{ "cells": [ { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "from scipy.stats import norm, t, chi2\n", "from scipy.stats import iqr, median_abs_deviation" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# Significane level\n", "ALPHA = 0.05" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reading and preprocessing data" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "DataWhr2024 = pd.read_csv(\"DataWhr2024.csv\")\n", "UnM49 = pd.read_csv(\"UnM49.csv\", sep=';')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "DataWhr2024.loc[DataWhr2024[\"Country name\"].str.startswith(\"Hong\"), \"Country name\"] = \"Hong Kong\"\n", "DataWhr2024.loc[DataWhr2024[\"Country name\"].str.startswith(\"Somaliland\"), \"Country name\"] = \"Somaliland\"\n", "DataWhr2024.loc[DataWhr2024[\"Country name\"].str.startswith(\"Taiwan\"), \"Country name\"] = \"Taiwan\"" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "UnM49 = UnM49[['Country or Area', 'Sub-region Name', 'Region Name']]\n", "UnM49 = UnM49.rename({'Country or Area':'Country name', 'Sub-region Name':'Subregion', 'Region Name':'Continent'}, axis=1)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "UnM49.loc[97, \"Country name\"] = \"Bolivia\"\n", "UnM49.loc[33, \"Country name\"] = \"Congo (Brazzaville)\"\n", "UnM49.loc[34, \"Country name\"] = \"Congo (Kinshasa)\"\n", "UnM49.loc[124, \"Country name\"] = \"Hong Kong\"\n", "UnM49.loc[125, \"Country name\"] = \"Macao\"\n", "UnM49.loc[126, \"Country name\"] = \"North Korea\"\n", "UnM49.loc[145, \"Country name\"] = \"Iran\"\n", "UnM49.loc[46, \"Country name\"] = \"Ivory Coast\"\n", "UnM49.loc[133, \"Country name\"] = \"Laos\"\n", "UnM49.loc[129, \"Country name\"] = \"South Korea\"\n", "UnM49.loc[173, \"Country name\"] = \"Moldova\"\n", "UnM49.loc[217, \"Country name\"] = \"Netherlands\"\n", "UnM49.loc[175, \"Country name\"] = \"Russia\"\n", "UnM49.loc[164, \"Country name\"] = \"Syria\"\n", "UnM49.loc[26, \"Country name\"] = \"Tanzania\"\n", "UnM49.loc[116, \"Country name\"] = \"United States\"\n", "UnM49.loc[193, \"Country name\"] = \"United Kingdom\"\n", "UnM49.loc[111, \"Country name\"] = \"Venezuela\"\n", "UnM49.loc[140, \"Country name\"] = \"Vietnam\"" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "_ = pd.DataFrame(\n", " {\n", " \"Country name\": [\"Kosovo\", \"Somaliland\", \"Taiwan\"],\n", " \"Subregion\": [\"Southern Europe\", \"Sub-Saharan Africa\", \"Eastern Asia\"],\n", " \"Continent\": [\"Europe\", \"Africa\", \"Asia\"],\n", " }\n", ")\n", "\n", "UnM49 = pd.concat([UnM49, _], axis=0)\n", "UnM49 = UnM49.reset_index(drop=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Merging the datasets" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "# Data\n", "Dat = pd.merge(DataWhr2024, UnM49)\n", "\n", "# Data of 2023\n", "Dat2023 = Dat[Dat['year'] == 2023]\n", "Dat2023 = Dat2023.reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "# Extract the 'Life Ladder' data\n", "Data1 = Dat2023['Life Ladder']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 1**" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Mean (X̄n) Median (X̃n) Std. Dev (σ̂1) MAD (σ̂2) IQR (σ̂3)\n", "0 5.6208 5.863 1.1395 1.1764 1.3399\n" ] } ], "source": [ "# Estimators for μ\n", "mean_estimator = np.mean(Data1)\n", "median_estimator = np.median(Data1)\n", "\n", "# Estimators for σ\n", "# Sample standard deviation (1 degree of freedom)\n", "std_dev_estimator = np.std(Data1, ddof=1)\n", "\n", "# Median Absolute Deviation (MAD), scaled\n", "mad = np.median(np.abs(Data1 - median_estimator))\n", "mad_estimator = mad * 1.4826\n", "\n", "# Interquartile Range (IQR), scaled\n", "q1 = np.percentile(Data1, 25)\n", "q3 = np.percentile(Data1, 75)\n", "iqr = q3 - q1\n", "iqr_estimator = iqr * 0.7413\n", "\n", "# Create a results table\n", "results = pd.DataFrame({\n", " 'Mean (X̄n)': [round(mean_estimator, 4)],\n", " 'Median (X̃n)': [round(median_estimator, 4)],\n", " 'Std. Dev (σ̂1)': [round(std_dev_estimator, 4)],\n", " 'MAD (σ̂2)': [round(mad_estimator, 4)],\n", " 'IQR (σ̂3)': [round(iqr_estimator, 4)]\n", "})\n", "\n", "# Print the results\n", "print(results)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 2**" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " MLE for μ MLE for σ\n", "0 5.6208 1.1353\n" ] } ], "source": [ "# MLE for μ (mean)\n", "mle_mu = np.mean(Data1)\n", "\n", "# MLE for σ (standard deviation)\n", "mle_sigma = np.sqrt(np.mean((Data1 - mle_mu) ** 2))\n", "\n", "# Report the results rounded to 4 decimal places\n", "results = pd.DataFrame({\n", " 'MLE for μ': [round(mle_mu, 4)],\n", " 'MLE for σ': [round(mle_sigma, 4)]\n", "})\n", "\n", "# Print the results\n", "print(results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 3**" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CI TypeLower BoundUpper Bound
0Mean (σ̂1)5.43075.8110
1Mean (σ̂2)5.42465.8171
2Mean (σ̂3)5.39735.8444
3Median (σ̂1)5.62476.1013
4Median (σ̂2)5.61706.1090
5Median (σ̂3)5.58286.1432
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" ], "text/plain": [ " CI Type Lower Bound Upper Bound\n", "0 Mean (σ̂1) 5.4307 5.8110\n", "1 Mean (σ̂2) 5.4246 5.8171\n", "2 Mean (σ̂3) 5.3973 5.8444\n", "3 Median (σ̂1) 5.6247 6.1013\n", "4 Median (σ̂2) 5.6170 6.1090\n", "5 Median (σ̂3) 5.5828 6.1432" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = len(Data1) # Sample size\n", "Z = 1.96 # Critical value for 95% confidence level\n", "\n", "# Mean and median estimators\n", "mean_estimator = np.mean(Data1)\n", "median_estimator = np.median(Data1)\n", "\n", "# Standard deviation estimators\n", "std_dev_estimator = np.std(Data1, ddof=1) # σ̂1\n", "mad = np.median(np.abs(Data1 - median_estimator))\n", "mad_estimator = mad * 1.4826 # σ̂2\n", "q1 = np.percentile(Data1, 25)\n", "q3 = np.percentile(Data1, 75)\n", "iqr = q3 - q1\n", "iqr_estimator = iqr * 0.7413 # σ̂3\n", "\n", "# CI for the mean\n", "def CI_mu_mean(hat_mu, hat_sigma, n, alpha=0.05):\n", " z_critical = norm.ppf(1 - alpha / 2) # Two-tailed Z-critical value\n", " margin_error = z_critical * (hat_sigma / np.sqrt(n))\n", " return hat_mu - margin_error, hat_mu + margin_error\n", "\n", "# CI for the median\n", "def CI_mu_median(hat_mu, hat_sigma, n, alpha=0.05):\n", " z_critical = norm.ppf(1 - alpha / 2)\n", " density_at_median = 1 / (np.sqrt(2 * np.pi) * hat_sigma) # f( ̃μ)\n", " std_error = 1 / (4 * n * density_at_median**2)**0.5 # Standard error for the median\n", " margin_error = z_critical * std_error\n", " return hat_mu - margin_error, hat_mu + margin_error\n", "\n", "ci_mean_sigma1 = CI_mu_mean(mean_estimator, std_dev_estimator, n)\n", "ci_mean_sigma2 = CI_mu_mean(mean_estimator, mad_estimator, n)\n", "ci_mean_sigma3 = CI_mu_mean(mean_estimator, iqr_estimator, n)\n", "ci_median_sigma1 = CI_mu_median(median_estimator, std_dev_estimator, n)\n", "ci_median_sigma2 = CI_mu_median(median_estimator, mad_estimator, n)\n", "ci_median_sigma3 = CI_mu_median(median_estimator, iqr_estimator, n)\n", "\n", "# Prepare the results in a table, rounded to 4 decimal places\n", "result_table = pd.DataFrame({\n", " 'CI Type': ['Mean (σ̂1)', 'Mean (σ̂2)', 'Mean (σ̂3)', \n", " 'Median (σ̂1)', 'Median (σ̂2)', 'Median (σ̂3)'],\n", " 'Lower Bound': [round(ci_mean_sigma1[0], 4), round(ci_mean_sigma2[0], 4), round(ci_mean_sigma3[0], 4),\n", " round(ci_median_sigma1[0], 4), round(ci_median_sigma2[0], 4), round(ci_median_sigma3[0], 4)],\n", " 'Upper Bound': [round(ci_mean_sigma1[1], 4), round(ci_mean_sigma2[1], 4), round(ci_mean_sigma3[1], 4),\n", " round(ci_median_sigma1[1], 4), round(ci_median_sigma2[1], 4), round(ci_median_sigma3[1], 4)]\n", "})\n", "\n", "result_table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 4**" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "95% Confidence Interval for σ: (np.float64(1.0191), np.float64(1.2924))\n" ] } ], "source": [ "# Degrees of freedom\n", "df = n - 1\n", "\n", "# Critical values for chi-squared distribution\n", "alpha = 0.05\n", "chi2_lower = chi2.ppf(alpha / 2, df)\n", "chi2_upper = chi2.ppf(1 - alpha / 2, df)\n", "\n", "# Confidence interval for σ²\n", "lower_bound_variance = (df * (std_dev_estimator ** 2)) / chi2_upper\n", "upper_bound_variance = (df * (std_dev_estimator ** 2)) / chi2_lower\n", "\n", "# Confidence interval for σ (square root of variance bounds)\n", "lower_bound_sigma = np.sqrt(lower_bound_variance)\n", "upper_bound_sigma = np.sqrt(upper_bound_variance)\n", "\n", "# Print results rounded to 4 decimals\n", "ci_sigma = (round(lower_bound_sigma, 4), round(upper_bound_sigma, 4))\n", "print(f\"95% Confidence Interval for σ: {ci_sigma}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 5**" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CI TypeLower BoundUpper Bound
0Mean (σ̂1)3.37947.8623
1Mean (σ̂2)3.30677.9350
2Mean (σ̂3)2.98528.2565
3Median (σ̂1)3.61708.1090
4Median (σ̂2)3.54418.1819
5Median (σ̂3)3.22198.5041
6IQR2.95738.2097
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" ], "text/plain": [ " CI Type Lower Bound Upper Bound\n", "0 Mean (σ̂1) 3.3794 7.8623\n", "1 Mean (σ̂2) 3.3067 7.9350\n", "2 Mean (σ̂3) 2.9852 8.2565\n", "3 Median (σ̂1) 3.6170 8.1090\n", "4 Median (σ̂2) 3.5441 8.1819\n", "5 Median (σ̂3) 3.2219 8.5041\n", "6 IQR 2.9573 8.2097" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = len(Data1) # Sample size\n", "Z = 1.96 # Critical value for 95% confidence level\n", "\n", "# Mean and median estimators\n", "mean_estimator = np.mean(Data1)\n", "median_estimator = np.median(Data1)\n", "\n", "# Standard deviation estimators\n", "std_dev_estimator = np.std(Data1, ddof=1) # σ̂1\n", "mad = np.median(np.abs(Data1 - median_estimator))\n", "mad_estimator = mad * 1.4826 # σ̂2\n", "q1 = np.percentile(Data1, 25)\n", "q3 = np.percentile(Data1, 75)\n", "iqr = q3 - q1\n", "iqr_estimator = iqr * 0.7413 # σ̂3\n", "\n", "\n", "def PI_mu_mean(hat_mu, hat_sigma, n, alpha=0.05):\n", " z_critical = norm.ppf(1 - alpha / 2)\n", " margin_error = z_critical * np.sqrt(hat_sigma**2 + (hat_sigma**2 / n))\n", " return hat_mu - margin_error, hat_mu + margin_error\n", "\n", "def PI_mu_median(hat_mu, hat_sigma, n, alpha=0.05):\n", " z_critical = norm.ppf(1 - alpha / 2)\n", " density_at_median = 1 / (np.sqrt(2 * np.pi) * hat_sigma) # f(μ̃)\n", " std_error = np.sqrt(1 / (4 * n * density_at_median**2) + hat_sigma**2)\n", " margin_error = z_critical * std_error\n", " return hat_mu - margin_error, hat_mu + margin_error\n", "\n", "def PI_iqr_nonparametric(q1, q3, iqr, alpha=0.05):\n", " # Critical Z-value\n", " z_critical = norm.ppf(1 - alpha / 2)\n", " # Inverse CDF of the normal distribution at 0.75\n", " phi_inv_3_4 = norm.ppf(0.75)\n", " # Delta calculation\n", " delta = 0.5 * ((z_critical / phi_inv_3_4) - 1)\n", " # Prediction interval\n", " lower_bound = q1 - delta * iqr\n", " upper_bound = q3 + delta * iqr\n", " return lower_bound, upper_bound\n", "\n", "pi_mean_sigma1 = PI_mu_mean(mean_estimator, std_dev_estimator, n)\n", "pi_mean_sigma2 = PI_mu_mean(mean_estimator, mad_estimator, n)\n", "pi_mean_sigma3 = PI_mu_mean(mean_estimator, iqr_estimator, n)\n", "pi_median_sigma1 = PI_mu_median(median_estimator, std_dev_estimator, n)\n", "pi_median_sigma2 = PI_mu_median(median_estimator, mad_estimator, n)\n", "pi_median_sigma3 = PI_mu_median(median_estimator, iqr_estimator, n)\n", "\n", "pi_iqr = PI_iqr_nonparametric(q1, q3, iqr)\n", "\n", "# Prepare the results in a table, rounded to 4 decimal places\n", "result_table = pd.DataFrame({\n", " 'CI Type': ['Mean (σ̂1)', 'Mean (σ̂2)', 'Mean (σ̂3)', \n", " 'Median (σ̂1)', 'Median (σ̂2)', 'Median (σ̂3)',\n", " 'IQR'],\n", " 'Lower Bound': [round(pi_mean_sigma1[0], 4), round(pi_mean_sigma2[0], 4), round(pi_mean_sigma3[0], 4),\n", " round(pi_median_sigma1[0], 4), round(pi_median_sigma2[0], 4), round(pi_median_sigma3[0], 4),\n", " round(pi_iqr[0], 4)],\n", " 'Upper Bound': [round(pi_mean_sigma1[1], 4), round(pi_mean_sigma2[1], 4), round(pi_mean_sigma3[1], 4),\n", " round(pi_median_sigma1[1], 4), round(pi_median_sigma2[1], 4), round(pi_median_sigma3[1], 4),\n", " round(pi_iqr[1], 4)]\n", "})\n", "\n", "result_table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 6**" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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TJ52WpfS51rx5cwMw5s6dm2R/ly5dSjFhJiJyKyWlRETSyP4LrP3RtWvXVG2XlqRUTEyMY93Efzgk/oPpdo8aNWqk+nxef/11AzDq1KmT7PJ3333XAIzy5cs7zc8NSSl7UqJmzZpGXFxckuUJCQlG1apVDcDYuXNnquLI7MTgjBkzDMB48cUXneY/8cQTBmD07Nkz2e3uv//+ZK/hhAkTDMC49957k91u+PDhyV7L9F4r+2vp7u6ebOLNrkWLFkZYWJijdUlqPPnkk7c9lztp2bKlwc1WZcnp1KmTARgDBw50mm9P2jz44IPJbteuXTsDSPKHdnqTUuXKlTMAY9q0aUm2uX79ulG0aNFk/+jv2bOnARjvvvtussfbuHFjsvd2es4vLi7OCAwMNIAkCaaUpDe+O/nhhx8MPz8/47777jN8fHwMwJg4cWKa9pGSlO7v06dPGx9//LHh5+dnAEaXLl0cyxJ/tv/555/J7rd+/foGYMyfPz/Z5d9//70BGA899JBj3tq1aw1utlJKrqXgggUL0pyUsrcS/N///pem65GepJS9hWhK77Nhw4YZgNG6detkY69Tp47TP0ns7J8Lr732WqrOwUhlUiqlz4mKFSsm+9reKSmVlZ+phw4dciTLEicrDcMwzp8/b7i7uxuenp5GZGRkClckqZdfftkAjI8++shpfkqfa5UrVzYA4+LFi6k+hohIclRTSkQknZo0aQLAggULePPNNzN131ar1TGd0uh7Dz30EH379k32kVwx6JTYa0D17ds32eUDBgwA4ODBg5w+fTqNZ2Ku3377DW5eq+QKT7u4uDhex7/++itLY7ly5Qrz5s1j1KhRDBo0iH79+tGvXz9++OEHuDl6Y2L21+WRRx5Jdn8pvV727R5++OE0bZfRa1WrVi3Kli2b7L4BVqxYwb59++jatWuK62Sm+Ph41q1bBzdr1CTH/t5euXJlsss7d+6c7Hx7LZdTp05lOM5Tp05x6NAhSOG19vLyokePHknmW61WFi9eDEDPnj2T3XfdunXx8/Nj69atxMTEJFmelvPbvHkzERERFC5cOFWvYWbEl5w//viDnj17Uq1aNf744w9+++03fH19efnll3nnnXeSrF++fHksFgsHDhxI1f4TCwkJcdQJKl68OEOHDuXKlSu0atUq2VpNQUFBNG7cOMn8CxcusHHjRry9vVO85vb6a4nvLfu93K5dOwoVKpRkm/vvvz9NhczPnj3Ltm3bcHFxcbz3s5I9/jvdf2vWrCEhISHJ8k6dOiX7/ZeZ919imX2/Z+VnamhoKE2aNCEqKooFCxY4Lfvqq6+Ii4vj/vvvp2DBgkm2jYyM5IsvvmDkyJEMHDjQ8V20evVqSOa7KCX16tWDm981a9euJT4+PlXbiYjcKmeOYysikgv079+fnj17MmzYMEaPHk1CQgKvvvpqpuz7woULjunkfqkkmeHK08v+i3ZKRaTz589PwYIFuXjxIidPnqR48eIZPmZ2OXLkCACvvvrqHV+biIiILItj4cKF9O/f/7ZF1aOjo51+PnnyJNzmdUlpfnq3y+i1yoz34q0CAwMBOH/+fJq3jYyMdCQ6Ujrn0NBQuM0fm6VLl052vn20ydQmUm7H/noVLlw4xRE7k4s/MjLS8Z4pVarUHY8TGRlJiRIlnOal5fzsxa/DwsJSTJRndny3MgyDYcOGER8fz7Rp0xyFnxcvXkyHDh0YOXIkrq6ujBgxAm4Wyj927BjFixdP1+icDz30EH5+flgsFry8vChVqhQtW7akfv36ya6f0j0QHh6OYRhcv34dT0/P2x4z8b11p3vZPqhD4kLWt2MfHbJYsWLZMirfnb5b7PdfTEwMkZGRBAUFOS3PjvsvK4+X1Z+pjz/+OH/++SezZs1yGnFy1qxZcPN3lFt98sknPPfcc1y9ejXF/d76XZSSiRMnsmPHDhYvXszixYsdheGbNWvGww8/fNtC7CIiiSkpJSKSAUOHDsXV1ZUhQ4YwZswYrFarYzS4jNiyZYtjulq1ahne393K3uKsUaNGjj+AUlKlSpUsieHUqVP07NmT69evM3LkSB5++GHKlCmDn58fLi4uLF26lLZt2952tLPskNFr5e3tnekx2Uc6Cw8PJzIyMtnWIlnJxSXnNihP3JoypdZviSWXDMnK88uM+G518OBB9u7dS1BQEDVq1HDMb9y4Mb///jvt27fn+eefx9XVleHDh/Ptt98SFxfnGB0trdKa+E/pHrBfCz8/Px566KE0x3G3yu77L7OPl9Wfqd27d+fpp59mxYoVnDx5kpIlS7JlyxZ27NhBiRIlaNOmjdP6mzdvZvDgwbi6uvL222/TuXNnSpcujY+PDxaLhZkzZzJ48OBUfxcVLVqUf/75h9WrV7N8+XLWrVvHhg0bWLduHW+++SYTJ07kpZdeStW+ROTupqSUiEgG2X/JGzRoEOPGjcNqtTJ+/PgM7dM+VHqNGjWS/Pc4s5UoUYJ9+/Y5/qt7q6ioKC5evOhYNzext9C4//77eeGFF0yJYeHChVy/fp2uXbvy9ttvJ1l+8ODBZLcrUaIEhw8f5ujRo8n+wWIfjj657fbt25fi8pTm54RrdavmzZuTL18+Ll++zBdffMFzzz2X6m0LFSqEp6cnsbGxHDlyhOrVqydZx/6eN/N9bT/2hQsXuHLlSrKtpZJ7zQoXLoy3tzfXr1/n3XffpXDhwlkap70VyYEDBzAM445JnqyI79KlSwDJdvVq2LAhS5YsoV27djz77LNcu3aNDz/8kMDAQF588cUMHzsj7PeWxWLh888/T3Xyw/7eSOmeJVELttSwv4ZnzpwhKioqy1tL2T/Djhw5QtWqVZMst99/Xl5eKbYIzs2y+jPVx8eHHj168NlnnzFnzhxGjx7t6Fbat2/fJO+zefPmYRgGTz/9NCNHjkyyv5S+i27HYrHQrFkzR/fTmJgYZs+ezVNPPcWoUaPo1q3bHRNyIiI591+AIiK5yBNPPMFnn32Gi4sLr732Gq+88kq69/Xbb7856gwl94tjZrP/Mjlnzpxkl3/++edwszZLbktKtW/fHhL9Mm4Ge0IvODg4yTLDMPj666+T3a5p06Zwsz5Icr744otM3S4nXKtb+fv788wzzwDw2muvER4eftv1r1y5wtatWwFwc3OjUaNGAMnW/yHRe7t58+aZHHnqlSxZ0lE3Jrn3QmxsLPPmzUsy39XVldatWwPw/fffZ3mcdevWpXDhwkRERPDTTz/dcf2siC8sLAx3d3ciIyNZtmxZkuUNGjRg6dKlBAQEMGrUKE6fPs2cOXOypava7RQvXpzq1atz+fJlfv/991RvZ7+Xf//9d8fnSGK//PKLI1GXGkWLFqVGjRpYrVbHe/9OPDw84GaNtrSyf7fc6f5r3LhxsjWXcro7XZvs+Ex9/PHH4eb3d2xsrOMzJLk6Xrf7LoqJiXH83pERXl5ePPnkk1SvXh2r1cqOHTsyvE8RyfuUlBIRyST9+/d3/Bf8jTfe4OWXX07T9pcuXeKNN97gwQcfxDAM+vTp41QnIqsMHDgQf39/tmzZwptvvun0y/PWrVuZMGECgOmtDdLj/vvv55577mHjxo30798/2bod//77L9OnT8+yIq32uhrz58/nzJkzjvkJCQmMGTMmxQLrTz/9NK6urnz//fdJCtl+++23KSYGBgwYgJ+fH+vXr+f99993WrZq1SqmT5+e7HZZfa1atmxJxYoVk5zLnYwZM4b77ruPS5cu0ahRIxYuXJhknYSEBBYsWECdOnUcxXoBnn/+eQCmTZvGihUrnLaZPXs2v/zyC+7u7gwfPjzN55OZnn32WQDGjRvHvn37HPMTEhJ44YUXUhxgYOzYsXh4ePDiiy8yZ84cpy5zdrt27eLHH3/McIxubm6MHj0agEGDBvHnn38mWWfTpk2OOkhZEV9AQABPPPEE3CyunNJ7oWjRoo6fE78fzGT/HO3fv3+ycRuGwYYNG1i6dKljXuPGjalduzZXrlzhqaeeIjY21rHsxIkT6Wp9Y+9ePnr06GSTEHv27GHv3r2OnwMDA/Hw8ODs2bPJJsZuZ/jw4bi5ufHTTz85Wv/aLV26lBkzZgDkmJaZaVWyZEkAdu/enezy7Pj+ue+++wgLC+PgwYO89NJLREZG0qhRo2RrqNm/i+bMmcPly5cd82NiYhg6dOgdk/63evfddx11yhLbt2+fo9VVcgkwEZFb5b5/S4iI5GB9+/bF1dWVfv368dZbb5GQkMCkSZOSrGf/L6ZhGFy5coXjx4+zfft24uLicHd3Z8yYMbzyyiu37SLzwgsvpFgYGeCZZ56hdu3ad4y5SJEifPXVV3Tv3p3Ro0fz5ZdfUqtWLc6fP8/q1auJj4+nf//+DBw4MNXXIbXOnDnDvffem+Ly2rVr8/HHH6d7/y4uLvz000907NiROXPmMH/+fGrUqEHp0qW5ceMGR44cYefOnSQkJNCvX780/7e+a9eut62F8/fff9O5c2fq1KnD5s2bqVChAk2bNsXX15cNGzZw+vRpXnrppWS79dWsWZOJEycycuRIHnzwQerXr09oaCgHDx5k06ZNPPfcc7z33ntJtitevDiffPIJjzzyCMOHD+fTTz+latWqnDp1ijVr1vDss88mu11WX6vDhw9z7NgxoqKi0rSdh4cHS5YsYcCAAXz//fd06dKFYsWKUadOHfz9/YmMjGTTpk1cvHgRT09Pp6LK7du355VXXmHChAm0bt2ahg0bUrp0afbt28eWLVtwdXVl+vTpWVZPLLWeeuopli1bxsKFC6lRowbNmzenQIECbNiwgTNnzjBkyBCmTZuWZLvatWszd+5cx+hZr7zyCpUrVyYwMJCLFy+yc+dOTp48Sc+ePXnwwQczHOfw4cPZv38/06dPp2nTptSqVYuwsDCio6MdXYBXrlzp+GM9K+KbPHkyp0+f5ueff6ZLly5UqFCBypUr4+bmxs6dO9m/fz8BAQG88847TJo0ibfffhs3NzdHUsgsnTt3ZurUqTz//PN06dKFcuXKERYWRkBAABEREWzfvp3z58/z0ksvOdUC+vLLL2nWrBnffvstf/75J40aNeLatWv88ccfVK9encKFC7N+/fpUx9G1a1feeOMNXnnlFbp160bFihWpUaMG169f59ChQ+zZs4dZs2Y5Ehju7u506dKF+fPnU7NmTRo1aoSPjw8An3766W2PVa1aNT766COGDBnCo48+ynvvvUfFihU5duwYf/31F4ZhMG7cuCS1j3KLBx54gPHjx/P++++za9cuSpUqhYuLC126dKFLly5Z/plq179/f/7v//6PqVOnQqLWU8mtN3XqVLZu3UpISAiNGzfG1dWVNWvWcP36dYYPH+7YR2pMmDCBF198kYoVK1KpUiW8vb05ffq0YyS+xx57LFW/g4iIYIiISJoEBwcbgDFr1qwU1/n6668NV1dXAzBGjBhhGIZhrFy50gCcHq6urkb+/PmNcuXKGQ8++KDx3nvvGefPn09xv+Hh4Un2kdJjwYIFaTqvPXv2GH379jVKlixpuLu7G/nz5zeaN29ufPvtt7eNJTg4OE3HMQzDmDVrVqrOoWnTpo5t7PNuNXbsWAMwxo4dm+LxYmJijOnTpxvNmzc3ChUqZLi5uRlBQUFGzZo1jaeeespYsmRJqmNPy2tgd/nyZWPUqFFGWFiY4eXlZQQFBRkPPPCA8c8//zjeF4nPNbGff/7ZaNSokeHr62v4+fkZ9913nzF//vw7Xv81a9YYbdu2Nfz9/Q0fHx+jVq1axowZM257LdNzreyvZd++fW973VJz39zJ+vXrjUGDBhmVKlUy/P39DTc3N6Nw4cJGkyZNjDfeeMM4efJkststXrzY6NChg+N8ihYtanTv3t3YsGFDsus3bdrUAIyVK1cmuzyl99ydXsu+ffumeA3i4uKM//3vf0blypUNT09Po1ChQsb9999vbNu27Y7XODw83HjuueeMqlWrGr6+voaXl5cRHBxsNGvWzHjrrbeMQ4cOZcr52S1evNi4//77jSJFihju7u5GYGCgUa9ePWP8+PFGZGRkhuNLjR9//NHo3LmzUaRIEcPNzc0ICAgw7r33XmPChAlGRESEYRiGsWHDBsPHx8cAjDFjxqRqv4nv7/Dw8FRtc6fXPbGdO3cagwYNMsqXL294eXkZPj4+RtmyZY22bdsa77//vnHq1Kkk2xw7dszo16+fUaRIEcPDw8MoW7as8dJLLxlXr15N8bW83XvNuHkv9e7d2yhRooTh7u5uFCxY0KhRo4YxcuRI49ixY07rRkZGGoMHDzZKly5tuLu7J/n8uNP78++//za6detmFC1a1HBzczMKFSpkdOzY0Vi6dGmy698p9tR+5iSW+Pv3VvbPppRe79vFs2DBAqNhw4ZGvnz5DIvFkux9k1WfqXanT592/L7h6+trXL58OcV1IyIijKFDhxqhoaGGp6enUbx4ceORRx4xDh48mOJxU3p/z5071+jfv79RtWpVo2DBgoanp6cRHBxstG/f3liwYIFhtVpTFb+IiMXIKYUjRERERERERETkrqGaUiIiIiIiIiIiku2UlBIRERERERERkWynpJSIiIiIiIiIiGQ7JaVERERERERERCTbKSklIiIiIiIiIiLZTkkpERERERERERHJdm5mB5BbWK1WTp8+Tb58+bBYLGaHIyIiIiIiIiKSIxmGweXLlylevDguLim3h1JSKpVOnz5NqVKlzA5DRERERERERCRXOHHiBCVLlkxxuZJSqZQvXz64eUH9/f2z9dhWq5WIiAgCAwNvm2EUkayn+1Ek59D9KJJz6H4UyVl0T4rZoqOjKVWqlCOXkhIlpVLJ3mXP39/flKRUTEwM/v7++kARMZnuR5GcQ/ejSM6h+1EkZ9E9KTnFncof6d0pIiIiIiIiIiLZTkkpERERERERERHJdkpKiYiIiIiIiIhItlNSSkREREREREREsp2SUiIiIiIiIiIiku00+p6IiIiIiIhkuoSEBOLi4swO465ktVqJi4sjJiZGo+9JpnJzc8PV1fWOo+qlen+ZshcRERERERERwDAMzp49y6VLl8wO5a5lGAZWq5XLly9nWvJAxM7V1ZWgoCACAgIy/P5SUkpEREREREQyjT0hFRQUhI+Pj5IiJjAMg/j4eNzc3HT9JdPY31fR0dGcOXOG69evU6xYsQztU0kpERERERERyRQJCQmOhFShQoXMDueupaSUZKV8+fLh6enJhQsXCAoKwtXVNd37UudSERERERERyRT2GlI+Pj5mhyIiWcjX1xfDMDJcN05JKREREREREclUap0jkrdl1j2upJSIiIiIiIiIiGQ7JaVERERERERE7hLx8fGMHDmSUqVK4eLiwgMPPGB2SHIXU1JKRERERERE5A5mz56NxWLhn3/+cZofFRVFvXr18PLy4vfffwdg3LhxWCwWx8PHx4fSpUvTuXNnZs2aRWxsbJL99+vXz2mbxA8vL69MO4/PP/+cd955h27dujFnzhyee+65FNdt1qyZIwYXFxf8/f0JCwvj0UcfZdmyZRmK4+OPP2b27NkZ2se0adPo3r07pUuXxmKx0K9fvwztT7KfRt8TERERERERSYfo6GjatGnDjh07WLBgAe3atXNaPm3aNPz8/IiNjeXUqVMsWbKExx9/nClTpvDrr79SqlQpp/U9PT359NNPkxwnI6Ob3eqPP/6gRIkSvPfee6lav2TJkkycOBGAq1evcujQIX788Ufmzp1Ljx49mDt3Lu7u7mmO4+OPP6Zw4cIZSiS9/fbbXL58mXr16nHmzJl070fMo6SUiIiIiIiISBpdvnyZtm3bsm3bNn788Ufat2+fZJ1u3bpRuHBhx89jxozhq6++4rHHHqN79+78/fffTuu7ubnxyCOPZGnc58+fJ3/+/KlePyAgIElMb731Fs888wwff/wxZcqU4e23386CSO9s9erVjlZSfn5+psQgGaPueyIiIiIiIiJpcOXKFdq1a8eWLVv44Ycf6NixY6q3ffjhh3niiSfYsGFDhrvAJXb16lWef/55SpUqhZeXF1WqVOHdd9/FMAwAjh49isViYeXKlezevdvRLW/VqlVpPparqyvvv/8+lStX5sMPPyQqKsqxbNasWbRo0YKgoCA8PT2pXLky06ZNc9q+TJky7N69m9WrVzviaNasGQAXL17khRdeoFq1avj5+eHv70/79u3Zvn17kjiCg4M10mMup5ZSIiIiIiIiIql09epV2rdvz6ZNm5g/fz6dOnVK8z4effRRZs6cydKlS2ndurXTsgsXLiRZ38PDA39//xT3ZxgGXbp0YeXKlQwYMIAaNWrw+++/M3LkSE6fPs17771HYGAgX375JW+88QZXrlxxdMmrVKlSmuPnZmKqd+/evPrqq6xdu9aRmJs2bRpVqlShS5cuuLm5sXDhQoYOHYrVauWpp54CYMqUKTz99NP4+fkxevRoAIoUKQLAkSNH+Omnn+jevTshISGcO3eOGTNm0LRpU/bs2UPx4sXTFa/kTEpKiYiIiIiIiKRS3759OX36NPPmzaNLly7p2kfVqlUBOHz4sNP8q1evEhgYmGT9tm3bOoqoJ+eXX37hjz/+YMKECYwePRrDMBg8eDB9+vRh6tSpDBs2jNDQUB555BE+/fRTXF1dM6WbYHLnsXr1ary9vR0/Dxs2jHbt2jF58mRHUuqBBx7glVdeoXDhwkniqFatGgcOHMDF5b+OXY8++igVK1bks88+49VXX81w3JJzKCklIiIiIiIiWcowIC7O7Cj+4+4O6e31de7cOby8vJIUKU8Le/2jy5cvO8338vJi4cKFSdZPXJcqOYsWLcLV1ZVnnnnGaf6IESOYP38+ixcvZtiwYemONyXJnUfihFRUVBRxcXE0bdqUJUuWEBUVRUBAwG336enp6ZhOSEjg0qVL+Pn5ERYWxpYtWzL9HMRcSkqJiIiIiIhIloqLgzffNDuK/4waBR4e6dt2xowZjBgxgnbt2rFmzRrCwsLSvI8rV64AkC9fPqf5rq6utGrVKs37O3bsGMWLF0+yP3vXvGPHjqV5n6mR3HmsW7eOsWPHsn79eq5du+a0fmqSUlarlalTp/Lxxx8THh5OQkKCY1mhQoUy/RzEXCp0LiIiIiIiIpJKlStXZtGiRVy/fp3WrVtz4sSJNO9j165dAJQrVy4LIsw+t57H4cOHadmyJRcuXGDy5Mn89ttvLFu2jOeeew5uJpzu5M0332TEiBE0adKEuXPnsmTJEpYtW0aVKlVStb3kLmopJSIiIiIiIlnK3d3WOimncHfP2Pb16tXjp59+omPHjrRu3Zo1a9YkWwsqJV9++SXcrBWVGYKDg1m+fDmXL192arW0b98+x/LMlpCQwNdff42Pjw+NGjUCYOHChcTGxvLLL79QunRpx7orV65Msn1Ko+bNnz+f5s2b89lnnznNv3Tp0h27MUruo5ZSIiIiIiIikqUsFlt3uZzySG89qcRatmzJN998w6FDh2jXrh3R0dGp2u7rr7/m008/pUGDBrRs2TLjgQAdOnQgISGBDz/80Gn+lClTsFgstG/fPlOOY5eQkMAzzzzD3r17eeaZZxwjA7q6usLN0QDtoqKimDVrVpJ9+Pr6cunSpSTzXV1dnbYHmDdvHqdOncrUc5CcQS2lREREREQk17D/rZoZSQWRjOratSuffPIJjz/+OF26dOH333/Hy8vLsXz+/Pn4+flx48YNTp06xZIlS1i3bh01atRg3rx5SfYXHx/P3LlzUzyWr69vsss6d+5M8+bNGT16NEePHqV69eosWbKEhQsX8uyzzxIaGpruc4yKinLEdO3aNQ4dOsSPP/7I4cOH6dWrF6+//rpj3TZt2uDh4UHnzp0ZPHgwV65c4ZNPPiEoKIgzZ8447bdOnTpMmzaNCRMmUK5cOYKCgmjRogWdOnXitddeo3///tx3333s3LmTr776irJlyyaJbeHChWzfvh2AuLg4duzYwYQJEwDo0qUL1atXT/d5S/ZQUkpERERERHKcGzdg3z64cAEuXYKoKNvz5cu2li6FCkHhwrZHoUJQujTcHAhMJFv179+fixcv8sILL9C9e3cWLFjgWDZkyBC4Oape4cKFqVmzJp9//jl9+vRxGmXOLjY2lkcffTTZ44SHh6eYlHJxceGXX35hzJgxfPfdd8yaNYvg4GAmTZrECy+8kKHzO3nypCMmPz8/ihUrRoMGDZg2bRqtW7d2WjcsLIz58+fzyiuv8MILL1C0aFGGDBlCYGAgjz/+uNO6Y8aM4dixY0yaNInLly/TtGlTWrRowahRo7h69Spff/013333HbVr1+a3337j//7v/5LE9sMPPzBnzhzHz1u3bmXr1q0AlCxZUkmpXMBi3NouTpIVHR1NQEAAUVFRjqaJ2cVqtXL+/HmCgoJwcVGPSxEz6X4UyTl0P4rkHJl1PxoGnD4NmzfDrl22xFRqubpClSpQvz6UKJHuECSDYmJiCA8PJyQkxKnFkGQvwzCIj4/Hzc0txdpNIhlxp3s9tTkUtZQSERERERFTJSTAli3wzz9w7tx/8wsVgrJlISAA8ue3Pfz9ISbG1oIqMtL2fO4cnD0LO3bYHqVK2ZJTlSrZklUiIpIz5dh/K3700UeUKVMGLy8v6tevz8aNG1Nc98cff6Ru3brkz58fX19fatas6RjNwK5fv35YLBanR7t27bLhTEREREREJCVnzsDMmfDbb7bkkpsbVK8O/fvDsGHQsSM0agRVq0LJkrakVFAQVK4MjRtD167w5JMwaBDUqGFLQp04AfPnw7Rptv2LiEjOlCNbSn333XeMGDGC6dOnU79+faZMmULbtm3Zv38/QUFBSdYvWLAgo0ePpmLFinh4ePDrr7/Sv39/goKCnIbYbNeunVPV/+T68IqIiIiISNaLj4c//4S1a8FqBR8faNLElljy9k77/ooXtyWoWre2df/buNHWiurTT6FFC7jvPhVHFxHJaXJkUmry5MkMHDiQ/v37AzB9+nR+++03Pv/882SLmzVr1szp5+HDhzNnzhzWrl3rlJTy9PSkaNGi2XAGIiIiIiKSktOn4aef4Px5289VqkCHDpBCDec08fODpk2hXj1YuBD27IFly+DQIVvSKpvLw4qIyG3kuO57N27cYPPmzbRq1coxz8XFhVatWrF+/fo7bm8YBitWrGD//v00adLEadmqVasICgoiLCyMIUOGEBkZmSXnICIiIiKSJtYEuHIUzi6Hw7Ng/wcQkeh33/ircHIhRO2DhFgzI82wLVtsrZfOn7clobp3tz0yIyGVmLe3bb/3328brS883Nadb+/ezD2OiIikX45rKXXhwgUSEhIoUqSI0/wiRYqwb9++FLeLioqiRIkSxMbG4urqyscff+w0PGW7du148MEHCQkJ4fDhw4waNYr27duzfv16XJOpfhgbG0ts7H9f+NHR0XBzZBGr1ZpJZ5s6VqsVwzCy/bgikpTuR5GcQ/ej5HpXT2A59DFErIWLm7FYnZNNRuXRGIXq2364cgKXP7vY5ltcwL8yBDbCCGwMRVuDZyEzzsAhtffjunWwfLmtD13lyoajdVRW3sY1athqUf3wA5w5Y+G77ww6d4ZatbLumHcz+3vB/hDz2K+/XgfJCvZ7PKUcSWp/P8txSan0ypcvH9u2bePKlSusWLGCESNGULZsWUfXvl69ejnWrVatGtWrVyc0NJRVq1bRsmXLJPubOHEi48ePTzI/IiKCmJiYLD4bZ1arlaioKAzD0JDXIibT/SiSc+h+lFzHGo9L3EWsnrYaqa7XzhC4d5JjsWHxIMG7NAlepbC65SOWEsTc7N/meu0S+f2q4nr9CC4J1yBqF0TtwnJoOobFlctlR3EteKh5p3aH+9Ew4M8/Pdi0yQOAevVu0LjxDa5ehatXsyfGzp3hjz882b7dnW+/Nbh4MZZq1eKz5+B3kbi4OKxWK/Hx8cTH6/qaxTAMEhISALComJpkgfj4eKxWK5GRkbi7uydZfvny5VTtJ8clpQoXLoyrqyvnEo8FC5w7d+629aBcXFwoV64cADVr1mTv3r1MnDgxSb0pu7Jly1K4cGEOHTqUbFLq5ZdfZsSIEY6fo6OjKVWqFIGBgfhnc0d0q9WKxWIhMDBQv3SLmEz3o0jOoftRco3YC3D4MyyHpkGhehgNv7fNNwIxLj6Nkb8GFG4IfqG4uLg66mt4Av/91hkEZbaDYWC9fhoiN2CJWAPnVmKJ2olf8br42QcEijkHcdGQr3y2neLt7kerFX79FfbsseDrC61bG9x3n0+2xZZYnz5QoABs2mRh3TpfChZUi6nMFhMTw+XLl3Fzc8PNLcf9uXnXSS5ZIJIZ3NzccHFxoVChQnh5eSVZnty8ZPeTBbFliIeHB3Xq1GHFihU88MADcPNLbsWKFQwbNizV+7FarU7d72518uRJIiMjKVasWLLLPT09kx2dz8XFxZRffC0Wi2nHFhFnuh9Fcg7dj5KjxV6Eve/A/vch4ZptnsUFCwa43CwfUfd90tyGwa+U7RHczfZz9EFcfEuD/T448D7seRuCe0H11yBfucw7p9tI7n6Mj7d1m9u71xZely5Qq5a5rTY6drTFsnGjhYULbSPy1a5takh5iouLCxaLxfEQcxiG4bj+eh0kK9jv8ZR+D0vt72Y5LikFMGLECPr27UvdunWpV68eU6ZM4erVq47R+B577DFKlCjBxIkT4WZXu7p16xIaGkpsbCyLFi3iyy+/ZNq0aQBcuXKF8ePH89BDD1G0aFEOHz7MyJEjKVeunNPofCIiIiIiGZYQC/sm2xJDcVG2eQVqQdhwCO75X0Iqs/jf0iLq+mnAgGPfwPHvIXQAVJ8AXoGZe9w7MAz4+WdbQsrVFbp1g0qVsjWEZFks0L697XnDBvjlF9t8JaZERLJfjkxK9ezZk4iICMaMGcPZs2epWbMmv//+u6P4+fHjx52yblevXmXo0KGcPHkSb29vKlasyNy5c+nZsycArq6u7Nixgzlz5nDp0iWKFy9OmzZteP3115NtDSUiIiIikm77JsP2Ubbp/NWg+htQopMtC5IdGsyBsGdhxytwehEcmgnH50HNSRD6OFiyp2Xhn3/Czp22Vkm9e0O57GmwlSoWC7RrZ3v++29YuBACAiA01OzIRETuLhZDpfhTJTo6moCAAKKiokypKXX+/HmCgoLUPUHEZLofRXIO3Y+SY8VdgRUtIOxpKPNwtiWBknV+DfzzNFzabvu50gtQ651MP8yt9+OuXTB/vm1Z585Qp06mHzJTGIatpdTWreDjA4MGQf78ZkeVu8XExBAeHk5ISEiqa8pI5jMMg/j4eNzc3NR9T7LEne711OZQ9BuciIiIiEhGRPwFG54A4+bw1+5+0HYDhDxqbkIKIKgxtPsHak8G32BbC6osduIE/PSTbfq++3JuQoqbLaY6doTixeHaNfj+e1sdLJGcqkyZMvTr18/x86pVq7BYLKxatSrTjmGxWBg3blym7S8r9evXjzJlymTLsW699rNnz8ZisfDPP/9ky/GbNWuW4kBuuZmSUiIiIiIi6WFYYdcEWN4YDn9m6yZnl5NaJri4QcXnoPNB8Cnx3/zDs2ytujLRpUvw7be2xE5YGLRqlam7zxJubtCjB3h7w+nTsHix2RFJTmVPQtgfXl5eVKhQgWHDhiUZPT6nW7RoUY5LPI0bN87p+vr4+FC6dGk6d+7MrFmzbjuQWVrs2bOHcePGcfTo0UzZX2bKybFllRxZU0pEREREJEeLi4b1feHkzSZBZR6B4N5mR3V7LomGhj/5M2x43Fb/qsmCTBmhLzbWNtLe1atQtCg89NB/AwLmdPnz2wqxz50LmzdDiRIqfC4pe+211wgJCSEmJoa1a9cybdo0Fi1axK5du/Dx8cnWWJo0acL169fx8PBI03aLFi3io48+SjYxdf36ddzczEsVTJs2DT8/P2JjYzl16hRLlizh8ccfZ8qUKfz666+UKlXKse4nn3yC1WpN0/737NnD+PHjadasWZpaWe3fvz/LywXcLralS5dm6bHNoqSUiIiIiEhaRB+EP7tA9D5w8YB7ptkKiOcmnkHgVRSidsHv90DDb6F4xkalXrbMk/PnLfj7Q58+kMa/kU0XGgrNm8Mff8CiRbbEWvHiZkclOVH79u2pW7cuAE888QSFChVi8uTJ/Pzzz/TunXxy+urVq/j6+mZ6LC4uLpleu8vsWmDdunWjcOHCjp/HjBnDV199xWOPPUb37t35+++/Hcvc3d1T2EvmMAyDmJgYvL29TR8kLa2Jx9wil/zvQkREREQkB7iwAZY1sCWkvEtAqzW5LyEFENgA2m2GQvdC3CVY3REOf57u3e3cCfv2uePiYtCjB2TzuECZpnFjW7fD+HhbfalM6i0keVyLFi0ACA8Ph5t1jvz8/Dh8+DAdOnQgX758PPzww3BzUIApU6ZQpUoVvLy8KFKkCIMHD+bff/912qdhGEyYMIGSJUvi4+ND8+bN2b17d5Jjp1RTasOGDXTp0oWCBQvi6+tL9erVmTp1qiO+jz76CG7Wj7I/7JKrKbV161bat2+Pv78/fn5+tGzZ0ik5RKLujevWrWPEiBEEBgbi6+tL165diYiIyNA1fvjhh3niiSfYsGEDy5Ytc8xPrqbUt99+S506dciXLx/+/v5Uq1bNce6zZ8+me/fuADRv3txx7vbrV6ZMGTp16sSSJUuoW7cu3t7ezJgxw7EscU0pu2vXrjF48GAKFSqEv78/jz32WJLXM6U6XYn3eafYkqspdf78eQYMGECRIkXw8vKiRo0azJkzx2mdo0ePYrFYePfdd5k5cyahoaF4enpyzz33sGnTpjS8CllDLaVERERERFLLGmerw1SwLjRdCN5FzY4o/XyKQ6tVsGEgHP0SNgyAa6eg6itpqokVFWVrWcTNpE6injW5jsUCXbvCjBnw77+wYgV06GB2VJLTHT58GIBChQo55sXHx9O2bVsaNWrEu+++6+jWN3jwYGbPnk3//v155plnCA8P58MPP2Tr1q2sW7fO0fJnzJgxTJgwgQ4dOtChQwe2bNlCmzZtuHHjxh3jWbZsGZ06daJYsWI888wzFCtWjL179/Lrr78yfPhwBg8ezOnTp1m2bBlffvnlHfe3e/duGjdujL+/PyNHjsTd3Z0ZM2bQrFkzVq9eTf369Z3Wf/rppylQoABjx47l6NGjTJkyhWHDhvHdd9+l+dom9uijjzJz5kyWLl1K69atUzz33r1707JlS95++20A9u7dy7p16xg+fDhNmjThmWee4f3332fUqFFUqlQJwPHMzW56vXv3ZvDgwQwcOJCwsLDbxjVs2DDy58/PuHHj2L9/P9OmTePYsWOOhGFqpSa2xK5fv06zZs04dOgQw4YNIyQkhHnz5tGvXz8uXbrE8OHDndb/+uuvuXz5MoMHD8ZisTBp0iQefPBBjhw5kuUtzm5HSSkRERERkdQKagQtlkKB2rZR9nI7V09oMAd8SsKeibBzDATeB0Vbpmpzw7CNtBcTY6FYsQQaN87yiLOclxd07gxffAGbNkHVqlC6tNlR5SHxV1NeZnEFV6/UrYsLuHmnb90MioqK4sKFC8TExLBu3Tpee+01vL296dSpk2Od2NhYunfvzsSJEx3z1q5dy6effspXX31Fnz59HPObN29Ou3btmDdvHn369CEiIoJJkybRsWNHFi5c6EhsjB49mjfffPO2sSUkJDB48GCKFSvGpk2bKFy4sGN7wzAAaNCgARUqVGDZsmU88sgjdzzfV155hbi4ONauXUvZsmUBeOyxxwgLC2PkyJGsXr3aaf1ChQqxdOlSx3GtVivvv/8+UVFRBAQEpOoaJ6dq1aqQKAmYnN9++w1/f3+WLFmCq6trkuVly5alcePGvP/++7Ru3TrZ0ewOHTrE77//Ttu2qevS7OHhwYoVKxyJneDgYEaOHMnChQvp0qVLqs8vNbElNnPmTPbu3cvcuXMdLfGefPJJmjZtyiuvvMLjjz9Ovnz5HOsfP36cgwcPUqBAAQDCwsK4//77WbJkidN7N7up+56IiIiIyO2EfwVRe/77OahJ3khI2VksUPNNqPsh1Hgz1QkpgPXrITwc3N0N2rePIZm/AXOlsmWhVi1b0m3hQlt3Pskk3/ul/FjzkPO6PwSlvO6q9s7r/lwm5XWXN8nUU2jVqhWBgYGUKlWKXr164efnx4IFCyhRooTTekOGDHH6ed68eQQEBNC6dWsuXLjgeNSpUwc/Pz9WrlwJwPLly7lx4wZPP/20U0ubZ5999o6xbd26lfDwcIYPH07+/PmdlqWl1Y5dQkICS5cu5YEHHnAkpACKFStGnz59WLt2LdHR0U7bDBo0yOlYjRs3JiEhgWPHjqX5+In5+dk+dy9fvpziOvnz5+fq1atOXfzSKiQkJNUJKW6eb+KWRkOGDMHNzY1F9iakWWTRokUULVrUqY6Zu7s7zzzzDFeuXEmSLOzZs6cjIcXN1wXgyJEjWRrnnaillIiIiIhISg5/Zuve5hVkq8HkUyIVG+VSFZ5y/jnuCrh6g0vymaZz52zd2wDatoWCBY1sCDL7tGkDBw5ARASsXQt3aLQgd5GPPvqIChUq4ObmRpEiRQgLC0syKpubmxslS5Z0mnfw4EGioqIICgpKdr/nz58HcCRvypcv77Q8MDDQKamQHHsrInurooyKiIjg2rVryXZhq1SpElarlRMnTlClShXH/NK3NC20x3xrnaW0unLlCoBT659bDR06lO+//5727dtTokQJ2rRpQ48ePWjXrl2qjxMSEpKmuG59nfz8/ChWrBhHjx5N037S6tixY5QvXz7Je8/e3e/WJGBWvS4ZpaSUiIiIiEhyDn0KGwfapkt3B++7aCi2uMuwsi0EVIZ6nySpMRUfDz/+CAkJtsLgtWvbkjd5ibe3rZ7UvHmwZg1UqQKBgWZHlQf0uJLyMsstCdCHzt9mR7d0+rn/dgmAzO0gVK9ePcfoeynx9PRMkiywWq0EBQXx1VdfJbtNYB55gyXXbY5E3QfTa9euXQCUK1cuxXWCgoLYtm0bS5YsYfHixSxevJhZs2bx2GOPJSkAnhJv78zr6nknCQkJ2XasrHpdMkpJKRERERGRWx37DjYOsk1XeAbqTElT8e9c78LfELkBLqwHj4JQa5LT4tWrbS2lfH2hS5e8e2kqV7Yl3fbvh19+gccfz7vnmm3cfM1f1yShoaEsX76chg0b3jbxERwcDDdbViXuMhcREXHHVi2hoaFwM4Fzu5pEqe3KFxgYiI+PD/v370+ybN++fbi4uFAqm0Y3sBdlv1PXOg8PDzp37kznzp2xWq0MHTqUGTNm8Oqrr1KuXLl0dWO8nYMHD9K8eXPHz1euXOHMmTN0SDRKQoECBbh06ZLTdjdu3ODMmTNO89ISW3BwMDt27MBqtTolQPft2+dYnhuoppSIiIiISGJnl8P6RwEDyg+9+xJSAMVa21pIAex9B/a87VgUGQl//WWb7tTJlpjKqywW6NgRPD3hxAlb4XOR9OrRowcJCQm8/vrrSZbFx8c7khatWrXC3d2dDz74wKkVy5QpU+54jNq1axMSEsLUqVOTJEES78v35o176zq3cnV1pU2bNvz8889O3dHOnTvH119/TaNGjfD3979jXBn19ddf8+mnn9KgQQNatky57l1kZKTTzy4uLlSvXh1uFp8nDeeeWjNnziQuLs7x87Rp04iPj6d9+//qnoWGhvLnn38m2e7WllJpia1Dhw6cPXvWaVTD+Ph4PvjgA/z8/GjatGmGziu7qKWUiIiIiIjdxa3wZ1ewxkHpHlDn/bsvIWUX+jjcuAhbX4Rt/wceBTBCB7Foka3bXvnyULGi2UFmPX9/aNUKfvsNli+HSpXgNiVtRFLUtGlTBg8ezMSJE9m2bRtt2rTB3d2dgwcPMm/ePKZOnUq3bt0IDAzkhRdeYOLEiXTq1IkOHTqwdetWFi9eTOHChW97DBcXF6ZNm0bnzp2555576NevH8WLF2ffvn3s3r2bJUuWAFCnTh0AnnnmGdq2bYurqyu9evVKdp8TJkxg2bJlNGrUiKFDh+Lm5saMGTOIjY1l0qRJyW6TEfPnz8fPz48bN25w6tQplixZwrp166hRowbz5s277bZPPPEEFy9epEWLFpQsWZJjx47xwQcfULNmTUetpZo1a+Lq6srbb79NVFQUnp6etGjRIsVaX3dy48YNWrZsSY8ePdi/fz8ff/wxjRo1chp574knnuDJJ5/koYceonXr1mzfvp0lS5YkeT3TEtugQYOYMWMG/fr1Y/PmzZQpU4b58+ezbt06pkyZctvaWzmJklIiIiIiIna+wZC/uq3Ad4MvUizyfdeo9ALERsKet2Djk5yIKMHhwx1xc4P27e+efF3durB9O5w8aeu6aOLo6ZLLTZ8+nTp16jBjxgxGjRqFm5sbZcqU4ZFHHqFhw4aO9SZMmICXlxfTp09n5cqV1K9fn6VLl9KxY8c7HqNt27b88ccfjB8/nsmTJ2O1WgkNDWXgwIGOdR588EGefvppvv32W+bOnYthGCkmpapUqcKaNWt4+eWXmThxIlarlfr16zN37lzq16+fSVfmP/ZRC728vChcuDA1a9bk888/p0+fPnh6et5220ceeYSZM2fy8ccfc+nSJYoWLUrPnj0ZN26co4tb0aJFmT59OhMnTmTAgAEkJCSwcuXKdCelPvzwQ7766ivGjBlDXFwcvXv35v3333fqijdw4EDCw8P57LPP+P3332ncuDHLli1L0uorLbF5e3uzatUq/u///o85c+YQHR1NWFgYs2bNol+/fuk6FzNYDLOrWuUS0dHRBAQEEBUVlS3NExOzWq2cP3+eoKCgJMXyRCR76X4UyTl0P0qWib8GRgK4547/Mmc5w7AVfD/8GdEJpZh65CCNmniSqITKXXE/HjsGs2aBiwsMHQp3aLBy14qJiSE8PJyQkBC8vLzMDueuZRgG8fHxuLm5ZXoNJRFSca+nNoeSN78xRERERERSyxoHpxb997ObjxJSiVkscM80Tnk9zhfHl+Kf35NGjcwOKvsFB9uKnlutsGKF2dGIiOQNSkqJiIiIyN1tywhY3RF2jDE7khwrItKdz3Z9xoW4irRvD+7uZkdkjlatbDm6vXtthc9FRCRjlJQSERERkbvXwelw4EPbdIFaZkeTIxkGLF5sayEUFgYVKgBn/4BNT9kW3kUCA6HWzbfJsmV33emLiGQ6JaVERERE5O4UsQ7+edo2XX0ClOpqdkQ50p49cOQIuLlBu3ZAzHlY3QkOfmx73GWaNbNdi+PH4cABs6MREcndlJQSERERkbtPTASs7QlGPJTuCVVGmR1RjpSQAMuX26YbNYICBQCvIFsSD2DLc3Dhb1NjzG7+/nDvvbbp5cttLchERCR9lJQSERERkbuLNQH+ehiunwL/ilD/E1uhIEliyxb491/w84P77ku0oOJzUOohW5H4td1tSb67SKNG4O0NERGwbZvZ0YiI5F5KSomIiIjI3eXcCji7DFx9oNF8jbSXghs3YPVq23TTpuDhkWihxQL3fg75KsC1k1jWPwxGglmhZjsvL2jSxDa9ciXExZkdkYhI7qSklIiIiIjcXYq1gcYLoP5nkL+K2dHkWBs2wJUrti57tWsns4K7PzT+EVx9sJxbge/R902I0jz33AP588Ply7B1q9nRiIjkTkpKiYiIiMjdp9QDUKaX2VHkWNeuwdq1tukWLcDVNYUV81eBe6YB4Hbt0F01HJ2bGzRsaJtet85Wf0tERNJGSSkRERERyfsMA3aOh2snzY4kV1i3DmJjoUgRqFr1DiuHPIq1+QqiKn9419XmqlkTfH0hKgp27TI7GhGR3EdJKRERERHJ+w7NgJ3jYEk9iL9mdjQ5WnS0reseQKtWqcgzWSxQpNl/K95FraXc3f8biW/t2rvq1EVEMoWSUiIiIiKSt0Xtgy0jbNOVRoKbj9kR5WirV0N8PAQHQ7lyadw49iKs7Qbhc7MoupznnnvA09M2Et/+/WZHI3ndwYMHadOmDQEBAVgsFn766Sdmz56NxWLh6NGjd9y+TJky9OvXL1tilcyRltfsxIkTeHl5sW7dujQfZ8+ePbi5ubErm5t9KiklIiIiInlXwg34qw8kXIeirSHsGbMjytEuXPivaHfLlunojRc+C078CP88ddd0lfTysiWmUGupu8bhw4cZPHgwZcuWxcvLC39/fxo2bMjUqVO5fv16lh67b9++7Ny5kzfeeIMvv/ySunXrZunxcqoyZcrQqVOndG27aNEixo0bl+kx5QSvvfYa9evXp6G94F0aVK5cmY4dOzJmzJgsiS0lSkqJiIiISN61ewL8uxU8C8G9s8GiX39vZ/VqsFohLAxKl07HDioMh0L3Qlw0bHjirsnQ3HuvrfD5yZNw7JjZ0UhW+u2336hWrRrff/89nTt35oMPPmDixImULl2aF198keHDh2fZsa9fv8769esZMGAAw4YN45FHHqFkyZI8+uijXL9+neDg4Cw7dl6yaNEixo8fb3YYmS4iIoI5c+bw5JNPpnsfTz75JAsWLODw4cOZGtvt6FtZRERERPKmf7fB7om26XumgU9xsyPK0SIj/yvW3bx5Onfi4gYNZoOrF5xZAoc/y8wQcyw/P6hVyza9Zo3Z0UhWCQ8Pp1evXgQHB7Nnzx6mTp3KwIEDeeqpp/jmm2/Ys2cPVapUybLjR0REAJA/f36n+a6urnh5eWG5ywYayEkMw8jyVnJ3MnfuXNzc3OjcuXO699GqVSsKFCjAnDlzMjW221FSSkRERETypl1vgBEPpR6C0t3NjibHs3c9q1ABihbNwI78w6D6BNv0lhFw9XhmhZij3Xefrbvj4cNw5ozZ0UhWmDRpEleuXOGzzz6jWLFiSZaXK1fOqaVUfHw8r7/+OqGhoXh6elKmTBlGjRpFbGys03b2rmhr166lXr16eHl5UbZsWb744gvHOuPGjXO0hHrxxRexWCyUKVMGINmaUoZh8Oabb1KqVCl8fHxo3rw5u3fvTva8Ll26xLPPPkupUqXw9PSkXLlyvP3221itVsc6R48exWKx8O677zJz5kzHOd1zzz1s2rQpyT737dtHjx49CAwMxNvbm7CwMEaPHu20zqlTp3j88ccpUqQInp6eVKlShc8//zxVr8WtUhtfv379+OijjwCwWCyOh53VamXKlClUqVIFLy8vihQpwuDBg/n333+djmd/zZYsWULdunXx9vZmxowZVK1alebJZPWtVislSpSgW7dujnnvvvsu9913H4UKFcLb25s6deowf/78dJ0/wE8//UT9+vXx8/NLEmtyNamaNWtGs2bNnOa5u7vTrFkzfv7553THkVZu2XYkEREREZHs1GAO+FeACqojdSdRUbB9u226ceNM2GHYs7baUhf+gg0DoPnSdBSoyl0KFICqVWHnTluCr7vyoHnOwoULKVu2LPfdd1+q1n/iiSeYM2cO3bp14/nnn2fDhg1MnDiRvXv3smDBAqd1Dx06RLdu3RgwYAB9+/bl888/p1+/ftSpU4cqVarw4IMPkj9/fp577jl69+5Nhw4dkiQfEhszZgxvvPEGHTp0oEOHDmzZsoU2bdpw48YNp/WuXbtG06ZNOXXqFIMHD6Z06dL89ddfvPzyy5w5c4YpU6Y4rf/1119z+fJlBg8ejMViYdKkSTz44IMcOXIEd3d3AHbs2EHjxo1xd3dn0KBBlClThsOHD7Nw4ULeeOMNAM6dO8e9996LxWJh2LBhBAYGsnjxYgYMGEB0dDTPPvtsql+XtMQ3ePBgTp8+zbJly/jyyy+TbD948GBmz55N//79eeaZZwgPD+fDDz9k69atrFu3znGOAPv376d3794MHjyYgQMHEhYWRs+ePRk3bhxnz56laKLs/tq1azl9+jS9evVyzJs6dSpdunTh4Ycf5saNG3z77bd0796dX3/9lY4dO6bpvOPi4ti0aRNDhgxJ13VLrE6dOvz8889ER0fj7++f4f3dkSGpEhUVZQBGVFRUth87ISHBOHPmjJGQkJDtxxYRZ7ofRXIO3Y8imee33wxj7FjDmD07fdsnez9G7TeMb70N48cShnHlaKbFmpOdPWu7juPGGUZkpNnRmOP69evGnj17jOvXr5sdSqay/z14//33p2r9bdu2GYDxxBNPOM1/4YUXDMD4448/HPOCg4MNwPjzzz8d886fP294enoazz//vGNeeHi4ARjvvPOO0z5nzZplAEZ4eLhjWw8PD6NDhw5O9+SoUaMMwOjbt69j3uuvv274+voaBw4ccNrn//3f/xmurq7G8ePHnY5dqFAh4+LFi471fv75ZwMwFi5c6JjXpEkTI1++fMaxY8ec9mm1Wh3TAwYMMIoVK2ZcuHDBaZ1evXoZAQEBxrVr1257fYODg42OHTsmuTapie+pp54ykkuFrFmzxgCMr776ymn+77//nmS+/TX7/fffndbdv3+/ARgffPCB0/yhQ4cafn5+Tud16zneuHHDqFq1qtGiRYsk55r4NUvOoUOHkj3u7bZv2rSp0bRp0yTzv/76awMwNmzYcNtj3uleT20ORS2lRERERCTvsMbDsW8guA+4uJodTa5w5Qps2WKbbtIkE3fsXwGa/QYF64J7vkzccc5VpAiUKweHDsGmTdC2rdkR5TyT109m8vrJpsYwosEIRjQYkaZtoqOjAciXL3Xv5UWLFtmONcL5OM8//zzvvvsuv/32m1M3r8qVK9M4UTPFwMBAwsLCOHLkSJriBFi+fDk3btxg6NChTl3Tnn32Wd58802ndefNm0fjxo0pUKAAFy5ccMxv1aoVb731Fn/++ScPP/ywY37Pnj0pUKCA42d7zPY4IyIi+PPPPxk+fDilbxktwR6LYRj88MMP9OjRA8MwnI7btm1bvv32W7Zs2ZKuEeTuFN/tzJs3j4CAAFq3bu0UU506dfDz82PlypX06dPHMT8kJIS2t9zkFSpUoGbNmnz33XcMGzYMgISEBObPn0/nzp3x9vZ2rJt4+t9//yUhIYHGjRvzzTffpPm8IyMjAZzOPb3s+0h8DbKSklIiIiIiknfsnwpbX4Bj30LTX/N8l7HMsH49xMdDyZJws0RN5imS3orpuVe9erak1Nat0KIFJOrtI0B0bDSnLp8yPYa0sndjunz5cqrWP3bsGC4uLpQrV85pftGiRcmfPz/Hbhmm8dYEDjeTA7fWMkrtsblZ4yqxwMDAJEmLgwcPsmPHDgIDA5Pd1/nz528bp31/9jjtyZ+qVaumGF9ERASXLl1i5syZzJw5M1XHTa07xXc7Bw8eJCoqiqCgoFTFFBISkux6PXv2ZNSoUZw6dYoSJUqwatUqzp8/T8+ePZ3W+/XXX5kwYQLbtm1zqjOWkYL1RiaMeGrfR3YVzldSSkRERETyhqvHYccY23SpB5WQSoXr120terjZSirLLplhwJHZ4FsKirbKooPkDOXK2epL/fuvrb5U7dpmR5Sz+Hv6UyJfCdNjSPM2/v4UL16cXfYhKlMptX/Yu7om37IzM5IMt2O1WmndujUjR45MdnmFChWcfs6MOO0F1B955BH69u2b7DrVq1dP9f4yKz6r1UpQUBBfffVVsstvTdwlbumUWM+ePXn55ZeZN28ezz77LN9//z0BAQG0a9fOsc6aNWvo0qULTZo04eOPP6ZYsWK4u7sza9Ysvv766zvGeqtChQpBKpNvdoZhJPv+tO+jcOHCaY4jPZSUEhEREZG8YfNwSLgGgY2gbH+zo8kVNmyAGzdso+2VL5+FBzrwIWx+BnxDoOMucPPJwoOZy8UF6taFZctsCb9atZQfTSw9Xedyik6dOjFz5kzWr19PgwYNbrtucHAwVquVgwcPUqlSJcf8c+fOcenSJcdIelnBvu9Dhw45JZUiIiKSJC1CQ0O5cuUKrVplTrK4bNmyALdN3gUGBpIvXz4SEhIy7bhpkVKiMDQ0lOXLl9OwYcMUE06pERISQr169Rxd+H788UceeOABPD09Hev88MMPeHl5sWTJEqf5s2bNStcxS5cujbe3N+Hh4ckuT66F37lz55yKsduFh4fj4uKSJCGZVVyy5SgiIiIiIlnp1K9w8iewuME908CiX3PvJDbWlpTi5oh7WZo4KdsffErB1XDY9XoWHihnqFUL3NzgzBk4edLsaCSzjBw5El9fX5544gnOnTuXZPnhw4eZOnUqAB06dABIMnrd5Mm2elppHV0tLVq1aoW7uzsff/yxUwuhW2MB6NGjB+vXr2fJkiVJll26dIn4+Pg0HTswMJAmTZrw+eefc/z4cadl9lhcXV156KGH+OGHH5JNXkVERKTpmGnl6+sLN88vsR49epCQkMDrryf9jIqPj0+y/u307NmTv//+m88//5wLFy4k6brn6uqKxWIhISHBMe/o0aP89NNP6TgjcHd3p27duvzzzz/JLl+/fj0xMTGOn3fv3s3BgweTbUG2efNmqlSpQkBAQLpiSSu1lBIRERGR3C3+GvzztG264nOQP+VaJvKff/6xdd8rXBgSNeTIGu5+UPdD+PN+2PsulOkD+atl8UHN4+MDVavCtm221lKlSpkdkWSG0NBQvv76a3r27EmlSpV47LHHqFq1Kjdu3OCvv/5i3rx59OvXD4AaNWrQt29fZs6cyaVLl2jatCkbN25kzpw5PPDAA05FzjNbYGAgzz//PG+99RadO3emQ4cObN26lcWLFyfpkvXiiy/yyy+/0KlTJ/r160edOnW4evUqO3fuZP78+Rw9ejTN3bjef/99GjVqRO3atRk0aBAhISEcPXqU3377jW3btgHw1ltvsXLlSurXr8/AgQOpXLkyFy9eZMuWLSxfvpyLFy9m6jVJrE6dOgA888wztG3bFldXV3r16kXTpk0ZPHgwEydOZNu2bbRp0wZ3d3cOHjzIvHnzmDp1Kt26dUvVMXr06MELL7zACy+8QMGCBZO0COvYsSOTJ0+mXbt29OnTh/Pnz/PRRx9Rrlw5duzYka7zuv/++xk9ejTR0dGOGmh2ly5dokWLFjz88MNER0fzwQcfkC9fPnbt2sWMGTMYPHgwAHFxcaxevZqhQ4emK4b0UFJKRERERHK33W/A1aO2ljhVx5gdTa6QkAB//22bbtjQ1uUsy5XsAiW7wskFsGkItFqTp/u11atnS0rt3m0bhe9m4wzJ5bp06cKOHTt45513+Pnnn5k2bRqenp5Ur16d//3vfwwcONCx7qeffkrZsmWZPXs2CxYsoGjRorz88suMHTs2y+OcMGECHh4efPLJJ47kz9KlS5O00PLx8WH16tW8+eabzJs3jy+++AJ/f38qVKjA+PHj09VapkaNGvz999+8+uqrTJs2jZiYGIKDg+nRo4djnSJFirBx40Zee+01fvzxRz7++GMKFSpElSpVePvttzPlGqTkwQcf5Omnn+bbb79l7ty5GIZBr169AJg+fTp16tRhxowZjBo1Cjc3N8qUKcMjjzySptEAS5YsyX333ce6det44okncL9lxIMWLVrw2Wef8dZbb/Hss88SEhLC22+/zdGjR9OdlHr00Uf5v//7P3755RceeeSRJOecL18+Xn75Zdzd3Rk6dCj16tXj4Ycf5scff3QkpVasWMHFixdTrPWVFSxGVldOyyOio6MJCAggKioqSdYxq1mtVs6fP09QUBAu2fIbg4ikRPejSM6h+1EcLm6GjUOgystQqqvZ0eQKO3bAjz+Cnx88+6ytq1lGpPp+vHYSFobZan81mAshD6e8bh7wySdw6hS0bGnrInk3iImJITw8nJCQELy8vMwO565lGAbx8fG4ubll2yhqYr4BAwZw4MAB1qxZ45hXpkwZmjVrxuzZs++4/QMPPIDFYmHBggV3XPdO93pqcyj6DU5EREREcreCdaDt31DyAbMjyRUMA9avt03Xq5fxhFSa+JSEqqNt09tegoTYO22Rq91zj+35n3/g5oBjIiJZZuzYsWzatIl169aledu9e/fy66+/JltTKyup+56IiIiI5E7WOHC52SVChc1T7fhxWwFuNzfbKHHZruIIuLjF9uzqmYoNcq+qVWHpUoiKggMHoGJFsyMSkbysdOnSTgXN06JSpUppLmyfGfTtLSIiIiK5T0IMLKoB216G+KtmR5Or2FtJ1ahhK8id7Vy9oPF8CLzPhINnLzc320h8YCt4LiIiztRSSkRERERyn31TIHovhEdBldFmR5NrXLwI+/fbpu+91+xobrp+FryLmh1FlqlbF/76Cw4fhshIKFTI7IhE5G5y9OhRs0O4LbWUEhEREZHc5foZ24h7ADXfAnc/syPKNf7+21ZTqnx5CAw0Oxpg1wT4JQROLTI7kixToACUK2eb3rrV7GhERHIWJaVEREREJHfZPhrir0ChelAmb4/elpmuX4dt22zTDRqYHc1NNy7ZumJueRYSbpgdTZaxd+Hbvl0Fz0VEElNSSkRERERyj4ub4cjNYa3rTFWB8zTYsgVu3IAiRSAkxOxobqo2BryC4PJBODTD7GiyTIUK4O0Nly/DkSNmRyMiknPoW1xEREREcgfDgM3DAcPWQqpwTimKlPMlJMCGDbbpe+8Fi8XsiG5y94dq423Tu8bDjSizI8oSbm5QrZpt2t5aTURElJQSERERkdwieq+tpZSrj62WlKTanj0QHQ1+fv8lR3KM0CfAvyLERsKeiWZHk2XsXfj27bN1pRQRESWlRERERCS3CKgMnQ9Bw6/Bp6TZ0eQq9lZS99xja7WTo7i4Qc1Jtul9U+DqcbMjyhJFi9q6TsbHw+7dZkcjIpIzKCklIiIiIrmHTwkoeb/ZUeQqp0/DyZPg6gp16pgdTQpKdIKgZrbpyA1mR5MlLBaoWdM2rVH4RERslJQSERERkZwt7gpE/GV2FLnWP//YnitXtnXfy5EsFqg3AzofgNLdzY4my1SrBi4ucOoURESYHY3kRmXKlKFfv36On1etWoXFYmHVqlWZdgyLxcK4ceMybX+S1Lhx47DkmOJ+5lJSSkRERERytn3/g2UNYfOzZkeS61y/Djt32qbvucfsaO7AvwL4ljY7iizl5wfly9umVfA895k9ezYWi8Xx8PLyokKFCgwbNoxz586ZHV6aLFq0KMclnuyJmgsXLqR529OnTzNu3Di26cbKdXJaj3IRERERkf9cPwd737FNBzY0O5pcZ9s2iIuz1TIqVcrsaNLgwt+20RYDG5gdSaarWRP274ft26FlS1vLKcldXnvtNUJCQoiJiWHt2rVMmzaNRYsWsWvXLnx8fLI1liZNmnD9+nU8PDzStN2iRYv46KOPkk1MXb9+HbccV3zu9k6fPs348eMpU6YMNe39ZCVX0EegiIiIiORcu16D+KtQ8B4o1c3saHIVw4BNm2zT9erZesjlCodnwdIG8M9TYFjNjibTVagAPj5w5QocPmx2NJIe7du355FHHuGJJ55g9uzZPPvss4SHh/Pzzz+nuM3Vq1ezJBYXFxe8vLxwycTsppeXV65LSmWVrHrd5D9KSomIiIhIzhR9EA7NtE3XmpSLsio5w5EjcPEieHraahnlGiU6g1s++HcrnPjB7GgynasrVK9um1ZPo7yhRYsWAISHhwPQr18//Pz8OHz4MB06dCBfvnw8/PDDAFitVqZMmUKVKlXw8vKiSJEiDB48mH///ddpn4ZhMGHCBEqWLImPjw/NmzdndzLDNqZUU2rDhg106dKFggUL4uvrS/Xq1Zk6daojvo8++ghu1o+yP+ySqym1detW2rdvj7+/P35+frRs2ZK///7baR1798Z169YxYsQIAgMD8fX1pWvXrkSks4has2bNqFq1Knv27KF58+b4+PhQokQJJk2a5HQN7rnZP7l///6O85k9e7bT9WjXrh0BAQH4+PjQtGlT1q1b53Qse/fBPXv20KdPHwoUKECjRo149913sVgsHDt2LEl8L7/8Mh4eHo7Xb82aNXTv3p3SpUvj6elJqVKleO6557h+/Xq6zv9uoKSUiIiIiORMO8eCEQ/FO0CRZmZHk+vYW0nVrAlp7NljLq/CUOl52/SOV8Eab3ZEmc7eu2jfPlvdL8ndDt9s8laoUCHHvPj4eNq2bUtQUBDvvvsuDz30EACDBw/mxRdfpGHDhkydOpX+/fvz1Vdf0bZtW+Li4hzbjxkzhldffZUaNWrwzjvvULZsWdq0aZOqljvLli2jadOm7N27l2eeeYb//e9/NG/enF9//dURQ+vWrQH48ssvHY+U7N69m8aNG7N9+3ZGjhzJq6++Snh4OM2aNWPDhqSjZT799NNs376dsWPHMmTIEBYuXMiwYcPSdE0T+/fff2nXrh01atTgf//7HxUrVuSll15i8eLFAFSqVInXXnsNgEGDBjnOp0mTJgD88ccfNGnShOjoaMaOHcubb77JpUuXaNGiBRs3bkxyvO7du3Pt2jXefPNNBg4cSI8ePbBYLHz//fdJ1v3+++9p06YNBQoUAGDevHlcu3aNIUOG8MEHH9C2bVs++OADHnvssXSff16nNnkiIiIikvNc2gXHvrVN13jD7GhynagoW90ickOB8+RUfA4OfADR+yH8Swjtb3ZEmapoUdvj7FnYvRvq1jU7IkmLqKgoLly4QExMDOvWreO1117D29ubTp06OdaJjY2le/fuTJw40TFv7dq1fPrpp3z11Vf06dPHMb958+a0a9eOefPm0adPHyIiIpg0aRIdO3Zk4cKFjlZMo0eP5s0337xtbAkJCQwePJhixYqxadMmChcu7NjeMAwAGjRoQIUKFVi2bBmPPPLIHc/3lVdeIS4ujrVr11K2bFkAHnvsMcLCwhg5ciSrV692Wr9QoUIsXbrUcVyr1cr7779PVFQUAQEBqbrGiZ0+fZovvviCRx99FIABAwYQHBzMZ599Rvv27SlSpAjt27dnzJgxNGjQwOmcDMPgySefpHnz5ixevNgR0+DBg6lSpQqvvPIKS5cudTpejRo1+Prrr53m3XvvvXz33Xe8+OKLjnmbNm3iyJEjTq3K3n77bby9vR0/Dxo0iHLlyjFq1CiOHz9O6dJ5ezCH9FBLKRERERHJea6fAZ8StjpSBVS0Nq3++cdWU6psWShc2Oxo0sHdHyq/bJveOQ4SYs2OKNPZu1Tu2mV2JJJWrVq1IjAwkFKlStGrVy/8/PxYsGABJUqUcFpvyJAhTj/PmzePgIAAWrduzYULFxyPOnXq4Ofnx8qVKwFYvnw5N27c4Omnn3bqVvfss3cegXTr1q2Eh4czfPhw8ufP77TMko4u0AkJCSxdupQHHnjAkZACKFasGH369GHt2rVER0c7bTNo0CCnYzVu3JiEhIRku7+lhp+fn1OiycPDg3r16nHkyJE7brtt2zYOHjxInz59iIyMdFzzq1ev0rJlS/7880+sVufadU8++WSS/fTs2ZPNmzc7WsUBfPfdd3h6enL//fc75iVOSF29epULFy5w3333YRgGW7duTdf553VqKSUiIiIiOU+x1tD5IMRFp2JlSSw+HrZssU3nylZSduWHwr734NpxODQDwp4xO6JMVaUKLFsGx47B5cuQL5/ZEWWTyZNtDzONGGF7pNNHH31EhQoVcHNzo0iRIoSFhSUpNO7m5kbJkiWd5h08eJCoqCiCgoKS3e/58+cBHMmb8uXLOy0PDAx0dBNLiT1pUrVq1XScWVIRERFcu3aNsLCwJMsqVaqE1WrlxIkTVKlSxTH/1tZA9phvrZuVWiVLlkySUCtQoAA7duy447YHDx4EoG/fvimuExUV5XRdQ0JCkqzTvXt3RowYwXfffceoUaMwDIN58+Y56mzZHT9+nDFjxvDLL78kOd+oqKg7xns3UlJKRERERHImVy/bQ9Jk7164ehX8/SGZvyNzDzdvqDYGdr4GnoFmR5Pp8ueHUqXgxAlbF7577zU7omwSHQ2nTpkfQwbUq1ePunfoc+np6ZkkUWW1WgkKCuKrr75KdpvAwLzxPnd1dU12vr37YHbuz94K6p133qFmzeRb3fr5+Tn9nLi1k13x4sVp3Lgx33//PaNGjeLvv//m+PHjvP322451EhISaN26NRcvXuSll16iYsWK+Pr6curUKfr165ekRZbYKCklIiIiIjlH5D8QtRvKPAwu+lU1PewFzuvUgUwcJd4cZftDyGN5NjlZtaotKbVr112UlPL3h1u6uZkSgwlCQ0NZvnw5DRs2TDbxYRccHAw3W/kk7jIXERFxx9ZGoaGhAOzatYtmzVIeICK1XfkCAwPx8fFhv71IXSL79u3DxcWFUqVKpWpfWSml87FfD39/f1q1apWhY/Ts2ZOhQ4eyf/9+vvvuO3x8fOjcubNj+c6dOzlw4ABz5sxxKmy+bNmyDB03r8vtX1MiIiIikpdsfxn+7mcbdU3SLCICjh+3JaNq1zY7mkzg4p5nE1Lc7MJnscDJk5DOnk25z4gRthM285GBrnsZ0aNHDxISEnj99deTLIuPj+fSpUtws2aVu7s7H3zwgVNroClTptzxGLVr1yYkJISpU6c69meXeF++vr4ASda5laurK23atOHnn3/m6NGjjvnnzp3j66+/plGjRk7d18yS0vnUqVOH0NBQ3n33Xa5cuZJku4iIiFQf46GHHsLV1ZVvvvmGefPm0alTJ8dxSdSiK/F1NgyDqVOnpuuc7hb695OIiIiI5Azn/4Szy22JiHKDzY4mV9q82fZcoUIeq1FkjYdj34A1DkIfNzuaTOPnB2XKQHi4rQtfo0ZmRyRZqWnTpgwePJiJEyeybds22rRpg7u7OwcPHmTevHlMnTqVbt26ERgYyAsvvMDEiRPp1KkTHTp0YOvWrSxevJjCdxi5wMXFhWnTptG5c2fuuece+vXrR/Hixdm3bx+7d+9myZIlcDNZA/DMM8/Qtm1bXF1d6dWrV7L7nDBhAsuWLaNRo0YMHToUNzc3ZsyYQWxsLJMmTcqCK5V2oaGh5M+fn+nTp5MvXz58fX2pX78+ISEhfPrpp7Rv354qVarQv39/SpQowalTp1i5ciX+/v4sXLgwVccICgqiefPmTJ48mcuXL9OzZ0+n5RUrViQ0NJQXXniBU6dO4e/vzw8//JDuWlp3C7WUEhERERHzGQZsf8U2HfoE+JUxO6JcJz4etm+3Td/8ezPvOLkA1j8G20ZCXNLWDrmZvR61RuG7O0yfPp2ZM2dy/vx5Ro0axcsvv8wff/zBI488QsOGDR3rTZgwgfHjx7N161ZefPFFDh8+zNKlS51a5qSkbdu2/PHHH5QvX57JkyczYsQIVqxY4dTV7MEHH+Tpp5/m999/59FHH6V3794p7q9KlSqsWbOGqlWrMnHiRMaPH09wcDArV66kfv36mXBVMs7d3Z05c+bg6urKk08+Se/evVm9ejUAzZo1Y/369dStW5cPP/yQp59+mtmzZ1O0aFGee+65NB2nZ8+eXL58mXz58tGhQ4ckMSxcuJCaNWs6rlP58uX54osvMvVc8xqLkd5qY3eZ6OhoAgICiIqKyvbmiVarlfPnzxMUFJSkWJ6IZC/djyI5h+7HPObscvijNbh4QpfD4GNyzZlcaOdO+OEHCAiA4cOzt55Ult+P1nj4rTJcPgg134LKL2X+MUxy7Rq8+y5YrfDUU5Dba13HxMQQHh5OSEgIXl55t+tlTmcYBvHx8bi5uaW6fpRIWtzpXk9tDkW/wYmIiIiIuQwDdo63TZcbrIRUOtm77tWqlQcKnN/KxQ2q3GxJt/edPNVayscHbtZiZvdus6MREcleee3rSkRERERym/OrIWKtrZVUHmoBk50iI+HoUVvR7Fq1zI4mi5TpA37lIDYSDn5sdjSZKnEXPvVjEZG7iZJSIiIiImIuNz8IagqhA8CnuNnR5Epbttiey5e3dd/Lk1zcoGrebC1VsSK4ucGFC3DunNnRiIhknxyblProo48oU6YMXl5e1K9fn40bN6a47o8//kjdunXJnz8/vr6+1KxZky+//NJpHcMwGDNmDMWKFcPb25tWrVpx8ODBbDgTEREREbmtQnWh1Sqo/Z7ZkeRKCQmwbZttunZts6PJYmUeBr9QiL0AB6eZHU2m8fS0JRRRwXMRucvkyKTUd999x4gRIxg7dixbtmyhRo0atG3blvPnzye7fsGCBRk9ejTr169nx44d9O/fn/79+zuGuwSYNGkS77//PtOnT2fDhg34+vrStm1bYmJisvHMRERERCRFrh5mR5Ar7dsHV69CvnxQoYLZ0WQxe2upoCZQ+F6zo8lU1arZntWFT0TuJjkyKTV58mQGDhxI//79qVy5MtOnT8fHx4fPP/882fWbNWtG165dqVSpEqGhoQwfPpzq1auzdu1auNlKasqUKbzyyivcf//9VK9enS+++ILTp0/z008/ZfPZiYiIiAgAF7fA9tEQc8HsSHI1e9e9PFngPDkhfaHVaghqbHYkmap8efDwgEuX4ORJs6MREckeOe5r68aNG2zevJlWrVo55rm4uNCqVSvWr19/x+0Nw2DFihXs37+fJk2aABAeHs7Zs2ed9hkQEED9+vVTtU8RERERyQK7Xofdb8K2kWZHkmv9+y8cPmybzrMFzm+VR4e3d3eHsDDb9N69ZkcjIpI93MwO4FYXLlwgISGBIkWKOM0vUqQI+/btS3G7qKgoSpQoQWxsLK6urnz88ce0bt0agLNnzzr2ces+7ctuFRsbS2xsrOPn6OhoAKxWK1arNQNnmHZWqxXDMLL9uCKSlO5HkZxD92Mud2kHLid/wsCCUfEF0OuYLv/8A4ZhoWxZg4AA8y6jKfdjzHksB97HyBcGIY9m33GzUFgY7NhhYc8eg5Ytc2f+LfF7wVA/RFPZr79eB8kKie/15D77U/t9kOOSUumVL18+tm3bxpUrV1ixYgUjRoygbNmyNGvWLF37mzhxIuPHj08yPyIiItvrUFmtVqKiojAMA5e7ok22SM6l+1Ek59D9mLsF7BqHNxAT1JmomIIQk3ztUEmZ1Qrr1vlw9aoLZcpc5/z5BBNjyf770efEJ/gfnEiCVzAXvFva6k3lcv7+EBPjy8mTFnbvvkZQUO5L1lqtVhISErhy5Qru7u5mh3PXMgyDhATbZ4IlN2Y3Jce7fPkyVquVS5cuJfu5f/ny5VTtJ8d9chcuXBhXV1fO3TIW6rlz5yhatGiK27m4uFCuXDkAatasyd69e5k4cSLNmjVzbHfu3DmKFSvmtM+aNWsmu7+XX36ZESNGOH6Ojo6mVKlSBAYG4u/vn+HzTAur1YrFYiEwMFC/dIuYTPejSM6h+zEXi96H5fwvAHjWHk9Q/iCzI8qVDh60tZIKDDS47z4fXF3Ni8WU+7HgsxjHpuIWc4yg2NUQ3Dt7jpvFatSA/fstXLjgQ9WqZkeTfpGRkbi4uODj46OkiEni4uLMDkHyGMMwiI+P5/Lly0RFRVGwYMEU8zReXl6p2meOS0p5eHhQp04dVqxYwQMPPAA3v+RWrFjBsGHDUr0fq9Xq6H4XEhJC0aJFWbFihSMJFR0dzYYNGxgyZEiy23t6euLp6ZlkvouLiym/+FosFtOOLSLOdD+K5By6H3Opfe8CBpS8H5eCyf+DUO5s+3Zb967q1S3khAYp2X4/euSDsOGwcwwue9+GMn1yZ3+3W1SuDAcO2BJTLVqYHU36FCtWDIvFQkREhNmh3LXs3apcXFyUFJRM5+rqSvHixQkICEjx/ZXa74Icl5QCGDFiBH379qVu3brUq1ePKVOmcPXqVfr37w/AY489RokSJZg4cSLc7GpXt25dQkNDiY2NZdGiRXz55ZdMmzYNbn5BPvvss0yYMIHy5csTEhLCq6++SvHixR2JLxERERHJBtdOwtG5tunKL5sdTa517Rrs32+bvmsKnCcnbBjsnQSXdsLp36BEJ7MjyrCwMNsoiufOwcWLULCg2RGlncVioVixYgQFBam1jkmsViuRkZEUKlRI/7iRTOXm5oarq2umJTtzZFKqZ8+eREREMGbMGM6ePUvNmjX5/fffHYXKjx8/7nRjXb16laFDh3Ly5Em8vb2pWLEic+fOpWfPno51Ro4cydWrVxk0aBCXLl2iUaNG/P7776luUiYiIiIimcEFyvaHK0ehcH2zg8m1du6EhAQoVgxuU+Ei7/MoAOWHwN53YPdEKN4x17eW8vaGMmXgyBHbKHwNG5odUfq5urriama/0ruY1WrF3d0dLy8vJaUkR7MYKsWfKtHR0QQEBBAVFWVKTanz588TFBSkDxQRk+l+FMk5dD/mcoYVLHrd0mvGDDhzBtq3h/o5ILdn6v14/Qz8HALWWGi1GoKaZO/xs8CmTfDbb1CqFAwYYHY0khvpO1LMltocit6dIiIiIpL9lJBKt7NnbQkpV1eoVs3saHIA72JQ/kkoNxh8g82OJlOEhdmeT5yAVA5gJSKSK+m3ARERERHJegkxsGEgXNxidiS53rZttuewMPDxMTuaHKLOFKg3Pc8kpfz9oWRJ2/S+fWZHIyKSdZSUEhEREZGsF/4FHP4U/nwArAlmR5NrJSTAjh226bu6wPldoFIl2/PevWZHIiKSdZSUEhEREZGsZU2APe/YpiuOABcVPk6vAwdsI+/lywehoWZHkwNF/gPresOVcLMjybCKFW3PR4/C9etmRyMikjWUlBIRERGRrHVyAVw5ZBspLfQJs6PJ1bZutT3XqAGqXZyM7aPg2Lewf6rZkWRYoUIQFARWqy0ZKSKSF+mrTERERESyjmHAnkm26QrDwN3P7IhyrStX4NAh23TNmmZHk0NVesH2fPhTuPGv2dFkmLrwiUhep6SUiIiIiGSd86vg4iZw9YIKT5sdTa62fbut1UypUlC4sNnR5FBFW0P+ahB/FQ7OMDuaDLMnpQ4dghs3zI5GRCTzKSklIiIiIllnz9u257KPg1eg2dHkWobx36h7aiV1GxYLVLzZWurA+5AQa3ZEGVKkCBQoAPHxcPiw2dGIiGQ+JaVEREREJGsYBhRtAz6lodLzZkeTq506BRER4O4OVauaHU0OF9wLvIvD9TNw7Buzo8kQi+W/guf795sdjYhI5lNSSkRERESyhsUClUbA/eHgV9bsaHI1eyupSpXA09PsaHI4Vw8IG26b3vs/W3I0F6tQwfZ88GCuPxURkSSUlBIRERGRrGXRr5wZERcHu3bZpmvVMjuaXKLcIAioDGX7gxFvdjQZUrq0LRF59aqtxZyISF7iZnYAIiIiIpIHHf4cXH2g9EPg4m52NLnavn0QEwP580OZMmZHk0t45IcOu2yt9XI5V1coVw5274YDB6BkSbMjEhHJPPq3lYiIiIhkrvjrsO0l+Ks3nPrV7Ghyva1bbc81a+aJHEv2yUMXy96F78ABsyMREclcSkqJiIiISOY6OhdiL4BvMJTobHY0uVpUFISH26Zr1DA7mlzIGg9Hv4Wdr5sdSYaUL2/LsZ09a3tPiIjkFUpKiYiIiEjmMQzY955tOmw4uKhaREZs22a7pCEhUKCA2dHkQpd22Frs7XoNrp4wO5p08/GBUqVs02otJSJ5iZJSIiIiIpJ5ziyB6L3glg9CB5gdTa5mGP+NulezptnR5FIFa0NQM1ux84Mfmx1NhqgLn4jkRUpKiYiIiEjmsbeSCh0A7v5mR5OrHTsG//5rG3mtcmWzo8nFKj5rez40A+KvmR1NutmTUuHhcOOG2dGIiGQOJaVEREREJHNc2gVnl4LFBcKeMTuaXM9e4LxKFXDXAIbpV7wT+IbAjX9t9c5yqcBA2wiM8fH/1RkTEcntlJQSERERkcyREAOBjaBkV/ALMTuaXC02FvbssU3XqmV2NLmciyuEPW2b3v++rV9kLmSxqAufiOQ9SkqJiIiISOYoVBdar4EGX5odSa63ezfExUHhwlCypNnR5AFlHwc3P4jaDedWmB1NuiVOSuXS3JqIiBMlpUREREQkc7l5mx1Brpe4wLnFYnY0eYBHAJTtB0FNwDX3vj/LlAEPD7h8Gc6eNTsaEZGMU1JKRERERDImIRb2/g9iLpgdSZ4QGQnHj9uSUTVqmB1NHlL7PWi1GgIbmh1Jurm5QWiobVpd+EQkL1BSSkREREQy5vg82PoCLG2gPkWZwN5Kqlw5yJfP7GjyEBc3syPIFKorJSJ5iZJSIiIiIpIxBz6wPZftp75mGWS1wvbttumaNc2OJo+KiYDdb0JctNmRpEv58rbnU6ds3fhERHIzJaVEREREJP0ubITIjeDiAeUGmh1NrnfkCERHg7c3hIWZHU0etbIdbB8Nh2eZHUm6+PlBiRK26YMHzY5GRCRjlJQSERERkfSzt5IK7gVeQWZHk+tt3Wp7rlbNVj9IskC5QbbnA++DNcHsaNLF3oXv0CGzIxERyRglpUREREQkfa6fg+Pf2aYrDDM7mlzv+nXYt882XauW2dHkYSGPgkcBuHIETv9mdjTpUq6c7fnwYUjInXk1ERFQUkpERERE0u3QTLDGQaH6UOges6PJ9XbutCUYihSBokXNjiYPc/OB0JtdTfdPNTuadClWDHx8IDbWVltKRCS3UlJKRERERNInPtpWS6rC02ZHkifYR92rVUv14rNchafA4grn/oBLu82OJs1cXCA01DatLnwikpspKSUiIiIi6VPrHbj/OJTubnYkud65c3D6tC3ZUK2a2dHcBXxLQ8n7bdMHPzY7mnSxd+FTUkpEcjMlpUREREQk/byLgKuH2VHkevZWUmFh4OtrdjR3ifJPgYsnkDubpdlbSp0+DVevmh2NiEj6KCklIiIiImlz+TBc2ml2FHlGQgLs2GGbrlnT7GjuIkWawwMn4Z4PzY4kXfz8bLWluFnwXEQkN1JSSkRERETSZvcbsKg67Hzd7EjyhIMHbS1d/PygfHmzo7mLWCzgVdjsKDLE3oXv4EGzIxERSR8lpUREREQk9WIj4ejXtumiLc2OJk/YutX2XL26raaUmODfHRC1x+wo0syelDp8GKxWs6MREUk7fe2JiIiISOod/hSssVCgNhRuYHY0ud6VK/+1cqlVy+xo7lJ734XFNWDHGLMjSbOSJcHTE65dgzNnzI5GRCTtlJQSERERkdSxxsOBmyOVhT1t6/4kGbJjh62FS4kSEBhodjR3qWLtbM8nf4JrJ82OJk1cXaFsWdu0RuETkdxISSkRERERSZ1Tv8K14+BZGIJ7mR1NrmcY/426p1ZSJspfFYKagpEAB2eYHU2a2bvwKSklIrmRklIiIiIikjoHp9meQweAq5fZ0eR6p0/D+fPg5gZVq5odzV2uwlO258OfQMINs6NJE3tS6uRJuH7d7GhERNJGSSkRERERubO4aLi0HbBAuUFmR5Mn2FtJVaoEXsrxmavkA+BdHGLOwYkfzI4mTQICbF0/DQOOHDE7GhGRtFFSSkRERETuzN0f7j8GLVeAX1mzo8n14uNh507bdM2aZkcjuLj/l2w9+JHZ0aRZ+fK2Z3XhE5HcRkkpEREREUkdV08o0tzsKPKEffsgJsbWyiUkxOxoBLAlpSxucPkwxFwwO5o0SVxXyjDMjkZEJPWUlBIRERGR27t+FqwJZkeRp2zdanuuUQNc9Bt5zuBdDFqtsrUI9CpsdjRpUro0uLvD5cu2OmUiIrmFvgJFRERE5PbWPAQLy0PEX2ZHkidERf1X+0dd93KYwIbg6mF2FGnm5vZfi7uDB82ORkQk9ZSUEhEREZGU/bsDLvwF106ollQm2b7d1sUqOBgKFjQ7GkmWNQGuHjc7ijQJDbU9q9i5iOQmSkqJiIiISMoOTrM9l+oK3kXNjibXM4z/Rt2rVcvsaCRZF7fAwlBY2TZXFWgqezNnfPw4xMWZHY2ISOooKSUiIiIiyYu7DEfn2qbLDzE7mjzh+HG4eBE8PKByZbOjkWTlKwexFyB6H0SsMTuaVCtcGPz9bSM7Hs9djbxE5C6mpJSIiIiIJO/oXIi/Av4VIaiZ2dHkCfZWUlWq2BJTkgO5+0NwH9v0welmR5NqFst/raXUhU9EcgslpUREREQkKcP4r+teuSdtf/FKhty4Abt326ZV4DyHKz/Y9nxiPsREmB1NqtnrSh0+bHYkIiKpo6SUiIiIiCR18R+4tBNcvaHsY2ZHkyfs2mVLTBUqBKVLmx2N3FbBOlCwLljj4Mhss6NJNXtLqbNn4epVs6MREbkzJaVEREREJKmCdaH1WqjzPngUMDuaPGHLFttz7dpqeJYrlH/S9nxoBhhWs6NJFV9fKHpzPAJ14ROR3EBJKRERERFJymKBwIZQ7gmzI8kTzp+HkyfBxQVq1DA7GkmV4F62+lJXDkPEOrOjSTXVlRKR3ERJKRERERFxZhhmR5DnbN1qe65QAfz8zI5GUsXNF+p+BG3WQ2Ajs6NJtcR1pXQri0hOp6SUiIiIiPzHsMLvdWDTUxBzwexo8oT4eNi+3TZdu7bZ0UiahDwChe/NVf0tS5cGNzeIjobISLOjERG5PSWlREREROQ/Z5bBv1vh6Ffg5m12NHnC/v1w7Rr4+0O5cmZHI+mWS+pKubv/V0hfo/CJSE6npJSIiIiI/OfQDNtzyGO27kuSYfYC5zVr2mpKSS4TcwE2PgmLa4I1wexoUkV1pUQkt9DXooiIiIjYXD8DpxbapssNMjuaPOHSpf9aq9SqZXY0ki5uvnD8e7i0E84sMTuaVLHXlTp6FBJyRx5NRO5SSkqJiIiIiM2R2WDEQ+EGkL+q2dHkCfYC52XLQoECZkcj6eLmDSF9bdP2loQ5XNGi4OMDsbFw6pTZ0YiIpExJKRERERGx1cs5/KltWq2kMoXV+l9SSgXOc7lyg23Pp3+FqyfMjuaOLJb/uvCprpSI5GRKSomIiIgInPsDrhwBd38o3d3saPKEw4dtI6B5e0PFimZHIxkSUBGCmt5M3n5mdjSporpSIpIbKCklIiIiIuBfEaqMgrBnVeA8k9gLnNeoAW5uZkcjGVbuSdvz4U/AGm92NHdkryt16hTExJgdjYhI8pSUEhERERHwKQk13oDq482OJE+4cgX277dNq8B5HlGqK3gWhuun4dSvZkdzRwEBUKiQrRvp0aNmRyMikjz9z0ZEREREJJNt3WpLBpQsCUWKmB2NZApXT6g0EuKioGAds6NJldBQiIy0deFTF1IRyYmUlBIRERG5mxkGbBoKJTpBsXbg4mp2RLme1QqbN9um77nH7GgkU1V+0ewI0qRsWdi4EcLDzY5ERCR56r4nIiIicjc7/yccmg7rekHCdbOjyRMOH4ZLl2wFzitXNjsauZsFB9tG4ouIgMuXzY5GRCQpJaVERERE7maHP7E9l+kD7n5mR5Mn/POP7blmTXB3NzsayXTWeDi5EP5+HKwJZkdzW97eUKyYbVqtpUQkJ1JSSkRERORuFRsJx+fbpkMHmh1NnhAVBQcO2Kbr5I6yQ5JW1jhY/xgcmQVnl5sdzR2FhNielZQSkZxISSkRERGRu1X4l2CNhQK1ck3h5pxuyxZbma6QEChc2OxoJEu4eUPIo7Zpe0vDHMyelDpyxPbeFBHJSZSUEhEREbkbGcZ/f1CXG2grPCMZkpBgS0oB1K1rdjSSpUKfsD2f/BlizpsdzW2VLg2urrZWfP/+a3Y0IiLOlJQSERERuRtd+Aui9oCrDwT3MTuaPGH/flsxaT8/qFjR7GgkSxWoDgXvASMewr8wO5rb8vCAkiVt0+rCJyI5jZJSIiIiInejhBjIXwOCe4JHgNnR5An2Aue1atlapkgeV+5mHbbDn+b4fnGqKyUiOVWOTUp99NFHlClTBi8vL+rXr8/GjRtTXPeTTz6hcePGFChQgAIFCtCqVask6/fr1w+LxeL0aNeuXTaciYiIiEgOVLQltN8KdT8yO5I8ITLSVrPHYlGB87tGcC9w84Xo/RCx1uxobitxUiqH589E5C6TI5NS3333HSNGjGDs2LFs2bKFGjVq0LZtW86fT76/9qpVq+jduzcrV65k/fr1lCpVijZt2nDq1Cmn9dq1a8eZM2ccj2+++SabzkhEREQkB7JYbEWbJcM2b7Y9lysH+fObHY1kC/d8tsRUgVpgvWF2NLdVsiS4u8PVq5DCn1QiIqbIkUmpyZMnM3DgQPr370/lypWZPn06Pj4+fP7558mu/9VXXzF06FBq1qxJxYoV+fTTT7FaraxYscJpPU9PT4oWLep4FChQIJvOSERERCSHMAw4+i3EXTY7kjwjPh62bbNNq8D5Xabuh9B+i63lYQ7m6grBwbZpdeETkZwkxyWlbty4webNm2nVqpVjnouLC61atWL9+vWp2se1a9eIi4ujYMGCTvNXrVpFUFAQYWFhDBkyhMjIyEyPX0RERCRHi9wEf/WGX8pCQqzZ0eQJu3bBtWsQEADly5sdjWQrVy+zI0g11ZUSkZzIzewAbnXhwgUSEhIoUqSI0/wiRYqwb9++VO3jpZdeonjx4k6JrXbt2vHggw8SEhLC4cOHGTVqFO3bt2f9+vW4JlOJMjY2ltjY/35Ri46OBsBqtWK1WjNwhmlntVoxDCPbjysiSel+FMk5dD+mj+XQTCyAUbQthsUddP0yxDDgr7/AMCzUrWsr1nM3XtK7/n6Mi4bTv0HpXrZusTlQcLDtfRoebhAfDy45rnmCZKa7/p4U06X2vZfjklIZ9dZbb/Htt9+yatUqvLz++89Fr169HNPVqlWjevXqhIaGsmrVKlq2TNrcduLEiYwfPz7J/IiICGJiYrLwDJKyWq1ERUVhGAYu+vYQMZXuR5GcQ/dj2lnirxB47BsswMVC3YhTcZkMO3HChSNHfHBzMyhZ8updW6/nrr4fE2IIWlcbl/h/uRBXiHj/mmZHlCwXF0hI8OXiRQs7dlyjeHElK/Kyu/qelBzh8uXUlQnIcUmpwoUL4+rqyrlz55zmnzt3jqJFi95223fffZe33nqL5cuXU7169duuW7ZsWQoXLsyhQ4eSTUq9/PLLjBgxwvFzdHQ0pUqVIjAwEH9//zSfV0ZYrVYsFguBgYH6QBExme5HkZxD92M6HPkVl4RrGPkqUKB85xzboiM3WbUKfH0t1K5tEBzsa3Y4prnb70dLiXZw7BsK/fsjRrk2ZoeToipVYN8+C5cv+xAUZHY0kpXu9ntSzJe4kdDt5LiklIeHB3Xq1GHFihU88MADcPOGWrFiBcOGDUtxu0mTJvHGG2+wZMkS6qaiwuTJkyeJjIykWLFiyS739PTE09MzyXwXFxdTbmqLxWLasUXEme5HkZxD92Mahc8GwFK2P5ZkyhdI2kRFwYEDttzevfda7vruUHf1/VhuIBz7Bsuxb7DUngzufmZHlKzQ0P9n787jo6zO/o9/ZiZ7IIFAFsKShX3fBBREFlFWEbUt+rTForWtre1jqbalrVvbp7i2Pm1dWvtT6WJF6yPuuERBVkF22bckbFlYkpCQdeb+/XFIAhIgy8zcM5Pv+/Wa131m5p5zrih3MnPNOdeBXbsgJ8fBuHF2RyO+1qqvSbFdY//dBeS/znnz5vHcc8+xcOFCduzYwZ133klZWRlz584FYM6cOcyfP7/u/EceeYT77ruP559/nvT0dPLy8sjLy6O0tBSA0tJS7r33XtasWUN2djZZWVlcf/319OjRg8mTJ9v2c4qIiIj4TcluKFwJDidkzLE7mpCwbp2pH5WRAV8qhyqtTdJ4aNMDakoh9xW7o7mg2mLnublm10gREbsFZFJq9uzZPP7449x///0MGTKETZs2sWTJkrri57m5uRw9erTu/GeeeYaqqiq+8pWv0KlTp7rb448/DoDL5WLLli3MnDmTXr16cfvttzN8+HCWL1/e4GwoERERkZBTuBxwQKcpEJNqdzRBr7oa1q837VGj7I5GbOdwQPfbTXvf3+yO5oI6doS2bU1C6uBBu6MREQnA5Xu17rrrrgsu11u6dOk597Ozsy/aV3R0NO+//75X4xMREREJKt1vh5RroaZxhUfl4rZuhfJyaNcOevWyOxoJCJnfgi2/gmOroWgbtOtvd0TncTjMbKktW+DAgfqZUyIidgnImVIiIiIi4gOxXSG+n91RBD3Lgs8+M+2RI2n1taTkjOgU6HydWSJ7bKXd0VxQbSLqEt/ri4j4RcDOlBIRERERL6kqgoh2dkcRMnJyID8fwsNh6FC7o5GAMuQRuOxPENPF7kguKD3dHA8fhqoqiIiwOyIRac30vY6IiIhIKKs4Bq93gk+mQk2Z3dGEhNpZUoMHQ3S03dFIQInrFdAJKTBLTuPjwe2GQ4fsjkZEWjslpURERERCWc5L4K6AigIIi7U7mqB38iTs3GnaKnAuF1VxzO4IGuRw1M+WOnDA7mhEpLVTUkpEREQklO173hwz59odSUhYtcrUlOrRAxIT7Y5GApK7Cj6ZYmYolgXmFne1SSnVlRIRuykpJSIiIhKqTmyEos3gjID0/7I7mqBXVgYbN5r2mDF2RyMByxUB7kqwauDAQrujadCX60qJiNhFSSkRERGRULX/zCypLrMgMsHuaILeZ59BTQ107lz/oV6kQd1vM8f9L4DlsTua89TWlfJ44GBgTuYSkVZCSSkRERGRUOSugOx/mXbmbXZHE/SqqmDdOtMeM8bU5RG5oK43QVhbKN0PBZ/aHc15zq4rpSV8ImInJaVEREREQtGhN6HqpNkJLGWS3dEEvfXrobwcOnSAPn3sjkYCXlgMpN9i2rV13QKMklIiEgiUlBIREREJRalTYORfYeCD4HTZHU1Qc7th9WrTHj0anHoHLY1RO0Px4H+gqtjuaM6julIiEgj0J1VEREQkFIXHQY87oPvtdkcS9LZuhZISaNMGBg+2OxoJGh1GQnw/cJdDzst2R3Me1ZUSkUCgpJSIiIiIyAVYFqxcadqXXw5hYXZHJEHD4YABD8CovwXk7peqKyUigUBJKREREZFQYlnw6Y2w689QU2Z3NEFv924oLITISLjsMrujkaCT9jUzWzG8rd2RNEhJKRGxm5JSIiIiIqGkcDkceh02z7c7kpBQO0vqsssgKsruaES8KyPDHFVXSkTsoqSUiIiISCjZ/4I5ps2GsFi7owlq2dmQmwsul1m6J9Is7grY9UfImgSearujOUe7duamulIiYhclpURERERCRfUpyHnFtGt3/pJmsSz45BPTHjoU2gbm6isJBo4w2LYA8rPg8Nt2R3MeLeETETspKSUiIiISKnJfBfdpiOsNHa+wO5qgduAA5OSYwuZXXWV3NBLUnGGQeatp73ve7mjOo6SUiNhJSSkRERGRULH/zAfejG+ZrbWkWSwLPv7YtIcPh7g4uyOSoJc51xyPvgunj9gdzTlqk1KqKyUidlBSSkRERCQUlOyCwpXgcELGHLujCWp798KhQxAeDmPH2h2NhIS43pA4BiwPZP/D7mjOobpSImInJaVEREREQoGnBrrMgs7XQUyq3dEErbNrSY0YAW3a2B2RhIzaOm/7njf/0AJI7WypAwfsjkREWhslpURERERCQbv+cNXrMPb/7I4kqO3aBUeOQEQEjBljdzQSUrp91eyIeWq3mdUYQFRXSkTsEmZ3ACIiIiLiRQ5959hcZ8+SGjUKYmPtjkhCSnhbSP8GVBaa5FQAqU1KHTli6kpFRNgdkYi0FkpKiYiIiAS77Jehwwho293uSILa9u2Qnw+RkTB6tN3RSEga8UxAbkJQW1eqqAhyc6FHD7sjEpHWQl+liYiIiASzimOwZg681QNO7bU7mqDl8cDSpaZ9xRUQHW13RBKSAjAhVUtL+ETEDkpKiYiIiASznJfAUw3th0FbTW9ors2bobAQoqLg8svtjkZC3qm9sOcZu6M4h5JSImIHLd8TERERCWb7njfH7rfZHUnQqqyErCzTvuoqk5gS8ZnK4/B2X7BqIHkixPW2OyJQXSkRsYlmSomIiIgEqxMboWgzOCMg7Ra7owlay5dDaSkkJJgC5yI+FdkBOk0x7f0v2B1Nndq6Uh6PqSslIuIPSkqJiIiIBKv9Z2ZJdbkBIhPsjiYonTwJq1eb9uTJ4HLZHZG0CrUzG/cvBE+N3dHU0RI+EfE3JaVEREREgpG7ArL/ZdpautdsH3wAbjdkZkKvXnZHI61G6nSITISKPDi6xO5o6igpJSL+pqSUiIiISDAq3gaWG2K6QPLVdkcTlLKzYccOsyHalCkBvTGahBpXBGR807T3/T+7o6lzdl2pykq7oxGR1kBJKREREZFglDAcbjgK494Gp9acNZXHA0vOTFC57DJISrI7Iml1Ms/McDz8NlQU2B0NnKkr1b69uT4OHrQ7GhFpDZSUEhEREQlWYTHQfrDdUQSljRshL8/stDdhgt3RSKvUrj8kjICwWCjaanc0dbSET0T8KczuAERERESkicrzICpZ682aqaICPv7YtMePh5gYuyOSVmvMSxDdGcKi7Y6kTnq6SdoqKSUi/qCZUiIiIiLBxLLgwyvhnX5QtM3uaILShx9CWRl07AgjRtgdjbRqbXsEVEIKIC3NHFVXSkT8QUkpERERkWBSuBxK98HpQ9Am3e5ogs7+/bB+vWnPmAEuleOSQGBZUBoYU5NUV0pE/ElJKREREZFgsv8Fc0ybbWrRSKNVVsKbb5r2yJH1tXNEbFV2EN7pC+8NgZpyu6MB1ZUSET9SUkpEREQkWFSfgpxXTDtzrt3RBJ2PPoKiIjMTZNIku6MROSOmM7groLoYDi22Oxo4Kyl14IDdkYhIqFNSSkRERCRY5L4K7tPQthd0HG13NEElOxvWrTPtmTMhIsLuiETOcDgh41umvf95u6OBs+pKHT2qulIi4ltKSomIiIgEi9oPrN1v0857TVBVBW+8YdrDh0Nmpt0RiXxJ5q3mmJcFZTl2R3NOXancXLujEZFQpqSUiIiISDAo2QOFK82sivRv2h1NUPn4Yzh5EuLi4Jpr7I5GpAFtMiB5ImDB/oV2RwOqKyUifqKklIiIiEgwaJMJEz6AwQ9DTKrd0QSN7Gz47DPTnjkToqLsjkjkAmrrxO1/ESyP3dEoKSUifqGklIiIiEgwcLqg0zXQ7167IwkaJSXw6qtgWTB0KPToYXdEIhfR9UYIj4OyA1C4wu5oVFdKRPxCSSkRERERCTlut0lIlZVBcjJMm2Z3RCKXEBYDw56EiVmQeKXd0dCunbl5PHDwoN3RiEioUlJKREREJNCt+z5suAfK9MmwsT74wHyQjoqC2bMhPNzuiEQaoftcSJloascFgNolfDn2114XkRAVGL/tRERERKRhFcdg399g5xNQdcLuaILCli31daRuvBESEuyOSCQ41S7hU10pEfEVJaVEREREAlnOS+CphvbDoP1gu6MJePn58NZbpn3VVdCrl90RiTRReR5s+Aksu97uSOpmSh0+DFVVdkcjIqFISSkRERGRQGVZsO//mXb32+yOJuBVVMCiRVBdDd27w/jxdkck0ky7/hcOvwnFO2wNo107iIszdaUOHbI1FBEJUUpKiYiIiASqkxuhaAs4IyDtFrujCWhVVfCvf8GJExAfDzfdBE6905VgFJ0CqWcq8+9/wdZQHI762VJawicivqA/1SIiIiKBqvYDaZcbIFKFkS6kuhr+/W9T2Dw6Gv7rvyAmxu6oRFog88zMyAN/N8t3baRi5yLiS0pKiYiIiAQidwVk/8u0M+faHU3Acrvh1VfhwAGIiIBvfAOSk+2OSqSFOk+HyESoyIcjS2wNpbbY+aFDJgEsIuJNSkqJiIiIBKKa05AxB9oPgZRJdkcTkDweeO012L0bwsPh61+Hzp3tjkrEC5zhkPFN07Z5CV9CArRtaxLAhw/bGoqIhCAlpUREREQCUWQCDH8SpmwAp8vuaAKOZcEbb8D27eBywezZ9TM6REJC7QzJw29BRYFtYTgc9deW6kqJiLeF2R2AiIiIiFyEw2F3BAGnqgpefx127DDFzL/6VejRw+6oRLys3QBT8Lxtb7A8toaSng5ffKGklIh4n5JSIiIiIoHm0FsQ3gaSxoFDE9vPVlRkiprn55sZUjfcAH362B2ViI+Mf8fuCOBLdaVqaiBMnyJFxEv0LkdEREQkkFgWbPgxZE2EnEV2RxNQsrPhr381Cak2beBb34IBA+yOSiT0dewIsbEmIaW6UiLiTUpKiYiIiASSwuVQug/C2kCXmXZHEzA+/xz+/nc4fRpSU+E734GuXe2OSsQPLA/kZcH+F20LweEwS/gAcnJsC0NEQpCSUiIiIiKBpHanrbTZEBZrdzS2Ky6GRYvg7bfNbnsDBsDcuRAXZ3dkIn5SsAw+ngTrfww15baFoWLnIuILWg0sIiIiEiiqT0HOK6adeZvd0djK7YbVq2HZMqiuNgXNJ0yAK69U7XdpZZLGQUw3OJ0LhxZD+i22hFE7U+rgQXN9urQpqIh4gWZKiYiIiASK3FfBfRriekPHK+yOxjYHDsAzz8BHH5mEVFoafO97MHasElLSCjmckPkt066dSWmDxESIiTHX5JEjtoUhIiFGM6VEREREAsX+580xc26ry7643bBzJ6xbV788KDYWrr0WBg1qdf85RM6V+S344teQ9xGU5UBsmt9DcDhMgnjHDnONqqabiHiDklIiIiIigaD6FFQUgsMFGXPsjsZvSkpg/XpzKy01jzmdMHw4TJwI0dF2RygSANpkQPIEyP8E9i+EgffbEkZtUionx8xcFBFpKSWlRERERAJBeFuYsROKt0N0J7uj8ZnKSsjNNR9qs7PNMiCPxzzXpo1JRg0frkLmIufJnHsmKfUiDPiVWdbnZ7V1pXJzzXXrVDEYEWkhJaVEREREAoXDAe36e7VLy4JTp+DkyfpbWRnU1Jx783ggPBzCwupv4eEQGQkREeZ2dvvs++Hh5vVn36qqzCyokhKzg15xMRw7Bnl59UmoWunpMGIE9Omj4skiF9T1Jlj3A3BFQPkRiOni9xCSk83sxfJyOHoUOnf2ewgiEmJalJQ6cuQIqamp3otGREREpDUqPwoR7cEV1eKuamrM7lj795tbfr55LJC0b28SUbW3+Hi7IxIJAmExMG0zxKbbVmTN4YBu3WDXLjPTUUkpEWmpFiWl0tPTmTp1KnfccQfTpk3DqfmbIiIiIk33+Q8hLwtGPQfdvtLkl1dVwZYtplB4To7ZHetsTqdJ/LRvDwkJZpncl2dFORz1s6aqq+uPVVXmVll57vHsttttxnG5zFhOp+kzLs7c4uPrb127Kgkl0mxtMuyOgPR0k5TKyYExY+yORkSCXYuSUpdffjlvvfUWb7/9Np06deK2227jtttuI712sbGIiIiIXFzFMTj8JniqoW3PJr305ElYuxY2boSKivrH27SBzExz69YN2rXzbe0Xj8cktbRDnoif1JSbJXxtu/t96LQzG//l5KiulIi0XIuSUp9++im7d+/mueee4x//+Ae//e1v+d3vfsekSZO44447uP766wkLU9kqERERkQvK/pdJSLUfBu0HN+olOTmwahXs3m1qRgF06ADDhkGPHpCU5N8EkT6UivjR0Q9hxVchvh9cu8rvw6ekmHpylZWmRpyquYhIS7T4LUSvXr147LHHOHToEK+++irXXHMNH330EV/72tfo3LkzP/vZz9i9e7d3ohUREREJJZYF+5837e63XfL00lJ47TV44QWzfMayoGdP+MY34K67zFKa5GTNWBIJae0GQk0pHFsNxTv8PrzTaWZgciZBLiLSEl77XissLIybbrqJ9957j+zsbB544AGcTiePP/44ffv2ZcKECbzyyitYtV/niYiIiLR2JzdC0RZwRkDaLRc8zeMxy/T+9CfYutUknYYPN4mor3/dzI5SIkqklYhOgdRppr3/RVtCqK3Wkp1ty/AiEkK8Ptna4/Gwfv161q1bR2FhIZZl0bVrV1auXMktt9zC4MGD2bNnj7eHFREREQk++18wxy43QGRCg6ccOQJ/+xu8+65ZLtO5M3znO3DdddCxo3/DFZEAkXlmZuWBv4PH/9tr1ialcnPrlxCLiDSH15JS+/fv5xe/+AVdu3blxhtv5IMPPuCmm24iKyuL7OxscnNzueeee9i5cyd33nnnJft76qmnSE9PJyoqilGjRrF27doLnvvcc88xduxY2rdvT/v27Zk0adJ551uWxf3330+nTp2Ijo5m0qRJSo6JiIiIfTzVkP2SaWfOPe9pyzKzo/72N5OYioqC6dPh9tuhUyf/hysiAaTzdIhMhIo8OLrE78N36gQREVBeDvn5fh9eREJIi5JS1dXVvPzyy1x99dX06tWLhx9+mKioKH73u99x8OBBFi1axIQJEwBISUnhkUce4fbbb2f16tUX7XfRokXMmzePBx54gA0bNjB48GAmT55MQUFBg+cvXbqUW265hU8++YTVq1fTtWtXrr32Wg4fPlx3zqOPPsof//hHnn32WT777DNiY2OZPHkyFWdvVSMiIiLiL85wU6R44K8hZdI5T9XUwJtvmtlRHg/072+W6o0YoaLiInLm90fGN0173/P+H151pUTESxxWC4o8JSYmcuLECVwuF9dddx3f/e53ufbaay/6mocffphf/OIXeDyeC54zatQoRowYwZ///Gc4sySwa9eu/PCHP+TnP//5JeNyu920b9+eP//5z8yZMwfLskhNTeUnP/kJ99xzDwDFxcUkJyfz4osvcvPNN1+yz5KSEuLj4ykuLiYuLu6S53uTx+OhoKCApKQknHonKmIrXY8igSNUr8dTp2DRIjh0yNSJuuYauOIK1YySwBaq12NAK/oC3h1oElQ3FkBEO78Ov3w5ZGVB374we7Zfh5ZG0DUpdmtsDqVF/zpjYmJ46KGHyMnJ4bXXXrtkQgrg+9//PgcOHLjg81VVVaxfv55Jk+q/MXQ6nUyaNOmSM6xqnT59murqahISTG2GAwcOkJeXd06f8fHxjBo1qtF9ioiIiPjaoUPw17+aY3S02VVv9GglpESkAe0GwPA/wbRtfk9IcVZdqZwc1ZUSkeYLa8mLs7OzcTTxXVJcXNxFs2THjh3D7XaTnJx8zuPJycns3LmzUWP87Gc/IzU1tS4JlZeXV9fHl/usfe7LKisrqaysrLtfUlICZzLOF5vl5QsejwfLsvw+roicT9ejSOAI6utx5xM4jq3B6jMPOl4BwN69ZoZUTY2DpCSL2bMhIcEs3xMJdEF9PQaznt83Rxv+u6ekQFgYlJU5yM+3SEryewhyEbomxW6N/bfXoqRU9+7d+fGPf8wPf/jDC57z1FNP8cQTT7B///6WDNVoDz/8MC+//DJLly4lKiqq2f0sWLCAhx566LzHCwsL/V6HyuPxUFxcjGVZmnopYjNdjyKBI2ivR8ui466nCSvPprjtOCo83TlwwMXixVG43Q4yM2uYPr2Cmhq4QDlNkYATtNdjKLEsv0+rjIuLIjc3jI0bKxk6tNqvY8vF6ZoUu506dapR57V4ptTJkycvek5RURE5Tah+17FjR1wuF/lf2sYhPz+flJSUi7728ccf5+GHH+ajjz5i0KBBdY/Xvi4/P59OZ21Xk5+fz5AhQxrsa/78+cybN6/ufklJCV27diUxMdGWmlIOh4PExET9QhGxma5HkcARtNdjwac4y7OxwtoS138uhTmxfPQRREU56N3b4qtfBZfLv+81RFoqaK/HUHBiPY7tv4OYbljD/uDXoQcNguPHHZSUxGimVIDRNSl2a+wkoRYlpRqjuLiYyMjIRp8fERHB8OHDycrKYtasWXDmgsrKyuKuu+664OseffRR/ud//of333+fyy677JznMjIySElJISsrqy4JVVJSwmeffcadd97ZYH+RkZENxu10Om25qB0Oh21ji8i5dD2KBI6gvB4PvAiAI202Bw61ZdEicLuhTx/42tccuFx2ByjSPEF5PYaCmmI4tBjC2+EY8jCERftt6IwMWLoUcnMdOByqfxdodE2KnRr7767JSalPP/30nPvZ2dnnPcaZHfAOHjzIv/71L3r16tWkMebNm8ett97KZZddxsiRI3nyyScpKytj7ty5AMyZM4fOnTuzYMECAB555BHuv/9+XnrpJdLT0+vqRLVp04Y2bdrgcDi4++67+e1vf0vPnj3JyMjgvvvuIzU1tS7xJSIiIuJz1acg91UADkfN5aWXoKYGeveGr30NJaREpOmSJ0JMNzida5JT6bf4bejOnWvrSsHx49Cxo9+GFpEQ0eSk1Pjx4+uKmzscDhYuXMjChQsbPNeyLBwOBw8//HCTxpg9ezaFhYXcf//95OXlMWTIEJYsWVJXqDw3N/ecrNszzzxDVVUVX/nKV87p54EHHuDBBx8E4Kc//SllZWV85zvfoaioiCuvvJIlS5a0qO6UiIiISJPkvgLu01RH9+bFt6+gpgZ69eLMkj27gxORoORwQua34Itfw/4X/JqUCguDLl0gO9vclJQSkaZyWFbTNvB88MEHcTgcWJbFr3/9a8aNG8f48ePPO8/lcpGQkMCECRPo27evN2O2RUlJCfHx8RQXF9tSU6qgoICkpCRNvRSxma5HkcARlNfjB2Pg2Co+LXmEj/N+So8ecPPN5oOdSDALyusxlJQegDczAQdcnw2x3fw29NKl5jZgAHxpjoDYSNek2K2xOZQmvwWqnXkEsGzZMubOncucOXOaH6mIiIhIa2BZuLt8jZP55awt/CZJSWaGlBJSItJibTIgeQLkfwL7F8LA+/w2dHq6Oebk2LIBoIgEuRa9Dfrkk0+8F4mIiIhICLNw8H/b/ptt+/6bmBi45RZowl4wIiIXl3nbmaTUCzDgl2ZZnx907myWH586BSdOQIcOfhlWREKE5vGJiIiI+MGyZbBtm/nwNns2tG9vd0QiElK63giJY6HPj8FT47dhw8NNXSnOzJYSEWmKJs2UyszMxOFw8NFHH5GRkUFmZmajXudwONi3b19zYxQREREJavvXrqRow24iHF9l6ow2pKXZHZGIhJywGLjm/F3R/SEtzSSksrNh2DBbQhCRINWkmVIejwePx3POfcuyLnk7+zUiIiIirUl+PlRtfpxZKbdxy6BfM3So3RGJiHjXl+tKiYg0VpNmSmVnZ1/0voiIiIjUq66Gd17L59b4twFIG3+r3SGJSKirPgW5r0BUMnSe4Zchu3QBpxOKi6GoSMuTRaTxVFNKRERExEfefx86V/8Tl6MGd/tRONv3tzskEQl1e/8Cn30btv3Ob0NGRJiC52CW8ImINJZPklIlJSV8+OGHLF++HEvzN0VERKQV2rEDPv/cYljc/wPA1fM2u0MSkdYg/RvgcMGx1VC8w3/DnrWET0SksVqUlHruuecYN24cJ0+erHts8+bN9OnThylTpjB+/HjGjh3L6dOnvRGriIiISFAoLoY334QuUZ+RGLkDXNHQbbbdYYlIaxCdAqnTTHv/i34btnYDB82UEpGmaFFS6h//+AeVlZW0P2vR8E9+8hMKCgqYO3cu06ZNY/Xq1TzzzDPeiFVEREQk4Hk88PrrUF4OYzo9bx7s+hWIiLc7NBFpLTLnmuOBv4Onxi9Ddu1q6koVFZnEvIhIY7QoKbV7924GDx5cd//48eN88sknfPvb3+Zvf/sbb731FiNGjOBf//qXN2IVERERCXgrVpiZAhER0L3LCcAB3bV0T0T8KHU6RCZCRR4cXeKXISMjoVMn09ZsKRFprBYlpYqKikhMTKy7v3z5cgBuvPHGuseuvPJK7dInIiIircLhw7B0qWlPnw4RV/8Hrs+GpKvsDk1EWhNXBGR807T3Pe+3YVVXSkSaqkVJqQ4dOnD06NG6+1lZWbhcLsaMGVP3mGVZVFdXtyxKERERkQDndsMbb5jlewMGwKBBZ56I7QYObXgsIn6WOdfM1PRUg+Xxy5CqKyUiTdWid0iDBg3ijTfe4IsvvmDv3r289NJLjBkzhtjY2LpzsrOz6VQ7j1NEREQkRK1YAQUFEBMD064uwFF+xO6QRKQ1azcAbjgC49/yW2K8WzdwOODECSgp8cuQIhLkWvTb6ac//SknT55k8ODB9O7dm6KiIubNm1f3vMfjYcWKFQwfPtwbsYqIiIgEpMJC+PRT0546FWJy/whvdIUtD9odmoi0ZtEpfh0uKqq+rpSW8IlIY4S15MUTJkzgzTff5IUXXgDg5ptv5rrrrqt7fuXKlaSmpp5TY0pEREQklHg88OabZvler14woJ8b3lpolsvE97M7PBEROH0ILAtiu/p8qLQ0OHLELOEbONDnw4lIkGtRUgpg+vTpTJ8+vcHnxo4dy8aNG1s6hIiIiEjAWrcODh40O0/NmAGO/I/MB8CIBOhyvd3hiUhrt/1R2DwfenwPRjzl8+HS02H1as2UEpHGUdVNERERkWYqKoKsLNOeNAni4oD9Z3a6Sv86uCJtjU9EhIRhZuZm9kvgrvD5cLV1pY4dg9JSnw8nIkGuxTOlANauXcu6desoKirC7Xaf97zD4eC+++7zxlAiIiIiAcGy4K23oKrKLFe57DKg8jgcWmxO6H6b3SGKiEDyRIjpBqdz4eBiSL/Zp8NFR0NyMuTlmdlS/fv7dDgRCXItSkqdOHGCWbNmsXLlSizLuuB5SkqJiIhIqPniC9i3D8LCYOZMMzOA7JfAUwXth0L7IXaHKCJidt7L/BZ88Wszk9PHSSnO1JXKyzN1pZSUEpGLaVFSat68eaxYsYLx48dz66230qVLF8LCvDL5SkRERCRgVVbCBx+Y9lVXQYcOZ57IfskcMzVLSkQCSG1SKu8jKMuF2G4+HS49HT77zCSlREQupkUZpLfffpuRI0eSlZWFw+HwXlQiIiIiAezTT+HUKUhIgNGjz3piwhLIeRm6fdXG6EREvqRNBiRPgPxPYP9CGOjbVSxpaeZYWAhlZRAb69PhRCSItajQeXl5OVdddZUSUiIiItJqHDtmdpYCmDLFLN+rExEPPb8LkQl2hSci0rDMueaY+4rPh4qJgaQk09YufCJyMS2aKTVkyBCyNSdTREREWgnLgvfeA48HevUyt7on9CWdiASyrjeBuxLSvuaX4dLToaDAJKX69fPLkCIShFo0U+qBBx7gzTffZM2aNd6LSERERCRA7dxpipu7XGaWVJ2cRbBkBGT/28boREQuIiwGenwbwuP8MlztEj7NYRCRi2nRTKm8vDymT5/OuHHj+PrXv86wYcOIi2v4l9ycOXNaMpSIiIiIraqr4f33TXvMGFNPqs7+5+HE51Cy067wRESaxvKYnfl8pDYpVVAA5eUQHe2zoUQkiLUoKfWtb30Lh8OBZVm8+OKLvPjii+fVl7IsC4fDoaSUiIiIBLUVK6CoCOLj4corz3qiLMfsaMWZHa5ERAJZziuw/RHoeaeZOeUjbdpAYqIpdp6TA336+GwoEQliLUpKvfDCC96LRERERCRAnTwJK1ea9uTJEBFx1pP7FwIWJE80O1yJiASyshw4ucHM8PRhUoozs6UKC80SPiWlRKQhLUpK3Xrrrd6LRERERCRAffQR1NRARgb07XvWEx437Pt/pp15m13hiYg0XsY3YfN8OLYaindCvO+yRenp8Pnn2oFPRC7Md4uIRURERELAwYOwbZvZXG/KlC9tspefBadzIbwddL3RxihFRBopOgVSp5n2ft+ufKmtK5WXBxUVPh1KRIKUV5JSr7/+Ol/72tcYNGgQPXr0qHt8586dPProoxw+fNgbw4iIiIj4lWXVFzcfMgSSk790wt7nzDHjmxCmKr4iEiQy55rjgb+Dp8Znw7RtCx06mN+lubk+G0ZEgliLlu95PB5uueUW/vOf/wAQHR1NeXl53fPt27fnl7/8JW63m/nz57c8WhERERE/2r4dDh2C8HCYOLGBEzK/BZ5K6O7buiwiIl6VOh0iE6EiD44ugc4zfDZUWhocP27qSvXq5bNhRCRItWim1B/+8AdeffVVvvvd73Ly5Enuueeec55PTk5m7NixvPPOOy2NU0RERMSvampMLSmAMWPMN/7n6Twdxr0J7Qf5OzwRkeZzRUD6N0x73/M+HSo93RxVV0pEGtKipNSLL77IiBEjePrpp4mLi8NxTpEFo0ePHhw4cKAlw4iIiIj43bp1Zte9tm1h9Gi7oxER8bLut0GX66H77T4dprau1JEjUFnp06FEJAi1KCm1d+9exo4de9FzOnTowPHjx1syjIiIiIhfnT4Ny5aZ9oQJEBHxpROOrYUt90Npth3hiYi0XLsBcNViM+PTh+LjoX171ZUSkYa1KCkVHR1NcXHxRc/JycmhXbt2LRlGRERExK8+/dTsFJWcbAqcn2fP0/DFb+CLX9sQnYhIcNESPhG5kBYlpYYOHcr7779PxQX29zxx4gRLlizh8ssvb8kwIiIiIn5z4oRZugdw7bXg/PK7paoiyH3FtLvf4ff4RES8qvQAbP4VHP/cZ0PULuHL1uRSEfmSFiWlfvSjH3Ho0CFuuukmDh06dM5z+/bt44YbbqC4uJgf/ehHLY1TRERExC8+/hjcbujRA7p3b+CEnH+Duxzi+0FHffEmIkFu60Ow7X/MDFAfqZ0pdeQIVFX5bBgRCUJhLXnx9ddfz89+9jMeeeQR0tLSiI2NBSApKYnjx49jWRb33XcfExvcQ1lEREQksBw5Al98AQ4HTJp0gZP2/s0cu99hThQRCWY97oADCyFnEQx/EsLjvD5Eu3amtlRxMRw8eIGEv4i0Si2aKQWwYMEC3n//fWbMmEFMTAwulwuPx8OUKVN47733eOihh7wTqYiIiIiPZWWZ48CBkJLSwAknNsDJDeA8azt1EZFg1nE0xPUF92nIfslnw6iulIg0pEUzpWpdc801XHPNNd7oSkRERMQWBw7Avn3gcpkd9xq078wsqS43QFRHf4YnIuIbDoeZLbVhHuz9K/T8nk+GSU+HzZtVV0pEztWipNThw4dZvHgx69at49ixY3Bm6d6IESOYNWsWnTp18lacIiIiIj5jWfDRR6Y9fLjZvrxBYbEQ1sZ8gBMRCRXp34RNP4eTG+HEekgY7vUhaoudHz4M1dUQHu71IUQkCDU7KfXAAw/w6KOPUlVVhWVZ5zy3cOFCfvKTnzB//nzuu+8+b8QpIiIi4jM7dpgPShERcNVVFzlx6GMw4AEIi/FjdCIiPhbVEbreZDZy2PscjPR+Uqp9+/q6Urm5qislIkazklK//OUvWbBgAZGRkXzjG99g/PjxpKamAnDkyBE++eQTXn31VR588EHcbjcPPvigt+MWERER8QqPx+y4B3DFFdCmzSVeEH6pE0REglCPO+Dw2z4pdM6ZVYJnL+FTUkpEaE5Sav/+/Tz66KNkZGTw3nvv0atXr/POmTt3Lr/61a+YPHkyv/vd77j11lvJyMjwVswiIiIiXrNpExw7BjExMHr0BU4qy4Xyo9BhpHbcE5HQlDQebjxqlin7SEaGSUodOOCzIUQkyDR5972FCxfi8Xj4xz/+0WBCqlavXr345z//SU1NDX//+99bGqeIiIiI11VXw9Klpn3VVRAZeYETd/8JPrgcPv+hP8MTEfEfh8OnCSnO2oHvyBGorPTpUCISJJqclFq5ciUDBgxg9AW/Sqw3ZswYBg4cyPLly5sbn4iIiIjPrF0LJSWmzslll13gJHcV7F9o2p2u9Wd4IiL+Z1lQuBpKvT+dqV07U1vK4zF1pUREmpyU2rFjByNHjmz0+SNHjmTnzp1NHUZERETEpyorYeVK054wAcIuVNTg8JtQWQjRnSB1mj9DFBHxvw3z4MPRsPMPPum+tqqLlvCJCM1JShUVFZGUlNTo85OSkigqKmrqMCIiIiI+tWYNnD4NHTvCoEEXOXHf38wxcy44m71xsYhIcOg0xRwP/ANqyr3efe0Svuxsr3ctIkGoyUmp8vJyIi9YcOF8ERERlJd7/5eZiIiISHOVl8OqVaY9fjw4L/SOqCwHjn5g2pm3+S0+ERHbdLoGYtOguggOvub17mtnSh09ChUVXu9eRIJMk5NSIiIiIsFu5UqzfC8lBfr3v8iJ+14ALEieCG21f7mItAIOJ2Tebtr7nvN6923bQocOpnRVTo7XuxeRINOsOej//Oc/WbNmTaPO3bt3b3OGEBEREfGJ0lL47DPTnjDBbDh1QflZ5tj9Dr/EJiISELrPhS8ehIJPoWQXxPX2avcZGXD8uKkr1du7XYtIkGlWUmrv3r1NSjY5LvpuT0RERMR/VqyA6mro3Bl69brEyVcvhbwPIHmCn6ITEQkAMV2g0zQ48jbsfQ6GPe7V7tPT4fPPVVdKRJqRlDqgbRJEREQkSBUXw7p1pn311ZeYJQXgdEHqVH+EJiISWHrcYZJSBcvMWjsvTjSoLXael2c2nIiJ8VrXIhJkmpyUSktL800kIiIiIj726afgdpsPRLXFdhtUXQquSHCG+zE6EZEAkjoNJnwAKY3J4DdNmzaQmAiFhWa2VL9+Xu1eRIKICp2LiIhIq3DiBGzcaNoTJ17iM9aOR2Fx1zOFzkVEWiFnmNmJz+Gbj4y1XwxoCZ9I66aklIiIiLQKS5eCxwM9e0K3bhc50VMD+/4fVORDmNaUiIjgroLqU17tsnYJn6rDiLRuSkqJiIhIyCsogK1bTXvCpWqWH34byo9AZCJ0ucEf4YmIBK59L8AbXWH7w17ttjYpVVhodkUVkdZJSSkREREJeUuXmjq9fftCauolTt77F3Psfhu4IvwRnohI4ApvCxUFsO958FR7rduYGEhJMW0t4RNpvZSUEhERkZB29Chs325qSF1yllRpNhx937S73+GP8EREAlvnmRCVBBV5cPgtr3ZdO1tKSSmR1ktJKREREQlpH39sjgMHQlLSJU7e9xxgQco10La7P8ITEQlsrgjIvN209zzr1a5ri52rrpRI66WklIiIiISs3FzYswecThg//hIne2rM8hSAHt/1R3giIsGhx3cAB+R9CCV7vNZtWpqZxXr8OJSUeK1bEQkiSkqJiIhISLKs+llSQ4dCQsIlXuAMgwnvQ997octMf4QoIhIc2qRD6lTT3vdXr3UbFVVf52//fq91KyJBREkpERERCUkHDpg6JS4XXHVVI1/UfhAMfRSc4T6OTkQkyPT4njnufwHcVV7rVkv4RFo3JaVEREQk5Jw9S+qyyyA+3u6IRESCXOo06PdzuHqpV3cmzcw0xwMHzO9uEWldlJQSERGRkLN7Nxw6BOHhMHZsI16w9SFY9U04ucUP0YmIBCGnC4YsgHYDvNpt164QFmZqSh0/7tWuRSQIKCklIiIiIeXsWVKjRkGbNpd4gbsK9jwN2f+E0n3+CFFERM4IDzeJKbSET6RVUlJKREREQsq2bZCfbwrojhnTiBccWgwVBRCdCp1n+CFCEZEgVvQFrPoGbLjHa13W1pVSsXOR1kdJKREREQkZbnf9LKnRoyE6uhEv2vusOXa/XQXORUQupfwoZP8L9j0HNWVe6bK2rlR2Nng8XulSRIKEklIiIiISMjZtghMnIDYWLr+8ES8o2Q35n4DDCd2/7YcIRUSCXMrV0KYHVJdA9r+90mVqKkRGQnk55OV5pUsRCRIBmZR66qmnSE9PJyoqilGjRrF27doLnrtt2zZuuukm0tPTcTgcPPnkk+ed8+CDD+JwOM659enTx8c/hYiIiPhTTQ0sW2baY8dCRGM2h9r7V3PsNBViu/k0PhGRkOBwQs/vmnbtTNMWcjohLc20VVdKpHUJuKTUokWLmDdvHg888AAbNmxg8ODBTJ48mYKCggbPP336NJmZmTz88MOkpKRcsN/+/ftz9OjRutuKFSt8+FOIiIiIv61bZ3Zvio+Hyy5rxAtqymH/C6bd47u+Dk9EJHRkfAucEXBiPRz/3Ctd1i7hU1JKpHUJuKTU73//e+644w7mzp1Lv379ePbZZ4mJieH5559v8PwRI0bw2GOPcfPNNxMZGXnBfsPCwkhJSam7dezY0Yc/hYiIiPhTZSUsX27a48aZ7cUvyXJDv59C0lWQOs3XIYqIhI6ojtDtq6btpdlStcXOc3JMfUARaR0CKilVVVXF+vXrmTRpUt1jTqeTSZMmsXr16hb1vWfPHlJTU8nMzOTrX/86ubm5XohYREREAsGaNXD6NHToAEOGNPJF4W2g389g0jJwunwcoYhIiOnxPXPM/jdUFbW4u6QkUw+wuhoOHWp5eCISHBrzPaLfHDt2DLfbTXJy8jmPJycns3Pnzmb3O2rUKF588UV69+7N0aNHeeihhxg7dixffPEFbdu2bfA1lZWVVFZW1t0vKSkBwOPx4PHzlhAejwfLsvw+roicT9ejSOCovR5LSz2sXAmW5WD8eOvMc3ZHJ9K66O9jK9ThChypM7ASrwTLO79409Jg2zYHe/dadO3qlShbLV2TYrfG/tsLqKSUr0ydOrWuPWjQIEaNGkVaWhqvvPIKt99+e4OvWbBgAQ899NB5jxcWFlJRUeHTeL/M4/FQXFyMZVk4nQE1uU2k1dH1KBI4aq/HZcsiOHEiksRENx07lnOBMpTniMl9Fk9kJyoSp5q6KCLSIvr72Er1ec4cT1YALf+M1K5dGGVlUWzZ4qZ///KWx9eK6ZoUu506dapR5wVUUqpjx464XC7y8/PPeTw/P/+iRcybql27dvTq1Yu9e/de8Jz58+czb968uvslJSV07dqVxMRE4uLivBZLY3g8HhwOB4mJifqFImIzXY8igcPj8VBW5mT37nbExjqZNcsiObnhGdDnqDqJY9kjONwVeCYth46j/RGuSEjT30fxhuHDYeVKByUlFu3atW3cLqrSIF2TYreoqKhGnRdQSamIiAiGDx9OVlYWs2bNgjMXU1ZWFnfddZfXxiktLWXfvn1885vfvOA5kZGRDRZOdzqdtlzUDofDtrFF5Fy6HkUCx2efReJ2O0lLc9CrlwOHoxEvyv47uCug3WCciWNo3ItE5FL097GVclfCwdegLBv6/6JFXXXoAAkJcPKkg4MHoWdPr0XZKumaFDs19t9dwP3rnDdvHs899xwLFy5kx44d3HnnnZSVlTF37lwA5syZw/z58+vOr6qqYtOmTWzatImqqioOHz7Mpk2bzpkFdc8997Bs2TKys7NZtWoVN9xwAy6Xi1tuucWWn1FERERa7uRJ2LLFfL929dWNzC1ZHtjzjGn3+r4SUiIiLVW8DVZ9HbY+COX5jXjBxdXuwnfgQMtDE5HAF1AzpQBmz55NYWEh999/P3l5eQwZMoQlS5bUFT/Pzc09J+N25MgRhg4dWnf/8ccf5/HHH2fcuHEsXboUgEOHDnHLLbdw/PhxEhMTufLKK1mzZg2JiYk2/IQiIiLiDUuXgsfjoHt3i7S0RiaX8rLg1B4Ij4O0//J1iCIioS9hGHQYBcc/g31/gwG/bFF3GRmwYQPs3++1CEUkgAVcUgrgrrvuuuByvdpEU6309HQsy7pofy+//LJX4xMRERF7FRTA1q2mPXFiE1645ylzzPgWhLfxSWwiIq1Or7tg9Wew91no9zNwNv9jZu1Mqbw8KCuD2FjvhSkigSfglu+JiIiIXMrHH4NlOejVq4bU1Ea+qCwXDr9l2j3v9GV4IiKtS7evQmQinD4Eh99sUVdt2sCZRTJawifSCigpJSIiIkHl8GHYuRMcDovRoysb/8Kqk2anveSJEN/HlyGKiLQurkjocYdp7/5zi7vr3t0c9+1rcVciEuCUlBIREZGgkpVljoMGQceOF1/Cf472g+Ga5TDuLZ/FJiLSavX4LjickP8JFG9vUVeZmea4fz9colKLiAS5gKwpJSIiItKQAwfMhxSXC8aPh6qqZnQSFuODyEREWrnYbtD5eqg6DjWnW9RVWpr5PV9cDMePQ8eOXotSRAKMZkqJiIhIULAs+Ogj077sMmjXrgkvPvAPqDjmq9BERARgzL9h0jLocFmLugkPh27dTFu78ImENiWlREREJChs327qSUVEwNixTXhh0VZYPQfeTIfqEh9GKCLSyrkivdaV6kqJtA5KSomIiEjAc7vra0mNHm12Z2q03U+bY6epEB7nk/hEROQsFYXmd28LCkLVJqWys83fABEJTaopJSIiIgFv/Xo4ccIko0aPbsILq4og+x+m3ev7vgpPRERqearhnX5QeQzi+kDKxGZ1k5ICMTFw+rSZJVu7nE9EQotmSomIiEhAq6yEZctMe/x4s3yv0fa/ADVlEN8fksb7KkQREanlDIdus01795+b3Y3DUb8Ln5bwiYQuJaVEREQkoK1cCWVl0KEDDB3ahBd63PUfiHr/yHzCERER36udmXr4DSjLbXY3tUkpFTsXCV1KSomIiEjAOnUKVq827UmTzBbhjXb0PSjdD+HtIP3rvgpRRES+LL4fJE8EywN7/9LsbmrrSh0+DBUV3gtPRAKHklIiIiISsJYuhepq6NoV+vRp4otLdoMzAnp8G8JifRShiIg0qNcPzHHvc+CubFYX8fFmlqzHYwqei0joUVJKREREAlJhIWzYYNrXXNOM1Xd958H1udD3Xl+EJyIiF9N5JsR0gcpCyH212d3UzpZSXSmR0KSklIiIiASkjz4yu4n36dOCXZeikyEqycuRiYjIJTnDoMf3zIzV06orJSINC7M7ABEREZEvO3AAdu0Cp9PUkmqSqmLzAajdQB9FJyIijdLrB9DjOxCV2Owu0tPN34Ljx6GoCNq182qEImIzzZQSERGRgOLxwPvvm/Zll0HHjk3sYP/z8O4gWPtdX4QnIiKNFdGuRQkpgKgo6NzZtDVbSiT0KCklIiIiAWXTJsjLMx9Exo9v4os9btj9Z9NOGO6L8EREpDlOboby/Ga9VHWlREKXklIiIiISMCor4eOPTXvcOIiJaWIHR9+D0v0Q3g7Sv+6LEEVEpKnWz4P3hsDuPzXr5bVJqf37zWxaEQkdSkqJiIhIwFi5EkpLISEBRo5sRge7/miOPb4NYbHeDk9ERJojcYw57v0LuCua/PLOnSEyEsrL4ehR74cnIvZRUkpEREQCQnExrFpl2tdeCy5XUzvYAXkfgsMJPX/gixBFRKQ5ulwPMd2g8hhkv9Tklzud9bvw7d3r/fBExD5KSomIiEhA+OgjqKkxOy317t2MDmprSXWeCW3SvR2eiIg0lzMMev/QtHc9CZbV5C569DBHJaVEQouSUiIiImK7Q4dg61ZwOGDyZHNsEo8bjp7Zsq/3j3wRooiItET328EVA0VboWBpk19em5Q6dMgs4xOR0KCklIiIiNjKsuD9M/mkwYOhU6dmdOJ0wfRtMPY1SGrqln0iIuJzEe0h81bT3vlkk18eHw+JieZvxv793g9PROyhpJSIiIjYassWOHgQIiLg6qtb0JErErre2IxpViIi4he9zsxkPbEeqkub/PLa2VL79nk5LhGxjZJSIiIiYpvKSvjwQ9MeNw7atm1GJ+V5ZvmeiIgEtvg+MPFDmLkfwts0+eVn15VqRlkqEQlASkqJiIiIbZYtg9JS6NABRo1qXh+OVbfA272gcKW3wxMREW9LmQSuiGa9NC0NwsOhpAQKC70emYjYQEkpERERscWxY7BmjWlPmQJhYU3vI6xkM47CT6EsF2K1456ISNDwuKH0QJNeEhZmdmhFu/CJhAwlpURERMTvLAveew88HujdG3r2bF4/sQf/ahppN0NMZ6/GKCIiPnJyC7zVEz6+psnLr89ewiciwU9JKREREfG7XbtMoVqXCyZPbmYnpw8RVfCmaff5sTfDExERX2rbHapOQuk+OPJuk17avbs55uRAVZVvwhMR/1FSSkRERPyquhref9+0R4+GhITm9ePY8xQOqwYrcRwkDPNqjCIi4kNhsdDjDtPe9b9NemmHDtCuHbjdkJ3tm/BExH+UlBIRERG/WrUKTp6EuDgYO7aZnVSXwl6zdM/qc7dX4xMRET/odRc4XJCfZZbzNZLDoSV8IqFESSkRERHxm5MnYfly0772Woho3gZMcPgtHNVF1ERnQOoMb4YoIiL+ENsNut5k2jt/36SXKiklEjqUlBIRERG/sCx4912oqYHMTOjfvwWdpd2MZ9JySnr9Fhx6OyMiEpT63mOOOS/B6SONfllGBjidcOKEuYlI8NK7OBEREfGLHTtgzx5T3HzaNLMEo9kcDug4mqoOE70YoYiI+FWHEZA4FjzVcPC1Rr8sMhK6dTNtzZYSCW5hdgcgIiIioa+yEpYsMe0xY6BjxxZ0Vl0K4W28FZqIiNhp6GNguSFxdJNe1qOHKXS+dy+MHOmz6ETExzRTSkRERHxu6VIoKYH27VtQ3BzgxHp4PQU2/MSsBxQRkeDWcVSTE1KcVVcqO9ssCxeR4KSklIiIiPhUXh589plpT58O4eEt6GznH6CmDCryW7j+T0REAk7lCfA0LsOUnAxt2kBVFeTk+DwyEfERJaVERETEZywL3nkHPB7o16/+m+1mOX0YchaZdp8feytEEREJBFsehMVd4eD/Nep0hwN69jTtPXt8G5qI+I6SUiIiIuIzGzbAwYMQEQFTprSws11/BKvGFMVNGO6lCEVEJGC4T8OOxxu9PLtXL3Pcvdu3YYmI7ygpJSIiIj5RWgoffWTaEyZAXFwLOqsqhr3Pmnbfe70Sn4iIBJBe3wdnJJxYB4UrGvWSzEyzo+uJE3D8uM8jFBEfUFJKREREfOK996C8HDp1glGjWtjZ3r9CdQnE9YXO070UoYiIBIyoJMi81bR3PN6ol0RGQlqaaWu2lEhwUlJKREREvG7XLti2DZxOmDnTHJvN8sCep027773g0NsXEZGQ1GeeOR5+C0p2NeolqislEtz0rk5ERES8qrLSFDcHuOIKM1OqRRxOuGY5DLgP0v/LGyGKiEggiusNna8DLLPbaiPU1pXKyTF/f0QkuCgpJSIiIl6VlQUlJZCQAOPHe6nTmC4w6NfgivRShyIiEpD63mOOOf+GmtOXPL1DB/P3xu2G/ft9H56IeJeSUiIiIuI1ubmwbp1pX3cdhIe3sEN3hTfCEhGRYJE4Fob9AaZvg7CYRr1Eu/CJBC8lpURERMQramrgrbfMTt5Dh0JGhhc6/fhaWDodSlQsRESkVXA4oM/dZoZsI51dV8qyfBeaiHhfmN0BiIiISGhYsQIKC6FNG7j2Wi90WLgKCpeDMwLCYr3QoYiIBJ2qYoiIv+gpaWkQEQGlpXD0KKSm+i06EWkhzZQSERGRFsvPh+XLTXvqVIiO9kKnOx4zx4xvQow+YYiItCqnj8An0+DdgeCuuuipYWGQmWna2oVPJLgoKSUiIiIt4nbD4sXm2KcP9OvnhU6Ld8KhN0y7zz1e6FBERIJKZAco2gSnD0LOS5c8XXWlRIKTklIiIiLSIqtWmeUS0dEwY4YpB9JiO58wW4J3uR7i+3ihQxERCSquSOj9Y9Pe/ghYnoueXltX6sgRKCvzQ3wi4hVKSomIiEizFRTA0qWmPXWqqSfVYuVH4cDfTbvvT73QoYiIBKWe34XweCjZCYfevOipbdtCp06m0LmW8IkEDyWlREREpFk8nvple717w8CBXup4z1/AUwWJYyBxtJc6FRGRoBMeB71+YNrbF1xya73aJXxKSokEDyWlREREpFlWrTLLJKKivLhsD6D/z2HkX2Dgr73UoYiIBK1ePwJXFBxfCwVLL3pq7RK+vXvNFyYiEviUlBIREZEmKyyETz4x7alTzbIJr3FFQY/vQMpEL3YqIiJBKToZMm8z7b1/veipnTtDbCxUVkJOjn/CE5GWUVJKREREmuTsZXu9esGgQV7q2F0FHn21LSIiX9L3HjOD9vIXLnqaw1G/hG/XLv+EJiIto6SUiIiINMmKFXD4sA+W7e15Gt7pCwf/z0sdiohISGiTYWbQuqIueWrv3ua4a9clS1CJSABQUkpEREQa7ciR+t32pk2DuDgvdeyuhB2Pwak9UHncS52KiEjI8bihuuSCT3fvDuHhUFQE+fl+jUxEmkFJKREREWmU6mp4/XWzfK9/fy/utgdwYCGUH4HozpAxx4sdi4hIyDj6gZlRu+GeC54SHg6ZmaatJXwigU9JKREREWmUjz82Bc7btIHp0724bM9TA9sfMe2+94Ar0ksdi4hISAmLNTNqD7wIpw9d8LQ+fcxx507/hSYizaOklIiIiFxSdjasWWPaM2dCTIwXO89ZBKX7IbIj9LjDix2LiEhISRwDSVeBpxq2P3rB03r1Ml+cHD0KxcV+jVBEmkhJKREREbmoykqzbM+yYPjw+p2NvMLywPbfmXafH5tvwUVERC5kwH3muO85KM9r8JTYWOja1bS1hE8ksCkpJSIiIhe1ZIn5prl9e5g82cud538CxdshPA56/sDLnYuISMhJvho6XA7uCtjx+AVPO3sXPhEJXEpKiYiIyAXt3AkbN5plEDfcABERXh4geSJc/QkM/yNExHu5cxERCTkOR/1sqT3PQEVhg6fV1pXKzoaKCj/GJyJNoqSUiIiINKisDN56y7RHj4Zu3XwwiMMByeMh81YfdC4iIiEpdSokDAf3aTj4nwZP6dABOnYEtxv27vV7hCLSSEpKiYiIyHksyySkysogORkmTPDBAFVFXu5URERaBYcDhj1pZtr2+N4FT6udLaUlfCKBS0kpEREROc/mzWbpnstllu2FhXl5gPyPYXEX2PqQlzsWEZFWIelKM9PW4bjgKbV1pfbsMTOmRCTwKCklIiIi5ygqgvfeM+0JEyAlxcsDWBZsfRBqyqDyhJc7FxGRVqfyBFSXnvdwly5mJ76KCsjJsSUyEbkEJaVERESkjmXB4sVQWWm20x492geD5H8ChSvAGQn9fuaDAUREpNXY9Wd4Iw12//G8pxyO+tlSO3f6PzQRuTQlpURERKTOmjVmp6KICLNsz+ntdwq1s6QAenwHYlK9PICIiLQqEe2hphR2/r7B2VK1Saldu8yfIBEJLEpKiYiICAAFBZCVZdrXXgsJCT4YJP8TKFyuWVIiIuIdabOhTQ+oPA57njnv6cxMCA+H4mLIy7MlQhG5iIBMSj311FOkp6cTFRXFqFGjWLt27QXP3bZtGzfddBPp6ek4HA6efPLJFvcpIiLS2rjd8H//BzU10LMnDB/ug0EsC744U9i8xx0Q09kHg4iISKviDIMBvzTtHY+dN1sqPNz8XQPYvt2G+ETkogIuKbVo0SLmzZvHAw88wIYNGxg8eDCTJ0+moKCgwfNPnz5NZmYmDz/8MCkXqMTa1D5FRERam6VLzTfIMTEwc+ZFNzNqvlN74NhqcEZAv5/7YAAREWmV0r9xZrZUIez+83lP9+1rjtu3awmfSKAJuKTU73//e+644w7mzp1Lv379ePbZZ4mJieH5559v8PwRI0bw2GOPcfPNNxMZGemVPkVERFqT3FxYscK0r7sO2rb10UBxveC6PXDF3zVLSkREvMcZBgMfMO0dj0F1yTlP9+oFLhccPw6FhfaEKCINC6ikVFVVFevXr2fSpEl1jzmdTiZNmsTq1asDpk8REZFQUVlplu1ZFgwZUv9tss/Eppn6HyIiIt6UdgvE9YaaU1Cw/JynIiOhRw/T1hI+kcASZncAZzt27Bhut5vk5ORzHk9OTmZnM/fwbG6flZWVVFZW1t0vKTHZdo/Hg8fjaVYszeXxeLAsy+/jisj5dD1KqHn3XTh50kG7dhaTJ4PP/mmX7DYzpbxI16NI4ND1KPZzwKgXIDIZ2qSf9wetd2/YudPBtm0WV11lW5B+o2tS7NbYf3sBlZQKJAsWLOChhx467/HCwkIqKir8GovH46G4uBjLsnB6fW9uEWkKXY8SSvbscbFyZTQOh8WMGeUUF/vmjWvEyZUkbPwK5UmzKO7/tNcKVul6FAkcuh4lMGTAaeD0+bWD27eH8vJYDhxwsHNnGQkJoV1cStek2O3UqVONOi+gklIdO3bE5XKRn59/zuP5+fkXLGLuqz7nz5/PvHnz6u6XlJTQtWtXEhMTiYuLa1YszeXxeHA4HCQmJuoXiojNdD1KqCgthVWrIDbWwZgxFsOHx/pmIMvCsfkJAKLiU4n80szlltD1KBI4dD1KwDm5CWK6QWRC3UP9+8P+/Q4KC2Po08fW6HxO16TYLSoqqlHnBVRSKiIiguHDh5OVlcWsWbPgzMWUlZXFXXfd5dc+IyMjGyyc7nQ6bbmoHQ6HbWOLyLl0PUqwsyx4+20oL4dOnWDiRAc+++d8+B04vhpc0TgG/AqHlwfS9SgSOHQ9SsDY9AvYvgD6zYchv6t7eMAAOHAAdu1yMG6crRH6ha5JsVNj/90F3L/OefPm8dxzz7Fw4UJ27NjBnXfeSVlZGXPnzgVgzpw5zJ8/v+78qqoqNm3axKZNm6iqquLw4cNs2rSJvXv3NrpPERGR1mT9eti92+xEdOONEOarr6gsD2z5lWn3uguiO/loIBERkbN0vNwcd/8RKuq32+vTx6wgP3IETp60LzwRqRdQM6UAZs+eTWFhIffffz95eXkMGTKEJUuW1BUqz83NPSfjduTIEYYOHVp3//HHH+fxxx9n3LhxLF26tFF9ioiItBbHj8P775v2pEmQlOTDwQ6+ZpZPhLWFfj/z4UAiIiJn6XwdJAyHE+thx6Mw9DEAYmMhLQ2ys2HHDhg92u5ARcRhWVZoV3jzkpKSEuLj4ykuLralplRBQQFJSUmaeiliM12PEsw8Hnj+eTh0CDIyYM4cr9Ucb2AwN7w7AEp2woAHYNCD3h9C16NIwND1KAHn8LuwbDq4omHmfog29YTXrjU7z3btCrffbneQvqNrUuzW2ByK/nWKiIi0EsuXm4RUVBTMmuXDhBRA6V6oKoKIBOg7rxEvEBER8aLUqdBhFLjLYVt9Xam+fc3x4EEoKbEvPBExlJQSERFpBQ4fhmXLTHvaNIiP9/GAcb1h5j4Y/x6E+3eGsYiICA4HDD6TjNr7LJQeAKBtWzNLCswSPhGxl5JSIiIiIa66Gv7v/8zyvf79YeBAPw0cFgMdR/ppMBERkS9JmQgp15hZu6X76h7u188clZQSsZ+SUiIiIiHuww9NgfO2bWHGDB8v26s5DTmvmJ33RERE7Dbq/5mZuymT6h6qXcKXkwOlpfaFJiJKSomIiIS0vXtNUVcwdaSio3084J6nYeVsWHa9jwcSERFphNiuEBZ7zkPt2kHnzmBZmi0lYjclpURERELU6dOweLFpjxoF3bv7eMCqYtj+sGl3vcHHg4mIiDSB5YHsl+GY+aZmwADz8Nat9oYl0topKSUiIhKCLAveftssS+jYESZNasSLWmrHo1B53BQ5z5jjhwFFREQa6YvfwqpbYOM8sCz69zfL2XNzobjY7uBEWi8lpURERELQli2wfTs4nXDjjRAe7uMBTx+GnX8w7cEPgzPMxwOKiIg0QffbwRUFhSvhyLvExUG3buapbdvsDk6k9VJSSkREJMQUFcG775r2+PGQmuqHQbc+CO5ySBwDXVRPSkREAkxMZ+j1I9PePB8sT90Svi++sDUykVZNSSkREZEQYlnw+utQWQldu8KVV/ph0OLtsP950x7yqI+39xMREWmmfj+D8Hgo2grZL9Gvn5lRfOSI2aVWRPxPSSkREZEQ8tlnZovriAi44QbzZtvnPFWQcBl0vRESR/thQBERkWaITDCJKYAt9xEbVUlmprmr2VIi9lBSSkREJEQcPw4ffWTa114LCQl+Grj9ELh2DVz+gp8GFBERaabeP4LoTlCWDXuePmcJn2XZHZxI66OklIiISAjweGDxYqipgcxMGD7czwE4HBAe5+dBRUREmigsFgb9BhKGQ8Jl9OkDYWFQWAgFBXYHJ9L6KCklIiISAtasgYMHITISrr/eT2WdDr0BW+6H6lN+GExERMRLMufC5LWQNJaoKOjZ0zysJXwi/qeklIiISJArLISPPzbtyZMhPt4Pg3qqYeO98MVvYNcf/TCgiIiIlzic5naGlvCJ2EdJKRERkSB29rK9Hj1g6FA/Dbz3OTi1B6KSTH0OERGRYFNdClsepG/Rt4iIgJMn4fBhu4MSaV2UlBIREQliq1aZN9BRUTBzpp+W7VUVw9YHTHvA/RDe1g+DioiIeFlZNmz7Dc6chVzRcxVoCZ+I3ykpJSIiEqQKCuCTT0x7yhSI81ed8W2/hcpjENcHenzHT4OKiIh4WbsBkHkbAJdH/ASw2LbNzEIWEf9QUkpERCQIud1m2Z7bDb16weDBfhq4ZA/s+l/THvZ7cIb7aWAREREfGPRrCIslumwNQxJe5dQpyMmxOyiR1kNJKRERkSC0ciUcOWKW7V13nZ+W7QFs+aUpct5pKqRO9dOgIiIiPhLdCfr+FIBrEn+Oy1HJ5s12ByXSeigpJSIiEmTy82HZMtOeNg3a+rOk09DHIf3rMOwJPw4qIiLiQ31/AtGpxFoHGNXuT2zfDlVVdgcl0jooKSUiIhJEzl6216cPDBzo5wBiu8Hof0J8Xz8PLCIi4iNhsTDotwCM6fA47upKdu60OyiR1kFJKRERkSCyYgUcPQrR0TBjhh+X7VWe8NNAIiIiNsiYA33v5YtOq3FbkWzaZHdAIq2DklIiIiJBIi/v3GV7bdr4aeCqYninL6y4WckpEREJTU4XDH2UXsMyADhwAEpK7A5KJPQpKSUiIhIE3G54/XWzTXXfvjBggB8H3/Y7qCiAok0Q7s8CViIiIv7Vvj2kpUFyxEa2bPbYHY5IyFNSSkREJAisWGEKnMfE+HnZ3ql9sOtJ0x76BDjD/TSwiIiIPWYkf5/vpQ2jfOe/sCy7oxEJbUpKiYiIBLjCQvj0U9OeOhViY/04+IYfg6cKUq6B1Gl+HFhERMQe7TqnAXB55E85mnvK7nBEQpqSUiIiIgHM44E33zTL93r18vOyvcPvwOG3wBEGw//Xj9OzRERE7BM+4G5KHT1oG5ZH+ef/Y3c4IiFNSSkREZEAtm4dHDwIkZEwfbof80LuClj/I9Pu82OI7+ungUVERGzmiqSkxx8ASK/4Pe6iPXZHJBKylJQSEREJUMXFkJVl2pMmQXy8Hwc/tRfclRCdCgPu8+PAIiIi9ksZNp39FVNxOaopXzHP7nBEQpaSUiIiIgHIsuDtt6GqCrp1g8su83MA7QbAjJ0w7m3tuCciIq2O0+XgSMofcFvhtCl5Gw6/a3dIIiFJSSkREZEAtHUr7NkDLhfMnGlTOafwNpAw1IaBRURE7NdzeG/WnLyb0pokKitq7A5HJCQpKSUiIhJgyspgyRLTHjcOOnb04+B5WbDvBbA8fhxUREQk8CQnw87w+/lz9i42HZtpdzgiIUlJKRERkQCzZAmcPm3eDI8Z48eB3ZWw9nvw2W2w649+HFhERCQwDRzahgpPO9avN0vrRcS7lJQSEREJIHv2mKV7DodZtudy+XHwHY9B6V6ISoHut/lxYBERkcA0aBCEhUFBgcWJjS/Byv9SdkrEi5SUEhERCRCVlaa4OcDll0Pnzn4cvGQPfPFb0x72BITH+XFwERGRwBQVBf37Q9uwI8Tv/Dbk/BuyX7I7LJGQoaSUiIhIgMjKguJiaN8eJkzw48CWBevuBE8lpFwLabf4cXAREZHANnw4nKrpzPITvzIPbJwHVSftDkskJCgpJSIiEgAOHoR160z7uusgIsKPg2f/C/KzwBUFI562aas/ERGRwNS1KyQmwopj91Ae0RcqCmDzL+0OSyQkKCklIiJis5oaePNNM2FpyBDIzPTj4O4K2HiPaQ+4H9p29+PgIiIigc/hgGHDwE0EHxY9bR7c8ywcW2t3aCJBT0kpERERmy1fDoWFEBsLkyf7eXBXFFz1BnSbDX1+4ufBRUREgsPgwWbzkQ2HxnM6ZQ5gwbrvgafG7tBEgpqSUiIiIjYqKIAVK0x72jSIjrYhiI6j4MqXweXPNYMiIiLBIyYG+vY17RWlj0FEezi5EfI/tjs0kaCmpJSIiIhNLAveegvcbujdG/r18+Pg7ko4tdePA4qIiAS3YcPMcf22JKqHPQdXfwKdrrU7LJGgpqSUiIiITTZsMAXOIyLMLCm/1hfftgDeGQC7/uTHQUVERIJXRgYkJEBlJXxRfBMkj7c7JJGgp6SUiIiIDUpL4cMPTXviRIiP9+PgJ7fAtv8BTyVEJftxYBERkeBVW/CcM18s1SnNhiPv2RWWSFBTUkpERMQG778PFRXQqROMHOnHgT018NltYNVAlxug21f9OLiIiEhwGzIEnE4z07mgADixEd4dACtvhtOH7Q5PJOgoKSUiIuJn+/bB1q3mG9frrjNvbv1m5xNwYr0p0DriKT+vGRQREQlubdqYOpAA69YB7QZBfH+oLoHPf2AKRopIoykpJSIi4kfV1fD226Y9ahSkpvpx8OKdsOUB0x72JER38uPgIiIioaF2hvPmzVBR5YJR/w+c4XDoDTj4H7vDEwkqSkqJiIj40aefwsmTEBcHEyb4cWCP2yzb81RCp6mQ8U0/Di4iIhI60tMhKQmqqmDTJqDdAOj3C/PklgfA8tgdokjQUFJKRETETwoKYOVK0546FSIj/Tm6BV2uh8hEGPkXLdsTERFpJoejfrbU2rVnVuz1nw99fgJXfwIOfcwWaSxdLSIiIn5gWWbZnsdjalH06ePnAJxh0O9ncP0BiO3q58FFRERCy6BBEBUFJ07A3r2AKxKGPQ7R2tVWpCmUlBIREfGDjRshNxciImDaND9OVPK4oaa8/n5YrJ8GFhERCV0REWYnPs7MlhKR5lFSSkRExMdKS+GDD0x7wgSIj/fj4DufgCXD4PjnfhxUREQk9I0cab5k2rMHjh+3OxqR4KSklIiIiI998AFUVECnTmbHPb85uQW23AclO6Foix8HFhERCX0JCdCjh2mvW2d3NCLBSUkpERERH9q3D7ZsMd+kXncdOP31l9ddCau/CZ4q6DwTMuf6aWAREZHWo/bLpo0bzW58ItI0SkqJiIj4SHU1vPOOaY8cCampfhx864NmdlRkIoz8q3bbExER8YHu3aFDB6ishM2b7Y5GJPgoKSUiIuIjy5ebXXnatoWJE/04cOFK2PGoaY/8i3YCEhER8RGHA0aMMO21a81uuyLSeEpKiYiI+EBhIaxcadrTpkFkpJ8Gri6F1XPA8kDGrdD1Bj8NLCIi0joNGWJ24ysshAMH7I5GJLgoKSUiIuJllgVvvQVuN/TuDX36+HFwdzm0yYSYbjD8f/04sIiISOsUFWUSUwCrVtkdjUhwUVJKRETEyzZuhNxc863ptGl+LucUlQgT3odrPoWIeD8OLCIi0npdfrn5e793L+Tn2x2NSPBQUkpERMSLysrgww9Ne8IEiPdXXqimvL7tcEJsmp8GFhERkYQE6NfPtDVbSqTxlJQSERHxog8+gPJySEmp3yba5zzVkDURPvsO1Jz206AiIiJytjFjzHHrVigutjsakeCgpJSIiIiX7N9vtoN2OOC668Dpr7+yWx+E42sg9xWoLPTToCIiInK21FTIyACPB9assTsakeCgpJSIiIgX1NTA22+b9ogR0LmznwbO+xi2LTDtUc9p2Z6IiIiNamdLrV9vZk6LyMUpKSUiIuIFy5fDiRPQti1MnOinQSuOwepvAhZ0/zZ0+6qfBhYREZGGdO8OyclQVQWff253NCKBT0kpERGRFioshBUrTHvqVLM1tM9ZHlj9DSg/AnF9YPiTfhhURERELsbhgNGjTfuzz8xMahG5MCWlREREWsCyzLI9txt69YK+ff008Bf/A0ffB1c0jFkEYbF+GlhEREQuZsAAs/tuaSls2WJ3NCKBTUkpERGRFtiwAXJyICICpk0z35D6RYfLICIBRjwD7Qf5aVARERG5FJcLLr/ctFeuNF9giUjDlJQSERFpplOn4MMPTXviRGjXzo+Dp06F63ZD5q1+HFREREQaY9gws5z/+HHYudPuaEQCl5JSIiIizbRkCVRUmC2gR470w4Ceaig7WH8/soMfBhUREZGmiow0u/ECfPqpZkuJXIiSUiIiIs2waxds2wZOJ8ycaY4+t2k+vDcYDr/rh8FERESkJa64wizvP3oUdu+2OxqRwBSwSamnnnqK9PR0oqKiGDVqFGvXrr3o+a+++ip9+vQhKiqKgQMH8u67575h/9a3voXD4TjnNmXKFB//FCIiEooqK6H2z8wVV0BKih8Gzf0P7HwCqk6Cp8oPA4qIiEhLxMTUz6ReulSzpUQaEpBJqUWLFjFv3jweeOABNmzYwODBg5k8eTIFBQUNnr9q1SpuueUWbr/9djZu3MisWbOYNWsWX3zxxTnnTZkyhaNHj9bd/v3vf/vpJxIRkVDy8cdQXAzt28P48X4Y8OQWWH2mdlTfe6DrLD8MKiIiIi01erRmS4lcTEAmpX7/+99zxx13MHfuXPr168ezzz5LTEwMzz//fIPn/+///i9Tpkzh3nvvpW/fvvzmN79h2LBh/PnPfz7nvMjISFJSUupu7du399NPJCIioeLwYaidvDtjBoSH+3jAyuPw6Sxwn4aUa2DwAh8PKCIiIt6i2VIiFxdwSamqqirWr1/PpEmT6h5zOp1MmjSJ1atXN/ia1atXn3M+wOTJk887f+nSpSQlJdG7d2/uvPNOjh8/7qOfQkREQpHbDW++ad5QDh4M3bv7eEBPDayYDWUHoE0mjHkZnGE+HlRERES86ezaUnv22B2NSGAJuHe2x44dw+12k5ycfM7jycnJ7LzAXpp5eXkNnp+Xl1d3f8qUKdx4441kZGSwb98+fvGLXzB16lRWr16Ny+U6r8/KykoqKyvr7peUlADg8XjweDwt/jmbwuPxYFmW38cVkfPpemzdVq6EvDwHMTEWkyaBz/8Z7PozzvwsrLBYrCtfh/B2fhg0eOh6FAkcuh5FLiw6Gi67DFaudPDxxxbdu4PD4dsxdU2K3Rr7by/gklK+cvPNN9e1Bw4cyKBBg+jevTtLly7l6quvPu/8BQsW8NBDD533eGFhIRUVFT6P92wej4fi4mIsy8Lpl+2dRORCdD22XidPOnjnnRjcbgdXXVVBWVkNZWU+HjT+BuJSP6cyYQKVVUlwgdqKrZWuR5HAoetR5OJ69HDw8ccx7N3rYM2acrp3d/t0PF2TYrdTp0416ryAS0p17NgRl8tFfn7+OY/n5+eTcoHtjVJSUpp0PkBmZiYdO3Zk7969DSal5s+fz7x58+rul5SU0LVrVxITE4mLi2vGT9Z8Ho8Hh8NBYmKifqGI2EzXY+tkWbBkCURFOcjIsBg/Psbn33DWSfk7UX4aKtjoehQJHLoeRS5twgRYtcrB1q0xXH65b2dL6ZoUu0VFNe4dbMAlpSIiIhg+fDhZWVnMmmV2F/J4PGRlZXHXXXc1+JorrriCrKws7r777rrHPvzwQ6644ooLjnPo0CGOHz9Op06dGnw+MjKSyMjI8x53Op22XNQOh8O2sUXkXLoeW5/NmyE72xQ1nznTQQOrvr2nNBv2/T8Y+CA4fTlQaND1KBI4dD2KXNyVV8Lnn5tSAPv2Qa9evh1P16TYqbH/7gLyX+e8efN47rnnWLhwITt27ODOO++krKyMuXPnAjBnzhzmz59fd/5///d/s2TJEp544gl27tzJgw8+yOeff16XxCotLeXee+9lzZo1ZGdnk5WVxfXXX0+PHj2YPHmybT+niIgEvrIyeP990x4/HhISfDhYVREsmw7bfgubf+7DgURERMTfYmPrd+LLylKZSBECcaYUwOzZsyksLOT+++8nLy+PIUOGsGTJkrpi5rm5uedk3UaPHs1LL73Er371K37xi1/Qs2dPFi9ezIABAwBwuVxs2bKFhQsXUlRURGpqKtdeey2/+c1vGpwNJSIiUuv99+H0aUhONrvn+IynGlZ8FYq3Q3Qq9P5vHw4mIiIidrjySli/HvLzYcsWGDLE7ohE7OWwLMuyO4hgUFJSQnx8PMXFxbbUlCooKCApKUlTL0Vspuuxddm3D/7xD1Pz4dvfhs6dfTSQZcHa78C+v0FYLExaDglDfTRY6ND1KBI4dD2KNN7KlfDhhxAXBz/8oSkP4G26JsVujc2h6F+niIhIAyor4a23THvkSB8mpAB2PGYSUg4njHlZCSkREZEQNmoUxMdDSQmsXWt3NCL2UlJKRESkAVlZUFQE7dpBA5u0ek/OK7DpZ6Y97A/QeYYPBxMRERG7hYWZnfgAli+H8nK7IxKxj5JSIiIiX5KdXf/N5cyZEBHhw8HCYsAVBb3ugt4/8uFAIiIiEigGDTL1KisqTGJKpLVSUkpEROQs1dXw5pumPXw4ZGb6eMDOM2Dy5zDsSR8PJCIiIoHC6YRrrjHtzz4zs7NFWiMlpURERM7y8cdw4oQpPlr7ZtHrTu2D0v3199v1B6fLR4OJiIhIIOreHTIywO027z9EWiMlpURERM44eBDWrDHtmTMhKsoHg5TnwSfXwgdjoOgLHwwgIiIiwcDhqP8CbOtWOHrU7ohE/E9JKREREaCmBt54AywLhgyBHj18MEhVMXwyxcySCouFyEQfDCIiIiLBIjUVBg407z+WLDFHkdZESSkRERHgk0/g2DFo2xYmT/bBANWlsHQaFG2GqGSY+AFEJ/tgIBEREQkmkyZBeDjk5MAXmkQtrYySUiIi0url5sKqVaY9YwZER3t5gJrTsOw6OLYKItrDhPehja8rqIuIiEgwiI+HsWNN+4MPoLLS7ohE/EdJKRERadWqquD11+uX7fXu7eUB3BXw6Q1QsBTC40xCqv1gLw8iIiIiwWz0aEhIgFOnYNkyu6MR8R8lpUREpFX74AM4edJ8Szllig8GcFdAdZGpITX+XegwwgeDiIiISDALC4OpU017zRooLLQ7IhH/UFJKRERarT174PPPTXvWLB/tthfRDiZ+CBOzIHGMDwYQERGRUNCzp5mx7fHAe++p6Lm0DkpKiYhIq1ReDm++adqXXw4ZGV7s3FMNh96ovx8eBx1HeXEAERERCUVTpphZU/v3w/btdkcj4ntKSomISKv07rumbkPHjnD11V7s2F0JK74Gn86Cnf/rxY5FREQk1LVvD1deadrvv29qX4qEMiWlRESk1fniC9i6FZxOuOEGsw2zV7grYPmNcGgxOCMhrpeXOhYREZHWYswYk5wqKVHRcwl9SkqJiEirUlwMb79t2mPHQufOXuq45jQsmwlH3gVXNIx/G1KneqlzERERaS3Cw+uLnq9aBYcP2x2RiO8oKSUiIq2GxwOvvQYVFdClC1x1lZc6ri6FpdMh78Mzu+y9BymTvNS5iIiItDa9esHAgabY+RtvgNttd0QivqGklIiItBqffgq5uRAZCTfdBC6XFzr1VMPHV0PBUghrCxPeh+RxXuhYREREWrOpUyE2FgoKYPlyu6MR8Q0lpUREpFXIza2vyzB9uqnV4BXOcOj2VYjsAFdnQeIYL3UsIiIirVlMDEybZtqffgr5+XZHJOJ9SkqJiEjIKy83y/YsCwYPhkGDvNCpZdW3+94D07dDhxFe6FhERETE6NcP+vY1JQgWLzZHkVCipJSIiIQ0yzKFzYuLISGh/hvHFilcBR9fA1XF9Y9FJXmhYxEREZF6Dod57xIdDUePmsLnIqFESSkREQlpGzfCtm3gdJo6UpGRLezw0Bvw8STIz4KtD3opShEREZGGtW0LU6aY9tKlUFhod0Qi3qOklIiIhKy8PHj3XdOeOBE6d25BZ5YFO34Pn94A7nJInQaD/8dboYqIiIhc0KBB0LMn1NTA669rNz4JHUpKiYhISKqogFdeMW/eevaEMS2pP+6phnV3wsafABb0+B5c9QaExXgxYhEREZGGORxw3XVmGd+RI/Dxx3ZHJOIdSkqJiEjIsSxTDPTECWjXDm680byZa5aqYlg6A/b+BXDAsN/DiKfBGeblqEVEREQuLC4Orr/etFeuhH377I5IpOWUlBIRkZCzahXs3AkuF3zta+ZbxWarKYPibeCKgasWQ58ftyDDJSIiItJ8ffrAiDOb/b7+OpSW2h2RSMsoKSUiIiElJweyskx76lRITW1hhzGpMP4duGY5dJnpjRBFREREmu3aayEpySSkFi82M8RFgpWSUiIiEjJOnYJXXwWPxxQEHT68GZ1YFmx/FA78s/6x9oMhYZg3QxURERFplvBw+MpXICwM9u6FNWvsjkik+ZSUEhGRkOB2m4RUaan59nDGjGassqsuhZWzYdPPYO0dUJrto2hFREREmi8pCaZMMe2PPjLFz0WCkZJSIiIS9CwL3n4bcnMhMtLUkYqIaGInJbvhgysg91VwhsOwP0Bsmo8iFhEREWmZ4cOhXz/zxdwrr8Dp03ZHJNJ0SkqJiEjQW7MGNm40M6O++lXo2LGJHWS/BEuGQ/EXEJUCV38CPb+nguYiIiISsBwOuO46SEiAoqL6EgYiwURJKRERCWp79sAHH5j25MnQo0cTXmxZsPa7sOrrUFMKSeNgynpIHOOrcEVERES8Jjoabr7ZzBA/cKD+PZFIsFBSSkREglZhIfznPya3NGwYjBrVxA4cDojqBDhgwP0wMcvsticiIiISJJKS4MYbTXvNGti82e6IRBpPSSkREQlKp0/DSy9BZSWkpcH06Y1cbWdZUHm8/v6A++DaNTDoIXC6fBmyiIiIiE/06QPjx5v2W2/B4cN2RyTSOEpKiYhI0KmpgUWL4ORJaN8eZs8GV2PySeVHYdkMyJoI7krzmNMFHUf6OmQRERERnxo3ziSnampM4fOyMtXGlMCnpJSIiAQVjwdeew1ycsxOe7fcAjExjXhh7n/g3YFw5F0o2QXH1/khWhERERH/cDjghhsgMRFKShy8/noUVVV2RyVycUpKiYhI0LAsePdd2LHDzIy65RZTR+Giqk7Cqjmw4qtm2V77oaaYedKVfopaRERExD8iI03h85gYi7w8F6++Cm633VGJXJiSUiIiEjSWLYPPPzffBN50E6SnX+IFua/B2/0g+x/gcEL/X5r6Ue36+yliEREREf/q0MF8cRcWZrF3r4M33zRf7IkEIiWlREQkKKxbB0uXmvb06dCv3yVeYFmw5ymoyIO4PjBpOQz+Lbgi/BGuiIiIiG26dIGZMytwOi02b4aPPrI7IpGGKSklIiIBb/t2s2wPzM4yl112gRMtT30Bc4cDRv4VBtwPUzdC4mi/xSsiIiJit8xMN9ddZ9orV8Lq1XZHJHI+JaVERCSg7dxpCptblklGjRt3gRNPbIAPr4QNP6l/rG0PGPQQuKL8Fa6IiIhIwBgyBCZNMu3334ctW+yOSORcSkqJiEjA2rmTugKdAwbAtGlmAtQ5Ko/D2jthyWVwbLWpH1VRYFPEIiIiIoFlzBi4/HLTfv11JaYksITZHYCIiEhDdu6EV14Bj8ckpG68EZxnf5XiccO+v8HmX0DVCfNY2n/B0Ech6lJb8omIiIi0Dg4HTJ4MVVWwYYNJTHk8ZhaViN2UlBIRkYBzdkJq4EC44YYvJaSKd8Kq/4KTG839dgPhsj9D0lV2hSwiIiISsBwOuO46837q88/hjTfM+6xhw+yOTFo7JaVERCSg7NhhluxdMCEFZiZU6QEIj4dBv4Ged4JTf9JERERELsThMDsYO52wdi28+aZ5v3XBDWRE/EDv4EVEJGBs2ABvv91AQqosF7L/Cf3mm3dUkQkw9jUzQyoq0e6wRURERIKCwwFTp5r3V2vWmPddNTX1NadE/E1JKRERsZ1lwaefwiefmPtDhsDMmeCszIPtj8Kep8FTCe0GQecZ5qSUibbGLCIiIhKMamtMuVywciUsWQIlJXDNNQ1sKCPiY0pKiYiIrTweeOcdWL/e3B87FiZecRTHxkdh77PgrjBPJE+AmC62xioiIiISChwOmDQJoqIgKwtWrYKiIjNLPTzc7uikNVFSSkREbFNdDf/5D+zaZd4czZhymuFhv4C3/lKfjOp4BQx4ADpdq6/vRERERLzE4TBfBrZrB4sXw/btZsbULbdAbKzd0Ulr8eXSsSIiIn5RUgIvvmgSUmFh8LWvwfCRUZD3kUlIdRwNEz6Aa1ZC6mQlpERERER8YOBAmDMHoqPh0CH429/g2DG7o5LWQjOlRETE77Kz4dVXLTq4V/K1zk/TdtJf6ZrRxnxXMvyPgAeSr1YiSkRERMQP0tLg9tvhX/+Ckyfhuedg1izo29fuyCTUKSklIiJ+Y1mwdk01uasWc0vCE3SJ/sw8UXUF8EPTVgFzEREREb/r2BG+/W1YtAhyc83xiitM7SmXy+7oJFQpKSUiIn5RVXyYvUueo1/lXxnV6SgAljMSR+atpl6UiIiIiNgqNhZuvbW++Pnq1WZJ31e+AvHxdkcnoUhJKRER8bn8gyfo+Gkm/RxVEAbVriTC+n4PR68fQFSS3eGJiASk8upySipLiIuMIzo82u5wRKSVcLng2muhWzdTAP3gQfjLX8zOfD172h2dhBoVOhcREe8rzYb9C/F4YPly+OuLCewpm8rByqso7PUy4V85iGPQQ0pIiYg0YEXuCm5cdCNtFrQh5YkU2ixow42LbmRl7kq7QxORVqRPH/jud6FTJzh92tSbevNNqKiwOzIJJZopJSIi3lFdCgf/A/sXQsFSAF75ZDw7D6YBsDVuEdOui9QWwyIiF/HMumf4wbs/wOV04bE8AHgsD2/tfovFOxfz9PSn+d5l37M7TBFpJdq3NwXQP/wQPvsMNmyAvXvhuus0a0q8Q0kpERFpPncV5H0IOYvg4GvgPg2AhYPs8gmczC8iMjKNadNg0KBIbaYnInIRK3JX8IN3f4CFRY2n5pznau9//53vMzBpIGO6jbEpShFpbcLCYOpU6NcP3ngDTpwws6aGDIHJkyFaq4ulBZSUEhGR5jv4f7Dqlrq71dG92Fh0KysPf4Pimm5kZsJ/Xa/CmCIijfH71b/H5XSdl5A6m8vp4g9r/qCklIj4XVoa3HknfPwxrFkDmzaZWVMTJ5oElVPFgaQZlJQSEZFLqy6FvI/g0OuQMBx6/8g83nk6tMmkquM0Vh75Oss2jwIcREXBtGthxAg0O0pEWoXHVj7Gk5892ezXW5bF0dKjlzyvxlPDazteI/WJVBwt+AV796i7uXfMvc1+vYi0TuHhZnZU7aypY8dMnal162DKFJO4EmkKJaVERKRhpQfg8Dtw5G3I/wQ8Vebxk5vrklLVtGVtwl6WLXVQVWUSUEOHwtVXo9pRItKq5JXmceTUEb+N15gE1sXkleZ5LRYRaX26djWzptauhWXL4OhReOEFk6y65hpTi0qkMZSUEhGR8314JRR+aZenNpmQOgO6fYXqavj8c1i5EkpLzTf1nTvDtGnmKCLS2qS0SSG1bWqzX9/YmVK1OrXp1KKZUiltUpr9WhERAJcLrrgCBg2CTz6B9eth+3bYtcss57vySiWn5NKUlBIRaa081XB8HeRlQdEmuPI/9WvtoruAwwWJV0LnGSYZFdeb6hoHn38OK1ZAWZk5tV07GD8eBg/WUj0Rab3uHXNvi5fD3bjoRt7a/VZdTamoaoirhJJIqAg354Q5w7i+9/X852v/8UbYIiItFhsLM2aYsg0ffAD79pkE1caNJmE1dix06GB3lBKolJQSEQBqaqCqCiorTfvLN48HLOvcm9N5/i08HCIizC0y0hxdLrt/OgGgphxOfG5mQBWugIJPoeZU/fMlOyG+r2kPeRhGPgMR5uutoiJY/7HZBvjsZNRVV5lklP4fi4i03Lwr5rF452LG5MCPV8OsXeCywO2Axb3h96NhdTc3P778x3aHKiJynuRk+OY3ITcXPv3UFEHftAk2b4b+/WHUKOjSRV9iyrmUlBIJQdXVcPq0SR6UlV28XVlpbm637+Jxuc5NUkVEQEyM+Vbl7FubNvXtmBjt4NFi5XkQ0Q5cUeb+F7+G7Q+fe05EAiRPhJSrISqp/vE26VgW7NtrClfu3m0SkWCmYV91lfnmS8koERHvubLblSwtn82VL7yM22kSUmCOM3fDDTthxc9v1s57IhLQunWDb3wDDh829aZ274YvvjC3Tp3MjKqBA82X2SJKSokEGbcbSkrMrbjY3Grbtcfy8ub3Hx5ubmFh9TeXyySIHI5zb5ZlZlCdfauuNkmuqiozw6o25vLypsXlcEB0NLRtC/Hx9be4uPp227ZKitQpPwon1sOJDWeO66H8MIx/D1KnmHM6joaoFEgcY9rJ46H9EHDUZ/8sCwoK6t84nDxZP0RmpnkT0bu3EoYiIj6xYgVXPboIAKfn3KfCz9y/6pGXYcYPYIwSUyIS2Dp3hv/6L1ME/bPPzHvLo0fNbn0ffGDqTg0aZBJVmj3VeikpJRJgLAtKS00yoPZ24oQ5FhWZ52pnrFyMy1U/4+js2Udnt2NiICrKzGCqncXkzWSD231ukqp2eWBV1bkztkpL69u1M7gsyxxPn4b8/Ib7dzjM7KraJFW7dufe4uPNzxRSPNXmFhZj7ud9BKvnmKTUeRxwajdwJinVeTrccOS8v/qWBYWFsG2buR07Vv9cZKR5wzBiBHTs6MsfTERE+P3vzR/w2m91GuJywR/+oKSUiASNTp1g1iy49lqznG/dOvPZZs0ac+vQwcycGjhQtadaIyWlRGxQU2MSTGcnnM6+VVdf/PVhYfUzhr58rG1HRtr/jYPLZW5RUU17ncdTn7T68iyws2eHud1w6pS5HTrUcF+xsfUJqi8nrdq1C+CklacGTu2F4m3n3k7thiGPQJ8z9USikk1CyuGEuL7QfhgkDDe39kMgvE19n2fNiCorg/37TSHK/fvNf89aYWHQo4dZ+9+7dwD/N2ptHnsMnnzS7igCigNI9HhwaOqehALLMlMILqWmBl57DVJT7f9DfxZdjyJedvfdcG/LNk8INDExMHq02bFv715Ta2rnTjh+HJYuNbfUVOjVy9w0g6p1UFJKxAdqZ/l8OdlUm4A6deris50cDpNESUgw9XvOvsXHm1/oofwL2uk0M6DatDEFExtiWSaxcnaiqqjo3FtlZf3sq8OHG+4nJub82VVn34+M9OEP6qmG0mwo3WuW1SUMNY8Xb4d3B4N1gW/KS3bVt+P6wjUrof1gCItt8HS328yEOnwYjhwxCbwvzz4LCzPL8/r3hz59fPxzS/Pk5Zn/gVLHAWgFr7RajUlg+ZGuRxEvy8uzOwKfcTigZ09zq6w0iamtW80XpUeOmNvSpeazQO156enmfbuEHiWlRJqpvLw++VG7tO7sW1XVxV8fEXF+0qn2fny8aiVdSu3SvTZtzHr1hlRUnP//pfZWW3urdonghT7rR0efm7CKiYGqqjC6djUz0mqLszf4xbDlqZ+dVFUEO/8AZTlwOtccy3LAOlNhvuedkPC0acd0NQkpV4zZDS9+AMT3N7d2/SGmW/0YzjBIHA1nvjw/edIsvzt+3ByPHTPvaRpaCZKSAt27m2RUt24qNhnwUlLM14dSxwI8Hg9Op5MQztNLa9HYmVK1AmwKga5HES9LSbE7Ar+IjDQ7OQ8ebL5I3r3b3PbtMyU+Nm40N4DERJOcSkszt7Zt7Y5evMFhWY2pTiMlJSXEx8dTXFxMXFycX8f2eDwUFBSQlJSEU1Oi/cKyTMKidslYQ4mniopL99O27bmJp7PboT7bKRhUVDQ8w6r21lBhdsuyKCsrIzY2lnBnJT1j36VNWB7to/JoF5VH27A82rgOE0MuhVFfITf5GaKiICa8hD5b48/vzxmNJ7YHNZ1uorrPAzgcZmaTdfowNWGdcHucuN0m1spKc6y91S5drL2VlV14Bl5UlMlnpKaaJF63biaZJhLM9PdRQs6NN8Jbb128plRYGFx/PfznP/6M7JJ0PYoElmC/JmtqICfHJKj27zez/r+sbdv697epqSZXHxurz1iBorE5lICdKfXUU0/x2GOPkZeXx+DBg/nTn/7EyJEjL3j+q6++yn333Ud2djY9e/bkkUceYdq0aXXPW5bFAw88wHPPPUdRURFjxozhmWeeoWfPnn76iSRQVFWZD++1H+RLSuqPte1Tpy7+frBWbKxJMJ293Kv2fny8ed8ogSsqCqIiakjuUFVfONxdAdn/hqrj1Jw+TnXpCWpOH8cqP4arOo8jTOH9wgdxOi2qy2uY3emm8zs+kxg6XZjLB5trH4xjauIPKXWnUFSdRnFNN05WdeeUu9OZRQ/A22d3coHpX5cQGWkKRHbsaI4dOpg/0AkJ+gMtIhLw5s2DxYsvfo7bDT/+sb8iEhGxRViYmdHfvbu5X1YGubmQnW2SVfn55jPbrl3mVis62syoOvumlSiBLSA/Mi9atIh58+bx7LPPMmrUKJ588kkmT57Mrl27SEpKOu/8VatWccstt7BgwQJmzJjBSy+9xKxZs9iwYQMDBgwA4NFHH+WPf/wjCxcuJCMjg/vuu4/Jkyezfft2oppahVkChsdjZoyUl9cfz97VraHbpYqIny021izROjvxdHZby51sZFlQXQLucqg+BTUl5n7tLTYNkq4y51afgnV3nvt8dTFUHjfHzG/B5S+c6dcDn90GZ35BfvmXZI/UHrQdV0FSUhxOZxusj8bjdrWnyplCBSmctjpxyt2JU+5uFMWnMahj/cymTdV/pNqCaidUO6DGBWEO86NYlvn3zJmaWrVF4mvbUVH1OyXWttu0Md8QxcWZY9u2moEnIhLUrrwSnn4avv/983fhCwszCamnn9bOeyLS6sTGQt++5saZiQa15TaPHjXHY8fM58HcXHM7W23N3trPc7Xvo2vLgdTetMGP/wXk8r1Ro0YxYsQI/vznP8OZqYddu3blhz/8IT//+c/PO3/27NmUlZXx9tv10wwuv/xyhgwZwrPPPotlWaSmpvKTn/yEEWQUeAAAEBlJREFUe+65B4Di4mKSk5N58cUXufnmmy8Zk5bvtZzHY95L1dTUH2tqzC+UxtxqP9ifnYC6VN2mCwkLM7904uLqP9CffaytFaSZThdgWeCpAk8luM8cPVXgPnOM7AgxZ2rfVJdC/sfnn1t7fsIwSLnanFtxDDb+BGpOg/v0+ce0m2Hw/5w5twD+7wJV0AEyboUrXjTtmnJ45SKVETtfB+PerP/ZPr0ewuMgogNEnrlFdIDoFDzRXSk4Heuz69GylFQSaaxQ+fsocp6VK+EPf4DXXzdvoJxOuOEGM0MqQBNSuh5FAktrvCarq01d1cLC+tuxY6YsR2MnJkREnJukOvtL4bOPX34sPNx8dmwl/6kbJWiX71VVVbF+/Xrmz59f95jT6WTSpEmsXr26wdesXr2aefPmnfPY5MmTWXxm+vOBAwfIy8tj0qRJdc/Hx8czatQoVq9e3aikVKjYutVMc/R46mdmXKzd2PO+nGhq6H7tLBBfOHv2SGysmS0SG3vhW0SEPvi3SNFmeG/ohZ/vNx+G/M60y4+aJM+F9PpRfVLKqoYDf7/wueVnFYB11SaZHBDW5kwSKR7C4kw7vv9Z50bB0MfN42ffapNOEe3rz3U46hNUDfF44HTBhZ9vIf27FBERxowxt9oCl3FxZk2KiIhcUHi4qQ//5Rrxtbt21+6IXlRkiqiffTt1yiSuqqrMjuknTjQvBqezPkEVFlbfrj2evRLiYse4OLj8cq/8Zwl4AZeUOnbsGG63m+Qv7QOfnJzMzp07G3xNXl5eg+fnndlGs/Z4sXO+rLKyksrKyrr7xcXFABQVFeHxZXalAR6Ph5KSEiIiIlqc5c7Kgrw8+z/1OhxW3fKkiAhzkYaHm3bt/S+3IyPN+7HajHR0dH0yqin/WcrLGy5gLU1QUoHzdP1dy+ECZwS4IsEZjlXuML/tAcrdOKKGg9M8hyvCnHvmvhXer/7cGqD7QxAWDc5oc3TFgCva1HyKTK4/17Jg8lHTz4UyObXnAnS6/fznLaACqCht9I/uzetRRFpG16O0CpGRZqeLs96XBiJdjyKBRdfk+WrLXXTr1vDzZ9ceLiszZWFqNxv68q2q6uy29z9fd+xo0aeP17v1q5KSEjhT3/tiAi4pFSgWLFjAQw89dN7jaWlptsQjEtjcQPmZG8DvztwaYxHwPR/GJiIiIiIiElzuvdfuCLzj1KlTxMefvwt5rYBLSnXs2BGXy0V+fv45j+fn55Py5Xl4Z6SkpFz0/Npjfn4+nTp1OuecIUOGNNjn/Pnzz1kS6PF4OHHiBB06dMDh5/U1JSUldO3alYMHD/q9npWInEvXo0jg0PUoEjh0PYoEFl2TYjfLsjh16hSpqakXPS/gklIREREMHz6crKwsZs2aBWcSQllZWdx1110NvuaKK64gKyuLu+++u+6xDz/8kCuuuAKAjIwMUlJSyMrKqktClZSU8Nlnn3HnnXc22GdkZCSRkZHnPNauXTuv/ZzNERcXp18oIgFC16NI4ND1KBI4dD2KBBZdk2Kni82QqhVwSSmAefPmceutt3LZZZcxcuRInnzyScrKypg7dy4Ac+bMoXPnzixYsACA//7v/2bcuHE88cQTTJ8+nZdffpnPP/+cv/71rwA4HA7uvvtufvvb39KzZ08yMjK47777SE1NrUt8iYiIiIiIiIiI/wRkUmr27NkUFhZy//33k5eXx5AhQ1iyZEldofLc3NxzirWNHj2al156iV/96lf84he/oGfPnixevJgBAwbUnfPTn/6UsrIyvvOd71BUVMSVV17JkiVLiIqKsuVnFBERERERERFpzRzWpUqhi+0qKytZsGAB8+fPP29JoYj4l65HkcCh61EkcOh6FAksuiYlWCgpJSIiIiIiIiIifudsxDkiIiIiIiIiIiJepaSUiIiIiIiIiIj4nZJSIiIiIiIiIiLid0pKBbinnnqK9PR0oqKiGDVqFGvXrrU7JJFWacGCBYwYMYK2bduSlJTErFmz2LVrl91hiQjw8MMP43A4uPvuu+0ORaRVOnz4MN/4xjfo0KED0dHRDBw4kM8//9zusERaHbfbzX333UdGRgbR0dF0796d3/zmN6iMtAQyJaUC2KJFi5g3bx4PPPAAGzZsYPDgwUyePJmCggK7QxNpdZYtW8YPfvAD1qxZw4cffkh1dTXXXnstZWVldocm0qqtW7eOv/zlLwwaNMjuUERapZMnT/L/27vfmKrqB47jnwt0xw0oMAO8S42tFCQnfzVhU7dYYsZITGfxgFuZPoDMyBbWSq2g2R/HiqkzHU/UpTNRR3MObxHISt2Ny3LBxSjrtl2hHmhRKsqlB7/f7ropqfzoHH6d92s7D873fs/5fu4DxvjsnC/5+fm65ZZbdPjwYX399dd69913lZCQYHY0wHI2btyoLVu2qK6uTp2dndq4caP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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calculate basic statistics\n", "mean_xn = np.mean(Data1)\n", "std1 = np.std(Data1, ddof=1) # Sample SD\n", "z_critical = norm.ppf(0.975) # 95% confidence level\n", "\n", "# Confidence interval for the mean (μ)\n", "ci_lower = mean_xn - z_critical * (std1 / np.sqrt(len(Data1)))\n", "ci_upper = mean_xn + z_critical * (std1 / np.sqrt(len(Data1)))\n", "\n", "# Prediction interval for Life Ladder\n", "pi_lower = mean_xn - z_critical * std1\n", "pi_upper = mean_xn + z_critical * std1\n", "\n", "# Prepare the KDE and prediction distribution\n", "x_vals = np.linspace(min(Data1), max(Data1), 1000)\n", "pdf = norm.pdf(x_vals, loc=mean_xn, scale=std1) # Prediction distribution (normal)\n", "\n", "# Plot the KDE\n", "plt.figure(figsize=(12, 7))\n", "sns.kdeplot(Data1, color=\"blue\", label=\"KDE of Data1\", alpha=0.5)\n", "\n", "# Overlay prediction distribution\n", "plt.plot(x_vals, pdf, color=\"orange\", linestyle=\"--\", label=\"Prediction Distribution\")\n", "\n", "# Plot intervals with shaded regions\n", "plt.hlines(y=0.01, xmin=ci_lower, xmax=ci_upper, color=\"green\", linewidth=2, label=\"Confidence Interval (μ)\")\n", "plt.hlines(y=0.005, xmin=pi_lower, xmax=pi_upper, color=\"red\", linewidth=2, label=\"Prediction Interval\")\n", "plt.fill_betweenx([0.01], ci_lower, ci_upper, color=\"green\", alpha=0.2)\n", "plt.fill_betweenx([0.005], pi_lower, pi_upper, color=\"red\", alpha=0.2)\n", "\n", "# Mark mean as a prominent dot for both intervals\n", "plt.scatter(mean_xn, 0.01, color=\"green\", zorder=5, s=50)\n", "plt.scatter(mean_xn, 0.005, color=\"red\", zorder=5, s=50)\n", "\n", "\n", "# Add labels, legend, and grid\n", "plt.title(\"KDE of Life Ladder: Confidence & Prediction Intervals\", fontsize=16)\n", "plt.ylabel(\"Density\", fontsize=14)\n", "plt.legend(fontsize=12)\n", "plt.grid(alpha=0.3)\n", "\n", "# Show the plot\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 7**" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "ALPHA = 0.05\n", "\n", "N = len(Data1)\n", "MU = Data1.mean()\n", "SIGMA = Data1.std()\n", "\n", "np.random.seed(111)\n", "YSample = norm.rvs(MU, SIGMA, size=N)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "def RelativeLogLikelihood(mu, sigma, data):\n", " n = len(data)\n", " sigma_hat = np.std(data)\n", "\n", " return n * (np.log(sigma_hat / sigma) + 0.5 * (1 - (np.mean(data**2) - 2 * mu * np.mean(data) + mu**2) / sigma**2))\n", "\n", "def RelativeLikelihood(mu, sigma, data):\n", " return np.exp(RelativeLogLikelihood(mu, sigma, data))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "def RelativeLogLikelihood(mu, sigma, data):\n", " n = len(data)\n", " sigma_hat = np.std(data)\n", "\n", " # Vectorized computation of the log-likelihood\n", " mean_data = np.mean(data)\n", " mean_squared_data = np.mean(data**2)\n", " \n", " return n * (np.log(sigma_hat / sigma) + 0.5 * (1 - (mean_squared_data - 2 * mu * mean_data + mu**2) / sigma**2))\n", "\n", "# Define RelativeLikelihood based on the log-likelihood\n", "def RelativeLikelihood(mu, sigma, data):\n", " # Vectorized calculation\n", " return np.exp(RelativeLogLikelihood(mu, sigma, data))\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "# Calculate the MLE of mu and sigma\n", "Mu_MLE = np.mean(YSample)\n", "Sigma_MLE = np.std(YSample)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "# Find confidence intervals for mu and sigma\n", "S = np.std(YSample, ddof=1)\n", "\n", "LowMu, UppMu = t.ppf(ALPHA/2, N-1, Mu_MLE, S/np.sqrt(N)), t.ppf(1 - ALPHA/2, N-1, Mu_MLE, S/np.sqrt(N))\n", "LowSigma, UppSigma = S * np.sqrt(N-1) / np.sqrt(chi2.ppf(1-ALPHA/2, N-1)), S * np.sqrt(N-1) / np.sqrt(chi2.ppf(ALPHA/2, N-1))\n", "\n", "# Find prediction interval for Y\n", "LowY, UppY = t.ppf(ALPHA/2, N-1, Mu_MLE, np.sqrt(S**2/N + S**2)), t.ppf(1 - ALPHA/2, N-1, Mu_MLE, np.sqrt(S**2/N + S**2))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "Probabilities = np.array([0.1, 0.5, 0.75, 0.89, 0.95])\n", "Probabilities = Probabilities[::-1] # We need to write the probabilities in a dicreasing order\n", "\n", "Levels = np.exp(-0.5 * chi2.ppf(Probabilities, 2))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "mu_vect = np.linspace(5.3, 6, 100) # Extending the range to cover 5.62\n", "sigma_vect = np.linspace(0.9, 1.3, 100) # Adjusting to cover 1.139\n", "\n", "mu_grid, sigma_grid = np.meshgrid(mu_vect, sigma_vect)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(6, 5))\n", "\n", "cnt = ax.contour(mu_grid, sigma_grid, RelativeLikelihood(mu_grid, sigma_grid, YSample), Levels)\n", "ax.scatter(x=Mu_MLE, y=Sigma_MLE, color='k')\n", "\n", "ax.plot([LowMu, LowMu, UppMu, UppMu, LowMu], [LowSigma, UppSigma, UppSigma, LowSigma, LowSigma], color='r', ls='--')\n", "\n", "ax.set_title(r\"Confidence regions for $\\mu$ and $\\sigma$\")\n", "ax.set_xlabel(r\"$\\mu$\")\n", "ax.set_ylabel(r\"$\\sigma$\")\n", "\n", "_, labels = cnt.legend_elements()\n", "ax.legend(_, Probabilities, loc=\"upper right\", frameon=False)\n", "\n", "ax.spines[['right', 'top']].set_visible(False)\n", "\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_vect = np.linspace(4, 16, 100)\n", "\n", "ax = sns.kdeplot(YSample, color=\"rebeccapurple\", label=\"KDE\")\n", "ax.plot(y_vect, t.pdf(y_vect, N-1, Mu_MLE, np.sqrt(S**2/N + S**2)), color=\"k\", ls=\"--\", label=\"Prediction distribution\")\n", "\n", "ax.hlines(0.025, LowY, UppY, color='red', label=\"Prediction interval\")\n", "ax.hlines(0.05, LowMu, UppMu, color='darkorange', label=\"Confidence interval\")\n", "ax.scatter(Mu_MLE, 0.025, color='red')\n", "ax.scatter(Mu_MLE, 0.05, color='darkorange')\n", "\n", "ax.legend(frameon=False)\n", "ax.spines[['right', 'top']].set_visible(False)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 2 }