8qFTYXnake6giQwJkGOUD96oM7dpGPdqHa1dqdo1r69k+QkO6tq7x8UBjQCCPlis/U8o8KBXmSEFhUmScZIts6KqanOYwKrmpc/0i9OCDD+qhhx5q2GKARiYhIUH79u3T9ddfr7lz5zZ0OQAAAAAAAGgBiu0OpWTmO9duzzgRtJdevz2/qH6mkw+wGIqNspUL3F3bcdEhsgVa6+TchmHomQn9NWF2knIL7T4fHxpk1dMTTq3RhwGAxoRAHi2LaUrJK6S1r0k/fymZpX4AGFap1yXSoKlSwnCJb/BoJGozTX1L8tBDDzX5DyTMnTuX0BgAAAAAAABo5PIK7WVGtec6w/d055ruKZn5stfTfPKhQVaPEe0do0PUqVTY3j7S5nU6+frSt2OUZk8eoGnz1vsUyocGWTV78gD17RhVh9UB9YNAHi3HwU3SJ7dIaTu87zft0vbPnLeYXtK4V6S4/vVZIQAAAAAAAAAAaECmaSo9t6jciPbS08kfyymst3rahAU5w/Yyo9pdoXt0aGCjH0E+vHuMFkwbqpkLNlVr+vqe7SP09IRTCePRbBDIo2X49TvpvUlSUU712qftkN64WJo4X+p2Tt3WBgAAAAAAAAAA6oXdYSo1M7/C9dsPpufVaHr1mrBaDMVG2sqF7aWnlg8Jqpvp5Otb345RWjxjhNbsOaZ5a5K1eFuqxywCARZDo/vEatKQeA3p2rrRf8gA8IWloQsA6tzBTb6F8S5FOc7jDm6qi6rqzEMPPSTDMNw/rDIyMvToo4/qtNNOU3R0tAzD8Dol9qeffqrx48erS5custlsio6O1sCBA/Xwww/r+PHj5drPnTtXhmG414+X5D5v6VtycrJ7f0JCggzD0JQpUyp9DFOmTJFhGEpISCi3Lzk52d2363F8/PHHuvjiixUXF6eAgACNGjWqwrbffPONLr30UsXGxio4OFiJiYmaPn269u/fX2lNlenbt68Mw9DEiRO97nc9V4ZhqH///l7brFmzxt3m66+/9tg3atQoGYbhflwurufT5eGHHy73/Ff1XC9YsEDnnnuuYmJiFBISop49e+qvf/2rjh07VuXjrkzZmnfu3Kk//vGPSkxMlM1mU4cOHTRhwgStWbOmwj58ea2l8u/9ihQUFOjVV1/VmDFj1LFjRwUHByssLEx9+vTR1KlTtXjxYplmxdNJ+Xqt+KKy97504hpzTc3//fffa+zYsYqLi1NISIh69eqlRx99VDk5nt/vFi1a5H7eQkJC1Lt3bz3++OMqLKz4k7xlr9d169bp6quvVufOnWWz2dS5c2fdcMMN+vnnnyt9TIcOHdKsWbN01VVXqXv37goLC1NwcLA6duyoyy+/XO+//74cjuqtl5WcnKy//e1vGjBggNq0aaPAwEC1bdtWw4cP10MPPaQ9e/a427reg/v27ZMkvfnmm+Wuj7LXFAAAAAAAAJq+/CK7fk3L1ordaXpv7W96eslOzXx/kybMTtKw//tOPf/xlc584juNfyVJM97fpKcW79Q7P/ymZbvS9MvhbL+G8bZAi7rFhGlEjxhdfUZn3X1BDz37h1O1YNpQrfr7Odr56IVa9fdztOCWoXr2D/119+ieumZwF43sEaOT2oU3mzDexTAMDe3WRrOuHaBND5yvb2eO0Ke3naVvZ47QxgfO10vXnq6h3doQxqPZYYQ8mjfTdE5T72sY71KUI306XZq+ukmuKb97925dcMEFHqF4WcePH9dVV12l7777zuP+goICrV+/XuvXr9esWbP02WefaciQIXVcsW9M09R1112nefPmVav9PffcoyeeeMLjvuTkZL3yyiv66KOPtGzZMvXq1cvnOkaOHKlt27Zp2bJlXveXvn/z5s06duyYWrdu7bVNQECAhg0b5nMNvnI4HJo8ebLmz5/vcf+uXbv01FNP6ZNPPtGKFSsUGxtb63N99dVXGj9+vEdInJKSog8++EAfffSRnn76ac2YMaPSPnx9rSuyadMmXXHFFdq7d6/H/YWFhdq+fbu2b9+u119/XXv37i0Xije2a+WJJ57Qvffe6/HhgZ9//lkPPPCAvv76ay1ZskShoaGaMWOGnn/+eY9jd+zYoXvvvVfLly/XwoULZbVW/ov9f//7X02bNk3FxcXu+/bv36+5c+fq3Xff1bx58zR+/Phyx9ntdnXq1Mlr4H7w4EF9/vnn+vzzz/X666/r448/Vnh4eIU1/Pvf/9a9996roqIij/uPHj2qlStXauXKlVq6dKmWLl1a6WMBAAAAAABozrLyi5SSka+cQrvCgqyKjbIpwhbY0GX5jWmayswr1v6SNdtdI9sPZpwY4X4ku/6mk28VGlhuOvlOrULc97UOCyJcrkCELbBZvTeByhDIo3lLXlHxmvHVdXi7lLxSShzun5rq0VVXXaUDBw7oT3/6ky677DK1atVKu3fvVnx8vCRnkHjeeedpw4YNslqtuuaaa3TxxRcrMTFRRUVFWr58uZ555hkdPnxYF198sTZu3Og+duzYsRo4cKBmzZqll19+WZK0ZcuWcjV07Nixzh7fc889p82bN2v48OGaPn26evToofT0dK8fQHjttde0evVqjRw5UtOmTXO3feutt/TWW28pLS1NN954o5KSknyuY9SoUZo1a5ZSUlL0888/6+STT/bYXzogNE1Ty5cv19ixY722Of300ysNJUtbsmSJCgsL1a9fP0nS9OnTdeutt3q0adWqlddj77//fq1evVpjx47Vddddp/j4eKWmpuqll17Sl19+qV9++UV33nmn3n333WrVUpGDBw/qmmuuUUBAgB577DH3iOTvv/9e//d//6fMzEzdeeedSkhIKPeclObLa12RHTt2aPjw4crOdq5RNG7cOE2cOFFdu3aV3W7Xrl27tGTJEn3yySfljq3NtVIXvvrqK61du1ZDhw7Vn/70J/Xo0UNHjhzRf/7zH3311VdavXq1Hn/8cbVu3VrPP/+8LrroIk2dOlUJCQnav3+/Hn/8ca1Zs0Zff/21XnvtNd1yyy0VnmvTpk1655131K5dO91zzz0644wzlJ+fr0WLFum5555TQUGBrr32WiUmJmrgwIEex7o+LHDOOefooosuUr9+/RQTE6OsrCzt2bNHr732mpKSkvTNN9/otttu05tvvum1hkcffVQPPPCAJCk6Olq33nqrzj77bLVp00bp6enasGGDPv74Y4//3LzxxhvKycnR6NGjdfDgQV1++eX65z//6dFvWFhYjZ5/AAAAAACAxsQ0TSXtOap5Sfu0ZLvnNOBWi6HRfdpr0pB4De3a+EceOxymDmcV6EB6rvf124/nKaeeppO3GFL7CqaT71SyHRZMzAaganynQOPhcEh5xzy+NnKznNvWQslSgxUWkmb5p7Y1s6R2vo+crlRI65o9Jh9s3bpVX331lS644AL3fQMGDHBvP/LII9qwYYOio6P17bffeuyTpGHDhunaa6/V0KFDdejQId177716++23JTlDsejoaLVr187dvm/fvnX6eMravHmzrrvuOveU8JVZvXq1br75Zs2ePduj7bnnnqugoCDNmTNHa9as0caNG3Xaaaf5VMfIkSPd20uXLvUI5H/77Tf31OtjxozRwoULtXTpUo/w2W63a9WqVZLk0xTaPXr08Pi6Xbt2Xl8Db6OTV69erX/+85+67777PO6/8MILdeGFF2rJkiX68MMP9fzzzysmJqbaNZW1e/duRUVFKSkpyWP2gaFDh+ryyy/XmWeeqczMTN1+++0aM2aMAgO9fyLSl9e6IpMmTVJ2drYsFovefvvtcksMDB48WJMnT9bRo0cVGhrqsa8210pdWLt2ra688kq9//77HqPbzzvvPA0bNkxr1qzR888/r6KiIs2YMUPPPvusu83pp5+u8847T71799a+ffv08ssvVxrI//TTT4qPj9eaNWs8ZkwYMWKERo8erQsuuEBFRUW69dZbtXbtWo9jrVardu7cqZNOOqlcvyNHjtQNN9ygBx98UI888ojmzZunf/zjH+revbtHu40bN7qn6O/Ro4f+97//qVOnTh5tzj77bN111136/fff3fclJiZKkvs9FR0dXe/fowAAAAAAAOra1gMZmrlgk3alZnvdb3eYWrQlRYu2pKhH+3A9M6G/+naMqucqT8gvsutQRr5zVHt6nva7w/ZcHUjPU0pGvorsFS8n6U/BARZ32B4XVWrd9pJ/Y6NsCrSy8jOA2iOQR+ORd0x6qpv7S4ukhvu1oIydi6SnFvm3z7/8KoW19W+fZUyZMsUjjC8tOztbL730kiTn6NOyAaNLfHy87r//ft1666364IMP9OqrrzaaUaXR0dF68cUXqxXQdujQQS+88ILXtnfffbfmzJkjSVqxYoXPgXy7du3Uq1cv7dixQ0uXLvUIN10j33v37q3x48e7A/nS1q9fr6ws54dPSof7dWnAgAG69957y91vGIZmzpypJUuWqLi4WElJSbrssstqda7777/f61IAffr00X333ae//e1vOnDggD777DNdddVVXvvw5bX2ZsmSJdqwYYMk6Y477igXxpfWpk0bj68b47USGhqqV199tdxU81arVX/84x+1Zs0aZWVlqXPnznryySe9Hn/99dfrkUce0ebNm5WRkaGoqIq/4z799NNely84++yzdfPNN+vll1/WunXr9OOPP3qMkjcMw2sYX9oDDzygWbNm6ciRI/r888911113eex/6qmn5HA4ZBiG3nvvvXJhfGmdO3eu9FwAAAAAAADNyYrdaZo2b3211zzflZqtCbOTNHvyAA3vXvNBOJXJyCvyHNFeZjstq6BOzutNVEige1R7p1Jhu2s6+bbhTCcPoH4QyAPN2LXXXlvhvmXLlikjI0OSKgxBXUaMGCFJKioq0vr1691fN7RLL71UERER1Wp71VVXKTg42Ou+nj17Kjw8XNnZ2dqzZ0+Nahk5cqR27NhRbh1519ejRo1yj34vu468q43Vaq2X9eMl6Zprrqnwl83SgXNNnw8XwzB0/fXXV7j/hhtu0N///neZpqlvv/22wveiL6+1NwsXLnRvV7VefVmN8Vo5//zz3e+fsk499VT39hVXXFHhrAOl2+3du1f9+/f32q5Vq1a6/PLLK6zlxhtvdC9b8e2335abtr40h8OhlJQUZWVleawF36lTJx05ckQ//fRTufZfffWVJOc15OuHZQAAAAAAAJqrrQcyfArjXXIL7Zo2b70WTBvq80h5h8PUkeyCUqPa89wj3V3bWQXFPvVZU4YhtYsILgnZQ0uNbLepY3SoOrYKUTjTyQNoJPhuBDRjp5xySoX7fvzxR/d2hw4dqt1nSkpKrWryp8oeX1ll13Uvq1WrVsrOznaPVPfVqFGj9Morr5RbR941Gn7UqFHq0qWLEhMTtXfvXo915F1tTjvtNEVGRtbo/L6q7PkoHfTW9PlwSUxMVNu2Fc8EERMTo4SEBO3du1dbtmypsJ0vr7U3GzdulCR16dLF57XdG+O1Una5gtKio6N9blfZ63zaaacpIKDiXxf69++voKAgFRYWen0NTdPU22+/rddff10//PCD8vLyKuzryJEjHl/v3btX6enpkqThw4dXeBwAAAAAAEBLYpqmZi7Y5HMY75JbaNddC37S1zOGewzaKSx26FCGM1jfX3bt9vQ8HUrPV6G9/PKYdSHIalFctM1z7Xb3+u2hio2yKSiA6eQBNA0E8kAz1qpVqwr3HT58uEZ95ubm1rQcv6vs8ZVVdk3wsiwW5y9vdnvNfon1to78/v37tWfPHhmG4d4/atQo7d27172OvN1u18qVK9376ktlz4fruZBq/ny4tGvXrso27du31969e3Xs2LEK2/jyWnvjCnp9CdRdGuO1Ut3Xzx+vc1WvYUBAgFq3bq2UlJRyr2F+fr6uuOIK9yj3qpQN60sH9DV57QAAAAAAAJqjpD1HK1wzvrp2pmbpjvc2yjSlAyXh++GsApn1s3y7ImwBzoA9uvza7c7p5INlsTCdPIDmgUAejUdIa+e66iUcDod71GZERIRHeFQtBVnSCwMks3aBoiTJEiDd/qMUXPMps8sJ8T7dtD+VXV+6tNIB3IYNGyqc1rqsytZvrm+VPb76Fhsbq549e2rnzp3udeRdU9H37t1bMTHONZlGjhypN954wz0qftOmTcrMzHTva278tQZTQ77WzeFaqY3avIb/+te/3GH8yJEjddttt+n0009XbGysQkJC3N/XR4wYoRUrVsisr//xAQAAAAAANGHz1+zzSz9f/HTIL/14E+OeTj5EnVxrt0eVhO6tQhRpq97f2ACgOSCQR+NhsUhhpaa2djhk2oOc22GRzv2+CGsr9bpE2v5Z7Ws7+RKpdWLt+2lE2rRp496OiYmpl/DQFb45HJVPa5STk1PntdSFkSNHaufOne4gvvR09S5l15F3tbFYLM1ySu7U1NRqt6loTXR/cE2bf+iQ7//JaIhrpTGp6jUsLi52j4wv/Rqapqk5c+ZIck43/91331X4waqKZkcovdxBTV47AAAAAACA5iYjt1CLt1b9N7e6FGg11CHqxKj2uOgToXvH6BB1iLYpOKDxDKYCgIZGII/mbdBU/wTyg6bWvo9G5rTTTnNvr1q1Sn/4wx9q1I8vo2cjIpwzDBw/frzSdrt27apRLQ1t1KhRevXVV93ryLuC+dKBfHx8vBISEpScnKzly5e72/Tv319RUVENUXad2rt3r44ePeoRapeWlpam5ORkSVLfvn3rrI7TTz9dK1eu1G+//aZ9+/b5tI68v66VpmrTpk0qLi6ucB35n376SYWFhZI8X8Njx44pJSVFkjR+/PgKw/js7Gzt3LnT677ExERFR0crPT1dy5cvr1H9/pqlAQAAAAAAoL6YpqnjuUXak5atPUdytPdIjvamOf/dcyRb9jqeZTA8OKBU2G5Tx+hQd9jeqVWIYphOHgB8QiCP5i1huBTTS0rbUfM+2vWWEob5r6ZG4rzzzlNoaKhyc3P1/PPPa8KECTUKrmw2m3u7oKBAwcHBFbZNTEzU5s2btWHDBpmm6fV827Zt0+bNm32uozEoPeX8O++8o927d3usH+8yatQozZ07V999951WrFjhvq+mbDab8vPzVVBQUOM+6oppmnrrrbd05513et0/d+5c9zTl5513Xp3Vcemll+r555+XJD377LN67rnnqn2sv66VpurYsWP64osvNG7cOK/7//vf/7q3S7+GxcXF7u3KZr2YM2eOR9vSLBaLxowZo7ffflvLli3Txo0bPT4gUR2u71GN8foAAAAAAAAtW26hXftzpLQ8Q78u36v96YXuAD4jr6heari8f5xO6xytjq1CFRdtU6foUEWGBLSov38BQF3zcQ5woIkxDGncK1JgWM2ODwyTxr7s7KeZiY6O1u233y5JWr16te68885Kp5JPTU11Tz9dWocOHdzbv/76a6XndAXTBw8e1Lvvvltuf1ZWlm666aZq1d8YxcXFqXv37pLkDn9Lrx/v4noe3nrrLaWnp3vcVxOu16Cq57+hPProo15HQO/YsUP/+te/JDkfw+WXX15nNZx33nkaMGCAJOmFF17Qe++9V2Hbo0ePKi8vz/21v66VpmzmzJlep65ftmyZXn31VUnSgAEDNGjQIPe+mJgYRUdHS5Leffddr4H4unXrdP/991d67rvvvlsWi0WmaWrixInav39/hW297Wvs1wcAAAAAAGjeiuwO7T2So+9+TtWcFXt07ydbdPWrazTksf9pwOPL9NTmAM3dbdXz3+/VxxsPaNPv6fUWxkvSn845SVPOStT5vdurT1yUokIDCeMBwM8YIY/mL66/NHG+9N4kqciHtckDw5zHxfWvq8oa3COPPKJly5bphx9+0H/+8x8tXbpUN998s/r376+wsDAdP35c27Zt07fffquvvvpK/fr109SpntP3n3nmme7tO++8U/fdd586dOjg/qUtISHBPdX1pEmT9NBDDykzM1M33XSTfvnlF40ePVqGYWj9+vV65plntH//fp122mnauHFj/T0RfjRy5Ejt3r1bGRkZkryPfHfd52pjsVg0YsSIGp/zzDPP1N69e/X5559r9uzZOuuss9yjgiMjIz3W4a5vJ510ktLS0jRkyBD97W9/cz/2pUuX6oknnnA/By+88IKCgoLqtJZ58+bpjDPOUHZ2tq6++mp98MEHmjhxorp27Sq73a5ffvlFS5Ys0YcffqitW7cqISHBfaw/rpWm6tRTT9X27ds1YMAA3XPPPTrjjDNUUFCgRYsW6dlnn3VPZ//SSy95HGexWHTttdfqpZde0ubNmzVs2DDNnDlT3bt3V0ZGhhYtWqRZs2YpPDxccXFxFS5V0b9/fz388MO6//77tWvXLvXr10+33Xabzj77bLVp00bp6enatGmTPv74Y1mtVn3//fcex5955pn6/vvvtW7dOj3xxBO66KKLFBbm/JBWSEiIOnbsWDdPHAAAAAAAaDFM01RqZoH2HMn2mF5+75Ec/XYsV8WOup1ivqYCLIbaR9qqbggAqBUCebQM3c6RblgkfXJL9aavb9fbOTK+GYfxkhQcHKxvvvlGU6ZM0ccff6yffvrJPRLYm8jIyHL3nXTSSZowYYIWLFigJUuWaMmSJR779+7d6w42Y2JiNGfOHF199dXKz8/Xgw8+qAcffNDdNiQkRPPmzdPChQubbCA/atQoj9HR3gL5hIQExcfHa9++fZKkU045xT2SuCbuvvtuffjhhyooKNAtt9zise/666/3mFK8vnXs2FHPPfecJkyYoHvuuafcfovFoieffFJXXnllndfSq1cvLV26VOPGjdPvv/+ujz/+WB9//HG1jvXHtdJU9e/fX7fffrumT5/u9TEHBQXpzTff1ODBg8vt+9e//qVVq1Zp06ZN+vHHH3XNNdd47G/durU++ugjPfDAAxUG8pL0j3/8QxaLRQ8++KDS09P1r3/9yz27QmneZpqYPn26Xn75ZR07dkz33HOPx/tw5MiRWrp0aWUPHwAAAAAAwC0jr6gkaM/W3rScE+u7H8lRbqHd7+cLtBqKbxOmxLZh6trW+W9i2zDNXr5H3/18uNb9j+4TqwhboB8qBQBUhkAeLUdcf+nWJCl5pbTuNWnHQsks9UuSJUA6+RJp0FTnmvEtZFqeiIgIffTRR1q5cqXefPNNrVixQgcPHlReXp4iIyPVrVs3nXHGGRozZowuuOACr33Mnz9fAwcO1IcffqidO3cqKyurwim9x48fr/j4eD3xxBNauXKlMjIy1L59e51zzjn6y1/+oj59+mjhwoV1+ZDrVOlA0Nv68S6jRo3Sm2++6d6ujf79+yspKUlPPfWUVq1apdTU1Ea1XvaYMWP0448/6qmnntJ3332nQ4cOKTo6WsOHD9ddd92loUOH1lstAwYM0M6dOzVnzhx9+umn2rp1q44dOyabzabExEQNHTpUf/jDHzxGx7v441ppqqZOnaq+ffvq2Wef1cqVK3XkyBHFxMTo3HPP1d/+9jf17t3b63FRUVFatWqVnnnmGS1YsEC7d+9WQECAOnfurDFjxujPf/6zOnXqVK0a7r33Xo0fP16zZs3St99+q99++025ublq1aqVevfurfPPP1/XXXddueM6duyotWvX6vHHH9eyZcu0f/9+5efn1+r5AAAAAAAAzVd+kV2/HcvVnpJR7nvSst2h+9Gcwjo5Z6sgUyd3aq2T2kU6Q/eYMHVrG664aJsCrOVXHrabpl8C+UlD4mvdBwCgaoZpNs6pUtD4GIbRSdLvkvT7779XO0SRpN27d7unNXatsV0Vh8OhzMxMSc7RphZL+V88aiU/U8o6JBVkS8HhUkQHydZ8RrUCLnV+LXkxatQoLVu2jBHITVhCQoL27dun66+/XnPnzm3ochpcQ1xH/lCTn79AXcnLy3PPpHPBBRcoJCSkgSsCmiauJaD2uI6A2uM6giRl5RcpJSNfOYV2hQVZFRtlazKjre0OUwfT85wj3EsCd9do9wPpeaqL2KR1WJB7hLtrxHtcRIB2bVitIKtv15Jpmhr93HLtSs2ucT0920fo6xnDWS8eTR4/k+Bv+/fvV+fOnV1fdjZNc39t+2SEPFouWyQBPAAAAAAAAABUk2maStpzVPOS9mnJ9lTZS62NbrUYGt2nvSYNidfQrm0aPOg1TVNHcwrda7rvcU01fyRHyUdzVVjsfYbP2rAFWpTYNtxjevnEGGf4Hh0aVK59Xl6ekq2+n8cwDD0zob8mzE6q0VT5oUFWPT3h1AZ/jZqk/Ewp86BUmCMFhUmRceQMAKpEIA8AAAAAAAAAACq19UCGZi7YVOGobLvD1KItKVq0JUU92ofrmQn91bdjVJ3XlVNQ7J5SvvQU83uO5Cgrv9jv57NaDHVuFeIc5R4TfmJ995gwtY+wyWKpn5C7b8cozZ48QNPmrfcplA8Nsmr25AH18to0G6YpJa+Q1r4m/fyl51K4hlXq5VoKd3iLWQoXgG8I5AEAAAAAAAAAQIVW7E7zKfjdlZqtCbOTNHvyAA3vHlPr8xfZHfrtWK72utZ1LzXaPTWzoNb9e9M+MrhklHupEe8xYercKlRBAY1jKbvh3WO0YNrQSj8oUVrP9hF6esKphPG+OLhJ+uQWKW2H9/2mXdr+mfMW00sa94oU178+KwTQBBDIAwAAAAAAAAAAr7YeyPB5FLYk5RbaNW3eei2YNrRaAbBpmkrJzC81vfyJ22/Hcj2mx/eXiOAAdY0JOxG8l2wntA1TeHDTiE/6dozS4hkjtGbPMc1bk6zF2zyXEgiwGBrdJ1aThsRrSNfWTFPvi1+/k96bJBXlVK992g7pjYulifOlbufUbW0AmpSm8RMFAAAAAAAAAADUK9M0NXPBphqtUy45Q/m7Fvykr2cMdwfBGblF2lMyut012n1PWo6Sj+Qor6hm56lMkNWihLahnqPdS4L3NmFBzSKgNgxDQ7u10dBubZSVX6TUzHxlF9gVHmxV+0ibImyBDV1i03Nwk29hvEtRjvO4GxYxUh6AG4E8AMDvli5d2tAloJaSk5MbugQAAAAAANDAkvYcrdZU6JXZmZql699Yq5wCu/YeydGxnEI/VXeCYUgdo0NOrOfeNkyJMc7wPS46RNZ6Wte9MYiwBRLA15ZpOqep9zWMdynKkT6dLk1fzZryACQRyAMAAAAAAAAAAC/mr9nnl36W7zril37ahAV5TDGf2DZMXWPC1KV1qGyBVr+cA1DyiorXjK+uw9ul5JVS4nD/1ASgSSOQBwAAAAAAAAAAHrLyi7R4W2q9nzc0yFoSuIeVml4+XIltwhQVyshv1IN1c/zXD4E8ABHIAwAAAAAAAACAEvlFdu1MydL3Ow/L7jDr5BwBFkNdWod6He3eLiK4Wazr3iDyM6XMg1JhjhQUJkXGSbbIhq6q8XPYpdxjUk6adDxZ2vGFf/rd8YXzNeE1AFo8AnkAAAAAAAAAAFqgjNwibTuUoe0HM7XtYKa2H8zUL2nZdRLE33BWgoZ3b6vEtuHq1CpEgVaL38/RIpmmc4r1ta9JP38pmfYT+wyr1OsSadBUKWF4y1rPvCjPGbBnpzn/zUmTcg5LOUek7MOl7kuTco9KpsP/NZh2KesQgTwAAnkAAAAAAAAAAPwlK79IKRn5yim0KyzIqtgomyJsDTvVummaOpSR7w7dtx3M0LaDmTqQnldvNVw7uItOahdRb+drEQ5ukj65peL1zk27tP0z5y2mlzTuFSmuf31W6D+mKeUddwbqOYfLhO0lQXtOWknYfkQqzGroip0Kshu6AgCNAIE86oXValVxcbHsdrscDocsFj79CABAXXI4HLLbnZ+Kt1qtDVwNAAAAADRvpmkqac9RzUvapyXbUz1GmFsthkb3aa9JQ+I1tGubOp+O3e4wtSctW9sPOUe9bzvoHAF/PLeoTs9bmQCLofaRtgY7f7P063fSe5OkopzqtU/bIb1xsTRxvtTtnLqtrbqKC6XcIydC9JxSI9e9he2O4oau2HfB4Q1dAYBGgEAe9cJms6mgoECmaSo7O1uRkUzRAgBAXcrOzpZpOv8AFBIS0sDVAAAAAEDztfVAhmYu2KRdqd5HwtodphZtSdGiLSnq0T5cz0zor74do/xy7vwiu35OyXKPeN9+MFM/p2Qqv6h2028bhpTYNkx5hXYdysivdZ2j+8Q2+CwBzcrBTb6F8S5FOc7jblhUNyPlTVMqyPKcDr5s2F56Cvn8dP/XUFu2aP/VZQmQIjr4py8ATRqBPOpFZGSkMjIyJEkpKSmSpPDwcEbKAwDgZw6HQ9nZ2e6ft5IUEcGUgAAAAABQF1bsTtO0eeuVW2ivurGkXanZmjA7SbMnD9Dw7jE+nSs9t9BjxPu2g5n6NS1btV3uPchqUc/YCPWJi1TvuEj1iYvUybGRCgsO0Opfj+ia136o3QkkTRoSX+s+UMI0ndPU+xrGuxTlSJ9Ol6avrt6a8vZiKe9Y+XXXs0uNXC+9Nru9oGZ11RVLoBTeTgprK4W1k8JinNvhru3St7aSNVBacJ1zmv/aOvkS1o8HIIlAHvUkLCxMISEhysvLk91u14EDB2QYRpVT6BYXO6egSUtLq48ygWaLawmovaZyHdntdvfIeMk5Oj4sLKwBKwIAAACA5mnrgQyfwniX3EK7ps1brwXThnodKW+apg6k57lD920HM7XjkH/We4+wBTiD9w5R6hMXqT4dI9UtJlyBVu8Dp4Z2baMe7cMrHP1fHT3bR2hI19Y1Ph5lJK+oeM346jq8Xdr6odQqsXzQXnZUe+4xSbX81Ie/BUeVCtXbloTp7bwH7bao6n3woLRBU/0TyA+aWvs+ADQLBPKoF4ZhqEuXLvrtt9+Ul+f8xdE0TXe44Y1pmu62ISEhdb62EtBccS0BtddUr6OQkBB16dKlydQLAAAAAE2FaZqauWCTz2G8S26hXXct+EkL/3SW9h7NdU45fyBT2w85b+l+WO89NtLmDN3jItU7zhnAd2rl2/9pDcPQMxP6a8LspBo91tAgq56ecCr/L/WndXP8089HjSgsNqylgvWSW0Wj2kPbSoG2uq0nYbgU06t2H3xo11tKGOa/mgA0aQTyqDcWi0Xx8fHKyclRVlaWe7R8RRwOhzv8YHp7oOa4loDaa0rXkdVqVUhIiCIiIhQWFsYfPQAAAACgDiTtOVqrUeOStDM1S70fWKyiWs45bxhS17Zh6hMX5Z5yvneHSLUJD65Vvy59O0Zp9uQBPs8GEBpk1ezJA7zOAgAfFRdImQektN3Sji8auprqCQzzPmK93Kj2GCmkldSY/tZiGNK4V6Q3Lq7Z0gCBYdLYl30fmQ+g2SKQR70yDEPh4eEKDw+vsm1eXp5+/vlnSdKAAQMUEhJS1+UBzRLXElB7XEcAAAAAgNLmr9nnl358DeODAizqFRuh3qVGvZ8cG6HQoLr9U//w7jFaMG2oZi7YVK0PIvRsH6GnJ5zqexifnyllHpQKc6SgMCkyrtmvwW1xFMlWdFyW35Ok/CNSxn7nc5B5wHnLOCDlHmnoMiUZUmjriqeGLxu2BzXx5fPi+ksT50vvTfItlA8Mcx4X17+uKgPQBBHIAwAAAAAAAACavKz8IqVk5Cun0K6wIKtio2yKsAXWyXkWb0v1e79lRdoC1CfuxFrvvTtEqVtMmAIqWO+9rvXtGKXFM0ZozZ5jmrcmWYu3pcpe6gMFARZDo/vEatKQeA3p2rr6M7aZpnNd9LWvST9/KZmlRuEbVqnXJc61uBOGN70Rx8WFUtZBZ8CeccAzZM88IFvGfl3qCtu3N0B9lkApItZzavjwGO9Be0hrydrCIqVu50g3LJI+uaV609e36+0cGU8YD6CMFvbdEwAAAAAAAADQXJimqaQ9RzUvaZ+WbPcMiK0WQ6P7tNekIfEa2rWN35b0SsnI9ziPP8RF2dS71JTzfeIi1THat/Xe64NhGBrarY2GdmujrPwipWbmK7vArvBgq9pH1uADEAc3VR52mnZp+2fOW0wv5zTijSXstBdJWYe8Bu3O20Ep+7Ckit8rDf7q3rJSandyQ1fRuMX1l25NkpJXSutek3Ys9PzQiCVAOtn1oZFhTe9DIwDqBYE8AAAAAAAAAKDJ2Xogo9Ip1O0OU4u2pGjRlhT1aB+uZyb0r/F65gfT8/TD3qNa8+sxLd91uDZll/PWjWdoRI8Yv/ZZHyJsgbWbgeDX73ybDjxth3NN74nznSOX65K9SMpK8R60Z7jC9lRVFrbXicBwqajqJQOqxRLgXBIAVTMMKXG485af6fwgRkG2FBwuRXRo9ssqAKg9AnkAAAAAAAAAQJOyYneaps1br9xCe9WNJe1KzdaE2UmaPXmAhnevOvw+kJ6nH/Yc1Zo9R7VmzzH9diy3tiVXKC7aVmd9N1oHN/m+NrfkbP/eJOc04jUdKW8vlrJTyo9mz9hfamR7qmQ6atZ/DZlBYTIiOzlD8qiOUmSpm+trW6S04DrnjAG1dfIlBMk1YYvkeQPgMwJ5AAAAAAAAAECTsfVAhk9hvEtuoV3T5q3XgmlDy42U3388Vz/sOeYM4Pce1e/H8vxZcoUCLIbaR7awQN40ndPU+xrGuxTlSJ9Ol6avLj89uMNeMrL9oJS5/8Ro9sz9J9Zxz06p97BdgaGlgvUToXtBcFut2rpPeYGtdM5FYxUSGlp1X4Om+ieQHzS19n0AAKqFQB4AAAAAAAAA0CSYpqmZCzb5HMa75BbaddeCn/TadQP0w95j+mGvM4Tff7x+AviyRveJrd20701R8oqK14yvrsPbpUV/kQKCS0a2H3SObs9K8Vzfuz4EhHiOaI/q6AzcIzud2LZFe11b3JGXp6xflzi/qO7a4wnDpZhetXsO2/V2rncOAKgXBPIAAAAAAAAAgAaTlV+klIx85RTaFRZkVWyUrcKQOmnP0QrXjK+unalZGvHUUp+PiwoJ1ODE1hrctY1CAi2695OttapDkiYNia91H03Oujl+6uc1//RTmQCb55TxkSUBe1TJKPfIjlJIq+qH6f5gGNK4V6Q3Lq7ZLAOBYdLYl+u3ZgBo4QjkAQAAAAAAAAD1yjRNJe05qnlJ+7Rke6rsDtO9z2oxNLpPe00aEq+hXdvIKBUczl+zr95qjA51BvBDurbR4MQ2Ojk2QhaL4a5/7urkWn04oGf7CA3p2tpf5TYNOUekHQsbugona7CXoL30lPKd6j9sr664/tLE+dJ7k3wL5QPDnMfF9a+rygAAXhDIAwAAAAAAAADqzdYDGZq5YFOFYbbdYWrRlhQt2pKiHu3D9cyE/urbMUpZ+UVavC21zupqFRqowYltNKRraw3p1kY92p0I4MsyDEPPTOivCbOTajR9fmiQVU9PONXjwwYe8jOd07AX5khBYSXTnkf6fJ4GUVwoHU+Wjv0qHdsjHf3VuX10j5TxW/3UYA0qP228x5TynaTQ1o0zbK+ubudINyySPrmletPXt+vtHBlPGA8A9Y5AHgAAAAAAAABQL1bsTtO0eeurHWLvSs3WhNlJmj15gGIjbR4j6WsrKiRQZ3ZroyFdnbfu7cIrDOC96dsxSrMnD/Dp8UjOMH725AHq2zHKc4dpOtdXX/ua9POXnmuhG1ap1yXSoKnONcQbOkguLpTS95WE7XtKAveS7YzfJdNRP3V0O1eK7ec5hXxkRymsbcM/R/Uhrr90a5KUvNI5hf+OhZ7vG0uAdLLrfTOsZTwnANAIEcgDAAAAAAAAAOrc1gMZPofXkpRbaNe0eev16OV9/VrP3CmDdFp8q1r1Mbx7jBZMG1rpiP/SeraP0NMTTi0fxh/cVPlIZ9Mubf/MeYvp5VxDvK5HOtuLpOP7yo90P7ZHSv+t/kL3ylz4uBTTs6GraFiGISUOd97yM6WsQ1JBthQcLkV0aDozKwBAM0YgDwAAAAAAAACoU6ZpauaCTTWa3l1yhvL3f7bVrzVFhPjnz+N9O0Zp8YwRWrPnmOatSdbibakeI/kDLIZG94nVpCHxGtK1dflp6n/9zre1wNN2SG9c7FwLvNs5tSveXuQM1z2mli8dutfs9aoXlgBn4IwTbJEE8ADQCBHIAwAAAAAAAADqVNKeo9UaQV6Zmob53gRYDLWPtPmtP8MwNLRbGw3t1kZZ+UVKzcxXdoFd4cFWtY+0KcIW6P3Ag5t8C+NdinKcx92wqOqR8vZi5/Tyx/aWCtxLQvfj++oudA9tK7XpJrXuJrXpKrXu6txe9qS088va93/yJYTPAIAmgUAeAAAAAAAAAFCn5q/Z19AleBjdJ7bikLyWImyB1evbNJ3T1PsaxrsU5UifTpemr5YcdinDNdK9zJru6fskR3HNzlGV0DYlgXu3ksC964ltW5T3Y4bc4p9AftDU2vcBAEA9IJAHAAAAAAAAANSZrPwiLd6W2tBleJg0JL6hS5CSV1S8Znx1Hd4uPdtbyk6THEX+qauskNYnRrqXDtxbd5VCon3vL2G4FNOrdo+9XW8pYVjNjwcAoB4RyAMAAAAAAAAA6kxKRr7Hmuq11alViPYfz6vx8T3bR2hI19Z+q6fG1s3xTz+ZB2vfR0irMiPdS00zH9Kq9v2XZhjSuFekNy6u2ewAgWHS2Jed/QAA0AQQyEsyDCNe0h2SxkjqLKlA0q+SFkh6yTTN3Do4Z6ikrZISS+7aZ5pmQjWPu13SeEndJAVL+l3Sl5KeN02zcc39BAAAAAAAAKBFy/Hj2u+SdOf5PXT/p1urvaZ8uHIVaxxTmPLlCAzT/409VUZDh7n5mdKOhfV7Tlt0mcDdNeo9UQqt5w8oxPWXJs6X3pvkWygfGOY8Lq5/XVUGAIDftfhA3jCMSyXNlxRZ6u5QSQNLblMNwxhjmuYvfj71IzoRxleLYRgnSVokqXuZXT1LblMNw7jWNM16/k0OAAAAAAAAALwLC7L6tb9TO0Vp9uQBmjZvfSWhvKmhlu2abP1GF1h+VIDhOLHrrb9KvS5xrkGeMLx+Rlrbi6Wju6WULVLKZum3NZLp3w8qSHKu2+4xtXyp7foO3avS7RzphkXSJ7dUb/r6dr2dI+MJ4wEATUyLDuQNwzhN0vuSQiRlS3pc0vclX0+UdLOkHpK+NAxjoGmaWX487wxJ+ZKKJEVU45gIOUfBu8L41yS9JylP0tmS7pHzQwXvG4Zxlmmam/xRKwAAAAAAAADUVEZukT7ddMBv/QVYDLWPtOmkdhFaMG2oZi7YpF2p2R5t+hh79Uzgy+pp2e+9E9Mubf/MeYvp5Zw+3Z8hb36GlLpNStnqDN9TtkiHd0j2Av+do6zLXpB6jnGG7g09+t8Xcf2lW5Ok5JXSutecswaU/qCCJUA62fXhiWFN67EBAFCiRQfykv4jZ/heLOkC0zSTSu37zjCM3ZKelDOUv0vSQ7U9oWEYVjnDdKukhyXdpGoE8pL+UlKHJP3VNM2nSu1LMgxjqaRlco7uf07SqNrWCgAAAAAAAAA1cTgzX3NW7tXba/b5dcr60X1iFWELlCT17RilxTNGaM2eY5q3JlmLt6VqqDZrduAzCjOqGX6n7XCuZT5xvnPEti9MU8rYXzLqfYuUWvLv8WTf+vGHzoOlsDb1f15/MAwpcbjzlp8pZR2SCrKl4HApooNki6y6DwAAGrEWG8gbhnGGpOElX75eJox3eVrSDZJ6SfqzYRj/Mk2zqJan/rOkAZJ2Svo/OQP5qmoNlHONe0naUVKXB9M0VxuG8bqkaZJGGoYxyDTNdbWsFQAAAAAAAACqbd/RHL2ybI8+Wr9fhXZH1Qf4aNKQeI+vDcPQ0G5tNLRbG+Uk/6iQ+f+RpdjHkehFOc61zG9YVPFI+eJCKe1nKXXriQA+ZYuUn16jx+FXlgBncN0c2CIJ4AEAzU6LDeQljS21/Ya3BqZpOgzDeEvOqeyj5ZwafklNT2gYRryca8dL0i2maRYa1Zti52xJUSXbb5qmWdFvsnPlDOQlaZwkAnkAAAAAAAAAVcrKL1JKRr5yCu0KC7IqNsrmHoleHdsPZurlZb/qy80H5TDrpsae7SM0pGsF66CbpsK+vF0qzq1Z50U50qfTpemrpbzjpYL3kn/TfpYctR2rJSmyoxTbz3nbs1Ta74c/4Z58CSE2AACNWEsO5IeV/JsjaX0l7ZaV2j5LtQjkJc2SFCZpnmmaS304blip7WUVtpJ+lJQr57T1Z/lcHQAAAAAAAIAWwzRNJe05qnlJ+7Rke6rspZJ0q8XQ6D7tNWlIvIZ2baOKBhat/y1dr6/eou93plV4HoshjTklTuf3aq+/f7xZuTWYwj40yKqnJ5xaYR1KXuGcfr42Dm+X/t1dyqn4sVSbJUCKOdkZvLfveyKEDy31gYLEEdKbl9b+XIOm1r4PAABQZ1pyIN+r5N9fTNMsrqTdz16O8ZlhGBMlXSzpuJzr0fuidwX1eDBNs9gwjF8knaJa1AoAAAAAAACgedt6IEMzF2zSrtRsr/vtDlOLtqRo0ZYU9Wgfrmcm9Fffjs5JPE3T1Lbjhr49YNGepA0VniPIatGVAzpp2oiuSmgbJuVnquNlkXry8w06WhSoFLO1shVaZa2hQVbNnjzAfX6v1s2psp9qqUkYb4uS2vc7EbrH9nWG8QHBlR+XMFyK6VW7DxK06y0lDKu6HQAAaDAtMpA3DMMmqW3Jl/sra2ua5nHDMHLkHNneuYbnayXpuZIv/26apq+/1XUq+TfHNM30Ktr+LmcgH2MYRrBpmtVeMMkwjE5VNIl1beTl5SkvL6+6XddIfn6+120AvuFaAmqP6wioPa4jwD+4loDa4zoCpFW/HtMd729RblH1RqrvSs3WhFeS9NyEvsrML9arK/Zq12Frhe1Dg6yaOKCjrh/aWe3Cg2T5bZXsS96QZffXGmDa9b5FUrBUbFq02DFQ8+3nK8nRW1L50e/d24Xp8bG91adDeMV/jyzIkm3HQi9H+58jqovM9n3liOktR/u+Mtv1kRnZSSo7cr/IIRVV/fdT4+L/KPidsTKKfJ9q3wwMVcFFz8nke1mTxc8koPa4juBvdZF/GqZZRwv6NGKGYcRIOlzy5fumaU6son2qpHaStpqm2a8G55sj6SZJSZLOMks96YZhJEuKl7TPNM2ECo7fJuco+VTTNGO9tSnV9n1JE0q+bGua5lEf6qz2m2HOnDlq27Zt1Q0BAAAAAAAANBq/Z0vPb7Oq0FGT+NqUt9DcJSzA1MgODg1rbyosUIrKTdbp+2YrMv9AlT3vdHTSzKLp2mYmymKYOqW1qWHtTZ0UaZbLusuKyDugc36+x8fHUjm7EaAsWydlhHRRRmgXZYZ0UYats4oDwvx6HkmKydyqM/b+RwGOao+tUrElWGsT/6y0yL5+rwcAgJbsyJEjmjrVvRxMZ9M0Kx3cXR0tcoS8JFup7cJqtHf9JhTi64kMwxgh6UZJxZJuMWv2CQhXvb7UKtWgXgAAAAAAAADNk2lK83+paRgvVRTGRweZOjvOoaHtTAWXDJz3NWTuadmvT2yPaEnHPyunVV/ZqvjLdWBxjlrn7FLb7J/VLuMnXx5EldbFT9ehVoNkGvXz5/O0yL5a2f2+an94IdPWSRvi/6iM0IS6Lw4AANRaSw3kS89ZEVSN9q7Ffnyao8AwjGBJr8r5m+p/TNPc7Mvxpbjq9aVWycd6VfWU/LGS1knSiBEj1KlTVTPc105+fr6WL18u1/lsNlsVRwDwhmsJqD2uI6D2uI4A/+BaAmqP6wgt2Q97jytlzUa/9dfOZurWs0/SuAGdFWS1uO83UjYr+J1bZPgw4luSgswCjUl9UQXnfioz9hTPnblHZPn9B1l/T5Ll9yQZh7fLUN3M/trvvKvVt22POum7UubNKvh9tQI2vCHLrq9kmCeWFDAtAbJ3v0j206cosPOZGlzVtAFoEviZBNQe1xH8bf/+Wg+IL6elBvJZpbbDq9HeNQ9Rto/nuU9STznXdX/Qx2NLc9XrS62Sj/VWNeWCUeqXvJCQEIWE1N8AfJvNVq/nA5orriWg9riOgNrjOgL8g2sJqD2uI7Q0CzZu90s/kbYAXdmlQKe0NnXhGfGe15FpSov+LNVgTXRJMopyZftqhjTpY2nfKmnfaue/aT/7pfYqWQJki0mUbA30vaHnec5bfqaUdUgqyJaCw2VEdFCALbLF/kG/JeBnElB7XEfwh7p4D7XIn9+maeYbhnFUUhtJlQ7zNgyjlU6E3L/7eKq/lfz7raRLDe+fWnT1HWYYhmst+8OmaX5Xqs1+SYNL2kSbppleyTldo9zTTNP07SOoAAAAAAAAAJqlrPwiLd6W6pe+cgrsOjnKlMXbnzuTV0hpO2p3gsPbpWdOrl0fNXXyJZItsmHOXZotsnHUAQAAaq1FBvIltksaLukkwzACTNMsrqBd6d/8fP1N0jXF/A0lt8q0lfRuyfYySaUD+e2SrixVzxpvHRiGESCpWw1rBQAAAAAAANBMpWTky+7wfYr3cOUq1jimMOUrRzalmK2VbYYqvVCK9fbX5XVzal9sdQXYpE6DpPizpOBwack/at/noKm17wMAAKCUlhzIr5QzkA+TNEDSDxW0G1lqe1VdF1WBlaW2R6qCQF7SQJ0Ycd9QtQIAAAAAAABoZHIK7VU3cjM11LJdk63f6ALLjwowHO49xaZFix0DVZB1jhTSy/Ow/Expx0L/FOxNYJjU+Qwp4SwpfpjU8XQpILikZFPa+HbtRue36y0lDPNPrQAAACVaciD/qaR7SrZvkJdA3jAMi6TrSr5Ml/S9LycwTdPrHPVlzpEsKV7SPtM0EypotlRShqQoSdcbhvGkaZrePs46pdT2J77UCgAAAAAAAKD5CguyVqtdH2Ovngl8WT0t+73uDzAcGmNdKx1cq8xjHWWc2kFKHOzcmXlQMn0J/qsQGFYSvp/lDMo7nCpZA723NQxp3CvSGxdLRTk1O9fYl539AAAA+JGloQtoKKZprpW0ouTLmwzDGOql2V2SXB/z/I9pmkWldxqGMcowDLPkNrcOay2U9HzJl70k3V22TUn9N5V8ucw0zXV1VQ8AAAAAAACApsUWaFVVUfMwyxYtCHqkwjC+rMj8Awp+Z6z0a8nqm4U1CMIrc92n0rUfSMNmSJ0GVhzGu8T1lybOd4brvggMcx4X179mdQIAAFSiJY+Ql6Q/yzm1e4ikJYZhPCbnKPgQSRMl/bGk3S5JTzdIhSc8JekPknpIetIwjJMkvScpT9LZku6V8/XMkzSjgWoEAAAAAAAA0IgU2R2al7RPz327S5WtIN/H2KvZgc8ozCjwqX+jKFd671ppyG1S8vLaFVuWLcr3Y7qdI92wSPrklupNX9+ut3NkPGE8AACoIy06kDdNc6NhGH+QNF9SpKTHvDTbJWmMaZpZ9VpcGaZpZhmGMUbSIknd5fywwB/LNMuUdK1pmpvquTwAAAAAAAAAjczSnYf16MLt+jWtqpHrpp4JfNnnMN6tKFda8VTNjq2IJUCK6FCzY+P6S7cmSckrpXWvOde1Lz2VviVAOvkSadBU51T4TFMPAADqUIsO5CXJNM0vDMM4Rc7R8mMkdZJUKOkXSR9IetE0zdwGLNHNNM1fDMM4TdJtksZLOklSkKTf5Qzq/2Oa5r4GLBEAAAAAAABAA/s1LVv/XLhd3+9Mq1b7oZbt1Z6mvt6cfIlki6z58YYhJQ533vIzpaxDUkG2FBzuDPpr0zcAAIAPWnwgL0klIfbMkpsvxy2Vqlx6qao+EnxsnyPpyZIbAAAAAAAAAEiSMvKK9Pz/duvN1ckqdnifoH5497b6Mfm48opOjBifZP2mvkqsvkFT/deXLZIAHgAANBgCeQAAAAAAAABowortDr237nc9880uHcsp9NqmX8coPXBpbw1KaK0Vu9M0bd565RbaFa5cjbb86KdKDOnS56WkF6UjO2veTbvezqnkAQAAmgECeQAAAAAAAABoolb/ckSPLNyun1OyvO6PiQjWX0b31FWnd5LF4pzsc3j3GC2YNlQzF2yS4/B+BRgOP1VjSl0GSx1Okd64WCqqau16LwLDpLEvs647AABoNgjkAQAAAAAAAKCJ2Xc0R//6coeWbE/1uj/IatFNwxN129knKTy4/J+B+3aM0uIZI7R1rV36yo+FFWRLnQZIE+dL703yLZQPDHMeF9ffjwUBAAA0LAJ5AAAAAAAAAGgisguK9eJ3v+i/K/eq0O59ZPvoPu1138W91aVNaKV9GYU56pe13L8FBoc7/+12jnTDIumTW6S0HVUf1663c2Q8YTwAAGhmCOQBAAAAAAAAoJFzOEx9uH6/nly8U0eyC7y2OTk2Qg9c2ltndmtbeWdZKdIPs6UfX5fyM/xXpCVAiuhw4uu4/tKtSVLySmnda9KOhZJp92x/8iXSoKnONeOZph4AADRDBPIAAAAAAAAA0IitSz6mh7/Ypq0HMr3ubx0WpLsu6KGJg7rIaqkk1D78s5T0grR5gWQv9H+hJ18i2SI97zMMKXG485afKWUdck5rHxzuDO/LtgcAAGhmCOQBAAAAAAAAoBE6kJ6nxxft0NLNvyrWOKZTjXzlyKYUs7WyFaoAi6EpZyboT+d2V1RIoPdOTFPat0pa9by0e3HdFjxoauX7bZEE8AAAoMUhkAcAAAAAAACARiS3sFivfP+LNq1cqIlarOeCf1SAcWK9+GLToo1hwxR3/u3q2L+X96ne7cXSjs+l1S9IBzdUfsIeF0mHt0vp+2pedLvezmnnAQAA4IFAHgAAAAAAAAAaAYfD1Gc/HdCnXy7SvYX/0Uzrfq/tAgyHBuUulz5bLq3uJY17xbleuyQV5kgb50tJL1UesFuDpf5XS0Nvl9p2lw5ukt64WCrK8b3wwDBp7MusAQ8AAOAFgTwAAAAAAAAANLBNv6fr4S+2KWz/Cs0OfEZhloLqHZi2wxmkX/6ilLpNWjdHyk+vuH1IK+fU8mf8UQpvd+L+uP7SxPnSe5N8CuXNwFAZE+ef+EAAAAAAPBDIAwAAAAAAAEADScnI15Nf/6yPNx5QH2Ov5gc9ozCjmmG8S1GO9OENlbdpleAcDd//GikozHubbudINyySPrnFGfRXIdPWSUF/+K9siYN9qxcAAKAFIZAHAAAAAAAAgFrIyi9SSka+cgrtCguyKjbKpghbYKXH5BfZ9dryPZq19FflFdklmXom8GXfw/iqxJ0unXWH1OsyyWKtRvv+0q1JUvJKad1r0o6Fkmk/sd8SoOLuF2mNva+Ohp+sC2JP8W+9AAAAzQyBPAAAAAAAAAD4yDRNJe05qnlJ+7Rke6rsDtO9z2oxNLpPe00aEq+hXdvIKLW2ummaWrQlRY8t2qED6Xnu+4datqunxfua8TXS4yLpzD9J8Wf6vra7YUiJw523/Ewp65BUkC0Fh0sRHVRkBurokiX+qxUAAKAZI5AHAAAAAAAAAB9sPZChmQs2aVdqttf9doczdF+0JUU92ofrmQn91bdjlLYeyNAjX2zX2uRj5Y6ZZP3GP8VFx0vXfiDF9PRPf7ZI5620vDzvbQEAAFAOgTwAAAAAAAAAVNOK3WmaNm+9cgvtVTeWtCs1W+NfSdIZCa21/Jc0mWb5NhFGri60rvdPgRn7pYgO/ukLAAAAtWZp6AIAAAAAAAAAoCnYeiDDpzDeJa/IrmW7vYfxgxNb65NJCbLKtz4rZNqdU8wDAACgUWCEPAAAAAAAAABUwTRNzVywyecwviKdWoXovot76cK+sTIObPBLn24F3qfSBwAAQP0jkAcAAAAAAACAKiTtOVrhmvG+CA6w6I5zu+umYYmyBVqddwaF1bpfz5OE+7c/AAAA1BiBPAAAAAAAAABUYf6afX7pZ9hJbXXb2SeduMM0pQM/+qVvSZIlgDXkAQAAGhECeQAAAAAAAACoRFZ+kRZvS/VLX0t3pSkrv0gRtkBp/3pp8T3S7z/4pW9J0smXSLZI//UHAACAWiGQBwAAAAAAAIBKpGTky+4w/dKX3WHqyME9itj0tLT5fb/06WHQVP/3CQAAgBojkAcAAAAAAACASuQU2v3ST4jyNS1goeLfXiTZ8/3Sp4d2vaWEYf7vFwAAADVGIA8AAAAAAAAAlQgLstbqeEMOjbWs0l8D31cH45jkLd9vlSANvEla+oRUlOP7SQLDpLEvS4ZRq1oBAADgXwTyAAAAAAAAAFCJ2CibrBajRtPWn27s0gOBb6m/ZY/3BsGR0oi/SIOnSQHBUmxf6b1JvoXygWHSxPlSXH+f6wMAAEDdsjR0AQAAAAAAAADQmEXYAnVB7/Y+HdNRaXoh8Hl9HPyQ9zDesEgDb5T+tEE66w5nGC9J3c6RblgkxfSq3ona9Xa273aOT/UBAACgfjBCHgAAAAAAAAAqkZFbpIMZedVqG6Y8TQ/4XDdbFynYKPLeqOsoafRjUvs+3vfH9ZduTZKSV0rrXpN2LJTMUvPcWwKkky+RBk11rhnPNPUAAACNFoE8AAAAAAAAAFRgV2qWbn7rR+07mltpO4scutK6XH8JWKB2RrrXNr9bOqrTH56W0ePCqkN0w5AShztv+ZlS1iGpIFsKDpciOki2yBo+IgAAANQnAnkAAAAAAAAA8OKrLYd01wc/KbfQXmm7IZbtuj9gnvpY9nndn26GaZZ5lS676QF17tzW90JskQTwAAAATRSBPAAAAAAAAACUYneYeuabnXrp+1/L7Yu0Baig2KGCYoe6GKm6N+AdXWhd57WfYtOiefbz9aplvJ6cPEp9axLGAwAAoEkjkAcAAAAAAACAEhl5Rfrzexu1dGdauX2ndorSy5MGKP34EW19935dXvCFgo1ir/18Z++vfxVfq4B2J+u1Caeqb8eoui4dAAAAjRCBPAAAAAAAAADIuV78H9/6Ucle1ou/akAn/fOyk2XbPF9x3z+m3oVHJC/LwO9ydNTj9skK7T1a/xwSryFdW8uoar14AAAANFsE8gAAAAAAAABavIrWiw+wGHrg0t6aHLNHxuujpMPbvR5vt7XSwdPvkuWUSXo+OlwRtsB6qBoAAACNHYE8AAAAAAAAgJYhP1PKPCgV5khBYVJknOxBERWuF982PEivj4nWqTv+Ji3+2nuflkBp8DRZR/xFnUOi67Z+AAAANDkE8gAAAAAAAACaL9OUkldIa1+Tfv5SMk+MgDcNqzaEnKkN6SMk9VbpOejPjLPo1S6LFf7FG5LD+zrxOvkS6fxHpDbd6vYxAAAAoMkikAcAAAAAAADQPB3cJH1yi5S2w+tuw7RrUO4KvRu0QjsdnTSzaLp2mp31VMJ6jc14S8am4977bd9XGv2Y1HVk3dUOAACAZoFAHgAAAAAAAEDz8+t30nuTpKKcajXvadmvj4IeUnFIG4WnpHhvFBYjnXO/dNokyWL1Y7EAAABorgjkAQAAAAAAADQvBzf5FMa72IwiKd9LGG8NkobcKg2/S7JF+qdGAAAAtAgE8gAAAAAAAACaD9N0TlPvYxhfod5jpfMfllol+Kc/AAAAtCgE8gAAAAAAAACaj+QVFa4Z75PW3aTLX5Tiz6x9XwAAAGixLA1dAAAAAAAAAAD4zbo5/uknth9hPAAAAGqNQB4AAAAAAABA85CfKe1Y6J++dnzh7A8AAACoBQJ5AAAAAAAAAM1D5kHJtPunL9MuZR3yT18AAABosQjkAQAAAAAAADQPhTn+7a8g27/9AQAAoMUhkAcAAAAAAADQLKz4Lc+/HQaH+7c/AAAAtDgE8gAAAAAAAACaNIfD1NNLdmr654dUbPrpT56WACmig3/6AgAAQIsV0NAFAAAAAAAAAEBNZeQV6c73N+m7nw/LIpt+NTuop3Gg9h2ffIlki6x9PwAAAGjRCOQBAAAAAAAANEm7U7P0x3nrtfdIjmJ0XM8FzlJPix/CeEkaNNU//QAAAKBFI5AHAAAAAAAA0OQs3paime9vUk6hXcMsW/Rs4EuKMTL903m73lLCMP/0BQAAgBaNQB4AAAAAAABAk+FwmHru2116/rtfZJVdfwn4QNOtX8himP45QWCYNPZlyTD80x8AAABaNAJ5AAAAAAAAAA0uK79IKRn5yim0KyzIqtgomyJsgR5tSq8X30FH9XzQCxpk2VW+s8iO0pDbpO//JRXlVL+IwDBp4nwprn/tHgwAAABQgkAeAAAAAAAAQIMwTVNJe45qXtI+LdmeKrvjxCh3q8XQ6D7tNWlIvIZ2baNfDme714s/17Je/w6crVZGdvlOe1zoHOEe2lpKOEv65BYpbUfVxbTr7TyOMB4AAAB+RCAPAAAAAAAAoN5tPZChmQs2aVeql1Bdkt1hatGWFC3akqK4KJuO5RTKXlyofwS8q6kBX5U/wBIonf+INGT6ienm4/pLtyZJySulda9JOxZKpr3UMQHSyZdIg6Y614xnmnoAAAD4GYE8AAAAAAAAgHq1Yneaps1br9xCe9WNJR3MyFcXI1UvBL2gUy17yjeIjpfGvyF1HFB+n2FIicOdt/xMKeuQVJAtBYdLER0kW2QtHw0AAABQMQJ5AAAAAAAAAPVm64EMn8J4SRpjWaPHA19TpJFXfmfvsdJlz0u2qKo7skUSwAMAAKBeEcgDAAAAAAAAqBemaWrmgk3VDuODVaj7A+ZpUsD/yu+0BksXPSENuIGp5gEAANBoEcgDAAAAAAAAqBdJe45WuGZ8Wd2MA3ox8AX1svxWbl9eZFeFXPOWFNvP3yUCAAAAfmVp6AIAAAAAAAAAtAD5mfp22TKdavyik4z9ClduhU2vsCzXF0H/8BrGf2QfpvtiXiSMBwAAQJPACHkAAAAAAAAAdcM0peQV0trXZP78pR4w7VKwc1exadFix0DNt5+vJEdvSYZCla9HAufqKuvycl3lmsG6v+gGfeQYIeuODD2cX6QIW2D9Ph4AAADARwTyAAAAAAAAAPzv4Cbpk1uktB2SpLKrvAcYDo2xrtUY61rtdHTSC8XjNCPgI51kOViuqx2Ozrq96A79anaUJNkdplIz8wnkAQAA0OgRyAMAAAAAAADwr1+/k96bJBXlVKt5T8t+vRD4goyyqb2kt4vP1SPFk1WgII/7swvs/qgUAAAAqFME8gAAAAAAAAD85+Amn8J4l7JhfJYZonuKpmqhY6jX9uHB1hoWCAAAANQfAnkAAAAAAAAA/mGazmnqfQzjy9rsSNCfiu7QPjPW6/4Ai6H2kbZanQMAAACoD5aGLgAAAAAAAABAM5G8wr1mfG08WTyxwjBekkb3iWX9eAAAADQJBPIAAAAAAAAA/GPdHL90M9H6faX7Jw2J98t5AAAAgLpGIA8AAAAAAACg9vIzpR0L/dLVhZZ1Cleu130920doSNfWfjkPAAAAUNcI5AEAAAAAAADUXuZBybT7pasAw6H2xvFy94cGWfX0hFNlGIZfzgMAAADUNQJ5AAAAAAAAALVXmOPX7sKV5/F1aJBVsycPUN+OUX49DwAAAFCXAhq6AAAAAAAAAADNQFCYX7vLVoh7u2f7CD094VTCeAAAADQ5BPIAAAAAAAAAai8yTjKsfpm2vsi06qjRWmP6dtCkIfEa0rU109QDAACgSSKQBwAAAAAAAFB7tkjZTx4j647Pa91V/kkXacX4yxVhC/RDYQAAAEDDYQ15AAAAAAAAALWWnluoVw6c5Je+IobdQhgPAACAZoFAHgAAAAAAAECt7Duao0dfeEWTM16ufWfteksJw2rfDwAAANAIMGU9AAAAAAAAgBpbv++4Pp/7lJ5wvKJAo5brxweGSWNfllgvHgAAAM0EgTwAAAAAAACAGvnyp4NK/uhePWz5RCqToZuyyJCj+p0FhkkT50tx/f1aIwAAANCQmLIeAAAAAAAAgE9M09Ts73ao6MObdJvlk3L7804aI+PGr6SYXtXrsF1v6YZFUrdz/FwpAAAA0LAYIQ8AAAAAAACg2orsDj3x0SqN3nqXzrDuLLe/8IzbFHLhPyWLRbo1SUpeKa17TdqxUDJLTWlvCZBOvkQaNNW5ZjzT1AMAAKAZIpAHAAAAAAAAUC1Z+UV6aO4Xuu3gPepqSfHY55BF5kVPKmjwzSfuNAwpcbjzlp8pZR2SCrKl4HApooNki6znRwAAAADULwJ5AAAAAAAAAFU6mJ6np+fM1X1Z/1RrS7bHvkJrqAL/8KYsPS6ouANbJAE8AAAAWhwCeQAAAAAAAACV2rI/Q++98aweK35BwUaxx768kPYKuf4jKbZfA1UHAAAANF4E8gAAAAAAAAAq9O22FG15/0H9y/KeVGaZ99w2fRR6/YdSZFzDFAcAAAA0cgTyAAAAAAAAALx6c8UuhSz5i+60Li23Ly/hPIVe/aZzPXgAAAAAXhHIAwAAAAAAAPBgd5h66rMfNGzDTA2zbiu3v+C0mxRyyZOSlT8vAgAAAJXhN2YAAAAAAAAAbrmFxXr4ra91029/VQ/rAY99DhlynP8vBZ95q2QYFfQAAAAAwIVAHgAAAAAAAIAk6XBmvp6Y87buyXhYMZYMj31FlmAFjH9dAb0ubaDqAAAAgKaHQB4AAAAAAACAdqZkae7rL+hfhc8qxCj02Jcf3Ea26z6UOp7eQNUBAAAATROBPAAAAAAAANDCrdh1WGvefkT/0nxZDNNjX250D4VO+UiK7tJA1QEAAABNF4E8AAAAAAAA0IIt+GGPihb+RX+xfltuX26n4Qqd9LZki2qAygAAAICmj0AeAAAAAAAAaIEcDlPPf7VBp665U2dbfyq3P7/ftQod+x/JGtgA1QEAAADNA4E8AAAAAAAA0MLkF9n1z3e+1TW//kW9rfvK7S8adb9sI++SDKMBqgMAAACaDwJ5AAAAAAAAoAU5ml2gx/67QH85er9iLcc99hUbQbJc8YoC+13ZQNUBAAAAzQuBPAAAAAAAANBC7EnL1itzXtYj+U8pzCjw2FcQGK3gye9LXYY0UHUAAABA80MgDwAAAAAAALQAa/ce07dv/UuPO16X1TA99uVGJCp0ykdSm24NVB0AAADQPBHIAwAAAAAAAM3cpxt+07FP/q57rV9KZZaFz+kwWGGT35NCWzdMcQAAAEAzRiAPAAAAAAAANFOmaerlb7aq64o7daN1Xbn9eb2uUtiVs6SA4AaoDgAAAGj+COQBAAAAAACAZqiw2KHHFyzV5T/fpf7WPeX3D/urQs69VzIML0cDAAAA8AcCeQAAAAAAAKCZycgr0j/f+Eh/Tr1PnSxHPPbZjQAZl72goNOuaaDqAAAAgJaDQB4AAAAAAABoRn4/lqvn58zR/TmPK9LI9dhXEBCh4GvflRKHN1B1AAAAQMtCIA8AAAAAAAA0Ext/O64v5v6fHrO/qkDD7rEvN6yTQqd8IsX0aKDqAAAAgJaHQB4AAAAAAABoBr7eckDJH9yjByyfSWWWhc9pd5rCrvtACo9pmOIAAACAFopAHgAAAAAAAGjCTNPUG0t/Vsx3M3SLdU25/bknXaKwP8yRAkMaoDoAAACgZSOQBwAAAAAAAJqoYrtDT36yShdsnqmB1l3l9hcM/pNCRz8iWSwNUB0AAAAAAnkAAAAAAACgCfp/9u47yq66Xh/w+z2TnpCQEDqIAiIgdiwogh0VGyoK0rsKooj1Wn541Xuv9YoNgdB7RxBFVJqCCmIFREDpPQTS68z398cM15xMElJmzplknmets84+57PLi4u9cOadvfeMuQvy5VMuyYfu/0ye2XikadaZjtSdv5nhL92/TekAAIBEIQ8AAAAAq5yHps7Ot44/OZ+b/pWMb8xoms3rGJ2hu52a8uw3tCkdAADwFIU8AAAAAKxCbnlwas454Vv56oIfZHhZ0DSbPXK9jNzngmS9bdqUDgAAWJhCHgAAAABWEVf9/ZHcfNbn85+Nc5PSPJs54bkZve8Fydj12xMOAADoRSEPAAAAAKuAM66/M8N/dkQ+0nFtr9msZ74xo3c/ORk+pvXBAACAJVLIAwAAAMAA1tVV87+X/j7b/eGIvLLj1l7zOS8+KKPe9rWk0dGGdAAAwNIo5AEAAABggJo9rzNfPv2n2f/uT2XzjgebZl1ppOtNX82IV364TekAAICno5AHAAAAgAHoselz8/UTTs+nnzgqExvTmmbzGyMy5H0nZciWb21TOgAAYFko5AEAAABggLnjkek5+YTv5stz/zcjyvym2ezhEzNyn/OTDV7UpnQAAMCyUsgDAAAAwABy/R2P5bdnHJUv1zPSKLVpNnPN52T0vhcka27cpnQAAMDyUMgDAAAAwABx/g13Zd6lR+bIjl8lpXk2c+MdM3qP05MRY9sTDgAAWG4KeQAAAABos1prvv+zP+b5v/1Yduz4a6/57OftldHv+t+kY2gb0gEAACtKIQ8AAAAAbTR3QWf+66xfZrc7jsxWHff1ms9/7VEZucPHklJ6bwwAAAxoCnkAAAAAaJMnZs7L/5x4dj4++QtZt/Fk02xBGZbGe47L0G12aU84AABgpSnkk5RSNklyeJKdk2ycZG6SfyY5N8kPaq2zVmLfWyV5fZKXJnleknWSTEzSmeSRJDcmOTPJJbXWupT9nJxkn2U87LNqrXevaGYAAAAA+t/dk2fm2Ek/zBdnfyOjy9ym2Zxh4zNir3OTjV/WpnQAAEBfGPSFfCnl7UlOTzJ2oa9HJdm253VgKWXnWuudK3iIzyXZYwmzZ/W83pfkmlLKe2qtj6/gcQAAAABYRfzh7in51Slfzle6TkpHab5GY+Yam2b0fhcmE57VpnQAAEBfGdSFfCnlRUnOSTIyyYwk/53kqp7PuyU5KMkWSS4rpWxba52+AodZkOT3Sa5L8rckDyd5LMn4JFsmOSTJNkl2THJpKWX7WmvXUvb3YJKdnuaYD6xATgAAAABa4NI/35fHL/xkPt34WbLIY+Fnrv+KjN7rrGTUhPaEAwAA+tSgLuSTHJ3u8n1BkjfVWn+70OzKUsodSb6e7lL+yCRHrcAxDqy1LljC7JellGPSfWv8dyfZLsnbklyylP3Nr7XevAI5AAAAAGijWmuO+9XN2fSaj2bfjpt6zWdt9d6Mfs8xyZBhbUgHAAD0h0a7A7RLKeVlSV7d8/GERcr4p3wryd97lj9aShm6vMdZShn/1LwzyTcW+urVS1oXAAAAgFXT/M6ufPXsq7LdtXvljYsp4+e++jMZ9b5JyngAAFjNDNpCPsm7Flo+aXEr9Nw6/tSej2smeW0/ZVn4Vvgj+ukYAAAAALTBtDnz84Xjzs2+tx2U5zfuapotKEPT+c4fZfjrP5uUsoQ9AAAAq6rBfMv67XveZybp/WfJ/3bNQsuvSnJFP2TZbaHl2/ph/wAAAAC0wf1PzMoPjj8u/zHzfzK2zG6azRkyNsP3PCvlmdsvYWsAAGBVN5gL+a163u98mtvKL1yQb7XEtZZTKWVikmcnOTDJfj1fT05yxtNsulYp5Zok2yQZk2RKkr8muTTJibXWWSuRaaOnWWW9pxZmz56d2bNnL23dlTZnzpzFLgPLx7kEK895BCvPeQR9w7kEK6+V59HND07L5Wd8O1/uPC5DSlfTbOaojdLxgbMzZ63Nk37+HQv0Nf89gr7hXIKV5zyir/VH/1lqrX2+04GulDIiyVP/a15Wa33b06w/I8noJL+rtW63Ese9OsmOSxhPTrJLrfU3S9j25CT7PM0hHkjyvlrr9SuYb5n/ZZg0aVImTpy4IocBAAAAWO397fGate86P4d0XNpr9uCIZ+cvm38084aObUMyAABgSSZPnpwDDzzwqY8b11rvX9l9DtYr5NdYaHnGMqw/M92F/Jj+iZPvJvlyrXXyUtapSX6X7ivh/5jkkXQ/b/55SQ5I8rIkGya5opTy6lrrn/opKwAAAABLcd2D87Pjg8dl547f95r9a42X5ZZND05XY1gbkgEAAK02WAv5EQstz1uG9ef2vI9cyePul+5ivyRZM8m2ST6U5LAkm5ZSDqy1PrKEbY+otT65mO9/W0o5PslXkvxHz/4nlVK2rct/+4ONn2a+XpIbk2SHHXbIRhs93R3uV86cOXNy7bXX5qnjjRgx4mm2ABbHuQQrz3kEK895BH3DuQQrrz/Po86umu9edmPe/9CReUnHHb3mM7Y9NOu/7nNZvzT67JjQDv57BH3DuQQrz3lEX7v//pW+IL6XwVrIL/wQiWX5c+ThPe8r9dCAWutdi3z161LKMUnOS/K2JDeWUl65uFsfLKGMf2pWk3yulPLyJK9P8uIkr0xy3XLmW+q/YaWU/1seOXJkRo5c2b9PWHYjRoxo6fFgdeVcgpXnPIKV5zyCvuFcgpXXl+fRzLkL8t+n/TgH3/+ZbNJ4tGnWmY7Unb+dMS/dt0+OBQOJ/x5B33AuwcpzHtEX+uPfocH657jTF1peltvQj+55X5bb2y+XWuucdF85PyvdV6h/fSV2d+xCy0t6Vj0AAAAAfeiRaXPyn98/Lp984CO9yvi5HaPT2PP8DFHGAwDAoDQor5Cvtc4ppTyeZK0kS73veillfP5dyN/XT3kml1KuS/LGJO8spQyttc5fgV3dutDyhn2TDgAAAIAlue3haTl30jfy5fk/yLDS2TSbNXL9jNr3wmTdrduUDgAAaLfBeoV88u/yevNSytL+MGHLhZb/3o95Hut5H5Vk4gruY3mfGQ8AAADACrrmH4/mymOOyBcXfLdXGT9jrW0y6sNXK+MBAGCQG8yF/G963kcneclS1lv41u/L9Uz25bTwFe0remv8hX/Ce3AlsgAAAACwFGdff2emnL5/PlzO7zWb+aydMuaQK5I11mtDMgAAYCAZzIX8xQst77e4FUopjSR793x8MslV/RGklLJRku16Pt5Ta52+tPWX4pCFlq9ZuVQAAAAALKqrq+Z/L/1dnnn5ntml49e95nNeckhG73VWMmz0YrYGAAAGm0FbyNdab0jy1E9NB5RStlvMakcm2apn+ehFn+teSnlNKaX2vE5edONSyhallNctLUcpZVySM5MM6/nq1MWs84pSyvpL2UcppXwlyRt6vvpL+vdqfgAAAIBBZ878znzplJ/kHTfum1c0mp9s2JVG5r/paxnx9q8njY42JQQAAAaapT07fTD4aLqL65FJriil/Fe6r4IfmWS3JAf3rHd7km+twP43SPKrUspf0n1F/k1JHk6yIMl6SV6V5ICe5SS5Ocn/LGY/b07ymVLK5Ul+keTWdF+xPzzJ85Psn+TlPevOSnJQrdXz5AEAAAD6yOMz5uYbJ5yWT045Kms1mm9uOK8xMkPed1KGbvmWNqUDAAAGqkFdyNda/1RKeX+S05OMTfJfi1nt9iQ7r8Rt5JPkBT2vpbksyX611llLmA9P8s6e15Lcm+QDtdYblz8iAAAAAItz56Mzcuqk7+RLc7+T4aXpBoqZNXydjNr3/GT9p/vVDwAAMBgN6kI+SWqtl5ZSnp/uq+V3TrJRknlJ7kxyXpLvL6UkfzrXJdkp3beS37Zn3+smGZVkWpK7kvwuyVm11qXdYv6kJI+k+znzz0+yTpK10n2l/eQkf0xyaZIza61zVjArAAAAAIv43T8n53enfTH/mTOS0jybseaWGbPfhcm4DdsTDgAAGPAGfSGfJLXWe5J8vOe1PNtdnV4/ijXN5ye5oue1svmO6XkBAAAA0AIX33RX5vz44/lY48pesxkbvyZj9jw9Gb5GG5IBAACrCoU8AAAAACyk1ppjfv6nbHPd4dmh42+95rOev0/GvPPbSYdfrQEAAEvnpwYAAAAA6DFvQVf+55xf5H3/ODJbdtzXNOtKyYLXfymjtj88KUu8aSIAAMD/UcgDAAAAQJKps+bnf048K0c89oWs03iyaTa/DEvjvcdn2HPf1ZZsAADAqkkhDwAAAMCgd+/js3Lc8d/LF2Z/K6PK3KbZ7GETMnLv85KNtm1TOgAAYFWlkAcAAABgUPvjvU/kypO+lP/sOjmNUptmM8ZuljH7XZiMf2Z7wgEAAKs0hTwAAAAAg9ZP/3p/Jp9/ZD7RuDxZ5LHwM9Z/ZcbsfWYycnx7wgEAAKu8RrsDAAAAAECr1Zqceu3fM+y8PbN34/Je85lbvz9jDvixMh4AAFgprpAHAAAAYFDprMkv/zk1H576xWzTcXev+dxXfzajX/fppJTeGwMAACwHhTwAAAAAg8aMuQvyi1vuzxfmfTMbNKY0zRaUock7f5DhL3x/m9IBAACrG4U8AAAAAIPCg0/OzveOOz5fn/f1rFFmN83mDBmX4XuelfLMV7UpHQAAsDpSyAMAAACw2rv5gan5yYlfzZcXHJ8hpatpNnP0Jhm934XJxM3blA4AAFhdKeQBAAAAWK396taHcvc5n8xnyqXJIo+Fn7HOthmzz7nJ6LXaEw4AAFitKeQBAAAAWG2d/uvbstYVH8kBHTf0mk3b7O0Zu9ukZOiINiQDAAAGg0a7AwAAAABAX+vsqvnmhb/Jc3+xR96ymDL+5rXfkaHvOVYZDwAA9CtXyAMAAACwWpk1b0H+59Qf58B7P51nNB5rmnWmI3/aeL88MHGHbFZcqwIAAPQvhTwAAAAAq41Hp8/J0cefkE9N/UrGNWY1zeZ0jEnee2IeuH1Om9IBAACDjT8DBgAAAGC1cPsj03Pcd7+So6Z+IeNKcxk/c+QGGXHIr1KfuUOb0gEAAIORK+QBAAAAWOX95vbHcvMZn87nywVJaZ7NWOv5GbPfBcmYdZLZs9sTEAAAGJQU8gAAAACs0s7/3T8z9LKP5IMd1/WazXjWWzJm9xOTYaPakAwAABjsFPIAAAAArJK6ump+8NMb8rIbDs/LO27rNZ+17Ycy5q1fTRodbUgHAACgkAcAAABgFTRnfme+duZPs9c/P5FNGw83zbrSSOebv55RrzioTekAAAC6KeQBAAAAWKVMmTkv35p0So6cclQmNGY0zeY2RmXo+0/O0Ofs1KZ0AAAA/6aQBwAAAGCVcdfkmTn1+G/ni3OOzvAyv2k2a/g6GbXvBcn6z29TOgAAgGYKeQAAAABWCTfe9Xh+d+rn8v/qWUlpns1Yc8uM2e/CZNyG7QkHAACwGAp5AAAAAAa8S/54d+Ze/LF8pHFVr9mMZ7wuY/Y4NRm+RhuSAQAALJlCHgAAAIABq9aa43/xp2z968OyfcctveYzX7B/xrzjG0mHX3MBAAADj59UAAAAABiQ5i3oyjfO+UV2/ccR2aLjgaZZV0rmv/7LGb39YUkpS9gDAABAeynkAQAAABhwps6en2+ceGY++ugXsnZjatNsXhmejveekOHPfXub0gEAACwbhTwAAAAAA8p9U2Zl0vHfy+dmfTMjy7ym2axha2Xk3uelbPSSNqUDAABYdgp5AAAAAAaMv9z7RH518lH5f52npFFq02zG2M0zZr8Lk/GbtCkdAADA8lHIAwAAADAg/Pyv92fy+Ufk440rkkUeCz99g+2zxl5nJCPXbEs2AACAFaGQBwAAAKCtaq059eqbs/GVh2WPjj/3ms/Yeves8Z7vJR1DWx8OAABgJSjkAQAAAGibBZ1d+fYF12Tnmz+a53bc02s+Z4fPZ8xrP5GUspitAQAABjaFPAAAAABtMXPugnz9lPPzwQc+m/UbU5pm88uwlHf9MCNesGub0gEAAKw8hTwAAAAALffw1Dn54fE/zKemfy1jypym2ewh4zJir3NSNtmuTekAAAD6hkIeAAAAgJa69cFp+ckJX87/W3B8Okptms0YvUnG7H9RstZmbUoHAADQdxTyAAAAALTMVbc9nLvPOjKfKj9JFnks/PR1X5o19jk3GTWhPeEAAAD6mEIeAAAAgJY467rbMuHnh2W/xo29ZjO22CVrvO/YZMjwNiQDAADoHwp5AAAAAPpVV1fN9y69LjvedHhe2Phnr/ns7T6RMW/6fFLKYrYGAABYdSnkAQAAAOg3c+Z35munXZwD7vlUNmpMbpotyJB0vf3ojHzJnm1KBwAA0L8U8gAAAAD0i8emz833Jk3KJ578SsaWWU2zOR1rZNgHzsiQzXZsUzoAAID+p5AHAAAAoM/d+ej0nHv8/+QL847J0NLZNJsxaqOM2e/CZO3ntCkdAABAayjkAQAAAOhT19/5WG4+/VP5j1yYLPJY+OkTX5g19j0/GbN2e8IBAAC0kEIeAAAAgD5z4Q3/zNCfHJaDG9f3mk3f9K1ZY/cTk6Ej25AMAACg9RTyAAAAAKy0WmuO+dmNeenvDs1LG7f3ms/c9tCs8davJI1GG9IBAAC0h0IeAAAAgJUyd0FnvnHmT7PHnUfmWY1HmmadaaTzzd/M6Fcc0KZ0AAAA7aOQBwAAAGCFPTlrXr59wik5YvJRGd+Y0TSb2xiVIbudmmFbvLFN6QAAANpLIQ8AAADACrnn8Zk57bhv5nNzvpvhZUHTbOaIdTN63wuT9bZpUzoAAID2U8gDAAAAsNxuuvvx/O6U/8jn69lJaZ5NH7911tjvwmTs+u0JBwAAMEAo5AEAAABYLpf9+e7MufDwHNq4ptds2jNen7F7nJoMH9OGZAAAAAOLQh4AAACAZVJrzQm//HO2uvbQvKrjll7zmS/cP2Pf8c2k0dGGdAAAAAOPQh4AAACApzW/syvfPveKvPvvH8+zOx5omnWlZP4bvprR2x/apnQAAAADk0IeAAAAgKWaNmd+vn3iGTn0kS9k7ca0ptm8xoh0vPeEDN/6bW1KBwAAMHAp5AEAAABYogeenJ0Tjjs6n5n5zYwo85tms4ZNzMh9zk/Z8EVtSgcAADCwKeQBAAAAWKy/3fdkfnXSF/P5zlPTKLVpNn3ss7PG/hcla27cpnQAAAADn0IeAAAAgF5+dfMDeezcj+ZjjV8kpXk2bYNXZ+zeZyQjxrUnHAAAwCqi0e4AAAAAAAwsp19zczrO2T27NX7RazbjuXtk7AEXKeMBAACWgSvkAQAAAEiSdHbVHH3h1XnLXw/PVh339prP3vGLGfOajyelLGZrAAAAFqWQBwAAACCz5i3IN04+L4c88Nms13iiaTa/DEt2OTYjn//uNqUDAABYNSnkAQAAAAa5R6fNyTHH/yCfmPa1jC5zm2azhqyZkXufm/KMl7cpHQAAwKpLIQ8AAAAwiN328LRcNulL+fz8E9JRatNs+phnZY39L0wmbNqmdAAAAKs2hTwAAADAIHXtbQ/nnrOOyJHlp8kij4Wftu7LM3afs5NRE9oTDgAAYDWgkAcAAAAYhM67/rasefmh2avxh16z6c95T8buekwyZHgbkgEAAKw+FPIAAAAAg0hXV80Pf3J9Xv2Hw/KCxr96zWe98pNZ442fS0pZzNYAAAAsD4U8AAAAwCAxZ35nvnn6xdn37k9lo8bkptmCDEnXO76XUS/+QJvSAQAArH4U8gAAAACDwOMz5ub7k47LEU98NWPL7KbZ7I6xGb7HmRmy6avblA4AAGD1pJAHAAAAWM3987EZOf/4/8p/zP1RhpbOptn0URtljf0vTiY+uz3hAAAAVmMKeQAAAIDV2O//+Vj+dton8+lclCzyWPipE1+Ucfudn4ye2J5wAAAAqzmFPAAAAMBq6pI//Csdl3w4BzZ+22s2bdO3Zdzuk5KhI9uQDAAAYHBQyAMAAACsZmqtmXT5jXnRbw/Nto3be81nvvQjGfuW/0wajTakAwAAGDwU8gAAAACrkXkLuvLtsy7LbnccmWc2HmmadaaRBW/5Vka/fP82pQMAABhcFPIAAAAAq4mps+bn6BNOyuGTj8qajZlNszmN0Rm6++kZ/uzXtSkdAADA4KOQBwAAAFgN3DdlVk4/7uv5zOzvZljpbJrNGLFexux3UbLu1m1KBwAAMDgp5AEAAABWcX+6Z0p+f/Kn89l6blKaZ9PGb5Ox+1+QrLFee8IBAAAMYgp5AAAAgFXY5X+5J7MvOCwfbFzbazbtGW/M2D1PSYaNbkMyAAAAFPIAAAAAq6Baa0698i/Z4poP5c2NW3vNZ7zwoIx9x9eSRkcb0gEAAJAo5AEAAABWOQs6u/Kd867ILrd+LJs1HmqadaWRuW/4asZs/+E2pQMAAOApCnkAAACAVciMuQvyvyedng899IVMbExrms0rI9J434kZudXObUoHAADAwhTyAAAAAKuIh6bOzonH/m8+OfPbGVHmN81mDpuYUfuen7LBi9qUDgAAgEUp5AEAAABWAbc88GSuPPHz+VznaUlpnk0bu0XGHnBRMm6j9oQDAABgsRTyAAAAAAPc1bc+kEfP+Ug+Un7VazZ1wx0zbq/TkxFj25AMAACApWm0OwAAAAAAS3b2r29O4+z3532LKeOnP3evjNv/QmU8AADAAOUKeQAAAIABqLOr5gcXX503/vkj2apxX6/5rB2Pyhqv+VhSSu+NAQAAGBAU8gAAAAADzOx5nfn2qefkwPs+m3UbTzbN5pVhKe8+LqOet0t7wgEAALDMFPIAAAAAA8hj0+fm2ON/kI9P/Z+MKnObZrOGjM/Ifc5N2fhlbUoHAADA8lDIAwAAAAwQdzwyPT85/v/lP+afmEapTbNpY56VsQdcnIx/ZluyAQAAsPwU8gAAAAADwHW3P5K7zzw8R+TyZJHHwk9d9xUZt+/Zycjx7QkHAADAClHIAwAAALTZhb/7R8b99IPZo/HHXrNpz9k143b9YTJkWBuSAQAAsDIU8gAAAABtUmvNsZddl1fdcGie17i713zmKz+dsW/8bFJK740BAAAY8BTyAAAAAG0wd0Fn/vf0i7PXXZ/Mho3Hm2bzMzT1Hd/P6Bfv1qZ0AAAA9AWFPAAAAECLPTFzXn4w6dh8dMpXs0aZ3TSb1TE2I/Y8K41nbd+mdAAAAPQVhTwAAABAC909eWYuOP4r+cycH2VI6WqaTRu1ccbuf3EycfP2hAMAAKBPKeQBAAAAWuTGuybn5lM+niPz42SRx8JPnfjijNvv/GT0Wu0JBwAAQJ9TyAMAAAC0wE/++K90XPzB7Nf4fa/Z1E3fnnG7T0qGjmhDMgAAAPqLQh4AAACgH9Vac9IVN+aF130oL27c2Ws+42Ufzbg3H5U0Gq0PBwAAQL9SyAMAAAD0k/mdXTn67Mvyvn8ckWc0HmuaLUhHFrz1fzPmZfu0KR0AAAD9TSEPAAAA0A+mzp6f751wYj7y2FEZ15jVNJvTGJ2hu5+eEc9+XZvSAQAA0AoKeQAAAIA+dv8Ts3LmcV/Lp2Z9L8NKZ9Ns+oj1M2a/C1PW3bpN6QAAAGgVhTwAAABAH/rrfU/k9yd9Mp/qOi8pzbMnxz8va+5/QbLGuu0JBwAAQEsp5AEAAAD6yC/+em9mn/+hHNT4Ta/Zk5vslDX3ODkZNqr1wQAAAGgLhTwAAADASqq15vSr/5Itrjokb2zc1ms+/UWHZM23/3fS6GhDOgAAANpFIQ8AAACwEhZ0duUHF1yRt938sWzWeKhp1pVG5r7xf7LGqw5pUzoAAADaSSEPAAAAsIJmzl2Qo086PYc89Pms1ZjeNJvbGJnGridl5FZvaVM6AAAA2k0hDwAAALACHpk2Jycd+60cOeN/M7zMb5rNGLZ2Ru93Qcr6L2hTOgAAAAYChTwAAADAcvr7g1Nz9QmfzWc6z0hK82zq2Odk3AEXJeM2bE84AAAABgyFPAAAAMByuPa2B/PYWYfmQ+XKXrMnN3xN1tz79GT4Gm1IBgAAwECjkAcAAABYRuf95pasf8XBeU/j5l6zqdvsnTV3+d+kw69bAAAA6OYnRAAAAICn0dVVc8wlV+UNf/xIntO4v3mWkjmv+VLG7Xh4UsoS9gAAAMBgpJAHAAAAWIo58zvznVPPyf73fjbrNJ5sms0rw5N3H59Rz3tne8IBAAAwoCnkAQAAAJZg8oy5Of747+VjT34tI8u8ptnMoeMzcu/z09h42zalAwAAYKBTyAMAAAAsxp2PTM9PJ30xn553UhqlNs2mjtks4w64KBm/SZvSAQAAsCpQyAMAAAAs4nd3PpJ/nX54Ds/lySKPhX9yvVdmzX3OSkau2ZZsAAAArDoU8gAAAAAL+fEN/8jYnxySDzT+1Gs29Tnvz5q7fj8ZMqwNyQAAAFjVKOQBAAAAktRac/xl1+VVN3w4z23c02s+41Wfzbg3fDopZTFbAwAAQG8KeQAAAGDQm7ugM98746Ls8a9PZv3GlKbZ/AxN1zt/kDEven+b0gEAALCqarQ7wEBQStmklPKtUsptpZSZpZQppZQbSymfLKWMWsl9b1VKOayUckop5Y+llPtLKXN6jvOvUso5pZR3lrJsf15fShlSSvlgKeXXpZTHSimzSyn/LKUcW0p57spkBQAAgMHoyVnz8u0f/CAf/NehWb80l/GzOsalse8lGa6MBwAAYAUM+ivkSylvT3J6krELfT0qybY9rwNLKTvXWu9cwUN8LskeS5g9q+f1viTXlFLeU2t9fClZJyb5aZKXLjLaNMnBSfYppRxWa520glkBAABgULn38Vm54Lgv5ZNzjsuQ0tU0mzrqGRl3wMXJWpu1JxwAAACrvEFdyJdSXpTknCQjk8xI8t9Jrur5vFuSg5JskeSyUsq2tdbpK3CYBUl+n+S6JH9L8nCSx5KMT7JlkkOSbJNkxySXllK2r7V2LbqTUkpHkovy7zL+wiTHJ5mS5OVJPp9knSTHllIeqLX+bAWyAgAAwKDxx3sezy0nfzRH1EuTRe5b9+TEl2TN/c9PRk1oTzgAAABWC4O6kE9ydLrL9wVJ3lRr/e1CsytLKXck+Xq6S/kjkxy1Asc4sNa6YAmzX5ZSjklybpJ3J9kuyduSXLKYdfdJsn3P8g9rrYcuNLuhlPKzJDel+0r/75ZStlrKcQEAAGBQ+9kf/5XGxYdkr8YNvWZPbvbOrLnbccnQEW1IBgAAwOpk0D5DvpTysiSv7vl4wiJl/FO+leTvPcsfLaUMXd7jPF0pXmvtTPKNhb569RJW/UTP+5Qkn1zMfu5M9xX+SbJ5kl2WLykAAACs/mqtOeUXN2T9i9+bnRZTxk9/2RFZc89TlPEAAAD0iUFbyCd510LLJy1uhZ5bx5/a83HNJK/tpywL3wq/10/8pZQtkmzV8/HcWuusJezn5IWWFfIAAACwkAWdXfnOWZfmdb/+QF7Y+GfzLB2Z/dbvZo23HpWUsvgdAAAAwHIazIX8U7d/n5nuW70vyTULLb+qn7LsttDybYuZb7/Q8jWLmSdJaq0PJ7m952N/ZQUAAIBVzvQ58/ONY4/PAf84JBs3HmuazW6MSfa8MCNftk+b0gEAALC6GszPkH/qivM7n+a28gsX5Fstca3lVEqZmOTZSQ5Msl/P15OTnLGY1bdeQp7FuS3dz7zfuJQyutY6czkybfQ0q6z31MLs2bMze/bsZd31CpkzZ85il4Hl41yClec8gpXnPIK+4VxaMQ9NnZMLTv52PjH7BxlaOptm04avn6F7np35E5+T+f38cy4Dg/MIVp7zCPqGcwlWnvOIvtYf/Weptfb5Tge6UsqIJE/9r3lZrfVtT7P+jCSjk/yu1rrdShz36iQ7LmE8OckutdbfLGa7s5O8v+fj2rXWyUs5xveTHNrzccta6z+WI98y/8swadKkTJw4cVlXBwAAgLa4b3rN0NsvyiGNi3vN7h+2aW7e4ojMHTqu9cEAAAAYcCZPnpwDDzzwqY8b11rvX9l9DtYr5NdYaHnGMqw/M92F/Jj+iZPvJvnyUor25cm78BXx/ZUXAAAABry/T1mQrf81Ke/ouL7X7I7RL80/Nj84nY3hbUgGAADAYDFYC/kRCy3PW4b15/a8j1zJ4+6X7mK/JFkzybZJPpTksCSbllIOrLU+spjtlifv3IWWlzfvxk8zXy/JjUmyww47ZKONnu4O9ytnzpw5ufbaa/PU8UaMGPE0WwCL41yClec8gpXnPIK+4Vxadudfd3Nef9OheWlH7xvHTXn+QdnozV/KRqXRhmS0m/MIVp7zCPqGcwlWnvOIvnb//St9QXwvg7WQX/ghEsOWYf2n/lx+pR4aUGu9a5Gvfl1KOSbJeUneluTGUsorF3Prg0XzLu0hGAv/af9y5X26Wy6UUv5veeTIkRk5cmX/PmHZjRgxoqXHg9WVcwlWnvMIVp7zCPqGc2nxOrtqjrngirz1b4dn08bDzbM0MveNX8uEVx3cpnQMNM4jWHnOI+gbziVYec4j+kJ//Ds0WP8UfPpCy8tyW/fRPe/Lcnv75VJrnZPuK+dnpfsK9a8vZrXlyTt6oeU+zwsAAAAD1ax5C/LNSSdnj5v371XGz2mMTOfuZ2eUMh4AAIAWGpSFfE8J/njPx6Xed72UMj7/Lrnv66c8k5Nc1/PxnaWUoYussvCV6093n/inbjtfF9kOAAAAVluPTp+TH37vf/KxBz6R8aX579OnD1snww+6IsOes1Ob0gEAADBYDcpCvsetPe+bl1KWduv+LRda/ns/5nms531UkomLzG5daHnLLN1T8/tqrTP7IhgAAAAMZP94aFouOvqIfGL6NzK8LGiaPTluq6xx2DUp6z+/TekAAAAYzAZzIf+bnvfRSV6ylPV2XGj5uiWutfI2XGh50VvN/2ah5R2zBKWU9ZJs0fOxP7MCAADAgHDdbQ/m1h/tlUMWnNlr9sSGr82aH/5lMnaDNiQDAACAwV3IX7zQ8n6LW6GU0kiyd8/HJ5Nc1R9BSikbJdmu5+M9tdaFnxmfWuvt+ffV+e8rpYxawq72XWj5oj4NCQAAAAPMRdffknLme7NLubrX7Mlt9s34/c9Pho9pfTAAAADoMWgL+VrrDUl+3fPxgFLKdotZ7cgkW/UsH11rnb/wsJTymlJK7XmdvOjGpZQtSimvW1qOUsq4JGcmGdbz1alLWPWbPe8Tknx9MfvZLMlnez7eGYU8AAAAq6murpofXXxltrl817yycUvzLCUzXvvlrPme7yQdS3tCHQAAAPS/wf6T6UfTfWv3kUmuKKX8V7qvgh+ZZLckB/esd3uSb63A/jdI8qtSyl/SfUX+TUkeTrIgyXpJXpXkgJ7lJLk5yf8sYV+nJNm/Z5tDe25Pf3ySJ5K8LMkXkoxN0pXk8FrrgiXsBwAAAFZZc+Z35nunnp197/1M1m5Ma5rNK8OT90zKmG3e0aZ0AAAA0GxQF/K11j+VUt6f5PR0l9n/tZjVbk+y86K3kV9OL+h5Lc1lSfartc5aQtbOUsq7kvw0yUuTvKfntbC5SQ6rtf5sJbICAADAgDRl5rxMOu7ofOTJr2dkmdc0mzF0Qkbtc0EaG724TekAAACgt0FdyCdJrfXSUsrz0321/M5JNkoyL923fT8vyfeXVJIvg+uS7JTkDUm27dn3uklGJZmW5K4kv0tyVq31umXIOrmU8sokByX5QLpvpz86yYNJfpXu2+rfspRdAAAAwCrprsdm5GfHfy6fmHtKGqU2zZ4cs3nWPPCiZM1ntCkdAAAALN6gL+STpNZ6T5KP97yWZ7urk5SlzOcnuaLn1Sd6bkV/TM8LAAAAVns3/PPR3H3aoflwruj1U/iU9V6VCfuelYwY155wAAAAsBQKeQAAAGDAuuzG2zPm0oPyvsafe82e2HL3TNj1e0nH0NYHAwAAgGWgkAcAAAAGnFprTvrZdXnF7z6crRv39JpP3/5zGf/6TyZliTeuAwAAgLZTyAMAAAADyrwFXfn+mRfmA//8RNZrPNE0m5+h6XzXj7LGC9/bpnQAAACw7BTyAAAAwIAxdfb8HDvphzl08lczusxtms0csmZG7HVOhm7yijalAwAAgOWjkAcAAAAGhPumzMpFxx2VI2cfl45Sm2ZPjtok4w64OGWtTduUDgAAAJafQh4AAABouz/f83huOfnwHF5/kizyWPgpE1+aCfufm4ya0J5wAAAAsIIU8gAAAEBb/eLP/0ouOjh7lBt7zZ7YfJdM2O3YZMjwNiQDAACAlaOQBwAAANqi1pozf3Vjtrn2kLyg8a9e82kvPzLj3/yFpJTFbA0AAAADn0IeAAAAaLkFnV055tyfZJfbjshGjcnNswzJ/J2PztiX7tmmdAAAANA3FPIAAABAS82YuyDHnDAphzxyVMaW2U2zWY0xGbbHWRm52Q5tSgcAAAB9RyEPAAAAtMzDU+fkrGO/mo/N/EGGls6m2dQRG2bsARelrP2cNqUDAACAvqWQBwAAAFri1geezA0nHJEjui5MFnks/JTxL8iEAy5IxqzdnnAAAADQDxTyAAAAQL+75tb7MvOcg7Jv+W2v2ZRnvjUT9jgxGTqyDckAAACg/yjkAQAAgH513rV/zqa/PCg7Nm7vNZv64kMz4W1fSRqNNiQDAACA/qWQBwAAAPpFV1fNsRf9PG/5y+F5ZuORpllnGpnzpm9m3CsPaFM6AAAA6H8KeQAAAKDPzZ7XmR+cfGoOeODzGd+Y0TSb0xiVxvtPzejnvLFN6QAAAKA1FPIAAABAn3ps+tycdtw38pFp/5vhZUHTbNqwdbPG/hemrLdNm9IBAABA6yjkAQAAgD5z5yPTcvXxn8zHF5ydlObZlHFbZ8IBFyZj129POAAAAGgxhTwAAADQJ357+4N59MwP5sBc02s2ZcPXZ8I+pyXDRrchGQAAALRHo90BAAAAgFXfj397S8rp78k7F1PGP/G8/TPhgPOU8QAAAAw6rpAHAAAAVlitNSdeelV2/MOh2bzxYNOsM43Mfu2XM37Hw9qUDgAAANpLIQ8AAACskLkLOvPD087KXnd/NhMb05pnZUTy3hMz5rk7tykdAAAAtJ9CHgAAAFhuT86alxOO+04OfeLrGVHmN82mD10ro/a5IB0bvahN6QAAAGBgUMgDAAAAy+Xux2bk58f/R46cd0pSmmdPjHl2xh94UbLmxu0JBwAAAAOIQh4AAABYZjfd9WjuOvXDOaT+otfs8fW2z1r7npWMGNuGZAAAADDwKOQBAACAZXL5Tbdn1I8PzHsbf+k1m7LlB7LWrt9NOoa2IRkAAAAMTAp5AAAAYKlqrTnt59flpdd/MFs17us1n/bqL2TC645MSlnM1gAAADB4KeQBAACAJZrf2ZVjzrog77/jE1m38WTTbF4Zls53HZuxL3h3e8IBAADAAKeQBwAAABZr2pz5OX7SD/PBx76a0WVu02zGkDUzYq9zM2yTl7cpHQAAAAx8CnkAAACglweenJ2Lf/TFfGz28ekotWn2xKhnZc0DL0qZ8Kw2pQMAAIBVg0IeAAAAaPK3e6fk5pMPy6FdlyWLPBb+8bVflrX2PzcZOb494QAAAGAVopAHAAAA/s+v/npXcsGB2b38odfs8c3fnbV2OzYZMqwNyQAAAGDVo5AHAAAAkiRnX3lDnnv1QXle4+5es6kv/2TWevPnklJ6bwgAAAAslkIeAAAABrnOrppjz7s077z1Y9mw8XjTbH6GZv7OR2fcS/doUzoAAABYdSnkAQAAYBCbOXdBjj3x+Bz08JeyRpndNJvVWCND9zgrozZ7dZvSAQAAwKpNIQ8AAACroelz5ufhqXMyc15nRg/ryHrjRmSNEUOb1nl02pycc+yXc/iMH2ZI6WqaTR2xYcYecHHK2lu0MjYAAACsVhTyAAAAsJqotea3/3o85113a2697baMqLMzMyPycJ2Q2Y3R2em562bPV2yS7TZdK/94eGpunPSxfKTzomSRx8I/PuFFWeuA85PRE9vzDwIAAACrCYU8AAAArAZuvv/JnHLWaXnttEvyjcYfMmTYv694X1Ab+XnXtjn9ljfmA3/bOs8a15FPzf5O9mr8rtd+Ht9k56y154nJ0BGtjA8AAACrJYU8AAAArOL++PurssZPD8s3yv1JR+/5kNKVnTtuyM4dN+TOrg0yb3ZHtm7c12u9J158WNZ625eTRqMFqQEAAGD1p5AHAACAVdhdv780W/70gIwqc5dp/c0bD/b6bkE6Mmenb2b8dvv3dTwAAAAY1BTyAAAAsIqqD/4p6/3swIxcxjJ+caZnZIbtfnrGPOcNfZgMAAAASBL3oAMAAIBVUa2Zfc5BGZk5K7yL+bUj7557VP445EV9GAwAAAB4ikIeAAAAVkGNe6/LqKl3rNQ+hpbOrFWm5/Tf3dNHqQAAAICFKeQBAABgVXTTSX2ymz07fpHLb3k40+fM75P9AQAAAP+mkAcAAIBVzJDO2Rl25+V9sq83N27MyK6ZeWTait/6HgAAAFg8hTwAAACsYkbOm5JSO/tkX0NKV9YtT2TG3L7ZHwAAAPBvCnkAAABYxXR09e3V7GMyO2OGd/TpPgEAAACFPAAAAKxyOhsj+nR/c8qorDu2b/cJAAAAKOQBAABglTN72ITU0jdXtM+vHdlmyy2zxoihfbI/AAAA4N8U8gAAALCKWdAxMl1bvKVP9vXzrm3z3ldt3Sf7AgAAAJop5AEAAGAVdOPYN/XJfq4Z+868YtMJfbIvAAAAoJlCHgAAAFYhtSZ/uu/xrPn7r6/0vm6vG2ef3fdIKaUPkgEAAACLGtLuAAAAAMCyWdDVlRvvvCcfnf6trNN4cqX2NasOz4y3fi8v3mjNPskGAAAA9KaQBwAAgFXAjLkLMunEY/O56f+TUWVu06wmWZ5r3GdnRB5566S8+OWv7dOMAAAAQDO3rAcAAIAB7qGps3Padz6Tj07+z15l/JRRz0p2OytZe6tl2tesNbfIiIMvz7Ne/vb+iAoAAAAsxBXyAAAAMIDdfN+U3HLSoflQ1097XQY/ee2XZ+L+5yQjxyfPeUty92+SG49P/v6TpHb+33q1MSQLttg5Q19+UEY9c/vEM+MBAACgJRTyAAAAMEBd87d/pev8A/L+8sdes0c3fXfW+cCxyZBh3V+Ukjzr1d2vOdOS6Q8lc2ckw8ekrLF+ho4Y2+L0AAAAgEIeAAAABqDzrrohW111ULZp3N1rdtPa78nW7/3+v8v4RY0Y2/0CAAAA2kohDwAAAANIZ1fNpPMvzdtv+Wg2aExpms3PkNyw0YGZvPYrs7XbzgMAAMCAp5AHAACAAWL2vM4cd9Jx2f/BL2WNMrtpNrMxNnXXUzL59ultSgcAAAAsr0a7AwAAAADJo9Pn5KSjv5hDH/yPXmX8EyM2zqgPX5mOZ27XpnQAAADAinCFPAAAALTZ7Q9PzY3HH54Pd16cLHIn+snjX5SJB16QjF4rmT17sdsDAAAAA5NCHgAAANrot7fdn+ln75898vtes8ee+fasvcekZOiINiQDAAAAVpZCHgAAANrkx9f9OZv8/IBs17iz12zKSw7P2jt/KWl42hwAAACsqhTyAAAA0GK11px08c/zhj8dlmc0HmuaLUhHZu/07UzYbt/2hAMAAAD6jEIeAAAAWmjO/M4cf+op2fvez2VcY1bTbFZjdDref3rWeM7r2pQOAAAA6EsKeQAAAGiRKTPn5czj/juHPHl0hpXOptmTw9bL2AMuTmPdrdqUDgAAAOhrCnkAAABogbsem5Frjvt4Dpt/TlKaZ5PHbZOJB12UjFmnPeEAAACAfqGQBwAAgH72h38+lEdOOyj75te9Zo9t9MasvfepybBRbUgGAAAA9KdGuwMAAADA6uxnN96arlN2yc6LKeMff/7BWXv/c5TxAAAAsJpyhTwAAAD0g1przvjZ1dnudx/KZo2HmmadaWTG6/47a+3wwTalAwAAAFpBIQ8AAAB9bH5nV44748zs9s/PZK3G9KbZ7DIyee9JGffct7QpHQAAANAqCnkAAADoQ1Nnz8+px38rBz/+zQwv85tnQ9fO6H0vyJANX9CmdAAAAEArKeQBAACgj9w/ZWZ+fuyn85G5pyWlefb4mC0y4aCLUsZt1J5wAAAAQMsp5AEAAKAP/OWex3LXyYfkgPqrXrNH19sx6+x3RjJ8jTYkAwAAANpFIQ8AAAAr6Vd/viMjLtov7yp/6zV7bKu9ss57v5N0+BEcAAAABhu/DQAAAIAVVGvNub+8Pi/89cF5TuP+pllXSqZu/8Ws/fojklKWsAcAAABgdaaQBwAAgBWwoLMrJ5xzQd79jyOzdmNq02xuGZ4F7zou41/wrvaEAwAAAAYEhTwAAAAsp5lzF+TESd/PgY9+NSPLvKbZ9I7xGbH3eRm9yUvblA4AAAAYKBTyAAAAsBwemTo7lxz7+Rw684Q0Sm2aPT5q00w46KKU8c9sTzgAAABgQFHIAwAAwDK69f4pufXED+egrp8lizwW/rG1X5G19z8nGblmW7IBAAAAA49CHgAAAJbBr2++K53n7Z/3lj/2mj22+a5Ze7cfJkOGtSEZAAAAMFC1vJAvpTSSbJ1k0yRrJOl4um1qraf2dy4AAABYkguvuSHP+dWBeW7jnl6zKa/4TNbe6TNJKYvZEgAAABjMWlbIl1JGJvl8koOSrLUcm9YkCnkAAABarqur5qQLL81b//bRrN+Y0jSbnyGZ+7YfZMK2u7UpHQAAADDQtaSQ7ynjr0zysvR6yh4AAAAMPHPmd2bSicdm3we/lDFlTtNsRsfYDN3j7IzZ9FVtSgcAAACsClp1hfwRSV7es3xzku8nuSnJlCRdLcoAAAAAy2TyjLk570dfygenH5MhpfnH1ikjNs6aB1ycxtqbtykdAAAAsKpoVSH//p7365O8rtY6r0XHBQAAgOVy5yPTcuPxh+VDC37c6x5vj41/cdY+6IJk1IT2hAMAAABWKa0q5DdL97Pgv66MBwAAYKD6/T/uz/Sz9svuuaHX7NFN3p519pyUDB3RhmQAAADAqqjRouM8VcLf26LjAQAAwHL5yfV/zogz3pE3LKaMf/wlH806+56mjAcAAACWS6uukL8t3c+QX69FxwMAAIBlUmvNqZf8PK+76bBs3HisabYgHZm107ey1nb7tSkdAAAAsCpr1RXyJ6f7yXu7tuh4AAAA8LTmLujMMSedlF3+uF+vMn5WY3QWfOD8jFXGAwAAACuoVYX88UmuTLJ3KWX3Fh0TAAAAlujJWfNywve+koPu+UTGlllNsyeGrZ/hh/wqI7Z4XZvSAQAAAKuDVt2yfuMkH0l3MX96KWWXJGem+1b2s5a2YZLUWj17HgAAgD5zz+QZufa4I/Lheed2389tIY+N3SZrH3xRMmad9oQDAAAAVhutKuTvTlJ7lkuS9/S8lkVN63ICAACwmvvjvx7Ow6cdkL3qb3rNHt1op6yz98nJsFGtDwYAAACsdlpZdJclLAMAAEBLXHHjrZnwk33z1vKPXrPHnn9I1nnX/ySNVj3dDQAAAFjdtaqQ369FxwEAAIBeaq056/Kr84rffjCbNh5umnWmkemv+++svcMH25QOAAAAWF21pJCvtZ7SiuMAAADAouZ3duWEM8/M++78dCY0ZjTNZpeRqbuenDW3fnOb0gEAAACrs5YU8qWUL/Ys/r7W+vNWHBMAAACmz5mfU4//Vg6c/I0MLwuaZlOHrp1R+16QoRu+oE3pAAAAgNVdq25Zf1SSmmSXFh0PAACAQe7BJ2bl58d+KofOOS0pzbPHxmyZiQddmDJuw/aEAwAAAAaFVhXyjyeZkOTeFh0PAACAQezm+ybnXycelP3qlb1mj663Y9bZ78xk+Jg2JAMAAAAGk0aLjnNnz/t6LToeAAAAg9Q1f7kj0ye9M+9YXBm/1d5Z56ALlfEAAABAS7SqkD8n3TcIfF+LjgcAAMAgdP4vf5P1L3hntis3N33flZInXv2lrPO+7yYdrbpZHAAAADDYtaqQ/2GSvyTZu5Syb4uOCQAAwCDR2VVz/NnnZcdffyBbNB5oms3N8Mze5eSMf/3HklIWvwMAAACAftCqywLWS3JgkhOSnFBK+UCSM5P8NckTSTqXtnGt1bPnAQAAWKxZ8xbkpEnfz/6PfDUjy7ym2bSOCRm5z/kZ/YyXtCkdAAAAMJi1qpC/O0ntWS5JXt/zWhY1rcsJAADAKuTRabNzyY8+nw/NPCGNUptmk0dtmrUOujhl/CZtSgcAAAAMdq0sussSlgEAAGC5/ePBJ3LzCR/MgZ2X9/op85GJ22XdA89JRoxrTzgAAACAtK6Q369FxwEAAGAQuP7Wu7LgnP3ynvKnXrNHNn9f1t39h0nH0DYkAwAAAPi3lhTytdZTWnEcAAAAVn+X/PrGbP6LA7J1455es8df/pms++bPJMWN2QAAAID282x2AAAAVgldXTWnXnRpdvrrR7N+Y0rTbF6GZu7bfpC1tn1/m9IBAAAA9KaQBwAAYMCbM78zJ550bPZ54KiMLnObZjMaYzN0z7OzxqavalM6AAAAgMVTyAMAADCgTZk5L+f/6KgcMu2H6Si1afb4iGdk/IEXpzFxszalAwAAAFiylhTypZQTV2LzWms9oM/CAAAAsMr41yNTc+Pxh+XgBZckizwW/tHxL846B12QjJrQnnAAAAAAT6NVV8jvm6Q+3UqLUXq2U8gDAAAMMn+444FMPWPfvD839Jo98sx3ZN09JyVDhrchGQAAAMCyaVUhf2+evpAfnWSt/LuEn5xkVj/nAgAAYAC6/Hd/zgY/3T+vb/yz1+yxF38s6779qKSU3hsCAAAADCAtKeRrrc9clvVKKeOT7J7kP5M8meQdtdZ/9F8yAAAABpJaa8689OfZ8aZDs1FjctNsfoZk1k7fztrb7dOmdAAAAADLp9HuAAurtT5Ra/1hklclWSfJz3pK+n5VStmklPKtUsptpZSZpZQppZQbSymfLKWMWsl9jyqlvLuUckzPPp8opcwvpTxeSvltKeWoUsp6y7Cfq0spdVleK5MXAACgXeYt6MqxJ5+Ut9+0XzYqzWX8zMaYLPjA+RmnjAcAAABWIa26Zf1yqbX+o5Ty3SRfSHJkks/317FKKW9PcnqSsQt9PSrJtj2vA0spO9da71yBfT8/yXVJxixmPCHJK3peR5RSDq61nrO8xwAAAFgdTJ09P2cf+1854ImjM7R0Ns2mDNsg4w64KB3rbtmmdAAAAAArZkAW8j1+me5C/t3pp0K+lPKiJOckGZlkRpL/TnJVz+fdkhyUZIskl5VStq21Tl/OQ4zNv8v465L8JMkfkjyeZO10/7Md1LPeGaWUabXWnz3NPv+QZL/lzAEAADBg3ff4jFx77MdyyLzzkkUeC//o2OdlnYMvSsas3Z5wAAAAACthIBfyM3ren9GPxzg63eX7giRvqrX+dqHZlaWUO5J8Pd2l/JFJjlrO/XclOTfJl2qtty5mfkUp5WdJLkrSkeR7pZRn11qXdtv5mbXWm5czBwAAwID0l7sezsOn7p896nW9Zo9suFPW3feUZOjINiQDAAAAWHkD6hnyi3hRz/v8/th5KeVlSV7d8/GERcr4p3wryd97lj9aShm6PMeotV5fa33/Esr4p9b5cZILez5uln//cwMAAKzWfnXTrVlw8juy02LK+Eefd0jWPeBsZTwAAACwShuQhXwp5Vnpvhq9JvlzPx3mXQstn7S4FWqtXUlO7fm4ZpLX9lOWqxZa3qyfjgEAADAg1FpzzuVXZdMfvysvKf9omnWmkSde97Ws856vJ40B+SMrAAAAwDJryS3rSyl7L8NqjSTjk2yb5J1JRqW7kP9RP8Xavud9ZpKblrLeNQstvyrJFf2QZfhCy539sH8AAIABYUFnV04668y8945PZ3xjRtNsdhmVrl1Pyvit39ymdAAAAAB9q1XPkD853eX6sio979+ttZ7T93GSJFv1vN9Za12wlPVuW8w2fW3HhZb/vsS1um1ZSvl9kuckGZFkcrr/oOCCJGfVWlf4Fv+llI2eZpX1nlqYPXt2Zs+evaKHWiZz5sxZ7DKwfJxLsPKcR7DynEckycy5C3Leqd/L/o9/K8NL849hTw5ZO0P2OCsd623T7z9rrMqcS7DynEew8pxH0DecS7DynEf0tf74nUSpdXl68hU8SCldy7H6k0muTfLDWmt/XI2eUsqIJE/9r3lZrfVtT7P+jCSjk/yu1rpdH2d5QboL9Y4kf6u1Pn8J612d5uJ+cW5N8t5a69OV+kvKssz/MkyaNCkTJ05ckcMAAACD0JNzaubedmkOruf3mt03ZJPc8pyPZ+6w8W1IBgAAANBt8uTJOfDAA5/6uHGt9f6V3WerrpB/1jKs05Vkeq31yX7OkiRrLLQ8Y4lr/dvMdBfyY/oyRClleJJJ6S7jk+RzS1m9K8mvkvw0yV+SPJ7uf44XJzkk3Vfvb53kqlLKy2qt9/ZlVgAAgBX10IwFWf/2k/POcm2v2T9GvDB3bPHhdHaMaEMyAAAAgP7VkkK+1npPK46zHBb+Tc+8ZVh/bs/7yD7O8f0k2/Ysn1JrvXQp6757CX+s8OtSyg+THJ9knyTrJvlOknevQJ6Nn2a+XpIbk2SHHXbIRhs93R3uV86cOXNy7bXdv7DbYYcdMmKEX9DBinAuwcpzHsHKcx4NXtfd8q+88NKD84pyc6/Zg1vsmWe882t5RqNjMVuyOM4lWHnOI1h5ziPoG84lWHnOI/ra/fev9AXxvbTqCvmBZuGHSAxbhvWH97z32UMDSimfTfLU/Q5uTHLo0tZf2p0Daq3zSykHJnlFup8tv0spZcNa6wPLk+npbrlQSvm/5ZEjR2bkyL7++4QlGzFiREuPB6sr5xKsPOcRrDzn0eBx0ZXXZZurD8qzG80/mnSl5IlXfykbvP6jbUq2enAuwcpzHsHKcx5B33AuwcpzHtEX+uPfoUaf73ExSil3lVL+WUrZfDm2eUYp5V+llH/2Q6TpCy0vy23oR/e8L8vt7Z9WKeWQJP/V8/G2JG+ttc5cmX3WWhckOWGhr57uefMAAAD9oqur5qRzzsv21+zWq4yfU4Zn1i6nZi1lPAAAADAItOoK+U2S1Czb1ehPGZrkmT3b9ala65xSyuNJ1kqy1Puul1LG59+F/H0re+xSyu5Jftjz8Z4kb6y1Tl7Z/fa4daHlDftonwAAAMts9rzOnHLCd7Pvw1/NiDK/aTa1Y0JG7nt+xmz8kjalAwAAAGitwXrL+qS7vH51ks1LKUN6rjBfnC0XWv77yhywlPKOJKem+84EDyV5/dPdJn459fkfLwAAACyrx6bNyaXHfi4HzzghjdL848ljozbLxIMvTlnzGW1KBwAAANB6Lbll/Qoa1/M+q5/2/5ue99FJlnZ5xsK3fr9uRQ9WSnl9knPT/UcQj6f7yvi+vh3/1gstP9jH+wYAAFiiOx56Ir85eu/sP3NSrzL+4YnbZe3Dr1LGAwAAAIPOQC7k9+x5v6ef9n/xQsv7LW6FUkojyd49H59MctWKHKiU8sokP04yPMnUJDvVWm9ZkX0t5RhDkuy/0FfX9uX+AQAAluR3f787Dx+7S3bp/Hmv2cObvz/rfejSZMS4xWwJAAAAsHrrl1vWl1KuXMLopFLKzKfZfHiSTZOsk+5bsF/Rl9meUmu9oZTy63Tftv6AUsoptdbfLrLakUm26lk+utba9ADEUspr8u+S/pRa676LHqeU8sIkl6X7SvyZSXautd60PFlLKa9N8qda65NLmA9NcvxCWS+tta708+4BAACezmW/+UM2vWK/bNW4t9ds8is+m/V2+nRSShuSAQAAALRffz1D/jXpLtMX/q1LSfLS5dzPv5L8dx9lWpyPpvs29COTXFFK+a90F+wjk+yW5OCe9W5P8q3l3XkpZbMkP0+yZs9Xn08ytZSyzVI2e7TW+ugi3+2T5JJSyiVJrk7yjyTTkoxJ9+32D86/b1f/aM8/FwAAQL+pteb0iy7NG/9yeNZrPNE0m5ehmf22YzJx213blA4AAABgYOivQv7adBfyT9mx5/NN6b5KfElqkjlJHkpyfZKza61Pd0X9Cqu1/qmU8v4kpycZm+S/FrPa7em+qn36Chzi1em+0v8p/7sM23wpyVGL+X5Mkg/0vJbkb0l2q7XetawBAQAAltfcBZ05+eRjs+d9R2V0mds0m94Yl2F7npNxm27XpnQAAAAAA0e/FPK11tcs/LmU0tWzuG+t9db+OOaKqrVeWkp5frqvKt85yUZJ5iW5M8l5Sb5fa53VxohJ8rUkf06yXbqvhF87yYQkc5M8kuQPSc5PclGttbNNGQEAgEHgiZnzcuGP/l8OnHZMOkptmk0e8YyMP/DH6Zi4aZvSAQAAAAws/XWF/KJOTffV70883YrtUGu9J8nHe17Ls93Vab4t/6Lzk5OcvBLRntrP35P8Pcl3VnZfAAAAK+ruR6flxuMPzQHzL+n1k9DD47fNegedl4ya0J5wAAAAAANQSwr5Wuu+rTgOAAAA/eNPd96fJ07fL7vmhl6zh5/5rqy353HJkOFtSAYAAAAwcDXadeBSSqOUMrGU8oxSSke7cgAAALB0V/z+Lxly2tvzusWU8Y++5Iist8/JyngAAACAxWjVLeuTJD3F+749r5cmGZruW9k/P8mtC633tiQ7JJlaa/1qKzMCAADQrdaasy+7Iq++8cPZqExums3PkMzc6X+zznZ7tykdAAAAwMDXskK+lLJOkouTvDxLee56j7uTXJKkllIuq7X+uV/DAQAA0GR+Z1dOPu3kvP+u/8jYMrtpNrMxJo3dzsiaW7ymPeEAAAAAVhEtuWV9z5XxlyZ5RbqviD83yWFLWr/WenOS3/d83KXfAwIAAPB/ps2Zn1O+/5/Z965P9CrjpwzbIMMP+VVGKuMBAAAAnlarrpDfJ923qJ+f5B211p8nSSnl+0vZ5pJ0X02/ff/HAwAAIEkeeGJmfv2jj+bAuef1urfZI2Ofl3UOvihlzNrtCQcAAACwimlVIb97uq+MP/apMn4Z/Knn/Tn9EwkAAICF3Xz3I3nwlP2yW72u1+yhDd+c9fc9ORk6svXBAAAAAFZRrSrkn9/zfslybPNoz/tafZwFAACARVz1x1sz7sf75E3l9l6zR573way/y38njZY89QwAAABgtdGqQn7NnvfHl2Objp73zr6NAgAAwMLO//lV2fb6Q/LM8kjT951pZOrrvpZ1dzi4TckAAAAAVm2turxhSs/7xsuxzbN73h/r4ywAAAAk6eyqOemM0/OG6/foVcbPKqMyZ9dzMkEZDwAAALDCWlXI39Lz/tLl2Ob96X7u/I19HwcAAGBwmzl3QU485mv5wO0fzZplZtPsiSHrZOhBV2T0c9/UpnQAAAAAq4dWFfIXJylJDiuljH+6lUsp703y9p6PF/RjLgAAgEHnkamzc9F3Ds9Bj/13hpcFTbNHx2yZNQ+/JkM3eF6b0gEAAACsPlpVyB+f5N4kY5NcUUrZenErlVLWKaV8NcmZ6b46/uYk57YoIwAAwGrvtgcm5w9H7549Z5/Ra/bQeq/NOodfmTJ2gzYkAwAAAFj9DGnFQWqtc0sp70xydZKXJPlbKeUfC61yeillTJJN030lfUnyeJL31FprKzICAACs7q6/+c4MOW/v7Fxu6TV7eMt9s/77vp00OtqQDAAAAGD11Kor5FNr/Uu6nyH/23QX7lsuNH5Bks178pQkNyR5ea31zlblAwAAWJ1dcvV1Wee8t+dli5TxnWlk8vb/mfV2O1oZDwAAANDHWnKF/FN6CvZXlVK2T/KOJNsmWSdJR7qviP9Tkktqrb9oZS4AAIDVVVdXzennn5+33vLxTCzTmmZzMjzzdpmUiS94R5vSAQAAAKzeWlLIl1Ke0bM4o9Y6pdb6myS/acWxAQAABqs58ztz+gnfzZ4PfTUjyvym2dSOCRmxz/kZ+4yXtCkdAAAAwOqvVbesvzvJXUl2a9HxAAAABrXHp8/JOd/5RA58+KheZfyjozbPGoddk+HKeAAAAIB+1apb1s9OMiLJjS06HgAAwKD1z0eezM3HH5x9Fvy81+yhia/M+geek4wY24ZkAAAAAINLqwr5B5Jslu5nxQMAANBPbrzt7sw7e++8M3/pNXto892y/u7fTzqGtiEZAAAAwODTqlvWX9Hzvn2LjgcAADDoXH79jVnjrLflVYsp4x99xeey/h4/UsYDAAAAtFCrCvmj033b+k+UUjZs0TEBAAAGhVprzrj4krzo5+/NluW+ptncDMvUt03KOm/+VFJKmxICAAAADE4tKeRrrXck+UCSUUl+V0r5QCllWCuODQAAsDqbt6ArJ5x4TN71pwOzbnmyaTatsWbqPpdk3La7ticcAAAAwCDXkmfIl1Ku7Fl8LMmzkpyW5IRSyh1JnkjSuZTNa6319f0cEQAAYJUzddb8XPijL2S/qT9KR6lNs8dGbJIJB/04HWs9q03pAAAAAGhJIZ/kNUkW/u1QSTI8yTZL2ab2rFeXsg4AAMCgdO9j03PjcR/KfvMv7f7JaSEPjd826x98fjJyfHvCAQAAAJCkdYX8tVGsAwAA9Ik///OBPHna3nlP/tBr9uAm78oGex2fDPGUMAAAAIB2a0khX2t9TSuOAwAAsLr71Q1/ybqX7ZvXlH/1mj3y4o9ng7d/MSllMVsCAAAA0GqtukIeAACAlVBrzXk/vSKvvOHD2ahMbprNy5DM2Ok7WXe7vdqUDgAAAIDFUcgDAAAMcAs6u3LqGSflvf/8XMaW2U2zGY010tjtjEzYYsc2pQMAAABgSRTyAAAAA9j0OfNz3nFfzd6PH50hpatp9viwDTL2gIszdN3ntCkdAAAAAEujkAcAABigHnxiZn79o8Oz/9zzk0UeC//Q2BdkvYMvSBmzdnvCAQAAAPC0FPIAAAAD0C33PJIHT9k37++6vtfswQ3fkg32PTkZOqL1wQAAAABYZgp5AACAAebXf/57xly0d95Ybu81e+j5H84G7/pq0mi0IRkAAAAAy0MhDwAAMIBc/Mur86JfH5RNyqNN33emkSdf97Wsv8PBbUoGAAAAwPJSyAMAAAwAnV01p59zRt5526eyZpnZNJtVRqVr11Oy1tZvalM6AAAAAFaEQh4AAKDNZs1bkLOP/0b2fPQbGVY6m2ZThqybMftfkGEbPK9N6QAAAABYUQp5AACANnp02uz86piPZ//ZZyalefbI6K2yziEXpYxdvz3hAAAAAFgpCnkAAIA2uePByblz0gHZvevqXrMH1n1dNjzg9GTY6NYHAwAAAKBPtK2QL6Wsm2SbJBN6vpqS5OZa6yPtygQAANAqv7/lzjTO2ytvya29Zg9uuV82fN+3kkZHG5IBAAAA0FdaWsiXUkqSg5MclmTrJaxza5LvJTm+1lpbGA8AAKAlLrv6umx51YHZrDzY9H1nGpny6i9lg9cf3qZkAAAAAPSllhXypZTxSS5J8sqnvlrCqlsnOSbJXqWUt9dan2xBPAAAgH7X1VVz1oXn581/OyJrlelNs9kZkXm7TMraL3h7m9IBAAAA0NdaUsj3XBn/4ySv6vnq8STnJvl9kod7vlsvycuSvC/JxHQX9z9OsmMrMgIAAPSnOfM7c8ZJ382eD3w1w8v8ptmTHWtl5D7nZ9wzXtymdAAAAAD0h1ZdIf+BJNsnqUnOTPLhWuv0xax3ainlM0l+kGSvJNuXUnavtZ7VopwAAAB9bsqMufnpjz6dA2ac1OteYY+O3CwTD/5xGuM3bk84AAAAAPpNo0XH+UDP+zW11r2WUMYnSWqtM2qt+yS5Jt2/qtqzFQEBAAD6w12PPJnrvrNH9pxxUq/ZAxNflXU+erUyHgAAAGA11apC/sXpvjr++8uxzfd63l/U93EAAAD63x9vvzsPHfOOvH3BL3rNHtx8t2z4oUuSEWPbkAwAAACAVmjVLesn9LzftRzbPLXuhKWuBQAAMABdcf2N2eTn++XF5b5es0de/rls8OZPJqUsZksAAAAAVhetKuSnJlkryQZJ/rSM26zf8z6tXxIBAAD0g1przrv0J3nNTYdlnfJk02xuhmXW247Jutu+tz3hAAAAAGipVt2y/uae9/2WY5un1r15qWsBAAAMEPM7u3LKST/M2246oFcZP62xZrr2viTjlfEAAAAAg0arCvnzk5Qku5RSjipl6fdlLKV8Icl70v3c+fNakA8AAGClTJ09P2d99z+y9z2fy6gyt2n22PBNMurDV2fkptu1KR0AAAAA7dCqW9Yfn+QjSZ6T5AtJ3l1KOTnJ75M8mu7ifd0kL0+yT5Jtera7rWdbAACAAeu+ydPzh+M+mL3n/aT7T5EX8uD4l2b9g85LGTW+PeEAAAAAaJuWFPK11vmllLck+VWSZyV5bpJvLGWTkuRfSd5Sa13QgogAAAAr5G//eiBPnLZ3dql/6DV7YJNdsuFexyVDhrUhGQAAAADt1qpb1qfWeneS5yf5VpKp6S7dF/eamuSbSV5Ya723VfkAAACW19V/+Esap+ycHRZTxj/04iOz4b4nKeMBAAAABrFW3bI+SVJrnZnkk6WUzyV5SbpvTT+hZzwlyc1Jbqq1zmtlLgAAgOVRa82Fl1+RV/zuQ9mwPN40m5chmb7T0Vl/uz3blA4AAACAgaKlhfxTegr33/a8AAAAVhkLOrty5pknZZc7P5c1yuym2fTGGim7nZm1ttihTekAAAAAGEjaUsgDAACsimbOXZDzj/ty9pj83QwpXU2zx4ZtmHEHXJxh627RpnQAAAAADDQKeQAAgGXw8JOz8utjDss+cy9ISvPswbEvyPqHXJgyemJ7wgEAAAAwIPVpIV9K2fup5VrrqYv7fkUsvC8AAIBW+/u9j+Shk/fJrl29n7p1/4ZvzUb7npQMHdGGZAAAAAAMZH19hfzJSWrP69TFfL8iFt0XAABAy1z3l79n9IV75XXljl6zB5//4Wz0rq8mjUYbkgEAAAAw0PXHLevLcn4PAAAwIF36q6vzgmsPyjPKo03fL0hHnnjd17PBDge2KRkAAAAAq4K+LuSftZzfAwAADDhdXTVnnXtG3vb3T2ZcmdU0m1lGpfO9p2bt576xTekAAAAAWFX0aSFfa71neb4HAAAYaObM78zZx389H3jkGxlWOptmjw9ZN2P2vzCjN9imTekAAAAAWJX0xy3rAQAAVkmTp8/JL485IvvOOrPXQ7ceGr111j3kojTGrteecAAAAACschTyAAAASe58aHL+OWn/7NZ5Ta/Z/eu+LhsdcEYybFQbkgEAAACwqlLIAwAAg94Nt96Zxrl7Zqf8vdfsgS0PyEbv+0bS6GhDMgAAAABWZX1ayJdSTuzL/fWotdYD+mG/AAAAufza67PFr/bPpuWhpu8708jkV385G77+sDYlAwAAAGBV19dXyO+bpPbh/krP/hTyAABAn6q15pwLz88b/3pE1irTm2azMyJzd5mUdV/w9jalAwAAAGB10NeF/L3p20IeAACgz81d0JmzTzo6u93/Xxle5jfNnuhYKyP2vSBrbvyiNqUDAAAAYHXRp4V8rfWZfbk/AACAvvbkzLm57Eefzj7TT+q+J9dCHh757Kx9yMXpWHOj9oQDAAAAYLXS11fIAwAADFj3PPpkbj7ugOyx4Je9ZvdP3D4bHXR2MnyNNiQDAAAAYHWkkAcAAAaFP91xT+aesWd2zl97ze7f/APZaPfvJR1+RAIAAACg7/htEwAAsNq78nd/yEY/2ycvKvc3fd+Vkkde/vls9OYjk1KWsDUAAAAArJiWF/KllAlJ9kvyhiTbJJnQM5qS5OYkv0xyUq11SquzAQAAq5daay649CfZ4abDsk55smk2J8My623HZP1t39uecAAAAACs9lpayJdSDknyzSSjnvpqofGGSTZI8qYkR5VSjqy1HtfKfAAAwOpjfmdXzjr1mOx691EZWeY1zaY21syQPc/NhE1f3qZ0AAAAAAwGLSvkSymfSfLV/LuEn5rkT0ke7vm8XpIXJRmXZHSSY0opa9Zav96qjAAAwOph+ux5+fGxX8ieTxybRqlNs0eHPzMTDv5xhqz1zPaEAwAAAGDQaEkhX0rZJsmX013GP5Tkk0nOq7XOX2S9IUl2TfKNdF8t/5VSymW11ltakRMAAFj1PThlem780Qez57yfNN+TK8kDa740GxxyXsrI8e0JBwAAAMCg0mjRcQ5L0pHksSTb1VrPXLSMT5Ja64Ja61lJtkvyaM82h7UoIwAAsIq75a4H86/vvTPvnPeTXrP7N9klGx72U2U8AAAAAC3TqkL+dUlqkv+utd77dCvXWu9L8rV0X8/y+n7OBgAArAauvemvKSe/JdvXm3rNHnzxJ7LRviclQ4a1IRkAAAAAg1WrniG/Yc/79cuxzXU97xv0cRYAAGA18+PLL89Lf/uhbFCmNH0/L0MybafvZoPt9mhTMgAAAAAGs1YV8p0rcLyOnveuPs4CAACsJjq7as4688S8647/yJgyp2k2vayRsvtZmbjFq9uUDgAAAIDBrlW3rH/qNvXLc/v5p9Z92lvcAwAAg8+seQtyxg+Oym53fKJXGf/Y0A0z/ENXZYwyHgAAAIA2alUh/4t0Pw/+E6WU5z3dyqWUbZJ8Mt3Pnb+in7MBAACrmEenzsrl3z4oez/+nQwpzTfVemDsCzLxY9dm2DrPblM6AAAAAOjWqkL+O0nmJhmT5DellE+UUtZadKVSylqllE8k+XWSNXq2+U6LMgIAAKuA2+9/NLcc/e68e86FvWb3bfjWbPiRK1JGT2xDMgAAAABo1pJnyNda7ymlHJLkpHSX8l9L8j+llLuSPJruK+HXTfKsdF9JX3q+O6TW6pb1AABAkuR3f/17Rl6wV15b7ug1u/95h2bjXb6SNFr1d8cAAAAAsHQtKeSTpNZ6ainl8STHJtkg3aX7Zkk27VmlLLT6g0kOrrX+tFX5AACAge2nV16d511zYDYujzV9vyAdmfK6b2SjHQ5oUzIAAAAAWLyWFfJJUmu9rJTyzCS7JHlDkm2STOgZT0lyc5JfJrm41jq/ldkAAICBqaur5tzzzshbbv1kxpVZTbOZZXQW7Hpq1tn6DW1KBwAAAABL1qeFfCnl+T2Lt9Va5y1unVrrgiTn9bwAAACWaM78zpw36WvZ7eFvZmjpbJpNHrJe1tj/woze4LltSgcAAAAAS9fXV8j/OUlXkucnufWpL0spX+xZ/GGtdXIfHxMAAFgNPT59Tq780cey18yzmh9wleTB0c/NeodclMbYddsTDgAAAACWQX/csr4s5rujktQk5ydRyAMAAEv1r4cfzz+P3ze7dl7ba3bfum/Ixgeclgwb1YZkAAAAALDs+rqQn9+zz5F9vF8AAGCQ+OPf/5l6zh55Y/7ea3bfVgdm412/kTQabUgGAAAAAMunr3+L9UjP+0v6eL8AAMAg8ItfX5/xZ781L1mkjO9MIw9t/9Vs/P5vKeMBAAAAWGX09RXy1yb5QJKvlVI2S3J7uq+af8o7SynbLu9Oa62n9lE+AACgxabPmZ+7H5uZe6YnwzuSGXMXZOQi99Sqteb8i87P6//ysUwoM5pmszIyc3c5Ieu/YOcWpgYAAACAldfXhfx/J9klybgkn1hkVpJ8ZQX2WZMo5AEAYBVSa81v//V4TvvtPbn+1ruydn08ozMnMzMiP/zrz/OqbZ6VPV+xSbbbdK3M76w556Tv5H33/1eGl/lN+5nSMTEj97kg45/xwvb8gwAAAADASujTQr7WekspZYd0F/M7JBm2yCqlL48HAAAMPDc/MDUfP+dPWWvyDdmr4xf53tA/ZEjp+r/5gtrIz2/bNt+75Y35wvhts0/XRdl79qm9flp4aOSzs84hP07Hmhu2+J8AAAAAAPpGX18hn1rrTUneVEoZkmRikhFJ/pXuK913SnJHXx8TAAAYGH59x2P5zmnn5Xv5QZ4z7P7FrjOkdGXnjhuyc8cNeXLGqKxZZvVa5761ts/GB5+dDF+jvyMDAAAAQL/p80L+KbXWBUkeTpJS/u9Slwdrrff01zEBAID2ufmBqTn5tJNyavlmRpe5y7TN4sr4ezfbI8/4wHeTjn77cQUAAAAAWqJVv+H6Us/7oy06HgAA0EK11vzwrAvy3eUo4xfVVZOHXvH5POPNn0iKp10BAAAAsOprVSF/Vc/7zGXdoJQyIsnLkqTWem1/hAIAAPrGb/85OR+d9q2MbqxYGZ8kD9W1cu+z98uGyngAAAAAVhONFh3n6iRXJnnWcmyz4ULbAQAAA9gNV/04z2ks/pnxy2rDxuP5/VWX9FEiAAAAAGi/VhXySbKil7m4PAYAAAaw6XPmZ4v7zu2TfT373rMzfc78PtkXAAAAALRbKwv55fVUts62pgAAAJbq0ccey5vKjX2yr50aN+bRyY/1yb4AAAAAoN0GciG/Sc/71LamAAAAlmreE/dnSOnqk30NKV2Z98QDfbIvAAAAAGi3If2x01LKM5YwWr+UMuNpNh+eZLMkX05Sk9zSl9kAAIC+NTpz+nR/YzK7T/cHAAAAAO3SL4V8krsW811JcsUK7OvUlcwCAAD0o7UmTOjT/U0Yv1af7g8AAAAA2qW/CvmynN8vzpwk3621ntgHeQAAgH4yeuIz0plGOrLyt63vTEdGT9y4D1IBAAAAQPv1VyG/3yKfT0r37ee/kGRpD4Ss6S7iH0ryp1rr093eHgAAaLcRY/P4xm/KOvddvtK7emKTnTJxxNg+CAUAAAAA7dcvhXyt9ZSFP5dSTupZvLjWemt/HBMAAGiPx6bNzj8emZF1+mBfa73mQ32wFwAAAAAYGPrrCvlFvTbdV78v7tnyAADAKurO+x/NAyfumR27fr/S+5oz/jkZ8cxX90EqAAAAABgYGi06zklJTk5yQIuOBwAA9LMb/3ZbZk16S5+U8Z1DRmXErsclpfRBMgAAAAAYGFp1hfxGSTqS/LlFxwMAAPrR5VddnW2uPiAblclN33emkTSGpKNr3jLvq3PIqHTsfkaywQv7OCUAAAAAtFerrpB/uOd9douOBwAA9INaa84797S88urde5XxM8roTN/1vHQc+IvUtbdatv2ts3U69v9Zstnr+iMuAAAAALRVq66Q/32Sdyd5bpKbWnRMAACgD81d0JkLJn0tuz70zQwtnU2zyUPWy5j9L8qYDbZOkpQP/za5+zfJjcen/v0nKfXf69fGkJQt35a89MCUZ27vNvUAAAAArLZaVcgfk+Q9SY4opZxVa53fouMCAAB94IkZc3PlMYfnAzPPThbpzx8YvXXWP+TiNMau++8vS0me9erkWa9OmTMtcx67K7//9a+yoGNEXrnTezNyzXVa+w8AAAAAAG3QklvW11qvTPLfSV6Q5CellI1bcVwAAGDl3fPw4/njd96b98w8u/ds3Tdkw4/+qrmMX9SIsakTt8iTozfLjBEbJsPX6Me0AAAAADBwtOQK+VLKF5PMTfK3JG9M8q9SynVJ/prkiSSdS9k8tdb/7PeQAABAL3++7c50nb1HXp/bes3u3fKAbPK+byaNlvydLwAAAACsclp1y/qjktSe5ZqkI8mre17Lol8L+VLKJkkOT7Jzko3T/ccD/0xybpIf1FpnrcS+RyV5c7r/EGHbJJsnGZNkWpLbk/w8yY9qrQ8vx/4OS7Jrks2SDE9yX5LLkny31nrPimYFAICFXXnd9dn0iv3yzNL8f1UXpJFHt/9ynvGGw9qUDAAAAABWDa0q5JNeT5rs9bktSilvT3J6krELfT0q3eX5tkkOLKXsXGu9cwX2/fwk16W7gF/UhCSv6HkdUUo5uNZ6ztPsb/MkP03y7EVGz+l5HVhK2aPW+pPlzQoAAE+pteaiH5+f1/zpY5lQZjTNZmVk5uxyQjZ4wc5tSgcAAAAAq45WPUO+sTKv/spVSnlRknPSXcbPSPK5JK9M8vokx/estkWSy0opK/Kgy7H5dxl/XZLPpvtK+Rcn2SnJsUm6etY7o5TylqVkXSPdV8E/VcYf35PzlT25Z/Ts55xSygtXICsAAGR+Z1fOOuF/s/OfPtirjJ/SMTHZ//JMUMYDAAAAwDJp5RXyA9HRSUYmWZDkTbXW3y40u7KUckeSr6e7lD8y3bfeXx5d6b7t/ZdqrbcuZn5FKeVnSS5K9238v1dKeXattS5m3U/25EiST9Vav7HQ7LellKuTXJPuq/u/k+Q1y5kVAIBBbtrsebn8mE/lA9NO6nU/qwdHbpF1Dr4oQ8Zv1J5wAAAAALAKaskV8gNRKeVl+fcz7E9YpIx/yreS/L1n+aOllKHLc4xa6/W11vcvoYx/ap0fJ7mw5+NmSV60mKxD0/2M+/Tk+dbijpXkhJ6PO5ZSXro8WQEAGNzunzw1139797xv2km9Zveu9eqs/7ErlfEAAAAAsJwGbSGf5F0LLff+rWOSWmtXklN7Pq6Z5LX9lOWqhZY3W8z8tUnG9Syf0pNrcU5eaHmXPsgFAMAgcPM/78mDP9g5b57/y16zezb7QJ7x4YtThq/IE5wAAAAAYHBreSFfSplQSjmylPKzUsp9pZSZPa/7er47spQyoQVRtu95n5nkpqWsd81Cy6/qpyzDF1ruXMx8+4WWr1nM/Cl/SDKrZ7m/sgIAsBq59oY/ZMSpb83L6t+avu9KyQMv/2I22fOHScdgf9IVAAAAAKyYlv5mrZRySJJvpvs550nzkyk3TLJBkjclOaqUcmSt9bh+jLNVz/udtdYFS1nvtsVs09d2XGj574uZb73Q8m2LmSdJaq0LSil3Jnl++i8rAACriUsuuzTb3XBo1i5Tm76fk2GZsfOPsuFL39OmZAAAAACwemhZIV9K+UySr+bfJfzUJH9K8nDP5/XS/fz0cUlGJzmmlLJmrfXr/ZBlRJKJPR/vX9q6tdYnSikzezJt3A9ZXpBk556Pf6u1Lq6Qf+phnTNrrU8+zS7vS3chv3YpZXitde5yZHm6h4Ku99TC7NmzM3v27GXd9QqZM2fOYpeB5eNcgpXnPGJ109lV8+NzT8i77/7PjCzzmmZPNsZnwa6nZfQzt+3T/7/nPIK+4VyClec8gpXnPIK+4VyClec8oq/1R/9Zaq19vtNeByllm3SX7x1JHkryySTn1VrnL7LekCS7JvlGuq+WX5DkRbXWW/o4z9pJHu35eE6tdbenWf+RJOskubnW+rw+zDE8yW+SbNvz1TtqrZcuZr1b0n2V/CO11vUWnS+y7jlJ3tfzcWKt9fHlyLPM/zJMmjQpEydOfPoVAeD/s3ffYXqVdf743/fMJDPpEIpUE3oJXQRDUwQLoqKCFMFGx13LWra5uz+3+d3VddeySxMFRUVFmggWVAQiQUAQQm+h15CQ3mbm/P7IRPJk0ueZeWYyr9d1Pddzzv25z30+0ZwkF+855wD9ysL2KjPv/2VOWXxJmpb759+TTVvl3p0+nUVt/p0HAAAAwOAzbdq0nHrqqUt3t66qapU3d6+JvnqH/F9mSRj/UpKJVVX9YPkwPlnyyPWqqi5JMjFLAvPmrmPrrW2Z7UUrnfWqpXeZD6tzH/+bV8P476wojO+ytN+16TWpf78AAAxgsxZ2pOWei3Na+w+6hfH3D5mQuyf8gzAeAAAAAOqorx5Z/+YkVZL/V1XVk6ubXFXVU6WU/0zylSSH9UI/yz6zYugazG/t+q7bMwpKKX+XZOmPV9yW5C9WMX1pv2vTa7L2/a7ukfybZUmvOeSQQ7LVVqt7wn3PLFiwIDfeeGOWnq+trW01RwAr4lqCnnMdsT545OnnM+sHJ+fA6o5utce2em/GH//1jG8e0mvndx1BfbiWoOdcR9BzriOoD9cS9JzriHp7+uke3xDfTV8F8lt2fd+8Fsf8vut7izr3kiSzl9keuQbzR3R9z6nHyUspZyT5YtfuA0neUVXV3FUcsrTftek1Wct+V/fIhVLKn7eHDRuWYcP67gb8tra2Pj0frK9cS9BzriMGosl/mpINrjgpB5bHu9We2udz2fZdn0+W+bdeb3MdQX24lqDnXEfQc64jqA/XEvSc64h66I3fQ331yPqOru+1+QGA5q7vzjr3kqqqFiRZ+m71Vd7mXUrZMK+G3E/19NyllBOSnN21+0SSt1RVNW01hy0NykeUUjZYzdyld7m/VFXVwlXOBABgvXftdddl/BXvzi7LhfELMyQvvPXsbP3uf+jTMB4AAAAABpO+CuSXPqZ+bR4/v3Tuah9xv47u6/revpSyqh8U2HmZ7ft7csJSyruTfDdL/nd/Lslhq7srvct9y2zvvLJJXb+O7bp2e9QrAAADW2dnlR9f8q0cMumkbF6m19RmldFZeMIVec0BJzaoOwAAAAAYHPoqkL8uSUny2VLK7qubXErZLcnnsuS987/qpZ4mdX2PSPK6Vcx74zLbv1/prNUopRyW5MdZ8pSAl7PkzvhH1/DwSctsv3Gls5J98+rd/OvcKwAAA9uCxR358TlfyNEPfCYjy4Ka2gtDtk7rmb/N6J0OblB3AAAAADB49FUg/9UkC7PkHeiTSimfLaVstPykUspGpZTPJrkpyaiuY77aSz1ducz2R1c0oZTSlORDXbuvJLl+XU5USjkgyVVJWpPMTPK2qqruXYslftd1XJJ8uJSVPlP0I8tsX7GWbQIAsB54efb8/PK/T83xL301zaWqqT01aq9s8skb0vqaHRrUHQAAAAAMLn0SyFdV9USSM7p2Ryb5zyQvlFIeLqX8vpQyqZTycJIXumpjsuTu+DOqquqVR9ZXVXVrlgT/SXJKKWXiCqZ9JskuXdtfq6pq8bLFUsqbSilV1+eiFZ2nlLJXkmuy5M71uUmOrKrqj2vZ66IkX+/a3SXJZ1dwnolJTunavaGqqtvW5hwAAAx8jz7zUu796nty1PzLu9Ue3+LIbP3JX6VpZLefiwUAAAAAesmq3p1eV1VVfbeU8nKS85JskSWPsN8uybZdU5a96/vZJKdXVXVtL7f1ySx5tPuwJL8qpXwxS+6CH5bk+CSnd817KMlX1nbxUsp2SX6ZZIOuoX9IMrPrkfwr82JVVS+uYPzLSY5LsmOSL5VStk/ywyTzkxya5O+z5P/P+Uk+tba9AgAwsP3x3gcy9NITc0ge6VZ7Yve/zPj3/Vuy0gctAQAAAAC9oc8C+SSpquqaUsr4JO9NcniS3ZKM7SpPT3JPkl8nuXL5u9F7qZ87SynHJflektFJvriCaQ9lyV3ts9fhFAcn2XSZ/f9Zg2P+OckXlh+sqmp2KeXIJNcm2SFLfljg9OWmzUpyYlVVf1qHXgEAGKB+fcPvsvNvT8lWZVrN+OK0ZNqhX864N57coM4AAAAAYHDr00A+Saqqak9yaden4aqqurqUskeW3C1/ZJKtkixK8kiW9Pi/VVXNa2CLf1ZV1SOllL2T/EWS9yfZPsnQJE9lSVD/ta7XAwAAMAhUVZXLL/t+3jLlcxldav/JOqeMyOKjv5vNdzu8Qd0BAAAAAH0eyPdHXSH2p7s+a3Pc71L7qP3l6xcluagHra1ozblJvtT1AQBgkFrU3pnLvv0fOeaZ/8qQ0lFTe6ll84w8+bKM3GJCg7oDAAAAAJIGB/KllJYkG3btzui6ex4AAFiFmXMX5TfnfDwnzPlhtx8PfXrEhGx+5pVpHrXpig8GAAAAAPpMnwfypZRdk5yZJe+Q3zGv/ifEqpTycJa8Q/68qqru6eveAACgv3vyhel5+Jsfzvvab+xWe3zTwzP+tO8lQ4Y1oDMAAAAAYHl9FsiXUpqSfDnJJ5I0pfuj3kuSnbIkpD+zlPK/ST5TVVVnX/UIAAD92V0PPZrOH3wgh+WBbrXHdzo144/7ctLU1IDOAAAAAIAV6cs75H+Q5P15NYi/N8mtSV7o2n9Nktcn2S1Jc5YE91skOa4PewQAgH7pdzdPzvhffiTjy/M14+1pygsH/VvGH/4XDeoMAAAAAFiZPgnkSynHJzk2SZXkriSnV1V120rmvj7JuUn2TnJMKeX4qqp+2Bd9AgBAf1NVVX7608tyyB2fzIZlTk1tboZlwXu+lS33OrJB3QEAAAAAq9JXz7M8vev7oSQHrSyMT5Ku2iFJHsySu+nP6P32AACg/2nv6MyPLvyfvP2OM7qF8S83b5Kc/ItsJIwHAAAAgH6rrwL5PbPk7vj/rKpq7uomd835z2WOBQCAQWX2/EW58ut/leOf/Oe0lvaa2jNtO2T0x2/IiNfu1ZjmAAAAAIA10lfvkB/a9X33WhyzdO6QOvcCAAD92rMvz8yU807OMYt+3a32+EYHZ9zpl6S0jmpAZwAAAADA2uirQP6JJLskGbMWx4xe5lgAABgU7nvsycy9+AN5WzWlW+3xbU/M+JO+kTQ1N6AzAAAAAGBt9dUj6y/LkvfBH70WxxyTJY+5v6JXOgIAgH7m97f/Ma3feXtev1wY35mSp/b7p4z/0NnCeAAAAAAYQPoqkP/vJI8lOaOUcuzqJpdSjklyRpKpSf6rl3sDAICGu+bnV2enq9+T7cozNePz05qXj/x2tn7HZxrUGQAAAACwrvokkK+qamaSw5PckeSSUsqVpZT3lFK2LKUMKaW0dG2/p5RyRZIfdc09rOtYAABYL3V0VvnxxWfnzbecnI3LrJrajKYN0/7Bn2WT17+vQd0BAAAAAD3RJ++QL6V0LLub5F1dn5UekmTfJI+VUlY2p6qqqk/6BwCA3jBv4eJcc97nc8zL56epVDW151q3ydjTrkzrxuMb0xwAAAAA0GN9FWgvn6qvNGVfyzkAADAgvThzTm4/57S8f8G13f7l+8QG++e1Z/w4ZdgGDekNAAAAAKiPvgrk/7mPzgMAAP3ew089l2kXfiDv6LyjW23qa4/ONh8+L2ke0oDOAAAAAIB66pNAvqoqgTwAACS57a4pGXXFiZmYJ7rVntj7c9nm3Z9PVv7aJgAAAABgAPEOdgAA6CO/+vV12eOm07JZmVEzvjBDMuOtX8+4Az7QoM4AAAAAgN4gkAcAgF7W2Vnl8h9/O0fc/3cZURbW1GaW0amO/0E22+ngBnUHAAAAAPQWgTwAAPSiBYs78tNv/kuOfuFraS5VTe2FIVtlg9OuSuum2zeoOwAAAACgNwnkAQCgl0yfPT+TzvlYjp13ebLca+GfHLV3tjrz8jSNGNuY5gAAAACAXieQBwCAXvD4cy/lyQtOyrs7bulWm7rFO7PNyd9OWlob0BkAAAAA0FcE8gAAUGd33vdghvz4hBySR7vVHt/t49nm6H9NSlnBkQAAAADA+kQgDwAAdfTbG3+XHX9zSrYq02rGF6clLx365Yx/48kN6gwAAAAA6GsCeQAAqIOqqnLV5T/Im+/+bEaXeTW1OWVEFh1zcbaYcFiDugMAAAAAGkEgDwAAPbS4ozNXfPs/8t6n/ytDSkdN7aWWzTLi5CsydotdG9QdAAAAANAoAnkAAOiBmfMW5vpzPpFjZ/8wWe618E8Nn5DNz7wiLaNf05jmAAAAAICGEsgDAMA6eurF6Xnk/A/lPe03datN3fTwjD/14pShwxvQGQAAAADQHwjkAQBgHdzz8GNp//4JOTQPdKtN3enUbHPcl5OmpgZ0BgAAAAD0FwJ5AABYSzfdcku2/vmHM748XzPenqY8f9C/Z5vDP9agzgAAAACA/kQgDwAAa6iqqvzs6sty0B8/mQ3LnJra3AzLvPd8K1vtdWSDugMAAAAA+huBPAAArIH2js5c8d2v5t2P/3taS3tN7eXmjTP0Q5dlk3F7NaY5AAAAAKBfEsgDAMBqzF2wOL8897N5/ysXJaW29kzbDtn0zKsyZIMtG9IbAAAAANB/CeQBAGAVnp8+K1PO/Wjet+jX3WpTNzok40//QUrrqAZ0BgAAAAD0dwJ5AABYiQemPpU53z0hb6mmdKs9tu1J2fakrydNzQ3oDAAAAAAYCATyAACwArf88Y/Z5KcfzL7lmZrxzpQ8vd8/Ztt3fKZBnQEAAAAAA4VAHgAAlvOLX16dfW/+WDYus2rG56c1s448N699/fsa1BkAAAAAMJAI5AEAoEtnZ5UrfnBOjnz4n9JWFtfUZjRtmOYTL81rtnt9g7oDAAAAAAYagTwAACRZsKg9Pzvv83nftPPSVKqa2rNDt8nY069K28bjGtQdAAAAADAQCeQBABj0ps2am9vOPjXHLLg2KbW1x8fsn3Fn/jhl2AYN6Q0AAAAAGLgE8gAADGqPPv1cXvz2B3JE5x3da689Jtt9+NykeUgDOgMAAAAABjqBPAAAg9btd0/JqMtPzMQ80a32+N5/ne3e/fdJKSs4EgAAAABg9QTyAAAMSr/+7XXZ7YbTslmZUTO+MEMy/a3fyPgDTmhQZwAAAADA+kIgDwDAoFJVVa768YV5y31/mxFlYU1tZhmdzuMvyeY7HdSg7gAAAACA9YlAHgCAQWNhe0eu/ua/5L3Pfy3NpaqpPT9k62xw6pVpe832DeoOAAAAAFjfCOQBABgUXpkzP5POOSvHzL0iWe618E+M3DtbnXV5mkeMbUxzAAAAAMB6SSAPAMB678nnX8qT3zwx7+z4Q7fao1u8M9ud/O2kpbUBnQEAAAAA6zOBPAAA67W77n8wLT86IQfl0W61x3b7eLY7+l+TUlZwJAAAAABAzwjkAQBYb90w6cZsf91Hs2WZVjO+OC154U1fzrZvOrlBnQEAAAAAg4FAHgCA9U5VVbn6ikvyprs+ndFlfk1tdhmRRUd/N1vtdniDugMAAAAABguBPAAA65XFHZ256sL/zFFPfTlDSkdN7cWWzTPi5Muz0Ra7Nqg7AAAAAGAwEcgDALDemD1/Ya4/+xM5ZvYPk+VeC//k8AnZ4swr0zJ608Y0BwAAAAAMOgJ5AADWC89Om5GHzvtg3r34pm61xzZ9S7Y59bspQ4c3oDMAAAAAYLASyAMAMODd98jULP7+cXlT9WC32qM7npbtjv9S0tTUgM4AAAAAgMFMIA8AwIB28x9uyZbXfijjygs14+1pyrMH/nu2e8vHGtQZAAAAADDYCeQBABiwrv3ZZZl42yeyYZlTMz43wzL3qG/ntXu/o0GdAQAAAAAI5AEAGIA6Oqtc9d3/yZFT/z2tpb2mNq15k7R+6LJsOm7PBnUHAAAAALCEQB4AgAFl3sLF+dU5n837XrkoKbW1p9p2zGvOuDJDN9yyIb0BAAAAACxLIA8AwIDx4oxZmXLuR/Kehb/pVnts7MHZ5owfprSObEBnAAAAAADdCeQBABgQHn7iqcz6zvE5rPOebrVHtz0p25309aSpuQGdAQAAAACsmEAeAIB+79Y778jGV56U15VnasY7UvLU6/8x2x35mQZ1BgAAAACwcgJ5AAD6tet+9bPs8/uzslGZVTM+P6155R3nZvx+72tQZwAAAAAAqyaQBwCgX+rsrPLTS87J2x/6p7SVxTW1GU0bpnzgx9l8+/0a1B0AAAAAwOoJ5AEA6HcWLGrPz8//fI566bw0laqm9szQbbPR6VembeNxDeoOAAAAAGDNCOQBAOhXXp41N7efc2reO//apNTWpo7ZP+POvDRNw8Y0pjkAAAAAgLUgkAcAoN+Y+sxzefHbH8jbOu7oVntk66Oz/UfOS5qHNKAzAAAAAIC1J5AHAKBf+NM992TYTz6Q/fNEt9rUvf462x/190kpKzgSAAAAAKB/EsgDANBw119/XXb93Wl5TZlRM74wQzLtLd/INgee0KDOAAAAAADWnUAeAICGqaoqV196YQ67928zoiysqb1SxqTzuO9ny50PblB3AAAAAAA9I5AHAKAhFrV35poLvpB3P/f1NJeqpvbckK2zwalXZthrtm9QdwAAAAAAPSeQBwCgz82cuyC/P/vMvHfuFclyr4V/fOTe2fqsy9M8YmxjmgMAAAAAqBOBPAAAferpF6bliW9+IO9o/0O32iObvzPbn3Jh0jK0AZ0BAAAAANSXQB4AgD4z5YEH0/TDE3JgHu1We3TCx7P9Mf+alLKCIwEAAAAABh6BPAAAfWLS72/INr86OVuWaTXji9OS59/4X9nu0I82qDMAAAAAgN4hkAcAoFdVVZWfX3VJDrrz0xld5tfUZpeRWXD0d7P1boc1qDsAAAAAgN4jkAcAoNe0d3Tm6ov+M+988ssZUjpqai80b54RJ1+eTbbctUHdAQAAAAD0LoE8AAC9Ys6CRbn+7I/nvbN+mCz3Wvgnhu+WLc68IkNGb9qY5gAAAAAA+oBAHgCAunvu5Rl56NwP5l2Lb+pWe2STt2S7076bMnR4AzoDAAAAAOg7AnkAAOrqgUcfy8LvHZ83Vg92qz2y42nZ/vgvJU1NDegMAAAAAKBvCeQBAKibW269JVtc86HsXF6oGW9PU5454N+y/Vv/okGdAQAAAAD0PYE8AAB18ctrL8v+f/h4Nihza8bnZHjmHvWtjNv7HQ3qDAAAAACgMQTyAAD0SGdnlZ9+76s54tF/S2tpr6lNa9okQz98WV4zbs8GdQcAAAAA0DgCeQAA1tn8he257txP5z0zvpOU2tpTbTtl0zOuSOuGWzamOQAAAACABhPIAwCwTl56ZXbuPucjeffCX3erPTL2kGx3xiUprSMb0BkAAAAAQP8gkAcAYK09+uRTmXnR8Tms855utYe3OSk7fPDrSVNzAzoDAAAAAOg/BPIAAKyVP955Rza86qTsk2dqxjtS8sTr/zE7HPmZBnUGAAAAANC/COQBAFhjv7nuZ9lr0lnZqMyqGZ+f1sx4x7nZdr/3NagzAAAAAID+RyAPAMBqVVWVqy85J2998J/SVhbX1KY3jU058cfZYrvXN6g7AAAAAID+SSAPAMAqLVzcnl+c//kc9dK5SamtPT1022x8xpVp22hcY5oDAAAAAOjHBPIAAKzUjNnzcvvZp+So+dd2qz065g3Z5swfp2nYmAZ0BgAAAADQ/wnkAQBYoSeefT4vfOv4vKXjzm61h7c6Jjt89NykeUgDOgMAAAAAGBgE8gAAdHPXvfdk2KUnZL882a326F5/nR2O+vuklBUcCQAAAADAUgJ5AABq3HjDddnpt6flNWVGzfiCDM1Lh3892x10QoM6AwAAAAAYWATyAAAkSaqqyjWXXZhDp/xtRpSFNbVXyph0HPeDbL3zQQ3qDgAAAABg4BHIAwCQxR2dufaCL+Sdz349zaWqqT3bsnXGnHplNths+wZ1BwAAAAAwMAnkAQAGuVnzFuTms8/MUXOuSJZ7LfzUkXtn6zMvT8vIsY1pDgAAAABgABPIAwAMYs+8OC1PnH9C3t5+a7faQ5u9Mzuc+u2UltYGdAYAAAAAMPAJ5AEA1kOzFyzO8zMXZO6ijowY2pzNxrRlVNuQmjn3P/RQqkuOzwHVo92Of2TCx7PjMf+alNKtBgAAAADAmhHIAwCsJ6qqyuTHXs6lv78v9z3wQNqq+Zmbtjxfjc38phF524TX5KQ3jMvEbTfK5Mk3ZfwvP5otyrSaNRalJc+98cvZ/tCTG/SrAAAAAABYfwjkAQDWA/c8/Uq+c8nFOXTWT/PlptvTMrTzz7X2qim/7Nw337v3LfnAlF3zzhEP5Ivt/5XRZX7NGrMyMguO/m7G7X5YX7cPAAAAALBeEsgDAAxwd/zh+oy69i/z5fJ00ty93lI6c2TzrTmy+dY8X22YjdtfSUupaua80Lx5hn/08my61a591DUAAAAAwPpPIA8AMIBN/cPV2fnaUzK8LFyj+ZuVGd3GHh+2W7Y484oMHbNpvdsDAAAAABjUmhrdAAAA66Z69s5s9vNT1ziMX5FflQPy2r+6ThgPAAAAANALBPIAAANRVWX+j07LsCxY5yVerkbljPln5Q9PzV/9ZAAAAAAA1ppAHgBgIHr8pgyf+XCPltiozM7+TQ/me7c8UaemAAAAAABYlkAeAGAAWnzLN+uyzknN1+UX9z6f2QsW12U9AAAAAABeJZAHABhoFsxKy0PX1GWptzfdlmGdc/PCrHV/9D0AAAAAACsmkAcAGGhmPZtSddRlqZbSmdeUGZmzsD7rAQAAAADwKoE8AMBAs2huXZcbmfkZ2dpc1zUBAAAAABDIAwAMPENH1HW5BWV4XjO6ra5rAgAAAAAgkAcAGHhGb5GU+tzRvrhqzm4775xRbUPqsh4AAAAAAK8SyAMADDRto5Nd3lmXpX7ZuW+OOXDXuqwFAAAAAEAtgTwAwABTVVVumb1JXda6YfRRecO2Y+uyFgAAAAAAtQTyAAADyKLFHfnZOX+TNzx1QY/XeqjaOh8+4cSUUurQGQAAAAAAy2tpdAMAAKyZmbPn5bZzTs675v28x2vNq1oz5x3fyD5bbdDzxgAAAAAAWCGBPADAAPDUcy/k+QuOzeEdf+pW6yzNaao61nit+WnLC++4IPvsf2gdOwQAAAAAYHkeWZ+klDKulPKVUsoDpZS5pZTppZTbSimfK6UM7+HaTaWUXUspHymlnN217sJSStX1edMarvO7ZY5Z5acn/QIA/c+U++7NgvMOz+tXEMY/vOffpOm03ySb7LJGa83bYMe0nf6LbLP/u+rcJQAAAAAAyxv0d8iXUt6V5HtJRi8zPDzJvl2fU0spR1ZV9cg6nuKDSS7qUZMAwKA16cbrsuNvTsumZUbN+IIMzYuHfz07HHTCkoGPTU4en5Tc9s3k/p8ly9wxXzW1pH3HIzNk/9MyfPxBiXfGAwAAAAD0iUEdyJdS9k7yoyTDksxJNBoCeQAAcftJREFU8v+SXN+1f3yS05LsmOSaUsq+VVXNXpfTLLO9OMmUJEOS7L6Obd+e5KPreCwAMEBUVZVfXn5hDrn7bzO8LKypvVLGZPGxP8hrdzno1cFSkm0OXvJZMCuZ/VyycE7SOjJl1OYZ0jY6AAAAAAD0rUEdyCf5WpaE7+1J3lpV1eRlar8tpTyc5EtZEsp/JskX1uEc9yX5RJLbkvypqqoFpZQvZN0D+blVVd2zjscCAANAe0dnfv6tf847nvlampd7G82zLVtn9KlXZoPNtl/5Am2jl3wAAAAAAGioQfsO+VLKfkkO7tr91nJh/FJfSXJ/1/YnSylD1vY8VVXdWlXVN6qquqWqqgXr2C4AMEjMnrcgv/2fj+Zdz361Wxj/6Mh9sumnbszIVYXxAAAAAAD0G4M2kE/ynmW2L1zRhKqqOpN8t2t3gySH9m5LAMBg9txL03LPf787b51zZbfag5u9K9t+6hdpGTm27xsDAAAAAGCdDOZAfulLV+cm+eMq5t2wzPaBvdcOADCYPfDwQ5l59lsysf0P3WoP7fqJ7HTGxSktrQ3oDAAAAACAdTWY3yG/S9f3I1VVta9i3gMrOKaRdi6l/CHJTknakkzLkh8ouCzJJVVVLW5kcwDA2rtl8o0Z94uPZPPycs34orTk2UO+nB3ffHKDOgMAAAAAoCcGZSBfSmlLsnHX7tOrmltV1YxSytwkI5Js3du9rYHXdH2W2rLr8+4kf1NKOaaqqvtXeORqlFK2Ws2UzZZuzJ8/P/Pnz1+X06yxBQsWrHAbWDuuJei53ryOfveLn+SgP30uo0rt36uzMjIz33VBXrPrm3r971zoC/4+gvpwLUHPuY6g51xHUB+uJeg51xH11hv/LbZUVVX3Rfu7UsomSV7s2v1RVVXHr2b+C0k2TXJPVVW71+H8X0jy/3XtHlpV1e/W4JjfJulMcm2Su5K8nGRUkn2SnJFX795/Icl+VVU9uQ59rfFvhgsuuCAbb7zx6icCACvUWSWvPHRDPjj3wrSUzpras9k0d+z4mXSM2LxB3QEAAAAADD7Tpk3LqaeeunR366qqVnlz95oYlHfIZ8mj3pdatAbzF3Z9D+uFXtbU+6qqemUF4zeVUs5O8s0kH86Su+e/muR9fdcaALA2FrZ3pun+n+Sj7T9LSm3tgaYd8vAun0zn0NGNaQ4AAAAAgLoZrIH8ss+sGLoG81u7vhv2vNiVhPFLa4tLKacmeUOWvFv+vaWULauqemYtT7O6R/JvluS2JDnkkEOy1Vare8J9zyxYsCA33nhjlp6vra1tNUcAK+Jagp6r53U07ZWZefzCk3Nw+++71R4Ye1he+5ELMm5II38GEHqHv4+gPlxL0HOuI+g51xHUh2sJes51RL09/XSPb4jvZrAG8rOX2R65BvNHdH3P6YVe6qKqqvZSyreSfKlr6I1JfrCWa6zyd1gpr97CN2zYsAwb1ndhQVtbW5+eD9ZXriXouZ5cR48+/njmfefYHFw92K324A6nZecTvpQ0NfW0Rej3/H0E9eFagp5zHUHPuY6gPlxL0HOuI+qhN34PDcpAvqqqBaWUl5NslGSVt3mXUjbMq4H8U73dWw/dt8z2lg3rAgDo5o+3/yGbXv3BbFdeqBlvT1OemPjv2eltH2tQZwAAAAAA9JbBfAvW0vB6+1LKqn4wYedltu/vxX7qoWp0AwBAd9f/4vJsd/V7s/VyYfycDM+L7/5+thPGAwAAAACslwZzID+p63tEktetYt4bl9nu/rLX/mXXZbafbVgXAECSpLOzyjXf+58cOPnUbFDm1tRebNok7R/+ebbY5x0N6g4AAAAAgN42mAP5K5fZ/uiKJpRSmpJ8qGv3lSTX925L667rLv+Tlxm6sVG9AADJgkXt+cX/fSpHPvKFDC0dNbUnWnfM6L+8IRtss1djmgMAAAAAoE8M2kC+qqpbk9zUtXtKKWXiCqZ9JskuXdtfq6pq8bLFUsqbSilV1+ei3uq1lHJoKWWDVdSHJLlgmV6vrqqqv7/vHgDWW9Nnzckt/31s3vHyRd1qD214SLb+q+vTNnbLvm8MAAAAAIA+tap3pw8Gn8ySx9APS/KrUsoXs+Qu+GFJjk9yete8h5J8ZV1PUkr5yHJDey2z/fZSyvhl9h+pqmpS7fR8OMlPSyk/TfK7JA8mmZVkZJY8bv/0vPq4+hez5NcFADTAE08/k+nfPjZv6rynW+2B8Sdl5w99PWlqbkBnAAAAAAD0tUEdyFdVdWcp5bgk30syOskXVzDtoSRHVlU1uwenunAVtb9Zbv87efX99ssameQDXZ+VmZLk+Kqqpq5dewBAPdw95U8ZddkJ2TvP1ox3VCWP7fuP2fldn2lQZwAAAAAANMKgDuSTpKqqq0spe2TJXeVHJtkqyaIkjyS5NMn/VlU1r4EtJsl/JvlTkolZcif8JknGJlmY5IUktyf5SZIrqqrqWMkaAEAvuvG312bCDWdkozKrZnxe2vLyEedkhze8r0GdAQAAAADQKIM+kE+SqqqeSPLprs/aHPe7JGUN5q12zmqOvz/J/Um+2pN1AID6q6oqv/jxuTn0vn9MW1lcU3u5jE0+8KNsvcN+DeoOAAAAAIBGEsgDAKyjxe0d+dU3/z5HvnButx/Re3rINhl7+lUZvsm4xjQHAAAAAEDDCeQBANbBzDnzcvs5p+TIudd2qz08+g3Z9qwfp3nYmAZ0BgAAAABAfyGQBwBYS08/90Kev+C4HNZxZ7faA1sek51PPi9p9s8sAAAAAIDBzn8pBgBYCw8+9GBGXPHB7Jsnu9f2+Ovs/N6/T0pZwZEAAAAAAAw2AnkAgDX04nOPZ+Idn8ymZUbN+IIMzfOHfS07HfyBBnUGAAAAAEB/JJAHAFiNqqry4tQ7c+KMszO8LKypvVLGZNH7v5/xux7coO4AAAAAAOivBPIAAKvQ3tGZ31z8/3LyjG+kuVQ1tWdats7oU6/Mpptt36DuAAAAAADozwTyAAArMXf+wtx89hl51+wrkuVeC//IiH0y7qzLMmTk2MY0BwAAAABAvyeQBwBYgRemvZzHzjshb1n8h261+1/zzux82rdTWlob0BkAAAAAAAOFQB4AYDkPPfJwOr9/bCZWj3Wr3bvjxzLhhC8mpazgSAAAAAAAeFVToxsAAOhPbv3DTRl18duy83Jh/KKqJT/f9Mxs+95/EsYDAAAAALBG3CEPANDlNz+7JPvd9lcZVebXjM/MyNw07hPp2GjnBnUGAAAAAMBAJJAHAAa9zs4qv/juf+StU7+UltJZU3u+efOUE36YjnufaFB3AAAAAAAMVAJ5AGBQW7BocW44+y/zjld+mCz3JPrHhu2WLc+8PJ1DRycCeQAAAAAA1pJAHgAYtKbNeCUPnnti3rZwUrfagxsdnh3P/F7KkGGZP3/+Co4GAAAAAIBVE8gDAIPSY088nrnfOTYHdj7YrXb/9qdmlw98OWlqakBnAAAAAACsLwTyAMCgc+cdf8jGP/1gds8LNePtacrj+/9bdjniLxrUGQAAAAAA6xOBPAAwqPzul1dkr5v/IhuUuTXjczI8M995Qbbf98gGdQYAAAAAwPpGIA8ADApVVeUXP/haDnvoXzK0dNTUXmzaJC0f/Em23GavxjQHAAAAAMB6SSAPAKz3Fi5uz/XnfSZHTLsoKbW1J1p3zKZnXJlhY7dsSG8AAAAAAKy/BPIAwHrtlVmzc9c5H87b5/+mW+3BDQ7J9mdekua2kQ3oDAAAAACA9Z1AHgBYbz31zDN5+VvH5o2d93Sr3T/upOzy4a8nTc0N6AwAAAAAgMFAIA8ArJem3POnjPzJCdkrz9aMd1Qlj77uH7PLuz/ToM4AAAAAABgsBPIAwHpn0u+uzS7Xn5GNyqya8Xlpy0tvPzs7Tjy6QZ0BAAAAADCYCOQBgPVGVVX51aXn5Y33/kPayuKa2stlbDpP+GHG7bh/g7oDAAAAAGCwEcgDAOuFxe0d+fUFf58jnj83KbW1p4Zsk7GnX5URm4xrTHMAAAAAAAxKAnkAYMCbNXdebj/7lBwx99putYdG7Z9tz/pxWoZv0PeNAQAAAAAwqAnkAYAB7dkXXsyz3zw2b26/s1vt/i2Pzs4nn5fSPKQBnQEAAAAAMNgJ5AGAAeuBB+5P8w+Pzb55sntt989ll/d9PillBUcCAAAAAEDvE8gDAAPSHyb9Jttcd0o2LTNqxhdkaJ5989ey8yEfaFBnAAAAAACwhEAeABhwfn3lRTngzr/O8LKwZnxGGZNFx3w/2044uEGdAQAAAADAqwTyAMCA0dFZ5Zff/ue87amvprlUNbVnWrbOqJOvzGu22L5B3QEAAAAAQC2BPAAwIMxbsDA3n31G3jHrimS518I/PHzvjDvr8gwdNbYxzQEAAAAAwAoI5AGAfu/Fl1/OY+eekMMX/6Fb7b5N35ldTv92SktrAzoDAAAAAICVE8gDAP3aI48+kvbvvT9vqB7rVrt/549n1+P+NSllBUcCAAAAAEBjCeQBgH7r9lsnZctrPpTNy8s144vSkicP+lJ2OfyUBnUGAAAAAACrJ5AHAPql313zw7zu1k9lVJlfMz4zozL3vRdl+z0Pb1BnAAAAAACwZgTyAEC/0tlZ5VcX/2cOf+w/01I6a2rPNW+etg9fni1eu2uDugMAAAAAgDUnkAcA+o0FixbnxnM+nrfPuCRZ7rXwj7ZNyJZnXZm2MZs2pjkAAAAAAFhLAnkAoF94ecYrefDck/LWhTd1q92/0eHZ6YzvpWnosAZ0BgAAAAAA60YgDwA03NQnn8jci96fAzof7Fa7d7vTMuHELyVNTQ3oDAAAAAAA1p1AHgBoqLvuvDVjrzopu+WFmvH2qimPveHfMuGIv2hQZwAAAAAA0DMCeQCgYW667srsPulj2aDMrRmfk+GZ8c4LsuPrj2xQZwAAAAAA0HMCeQCgz1VVlesu+Vre9OC/ZGjpqKm92LRJmk+6NFtvu3eDugMAAAAAgPoQyAMAfWrR4o5cf96n87ZpFyWltvb40B2z6ZlXZPjYrRrSGwAAAAAA1JNAHgDoMzNnzcld53wob5v/m261B8YcnB3O+mGa20Y2oDMAAAAAAKg/gTwA0CeeefaZvPStY3NIxz3dave+9sRM+Mg3kqbmBnQGAAAAAAC9QyAPAPS6e+/5U0b85ITslWdrxjuqkof3+YdMOOqzDeoMAAAAAAB6j0AeAOhVt9xwbXb87ekZW2bXjM9LW15829nZ+YCjG9QZAAAAAAD0LoE8ANArqqrKby47PwdP+Xxay+Ka2stlbDpO+GHG77h/g7oDAAAAAIDeJ5AHAOquvb0jv/7W5/P2585JSm3tySHbZOxpV2bkpuMb0hsAAAAAAPQVgTwAUFdz5s3PbWefkrfPuaZb7cFR+2fbs36cIcM36PvGAAAAAACgjwnkAYC6ef7FF/PM+cfm0PY7u9Xu3fzo7HrqeSnNQxrQGQAAAAAA9D2BPABQFw8+dH+aLjk2r6ue7Fa7f7fPZcLRn09KWcGRAAAAAACwfhLIAwA9dtvNv824X56cTcuMmvEFGZpnDv1adnnjBxrUGQAAAAAANI5AHgDokeuvuij73/HXGV4W1ozPKGOy4JjvZbsJhzSoMwAAAAAAaCyBPACwTjo6q1x34b/kLU/+T5pLVVN7unnrjD7limy+xQ4N6g4AAAAAABpPIA8ArLX5Cxbl5nNOz9tnXpEs91r4h4fvndeedVlaR23UmOYAAAAAAKCfEMgDAGvlpenT89i5J+SwRbd0q9276ZHZ9fQLU1paG9AZAAAAAAD0LwJ5AGCNPfbYI1l08fuzf/VYt9q9O/1lJhz/b0kpKzgSAAAAAAAGH4E8ALBG7rzt99nsmg9m27xcM74oLXniwC9lwltOaVBnAAAAAADQPwnkAYDVuuHnP8zrbvlURpb5NeMzMzKz33NRdtjrLQ3qDAAAAAAA+i+BPACwUlVV5brvfSlvfuQ/0lI6a2rPNW2e1o9cnq1eu2uDugMAAAAAgP5NIA8ArNDCxYtz0zkfz1unX5Is91r4R9smZMuzrkzbmE0b0xwAAAAAAAwAAnkAGGRmL1ic52cuyNxFHRkxtDmbjWnLqLYhNXNmvDIz9597Yg5fcFO34+8be3h2PvN7aRo6rK9aBgAAAACAAUkgDwCDQFVVmfzYy7l48hO5+b6p2aR6OSOyIHPTlpfKRjlwwjY56Q3jMnHbjfLU009l1oXH5IDOB7utM2XbU7P7SV9Ompoa8KsAAAAAAICBRSAPAOu5e56ZmU//6M5sNO3WfLD5unxjyO0174Nvr5ryywf2zTfufUv+b+Rr8h+L/j275YWaNRZXzXl0/3/N7u/4i75uHwAAAAAABiyBPACsx256+KV89eJL8438X3Ya+vQK57SUzhzZfGuObL41HQtLmktVU5+T4Xn5yAuy835H9kXLAAAAAACw3hDIA8B66p5nZuaiiy/Md8t/ZURZuEbHLB/Gv9i0SZpO/EnGbbdXL3QIAAAAAADrN4E8AKyHqqrK2Zdclq+vRRi/vEeqLbP5x36VERtvVefuAAAAAABgcGhqdAMAQP1NfnRaPjnrK+scxidJZ5Xc/UpbHbsCAAAAAIDBRSAPAOuhW6+/Kjs1rfid8Wtqx6Zn8ofrf1qnjgAAAAAAYPARyAPAemb2gsXZ8akf12WtHZ78YWYvWFyXtQAAAAAAYLARyAPAeubFl17KW8ttdVnrbU235cVpL9VlLQAAAAAAGGwE8gCwnlk04+m0lM66rNVSOrNoxjN1WQsAAAAAAAYbgTwArGdGZEFd1xuZ+XVdDwAAAAAABguBPACsZzYaO7au643dcKO6rgcAAAAAAIOFQB4A1jMjNn5tOur0V3xHmjNi463rshYAAAAAAAw2AnkAWN+0jc4LmxxYl6VmjHtb0ja6LmsBAAAAAMBgI5AHgPXMH2+5PsNevLMua230prPqsg4AAAAAAAxGAnkAWI/c8NPvZpefH5cNy5wer7Vgw51Sxh9ch64AAAAAAGBwaml0AwBAz3V2Vvn1d/4lhz3+P2kuVY/X62gZnrb3n5+UUofuAAAAAABgcBLIA8AAt2Dhokw+54y89ZXLk+Xy86dbt8vmHc+muX3+Gq/X0TI8zSd8P9lir/o2CgAAAAAAg4xH1gPAADZt+vTc/ZV35dBXLu9Wu2eTI7PFZyen+eRfpNpklzVar9p01zSf/PNkuzfXu1UAAAAAABh03CEPAAPU1KmPZtF3j8l+1WPdavfs+JfZ7YR/W/LI+S32SvnY5OTxSclt30x1/89Sqo4/z62aWlJ2fmfy+lNTxh/kMfUAAAAAAFAnAnkAGIDu+uPvs+nVH8w2eblmfFHVkqkHfim7vfWU2gNKSbY5ONnm4JQFs5LZzyUL5yStI1NGbZ60je7D7gEAAAAAYHAQyAPAADPpFz/KXpM/mZGl9r3wMzMys95zUXba+y2rXqBttAAeAAAAAAD6gEAeAAaIqqry6+99KYc+8h9pKZ01teeaNs/QD1+WrcdNaFB3AAAAAADA8gTyADAALFy8OJPO/UTe8vIPkuVe8f5I64RsceblGb7hZo1pDgAAAAAAWCGBPAD0c6/MnJn7zzkxhy24qVvt3g0Py85nfT/NQ4c1oDMAAAAAAGBVBPIA0I89/fSTmfntYzKx88FutSnbnJLdP/hfSVNTAzoDAAAAAABWRyAPAP3UvXffljGXn5gJeaFmvL1qysP7/Ut2P/LjDeoMAAAAAABYEwJ5AOiHJv/mquxy41nZoMytGZ+T4XnpHednl/3f1aDOAAAAAACANSWQB4B+pKqq/PbHX8/B9/1zhpaOmtoLZZOUE3+cbbbfp0HdAQAAAAAAa0MgDwD9xOL2jtzwzc/m8Be+nZTa2tShO2ST06/IyI23bkxzAAAAAADAWhPIA0A/MHvu3Nx19odz+NzrutXuG31wdjzrkrQMG9WAzgAAAAAAgHUlkAeABnvu+efy4gXvz0HtU7rV7t7qA9n9o99IafZXNgAAAAAADDT+6z4ANNCD99+d1h8dlz3zbM14R1XywF6fzx7v/VyDOgMAAAAAAHpKIA8ADXLbTb/Mtr8+NRuVWTXj89KWZ9/yf5lw0DEN6gwAAAAAAKgHgTwANMDvLj8/b7jr79NWFteMTysbZvGxP8z2u7yhQZ0BAAAAAAD1IpAHgD7U0dGZ67/9Dzn8mf9LSm3tiZbx2fDUq7LxZuMb0hsAAAAAAFBfAnkA6CPz5s/PbWefksNnX9Otdv+I/bLdxy7N0BEb9H1jAAAAAABArxDIA0AfeGnaS3nqvGPzxsV3dKvdvdn7svtp56c0D2lAZwAAAAAAQG8RyANAL3v0kQdTff/92ad6olvtnl0/kz3e/49JKSs4EgAAAAAAGMgE8gDQi+685fps+fOPZtMyo2Z8QYbkyTf+T3Y79IMN6gwAAAAAAOhtAnkA6CU3/ezivO62z2R4WVgzPiNjMvfoi7Pj7m9sUGcAAAAAAEBfEMgDQJ11dlb57Xf/NYdO/e80l6qm9nTz1hnx0cuy1VY7Nag7AAAAAACgrwjkAaCOFixclMnnnJHDX7k8We618A8N2yuvPevytI3eqDHNAQAAAAAAfUogDwB1Mn3GjDxy7vE5dOEt3WpTNn5HJpxxUZqGtDagMwAAAAAAoBEE8gBQB08+8Vjmf+fo7Nf5WLfa3Tt8LHt84ItJKSs4EgAAAAAAWF8J5AGgh6bccXM2/ulJeW1erhlfVLXk0QP+I3u87bQGdQYAAAAAADSSQB4AeuDmX/04e/z+ExlZ5teMz8rITH/3hdnldW9tUGcAAAAAAECjCeQBYB1UVZXrv//lHPLw/0tL6aypPde0WYZ86LKMH79bg7oDAAAAAAD6A4E8AKylRYvbM+m8j+fN036QLPda+EdaJ2TzMy/PiA03a0xzAAAAAABAvyGQB4C1MHPWrNx3zol58/wbu9Xu2eCw7PKx76d56LAGdAYAAAAAAPQ3TY1uoD8opYwrpXyllPJAKWVuKWV6KeW2UsrnSinDe7h2Uyll11LKR0opZ3etu7CUUnV93rSW6w0vpfx11zrTu/p9oKv/cT3pFYBVe+bpJ/P0196SiSsI4+8af3J2+8RPhPEAAAAAAMCfDfo75Esp70ryvSSjlxkenmTfrs+ppZQjq6p6ZB1P8cEkF/WoyS6llO2TXJtkh+VKO3V9Ti2lnFhV1c/qcT4AXnX/lD9m1GUnZEJeqBlvr5ry4L7/kj3f9fEGdQYAAAAAAPRXg/oO+VLK3kl+lCVh/Jwkn09yQJLDknyza9qOSa4ppYxa19Mss704yR1JpqxDr6OSXJNXw/hvdvV5QJb0PSdLfh0/KqXstY69ArACf7j+p9n8J+/KVsuF8XMyPE8e8d1MEMYDAAAAAAArMNjvkP9akmFJ2pO8taqqycvUfltKeTjJl7IklP9Mki+swznuS/KJJLcl+VNVVQtKKV9IsvtarvO5rj6S5K+rqvryMrXJpZTfJbkhS+7u/2qSN61DrwAso6qq3HDp/+aAe/+/DC0dNbUXy8bp/MCl2XaHfRrUHQAAAAAA0N8N2jvkSyn7JTm4a/dby4XxS30lyf1d258spQxZ2/NUVXVrVVXfqKrqlqqqFqxjr0OyJNRPVz9fWcF5bk7yra7dN5ZSXr8u5wJgifb2jlx/3mfypvv+oVsY/9iQHTLsL36XzYTxAAAAAADAKgzaQD7Je5bZvnBFE6qq6kzy3a7dDZIc2rstrdShScZ0bX+nq68VuWiZ7ff2akcA67E58+blD/9zXN78/Le61e4bdVC2/vT1GbXx1g3oDAAAAAAAGEgGcyB/UNf33CR/XMW8G5bZPrD32lmlg5bZvmGls5Lbk8zr2m5UrwAD2gsvPJ9H//utOXDudd1qd215Qnb51FUZMmxUAzoDAAAAAAAGmsH8Dvldur4fqaqqfRXzHljBMX1t12W2H1jZpKqq2kspjyTZI+vQayllq9VM2Wzpxvz58zN//vy1PcVaWbBgwQq3gbXjWlpzjz18b0Zc/sHsmWdrxjuqkikT/jo7veuvsmDR4iSLG9MgDeM6gp5zHUF9uJag51xH0HOuI6gP1xL0nOuIeuuN/LNUVVX3Rfu7UkpbkqX/a15TVdU7VzN/TpIRSW6pqmpiHc7/hST/X9fuoVVV/W41829Jsn+SuVVVjVzN3J8lObJrt62qqoVr0dca/2a44IILsvHGG6/pdIB+75XnHs07n/ufbFRm1YzPq1pz3eYfSzbfu0GdAQAAAAAAfWHatGk59dRTl+5uXVXV0z1dc7DeIb/ss4bnrMH8uVkSyK8yDO9FS/td016XGplkjQN5gMFq1tRbc9yM89JWau98fykbZNI2n07ThuMb0xgAAAAAADCgDdZAvm2Z7UVrMH9pqD2sF3pZE0v7XZtek7Xvd+vV1DdLcluSHHLIIdlqq9U94b5nFixYkBtvvDFLz9fW1raaI4AVcS2tXGdnZ27+/r/kqFfOTUpt7Ynm8Wn94I/y9teMa0xz9CuuI+g51xHUh2sJes51BD3nOoL6cC1Bz7mOqLenn+7xDfHdDNZAftmXSAxdg/mtXd+9+9L0lVva79r0mqxlv6t75EIpr6ZVw4YNy7BhfffzCW1tbX16PlhfuZZeNX/+gvzxvNPzllk/61a7f8Trs+3HLk3riA0b0Bn9nesIes51BPXhWoKecx1Bz7mOoD5cS9BzriPqoTd+Dw3WQH72Mttr8hj6EV3fa/LI+N6wtN+16TVpXL8A/dq0adPy1HnvzyGL7+hWu/s1783up52f0rImPwMFAAAAAACwck2NbqARqqpakOTlrt1VPne9lLJhXg25n+rNvlZh6Z3rI0opG6xm7tLHzr9UVZX3xwMs5/FHH8wr//fm7L2CMP6uXT6dPc68UBgPAAAAAADUxaAM5Lvc1/W9fSllVU8K2HmZ7ft7sZ9VuW+Z7Z1XNqnr17Fd126jegXot+669foMv/ht2b56omZ8QYbkwYP/N3se9/8lpazkaAAAAAAAgLUzmAP5SV3fI5K8bhXz3rjM9u97r51VmrTM9htXOivZN6/ezd+oXgH6pd9fc3F2uOa4bJoZNeMzMjovvfcn2emwDzaoMwAAAAAAYH01mAP5K5fZ/uiKJpRSmpJ8qGv3lSTX925LK/W7JDO7tj9cykpv3/zIMttX9GZDAANFVVX57Xf+JW+49eMZXmrf5PFU89bpPOXX2XrPNzWmOQAAAAAAYL02aAP5qqpuTXJT1+4ppZSJK5j2mSS7dG1/raqqxcsWSylvKqVUXZ+LerHXRUm+3rW7S5LPLj+nq/9TunZvqKrqtt7qB2CgWLhoUW78+il589SvpLlUNbUH2/bKxp+4IRttvVODugMAAAAAANZ3q3p3+mDwySx5tPuwJL8qpXwxS+6CH5bk+CSnd817KMlX1vUkpZSPLDe01zLbby+ljF9m/5Gqqialuy8nOS7Jjkm+VErZPskPk8xPcmiSv8+S/z/nJ/nUuvYKsL545ZUZefic4/PGhbd0q03Z6IhMOPM7aRrS2oDOAAAAAACAwWJQB/JVVd1ZSjkuyfeSjE7yxRVMeyjJkVVVze7BqS5cRe1vltv/TmrfGZ8kqapqdinlyCTXJtkhS35Y4PTlps1KcmJVVX9a91YBBr6nnngs875zTF7f+Wi32l3bn5U9T/x/yUrf/gEAAAAAAFAfg/aR9UtVVXV1kj2S/E+WhO/zsuR98bdnSVi+d1VVjzSswWV09bF3lvR1e5b0OS/Jg1nS/x5VVf2sYQ0C9AP3/mlyWi58S3ZaLoxfXDXnvjf8V/Y86T+E8QAAAAAAQJ8Y1HfIL1VV1RNJPt31WZvjfpdktalOVVV1S36qqpqb5EtdHwCWccuvLs2E3388o8r8mvGZGZnp7/p2dt33bQ3qDAAAAAAAGIwE8gAMeFVV5XeX/FcOfvCLaSmdNbXnmjZL8wd/km222b1B3QEAAAAAAIOVQB6AAW1xe3smnfeJHPrS97s9s+Th1l2z+ZlXZOSGmzWmOQAAAAAAYFATyAMwYM2aPSv3nX1SDp1/Q7falA0OzS5nfj8tbSMa0BkAAAAAAIBAHoAB6rlnn8qMbx2TN3Q80K32p3EfzZ4f/kpKU3MDOgMAAAAAAFhCIA/AgPPgPXdkxE+Oz655oWa8vWrKA6/7QvZ69ycb1BkAAAAAAMCrBPIA9BuzZ07P9GenZuH82WkdNipjt9gmo8aMrZlz+++uzvbXn5ENytya8TkZnuePOD+7veFdfdkyAAAAAADASgnkAWioqrMz906+JotuPj97zJmUcaXzz7X2qil3jDooQyeengkTj8yNl5+diVP+KUNLR80aL5aN0/mBS7P9Dvv0dfsAAAAAAAArJZAHoGEeuWtSWq46K7t1PrlkoNTWW0pn9plzY3LdjZl+3ei8MbO6zXlsyA7Z+LQrMnrTrfumaQAAAAAAgDUkkAegIabceEW2+80ZGV4WrtH8sZnVbeyeUQdmx7N+lKHDR9W7PQAAAAAAgB5ranQDAAw+j9w1aa3C+BX50xYnZMKnfiqMBwAAAAAA+i13yAPQp6rOzrRcdVaPwvhp2TB7nnp2SpOfKwMAAAAAAPovSQYAfereyddk/NJ3xq+jjTMj903+eZ06AgAAAAAA6B0CeQD61KLJ59dlnYWTz6vLOgAAAAAAAL1FIA9An5k9c3r2mD2pLmvtMfumzJ45vS5rAQAAAAAA9AaBPAB9ZvqzU9NSOuuyVkvpzPTnHq/LWgAAAAAAAL1BIA9An1k4f3Z915s3q67rAQAAAAAA1JNAHoA+0zpsVH3XGz66rusBAAAAAADUk0AegD4zdott0l7V56+exVVzxm4+vi5rAQAAAAAA9AaBPAB9ZtSYsbl71EF1WWvKqIMyaszYuqwFAAAAAADQGwTyAPSZqqoyd8jGdVmrdeIZdVkHAAAAAACgt7Q0ugEABodFixbnlnPPzCEzLu/xWlObxmXXiUfUoSsAAAAAAIDeI5AHoNfNfOWVPHzucTlkwS09Xmte1ZqOo85OafKQFwAAAAAAoH8TyAPQq55+cmrmXnR09u18tFttUdWcoaVjjdeaV7Xm0cPOy+571uc99AAAAAAAAL3J7YUA9Jr777olzd8+PDstF8Yvqppzz/5fzpPv+2keb3rtGq01tWlcnn3f5dn9kPf2RqsAAAAAAAB15w55AHrFrb/+SXa56S8zqsyvGZ+ZkXn5Xd/Obvu+LUlS7X5X7p388yycfG72mD0pLaXzz3MXV82ZMuqgtE48I7tOPMJj6gEAAAAAgAFFIA9AXVVVlRt/9N854P5/z5DlHkf/bNNmaTnp0my77R5/HitNTZlw4JHJgUdm9szpmf7c41k4b1Zah4/O2M3HZ58xY/v6lwAAAAAAAFAXAnkA6qa9vT2Tzv9U3vTixUmprT08dJdsfuYVGTl285UeP2rM2IwSwAMAAAAAAOsJgTwAdTF7zuzce/ZJedO833Wr3T3m0Oxy1vczpG1E3zcGAAAAAADQIAJ5AHrsheeeyrQL3p83dNzfrXbnaz+SvT7y3ylNzQ3oDAAAAAAAoHEE8gD0yEP33pFhl56QCXm+Zry9asp9+3whex/1ycY0BgAAAAAA0GACeQDW2R03/izb/eb0jClza8bnZFiefdt52eOAoxrUGQAAAAAAQOMJ5AFYJzdd9n/Z7+5/Smtprxl/oWycjuN/lB132rdBnQEAAAAAAPQPAnkA1kpHR2du+tbn8qZnL0hKbe2xlu2z0WlXZMxrXtuY5gAAAAAAAPoRgTwAa2z+/Pm58+wP5U2zf9Wtds/IA7LDx36U1uGjG9AZAAAAAABA/yOQB2CNvPTS83n+/GNywOIp3Wp3bn5c9jr17JRmf60AAAAAAAAsJTkBYLWmPnxPyg+Oze7VMzXjHVXJlN3+Nnu//28b1BkAAAAAAED/JZAHYJXuuuXX2eoXH81GmVUzPj+teeLQ/81ebzq2QZ0BAAAAAAD0bwJ5AFbq91d/O6+7/a/TVhbXjL+cDTPvmB9k590OaFBnAAAAAAAA/Z9AHoBuqs7O3PCd/y+HPP6NNJWqpvZ48/iMPuXybL3Fdg3qDgAAAAAAYGAQyANQY8HChbn97FPypplXJ6W2dt+wfbPtX/wkbSM3bExzAAAAAAAAA4hAHoA/mz795Txx7rE5aNHt3Wp3bnJU9jz9gjQNGdqAzgAAAAAAAAYegTwASZInpz6URRe/P3t3Pt6tdueOn8reJ3whKaVbDQAAAAAAgBUTyAOQe/94Uza5+oN5bWbUjC+shuSRg76Svd/y4QZ1BgAAAAAAMHAJ5AEGuT/84vvZbfJfZURZWDM+I6Mz8z3fzYS9D21QZwAAAAAAAAObQB5gkKqqKjd9/4s58OEvp7lUNbWnmrZK24cvy/hxOzeoOwAAAAAAgIFPIA8wCC1evDi3nHtWDnn50mS518I/0LpHtj7r8ozYYJPGNAcAAAAAALCeEMgDDDIzZ76Sh885PgcvmNyt9qcN357dzrwoLa3DGtAZAAAAAADA+kUgDzCIPP/sk1n0/Q9k385HutXu2PbM7H3S/0tpampAZwAAAAAAAOsfgTzAIDH75afTdvGns02m1YwvqprzwP5fzD7vOLNBnQEAAAAAAKyfBPIAg8DsZ+7NUS98PaPK/JrxWRmRF4/8VvbY74gGdQYAAAAAALD+EsgDrMeqqsotV/xvjnvhvzKkdNTUni2bpZz442y//Z4N6g4AAAAAAGD9JpAHWE+1t7fn5m9+Koe+cHFSamsPD90lrznjiozeaPPGNAcAAAAAADAICOQB1kNz587JPf93Yg6Z97tutbtGH5pdP/b9DGkb0feNAQAAAAAADCICeYD1zIsvPJNp3zw6+7ff361225YfzL6nfC2lqbkBnQEAAAAAAAwuTY1uAID6eeS+P2XROW/OrsuF8e1VU67a8OTsdtKXhfEAAAAAAAB9xB3yAOuJO2+6Jtv8+vRsUObUjM/JsPxyi79Iy2Z7NKgzAAAAAACAwUkgD7Ae+P3lZ2ffu/4xraW9ZvzFslHmvOe7aXn85QZ1BgAAAAAAMHh5ZD3AANbZ0Zkbv/m5HHj333UL4x9r2S5Dz/xdNt/xdQ3qDgAAAAAAYHBzhzzAALVgwfzc+X8fziGzf9mtNmXEAdnhYz9M24gxmT9/fgO6AwAAAAAAQCAPMAC9PO2FPHPeMZm4+O5utTs2OzZ7nXpOmlr8EQ8AAAAAANBI0hqAAebxR+5Lvn9M9qieqRnvrEru2u1vss/7/65BnQEAAAAAALAsgTzAADLllt9ki198NBtlZs34vKo1T7zp69n70OMb1BkAAAAAAADLE8gD9AOzZ07P9GenZuH82WkdNipjt9gmo8aMrZkz+WcXZu/bPpe2srhmfFo2zNz3fz+77HZgX7YMAAAAAADAagjkARqk6uzMvZOvyaKbz88ecyZlXOn8c629asodow7K0ImnZ9f9j8hN3/uXHDz162kqVc0ajzePy8iTL8+4Lbfv6/YBAAAAAABYDYE8QAM8ctektFx1VnbrfHLJQKmtt5TO7DPnxuS6GzPzuhF5Y+Z2m3PfsNdlm7Muy7DRG/ZN0wAAAAAAAKwVgTxAH5ty4xXZ7jdnZHhZuEbzx2Rut7E7Nj4qe55xQZqHDK13ewAAAAAAANRJU6MbABhMHrlr0lqF8Svyxx0+mX3+4jvCeAAAAAAAgH5OIA/QR6rOzrRcdVaPwvjns3H2OeELSSmrnQsAAAAAAEBjCeQB+si9k6/J+KXvjF9Hm2Va7pv88zp1BAAAAAAAQG8SyAP0kUWTz6/LOgsnn1eXdQAAAAAAAOhdAnmAPjB75vTsMXtSXdbaY/ZNmT1zel3WAgAAAAAAoPcI5AH6wPRnp6aldNZlrZbSmenPPV6XtQAAAAAAAOg9AnmAPrBw/uz6rjdvVl3XAwAAAAAAoP4E8gB9oHXYqPquN3x0XdcDAAAAAACg/gTyAH1g7BbbpL2qzx+5i6vmjN18fF3WAgAAAAAAoPcI5AH6wKgxY3P3qIPqstaUUQdl1JixdVkLAAAAAACA3iOQB+gDHR0dWVwNqctarRPPqMs6AAAAAAAA9K6WRjcAsL6bN29OpvzfSdl/7vU9Xmtq07jsOvGIOnQFAAAAAABAbxPIA/Sil154Ji9985js335fj9eaV7Wm46izU5o83AQAAAAAAGAgEMgD9JLHHrgrQ350XHatnqsZ76hK2tOc1tK+xmvNq1rz6GHnZfc96/MeegAAAAAAAHqfQB6gF9z1+59n3K9OzQZlTs34nAzLU4efkyGjN0nLVWdlfOeTq11ratO4dBx1tjAeAAAAAABggBHIA9TZ5CvPzevu/HyGLncH/Itloyw89ofZZZf9kiTV7nfl3sk/z8LJ52aP2ZPSUjr/PHdx1Zwpow5K68QzsuvEIzymHgAAAAAAYAASyAPUSWdHZyZd9Hc55Klzk1Jbe6xlu2xw6hXZdLNxfx4rTU2ZcOCRyYFHZvbM6Zn+3ONZOG9WWoePztjNx2efMWP7+FcAAAAAAABAPQnkAepgwYL5ufPsj+SQWb/oVpsyYmJ2+NiP0jZizEqPHzVmbEYJ4AEAAAAAANYrAnmAHprx8kt56ryjM3HRXd1qf3zN+7P3aeemqcUftwAAAAAAAIONhAigB5589P50fu+Y7FE9XTPeWZXcuetf53XH/X2DOgMAAAAAAKDRBPIA6+jeW3+bza79SDbKzJrxeVVrpr7xq3ndmz/QoM4AAAAAAADoDwTyAOvg1mu/k93/8NkMK4tqxqdlg8w5+nuZsMfBDeoMAAAAAACA/kIgD7AWqs7O/P7if84Bj30tTaWqqT3R/NoM/+jlGb/VDg3qDgAAAAAAgP5EIA+whhYtWpTbzzktB824Mim1tfva9sm4sy7LiDFjG9IbAAAAAAAA/Y9AHmANzHxleh4757gcsPDWbrU7NnpX9jzz22keMrQBnQEAAAAAANBfCeQBVuPZJx/NvIuOzt6dU7vVbt/+49n3xH9NSlnBkQAAAAAAAAxmAnmAVXjgTzdnwytPzPaZXjO+sBqSByd+Kfu+/eQGdQYAAAAAAEB/J5AHWInbr/tRdpn0iYwoC2rGX8movPyui7LHvoc3qDMAAAAAAAAGAoE8wHKqqsrvL/nPTHzwP9JcqpraU01bpuWDl2a7bSY0qDsAAAAAAAAGCoE8wDLa29tzy3l/kYNe+mGy3GvhH2jdLVueeXlGbfiaxjQHAAAAAADAgCKQB+gyZ86sPPh/x+eg+b/vVrtzg7dkt7MuzpDWYQ3oDAAAAAAAgIFIIA+Q5MVnn8yMbx2d13U81K1227jTsu+Hv5TS1NSAzgAAAAAAABioBPLAoPfIvbdn+KUnZKe8WDO+uGrOPa/7t7z+3R9rUGcAAAAAAAAMZAJ5YFC784arst1vz8zoMq9mfHaG59m3fzN7T3xngzoDAAAAAABgoBPIA4PW73/y9ew35QsZUjpqxp8rm6bzhEuz0457NaYxAAAAAAAA1gsCeWDQ6ezozM0X/FUOeu6ipNTWHh6yUzY57fJssOlWDekNAAAAAACA9YdAHhhU5s+blylnn5SD5vymW+1Pow7JLh/7QVqHjWpAZwAAAAAAAKxvBPLAoDHtxefywvlHZ7/2e7vVbt/ipLzu1K+nNDU3oDMAAAAAAADWRwJ5YMCbPXN6pj87NQvnz07rsFEZu8U2GTVmbM2cJx6+O00/ODYTqudqxturpty1x+ez79Gf7cuWAQAAAAAAGAQE8sCAVHV25t7J12TRzednjzmTMq50/rnWXjXljlEHZejE0zNh4pG55w+/yla/PDUbZnbNGnPTlicOOzuvO+Tovm4fAAAAAACAQUAgDww4j9w1KS1XnZXdOp9cMlBq6y2lM/vMuTG57sa8cN1G2al6JUNLR82cF7NRFhx7SXbddf8+6hoAAAAAAIDBRiAPDChTbrwi2/3mjAwvC9do/mvycrfA/rGWbTPm5Cvy2i3G179BAAAAAAAA6CKQBwaMR+6atFZh/IpMGb5/tj/rxxk2aoP6NQYAAAAAAAArIJAHBoSqszMtV53VozD+lYzMrp+6Os1DW+vYGQAAAAAAAKxYU6MbAFgT906+JuOXvjN+HW2QOXngtl/XqSMAAAAAAABYNYE8MCAsmnx+XdZZOPm8uqwDAAAAAAAAqyOQB/q92TOnZ4/Zk+qy1h6zb8rsmdPrshYAAAAAAACsikAe6PemPzs1LaWzLmu1lM5Mf+7xuqwFAAAAAAAAqyKQB/q9hfNn13e9ebPquh4AAAAAAACsiEA+SSllXCnlK6WUB0opc0sp00spt5VSPldKGV7H8xxRSrmilPJ0KWVh1/cVpZQj1uDYi0op1Rp+xterZ+gPWoeNqu96w0fXdT0AAAAAAABYkZZGN9BopZR3JflekmUTuuFJ9u36nFpKObKqqkd6cI6mJOcnOWW50pZdn/eUUi5IckZVVfV5LjesR8ZusU3aq6a6PLZ+cdWcsZuP73lTAAAAAAAAsBqD+g75UsreSX6UJWH8nCSfT3JAksOSfLNr2o5Jriml9OQW3X/Pq2H8nUlOSLJf1/edXeOnJvm3NVjr2SS7r+bzTA96hX5n1JixuXvUQXVZa8qogzJqzNi6rAUAAAAAAACrMtjvkP9akmFJ2pO8taqqycvUfltKeTjJl7IklP9Mki+s7QlKKTsm+WzX7u1JDqmqan7X/m2llJ8muSFL7sb/XCnl26u5G39xVVX3rG0fMNDN33iPZM6NPV6ndeIZdegGAAAAAAAAVm/Q3iFfStkvycFdu99aLoxf6itJ7u/a/mQpZcg6nOpTefUHHz6+TBifJKmqal6Sj3fttiT5q3U4B6zXbv7Rl7P/1P/r8TpTm8Zl14lH1KEjAAAAAAAAWL1BG8gnec8y2xeuaELX+9y/27W7QZJD1+YEpZSS5Kiu3QeqqrplJee5JcmDXbtHdR0Hg15HR0d+f/ZZOeD+f0tLqXq01ryqNR1HnZ3SNJj/2AMAAAAAAKAvDeZkaukLqecm+eMq5t2wzPaBa3mObZJssYJ1VnWeLZOMX8vzwHpn7pzZufMrR+XAF3/QrdZerd0fXfOq1jx62HnZfs/6vIceAAAAAAAA1sRgfof8Ll3fj1RV1b6KeQ+s4Jg1tetK1lmT80xdybyNSik3JNktycgk05PcneTqJN/uegT+OimlbLWaKZst3Zg/f37mz5+/qrk9tmDBghVus/6b9sLTmXvxidm348FutVu2/EhG7/HuDPvlp7JN55OrXWtq07jMP+Jr2X63N/T679n+yrUEPec6gp5zHUF9uJag51xH0HOuI6gP1xL0nOuIeuuNLKlUVc8eAz0QlVLakiz9X/OaqqreuZr5c5KMSHJLVVUT1+I8ZyY5p2v3/VVV/WQVc49JcmnX7plVVZ23XP2iJB9ezSmfSXJsVVU3r2mPy51jjX8zXHDBBdl4443X5TSwSvOmP5NDHv/vbFleqhlfXDXn6rEnp3n8wUmSqrPKwhcfzFYv/jr7t9+eltJZM/fWltfl6U0PT+umO6U0eQsEAAAAAAAAqzZt2rSceuqpS3e3rqrq6Z6uOVjvkB+1zPacNZg/N0sC+ZG9eJ65y2yv6DxVkluy5E74O5K8kKQtye5JTkmyX5Y87v5XpZSDq6q6cy17hYab99z9ecdzX8uYUvugh1nV8Pxyi49n6GYT/jxWmkraNts50zbbOVctnJ/2edOT9gVJS1taho9NS+uwtPX1LwAAAAAAAACWMVgD+WVzukVrMH9h1/ewXjzPwmW2V3Sev6qq6pUVjE8upXwzyb8l+fss+cGBC0op+1Zr//iDrVdT3yzJbUlyyCGHZKutVveE+55ZsGBBbrzxxiw9X1ubeHV99serz80Rz30pQ0tHzfhzZZPMPfp7eef2ezaos4HPtQQ95zqCnnMdQX24lqDnXEfQc64jqA/XEvSc64h6e/rpHt8Q381gDeSXfYnE0DWY39r1vbYvDVib87Qus93tPCsJ45fWqiSfL6Xsn+SwJPskOSDJ79e40yXrrPJ3WCmvPvZ72LBhGTZsbX8+Yd21tbX16fnoO50dnbn525/NQc98K1nuyfIPt+yYjU+/Iptv2rs//DGYuJag51xH0HOuI6gP1xL0nOsIes51BPXhWoKecx1RD73xe6ip7isODLOX2V6Tx9CP6Ppek8fbr+t5RiyzvbbnWWrZ986/cR3XgD6zYP683P7V9y8J45fzp5EHZ+tP/zYbCuMBAAAAAAAYoAblHfJVVS0opbycZKMkq0z7Sikb5tWw/Km1PNWyd5yvLlVc9nHxa3uepe5bZnvLdVwD+sT0l57Pc+cfnf0W39OtdtvmH8jrTvlGmloG5R9RAAAAAAAArCcG6x3yyavh9fallFWlfjsvs33/Op5j+XXqfZ6l1vad8dAQTzw8JXPOPjQTlgvjO6qS23f7h7z+jHOE8QAAAAAAAAx4gzmQn9T1PSLJ61Yxb9lHv6/VO9mTTE3y7ArWWZFDur6fSfL4Wp5nqV2X2X52pbOgge655VcZ9f0j8tqq9rfo3Kot9x/6zex7zOca1BkAAAAAAADU12AO5K9cZvujK5pQSmlK8qGu3VeSXL82J6iqqkpyVdfuzqWUN6zkPG/Iq3fIX9V13Lo4Y5ntG9ZxDeg1t/7sm9nh5x/I2MyuGX8xG+Wl91+V3d70/gZ1BgAAAAAAAPU3aAP5qqpuTXJT1+4ppZSJK5j2mSS7dG1/raqqxcsWSylvKqVUXZ+LVnKqrybp6Nr+Rill2HJrDEvyja7d9q75WW7OG0opm6/s11KW+Lckh3cN3ZW1v5sfek3V2ZnfX/h32e/2z6a11FxGeax5mzSd9uuM322FP68CAAAAAAAAA9Zgf0nzJ7MkuB6W5FellC9myV3ww5Icn+T0rnkPJfnKupygqqqHSilfTvK3SfZN8vtSyn8meTTJdkn+JsneXdO/XFXVwytY5u1J/raU8osk12XJu+lfSdKaZI8kJyfZv2vuvCSn9eAue6irRQsX5o5zPpoDX7mmW23KsP2y3cd+nOGjNmxAZwAAAAAAANC7BnUgX1XVnaWU45J8L8noJF9cwbSHkhxZVdXsFdTW1OeTbJolwfneSX64gjnfSvIPq1ijNclRXZ+VeTLJB6qqum0d+4S6mjnj5Txx7jF5w8I7utVu2+R92eeM89PcMqQBnQEAAAAAAEDvG9SBfJJUVXV1KWWPLLlb/sgkWyVZlOSRJJcm+d+qqub18BydWfJY/Muy5K771yfZOMm0JLclOa+qqp+vYokLk7yQZGKW3BG/aZKNsuQR99OS3JHk6iQ/qKpqQU96hTUxe+b0TH92ahbOn53WYaMydottMmrM2Jo5zzz+UBZ+95js0flEzXhnVXL7Tp/Ofif8Y1JKX7YNAAAAAAAAfWrQB/JJUlXVE0k+3fVZm+N+l2SNE8Wqqq5Ncu1aNZc/93dO1wcaourszL2Tr8mim8/PHnMmZVzp/HOtvWrKHaMOytCJp2fCxCPzwJ8mZZOffjBb5pWaNeZXQ/PQgV/Jfm/9UB93DwAAAAAAAH1PIA+s1iN3TUrLVWdlt84nlwws92MoLaUz+8y5Mbnuxjx/3cYZX83MsLK4Zs7LGZNX3nNx9tz7jX3UNQAAAAAAADSWQB5YpSk3XpHtfnNGhpeFazR/s0zrFtg/0bR12j58WbYbt1MvdAgAAAAAAAD9k0AeWKlH7pq0VmH8itzbuldee9ZlGbXBxnXsDAAAAAAAAPo/gTywQlVnZ1quOqtHYfysjMgOf/XzDG0bXsfOAAAAAAAAYGBoanQDQP907+RrMn7pO+PX0ejMzcN/vL5OHQEAAAAAAMDAIpAHVmjR5PPrss7CyefVZR0AAAAAAAAYaATyQDezZ07PHrMn1WWtPWbflNkzp9dlLQAAAAAAABhIBPJAN9OfnZqW0lmXtVpKZ6Y/93hd1gIAAAAAAICBRCAPdLNw/uz6rjdvVl3XAwAAAAAAgIFAIA900zpsVH3XGz66rusBAAAAAADAQCCQB7oZu8U2aa/q88fD4qo5YzcfX5e1AAAAAAAAYCARyAPdjBozNnePOqgua00ZdVBGjRlbl7UAAAAAAABgIBHIAyu0YLN967JO68Qz6rIOAAAAAAAADDQCeaCbP1zxv9n34a/2eJ2pTeOy68Qjet4QAAAAAAAADEAtjW4A6D+qzs5M/vbncsDTFySlZ2vNq1rTcdTZKU1+7gcAAAAAAIDBSSAPJEkWzJ+Xu8/5cA6Y9atutfaqKS2lc43Xmle15tHDzsvue9bnPfQAAAAAAAAwELl1FciMaS/k0f9+a/ZbQRh/62bHZ+q7r8zjTa9do7WmNo3Ls++7PLsf8t56twkAAAAAAAADijvkYZB76tF7U33v/ZlQPVMz3lGV3DHh77LfsX+TJKn2viv3Tv55Fk4+N3vMnlRzx/ziqjlTRh2U1olnZNeJR3hMPQAAAAAAAEQgD4Pafbf+Optd+5GMzeya8XlVax554zfy+jcf9+ex0tSUCQcemRx4ZGbPnJ7pzz2ehfNmpXX46IzdfHz2GTO2r9sHAAAAAACAfk0gD4PUbdd8K3vc+jdpLYtrxl/K2Mw+5vvZY/cDVnrsqDFjM0oADwAAAAAAAKskkIdBpurszOSL/ykHTP1GUmprjzWPz6iTr8i2W27bmOYAAAAAAABgPSKQh/XE7AWL8/zMBZm7qCMjhjZnszFtGdU2pGbO4kULc8c5p+SAGVd3O37KsNdn27MuzYjRG/ZVywAAAAAAALBeE8jDAFZVVSY/9nIunvxEbr5vajapXs6ILMjctOWlslEOnLBNTnrDuEzcdqPMmjk9T5zz/uy/8I/d1rl14/dknzO+mZYhQxvwqwAAAAAAAID1k0AeBqh7npmZT//ozmw07dZ8sPm6fGPI7WkpnX+ut1dN+eUD++Yb974lXxszPl9c8K/Zo3qy2zp/2OGvst8J/5TS1NSX7QMAAAAAAMB6TyAPA9BND7+Ur158ab6R/8tOQ59e4ZyW0pkjm2/Nkc23pn1eU01YnyQLqiF54ICvZP+3fbgvWgYAAAAAAIBBRyAPA8w9z8zMRRdfmO+W/8qIsnCNjlk+jJ+e0Xn53d/JXq97c2+0CAAAAAAAAEQgDwNKVVU5+5LL8vW1COOX91S1cZo+/LPssO0ude4OAAAAAAAAWJaXRsMAMvnRafnkrK+scxifJAuqoXmyc5M6dgUAAAAAAACsiEAeBpBbr78qOzWt+J3xa2qHpmfzh+t/WqeOAAAAAAAAgJURyMMAMXvB4uz41I/rstYOT/4wsxcsrstaAAAAAAAAwIoJ5GGAePGll/LWcltd1npb0215cdpLdVkLAAAAAAAAWDGBPAwQi2Y8nZbSWZe1WkpnFs14pi5rAQAAAAAAACsmkIcBYkQW1HW9kZlf1/UAAAAAAACAWgJ5GCA2Gju2ruuN3XCjuq4HAAAAAAAA1BLIwwAxYuPXpqNOl2xHmjNi463rshYAAAAAAACwYgJ5GCjaRmfa5m+sy1Izxr0taRtdl7UAAAAAAACAFRPIwwDxxIN/StNzf6rLWhu96ay6rAMAAAAAAACsnEAeBoB7b742G1zyjmySGT1ea8GGO6WMP7gOXQEAAAAAAACrIpCHfu7Wq87JDr88KWMyt8drdbQMT9v7z09KqUNnAAAAAAAAwKq0NLoBGKxmz5ye6c9OzcL5s9M6bFTGbrFNRo0Z++d61dmZWy78m0x86vxkufz8maYtslmZkeaO+Wt8vo6W4Wk+4fvJFnvV6VcAAAAAAAAArIpAHvpQ1dmZeydfk0U3n5895kzKuNL551p71ZQ7Rh2UoRNPz/b7vDlTzv1oJs78Zbc1/jT8gOz0sR+ledZjqa44M+Wl+1d/3k13TfN7zhHGAwAAAAAAQB8SyEMfeeSuSWm56qzs1vnkkoHl7npvKZ3ZZ86NyXU3Zt6vWvP6srDbGn/Y9Njse/o5aW5pSUbulfKxycnjk5Lbvpnq/p+lVB1/nls1taTs/M7k9aemjD/IY+oBAAAAAACgjwnkoQ9MufGKbPebMzJ8BSH7iiw/r6Mq+eMuf539j//72omlJNscnGxzcMqCWcns55KFc5LWkSmjNk/aRtfrlwAAAAAAAACsJYE89LJH7pq0VmH88uZXQ/LQId/IfoedsOqJbaMF8AAAAAAAANCPNDW6AVifVZ2dabnqrHUO45Pk5aaNssehx9WxKwAAAAAAAKAvCOShF907+ZqMX/rO+HW0VfV87pv88zp1BAAAAAAAAPQVgTz0okWTz6/LOgsnn1eXdQAAAAAAAIC+I5CHXjJ75vTsMXtSXdbaY/ZNmT1zel3WAgAAAAAAAPqGQB56yfRnp6aldNZlrZbSmenPPV6XtQAAAAAAAIC+IZCHXrJw/uz6rjdvVl3XAwAAAAAAAHqXQB56SeuwUfVdb/jouq4HAAAAAAAA9C6BPPSSsVtsk/aqPpfY4qo5YzcfX5e1AAAAAAAAgL4hkIdeMmrM2Nw96qC6rDVl1EEZNWZsXdYCAAAAAAAA+oZAHnrR0Imn12Wd1oln1GUdAAAAAAAAoO8I5KEXTZh4ZB5vem2P1pjaNC67TjyiTh0BAAAAAAAAfUUgD72oNDWl/ahzMq9qXafj51Wt6Tjq7JQmlyoAAAAAAAAMNFI+6GXb73lQHj3svLUO5edVrXn0sPOy/Z71eQ89AAAAAAAA0LcE8tAHdj/kvXn2fZev8ePrpzaNy7Pvuzy7H/LeXu4MAAAAAAAA6C0tjW4ABovt9zwo1e535d7JP8/Cyedmj9mT0lI6/1xfXDVnyqiD0jrxjOw68QiPqQcAAAAAAIABTiAPfag0NWXCgUcmBx6Z2TOnZ/pzj2fhvFlpHT46Yzcfn33GjG10iwAAAAAAAECdCOShQUaNGZtRAngAAAAAAABYb3kmNgAAAAAAAAD0AoE8AAAAAAAAAPQCgTwAAAAAAAAA9AKBPAAAAAAAAAD0AoE8AAAAAP9/e/cdLktVJmr8/chJRQcVMZFkgAEFAUUQAdFrQAHRUUSvoJhGHcVRx4Bc0THMmAPKRfR6cEzoOKKYUJEDiCCICSOiHCRnkRy/+0etnl003b07VYdz3t/z1NNVXavWWt27v13d9VWtkiRJkiQ1wIS8JEmSJEmSJEmSJEkNMCEvSZIkSZIkSZIkSVIDTMhLkiRJkiRJkiRJktQAE/KSJEmSJEmSJEmSJDXAhLwkSZIkSZIkSZIkSQ0wIS9JkiRJkiRJkiRJUgNMyEuSJEmSJEmSJEmS1AAT8pIkSZIkSZIkSZIkNcCEvCRJkiRJkiRJkiRJDTAhL0mSJEmSJEmSJElSA0zIS5IkSZIkSZIkSZLUABPykiRJkiRJkiRJkiQ1wIS8JEmSJEmSJEmSJEkNMCEvSZIkSZIkSZIkSVIDTMhLkiRJkiRJkiRJktQAE/KSJEmSJEmSJEmSJDXAhLwkSZIkSZIkSZIkSQ0wIS9JkiRJkiRJkiRJUgNMyEuSJEmSJEmSJEmS1AAT8pIkSZIkSZIkSZIkNcCEvCRJkiRJkiRJkiRJDTAhL0mSJEmSJEmSJElSA0zIS5IkSZIkSZIkSZLUABPykiRJkiRJkiRJkiQ1wIS8JEmSJEmSJEmSJEkNMCEvSZIkSZIkSZIkSVIDTMhLkiRJkiRJkiRJktQAE/KSJEmSJEmSJEmSJDXAhLwkSZIkSZIkSZIkSQ0wIS9JkiRJkiRJkiRJUgNMyEuSJEmSJEmSJEmS1AAT8pIkSZIkSZIkSZIkNcCEvCRJkiRJkiRJkiRJDTAhL0mSJEmSJEmSJElSA0zIS5IkSZIkSZIkSZLUgFWm3QHNlZVbM5dccknjjd10001ceeWVAFx44YWsueaajbcpLY+MJWl0xpE0OuNIGg9jSRqdcSSNzjiSxsNYkkZnHGnc2nKgK3crN4jIzHHUoxVARGwPnDntfkiSJEmSJEmSJElSw3bIzJ+OWolD1kuSJEmSJEmSJEmS1ACvkFffImJ1YOuyeAVwR8NNrs/CFfk7AJc23J60vDKWpNEZR9LojCNpPIwlaXTGkTQ640gaD2NJGp1xpHFbGbhvmT87M28ZtULvIa++lQ/cyMMy9Csi6ouXZuaFk2pbWp4YS9LojCNpdMaRNB7GkjQ640ganXEkjYexJI3OOFJDzh9nZQ5ZL0mSJEmSJEmSJElSA0zIS5IkSZIkSZIkSZLUABPykiRJkiRJkiRJkiQ1wIS8JEmSJEmSJEmSJEkNMCEvSZIkSZIkSZIkSVIDTMhLkiRJkiRJkiRJktQAE/KSJEmSJEmSJEmSJDUgMnPafZAkSZIkSZIkSZIkabnjFfKSJEmSJEmSJEmSJDXAhLwkSZIkSZIkSZIkSQ0wIS9JkiRJkiRJkiRJUgNMyEuSJEmSJEmSJEmS1AAT8pIkSZIkSZIkSZIkNcCEvCRJkiRJkiRJkiRJDTAhL0mSJEmSJEmSJElSA0zIS5IkSZIkSZIkSZLUABPykiRJkiRJkiRJkiQ1wIS8JEmSJEmSJEmSJEkNMCGvkUTEQyPiAxHx+4i4ISKujogzI+INEbHWGNt5SkR8LSIujIhbyuPXIuIpA9SxSkS8PCJOiYgrIuKmiPhTRBwZEf8wrr5Kw5iHWIqIJRGRfU4bjqvPUr+ajKOIWCkitoyIAyPiE6XeW2qf+d0GrG+tiPjXUs/Vpb+/L/1/6Ch9lUYxD3EUEUv73R+N0l9pFA3H0loRsW9EHFHqvCYibouIqyLitIg4LCLWH7A+90maOfMQR+6TNOsajqMtIuJVEXF0RPwsquMLN5d2/hwRx0TE3hERfdbncTvNrHmIpfC4nWZck3HUo821Shy1PvvLBtjO30gaq8j0N4GGExFPBz4H3LNLkXOAPTPz3BHaWAn4JHBQj2KfAl6WmXf2qGc94NvADl2K3AK8KjM/NWxfpWHNSyxFxBLggD6b3Cgzlw3SR2kUTcdRRBwALOlRZPfMXNpnXZtS7ZMe1qXI34DnZeY3B+mjNKp5iaOIWArs2k+bmdnXAWBpnJqMpYh4OHAqsM4iRf8GvDQzj1mkPvdJmknzEkfukzTLJvDd7nPA8/ooehLwzMy8qkddHrfTzJqXWPK4nWbZJI5/d2n3/cDrak+dn5kbLrKNv5HUiFWm3QHNp4jYFjgGWBO4HngPcGJZ3g94CbAZ8K2I2D4zrxuyqXexkED8OfBe4E/AJsC/AtsCLwauAN7Spa8rA19j4Uv9fwNHAVcDjwbeCtwPODIiLsrM7wzZV2lg8xRLNRcDT1qkzEVD9lMa2ITiqH4A9TbgbGBVYOsB+3oP4FssfKk/CvgScBOwO/Bmqh8nx0TEzpn5iyH6Kg1snuKo5qfAC4fcVmrEBGLpniwkEU8FvkkVC1cB9wX2LW3cE/h8RPyt2+8b90maVfMURzXukzRTJvTd7nbgJ1RxdDZwKdUxhXsDmwMvA7aiOmnluIh4bKcLADxup1k2T7FU43E7zZQJHv/u1O7BwM1UxyDu0cc2/kZSczLTyWngCTgZSKp/ZI/psP4NZX0Chw3Zxmal/gTOBNZsW79Web7Vj0271POiWl8+3mH9psC1Zf0fgVWm/f46rTjTnMXSklJm2bTfNyen+jShOHoU8M/AjsAa5bnDavXu1mc976ht84YO63eqxevSab+3TivONGdxtNQYcZrVqelYKvuJY4Ate5TZG7iztHEuZWS8DuXcJznN5DRnceQ+yWkmpwl9t+t5/AxYGfhqrZ29upTzuJ3TzE5zFktL8Lid0wxOk4ijDnWuTHXCZAKHAsv6iQ9/Izk1OXkPeQ0sIh4F7FIWP52Zp3Uo9gHgd2X+NRGx6hBNHczCKA7/nJk31Vdm5o1UB3Up5V7bpZ7Xl8erqf6530VWw6C8pyxuCjxjiL5KA5vDWJJmzqTiKDPPyMyPZebpmXnzkH1dFXh1Wfxd6Vd7Oz8GPl0Wd42IbkM2SmMzT3EkzbJJxFJm/jgzn5OZv+1R5utUVxdCNRrSth366j5JM2me4kiaVRP8bnf7IuvvAN5Xe2qXLkU9bqeZNIexJM2cCR7/bvcaYDvgD8B/9LOBv5HUNBPyGsY+tfnPdCqQ1bA5ny2L61IN59G3iAiqM9IBfp+Zp3dp53Sqf6oAe5ft6vVsBmxRFr9cEo+dLKnN+8Vek7JPbX6mY0maYfvU5huJozHaHbhXmT86uw8xt6Q27z5Jk7BPbX7W40iaZfvU5qcdSyfW5jfpsN59kmbVPrX5WY8jaVbtU5ufdhzVhx1eo32lx+004/apzc90LEkzbJ/a/ETiKCIeSnWlO8DLM/PWPjf1N5IaZUJew3hsebwBOKtHuZNq8zsP2MZGwAYd6unVzgOBDdvWPbZDubvJzEuBc8rioH2VhjVPsSTNqknE0bj0tU+iGlKrdSDKfZImYZ7iSJplsxRLq9fm7+iw3n2SZtU8xZE0q2Ypjvarzf++w3qP22mWzVMsSbNqGnH0CWBt4D8zc+kA2/kbSY0yIa9htM5cPXeRIXXqXw626Fqqsy271DNoO8PU8+CIWHuRstI4zFMs1f1dRJwUEVdFxC0RcUlEHB8Rr4qItQbsnzSqScTRuPQVj+V1nFsWp9VXrVjmKY7qNo+In0TEXyPi5oi4MCK+HhEvGNMQd9KgZimWdq3N/67DevdJmlXzFEd17pM0S6YaRxGxXkQ8JiI+DRxSnr4S+HyH4h630yybp1iq87idZslE4ygi9gOeClwDvG7Azf2NpEaZkNdAImINYL2yeGGvspl5DdWZTwAPHrCpB9Xme7YDXFCbb29nmHqibTtp7OYwlurWAR4H3AdYDVgf+F/Ax4BzImKnAfsoDWWCcTQurXi8ITP/ukjZVjzeNyJW71lSGsEcxlHd/YFHUQ0ptzrVCC97AUcDv4gIfxhrYmYpliLiEcCeZfHszOyUSHSfpJkzh3FU5z5JM2FacRQRSyMiIyKBK4AfAy+iOsZ2JfCMLvsbj9tpJs1hLNV53E4zYdJxFBH3Bj5cFt+UmVcMWIW/kdQoE/Ia1D1q89f3Ub71T3SdBtu5oTbf3s646pHGbd5iCSCB06nOyn0K8EhgJ+BlwBmlzAOB70XEtgP2UxrGpOJoXFr9HaSv4D5JzZq3OAK4EziB6mz3JwDbUh1wOpiFKxi3BE6MiIdMo4NaIc1ELJWDQZ8CVi5PHdKlqPskzaJ5iyNwn6TZMxNxVPNRYIvM/FGX9R6306yat1gCj9tp9kw6jt5HdZLkacBRQ2zvbyQ1apVpd0BzZ43a/K19lL+lPK7ZYDu31Obb2xlXPdK4zVssAby2y9mBp0XEUcA7gbdQ3aPnUxGxfWbmIJ2VBjSpOBqXVn8H6Su4T1Kz5i2OAPbtsj86JSI+QfXD+wCqH+IfBvadXNe0ApuVWDoc2L7MH52Zx3Up5z5Js2je4gjcJ2n2TCuOXkh1LCCAdali6J+AVwEbR8SLM/OyDtt53E6zat5iCTxup9kzsTiKiMdRjSZxO/DyIT/b/kZSo7xCXoO6uTa/Wh/lW8N13NRgO/UhQdrbGVc90rjNWyzRa6ierBxCdXUILJyFKzVpUnE0Lq3+DtJXcJ+kZs1bHC22P7oNeDHwh/LUMyLigZPol1Z4U4+liHgz1ecf4EzglT2Ku0/SLJq3OHKfpFk0lTjKzPMy89eZeXZmnpKZHwIeDnwbeBpwZkR0Gmbe43aaVfMWSx630yyaSByV0Y0+SXUiy0cy81eDbF/jbyQ1yoS8BnVdbb6foTjWLo/9DPMxbDtr1+bb2xlXPdK4zVss9evI2vyuQ9Yh9WtScTQurf4O0ldwn6RmzVscLSozbwc+XXvK/ZEmYaqxFBEvA95dFn8PPDUzb+ixifskzaJ5i6NFuU/SFMzMd7vMvJnqat8bqe4H/N4OxTxup1k1b7HUL4/baZImFUeHAH9PdV/3tw24bZ2/kdQoE/IaSPkCcFVZ7Hg2XktE3JuFf0wXDNjUhbX5nu1QfRFpaW9nmHqybTtp7OYwlvr129q8V3+oUROMo3FpxePaEbHuImVb8XhFZt7Ss6Q0gjmMo365P9JETTOWIuK5wCfK4vnAEzPzykU2c5+kmTOHcdQv90mamFn7blfi6NSyuHdErNpWxON2mklzGEv9cp+kiZlgHL2xPP4AeHpE7Nc+1epeu/b849vq8TeSGmVCXsNo7bg3jYhVepTbvDb/uyHbaK9n0HaGqeeCUc+Cl/o0T7HUL+89pUmbRByNS1/xWF7HJmVxWn3VimWe4qhf7o80DROPpYjYC/gs1W/7S4A9MrOfJIX7JM2qeYqjfrlP0qTN2ne7K8rjWsB6bes8bqdZNk+x1C/3SZq0ScRRa4j5FwJf7DK1Yma92nP/p0tf2/tzF/5G0rBMyGsYPyqPawPb9ShXH/bm1K6lOjsPuLhDPZ08rjxeBCxrW/ej2nzXeiJifWCzsjhoX6VhzVMs9WvL2vzFXUtJ4zOJOBqXvvZJwPYsnLnrPkmTME9x1C/3R5qGicZSROwBfBlYherKkydm5p/63Nx9kmbVPMVRv9wnadJm7btd/Src9mF9PW6nWTZPsdQv90matFmLo178jaRGmZDXMI6tzb+wU4GIWAl4QVn8K3DiIA1kZgJfL4ubR8SOXdrZkYWzlb5etqvXcw4LZyk9OyLW6tLkgbX5rw3SV2kEx9bmZzqWBvCy2vxJQ9YhDeLY2nwjcTRGS4Fry/wBERFdyh1Ym3efpEk4tjY/63G0qHK2+otqT508rb5ohXNsbb7RWIqInai+461OtW95Umb+ZoAqluI+SbPp2Nr8rMdRP224T9I0HFubn+p3u4h4EPCYsnh+ZtbvJ+xxO826Y2vzMx1LA/C4nSbt2Np8U8e/Y7GJ6pZEUMVP6/nd2qpair+R1CAT8hpYZp4BnFIWD4qIx3Qo9jpgizL/kcy8rb4yInaLiCzTki5NfRi4o8x/LCLWbKtjTeBjZfH2Ur6T95fH+wDvbV8ZEZsAby6L5+I/UU3IPMVSROwYEQ/o9lqi8k7gCeWpX+IZgpqACcbROPp6K/DRsrgF8Pr2MqX/B5XFkzLzzKb6I7XMUxxFxO697uVW7qX4qVpfj8vMWb/fvZYTk4qliNgG+BbVVRk3AHtm5lkD9tV9kmbSPMWR+yTNqknEUURs1uHeu+1l7gV8gYWhhD/bpajH7TST5imWPG6nWTVPxxv8jaSm9bpng9TLa6h22msC34uId1OdubQmsB/w0lLuHOADwzSQmedExPuAN1ENA3JqRPwH8Ceqe3S8Edi2FH9fZv6xS1VHU52RvjPwyjLM1VHANcCjgEOBewJ3Aq/OzNuH6a80pHmJpScDb4qI7wLfp7qnzl+priZ5OFWMPbqUvRF4yQhX2UuDajyOACLiwLantqnNPzkiNqwtn5uZP+Lu3gc8h2q4xfdGxKbAl4CbgN2Bt1B9P7sJOHjYvkpDmJc4OgD4RkR8g+rs9T8AfwPWoRr+7qUsDMN4OdXrkiap0VgqSYnjgXXLU28Fro2IrXpsdnlmXt7hefdJmlXzEkfukzTLmv5utwFwQkT8kurqx7OAS6lO8l+f6hjcQWUe4NfAv3epy+N2mmXzEkset9Msm8jxhjHxN5Kak5lOTkNNwNOphvDILtMfgE27bLtbrdySHm2sBHy6RxtJdcb5Sov0dT3gjB513Ay8eNrvqdOKOc1DLAGHLbJtazof2Hna76nTijdNKI76iYF+6tmU6kdGt22vBZ427ffUacWb5iGOgCV9bvsrYMtpv6dOK+bUZCxRDY84SBwlcFiPvrpPcprJaR7iyH2S06xPDcfRbn1+/hP4JnDfRfrqcTunmZ3mIZbwuJ3TjE9NxlGf7S8r2y/ro6y/kZwambxCXkPLzOMi4uFUZzjtCTwIuJVq+KivAIdn5o0jtnEn1VAmX6U6U2oHqi/pVwJnAkdm5nf6qOfKqO4P9xJgf6ohR9YGLgZOoBoKZaz3ipP6NSex9BngMqr7VT0cuB/wd1Rn7F4J/Aw4DvhCZt48Sl+lYUwijsYlM8+NiG2BVwL/SPVFfzXgAuDbVPuk83tUITViTuLoP4BfUO2PtgTuSzW86S1U+6mfAv8FfC0z7+hSh9SoOYklwH2SZtecxJH7JM20huPoVOBJVMNfb1/qvj+wFtVIEecBpwNfzMxFh8X2uJ1m2ZzEksftNNPm5Lsd4G8kNScyc9p9kCRJkiRJkiRJkiRpubPStDsgSZIkSZIkSZIkSdLyyIS8JEmSJEmSJEmSJEkNMCEvSZIkSZIkSZIkSVIDTMhLkiRJkiRJkiRJktQAE/KSJEmSJEmSJEmSJDXAhLwkSZIkSZIkSZIkSQ0wIS9JkiRJkiRJkiRJUgNMyEuSJEmSJEmSJEmS1AAT8pIkSZIkSZIkSZIkNcCEvCRJkiRJkiRJkiRJDTAhL0mSJEmSJEmSJElSA0zIS5IkSZIkSZIkSZLUABPykiRJkiRJkiRJkiQ1wIS8JEmSJEmSJEmSJEkNMCEvSZIkSZIkSZIkSVIDTMhLkiRJkiRJkiRJktQAE/KSJEmSJEmSJEmSJDXAhLwkSZIkSTURsVVEfC4iLoiIWyMiy7TNGNtYVupc0mHdhrU2DxxXm01qss+1eg8bZ7092ntHae+bk2hvFL0+R1Poy2Gtv9W0+zKoiFgrIi4v/d9t2v2RJEmStHwxIS9JkiRJUhER2wFnAM8DHgSsOt0eaZIi4iHAG8ri26fZF01OZt4IfLAsfjgiYpr9kSRJkrR8MSEvSZIkSVpuRcSBtSusN+xjk/cAawJ/A14BPArYuky/b6yjmhVvBdYAvpuZZ46z4ohYWj6HS8dZr8bm48DVwCOAf5xyXyRJkiQtR1aZdgckSZIkSZoFEbEqsGtZ/GRmHjGNfmTmMmCurtCdxz63i4gHAgeWxQ9MsSuagsy8LiI+CbyJ6sSML0+5S5IkSZKWE14hL0mSJElSZT1gtTJ/zjQ7oql4BdUtCi4Gfjjlvmg6vlAet/Ze8pIkSZLGxYS8JEmSJEmV1Wvzt02tF5q4iFiJhavjv5SZd06xO5qSzDwbOLssHjTNvkiSJElafpiQlyRJkiQtKiIOa92LvSyvGxFvj4jfRMT1EXF1RJwYEc/ts74NI+JDZfvrIuLGiPhjRBwZEVsvsm3rnvCHleXHR8RXIuKCiLgtIpZFxG6lr5+pbXpebdvWtFvrtQHn1cp+pq3cYeN8DX28N612D+xRbrWIeEV536+IiFsj4tKI+HZEPL8kmYfWfs/ziHhYRBxeXuONZd2G/fY5IjaIiH+PiJ9FxLXlb3VZRJwdEV+MiAMj4p5D9HOliDii1v7hETHo8PmPBTYo819dpL11I+KQiDgtIq4pr+OKiPhtRHwtIv4pIu5fK7+kfL5at0PYtcPncFmXtp5S/p5XlPf8nIj4YBlef6Ii4kER8fGI+HNE3BwRF0fENyLiCQPWc6+IeHNEnFr73F4SEcdFxLM6/e0i4v/U3quH9dHG8aXsJRGx8iD9Y+Hvv09ErDHgtpIkSZJ0N95DXpIkSZI0kIjYCPg+sEnt6bWB3YDdImIf4HmZeXuX7V8AfJK7XpEOsGmZDoqIQzPzPX305V3AWwZ9DaMa52sYsv0Nge8Am7etuj/wlDK9LCL2zsyrx9De3sDnqf7Ow2y/C/BNoD3hfr8ybQXsB1xZyvVb76rAZ8u2AO/MzEOH6OLu5fE24Kwe7W0B/ICF5H3LemXaAtgHWBk4fIh+1Nv6IPDatqcfVp57fkQ8dZT6B+xLp7/fA4CnA0/vdMJKl3r2AI4B/q5t1frA08r07Yh4TmZeX1v/BeDtZX7/2nynNu4P7FEWv5SZd/TTt5rTy+M6wC5U/+skSZIkaWgm5CVJkiRJgzoG2Aj4v8B/AdcCDwfeCGwGPJvqPtztyUQiYk9gCRDA9cAHqBKctwM7AW+mSmy+OyL+mplH9OjHvsDWVENMfwj4NbAmsA1wZlm3N/DOUv5JpV915wG/La9jA+D48vxbga/Xyl3e0GsYWESsA5wAbFyeOhb4f1SvbSPgVVRXYz8WOC4iHjdEUrLuIcDngBuBfwNOAe4AdqB6/Yv1d3XgS1TJ3OuAI4ATqd7T1UqfdwKeMUinImItqquZnwwk8C+Z+eFB6qjZpTyenZm39Cj3n1Sfk9uAo6hOiriUagTCBwE7cvfXcQjwfqrRGrYHfgq8sK3MrfWFiDiYhfi5GHgPcAawBrAncDDwFWCtPl7bSCLiISwk4++kOhGlHvdvAg6jel296tmZ6v1aFbgM+BjwS6rXtwHwHOD5wFOBo4FntrbNzHMj4ifAo1kkIV/qaV0V//m+X+iCM2rzu2JCXpIkSdKITMhLkiRJkga1A7B/Zn6x9txPI+IrVMnaRwCvjohPZ+avWwXK1cyfZCGRvUtm/qJWx+kR8VXgNKqrb98fEV/JzCu79GNrqsT0nm1J1JPL468jYvva8+dk5rIO9dwAXB4R9eTyRfW+N/gahvE2FpLx7VeEn1Xa/0/geVSJ7pdSJcGHtRFV0vQxmfmX2vM/6XP7nVm4onz/zGy/Av504IsR8Vr6TDBHxLpUSeKdqU4OeHFmLumzP+11BVUiHeDnPcptDGxXFv8lM9uvgD8D+O+IeCOwbuvJzLwIuCgibihP3dDps1Vr537Au8ri+cCOmXlprcjJEXE81ckjkziu8wEWrox/fo+43/5uWxYlbj5HlYz/LvDMzLyxVuRnwDcj4mSq+No3Ip6YmfVk+OepEvKbRcT2mdntBID9y+M5Pcp0lZnXRMR5VJ/77RYrL0mSJEmL8R7ykiRJkqRBfbMtKQdAZl5HlfyF6vfmy9uKPIOFxOw72xLZrTrOB95QFtfi7lcS191JlYjtdUXzuI37NQykXG3+4rL4G6ork9vbT+AVwFXlqVeNoek3tSXjB7F+bf7kboUy8/bM/NtilZUhyZdSJeNvAZ41bDK+uDcLQ/Ff3qNcv68jM/OaEfpzAAsnJryuLRnfauOHVFfoNyoi1mfhiv9+4r6b/YANgZuBF7Ql4+t1HcXCFeoHtq0+hurkC6hONunU302okvYw3NXxLa3PwcY9S0mSJElSH0zIS5IkSZIG9ZluKzLzDKpEMcAT2la3lpNqiPVuvkI1HHanOupO7XLFe5PG/RoGtR0LV18v6TYUfUlsf7ksbhkRDxihzVupXs+wLqnNj3RyQkRsCPyIahSG64GnZuaxo9QJ3Lc23yuRXn8dB47YZi+tz8s13PW2Ce16ff7GZXcWhn/vN+472as8npSZVyzSZutkh8e0tXE5C8PHPyciOh3T2r82/4VF2unl6vK4fs9SkiRJktQHE/KSJEmSpEGducj61hWum0XEarXntyqP5/VKymXmrSwMHb5Vt3LArxbpRxPG/RqGbR8WHzK+vn6UPvwxM28eYfsfAX8u8x+OiDMi4s0RsXPb52MxWwCnAptSXf2/R7lSfFT3qc13Tchn5nlUQ7MDvDYifhMR74iIx5f72Y/L1uXx55l5e49yv6Dt3vMN2Lo232/cd9Iazv5JEZG9JuD1pWynZHjrqvcHAI/vsL6VkP9JZp67SH97aX0O1u5ZSpIkSZL6YEJekiRJkjSoXsN6A1xWHoNqOPCWVuJzse0BWsN036dHmVGGBR/WuF/DsO3304f6UOej9GGk9zkzbwOeDvyuPLUD8G6qRP1fI+K7EbF/RKzcrY7i2SzcLuCfylXZ41A/2WDNRco+FzitzG8JHAqcQPU6To6Il0fEGiP2p6/PWEnWX92rzBgM8nm7rMe6+w3Rdqe/xbFAa7j7uwxbHxGPBDYvi6MMV19v+7YR65EkSZIkE/KSJEmSpIHllLdv6Thc+4SM6zWMYlJ9GPl9zszfUl1t/QyqodZbVy+vCTyJKoH6k4jolbg9HrihzB8eEVuO2q+iPtJBzxMXMvOizNyJalj5T1AN057AqsAuwBHAryNiszH0axY+Y3Wj9Kd1ssV3qD4H/U537UDm9SwM479v28kPravj76C63/woWp+Dv45YjyRJkiSZkJckSZIkDez+fa5P7np19dVt63tpDVfd9BXAg5r2a6jXtVgf6kN+T/19zMw7MvPYzDwoMx9GdbX7i4CzSpHtgCN7VHE6sCfVFdL3A06IiL8fQ9fqCfl7dy1Vk5knZOYrM3MrqnvQ7we0hs/fhNESwq2Y6fn3jYhVGO/oC736Av3HfSdXlcfVMvPX/U5d6mpd/X5P4GkA5X7y+5Xnv1/uNz+K1ufgLyPWI0mSJEkm5CVJkiRJA9uhz/V/LPdSb2kl2DaKiPt22zgiVgW2bdtmWOO+yngar6FT+wCPXqTso7psNxMy85LM/AzwGOBn5emnRUTXYeMz8ySq4e9vojrh4MSIeNiI/bgF+GNZHPjK9sy8KjOPycw9gG+Up7fp0K9+P4tn1+pYpUe5RwCrDdDVYZxdm+837jv5eXncPiJG7fPxwJVlvnVV/K7AA8v8SMPVl+T+xmXxN6PUJUmSJElgQl6SJEmSNLgDuq2IiB2ArcriD9pWt5YDeGGP+p8F3KtLHYOq3x989RHrgum8hrqzWBhG+4CSPLybiLgH1T3XAX6bmZeMsQ9jVe4xf1JZXAVYd5HyPwT2pvrbPoAqKb/JiN04pTwulnRezAm1+fXa1rU+i4t9Dlufl/tQnXzQzYsG6NewTmThlgX9xn0nrRMV7kXvuFlUZt4OfLksPjUi1mUhMX8j1X3mR7ElsE6Z/8mIdUmSJEmSCXlJkiRJ0sD2iohntz8ZEeuwMOT4ndx9+PFjgYvL/CERcbd7REfEg4H3l8Ubgc+M2Nd6InrUpC1M5zX8j3I196fK4lbAoR3aD+BwFhLCh4+r/WFExC4RsWmP9atRXeEMcD13HUK+o8z8PrAPcAvVldEnRsTGPTfqrZWQXy8iNurSz20iYptuFZT3/QmtLgLL2oq0Posbl7LdHE01AgDAByPibkPBR8SuwEt71NEqtywiMiKGGiminMjRumd7P3HfzdHABWX+/RHxuF6FI+Kx5TV207oKfnWqZPwzy/LXy33m2+vbrfU+RMSSRfpaH1nie4uUlSRJkqRFmZCXJEmSJA3qp8AXIuLjEbF7RGwXES8sz7eGaf94Zv6qvlEZvv6lVMnKewKnRsShEbFTRDw6Il5b6tigbPL6zLyS0fychSuT/y0inhgRm0XEpmXqOjx6J1N6De3eAfy5zB8WEf8VEXtGxCMj4plU9zJ/QVl/GvDJMbc/qD2AP0TE0oh4Q0Q8qfR15/K5OQV4ZCn76XIF9KIy83hgX+BW4MHADyPioUP28dvAbbX+drIN8POIOKP8zfcsn/0dI+K5VEOpt65o/0aHUQl+XB7vR5Vo3672OfyffmfmZSycaLEhcFZEvDIidignN7yntHURfZy8MAavA64r853i/iyquP9ptwrKiSTPpjqBYh2qv9XnIuJZpZ4dImKviHh7RPyK6jNxt5NdavX9GDivLL6LhXu+jzRcfdH6+/8yM8/rWVKSJEmS+tDrXmSSJEmSJHXybKqhuV9RpnZfBf6l04aZ+a2SxDsSuAdVcvkdbcXuAA7NzCNG7WhmXhcRHwX+lSrp237F6+7A0gHrnOhr6ND+dRGxB/AdYHOqq4Of2aHoqcBemXlHh3WTthLVVfC9rnr+OvDmQSrNzG9HxLOoPnMPpbpSftfMvGCRTdvruTwivkH1Pu7PwigEnexA76Htfwwc1OH5L1G9vo2Bg8vUcj5V8r3Vnw9ExEOAV1ONANA+ysGVwD8CX+nRj7HIzGURsRfVsPP3oHPcv4PqJJXte9RzekTsRjXc/IOB55Wpm78t0rUvAIewcIuDK6lOVBhaRKxFdTsEgM+NUpckSZIktXiFvCRJkiRpIOWq0e2AdwO/oxqW/VrgZOD5mfmsXlc5Z+bRVInkj5Ttb6AaovtPwFHAtpn5njF2+U3AS6iuur2ahXtiD20Kr6G9/WXAI4BXUd1//SqqK7wvA74L/G/gcZl5dVN9GMD7qRLdRwCnA3+hGrXgZqph3b8MPC0z98nMm7pV0k1mHkd1kshtwEZUSfkHDdHP1kgCu0bEBh3WfxF4KvAh4EdUV2jfSHWF/oVUCevnAbtk5lUd+nk9sBMLn5kbe3UmM18D7EmVZL6a6v06F/go1efrzAFf39AycynwD1R/w/OpXvNlwLeAJ2fm2/qs53TgYcDLy7YXl7puphrS/ntUSfbNM/Ozi1TXfjX8l/sdXaGHvYG1S3/GdqsJSZIkSSu2yBzqNmKSJEmSpBVIRBwGvA0gM3vd/1qaS+W+7mdTJZ7fmpnvmnKXNGER8QOqIeuPzMyXT7s/kiRJkpYPXiEvSZIkSZJWeFldsdAaMv/giFh7mv3RZEXEjlTJ+FupRv+QJEmSpLEwIS9JkiRJksT/DH9/CrAe8Mopd0eT1Rp2/yOZ+Zep9kSSJEnScmWVaXdAkiRJkiRphryS6p7310+7I5qMiFgLOL1MH5pydyRJkiQtZ0zIS5IkSZIkFZl5NtW95LWCyMwbgbdPux+SJEmSlk8OWS9JkiRJkiRJkiRJUgMiM6fdB0mSJEmSJEmSJEmSljteIS9JkiRJkiRJkiRJUgNMyEuSJEmSJEmSJEmS1AAT8pIkSZIkSZIkSZIkNcCEvCRJkiRJkiRJkiRJDTAhL0mSJEmSJEmSJElSA0zIS5IkSZIkSZIkSZLUABPykiRJkiRJkiRJkiQ1wIS8JEmSJEmSJEmSJEkNMCEvSZIkSZIkSZIkSVIDTMhLkiRJkiRJkiRJktQAE/KSJEmSJEmSJEmSJDXAhLwkSZIkSZIkSZIkSQ0wIS9JkiRJkiRJkiRJUgNMyEuSJEmSJEmSJEmS1AAT8pIkSZIkSZIkSZIkNcCEvCRJkiRJkiRJkiRJDTAhL0mSJEmSJEmSJElSA0zIS5IkSZIkSZIkSZLUgP8PCCHvp6h/pVcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "deltas = np.logspace(start=-0.5, stop=2, num=20)[::-1]\n",
    "\n",
    "ax = plt.gca()\n",
    "for a in [a1, a2]:\n",
    "    df_result = EfficientFrontier(N, m, G, deltas, a, beta, rf)\n",
    "    df_result.plot(ax=ax, x=\"risk\", y=\"return\", style=\"-o\", \n",
    "                   xlabel=\"portfolio risk (std. dev.)\", ylabel=\"portfolio return\", grid=True)\n",
    "ax.legend([\"return without price impact\", \"return with price impact\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>delta</th>\n",
       "      <th>obj</th>\n",
       "      <th>return</th>\n",
       "      <th>risk</th>\n",
       "      <th>t_resid</th>\n",
       "      <th>x_sum</th>\n",
       "      <th>xf</th>\n",
       "      <th>tcost</th>\n",
       "      <th>PM</th>\n",
       "      <th>LMT</th>\n",
       "      <th>MCD</th>\n",
       "      <th>MMM</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>TXN</th>\n",
       "      <th>CSCO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.018790</td>\n",
       "      <td>0.027579</td>\n",
       "      <td>0.013258</td>\n",
       "      <td>1.409437e-06</td>\n",
       "      <td>0.047430</td>\n",
       "      <td>9.523007e-01</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>7.384062e-10</td>\n",
       "      <td>3.492337e-09</td>\n",
       "      <td>1.560750e-08</td>\n",
       "      <td>9.255996e-10</td>\n",
       "      <td>0.005845</td>\n",
       "      <td>0.030481</td>\n",
       "      <td>1.110292e-02</td>\n",
       "      <td>2.005896e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73.861998</td>\n",
       "      <td>0.021900</td>\n",
       "      <td>0.033798</td>\n",
       "      <td>0.017949</td>\n",
       "      <td>8.868563e-07</td>\n",
       "      <td>0.064211</td>\n",
       "      <td>9.353648e-01</td>\n",
       "      <td>0.000425</td>\n",
       "      <td>4.115499e-10</td>\n",
       "      <td>1.694335e-09</td>\n",
       "      <td>7.233661e-09</td>\n",
       "      <td>5.168015e-10</td>\n",
       "      <td>0.007915</td>\n",
       "      <td>0.041267</td>\n",
       "      <td>1.502848e-02</td>\n",
       "      <td>1.036294e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54.555948</td>\n",
       "      <td>0.026111</td>\n",
       "      <td>0.042218</td>\n",
       "      <td>0.024300</td>\n",
       "      <td>2.066310e-06</td>\n",
       "      <td>0.086929</td>\n",
       "      <td>9.124021e-01</td>\n",
       "      <td>0.000669</td>\n",
       "      <td>9.675016e-10</td>\n",
       "      <td>7.633847e-09</td>\n",
       "      <td>4.879841e-08</td>\n",
       "      <td>1.297328e-09</td>\n",
       "      <td>0.010719</td>\n",
       "      <td>0.055868</td>\n",
       "      <td>2.034184e-02</td>\n",
       "      <td>3.466895e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.296113</td>\n",
       "      <td>0.031810</td>\n",
       "      <td>0.053615</td>\n",
       "      <td>0.032897</td>\n",
       "      <td>4.129274e-06</td>\n",
       "      <td>0.117684</td>\n",
       "      <td>8.812629e-01</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>1.669839e-09</td>\n",
       "      <td>9.676767e-09</td>\n",
       "      <td>6.182178e-08</td>\n",
       "      <td>2.131469e-09</td>\n",
       "      <td>0.014515</td>\n",
       "      <td>0.075637</td>\n",
       "      <td>2.753150e-02</td>\n",
       "      <td>5.142045e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.763514</td>\n",
       "      <td>0.039526</td>\n",
       "      <td>0.069044</td>\n",
       "      <td>0.044536</td>\n",
       "      <td>5.611713e-07</td>\n",
       "      <td>0.159317</td>\n",
       "      <td>8.390245e-01</td>\n",
       "      <td>0.001659</td>\n",
       "      <td>2.639029e-10</td>\n",
       "      <td>1.555416e-09</td>\n",
       "      <td>7.700856e-09</td>\n",
       "      <td>3.397585e-10</td>\n",
       "      <td>0.019658</td>\n",
       "      <td>0.102398</td>\n",
       "      <td>3.726174e-02</td>\n",
       "      <td>8.681476e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.983926</td>\n",
       "      <td>0.049971</td>\n",
       "      <td>0.089929</td>\n",
       "      <td>0.060293</td>\n",
       "      <td>1.525075e-06</td>\n",
       "      <td>0.215676</td>\n",
       "      <td>7.817115e-01</td>\n",
       "      <td>0.002612</td>\n",
       "      <td>6.768740e-10</td>\n",
       "      <td>3.982106e-09</td>\n",
       "      <td>1.937252e-08</td>\n",
       "      <td>8.770383e-10</td>\n",
       "      <td>0.026622</td>\n",
       "      <td>0.138627</td>\n",
       "      <td>5.042681e-02</td>\n",
       "      <td>2.267352e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16.237767</td>\n",
       "      <td>0.064110</td>\n",
       "      <td>0.118199</td>\n",
       "      <td>0.081622</td>\n",
       "      <td>8.943029e-07</td>\n",
       "      <td>0.291968</td>\n",
       "      <td>7.039184e-01</td>\n",
       "      <td>0.004114</td>\n",
       "      <td>4.653877e-10</td>\n",
       "      <td>2.333089e-09</td>\n",
       "      <td>1.235061e-08</td>\n",
       "      <td>5.979785e-10</td>\n",
       "      <td>0.036056</td>\n",
       "      <td>0.187675</td>\n",
       "      <td>6.823738e-02</td>\n",
       "      <td>1.386821e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.993539</td>\n",
       "      <td>0.083249</td>\n",
       "      <td>0.156465</td>\n",
       "      <td>0.110496</td>\n",
       "      <td>1.668722e-06</td>\n",
       "      <td>0.395242</td>\n",
       "      <td>5.982799e-01</td>\n",
       "      <td>0.006478</td>\n",
       "      <td>7.340574e-10</td>\n",
       "      <td>4.225165e-09</td>\n",
       "      <td>2.160286e-08</td>\n",
       "      <td>9.562667e-10</td>\n",
       "      <td>0.048836</td>\n",
       "      <td>0.254072</td>\n",
       "      <td>9.233388e-02</td>\n",
       "      <td>2.417856e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.858668</td>\n",
       "      <td>0.109156</td>\n",
       "      <td>0.208260</td>\n",
       "      <td>0.149581</td>\n",
       "      <td>3.317164e-06</td>\n",
       "      <td>0.535034</td>\n",
       "      <td>4.547640e-01</td>\n",
       "      <td>0.010201</td>\n",
       "      <td>1.031830e-09</td>\n",
       "      <td>6.343991e-09</td>\n",
       "      <td>3.181106e-08</td>\n",
       "      <td>1.335507e-09</td>\n",
       "      <td>0.066151</td>\n",
       "      <td>0.343956</td>\n",
       "      <td>1.249278e-01</td>\n",
       "      <td>3.566057e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.543189</td>\n",
       "      <td>0.144222</td>\n",
       "      <td>0.278363</td>\n",
       "      <td>0.202489</td>\n",
       "      <td>9.939790e-07</td>\n",
       "      <td>0.724252</td>\n",
       "      <td>2.596848e-01</td>\n",
       "      <td>0.016063</td>\n",
       "      <td>3.768487e-10</td>\n",
       "      <td>1.650911e-09</td>\n",
       "      <td>5.697224e-09</td>\n",
       "      <td>4.980522e-10</td>\n",
       "      <td>0.089609</td>\n",
       "      <td>0.465635</td>\n",
       "      <td>1.690072e-01</td>\n",
       "      <td>1.182601e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.832930</td>\n",
       "      <td>0.191679</td>\n",
       "      <td>0.371374</td>\n",
       "      <td>0.272695</td>\n",
       "      <td>1.993875e-06</td>\n",
       "      <td>0.974965</td>\n",
       "      <td>7.627774e-08</td>\n",
       "      <td>0.025035</td>\n",
       "      <td>8.861361e-10</td>\n",
       "      <td>4.402298e-09</td>\n",
       "      <td>1.350758e-08</td>\n",
       "      <td>1.176195e-09</td>\n",
       "      <td>0.121562</td>\n",
       "      <td>0.627468</td>\n",
       "      <td>2.259342e-01</td>\n",
       "      <td>2.972946e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.569699</td>\n",
       "      <td>0.239828</td>\n",
       "      <td>0.380176</td>\n",
       "      <td>0.280416</td>\n",
       "      <td>4.436191e-07</td>\n",
       "      <td>0.977195</td>\n",
       "      <td>1.746777e-09</td>\n",
       "      <td>0.022805</td>\n",
       "      <td>8.126131e-10</td>\n",
       "      <td>2.452980e-09</td>\n",
       "      <td>3.523080e-09</td>\n",
       "      <td>1.059763e-09</td>\n",
       "      <td>0.173104</td>\n",
       "      <td>0.663382</td>\n",
       "      <td>1.407082e-01</td>\n",
       "      <td>2.219582e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.636651</td>\n",
       "      <td>0.277829</td>\n",
       "      <td>0.389766</td>\n",
       "      <td>0.291391</td>\n",
       "      <td>1.264736e-07</td>\n",
       "      <td>0.978061</td>\n",
       "      <td>4.937735e-10</td>\n",
       "      <td>0.021939</td>\n",
       "      <td>3.402430e-10</td>\n",
       "      <td>8.511100e-10</td>\n",
       "      <td>1.054809e-09</td>\n",
       "      <td>4.537145e-10</td>\n",
       "      <td>0.236475</td>\n",
       "      <td>0.698696</td>\n",
       "      <td>4.289079e-02</td>\n",
       "      <td>8.544320e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.947483</td>\n",
       "      <td>0.308000</td>\n",
       "      <td>0.394862</td>\n",
       "      <td>0.298671</td>\n",
       "      <td>1.232131e-07</td>\n",
       "      <td>0.977823</td>\n",
       "      <td>3.750815e-10</td>\n",
       "      <td>0.022177</td>\n",
       "      <td>3.485414e-10</td>\n",
       "      <td>7.946996e-10</td>\n",
       "      <td>9.392737e-10</td>\n",
       "      <td>4.657178e-10</td>\n",
       "      <td>0.295710</td>\n",
       "      <td>0.682113</td>\n",
       "      <td>7.799778e-08</td>\n",
       "      <td>9.132036e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.438450</td>\n",
       "      <td>0.331011</td>\n",
       "      <td>0.397164</td>\n",
       "      <td>0.303279</td>\n",
       "      <td>7.321960e-08</td>\n",
       "      <td>0.978178</td>\n",
       "      <td>1.678265e-10</td>\n",
       "      <td>0.021822</td>\n",
       "      <td>1.514440e-10</td>\n",
       "      <td>3.807351e-10</td>\n",
       "      <td>4.635071e-10</td>\n",
       "      <td>2.018823e-10</td>\n",
       "      <td>0.355729</td>\n",
       "      <td>0.622449</td>\n",
       "      <td>7.407283e-09</td>\n",
       "      <td>4.371606e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.062468</td>\n",
       "      <td>0.348665</td>\n",
       "      <td>0.399864</td>\n",
       "      <td>0.310450</td>\n",
       "      <td>1.299169e-07</td>\n",
       "      <td>0.978274</td>\n",
       "      <td>5.306089e-11</td>\n",
       "      <td>0.021726</td>\n",
       "      <td>2.910744e-11</td>\n",
       "      <td>2.537720e-10</td>\n",
       "      <td>3.142506e-10</td>\n",
       "      <td>8.745874e-11</td>\n",
       "      <td>0.429602</td>\n",
       "      <td>0.548672</td>\n",
       "      <td>6.277232e-09</td>\n",
       "      <td>2.841540e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.784760</td>\n",
       "      <td>0.362459</td>\n",
       "      <td>0.402909</td>\n",
       "      <td>0.321075</td>\n",
       "      <td>3.061959e-07</td>\n",
       "      <td>0.977905</td>\n",
       "      <td>-3.170491e-10</td>\n",
       "      <td>0.022095</td>\n",
       "      <td>-2.744697e-10</td>\n",
       "      <td>-7.329988e-11</td>\n",
       "      <td>-4.811559e-12</td>\n",
       "      <td>-2.303274e-10</td>\n",
       "      <td>0.518113</td>\n",
       "      <td>0.459792</td>\n",
       "      <td>4.017685e-09</td>\n",
       "      <td>1.001661e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.579639</td>\n",
       "      <td>0.373478</td>\n",
       "      <td>0.406173</td>\n",
       "      <td>0.335877</td>\n",
       "      <td>6.255417e-08</td>\n",
       "      <td>0.976819</td>\n",
       "      <td>-4.685341e-11</td>\n",
       "      <td>0.023181</td>\n",
       "      <td>-2.786973e-11</td>\n",
       "      <td>3.028225e-11</td>\n",
       "      <td>5.233638e-11</td>\n",
       "      <td>-1.620368e-11</td>\n",
       "      <td>0.620522</td>\n",
       "      <td>0.356297</td>\n",
       "      <td>5.972721e-10</td>\n",
       "      <td>5.911773e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.428133</td>\n",
       "      <td>0.382473</td>\n",
       "      <td>0.409414</td>\n",
       "      <td>0.354756</td>\n",
       "      <td>5.859351e-08</td>\n",
       "      <td>0.974797</td>\n",
       "      <td>-5.776113e-11</td>\n",
       "      <td>0.025203</td>\n",
       "      <td>-4.518043e-11</td>\n",
       "      <td>-1.648955e-11</td>\n",
       "      <td>-6.542901e-12</td>\n",
       "      <td>-3.911094e-11</td>\n",
       "      <td>0.732480</td>\n",
       "      <td>0.242317</td>\n",
       "      <td>4.754135e-10</td>\n",
       "      <td>-2.822046e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.316228</td>\n",
       "      <td>0.389920</td>\n",
       "      <td>0.412270</td>\n",
       "      <td>0.375966</td>\n",
       "      <td>1.067333e-07</td>\n",
       "      <td>0.971827</td>\n",
       "      <td>-1.564786e-10</td>\n",
       "      <td>0.028173</td>\n",
       "      <td>-1.324343e-10</td>\n",
       "      <td>-7.528260e-11</td>\n",
       "      <td>-5.792961e-11</td>\n",
       "      <td>-1.192095e-10</td>\n",
       "      <td>0.844104</td>\n",
       "      <td>0.127723</td>\n",
       "      <td>1.114003e-09</td>\n",
       "      <td>-5.213990e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         delta       obj    return      risk       t_resid     x_sum  \\\n",
       "0   100.000000  0.018790  0.027579  0.013258  1.409437e-06  0.047430   \n",
       "1    73.861998  0.021900  0.033798  0.017949  8.868563e-07  0.064211   \n",
       "2    54.555948  0.026111  0.042218  0.024300  2.066310e-06  0.086929   \n",
       "3    40.296113  0.031810  0.053615  0.032897  4.129274e-06  0.117684   \n",
       "4    29.763514  0.039526  0.069044  0.044536  5.611713e-07  0.159317   \n",
       "5    21.983926  0.049971  0.089929  0.060293  1.525075e-06  0.215676   \n",
       "6    16.237767  0.064110  0.118199  0.081622  8.943029e-07  0.291968   \n",
       "7    11.993539  0.083249  0.156465  0.110496  1.668722e-06  0.395242   \n",
       "8     8.858668  0.109156  0.208260  0.149581  3.317164e-06  0.535034   \n",
       "9     6.543189  0.144222  0.278363  0.202489  9.939790e-07  0.724252   \n",
       "10    4.832930  0.191679  0.371374  0.272695  1.993875e-06  0.974965   \n",
       "11    3.569699  0.239828  0.380176  0.280416  4.436191e-07  0.977195   \n",
       "12    2.636651  0.277829  0.389766  0.291391  1.264736e-07  0.978061   \n",
       "13    1.947483  0.308000  0.394862  0.298671  1.232131e-07  0.977823   \n",
       "14    1.438450  0.331011  0.397164  0.303279  7.321960e-08  0.978178   \n",
       "15    1.062468  0.348665  0.399864  0.310450  1.299169e-07  0.978274   \n",
       "16    0.784760  0.362459  0.402909  0.321075  3.061959e-07  0.977905   \n",
       "17    0.579639  0.373478  0.406173  0.335877  6.255417e-08  0.976819   \n",
       "18    0.428133  0.382473  0.409414  0.354756  5.859351e-08  0.974797   \n",
       "19    0.316228  0.389920  0.412270  0.375966  1.067333e-07  0.971827   \n",
       "\n",
       "              xf     tcost            PM           LMT           MCD  \\\n",
       "0   9.523007e-01  0.000270  7.384062e-10  3.492337e-09  1.560750e-08   \n",
       "1   9.353648e-01  0.000425  4.115499e-10  1.694335e-09  7.233661e-09   \n",
       "2   9.124021e-01  0.000669  9.675016e-10  7.633847e-09  4.879841e-08   \n",
       "3   8.812629e-01  0.001053  1.669839e-09  9.676767e-09  6.182178e-08   \n",
       "4   8.390245e-01  0.001659  2.639029e-10  1.555416e-09  7.700856e-09   \n",
       "5   7.817115e-01  0.002612  6.768740e-10  3.982106e-09  1.937252e-08   \n",
       "6   7.039184e-01  0.004114  4.653877e-10  2.333089e-09  1.235061e-08   \n",
       "7   5.982799e-01  0.006478  7.340574e-10  4.225165e-09  2.160286e-08   \n",
       "8   4.547640e-01  0.010201  1.031830e-09  6.343991e-09  3.181106e-08   \n",
       "9   2.596848e-01  0.016063  3.768487e-10  1.650911e-09  5.697224e-09   \n",
       "10  7.627774e-08  0.025035  8.861361e-10  4.402298e-09  1.350758e-08   \n",
       "11  1.746777e-09  0.022805  8.126131e-10  2.452980e-09  3.523080e-09   \n",
       "12  4.937735e-10  0.021939  3.402430e-10  8.511100e-10  1.054809e-09   \n",
       "13  3.750815e-10  0.022177  3.485414e-10  7.946996e-10  9.392737e-10   \n",
       "14  1.678265e-10  0.021822  1.514440e-10  3.807351e-10  4.635071e-10   \n",
       "15  5.306089e-11  0.021726  2.910744e-11  2.537720e-10  3.142506e-10   \n",
       "16 -3.170491e-10  0.022095 -2.744697e-10 -7.329988e-11 -4.811559e-12   \n",
       "17 -4.685341e-11  0.023181 -2.786973e-11  3.028225e-11  5.233638e-11   \n",
       "18 -5.776113e-11  0.025203 -4.518043e-11 -1.648955e-11 -6.542901e-12   \n",
       "19 -1.564786e-10  0.028173 -1.324343e-10 -7.528260e-11 -5.792961e-11   \n",
       "\n",
       "             MMM      AAPL      MSFT           TXN          CSCO  \n",
       "0   9.255996e-10  0.005845  0.030481  1.110292e-02  2.005896e-09  \n",
       "1   5.168015e-10  0.007915  0.041267  1.502848e-02  1.036294e-09  \n",
       "2   1.297328e-09  0.010719  0.055868  2.034184e-02  3.466895e-09  \n",
       "3   2.131469e-09  0.014515  0.075637  2.753150e-02  5.142045e-09  \n",
       "4   3.397585e-10  0.019658  0.102398  3.726174e-02  8.681476e-10  \n",
       "5   8.770383e-10  0.026622  0.138627  5.042681e-02  2.267352e-09  \n",
       "6   5.979785e-10  0.036056  0.187675  6.823738e-02  1.386821e-09  \n",
       "7   9.562667e-10  0.048836  0.254072  9.233388e-02  2.417856e-09  \n",
       "8   1.335507e-09  0.066151  0.343956  1.249278e-01  3.566057e-09  \n",
       "9   4.980522e-10  0.089609  0.465635  1.690072e-01  1.182601e-09  \n",
       "10  1.176195e-09  0.121562  0.627468  2.259342e-01  2.972946e-09  \n",
       "11  1.059763e-09  0.173104  0.663382  1.407082e-01  2.219582e-09  \n",
       "12  4.537145e-10  0.236475  0.698696  4.289079e-02  8.544320e-10  \n",
       "13  4.657178e-10  0.295710  0.682113  7.799778e-08  9.132036e-10  \n",
       "14  2.018823e-10  0.355729  0.622449  7.407283e-09  4.371606e-10  \n",
       "15  8.745874e-11  0.429602  0.548672  6.277232e-09  2.841540e-10  \n",
       "16 -2.303274e-10  0.518113  0.459792  4.017685e-09  1.001661e-11  \n",
       "17 -1.620368e-11  0.620522  0.356297  5.972721e-10  5.911773e-11  \n",
       "18 -3.911094e-11  0.732480  0.242317  4.754135e-10 -2.822046e-12  \n",
       "19 -1.192095e-10  0.844104  0.127723  1.114003e-09 -5.213990e-11  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}