{ "cells": [ { "cell_type": "markdown", "id": "56385a99-ef52-4451-80f5-46bbcf9a688a", "metadata": {}, "source": [ "# A Data Science Regression Case Study Project" ] }, { "cell_type": "markdown", "id": "2625b7eb-58ac-4535-8312-5a0e48c26821", "metadata": {}, "source": [ "A comprehensive roadmap towards approaching a data science regression project, covering all major steps - from exploratory data analysis and data preprocessing to hyperparameter tuning - to create the most effective predictive model. More specifically, the following steps are demonstrated:\n", "* Exploratory Data Analysis (EDA)\n", "* dataset splitting early on to avoid data leakage\n", "* missing values imputation\n", "* encoding (categorical -> numerical data)\n", "* feature scaling, encoding, unskewing\n", "* feature extraction\n", "* automated hyperparameter tuning using GridSearchCV with pipelines and parameter grids\n", "\n", "We will use the Student Performance dataset (https://archive.ics.uci.edu/dataset/320/student+performance) since it has various preprocessing needs.\n", "\n", "This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. In this study we will use the maths related data only.\n", "\n", "Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details)." ] }, { "cell_type": "code", "execution_count": 1, "id": "d120c477-487e-481a-9f01-cf4d3613f3ba", "metadata": {}, "outputs": [], "source": [ "# Import libraries\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split, GridSearchCV\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.preprocessing import StandardScaler, RobustScaler, PowerTransformer\n", "from scipy.stats import shapiro, anderson, kstest, normaltest, jarque_bera, normaltest, boxcox, yeojohnson\n", "from statsmodels.stats.diagnostic import lilliefors\n", "from scipy.special import inv_boxcox\n", "from category_encoders import OrdinalEncoder, OneHotEncoder\n", "from sklearn.decomposition import PCA, FactorAnalysis\n", "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n", "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n", "from sklearn.feature_selection import SelectKBest\n", "from sklearn.compose import ColumnTransformer, TransformedTargetRegressor\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n", "from xgboost import XGBRegressor\n", "from catboost import CatBoostRegressor\n", "from sklearn.svm import SVR\n", "from sklearn.neighbors import KNeighborsRegressor\n", "from sklearn.tree import DecisionTreeRegressor\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.metrics import mean_squared_error, r2_score" ] }, { "cell_type": "code", "execution_count": 2, "id": "4a7305f2-1091-4978-888e-fb442c66cf5c", "metadata": {}, "outputs": [], "source": [ "# Load dataset\n", "url = \"students-mat.csv\"\n", "df = pd.read_csv(url)" ] }, { "cell_type": "markdown", "id": "2d8ebe5c-7d48-4d1d-acc9-58ea829f1f41", "metadata": {}, "source": [ "## Exploratory Data Analysis" ] }, { "cell_type": "code", "execution_count": 3, "id": "9bcedd15-7b3b-4fa5-a182-c17d59f19fee", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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schoolsexageaddressfamsizePstatusMeduFeduMjobFjob...famrelfreetimegooutDalcWalchealthabsencesG1G2G3
0GPF18.0UGT3A4.04at_hometeacher...4341136566
1GPF17.0UGT3T1.01at_homeother...5331134556
2GPF15.0ULE3T1.01at_homeother...432233107810
3GPF15.0UGT3T4.02healthservices...3221152151415
4GPF16.0UGT3T3.03otherother...432125461010
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5 rows × 33 columns

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" ], "text/plain": [ " school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n", "0 GP F 18.0 U GT3 A 4.0 4 at_home teacher \n", "1 GP F 17.0 U GT3 T 1.0 1 at_home other \n", "2 GP F 15.0 U LE3 T 1.0 1 at_home other \n", "3 GP F 15.0 U GT3 T 4.0 2 health services \n", "4 GP F 16.0 U GT3 T 3.0 3 other other \n", "\n", " ... famrel freetime goout Dalc Walc health absences G1 G2 G3 \n", "0 ... 4 3 4 1 1 3 6 5 6 6 \n", "1 ... 5 3 3 1 1 3 4 5 5 6 \n", "2 ... 4 3 2 2 3 3 10 7 8 10 \n", "3 ... 3 2 2 1 1 5 2 15 14 15 \n", "4 ... 4 3 2 1 2 5 4 6 10 10 \n", "\n", "[5 rows x 33 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# print 5 first observations to start getting familiar with the dataset\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "07b9a4fb-33a7-4a0d-8300-986ce9a05787", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "school object\n", "sex object\n", "age float64\n", "address object\n", "famsize object\n", "Pstatus object\n", "Medu float64\n", "Fedu int64\n", "Mjob object\n", "Fjob object\n", "reason object\n", "guardian object\n", "traveltime float64\n", "studytime float64\n", "failures int64\n", "schoolsup object\n", "famsup object\n", "paid object\n", "activities object\n", "nursery object\n", "higher object\n", "internet object\n", "romantic object\n", "famrel int64\n", "freetime int64\n", "goout int64\n", "Dalc int64\n", "Walc int64\n", "health int64\n", "absences int64\n", "G1 int64\n", "G2 int64\n", "G3 int64\n", "dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# print column types\n", "df.dtypes" ] }, { "cell_type": "code", "execution_count": 5, "id": "f88a43a5-400a-4b7d-a6a9-26aef857d796", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(395, 33)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 6, "id": "eb960a5e-6cb4-450c-bc19-00a59ee4dc3d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing Values per Column:\n" ] }, { "data": { "text/plain": [ "school 0\n", "sex 0\n", "age 28\n", "address 0\n", "famsize 2\n", "Pstatus 11\n", "Medu 2\n", "Fedu 0\n", "Mjob 4\n", "Fjob 0\n", "reason 10\n", "guardian 0\n", "traveltime 9\n", "studytime 5\n", "failures 0\n", "schoolsup 0\n", "famsup 0\n", "paid 0\n", "activities 7\n", "nursery 0\n", "higher 0\n", "internet 0\n", "romantic 17\n", "famrel 0\n", "freetime 0\n", "goout 0\n", "Dalc 0\n", "Walc 0\n", "health 0\n", "absences 0\n", "G1 0\n", "G2 0\n", "G3 0\n", "dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Missing values\n", "missing_values = df.isnull().sum()\n", "print(\"Missing Values per Column:\")\n", "missing_values" ] }, { "cell_type": "code", "execution_count": 7, "id": "f768622a-713e-4495-aec5-f209e0f777c6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Summary Statistics:\n" ] }, { "data": { "text/html": [ "
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ageMeduFedutraveltimestudytimefailuresfamrelfreetimegooutDalcWalchealthabsencesG1G2G3
count367.000000393.000000395.000000386.000000390.000000395.000000395.000000395.000000395.000000395.000000395.000000395.000000395.000000395.000000395.000000395.000000
mean16.7057222.7531812.5215191.4481872.0384620.3341773.9443043.2354433.1088611.4810132.2911393.5544305.70886110.90886110.71392410.415190
std1.2503751.0938821.0882010.6977960.8429700.7436510.8966590.9988621.1132780.8907411.2878971.3903038.0030963.3191953.7615054.581443
min15.0000000.0000000.0000001.0000001.0000000.0000001.0000001.0000001.0000001.0000001.0000001.0000000.0000003.0000000.0000000.000000
25%16.0000002.0000002.0000001.0000001.0000000.0000004.0000003.0000002.0000001.0000001.0000003.0000000.0000008.0000009.0000008.000000
50%17.0000003.0000002.0000001.0000002.0000000.0000004.0000003.0000003.0000001.0000002.0000004.0000004.00000011.00000011.00000011.000000
75%18.0000004.0000003.0000002.0000002.0000000.0000005.0000004.0000004.0000002.0000003.0000005.0000008.00000013.00000013.00000014.000000
max21.0000004.0000004.0000004.0000004.0000003.0000005.0000005.0000005.0000005.0000005.0000005.00000075.00000019.00000019.00000020.000000
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" ], "text/plain": [ " age Medu Fedu traveltime studytime failures \\\n", "count 367.000000 393.000000 395.000000 386.000000 390.000000 395.000000 \n", "mean 16.705722 2.753181 2.521519 1.448187 2.038462 0.334177 \n", "std 1.250375 1.093882 1.088201 0.697796 0.842970 0.743651 \n", "min 15.000000 0.000000 0.000000 1.000000 1.000000 0.000000 \n", "25% 16.000000 2.000000 2.000000 1.000000 1.000000 0.000000 \n", "50% 17.000000 3.000000 2.000000 1.000000 2.000000 0.000000 \n", "75% 18.000000 4.000000 3.000000 2.000000 2.000000 0.000000 \n", "max 21.000000 4.000000 4.000000 4.000000 4.000000 3.000000 \n", "\n", " famrel freetime goout Dalc Walc health \\\n", "count 395.000000 395.000000 395.000000 395.000000 395.000000 395.000000 \n", "mean 3.944304 3.235443 3.108861 1.481013 2.291139 3.554430 \n", "std 0.896659 0.998862 1.113278 0.890741 1.287897 1.390303 \n", "min 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n", "25% 4.000000 3.000000 2.000000 1.000000 1.000000 3.000000 \n", "50% 4.000000 3.000000 3.000000 1.000000 2.000000 4.000000 \n", "75% 5.000000 4.000000 4.000000 2.000000 3.000000 5.000000 \n", "max 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 \n", "\n", " absences G1 G2 G3 \n", "count 395.000000 395.000000 395.000000 395.000000 \n", "mean 5.708861 10.908861 10.713924 10.415190 \n", "std 8.003096 3.319195 3.761505 4.581443 \n", "min 0.000000 3.000000 0.000000 0.000000 \n", "25% 0.000000 8.000000 9.000000 8.000000 \n", "50% 4.000000 11.000000 11.000000 11.000000 \n", "75% 8.000000 13.000000 13.000000 14.000000 \n", "max 75.000000 19.000000 19.000000 20.000000 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Summary statistics for numerical features\n", "print(\"Summary Statistics:\")\n", "df.describe()" ] }, { "cell_type": "markdown", "id": "b2b9d64d-d7a8-4104-8cd4-f7187cfd60d9", "metadata": {}, "source": [ "#### Target Variable Analysis (G3)\n", "\n", "The dataset contains the final grade (G3) as the primary target variable. We can perform the following tasks as part of the EDA:\n", "\n", "* Distribution of G3:\n", " * Plot a histogram or density plot to observe the grade distribution.\n", " * Check for normality, skewness and outliers.\n", "* Bin G3 into categories:\n", " * Create categories like \"Low,\" \"Average,\" and \"High\" performance to analyze patterns." ] }, { "cell_type": "code", "execution_count": 8, "id": "9ddb7de8-310b-45c5-9e2e-736884f232dc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Frequency')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Distribution of the target variable\n", "fig,ax = plt.subplots(figsize=(6,3))\n", "sns.histplot(df['G3'], kde=True)\n", "ax.set_title(\"Distribution of Final Grades (G3)\")\n", "ax.set_xlabel(\"Grade\")\n", "ax.set_ylabel(\"Frequency\")" ] }, { "cell_type": "markdown", "id": "c3dc263d-df3c-4ef8-9261-25de1c06c38b", "metadata": {}, "source": [ "The target variable does not seem to involve outliers. This visual observation can be also confirmed by the following boxplot:" ] }, { "cell_type": "code", "execution_count": 9, "id": "ee5cd532-52fa-49c5-b85b-849d3c17859b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(figsize=(6,3))\n", "sns.boxplot(data=df, y='G3')" ] }, { "cell_type": "markdown", "id": "61b9338f-384e-4ded-96ed-6c3b3e263bac", "metadata": {}, "source": [ "The target variable can be said that it does not follow the normal distribution based on the output of the normality tests shown below. All of them are" ] }, { "cell_type": "code", "execution_count": 10, "id": "6cd1b5c5-8953-4118-965a-2b279d485831", "metadata": {}, "outputs": [], "source": [ "def run_normality_tests(data):\n", " \"\"\"\n", " Perform various normality tests (Shapiro-Wilk, Anderson-Darling, Kolmogorov-Smirnov, D'Agostino and Pearson, Jarque-Bera, and Lilliefors) on a given dataset.\n", " Choosing the Right Test:\n", " * For small sample sizes (< 50), the Shapiro-Wilk Test is recommended.\n", " * For larger samples, use the D’Agostino and Pearson’s Test or Jarque-Bera Test.\n", " * If you need critical values, use the Anderson-Darling Test.\n", " * Use the Kolmogorov-Smirnov Test or Lilliefors Test for additional validation, especially when comparing with a reference distribution.\n", " \n", " All tests compute a p-value. The p-value (probability value) is a statistical measure that helps you determine whether the observed data is consistent with a \n", " null hypothesis. In the context of normality tests, the null hypothesis is usually: \"The data follows a normal distribution.\"\n", " \n", " How to Interpret the p-value:\n", " * High p-value (>= 0.05): There is not enough evidence to reject the null hypothesis. This suggests that the data might follow a normal distribution.\n", " * Low p-value (< 0.05): The null hypothesis is rejected. This indicates that the data does not follow a normal distribution.\n", " \n", " In Practice\n", " * If a test returns a p-value of 0.03, it means there is a 3% probability that the data could have arisen under the assumption of a normal distribution. In this case, you would reject the null hypothesis (assuming a 5% significance threshold).\n", " * If a test returns a p-value of 0.2, it suggests there is no strong evidence against the data being normal.\n", "\n", " Caveats\n", " * A high p-value does not prove normality, it simply suggests that the data is not inconsistent with a normal distribution.\n", " * A low p-value indicates a deviation from normality but does not specify the nature of that deviation (e.g., skewness, kurtosis).\n", "\n", " Parameters:\n", " data (array-like): The dataset to test for normality.\n", "\n", " Returns:\n", " dict: A dictionary containing test results with the test name, statistic, p-value, and whether the data is considered normal at a 5% significance level.\n", " \"\"\"\n", " results = {}\n", "\n", " # Shapiro-Wilk Test\n", " try:\n", " stat, p = shapiro(data)\n", " results['Shapiro-Wilk Test'] = {'Statistic': stat, 'p-value': p, 'Normal': p > 0.05}\n", " except Exception as e:\n", " results['Shapiro-Wilk Test'] = {'Error': str(e)}\n", "\n", " # Anderson-Darling Test\n", " try:\n", " result = anderson(data, dist='norm')\n", " results['Anderson-Darling Test'] = {\n", " 'Statistic': result.statistic,\n", " 'Critical Values': result.critical_values,\n", " 'Significance Levels': result.significance_level,\n", " 'Normal': result.statistic < result.critical_values[2] # At 5% level\n", " }\n", " except Exception as e:\n", " results['Anderson-Darling Test'] = {'Error': str(e)}\n", "\n", " # Kolmogorov-Smirnov Test\n", " try:\n", " standardized_data = (data - np.mean(data)) / np.std(data)\n", " stat, p = kstest(standardized_data, 'norm')\n", " results['Kolmogorov-Smirnov Test'] = {'Statistic': stat, 'p-value': p, 'Normal': p > 0.05}\n", " except Exception as e:\n", " results['Kolmogorov-Smirnov Test'] = {'Error': str(e)}\n", "\n", " # D’Agostino and Pearson’s Test\n", " try:\n", " stat, p = normaltest(data)\n", " results['D’Agostino and Pearson’s Test'] = {'Statistic': stat, 'p-value': p, 'Normal': p > 0.05}\n", " except Exception as e:\n", " results['D’Agostino and Pearson’s Test'] = {'Error': str(e)}\n", "\n", " # Jarque-Bera Test\n", " try:\n", " stat, p = jarque_bera(data)\n", " results['Jarque-Bera Test'] = {'Statistic': stat, 'p-value': p, 'Normal': p > 0.05}\n", " except Exception as e:\n", " results['Jarque-Bera Test'] = {'Error': str(e)}\n", "\n", " # Lilliefors Test\n", " try:\n", " stat, p = lilliefors(data)\n", " results['Lilliefors Test'] = {'Statistic': stat, 'p-value': p, 'Normal': p > 0.05}\n", " except Exception as e:\n", " results['Lilliefors Test'] = {'Error': str(e)}\n", "\n", " return results" ] }, { "cell_type": "code", "execution_count": 11, "id": "aa1c3165-5db9-4260-91e3-129431943565", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shapiro-Wilk Test: {'Statistic': 0.928729849872698, 'p-value': 8.835916843805374e-13, 'Normal': False}\n", "Anderson-Darling Test: {'Statistic': 8.30320458141631, 'Critical Values': array([0.57 , 0.65 , 0.779, 0.909, 1.081]), 'Significance Levels': array([15. , 10. , 5. , 2.5, 1. ]), 'Normal': False}\n", "Kolmogorov-Smirnov Test: {'Statistic': 0.134735987568745, 'p-value': 1.0269383080656354e-06, 'Normal': False}\n", "D’Agostino and Pearson’s Test: {'Statistic': 32.05917094576832, 'p-value': 1.092545372561713e-07, 'Normal': False}\n", "Jarque-Bera Test: {'Statistic': 37.48826984009276, 'p-value': 7.236451174084543e-09, 'Normal': False}\n", "Lilliefors Test: {'Statistic': 0.1347816508803158, 'p-value': 0.0009999999999998899, 'Normal': False}\n" ] } ], "source": [ "results = run_normality_tests(df['G3'])\n", "for test, result in results.items():\n", " print(f\"{test}: {result}\")" ] }, { "cell_type": "markdown", "id": "72fa70f2-2d9b-4615-8a69-82c91d65f78d", "metadata": {}, "source": [ "From the aforementioned resuts, **G3 deviates from normality**." ] }, { "cell_type": "markdown", "id": "e2f1de86-39bf-4d42-9256-fa57d4e82784", "metadata": {}, "source": [ "From the histogram and the kernel density estimate (KDE) curve shown above, we can say that the distribution appears to be fairly symmetric around its central peak, which is approximately 10-12. There are slight tails on both ends, but no extreme elongation on either side. The distribution is approximately symmetric, suggesting that the skewness value would be close to zero. However, the exact skewness value would need to be calculated. Below, we are using the .skew() function to reveal if the target variable is skewed:\n", "\n", "* If the skewness is between -0.5 and 0.5, the data are fairly symmetrical\n", "* If the skewness is between -1 and -0.5 or between 0.5 and 1, the data are moderately left-skewed or right-skewed respectively\n", "* If the skewness is less than -1 or greater than 1, the data are highly left-skewed or right-skewed respectively" ] }, { "cell_type": "code", "execution_count": 12, "id": "19b55a30-be14-4d07-8f2f-b56570e8ccd6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "skewness: -0.7326723530443435\n" ] } ], "source": [ "print('skewness:', df['G3'].skew())" ] }, { "cell_type": "markdown", "id": "932fd966-75f6-4e26-8acb-515f44a56cd2", "metadata": {}, "source": [ "As shown above, the **target variable is moderately left-skewed**, therefore unskewing techniques such as Log, Sqrt, Box-Cox and Yeo-Johnson can be applied. Only the latter technique can be used if the variable to be transformed contains both negative, zero and positive values." ] }, { "cell_type": "markdown", "id": "87e76d83-fe80-4a26-958f-f569aa24186a", "metadata": {}, "source": [ "#### Categorical Features Analysis" ] }, { "cell_type": "code", "execution_count": 13, "id": "1e1f03fa-38c5-4b96-a290-8ddb0fbc5809", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "categorical_features = ['school', 'sex', 'address', 'famsize', 'Pstatus', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery', 'higher', 'internet', 'romantic']\n", "nrows, ncols = 4,4\n", "fig, axs = plt.subplots(nrows, ncols, figsize=(18,21))\n", "i,j = 0,0\n", "for feature in categorical_features:\n", " sns.countplot(x=feature, data=df, ax=axs[i,j])\n", " axs[i,j].set_title(f\"Distribution of {feature}\")\n", " j = j + 1\n", " if j % ncols == 0:\n", " i = i + 1\n", " j = 0" ] }, { "cell_type": "markdown", "id": "3d4f50f4-3a6e-4bc8-be98-0f667236a275", "metadata": {}, "source": [ "The bar plots shown above depict the distributions of several categorical variables in the dataset. Here are observations and insights for each feature:\n", "\n", "1. School (school)\n", " * GP (Gabriel Pereira) has significantly more students than MS (Mousinho da Silveira).\n", " * This imbalance might affect analyses involving the school variable. Stratified sampling might be necessary for fair comparisons.\n", "2. Sex (sex)\n", " * The distribution of males (M) and females (F) is relatively balanced, with a slight majority of female students.\n", " * This balance is good for avoiding gender-related biases in analysis or modeling.\n", "3. Address (address)\n", " * A large majority of students live in urban (U) areas compared to rural (R) areas.\n", " * The dataset's findings may reflect urban education trends more than rural ones.\n", "4. Family Size (famsize)\n", " * Students with families greater than 3 members (GT3) are more frequent than those with families of 3 or fewer (LE3).\n", " * Family size could be an interesting factor to explore when analyzing its impact on student performance.\n", "5. Parental Status (Pstatus)\n", " * Most students' parents are living together (A), while a smaller proportion have separated parents (T).\n", " * This variable might be useful for exploring how family dynamics impact academic performance.\n", "6. School Support (schoolsup)\n", " * Most students do not receive additional school support (no), but a noticeable minority does (yes).\n", " * Investigating the performance difference between students with and without school support could be valuable.\n", "7. Family Support (famsup)\n", " * Similar to schoolsup, most students do not receive family support (no), though a significant proportion does (yes).\n", " * A comparison between students with and without family support could yield insights into its importance for academic outcomes.\n", "8. Paid Extra Classes (paid)\n", " * The distribution is relatively balanced, but slightly more students do not take paid extra classes (no).\n", " * Analysis could focus on whether paid classes lead to better academic performance (e.g., higher G3 scores).\n", "9. Extracurricular Activities (activities)\n", " * The number of students participating in extracurricular activities (yes) is roughly equal to those not participating (no).\n", " * This variable can be useful for understanding the role of extracurricular engagement on academic outcomes.\n", "10. Nursery School Attendance (nursery)\n", " * The majority of students attended nursery school (yes), while a minority did not (no).\n", " * It could be interesting to analyze whether early education correlates with better grades or other variables.\n", "11. Higher Education Intentions (higher)\n", " * Almost all students aim for higher education (yes), with very few not planning to continue education (no).\n", " * This high proportion could reduce variability, making it less impactful as a predictive feature.\n", "12. Internet Access (internet)\n", " * Most students have internet access at home (yes), while a smaller group does not (no).\n", " * Exploring the relationship between internet access and academic performance could provide insights, particularly in terms of access to learning resources.\n", "13. Romantic Relationship (romantic)\n", " * A majority of students are not in a romantic relationship (no), while a smaller proportion are (yes).\n", " * This could be explored to determine if romantic relationships impact academic performance or study habits.\n", "\n", "*General Observations:*\n", "\n", "* Imbalanced Features: Some features (e.g., higher, nursery, address, Pstatus) are highly imbalanced, which may reduce their usefulness for certain analyses or predictive modeling.\n", "* Visualization Options: Grouped bar plots or stacked plots could be used to visualize interactions between categorical features (e.g., schoolsup vs famsup).\n", "* Insights from Interactions: Exploring the relationships between these categorical features and the target variable (G3) could reveal interesting patterns. In order to elaborate more on this task, we can use the numerical version of the target variable or provide a categorization of students into dinstict categories (using binning on the G3 -- see below) thus making it easier to spot broad trends or patterns." ] }, { "cell_type": "markdown", "id": "2276393e-10c3-46bf-8b08-aba4727c9e11", "metadata": {}, "source": [ "#### Numerical Features Analysis" ] }, { "cell_type": "code", "execution_count": 14, "id": "40196f0c-5aaf-4f44-b60f-9c0dfd45e045", "metadata": {}, "outputs": [ { "data": { "image/png": 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9NQC0f5eUJJekbt266eOPP1ZZWZkOHTokm82mtLQ0RUZGuiI+AEAb0V9fuq6RwZKk/Ip62Ww2GYbh4YgAdAT01wDg/eirAaB9u+QkeYvIyEhdddVVzowFAOAC9NcXLzbMLJOfobpGq8prGxUZGujpkAB0IPTXAOD96KsBoH26pI07AQBoz/z9/BQfbq9FfrKizsPRAAAAAAAAdyBJDgDAdyRF2EuunCyv93AkAAAAAADAHUiSAwDwHYmdgyQxkxwAAAAAgI6CJDkAAN/RMpO8vLZRtQ1NHo4GAAAAAAC4GklyAAC+IyjApKjTG3bmV1ByBQAAAACA9o4kOQAA35MUYS+5kk9dcgAAAAAA2j2S5AAAfE9i59Obd1KXHAAAAACAdo8kOQAA39Myk7yo0qIma7OHowEAAAAAAK7k7+kAgAupa7DqWGmNKuubFBTgp9SYUIUFBXg6LADtWERwgEICTaptsKqwyqIup2eWAwAAAACA9ockObyWtdmmLUdPafPRMlltNsf7aw4Wq09CuEakxyrQn8UQAJzPMAwlRgTpcHGN8svrSJIDAAAAANCOkWGEV2q0Nuvd7Se0KeeUrDabYjoFqk9imBIjgmSzSXvzK/XW5jxV1Td6OlQA7VRihD0xXlDJ5p0AAAAAALRnzCSH17E22/R+1kkdL69ToMlPN/aOU3p8JxmGIUk6UVanT3bn61Rtg97LOqm7BndVUIDJw1EDaG/iw82SpMJKi4cjAQAAAAAArsRMcnid9YdKHAnyyZlJ6pUQ5kiQS1KXyGBNGZKsULNJpTUN+nR3gWzfKccCAM4QF2bfvLPa0qQaS5OHowEAAAAAAK5CkhxeJaekRll55ZKk8f3iHeUOvi88OEC3Degik5+hY6dqtfN4hRujBNARBPr7KSokUJJUWEXJFQAAAAAA2iuS5PAajdZmfXmgSJKUmdxZPWI7nbd9bJhZ110RI8k++7yWiZ4AnIySKwAAAAAAtH/UJIfX+CbnlKrqmxQW5K9rekZf1GcGdI3QoaJqnSiv0+5y6pIDcK748CDtK6hSIZt3AgAAAB1Gbm6uSkpKLvs6MTExSklJcUJEAFyNJDm8QnV9k7afLrMyIj1WAaaLW+RgGIZuSI/Rm9/kKa/WpMCk3i6MEkBHEx9ur0teVGmRzWZrtT8CAAAAgPYnNzdXvfv0UV1t7WVfKzgkRPv37SNRDvgAkuTwCpuPnpK12abEiCD1iAm9pM/GhQWpb2K49uZXqvN197koQgAdUUynQPkZUl2jVVX1TQoPDvB0SAAAAABcqKSkRHW1tbr/F79XfErPNl+nMPewlj77pEpKSkiSAz6AJDk8rqq+UbtP2jfeHN4zuk0zNYemRmlffoWCUwfpYGmDBjk7SAAdkr/JTzGdzCqqsqiwsp4kOQAAANBBxKf0VNe0fp4OA4CbeP3Gnd27d5dhGGccjz76qCRp2rRpZ5wbNmyYh6PGpcjKK1ezTeraOVhdI0PadI3w4AClhDZLkv6xt9qZ4QHo4OLYvBNAO8KzNdC+WJttOlpao4OFVSqu4lkFAIC28vok+ebNm5Wfn+84Vq1aJUn6wQ9+4Ghz0003tWrz8ccfeypcXCJLk1W7T1RKkgZ1i7ysa/UKt8pma9bWfIuOFJMoB+AcLXXJ2bwTQHvAszXQfuTXGXr9Xzl6L+ukPtldoGXf5OrDnSdVY2nydGhwkhMnTuiHP/yhoqOjFRISooEDB2rr1q2O8zabTXPmzFFSUpKCg4M1cuRI7dmzx4MRA4Dv8vokeWxsrBISEhzHhx9+qJ49e2rEiBGONmazuVWbqKgoD0aMS7HnZKUarM2KCglU9+i2zSJvERYg1R3eIkl6Y+MxZ4QHAIoPO715Z5V9804A8GU8WwPtQ2i/G7Wx2F81DVYFB5iUFBEkP0M6XFyjf249rroGq6dDxGUqKyvTtddeq4CAAH3yySfau3evnn/+eXXu3NnR5rnnntMLL7ygl156SZs3b1ZCQoLGjh2rqqoqzwUOAD7K65Pk39XQ0KAlS5boJz/5Sau61WvWrFFcXJzS09P10EMPqaio6LzXsVgsqqysbHXA/Ww2m3Yet9ciH5jSuU21yL+vauv7kqR/bj2uqvrGy74eAESHBsrfz1CDtVlltfQrANoPZz1bSzxfA+50+FSjoic8LpsM9UkM04PXpeoHQ5J1z1UpCgvyV3ldoz7YeVLWZgb3fdmzzz6r5ORkvf7667r66qvVvXt3jR49Wj172jeStNlsWrBggZ5++mndcccdysjI0OLFi1VbW6tly5Z5OHoA8D0+lSR/9913VV5ermnTpjnemzBhgpYuXaovvvhCzz//vDZv3qwbb7xRFsu567HNnz9fERERjiM5OdkN0eP7jpfVqaKuUYEmP/WKD3PKNeuPZqlLmEnVlia9v+OkU64JoGPz8zMUG2avS15EyRUA7Yiznq0lnq8Bd6ltaNILm8pkmAKUFNyssX3iZfKzD3LFhpk1eWAXmf39lF9Rr+25ZR6OFpfj/fff15AhQ/SDH/xAcXFxyszM1F//+lfH+ZycHBUUFGjcuHGO98xms0aMGKENGzac87oMagLA2flUkvzVV1/VhAkTlJSU5Hjv7rvv1i233KKMjAxNmjRJn3zyiQ4ePKiPPvronNeZPXu2KioqHEdeXp47wsf37D5hn0XeKyFMgf7O+19xbA972Za/b+a/KwDniGtJklezIRaA9sNZz9YSz9eAu7z6VY7yq61qqirR4KimM1bjRoUGakR6rCTp65xTqqhjFZyvOnLkiF5++WWlpaXps88+009/+lM98cQTeuONNyRJBQUFkqT4+PhWn4uPj3ecOxsGNQHg7HwmSX7s2DGtXr1a//Zv/3bedomJierWrZuys7PP2cZsNis8PLzVAfeqb7Tq0OnNNft3iXDqtUd0C1aAydCO4xXaX8CoOIDL1zKTvLiSJDmA9sGZz9YSz9eAO5RWW/SXdUckSWVfvqZA09nb9U4IU9fIYDU127TxSKkbI4QzNTc3a9CgQZo3b54yMzP18MMP66GHHtLLL7/cqt33B0psNtt5S5kyqAkAZ+czSfLXX39dcXFxuuWWW87brrS0VHl5eUpMTHRTZGiL7KJqNdukmE6BjuSTs0QEmTSmj300/e+bjzv12gA6priWzTur2bwTQPvAszXge/689rCqLU3qEemv2n1fnbOdYRi67ooYSdLBgiqV1za4K0Q4UWJiovr27dvqvT59+ig3N1eSlJCQIElnzBovKio6Y3b5dzGoCQBn5xNJ8ubmZr3++uuaOnWq/P39He9XV1dr1qxZ2rhxo44ePao1a9Zo0qRJiomJ0e233+7BiHEhBwrsu233SnBOLfLvu2twV0nSh2xYA8AJokIDZTIMNTQ1q7K+ydPhAMBl4dka8D1V9Y168xv7jN97M8Iknf83Tnx4kLpFh8gmacsxapP7omuvvVYHDhxo9d7BgwfVrVs3SVJqaqoSEhK0atUqx/mGhgatXbtWw4cPd2usANAe+ESSfPXq1crNzdVPfvKTVu+bTCbt2rVLt912m9LT0zV16lSlp6dr48aNCgtzTfIVl6+qvlEnyuskyWkbdn7f9WmxCg/yV1GVRd/knHLJPQB0HCY/Q9GdAiVJRVVs3gnAt/FsDfief249rmpLk3rGhioz4eJW4l7dPUqStD+/SnUNVleGBxf4+c9/rk2bNmnevHk6dOiQli1bpldeeUWPPvqoJPuKgenTp2vevHlasWKFdu/erWnTpikkJET33Xefh6MHAN/jf+Emnjdu3LizLm8PDg7WZ5995oGIcDkOFNpnkXfpHKywoACX3CPQ308TMhL11pY8fbDzpK7pGe2S+wDoOGLDzCqqsqi4yqK0OJJFAHwXz9aAb2lutmnxhqOSpGnXpsrPuLg640mdgxV3+vllX36lBnWLdGGUcLarrrpKK1as0OzZs/Xb3/5WqampWrBgge6//35Hm6eeekp1dXV65JFHVFZWpqFDh2rlypUMbAJAG/jETHK0L64utdJi0oAkSdInu/LVaG126b0AtH9xp/dPKKpi804AAOA+X+ec0tHSWoWZ/XXnoC6X9NmMLhGSpF0nK9hXxQdNnDhRu3btUn19vfbt26eHHnqo1XnDMDRnzhzl5+ervr5ea9euVUZGhoeiBQDfRpIcblXRYKikukF+hpQW18ml9xrWI0oxnQJVVtuofx0qcem9ALR/js07K9m8EwAAuM/b245LkiYOSFRI4KUtBu8VH6YAk6Hy2m9LXgIAgDORJIdb5dXa/5frHh2qoACTS+/lb/LTzf0TJUkf7Mh36b0AtH/RnQJlSKprtKqGup4AAMANahua9Mku+2+ZOwd1veTPB/r7Kf30PlAtK3oBwNUampp1uLha+/IrdbysVs1MMoIP8Ima5Gg/jp9Okru61EqLSQOS9MbGY1q5p0D1jRkuT8wDaL8CTH6KCg1UaU2Diqss6mTmr1AAAOBaK/cUqqbBqu7RIRrcxpriveLDtOdkpbKLqjWyV5xMfoaTowQAO2uzTZuOlGrH8XI1Wr9NjIcEmnR9Wox6xYfJMOiD4J34hQ+3CYjpppomQyY/Q92jQ91yz8EpkUqMCFJ+Rb3WHizW+H4JbrkvgPYpNsys0poGFVXVKzXGPf0YAAAdSW5urkpKnFMqMSYmRikpKU65lqd8uNM+i/zWgV3anFjqEhms0ECTahqsOlZaox6xri17CaBjqmuw6r0dJ1RYad/DKSI4QOHB/iqutKi2warP9hTqeFmdRveOI1EOr0SSHG4Tkn6NJCklKkSB/u6p9OPnZ2hCRqJe+1eOVu4pJEkO4LLEhpm1v6BKxWzeCQCA0+Xm5qp3nz6qq611yvWCQ0K0f98+n02UV9U3al12sSTp5v5t/x3jZxhKiw9TVl65DhZWkyQH4HQNTc2OBLnZ309j+sSrZ2yoDMOQtdmmLUdP6eujp7TnZKUkkSiHVyJJDrcJThsmSeoZ697Zl2P7xuu1f+Xoi/2FarI2y99EKX4AbRMXZpYkFZEkBwDA6UpKSlRXW6v7f/F7xaf0vKxrFeYe1tJnn1RJSYnPJsm/2F+khqZm9YgJVa/4yytXmR7fSVl55copqZG12UbJFQBOY7PZtHJvgQorLQoK8NMPBicrKjTQcd7kZ2hoj2h1DgnUZ3sKtOdkpWI6mTUwubPnggbOgiQ53KKopknmhCsk2dxeouCq7pGKCA5QWW2jtuWW6+rUKLfeH0D7EXs6SV5V36S6RquC2ecAAACni0/pqa5p/Twdhsd9sqtAkjShf8Jlz7hMCA9SSKBJtQ1WHS+rVTc3lb8E0P7tya/U4eIa+RnSbQO6tEqQf1evhDDVNVq19mCx1meXKDEiyM2RAufHlFq4xeYT9lmXMWabQgLdOzbjb/LTjb3jJEmr9ha49d4A2hezv0kRwQGSRMkVAADgMvWNVkepFWeUjDQMQz1Or+g9VFx92dcDAEmqa5LWHbT3VcN7xijhAonvAV0j1DM2VFabTZ/vL1Kz7bzNAbdiJjnc4usT9ZKkpOBmj9x/bN94rdh+Qqv2FupXN/eh9pWcuynSubSHzZKA74sLM6uirlHFVRalRIV4OhwAANAOfZ1zSrUNVsWHm9W/S4RTrtkztpN2n6jUkeIa3diLzBSAy7e73KRGq00J4UEalNL5gu0Nw9CNveN0vOyYiqssOuKm/eqAi0GSHC5XVtOgvSUNkqSkEM8kyW9Ij1WgyU9HS2t1qKhaaZdZ08/XOXtTpHPx9c2SgLOJDTMru6haRVX1ng4FAAC0U1/sK5Qk3ejEze26RgYrwGSotsHK/ioALltgYrpya+3lJ0f0ir3oviok0F/De0brywPF2ltukp+Z8k/wDiTJ4XItS2gaCo8oNKWrR2LoZPbXNT2jtfZgsVbtK+zwSXJnbop0Lu1hsyR8a926dfr973+vrVu3Kj8/XytWrNDkyZMd5202m5555hm98sorKisr09ChQ/XHP/5R/fp9W0/UYrFo1qxZevPNN1VXV6fRo0frT3/6k7p29Uy/0FYtm3dSbgUAALiC7XQZAkm6sXe8067r7+en5MgQHSmp0bHSWiU57coAOqLO190vSeqTGKaE8EurL57RJUI7j1eotKZB4Vff4YrwgEtGkhwu9/npWRC1hzZJV93lsTjG9o23J8n3FuqRkVd4LA5vwqZIuFg1NTUaMGCAfvzjH+vOO+884/xzzz2nF154QYsWLVJ6errmzp2rsWPH6sCBAwoLsw9KTZ8+XR988IGWL1+u6OhozZw5UxMnTtTWrVtlMvnOBpgtm3eW1TaqoalZgSwRBAAATnSoqFrHy+oU6O+na6+Iduq1u0WfTpKfqlFSuFMvDaADOVDSoOAeg2XIpqGpl95P+RmGrukZrQ935itsyK0qr7e6IErg0vDLHi7VaG3W+mx73eu6w1s8GsuYPvZZGFl55ZRJAC7RhAkTNHfuXN1xx5mj/DabTQsWLNDTTz+tO+64QxkZGVq8eLFqa2u1bNkySVJFRYVeffVVPf/88xozZowyMzO1ZMkS7dq1S6tXr3b317ksIYH+6mS2jzGXVDObHAAAONe607+fhqZGKSTQufPaukXbyxrkV9Sr0TOVMAG0A//YZ98AuFtosyKCA9p0jR4xoYoMbJZfYLA+yq5xZnhAm5Akh0ttO1amKkuTws1+aig45NFYEiKC1L9LhGw2ae2BYo/GArQnOTk5Kigo0Lhx4xzvmc1mjRgxQhs2bJAkbd26VY2Nja3aJCUlKSMjw9HmbCwWiyorK1sd3qBlNjn1PAEAgLP965A9SX59WozTrx0RHKDOIQGy2aSieufUOgfQsRwqqtK2fItstmb1Cm/7DHDDMByf//RQraotTc4KEWgTkuRwqS9PJ6MHxgdKNs9PVRjZK1aStOYgSXLAWQoKCiRJ8fGta2bGx8c7zhUUFCgwMFCRkZHnbHM28+fPV0REhONITk52cvRtE0tdcgAA4AINTc3adKRUknTtFc5PkktS9yj7bPLCetIBAC7dq+uPSpLqsr9Wp7ZNIndICrapsfS4ahptWv5N7uUHB1wG/laES605YN9wZlDipW3i4CotSfKvDharyer5pD3Qnnx/N3ObzXbBHc4v1Gb27NmqqKhwHHl5eU6J9XLFOWaSU7oJAAA4z/bcMtU2WBUdGqg+Ca4pGt4tOkSSVFhHOgDApSmradA7245Lkio3v3vZ1zMMqXLzCknSGxuPqbnZdtnXBNqKvxXhMvkVddpfUCXDkAYmmD0djiRpYHKkIoIDVFnfpKy8ck+HA7QLCQkJknTGjPCioiLH7PKEhAQ1NDSorKzsnG3Oxmw2Kzw8vNXhDVpmkp+qaVBTMwNuAADAOdafLrVy7RUx8vNzTTmULpHBMvkZqrUa8o/u6pJ7AGif3t52XJamZqV29pfl+B6nXLNmzxqFBBjKPVXr6AMBTyBJDpdpqfs9oGtnhZu94381k5/hqO23hrrkgFOkpqYqISFBq1atcrzX0NCgtWvXavjw4ZKkwYMHKyAgoFWb/Px87d6929HGl4SZ/RUU4Kdmm1Ra3eDpcAAAQDvx1elNO69zQT3yFgEmP3XpHCxJCk4d7LL7AGhfbDablp0uiTKuZ4jzrttk0aju9j5p6dfHnHZd4FJ5R+YS7VJLEnpUrzgPR9LayNPxrDlY5OFIAN9RXV2trKwsZWVlSbJv1pmVlaXc3FwZhqHp06dr3rx5WrFihXbv3q1p06YpJCRE9913nyQpIiJCDz74oGbOnKnPP/9c27dv1w9/+EP1799fY8aM8eA3axvDMBQXZi8jRV1yAADgDBW1jdp5vFySazbt/K6WkivBPUiSA7g4m46c0pHiGoUGmnRDSrBTrz2uh71PWr2vSAUVlLSEZ/h7OgC0Tw1NzY5lMiN7xcpaXOXhiL51Q7r9gXP3iUoVVdU7El0Azm3Lli0aNWqU4/WMGTMkSVOnTtWiRYv01FNPqa6uTo888ojKyso0dOhQrVy5UmFhYY7PvPjii/L399eUKVNUV1en0aNHa9GiRTKZTG7/Ps4QG2ZW7qlaFZEkBwAATrDxSImabVLP2FAlRjg3AfV93aJC9JUkc9e+arRSAxjAhf1ji31/qFsHdlFwQKNTr50cEaCrU6P0Tc4pvbU5Tz8bk+bU6wMXg5nkcImtx8pUbWlSdGig+neJ8HQ4rcSFBalfkr2u8bqD1LsCLsbIkSNls9nOOBYtWiTJPrN6zpw5ys/PV319vdauXauMjIxW1wgKCtLChQtVWlqq2tpaffDBB0pOTvbAt3EONu8EAADO1FJq5fq0WJffKyo0UGY/m/wCgpR9yrnJLgDtT21Dkz7dY9+D6gdDXLOXwf1DUyRJyzfnqsnKvk9wP5LkcImWUiYj0mNdtuHM5RjZy/7gueYAJVcAtE3L5p0l1Q3swg4AAC7bv06vxL3uCteWWpHsExxig+xJqN1FrIoDcH6f7SlQbYNV3aNDlJnc2SX3uCkjQVGhgcqvqNcX+8nVwP1IksMl1uy31yMf0cv1syDaoqUu+VfZJYxQAmiTzsEBCjT5ydpsU2kNm3cCAIC2O1lep6OltTL5GRraI8ot94w12wf5dxfxHAPg/FZsPylJmpzZRYbhmomQZn+T7hpsn6X+z63HXXIP4HxIksPpTpbX6UBhlfwM6QY3LBVsi8zkzgoP8ldFXaN2nN4cBwAuhWEYjtnkbN4JAAAux9c5pZKkjKRwhQUFuOWeLTPJD5Q2qL7R6pZ7AvA9RZX1Wp9tnwh5e2YXl97rzkH2JPmXB4pUxkQkuBlJcjjd2oP2znNgcmdFhgZ6OJqz8zf5OWr9rTlQ7OFoAPiquHDqkgMAgMv39ZFTkqShPaLdds9O/lJTVakam6VtuWVuuy8A3/L+jpNqtkmDUjqrW3SoS+/VKyFM/ZLC1Wi16cOdJ116L+D7SJLD6b48XTuqpaSJtxrRiyQ5gMvz7eadzCQHAABt93WOPUk+zE2lViTJMCRL7i5J0qbDpW67LwDf8s62E5Kk2we5ZsPO72uZrf7O9hNuuR/QgiQ5nKqhqdmx4cwoL0+Sj0y3J8l3naigVAKANokLC5JkL7fSbGPzTgAAcOkKK+uVU1IjP0Ma0t19SXJJqs/dKUnaQJIcwFkcLKzS3vxKBZgMTeyf6JZ73jowSSY/Q9tzy3WkuNot9wQkkuRwsi3HTqmmwaqYToHqlxTu6XDOKy48SH0T7TGuO8hscgCXLjIkQAEmQ03NNmrmAQCANtl0xJ6g7pcUoXA31SNv0ZIk33G8XLUNTW69NwDv9+HOfEnSiPRYt5XTjQsL0g1pMZKkd5lNDjciSQ6naildckN6rPz8XLPjsTONPF1yZV02SXIAl+67m3dScgUAALTFppZ65KnunUUuSU3lBYoJManRatOWo9QlB9DaJ7vsSfJbrnTPLPIWLaVd3tl+Qs3NrNiFe5Akh1OtOWCvR+7tpVZajDhdcuWr7BI6XgBt0lJypaiSJDkAALh0X+fYZ5K7c9PO7+ofZ58duvEIJVcAfCu7sErZRdUKMBka3Sferfce1zdeYWZ/HS+r0+ajp9x6b3RcJMnhNCfK63SwsFp+hnT96aUx3m5Qt0h1MvvrVE2Ddp+s8HQ4AHzQt5t31ns4EgAA4GuKKut1pLhGhiFd7eZ65C0yWpLk1CUH8B2f7C6QJF2fFuv2UlBBASbdlJEgSXo366Rb742Oy6uT5HPmzJFhGK2OhIQEx3mbzaY5c+YoKSlJwcHBGjlypPbs2ePBiDu2llnkg1Ii1TnEPbWqLleAyU/De9pnbKw9QMkVAJeuJUleXM3mnQC8H8/XgHf5Osc+Q7JPQrgiQtybhGqREWt/ltl1okLVFuqSA7D7+HSplQkZCRdo6Rq3Z3ZxxGFpsnokBnQsXp0kl6R+/fopPz/fcezatctx7rnnntMLL7ygl156SZs3b1ZCQoLGjh2rqqoqD0bccbXUI2+p8+0rRpyOdy2bdwJog8jQQPn7GWq02lRe2+jpcADggni+BrxHy6adQ3t4Zha5JMWGmpQSFSJrs02bcyhr4K3mz58vwzA0ffp0x3sMbMJVjhRXa39Blfz9DI3t695SKy2G9ohWfLhZFXWNjnwT4EpenyT39/dXQkKC44iNtSc0bTabFixYoKefflp33HGHMjIytHjxYtXW1mrZsmUejrrjsTRZteFQiSRppI/UI29xQ5r9/6ltuWWqIMEF4BL5tdq8k5IrALwfz9eA92iZST7MQ/XIW1xz+v4bDpd4NA6c3ebNm/XKK6/oyiuvbPU+A5twlZZSK8OviPFYpQCTn6HbBtpnk7+XdcIjMTiDX2hn7a/w09+35OmvXx3Ra//K0bvbT2hvfqWarM2eDg/f4fVJ8uzsbCUlJSk1NVX33HOPjhw5IknKyclRQUGBxo0b52hrNps1YsQIbdiw4bzXtFgsqqysbHXg8mw5WqaaBqtiw8zqmxju6XAuSXJUiHrGhqrZJv2Lh0IAbeCoS87mnQB8AM/XgHcorrLoUFG1JM/VI29xzekSlGze6X2qq6t1//33669//asiIyMd7zOwCVf6ZLe91MrNHiq10mLy6ST56n1FqqjzrUmNzc02fXCwRl0eekV7KvyVX1Gv2garquqbdOxUrVbtLdSSr3OVd6rW06HiNH9PB3A+Q4cO1RtvvKH09HQVFhZq7ty5Gj58uPbs2aOCAvuoVnx862Uf8fHxOnbs2HmvO3/+fD3zzDMui7sjaqlHPiI9Vn5+hoejuXQj0uN0uDhHaw8U6+b+iZ4OB4CPiQsPklShoiqLevjWOCGADobna8B7fHN6FnnvhDBFhnp2T6eWJPmek5WqqG30WH10nOnRRx/VLbfcojFjxmju3LmO9y80sPnwww+f9XoWi0UWy7cTOxjUxPflltZq94lKmfwMjevn2SR5n8Qwpcd30sHCan26O193X5Xi0XguVl2DVT9/K0uf7qmUnzlEkYHNGtQjQXFhZlltNuWV1WlnXrkq6hq1YvsJjewVqyu7dvZ02JcsNzdXJSWXP9k0JiZGKSme/2/r1UnyCRMmOP65f//+uuaaa9SzZ08tXrxYw4YNkyQZRuuErM1mO+O975s9e7ZmzJjheF1ZWank5GQnRt7xfHm6PtQoHyu10mJEr1i99q8crT1YfFH/DwHAdzk276yyyBbm4WAA4Dx4vga8x9c59lnbni61Iknx4UHqERuqI8U1+jqn1OOJMdgtX75c27Zt0+bNm88419aBTQY1cSEts8iH9YhSlIcH8AzD0OTMLnru0wN6d/tJn0iS1zVYNfW1b/TN0VPy95MKP/mj7vi3h5TcJcLRJjEiWAO6RmjtwWLty6/SlweK1dRs06CUyPNc2bvk5uaqd58+qqu9/JnwwSEh2r9vn8cT5V6dJP++0NBQ9e/fX9nZ2Zo8ebIk+18MiYnfzvwtKio64y+J7zObzTKbza4MtUPJO1WrQ0XVMvkZuj49xtPhtMnQ1CiZ/f1UUFmvg4XV6pVAlgvAxYsKsW/e2WBtVnWTp6MBgIvH8zXgOY5NO1M9W2qlxTU9onWkuEYbDpMk9wZ5eXn62c9+ppUrVyooKOic7S51YJNBTVzIx6frkU/I8I5V9rcOSNJznx7QppxS5VfUKTEi2NMhnVOTtVk/XbJV3xw9pTCzv345PEI/nP+JDOOhM9qa/U0a2yde4UEB+jrnlL7KLlFIoEm9E3xjaXJJSYnqamt1/y9+r/iUnm2+TmHuYS199kmVlJSQJL8UFotF+/bt0/XXX6/U1FQlJCRo1apVyszMlCQ1NDRo7dq1evbZZz0cacfy5elSK0O6RSo8yDeX5QUFmDSsR7TWHizW2oNFJMkBXBI/P0MxncwqqKxXeYPXb/cBAA48XwOeUVpt0cHC0/XIvSRJPrxnjJZ+netI3sOztm7dqqKiIg0ePNjxntVq1bp16/TSSy/pwIEDki59YJNBTZzP8bJa7cgrl2FI471ksKxrZIiuTo3SNzmn9H7WST08ou0JWVeb9/F+rT1YrOAAk17/8VXyO3X0vO0Nw9DQ1ChZmpqVlVeu1fuKFNPJrJhOvvNnND6lp7qm9fN0GE7h1b/kZ82apbVr1yonJ0dff/217rrrLlVWVmrq1KkyDEPTp0/XvHnztGLFCu3evVvTpk1TSEiI7rvvPk+H3qF8ud+eJB/V2zdLrbQYkR4rSVp3kM07AVy6uHD7g0xZA+WaAHgvnq8B79BSjzw9vpOivSQZMqyHPVm/v6BKpdVsRu5po0eP1q5du5SVleU4hgwZovvvv19ZWVnq0aOHY2CzRcvA5vDhwz0YOXzZp6dnkV/dPUqxYd7RN0nfbuC5YvsJD0dybu/vOKnX/pUjSXrx7gEacpEbMhuGoRvSYtQtOkTWZps+3pWvRmuzK0PFOXj1TPLjx4/r3nvvVUlJiWJjYzVs2DBt2rRJ3bp1kyQ99dRTqqur0yOPPKKysjINHTpUK1euVFgYs4Ddpa7Bqg2H7TMNfLUeeYsbTifJv8k5pdqGJoUEevUfDwBepqUueTlJcgBejOdrwDu0zNa+xgvqkbeI7mRW74Qw7S+o0qYjp3TLld5RaqGjCgsLU0ZGRqv3QkNDFR0d7Xi/ZWAzLS1NaWlpmjdvHgObuCyfnE6S39zfu/7839I/UXPe36P9BVXaX1DpdSVJjpfV6ul3dkmSHh3VUzddYqkawzA0vm+Cln2Tq7LaRm08Uqob0mJdESrOw6uzgMuXLz/vecMwNGfOHM2ZM8c9AeEMm46UytLUrKSIIKXHd/J0OJelZ2younQO1onyOm06Uqobe5+/9iYAfFdcmL1WpH0mOYlyAN6J52vAO2w84j2bdn7XsB7R2l9QpY1HSkiS+wAGNuFMBRX12nqsTJL3lFppERESoJG9YrVyb6He3X5Sv5zgPUlym82mWf/YoSpLkzJTOuvnY9LbdJ3gQJNG947TeztOKiu3XOlxYUqIOPd+BHA+ry63Au/XUo98VO+4824O4gsMw9CIXvaRurUHij0cDQBfEx1q37yzyWbIP6qLp8MBAABequQ79ciHelmSfHhPezwtq4XhXdasWaMFCxY4XrcMbObn56u+vl5r1649Y/Y5cLE+3Z0vSRrcLdIrk7O3Z9p/Y72fdULNzTYPR/Otf2w9rk1HTik4wKQFdw+Uv6ntqdbuMaHqnRAmm+z5tmab93zPjoAkeRvllNTovQPVCu13o0oshqxe9AfUXWw2m75oqUfu46VWWrTUJV97kCQ5gEvj52c4Sq6Yk9o2ewAAALR/LfXIeyeEKSo00MPRtDY0NVqGIR0prlFhZb2nwwHgRh+fLrUyIcO7ZpG3GNU7TmFB/jpZUa9vjp7ydDiSpFM1DZr/8T5J0vQxaeoWHXrZ17w+LUaB/n4qqrJo78nKy74eLh5J8jbaebxci3dUKWbiDK0tDNAr645ozYEiVdY3ejo0tzlcXK3jZXUKNPlp+BXeNQOirYb3jJa/n6GjpbU6WlLj6XAA+JiWGRfmxF4ejgQAAHirTV5aakWylzTol2QvY9ASJ4D2r7jKos2nE88TvKweeYugAJNuPl3r+10v2cBz3sf7VFbbqN4JYfrJdalOuWZIoL+Gpto3/dxwuFQNTWzi6S4kydsoPjxI16cEqS5nmwL9bGqwNmvH8Qr9beMxbTtWJlsHWBLx5X77bOuhPaLazSaXYUEBGtwtUpK0LpvZ5AAuTUK4PUkemESSHAAAnN23SfIoD0dydsN7xkiSNhwiSQ50FJ/tKZDNJg1I7qwunYM9Hc45TT5dcuWjXfmqb7R6NJZNR0r1z63HZRjS727vr4DLKLPyfQO6dlZEcIDqGq3acbzcadfF+ZEkb6NhPaL182GRKvr7bzSxS6MmD0xSUucgNTXb9NWhEn2wM1+WJs/+gXW1lnrkN/ZuH6VWWlCXHEBbxZ+eSR4Y212WpvY/WAoAAC7Nd+uRX53qfTPJJema0zPcNzKTHOgwPjldj/xmLy210mJoapQSI4JUVd+kNadzUp5gbbZpzvt7JEn3Xp3imGzpLCY/wzGbfOuxMjUymdwtSJI7gWFI3aJDddegrrqxd5xMfoZySmr09rYTqmton4nyqvpGx1Kc9lKPvEVLXfINh0s9PjIJwLeEmf1l9rPJMPnrSHnHKb8FAAAuztdHvLceeYurUqNk8jOUe6pWx8tqPR0OABcrrbZo0+m+aUKGd5ZaaeHnZ+jWgUmSpBUeLLny1uY87S+oUkRwgJ4c55pVxL0SwhQZEiBLU7Oyq0jfugP/lp3IMAz17xKhHwzuquAAk4qrLHp7+/F2mWj916ESNVptSo0JVfeYy9+YwJv0TQxXQniQ6hqtzJ4AcEkMw1CU2T6DPLu0wcPRAAAAb+PN9chbdDL768quEZKkjYd96/eQtdmmo6U1yq70U+eRP9apuvb3Wxxwts/2FMrabFNGl3ClRId4OpwLmjzQXnLly/3FKq91/2+uqvpGvbDqgCTpZ6PTFOmiAU8/w3D8XZFdaZJfUCeX3AffIknuAvHhQbprcFeFBppUWt2gD3aeVJO1fa2N+GK/fVnLyNOlSdoTwzB0Yx/77Pgv9nlu+Q4A3xQVaO/vD55iJjkAAGjNF5LkkjS8p2+VXGm22ZSVV67X/pWj97JOame5vyKG3qn8qiZPhwZ4vQ93npQk3dI/ycORXJw+ieHqkxiuBmuz3t7m/tnkf1pzWCXVDeoRE6oHrunm0nulxXVSTKdANdkMhV99h0vvBZLkLhMVGqjJmV0U6O+nk+X1WnOw/dS3tjbb9Pnp5PGYPvEejsY1Rp+us/7F/qIOsQkrAOf5diY5SXIAAPCtkmqLsovs9chbas16q2t62Dfv3Hi41Ot/D9U1WvXOthNae7BYtQ1WBQeY1CXEqspvVigiiJQHcD7FVRbH4N3EK7271Mp33Tc0RZK09Otjbu2j8k7V6tX1OZKkX93cx6mbdZ6NYRgaenr/irBBt6iW4uQuxd8YLhTTyaxb+ts7mT0nK7U3v9LDETnHttwyldY0KCI4QFd7+cNdWw3vGSOzv59OlNfpQGGVp8MB4EMiA22y2ZpVXGtVcZXF0+EAAAAv8d165K5anu8sg7tFKtDkp/yKeuWU1Hg6nHOqrm/SP7bk6UR5nQJNfhrZK1YPXpeqYTFWlX35qrqGB3g6RMCrfbo7X802aUByZyVHeX+plRa3Z3ZRaKBJR4prHPXU3eF/Pt2vhqZmXXtFtEb3cc/+fD1jQxXmb5OfOVQrD7NPhCuRJHexlKgQDethTyR/ub9IJdW+nzBZuadAkn22tatHzTwlONCka6+wz574nJIrAC5BgJ/UWJonScrKK/dsMAAAwGtsPFIiyftLrUj230NDukdKktZ56apoS6NV72adUFltozqZ/TVlSFcN6NpZJj/D06EBPuODnfmSpIn9fWcWuWTfO+G2THtt8iVfH3PLPbccPaWPdubLMKT/vKWvDMM9fY1hGEoPt++v8GF2jRqamE3uKu0zw+llru4epW5RIWpqtumjXflq9OH65DabTSv3FkqSxvVrn6VWWtz4nZIrAHApGk4elCRl5ZV5OBIAAOAtWmY7+kKSXJJGpNv3n1rrhUnyZptNH+8uUGlNg0IDTfrB4K6K7mT2dFiATymsrNfmo/Z+6WYfKrXS4v7TJVc+213g8hW8zc02/fdH+yRJdw9JVp/EcJfe7/uSQ5vVVFWqU3XNei/L/XXYOwqS5G5gGIbG90tQJ7O/ymsbtcHHdgj/roOF1TpWWiuzv59uSG9/m3Z+V0uSfFtumU7VuH/HZAC+y5JvT5Jvzy33bCAAAMArFFdZdKioWoYhx0pjbzeil/333qYjp1TfaPVwNK19k3NKuadq5e9n6LaBXRQeTFkV4FJ9vCtfNps0KKWzunQO9nQ4l6xfUoQyUzqrqdmmv2/Jc+m93t9xUjvyyhUaaNKMcekuvdfZmAypasv7kqRX1h1Rc7N37xXhq0iSu0lwoEljTtcrysor18nyOg9H1DYtpVauT4tRSKC/h6NxraTOweqTGC6bTVpzgNnkAC6e5fheSfb+3pdXDwEAAOfYcNheaqV3Qrg6h3h3PfIWveLDFB9uVl2jVVuOes/quJPldfo6xz779cbecYoNYwY50BYftpRauTLJw5G03f1Du0mSln2dqyYX/e6qa7Dq2U/3S5IeGXWF4sKCXHKfC6nK+kTB/oayi6r1JTkqlyBJ7kbdokPV9/SSjNX7Cl32B9iVHKVW+iZ4OBL3GH16NvnqfYUejgSAL2ksyVVogKHaBqv2nmwfmzYDAIC2+yrbniS/Pi3Gw5FcPMMwdENaS8kV70jINFmbHb/N+iSEub3kAdBe5J2q1dZjZTIM6WYfq0f+XROvTFR0aKBOlNfpo135LrnHX786ovyKenXpHKwHr0t1yT0uhq2hVuN7hjhigvORJHez69NiFBpoUlltozbluG8HXmc4WV6nXScq5GfIbbv4etrYvva662sOFHvdEkMA3sym3jH2WWItdf4AAEDHZLPZ9FW2va53S9LZV7SUXPGWuuTfHD2lstpGhZpN7b78J+BKK7bb61oP7xmthAjPzIx2hqAAk6YO7y7JXobEZnNuGZLCynq9vOawJOkXE3orKMDk1OtfqpvTQmXyM7TpyCntPlHh0VjaI5LkbhYUYNKo79S6Lq127eYCzvTpbnuplcHdIjvMpihXdo1QYkSQahusjtkfAHAx+pxOknvT8mQAAOB+BwurVVhpkdnfT0O6R3o6nEty3RUx8jPs38HTJUPLaxu07Vi5JGlUrziPJ6sAX2Wz2fTOtuOSpDsHdfVwNJfvgWHdFBxg0p6TlVrn5LzN/3yyX3WNVmWmdNYkL9jcNCbEpFtOz/x/bX2Oh6Npf0iSe0DP2E7qGRtqr3V9sNjpI12u8sHOk5Lk+APZEbRsuip9O0gAABejT+y3M8l9pZ8HAADO1zKLfGiPaJ9L7HYOCdTA5M6SpHUenk3+VXaJrDabUqJC1CMm1KOxAL5sW26ZjpbWKiTQ5Mh3+LLI0EDdNzRFkvTCqoNO++219mCxVmw/IcOQ5kzqJ8MwnHLdy9VS8uWDnSdVWFnv4WjaF5LkHnJ9WqxMfoaOl9XpUFG1p8O5oLxTtdqeWy4/Q7rZC0bP3OmmDPtfGp/vL2QDPgAX7YrIAAX6+6m0pkE5JTWeDgcAAHhIy8zGG3yoHvl3jUi3r4Rel+25JPmx0hodKamRnyGNSI/1mmQV4Ive3mYvtTIhI1GhZn8PR+McPx3RU8EBJu3IK9cX+y9/D4XahiY9vWKXJGna8O4acHqw0BsMSO6sq7pHqtFq0xsbj3o6nHaFJLmHRAQHaHA3+1K7rw6VeH3ytWXX42E9oj22k6+nXNU9StGhgSqvbdQ3PlZHHoDnBJgMDezaWRJ1yQEA6KjqG636+kipJPlsDe2WuuRfHSxRQ5P7f7dam22OmuhXdu2sqNBAt8cAtBf1jVZ9uMNeJeDOQV08HI3zxIaZ9aPh3STZS6Rcbo7tD6uzdbysTkkRQZo5rpczQnSqltnkS7/OVV0D++c5C0lyDxrSLVJhQf6qqm/y+pq1H5zuRCcNSPJwJO5n8jMcG3hScgXApbgq1T4YutnL+3gAAOAam4+ekqWpWQnhQUqL6+TpcNrkyi4RiulkVpWlSV/nlLr9/rtPVKistlHBASYNS41y+/2B9uTzfUWqrG9SUkSQhvWI9nQ4TvXIiCsUGRKg7KJqLdl0rM3X2Xm8XP93ut73f0/OUCcvnG0/tm+CkqOCVV7bqLdP15fH5SNJ7kEBJj9df3rJ3dbcMlXUNXo4orM7VFStvfmV8vczdFM7qFfVFuNPl1z5bE+BmpupLQzg4gzpbv8hx0xyAAA6ppY63tenxfhsiRA/P0Nj+9pLrqzcU+jWezdam/XN6eeooT2iZPaxmu6At/nn1jxJ0u2DusjPzzf7pHOJCAnQrPH2Wd8vrDqogopLr9ddbWnSE29ul7XZpolXJmp0n3hnh+kUJj9DPx5un03+2r9yyFM5CUlyD7sitpOSI4NlbbZ5fCOUc2mZRX59WowiO+jStuE9oxVm9ldRlUXb85gRCnzfnDlzZBhGqyMh4dtBNZvNpjlz5igpKUnBwcEaOXKk9uzZ48GI3WNwt0gZhnSstFZFbKoCAECH89XpeuTX+2iplRYtK2tX7S1064bk2/PKVdtgVURwgDKSItx2X6A9yi2t1ZrTeae7Bid7OBrXuOeqFF3ZNUJV9U166u2dl9Rf2Ww2/fLtnTpaWqukiCDNnZzhwkgv35SrkhVm9teR4hqtOXj5ddhBktzjDMPQiPRY+RnSkZIaHSv1rs3dbDabPtjZcUuttDD7mzS6j332REt9dgCt9evXT/n5+Y5j165djnPPPfecXnjhBb300kvavHmzEhISNHbsWFVVVXkwYtcLDwpQ74RwSdKWYwywAQDQkZwsr9P+gioZhnTdFb65aWeL4T1jFBJoUkFlvXYer3DLPesardp6umTdsB5RMrWzWa+Auy35+phsNvvmt6kxoZ4OxyVMfoZemDJAZn8/rTtYrL9+deSiP/uHz7P14c58+fsZ+sO9meoc4t2TRDuZ/XXP1fbBjldPl4fB5fG+wjodUHQns67s2llZeeVal12i+yNDvGbZy+4TlTpSXKNAfz/H7IGO6taBSXo366Q+2JGvp2/uI38TY0zAd/n7+7eaPd7CZrNpwYIFevrpp3XHHXdIkhYvXqz4+HgtW7ZMDz/88DmvabFYZLFYHK8rKyudH7iLXd09UvvyK7XxcKlu7p/o6XAuW25urkpKStx2v5iYGKWkpLjtfgAAOMuqvfbSJINTIn1+s8mgAJNG9YrTR7vy9fGufA1I7uzye249WqYGa7NiOgWqV3yYy+8HtGd1DVa9tdleamXq6Q0u26sr4sL09C199Jv39mj+J/uVEhWqmzLOXzr4/746ogWrsyVJcydn6KruvrH/wdTh3fXq+hz961Cp9p6sVN+kcE+H5NNIknuJoalR2l9QqVM1Ddp1osItDx0X483NuZKkm/olKCwowMPReNb1abGKDAlQSbVFGw6X+uzu9ICrZGdnKykpSWazWUOHDtW8efPUo0cP5eTkqKCgQOPGjXO0NZvNGjFihDZs2HDeJPn8+fP1zDPPuCN8lxl+RYwWbzymfx1yX2LZVXJzc9W7Tx/V1da67Z7BISHav28fiXIAgM9ZubdAkjS+nezrdMuVifpoV74+3JmvX07o7dIa67VNUlZBuST7LHZfrecOeIv3sk6ooq5RKVEhGpEe5+lwXO6BYd10oKBKS7/O1WPLtum5u67UHYO6ntGu0dqs/115QH9Za59x/sSNV+ieq33nd0fXyBBN6J+oj3bm67V/5eh/fzDA0yH5NJLkXiIowKRrekTrywPF2nikVOkJYQr28KYktQ1Nej/LXmrlnqvaZ72qSxFg8tMtVyZqyaZcvZt1giQ58B1Dhw7VG2+8ofT0dBUWFmru3LkaPny49uzZo4IC+w/E+PjWq1Hi4+N17Nj5dx2fPXu2ZsyY4XhdWVmp5GTf6o+u6Rktk5+hIyU1OlFepy6dgz0dUpuVlJSorrZW9//i94pP6eny+xXmHtbSZ59USUkJSXIAgE+pqG3UpiP2DSfby4rcUb3iFBJo0onyOmXllSszJdJl99pXYZK12aakzkHqHh3isvsAHYHNZtPijfbfXQ8M69YhShcZhqFnbu2nyvomfbDjpGb8fYc+31+k/xjRU/2SwlXf2Kyvsov1h8+zteekfbXy9DFp+tnoNA9HfukevC5VH+3M1/tZJ/XUTb0UFxbk6ZB8FklyL5KRFKGdJypUWt2gr4+UamQvz47ufbgzX9WWJnWLDtGwHtEejcVbTB7YRUs25eqz3QWqm2xVcCC7qwOSNGHCBMc/9+/fX9dcc4169uypxYsXa9iwYZJ0xgwgm812wVlBZrNZZrPZ+QG7UXhQgAZ0jdC23HL9K7tEU9rBoGN8Sk91Tevn6TAAAPBan+8vlLXZpl7xYereTmr/BgeaNKZPvN7fYS9B6aokuX9UFx2rsZe2vJZZ5MBl23KsTPvyKxUU4KcfDDlzNnV75W/y0x/uHqiUqGC9vOawPtqZr49O1xxvttnUfHpPzzCzv/7nzit1y5W+WRpzUEqkBqV01rbcci3ZeEwzxvXydEg+i6LKXsTPz9CINPvs5J0nKlRSbbnAJ1xr+Tf2Uit3X5XsNTXSPW1wt0h1jQxWTYNVq/cVejocwGuFhoaqf//+ys7OdtQpb5lR3qKoqOiM2eXtVctmXV+1g5IrAADgwlbusf9WGN+vfT3r3DogSZL0/o4TarQ2u+QenW/4kWwylBoTqiQfXoEHeIu/rrOXErltQBev34zS2fz8DD05vrc+ePw6TchIUHCASU3N9gR5YkSQHr6hh9Y8OdJnE+QtHryuhyRpyde5qm+0ejga38VMci+THBWinrGhOlxco3XZxbrKQ5MODhZWaVtuuUx+hu4a3HFGGi/EMAzdNjBJf/zysN7LOqFJpx8SAbRmsVi0b98+XX/99UpNTVVCQoJWrVqlzMxMSVJDQ4PWrl2rZ5991sORusd1abH6f18c0oZDJWputjHwCABAO1bfaNXag8WSpHHtpB55ixG9YhXTKVAl1Q1ae6BYY5xcSuZASYNCe10ryabhPVnNDO9nabLqZHm9iqssqm1okmQvpxsdGig/L8hV7i+o1Mq9hTIM6aEbeng6HI/plxShl384WJYmq07VNMjPMBQXZm43K1XG94tXl87BOlFepxXbT+heH6qr7k1Iknuh69NidbSkVnmn6tTVQ4mU5d/Ydz0e3TuOekbfc9vALvrjl4e15kCxTtU0+PxO9ZK97EVhlUXHy2pVUtWgakuTrM02BfgbCg8KUHx4kLpFhyi8g2/einObNWuWJk2apJSUFBUVFWnu3LmqrKzU1KlTZRiGpk+frnnz5iktLU1paWmaN2+eQkJCdN9993k6dLfITOms0ECTSmsatK+gUv2SIjwdEgAAcJH12SWqa7SqS+dg9UsK93Q4ThVg8tPkgV30f+tz9M+tx52aJLfZbHpjp702cPfQZsV08u2Se2jfCirrte1YmY4U18hqs52jVYDi752v9bl1GjDQ5pFa4C99cUiSdHP/RF0R18nt9/c2Zn+TEiPa3woVf5Offnxtd839aJ9eXZ+je65KbjcDAO5EktwLRQQHKDOls7YcK9POcn/J5N7/THUNVr2z/bgkMfp0FunxYeqXFK49Jyv1zrbj+rfrfXc0tqFZ2nL0lHadqFBlfdM5WtU5NrJIigjSwOTO6hnXSX4dqMPNzc1VSYlry2TExMT49MaEx48f17333quSkhLFxsZq2LBh2rRpk7p16yZJeuqpp1RXV6dHHnlEZWVlGjp0qFauXKmwsDAPR+4eASY/De0RrS/2F2l9dglJcgAA2rHP9thLzI3tG98ukxR3Demq/1ufo8/3F6q4yqLYMOcks1fvK9K+kkY1N1rUJ6L9/XtD+1BtadLag8U6VFTteC8iOECJEUEKC7LnbmobrCqsrFdJdYOCUvrrhU3l+vjoV5p9cx/dkOa+Ovt7Tlboo135kqRHR17hlnvCc6ZclawFq7N1qKhaaw8We3yfQ1/k1Uny+fPn65133tH+/fsVHBys4cOH69lnn1WvXt8WoZ82bZoWL17c6nNDhw7Vpk2b3B2uU13VPUp78ytV02BV+JDb3Hrvf247rvLaRqVEheiG9Fi33ttX3Dc0RU+v2K2lX+fqwetSfe7ht9lmU6cB4/XZyQA1NJdKkgJMhpIjQ5QQEaTwoAD5mww1NDWrrLZBJ8rqdLKi/vRRoNhOZt2QHqOuke1/p/nc3Fz17tNHdbW1Lr1PcEiI9u/b57OJ8uXLl5/3vGEYmjNnjubMmeOegLzQdVfE2JPkh0r08Iieng4HQAfVkZ+v4bsampqVXVSlo6W1Kqm2qKGpWQEmP0WGBKhbdKjS4jop1OwdP20tTVat3GuvRz6undUjb9E7IVyZKZ21Pbdcb36TqydGp132NZuszfqfT/ZJkqq2vKeQnpMv+5qAsx0qqtaqfYVqaGqWIal3QpgGpnRWbKezl+04uG+P3vz7P9Vl1P3aX1Clqa99oxHpsfqfO/u7ZTbz/3yyXzabNGlAkvq2s1UtOFN4UICmDEnWa//K0f99lUOSvA2840niHNauXatHH31UV111lZqamvT0009r3Lhx2rt3r0JDvy3WfdNNN+n11193vA4M9P3yF4H+frr2ihit2luoiOH3qqjmXLN8nau52abX1udIkn5ybXePLAfyBbcN7KL5H+9XTkmNNh4u1fDTm/L5gr0nKzX781JF3/S4GpqlqJBADe4WqbT4TgownXsv32pLk3Ydr1DW8XIVV1v09rYTGpTSWdf0jJa/X/vdA7ikpER1tbW6/xe/V3yKaxKbhbmHtfTZJ1VSUuKzSXJc2PVp9n7im5xTqm+0KijA5OGIAHREHfn5Gr6nydqsbXnl2nq0TA1nbBJpVUVdo46W1mp9don6JIXpmh7RCgn07E/cNQeKVVHXqITwIA1Nbb81tacN767tuVlasumYfjqipwL9L+/3wBsbj+lwcY3CAg3lbvqndPdk5wTqwy5mUNNms+mZZ57RK6+84lit+cc//lH9+vXzYOTtj81m04bDpdpyrEySFB9u1uje8RdcRRHiL1X8a5ne+/3Pta40RG9sPKq1B4s1/sV1mnt7f8dGuK6w5kCRvsouUYDJ0JPjel34A2gXfnxtd72x8ajWHyrRttwyDUqJ9HRIPsWrk+Sffvppq9evv/664uLitHXrVt1www2O981msxIS2teGKJLUJyFM2w/nq0RBemVbpcZfZ3P5jOVV+wqVU1KjsCB//WBIskvv5cs6mf01OTNJSzblaunXuT6TJP/75jz953u71dDUrGZLrQbGB2pE5hUXtYlgJ7O/rukZrYEpnbXhUIl2n6zUttxy5Z6q1YSMxHZRm/184lN6qmsaD5touyviOik+3KzCSos2Hz2l69NYqQPA/Tr68zV8R3GVRZ/uKdCpmgZJUmRIgHolhCkxIljBASZZmqwqqrIou7BaBZX12n2iUtmF1RrVK069EjxXzu3d7SckSbcNTGrXE44mZCRqbtg+FVVZ9PGufE3O7NLmaxVU1OuFVQclSff1D9OvGly7gtNXXMyg5nPPPacXXnhBixYtUnp6uubOnauxY8fqwIEDHaasoatZm21aubdABwvt5VUGpXTW8J4xl/TnO8zsp19P7Kv7hqZoxltZ2nG8Qk+8uV1f7CvU3Nv7q5OTV8LUN1r1m/f2SJJ+dE13pUS3/xXgsEuOCtEdg7ro71uO6w+rs7X4J1d7OiSf4lPTPysqKiRJUVFRrd5fs2aN4uLilJ6eroceekhFRUXnvY7FYlFlZWWrwxsZhqHMqCbZmhq1Ld+ij3cVuPR+NptNC7/IliQ9MKyb1yxZ9Fb3XW2vtfzZngIVVdV7OJrzq2+06hf/3Kmn3t6phqZmDU406+T//VRp4c0XlSD/ruAAk0b3idekKxMVHGBSSXWD3tqSp7xTPMwC52MYhkadXvK26vQybADwtI72fA3fcLzW0Ftb8nSqpkEhgSaN7xevB4Z109DUaKVEhSg2zKyukSEalBKpu69K1l2DuiqmU6AsTc36dE+BvthfpOZz7aPnQhV1jfp8n/3Pym0D25409gWB/n6aeo3999BLXx6S9TL+hf/3h3tVbWlSZkpnje1BMq/Fp59+qmnTpqlfv34aMGCAXn/9deXm5mrr1q2S7L/fFyxYoKefflp33HGHMjIytHjxYtXW1mrZsmXnvC799cVrttl/7x8srJafIY3vG6/r02LbPADWM7aT/vkfw/Wz0Wky+Rl6N+ukbl24Xvvynfvf4P99nq3cU7VKCA/Sz8emO/Xa8H6PjbL//7X2YLGy8so9HY5P8Zkkuc1m04wZM3TdddcpIyPD8f6ECRO0dOlSffHFF3r++ee1efNm3XjjjbJYLOe81vz58xUREeE4kpO9d8Z0eIBUsekfkqQ5H+xRRV2jy+715YEi7T5RqZBAk09vRukufZPCNSils5qabfrHluOeDuecKmob9cCrX+utLXnyM6Qnx/fS7OsiZa0+dVnX7RHbSfcPTVFiRJAampr1btYJ7S/gAQs4n5bapCv3FMpm88CvdwD4jo76fA3v1mngBH1dEiBrs03dokP0w6Hd1Dsh/LwrartEBuveq1J0dXf7YM+uExXaVOIvw9+9Kx3f3X5CDdZm9U4IU5/E9j+L90fDuysiOECHiqodmwNeqrUHi/XRrnz5GdLcyRny87G9ntzp+4OaOTk5Kigo0Lhx4xxtzGazRowYoQ0bNpzzOvTXF8nw0+ZSk7KL7AnyiVcmqXfi5df1DjD56edj0/XWvw9TYkSQjpTUaPIf/6VlX+c65ffBpiOl+vPaw5KkObf2c/osdXi/lOgQ3X56dc//+zzbw9H4Fp9Jkj/22GPauXOn3nzzzVbv33333brllluUkZGhSZMm6ZNPPtHBgwf10UcfnfNas2fPVkVFhePIy8tzdfiXpWLTP5QUZlJxlUW//WCvS+7R3GzT8yvtS9weuKZbuy+d4Sz3D7XPnlj2da6azqiT6HkFFfWa8peN2ny0TGFB/lr8k6v16KgrnPbwGWr21x2ZXZQW1+n0KHuhdjBSCZzT8J4xCg00qaCyXrtOVHg6HAAdXEd+voZ3+vRQjaLHPypJGtA1QrcOSFJw4MXt4eHnZ+iantGaeGWiTH6G8uv8FPeDZ1TT4J5ndJvNpje/yZUk3XNVssvLZHqD8KAA/dt1qZKkF1cdlKXJekmfr7Y06dfv7pYk/fjaVPVLinB6jO3F2QY1CwrsK83j41tvEBsfH+84dzb01xdms9kUfdNjOl5rkp8h3dI/UakxoRf+4CUY0j1KHz1xvUb1ipWlqVm/WrFLP1uepWpL2/ejK6qs1/TlWWq2SXcN7qqbMiib1lHZ8z7SF/uLtPN4uafD8Rk+MaT0+OOP6/3339e6devUtWvX87ZNTExUt27dlJ197tESs9kss/n8Gyx4FWujHh3SWb9eU6q3tx3XqN6xmnilczd4eDfrhPacrFSY2V8P3+CazQnbo1uuTNS8j/fpRHmdPtqV71XLKo+V1ui+v36tE+V1igsza/FPrlYfJ4x8f5+/yU8TMhLUKbtE2/PKteZgsQxDirrwR4EOJyjApJG94vTRrnyt3FOoK7t29nRIADqoDv98Da+zam+h/m+7fVViephVI9Jj25Ro7hnbSZMHJum97ccVlNJfc9ae0rsDGxUWFODskFvZnleu/QVVCgrw0+2Dzv9nqj2Zdm13vbHpmHJKavTXdUf02I1pF/3Z/3pvj3JP1SopgpIQF9IyqLl+/fozzn3/z4nNdv69zJzVX+fm5qqkpOSyryNJMTExSklJccq1nOHj7Fp1unKcDNk0ISNJPWI7ueQ+UaGBenXqVXrlqyP6/WcH9P6Ok9p9okIv3TdIfZMu7bd7jaVJP1m8WQWV9eoRG6pnbmU/rY4sNSZUkwd20TvbT+j3nx3QGz+5ukMM3l4ur06S22w2Pf7441qxYoXWrFmj1NTUC36mtLRUeXl5SkxMdEOE7tMnNlCPjrpCC784pF+9s0uDUiKV1DnYKdeubWjS/352QJL0yKgrmEV+CYICTPrRNd314uqDemXdEd06IMkrOp7c0lrd88om5VfUKzUmVG/85GolR7muvp9hGLo+LUZ+hqGtuWX68kCxBkX5zEIVwK3G9Yu3J8n3FmjWeHaaB+BePF/DG+08Xq4n3tyuZptUlfWpMibdeFnP1F0jQzQivkkrj9TqsCL04OItWvzjqy96Vnpb/G3jMUn2kgwRwa5NyHuTsKAAPX1zH01/K0sLvzikCf0T1fMiEorLv8nV29uOy8+QFtyTSUmI8zjXoGbL5soFBQWt+ueioqIzZpc7W25urnr36aO6WufsSxUcEqL9+/Z5RaJ84+FSvb7DPmDXv7NVV8S5JkHews/P0E9H9NSQbpF6/M3t9vIrf/qXfnlTb00d3v2i6p+X1TTox4s2a/eJSkWHBur1aVexxxw0fUy6PtyZr6+yS7TmYLFjfyycm1f/qXn00Ue1bNkyvffeewoLC3MsGYqIiFBwcLCqq6s1Z84c3XnnnUpMTNTRo0f1q1/9SjExMbr99ts9HL3zPTE6TeuyS7Qjr1wz/75DS/5tqFN2TF+wOlsnK+rVpXOwfnxt98sPtIP50TXd9Oe1h7XnZKW+yi7RDemxHo0n71St7v2rPUHeMzZUb/77MMWFBbn8voZh6NorotVss2l7Xrm2nfJXSO/rXH5fwNeM7BUnfz9DBwurlVNS4/SlmwBwPjxfw9ucKK/TTxZtUV2jVQMTzHpv1csybr3xsq/bOdCmor//Rj0f+n/6JueU/mPpVr3ywBAF+jt/IsfJ8jp9sOOkJGnqNd2dfn1vd9vAJP1ja57+dahU/7Fkq9599FqFBJ471bA+u0T/ebrMys/HpOvqVNagns2FBjVTU1OVkJCgVatWKTMzU5LU0NCgtWvX6tlnn3VpbCUlJaqrrdX9v/i94lMubyV6Ye5hLX32SZWUlHg8SX6ivE6PLtumZptUvedLXXHTtW6795DuUfr4ies18x879MX+Iv32w716e9txPTm+13lX1mw+ekoz/75DuadqFREcoNemXaVu0fy+gL02+Y+v7a6/rDui3320T9ddEaMAE5MZz8er/+28/PLLqqio0MiRI5WYmOg43nrrLUmSyWTSrl27dNtttyk9PV1Tp05Venq6Nm7cqLCw9rdRSoDJTwvuHqiQQJM2HinV8ysPXPY1d5+o0KvrcyTZN0oJCnDd7Ir2KjI0UPdcbd/sZMHqgx7djO94mX0G+YnyOvWICdWbD7knQd6iZUb5lV3s9QRjbpmpPcXn3uQL6IgiggN0Tc9oSdLKPeeuFwkArsDzNbxJk7VZP3tzu0qqLeqdEKZZ13SWmi+trvX5NBQe1tPXRSkowE9rDhRrxt+z1Nzs/Gf11/+Vo6Zmm67pEa3+XTteXW3DMPTi3QMVG2bWwcJqPbJ0m+obz/7f8cv9RXpw8WY1Ndt028AkPXbjFW6O1nc8+uijWrJkiZYtW+YY1CwoKFBdXZ0k+7/36dOna968eVqxYoV2796tadOmKSQkRPfdd59bYoxP6amuaf0u67jcJLuz1Dda9dO/bdWpmgaldvbXqU9fkrsXiUeGBur/fjREv7s9Q+FB/tpzslLTXt+smxZ8pT+tOaStx07pRHmdjhRX6/0dJ/Xgos36wZ83KvdUrbp0DtY/fnqNBiR3dm/Q8Got1SIOFVVr+el9M3BuXj2T/ELJxuDgYH322WduisY7pMaEav4d/fWz5Vn605rDSo8P0+TMttXBrrY06Yk3t8vabNPEKxM1qjdLL9rqP0b21Jvf5GpbbrnWHizWSA8sYzlRXqd7/2pPkKfGnJ5BHu6+BHkLwzA0olesSk6V6WRdgP5nfZmGDaxSWjw/rIEW4/rG66vsEq3cW6iHR3jHDwMAHQPP1/AmL315SFuOlamT2V9//dEQFR/d7/R79IkN1F8eGKJ/W7xZH+7MV3RooObc2s9pJRJLqy1a9rU98fDvN/RwyjV9UVxYkP50/yA98OrXWnOgWPf+dZOevfNKpZ/+DVBZ36g/fnlIf113RM02aXTvOD1755VeUarSW7388suSpJEjR7Z6//XXX9e0adMkSU899ZTq6ur0yCOPqKysTEOHDtXKlSsZ1LxENptNv1qxS7tOVCgyJEC/uLaz1jR5ZrKXn5+h+4d20039EvTymsNa8vUxHSis0nOfnn2SpGFIPxjcVf85sa/CXbz3AnxPRHCAfj4mTb9+b49eWHVQtw7s0qFKgl0qr55JjrO7bWAXPTzC/gA26x87tPZg8SVfo7nZptnv7NKRkholRQRp7uQMZ4fZocSFBemBYd0kSb//7IBLZqicT35Fne59ZZPyTtWpW3SI3nxomOI9kCBv4WcYujq6SfUn9qmm0aZpr29WYWW9x+IBvM3YvvYakttyy5RfUefhaAAAcL8tR0/p/31u3wz2d7dnuHT/nBHpsfrfHwyQYUiLNx7Twi8OOe3af1pzWDUNVmV0CdcID5dd9LSrukfpjZ8MVZjZX9tzyzV+wTrd/Iev9IM/b9DVv1utv6y1J8inDOmqPz8wmFXMF2Cz2c56tCTIJfsEpTlz5ig/P1/19fVau3atMjL4bX+pFm04qne2nZDJz9Af7xukuFDPzyeN7mTWf07sq69/NUb/PTlDY/rEKSE8SP5+hsLM/uqTGK6Hrk/VFzNH6rm7BpAgxznde3WKrojrpLLaRv3+M+cPRrcnJMl91C/G99akAUlqarbp39/Yoi8PFF3S55/9bL8+2HFSJj9D/+/eTHUOYbPOy/XTET0VdnpJ1NvbjrvtvgUV9br3lU3KPVWrlCh7gjzh/7N353FRlfsfwD/DbOzDpsAIiPsGouK+hKZi5JKZmVqmZTfvTU1yKa1M7Fdy1ZtamC03E9Nc7i0xy1LRXDLUFEXFfUEBBRFkXwaYeX5/EHMbAQUEZob5vF+v86o55znnfM/DzHeO3znnOSrjFcjLSa2Au9//HzztpbiVVYiX1h1HnqbU2GERmQQPlTV6+rpACGBH3G1jh0NERNSgcopKMGtLHHQCeLprMzzVpXZ3xtbEU12aYdGIjgCAFdGXsfHozUfe5q2sQmz4czvzhrWHVR08L8rc9Wzhgl9CB2BYJ3cIAZxPycHxG5koKtGhTVN7fDkpEMvGBnBcXDIZR65l4IOdFwAAC0Lao29rNyNHZEhlI8ek3s3x1eQeOPr2YFxd8iTOLh6GX2YNwDvDO/L5RvRQMqkV3n+qEwBg49FE/JFwz8gRmS5+M5kpKysJPno2AIPbN4WmVIdXvzmBb4/dfOgttDqdwIc7z+OLg9cBAP8c44/uvnxQSl1wtVdi5p9j6i3ffQm5RSX1vs/U7CKM//IIbmQUwMvZBptf7Q21k02977e6dIU5WPiYC9zsFTifkoOZm06iVKszdlhEJqF8qKyoU7eMHAkREVHDEULg3ah43MoqhLeLjf4f7g1hSr8W+vP1hT/E46cztf+hWgiBRT+cQ3GpDr1buuCxNqZVWDMmL2dbfDGpO/54ZzA+e74b1jzfDT/N7I89bzyG4E4exg6PSK/8QZ1ancDoLmpM7d/i4SsRmaG+rdwwvkfZs/TmfXeaFzBWgUVyM6aQWeHzSYEYFaBGiVbgnah4vPbtSdzKqvzW/VtZhZgSeRz//q3sQZ3vDu+AZ7t7N2TIjd7kvr5o7mqLtFwNlu9+9AerPsj9BfItr/ZGMxMqkJfzsJfhq8k9oJRZYf+lu/pf6Yks3XB/TyikVriYmosLKTnGDoeIiKhBRJ26hR1/3tH68fiucGjgIQJmD22LCT19IAQwa0scfoir3Y/Vu+JTsffCHcilErz/lB/H1q5EUwdrhPh74kl/T/g1U7GPyKT89UGdHT0dET6GY+RT47bgyQ5o5mSDmxkFCNtxztjhmCTjD7REj0QutcLH47ugk9oRy3Zfwi9/nqwN6+SB/q3d4O5ojYz8Yvx+NR07z6SgWKuDQmaFf47xx5huXsYOv9FRyqT4cLQ/Xlh7DBuO3sSoAHW9XKlfWYHcy7n+xnF8VF28nbDyuS547duTiIy5AV9XW0zpx1/pybKpbOUY1L4Jdp+7g+9ik7Hwz1vAiYio8UpMTER6evojb8fNzQ0+Pj51EFHDupmRj4Xb4wEAoYPboJuPc4PHIJFI8MFoP5RodfguNhmhW+NQXKqr0cVDSfcKMH/bWQBlQy625QPqiczK/Q/q/GJSIGwUHCOfGjeVjRwrn+uC8V8ewXexyejh64znejT8uYROJ5BZUIyswhJoSnS4l2cFu06DkF9s/FEHWCRvBCQSCaYFtcKANk2w+MdzOJZwDz+dScFPZ1IqtO3ZwgVLnvZH66b2RojUMvRv44Znunnh+5NlJ907Xx9Qp08PvpmRjxe//gM3zaRAXu5Jf0+89UR7LN11Ee//dB4+rrZ4vL27scMiMqrnenhj97k72HYyGfOGteMDrIiIGrHExES079ABhQUFj7wtG1tbXLxwwawK5SVaHWZtiUN+sRY9fV3w2qDWRotFaiXBsmc6Qy61wuY/EjHvuzPILCjG3wa0fOiVpDlFJfj7xlhkF5YgwNsJMx433nEQUe3c/6DO+nxwMJEp6dnCBW8MaYuPoi/j3e3xaNnEHj3qeQhmIQRSc4qQkJ6PmxkFyMgvhlb316GiZXAbMQeZRdp6jaM6WCRvRDqqHbF1Wh/E38rGT2dScPZWFnKLSmEjl6KzlwpP+nuiqxGu1rBEi0Z1xB83MpB0rxBvfXcGa57vVicP8jmdlIWXI48jI78Y3i422Pw38yiQl/t7UEvcSM/H1hNJmLnpFP77977oqHY0dlhERhPUtik8VdZIyS7C7nOpDfLgsvpQXKpDWm4RMvNLUFBSCq1OQGolgZ1CBmc7BZo6KPmALiKyeOnp6SgsKMDzby2Hu0+rWm/nTuI1fLt0HtLT082qSP7x3iuIS8qCo7UMK8d3gdTID7m0spJgydN+UMqsEBlzA0t+vojTSdn4v9F+cLFTVLrOvfxivLTuD5y7nQMXOwU+e74blDL+wE1kTkz9QZ1E9W36oNY4dzsHu86lYmrkcWyd1gcdPOu+LpOnKUX8rWzE385GvsawAC6XSuBsq4CNQgpNfh4SLsTBWvZEncdQUyySN0J+zVTwa6YydhgWzdFajogJ3fDs5zHYdS4VH0Vfwrxh7R9pm3vP38HMzadQWKJFJ7Uj1r3UA00drOso4oYhkUjwwdN+SMosQMy1DExdfxzbp/eDu6N5HQdRXZFaSTCuuzc+3ncF3x5LNKsiuaZUiyt38nAhJQcpOUV40HOjrSSA2skG7T0c0NbdgQVzIrJo7j6t4NWm4R5WaQqOXs/ApweuAgCWjPE3mefoSCQSLBrZES2b2GHxj+ex82wKjl7PwD8GtsKz3b31d4OWaHX4+WwKPth5AXdzNXC2lWPD1J5Qm8hxEFH18EGdRGU/Eq98rgvS1x7DiZuZeP6rY4h8qQc6ezk98raFELiVVYgzydm4djcP5ReMK6RW8HW1RYsmdvBU2cDRWqa/cyv5yjkcW/we3N4a/sj7f1QskhPVky7eTggf0xlz/3san+6/BnulHP8YWPOrhkq0Ovxr9yV8ceg6AGBAGzd89kIg7JXm+fGVS63w2fOBGPPZ77h2Nx9T1x/Hf6b1ga3CPI+H6FGN7+mNT/dfxR8J9xB/K9vkf+SUOjZBbIYUyckJKP3LbXIO1jK42ilgp5RBZiVBqU4gT1OK9DwN8jVaJGcWIjmzEL9dSUcHT0d0b+4MOzPNY0REVH3ZBSV4Y2schADGdffCiM5qY4dkQCKR4MU+vgjwcsK8707j8p08fLDzAv75y0W0cXeAjdwK1+7mI7uwBADQuqk91jzfjeOQE5mZwmItpm04wQd1EgGwUUixdkoPvPDVMZy9lY3xXx7F0mc6Y2RA7b6ji0t1uJiagzPJ2cjIL9bPVztZI8DLCa2a2Bv9DrLq4L9OierR2EAv3M4qxIroy1i66yIyC4rx5rB2kFXzKsob6fl44z9xOJWYBQCY3Kc53hneEQqZeV+FqbKVY92Unhi95nfE38rBrC1x+PyFQLNImkR1zVNlgyf9PbHj9G2sPZyAlc91MXZIlcrI02DtqWw0+9uXuJEvBSDgYqtAR7Uj2jS1h+MDnr2QVVCMq2l5OHsrGzlFpYhLysK529kI9HFGt+bOvLKciKiREkJgQdQZpGQXoYWbHRaNNN0r6AO8nbDz9QHYdjIZaw8n4PKfd0uVc7NX4oXePvh7UCs+Q4TIzAghMPe/pxF/q2yoJD6ok6jsQZ6bX+2Nv2+IxeGr6Zi5+RR+vZiGBSHt0bQad/sLIaDwbIvYDClu3bqOEm3ZBVQyKwnaezqgczMnNHFQ1vdh1CkWyYnq2euD20BqJcHy3Zfw5aHrOJWYiQ9G+6OdR9VXn9zLL8aXh67j68MJKNbq4Ggtw7KxnfGEn2cDRl6/fFxt8e8XAzHh38cQff4O/vnLBbwzvKOxwyIyilcGtMCO07fx4+nbePOJdvBUmc7t20IIRJ26hfd/Oo+sghJIZHI0UeoQ5OcDtcq6WlfgONkq0N3XBYHNnXEzowDHEu4hNacIRxPuIf52DgZ3aApfV7sGOBoiImpI/z2RjJ/PpkJmJcHH47uY/B1EcqkVnuvhg+d6+CAxowDX0vNQWKyFj4st2nlwuDAic/XJvqvYeTYFcqkEnz3PB3USlbNXyhD5Ug+s3HsZaw5cQ9SpW/j5bAqe7toMwzt7IrC5s8Fd/1kFxTiVlIWDl+5i95m78HxxBW7kA4CAs60cnb2c0MHTwWyf12HaZylEjcT0Qa3Rws0O8/57GsdvZCLk40MY3MEdIzp7opNaBUcbGXIKS3Dudg5+vZiG3edSUVSiA1A2vMqSp/0b5Rd5YHMXLB/bGbO2xOHfvyXA180Oz/dqbuywiBpcZy8n9GrhgmMJ9/DZgWt4/yk/Y4cEAEjJLsRb35/Foct3AQDNVTL88cVbGDNvUa3Gk5VIJPB1s0NzV1tcScvD71fTkVNUih/ibsNP7YgBbZqY/Z0yRERU5mpaHhbtOAcAmDusXZ2MddqQfFxt4ePa+M6/iSzNT2duY+XeywCAD0b7oVdLVyNHRGRaZFIrzBvWHsEdPRD24zmcSszCluNJ2HI8CUDZnVRKmRXyNKX6ocfK6Uo08FXJ0LN9c6idqncBlSljkZyogTzp7wn/Zip8uPMCdp1LRfT5O4g+f6fK9v7NVHh9cBsM6dDU7BPNgzzVpRluZhRgRfRlvPfDOXg72+Kxtk2MHRZRg5s1pA0m/vsYtvyRhH8MbGX0q8mjz9/BvO9OI6ugBAqZFWYNboMeDtno9fZpPGpKkkgkaOvugBZudoi5moG45CzE385BUmYhhvt7mt1teUREZKioRIsZm06isESLfq1d8eqAlsYOiYgsUMzVdMzeehoA8HK/Fniuh4+RIyIyXQHeTtj2j744fiMT38Um4eDlu7iTo0F6nsagnZezDQa0cYO3LA8znh2CZ1d+i2bOpnMn9KNgkZyoAXm72OLzSYG4mpaL/55IxtHrGbh2Nx95mlI4WMvQws0OPXxdMDJAjQAvVaMujv/VzMdb40Z6PraduoXXvj2JTX/rZXZXGxE9qj4tXdGzhQv+SLiHT/ZdRfgYf6PEoSnVIvzni4iMuQGg7Ae7VeO7oFUTe5w8ebJO9yWXWiGoXRO0amqHPefvILuwBFtPJOHxdk3RUe1Yp/siIqKGs/jHc7iYmgs3eyVWPtcFVnzuDBE1sLPJ2fjbNydQrNUhxM8D7wzvYOyQiEyeRCJBzxYu6NnCBUDZUMAp2YUoLtXBwVoGd0drOFiXPYvq5MmTEMWFxgy3zrFITmQErZs6YMGT//uSFkJYTEG8MhKJBOHP+ON2diGOXr+HF7/+A1te7Y32HiySkeWQSCSYN6wdnv38CLYeT8SLfZqjg2fDfgYS0vMxY9NJnLtd9qCyV/q3wJtPtK/3IVC8nG0xsacPdp9LxY2MAkRfuIOU7EK0Ns+h7IiILNoPcbew+Y8kSCTAx+O7oKnDwx/+RURUl66m5WLKuj+QX6xF31auWDW+C6T8sY6oxlzsFHCxUxg7jAbDgT+JTIAlF8jLKWVSfDW5B7p4OyGroAQvfPUHrt/NM3ZYdUpTosWtzELEJWXhwKU0/Hw2Bf+NTcLeFBk8p36Kw4mN61dYqrkevi4Y7u8JnSi7Ck8I0WD73nkmBSMjDuPc7Ry42Cnw9ZTueHdExwYbI9xaLsWoADV6tyy7aiH+dg4O3pFB6uDWIPsnIqJHd+52NuZ/fxYAMHNQa/RrzRxORA0r/lY2xn1xFBn5xfBvpsKXL3Y324cIElHD4pXkRGQy7JUyrH+pJyb8+yjOp+Rg/JdH8e0rvdDG3cHYodWKVieQnFmAxHtlU3pecRUtraBwa45sja5B4yPTND+kPfZeuIOj1+/hPyeS6n3sxOJSHZb8fEE/vErPFi74ZHxXeKga/so/iUSCXi1c4eFojV3xqcgsBjwnr8SF9GJ0a/BoiIioJu7mavC39SdQWKJF/9ZueH1wG2OHREQWJvbmPUxZdxy5RaXwb6bC+pd7wl7JshcRVQ+zBRGZFJWtHBum9sTEfx/DpTu5GPfFEXzzci/4e6mMHVq1CCFwO6sIl+7k4kpaLopKDAvfDtYyuNkr4WKngL1SBluFFDl3kvD9J++h94i1RoqaTIm3iy3mBrfDhz9fwAc/XUD/Nk3QzKl+HoSSnFmA6ZtO4XRSFgDgHwNbYc7QtpBJjXujWXNXO0zo6YNtx68j284Ziw5kwMopERN6Nq6HLSUmJiI9Pb3B9ufm5gYfn8bVh0RkGjSlWvx9YyxuZxehpZsdPp3YzejfJURkWXacvo23vjuDwhItevq6YO2U7vqxk4mIqoNFciIyOa72Smyd1huTv/4Dp5OzMeHfR/Hli4Ho28p0b9nN15Qi/lY2zqXkILeoVD/fRi5FCzc7+LjYwtvFBraKimk3OUdAk3gWrra8DZDKvNy/BX6OT8GpxCy89u1J/Gda7zq/TXTfhTuY/Z/TyC4sgcpGjhXjAjC4g3ud7uNRONrIMdC9FJv2/gG79v2xYNtZXEjJwcIRHSFvBIWXxMREtO/QAYUFBQ22TxtbW1y8cKFRFsob8gcH/thAZEirE5j33zOIvZkJR2sZvprcHSpbFqaIqGGUaMvuilz3+w0AwMB2TfDZ84GwUfDfVkRUMyySE5FJcrJV4Nu/9cbUyOM4lnAPL679A2GjOuGF3s2NHZpe+VXjZ5KzcPVuHnR/Dh+tkFqhVVM7tHN3gLezLaz4kBiqIamVBJ+M74oREYdxOikLC7fHY+kznevk+QUFxaVY8vMFbDyaCAAI8FJh9cRu8HaxfeRt1zWZFZD+wz/xt7EHsCk+D98cuYlLqblY83w3uNorjR3eI0lPT0dhQQGef2s53H1a1fv+7iRew7dL5yE9Pb3RFXgb+geHxvxjA1FN6XQCb287ix2nb0NmJcHqid3Qsom9scMiIgtx7nY23omKR9yfd0VOH9QKs4e240M6iahWWCQnIpNlr5Rh/cs98eZ3Z7Dj9G28uz0eF1Nz8N6ITg32MMHKlGp1uHQnF3FJWQbjjHuqrNHZS4XWTex5izE9Mm8XW6wa3wVTI4/jPyeS4WyrwPyQ9o9UKD9+4x7m/fc0bmSUFRNf6ueLBSEdjPp5qo6xHR0wqFt7vLE1DscS7mHU6t/x5YuB6KQ2j2GYHsTdpxW82nQydhhmrSF/cGjMPzYQ1ZQQAot/PIetJ5JgJQE+Ht8Vj7VtYuywiKgBxKVq4ND9KVzJsUJ6UhakEgms5VawUUhhp5TB0Vper4Xq7MISrIy+jG+O3IBOAA5KGT4aF4DgTh71tk8iavxYJCcik2Ytl+Lj8V3Q3tMBy3dfwsajiYi9mYWVzwWgvYdjg8aSrynFmeRsnL2VjcISLQBAZiVBOw8HBHg5oYmDeV/ZSqZnULumCB/jj7e+P4svDl1HTlEJ3n/Kr8bDjdzJKcI/f7mIqFO3AJT9oPOvZwPQr7XpDmF0v6Ed3RH1Wl/87ZsTuJFRgLGfHcG/ng3A8M6exg6NTAR/cGg86mr4HA6NU3+KS3VYuD0eW08kQSIBPhrHfExkSQ4nFsJl8N9wJgtA1t0KyyUSwNFaDicbOZxtFXCxV8DVrmxSyms/DMrVtDysj7mB708mo6C47N9jwzt74t3hHeCpqp9n+BCR5WCRnIhMnkQiwWsDW6OduwPmfXcGF1JyMCrid7wxtC1e7u9b52M130/h0QZ/pEtxKylBP6SKvVKGAG8V/NQqWD/CiR7RwzzXwwfFpTos2nEOm/9IwvnbOfjXswFo4+7w0HVvZxViw9GbWB9zAwXFWkgkwLhAb7w9vANUNuY3Xmwbdwf8ML0/Zmw+id+upGP6ppO4kNIas4e25bBGRI1EXQ6fY85D4wghUKzVQfOXB4BLrSSwMYFzjqyCYvxj40kcuZ4BKwmw5Gl/PN3Vy9hhEVEDauuqwI6dP6NDj8dgY69CqU6gqESLwhItcotKUaoTyC4sQXZhCW7eM8zn9koZXOwUcP2zcF6qkUDq2ATFWmHQLk9TiluZhbiRkY+j1zNw+Eo6rqTl6Ze393DAO8M7YEAb3sFCRHWDRXIiMhuDO7hjd+hjWLDtDPZeSMPSXRex6Y+bmDesPUb4e9ZpkexefjF+iLuFb367C8/JK5H057mdp8oaXb2d0KqJPYty1GAm9fGFp8oGs/8Th9PJ2Ri26hBGBajxbHdvBDZ3NvihJqugGEevZ+DH0ynYdS4V2j9/2enm44SwUZ3Q2cvJSEdRN1S2cqyb0gPLdl/Cl4euY/X+qzhzKxsfPRvAuzmIGoG6Gj7HnIbGydeU4lZWIdJyNcjI0yCzoAR5mlJ9/r6fwkoOjxdX4JNjWeidew2dvVTo4u1U6cPB61r8rWzM3HwKCen5sFfKEDGhKwa1b1rv+yUi0xLcyhYLfvoIPUP6wauN4V0kQgjkF2uRVVCMrIISZBYUIyO/GBl5xcjTlOqnRH3xXA6vf6zD+O9Tofjhlz83AhRrdbifRAIMbu+Ol/v5ok8r1zp5Xg8RUTkWyYnIrDRxUOLfL3bH9ydvYdmui0i6V4jXN5/C6l+v4PlezTG6a7NaXyGbU1SCQ5fv4qfTKdh38Q5K/ryaQWhL0NzBCn07+cLd0bouD4eo2oZ0dMfuNx7Doh/OYc/5O9gedxvb48oelKZ2soFSZoXMghKk52kM1uvd0gUv9WuB4I7ujeYfEjKpFd5+sgM6eDpg/vdncejyXYR8fAjLnw3AoHYs1hA1Bo15+BwhgNTsIlxKzcXNe/nILCipsq3USoLyzF36Z9G8WCeB0rMtDtwsxIGbFwGUDf/WqZkKPZo7o1dLV/Rs4VKndwwVFmvx6f6r+PzgNZTqBJo52WDtlO4NPvQdEZk+iUQCe6UM9koZvJwNl2lKtMjIL8a9P4vmGfkapOcUoKC4FBKpHMWlhoVxJ1s5mjnZoLOXCv1bN0HfVq5wtlM04NEQkSVhkZyIzI5EIsHYQC886e+Brw8n4POD13H5Th4W7TiH8F8uoH/rJujX2hV9WrmihZtdpcOx6HQC6XkaxN/ORlxiFv64cQ8nbmTq/wEKAP7NVOjVVGDRlOEY+9F6FsjJ6DxVNvjyxe44k5yFzX8kYs+5O8jIL/7LlThlWrrZYWC7phgb6IWO6sZbwHi6qxc6eqrw+uZTuHQnFy+tO46JvXwwP6Q9HK3NbziZqpTqdMgpLEV2YQlyikpQWFx2O3NJqQ5aIaDTAVZWgMzKCgpp+UOzpPrbme2VPN17VFqdQKlOhyItIHVogqLSile3ET1MUnYJnAa8gN0pcuQnJRksa+KghIejNVztFXCzU8LeWgZbhdTgGRQ6IVBYrEXC1cvY/NkyzFoYjhwrB5y8mYmU7CKcTsrC6aQsfHU4AVYSwN/LCX1buaJvK1d0b+4CG0XNh2rJ05TivyeS8PnBa7iTU/YjbIifBz582h8uLFQRUQ0p5VKonWygdvrf+OHJV85hxfQxOHTkOFq06wiJpOzHQQdrGRwa0fkcEZk+/quJiMyWrUKGGY+3waQ+vog6mYxNfyTi8p087L1wB3sv3NG3c3dUoomDElYSCYQoexp6anZRpbfwtWpihyEd3DG6azN08HTEyZMnsbAotyEPi+ihOns5obOXE5Y8LXA7uwip2YXQlOjgaCOHj6ttoyoQP0w7Dwf8MKMf/vnLRUTG3MCmY4nYd+EO3n/KD8M6eRg7vBor1eqQlqspm3KKkJarwb38YlQ+6EL1KKRWsJfK4DJsBqKvF8DRKxetmtg3mjsL6oIQAjlFpcjI0yA9rxhZBcXIKy5FvkaLfE0pNPqiuAJer61DXKoGfY0aMZmLW1mF+PH0bfwQdxsXUnKg6jse+aWAXCpByyb2aNPUHs2cbKr1fBMriQR2ShmcFAKFl4/g2Y4O6NatG4QQuJVViBM3MnEs4R6OXc/A9fR8fdH8swPXoJBaoauPEwKbO6Oj2hEdPB0r3a9OV7atk4mZ2H8xDXvO39E/HK+Zkw3eHd4BT/h5MH8QUZ2zU1jBy9nW2GEQkQVjkZyIzJ7KRo4p/Vpgcl9fxN/KweGr6Yi5lo6TNzORX6zFnRyN/uqnv5JIgFZN7BHg5YQu3ioMaNMEvm52RjgCotqRSCRo5mSDZn+5GscSWculCBvVCcM6eeDtqLNISM/HtA2xGNDG7c9hWUz3anqdKHvAanJmIZIyC5CSXVTpOMRyqQQqGzlUNnLYKKSwlcugkFnBSgJYWUmg0wmU6ARKSnUoKC4r7OYUlSCrsATFWh3uaa3g0OUJfHYiG5+dOAQnWzm6N3dG75aueKxtE7RpallF81KtDqk5RUjOLMStzELcyS3SD7H1MKK0GFUMFU0EoOy5Jj+fTcGOuNv448Y9/XyZFZBz6RgG9g5Ed/92BleJPwqJRAIvZ1t4OdtidNdmAMryypFrGYi5loGYa+lIyS4qK6An3DNY19FaBmc7BUq1AiVaXVnOuO9OiZZudnipfwuM6+5V7w9LJyIiIjIWFsmJqNGQSCTw91LB30uFfwxsBSEE7uUXIzmzEPfyi/Xt7JQyeKqs4aGyrrN/oBKR8fVp5YpfZg3AJ/uu4N+/XcdvV9Lx5Ce/YUxXL/xjYCu0bmpv7BCh0wmcT8nB9ot5aDo2DDuS5dAmJRu0sVVI4e5ojaYOyj8na9gppbUqYpfqdMgqKMGVq9cR/VMU+ox6HtcyS5FVUIK9F9Kw90IasPMC3B2VGNCmCQa0cUP/1m5wtW9cD0H9a1E8ObMQqTkVf4yQSiRwtpPDzV4JZzsFHJQy2CllsFNIYaeUQSaVIOXqBaycMQZ9J8Ya6UjIVOUWlWDfhTTsOH0bhy7f1Q/fJpEAvVq44KkuzaDWpmFg+P/Be/C2ej//UDvZ4JlALzwT6AUhBG5kFODItQycvZWN87ezcelOLopKdMgpKkVOUanBunKpBB09HdGrpSuCO7ojsLmzRf2IRkRERJaJRXIiarQkEglc7ZWNrthDRFWzlkvx5hPtMb6HD5buvoidZ1Lw/clkfH8yGUM7uuPlfi3Qq4ULrKwapuAjhMDNjAL9HS5HrmXoH9Jn06o7tAKwllv9eRWoDbydbeFsK6+zgpTMygpu9koU2emQdWg9Plj5Ovw6d8G529k4fuMeDl/NwLHrGbiTo8F3scn4LrasYO/XzBH9WzdB/9Zu6O7rXK2hIExJqVaHlOwiJGeVXSleWVHcViGFl7MNvJxsoXayhrOt4qHvC9YJ69eZ5CzsSyiAbYcg3CqQoDg9DzIrKyhlVrBVSGEjl0JmQj9uZxeUYO+FO/glPgWHLqcbDOPm18wRTwU0w4gAT3iqyu72OXky3ShxSiQStHCzQ4u/3C0nhEBOYSnScouQVVgCudQKcqkEjtZyeKqsTaqfiYiIiBoCi+RERETU6Pi42uLTid3wSv9MrDlwDdHn7+gnL2cbjOnaDMGdPNDR07FOC+Y6ncDVu3k4lZiJEzcyEXMtA7eyCg3a2CtlaO8ixZ6NEXj2+ZfQqWOHBr1KUyGzQlcfZ3T1ccarj7VCUYkWJ25k4rcrd3HoSjoupOQg/lbZ9PnBa1DIrNDD1xn9WpddZd5JrYK0gX5kqC6JwgZ3CiVIup5RVhTPLoJWGBbF7RRSNHO20f8g4WRTdz9GUN34JT4Vnx3PRpNR83A0HUB6SoU2cqkE9koZHG3kcLSWw9H6f//v8OfDLuvr76op1eJUYhZirmXgyLV0nErMMnjgd0s3O4wIUGNUgNok7lx5EIlEApWtHCpby3mGBREREdGDsEhOREREjVZXH2f8+8XuuJqWh7WHE/DT6dtIzizEJ79exSe/XoWbvQL9W7uhs5cT/Jqp0M7dAY42socW2cqHc7p2Nx9X0/Jw7W4eLqbm4ExSNnI1FYcu6OrjjP6t3dCvtSs6eznh7Ok4fB+6A04vTTF6odZaLkX/Nm7o38YNCwCk5Rbh96vp+P1qBg5fSUdqThF+v5qB369mYBkuQWUjRzcfJ3T1cUY3H2d09lY16MNidTqBpMwCnErMwomb93D44l14z9qCw3elwN3/jbdsp5DqC+LNWBQ3Cy3c7NDNU4nfj/wBr3b+kCpsoNUJFJVoUViihU4AJVqBzIIS/R0Z95NZlV0NLdfJ4DL079h+MQ+p8hR4OdvA1V4JZ1s5bOQPLqQXlWiRml02PM+NjHxcTM3B+ds5OHc75y8PcS3Tzt0BIf4eCPHzRFt3yxrbn4iIiKgxaTRF8jVr1mD58uVISUlBp06dsGrVKgwYMMDYYRER0X2Yr8kYWje1R/gYfywa2RF7zt/BjrjbiLmWjvS8YmyPu43tcbf1be0UUnioyobfsJZLYS23QqlOQFOiQ0FxKdLzipGep6lQLCtnI5eis5cKXX2c0bulC3q2cIGtwnxOuZo6WOPprl54umvZWMbX0/Px+9V0HL6SjiPXM5BdWIL9l+5i/6W7AMqGIPFxsUVbdwe0dbdHW3cH+LrawdPJGm52ylpfqV9YrEVyZgGSMwuReK8Al+7k4mJKDi6l5iK/WGvQVmIlha1UwLuJI7ycWBSvKw2dr8d190Zrq7sIDH0HL3y6DV5tfPTLhBAoLtWhoESLvKKyB9PmFP753z//P09TilKdwL2CYgBWcOg2At+cycU3Z04a7EchtYLKVg6F1AoSSdl7WAIJikq0yCkqQVFJ5Z9tAHCzV6JvK9c/Jzf4uNrWV3cQEVUbz6+JiB6d+fyL7QG2bt2K0NBQrFmzBv369cMXX3yBkJAQnD9/Hj4+Pg/fABERNQjmazI2a7kUo/4cDqG4VIcTN+/h6PV7OH87G+du5yAluwj5xVpcu5sPIP+B25JIgGZONmjd1B6tmtijdVN7dPYquxq9sYznK5FI0KpJ2fG92McXpVodzt3OwanETJxMzMKppEwk3SvEzYwC3MwoQPT5Owbry6USuDtaw81eCQdrGewUMthby6CUWUGgrPCp0wElWh2yC0uQXViCrMISZOYXI+MvD1y+n0JqhQ5qR3Rv7gxnbSZCXxiFWUu/glcbj3ruEcthavlaIpFAKZdCKZfC2VZRaRutTiC3qAQ5RaW4efMm9v/0PUaMn4x8WONWVtlDvEu0AsVaHe7mah64Pxv5n2PWO9ugnYcjOng6oJNahVZN7PjjCxGZFFPL10RE5qpRFMlXrFiBqVOn4pVXXgEArFq1Crt378Znn32G8PBwI0dHRETlmK/JlChkVujbyg19W7np5xUWa5GSXYiU7CLk/nlFaVGJFjJp2cMDreVSuNor0MReiSYOSrN7oOWjkkmtEODthABvJ0zpVzYvPU+Dy6m5uHwnF5fu5OHKnVwkZxYiLbcIJVqB5MxCJGcWPnjDVXBQyuDlUjZkSpum9mjv6YgOHg7wdbOD/M8fIk6ePAlt3r2HbIlqyhzztdRKAidbBZxsFbDK0CHqtw14Y1UounXrBqDsR5mCYq3+h5hSnYAQAuWjiiukVlDZyP8c4/zhwy4REZkCc8zXRESmyOyL5MXFxYiNjcX8+fMN5gcHByMmJqbSdTQaDTSa/109kp2dDQDIycmp0b7z8vIAAMlXzkFTWFCjdavrbnICACA2Nla/v/pgZWUFna7qW0sf1aVLlwCwr0xlH/x7VF9D9lVeXl6N8lB5W3Hfw+lMVWPP1+Ua6r3/Vw2Rl4CG+Tz8VUP35f39aPvndL+8dCAPQMIj7Ksx9mVLAC3dgBA3wMpKieJSGbKKdMgo1CGvWIfCEh0KSwWKSgSKdQJWfw5zYQUJpBLAVmEFO4UE9nIr2Cms4GZrBTu5FQABoKBsyk5DSjaQcul/+23IvmS+Np98Xdv3fGU/t9RVjq2r92pdfp7r8vujLrZVl59nU+wnvpcerq6Pjfm6orrI13V5bm0J70NTOra62pYp9hHAfF0dpnpsJpGvhZm7deuWACB+//13g/kffvihaNu2baXrLFq0SKDsX1ycOHHiZPZTUlJSQ6TbR8Z8zYkTJ0ufmK85ceLEyTwm5mtOnDhxMo+pLvO12V9JXu7+2yGFEFXeIrlgwQLMnj1b/1qn0+HevXtwdXWt0W2VOTk58Pb2RlJSEhwdHWsXuBEw7oZljnGbY8yA5cUthEBubi7UanU9Rlf3mK+rzxzjNseYAcbd0Cwtbubrxp+v6wv7oyL2SUXsk4qYr+s3X/M9VxH7pCL2SUXsk4pMKV+bfZHczc0NUqkUqampBvPT0tLg7u5e6TpKpRJKpdJgnpOTU61jcHR0NMs3N+NuWOYYtznGDFhW3CqVqp6iqXvM17VnjnGbY8wA425olhQ383XNmOt7o76wPypin1TEPqmI+bqiuszXfM9VxD6piH1SEfukIlPI11Z1ujUjUCgUCAwMRHR0tMH86Oho9O3b10hRERHR/ZiviYjMA/M1EZF5YL4mIqo7Zn8lOQDMnj0bkyZNQvfu3dGnTx98+eWXSExMxN///ndjh0ZERH/BfE1EZB6Yr4mIzAPzNRFR3WgURfLnnnsOGRkZeP/995GSkgI/Pz/8/PPPaN68eb3uV6lUYtGiRRVuVTJ1jLthmWPc5hgzwLjNAfN1zZhj3OYYM8C4GxrjNn3M16aB/VER+6Qi9klFltQnxsjXltS/1cU+qYh9UhH7pCJT6hOJEEIYOwgiIiIiIiIiIiIiImMw+zHJiYiIiIiIiIiIiIhqi0VyIiIiIiIiIiIiIrJYLJITERERERERERERkcVikZyIiIiIiIiIiIiILBaL5FU4dOgQRo4cCbVaDYlEgu3btz90nYMHDyIwMBDW1tZo2bIlPv/88/oP9D41jfvAgQOQSCQVposXLzZMwH8KDw9Hjx494ODggKZNm2L06NG4dOnSQ9czZp/XJmZT6O/PPvsMnTt3hqOjIxwdHdGnTx/88ssvD1zHFN7bNY3bFPq6MuHh4ZBIJAgNDX1gO1Poc3OyZs0atGjRAtbW1ggMDMRvv/32wPam0r81idtU3tP8fmy4/jbH70aA349UO7XJLY1ZbT//jVltPqOWprrnmY1ZWFhYhe8SDw8PY4fVqDBfG2K+roj5+sGYq8uYYr5mkbwK+fn5CAgIwOrVq6vVPiEhAU8++SQGDBiAU6dO4e2338brr7+O77//vp4jNVTTuMtdunQJKSkp+qlNmzb1FGHlDh48iOnTp+Po0aOIjo5GaWkpgoODkZ+fX+U6xu7z2sRczpj97eXlhX/+8584ceIETpw4gccffxxPPfUUzp07V2l7Y/dzbeMuZ+z39l8dP34cX375JTp37vzAdqbS5+Zi69atCA0NxTvvvINTp05hwIABCAkJQWJiYqXtTaV/axp3OWO/p/n92HD9bY7fjbWNuxy/Hy1XbT+jjdWjfI4aq9qeC1qK6p5nWoJOnToZfJecPXvW2CE1KszXhpivK2K+rhpztSGTy9eCHgqAiIqKemCbN998U7Rv395g3rRp00Tv3r3rMbIHq07c+/fvFwBEZmZmg8RUXWlpaQKAOHjwYJVtTK3PqxOzqfa3s7Oz+OqrrypdZmr9/FcPitvU+jo3N1e0adNGREdHi6CgIDFr1qwq25pyn5uinj17ir///e8G89q3by/mz59faXtT6d+axm1q72kh+P3Y0Mzxu1EIfj9SzVXnM2ppqvM5skQP+oxakpqcZzZ2ixYtEgEBAcYOw2IwX1fEfF055mvm6vuZYr7mleR15MiRIwgODjaYN2zYMJw4cQIlJSVGiqr6unbtCk9PTwwePBj79+83djjIzs4GALi4uFTZxtT6vDoxlzOV/tZqtdiyZQvy8/PRp0+fStuYWj8D1Yu7nKn09fTp0zF8+HAMGTLkoW1Nsc9NVXFxMWJjYyv0V3BwMGJiYipdxxT6tzZxlzOV93R1mUJ/PwpT6m9z/G4E+P1IVBdq8jmyBDU5F7QENTnPtARXrlyBWq1GixYtMH78eFy/ft3YIZEFYb42xHz9P8zVFZlavpYZde+NSGpqKtzd3Q3mubu7o7S0FOnp6fD09DRSZA/m6emJL7/8EoGBgdBoNNiwYQMGDx6MAwcO4LHHHjNKTEIIzJ49G/3794efn1+V7Uypz6sbs6n099mzZ9GnTx8UFRXB3t4eUVFR6NixY6VtTamfaxK3qfQ1AGzZsgUnT57E8ePHq9XelPrc1KWnp0Or1VbaX6mpqZWuYwr9W5u4Tek9XROm0N+1YWr9bY7fjQC/H4nqQnU/R5agJp9RS1HT88zGrlevXvjmm2/Qtm1b3LlzBx988AH69u2Lc+fOwdXV1djhUSPHfP0/zNeGmKsrMsV8zSJ5HZJIJAavhRCVzjcl7dq1Q7t27fSv+/Tpg6SkJPzrX/8yWtFlxowZOHPmDA4fPvzQtqbS59WN2VT6u127doiLi0NWVha+//57TJ48GQcPHqzyS8tU+rkmcZtKXyclJWHWrFnYs2cPrK2tq72eqfS5uaisvx7UV6bSvzWJ21Te07VhKv1dE6bW3+b43Qjw+5GoLtTk89/Y1fQz2tjV9jyzMQsJCdH/v7+/P/r06YNWrVph/fr1mD17thEjI0vAfP0/zNf/w1xdOVPM1xxupY54eHhUuPovLS0NMpnM7H6x7t27N65cuWKUfc+cORM7duzA/v374eXl9cC2ptLnNYm5Msbob4VCgdatW6N79+4IDw9HQEAAPv7440rbmko/AzWLuzLG6OvY2FikpaUhMDAQMpkMMpkMBw8exCeffAKZTAatVlthHVPqc1Pn5uYGqVRaaX/df4VnOVPo39rEXRlj5uvqMoX+rivG6m9z/G4E+P1IVBce9XPU2DzquWBjU5vzTEtjZ2cHf39/kz9fIvPHfG2I+fp/mKurxxTyNYvkdaRPnz6Ijo42mLdnzx50794dcrncSFHVzqlTp4xyO/aMGTOwbds2/Prrr2jRosVD1zF2n9cm5soYo7/vJ4SARqOpdJmx+/lBHhR3ZYzR14MHD8bZs2cRFxenn7p3747nn38ecXFxkEqlFdYx5T43NQqFAoGBgRX6Kzo6Gn379q10HVPo39rEXRlTyB8PYwr9XVcaur/N8bsR4PejOb63yfTU1eeosavpuWBjU5vzTEuj0Whw4cIFo3+fUOPFfF09lpyvmaurxyTydQM9INTs5ObmilOnTolTp04JAGLFihXi1KlT4ubNm0IIIebPny8mTZqkb3/9+nVha2sr3njjDXH+/Hmxdu1aIZfLxXfffWfSca9cuVJERUWJy5cvi/j4eDF//nwBQHz//fcNGvc//vEPoVKpxIEDB0RKSop+Kigo0LcxtT6vTcym0N8LFiwQhw4dEgkJCeLMmTPi7bffFlZWVmLPnj2Vxmzsfq5t3KbQ11W5/0nWptrn5mLLli1CLpeLtWvXivPnz4vQ0FBhZ2cnbty4IYQw3f6tadym8p7m92PD9bc5fjfWNm5T6G9z/X5sLB72GbU01fkcWZqHfUapzP3nmZZmzpw54sCBA+L69evi6NGjYsSIEcLBwUF/fkWPjvnaEPN1RczXD2fpuVoI08zXLJJXYf/+/QJAhWny5MlCCCEmT54sgoKCDNY5cOCA6Nq1q1AoFMLX11d89tlnJh/30qVLRatWrYS1tbVwdnYW/fv3Fzt37mzwuCuLGYBYt26dvo2p9XltYjaF/n755ZdF8+bNhUKhEE2aNBGDBw82+LIytX4uV9O4TaGvq3L/F6Kp9rk5+fTTT/Xvj27duomDBw/ql5ly/9YkblN5T/P7seGY43djbeM2hf421+/HxuJhn1FLU53PkaV52GeUylh64eW5554Tnp6eQi6XC7VaLcaMGSPOnTtn7LAaFeZrQ8zXFTFfP5yl52ohTDNfS4T48wlDREREREREREREREQWhmOSExEREREREREREZHFYpGciIiIiIiIiIiIiCwWi+REREREREREREREZLFYJCciIiIiIiIiIiIii8UiORERERERERERERFZLBbJiYiIiIiIiIiIiMhisUhORERERERERERERBaLRXIiIiIiIiIiIiIislgskhM9osjISDg5OT3ydgYOHIjQ0NBH3g4RUWMlhMCrr74KFxcXSCQSxMXFPbD9jRs3DNodOHAAEokEWVlZ9R4rERGVqWnubmhTpkzB6NGjjR0GEVGDMHZOZt2DTJnM2AEQGcOUKVOQlZWF7du3N/i+Dxw4gEGDBiEzM9OguL5t2zbI5fIGj4eIyFzs2rULkZGROHDgAFq2bAk3N7cHtvf29kZKSspD2xERUf2pae4mIqL601A5mXUPMkcskhOZCBcXF2OHQERk0q5duwZPT0/07du3Wu2lUik8PDzqNIbi4mIoFIo63SYRUWNW09xdV5iviYgqelhOru/cyboHmTIOt0KN2nfffQd/f3/Y2NjA1dUVQ4YMwbx587B+/Xr88MMPkEgkkEgkOHDgQKW34cfFxUEikeDGjRv6eZGRkfDx8YGtrS2efvppZGRk6JfduHEDVlZWOHHihEEcERERaN68ORISEjBo0CAAgLOzMyQSCaZMmQKg4m1Hvr6++OCDD/Diiy/C3t4ezZs3xw8//IC7d+/iqaeegr29Pfz9/SvsKyYmBo899hhsbGzg7e2N119/Hfn5+XXToURERjJlyhTMnDkTiYmJkEgk8PX1xa5du9C/f384OTnB1dUVI0aMwLVr1/Tr3D/cyv3CwsLQpUsXg3mrVq2Cr6+vwX5Hjx6N8PBwqNVqtG3bFgBw69YtPPfcc3B2doarqyueeuopg++KAwcOoGfPnrCzs4OTkxP69euHmzdv1lV3EBGZhUfJ3f/5z38wYMAA2NjYoEePHrh8+TKOHz+O7t27w97eHk888QTu3r1rsK/a5GsiIktRWU4eOHAgZsyYgdmzZ8PNzQ1Dhw4FAJw/fx5PPvkk7O3t4e7ujkmTJiE9PV2/LSEEli1bhpYtW8LGxgYBAQH47rvvAJTlcdY9yByxSE6NVkpKCiZMmICXX34ZFy5cwIEDBzBmzBgsWrQI48aNwxNPPIGUlBSkpKRU+8qWY8eO4eWXX8Zrr72GuLg4DBo0CB988IF+ua+vL4YMGYJ169YZrLdu3TpMmTIFPj4++P777wEAly5dQkpKCj7++OMq97dy5Ur069cPp06dwvDhwzFp0iS8+OKLeOGFF3Dy5Em0bt0aL774IoQQAICzZ89i2LBhGDNmDM6cOYOtW7fi8OHDmDFjRk27j4jIpHz88cd4//334eXlhZSUFBw/fhz5+fmYPXs2jh8/jn379sHKygpPP/00dDpdne573759uHDhAqKjo/HTTz+hoKAAgwYNgr29PQ4dOoTDhw/rCzbFxcUoLS3F6NGjERQUhDNnzuDIkSN49dVXIZFI6jQuIiJT9yi5e9GiRXj33Xdx8uRJyGQyTJgwAW+++SY+/vhj/Pbbb7h27Rree+89g3Vqmq+JiCxJZTkZANavXw+ZTIbff/8dX3zxBVJSUhAUFIQuXbrgxIkT2LVrF+7cuYNx48bpt/Xuu+9i3bp1+Oyzz3Du3Dm88cYbeOGFF3Dw4EF4e3uz7kHmSRA1UrGxsQKAuHHjRoVlkydPFk899ZTBvP379wsAIjMzUz/v1KlTAoBISEgQQggxYcIE8cQTTxis99xzzwmVSqV/vXXrVuHs7CyKioqEEELExcUJiUSi30Zl+xFCiKCgIDFr1iz96+bNm4sXXnhB/zolJUUAEAsXLtTPO3LkiAAgUlJShBBCTJo0Sbz66qsG2/3tt9+ElZWVKCwsrNAPRETmZOXKlaJ58+ZVLk9LSxMAxNmzZ4UQQiQkJAgA4tSpU0KIivl30aJFIiAg4IH7mDx5snB3dxcajUY/b+3ataJdu3ZCp9Pp52k0GmFjYyN2794tMjIyBABx4MCBRzpeIqLGoLa5+6uvvtK32bx5swAg9u3bp58XHh4u2rVrp39dm3xdvt79/y4gImqs7s/JQUFBokuXLgZtFi5cKIKDgw3mJSUlCQDi0qVLIi8vT1hbW4uYmBiDNlOnThUTJkwQQrDuQeaJV5JToxUQEIDBgwfD398fzz77LP79738jMzPzkbZ54cIF9OnTx2De/a9Hjx4NmUyGqKgoAMDXX3+NQYMGGdy+X12dO3fW/7+7uzsAwN/fv8K8tLQ0AEBsbCwiIyNhb2+vn4YNGwadToeEhIQa75+IyJRdu3YNEydORMuWLeHo6IgWLVoAABITE+t0P/7+/gZjM8bGxuLq1atwcHDQ51oXFxcUFRXh2rVrcHFxwZQpUzBs2DCMHDkSH3/8MVJSUuo0JiIic1Xd3F2d8+Dyc+ByNc3XREQEdO/e3eB1bGws9u/fb1BXaN++PYCyHH7+/HkUFRVh6NChBm2++eabWuVW1j3IVPDBndRoSaVSREdHIyYmBnv27EFERATeeecdHDt2rNL2VlZlvxmJP2/hAYCSkhKDNn9dVhWFQoFJkyZh3bp1GDNmDDZt2oRVq1bV6hj++tTn8tv0K5tXfnuqTqfDtGnT8Prrr1fYlo+PT61iICIyVSNHjoS3tzf+/e9/Q61WQ6fTwc/Pr9q30FtZWVXI6/fnfQCws7MzeK3T6RAYGIhvv/22QtsmTZoAKBtm6/XXX8euXbuwdetWvPvuu4iOjkbv3r2re3hERI1SdXN3dc6D7x+ipTb5mojI0lWWO0eOHImlS5dWaOvp6Yn4+HgAwM6dO9GsWTOD5Uqlssb7Z92DTAWL5NSoSSQS9OvXD/369cN7772H5s2bIyoqCgqFAlqt1qBt+YlySkoKnJ2dAaDCw946duyIo0ePGsy7/zUAvPLKK/Dz88OaNWtQUlKCMWPG6JeVX91y//7rQrdu3XDu3Dm0bt26zrdNRGRKMjIycOHCBXzxxRcYMGAAAODw4cM12kaTJk2QmpoKIYT+5Luqh3z+Vbdu3bB161Y0bdoUjo6OVbbr2rUrunbtigULFqBPnz7YtGkTi+REZNHqInfXRHXzNRER/U+3bt3w/fffw9fXFzJZxbJhx44doVQqkZiYiKCgoEq3wboHmSMOt0KN1rFjx7BkyRKcOHECiYmJ2LZtG+7evYsOHTrA19cXZ86cwaVLl5Ceno6SkhK0bt0a3t7eCAsLw+XLl7Fz50589NFHBtssvypw2bJluHz5MlavXo1du3ZV2HeHDh3Qu3dvvPXWW5gwYQJsbGz0y5o3bw6JRIKffvoJd+/eRV5eXp0d81tvvYUjR45g+vTpiIuLw5UrV7Bjxw7MnDmzzvZBRGQKnJ2d4erqii+//BJXr17Fr7/+itmzZ9doGwMHDsTdu3exbNkyXLt2DZ9++il++eWXh673/PPPw83NDU899RR+++03JCQk4ODBg5g1axaSk5ORkJCABQsW4MiRI7h58yb27NmDy5cvo0OHDrU9XCKiRqEucndNPCxfExFRRdOnT8e9e/cwYcIE/PHHH7h+/Tr27NmDl19+GVqtFg4ODpg7dy7eeOMNrF+/HteuXcOpU6fw6aefYv369QBY9yDzxCI5NVqOjo44dOgQnnzySbRt2xbvvvsuPvroI4SEhOBvf/sb2rVrh+7du6NJkyb4/fffIZfLsXnzZly8eBEBAQFYunQpPvjgA4Nt9u7dG1999RUiIiLQpUsX7NmzB++++26l+586dSqKi4vx8ssvG8xv1qwZFi9ejPnz58Pd3b1On8DcuXNnHDx4EFeuXMGAAQPQtWtXLFy4EJ6ennW2DyIiU2BlZYUtW7YgNjYWfn5+eOONN7B8+fIabaNDhw5Ys2YNPv30UwQEBOCPP/7A3LlzH7qera0tDh06BB8fH4wZMwYdOnTAyy+/jMLCQjg6OsLW1hYXL17EM888g7Zt2+LVV1/FjBkzMG3atNoeLhFRo1AXubsmHpaviYioIrVajd9//x1arRbDhg2Dn58fZs2aBZVKpR+m9v/+7//w3nvvITw8HB06dMCwYcPw448/6p8zwboHmSOJqM4gy0RUYx9++CG2bNmCs2fPGjsUIiIiIiIiIiIiqgKvJCeqY3l5eTh+/DgiIiIqfZAEERERERERERERmQ4WyYnq2IwZM9C/f38EBQVVGGqFiIiIiIiIiIiITAuHWyEiIiIiIiIiIiIii8UryYmIiIiIiIiIiIjIYrFITkREREREREREREQWi0VyIiIiIiIiIiIiIrJYLJITERERERERERERkcVikZyIiIiIiIiIiIiILBaL5ERERERERERERERksVgkJwORkZGQSCT6ydraGh4eHhg0aBDCw8ORlpZWYZ2wsDBIJJIa7aegoABhYWE4cOBAjdarbF++vr4YMWJEjbbzMJs2bcKqVasqXSaRSBAWFlan+6tr+/btQ/fu3WFnZweJRILt27cbO6RHsmTJErM/BqKGwjxexlLy+I0bNwz+3nK5HK6urujRowfeeOMNnDt37pHiMIe+IjI3zNNlLCFP3717F1ZWVvjHP/5RYdmsWbMgkUiwYMGCCsumTp0KqVSKzMzMasdTm/cIEZVhXi5jCXkZAA4cOACJRILvvvuuYQPE/87dIyMj9fNiYmIQFhaGrKysCu3r4+9MVWORnCq1bt06HDlyBNHR0fj000/RpUsXLF26FB06dMDevXsN2r7yyis4cuRIjbZfUFCAxYsX1/jLoTb7qo0HfTkcOXIEr7zySr3HUFtCCIwbNw5yuRw7duzAkSNHEBQUZOywHgmL5EQ1xzxuWXl85syZOHLkCA4ePIgNGzZg9OjR2LFjBwICArB8+fIGipyIaoJ5uvHn6SZNmqBTp07Yv39/hWUHDhyAnZ1dlcu6dOkCZ2fneomfiCrHvNz487IpiomJweLFiystklPDkhk7ADJNfn5+6N69u/71M888gzfeeAP9+/fHmDFjcOXKFbi7uwMAvLy84OXlVa/xFBQUwNbWtkH29TC9e/c26v4f5vbt27h37x6efvppDB482NjhEJGRMI9XrTHmcR8fH4PjevLJJzF79myMGTMGb775Jvz8/BASElJfIRNRLTBPV60x5elBgwYhIiICqamp8PDwAADcu3cPZ8+exZw5c7Bq1Srk5ubCwcEBAJCcnIzr169jzpw59X4cRGSIeblqjSkvE1WFV5JTtfn4+OCjjz5Cbm4uvvjiC/38ym79+fXXXzFw4EC4urrCxsYGPj4+eOaZZ1BQUIAbN26gSZMmAIDFixfrb2maMmWKwfZOnjyJsWPHwtnZGa1atapyX+WioqLQuXNnWFtbo2XLlvjkk08MlpffQnXjxg2D+eW32pT/mjtw4EDs3LkTN2/eNLjlqlxltxnFx8fjqaeegrOzM6ytrdGlSxesX7++0v1s3rwZ77zzDtRqNRwdHTFkyBBcunSp6o7/i8OHD2Pw4MFwcHCAra0t+vbti507d+qXh4WF6b8833rrLUgkEvj6+j5wm+fOnUNwcDBsbW3RpEkTTJ8+HTt37jTok3Jff/01AgICYG1tDRcXFzz99NO4cOFChW3u2LEDffr0ga2tLRwcHDB06NAKv3xPmTKl0tju/xtLJBLk5+dj/fr1+r/FwIEDH9xRRFQp5vEyjS2PV8XGxgZr166FXC43uJr87t27eO2119CxY0fY29ujadOmePzxx/Hbb79Va7u3bt3Cq6++Cm9vbygUCqjVaowdOxZ37typVZxE9D/M02UaU54eNGiQPrZyBw8ehEwmw9y5cwHAIP+WX1levt7WrVsRHBwMT09P2NjYoEOHDpg/fz7y8/OrdTybNm1Cnz59YG9vD3t7e3Tp0gVr166t1rpExLxcrjHl5XIlJSXVimnv3r0YPHgwHB0dYWtri379+mHfvn0Gba5evYqXXnoJbdq0ga2tLZo1a4aRI0fi7NmzD4whLCwM8+bNAwC0aNFC3+/312J27dqFbt26wcbGBu3bt8fXX3/90OOjmmORnGrkySefhFQqxaFDh6psc+PGDQwfPhwKhQJff/01du3ahX/+85+ws7NDcXExPD09sWvXLgBl4+0dOXIER44cwcKFCw22M2bMGLRu3Rr//e9/8fnnnz8wrri4OISGhuKNN95AVFQU+vbti1mzZuFf//pXjY9xzZo16NevHzw8PPSxPejWpkuXLqFv3744d+4cPvnkE2zbtg0dO3bElClTsGzZsgrt3377bdy8eRNfffUVvvzyS1y5cgUjR46EVqt9YFwHDx7E448/juzsbKxduxabN2+Gg4MDRo4cia1btwIouw1r27ZtAP53631UVFSV20xJSUFQUBAuXbqEzz77DN988w1yc3MxY8aMCm3Dw8MxdepUdOrUCdu2bcPHH3+MM2fOoE+fPrhy5Yq+3aZNm/DUU0/B0dERmzdvxtq1a5GZmYmBAwfi8OHDDzzGyhw5cgQ2NjZ48skn9X+LNWvW1Hg7RFSGebwic87jD6NWqxEYGIiYmBiUlpYCKLuCEQAWLVqEnTt3Yt26dWjZsiUGDhz40Nt/b926hR49eiAqKgqzZ8/GL7/8glWrVkGlUtVo7FwiqhrzdEXmnKeDgoJgZWVlMKzK/v370b17d7i7uyMwMNAg9+7fvx9SqRQDBgwAAFy5cgVPPvkk1q5di127diE0NBT/+c9/MHLkyAceCwC89957eP7556FWqxEZGYmoqChMnjwZN2/efOi6RPQ/zMsVmXNerklMGzduRHBwMBwdHbF+/Xr85z//gYuLC4YNG2ZQKL99+zZcXV3xz3/+E7t27cKnn34KmUyGXr16PfDHgFdeeQUzZ84EAGzbtk3f7926ddO3OX36NObMmYM33ngDP/zwAzp37oypU6c+8P1ItSSI/mLdunUCgDh+/HiVbdzd3UWHDh30rxctWiT++lb67rvvBAARFxdX5Tbu3r0rAIhFixZVWFa+vffee6/KZX/VvHlzIZFIKuxv6NChwtHRUeTn5xscW0JCgkG7/fv3CwBi//79+nnDhw8XzZs3rzT2++MeP368UCqVIjEx0aBdSEiIsLW1FVlZWQb7efLJJw3a/ec//xEAxJEjRyrdX7nevXuLpk2bitzcXP280tJS4efnJ7y8vIROpxNCCJGQkCAAiOXLlz9we0IIMW/ePCGRSMS5c+cM5g8bNsygTzIzM4WNjU2F2BMTE4VSqRQTJ04UQgih1WqFWq0W/v7+QqvV6tvl5uaKpk2bir59++rnTZ48udI+ruxvbGdnJyZPnvzQ4yEi5vFylpLHq9P2ueeeEwDEnTt3Kl1eWloqSkpKxODBg8XTTz9tsOz+vnr55ZeFXC4X58+ff2hsRFQ55ukylpKnhRCiS5cuom3btvrX/v7+Yv78+UIIId58803RvXt3/bIWLVqInj17VrodnU4nSkpKxMGDBwUAcfr0af2y+/9u169fF1KpVDz//PPVipHIkjEvl7GUvFzdmPLz84WLi4sYOXKkQTutVisCAgKqzNXlMRYXF4s2bdqIN954Qz+/PM5169bp5y1fvrzSv5EQZX9na2trcfPmTf28wsJC4eLiIqZNm/bQY6Wa4ZXkVGNCiAcu79KlCxQKBV599VWsX78e169fr9V+nnnmmWq37dSpEwICAgzmTZw4ETk5OTh58mSt9l9dv/76KwYPHgxvb2+D+VOmTEFBQUGFX19HjRpl8Lpz584A8MArOvLz83Hs2DGMHTsW9vb2+vlSqRSTJk1CcnJytW9V+quDBw/Cz88PHTt2NJg/YcIEg9dHjhxBYWGh/lawct7e3nj88cf1v6BeunQJt2/fxqRJk2Bl9b/0Ym9vj2eeeQZHjx5FQUFBjeMkorrFPG7InPN4dVT29/7888/RrVs3WFtbQyaTQS6XY9++fZUOofVXv/zyCwYNGoQOHTrUS6xEVIZ52pC55+lBgwbh8uXLuH37NjIyMhAfH68fPjAoKAinTp1CdnY2EhMTkZCQoB9qBQCuX7+OiRMnwsPDA1KpFHK5XP8wugfl7OjoaGi1WkyfPr1WMRORIeZlQ+ael6sTU0xMDO7du4fJkyejtLRUP+l0OjzxxBM4fvy4fuir0tJSLFmyBB07doRCoYBMJoNCocCVK1ceen79MF26dIGPj4/+tbW1Ndq2bcu7guoBi+RUI/n5+cjIyIBara6yTatWrbB37140bdoU06dPR6tWrdCqVSt8/PHHNdqXp6dntduWPwSnsnkZGRk12m9NZWRkVBpreR/dv39XV1eD10qlEgBQWFhY5T4yMzMhhKjRfqojIyND/+CRv7p/Xvm2q9p/+fKHtdPpdLwdn8jImMcrMuc8Xh03b96EUqmEi4sLAGDFihX4xz/+gV69euH777/H0aNHcfz4cTzxxBMPPAagbDxzYz84iqixY56uyNzz9F/HJT9w4ACkUin69esHAOjfvz+AsnHJ7x+PPC8vDwMGDMCxY8fwwQcf4MCBAzh+/Lh+WIEHHc/du3cBgDmbqA4wL1dk7nm5OjGVP29n7NixkMvlBtPSpUshhNAPYzh79mwsXLgQo0ePxo8//ohjx47h+PHjCAgIeOj5dU3jLI/1UbdLFcmMHQCZl507d0Kr1T70wYkDBgzAgAEDoNVqceLECURERCA0NBTu7u4YP358tfZV1YMpKpOamlrlvPKEYm1tDQDQaDQG7dLT06u9n8q4uroiJSWlwvzbt28DANzc3B5p+wDg7OwMKyurOt+Pq6trpQ9au78/y/uwqv2X7/th7aysrODs7Ayg7O9x/98CePS/BxE9GPN4Reacxx/m1q1biI2NRVBQEGSystO+jRs3YuDAgfjss88M2ubm5j50e02aNEFycnKdx0lE/8M8XZG55+nHHnsMUqkUBw4cgFKpRLdu3fRXRTo6OqJLly7Yv38/7t27B5lMpi+g//rrr7h9+zYOHDigv3ocALKysh66z/IHBCYnJ1e40pOIaoZ5uSJzz8vVUb7tiIgI9O7du9I25RcYbty4ES+++CKWLFlisDw9PR1OTk71FiPVLV5JTtWWmJiIuXPnQqVSYdq0adVaRyqVolevXvj0008BQH/LT3V+NayJc+fO4fTp0wbzNm3aBAcHB/0DD8qfbnzmzBmDdjt27KiwvZr8Kjd48GD9CexfffPNN7C1ta0ymdaEnZ0devXqhW3bthnEpdPpsHHjRnh5eaFt27Y13m5QUBDi4+Nx/vx5g/lbtmwxeN2nTx/Y2Nhg48aNBvOTk5P1t1kBQLt27dCsWTNs2rTJ4Ha0/Px8fP/99+jTpw9sbW0BlP090tLSDIr0xcXF2L17d4U4+SspUd1gHq+cOefxByksLMQrr7yC0tJSvPnmm/r5EolE//crd+bMmQc+nKlcSEgI9u/fX29DwxBZOubpypl7nlapVOjatav+SvL7C21BQUHYv38/Dhw4gJ49e+oL6OXFsvtz9hdffPHQfQYHB0MqlVb4QZSIaoZ5uXLmnpero1+/fnBycsL58+fRvXv3SieFQgGg8vPrnTt34tatWw/dT12/L6j2eCU5VSo+Pl4/3lJaWhp+++03rFu3DlKpFFFRUforEyrz+eef49dff8Xw4cPh4+ODoqIifP311wCAIUOGAAAcHBzQvHlz/PDDDxg8eDBcXFzg5uamT+A1pVarMWrUKISFhcHT0xMbN25EdHQ0li5dqi/K9ujRA+3atcPcuXNRWloKZ2dnREVF4fDhwxW25+/vj23btuGzzz5DYGAgrKys0L1790r3vWjRIvz0008YNGgQ3nvvPbi4uODbb7/Fzp07sWzZMqhUqlod0/3Cw8MxdOhQDBo0CHPnzoVCocCaNWsQHx+PzZs31+gX53KhoaH4+uuvERISgvfffx/u7u7YtGkTLl68CAD6ccWdnJywcOFCvP3223jxxRcxYcIEZGRkYPHixbC2tsaiRYv07ZctW4bnn38eI0aMwLRp06DRaLB8+XJkZWXhn//8p37fzz33HN577z2MHz8e8+bNQ1FRET755JNKn27t7++PAwcO4Mcff4SnpyccHBzQrl272nQjkcVgHreMPF4uMTERR48ehU6nQ3Z2Nk6dOoWvv/4aN2/exEcffYTg4GB92xEjRuD//u//sGjRIgQFBeHSpUt4//330aJFC5SWlj5wP++//z5++eUXPPbYY3j77bfh7++PrKws7Nq1C7Nnz0b79u1rfQxEloZ52rLy9KBBg7B8+XJIJBIsXbrUYFlQUBBWrlwJIQSef/55/fy+ffvC2dkZf//737Fo0SLI5XJ8++23FYpilfH19cXbb7+N//u//0NhYSEmTJgAlUqF8+fPIz09HYsXL671sRA1VszLlpWXH8be3h4RERGYPHky7t27h7Fjx6Jp06a4e/cuTp8+jbt37+p/iBwxYgQiIyPRvn17dO7cGbGxsVi+fHm1hrzy9/cHAHz88ceYPHky5HI52rVrBwcHh3o7NqqCcZ4XSqaq/MnH5ZNCoRBNmzYVQUFBYsmSJSItLa3COvc/afnIkSPi6aefFs2bNxdKpVK4urqKoKAgsWPHDoP19u7dK7p27SqUSqUAICZPnmywvbt37z50X0KUPe13+PDh4rvvvhOdOnUSCoVC+Pr6ihUrVlRY//LlyyI4OFg4OjqKJk2aiJkzZ4qdO3dWeKrzvXv3xNixY4WTk5OQSCQG+0QlT6M+e/asGDlypFCpVEKhUIiAgACDpxUL8b8nKP/3v/81mF/Z042r8ttvv4nHH39c2NnZCRsbG9G7d2/x448/Vrq96jzVWQgh4uPjxZAhQ4S1tbVwcXERU6dOFevXrxcAxOnTpw3afvXVV6Jz585CoVAIlUolnnrqKXHu3LkK29y+fbvo1auXsLa2FnZ2dmLw4MHi999/r9Du559/Fl26dBE2NjaiZcuWYvXq1ZX+jePi4kS/fv2Era2tACCCgoKqdWxEloh5vIyl5PHytuWTVCoVzs7OIjAwUISGhlaaozUajZg7d65o1qyZsLa2Ft26dRPbt28XkydPFs2bNzdoW1lfJSUliZdffll4eHgIuVwu1Gq1GDdunLhz585D4yUi5ulylpKny/3888/6PJ2dnW2w7N69e8LKykoAENHR0QbLYmJiRJ8+fYStra1o0qSJeOWVV8TJkycrHE9lfzchhPjmm29Ejx49hLW1tbC3txddu3atVj8QWRLm5TKWkpdrGtPBgwfF8OHDhYuLi5DL5aJZs2Zi+PDhButnZmaKqVOniqZNmwpbW1vRv39/8dtvv4mgoCCD+kVV+1iwYIFQq9X674Lyv0v53/l+92+X6oZEiIc8opeILM6rr76KzZs3IyMjQ3/7EBERERERERERUWPE4VaILNz7778PtVqNli1bIi8vDz/99BO++uorvPvuuyyQExERERERERFRo8ciOZGFk8vlWL58OZKTk1FaWoo2bdpgxYoVmDVrlrFDIyIiIiIiIiIiqnccboWIiIiIiIiIiIiILJaVsQMgIiIiIiIiIiIiIjIWFsmJiIiIiIiIiIiIyGJxTHIAOp0Ot2/fhoODAyQSibHDISKqFiEEcnNzoVarYWVlGb95Ml8TkTlivma+JiLzwHzNfE1E5qE+8jWL5ABu374Nb29vY4dBRFQrSUlJ8PLyMnYYDYL5mojMGfM1EZF5YL4mIjIPdZmvWSQH4ODgAKCsYx0dHY0cDRFR9eTk5MDb21ufwywB8zURmSPma+ZrIjIPzNfM10RkHuojX7NIDuhvKXJ0dOSXAhGZHUu6LZL5mojMGfM1EZF5YL4mIjIPdZmvLWOQLSIiIiIiIiIiIiKiSrBITkREREREREREREQWi0VyIiIiIiIiIiIiIrJYLJITERERERERERERkcVikZyIiIiIiIiIiIiILBaL5ERERERERERERERksVgkJyIiIiIiIiIiIiKLxSI5EREREREREREREVksFsmJiIiIiIiIiIiIyGKxSE5EREREREREREREFotFciIiIiIiIiIiIiKyWCySExEREREREREREZHFYpGciIiIiIiIiIiIiCyWzNgBEFmCxMREpKenN8i+3Nzc4OPj0yD7IjIndfU55GeMiKh+8byJiIiIyLgs8XyMRXKiepaYmIj2HTqgsKCgQfZnY2uLixcumESCITIVdfk55GeMiKj+8LyJiIiIyLgs9XyMRXKiepaeno7CggI8/9ZyuPu0qtd93Um8hm+XzkN6errRkwuRKamrzyE/Y0RE9YvnTURERETGZannYyySEzUQd59W8GrTydhhEFk0fg6JiMwD8zURERGRcVna+Rgf3ElEREREREREREREFotFciIiIiIiIiIiIiKyWCySExEREREREREREZHFYpGciIiIiIiIiIiIiCwWi+REREREREREREREZLFYJCciIiIiIiIiIiIii8UiORERERERERERERFZLBbJiYiIiIiIiIga0KFDhzBy5Eio1WpIJBJs3769yrbTpk2DRCLBqlWrDOZrNBrMnDkTbm5usLOzw6hRo5CcnFy/gRMRNVIskhMRERERERERNaD8/HwEBARg9erVD2y3fft2HDt2DGq1usKy0NBQREVFYcuWLTh8+DDy8vIwYsQIaLXa+gqbiKjRkhk7ACIiIiIiIiIiSxISEoKQkJAHtrl16xZmzJiB3bt3Y/jw4QbLsrOzsXbtWmzYsAFDhgwBAGzcuBHe3t7Yu3cvhg0bVm+xExE1RrySnIiIiIiIiIjIhOh0OkyaNAnz5s1Dp06dKiyPjY1FSUkJgoOD9fPUajX8/PwQExNT5XY1Gg1ycnIMJiIiYpGciIiIiIiIiMikLF26FDKZDK+//nqly1NTU6FQKODs7Gww393dHampqVVuNzw8HCqVSj95e3vXadxEROaKRXIiIiIiIiIiIhMRGxuLjz/+GJGRkZBIJDVaVwjxwHUWLFiA7Oxs/ZSUlPSo4RIRNQoskhMRERERERERmYjffvsNaWlp8PHxgUwmg0wmw82bNzFnzhz4+voCADw8PFBcXIzMzEyDddPS0uDu7l7ltpVKJRwdHQ0mIiJikZyIiIiIiIiIyGRMmjQJZ86cQVxcnH5Sq9WYN28edu/eDQAIDAyEXC5HdHS0fr2UlBTEx8ejb9++xgqdiMhsyYwdABERERERERGRJcnLy8PVq1f1rxMSEhAXFwcXFxf4+PjA1dXVoL1cLoeHhwfatWsHAFCpVJg6dSrmzJkDV1dXuLi4YO7cufD398eQIUMa9FiIiBoDFsmJiIiIiIiIiBrQiRMnMGjQIP3r2bNnAwAmT56MyMjIam1j5cqVkMlkGDduHAoLCzF48GBERkZCKpXWR8hERI0ai+RERERERERERA1o4MCBEEJUu/2NGzcqzLO2tkZERAQiIiLqMDIiIsvEMcmJiIiIiBqJQ4cOYeTIkVCr1ZBIJNi+fXuVbadNmwaJRIJVq1YZzNdoNJg5cybc3NxgZ2eHUaNGITk5uX4DJyIiIiIyIqMWyR90El9SUoK33noL/v7+sLOzg1qtxosvvojbt28bbIMn8UREREREZfLz8xEQEIDVq1c/sN327dtx7NgxqNXqCstCQ0MRFRWFLVu24PDhw8jLy8OIESOg1WrrK2wiIiIiIqMyapH8QSfxBQUFOHnyJBYuXIiTJ09i27ZtuHz5MkaNGmXQjifxRERERERlQkJC8MEHH2DMmDFVtrl16xZmzJiBb7/9FnK53GBZdnY21q5di48++ghDhgxB165dsXHjRpw9exZ79+6t7/CJiIiIiIzCqGOSh4SEICQkpNJlKpUK0dHRBvMiIiLQs2dPJCYmwsfHR38Sv2HDBv3Tmzdu3Ahvb2/s3bsXw4YNq/djICIiIiIyFzqdDpMmTcK8efPQqVOnCstjY2NRUlKC4OBg/Ty1Wg0/Pz/ExMRUeX6t0Wig0Wj0r3Nycuo+eCIiIiKiemJWY5JnZ2dDIpHAyckJwMNP4qui0WiQk5NjMBERERERNXZLly6FTCbD66+/Xuny1NRUKBQKODs7G8x3d3dHampqldsNDw+HSqXST97e3nUaNxERERFRfTKbInlRURHmz5+PiRMnwtHREQBP4omIiIiIqis2NhYff/wxIiMjIZFIarSuEOKB6yxYsADZ2dn6KSkp6VHDJSIiIiJqMGZRJC8pKcH48eOh0+mwZs2ah7bnSTwRERERkaHffvsNaWlp8PHxgUwmg0wmw82bNzFnzhz4+voCADw8PFBcXIzMzEyDddPS0uDu7l7ltpVKJRwdHQ0mIiIiIiJzYfJF8pKSEowbNw4JCQmIjo42OOHmSTwRUf0LDw9Hjx494ODggKZNm2L06NG4dOmSQZspU6ZAIpEYTL179zZoo9FoMHPmTLi5ucHOzg6jRo1CcnJyQx4KEZFFmzRpEs6cOYO4uDj9pFarMW/ePOzevRsAEBgYCLlcbvBsoJSUFMTHx6Nv377GCp2IiIiIqF6ZdJG8vEB+5coV7N27F66urgbLeRJPRFT/Dh48iOnTp+Po0aOIjo5GaWkpgoODkZ+fb9DuiSeeQEpKin76+eefDZaHhoYiKioKW7ZsweHDh5GXl4cRI0ZAq9U25OEQETVqeXl5+gI4ACQkJCAuLg6JiYlwdXWFn5+fwSSXy+Hh4YF27doBAFQqFaZOnYo5c+Zg3759OHXqFF544QX4+/tjyJAhRjwyIiIiIqL6IzPmzvPy8nD16lX96/KTeBcXF6jVaowdOxYnT57ETz/9BK1Wqx9n3MXFBQqFwuAk3tXVFS4uLpg7dy5P4omI6tCuXbsMXq9btw5NmzZFbGwsHnvsMf18pVIJDw+PSreRnZ2NtWvXYsOGDfr8vHHjRnh7e2Pv3r0YNmxY/R0AEZEFOXHiBAYNGqR/PXv2bADA5MmTERkZWa1trFy5EjKZDOPGjUNhYSEGDx6MyMhISKXS+giZiIiIiMjojFokf9BJfFhYGHbs2AEA6NKli8F6+/fvx8CBAwHwJJ6IqKFlZ2cDKPvB8q8OHDiApk2bwsnJCUFBQfjwww/RtGlTAGUPiyspKUFwcLC+vVqthp+fH2JiYqoskms0Gmg0Gv3rnJycuj4cIqJGZeDAgRBCVLv9jRs3KsyztrZGREQEIiIi6jAyIiIiIiLTZdQi+cNO4qtzgs+TeCKihiOEwOzZs9G/f3/4+fnp54eEhODZZ59F8+bNkZCQgIULF+Lxxx9HbGwslEolUlNToVAo4OzsbLA9d3d3/V1ClQkPD8fixYvr7XiIiIiIiIiIiIxaJCciIvMyY8YMnDlzBocPHzaY/9xzz+n/38/PD927d0fz5s2xc+dOjBkzpsrtCSEgkUiqXL5gwQL9XUZA2ZXk3t7ej3AERERERERERESGTPrBnUREZDpmzpyJHTt2YP/+/fDy8npgW09PTzRv3hxXrlwBAHh4eKC4uBiZmZkG7dLS0uDu7l7ldpRKJRwdHQ0mIiIiIiIiIqK6xCI5ERE9kBACM2bMwLZt2/Drr7+iRYsWD10nIyMDSUlJ8PT0BAAEBgZCLpcjOjpa3yYlJQXx8fHo27dvvcVORERERERERPQwHG6FiIgeaPr06di0aRN++OEHODg46McQV6lUsLGxQV5eHsLCwvDMM8/A09MTN27cwNtvvw03Nzc8/fTT+rZTp07FnDlz4OrqChcXF8ydOxf+/v4YMmSIMQ+PiIiIiIiIiCwci+RERPRAn332GYCyhy3/1bp16zBlyhRIpVKcPXsW33zzDbKysuDp6YlBgwZh69atcHBw0LdfuXIlZDIZxo0bh8LCQgwePBiRkZGQSqUNeThERERERERERAZYJCciogcSQjxwuY2NDXbv3v3Q7VhbWyMiIgIRERF1FRoRERERERER0SPjmOREREREREREREREZLFYJCciIiIiIiIiIiIii8UiORERERERERERERFZLBbJiYiIiIiIiIiIiMhisUhORERERERERERERBaLRXIiIiIiIiIiIiIislgskhMRERERERERERGRxWKRnIiIiIiIiIiIiIgsFovkRERERERERERERGSxWCQnIiIiIiIiIiIiIovFIjkRERERERERERERWSwWyYmIiIiIiIiIiIjIYrFITkREREREREREREQWi0VyIiIiIiIiIiIiIrJYLJITERERERERERERkcVikZyIiIiIiIiIiIiILBaL5ERERERERERERERksVgkJyIiIiIiIiJqQIcOHcLIkSOhVqshkUiwfft2/bKSkhK89dZb8Pf3h52dHdRqNV588UXcvn3bYBsajQYzZ86Em5sb7OzsMGrUKCQnJzfwkRARNQ4skhMRERERERERNaD8/HwEBARg9erVFZYVFBTg5MmTWLhwIU6ePIlt27bh8uXLGDVqlEG70NBQREVFYcuWLTh8+DDy8vIwYsQIaLXahjoMIqJGQ2bsAIiIiIiIiIiILElISAhCQkIqXaZSqRAdHW0wLyIiAj179kRiYiJ8fHyQnZ2NtWvXYsOGDRgyZAgAYOPGjfD29sbevXsxbNiwej8GIqLGhFeSExERERERERGZsOzsbEgkEjg5OQEAYmNjUVJSguDgYH0btVoNPz8/xMTEVLkdjUaDnJwcg4mIiFgkJyIiIiIiIiIyWUVFRZg/fz4mTpwIR0dHAEBqaioUCgWcnZ0N2rq7uyM1NbXKbYWHh0OlUuknb2/veo2diMhcsEhORERERERERGSCSkpKMH78eOh0OqxZs+ah7YUQkEgkVS5fsGABsrOz9VNSUlJdhktEZLZYJCciIiIiIiIiMjElJSUYN24cEhISEB0drb+KHAA8PDxQXFyMzMxMg3XS0tLg7u5e5TaVSiUcHR0NJiIiYpGciIiIiIiIiMiklBfIr1y5gr1798LV1dVgeWBgIORyucEDPlNSUhAfH4++ffs2dLhERGZPZuwAiIiIiIiIiIgsSV5eHq5evap/nZCQgLi4OLi4uECtVmPs2LE4efIkfvrpJ2i1Wv044y4uLlAoFFCpVJg6dSrmzJkDV1dXuLi4YO7cufD398eQIUOMdVhERGaLV5ITERERETUShw4dwsiRI6FWqyGRSLB9+3b9spKSErz11lvw9/eHnZ0d1Go1XnzxRdy+fdtgGxqNBjNnzoSbmxvs7OwwatQoJCcnN/CREBE1bidOnEDXrl3RtWtXAMDs2bPRtWtXvPfee0hOTsaOHTuQnJyMLl26wNPTUz/FxMTot7Fy5UqMHj0a48aNQ79+/WBra4sff/wRUqnUWIdFRGS2WCQnIiIiImok8vPzERAQgNWrV1dYVlBQgJMnT2LhwoU4efIktm3bhsuXL2PUqFEG7UJDQxEVFYUtW7bg8OHDyMvLw4gRI6DVahvqMIiIGr2BAwdCCFFhioyMhK+vb6XLhBAYOHCgfhvW1taIiIhARkYGCgoK8OOPP8Lb29t4B0VEZMY43AoRERERUSMREhKCkJCQSpepVCqDsWsBICIiAj179kRiYiJ8fHyQnZ2NtWvXYsOGDfrb9Tdu3Ahvb2/s3bsXw4YNq/djICIiIiJqaLySnIiIiIjIQmVnZ0MikcDJyQkAEBsbi5KSEgQHB+vbqNVq+Pn5Gdzifz+NRoOcnByDiYiIiIjIXPBKcqpSYmIi0tPTG2Rfbm5u8PHxaZB9ERERERFQVFSE+fPnY+LEiXB0dAQApKamQqFQwNnZ2aCtu7u7/qFxlQkPD8fixYvrNV4iIiIiovrCIjlVKjExEe07dEBhQUGD7M/G1hYXL1xgoZyIiIioAZSUlGD8+PHQ6XRYs2bNQ9sLISCRSKpcvmDBAsyePVv/Oicnh+PiEhEREZHZYJGcKpWeno7CggI8/9ZyuPu0qtd93Um8hm+XzkN6ejqL5ERERET1rKSkBOPGjUNCQgJ+/fVX/VXkAODh4YHi4mJkZmYaXE2elpaGvn37VrlNpVIJpVJZr3ETEREREdUXFsnpgdx9WsGrTSdjh0FEREREdaC8QH7lyhXs378frq6uBssDAwMhl8sRHR2NcePGAQBSUlIQHx+PZcuWGSNkIiIiIqJ6Z9QHdx46dAgjR46EWq2GRCLB9u3bDZYLIRAWFga1Wg0bGxsMHDgQ586dM2ij0Wgwc+ZMuLm5wc7ODqNGjUJycnIDHgURERERkWnIy8tDXFwc4uLiAAAJCQmIi4tDYmIiSktLMXbsWJw4cQLffvsttFotUlNTkZqaiuLiYgCASqXC1KlTMWfOHOzbtw+nTp3CCy+8AH9/fwwZMsSIR0ZEREREVH+MWiTPz89HQEAAVq9eXenyZcuWYcWKFVi9ejWOHz8ODw8PDB06FLm5ufo2oaGhiIqKwpYtW3D48GHk5eVhxIgR0Gq1DXUYREREREQm4cSJE+jatSu6du0KAJg9eza6du2K9957D8nJydixYweSk5PRpUsXeHp66qeYmBj9NlauXInRo0dj3Lhx6NevH2xtbfHjjz9CKpUa67CIiIiIiOqVUYdbCQkJQUhISKXLhBBYtWoV3nnnHYwZMwYAsH79eri7u2PTpk2YNm0asrOzsXbtWmzYsEF/ZcvGjRvh7e2NvXv3YtiwYQ12LERERERExjZw4EAIIapc/qBl5aytrREREYGIiIi6DI2IiIiIyGQZ9UryB0lISEBqaiqCg4P185RKJYKCgvRXusTGxqKkpMSgjVqthp+fn8HVMPfTaDTIyckxmIiIiIiIiIiIiIjI8phskTw1NRUA4O7ubjDf3d1dvyw1NRUKhQLOzs5VtqlMeHg4VCqVfvL29q7j6ImIiIiIiIiIiIjIHJhskbycRCIxeC2EqDDvfg9rs2DBAmRnZ+unpKSkOomViIiIiIiIiIiIiMyLyRbJPTw8AKDCFeFpaWn6q8s9PDxQXFyMzMzMKttURqlUwtHR0WAiIiIiIiIiIiIiIstjskXyFi1awMPDA9HR0fp5xcXFOHjwIPr27QsACAwMhFwuN2iTkpKC+Ph4fRsiIiIiIiIiIiIioqrIjLnzvLw8XL16Vf86ISEBcXFxcHFxgY+PD0JDQ7FkyRK0adMGbdq0wZIlS2Bra4uJEycCAFQqFaZOnYo5c+bA1dUVLi4umDt3Lvz9/TFkyBBjHRYRERERERERERERmQmjFslPnDiBQYMG6V/Pnj0bADB58mRERkbizTffRGFhIV577TVkZmaiV69e2LNnDxwcHPTrrFy5EjKZDOPGjUNhYSEGDx6MyMhISKXSBj8eIiIiIiIiIiIiIjIvRi2SDxw4EEKIKpdLJBKEhYUhLCysyjbW1taIiIhAREREPURIRERERERERERERI2ZyY5JTkRERERERERERERU31gkJyIiIiIiIiIiIiKLxSI5EREREREREREREVksFsmJiIiIiIiIiIiIyGKxSE5ERA8UHh6OHj16wMHBAU2bNsXo0aNx6dIlgzZCCISFhUGtVsPGxgYDBw7EuXPnDNpoNBrMnDkTbm5usLOzw6hRo5CcnNyQh0JEREREREREVAGL5ERE9EAHDx7E9OnTcfToUURHR6O0tBTBwcHIz8/Xt1m2bBlWrFiB1atX4/jx4/Dw8MDQoUORm5urbxMaGoqoqChs2bIFhw8fRl5eHkaMGAGtVmuMwyIiIiIiIiIiAgDIjB0AERGZtl27dhm8XrduHZo2bYrY2Fg89thjEEJg1apVeOeddzBmzBgAwPr16+Hu7o5NmzZh2rRpyM7Oxtq1a7FhwwYMGTIEALBx40Z4e3tj7969GDZsWIMfFxERERERERERwCvJiYiohrKzswEALi4uAICEhASkpqYiODhY30apVCIoKAgxMTEAgNjYWJSUlBi0UavV8PPz07epjEajQU5OjsFERERERERERFSXWCQnIqJqE0Jg9uzZ6N+/P/z8/AAAqampAAB3d3eDtu7u7vplqampUCgUcHZ2rrJNZcLDw6FSqfSTt7d3XR4OERERERERERGL5EREVH0zZszAmTNnsHnz5grLJBKJwWshRIV593tYmwULFiA7O1s/JSUl1S5wIiIiIiIiIqIqsEhORETVMnPmTOzYsQP79++Hl5eXfr6HhwcAVLgiPC0tTX91uYeHB4qLi5GZmVllm8oolUo4OjoaTEREREREREREdYlFciIieiAhBGbMmIFt27bh119/RYsWLQyWt2jRAh4eHoiOjtbPKy4uxsGDB9G3b18AQGBgIORyuUGblJQUxMfH69sQERERERERERmDzNgBEBGRaZs+fTo2bdqEH374AQ4ODvorxlUqFWxsbCCRSBAaGoolS5agTZs2aNOmDZYsWQJbW1tMnDhR33bq1KmYM2cOXF1d4eLigrlz58Lf3x9Dhgwx5uERERERERERkYVjkZyIiB7os88+AwAMHDjQYP66deswZcoUAMCbb76JwsJCvPbaa8jMzESvXr2wZ88eODg46NuvXLkSMpkM48aNQ2FhIQYPHozIyEhIpdKGOhQiIiIiIiIiogpYJCciogcSQjy0jUQiQVhYGMLCwqpsY21tjYiICERERNRhdEREREREREREj4ZjkhMRERERERERERGRxWKRnIiIiIiIiIiIiIgsFovkRERERERERERERGSxWCQnIiIiIiIiImpAhw4dwsiRI6FWqyGRSLB9+3aD5UIIhIWFQa1Ww8bGBgMHDsS5c+cM2mg0GsycORNubm6ws7PDqFGjkJyc3IBHQUTUeLBITkRERERERETUgPLz8xEQEIDVq1dXunzZsmVYsWIFVq9ejePHj8PDwwNDhw5Fbm6uvk1oaCiioqKwZcsWHD58GHl5eRgxYgS0Wm1DHQYRUaMhM3YARERERERERESWJCQkBCEhIZUuE0Jg1apVeOeddzBmzBgAwPr16+Hu7o5NmzZh2rRpyM7Oxtq1a7FhwwYMGTIEALBx40Z4e3tj7969GDZsWIMdCxFRY8AryYmIiIiIiIiITERCQgJSU1MRHBysn6dUKhEUFISYmBgAQGxsLEpKSgzaqNVq+Pn56dtURqPRICcnx2AiIiIWyYmIiIiIiIiITEZqaioAwN3d3WC+u7u7fllqaioUCgWcnZ2rbFOZ8PBwqFQq/eTt7V3H0RMRmScWyYmIiIiIiIiITIxEIjF4LYSoMO9+D2uzYMECZGdn66ekpKQ6iZWIyNyxSE5EREREREREZCI8PDwAoMIV4Wlpafqryz08PFBcXIzMzMwq21RGqVTC0dHRYCIiIhbJiYiIiIgajUOHDmHkyJFQq9WQSCTYvn27wXIhBMLCwqBWq2FjY4OBAwfi3LlzBm00Gg1mzpwJNzc32NnZYdSoUUhOTm7AoyAismwtWrSAh4cHoqOj9fOKi4tx8OBB9O3bFwAQGBgIuVxu0CYlJQXx8fH6NkREVH0skhMRERERNRL5+fkICAjA6tWrK12+bNkyrFixAqtXr8bx48fh4eGBoUOHIjc3V98mNDQUUVFR2LJlCw4fPoy8vDyMGDECWq22oQ6DiKjRy8vLQ1xcHOLi4gCUPawzLi4OiYmJkEgkCA0NxZIlSxAVFYX4+HhMmTIFtra2mDhxIgBApVJh6tSpmDNnDvbt24dTp07hhRdegL+/P4YMGWLEIyMiMk8yYwdARERERER1IyQkBCEhIZUuE0Jg1apVeOeddzBmzBgAwPr16+Hu7o5NmzZh2rRpyM7Oxtq1a7FhwwZ9kWXjxo3w9vbG3r17MWzYsAY7FiKixuzEiRMYNGiQ/vXs2bMBAJMnT0ZkZCTefPNNFBYW4rXXXkNmZiZ69eqFPXv2wMHBQb/OypUrIZPJMG7cOBQWFmLw4MGIjIyEVCpt8OMhIjJ3LJIT0SNJTExEenp6g+zLzc0NPj4+DbIvIiKixiYhIQGpqakIDg7Wz1MqlQgKCkJMTAymTZuG2NhYlJSUGLRRq9Xw8/NDTExMlUVyjUYDjUajf52Tk1N/B0JE1AgMHDgQQogql0skEoSFhSEsLKzKNtbW1oiIiEBEREQ9REhEZFlYJCeiWktMTET7Dh1QWFDQIPuzsbXFxQsXWCgnIiKqhfIHwN3/QDd3d3fcvHlT30ahUMDZ2blCm/sfIPdX4eHhWLx4cR1HTERERETUMFgkJ6JaS09PR2FBAZ5/azncfVrV677uJF7Dt0vnIT09nUVyIiKiRyCRSAxeCyEqzLvfw9osWLBAP1QAUHYlube396MFSkRERETUQFgkJ6JH5u7TCl5tOhk7DCIiInoADw8PAGVXi3t6eurnp6Wl6a8u9/DwQHFxMTIzMw2uJk9LS0Pfvn2r3LZSqYRSqaynyImIiIiI6peVsQMgIiIiIqL616JFC3h4eCA6Olo/r7i4GAcPHtQXwAMDAyGXyw3apKSkID4+/oFFciIiIiIic8YryYmIiIiIGom8vDxcvXpV/zohIQFxcXFwcXGBj48PQkNDsWTJErRp0wZt2rTBkiVLYGtri4kTJwIAVCoVpk6dijlz5sDV1RUuLi6YO3cu/P39MWTIEGMdFhERERFRvWKRnIiIiIiokThx4gQGDRqkf10+TvjkyZMRGRmJN998E4WFhXjttdeQmZmJXr16Yc+ePXBwcNCvs3LlSshkMowbNw6FhYUYPHgwIiMjIZVKG/x4iIiIiIgaAovkRERERESNxMCBAyGEqHK5RCJBWFgYwsLCqmxjbW2NiIgIRERE1EOERERERESmh2OSExEREREREREREZHFYpGciIiIiIiIiIiIiCwWi+REREREREREREREZLFYJCciIiIiIiIiIiIii8UiORERERERERERERFZLJmxA3iQ0tJShIWF4dtvv0Vqaio8PT0xZcoUvPvuu7CyKqvvCyGwePFifPnll8jMzESvXr3w6aefolOnTkaOnoiIiIiIiIiIyDIlJiYiPT29wfbn5uYGHx+fBtsfNS4mXSRfunQpPv/8c6xfvx6dOnXCiRMn8NJLL0GlUmHWrFkAgGXLlmHFihWIjIxE27Zt8cEHH2Do0KG4dOkSHBwcjHwEREREREREREREliUxMRHtO3RAYUFBg+3TxtYWFy9cYKGcasWki+RHjhzBU089heHDhwMAfH19sXnzZpw4cQJA2VXkq1atwjvvvIMxY8YAANavXw93d3ds2rQJ06ZNM1rsRERERERERERElig9PR2FBQV4/q3lcPdpVe/7u5N4Dd8unYf09HQWyalWTLpI3r9/f3z++ee4fPky2rZti9OnT+Pw4cNYtWoVACAhIQGpqakIDg7Wr6NUKhEUFISYmJgqi+QajQYajUb/Oicnp16Pg4iIiIiIiIiIyNK4+7SCVxsOiUymz6SL5G+99Rays7PRvn17SKVSaLVafPjhh5gwYQIAIDU1FQDg7u5usJ67uztu3rxZ5XbDw8OxePHi+guciIiIiIiIiIiIiMyClbEDeJCtW7di48aN2LRpE06ePIn169fjX//6F9avX2/QTiKRGLwWQlSY91cLFixAdna2fkpKSqqX+ImIiIiIiIiIiIjItJn0leTz5s3D/PnzMX78eACAv78/bt68ifDwcEyePBkeHh4Ayq4o9/T01K+XlpZW4eryv1IqlVAqlfUbPBERERERERERERGZPJO+krygoABWVoYhSqVS6HQ6AECLFi3g4eGB6Oho/fLi4mIcPHgQffv2bdBYiYiIiIiIiIiIiMj8mPSV5CNHjsSHH34IHx8fdOrUCadOncKKFSvw8ssvAygbZiU0NBRLlixBmzZt0KZNGyxZsgS2traYOHGikaMnIiIiIiIiIiIiIlNn0kXyiIgILFy4EK+99hrS0tKgVqsxbdo0vPfee/o2b775JgoLC/Haa68hMzMTvXr1wp49e+Dg4GDEyImIiIiIiIiIiIjIHJh0kdzBwQGrVq3CqlWrqmwjkUgQFhaGsLCwBouLiIiIiIiIiIiIiBoHkx6TnIiIiIiIiIiIiIioPrFITkREREREREREREQWi0VyIiIiIiIiIiIiIszJTQoAAQAASURBVLJYLJITERERERERERERkcVikZyIiIiIiIiIiIiILFatiuQtW7ZERkZGhflZWVlo2bLlIwdFRER1g/maiMg8MF8TEZk+5moiosarVkXyGzduQKvVVpiv0Whw69atRw6KiIjqBvM1EZF5YL4mIjJ9zNVERI2XrCaNd+zYof//3bt3Q6VS6V9rtVrs27cPvr6+dRYcERHVDvM1EZF5YL4mIjJ9zNVERI1fjYrko0ePBgBIJBJMnjzZYJlcLoevry8++uijOguOiIhqh/maiMg8MF8TEZk+5moiosavRkVynU4HAGjRogWOHz8ONze3egmKiIgeDfM1EZF5YL62LImJiUhPT2+Qfbm5ucHHx6dB9kXU2DFXExE1fjUqkpdLSEio6ziIiKgeMF8TEZkH5uvGLzExEe07dEBhQUGD7M/G1hYXL1xgoZyoDjFXExE1XrUqkgPAvn37sG/fPqSlpel/VS339ddfP3JgRERUN5iviYjMA/N145aeno7CggI8/9ZyuPu0qtd93Um8hm+XzkN6ejqL5ER1jLmaiKhxqlWRfPHixXj//ffRvXt3eHp6QiKR1HVcRERUB5iviYjMA/O15XD3aQWvNp2MHQYR1UJD5urS0lKEhYXh22+/RWpqKjw9PTFlyhS8++67sLKyAgAIIbB48WJ8+eWXyMzMRK9evfDpp5+iUyfmGCKimqpVkfzzzz9HZGQkJk2aVNfxEBFRHaqrfH3o0CEsX74csbGxSElJQVRUlP4BRgAwZcoUrF+/3mCdXr164ejRo/rXGo0Gc+fOxebNm1FYWIjBgwdjzZo18PLyeqTYiIgaA55fExGZvobM1UuXLsXnn3+O9evXo1OnTjhx4gReeuklqFQqzJo1CwCwbNkyrFixApGRkWjbti0++OADDB06FJcuXYKDg0O9x0hE1JhY1Wal4uJi9O3bt65jISKiOlZX+To/Px8BAQFYvXp1lW2eeOIJpKSk6Keff/7ZYHloaCiioqKwZcsWHD58GHl5eRgxYgS0Wu0jx0dEZO54fk1EZPoaMlcfOXIETz31FIYPHw5fX1+MHTsWwcHBOHHiBICyq8hXrVqFd955B2PGjIGfnx/Wr1+PgoICbNq0qcrtajQa5OTkGExERFTLIvkrr7zywKRLRESmoa7ydUhICD744AOMGTOmyjZKpRIeHh76ycXFRb8sOzsba9euxUcffYQhQ4aga9eu2LhxI86ePYu9e/c+cnxEROaO59dERKavIXN1//79sW/fPly+fBkAcPr0aRw+fBhPPvkkgLKHiKampiI4OFi/jlKpRFBQEGJiYqrcbnh4OFQqlX7y9vau3wMhIjITtRpupaioCF9++SX27t2Lzp07Qy6XGyxfsWJFnQRHRESPpiHz9YEDB9C0aVM4OTkhKCgIH374IZo2bQoAiI2NRUlJicFJvFqthp+fH2JiYjBs2LBKt6nRaKDRaPSveaULETVWPL8mIjJ9DZmr33rrLWRnZ6N9+/aQSqXQarX48MMPMWHCBABAamoqAMDd3d1gPXd3d9y8ebPK7S5YsACzZ8/Wv87JyWGhnIgItSySnzlzBl26dAEAxMfHGyzjQ4aIiExHQ+XrkJAQPPvss2jevDkSEhKwcOFCPP7444iNjYVSqURqaioUCgWcnZ0N1nN3d9ef4FcmPDwcixcvrrM4iYhMFc+viYhMX0Pm6q1bt2Ljxo3YtGkTOnXqhLi4OISGhkKtVmPy5MlV7lcI8cBYlEollEplncZKRNQY1KpIvn///rqOg4iI6kFD5evnnntO//9+fn7o3r07mjdvjp07dz5wiJaHncTzShcishQNla9LS0sRFhaGb7/9FqmpqfD09MSUKVPw7rvvwsqqbCRGIQQWL16ML7/8EpmZmejVqxc+/fRTdOrUqUFiJCIyVQ1ZC5k3bx7mz5+P8ePHAwD8/f1x8+ZNhIeHY/LkyfDw8AAAfS4vl5aWVuHqciIierhajUlORET0IJ6enmjevDmuXLkCAPDw8EBxcTEyMzMN2j3sJF6pVMLR0dFgIiKi2lu6dCk+//xzrF69GhcuXMCyZcuwfPlyRERE6NssW7YMK1aswOrVq3H8+HF4eHhg6NChyM3NNWLkRESWpaCgQP/jZTmpVAqdTgcAaNGiBTw8PBAdHa1fXlxcjIMHD/JB0EREtVCrK8kHDRr0wCv/fv3111oHREREdcdY+TojIwNJSUn6q1oCAwMhl8v/n707j4+ivv84/po9stnc951ACAn3DXJ4ACpYr9pqtVat2tPWk9JWq/bA/ixUWy2tWltaq7aV0kPxrApqARG55b4h5L6vzbHZzR6/PwLRGFGOJLtJ3s/HYw07mZ357Jh8MvuZ73y+rFy5kmuuuQaAsrIydu3axcMPP9wjMYiI9CW9la/ff/99rrjiCi699FIABg8ezD/+8Q82b94MtI8iX7x4Mffff3/HnUDPPvssycnJLF26lFtuuaVb4hAR6Yt689z68ssv5xe/+AVZWVmMGjWKDz74gEcffZSvf/3rQHublXnz5rFw4UJyc3PJzc1l4cKFhIWFcd1113VbHCIiA8VpFcmP9+A6rq2tjW3btrFr165OvbFERCSwuitfNzU1cejQoY7n+fn5bNu2jbi4OOLi4liwYAFXXXUVqampHD16lPvuu4+EhAS++MUvAhAdHc03vvENvv/97xMfH09cXBw/+MEPGDNmDBdeeGG3vFcRkb6st86vzznnHP7whz9w4MAB8vLy2L59O2vXrmXx4sVAe34vLy/vNNGyzWZj5syZrFu37oRFck20LL2psLCQ6urqXttfQkICWVlZvbY/CV69WQt57LHH+MlPfsKtt95KZWUlaWlp3HLLLfz0pz/tWOfuu+/G6XRy6623drTHWrFiBZGRkd0ai4jIQHBaRfLf/OY3n7h8wYIFNDU1nVFAIiLSfborX2/evJnZs2d3PD/eJ/ymm27iySefZOfOnfz1r3+lvr6e1NRUZs+ezT//+c9OJ+i/+c1vsFgsXHPNNTidTi644AKeeeYZzGbzab47EZH+o7fOr++55x4aGhoYPnw4ZrMZr9fLL37xC77yla8AdEym/PFWWMnJyRQUFJxwu5poWXpLYWEhw0eMwNnS0mv7tIeFsW/vXhXKpVdrIZGRkSxevLjjIuYnMQyDBQsWsGDBgm7dt4jIQHRaRfITueGGGzjrrLP49a9/3Z2bFRGRbnaq+XrWrFn4/f4Tfv/NN9/8zG2Ehoby2GOPdep7KyIin667z6//+c9/8ve//52lS5cyatQotm3bxrx580hLS+s0CvLj7QQ00bIEi+rqapwtLVx/z69Izsrp8f1VFB7muYd+SHV1tYrkckKqhYiI9H3dWiR///33CQ0N7c5NiohID1C+FhHpG7o7X//whz/kRz/6Eddeey0AY8aMoaCggEWLFnHTTTeRkpICtI8oPz6vBJzcRMs2m63b4hT5LMlZOWTkjgp0GCKAzq1FRPqD0yqSH5/E5zi/309ZWRmbN2/mJz/5SbcEJiIiZ075WkSkb+itfN3S0oLJZOq0zGw24/P5AMjOziYlJYWVK1cyYcIEANxuN6tXr+ahhx7qtjhERPoinVuLiPRfp1Ukj46O7vTcZDIxbNgwfv7zn3ea5EdERAJL+VpEpG/orXx9+eWX84tf/IKsrCxGjRrFBx98wKOPPsrXv/51oL3Nyrx581i4cCG5ubnk5uaycOFCwsLCuO6667otDhGRvkjn1iIi/ddpFcmffvrp7o5DRER6gPK1iEjf0Fv5+rHHHuMnP/kJt956K5WVlaSlpXHLLbfw05/+tGOdu+++G6fTya233kpdXR1Tp05lxYoVnSZjFhEZiHRuLSLSf51RT/ItW7awd+9eDMNg5MiRHbdkiohIcFG+FhHpG3o6X0dGRrJ48WIWL158wnUMw2DBggUsWLCgW/ctItJf6NxaRKT/Oa0ieWVlJddeey2rVq0iJiYGv99PQ0MDs2fPZtmyZSQmJnZ3nCIichqUr0VE+gblaxGR4KdcLSLSf5k+e5Wu7rjjDhwOB7t376a2tpa6ujp27dqFw+Hgzjvv7O4YRUTkNClfi4j0DcrXIiLBT7laRKT/Oq2R5G+88QZvvfUWI0aM6Fg2cuRInnjiCU1WISISRJSvRUT6BuVrEZHgp1wtItJ/ndZIcp/Ph9Vq7bLcarXi8/nOOCgREekeytciIn2D8rWISPBTrhYR6b9Oq0h+/vnnc9ddd1FaWtqxrKSkhO9973tccMEF3RaciIicGeVrEZG+QflaRCT4KVeLiPRfp1Ukf/zxx2lsbGTw4MHk5OQwdOhQsrOzaWxs5LHHHuvuGEVE5DQpX4uI9A3K1yIiwU+5WkSk/zqtnuSZmZls3bqVlStXsm/fPvx+PyNHjuTCCy/s7vhEROQMKF+LiPQNytciIsFPuVpEpP86pZHk77zzDiNHjsThcAAwZ84c7rjjDu68806mTJnCqFGjePfdd3skUJG+xO/3s6ukgd+9fZBntjmIOuuLtHgCHZUMJMrXIiJ9g/K1iEjwU64WEen/TqlIvnjxYr71rW8RFRXV5XvR0dHccsstPProo90WnEhfVN7Qyjef3cxlj63l0ZUHePlAM7Gzv8HrpVbe2VeJx6sJXaTnKV+LiPQNytciIsFPuVpEpP87pSL59u3b+dznPnfC78+dO5ctW7accVAifdXBikYue2wtb++rxGo2uGhUMl8YFk5rwQ7AYGdJA//aXIyzzRvoUKWfU74WEekblK9FRIKfcrWISP93SkXyiooKrFbrCb9vsVioqqo646BE+qKj1c185U/rqW5yMTwlktfuPJc/fnUyN46LomLZfZyT2IbdaqaqycVL20pwezSiXHqO8rWISN+gfC0iEvyUq0VE+r9TKpKnp6ezc+fOE35/x44dpKamnnFQH1VSUsINN9xAfHw8YWFhjB8/vtMVWr/fz4IFC0hLS8NutzNr1ix2797drTGIfJbWNi/ffW4r1U1uRqVFsezb08hLjuy0TrLdz1UT0wm1mqhwuPjf/soARSsDQSDytYiInDrlaxGR4KdcLSLS/51SkfySSy7hpz/9Ka2trV2+53Q6+dnPfsZll13WbcHV1dVx9tlnY7Vaef3119mzZw+PPPIIMTExHes8/PDDPProozz++ONs2rSJlJQU5syZQ2NjY7fFIfJZfvHaXvaWOYgPD+EvN08hJizkE9eLj7Bx2dg0DGBfeSP7yh29G6gMGL2dr0VE5PQoX4uIBD/lahGR/s9yKiv/+Mc/5oUXXiAvL4/bb7+dYcOGYRgGe/fu5YknnsDr9XL//fd3W3APPfQQmZmZPP300x3LBg8e3PFvv9/P4sWLuf/++7nyyisBePbZZ0lOTmbp0qXccsst3RaLyIlsKajlb+sLAFh87XiSo0I/df30GDtnZcexIb+W1furGBQfjt1q7o1QZQDp7XwtIiKnR/laRCT4KVeLiPR/p1QkT05OZt26dXz3u9/l3nvvxe/3A2AYBhdddBG///3vSU5O7rbgXn75ZS666CKuvvpqVq9eTXp6Orfeeivf+ta3AMjPz6e8vJy5c+d2vMZmszFz5kzWrVt3wiK5y+XC5XJ1PHc4NJpXTo/H6+P+5bsAuHpSBufmJp7U684aHMehqiZqmty8f7iG84cn9WSYMgD1dr4WEZHTo3wtIhL8lKtFRPq/UyqSAwwaNIj//ve/1NXVcejQIfx+P7m5ucTGxnZ7cEeOHOHJJ59k/vz53HfffWzcuJE777wTm83GjTfeSHl5OUCXP0bJyckUFBSccLuLFi3igQce6PZ4ZeD5z5Zi9pU3EhNm5d5LRpz060wmg1l5iTy/tYRdJQ2MzYgmIcLWg5HKQNSb+VpERE6f8rWISPBTrhYR6d9OuUh+XGxsLFOmTOnOWLrw+XxMnjyZhQsXAjBhwgR2797Nk08+yY033tixnmEYnV7n9/u7LPuoe++9l/nz53c8dzgcZGZmdnP00t+1tnn57dsHAbh99lDiwj+5D/mJZMSGkZMYzuGqZjbk13LpGE30Ij2jN/K1iIicOeVrEZHgp1wtItI/ndLEnb0tNTWVkSNHdlo2YsQICgsLAUhJSQHoGFF+XGVl5afe6mSz2YiKiur0EDlVz20opKyhlbToUG6YNui0tjFtSDwAhyqbqGp0fcbaIiIiIiIiIiIi0t1OeyR5bzj77LPZv39/p2UHDhxg0KD2gmR2djYpKSmsXLmSCRMmAOB2u1m9ejUPPfRQr8crA4fb4+NPa44AcMcFuYSe5sSbCRE28pIiOFDZxOajtVys0eQiIiIiIiIiIkGtsLCQ6urqXttfQkICWVlZvba/gSioi+Tf+973mDFjBgsXLuSaa65h48aNLFmyhCVLlgDtbVbmzZvHwoULyc3NJTc3l4ULFxIWFsZ1110X4OilP3vxgxLKHa0kRdq4cmL6GW1r8uA4DlQ2cbCqibOdbUTZrd0UpYiIiIiIiIiIdKfCwkKGjxiBs6Wl1/ZpDwtj3969KpT3oKAukk+ZMoXly5dz77338vOf/5zs7GwWL17M9ddf37HO3XffjdPp5NZbb6Wuro6pU6eyYsUKIiMjAxi59Gd+v58/rjkMwDfPzcZmOb1R5MclRtrIjLVTVOdkW3E95+UmdkeYIiIiIiIiIiLSzaqrq3G2tHD9Pb8iOSunx/dXUXiY5x76IdXV1SqS96CgLpIDXHbZZVx22WUn/L5hGCxYsIAFCxb0XlAyoL1/uIbDVc2Eh5j5ylndk5wmZsVSVOdkd4mD6UPisZqDeroAEREREREREZEBLTkrh4zcUYEOQ7qJKnEip+jvGwoA+OLEdCJDu6c1yqD4MKLtVtxeHwcrmrplmyIiIiIiIiIiIvLZVCQXOQWVjlZW7K4A4IZpg7ptu4ZhMCotCoBdpQ3dtl0RERERERERERH5dCqSi5yCZZuK8Pj8TB4Uy/CUqG7d9sjUKAwDyhpaqWlydeu2RURERERERERE5JOpSC5ykjxeH//YWAh07yjy48JtFrLjwwHYXebo9u2LiIiIiIiIiIhIVyqSi5ykd/ZVUtbQSlx4CBePSemRfYxOjwZgb5kDj8/XI/sQERERERERERGRD6lILnKSlm0qAuDqyRnYLOYe2ceguDAibBZa23wcqWrukX2IiIiIiIiIiIjIh1QkFzkJNU0uVh+oAuDqSZk9th+TyWBkanuv892larkiIiIiA4fb46OooQ177nSKWwxK6514ff5AhyUiEjAlJSXccMMNxMfHExYWxvjx49myZUvH9/1+PwsWLCAtLQ273c6sWbPYvXt3ACMWEem7VCQXOQmv7SzD6/MzJj2aoUkRPbqvEamRABTVtdDs8vTovkRERESCxU9f2sVdb1aTdOX9bKi28u8txSxZc4TV+6t0TiQiA05dXR1nn302VquV119/nT179vDII48QExPTsc7DDz/Mo48+yuOPP86mTZtISUlhzpw5NDY2Bi5wEZE+yhLoAET6ghc/KAHgivFpPb6vmLAQkqNsVDhcHKpsYlxmTI/vU0RERCTQchIjsFsM6ov2kzZ4KC0+K842L9uK69lT5mDWsESGp0RiGEagQxUR6XEPPfQQmZmZPP300x3LBg8e3PFvv9/P4sWLuf/++7nyyisBePbZZ0lOTmbp0qXccsstvR2yiEifppHkIp+hoKaZrYX1mAz4/LieL5IDDEtuH02+v0IjAERERGRguPnswfz9i8mU/3U+s5I9fPPcbL4wPo3kKBtur48VeypYfaAKv18tWESk/3v55ZeZPHkyV199NUlJSUyYMIE//elPHd/Pz8+nvLycuXPndiyz2WzMnDmTdevWnXC7LpcLh8PR6SEiIhpJLvKZXtpWCsDZQxNIigrtlX3mJkey5mA1ZQ2tOJxtRNmtvbJf6aywsJDq6upe2VdCQgJZWVm9si8REZFgZDWbOo0SNxkGg+LDyYwLY9PRWtYfqWV7cQOtHh9zRyZj0ohyEenHjhw5wpNPPsn8+fO577772LhxI3feeSc2m40bb7yR8vJyAJKTkzu9Ljk5mYKCghNud9GiRTzwwAM9GruISF+kIrnIp/D7/by47XirlfRe22+EzUJGrJ3iOif7KxqZMjiu1/Yt7QoLCxk+YgTOlpZe2Z89LIx9e/eqUC4iIj2upKSEe+65h9dffx2n00leXh5PPfUUkyZNAtrPfx544AGWLFlCXV0dU6dO5YknnmDUqFEBiddkGEzNjifGHsKKPeXsL28kxGxi9rBEtV4RkX7L5/MxefJkFi5cCMCECRPYvXs3Tz75JDfeeGPHeh/Pg36//1Nz47333sv8+fM7njscDjIzM7s5ehGRvkdFcpFPsbOkgSNVzYRaTVw0KvmzX9CNhiVHUlzn5ICK5AFRXV2Ns6WF6+/5FclZOT26r4rCwzz30A+prq5WkVxERHrU8YngZs+ezeuvv05SUhKHDx/+xIngnnnmGfLy8njwwQeZM2cO+/fvJzIyMmCxD0tp3/cbu8vZWdJAbJiVCVmxAYtHRKQnpaamMnLkyE7LRowYwfPPPw9ASkoKAOXl5aSmpnasU1lZ2WV0+UfZbDZsNlsPRCwi0repSC7yKV78oL3VyoUjkokM7d2WJ0OTIvjf/kqqm9zUNLmIj9CJTCAkZ+WQkRuYkXMiIiLdra9PBDcsJZJmt4d3D1bz7qFqEiNtZMSGBTQmEZGecPbZZ7N///5Oyw4cOMCgQYMAyM7OJiUlhZUrVzJhwgQA3G43q1ev5qGHHur1eEVE+jpN3ClyAh6vj1d2tBfJv9CLrVaOC7WaGRwfDmgCT5EzUVDTzCsHmrEmZqO53kRkoOsPE8FNyIxheEokfj+8ubsCV5u3x/YlIhIo3/ve91i/fj0LFy7k0KFDLF26lCVLlnDbbbcB7W1W5s2bx8KFC1m+fDm7du3i5ptvJiwsjOuuuy7A0YuI9D0qkoucwLrDNVQ1uogNs3JeXmJAYshLbr+t+GBlE35V90ROy9t7K3l6m4O0rz/GynILNU2uQIckIhIwxyeCy83N5c033+Q73/kOd955J3/9618BPnUiuOPf+ySLFi0iOjq649GT/W0Nw+D84UlE2600uTysPlDVY/sSEQmUKVOmsHz5cv7xj38wevRo/u///o/Fixdz/fXXd6xz9913M2/ePG699VYmT55MSUkJK1asCGhrLBGRvkrtVkRO4PiEnZeOTSXEEpjrSdkJ4ZhNBvUtbVQ3uUmMVMsVkVOVHmtnQoqNLYX1NGLnn5uLuGJ8Oukx9kCHJiLS6/rLRHBWs4m5I5P5z5Zi9pY3kpMUQU5iRI/tT0QkEC677DIuu+yyE37fMAwWLFjAggULei8oEZHTVNXo4mBlI+WOVhqdHgwDrD4LMTNvprLZE+jwNJJc5JM43V7e3NU+WioQrVaOC7GYGBzf3mfzYKVaroicjotGpfCT8+Io+cM3SLT5aPP6+e/OMppdgf8jLCLS2040EVxhYSHQeSK4jzqZieCioqI6PXpaWoydiYPaJ+58e28lLW7ldREREZFgU+FoZfkHJSzdWMimo3UU1Tqpd7ZR19JGZauJ6GlfotUT+O4JKpJLt2p2eahuclHf4u7T7UFW7q2g2e0lI9bOpGMfvgIlN+lYy5UKtVwRORM+p4MZiR7iw0NocXtZsadCv1MiMuCcykRwxx2fCG7GjBm9GuvJmDYkjviIEJxtXtYeqg50OCIiIiJyjNfn571D1fxzUxGFtS2YDMhJDOeC4UlcNTGdqyamMyHOQ+PWV0mPDHyzk8BHIH2e3+9nf0UjWwrqqG5ydywPt5kZmRrFxKxYQq3mAEZ46l76oL3VyhfGp3/qrcW9oaPlilMtV0TOlMUEl45J5bkNhRTWtnC0poXshPBAhyUi0mu+973vMWPGDBYuXMg111zDxo0bWbJkCUuWLAE6TwSXm5tLbm4uCxcuDNqJ4CwmExcOT+afm4vYW9bImPRoUqPVTktEREQkkJxuL6/tLKOk3glAXnIEZ+ckEGW3dl6x2seLK/+A+ZffCkCUnWkkuZyRFreH57eW8Obuio4Cud1qxmwyaHZ52XS0jr++X0BBTXOAIz15tc3ujgmgvjAhLcDRdG65cqBCLVdEzlRseAjjs2IAePdgFV6fRpOLyMDRHyeCS4kOZWRqe3uXVfur8OkuIREREZGAaWr18O8tRZTUO7GaDS4Zk8LFo1O7FsiDjEaSy2lzONt4fmsxjlYPVrPBlMFxjE6Lxh5ixuP1cbSmhfeP1FDb7ObFbaXMyktkXGZMoMP+TK/tKMXj8zM6PYqhScHxYTAvOZLDVc0crGxiRk58wEe3i/R1UwbHsqfUQV1LG4cqmxiWEhy/6yIivaE/TgQ3IyeeQ1VNVDa62F3qYEx6dKBDEhERERlwml0e/rO1mAZnGxE2C18Yn0Z8RN/oiKCR5HJanG4vL24rwdHqIdpu5dopWUwZHIc9pL2tisVsYmhSBF+ZksnotGMjew5Usa2oPoBRn5wXt5UCgZ2w8+MGx4djMRk0ONuoanIFOhyRPs9mMTMus72AsrWwTr3JRUT6uHCbhelD4gFYd6ia1jZvgCMSERERGVhcbV6WbyuhwdlGVKiFqydl9JkCOahILqfB7/fz+u4y6lrarwpdNTGduPCQT1zXYjZx/vAkJh+b/HL1gSoOVzX1ZrinpLCmhS0FdRgGXD4u8K1WjmtvudLeN/lgRfAeP+m/1qxZw+WXX05aWhqGYfDiiy92+r7f72fBggWkpaVht9uZNWsWu3fv7rSOy+XijjvuICEhgfDwcD7/+c9TXFzci++iszHp0ZhNBpWNLkrrWwMWh4iIdI+x6dHEh4fQ6vGx+WhdoMMRERERGTB8fj+v7y6npslNeIiZKydmBH17lY9TkVxO2eaCOopqnVhMBl8Yn0Zk6Kf/0BuGwYyc+I7bXt/cXU5NkI6Gfmlb+4SdZ+ckkBwVGuBoOstNjgDgYGWTRr1Kr2tubmbcuHE8/vjjn/j9hx9+mEcffZTHH3+cTZs2kZKSwpw5c2hs/LCP/rx581i+fDnLli1j7dq1NDU1cdlll+H1Bma0X1iIhRHH2qxsL64PSAwiItJ9TCaDs4cmALCtuJ7G1rYARyQiIiIyMLx/uIaCmhYsJoPPj08juo8VyEFFcjlFNU0u1h+pAWDWsMSTvm3CMAxm5iWSEWunzdt+dcnj9fVkqKfM7/fz4rEi+RXjg2cU+XHZCR9pudIYnBcZpP+6+OKLefDBB7nyyiu7fM/v97N48WLuv/9+rrzySkaPHs2zzz5LS0sLS5cuBaChoYGnnnqKRx55hAsvvJAJEybw97//nZ07d/LWW2/19tvpcPzi3ZHqZt2aLyLSDwyODyM9xo7X5+f9Y+esIiIiItJzDlY0srmg/S6+C0ckkxQZXINOT5aK5HLS/H4/7+yvxOeHIQnhjEyNOqXXm00GnxuVQliImZomN+8dCq4PLrtKHByuasZmMfG50SmBDqcLq9lEdkJ7y5UDlWq5IsEjPz+f8vJy5s6d27HMZrMxc+ZM1q1bB8CWLVtoa2vrtE5aWhqjR4/uWOeTuFwuHA5Hp0d3Soy0kRARgtfn50BF42e/QEREgpphGJxzbDT53rJGqoP07kURERGR/qCuxc3KvRUATMyKYdixu7X7IhXJ5aTtr2iktL4Vi8lg5rBEDMM45W2E2yzMHZkMtN8GW94QPH2Aj48iv3Bk8me2kAmU3KRjLVcqGtVyRYJGeXk5AMnJyZ2WJycnd3yvvLyckJAQYmNjT7jOJ1m0aBHR0dEdj8zMzG6N3TAMRhy74LenrHsL8CIiEhgp0aEMPXbO9N6h6gBHIyIiItI/eX1+3txdTpvXT0asnbNzEgId0hlRkVxOisfn4/3D7SO/p2THEXUGReRB8eGMSG2/svTWvgp8QVDr9Xh9vLy9FIAvjE8PcDQnNvhYyxVHq4dKtVyRIPPxC2d+v/8zL6Z91jr33nsvDQ0NHY+ioqJuifWjhqdEYhhQ4XDR4FT/WhGR/mBGTjyGAUdrWiiuawl0OCIiIiL9zsb8WiocLmwWE3NHJmMynfpg2mCiIrmclJ3FDThaPYTbzEzIjDnj7Z07NJFQq4maJjcHHIH/MXz3YDVVjS5iw6zMzEsMdDgn9NGWKwfVckWCREpKe3uij48Ir6ys7BhdnpKSgtvtpq6u7oTrfBKbzUZUVFSnR3cLC7GQEWMH4JB+r0RE+oXYsBBGp7XPO/HeoRrdgSciIiLSjcoanGw6WgvA+cOTgrYjw6kIfHVSgp7H6+towD8tOx6r+cx/bOwhZs7LbS9G720wY4kN7ESZ/9lSDMAXJqQTYgnuX4vcZLVckeCSnZ1NSkoKK1eu7FjmdrtZvXo1M2bMAGDSpElYrdZO65SVlbFr166OdQLp+G35ByvVl1xEpL+Ymh2H1WxQ7mjlUJUugoqIiIh0B4/Xx5u7K/DTfmd2XnLf7UP+UcFdDZSgsLvUQYvbS2SopaN3b3cYnhJJZpwdHwaxs7/Rbds9VfUtblbuaZ9k4EuTMgIWx8kaHP9hy5UKtVyRXtLU1MS2bdvYtm0b0D5Z57Zt2ygsLMQwDObNm8fChQtZvnw5u3bt4uabbyYsLIzrrrsOgOjoaL7xjW/w/e9/n7fffpsPPviAG264gTFjxnDhhRcG8J21y0mMwKC95YpDLVdERPqFcJuFCVntc2G8f7gGXzD0+BMRERHp4zYeraXB2UaEzcKsYcHbjeFUqUgun8rnp2MU+aRBsZi7sb+QYRjMykvCwE9Y7lR2VASm4PvStlLcXh8jU6MYdey23GBmNZsYcrzlSoVGvUrv2Lx5MxMmTGDChAkAzJ8/nwkTJvDTn/4UgLvvvpt58+Zx6623MnnyZEpKSlixYgWRkR9eUf7Nb37DF77wBa655hrOPvtswsLCeOWVVzCbzQF5Tx8VbrOQdqzlypHq5gBHIyIi3WViVgx2q5m6ljZN0CwiIiJyhmqb3Ww5ViecmZeIzRL4z/PdRUVy+VQlLSaaXB7CQsyM6sZR5MfFhYcwJMIHwNPbHHgDMMLneKuVqycH/yjy43KP3cpysLJJLVekV8yaNQu/39/l8cwzzwDtF70WLFhAWVkZra2trF69mtGjR3faRmhoKI899hg1NTW0tLTwyiuvkJmZGYB388mO9/s/WqMiuYhIf2GzmJkyuH00+fr8Gtq8vgBHJCIiItI3+f1+3tlXic/f/vk5JzE80CF1K0ugA5Dgdrip/TrKmPRoLN3Qi/yTjIj2cqCqhQIi+eemIq6bmtUj+/kk+8od7CxpwGo2uGJ8eq/t90wNjg/DajZobPVQ4XCREh0a6JBE+rxB8WGsPQTFdU48Xl+P5TwREeldYzKi+aConsZWD9uL6pk8OC7QIYmcUGFhIdXV1b22v4SEBLKyeu/zl4iI9F17yxopqXdiMRnMykvEMLqv20QwUJFcTsiaNIQalwmT0V4k7yk2MzSs+wdxF3ybR1fu5/Jxqb02K+6/N7ePIr9wRDJx4SG9ss/uYDGbyE4I50BFEwcrG1UkF+kG8eEhRNgsNLk8FNc5GZzQv66Ki4gMVBaTielD4lmxp4LNBXWMTo8m1Np/bg2W/qOwsJDhI0bgbGnptX3aw8LYt3evCuUiIvKpnG4v7x6qAmDakHii7L1Tt+tNKpLLCUVNugyAoUkRhNt69kelcet/GXXFdylrcvPE/w7zo4uH9+j+oP0XvC+2WjkuNynyWJG8iXOGJvS7K3givc0wDAbHh7Gr1MHRmmYVyUVE+pFhKZFsKaijptnN5oI6zhmaEOiQRLqorq7G2dLC9ff8iuSsnB7fX0XhYZ576IdUV1erSC4iIp9q7aFqWtt8xEeEMD4zJtDh9AgVyeUTNbp8hI2YCcC4jJie36HPw83jo1i0to6/rM3n+qlZZMaF9eguX9xWQoOzjay4MGbmJfXovnqCWq6IdL/BCeHHiuS9N4JLRER6nskwmDE0nle2l7GtqJ7xvXF+K3KakrNyyMgdFegwREREACipc3ZMgH7+sCTMpv45SFMNV+UTvZ3fgslqI9rqI7WXiq+TU22cPTQet9fHotf39ui+/H4/z7x3FIAbpw/qk7/gFrOJIQkRAByobAxwNCL9Q2ZsGCYDGpxt1LW4Ax2OiIh0o+z4cNKiQ/H6/GzIrwl0OCIiIiJBz+trn6wTYHRaFGkx9gBH1HM0kly68Pn8vHm4fRRlTqSv19p4GIbBjy8dyaW/e5f/7ixnw5Eapg6J75F9vX+khv0VjYSFmLl6cmaP7KM35CZHsL+ikQMVjUF727Df76ewtoVDVU1UOly4PD5sFhNJUTaGJUeSHmNXqxgJGiEWE2kxdorrnBTUtBAb1nfmKhARkU9nGAZnD03g31uK2V3mIC0l0BGJiPRdmmRWZGDYUlhHbYsbu9XM2UFad+ouKpJLFxvya6lo9uJzNZMZ1ruN+EekRnHtWVks3VDIz1/dw8u3n9Mjo7yPjyK/amIG0X14soFB8WGEWk00u7wU1rQE3S90cV0Lqw5UUdPUdURuZaOLXSUOkqNsnD88iaRItYuR4DA4PpziOidHa5r7ba81EZGBKi3GTnZCOPnVzeyqD7YzJxGRvkGTzIoMDE1tsLG4FoDzchP6/cTnferMcNGiRdx3333cddddLF68GGgfpfrAAw+wZMkS6urqmDp1Kk888QSjRqmH2+k6Ppll8953seSe3+v7//6cPF7ZXsruUgf/2VLEl6d07x/BotoW3tpbAcBNMwZ167Z7m8VkYnhKFNuK6tld5mBckNSZvT4/7x2u5oPCegCsZoPhKVFkxtkJC7HQ4vZQWNvC/vJGKhwulm0qYlZeImPVH1SCwKD4MNYeguI6Jx6vD4tZnclERPqTs3PiOVrdTKnThC1zdKDDERHpczTJrMjAsK3OgtfnJzPWzrCUyECH0+P6TJF806ZNLFmyhLFjx3Za/vDDD/Poo4/yzDPPkJeXx4MPPsicOXPYv38/kZH9/39gd2t2eXh9V1n7v3e9BZ/v/SJ5fISNuy7I5cHX9vKrN/dzyZhUIkO7b7T3X97Lx+eHc3MTGJrU939GRqa2F8mPVDUxPC3Q0YDL4+XVHWUU1zkBGJ0exdk5Xa845iZFMi07nv/tr+RwVTP/219FY6uHGTnxar8iARUfHkKEzUKTy0NpQytZPTyJsIiI9K74CBuj06PZWdJA3AXfwuvzBzokEZE+SZPMivRfYcPPpaLVhNkwmD08aUDUafrE8Limpiauv/56/vSnPxEbG9ux3O/3s3jxYu6//36uvPJKRo8ezbPPPktLSwtLly4NYMR91+u7ymlxe0mNMOMq2RewOG6cPpghCeFUN7l5ZMWBbttupaOVpRsKAfj2eUO6bbuBlBhpIynShs8PRS2B/ZVubfPywtYSiuucWM0Gl45J5YLhySe8JSfcZuHSMalMz2nvPb+5oI4N+bW9GbJIF4ZhkBHbPhlJcV3v3UIqIiK9Z/qQeKyGn5DkHN7JdwY6HBEREZGg0ez2EXvBtwCYPDh2wMzV1SdGkt92221ceumlXHjhhTz44IMdy/Pz8ykvL2fu3Lkdy2w2GzNnzmTdunXccsstn7g9l8uFy+XqeO5wOHou+D7mP1uKAJg92M76AMYRYjHxwBWj+OpTG3n2/aNcMT6NCVmxn/3Cz/DHNUdweXxMzIoJ2okuT8fI1CgqG6s42hS4Irnb4+OlbaVUNrqwW81cMT6N5KjP7v9iGAZnDY7DajJYc7CaDfm1RNutjEiN6oWoRT5ZRqydfeWNHXdEiIhI/2IPMTMi2suOegvP7WrkO5e19el5akREpHv15sSkmpRUgs3SXY1YIuKIsPiZPOjMa3F9RdAXyZctW8bWrVvZtGlTl++Vl5cDkJyc3Gl5cnIyBQUFJ9zmokWLeOCBB7o30H6gqLaF9UdqMQyYOSiMRQGO59zcRK6cmM4LW0v40fM7eeWOcwixnH4RuLiuhb+tb/+5uPOC3H51q8iwlEjePVhNQ5sJa1Lvj5D3++GN3eWUO1qxWUxcOTGdhAjbKW1jQlYsLW4vmwvqeHtfJfHhISSdRJFdpCdkxLa3WKlwtOL2+M4o94iISHDKifSx5XARjvhMFr91gJ9drpYBIiLS+xOTalJSCSbbiup541D7z/6EOM+AmqMrqIvkRUVF3HXXXaxYsYLQ0BMXyz5e7PT7/Z9aAL333nuZP39+x3OHw0FmZuaZB9zHvbC1BICzcxJIDA+OGWt/fOlIVu2vYn9FI0vWHOb283NPe1u/enM/bo+PaUPimJmX2I1RBl6o1UxOYjgHKpuIHHdRr+9/n8NEfkMzZpPBFePTTrlAftyMnHhqmt3kVzfz+u5yrjsrC+sASsgSPKLtViJDLTS2eihrcDIoPjzQIYmISDczGVD71hKSv/x/PLvuKFeMT2d8ZkygwxIRkQDrzYlJNSmpBJM2r497X9iJH2ja9Q5Jl5wT6JB6VVAXybds2UJlZSWTJk3qWOb1elmzZg2PP/44+/fvB9pHlKempnasU1lZ2WV0+UfZbDZsttMr4vVXfr+fF7e1F8mvnJgOVAY2oGPiwkP42eUjuWvZNn739iEuGJF8Wm04Nh2t5aVtpRhGe+G9P40iP250ejQHKpsIH30+zW5fr+3XPmQyexraL6rMHpZIarT9tLdlGAZzRybz3IZC6lvaWHOwiguGn/h3WaQnZcTa2VvWSFGdiuQiIv1V69EPmDnIzuoCJz96fgev3HGOLtCLiAigiUll4PnTu0fYW+YgIsSg6H9PwQArkgf1GeAFF1zAzp072bZtW8dj8uTJXH/99Wzbto0hQ4aQkpLCypUrO17jdrtZvXo1M2bMCGDkfc/uUgf51c2EWk1cNCol0OF08vlxaVwwPAm318ftS7fS7PKc0utb27zc858dAHx5ciaj06N7IsyAy4i1E2X1YQqx87+jvdNHuazRQ/zlPwAMxqRHMyrtzI9tqNXM3JHthfFdJQ5K69UTWgIj81jLFU3eKSLSv31tfBRx4SHsK29kyZojgQ5HREREpNcdrW7mt28dBNrPjXwtDQGOqPcFdZE8MjKS0aNHd3qEh4cTHx/P6NGjMQyDefPmsXDhQpYvX86uXbu4+eabCQsL47rrrgt0+H3KK9tLAbhgeDLhtuC6wcAwDH519TiSo2wcrmrmh//Zjs/nP+nX//L1fRypbiY5ysa9l4zowUgDyzAMciLaR5C/fqgZ7ykco9PR4vbw8Lo6zKERxIX4urWFTWZcGCOP3THwv/2Vp/T/W6S7ZMS23xVR2ejC5fEGOBoREekpUTYTP71sJAC/ffsghyqbAhyRiIiISO/x+/3ct3wnLo+Pc4YmMGvQ6XcI6MuCukh+Mu6++27mzZvHrbfeyuTJkykpKWHFihVERkYGOrQ+w+fz8+qOMgAuH5f6GWsHRlx4CI99ZSJWs8F/d5bzyzf24fd/duH0ha3FPLPuKACLrhxDtN3aw5EGVla4D6+zkbImLyt2l/fYfvx+P/c8v5OCBg/epjqmJXgwm7q3hc05QxMItZiobnKzvbi+W7ctcjIiQ61E2634/VBa3xrocEREpAddMT6NmXmJuD0+7vjHB7S26eKoiIiIDAz/3lLMusM1hFpNLPzimH7Zovhk9Lki+apVq1i8eHHHc8MwWLBgAWVlZbS2trJ69WpGjx4duAD7oK2FdZTUO4mwWZg1LCnQ4ZzQWdlx/PLKsQAsWXOEhf/d+6kjjFfsLudHz+8E4I7zh3L+AOhtbTFB49ZXAPj9qsMndSHhdDy1Np9XtpdiNqDqpV9i74GbD+whZs4emgDA+0dqcJ5alx2RbnF8NLlaroiI9G+GYfDwl8YSHx7C3jIHv3htb6BDEhEREelxVY2ujvOe712YR1Z8WIAjCpw+VySX7ne81crcUcmEWs0BjubTXTUpg59d3n477J/ezedrz2yivKHzCE+vz88fVx/mu89txe31cemYVL53YV4gwg2Ixi2vEmKGnSUNrDlY3e3bf/9wDYte3wfAzeOicBXv7vZ9HDcqLYqUqFDavH521Qf3z6b0Tx8WydUbX0Skv0uOCuWRa8YB8Lf1Bby+syzAEYmIfGjRokUdLWeP8/v9LFiwgLS0NOx2O7NmzWL37p77fCYi/c+CV3bT4GxjVFoU3zgnO9DhBJSK5AOcx+vjtZ3HW62kBTiak/O1s7P59dXjsFlMrD5QxXkP/4/bl27lif8dYtF/93L+I6tY9Po+vD4/V05I57fXjsfUza1AgpnP6eCinHAAHlmxv1tHk5c1OLl96Va8Pj9fnJDOJbk9e4XRMAxmDWvvdV7YYsKaOLhH9yfycRnHJu9UX3IRkYFh1rAkvjMzB4C7n9/BocrGAEckIgKbNm1iyZIljB07ttPyhx9+mEcffZTHH3+cTZs2kZKSwpw5c2hsVO4Skc/20rYSXttRhtlk8NBVY7GYB3aZeGC/e2FDfi3VTW5iw6ycc6y1RV/wpUkZvHz7OUwZHIvb6+PVHWX86s39/HHNEQpqWogKtfDwl8byyDXjBuQv+ReHhxMWYmZHcQNv7Oqe3uQuj5fv/H0rNc1uRqRG9VqfquSoUHKTIgCDmPNu7PH9iXxUhM3SMZeB+pKLiAwM35+bx+RBsTS2erjpL13vWhQR6U1NTU1cf/31/OlPfyI2NrZjud/vZ/Hixdx///1ceeWVjB49mmeffZaWlhaWLl0awIhFpC8oa3Dykxd3Ae0tikenRwc4osAbeNVD6eTlbe2tVi4ek4q1jxWTh6VE8u/vzOD5705n3oW5fHFCOjdNH8Sj14xj/X0XcM3kzAE72UBMqJlvnjsEgEWv7zvjyaf8fj8/e2k324vqibZb+eMNk7CH9F77k+k58Rj4CRt6Fnur3L22XxGA9Jj2lisl9Wq5IiL9j27f78pqNrHkxskMSQinpN7JzU9vpMHZFuiwRGSAuu2227j00ku58MILOy3Pz8+nvLycuXPndiyz2WzMnDmTdevWnXB7LpcLh8PR6SEiA4vP5+eH/96Bo9XDuIxobps9NNAhBYUemG5P+gq3x8fru461WhnbN1qtfJJJg+KYNCgu0GEEnW+fN4R/biqksLaFJ1cd5ntzTr8v+x/XHGHZpiIMA3577fhen8ghNiyEwRE+8pvM/G2ng+vm+gfsBRDpfRmxdvaUOSipczI4JtDRfLLCwkKqq898DoKEhASysrK6ISIR6Qs+6/b9Z555hry8PB588EHmzJnD/v37iYyMDFC0vSsuPIRnv34WVz65jn3ljXzr2c08dfNkIkOtgQ5NRAaQZcuWsXXrVjZt2tTle+Xl7XcMJycnd1qenJxMQUHBCbe5aNEiHnjgge4NVET6lKfXHWXtoWpCrSYe/fL4PjdotqeoSD6AvXuwCkerh+QoG2dlq8jc30TYLPz0slHctnQrT646zMVjUhieEnXK23lleym/PDZR508uHcmsYUndHepJGRHl5XCdh33V8M6+Si4YkfzZLxLpBsdHklc0tuI59V+hHldYWMjwESNwtrSc8bbsYWHs27tXhXKRAeCjt+8/+OCDHcs/fvs+wLPPPktycjJLly7llltuCVTIvS4zLoxnvjaFa/+4no1Ha7n+zxt46qYpJEbaAh2aiAwARUVF3HXXXaxYsYLQ0NATrvfxwUN+/6cPKLr33nuZP39+x3OHw0FmZuaZBywifcIHhXX88vW9ANx3yQhyEiMCHFHwUJF8AHt5e3urlUvHpGEeQBNbDiSXjEnhguFJvL2vkjuWfsDLt59zSm1SNhyp4fv/2g7A184ezNcDONOx3QKNW18leupV/O7tg5w/PEmjyaVXRNmtRIZaaGz1UOMKvp+56upqnC0tXH/Pr0jOyjnt7VQUHua5h35IdXW1iuQiA8BHb9//aJH8s27fP1GR3OVy4XK5Op73l9v3R6VFs/Rb07jp6Y3sKG7g8sfW8uQNE5mQFfvZLxYROQNbtmyhsrKSSZMmdSzzer2sWbOGxx9/nP379wPtI8pTU1M71qmsrOwyuvyjbDYbNpsu9okMRHXNbm57bittXj+Xjknlq9MGBTqkoKIi+QDldHtZuacCgMvGpX7G2tJXGYbBw18ay+d++y4HK5v4wb+387uvTDipiyLrj9Tw9Wc24fb6uGhUMj++dGQvRPzpHBtfIHHGVWwvbmDNwWpm5iUGOiQZINJj7Owrb6TaFby3oSVn5ZCROyrQYYhIH6Db90/NmIxo/vOd6Xzrr5s5XNXMl/7wPt+dmcNts4f26hwtIjKwXHDBBezcubPTsq997WsMHz6ce+65hyFDhpCSksLKlSuZMGECAG63m9WrV/PQQw8FImQRCWI+n5/v/WsbpQ2tZCeE88urxmjg4ccE76d96VH/219Ji9tLRqydCZkxgQ5HelB8hI3fXTsBq9ngtZ1l/PjFnXi8vk99zfIPirnpLxtpcXs5Z2gCi798coX1nuZraeCinHAAfvf2Qfx+f4AjkoHieMuV6iAcSS4iciqO377/97//vdtv329oaOh4FBUVdVvMwWBIYgTLbzubz49Lw+vz8/j/DnH+I6t4dt1RWtyeQIcnIv1QZGQko0eP7vQIDw8nPj6e0aNHd0y6vHDhQpYvX86uXbu4+eabCQsL47rrrgt0+CISZH69Yj+r9ldhs5j4/fUTNc/KJ9BI8gHq1R3HWq2MTdWVowFgek48j14znjuXfcA/NhZRWt/KL68aQ2q0vdN6NU0ufvHfvbywtQSAC0ck8/h1Ewi1Bs8oqSuGhfPmESdbCup4/3ANM4YmBDokGQDSY9t/V2pdBoYlJMDRiIicPt2+f/qiQq387isTuGRMCv/36l5K6p387OXdPPzGPi4cmcw5QxMYnhJFVlwY0WGdP3i2tnmpbvESkpxDudPAUeqg2e2hxe3F6fbi8fnw+tov/tssZmxWExE2CwkRNhIiQoiwWXTOLiJd3H333TidTm699Vbq6uqYOnUqK1asGDCTLEtw8/v9GCF2Wr3Q4vYQajFjCoLBdwPRvzYX8ftVhwFYdOUYRqQG4WRbQUBF8gGo2eXhnX2VAFw+Ni3A0UhvuXxce+/5+f/axuoDVcz+9SouGZPKhMwYfP72yRte31WOy+PDMOC2WUOZPycv6P6IxdnNfGVKJs++X8Bv3z6oIrn0ihi7lfAQM81uLyGpwwIdjojIadPt+2fuc6NTmTUsiX9tLuKptfkU1LTw0rZSXtpW2rFOeIgZi9mEyQC3x0ez2wtA6s2/5b0qoKrilPYZFWphSGIEOYnhpMfYVTAXGaBWrVrV6blhGCxYsIAFCxYEJB6Rj3J7fKw+UMWaA1V8UFTHgXIHWd/7N6+VACX5QPvnquSoUJKjbAxJjCDartHMPW3doWrue6H93O+O84dy5cSMAEcUvFQkH4De2ltBa5uP7IRwRqXp6tFAcsmYVAbHh7Pg5d1sPFrLC1tLOkaNHzc2I5oFnx/FxCCekOo7s3L4x8YiNuTXsuFIDVOHxAc6JOnnDMMgPcbOgcomQjPV91tE+q7jt+9/1Edv3wc6bt/Pzc0lNzeXhQsX6vb9jwm1mrlx+mC+Om0QWwvreHtvJZuO1pJf3UJ1k+tYUdzb6TUWE7Q21BAfF0tMZARhNjNhIRbCrGYsZqOjtZ3L48PV5qPB2UZ1k4vaFjeOVg/biurZVlRPjN3K+MwYRqRGEWJR90wREQms/Opm/rGxkOe3FFPT7P6ENfxA+9+4emcb9c429lc0suZgNSlRoQxPiWRkWhRWs/6mdbddJQ3c8vcteHx+Lh+Xxvw5eYEOKaipSD4AvbqjDIDL1GplQBqZFsU/b5nGxvxa3t5XSUFNM14fDEuJ4PzhSUzMig36n4vUaDtfmpzB0g2FPPbOIRXJpVekx7YXyW2ZYwIdiohIj9Lt+yfPMAwmDYpj0qC4jmUtbg8VDtex9il+LCYTcREhHNy9g8mTL+PLT7xARm76Se+jzeujsLaFw1VNHK5qpt7ZxqoDVbx/pIazBscxNjMai0mFBRER6V2Vja389q2DLNtU1NEyLDHSxsWjU5g2JB5fbRGfv/A8vvfbf5CWMxJnm5fqJhcVDhfFdS0U1zkpd7RS7mhlfX4NEzJjGZcZjc0SPO1e+7JdJQ1c/+cNNLZ6mDI4ll99aWzQ13oCTUXyAabB2cbq/VUAXKZWKwOWYRhMHRLfp4vL352Zw782FbH2UDVbCuqYNCh4R75L/3B88k5b+jDavJo0VkT6D92+373CQixkJ3T9mHW6H0ytZhM5iRHkJEbg9vjYW+bgg6J6GpxtvHuomh0lDZwzNIGcxHB9+BURkR7n9fn507tH+N3bB2k51k5sZl4iN0wbxOxhiViOjQjfurUMv8cFgMlkEG6zEG6zMCg+nLOy42h2eThQ0cj24gYanG28f6SGrYV1TB8Sz5j06KBr/dqX7Cl1cMNTG2hwtjExK4a/3DwlqOaaC1Yqkg8wK/dU4Pb6yE2KYFiKRgNJ35UZF8aVE9P51+ZiHnvnIM987axAhyT9XFx4CCEmP25rKIfr2pga6IBERGTACbGYGJcZw5iMaPaWOVh3uIYGZxuv7SxjaGIEs4cnEhaij3giItIzCmqa+f6/trO5oA6AcZkx3Hfx8NMagBduszAhK5ZxGTEcqGxk09E6apvdrDpQxc7SBmbnJZEea+/ut9DvrTtczXf+tgVHq4dxmTE88/WziAxV7/eToTOoAebVHe0TCmkUufQHt80eyvNbS1i1v4rtRfWMy4wJdEjSjxmGQYLNT6nTYE/VJ/XaExER6R0mw2BUWjS5SZFsLqhlS0Edh6qaKK5v4fxhSaikICIi3e2lbSXc+8JOWtxeImwWfnrZSK6enHHGdzGZTAbDU6LIS4pkZ2kD7x+uoabJzX+2FjM+M4YZOfHqV36S/rOlmB89vwOPz8+kQbH85eYpRKlAftL0UzaA1DW7WXuwGoDLxqUGOBqRMzcoPpwrxrdf8HnsnUMBjkYGggSbD4DdKpKLiEgQCLGYmJGTwLVTskiICKG1zcd/d5WztdYMZn0oFhGRM+f1+Vn4373ctWwbLW4vU7PjeP2uc7lmSma3tvkymQzGZcRw04zBjEqLAmBbUT1LNxRSWu/stv30Ry6Plwdf3cMP/r0dj8/PZWNTee6bU4m261zgVGgk+QDy5u5yPD4/I1KjyEmMCHQ4It3ittlDWf5BCW/trWB3aQOj0qIDHZL0Y4mh7b3I91a78Xh9Hf32REREAikx0sa1U7LYkF/DpqN15DeZSf3qI5Q2epgY6ODOgN/vp6jWyZ6yBsobWnG2+agoayJ85CxqXAbJXp9GF4qI9CBHaxu3L/2ANQfa57a7dVYO3587DHMP9gu3W81cOCKZoYkRvLWvgnpnG//ZUszErFim5cRpsuqPOVTZxJ3/+IA9ZQ4Abpudw/fnDFNP99OgIvkA8uqOMgAu1yhy6UdyEiO4fGwaL28v5fF3DvHkDZMCHZL0Y9FWP97WJlpDI9hT5mBsRkygQxIREQHAbDKYkZNAeoyd13eUQPIQfrCyGm90CVeMTw90eCetsbWNt/ZW8NqOMjYcqaXR5emyTsLlP2BVBaypPExqtJ2hx+ZbsmtSMhGRblPpaOWmpzext8yB3WrmV1eP7dXWvYMTwrlh6iDWHKxib1kjWwrrKKht5qJRKSRE2HotjmDV2ublqbX5PPbOQVrbfMSGWXn4S+OYMzI50KH1WSqSDxBVjS7WHT7WamWM+pFL/3L7+UN5ZUcpr+8qZ395oyallR5jGOAq3kPY0LPYcKRWRXIREQk6g+LDuSCljRfW74esMdy1bBubj9bx48tGYLMEbxF5T6mDP797hFd3lOH2+jqWh5hN5KVEkBUXRliIhYqqala+u4GY7LG0+gxK6p2U1DtZe6iaMenRTB4US7hNH3NFRM5EfnUzX31qA8V1ThIibDzztSmMTu/9u7ZDrWbmjkwhJzGCt/dWUt3kZtnGImYMjWdCZky3tnvpK/x+Pyv2VPCL1/ZSWNsCwLm5CTxy9TiSokIDHF3fprOHAeKNXWX4/DAuI5qs+LBAhyPSrfKSI7l4dAr/3VnOY+8c5PHr+vKNxRLsXEU724vk+bV867whgQ5HRESkC7sFKpbdz/effof/7G3ib+sL2FFczxPXTyQjNrg+C3xQWMcjKw6w9lB1x7KhSRFcOiaVOSOTGZYS2amlytatW/n7bfdz/RMvEJmRx5GqJvaWN1LV6GJbUT27ShoYnxnD1Ow4tUUTETkN+8sbuf7P66lucjMoPoy/fX1qwOtIOYkRpESF8va+SvKrm3n3YDX51c3MHZlM5ECZmNIwsbbQyX1r3mVfeSMAyVE27r14BFeMTxuQFwy6m4rkA8Qrx1qt9OatMSK96fbZufx3Zzmv7SxjXmUTQ5PUd196RmvRbgA2Ha3F5/Or15uIiAQnv4/rxkRy6bQRfO+f29he3MBlj63lN18ez+xhSYGOjqLaFh56Y19HS0izyeCSMal885xsxmZEn9SH/Wi7lQlZsYzPjKGwtoX1R2opd7SyuaCOQ1VNzBmRTFqMvaffiohIv7Gn1MENT22gttnNqLQonvnaWSRGBkdrk3CbhcvHprKr1MGaA1UU1zl5bkMhs4cl9eu7yRucbeypN5N+y595dH09AOEhZm4+ezC3zhqqu6e6kY7kAFDhaGXT0VoALh2rfuTSP41Mi2LuyGRW7KngsXcO8ttrJwQ6JOmn3OWHCLUYNDjb2FfeyMhjM6+LiIgEo9nDknj1jnO49bmt7Chu4OvPbOKO2UO568K8Hp147UQaWtp4YtUhnnnvKG6vD8OAqydlcMf5uWTGnd5IRcMwGBQfTlZcGEeqm/nf/krqW9r495ZiJg2KZcaQeF3UFhH5DLtKGrjhqQ3Ut7QxLiOav359KtFhwTVK2zAMxqRHkxFrZ8XuCsodrbyxu5wj1U0MC65Qz0h9i5vDVc0crmqirKEVMGOJTiIyxOBbM3O5cfogYsJCAh1mv6Mi+QDwyvZS/H6YNChWIymkX7vzglxW7Kng5e2l3HJejoqX0jP8PoYnhLCt3MXG/Br9nImISNDLiA3j39+ZzoOv7uVv6wv43TuHWJ9fyyNXjzvtwvSpcnt8PLehgN++fZD6ljYAzhmawH2XjOi2v6WGYZCTGEFGjJ01B6vZU+ZgS0EdFY5WLh6dQliIPv6KiHySncXtBfIGZxvjM2N49utnEW0P3qpzbFgIV0/KYOPRWjYereVARRNFZiv2oWcFOrRT5vf7qXe2tc+xUdc+z0Zja+dJq5NCfez+169Z9uyjTJuSG6BI+z81aRsAln9QAsAXxqvVivRvo9OjuWxsKn4//HrF/kCHI/3YyIT2q/Yb8msDHImIiMjJsVnM/N8XRvPba8cTHmJmY34tF//2Xf6zpRi/399j+/X7/by2o4y5v1nNA6/sob6ljdykCJ7+2hT+9o2zeuRis81qZs7IZC4ZnYLVbFBc5+QfG4uobGzt9n2JiPR124vque7P62lwtjExK4a/fSO4C+THmUwG04bEc82kTGLsVpxeg6SrfsqitbUU17UEOrwT8vv9VDe52F5cz+s7y3hqbT5/fb+At/dWsq+8kcZWD4YBGbF2ZuUl8vWzB3NukoeWvWsIMeuuqJ6kS+n93IGKRnaXOrCaDfUjlwHhB3OH8cauct7ZV8nG/FrOyo4LdEjSD41Kai+Sb8yvxe/3a5IUERHpM64Yn86EzFjm/2sbmwvq+MG/t7P8g2Ie+Pzobp/T5b1D1Tz0xj52FDcAkBARwvw5w7hmckavTKqZmxxJXHgIr+4oo97Zxn+2FHPpmFQGxYf3+L5FRPqCDwrruPGpjTS6PEwZHMvTXzuLiD7W4zolOpTrpmbx9tYD7Kvzs6kULnhkNV87O5vvzswJeMsYn99PdaOrfaT4sUdrm6/TOmbDIDnaRnqMnfQYO6nRdkIsH/6dbOjtoAeovvWTL6fsha3to8hnDUsiNlz9iqT/G5wQzpenZPLchkIeemMf//nOdBUwpdsNjbVis5ioaXZzuKqJoUn9d6IYERHpf7Liw/jnLdP545rDLH7rIO8dquHi367ha2dn8+3zhpAQcfqTtPn9fjbm1/L4/w7x7sFqoH2CsW+eO4RvnTek14sv8RE2rj0rk1d3lFFc5+Tl7aVcMDxZ7dJEZMDbWljHTccK5Gdlx/H0zVP67CSQVrOJ0TFe/vebecz50Z/ZXeXmD6sPs3RDAd88dwg3TBtEXC/VxDw+H5UOF6XHCuKl9a24vZ2L4haTQWpMaEdRPCUqtFcuHsun65s//XJSfD4/L21rL5JfOSE9wNGI9J47L8jl+a3FbCmoY+WeCuaOSgl0SNLPWM0GE7Nief9IDeuP1KpILiIifY7ZZHDrrKFcNiaNB17Zzdv7Klmy5gjPrjvKl6dkcvOMwQxJPPmR5Y7WNt7cVc4z646yu9QBtP+9vH7qIG4/f+gZFd7PlM1i5gvj01m5t4L95Y2s3FtBa5uXiYNiAxaTiEggbT02grzJ5WFqdhxPf21Kv5i3oa26kJ/PiqM+LIOH39jP/opGHl15gCf+d4grJ6ZzzeRMxmfGdOtAutY2L6UNTsrqWymtd1LR6MLr69zGLMRsIu14UTzWTlJkaEAmz5ZP1/d/A+SE1h+poayhlahQC+ePSAp0OCK9JjkqlK+fnc3vVx3m/17bw3l5iYRazYEOqxOvz0+Dsw23x0e928AcHtvlD6kEt6lD4nj/SA0b82u5YdqgQIcjIiJyWrLiw3jq5im8s6+C3751kO3FDfz1/QL++n4Bw1MiuWhUClOHxDHsWOuS44WFumY3+8ob2VfuYPWBKt47VE2bt/1cxmYxceXEdL47cyhZ8b0zMehnMZsMLhqZTESIhS2Fdbx7qJo2r4+zsuN016GIDChbCuq46S/tBfJpQ+L4y839o0B+nGEYXDAimVnDknh1Ryl/evcIu0oc/GNjEf/YWER6jJ2LR6cwbUg8EwfFntII8/oWN3vKHKzc30T8JfNYUWahsfBIl/XsVvOHRfEYOwmRNkz6WxP0+s9vgXTxwrEJOy8dm4bNElwFQpGedtvsoSz/oISiWidPrjrM9+bkBTokml0e9pY5OFLdTIWjlQ9r4lYybv8bL+1vZsrkQEYop+J4v/sN+TXqSy4iIn3e+cOTmT0sifcP17Dk3SOsPVh9rAjeCG+3r2M1G4RazLi8PtweX5dtDE2K4IsT0rnurKygbPVoGAbn5CYQYjG13w2WX0ubz8/ZOfH6Oy4iA8KWglpu+ssmmlwepg+J56mbJ/erAvlHmU0GV4xP5/Pj0th0tI7nNhTw1p4KSuqd/HltPn9emw9AZpydjJgwMmLtxIaHYDUbWM0mnG1emlo91DvbKK5zUlLXQnWTu2P7EWMupLGt/d+xYVZSo+2kxYSSFmMnxm7V35U+qH/+JghOt5c3dpUDcOVEtVqRgSfcZuEnl43k1ue28uTqw1w5MT1gkzQ1ONvYmF/LvnIHHx0sbjUb2K1m3O42nB4fsXb1IOtLJmbFEmI2UeFwUVjboknARESkzzMMgxlDE5gxNIH6Fjdv763k7X0VbC9qoKTeSZvXT5vX07F+ZpydYclRTMiK4aJRyX2m/dhZ2XFYzAbvHqxmS0EdHq+PmXmJKmiISL82kArkH2UYBmdlx3FWdhytbV5W7a/if/sq2VJYx6HKJopqnRTVOk96e5lxdtLsPt5c9hQXf/HLjB6RNyCO40Cg/4v91H93ltHk8pAZZ2eyeu3JAHXx6BTOzU3g3YPVLHh5N3+5eUqvfvjx+HxsPlrH5oK6jlYqqdGhDE+JZFB8OFGhFgzDoPjgbh69/Uucu3Fjr8XW3RYsWMADDzzQaVlycjLl5e0X6/x+Pw888ABLliyhrq6OqVOn8sQTTzBq1KhAhNstQq1mxmVGs+loHRuO1KpILiIi/UpMWAhXTcrgqkkZALg8Xmqa3Lg8PkIsJmLDrH26KDAxKxarycQ7+yvZXtxAm9fPBSOSdDu8iPRL6w5X861nN9Ps9jJ9SDx/uXkK9pCB13Eg1Grmc6NT+Nzo9nnL6prdHKxsoqS+heJaJ40uD26PD7fXh91qJjLUQlSolbQYO5lxdjLjwogKtbJ161b+decy0q6/pk//LZTO9H+yn1q2qRCAL0/O1IgIGbAMw2DB50fxucVr+N/+Kl7ZUcbnx6X1yr5L6py8ta+C+pb2+68yY+3MyEkgJTr0k1/g92Hp4xN3jBo1irfeeqvjudn84UnXww8/zKOPPsozzzxDXl4eDz74IHPmzGH//v1ERvaNUWef5KzsODYdrWN9fg3XTMkMdDgiIiI9xmYxkxZjD3QY3WpMRjQWs8HKPRXsKXPg8fmYOzJFk6mJSL/yxq4y7vzHNtxeHzNy4nnqpoFZIP8kseEhx9poxgU6FAkCKpL3Q4cqG9l0tA6zyeDqySrayMCWkxjBbbOHsvitg/zkxV1MzY4jOeoEhepu4PP72ZRfy4b8WvxAWIiZmXmJ5CZF9PsLVhaLhZSUlC7L/X4/ixcv5v777+fKK68E4NlnnyU5OZmlS5dyyy239Hao3WZqdjxP/O8wG/NrAx2KiIiInIYRqVFYTAZv7C7nQEUTHm8ZF49OwWJWG7yeUFhYSHV1da/sKyEhgaysrF7Zl0iw+uemQu59YSc+P1w0KpnfXjuBUKsK5CKfREXyfmjZxiIAzh+e1KPFQJG+4rbZQ3l7byU7SxqYt2wbf//m1B4ZIdTk8vDmrnKK69v7mY1IiWRmXiK2AXIScvDgQdLS0rDZbEydOpWFCxcyZMgQ8vPzKS8vZ+7cuR3r2mw2Zs6cybp16z61SO5yuXC5XB3PHQ5Hj76HUzVxUCxmk0FxnZPiuhYyYsMCHZKIiIicotzkSCxmE6/tLONIdTMvbSvlsnGp2CwD4xyutxQWFjJ8xAicLS29sj97WBj79u5VoVwGJL/fzx/XHOGXr+8D2rsM/OKLo3UBUORTqEjez7g8Xp7fWgzAV87SKHIRAKvZxG++PJ7PP76W94/U8MiK/dz9ueHduo/86mZW7qnA2ebFajaYPSyJEalR3bqPYDZ16lT++te/kpeXR0VFBQ8++CAzZsxg9+7dHX3Jk5OTO70mOTmZgoKCT93uokWLuvQ6DyYRNgvjMqLZWljP2oPVXHuWPoSJiIj0RdkJ4VwxLo1Xd5RRXO/k+a0lXNFLbfoGiurqapwtLVx/z69Izsrp0X1VFB7muYd+SHV1tYrkMuC0eX0seHk3z21ob8P73Vk53H3RsH5/Z7PImVKR/AwF2+1ib+6uoK6ljdToUGbmJfVKXCJ9wdCkCB66aix3/OMDfr/qMEMSI/jSsYmozoTH5+P9wzVsLawHIDHCxsVjUogNCznjbfclF198cce/x4wZw/Tp08nJyeHZZ59l2rRpAF1Oyvx+/2eeqN17773Mnz+/47nD4SAzM7guAJ6Xl8jWwnrWHKxSkVxERKQPy4wL46qJ6by4rZSqRhf/3lLMtNhAR9X/JGflkJHbdydvFwlmdc1uvvvcFtYfqcUw4P5LRvDNc4cEOiyRPkFF8jMQjLeLLdvYfqXw6smZmnBG5GMuH5fG7lIHf1h9mB89v4MYu5ULRyZ/9gtPoKbJxZu7K6hqam8HMi4jmnOGJugWNiA8PJwxY8Zw8OBBvvCFLwBQXl5OampqxzqVlZVdRpd/nM1mw2az9WSoZ+y8vEQWv3WQtQer8Xh9+v8vIiLShyVFhXLN5AyWf1BCg7ON1S4rIck9O+pZRKQ77Ciu57alWymqdRIeYuZ3X5nABSNO//OuyECjIvkZCLbbxfaXN7LucA0mA66ZfOYjZEX6o7svGkZZg5OXtpXynb9v4dEvj+fzp3grrc/nZ3txPe8drsHr8xNqNXHhiGRyEiN6KOq+x+VysXfvXs4991yys7NJSUlh5cqVTJgwAQC3283q1at56KGHAhzpmRuXEUO03UqDs43txQ1MGqQhZyIiIn1ZTFgI10zO5MVtJVQ3uUm+/mHWFDiZODHQkYmIdOX3+/nLe0f55et7afP6yYyz8+cbpzAsJTLQoYn0KSqSd4NguV3sqbVHAPjc6BRNHidyAiaTwSNXj8Pvh5e3l3LnPz5gd0kD8+fmndTkTIW1Law5UEVNsxuAQfFhzBmRTLhtYKfTH/zgB1x++eVkZWVRWVnJgw8+iMPh4KabbsIwDObNm8fChQvJzc0lNzeXhQsXEhYWxnXXXRfo0M+Y2WRwztAEXttZxpoDVSqSi4iI9APhNgtfmpTBixsOUY6NxRvqcdr28oOLhmHVXWMiEiRK653ct3wnq/ZXAfC5USk8dNVYosOsAY5MpO8Z2FWdfqSq0cWL20oB+MY56jcl8mksxybyTIkOZcmaI/xxzRFW7Klg3oW5XDw6lRBL5w8+Xp+fgtpmthc1UFjb3l4p1GJiRk4Co9OjNAEKUFxczFe+8hWqq6tJTExk2rRprF+/nkGDBgFw991343Q6ufXWW6mrq2Pq1KmsWLGCyMj+MbrhvLxjRfKDVXxvTl6gwxEREZFuYLOYmZHo4el/vUj09Gv445ojbMivZfGXxzM4ITzQ4YnIAObz+fn7hgIeen0fzW4vIWYTP7lsBDdMG6TPpyKnKaiL5IsWLeKFF15g37592O12ZsyYwUMPPcSwYcM61vH7/TzwwAMsWbKko/DyxBNPMGpU4Ed296a/ry/A7fExIStGoxhFToLZZHDfJSOYmBXLT1/aRX51M3ct28aPbbuYkh2Hta2JuLm38W6lhfrSI7g9PgAMA8alxzB1SByh1s8eeT5QLFu27FO/bxgGCxYsYMGCBb0TUC87Ly8RgO1F9dS3uIkZYBO3ioiI9FeGAfVr/sr/zf82f/ygiW1F9Vzyu3e5/9IRfGVKFibNAyUivey9Q9Us/O9edpc6AJg0KJaHrhrD0KT+MQBJJFCC+j6x1atXc9ttt7F+/XpWrlyJx+Nh7ty5NDc3d6zz8MMP8+ijj/L444+zadMmUlJSmDNnDo2NjQGMvHe1tnn5+/oCAL5xTnaAoxHpWz43OoW3vj+T+XPySIy00ejy8M6+St483ELkhIupbDXh9vgICzEzPjOGm6YPZuawRBXIpZPUaDu5SRH4/LD2UHWgwxEREZFuNiPTzhvzzmNqdhwtbi/3L9/FFU+8x5aC2kCHJiIDxJaCWm76y0au//MGdpc6iLBZeODzo/j3LdNVIBfpBkE9kvyNN97o9Pzpp58mKSmJLVu2cN555+H3+1m8eDH3338/V155JQDPPvssycnJLF26lFtuueUTt+tyuXC5XB3PHQ5Hz72JXvDiByXUNLtJj7HzuVEpgQ5HpM+JCrVy5wW53DZ7KLtKGthRXM/2AwX8eckfmH3FteTlZJMUacOk29bkU8zMS+RgZRNrDlRx2dhTmwxWREREgl96jJ2l35rGM+uOsnjlAXaWNHDVk+9z4YhkvnVuNmdlx6nNgYh0K7fHx1t7K/jzu0fYWlgPgMVkcMO0Qdxx/lDiI2yBDVCkHwnqIvnHNTQ0ABAXFwdAfn4+5eXlzJ07t2Mdm83GzJkzWbdu3QmL5IsWLeKBBx7o+YB7QZvXxx9WHwbg5hmDsWgSGZHTZjYZjMuMYVxmDKNstTyybhlDrr+GlKjQQIcmfcB5eYn8eW0+aw5U4/f79SFZRESkHzKbDL5xTjafH5fGIyv288/NRby1t4K39lYwNiOaK8anM3dkMplxYWe0H6/PT5PLQ1Orh0ZXGy6PjzaPjzafH5MBJsPAajYRYbMQYbMQE2bVnY4i/YTX52drYR2vbC/l5e2l1Le0ARBiNnHlxHS+MzNH8yKI9IA+UyT3+/3Mnz+fc845h9GjRwNQXl4OQHJycqd1k5OTKSgoOOG27r33XubPn9/x3OFwkJmZ2QNR97wXthZztKaF+PAQrpuaFehwREQGrLOy4wgLMVPuaGVHcQPjMmMCHZKIiIj0kMRIG7+8aizfPHcIT63N54WtxewobmBHcQP/9+oecpMiGJMezYjUKAYnhBMbZiUmzIrJMPD6/Lg8Pmqa3VQ3uqhqclHV6GLP0TpSvvprXiux0lp46JRjirZbSY60kRkXRnZCOOG2PvNxX2TAK230cHhzEesO17BqfyV1xwrjAMlRNq6ZnMlXpw8iKVIDuER6Sp/5q3n77bezY8cO1q5d2+V7Hx+t91kj+Gw2GzZb378lxeXx8ru320+evjsrRydBIiIBFGo1M3t4Eq/tKOON3eUqkouIiAwAQ5MiWHTlGH4wN4+XtpWyYk85G/NrOVjZxMHKJvig5JS2Z0sbTqu3/d9mk0GEzUKkzYLNaiLEYsJiMuH3+/H529swNLk8HY8GZxsNzjYOVDYBkBIVyojUSIanRBFi0R3Hfc2iRYt44YUX2LdvH3a7nRkzZvDQQw8xbNiwjnX8fj8PPPAAS5Ysoa6ujqlTp/LEE08watSoAEYun8bn99PgbKOmyU1Nk4uCKgsZdzzH7a9XAVUd60WFWjh/eBJfnJjBOUMTMGuSYJEe1yeqqnfccQcvv/wya9asISMjo2N5Skp7/+3y8nJSU1M7lldWVnYZXd4fLdtYREm9k5SoUG6YNijQ4YiIDHifG5XSXiTfVc7dFw1TyxUREZEBIj7CxtfPyebr52RT2+xmS0Ede8sc7Cl1UNbgpN7ZRl2zG78fLOb2Vilx4SEkRtpIjLCREGnD66hm0U/v4drv3k1OXh52q/mkzyVa27xUOFopb2glv6aZCoeLckcr5Y5W1h6qZnhKFJMGxRJtt/bwkZDusnr1am677TamTJmCx+Ph/vvvZ+7cuezZs4fw8PZWGw8//DCPPvoozzzzDHl5eTz44IPMmTOH/fv3ExmpiRwDye/309jqobrZRU2Tm9pmd/vXFjden/8ja5owh0VjNcH4rFgmDYpj9rBEJg2KVTtdkV4W1EVyv9/PHXfcwfLly1m1ahXZ2dmdvp+dnU1KSgorV65kwoQJALjdblavXs1DDz0UiJB7jdPt5fH/tY8iv/38oeo/JyISBGYNSyTEbCK/upmDlU3kJevDiYgEF41MFOl5ceEhzBmZzJyRpzZwa+vWrfz04PvE2vyEhZzaR/VQq5lB8eEMig9n6pB4mlweDlQ0srOkgfqWNnaWNLC7tIERqVFMGRynYnkf8MYbb3R6/vTTT5OUlMSWLVs477zz8Pv9LF68mPvvv58rr7wSgGeffZbk5GSWLl16wjnapPu1tnmpbHRR0+Si5lgxvKbZRZvX/4nrW0wGceEhxEeEYHHWseKxe1jz4nNMnTKplyMXkY8K6iL5bbfdxtKlS3nppZeIjIzs6EEeHR2N3W7HMAzmzZvHwoULyc3NJTc3l4ULFxIWFsZ1110X4Oh71l/ey6eq0UVmnJ1rJvfNfuoiIv1NZKiVc3ITeGdfJW/sKleRXESCjkYmigwMETYLE7NimZAZQ3Gdk80FdRTWtrC71MG+skbGZ8WQ7gt0lHIqGhoaAIiLiwMgPz+f8vJy5s6d27GOzWZj5syZrFu37oRFcpfLhcvl6njucDh6MOr+x+vzU93UfqdGRUMrZY7Wjok1P85kQGx4CPHhIcRH2EgIDyEuPIRou7XjLpHigzW8Wrofq1l3oIoEWlAXyZ988kkAZs2a1Wn5008/zc033wzA3XffjdPp5NZbb+0Y6bJixYp+fQJf1uDk8XfaR5F/f84w9ZcTEQkinxuV0lEkv/OC3ECHIyLSiUYmigwshmGQGRdGZlwYZQ1O3j9SQ1Gtky0FdewyWQkffT5+/yePdpXg4ff7mT9/Pueccw6jR48G6BhE+PFWs8nJyRQUFJxwW4sWLeKBBx7ouWD7GZ/fT1Wji6LaForqnJTWO/H4uv7ORNutJESEEB9uIz6ivTAeExaiXuIifUhQF8lP5o+1YRgsWLCABQsW9HxAQeLB1/bibPMyeVAsV4xPC3Q4IiLyEReOTMb0Auwpc1BY00JWfFigQxIROSGNTBQZOFKj7XxxfDpHa1pYc7CK+pY2Ei6dzwNranl8sM5Zgtntt9/Ojh07WLt2bZfvfbxvvd/v/9Re9vfeey/z58/veO5wOMjMPLW704/UtRE+Zg676s1s31lGs8uD2+ujzePDT3s7EYvZhN1qJtxmJsJmITasvXAcGx6CNYh7bfv9fgoa2oicdDnrqizUlB7B7el824XNYiIlKpTk6FBSotof9hC1wBXp64K6SC5dbSxp5bUddZgMWPD5UZoUTkQkyMSFhzA1O573j9Tw5u5yvnXekECHJCLyiTQyUWTgMQyD7IRwsuLCWPXBPnZUedlRARctXsP35+Zx84zBmiwwyNxxxx28/PLLrFmzhoyMjI7lKSkpQHveTk1N7VheWVnZJYd/lM1mw2aznVFML+5rIuGSu9jvABxNp/z646Ouk6JCSY60kRhpO+U+/N3F7/dzpLqZ9w/X8P6RGjYcqaG6yU3chbdQ5gTwEWIxkRFjJzMujIxYO/HhIarFiPRDKpL3ISZbOH/c0j7a51vnDWF0enSAIxIRkU/yudEpvH+khld3lKpILiJBK9hGJopI7zGbDIZF+Xjzodu56CfPsqvSzYOv7eWV7aX86upxmlclCPj9fu644w6WL1/OqlWryM7O7vT97OxsUlJSWLlyJRMmTADA7XazevVqHnrooR6NLS8+hJVr1jFqzDgyUpOJCLUQYjYRYjFhYODx+fB4/TjbvDS5PDS2eqhtdlPb7MbZ5qXB2UaDs43DVc0d24ywWUiOai+YJ0WGkhRpI9zW/SWrNq+P/eWNbC+uZ2N+Le8frqGy0dVpnRAzNBzaypQJYxmVm01ShA2T2qaI9HsqkvcRfj/EX3wXda0+hiSG870L8wIdkoiInMAlY1L5+at72F7cwKHKJoYmRQQ6JBGRToJxZKKI9D5PfRkPzIzjkC+RB1/by/biBi773VruujCXW84bolHlAXTbbbexdOlSXnrpJSIjIzvu9ImOjsZut2MYBvPmzWPhwoXk5uaSm5vLwoULCQsL47rrruvR2C7LC+dn//opN8x8gYys2FN6bYvbQ02Tm8pGF5WNrVQ2uqhvaaPJ5aGpytOlcJ4YaSO0zYQ9bzqHat1kNLaSEP7ZRWu3x0dxXQsFNS3kVzeTX93MrtIG9pQ6cH2sfUqIxcTErBimDYln+pB4qDnKtLN+yrCZL5ASFXpK709E+i4VyfuIw00mwobNwGKC3107gVCr+l2JiASrxEgbM/MSeWdfJS9sLebuzw0PdEgiIkBwj0wUkcAwDIMvT8li1rAk7n1hJ+/sq+RXb+5nxe5yjSoPoCeffBKAWbNmdVr+9NNPc/PNNwNw991343Q6ufXWW6mrq2Pq1KmsWLGCyMjg/X8WFmIhLM5CZtyHPfBdHi9Vja5jhXMXlY5W6o4Xzl0ewELSF+/n7rdq4K23CTGbSIgIIdxmISzEjMVswuPz4/X5aGz1UNfsxtHqOWEMkaEWxmXEtBfGc+KZmBXbqcaytf7E7cVEpP9SkbwPKKhpZkdde8K+cWyU2qyIiPQBV03M4J19lSz/oITvzx2mme1FJCgE88hEEQms5KhQnrppMi9sLeGBV3ZrVHmA+f3+z1zHMAwWLFjAggULej6gHmSzmMmIDSMj9sPCudvjo6qpvWB+tKSCg4cOkZozgrpWH26vj9KG1s/crt1qZlB8GIPjwxmUEMaIlCjGZkQzOD5c7VNEpAsVyYNcZWMr/91Vjh+Dpp1vc+nV1wc6JBEROQkXjEgi2m6lrKGVNQermD0sKdAhiYj025GJItI9DMPgqkkZnD00gfuWdx5V/uurx5GrUeXSS0IsJtJj7KTH2El0lfLuz77Pa1u2MGbceCobXVQ1umhxe2hxefH4fFhMJswmg4hQC7FhVmLCQjTBpoicEhXJg1hVo4sXPyjF7fERb/NR8ObjGP93Q6DDEhGRkxBqNXPVxAz+8l4+z60vCJoiucvjxeH04PP7CbWaibBZNMpdZAAZSCMTReT0pUS3jyp//iOjyi/93Vrmzcnl2+dqVLkEjtX8YfFcRKQ7qUgepIpqW3h1Rxlur4+kSBvTohvZ6m0LdFgiInIKrp+WxV/ey+ftfZUU1bZ06r3YW/x+PxWNLvaWOiiobaHB2flvidlkkBYdSgImjBB92BAREZF2hmHwpUkZnPORUeUPv7GfN3eV88urxjIiNSrQIYqIiHQbXf4NMj6/n81Ha1m+rQS310d6jJ0rJ6Rj1f8pEZE+JycxgrOHxuP3w9/X9/4EQMV1Lfx7SzH/3FTEjpKGjgK5/SMjyL0+P0V1Tj6os5D+3ad5fm8TrW3eXo9VREREgtPxUeW/vnockaGWY6PK3+X+5TupbXYHOjwREZFuoZHkQaTC0cqq/VWUO9onoBieEskFw5N0K5uISB/29bOzee9QDc9tKOTW2UOJtlt7fJ+tbV7WHKxib1kj0D5aPDcpgtzkCNJj7Ngs7ZNB+/1+6lrayK9uZtvRKppCI3huZyPryt7lV18ay+TBcT0eq4iIiAS/j44q//mru/nvznKe21DIK9tLuevCPG6cPgirPreKiEgfpr9iAebz+SmoaeaV7aUs21REuaOVELOJC4YnMXdksgrkIiJ93OxhSQxLjqTJ5emV0eSHq5r46/sFHQXyMenR3DxjMBeNSmFIQkRHgRzaP/DGhYcwaVAsc1PbqH7l18TZTeRXN3PtkvU8817+SfUvFhERkYEhJTqU318/iWXfnsbI1CgcrR7+79U9nP/IKv6xsRC3xxfoEEVERE6LRpL3MrfHR22zm5pmF4W1LRTUtOD6yInE8JRIZuTEExna8yMNRUSk55lMBt+dlcO8f27jz+8e4avTBxHVAzne74d1h6vZdLQOgLiwEC4cmURq9Mn1GTcMaN6zit9e9Cv+nW/m5e2lLHhlDztLHPzii6MJtZo/eyNBrLCwkOrq6m7ZVkJCAllZWd2yLRERkb5o2pB4XrnjHP69uYhfr9hPUa2Te1/YyWNvH+SmGYO5enImceEhgQ5TRETkpKlI3g08PqhqdNHi9tDi9uJ0e2lpa//a2ubF2fbRr12vrIdaTOQlRzIuM0YnEiIi/dBlY1N57J2DHK5q5o+rD/PDi4Z36/aNkDDWVVkob20vkI/PjOHsofFYTKd+N1J4iInfXjuesRnRLPzvXp7fWkxxXQt/uXkK4ba+edpQWFjI8BEjcLa0dMv27GFh7Nu7V4VyEREZ0Mwmg2vPyuLz49NYuqGQJWuOUNrQyqLX9/HIigNcPCaFz49L45zchE53somIiASjvvlpNwj8b38lv1ldQ/p3n+al4hAoLjzp14aFmIkLDyElKpTshHBSokMxGUYPRisiIoFkMZu453PD+fbftvDnd/O5buog0mNOboT3Zyl2eEi98VHKW02YTQYXDk9ieGrUGW3TMAy+ee4QRqRGccvftrAhv5Yb/7KRp782pUdGwfe06upqnC0tXH/Pr0jOyjmjbVUUHua5h35IdXW1iuQiIiJAWIiFb547hBumDeKlbSX8fX0hO0saeGlbKS9tKyXCZmHmsETSLc1YE7JQJzcREQlGKpKfpqZWDzsq3FiiEgGwW82E28zYQ8yEWS3YQ9r/bbeaCbWasFvNx9ax9Plb1kVE5NTNGZnMWYPj2Hi0lh8v38lfbp6CcYYXSN/aU8E9b1Vjjc/AbvZzxcRMkqNCuyliOHtoAn//5lRufGoDWwrquOHPG/jb16cSHdb3CuUAyVk5ZOSOCnQYIiIi/VKo1cyXp2Tx5SlZbC+qZ/kHJbyxq5xyRyuv7SgDIO0bv+flYj/xDYXEh9uICrUQHmohPMRCiMVEiNmExWxgNZuwmAxMhoHZZGAyOOPzJhERkU+jIvlpmjI4jtumRPPjO7/Ft3+0kCHD9aFbREROzDAMFl45mkt+u5b/7a/iha0lXDUp47S25fP5efx/h3h05QEAWgt3cun0Yd1aID9ufGYMS781ja8+tYEdxQ1849lN/O0bU7GH6IKviIiIfLJxmTGMy4zhp5eN5IOietYerOatHUfZXuLAYw2lwuGiwuE6pW2aDI4VzI1OX82GgckEIRYTETYLETYLvkYToYPG0ezWRKIiInJyTr1ZqQDts3pfkB2Gu3QfqhOIiMjJGJoUyZ0XDAXgxy/uYm+Z45S3Udfs5tt/29xRIL94aBgV//wxoT34t2h0ejRLvzWNyFALmwvquH3pVtq8+tApIiIin85kMpg0KJa7LszlgVnxFC3+MnNS3VwyOoWp2XGMSotiUHwYiZE2YuxWwkPMhJhNfNKYcZ8f2rx+XB4fLW4vTS4PDc42alvcVDe5Ka1v5UBFE1sL69lWZyH52l+wt9rd6+9ZRET6Jo0kFxER6UXfnTWUDfm1vHuwmm/9dTPLvj2NjNiwk3rthiM13LVsG+WOVkLMJh78wmiGmqv4g8/bw1HDiNQonrppCl99agNv76vknud38OsvjcNk0q3PIiIicpJ8XqKskJEcSe5nrer34/P58fr9eH1+fD7wHlvm8fnxHV9+7KvL46PJ5aGx1UNFdS2FxSWkRyb2ytsSEZG+TyPJRUREepHZZPDYVyYwOD6M4jonX/7jevaVf/qI8iaXhwdf3cNX/rSeckcrQxLCeeHWGVwzJbOXom53VnYcT1w3EbPJ4IWtJSx6fS9+zb4lIiIiPcBkGFjMJmwWM2EhFiJCLUTbrcSGh5AYaSM5KpS0GDsZsWEMig8nLzmSiVmxzMxLZEaih9I/f5fUSI0LFBGRk6MiuYiISC+LCQvhH9+expCEcErqnXz+8fdY/NYB6po73xJcWu/k8XcOct7D/+PPa/Px+eGqiRm8csc5jE6PDkjsF45M5qGrxgLwp3fz+eOaIwGJQ0RERERERKS76LKqiIhIAKRG2/nXd6Zz93928M6+Sha/dZDH3jlEXnIkkaEWKhytFNS0dKyfnRDOTy8fyexhSQGMut2XJmVQ2+xi4X/38cvX95EYYTvtSUhFREREREREAk1FchERkQBJiLDx1E2TeXVHGX9YfZjdpY5Ok3kaBkwZFMdXpmZy+dg0LObguQHs2+flUNXo4k/v5nP38zuIiwgJigK+iIiIiIiIyKlSkVxERCSADMPg8nFpXD4ujdJ6J3tKHTjbvMSFhzAiNYq48JBAh3hC9148gqpGFy9uK+XWv29l6bemMiErNtBhiYiIiIiIiJwSFclFRESCRFqMnbQYe6DDOGkmk8HDXxpHbUsbaw5U8fVnNvGf784gJzEi0KGJiIiIiIiInLTguW9bRERE+pwQi4knr5/IuIxo6lrauPGpjVQ4WgMdloiIiIiIiMhJU5FcREREzki4zcJfbp5CdkI4JfVObnxqI7XN7kCHJSIiIiIiInJSVCQXERGRMxYfYeOvXz+LpEgb+ysauf7PG1QoFxERERERkT5BRXIRERHpFplxYSz91jQSImzsLXOoUC4iIiIiIiJ9gorkIiIi0m2GJkWw7NsfFsqvXfI+JfXOQIclIiIiIiIickIqkouIiEi3Ol4oT46ycaCiiSt//x67SxsCHZaIiIiIiIjIJ7IEOgARERHpf4YmRfDCrWfztac3cqCiiWv+8D6/+fJ45o5KCWhcfr+fFreXuhY3dS1tNLs8uDw+XB4vBgZmk4HVbBBhsxBltxIVaiU+PASTyQho3CIiIiIiItJzVCQXERGRHpEeY+ff35nBd/62hfeP1PDtv23hxumDuO+SEYRazb0Sg9/v50hdG1FnfZH3qyzUl+fT4vae0jYsJoPESBtp0XbCWg0w6fRJRERERESkP9GnPBEREekx0XYrz379LH715j7+9G4+f32/gA1Hannwi6OZMjiuR/bZ4Gzj3YNVrNpfxeoDVVQ1uoid/Q1KnQBeDCDKbiUmzEqkzYLNasZmae9A5/X5afP6aGz14Ghto665DbfXR1lDK2UNrYCVzDuX8uv367jZVs7MYYnYLL1T8O8NhYWFVFdXn/F2EhISyMrK6oaIuk9/fm8iIiIiInJmVCQXERGRHhViMXH/pSM5JzeR7/9rO/srGrn6D+/zuVEp3HPxcLITws94H/nVzby9t4K39law6WgdXp+/43uhFoPafRs4a/JERuQMJinShsV8ctOy+P1+6lvaKHe0UlTbwpFKBy5bGOuKWln3ty1EhVq4eHQqV0xIY1p2fJ9uy1JYWMjwESNwtrSc8bbsYWHs27s3aIrJ/fm9iYiIiIjImVORXERERHrFzLxE3ph3Lo+sOMA/NxXyxu5y3txTzrm5iVw7JZMLRiSd9KjshpY2Nh2tZf2RGt7ZV8mR6uZO389JDGf2sCRmDUvCWl/AtF/8nGHnv0BajP2UYjYMg9jwEGLDQxiRGkWRtYYnfvEjvv3gH9lY7qHC4eKfm4v45+Yi0mPsXDUxnasmZTAo/swL/72turoaZ0sL19/zK5Kzck57OxWFh3nuoR9SXV0dNIXk/vzeRERERETkzKlILiIiIr0mIcLGoivHcPOMwfzy9b38b38Vaw60P0IsJsakRzMhM4b0WDuxYSGE2yy0uD00tnqodLRysLKJAxWNHKluxv/hYHGsZoOp2fGcPzyJC0YkdSpSb91a2G3xGwa4yw7wtfFRLB4/gY35tby0rYTXdpRRUu/kd+8c4nfvHGLK4Fi+NCmDS8akEhlq7bb994bkrBwyckcFOowe0Z/fm4iIiIiInD4VyUVERKTXDUuJ5OmvnUVBTTP/3lzMf7YUU+5oZUtBHVsK6k5qG0MSw5maHcc5QxM5Ly+h14vRZpPB9Jx4pufEs+Dzo3hzdznPby1h7cEqNh2tY9PROn728m4+NyqFqyZlMCMnAXMfbsciIiIiIiLSX6lILiIiIgEzKD6cH1w0jO/PzeNoTQtbC+rYWdJAVaOLuhY3zS4PYSEWouwW4sJtDE2KIDcpghGpUSRG2gIdfodQq5krxqdzxfh0yhtaWf5BCf/ZUsThqmZe3FbKi9tKSY0O5YsT0vnihHSGJkVgGCqYi4iIiIiIBAMVyUVERCTgDMMgOyGc7IRwrpqUEehwzkhKdCjfnZXDd2YOYXtxA//ZUsTL20opa2jl96sO8/tVh0mPsTNrWCKzhiUxIyeecFvvn5K1eX3UNLmpbGylpsnN7iIn4aMv4HCjiYpjo/mNY/8xAIvJRIjl2MNsItRqItxmwWYx9ZuCv8fnw+n20trmw+Vp/+r2+vD5/Hh9fmodJqKmfokSh4eJgQ5WRERERES6jYrkIiIiIj3AMAzGZ8YwPjOGH186krf3VvL81mLWHqympN7JcxsKeW5DIWaTwbDkSMZnxTA6LZqhSRHkJIYTFx5yysVnn89PvbONmiYX1ccK4FWNLqqaXFQ5jn1tdFHZ6KK22d3l9QmXfo9tdUBd9Unv02wyCAsxEx5iIdxmBqeZqLO+yNpCJ/74WlKi7SRH2rCYTaf0XrqdYaLFA6X1ThpbPTS62mhqbe933+Rq/+ps837GRizEzrqZgoa2XglZRERERER6R78pkv/+97/nV7/6FWVlZYwaNYrFixdz7rnnBjosERH5GOVrGYhCrWYuHZvKpWNTaXF7WH+khlX7q1i1v4rC2hb2lDnYU+b42GtMJEWGkhhpIyzEjM1ixmY1YbOY8Hj9tLZ5afX4aG3z4nC2Ud3kprbZhc9/giA+gdlkkBARQny4Ddpa2bJxHUNHTSQyOhoD8B9/+P14vH7cXh9uT/voaqfbi8vjw+vztxedWz3Ht0rs7G/w6Pp6Hl3/PgAmA5IiQ0mNCSUt2k5qdCipMce+RoeSFmMnMcKG6TR7tre2ealpdlPX7Ka6yUV5Qysl9U5K6pwU1zs5WtlA1vdf4PVSC5QWf+q2TAbYLGZCrSZCrWZCLCbMhoHZZNDa1MDe9e+QcMG1pxVnX6N8LSLSNyhfi4icuX5RJP/nP//JvHnz+P3vf8/ZZ5/NH//4Ry6++GL27NlDVlZWoMMTEZFjlK9FICzEwvnDkzl/eDJ+v5+yhla2FdWzraie/eWNHK5qoqTeSWubj8LaFgprW055H9F2K/HhISRG2kiMtHUU29v/bev4d1xYSEdheuvWrUz64QJumPUCGbkpJ7Ufj9dHi9tLs9tDs8tLs8tDaXk5H7z/LlNmzsXhNVPhaKXN66fc0Uq5o5UPqP/EbVlMBlF2KxE2C+E2C5E2CyEWE4bRXsw3GQZe34cXB1xtXppcHmqb3bS4P2sEOBhmCwZ+ouwhRNgsRIZaPvbVSkSohdBPaR9TfLCWta//lrwHbzyp49OXKV+LiPQNytciIt2jXxTJH330Ub7xjW/wzW9+E4DFixfz5ptv8uSTT7Jo0aIARyciIscpX4t0ZhgGaTF20mLsXDImtWN5a5uXCkcrlY0uqhtdONvaR227jn21HOsJHnpsdHl7UdxGfEQIsWEhhFh6p7WJxWwiym4iym7tWBbfWsLKV3/Ngw98hYkTJ+Lz+aludlFW30pZg5PS418bWilvaKWs3klFowuPz09ts/sT28CcDKvZIC48hLhwGylRNtJj7aTHhJEea6epopAbr7qMO3/5JzLz8rrr7fdrytciIn2D8rWISPfo80Vyt9vNli1b+NGPftRp+dy5c1m3bt0nvsblcuFyuTqeNzQ0AOBwOD5x/RNpamoCoPjgblzOUx/ldSqqivMB2LJlS8d+e9L+/fuB/vneAEwmEz6fr1f21Z+Ppd5b9zj+3pqamk4pDx1f1+8/hd4KAdQf8nV3/hx2Vx7qrp/VYHxv3bWt7vx9DsRxCj/26GAAPsDV/jCZTPjqfVQDJ99JvKve+FkKA4YCQyOBSCADwIrXZ6HB5aOlzY+zzY/T68fZ5sPjAzDw+nz4/WAYEGI2sJoNQswGoWaDKJtBpM2E3WIcGwHuB1qPPeqgASoK9uNtqqHk0G7crWf+3pSvuwq2fH0yBsK5RX88v+7N4wi9eyz783sDnV/3hEDl697M1TAwflaVr8+c8nX3GbD52t/HlZSU+AH/e++912n5L37xC39eXt4nvuZnP/tZR4tNPfTQQ4++/igqKuqNdHvGlK/10EOPgf5QvtZDDz306BsP5Ws99NBDj77x6M583edHkh/38d6Rfr//hP0k7733XubPn9/x3OfzUVtbS3x8/Alf80kcDgeZmZkUFRURFRV1eoH3MzomXemYdKbj0dXpHhO/309jYyNpaWk9GF33U74ODjomnel4dKVj0pXytfJ1b9Px6ErHpCsdk66Ur3s2X+tnrisdk650TLrSMekqmPJ1ny+SJyQkYDabKS8v77S8srKS5OTkT3yNzWbDZrN1WhYTE3PaMURFRemH+2N0TLrSMelMx6Or0zkm0dHRPRRN91O+Dk46Jp3peHSlY9KV8nVXytc9S8ejKx2TrnRMulK+7qo787V+5rrSMelKx6QrHZOugiFf986sTj0oJCSESZMmsXLlyk7LV65cyYwZMwIUlYiIfJzytYhI36B8LSLSNyhfi4h0nz4/khxg/vz5fPWrX2Xy5MlMnz6dJUuWUFhYyHe+851AhyYiIh+hfC0i0jcoX4uI9A3K1yIi3aNfFMm//OUvU1NTw89//nPKysoYPXo0//3vfxk0aFCP7tdms/Gzn/2sy61KA5mOSVc6Jp3peHQ1kI6J8nXw0DHpTMejKx2TrgbSMVG+Dg46Hl3pmHSlY9LVQDomgcjXA+n4niwdk650TLrSMekqmI6J4ff7/YEOQkREREREREREREQkEPp8T3IRERERERERERERkdOlIrmIiIiIiIiIiIiIDFgqkouIiIiIiIiIiIjIgKUiuYiIiIiIiIiIiIgMWCqSn4Y1a9Zw+eWXk5aWhmEYvPjii4EOKaAWLVrElClTiIyMJCkpiS984Qvs378/0GEF1JNPPsnYsWOJiooiKiqK6dOn8/rrrwc6rKCyaNEiDMNg3rx5gQ4lYBYsWIBhGJ0eKSkpgQ6rX1G+7kz5uivl68+mfK183RuUrztTvu5K+fqzKV8rX/cG5evOlK+7Ur7+dMrV7YIxX6tIfhqam5sZN24cjz/+eKBDCQqrV6/mtttuY/369axcuRKPx8PcuXNpbm4OdGgBk5GRwS9/+Us2b97M5s2bOf/887niiivYvXt3oEMLCps2bWLJkiWMHTs20KEE3KhRoygrK+t47Ny5M9Ah9SvK150pX3elfP3plK8/pHzds5SvO1O+7kr5+tMpX39I+bpnKV93pnzdlfL1iSlXdxZs+doS0L33URdffDEXX3xxoMMIGm+88Uan508//TRJSUls2bKF8847L0BRBdbll1/e6fkvfvELnnzySdavX8+oUaMCFFVwaGpq4vrrr+dPf/oTDz74YKDDCTiLxRLwq6X9mfJ1Z8rXXSlfn5jydWfK1z1L+boz5euulK9PTPm6M+XrnqV83ZnydVfK159MubqrYMvXGkku3a6hoQGAuLi4AEcSHLxeL8uWLaO5uZnp06cHOpyAu+2227j00ku58MILAx1KUDh48CBpaWlkZ2dz7bXXcuTIkUCHJAOI8nVnytedKV93pnwtgaR83ZnydWfK150pX0sgKV93pnz9IeXqroItX2skuXQrv9/P/PnzOeeccxg9enSgwwmonTt3Mn36dFpbW4mIiGD58uWMHDky0GEF1LJly9i6dSubNm0KdChBYerUqfz1r38lLy+PiooKHnzwQWbMmMHu3buJj48PdHjSzylff0j5uivl686UryWQlK8/pHzdlfJ1Z8rXEkjK1x9Svu5MubqrYMzXKpJLt7r99tvZsWMHa9euDXQoATds2DC2bdtGfX09zz//PDfddBOrV68esH8YioqKuOuuu1ixYgWhoaGBDicofPQ2xTFjxjB9+nRycnJ49tlnmT9/fgAjk4FA+fpDytedKV93pXwtgaR8/SHl686Ur7tSvpZAUr7+kPL1h5SrP1kw5msVyaXb3HHHHbz88susWbOGjIyMQIcTcCEhIQwdOhSAyZMns2nTJn7729/yxz/+McCRBcaWLVuorKxk0qRJHcu8Xi9r1qzh8ccfx+VyYTabAxhh4IWHhzNmzBgOHjwY6FCkn1O+7kz5ujPl68+mfC29Rfm6M+XrzpSvP5vytfQW5evOlK8/pFx9coIhX6tILmfM7/dzxx13sHz5clatWkV2dnagQwpKfr8fl8sV6DAC5oILLugyU/HXvvY1hg8fzj333KM/CoDL5WLv3r2ce+65gQ5F+inl65OjfK18/VmUr6WnKV+fHOVr5evPonwtPU35+uQM5HytXH1ygiFfq0h+Gpqamjh06FDH8/z8fLZt20ZcXBxZWVkBjCwwbrvtNpYuXcpLL71EZGQk5eXlAERHR2O32wMcXWDcd999XHzxxWRmZtLY2MiyZctYtWpVl5mvB5LIyMgufdnCw8OJj48fsP3afvCDH3D55ZeTlZVFZWUlDz74IA6Hg5tuuinQofUbytedKV93pXzdlfJ1V8rXPU/5ujPl666Ur7tSvu5K+brnKV93pnzdlfJ1Z8rVnywY87WK5Kdh8+bNzJ49u+P58V45N910E88880yAogqcJ598EoBZs2Z1Wv70009z8803935AQaCiooKvfvWrlJWVER0dzdixY3njjTeYM2dOoEOTIFJcXMxXvvIVqqurSUxMZNq0aaxfv55BgwYFOrR+Q/m6M+XrrpSv5WQoX/c85evOlK+7Ur6Wk6F83fOUrztTvu5K+VpORjDma8Pv9/sDtncREREREZH/Z+++w6Mq8zaOfydt0jukQBqQ0DuIgEoTlGbBDiisDcXVRURdllWDBRSVRUWxAwqoq68gu6ISEBAENFQBQxEDoSQkgZDec94/IrOOSZCS5KTcn+s6l8yp9wn4zOQ3z3keERERERETOZgdQERERERERERERETELCqSi4iIiIiIiIiIiEijpSK5iIiIiIiIiIiIiDRaKpKLiIiIiIiIiIiISKOlIrmIiIiIiIiIiIiINFoqkouIiIiIiIiIiIhIo6UiuYiIiIiIiIiIiIg0WiqSi4iIiIiIiIiIiEijpSK5iIiISCUWLFiAr6+v2TFEROQcrF27FovFwunTp82OIiJSb/Xv359JkybV6DUiIyOZM2fOWfeJjY2lS5cuNZpD5I9UJBepww4dOoTFYmHHjh1mRxERqTfGjx+PxWLBYrHg7OxMUFAQgwcP5v3336esrMzseCIijd6bb76Jl5cXJSUltnU5OTk4Oztz+eWX2+27fv16LBYL+/fvr+2YIiJSCywWC8uWLTM7hoiK5CIiItLwXH311SQnJ3Po0CG++uorBgwYwN/+9jdGjBhhV5QREZHaN2DAAHJyctiyZYtt3fr16wkODiY+Pp68vDzb+rVr1xIaGkpMTIwZUUVERKSRUJFcpArZ2dmMGTMGDw8PQkJC+Ne//mX36FFGRgZ33HEHfn5+uLu7M3ToUA4cOGB3jv/7v/+jffv2WK1WIiMjefnll+22V/aNqa+vLwsWLAAgKioKgK5du2KxWOjfv39N3KqISINjtVoJDg6mWbNmdOvWjX/84x988cUXfPXVV7Y2dvbs2XTs2BEPDw/CwsKYOHEiOTk5Zz3v8uXL6dGjB66urgQGBjJq1KhauBsRkYaldevWhIaGsnbtWtu6tWvXcu2119KyZUs2btxot37AgAEsWrSIHj164OXlRXBwMKNHjyY1NfWs1/n+++/p168f7u7u+Pn5cdVVV5GRkVFTtyUi0iCUlZXx2GOP4e/vT3BwMLGxsbZtmZmZ3HvvvTRt2hRvb28GDhzIzp07bdsPHjzItddeS1BQEJ6envTs2ZNVq1ZVea3IyEgArr/+eiwWi+31GR9++CGRkZH4+Phw6623kp2dXZ23KmJHRXKRKkyePJnvv/+e5cuXExcXx/r169m2bZtt+/jx49myZQvLly9n06ZNGIbBsGHDKC4uBmDr1q3cfPPN3HrrrezatYvY2FieeOIJW3HmXPz4448ArFq1iuTkZD7//PNqvUcRkcZk4MCBdO7c2daWOjg48Oqrr7J7924WLlzIt99+y2OPPVbl8V9++SWjRo1i+PDhbN++ndWrV9OjR4/aii8i0qD079+fNWvW2F6vWbOG/v37069fP9v6oqIiNm3axIABAygqKuKZZ55h586dLFu2jMTERMaPH1/l+Xfs2MGgQYNo3749mzZtYsOGDYwcOZLS0tKavjURkXpt4cKFeHh48MMPPzBr1iyefvpp4uLiMAyD4cOHk5KSwooVK9i6dSvdunVj0KBBnDp1CigfOmvYsGGsWrWK7du3c9VVVzFy5EiSkpIqvVZ8fDwA8+fPJzk52fYaygvuy5Yt47///S///e9/WbduHc8//3zN/wCk0XIyO4BIXZSdnc3ChQtZsmQJgwYNAsob7dDQUAAOHDjA8uXL+f777+nTpw8AixcvJiwsjGXLlnHTTTcxe/ZsBg0axBNPPAFATEwMP//8My+++OJZP9D/XpMmTQAICAggODi4mu9SRKTxadOmDT/99BOA3aREUVFRPPPMM9x///288cYblR773HPPceuttzJ9+nTbus6dO9doXhGRhqp///48/PDDlJSUkJ+fz/bt27niiisoLS3l1VdfBWDz5s3k5+czYMAAWrRoYTu2RYsWvPrqq1xyySXk5OTg6elZ4fyzZs2iR48edm16+/bta/7GRETquU6dOvHUU08BEB0dzdy5c1m9ejWOjo7s2rWL1NRUrFYrAC+99BLLli3js88+495776Vz5852n4+fffZZli5dyvLly/nrX/9a4Vpnah6+vr4Vah5lZWUsWLAALy8vAG6//XZWr17Nc889VyP3LaKe5CKV+PXXXykuLuaSSy6xrfPx8aF169YAJCQk4OTkRK9evWzbAwICaN26NQkJCbZ9+vbta3fevn37cuDAAfVgERExiWEYWCwWoLzX4uDBg2nWrBleXl7ccccdnDx5ktzc3EqPPdMrUURELt6AAQPIzc0lPj6e9evXExMTQ9OmTenXrx/x8fHk5uaydu1awsPDadGiBdu3b+faa68lIiICLy8v2zCEVfVOVJstInJhOnXqZPc6JCSE1NRUtm7dSk5ODgEBAXh6etqWxMREDh48CEBubi6PPfYY7dq1w9fXF09PT/bu3VtlW302kZGRtgL573OI1BT1JBephGEYALZCyh/Xn/lvZcedOeb3f/7j8WdYLJYK684M1yIiItUvISGBqKgoDh8+zLBhw7jvvvt45pln8Pf3Z8OGDdx1111VtsNubm61nFZEpOFq1aoVzZs3Z82aNWRkZNCvXz8AgoODiYqK4vvvv2fNmjUMHDiQ3NxchgwZwpAhQ1i0aBFNmjQhKSmJq666iqKiokrPrzZbROTCODs72722WCyUlZVRVlZGSEiI3XwSZ/j6+gLw6KOP8s033/DSSy/RqlUr3NzcuPHGG6tsqy8kh0hNUU9ykUq0bNkSZ2dn25jgAFlZWbaJOdu1a0dJSQk//PCDbfvJkyfZv38/bdu2te2zYcMGu/Nu3LiRmJgYHB0dgfJHi5KTk23bDxw4QF5enu21i4sLgHqei4hUg2+//ZZdu3Zxww03sGXLFkpKSnj55Ze59NJLiYmJ4fjx42c9vlOnTqxevbqW0oqINHwDBgxg7dq1rF271m6C+n79+vHNN9+wefNmBgwYwN69e0lPT+f555/n8ssvp02bNn/am1BttohI9erWrRspKSk4OTnRqlUruyUwMBCA9evXM378eK6//no6duxIcHAwhw4dOut5nZ2dVfOQOkFFcpFKeHl5MW7cOB599FHWrFnDnj17uPPOO3FwcMBisRAdHc21117LPffcw4YNG9i5cydjx46lWbNmXHvttQA88sgjrF69mmeeeYb9+/ezcOFC5s6dy5QpU2zXGThwIHPnzmXbtm1s2bKF++67z+7b0qZNm+Lm5sbXX3/NiRMnyMzMrPWfhYhIfVRYWEhKSgrHjh1j27ZtzJgxg2uvvZYRI0Zwxx130LJlS0pKSnjttdf49ddf+fDDD3nzzTfPes6nnnqKjz76iKeeeoqEhAR27drFrFmzaumOREQangEDBrBhwwZ27Nhh60kO5UXyd955h4KCAgYMGEB4eDguLi62Nnv58uU888wzZz331KlTiY+PZ+LEifz000/s3buXefPmkZ6eXtO3JSLSIF155ZX07t2b6667jm+++YZDhw6xceNG/vnPf7Jlyxag/Cmhzz//nB07drBz505Gjx79p72/IyMjWb16NSkpKWRkZNTGrYhUSkVykSrMnj2b3r17M2LECK688kr69u1L27ZtcXV1Bcon8uzevTsjRoygd+/eGIbBihUrbEXubt268e9//5uPP/6YDh068OSTT/L000/bTdr58ssvExYWxhVXXMHo0aOZMmUK7u7utu1OTk68+uqrvPXWW4SGhtoK8CIicnZff/01ISEhREZGcvXVV7NmzRpeffVVvvjiCxwdHenSpQuzZ8/mhRdeoEOHDixevJiZM2ee9Zz9+/fn008/Zfny5XTp0oWBAwfaPVEkIiLnZ8CAAeTn59OqVSuCgoJs6/v160d2djYtW7YkLCyMJk2asGDBAj799FPatWvH888/z0svvXTWc8fExLBy5Up27tzJJZdcQu/evfniiy9wctKIoyIiF8JisbBixQquuOIK7rzzTmJiYrj11ls5dOiQrQ3/17/+hZ+fH3369GHkyJFcddVVdOvW7aznffnll4mLiyMsLIyuXbvWxq2IVMpiVDW4sojYyc3NpVmzZrz88svcddddZscRERERERERERGRaqCv0UWqsH37dvbu3csll1xCZmYmTz/9NIB6c4uIiIiIiIiIiDQgKpKLnMVLL73Evn37cHFxoXv37qxfv942IYWIiIiIiIiIiIjUfxpuRUREREREREREREQaLU3cKSIiIiIiIiIiIiKNlorkIiIiIiIiIiIiItJoqUguIiIiIiIiIiIiIo2WiuQiIiIiIiIiIiIi0mipSC4iIiIiIiIiIiIijZaK5A3QggULsFgstsXV1ZXg4GAGDBjAzJkzSU1NrXBMbGwsFovlvK6Tl5dHbGwsa9euPa/jKrtWZGQkI0aMOK/z/JklS5YwZ86cSrdZLBZiY2Or9XrVbfXq1fTo0QMPDw8sFgvLli276HNaLBb++te/Xnw4EamX9P5QrrG9P5w4cYJ//OMfdOnSBW9vb1xcXGjevDmjRo1i+fLllJaW2u3/z3/+kxEjRtCsWTMsFgvjx4+vuZsRkQrUVpdTW111W71161YeeOABOnbsiJeXF0FBQVx55ZV8++23NXxXIiIiDZeK5A3Y/Pnz2bRpE3Fxcbz++ut06dKFF154gbZt27Jq1Sq7fe+++242bdp0XufPy8tj+vTp5/3B+kKudSHO9sF606ZN3H333TWe4UIZhsHNN9+Ms7Mzy5cvZ9OmTfTr18/sWCLSQOj9ofG8P2zevJmOHTvyzjvvcM011/Dxxx+zatUqnn/+eZydnRk1ahQLFiywO+Zf//oXJ0+e5JprrsHFxaWG70hEqqK2Wm11VW31Rx99xI8//sidd97JF198wbvvvovVamXQoEF88MEHtXCHIiIiDY+T2QGk5nTo0IEePXrYXt9www08/PDDXHbZZYwaNYoDBw4QFBQEQPPmzWnevHmN5snLy8Pd3b1WrvVnLr30UlOv/2eOHz/OqVOnuP766xk0aJDZcUSkgdH7Q9Ua0vvD6dOnue666/D09OT7778nJCTEbvvYsWP56aefOHnypN367OxsHBzK+1F8+OGH1XsDInLO1FZXrbG31Y899hgvvfSS3X7Dhg2jW7duPP3009xxxx3VdzMiIiKNhHqSNzLh4eG8/PLLZGdn89Zbb9nWV/bY5Lfffkv//v0JCAjAzc2N8PBwbrjhBvLy8jh06BBNmjQBYPr06bbHQc88kn3mfNu2bePGG2/Ez8+Pli1bVnmtM5YuXUqnTp1wdXWlRYsWvPrqq3bbzzx+eujQIbv1a9euxWKx2HrC9O/fny+//JLDhw/bPa56RmWPaO7evZtrr70WPz8/XF1d6dKlCwsXLqz0Oh999BHTpk0jNDQUb29vrrzySvbt21f1D/53NmzYwKBBg/Dy8sLd3Z0+ffrw5Zdf2rbHxsbafvF4/PHHsVgsREZGVnm+goICHnnkEbp06YKPjw/+/v707t2bL774ospj3nrrLWJiYrBarbRr146PP/7YbnteXh5TpkwhKioKV1dX/P396dGjBx999JHdflu2bOGaa67B398fV1dXunbtyr///W+7fc78na1Zs4b777+fwMBAAgICGDVqFMePH6+QbcmSJfTu3RtPT088PT3p0qUL7733nt0+q1atYtCgQXh7e+Pu7k7fvn1ZvXq13T5paWnce++9hIWFYbVaadKkCX379q3Q80pEyun9oVxDen945513OHHiBLNmzapQdDmjU6dODBgwwG7dmQK5iNQ9aqvLNfa2umnTphX2cXR0pHv37hw5cuSc7kNERETs6begRmjYsGE4Ojry3XffVbnPoUOHGD58OC4uLrz//vt8/fXXPP/883h4eFBUVERISAhff/01AHfddRebNm1i06ZNPPHEE3bnGTVqFK1ateLTTz/lzTffPGuuHTt2MGnSJB5++GGWLl1Knz59+Nvf/lahl8S5eOONN+jbty/BwcG2bGd7LHTfvn306dOHPXv28Oqrr/L555/Trl07xo8fz6xZsyrs/49//IPDhw/z7rvv8vbbb3PgwAFGjhxZYVzXP1q3bh0DBw4kMzOT9957j48++ggvLy9GjhzJJ598ApQ/wvr5558D8OCDD7Jp0yaWLl1a5TkLCws5deoUU6ZMYdmyZXz00Ue2HkaVPW65fPlyXn31VZ5++mk+++wzIiIiuO222/jss89s+0yePJl58+bx0EMP8fXXX/Phhx9y00032fVgWbNmDX379uX06dO8+eabfPHFF3Tp0oVbbrmlwqP7Z+7L2dmZJUuWMGvWLNauXcvYsWPt9nnyyScZM2YMoaGhLFiwgKVLlzJu3DgOHz5s22fRokUMGTIEb29vFi5cyL///W/8/f256qqr7Arlt99+O8uWLePJJ59k5cqVvPvuu1x55ZUVekyKyP/o/aGi+vz+EBcXh6OjI8OGDTuXH42I1BNqqytSWw0lJSWsX7+e9u3bX9R5REREGi1DGpz58+cbgBEfH1/lPkFBQUbbtm1tr5966inj9/8cPvvsMwMwduzYUeU50tLSDMB46qmnKmw7c74nn3yyym2/FxERYVgslgrXGzx4sOHt7W3k5uba3VtiYqLdfmvWrDEAY82aNbZ1w4cPNyIiIirN/sfct956q2G1Wo2kpCS7/YYOHWq4u7sbp0+ftrvOsGHD7Pb797//bQDGpk2bKr3eGZdeeqnRtGlTIzs727aupKTE6NChg9G8eXOjrKzMMAzDSExMNADjxRdfPOv5KlNSUmIUFxcbd911l9G1a9cK9+3m5makpKTY7d+mTRujVatWtnUdOnQwrrvuurNep02bNkbXrl2N4uJiu/UjRowwQkJCjNLSUsMw/vd3NnHiRLv9Zs2aZQBGcnKyYRiG8euvvxqOjo7GmDFjqrxmbm6u4e/vb4wcOdJufWlpqdG5c2fjkksusa3z9PQ0Jk2adNZ7EGls9P5QrrG8P7Rp08YIDg6usL60tNQoLi62LWfa68p4eHgY48aN+9NriUj1UVtdTm31ubfVhmEY06ZNMwBj2bJlf3pNERERqUg9yRspwzDOur1Lly64uLhw7733snDhQn799dcLus4NN9xwzvu2b9+ezp07260bPXo0WVlZbNu27YKuf66+/fZbBg0aRFhYmN368ePHk5eXV6HnyjXXXGP3ulOnTgB2PZ7/KDc3lx9++IEbb7wRT09P23pHR0duv/12jh49es6Pef7Rp59+St++ffH09MTJyQlnZ2fee+89EhISKuw7aNAg2/iVZ65/yy238Msvv3D06FEALrnkEr766iv+/ve/s3btWvLz8+3O8csvv7B3717GjBkDlPdcObMMGzaM5OTkCvfyZz+zuLg4SktLeeCBB6q8z40bN3Lq1CnGjRtnd82ysjKuvvpq4uPjyc3Ntd3DggULePbZZ9m8eTPFxcXn9LMUaez0/mCvvr8/VGby5Mk4Ozvblj9mFpG6T221vcbeVr/77rs899xzPPLII1x77bXVlkFERKQxUZG8EcrNzeXkyZOEhoZWuU/Lli1ZtWoVTZs25YEHHqBly5a0bNmSV1555byuVdW4epUJDg6ucl1ND5Fx8uTJSrOe+Rn98foBAQF2r61WK0CFYvLvZWRkYBjGeV3nXHz++efcfPPNNGvWjEWLFrFp0ybi4+O58847KSgoqLD/ufycX331VR5//HGWLVvGgAED8Pf357rrruPAgQMAnDhxAoApU6bYfXh3dnZm4sSJAKSnp9td489+ZmlpaQBnnQjqzHVvvPHGCtd94YUXMAyDU6dOAfDJJ58wbtw43n33XXr37o2/vz933HEHKSkpVZ5fpLHT+0NF9fn9ITw8nLS0NPLy8uzWP/LII8THxxMfH39efw8iUjeora6oMbfV8+fPZ8KECdx77728+OKL5319ERERKedkdgCpfV9++SWlpaX079//rPtdfvnlXH755ZSWlrJlyxZee+01Jk2aRFBQELfeeus5XauqSX0qU1nx8sy6Mx9kXV1dgfJxuH/vjwXZ8xUQEEBycnKF9WcmlgwMDLyo8wP4+fnh4OBQ7ddZtGgRUVFRfPLJJ3Y/7z/+jM44l5+zh4cH06dPZ/r06Zw4ccLWq3zkyJHs3bvXlnPq1KmMGjWq0uu0bt36vO7jzORRR48erdAL6Iwz133ttde49NJLK93nTC/5wMBA5syZw5w5c0hKSmL58uX8/e9/JzU11TYGp4jY0/tDRfX5/WHw4MGsXLmSFStWcOONN9rWh4WF2dpZFxeXC0wtImZRW11RY22r58+fz9133824ceN48803z+vvS0REROypJ3kjk5SUxJQpU/Dx8WHChAnndIyjoyO9evXi9ddfB7A9LnkuPS7Ox549e9i5c6fduiVLluDl5UW3bt0AbDPD//TTT3b7LV++vML5rFbrOWcbNGgQ3377re0D7hkffPAB7u7uVRZkz4eHhwe9evXi888/t8tVVlbGokWLaN68OTExMed9XovFgouLi92H4pSUFL744otK91+9erWtRzZAaWkpn3zyCS1btqy0F3dQUBDjx4/ntttuY9++feTl5dG6dWuio6PZuXMnPXr0qHTx8vI6r/sYMmQIjo6OzJs3r8p9+vbti6+vLz///HOV163sl4jw8HD++te/Mnjw4Bp/3FekvtL7Q+Xq8/vD3XffTVBQEI899lilRR0RqX/UVleuMbbVCxYs4O6772bs2LG8++67KpCLiIhcJPUkb8B2795tG7M5NTWV9evXM3/+fBwdHVm6dKmt525l3nzzTb799luGDx9OeHg4BQUFvP/++wBceeWVAHh5eREREcEXX3zBoEGD8Pf3JzAw0Pbh93yFhoZyzTXXEBsbS0hICIsWLSIuLo4XXngBd3d3AHr27Enr1q2ZMmUKJSUl+Pn5sXTpUjZs2FDhfB07duTzzz9n3rx5dO/eHQcHB3r06FHptZ966in++9//MmDAAJ588kn8/f1ZvHgxX375JbNmzcLHx+eC7umPZs6cyeDBgxkwYABTpkzBxcWFN954g927d/PRRx9d0IfbESNG8PnnnzNx4kRuvPFGjhw5wjPPPENISIhteJTfCwwMZODAgTzxxBN4eHjwxhtvsHfvXj7++GPbPr169WLEiBF06tQJPz8/EhIS+PDDD+ndu7ft7+Ktt95i6NChXHXVVYwfP55mzZpx6tQpEhIS2LZtG59++ul53UdkZCT/+Mc/eOaZZ8jPz+e2227Dx8eHn3/+mfT0dKZPn46npyevvfYa48aN49SpU9x44400bdqUtLQ0du7cSVpaGvPmzSMzM5MBAwYwevRo2rRpg5eXF/Hx8Xz99ddV9nwXaUz0/tA43h98fX1ZtmwZI0eOpHPnztx///1ceumleHp6cvLkSb777jtSUlLo06eP3XHr1q2zDYFVWlrK4cOH+eyzzwDo16/fWf99iEj1UVuttrqqtvrTTz/lrrvuokuXLkyYMIEff/zR7pxdu3a1fQkiIiIi58jESUOlhpyZNf7M4uLiYjRt2tTo16+fMWPGDCM1NbXCMX+cpX7Tpk3G9ddfb0RERBhWq9UICAgw+vXrZyxfvtzuuFWrVhldu3Y1rFarARjjxo2zO19aWtqfXsswDCMiIsIYPny48dlnnxnt27c3XFxcjMjISGP27NkVjt+/f78xZMgQw9vb22jSpInx4IMPGl9++aUBGGvWrLHtd+rUKePGG280fH19DYvFYndNwHjqqafszrtr1y5j5MiRho+Pj+Hi4mJ07tzZmD9/vt0+a9asMQDj008/tVt/Zgb7P+5fmfXr1xsDBw40PDw8DDc3N+PSSy81/vOf/1R6vhdffPFPz2cYhvH8888bkZGRhtVqNdq2bWu88847lf6cAeOBBx4w3njjDaNly5aGs7Oz0aZNG2Px4sV2+/397383evToYfj5+RlWq9Vo0aKF8fDDDxvp6el2++3cudO4+eabjaZNmxrOzs5GcHCwMXDgQOPNN9+07XPm32N8fLzdsWd+lr//OzMMw/jggw+Mnj17Gq6uroanp6fRtWvXCj/XdevWGcOHDzf8/f0NZ2dno1mzZsbw4cNtfy8FBQXGfffdZ3Tq1Mnw9vY23NzcjNatWxtPPfWUkZube04/U5GGSO8P5RrT+4NhGEZKSooxdepUo1OnToaHh4fh7OxshIaGGiNHjjQ++OADo7i42G7/fv362f07+f3yxzZbRKqf2upyaqurbqvHjRtXZTsNGImJied8XRERESlnMYw/mRpdRERERERERERERKSB0pjkIiIiIiIiIiIiItJoqUguIiIiIiIiIiIiIo2WiuQiIiIiIiIiIiIi0mipSC4iIiIiIiIiYqLvvvuOkSNHEhoaisViYdmyZX96zLp16+jevTuurq60aNGCN998s+aDiog0UCqSi4iIiIiIiIiYKDc3l86dOzN37txz2j8xMZFhw4Zx+eWXs337dv7xj3/w0EMP8X//9381nFREpGGyGIZhmB1CRERERERERETAYrGwdOlSrrvuuir3efzxx1m+fDkJCQm2dffddx87d+5k06ZNtZBSRKRhcTI7QF1QVlbG8ePH8fLywmKxmB1HROScGIZBdnY2oaGhODg0jgeD1F6LSH2k9lrttYjUD/Wpvd60aRNDhgyxW3fVVVfx3nvvUVxcjLOzc6XHFRYWUlhYaHtdVlbGqVOnCAgIUHstIvVGTbTXKpIDx48fJywszOwYIiIX5MiRIzRv3tzsGLVC7bWI1Gdqr0VE6of60F6npKQQFBRkty4oKIiSkhLS09MJCQmp9LiZM2cyffr02ogoIlLjqrO9VpEc8PLyAsp/sN7e3ianERE5N1lZWYSFhdnasMZA7bWI1Edqr9Vei0j9UN/a6z/2/D4zmu7ZeoRPnTqVyZMn215nZmYSHh6u9lpE6pWaaK9VJOd/byDe3t56UxCReqcxPRap9lpE6jO11yIi9UN9aK+Dg4NJSUmxW5eamoqTkxMBAQFVHme1WrFarRXWq70WkfqoOtvruj3IloiIiIiIiIiI2OnduzdxcXF261auXEmPHj2qHI9cRESqpiK5iIhctJKSEv75z38SFRWFm5sbLVq04Omnn6asrMy2j2EYxMbGEhoaipubG/3792fPnj0mphYRERERqRtycnLYsWMHO3bsACAxMZEdO3aQlJQElA+Tcscdd9j2v++++zh8+DCTJ08mISGB999/n/fee48pU6aYEV9EpN5TkVxERC7aCy+8wJtvvsncuXNJSEhg1qxZvPjii7z22mu2fWbNmsXs2bOZO3cu8fHxBAcHM3jwYLKzs01MLiIiIiJivi1bttC1a1e6du0KwOTJk+natStPPvkkAMnJybaCOUBUVBQrVqxg7dq1dOnShWeeeYZXX32VG264wZT8IiL1ncYkFxGRi7Zp0yauvfZahg8fDkBkZCQfffQRW7ZsAcp7kc+ZM4dp06YxatQoABYuXEhQUBBLlixhwoQJpmUXERERETFb//79bRNvVmbBggUV1vXr149t27bVYCoRkcZDPclFROSiXXbZZaxevZr9+/cDsHPnTjZs2MCwYcOA8sdFU1JSGDJkiO0Yq9VKv3792LhxY5XnLSwsJCsry24REREREREREalO6kkuIiIX7fHHHyczM5M2bdrg6OhIaWkpzz33HLfddhsAKSkpAAQFBdkdFxQUxOHDh6s878yZM5k+fXrNBRcRERERERGRRk89yUVE5KJ98sknLFq0iCVLlrBt2zYWLlzISy+9xMKFC+32s1gsdq8Nw6iw7vemTp1KZmambTly5EiN5BcRERERERGRxks9yUVE5KI9+uij/P3vf+fWW28FoGPHjhw+fJiZM2cybtw4goODgfIe5SEhIbbjUlNTK/Qu/z2r1YrVaq3Z8CIiIiIiIiLSqKknuYiIXLS8vDwcHOzfUhwdHSkrKwMgKiqK4OBg4uLibNuLiopYt24dffr0qdWsIiIiIiIiIiK/p57kIiJy0UaOHMlzzz1HeHg47du3Z/v27cyePZs777wTKB9mZdKkScyYMYPo6Giio6OZMWMG7u7ujB492uT0IiIiIiIiItKYqSe5iIhctNdee40bb7yRiRMn0rZtW6ZMmcKECRN45plnbPs89thjTJo0iYkTJ9KjRw+OHTvGypUr8fLyMjG5iEjjEhsbi8VisVvODIkF5XNFxMbGEhoaipubG/3792fPnj0mJhYRERERqXnqSS4iIhfNy8uLOXPmMGfOnCr3sVgsxMbGEhsbW2u5RESkovbt27Nq1Srba0dHR9ufZ82axezZs1mwYAExMTE8++yzDB48mH379ulLTRERERFpsNSTXERERESkEXFyciI4ONi2NGnSBCjvRT5nzhymTZvGqFGj6NChAwsXLiQvL48lS5aYnFpEREREpOaoJ7mIiEgjlZSURHp6erWfNzAwkPDw8Go/r4hUjwMHDhAaGorVaqVXr17MmDGDFi1akJiYSEpKCkOGDLHta7Va6devHxs3bmTChAlVnrOwsJDCwkLb66ysrBq9B6n/LuY9SO8zIiIiUt1UJL9IA64cwom0qj/cBTUJZM2qlbWYSERE5M8lJSXRpm1b8vPyqv3cbu7u7E1IUAFDpA7q1asXH3zwATExMZw4cYJnn32WPn36sGfPHlJSUgAICgqyOyYoKIjDhw+f9bwzZ85k+vTpNZZbGpaLfQ/S+4yIiIhUNxXJL9KJtHTueenjKre/M+XWWkwjIiJybtLT08nPy2PM4y8SFN6y2s57Iukgi194lPT0dBUvROqgoUOH2v7csWNHevfuTcuWLVm4cCGXXnopUD6HxO8ZhlFh3R9NnTqVyZMn215nZWURFhZWjcmlIbmY9yC9z4iIiEhNUJFcRESkEQsKb0nz6PZmxxARk3h4eNCxY0cOHDjAddddB0BKSgohISG2fVJTUyv0Lv8jq9WK1WqtyajSAOk9SEREROoKTdwpIiIiItJIFRYWkpCQQEhICFFRUQQHBxMXF2fbXlRUxLp16+jTp4+JKUVEREREapZ6kouIiIiINBJTpkxh5MiRhIeHk5qayrPPPktWVhbjxo3DYrEwadIkZsyYQXR0NNHR0cyYMQN3d3dGjx5tdnQRERERkRqjIrmIiIiISCNx9OhRbrvtNtLT02nSpAmXXnopmzdvJiIiAoDHHnuM/Px8Jk6cSEZGBr169WLlypV4eXmZnFxEREREpOaoSC4iIiIi0kh8/HHVE85D+aSdsbGxxMbG1k4gEREREZE6QGOSi4iIiIiIiIiIiEijpSK5iIiIiIiIiIiIiDRaKpKLiIiIiIiIiIiISKOlIrmIiIiIiIiIiIiINFqmFsm/++47Ro4cSWhoKBaLhWXLltltt1gslS4vvviibZ/+/ftX2H7rrbfW8p2IiIiIiIiIiIiISH1kapE8NzeXzp07M3fu3Eq3Jycn2y3vv/8+FouFG264wW6/e+65x26/t956qzbii4iIiIiIiIiIiEg952TmxYcOHcrQoUOr3B4cHGz3+osvvmDAgAG0aNHCbr27u3uFfUVERERERERERERE/ky9GZP8xIkTfPnll9x1110Vti1evJjAwEDat2/PlClTyM7OPuu5CgsLycrKsltEREREREREREREpPExtSf5+Vi4cCFeXl6MGjXKbv2YMWOIiooiODiY3bt3M3XqVHbu3ElcXFyV55o5cybTp0+v6cgiIiIiIiIiIiIiUsfVmyL5+++/z5gxY3B1dbVbf88999j+3KFDB6Kjo+nRowfbtm2jW7dulZ5r6tSpTJ482fY6KyuLsLCwmgkuIiIi9UJSUhLp6ek1cu7AwEDCw8Nr5NwiIiIiIiJycepFkXz9+vXs27ePTz755E/37datG87Ozhw4cKDKIrnVasVqtVZ3TBEREamnkpKSaNO2Lfl5eTVyfjd3d/YmJKhQLiIiIiIiUgfViyL5e++9R/fu3encufOf7rtnzx6Ki4sJCQmphWQiIiLSEKSnp5Ofl8eYx18kKLxltZ77RNJBFr/wKOnp6SqSi4iIiIiI1EGmFslzcnL45ZdfbK8TExPZsWMH/v7+tl8is7Ky+PTTT3n55ZcrHH/w4EEWL17MsGHDCAwM5Oeff+aRRx6ha9eu9O3bt9buQ0RERBqGoPCWNI9ub3YMERERERERqUWmFsm3bNnCgAEDbK/PjBM+btw4FixYAMDHH3+MYRjcdtttFY53cXFh9erVvPLKK+Tk5BAWFsbw4cN56qmncHR0rJV7EBEREREREREREZH6y9Qief/+/TEM46z73Hvvvdx7772VbgsLC2PdunU1EU1EREREREREREREGgEHswOIiIiIiIiIiIiIiJhFRXIRERERERERERERabRUJBcRERERERERERGRRktFchERERERERERERFptEyduFNERERERETkfCUkJFzwsYGBgYSHh1djGhEREanvVCQXERERERGReiHrVBoAY8eOveBzuLm7szchQYVyERERsVGRXEREREREROqF/JwsAIZPmEbrTt3P+/gTSQdZ/MKjpKenq0guIiIiNiqSi4iIiIiISL0SEBpB8+j2ZscQERGRBkITd4qIyEWLjIzEYrFUWB544AEADMMgNjaW0NBQ3Nzc6N+/P3v27DE5tYiIiIiIiIiIiuQiIlIN4uPjSU5Oti1xcXEA3HTTTQDMmjWL2bNnM3fuXOLj4wkODmbw4MFkZ2ebGVtERERERERERMOtiIjIxWvSpInd6+eff56WLVvSr18/DMNgzpw5TJs2jVGjRgGwcOFCgoKCWLJkCRMmTDAjsoiIiNRTTj5BHCzy5qdNh8guKMHZ0YFATxdaB3vROsgLJ0f1BRMREZHzo08PIiJSrYqKili0aBF33nknFouFxMREUlJSGDJkiG0fq9VKv3792Lhx41nPVVhYSFZWlt0iIiIijZNhGBzHj5C73uBoiScZecWUlBnkF5dyJCOfVQmpfLD5MAfTcsyOKiIiIvWMepKLiEi1WrZsGadPn2b8+PEApKSkABAUFGS3X1BQEIcPHz7ruWbOnMn06dNrJKeIiIjUH4ZhsHZ/Gr8SjIMz+DgUcln7SAI9XSgsKSPpVB4/Hc0ku6CE//6UTOfmPlwe3QRHB4vZ0UVERKQeUE9yERGpVu+99x5Dhw4lNDTUbr3FYv9LqmEYFdb90dSpU8nMzLQtR44cqfa8IiIiUvd9f/AkPx3NBAxOrXqbztaTtGrqia+7C0HervSM9OeO3hF0D/cDYOfRTP7z03GKS8vMDS4iIiL1gorkIiJSbQ4fPsyqVau4++67beuCg4OB//UoPyM1NbVC7/I/slqteHt72y0iIiLSuBxMy2Hr4QwAWpFM9tblVPY9u7OjA5dFBzKyUwhODhYOn8zjix3HKVGhXERERP6EiuQiIlJt5s+fT9OmTRk+fLhtXVRUFMHBwcTFxdnWFRUVsW7dOvr06WNGTBEREakncgtLiPv5BADdwn0JJvNPj2nRxJPruzbDxdGBY6fz+XpPCmVlRk1HFRERkXpMRXIREakWZWVlzJ8/n3HjxuHk9L8pLywWC5MmTWLGjBksXbqU3bt3M378eNzd3Rk9erSJiUVERKSuW38gncKSMpp6WenTMvCcjwv1dWNk5xAcHSwcTMvl232pGIYK5SIiIlI5TdwpIiLVYtWqVSQlJXHnnXdW2PbYY4+Rn5/PxIkTycjIoFevXqxcuRIvLy8TkoqIiEh9cDQjj30nsgEY2KbpeU/C2dzPnavbB7NiVzJ7jmfhZXWiWU0EFRERkXpPPclFRKRaDBkyBMMwiImJqbDNYrEQGxtLcnIyBQUFrFu3jg4dOpiQUkREROoDwzDYePAkAB2b+RDk7XpB52nV1JMBbZoCsDnxFMfzzq/QLiIiIo2DiuQiIiIiIiJSpySdyiM5swBHBwu9ovwv6lwdm/nQubkPAPEnnXAKaF4dEUVERKQBUZFcRERERERE6pQfEk8B0KmZDx7Wix8l9PLoJjTzdaPEsNB01BPkFpVd9DlFRESk4VCRXEREREREROqMlMyC8l7kFgvdI/yq5ZyODhaGdQzGzdHA2b8Zr/14mrIyTeQpIiIi5VQkFxERERERkTpjx5HTAMQEe1ZLL/Iz3F2c6B1YglFSzI/HC3nzu4PVdm4RERGp31QkFxERERERkToht7CEA6nZAHRu7lvt5/ezGpxa9SYAL32zj+9/Sa/2a4iIiEj9oyK5iIiIiIiI1Ak/J2dRZkCIjytB3q41co2cnd8wMNKNMgMe/Gg7x0/n18h1REREpP5QkVxERERERERMZxgGPx/PAqB9qHeNXuuebj60D/XmVG4RExdvo7CktEavJyIiInWbiuQiIiIiIiJiuuTMAk7nF+PsaCG6qVeNXsvqZGHemO54uzqx48hpnv1vQo1eT0REROo2FclFRERERETEdAnJ5b3IWzX1xMWp5n9VDQ9wZ86tXQD4cPNhlm4/WuPXFBERkbpJRXIRERERERExVWmZwYHUHADaBtfsUCu/N7BNEA8NigZg6ue7bIV6EbO88cYbREVF4erqSvfu3Vm/fv1Z91+8eDGdO3fG3d2dkJAQ/vKXv3Dy5MlaSisi0nCoSC4iIiIiIiKmSjqVR2FJGe4ujjTzc6vVa/9tUDRXxDShoLiM+xdtJTO/uFavL3LGJ598wqRJk5g2bRrbt2/n8ssvZ+jQoSQlJVW6/4YNG7jjjju466672LNnD59++inx8fHcfffdtZxcRKT+U5FcRERERERETHUgNRsoH2rFwWKp1Ws7Olh45ZYuNPN149DJPB75907KyoxazSACMHv2bO666y7uvvtu2rZty5w5cwgLC2PevHmV7r9582YiIyN56KGHiIqK4rLLLmPChAls2bKllpOLiNR/KpKLiIiIiIiIaUrKyjiYlgtATA1P2FkVPw8X5o3thoujA6sSTvDG2l9MySGNV1FREVu3bmXIkCF264cMGcLGjRsrPaZPnz4cPXqUFStWYBgGJ06c4LPPPmP48OFVXqewsJCsrCy7RUREVCQXEREREREREx3NyKfot6FWQn1dTcvRqbkvT1/bHoCXVu7n693JpmWRxic9PZ3S0lKCgoLs1gcFBZGSklLpMX369GHx4sXccsstuLi4EBwcjK+vL6+99lqV15k5cyY+Pj62JSwsrFrvQ0SkvlKRXEREREREREzz62+9yFsEemCp5aFW/ujWS8IZ3ycSgIc/2cnuY5mm5pHG54//DxiGUeX/Fz///DMPPfQQTz75JFu3buXrr78mMTGR++67r8rzT506lczMTNty5MiRas0vIlJfmVok/+677xg5ciShoaFYLBaWLVtmt338+PFYLBa75dJLL7Xbp7CwkAcffJDAwEA8PDy45pprOHr0aC3ehYiIiIiIiFwIwzBITP+tSN7E0+Q05f45vC1XxDQhv7iUuxduITWrwOxI0ggEBgbi6OhYodd4ampqhd7lZ8ycOZO+ffvy6KOP0qlTJ6666ireeOMN3n//fZKTK38Swmq14u3tbbeIiIjJRfLc3Fw6d+7M3Llzq9zn6quvJjk52basWLHCbvukSZNYunQpH3/8MRs2bCAnJ4cRI0ZQWlpa0/FFRERERETkIqRmF5JTWIKzo4UwPzez4wDg5OjA3NFdadXUk5SsAu75YAsFxfr9UmqWi4sL3bt3Jy4uzm59XFwcffr0qfSYvLw8HBzsyzqOjo5A+RdQIiJy7pzMvPjQoUMZOnToWfexWq0EBwdXui0zM5P33nuPDz/8kCuvvBKARYsWERYWxqpVq7jqqquqPbOIiIiIiIhUjzO9yCP8PXByrDujgXq7OvPeuB5c+/r37DyayZRPd/LabV1NHw5GGrbJkydz++2306NHD3r37s3bb79NUlKSbfiUqVOncuzYMT744AMARo4cyT333MO8efO46qqrSE5OZtKkSVxyySWEhoaaeSsiIvWOqUXyc7F27VqaNm2Kr68v/fr147nnnqNp06YAbN26leLiYrvZn0NDQ+nQoQMbN26sskheWFhIYWGh7bVmcxYREREREal9SafyAIgIdDc5SUURAR68ObY7Y9/9gf/+lEx0Uy+uj7GSnp5+QecLDAwkPDy8mlNKQ3LLLbdw8uRJnn76aZKTk+nQoQMrVqwgIiICgOTkZJKSkmz7jx8/nuzsbObOncsjjzyCr68vAwcO5IUXXjDrFkRE6q06XSQfOnQoN910ExERESQmJvLEE08wcOBAtm7ditVqJSUlBRcXF/z8/OyOO9vsz1A+btf06dNrOr6IiIiIiIhUoagMUjLLx/uO8K97RXKAS1sE8Nz1HXj8/3bxr1X7iX3kX5z+afUFncvN3Z29CQkqlMtZTZw4kYkTJ1a6bcGCBRXWPfjggzz44IM1nEpEpOGr00XyW265xfbnDh060KNHDyIiIvjyyy8ZNWpUlcedbfZnKH9EafLkybbXWVlZhIWFVU9oERERERER+VNpBRYMwN/dBS9XZ7PjVOmWnuHsP5HDexsS8bryfvoNvY6W4c3O6xwnkg6y+IVHSU9PV5FcRESkDqrTRfI/CgkJISIiggMHDgAQHBxMUVERGRkZdr3JU1NTq5zYAsrHObdarTWeV0RERERERCp3oqB8DPLwgLrZi/z3/jGsLdt/Oc62FNjrGEn38BZ4WuvVr9MiIiJyFnVnZpRzcPLkSY4cOUJISAgA3bt3x9nZ2W725+TkZHbv3n3WIrmIiIiIiJQPQ2ixWJg0aZJtnWEYxMbGEhoaipubG/3792fPnj3mhZQG60T+b0XyOjrUyu85Olh4+FJfitKTKCi18N+fjlNSWmZ2LBEREakmphbJc3Jy2LFjBzt27AAgMTGRHTt2kJSURE5ODlOmTGHTpk0cOnSItWvXMnLkSAIDA7n++usB8PHx4a677uKRRx5h9erVbN++nbFjx9KxY0euvPJKE+9MRERERKRui4+P5+2336ZTp05262fNmsXs2bOZO3cu8fHxBAcHM3jwYLKzs01KKg2Rk18oeaUWHCzQ3M/N7DjnxMPFgbT/exoXB4MTWYXEJZzAMAyzY4mIiEg1MLVIvmXLFrp27UrXrl0BmDx5Ml27duXJJ5/E0dGRXbt2ce211xITE8O4ceOIiYlh06ZNeHl52c7xr3/9i+uuu46bb76Zvn374u7uzn/+8x8cHR3Nui0RERERkTotJyeHMWPG8M4779gNW2gYBnPmzGHatGmMGjWKDh06sHDhQvLy8liyZImJiaWhcY0s/x0w1NcNZ8f684BzyekULg0swcEC+0/ksC3ptNmRREREpBqYOoha//79z/rN+zfffPOn53B1deW1117jtddeq85oIiIiIiIN1gMPPMDw4cO58sorefbZZ23rExMTSUlJYciQIbZ1VquVfv36sXHjRiZMmFDp+QoLCyksLLS9zsrKqrnw0iC4RZUXyevDUCt/1MTVoF9ME9bsS+P7g+kEeVtp7lf/7kNERET+p/58ZS8iIiIiIhft448/Ztu2bcycObPCtpSUFACCgoLs1gcFBdm2VWbmzJn4+PjYlrCwsOoNLQ1KSZmBa3j5MD8R9WDSzsp0bOZDm2AvDAO+2p1CbmGJ2ZFERETkIqhILiIiIiLSSBw5coS//e1vLFq0CFdX1yr3s1gsdq8Nw6iw7vemTp1KZmambTly5Ei1ZZaG58DJYhys7lgdDJp4Ws2Oc0EsFgsD2zQlwNOFvKJSVuxOpqxM45OLiIjUVyqSi4iIiIg0Elu3biU1NZXu3bvj5OSEk5MT69at49VXX8XJycnWg/yPvcZTU1Mr9C7/PavVire3t90iUpU9aeVD8wS6lp31y5e6ztnRgeEdQ3BxdOD46QLiD58yO5KIiIhcIBXJRUREREQaiUGDBrFr1y527NhhW3r06MGYMWPYsWMHLVq0IDg4mLi4ONsxRUVFrFu3jj59+piYXBqSn9OKAAi01v+e137uLgxo0wSAHxJPkZJZYHIiERERuRCmTtwpIiIiIiK1x8vLiw4dOtit8/DwICAgwLZ+0qRJzJgxg+joaKKjo5kxYwbu7u6MHj3ajMjSwBSXlrH3ZDEATRpAkRygdZAXiem57D+Rw9d7Uhh9STguTuqPJiIiUp+oSC4iIiIiIjaPPfYY+fn5TJw4kYyMDHr16sXKlSvx8vIyO5o0AHuOZ1FQYlCan423c/0cj/yPLBYLA1o35fjpAjLzi1l/II1BbasenkhERETqHn29LSIi1eLYsWOMHTuWgIAA3N3d6dKlC1u3brVtNwyD2NhYQkNDcXNzo3///uzZs8fExCIiArB27VrmzJlje22xWIiNjSU5OZmCggLWrVtXofe5yIX6MfEkAIVH91CPhyOvwNXZkSHtygvju49ncTQjz+REIiIicj5UJBcRkYuWkZFB3759cXZ25quvvuLnn3/m5ZdfxtfX17bPrFmzmD17NnPnziU+Pp7g4GAGDx5Mdna2ecFFRESkVv3wa/nklgVHdpucpPqF+bvToVn5pLWr96ZSUlpmciIRERE5VxpuRURELtoLL7xAWFgY8+fPt62LjIy0/dkwDObMmcO0adMYNWoUAAsXLiQoKIglS5YwYcKE2o4sIiIitay0zODHQ+VF8sIGWCQHuKxlIL+m5XI6r5j4Qxn0bhlgdiQRERE5B+pJLiIiF2358uX06NGDm266iaZNm9K1a1feeecd2/bExERSUlIYMmSIbZ3VaqVfv35s3LixyvMWFhaSlZVlt4iIiEj9tC8lm+yCEtycLBSd+NXsODXC6uxI/5gmAGw5fIqTOYUmJxIREZFzoSK5iIhctF9//ZV58+YRHR3NN998w3333cdDDz3EBx98AEBKSgoAQUH2k1gFBQXZtlVm5syZ+Pj42JawsLCauwkRERGpUT/8Nh55m0AXMBruUCStmnoSFehBmQFr9qVhGIbZkURERORPqEguIiIXraysjG7dujFjxgy6du3KhAkTuOeee5g3b57dfpY/zNBlGEaFdb83depUMjMzbcuRI0dqJL+IiIjUvB8Ty4daadfExeQkNctisdA/pgmODhaOnc7nYFqu2ZFERETkT6hILiIiFy0kJIR27drZrWvbti1JSUkABAcHA1ToNZ6amlqhd/nvWa1WvL297RYRERGpfwzDaDRFcgBvN2e6hfsCsOGXdErVmVxERKROU5FcREQuWt++fdm3b5/duv379xMREQFAVFQUwcHBxMXF2bYXFRWxbt06+vTpU6tZRUREpPYlncrjZG4RLo4OtPJzNjtOregR4Y+7iyOZ+cX8kq1fvUVEROoyvVOLiMhFe/jhh9m8eTMzZszgl19+YcmSJbz99ts88MADQPljx5MmTWLGjBksXbqU3bt3M378eNzd3Rk9erTJ6UVERKSm7ThyGoB2od44O1Y91FpD4uLkQN+WgQDszXTEwd3X3EAiIiJSJSezA4iISP3Xs2dPli5dytSpU3n66aeJiopizpw5jBkzxrbPY489Rn5+PhMnTiQjI4NevXqxcuVKvLy8TEwuIiIitWF70mkAuoT5AoVmRqlVbUO82Hn0NKnZhfj0ucXsOCIiIlIFFclFRKRajBgxghEjRlS53WKxEBsbS2xsbO2FEhERkTrhTE/yruG+UHbC1Cy1yWKxcFmrQD7ffgyvLleTmltidiQRERGphIZbERERERERkRpTWFLKz8ezAOjc3NfcMCYI83enibUMi6Mzn/6cY3YcERERqYSK5CIiIiIiIlJjEpKzKSotw8/dmYgAd7PjmKK9bykAaw7lczBNhXIREZG6RsOtiIiIiIiISI3Z+dtQK53DfLFYGseknX8UYDXIO/AD7tG9ePLfP/BIb7/zPkdgYCDh4eE1kE5ERERUJBcREREREZEac2Y88vJJOxunrFNpnF7/Ie7Rvfj+SAGfxo6iOP3weZ3Dzd2dvQkJKpSLiIjUABXJRUREREREpMaoSA75OVkUpx3CuyidLJdAej34KpcElp7z8SeSDrL4hUdJT09XkVxERKQGqEguIiIiIiIiNeJ0XhGJ6blA4y6SnxHtXcbWAjia58jAZi3wdXcxO5KIiIigiTtFRERERESkhpzpRR4Z4K6CMODpUEJkgDsGsOVwhtlxRERE5DcqkouIiIiIiEiN0FArFV0S5Q9AQnIW2QXFJqcRERERUJFcREREREREashOFckrCPFxo7mvG2UGbDt82uw4IiIigorkIiIiIiIiUgMMw/hfT/JwP3PD1DE9f+tNvvt4JnlFJSanEREREU3cKSIiIvVedkExGw+eZM/xLE7lFmLBQjM/N7qF+9Et3BcnR/ULEBGpbUmn8sjIK8bF0YG2IV5mx6lTwvzcCPK2ciKrkO1Jp+nbKtDsSCIiIo2aiuQiIiJSb+0/kc0ba35hxe4UikrKKt0n0NPK7ZdGcOdlkXi5OtdyQhGRxutML/J2od5YnRzNDVPHWCwWekb689+fkvnpaCY9IvywOutnJCIiYhYVyUVERKTeySoo5qVv9vHh5sMYRvm6qEAPekb6EeLjRmmZQWJ6Lt8fTCc9p5B/rdrPh5sP89TIdozsHGpueBGRRmJ70mlA45FXpUWgBwEeLpzMLeKnY5n0jPQ3O5KIiEijpSK5iIiI1CvbkjJ4cMl2jp3OB+Cq9kFM7N+KTs19sFgsdvsWl5axYlcyr6w6wK/puTz40XbWH0jj6Ws74KoeeyIiNWqHJu08K4vFQvcIP1b+fIKdR07TNdwXJwcNDyYiImIGvQOLiIhIvbF0+1FueWsTx07nE+7vzpK7e/HW7T3oHOZboUAO4OzowLVdmvHVpMt5aGArHCzw7y1HGfvuD5zKLTLhDkREGoeikjJ+Pp4FqEh+NjFBXnhYHcktKmV/So7ZcURERBot9SQXERGROs8wDOZ++wsvx+0H4Or2wcy6qRPe5zjGuNXJkclDWtOrRQD3LdrKlsMZjH5nMx/feym+7i41GV1EpFFKSM6iqLQMP3dnIgLczY5TQUJCQq0c82ccHSx0CfPl+19Osi0pg7YhXpV+6SsiIiI1S0VyERERqdPKygye+GI3i39IAmDCFS14/Oo2ODicfxGhb6tAPr+/D6Pf/YG9Kdnc/t6PLL6nV3VHFhFp9M4MtVLVkz5myTqVBsDYsWMv+Bw5OdXb47tjqA8/Jp7iZG4RSafyiAjwqNbzi4iIyJ9TkVxERETqLMMweHJ5eYHcwQLTr2nP7b0jL+qc0UFeLL67F7e+vZldxzIZ//6PPNLdWj2BRUQEqLvjkefnlA8BM3zCNFp36n5exyb8uI6vFr5CQUFBtWayOjvSPtSHHUdOsy3ptIrkIiIiJjB1TPLvvvuOkSNHEhoaisViYdmyZbZtxcXFPP7443Ts2BEPDw9CQ0O54447OH78uN05+vfvj8VisVtuvfXWWr4TERERqW6GYfD0f39m0eYkLBZ46abOF10gPyMmyIsP77oEb1cntiWd5tUfTwN1p6ejiEh99/ue5HVRQGgEzaPbn9fiH9y8xvJ0DfPFYoGkU3mkZRfW2HVERESkcqYWyXNzc+ncuTNz586tsC0vL49t27bxxBNPsG3bNj7//HP279/PNddcU2Hfe+65h+TkZNvy1ltv1UZ8ERERqSGGYTDzq73M//4QAC+M6sSobtVbnGgf6sP8v/TExdGBH44V4nPZbdV6fhGRxup0XhGJ6bkAdGnua26YesLbzZnoJp4AbEvKMDmNiIhI42PqcCtDhw5l6NChlW7z8fEhLi7Obt1rr73GJZdcQlJSEuHh4bb17u7uBAcH12hWERERqR2GYfDiN/t4+7tfAXju+g7c3DOsRq7VPcKfGaM6MuXTnfj2Hc3RvGJqrp+giEjjsPNoJgCRAe74eWhy5HPVLcKP/ak57D+RTZ+WAXid4+TUIiIicvFM7Ul+vjIzM7FYLPj6+tqtX7x4MYGBgbRv354pU6aQnZ191vMUFhaSlZVlt4iIiEjd8MrqA7yx9iBQPgb5mF4RNXq9G7s3Z2RM+fivW046cTJHj7mLiFyMHUmngbo3HnldF+TtSjNfN8qM/33RICIiIrWj3kzcWVBQwN///ndGjx6Nt7e3bf2YMWOIiooiODiY3bt3M3XqVHbu3FmhF/rvzZw5k+nTp9dGbBERETkPc789wJxVBwD45/C2jOsTWSvXvaOTF//+Zj1uUd34ancKt/QMw9mxXvUlEBGpM3YcKR8uREXy89ct3Jdjp/PZdSyTSyL9cXGqnveipKQk0tPTL+jYwMBAuye5RUREGqJ6USQvLi7m1ltvpaysjDfeeMNu2z333GP7c4cOHYiOjqZHjx5s27aNbt26VXq+qVOnMnnyZNvrrKwswsJq5jFuEREROTevr/mFl1buB+Dxq9tw9+Utau3ajg4W0v/7MtF/W8TJ3CK+25/GoLZBtXZ9EZGGwjAM26SdXcL9zA1TD0UFeuDn7kxGXjF7jmfStRp+hklJSbRp25b8vLwLOt7N3Z29CQkqlIuISINW54vkxcXF3HzzzSQmJvLtt9/a9SKvTLdu3XB2dubAgQNVFsmtVitWq7Um4oqIiMgFeH3NL7z4zT4AHr2qNff3b1nrGcryMukZWML6VGd2H88izN+dmCCvWs8hIlKfJZ3KIyOvGBdHB9qGqA09XxaLha7hfny7N5XtR07TubkvDg6Wizpneno6+Xl5jHn8RYLCz+/99UTSQRa/8Cjp6ekqkouISINWp4vkZwrkBw4cYM2aNQQEBPzpMXv27KG4uJiQkJBaSCgiIiIX64219gXyBwa0Mi1LU1eDnpF+xB/KYHVCKkHervi4aeI0EZFzdaYXedtQb6xOjuaGqafaBnux6eBJsgtKOJiWQ3Q1fWEbFN6S5tHtq+VcIiIiDY2pg23m5OSwY8cOduzYAUBiYiI7duwgKSmJkpISbrzxRrZs2cLixYspLS0lJSWFlJQUioqKADh48CBPP/00W7Zs4dChQ6xYsYKbbrqJrl270rdvXxPvTERERP6MYRj8K24/s74uL5BPGRJjaoH8jEujAgjxcaWotIyvdidTWmaYHUlEpN44UyTvqvHIL5iTowMdm/kAsP23n6eIiIjULFOL5Fu2bKFr16507doVgMmTJ9O1a1eefPJJjh49yvLlyzl69ChdunQhJCTEtmzcuBEAFxcXVq9ezVVXXUXr1q156KGHGDJkCKtWrcLRUb0WRERE6qqS0jL+/n+7eGV1+SSdU4bE8NeB0SanKufgYOHqDsFYnRw4kVXIpl9Pmh1JRKTesI1HriL5RenU3AdHi4XkzAJSMgvMjiMiItLgmTrcSv/+/TGMqntnnW0bQFhYGOvWravuWCIiIlKDsguKeeij7azZl4aDBZ65rgNjekWYHcuOt6szg9sF8d+fktl6OIMIf3fC/N3NjiUiUqcVlZSx53gWoCL5xfKwOhET7ElCcjbbkzLo6Gp2IhERkYbN1J7kIiIi0rjsTcnimrnfs2ZfGq7ODrx1e486VyA/o2UTTzqElk8Y/s3PKeQXl5qcSESkbktIzqKopAxfd2ciAvTF4sXqGuYHwIG0HPJKTA4jIiLSwKlILiIiIjXOMAz+veUI173+PYnpuYT6uPLxvb0Z3C7I7GhndUVME/zcncktLGV1wok/fcpNRKQxOzPUSufmvlgsFnPDNABNvKw093PDMOBgtoYTFRERqUmmDrciIiIiDd/x0/lMW7qLNfvSgPLC85xbuuDv4XLe50pKSiI9Pb26I5KQkFDpemdHB67uEMwn8Uc4mJbLnuNZdPhtMjUREbG3U+ORV7uu4b4czcgnMccBi7PGXBEREakpKpKLiMhFi42NZfr06XbrgoKCSElJAcp7EU+fPp23336bjIwMevXqxeuvv0779u3NiCu1pKikjMU/HObllfvJKSzBxdGBv10Zzf39WuLgcP49DJOSkmjTti35eXk1kLZcTk5OhXVNvVzp0zKQDb+ks25/Gs183fC7gAK/iEhDZ5u0M9zX1BwNSVSAB77uzpzOK8az45VmxxEREWmwVCQXEZFq0b59e1atWmV77ej4v8eCZ82axezZs1mwYAExMTE8++yzDB48mH379uHl5WVGXKlhG4/kM3n1Og6dLC9od4/w44UbOtKq6YX/faenp5Ofl8eYx18kKLxldUUFIOHHdXy18BUKCgoq3d4t3JfDJ3M5kpHP13tSuLlHGI4XUOgXEWmoMvOK+TU9F4AuzX3NDdOAWCwWuoT5snZfGl49rqG0TMN+iYiI1AQVyUVEpFo4OTkRHBxcYb1hGMyZM4dp06YxatQoABYuXEhQUBBLlixhwoQJtR1VatDJQgtBY1/kpU2nAQj0dOHhwTHc2jO82orKQeEtaR5dvU8hnEg6eNbtFouFIe2CWfzDYVKzC9l08CSXRQdWawYRkfpsx9HTAEQGuOtpm2rWLsSb7w+kgl8oW5ML6Wl2IBERkQZIRXIREakWBw4cIDQ0FKvVSq9evZgxYwYtWrQgMTGRlJQUhgwZYtvXarXSr18/Nm7ceNYieWFhIYWFhbbXWVlZNXoPcuEycov4/mA6B9OccW3WFqujhQn9W3HvFS3wtDaMjxuerk5c2S6I//6UzNakDMID3An3dzc7loiIaX4/T8RXe7IBCPcoY9u2bWc9rqp5IKRyzo4ORHmWsT/Lkf/sz+U+swOJiIg0QA5mBxARkfqvV69efPDBB3zzzTe88847pKSk0KdPH06ePGkblzwoKMjumN+PWV6VmTNn4uPjY1vCwsJq7B7kwuQWlvDt3lQ+/OEwB9NyAYPsnd/w+rAmTB4c02AK5Ge0bOJJh1BvAFb+nEJ+canJiUREzHFmnoju3bvTvXt33l++BoBl779iW1fVMnbsWKDyeSCkci09SzFKS9iTVsTuY5lmx5Ea9MYbbxAVFYWrqyvdu3dn/fr1Z92/sLCQadOmERERgdVqpWXLlrz//vu1lFZEpOFoWL+5ioiIKYYOHWr7c8eOHenduzctW7Zk4cKFXHrppUD5cBW/ZxhGhXV/NHXqVCZPnmx7nZWVpUJ5HWEYBruPZ7HhQDpFpWUARAV60Mo5g3dfeA3/58abG7AGXRHThGOn88nIK2Z1wgmGdwz503/LIiINze/niWga1pL/HnOmqAxuGHMn/nf+5azH/tk8EFKRuxPk7duAR7v+vL8hkdm3dDE7ktSATz75hEmTJvHGG2/Qt29f3nrrLYYOHcrPP/9MeHh4pcfcfPPNnDhxgvfee49WrVqRmppKSUlJLScXEan/VCQXEZFq5+HhQceOHTlw4ADXXXcdACkpKYSEhNj2SU1NrdC7/I+sVitWq7Umo8oFyMovZtXeExw5lQ9AUy8rl0cH0tzPnaMHMkxOV/OcHR24ukMwn8Qf4WBaLruPZ9GxmY/ZsURETBEU3hKv5jEUHTmEo8VCu3ZtcHI4+wPLfzYPhFQuK34ZHu36s3zncR4f2oYgb1ezI0k1mz17NnfddRd33303AHPmzOGbb75h3rx5zJw5s8L+X3/9NevWrePXX3/F398fgMjIyNqMLCLSYGi4FRERqXaFhYUkJCQQEhJCVFQUwcHBxMXF2bYXFRWxbt06+vTpY2JKuRBJp/JY8mMSR07l4+Rg4fLoQG7pGUZzv8Y1NndTL1f6tCyfuPO7/Wmcyi0yOZGIiHlSMst7hAd6ufxpgVwuXFHKL7QNdKakzOCDTYfMjiPVrKioiK1bt9rN4wMwZMgQNm7cWOkxy5cvp0ePHsyaNYtmzZoRExPDlClTyM/Pr/I6hYWFZGVl2S0iIqIiuYiIVIMpU6awbt06EhMT+eGHH7jxxhvJyspi3LhxWCwWJk2axIwZM1i6dCm7d+9m/PjxuLu7M3r0aLOjy3nYdTSTZTuOUVhSRpC3ldG9wukW7odDIx1qpFu4L2H+bpSUGXy9O4WS34adERFpbFKyyovkwerZXONGxngAsPiHJPKLNC9GQ5Kenk5pael5zePz66+/smHDBnbv3s3SpUuZM2cOn332GQ888ECV19GcPyIilVORXERELtrRo0e57bbbaN26NaNGjcLFxYXNmzcTEREBwGOPPcakSZOYOHEiPXr04NixY6xcuRIvLy+Tk8u5MAyDDb+k8+2+VAwDWgd7cWO35vi5u5gdzVQWi4Uh7YJxdXYgLaeQtfvTzI4kImKKMz3Jg31UJK9pPUNdCfN343ReMZ9vP2p2HKkB5zOPT1lZGRaLhcWLF3PJJZcwbNgwZs+ezYIFC6rsTT516lQyMzNty5EjR6r9HkRE6iONSS4iIhft448/Put2i8VCbGwssbGxtRNIqo1hGHx/8CRbD5ePNd67RQA9I/00UeVvPK1OXN0+mGU7jrPneBbBPq50CNX45CLSeJQZkJZTCKAxsmuBo4OF8X2ieOa/P/P+hkRu6xmOg4PekxuCwMBAHB0dK/QaP9s8PiEhITRr1gwfn/999mjbti2GYXD06FGio6MrHKM5f0REKqee5CIiIlKl7UdO2wrkA1o34ZIofxXI/yAiwIPeLQIAWLsvjRO/DTsgItIYZBZZKC0zcHVywNfN2ew4jcLNPZrjaXXiYFou6w7oKaaGwsXFhe7du9vN4wMQFxdX5Tw+ffv25fjx4+Tk5NjW7d+/HwcHB5o3b16jeUVEGhoVyUVERKRSB9NyWH8gHYDLWgXSqbmvuYHqsJ6RfkQFelBaZrBiVzIFxRonVkQah1NF5V+cBvm46kvUWuLl6swtPcvHkX5/Q6LJaaQ6TZ48mXfffZf333+fhIQEHn74YZKSkrjvvvuA8qFS7rjjDtv+o0ePJiAggL/85S/8/PPPfPfddzz66KPceeeduLm5mXUbIiL1korkIiIiUkFGXhEr95wAoFMzH7qF+5obqI6zWCxc1S4IHzdnsgpK+HpPCmWGYXYsEZEad6qwvDCuSTtr1/g+kThYYP2BdPamZJkdR6rJLbfcwpw5c3j66afp0qUL3333HStWrLDN85OcnExSUpJtf09PT+Li4jh9+jQ9evRgzJgxjBw5kldffdWsWxARqbdUJBcRERE7JWVlrNiVTFFpGaE+rlwR00S9A8+B1dmR4R1DcHKwcPhkHt//km52JJEK5s2bR6dOnfD29sbb25vevXvz1Vdf2bYbhkFsbCyhoaG4ubnRv39/9uzZY2JiqetOFZX/Sqkiee0K83fn6g7BgHqTNzQTJ07k0KFDFBYWsnXrVq644grbtgULFrB27Vq7/du0aUNcXBx5eXkcOXKEl19+Wb3IRUQugIrkIiIiYmfzr6dIzynCzdmRYR1DcNSEYOesiZeVwe3KJ9falnSa3cczTU4kYq958+Y8//zzbNmyhS1btjBw4ECuvfZaWyF81qxZzJ49m7lz5xIfH09wcDCDBw8mOzvb5ORSFzlYPcgp+d9wK1K77rosCoBlO46T/tvkqSIiInJhVCQXERERm5TMAttEnYPaNsXD6mRyovonJsiLXlH+AKzZm0pagb5kkLpj5MiRDBs2jJiYGGJiYnjuuefw9PRk8+bNGIbBnDlzmDZtGqNGjaJDhw4sXLiQvLw8lixZYnZ0qYNcQmIA8HFzxs3Z0eQ0jU+3cD86h/lSVFLGos2HzY4jIiJSr6lILiIiIgCUlRms3ls+DnmbYC9aNvE0OVH91SvKn5imnpQZsDndCSffELMjiVRQWlrKxx9/TG5uLr179yYxMZGUlBSGDBli28dqtdKvXz82btx41nMVFhaSlZVlt0jDZw1tDWioFbNYLBZbb/JFmw9r0mgREZGLoCK5iIiIALDj6GnSc4pwdXLgiugmZsep1ywWC4PbBRHkbaWozELTG58kp6jM7FgiAOzatQtPT0+sViv33XcfS5cupV27dqSkpAAQFBRkt39QUJBtW1VmzpyJj4+PbQkLC6ux/FJ3nOlJHqyhVkwztEMwIT6upOcUsXzncbPjiIiI1FsqkouIiAj5RaX8kHgKgL6tAnFz0WPzF8vJ0YGRnUJxczRwDghj5oZT6uUndULr1q3ZsWMHmzdv5v7772fcuHH8/PPPtu1/nKjXMIw/nbx36tSpZGZm2pYjR47USHapOwzDUE/yOsDZ0YFxfSKB8gk8DcMwN5CIiEg9pYFGRUREhB8ST1JUUkYTTyvtQ73NjtNgeFid6NukhJWHCklI9+Shj7bzxphuODnWj34KSUlJpKen18i5AwMDCQ8Pr5Fzy9m5uLjQqlUrAHr06EF8fDyvvPIKjz/+OAApKSmEhPxviKDU1NQKvcv/yGq1YrVaay601DkncktxdPfBAYNALxez4zRqt/UM55VVB9ibks3Ggyfp2yrQ7EgiIiL1jorkIiIijVxWfjG7jmUCcHl04J/2GJXz4+NikPp/zxB2+wus/PkET3yxhxnXd6jzP+ekpCTatG1Lfl5ejZzfzd2dvQkJKpTXAYZhUFhYSFRUFMHBwcTFxdG1a1cAioqKWLduHS+88ILJKaWuOXCqGChv45wc6scXfw2Vj7szN3ZvzoebD/PhpsMqkouIiFwAFclFREQaufjDpygzINzfnTB/d7PjNEiFR/fw8KW+vLTpNB/9mERTLysPD44xO9ZZpaenk5+Xx5jHXyQovGW1nvtE0kEWv/Ao6enpKpLXsn/84x8MHTqUsLAwsrOz+fjjj1m7di1ff/01FouFSZMmMWPGDKKjo4mOjmbGjBm4u7szevRos6NLHbP/ZBEA/i4a3qMuGHtpBB9uPkxcwglSMgs0TryIiMh5UpFcRESkEcsrgYTkbAAuifQ3OU3DdmlzN56+tjn/XLabV1YfoImXlbGXRpgd608FhbekeXR7s2NINTlx4gS33347ycnJ+Pj40KlTJ77++msGDx4MwGOPPUZ+fj4TJ04kIyODXr16sXLlSry8vExOLnXN/pPlPcn9rSqS1wWtg724JNKfHw+d4uP4JCZdWbe/iBUREalrVCQXERFpxPZnOVJqGDTzdaOZn5vZcRq8sZdGkJpdyKurD/DkF7sJ9LRydYdgs2NJI/Lee++ddbvFYiE2NpbY2NjaCST1UkFxKYmnzxTJy0xOI2eMuTS8vEj+4xH+OqBVvZn/QkREpC64oHfNFi1acPLkyQrrT58+TYsWLS46lIiI1B616Y2Xg4cvibnlHwUuiVIv8try8JXR3HZJOGUGPPTxdjYerJmJMaVhUVstdcme41mUlEFpbgYejmankTOu7hBMgIcLKVkFrEpINTuOiIhIvXJBRfJDhw5RWlpaYX1hYSHHjh276FAiIlJ71KY3Xt49r6fMsBDs7UqYepHXGovFwjPXtueq9kEUlZRx7wdb2XU00+xYUseprZa6ZHtSBgCFx/dRx+cgblSsTo7c3DMMgMU/HDY5jYiISP1yXsOtLF++3Pbnb775Bh8fH9vr0tJSVq9eTWRkZLWFExGRmqM2vXErKCnDq8vVQHkvcouqHLXKydGBV27typ0L4tl48CTj5v/Ivyf0plVTT7OjSR2jtlrqou1JpwEoPLYX6GZqFrE3+pJw3lx3kPUH0jmUnktkoIfZkUREROqF8yqSX3fddUB5D6hx48bZbXN2diYyMpKXX3652sKJiEjNUZveuH13uAAHqwceTgaRAe5mx2mUXJ0defuOHox+ZzM/Hc3kjvd+4NP7+9DMV7365X/UVktd9Pue5FK3hPm7c0V0E9btT+OzrUeZclVrsyOJiIjUC+c13EpZWRllZWWEh4eTmppqe11WVkZhYSH79u1jxIgRNZVVRESqkdr0xsswDL76JReAFp6l6kVuIk+rE/PH96RFEw+OZxZw+3s/cDKn0OxYUoeorZa6JiWzgOOZBThYoCjlgNlxpBI39ygfcuWzrUcpLTNMTiMiIlI/XNCY5ImJiQQGBlZ3FhERMYHa9MZnW1IGhzNLKCsuJNKjzOw4jV6Ap5VFd/Ui1MeVX9NyGT8/npzCErNjSR2jtlrqijO9yCN8nDCKC0xOI5W5sl1T/NydSckq4LsDaWbHERERqRfOa7iV31u9ejWrV6+29Wj5vffff/+ig4mISO1Rm964fLCpfDKvvITvcGnZz+Q0AhDq68aHd/fipjc3setYJvcs3ML8v/TE1dnR7GhSh6itlrpg+5HTAMQEuLDW1CRSFauTI9d1bcb87w/x6ZYj3N1WT4yJiIj8mQvqST59+nSGDBnC6tWrSU9PJyMjw245V9999x0jR44kNDQUi8XCsmXL7LYbhkFsbCyhoaG4ubnRv39/9uzZY7dPYWEhDz74IIGBgXh4eHDNNddw9OjRC7ktEZFGqbradKkf0nMKWbErGYDsbf81OY38Xssmniz8yyV4uDiy6deTPPTRdkpK1dNfyqmtlrpi2+Hyf28xAc4mJ5Gzual7+ZArcT+fIKtQ7yUiIiJ/5oJ6kr/55pssWLCA22+//aIunpubS+fOnfnLX/7CDTfcUGH7rFmzmD17NgsWLCAmJoZnn32WwYMHs2/fPry8vACYNGkS//nPf/j4448JCAjgkUceYcSIEWzduhVHR/W+EhH5M9XVpkv98O8tRyguNYj2d+bwiYNmx5E/6Njch3fG9WD8/HhW/nyCqZ/vYtaNnTRuvKitljqhqKSMXccyAWgd4GJyGjmbdqHedGzmw65jmXx3ON/sOCIiInXeBfUkLyoqok+fPhd98aFDh/Lss88yatSoCtsMw2DOnDlMmzaNUaNG0aFDBxYuXEheXh5LliwBIDMzk/fee4+XX36ZK6+8kq5du7Jo0SJ27drFqlWrLjqfiEhjUF1tutR9hmHwf1vLn7Ya3MLd5DRSlT4tA3nttq44WODTrUeZsSIBw9DEa42d2mqpC/amZFFYUoaPmzMhnuqQVNfd3LO8N/nqxDyTk4iIiNR9F1Qkv/vuu22F6pqSmJhISkoKQ4YMsa2zWq3069ePjRs3ArB161aKi4vt9gkNDaVDhw62fSpTWFhIVlaW3SIi0ljVRpsudcOuY5kcTMvF6uRAnzBXs+PIWVzVPpjnb+gEwDvrE3ljrXr9N3Zqq6UuODPUStdwXz3hUg9c0ykUF0cHDmeW4BwYYXYcERGROu2ChlspKCjg7bffZtWqVXTq1AlnZ/vx6GbPnn3RwVJSUgAICgqyWx8UFMThw4dt+7i4uODn51dhnzPHV2bmzJlMnz79ojOKiDQEtdGmS93w+bZjAAxuF4S7hpKt827uEUZmXjHPrUjgxW/24efuwuhe4WbHEpOorZa64Myknd3C/YBsU7PIn/Nxd2ZAmyZ8s+cEHu36mx1HRESkTrugIvlPP/1Ely5dANi9e7fdturuUfDH8xmG8afX+LN9pk6dyuTJk22vs7KyCAsLu7igIiL1VG226WKe4tIy/rPzOACjujWDvGMmJ5Jzcc8VLcjIK+KNtQf557JdBHlbGdQ26M8PlAZHbbXUBduTTgPlPcnJVpG8Pri2S7PfiuT90MhdIiIiVbugIvmaNWuqO0cFwcHBQHlv8ZCQENv61NRUW+/y4OBgioqKyMjIsOtNnpqaetYxG61WK1artYaSi4jUL7XRpov51h9I42RuEQEeLlwe3YRdO1Ukry8evao16TmF/HvLUf66ZDuf3tebDs18zI4ltUxttZgtPaeQpFN5WCzQOcyXX34+YnYkOQcD2zTF3dlCnk9TThYWo65hIiIilbugMclrQ1RUFMHBwcTFxdnWFRUVsW7dOlsBvHv37jg7O9vtk5yczO7duzWxkYiIyO+cGWplZOdQnB3r7Nu/VMJisfDc9R25rFUg+cWl3LkgnmOn882OJSKNzJle5NFNPfF21Zhd9YWrsyO9mpXPQ5KUp/d/ERGRqlxQT/IBAwac9bHOb7/99pzOk5OTwy+//GJ7nZiYyI4dO/D39yc8PJxJkyYxY8YMoqOjiY6OZsaMGbi7uzN69GgAfHx8uOuuu3jkkUcICAjA39+fKVOm0LFjR6688soLuTURkUanutp0qbuyCoqJ+/kEANd3bWZyGrkQzo4OvDG2GzfN28S+E9ncOT+eT+/vrUJVI6K2Wsy2Lem3STvD/P5kT6lrrohwY82hfI7lOVBaZuDooCGaRERE/uiCiuRnxkM8o7i4mB07drB7927GjRt3zufZsmULAwYMsL0+M074uHHjWLBgAY899hj5+flMnDiRjIwMevXqxcqVK/Hy8rId869//QsnJyduvvlm8vPzGTRoEAsWLMDR0fFCbk1EpNGprjZd6q64PScoLCmjRRMPOjXXMB31lberM+//pSfXv/49+05kM3HRNub/paeeDGgk1FaL2bYeLi+Sd4vwNTeInLcOTVwoyTkFnv4cPpVLi0BPsyOJiIjUORdUJP/Xv/5V6frY2FhycnLO+Tz9+/fHOMvsIRaLhdjYWGJjY6vcx9XVlddee43XXnvtnK8rIiL/U11tutRdX+1OBmBEp1BN8FfPNfN14/3xPbn5rU1s+CWd575MIPaa9mbHklqgtlrMVFhSys4jpwHoEelvbhg5b44OFvISvsO753XsS8lWkVxERKQS1dr1aOzYsbz//vvVeUoRETHJxbTpM2fOxGKxMGnSJNs6wzCIjY0lNDQUNzc3+vfvz549e6oprVQlp7CE7w6kAzCsY7DJaaQ6dGjmw5xbugCwYOMhPolPMjeQmEqfv6U27D6WRWFJGQEeLrQI9DA7jlyA3L3rAUhMz6WktMzkNCIiInVPtRbJN23ahKura3WeUkRETHKhbXp8fDxvv/02nTp1sls/a9YsZs+ezdy5c4mPjyc4OJjBgweTnZ1dXZGlEt/uTaWopIyoQA9aB3n9+QFSLwxpH8zkwTEA/HPZbrYcOmVyIjGLPn9LbYj/rY3pEemnJ5LqqaLj+3FzNCguNUg6lWd2HBERkTrngoZbGTVqlN1rwzBITk5my5YtPPHEE9USTEREakd1tuk5OTmMGTOGd955h2effdbunHPmzGHatGm26y1cuJCgoCCWLFnChAkTLv5GpFJf/zbUytUdglXYaGAeHNiKvSlZrNiVwn2LtvLFXy+jma+b2bGkhujzt5jpzBdxPTXUSj1mEOpWxsEcR35Jy6FFEw25IiIi8nsX1JPcx8fHbvH396d///6sWLGCp556qroziohIDarONv2BBx5g+PDhXHnllXbrExMTSUlJYciQIbZ1VquVfv36sXHjxirPV1hYSFZWlt0i5y6/qJQ1e9MAGNpBQ600NBaLhZdu6kzbEG/Sc4q494Mt5BeVmh1Laog+f4tZysoMtvw2aafGI6/fmrmXD7Pya1oupWVVzw0mIiLSGF1QT/L58+dXdw4RETFJdbXpH3/8Mdu2bSM+Pr7CtpSUFACCgoLs1gcFBXH48OEqzzlz5kymT59eLfkao3X708gvLqWZrxsdm/mYHUdqgLuLE+/c0Z1r5n7PnuNZPPHFbl68sZOeGmiA9PlbzHIwLYfTecW4OjvQPtTb7DhyEQKtBm7OjuQXl3I0I4+IAI0vLyIicsYFFcnP2Lp1KwkJCVgsFtq1a0fXrl2rK5eIiNSyi2nTjxw5wt/+9jdWrlx51rFx/1i4MwzjrMW8qVOnMnnyZNvrrKwswsLCzjlXY6ehVhqH5n7uzB3dlbHv/sBnW4/SI8KPWy8JNzuW1BB9/pbaFn+ovBd51zA/nB2rdUorqWUWC7Rs6sHuY1n8kpqjIrmIiMjvXFCRPDU1lVtvvZW1a9fi6+uLYRhkZmYyYMAAPv74Y5o0aVLdOUVEpIZUR5u+detWUlNT6d69u21daWkp3333HXPnzmXfvn1AeY/ykJAQu2v/sXf571mtVqxW60XcXeNVVFLG6oRUQEOtNAZ9WgbyyJDWvPjNPp5cvocOzXzooKcHGhR9/haz/G88cj+Tk0h1aNXEk93HsjiYlsuANgYO+hJdREQEuMAxyR988EGysrLYs2cPp06dIiMjg927d5OVlcVDDz1U3RlFRKQGVUebPmjQIHbt2sWOHTtsS48ePRgzZgw7duygRYsWBAcHExcXZzumqKiIdevW0adPn5q6tUbtx8RTZBeW0MTLSrdwFTYag/v7tWRQm6YUlZRx/+KtZOYVmx1JqpE+f4tZ4g+XF8k1HnnD0NzPHauTA/nFpRw/nW92HBERkTrjgnqSf/3116xatYq2bdva1rVr147XX3/dblI2ERGp+6qjTffy8qJDhw526zw8PAgICLCtnzRpEjNmzCA6Opro6GhmzJiBu7s7o0ePrr6bEZvVe08AMLB1Uxwc1EusMXBwsDD75i6MmLueI6fyeeTTHbx9ew/9/TcQ+vwtZkjJLODIqXwcLNA13NfsOFINHB0stGjiQUJyNgfTcmnu5252JBERkTrhgnqSl5WV4ezsXGG9s7MzZWVlFx1KRERqT2216Y899hiTJk1i4sSJ9OjRg2PHjrFy5Uq8vLyq7RpSzjAM21ArA9s2NTmN1CYfd2fmjemOi5MDqxJSeWf9r2ZHkmqiz99ihvjfhlppG+KNl2vFf39SP7UI9AQgMT0XwzBMTiMiIlI3XFCRfODAgfztb3/j+PHjtnXHjh3j4YcfZtCgQdUWTkREal5Ntelr165lzpw5ttcWi4XY2FiSk5MpKChg3bp1FXqfS/U4mJZL0qk8XBwduKxVoNlxpJZ1aObDUyPbAfDiN/vYeeS0uYGkWujzt5jhh8STAPTUUCsNSri/O44WC5n5xZzKLTI7joiISJ1wQUXyuXPnkp2dTWRkJC1btqRVq1ZERUWRnZ3Na6+9Vt0ZRUSkBqlNb3i+/W2olUtbBuBhvaCR1aSeG31JOEM7BFNSZvDQx9vJKSwxO5JcJLXVYobNv5b3JO/dMsDkJFKdXJwcaO7vBpT3JhcREZELHJM8LCyMbdu2ERcXx969ezEMg3bt2nHllVdWdz4REalhatMbHttQK62bmJxEzGKxWHh+VCd2HjnN4ZN5PLlsN7Nv6WJ2LLkIaqultqVlF/JLag4WC/SKUk/yhiYq0IPDJ/P4NT1Xk7KKiIhwnj3Jv/32W9q1a0dWVhYAgwcP5sEHH+Shhx6iZ8+etG/fnvXr19dIUBERqV5q0xumzLxithzOAGBgmyCT04iZfNydeeW2rjhY4PPtx/h821GzI8kFUFstZjkz1EqbYG983V1MTiPVLSrQAyifnDW/qNTkNCIiIuY7ryL5nDlzuOeee/D29q6wzcfHhwkTJjB79uxqCyciIjVHbXrD9N2BNErLDKKbehIe4G52HDFZz0h//jYoBoAnlu3mkB6rr3fUVotZNv9aXiS/tIV6GTdE3q7OBHq6YACHTuq9QURE5LyK5Dt37uTqq6+ucvuQIUPYunXrRYcSEZGapza9Yfp2729DrbRtanISqSv+OrAVl0T5k1tUyqRPdlBSWmZ2JDkPaqvFLGfGI7+0hcYjb6haBHoC8Ku+QBURETm/IvmJEydwdnaucruTkxNpaWkXHUpERGqe2vSGp6zMYN3+8r+zga1VJJdyjg4W5tzSBS9XJ3YcOc28tQfNjiTnQW21mEHjkTcOZ4ZcSTqZR2mZYXIaERERc51XkbxZs2bs2rWryu0//fQTISEhFx1KRERqntr0hmfP8SxO5RbhaXWiW4Sf2XGkDgn1dWP6Ne0BeGX1AXYfyzQ5kZwrtdViBo1H3jgEeVtxd3GkqLSMY6fzzY4jIiJiqvMqkg8bNownn3ySgoKCCtvy8/N56qmnGDFiRLWFExGRmqM2veH57kB5b9LeLQNwdjyvt3hpBK7v2oyhHYIpKTN4+JMdFBRrorb6QG21mEHjkTcOFouFiN/mLzmscclFRKSRczqfnf/5z3/y+eefExMTw1//+ldat26NxWIhISGB119/ndLSUqZNm1ZTWUVEpBqpTW941v9WJL8iOtDkJFIXWSwWnru+I/GHMjiQmsNL3+zjnyPamR1L/oTaajHDmfHIe2s88gYvwt+DhORsDp/K43Kzw4iIiJjovIrkQUFBbNy4kfvvv5+pU6diGOXjllksFq666ireeOMNgoKCaiSoiIhUL7XpDUtuYQlbD2cAcHl0E5PTSF3l7+HCrBs7cueCLbz3fSID2zalT0t9qVKXqa2W2vb78cgv0XjkDV74bz3JT+YUkVNQgqfreZUIREREGozzfgeMiIhgxYoVZGRk8Msvv2AYBtHR0fj5aexTEZH6Rm16w/Fj4imKSw2a+7nZHp0WqczANkHcdkk4H/2YxGOf/cQ3k67Aw6qiSF2mtlpq08aD6QC01XjkdVJCQkK1HuPm7EiQt5UTWYUcPpVL+1Cfi4knIiJSb13wb0R+fn707NmzOrOIiIhJ1KbXf2fGI788ugkWi8XkNFLX/XN4W9YfSONoRj4vfrOP2N8m9ZS6TW211Ib1B8qL5Jdr6K46JetU+fv82LFjL/gcOTk5la6P8PcoL5KfzFORXEREGi11GxIREWkAzhQ1NB65nAsPqxPPj+rE2Pd+YMHGQwzrGKJhFUQEwzDY8Nv7yWV6P6lT8nOyABg+YRqtO3U/r2MTflzHVwtfqXQCYICIAHd+PHSKpFN5lBkGDvqyXUREGiEVyUVEROq546fz+SU1BwcLGl9aztll0YHc0iOMT7Yc4fH/+4mv/nY5rs6OZscSERMdTMslJasAFycHekbqi7O6KCA0gubR5/f0z4mkg2fdHuztiouTA4UlZZzIKiDEx+1iIoqIiNRLDmYHEBERkYtzptdf5zBffNydTU4j9ck/hrclyNtKYnou/4rbb3YcETHZht+G7uoZ6acvzRoRBwcL4f7l85kcPplnchoRERFzqEguIiJSz/1+PHKR8+Hj5syM6zsC8M76X9lx5LS5gUTEVBt++W2olVZ6P2lsIlQkFxGRRk5FchERkXqstMywFTU0yZpciEFtg7iuSyhlBjz22U6KSsrMjiQiJiguLWPzr6cAvZ80RhEB5UXyE1kFFBSXmpxGRESk9qlILiIiUo/tOZ7J6bxiPK1OdAnzNTuO1FNPjWyPv4cL+0/k8O6GX82OIyIm2HnkNDmFJfi5O9MuxNvsOFLLvFyd8fdwwQCSTqk3uYiIND4qkouIiNRj638bj7x3ywCcHfW2LhfGz8OFacPaAvDq6gMcUYFEpNE5837Sp1UgDg4Wk9OIGc70JteQKyIi0hjpt2kREZF6bP1v45FfoUfj5SKN6taM3i0CKCgu44kvdmMYhtmRRKQW2YbuaqX3k8bKNi75qVy9B4iISKOjIrmIiEg9lVdUwtbDGQBcpkk75SJZLBaevb4DLo4OrN2XxqajBWZHEpFakplXbJu4t6+K5I1WM183nBws5BaWcjK3yOw4jdYbb7xBVFQUrq6udO/enfXr15/Tcd9//z1OTk506dKlZgOKiDRQKpKLiIjUU1sOZVBcatDM143I3x6RFrkYLZt4cl//lgC8tz0Li4v+XYk0But/SaO0zKBVU0/C/PX/fWPl5OhAMz83AJI05IopPvnkEyZNmsS0adPYvn07l19+OUOHDiUpKemsx2VmZnLHHXcwaNCgWkoqItLwOJkdQERERC7Mpl9PAnBpiwAsFo0fW9clJCTUyHkDAwMJDw+vtvNN7N+S/+w8TmJ6Lr5X3F5t5xWRumvN3vKhuwa01lNJjV24vzuHT+aRdCqPbhF+ZsdpdGbPns1dd93F3XffDcCcOXP45ptvmDdvHjNnzqzyuAkTJjB69GgcHR1ZtmxZLaUVEWlYVCQXERGppzYdLC+S924ZYHISOZusU+XFp7Fjx9bI+d3c3dmbkFBthXJXZ0eeubYDY9/7Aa9uwzldVErzajmziNQ1SUlJpKalsWpPKgDNHTLZtm3bOR1bU1/8ibki/N1ZDxw9nU9JaZnZcRqVoqIitm7dyt///ne79UOGDGHjxo1VHjd//nwOHjzIokWLePbZZ//0OoWFhRQWFtpeZ2VlXXhoEZEGpM4XySMjIzl8+HCF9RMnTuT1119n/PjxLFy40G5br1692Lx5c21FFBERqXU5hSXsOpYJqEhe1+XnlP/yOXzCNFp36l6t5z6RdJDFLzxKenp6tfYmvyw6kMvCXNlwpIAdGdDeMPS0gkgDk5SURJu2bSn1CiFk/CuUFeYxfsR1UFZyXufJycmpmYBiCn8PFzysjuQWlnI8s0Djs9ai9PR0SktLCQoKslsfFBRESkpKpcccOHCAv//976xfvx4np3Mr78ycOZPp06dfdF4RkYamzhfJ4+PjKS0ttb3evXs3gwcP5qabbrKtu/rqq5k/f77ttYuLS61mFBERqW3xiacoLTMI93enma+b2XHkHASERtA8ur3ZMc7ZHZ29+e7X05zElf0ncmgd7GV2JBGpRunp6eTn5XH5g/8kCWju68pNr/37nI9P+HEdXy18hYICTfLbkFgsFsL93UlIzibpVB6RZgdqhP74pbRRxRfVpaWljB49munTpxMTE3PO5586dSqTJ0+2vc7KyiIsLOzCA4uINBB1vkjepIn9uHjPP/88LVu2pF+/frZ1VquV4ODg2o4mIiJimjPjkfduoV7kUjMC3R3J2vwZvpePZcMv6bRo4oGzo/oUijQ0OS4BUARtI4Jp3sznnI87kXSwBlOJmWxF8pN5RPqbnabxCAwMxNHRsUKv8dTU1Aq9ywGys7PZsmUL27dv569//SsAZWVlGIaBk5MTK1euZODAgRWOs1qtWK3WmrkJEZF6rF79plNUVMSiRYu488477b5JXbt2LU2bNiUmJoZ77rmH1NTUs56nsLCQrKwsu0VERKQ+0XjkUhuyfvwcd0eDnMISthzKMDuOVIOZM2fSs2dPvLy8aNq0Kddddx379u2z28cwDGJjYwkNDcXNzY3+/fuzZ88ekxJLTXJw8+ZUUfnvVZEBHiankboizM8dgLScQgpK/2RnqTYuLi50796duLg4u/VxcXH06dOnwv7e3t7s2rWLHTt22Jb77ruP1q1bs2PHDnr16lVb0UVEGoR6VSRftmwZp0+fZvz48bZ1Q4cOZfHixXz77be8/PLLxMfHM3DgQLuJKP5o5syZ+Pj42BY9WiQiIvVJZn4xe45rPHKpeUZJEZ38yscn3pqUQWZ+scmJ5GKtW7eOBx54gM2bNxMXF0dJSQlDhgwhNzfXts+sWbOYPXs2c+fOJT4+nuDgYAYPHkx2draJyaUmuEV1AywEerrg6VrnHzKWWuJhdaKJZ3lP49SCelUyqPcmT57Mu+++y/vvv09CQgIPP/wwSUlJ3HfffUD5UCl33HEHAA4ODnTo0MFuadq0Ka6urnTo0AEPD33xJSJyPurVJ6H33nuPoUOHEhoaalt3yy232P7coUMHevToQUREBF9++SWjRo2q9Dw1OQZXek4hRzPy8XN3JszfvVrOKSIi8ns/Jp6izIAWgR4EebuaHUcauFA3g+Z+bhzNyGf9gTRGdAr984Okzvr666/tXs+fP5+mTZuydetWrrjiCgzDYM6cOUybNs32WXrhwoUEBQWxZMkSJkyYYEZsqSFuLXsA6kUuFYX7u5OWU0hqgSZtrk233HILJ0+e5OmnnyY5OZkOHTqwYsUKIiIiAEhOTiYpKcnklCIiDVO9KZIfPnyYVatW8fnnn591v5CQECIiIjhw4ECV+9TUGFzHMvL5YucxiksNACID3DEs+uZdRESq18aD6QBcql7kUgssFugX04QlPyZxMC2Xoxl5NPdTR4CGIjOz/KkUf//ygYcTExNJSUlhyJAhtn2sViv9+vVj48aNVRbJCwsL7Z7k1HCGdV9JmYFbi/IieVSgiuRiLzzAna1JGZxQT/JaN3HiRCZOnFjptgULFpz12NjYWGJjY6s/lIhII1BviuRnerkMHz78rPudPHmSI0eOEBKcEdFFAABgqUlEQVQSUkvJymUVFNsK5P4eLmTmF3PoZB5Ora+q1RwiItLw2cYjr8OTdiYkJNSLc8q5CfS00jHUh5+OZbLhl3Ru6RFmNz+M1E+GYTB58mQuu+wyOnToAGCbMO6Pk8QFBQVx+PDhKs81c+ZMpk+fXnNhpdrtSSvCwdUTq4NBsI+eShJ7oT6uODpYKCgF5wANTyoiIg1fvSiSl5WVMX/+fMaNG4eT0/8i5+TkEBsbyw033EBISAiHDh3iH//4B4GBgVx//fW1mnF70mmKSw2CvV25oVszEtNzWbE7hZKoPuw5nkn70HOfKV5EpL6ZN28e8+bN49ChQwC0b9+eJ598kqFDhwLlhZjp06fz9ttvk5GRQa9evXj99ddp3769ianrp1O5RexNKR8X+NI6WCTPOpUGwNixY2vsGjk5OTV2bqnaJVH+JKRkcSKrkAOpOcQEeZkdSS7SX//6V3766Sc2bNhQYdsfvwQxDOOsX4zU5HCGUjN+PFYAQIhbGQ760kv+wMnRgWa+biSdysM1qqvZcURERGpcvSiSr1q1iqSkJO6880679Y6OjuzatYsPPviA06dPExISwoABA/jkk0/w8qq9X9wKikttE6hd2sIfJ0cHooO8iEnLYf+JHN5Yc5DXx3SrtTwiIrWtefPmPP/887Rq1QooH7/22muvZfv27bRv3942CdyCBQuIiYnh2WefZfDgwezbt69W2+uG4Idfy3uRRzf1pIlX9Q8ddrHyc8qHWBg+YRqtO3Wv1nMn/LiOrxa+QkFBQbWeV86Nh9WJbuF+/JB4io0HT9KyiSeODiqs1VcPPvggy5cv57vvvqN58+a29cHBwUB5j/LfP5mZmppaoXf579XUcIZSMwzDIP74/4rkIpUJ93cvL5JHqkguIiINX70okg8ZMgTDMCqsd3Nz45tvvjEhkb09x7MoLjUI8HQh/HeTdfaM9Gf/iRxW7E7ml9QcWjX1NDGliEjNGTlypN3r5557jnnz5rF582batWunSeCq0abfiuS96/h45AGhETSPrt4nBU4kHazW88n56xbux65jmWTmF7P7WCadw3zNjiTnyTAMHnzwQZYuXcratWuJioqy2x4VFUVwcDBxcXF07VpeGCsqKmLdunW88MILZkSWGrDneBbpeWWUFRUQ5Koxp6VyZ363dQ3raJt3S0REpKGqF0Xyuu6X1PLHvjs187F7DDXQ04rjib2UBrVhyQ9JPDmynVkRRURqTWlpKZ9++im5ubn07t37gieBA00EV5n6MB651L7aGgPexcmBXlH+rNmXxg+Jp2gT4oXVybHary0154EHHmDJkiV88cUXeHl52cYg9/Hxwc3NDYvFwqRJk5gxYwbR0dFER0czY8YM3N3dGT16tMnppbqs/PkEAAWJ23Bs1cPkNFJXBXq6YHUwKHRxZd/JInqZHUhERKQGqUh+kQxnd1Kyyh9VrGxWeMejWygNasPyncf5x7A2ODmqp4aINEy7du2id+/eFBQU4OnpydKlS2nXrh0bN24Ezn8SONBEcH+Ull0+FjTA/7d33/FR1Pn/wF+zNb33SgiBAKEmShcQiKCiiIVTT+EsJweoyCnKcafxvgqeheM8BU9FwB/VU1A8ioQWeguJtAAhPSG91012d35/LFkJBEjCJrPl9Xw85iGZnd19zZh8dve9n3nPEBbJCdL0gO8b4IqknApU1DXhVFaF2Z/VQC0tX74cADBmzJgW61euXIkZM2YAAObPn4/6+nrMmjXLeB2JnTt3sj2WFYm/WiSvu3wUmMAiObVOEAT42umRXSdHcoEGz0odiIiIqBOxSH6HdN6G/rteTio42ylvuF1efBnuDkqU1GhwKK0Uo3t6d3VEIqIu0atXLyQnJ6OiogI//PADpk+fjoSEBOPt7b0IHMALwV3v6NVWK739XeDhqJI4DZkDKXrAy2UCRoR7YeuZfJzKLkf/IFc4qvmW0lK01sLweoIgIC4uDnFxcZ0fiLpcTlkdUvKrIBOA+rSTUschM+djJyK7Dvi1sFHqKERERJ2Kn2jukM67JwCgm+eNs8gBQBB1eLB/AP7f0Sz8mJTHIjkRWS2VSmW8cGdMTAxOnDiBf/3rX3jzzTcBtP8icAAvBHc9Yz9yziKn63R1D/hwb0f4udihoKoBJzLLMKaXj0mfm4g6T/Ms8t5eKmTUs40Z3ZrP1Qu7ppc3oay2kV/SExGR1WLvjzug14vQeRkKQt1aabXS7OGBAQCAXSmFaNLx6vFEZBtEUYRGo2lxEbhmzReBGz58uIQJLc/RNMu4aCdZP0EQjL+HZ69UoaZBK3EiImqrbWfyAQBDAu0kTkKWwF4ONBZnQgRw6HKJ1HGIiIg6DYvkdyCjtBZQOUIuE+DncvM3mYNC3OHpqEJ1gxYnMsq6MCERUdf4y1/+ggMHDiAzMxNnzpzBwoULsW/fPjz99NMtLgK3efNmnD17FjNmzOBF4NqpsKoB6SW1kAnA3WEeUschQrC7PQLc7KDTiziRxfc3RJagoLIBJ7PKAQDDglgkp7ZpyEgCABxMZZGciIisF4vkdyA5uwIA4OOshlx28766cpmAsZGG05B3pRR1RTQioi5VWFiIZ555Br169cK4ceNw7Ngx7NixAxMmTABguAjc3LlzMWvWLMTExCAvL48XgWunI1dnkfcNcIWr/Y3XwCDqaoIgYGiYYTb5ubwqVDc0SZyIiG6neRZ5TKg7PB3kEqchS1GfaSiSH0gtbtN1DYiIiCwRi+R3IDmnAgDg53r7WRjjexuK5LsvFPKNBRFZnRUrViAzMxMajQZFRUXYtWuXsUAO/HYRuPz8fDQ0NCAhIQFRUVESJrY8zRftZKsVMifBHg4IdLOHThRxIrNc6jhEdBvNRfL7+/nfZkui32hyzkEhA65UGs5qIyIiskYskt8BY5H8Fq1Wmo2M8IZKLkNWaR3SivnGgoiI2ucoL9pJZmpod0P7n3NXKlFVz9nkRObq2lYrLJJTe4haDfp4GS7YeeBSscRpiIiIOgeL5B3U0KRDSr7havBtKZI7qRWI6eYOADicxl5uRETUdvmV9cgsrYNMgPG1hMhcBLk7IMjdHnoROJHJ3uRE5uraVittOROW6Fr9fdUAgIO8eCcREVkpFsk76GxeJbR6EdBUw9lO0ab7jOjhBYBXBSciovY5lm4oPPYLdIWzHfuRk/lp7k1+Pr+Ks8mJzBRbrdCdGOhnKJIfSStFk04vcRoiIiLTY5G8g5pbrcgrciEIN79o57Wa+8geTS+DTs++5ERE1DbNrVaGstUKmalAd3sEN88mz+JsciJzw1YrdKe6uSng6ahCbaMOSdkVUschIiIyORbJO+jpIaH478xhUKTtb/N9+ge6wlmtQGV9E85fqerEdEREZE1YJCdLcHeYoTd5ypVq1Gq0Eqchomux1QrdKZkgGM+MPpDKvuRERGR9WCTvIHuVHHd184C8MrfN91HIZRhy9eJWh9iXnIiI2oD9yMlSBLrZw9/VDjpRRNLVM+6IyDyw1QqZwsgIQ5F8fyo/yxIRkfVhkbyLDQs3vLE4nFYqcRIiIrIE7EdOlkIQBMSEGr7IOZNbCU2TTuJERASw1QqZzqirRfIzuRWoqGuUOA0REZFpsUjexUb0MJwqfyKjDI1aXvCEiIhuja1WyJKEeTnC01GFRp0ep/MqpY5DRGCrFTIdf1d79PBxgl7kpC8iIrI+LJJ3sZ4+zvB0VKG+SWe8+CcREdHNsEhOluTa2eRJ2RXQ6jghgEhqP5++AoCzyMk0mmeTH2DLFSIisjIskncxmUzAsHBDoePQZb6xICKim2M/crJEEb7OcLZToL5Jh3P5vFA5kZSySmuRlF0BmQA8OIBFcrpzvxXJiyGKosRpiIiITIdF8k6WnZWFPgMGt1h2rl0OAPjix33ShiMiIrPGfuRkieQyAdEhhi91TmWVQ69nEYVIKluSDbPIR/Twgo8zW63QnRsS5gmlXEBueT2ySuukjkNERGQyCqkDWDutXsSLH29osa6irhGrj2RB4+iH+kYd7FVyidIREZE5Y6sVslR9AlxwLKMMVQ1aXCqqRqSfi9SRiGyOKIr4MTkPAPDwwECJ05C1cFQrEB3qjqPpZTiQWoxuXo5SRyIiIjIJziSXgKu9Ek5qBSCT41R2udRxiIjITLFITpZKKZdhYIgbACAxq5yn5BNJ4NyVKqQV10KtkOG+vr5SxyErMirCGwD7khMRkXVhkVwCgiAgyN0ewG8FECIiomuxHzlZuv6BrlDIBJTUNCK3vF7qOEQ256ers8jH9/Zlyy4yqea+5EfSStHECzQTEZGVYJFcIoFuhiJ5c79ZIiKia7EfOVk6O6UcfQIMbVZ45hxR19LpRWz51dCP/OGBARKnIWvTN8AVHo4qVGu0OJnJ8Z2IiKwDi+QSCbw6kzw5pwINTTqJ0xARkblhqxWyBoOC3QAAmaV1KKttlDYMkQ05llGKwioNXOwUGN3LW+o4ZGXkMgFjrv5e7U4plDgNERGRabBILhE3eyWEhio06vScXUVERDdgkZysgZuDCuHehou6JfH9DlGX+SnJMIv8gf7+UCvkEqchazS+t6HP/e4LRRInISIiMg0WySUiCAJkZZkA2HKFiIhaYj9ysiaDgg2/wykF1ahr1Eqchsj6abQ6bDubDwB4aECgxGnIWo2K8IJSLiCjpBZpxTVSxyEiIrpjCqkD2DJ5WQZ0Af158U4iImrh2n7k5UX5SC0pMflzpKSkmPwxiVoT4GYHH2c1iqo1OJNXiSFhPDuCqDPtvVCM6gYt/F3tMCTMQ+o4ZKWc7ZQY2t0TB1JLsDulEOHeTlJHIiIiuiMskkuoeSZ50tW+5HZKngpJRES/tVrp46VEZO/eqK+r67Tnqqnh7C/qXIIgYHCIO3acK8CvOZWIDuHZEUSd6afkPADAQwMCIJMJEqchazYu0gcHUkuwK6UIf7wnXOo4REREd4RFcgkJtSXwdlajuFqD5JwK9p0lIiIAvxXJuzvrUF9Xh6ff/Ai+Iab98JlyPAHbV/8LDQ0NJn1cotb08HGC02UFajRaXCyshqvUgYjMTHZ2Nko6eNaQl5cXQkJCAACV9U3GHtEPDQwwWT6i1ozr7Yu4n88jMascFXWNcHNQmfw5TPW3QUREdDsskktIADAkzAP/O52PY+llLJITEVGLfuS9vQwfNn1DwhEU0dekz1OYnWbSxyO6FblMwMBgNxy8XIKk7AqMZgcIIqPs7Ow7OmvI3sEBF1JSEBISgu1n8tGo1aOXrzP6+LuYOClRS8EeDujp64RLhTVIuFSMhweatge+Kf82iIiIbodFcokN6e5pKJJnlAKIkDoOERFJ7Np+5A5KXl+brEdUgAuOZZSitLYRRQ5sAUHUrKSkpMNnDRVmp2HtP95ASUkJQkJCsDnJ0GplyqBACAL/zqjzjevti0uFNdiVUmTyIrkp/zaIiIhuh0VyiQ3rbphKlZhVDo1WB7WCfcmJiGxZc6sVw9lF9dKGITIhtVKOvgGuSM6pQGo13+8QXe9OzxrKq6jHsYwyCALwMFutUBcZ39sXy/elYd+Fok77PNsZZ9QRERFdj1PUJBbu7QQvJxU0Wj1O51ZKHYeIiCTWskhOZF0GBrtBAFDYIIPSizP7iEyp+YKdQ8M8EeBmL3EashWDgt3g52KHao0WBy51rHc4ERGROWCRXGKCIGBImKEQcuxqYYSIiGzTtf3IY7q5Sx2HyORc7ZUI93YCADjHPCxxGiLrIYoiNp8yFMkfGWTalhdEtyKTCZgY5QcA2HYmX+I0REREHWfWRfK4uDgIgtBi8fPzM94uiiLi4uIQEBAAe3t7jBkzBufOnZMwcccMudpy5ejVPrRERGSbru1H7mynlDgNUecYFOIGAHDqOxaVDTppwxBZiYwKLVKLaqBSyDCxn9/t70BkQg/09wcAxKcUQqPluE5ERJbJ7HuS9+3bF7t27TL+LJf/1uPsww8/xJIlS7Bq1Sr07NkT7733HiZMmICLFy/C2dlZirgd0nxKfWJWORq1eqgUZv3dBRERdZLDaYbTlNlqhayZv6sd3FV6lEOFX9LqMHa41ImILN/mpFwAdojxU+Hy+TNtuk9KSkrnhiKbER3iDh9nNYqqNTh0uQT3RvpKHYmIiKjdzL5IrlAoWswebyaKIpYuXYqFCxdi6tSpAIDVq1fD19cX69atw0svvdTVUTsswscJHo4qlNU24kxeBaJDPaSOREREXUwURRy6bGi7NbyHl8RpiDqPIAiIcNbjeKkM2y/XIa5JBzslL+RJ1BFVZcWAIENCZh0UTnb4celfsP7l4+16jJqamk5KR7ZCJhMwKcoPq49kYevpAhbJiYjIIpl9kTw1NRUBAQFQq9UYMmQIFi1ahO7duyMjIwMFBQWIjY01bqtWqzF69GgcPnz4lkVyjUYDjUZj/LmqqqpT9+F2BEHA3d08sONcAY6ml7FITkRkg7JK65BXUQ+lXMBd7EdOVi7QQQ9tRjEqXbyx5dcreCImWOpIRBapvqYKdqEDoHDygEom4qXX3oJMaNt9U44nYPvqf6GhoaFzQ5JNuL+fP1YfyUL8+QI0avvx7GgiIrI4Zl0kHzJkCL799lv07NkThYWFeO+99zB8+HCcO3cOBQUFAABf35bfUvv6+iIrK+uWj7t48WK8++67nZa7I4Z2NxTJj2WUYfZYqdMQEVFXO3S11cqgEHc4qMz65ZnojskEoDrxZ7iPfQ7fHMzA49FBEIQ2VvaIqAXHvoYPD5H+bgjp6dPm+xVmp3VWJLJBMd084OWkRkmNBofSSjC2V9t/F4mIiMyBWX+9O2nSJDz66KPo168fxo8fj61btwIwtFVpdv0HKlEUb/sha8GCBaisrDQuOTk5pg/fTkOu9p89mVmGJp1e4jRERNTVDl9ttTIinK1WyDZU//oL7BQCLhRU43BaqdRxiCySDgIceg4DAET6W841mcj6yK+2XAGAn3+9InEaIiKi9jPrIvn1HB0d0a9fP6Smphr7lDfPKG9WVFR0w+zy66nVari4uLRYpNbL1xluDkrUNepwNq9S6jhERO2yePFi3HXXXXB2doaPjw+mTJmCixcvtthGFEXExcUhICAA9vb2GDNmDM6dOydRYvOi14vGi3aOjOBFO8k2iJpajO1mDwD4+kC6xGmILFMpnCFT2cNO0MLPxU7qOGTjHh4YAADYcbYAtRqtxGmIiIjax6KK5BqNBikpKfD390dYWBj8/PwQHx9vvL2xsREJCQkYPny4hCk7RiYz9CUHgKPpZRKnISJqn4SEBMyePRtHjx5FfHw8tFotYmNjUVtba9zmww8/xJIlS/DZZ5/hxIkT8PPzw4QJE1BdXS1hcvNwPr8K5XVNcFTJ0T/ITeo4RF3mwQhHCAKw92IxLhfx4oFE7VUMVwCAr7yeLYtIctGh7ujm6YC6Rh22ny24/R2IiIjMiFk3PX399dcxefJkhISEoKioCO+99x6qqqowffp0CIKAuXPnYtGiRYiIiEBERAQWLVoEBwcHPPXUU1JH75Ah3T2x83whjmWU4k9jwqWOQ0TUZjt27Gjx88qVK+Hj44PExETcc889EEURS5cuxcKFCzF16lQAhtZZvr6+WLdu3S0vtmwLmmeRD+nuCaXcor6/Jroj/s4KjO/ti/jzhfjmUAYWPdJP6khkBrKzs1FSUtKh+3p5eSEkJMTEicxTrUaLcjgCAHwUdRKnIWuXkpLSpu2G+cuQWQqsSkhBd6EIgG39XRIRkeUy6yJ5bm4unnzySZSUlMDb2xtDhw7F0aNHERoaCgCYP38+6uvrMWvWLJSXl2PIkCHYuXMnnJ0tsx/f0O6GmeQnM8uh1emhYKGEiCxUZaWhbZSHh2Fcy8jIQEFBAWJjY43bqNVqjB49GocPH75pkVyj0UCj0Rh/rqqq6sTU0jl0tR/58HC2WiHb8/zIMMSfL8SmU7l4PbYXPBxVUkciCWVnZyOyd2/U13Ws6Gvv4IALKSk2UZC7VFgNQIDmygU49JC+fSRZp6qyYgDA73//+zZtL3f2RuCfVuBsUSPuHjsJuqoim/q7JCIiy2XWRfINGzbc8nZBEBAXF4e4uLiuCdTJIv1c4GKnQFWDFueuVGFAsJvUkYiI2k0URcybNw8jR45EVFQUgN+uH3H9NSN8fX2RlZV108davHgx3n333c4LawYatXoczzC02RrRgxftJNszJMwDUYEuOJtXhXXHsjDn3gipI5GESkpKUF9Xh6ff/Ai+Ie07s7IwOw1r//EGSkpKbKIYd6HA0K6s5uxeoMfDEqcha1VfY5ig8MBLC9Grf3Sb7rO/ECjWALFvfQmPylSb+rskIiLLZdZFclsjlwm4O8wTu1IMLVdYJCciSzRnzhycPn0aBw8evOG26/uliqJ4yx6qCxYswLx584w/V1VVITg42HRhzUBSdjnqm3TwdFShl69lnglFdCcEQcDzI8Pw2sZf8e2RLPzxnnCoFDybztb5hoQjKKKv1DHMVlltI4qqNRAgou7CAQAsklPn8gwIbfPf5CCnKuw8X4i8RntEBrONKBERWQZ+AjEzzS1XjvHinURkgV5++WVs2bIFe/fuRVBQkHG9n58fgN9mlDcrKiq6YXb5tdRqNVxcXFos1uZQmqHVyrBwT8hkvOga2aYH+gXA10WNomoN/nf6itRxiMzehQLD7F431EBfb52tyMhy9fBxglIuoLK+CaUavrchIiLLwCK5mRna3dCP9lhGGZp0eonTEBG1jSiKmDNnDjZt2oQ9e/YgLCysxe1hYWHw8/NDfHy8cV1jYyMSEhIwfPjwro5rVg5fNlycbiRbrZANUylkeHZYNwDA1wcyIIqitIGIzJgoirh4tdWKDyolTkN0I6Vchp5Xz45Lq2HJgYiILANfscxMH38XeDiqUKPRIim7Quo4RERtMnv2bKxZswbr1q2Ds7MzCgoKUFBQgPr6egCGdgpz587FokWLsHnzZpw9exYzZsyAg4MDnnrqKYnTS6dWo0VyTgUA9iMnenpICOyUMpzPr8JRnlFHdFNXKhpQ1aCFSi6DB2qkjkPUqv5BrgCAvDoZ5E4eEqchIiK6PRbJzYxMJmBUhKFQknCpSOI0RERts3z5clRWVmLMmDHw9/c3Lhs3bjRuM3/+fMydOxezZs1CTEwM8vLysHPnTjg7224f7uMZZdDqRQR72CPYw0HqOESScnNQ4bFoQ5umFQfTJU5DZL6aW6308HGCHDzrgsyTj7MdAtzsIEKA08BJUschIiK6LRbJzdDont4AgP2XSiROQkTUNqIotrrMmDHDuI0gCIiLi0N+fj4aGhqQkJCAqKgo6UKbgYNXW62MCOcsciIA+MMIQ6um3ReKkFFSK3EaIvOj1euRWmSYPR7pZ7tfMpNlGBjkBgBwHjgJTTp+oUNEROaNRXIzNCrCUCQ/k1eJkhqNxGmIiKizHEgtBsBWK0TNwr2dMC7SB6IIfH2As8mJrpdZUgeNVg8ntQKB7vZSxyG6pXBvJ9jLRcgd3XAwp17qOERERLfEIrkZ8nZWo2+AC4DfCihERGRdrlTU41JhDWQCL9pJdK0/3tMdAPDfxFwUVTVInIbIvDS3Wunl5wyZIEichujWZDIB3Z10AIBtqXW8KDMREZk1FsnN1D1suUJEZNWavwQdEOwGd0eVxGmIzMfdYR6IDnVHo1aPFQczpI5DZDYamnTILKkDwFYrZDnCnPQQtY1IK2/iRZmJiMissUhupn7rS14MvZ7fuBMRWZuES4Yi+T1XW2wRkYEgCJg9NhwAsOZoFirrmiRORGQeUotqoBNFeDmp4OWkljoOUZuo5UDN6Z0AgH/tviRxGiIioptjkdxMDQ5xh6NKjtLaRpzPr5I6DhERmZBWp8fBVMOZQqN7sUhOdL2xvXwQ6eeM2kYdVh/JlDoOkVm4cPUzQaSfi8RJiNqn8uj3UMiAo+llOJZeKnUcIiKiVrFIbqZUChmGX+1R2zzbkIiIrMOvuRWoatDC1V6JAUFuUschMjuCIGDW2B4AgG8OZaBWo5U4EZG0KuubcKXS0KO/ly9brZBl0VWXYFyYAwDgX7tTJU5j/pYtW4awsDDY2dkhOjoaBw4cuOm2mzZtwoQJE+Dt7Q0XFxcMGzYMv/zySxemJSKyHiySm7Hmliu7UwolTkJERKaUcNHw5efICC/IZbzwGlFrHujnj26eDqioa8L649lSxyGS1MWCagBAsLs9nOwUEqchar+pkU5QygUcTivF8Qz2Jr+ZjRs3Yu7cuVi4cCGSkpIwatQoTJo0CdnZrb8O7t+/HxMmTMC2bduQmJiIsWPHYvLkyUhKSuri5ERElo9FcjM2vrcvAOBUdgWKqhokTkNERKaS0NxqpSdbrRDdjFwm4KXRht7kXx/IgEarkzgRkTREUcSFArZaIcvm7SjH4zHBAICluy5BFHndrdYsWbIEzz//PF544QX07t0bS5cuRXBwMJYvX97q9kuXLsX8+fNx1113ISIiAosWLUJERAR+/vnnmz6HRqNBVVVVi4WIiFgkN2t+rnYYGOwGANh5nrPJiYisQVltI07nVgBgkZzodqYODoSvixoFVQ3YdCpP6jhWY//+/Zg8eTICAgIgCAJ+/PHHFreLooi4uDgEBATA3t4eY8aMwblz56QJSyiq1qC8rglymYBwH0ep4xB12Kwx4VDJZTicVoo9F4qkjmN2GhsbkZiYiNjY2BbrY2Njcfjw4TY9hl6vR3V1NTw8PG66zeLFi+Hq6mpcgoOD7yg3EZG1YJHczN3X1w8A8Mu5AomTEBGRKRxILYYoApF+zvB1sZM6DpFZUyvkeHFUdwDAsn2X0aTTS5zIOtTW1mLAgAH47LPPWr39ww8/xJIlS/DZZ5/hxIkT8PPzw4QJE1BdXd3FSQkALlxttRLu5Qi1Qi5xGqKOC3J3wB9GdgMAvL81BY1ajunXKikpgU6ng6+vb4v1vr6+KChoWz3gk08+QW1tLZ544ombbrNgwQJUVlYal5ycnDvKTURkLVgkN3P39TW8QB5JK0VlfZPEaYiI6E41z5wa3YuzyIna4ukhofByUiOnrB7fJ+ZKHccqTJo0Ce+99x6mTp16w22iKGLp0qVYuHAhpk6diqioKKxevRp1dXVYt26dBGltm14v4lKhoUjey58X7CTLN2dsD3g5qZFeUotvj2RKHccsCULL69WIonjDutasX78ecXFx2LhxI3x8fG66nVqthouLS4uFiIhYJDd73b2dEOHjBK1exF6ekkZEZNG0Oj32Xb1oZ/N1J4jo1uxVcswaY+hN/u/dqexN3skyMjJQUFDQ4nR/tVqN0aNH3/J0f/a47Rw55XWoa9TBTilDqAdbrZDlc7ZT4o37egIA/rU7FaU1GokTmQ8vLy/I5fIbZo0XFRXdMLv8ehs3bsTzzz+P7777DuPHj+/MmEREVotFcgvAlitERNYhMasclfVNcHdQYnCIu9RxiCzGU0NC4OuixpXKBmw8wdPCO1Nzcaa9p/uzx23naG610tPXGXLZ7WeSElmCx6KD0TfABdUNWny886LUccyGSqVCdHQ04uPjW6yPj4/H8OHDb3q/9evXY8aMGVi3bh0eeOCBzo5JRGS1WCSXUHZWFvoMGHzTZex4wwye5iL5vovFaGji7CkiIku1++oZQWN7+bDYQdQOdko55oztAQD4fO9lvh/qAu093Z89bk2vUavH5aIaAIbrWBBZC7lMQNxDfQEA64/n4EhaqcSJzMe8efPw9ddf45tvvkFKSgpee+01ZGdnY+bMmQAMY+2zzz5r3H79+vV49tln8cknn2Do0KEoKChAQUEBKisrpdoFIiKLpZA6gC3T6kW8+PGGm97+1eu/AwBEBbog0M0eeRX12H+pGLFXi+ZERGRZdqUUAgDu7X3zPpFE1Lon7grGFwnpyKuox5qjWXjh6gU9ybT8/AzvMwsKCuDv729cf7vT/dVqNdRqdafnsyXpJTXQ6kW42ivhxws9k5W5q5sHnh4SgrXHsrFg02lsf/Ue2Kt4Ydpp06ahtLQUf//735Gfn4+oqChs27YNoaGhAID8/HxkZ2cbt//Pf/4DrVaL2bNnY/bs2cb106dPx6pVq7o6PhGRReNMcgsgCIJxNvmWX69InIaIiDoio6QW6cW1UMgE3NOTF+0kai+1Qo5Xxhlmk/97z2VU1vGC5p0hLCwMfn5+LU73b2xsREJCwi1P9yfTa261Eunn3KaL9hFZmrcmRcLf1Q6ZpXVYuuuS1HHMxqxZs5CZmQmNRoPExETcc889xttWrVqFffv2GX/et28fRFG8YWGBnIio/VgktxBTBwcCAHaeL0RVAz8UEhFZmt1XZ5EP6e4BFzulxGmILNNj0cHo6euEyvomLEu4LHUci1VTU4Pk5GQkJycDMFysMzk5GdnZ2RAEAXPnzsWiRYuwefNmnD17FjNmzICDgwOeeuopaYPbkFqNFtmldQCAXmy1QlbK2U6J96ZEAQC+OpCO5JwKaQMREZFNY5HcQvQNcEGEjxMatXpsP5MvdRwiImqn3SmGfuTjIm/eroCIbk0uE7BgUm8AwMpDmcirqJc4kWU6efIkBg0ahEGDBgEw9MAdNGgQ3n77bQDA/PnzMXfuXMyaNQsxMTHIy8vDzp074ezMYm1XuVRYDRGAn4sd3B1UUsch6jTjevvioQEB0IvAaxuTUavRSh2JiIhsFIvkFkIQBEwdHAQA+OFUnsRpiIioPSrrm3AiswwAMI79yInuyJhe3hjW3RONWj0++eWi1HEs0pgxY255er4gCIiLi0N+fj4aGhqQkJCAqKgoaUPbmGtbrRBZu78/3Bf+rnbIKKnF338+L3UcIiKyUSySW5ApgwIgCMDxjDLklNVJHYeIiNpoz4VCaPUiInycEOrpKHUcIosmCAIW3B8JANicnIczuZUSJyIyrbLaRhRVayATgAhfJ6njEHU6NwcVljwxEIIAbDyZwzOniYhIEiySWxB/V3sMD/cEAPyYxNnkRESWYvuZAgDApCg/iZMQWYf+QW6YMjAAogi8s+Us9HpR6khEJnPx6izyUE9HOKgUEqch6hrDwj0xc3Q4AOCtTWdwhe20iIioi7FIbmGmDjK0XNmclAdR5AdCIiJzV6vRIuFSMQBgUj9/idMQWY+3JvWGg0qOU9kV2MzJA2QlRFHEhYIqAGy1QrbntfE90T/IFZX1TZj3XTJ0/AKUiIi6EIvkFmZilB/slXKkl9QiMatc6jhERHQbey8WQaPVo5unAwseRCbk52qHl++NAAAs3n4B1Q1NEiciunP5lQ2oatBCJZchzIvtuci2qBQy/Ot3g+CgkuNoehl+vFgrdSQiIrIhLJJbGEe1Ag8NCAAArDqcKW0YIiK6LWOrlX7+EARB4jRE1uW5kd0Q5uWIkhoNPt2dKnUcojvWfMHOcB9HKOX8qEa2J8zLEXEP9QUArD9bDVVAL4kTERGRreA7Lws0fXg3AMD2swXIr2SvNiIic1XfqMOeC0UA2I+cqDOoFXK8PbkPAOCbQ5lIya+SOBFRx+lF4FKhoUge6ecicRoi6TweHYTJAwKgFwHvyW+gSS91IiIisgUsklugPgEuGBLmAZ1exJqjWVLHISKim0i4VIz6Jh0C3ezRL9BV6jhEVmlsLx9M7OsHnV7EWz+cZg9bslgF9QI0Wj0c1XIEudtLHYdIMoIg4P1HouDjKIfCzQ+nyuS8HhcREXU6FsnNWHZWFvoMGNzqkvTDZwCAdcey0dCkkzgpERG1ZvvZfADA/f382GqFqBO9+3BfONsp8GtuJVazHR1ZqOxaOQCgl68zZHzNIBvnYqfEa0PdIOp1yK2T4zzPFCIiok6mkDoA3ZxWL+LFjze0epteL+KzrSdQDnds+fUKnogJ7uJ0RER0Kw1NOuxOMbRamRjlL3EaIuvm62KHtyZFYuHms/h450XE9vVFkLuD1LGI2kxm74Ir9YbCOFutEBn08lSh4sAauI+ejn0XixHgag93R5XUsYiIyEpxJrmFkskEKLKPAQC+OZjB08+IiMzMrpRC1Gi0CHSzx6BgN6njEFm9J+8Kwd3dPFDXqMNfNp/leyOyKI59RkOEAB9nNbyd1VLHITIbVcd+gLdaD61exPazBdDq2aCciIg6h1kXyRcvXoy77roLzs7O8PHxwZQpU3Dx4sUW28yYMQOCILRYhg4dKlHirqXIPQUHlRwXCqqx83yh1HGIiOgaPyblAQAeGRQImYynzRN1NplMwKKp/aBSyLD/UjHWHc+WOhJRmzn1Gw8A6OPPWeRELYh63OWphb1SjuIaDQ5dLpU6ERERWSmzLpInJCRg9uzZOHr0KOLj46HVahEbG4va2toW202cOBH5+fnGZdu2bRIl7lpCUz3+MKIbAOCf8Zeg54WqiIjMQmmNBvsuFgMApgwKkDgNke3o4eOE+ff1AgC8vzUFWaW1t7kHkfTSy5ug8g2HDCJ6+TlLHYfI7NgrgAl9fAEAyTkVyCjh2E5ERKZn1j3Jd+zY0eLnlStXwsfHB4mJibjnnnuM69VqNfz8/Lo6nln446hwfHskCxcKqrH1TD4mD2AxhohIalvP5EOrF9Ev0BU9fFjwIOpKz40Iw/+SspF8pRYvrTyE/xvjCbkJz+bw8vJCSEiIyR6PaE9GHQAgwEEPO6Vc4jRE5inMyxEDg92QnFOB+POFeGpICJzUZl3OICIiC2NRryqVlZUAAA8Pjxbr9+3bBx8fH7i5uWH06NF4//334ePjc9PH0Wg00Gg0xp+rqiz3StmuDkq8OKo7lsRfwj93XcKkKD8o5GZ9ggARWan9+/fjo48+QmJiIvLz87F582ZMmTLFeLsoinj33Xfx5Zdfory8HEOGDMHnn3+Ovn37She6k2y+2mplyqBAiZMQ2Z7c3BzsWjQDHk9+hAslDhj/8j9QdfS/Jnt8ewcHXEhJYaGcTEKj1WF/dj0AINSRvZaJbmVED0/kldejuEaDXSmFeHhAAASBLe2IiMg0LKZILooi5s2bh5EjRyIqKsq4ftKkSXj88ccRGhqKjIwM/O1vf8O9996LxMREqNWtX/Rm8eLFePfdd7sqeqf7w4hu+OZQBtKLa/Fj8hU8Fh0kdSQiskG1tbUYMGAA/vCHP+DRRx+94fYPP/wQS5YswapVq9CzZ0+89957mDBhAi5evAhnZ+uZbZ1ZUouk7ArIZQIe4tk9RF2upKQEtYVZGKauQCoc4DH6WTzyxFPwVN95W7rC7DSs/ccbKCkpYZGcTGLX+SLUNIrQVpfAN5j9yIluRSGTYWKUH9Ydz0ZWaR3OXalCVKCr1LGIiMhKWEyRfM6cOTh9+jQOHjzYYv20adOM/46KikJMTAxCQ0OxdetWTJ06tdXHWrBgAebNm2f8uaqqCsHBwZ0TvAs42ykxc3Q4Pth+AR//chETo/x46hkRdblJkyZh0qRJrd4miiKWLl2KhQsXGsfm1atXw9fXF+vWrcNLL73UlVE7VfMs8pE9vODt3PqXtUTU+foFe0HW6IyLhdU4WWGPp4eEsJUFmZ3vTuYAAGrP7IbQ9xGJ0xCZPw9HFYaHe+JAagn2pxYjxMMBLvZKqWMREZEVsIi+HC+//DK2bNmCvXv3Iijo1rOk/f39ERoaitTU1Jtuo1ar4eLi0mKxdDOGd0OIhwMKqhrwz/hLUschImohIyMDBQUFiI2NNa5Tq9UYPXo0Dh8+fNP7aTQaVFVVtVjMmU4v4vvEXADAI2y1QiQpQQDujfSBq70SNRot4s8XQhR5kXMyH/mV9TiQarjIc82ZXRKnIbIcA4PdEOBmhyadyLGdiIhMxqyL5KIoYs6cOdi0aRP27NmDsLCw296ntLQUOTk58Pf374KE5sNOKcf/TTG0oVl5KANn8yolTkRE9JuCggIAgK+vb4v1vr6+xttas3jxYri6uhoXcz/rZ/+lYuRV1MPNQYmJUbZ5QWkic6JSyHB/Pz/IBQHpJbVIzqmQOhKR0aZTedCLQB8vFbQV+VLHIbIYMkHAhN6+UMoF5FbU49dcfvYlIqI7Z9ZF8tmzZ2PNmjVYt24dnJ2dUVBQgIKCAtTXGy5uU1NTg9dffx1HjhxBZmYm9u3bh8mTJ8PLywuPPGJ7pyuO7umNB/v7Qy8CCzefgU7Pb9SJyLxcf3ElURRvecGlBQsWoLKy0rjk5OR0dsQ7svZYFgDg0cFBbOtAZCZ8nO0wqqcXAODg5RIUVDVInIgI0OtF/Pdqq5V7w+wlTkNkedwcVBjZwzC2H7pcgvK6RokTERGRpTPrIvny5ctRWVmJMWPGwN/f37hs3LgRACCXy3HmzBk8/PDD6NmzJ6ZPn46ePXviyJEjVnURuPZ4+8E+cFYr8GtuJVYeypA6DhERAMDPzzCr+vpZ40VFRTfMLr+WJbXHulJRjz0XigAATw3hBf2IzEn/QFf08HaCXgS2nclHXaNW6khk4w6llSCztA5OagWGBdlJHYfIIvULdEWIhwO0erZdISKiO2fWRXJRFFtdZsyYAQCwt7fHL7/8gqKiIjQ2NiIrKwurVq0y+9PxO5OPix3enBQJAPjHjgs4nVshbSAiIgBhYWHw8/NDfHy8cV1jYyMSEhIwfPhwCZOZzoYTOdCLwLDungj3dpI6DhFdQxAEjO/tAzd7JaobtNh+poBn3JGk1hxtPvMoEPZKs/5IRmS2msd2lVyG/MoGnGbbFSIiugN8R2aFnh4Sgol9/dCkEzFnXRKqGpqkjkRENqCmpgbJyclITk4GYLhYZ3JyMrKzsyEIAubOnYtFixZh8+bNOHv2LGbMmAEHBwc89dRT0gY3Aa1Oj40nsgEATw/lLHIic6RWyvFgf39jD9uDl0ukjkQ2Kr+yHvHnCwEAvx8aKnEaIsvmbKfEiB6eAAxnaFTV87MvERF1DIvkVkgQBPzjsf4IcrdHdlkd3vrhNE89I6JOd/LkSQwaNAiDBg0CAMybNw+DBg3C22+/DQCYP38+5s6di1mzZiEmJgZ5eXnYuXOnVbTH2pVShMIqDbycVIjtwwt2EpkrTyc17utr+BtNzqlASn6VxInIFq0/lg29CAwJ80CEr+W/BhJJrV+gKwLc7NCkE7HnYhE/+xIRUYcopA5AHZedlYU+Awbf9HbXbn2h6Psktp0pwLJ9aZg9tkcXpiMiWzNmzJhbfigRBAFxcXGIi4vrulBdZMXBdADAEzHBUCn4/TOROQv3dsLd3TxwPLMMuy8UwcNRBV8X9oSmrtGk02P9CcMFO58ZxlnkRKZgaLvii7XHspFVWocLBdXg109ERNReLJJbMK1exIsfb7jp7V+9/ju881Yf/O2nc/jol4vwd7XD1MFBXZiQiMj6JWaV4URmOVRyGaYP7yZ1HCKLkpKSIsljDu3ugeIaDTJKavG/0/mYFhMMJzu+LabOt/NcIYqrNfB2VvPMIyITcndQYUiYBw6nlWL/pWKMu/l14YmIiFrFTwNW7plh3ZBbXo//7E/H/O9Pw8fZDiMjvKSORURkNb5IMMwif2RQIGejErVRVVkxAOD3v/99pz1HTU3NTW8TBAH39fXFxhM5KK9rwpbTV/DY4CCeCUKdbvXhTADAk3fxzCOyLR35UrS99xkc4o7UwhoU12jwa7m83c9HRES2jUVyG/DmxEhcqWzAz79ewUv/7yS+ff5uRId6SB2LiMjiXS6qQfz5QggC8OI93aWOQ2Qx6msMvcAfeGkhevWPNuljpxxPwPbV/0JDQ8Mtt1Mr5Hh4YCA2nshBcbUGO84V4MH+/pAJgknzUNvtvViETclVcBn6OPLqBHg0auGgsp6PK8k5FTieWQalXMBTQ9hqhWyDKb4UvdWXnteSywSM7+2DDSdzkFsnh32Puzv8nEREZHus510n3ZRMJuDjx/ujrFaDQ5dL8eyK41j93N2I6cZCORHRnfhqv2EW+YTevujh4yRxGiLL4xkQiqCIviZ9zMLstDZv62qvxOQB/vjhVB4ySmpxMLUE9/T0Nmkearuj6aX4+VIt3EdPx9ES4NiBDHTzcsTgEDcEuTtIHe+ONb9mPDQgEH6uPPOIbMOdfCna1i89r+XjYofBIe5IzCqHR+xs1Dbq2/WcRERku1gktxFqhRxfP3sXnl99AofTSjH9m+NY9dzduIuFciKiDimobMDmpDwAwEujwyVOQ0Qd5e9qj9g+vth+tgBJORVwc1Cif5Cb1LFs0tDunigsKMSa77cgYPBYVDXJkFFSi4ySWnT3csTYXj4W2zs+u7QO28/mAwD+yDOPyAZ15EvR9nzpea2hYR64mFeGGmdPHMppwKihHXoYIiKyMZb5LpM6xF4lx4rpvxXKn1lxDMt/H42xvXykjkZEZHE+3ZOKRp0ed3fzgLdQjVOnMkz+HJ1xUUMiulFPX2dU1DfhSFop9l0qhou9Et08HaWOZXPG9vKBa20u/vXcPzH9gVFwDIjAqZxynLtShfSSWuRVZGFCH1+Ee1vemTvfHMqAXgRG9/RGLz9nqeMQWTWFXIZoTy3+u+wDxD6xTOo4RERkIVgktzHNhfKZaxKRcKkYL64+iY8fH4ApgwKljkZEZDHSi2uw8UQOAOCZgW6I7N0b9XV1nfZ8be3FSUQdd1eoOyrqGpGSX43tZwrwWHQQvJ3VUseyae6OKoyL9MXAIDfsPF+IomoN/nc6H8O6e+Kubu4QLKR/fHlto/E1g7PIibqGl1pE/eVjUscgIiILwiK5DbJXyfHVszF44/tf8VPyFczdmIyy2kY8NzJM6mhERBbhk52XoNOLuDfSB4GqetTX1eHpNz+Cb4hp2650pBcnEXWMIAgYF+mL6gYtcsvr8WNyHp6ICYarvVLqaDbP00mNJ2KCcTC1BMm5FTiSXoqGJh1GRXhZRKF89ZFM1Dfp0MffBcPDPaWOQ0REREStYJHcimVnZaHPgME3vT3/Sj48xz0Pbbdh+Pv/zmPR0uVQpu5C80cNX28v7N21s2vCEhFZiNO5Fdh6Jh+CAMyf2At1Vy4DAHxDwiW9ACER3Tm5TMCD/fzx/alclNQ0YnNSHh6PDoKjmm+ZpSaXCRjdyxuuDkokXCpGUk4FGnV63BvpA5kZF8or6hqx4oChHdesseEWUdQnIiIiskV8x2/FtHoRL3684aa3L3h0KGY99wxOZJXjSFoptD1Go9c9k3FvLx/IZAK+ev13XZiWiMj8iaKIf+y4AAB4ZGAgIv1ccOqKxKGIyKTUSjmmDAzEdydzUFnfhJ+Sr+DR6ECoFXKpoxGAgcFuUMoF7E4pwrkrVRBFYHxvH7MtPn91IB3VGi0i/Zxxf5S/1HGIiIiI6CZkUgcgaQmCgLu7eeDeSB8IAM5dqcLWM/lo0umljkZEZHa2ny3AoculUMoFvDahp9RxiKiTOKoVeGRQIOyVchTXaPC/X/Oh5Xsjs9E3wBUTo/wgCMD5/CocvFwCURSljnWD0hoNVh7KBADMm9ATMpl5FvKJiIiIiEVyuqpfoCvu7+cPuUxAekktNp3Kg6h0kDoWEZHZqGpoQtyWcwCAP40OR7AHx0gia+bmoMKUQQFQyWXIrajHjnMFMMM6rM3q6euM8b19AQCnsiuQmFUucaIbfZGQhrpGHfoHuWJCH1+p4xARERHRLbBITkY9fJzwyKBAqBUyFFQ1oGHoi8gpq5M6FhGRWfhox0UUVWvQ3csRs8b2kDoOEXUBH2c7TB7gD7kgIK24FqfK2HLFnPTxd8GoCC8AwKG0UmTWmM9Hm4LKBnx7JAuAYRa5ubaDISIiIiID83knSWYh0M0ej0cHwUmtgOjkhUeWHcbZvEqpYxERSSoxqxxrjhmKHe89EgU7JQtlRLYiyN3B0NoDQGatHO7jXjTL1h62anCIO2JC3QEAp8rksO8eI3Eig0XbUqDR6hET6o7RPb2ljkNEREREt8EiOd3A00mNaTHBEKoKUFKjwbT/HMH+S8VSxyIikkRdoxZv/nAaogg8OjgIw8O9pI5ERF2sh48TxvX2AQC4xDyMHy/WSpyIrjU83BO9/Z0hQoDXlLdwqbRR0jyH00qw5dcrkAlA3EN9OYuciIiIyAKwSE6tcrJTwO7Y1xge7onaRh2eW3UCPyTmSh2LiKjLvf3TOVwuqoGvixoLH+gtdRwikkjfAFcMdNeiqTwfI4LtpI5D1xAEAeMifeFrp4dMaYf3D5QhvbhGkixNOj3e/slw/YrfDw1FVKCrJDmIiIiIqH1YJKebykm7hKTPX4b8yq/Q6kX8+b+/osdjb6D3gGj0GTAYY8fHSh2RiKhTfZ+Yi+8TcyETgE9/NwgejiqpIxGRhMKd9chfOQc+jgqpo9B15DIBQ7200Fy5hOpGEc9+cxxFVQ1dnmPloQxcLqqBp6MKf57Qq8ufn4iIiIg6hkVyuimtXsQfP1qL2b9/FNFXez1qe4yB3zOf4NkP1qKwuETihEREnSe1sBp/+/EsAOC18T0xpLunxImIyByITRqpI9BNKGRA0fdx8HeSI7e8HjNWnkB1Q1OXPX92aR2W7koFALw1KRKuDsoue24iIiIiujMsktNtCYKAkT28ENvHF3KZgIySWnx3Ihd6J1+poxERdYrCqgbMWHkC9U06jIrwwqyxPaSOREREbaCvr8Lf7vGAl5MK5/OrMHNNIhq1+k5/Xp1exGvfJaOuUYchYR54dHBQpz8nEREREZkOi+TUZr39XfBYdBAc1XKU1TWiYfhL+H9HsyCKotTRiIhMpqqhCdO/OY68inp083TA0mkDIZfxomtERJbCz0mBVX+4G44qOQ5dLsXr//0Ven3nvl/91+5UJGaVw0mtwCdPDICMrxtEREREFoVFcmoXPxc7PHV3CLp5OgByJf7241m8sPok8irqpY5GRHTHGpp0+OO3J3GhoBpeTmp8+9wQeDqppY5FRETtFBXoii+eiYZCJmDLr1cw/4fT0HVSoXzvhSJ8utvQZuW9KVEIcnfolOchIiIios7DIjm1m4NKgYcGBECZsg1KuYDdF4owYUkCvj6QDq2u809nJSLqDM0zyI+ml8FJrcCqP9yFEE8WOoiILNWoCG/88+rZQN8n5uLVDUloMvF71fNXqvDy+iQAwDNDQzFlUKBJH5+IiIiIugaL5NQhgiBAmXkEW18ZhZhQd9Q16vDe1hTct3Q/tp/JZwsWIrIoRdUNmPafoziWYSiQfz09BlGBrlLHIiKiOzR5QAA+f2oQlHIB/zudjz+tSUStRmuSx84qrcUfVh1HjUaLIWEe+OuDvU3yuERERETU9VgkpzvS09cZ3700DB9M7Qd3ByXSimvxp7Wn8PDnh7DjbH6nndZKRGQqZ/Mq8djyI0jJr4KXkwob/jgUQ7t7Sh2LiIhMZGKUP758JgZqhQy7UorwyLJDyCipvaPHTCuuwbT/HEVhlQY9fZ3w5bMxUCvkJkpMRERERF2NRXK6YzKZgN/dHYKE+WPxyr094KCS43RuJWauOYV7P9mH1YczUVnfJHVMIqIWRFHE/zuSianLDiO7rA7BHvb478zhnEFORGSFxkb6YN2LQ+DtrMalwho89O+D2HG2oEOPlXCpGI98fggFVQ2I8HHCmheGwNVeaeLERERERNSVFFIHIMuVnZWFPgMG37BeVDlCEToU+m5DkVUKvLPlHBZtS8H9/fzxREwwhnb3gCAIEiQmIjK4UlGPd7acQ/z5QgDA+N6++Pjx/nBzUEmcjIiIOkt0qAe2vjwSf1p7ColZ5Zi5JhEP9PPHW5MiEexx+2tQVDc04ZOdl7DqcObVx3PHf56Jhhcv8ExERERk8Vgkpw7T6kW8+PGGm97+5ZvPYMHnG7H2aDYuFlZjc1IeNiflIdTTAQ8NCMBDAwIQ4evchYmJyJZlZ2cjv7AYW1Nr8d35GjRoRcgF4Nn+zniwp4D0C2c79LgpKSkmTkpERJ3Fx8UO618cik/iL+LrAxnYeiYfO88XYPKAADwRE4zoUHco5S1Pts0pq8OmU3lYdTgD5XWGsyOfGhKCdyb3YYsVIiIiIivBIjl1mpz0y/hg5lSIANSugdAGRUMX0B9ZpcC/91zGv/dcRqSfMyYPCMDk/gEI8bz9DB4ioo5Iy8jEXdNegUP0FChcfQEADbnnUPbLMsSVZCHOBM9RU1NjgkchIqLOplLIsGBSbzw0IAAfbL+AA6kl2HQqD5tO5cFRJUcPX2e4OyjRqNUjq7QOeRX1xvt293bE2w/2wZhePhLuARERERGZGovk1Glam2neqNUjvaQGW7ZuhzqkPy4UVONCwUV89MtFCJV5UBRdhLzoAvzUTdi3a6dEyYnImvyUnIf/23IBLve+BABQy0REuekQGhwBYfg/7/jxU44nYPvqf6GhoeGOH4uIiLpO3wBX/L/nhyApuxzrjmVj94UilNU24tecihbbyWUC7u7mgd/dHYwH+vlDIb/xsk7Z2dkoKSnpUA6ekUREREQkPRbJqUupFDJE+rlg9bZ/4p0Nh5BWXIOLhdXILauH6BqIJtdANEXci6z6SizcfAbje/vi7jAPOKr5q0pEHVNe24iSOh20NWUYHOSCkQN73XAq/Z0ozE4z2WMREVHXGxTijkEh7tDpRaQV1yC9uAbVDVoo5AL8Xe0RFegKp1u8F83OzkZk796or6u7oxw8I4mIiIhIOqw8kmTslHL0DXBF3wBX1DVqkVlSh/SSGmSV1kFr74q1x7Kx9lg2FDIB/YNcMbS7J4Z298SAYDe42iuljk9EFuJ3d4fgSl4u/vr0VEz71waTFsiJiMh6yGUCevo6o2c7r5lTUlKC+ro6PP3mR/ANCW/38/KMJCIiIiLpsUhOZsFBpUCfABf0CXCBVqfH1x+9g8fm/BX7LhYjr6Iep7IrcCq7Asv2GWZshno6ICrQFf0CXdHT1wlhXk4Icrdn8YuIbmCnlGNiD0cs1DZKHYWIiKyYb0g4giL6tvt+PCOJiIiISHpWU1FctmwZwsLCYGdnh+joaBw4cEDqSNRBCrkMeYm7sDluOsrXvw67fZ9AdXoT5HnJEOrKAQBZpXXYejofH2y/gOdWncTYj/eh9992YOzH+/DcqhOI23IOX+5Pw5Zfr+BkZhlyy+vQpNNLvGdEBHC8JiKyFByviYi6XnvH3oSEBERHR8POzg7du3fHF1980UVJiYisi1XMJN+4cSPmzp2LZcuWYcSIEfjPf/6DSZMm4fz58wgJCZE6HnVAaxf9bFbfpMPqj9/Gq28vxrkrVUgvrkVmSS3qm3TIKKlFRkltq/cTBMDLSY0AVzv4udrB39Ue3s5qeDup4emkgqeTGl5OKng5qWGnlHcst06PGo0W1Q1a1GiuLg1aVDU0Gf99fbFeLpPBSS2Ho1oBR7UCTlcXF3slXOwM/zXHGfKiKKK2UYeq+iZUNTShql5r/Hd1gxZ1jTroRRGiKEIvGu5jr5TDTiWHg1IOe5UcTmoFnO0UcLYz7KuznRJ2ShkEQZB256jTcLwmIrIMHK+JiLpee8fejIwM3H///XjxxRexZs0aHDp0CLNmzYK3tzceffRRCfaAiMhyWUWRfMmSJXj++efxwgsvAACWLl2KX375BcuXL8fixYslTkemZq+UI+/UHnz26hMt1tupnSE6ekHv6IkavQoOXgEQ7Vwg2rka/itToLhag+JqDX7Nrbzlcwi6Rvh5uMBeJYeDSg4HpQJKhQBRBMSrBd+mqwXx2kYtajU61Gq00Gg7Z7a6o0oOV3uloXBurzT8204JF3sFVAoZ1HIZVIqri1wGlUIOpVxotdh8/RqdKKJJp0eTVo8mnYhGnR6NWj2adHrUNeqMBfDqq8Xv5iJ4dUOTsfhtSgqZYCycG/77279drlmnlMugkAmQyQTIhd/+K7+6TnZ1R8VrMl4bVxRbD99ye7H19SKg1evRqBPRpNWj0Xj8rq7T6aHR6qBp0kOj1aOhSQeNVo9jJ0+hUQ+IMgVwdRHlSsO/5Qq8/+hAPD0k1ARH0XxxvCYisgwcr4mIul57x94vvvgCISEhWLp0KQCgd+/eOHnyJD7++GMWyYmI2snii+SNjY1ITEzEW2+91WJ9bGwsDh8+3Op9NBoNNBqN8efKSkPBtKqqqt3Pr9Pp0FB78yvRi6J409tvdZu1336nj92k0+OZd7++6e1xz4zDK/9vd4vHq9fqUHt1hvd/v/wnRv1uFuqbtKhv1KG+UYe6Jh0aGvXQXa2G5hWV3fTxb0vXCHt7O6hkhsK1UiGD+moBO2nfNkTf+4BxU70oXi1KG/5bnJ8Ln4AgQwFeowMAVGuA6uqOx+lMSrkAFzsFnOyUcFYrcP5MMsIi+0FxtUh/bVH+1P6d6DNsHLRXi/GGorIeGq2h2AwAjQBK64FSSfamk6k9W1+v1wP6RpRXVLZrHGre9mYFf3Mj5XhdU2MYT3JTz0FTX9eu+95Ocy/ZgsxLSHN0sPnHtsTMfGw+dlsV52YAMIwpHK9bMofxuvn/T2JiovFx2kMmk0Gvb/+Eh4sXLwLo+GvMnfzOSnVfKZ+buZm7LSxxvO7I2HvkyBHExsa2WHffffdhxYoVaGpqglKpvOE+pqyHEBFJpVPGa9HC5eXliQDEQ4cOtVj//vvviz179mz1Pu+8844Iw6RSLly4cLH4JScnpyuG2zvG8ZoLFy62vnC85sKFCxfLWKQYrzsy9kZERIjvv/9+i3WHDh0SAYhXrlxp9T4cr7lw4WJNS1pammkGYVEULX4mebPr20qIonjTvsYLFizAvHnzjD/r9XqUlZXB09OzXb2Qq6qqEBwcjJycHLi4uHQsuJXjMWobHqfb4zG6kSiKqK6uRkBAgNRR2kWK8dpc8Pe4c/C4dg4eV9PheM3316bC43N7PEa3xuNza+YwXrdn7L3Z9q2tb3b9eF1RUYHQ0FBkZ2fD1dW1o7Ethi3+DXCfrX+fbW1/AcNZMCEhIfDw8DDZY1p8kdzLywtyuRwFBQUt1hcVFcHX17fV+6jVaqjV6hbr3NzcOpzBxcXFZn4JO4rHqG14nG6Px6glS3ojaw7jtbng73Hn4HHtHDyupsHxun34e3drPD63x2N0azw+NyfVeN2RsdfPz6/V7RUKBTw9W2/12Np4DRj225Z+J2zxb4D7bP1sbX8BQ7s8kz2WyR5JIiqVCtHR0YiPj2+xPj4+HsOHD5coFRERXY/jNRGRZeB4TUTU9Toy9g4bNuyG7Xfu3ImYmJhW+5ETEdHNWfxMcgCYN28ennnmGcTExGDYsGH48ssvkZ2djZkzZ0odjYiIrsHxmojIMnC8JiLqercbexcsWIC8vDx8++23AICZM2fis88+w7x58/Diiy/iyJEjWLFiBdavXy/lbhARWSSrKJJPmzYNpaWl+Pvf/478/HxERUVh27ZtCA0N7dTnVavVeOedd1o9VYkMeIzahsfp9niMrINU47W54O9x5+Bx7Rw8rraN76/NE4/P7fEY3RqPj3m73dibn5+P7Oxs4/ZhYWHYtm0bXnvtNXz++ecICAjAp59+ikcffbTNz2lrvxO2tr8A99kW2Nr+Ap2zz4LYfFUHIiIiIiIiIiIiIiIbY/E9yYmIiIiIiIiIiIiIOopFciIiIiIiIiIiIiKyWSySExEREREREREREZHNYpGciIiIiIiIiIiIiGwWi+QdtGzZMoSFhcHOzg7R0dE4cOCA1JEks3jxYtx1111wdnaGj48PpkyZgosXL7bYRhRFxMXFISAgAPb29hgzZgzOnTsnUWLzsHjxYgiCgLlz5xrX8TgBeXl5+P3vfw9PT084ODhg4MCBSExMNN7OY0SWKC4uDoIgtFj8/PykjmVx9u/fj8mTJyMgIACCIODHH39scTvHh4653XGdMWPGDb+/Q4cOlSYsWTW+v745vo60xNeD2+PYTs3aO7YmJCQgOjoadnZ26N69O7744osuSmo67dnnTZs2YcKECfD29oaLiwuGDRuGX375pQvTmkZHX0MPHToEhUKBgQMHdm5AE2vv/mo0GixcuBChoaFQq9UIDw/HN99800VpTaO9+7x27VoMGDAADg4O8Pf3xx/+8AeUlpZ2Udo7c7vXsNaYYuxikbwDNm7ciLlz52LhwoVISkrCqFGjMGnSJGRnZ0sdTRIJCQmYPXs2jh49ivj4eGi1WsTGxqK2tta4zYcffoglS5bgs88+w4kTJ+Dn54cJEyagurpawuTSOXHiBL788kv079+/xXpbP07l5eUYMWIElEoltm/fjvPnz+OTTz6Bm5ubcRtbP0Zkufr27Yv8/HzjcubMGakjWZza2loMGDAAn332Wau3c3zomNsdVwCYOHFii9/fbdu2dWFCsgV8f317fB35DV8Pbo9jOwHtH1szMjJw//33Y9SoUUhKSsJf/vIXvPLKK/jhhx+6OHnHtXef9+/fjwkTJmDbtm1ITEzE2LFjMXnyZCQlJXVx8o7r6GtoZWUlnn32WYwbN66LkppGR/b3iSeewO7du7FixQpcvHgR69evR2RkZBemvjPt3eeDBw/i2WefxfPPP49z587hv//9L06cOIEXXnihi5N3TFtew65lsrFLpHa7++67xZkzZ7ZYFxkZKb711lsSJTIvRUVFIgAxISFBFEVR1Ov1op+fn/jBBx8Yt2loaBBdXV3FL774QqqYkqmurhYjIiLE+Ph4cfTo0eKrr74qiiKPkyiK4ptvvimOHDnyprfzGJGleuedd8QBAwZIHcOqABA3b95s/Jnjg2lcf1xFURSnT58uPvzww5LkIdvB99e3xteRm+Prwe1xbLdd7R1b58+fL0ZGRrZY99JLL4lDhw7ttIymZorXkz59+ojvvvuuqaN1mo7u87Rp08S//vWvFvca09793b59u+jq6iqWlpZ2RbxO0d59/uijj8Tu3bu3WPfpp5+KQUFBnZaxs7T2GnY9U41dnEneTo2NjUhMTERsbGyL9bGxsTh8+LBEqcxLZWUlAMDDwwOA4RudgoKCFsdMrVZj9OjRNnnMZs+ejQceeADjx49vsZ7HCdiyZQtiYmLw+OOPw8fHB4MGDcJXX31lvJ3HiCxZamoqAgICEBYWht/97ndIT0+XOpJV4fjQufbt2wcfHx/07NkTL774IoqKiqSORFaE76/bhq8jbcPXg7bj2G7dOjK2Hjly5Ibt77vvPpw8eRJNTU2dltVUTPF6otfrUV1dbaxnmLuO7vPKlSuRlpaGd955p7MjmlRH9re5zvDhhx8iMDAQPXv2xOuvv476+vquiHzHOrLPw4cPR25uLrZt2wZRFFFYWIjvv/8eDzzwQFdE7nKmGrtYJG+nkpIS6HQ6+Pr6tljv6+uLgoICiVKZD1EUMW/ePIwcORJRUVEAYDwuPGbAhg0bcOrUKSxevPiG23icgPT0dCxfvhwRERH45ZdfMHPmTLzyyiv49ttvAfAYkeUaMmQIvv32W/zyyy/46quvUFBQgOHDh1tMTzhLwPGh80yaNAlr167Fnj178Mknn+DEiRO49957odFopI5GVoLvr2+PryNtx9eDtuHYbv06MrYWFBS0ur1Wq0VJSUmnZTUVU7yefPLJJ6itrcUTTzzRGRFNriP7nJqairfeegtr166FQqHoipgm05H9TU9Px8GDB3H27Fls3rwZS5cuxffff4/Zs2d3ReQ71pF9Hj58ONauXYtp06ZBpVLBz88Pbm5u+Pe//90VkbucqcYuy/prMCOCILT4WRTFG9bZojlz5uD06dM4ePDgDbfZ+jHLycnBq6++ip07d8LOzu6m29nycdLr9YiJicGiRYsAAIMGDcK5c+ewfPlyPPvss8btbPkYkWWaNGmS8d/9+vXDsGHDEB4ejtWrV2PevHkSJrM+HB9Mb9q0acZ/R0VFISYmBqGhodi6dSumTp0qYTKyNvz7vTm+jrQff59ujWO77Wjv30Jr27e23px19O9//fr1iIuLw08//QQfH5/Oitcp2rrPOp0OTz31FN5991307Nmzq+KZXHv+H+v1egiCgLVr18LV1RUAsGTJEjz22GP4/PPPYW9v3+l5TaE9+3z+/Hm88sorePvtt3HfffchPz8fb7zxBmbOnIkVK1Z0RdwuZ4qxizPJ28nLywtyufyGb2uKiopu+NbC1rz88svYsmUL9u7di6CgION6Pz8/ALD5Y5aYmIiioiJER0dDoVBAoVAgISEBn376KRQKhfFY2PJx8vf3R58+fVqs6927t/FiFPxdImvh6OiIfv36ITU1VeooVoPjQ9fx9/dHaGgof3/JZPj+uv34OnJzfD3oGI7t1qcjY6ufn1+r2ysUCnh6enZaVlO5k9eTjRs34vnnn8d33313Q2tUc9befa6ursbJkycxZ84cY13i73//O3799VcoFArs2bOnq6J3SEf+H/v7+yMwMNBYIAcMdQZRFJGbm9upeU2hI/u8ePFijBgxAm+88Qb69++P++67D8uWLcM333yD/Pz8rojdpUw1drFI3k4qlQrR0dGIj49vsT4+Ph7Dhw+XKJW0RFHEnDlzsGnTJuzZswdhYWEtbg8LC4Ofn1+LY9bY2IiEhASbOmbjxo3DmTNnkJycbFxiYmLw9NNPIzk5Gd27d7f54zRixAhcvHixxbpLly4hNDQUAH+XyHpoNBqkpKTA399f6ihWg+ND1yktLUVOTg5/f8lk+P66/fg6cnN8PegYju3WpyNj67Bhw27YfufOnYiJiYFSqey0rKbS0deT9evXY8aMGVi3bp3F9Wxu7z67uLjcUJeYOXMmevXqheTkZAwZMqSrondIR/4fjxgxAleuXEFNTY1x3aVLlyCTyVpM8DRXHdnnuro6yGQtS75yuRzAbzOsrYnJxq52XeaTRFEUxQ0bNohKpVJcsWKFeP78eXHu3Lmio6OjmJmZKXU0SfzpT38SXV1dxX379on5+fnGpa6uzrjNBx98ILq6uoqbNm0Sz5w5Iz755JOiv7+/WFVVJWFy6Y0ePVp89dVXjT/b+nE6fvy4qFAoxPfff19MTU0V165dKzo4OIhr1qwxbmPrx4gs05///Gdx3759Ynp6unj06FHxwQcfFJ2dnW32daOjqqurxaSkJDEpKUkEIC5ZskRMSkoSs7KyRFHk+NBRtzqu1dXV4p///Gfx8OHDYkZGhrh3715x2LBhYmBgII8rmRTfX98aX0da4uvB7XFsJ1G8/dj61ltvic8884xx+/T0dNHBwUF87bXXxPPnz4srVqwQlUql+P3330u1C+3W3n1et26dqFAoxM8//7xFPaOiokKqXWi39u7z9d555x1xwIABXZT2zrV3f6urq8WgoCDxscceE8+dOycmJCSIERER4gsvvCDVLrRbe/d55cqVokKhEJctWyampaWJBw8eFGNiYsS7775bql1ol9u9znfW2MUieQd9/vnnYmhoqKhSqcTBgweLCQkJUkeSDIBWl5UrVxq30ev14jvvvCP6+fmJarVavOeee8QzZ85IF9pMXF8k53ESxZ9//lmMiooS1Wq1GBkZKX755ZctbucxIks0bdo00d/fX1QqlWJAQIA4depU8dy5c1LHsjh79+5t9fVm+vTpoihyfOioWx3Xuro6MTY2VvT29haVSqUYEhIiTp8+XczOzpY6Nlkhvr++Ob6OtMTXg9vj2E7NbjW2Tp8+XRw9enSL7fft2ycOGjRIVKlUYrdu3cTly5d3ceI71559Hj169C3HE0vR3v/P17K0Irkotn9/U1JSxPHjx4v29vZiUFCQOG/evBYTOy1Be/f5008/Ffv06SPa29uL/v7+4tNPPy3m5uZ2ceqOud3rfGeNXYIoWuE8eyIiIiIiIiIiIiKiNmBPciIiIiIiIiIiIiKyWSySExEREREREREREZHNYpGciIiIiIiIiIiIiGwWi+REREREREREREREZLNYJCciIiIiIiIiIiIim8UiORERERERERERERHZLBbJiYiIiIiIiIiIiMhmsUhORERERERERERERDaLRXIiAJmZmRAEAcnJyVJHISIiIiIiIiIioi7EIjkRERHZjIKCArz66qvo0aMH7Ozs4Ovri5EjR+KLL75AXV0dAODLL7/EmDFj4OLiAkEQUFFRIW1oIiIbdLvxuqysDC+//DJ69eoFBwcHhISE4JVXXkFlZaXU0YmIiMgCKaQOQERERNQV0tPTMWLECLi5uWHRokXo168ftFotLl26hG+++QYBAQF46KGHUFdXh4kTJ2LixIlYsGCB1LGJiGxOW8br7t2748qVK/j444/Rp08fZGVlYebMmbhy5Qq+//57qXeBiIiILAxnkpPN2LFjB0aOHAk3Nzd4enriwQcfRFpaWottLly4gOHDh8POzg59+/bFvn37jLeVl5fj6aefhre3N+zt7REREYGVK1cab8/Ly8O0adPg7u4OT09PPPzww8jMzDTePmPGDEyZMgUff/wx/P394enpidmzZ6Opqcm4jUajwfz58xEcHAy1Wo2IiAisWLHCePv58+dx//33w8nJCb6+vnjmmWdQUlJivP37779Hv379YG9vD09PT4wfPx61tbUmPIpERJZr1qxZUCgUOHnyJJ544gn07t0b/fr1w6OPPoqtW7di8uTJAIC5c+firbfewtChQyVOTERkm9oyXkdFReGHH37A5MmTER4ejnvvvRfvv/8+fv75Z2i1Wql3gYiIiCwMi+RkM2prazFv3jycOHECu3fvhkwmwyOPPAK9Xm/c5o033sCf//xnJCUlYfjw4XjooYdQWloKAPjb3/6G8+fPY/v27UhJScHy5cvh5eUFAKirq8PYsWPh5OSE/fv34+DBg3BycsLEiRPR2NhofPy9e/ciLS0Ne/fuxerVq7Fq1SqsWrXKePuzzz6LDRs24NNPP0VKSgq++OILODk5AQDy8/MxevRoDBw4ECdPnsSOHTtQWFiIJ554wnj7k08+ieeeew4pKSnYt28fpk6dClEUO/vQEhGZvdLSUuzcuROzZ8+Go6Njq9sIgtDFqYiI6Hp3Ml5XVlbCxcUFCgVPmCYiIqL24bsHshmPPvpoi59XrFgBHx8fnD9/3liInjNnjnG75cuXY8eOHVixYgXmz5+P7OxsDBo0CDExMQCAbt26GR9rw4YNkMlk+Prrr41v2leuXAk3Nzfs27cPsbGxAAB3d3d89tlnkMvliIyMxAMPPIDdu3fjxRdfxKVLl/Ddd98hPj4e48ePBwB0797d+BzLly/H4MGDsWjRIuO6b775BsHBwbh06RJqamqg1WoxdepUhIaGAgD69etnykNIRGSxLl++DFEU0atXrxbrvby80NDQAACYPXs2/vGPf0gRj4iIruroeF1aWor/+7//w0svvdRlWYmIiMh6cCY52Yy0tDQ89dRT6N69O1xcXBAWFgYAyM7ONm4zbNgw478VCgViYmKQkpICAPjTn/6EDRs2YODAgZg/fz4OHz5s3DYxMRGXL1+Gs7MznJyc4OTkBA8PDzQ0NLRo6dK3b1/I5XLjz/7+/igqKgIAJCcnQy6XY/To0a3mT0xMxN69e42P7+TkhMjISOO+DRgwAOPGjUO/fv3w+OOP46uvvkJ5efmdHjYiIqty/ezD48ePIzk5GX379oVGo5EoFRERXa8943VVVRUeeOAB9OnTB++8805XxiQiIiIrwZnkZDMmT56M4OBgfPXVVwgICIBer0dUVFSLdiitaX6DPmnSJGRlZWHr1q3YtWsXxo0bh9mzZ+Pjjz+GXq9HdHQ01q5de8P9vb29jf9WKpU3PHZzuxd7e/tb5tDr9Zg8eXKrsxz9/f0hl8sRHx+Pw4cPY+fOnfj3v/+NhQsX4tixY8YvBIiIbFWPHj0gCAIuXLjQYn3zGTu3G4OJiKhrtHe8rq6uxsSJE+Hk5ITNmzff8H6biIiIqC04k5xsQmlpKVJSUvDXv/4V48aNQ+/evVudZX306FHjv7VaLRITE42ztQFDwXvGjBlYs2YNli5dii+//BIAMHjwYKSmpsLHxwc9evRosbi6urYpY79+/aDX65GQkNDq7YMHD8a5c+fQrVu3G56juV+jIAgYMWIE3n33XSQlJUGlUmHz5s1tPk5ERNbK09MTEyZMwGeffcYLGhMRmbH2jNdVVVWIjY2FSqXCli1bYGdn10UpiYiIyNqwSE42wd3dHZ6envjyyy9x+fJl7NmzB/Pmzbthu88//xybN2/GhQsXMHv2bJSXl+O5554DALz99tv46aefcPnyZZw7dw7/+9//0Lt3bwDA008/DS8vLzz88MM4cOAAMjIykJCQgFdffRW5ubltytitWzdMnz4dzz33HH788UdkZGRg3759+O677wAYei+WlZXhySefxPHjx5Geno6dO3fiueeeg06nw7Fjx7Bo0SKcPHkS2dnZ2LRpE4qLi40ZiYhs3bJly6DVahETE4ONGzciJSUFFy9exJo1a3DhwgVjO6yCggIkJyfj8uXLAIAzZ84gOTkZZWVlUsYnIrIZbRmvq6urERsbi9raWqxYsQJVVVUoKChAQUEBdDqd1LtAREREFobtVsgmyGQybNiwAa+88gqioqLQq1cvfPrppxgzZkyL7T744AP84x//QFJSEsLDw/HTTz/By8sLAKBSqbBgwQJkZmbC3t4eo0aNwoYNGwAADg4O2L9/P958801MnToV1dXVCAwMxLhx4+Di4tLmnMuXL8df/vIXzJo1C6WlpQgJCcFf/vIXAEBAQAAOHTqEN998E/fddx80Gg1CQ0MxceJEyGQyuLi4YP/+/Vi6dCmqqqoQGhqKTz75BJMmTTLNQSQisnDh4eFISkrCokWLsGDBAuTm5kKtVqNPnz54/fXXMWvWLADAF198gXfffdd4v3vuuQeA4YLMM2bMkCI6EZFNact4ffz4cRw7dgyAoUXLtTIyMtCtWzcJkhMREZGlEkRRFKUOQUREREREREREREQkBbZbISIiIiIiIiIiIiKbxSI5EREREREREREREdksFsmJiIiIiIiIiIiIyGaxSE5ERERERERERERENotFciIiIiIiIiIiIiKyWSySExEREREREREREZHNYpGciIiIiIiIiIiIiGwWi+REREREREREREREZLNYJCciIiIiIiIiIiIim8UiORERERERERERERHZLBbJiYiIiIiIiIiIiMhm/X+rHGOv3zl4ygAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "numerical_features = ['age', 'Medu', 'Fedu', 'traveltime', 'studytime', 'failures', 'famrel', 'freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences', 'G1', 'G2']\n", "nrows,ncols = 4,4\n", "fig, axs = plt.subplots(nrows, ncols, figsize=(18,21))\n", "i,j = 0,0\n", "for feature in numerical_features:\n", " sns.histplot(df[feature], kde=True, ax=axs[i,j])\n", " axs[i,j].set_title(f\"Distribution of {feature}\")\n", " j = j + 1\n", " if j % ncols == 0:\n", " i = i + 1\n", " j = 0" ] }, { "cell_type": "markdown", "id": "77c0e735-7e5b-4be5-90f2-125202804da5", "metadata": {}, "source": [ "Age and absences seem to be right skewed, while G1 and G2 are close to normal distribution. We can confirm these observations by using statistical measures as shown below:" ] }, { "cell_type": "code", "execution_count": 15, "id": "4c549ec7-0666-4ffd-b8e9-196fa057855d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Age skewness: 0.3099070931477198\n", "Absences skewness: 3.6715789504758862\n", "Normality tests for G1\n", "======================\n", "Shapiro-Wilk Test: {'Statistic': 0.9749134463536321, 'p-value': 2.4541585453322648e-06, 'Normal': False}\n", "Anderson-Darling Test: {'Statistic': 3.2267436964076524, 'Critical Values': array([0.57 , 0.65 , 0.779, 0.909, 1.081]), 'Significance Levels': array([15. , 10. , 5. , 2.5, 1. ]), 'Normal': False}\n", "Kolmogorov-Smirnov Test: {'Statistic': 0.09662942363084004, 'p-value': 0.0011602978195641197, 'Normal': False}\n", "D’Agostino and Pearson’s Test: {'Statistic': 22.612048290638576, 'p-value': 1.2298612026597037e-05, 'Normal': False}\n", "Jarque-Bera Test: {'Statistic': 11.852351204527706, 'p-value': 0.002668668547586327, 'Normal': False}\n", "Lilliefors Test: {'Statistic': 0.09649598812059312, 'p-value': 0.0009999999999998899, 'Normal': False}\n", "Normality tests for G2\n", "======================\n", "Shapiro-Wilk Test: {'Statistic': 0.9691414513461054, 'p-value': 2.083959796331057e-07, 'Normal': False}\n", "Anderson-Darling Test: {'Statistic': 2.5272443802065254, 'Critical Values': array([0.57 , 0.65 , 0.779, 0.909, 1.081]), 'Significance Levels': array([15. , 10. , 5. , 2.5, 1. ]), 'Normal': False}\n", "Kolmogorov-Smirnov Test: {'Statistic': 0.0810756194160844, 'p-value': 0.010477220635139107, 'Normal': False}\n", "D’Agostino and Pearson’s Test: {'Statistic': 16.269029207533304, 'p-value': 0.00029324133947711663, 'Normal': False}\n", "Jarque-Bera Test: {'Statistic': 18.189764583950865, 'p-value': 0.00011223874630804778, 'Normal': False}\n", "Lilliefors Test: {'Statistic': 0.08128339674685511, 'p-value': 0.0009999999999998899, 'Normal': False}\n" ] } ], "source": [ "print('Age skewness:', df['age'].skew())\n", "print('Absences skewness:', df['absences'].skew())\n", "print('Normality tests for G1\\n======================')\n", "results = run_normality_tests(df['G1'])\n", "for test, result in results.items():\n", " print(f\"{test}: {result}\")\n", "print('Normality tests for G2\\n======================')\n", "results = run_normality_tests(df['G2'])\n", "for test, result in results.items():\n", " print(f\"{test}: {result}\")" ] }, { "cell_type": "markdown", "id": "dd940a5b-8c1a-42ba-85db-566d46894b45", "metadata": {}, "source": [ "Based on the results, age feature is moderately right skewed while absences feature is highly right skewed. We can apply unskewing transformations (log, sqrt, Box-Cox, Yeo-Johnson) when experimenting with machine learning modelling.\n", "\n", "G1 and G2 do not follow the normal distribution based on the normality tests. Since the values are on a different scale compared with the rest features, we can apply scaling *if we intend to use distance-based machine learning for predictive modelling*. Standard scaler, robust scaler, min max scaler can be used. Standard scaler is more appropriate when feature is approximately normal. Robust scaler is more applicable when feature has significant outliers. Max-min scaler is more appropriate when feature values need to be scaled within a specific range (this is usually needed in neural netwrorks.\n", "\n", "The rest features have discrete numerical values, with low spread. Whether or not to transform such features depends on the context of the problem, the machine learning algorithm being used, and the role of the feature in your analysis. Here's a detailed explanation:\n", "\n", "1. When Transformation is NOT Necessary\n", " * Tree-Based Models (e.g., Decision Trees, Random Forest, XGBoost): These models are invariant to scaling because they split data based on thresholds. Transforming a low-spread numerical feature (like 1-6) will not affect their performance.\n", " * Low Variability and No Skew: If the distribution is already uniform or balanced, and there's no skew, scaling is usually not required.\n", " * Feature Represents Ordinal Categories: If the feature represents an ordinal variable (e.g., levels like 1=low, 2=medium, 3=high), it already carries meaningful rank information. Scaling or normalizing may distort this interpretation.\n", "2. When Transformation Might Be Necessary\n", " * Distance-Based Models (e.g., K-Nearest Neighbors, K-Means, SVMs): These models calculate distances between data points. A feature with a low spread might have less influence compared to other features with larger spreads. Scaling ensures all features are treated equally in terms of their contribution to distance metrics.\n", " * Gradient-Based Models (e.g., Neural Networks, Logistic Regression): Features with low spread may still need scaling (e.g., MinMaxScaler or StandardScaler) to improve optimization and training stability.\n", " * If Combined with Other Scaled Features: If other numerical features are scaled, it’s usually a good idea to scale this feature too, to maintain consistency.\n", " * If the Feature is Highly Skewed: Even low-spread features can exhibit skewness (e.g., a highly imbalanced frequency distribution). Applying transformations like log scaling or normalization can help." ] }, { "cell_type": "markdown", "id": "414662e2-9f18-406f-8bc3-894bc3aeadb2", "metadata": {}, "source": [ "#### Exploring relationships between features and the target variable (G3)" ] }, { "cell_type": "markdown", "id": "199bfb0d-a1b0-4f09-9b2a-932e3ecd6765", "metadata": {}, "source": [ "#### - Keep the target variable as continuous numerical variable: This approach is more precise and is better suited for statistical comparisons and detailed analyses." ] }, { "cell_type": "markdown", "id": "1c9de1b7-fac0-4481-af7c-5a3a6be99dd8", "metadata": {}, "source": [ "1. Using plots: Visualize the distribution of G3 for each category of a categorical variable or statistical measures (mean, std, median) of G3 for each category of a categorical variable." ] }, { "cell_type": "code", "execution_count": 16, "id": "9f814d38-c98c-4cb9-896d-d6dece41044d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'G3 Distribution by School')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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vXj0TFBRkunXrZl588UVz7ty5Uu3HjBljwsPDTf369V3f62PHjhlj/v16vfXWW27rlXzfSr7nJV555RVzww03GD8/PxMSEmLuuusus2fPnlJ1VuT15i4PVIbNmAp0OQeAq1Tnzp1ls9m0Y8cOT5cCeDUueQDARXJycvSvf/1Lf/vb3/Tll1/q3Xff9XRJgNcjUADARf75z3+qd+/eaty4sWbNmlXqAWcASuOSBwAAsIzbRgEAgGUECgAAYBmBAgAAWFbnO2UWFxcrKytLQUFBlR6KGACAq5kxRrm5uXI6nW4PkytLnQ8UWVlZioyM9HQZAADUWocOHbrsg+zqfKAICgqS9MuLceHwygAA4NJycnIUGRnp+iy9lDofKEoucwQHBxMoAAC4AhXpMkCnTAAAYBmBAgAAWEagAAAAlnk0UKSkpKhLly4KCgpSaGiofve732nfvn1ubYwxSkxMlNPplL+/v3r16qU9e/Z4qGIAAFAWjwaKLVu2aPLkyfr888+Vmpqqc+fOqV+/fjpz5oyrzdy5c/X8889r/vz52rFjh8LDwxUTE6Pc3FwPVg4AAC7kVQ8HO3HihEJDQ7VlyxbddtttMsbI6XTqkUce0RNPPCFJKigoUFhYmJ599lmNHz/+stvMyclRSEiIsrOzucsDAIBKqMxnqFfdNpqdnS1JatSokSQpIyNDR48eVb9+/Vxt7Ha7evbsqbS0tDIDRUFBgQoKClzTOTk51Vx17ZSfn6+DBw96uoyrUlRUlBwOh6fLAIAq5TWBwhij+Ph43XrrrerQoYMk6ejRo5KksLAwt7ZhYWHKzMwsczspKSlKSkqq3mLrgIMHD2rcuHGeLuOqtHjxYrVu3drTZQBAlfKaQDFlyhTt3r1bn332WallFw+oYYwpd5CNGTNmKD4+3jVdMsoX3EVFRWnx4sWeLqPSMjMzNWfOHM2cOVPR0dGeLueKREVFeboEAKhyXhEopk6dqnXr1mnr1q1uY4WHh4dL+uVMRUREhGv+8ePHS521KGG322W326u34DrA4XDU6r+So6Oja3X9AFDXePQuD2OMpkyZojVr1uiTTz5Rs2bN3JY3a9ZM4eHhSk1Ndc0rLCzUli1b1L1795ouFwAAlMOjZygmT56slStX6r333lNQUJCrz0RISIj8/f1ls9n0yCOPKDk5Wa1atVKrVq2UnJysBg0a6IEHHvBk6QAA4AIeDRQLFy6UJPXq1ctt/tKlSzVq1ChJ0uOPP66zZ89q0qRJOnXqlLp166YPP/ywQk8+AwAANcOjgaIiQ2DYbDYlJiYqMTGx+gsCAABXhGd5AAAAywgUAADAMgIFAACwzCvGoQAA/BtD43sOQ+NfOQIFAHgZhsb3HIbGv3IECgDwMrV1aHyp9g+Pz9D4V45AAQBeprYPjS8xPP7ViE6ZAADAMgIFAACwjEABAAAsI1AAAADLCBQAAMAyAgUAALCMQAEAACwjUAAAAMsIFAAAwDICBQAAsIxAAQAALCNQAAAAywgUAADAMgIFAACwjEABAAAsI1AAAADLCBQAAMAyAgUAALCMQAEAACwjUAAAAMsIFAAAwDICBQAAsIxAAQAALCNQAAAAywgUAADAMgIFAACwjEABAAAsI1AAAADLCBQAAMAyAgUAALCMQAEAACwjUAAAAMsIFAAAwDICBQAAsIxAAQAALPNooNi6davuuOMOOZ1O2Ww2rV271m35qFGjZLPZ3L5uvvlmzxQLAADK5dFAcebMGXXq1Enz588vt81vf/tbHTlyxPW1fv36GqwQAABURD1P7jwuLk5xcXGXbGO32xUeHl5DFQEAgCvh9X0oNm/erNDQULVu3Vpjx47V8ePHL9m+oKBAOTk5bl8AAKB6eXWgiIuL0+uvv65PPvlEzz33nHbs2KE+ffqooKCg3HVSUlIUEhLi+oqMjKzBigEAuDp59JLH5QwZMsT1/w4dOqhz586Kjo7W3//+d91zzz1lrjNjxgzFx8e7pnNycggVAABUM68OFBeLiIhQdHS0Dhw4UG4bu90uu91eg1UBAACvvuRxsZMnT+rQoUOKiIjwdCkAAOACHj1DkZeXp2+//dY1nZGRoV27dqlRo0Zq1KiREhMTNWjQIEVEROj7779XQkKCmjRporvvvtuDVQMAgIt5NFDs3LlTvXv3dk2X9H0YOXKkFi5cqK+//lqvvfaaTp8+rYiICPXu3VtvvvmmgoKCPFUyAAAog0cDRa9evWSMKXf5Bx98UIPVAACAK1Wr+lAAAADvRKAAAACW1arbRr3RsWPHlJ2d7ekyrhqZmZlu/6JmhISEKCwszNNlAPBiBAoLjh07pgeHj1BRYfkjd6J6zJkzx9MlXFXq+9m14q+vESoAlItAYUF2draKCgt0tnlPFTtCPF0OUC188rOl77YoOzubQAGgXASKKlDsCFFxQBNPlwEAgMfQKRMAAFhGoAAAAJYRKAAAgGUECgAAYBmBAgAAWEagAAAAlhEoAACAZQQKAABgGYECAABYxkiZAOo0HuBXs3iAn2d4wwP8CBQA6iwe4Oc5PMCvZnnDA/wIFADqLB7gh6uBtzzAj0ABoM7jAX5A9aNTJgAAsIxAAQAALCNQAAAAywgUAADAMgIFAACwjEABAAAsI1AAAADLCBQAAMAyAgUAALCMQAEAACwjUAAAAMsIFAAAwDICBQAAsIxAAQAALCNQAAAAy+p5uoC6wOfsaU+XAFQbfr4BVASBogr4Z2z1dAkAAHgUgaIKnG12m4r9G3q6DKBa+Jw9TWgGcFkEiipQ7N9QxQFNPF0GAAAeQ6dMAABgWaUDhTFGGRkZOnfunCSpsLBQb775pl577TX9+OOPVV4gAADwfpW65LFv3z7Fxsbq0KFDat68uT788EPde++92rt3r4wxatCggdLS0tSqVavqqhcAAHihSp2heOKJJ9SpUyft2rVLAwcO1MCBA9W0aVOdOnVKp06dUo8ePfTMM89UeHtbt27VHXfcIafTKZvNprVr17otN8YoMTFRTqdT/v7+6tWrl/bs2VOZkgEAQA2oVKBIS0tTUlKSOnbsqNmzZys9PV2PPfaY6tevLz8/Pz3xxBPaurXivcHPnDmjTp06af78+WUunzt3rp5//nnNnz9fO3bsUHh4uGJiYpSbm1uZsgEAQDWr1CWPvLw8NWrUSJIUEBCggIAARUREuJY3bdpUx44dq/D24uLiFBcXV+YyY4zmzZunmTNn6p577pEkLV++XGFhYVq5cqXGjx9fmdIBAEA1qlSgcDqdOnjwoKKioiT9cgYhNDTUtfzEiRO65pprqqSwjIwMHT16VP369XPNs9vt6tmzp9LS0soNFAUFBSooKHBN5+TkVEk9AGovRvtEXeYtP9+VChS333679u7dq1tvvVWSNHHiRLflH374oW688cYqKezo0aOSpLCwMLf5YWFhyszMLHe9lJQUJSUlVUkNAOoGBuYCql+lAsWiRYsuuXzIkCEaOXKkpYIuZrPZ3KaNMaXmXWjGjBmKj493Tefk5CgyMrJKawJQuzCaLeoybxnNtlKBIj8/Xx999JEGDhwo6ZcP7wsvL/j6+uq//uu/qqSw8PBwSb+cqbiwn8bx48dLnbW4kN1ul91ur5IaANQNjGYLVL9K3eWxfPlyvfzyy67p+fPnKy0tTV999ZW++uorrVixQgsXLqySwpo1a6bw8HClpqa65hUWFmrLli3q3r17lewDAABUjUqdoXj99df16KOPus1buXKlmjdvLklasWKFFixYUKpNefLy8vTtt9+6pjMyMrRr1y41atRIUVFReuSRR5ScnKxWrVqpVatWSk5OVoMGDfTAAw9UpmwAAFDNKhUo9u/fr9atW7umHQ6HfHz+fZKja9eumjx5coW3t3PnTvXu3ds1XdL3YeTIkVq2bJkef/xxnT17VpMmTdKpU6fUrVs3ffjhhwoKCqpM2QAAoJpVKlBkZ2erXr1/r3LixAm35cXFxW59Ki6nV69eMsaUu9xmsykxMVGJiYmVKRMAANSwSvWhaNq0qf71r3+Vu3z37t1q2rSp5aIAAEDtUqlA0b9/fz399NPKz88vtezs2bNKSkrSgAEDqqw4AABQO1TqkkdCQoJWr16tNm3aaMqUKWrdurVsNpv27t2r+fPn69y5c0pISKiuWgEAgJeqVKAICwtTWlqaJk6cqCeffNLV/8FmsykmJkYvvfTSJceIAAAAdVOlAoX0y/gQGzdu1E8//eS65bNly5auh4YBAICrT6UDRYlGjRqpa9euVVkLAACopSrVKRMAAKAsBAoAAGAZgQIAAFhGoAAAAJYRKAAAgGUECgAAYBmBAgAAWHbF41Dg33zysz1dAlBt6sLPd104BqA83vLzTaCwICQkRPX97NJ3WzxdClCt6vvZFRIS4ukyKo33KK4W3vAetZmSB3LUUTk5OQoJCVF2draCg4OrfPvHjh1TdrZ3pMOrQWZmpubMmaOZM2cqOjra0+VcNUJCQmrtc3p4j9Ys3qOeUV3v0cp8hnKGwqKwsLBa+4u2NouOjlbr1q09XQZqAd6jnsF79OpDp0wAAGAZgQIAAFhGoAAAAJYRKAAAgGUECgAAYBmBAgAAWEagAAAAlhEoAACAZQQKAABgGYECAABYRqAAAACWESgAAIBlBAoAAGAZgQIAAFhGoAAAAJYRKAAAgGUECgAAYBmBAgAAWEagAAAAlhEoAACAZQQKAABgGYECAABYRqAAAACWESgAAIBlBAoAAGCZVweKxMRE2Ww2t6/w8HBPlwUAAC5Sz9MFXE779u310UcfuaZ9fX09WA0AACiL1weKevXqcVYCAAAv5/WB4sCBA3I6nbLb7erWrZuSk5PVvHnzctsXFBSooKDANZ2Tk1MTZQJAlcnPz9fBgwc9XcYVyczMdPu3tomKipLD4fB0GbWSVweKbt266bXXXlPr1q117NgxzZ49W927d9eePXvUuHHjMtdJSUlRUlJSDVcKAFXn4MGDGjdunKfLsGTOnDmeLuGKLF68WK1bt/Z0GbWSzRhjPF1ERZ05c0YtWrTQ448/rvj4+DLblHWGIjIyUtnZ2QoODq6pUlFN9u/fr3HjxvGmR51Wm89Q1HacoXCXk5OjkJCQCn2GevUZiosFBASoY8eOOnDgQLlt7Ha77HZ7DVYFAFXL4XAQmFHrePVtoxcrKChQenq6IiIiPF0KAAC4gFcHiscee0xbtmxRRkaGvvjiCw0ePFg5OTkaOXKkp0sDAAAX8OpLHocPH9bQoUP1448/6le/+pVuvvlmff7554qOjvZ0aQAA4AJeHSjeeOMNT5cAAAAqwKsveQAAgNqBQAEAACwjUAAAAMsIFAAAwDICBQAAsIxAAQAALCNQAAAAywgUAADAMgIFAACwjEABAAAsI1AAAADLCBQAAMAyAgUAALCMQAEAACwjUAAAAMsIFAAAwDICBQAAsIxAAQAALCNQAAAAywgUAADAMgIFAACwjEABAAAsI1AAAADLCBQAAMAyAgUAALCMQAEAACwjUAAAAMsIFAAAwDICBQAAsIxAAQAALCNQAAAAywgUAADAMgIFAACwjEABAAAsI1AAAADLCBQAAMAyAgUAALCMQAEAACwjUAAAAMsIFAAAwLJ6ni4AAFA35OXlKSUlRVlZWXI6nZoxY4YCAwM9XRZqSK04Q/HSSy+pWbNmcjgcuummm/Tpp596uiQAwAUmTJiggQMHatu2bcrIyNC2bds0cOBATZgwwdOloYZ4faB488039cgjj2jmzJn66quv9Jvf/EZxcXE6ePCgp0sDAOiXMLF3717ZbDb169dPr7zyivr16yebzaa9e/cSKq4SXh8onn/+eT300EN6+OGH1bZtW82bN0+RkZFauHChp0sDgKteXl6eK0xs2LBBCQkJatmypRISErRhwwZXqMjLy/N0qahmXt2HorCwUF9++aWefPJJt/n9+vVTWlpamesUFBSooKDANZ2Tk1OtNdZW+fn5tfIsT2Zmptu/tVFUVJQcDoenywCqREpKiiQpJiam1M+1w+HQ7bffrtTUVKWkpGjOnDmeKBE1xKsDxY8//qjz588rLCzMbX5YWJiOHj1a5jopKSlKSkqqifJqtYMHD2rcuHGeLuOK1eZfTIsXL1br1q09XQZQJbKysiRJ9913X5nL7733XqWmprraoe7y6kBRwmazuU0bY0rNKzFjxgzFx8e7pnNychQZGVmt9dVGUVFRWrx4safLuCpFRUV5ugSgyjidTmVkZGj16tVKSEgotfytt95ytUPd5tWBokmTJvL19S11NuL48eOlzlqUsNvtstvtNVFereZwOPgrGYBlM2bM0MCBA5Wamqr4+Hi3yx75+fn66KOPXO1Qt3l1p0w/Pz/ddNNNSk1NdZufmpqq7t27e6gqAECJwMBAXX/99TLGKC4uTnPmzNH+/fs1Z84cxcXFyRij66+/nvEorgI2Y4zxdBGX8uabb2r48OFatGiRbrnlFi1evFh/+ctftGfPHkVHR192/ZycHIWEhCg7O1vBwcE1UDEAXH1Kbh292PXXX69FixZ5oCJUhcp8hnr1JQ9JGjJkiE6ePKlnnnlGR44cUYcOHbR+/foKhQkAQM1YtGgRI2Ve5bz+DIVVnKEAAODKVOYz1Kv7UAAAgNqBQAEAACwjUAAAAMu8vlOmVSVdRBiCGwCAyin57KxId8s6Hyhyc3MlidEyAQC4Qrm5uQoJCblkmzp/l0dxcbGysrIUFBRU7nDdqD1KhlI/dOgQd+0AXoj3aN1ijFFubq6cTqd8fC7dS6LOn6Hw8fFR06ZNPV0GqlhwcDC/rAAvxnu07rjcmYkSdMoEAACWESgAAIBlBArUKna7XbNmzeKJsoCX4j169arznTIBAED14wwFAACwjEABAAAsI1AAAADLCBQAAMAyAgW80tGjRzVt2jS1bNlSDodDYWFhuvXWW7Vo0SL9/PPPkqTrrrtONptNNptNDRo0UIcOHfTyyy97uHKgbhs1apRsNpsmTJhQatmkSZNks9k0atQoSdLx48c1fvx4RUVFyW63Kzw8XLGxsdq+fXsNV42aUOdHykTt891336lHjx5q2LChkpOT1bFjR507d0779+/XkiVL5HQ6deedd0qSnnnmGY0dO1Z5eXlatmyZJkyYoIYNG2rIkCEePgqg7oqMjNQbb7yhF154Qf7+/pKk/Px8rVq1SlFRUa52gwYNUlFRkZYvX67mzZvr2LFj+vjjj/XTTz95qnRUIwIFvM6kSZNUr1497dy5UwEBAa75HTt21KBBg9yeehcUFKTw8HBJ0uzZs7V69WqtXbuWQAFUoxtvvFHfffed1qxZo2HDhkmS1qxZo8jISDVv3lySdPr0aX322WfavHmzevbsKUmKjo5W165dPVY3qheXPOBVTp48qQ8//FCTJ092CxMXutRD3hwOh4qKiqqrPAD/3+jRo7V06VLX9JIlSzRmzBjXdGBgoAIDA7V27VoVFBR4okTUMAIFvMq3334rY4zatGnjNr9JkyauX1BPPPFEqfXOnTunZcuW6euvv1bfvn1rqlzgqjV8+HB99tln+v7775WZmalt27bpwQcfdC2vV6+eli1bpuXLl6thw4bq0aOHEhIStHv3bg9WjepEoIBXuvgsxD/+8Q/t2rVL7du3d/tr54knnlBgYKD8/f01efJkTZ8+XePHj6/pcoGrTpMmTTRgwAAtX75cS5cu1YABA9SkSRO3NoMGDVJWVpbWrVun2NhYbd68WTfeeKOWLVvmmaJRrehDAa/SsmVL2Ww27d27121+yXXZkg5gJaZPn65Ro0apQYMGioiIuOTlEABVa8yYMZoyZYokacGCBWW2cTgciomJUUxMjJ5++mk9/PDDmjVrlutOENQdnKGAV2ncuLFiYmI0f/58nTlz5rLtmzRpopYtW8rpdBImgBr229/+VoWFhSosLFRsbGyF1mnXrl2F3tuofQgU8DovvfSSzp07p86dO+vNN99Uenq69u3bpxUrVmjv3r3y9fX1dIkAJPn6+io9PV3p6eml3pcnT55Unz59tGLFCu3evVsZGRl66623NHfuXN11110eqhjViUse8DotWrTQV199peTkZM2YMUOHDx+W3W5Xu3bt9Nhjj2nSpEmeLhHA/xccHFzm/MDAQHXr1k0vvPCC/u///k9FRUWKjIzU2LFjlZCQUMNVoibw+HIAAGAZlzwAAIBlBAoAAGAZgQIAAFhGoAAAAJYRKAAAgGUECgAAYBmBAgAAWEagAAAAlhEoANQIm82mtWvXVus+Nm/eLJvNptOnT1frfgCURqAAAACWESgAAIBlBAoAlfL222+rY8eO8vf3V+PGjXX77be7Hke9ZMkStW/fXna7XREREZoyZYrbuj/++KPuvvtuNWjQQK1atdK6devclm/ZskVdu3Z1rf/kk0/q3LlzruUFBQX6/e9/r9DQUDkcDt16663asWNH9R80gMsiUACosCNHjmjo0KEaM2aM0tPTtXnzZt1zzz0yxmjhwoWaPHmyxo0bp6+//lrr1q1Ty5Yt3dZPSkrSfffdp927d6t///4aNmyYfvrpJ0nSDz/8oP79+6tLly763//9Xy1cuFCvvvqqZs+e7Vr/8ccf1zvvvKPly5frn//8p1q2bKnY2FjXNgB4kAGACvryyy+NJPP999+XWuZ0Os3MmTPLXVeSeeqpp1zTeXl5xmazmQ0bNhhjjElISDBt2rQxxcXFrjYLFiwwgYGB5vz58yYvL8/Ur1/fvP76667lhYWFxul0mrlz5xpjjNm0aZORZE6dOmX1UAFUUj0P5xkAtUinTp3Ut29fdezYUbGxserXr58GDx6soqIiZWVlqW/fvpdc/4YbbnD9PyAgQEFBQTp+/LgkKT09XbfccotsNpurTY8ePZSXl6fDhw/r9OnTKioqUo8ePVzL69evr65duyo9Pb2KjxRAZXHJA0CF+fr6KjU1VRs2bFC7du304osvqk2bNjp27FiF1q9fv77btM1mU3FxsSTJGOMWJkrmlbS78P8Xt7l4HoCaR6AAUCk2m009evRQUlKSvvrqK/n5+Sk1NVXXXXedPv744yvebrt27ZSWluYKDpKUlpamoKAgXXvttWrZsqX8/Pz02WefuZYXFRVp586datu2raVjAmAdlzwAVNgXX3yhjz/+WP369VNoaKi++OILnThxQm3btlViYqImTJig0NBQxcXFKTc3V9u2bdPUqVMrtO1JkyZp3rx5mjp1qqZMmaJ9+/Zp1qxZio+Pl4+PjwICAjRx4kRNnz5djRo1UlRUlObOnauff/5ZDz30UDUfOYDLIVAAqLDg4GBt3bpV8+bNU05OjqKjo/Xcc88pLi5OkpSfn68XXnhBjz32mJo0aaLBgwdXeNvXXnut1q9fr+nTp6tTp05q1KiRHnroIT311FOuNn/84x9VXFys4cOHKzc3V507d9YHH3yga665psqPFUDl2MyF5xcBAACuAH0oAACAZQQKAABgGYECAABYRqAAAACWESgAAIBlBAoAAGAZgQIAAFhGoAAAAJYRKAAAgGUECgAAYBmBAgAAWPb/AOLypzNAhLy6AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Boxplots: Visualize the distribution of G3 for each category of a categorical variable.\n", "fig,ax = plt.subplots(figsize=(6,3))\n", "sns.boxplot(x='school', y='G3', data=df)\n", "ax.set_title(\"G3 Distribution by School\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "72ef7b30-4259-4704-aae7-9ba2971b697f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'G3 median and std by Free Time')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Barplots: Visualize statistical measures (mean, std, median) of G3 for each category of a categorical variable.\n", "fig, axs = plt.subplots(1, 2, figsize=(12,3))\n", "sns.barplot(y='G3', x='school', data=df, ax=axs[0])\n", "axs[0].set_title(\"G3 mean and std by School\")\n", "\n", "sns.barplot(y='G3', x='freetime', data=df, estimator='median', errorbar=None, ax=axs[1])\n", "axs[1].set_title(\"G3 median and std by Free Time\")" ] }, { "cell_type": "markdown", "id": "f2690fc9-22d8-4ec0-a5a9-339919749cb1", "metadata": {}, "source": [ "2. Using Group Statistics: Compute summary statistics like mean, median, and standard deviation for G3 across categories." ] }, { "cell_type": "code", "execution_count": 18, "id": "5067b22c-1fe4-4252-b84a-11f85bfabb62", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
meanstdmedian
school
GP10.4899714.62539711.0
MS9.8478264.23722910.0
\n", "
" ], "text/plain": [ " mean std median\n", "school \n", "GP 10.489971 4.625397 11.0\n", "MS 9.847826 4.237229 10.0" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('school')['G3'].agg(['mean', 'std', 'median'])" ] }, { "cell_type": "markdown", "id": "ba5443b5-7d81-4eaa-9796-d936a7419068", "metadata": {}, "source": [ "#### - Convert target variable into categorical (e.g., Low, Average, High) using binning: This approach simplifies the analysis, making it easier to spot broad trends or patterns." ] }, { "cell_type": "code", "execution_count": 19, "id": "f2917aac-c1af-4951-8ac1-4e8eb5ae5b81", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " G3 G3_category\n", "0 6 Low\n", "1 6 Low\n", "2 10 Low\n", "3 15 Average\n", "4 10 Low\n", "5 15 Average\n", "6 11 Average\n", "7 6 Low\n", "8 19 High\n", "9 15 Average\n" ] }, { "data": { "text/plain": [ "Text(0, 0.5, 'Count')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Define bins and labels\n", "bins = [0, 10, 15, 20] # Define bin edges\n", "labels = ['Low', 'Average', 'High'] # Define category labels\n", "\n", "# Binning G3\n", "df['G3_category'] = pd.cut(df['G3'], bins=bins, labels=labels, include_lowest=True)\n", "\n", "# Display the new column\n", "print(df[['G3', 'G3_category']].head(10))\n", "\n", "# Visualize the distribution of categories\n", "fig,ax = plt.subplots(figsize=(6,3))\n", "sns.countplot(x='G3_category', data=df)\n", "ax.set_title(\"Distribution of G3 Categories\")\n", "ax.set_xlabel(\"Performance Category\")\n", "ax.set_ylabel(\"Count\")" ] }, { "cell_type": "markdown", "id": "e68f12da-97fc-4a7a-ad31-2f6edd951a33", "metadata": {}, "source": [ "The newly binned feature G3_category can be utilized in several ways depending on your analysis goals. Here are some ideas:\n", "\n", "1. **Analyzing Categorical Performance Trends:** You can now group and analyze the data based on the performance categories (Low, Average, High)." ] }, { "cell_type": "code", "execution_count": 20, "id": "c8b58430-867b-42b3-855b-902d260a4c08", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ageMeduFedutraveltimestudytimefailuresfamrelfreetimegooutDalcWalchealthabsencesG1G2G3
G3_category
Low16.8869052.5652172.3225811.5549451.9508200.5913983.9569893.2365593.2903231.5483872.4677423.5698926.1881728.2580657.6989256.774194
Average16.5838512.8224852.6745561.3795182.0958080.1242603.8994083.2071012.9467461.4556212.1893493.5976335.51479312.53254412.61538512.810651
High16.4210533.3250002.8000001.2368422.2000000.0250004.0750003.3500002.9500001.2750001.9000003.3000004.30000016.37500016.70000017.225000
\n", "
" ], "text/plain": [ " age Medu Fedu traveltime studytime failures \\\n", "G3_category \n", "Low 16.886905 2.565217 2.322581 1.554945 1.950820 0.591398 \n", "Average 16.583851 2.822485 2.674556 1.379518 2.095808 0.124260 \n", "High 16.421053 3.325000 2.800000 1.236842 2.200000 0.025000 \n", "\n", " famrel freetime goout Dalc Walc health \\\n", "G3_category \n", "Low 3.956989 3.236559 3.290323 1.548387 2.467742 3.569892 \n", "Average 3.899408 3.207101 2.946746 1.455621 2.189349 3.597633 \n", "High 4.075000 3.350000 2.950000 1.275000 1.900000 3.300000 \n", "\n", " absences G1 G2 G3 \n", "G3_category \n", "Low 6.188172 8.258065 7.698925 6.774194 \n", "Average 5.514793 12.532544 12.615385 12.810651 \n", "High 4.300000 16.375000 16.700000 17.225000 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Group by the new category and calculate mean of other variables\n", "grouped_data = df.groupby('G3_category', observed=True).mean(numeric_only=True)\n", "grouped_data" ] }, { "cell_type": "code", "execution_count": 21, "id": "69d1ed6c-c005-4513-b40e-eda850956e7d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Travel Time')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize study time across G3 categories\n", "fig, axs = plt.subplots(1,2, figsize=(12,3))\n", "sns.boxplot(x='G3_category', y='studytime', data=df, ax=axs[0])\n", "axs[0].set_title(\"Study Time Across G3 Categories\")\n", "axs[0].set_xlabel(\"Performance Category\")\n", "axs[0].set_ylabel(\"Study Time\")\n", "\n", "sns.boxplot(x='G3_category', y='traveltime', data=df, ax=axs[1])\n", "axs[1].set_title(\"Free Time Across G3 Categories\")\n", "axs[1].set_xlabel(\"Performance Category\")\n", "axs[1].set_ylabel(\"Travel Time\")" ] }, { "cell_type": "markdown", "id": "851336a2-e206-4335-a368-c788043301c0", "metadata": {}, "source": [ "2. Feature Interaction" ] }, { "cell_type": "markdown", "id": "bb296ead-c687-4691-8847-2d11fafb1cba", "metadata": {}, "source": [ "You can compare G3_category with other variables to find patterns and relationships." ] }, { "cell_type": "code", "execution_count": 22, "id": "78e6a1e1-9c65-4f7b-aa00-238f93a7c715", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Comparing with Categorical Variables\n", "# Example: Compare G3_category with 'school'\n", "fig,ax = plt.subplots(figsize=(6,3))\n", "sns.countplot(x='G3_category', hue='school', data=df)\n", "ax.set_title(\"G3 Categories by School\")\n", "ax.set_xlabel(\"Performance Category\")\n", "ax.set_ylabel(\"Count\")\n", "ax.legend(title=\"School\")" ] }, { "cell_type": "code", "execution_count": 23, "id": "2079b577-b3e2-4f4c-93cd-2caa1a13c7db", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Average Absences')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Comparing with Numerical Variables\n", "# Example: Compare absences across performance categories\n", "fig,ax = plt.subplots(figsize=(6,3))\n", "sns.barplot(x='G3_category', y='absences', data=df)\n", "ax.set_title(\"Average Absences Across G3 Categories\")\n", "ax.set_xlabel(\"Performance Category\")\n", "ax.set_ylabel(\"Average Absences\")" ] }, { "cell_type": "markdown", "id": "89e22e2a-3acf-4cba-9ab3-37728baee8d5", "metadata": {}, "source": [ "3. Use in Machine Learning Models: You can use G3_category as a target variable instead of the continuous G3 for classification tasks.\n", "\n", "We wil use this approach later." ] }, { "cell_type": "markdown", "id": "af75665d-db7f-4f38-b726-263856e2f52b", "metadata": {}, "source": [ "#### Example 1: Study Habits and Lifestyle on the target variable" ] }, { "cell_type": "code", "execution_count": 24, "id": "bdc1aa6f-b563-41db-8cb5-d9fbf32bda0d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "study_features = ['studytime', 'failures', 'freetime', 'Walc', 'Dalc', 'goout']\n", "nrows,ncols = 2,3\n", "fig, axs = plt.subplots(nrows, ncols, figsize=(14,10))\n", "i,j = 0,0\n", "for feature in study_features:\n", " sns.barplot(x=feature, y='G3', data=df, ax=axs[i,j])\n", " axs[i,j].set_title(f\"Impact of {feature} on G3\")\n", " j = j + 1\n", " if j % ncols == 0:\n", " i = i + 1\n", " j = 0" ] }, { "cell_type": "markdown", "id": "6c1b4c36-de79-4fc4-bc0a-2fdc5832caf8", "metadata": {}, "source": [ "#### Example 2: Study Schooling and Family Influence on the target variable" ] }, { "cell_type": "code", "execution_count": 25, "id": "95938eda-0dbb-4f06-85b3-5bd79b97ce4e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'G3 by Family Support')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,axs = plt.subplots(1, 2, figsize=(12,3))\n", "sns.boxplot(x='school', y='G3', data=df, ax=axs[0])\n", "axs[0].set_title(\"G3 by School\")\n", "\n", "sns.boxplot(x='famsup', y='G3', data=df, ax=axs[1])\n", "axs[1].set_title(\"G3 by Family Support\")" ] }, { "cell_type": "markdown", "id": "9bcfd47a-5b80-4e4d-af60-d0f32bf5e9a1", "metadata": {}, "source": [ "#### Example 3: Study Absences Influence on the target variable" ] }, { "cell_type": "code", "execution_count": 26, "id": "c3020e97-2db2-47e6-ba17-8f9f2353777a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Absences vs G3')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,axs = plt.subplots(figsize=(6,3))\n", "sns.scatterplot(x='absences', y='G3', data=df)\n", "ax.set_title(\"Absences vs G3\")" ] }, { "cell_type": "markdown", "id": "547f660a-1611-4d87-9921-d6778ee3dcf5", "metadata": {}, "source": [ "#### Correlation Matrix\n", "\n", "Correlation matrix a valuable tool for revealing the linear relationships between numerical variables. A correlation matrix displays the pairwise correlation coefficients between numerical features a dataset." ] }, { "cell_type": "code", "execution_count": 27, "id": "88c70480-dd77-441e-b28d-bccda39ac07e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Correlation Matrix')" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corr_matrix = df.corr(numeric_only=True)\n", "fig,ax = plt.subplots(figsize=(12, 8))\n", "sns.heatmap(corr_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n", "ax.set_title(\"Correlation Matrix\")" ] }, { "cell_type": "markdown", "id": "adbfbdff-757e-4d11-a1f0-191bdc19e445", "metadata": {}, "source": [ "From the correlation matrix heatmap provided, here are some key observations:\n", "\n", "1. Correlation of G3 (Final Grade)\n", " * G3 has strong positive correlations with:\n", " - G1 (First Period Grade): 0.80\n", " - G2 (Second Period Grade): 0.90\n", " - This suggests that the performance in earlier periods strongly predicts the final grade.\n", " * G3 has moderate negative correlation with:\n", " - failures (Number of Past Failures): -0.36\n", " - This indicates that students with more past failures tend to have lower final grades.\n", "2. Correlation Between Other Features\n", " * Medu (Mother's Education) and Fedu (Father's Education):\n", " - Strong positive correlation (0.63) suggests that parental education levels are related.\n", " - Walc (Weekend Alcohol Consumption) and Dalc (Workday Alcohol Consumption): Strong positive correlation (0.65) indicates that students who consume alcohol on workdays tend to consume it on weekends as well.\n", " - G1 and G2: High positive correlation (0.85) suggests strong consistency in student performance between the first two periods.\n", "3. Weak or No Correlation\n", " * Many features, such as health, famrel (Family Relationship Quality), and freetime, have very low or negligible correlations with grades (close to 0).\n", " - These features may not contribute significantly to predicting G3 and could be less relevant for modeling.\n", "4. Multicollinearity\n", " * High correlations between G1, G2, and G3 suggest potential multicollinearity if these features are included in a regression model.\n", " - Consider using only one of these features or techniques like principal component analysis (PCA) to address this.\n", "5. Interesting Patterns\n", " * absences: Weak correlation with G3 (0.03) indicates that the number of absences may not significantly affect grades.\n", " * failures: Negative correlation with grades suggests that reducing the number of failures could be a key to better performance.\n", " * Dalc and Walc: Negligible or weak correlations with grades (0.05) indicate that alcohol consumption may not have a strong direct impact on academic performance.\n", "\n", "What to do next:\n", "\n", " * Highly correlated features such as Medu and Fedu can be dropped since they may contain redundant information. Currenty, we will not drop them though." ] }, { "cell_type": "markdown", "id": "d15ba24f-86ce-478f-8c40-77a72137821a", "metadata": {}, "source": [ "### Split dataset" ] }, { "cell_type": "code", "execution_count": 28, "id": "3014e871-30c7-4bbc-9b3d-fd454ff092d8", "metadata": {}, "outputs": [], "source": [ "# Split features and target (keep G3 as target variable to solve a regression problem; G3_category can be used to solve a classification problem)\n", "X = df.drop(['G3', 'G3_category'] , axis=1)\n", "y = df['G3']" ] }, { "cell_type": "code", "execution_count": 29, "id": "caef0c46-2427-43d8-bdd8-00fe2589c879", "metadata": {}, "outputs": [], "source": [ "# Split the data into training and test sets before scaling, encoding to avoid data leakage\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" ] }, { "cell_type": "code", "execution_count": 30, "id": "49224c51-f760-4f67-8306-0379a9585cee", "metadata": {}, "outputs": [], "source": [ "# Apply PowerTransformer with Yeo-Johnson method on the target variable\n", "# We could apply Box-Cox if all values were positive\n", "yj = PowerTransformer(method='yeo-johnson')\n", "\n", "# fit_transform() and transform() need 2D input data but y_train and Y_test are 1D Pandas Series. So, convert them to DataFrames with a single column.to_frame()\n", "y_train_yj = yj.fit_transform(y_train.to_frame()) # Fit and transform training data\n", "y_test_yj = yj.transform(y_test.to_frame()) # Transform test data\n", "\n", "# alternative solution: y_train and y_test can be cnverted to 2D NumPy arrays using column.reshape()\n", "# y_train_yj = yj.fit_transform(y_train.values.reshape(-1, 1)) # Fit and transform training data\n", "# y_test_yj = yj.transform(y_test.values.reshape(-1, 1)) # Transform test data\n", "\n", "# Convert the results back to pandas Series with the original index\n", "y_train_yj = pd.Series(y_train_yj.flatten(), index=y_train.index, name='y_train_yj')\n", "y_test_yj = pd.Series(y_test_yj.flatten(), index=y_test.index, name='y_test_yj')" ] }, { "cell_type": "markdown", "id": "354f2c12-e3ae-481d-9a2d-64c10b80fff8", "metadata": {}, "source": [ "## Data Preprocessing and hyperparameter tuning" ] }, { "cell_type": "markdown", "id": "c571f2ba-1d36-48c8-801e-0218ca38b35d", "metadata": {}, "source": [ "In a data science project, particularly after completing exploratory data analysis (EDA) and data splitting, there are basically 2 approaches to follow:\n", "\n", "#### Approach A: Create Different Dataset Versions (based on different pre-processing strategies) and Evaluate Multiple Estimators\n", "\n", "Process: \n", "* Perform imputation, scaling, encoding and transformations on the original dataset to create multiple versions of the dataset. \n", "* Apply various estimators (regressors or classifiers depending on the target variable) with default hyperparameters on each version of the dataset. \n", "* Use cross-validation to evaluate performance and identify the best-performing models.\n", "\n", "Advantages:\n", "* Model Diversity: This approach allows you to explore how different preprocessing strategies (scaling and encoding) impact model performance across various estimators.\n", "* Insightful Results: By evaluating multiple estimators on different dataset versions, you gain insights into which preprocessing methods work best with specific models.\n", "* Initial Benchmarking: It helps identify a few strong candidates for further hyperparameter tuning without getting bogged down in complex grid searches early on.\n", "\n", "Disadvantages:\n", "* Time-Consuming: This approach can be computationally expensive and time-consuming, especially with a large number of dataset versions and models.\n", "* Overhead: Managing and comparing multiple datasets can become cumbersome.\n", "\n", "#### Approach B: Use Pipelines (to apply different pre-processing strategies) and GridSearchCV for Hyperparameter Tuning\n", "\n", "Process:\n", "* Define a pipeline that incorporates preprocessing steps (imputation, scaling, encoding, transformations) and estimators.\n", "* Perform GridSearchCV to tune hyperparameters (use a grid of different combination of hyperparameters) while evaluating different preprocessing techniques in the pipeline.\n", "\n", "Advantages:\n", "* Efficiency: This approach is more streamlined and efficient, allowing you to optimize hyperparameters and preprocessing steps in a single step.\n", "* Integrated Approach: By using pipelines, you ensure that the same preprocessing steps are applied consistently during both training and evaluation, reducing the risk of data leakage.\n", "\n", "Disadvantages:\n", "* Limited Exploration: You may miss out on valuable insights that come from exploring a wider range of models and preprocessing strategies manually (intermediate results are not apparent).\n", "* Complexity: Setting up pipelines and managing the hyperparameter search space can become complex, particularly if there are many preprocessing options to consider.\n", "\n", "#### Recommended Approach\n", "\n", "In practice, a hybrid approach often works best:\n", "\n", "* Start with Approach A: Conduct initial experiments with different preprocessing strategies and a larger variety of models using default hyperparameters to understand which combinations yield the best performance.\n", "* Narrow Down: Based on the initial findings, narrow down to the top 2-3 models and their best-performing preprocessing methods.\n", "* Then Move to Approach B: Utilize GridSearchCV with pipelines for these selected models and preprocessors to fine-tune hyperparameters.\n", "\n", "This combined strategy allows you to benefit from both broad exploration and focused optimization, leading to more robust model development and potentially better performance." ] }, { "cell_type": "markdown", "id": "ffed93ee-4f99-4c4f-8cbb-dc9671979d2a", "metadata": {}, "source": [ "#### APPROACH A\n", "\n", "##### *Dataset V1*\n", "1. Apply simple imputer with mean strategy on numerical features\n", "2. Apply simple imputer with most frequent strategy and ordinal encoding on categorical features" ] }, { "cell_type": "code", "execution_count": 31, "id": "4b7428d5-98f2-4ed3-ad58-e2ef5dc0cdf0", "metadata": {}, "outputs": [], "source": [ "X_train_V_1 = X_train.copy()\n", "X_test_V_1 = X_test.copy()\n", "\n", "# Identify numerical and categorical features\n", "num_features = X.select_dtypes(include=['int64', 'float64']).columns\n", "cat_features = X.select_dtypes(include=['object']).columns\n", "\n", "# Apply simple imputer with mean strategy on numerical features\n", "si1 = SimpleImputer(strategy='mean')\n", "# fit (train) imputer on the training dataset\n", "si1.fit(X_train_V_1[num_features])\n", "# apply imputation on both training and test datasets\n", "X_train_V_1[num_features] = si1.transform(X_train_V_1[num_features])\n", "X_test_V_1[num_features] = si1.transform(X_test_V_1[num_features])\n", "\n", "# ALTERNATIVELY: you could fit and transform training data at the same time and then transform test data separately\n", "#X_train_V_1[num_features] = si1.fit_transform(X_train_V_1[num_features])\n", "#X_test_V_1[num_features] = si1.transform(X_test_V_1[num_features])\n", "\n", "# Apply simple imputer with most_frequent strategy on categorical features\n", "si2 = SimpleImputer(strategy='most_frequent')\n", "si2.fit(X_train_V_1[cat_features])\n", "X_train_V_1[cat_features] = si2.transform(X_train_V_1[cat_features])\n", "X_test_V_1[cat_features] = si2.transform(X_test_V_1[cat_features])\n", "\n", "# Apply ordinal encoding on categorical features\n", "ordinal_encoder = OrdinalEncoder(return_df=True,\n", " handle_unknown='value', # Handle unknown categories in the test set (at transform time) --> Encode unseen categories with -1\n", " )\n", "X_train_V_1[cat_features] = ordinal_encoder.fit_transform(X_train_V_1[cat_features])\n", "X_test_V_1[cat_features] = ordinal_encoder.transform(X_test_V_1[cat_features])\n", "\n", "#print(X_train_V_1)\n", "#print(X_test_V_1)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "96bda599-64d4-45fc-96c3-1c5abf15e078", "metadata": {}, "source": [ "##### Investigate feature elimination (feature selection / feature extraction) techniques\n", "\n", "Since the number of features is relatively high, we can inverstigate whether feature selection and extraction can provide smaller feature sets which can reduce (a) computational costs during the training process of predictive modelling techniques as well as (b) reduce noise by discarding irrelevant or redundant features." ] }, { "cell_type": "markdown", "id": "cec0ec41-4fa0-4c55-8aa0-d44490062f30", "metadata": {}, "source": [ "###### Feature Selection" ] }, { "cell_type": "code", "execution_count": 32, "id": "b2718907-0276-40df-b604-dbd7a2e6bd0f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 32 out of 32 | elapsed: 15.6s finished\n", "\n", "[2024-12-21 17:20:45] Features: 1/15 -- score: 0.817115432024002[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 31 out of 31 | elapsed: 7.8s finished\n", "\n", "[2024-12-21 17:20:53] Features: 2/15 -- score: 0.860634183883667[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 8.0s finished\n", "\n", "[2024-12-21 17:21:01] Features: 3/15 -- score: 0.8697828352451324[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 29 out of 29 | elapsed: 6.8s finished\n", "\n", "[2024-12-21 17:21:08] Features: 4/15 -- score: 0.9112393438816071[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 28 out of 28 | elapsed: 7.2s finished\n", "\n", "[2024-12-21 17:21:15] Features: 5/15 -- score: 0.9189259469509125[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 27 out of 27 | elapsed: 7.0s finished\n", "\n", "[2024-12-21 17:21:23] Features: 6/15 -- score: 0.9236857950687408[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 26 out of 26 | elapsed: 6.3s finished\n", "\n", "[2024-12-21 17:21:29] Features: 7/15 -- score: 0.9255279183387757[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 23 out of 25 | elapsed: 5.2s remaining: 0.4s\n", "[Parallel(n_jobs=-1)]: Done 25 out of 25 | elapsed: 5.7s finished\n", "\n", "[2024-12-21 17:21:35] Features: 8/15 -- score: 0.9284655809402466[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 22 out of 24 | elapsed: 5.1s remaining: 0.4s\n", "[Parallel(n_jobs=-1)]: Done 24 out of 24 | elapsed: 5.1s finished\n", "\n", "[2024-12-21 17:21:40] Features: 9/15 -- score: 0.9313021063804626[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 20 out of 23 | elapsed: 4.9s remaining: 0.7s\n", "[Parallel(n_jobs=-1)]: Done 23 out of 23 | elapsed: 5.2s finished\n", "\n", "[2024-12-21 17:21:45] Features: 10/15 -- score: 0.9306594133377075[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 19 out of 22 | elapsed: 4.9s remaining: 0.7s\n", "[Parallel(n_jobs=-1)]: Done 22 out of 22 | elapsed: 5.2s finished\n", "\n", "[2024-12-21 17:21:51] Features: 11/15 -- score: 0.9307812035083771[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 17 out of 21 | elapsed: 4.6s remaining: 1.0s\n", "[Parallel(n_jobs=-1)]: Done 21 out of 21 | elapsed: 4.8s finished\n", "\n", "[2024-12-21 17:21:55] Features: 12/15 -- score: 0.9294819951057434[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 16 out of 20 | elapsed: 3.7s remaining: 0.9s\n", "[Parallel(n_jobs=-1)]: Done 20 out of 20 | elapsed: 4.8s finished\n", "\n", "[2024-12-21 17:22:00] Features: 13/15 -- score: 0.9280512034893036[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 14 out of 19 | elapsed: 3.6s remaining: 1.2s\n", "[Parallel(n_jobs=-1)]: Done 19 out of 19 | elapsed: 4.5s finished\n", "\n", "[2024-12-21 17:22:05] Features: 14/15 -- score: 0.9256120800971985[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 13 out of 18 | elapsed: 3.5s remaining: 1.3s\n", "[Parallel(n_jobs=-1)]: Done 18 out of 18 | elapsed: 4.4s finished\n", "\n", "[2024-12-21 17:22:10] Features: 15/15 -- score: 0.9231548368930816" ] }, { "name": "stdout", "output_type": "stream", "text": [ "best combination (ACC: 0.931): (2, 5, 13, 16, 18, 22, 23, 29, 31)\n", "\n", "all subsets:\n", " {1: {'feature_idx': (31,), 'cv_scores': array([0.77579939, 0.86253583, 0.84173697, 0.8130424 , 0.88349551,\n", " 0.8808679 , 0.87218249, 0.69840753, 0.73705035, 0.80603594]), 'avg_score': 0.817115432024002, 'feature_names': ('G2',)}, 2: {'feature_idx': (29, 31), 'cv_scores': array([0.90499926, 0.93289387, 0.88985217, 0.89161789, 0.8707273 ,\n", " 0.87871844, 0.89699674, 0.7119745 , 0.79670703, 0.83185464]), 'avg_score': 0.860634183883667, 'feature_names': ('absences', 'G2')}, 3: {'feature_idx': (2, 29, 31), 'cv_scores': array([0.87962115, 0.88843262, 0.92747635, 0.80342031, 0.8078413 ,\n", " 0.88313007, 0.92506194, 0.85417998, 0.8321954 , 0.89646924]), 'avg_score': 0.8697828352451324, 'feature_names': ('age', 'absences', 'G2')}, 4: {'feature_idx': (2, 18, 29, 31), 'cv_scores': array([0.9549005 , 0.8153463 , 0.90815669, 0.76249123, 0.91165912,\n", " 0.93722373, 0.96148574, 0.94771677, 0.96696621, 0.94644713]), 'avg_score': 0.9112393438816071, 'feature_names': ('age', 'activities', 'absences', 'G2')}, 5: {'feature_idx': (2, 18, 23, 29, 31), 'cv_scores': array([0.96820235, 0.88167435, 0.92399436, 0.89558727, 0.90423793,\n", " 0.93580204, 0.89052409, 0.941167 , 0.89593947, 0.95213062]), 'avg_score': 0.9189259469509125, 'feature_names': ('age', 'activities', 'famrel', 'absences', 'G2')}, 6: {'feature_idx': (2, 13, 18, 23, 29, 31), 'cv_scores': array([0.96051043, 0.90625584, 0.90869921, 0.92737722, 0.903907 ,\n", " 0.91881752, 0.9558022 , 0.95298284, 0.84972507, 0.9527806 ]), 'avg_score': 0.9236857950687408, 'feature_names': ('age', 'studytime', 'activities', 'famrel', 'absences', 'G2')}, 7: {'feature_idx': (2, 5, 13, 18, 23, 29, 31), 'cv_scores': array([0.95700979, 0.89308047, 0.92035884, 0.92718244, 0.91656435,\n", " 0.92125958, 0.9508757 , 0.95201188, 0.86427367, 0.95266247]), 'avg_score': 0.9255279183387757, 'feature_names': ('age', 'Pstatus', 'studytime', 'activities', 'famrel', 'absences', 'G2')}, 8: {'feature_idx': (2, 5, 13, 18, 22, 23, 29, 31), 'cv_scores': array([0.9556163 , 0.9253819 , 0.92733252, 0.93810165, 0.90803671,\n", " 0.9319976 , 0.9579792 , 0.93659604, 0.85594761, 0.94766629]), 'avg_score': 0.9284655809402466, 'feature_names': ('age', 'Pstatus', 'studytime', 'activities', 'romantic', 'famrel', 'absences', 'G2')}, 9: {'feature_idx': (2, 5, 13, 16, 18, 22, 23, 29, 31), 'cv_scores': array([0.96518195, 0.92189324, 0.93184626, 0.93608987, 0.92552394,\n", " 0.93448108, 0.9607535 , 0.94144869, 0.84616566, 0.94963688]), 'avg_score': 0.9313021063804626, 'feature_names': ('age', 'Pstatus', 'studytime', 'famsup', 'activities', 'romantic', 'famrel', 'absences', 'G2')}, 10: {'feature_idx': (2, 5, 13, 16, 18, 21, 22, 23, 29, 31), 'cv_scores': array([0.96462959, 0.91688532, 0.93084407, 0.92789108, 0.92643112,\n", " 0.93276322, 0.9592967 , 0.955033 , 0.83902448, 0.95379555]), 'avg_score': 0.9306594133377075, 'feature_names': ('age', 'Pstatus', 'studytime', 'famsup', 'activities', 'internet', 'romantic', 'famrel', 'absences', 'G2')}, 11: {'feature_idx': (2, 5, 13, 16, 18, 19, 21, 22, 23, 29, 31), 'cv_scores': array([0.96037394, 0.92216676, 0.93164957, 0.92827743, 0.92703021,\n", " 0.9286716 , 0.9644165 , 0.9499802 , 0.84643912, 0.9488067 ]), 'avg_score': 0.9307812035083771, 'feature_names': ('age', 'Pstatus', 'studytime', 'famsup', 'activities', 'nursery', 'internet', 'romantic', 'famrel', 'absences', 'G2')}, 12: {'feature_idx': (2, 5, 13, 16, 18, 19, 21, 22, 23, 26, 29, 31), 'cv_scores': array([0.96359503, 0.92307472, 0.92836916, 0.91842896, 0.93237907,\n", " 0.92523211, 0.96160555, 0.95372361, 0.84004724, 0.9483645 ]), 'avg_score': 0.9294819951057434, 'feature_names': ('age', 'Pstatus', 'studytime', 'famsup', 'activities', 'nursery', 'internet', 'romantic', 'famrel', 'Dalc', 'absences', 'G2')}, 13: {'feature_idx': (2, 5, 13, 15, 16, 18, 19, 21, 22, 23, 26, 29, 31), 'cv_scores': array([0.97346115, 0.91335988, 0.91748464, 0.91604489, 0.92345822,\n", " 0.93619871, 0.95765889, 0.95021462, 0.8618964 , 0.93073463]), 'avg_score': 0.9280512034893036, 'feature_names': ('age', 'Pstatus', 'studytime', 'schoolsup', 'famsup', 'activities', 'nursery', 'internet', 'romantic', 'famrel', 'Dalc', 'absences', 'G2')}, 14: {'feature_idx': (2, 4, 5, 13, 15, 16, 18, 19, 21, 22, 23, 26, 29, 31), 'cv_scores': array([0.97114277, 0.91794628, 0.91787899, 0.92001712, 0.92026305,\n", " 0.93640554, 0.9601025 , 0.94475001, 0.83511055, 0.932504 ]), 'avg_score': 0.9256120800971985, 'feature_names': ('age', 'famsize', 'Pstatus', 'studytime', 'schoolsup', 'famsup', 'activities', 'nursery', 'internet', 'romantic', 'famrel', 'Dalc', 'absences', 'G2')}, 15: {'feature_idx': (2, 4, 5, 8, 13, 15, 16, 18, 19, 21, 22, 23, 26, 29, 31), 'cv_scores': array([0.97480685, 0.89697331, 0.9035058 , 0.91386986, 0.92627805,\n", " 0.94540077, 0.95562553, 0.94316375, 0.85123479, 0.92068964]), 'avg_score': 0.9231548368930816, 'feature_names': ('age', 'famsize', 'Pstatus', 'Mjob', 'studytime', 'schoolsup', 'famsup', 'activities', 'nursery', 'internet', 'romantic', 'famrel', 'Dalc', 'absences', 'G2')}}\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xgb = XGBRegressor()\n", "\n", "# Sequential Forward Selection\n", "sfs = SFS(xgb, \n", " k_features=(5,15), \n", " forward=True, \n", " floating=False, \n", " verbose=2,\n", " scoring='r2',\n", " cv=10,\n", " n_jobs=-1)\n", " \n", "sfs = sfs.fit(X_train_V_1, y_train)\n", "\n", "print('best combination (ACC: %.3f): %s\\n' % (sfs.k_score_, sfs.k_feature_idx_))\n", "print('all subsets:\\n', sfs.subsets_)\n", "plot_sfs(sfs.get_metric_dict(), kind='std_err')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "a7e69544-3fcd-48b1-bbc0-6f123b15fd7a", "metadata": {}, "source": [ "Based on the previous feature selection analysis, there is a smaleler featureset which achieves the highest performance (r2 score).\n", "Therefore, it is worth creating another dataset consisting of the best performing features." ] }, { "cell_type": "markdown", "id": "460647c4-f6e6-4d84-b74f-1caabb87b0b8", "metadata": {}, "source": [ "##### *Dataset V2 (the \"best performing\" selected features from V1)*\n", "\n", "1. Apply simple imputer with mean strategy on numerical features\n", "2. Apply simple imputer with most frequent strategy and ordinal encoding on categorical features\n", "3. Sequential feature selection" ] }, { "cell_type": "code", "execution_count": 33, "id": "710802ff-bf3f-49a1-a13a-d7c7fee80711", "metadata": {}, "outputs": [], "source": [ "#X_train_V_2 = sfs.transform(X_train_V_1) # this returns a numpy 2D array, not a pandas dataframe\n", "X_train_V_2 = X_train_V_1.iloc[:, list(sfs.k_feature_idx_)]\n", "X_test_V_2 = X_test_V_1.iloc[:, list(sfs.k_feature_idx_)]" ] }, { "cell_type": "markdown", "id": "64c52f7a-9613-4e1a-888c-09a4d8f471ae", "metadata": {}, "source": [ "###### Feature Extraction" ] }, { "cell_type": "markdown", "id": "725a00cb-d2a6-4954-9a81-196fc2fe22b7", "metadata": {}, "source": [ "Beyond feature selection, we can explore feature extraction using PCA. However, PCA is suitable for continuous numerical features but not appropriate for discrete numerical features. Since our dataset contains both numerical types, we will skip this step." ] }, { "cell_type": "markdown", "id": "c856e59e-34b4-47e4-bb22-3077d5747279", "metadata": {}, "source": [ "##### *Dataset V3 (the same as V1 but with unskewing on age and absences)*\n", "\n", "1. Apply simple imputer with mean strategy on numerical features\n", "2. Apply yeo johnson (unskewing) on age and absences\n", "3. Apply simple imputer with most frequent strategy and ordinal encoding on categorical features" ] }, { "cell_type": "code", "execution_count": 34, "id": "35f62450-0935-4a41-b8f7-0de657546db4", "metadata": {}, "outputs": [], "source": [ "X_train_V_3 = X_train_V_1.copy()\n", "X_test_V_3 = X_test_V_1.copy()\n", "\n", "# Initialize the PowerTransformer with Yeo-Johnson method\n", "# We could apply Box-Cox if all data were positive; Fare column contains zero values\n", "transformer = PowerTransformer(method='yeo-johnson')\n", "\n", "features_to_transform = ['age', 'absences']\n", "X_train_V_3[features_to_transform] = transformer.fit_transform(X_train_V_3[features_to_transform]) # Fit and transform training data (age and absences columns only)\n", "X_test_V_3[features_to_transform] = transformer.transform(X_test_V_3[features_to_transform]) # Transform test data\n", "\n", "#print(X_train_V_3)\n", "#print(X_test_V_3)" ] }, { "cell_type": "markdown", "id": "5e0461c5-d6dc-42a2-a7fb-04b8fadfe4a9", "metadata": {}, "source": [ "##### *Dataset V4* (the same as V3 but one hot on categorical)\n", "1. Apply simple imputer with mean strategy on numerical features\n", "2. Apply yeo johnson (unskewing) on age and absences\n", "3. Apply simple imputer with most frequent strategy and one hot encoding on categorical features" ] }, { "cell_type": "code", "execution_count": 35, "id": "a144dc00-cb0c-4193-884c-122b05f2f074", "metadata": {}, "outputs": [], "source": [ "X_train_V_4 = X_train_V_3.copy()\n", "X_test_V_4 = X_test_V_3.copy()\n", "\n", "# Apply ordinal encoding on categorical features\n", "onehot_encoder = OneHotEncoder(return_df=True,\n", " handle_unknown='value', # Handle unknown categories in the test set (at transform time) --> Encode a new value as 0 in every dummy column\n", " )\n", "new_cols_train = onehot_encoder.fit_transform(X_train[cat_features])\n", "# Concatenate the original DataFrame with the encoded DataFrame\n", "X_train_V_4 = pd.concat([X_train_V_4, new_cols_train], axis=1)\n", "# Drop the original categorical column if you no longer need it\n", "X_train_V_4 = X_train_V_4.drop(columns=cat_features)\n", "\n", "# apply the same encoding on the test dataset\n", "new_cols_test = onehot_encoder.transform(X_test[cat_features])\n", "X_test_V_4 = pd.concat([X_test_V_4, new_cols_test], axis=1)\n", "X_test_V_4 = X_test_V_4.drop(columns=cat_features)\n", "\n", "#print(X_train_V_4)\n", "#print(X_test_V_4)" ] }, { "cell_type": "markdown", "id": "7d79b62f-b395-4338-a8d7-38ff15f44f9a", "metadata": {}, "source": [ "##### *Dataset V5 (the same as V3 + scaling)*\n", "1. Apply simple imputer with mean strategy on numerical features\n", "2. Apply yeo johnson (unskewing) on age and absences\n", "3. Apply simple imputer with most frequent strategy and ordinal encoding on categorical features\n", "4. Apply robust scaling on all features except age and absences" ] }, { "cell_type": "code", "execution_count": 36, "id": "fdf11d6c-234a-452b-987d-c769509655f1", "metadata": {}, "outputs": [], "source": [ "X_train_V_5 = X_train_V_3.copy()\n", "X_test_V_5 = X_test_V_3.copy()\n", "\n", "features_to_scale = X_train_V_5.select_dtypes(include=['int32', 'int64', 'float64']).columns.drop(['age', 'absences'])\n", "#print(features_to_scale)\n", "\n", "sc = StandardScaler()\n", "sc.fit(X_train_V_5[features_to_scale])\n", "\n", "X_train_V_5[features_to_scale] = sc.transform(X_train_V_5[features_to_scale])\n", "X_test_V_5[features_to_scale] = sc.transform(X_test_V_5[features_to_scale])\n", "\n", "#print(X_train_V_5)\n", "#print(X_test_V_5)" ] }, { "cell_type": "markdown", "id": "aadb32ea-c4d2-4ba2-9e19-ac3b44530ca2", "metadata": {}, "source": [ "#### Model Selection" ] }, { "cell_type": "markdown", "id": "6b983d91-43ab-4576-8bee-cc0e3a690d24", "metadata": {}, "source": [ "Train 7 regressors using training datasets: RandomForestRegressor, AdaBoostRegressor, XGBoostRegressor, CatBoostRegressor, SVR, KNeighborsRegressor and DecisionTreeRegressor using 10-fold CV.\n", "\n", "Make predictions using test datasets and choose the top 2 best performing models." ] }, { "cell_type": "code", "execution_count": 37, "id": "979342a4-0e90-4e29-b9b5-245daea771c3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "v1 original: ... RandomForest ... AdaBoost ... XGBoost ... CatBoost ... SVR ... KNeighbors ... DecisionTree ... \n", "v1 unskewed: ... RandomForest ... AdaBoost ... XGBoost ... CatBoost ... SVR ... KNeighbors ... DecisionTree ... \n", "v2 original: ... RandomForest ... AdaBoost ... XGBoost ... CatBoost ... SVR ... KNeighbors ... DecisionTree ... \n", "v2 unskewed: ... RandomForest ... AdaBoost ... XGBoost ... CatBoost ... SVR ... KNeighbors ... DecisionTree ... \n", "v3 original: ... RandomForest ... AdaBoost ... XGBoost ... CatBoost ... SVR ... KNeighbors ... DecisionTree ... \n", "v3 unskewed: ... RandomForest ... AdaBoost ... XGBoost ... CatBoost ... SVR ... KNeighbors ... DecisionTree ... \n", "v4 original: ... RandomForest ... AdaBoost ... XGBoost ... CatBoost ... SVR ... KNeighbors ... DecisionTree ... \n", "v4 unskewed: ... RandomForest ... AdaBoost ... XGBoost ... CatBoost ... SVR ... KNeighbors ... DecisionTree ... \n", "v5 original: ... RandomForest ... AdaBoost ... XGBoost ... CatBoost ... SVR ... KNeighbors ... DecisionTree ... \n", "v5 unskewed: ... RandomForest ... AdaBoost ... XGBoost ... CatBoost ... SVR ... KNeighbors ... DecisionTree ... \n" ] } ], "source": [ "featuresets = {\n", " 'v1': X_train_V_1,\n", " 'v2': X_train_V_2, \n", " 'v3': X_train_V_3,\n", " 'v4': X_train_V_4,\n", " 'v5': X_train_V_5\n", "}\n", "\n", "targets = {\n", " 'original': y_train,\n", " 'unskewed': y_train_yj\n", "}\n", "\n", "# Define regressors\n", "regressors = {\n", " \"RandomForest\": RandomForestRegressor(),\n", " \"AdaBoost\": AdaBoostRegressor(),\n", " \"XGBoost\": XGBRegressor(),\n", " \"CatBoost\": CatBoostRegressor(silent=True),\n", " \"SVR\": SVR(),\n", " \"KNeighbors\": KNeighborsRegressor(),\n", " \"DecisionTree\": DecisionTreeRegressor(),\n", "}\n", "\n", "# Dictionary to store results\n", "results = []\n", "\n", "# Loop over each featureset version\n", "for feature_name, X_data in featuresets.items():\n", " # loop over each target version\n", " for target_name, y_data in targets.items():\n", " # Loop over each regressor\n", " print(feature_name+\" \"+target_name+\":\", end=' ... ')\n", " for rgs_name, rgs in regressors.items():\n", " print(rgs_name, end=' ... ')\n", " # Perform 10-fold cross-validation\n", " scores = cross_val_score(rgs, X_data, y_data, cv=10, scoring='r2', n_jobs=-1)\n", " # Store the average score for this classifier and dataset version\n", " avg_score = scores.mean()\n", " results.append({\n", " 'featureset': feature_name,\n", " 'target': target_name,\n", " 'regressor': rgs_name,\n", " 'score': avg_score\n", " })\n", " print()" ] }, { "cell_type": "code", "execution_count": 38, "id": "8aeb69e9-86ac-41ea-8dee-c96380cec049", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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featuresettargetregressorscore
0v1originalRandomForest0.905500
1v1originalAdaBoost0.896157
2v1originalXGBoost0.879765
3v1originalCatBoost0.897801
4v1originalSVR0.801961
...............
65v5unskewedXGBoost0.890280
66v5unskewedCatBoost0.909834
67v5unskewedSVR0.814789
68v5unskewedKNeighbors0.548015
69v5unskewedDecisionTree0.842905
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70 rows × 4 columns

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" ], "text/plain": [ " featureset target regressor score\n", "0 v1 original RandomForest 0.905500\n", "1 v1 original AdaBoost 0.896157\n", "2 v1 original XGBoost 0.879765\n", "3 v1 original CatBoost 0.897801\n", "4 v1 original SVR 0.801961\n", ".. ... ... ... ...\n", "65 v5 unskewed XGBoost 0.890280\n", "66 v5 unskewed CatBoost 0.909834\n", "67 v5 unskewed SVR 0.814789\n", "68 v5 unskewed KNeighbors 0.548015\n", "69 v5 unskewed DecisionTree 0.842905\n", "\n", "[70 rows x 4 columns]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert results to DataFrame for easier analysis\n", "results_df = pd.DataFrame(results)\n", "results_df" ] }, { "cell_type": "code", "execution_count": 39, "id": "588e0114-ca59-419a-a391-50ef2a99abde", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mean
regressortarget
RandomForestunskewed0.918344
AdaBoostunskewed0.914237
CatBoostunskewed0.912251
RandomForestoriginal0.908157
CatBoostoriginal0.900978
XGBoostunskewed0.900431
AdaBoostoriginal0.897475
XGBoostoriginal0.888852
SVRunskewed0.866894
DecisionTreeunskewed0.846367
original0.826440
KNeighborsunskewed0.796752
original0.781158
SVRoriginal0.767598
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" ], "text/plain": [ " mean\n", "regressor target \n", "RandomForest unskewed 0.918344\n", "AdaBoost unskewed 0.914237\n", "CatBoost unskewed 0.912251\n", "RandomForest original 0.908157\n", "CatBoost original 0.900978\n", "XGBoost unskewed 0.900431\n", "AdaBoost original 0.897475\n", "XGBoost original 0.888852\n", "SVR unskewed 0.866894\n", "DecisionTree unskewed 0.846367\n", " original 0.826440\n", "KNeighbors unskewed 0.796752\n", " original 0.781158\n", "SVR original 0.767598" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Best performing regressors: RandomForest, AdaBoost and CatBoost (almost same performance) mostly on the unskewed target variable\n", "results_df.groupby(['regressor', 'target'])['score'].agg(['mean']).sort_values(by='mean', ascending=False)" ] }, { "cell_type": "code", "execution_count": 40, "id": "824de2b9-01f7-470f-9636-32ba7556631a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
mean
featuresettarget
v2unskewed0.904344
original0.890340
v4unskewed0.888800
v1unskewed0.886155
v3unskewed0.883985
v1original0.862361
v4original0.862232
v3original0.860846
v5unskewed0.833343
original0.788976
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" ], "text/plain": [ " mean\n", "featureset target \n", "v2 unskewed 0.904344\n", " original 0.890340\n", "v4 unskewed 0.888800\n", "v1 unskewed 0.886155\n", "v3 unskewed 0.883985\n", "v1 original 0.862361\n", "v4 original 0.862232\n", "v3 original 0.860846\n", "v5 unskewed 0.833343\n", " original 0.788976" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Best performing featuresets: v2, v4 also in conjuction with the unskewed target variable\n", "results_df.groupby(['featureset', 'target'])['score'].agg(['mean']).sort_values(by='mean', ascending=False)" ] }, { "cell_type": "markdown", "id": "603637a4-9ccc-4e0a-baff-a7d8acd6c99e", "metadata": {}, "source": [ "#### APPROACH B\n", "\n", "Use pipelines and GridSearchCV to fine tune the hyperparameters of the top 2 best performing models. Pipelines should involve the best performing datasets versions.\n", "\n", "One of the two best performing featuresets involves feature selection. Feature selection is a computationally expensive process, thus it is recommended to avoid incorporating it as transformer in the pipeline of GridSearchCV. In this case, feature selection transformer will be performed at every fold making the whole process even heavier. To simplify things, two training datasets will be fed into the Grid Search CV process. The original training dataset (X_train) with all features (that will be used in v4 pipeline) and a subset of the original dataset involving only the features were selected from the sequential feature selection process (that will be used in v2 pipeline)." ] }, { "cell_type": "code", "execution_count": 41, "id": "f3dc3c6e-0cfe-4451-aaa9-27ec622a147b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running GridSearchCV for rf_v2_pipeline...\n", "Best Parameters for rf_v2_pipeline: {'estimator__regressor__bootstrap': True, 'estimator__regressor__max_depth': 10, 'estimator__regressor__max_features': None, 'estimator__regressor__min_samples_leaf': 1, 'estimator__regressor__min_samples_split': 2, 'estimator__regressor__n_estimators': 100}\n", "Best Cross-Validated Score for rf_v2_pipeline: 0.9236276075444458\n", "Performance on the test dataset: 0.8189319965815592\n", "\n", "Running GridSearchCV for ada_v2_pipeline...\n", "Best Parameters for ada_v2_pipeline: {'estimator__regressor__learning_rate': 0.1, 'estimator__regressor__loss': 'square', 'estimator__regressor__n_estimators': 200}\n", "Best Cross-Validated Score for ada_v2_pipeline: 0.9130031481001308\n", "Performance on the test dataset: 0.8419416712987652\n", "\n", "Running GridSearchCV for rf_v4_pipeline...\n", "Best Parameters for rf_v4_pipeline: {'estimator__regressor__bootstrap': True, 'estimator__regressor__max_depth': 20, 'estimator__regressor__max_features': None, 'estimator__regressor__min_samples_leaf': 1, 'estimator__regressor__min_samples_split': 5, 'estimator__regressor__n_estimators': 200}\n", "Best Cross-Validated Score for rf_v4_pipeline: 0.9034421738979613\n", "Performance on the test dataset: 0.7980490975469252\n", "\n", "Running GridSearchCV for ada_v4_pipeline...\n", "Best Parameters for ada_v4_pipeline: {'estimator__regressor__learning_rate': 1.0, 'estimator__regressor__loss': 'square', 'estimator__regressor__n_estimators': 50}\n", "Best Cross-Validated Score for ada_v4_pipeline: 0.9072969611812949\n", "Performance on the test dataset: 0.8129730834992104\n", "\n" ] } ], "source": [ "# Preprocessing pipeline for numerical features that need only imputing (not scaling, nor unskewing)\n", "num_pipeline1 = Pipeline([\n", " ('imputer', SimpleImputer(strategy='mean')),\n", "])\n", "\n", "# Preprocessing pipeline for numerical features that need imputing + unskewing\n", "num_pipeline2 = Pipeline([\n", " ('imputer', SimpleImputer(strategy='mean')),\n", " ('unskewer', PowerTransformer(method='yeo-johnson'))\n", "])\n", "\n", "# Preprocessing pipeline for categorical features (ordinal encoding)\n", "cat_pipeline1 = Pipeline([\n", " ('imputer', SimpleImputer(strategy='most_frequent')),\n", " ('ordinal', OrdinalEncoder(handle_unknown='value'))\n", "])\n", "\n", "# Preprocessing pipeline for categorical features (one-hot encoding)\n", "cat_pipeline2 = Pipeline([\n", " ('imputer', SimpleImputer(strategy='most_frequent')),\n", " ('ordinal', OneHotEncoder(handle_unknown='value'))\n", "])\n", "\n", "# preprocessing pipeline for creating featureset v2\n", "preprocessor1 = ColumnTransformer(\n", " transformers=[\n", " ('num', num_pipeline1, X.iloc[:, list(sfs.k_feature_idx_)].select_dtypes(include=['int64', 'float64']).columns), # all numerical features that were selected by sfs\n", " ('cat', cat_pipeline1, X.iloc[:, list(sfs.k_feature_idx_)].select_dtypes(include=['object']).columns) # all categorical features that were selected by sfs\n", " ],\n", " remainder='passthrough'\n", ")\n", "\n", "# preprocessing pipeline for creating featureset v4\n", "preprocessor2 = ColumnTransformer(\n", " transformers=[\n", " ('num1', num_pipeline1, list(set(num_features) - set(features_to_transform))), # all numerical features minus features to be unskewed\n", " ('num2', num_pipeline2, features_to_transform), # numerical features to be unskewed\n", " ('cat', cat_pipeline2, cat_features) # all categorical features\n", " ],\n", " remainder='passthrough'\n", ")\n", "\n", "# IMPORTANT NOTICE: avoid using multiple consecutive column transformers since they alter column order\n", "\n", "rgs1 = TransformedTargetRegressor(\n", " regressor=RandomForestRegressor(),\n", " transformer=PowerTransformer(method='yeo-johnson')\n", ")\n", "\n", "rgs2 = TransformedTargetRegressor(\n", " regressor=AdaBoostRegressor(),\n", " transformer=PowerTransformer(method='yeo-johnson')\n", ") \n", "\n", "# Create 4 pipelines with different preprocessing steps\n", "# v2\n", "pipeline1 = Pipeline([\n", " ('preprocessor', preprocessor1),\n", " ('estimator', rgs1)\n", "])\n", "pipeline2 = Pipeline([\n", " ('preprocessor', preprocessor1),\n", " ('estimator', rgs2)\n", "])\n", "# v4\n", "pipeline3 = Pipeline([\n", " ('preprocessor', preprocessor2),\n", " ('estimator', rgs1)\n", "])\n", "pipeline4 = Pipeline([\n", " ('preprocessor', preprocessor2),\n", " ('estimator', rgs2)\n", "])\n", "\n", "# Define different pipelines with different regressors and preprocessing steps\n", "pipelines = {\n", " 'rf_v2_pipeline': pipeline1,\n", " 'ada_v2_pipeline': pipeline2,\n", " 'rf_v4_pipeline': pipeline3,\n", " 'ada_v4_pipeline': pipeline4\n", "}\n", "\n", "# Set up parameter grid for GridSearchCV to explore both pipelines\n", "param_grid1 = [\n", " {\n", " 'estimator__regressor__n_estimators': [100, 200, 500], # Number of trees\n", " 'estimator__regressor__max_depth': [None, 10, 20, 30], # Maximum depth of the tree\n", " 'estimator__regressor__min_samples_split': [2, 5, 10], # Minimum number of samples to split a node\n", " 'estimator__regressor__min_samples_leaf': [1, 2, 4], # Minimum samples in leaf nodes\n", " 'estimator__regressor__max_features': ['sqrt', 'log2', None], # Features to consider for best split\n", " 'estimator__regressor__bootstrap': [True, False] # Use bootstrap sampling\n", " }\n", "]\n", "\n", "param_grid2 = [\n", " {\n", " 'estimator__regressor__n_estimators': [50, 100, 200], # Number of boosting stages\n", " 'estimator__regressor__learning_rate': [0.01, 0.1, 0.5, 1.0], # Shrinks the contribution of each estimator\n", " 'estimator__regressor__loss': ['linear', 'square', 'exponential'] # Loss function to optimize\n", " }\n", "]\n", "\n", "param_grids = {\n", " 'rf_v2_pipeline': param_grid1,\n", " 'ada_v2_pipeline': param_grid2,\n", " 'rf_v4_pipeline': param_grid1,\n", " 'ada_v4_pipeline': param_grid2\n", "}\n", "\n", "# create 2 input featesets: the first contains ony the features selected in sfs, and the second all features\n", "X_train_all = {\n", " 'rf_v2_pipeline': X_train.iloc[:, list(sfs.k_feature_idx_)],\n", " 'ada_v2_pipeline': X_train.iloc[:, list(sfs.k_feature_idx_)],\n", " 'rf_v4_pipeline': X_train,\n", " 'ada_v4_pipeline': X_train\n", "}\n", "\n", "X_test_all = {\n", " 'rf_v2_pipeline': X_test.iloc[:, list(sfs.k_feature_idx_)],\n", " 'ada_v2_pipeline': X_test.iloc[:, list(sfs.k_feature_idx_)],\n", " 'rf_v4_pipeline': X_test,\n", " 'ada_v4_pipeline': X_test\n", "}\n", "\n", "# Loop through each pipeline and perform GridSearchCV\n", "best_estimators = {}\n", "for pipeline_name, pipeline in pipelines.items():\n", " print(f\"Running GridSearchCV for {pipeline_name}...\")\n", " grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grids[pipeline_name], cv=5, scoring='r2', n_jobs=-1)\n", " grid_search.fit(X_train_all[pipeline_name], y_train)\n", " \n", " # Store the best estimator and results for each pipeline\n", " best_estimators[pipeline_name] = grid_search.best_estimator_\n", " print(f\"Best Parameters for {pipeline_name}: {grid_search.best_params_}\")\n", " print(f\"Best Cross-Validated Score for {pipeline_name}: {grid_search.best_score_}\")\n", " \n", " # Make predictions using the best estimator\n", " y_pred = grid_search.best_estimator_.predict(X_test_all[pipeline_name]) # Use X_test for evaluation\n", " print(f\"Performance on the test dataset: {r2_score(y_test, y_pred)}\") # Evaluate on the test dataset\n", " print(\"\")" ] }, { "cell_type": "markdown", "id": "9b9fdc1c-928f-463c-a531-50e017419863", "metadata": {}, "source": [ "### Μeasure the performance of the best model" ] }, { "cell_type": "code", "execution_count": 42, "id": "9b65f85a-c42f-42b5-9189-cf7d8c60318b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R2 score on training dataset: 0.9909947651256975\n", "R2 score on training dataset: 0.865787794941355\n" ] } ], "source": [ "# Create the best model (using the best hyper-parameter values)\n", "best_model = RandomForestRegressor(n_estimators=500, min_samples_split=2, min_samples_leaf=1, max_features=None, max_depth=20, bootstrap=True)\n", "# train the model on the dataset that achieves the highest score\n", "best_model.fit(X_train_V_2, y_train_yj)\n", "# make prediction on the train dataset in order to check for overfitting \n", "# (overfitting arises when high performance on training dataset and low performance on test dataset => model does not generalize\n", "y_train_pred = best_model.predict(X_train_V_2)\n", "print(\"R2 score on training dataset:\", r2_score(y_train_yj, y_train_pred))\n", "\n", "# make prediction on the test dataset\n", "y_test_pred = best_model.predict(X_test_V_2)\n", "print(\"R2 score on training dataset:\", r2_score(y_test_yj, y_test_pred))" ] }, { "cell_type": "markdown", "id": "37615b8c-1eae-4dc8-8054-88666e79a120", "metadata": {}, "source": [ "Determining if a model is overfitting based on the difference in r2 scores between the training and test datasets involves some judgment. Here are general guidelines:\n", "\n", "Key Considerations:\n", "\n", "* Small Gap: A small difference between r2 scores on training and test datasets (e.g., within 5-10%) suggests the model generalizes well.\n", " * Example: Training r2: 0.85, Test r2: 0.80. This is acceptable.\n", "* Large Gap: A large difference indicates potential overfitting. If the training r2 is very high and the test r2 is significantly lower, your model likely memorized the training data rather than learning patterns.\n", " * Example: Training r2: 0.95, Test r2: 0.50. This is concerning.\n", "* Negative Test r2: A negative r2 score means the model performs worse than a naive baseline (mean of the target values) on unseen data. This strongly suggests overfitting or other issues, such as data leakage, inappropriate preprocessing, or insufficient training data.\n", "\n", "In our case, the difference in r2 scores between training and test datasets is considered to be marginally acceptable without raising an overfitting concern.\n", "\n", "We can further enhance our analysis by approaching the problem from a classification perspective, utilizing the G3_category column as the target variable. However, this exploration is left as an exercise for the students." ] }, { "cell_type": "code", "execution_count": null, "id": "5e12a5b9-904e-49bb-b825-9c0ac90fc954", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }