{ "cells": [ { "cell_type": "markdown", "id": "sonic-particular", "metadata": { "_kg_hide-input": false, "_kg_hide-output": true, "execution": { "iopub.execute_input": "2021-06-10T18:17:05.828417Z", "iopub.status.busy": "2021-06-10T18:17:05.827889Z", "iopub.status.idle": "2021-06-10T18:17:40.26371Z", "shell.execute_reply": "2021-06-10T18:17:40.262629Z", "shell.execute_reply.started": "2021-06-10T18:17:05.82828Z" }, "id": "ptkHaiID_EWG", "papermill": { "duration": 0.072838, "end_time": "2021-06-18T10:28:22.337707", "exception": false, "start_time": "2021-06-18T10:28:22.264869", "status": "completed" }, "tags": [] }, "source": [ "# **Deep Learning approach to predict real or fake tweets about disaster**\n", "---" ] }, { "cell_type": "markdown", "id": "sexual-combat", "metadata": { "papermill": { "duration": 0.070136, "end_time": "2021-06-18T10:28:22.478357", "exception": false, "start_time": "2021-06-18T10:28:22.408221", "status": "completed" }, "tags": [] }, "source": [ "# Project Description\n", "\n", "[Twitter](https://twitter.com/?lang=en) has become an important communication channel in times of emergency. \n", "The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. \n", "Because of this, more agencies are interested in programatically monitoring Twitter (i.e. disaster relief organizations and news agencies).\n", "\n", "# Objective \n", "\n", "\n", "[Sentiment analysis](https://en.wikipedia.org/wiki/Sentiment_analysis) is a common use case of [NLP](https://machinelearningmastery.com/natural-language-processing/) where the idea is to classify the tweet as positive, negative or neutral depending upon the text in the tweet. \n", "This problem goes a way ahead and expects us to also determine the words in the tweet which decide the polarity of the tweet.\n", "\n", "In this project [Deep Learning](https://en.wikipedia.org/wiki/Deep_learning) models are implemented for predicting that a tweet regarding a disaster is real or fake \n", "Whole code below is in [Python](https://www.python.org/) using various libraries. Open source library [Tensorflow](https://www.tensorflow.org/) and [Transformers](https://huggingface.co/transformers/model_doc/auto.html) are used for creating the models and tuning them with [Keras Tuner](https://www.tensorflow.org/tutorials/keras/keras_tuner).\n", "

\n", "
\n", " \"Tweets\"\n", "

" ] }, { "cell_type": "markdown", "id": "unlikely-fiction", "metadata": { "papermill": { "duration": 0.069221, "end_time": "2021-06-18T10:28:22.617986", "exception": false, "start_time": "2021-06-18T10:28:22.548765", "status": "completed" }, "tags": [] }, "source": [ "# Table of Contents\n", "\n", "1. Dependancies and Dataset\n", "\n", "2. Data Exploration\n", "\n", "3. Data Cleaning\n", "\n", "4. Extra Data Exploration and Analysis with Cleaned Text\n", "\n", "5. Data Preprocessing and Creating Word Embedding Matrix\n", "\n", "6. Training and Tuning Deep Learning Models\n", "\n", "7. Conclusion" ] }, { "cell_type": "markdown", "id": "stainless-accountability", "metadata": { "papermill": { "duration": 0.06895, "end_time": "2021-06-18T10:28:22.756378", "exception": false, "start_time": "2021-06-18T10:28:22.687428", "status": "completed" }, "tags": [] }, "source": [ "# 1. Dependancies and Dataset" ] }, { "cell_type": "markdown", "id": "close-african", "metadata": { "papermill": { "duration": 0.069077, "end_time": "2021-06-18T10:28:22.894985", "exception": false, "start_time": "2021-06-18T10:28:22.825908", "status": "completed" }, "tags": [] }, "source": [ "## 1.1 Importing dependancies" ] }, { "cell_type": "code", "execution_count": 1, "id": "fiscal-breast", "metadata": { "_kg_hide-input": false, "_kg_hide-output": true, "execution": { "iopub.execute_input": "2021-06-18T10:28:23.061279Z", "iopub.status.busy": "2021-06-18T10:28:23.058355Z", "iopub.status.idle": "2021-06-18T10:28:43.662748Z", "shell.execute_reply": "2021-06-18T10:28:43.661906Z" }, "id": "ptkHaiID_EWG", "papermill": { "duration": 20.697931, "end_time": "2021-06-18T10:28:43.662903", "exception": false, "start_time": "2021-06-18T10:28:22.964972", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting chart_studio\r\n", " Downloading chart_studio-1.1.0-py3-none-any.whl (64 kB)\r\n", "\u001b[K |████████████████████████████████| 64 kB 295 kB/s \r\n", "\u001b[?25hRequirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from chart_studio) (2.25.1)\r\n", "Requirement already satisfied: plotly in /opt/conda/lib/python3.7/site-packages (from chart_studio) (4.14.3)\r\n", "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from chart_studio) (1.15.0)\r\n", "Requirement already satisfied: retrying>=1.3.3 in /opt/conda/lib/python3.7/site-packages (from chart_studio) (1.3.3)\r\n", "Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->chart_studio) (2.10)\r\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->chart_studio) (1.26.4)\r\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->chart_studio) (2020.12.5)\r\n", "Requirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests->chart_studio) (4.0.0)\r\n", "Installing collected packages: chart-studio\r\n", "Successfully installed chart-studio-1.1.0\r\n" ] }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import tensorflow as tf\n", "from tensorflow.compat.v1.keras.layers import CuDNNLSTM\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "from keras.layers.merge import concatenate\n", "from keras.models import Model\n", "import keras\n", "from keras.preprocessing.text import Tokenizer\n", "from keras.preprocessing.sequence import pad_sequences\n", "from keras.utils.vis_utils import plot_model\n", "from keras.models import Sequential\n", "from keras import layers\n", "from keras.layers.merge import concatenate\n", "from keras.callbacks import *\n", "from keras.layers import *\n", "from keras.models import Sequential,Model\n", "import kerastuner as kt\n", "\n", "import transformers\n", "from transformers import AutoTokenizer, TFAutoModel\n", "from transformers import AutoConfig, AutoModel\n", "\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import OneHotEncoder\n", "\n", "import nltk\n", "import re\n", "from nltk.tokenize import word_tokenize\n", "from nltk.corpus import stopwords\n", "from nltk.stem import WordNetLemmatizer,PorterStemmer,LancasterStemmer\n", "from nltk.stem.snowball import SnowballStemmer\n", "from wordcloud import WordCloud,STOPWORDS\n", "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = 'all'\n", "!pip install chart_studio\n", "from IPython.display import HTML\n", "import plotly\n", "import cufflinks\n", "import plotly.graph_objs as go\n", "import chart_studio.plotly as py\n", "import plotly.figure_factory as ff\n", "import plotly.express as px\n", "import plotly.figure_factory as ff\n", "from plotly.offline import iplot\n", "from plotly.subplots import make_subplots\n", "\n", "plotly.offline.init_notebook_mode(connected=True)\n", "cufflinks.go_offline()\n", "cufflinks.set_config_file(world_readable=True, theme='pearl')\n", "\n", "from string import punctuation\n", "from collections import defaultdict\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "id": "practical-scroll", "metadata": { "papermill": { "duration": 0.071938, "end_time": "2021-06-18T10:28:43.807984", "exception": false, "start_time": "2021-06-18T10:28:43.736046", "status": "completed" }, "tags": [] }, "source": [ "## 1.2 Reading and preparation of data" ] }, { "cell_type": "markdown", "id": "happy-beaver", "metadata": { "papermill": { "duration": 0.072783, "end_time": "2021-06-18T10:28:43.960441", "exception": false, "start_time": "2021-06-18T10:28:43.887658", "status": "completed" }, "tags": [] }, "source": [ "Reading [data](https://www.kaggle.com/c/nlp-getting-started/data) and choosing important columns using [pandas](https://pandas.pydata.org/)" ] }, { "cell_type": "code", "execution_count": 2, "id": "technical-elements", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:28:44.113137Z", "iopub.status.busy": "2021-06-18T10:28:44.112427Z", "iopub.status.idle": "2021-06-18T10:28:44.148946Z", "shell.execute_reply": "2021-06-18T10:28:44.148417Z" }, "id": "maaZD2m7GrYZ", "papermill": { "duration": 0.115656, "end_time": "2021-06-18T10:28:44.149083", "exception": false, "start_time": "2021-06-18T10:28:44.033427", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "data = pd.read_csv('../input/nlp-getting-started/train.csv')" ] }, { "cell_type": "markdown", "id": "unique-variance", "metadata": { "papermill": { "duration": 0.072695, "end_time": "2021-06-18T10:28:44.295379", "exception": false, "start_time": "2021-06-18T10:28:44.222684", "status": "completed" }, "tags": [] }, "source": [ "Displaying first 10 rows of our data using [DataFrame.head()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html)" ] }, { "cell_type": "code", "execution_count": 3, "id": "norman-worker", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:28:44.456987Z", "iopub.status.busy": "2021-06-18T10:28:44.456325Z", "iopub.status.idle": "2021-06-18T10:28:44.465166Z", "shell.execute_reply": "2021-06-18T10:28:44.464760Z" }, "id": "NluCZIHYHDQU", "outputId": "5e7f9692-a804-44e3-f2bf-f91d8de296a4", "papermill": { "duration": 0.096242, "end_time": "2021-06-18T10:28:44.465279", "exception": false, "start_time": "2021-06-18T10:28:44.369037", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { 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idkeywordlocationtexttarget
01NaNNaNOur Deeds are the Reason of this #earthquake M...1
14NaNNaNForest fire near La Ronge Sask. Canada1
25NaNNaNAll residents asked to 'shelter in place' are ...1
36NaNNaN13,000 people receive #wildfires evacuation or...1
47NaNNaNJust got sent this photo from Ruby #Alaska as ...1
58NaNNaN#RockyFire Update => California Hwy. 20 closed...1
610NaNNaN#flood #disaster Heavy rain causes flash flood...1
713NaNNaNI'm on top of the hill and I can see a fire in...1
814NaNNaNThere's an emergency evacuation happening now ...1
915NaNNaNI'm afraid that the tornado is coming to our a...1
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" ], "text/plain": [ " id keyword location text \\\n", "0 1 NaN NaN Our Deeds are the Reason of this #earthquake M... \n", "1 4 NaN NaN Forest fire near La Ronge Sask. Canada \n", "2 5 NaN NaN All residents asked to 'shelter in place' are ... \n", "3 6 NaN NaN 13,000 people receive #wildfires evacuation or... \n", "4 7 NaN NaN Just got sent this photo from Ruby #Alaska as ... \n", "5 8 NaN NaN #RockyFire Update => California Hwy. 20 closed... \n", "6 10 NaN NaN #flood #disaster Heavy rain causes flash flood... \n", "7 13 NaN NaN I'm on top of the hill and I can see a fire in... \n", "8 14 NaN NaN There's an emergency evacuation happening now ... \n", "9 15 NaN NaN I'm afraid that the tornado is coming to our a... \n", "\n", " target \n", "0 1 \n", "1 1 \n", "2 1 \n", "3 1 \n", "4 1 \n", "5 1 \n", "6 1 \n", "7 1 \n", "8 1 \n", "9 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head(10)" ] }, { "cell_type": "markdown", "id": "technical-programming", "metadata": { "papermill": { "duration": 0.073176, "end_time": "2021-06-18T10:28:44.612012", "exception": false, "start_time": "2021-06-18T10:28:44.538836", "status": "completed" }, "tags": [] }, "source": [ "Concise summarization of total information provided by the data using [DataFrame.info()](https://pandas.pydata.org/pandas-docs/version/0.23/generated/pandas.DataFrame.info.html)" ] }, { "cell_type": "code", "execution_count": 4, "id": "centered-enemy", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:28:44.774486Z", "iopub.status.busy": "2021-06-18T10:28:44.766284Z", "iopub.status.idle": "2021-06-18T10:28:44.779397Z", "shell.execute_reply": "2021-06-18T10:28:44.779944Z" }, "id": "j7WlTIr9HE9a", "outputId": "efaeacad-dcbf-479b-fe93-feb5ba103831", "papermill": { "duration": 0.094552, "end_time": "2021-06-18T10:28:44.780135", "exception": false, "start_time": "2021-06-18T10:28:44.685583", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 7613 entries, 0 to 7612\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 7613 non-null int64 \n", " 1 keyword 7552 non-null object\n", " 2 location 5080 non-null object\n", " 3 text 7613 non-null object\n", " 4 target 7613 non-null int64 \n", "dtypes: int64(2), object(3)\n", "memory usage: 297.5+ KB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "markdown", "id": "stylish-syndicate", "metadata": { "papermill": { "duration": 0.074237, "end_time": "2021-06-18T10:28:44.929094", "exception": false, "start_time": "2021-06-18T10:28:44.854857", "status": "completed" }, "tags": [] }, "source": [ "We only use text and target column of dataset for rest of our work as there lot's of null values inside other columns" ] }, { "cell_type": "code", "execution_count": 5, "id": "lightweight-eating", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:28:45.087902Z", "iopub.status.busy": "2021-06-18T10:28:45.087196Z", "iopub.status.idle": "2021-06-18T10:28:45.090124Z", "shell.execute_reply": "2021-06-18T10:28:45.090526Z" }, "id": "pYXi7Ltic6iG", "outputId": "a0ba0fd6-5162-4946-87b6-e07d4932dce1", "papermill": { "duration": 0.086521, "end_time": "2021-06-18T10:28:45.090658", "exception": false, "start_time": "2021-06-18T10:28:45.004137", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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0Our Deeds are the Reason of this #earthquake M...1
1Forest fire near La Ronge Sask. Canada1
2All residents asked to 'shelter in place' are ...1
313,000 people receive #wildfires evacuation or...1
4Just got sent this photo from Ruby #Alaska as ...1
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.bar(x=[\"0\",\"1\"], y=data[\"target\"].value_counts(),\n", " color=[\"red\", \"goldenrod\"])\n", "\n", "#Change this value for bar widths\n", "for dt in fig.data:\n", " dt[\"width\"] = 0.4 \n", "\n", "fig.update_layout(\n", " title_text = \"Counts for Disaster and Non-Disaster Tweets\",\n", " title_x=0.5,\n", " width=800,\n", " height=550,\n", " xaxis_title=\"Targets\",\n", " yaxis_title=\"Count\",\n", " showlegend=False\n", ").show()\n", "\n", "# py.plot(fig,filename='Counts for Disaster and Non-Disaster Tweets',auto_open=False,show_link=False)" ] }, { "cell_type": "markdown", "id": "requested-wisconsin", "metadata": { "papermill": { "duration": 0.08121, "end_time": "2021-06-18T10:28:46.632431", "exception": false, "start_time": "2021-06-18T10:28:46.551221", "status": "completed" }, "tags": [] }, "source": [ "The plot shows that our data is quite balanced, you can also click on the plot to explore more about [interactive plots](https://plotly.com/)" ] }, { "cell_type": "markdown", "id": "essential-blowing", "metadata": { "_kg_hide-input": true, "_kg_hide-output": true, "id": "q5FZ58sah2jH", "papermill": { "duration": 0.07927, "end_time": "2021-06-18T10:28:46.791992", "exception": false, "start_time": "2021-06-18T10:28:46.712722", "status": "completed" }, "tags": [] }, "source": [ "\n", "
\n", " \"Counts\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "contained-organizer", "metadata": { "papermill": { "duration": 0.07912, "end_time": "2021-06-18T10:28:46.952858", "exception": false, "start_time": "2021-06-18T10:28:46.873738", "status": "completed" }, "tags": [] }, "source": [ "## 2.2 Visualising lengths of tweets" ] }, { "cell_type": "markdown", "id": "saved-hayes", "metadata": { "papermill": { "duration": 0.079787, "end_time": "2021-06-18T10:28:47.113350", "exception": false, "start_time": "2021-06-18T10:28:47.033563", "status": "completed" }, "tags": [] }, "source": [ "Analyzing lengths of words in a tweets according to it being real or fake target value by ploting [histograms](https://www.investopedia.com/terms/h/histogram.asp#:~:text=A%20histogram%20is%20a%20bar,used%20to%20visualize%20data%20distributions.)" ] }, { "cell_type": "code", "execution_count": 7, "id": "nominated-burning", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:28:47.281643Z", "iopub.status.busy": 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"xaxis": { "anchor": "y", "domain": [ 0, 0.45 ] }, "xaxis2": { "anchor": "y2", "domain": [ 0.55, 1 ] }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ] }, "yaxis2": { "anchor": "x2", "domain": [ 0, 1 ] } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "word_len_dis = data[data['target']==1]['text'].str.split().map(lambda x : len(x))\n", "\n", "word_len_non_dis = data[data['target']==0]['text'].str.split().map(lambda x : len(x))\n", "\n", "fig = make_subplots(rows=1, cols=2,\n", " subplot_titles=(\"Disaster Tweets\", \"Non-Disaster Tweets\"))\n", "\n", "fig.add_trace(\n", " \n", " go.Histogram(x=word_len_dis,marker_line=dict(color='black'),\n", " marker_line_width=1.2),row=1, col=1\n", " \n", ").add_trace(\n", " \n", " go.Histogram(x=word_len_non_dis,marker_line=dict(color='black'),\n", " marker_line_width=1.2),row=1, col=2\n", " \n", ").update_layout(title_text=\"Length of words in Tweets\",\n", " title_x=0.5,showlegend=False).show()\n", "\n", "# py.plot(fig,filename='Length of words in Tweets',auto_open=False,show_link=False)" ] }, { "cell_type": "markdown", "id": "boring-centre", "metadata": { "papermill": { "duration": 0.098058, "end_time": "2021-06-18T10:28:47.594382", "exception": false, "start_time": "2021-06-18T10:28:47.496324", "status": "completed" }, "tags": [] }, "source": [ "From the plot we can say that the number of words in the tweets ranges from 2 to 30 in both cases" ] }, { "cell_type": "markdown", "id": "national-photograph", "metadata": { "_kg_hide-input": true, "_kg_hide-output": true, "id": "xM2GJvWTmOSh", "papermill": { "duration": 0.097388, "end_time": "2021-06-18T10:28:47.789906", "exception": false, "start_time": "2021-06-18T10:28:47.692518", "status": "completed" }, "tags": [] }, "source": [ "
\n", " \"Length\n", " \n", "
\n" ] }, { "cell_type": "markdown", "id": "enabling-insulin", "metadata": { "papermill": { "duration": 0.160608, "end_time": "2021-06-18T10:28:48.060731", "exception": false, "start_time": "2021-06-18T10:28:47.900123", "status": "completed" }, "tags": [] }, "source": [ "## 2.3 Visualising average word lengths of tweets" ] }, { "cell_type": "markdown", "id": "typical-tucson", "metadata": { "papermill": { "duration": 0.160752, "end_time": "2021-06-18T10:28:48.382377", "exception": false, "start_time": "2021-06-18T10:28:48.221625", "status": "completed" }, "tags": [] }, "source": [ "Checking average word length for both type of tweets" ] }, { "cell_type": "code", "execution_count": 8, "id": "outstanding-alberta", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:28:48.600345Z", "iopub.status.busy": "2021-06-18T10:28:48.599534Z", "iopub.status.idle": "2021-06-18T10:28:49.068165Z", "shell.execute_reply": "2021-06-18T10:28:49.068687Z" }, "id": "FV1c2BhymfUN", "papermill": { 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def avgwordlen(strlist):\n", " sum=[]\n", " for i in strlist:\n", " sum.append(len(i))\n", " return sum\n", "\n", "non_dis_data = data[data['target']==0]['text'].str.split()\n", "dis_data = data[data['target']==1]['text'].str.split()\n", "\n", "avgword_len_dis = dis_data.apply(avgwordlen).map(lambda x: np.mean(x))\n", "avgword_len_non_dis = non_dis_data.apply(avgwordlen).map(lambda x: np.mean(x))\n", "\n", "group_labels = ['Disaster', 'Non-Disaster']\n", "colors = ['rgb(0, 0, 100)', 'rgb(0, 200, 200)']\n", "\n", "fig = ff.create_distplot([avgword_len_dis, avgword_len_non_dis], \n", " group_labels, bin_size=.2, colors=colors,)\n", "\n", "fig.update_layout(title_text=\"Average word length in tweets\",title_x=0.5,\n", " xaxis_title=\"Text\",yaxis_title=\"Density\").show()\n", "\n", "# py.plot(fig,filename='Average word length in tweets',auto_open=False,show_link=False)" ] }, { "cell_type": "markdown", "id": "informational-citation", "metadata": { "papermill": { "duration": 0.16611, "end_time": "2021-06-18T10:28:49.402566", "exception": false, "start_time": "2021-06-18T10:28:49.236456", "status": "completed" }, "tags": [] }, "source": [ "The average word countss for real disaster tweets are found to be in the range(5-7.5) \n", "while for fake disaster tweets are in the range of (4-6)." ] }, { "cell_type": "markdown", "id": "lightweight-essay", "metadata": { "_kg_hide-input": true, "_kg_hide-output": true, "id": "WsevjsJpqbAU", "papermill": { "duration": 0.165388, "end_time": "2021-06-18T10:28:49.773700", "exception": false, "start_time": "2021-06-18T10:28:49.608312", "status": "completed" }, "tags": [] }, "source": [ "
\n", " \"Average\n", " \n", "
\n" ] }, { "cell_type": "markdown", "id": "loaded-coordinate", "metadata": { "papermill": { "duration": 0.166509, "end_time": "2021-06-18T10:28:50.105995", "exception": false, "start_time": "2021-06-18T10:28:49.939486", "status": "completed" }, "tags": [] }, "source": [ "## 2.4 Visualising most common stop words in the text data" ] }, { "cell_type": "markdown", "id": "friendly-morocco", "metadata": { "papermill": { "duration": 0.168423, "end_time": "2021-06-18T10:28:50.442465", "exception": false, "start_time": "2021-06-18T10:28:50.274042", "status": "completed" }, "tags": [] }, "source": [ "### What is a corpus?\n", "\n", "In linguistics and NLP, corpus (literally Latin for body) refers to a collection of texts. \n", "Such collections may be formed of a single language of texts, or can span multiple languages\n", " \n", "Function for creating sample [corpus](https://21centurytext.wordpress.com/home-2/special-section-window-to-corpus/what-is-corpus/) for further analysis. " ] }, { "cell_type": "code", "execution_count": 9, "id": "confused-brook", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:28:50.781949Z", "iopub.status.busy": "2021-06-18T10:28:50.781093Z", "iopub.status.idle": "2021-06-18T10:28:50.783738Z", "shell.execute_reply": "2021-06-18T10:28:50.783301Z" }, "id": "rtAFdb6xpDK9", "papermill": { "duration": 0.174409, "end_time": "2021-06-18T10:28:50.783858", "exception": false, "start_time": "2021-06-18T10:28:50.609449", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def create_corpus(target):\n", " corpus = []\n", " for i in data[data['target']==target]['text'].str.split():\n", " for x in i:\n", " corpus.append(x)\n", " return corpus" ] }, { "cell_type": "markdown", "id": "chinese-grade", "metadata": { "papermill": { "duration": 0.166884, "end_time": "2021-06-18T10:28:51.117626", "exception": false, "start_time": "2021-06-18T10:28:50.950742", "status": "completed" }, "tags": [] }, "source": [ "### What are stopwords?\n", "\n", "In computing, stop words are words that are filtered out before or after the natural language data (text) are processed. \n", "While “stop words” typically refers to the most common words in a language, all-natural language processing tools don't use a single universal list of stop words. \n", "\n", "Analysing most occuring [stop words](https://en.wikipedia.org/wiki/Stop_word) in the text using corpus creating function(create_corpus)" ] }, { "cell_type": "code", "execution_count": 10, "id": "unlike-lafayette", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:28:51.485104Z", "iopub.status.busy": "2021-06-18T10:28:51.460283Z", "iopub.status.idle": "2021-06-18T10:28:51.554802Z", "shell.execute_reply": "2021-06-18T10:28:51.554341Z" }, "id": "mAAtFo91xRBw", "papermill": { "duration": 0.270424, "end_time": "2021-06-18T10:28:51.554935", "exception": false, "start_time": "2021-06-18T10:28:51.284511", "status": "completed" }, "tags": [] }, "outputs": [ { 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"xaxis": { "anchor": "y", "domain": [ 0, 0.45 ] }, "xaxis2": { "anchor": "y2", "domain": [ 0.55, 1 ] }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ] }, "yaxis2": { "anchor": "x2", "domain": [ 0, 1 ] } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "values_list = []\n", "\n", "def analyze_stopwords(data,func,targetlist):\n", " \n", " for label in range(0,len(targetlist)):\n", " corpus = func(targetlist[label])\n", " dic = defaultdict(int)\n", " \n", " for word in corpus:\n", " dic[word] += 1\n", " \n", " top = sorted(dic.items(),key = lambda x: x[1],reverse=True)[:10]\n", " x_items,y_values = zip(*top)\n", " values_list.append(x_items)\n", " values_list.append(y_values)\n", "\n", "#analyzing stopwords for 0 and 1 target labels\n", "analyze_stopwords(data,create_corpus,[0,1])\n", "\n", "fig = make_subplots(rows=1, cols=2,subplot_titles=(\"Disaster Tweets\", \"Non-Disaster Tweets\"))\n", "\n", "fig.add_trace(\n", " go.Bar(x=values_list[1],y=values_list[0],orientation='h',marker=dict(color= 'rgba(152, 255, 74,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=1, col=1\n", ").add_trace(\n", " go.Bar(x=values_list[3],y=values_list[2],orientation='h',marker=dict(color= 'rgba(255, 143, 92,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=1, col=2\n", ").update_layout(title_text=\"Top stop words in the text\",title_x=0.5,showlegend=False).show()\n", "\n", "# py.plot(fig,filename='Top stop words in the text',auto_open=False,show_link=False)" ] }, { "cell_type": "markdown", "id": "developing-gambling", "metadata": { "papermill": { "duration": 0.189694, "end_time": "2021-06-18T10:28:51.917442", "exception": false, "start_time": "2021-06-18T10:28:51.727748", "status": "completed" }, "tags": [] }, "source": [ "[The Bar Charts](https://plotly.com/python/bar-charts/) displays the top 10 stop words in tweets where **'the'** is most frequent in both groups" ] }, { "cell_type": "markdown", "id": "norwegian-adolescent", "metadata": { "_kg_hide-input": true, "_kg_hide-output": true, "id": "zPUJ70ZnqipE", "papermill": { "duration": 0.173551, "end_time": "2021-06-18T10:28:52.264355", "exception": false, "start_time": "2021-06-18T10:28:52.090804", "status": "completed" }, "tags": [] }, "source": [ "
\n", " \"Top\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "referenced-excess", "metadata": { "papermill": { "duration": 0.171905, "end_time": "2021-06-18T10:28:52.609034", "exception": false, "start_time": "2021-06-18T10:28:52.437129", "status": "completed" }, "tags": [] }, "source": [ "## 2.5 Visualising most common punctuations in the text data" ] }, { "cell_type": "markdown", "id": "terminal-appendix", "metadata": { "papermill": { "duration": 0.172322, "end_time": "2021-06-18T10:28:52.953359", "exception": false, "start_time": "2021-06-18T10:28:52.781037", "status": "completed" }, "tags": [] }, "source": [ "Now let's have a look at the punctuations inside our data" ] }, { "cell_type": "code", "execution_count": 11, "id": "potential-invention", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:28:53.310044Z", "iopub.status.busy": "2021-06-18T10:28:53.309231Z", "iopub.status.idle": "2021-06-18T10:28:53.377116Z", "shell.execute_reply": "2021-06-18T10:28:53.377538Z" }, "id": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "values_list = []\n", "\n", "def analyze_punctuations(data,func,targetlist):\n", " \n", " for label in range(0,len(targetlist)):\n", " corpus = func(targetlist[label])\n", " dic = defaultdict(int)\n", " \n", " for word in corpus:\n", " if word in punctuation:\n", " dic[word] += 1 \n", " \n", " x_items, y_values = zip(*dic.items())\n", " \n", " values_list.append(x_items)\n", " values_list.append(y_values)\n", "\n", "#analyzing punctuations for 0 and 1 target labels\n", "analyze_punctuations(data,create_corpus,[0,1])\n", "\n", "fig = make_subplots(rows=1, cols=2,\n", " subplot_titles=(\"Disaster Tweets\", \"Non-Disaster Tweets\"))\n", " \n", "fig.add_trace(\n", " \n", " go.Bar(x=values_list[0],y=values_list[1],\n", " marker=dict(color= 'rgba(196, 94, 255,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=1, col=1\n", " \n", ").add_trace(\n", " \n", " go.Bar(x=values_list[2],y=values_list[3],\n", " marker=dict(color= 'rgba(255, 163, 102,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=1, col=2\n", " \n", ").update_layout(title_text=\"Top Punctuations in the text\",\n", " title_x=0.5,showlegend=False).show()\n", "\n", "# py.plot(fig,filename='Top Punctuations in the text',auto_open=False,show_link=False)" ] }, { "cell_type": "markdown", "id": "therapeutic-income", "metadata": { "papermill": { "duration": 0.179297, "end_time": "2021-06-18T10:28:53.736923", "exception": false, "start_time": "2021-06-18T10:28:53.557626", "status": "completed" }, "tags": [] }, "source": [ "As observed from the plots the most occuring punctuations in both disaster/non-disaster tweets is '-' (350+) and '|' \n", "while the least occuring for non-disaster are '%' , '/:' , '_' and for disaster tweets is '=>' , ')'." ] }, { "cell_type": "markdown", "id": "advised-kitty", "metadata": { "_kg_hide-input": true, "id": "cpQiMex1qqbg", "papermill": { "duration": 0.178119, "end_time": "2021-06-18T10:28:54.100255", "exception": false, "start_time": "2021-06-18T10:28:53.922136", "status": "completed" }, "tags": [] }, "source": [ "
\n", " \"Top\n", " \n", "
\n" ] }, { "cell_type": "markdown", "id": "atomic-trust", "metadata": { "papermill": { "duration": 0.178519, "end_time": "2021-06-18T10:28:54.458252", "exception": false, "start_time": "2021-06-18T10:28:54.279733", "status": "completed" }, "tags": [] }, "source": [ "# 3. Data Cleaning" ] }, { "cell_type": "markdown", "id": "recreational-output", "metadata": { "papermill": { "duration": 0.177459, "end_time": "2021-06-18T10:28:54.815803", "exception": false, "start_time": "2021-06-18T10:28:54.638344", "status": "completed" }, "tags": [] }, "source": [ "## 3.1 Removing unwanted text using regular expressions" ] }, { "cell_type": "markdown", "id": "fantastic-blair", "metadata": { "papermill": { "duration": 0.179031, "end_time": "2021-06-18T10:28:55.172108", "exception": false, "start_time": "2021-06-18T10:28:54.993077", "status": "completed" }, "tags": [] }, "source": [ "### What is lemmatizing? \n", "Lemmatization is the process of grouping together the different inflected forms of a word so they can be analysed as a single item. \n", "Lemmatization is similar to stemming but it brings context to the words. So it links words with similar meaning to one word.\n", " \n", "Text preprocessing includes both Stemming as well as Lemmatization. Many times people find these two terms confusing. Some treat these two as same. \n", "Actually, lemmatization is preferred over Stemming because lemmatization does morphological analysis of the words.\n", "\n", "Function for cleaning the data, we use [RegEx](https://docs.python.org/3/library/re.html) i.e. re python library and [WordNetLemmatizer()](https://www.nltk.org/_modules/nltk/stem/wordnet.html)." ] }, { "cell_type": "code", "execution_count": 12, "id": "sudden-logic", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:28:55.564845Z", "iopub.status.busy": "2021-06-18T10:28:55.564210Z", "iopub.status.idle": "2021-06-18T10:29:09.697653Z", "shell.execute_reply": "2021-06-18T10:29:09.697184Z" }, "id": "sckzCfiLegfL", "papermill": { "duration": 14.346529, "end_time": "2021-06-18T10:29:09.697853", "exception": false, "start_time": "2021-06-18T10:28:55.351324", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "lemmatizer = WordNetLemmatizer()\n", "# stemmer = LancasterStemmer()\n", "\n", "def preprocess_data(data):\n", " \n", " #removal of url\n", " text = re.sub(r'https?://\\S+|www\\.\\S+|http?://\\S+',' ',data) \n", " \n", " #decontraction\n", " text = re.sub(r\"won\\'t\", \" will not\", text)\n", " text = re.sub(r\"won\\'t've\", \" will not have\", text)\n", " text = re.sub(r\"can\\'t\", \" can not\", text)\n", " text = re.sub(r\"don\\'t\", \" do not\", text) \n", " text = re.sub(r\"can\\'t've\", \" can not have\", text)\n", " text = re.sub(r\"ma\\'am\", \" madam\", text)\n", " text = re.sub(r\"let\\'s\", \" let us\", text)\n", " text = re.sub(r\"ain\\'t\", \" am not\", text)\n", " text = re.sub(r\"shan\\'t\", \" shall not\", text)\n", " text = re.sub(r\"sha\\n't\", \" shall not\", text)\n", " text = re.sub(r\"o\\'clock\", \" of the clock\", text)\n", " text = re.sub(r\"y\\'all\", \" you all\", text)\n", " text = re.sub(r\"n\\'t\", \" not\", text)\n", " text = re.sub(r\"n\\'t've\", \" not have\", text)\n", " text = re.sub(r\"\\'re\", \" are\", text)\n", " text = re.sub(r\"\\'s\", \" is\", text)\n", " text = re.sub(r\"\\'d\", \" would\", text)\n", " text = re.sub(r\"\\'d've\", \" would have\", text)\n", " text = re.sub(r\"\\'ll\", \" will\", text)\n", " text = re.sub(r\"\\'ll've\", \" will have\", text)\n", " text = re.sub(r\"\\'t\", \" not\", text)\n", " text = re.sub(r\"\\'ve\", \" have\", text)\n", " text = re.sub(r\"\\'m\", \" am\", text)\n", " text = re.sub(r\"\\'re\", \" are\", text)\n", " \n", " #removal of html tags\n", " text = re.sub(r'<.*?>',' ',text) \n", " \n", " # Match all digits in the string and replace them by empty string\n", " text = re.sub(r'[0-9]', '', text)\n", " text = re.sub(\"[\"\n", " u\"\\U0001F600-\\U0001F64F\" # removal of emoticons\n", " u\"\\U0001F300-\\U0001F5FF\" # symbols & pictographs\n", " u\"\\U0001F680-\\U0001F6FF\" # transport & map symbols\n", " u\"\\U0001F1E0-\\U0001F1FF\" # flags (iOS)\n", " u\"\\U00002702-\\U000027B0\"\n", " u\"\\U000024C2-\\U0001F251\"\n", " \"]+\",' ',text)\n", " \n", " # filtering out miscellaneous text.\n", " text = re.sub('[^a-zA-Z]',' ',text) \n", " text = re.sub(r\"\\([^()]*\\)\", \"\", text)\n", " \n", " # remove mentions\n", " text = re.sub('@\\S+', '', text) \n", " \n", " # remove punctuations\n", " text = re.sub('[%s]' % re.escape(\"\"\"!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\"\"\"), '', text) \n", " \n", "\n", " # Lowering all the words in text\n", " text = text.lower()\n", " text = text.split()\n", " \n", " text = [lemmatizer.lemmatize(words) for words in text if words not in stopwords.words('english')]\n", " \n", " # Removal of words with length<2\n", " text = [i for i in text if len(i)>=2] \n", " text = ' '.join(text)\n", " return text\n", "\n", "data[\"Cleaned_text\"] = data[\"text\"].apply(preprocess_data)" ] }, { "cell_type": "markdown", "id": "continental-structure", "metadata": { "papermill": { "duration": 0.303481, "end_time": "2021-06-18T10:29:10.180729", "exception": false, "start_time": "2021-06-18T10:29:09.877248", "status": "completed" }, "tags": [] }, "source": [ "Displaying Cleaned Data " ] }, { "cell_type": "code", "execution_count": 13, "id": "related-nightlife", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:10.629619Z", "iopub.status.busy": "2021-06-18T10:29:10.628898Z", "iopub.status.idle": "2021-06-18T10:29:10.631984Z", "shell.execute_reply": "2021-06-18T10:29:10.632384Z" }, "id": "iwX5VY5bK13S", "outputId": "7ecbefb1-5c8c-4e22-aebb-8264be851a15", "papermill": { "duration": 0.195682, "end_time": "2021-06-18T10:29:10.632540", "exception": false, "start_time": "2021-06-18T10:29:10.436858", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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texttargetCleaned_text
0Our Deeds are the Reason of this #earthquake M...1deed reason earthquake may allah forgive
1Forest fire near La Ronge Sask. Canada1forest fire near la ronge sask canada
2All residents asked to 'shelter in place' are ...1resident asked ishelter place notified officer...
313,000 people receive #wildfires evacuation or...1people receive wildfire evacuation order calif...
4Just got sent this photo from Ruby #Alaska as ...1got sent photo ruby alaska smoke wildfire pour...
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" ], "text/plain": [ " text target \\\n", "0 Our Deeds are the Reason of this #earthquake M... 1 \n", "1 Forest fire near La Ronge Sask. Canada 1 \n", "2 All residents asked to 'shelter in place' are ... 1 \n", "3 13,000 people receive #wildfires evacuation or... 1 \n", "4 Just got sent this photo from Ruby #Alaska as ... 1 \n", "\n", " Cleaned_text \n", "0 deed reason earthquake may allah forgive \n", "1 forest fire near la ronge sask canada \n", "2 resident asked ishelter place notified officer... \n", "3 people receive wildfire evacuation order calif... \n", "4 got sent photo ruby alaska smoke wildfire pour... " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "id": "indian-navigator", "metadata": { "papermill": { "duration": 0.285346, "end_time": "2021-06-18T10:29:11.188322", "exception": false, "start_time": "2021-06-18T10:29:10.902976", "status": "completed" }, "tags": [] }, "source": [ "# 4. Extra Data Exploration and Analysis with Cleaned Text" ] }, { "cell_type": "markdown", "id": "toxic-excess", "metadata": { "papermill": { "duration": 0.291296, "end_time": "2021-06-18T10:29:11.773887", "exception": false, "start_time": "2021-06-18T10:29:11.482591", "status": "completed" }, "tags": [] }, "source": [ "## 4.1 Creating function and data for visualising words" ] }, { "cell_type": "markdown", "id": "possible-label", "metadata": { "papermill": { "duration": 0.178141, "end_time": "2021-06-18T10:29:12.129531", "exception": false, "start_time": "2021-06-18T10:29:11.951390", "status": "completed" }, "tags": [] }, "source": [ "Using the popular [WordCloud](https://www.python-graph-gallery.com/wordcloud/) python library for visulaising the cleaned data" ] }, { "cell_type": "code", "execution_count": 14, "id": "guilty-dress", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:12.494878Z", "iopub.status.busy": "2021-06-18T10:29:12.493941Z", "iopub.status.idle": "2021-06-18T10:29:12.498589Z", "shell.execute_reply": "2021-06-18T10:29:12.498155Z" }, "id": "rA860VD7K5zQ", "papermill": { "duration": 0.190082, "end_time": "2021-06-18T10:29:12.498723", "exception": false, "start_time": "2021-06-18T10:29:12.308641", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def wordcloud(data,title):\n", " words = ' '.join(data['Cleaned_text'].astype('str').tolist())\n", " stopwords = set(STOPWORDS)\n", " wc = WordCloud(stopwords = stopwords,width= 512, height = 512).generate(words)\n", " plt.figure(figsize=(10,8),frameon=True)\n", " plt.imshow(wc)\n", " plt.axis('off')\n", " plt.title(title,fontsize=20)\n", " plt.show()\n", "\n", "data_disaster = data[data['target'] == 1]\n", "data_non_disaster = data[data['target'] == 0]" ] }, { "cell_type": "markdown", "id": "round-trainer", "metadata": { "papermill": { "duration": 0.212647, "end_time": "2021-06-18T10:29:12.888790", "exception": false, "start_time": "2021-06-18T10:29:12.676143", "status": "completed" }, "tags": [] }, "source": [ "## 4.2 Visualising words inside Real Disaster Tweets" ] }, { "cell_type": "markdown", "id": "million-reconstruction", "metadata": { "papermill": { "duration": 0.178212, "end_time": "2021-06-18T10:29:13.244650", "exception": false, "start_time": "2021-06-18T10:29:13.066438", "status": "completed" }, "tags": [] }, "source": [ "we can see that most common words in disaster tweets are fire,storm,flood , police etc. " ] }, { "cell_type": "code", "execution_count": 15, "id": "analyzed-liabilities", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:13.637277Z", "iopub.status.busy": "2021-06-18T10:29:13.621969Z", "iopub.status.idle": "2021-06-18T10:29:14.709299Z", "shell.execute_reply": "2021-06-18T10:29:14.708861Z" }, "id": "zH60osphMhQ0", "outputId": "9f045705-2762-4571-ebf5-3e946c14e2a4", "papermill": { "duration": 1.285186, "end_time": "2021-06-18T10:29:14.709422", "exception": false, "start_time": "2021-06-18T10:29:13.424236", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "wordcloud(data_disaster,\"Disaster Tweets\")" ] }, { "cell_type": "markdown", "id": "sustained-envelope", "metadata": { "papermill": { "duration": 0.185909, "end_time": "2021-06-18T10:29:15.080853", "exception": false, "start_time": "2021-06-18T10:29:14.894944", "status": "completed" }, "tags": [] }, "source": [ " \n", "now let's have a look on Non-Disaster tweets" ] }, { "cell_type": "markdown", "id": "correct-scientist", "metadata": { "papermill": { "duration": 0.183961, "end_time": "2021-06-18T10:29:15.449054", "exception": false, "start_time": "2021-06-18T10:29:15.265093", "status": "completed" }, "tags": [] }, "source": [ "## 4.3 Visualising words inside Fake Disaster Tweets" ] }, { "cell_type": "markdown", "id": "central-james", "metadata": { "papermill": { "duration": 0.18322, "end_time": "2021-06-18T10:29:15.816491", "exception": false, "start_time": "2021-06-18T10:29:15.633271", "status": "completed" }, "tags": [] }, "source": [ "love,new,time etc are the most common words in wordcloud of Non-disaster tweets" ] }, { "cell_type": "code", "execution_count": 16, "id": "typical-indication", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:16.209116Z", "iopub.status.busy": "2021-06-18T10:29:16.204054Z", "iopub.status.idle": "2021-06-18T10:29:17.294564Z", "shell.execute_reply": "2021-06-18T10:29:17.294990Z" }, "id": "6USUwvWKNFXX", "outputId": "9c6d292d-3f95-4252-ad04-d85b51e3ba75", "papermill": { "duration": 1.29413, "end_time": "2021-06-18T10:29:17.295135", "exception": false, "start_time": "2021-06-18T10:29:16.001005", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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Iu2jYREgpkZbpCsSJMQpXLrh9914al+8ReGI++n7sEEiJYzqUpwuMPneFQGeElvt60MNeslcTzLwzQsP+FhzDIXN5mq4P7yF1bgK7atHyQC/ScvC3hBj62nmMbB0JQ4QgcriH0L5OtLDvZlZMQNqSkf/9wu176PcAQiJGl7oTvwgRU5oZtq8wYF3AWZqXekU86vkUVcq8ZTy34PgdUYfaFty4YbF9h5eGBuU9JQS1hjiNDz1OYOdetEjEnZxvg/CbheLx4mvvxNvaTnDXHkqD/WTeeJnK+MgPnDrK19FF7sTqZTwtbTQ8/CTBHbtRQ+FNn8DnQ2ga3uZWPPFmAtt3Uj50lOQLz2FMbyx1XktgF03+XiSSZHmQXHUaVejsiD2AKnSy1QlGC+dp8HXRGtiJIjQS5QEqVh6fFgYgpMcZzp2mJ3I3/dk1szfNQY1Eid37EKG9B9Eb4whNvy2LMgAhBEL34G1pw9PUQmDHLoxjD5F69XlKN66+J8elpvvpu+uTzIycIpcawLHNO3ZvT8RH4uQY+cEUe37pXtIXp2i6uxMjV2HmnRF2/sw9VGYKBDqi2GWTzOVpGva1UBhJYxUMGva1MPb8ddIXJjGL9bU7dt8Oun/pcZySiZkuLNj/SHvzFnrvVRRlluvWGSJKHJ8I3rH7bpoQlBJSCYdi8earU1X4K78a5MBBnYlxmy/8YQlFgV/5ayGkLfH5BV/5swovPF/lL/5KgB07NXp6VQb6bX77NwtMTt7aixe6TuTwMRqf+BBaMHRLKrn139wl2mjhKOGDRwju3kfmjZdJvfYC0tic1bcWjrn2yUqZhTZIgaJ7UP0BrGLeJZdsEL72rhXPCVUjuPcATR/4KHpDfB7Z5fZDKApqKEJo3134OrpIfP/bFM6fdskg60BHaA/DuTPkjRlsaeFR/HjUAFfTL2M7Fjti95M1pmjy9zKaP0/VLrCn8XFS5WGEUPAofiKeFnTVj6Z46rupqhLs20XThz6Op7nFJbzcqXGJ23daKIIaDNPe1k7+3ClSr72AlUnfUr3hozuxixVKV8fAWUGoqgre9ga8vS1YqQLlgUlkZXkhoSgaLZ1309R+F+XCNNMjJ5kZO41llJG3WVVdTZcojKQpjGSwKxb+5iCaXyNzeZriSJbydIFwXy0hSO3VCfXmO6ymyuT7kxjZ+rUJsQd2Urg8wfD/eh5p3ZlNhIJSC/LqzrUCgUBB4sypIUXtP4f587FAmedcML88C0q59TnYc/9e6RqJxMLEkmZdKtCF9cla+9a/mLutxJh7jnlob1f5a7+S4UPPeLn3fg9XL1v09qr8zE+kOHavh/0HNU68Lbj3Pg//6p/n2bXbFYS3JAAVBW9rO42PPU1wzwGXXXcHJ5kFqAkGxeuj8YkPEdixi5nvfovq2DDSujWje8O+YziWiVXIYeRSVBLjSMsk0LGN1kc+hhaMYGQSjD//RczcxiY4T3MrQteR5sKJSvH5idxzH/EnPozi9b4r/Tt7Ty3WSMtHP40ebSDz1ms4lfIaV97E5eRLdIXvoi24m4niFcpmlrKVxbDdOmxp4VWD2I6F5VSwHIOqXUQRGkIoqIqHql0i7ushV51eq8FokSgNDz1B7L6HXZbnuzUucftP8QeI3vcw3vZOEs8/S2V4YN0LiVkE7+p1SU6mhRoJAGBMpDGTOaRpg4Dog3tp/0sfdomtHp38O9cY++/fwikuoy4UtcWOohOMdtIX7aR334dIjJ1jZvwspdwEllHCcTafvCJt5+Z8KiVW2cCqWAQ6Ixj5CsH2CBMv9xPubcDXGCTQHibQGXVZu9xMTLAeaEEv2VND2KVqXXO5UDU0TwCznFtyTtG9CAS2uboQvsfzNLY0OWW+CECPuocd2mGGrMvcsM8gUNim7ScmmrhovkWVEh58tKt9tKvb8Ag/tjSZccYYtq5Qobig/jZlG9u0/Vwy3yaqNNKu9uEVAQxZYdC6yIQzUHf/zIeGTpe6iw51B5rQKDo5huwrG6Ij3lYhGIsKEgn3gyqVJG3tAlWDwX4LywLDlHNEvxvXLX70x31MTzu8+P16Em4vD6GqBHbupfHRp/F1976rk8x8zLbD191H26d+ktTLz1O4dPaWCApC1Wjcf4xqegZF85C+8CbZyycJ9e6hMHyV7JVTNB19gtj+e5k5/p2N3UPX8DS3UR0fmTumBII0Pvo0DQ8+dltVn3VBuNOO6g/Q8MiTCFUl/fpLdZNmfFqYVGWYgNZAg7eTspldUqZqFXE8NlFvO5ZTRRUa6eoYbcHdmE6VnDFFT+QIFxNLsgbdhKri6+yh6ckP4+/b+e73Ww2z49Lf00frj3yG9CvfJ3f25IZthdFHDxB76tDcTsZKF5n+4qvk376KdCTxj99HpX+S1HMn8fW1Ev/YvUQf2kf6u6eXVjY3o4k5BY6q+WjtvZemriMUMqOkJi9RyIxQzk9jVPNL69gAHNOmMJTBLBogJZnL01RmSpi5UVof6iXykX1MvNJP/kYSp2rR8cQOOp7axczboxi5CnbFIj+YwjHXt5AvXp0guLOVdDSIlSmuWd4XbaV534MMv/alZc61oKgahanVhUzByRBXOnB3dgK/COFgE1EawRZoaPgJUpJ5bEwUVLbrd9GsdDJtj1CUWbwEaFG7CepRLplvLRGEuvDQpe4EAeP2ABIbnwhRlfUvVhejR9tLr7qPaWeErJ3EK/xs1w6iCR1Drm9O3TQhGAgKnv6Ql4OHdHw+QTrtcO6cyf0PefjMT/tpblE4e9qkXJJLNCWaDuGwYGREYpnQ2aUyPmazXq2hUFWi9z5M7P5H8MSbNuvRNhVCCDzxZuJPP4MWayBz/BWc8kZzeEpSZ98gfe443qY2mo89SfbySRSvj/L4IEYmSfbySRqPPHIrLcbX0TknBNVAkIZHnyZ2/yN3TrVcJ1Sfn9iDj+GYJunXX4Q6CDMeNYCm6FiySqo4guVUmSn1Y0s3o9FMaYCylWW6eJ2ItwWfN4znCZvil1OkKqNYTtW1D6phytZSAQo10tCufTQ+/gG8bZ3vGQG4GJ7GJpo+/AnUUNgdlxtg3wpdJfXtk1QGpxCqQvBwHw1PH6bSP4GZyONpa2Dmy6+TO36F/Inr+PraVhaCq0BVdaLxPiKN26iW0+RTI+TTQ+RSg5RyUzjOxu2HVtFk4uX+ub8H//zC3L/7v3h2QdniaJZrf3hySR0TL9xY932rk1manzlCj0+nMpLEqcxm1QLpSKa+ttQ4r3oCNG6/GxAUpgcwCmn8jR0EGjsopcbnygWbe/CEGtG8QWyzQm70Mla1SF6maRPb8OJDIPAKPylnkrDSiIKCio5XBEg649hYNCpttCvbGLauMmCfnyOtZGSCg/qDdKjb6bfPLWijBz+KULhqnlokIDc2f/gI0qXuIuvMcMU8gYUrKMpqgf3a/euub9OEoGVKLl4wGey3qRqSQsEhm5H80f8p0dCocP6sZGjQRgj4/O+5k/6NaxYz0w47d+mMjzmcPGHQ0KBw+IjO4IDF2Og6VlKKQsPDT9Lw0OOogTtnVN0o9EiMhgcfQ/X7SXznm0hr/R+tY5pYpTyOZWKXi2ihBjyxZlTdOyegbKOCUDbm+gGAEHhrdkGh68Tuf4To0Qdcp/f3mBAEVxA2PvIkVjpJ/sKZNctPl64vOZasDM/79xAAlmVQsjIoHpVdTzwDX4Z0ZXSu3HD+9PI3UBQCO/fS9MGPoceb35N9Nh+K10fDw0+geH2kXv7eulTLAIWzAyS+ehw7715XHU3S9tmnUUN+zEQeRVdxyq6mR5o2levjxJ4+suH2CiHwBRrx+htobNtLpZiimBsnNXWFzPQVLHPju407DW9bDGMmh68thrc54qqQa5C2s6wQ1H0hqvkk/sZOIh27SF4/iVUtovnDBOJdlBIjgKBx5zHSA2cQikKobQeZIVdQ5Z0UAggqUSxpoKAy44wRU1rwizAC0IRORZaRSFrULmxsZpzRBazNjDNNWRaIKI14bB8GNxdQDhZZJ7lkh7hRX+qoEkdDI+lMzAlAgGl7mH3asXXXt2lC0DDg3JmluvmBfpuB/oU2hksX3XLZrCSbdc9t/wk/Xd0qqgZnz5gkkxsTgIo/sOFnWE6HLy0LxzTAtlE8XoS+lFkqYEO7ItXnJ3r0QYRQmH72z+vaucxHZWaMpqNPEu7bjycWxy7n6f7Yz6N6fNhmleLINUI9uzHzmXW3bT687Z0ABHbsIXL3vfXbAOUy5m0pb/ap4yB0D4quw6Ld0a0IC8UfoPGpZyiPDS9L9gi0h9n+yb0E28KUE0XO/847NB9qw9cUYOjb19j54wfIXEkC0PvMToSmIi2Hi//nJNW0+3Frfp2eD+2kNJln6p0xpL38B+3v7qPlo59Gi8Y29EzL2pWkRJoG0jSRgFJznEcs3WGu956z9uvo0ftxKmXSr71Qt41QWjZ2rrSA1OFUDHeuUxU36o1gwdxn5cuovuVdaSS40Zhqz7XaswghUDUvwWg7/nALDa37MKsFkhPnmRk9RSk/VdczvJuY/vZpEs+fX9c1ZilDcWYYhEKopRdF92AWsxiFNIo2S9SSmKUcjdsOI6VDduQijuUKj4LM4uAQFFHKFADIOUkcbKJKIyUnj4I6J9QCRJA4VORC7ZVEUnaK+JQAuvAsUEnaWFTlRrVdS+ETAUBQlguFqoWJKde/mXhPRIwZGbb5R3//pirJcdYhDxSFyJF7aXjgURR/YN0f/VyYLsvEzGWpDA9QHrxBNTGNlU7hVCtz36wAUFS0SARPYzPejm4C23fibW13I59o6yfgCE0jcvR+HNMg8fyz6wpzVRi6gmOZhHp3Uxi6QvbqKdcgrig03/dBdvzsr2OV8ox954/X1abF0CJR/Nt3ErvvYbRZ5+1VIKUE28YxTcx0gvJgP+XhQYzUDFYm7U7g88ormu5GlWnrwNe9jeCuvWjBsBsRZYOqQ09jE42PfZDpb3xpyWAKtARBwvnfeZtqpoK0HVS/jh50Jw1PxIvq0xCqQAt6ePtfv8Tun7qLaF8jM9kJFF2l++ntOIbN9MnxFQWgp6WN9p/8BdRgaEPjUloW0jKpTo5THup3AzAkZrByWbCtBeNSaDpqOIKnqQVfVw+BbTvwNLUiPJ5179qFECg+P7H7HsZMJchfPFvXB2lMZWi67z4Ce7ooXx8HVSF093a8PU3En7mH8uA0COGSZmrN0RvD2MXl1a5mtcDJF/4TLd1HiXccwOuLoij6mixkRVFRPAE03U/Xrido73uIXGqQqcE3yaUGsawq8jaQaW4VdqGKo5qoQS/e9hiKrlGdzmJmSkhj+fbqgSiqx4/mCyEdibQtFM2DoukoqlaLdGWjqDqpwTOU0xPuwqIGB5uSkyOkRJGOg4VJRZbIOxliohlH2FjSwJhvv5Nz/6sL89mnm4nlGKTrcayfxXtCCAJsiCgpBP6ePmL3P4IWjqzr0lnhZ6aTlIcHyJ87RXloYFW1pASwbcxkAjOZoHjtEqmXvoMWjRE+cJjgngN4W9vXJYyFEAhNJ3LkXozENPkzJ9bFziuN9VMau2m/sGsdOfHiV0ieehmrmMcxb4FoJARaKEz7j/88aiC46nPJ2g7FSMxQunGF/PnTVKcm1pxAHdvGmJ7EmJ4kf/YkM5pGcMduIkeO4e/Z7gqRdQhDIQSoKoG+Hfi7t1Ee6l9wPn0lgR72svMnDlKeKXL9ixdAyjmKu+bTQYC0HAqjORzDxioaKLqKEOBt8BM/2MrYy4MrzgVaNEbrj3xm3QJQSolTLlGdnqRw+TyFS+ew0qtHLZGAtG03OkximuLl8yQVBU9TK+G77ia4ex+eeDOKp04XDmrvPRKl8dGnMFIJquOja15TONVP+O4ddP/dH8UpuWNOeDRyr11Cb4kS/+gxSldGCR3aRvnGBEhJ5IE9FC+OrFCjpFyYZujSs4xef4FY007iHXcRinage8Noug+xzO53/jOAQNN9NLbupbF1D6XcNImJc6SmLmOUM5jV4m13t6gXikcj/uR+2j59L4pPd1+sI0m/eY2JL76JmV6483Esg/xkP22HngIg1X8KxzZp2f8IgYZOEBDrvYvi9BAgaeg7QuP2Izi2xdTZF+ZYpTmZJqw0YAmTklPAwiAnE7QqvZRlEYPK3M6uJPMElSi+GsNzFgKBTwSwpIEpb28AhqqsALK2I7wJFRVdeNa963zPCMGNQIvGaHj4CXxtHeu+1splKV4+T+7sSSqjQ7fkLGxlM6Rff4nc2ZOE9h4kfPAI/t7t6wrDpgVDNDzwGFYmTWngen3tEQJPrBlPbGl0luLoDYxMYr2PsvxtFBUtFF61jJQSY2qC/IUzFC6cxkjMbPyGlkXxykXKg/0E9+wjeuwh/D19694VatEGgvsOUhkfWeDioYc8CAHJM5P0fGgnN750gWq2QsvRDtof7CbQ5gouyfI09/JMkct/eIauJ/sw8lVSF6cX7AbVYJD4Ex/C195VvwCUEuk4lIf6yZ8/TeHyeezCLbAdHQdjeoLk8xNkTxwnes99hPYfcl1e1iGUve1dNDzwKNPf+vM17YNmIsfE558n+uBevF1NSNumfG2C3NtXQYK3K445naX5xx6i61c/huLVMaazJL/1zprtsM0KyYnzJCfO4w+1EGveQbihl2CkHX+oCaHUo4URBCKtdIdb3N1h4gaZxA0K2TFKuUlsa+OLxc1A9GgfrZ+4h5nvnqN4fRIsB19nI83PHKLlo0cY+6PXFpSv5maYOPXcknqmL7yy4O9AvBPpOORGLyEUFV+0GaHenPpzTpI2tYeqKJGSkzg4ZO0k29QDhJQoFVnCwv1+pp1RWtUe4koHBTsz5zsYEY0ElQhj9nVMbm8/5pwkNjaNShsT9sBc2+JKBwrr5z/84ApBRSV69H6CO/euyx4npaQ81E/mrdcoXrmwxP/tVmAX8mTfeYPy4A0id99H9NgDqPXaKIXA09JG7P5HMJIJrOzafn2+lk6a7nkCoajIRdE0qslJ7PLaNOvNgGOaFC6edZ99eGDToo841Qr5s6cwpqdoePRpwgePrGsCVzQNf9c2PPFmqpPjC84JRUE6kitfOINt2mT7U3hCXlAEQ89eJVuLGWkW3VXtzOlJrJKJbThc/dNzFEazjL8ytDQZmaIQOnCE4N67EFr9n5djGmTefI3cmXc2HP1mJViZFMmXvkt5eICGh54g0LdzXQu00IEjlEeHyb712pplzakMiT8/DoqobVFvjoXSRXeFPvOl1yicG0LxalSGZ6gMrO95y4Vp13l+9DSBcCuhaAeRxm1E4tvw+qOsxToUQqB7AsQ77iLWupdKIUEhO0YuOUA22U+lmFxXezYLsQd2kj0xwORX3p4LNpC/MIpj2XR85gHGvvDahrgklcw0qsePouo4jkVu7ApG8eb8kpMpNLz4RZBybReVl2kEgrBoIO3c9H9NO1NM2yN0qNtRhELRyeERXtrUbRSdHBP24IZUkgBBEUVFI6I0oKK54dNEMyYGpqxQwW1biTwT9gCd6k52aIfIOSk8wkez0rkhAfwDKwT9XT3E7n14XR+zdBxKN66S/P63qUyMrpuIUi+MxDSpV76HmUkRf/LDa+6iZiEUhcCOPYT2HyL71mtrRnoJdmzHKhXIXHgL21i4SreKm+MztRYcwyB76k3Sr75Yl+DeCKqT4yS+83UUXSe09+C6rvW2tuFt7XDVsrUJuZIoMfbSQv8pq2gy8cbwkuvL0+5CInvjpkpy/BWXMZobXPq83pZ2IoeOogbqJ2jZ5RKJ732L/NmTtyUouHsTm9KNa9jFAs0f+sS6fBUVXafxkacoXj7v2iPrwUoRYwAzmcd8/dLc33p7K4rXhzEx6ZKQDBO1MYY1k0QNh5CWhd7SjJXJYmeyqLEodjaHg0nRmKEwPEJy/BzeQAPhhh7iHXcRjnWhqGvHrlVVnWC0nUCkjcbWvVTKGQrpYRJjZ8ilR+6o7VD1eyj1Ty8RdGa6iOqvX5W9GI5tkp9YyoKeRVWWsHDt9NUa2cSkSknm0YVOWRZu1oXNDescHWqRVrWbdnU7UtqknWlG7KsUZGZDbVTRuEt/CBUNVWh48OERXmJ6Mw42KWeSy9ZNjcGgdREpHVrUHpqVbsqywKh9nRa6l6hJ18ImC8HZFdhtjkOoqjQ98yPrYoJKKamMDjPzna+7K+01dyt1PIsQKxqJnUqF3Km3EYpC/MlnUPz+unYxisdD4yNPUrp+GWNmdUabonswMtNU01MbjvKxUbg2VZvCxTOkXvyOm+LodqDWZVY2w8x3voEn3oze1FK/zdXjxdvRhXLlwrrp/uuF8HgJHTiMr6u7rvZJKZGGQeL5b5E7/famaiWWv6FDdWKcme98nfaf+Pl1uWy4kW4eZ+bbX1u7sJj730oNufnJqCp6ZydCV/Hu7MOcSSArFcJPPkbyD/8E/+GDVK/3o8aihB59kNQX/xxvbzd2vlDLT6lQuXYDo5rHqOYpZMaYGnmHQKiVps7DNHceQfcG5tqz0vMKIdC9ITRPkGCknebOuynmJ5kZPcnM6Flsy7VD3U6Uh5M0PLCLxPfOY5ddDYTQVBoe3EVpYOa23d7C5PXqN+bClsX3xjnwcwcQ5RIXv3CR1MBCs0qVEonmYdp/KoZt5Tj3B+coZ0toUZW7f+pu9KDOhS9coDhZZMoZJlEdx2b1sW1jcdJYOUC4s4hYY1Dhhn2OQfvSXEg3G5OEMzYXtade3LIQVDUffn8jsYbt+P1xVM1DJnWD6alzcw6rqupBUXRsu7opIY4ih4/ha+uo+wOWUmJlUiS+902MqYk1yyuKTvvOh3Fsk4kbNRWQEChCxXFsQKJqXrYf+TSl7CQzo6ewzApeXwTTKGIZ7rZdWibZU2+hNTQSu/chRJ2Bu9VQmMbHP8jkl7+w6m61kpwg1LMbTzSOWcwvEOyOaYC8vUF3y8ODzHz3mziVEqpXxbGcOduYoimgCKTlIB2J0ARCUXAMN3yWEMI9rtZ+C4GiKTiORFpuuxVdofuxHoxclcmTk5ipBKlXX6Dl4z/mugTUASEE3o5uVH/gtgtBT1Mz0buP1e2XKW2bzDuvkz9zsi4BKDQFLehF2g5WYaN2F0l1YozE88/S9qmfRHh99V0mBOGDd5M99Q7G1PgKZUBrCBG+ewfenmY3kfIyqAxOk/7uKfcSVUUoAi0ed7U6M0k8nZ1Ubwzg378XJ1dAKCpKKIQaawChUB0aIXjvPdjpDOZMYgGjWkob27TJp4fIp4cYuvRt4m37ae46QjDaiaa7akGxgqAWQiCEiuLxuyrWxm307vsIM2OnSYyeoVSYwjart4VMM/PcGaJHejn4Xz5L4fI40rLx9zahBn3c+Ddf3/T7zYdJ1X1/fo3eJ3sZeXmEibcnqGarqAEVaQlsw8Yb9WIWTfKTOa58+xLtx9oxRRUbCztrcePZG3Q/2o1Q3L51sOvOAjHft7AeLFe3tYawXQ4bF4JCEAq20dnzCC2td9UGVs1B2zZIzFzCcUyEotLcdoj2jmOMDr9GYvoC8hYmZzUYInr0fjc6d52QlkXq1RcoD9YXxcFxTNKTV4jEtwEuMSTc2IuqeTHKWSqFBOHGXgSCUm4Cs1ok3NBNtGUn5dw0yYmLyNoCQBoGmddfwtfa4aqg6hCCQghC++/C92YPlZHBldtpVAm09RJo30YlObmA2Zo48RJG5hbIKWvAymVJfO9b2Pkc4Z4Isb4Y0paMvjaCL+qj5XALtumQvJTArto0H3Z3b9mhLNKR6H6d3HCW+P4mStNFgm0hPCEPQhWMvTaKr9FH454mottiJC/WnqNGHCkP9RPcsbtuW7C3tQ01EMBM3z5bj1A1wnfdgxaJ1VVeOg6V4QFyJ9+qP8Rba4SOTx7BMUwG/8/xBc7U60Xh4lkKew8SPnRP3WNS8fsJHzxCcnpiWU2KpyVGx1/9KP5dHVipPEJT0WJBzFQBoSnozVHMmSxW6qaq3rejD7UxRvXaDTzberCzOXw7t1N44y1iH/sQue+9iKevFzuVwk6l3PRGlQo4EiUUwr6yspoPwLENZsZOMzN+lmC4lca2fUSbduANNOL1RVZVmc72i+4J0NH3EK3dx8inh0iOnyefGaVaSmEam6cBMWbyXP+Nr9H8kcMEd7SCIihcHGPm+fOUbqwRl3YToHpU2u5po+lgE4HWAMHWINe/cZ3eJ3vJjeSYeHuCo796lMtfvEzq2iqM5fdeQpJVsWEhGAg0s333R2ho3IFlVSnmJgFBJNq9oJx0bMxqkVC4g6bm/WRS/ZjmxgdOaP8hPOtQhwEUr14kd+qtDd8zGGmnddt9FLMTxFp2MXr1RUyjiGkUKReTIB0cx8YyylSKySWrRCuXJfXq92lv60BZw81gFkLVaHz0KSa++Acr7hKkZZG5cmrZc7POsLcDUkoyx192BbQAT8iDN+Jjx0d3MH58DH9zgNZ72ul/7gZWxaLtaDuBlgD50TytR9qoZCvoAZ3CeJ6Ww63MnJ+h5VALiQsz9DzRS/p6mvi+JlRdqe0ib9quzEyaUv81/D19dVP+VZ8fPdZIZXy0LtJOMKKg6wvfUangYFRXvlaPNxE5fE9d7QFwKmWyp95aVuWt+DRCO1rQo35Un055LE3+yhTlsQzJ4zeI7G27ed+GANH97Sh+D8ZMnsy5MfwdMYLb4ghVwbFsUm8NLhWYUpJ6+XsEdu5BC4bqarPQdALbdpCNNSzrthE60oe3K07iz9+gcHoAT0cjjR+6m8RX3kBaDpEH9qA3R8i8fNMh3JiYRI1FET4vxuAw1swMlStXsZIpKldvYIxPgqqihsNU+wfdoPO2jZVIuEIwX6ftWzoUcxMUcxOMD7xOuKGXSEMPoVgXoVgnuje0qrsFgKp5iDXvIta0k3IxSS41QC45SD4zQrkws8D/bqMwEnnG/uDVBdqSOwW7ajP66ijxvXGmT08z8c4EemDhIuG9HvFoI9iQEFRVDy2th4g1bCeTHmR89E3KpRn8gWYOHPqZJeWr1SyVcppgqB1V825YCKqBIIEdu1HqVeEAdqlI6uXnbyljg8cfpZybIpcYQAiXrm0CllGiWkwjpYNlljCrBSql9LJqyFL/NfIXzhC996G67+vr3oa/exul/mvLni9NDFKaGNzgU20clbFhcqddI7Uv5iPUHiI/msMomK4K1HbIj+VJXnRtCYHWIOlrKcrJMv54AFVXqLlwofndIVicLpIdzFCcbMIT9qD7ddLXkkhH4syfwB2byvgIVi6Dp6ml7jZ7WtoQl8/XZTu9694Aze06PTs9jA8ZBEIKb79Y5Oq5lXdskbvvRQvV56sqpaQ8Mkjx8oVlz6t+D02P7KQ0kMBIl+j4xGFuTLyMlVt6f2k7mPkqStWi60fvIXd5kvCeVqIHO0m8co2Wp/ZSHk1TGloqtIxUgsKFs8Tuq29MCiHQ4034e/rILyME9XgEcyZL+junsbJFV/1dMqiOpzDGU5T7J+n5Bz9O9KH9JL7qJmq2M1kKx99esDgpn3dJM/mXXgWgen2en6cA366dqJEw5cvXNsREts0KmekrZGeu4/FH8YeaCMe6iDbtIBTrRtPXmF+EwB9qwheME287SKkwQzE7RmbmGpmZa7fkauHtbCD+2F58bQ0IVWAkC2TevkH+4irpqW4jpJQgXPOGUASBlsByWuQfaGxICGqan8amPRjVHCODL5FKXgOkG81hGdh2FcPIEw53rVimHnjbOvC2tK3LXyx/9iRGYn0hk3zBRpq7j+ALxilkRsinhog27yDWspNqyQ3Wy6IPxTLLeAMx2voeZPzGy0t3YlKSeu1FwnfdXZfbhBAC1ecjdPDwikJwJcT230t5cphq6jaEipKSzJuvYpeKsw0l0hMltqMB1avOFlkwOY2/Mcq+nzpINVchO5AheTnJgZ87SLA1RLClFudVMqdGcUxXiG7/yE40n8bIqwtZm8b0FGZ2fUJQjze7odnqEIIXTpQ4dF+AQs7i3Jtl9hz2oXtWiVDi8RK5q/5dILZN9p3jq6pBnbJJ/to0xaEkTY/uxNcSobCMEPQ2hWi4uweQhHa2IFQFaTmURlKkTw4Tu7sHPRaAZYQgtk3+/Gki99yHUqc7hxoI4m3rpHD+zBL2spQSp2Iia3ZsadugCFS/171doUx1JIF/ZxuLLqzr3m5ZMEbHMSamcAqFtcuvVpW0qZZSVEspcskBpoZP4PFFiMb7aGjZQ7ixB1XzsNKsL4RA8/iJNPYQinUQbz9ApZwhMXqambEzmOvMahHY1cq2X/0Qqt9LqX8a6TiE7+qm8ZE9DP/+S6RfvbKu+nRPkO6+J2iI71yxzMXTf0S5tLI/sVWxKE4X2f7Mdjof6HQzYwho3NXIvs/sI9wVJtAS4MIfXUDRFA787AGi26KEe8Jc/MJFcsNL0zy917AhIaioOoFgM5nUDYrFadZSAjuOjW0ZqJpnTZXDyjdV8HX1uMlb64RdKZM/f3rdrLtKMc3wpe8ihHCJPFIyeP5bKKpWy24tMSo5Rq++MKf6tKpFl0QjlBVVkVY2Tf78GWLHHqjPnqW46Xc8TS0YiZpNYPY6KanpTJZc5mvuwMjeHvtXZWqcyvDgTXeDVJmL//eCS5SV4Bg22cEM+ZGbg78wXuDkb7+NUAV21UbakhP/5R2EAtKSOJbDjHAj5V/64ws4toMQgsTFGQRgm/YCZ3S7kMPKpJCOXTcJRQtHlo2tuRzyGYehawY/9suNVCuSbbu9nH9nZVJNcM9+1DpVigBGcobitUurltEiPrSQF82n42kIYGZLoAgUzWVEKroKmkJ0fzul0RTZM6M0P7YbcO2NjuGOS1nry5VgppNUJ8fwd/XW1XahKHhb2tAi0SU2VjtXRosGEbo7rTgVA0VT8HbFqQxNgaqiRQM45q2R45zS5sWhnKvTNjHsLEYlSzE7xsTgcXRvkEhjL42t+2jqPLzq3KUoGro3jO4NE4p20LH9ESaH3mJq+G3MaoF6DGUtHzyEna9w5R9/EafqzllCVej82Ufo/MwD6xaCQqj4/DGCoVbmVC8sVGkuZxM99/lzN783CWOvjTHx1k1C4WyKqDf+zRuuylZK7Np4e+s/vrXk2HsdGxKCQggURcO2jbrYnkIIhKLWym6MFKOFI/g6e5YEWl4N5YEbmJnVQ04tD4l0rAXDVjoW9qJnXWwDcOw1hK3jkD97gsiRY27Q6DUghECPNuDv3T4nBCM7D+EYFQpDV/C3deNv7V58Ef6WLvI31heItx5IKSlevYRVXLgCtyuLxoAEx1r4nu3qwr5ack0Ns9dJKVcsA2CkkkjTQnjrFIKhcC0KTH0YHTD4yu+n2HPIx1svFRm4vIKKSwjCd929rnGZP3tyTR9Vu2LS+oF9KB89yMzL16kmi8SOdNH8xG70sI+en7qXsa+cojSaoeXpvQT7mshdnkA6ErtozE10ZrqEvULmdnADElTHR+sWggCepha0aGyJECz3TxJ78hCethhWKo+VKVIZTdLyk4/i2+HaLYN3bWPmi6/Wfa87CSFUNI8fjzdMMNpJtHkHkYYefME49egAZ/tcCA1voIHefR+mpftuRq+/THrqUk0YrgwtFiB9/LobS3VWBpk2mbeuE39s75Lg42vBtqrMTJ6nWsmhewLoehCPN0wg1IqyyuJxcR5E6cgl3y8s/aZXOnYrcBce4raGttuQEJSOg2kW0T0hNNWLyRovV/Pj88WoVnMbdpHQQhHXrlOvW4RtUx7uxy7emspks+HGYRzB37u9rvKKz4+3rR2haUjLItDei1N1hWC4bz+hnl2YhXkqBwFaKHpb2u5UK8jUOD6fA96bQ8esOljG7XXHWAwrm3HtvF5vXeXVYHBdkYU8XkFbt0656JCcMglFFDLJpR+iFonhbWmv20ziWBaFFWyB82GXDKa+e3GBLS9zcoTMyYVxNo10iczphcfSJ2+qj8e+enr19hhVqtOTSCnr9xkMR2qxehfOypWBKab/5GWMSTeIgF2okHnpHHo8TPjYTqRpk3vtEpmXNn+BtnG4/oH+YBPBaDux5p2EG3vxeEPcivFrti8D4VZ2HfkxkuPnGR94jUJ6dMVch8Vrk3jboqh+D3ap5ieoqwS2t7g2wXWaBG27yszkGWYmb6YU8/qiHLn/r+LzN2zswe4khKC14x4URSMxcwmjUmeghnViQ0LQsqvkc+NEot1Eoj1UKpkVJbWiaDTGd+P3x5mePotlbiAihhDojXH0aP0vzirkMKbvvBP5WnCqFUpD/XULQaEo6LMr72SCyVe+MXdOOg4z77xAvv8ic1+IELQ99snlU/DcIozEDK0dJqHuJpp6/KTGK3j9CsPn8wyevj0DdCXYpcK62HhC965rTuvZ6WHXAR+KKijkHCxLkkmVl0xE/u5eFK+3bgFrJqYxs6trJ+yySeb0CEZ689V+S+A4WLksslpF+OojnAlNQ29sQujaAlODUzHIv73Qfl2+Os7E734HT1sjjmFSHZ7BKd/eAMv1QAiFQKSNaLyPcEMvoVgXvmC87oWAbRkUc+Nomg9fqHnVnZUQCvGOgwQirYzdeJXE2OllyTPViQzxJ/ajBrxUp7JgO+iNQaJHt1O4NEbbp27mysu8009ldCNarh8c+HwN9O1+BiFUSsXp95gQNMskpi8Qa+ijd/tTeLxhZqYvLLS5CEEg0Exrx1HaOu7BsiskZy7T2G6je5Z+bBNDVWxz+Ylb6B58nT3risVoppIL1TWKgr93O6GDh9Ei0TlyjVXIk375+5jpJP7ePoK796E1NIIjqYwOkT93Gjufw9PSSvS+hykPDxLYsYvcqXfwNDXj79tB8colCmfd7NKK10v40FH8fa6QKw3coHDhzJwdQxom1fFRHMOom+LviTejxxoxk4kFzNPs5ZPYRmUhG1VKKlOjtyVuqJlKMHVhEs9ulXzSYOhMlpa+AIGotqqqRgkH8O7oQmuKoXg9GGPTVC70I2/BNuRUK3MEjHrghterXwoaVUkgrKBpgt5dXgauVJd9Pl9Xb/0O50B5ZHDtrBoVk9zFtYM6bBaccgmrWMBTpxAEVyWq6B7sOuzt5kwOc+a9QZBQVA+x5p3E2w8SjHbgCzSgar66hZ9RyZOevkxy4iKV4gyK6nFDtbUfINK4DUXVWN4JX8EfaqFnzwdQVZ3Jobdw7IWLAW97A1a+QmB7C/6uOFI6KLqGXaziaYnibb0ZF7U0MP1DLwQjDb0oqr6u73wj2Jg6VNokZy4RiXTT1nmU3u1P09nzMLOzRHPLQWINO9E0D5ruBwTjo2+STl7lk78So6nDQ6xZQ1EEpimxLcnv/bMRcsnlJ0VF0/C2ti17bvn2SaxsZkGcQ09zK/EPf4zipQsUr1wk9tBjeJpamHnu627iVSkJ7NyD0DTKN66jhsNEjz0AikL2+Guo/gDhQ/e4+bp0Dy0f+zSV0SGsbIaGR56gMjqEncsS/+DH8Pf2kTt9opbr8BhaMET69ZeRRhWQWPnsuij+Wjhac8JeKGmM7PKsruy1M5u+A5aOg5lNkxpMoTo+Hv8LPSSGSzR2+EhPLC8ghK4RfuoYkafvRwkH3QgiiqDw+hmq10fmhKDW2kjokbtR/F4yX3kBp7h2ZBdp2etiFbp26frtdmMDBq9+O8++u/0MX69y9dzSNileL3pT87ri11Ynx277R71eOKaBU11fNB0tGl19UaoItGgQNeDFqRiYmSLY795ze7wRmjoP0dJ1D95AA6ruW3X3NgtXoyKpljNMDZ8gOXGOajmDbbrfMkAxN0Fi7Ay+UDOtPcdoaj+Eqrtq+vnCVQiBxxehe8/TVIopUlOXmP/hTD97isT3ztX1PNYKORh/mBBt6HMjjTm3NyvFhp3lTbNI//VvY1ol2jqOousBFKEgpUT3hNA9QdeB3CozOfY2w4MvY9tV/vx/TOLxKfzUr3fwh/9mDCkln/zl1lW1SULT0Zta626btG3MbBrHqHWeEPj7diANg+zbr+OUSkjLpP0nfwFsZ47qnfzeszfvqWogJb6ObvK1FbJjGuROvoUeb6bpAx8hc/wVrFyO4N6D6LFGPE2tBHbuZuorf+IyKAE7nyX20OMULp7FmHZdFuxiETOTqlsICkVBb4wjPDrSMOYdV92PVC42ZDublslhFk6l4jpIS8nMUJlX/2iUvnuijF4uMLSMKlQJ+Ij9+AcIP3EU4dGhlm1c6FqNPXjzhUvLxtPTRuCevVRvjFJ84+ya7ZG18HV1QyzPpF2xuALXz1e4fKaCqi7fnVoktmaOxfmQUrrO8e8xIShNE6e6volGC4aXFf5aLETjx47R8NQhtEjA7TghsAtlsq9fJvHl1zGTtz+4uxAKiqrjCzbR1ns/8fYD6N4ga8UQhVpMV+ng2Cal/BSTg8dJTlyoqTCXSeTq2G7wjFSRfHqYkavfp3PHYzR1HkL3BBawSoUQaHqAHYc/TeGlUYzqzR2yXahiKwZKbbG48CbMMUbfD9D0AMFQ68a9CdZzr1u52LLKDFx/jsnxE8Sb9xIKdaB73EnBNEsU8uMkZy5TKt4M+eM4YNuSQEilfZsXKSEa11edn9RAYF1Jc2W1sjREluO4MQg9XmS1iuIP4NQyd89C8fpQw2EUj5udXfF43YzxszsIx8YuFNDCUcxcFscw5rLSC1XF09wMjoPqD+Dr3ubW6Qug+gMo/uDcfexyCSubqft5EAJPvAnF48WeJwTD2w9gFjKUJ28SIYTmIbxtL+WpEcz85mV1cIwKVt79YBVVoAdclejohRyegIpZnTexqwqB+w4Seviwq1a+PEj18iBKOEDkA/cvqdtO5zEnEiAhcGQ3xTfPoXkXDX4JVuUWd7d1ykBNg3sfD2Eakstnyuy/208h73Dx5EKboBaOovrWly3Cvg30/luFtK1Vk0kvBzUYBnXh9KE3R9ywadvbMKYyFM8N4pQNhFfH2xYj+tA+Anu7GPkPX8EYvz2qPFXz4fFHiTR009R5hFjTjvpjuUqJbRsYpQz59BAzo6fJJvvXF+ZROlRLKQbOf43U1EW6dj1JpHEbQijz2KMCrz9K+/aHGbp0c+GtBrzEjvUROtCF4vfMlZeOxMqXGfnfKweYfjeh6QF0PYCqeVGEikTiOBa2VcEwCmuz5nHjNauaF03zoqpewrEevL7YnAYnGGqrxW1eCtuuUi4mVyQcrdn+DV21COVSgtGh+mnPtiU59VKWhz7u2t4GL5YoF1YeaJ51Osg7pjE3YQMgJaUb1wju2U/jkx/CmJ7C19NL/szJOZWpFm0gcu8D6NGGuZ2hp7kVp1xmbvaU3AxjtOSFuG4gis9P9P6HF0SoqU6O11ShtUurVaxiYV2MPL2hybXBzDvma+4ARaE8OcLs7CwUhWDPLqxSbpOFoIFdS8/U1O3nrqea8Ic0ctMGobiHiy8n5gSEGgkRuHsPis9L7ntvkvnK93EKZfyHdy8rBHEcrEQGp1LFs60D1aPS81AHelBz/QQNB82vcukr9cV+vVUEwipdfR48XkEwohBpUEmfrizZBGiRKIrfX3e9drHwniNqgas5WK+KVvH5luwEw0d34u1sYvpPXyXzwtkFBBjh1Ynct5u2zz5Nw1OHmPrDFzej6XPwBhoIRTsJN26joXkX/nBLXepOqO3QKzmK2XGyqQEy01cp5SZvKcaxlA6Z6atUSml6936Ipo5DS8q0dN/D+I1XMA2Xwd74yB46fvIBClcmXFZobxPFG1MEd7SSeuXyhttyu6DpAWKNfcTiu4hEu/H6Y2iqF4nENEqUizNkM4OkZq5QyI2vSJ4UikpHz4OEIh34/I34/A14PMG5xYum+di5/0dWbEc2PcjVC1+mVNhYcJB3JZ+gY8Pxb2e4+GYBRREUshamsYJqSwj0WOP66jfNJVm57VLR3X2pKk6lTO7k21SG+uf0XIGduwnu3k/yu9+gMubmGow99Bi+ju5l7rA8rGwGu1gk9cJ3l+xEFzj4OjZ2sYi0rLqzIWjhyFxZRffiicbRAmE8ZtUVhrXn0MMxPOHYpscclIYxly5JqAKz4uAPQ3NfgGrBWiAg1HAAz7YOzKkkhZdO4BTWtjc5hRKyaqI1RpESqrkqodYAxZkSpWSFHR/ocTNO2Jv7XMuhmLc5f6JMMKQwM2FSKUtS00vt1WoohLoOUowacDPO37acgRuE4vXibWlf1zVCUZb4urph0zJkX724hAEqqybZ1y8Re+wgnvb1fc8rQVE0gtEOYs27CTf2EIp2oHvDdS8sHcemXJghPX2FXHKQQnYMo5zZlLbNolKYYejis/iCccKxrgXndE+QcHwbqQnXZSR8qIfM2zcY+T+vEOhpovnDhxn53Is0feAutHD94+xOwOuL0bntEVraDuHxRpb0uer34PPHiMV3EG/ey9jQ68xMnVt2V6goGu3d9xEINt+p5i/AuyIEVQ2e+flmDj4YweNTyMyYfP5fjZJLLU+M0SLr83uTprnEP1DxePC0tJF+7UWKl5bxU5ISRddQvD7XMb+rh+DufbWdYn0Tb6n/GqG7jtDw8OPkTr6FXS6hRRtQfT4KF85il24+n1Mu4hjVupzmATfwdk39pAXDxI8+Tqh7FwhBw/77bj4GDoWhq5i5zVU3OZY1N3knhkr0n8zQdyRKtWgxeGahTVB4dNRoiMq569h1kFygRnRxHNA1HMth6nySQLMfza+h6gp6QLtjwYRtC66cKaMoAtuWCJYx4ymKqwpdBylGC4aIHD66qW19NyG0hWPXLrrZHVZTO0spsXO3ltJKUT00tu6lqeMugtEOPL7ogiw2a0FKh2xigMTYaXKpQZfocgvxPtdCpZRi7NqL7L33Z5nfOUIIoo03haDiUSkMp3DKBtJ2cIN2SHJnhtj59z7B6OdfXrev4O2Apvvp3fk0LW2HUTUvjmORnL5CLjOMaZZQFJVAsIWm1gN4vBFCkS627fygm11o6gKLH8K2DC6d+b8oyk1xFI520d33OF5fFMuqMHj1OfK5sWXbY9tVKqWNz3erCkFV9dDedT+WWSExcwHLvLmbETUSzEbeiqoJth8M8O0/mKGxVaehdTVBIOpOTwOAlEjbxq4sXG1L28Yul2j91GeQn/gxpG1jJKZJv/Q85cEbFC9fQG9uIf70MwhFpXjjGrkzJ/A0Ns9We5OMIeU88omszZASu5Bn5ut/RuzBx4h/+GMoXj9WLkvh3Oml0WWM6rqCegtVdR2+ASObZOLFP6f53qcwsikKQ5dvvgvbwTaqyM3MIiEl0rbmiEa2JRk4lWXkfA4pwaouozaSskbaqW98CK8HNBVZctWOVtli9PgkbXc3E2wNcOJ3z9/RCSAWV/nb/7odj09Qyjt8/Q/TnHzt5vhXdN1VCf4QRtWvF0LX3Ug5tXdcOD9E9NEDhI/uJPvS+UW+qoLQ4T58fa2Mff3NDdzMZVa2dN1DS/c9eP0xFKUm+NZ4B7Pfhm1VSU1dZnLwTUr5SWzbvPlNK4o7vmZVoHP11hjZzry5TlGWroqEcnM+WAa51CDlQhJ/qGnBM/mCN3fFZrKAtz0GQuBULYSu4WtvQA16FmRSeXchaGk/QmvHPQihUi4muXbpq+Qygzi2xZxZRqiMDLzI7rs+Q0PjDrz+Brq2PUohN06lvFhgSQqLBJyi6nOBVaTjUCxMkssM3ZYnWlUIKqqH9o5j2HaVQn6MQk0I+vyNHDz880xPnmF0+LV1GySlhGrJoZS3CEVVfH6FFdX3ArRwuP66qRn6zXn2CFWj+RM/jl0qMvEnbmoioWmEjxyl6WOfYux3fxu7VCT53DdIPveNZeu1clmG/vNvAFC6cZXSjatz50b+x39eUC7x3NfhudWTYDpVY11kBCEEWijMbJBOp1qmMj2GWchg5jbP9rccJIBlzQnt9t0hGjp8GGWbhz7TydtfneDSyzfVv9IwsXNFtHgUxe/DZg0fMVXF09mMGvJTvjw4e0ds06GaqTL+zpSbpPcOYsd+H9/5UhZfUCE9Y1EqLZz0hO5B8dVvD/xhhFBUWn/uCXeCloDjYOdKdP61jxH/6DEqQ9M4JQPFp+Ptaca/s538W1fXxQ7VPSECkVZauo8Sbz+AqrlqwXoWH1K6gs+oZElOXGBq+G0qxRThnQfpeOSncBstKE+PEtlxgGo6weizf4Si6cT2HSW65260UBirVCRz8W3S599CC4Tp/dRfZPLlb1AcducAPRyj85mfIfHO9ykMLG+7c5mmkwuFIALNc5Mwlz01SOMjewAwM0XsUpXtf/ujKH6d/Nnh98wusGf7UyiKhmmU6L/6LOnEVRY3TkqHaiXL1XNf5Mj9fwWfv4FIrJdY4w4mxzNLGO3vJlYXgkLB642SzQ5imTdVGIqiEQq3k0n3b2gl7Nhw4a0CpZxNx3YfHr+Cba38hpVlnOtXrb9aXbADUfx+/N09zHz7a1QnxlzVp9eLmUzgbeuYUzPeSTimsW6ShPD65oQgQPbq6dvQsuXhzGurL6wSafIQbvLw5p+NI5TaQrg2ru18CWNgjMDde/Af3IE1k0YaKwh8RcG3pxf/XTtBUSiduAxSonlVuu9vY8cHe3n7f5xh7yd3cPy/nr4jNkGA8UGD9h4PpiXZc9jH6TcWsjqFqtUd7OCHGaG7tiE8C7+f6lgSoWv4d3YsPD6axNMRJ/6xe5n4nefWrFvT/ew59jNuQutVkt8uhmNbVCsZyvkZkpMXSU1eXJDRQSgK3sYWJl78Kg0H7sUTjTP+vS/R/Ym/gLexFSM1TSU5SeWVb2DkMwQ7+2i851HKE8NUZsYpDF6m8dADFEfcVE6Bjj6kZVCZGV+xTVI6VMtLF4OqenMMZU8OkDnRD47EypWZ+voJrHwFpGT6W6frfv7biea2Q3i87qYkk+4nnx1mNelsmkUS0xfp6n0YIQTx5j1MT5zG+UERgiBqHvv2LTGlFsO2JG98K02sWeeVr6Uo5W0KmZUFwmxU+rog5ZIdllMqUbx0nvDBI/h7+gCJ0D1ooTDZt15/d+KLOva6/cWURTYYPdyAYxkLo8MIBT0UwSqXNk8lKuWCFESTV4u0bgswcj7HzHAJX1BbsLCzcwVKpy7j29NL9BOPoQR8VC4NoDW6tl3h0dHiUURrI55tHYQeOoxnWwfVgXFKJy+BBKEpOLakOF0i1BZE0ZVaAOw7IwTHBk0mRkx8foXpMZOpsYVjSqjKu7J4eq9h7L9/az3ulwDYxfrsb4qiEWteOQ3QfEgpcWyDQnaMXHKQbLKfXHJwSVSW2bJWqUBx+Bqhnl0YmSSV6XGsUgHNH6TqOFiFHL7WTnxt3aheP0IoaAE3U0jm4jv0fOqX8cbbMLMp/O09lCaGsCsr2zqllMtqzObHUpbzgs4LVaE6mWXsD16p6/nvFOItewFXqJcK0xhrpIuSjk2pcNNFLhjpdJn+7x0ZuLoQlLgDS9N8bl6tTbIdKyo8+slGevf5Of1KjkiDxqmXchSzK1BotfXlIFy8w5KOTfL7z+FpbUf1+0EoSNPAzGbmHNjvNKTjrDu+pxuh4+aME95xACOdoDB0UwWjaDqR3XdTGLpMNbF5obfm2zTLeYuTz04RiXsxyg7ZyUUh2myH0qkreDpbCT91L7EfeQLzgUPMrhh9u3vRfuHjCJ8HrbkBNejHnEiQ/eqL2Gl3tWyVTVL9GcLtQSKdIQZfHF2SmeJ2w7HdjPKmIYnEVIq5efcXyroyR/ywotI/xbutpzOrRdLTV0hPX6GYHadSTK4ZqN+xzJtO8ZbhfouOA4qCt6mV+N2PYRWzVFPTbnxUKefedzU1TWnsBg0H7iV94W30SAP5/ktrmjfEMowhe9FC1d8dJ/7UAXydDZRuTDH1zdME+pqxi1VK/dNLrr+TEIpKKHyTRRxv3ovPvzrTVwgWBOv2eMMI3lvfzepC0LEpl1OEwh00t97F2MjxBeSYjULVBPvuDXHm1RzhmEbXTj8XjhcospwQFOtbcUu5bGBlu1igvM7ktLcVjrNuvbjQ9AXMO80fwioVWBBOTQh8Dc2UJ26PERkgENH4yK9tJz1RwRNQuf5WhutvLrRLOrkima++iJXIEP3kY+idswQjiRoLo8Zqdl5HUrk6TObPX6B84cZcaC3pQHYkz6Wv3kBRBUbhzgVd/om/1Eh7rz6nUW9s1vjOl7JMDN+c5IQi1hUubQubh9nFY6WUYmb0NMmJ81TLGSyjxGYIZF9TO6o/QOLEixjpGfRwjMYjjywokzr9Op3P/BRGLo20LYzMzKp1CiGWyd0nF8yn/p442//2RxG6hl0oE9rbycx3zxPoaya4s53+//jNW362W4Gm+VCUWSd+QSjSQSjSseZ186Eo2nuOTLaqdLFtg2TiMr19T9Lb9zQ9vU9g2WWoSfK2jmM0tRxkPQPv7Mnfw7RmkBLKBYdYs14jVq1Ux+qU6yV4b/XvitjopyoAxR8i2NGHt7HVjWrDbP8JPLE4ejSOcxsp3227Qlx+Ncm1N9N07g3jj2ioulgSAN0plsl9700Kr58mcM8+fHu2obU2uuHfKlXMiQTls1cpXxxAVowFdlxv1EPzvjjp/gylZIXDP7ePM1+4jFzFdrxZGB0weO5LWSo1Msyewz7KxaVswDsR0mkLs5A1ladFuTDNxMAbpCYvYZmlTTXVgGuvV71+PJEGFFWj6d6nEIvCmFUSExiZJI2HHiBz6QRmPrNGrQJNX8htkFJSKd4klDV/6BDVRJ7+//QtgjvbaP/0ve69xtK0fvzoqkHq7wQ09Wa2FHcXbb/n4uBuBKsKQccxmRw/QSDYTCzWh6p60LXAXLYIVdXdqAzrCuHokmCunCzywZ9uQvcqvP3dzKoRY6RpQt1MvPWt0FUVWptVVEUwNWOxEn9jsyEUZWHWjTogLROJ6/MY2rYHf2sX/rZuQj27agXAMatkLr2DkV59ZXormBkq8YFf6cUf0Wno8IEDqq5w+dUkZmXRe3QcnEKZwssnKbx8sq76hSKIdoVpO9REqDVANW8Q6ayfIbwWdI8gEBQU8w7Leam88b2FNuLrF6tLfRRrqrT1YIFbzQ8DbkO6rqW3cE0yZrVAPj3M1PAJMjPXuJU+dKoVzEwSkFiFHHbF3UFWMwkco0p5cgRPrInm+z+AY1lkL52gPDWyIL6qdGxy187RfP9TlCeG1+wLoShoegB7no1S1pz1Z+Fpi5E75Yaak9ZNbZZdMlD86zMJ3Q7Yjslsvzu2wcjAy0yNn1p3PaZ5a36im4019YyVcoprl75KtGEbwWArqubF4wnT1nEPhfwE2czgijHdloPXX0X1aJx+OcfI1TKKKkhOGhjL+ZrVsN64hvXGCgQI+AWf+kiI3ds9/Nb/TtM/tPH0PuuBUJR1qwWk5domzGyK8ee/RNPRJ6hmkuQHL978CJ3bP8maFZvT355GUQXpCdevz7HlpjmzS0dSSpbJjRUwSyZGweTU5y9s2i5wx14Pj304yFe/kGNqbO33HQy5C7dK6eY4l46zbnavMT1JeWRwXf6hdxq+9hiBna3kz49ipldPx2VmUtzOseZIm8zMNXKpQVKTF285lNksisNX59wbkidfnjs+9uwX5v6dPPESyRMvrVqP6vVRSUxRmVneiXs+HNsiNXWJfGa0dsQNfJ9L3TRbmKkCgW3NqP6bjFGhKgR3tVEZT7/rayfLLOPYFlKTteD9NpVycu0L3+Ooy9jm0lwvkMDNiB0MttLSdohsZpCB688tWN2shcd/tJFYU5xAWEUobnQOx5Z863PTFHPLTyrOevLOCbFsKDJNg0P7vOzb7cHvE0xM2Xz7+0XyBcl3Xizh9dwUSLoGjzzgZ+c2D8m0zfdeLlEoOnz6oyHiDSq5vM33Xy0znbB54mE/u/o8GIbk9bfLXBswOXLQy7HDXqqm5K0TFa7cWEaIq+q6iRWLJ8/C0BXsamUBc/NOQDrQvjPkBs6u2AyfyzFyYXMzAxSmSvQ/P4xjSRzb2VQtdyyu0t6tr2iS3XvERyHrcNd9fhwHOrd5OP16kXRifug7Z5n4saujPDpE4nvfwim/94Joz6L5Q3fR+pEnmfjiq+RO3T678kpQNAXNp2IUTWyzwvUzf0a1lGE1CaB6VFSPglHYmBon3BlCD+ikrtchaISConvwRBsJ7zxI5tKJVVmhs3Bsg4mB11ctk3rlMj1/6Sm6f/FxHMvB0xqh9ZP3ED7QzdTXTqzjiW4PHMeiWJjE4w2jCBWfvxFN82NZ762d3XqxIaOG41iYxsaStl49VeTC8TzRZp0Tz2c5+UIGj19B86w8zTmV9U0aimdppm9NFRw97GPHNp2zl6r8+CdC9HQtEpa1D6CvR+eZJ4OcuVBh/24Pxw57UQTcGDA5c6HK7h0eDuxxV2t/45djJFM25y9XSWdtGmMKf/FnIpy/bFAuST7ydJBIaGk3K7pn3RR7u1JZoHapJCZuBsm+g8bm9t0hNK9CqEGnnLcIxzffX05RBO13t/DI3z3Kg3/zbh74m3dvWt3VskO55KwY8Sw5ZRFv0ygXHQYuVxkfMjCqi5yBN7ATVDR9XYHgfxjhCXtoPtBEoMkPwhVALYea8YR0FFXQvD9O39PbaD3UjMTBdnLE9zQQ6QojVHeMh9qDNB9swhvxoHpVuh/uZNtTvTRsd11wvBEPofYQTfviCFWg6ArR3gihtiBCEXgjHgLNfhp2xFC9KuH2EE17Gmk52Iyvwc0DqPk14nsa8cW8C9qvhcK0PfFJ2p74JOWpEXLX6sv/Vw8KF8cY+f0XUcN+okd6Uf0e/L1NTD97mvTx9wapLzV9yf2HEIRj3fgD8dtynwXseSFQ1Nvnk7shRydH2pjmxoTgxEAVj0+gqnD1tFvH/c/EUNQVJnHJwowQa0AIl02q6J6b+QRrqFYlNwZN3jpZ5dMfsdi5XWdgeOnq8dABLx98PEB7i0okrHBj0CQYUPjMj4Tw6IJD+72cvejW/V//d4ZPfzREKuPhd7+Qo6VJ5YFjPmIRBV0TXB808fsFuUWuiIrHu3pS0sXdIKWbxWFRKKpw3z4ajzyCFggz+Gf/HUX34mtupzQ+WLN1bD4sw6aUM6kWLZp6/MwMlef78KNEgkSeeRjV5yH/4gmMkcl124/0kI4e0MmPFxl5c4LtT/UsuMetYOCqQTppc+Coj8R0gcXa9pkJi0zSdv3JTJgYMXEWOelLa/05+ITHs/7FymIyxGaRI+Y3Y7X6RB1l6oQe1Lj/144yenwcx3SoZKo07mxAURV2fGAbb/32SULtQYLNfgpTRRDgjXqJ9kRo3h/n8leuUs5UOfwLBxl6aQRfg49Kukq0N4J0QPW639OOD/fhjXjJjeZJ38jQdqSFhp0xdL/GwPNDNOyI0XygidxwnobtMarZCu3H2qjmDLY90c2Zz5+n9/Fu7KrN3k/t4q3/chKz5A4Sq5hn+rVvg6LgVMtL5phbgRrykT8/SvH6FEotAIE0bexSdYEP4buJqYnTdG9/Aq8vSjDYQmvnMcql5KbvBm27OsfyV4Ry24QtbFAI2laFyfETVKu5DXn+2xZMjxr8rd/sw7Yk6WkTo7xSPXJ9ufdw42wqPt+SARoICMIhhXiDQnenzuhYHlWFcEjB73PPaRpc6zd49c0y//I/pRACsnmHB4/6iEZU/sV/SPI3fjnG7Oxw4YrBxaspPvWREE8/GuDZ54tcHzD5+/8igWWDYUqyuaXPpng86xKCOA52aeHCI9C1nfg9j1GaHKbx4AMu0UYIwn0HMOcM/puPsUsFpm6U0P0q2++JMnoxv0A4qeEgwaP70NvjlC8PukJwnXBMB6NgkJ8o0rwvTrgjiNAE0rz12TgcU2lsUvngjzTy078SY2bCmo2gBRJ+5z+mGLhq0NSmEYlpCMESZ3nHNOpSg82HOi8I+koQqkLrJ+/B1xFDGjaNj+8ld3qIya+epPOnHsC/rYmxP3qDxIsX0YJeen75CYRHY/h3XsRMuSstLeKn+7OPogV9DP7P788dV7wa0Xv6aHnmEIG+ZoSmYqaLJF++zNTXT2KXamYNAb62GE1/ex+RI71gS/KXxhj5P69gTNW/IF2M+J5GZi4mGPj+kBuuU1cQiiDSHSbYEsA2bPITRRxLMnV6ChxQVAVvxEOkK4zq1bBKBWzTIdgaIHElRTVXJT9ewCgYJC7dtE+NvD5G4nIS3a8R7Y0w9NII0e4I0d4omldj6vQ0udEC8T2N6EEPE+9MMn5ikmhPhPieRuK7G5GOJNDkJ9obuVm342AVN94Hq6HnLz1FeH8nxf5pStcnKV6bpDycRDoSadk4lXc/qa5llhm8/j127fskiqrT0XM/mu5j8Pr3MI18jS1a+5iEcIMMaF4isV4isV7Ghl6lUl47zGOlnMY0S0gpUVQPLW2HSSWuUi2n59mFxRyvYi2/0NWwISFoWRVGh1/b8E1tS/Ll356ktduDogmmh6tL1E3zYeYy66pf0XXUYGguV+AsDEOyf7ePf/x34rzxTplrAwZd7Ro/+SMhWps1furTCv/7j7JcvmZy/ESFf/hrriPof/v9DGcuVPn0R0P86i/FQMDImIkQ8A9+rRFFQCpj86WvF5iasfmfn8/yd3+1EYnk9bcrfPmbhSU7GMUfdNW2dcIulZbYBAOt3eQHL5M689pcJgmnWnbT3KwzwMB6ICWYVQez6nDj7QyqZ6GKT3h1tMYIViqHNZnY0PbNLFmMvTMFEloOxpm5kMTZBAEI4PO5H8/J15dPaWSZkp6dXj71Cw1cO++WKZcccul5xBjTxCmX1pUTUg2G6lv4CEHD/TtJvXGN1EuXafnYEfSGIKWhBMKj0flzD82px4Si1JjGi+pQFFDF3HGhqzR94CCdP/kAxRvTTHz5HRzDwt/jrrCd6s2xpXh1Wj9xN+XhJBN/9jaepjDNHziI3hDg6j/9Co6xsQmnmjXwN/qIdIUx8gbR3iix3ijDr44S6Qy7uSOrNp6Qh2BrkMJkkY7aDi11PYO0HYSicPnLV2g/2kb3Q51c++YNzKJJsClAoMlPKVHGsaSbhaFG2JplF/ub/BTGC3iCOsHWILbhsnUd08E27BqxS2KWLZJXUqQHslRzVXKjm2vvXgkjv/sC/p4mAtua8ffGid7Thxr0YherlEeTDP7W2qHmZiGEgs/fWEvmqyAUFSEUPN7IgmwNoXA7iqIhpXPzx7ExjeIKuzvJzOQZAsEm2rruQ9d8tLQfId6yj1xqgFIpgW2bKIqKpgfwB5oIhlrRPQFKhRnGR47X1X7LLJFOXCEc6UBRPYRj3ew//DOkZi5TKaeQ0g2rp3uCGNUc0xOnN2yie9fiPtmWZHygDlWClJipxLrqdkOiRaiykLVlGJJnny/yp1+7qZscGrX4B/9yKcPpC1/O84UvLxz8v/b/LHU7+Dv/79JjL75e5sXXV9klKApqMLiunaBVzC9lFc4yi+ZX7fHWmFu3R33SfSBMpNkzJ9fiXX4yU1XOfe9mPwhFcR1+swWcDfqcqB6VUGuAcrrC1Nn1vf+1cO2iwW/8vZWjb8RbNfbd7aH/cpUzx0s4jiSdWGT/kxKnXEZaJkKvz16hhcJ1pc4SgFWsMv2tM1TG08Sf2Id0JKOff4WGh3az7S8/hRr0rmtnoMcCtH3yHnJnRxj+3RcXMj+X0TOXR1MM/JfvzIU4E5pC0xP78ffGKV7bWJSlzECG/J5GWu5qJtOfIT+eJ7YtSmxbhMkz00ggP5Yn1helcWcD+dE86f4MDTtilGZKVPMGekCjaW+cat4gM+AuchOXk/Q93Uu0N0opUSYzkKGSqWU8qdpMnZ2h/e4WHMshdSNNsCVAIO7HyBukB7JYZQs9qONYDsmraXLDORRVEOuNIKUkP15YX8xaAYpHQ9rOutSYZrYEQzNYxSqVqQy+kST+3ma87TG8rbF19DRoniB7Dn0GVdFRVQ+K6qn91hckG95z10/g2Ca2bWDbJk7t9/jQa0xNLO/+YFtVRgZexjLLtHTcTSDQhKb5aGzZx0rxYxzHplrNLRvIZCVMjr6DP9hMc9shVFVf0Tk/OX2J5PQlTH7AhOB6YCZmcEwDpc7JRtE9qJHIwjosyfGTFZw7lJNuNSheH1owtC4XCTOdxDEXsnArM2M0HLgPu1pGqCqhnl34W7qQjo1VuD0qm9btQSzToVpzF1jO7U1aNk7p1hLHKrpCbFsEoYoNs/42ip6dHoJhhWijwr57fDgOXDxRXuIwbxVyOJVK/ePS50cLR6lOTrCakU0CZrqIUzWRtoORKmAkCzhVy7UPOQ6qz7MuIejrakSPBsidGVrq+rBIADqmTe7U4IIYn8XrUzQ/fRA9FmSjkA5c/1b/Arvm1a9fX+BaYxRMbjw3ULsAps8nmL6QWNBd158bcOPI1q4rJytc/OKVufOTpxcucArjBa5NFObqsE2b8ROTjL8zueQ1jL3lhhqcOjPD9LlELR3Y6s+l+HS8bVGcikl1KoveGCJ8oBMrU6JwdRKnauJrb0BoCpXJrCsgTQunaqEGPEhH4lRMWj9+D6G9HThlA7tiYhUq5M+NkHzhAkZiffGNVUUjGutds5wQCqrmRdUWaqWS07FVrzONAmNDr5FNDxJr3EE41k0g2ILuCaAoOkgHy6piVHOUijMUcmPkMsNrxhqdD8PIM3D12xSyozQ07XZ3lDVmquOYWFaFaiVDLjN0S/kg75gQ1PUgXl+UUnFm3amX7GoFM53C29JWV3nF60VvWGhItW24PvDu69QBVH8ALRpb1zVmKok0FgrB4lg/iu4luucehKLS8uAzlKdGSJ15DbOQXaGmW8OFFxNUS/YcUSQY09EWqUOdQonq8CTenjYUf/0q3/mQtkOoNcDOD/dSSVdxLIc3fnP9jrkrIRxT2HvIR0ubusRT5dyJKgOXb35UHq/rWL8YZjaNXSqihSNLzi0HIQSeljaKN67U/DlXhrScuUle2g7SrK2gndqkvB6CjQBPPIRdNTGzddgxHQdjZuFkJU3bNfOsktIqsLcLKSXl6xNz4e+WxbxHX9a3dPGhZf5eEmFqrbXtvPPjb0+643eNa+rxexWqIHZsO4pPx8qWqM7k8DSGCG5vcdXXmoKwVYK721D8HgJ9zZTH0njiIXKnhog/sZ/08es4FRN/bxMND+ykPJSoEWQmKVwaxy6sf0FZreY4dfy/rfu6WdRjt7Ntg2x6gHxuFF0PumHVVG0ut6KUjrvDtCquj+EG7HZGNcf4yJskpi/W6ndzSErp4Di2G0jBLC/IcrRe3DEh2NRygL4dH+LMyd+hWFifOkVaFsbMdN1CEFVFj8ZQvL65bOjvJaiBIHp09cCz8yFrKmFnkRCUlknuxnkKw1fdAAHC7SvHMpas7jcL5fzCgVwt25jGoh1SOu9mkdjVQ/DofsyxmZVTKa0Aq2Jz+ev9XP/OkDsZbeLjNLWq/KX/X5yHng5gWZJAUME0JNKBwesGF09XyabsuRyXew75CUUd+i8tXG1amQz2Ou2Cvq4eV129ZriptSTBPCy6tVAEimee/4cEadgomopaZ+QRZzUhtgLCR3fS+NFjVMcSpJ8/S+74Fex8aU2Bf6dRzW1eHFpXTSoJbIuTeGEGbImRzFMZy1C8MoFdqCI0BcWrEdzWTDWRI/XGdZqe2EfxygT+njjTz54BYPh3vs/4H79BcE8b4X2ddP7kg+iNISrjafIXRhn/v6v7GS5ol2OvmIRWCFA0dye9TlfXZeHYJlU7s1n5FZZASptq5fbVf8eEoKb50D1BNuKaKE2TysQoof131TXZCCHQog1okSjGzAaFoCJQvRqOYbtG9nov82loQS9GcmX9tBaOoMVidddpFwtYuQyLvbs9jS041Qp2pYRj3syhKBQVLRxFC4Ywcxms0mLXio3DH9HQfcrcnNy6I4gQgqtvzMsWbdsUXzuD3tJI+Kl7sXNFim+dw6kYNcLCGm2phYzSvCrx3Q0kr6ZdZ+ji5uzke7Z76Nmu8xt/d5o3Xyrxf1/q4Vc+OcbDHwzQ1KJx4N4AsWadD/94lErZoaFZ49k/ziwVgvksVj7rPk+dQtDfux2h6+uOgrQcpCOxywa+huAcpR5cqr2vLYaRuqlCKw0lUDwa/p4mhKYuCMu1Wci8ehGha4SP7qDt556g9WceJ3/yBtmXzlMZnsYuVJAbJNXUjzsZPNiNa5w9MUhlPEPnzzzI9f/v6+7OXUrUgBehKjQ8sBPFq5M+fg1fZwPStCgPJ2n9+N1kTw7e/B6kxKmaFC9PUB5OkjszTHBnG9F7t9P84UOM//Hrt7wYVDXBsY+38sm/08elV1J85d/doJh570YwuhO4Q0JQzEUP38gQlZZJdWIUaRgIb33qNb0hjt7QiDGz8q5T9emoAR2raLjsOEWgR3xI20HRVeLHeikMJimNZlz6uAA14HGJCyXDpXn7NFSvjpmvgCMJ9TWx7Wfu5ew/+cayK2Che/C2d9ZtRwIwUwnMTGbJ8eiuwyiaB6tcwMxnKAxfxamWCXTtoPnYUwhNwzFNJl/+KtXk+t0UlsOxT7QRbfVi1XZ/kSYP/ScXql6F14PeFqdyaQBPbzuNP/sRgg8donJ1GDuTXzXCjZSS/HffRPMqdN3Xxo4P9PD2/zzLnk/u4M1NSqrr9QsmRk2unK9iWVAtSxQV3nm1zM//agNvvZyjVJL83r+fYXLEZNtuL9ZyzFQpqY6NEtq93014XAe0UBhfZzel61fWLrwGHMOiPJIkdu92okd6yesqwqMRO7YdX1fjAiFoJPKk37pBwwM7qUxmKF6bQto2asD9nkr90zdVrhvs4urQNJOf+x7Tf/IyoUPbCB3Zjm97O52/+jHMZJ78qRuULg5jTGaw0vlb8n1TVA8ebxjNE0DVvbU4xnotqPlKs4xc5dxy5+f5zSxwmHTVcYnxMwhNJbinHV9HjPy5Edeft2RgZUuE7+rGSBYoDyUJbGuGnjjGZBYkZN7pZ9c/+ARjf/rm3N3iTx4gtL8TPeJHDbis0Mp4mulnz1AenNkUbYjHr7L7/hixFi/7Hm7kuf81vCUE1yoghEutXa8db0EdiuoaS28BVjZDdWYSf9faxl5wJxtPcyvFG1eXnXSFptD69B6MZJHyRJbiSJr4sV5Un4ZdsSjcmEGP+QnvaiGyp5Wp719BD/uIHGxHmg75a9NYhSrx+7dhlwzMfJXMuXFylyaxVkkaqni9+Hu31/3cUkqMZAIru1RHLxSVYM8uShND+Nt60QIhUmdeI9i9k8LwFbJXT9N4+GGie+9h+rVv1X3P1XDplSTZ6epcoOxYmxdvYGHoFb29iea/9hMoAR+K3weqgm9XD75dPWs/ryPJP/8WQq0l1Z0pE2oLom5iUl3TcGvxeN2JLTljs323h/6rBv6AgscruHi6iu4RRBpUClmb4goB3stD/djVCkqdQhAgcuTeTRGC0rTJnhwktLeD1k/c7bJILQczWyJ3egjmZT6wS1UmvvgWbT96jLZP3oNTtZCmjdBVClcmqIyksGeF4C1uppyyQe7Nq+TevobeHCWwqwP/zg6i9+8h/pGjlK6NU7o0SvH8EJX+yXXtSlXdT6xpB+GGXgKRNvzBRnRvCFXz3dEUPbZlkBg/gzRtcmeGyJ0bmZcGzCbzdj+cGABHYqaLjH7h9blFsR4PETnQxfRz5xbsjO1SlcLFMaqTWapTGYxkYdN3zrbpMDNUopy3GLtamCO4vZ+xqhBUNR/btj+NqnoZHX6NUtHdVXm8Efp2frjumwghCIfWl3dqMax8jurkeN1CUKgq/t7t5M+dWuIvCICUxA60kzk/Tv7GDN7GAMG+RpJvDGJVagk3TZvSaJqGw114m8NE97VRSRRQNIVgXxzN70HaDqXxLLGDHZRG0hjp1R3U9cYmfB3ddT+3NAyM6Umc5RyzpSR9/k0yl97BE22i9eGPkDrzGoruoTIzjpnPkr9xgfjRx+u+31pIDLvt0L0KzdsClHPW3LF5DXMjXWSL2Nl10pZrbDyzZJK6kSHUFiDSGWLghZFNS6qbSthYpiQWVxkftjj9Zpmf/+sNDFwzaGpVKZckDc0qH/vpGJmESwg5/UaJ0f6ltqTq1ARWJo0WjtY9CQd27EaPNdYCUC+EdByyJwYwJtIuO1NC/tQA5VG3bPHGFP2/9RxGwmX/VsYzjHz+FXydDagBL07ZoDyaQvFoaGEfkd0tlIY1ymMZSkMzjPz+y/i6GtHCrtCwywaVySx2xX223JlhbvyHZykNLHT9KVwap/8/PUvh6jo0Co7ELlRwqiZq0IsS8KL4POjRIA1PHyL22AFyb14l8eXXcaprL7Ij8T7atz9MpKEHj6/+/r7tkCxPBJqvCZr3b2lYVCYyc+90FqmXL3O7YVQdjn9lkpFLBdITVQqp9wZZ8N3EqkJQU720d96HEArp1LU5IahpPtra1xvL8dYGrFMuUxkdxj5wGNUfqOsaf+8O9IbGZYWgtCU3fv8NIntaaX54B4njA0jLoZosYBUNPI0BzEKV6kwBM1tB9WooXg0jXULz6egNfrSgh+JI2mXcORKhrmHvVBTCBw6j1KnSlVJi5bNURpc3cDum4aZXcty8Xno4hjfejurxzeW6cyxj0zM5ewMqz/z1PpIjZbwhjYGTWfpPZObOG6PTTP37/7PxG9RII9nRPJe/dgNFUTCKm0dmGBs0+dxvpskk3VXw176Qo3eHhz0HvHznq3lG+g12HfIzcNng4kl3UbMcOxRcVX3h6kV8XT0g6steovr9RI89QOJ7y+zOhUDzKgTaQwjZQfrkMMZkGkU6hHe1UBxJU7g0hqKrOJqCHvXjlKtUhmbwdzZQmcphTOdRAx70gE5oWxwjU4IJgb89iuLVKFweR5o2/o4oiq6ieRSqNbtUdSpLdWrp92Ik8hiJ+untejxC9ImDRO7bjaetAVm1yL15mewrFzFTeZSAl4Yn7qLxw3fjlKsk/nx1J+qmjrvo3vNBAuEW5kcK+UGEla9g5TfHPLFuSMhMGWSmli7A3q9YM6luJnUDjzdEtZJZcE4IhWo1R7WSXdMxWwgFrzeK11cflXx5SCqjQxiJaXxdvXV9BIrPR+jAESrjo25OwnnwNAbo+uRdIKE0nqUylQMp6fuF+7HLJuPPXpijqTuWjWPZpE4M0/3pw9hVi6kXrpA+NcqOX3oQ6+5uSiNp7KpJ+4f3E+yK0f2pw0x8+6JrO6xBDQSJHD66rmc2kgkqY8PLni1NDtHy4DNE99yNHolRTU7S/bGfd9WGtkV5apjIzruoZjbX2bzrQJhrb6a5ejxN594QgYi+0N/asrFm1qZYrwZf1EPTvjjp/iylZJnDP7+PM3+0OUl1qxXJxMhNNVNy2uZf/fqUuzOyJLYNE0MGn/31Zjw+gWVKrp6tLAmdNovcqXeIP/IUot6cl0IhdOAI2ZNvLQkEIRSBHvWjhX2AK4xUr0ZgVwve5jCexiBCFQhVIXNujKYH+ihP5mh+eAepd4bp/MQhrv32izQe60ULewntaiFzYZzQjmZaHt+NVajgjYdInRhiz9/5IFPPX6I4lNoUe5PwagT399Dw9BFC9+yo2UyTTP3hi+TeuuJS/eftmCb/6EX0pgixJ+5aVQhGm3bQvecDBMKt777wkxKJXJfT90agexU3UbUlsarOqlwyIVxbn1DcSE6Lk1urmkDzKEvSlzq2xKg4db97obh1KapAKKKWzBscR+JYEvs2JrxuOtJO5xPbufz5E1TTm8v4X1UIWlaZ82dWXtFPjZ9isP+7a/p/KIpG7/an6e17cmOtrMFITFMdH8XX0QV1ZGAQQhA5cozsieMYUxML60qVuPG54whFmSMEjHz5DEJT5gz2lSl35Tv+zfNz1135Ly+6o66m3rjyWy8iVGWOQTrx3EUmnru4XGNoeOhxlDp3seCqQotXzq+Yf6401s/ky18j2LUDI5si338eRdcRQiV+z+P0fOIXMYs5Jl/8St33rAfT/UUe+/lupCOJtvlIDpc31SNDKILIoqS64Y7QprlJ6Dr4QwqleUl1TQM8XghFFQo5h0rZ4dVv59E8glW5FoCdz5K/eJboPffXdX8hBFokSuz+R0h875sLFmjScqhM5dBCPnIXJ3BMG7tqkXijH2naqAEPiq7O1SN0FaEIclemSJ8eIbS9CX9nDEVTSL456I5v26HhcBd62ItQBXrEi6KpWPkyE89e2Gg3LkHzjz5E/GP3YufL5I5fJvPCWYqXRmAlNbbtUBlJENi3snlA94Zp672PYKR909q5Xswm9rXMCrZZoVScIZfsv233070Kn/hbfTz12W5unMjwh//oMjPDK0/8bTuD/LX/dRexFi9/9hvXefVPxrGMmx/L0Y+28Om/t4NI00IyXv+pLL/7ty+SmVzd+UDVBLE2LzuORtn/aCM9B8LEWj2omkK5YDEzWObCyylOfGuaxGh5xRRltwK7alFJlnBug6DdMDtUSoltV+tKqCulg2O7H/otPYLjULh0juDeA2iRWF2rQtXnp/HRp5j66p8u2Q1iyyXpcNZkrEmWUPzrcaHwtncROXJszXJzdUqJmUlTvHpp1XKV6VEq06Nzf8/6Ek4ff470+eNY5RLS2jxVIkA+afLGF8fp2BNi7GKe8avri2axFqQjKSVqSXWLblLd05+/uCnMUICeHR4eeybIt/8sv2BH2LVN56mPB3n2z/KkZhxsW2KXXfm3OIvEYmRPHCe0/zCqrz6CjKLrBPfspzxwncLVi3MqYADHsNGCHkI7msmcG3P9/GbHpQAjWya6vx2raKBH/JTHMu752rh0TAvHdgjtaMbXHCJ3FYpDKeyySeHGDOWJLI5hbXpmgsrQDNN/8gr5d65jTNSnbquOJkh//8wKZwXhhh4aWveuWodtm5iVPLZVceej2iwcjHXWQoS584RRzWNWCnOaKyEUFFVDUXQ0b3COwT4fUkocq8rE4JtkkzcoZsZrUU9u367HNBwuvJzioR9vp3t/iJZtARIjlWUXmooK2++O0NjuIzFSZvBsfoEABEiMlDn7fIJwXMfjV2nu8dPUXafWAjcgxif/Vh93P9MMQC5hkBitgAO+sEbvoTDbjkQ48HgjX/rX1xk6t/mxVtOXZkhfWhqicjOwMSEoJaZZxDTL1DMYXO9+a2mUhw2gPDxAuf864cNH6/bNCu07RLn/GtmTb93y/TcCNRii8bGnUQPrCZUmyZ44vjypp67LJWY+s7Fr10BDu5dAVOf89xM0dPho7g0weX1jcftWQmF6Nqmug2NJNP/mefPEW1R6dyx1UalWHLr7PDQ2aYRj0L3Dy5UzLulnWReJ+ddOjlO4cJro0QfqboceayT2wKMYmSTG5E1NhZEqUhpKofg0EIL81ak5xrGiqxjZMnrYncQyZ0epTOZQvHkc0yZ1cpjKZA67bBHojJG9MEFlMo9dSiDULvSIj2otBNfk85tLxMi9cWndsiH3xmVybyzfDlXzEO84iKYvP2Eb1QLZmevk0kNUCslaVBJzTgjuf+CXagQat3x25jqTQ2+7IbZELWSYqqOoXjz+CIFQC+GGXoLR9gXxNV3ei0EhM4a5jrBfG4aEmaEyg2dz7Hu4kf2PNXL1rcwcI3s+VE3h2MdbAeg/mSU5upRA138qx+C5PB6fQjCm88TPdfLUZ+sn5xWzJiOXCiBg9HKB6cEyhaSBIyEU09n9QIzHfqaTngNhHvyxdiauFxdkBQr3NhDd3kA5UaTpSAeqR2Xq7VGSZydpPNiKvynI5PFh7IqFFtRpu7+HwmiWzNUECNj3i8fQfBq5gRTjrwxiFtxFvSfipf2hXiqpEtGdcTSfTnYgxcSrgzhm/Qu8Dc0slUqG82f+oK7QOrNwhaB9y66s0rJIv/4Swb0HUXz10aKFptHw8FMY6RTlgeu32IL1QWg60WMPEti+e10JVY3pKXJn3v1s0suhsdNPsEFn9GKecNxDOO5h6kbxllWiemcLStBP9aqbaseq3Nyl9z3RxbVnB2/tBjWomsBxXFeJ+TBNd2WtaZBJOzQ2a2i6cGOUrzHMpGmSPfkmge270RvqiwYkFAV/bx9NT3yY6W99ZW7B4xi2uwOsuagVB5cGeJ95Zfkkq7mLrjC18lXKowu/z+TxgQVxO5PHB+pqZ93Y5M2RqvlobNmz9DZSUi7MMHrtBdLTVzGrhWVvbptVmLcxN80yhewY9gohtlTNhzfQQEPLLtq2PYg/GEcIgap56dz+CKriYez6ixvOVrAe5BIGV49n2P1AA4eebuK5/zGMWVmq0WnbHqDvcJhy3qL/dI5Cenm7tWNJKgUb25JLoj6tBduUHP/yJG99TVBIGUuizFx/J0NLT4CDT8Zp3xmgoc3L1ECtjwWEuiLs+8WjTLw6RHEyh+LR5lT6sV1xGnY3MXNq3BWCfp22B7uZfkdxhaCExJkJmu5qpfPxPqZPjM0JQT3kYddPH6YwnCFxZgLTkez5+bvBkYy9VP/Y3lhSXcdcMSTPSpgcP0Fy5hLV6q0Hdq5OT5A9cZyGh5+oq7wQAj3eRPzxDzKdz2EkVs4gsKkQgtC+g0TvfbBuRii4NPnUK8/jlG9PPsBbxcS1Aj/6/+whHPcQafEydCZ76zZBAQ0/+hS+fX1M/L3/wO6P9CItSfs9LUhH0tAX3TQhWCk5BEOChiaVxNTNLzrerOLzKRhVSTFvMzbo5hSUEmYmTWB11X91apL8uZM0PPJU3QseoWoE9xyg1eNl4ot/iFOeN8HeDo3bButUwxEUVcfMLBTIO//LX0bxLtpVL+dvPu/vwpl+xn/7m2veMxTtQPMsDNgtpcQ0igxdeo7U5EWkXPmd2PZCgbCcunNBeatCKTdBuTBDITPO9rs+QSDshmpUNR9tfQ9gmUXG+1/HsTfXxLAYluEwfCFPaqxCU5ePPQ828PbXlwb+OPqxFjSPwsT1wv+fvf8Os+s8z3vh3+p77b739ArMoAMESLBXUSQliuqWLMlSLMnKcYnl2CeJj+045cR27MTdTpzEPnYUW5abZFtdVCMldoIkQPRepvfZs3tZfX1/rGkbM8DsGQxoyZ9vXrgIrFl93vU+79Pum5FTpZuSjwOuaVwBaiWXC6/kuOWRJkIRCT2uAPULDc/xmHp1lOyZ6YDecR3jcPb1cSRFJLW7ZeUPfZ+ZoxMMPRlEE2K9SVrv7FqXEdzc2vnrwHVNDCN33UHbMHyf7PNPY05NNPw2BUFA79tG89veg9ravn6F73VCkGSiu/fR/Pi71tVD5vs+5bMnqFxYpbjmewTVgsPf/+fzTF6qcPjLk5x7fqWnsl4IioIYCyPGItg1h8vfHGbuSp6Xf+91nvmVV7j0zaEbv/F5TIw4GDWfH/mZFLffH6KtU+b2+3V++JMpKmWP2SmHfMblxW+WeP35Cq8/X2Vueu3Vs2+ZlE4dxxgdWlfoX5Akwtt20vPPfyqofG6g6OtmQ5BkRD2MvnUb7e//CH0//W/Rt64kefAqJl7FwKsYuOUaeB5KIowUUgKn07DAcRFVGSUZwS1VqV0cX3nBVRBL9a7y3fjMjh1nbvLUmnPJ1QQfoqQ01C7kew6FzGUuHv0cRjXIbQqCgCSpdO94hERzP28EPdvExQrj54Mw5D0/0LaiulMJidzxzlZ8DyYuVRjf5Nz8apBVkVBUIpKUiaYVYk0KsWYFZz78KEoCkrzy3dRmK1Qmi0F6w15DYmodc7NVMCiPFfAsF89yMbJV1Pj6SPv/4b+2DcIzDea+8w3a3/cRpEgD8i6CgIBAdOceBFkm+/zTwWR1jcrLG4EUiRLds5+mR59AjsbWdaydmyN36IXvSeLv5TDKbl1v4I1CUBVY1mdplqw6HcHL3xratGtNTzj8/acL/LN/keTn/2sriipgWz5Dly0+96kCM5Mu3f0Kn/jZVkK6QCHn8t0vF3j9hbU9c3N6gsLrr6Kkm5GisYYXP4IgoLa10/nhT5A79Bzl86ex87nrUsxtNgRNQw5HkWIx9C3biO7eh9bRjSjLAeH3Ko8y8IufXvy70pqg88efoHz0MpmvHQ6KY+bXAnI6Rvrxg0QP9lM9N7byRKtAj7Wu2OY6FlNDjQmzeqt4guuZYCuFCYbOfJ0dt38IWdaCql5FZ8vut70h+cFixmLoZJHd96fYfmeSlh6dmeElD2vfQ03E0irVgs2FQ7kVBTGbiUhSpn1bhB13J+ndF6O5O0QkqaCGRWRNRFGuv7jwXe/aiiHikvizKIso4cbZxTzXw7Nv7Bv5vjWC+D614QEKR14mee9DjdNWCQKRbTtR003kjxyievE85uxUXXXeRiFqIULdvcT2HyR+4I51ieYCuNUKhcMvY05P3PC9fL9B0BQE6drN5oneGNW5zVsYnDpi8BsDM/TvVInGJcpFl8FLFvm5YBw0tym8+M0SqhZQtVlm4xNM6exJ1LZ2knc/GIyBdRhCOZ6g6dG3E9m5l/L50xgjQ5gzU/j2TQi/SRJyPImSSKKkmlBb2wl1dKF1dDVMSLEcsTu2I+oKM3/3Is5VmoVOtsTs518icksvqUcPMPWZ765xNoFQOLVia7U0Ta3cWDpjhRGUFrhFG8fc5GmaJvfR2nP74rZosouWrluZGHhxXefaCC68kue+93fQskXnrve08eT/GAICj+vgEy1IskB+2uLiKzfWl3s9pLs0Hv7hbu55bxuRlEJmtEZ23GB6sIpleDi2R+vWMDvvTq773FbRREuFiXTEwPdJ7W4l2p1YuaMgEFjKzffA3zAjKIoKkqzh2NVNUz33TIP8668gp5qI7TuwrjCSnEzT9MgTRHfuozp4ierlC0FT/QbY/cVwmHDfDiLbdqH3bUdJpddVBAPg2Tblc6conji6QjfwexWCqhC+bSdKTzvm5RFqZwbAcZESUcJ37FnXuaR4BCkZeM2KLrP3fdvqGuM772pj8thzm3r/hazHsVdWN6yDF0ySTQ6hsMjO/SGKucZXm75lknv5OaRIlPitd677sxUVhXDfdkLdW7Bmp7FmpjAmxzAnJ7DmZnDL61cFEVUVKRpHjsVRUmnkZDr4fzyBHE8ixxOIqnZDzehyOhaorlwj1OXP06ipDaqkK9rKKEo535gXCSuNoCSp6zaC4DM5+DLpjr3I8tJCu33rPUwNvbIhjbz1YOJihfELZZp7dfY/0sR3Pz1GreTQ2qfTuy+K7/tcfDVPfvrmzBlaROLu97Tz4A91IMki3/2zMc68MEd+0qRacrAND9fxuPf97Rsygtmz07Qc7GT3xw7i1BzsikV5fKlupO3eHjof2EKkK0FsS5L9n7yHykSRc39+dNOe8Q0zgk0te+jZ8hDnTn+OWnXzGEycfI7ci8+gpJsIda2WQ1gdgiAgyDKh3q1oHV3EDtyOnctijo8GzDTZDHYhh2+aVx8Y6AGm0qgt7ei9fWjtncjxeNACcR1v5lrwfR9zYpTsC9/FvUmK8DcDUjpO6iNPICVjuJk8k//1T3FzReTmJKkPPb6+k4kCYmgplu8YLuOHl6ilIu3r90xuBIWsSzHnIskwMWRRraxv4eaWimSeehI5EiOy4/p9bteCqCiEOrvROrqI7N6HV6vimQZOpYJbLGCXCsE2xyEoYRURJBFBkhE0DUkPI+k6UjSGFAojyEqQe1XVwOApyqYysLilGqGeZkJb26icGlrx81BXE/qOToqvNkYefrXaOYBZyzd+P1cVr0jyRowg1CoZKvmJ+VxgAC2cJhLvoJQfRZXCtIV3MFW5gO1tLFqhSRFcz8bx6+/ZsTxOPjPHvoebSHWE2H5nglPPzLHz7iTRtIJr+6sWzGwWEi0q+x5KE4rIHP3GDE//nxFKq/CNavo15j0fMicnKY3kMfMr3011usy5T79OqCmMIAqYhUCNxzGCxUXh8hxWwVzKh/rgWi6e41GbrXLiv79MbXYp6jD4lXNI6vrm4DfMCGpagli8C2kdahKrfZ+rLYDNqQlmnvwinR/8YeRU87o+bEEQEFQVNd2MkmoivKV/kYsTzwtEai0T33URFDVgZJED9WRBFAOjJ4obnkx838fKzDD95b9dQaH1/QBBCCi8kKWlSIUkIcXC8+9wHR6LKATk2TWHc1+8vESYLcCZv7u46fe+FnwfHBtKhXoDKCDis7ZRdEtFZr76d7T+wIcJ923b0AQM80UZIR1pnpZN9f2gF87zlxXgLPxfWDgoGJMLf+bPczNRPnaF5Jv20fuLH6D02kWqlyZwKwZiSEHvbyd+zy58xyP/XGMsNct79RbgOo0bGdepX8DKSjgQn14nPNemlBupM4KCIBJr2kopP4ooSOhyDEmQcRDnlU7mtT3nC3FW2xZs9wCBFr2fsj1HwZxaMbZOPTPHe/61TbxFZd/Dac69nKP/9gShqMzgsSKj525eblINSyTagsXI5dcLVIure769+65d+2CXLOzSNTxVH4y5Ksbc6vl2I1PFyKz+M89zKQ3n67bVZtbfvvKG6gmuJ567Z7dMT5eEJAmIEriOjyAKHDthMTV19QTkY46PMPG5z9D5oR9BTjdt6IMXBAFkGWHhtfjB0L36s9msycT3PKzZaSY++2nsbAZR0fBdu051XNLC4Hu41tLHL6k6rrVKr5MgIMoqvuvUcxsu3O8mq807Mzlm/uBvULd2Yl4ZDbQCl6H62hlKzx/FM9cO1cjpBMn3PYLS3gwEJdVaTCHRG0dSRArjN7/yrREIiOxpfpThwlEq9tqsKHY+x8yX/5aWt72H8M49iOvME696D4IQkHWLb6yE7FowRzOM/+HXaf/YI0Rv6yN21w4EUQiEQWwHe6bA1J9/h9qFxkKanu8iUr9oFqXGdTgdq37yVLQI4gYqb33PpVa5iuNVEObJvOfPLYXZlrwXWdTIm5OMFI+TDnXTFtmBJKpUrCyDhcMkQx10RfchCjJVO8+V/Cs06b10xvbieBamU+Ls3HfrDKFRcjj6rVke+0Q3Xbui7Lk/ResWHUGAQ5+fvGltERAIDSzXDr16HSeI0LM3xi2PNN28m7jJeEOM4AIzw3qMR1urxI6dCg/cqzEy6pDNefR2S+RyHlNTq0+q5uQ4k1/8a1oefzehrp4bLzXfoAhwI/BsG2NsmNlvfgV7bhZJi5Do2Y2Rn8HITwfqD5JMqv82ADLnXgZA1mPEe/aQvRiw3wiSgqSGwPcRJIlY5w6M/DRmfhbXNhBllVCqHc+xMIsZfHcTcxiehzkwjjmwesm7PZnBOD+I34BMjpOO41WW2FbkkETP/Z1EWnSMgsWOt/fxwm++tqkffEiKIUsaiqhhuwaCIFKxc2hSBE0KgyBgOCUMp4QqhdHlBAIgzqtFyKKGLieQBBnLq1K186tex87NMfvU10ibBtE9tyBqb6z23RsJY2CKoV/9LHp/O2pnGimk4lkO1nSO2uXJJeHeBuBYtbo8HHBN9pjVsERvFrxrSdZQtTgVJllPw6Tv+zj21R6ogBZKLvuXwOX8qziexe6mR4iqzTSFt5I3J6naebbG7yCsJJEFlZI1S9GapWrn8PHI1IaIqS0UzGmyxupk+Ye/Ms3DH+kk3qJyxztaSXeGKGYsTj+3dnuSIIAoC8iKQCgqo86HLiVZIByXMcoOrh0QYF9ND1gpOExertC6Vee2tzZz6bU8M0NVXMdHi0h07ojwxE9uoVZyCEW+P+ssr68nKKm0tq9XMmklRFEiFu9e1zHPPm/y7PMmt+xV+A+/VMB14Qfeo7NWys0YHWbmq58n9dCjRHftW1eT+hsF16hRPneK3EvPYs0uxfMlLUy4uQs93UF+6CSuZVCZGSLcsiRGq8VbFlezgiQT7w4YNfRUB/nBE8ihCOGmbsJN3eQGjyNrERK9+7DKWVyzil1943KOnmE2LJjqm/bivqIkkOpPoEQUckNFqrM1mrYn5z3azfNm03ovmhwhoqQwnDI+HtOVS8iiRkRJIosaLeE+ruReoT2yC1GQsNwqmhxBFESa9F5CchzHM+nQ9nA5+zK2tzobiT03S+bpJ7Fmp0jedT9yMv2P1hDi+dQuT1K7PLn2vteBY9eA+gpRTU82fLxZza/YFkl0kJ+9tM5+ZR9vFf5dWdWX7eEu/s33XSRBRhJkdDmOgMB09RKOZ5I1RnE8i4iapi28nfPZZ4HAiF5vyT1xqcLgiSJbD8TZdX+KSFLhxc9NXDM8uYD2bWH2P9JEOKGgaCKhiETP3iB0me4K8cRPbqGct7END9sIGvTPv5xdpD0rZSyOfXOW3n0x2vrD/PCv7WLsfBnb9IilFTp3RsiMGnz1vw3y4V/euY53uhJKawK1LUnl1PqIWBaQfGQ/+edOry8Fw1p6grLOtp1v39ANXY0gHLp+TE65/OLPxcjlfbq7JP7+C2v0avk+5vQEmaefxJ6bJXHX/UiR9XB23jz4vo9bLpJ75QVKJ15fwQvqey5GYZZwcw9avJlqZmXYyCzO0LQzIOIWZRU93UFp8jJ6up25i6/iuw5GcZZI6xaUcBKnVsIxShj5aRzjjWGg8ao1aqcvY0/Ori42utoxlr24ryiLpLYmkCSRWHuEcDpEZaa6aQTay1GxsjheQE7s+TaqFEYWVGRRQxRkYmoLqhRGk8OMF89gOCVaIn3IokYy1IksahhOiZAcR5dj2KuFqefhlorkX3kBc2KM1P0PE962a0OFVN8PEDQFraspEPo1LMypHF55PUUjPmatQDRRL8Yduerf18NqrRTxpq1MDLy4gjj/+hBWDaOKy+obPN+jK7YPSVCoOUVK1ixqTSeuthGS47i+heXWSGqdNOu9IIiIwtI5DadEW2Q7ca2VwcIRrl7sea7PoS9MsfOeFKouYRkux789u6Z8UefOCG/98V4iiZW1GLF04FUux6tfnmLweGHRCLqOz+nn5nBdn7vf00bfrXH2vakJy3DJjhu88sVpjn5jhqmBKo98vHvVRvlGobYlid3Wt2EjmH7rrRRePLtumavrWyZBQJZD8wTYLhtfhS+JYK73DP/zD8vsv0UhFhV49nmDK1caC+c5+RzZl56lOnSF5sffHcgvcfOLA1bDQvFCbeAimae/jjkzvWrfl+86OEYFzzbrEvjL79g1l0jLXauGZ1uE051MnXgG33NxbQO7UsA1KoiSjOfYuJaJa9bwb3I59wLsyTlm/+jv8arrmPRsB2c2hz09h2u6DD0/higLJHriKLrM0PONl8avB57v4vkevu/hA2E5QURpYrx0CkUKkda7g9yUIAeFLYKALGh4vovtGhSMKfLmJBOls1ju2kl533GoDlzCyswQ2bmX1AOPoKSDfMr3wkJtAcsZb7xajdKZ41QHVucrrYMkkn78IOm33Y4U0wMpJ8/Dq1oUXj7L7OdfxrcaG4fV4iRN7fWtNnqkCUkONVQgYxklbKuCuqzVIpHuQ1bCWG7jxPSCIKKuooW64E2aToUruVcQBBFBEHE8E9e3ydSGKZjTCAS9pj4eJWuGmhtEY9xljDbT1ctkjbGg4EZrwXBK2G6NRKgT26tRtXKcerrAb733HOBjWGVqMxqqFMZyqqiSjiyFqNlFfN8lHgqM78ChDL//kVMIIoiCiONdPz9fKzlU8vXpC6PscvLpDJcP5wlFZERZwPd8bMujVnAwqgEn9J/89GlESaAwe/1rdHZIKApYlk9mzmNB3EcQBPT+Nrp+6u24ZYO5J49gZ0s0v/NOwjs6sTJFsk+fwJrMkX78NqL7erELVXLPnMIYnI+oiQKR/VsIbW0h99QJPGPtVExD7tn0xFGuXPrGipLjRiGKClv6HqFn60PrzrFVqx6W7TM+6TE07C4aUSWWxPc8XKOKIIqEWjqxClmcylK4z7dMaoOXGf+zPyRx9/0k734AORYHUXpDJhzfD6Sa7LlZcq88T+nkses0PftIqk7TzrsQZZXc4Am0RAvpHXejRuJUs5PYpSypHXeiJVpJ9d9GcewCajSJpOookQSZc4fwXXdeuir4v+85eLZBy94HmT33EkbuDVC0dt0VRTKNIPNnXwkKKVwPx/DZ8lA3TTuSWGWbLQ918dLvbi6huO+7+Lh4vjs/6XsYXo1opI3+5vuxrQplK4vl1sjVxtmeuh/bNzGEKq7iM2sM0R3ZR1t0J55nc2b2qYav7RQLFF5/herARRJ33U/81juD6k/pjRmb14IfqKTiuw52Nkvx5OuUTr6OUy6tSSghKBJtH3uU1KMHcHJlSkev4FUMRF0jvKOD5vfcQ2hrG2O//6WGJqdyfmWuWRAlYqle8rNrVwv7vkc5N0Z6mSEVZY3WnjsYu7RWs/4SREle1QN15om4fTwsb2WUxfMdTLe+oMvxLZxVQquub+O6NpocRZNjtES3MTB3CElUcH0LEND9NsRMO1PF82hymoiSoD3VwlD2VVQ5QnNkG5nKAFG1CR+PmNZKtmpiTHroapJMZQTD2Vg0yHV8SnM2pbnVf28+kJ24vi4hQDot8NSTLSQSIqfOWPzcv81z5tz8okgAt2Yx8amnaHrnneg7O1HyFfRt7Yz+ryeJ37md6IGtGMkI0f1bGP0fXyO8s4vYbX3YswVwPaIH+oje0sv0515saIxBA0bQ930c18Rxahtucvd9F9dd+wWthn/9f8doa5WYm/PY1mczPePywksWodYuEjsPYkyP4tkWCALx/luYeunJFVWQnmWSe/EZSqeOkbjrfiI79qAkkoh6+KZMOL7r4lYr2Pkc5TMnKJ44glu5fnWja1aZOf0cyyXaXbPK+Ctfqttv5uQzzJx8BoBI21bK04NUM2MkevciqTq5geMAZItLCfP80Cnyw2e4qWVkmwHXZSFVo4QVfM/nylMjlGcqHPzEPkRZXGqb2ARMVlaX8LHevA1r6ALG2aWJdqZ6mZnq5YAU/aF7CO14jNJzhzh3ufHJdAV8Hzs7R+ZbXyV/6Hnid9xLdPc+5GgsGJvS9QmfNwu+5+HbFq5Rw61WMEaGKJ8/TW14cF3kEdFb+4nfuZ3MFw+R+dIr9TqbgkDqsVtp++ibSb7pFrLfPrbm+SrFSTzXqQtFCoJIun1PQ0YQfApzV+qMoCAIdPTdR2biJEalsZYkNZQg3Xp1r6ePZW5+xXIs1EpUbUaRQoCAJCoofghBEPF8l1x1jJpdmPf0ophuBUnUiIfa0ZUEoiChymEy5UFkMUTFypEItaPLCSz3H56Q/757NGIxgVBIQFMDhfpF+D5OroJvObiFKpKuIqoy1nQB33JxijWU5gRaWxJrJtjmlg0QQAypCIpM+tH9lM+MNKTxuoC1PUHfx3WsG2J5CcKp9ob0BHdul/n3/6nAj/7zaF2+07dtKiOXwPcId/WTP/86sh697rmcQp65p79O8fVX0Pt2EO7bjtrUgpxIbLjRHeY9PtvGKRdxCnmsmSmqg5epDQ+safxWOVnDuxqFWeRQhEhLL3a1TO16Xt7y35/vY06OUzh+uLELeT7G2Mbi9BuFXbUxyxZtB5pJVxNUZ2t1BtCrVSmfPYkYboA3dh4rRJWXQVAUlO4ORE1FikeDGLQoonS0IiXieIaBPTqJb9tUDx+vGytiWEdpb8UcGEaMxxD1EE5mDqW9FTESRgxpeOUq1tgEYiSC0ha0gdizc7hzAd2VUyyQfeZbFF57Eb23H723D7WtI2B0icURQ/rmtea4Lt68wXNKRZxiHmt2BmNiFGN8FM+4dl7zegj1teEUKmSfPr5yEvJ9cs+cJPHQPvQ93dCAEXSsCuXCOPH0lmVbBRJNfQ2HRPOzl3EdC0leaq1QQ3H697+HwTNfo1a6PgWbokbp3vEwql4fDvV9n2rhxgp/VoPr2lSsOQyniCTI+L6PIoWDoixnKdyer40vRjBst4rlVCkYk9hujbnKEPFQe9B875kIgkjBmNw0pq4bwf33aSjKNcaxIKA0xYje1ofWlaZ0fBA7UySyt4fY7f1o3U3YmSK1wWnCe7qJ3d6P0hLHLdVwSzV8z2Psf32d9NsOEr97B8XXLjVUmX5dI+jYNS5f/Bql4ur5GFGVECQR17DBBzmiIutK0Ph41WTues6GfgnnLzp8+INhbtmnUKt5XLwUuM7G7ARyNIFvW5SHzqNEk9iVYkNJRzuXxc69SunUMdTmFpRUE0q6GTXVhBSPI0fjSJEooqoiyHLQaiEIQf+d4+A7Nm6thlsu4ZRLOKUCdjYTnDc7h53PbgoX6VpwjQqF4cYaj+vg+5TPnqR89uTm39QmwXN8Zk7PYWRN5JDE8Av1oTGnWGD2m1/ZtOspHa2Eb9+POTCClIiDKCKlk0Tuug1rdILQzn6qgDU4uuJYKZ0k+sBdmAPDqB2tKO2tVA6XiL/1TVSPn0Ht7UIQRexMlsj9d+DMzCGoCuqWbkrPHcI3lqIkbqVC+dwpyufPIMfjKMk0cjKFkkghJ1PI0RhSOIoUjgR6mrKMIElL7UC+H3h2roNvWXiWiWcauNUKbqWCUynhFIu45WJgAAt5nFJhU4jkBUXCrVnXrc5zyzVEtTHCDNe1yc1cqDOCgiCghuIkmvvJTq2ttGJWcxSzw6Rad9SdI9m8nf5b3s30yBHys5dW9BQKokws1UNbz520dK9SIe97FOY2WY8RyNXqx1emcmXx79Vl+TzTqTBbvrJsv4G64ypW0MMqiyFs16BkzjRE8HAzEY0K3Lpf4VqtsuZ4ltx3TiKGVCpnRqhdmcKrmuS+ewq1JY45Nkf18iRusUb2qRNo7UnsbDnYz7SZe/J13KpJ7qkTaL0BaUojLsV1jaDrmoyPvnzNn2tNYbrfshNZVxj68hnaH+rDs11yp6coXqnvX8llLnLBrmEY+QZuawmf+csKd9yuUiz5HDthcflyYNmdWpnS0DlkVQ8YXgDPMlhX/49tYU6OY06OgyghalrwZ4FSSpICDlBBJBDB8pYxydh4ljX/x8S3golMUnXabnmYcGsvgihRGD5L9vKRxf48UVZJ77gLxyiTHzyxrnfxvQYxHCJy74HAOzp2Hq+8MQ/iWnBNl9xg4wUMG4YoIrc048zlqB07hba1G3wftbuT0M7+4OdNKeSmFNbQWgU6wqJemmdaqN2dASvGxSuIYZ3Qjn7sSDgYU0LgRbrGKqkC3wsMVCEPw4AoIek6sQfuRYqEMV4ZxC0Ug2IdUZwv2gH9tluQEjFK330B33VRt/YS2r2d3Ne+GoxV2wry0ptAnBC+7Ras8Umc2eBbd7Il1La9qK0JaqWVY0FpiqH3t1N4sTGZMN9zKGWHcW0DSVnqF5QUnXT7XnLT59dcWLuOSWbiOMnm/rpiM1GSSbbsIBxrx6jMUS3PYBslPM9BVsPokRb0WAt6OM2KDnGgVp6lUvjeJ7r3fJuCMVlXhPMPhV07Fdpar53ztjNF7MzKFq7qhXGqF+oXwau14BReOgeAU6zinF6933I13FB3oxxSKA1lMTIV2u7tRU2EyBwZQ9JXrvSq1Qy12ty6vcHZjMfgkMvsrAc+hEIChumjpVppufsxzFwQ1y9cOIp7Iy0AnhvwMN6gkG3LLQ+R3nY7s2dfxHNszFK2rkFdECVCyRasihJ8XDcjRCEIxDq2U54eXLU5XtZEPvxre/jLn6/3IiVZYPeDaYyKy5XD+bqftW0Ls/2uFMe+MU21MG/QIzqxR+8EUcQancaaN4JKdyvpj72L6utnKT9/NNCU+16G7+NbJlKkOeAwDQe5YrdQxJqcpvidFxAQ8Awz8PCvoiPzLRsxrAeMPdFw8HffR0CgduocTiaLZ1qIER2vUqH0bOD9+b6PV2l0vPmI6ThiKoo5NkH18kUETcW37EWydq9axZybJv7Wh6kNB16KOT2JsqMHO3eNpmpZRm5Oz4dws3ilMnI6iZROYY1O4JsmclsrSnMKc3Qcr1hGjISR21pQutpx5pZYc8rHB0k+coCen3s/c197jcrZUdyKgRTW0Hd00fTOu0AQgl6uBmFU5ijlR0k0b1+cPAVBJJroIhxvX9MQ+b5HcW6IYm6EeHpr3QQsCCKankANxYmle4N0je8HVZ4LC4tVz+kHwrrfA4ZlLXi+C5uh4boJOHCLQjLxhknYNowbMoJmtkr7Q3203NEdlI7rCnprlOrUapWB/oZygr/5awmSSYl8IfhF/tXfVDl2wkaJp8ifO0J1chj8lYzxAJIEkbBAqexvNmPY6hAEoq19FEbOMnv2pVVX265VY+zQl9nMpu+rocWbaTvwCLVnJ3HclTlJQYBoWgUhIL51XR/H9JAUkaETRaza0kejhiUUTSSSVAjF5LpEtqDIyC0p7LGZOuULMRxC39ePMzGLIEk38Uk3Cb6PNTKOtms7TR/7IHgenmlhj09ij06Q/uC7Ach/7WncQoHkDzyB3NKM1t8LooB5eQgnX6D5xz6CVzOxBoaR5o1i/PE3gShhjU5QeuYlyi8eJvGuxxAEEXN4nNJ3G5TjEQQEUcRzHNxSBTwfffcOnNk5BE0DAYwLV/BMq+59+7Z9Xa9PTidJvutxqsdP42TzyC1p4o8/gjU4irqlh/KLr6C0NSPIMukfej9zf/E5wgf347seWk83tVNLxUXWVI7JT32bjp94grYffjOCLM0XegV5SGu2wOjvfRFzrHGOXLNWIDd9kXhTH4Ig4/sejm1gmeWGBHIhIMCeGX2dSKwdSVnJ1iMIAoLQ2FTo+z6l3AizE+uL4ggChDQBVQNFFhADBx7PA8cF0/Sp1fybmkURhMCJ0DSQZQFJXKrDc1ywbR/D8LkZIjayDPv3KcTj3zttQAu4ISNoFQyGvnQaNR7CmKuiJUPEtqYpDW+etlUyKfET/zK7YnCY+QwtdzyMHIkHOa7Ry3XtEQDdnRKf/PEY/+W3CpTKN28qFkQZLdGCGomjhGPYtQLRjm34rotZzODUgkVBKNWBHAoKOaxSFqtczz0paRHkUASnVkKJJpFVHc+1MYtzuGbgMYiyihZvRlQCJhzPNjFLc0FvoSQTSrQS796NGksRae8L+gNdG6Mwu3gOCAZ/c4/OLY+2MHKqyNDxAnve1MStb23h+LdmOPnULLIq8Naf2IIWkRFFAaNylVcpikHRh2lfJZ+zQN68yS/6JsItlMj//ddWbC89e4jSs4fqtmX/+osr9st9rj4/Gdq7AyeXp/bcWaREPCiS0VSMc5cwzjXQb7fiBl2cTBY3M4c1MroshxcYR3+jSw3PwxqfpHrkOAD63UH+S4xG8EwTKRYLvCJNQ1BkxEgE33Ewzl9GjkdXGNjapQmGfukvid25A72vHTGs4dUsjKFpSocv4VbWp7Lg+y7F3BCVwiSKGqZcnGRm5Aj5mYuNyxj5PtnJM0ST3bT13IEgNU7iX38aH6OSYfDM1/CcxqrdRTHoi9uxTeaB+zUO7Ffo75NJJkUiEYFSyWdyyuXI6xbPv2Ayl13ba5ue8RgYdBrWW1YU6OqU2LFd5qEHQtx6QGFrr0Q6HfTrGabP9LTHpcs2h161OPy6xaVLNrn8xsaUIEBYF4jGBOJRkVhMoG+rzK0HFCRpaVKIREQO3qqQTDQ2UUxOBc+92Q7NDRlBUZVI7GxBiWrEt/tYBZPpQ5tbRVipenz4Q2Eysx4+PqdO20xNe3hGldzZoLpREOVrcmJqKjzycAh8GB51OHHKpm+rzO6dMrIEFy45jIy57Nkl09wkEg6LDI84HD9pk0qJHNinkEiITE65HH599SWSqKjEe3ajpzuQwzHCQjeCKONaBrkrxyjPG8Fo21aiHf1E2/uZu/Aak0e/VXeeaHsfTTvvopadQAknkFQdz3WYu/AK5ckrIAi03PImoq1b8RxzsRhi8vVvUctOICkh4r17iXftRNZjpLffge/a2LUy2YuvUVtmBCMJmXve18HwqRKDx/L4Hpx5ZpZwYmlINHXrNPWE+YufP82W/XH2PdJy1ZMH3r0gB0oa/4QlWIOjSMkkWl9vUI07MIxb2ES2f9/HqxqoW7oQFAV7JoMY0lB7upATceTWZtxCEbmlGSkWRd3SjTU2uUKp3vf9OlYfe2oWKZnEHBrBK5YQ9RBKdyfmlaEghGuagIDWvwUplVz11ryqReH5MxSe30DR1iqoFqcYv/wcjm1QmBvYEOmDbVUYv/w8oijT0nUrgri+9hPf96gUJxm7+Myq/YurIRYVeOTNGh/8wTBvekAjEln5jaRTAumUyL49Cj/y0cYqnf/6sxX+3/9coFBY2xq0tYo88XiI97xL5967NVR1JSlDVBaI9ots65d521t1JiddnvqOwRe+XOWV16yGja2uC9x9p8qO7TJdnRLdXRI9PTI93RLNTWKdAQTY1i/z+7+9Ujj5WvjTP6/wH34pz3WKvDeEGzKCoaYI4fYY5ZE8nung1K59d8lkP62t+xumT5uaOko+P8hLhyxaW0QkMVh0XgoFH4Br1qhNBZVUoZaua5Jlt7ZKeF4Qinjfu3WyOY/3vENnbMKhVvX5oR8M83dfrPKut+uMjDpMz3j82Cei/OdfL3Dn7Sod7RIjoy4f/XCE8QmXicmVI8I1a2TOHUJSQ2ixJspTA0yffBZ8r45zMHPhFYpj5+m+733XfO5Qqh2jMMvs2ZdwjAqirOIYQUhTUnVa9j7AzKlnyQ2cQJQklHBi0aN0zAqZsy/hGhXkUISJw0/iGJUV9wHguVCYtYi3qIQiMrXSyonFZ5kAxSpune+4eIUySnsTSksKZ3pu05Uqvl/h1QwqLzfYgtLoOU0zCHlWa4FhHRrBtwPVEWcuF7Q+1GpUDh8LDJvvg+NQeuGVa1Z/euUKtTPnFv9tjY4jaCoQVJk6mTmEC5fxHYfSS6/iVaqYVwaREnGqJ07jZK8d9ZGiIQRNCfq+Ksa6OR0XEBS33Hgls1HJMHL+W9TKs7RvvXdV5fqr4fs+nmsxN3WW6eHXKGaHGqLliscFPvGxCD/6I1E6OkRE8Y0Pi/T3Sfyrfxnj7W/TSaXEVaXproYgQGenxMc/Guauu1T+/C8qfPZvq1Sqa//u0imRn/oXUR56IGiD+B4iQLoubsgIOjULLR3BqdrYJTPQg7sGwuEW2jtuR2pQCqVYHCWfH+RrX6+xe5eCbfsMjzgYhk9y9+1YhSzJPXfgey5qPM3s4e+uCIcCZOZcXnjJIBQSOLA/xs7tMqoKL75ski943H5QZVu/TM3wOXbC5tQZm7c/HmLPLoXbb1PZu1thfMKlvU2io01c1QiCj2cbQfWo7+K5Nq65CoWWHxDxXu8j8l2H4shZanMrV5uebWIVMyT7bsWpVSiMncNc1hSP7+NaNVzbxPc9XLNWFwJdjlrJ4chXJ7nrvR3sfjDN6WcyvOXHt7Ll1jhm1cU2PAaO5pkbrfGhX9mDbbpUrhLT9A0La2QK/badpD/2DmqnLuNm8kjNSQDU3nZij91dly9cEz4Uv/3KPxnT1eC42FNLfW1eqYxxvj60ag3XV6/aUzN1x1wN37Swx5f1l/o+5qWBIJQ9/yswL9WX3zuzc4sVoVdDUGWSb9pH/IG9KOnoPG2aj5MvUzp8idxTx/DMN4a+bzWYtQITAy+Sm7lIU/temjpuQY+1rtAu9H2PWjlDcW6QzMRJKsVJbLNCI7l8VYV3v1Pnkz8RpblpSWvUcXzOnrf59lMGl684FEs+8ZjA9m0yb3ksxL69Cso1uDd93yeT8RgedTl1xl4zb9fWKvIbv5bkgfs0NK3+nJ7vUy75zMx61GoeyaRIa4tUt58gCOzZJfOz/ypGWBf41KfL1NYo/pZkSCREVPX7xPrN44aMoGd5FC9nUGIqakpfXQV3EUHobKE4ptFQxMc/GuGeu1TOn7c5f8FhZtbllSMnCXdsJXv6NazcDNGeHYHHswpqtSD57Pvz3k/RJxIRiEQETFOgpVlkNuNxX1wkHhMIhQIZp4lJl4kJlwsXbb7znIkkQr5w8/tsHLNapx24HL7nMvD0p2necz9ttz5C222PMnf+EJkLr+HZjTPy2IbHn/7MScyqy6G/DYytY/l851NDiFKg/+ZYHq7t880/HESShUDD1VvSFgNwixUqr50mtHsrSmcLSltTUO06/7vVtvei9XevryDK9yk+/SrcBLLsf8I6sIHXL4Y1On7scRL37gYRrMkcTrmKqKvo2zsI7+omenAbY7//pYDp4xrQe9K0v/NWBv/42brFUPq+7bS98wDZFy8x/c1TG3ioAFt+/EFmvn2G0UvfZfzK8yhqhFCkCVnR8T0P26pgGUVsqxLQDq4z/NrbI/N/fTyyaAB936da9fnjT5X5/z5Vplz2cd1gXhKFwHj88f8p82P/PMpP/USUaHSJa/mZ5ww+9acVTp21KZc93PkClusZQUWB3/71JA8/pC2GIH0/OO7FQyZ/+ukKrx+1MIwgkywKkEyIvO3xEB/9SITdu2REMZijW1tEfvInooxNuHzla7XrhkYLeY/P/GWFp76z8nebSgZh2a1blkzO1LTL179ZY3pmDTo+AFHi2HHj2te/gUr7G/MEqxbTLw/N34OA3nptxhbDyJHPXUHV4siyhiBI81VZIpKkIUnaqobx7jsU/t1/zPOj/zyKJAVVRr7rUBm7PK+eLVKZGFw1UW07MJtx8bwgFZLJuoyMOjzzvMnP/GQMTRP4xrdrDAw61Go+P/SBCB//aJSvf6vGpSsOtu3zkQ9FePObQrgu/Ny/37yCn2vCX1KgXg2OUWHq2FPMnH6eVP9ttB98C45pkL10NfP89RcZZjUYTY61dMwCc3zd9UyPa9YAeB6V184gqAqxR+9CikVAEhFkGSkqB8QChrVuI3gzykklUUEUFCRRRmBhdR64O74fkBt7noPnO7ievfFCk2tCQBYVJFFFFKT58vvg+p7vLV7XXYPg+EYgCgqyqCCK8rwm4tLzL7/+Rp89fvdOorf2kX36OLN/9wJuaWkyFMMaTe+6m+b33E3qbbeT+fy1+48FSUQKa6hNUcDHKZt4hk320GXkeAhRXZq2RF1BjmggCLgVE7dqIWoycjQEooBbtXArJoIqocTmqcgiGkhCQDjvubiOiVFdWyC5EcgyvPmhoABmUTTAh7/7fJXf+J3SigI/1wfXCsikf/e/lUinRH70E5FFf2LfXoVSxWNy0m0oOCKK8BM/GuVtbw3VGcC5rMd/+c0if/U3lVUrUIsll//9pxWe/EaNf/tzcd73Hh1dXzKEP/2TUS5csDl7/hoLAkGgWBP568+tHnnaukVi7265zgjOzLh85q+qnD5z/SSfpOo077yDmbMvrmrn1EiSll33MXPuReza+vPuGzaCckRFiWkoUQ0pJCOpEukDHVz89JFV989mL5HNBqEbQRCRZR1ZDiHLITo676S9/Q6kVaq2pmaCkGVnh8TMTGDEAEQ1RLhjy+JkVp0ewb0q7zEx6fK7fxC8lErV53/+f0FubWbW5LkXlmb25iaRUsXjS5+qLpG5AkMjLr/+O5uovyeKCLISVNtJEoKkBEn+Bo2EpIXRYk04RhnPdShPD2GV8yjheF34yrVq8/2IbRh5AmJay9hcQd15+KZF6TuvUT50EqUlhRgOofZ1kf5nT1A7dZnyc0eD/rrGz7hpbDsCAiElQTTUQlLvJBpqJaymUOUwkqgs8jF6noPlVKjZRapmlpIxTdXOU7MKmHbpBpk2BMJqiniojWSkO+B4VJMoUghRkPB8B8sJVAKKtSly1VFKxgyGXWSzVgOaHCUWaiWhd5EIdxLR0sE7EFR838X2DGpWgZIxQ74yStGYpmLOrfu51c409lyR2c+/VGcAAbyqyeznXyK6fwv61ra1z9UcpeWxvShJncrlGTLPng+o2Ja9ElGVaXlkD6H2RMBxXKgx89QZkndsJbKtNVCvsBwmv3SM2N5OkndswcnXCLUn1vVc64GmCTz6SH0bRqnk89//V3nNYe158D/+sMwH3hcmlQqOb22ReO87dY4dt6nV1h4PO3fI/OgnInV1armcx3/9zQKf/Vx1zXuYmPT45V8toCoCP/g+fb4dVuDWAyof+kCYX//tIuZVn7Mgi8T39yDHdQrHhhFkETu7MjK3kdGsRtMoepTy9NDiul6NpFAjSXzfW6yHEEQZPd2J5thYlfyKyvvrYU0jqGnQ1LSyqsnXFZxUgkh3EtdyEEQBWW8s3+f7HrZdwbaDF5Ws9l+zif6vP1vlkYc1sllvvow3mMhDzR3oLZ3UZsYXTtrQtVdDterz6mGLmdmbF+7U4s2kd9yBrMfQ0x0o4QSCJOOaNaaOf6chV14Jx2m79dFAHcJ1ECQZu1qkOHqu7vlrc+NUpodov+0x7GoRq1oge+kIZmH2pj2fXzWwhgMGB9928C0bN1fEuDCEV9lcJplGIIkK7Ym9tMV3kQx3I4urRxpEQQJRRZXDREMtENuG7/uYTolibYq58hCThbPYGyAflsUQHYk9tCV2k9A7kaWVAs+SoKKrKrqaIB3ZQrd3G9nKMJP5M2TKV26I6UMUZJqifXQk9tIU7UOVwyv2EQQZTYyiyVGS4S66Ugco1CaZLpxnunAew2l8EehbDl7FvO5s55RqgXbkGvBsh4m/fw19SzNND+xAjoWw8/W/A601htYaZ+JLR3GrJr0/8iDxW7oIdSaZeeo0tbEs/Z98jNjeDvTuFLnXBikcHSa6p6PhZ1ovFAUO3la/mD9x0mJsvLESy4lJl+MnraCifR5vfjiE9jvFNXNyoggf+kCY1pYlVhbX9fnaN2p87RsGdoNr4Fze5zd+p8gdt6v09y2ZiB/6YJjP/l2V8xfqT6R3p4nt6USOapgzBeRIiHxucMU42EimUA5F0JPtxDt2MHr4K3iuS9OOu7CrAVtSpLmH4uRl5FAYLZoGBBJdu5k8+fSqQsirXmOtHTo6JD78QzrhsEgqKTCb8UilRF56yeSLT06RvziLXTIRRJFYf3oDj3l9nDlrMzjkENIEiqVAeyrU0oWaaEZUQ/ieg+/5N2YEa4ERvFF4rs3kkW8uVnMuh2ubVDOBXlhp7MLSMct0GquzI0y+/k3M0uphV6s4x8ypZ5FCkcCLcSys0hxmqX7VY1eLTB37NlqiFVFWcM0qTm3zGe+vBd928Mr/cIz1kqiwo/Vh2pP70OTGCbYXIAgCISWOJseQRY3JwvpL/XUlwbbWh2iJ7UCV9bUPmL+uIoVoje0kHmonmm9hcPYQnr9+D14WQ/SkD9Kdug1dTTacgxcFiaTeRVRrJq63MTD7MhXzGmwzV6F6YZz4PbsI7+ik9PrlFT8P9bUR6m1h5m+eW/s+5CBcK4gCiEJAV3gVPNdDkBaUCARERcaz3YC5TgzCzaIm49veItkABOHWmwU9JNCUrj//5QY1UJfv/8jDS//u7VkoWrn+HNfdJXHPXSrasrXW1LTHt582yOXWt8AfHnH5689W+I//bslrbmmWeN97Am9wOXzHQ1RlBE0mvLUZa66yaSmNamYUq5QlnO4CIBRvRhRl5q68jqSEaN19H2okiWtb5EfP4toG3Xe8EyWcwCw2tuhf0whOTLj82aerfOgDOt/4hs3QkENnl8TtB5X5atDgj+96WLkq0S0pKqP5wDBtAv7jL8b51V8vUqn4vOlBjXzBY7CSQBDBqRRR4oHhtYpzYL7xHkcdfJ/y1MCqP3JqpTXJru1qEbt67ZW359pUZhrrw7TKOazyG5DDXAW+7eBuMo/oetDf/ADd6YNI88rfPkt5Rs93MOwijhv0u8mSRkiJ1al8Q2CQPN8hWxnBdtf3LCElzq6Ot9IS3bZIvbU8L2q7VUynguc7yFKIkLx0/SBPLqCrCbY23YOAwOWZ59d1fUlU6E4fpK/5XmQptFicsQDXszCdMo5rIIkqqhJFEZc8jwVj3B7fgyBIXJj8Dqazeq4l9fjBRaMiiAJOvkLnJ99O9ewoxsgsXs1C0BRCvc1E9vZSvTRB5cwavI4+OCWDrT/5CHJEI/vyZTzDoeN9t5O+L3invu+TefY81cEMvT/yIIIkUjo3QfHkGIIs0fG+OxBViepghuLZcRCg5S37SN/TfxWxw+YiFlvZDrFexqpKpX5nWYawLsIa4el9exU6O6S6XOTFSzbnL9gb8hG+8qTBv/lXMSLhJaP+3nfr/MbvFOvOV5vIkT10mfj+bqxMmdLZxvooNwLHqiFr4XlqOwlR1vAcC1kNIYoSniAE/dUNeoHQgBG0LJie9hBFgXLFo1DwSSZ9kkmRUFOE/g8cQI1rXPqLo3S9dQdWwSAXD5E9dWMyI6IkENYFurukRaqdvq0SwyNw8vQ5BFEm6GVyEURp1ZXiP+EfBm6+ROHJF/AKFfwGQl+biWS4m670rYtGZaHoY7Z0mbHsMfK18UUVeVhYwklEtDSpSA/N0W3E9TYkUcN2DaYKjZE9L0AWNfpa7qMl2j+vNB4YIMutMl04x3j+FGVjlnnTjADIUoimSB99zfcSCTXNF62ALKn0Nd9LsTbFTKkR/bzgiZpj29jWcj+SqC5e3/VtcpVRRrNHyVfH5gtg5hWjBJmE3klv052kI1vm86UCoijTHt9NxZhjMLO6R9r2kTchhpalQYTAM4vds5PYXTtWbr9jO77rMfZ7X7rmE1RHMlz6nW8sPo8/3/M49ZXjTH1tnq7M8/Fdj9lnz5N5/mJAWu564PnkXh0gf2RwXvkl2FY4PkLx1FL7yHr05tYD05gnkFjmeccT4nKZ0DURv4pBxXGgXF77frdvk2luWmr18H2fwSGX0bGNcYfOZV1OnLC5/74l17KnR2Jbv7zo3YY6kkgxDUESKJ4ZB3xETcGtrDRCN+oW+YBZnKU8M8jWBz+E77nkBk9glrOYpSztBx5D1iIURs9iVxsn3m+4MOa1IxY/8vEI1YpHLC7ypS/XEASZzNExrHyN9ge3giiQPze9KT1ene0SH/2hGHfervJHf5BGEGByyuXlV8oIgkisbzeuUaM2O0Zsyy7M7AxGZvP1vf4J64dXNai89A+jkNEW31WX//N8m4HZQwzPvbZqfm1evpiiMUXRmGJ47jAhJUFLbDuaHKFiNRYKBBAQaYntoD2+Z5EUwvc9isY0l6afY668MkrgA5ZTYbJwmtnSRXa2P0Zn8pZFL1YQJHZ1PEbJmKFm59e8B11NsrP1kbr8o+VWGZh5idHs6ysqP30CkuW5yiDZyhC9TXexrfUBFEmfv75IX8u9ZCvD5KorPbix//G1dYcXndwaoXmfVb013/Xg6vnc81f23fo+vnPVHHSNc242CkWPmVmPttYlY3TLXhlJCozZWpAkuPWW+tqK8UkX5+rnuQqaCh3tErq+ZEBrNZ+Jicbp1a6GbcPlAafOCEoi3LJPWTKCXSkiO9qI7myndGYctSlK8dQY2ZdXUgOuNycoSgpKOIEciuDj4897d3NXXmfuyut1+04c/9Zqp2gIDRvBQ4csJidcenokzp13yGY9xJBPck8ryd2tmHkD1fMJd8RXyChtBGPjDr/xO0VmZlz+/K8qQZ/f/BiWwlG0dBv4PnI4ihyJ/5MB/B5DPCmyZYdGKCxSrXicOVJDj4j079EIR0Vysw4D50ya2mW6tqpIkoDr+px9vYbr+uy4JUQkJqGHBUauWIwOWGvWDgmIRLXmRU8KoGTMMlu6vK4CE8MuMJp9fe0dr4KmxOhKHVgsQPF9n5pd4OLUM2QrQ2se73gWV2ZeQJF02uK7FkOjmhylK3WAKzMvrNm+0Nd8L2FtiQnFcU0uTz/PWG5tEVsfn+G515AljW0tDyyGciVRoa/lXvIj4/hXKRKUj15Z7VT/fwvLhpcPmbzvvUtFSLfsVbntgMqRo2uH6A7eqrJ3T/20/PIhE3ONQ6MxkVSqfjFSrflMrdGDdz24js/ISL3lFgTo37r0feWPDOIUazglg/zhASLb2vCszalCl7QIie7diKJMfuQ07jp6odeDho3g3j0yj7xZQw8LnDtn88ibNZ550WHm1VF818MumeitEWJbm6iMb54G3Be+Ul2xkvGMGpXxQURFwS4V8KaGccrrb2WQ5TCxWCe63oSqRufVpwU818aySlSrGcrlycUq1n+MUJQwkUg74XAzqhZDElUQBDzXwrIrGLUcpdIklrW+3+nOAzq33htm4LyJ6/pIMuw5GGLXAZ2xQYvH3pugVs3Tt0vj3kcjvPKdMnc+HCOfccjOurz9h5Iceb7CQ2+P8dQXCowOrD2BSPM9cMtDUbZr4LjrI23eGARS4W6S4e66rWPZ4w0ZwAWYToWpwhlS4W40Jei7Dao8tzKeO3ldbzCqtdCe2F23baZ4cd2FPWPZY7TFdxELtS5uS0e2EA+1Uah972vo/UPCMHy+8mSNx98SWuQKDYXgF38+xn/85cKKysrl2Lld5hf+n1gdc0u+4PHNbxsYxvUXP6GQQCR8VRjVXplfXA88D/LFlce3tNSz69TGs+hbmonf2guuT230xp0gALuaZ+ZcYyorqbRALC4yNuIiio153Qto2Ag+8IDGufMOH/mwjh4WufWAwqFzKt1v3YUUVhj64mk63tSHa7mE5yqb4g0CfPyHI/zB/wrCJ/fcpVIsepy74FCdHEIOhYM2gXJ+XSFYTUvS3nGQdHoHmhpHVnQkSUGYbyD2fRfXtXCcGoZRIDt3gcnJI9h24xWPqfR2tvS+uW7b1PQxpibX72EshyCI7Nz1XvRQ0+I2z3c5e+ZvcJzGJ3tZ1mnvuJ3mpt1ooRSKoiNJ6mITd/AObBzHwLJK5PODTE0eoVptTAZnfNiiZ5vK9r0ar3zHRlUFuraqDJw3efW7ZTp6FXbuD2HWPMYGLV78Vpm+3RqpFpm5aQdBhL7dGqNXTAbPmw2RQXi+u6IxX5XDKHKYmn1zxXklQaY9sQdpGTeu7RqM5Y6v80w+ZSND2cwsGsHAG4wRDbVc1wh2JPchi0uhK8ezmSicWXcDvu0aZEpX6oygKEg0Rfv/yQiuAc+D1w5bfPtpgx94T3i+6V3ggfs0fvc3knz+SzW++6zB6JiL6y6pTDzyZo0Pvj/MnberS6F8z+cb36xx+Ii1Zn+fLLGCrsx1fQxj456g50O1Un+8IEAkctV1Khb5wwPE9nRiTBWojW0O8UCj2LNP5h3v1kmnBX77v5b4gQ+E+fSnGndcGjaCmiYwMuLgecELRwAppFAcmAtEde/bghIPUTgyuqqo7rohBANk104FaX7h0d8nMznlcu6CgxyO0vHQu6llJjAzU1iFOczs9PVPKYg0Ne1ma9+jhMOtK7yGpf1kRFFGUcKEQmni8W7a2g9y6eJXyedXr/68GrVqlmisA0VZCovIin7DRlDXm2hvv6OO6zA7dxF3FT3FayEe72HHzvcQibQiisoa70AnFEoSi3XS1LSLoaHvkJk9tyIsdjVmxm2+/fkCsYTEz/92B//uE6PUKh7xpIgoQUuHwsWTBqGwiDHfBOw4IIoCPlApejzzlSLZWYdaA0UBsFD5WcDz3cWQaExrpS2+i6qZxfFuTjgFQBJV0tGtdduylaF1V5YCGE4Jwy7iEwjzQmDMo1ozs6XVZZiCfOQ2lmdeysYsNSu/7ut7vkuhNll3fRBIR7YwMNug/iEQTfaw684fXvf1vx/geTbHvvu7q/5sZtbjU39WYVu/zP5bFEBAUQTuulPlln0KP/+zMQzDp1L1CesCekhA1wXCYSEY/36gK/jSIYs/+pMymbm1x/9qHTA+N16esdrxV88XUkSj+2MPUBvOEO5vRY6FKJ4cvbELrwO79ii8dsjiPe8LIQjQu0Va+6BlaNgIvvKqySc/GaW/X+bf/Osof/4XVcysE4jq3hmEgCRdQW+NXUNUd33Y0iPzL38iwcMPaXz18y2IIoxPuPzufw/CnnpzB6XBcwiKgqgo11SRWIAoKrS1H6S//3EUJVz3iww4Tb3Fhv2gqk9czMlIkkok0sYt+3+YgSvfYnLq6JpyLrZdIZM5T0fH7YvbIpFW4vEeisWND5D29oN1ite+7zE1deyaZAPLIQgSLS23sHPXe5Hl0CrvYMmTuvodCIJMJNLGnj0f4LLyTaYmj1yXU/HgAxHe9oEEvg8vP1XGMnxOvVbjXR9N8u//WycXTxucPVrjljt1atXg3qtlD8v0SKQk0i0SH/2ZJvSoyJN/VeDw82UaIO9ntjxAa2wnwnxrgCCIQZ5MSXEl8zI1KzefH9xcWrRkuAtJqF/8Faob85pczwoM9kL5JkFIVJUjCIirMrlEQy0oUn0zfNXaqOH3MZ1y3fUBdDWOKMgN9y2KktyQUsP3I1YT8V6A78Orhy3+4y8X+H//fYJb9iqEQsECL+Atrm+ZWf4dep5Pqezz0ssmv/rrxUVykLXgOAE36HJIkkBIW8U6NghBgHC4Ps/o+ywuWhcQ2d5K/rUB8keHiO7sQArJdQxWNxsL6TJJhnSzuG6ppYaN4NkzDn/xFxX6+xVGxxxGR12sgs3QF04h6QpGpoKWCpPY2bIporrDIw7/4ZcKTE27/OEfl+sKYwBqs5M0HXwQQRBxyoUl5phVIdDadoBt255AUYKqN3+eo9MwChhGlloti2UFYVdFiaDraXQ9jabFFz1GRQmzZeujeL7LzPSJ6xoB17WYy5ynrW0/4mKln0hb220bNoKSpNLSekvdtlotS6EwxFojLjCA+9i+4x11BtB1LQwjj2kWqFZncezAc1HUKLrehK6nCIVSiwZRkjR27HgHjl1lZubUNa975LkKR56rD0lMj9v8n9+sb2A9+tJSiPnLfx6Mm7e8P8HZowanDlfZe7uOHg2UuBsxgpnSZXKJUVpiO+afO2iCaE/uoTm2janiOWaKFygbGUynhLeGR9so4nr7ihWyLGmkI1s3dD5NirDcCgmCMJ/zlHBXiY1FtCYkoT6yIYkKSb0TZwOsM1GtCd93FxXXBUFAQESWNKx1JFzWo9n3jw0vv2Lx0/86y6/9cpK3PDovgu35OE5QBbqQu7Jsj0rFJ5/3GBlz+eqTNb745RrVBuSLFmCaPtWrjJMsQzi88fcvihCNrDx+Llf/zVSHM3S88yAIoCQj1EYyb5gBBDh13OJdP6ATj4t87BMRvvj364u+NGwEH3lEo6NDxHGCpsxIWCCTdUjsbEFvi+GaDnbZZOaVzRXV/ZvPBYUxAYfdknvu1EpURi6hpduwijmc2rVjwMlkH/39b1s0gACe5zA7e4bp6eMUCyM4Tv2LkySNRGILra0HaGnZhyQHZfehUIKengcxajny+UGu/dv2qVSmKJeniMd75rcJpFLb0LQ4prn+Qp5UajualqibWOYy57HttX/p8Xg3vVvehKrGlnrXrBIz0yeZmTlFuTyxwqiLokoi0Ut7+0GaW/Yhy9r8doVt257AMPIUi2s0Pm8AJ16ucPej0fmcoc/JV2s4Dc7jrmdzafrZIDwZ2VL3rmRJpSt1gPb4bgq1CbKVEYq1SQq1yQ2FLZcjrK70eLa1Psi2GzprPSRRRhRkXFa+jJASm89pL6Etvou2+K5Nu74gCCiihsU/3kKxzUQqJfLWx3T27A6mWcPwOfy6xZGjFqIYpJUM06dc9pmecRkYcDh73lmX8VtAqeyTy3n4/lJoNBQSaG5eX2hwOSRJoLPzaokpGB2tN4JOvsb0t06i9zRRvjD5hucEhwZd/ux/V2htk8jNueTz63t/DRvBZFJAkQUymYDNvFzxCbVEifYmSexoYfqVYVJ725h9bXNjwQcPqrz5IQ1dF6hUPP7qswF3Xai5AzXZhJmfJaBXWr1XSVbC9PW/FVVdUrjwPJfxsUOMjr6w6P1dDdc1yWYvUi5PYlklenofXFwVRyJtdHXfS6Uyfd3KUdMskMsNEIt1L4YVVS1GKrWdqamj63wTwgpRYtuukstfwXWvX/igKBHa228nElnyVhynxvDwc0xPHVuxAFiA51nkcpepVKawnSpdXfct5iK1UILe3oc4e/ZzVxlPgaTaTkrrQkBgsnqRmlukK7IHTQxj+yaztUEAmkO9jFXOEpaT6FKMnDVJUu0gWmnlzFd98uYEeWuKuNLC1liwkFjYdj2UzQznJ79Nd+q2FdRpwjxLTFO0j1SkF8MqUjJnyJaHmS1f3lAODYL2iJsNAQmB1ce5IoXrwuQ36w6uNrT/hNWh6/DPfijM//1TUdLpwHl45jmDX//tIhcubrx371owDJ/JKRfD9NFDwTce1gW6OqUgirKB+hhFCRrwl8P34fyFlYswK1PGypTR2hOE2pObViHaCA7eoTCX8Ri84qCq8MQ7Q3zzyXUUCTa6Y1NaRAsJREsivg96yAP8gCHm/AxKREXWFQRR2DTKNIAPvl/nL/6qwuNv0Rkb84hF52moHIdY3z7M7HTg1RQyuKt4g22tB4jFOuu2ZTLnGBs7dE0DuByWVWJk5Dki0XaampZ6t5qadpNMnmR29gzX8gZd16JYHMWySmhaHABZDpFK72Bm5uS6dMrC4RaisU6WJ2lKpXGqldlrXn8B0Wg7zS17Fw2Y73vMzJxkcuIIXgOVg5ZVZnLiCLFYF8lk3/xWgXiih2Sqn+zcEpuJIqj0RPYzXD6O69tYXiBEmjPHERBp07cRVZownTJJrZOxylk0KUxUSVOwpumN7me8eo6KncP2DGRBoyOym8nKeURBplXfRtUpYHnX99zKZobLMy+QKQ/Qk76DpmhfXeUmBBWPYS2FriZJR7bQmdrPVOEs47mT6/YMZbEx8vibBUlUlhWx/BM2Hz6uY1ItzVItTlEpXj/fe+t+lY98KEw6HVRbz2Vdfvv3Spw9tzk9dKvhyoBDNuvS1RmMc0kS6O2RaG0RmZpevxUMh0VuPVA/rgtFj7Pnl4xg2ztvJby1ZTFEpyTDZF+98oYawZ5eGd93GBl20UICDz6s3RwjODPrIQgwNhbo82XmPKpTJcxcMFl0PNTPxHevbKoBhMC1P3/B4eCtLrMZF2W+9kBUVPLnjlAevoiPv6qorCRptLYdqKuAtKwK01NHMc18w/fgOAYDV75FOr190RuUJIWu7nvIZM7jX6dQoFyaoFKZWQxDgkAk0kok0kap1DjHXiq9bdk5wPddioVhDOP6+VdJUmlu2YuiLHlDrmsxMvx8QwZwAdVqhmz2MonElsX8oKJESKd31hlBdT6XVbSX1MwlQaE3ehsCEFWaqDoFAubOBQiL/7pSfJXuyC20hLYwUg6EU+NKC3o8DvgYbmW+AGVtI+V4JpnyINnKCMlwF73pO2mOBdyTAkvCpQtcmfFQOzGthfb4Hs5PPU2+GtBANYRVcl+5ygjOJuoDloypaxalCAgrKDkqZoaqVWCzEjS2W1tXu4VtVpgdP7nh6/m+i2sbOFYFyyxjmxWiyS66tj+8oqhr/m9BgZgfsMhYZhnbquDYxjzRvhuIyEoSkqShaFFULYokB3qEAuK8RunyF+ljW1UuHv0c5fwYrmPNX+PaRiVQklDZuUNeTD2cPmNzag3dvBvF6TM2k1MenR3+/GIddmyX2b5NZmp6/ePwoQdUmtL1g+qFF806NQtrpkTu1Ss45WD+Dfe3rBqVc72V3qgw3wGwUWgheOe7dX7kRyPIClQrPpIk8NUvrY+8v2EjODvrsWunvMhKcOWKw4WLDkpMI9KVoDJZpDq9idp78zh6zML3IZkU2blT4TN/GXh7rllDb+0m0tWP7znMnTyElasvukgkeglpybptxeIo5fL1w2mroVqdIZe9TFPzUjNyMtmPrqepVmeueZxh5CgVx0gktsz3Igroepp4opdSaYJGJqggP7kVWV4iOTZqeQqF0TWrQmU5RFPT7roPO5u9tKbxvBq+71KtzGBZ5UWvVhAkwnozihJe7KG0vCqiKBJV0jiejeXViCutSILMQPEw3dFb8AHbt5AFjZAUQ5fjKPP9bY5nM1Q6RkJtp03fxkj5BGVnjrHyaWzPwsfDdNeTk/LnibCHyVaGCWtNdCX30xzdRkiJo0gLE+BSFWxc7+DOrR/h3MS3mCicWbMdBFjVq7849Qz52s0jE667vu8ExmDZnDWZPzvP+bnJsbcGUSvPcOHIX665nxLXkHUVq2jg1lY3FKKk0NS5n/Ytd9dt930Px6phmSWKc4MUs4OU8xMY1eyaFdwACCJqKE403kG8qY9EyzZCegpZ0RHEoBdMVnS6tj3E4JmvUylOril7pocEOjvqibRrho8osulh0OUYGnY5etRi/z5lUUmib6vMg/drHD1uryvXGIkI/OgnovXEE7bP3/xtvYHJHZ5vGRNAVGRqQ5lV+ypMw8e06rdrIYFYdOPRC9OAL/xdjbmMx/S0y8ULDvNroHWhYSN45qyNbftEowKeByMjLlpSp/utOzFzNQRZpPn2Li595sb64K7GH/1JMOH9wR+WSacExieCUWSXCpRHLiFpGo5RxamsbMuIRjuRl7VD+L5HpTK9oaIU3/eZzZytM4Ig0NS087pGEAKj09Z2K5IeFE/Icoh4vJsZJdwQG0002kEk0rrsOXwq1VlK5bUnWF1vRtfrizZy2dV7zdaCZZWx7eoyIyigqBG0UHLRCDqeyWj5DJ36HnQhwmDlGCU7g+VWaAvvwPFMqk4ewy0xZ47SHdmL49sUrGl8fNr17Uiigus7zNQGsT2T8fJZWvR+BEQMp8hU9fKqxSGNoGrOcWn6WUbmDtMU7acltn1R6Ha5Zygis6v9MRzPYrp4fs3zrhY+VVbR77tZCFQxri6RV4Mc3j+QEWwU6f0ddDyyg8lnLjP90uCKn4uSSmv3QXr3PI6s6ixYetuqUswOkRk/QXb6HK69AWYg38Oq5cnW8mSnzyFJGqnWnTR330ayZTuSHEIQRBLN2+nf/26Gzn6TUm7kuobQcVmh/Xf3HSpvf1uIo8ds5rIulrUpFMsr8HdfqPK2x0P09kjz1dwC7/8BnVcPWzz3gtlQbjAUCkhKDt5a3/LzyqsWh4+s9CgFRSK+rwsprOIaNtWhzIrFTKUaFAAtJxdvTkv0bZV5+RXrht7FmdM2kgSx2HzFuwPlcuMnbNgIvu3xEKmkwEMPahw7YbNrv8xffTeM53iUR/IIkoAaD920/pB83qOrQ6ajXWJo2CXU3I7W1EptagS9rQenVsFcxh8qCBK6nkaSlmLajmNg1LINrexXwqdUHMPz3LpG9WSyj9HR6zcQl0pjVKqzaKHEYvFCPN6DrjetaQQFQZyndlvSanRdi0JhCNta24AmElu4Ok5WLm+MZ9V1Tbyryu0lSUNZppfn45M1xyibGbrEbTiegYPJpeIrK843Ul4ZKhssrywYKtqzFO3Ayw8TIyGkyfrXJ0ZYC6ZTYSJ/iuniBVLhHlrjO2mN7ahjaZGlEL1Nd1KoTWKswThTXaWgJqwmb+ge1wPDKeP5LhJLE5cmR4MFxSaGZFeDEtNI39qJ73ioqTDTLw7gVG1St7QT6UxgFQ3mjo/jVC3aHuhDjYdwqhZzxyew8jVmXh1Bb129sEgQRFKtO+na8TCqFmVhgrGMEpODh5geOYxlbF4EynVNMpOnKOaGadtyN13bHpo3hALxdB+9u97CwKmvUCtfL/rjMzDoUCx5xGNB6qClReTXfzXJ4dctxscdqjV/pUHyA+7RWs0nm3UZGXW5eMkhuw4twBOnbP76c1V+4Wdjy0hGFH7hZ2O4rs8LL13f4MTjAu9/b5if/mS0LlQ5O+vyv/+svKo3qXeliO7uoHRuEr0rhSCK5LP1i5lKxWd41MUwQZ8PaCWTAo89GuK5F8wNK10A9PRKHLxDRRAg3SRy4bzNV75wE3KCyaTIV75Wo7lZ4jN/UeUDn2gh1BJMGNEtKQQRajOlTTOAtx5Quev2UN22g7epPPu8ydBwIEQrSDKuFWSXVvRoKTqKGqnb7jjGhrzABdhODcsqEQolF7dFou1rHhf0DJ4jmexDkhb4BNPEYp2USitJiZdDUSLzodRlxtyuMpdZ2zsBgegq99ff/zacNSpKV4Msa+h6U902UZQRpdWLQiRkOsV+wKfmlxn3B9CJ0Cx2oRKi7OeZ9kcIE6VV7EFGpUKRSW+QEGGaxU7wISSEGfYuoKLRI+4kJIRJ+M2Me5exuDEWGNezyJSvUKiNU6xN0d9yH/q88RIEgYiaJhXuZrJwfSNYMlYa5WS4i+G5wzd0f42iamVXhD3Dagr5DWhpUKIabfdtZfrQEJXRHJ7tonfEaLt/K1MvDtB8Rw9OzSZ7YhxjtoJdMGi+qycwjkevH83Qwik6+u4nFG5iYTHn2CYTgy8zOfAy7jqoAtcDyygyceVFfM9jy563AcF4SDZvo2v7Q1w5+eVrhls9D15+xeSll00ef0sISQqYYDraJd7zzmsLLPt+QHVm21CueGSzHsMjLt9+2uBv/766og/wWtf+00+XuWWfwrvfsXStO+/Q+I1fS/LkNwy+8mSNCxdslquc6TrcdYfG+96r87a3hGhuERfnTsMMwqAvvmyymgKVZ7uIaiBtJ8gioc4k0d0dVC5P1yl3vHbY4oc+4NE133YhigJvfpPGv/uFOH/yqTInTq2ue6hp0NwkYVo+mczKGxgfc3EcC0GA5haR2+9UgZtgBGdnXVwn+EX9zL+MMputMvVCfQXQZhbFPHi/Rj6nMJddeuhEXFxc3RiZKSQ9SrR3J+bcFGauntNSlrQ6ORkI8jbXagdoBL7nYluVOiMoyzqSpOG615+MZ2ZOsbXvscXGe1GUSKd3MDNz6rreoBZKkEhuWboH36NYHKNSuX4Idun45IptqfT2ho5tBKIo1bVtLIeCyqw/RtkvskO6jaw7Q1psxfCrzPhj7JBupehmMagx6Q0hILBXuocphpBQiApJJv0hZr1xHCxcHPL+DBESTHgD2Gyeh2O7BhP5U0S0FL3pOxefSZF1IlrTGkdDrhrkZ5e3KaQivYiCgufffE3FkjGN61r40lL4P6o1o8kRqtbN79uyigb5c9OYc0FYPL6tmeY7e1GTOkosRHW8gBRSaH+oH1ERiW9vpnj5+jy0giiRbttDvKmvLhVQyo/eVAO4ANcxmBx8iabOfcSSPYv31NJ9O7NjJyhkLl/z2LFxl1/7jUB89vG3hJDltXNfggCyLCDLoOsSLc0SO7bL3HO3yuNvCfHT/zrXkFeYy/v80q8WaG4Sufduta5I5id/PMqHPhBmZtZlcsqlUvFJp0Q62iWam0USCRFVEeZ7sn1cF779dI0/+0yF4ipk2gBu1QraI1rj+LaLj0+oPUF1cLbOCL50yODMWYv29hDSfL40FhV577t1HnpAY2zMZXTMwbQgpAUCxU1NIom4iOf5/NlnKvzxKpygM9MeM/PVr9GYwMOPaCv2uR4aNoKf/VwNQYDf/f0Se/YonD1j42ygqbNRHD9p8eKLFWaWSYE88maN3Pwg8D2PyuhlKmNXAlHdq2iMRFFZZGpZgO+562pLWAm/LrS00C4hy/qaRtC2K2Rmz9LRedfitoXG+WsZQUGQSCa3oqrxpTvwPaanj9OYyy2gLqsKvVm41udtUqPmVzCp4vo2mqATJUlUTGDNGzAFFU3QaRd68fCJCPHFUn/Tr1Hxizjz+/q42Fg42JgNVIeuF57vkK+O057YS0icz3siIksh1orzm3aZfHWCVGRJRUKVwrQndjORP7Xp93o1HNckWxmha1kIVhJVWuM7KdQmG6Y62yh818NbNuFVxgvkzkxy8U9fBUHAqVqk9rajxDUu/tlr9H3wNpivaJV1BVGVkDQZQRHx7eA8shyitffOuvSD77mMX3p2hQGU4jES736c6tFTODMZ5JYmxLBO7dxFovfcQeXoSWIP3YvvONhTM9TOXkDfvQO5rQU5maT80ivYkysXlq5jMnbxGXbf9bFFQyxJCt3b30Qhc4XVxkRIE9i9W+aHPxLh7rvUxYU7LOQBrzWOhBVFxqIokIgLPPaIxp/8YZof+8ks+cLahnBkxOXf/Hye//IrCd70oIYsM1+UB50hic4OkVv3L82PARlJfcWtYcB3nzX4T79SZGz82tEqz3LIPHcez7SRIvMFbiUjYOBehloNfvnXiuzaqdDTIyHOX09TBdrbRNrbRO64Xalj61u4JdOEaHT1UtK3vE3jsbcGUcN4QuTUifUtjhs2gvGYwG23KSQSIt9+yqC5WaJQvHkJ9yNHLabnDeBC7PzZ581Fdznc3ouohZBDYaK9O8kcfa5OU1BYoJhZBh8PrxHurWsgILZduaq/lid0NaamjtLeccdiw7Eka7S07KNcnoZV+CBlWaO5eW/d4DSMAtlsYyrjghDwN179DI5T27SsvOPUrvlO/fn/Fvf1bUpCjoI3R8kPqlNNarSxhZKfp+BnSIttK85y9TkVVCLEqVJelUfzRuCvdkXPWbH1aniew1TxDIlwxzI9Q4He9J1kK0MY9o3z6a6FicIp2pN7kIUgPC0IAh2JW5gqnKVQu3l6m67tUpsp1636K8NZcqen2PmJe0DwGfz8SYpXMnQ+uoP+D92GIArUZkqIisTW9+8n0pMi0pvCLptkjo3h2x6RRAfRRH2Pr2kUyWdWahh6NQM3m0dKxrEnp4NZVBSD5ht5vj/WtqmeOIMzPYsY1lF6OhFDIRAEpHgce2pm1V9zKTeKbZVRtaW8Zby5n1CkCaNS7832dEv86CcifPyHI8TjwaRdrXrMzLqUyz6G6QcCuVddRxRBkgUUORAriMdF0ilxUVZJkgTuukPhEx8L8wd/WG6owOXyFYf/59/m+al/EeVtbw3R1SkhSUtUgtditKvVfCYmXT7/pSp/9CdlSqXrj31JV2l/122UzowT6klRvjhF6fTqYe6Llxx+8mdy/Oovxdm1UyEaEesM8LVZ9q59D8eP2owMOfgEbRJTk+ubExo2gm9/IkRrq8httykcP27zgz+o83u/v3az+UZx1x0a1YrI3t0KzzwXeFnLKQtFRSXU3IFnGpSGL3K1P7LADVoP4QZZNQQEVjJmNEJeDUFze7k8sYxGDVpa9zM6+uKqMkjhcAvxeL0+3fT0sXV5s1fbOt93GRx4+gY94iV4nr1qoY2LS8nP4WDj45P3Z7EwmPOmaBLbaRY68fAY9y5T9OdIi23EhSYmvUF8fBwsSn4O7yojV/GLhIU4TWIHpje46CVC0Pzu+/6GDaMgiES0JhRxKRfteg6mu3bfkY9HpjRIZ3KGhN4xfz6BaKiFvub7GZh9KSCl3sh9zfevrVXQla+MMlcerKNK05QIO9oe5fzktyibjclgrXZ9QRCv6U2amQojX6nXLPRsj/FvX2D82xfqtp/63WdXHH/pz4+set5ky84V20q5odUrM2UJ4/IgWl8v6pZu3GwedUsP6pZuxFhQu+C7S/Xzvu3gTEyDouBmc1ijE9ecZz3XopyfIN229F4FQSLZsoOpZUaws0PiV385wbvevsBPDBcv2XztGwbffdbgyhWHXN67ZpvEAldnc7PInl0Kb38ixBNv1UkmgzkrHBZ5y2MhPvf3VSanGhvjY+Muv/JfCjz1HYMnHg+xd49CZ4dEKiUS1gNOXtuBcjnIQY6OuZw4afP1b9Y4dqKxML5drFEdytD08G6qw7MY49dvvzp8xOLHP5njA+8Pc89dKr09Es3NEmFdQFECp8c0fUzTp1Tyycx5gXrQ+dXvJ5kSyeeDkKiiwt33qRx6sXFvsGEj2NIi8dWv1dizR1mcWEVNQtZV7KKx6U3ytx1QyMwqfPQjES5ddhavmc151Go+tZkxRE3Hys6AwAruUN93V3gogiDWhVbWC0EQEKWVMlFrhUIX4HkuU5NH64xgONxCPLGF7NyFq/YWaG27tS6k6zgGM9ONNx8Hnmv9YBAEkZmZE+vSRtwIXGwy/hKrxoS/VC024dVXjpXIUfLqPxyTGqa/MuRpUmPMW73FI653kI5soWzOkquMrov1RUAkFe6lLb4Ladk7t9wqJaOx/KthFxjPnUBXkqjzFbOSKNOR3IcgiIxmX6dszK6pDr9wRyElTircRTTUylTh7Jr34eMzMPsysVBbXWVqOtLL7va3MDR3mGxluOHQqCLpJPQOUuEestUR5sor2xduJqLJ7hXbaqXZVfacT01IEubwGM5MBq9SQUzEwfepnbmAbxgYFy/jloKFiG/bmIMjKF3tCKpyPRcE3/ewr6pAFQSBaLJr8d+iCD/zU1He/vjSAuryFZtf/60i33zKaEjk1fOgWPIpllwGBl2OHreJRkTe8UQIUQw8t9YWiZ07FCanGi8IM0145jmTF14y6e+T6emWaGoSiYTFeSPoUyr6zGZchoaDXOF6+hnlqIZn2Yz+5Uvo3WnkeBg7d/35ZXTM5Q/+V4n2NomtWyTaWiUikUByyvOCUKxh+hSLHtMzLjMz3mIq7GrcdlBhcMBlZtpCVQWeeEfo5hjBCxdtPvbRMLt2yvzYj4Z5+ZCFIIm03NlNdGua3OlJMkfH8azNCZEeO2Fx/30au3cq/NS/WOL9/OzfVTl5ysY1qhQvnwyWW6K4wuVxXXMFp6YoykjS+pKmyyEIIvLydgDfx/PcdRTb+ORyVzCMfF1xTUfHHSuMoCSptLTUK0bkspcxjPy67tkyy4TDLfXPsKy5/R8TdCVBd+o2EARMu0jJmCFXGaVkTFO1cqs2jQuIxPQ2WuM7aYvvIqym6/pKS7VpCtXGGt4932W6cJ54qI3O5BLPqyKF6EzeQjLcxVx5kGxlmGJtGtut4fkOoiAhCgqaEiWsJolozST0DnQ1SUiOgSCQrTRGTF+qTTMw8yK7Oh5DkYKxKggC6ehWwlqaXHWMTGmAQm0C0ynjelYQ3xAVFCmMriYIq2kSejuRUAshOYoqRzGnyszxRhpBAT2ysiDpWm1BXrWGcf5SXerWOH2u/tjReqozt1DELaxdLe77/qK6yvL7C4WX2pb27VV44q2hxbYC1/X58ldrfOdZc10q58sxOeVy5KjFww9piz1wkbBAc9PGolmOE4QjL17a3PywUzIonBxDSeoUTowgiNdeUCyH6wbyeAu93xuF70N0XmkmERdo8PKLaNgIfvcZk8FBh2PHbS5csBkYdHFtmHpxEOn1cXrfvovt/+x2Rr95nslnruBcg/mhUbz6msU3v1UinRT5rd8rLtq4uj6VhY2rBMhtu4ZjV+uaM2VZqyPSXi8EUSYUStRtM4xcw+FQCLhIs9mLdHTctXhfqVQ/mpbANJfK8FPp7ajqUlGL7/tMz1xfvmk1VGtzJFN9dduikXZqDSrEf79BFCQ0JUpIjhHXO+hI7MPzXVzfwbRLOK4RFDcJIqoUJqTGkQQ1kCkSpLoqRMupMJB5eV2afJZb5crsS6hyNKBom2/fkUSFqNZCRE3Tlbp1UbtxUbh2kbVGnDeKAVuJIAhYTuMLFh+PqeJ5VDlCf8v9SOJCdaBISEnQnojTGtuB57uBhuYy4dwlDcngHoIw6D8cH6msXLud4Jq4KbV6q6VW6u/vzjtUEoml9zWb8Th12qbWQFvD9ZDLeZiWz0I2UlYCdYjvJUi6wpYffwTfcpj51ilEXaV4YvPVZa6F149YfPJnonz8/4qgavA/fm99aYeGjWAiITA07HLx0pLVFhWR9P4O2u7bQnWyyLFfe5rEzhaSe9vIvD62rhu5Go4TxIN/57+V1i2NAUGuyjDzeJ6DNB/ClKQQmpZY48hrQ9fTKypOS6X1PafjmGTnLtHWdts8o4eAJKq0tOxlbOzQ4n5trQdYnueslKcolyZZ31fuUy6NAXfWbU0mtzI7e3pd9/39hkD/TkKUFkjD/cCrWnVn6sinPd/DdEqcGv0axQ0UlBh2kdNjX2V351tpie1EXjREAS2bOP/ZLYRFr0d8vZDb9tdRyOR6FsNzh/F8ly1NdxFSYiwYVAEBcT4a0sj1F+5hswuQGsIqqQtF2/gidqMQBBFJWcn+s7y+oLNDRFGWk9t7FIo3/s7i8aBlYQGOE4QJv5cQ3tpC7tUrhHubEDUZUZHqde9WgaaLuK6PcxWVWigiYlSC96aGRLSwSCnrrNjHtnzceRHhoQGX//SLBdraJeYyLpV1tsU27Fd/5MNhurqWBqUgCWjpML7nc/mvjzHy5LlAUeLcNOVNENVdwMTkxl3lcmkSZ9kqWpIUwpFWlFUG9NoQaErvXLEyzmWv3Su0Onyq1dl53tD5M4syTU27FsNnqhojnljeG+iTzV1qSPXiahQKI7hXtY+k0jvqmu//scBwSpSMaSynssJoLBmhVf7MGwHPd6lZBaaL5zk+/Hly1Y1rY9qewemxr3Fx6jvkq2NYTnXlPSFc0wD5vo/tmlTMOTLlAWprMNZcDc93GJ57jTMTX2e2dAXTKa2IWKx1fdezqVp5spVBSsbqubibidVo0GKr5AlvNgRRJhK7umo5WNAuQBLrqy0lKaAsuxFEIgI7tst1wrjlsl/XNva9gOrgLOGtzSjpCPrWZpyyscIACgLoMYloUkaSBe5+IsWeu2OE49JiCDnZonDnW5dCzFv2hPmx/9LHwlpDkgUiCYlHPtRC764lLzwWF7j7PpWH3qzx5reEaG1bX7i4YU9wctKjq0uiVPLxXB8pFUXta0dL6+itERAEXMNh4rvrNQo3D8XiKKZZRFXji8YrHutC15ux7fW564oSprllT902yypRKKx/oqwZOYqFEeLxHkQxCMGF9OZFZYl0egfyvIgvBD2GhfxwwwU4y2GYBYrFEVKpJXlXXU/T3Lx3vt/wexuxlIQkCRTmnOstLIFAteGsXSQd7iUWakVXk2hyFEXSkaUQkighzIf5FtplXM/CcioYTomykSFfHSNbGV5XCPRa8PEZyx0nUx6kOdpHItxJWE3PE3eH5uWPRHzfw/NdHM/EdmuYdpmaXaBsZihUJygak+sKuS/HXHmQQnWCpuhWkuEeolozITWOKoWRJRUBKWhk8R1cz8ZyjeB92EUq5hzF2hT56ljD70OJpxBkJShYu0GYtTxqqN57j6V6UfUkVi1/w+dvFHqkmXD8auYlH9tYWphkMi624xOaX1S0NIts6Q1aEjZCmi1L8OB9Gvffqy422vu+z8yMy5WBm0++sB44JYOpLx9D70lh5aqYU/n6HQTo2q7TvUPHtnxOv1RAUUW27A0TSysMnqowOWjQ0R+qW0hcOlamVgpeniTD1n1hmjtU2reGuPD6kkNw590q/dtkBq44tHdIvO+DOn/8Pxt3Bxs2gooC7323zoP3e3iez8CEyAuDFqHmCNWJIggC6QMda7rBbyRMs0B27hLRaAcLEkh6uImmpp1UKlNritEuR0fHnYRC6bptM9MnN1Rg4rkWheIIrdaBxQIZVQmTSGylVJogld62GMKFBTmmjXFluo7JzMwpEomti5WxgiDR1X0vxeIotdobp/u1XqiawOMfbkbVBT7/h9NYxtrjqmblGbfyCIKIJkfnJ3sNSVTnc36BjFIw8bu4noPt1rCcKqZTvilN5YZdYCx3nKnCOUJKHFUO7kkU5KvuxcZ2jcAQOmXcVXpSNwLHM5kuXmC2dBlNiaPJkXojPC9B5PkOtmvOv49KQ9W1kqYjyAqiqmEXc6jpFuRoAkFRsAs5PKOKqGooiTR2MYdnmUiajhjSwfexi3nwPeRoPDhHIYvvBrSIleIksVRP3fVkNUxH330Mn/3GprybtSAIIp3bHkCS6yMnvu9TKS59k6fOBPm/+W4MolGR9/9AmCNHLc6dX3sBtxyKAk88rvMzn4zS37c0RRuGz0uHzA1pA95sOKUapbOrjxdNF+ndFaZSdJgcNHAdH9+HQiagIGvp0ZgZNZkZMbn7bWle+OLKeoVIXKZ7u87kkIFRrX/+dJPIiWMWR16zicYEfuHfr0/gumEj+O2nTF5+ecloVKo+Jd8msaMFJaqBLCLr6veMAVzA5ORh2toPEgol5+nKZDo676ZQHJ3XwVv7flOpbXT33F+3zTSLTE0d2/AKvVgYpVbLLhpBSQ4RjXUSibYSDreyEKn2PIdicXzd0kcL8H2XfG6AYnGYZLIfCEKDsVgXW7Y+ysDAt7A2yKcqisqq5AGbhaYOhf33RzFrHqK0PmZ23/cw7CKGvXGuWEGSadv5IFokxeTZZ7Cq+Q2fCwJjVDZnwQykgZr77yKc7GDy7DOYlZtPbRaEe3PUrM1LV4S3bCfU2oldKuCUi/ieS7h3G55ZI7ZtL7njh4ht34dnmST23cHcq88Q33Urgiwj6WHyp4+A55HYdydOuYCTbKY8eB58n0Lm8grpJBBo7T5IOTfC3OSZVe9pM9G25W7S7XtX/sD3KS4jiT7yusXlyw4tzeIiTdk9d6n8999N8b//tMzXnjTW5P5saxW55y6VD30gzMHbVFqal+SYfN/nyqDDX/x1taFG+dgjd6H1d+GZFuWXjmMNThB98CCh3Vtxy1Uqh04ipWKoPe2o3a2Yw1OIukbp6VeR29JE79kPskzt5EWqr59D27WFyN23IKgKtRMXqZ2+QvjOPWhbOhB0DXeuQP6LzyA1JYg+cBtKaxprZIriU4eCsLAIhYzNzKiJKIJje8yMWciKgKwICCKUC841c9+iLOD5kJ+xqeSduqlgasLlbe/Q2XdApbdX4vw6hYsbNoKG4ZOfb/SUZQiHBcxilbGnLtJ0sBPPdrny2WPruPQySdVVKtCCpHO9uOVGYBh5hgafZtfu9y8ytahqlN27f5Dz5/6OXG7gmk3IAW1ZHzt2vrtO0NbzPMbHXqFa3XiexLKKFArDxOPdiwUyup6ipeUWNG3hWj6mWSRfGNywsYVAEHdy8ijhcBvKvLSUIEi0tR1AljUGB56mWp2dv8Zq73mBoFxAkhQSiS20d9wOCJw5/dcbvq+10Naj0dqtMnrp5nJEXgsCAooWRglFb5BkYbWTCyihCGokOa9b9/0JUVaoTY3hlPKoTa34rkttbIDq6ABKshm9vQfPMikPXUDSw+gdPfiuTW16FC3dhqxHCbV1obW0oSSSWNlZquODeKZBITOIZZTqQqKCIKCGEmzZ8wSe55CbubSmvt9GIAgSrT23s2XP21YUw0EQqi3ODS3+u2bAL/9agc9/tploNDCCiiJw636F3/vNFL/8HzxOnLYZHHTI5Twcx0fTBCJRkc52if5+ie5OmZAuoKoscmvCvIzbrMfP/kKh4XaCyD23kP3ct3FmsniGidLdhr5/G5k/+yrqlg70/dvxbQc3X8J0PQRRwJnNom3vQd/XT/Hp1/Bdl8Q7H8LNlQjv3075+aM4mTzpH347Tq6I2tWKcXGE2qlLtHzygygdzWj9Xbj5EqXvHib98Xeinu+gOjJJMWPz0PuaeeC9Pl/9kylcx8dzfDwRPBHiaZmH3tdM7+4wdz2e4tLxMgceStDZH+Lxj7bx0lcyWIbH4x9rI9micPqlpcXtKy9bZDIeu/cqnDxucer4+hbmDRvBf/HjEb78lRpXBlw62iU+/EM6v/17ZTzHw8zW8Gx3VRJJUVTQtPjixCuKKrKsIsshZDmEJIdIpvpXNLGn0jsQRAnXMXFdE8cxcOb/7vsevu/humZDxSJTU8eIJ3rp6LhjkcBaVaPsP/BxpqeOMz1zEqOWXWw/EEQJTY3R2nqA1rZb6wppPM9lbu4809MnbtgLmsucp739dkQxENvVtARNTbsXVeB9H2q1OYqFGy039pmdOU0k0kZX592I80YXJJqb95JM9jE3d565uUtUKzPzz+UDIpKkEtLTRMItxGJdJJJbFnsls9fSJRQgnpRINCnMTlgYVQ9ZEYilJEJhCVECzwWj6lLKuTj2kuGNxCVCYRFNF9l5MEJTm8rshEX3thBG1asbYlOjJvZNrJTzXJvR41+/Oed2LMZPPXVTzv2GQhCQw1F82wq4Ij0Pz7bxvYCdxXNspFAYORxF0kIYRg1Jj+DbTrDoEgWcSpHSpTNUhy/jWSaePc8QZVeZHTtO57YH6hYhgiCgR1vZftsHmbjyAnMTp7HMIp5741EJUVLRwknat9xNW+/dSMty8wvwfY+poVdWLJ6PHrf5N7+Q51f+3wRtbSLyvHpEKASaJvKWRzRYg9z5av7OatXn3HmHf/ef8hxbx+Q+95dPEn3odsCn/NxR5HQcJ5PHN0y8cpDCEXQNd2ou6LP2PHzXRUrGkNuaSL7vETzLxsuXEKM6nuXgVWp4NRM3X0JOJXCLFdxcEd+w8Mo1xGgYuS1NaNdWtF1b8G0H5ouDTr9c5OyrJXwvCIW+/NWVkY8v/9EkX/6jpWrs5z+f4fnPL4VGX/16lsPfzK7whJMpkbFRlwvr9AAX0LARtB0WS4BVNRjvajxEx5v7MWYrKFGV3nfs4fLfHKtzJhKJLezZ+0EkSZtXVm9sRd3SspeWlpVhiAX+Ttcxmc2c5eKFLzVwNp/BgaeRRIXmln2LRSeCINPReSdt7bfNC8YGVYWKEkZV40hX8W56nks+P8jI8PMYxo2Hr0qlMSqV6UWRWk1LoGmJxQWB59lk5y5tqCDmariuycjw84iiTHv7QSRp6eNWlDDt7bfT3n774vv1fX9+wbB6r9j1SvYVReDxjzTzkZ/t4Ld+apCzr5W55/EE978zRd8enVBYpFp2GTpX4zO/McHwhSVP7/2fbGX37VHat6jE0zKiKHDLPTF+8wsr4/w/954LDF4jD3EjEGWNeNu2Rd5V2yhTyY7jOfW/B0kJEWnqwSjO4Ps+eqINSdbwXBujlMEsZ1n+McihKNF0D6K85Fm4jkVlbgTHXMoti7JKJNWN59pUcuN1nk441YUWTZEbO7u4XZQ1ok09GOU57FqJcKoDJRSMKatWoJqbAN9DCcWINPdSzY6vCO1Kqk4k3YNjValm19H2I4CabkEQRWqTo/iei6hq+J6LlZvFzEwhSjLhnm04tSrGzASCJOFaNexiDrdawZiZILX/bqI79mFMjmJMB9f3XJvZ8eOk2nYRjrXWX1YQ0EJxtu59By1dtzI3eZpSbgzLKGJbZRy7ht8AT7AgyoHsmhaIQ8fSW2jq2Ec41nrNuapSmGBmdHXx8K8+WSOX8/jnH49w+0GV1tbAGK6n39LzfOayHlcGHJ593uRvPldhfGJ93q6UiFE7cZHQri0oHc2Yg+OE79hDaG8/clMCr2asypLj5ouYl0YxRybxqwaeYeHM5lC6WtG29yCXqwghFXtqFrm5vt3Mt2zMgQm8soE1MgWiEPx/4bncG1+wrhYKfvgRjaFBh6NHNrYIatgIXrpk8653hrj/Po90WuTSZQdJk3FrDuWhLIIkEu5YOVFJkro4yW8GArVkFVGU16WQYNtlBgaewrJKtLffjqJGFwemKMqEQsk6Fper4Xk2MzOnGRt9ed29gdeC73vMTB8nldo27yUvY8v3fWy7SiZzdlOuBcE7GBx4ilptjs6Ou4hEW7nafV94vw3cfUPGua1XY9ftYR55fxNTIybnjpQRJYGmdoUtu3TKV5GwFzIOZ4+UOfc6bLslzIH7Y0wOmRz+bqHOYwTIZ26OMoIkKyQ7d6OGk4RTnRilDEOvfR7jKsouLZKi57Z3Upy6jBpOoEaSiKKErEUwSrOMHv96YIDmIWsR4h07UfU4oqSgRdOIssaVl/+K8uzQ4n5KKE7X/rdg1YoMHf4CnrOUi2/dfg/N/Xdx9PO/vGiU1XCcntveSWbwdQRRItGxC1FWkNUw5blhho98Cc+x0KJpeg++i9nLrzJ14cU6PbxYy1a23Pk+pi+8SDU7TqPpB99xqQycpzKyUlGhfCUYu+XB83UFc9XRAQCcYn5x3+zrL4AgrghtVkvTTA2/Ss/OR1HUld97QF/WTSTRhWNVqFUymNUcllHCtqu4tonnWgFbkB8QAwiihCipyIqGrERQQzG0cAo92rzqNZbDrBUYvfhdbGv1gjjXhedeMDl73ubeu1Ruu1Vl106Zzk6Z1haRSERA0wK5JM8D2/KpVH0KRZ9MxmVyymNg0ObiRYdjJ2yGhp0NVZdKyRgCPtbIFOblUdxCmfLzR1E6mvEMC+vsAIIewrftwJvzAd/DNyyskWm0bd2gawjFCtbwZJAX7OtESsYoP38MZzqLoY3gZoOwZOXIGZxsEWc2B56H1BS/ScQFKzE769HcLNLeIWLb4Ng+hcJNUJZ/4QWLuaxHU1rk7Dmb02ccDMOiPJIjvb8Dz/WYPjTyhj34RmCaeYaHn6dQHKWj4w7S6Z1rcol6nkuxOMLkxBGy2ctY1uapWANkMufpu0qodwH53CC12uYWTDhOjfGxVygWRmlu2Utr635CoVTDK1XXtSiVJpibOz9fWHR9PPbBNIoq8he/NcHlk1WqJRdBFIjEJWJJibmp+tXb1z+TASGI0Lz9o80cuD/GxKDBF/5wmtpVVWFXG8XNgm1WGDv5LWQtQtf+t6KErl9t1rLtLrIjJxg5+gK+5xBr7adz76O07XyQwVf/dnE/szjLxOmnEEQZUVJo2nobLdvuXeWMG3uu5r47qBWnmTjzNI5VQ5LVoOrTDYydUcpQnh0i0bmbzNAx7FpQ4i8IItGmXgRBJD95YV3Xr4xeme8BWOOYRgrmVsntea7F7NgxQuE0bb13rajSXIAgCChaNGimT28NjvVcPNdeZOfB//+x99/xkWX5eR/8PTdVrkIBhYxG6pwm9OS8M5vzLpdcZiqQpmxZtmW/ryVLsl9Lll5F27Kl15REkS9NLrXkMu1yw2ya3ZnZyaFnOucG0MhAoXK6+fiPiwYajZx6Zsl95oNBo+reU6eq7j2/80vPIwNiBKEEpAWqhiKUNXlDb4Vj15m8/hKF2fUL6rJZn288a/Lc8xaZlkCnLx4XhIzAAKqKwJ/X67NtScOU1GqSUjkgsrbXKVx/6lNJHv9YAj20dO7n3qrzzd8vUHvl1LJzrOvjWNdv3cCv3nvqZpcWT7mzedzZpWuRfctYjXcXaR8bZ1ZJk6yAwcMhPvmLaVraNf7st/KceWPz1fbhsODpD4W478EQvieZnPT40u9sfJwNG8FaXfLWWw6qGuxgbupila5kQUo8y6N8fXlpaz5/lddf+1erjLrRir9bj1v892ZaHG7CdevMzV2kVBwmEmkl03qYVKqPSKQFfZ4GyXUaNMw85fIYudwVatXp+VCpT0ePxhd/vZmj94WpV3z+x1+fpLYNZgjXbdA28AccfyDO//b3boYOgvcYKEsE71XV4L/5x21870/LnHt7e4UiUgaGvVabZmryTZKpXpqaBonHOwmHmlC1oF/H9x1c18RsFKjX5yhXximXA1kZ1zU3ROHWPRji//q7o/zo60U8d/G7zq5Cx3nTsAllsb9KSnCc5ewSuwYpca0avufg2ea6RlD6HuNnvodrBflpszJHy567SXUe4NbrVUp/MewpFFxzZxXfw4kWrr3y+1jVldteXKtOJTtCT9chwvGWBSNoRJuINvdQmR3GaWxuk+fVdl8iyrGqjF5+DqFotPfev2qI/nYEgs/bLToKclieazI59DLTo2/ib2LdaTQkY+MeY+M7KzvX2atz35MxIrGluVKz7s/3Fb6PvZFbkEyr3P1wlO4Bgxe/uTUH45WXLE69s/idbHZzvGEj+PM/F+Glly3uOq7z678W57d+p8Z3nvfZ9/P34Ds+QlNof6SPK7fJovi+s+PeDBD0Jqk6angxfOHb5nxOIox0nYWcgNB0hKLi2yaKbiD9INToODcoV8aC3aUQ+K5zy471JlXV4gdqhAVPfzpBoknlf/97sygK1Kvbr0zTjDKRhLvm5yQEhCM7W6HoeTaNRp5GI8/M9OmFCtBlLyzlQnWqUFVAbijfAjA5bPHWD8tLDOBfNFSyw3i3hMd818axa0SMrjXO2nnUClPYtbXaHyTV3ChWrUBL/z1UczeQvkc4mSGa6mD09LMrsrS8H+DaNa6f+SpWbY7u/U+j6eGdr9hdAVJKbLPM6OXnmB09ua6c1RJspGd6Xpnm1pD3RvBH/z7HN36/QFOzSkevwU/9ajP3PXHnKeXeD/BcSSEvF4jKI5HNMfVsSkopkVD41Ccj/N2/X+Izn4vx8rUQlRtF8memEAp0PrUXFLFMUXinoYYidNz/UUJNbWjhGEaqBadSYPylP8MszjLwkb/C7KnnKY8GLPKtx58g3rmXke/9Lu33fQTpOsyeeh7ftVE0ndZ7n0YIwczJ59a8GEMhQVuXzoWTDS6fubOLhevAP/3b0+sfuGWszE8Z7duHU8jjlPLoqTSJw3eDqlI+8zZuZX0qr/Fr5q6FLd8vcK368n23lBsOs+0UnEZ53f2/WZqhnh+nqfsIE2e/h+uYRNPduHYdsziz+32+QqCmm8DzNqTgsATSZ/zai5TzI3Tvf5pEU0+Q21d23hj6vodjVagUxhi7/By18uY5ZKOZPdjVPG5j9Qr2UDJD67EnGH/lzzY5P6iVfWpln0rJJz+zO/nxHwd89BMRRm+4nHrHIRYT/Ff/XZx//o83HqHYsBEsFnw++pEwb71l02hIws0R4r1JYnuaUEMaQhV4lrvrBhAg2XeUWEc/17/5mwhVpfvRz1IZu0Jt5gaqEV71PN+1qU0N0XL4YbRoErs8hxaJE2nuJH/pzTUN4InHo/QOGgweDhGNK3zSSjEz4XDylTqxuMLhe8JcPW9RzAU7xYN3hQiFFc68GVQvRqKCg3eHaevUEQJmpxwun7GWeZKKAofvDRNLqJx8uYbnQt8+g8MnAk2xd16pMT22eMEfPRGmXvNJNau0dek0aj5XzprMTgYsFZoOh+4O07lHRzeCKjXL9Hnj+RqV0vpebPOjz1B69w3cRpXk3Q8QynQgVAUtnmT2u19d93yrId9v/Ak7j13oU1sNQlFXNa4b6SWV0qc4eZn0nuM0dR+lOHmRZPt+KtkRrDW9yJ2BCIVIPvMEXrFE+bkXtzRGOX+D2jtfId12gHTbQWKpLiKxFlRt6zJpMM+X6lo0qlmqpQkKM5cozF5ZUkC0IQgFI9FMy8EHaeSnsMpZKuNXUI0IkUwPqm7gNKrUs4utT0LViWa6sUpZXKtBpLkTPZbCs00ac+Ob9hT/MkBVYWBQ49hdOnt6Vbp7VKJRwWb3RBs2gs+/YHH8uM7rb9i4ruS7Xy9THDMpXlrkCPSsO7MbUVQd6ftI30cIEfQkbXDTXZu5QfPBB4h19ONUC0Rbu0EI6tm1Kz5DIUE4IlAV0A1BOCowQgH9cEubxuf/aprf/7c5ijkPIeCJjyZIZ1TOvNnACAu+8KtpDt4VZuSKjedKYkmF0Ws29epiwFVR4L7Ho3zsZ1K89sPF3aNQgj6jn/vP01RLHtNji889/ekkvft0hq/Y1Co+Pf06h+8N80e/WaCY87j7oSif+Lkk50+adO7ReeYzSf7w3+c3/HnpqWas2Sn0VDNGOkPh7ZfxzAbdP/XLGzpfLvzvJ9gwpET6PsptPWqqHkYLxbYtb1SZvY5ZmSMz+ACN0gzheDNzwyfxlmnmvX/hOQ3mJk5TmL1MNN5GJN5GNNFGJNFKONqMEU6h6ZE1PyspfVy7jmWWsep56pVZ6pVZGpUZ6tXspnJ/t0MIgWKEEKq2GLYVQWWqUFTajj/J6ItfAYL1LNlzEC0cwyrnMGIp0vvupTY9QqI7yCvXZobed2xc7wcELY4SoQQ0i64LX/3jzV3HGzaCtyd333ylDrw3wqylkXMk+g4x+Ilfw7Mb2OUc1amhVXfk4hYeTs+sUZm4SmrwOOWR8yR6j9CYG8eprx3ae/35GhdPmezZa3D5jMk3v1ziZsHZejhyT5gnPhbn3/2TLFfOWkhfomqCRm1xvp4Hdz0U4SNfSPLDb1R44/kq80V9jFyxGR+y+dgXV241SaZVvva7RQpzLnc/FOWTP5ci3apSKng88FSMfNbj618qEm9SuffRKBdPNagUN+a9+GYdNZ5ATzXjOzb27DRKNIrQlrNo/AQ7A9eu49p1Em2DaKE4nmOBECTa9xJt6tz2+J5jUhg7R/fxD5MZuA/HqlLLj+3AzO88PMekUhilUhhF1ULBRkELo6gGqmag6VE0PYSY73mV0guI0x0T167jeTaea+O5Fq7T2JbhW4D0sUpZ7HKe2sww9dnA49NCUaKZQI0+0tK9EMaNZnqQvk/2/Eu4jQqpvqPEO/chhIoeS2GVstRnlc3lIzeB4w9Guf/JGHv2hQhFBNWSx5WzJi89W2F2YmO9d8cfmh9j7y1jnDF56dtlZie25xylmlX+2t9ppbs/xMy4ze//mzmmRx08D65cdvmD369Tq0rmsluLyGzYCL6f4JpVtEicmbe+R31uHOm5+M7ixev7HmKhlFoQTi9ttC0NnyNz9FFinf3E2noZu/TGutZM+sGO46bhW5e/75YNaP+BEPmsx5Wzt4Y/bxUHhtYOjb/y37Tw0nervPF8Dfe2a28tj+rKGYvZieCiyM8GF5xhCJBQLnocOB6EcLt6dRRFUMhu/GYqnX2Hrs/9Il6tSu6VH+CZdcJ7+pf0eO0Ggoq8IE8Zjih3NL0Wa+7BiKXRwwmiTZ1o4RjNvXdjVrO4doPy1PqtIduB55iUZ66T7NjPwad+ldL0FfRwjHCyfb7sf/sRl7mRk3Qf+xAtffeQu/EuZmVpZbeSTJD84JNEDh8MhH2npil87Vmk4xB78AR6poXQQB+V195CSyQI7R+g9J0fYF68AkIQf+ox4g+eQGgajXMXKT//En5leW5MhAxafumLuHN5it/8Lngeob0DJD/0AfSOVpyJacovvoJ1bW1PKBXrplSbwHMt7CWl/7c2qt9aqbuyUK6q6LQ2HcJyKhQqIwuPh/QEfW0PAYKRmVex3Q1U9s57fjd/xzoGcM0q5bFLNA3ctRDWrs+NUc+OEu/ah10t4NRKmIVpZk7/EADftTZciLYZJNMqv/p323jkwwnCEYGiBZEtKeGhDyb49C818we/Mcf3/6TIai+fTKv82v/QxsM3x1BvG+OXm/mD/yvL9/+0tOoYayEcFfy3/7yT+5+Kk591+caX8sxNLV0cp6e8hUpyoQQSVNXKDvYJKmLneQ39be5o9EiCUCKN71oouhEkvoQIqkM9F6uYpWnvXUjXxkhliLR0YZcXKy/dj5jKxwABAABJREFUepnyjQt0PPhJnFqJ+vTWdeNuQkqWGL5YYuOBaUUJaMauX7Q48ViUs282uHbB2nD0o1b1Fm7nxVMEUsJ3/qjMvY908G/+dA/ZaY/f/T9zTI1vnFmh+PYrWNNj+K6LNR30NfhmnbkffXfDY2wJEmolj0bVp/9IhK7+EOPXLXwvCH0oisBq+DsaIRKKQEpJ+8HHae49gARc00MArfsCImfpuZz55r8CZFA80SjjrtA47VjVdSo118bc0Fv4vkPr4IOkug5iVQtMnn8OVQvTduARbv2mpe9jN8q4Vo2Nxp5ds0pp6hKJ9n2Upq4sjaKoKrF7jqG3Zpj7vT8EKQn17cGvVhHhMEZ3J165SuWV12n65Eco//BlGucvEzl8EGv4BvGH7iP+yAMUvvYt/IZJ8pknSX30gxT/fCkFnZpMkv6pT+FXaxS/FRhAvbuL1Cc+Qu2tk1hfHSF8cD+pD3+AomVhjy5PWQgEmhpmb+dTnBv5czzfxfMtNDUMyIAvWEocr4EitPnHwfMdPN9BVQx86SKlh65GkICqGBhqFEOL40sX1zOxnApT+bO0JPeyeKOLBTUQKT0cb7GlCaA2M0zmyGOk955g/NU/wyrnyBx+lHBTB2Z5Dul7AbNOrUz+6lu0Hf8AsfZ+qlPXqc3coPvhzwAwc/qHmPnNF+ashaaMyn/xP3Xw2McSOLbk8hmTU6/UqJY92rt17nsqTne/wV//O62EwoJn/1NhofpycQyN//IftvPIRxI4luTyKZNTr86P0aNz/1NxuvoM/vrfacMIKTz7BwU2s39ralH5r/+/nTz4dJzZSYff+9dZXv52ZZkD8pnPRxm+7vL2mzbRqOC//3sJ/ue/v/GiqzWNoKoY7Gm5b+Oz3iBuzL25LddejzfRyE3T+dAnF3Z0Zn6a6be+g2c1yJ55kczRR2k+/CBmYZbJV/+caHv/kjGKQ2dpOfoYU+de3hY5NQQ9bL4nae3UiMYEybTK/mNhblwJGD1Grlp8/ItJDt0d4vIZC9+XGCGFetXHsQPvcuSqxW/84yw/82tpPvdXm/i9/yPHzC1hhJvdC5v1iKJxgW1L/s6vTGwpLCEMncb40k1CY2xk0+NsBaNXG4xcbHDo/hh/+1/389q3i9TKLqGoSjSm8LX/OLsqa4yRMFBUgVUJIgSKqqDHNJy6i2d5CFVgxHXchotn++gxnaa+BHbVYeStP4LYHpCSybdnsGsOvuuj6ApGXA8Y7V2J3Zhj+K3fQzVUuE15feTNP13zvQmYZ0iRK9otKX1yw++QG35n2XPZoTeX/G1Vc1z90e+s+XrLXl8oqEYUu1agPDt0+4vjmxZIidaUwpmeofp6oPYgwmGk62ENjWAN3aDpEx+h/s4p9M4O1EP7UUIh4o89ROX5VwKvEKi+8gbpL3waJRLGtwNeUTWZpOmzH8crlgIP0A3Wg/ChfUjHRpoWWksL0rYRIQO9vXVFI6hpEdrTR4hHOtjT+gCVxjSzxUv0tz+K61koiorjNpjMnaat6RCJaDtIieXWmM6fpyN9lEL1BuX6JAd6PsL43ElURSeV6iIV60ZRNK5OPLeirFVYT9CduS/oRxQqo7Nv0LCLC8+XRy9SL07hW4HIbG16iNr00s/advJMn/wOwILnB5C79Bq5S69t6jvdKDQNnvpkkvueiuF5kj/6Dzn+5DdzOLf04aZ/M8ff/EcdPPbRBB//2Saunzc5//Zirk3Tg3qEE0/E8BzJV/5djj/9raVj/PFv5vkv/2E7j340wcd/ronrF0wunNxYvq69R+ev/r9aeeADcWYmHL78b+d4/uvlFTNenitRVQIKP33zqdM1jaCuhjnY+cHNjbgBjOVO4m3RCKqhCF2PfIbs2RepTlwPdqlNrfQ88QX0WBOeFeQIJ1/7xpLzyqOXlvytx1K4jSqlke3LsZQKHpfPmjz1iQQHjgdVnIVbFueL75q8+GyVz/2VNFOjTkDrk/d4/psVslOLx9WrPt/4cpFf+a9b+Nm/0czv/G9z+BLueyxKd79Bc0bjsY/EyXRoXL9gcer19S+oUEQh067x8S+mMGs+tarP6TfqjF3fmDfY8ugz2Lks5tQ4dn6OLcU0toixqybf+J1ZzHqGgaMRPvvrbQjArPtkJ22e/dJycgYAoQo67mlFj2jUsg3qcw3aj2WwazahhMHQD8ZoOZAm2hJBNRRGXhwj0Rmj78k9lMcqmKVg89JyII0ErKLF7IUc3fcH6uKKrjDy4jhtxzLE26N4tseNlybwnY1vphTNIJJsxbN3KA+1SSTb9xFr6WXy/HNLehwB8H0a5y+hRMJE7zkO4i7s0TGqr74VPO95C4TLwILBBIESCaNEIri5xciLV6kgVAURDoPtBAT1A70IXafy/I8WxgFQ43G0dBPhIwcX2BKcyWnc3MpetePWGc+epKvlHq5PvbDkOdMuMV04B0DESBMNpRmdfQvLKXOg5yPEw5lVPh1JrnydmcIFjvV/jlg4Q7l+uycmSEY7iYVbmC1epCW5l3ikbcEIClVDjcWJH7kLc/wG5uQYWiIZEDFUKyi6gdB1pOsgNB2vWkGJRFGjMdxKGWlbgdxUPBEICdSq+JYV5OPn/95qVXKmU+e+J+PEkyqv/6DCn91mvAAKcx7/97/Ksu9omN59IU48HuP6BROzLhfGOPFEjFhC5fXnKvzZb68wRtbld/7XLHuPhunbH+Lex2IMXTAxV5GUsufJ8Dt6dX7xb2V45CMJspMOv/u/ZfnRs+VVjdvUlMeJ+w329Ko0pRWuXd1BKSVfepTqy6k9JIHOnapoxMKtaIqBlBLLreK4dXzpoQgFXYsS0gJZIMupka/doGZmtxcOFQpqKBzE2qWPUHXC6faAHmrdMmKBYhjosSYyRx+heO0d3MbG+0kaNck3v1yiUlw6/1rZ51t/WGLv4RDRmEI+61GYc0k0BaFky5R89f8usPdImOY2FSTMzbhUSsE4b75Y4+qFYOHNz3r8wb/LM3g4hOsGXkIp76GqDv/hn2VBgutJqvMsNd/909J8vjKYy/S4wx//VoHJURtNh4/8VJJzbzcoFzyQ0H/A4OFnYvx//sbkhuyZW6sSP3CU1F0PYOdmqA1dCdj+rZV5Q5WwgdQV3vh+ielRi9kJG8de+2bVWptQQjr2+FJuTs+FMxd15v5YIfX740SigRvsuIKaqS60o6yEcFOIVG+SRFec6XdnibVFmflBlsOf3096uExTX4K5SwV6Hu6kaSCFWbRo5BsUhks4teAmsioO9Wyd5r1NSCnpf3oP1cka6X1NZC/maepL4jZc5i7l8d2132M42Uqy40DAz+h5RNOdpHuOURg/j10rrnnuTsGIpkh2HEQLRWnpu4dGaZq54ZWJoP1ancpLr6OmkhjdnTR95uM42Rz2+GSwANy6It3yb+k4+KaF2pxeeEyNx5Guh9+Y37QJgTU8ij06Rvyxh3ALJcxLAdWWb5o42TlK3/oefn1xkyfXIdBUVmicN+3F3KDEJwieAggUoeJLb0HdBgRh42bhmVg4UhHqKpEiiU8gRGy7dabyZ6nfJNVXVMK9/ehNaUIdnZiT4yiGQbi7FzWewBwbIdTehe/YhLv2YM1M0hgdRk+lUSJREsfvJf+j5wjv6UdPpdEzrZTffh01EiW6/xByPjVhTa1Cu7QOMh06e48EYeEXvl7GXoWJKTfj8M5LNT75i2mO3BclmS5h1oPNc+stY/zwz8vLDOCSMV6u8clfSHP0/gjf+xMVs7GykapXfJrbVH7hv8zw+MeTzI47/Pa/mOXN56trenen3gkci/YOlaHrLu+8vblN5ZpG0HHrnLqxchOnoUUZaH2UeLidycJZJgtnMJ0y/jxP382LK6wn6Wm+h0x8kLqVYyy3SdaF2+CZdabf/h7Nhx6i7e6nAYldKzF76gWcanHNcxUjROvxJ0gNHKcxN8Hc2Zc39dqOLbl0euUm+ULW4+3s7bmhRW+rUZece3tlz21syGFsaPHYmQl3SSj09Bure3xXzy01RtWSz9m3guP79xs88qEY/+3PjlPKB+MNHAjxT3+nm1hC2VCFaOmd16lePouRzhDbf4SWJz5My+Mfonr1ApXzp7Dzi4ZLiYRIPHkcPJ/p8yOMfTOPmooROtiHNTyN77iosTBaJoVft3CmcwhDR29rwp1bXLCMrha0TAqvVENJxphzEkznfewzc7ilKpH9PYQGO9EPzmGfGV5WpdT9QAdCCCbemKJlfxoE1Gbq1Oca2FUbParhe5J6roFdc9BCGm6jhl11sErWgkErj1Vo5E28Xh8jbjB3qcDoy0FIrpFr4Nkelekalcn1iyRUzSDVcYBYczdC0fDsOrnRU8xeeW3FnOJuwIim6TryNIqqUc2PM3H2u7jmCo3cqkp4/16k6+LmC/imiRIOb6g1Qzou1TfeJvHUo7i5PNI0iT3yAOaV60jTCpq7pI9XLFE7eRqEIP35TzH3u3+AMzmNefka0eNHiD1wL/XT5xC6gZpO4UxO4xWKq70q5fo0x/o/R7Z0lZnC+flHF2HZFarmLP0dj6EIlUpjllJtgpCeoLf1fuz0kQVCeF+6tCT30pLci+VUqZk5YuFWetseJhpuJmykGJl5lXJtkmS0i47mYwAMTQZ9j2o4jN6UxpqaRI0ng42PJNBT7OnFLRXRMxlKJ98kOrgf37JQYwlQVYzmDNHB/RReeR4tFkd6Lmo4gm9bxI/eTaRvMAhLaxr23CzS2aRyggiKWdIZFcf2uXHVWtWhdB3JyOXgM+no1Rcp2ubHaGoJxhi9unr9guvIhbRQZ69BJLp6rYRjS372v8jw1KeTZCddfvOfzfDuS7V1w5uNumR8zMNx4NwZZ9MpozWNoERiOstbB4RQyCQGyST3MjL3OiPZ13G9lY1D3cpRbcywt+Mpelvup1AbJVcd3twsb5tVafgs5dGLC/03UvpIz103GOzbJrOnnid75sWgz3AHquzezyjmPRQhuO/xKGfeqNPUovK5v5Jm5Iq1oUZ5CApB3HIJt1LGnBqn0tZJ5vEP0/zQUzSdeJT6yFXyr7+ANTOJCOnomXmDJkFrSZH60L1Y1ybJ/PwzFJ59g+RTd+PmykQO9ZL9ve8Fgp/tadRkFGemgNaSIP3Zx6ifHyE80Il5bZLo4T4qr1+g5eeeJvt730NNxVBjYVa7/e2qzcFP76WerVOZqiF9iTdv2DzXpzpdI9wU5q5fOoxqqFz51hBCBIUxx37uEGe/fBHf8/FdH+mD9Hyy5+fIHGzm8Of349QcTn3pQvC8t7HPsZaf5Porvx/kAYNPdqEw4k6hmrvBue/86+DVfX/NJnC1JU3i8YdQojH8eo3yj16lcfEKSiyKdN2gNxeJb9kgg/FwA2XwyvMvI3xJ8898JqgOvXiF8g9eRDoOQlWC810PPI/aW++iNaVIf+6TzH3pK9g3xih89ZsknnmSxJOP4ls25sXLOJNrsyVdGv0WiMU2gutTLy7x4CQ+s4WLZItB9WpAqu0zU7jAbHExVeJLj3J9kklOzR/nI6VHzZzj4ui3gr5kKfFl8NkNT7+8sA4t0ReVAafszTBx/OBRPLNBfegq0nUAgd+oB/lP1yXU2Y0Qgsq5dwn39CJdF69eIzKwl/Lpk3iWiW/bVM6+Q+PGcEALeXulygagKBCNKyiqoDZfk7AafMlCtCqWULjZFbWpMfxbxkgqaPrqFuqjP5Pi6c+mUDWIJhR8dwNV+MCDjxh87gsRWloU/v5/X+TX/2aCf/a/7FBhzGow1CgdTUewnArZ0pVVDeBN2F6dXGWI1sQ+eprvo1Ab3V5IVMrgItjKqZ7LLrXbvO9QzHn8638ww6d/qYnP/koTVt3nwrsm/+GfZjfcwH4ztxFq7SBx+C7CHT2Y0+OM/eFvgYTU3ffT+vTHmX3um9hzMzjTeeyJLM50nti9+zGvTlA/O4TW2oSxpxVnpkDjwghKSEdrimON1nBmC6iJm8LFAum4+JU6jpRI16V+foT6uWFCfR0oYQMnW0SoCo1LYyveJbPncjz/P7+K7/m3vM8gf3jmSwGVXnm8yvDzo8hbNM6ufGuIq98ZRnqSytSid1eZDLylk//xDIquBJ6ihOvf20xVsdwR0ddtQcqNMY94HrVX3qD2yhvLnvIrVQp/tphvn/yH/xwA88JlzAuLSgLlH/6I8g9/tHwKpkXxa7dUifo+pe/+cMkx1vURrOsj68/z1nlJj1tv7JWiTRKJlO5t3Un+snCnlDIIn8qlZ/u3nXvzdW5/La9Rxy0XSRy7ByUcQboObrlI/PBxpOvi5HN41Qr4Pm6ljG9buKUC4a49JO+6D69eQ6gqajyBFk+SOHoXXrVM/fplmh5+gnBnD9XL57cWDpVBqxewIWaVm3u2JR0lt46xAt3w7VDmXbP1UphPfzbF7KSD50m6+wx+9e+18S/+9gSjV9e+ZvfsUfnTr9T5mZ+L4nuB3u1msCUjqCoGiXA7FXNmvix4fdhuFdczScd6gp3TXxZL9B7j5Mt1Tr689XBb6sTDRHsHUXSDxvgI+ddfxM7NLnjdTnGO1qc/gZZIYc/N4NdNjO5WnNkibr5MeF83oYFOlFgYb2gSNRJG3qTWE6DEwhjtzSixMEo8gluoojXFMfa0UXv3KmossiwfJC0HNREl1NdO48r4ihGA9XJ0wBIDGDwAch2i780Uv/wEf0khJfWhq9SHry25Nhuji6H7xmgQDSu88sLC8/VrlxeOV+MJhKpSPnUSo7UNNZ7AGbtB7rlvz/Mzb+069H2olgPvLRxRlqhQ3A5FEaSag7qGatHDmecAXjJGVFkzxKkoguT8GJWSt6bX+PaPqvze/54lmVb5G/9jO4MHQ/z6P2jn//z7U2QnV/d6y2VJb79GS0bh/ocMsrOb+2y2xDwrhEBVdBQ2JmkSnKMihIKmhNgwZ9dP8J5DiyeoXDrL9Lf/lNzLz2HPLSVZ9k2T2tCVBTLtxpVx/IaFEjawxrPYUznUWJjGpVHssSzm8BR+zaRxaRS3WEVoKm458AYVXSNycA/m0BRuoUJ4sAuvbmENTyNdj/r5YfyaiTNXwp7KoUQ2ueX7CX6CO4nbN2frGa5bjvcadczJCYSq4ORzWNM3xZk3wtSxNsp5j+ykg6oJ9h0Nr+oR6oZg39FAXm5ixKFxC89xKe+RnZof49jaY+w9GhTQTA47S1iybsePvlVm6KLF+bcb/Ml/zDE343Lf4zF++W+3LhjjlfDuSRvXkQwPebR3qHzzz3eJNu1W+NLHcRtEwxkiRpqalWet+JpAEA+3EtIT2N7GG3p/gvce+Vd+iG+vriAvPZfq1fML3pqbK1N57fzCV9y4cGOJpIw9FnDNmtcXFddrby2G0Yy+NvAlajSMV7dwZgpIKwiHmJcXqb3qZ4Y2Lkf5E2wJRipM52N9tNzViZ4MrVhw4Fkeb/3D5+785P6iw/OwZybZjeaZmQmbS6cbdPUbfOgLKV74RolGbfmN1N4dtEF4nuTCyTqlwmJEZmbc5srpBl19Bh+eH+Nm+8SSMXp07ns8hudKzr1dD6rUV8HNPYBjS175boV0q84v/tcZnvxkklLO4/f/TRZrhfaKuazPd581eflHNqYpMTbJ6LglI+h5FqXGBG3JQ+xrfxLHbVBqrByfFii0JAbpbbkfTQkxU7q07eb09xIKKhEtSVRrIqTGFn+UCJoSsEcoQkUVGgKBT5BY96WPLz18XGzfxPEa2H4D22tg+3VqbpGGW8aT73He6Db4tk108AChljaEuni5SM+j8NZLwb9vr1C7/TrdRPdq49wI1vWpIC/jegsGcEVswgCqQqc51E2T0UlcbyakxtGUwJP0pIvjmzTcMlUnR8meoWTPzJfV31lowqAp1Lkwz7CWQBMGIPCkgydtLLdOzS1Qc/OU7Fnqbomd3g1oMYN9P3sXgz91DDWk4jYcWGH9chvvr+t1KwgpUeJ6C1E9TVRLElbiGOrS+xkp8aWHJx0c38L2G5hehbpboubkqTp5XPk+UXpYJ9BWnPN484dV7n44ysG7I/z1v9vGb/2z2SUGJtOp8bf+cQfpjBr0JL9axzblkjHe+GGV4w9HOXhPhL/+d9r47X+xdIzWTo2/9b900JRRuXbO5PRrtYVewPVg1iXf+FKejh6dT/xCEx/+6UC159k/KKzY2mXbYOd9IlHBT/9clN/+DxsXrN6SEbS9BhOFszTF9pCMdPLA3l8mXx2hWB+jYZfw5nsII0YTzbH++TygiuVUGMud3DZt2m5DQUXMG7Ko1kST0UHCyJDQW4hoKVShsdhzBFsP78ol//elj+VVqbkFSvYsRXuSqp3Dk+68Ab3zn1vzw0/R/NCTONXKQqm2Fk9SPvv2+idvAdL18Co70zKgCI241kxP/Bjtkb3oSnihT2xFhGC+fALbM5lpXGOifpGak9/VzYkqdOJ6C72x47RGBtCU0NrzNG7OEyQeNafAbGOIyfplLK+KJ7df9RztSNDzwX1MvTTCxd9+k8bce0OWv5MQCBShoQqddKiLTLiXdKibiJZEoGzhfr555wai0xUnR8EaZ84ao2JncaWzUEW6kwiFBYm0SiyhkkgqZDp12nsC96e1S+e+J+LkZhxqlUBvsFx0l3hpUsKr36swcDjEp34hzSd+Ps2Jx+O8+1KVcjGgPLvvyTjJtMrcdMDXefn00hCjlPDKdysMHgnziZ9v4pO/mOa+J+K883KVSnGRNi3RFIzx9S8VuLJJDVarIfmP/3SGjj06Jx6P8fm/lmZq1Oad+baJdLPgF345tqTP0TAE+w5szqxtkUBbkq8OMzT7Cr0t9xMx0mQSe2lN7lt+pJRI6VG1sgzNvkLFnF1hvKVQ44FCslddXYxyJyFQMJQIuhomqqZoCnWQNNpJ6BkMNbLE3O30Ky/+P2jM1ZQ0MT1NW2Qw0DeTDmUnS8GaJG+O0/DK2F4DV64eotxJJI/dy9Q3/wjpuUR695J76fs0P/QESjiypfE0ESKqpTbMSSuRVJ3cpoyQQCGut9ATO0pX9CC6urrG5EpnCyCsxehL3E137DCzjSHGa+cp2TM7agxVoZMy2tkTP0ZbZO/85mpz8wSFpNFG0mijP3GCmcY1xqvnqThz2/JMtIiG9HzGvneF+vT27sOo1oShbP56sfw6DXd94eb1oAiNsBonZbTRGhkkE+5FFxvre1wbt9y/QqUp1EFTqIN+eYKGV2WuMULWHKbqFjDdyo5FFj71S2n++t9tQ1WXz//ofVGO3hdd+FtK+PL/L8sf/fvcEi/NtiT/6f+co1L0+ODnUrR06Hzs59IoCriupFb2uXSqwdd/r8CL31yZrcW2JF/611nKBZdnPpeipV3n4yuN8bsFXvzW6owva8EyJf/2f5rm7/4fXRy6J8LP/c0WClmXoUsW4bCgtV3h5RcX18JQSNDVvTm+6y2rSHi+w1juHWpmjrbkfuLhNgwthqYaCJRAq8u3sJwqFXOGmdIlSo3JDYVCQz29ANQvXdjq9NaFQCGiJYlrzcT0ZpJGK0m9jaiWXNT/eo8hhEATBs2hbppD3fQn7qXizFG0pinaU5Ts6fmba/cSY0LTcUoF1GgMoShBxdr5U+z5xb/B3Avf2fR4qVA7R5o+QExPr38w4Pkub899lYI1uf7BgK6EaY/spS9xLwm9ZdPzux2aYtAVO0Q61MVE7SITtfM0vI2zDK2GsJqgJ3aE7tgRwlpiRzZammLQFT1MS6iXyfpFJmqXqLkFthIq9V0fp2ovVvJuEYrQ2Jd8kK7Y4U2dJ6VkunGV07lvb+O1VVJGOy2hPbRGBojrLZvcaGwNQihEtSS9ibvojh+hZE0zaw5TsCaoOLlte4dDFy2e/XJhw0b8yhlzReJq25L8yW/mefP5KsceiNLRY6CHBI2qx/iQzek36uSml58oQgbhQ3uxb0xgF8v88X/I8+YPgzHa9xjoRjDG2JDNmVXGuIm5aZcXvl6mKaMxMbzypm120uE3/8ksT38miaIJmlo0FGFRLEi+/Lt1rlxeHN8wwNpgyHXh/cg1zLMQYkOjKUIlYjRhaEGe5aZul+vbWE6VhlPaFEtMqLcPI5OhcWMkyDf5/o55hSE1RtrooinUSULPENPShNTo+8bwbQRSShzfpOzMkrfGmakPUXPz65+4BXR94VeoXjmPOT1B84NPUh8bRjEMmu55iJHf+t83PV5LuHdTRhDgXP77TNQurmvsQ2qM/vi9dMeOoiuhHdjpL4UvPbLmDa6X36Bsrx/RWA1JvZW9qYfIhHpRld3RZfSkS8Ec53rl7fkNxOYWhkhbjGN/8xFqE2Wu/MEp3OrWvEpFaBxLP7MlI1hzC7wy/Z+25EGljHY6owdpCe8hrjW/p/f3reHSOfMGU7VLVLe4OdlxaBrzXIwbPkVtbqL5r/005W89j3Xp+u7NbRNoblFIJgWjNzx0HW5ndZRSrroY7Mi2yJceNStHzcrtxHAohkHkwEHCfQNI6ePVahS+vz3pnqTeTmfsAM2hLkJqHEOJBgyBd1KobocghMBQI7SovTQZnXRGDzFZu8RY9eyOh0kLb76EZzZwinnM6XGaH3gcFEHutRd29HXWQlzPLGysVoOhRBlM3E937Aiq0Hfle1WESlt4gJAS4VLxJYr25uVt0kYXh9JPkdRbd/XaU4VGS7iXsJbkfP6HFOz1G6vbHugh0hakIoQqsMsWXR8YJLU/Q/7cFFbBXOYZ+q7P2Hd3QV9RBF59WE3Q8DYeEtWUUOBhR48Q09NBru89vseDdUYlabQS09K0hvsYr51nvHoO/z0ovroV8ScfwBmfwboytP7B71McPa7zyc+ESTcr/It/UuYLX7wDhTG7jcbwENb42EJp/Vre6lpQhU4m3Mue+F2kjPb5is33/qbYKQgEmmIQF83sSz5ER3Qf10qvM2fe2LEQqTk5Fozl+5ROv0n5wimAQB7mDiGutyBQWLE8keB77o3fRU/sGIqi7mION9iApIwODqQe40Lhh1Q34YGnjA4Op58OwrR34BIUQiGmpbk781FOZr9BxcmueXz/Z47Q/mDPrQOgGCqx7iSt93bOM34sva7curMrRlDMk1zH9eYNG8GoluJA6nFaw/0oQn3f3efB/aqT0Fs5mHqcllAfF4svYnobp/hagHJThksG66S4pYFeVcDzg2tMKPP6azLgQbu5lgoBikLkxDG8ci3gdEUG593+Ojc/R+kHYyy8IRG8FvMtUNvsX9wqDhzUeOUli89+PoIAunvuUE7wdqiKgaroCKEgEPMhAG9e5HLzoRStuQWtKU3j6mWUaAxvFdWC26GgElJjZML99CXuIqal4cfU49sohBAoqCT1No43f5jhyjtM1C5g+5trGl0JPb/w63i1CtbsFObMJE4+i2eZKEYI39z++BtBQm9BEeqKRSkCQXtkHwOJE8HCdwesixCCdKiLgeR9XC6+gu2vXzkZ05o52PTYggG8E/OEYK4hJc5dzR/h5NzXMdfIZ177ymnGv391U+NvhJlnq1BQSOjNZM21uYYFCulQF0fTzxDVmt7397oQAlXotEUGSBgtXCr8iDlrdFO5wvTPfxp7ZJzaG6dJPPMokXsPU/jSV0EotPzqF5n+Z79BaLCXxEeeQGtrAdfDvHCN0te+i3RcIvccIfmxpzD6ujG62vF/+mO403OUvvY97JFxUASRoweIf/BRtEwz0vOonzxH+dsvBO9B04g9eDepz34YNRrBHpuk+EffwittP1++WbguaKpANwSt7SrWGqw0K2HbRtDQoiTCHbQm95OKdBI2kihCw/MdGk6Jcn2Sucp1yo1pHG9ji2Z4Ty+xo8fQWzJYYzdo+dgnmf3Kf1r7jQiDqNZEJtxLZ+zgvPfw/r4ZdhI3b3xDjbIv9TBRrYnh8tvUNxFKWgnT3/ojQpl2jEw7qWMn0FLpoJG3mGP6G1/Ziamvi5u9mI6/3PtMGm0caHp013Jrq0GIwPiW7Nn5sNbaodr+xL00GV3vyQIthCCmp9mbfJDLxZdXDZnnz83c4ZmtDSFUYusUN2kiRHtkLwfTT2Aom6kCfu8hhCCqpTiSfprhykkmaxdxNpjOcGfm0FpbEJqK3tOBrJuoLWlEyMCZmA7qKAolqi++gTM5g5pKkvm1n8W6cp3G6Us0Tl/EvHiVjn/wtyh94wfU3zlP4AkG17G+p4v0L36Oyg9fpXHqAkLXA89y/nk1FcfLFSh8+esgfZp/+fPEnniQ8jd/sEuf1uo4c8rmE5+OEIkKvvjzEf78zzYXpdqWEYyFMvS23E9n01E0dXnJccRIkY7uobPpGFPFc4zOvU3DKa47rhKJ0hgeAkVdUI5fD82hbvanHpnPH/3lMX4rQRUaPfGjGEqYK6VX5ysEtwgJvuviNWpY2WmcchE1GsO372xjcNJop+oszTnrSoQDqccIq/E7Opeb0BSD3vhdFK0pys7KhTIKKp3RA3RE96+oeXenoAiVtsgABWuCyfpl3hdFGetAIIioSVShrxgF0JUwe2LH6U+cQBeh92CGO4OwFmcw+QCGEmGk+u6Km73b4UzNEnv0foSho7U0YV64itaSRk3GscemwJf4tSBCETl2EKFpSM9DzcxvKnwf6QSE4NLzAnfqFkTvO46bzVN57pUVw5x+tU7j1EWc0SDXbF4eQu/IoMSjhPb1IR0H8/LwQsGNiIbRM804M3MLBBhC10FTkY1b3q+qooRDC3MHCO3tRc00YV0fw5tbvpbdGPH43f9/jdZvKhTzPqXSHfIEw3qKwbZHaU8dRgiVQn2Mcn0Sy6nhy6BZPqwnSUW7SUY66Gk+gSI0rs28uK5H6FUqRA8fJtTVTdOTHwjyg+vA8usYavQntKTzEAjaIntRhMbl0ktUna1Vj7Z9+DMITcecHMWancStlHFrFbzanQ17pPQ2JlnaMtMXv4t0qOuOzuN2xPQ0vfG7OF/44YpVjAkjQ3/iXnTlvV+kDSVKe3QfBWuSxjp5KCMVJrWvheLlLM4qlaHJwWYUXaV4ee1c41YhhEBXQkS0xLLrV1dC9Mbvoi9+D7oa+rGP+oTUKH2Ju9HUEFeKr6zbj+pMZdFamtBam5GmhTOVRe9qQ9/TReX51xCGTuKjT6LEY9jXb+BVqkjXRagb24ipyThusbRqnk9aNt4thko6bmBoXQ8lGkFr68a6PhZIZgF4Pr5pLRlP62hBa26icXpRykptShA+OEDt1XeXjG3s6cSv1lc0ggD1muTG8NbIRLZkBIVQySQGaUsewvVsrkz9gHxtBNez8KVLoKAVJLZ1LUp78iCDbY/Rlb6Luep1suVrrLUTtacnka6Nm8vhlopYE+PrzqlszzJnjtIVPfQTQ3gTImhJ2Ccf5kLh+S3lCKtXzhPu7iPS3UekuxdzapzG+AgN0wTuTMM+QNJoXfq33kZn9OB8wcx7B4GgM3qAsdpZSvbMsuf2px4lrCXeo9kthRCCltAeUqEOGvUKa92Dsa4k+3/+bs79xmurGsHOx/ppuauDV//7Z1d8fiegKQYRNbXECAbe9UH64vf+hTCANxFUth5FoHCx8MKarSFeoYT0fcKH92GNjOOVyoHH1JTAy+YQuk7k+CFKX3+OxpmLgezZ5z6ydBAJSIlYgf3azRWJHD8QFMx4K8lSsSIdojQt3Jk51KbFa15Jxog/+QCKYVB+7lWkU0Xv7yL18adQUwkixw9Q/MbzKLEwqY89ib6nk9DgHsrPvYY7ncWZyeIVl27aIieOELn7MH7DpP7GKezhLchK3ZzfVk7S1TBtyYMoisq1mReZLp3HdMq4vjWvLO/hSxfXt2jYBcby7zCWfwdFUelsOrZuWEj6PnY2S+XdkzSGrm9IPFIiuVE59Z5Qi71fIeb/y4T76Y3fvWGWlltRPvs22e//OZNf/RKzz30Dt1al+ZGn6f/Vv73zE14DCT2zMH+BQk/8aEB39T4IfStCoz9+YtnjXbHDtIR63leLtCp02sID6OvkzxRNwUiG1vEcJMl92yckWAuaYhDRkrc8ImgJ97I/9WjQC/o++mx3Agoq3dFD7E89svYGT0qc8Smi9x0PPL1SFSURQ1r2vNCxj3QctI4MenvrQgHLEvg+zuwckRPH0Hs60DoyiFDAp1t/5W2URJz0Fz+JsbeX0IEBIncfnq8i3Rz8co3GqUv4lo3QgvOd0Slqr5+icfoSha88i1+q4E7NUXnxTayrIxS+8izuzNyK46npFLFHT1D84+9gXrhG+NBeRGTr+eAtGUFNMUhGOqiac5Tqk+tygXq+TbE2huVUFvUE10B0/wGi+/YvlP9mPvP5Dc2r7Mwy19iM0OlffAgRlGV3x46QCfWxWTc5fuA46YeeovWDn6b9I58lcfA49twMcy9/f3cmvApulssDNBkdpEPdWzLquwEhBK2RPuLaokEIKTEGE/fxfgtLCCHIhPuIrJVHXRBKna+qFiz7UUMaoUwU397dTacqDCLqohFM6C0cbX4GTRjviw3QTuOmTF1P7Ag9saMorH6NW1eG8Rsm9sg4fqWCm81jDY0ibQe/YVH6+nOEDwyS/qXP4leqlL/3I/zK0v654p9+B3yf5l/5KRLPPIaaDDw4N19k7je+hAiHSP/sp0l95kOoLU1BN4Tn4WZzyFsq9v1yFTe3eu2B9LylnqMvkZ6HvJmbhHmx9FseW6U1Tk0n0dszNP30R4meOILfsFZUONkothgOVdDUMFUzu+H2B9tr4HgWsVAzqy4MQqA1pQl194CqgaohVHVTu48b1XdpCe9ZUAjYTfjSw/EtXN/Gl+48y7+LL/1AsZqgL+dm20igLqGjKTq6Ep4ndL4zfYsRLUF37DAlZwbL23gjabirB69WpXrpDFZuFqdUWDE8svsQJPQ2qnaBlnAvMS215tE3WXUsr4bjm3jSwcdHQUVXQoTVOIYa27FiFUVodEYPcrX8KiDojh8lrCY2/N160sX0qjheI7ie5iMaitAwlAgRNYG2Q6E/Q43QGhmg4uSWhdy6nhpAjxrE9qQwmsJ0PNZHau9t3p4iiLTF6P7AINOvrZ+v3w4UoRDWYuhKGFXoHE5/gJAS2/Y9I6XElTaOb+H5Nq608aSLlP48hbof3LPc1EHV0UQYXQnNsxHtbhjeUKP0Ju6m7hbJWeOsFLquv32W+ttnF/4uf2NpZaZ57grmubV7ON3pLLn/+IcrPueMTZH/nT9e9rhfqlD4va8ueaz6wusAKKk4em8nWmua0N49WFdvgKZi9HWhtTZj9PdgmRZ+3UTaLmoqQejQIPbQGNJ2wHFRwiHCh/dijUwgLZvQYC9aWwZUFWcqizubw7oyTP2dCyAlbq6AX9963/KWjKAkMACKom74YrgpSbJeL4xiGIhQCEXT0ZIpEILKm29seG4le5acOUp7dDmZ93YgpcTyatS9Eg23jOXVgh+/jjtvCF1p4/l2IJuEP8+TetMIKqhCQ1NCaIpBSIkSVuNEtBQJvYW43oKhbo2UeqPIhPvIhPuZrF3YcDN99gffXPHx5PH7KJ89uZPTWxMCQVJvpaRN0RLeg7IGB2TNKTJnjlCyZ6i7JSyvjift4JoVGoYaIaY10RTqpD2yj4i6/bCqQKEl3MONamSev3RwzTnehO01KFgTFKxJqm5+3hCaeLgIgvBlSI2T0FtoCfcG5M870ArQFhlkuPLOMi7f5EAzzcc7SA42E26Osu+LdyG9pdeKlBKnapN9Z5Jrf3h623NZD4YSI6ql6Ioepsno2PJ35fluIEHl5Kl5RUy3guXVcXwzuIelE6Ry5u9dIRQUFJT5+9ZQI4SU6MI9m9Bbd5VrOK4105u4m5pbXLO/8/0EoWn4tQbW0DjC0EFRgpC662EPjQX+z7xT40zMoEQjqIkYKMF36uZLNE5dQknEEKqKFAFXqT02GRTf6DpeoUzlh6+j93QETkZ5e5SaWxPV9R0adoGo0UJYT1CzVo7dLkIQC7UQ0uNUzdnVSbSlxJ6Zpvx6sJt2C5uvaPSky0T9Ii3h3m17g45vUbZnKFrTVJy5YIHyzcD7k9bGJaFuXUNuO0UTBoYaJaImaA710B7dS0xL78qNpSo6A4l7ma1f23A/0mpofvTpO2oEIVi443ozSb11xec932GyfomJ2kWqTm5lFQVpY/t1qk6OOXOM2foQ+1KP0BLuWX7sJiCEmFcqaCeqpddt2pbSp2hPc6NyiqI9heXVVtyYOFiYXpWSPUPWHKE9spe9yQcx1Oi2vMKEniGqpZa1nVz9w9OEvneV1ns62fszx7n+J2epjt1eSSrxLI9GtoqV333ChKTRyuH0B4L7YpMZHCkltt9gtjFEzhyl5hawvTqOb61fP3D7fTtfsClQCKlRQmqcJqODzugBkkbbjofnb4auu2KHGSmf/LGod/ByReq54pLHpAn1t88tO9av1Gi8c/62Yy0apy4ueez2vyEwoM7EzvS1bskIup5FoTZGb8v99GUeompmsdzVrXEi3EZ3891oSojZ0pV1lSTcYnEr05qHpGxnKVgTZML9G9o13krL5kqLbOMGc+YNSvY0tt+YD3V67EZvlSttXNem7hYp2FOM187TEd3PQOIEuhLZ8VBpTGumNTLIZH35hQUgdINQa/vagwgFLXqHKx4FhNU4YTW+7DORUuJIi+HyScZr5zbUZwXgSZuCPcnZ/Hc51vxhWkJ7tvV560qEntgxdCU8L4S7MqT0mWlc53LxZUxvoyogQSRivHoexzc5lv4wqrL1Nl+BQibct8wIeqZLfbJMIaJRHilQvDJH6erOcAJvFZpikDKCa3Kjhv+mDNl47RzjtfOYbhVXOuzEPSzxMb0qplelbGeZql+hLTLA3uRDK16f24EqNAbi95I3Ryna0zs27k+wiK0ZQd9ipnSR1sQ+Mom9PLj3V7gx9xa56jCOW59vkYCwkaQ1eYCe9D2E9Dil+kSgLL8OaWx4YHD+X4Lmj36M2pkzlF750YbnZ3lVZhvDNId6UMXKTCI3DZ8vXRxpUbJnmKpfYa4xsmM3y2bhS5eGV2a4cpKp+hWOND1FS7gXRdF2rApOCEFf4h6m6pdX/B5CbZ30/tJ/jr8WTZ2Yb3S9gxCIFVPJNxe70cppblTf3ZJgs+lVuVh4gbtbPk5iG2QLQUN6cO2uNoYvfWYb1zmT/+6W5urjMVW/QkxrZm/ygW1FDJpDPYxU3lnxOTNbY+x7V++Ip7ceNnPtSynxpUvOGuNS8WXqbpHN3MuKouP7G9eMlHjYfp3x2nlmGkMcSD1CZ/RQwFO8Q8ZQU0IcST/DG7N/tGHBZIFCUqSpyTIe7qa4hD/2U3E8V3LxjEW56OM6EseRuM6qtSo/1tjyVrJUn2A4+xoDbY8QMZo43P3RYEHyb+ZedFQluBB836PcmObq9As0nPVpvJRQGKFphPv7mf3DL9P80Y9vam4SScGepOxkaTI6l12MnnSD/J5bYrYxRLYxvG7z8J2G6VU4lf8OffF76EvcvSPFADcR15tpDvWQs0ZXfL5y8Qyzz31j9QGEoP8/++92ZC7bh2S2McRw5eSWjMpN1N0SI5V3ONT05JZzs+t9P1JKcuYo5/M/2NZcAYYrJ2mLDJA02rY8RspoQ0FdMcxmly1mXlv5+ni/QkpJw6swWj3NePXcmqLCQgT1DEIoeJ4NSAw9Tkt6P1OzQaO2IjRU1UDizx8DqmoAAt9z8KWHpoaQSDzPxvEbXCi8QNnOMpA4Md/Cs/20hhCChN5CT+wYN6qnNnSOisZB5QSWrJOTM5RlDpM69gZ6ez/yuRgPPRnF8ySToy7XLtpcvWBz/bLN3IxLtexTq/hUKz7OnSWO2hVs2Qj60mOycAbHq9OeOkwy0klYDyrYbsL1LOp2kWJtjMniWUr1jemaSdsi3N+Pmy/gN+qLLOabQM0pkDfHSeqtqEKfrwazKNtZivY0eWucgjW5bYHL3YQvXUarp3F8k73JB3ZMfFVBoT26d0Uj6NUqVK9fwquvnWy2Z9/70EygOVfkWvmNbSu+S3xy1jg5a4yOyL5dycnW3DzXym9sOx8LwUZupHKKY80f2lKF603B5rjRsi1txPcLpJRUnRxDlbeZaVxbZ5MhSCX3ENITIATlyjgNM08s2oo/n6oRQqM5vQ+EIBJOMz17mnAoRSScJhxqYnbuHI5r0ZTqR1NDFMojWFYJic947QKWV2Nf6qH5yMJOXEuCPfFjZM2Ree92bbjYnPdeJy3aSIkMGdGBjUVRzlGRRepU8Fh77VNVwZ4BnT0DOk9/IoaUUJjzGBtxmLjhMD7iMDPhkZ1xyc16zM16VCv+jwMj3xJsizvUly4zpUsUamPEw62E9SS6GkYIFd93cbw6dbtIzZzD9Td+41tTU0E5bDaLb9tU3n5z03OT+MyY1+mI7keXYbLmCDlzdKFicCtCne8FPOkwVb+MKlT2Jh/aoQpSQZPRia6El+XPnGI+aINYB9nnd48lZDO4UT21oUVhI7C8KnONGzSHegip0R0Z8yY832W8doGKs14R2UYhKVqT1Jw8CSOzpRGEUEjqmdWNoIBoR4J4Two1pK0YjvZd/z33GAMPsMSl0kvkzbENhf6ikUzQDiF9IuFmTKtIwyzQmjnKTPY0mmqQTOwhV7hMe+YYk9MniUXbkdIjFmvHnXkHRSgYeoymVB+WXcaygiiXxCNrjiCRHGx6fL7QbZvVx0IQ0VL0xI5ytfTahtavGhVqssKMHCMmkrSITvqVw7jYVGSROTlJTs5suOBGCGhuVWluVbn7gTBSSupVSS7rkcsGhnBm0mN8JDCQYyMOc9Pe+z6EuiNSSrZbI18Nes+C3I2y0B6wFfj1Go0rlxc8wMa1zcm73ETFnuNy8WUsr0bDK2P75pbn9F4iSPBfIKY3B5p529xZCiEIqVFSRjtz5grkAhu4as2p3e0P2wgqzhzT9a1dG6shZ43ScI9jqJEdZSMpOTPMmTd2NPJg+w3y1sSWjSAIolrzys+ogq4nBtj/8/cQaoki1JU/DTNff8+NoCstLuRfIGeNsdH72/c9GmZ+PmUTaOI5bn2haM/1THzfJhZpZWzyNTzPxnUbxKKtTM+exvNsWpoP4Lh1qrWZZZqnEp858waiKDje8mE0sf0eTwWVTLiPmca1ZRR9q0MQJkar6KZZtFGVRfJyFoMQfeIgURKMyitLNw4bXCKFEMQSglhCoXdwPtrmQL3mU6sGP4Wsx7VLDlfPW1y9YDM24uBuL2iz49hxUd2gQXx7+Q6jq5v00x/E6AykZxrDw2T/ZOWGzrXn4pM1hzecFBYLApTMi/n6qz4vpX+LsRALjwshkL4HQpn/922bAbHYHL90jLXhSYerpddpDvXsyM5SFTpJvW1lI3grFIXY3kOEMu0Idf5yESBdh/wdVJe/HVJKJmsXNxVh2AiCdoRpkkYrYodK3n3pUjAnqG2RxHw1uNKhZE8j5fEthdwEgpjetOJz0fYEg184hpEKMfqdy9QmShz/rx5j+Kvn8RyPnqcHaWRrvPMvX9zmu9gepJRcLr6yKQM4f2ZwjwcEmhh6jM72e0nGu2htOUK+eJ1wOE0k0kLISFCtzaJrEZKJHgwjTrU2E1ASNh9EzIdUl79CsP4Mld/mQOrRLaV1boUQgpiWpiXUu64RFAiaRRvdYi9x0URBznLRO0mNMj4eAkFBZtmv3s2Ydw15izdo2xLHlgglaN+7eWmtt+YIIdANSBkqqXSgACQPwr0PR3BdietIqmWfK+dtzp+yuHDK4volm1rVD/SBN74c7ih2zAhqSghF0ee9lEDJ2Jcenu9sWlRXTzdTPX0KY3KCyql3SD7y2JbntREDKFSNVNch2vY9RDjVBlJilrNMXXiBysz1IGzUeYCOI08RijfjORb5G6fJXnsD16qTGThBc/89OGaVWKaX8ZPfpGXwBJFUBzOXXyZ77U2QklhmD13HPkgk1Y7vuRTGzjJz+VVcc2ONsI7f4GrpNe5u+RhiDTqljUAVGjE9ve5xLY8+Q9O9D+FUyqiRGF6jht7UfMd7BG+H61vMWTc2VfW2UcyZo3TFDu1Y31fDrZCzRndhrnK+VL9GZIsk3REtiUBZFl4zUiEibXHO/4c3mHj+OkJVOPTX7mf2rTHmTk0x+uwlHvnnH6f7iQGu/+nyHrA7ASkl042rzDSubzK9IZmePbXs0ZGxFxkZC4x6OjXIXO4S5eoErS1HaE4PIpFcG/4uqWQfsWgr2dwF5vKX1mz5kkiGK++QMtppj+zb9uZVESrpUDfRxhXq7upFhho6B5R7yfoTXPVP02ApS5REUqWEy/K1+R/857N092nsPxJi/xGD/UcNuvboRGKCUEhghAIBW1Vd3ygKAYoBuhEcG08qdPRoPPnRIN1gNiQ3rjlcPGtx8VTgLRbzHpYpsSyJbcpdF6zfAVHdGMlIJ62JfSSjQXHMTVFd0ylRqk/Ni+pObVhU13ecec5CBS3djBbfxZ40IUh1HqT7+IcoT19j6sKLSN9Fj6awa0UAEu2D9D/8M8xdf4vJM99DjzbRfvBxVD3MxJnvIhQFI5YmN/Iuqh6m76GfYvLM96kXpmjqPkxp4hKKHmLwsV+gPHWZ6Ys/Qg/H6TjyAUAwceZ7wTZoA8g2hilYk7SE92zzbQcNv4YSXVMZPXHoLqa++ccgfSK9e8m99H2a7nsEPbW+Ad1NlOwZHG/rVElrjz2N69tBCGsH6LnqbonSLhWf2H4D06tsyQgKIVDRMNQolre0EEqoCl7DxcrXFxwsr+GiJ4LCt8ZsjalXb9D78YPvmRFsuCXGqmdxtqCOsh5q9VnSTYO0pPfhuSbF0g2SiW7SqYGAxaoaqBas1/McQHKt9AZJo43oOnR/60EIQcpoI6m3UnfLrOb9Oji8472IxeqfjYvNRW95E77nweiQy+iQyw++GRjPWFzQ3afTO6jTt1enZ0An06aSSCkkUsHvaExsyFu8FZGo4NBdIQ7dFeLzvwiuK5mZcBm+YjN0xWH4qs3cjEep4JGd8SgXdt4ibltUty/zAB2pI6uK6jZF99DZdHRTorpOdgaEwHdswr19NIaubWeaa0LVw6Q6D9AozjB14QVcc3lVZGbfg1jVHJNnvrdw0WtGmM6jzzBz6WUArGqe0sRl9FCMSKqNuetvEcv0EW8dQNFDtPTfC8D4u9/Gc4LFO5xsJbP3AabO/xDf3Zi37OMxVj03TyC9vdygrkQIq/E1jaBQVbxqGSUSRagKQtOoXj7Lnl/6L1alVLsTKNkz8/2cOw/bb1B1C4TV7W++fOlSsqe3Xb26GhzfwvJW//7WgxAKhhJZZgR9x0f6Plpkvh9USqxig+RAM1MvjQBBY30ovbtUf6vBlx6z5vCuVbbaTpWZ7Jklj+ULW1+H6l6RsepZ9qUeRt0And5aMNQILeFectYYzi3pgCTNJMX6m9MGdXJyComkwcYox2pVyZXzNlfOB+uUokAyrdDepdHeqdHWpdHWodLaqdHaodLaodHarmKE1jeMt0LTAmPb3afz+IeDTWQx7zM17vCNP6zy9T/Yefq4bYrqPkZ76lAgqlsbpdSYwnKqSOmiCI2wkaQp2k0y0rkpUV23UFj47czO4DV2r2FXUXWMeJra3CiutQKxtBCE4i3U8xNLdn2NchahqIRiTQBIzw16hzwXx6wt5BpABgrZqTY0I0L/wz+zMEY40YIRbULVwxs2ghB4KlUnt0xjb7PQ5ynbWGN9tmanCHf10pi8gRZLkjp+P0oodMeV5W+FLz2qbn7XDAtA2ZohE+rd9jiedMlb6+thbhWub625iVkPCirGClykTtXCrljE96QQbylIX5I/P0P303sx52rYFYuOx/pozG6Pt3GraLhlso3hNXsB30/wpUe2MUxbZIB0qHvb42XCfYxU3lliBMMiumAEJRAiTEwkqcoSLi4hwkREjCH/wiqjbhy+D8WcTzFnc/ls8B1oGiSbVJJphVSTQiqt0tGj0dOn09Ov09Wn0d6pohsbN4xCCNItKukWlbNv745+6TZEdffSnjyE61lcvkVUV0oPOb/wK0JDVyO0pQ6yt+1xupo3Jqob6glCfdLzaf7ox6ieepfqqZWZLbYPCb6PmCcDl7cX9chA8kPRltJgKaoGQuAtlDoFBm81sUnfd7EbZUoTi3RlNyP6nr25sJ7jmxSsSRJGZlsVZ6qir6t4nn/9RXyrgVMu0pgYIf3A44Bg7sXvbvl1t4ub5OW7ibIzCwvcR1uDJGCz2c0+PE86OJ6JlHJLoVtFKCu23Zi5OmPfu4pTtYMMvy8Z+95Vup4a5PjfehTf81EjGif/8Q934F1sDlL6ATWiPbXi80Y8jWs18J3dCZdvFXW3RNa8Qcro2Ha+OawmSBrt1NwSN9fSnJyiILNA0Cw/qBxh2j9PQc7gI1FR6VIG0Ha+HhIA14X8nEd+bnEN1XUIhRVCYYERFkQigo4ejb7BIKTa0x/8tHao3K7te6eksrb0aQSiugcQisK1qR8xXbqwzHjcVJpwfYvx/LsYWpT+1ofpbDpGrjK0ZjOrmkgGTaqDg+S+/S3SH3hm14yg51jUC1PEMr2EEhmscnARIURQ9ix9KrNDtPTfixqK4tsmCIVE215cq4ZVzRHPrE++XJkZIp7ppzx9DWc+5HrzS/a9zXk0QVXgFMjj26o4C2Sd1iYZv7UVonTqLcpng+9Beu8dyYA5L4+0m6g56/dKrgsZtHHsVtj2Jpx5MvethNmEUNDEck/QM11Gv30ZCAwgQOVGgdf//nfofmoQRVOYfm2U4pXs9ia/BbjSZs4cWbXdpPXwY6QH7qEwfJrc1TexKnmkv7o+3Z2Cj0feHKcWOUBcb9nWIi+EoC0ywEz92kJOz5v/7yaiIsF1/xwWwb3iACWZo0sMMCZ3L8V0KxwHHMeneksUc/iqw+svNlCUoCFf0yASU+jp1+nbp/PQkxEefCJCJPo+NoK3i+ou855uw01RXbPpyKKo7hrnSM8j1NWFX6/jlUvbLi1eC75rUxg/T6Ktn94Tn6IyO4T0PNRwjMr0NcrTV5m9/AqJtn72Pv5LlKeuYERTJNoGmTr7g+Dm2gByw++Q6jzAwKM/S2nqCtJzCcXSOFaNqfPPb7gwJoDE9GrbqgqEoEJUF2t7gktf1ke67z3JgH0HjGDdK+Pjo25Nd3oBd4L02PUtPOmgbuF2FihoqxBx3zR+iw9A9UaRy7+3W1GZ9RFImtXJmiNrHYQRTdFx/CnajjxOdeo6uaF3qGVHcRvlTUdebocwNISyfE3yLWfdLo2yPUvZnp0XiN7eutYS7kNVdHx/pbU0iEw1iQwFmUXOe4LNom3FitA7ASEgFBZEooJIVCEaD3KKgwd0+vfrDOw36O7XiSd2V6vxdtxRUV3Xs9cW1Z2Hk53BaGujMTyMdBzM0d1Vi6/nxxl562uku48QTmaQUmJW5rBqQV+XY1a4/vKXyQzeRzjZiueYjL/7LOWZ60jfx6zMocwMBf8uZylNXQYJjlmlMn0Vz2nguzZDr/0RLf33EE13IRQVu16iPH1tkwYwgOM3sLzqtoygEAqqoq9YIr8WAqFRsWvKGuvB9gPh2d2ELz1sr05ES65/8KqQVG9niNECPl3pufNaa2oQdTAtUBSUcHieKlBBa83g1+v4lbXzboFg8NZ6cwViVZL5Ww5CixoomoLbcHZdTX5tSPLWOPYaFaGVqWvo0SSRdAehZCvJnoMkuvZjlbOUJi5TnR7GLE4veoibROLEIFoyihLSg+/O98GXFF44h19fO2/l45G3xmmNDGyb/UkXIVpCe5huLCeMcHGZ8cfpVgZpld34+OgE9/qIv7KCzI5DQKpJCVhmMiqZNo2efo09gzq9AzpdvRrxpLqbPs6GsA1RXXdrorobuOjcQoHSy4uqEVsNhaY69qNoBsXJS0ED+yqINnWiR1NMX34ZuUpo0mmUA49tBVRmhqjMDAX/nr5KZTq4KK3KHNMXF9+H75hkr76+7Pzwgf2YVzbHfLLdqsCbuKlsv3akSBBVkzTrXUSVJNr8ojlqXqDqBRsFFQ1NCVgjbLl7XtpNfbjdDjFCkHvcnhGEym0N8qH+PSjxGO7sHELX8E2b0J5u6ueChSl8cB+NC5dACKJ3HcEauoG1nhH0nQ2W6q8AsboRFKqg6WAr7Q/uIdqZACkZ/e5V5t6dRI8btNzdSelajsbMnSuOkUiyjZE1jylPXKY6M0w43UG0pZt4Wz/xtn5CyVbam9pp2fcA9dw49bkxqjPDVGdH8KyN30tepQFCkL5ngPJbV5GeT+xoL0pIX9cIAuStcSy/jq4sr6jfLFoj/SsaQYnPhByi6heJkkBBxcWmJPPU2R2BXkWBdItKd59GT39Q4dnZo9HepdHWFRjBoChmc+NKKWnUJeXiCte4IuD2iIWqoBgafmNjG+VtiOoW50V1kzsnqrviqYLEifuXGMWNIp7pRw/FgvDjGjvlcLKNaLqLanYE7zYjKJT5nfpGcmCqit7WBkLgTE2BoqC3tOA7Dl6xiJpMInQd6QZjecUiekf7giyR0DREKITW1IRXr+MVCghNQ2ttRQkZuMUiXjEop/F8Z0dCgopQ54VKV/98ukMH6A0fIaIm0YURMO9Ll6w9umAEE1qGvdF7kUjOVH64a1V7vnTn+wN32wOV295kuNLGvE1nU003oSbi6K0Z7Mlp1CaN0L4BzCvX8E0TETICde6GibQd3Oz6XKOedLesShF4gsuXAaEI2h/q5dBfu59EXxNu3UENa+TOzTD37iRSSvb+zHHm3p3k8u/eufCo69sUrIl1j/Ndm3p2lHp2lOLIWYxEM7HMHlJ7jpLoHCTRuY94+wDpgXuwKnNUJq9SvHEOs7R+EVPt/BhCV0k9dIDC80GPZLi/bcUQ6UpoeFXK9gxxLc12Q6JNoS5ukpPcDh+PgsxSJg+IdQmzNwshINOusu+Qwb6jBvsOGrR3aSSbFJJplURSIagf3IIAQtXnxjWbqxdtrp63GRsKuEhvhZaKknxgH/kfnl00hIogef9eQp1psl/bGOf0NkR1R+lteYD+1oeomrNriuomw+30zIvqzpQur2oEhaYROXgYt5AneuDg/IOCyMDglozgRlGcuEBp6jKes3wX17znLqSUFMbOrG28FYX4/fctLGDO7CzhwUGMrk4UQ6d2+izx+07gFgqEBwdoXLtO4+IlhKIQu+suGucvoCaTxB96EDebJbx/P/mv/TlGVyehvl6UaIza6dOLRlC6O0IZJoSyZoVpd+ggh2KPLIjESlbmNqp7ReJqGl2EaNY7mbV3J4TtSWdHVBg2AmebHq3pVpdvvhwXr1giNNiPf+kqob0DCC2oNNaamzF6e/BKZczL14Lc+KH91N9c28j48xXZW0FQxb28UjHcGmPwc0dxqhav/p1naUxXeeZ3Ftt73JqDma3R/nDfHTWCRXtq0xss16zimlUauQnyQ+9iRJtI772X5v7jGIkMRryJWGs/rUcepzY7Su7qm1Smrq3ZtiR9iVtp0Pv//hy+64Ht4jsbNTKSnDlGR/TAtnLOQghCSpSY1kTNXV7IZRCiTzlEq+hCQcWiwYh/gaxcuap27dcKfre0qRy7L8TxE2GO3hOivVsjFBGEDIEeWp9F5iYWuFZlYL5tU3Llgs3ZkybnTloMXbapVnxsW2JbckW+UWFopJ84gle3KL0WFHG1fOguMp+6n+k/eHnD720borqXaE3soyU+yIN7/wqjubfIVUew3cXS9bCepC15gO703YT0OMX6OLNriOpKKfHKRbRUE0LTsKenAz3CnjXYUYSCqoWClgWCVgTPMRcXagGqEUGVYaT08Vx7SchTDwdyKrdf8EIoqKEoibZBnEaZajSF9Fxcx1wxZKrGYiAljYuXcPN5RCiE3tZK/ew5tEwGrTWDEotRf/U11GQSv95AGAb25FSQFwJQFNxcjsaVq2ipFGoyCYqKbzsgGvj1Rc/En68Eu9mOslUozPOhrrCGRpQkB2IPoAqNOWec4fopSm6Wh5s+S1RdynxhS5OKm6fF6CatdeyiEXR3vSjmJtxNiKuuhJU0Kuunzga/3z0LUmKPTwYbeV/ilcoU/vjPF67d6suvg7Z+Kb2Pv/VwKLeQQ96CUDpCtCvB+X//BrlTUyja8mMaM1UyJ7bf87YZFFdpi9gIpO/hWXUaVp3G21NMn/4BiY5B0oP3kOo+hB5J0tR3jKbeo5jlWeYuv0Fh+BROo7o8b+/5TH/phQUP0BzN4tc3bpwDGTdvIb++VQih0BTqWGYEFVT6lIPESDLkn8eWNgmRYq9yF8JXmZWr967qOgE1WkjQnFE5cm+Yu+4Lcfy+MJ17NJT5JePmtDcyfyklnguOLbEdiVmXXL9kc+Ztk7NvW1w+b2E1ZMAhusH9nDNXYearr9P6yfvxLZf40T2kHtrP2G98h9r5jRP8b7lhpHhTVLf1USJGikNdH1kiqqsKPVBEnxfVLTWmuDb9Ag1nDfFaz8MaG0NLN2NPT+EW8iAESjy+8vFCkMj00bbvEfRoEgFY1QJjp5/FmefjNCIpeu76GOF4CyApTFwke/0NPMdEUXUGHv4i4XgLlewIY6e+tdAwH4o30338IyTb9uL7LqmOA7iOyfTlH1GeXiEG77qAQGtpBlXFK5fw63W0TAtaKombzwffru9xkwxPqCpqUxNKKISaSiEUBel5SN9f6Df0azWMzk4aly/j127j/5PedlvZYA0T2hnaiyZClN0cZyvPY8ugGGG1a7TmFciwh4i6vTzaWgg84DtT3bbdZvyGu0buZWEnLJd+oLevAO76YU4p/W3xkq50BSiqgvQkTm31z1rRlTteF1W0tl9tK1QNI9ZEKNVKoms/kXQXqh5C+i5Oo4Ki6oSTbex56LO0HnyYyVPfpzx+cVlVqdHeRKgzjTk2h55OYDtFpLPC96Uo3E6A2fDK1N0SSX17hBcChSajk4na0mIXFZUkzVz2T1GlCEBeTuP4NhnRtaYR/NBn4jz6TIQDR0N07tE27N3dCinBMn2qZZ9KyadY8BgbdhZYZ65fsjHr27x4pKR65gZqNETnLzyOEjIY/udfxRrbnFzZlo2glB6ThbPYbp2O1GGS0a5VRHULFGvjGxPVFQI1nljImamJYDGtnT2z4uGKqpPqPIjnmoy//h0kknCsGdde9Jgi6S7mTn2L8ewIidZBMgP3UckOU8uN4nsOV174bVoHHyDeOrBkbLMyx/Abf0z/A1/ALM8yfeUVpO/Oq0Ish99oYE9Oore3oSYS1M8UsIZHCA8OIB0Xe3IKJRbDt22ssTG8cgXp2OitrZijo+iZDE4uhzObRToO9tg4vm0TamvDLRTQMxm8cgXrxo2FhVLO/7eTkj+3Iqm1IBDM2sMLBnAtWPPHbKrtYpPwpXfHWEK2K3t0OxXZbiGIrGwxHCrEfE54KVzTwXc9En1N5M9NL7PN4ZYo6SPtFC/fuT5B17epultX4tDCcSLNXcRae0l2HSCa6UHVQ7hmjcrMCLXsCNXZEbRQjHj7APH2AcLJVvoe/QJTp39A9uIrCxEjoalkPnU/ft1CKIJQT4bcsydxcss3PqG9vVhXR5Y9XrAmdsAIiqDnEHHbRkigoCzLA3o464ZgP/zZQFl+M5BSUi75zM14zE27ZGc8psYcxoZdRocDEd56dfs7ptiRPYS6llLDCSGwZ8toTTHiR3qIHezCmsxTu7AxpqZti+rOli9TrI8TD2UI6Ql0NYIi1CB34zZo2EWq1tyGWimEppF88GGEpqJEovimiRIO4+TmKL20XLJFeh714hSZgftp2/8I5akrlLPXlxiqen6C4uRlfNeiXpzEd4+h6rcv0it/Ob7vBr1x0p83gGvvyu3xceyJiQUj5RYKVE8uhinqpwNj3rh0eeExN1/AvLroWXrFYnDMlSsIXUcJh/ArVUQ4tGw3GcTVd28rHuQBBQ1/o4v5vETULs7ppjLJncDWQ4wB1irj32ls5xMPollLiysaszVyZ6YZ+OxRjFSY0rUcQhXEe1L0fHAfHY/1Ed+T4u1/cucYY+puEW/TLQ2CUKKFZM8hEh2DRFq6CcWbg+K1eonC0LuUp65Rz41jlecW7t3ijXNEm7vIHHqY5sETZA48SH1ujMrUtZvDooR0ii9dwMgsRj6ErhH/0KPgLV474cODZP/NyLIvaeOagGu8OyHQlTC6EllCn+fjUadKj7KXWX8cF4eIiNEp+plbLye4gYvJdSRzMx6jwzZjw4Gxm51yyWc9clmPfNbDbOz8OhDqShM7fBs5iZS45TrScYke7JpvkZR3xgjehO3WyLu3iuqKLS3Q0nWpnHqHcG8fQtcxh4cQuk7y/gdXPl56FCcvYtUKpDr203XsQ7TUTjDy9tfw3aB4wjEri/H8+d+7Ssezg6wU0nGoX7iIEgqMtlep3FHWi6ANQaKvwCiyEqJKApBY2+CyXA8SH28HhWnXgr9dI7hLKhe3Y5GndqtYfj84VYuhPzuHkQyx96eP4zs+WkSj/1OHkb7Es1wu/NZb5E5vPUe3WQSqCRv8ToQg3tZP894TxNsH0aNJVCOMEAqN4gy5q29TmbiMVSvg2Y1l95XvmFRnhrAqc0SbezDiTUQzexaMoPQl9nSRzGceRI0Y1C6O45tOsPbZDub5RUYWNZ1c8eup2DvjRatCI6ImlhhBD5dR/yp9ykGOqA8gEPh4zMhxpuU6IsirLI/DV2wunbW4fM5m+IpNPutRq/rUa5J61ce7A+2jxVcvUX77+rrH+fbGN8q7JKq7xRtSStzcHH57B8J1cHK5wCtcLScI+K5DLT9GvTBJdvhtDn3gPyPVeYDC2NmFMTeGFb55GbwfoagrP38H4NfrSwpi7iQqboFWo5dWYw+j5nnWWmh1EabZ6EYiKbu7FyLzpberxNk7iTvpCe44JFTHSrz7r35E08FW2u7rJtKRwHc8ykN5Zt4Yoz5VRnp3blPWcEvLFNxXQrLnEJ13fZBwcyeqZiy0OFWmrpO9+CqV6Wv4jrVuZAfAqVeoTl8nc+gRtNAtIULPJ/vnbwSFMbqKOTyLVws2PdUX31ySy62+8MbK78er4Pom+jab5hWhEtYSlJylnmWFAhf9t9ExUNGwMXFxN6m9uIhITGCEFRDQqEtmplwsUyL9ZUGqXYNft5cWIK3UJyhWbhlZDVsygqrQSUW7qNl5LGdzjZeqoqMpYRRFDao1fQfXM5eE0JzcHKlHHiO6/yBqLEbt4sqs56oeJt19FATYjTKhWDNIH7u2sbyBZkTRQjH0SArNCBOOt2CrBnb9ZghTYpbnSLbvo16YwnMaNCpzGxbB/XFH1h5hT/gQab2DgcjdTJiXcaV9y3ZALKgQDEZPEFHi2H6DGXt41+Yk8VftiROqCKi+3luKyAW4d6iVYzfhmS6501N31ONbDXW3tKFQe7LrALH2AXzXxq4VqUxdZ+7K69TnxlbN6a+FwFP0l52rhHTqF4OQmxoPLy7Ivo+WSaM0BZXnQlVwZ3LLxpXSp+oWSW/bCAae4HIE92fgBfqoaPj4eOsZwVU+4vauoPH9mU8Em4F6TTJy1eHK+cA7vH7JIp/1sSwf25SYZlARulvQklGanjxC+c0r2LNBwaXQFJoePYTXsCm/tTF+1C0ZwbCR4r7BX+Dq9POMZJczoKx6np6iO30XbalDhPU4rmdRakwxVThHrrpIqu3MTJP/zrNoTU34jQZuqbjieNL38D2HVOdBVD2Ma9cYPfUtavmgmdasZHEa5YXcjufa1PJjCwTWqa6DpHuOo8zTl3ceeRq7XuTGya9z80qYG34bIQSZgRM4jQqzQ2/8pTGCZW+OcesSA5G72Bs9QbPeScGZQhdhBIIWo5OElqZF76FJb8eXPsON05j+7ik8SBkYQdVQCCUNXNPDrjpEmkM09cSp5UwqUzWkD0Ys4Hi0qg6KpqAoAj2qYVVsfHf3LeWdyl3+ZYHl1TZkBJ1GlcrUNSqTVyjcOItV2l5kwnNtanNjQc5wHkJXaf+FJ5n4jW8DkPn0A+S++y5uvgpCQUnE0NsyiLBBaH8f1uWRZVEpicT0KkDntuanCo3wMvpEQVpk6BKDREUMMV8kU5BzTPjX1xTbbdQlnifnWyFu2fLelkaKxQVH7w1x9N75dI0X5AlvXHe4cc1m6IrN5KhLIedRzPuUCt6K/X5bhRoPkTwxsMTYSRl8N4kD/btrBGHeBddTNMf6iIaaMdQoEonlVinVJ6hb+SUXrKaGGWh9mO7me1CEhi89NCVENNRMU7Sby1M/YKa0WObrmw3s6bXDSb7nkB87Q35s5erR3I1TS/52GmWmLi4W2ORG3iU38u6ar+GYFSYv3Hm5mPcLhhunEUBX6MB8aHRRY68/cjcQ3MwNr8K4eYlxc/d4CaWUQZ5OlfQ+2IEaUmgUbSZPzdEykKTngTZy18vU5hpE02G672vFtTxy10toIY3MvhRW1cF3fUZemdpVj9GXq3us71f0fHAfsZ7Ntbf4js/VL5/anQndgoAub2NMQYWRU+SuvYXb2InNqiR//R1KYxdw6gFRhZaOkzgxSLivjcynHwAhMNpSi1Pzfdx8CWnZwXM9HYGXuELoeM02mg1CoKDfpgmpodEnDlGlxJQ/jIdHiAidSh/dyiBD/vlVx/vmH1UYumLT3Rvwe3bt0WhqUddtlVBVseAtPvhEJKgYLfpM3HCYuOEyMeowOeoyOeYyOeowN+ttz1Ocb1Zckv+TEul6CGPjpm1bOcG25AFa4gOE9DiaEgIkjmdSNbMMZ18lVxlaMITJSAct8QFURWck+wb56g001aAzfYyW+AD72p8iXx1ZV3D3xxEKKorQUBUdVWjzf9/2M3/M4mMKCtoqxwU/CT2zYnn7TsKTDkP1U+SdaVr0btJ6B2EljiY0POnS8GsUnWnmnDEKzjT+FvMNG4WPB1KS7k+ghlQKL04ifYlVdahmG8xdLeK7kj0PtVPPm/iOpGVvCqEIwkmDmfN5+h/vZOZcnkZx98KV/ntELr4ddD45QNsD85V3UiJ9iRY1UEMq0pO4poMQAjUceNhmrk7hUvaOGEFPOhvOBduVrbdRrDhedel40nZxsmXcYhV7ugAI6lcmAk5RAEVBa06hd7aCBPPCtSXVogvjIDFXIFTYLMQ8/2vgXARWRaCgC4MJ7zoNbonM+NAu1iAfAV57vsEbLzZIpRXSmYD8uqNHY/Cgwb5DBnsPGTQ1r0/iIIQglVZJpVWO3AO+J6mUfQq5QHMwO+0xfMXm2sWgb3B2anObRt92QUJ0f1fg9UmJGjaIH+/Dni2tP8A8tmUEI0ZqIVEdGC+JrkZJx3oJ6XFODv8hDTvIr0WNNCE9Qc2aYzj76jyzjKBqZjF6YgtGcrq0fdXjOwUFlYiWJKTGlv0YShhNGPMXZ1BYE/RkBbup4Hewk1l8RMwfd+vf3PL4beeyy5Wu8/BwyTnjFJ0ZNEVfyDPIIEOHK+07VKwiA4owH85/fZimPQnu/fn9PPeP38ZpuDg1F7NkIz2JHlZp5CwQEEroCEVg1xwaRQvf89mmpun6M5Xej5kJhLP/5hXUSMBjq2gK3U8P0vnEAKPPXiL77iSe5SEEGE0Rej+yn8yJLi79ztt3ZG6ub227ZWWn4NVMqmdv4DUsGldvy5UKgZqM4UxlcWdyQXuVvZq7I3dMHFoVGpowsOeNoMSnLqvoIkxDLr5GSESosb7h9X0o5HwKOZ+hyw6qCuGIIBxViMYEe/p1jt8f5vj9IQ4dDxGOLF2HVlqXFHXRKPbtBd+XWGYUsy5pNHxmJl3Ov2Nx9qTFxdMWhZy3Zl2jW6pTOXuDrr/6NM1PHcWtmUQG2kDC7AZ5Q2GbRlBKn+nSRa7P/IiGHVjeeLiV/R1P0xzvp6f5Xq5OB6FETQmhKgY1K4/r3dyBSyrmDIXaDeLhDC3xwfeVEbypsCBQMJQISaOduJ4mrjUT05uJaqnbuDcXzdaScd5rrZAdgofznua5AvUSHyOmsf9De4i3RbCqwXwaRZt4W4QTv3yQt377AsM/muTErxzCabgMvThBOBli4LFOUj1xanMN6rndLVrZagXeewkzt1iFHN+TovlYB9f+8DTjz11bqi04VqJ4OcuD/8uHGfz8Md75Zyurq+wkHGlv7TMVIlC62eg9KOWGqkaREnN0DjUWChhhpMSrWQhdI/bIvTgTMziTs3jFMkIRq8qn7hQFoCo02pRuDBlsYm4SSR1R7qcg5/CkQ1TEiYsUF72Tmx7f86BWldSqHjlgfMTljR81UNSAYu3Q8RD3PBjm7gfDDBwwiEQFqhYI5iorEIsLEYRPozFBNAZSKnTt0bj7/jC+H9CrjQ45nH7L5NSbJpfO2BRzHra9eB1K2yX37Xewpwok79+HGg9TPnmd/HNncOY2HmbelhEs1Me5PvMSNWux8qncmGJo5iWiRhOtib0LRjC4GAWeZ3N7mKjcmML3XWLhzHams20EAqMGuhImrMZJGm2kjDaSehtRrekvjDHbKBQ0/E0yzwsUQkoM09+N4iGJxMOuuZz/2jCKJvDsYGG0yjbv/P7lgKbPk9g1lxf+5TuBTJQv6X+8k6s/GGfk5ck7UhRzewFHJBZ4+OuxZmg6hCMKnisx57kU3wvoiRChVJj6THW5uC7g2x6lazl6P3rgjszHl5ss7RcKoXiaWFs/0eYu1FBkRY7U22FXckyd+v76w2sKXb/6QSL9baixMG65wej/+jXcqoXemUHvasMrVZGNgMe49K0XVhzH8S2klNteWxShEVGSRPylRCB1WSFECOZZnKqyREq0UJDrq2WsBSkDw+h5gcE6+arJyVcDg55sUth/xODovWEO323Q3asTjQkiMYVoTEE3ljsGN/9WNVAB3RAcuivEobtC/OyvprAsny/9XyV+598Ul5znmw6l169Qev3Klt/LNmjTJDUzi+UsZxOpmFlsr0E81MLtTBQr3dOmU8XHJ6THtjqdbUAQVmPEtDQxPU1SbyNltBPTm1E2qJX4FxVdoX3MOeOYG2SMUYVGp7GXZr2bM9XdKSa6GX6XvsSzl15N0r/N+MjF4yuTNYQm7ogBDF56aQP7A49HiSUUnv9WNWAbUaBRW2rkUs0KT38iTt8+g1rF5+SrDc681djVMvNVISVaRCfaFievimX9gFpUJ9aVxFuJK3MX4EtvQz2CN5Ho2kf3iY8TzezZMMGz7zlUJjeo6ykEajTM5G89h55JYnSmkZ6PtGwKX/k2Rm8XXqmMXw3SRKsh6Ht1FzQ6twpVaGSZpOi/960s5aK/YBSFgFRaoXdvIKS7Z0Cno0ejOaOSzqg0NaskUgqqurbOYCikkEgtX4/VWIjI3g6MTJLyO0O45TpGWwqvZi3maNfBFo2gDHZmUq5csrwQHVRueWh1g+JLF6REWUHXbLegCI2U0U5zqJuU0U5cbyGsxleUlfnLis7QPpr1Li7XX8Nap/HbEBH6IsfYEz5CzVsu67JT2GqYMTe0/QKEzeD2+6KjR+Ohp6L07TVINCkoSsDA8aPv1JieCKzcp342yed/JcXQJZvWDpVHnonyL/+HLJfP3vl+QzPfoDpeYvCnjxFKRyhdz+FUbYQqCDdHydzTSftDexj66upVhjuJzchFGfE0HcefJprpQfou1ewoTq1ENLOHcDJD4cY5pO+hhaJE0u1okSSNwhTZS69iFjZI0C3BLVSRQLg3g9HZjFCDNc6vNbCu3UC6AVm+CBlrDCPxpI3G9oyguBn2fZ9BSijmfYp5izNvBddxOCLItKtk2jUybSrNbSptHRod3SptXRod3TrpZgVlnWpUJaSTfuooyfv3EtnXiTVTwquZJO4dwDedBa3H9bA1UV3pYTlV4uEMIT1G3Vp6kybDHRhqFAWFVKSTqpXF0IKGUE0N+ChvRSDqKZB3IN+kKSHawoN0RPcR09KE1Tiqsr0L8C8yOkKDgOBS7ZVVFeOjSpK90ftoDw2goFBwts/0vxJuEob/uOKuB8IcPB5ifMRBAA88EaGtU+P3/12BYs7nmU/FeefVOr/zfxRoblX5b/9RKx/7QoLL56w7XmhqztW4+genOfxrD3DwV07g1Gx82wMBWkRHi+iMPXeVoT+7c0Zwox9CrLWXcKodgIm3vkVp/BK+Y9L9wKcIJTNMn/4BTqOEouoYiRbajz9FpKkTz7aozmyM6EF6HnPfehu3YlJPRmkMzeDe9DykRN5Sth89cZTaa6u0Ykm5Q600KxOhvx9hNiTjIy7jIy6hcGAQewd1Bg4YNOoSwxAkUwbKOv6ImowQP9bL3LffpfWzgSmTvkRaLtG9HbtrBF3PolgfpyN1hL1tTzKcfYWaOYcQCsloF4NtjxE2kggEd/V+jkJtjJZ4P1JKokZzEJ645XqOhppRFJWatXu7dUVotEUG6YvfTUJvQRVGUJz5HtGh/ThgqP4udyWeoT3Uj8TlUu31ZYK2cTXNwdjDtOjdgOBa/S1umLu5MP74GsFi3uNf/g9Zrpy3EMA9D0X4wl9NsWfAoJgzaevUeO7rVabGXKbHXd56uc6DT0RX0Q3fXUhPkjszxZv/43dJH+ug5Ug7oeYIvuNRmyyTfXeS6mgRt36HyMzxN/wZhBIZ1FCE2uwNspdfX9D/9OwgP+e5Fk49WGusSh67WmDfh3+V7vs/QX1udElT/EoI97aihIONs5aMLtClISXC0Ik/cT/ScQkdGgTfR+9pp/b6uyt+iTcrrLeLQDPi/beWiflCdk2F1g6V/v0GgwcNBg8Y9PTrtLSqGCFQtaCQRlVB04OCmvWgGBpK2KB2eZIWc/46lODfiT5BxzOZKp6nOd5PZ9NROpuO4PoOAoGqBF6d5Va4PPUchzs/Smf6OCCp2wUiRoru9D1MFs8ipY+hRmlPHkJTDAq1jQshbhSq0EkabRxMPU7KaOPWVoOfYG3k3AlOV5/j7viH6Ajtw/FtrjfewZEWAkFKa+Vw7HGSWgZbmlyuvcqkdZ07vWRr8RRNB+6ldPUUTqW44jGx3oNo0TilS5uvjNsJSAmn3jC5esGmlA8WvYunLQpzHomkspAntE1/4fipMZfmtvcuPC99iVU0mX55hOlXRm578j2Z0oagGmEURaOWm0DeklD1PRcpfbRQhMWtnMQqZykMn6bt2JM0D967bmFM4sH9GG0pwj0t+A0b3/FQDJWxf/ssbqFK9dV3MbrbqL97Ab9aJ/GhR9b8vHai9eO9DIcKERR06bpA0wWhkKC9W2PfYYP9hw0GDhj07dVJpJQFo3jrErzV9Vg6Hr7lEO4OQtGKoaGno8SP7MGa2nhKZss5wUL1BkOzL9PTfC8RPYWqBHFvxzOp2wWGZ18lW7mK4zboaT6B61tMFs6wr/0pDnZ9iLbkfkynTDzcRiLcjuOZO9oeoaAS09N0x46yJ34URWg/8fq2gLwzxdnqixyOP0pP+BC2tJiwLpHS2jgUe5SwEqPi5bhaf5s5e5TdXh1XGl2LxEkdPkFtcmhVIxjt2EPq4In3zAiaDZ9IJCgJL84XU0cTQbI/3RrkRIQATVu8Rj13+1WDO4b3sdFbBiFAgO9Y3Dpx3wl6DY1oEzVuLDmlPjeGEIJoy20yPSsg+yevInSVrl/7MBP//jsgoe2Ljy08Lxsm1rVFpYbaq2uxUsmdMYLcGSMoBEFbQ1whllCIxRWaW1X6BnX69un07Q2KXwKDt71r1/MCdYpqJRDnnRpfWiHmlurULozR9lMPE96TIfOxexC6hvR9Zr+2Mmn5SthyJYonHcZz71KsT9AU7SakBUoPplOiUBujZuUBSa46TK66GGcfzr7KAfUZmuMDCx9Swy5xY+4NqubOqA9owqA9uo/e+N0k9Mxf+irP7SLnjHOl9gb7o/czEDlOXE2RMXrRhE7emWSo8S4FZ+p9na+Tvo8a3pxQ6E7ixnWHT/yMxs/+WhMXT5uA4K77w7S0aTzzyTj3PxbBdWDgoEEoElSxDh40yGd/vKjXdg8bX1A920R6HqFEmlur0x2zgvRcYm19FEZOLzlHznfWqaENklnLQFg3+cgh8Hz09C1KN5qG2pTAr9SQlo3W2oxdn1xR0UYuvPb2IG4h19gxCEgkAyPX0howx7S0qbR3aXR0a7R3B7+TTcqKvYCbgZQSx5bMzXrMzXhkp11mpzymxgPKtclRl5nJpUbQtxxyz53BmikS3d+FGjWwZ0uU3riKPV3c8Gtvr1ken0pjmkpj44UQc5XruJ4VsMpoMRzfolyfJFe9wU5sNw0lykDyPjojBwhrq0sw/QQbh8Qna48iEOyPPUBneD9IybQ9zFD9HSreztJUbRjzxOfBbxFUIyu3bXiEQI8liXb245m7R+y9Hq6cs/jWV8p8/ldSfPDTcaSEyTGHr/x2EenD8fvD/N//tsAHPxXjb/+jDK4jeeQDMZ79k8p71iv4fsJmlni7VsRzTGKtfQhFWWh+t0pzeI5FU+8xZs6/iFNbpNaKt/XP95hurB9Feh75758KpJSEoPTaZbxqI8h/taYJH96LO5PDq1SJf+BBCr//9aBadJV3t13spLz2iUfCHDoeonOPRktr0MbQ1KKQalZJJhWEsnY7w0YgpaRaloyPOIwOOYwNO0yMOuSzAaVafs6jXPRZj7fAb9iU37xG+c2NkWWvhDvXk3ALivVxyo0pFKEFcko7RLkVUuIcb/kwaaPzx6Li86ZKuifd+R9n/sfFlw6eHzzm3vxbOng39fSkpD26n0y4d/0X2om54jFr38DH51DsEcJKnLpXeu8MILDn47+EkWpBaDpGspnuj/ws0rGXHXfz+ZlXv/0ezDKAWZd8/8+rnHrDJNUcGOpiziM74+F7kleeq2E2JHPTLr/0N5tIZzTe+FGdr3954xyIf5EhbiaTNoBGYQrXrBJOtRFr66M6PQRAPTeO26gQbemh77EvMnf5NZxGlXj7AM17TyClpJHfYJ+d5P9h77/jJMmy+z70e8Omzyzvq9pPm+numenpsTuzO7MWu7AruIUhQT2QBEiJovgRREhPoiiJT098ICmKegRBECJAeI9drMHamR1vekybaW+qunyWSZ8ZPq7+iLJdtruqunsA/PbTu1uRkTduRMa9595zfud3sK5PEjo+iqkROt6CFRKKiprLoOg6QaOBdeYSco2Ks2LbOArb41YF+IGfSPPkxxLEEmJD0ezNYr7CxLWLLlfOO1y94DI+4tOohjTqUWFex75NMy4iklLTR49gtEf6wPOonr3J7NfXL44wj3tiBCEyANupsh9XMxxr+Qw5o+OuBYgXk3fnHRpyLu/Hx/LLWH4VJ6jjBDXsoI4XNnBDBz908EN3rkL6/DcX/3uZg0TKW48AElUYpI02YOtGMK5kiIn1B1Ck1eJT8vN8UH2JY+nn6TL3UvLyK0onBdKnEe785D399ndI7zpEas/huY2gGklOLO23DPGqJWbf/S6ly5sbFDsFx45WvqNDKz8rF6Pn/+q365x6tYGiCDxX4jp/vQ2E9fOMb4VdnMAqTqInc8RznQtGMHAtZq68Te9jHWR6DpDq3BMxOhUVoaj4Tp3Ctc1poQpNpefvfQ9q3CC0okX8xH96Ab9Ux8tPU3vpLaTjETpuJMS5zs+osHXyU8SeXRzDAkGcJDYWIQE6Ju2iD0tWKZBfp6XIBZpMb/55LwhYRKUUCUPwvUj27MJphwunHS6edZjJB/ieJPAlfhCJW2wFWi5F99/8GEJVaFzPLxMp90ub9/psyQjqehKBwPXqrPYr61oSIRTc2yy8e7tIak0cbnqOnNHBTleAl1ISMr+D83BDi6o3S9WdpubN0vBLWEGNnSeIbJ825aHUU+TUjtu4duR8ESg8kvnMis9L/iRvlf982/q3FuzpMezpMSo3PqDruc+Tf/UrWJPDG3/xPkYYREoy8+9PzMyhKgauV0cSkkp04Hl16tb2xM8/LLidmJcMQyZOf5uxd76KW1vOEpy5/CaxTCvNex9B0U2EUJCBh9eoMPbOV7GKm9wJChCKws3//U9XfhZKhFBQO1sRigKqgnPxxtp3tg07QSnlMkUdFY39ynGG5VUqssBh5VF0YaKicTO8xKRcZ5xs0B05p8Tk2Iv/6tWAG1c8Lp1zuXzW4coFl1plZ/Vz1bgBisLIL38dN3/ni+4tGcH+zifQVJNrI98hCFaqWvR3PUkq2cWZS7+1lcusC1NNsSfzGE1m947uAIPQwwqqWH6Zml+g7OSpeFM0/AofLurcSrihjS22L162kbrMdiN0baz8MNK/F/pim0ciKejdrdPUqi3kQc3TNub/t1YOOXNqUZQgGW+jrekBGvYsfuAihMDM7mVo9KWFsjl/FXC7Y9surcVTkIye+grlsUuk2nehaAZuo0J1/DJ26Tb1NMOQ1EO7CRvR3GcN5pFeAELMFdVtQZgG5oFdOJcHo6rzK+5ru3aC8padoIIuTBphlWbRjiIUzgav0yzaaRYd6xvBW9uWkT5opRxQKYaUCgGz0wHDNzyGrnoMXXMZGfRYJRKxo5BhSGi7mxdHXwM76g71A5u4mdux9lWh05s8Qnt8947InUkpsYMaJWeckhsZvZo3gxfefRmrncSofXFb1Sbu9vPxGzWKH7yN37i70mi3g1RG4XM/luZTP5imu1+PVu2rrJ0un3P4r39qcTcSBA6WUySUAbnMADPFy+javWO53isoQt3GdBFJdfzq5nVCV2shlDhjBZJH+wlqNkhwxmYjLdUwJCiUI9UYITD6uuYm6lWMIMq28BeineDSnVe0M9TQaRYdzIZ5fFxcnI0l2iTUqiH5MZ/JcZ/8qM/kWMTOnBzzyY/5FGYC1ghz7ijM3pYFJq6aMJBeQNv3Pkrt3DCBtais5BVrOKOz67S0iB0zgkIoJOPtc1UjduQKtMT66E8dQ1fMjU+/DURVrBuM1y8zY9+k5s3ghI2Nv/ghRcnPf6gNuwx83OLWVPF3GoeOm/zAT0S6oL/7KyXq1dVdRbXK8pml2sijaXHC0KdWzxMzszhu+UNZqmkrUIV+f8mChZLquaFlhjmcrxuoKKhLiupa56+uWlQX5irXbFE8G0CynGMREtKgykH1EUIZMi4HCQmJk8Bj/Tn5d3+1jKZXKBUCysWQcjFYIfZ+r5A9uY/0I3sW/lYTJol9XST2dyFdf2GZUX33OlM7ZQSFUNg/8Gl0LUEq2YUiFA7pKeQykovANNKkk92MTL55u5fYFDShszfzOKa6favieZ963rrOYPUdal6RQN7lPf5f4y8legZ0HCvkt365yJUPnE1PKEHgUK6OoOtJkCGWXcAP7PumwOzdgib0+0ogWqgKLZ98CIRAyyaQXsDYv/9GRMgIArzRSbz8LAiQ3trsd1XZnvuaZ5cv/I3PUHiRnGijIavU5grp+vjkw/WVud593b4vDN5qmP3OWUqvX9rwvMDa/Lx920ZQSkm1Pklb80FMPYWqGhhacsVG3w9sxqfeZWjsldu9xKYwkH6IjN62be1Fuz+LK6XXGG9c+iu30v6wQ8800Xz0SdJ7j6InUqvGCcLA5/Kv/tO73zmi7sxOB5Q2qJZ9KxKxVvb2P0+1ETH6pgsXcXaYaHY/QlWMbd8JCkWNfpjNFtJdAukHkVoMgKLQ/kNPLL5zmkr80QdJP/c4Yd1CqBpT/8d/XNX9vV1erFD6+Lcs2C3qy6rKA+TlxtKU96sBBAgqFkFlkXMgNDVKP1nSZ6Ept3UPd+AOlUxMv8/E9Pvs7vkoupZgcOylJdXio3N2cqWa0LIMpB/etvZCGVLxprlSepWCM8aHnejyVw1qLEH7458is+8oXrWINT2GDFe+f/KeFOaLMDnqY5iC9m6NqQl/04PUNNLkZ85RrAwhYQfDC/c3VKFvXvlJCBLNPYS+i1svEfq3PDOhkGjuJtN7ECORxanOUhm/GjFDNztvCdDbsnOXE6iZxEKemmIaSMel/sZpnGvDJB87Fgk5rGhbYCjb48m6dScYta4sWcwLdIyI2X6bhbI3C02HWEygmwJNEyhK9GyklAQB+L7Ec8GxQraDw6ZlEzQ99yDl1y/jTkXsUKEp5J4+ROh4my60u6WYYN2awTTShKF/izt0Z9GbfBBNGNsSKA9lSNmd5Gr5jb82gGsgoWZJqTl0YSI2QUBywwZT7s0Nz9suaIkUid69FD54k5l3XiSw7p0yzFq4fsllasLnC38nx3e+XGNyzItyAG953WxLMjK4OJk17FkGup9CVU2kDClVh7Gd0t3t/H0ARSib3jUZyRz9T30eRY9x87U/pp5fnp6Q6XmA/ic/j5luXjjmVGYYefvPKQ9vsgKKotD0saNANPF6U6UFF1zoePjTRcJaIyLFrDGnCCCupTd3vXUwn5vsLylFp6DQJnooy1lsGrTSRZvSjSNtxuUNbLbOcVAUaG5T6erVaO/SaOvS6OhSybWopNIKhiFQNAg8cBxJrRLFGPPjkSTa9KTP+IhPaTZglTXrhlCTJuljA5Rfv7z4LEIJqiB1tP/uGMFSdQhF0QnvQh3AecTVDG2xXduikyelxPLLXCu/RcEZQxUqu5IPM+uMUFpSEy+mpOhLHGHMukQjKKOg0mR00aR3oQqNelBi2rm5QJ4RKLQYvWT0VnTFxJc+RXecoju+kPSe0zsxlQQVf4Z2c4CYksIOG0w5N7CCjd1dO6IVuAra9H52xY+R0poxhLmp+EXRm7irRlAoKjLwqY9cuy8NIMDeg1HpmH2HDI4/HmNq3Md1VpINBq+4/PN/vJgD6PkWY/l3AIGiqH9ld4IQpUMJxIZam/HmHvR4Bi2ewreWM4b1ZJaeRz+LkWrCrZeoTw8Ty7YRy7bTc+KzNGbH8OqljTsThBS+9T5aLglCEDScSDUGwPfxxvOo6RRhtY5z+Qarz/KCmLp1I4iU+KG3LISjotGt7KYR1BAo9Kv7mQknSYkMnfQzJDeOq60FVYN9hwweeybOoWNmVC2+RyOR2txOXUpJvSbJj/kM3/C4dM7hndcsLp9zb88VK6JyFOGtcnShROg7XEppHlGS/N1Fa2yAmJbanl0gAderpyg4o4AklCFptZl0opnT5UUj2Gbuoiu+n6H6GQSCjtge+hNHKHvTeKFDR2wvGb2Na7VTuKEFCHriB3HCOm5ok9Hb6DD3cK78bap+xFhKa820x3YTyIC6X8STDprQUTfJFBNC2dSubCuIKykOJB8jpUYrZk86OIFFQk0jUGiEZVQ0DCWBKlRqfpFJ9wZFb5MJx9uEMAgIHRtFW7uC971GrRLy7msW7762fg7l1MQtIsGhR92K6tulEp3oWhzP/8vLVF4PMTXFUkHstWCmm1GNOFYpj1NdLuvXsvdRYtl2fLvG6NtfppYfxEg1sfvZL2CmW8j2HWLm0hsbd0ZVaP7Uw+i55AIrNP+HrxFUGqAomAd2k3ziOJWvv0Ls+EFqL761ItgmEMS3wQiGMsANbp2LBRo6DhbtogdbNhiXN8jSSrcYuGOHV3e/xo/951lOPB2jo1sjnrj9ZH8hBKm0IHUwqi342LNxvufzKd570+aP/1OFm9c2t6kKXR+kJPlAD+W3rkAoUeMG6Yd2Y2+SGQrbliIxtytZ42Fsl6tUoJAzu9DE1ic7KSUz1k0m6pcXVlCSkBHrAkezz5PWWqj6swhUOmK7mXaG8KRNQs3SHT/AlD3EiHWRUPpk3FYOZ56lSe8m71xHEnCh8hIh4ULNxBO5z9Ji9C4YQYCM3sbFyqtMOzeRMkQgCNncsxJzZTR3Eu3GLhJqhgCPG43TjDtXCGTAyczniGtp3ir9OQJBQs1yIHGSlNZMwRujsEOV5Vciet+CRpVGfpjMgeNY+RG8WmmdQX5v3N0XTttcOb9xGsrSDUNn23Ecp0JPx6PRe2SkGJl4k4a9+QH+lwkxNTUXY1r/PC2WRNF0GjOjywyPaibIDTyIEArV8asUh86CDPEaFSrjV2nZf5JU+65NGUGhCMyuJsZ+9ZsL1wjq0e+rxAwU08CfLoKUaE2ZqKqsd0sgTAjiWub2HsIqCAmwg9otRyW+9EiKNDnRxrQcm4sFSriDeUNV4VM/mOJv/Vc52js1dGN7vFBCQDKlkNin0zOg8+jTcX7nV0p880s13A2Gi19qUHl/kO6/9TwtnziGX7OJ724ntFzyf/DapvuwJSOoCJWu9kfo73yCmJlFKFEtp/kCj2EYULemefvcv9vKZRaQ0LIktCzbIY0W4nOl/NoKFuisO4oVVOiK7aNam6XJ6CCuprlcjQaGqSQxlSRWWEMTGggtIixIn4SWASdaqYYEUW6TEHMi4T66srxMix3UKLv5OxIQF0JFETsr/ZrRWhEoTLvD3LAWdTcDPIQU+NJBInF9i7O1FzmR/SxHkh/l7cqXdlw1JnfoBLG2nrm/JKphkuzezZ4f/wc0xgfxa5UV5CwZBuRf/eqO9mstBEEkInw7mJw+QzbVy+DoS9StabKp3lsIaH+1EFMzcwzR9ReKQtVAKPhWlaWLnnTXPoxkDhkGTF96YwlRRWKVJuYqjuQ21xkJQcNBGBrSXVniBynRu9uJHzuIMPSVBpAo7SOaz7aGIPRXhFACfMrMckR5nIosUJazSCQJUljyVoO5PnLNCj/5d7P8wE9mSCR3JgQjhEA3oG+3xj/4Jy30DOj84X8sU5xdO1goPZ/Zb5zGGSuQeXQvasKk+N0PKLxwDr+0eW/JlmbRpuwe+jufwHbLFMqDdLQeYaZ4BSEUUolOpAy5PvLtrVxiGRJabmE1uFVMNq7S8FfTm5OMWhfpix8mrmZoNnqp+QUaQXSuIhQMJc7+1En8W4yXF0YV12NqioHEUWJqGlVoCBSSWo6CO7bsfD90Ce8wFUMVGrqys+6/iLkmmPWW9zuYk+vShI43R8u2wxqT9jV2Jx6i2zjAoH3m1ua2FbG2HlIDDyzvlxMZXrO5A7N5pRaqDHzy3BsjuBk0t6l09mhcOL1o6Cr1ibk4rKTamNy66vCHGCm9aVNpEjLwQYYo+iKRRqga6a79qLEkdmGC2tTgsu+EfjSWFXWzU6IEAd3/r0/gF2ogJfk/ei2i7wcB9sXrhK6HmkpQe/nUGvfTzHYt6G2/csuxkKHwEtOM4WDjEknxVWQJbwMB7aVo7VD5qZ/P8n0/liYWvxtFewXJlOALfydLLC74rX9XXrempnR9qu/doPreWtqsG2NLRjCVaMfzLS7e+HOCwCGb7uPm+GvUrSkyyW4O7P7ebZV4imuZbaEUB9Jf5ga9FTPOMN3xB+iO7SejtZJ3bizoNPrSxQoqjFmXKbhjy4L0fuggCdmTfISs3s6FyisLxvPRpu/bcr+XQhM6uhLb1jZvxTzxxpfLdx+ejPT6DCWBt4SoUfKnEChR/qbNjmL2zKuULr13m9+6v5m/jz4d5/N/I8vPfX5x0aFrCXKZfnzfpm5N47h/9XIE5xFTUxhqDN9ffzfsWzVC3yXe1LWQmpBo7ibV3o8iFKYvv7EiPqfq0Vhaq+TRrZB+yNQfv77wtxIzCOcTtIVAa21CzaZAgnlgN/bZlUSUqArM1iClnNsJrtzdhQRUKc0FThRCQsrMbLrtZFrw4z+7sQEMAklhOiqGWykF1KsS247SIGQoUVSBroMZV0ilFbJNCm2dGtmmtYvx6rrg8z+dQUr41X9ZxG7s3NjdmjtU0XHcCkEQKWCE0kfTopepUh+nULpGd/sjTM5sfVegoGKqiW3RCK17BWp+cc3PvdBh1hmhO/4AVlCh7OUXjF3DL1P28rQYPThhHSuoYSgmhhKn4k3jBQ6q0CM5W+ljKglazD50xdzWKVhTjBXu1e2GJx1AElOWFyeeZ8Gm1CbqQWnheBTXBGOHjTOAVy7gce9qGe4EVitf4/sWtlPGNNL0dDxKELhMzZ7HctZ+f7cKNZfDaGvDunrn2po7ASEUUnrLGh6cRVilSTy7TrKtn/bDH8EpT9O89xFi2Q6sUp7yyIUV3zHTrSDBdzZP9vOmFndf2acO4hdqBF6AMA20rjak5RCUqyQeP479wZUVDNGc0bXpa60HO6itqmylodMuemkSbQgUXGwm5U0qcnPvzvf9eJrv/8LqBlDKqD7ge2/YnHvPZmLEpzgTUKuGWHWJ40Qlk6SMUik0TWDEBImkIJ1VaW5T6d2lc/ykyUOPxUhnlRUePk0X/MAX0kxN+Pz+r+2cLvCWjGAQOKiqEZUjkT5B4JCMt1KuRgrlfmBj6NtT3V1VDEwluS2u0JI7iR+uTTUPCSi4Y/TGD1Pzi9hLmFeedBhqnKU3fog9yYfRlRh+6FLy8lS8aJU1WD/NA+kneTD7PL50mXaGovSIbXRlGUp8WyXjVkMjKCOR5PR2WBLiqwWFiCVr7GbKHVpYIKS1FkBsa53IzUCNJ0l076Z28zLSXzu+qugmsfYekBJ7ZoLQ3dnt6mPPJqjb8P6bFr27NPYeXCfPTcCRh1cuHiQQMzNkUr1YdpG6NUVX23GGxl7dsSoSaiKB3t6OPTyMYhgE9TpqMonW2oo7Po50HJRYDL2zE296mtCyUBMJ1HSa0LbxS6Udkx3J6O1MWeu7vupTN2nMjNC06xhdD32K0HfRzARCUciffxnPWr5rEkIh0dYHSJzKxjulzGP7qZ4ZpPmTDy04M1MP76FxdZzAcjEP7EJvbyF0PYSqoJjGKsxQZVuMoCSk4q3UzRUodCu7aRXdlMIpPDwSIs0B5WGuhWcpyfXv84EHDX7653MkV0l7sBshf/GnNb72JzXGhz2qpXATYtoSKhBRuqL3VjfgW19S6d+j80M/leG570mg3FLE14wLfvhnMpx7x+H86Z2Jh2/JCDbsIt1tWXQtgWUXqTXy9HY8FlVM9126209sG5NNE/q2TPoSSd0rLcS11kIgA5ywHhnMW1ZZdlDjeu1dBsXpOZdhlF4xz+ys+jO8X/qLufiFJJDBipy+UesS4/bVOyLFKEIjoTftuJZi0ZugP3aEjNo2l8cYTR4z7hhhIqTN6GNv4hEmnRsk1RwD8QcBuWx3eDegp7K0HH8atzRD6DkoRhyvUiD0nIXJR8+00Pe9fwMz2wICGhPDTL70JZzC5uMjt4uf+8UWalaMn/uhMR77aIK/+wstBOHq1SMADFNw7cLydy1u5gjDgKHRlwhCDyklqmLsuKyfEo+TfPBBvOlpEILc889j37hB/Nlnqb75JqkTJ/CLRTKPP07xG98gefw40vfR29spvfgiQam0I/1qjvVBZX094tB3GXvna+iJDMm2ARRNJ/RdZi68SenmuRVx1XhLD0Y8jQx8alNDG/YhqDsIoZB+eA+lV6JdpXT9uZpYAjUZJyhHY0WYJs714RVGMGO0oSuxLS/qJZKSs5KNraHRShc3gvOUmQUkQir0iX10ioF1jaCqws/+101km5bPL1JKSrMB/99/PMOpV+0tF3z2XJidCihMB1w47fDmd5P8V/+kmWR6cVcohKCtU+PzfyPDpXPTO1K5YktGsFIbZXz6/bkq8T4T02fIpXfxwK7PARLXazA09vK2dFQR6qZz6NaDlBI7rCHXYJipQkNBpdnowQ1tKt7qxUslIcE6O7tbjeytr8tG318PqtBJ6607nixf8CaoBSVUoZJUswtG0A6rTDhX6Y0dYm/8BHvjJxa+44R1Jp07D1LfGQTxjj4GfvBvo5pxEBC6LtOnvk3xg7eQvkfLwx9BT2bIv/4XqGac3OGT5I48xvSb34yM5Q7g1//PIs5cTp+mCa5fdvnOl2s0aqv/7k8+n6C9c/mQtJwCoQzQ9RQ6EtetMVvaYTelohAbGKBhWTijoySPHEGJxzG6uhCqitnfj9bUhBKLRfGvpiaCeh1nZITQcdBbWnbMCGaNNnQRw5Pr7+LdWoErf/ErpLv2oplJrNJkVC9wlTGnGnHKIxcIw4DK2OVVWluO+vlhUASzX3+P6qlrQCSoHToe+AH1N05v2Eaz2bt5Gbh1EMqAsrtaSpJAExoWtSUpYAF1KmRpWbfNk8/EOfKIucJAT437/JP/cpoP3tve8SIlOLbk639ao1oJ+YV/1kJL++I4UFU48rDJ0UdjnH5r+703W0yWrzE8sRgcrjXynL/+J+TSA4CkVLm5bTtBIZRtSQnwQ4dgDVeoJgx644eIqSnSWguj1gWs8P4jIuiKSdbo3PHrhARcrr+BHdYXDOA8rjXeRVdi5LQOdGEQEmKHNUbsi5T8ndtdrQWhG3izeUoXThG6NmZrF01HHscrF6gOXiDW0kl99AaFM6+BohAGAZl9D6LG4jtmBF/5Rm1OPCHC9Usu3/xilWppdSMYTwg+9YPLk6cT8VZ2dT+Domi4foOpmQ8oVgZX/f62QUq8qSmk52F0d+NXKrhjY1jXryMdBxmGqOk0Xj5P6Lr4pRJGdzdGRwdaNoszOrpjXRMoNMd6yFvXN3Ef4aZqBlbHr1Ad35zE1gJCuWAAAYovnN0070qg0GT2bFkQXEpJ3Ssue8cWPiPEkTatopNZmSckQMekRXRSk2vHVFUVnv1kgkRyed+qlYBf/ZclLp7ZuRSdMIRTr1j84a9X+Jv/RW6hD0IIWjtUjp80OfuOzW1qnW+IbU40kzSsGRrW5hlIm4VA2RZSTLRrXX0Smtfgs4M6M84wBXd8y9fbCWSMtm1RmtgM1jJorrS4UHuVpjn5t1D6VIMiFX/1nfNOw6sUmX7729SGLgKgmHG6PvoDxDv7qQ5eQDFMgvmag2GIW8ij6I9EOWV3AdcvukyN+zjW2jNltRKuCKUZWpLZ0lWEUFEV/a6UUPLLZWrvv0/QaKCYJu7kJGoigZbLEVSrOKOjuOPjqOk0wjSjGCCgxGK4+Tz+7M4l8wsEbbFdmzOCdxO34RlM6y2k9OZt4TdEalcrEeAzIW/SJQZoEV2EhGhouDgMy7UXBm2dGvsOG2j68r699ZLFqVesHS+ka1uSV77Z4PFn4zzy5CLxLxZX2HvQIJtT1s0dvBNsywyQSnSSS/eDEMwWr2A5RYRQ0NQYfuBsi2KMQGyP+4BgzXhKID1GrE0K6N4jCBQ64vu2scr2ncOTNlPu0L3uRqSdaNXwG4u7dhn4eLUyim4Q5WKJSFx3DqEbxXW4SzXqzr5jIYRYN47y3usW+bHlbvRqfRLbLaOpJplkN653e4nOd4KwVsOpLb9O4+LFhbJDAM7w8MLfSjxOUKvhjI3hz2z/Ang5BDmzG1NJ4oQbMzlNAz776QQPHtKZyAf8yZcaHD2iE48JvvEdmwcP6/R2q7z8msNTT5g8cdKkUgn52jctrt3w+bmfTTExEXBgn875Sx5DN30O7NP46tctYnHBx56Jceacy/DI5ue45ljftqU3TdurewUkkhk5jiVrJEQKdU5CrS4r64pnD+zVybWoy8S/GrWQt1+2KM7eHcLb2E2Ps+84HHnYxIzN7wahq1ejrVOjOLu9+rlbngF6Oh7j2IEfZ2//J+nrfJx4LNKZVBWD4wd/ko6WB7fcSWAu5eD+zvO6G0hoWdpju+91N+4rhL6HUFQS3bsWdnZ6Kkt69yHiHX3kDp1AjSVQY4vEKqHp0YR+l94pz2VDIsHsVMDZU8tjHn5gYdkFavU8U4WL2O7OUcU3xK3Pau7v0LZpXLyIX9j5lBUhRJR2FOvb1Pn79uiceMjgP/5WjWxWcOJhg8GbPl/4kSSqCo+dMFBUQVNO4Ud/KMGv/1aNkTGfZ54ySSQExx/USacVfuv367z2hs3wiM/HPxbDjAlyWYWHjxlMT29+Z6IJk5yxPdKPll+m6q696AgJqVIiL0eZkEPMyskNq0e0d2krGKGTYz4jg966lR4+86kYjz+2/J6EgL/1N5ILf8djgs4OBWUDq+P7cPWCQ7m4/IJNreoKss52YEs7wVx6gN6Ok1TqY8wUr7C3/xMLjEU/sHGcCu3Nh5icOYOqC1RVIBRQFIFVD9D0SAcw8CRmXMFzQxRFoBliYZVpN0KQkY97LTLL7UBBXdcXLzSBZmrIUOI7PmuR8IQqUFSFwL3zPqmGigwlob/5QdSfOr7jcmlLYShxOozdNGkdmEpyTgFn/V1oJZjlfG17CFGbgV+vULt5iY6nvoeWh58hdB30dBN+vYJXL9P57PfjWzWSvXuJtffg16ok+/YSeM6CUsj9COWWquPpZBeu36BWv7sC5RtCSqR796pbaIpBa6yficaVDVmyBw/oPP9Rk55uBVUVXLvuMz0TcmPQ5/u+J048Lhgc8hno13j4uME/+ydZFEXw+lsOmhrZ+Xfec5jML47z1990ePYpE8eRnDvvYtmbX0jlzC4yRtu2eHKm7ME1We4qGr1iH1NyBIv6hpU35tHUqhBPLO/b7HRAYWbx/k0Tens1wkBSq0tamxWyGUFbq45pCC5e8pieCXnsUYNrN/yF73zy4yb79+ucOevynRcdUknBoUM6liW5fNnDXTIUJ0Z96tUQlmSRZLIqqcx9ZgQzqW78wObK0NeRMmBv/yeWfV63Z2hrOgDAw883sedoksCXJNIqf/pvxnj4+RxWLeDdbxX50V/o482vzNLcafDIJ5qoFX3STTq/87/dpDwTUcO3I/9MVXTUdWKLAx8doP/5fopXi1z/2nUaU6uvnDJ9GZoPNDP07aFlbrbbwQM//ADVkSojr45sKqaQ1JroShy4o2vdCTJaK8dSz5NUc8zroUq58XDy5N3Vtww9h9n3XyF0bFK7D6HoJtXB8xROv4ZTnCbe3otbLdBy7GkGfvBvRxF4RYlqD9p3rxKKERO0tqsk09GEvBrsRsjQnIr+rp5n0bUYcm7HZZoZxvPvsvMO0fsbQihkjHZyZhdFZ2zdc89fcnnxFYff+r0aAsHwqI/jSL7+bYt/9F9k+NaLNjeHfWZnBe+edvnX/7YCEmYKIZXq3E73Fjv7539h8T/9d1ny+YD/8J82/2uoQqfF7CWuboNotgyYsYfWXAQoqLQrPUwFG1eSXwozpqyIBzZqIY36Eg3WtMIPfl8cGUouXPI5dFBjdjbkwH6NUEra2xW+9GWLmdmQX/yFNK+86qAIgWEKbFsyMxtiGPDx52Ls36fR2anym79d58y5RStYLYU4t3hOjJjYNuHupdiSEVRVE8etIqXPahp4MgyWlfuplXxe/IMprGqAEVtu0edXRooqGLtq8ee/Ms5P/GI/7f0m5RmPkGDdBPfNQhM62irFORVdIbc3x57P7mHirQmmz09jzVjk9uRQdAUjHRXxnXx3EiNjkGhLUJuoLUxQelKnaV8TRsrALtrMXpol2Zkk3ZtG0RRCL2TyvUmEKmg92Iqe1El2JKmObo59qgqd3ZlH0ZSV1OWdQl/sMAk1gx3WmPFGsIP6pqpS2yvKuuw8AqvOzLvfZea9lyLR8iUzV300YvFNvfUt3GoRPZ3Dmc1TvXFh3eT67UQ8KXj+e1N89kfS7NpvoK4hF3XprMM//MmIkDVduECtMbUQU0/G2+9q7c77GXEtS6vZT9nNrysacH3Q57sv23zyuTgS+NM/b1BvBFy97vPK6zZnP3BpWJKGJfmN367xyefjSCl5/U2X2YLLa286lCvLDU29LnFdychYQKWy+QVwSmumNTawLeO35hWoe6U1P5dIXGnfNgNVVVcWAwoCCJeIv4cBGAZMT0sG+lXy+ZAwlHz3ZZupqZBduzQMXXBj0Mdzo+9ZtmR8IsCyJB+c90gkBH29KrYjuXLVmy8NuOBx9wO5IptFVVlz8bgVbMkIer6FqafmCusufxFV1aQpM0CtsahmUJp28d25nJW5u9V0gRCQbdMRAnw3pFqMEk8dK1xYlQShhxduPUdECIW4lkER2vLBI0BRFRRNWfj/COh6tAsjZTB7ZRY59yIIIcjuzpJsTzJzcQYktD7YStPeJmoTNfZ//36cskP7sXbaHmxj/NQ4uz+5m9pEDS2msesTu5h8d5JYUyy6zibQFt9NW2xgW8hBm4UpkoBgzLnC9cZ7O56gvS2QcuHduhWha1M4/epd7lCEg0dNfuxnc0xP+vzmvylSq4Wr7v5LS8gH1Tm3pyI0DD2J69Xwgx0WZf2QQBUabfFdTNk3KLtrp+R4HnzrRZtvvbj8uRVLIf/2Pyzfxb15yuXNU8sX2r/zB8s9Qbv6VZ57NoaqCl54afO/hSp0OhP750SztwYpJdPW4KqpEfMI8ZmRE3SKPmZlHo/F+wrwcVj9u74n5x0li33Xlhsfx5FMT4dUKiEPHTf4869a9PeqzNe2FUAyKXj8pEFPt8YTjxm8f8alUAh56gmTMITvvGhz6YrH7l0aMzMhQzeDZSFnXRcotzjswluM8XZha5XlKzfpbD3O3r6PM1W4gBAKppGmJbuPzrbjpJPdnL36B6t+13clhUmXp3+glf5DSTRNLEwKq01igfTW/dFvBymtGfUWIxi6ITPnZ6hP1sm/n6dwZTHIXx4uM/bG2IIRtIs2xatFYrmI4aWaKm2H22g93Eq1rUqyI0myI0kYhJQGSwx/d5iO4x0kWhOYOZN6vs7wS8M07WvalCs1rbfSnzyKscNaobeiEkzTQhdShh8OA7gRli417zL69hjIAP7Tvynywbv2uiSDpdC1OAM9z+K6VRRFY6Z4mVrj7uVhxjtSZHY1oSf1FVuE/JsjePV7V+k+pbfQlXiAuldcoeq0UygUQ956x+XFl21mboOqn9Jb6Eke3haVJzuoUnDG1lWbUlDpU/ZjEqOTgblqNdG7X5TTXA7fX/V7VkPiezKaj+eQTCrEkwLmsp8sW/LnX7EIQ3j3fY/JyYDr1308T+L5MDYRUKuFXLnm80//WZmJyQDfhxuDPl/5qoXngevCm2+7jIxGxs+ylz/LbJOKGVv+vjm23LJKzWrYkhGsNfIMjn6XAwOfoa3pIKpmsn/gM9FEIwTXh79FqTIEwJkXiyDAm7sJKeHyqSo3L9TniC/gNEIUJSpYCfDV/zCOP7edDqSPG1pIKbfsTsiZXeiKuemdpd/wl6/axZz7VkR9lb6kMdNg9LVRhl8aRqgC3/LpaerBtyNDG/ohKODVvWgHqCmYGROxwfZeV2L0pR4kZ3bvuEzarbhpnSOpZumLH8aXLnl3aE48e/0XUS757/sF8Y4+ej7141z7rV+6J9fXNJjO+0yO+Zs2gACpZBfFyg1KlWGaMgOo6nzKx84/35ZjnRz7+08Qb0/hN7wVC4jS5Zl7agQVodKbPMKUNUjBub3Y152iUpVcuHR7LmlVaBzMPYOubj0tQkpJyZ2g6q2fiuLjczZYvbBssA7BsFQMsC1JbMl6u6U9ErweHZqby0KYmmPEzi8E6kuqPNTn4oejYwGjY4vXCgK4dGVx41GtSi5dXt2V3d2nkbpFUL5SjozrdmOLNEPJbOkKp6rDtDcfIpXsRBEqDbvIdPESll1gfrDO+4aXIvAl9fLyHySYaxfAri+9YYntV/FCG0Pd2o4ormbIGh00/Aq3TiZO2SH0Fq/rNbyIJboE3Y93s+9z+4g1x3j47z7Mxd+/yMSpCfZ//35O/sOTALz5S28uGEAAt+oS2AGl0RJdJ7t46r9/isAN8GprDyhV6PQkj9CTOHJX3aDz8KTLlfrbHEk9y8HkUxxMPokvfQK8dXeGZW+a09Vv3cWebgw1lkBL3h2BgdUwNRlEtPpmlanxzQtfN6wZdvU8i6ElSMRb8TwLQ08yU7yy44nzqd4stdEKb/4P38KaWSXOex+sc1Shc6TpOd6Z/iJWcA/TR9aAQGV/9klyRte2yBz60qFgj+GGGxWNlTTugEI1NRFQr4bkmhd9kV19Gn27dT5417mtBdydQtPggaPmsj5AlEJU2uZEedimZHk/sBmffn9hu7xTsIIqbtjYshEUQtCbPMq0NbTCjfLeLy+vUXftK9e4FeNvjjP+5ko1mdO/enrZ36OvLao5nPvNc2teYzWoQqc3eYT9mcdRlbuXErEUWa2No6mPkVRzc2o63oJI+LppJltPP90QaiyBUDX8egUUFS2xvoHTM813LTF+Hp29Or6IFjmlmYBGLeQLfyfLl3+vwux0gO9FpWaWbuw8VzI9uXRhKGhYMwihUKmNEYY+YRisGffcTlQGCzQfbqf1oS4ak9WFcMA8ytdnCZy7WzHkVgghSGg5Djc9x/nid7BXqat3r6Cg0pk8QG/ywW0hw0gpqXmzzG5y12sSx8NdGLMABjEk4bIY4VKM3PAozgZ092sLfY7FFZ74aJy3XrKYye/8771rv8HRE+YylqqUkrFhn6mJ7a+cssV6ghog7hpjzfIrOEGdlL6+AOxm0GR20x7fw3hjZbHLew1TSdCTfJA9mROoytZFw+8UfbFDC+zQvDNIPSwTSG/DnKPtit2uh9zBE8Q6ehn/9h+iJbN0Pvv9656vp3Mot0badxh/579tJp6JkoWlhGyzyon9BieeTjA65GE3Vsqk3bzu8W/+50VXl5QBjldD1+LEzSZmSld3RJZwNYRuQKIzzcFjXVhTUa28pTjzr1+jkb/3RkcIQUusn/3Zp7hROUV9nVqhdwsKKh3xvezLPL4twv8Q8SImG9do+KUNz1XRGFAeYCy8Tp1FBnqTaMMgxsga0mnTkz5DVz0OHjXRlnT7sWcTPPW8zVf/qEqwMxW8AEimBM9/Lsn+w8sT7xs1ydXzDpU1dHe3gi0ZwabMHlpzB7gx+iKev/O0eCeoU/MKNJk9W9YRFQj2ZE5S8WaobeBfv5tIak3szpygM34A9S4mxa8GXcQAwZh9mRvW6WUrynuNMAiiKuASVDNOsm8v1uQIgb2GmygM77r3bnLUx0guLhDHhz0unl4/Dj09cSvL2iCT7MJ2KzhulSC4eykS2f2thF7AB//+LezZxgqDbRd3frGzWShCoTOxH1XoXK+8TXWN6i93pS+odCT2sjfzOHE1s227wIo3w3hj4yoX833IiGaGWS4MHuCTE61rGsEggFe+VefZTyfI5Bbn2FRa4af/XpZyMeClr2/kir0zaBp8zw+n+N4fTS0r5CslTIz5nHrV3hFe25Zm2XSyk2S85a7lrUlCis44nYkDW64tKIQgqTXxQPYpPih+B+ce5LYt6w8KLWYfe7InyRodKELd8VJJG2HaG6ZF744k6+4jAwhQvvQulatnkHOS8l65wMyp72DPrs6cTO85TOcz33c3u8jv/koJT97ehBHc4nJ03Sql6jCJWDPpZBcNexbHXb+y+nahdGWGzK6IwWwXLELvlvJgwf3FGFaFRnt8NzE1ybXKW8zaw5tWStkuCBT6UkfZnT6BqW5PEXCI8v6GKu/g3ZaXRayYQzYTqjj1qs3V8y6PPLW83mF3n84//J9aaO/S+OLvVFfledwp4gnB3/qvcnzuR9PkmpZvcDxX8vbLFoNXd4aEtcWthsD16tsikL1ZFJwxvNDCUOLb8oK1xPo5mH2Gy+VX71E8QaALg4H0wwykjt/VZPiNMG5fQUWlP/YgIJjxRmgEVcINCgHfDaMZeg7Ml0CSEt+q41VLBNbqv6FXLd/19IhaJcDdIpMgZuZozu4hDAPqjTyOe/dKe2X3NLPrsw+w5/sPIcOV5uSlv/8lqjdLd60/m4EiVLJGJ8dbvoebtdMMVd+fE9nY6d9eYKpJDmSfoiuxH4G6fQZQSqbtG0zbQ5v/DiGetGkXvYzK60hCVHS6xS4qrO8udmzJ//2vizx4ogvzFkJrW6fKz/1CE09+LMFv/XKJi2cdPFfednUJIUBRIRZTePzZOD/197LsOWCsUISRUjI+4vEHv1beMTfs1irLWzPk0n1oWhzPvzuuETdskG9cY3fm5JZ3SkIIkHNuFMVYcKNshzzbRlCEhqHEaI0NsCd9kriWWezTJjFPjtgpo9kXO0Kr0YOuGOxLnGAfi8Vz12OHFr08pypf3pE+rQanOMX4t/8Ir772DiloVLFnVys+endgxgSaLmjUVsYB55FICsIwKiczj7o1w9jUu8TNZhy3gh/cPUm6/KlRXv1vvrrm5/WJ+6/WJkTjQcNgb/oxuhMHGaq+x7Q9hBtY6+bW3QkUVAw1QVtsF3szJzHV1LaPx0ZQ5lLp1dva1fp4jMrr7FOO0UYPvvSIiyS2rDMWblz0+swphz/69TI/+p9nMczF+xFCYMbhsWdjPPJkJxfPOrz+QoOz7zjMTkUKMb4XeTTm9emFiJLvVTXShTZMQU+/xkOPx3j6+QT9e/UoNW6V51Yphfxf/2uBmamdm5O3ZASLlUGasrvobj/BdOEint9YsSuUMtz21eto/SI9qSPE1NSW24oevKA9vpuklmOkfo5Ze5S6X1xXjukOr4apJkhoOZqMLtrje8kYbXcU3wxlQNWdQREqSb15R1IoslorCSWDFzp4bH7ydW/TBbhVyMDHq66/urVnxhn641++Sz1aiRNPx+nbrfPVP6xSq6y+gPj059O4ruSrf7A4XhKxZjpajlKpjdKU3Y0iVMq1u5MT55Zt3PIqMUwBTQfbIjnALQjIbxZLJ//NLnznJ9SEluVg7ll6vQeZsYcoOhNYfhknbNyxApVAwVSTxLUo1aozvv+Ox/FG8EKbG5W3sf3bn0MLMs/54C1yohUVnUl5kxk5gc/mFgK/8+/LdPZoPPvp5ApDCKAbcOzRGMcejSFDSWEmYGoioFQIqNdCPBfCUKKoAjMmSGcUmlpV2rtU0hllw8VCYSbgN///Jd56eWc3WFsW0Na1BF2tD9GaO4DrVVfIp7lenYs3vrSlTt4KKygzVr/InvSj27rqSupNPJD9COXEFEVnjLKTp+rNYPnlOcWF24cmDGJamqTWNFdMs4W03kZCy9xx8ruUIRV3ikulV0hqTRzMPYOyDYm4t+KmfQ5NrNRZ3Qj+XRbQ/jCgd5fOoYdifOuLa7vc9x02aevUlhlBTYvhejVqjTyaFlsp7HgPoOgqB378OBd/410qgzvPxKy6MxScUTri+4jdwU5LESoZo42M0YYTNKh7RRp+CcuvYAdV3NDCDSw8aS8U3Q5lgBACRagoaOiKiakmMNUkCS1HSm8iqbVsaRxvhFAGTDauMm2tLZS9HiSSGuV1K8mvh0o55Nf+dSRy8swnEwu1/VaDUAQt7Rot7dtD5suP+/zB/13mz/+guuNRjK2lSAgN37eZnDm75jk75SYdqZ2lLbaLjNG2re0KoZAzOsno7bjxBk5Qww7rNPwSDb+ME9TxQhs/dAllgETODRQVVWjoagxDiUcrRTVNTMugK/PHEgg2XgGtBykldlDjWuUtyu4kfugSSA+d7TeC5XtUJf6vKhSFZStugFpjipiZoyW3H9er07B3rmo7RCXChKIQemvv8FRDRUuslFHbKVh+mZvV09T9Egezz2yJNR0ZsgRNshtJiB+6+NIlCL2F9J9IJnA+1KCgoKAIDU0x0ISJrhg7rt4kpaTsTDJcO3dXUo5W7wSMDvr86r8oUi2HfPqHUiSSO3/fw9c9fvPflnjpmw0ca+fj+FsygrOlqxQqq1c2XsAOmXE7qHOjeopjzZ/eETeEIhRiWoqYliIjI6JHKIOFASKZF0COnN7zbprIyEUDR4goOL5dLM/5hPULpe8uMN/qfgEnbGDKrcciks063/sP93Lh5Vmuvllg4HiWh7+ng9//Hy/eF+oga0GNp2g6fJLGxBCN8SHup87OGzVVEygK6KZYYehUFfYdMnj06Tin317uogtDj1JliJjZNJcov4NJWsDJf/JxVFPjjV/8On2f3MeRnz254hyhCMzmu6djqyo6QgjG6udJajkGUg9t+V2PxqWKocYxuLuavBshWuhWuVF9956nb0kJYzd9fuX/V+TKeYef/UdNNLdG8+12euGklIQBvP2yxa/9H0WuX3Lx7lI20JaMYCgDbpsWtG2QzNojjNbP05d8cEdXZkIIVLR7mrcnkQShxweFbzNtDS47XnImyOhtrFbOarOIZzQefK4VoUBh1MKpB1x5vcDJ7+9cOCfXadL9QIrylMPElTrdD6QQAjRToTRpU867tO9KkO0wGbtUpVa4O2+xlkjSdPQJ/EaVxvgGi7K7CEUV/O5L/ZgxgRlXUFV48rnEqutCKaFaDvny7y2X/krEW9nT+/yCaPZ08SL1JZVZthsTrwyh6NFY0lMGjXyNmTMThEvSIVRTo/e5PTvWh1uhCh1V6IQy4ErpNWJqivb43nsiJbjTkFLiS4ebjXNobW1kGyql2ZWqVXcKRdFpajuA61SoljYfW65VQr78+zVef8Hip38+y3OfS5HKKBj6otbz7WLe8DmOZHzY47f/XZkXv17Hv8tytFua1WNGllAGuN7acQ7TyKBr8QWCzHaWgvFCm+HaWZJaM833QGD6bkFKiRPWuVJ+nby1ckAUnDH6Uke3vN90GyGNsk+j4i9scuchBCSbdOJpjWd+oo/f+sfnOfkDXZQmbJr7YoxdrDF5tcZTP9bLyPkKvUfSvPzbI3jWzueSCaEigwC/cX+xFcNQ8o9+apzjj8V5/vtSdPVpXDnv4t5Sidz3JbN5nxe/VuPKB8tnAFNPMVU4T7EcGfedZoeOfHvx/Qq9kPFXhrj+Z+eR/uLvqCV0snu2XhJos4iMYDRVhQRcLL2EIlRaYwM74gW6l/Clw2DlXUYa5+jIPowZy5BIdRD4Do5dQtcTIASeW8eMN+G7DUIZYMayIEMU1cBuzCIUDcNMR7U1pcSxioShj6bHserTOHa02FJUHTOWA6LFvutU8T0LI5ZFVQ0URSMIXByrhJQBM/mAf/2/FPij36jy/OeSPPp0jLZOjVRGIZGMvBzKOkYx8CWWJWnUQsrFgMGrHi9/o84b37WwG/fGg7MlI9jT8SihDCmUr6MqOp5vUbemF2TUkvEO9vQ9RzLeipQhs6XrjEy8juNt32RV8woMVd/DUGOktLuXuH+3IKWk4Ze5UTnFpHV1VZp0yZ0glMGWJgSr4jM1WCfdalAcX7lQ0WMKyZxB4EX1+hQFPDtg8HQZux4Q+pLew2nqJQ+nHlCbdVE1Be8ulGAKfRe/UUXRb5/Es6OQMHTNY+iah+dJjj4a4z/8UoHCzOa9Jw27QH/zQVRFR8qQUnUY2yntXJ+XoHhxCoRYZgABAjegdGUG37pLO31FXyY95gR1rpReQ+YkrbGBe66stF1wgjo3q6cZrL6Houqomkk82YZupIgnWxm68g1yLfsQQjA1fpqeXU8xM/EBrlOhf+/HqJXHEYrG1Pj7JNOddA88Sblwg0Sqg4mRU9TKo6Sy3TS3H2J28jzFmSvEEy3sPvhZClOXMOM5apVxZibO0b/nORq1PIl0B6EMGL76bXwvik3KEEaHorjdH/66YNc+nV37DLp6NVraVZIpBSMm0DRQFEEQRGkTthVSLYfkxwNGb3pcv+SSH/cJ77EOx5bfns6Wo7Q3H0RVY3heg8mZ04xNvUcYegx0P0XCbGZ4/HUMI01n61Fcr8rIxJvbWJ9OMmPfRCkrPJB7Zttkiu4HSCmp+bNcL7/NtD24ZsqGE9SpewWyZueqn98JjLhK/4Npcl0xHniqmakbDboOJAncEN9bNMQynCtiK2DkQpV0m4nnhhQmbOzazsav5uHXq9RuXiLZswcrP7JhusS9wPB1F90QeN7trXY932Is/86yv+8WytcLqx6XfsiNL17ArdydAr+q0FFuMXQ1v8DV8huEMqA9vudDbwgbfpmh6nuM1S8uzI1hGFCavc7s1AUOHPsR4sm2Ffov8/A9h8L0JWxr/t2XuE6VsaHX6B54CjOWoV4RlGdvLOz85iHDgPGbb5Bt3k0y3YWi6ghVRcoQu1HAahQWDOCtsBuSS2ddLp2NPBhCgVg8SonQ5uLgQRAV67UaG9cDVJIx9PZmnMGVBQrWg7mvl7DawMuv/s6uhy2/OYaR4ub4a1TrE2SS3bQ1H6Zan6RUvUk21Ue+cJ7x6fdQFA1NM2ltOsBo/hRyG2tySEKmrEEC6XOk6fmFxPMPM6SUlN1JLpVeoezmN1w0zDqjWzaC0zctqoXoZfbdkKmhBl/8369Sm3WpFT3Ofmsa3VS4/m6JRsXn9T8Yo172KIzbSCmxyj5WxUcogkbZu2v8FCklTnGGeOcAvZ/5Atb02JxCzC3PLAyZvUeV5a9ecLl53aNevb33Pgy9uyaYfTuwZ+9eLuhSd+hS1LxZLpdexQ0a9KeOfSjDIRJJwytyrfIWU9YNgiULXUVREXMVZDQtRuA7hFKizdWUNOO5hQV/GHoEwRJXupT4XvQbyTBYl5znufWoJ3Kx8G4YeFiNArZVwHM2oaQlQO9oQc0kcMZmcKwQoSgEDRs1myKoNFCSMbSsiZpN4g5NoqTiBKUawtQRpk5YtTB625Fu5GFQm9KENQvpByipGNILQAiMnja8fIGw2kAYOnpnM7EH+nGuj+FNFW573tmyEZyavcBY/hSe36BaH8cwUsRjzZSqN1FVYyFeGIY+leoYbU2HdkQTUxIyYw/z3syXebD5E2T09g/ljjBSgZFMNK5yqfTyJuqGRSg4I+yWJ7Z0z64V4FqRbyIMJJVpl8r04sC61U1amPvbqS/6M6YG726iPICRbaHnkz+KomkIRSXe0b9qrT3p+/fMCDq2xLFXH52xAwfIPPUk1tVrVF9bvRDq/QlB0mxGUyLFf8ur4O6AkL4qtDUrMdhBlSvl16j7RQ5kn0YV+odm3Ef5vtNcKL1IxZ1esdD1fYfm9gdo6zqKVZ+mUcsjhELf3o+Sbd6FkMyx1aP/XTr5LxwjmhslEqFo7D74OZLpjrmdncBza0vGSiTxoqo6QlHp6n8ckFTLo0wMv0ngrx2PVjNJct/7FPV3LqKmYmitOZRUnMbZ6yRPHqL+1gUyzz8SXXNyFndshvihAdzhPMZAJ95UEadmoaQT6O1NOIMT6N2tqKk41rnrpJ8+RuP0VRLH9+FXGmQ/foKZ3/km8YMD6J3NURujd0YY25IRDEIP328s0LbD0AcZomkxFKGteBn9wI7KL+3YOyqpejOcnvkqB3IfoS2260M1KEIZYgdVBivvMla/cFv6m2V3Cl86c5Uf/mrBqxQY+dpvbnziXdYOXQFFQYlFCe9CUQgdB+m62FeuoKZSqOnFmojCMKJ/QhC6LtJ1EYaB9H0IAkQsBkGAvFs88lVwoOt5YnpmwUU7VbnMbG0n2LkiSpNArBoTD6TPcO0cFW+aB7IfIaO3RwL09+m4l1Lihw4T1lWulF5bVVwiCBwmht9kYvjNZcdrlTEuvv+7K86/eXV5Eety4QblQiSPNjlyauH4tfN/tuK7Vz/4UwAqxZtUijdpajuIVZ9hePzbGLEsueY9aHpiXSMYWi6h7aDm0jg3JhCqgtBUhABh6CAiwfX6u5fxxqZBgD9bwdzTjdqUxro4BEGINzaN0RmVynMuj9D6M5/BHZpAScQQpoHe3YbW6iFiJkZ/B0o6Qf39qwQNh9C9s/DLloyg7ZRoanuYpuxubKdEPNZCOtWDaWbn8uVU4maO+aqhppEhDHbeTWYFVS4UX6A7cYie5CFSesuWk9R3ElJK3LBBwRnlZvUMZXfyttXvQxlQdvO0xgZ2qJf3L0LPoT68emmY+wl6czPZT3wCvzCLkkjgTU1RfevtKGhyi4E2+3qJ79+PiMWQfkD5hRdIPvwQ7sgIzvAITZ/4BPUPPsAZGro3NwPoSozzI1/Zdj3OWyGEQFMMBApyzYVhlCp0Zvbr9KeO0h7fS0LLbWue7lYRpT+4VNwpRmrnyFvX7nqVi82gWhomlemis/9xkNHfjl1a/0sCqq+eI35kN7FDA/izZYy+DvSuVtTMXMWfIFx8zyUE1QaJ4/vwpoqEDSdybbY3oWYSqOkEQa2BN1Eg/cxDNM5eI2zYODcn8EanCRsO3mwZrSmN0dWC1pTGv4N4IGxZO3SI5uxeHtj1WTzfQlUNKrUxHLdKW9MDTM2eJ5ceoLfjJEHo0dl6jEp9fFVX1XbDCx1u1k5TcifpjO+jNT5wX7JH3cCi4IwyZUUq8XeqZxjKgKIz/lfSCH6YIDSN8ksvoyTiZJ95BjWVIiivlLUK6g3s4WEQCk2f+hTFr36VoFRCzeZQs2VEPI5z8+Y9uINF+KFHT/ND2F4FkNTsaRruzpCSNDGn0rKBuL0dVLlWfosZe5iOxD5azX4Seu6eGkIpJaEMKLmTTFs3mLSuYQf3VzrPUvheg5HrL97WdxRTx+hoxp8q4o5O4RerGF2taM0ZnGujhLaLfX2MoLoYLvFnytjXx/Cni0jbRUnFEaaBly9G8cK6RfXN86ROHsS+MR7tFEdnUFsyqDmJOzGDc2Mcc3c3/nQJf7ZyRxusLRlBx60wOPpdZlLdmHoKx61RqY/h+RYxI4Pj1uhsO0ZPx0l0LUbdmmV08q0dEKZeG2V3kqo3Td66Tkusj/b4HtJ66z3NL5JSYgUVpq0hZu1hyl5+y/UMJSFlN08ow7+UScR/WSBUZU5uTERUulUWhEoySeLgQbxigdCyEXo0TJ2RURJHDpN8+GHq779/z927xfpN4kYWXY0BcgWDczuhCX1TtfAgyiUsOKOU3TyTxlVazF7a43tJ6S13fWz4oUvBGWXSukbZydPwS9vIjI8gNJ3k3gOYnT00hq5jjQzCNhIPN4Og0qD29oXoj7n3svrKmXknIAD2peWLNul6NN5dLBIc1izq823Mtztbpvz1txb+tq8MR+Nn7hrexCzexNakBLf81lpOEcspIoSybIdXtyLdyfGp95gtXUMRKn5g47p3v2ZftAqboOpNM16/RNbooCOxl5ZYP4Zy9yST3NCmYI8ybd+g5EziBI1tFZu2/Sq2XyGh57atzb/G9qPpM59GicWxb9wgbFikTp4kefw4imlAGFI/cwY0jcShQwSVCn4x2l0FlQrS89DbO6i+8eYGV9l55JK9qEKnYk1SsSZ2bBcI89Jpym2t9APpUXTGqLh5xhuXyBqddMSjcR/tLHdmdyhlSMMvk7euM2XdwPLLuKG1Y65PNZHA7O6jfu0S7vTUXTeAC1hVCukuXWcLEHKdBoUQ95/DelsgFkRxm8wuWmMDNJndxLUMykIxzOjfCjfKkpXNIuZfb7nwl5SSQLpUvVnK7mS0MnXykUo94WqNbAsiKvnt1CQMN03ASRw4hJpIUDt/doGQYfb2E+vppXrmfUJ74xw2gZjbhd/ZBBTKcJ240PZCoGzBYyCX0d0B9NZWMs8+Q/Hr30B6HjIIoglLVRFKtEORYRjFCOePzY1P6fugqmSefBK/VKJx/vyyyUAR2h27/DQjTjzTQRB41EojyE1mL6uKgaHFaUntoTN3mMnSRUYL7y07RxEaDzY9T3fy0B31bR6TjStcKL60abb06ojGvarotJi9tJh9NJk9xLQ0Qihzz29RBXh9yIX/ICWBDKj5M8zao8zaN6l6s3MC+xsbJDWZJnfiCczOboSiMvml3yfevxslnqRy+m2an/0k1tB13NkpWp/7DH69htHazux3v44MQlo++inMzh68mSkKb3wXe3yE3CNPkNi9n8BuUH73TdJHH2H25W/R8pGP4xamCR0bv1rBurlxbcFboShz/1QREV/ukqfZ96Ok+zuBlHLNXn64M0zvGIuC2NP20ELFZk2YpPRmkloOU00RU5MYahJNMVCENlctQlmiNB/ghx6edPBDByeo4wR1LL9Kwy9jB5VtW/0psThKPI4QgsC2CBsNlFgcoWkIVQVFIahUIJFAaBrS91F0Hb9agVCiplIIVSX0PIJqBRQFNZlC0TRUVSWo1wgtGzWZRDFMZODj12oQBqipNIpuRG0IBRDRd00TLZ1BaBF9XUkkUMw4yJCgUUcGAWoiGfVPKEjXJqjXVxiH+xWSkGAb49cyCPDLZaTrRkZtHkEQGcSluOWYkkySPnkSJR6ndvr0itXwVkIMueYH2P/wj1Ivj3Hp1G/P5Y1tjIHWk6RjHTh+jZvTb1FsjN5xHzaCsi2V2ufGfRgwaV1jck6C0FSSJPVmklqWuJbBVJPoSgxNmKhCm3vnJaEMCWQ03r3AwgqqWH6Ful+g7hXv+L2O9Q0Q+j7jf/Lb0QJozsoINVoYCVWbWyQJhFCoXfoAZ2LxWc+8+BfkHnmC4tuvEtSq6C2tJA8cZuov/oz4wF5ivQMgJVoqjZpKo7kOrnfLO7gBVBXSWYVsk8r+wwYHHjTp263T2q6QSCnoRpQYv5P4yh/U+I3/q7Tt7d5XRjA9kKNpfzNu1aFwcQa3sj2uQjWm0f5IF6EXUrw0jVtdXaHVlw4ld4KSO7HlawpV0PxgG8nOFLWxCqVrBULvzidUs6ubWN8AihkDRTD7ja+SPHgYo7WdoFFHaBq182fJnHgcNZEgtG3UeILapfM4YyOkjhxDiSfQslmmv/ynqIkkTc88R1Cvk9i3n8JL38EZGyX35DPIwEeJxaieeR+vMEvzc58iqFXRslncyQkUw6DpY58gtCyUWIzQjv4388hjRFU1FLzpKeyJUZqf/QTuzBSKaSCDgOJL37mtwfeXCX6xSPk7L9zRd8N6nfJ3v7u9HQIURSOebkczbj8sULWnCUIfTTVIxlqxvSrVbdQGXoYF78z2wwnrOE6dgrO9xYrNVAvxXCeBa1GdWmPHJUSkzuI5y92YUiKUyAuhxmILtx44Nn51/fqAajyBlsqQPvoIQoKdHyNo1EnuO4hfLkYL4Fgcu7FxaEoIaGlTeejxGE89n+CxZ+Pkmu8N0z6T2xkre18xKHo+0s9T/+vHOfZzj5Hq2T7Vl/ZHunnin3yMk7/4EdpPdG9bu+tBjWkc+JEjPPW/fpy9P3AQLb56su9mEXouXrGAOzVJfGDPgg/Cr1Yon3qT4ssvIH2foF7DGryOXylTPfc+ZkdnRM2ulHHzE5hdPQhVi0gZiopXLmINDWKPjmB0dmH29eNXqwjdxGhrx+zoQmgqhRe/SWPwOlKG6C2tqIkkhRe+Qf3yBWQYYnZ0osbjFL77baqn30FvbkFLZ0ARlE+9Qen1V+Z2s4nteLx/jW2CqsdIN/Xd2XeFShC6uH4DgRrlAP81AFBUg/b9T7D36Z9g9xM/ih5fYz6TEq8wM+cSfZLsiSdQNB2/VsVo6yDz8GNo6SwsddJu4Fxyp6do3LhCUK/i16v4pSLWyBDpQ8eoX79M0KijJlL49fWNoKLAwaMGP/+Lzfyj/7mFT/1giqaW+zf/8k6x7lsrEMS09Irjll9Z5ezlSHSmGPjUXvLvjFO4cG+Ls6b7MxgZE0VXiLcl72lf7gRqKk1y30HssRGk76Hoiz9b0Kgvi+HIIED6fhRzCkOErhMf2I2WyWIPD0VlTwQQBoS2jV8oYA/eILSt6DuOg5OfwM1P4FfKqMkUQo8MuFCiWokyCBaPichFE3rewrF5d44MQ8JGI3LxCBElee+0z+SvcVvQ9DipXO8dfbfuzJKMtaEKjanaJerOzhb8/TBB0TSMZDOqHkNRdTQjgWetPm+6U5NUXRclFgcEYeDj5Ccov/82QghmJ8fxSwVC16H03puE9vK4aFCrUT7zDqEVHQ8dm+Lbr0aLUMCv1wgadaZf+AvssRG8YgFF15Hu+jWLDh0z+Uf/Swv7Dhto2l8uw7cU6xpBVeh0JQ8SV9NIJH7oYqoJzs1+c8OGOx7t5sCPPYhbce65EZx8a5T6WAWrYDFzNn9P+3InCF0Hoamkj58gqFXxCnNJoVKuwciSC6WQZBASOg7x3fvQc80EDStytRgGemsL2dyToCgUX34BNz+BPTZC7omPAFB85UXcqUmCep2OH/lJCEPsm4N4M9N4M9N0/thPEboe3vQUbn4SdypPxw9/AekH1C+ei9iMS5at65Gw/hr3AEIhme3GiC3ZpdzGKj9uNtOa2oPj10jHOhgrnqFqf/jG107Adyxq04Ok23dTmxnGrq49B8ogwJ2dXnFsadxvHu7MSmkw6Xt4t3zfr5TwK6Vlx6yhawufbYTmNpX//pdaGdj34VHculOsawR96TJa/YCc2UnNK+AEdfZkHp1Tblg/vtX+UBdGylwI7t5LVAZLfPXH/wgAGXz4JmLpusx+5xtz+THzByXVs+8v/H8Av1yi/NZr0TkiOm4NXQcpaVy/sngcSO89QPXM+1iD10kdOYbR3oEzMUbx5RcWJ8K5GMXM1760eGzO8M5+62srjpXeeGX170oJDsx+86u3RW9O9zxA35M/hJFuZvDbv0F55MLGX5qDEc/yyPP/DYqqUZi4yI1zX8S1b12JC449+/cWdkK14ijXz/4Z9fItCvZCYd/xz9PedwIpAy6f+h0K+YtLPlZRVZN4up3mjoOkm/uJJVvR9QSSEN+zsGozVGYHmR0/h12fXbdCfLqpjz3Hfohktoti/hIX3/pPIBTMeJbmjkO0dD+40L7v29i1aYpTV5geO41rldcUo9D0OJmWPaRyPSSzPaSyXRixzILwdKZ1Lyc//T+wlr/NaRR5/4V/tdD39vQ+Lo5/gyD0aEntJmm2/LURXIAkf/k1pq68PrcgvUdpC3cAVYV/8D820793fQM4XxQ3DCGUcseVwHx/Zy6woRPflw4Chdb4AH7oRO6wDQxgrClOdncTinr/rCA+jMZvGVbb9a1VohyWGUtgMeg+n7g6PET64UeJ9fUjPZ/y268v+e4q19nKsfX6uw4EkYFRVO22edhh4GE3CqSy3cRTLajaylqDsWQLRiyzEMsy4lnMeG6FEdSNZHSeqhH4IfXKInFKUXU6+k/SuetxktkuViNvaHqcWKKZpvYD9O7/GGPXX2H8+qv4a7IwRXTfikYi04VQNJraD9B/8FOkst3LnoWqm5jxLNm2fXTteZrB819ldvwcchUjm27exeEnfmbNZxa5ttdOCbm1SoMfuiSMJvzAwdDiBOsY9r+akB9K78cjT8Y5+ZH4qsVxpYzKIc3kAwrTAROjPoWZgHotJNjhn//C6Z0pKL2hEQxlwIw9TNbsACmZaqzOckp2pUi0p4g1x2l6oJVEZwqEoO14J4TLXwQZSmY+mFqzXtmcBjp6Uiezp5lUVwo1piGDELtoU75eoJFfm8atJw06Hu0m1ryc8ebbPjNn89TG1o9p5g600HqkndKNIjNnJtGTOtm9zSQ75/rhL+nH1BaUXoSg/ZEu0v1ZpB9SuDxD6ersjq+oAJyJMZyJsZ2/0D2CDAMalQlS2W7MRNOqRjCZ7UJVjYXKHYaZwoznuDUZ1Iil0c0oluzaFRxrkZ2nqgZtfY+QyHQhJYSBg2OV8Jwage8CAt1IEEu1oOsJVM2kb//zqIrO0MWvr2qslsKMZWntOcbAwU9hJppxnSp2ParvJoSCHksTT0ZG3ohl2HvsB0GGzIydWdGWa5XI31wUU0YI4qlWMs27Fu6tPHOdcI3ZzHPry3aZk6WLtGcfIAx9gtCjVL+y7r3sKIRCPNOOmWlFMxIoytrG3KkVKE9cXvUzI5Ejlm1Hj6VRVI0w8PEaZaxyHs9eX+osnu0k1b57xTJIIpm+ur64gWYkSHfuQzeTzN48Q+Ba6PE08VznXF90wsDDbZSxypP49vqkFiEUYtkOYulWVCO+7vOwa7NUJqLfTtXg+e9Nkkyv7sEbvuHx0tcbvP2yxZXzDvXa9k1WWnMzZncPQaWKMzGO9NaPWc7D6OomaDQIyqU7u+5GJwgEaaOVllgfI9WztMZ3MdlY+bIf/IljtD8SGR49baDMuUEHPrmXgU/uXXZu6AWc/rdvr2kEw0CS7M4w8Kl9dDzaTbIzvdwI3ihw9Y8vMPHG6pRmsynGAz9xlLZjy+vrWdN13vs/39zQCHY93suxnz/J4FevENgeuz97gPYT3ZERNOf7YVG+XuTKH59n8s07yI8S0PNMP0f/zqNkduXIvzNO8cpfEwu2C2HoUy9PQB+oWgwjlgXGWGrckpluFNUg8CPDlcx0Eku2oGrGMsV8I5ZBN1IA1Eqjy9rw3DpTw++gaTFKM9eoFUexG1ENtiBwEUQTXCrXR/eep0lkOkEIWnuPMzt5gcrs+snKQlHZdeh70M0khckLTI++R6M6ReDZUW01M02u/QCdu57AjGfR9Dhdu5+iMju4wv1br0xy/cyfLm2cjoGTC0bQqk0zeP6r+M7qC7v5/FhNjWGocVy/xmz1Bppq4vr1O9a93SoUzaTjgY+Q6zmEmW5BMxIIZW0W4+zg+yuNoBC07j5B88BxYpkO9FhqwQi6VoVGYZSZG+9Qmby6pphAumMvA4/+wEJqwzxkGDB99S3WW93qiSzdD36cRFMPdq2ADFzaD3yEZEsfejyFoswZQatMbfom+cuv0CiMr9qmopl0HnyWbPfBuecRX/N5SCmZHXx3wQi2d2nsO7g6EebMKZvf+DdF3n3d5ta01u1A+pFHCRoN/PLGxMuliO/dj5ufxNopI6gpJoYSW1BTyBhtTDWuzameLGL6bJ76ZLQ6SfVm6HtuN0baZOyVIWbOLQ/mykAyfXpyzWuaGZMDP3qEpgOtNKZqjL58k8D1ye5uoumBVrqf7CfVm6ExVV/VkDam6rz3L18n3p7EyJi0HG5n3+dvT7FCCEHT/hYe+vuP03qsg/pkjdGXhgjcgOyeqB9dT6VI9WR4berblG/chmSUgO4n+zj6t0+Q3d3E1PsTvPsvX6c2sn7+z19j85BhQKOaR8oQIRQS6XaK+UvIOQFmoagk0m0oqk61MESjOrVgBDU9vswI6ubiTrBWHF5xrenR9xeMju/Z3DoxOVYp6ksYsOvIZzFiaTQ9QbZ1z8ZGUAiMeIbS9FUGP/gKdn12WftOo0i9mkfVYnTteQpV1Yklm0nl+ihMnr/1qSyPRQqx8DyijyUy8NeNVwJk4120pfehqTF0LY7j1dBUk7HC6R0qpbQeBN1HP0H7/idRVJ3a9CClsUvI0Cee66R196Momo5rVZm5/jbVqevY1VsXm4LuIx+n/YGnMWJpfLdBbWYIz66hx9Kk2nYRS7WQaOpm9PTXKY1+sGrctTj6AW6jhGYm0YwEzQPHSbX23/YdNfcfJdXaTyzdjl2dpjQ6AkKQaumNdrvJZjQzwdBbf7wK41TQe/zTtO17HKGoVKduUB6/jAxD4k2dtO4+gaLquI0y09ffpjo1iFNbfB79e/S5PMDlrY4Mevzavyry/lv2toc3lXic9ImTpI4exx4dIbQaBNUyycOPU3038lykTzxK7ewZhKaRPvkYajxBYFuUvrNYQkrN5sg++TTll18kaGxeWWhDIxhID0VoxLUsbfFd6Iq5wgACjLxwI6LfA+0nuul8rBcjbZJ/b4Lrf3Zx+ckSwnDtVVGqO02iI8Xl3z/HlT/8AK8+V2lYV9j3w4c59JPHSfVk2PdDh3j3X6wsQhq6AcUrsxSvziIUgVtybtsIAmT3NSP9kEu/9wFX/+gDvMZcPwyF/T/6IAe/cJRUb4a9P3iI9/7V62s3tPRWBbQe6+TYz58ks7uJqdOTvPE/vYA9uwnJsbkV5uJKNNIsml/hzbv1Nh97W/79hTZu6y3fhjbE0uTbuTjKNsRSPKeGa1cw4zkSmQ6EoiyosJjx3AIrslK4idMoApJ4qhVNj+NYpahrQsWMZVHm3KaV4krvQ+A7NKrrE0JkGDA7cY7uvU+jm9EuI5Zs2fAepJT4rsXY1Zex66tXmA99h+nR92nvexhV1dH0OIl0+ypGcHtQqA9Taoyxv/NjXB37LkHgkkv07qiA9lpItfbTMvAQqmZSHD3P4Jt/SDi3gBFCpTJxlb0f+Sk0I4FbL1KZuLrCgDUPHKP9gafRYykqUzcYPvXFaLEhJUIIYuk2+h/9IVJtA/Qe/zR2ZQqrvHIR7zXKlBqVufGgEEu33pERbN1zksC1GD39VWYG34vKzwGamaDn2Kdp3XOSXPdB0m27KAyfY+kEk2rfRfPAQyiqTmH4LENv/wmhH7kVhaJSnbzOnqe+gGYmcaqzVCavLhurHd3aCleolJLvfKXOxTPOjvB7Qtum8vabmP39VN58HXdyAjWVQmtqZl6GRmtqRug6mZOPYw8PYd8cQoYyGs8yRG9pIXnkCKWXXyKwNp5Ll2LDt1ZKyaw9TCA9NMXkSmn1yV4GcoF8Iv1wgSQhA3n7SimKYPSFG1z5w/PLjENgw5U/OE/vs7toeqCV1qPtKIZK6K6xN5dRv8Lgzn45oQiGvzvE1T/6ALuwvB+Xf/ccfc/uIruvmZYH1+/HgsEX0Hy4nZO/8DSp/iyTb41y6p+/uikDmO7eT/8zP4YWS3Dud/9ntFiSpj0P0bTrOGa2DYSCWytQHr5A4do72KWpdQ2RnsyR7TtE056HiDV1oeomvl2jNjlI4fp71CavE3rrB6JXa8Oza9QnBylcf5fa5I112xCKRqKtl7aDT5Hq2otqJvDtGpWRS8xcfjMaAFsYdb5nYddnIyOY7lhG7IglWtDNKAe2WrgZxbvCkFiiGc1MLZyn6jHMRFMkV+c5WNU7q14NkbG060WS2W5ARHHKJYr4a8Guz1KaubbuOY3KJEHgIueURu5EAWazkDIgkAGqoqMKDamEaOrKmOvdQKp9D5qZJAw8pq68SuAu7gAkAeXJq1SnB0m37yHR1IOix5adoxoJWvc+hh5L41plbrz++7j15d6lemGU4Xe/yJ6nf5JYtoO2fY8x8v5X13CLzs17MryjknFCCCQwM/gOk5deWfaZ67vkL79GqnWAeLaDTNcBiqPnl/Uj07EP1YgT+C5TV94gcBfnFhlGz6M2c5NU2y4SzT2oI+ci1/ocsjmFWHz5NnBqIuDcOzZWY4fIClJGUoJBQOjYkS7x3AIkCrAKhKahGDpC0/BLJcKlhk5RSR07jj04SNio3/YCel0jqAiVllhf9KIDQejSEutjsrGzBUwD22fy7THswsotrd/wqAyVaNrfgmpqmFkTa3ororob9WMUu7jSSPkNj/JQiezeZrQN+hHYPjKUtBxp5+Q/foZUb4aJN0Y4++9OYd0GsUYIgVA0cgMP0rT3BOmuPfh2Ha9RRqg6ZrqFzuPPke7ey+gbX6Q+NcxqMYNEWz89J7+XVNceAtchcOoETh1FM2nac5xM3yFmL79B/txL+NbqZIBk2wDdJz+3bhszl95g6tx3Vw/iKwrN+x+l57HvRTViBI6F14hcO017HibTc4Dy6MUtcYQCz8auF8i27iWWbEVRdAIio2wmmyIXp5TUyuMoioZjlYklm0mk2qjM3EDKAE2LjCBAvTKxoatwI4SBM6csJ+bk2dcrFBuhXBjccDEgZUA4py4shIKibk2haDPIly+zq+0J/NBBSsl48YMdv+at0GNphKIiAx+ntjI0ImWAWy9FhXnNiDCz9GlHbsdWAApD768wgPOwK9OUxy4SO/gMzQMPM/7Bd9aMnW4FUkoCz56LIa6Eb1exynni2Q7MdOsK1rQ+l/IipYfbWOV5hD5uo4wQAt1MrohfxhICTV/eZn7cZ3ry7jJ/Q8cBBczeSM1ISSQJXQ+/Usbs6UUYBtL3cScnAEnpO99Ga2sl/ehjVN95m9DefHx6Y2KM0EjqzThBAzewaIn1M9m4xk5SGO2CFbE/17iEV3ejlDdFoBo7VxfQmmlERmqtftScOY0/gbJOP3zbp/lgG8f//knSA1km3hjh3H94l8pQ6bb7JBSVzoc+iaIbTF98nXp+CN+uoxgm6a69NO87QbJtgNaDT2EVJhdcQ/OI5drpe+o/I97STT0/RHHwDHZpChl4aLE06e59NO0+Tuuhj+A7FlMfvIS8hS0Yy3XQ+/TniTd1UcsPUVraRnyujV3HaTv8EQKnEbVxy6o53b2fnpOfQ9VN6lPDFK69g1OeBiGIZdrI7T5Gy75HYR1W20bwfRu7UUBKiabHiCWb8dwaQtGIJVpQtRiuVcK1q+hGAqs2RSzZTDLbPZcOEaDqMWJzRrBW3qggtIgYpokmdDOFqpkoqoGiqHNqO+ocMeZ27kJi11Z3g644c2EFvHM6m0sxU71GuTGOrpo4fp0g3BybbzshQz9a+QtWNfwCgTIn8B6G/gpBezPdgh6LPALl8dUZoxAtqKxKnjBwMRIZ4tl2qlM7E/90G6U1k+vDICCY866omsGtv7MMPeYTgoWyyvMQYuE5haG/IoVD1QS3ZkZUyyHVys7nOTYuXCCoz6neWBa1Dz5Ab21Fui6199+Njp1+j/i+/RgdnUjHwc1P4owME1SrNK5dIXX8YYRhwHYZwVAGFKxhNKERygA3tOYe+c5y+P2Gh2+tXTNj2Ty0g2oGvrVBP+bzQzfoQrovS8ej3TTtbyHwQ6rDZSqDd1Z7TQiBnkiTP/MC0xdeW+bKqE8OIoRK25FnyPYfZvzdry03gkLQ/uDHSLR0Y1dmGH3zSzRmR5e5D6oT15CBT9uRZ2g7/BFKg2dwKjO3tPFREs3d2OVpxlZpozZxDRkEtB3+CG1H5tpYQkYQqk77kWdRzThuvcT4O1+jNnl9oY3q2FUahXH6nvwhEq13JukFkfvHsUoEvoOmx0hmu6kWh9GNJPFkC0IIauUxkGEU16tN09RxkGS2B6FogIOmxzHj2ej5lsfWNIKZlt20dD1IItOJbqbQ9DiqqiMUbUFubr4Cx+0qcHjezng6toqepuOMl87hBQ2SZguqYlCxti4+fztolCYIfBdVN8n1HMEqL4/NGvEMqdZ+pJTYlakV7nnNTKJoBsAqhJnl8J0GgeegaiaxzM4ZQae23tywGC+/NW8ToF4cJwx8FFUj13OIycpy970Rz5Fs6Y0Ke5cjo74UgS8JJSxderqOxHXXn/P17lZie7qpvnp23fPWQ/3c8rQe+8Y17BsrwwC10+8vP29o8Xeovf/ubV93QzkXX7oU7XFAkNSauFldmX+03ZChRK5DnLlbWBrn3Aq6n+6n5XA7nuWjGio9zwzQ9cSdiRZLKXFrZWYuv7nMAAIErkUtP0jgWqhmAiORXfZ5vKmTVNceAMpDZ2nMLDdeAIFTpzJ+Fa9RQU9kSHftu6WNLlJdUcpLaY02fLtOdaGNLKlb22juJpbrAAm1ievUJm/c0obEmh2L3KFbJMi4VhnPidyxyUyUMqObyQVSSrU4HBXYCVys2jRSShKZDlTNjMgNyWYURSMM/Yj8cosRVFSDXUc+x4ETX6Br91Pk2vaTzHRhxnNoRgIZhrh2Fas6TbU4gu/Wb/ueNlvf724jE+9aqPYe17MkzY2JPtuN8vhlrHIeoSi0P/AUXYefI5Zpx0g1k+s5zK4nfww9nsUqTVKZvL5AMoF5IYZFVZTAX3/3IMNg4bdQ9Z2Lud46rm8HpbEL2JUphKrT8cBH6Dz0UWKZNsxUM029D7LriR9Bj2doFMep5m+s8PI4lsT3bnk/BRsu3NRsEnNX57rn3K/YFJ3LCRvYfgVV0Wn4O0/jnyc5/mVB6Aec//X3GH1piI/+i8+Q7sty8KeO0cjXKF1bSzBgLUgaM8P49urxiNCzCT0HzUygmssrNsSbe1CNBFJK6jOjKLqxehuBR+BaGKkc8Zae5W209KCZiah69szY2m34i20kWnuYXZJaGmvqQNVNpAypTdxYNd4V+i5OaZpwbpV/p3DtCp5TJZ5qJZHpAiIFGDPRhJSSamF44YVzGlGSu26mSGY68Zwa8VQbCIFTL+K7y3dkQqgMHPo0nbuemHMxSez6DNOjZyhNXaZRm5pLPF9U4jnw6E/Q0nWE23NX3l+DwdTT9Lc8SlvmANlEVJXFCywGp9ZhSO8QArfB4Jt/yP5n/ybxXCc9D32G7mOfWvhcCAWnNsv4B9+iNjO07LsyDCPDNrcoiWLGa2NxN88C4/J+Q+A0uPH677P/2Z8hlm2n96HP0nP8MwufC6FgV6YZP/dN6oWVTOfibIDdkJixxWOJpCCeEKzruxICc3cXHf/l5xGqSuHPXsYdztP6U59C72gmsGzKX3sLZ3CC9p//QbzRKYxdnTTORQtgs78DY1cnzo0JFFOn+KVX8WdvL1/wTrGhEVRQaY/vIaam8EOHttweLhVf2vwV7h/ltHuG0ReHuPHly7hlh7f+Py/x5D99jrbjnRz8yWOc+eW3b4/YIyV2ZW23jZxjpgErXMVGqglF01FUjT0f/5lNXU6LLa+6YaaaEKqGours+cTm2lDN5W3o8YjMgAxXJTPMI3AtAtfeshF07SpSSuKpNoSiYswllbt2ZS41IoLnVHGsMkYsTaqpj8rsIPFkRJqwatPLcgcBsq17ae48jKoZSBlSnLrM9TN/tqzNv4xwvCpXJ1/EC2yGZ08RhgH30lA7tVlGz3yNvU//JL5j4Ts1pJR4To3a1BCFm6eX5cItQhK4FjL0EYqGkcziWWsv8lU9tuA6ddc5717Drs4weuYv2PPUj+M7DXwn8j54do3a1A1mh8/grjHupiYCatWQbPOiQzTXrJJtUhhfmSK7AAGElkP+336R7CdOYHS34o7NUPr62yAlqaePove04gxOIHQNN1+k+OVo0ZT+6EPYN8bxZiPCjluz0Npy948RVBUNKQMKzghOUCdpNG0ooB36YbS6EgI9sfpO4a8WFsVlCxen+eDX3uXoz51k4JN7sWYanP/19/Eba8ceV7R2h6tQRdPnmGMSu5RHBhtf070lPiEW2ggXyDC320bkgopW1Ovdy1L3053C9yxcOxKVntfwTKTaEULQqEwQLImJuHYV1ypBUy+pXB9CURfcplZtZk4GbRHJbNdCvDAIPEYuf2d9AygUdCPBfbUy3ILtGi+eXWDLzotpbKQrvBNItQ6w+/EfwbNrDL39J1QmrrLZG7Nrs3h2DSORI922h/rM6jO9UDXMVDOqbkbx4+L9KzmYbt/Frsf+M9xGmaFTf0p18jqbfR5D11wKMwHd/dqCC7SzR6O7T+fSWXfN7AMpJV6+AGFI6HigKOgtWXI/8DTuzTyx3V00yrX5k3FHl8cqw4aDYhpROlsoF3LO7wY2FtAOXeygRlpvI6W3UrInN3zRnaJN4AQgoO2hTq7+sX5bk/xfZoReyOjLN0l0pDjw40fZ/8NHcCsOl3737Kbjj3eSfwTzsSWJDAPyZ17ALq2t2jMP31meHiLDcBvaCJDIaOJcj/0pbp9Eshqs2gyh76BoBolMJ/F0OxDJiC2NEXluDdsqIWVIKtuFouqYiRxShlj1mWUGEyHQ9Hgk7i2j5xFJqq2NeKoVw8zcTyZwbhcXQdymULnrL7rk40YOVTHuSRWJ9v1PoJpJqtNDt2UAARqFUezqDEYiR8uuh5gZfGfVlB4z2Uymcx9CKFQmrq4Zjrgf0L7/KTQzQSV/9bYMIMD0ZMClcy4PHDUx5vYvmZzCQ4/HOPWqRaW0ztxzy2WUmIGWTeK4HkG1QeguiT+uENrfdBe3HRsnyyOpuNPYQQ0FFV9uvAupTVSpDBbJ7MrR/lAnj/7C0+TfHce3fLSYhpExyb8zRunq7cbDNgkBWkxDS+joCWOhSr3QFJJdKdJ9WTzLm2Oh+tuiTnI78Gou1790iWRXmoFP7+OBLxzFLtoMfmVtiva2XNeqRcwxzUAGXkRquU34VnWuDZ3Qd++sDScqBCxUHSORYa3pRNGMBffTVmDVZ/B9B0MzSaTbiSdbkWEYJZgv2d3JMMCuzxL4DrqRJJXtRtVi+G4d16osj11K5hKiFxMSVNXAD1cXPlAUjfbehzHimR1lNN8WJAukIYBYonlT+YVJs5VMrGP5sVgLdadwT4ygZiYRQmAmm0g092CVJjbtQXDrZQo3T5Ns7iGe66Ln2KcYP/utZWLZWixN5+GPkWzpw7PrTF19Y02R8fsBmhkVLzBTzSSau7FKk5t+HmEI3/pSjY9/LklTq4qYI8V89NMJXv5mnXdetVedLt3hPOW5nV7jbMT0DmsWhT97BYIQZ2hywb1Z/NKr+IXF59s4ex38AHdYW4jPSvfubZo2RYyRhDhBNFX1p48zvAFDNHQDzv/GaXL7W0j3Zen/xF66PzIwl88jkEGINVPfESOopw2e+eefItmdnksuF6hmtNswszEO/82HeODHjkaxs1DSmKzxzr94jfJtE1S2Brtgce4/vEu6L0PrsU4O/XRElMmf2jk3i1WciCjeeox07wMUB2+f6WsVJgiXtFEaun1KtFOeIfQ9VN0k0dZP8cbplScJBT2RRd0G5RO7NkPgOwgEiXQnZjKqxhBVg1g+ou36DL5noWoxmjsORTGKOXLNckhcu0zg2WhGHEXVae97hPEbK2X8FNWgY+AkHQOPLciv3R+FSiVOo4DnNtCNBLqZpmvXkwxd/Pq6yfnZeBepWBuWt6SahmKuStm/GyiNnifTdYBEUzcPPPezkYt2YaaWeE6DxuwIMzfeoT47cosnRTJ7410STT2073uctr2PkWrdRXXqBp5dRY+lyXYdwEw1g4T8lVfnUiOWvzeKoqEa8eifHkc1YtF3ABDkeg9HMW7PJnBtfM9apuayrc9j5AMyHXtJNPfxwHN/e+XzsOvUZ4ej51EYW/FbXzrr8K0/r/Ejfyuz8J42t6n8/D9u5hev5ZmaWGlQw7pNWI9YrUFxcaw411bOZ+7w8oXS0vPvBdY1goYS50jLJ5YlwSb0pg2NIED5eoHv/PyX2f/5w3Q81kO8NYEMJU7JpnStQPn6ytiJb/tYsw3csk3orz0I/YaLW3TxqxJ5iySaAFRDRdEWB2TgBgRLZM/mKb9CESi6ssL/7FtRP5xb+qGqJmHgLbiDvbqLXbBwSvbKlA4Z7fis2QZezV11t2lN13nzn73Es7/0KYx0jL3ff5DaaJn6xPplUu4UjambWIVxjGSWTO9B0t37qU5cX3XCE3MJ3vMqJPOoTw1hFSfQk1myvQcpdu2bS3HYfBuN6eGFFIxM70GmL7yKW12+CDFSOdLd+7Zl12Rbc8xOIci1H4jUYRrFZbugeVj1GXzXwow30dz1IACOXcFd5dxKYZhGbYp0Uz9C0eg7+CnMZCuFyfP4bh1VNUnlemnpPkq6eYDQdyjPXCPTsmfdun13E55To5i/RFvvQwih0LP/oySzXcxOnMdz6yiKhqYnMGJpwjBg5PK3qNpTlBqjNNzFMZyNd98T6TShaJQnrlCfHSHV2h8ZoaVjTUTlkZLNPbTsfoThd7/MzPW3lxnCMPC4eerP8O0q7QeeJp5pJ5HrZL6klgxDPLvG1JU3mLry2goBCoCuBz9O56Fnl+ykxeK7KwT7n/2ZueNR35x6gQ+++q+2nWUqVI3SxCVaCidINveu/TxaemnZ8yjDp77IzOC7y8ZvEMCv/R8lHnwkxqHjBooShSUOHDH457/Wwf/yD6cZHfLw/pJEuDaoLO8xWHmHkrOYANuTPLLquUZrCi0dw52p4VejFYFTtLn0Rxe4/McX8cobr3qu/ckFrv3JxtXDz/7KOwz/3iwtLQdWGAy36vKtn/3Shm2sh6t/fJ6rf7xcfFgIhb17PsPY2BvUG1FQ9+wvn+LsL59arQn8hsc7v/Qa7/zSyp3BUtTHqvzFT/zJlvq7WcgwYOrMi8RznRjpZnqf+EHy576LNTu6KLmlRivaeHP3QlJ+eIvLMH/mBWK5DoxUM71P/iBT5166rTYCz2b26iliuQ7MTCvdJz7L1AcvLcRiVDNB096HSXXtjZh7W5UAk5JGdZJ088AcMYWo3JG70rA5jdLCcd1MRnmZdnlVg9moTDB189ScSkwzupGgZ+9H6Nn7kVsuH+JYZUavfhermufwE/1bUsLZTnhunfzNt0mkO0hmOhGKSlPHQZo6Di47T0pJvTQ2ZwQXV/KqErmrK9bGseHthqrHadv3OF1HniMMPIojH2BXZyMjFYmmIBQNM5El2TpALN1C38OfpZq/hl29RYVHhoyd/RaF4XM09x0l3tQdkWBci0ZxnNLYxXXJMG69SG3m5pzIwsbw5xjL8wh9l0ZhjMBzsCtr69PKMMCuTlPJX8euTC8zXqqRoH3/k3Qe/hihZ1McXeN5JHPR80i10H/i+6jmr+HUl29KGrWQ/+2/nea/++etHDxqoumRIdx/2OBf/WYnv/erZd56xWJqwseq318pPLeLDRRj/AUDqCkmAsGUtbqQb3JXK63PHmD21asU3l7M4DfbIrfkZozg7cLQU7Q0P0AofaqVMYSikEx2UCoNYppZNNWkYc2Qy+3B9y0MPYXtlHGcMulUF4qiIYSGZc9Sr+cxzRypVAcChYY1Q6MxTSzWRCrZgQTUu6DHuNOo5W8wduordB5/nnhzD/0f+WHcajEyQEKg6nH0ZBbNiFGduMaUstLFVZu8wdjbX6Xz+HNRG0//MG5tSRtGHD2x2EZ+ld1c4crbJJq7ad53gqY9x0m09eGUZwCJmWlF1U3KQ+dItPYSb+7e8n3XyxNzq381mkgahRV5fxDJcFnVKbItexFqlCTvNEorlDXmkR8+hedZtPU+RDLTiW6mIzkrCUHg4NpV6pUJZsbOUMxfAgS+Z6Oo9w9rujI7xOAHX6G9/wSpXE9UNUOLxnsY+gS+g+fWVxB/UmYbTakBgtClWLuJomjUnbtVE1PQ1HuErsMfQwiVm+/8MaXR86u7cYWgqe8o/Se+HzPZRKp990ojCIDEKk0wVrp91Zvp628zff3t27+NOTi1WQbf/MMNzws8m4nzLzBx/oVbPhE09x+l89CzCODmqS9SGr+4xvNQaNn1EH0Pfy96PEOqfTfOKgpWN695/NL/e4af/vkcT3wsQSoTVXtp79L4+f+umU//kMu7r1tcu+SRH/cozobUKiFWI8R3JUHEods2bFNxmRXYVFHdnNlF2mhFSkkoA8bqK3drpbMjxHublh3LHOkhc6SbyvloBRXvbSLW00TxnUFUUyfzYA/1G9NoSZPsQ30ITaV2eZLKBxvHxYQQc4VOJU3Z3SDBD2w6249TKg2STLQTi+WwnRJdHY9QqgxhW0WQIbqepK/3I4xPnELX46RSR3DdOh3tx7DsIgJJZ8dDjI2/RVvrEYLAxnGqmEZ6k4/1/kZp8CxurUi27zDp7n2Y2TbMTEs0cXsWbnWG0uw45ZELK1yZi22cwasVyGzUxvCFVdMoQt9l/J2vYZfyZAceJN7UidGzP1p5T49QvPE+jdkxek5+bluMYDF/GSn/jPTeB9EzOaqNiQWXWHJgP7G2bmo3r+JMj5O/+Q618jhCqMjQX5f1KWXI7PhZKoUhEqm2yAiqOhIIAxfPrmLVZ5YVuL1x7ktoegKnUViV6RsYMDn9PvLG64CkvslJeeTyt9CMJCBp3Ea1CykDyjPXqFcmSKTaMGKZOUKSQIY+QeDiO/W5GOoiupuOUbbGaUntxvYqGGr8rhlBRTNItvShxVJU8tcoj11YO44pJU6tiGfXMJNNaHps9fN2CIlsJ227HmPm5rvUSzsT81d1k2RLP5qZpDJxZW0DCCBD7OosvlNHj2dQ13gesURE+Tp/2uHQcZNUZnFBbBiCg8dMHjhqUq2EzOR9SoWAejXEbkg8TxJusxF8+1WL73x5CY1OU9GyafzZEgBKKo7W1ow7PMntVP3dVFFdXYlTcvK4QYO+9DEEYoUQ7WqwxotkHuwm3ttM5fw4XsWm/6cPUjk7gt6UJPdwP/UbM7R+7AFK791EegFtzx/CGivhFdenIEspse0is4UraFocM5YlWJqGIZaKCEuq1TGq1TljHG/BDywKxWsYRorurkdJJFrIZQdIxFuQMkAoGvF4C5pmMjN7Edsu0dF+fMN73inUp0e4/q1fR1FVnOpKEo9QNTJ7j2LPjDP03d9FNWPUp26imDFCz0NRNbRECr9eRWgarlVh5upbFK6/S7y1B9+qI30XxYghZYhbmcWtFTFzbVFeT7W0zJjlDj1KfeQq05ffoDh0BkXTUY0Yoe+jqCoIgTU7jm/XSe8+TH30OqFro8aSxDt6ccsF/HqZav4a5eELaIkUWiyJBKzpUQLPxsi0kD/3XSbPvIBd3Jq7zbGK5G+eoli9SfbgQzjh4vvlFmdI9OzGyDThTI9Tr0T/bgeeXaHs1TFa2hBSx52eRGgGyBDpe6jJJIFloaXSVOxxFBnHq8ygJhIEjTqKGUNoGqHnIuMGM2Pn8IuzaNkmfKcMioIaixO6DkI3MNo6cPMThM6ixNbsxNaqOPhunUph89R/XYtTqA3Rktq9kCd4t6CoGopuIITYVFzNSGYXhLLtWxLFE9kucp0HGb/y0rqEoDuFkWiibeAE1dmhHTOCQtVQteh53JrPuhrMZBPanIjFrYIVn/vRFI8/G6e7TyeRFCRSCtmm1d33QkAmq5DJ7rxno14LlxlBNZui+We+H/viILVX3yf72Y8gHRdjoJvad1cPU62GDelcgfRRFY0ms4umWC9xLU1rfBdpvW3Dxr1SA6+06Ab1KxaNmwVyDw+QfqCTxs0CSkwjvb+D3v+Hvf8Os+w6zzvR39rp5Fw5d07obqABNAIBIjBBjCIpKlnJVvKVk3Q9nvHo6soK9vjaln01si2PRfuxZGsoWxJpRoAkAIIgMtAAGp1zV44n5x3X/LGrq7u6wjlVXQ2SEl88RfY5Z5+1w9l7fetL7/sj99L/mXtRgzpaGw32QggMI4qiaOh6GM+1cF0TTQ8jhIqhR9G06ysc56ZktuteL1aRUuI4TUyrwsTUy1wZfYbLV75OvTaPouioi0oAhrGc+eTdhGc3aeQmqc2PrS5vJARGPE24dwRhaGjROFK6xIf3YcRSxLffQbhn2A8N7T9KpH8bseE9WLUiaCp2s0I9P0Ogswcj3YmR6iAysINAupvkvntWTA5GNEm4d4TY0G70WBLHqqMEg1jVPGY1jxKK+JOTlGihqK9xF0kQHdxJuHcbaigCQiHctx3HqiF0DREwCPcPI6VDdHAXkf7toApqc6NrVtIJTafn8R8m2NVP1yMfxUh10vehz6AYATL3PMrAx3+Wnsc+jhb1m9qdWhm3uXwsp1bGbdw6SbXR0U3s4F1okShCMwiNbCM4OIwWSxDevgdFN8g8/mHCO/eixWIooRCh7btQY3HiR+5HCYVBgtHRSaDLb0EI9PYT3rkHPZle/F6C1AOPoIYiZB77EEL/7oXoc5Ur7B/4MMnIIF2JPVTN9tQutgKu3cRulJHSI969k2jniF+dKsQNfwpC0Yh1badv/2MY4ThmNU9lfnTZWPHOHSS6dn6PVOxuDq7VxG5WkNIj0buLaMfw6tdD1Yj37KRn/yPooThWNU9lYXTZWI88EeHRH4qw95DB8E6Dzh4NI/C9d22EUHAWCnj1BlomgRqPUnn+TfS+1rbpRrTO4kpJzS7gab57OV+/vMihd8NFEWAkw6hhAy0WRAnpeE0bPRFGjQT8is2IgVu3mH/mDNt++RGccpOx//YynulQG8+Rfe48VqGG9GRLLxB8I2aZFXbv/CiOay56ayWazSL79nwKxzUplcb8wgaripTX3WPPc30RVSRSujh2HcuqMjv3NgP9D6AoGo1GjrHx58kXLjHY/yCOa1KpzixrLr7x/JWg4RN/mzd4o4pAjfkl/m6lAZskBRcB3a+ybJrrhhekbdHMzqAaAdBU1EAYJRBCCIFTL/tcnLaJompUR88T6d+Ooup4ru2vqhWBdBxqk5fRQhFcs0F0cA9WcWFln5GUNOYnCXX2oRgGqh5EDUb8FpjFOIhQNVjsKVR0w8+xOTbNhSnfq5QSudi3KFSNxpwfdlT0ANKx0UIRqo0qLWMqnocWTWDEkujROJ7jEOjoxUh3MfutL5I6eB/h3mHKl26v3p3XrHNNAFS6jk8Np/ptQdca0aXrUD5+DGmZ/vtD2zAynejJJHY+C56HXSwgVH/l3bhygY4PfIxK402QEj2VQYvFCfYPIHQdLZbwv/ddwHz5PHUzT9CIUzNzNKx3j0pMei6l6fMkevcQTvez532/RHnmEvWCX1wiVBU9FCeSHiCSWuQ3bVQYf/PLuLb/O2lGCFULEu/cvkSqfu0+t63aciLrRXKEa0LI0nNwrPqq/YKaEfa3Qyy15twMoaioWnCJxQkpcR1rMU/t3++qFkQzfHq/m58/zQij6kGfoUhKpOdQnDxDvHsnoVQve9//y5RnL1EvTC9dDyOUIJIZIJz0OXTteomxY19cqayhgqp+7xm9myEdd7G9QqD3dfoL6VVqGFqhrWZ5x7umJCGJGh3km8uJV4Wm0vHwbvRUBDVsEJ0vUzk3S+ahXQS740gpiR8YoPTOOOZ8mdrlBZxqE3sx9LLwzFnSD+xA0VXMhQqzT51qGZYoVyYpV1bmai5dfnLle1eeWvbaNItcGX1m8d9lJqZ8DrtSqUapNLZs20LhEoXC+qreIqCTeOgAdr5C9c3r26qRIKn334WWjJD9wss4hc21PkQODqOnYpReOI3XXCPU4XnY9QqBZCfN/CzO9CiRvm2AwLUtRKOGt1jTbBYW/Ie4XkGoKoqqEkiksUpZrHIezzZxhEAPR3Hq/mpbMYJ41vVJwSzOE+oawLOamIV5Ql2DKLqBdF20cBQ1EPI9QFVDIAikumjMTyJdl0A8g1mYRw2GEZqGHk3iNuv+6r5aQrouSiDo6yRqBoh1FOY9D6uYI9jZg10uEuodwszOoIWjBDu6Sd/5IABO8/rkcrvgmSa186eJ7juIlc/h1esYPb3gSZSgH5WQ7g09W1LilEuEd+ymMX4VpL+dlkj6CgfBIJ5p4lTLBIe3Uz1/2idAnpmkOT6KdOzvmgEE6E7sI6BHEChEAhny1TFKjY2FkW8FlfmrTL7zdbp2P0go0U20c5hE767F+0XieQ6ebdIoL9AozZK9/Drl2UsgJaoWpHPbUZLdu4mmfIq8Xff/1NLY0+e/TXb8LcD3OOKdO+jafh+hWDeKUHCcJrnJE2TH3lxWORxO9tO/9zEiiV481y+yqpdnuTlUnOjaRff2+zFC8aUiqWZlganzz1HNjwOCVN9+Bg98iCtv/gWl+evzilBUhu74IRJduzjx7P+Ja/uRjfLc5cXr8QDBeDfRzhESvbuXnh/P87UIG+V5GsVZFi69RmX+Ciuei+99+weAZ1pIx0FLxfGaJtbkHKG79+HmN7YYa6ue11BDdIW2owiVulNc8bm0XaY+v1LHafarq/cTTnzu1WWva1cWqF1ZXUTy+waqSmhnH1osjJ0vUzsxiltpUDl2kehdO5Y2E4ZG5MAwWjKCna9QPzuBdFwid4ygZ2I4pRr1c5N4dZPIwRH0TBy9J4lTqmP0pVECOvWzE6jxMEZPCnMyi1c3/ZXxxePLDqmZ9VWXAZza9RujdMn/XeqzvsEvnr+uz1UZO7v0by28g8bc+GI4c/kKq3RpeZO8VS5c31e9TDN7fTLMvvPC0r+r4+epjl9nxskdv/6Zf5xlhKohbRsz5zNdCCHW4Sz0sEp54nsOUTrzJuk730Pu2PPY1RK1icuYef++svLzCFUltn0/wa5+1EAQp1LCrhSJbt9HsLsfLZbAKuUwc/Obyg0pgQBaNEpz4ipupYRTzKMmEghFwZydQjo29Uvnlq3qzZkptFicxqjPpi90A6/Z9MVPdQOvaVI9/Q5Gdy92bsEPL8fiaKm0Lyq6+N5WwBABbGm1le8HiIV6qDezWK4/Cdvu5iWANgdJafoctdwE4fQAwWgaLRD2lealh+fY2M0KZiVPozS7zLPzXIvC1GkqC5cZPvRRNCPClTc/v/TbmI3i0raheDdDBz+M1Sgxde5ZXLtJONFL9/YHULUA0+efw3NtVC3AyOGPoRlhps49h21W/aKY4bsXWwavX1ehqFQLkzTG5/38dyhO766HGTzwIc6++J9BetQKk5j1Isne/VRyY0sUf8FYJ+FkH/npUzflQyXFqTNUs+NEMgMEImm0QGjF9WhWcjRLc7ck2fS9ANloUn7mVdRIyI+qaCpKLIIzs7GFYVtGUBUailD8NonvEivE9zqUgM8haWfLpD54J+Zkbhk10DWEtvcQ3j9I9cRVogdH8BoW0naI3buL8uvnCe/qR7oe1lSO1PvvpPD02+jdCZSAjrQcUh+/j/rZCQJ9aYLbemiOr1cFeGuTY31mDC0cwyxmcZutcmZb52VJ16E2dRnF8L3BJaMhINCTJLKrG7tQp3p+Gj0RppmbQplUsCrzFE69gmsVMXpClC8dR1F9D8xzbKQnsYo5nPpbSNfFs02k9HAqRUpn30Z63mKhyebOxSkVqZZLy4xS5fixZdvULpxd9tozm1ROXl+EuJUy1dPHl21jZeexstd/5/rl836OZ4vrxbvVEWadUWxWNoOvBlXR0LXQUlO42maP3FbDMWuUZ86zEc0BKT2a1QU/r2Q3UVSdWnFyFXoxQcfQEVTNYPrC81SyfvtXaf4SmhEi3X+A4uxZqvkJYh0jRFL9TJ55moWxNwFJaf4iwWgnmYGD3OhiFWfOUZg5c8NvKIgk++gcvmdp0des5ajmx0j338HM+W9jLRrBaHIAPRgjP316WZrn+vWoUpo+t4GrsRxvvNAgN/e9p2F59p3l96USCRF7/D6USBBcFwkUP/+sz/22AbRukRCChlOhYuWQeCSM7lZf+WsJz7Sx5grUTo0Se2AvRm96pRFUBIHBTqyZAvWTYxidSYLDXT4jxVyR+skx1HAQoy+DUBWcapPaqTGUSBAtFcXOlbGm84T2DqDGw1hzBWTz9tE2SNfBrnx3ZIFcy6eXWgYhMDJRwts6qcsFX0j1Q4covzNO7EAMJdSBZzXpfOgOapfmCB5KMfvFY8gbWH8aM2PcjMbsSl21TePd4qG9aT9BEWFA24WKhiY0Fpwp5r0JUko3PeowQggWnEny3hyD2h4W3ElsTLZpdzDqnCGldDGg7SKhdGDKBhftt1oegu00adplTMcPNb/7nuC7AAGJzh1Yzeqyyk7pORTnLtA1cpRgtJNqfoJIagCkpJId5dpCSnoulexV0v13rBg303+YZPdujHAKVQ8QCKdQ9eCiSo+voFKau0Bm4BDJ3n3MX30NTQ8RzQzRqMzTXFUe6tbxpc9V0HRBLK6ga5DPr21UHv9AkLeOWRQLq28TjyuoGhTWGaNdWObye14EAyiREOWnX7n+PGzQAEIb1aGedDHdGkEthiddita7zwzRFoQAVUVoGsLQEQEDEQgggjf9BQL+Z4aO0DVYLOe/VShBAy0WRgka6B1xnHwZFIGiawhNRRg6IHDyFfTOOCKoo3fGcQoV7NkienfSfy8dw6s0sLNljI444oZxkVB45jipxw6jpWLUz2+cvHrDEAJUBTQNoesIw1j92q52XTeRpF4XnsRaqNCcKFA9O4XXtFFCOsGhDK7lomeiRPf0Ujk1SfmdcdyGRaAnubXHcDOE2Prz3AQUVFJKN1fsk4w7F4ipGcIiTq+2jVHnDGP2WTJqH5owyLpTDOt72aPfy6w7iinrzLnjVLwCV+wTXLKPt7VPy6kSCWRIhQdJhYcI6vHbeo5RYuzlyKqfpemmi4Fl7yXZGqV7zQjjufaKVgy7WfVzt4sk75oe8ivNbwozOtbyQj9VC7Drvp9k212fBCA7/hZTZ56mNH9xhSdazl6lUVmga9tREApGOEUsPUxp9sKKcbcK9Zrflf7hjwV5+NEAHZ0K0ajAtSU7d6k06x7xhCAWFSzMOWTnXMpFD6sp2bFTY+9+Ddfxx/ihjy+O0aFQKXmEw3D4Lp1gEMpFj2AA+vtVDh7SadQk5aK35l+zcdMC0/PwGk28egOvWserbq7Cu7WortDoCm+nN7yHc4XvMBg9yIXii23nDW4bhEAJBVFCIZR4FL2zA72zA62rAy2TQYmEEIaBEgj4BkiCtG2kY+M1mrjlCm6pjJMvYM/MYc8t4NXqeI0GXr2xsRWFJ7FmC4T39NPz8x+kdnIUa7aI0Z8h+fghtGQULR4i99XXqZ2dwBjspPcXPoQ5maN2ZgJpOQRGuun9hQ9hzxUpv3oOp1ildmqM3r/1Qbxak/rFKaTn4ZRqeJaNW63j1doLW230uopgACUURA2H0To70Doz6B1p1I4MajyOEvANoWIYoChI20FaFtKycatVnHwBJ1fAyeawp2dxq1W8RhPZbCLtW2Pfl64HqkBPRbAKdZpTRQLdCepX5zHSUZpTBfRkGD0VQdFV3LUKibYAImAQGBhAScSpnziFlkzgZHPc2J8Kws/JeLdfdaAuyzhYuDKAlC4BESQkImzXDuLhYUp/kmjIKp6UqEJS9YqLR+ot/q/XtibgeO5Y6422FAIVlRARQGBj4mCjodOkhs0ibR8CnQDb2Mc5juPhYLK6l9rOPGZbVb/CWQss4w3Vg1G/+GbRODq2n8vVA1GaN5AVXG9G9/eV7NlLrGM7s5deYPLMM0vbJbp3rzw+zyU78Q7b7vwE8cwIRjiBoupUC6uFbrcOqiYIBgXZBZdGTXL0QYN8zuOn/maE/+OflLjnvgDf+maD9zwS4epll3rdZWhE44MfDvLKiya6DooiCAYEC3Mu9bqku0fhZ38hyukTNv/gH8X5F79d5rEPBMl0KJw+aS+jkWsLnofe10XHL3wKr1IDKcn96dfA3eJwqCJUXM+hZhcw1NBia4RPLHsz1Eya4I6R1nuVEmt6Dntq45VkIhhA7+pE7+8jMNiPMTyI3tuFEmhN3is0FQiixmLoXct7STzbxsnmscYnMUfHcWbnsGfmcKvVlikiaTlUXj1H5dXlcXhrMsvMH319xfa5L76y4r3811ZSLuW+8tqy11o6RnBbN9LxqJ/ZwhAeIEJB9O5O9J4ejME+jIE+jL5eRCjYsn9KqCoE/euvdaQJjAwtfSZdF2chhzU9gzU+iTUxjT2/gFssbSp04ZQbOKUG4e3dNKcKVE5N4NZNmtMFnEqTyqkJMu/dS2xvH83pAnb2euWeEosS2rdyollzX/ki5qUra34e3LmTwPAgRn8v5oVLJB5/hNyff8FXEEh2+qXnQkHVDBqFObw1ChGEYRDctxslsLGGY+l62DOz2NN+dObmCb0pGxS8BSpuAQ8HW1rY0qRLHaQuy0jPo0/bwZRzCQ8XW1qklC7qskrJW16opoRDhO7Y1+aBgTk2gTO/9cVuQcJ00keAEFXKzDJGhBj9bKNMkUkuo6KSJEOYGJ300aTGPGs0qS+16fj1DpKbW4GgOHuBjsE7iaYGKC9cBnwOzlTPXuxmmWbVP89afsKvJM2MUMn51b5CKMQyI8ueIc0IogiFauF6JEcPxgjFuvy2mptQnDmLtedRunc8gOtY1IpTS/u8XSjkPKYmXa5edpmZ9o3YvgM6Tz/V5JH3BTl/xqaQl5SK3lIkcmHe5cplh1RaQdME2YVrYzjMTrvc+4CB50EgILhwziYUFjQakldfsnjrjY0vVt1qneLnn156LYLGpuaUtkR1G26ZmJchpneSb06uvlJUBIHtw3T87E+03Kn0PKovv07+z9onjhahIMHdOwnu2Ulg2whGfw8oSssJul0ouo7R243R203k3rtwcgWssXGaF6/QOHXWn7S/yxC6ihoyKL90ZtPtFjdDTacI7d2FsW2YwFA/Wnc3ir51BQ5CVdF7utB7ugjfeRC3WMKamsEaHadx+hzWxMYYNDzLofj65aXCkMZ4jsb48tzIwtOnQBEr+jKVcIjMz/x4W/eMlBLz0lXmfv8/rLmNGg3TOH8BLZXwDdDi7tRA0CcPj8RBQmX6IoFEhkZ29XPVe7tJ/9gn0RIbCydKx6HywisU/vLLWLLJnDOGRGLJJjlvBlPWmXGuEFPSaBh4uAgp/M/dGVwcMmrvUh/blHOZqJJAY2UDfmDb8IauXfY//bfbYgRdHMa5SII0STpQ0SiRJ0QEDX8R4eAwzxRD7GacCy3HrJdniXftoHv7A9TLswhFpVGex6zlAElu4jiJrp307X6EQDiJs1gdmu4/SHb8LeolfxFSyY1SK0zSMXI3jtPEahQJxbqIpPoX9+Rfu3ppDscx6Ry6G+m6CFXzc4OhxKo0eo5VIzf5Dj07H8KsF1kYO4a9ivDvVqOQczn6gEGl7DE96XL0/gBPfaXB//5bCb75tQY7d2uM7NC49wGD555uEgoJqmWPA4cMzp+xyS54FPIuRx8IUClLJsdcsgsularH/LxkYd7F824hje56OPPX2W4iD96JPTm34QHbmO0kVSuL6VQRQsFyb51ZAyHQe3varnAzRoaIP/4wge0jqPHYUiPx7YJQFPTODFpHmuDe3UTuvYvaa8eovfH2LYfzbgX2XJHSXHFLxlJCQSL3HiF85BB6bw9KJHzbGTOEoqClU2jpFME9OwkfOUzz4hVqr76xYWPY8r5ZhZjAzRdxy2W0RKKtXWiZNCIYRDZX9+CcXJ7Q/n0YfX0kP/QBzLFxAOx6BatawK6XUTQdPZKgNnt11TEAjIE+lM0wv6iqf4yhIE7DN3wADhYlzy8Tb8gqDXf5hJn3ruf1F9zr3khVFqi6qxdCGYP9q76/KqTEHNvaSMU1WIuVq3Lxv/WgoLRF8Tg/egw9EKVr+1H8vtomU+eeXTSC0KhmGT/5JN3b76dvz2MIRcWx6sxefons2FtLrQuuY3L1+Jfp3/c4fbvfi+tY1IvTTJ15lh1Hf3xpf9XCJFNnn6V75wOM3PkJHLtJJXuV+dE36N/7+BrH+Aa9u96LEGKxQvX2p6PefMNifs6jWPSoTXp87k9qzEy7/OHvV1hY8LAs+L//S41aVWKaEvC4dNHh4gWHqQnfo37zdYv5WY9S0SOf83j2G01CYUGzIZESXn3JxGxu/FxCR/bRPHeVyL3XVY0i9x+m9vpJ2GCYuE1RXYm1hmL2ZqFGI6iJGG5x7cJmEQyQ+ODjRB+415+kb7PxW7F/IVCjEZQd2zD6ewkdvIPCF76Cs3B7GpTVeAQUgVu8vau80IG9JD72BHpnxi9o+S7QRSmGgd7Xg97dSWj/birPv0z11WNrGpytgPRc7InptoygEAJh6OidmTUNdPPqGE6lgj01hb2Qw55dDEu6DvWFCVTDzwU5zRqevXa4xxjoWyyc2hiEEKjxGFoqid1CysjQY7iuietZCKERj/RQbSzguu3llfUNGEEnX8Qt3x6h1JsNmkAwzB7SdKGgIJHMMo6LQ555DnI/BRaYYG3CC6teYPzkk9dDotJbakD3d+pRyY1RL8+iqsYiY4yL6zRXMMbUS9NcefPzvpII4DoWrtPkxDP/BsesLw7nsDB2jML06cUePonrmCA9chPH8byVFd92s4rrWn7z/SZULjaDckly+uT1Y7l62T/XU+/47+VzvmG7BsuUVMrOumOMjy43UNn5TVaNui5KKEhw73aaZ/wQ9Wab/NuOe+kEiCkpCt48GnrbvUSr4doEo3V2rm4EFYHe1UXqRz5OcPeOLQ17bgZCCAgGCR3ch97VQeFLT9I4fa41U7nAr5bUVFAE0naRTQthaIuFOg5KKIBnOSAgdGAbIqBTO3bOV2neynJ7RUFNxEl88DGiDxwFTf2ucyUKIUDT0Do7SH36Y4QO7KXwxa9hz8xtKrbfElJijk+2ndsSqorW07WmEVR0HTdfpDa34F/fWBS3VEYNRsnsPYrTqOKaDUqjJ9feRyiI1tnhV9NuAmosipZOLeUF10IqMUJ3x0FyhYsoi83THel9XB5/et3v+QcpMPp72z4ma3zitvx+Ncqcxa8jKFOgjO+1jnGesaWw5/Vn5hInWat+AUCoAkUVeLaH65hIaS1NpJ4nl4iKhCqQrsR1moDPhysl3PnhXvLTDSZOFBerIX1yFmhiNRtLr/WAwnt+KsM7T1rkJ32DID13VY1Kq7F62iXZ7YtB56dW7w3cSijK7Xn8Ng0Bvf0a8ZTC+ZP+YrLxzgVEKEj1hTdpnvHz9momuak5sy0jqGHQq46QUfooe3mGtD1ccU7eUoWoMAz0zg7Mi5eXf6CqBHdtJ/nRD6w5Q04AAPyESURBVGEMD61Qff9u4ZrB0Hu6SH364yihEPW33kGuI6+shALEHr8boyu1yKcJ+b/4NpEju5GOS/WVU6R/7H1UXz6FdBwi9+33Wyx6MxS+9AKyvjXVn0LXCe7eSeKJ92GMDG6KX+92QiwS/Ib270Hv7abwha/SOHse2dhir9CTWGMT/oPSxgJAaCp6d9ean4fvPIS9kMW8fAWh6ySf+CC5//GXID3M4jzN4kJLhQO9I4MajWx6QaJEo2iZdMvtXNdmduEdFKGRiG1nbOpFujJ3tPwegJZJoYTbD5ebo7cnFLr+bLPWp6u/b4RUBg8nMIIqk6fKVLIm+x/volG2MasOlQWTQEwjO1Zn8I4Ec5erRFIGPTujNCoO4+8U0QyF3t0xQnGNqVNl6iWbocMJjJBGbqJOfqrB4IEEobhGJOlLU20EqhZEC4RRtSD9ex+jXpqlONNadPxWcfDuIFcuWFRK331LKATsPmDwN/9+iqlxm/Mn/RygCPmc1NbVaZSQH3GpPP3KhitDoV3GGFRsadGkRljEViWE3SgUQ0fr6rjpTUFw906Sn/ghAhvJQbzL0DvSJD/+BKgKtVffWJcYW6gKtbcvUH/7It3/4DMYfTf1Lgl8D+XKNLXXzyIMncq3WzcqtwsRMIgcPUL80YfRe9ae0L9XoCYTpH/i01Sef4nqi69ueUGSvZDFa5pLD876B6Oid6/DSO95vpe/GNm4tgr1HAunWSOQ6PA5XatrEw5onRmUWHSjp7EExdDRMimEYSCttQ1urTFPZ2ovjmdRqc0Ri/RiO+3l9/XeHoTa5sJJSt8TfLdIAzYDAd27ogwfSnL+xSyO7U+c9//YIM/84WVqRZtET5D0YJjCdJMd96Wp5Ez69sTo2hFl9C3/91QWPclEVxBnl4fddDn6I4PMXqwyeCjBue8sMHIkyaVXc0TTG5cainWM0L3jQfRAFOk5TJz+hh82vc34zN+Kc+WcxZ99tkSj/t37HTUd7n8kzM/9vST7Dgf4H//5+lwQOXoQoeuL/dP+olbaDuWnX96wUEFbRtCiSV1WCXlRokqSBXf61vsENQ0tnQJNA8ePIwe2DZP6xA9hDNy6iOrthpZMkPzwB/BqdRrvrKNOsChh4v9bQbqLV073J08tFb++QBRiSz1fYRgkPvwBokfvRo1/fwgCCyFQwyHijz2EGgpReuY53MLWGULZNLHnFwgMD7Z1LFoijhIO+b2jN8GeXyB86A6C27ejhIKYV0f9fXguzfwMKCrhzDqLOSHQOjKokfBmT2dpDCUaxs2vbQQbzQJz2ZMoioYnXXQthGW312xt9PW0nY93qzWc3Eq9y+8lCCAc18mO1Zk6cz0dUy/aSwYukjL8dAZghFWkJxk/UcJqugwdTjJxsoRtusxerqIHFIQC6f4wc5eqjL1dwHUkwYhGaa7J/JUapbmNRzWa1QVyE8eR0qNRnqdear+l7In3B3nzuMVCduOeUTgs+IlfTKBq8F//fWlThSu3inBE8JEfjfGZvxmnb3ClmbJnFlATUfT+HTTeuQCKIHxk32KEZ4uNoIJKUIRxsMhJPyFrcutFMkII1FgENR7FzRfR0inSP/5pjN7uLWFweTegJhOkfvgjOPMLfh5rVQii9+8ncvcenHwZa2IOEdCIv/8egiN9viex+Ju5hTKRx+9GH+gk/xfPIRu3kHfVNJI//BGi99+z4f6z7wUowSCRB+4BVaH4tW/iVbamWEjaNvbMXFtGECEQoSBqKrmqETQnJnHrdbR4HK/ZxF5sCdAjCWL9u0F6BFO9VKZWL9NXQkG0joy/ELwF+IY0gpsvrrlNJNxFf9cRmlYZEMzMv7UhT5BV+tdWgz07h7RuH5XfVkBKyI7XePAnh8kMhznz3Dxzl6p4N3gQpbkmh57oIdEVJNUbQtMVEkMhenbHyAyG/XWtBHnDd64cy/PI39zG0J1J5q/UGH+nyHt+apj7O4MkujeuZt+s5pZRo2kaDParRCMKcwsu8wse3V0KQ4MaZ87Z1GqSrk6FkSGNOw8ZXLhks6kaPgGhiODTP+u37PzJvyti3X4HdAmJlMJP/GKCj/5YjGR69XoQ88IYaiaJMdRL47jfnx25bzk/a7to+fQFRIgeZYSoksSSDTwkCgrnnPaVe9eCEomgJhJ4lSqpT38cvWfjBnCJZcCT+HfltX6tG1cDi0u6GwUm4ZYLQ4QQaB1pkh97guwff27Vh182TKovnaRxYcInebUdmucnMK9er/C61nbRODdO88q0r+vXvIW7TtdIfuwJovffs6mqw2XHf4Psz9If3HSNxTJv9trfrV5fJRAg+sC9uNUa5W98a938a7uQlo09O9d2XlAJBv3Ck6lVKvJcF2d+YalaWEslcfIWTqNK8eqJRdmotfNjSiyK3pG55eukdaRRousLPocCSebzZ6hU/fNwV6lAXA3C0NE60n7fZRuwp2e/q21E7SI/0eCb//YiQhFYdRckfOG3Ti99XsmaPPOHl1AUgedJ7KZLYbrJ1JkSni0x6y7Hn5rxxUauFdM4kqd+/wKqrmA3XRzT47k/uoyqKUtj3Ao6Mgq/8/9J8tTTDcqvevT2qPza34nx2jGL++8N8Pkv1fmZn4hw+pzNffcG+PMvbJ5WTQhBNKbwyZ+KU69K/vy/lLGt2+8RZjpVfv7XkjzxqShGYP05RFoWSjhEx9/+DELXcbLF21MY05BVpt0rxGWakpfFw2NA3dVW/00rKNEwencnwZ3bCe7duSEjLh3HpzirNbBn5jAnJnHms9jZHLJWx7MspOMiVBURMNDicdRMCmOgl8DIkB9CCoduvUVACAI7txO57x6qL7667EeQnsStNXBrjeVenechb6Dzig/EUDRBI99EKILMrhSVmSrlyU14P7pO/PGHiT541K9M3eS5SdfFM01ko7nEpGPNzOLkCrilsv+ZbftrC8PwqxyTCbRM2mecGRxATSZQQkH/800eh9A0Eh98DDdfoPrqsVsuW5OOgz2/gPS8tkJ814zg8oMSvqK7lAjD97KFoRN76EEKX/6aXxW4+HzIdSqI1VjMNzC3CGEYfn79wuU1K5abZon+nnsJBlJI6ZErXMBpg/D6Wq9ku03y1vQs0vneN4JSgllbfq2s+g2v5U2vAcfycKzr959rr5z//O9c/57d9LDbpKFr55gvjzp87i98D/7THw+RTCgc2KfjupK77zS4fMXhW99uctfBW1j8Lp6WEIJ4UuEnfilBteLy1BdqWLcpNCoE9I9o/OpvZrj/0dDS/teDV6lT+ItvYgx245k29vT87WGMAbAx0YRORu0DJA72lnCHqpEIsUfe4+ddAoG2VuZeo4E9u4A5Nk7z9DnM0fFVQ1XXIAFqNdx8AUbHqL953G/W7+4kuG8PoQN7MQb6UKORTYVhr+WwwnfeQePMOdzc9SII2bSovnhinW/7yOxK0n+0h/ylIoquYlUt+o/28PofHt9YeFtVCd+xl+iDR1FCoQ2fi08h5eJk85jjEzTPX8K8eBnnhnNa8RWARhNKZZzZG2SdNA2jr4fg3l0E9+xE7+tF22ReUqgqqU9+FKdUpnl68xIx1+BVajjFEnobVZUiYKCmksuIHZRgkMDIMGgqgeEhpGX57RSL4ymaTmxgj1+FKwRmeZWYlBCoyQTqBlliVj1GIXyKO11f0+hadpX5nO/pKEJpmx9Uy6RRgq0pCcH3sp1sbrkhlh4lax5V3FpIvmTP4cnvfeN6OyElODc48BNTLm+/Y/HCKyaViodhCB59OMjePTqp1C30VN8wDQohSGVUfu7vpbBMeObLVbYgILMMugGH7gnyd349zZ47Vt5rUkKj7pFbuOne1lSMwW70vi6aZ64QGO7DvDJ5OxhjfKqiOXecsPAnsYbcGvZyoWkEhgZab4gfMjTHJ6m/c5LGqbM4c7dAySQl9uw89uw8tTePEz64n+h992AMDyA2k58RAmOwn9CeXZvyVqyqRWmigqKrdN2R4fyXLxPv33jFoNHXQ+zx96J3bJw9X0qJk8vTOH6K+qkzWOOTSPMWyKcdx/cexyepvnqM0L7dhI8cJrhre1s8rzdDhIIkP/JBsrn8cmO7Cbi1Gm6u0J4RVFXURNxXeW/4iy3PsrCmpzEGB7DGJ3CyOZ+sPewvPLRQFCkdgsl+KpOr5wOFrqP3dre836Tj+OooLRZoRr/fcL8W2YBlV7FKfmQhFumj3bCL1pFp+/dy8gXc6vK5wcNjrHqcserxtsb4rkMRhLZ1ETs4RP3qPNVTE22V3ccODxPe07ciutC4Ok/52GWk0zoU6o/Rv6ISt3F1jvKxy9Rqkhdevv77vnncortLZXhIY3bW5cRpi0uXbTozCi++0qRQ3JwHajYknidRbgiBd/Vq/OI/TCEEPPX56pYV/0Zigsc/EuVv/HKCgZGVz4KUknpV8qXPlfnWV29S44iGCd9zACEE9kyW2KP3YI5Ot+7fvgltz/guDhVZQCDoVAaY925PL9AKSIlnmlRffp3qq8ewZ+c3fJLrwStXqL56DHNsgsQHHiN85x2bMoRqOExwz04ap8/hljYi7wnZ8wW0sI7TcMhdLBDrCVMcL2/ICxSBAIkPPb6MvLptSEnj1FnKz34Ha2Ly1vKRq8ArV6i9/hbm5VEi9x0h9tADG65WvebtxB99iMIXv3ZLx+jV6jj59nUS1XgMJRpZMoK4Lm6pjGlfRTqu35qgKHimf0xOs0p9waG+MMVaCvUiYLTVgF576x2CO3egpZPrbqf3dqEEA3irMLV0ZQ7QNItkUruR0iMW6eHS2NM0WrHFCL9HULRZWOXk8utGZb4foEaCdHzwMJkPHqZ+aZbLv/0XuNXWYePooRG6P3UUJbA8DJl79iSVd0bbNIIjdH1ylTGeOUnl+Ci1us3zL13/zVwXvvJUY1lz+3MvmLest/zFz1XYuc+gd1Bbtvjq7rtuCL/2F7deqJZMK3zyp+N84idjdHStvtCrlDz+6PcKPPvVGuWbjbqqIk0Le6HAYg5iU2g52weJkFA6CIkoYSWKQKBhvCtGUEqJtGzyf/5F6u+cvn2UWq6LPTlN4S+/DALCRw5vPIe1mBtU06kNG0GzbDF/IksgbiA9SWm0hFXbWMwhcu9dhA7u31AjvJQSXJfyt1+k8u2XcAvFDe1zQ5ASJ5uj/MzzOIUSqY//EMpGm8Q1ldDB/TQvXKb+1jubPhSv3sDJF5CebKslRY3HUKORFXR5yyZ8KXErvgGSniQxfIBgshvXajDzxpMrxlTaMYJS0jh5FsUwWhpBJRDA6O3BmV8Zei2UrhKL9FEoXaHRLOKk965KzbVizFAINZFoSy9RSomT/S4aQUUQOzxCZHcf+edOYc1vrq1G6CpaKoIwNPRMrO3+yIUvvUHx5XPo8TBGb5LMBw4R2bOxXuf5L71B4aVz6IkwRk+SzAcPE9ndul3s5sDTrXppx15s8M/+UZZ/+oddK6ozO3tU/tavpnAcyTf+5+YjgomUwi/+wxQf+ESUcGRlAYyUktlJh3/9mzmOvdRgNdZB2fRVWqIPH0HaDo0zV9ZcdK6HlkawSQ08MEWdSfcCEkmfun3DO9oopJR4tRrZP/7vNM9deFeab91Khdzn/hItlcLYNrRhQ6gm4gS3j/g0WxsoDsjsTnH4Z/ZTuOo/uBefvIqTa9/gq+kUyY9+aEMe7LUFRuX5Fyk//e13bfKSpkXtlTfA80h/5ocRbeabYDH/mogTvvMOzMtXN7zYWILn4eYLeI1GWz16ajy6oqH95mstDIP4ww9RfOobaIEQjfwMIH2moFWW5moy6eca14GUEmtiCi2TInznwfUPUgiMbUPUT5xesS/bqZMvXeZaaGEue7Ktohg1FkONR9t7DjwPJ5e/rdyv60FoKun3HyTYn6b81uVNG0G33KD0+iWCgx3knjmB2yZrk1Ou45TrNAB9LEr0jqENG0GnVMcpLY6RjhI9NNyWEdxquC6883qTX//bc/z2v+2is/u6lyaEoLtP5ed/NUWjLnnx6fqGsj9CQEePxm/8qw7ueiCIqq68t1xXcu6kye//Vo5zJ6w1x/dqDUpPvUDj1EW/6ntybsON8tBmOLRJjeYNecA5d2zDO9oovEqV/Oe/Qqg+jaVAtDOEWXPQgyqNokUkE0ALqkhXUs018RxJrDOIUARCgUbJxqo5RDoCqJqCUAVmxaaWMwknDYJxHaEKGkWLeuH6MkM2TXJ/9nm6/s7PoyXbUxu4BiEEgV3bUV55HW8DRjCUCnDp66PMnVzwq9I24gVqGsmPfghlow3Xjkv9zeOUn/3Ou796l5Laq8fQOzLEP/Dohoy3EILgnl0Eto9QP35y04sjp1DEq9XaMoJKOIwaiy4ZMyUaJfHow3g3TPhC05bYZaxaESklqhFAC0RWHqMiCLSxyHKyObxGA3NsAilly+0D24ZXNbgAmhZCVXRMq3yDJmiL845HUWPtha29Wg23WGxr29sBJaAROzCEU6qx6bgYIB2X/DMnyT+zNt9ryzE2MRGvGGOp1eu7Aynh1Jsm/7//Lcuv/laGgWFtKUcohKB/WOcXfi1FsyF586VGWxkq3YC9BwP8L/+sgx17VlauSymxLMmxFxv80e8VuHS2xTyoKGjpBGoihnl5HLUjiTOz8cbITZFIBsXmaZ7agddoUnn+JRonz/Dor+whnA7w8C/tYceDXRz+xBCxriDD93aw93193P1j2xi5t5NkX5gP/K8H2f1oD4d/eJg7PjxAciDMY39vP3vf38vBjwxw949uQwsodO6Ks+vRXg780AD3/fTOFfu35xeoPP/SuuXtayEwPIgS3FhjbHmqRt/d3Qw/NMDQg/0EE+17R8Gd2wjt273hMKh5dYzSN76FV92aIqfNoPTMt2mcPr/h76nRCOEjhzZu+G+Aky/iVdtrFheKskhN5udqhKqAqmCOjV//G59YWkwoqk4w1e1TiK1GmSYUAttHWu7XXmw3cBZybS1UjP7eNYkR0olt9HffTTzaRya1B11rXT2sxmJtU7q5ldqWMvtsFMHhTozOW6+0/QGuw/PgzZcb/Md/mWfssr2MTABgx16DX/qHKQ7fF2xZWB+JCh77cIRf/1drG8BmXfKNL1T5g9/NtzaAgBoLE33v3YQO7EDLpEh+7NFNEdG3kRMM06kOLJVUCxSiIkXZybX45uYgPY/mhUvU3ngLaZrkx6t0bI+h6AqRjgBWw8Fz/cbT8mydcMogORAmP15DAMf++1VSQxH2faAPI6KDgLe/MIZmqDzwczsJJQ08R1LLNWmWFQ5+bHAl0bzj0Dh1lvCRQwQG26tevQY1FkXv7d5Q4UUj3+D8l30icTWg4pjtGV8RDBA5eveGjYFXrVF88mmcbA5VhY99KsTuvTqmKfncn9RYmHt3iHNl06T87PMYA31omVTrL9yA4N5d6F2dmJs04m6xhFuptuVhgV8lKQwDaVq4tTqVF19ZTg+2mKQHUPQAwVQ3Tr2y6mpeaCrGcOsCJmt6Fhx3keVmFnXn+mkIEQyi9/ViXl6pXeh6NsXKGIFAgkS0n0Lpyvo7X1QdaYtjFXArVZzNeIICwjt6Ce/sQc9EUYL6Yn9tE2uhTHMsS3Mii7SXPxNKyCC0rYtAXwqjK0H0Dv851jvjdH3yKE5h+X1hZcsUXjiLnV1ZOBTZP0Di3p0oxvLpsDmdJ/f0CaT117c1w7Hh5W81EELwy/8oxcDI8mKZfYcD/N1fT/MHv5vn+Gurh8LjSYVP/GSMH/4bcbr7Vi+AKRc9/uyzZb7y38sU823OP5qGbJqYE7Pc1sIYIRQcaVOVRf81goCyiR60NuEWy1RfeWPJiEyfLLDn8V5mzxWJpAOUZxskekNsf7CLY5+7QqI3jKr5XlC9aOG5EulJpAeKImiWLDxHIjWJ60ji3SG2PdDJ2BuLLB+GuqrYipPL0zxzgcBA/8b6B4XAGBrwpZbahF13lvKB3Yc70cMadhsh0cC2YQLbhzess1h96TXMS/4k6Hlw+qRNsyH5jX+a4KmvNLbcCIbCAseRqya3rakZ6sdPEn/fezc0phoOE77rkC9kuwnmeGlZvhFz3bZoy7RMBiVg4FUAx1kygGoigd7ThVMs0rziGx/p2ghFxYhncK2VHpze3YUab+1h2dOzfjTCdrCmZgi2MIIAgR0jqxrBam0W17WQSFzXwnHWz92JgOE3yrdZFOOVyxvWEFSjQbp/5H4SR3eip6MoIcO/l6VE2i5u3cQp1ck9e5KFLx9bVmEZ6E/T93OPEhzIoIYDCN1/BvRkhPSjB1bsq3ZhmurJ8VWNYGhbNx0fPYIWWW7wK++MUnj+DO5fYyMIvk7gi8/UMJse/+s/71hRybn7gMGv/VaG3/uNLCffXJ5DjScVful/SfH4RyIk1uhdnJ6w+ey/LvCdb9RpNtqPAUvTAiEIH70DaVpYExtXlYe2GGNqNGV9WXOt0ybl0kYhPQ/z6iiNs+eXrNL06QKP/f39HPuzKxz6+CB2wyE/XiPeFeLen9yOqiuMvr6AX4SwnK1FSrm8WMiTNCs2mq5w56eGqedNChOrexLStDDHJ3DKFbQNNjTr/X1r5mZuxv5P7aI4XmbfJ3fh2i7Rngiv/7u3qS+0CH9pGsE9O9uS0bkRTjZP+dsvLh2blHDxnEMx7902DbFf+rtRXvqOybFXV1pB2WzSOHeB8JFDaC0KRW5G5OhdlJ78Jt4mJZfsuXmkZbeVk9Q60kvsMNdgDA4QPXoP0myiplLUT56mfvwErtUke+Zlf6NV7oHAjpGW+3PrdV9BQ0qf5WZmrr284BphVtMqI/Cb94vlUbwW6ttKINC2dy4tG3sht7HFiCLo+tR9dH70bpSgTnMyT+3FcziVJkpIJzTYQXjfALqm+jqcN7UY2NkyC186hhL0Q9SJoztJPbwPc65I/pmTmLPFZds75Trm3Orh2vyzJ6i8fRU1EcLojJN530ES965Mk3yvQhgakXv34FWb1N+5Lk2nJiKkPnI/aCqFr7yMW9h8W4NtwavPN/idX13gn/2HLmKJG6tGBdv36Pzab2f4l7+e5dwJ/zlPdyr85r/p5PDREEbg5vAngOTyOZt/93/kefuVxkZqCQG/1an8zKsEhnuRtoM1NnO7GGPkCnYYl63r01vai5R+LvC5F+GGG76Ws/jsjz6H9OC5f3v22iHxP/7eK0uG8hqN5ZO/cxwpIXe1ykv/+QJImD5VWCw2cfjWH5wBCd/6gzPLyMbXslXOfBY3l9+wETT6etre9sz/vEjP4U6O/dEJSuNlBu7rpd5GZajekSGwbXhDFaxSSiovvoJXW5kLW8tcqypEYwLD8PdjNiXVqly611TVj/cHFm9yy5JUyv7n4YggGhU8/sEgl847dPcoWBZUyt6yG96emcMan0JNJjZ0PkokQnD3TurrqXisA3t2Dm+Rf7D1vsKoifgy8Vq9r4faOyd9PcFgkNTHP0L9uM8Q5NlrVxUGd+5ouT8nm1/Wl2jPL/g0dcb6PXvGYD9C11bwd8Yifeze9gSe9JDS4/zlr9Aw1w7ZK8GAT+7dBqRpYs9tjMAg0JsidmgYJWRQeOEsY7/3ZeQyIypQIwHCu3tpTqxMvTjFOsWXrkdbjEyU1MP7/OrONy5Rv9C++rrXtDGn8zAN5niWyJ6+7ysjiBAoAQMhFNR4GOm4eHUTt1Sj/OJJwnftuu7RC/+3FbqKdD28uokSCeLVmiihgH+P6Rpew1oxMXouvP1qk9/8O/P8+u910tnje4T+IyvYfcDg7/9Ghj/4pzkUVfCP/3kH29fI/7muX3jz7/5ZjrMnNknKIcErVWmcuAiA1pPBq298QdzSCKpoS0wxwGKz/CCX3c33aa0Fa3wSc3R8xftL3pxc5b0bt7vxN5M3/f9N77XjNTuFIk6hiNFm3uga1GTc761qo7BGC6hkz/uTkRbUyF0sYFVaeNpCoPd0YVzzONuEWyzROHVuQ6ulnj6VX/nVGF09CpomyM57/Md/W+HSBQdFgcNHDH70b4Tp7PZDHTPTLr//L8pk5z0eeTzAhz8RZmhY4+d/Jcpn/kaYi+dt/uSzNSbHr18bt1jCmpwitH836BvjPAwdOrB5Izi3gLSstjwsIQR6TzfN85euXz/bQUvEcDNplGgU2WxdvCI0DWO4dZ7ZN4LXH2ivVsfJFXyVlXWOUQkY6H29vnjwDQgE4kzNvYnjNAkGksgW/VQiFFrJmboGPMvCnt0Yg5MSMpZycPVLs0jn5uORuNUmlbdWhnZXg1zzxcbwvSyDuB6UoE7gwAjBnX2gaWT/9Gmfn1gun+zURJTkE/f6OnxCofjUa3T81AdY+JOvk/nUw1TfOE9o/4jvOZZWRsmkhLdea/L//60cv/KP0/QvVo1eM4QH7wnwj/5ZB8m0umb+zzIlrzzX4D/+qwLjVzYXVVQzKxfMiY+8l9wff3nrGWOCIkK3OkRT+t6DQBAQG5cFaQc3E1B/tyEbTZxCyQ/zaO3n3YSuo0TCy9k7hECoGtJ1lp1j75FujKhBOBOkUWiihzTGX5qmNr925aIIBgju2tF20cI1NM9dwC1vrLeumPd48ssNrlxy0DX4B/9bnEffH2T0ShXDEDz43gD1uuQ3/1ERT8LAoEqx4E9o33yyyfPfMnny+S7+z39R5uUXTaS3SgullNjTs7jV2oZDosHdOxDBwKYYZLxKFbdQROvsaL0x+DRnquL3/gHm+AShA/sI33kIoajU3m7NE6v39aCEQq3bI3K5ZS0YXr2Bm8vDOkYQAFXFGBpYYQRr9XkMPYquhdG04Prcv0KgpZNtcYZKKX3ygezGStOdfBW7UCUku0m/dz/NsQVq56ZxK9/fjDPfNUion7hC5fl36P67P4wx0IF5aaX+YGB7L0Z/J5UXTxK+cweB4S7sbAm9J40wdNRUDK/WvMkrXw7XgVe/3SAQKPC3fjXF4Ii2RDqhKIK9B9e+b8pFlyf/ssqf/l9FCpvQOryGxEfei7OwPJKh93VuqjimdbO8rDHunMfi2gMpSIr2Jo2NwC2VaZ6/uOXj3ircQtEnSG6jpPxGqIkYXrmCEg4T3X2A4NAIiqaT+/Y38CwbI9OBOTtFdbZG98Ew0pOUJ6v0HO7EiOnU1okuKaEQwd2tQ2o3wrNszMujG86f2Y4kHBF85ifCRGOCwSGVfM5DKH7o8/xZm09+JsyP/0yEV140eeMVc4nk13XBtiRSgu2wamHM0n5m53Ar1Q0ZQSEESiSC0deLeWV0Q+d1DdbEFIGdO2CVpt2bYfT1LGNPcbI56idPY/T34SxkfYmmFghsG27JQiIdBzdfXCbN5dUb2Nkcre5CoaoYq1Q0N5p5Gs0Cmhqg3phfV1BXqCpGX2tKN/9gJc58dsM8s3axRuH5M4R39BDa3s3g/+tD1M5PUz01Tvmtq1izxe+pBfH3E4QQazeNL3qGnmVTff089nQWoWlE7tyJOT6P3pXEXii2lMOyTMl3vuE3yv/KP07T3d+a33ZmwubPPlviqS9UqVdv7bf1Gib14+eWef3GQPemogAtjaCLg4uz2BqRQAIlufXtEc1zF5e4F7+X4FZrm5KGUUNh3ECQ1NGHiN5xGKeQJzSyk+LrLyI0m9ihI3i2ReHKJFpIY9tjg+hhjUCsteyQlk6iLTZmt30ehQL2QnbDieNf/JUYh+7S+R9/WmduxiUYVpY0Bh0HXnjOZGbK5f6HAvzy34vy8U+F+N3fKFHb4E3u807WaVfn7xqEomAM9m/aCJpjk8SkRzsts3pP9zJxWWNwgOh99/oN9Hcdpn78BPUT64dmjZGhlr1Mbq2OUyovMwLSNHFyBV8ebL2ohKKg93T67RzWzYZJ4rhN4rFBbKe5tqiuqqK38jivwfN8hqSNwpMUXzqHna/S85kHiN05gtEVJ35kG10fu4fKqQmyT75J48qtkaX/dYF0XMIHRgjtHcKtmVhTWfTeDMmP3I/R14HRmaDwlVcwL89g7hwgeu9eAPJfXKB5aYrkR+5j/j9+lcQH78G8PN1WW4hpSr7zjRqNhuQ3fq9jzepPgKsXLf7Tvy7wynMNTPPWFzflr7+IV1l+/xa//NymeKXboupQUBlQdyJQ/Jwg/Vx1N5eHWQ1SSr+lYJXVixAKCOUGL3flBCnxkGtWuwmE4h+3vy2wWBzQDrx6fVNGUOgaejpDcGgbC9/8Ko2rl9n2q7/uj2k2UHQDNeKXyefOF1A0QWo4wfhLUxTH1glZKgqBnds33BbhZPN+teEG8cTHgvzpf6nx/LNNFMV3hG4sprRMydlTNhfO2nz5L+t89k8zPPhwgKef8j1Oz/Pzt7ru5w2WafHeAGk7uMVS2zp/S1AUjMHNU0tZE5M+h2gb24pgAL0zjTXuT/pGXy/102cwL19FCYVI/tAH1jWCIhDwjUsrnbRyZSUlnJS4hSJurbZuoZYQAjUSQevMLAkB79/1af85WkQokOLMxc+vaQSFqvpq8m1AenJzRhDwTIfKO6PUzk8T2dtH50eOED00QqA/TaAvReo9e1j4yjHmPv8anvm9rVb/3YQ0bUrPHaf8nRP+veV5SMvBnsuz8MffQAiBXHwPKSl88UVYjEZI0wYkU7/73/CaNgt/8k2/LadNL9y24dXn6vyTv7/Ab/9BJ/HkSiX47JzDf/vDEt95uk6LouS2cc0AKpEQel+nX0E9vTllobaMYECEaMo6RW8BD48hbfeWiOpeg7RtrMnlD5KiaEQSfXQP3kOyYyeBYByhrF40US6McvGdz9OoXl81CqFgBBN09N5BpucOwrFuVFXHMiuU86PMTRyjUpzAddb3PqVlb4KPzhddVYwA0rKw5maRN8QCfSYauTTZe47H3DtZ5t7JEkwFEaqAtZ55RRDau2tDR3NNJmk1rs1wWJDuUOjrV1E1wdCIRrMhyS541GuS82cd7r3f4MpFhz37dXbv1Tjxtn8u0ZjgsQ8GURVYmHfp7dNwHJiavKHoxYVTJyw++skgti0pFz2uXHao11ZeUyebR7ruBo2g8CfsG6n0NwBnIYdXq6EYyZbbCiHQB/qXjKBnmghVRQmHUMJhPNNCTcTxGs1VvDA/p6iEWgvUuqsZQcAplvAqVWhRrayEgujdXUtGcCF3hoX82aXPO1J7sJ21c29KONi+2K/jrHh2NwQJXsOi8vYoleOjBPrTZN53kORD+wj0puj61H14jsvc51/bVD/oXxs4K9tI8CSyaa2YpaXtrJhfvIZ/v94Ygm8XnueTbv9//848v/bbGYa268s4QZNplTvuDvDiMzVqla0LcSvxCMlPvd+vjtVUvIZJ/nNPbngeaItry5INFFTSSg8ZpQdTNrfMAALYs/PLChuEotLRd4i9d/8U3YP34NpNSrmrFLMXadayS16c1SyTnTlJfvYMjn19VSuEQjw9wp67foxt+z5MONaF1SxRr84jpUdH70H2H/05Bnc+hm60aFp23aXwX9sQfhWgrwWnoMUTN6z+BVo0htA0vGaTzn0Zug93Lv3t+eh2Yr1rH5PQdYzBjRHzStvBKZRWjfNv26HxM78Q4Sd+NsIbr5h85BMh/tbfjrJjp78++v1/UWZ+1uPTPxFG1+GP/6jG28dsPA9s26c6OnzE4BM/Embvfp1/88/LnDm5/EH6N//crxb9+KdD3HWvQTC4uhFwK9UNT3RCCEQgcEsUaubYZNvbBgaue51eo0n4jv3EH3mYxPseQagqsYcfXDOfZvR2t6TUk1Liliu+sbsJbqHoX6MWEMHgEo8psGQAhVDR1CClysS6zfJ6X2/bTfJ2Lr9qy82mIMGczDP9J89z5Xf/ksboPGo4QOzgUPuUaO3Rov4AWwwp4e3Xmvyb38xx6k0T27o+Z2q64D2Phzn6cGgzrGZrQugabrlK/r98kexnP48SCW5KGL1NUV2XrDdFRPg3YsVbWXV0K7Bn55ZN0OFoF73D9xMIJpiffIvpqy/QrBcQQhCJ9zOy9wmiyX6a9RyXTvxPnJuS/MFIB0O7P0g8PUIpN8rC1FvUK3N4noNuREl0bKd78F76t78XhGD8wjN47uorIOl5m0vQC4FdKuCUiqTe8yiNscsIXSe69wBqJIZbr2PlFhj68WHKU9UlQxvKBFG0tX9IvbMDsUFRWtlsrimTdPqkzemTa6/+Jsdd/uXvrh6edR14+qnmUuhzLYxecfkXv9O6KtWr1ZYqLzcCoamoseiqhqMdWKPjRO68o61tjYHrCxBzbHzV/ril/r6boPf2tKy4lLbtyzytEoJ3K1XcUtkPGa9jpIShL9K86Usre0OPkkntBunhSZdc4QLOGnqCgaH2F1nWKi1NW4HmeJbii+cIb+9GCQdQQ+v3R3qmf72Erq2gP/sB3h14Lhx/rcm//+d5fvbvJrnvvSE03Z/Luno1PvZjMc6dsJiZvHUGntCRfaixCFomSfS9dy+FdzdTGdPW3aITICLiVGQBD5ceZYQZr73+nXbg5PJ+68AiwrFuwrEeXMdk8tJz1G8IcxazF5mb7CSS6CUQShGOdlK+gSdQKBodvQeJp4epV+YYv/BNSrmr3HhxyoVRPNdmcNf76Oy7k1LuCoX5jRM5t4Jbq1J89QXidx0ltv8w0rYIb9tJfewq5eNv4FYqnPvS5WXtEPOncjSLa4doN+oFAnhNc8OUVt8NeE1zUwsOoWmoseiaEeRWMEfbV0XRerqWvHzZbOK2KR2khMN+iLHFUlg2TZyFNQrPXBdnIes3NK+zEBJCoCYTqIn40ljRSDeeZ1OtzZJJ7ULTQmsaQX0DfLnm1c0pygQHM2ipKPVLM3j1laFjNRokut8/DrfSxKmtf53NKZ/GzuiIE9rWTe3cdFtCtn8V0d2vtcMEeEtwHUmj7v9ZNxS6eB6cfttvgq+Uknzoh6O+so+Au+4P8dEfi/GNL1ZxnY0/59WyR2mx/UrRdXA9rMuT/oJQCJpnrt4+KSVN6BgiiJAK4BFR4ghva3KCUkq/HPyGG1bTw6hagEZ1ActcOXnXytNIKVFUDSO4XOpFVXU6+w4ihEJ+7iyV4iQ3rw5cx2T66sv0jTyIEYyT7tp7W4wgUmJl58k//7QfBlNV8Dy8ZhPP9B/qm/sBG/kmrrX2w2sMbLwIRNo2bu27pxbRNhxnc0ZQVVEikU3v1p6ZwzOtNRUYboRiGGhdHcuYY9qBmkqiJuIt84GeaWJn166+tucW/HaEFtEALRFHTSZwFnKk4tsIBVKEgmmk52Ho0XVD/Bu5x6zRzYlrh0a66PnJh0AI6hdnaI5ncSoNv9q3O0H8zhGCI524tSaVU2Orcn7eiNqFacyZAkZPkp4ffYDgUAeNK3N+z2MsiFu3lqpRb4bQVZSwgRoOYmSiGBl/TlGjQUI7e7CzFdy6iVcz8Sx7dVJ0XUUNB1CjQQLdCbSEH57Xk2HCO7qxCzXcmolbM9dsPxC6ihoJoEaCBLqTy8fY2dPWGAD/5Pc76e69vVZQSp8VyvPAcST1qqRe9ahVPWoVj2rVrykoFjxSGX/hZwQEn/mbcZ74VHRTrQxf+fMKf/wHRQDqb51Z68A2PG57eoKyTpcyxKCWQEjwpLt1OUFP+uXgN4TBPNdGSg9VCyLEypWzbkR80mspcW8KY2paiHC0B8+1qVfn8dzV+5ccu06lNEWqczfBcBrdiGJbm+fWWxPCbym4uf1DCQTxbAtV8yvsVMM/z4GjPeQuFihPrn4s7Vbt3Qi/2ou26MG+q9D0zeVzFGVJ5mgz8FUa5giMDLbeWAiM/t4NG0Et7RvBdY9DykVPcO3Gc3tuHs80UVlf509N+kYQ/GfCdS2qtTkURaVam8NdwwtU0ynUaHvySV65grNGmL0VpCdRFtXbA32ppZ99qXpYSry6Rfbrb5P96lstV/hutcnkZ59h8FeeQEuE6fjQYX+8xbGqp8Ypv30VbjKCmSfupO9nHkENLy4qBEuh5tC2bnb+9o8tm1gXvvoms3/2Im7t+vXr/rEH6fnRB6/3fwrhF7cBsbu2ET00fANblWT6v32Hha8cW6aM0fPj76H7Rx+4gd7shjGObCN6+KYx/ut3WPjqsRXqGgCd3So9A+9eSHhpQSWv/37X/l+5afqORBUi0U0p+BFPXP9eqz7GjaCtKyXxGHfPLeUErylKbAWkafqd1Deg2chj1vOEIh2ku/eRnTnpV3EK0PUIHb2+p2ebFeqV5RyBejCKUBRcy8S1W4RQ6kW/4VoNoGqBLTeCQtOI7N5P/K570eLJFXmc+ae+yOBel0axwfYPjGDXbOL9Ud787Elg9WNRU4kNH0dgaIC+X/+1zZzC9wcW2Xg2C+l6WJNT7RlBRaD39QJvb+j4tEzKF+Zd90AkTrm8rsajry3YbEn1pgSDPu2ZprKQX2PVvAqMgb62FyLm+OSmcrgAxVcvYGXLxO4cITTUiZaOoBga0pM4xRrN0QWKr1zwvbl24ElKr1/CWvhzUu/dT2h7F2o4gNe0sXNVKidGcYorr6tbbdIczy4RcbeCXaiuEM11S3WaYwugtHfhnPLKnLFTqtMc3cgY9Vuih9tKLN2H4vuzJqntmUPibanxuwbPNFcUAdRK02RnT9E3/ADb9v0Q4Vg3jVoWIQTx1AiZnv1YVo2Z0VcxG8t73673Q7X2VZc0EsWN39s66KkMiXsfxCkVqV88h3dTI6edz3LhyQLJbQne+PfHqWcbdO5PU51bo9pO11HD4e/LG+12QgixfgN5K3ge1mSbxV5CoPe3yaZy7SvBAFpnR2u1Cs/Dnlrfw5S2jT037/dGrhdaFQK9uxMlFNpQwZBvBNu7w6yJqU21pQDgetTPT1M/v4VFdp6kcXmOxuU2DSdQfPEcxRfblz1bDdmvHyf79eO3NsZTb5N9agMLqx9gy9C2EdQjOsFMGKtiYhY2J12zGuQqRtCxG8xcfQVVNegZuo/+7Q/5Lrf0sMwqhYULZKdPkJ1d2ZhsW7XFfKGOqq6/ujMC/srcc501w0O3stpSQmGk45B/8VvY2bWZLypTVQIxg3AmRLNgYlVXL/HQ4jF/pbiJMuC/0hBiU4rSS/C8JRX3tmSV0qkN8ZWqkfCyloW1IF0Pa6q1+oE1PknkyKFlFG6rQe/qQgkFN2YEB9vXz7TGJ0FKVDVAV88hVDXI/OxxVC1AZ9cdqKpBpTxFIX8J1zUJR7ro6NqPqgapVWfJ5y4sa226EaFQhmi8n1Aojab7XKuuY2GaJcqlSWrVRSHVm8/ZiNLVfZBQKMPo1W/h2A3CkQ4SyW0EQykURcVxmtQqc5SKo9jr0Mf9AH890JYRTGxLMfLhXSiagh4xmHpxjKnvbK4q7GZIx101pKIZIULRLhy7wdWzT2I2Cn5uzbWwzCpWs8xqD4Fj1Wk28gRDKQKhJEJRV2WTEUIlEu9DSollVrHN2/AweB6e1briMdIdZvjhfhRdIZwOceYLFymtwhqjxGM/MICrQdB2GGktuNUaTqGI3oJMWwjhywxl0kvN6K2gRCLoXV2tN/Rc7KnWnpE1MeW3SbTYTuvqQAm1nwcWutaWsQafScnJ5heNoEG6Yy+RSDfNZoG+gaMkEiMIRcU0y4xdeZZqeYpd+z5BNNaHECq2XScwnmBq4pVleftAIEH/0HtIpbdjGHE0PYii+NOUlB6OY2I2i8zNvM305Kt43vIFtK6F6Oi6g2RqO/PzJwkFUwwMP0wwmFysMRB4nott1ygXRxm7+u1Fg/oD/HVFW0Ywvj1F/swCc8em0EI6B3/pnq0zgq67MuktBF0Dd5PIbGfy0nMsTL3dNs2Z61rk587RN/Igyc5dLMycxKznV2yX7t5LIBjHsRuUC6NIuUZF5i3MrXYxj1uvETtwiOLrL63kRl00jtWZKue/7IthDr2nf4mR/Wb4PWY/MILtQu/sIjAwSPXtNwHIfPhj5J78yqrbStPEns+2NILg53q1zo72jKDwydRb5XL94ikLe761GoM9OeVT8bTgWVXCIbTODNbEZFul41pHBhEItCUbZs9nl6lcAASCcQaH3wt4TE29Sjq9i0i0h56+I9B7BN2IMT31OvFYP/HkCH0D97Ewd5Jm4/rz6XkOsXg/0Vgfjt2gkL9EveZfk3Cki3R6J3qsD92IYJkV5udWl3QTQjAw+CDpzG4AisVRatU5VFUnndlDKJyho+sgjmNy9dI3b79HeO2atqpebFOMe73v/M7/e2FJ33NLcIP26pr7F2yqPWEjmJveumKYG7G+ERRgxAMIAa7rIVQFoQqs8taFQ1mlGV0RGpoeRAiFRGYHsdQFzGZpKf/gF3y5eK6N61jc+At5rs3C5FtkuveT7NhJ/7aHmLr8PI5zrZhAIRLvZWTvh335m+IEuZmTW3c+N0ANhgh0dhM4eBfJ+x/BrVWWGfOFp75E4+olwpkQ3Ye7EAIyu1PkrxRXHU9o2g9sYJvwewdjaLE4aizu58g61/bGPNPEmVuAA3tbj63r6F0dtCP6IzQdY6C/LcNijk20RQDsNU3smTmMbcPr3g5CCAKD/TTeOY30WndR6t1diDb1HO3ZuRXUcIqio2kGJ976L5hmmWziFHfc+dMkktuw7QbH3/gP1OsLRGP97D3waSLRXkLhzDIjaNs1psZfZnL8JQq5i3jLjluQSI5w8K6fIxBIkErv8EOqa1DAdXYfotnIce70X1IqjnFtnlDVb7D3wI/S0XWAdMce5maOUypuXd/zCgiIPfIggd07qD7/sq9JCSAEwQN7wHVpnvUVdLp+9ZeY/4PPts2cJHSN5Kc+SuXbL/n3L3Dy2NYJESiRMFoyjrVOrtoY6if60P3kP/f5Ldvvu4l1jaAeMdj/03eiBlQCiSA9RwcIJIPkz22OqHRVXGNYXvaWTSl3hXTXXuLpEQ6/51eWf+46mM0Sxewl5iaOUS1OLvPkauUZJi5+i8Fdj9O3zfcIK/lRXMciEE6SyGxD08PUStOMnn0K27o9q0DPsWmMXqYxennVz92KH/J0bQ+7bqMaKpe+MUrx6ursKu1OUD8AGL39xO+5Hz2TwejtR6gK9Qtr94JK0/Ib0VuwscBi2LBNDUKha23n2cxL7U/EzcujGNuGW25nDA74Wph2G0ZwkQigJaTEnl1YYQSFEORzlxaNksSyKlQrs6QzuyjkL9I0iwDYVoVGPUc01kcwmORmVyO7cHqtHVMujbEwd5Le/nsIBJPoenhNIyilx5VL36BUHF32vutajI8+R7pjN4FAnEAwseIYthQSqq+8cd1jWoSaiOOVq8uI7f0okECJRlCCAZx8ASUcXookeKUKbrmCEouiJhN+I/pav5mqosajPpdt00RNp5C2jWyaaB0phG74Ye1cASUSRui6r6+ZSeM1fU3B4J6dBLYPIV99C2d+ARQVNRHDmVtAiUX9xZ1QEAEdfaAXJD7jUaOJEo+hJeNIz8MtlLaOXm+Lse4d7zRsLn7+DHpEJzoQR48YCAFWZWPaYRtFKNJBIJTE8xya9TyOVVvmQSmqTiCUpGfoKNFEH5dPfZlK4Xp41vMc5qfexrEbdPYfJhrvp3PgCIpQ/JxCo0h2+hSz469RLd0C+W8LOMUCuW9/EwBhGChGAM+ykNbylZpruxgRHZDE+qJU5+qYpZWrOaFr/MAVbA/mxBi5UhE900Fz9ErrEJPn4RSKePUGanT9xnuhKH7jezCIbMEYsxGuV/Ny+0bQvHwV3v9Iy+30/l4Uw8BtQ0dS6+poq8rWsx2cbG7VXi2zWcBbzMF7rrNU+FKvzS/l/j3PxV3MA6rqBikApaRe9xfhiqojbm5Eu/FYzCK5hbOrfmZZNcxmkXCkC8OIIISydkrkNkHLpIgcvQvz0lVqbxz33/QkWjpJcP8enFwBt94gcvSI7yyoCkJVqb78BpF770ToGl6tgZpYvWdUaBqh/buxF/KYFy4Tf/97qb32JsIwCO7diVuuomVSVJ9/Gb2/F60jTeVbLxK57y7MqxPYcwvofd1oXZ0EhvpxS2W0TJrIfUco/I8vEty1DaEbONkcWiqJMTSAlklhT87QOHeR2MP341ZrKAEDt1qj/sbbW9rft1VY1whKV1KbqdBz3wDR/jiNrN+b4jm3j83dCCYY2v1+0t37WJg6Tnbm1HIjKARC0QiGUozse4JIvI9U1x5qpelloRPPtcjOnKBSHCcU6UAzwgih4Lk2VrNMvTrfUkFiK6CGw0T3HyY0sgMlEERaJo2JMSqnjy95gpmdKbSgSvZcnu7DXYQzwVWNIIryAxu4AbjVCtJ10ZJJf5JD4uRX5oeXtq/4q/JWRhAh/HxbIobdwghqHZmWTfIAXrWGPdu+dp41PulXs7aIDqiRMFp356qqFDdCCYdQk8mWFafgC2C7ldUZXBy7yTWPSiLxFg2LbdeXvX/teRaKuqoTJoRKLN5PONJFIBBH04IIVUMROpFo9+I2Yt0wc7U8vWSQV+AGog0hfEHYd1vD17w6jt5zU4heUUh86DGs6VmaZ86jZVIE9+70K3EBNR5D7+lCjceofOdVvGqNwBoC29I0sRdyqPE4WlcHSLDnskSO3oU1NknjxBkSH34/xvDgTRX6/jV18wUaZy6AEFRffsP/KJNevt3i5XerNWovv0Fg9w6MwT6M/l6C+3bROHkWJRz01VOCQaR9GwhJbhFtFcZ4tosa0FANFel6axZu3DoE8eQQmd6DVIuTTF99mXp19Z6fWmmaeGqI/h3vJRhOoeqBVTXHzEYRs1Hc/CHdwoMhjADxex4kduAQzakJ7IV51Hh8sXk+Tv6FZ+nYGSE+ECUxHKeRbxJMrLMyXmR++QHag5ZIknz4EYSmLxZguWS/8sU1t/cWjSBt0IYp4RBKIg5z66cGAtuH21JkMMcmkG2ELJeOtd7AmV1oraUoBIGRYcwLq4fkr0FNJVHDobZyl26xiLtGQ78nnVUp2dbW+1yJdMceBoYeIhTOoKoBVEVb9PiuGb02WzisGus/MNc+u/X5bOCejxHr8Y2RazWZP/cixfHNaa5WXzlG+O5D6N2duPU6smnSOHUOr97wtQJdD6FrPkONAGUdCj1rcobIkU4iR++iduxtXxXHdhCLRO5KNOwTviuKTxsoBGoqgRi/7mHfKG0mbQsl5CuhqNHIUphfCYd8B0X3TYpXryNNi/qJM0v79OprZ9EzO+6mc+97lvq15898h9yVtzZ1/TaKtoxgqCOMETdoZus+RZV6e4ygUBRC8S5UVadRW8Bal8FFXpeDkfL2GYdbOFUjnSE8tI3sM0/RuHrRF28VguDgMOmHHsfo7CGUtPFcSeFKCSNqUJmuYpZXDzf7q7WNnaiUcvHB+atLJuxrPq6MTijBIHahQPnVl2nnurmVKk6p3JKNBRYJsRMJ1o0lCEFg+0jL/QKYV8Y2VBUoPQ9zbLwtQeHgjhFaaXhoqSSinXYKKXHyRbzKGnn0NU9h3fLCpc+7e+9i244PEggmcByT3MIZioUrNBp5bKuG5zr09N/NyPb3tTxUT757obdAPEM4M4AQArtZRQtcjyaIgEHq0x9F7+vBq+0EodC8eIXUJ57AGB7wydjBL2CybX9B5HqE7z5E9YVXqb3+FrHHH0IoCvb0HKVvPId5ZYzkD38Yr17HLRXXvHdkvYHXaKJ1d2BPz/njX75K7NH3ELnrINbULOalqyiJONH776bj534cVLHkGXqVKmoyQcfP/yTFL30de86v1O34xZ8Cx6Fx5oJPX1kqk/npz/he44uvYc/MU33lDZIf/QBIaJ694OdF14AeihHJDCyFt7VQjNuap70B7XGH5hvUZio08nWkK3Hqt1HlebHMVtUCKOvE+/VAlFTnbqT0MBuldyW0uVEogSDScbAWZpduKglYuQXcRh01FGbshbWKAFZiM4KXeB61Y8fJ/dn3Z+VW27jJyKvxBEoo7CsqxKJL/KmuXVpjAJCWhZvL+yvlFlykSiiImmwR5lQUjKHWVGzS87BGN2YE8TzMq2PEHrq/5abG8IBPJrDOQkhNJpZW+Ose66LCvVff+iKHUDhDd+9dBIJJms08p47/V2orIkFiRW/g9zqkaZH/3BdWvF/4/FdXvDf/B/8J8CWqrslUNUrnaJxazmpTf/ME9TdPrL9jRaBEIqjxGM0LV5YiDc58lsKff2nZpm42T/Y/f27FEM5Cjtwf//dl7938GiB3ZWXLXP3YO9SPrd7C8r2EtoxgI1snNpgguTON03CoTrXWhtsMpOdRLU3h2A0SmR109B6imL2IbdVBeghFRdWCBEJJugeOEEsN06hlKeev3lRK/b2BayrpaiiMUywsva8GQyi6vqpm3Lrj2c7GF0ZisXpsA6G2vwpI3P/goup7hORDj/qqHZ5H/umvr/s9O1dANhrQwggKTUONx5dp9t0MLZ1Cjbcmo/Yq1XWVI1aFlD7LjW23zAsq4TB6Vwf2zBp0YovSS620DsHXSnRyhU2x9a8Nf6xAMEk43IkQgumJ11YxgIttH4GN8+f+dYQwAgT37QYk1tXxzVPc/RVHW0bQrll4jofneKiG6odFbwsk1dIkcxNv0j14DyN7Pki19w6sZhkpPYSioRsRwrEudCNCo7bA1OUXKOVHb9PxcEveuFMu4tSrpB54hNqlc3iNOkowTHj7Tjzbxs5vrNXEazY31UirBP76tVaUXn0JRTcQgQBu1S/iUMMR9K5u7OzCmhOCk83hNhptFbOoyThKOIS7hhEMbBtqqzXCmpr25ZE2CK9aw17IYfS1UBYRgsC24TWNoAgG0BLxZbmfNfdZq+Pk1i4u2hz8sJci1KVw2FqpEE0Pk0yvXgjyAyyHbDapv/7u5NW+n9GWEUzt7kBKyfRL4xjxAMMf2kn+XGtmi83AtmpMXHqOWnmGTM9+IvE+YslBhFDxPBvbqlErzVDOX6WQvUitPLOmKvyW4BZygk6lQun1l0kcfQ/p974foWpI16E5PUHxtRexS8UNjeeWyhs2ggJA05eEYP+6wC2XUbt7SD78CI1LF6meOkHiPY/gNerYC/PUTq9OkOBkc8h1Evg3Qk3EUUIh3OLqkRFjZKitcazJ6U2Fuj3Lwp6eac8IjgxTffn1VT9Ww2Gfkq+dfd4WI+jf045j4tgNAoE4qcxO5mbe5sZVqKaF2bbjg4TD7fVo/gA/QDtoT1Q3qNFYqFG+WkQJqOz4eGtWjVuBbVaYn3yL3NwZVFW/QVPQL62+Rnh9W43fVkB6NKcnsL7+JZRAEEXX8Wwbz2ziNRsbNmhuuQJea7qsZRACYei+x/J9oC6/lRCahlMuo4QjqJEoeipN/q03iBy4A9Ywgl6thlsqt9U0r8ZivljyGggMt5EPlBJranZDlaFL3zUt7Jm5tgp5jOGBNSm5lHCotczT4rG61TpOsbjhY10fvifYqGepVmYIR7ro7LoD9nvkcufxXJtw1H8vEukhn7uwRIf2A/wAt4q2jGB1qsy2j+6m885eIr1RZl+7fQ3m1yCli2PVuF2+i9A1hK7i1W9fQY0aiaJnOrHmZnDKxVuPyXseTrGMvkE5JcXQUaKRLTOCOzmIjkGFIv1sQ0WjRJbLnKGJHyrX0OllhF6GMAhQocQY5ymSRcfgLh5mljHGubQ0bowkh3iAM7xJgXk0DPoYoYchDAwqlBjlPCWuRyHu4r1kmUbi0c82NHTmmOQSp/BsGyefQ2gaRlcXKAJptwg7SrCmZwge2Isw1leaV+PRNYtJlFgULZVseS3dYgm3WNxUju2arBKuCy2YXpRoBC2Txlkl96iEw6jteIKO47OGOFtdaeyfu23XmBx/iWAoTTTWS3fvXXT13oW/+JV4ns346LeZnzvBkaO/sv6QP8AP0CbaMoL5c1nMiklmXyeT3xmlcPb2hELfTcQfO0zysTsZ+9//8/ob3kJOUE+m6f74Z7ALeapnT9KcHMMpl3wvcJNwcnn0VuGvmyAMA6VNtfC2xkPQQS86Bpc5jYLKCHsYYQ/nOI6CwgDb6aSPSS7ToEYnfeziEOd5mzIF8szTzRATXFnSdeykFxuLIlkUVAbYRgd9THKJBjW66Gc3BznHcSr4hUYqKt0MUKbARU6hoOAtjuc1m2jJlM/wEo5g57JEDxzEa8GeYk/N+hP9+jYQJRj0w4iKWEEebPT1gKa19NCcbH7tdoM24C6qu69H4yaE8PlO+3pWN4KREEorggD8wixrZiWHpCddGvUFSsXxZUTU0nNp1HOUiuNYVnWpf1BKj0Yj779vlpc9Y5XyBGdO/N909d5JIjGMpgdxHYt6bZ6F+VOUSxPoeojs/Gmk9JYa3peuh+dQr82jKDrNRoG14EmHanUWz/OwzPKqvY23gq0e7we4fWjLCAZSQdK7OwgkgmgRA7fhUB4t3uZD+/6HOT/D/Nc+T3j7LmIHDhM/fDfW3Az18auYM9PYxVxbhMk3wp6aJnRw/4a+owSDaKkWPW0bhILgAu8seX4xEqTpRgABQqRFNzNynBnGkEhqVDgkHiAluyhTYJZxehkmSYYCCxgESNLJHBNIPEJESYtuZlcZIy07l4ygQODhcYUzONw0IVarlF76jv9C+t6/lkxjza4vV2RNz7adP9VSKT/felNOT+/taYuCzFnI4lY3z6LhVqo4uUJrCShNQ+/ppnHippYcVUWNx1Ha4KWVtr1qcY1j17lycWXVreM0GLvyLGNXnl1+zE6T8avPMX71uVX3Y5olJkafZ2KN47DtOufPrN7yYzYLXDz3pVU/WzaGVeP86b9sud2mIFnGh6zoQUKJLvRwAlX3K3A9x8IxazRLC9iNCrey2lYDYYxIEj0YQzVCKKoGQiA9d2k/Vq2EVdu6ql5FMzAiKYxIAkUPomo6CBWki+fYuHYTu1nFrpdxmt97LDE3oi0jmNnfRaQvRvFSnkAiwPaP7+H4H7x2u4/t9uJdWKhJ26Zx9TKN0StoyRTBnj6CfYPEDx2Bawbx8gXqY1faDpWao2tNDWtDCYXQ0qkNf289WJhLBhDAwUZbvJ00NEJE6KCXCH6YTUElQBCDIAKFKmXK5OlliAILJMigopHFlydS0RfH6FljDOHTbyGpU1lhAAG0eJzYXXf7341GcatVis890/LcnHzB5zy8RhC8DrTMGkawr6clGbV0XN8TbIPXcy14lSpubm2P5xp8I7hSRUPoWlv3xpLUUwtqt774HcRDPYibAvZTpZOUm7dfty8W6KYvsR9FLL/2pcYMs5Vz71IDvecz5AiFeN9u0iOHCKUHMCIJVD3kq/LYJk6zSr0wS3nqHIXxk7hm+1X3QlEJZwaJ9WwnnOrDiKbRwzGfHlLVF3UTHd8INqtYtSK13CSFq+/QKLTWrFwLimaQHNxPrHcXwXgnRiTpG17NQCgK0vPwHAvXamA3q1jVAo3iLMXxUzQK7elvbgSqESQ5dJBw+hpJvcRpVMheOoZdX7sn+Ea0ZQSFIsifyzL/5jRqQKXjYPetHPemYQx1knzfEYyBThRDQ3qSxrlx8l95Fa/aIHJkF4lHDqFEgjQuTlJ69jhO1r8QSihA8ol7Ce8bwp4v4Fab7RWXbAU5jpQ4hTwN00RLdRBJpVGCYdRQmMieAzQmRsk+/TW8RuuHwBpf1IbbCGuPpi6qobcmfG4XLut7sC4uJnXqXF8FVmWJCkUWl8pMM8oO7iBCnAQZqpQwuXZ8EheXJo01xmh9LG6tSnWxAEYJBIjf92CbJ+diT82g97a+z68ZwRshdB29I9OSh9Nn8M/dUq7Ya5rY+cUGf32dx1kRaKkkSji8rNFd6HpbuUsAZ2Gh5f2TCg/SnzyIIpZ7wcXGNOXmHLd79RkyEvQnD2Ooy9lvpkqnmK9efFeMoJQSBHTuuZ/ufQ8TiHesoHvTAmG0QJhAvNM3ZJl+Zk4829bEbUSSdN/xKPG+PRg3eJc3Q1UMVM1AD0YJJXuI9ewk0beH2VPfpjB6fMPnFc4M0HfnhwhnBtBD0SWKsxshVAVF1fxzi2WQHUNEqkPU89NbbgQVVSez4156D70PLeine1y7ycTrX8K12k85rWsEjViAg798D4F4gGA6RO0juwl1hCle2mBj7xZATUZJPXEUa65A/quvEn/4IImH7qD4zTfxak0id+6g86feR+Gp17HnS8Qf3E/mk+8h++fP45brJB47TPTe3RS++ioioJP54fcgrXdhVSgERqaT+F1HiR44jHQdKifeonL6HdxaFSOdoesjnyb1nkfJPfNky+G8RgNnIbvqqn7tQxBonR1oqQT2zFZpQa49mdlYNGSVKmWmGV3K+fnf8v8DKLCAi0Mvw0SIM83okkfnYC+OUWKG0aXv3DzGukdo29gLi56LEOtWct4Mc2yc8N2HWy6U1HRqRVGKmk760jQtvutWKti32m4gpd/bWK+jrdPbKK6RfqeTWDcaQU1DTSfb2o85Ntlys/Pz3+JS9kUMNUQs2M3OjocIG22Mv0XIVi/z4pXPoitBDC3Cnq7HSYZ637X9AyAlsd5dJPr2oIcTgPTlhKwmrmOi6iFU3fAliIRAD0bp2HUf0nWYOv4NPHv9xIVrmxiRJMF45+Lu/EUl0lfQcc0GnmuhGWFUI3Sd31PVCGcG6D30OHajTHXuStunFO/fy+A9HyOY7OIah+uN+10+H1wj1vb36zSr1HOt750NQSikd95D/11PoNwQYh576c8pTpzeEFdtSymlq1+7sMIbcpvvfr+ZGg2iRIKYo7M4CyUqL58m+fhd/upXShKP30n19fOUnn0bPIlXqdP5Mx9A707iVhvEH76D8gunqLx8BoDg9l5Cu1uXsN/KwlVLpul43xOEhrbhVMrkv/MMlVNvL9Nha07VKbz6Aom772trTOl6NC9c2pARBNC7OtDSqbVZQ7YQJg3mmGCQnQQIUqOMRoAgIRaYprhY3enikGWGPrZRJk/tBobLJvWlMYKEqFFGJ0DgpjHWg97RSfKRxwHfE7yRtaflObQZdtYScRRdW+aLapl0SwoyKaVf1JK99Z47ZyGHV61BiwZ/JRxCy6SxJq+Hw9r2BKXEGmt9TRzPxPFMTMcXkHa92yu7djM86WI5NSxq1Kw8trt6dMUIqRgRFbvpYdWcLSXA0cNxMtvvRigqTrPKwoVXyV9+i2bZJ8cQikK0c4Tew+8j2r0dRdVRVI2ufQ9TXRijMLo+1ZhrNShNnCHSMYQQCs3SAqXJM1RmLtEozeE5i+oYiko43UfX/odJDuxH0QMIIQilekkNH6Kem8RzWv8+kY4h+u96gmCye8n4ubZJozBDceI0ldnLWLUirm2iGUECsQ4incPE+3YRiHVQmj7fdmiyHQhVI7PzXobu++SSN+o0Koy98nmKE6fZ6KS9rhH0HI/82S0U0L0FuJUGbrFK5I5tPgn1rgEaFydx8hVE0ECNR7COXVyq0nPKdaTjosUji/pvUeyF6z+EPVcktGug9Y5vIRyqBoMgJfNP/k/qVy6u2QvmlIqYLYo1liA9GmfOE33P/T6LfLvHEo9hDPTRvHBpfU0v4Ycz1ns4alRQhOKT3EqJ06jSoEaRHKoRwrHqzDGFhUkX/fQGdmOaJcrkadwQ2pRI5pkiTpoCC5g3aLVL5OIYFl300cUgDtaKMUrkqbOGrE+pSOnlF1BDITzTxJxpPxdiT88iLXuJbX8tCF1HTSSwb1CT0DJpRCuv0/MWiahvvWjAyeXxqrWW/YJKOOR7rje+FwqitNMj6HpYE7e/NerdgFAF9/7IIJ07olx5Pcf55xcwa1u3sBdCQagKzfICE699kdL0+WUFKdJzqcxdpvatSQbv/RiZnfeiqBpCUei780NUZi7hmOtXDBcnzqAaIarzY9QWxlht4peeQy07ztjLf4Fz5Ifo3PMgQtUQQiHSMUgglmkZotSCUTr33E84079kAO16iflzL5G98NqK47TsJlatSGX2ErMnnyUQ71wyylsBVQ+S2XkP/Uc+smQAzXKWqbe/Tmnq7KrXoRXaygl+L8At1WheniZ2v18ZaRcqLHzuW/5EZWhIy0FNhJeIx5WgDkLg1k2QEmlaKOHrE5oSbFH/vgUw52eZ+/JfIlvcBM2pMcy5NicYCfbMHE4+37a6OQBCENy/h+rrb+EWimtupodjBDv6qIytLkYKMM1VkIJgqAcpPZxGlXmmmGeKjr0Pkz3xAhKPPPPkmadjz8NkT6zOIF+jzDu8tMapeuSZI8/a3utF1lk1KwpaKo2e8a+T0DSaY6Nrb3/jvi0Le2aWQBvq7VpnBi5eXiIx0NKpdeVt/PFt7OmtKRTxajWcfIGA5/lE2WtAGAZaKnGdTFsI32C3oyFYKP6VIFswwirbj2bY895Ozj43z9zFKlbdYeeDHZhVm3hXkErWZOJEkVR/iN69cYQimL9cZf5SlURPkL79cTRDITdWZ/rs6mxBrmORvfAa5dnLa1Zkeo7J7MnnCCa7iXVvByCY6CIxuJ/cpbUVF8APMc6dfr6tc/Yci9lT3yY5eAAjmvb5V2Np9FCshREURDIDJIfuWDI4nmsz/c7T5C690VbI0SzfihO1/LqpRoiOXffRc8djKJpfzdwszTN9/JsUJ05tKAR6I75vjKDQVPTuNI1z4xSeemOZNyMth9o7l4ndv4/GxSncfIXoPXtwqw2cbAnpedRPjxG9ZzfNq7Moukb44Lb2dBFvIUyihsIE+gYwpyZw62uv7KTjbIjSzKvXMS9d3ZgRBIyhAYyBfhrF0qoPphHPkN5/H6HOfoKpboqX3iax4xD5M6+T2H6QZn4WPZakPjNKMNNHuHuI6tRF/1yDEeIj+8kcfBChqDSyUzQWJokO7SFzh/+eVc5RunKSQLqb2OAeFE2nPjdOdfIikb7taKEYWiiC0AyyJ17w2XFuAVoiidHdQ+PSBYSmE7v7aNtGECkxRyfaM4IdmSU2FhEMoCbjLb10zzSxpraoUGCxwT9sO+vyfwpF8dshQiG8ahWEQO/MtLULc3R8i0mz14ZAIRkeIBUaIKjHEELBdhqUzXmy1cs43uabfaQnaVZsHMejXrKxmy4SuOMDPcxeLC8aRRcjrHH4I33Mnq8gFMmhH+rljT+fYP/j3dTyJs2aw5Ef7qc016SWXxk1MctZKnNXkC1Yrax6kcLVd4h2Di/xpmZ23N3SCG4Udr1EdWGMdNQXxVWN8FIubS0omk5y+NAyWajC1ePkLh3btMFpB1L6OdQboepBOnYdpfvAI2hB/3jMSpapN5+kNH0O6W7ek/++MYJSSrxGk+SH7iX+6GFwXJqjc+S/8grWxAKl544jdI2un34/StDAms5T/Pob2NkyeJL8k6/T+ZOP0/cPPoU1k6NxZozInW0Q8d5COFRPpYnfdZRcIb+uEdwovEaT5rmLhO8+jNKC1eRGKIZB7L0P0Dx3YdXQrF0vU58bQw2EKV05gdOoEeroRwtFiA7t8SfNcIzmwhTN3Ayhzn6MWJr67Bie1aQ6dYmOw++ldOUErmXi2ib1mat4rkPpykk8x0LRAyS2H6I2dRnXrNNx6GHMwjxGogMjlqJ48bg/cWzBQyZUFc8yMSd8SZrEex7e0PetsfG2ttM60kv3iRqNosZi6xfULEYm7Omtq5azpmb837RF+FZNxFAi4SUjqHa0ZwTbvRa3iqAWY3vHe+iMbEfXQihCQwCe9HA8k1rqLs7Pf4tSY3PXzm56TJwoUlnwvb38xGLOUML06TLj7xQB6BiJMHxnisxg2Gcq9CSZ4TCDh5MIBayaSyCiEUkZqxvBSg6z3DpvLT2XemEGq1YkEPN/i1CyBz0Ux25srVqPWbrulQlFRVFbqI9oOon+PUuvPddh7swLyNssZSU9d5lRE4pGZuc99Bx8H1rALzizakVGX/pzavOjSHlrTFzfN0YwvHeQ0J5BFv7km1gzeYShk3jsMOmPPcDs//UVvLpJ/iuvUPj6G/6i3PWQtru0erWnc8z8wf9EqArSk0jXI/fF1cNwWwWhan4/m7XFxQFSYk1NY01MEdw+0j6PKBDcu4vgrh00zpxb8Zl0bOxaGddqYJX9go36whTh3m14tokejuNaDTzHxmnWl/U1Sc/FqZWQrrP0XQC7Vl58z68oNuIZQp19RHqG8RwLz7ZQDH/ibubnsErZLVtleo0GejJF5sMfQwmHl4xhu7DGJ9viENU7Mlyzgko0gppYn4JMAvbCAl5t69RY7OkZpGW1zAuq8ThqJIyD3/rUTjRBLnrFtxsBLcrursfoie9FSkm+PkauNoYnHWKBTrpiu0iFBjjU9wlOTX+NQmNrj8lsXL/v6kWL0lyTF//4KrWC5RdBCijNNjnzrTlyYzWEgEZ5pUGQ0sNuVFrm9a7BblQwK3kCsYxfxavqBJPdW24EHft6ZbgQYjHEubZwbSCWWaxu9VHLjmFWt5o8fSWk51zXjBQKHbvupf/ujywZbbOa5/Jzf7JlFaffH0ZQUzEGu3DLdeqnR/FMGyWgY01lCe8fRmiq3+7gekjXWjOCKS1n2WdyyzkQl8Ot13EbNbRYzOcO3UI4CznMS1cJjAy1JYFzDUJRSH3yI5jjE35F4c2QHopuoMfSOPUS9bkxuo+8j8KFNwl1DeKaNd8gRhKoQb8xVw2EcM2mXzItJUY8g2vWcc1FknDPw0h04JoNnEaVZm6W6sRF7GrRT7RXi4R7RpaxbGwFnGKB4vPPERgaQjabNCc39tC4i4oJrQyFuphXk66LGlv0BNeD5/lK8lsIr1LDyeZXFL7cDDXue4IACAUtk249drW2Kt3aVkIIle7YHrpju3E9m3NzzzBVWk5yPpo/xuH+jxMLdLG940FOzXwN09l4YZGUUJ5r4trX77fyQhPHvP66XrR564uTPPBTI2i6YOFqjZf+6ygnvz7DnR/vIxjRqORMnv33l1aM77nOhgyYZzdwzBvOQ1EIxjupzFxs/6SEgqLpKKqOUHxJKiGE34aB8KM4ofaUQq4hnBlctqCq56Zvaxj0GqTre4JCUUlvu4vBo5/0F6JS0ijOMPbKX25py8X3hxF0XczxecL7h0l+4G7cWhM1HiG0q4/Kq2dvb7/fregJVsu4tRqxw/egxuJ4jeUNnOb8bFsN8qseluPQvHCJ8J0H0bs6NuQNat2dJD74OMWvfRNpLs+vWNUSVilHavcRCufewMzNYlUL1KYvIxQVu1bCcx1Sw3vRAhGk7hLq6Kc2O4p0HXJnXiW9916q05epTvoPcfbkS6T33kt9YYLylVMUL75NfHgfSv8OnEaV3JnXsMo5PMfeUs5FEQgS6O9HDYYgFCaaSFJ5+822vy8dF3t6tqURVMKhRZUOZ7mRWXNgiXX1Fo2gEIS2d2NO5/EafqTBvDpGYNf2de8FJRT0PVVFQRhGS68V/Hyj3CC930YRUMN0x3ahKjqT+RPMVs6v2KZmZbkw/22ODH6GeLCbTHiY6fLpVUZbH54jVxivb/2HlcZs7O0iY28Xl703fba8ZjHMNUjPxbXa78f1HHvZ9gKBGgit843r0MNxgvFOAvFOQokujFgGPRhFNYIoWsA3iqq6WH2qbWSawAgvb7kxK7ktX6iuBm/R0KaGDzJwz8cQit9PadVLTL/zNLXs1kYAvj+MoITmpSkKX4fgSDdqPIxXb5L/yqs0Lt7msu1byAlq0RiB3n6MTAfhbTtXEGdnn3mSxlj7Das3w7w6RvPCJZ8NZj22kJuhKETuuxt7foHaq8eWFeW4jSrZd76zbPPpF74IQPHi20vv5U6/surQhbMrNeuKF5cLe5qFeRYKy+m3alOX2z/+NqGnUoR27cEcH/NXsN4GDazrYE3NED58R8tN1XTKJ+xOp1qGTz3TwprYPHUVgNAUUh+4k9xX3sCc8r008+pY6+KVaxWhuo6WSbVktQGwJ6Y3zHG7UehamHiwF9dzyNWu4nqrF5Tk6+M07BJhPUEs2IVSOf8uUaFtANLbUN5Meu7SxA/4uVqtRW7XCJLo30dy6A4iHYMYkdb33UahGssNsWs33h1icOkR69lBtHs7WjCy5I0KVSMQSaGoelv9je3i+8MI4ocyG2fGaJzZ2jDS7YRTKVF48Vtrfm4u3FrjujQtqi+9Tmj/XtR0siVDyTUIIVAiYeLvey9etUr9xJlbl3n6XoSU2AvzVE++szmpokVPsB39xmsyRVqmNQ+nPT27IioQ3j9E7Mh2pO1SefsK9lyB+P17UeMhlJBB49Is5ZfPEtrTT+zunUjHRQ0v70U0Ryf837FFeNw3gpofQWgD1tQ00r2990dAjaCpAUy7hunWWSsEI6VHzcwSMVIEtCiaYmDdQmXgbYHcmIqE9L+w7L1rlaKrQQtG6TnwCOntd6OH49efeymxm1XMcharVsSxGnh2E9e18RybWPd2EgP72p4nFG150Z3nurwbpMtaMEJq252oWmDZserBKF0HHsGsFihOnNqyauXvGyP4XcMtXGev2bwlT68dWJNT1I69TfwDj24oJCqEQOvIkPz4h/Esm+bZC+9aCfytQgQCfnhunbaSjo99EiUQQEulCW3bgduoI12X3FdbKwwsQUpf769aayk6q2VSKIbRVo7NvLz8nlDjYVLvP8z8f/8Oekec2JEdVI5dJHbPTuY+9zxISeqDR2hemSV+3x4qb1zE+X/Y++swS7L0uhf+7cDD5yRjZVYWc3VBVzPDdA/PtDSjAc1IlmYsWfKVZVnme68tfzJ8si3ZsmRZAxrSMPZQM0N1dVUXMyfDyTwMgfv+EVmZlZWcBd1jaz1Pd2VGnogTETtiv/ultTIFov/gPVOO45dKOMNpjLa5acK0mlTgCS6gPcK3LJyR9HVeJAk01QgUQaSDlHN7nZdaJFShT+MofUdACJQ5jNj0jytXeHFyVk9HqDr1q3fRsO52lHFvUfoexXQPI8dfoZTuwXMspO8GVZPjeXqJRNV0ku3rF3xeV56DomrMVUhzrRDkNQPTlOs/hZUbpmHdHQghMCJJlu36AFYhfc24SN9xRlBRwDAFVlUuak6+1P97zXG1BNpCIHQDxTRRzBBudgzpeQhVHS/MucoHSkpyTz1HeOO6YPJbjCFUFLTGeup+9aNkv/8TygcPT1NDeEdAVQNS6pZG4nfehhKNkH3853M2mxf27516Ly7xHC4SfrkSFJzMYwT1ulqEaaDVzmMEpaR6ZqoRNBoShDobaf61B0CC1ZtGaAr2cC5gOVIUpOOi18UD7zSdx82WcEfy045tnT0/rxFUL4VD6+f3BN2RUfzyteKbnQ0S1wvywYpQEcwd1lNFUCXoSRf/BuSoFouAMWZ+aaqJz1/ZriADftCZEKlpoX71LpRxL8n3XNKn99C77yfzco4uNpTpXqFqoZrhcdaYRR1mSXAqeQYOP8fo6T0IRUU1QtR2bUMoKkY0xYq7P8mpp//6mtCxveOMYOdylUfeE+I736yQHln4A37zrQYH9ztUyu8gb0ZRCLV3UHPr3YTaO1GjMXr/5i/wLYvo6vUUjh7EzS2cz3I2yKrF2Ld+QMNnPzV/ZeIVEEKgJeLU/epHMDrbKb76Bm56bFaKtxsGTUWNRlFTScwVy4ls2YC5agVCUbDOXZh3d6u3B6HrKGYIrxgwnWi1C+uJuxx+pYKbHsXs6pjzc2pdDWo0gpqY21j6tj2FuxPAHspRPTvA2BNv4bseftkCNZClGRckAMAtVgK2j44G1FgINTW9AKd67gLxu+dWy1DjMZRwaEGN8u5I+popj8wFxyvhSxddDaOrs1POCSGIGDVBVbFXwZM3lpt0IRCqNqFqsBCoegjNnBzLgIVpevFNIJ/URihRDwQUZpXsIL37fozvzH8fVH3hBPIAdik75fdwsgmEAvMoyFwLDB9/hZETr00U4vQffBotHCfRvAqhqIRSjXTe9hgXX/vuVbeSvGOM4Jq1Gss6VeIJBd0IXvvWdpVVq4OKpgvnXS6e92hrV1mxUkPTYWTY4+Rxl+UrND7+qQj1DRbpEY/dr9q0tKk0NSmYpiASFbz8gkUoLNiwUScWFwwP+Rw/6rC8S8WyoLfHY3mXiqIKzp25NjkGLZEktetOfNdh9PknaXjX+wCQvo/Z0kZ1oHdGI6ibCqqhUC0s/Dysiz3kn3mR5KMPoczTMD0ThKoSv+cOQmtWUtrzFtWTZ7AHhuYMOV5riFAIrTaF1lCP3tyI2dGO2dWJkogvOI9xOfS6ekIdneT37Aag9uFHGf7m1xZ1DL9SDdoD5skLBufcNK8nbvf0Ia0rxX8rjP58H9FNnSDA6klTOTdI+URvwIzkKZRP9OKOFsjtPkF0Qwd6XZzcK8fwSlMNlH2xF+m6c2oZCkVBb24MmG7mgTOcxr8BRtDyyuSrg9RElpEKtzNW7p6x4CVmNBA1anF9i6I1OmsBzdsJoagY4ThC1RbEZKKaEfTwZCWm9H0quen1AkLVCNe0cnl4KnP+wIIMIIARW5ymaHm0B+l7E/nJSP0yFFXDm4cF51pA+t6U9IyVH2Xg0LNoRpRIXStCKCRaVtO85X4GDj5zVcK97wgjmEgqfPzTEV5/xcY0BYmkQjQqePR9YS6cc9F0eM/7w3ztb0o8/GgITYNzZ93JqiEB0ZgypR5gzVqNm2812L/XRspA7eam7QYbNmqcPePy3g+GKBZ9GptUVq3R+OrflHnkvWH2vH7FA3U1KhLxJIoZIv30T7CHB6l/8N1AkGdB+hNN4ldCD6usv7seVVfoOZJj6GwJ353nRDyP4hv70Brqid6yY0FK4VdCKApGWyvau+uJbNsSVJ+eOY99sWdOvtGlQomEUetq0Rvq0Vua0Opr0epq0err0BLzsK7MA6OphfiOmzEamtBSNaCoS8prScfBHcvg2w6KOTs7j5ZMEL3t5nmPZ53vnrHXqnp+iOr5qRNf/tWAv1UC+dcDcgOvOEj17OxhYL9QxE2PBgZ5DsTvCsLKc91h6bi4o2M3JERuu2WGC6dJhJppTW4kW+0jXZxaMRzS4qxquBMhVErWEJnKjWGxWSyEEOjRFEY0tSDWGCOSnGCLkVLi2WWquemcm0IoUyjMgBmN5UxQNDMQnl0E7GIWqzBKKBko1oSTTUTql1HoP7Wo41wbSEpDFxg4/CzLbn4/RjSFohnUdm3DLmUZOfE6vrs0Or13hBFsbVNRFHjmqSpdKzRaW1WamlV23KzT1KwgZZArTCQV9u6xueNug527DH70vQqOA6dPuYwMe+x+zWJoYHKiG+j32P2aTakoiUQFHZ0qJ467vPS8xYqVGhs26bz1ps3qtRrrNmh4nuTc2Ste+KvICQohgsbPK0KLAa+kmHVSrhZczu7NEKsx2PxQE7d/3GTfj/rpPpSb0xj6hSL5Z19EjccJb16/5JJpxTQwl3dgtLcS2bYlkPwZGsHu6cPu68cZGcXL5RfmJaoKSjiMmkygpVKoNUm0ulr0pgbUZAIlZCLCYZRwGKFrS/L4ZoKbz1G9cB5pWZRPnkAK8IpLWy26uTx+sYhizpzvE0KApmF2zi/NZV3shutYaSk9D6u7b14juBBOVK9YxJuFZ3YuqIqOJgxCemJC4T2kxQjrSRyviiftaVRXvnQZLJwkFWmnMbaKDU0PMRxZTrbSjy9dIkYNjfHVpMJtOH6V7sx+StbM7CWKUNEUE001UZVg4aIpJmE9RdUt4Pn2dW+rCCUbCaea5zWCim4Sb16Belk4ND9watbCmCuLhmYStp0JqWUb0MPxRa0rPdcm232E5s3jcmSqRuuWBzk9dA7/bajIldIj13sMzYzSsesDCE1HM6M0rb8LqzBGtvvIkvoY3xFGMJ/zqa1TMXQIhQSxuCCf8xlN+3zlCyVyOYn0Jfm8ZGTY5+IFl+YWld/7J3F+5zczIEFVBYYuUJRJ21KtSLxxo+G6klLZp7ZWQdOgpU1l/1sO6RGfC+c8PvBYhOefqVIoXLucolcpoxg6kRWryR8K8lJCVTFb2lFCIdzCzLFsoUDb+jhr76xn8HSRQ08Nsfq2OnKDVTL9c4em3OE02Z89iVqTxFgWrPyWaliEpqGlkqjJBEZbK+GtG8HzgnJ538crFPErlYCuy3EDLU1VQ2gawtBRIpEgNKsoQXP25f+qSlA0dI2M3pXwK2XKZ05h9fUuSkdwJnjZHF6+sKDKzzmPUyrjDqevaxWu9Dzs7l64edtVedIAXq6wYOUIgcKK+ttYltoWFIYQ0HJp40ZoZcOddNXdCuOCyEUrzYmhZylYk/2iFSfLyeHnEQjqo10sq9lOe2prcHyhIISK61U5NvQ0Q4UTU8SaAZKhVm5q/+A432hw7ZoaRFsaol3URNonKiUdv8r50d30ZQ9d1T2aDXo4TmrZJorDF3Grs9/DUKIhKPiYGCvJ6JmZCR2k7+FUph4rWtceTP5zwIzX0bj+zvGWh4U/E9JzyHQfpW7VzejhGCCINi6ndduj9L31sxvCHjP9nFxGz+zBjKZo3vJA4HVHkrTveA9WPk0ls/j+23eEEezv83nxuSp//J9TjKZ9ui+6DA/5fO/bFX7zt2NoOpw+6fKlz5X46CfCbLnJwPfhZz8Oeq2khJefr/IH/zxOz0WP//KfClQrklJpssLUtuDN3Q6/8okI/+nPUhw95HBgn43rwuiYj+dKBgc8ruUCxx5LUzhykNq7HyB1650ooRCN7/4wqBrZPa9gDc8c1orXm7iWz4/+/Uk8N3jRFUVQWWCO0OkdYPTr36X+k7+M3rq4itGZIIQAVSDUyXCglHJBOnQT+78NkJaFa80eIhGGEXjplxklMS7Rcrn8lZfL4xWK8/JyzgdncAi/cp3za56H09cfcJ4ugk5vxkPl8ri5xRQdCHzpwWXeiuPNfL2Sme9l2R5jf+/3aYyvpjm+lqhRh6KoVJ0iY+Vu+rIHsbyZOTkDIQ+JJyfHzp1F1Ff63rxVqEuGlAigbuV27HKWkZOv4VZLU4yGUDXMWB0dtz02wc8ppSTbfZTiyMy90L7nTMvT1a26mfTZfTOyuQhVI5RopG3bu4jWLxtfACzufaxmB0mfeZOmDXcHrDNCoXHdHWhmhKGjL2KXsvieM9mOIZTx6tig4lUzo0Tq2imnuydEha8W0vfoP/g0RqyG2q6bEIqKGa+j685f4fSzX1h0xeg7wgiC5DvfqPCdb0xtIB7bbfPm7qkP8Ve+WAamU409/oMqj/9g8oV748rcHtDf6/Ff/9PkSkoIaGtXWbUqyBOmh69xmMrzyB/YizOWJrJqLVosjlcuUz57ksqFs7MqJWQHqmQHgmsxoyqhuE7/icVpuTk9fYz8zdep++iHMFd2XfWEeCXeLsO2UAjDQNENfKuKdF0Uw0QYBl6lDJ6H0HWiq9dTPnc6oK4TAsUwiaxai1+tUj5/ZmJ8/FI5CAsuoBF9LjgDg8jq0mWAFgqvUMIdzSy4GX4mSN/Hy+XwSwsjgZb4nE2/wtn0K0v+zsuPNVQ4ydAM1GlzIVvp56Wz//Oqv/9q4bkWTimHEa+jdeuDxBqXk7l4mGpuBCk9FEUjUt9O/epbMMf1/QDs4hgDh56ePaQnJeWxAUqjveOq8oEXtOKujzN0/GWsfBrfcwPJLCNEtG4ZtSt3EE41Iz2H/MAZku3rWIw36DlVRs+8STjZSKJtXWAINZ26VTcTb1lFYeAM5bE+3GoJ33NRNAM9HMeM1xGpbSVc24ZTznHxte9cMyMIAcl2376fopkREq1rEIpKuLaVjls+RPfu703zmOfCO8QIvj0QAlpaFYQCe9+wKV/j9gqh6SiGQeXC2cDozfd5Bdo3JqhpC08U5MTrTayyy94fLN7NdweHGf3G90g+8iCRLRtQwgvjI/yFh6qR2Lwdt5DDHk3jjKUxW9pQI1GEblA4tA81EkMNT+Zh9JpaQsuWY9TWU71SgV5KnOE00rYRS7yH0vdxBoaDoqjrDL9awRkavjojaNk46bHrmr/83xFSSsqjffQffJqWLQ8Sb+oi0bKaePOqQB3BdVA0fZzHczIEWi2MMXDgSSqZuYWWrUKa0dN7MKI1GJGgajra0MHymo9gV/L4ThWhauih+ETbhWdXGT27j4HDz7L20d8hFF9cq1A1N0z/wafxPYdUx+YJcm4zVou5ete8+1+vsiq7nKf/wFOBwW/oRAiFZPt6mrc8yMCBpxas4vELYQTVcIzUhh2U+y9QHemfV6l9ofB92LvHYe+e6zNMRkMjdfc+TOXCOYqnjuGMzr0SUlRBTWsY6UEpG3iyqq5cVb++O5wm+6Of4QwMEr/7drSa1FWHR38REOrsonLuNP5AwC2rmCG0ZA2RlaspHNqHVyqiJVMoRuAthto6kbY9nqedvhhyhkfwLXvJCwm/HChSXG8OTgjaOpyhYdi8YenHqFZxR9IoEROjuQa/bGEPZkBTMOqT+I6HO1ZACRtEt67Ar9pUzw3i5a5ON3PDJp2t23QaGhU0TfC9b5Xxfbj7PpNESuH4UYdXXrDYul1n8xYDMwTlkuSbf1tm7TqNnbeYGAZY1SC6JCV88JfC1NYpjAz7PPHTCvncwha7QsBd95rcdqdBqkahkJfsfcPi5RdsKpVZaN18j8LgOQqDZ/DsCo3r7qR2xU2BB6Xq0zT8pO9RSvcwePQF8n0n582zSc9l7MJBQNC8+b4JpXhF06cZNykldnGMkZOvkz7zJq5VJt93ktC6uftIZ0J5tJfevT+lMHye+pU3E6lrX1A0SPo+dnFswQZpcZCUx3oZOPQcy25+H6FEA0JRqVuxHaeSZ/jYywviGH3HG0HFDBPrWotR10ip9yyKpoNu4FXLaNEEvm2h6Dp6vAavWsbOzl+SfKPgFgpYQwPEt24nuet2Kj0XKRzcR+XCmRlZ+T1XcuLlNK7tT1SBmlGVUOzqhsnL5Sm8+Bp23wA1H3gUva0VeOeHNJcMz2X0uScIt3USWbkGe3AANZ6gdPIoobZlIATSdSYXU0IgNA0nM8qUyqrL4A6nryqU6Y5lg4raGwBp2bgjo0jXQ2hLC9/61Sre2BiJ2zfgW05gBIezhFe3E+pqQo2GyL1yFDyP8OpWnMEMdm/6qtqohQhIL7ovuBzY7/CH/yLOlz8veeDhEJmMz8svWPz9340x2OexZl3Q7/v49yv87j+O89orFh3LNZZ1qHz58yU+/ZtRulaqnDgWVIOrGnzy16I07lXJ5xaWW//4pyL8+mejNDapaBp4Lrz7fSG++NclvvKF0pTi6J43fkT//ieDJv5KPghdjvbSu/fHjJ7bR03nFmJNXRiRJELRcK0ilcwQ2YuHyA+ewSnnFlxo4tkV0mf3Uhg6S83yrSRaVhNKNk4QXrtWiWpuhMLAaXK9x6nmh/HHn/X+A08ycvI14FIz/MKjX3YpQ/rkbnI9x4nUtpFoWUW0vgM9kgi8TqHiezZutYRVHKUy2k9h6ByVTP+84cn0mTfJ9Z6YWKA75ZkXo1dC+j75/pOcfmZoCtepZ1fwF9jP+I43gkJVUTQd36riVyuY9c1o0TiFc8dJrttGeeAi8RXrsUaHiS1fy8Cz38e3KvMf+AbAK+YZfeEpMq+9SGT5KhLbdtL82Mdxsxnyh/ZROPTWVMV5CXZ56ovgORK7cvXeg7RtqsdPMXihm8R9d5G4904Imdecef56QUoJvkTaVlBgUp59jLVEgtTO2wMasaF+fMci1NKGnkiB9BGaRrhzJaFlXSi6QfaNV7AG+0juvB0hBMVTx7jyBXRHx/DKZbSlFMdIiTeWuWFGEClxM9mgorU2tYTdJbJcwRkaQa5qwOxsJP/yERTTILp5OXpjCjViYvWMUHzrDPZAhuq5AZz01VFYSQlDAx4PPBwCCd/9ZhmhQCQqOHbUo/uiR1+vx+q1GuWSTyHn033RI5fxSaYULEty4ZxL90WPzKhPPKlQV6/w65+NIn3YvtPg8e8vTLqso1PlQ78cpq1dnRhvxYD6BpW//7sxfvyDCiOX1RDYpQzM4Oy4VonCwGmKQ+eCdobLnh0p/WlN4QBdf/xJMs8cJPv84SsPN7mv51DNDTN46FkGj7ww7ZkMju1Pyy+61eKUxvKaB7dSc99mzv2rhZFISN/DLo5hFzPkeo+NF8Jc+T7ICW1R6fssxJi51RJudWneovS9oDBoiXjHG0GvXKQ6HIS07NwoWjw50Rsj1CCubo0NU+o+hRZNoMUS2O8QIwiA7+NXKxRPHKZ44ghmcyvJ7buoueM+3FyG4vGp5c1mTEMA4WRwbamWENGUzuGnh2c+/mIwPrnlfvoU5f2HSb7rPsyVK1Dj0YCf8x3oGUrfx69U8YslqqfPUnx9D/bF3jnbDNx8nvRzP5/o0wQY/tkPpnh45TMnKJ85MblPIc/wj79zqcRw+kF9H6d/MOgFXGRxjJQSdyyz5D7FpcDL5vCyuSUZQXwfZ3gE6XoU951G703T8LF76fuzH2APjFE51YvVk8bNFQPFBMdFTUQRhj6NDWexCIcF+/bY7H/LxnXAtiTFok/ncpV83mdZh8pzz1RZvUabGM6J4ZKT0eZLBD8bNulYluTbXy+TTCkL7k5ZtUajrm7mdyKREGzaqvP80wuPDEjfQ074yQKBmNbicQmKqSO0hS1OpfQDMfEFn8lUCE1BMRdPrAFy3HP1EIaGlghjpxdWjKKYGr7lzrvtRuEdbwSvhFetEG7pINzQihYZL9H3/Ruh8LFkCE1Hr6lFb2gi0tGFXteANdSPk5++cm7ojBCt0Vmxs4Zq0SOS0sn0XXuj7vQPMPq172CuXE7kps2YyzvQmhpRjKW8ENcWUkr8Uhl3LIMzOIx15hzVE6eDnNpC4V8xMSyULWaOWdLuXVrrgaxWcUdGb2iRiZfL42ZzmAuQgboS0vOwe/sRukpoRQtabZzi3tPIqkPlTB+xrSvRG2sovHEcL18OBK83duKMZLF7l56OSCQFZkjQ1KzQ1BJixUqd//UXRXa/anP3/Sbv/UCYZ56scuaUSyQiJoqrjx52GBv1sSxJdTxXd+pkUO2dzfps2KRz970mp046pNMLG4NoVEGb5VUQQlBTM7OR0hRzoi1DESq+9FCEiq6Gsb0yUvpEjBpMPU7ZHsNyFlf1fc1xDebNcEcdTe/Zyrn/9tSCPl9/73qGn5zq5dbduYaR548vXvPzGuAXwgg6hey4lhXY2TTWWA2KYVK8eAo7N4ZXKeHbVcq9Z/BK13i1fRVjopghwl2rCHcsx2xuBaFgDfaR27ebal8Pbj47bZ/eo3ni9Qb9J4sURiyiNTqxutnpuq4G0nGonjhN9cx5jPbWgCVmWRtG5zL0hro5+Sev+bm4Lm4mizOcxukbwBkYxBkYCkJy9juDJNnp6QsM2SLXCV6pjDN87crDF4JLiwjp+YvPC3oedu8A0nYpHTwXEBuMG3BnIENmcLyZe3zBUD03GFC+zbCAiMYEnctVIpHAEFercOTQzN5iKCSIRARjoz6loiQe9/A8SW+Px9e/PDWM+dabk8f40fenLxKffmKyXeov/9vi54SqJeesYcrlZjamiXALtlfE811MLUbZHqMuvhLXsxAIxooXSEXbCRtJlJIyqxHUUzFqH9mOXhPFHs6Re/1kQKwOqMkI8a1dmO11eCWL0pGLVMap9JSwQWzLcsxl9SiGhj2UpbD/HO5YcA+EqZHYuRqzox43U0KNX5uKcT0Rpv6BjYCkcLgXazhPZGUjsTXNSMejeGIAe6xIYmM7LR/cgfQl1nCe4skB4utaaH7/doSu4WRKZN44i9GYILGhDTViUOkZJX+4l3BHHWZTEjWso0ZNRp4+gnSvfmF57WY5X2KdOsvQX3x+cbuVK7jz8FK6pQJuKXhYpOtQPHts6t/H/60MXHsuQWd4hNG//Q5ikR6S0zeAUd9I3T0PYQ0NkH9rD9bwIE42g7TnDqMUx2w6t6bovCmJoghGexaWx1gyXBf7Qjf2xR6UaBS1Jone1BAYxLZWjLYW1MTi1Cnmg1+1cNOjOEMjOAOD2P2DE9p9fr7w9qtYzAC7f4CRz315TjV2RRFsf18LpYzNqddG8VyJtGzs/mujfbZgSEnxldepnji1+Gpgz8fu6Zvy+5XHnun7ZsLGzTq/849iNDQEVIG9PR6f/fTMHv1o2ufpJ6okksH9PXjAofvCVEvUtPZOsv0nsArXtwCu+7xLIe/T3DJ9AZHLBuT7M8Fy8jQm15GrDKAqOjFFpyGxirKVxdAi5CsDOF4FbEm+MjPvp6KrxG9eTWHvabxilZoHtqJGQ6Qf34MSMal96CaMhiSVc4Oo8TBNH7+b4e+8SvlEH0JTCa9swc2V8EoWsW0rUJNRxn62F99ySOxaS90j2yi8dRY1YpDYtTpY5FwltGSESnea2JoWkts6yew9T/OjWxh+9hhmfZzUrhWknztGdSiHUBTKF0bwihbS9bBGCiAEle40bqGKYmrU7FqBkytTPT9C0yObqQ7kCLfVEFvfSmbPOeRQHnmNvMZrutT3cvkbl/y/QZDVKtYV+m8L39dm8IffxM1l8a3qgumyWtbGWbYpQffhHEgoZ2+QQZASv1jELxZx+vqpHD6G0HSEqaPVpNCbGtHqalHratFqa1BjEYRhBI3phg6qOi526yFdF79q4ZdK+KUSXqGIO5bBTWeCIpNsFt92wHWRjot03etHJ7YYGzDHKUg78Jzn/CpVEL3PwR6uUD02MEHb93bAHRkNwrBvE4SAlas0tu8w0A0xnxAHngc93UGeaTaEU80URy5wvbstz5wOqko7OjUu57n3PPjiXxcZGZqF99ctoKoGUbOWbKkHU08wVrjIcO4ECHDcKhGzFmWcAm4mSCmxetOkH98TePIhg/i2FaQf34PZWkt4eSPpn+2jcrofxdQxWmpJ3raO8ok+vGKV9I/ewHeC90naDpH1y1AiJr7jUfvQFooHL5D+8ZtBr197A6HlDVd9v+yRAqXTQyimTmxNM9HlDcTWt6FGTYSqUh3IBIaudwzPciidnlwAVAeyeBWb4ukhpONh1MWIr2lBS4bxShZKSEdLBB5rdTBL6cwQfvXazYk3JN5VV6tQU6Nw5uzsic+tW3Q+9pEI//xfX71I4uXYdpPOpg06X/36dG/qT/5Dii9/tciRY9cnIetbVezhQRTdQI3FA77QzGWiut70yjCA3GCVFbtqqeuIBKTee8bm5Qy99icfeDDSsqEE3lh2XMdPjBuVy2azy36Pda1F0Q3ypw5NNSiXrvMGqtdrDUmaf/MR4jtWL3if4a89x+jjry85NyE9yVN/cTYoGHkH56lvBBJJwZp12qy5tYUi1rCchtW3gAQjmgIgWt9B3fJtSOljRms4v/s7qJpJ84Z7MCIpqvlhBo+/SOfNH6bv4JM0rbuD0fP7qVuxg0z3YRrX3oHvWCiqxsCxFymNTo0ieR782Z8U6Ov1eP+HwzQ2KQz0+3zrb8s8/USV2QIVnu+SK/dRG1tO79gBStYoq5rvpbPhFip2jt6x/ZStMZY33kbEqOH8yOvTjiE9n8rZwYmJ3s0UUeKBFqAaCxG/eTWxrV0Bh68AFIX8GydBgBoNUfvwTcRu6kJLRVFjIarnhxBqUMVpNNcy+tN9SNtFAtWLw4Q6lk6qcAlGfQwtFkKviSJdj0rfGNX+DBc/9wK+L5GOh1cKli5CgBYP49tOUAzjB6KZWiIwem7ZotKfofjcUUrnRxCKwMlWCDUlkI53zfOG8xpBXQ+MWL4gKZclrS0qlapPtQKtLQpmSJDLSfoHPGpSQTJ5eNinvU0lX/CRPtx5h8mGdRrf/1GFCxdddF1QV6twsdujri5wxVUFYlGFNas1pIT+AY9SSVJfr9BQr+B5MDTkkcvPkHeICpJJhaEhDymhpVkll/dRBJTLkhdfnlw3xuOCluZxfayIQAgImdDcrBIJCwolSU+Ph2FAW6tKOCwoFCS9fd7iJzVFIdzeSeq2uwm1LUONxhckqhtJ6px4Kc35tzJIT1LOL3zVI3QDPZZEaBrSdbAzaRAKWiyBaoZBCKzRIYQAPVGD9CWKbmBn00jXRYsnUc0w0nNw8lmk52LU1CM0Hel5OPkMaigSsF7oetBv53k4xRxqKIKTy+CVi+D5gbhoPIlQVYSi4lXKuMUcajiKGg6OIaXEzo4iF6iJthgIAsWOReXElLndxni9QbIphKoLXNsnO1CllHWCCTqs0rI2hlAEmf4K+SFr8pkR0LA8QnHUJpLSidUYOJbPWG+FajFYhMXqDFLNIRRNUM46ZAequLaPqgvqOyJYZY9ojY6qK5RzDqPd5aWQ5t8wpGoU1q3Xr6rqWNEMorXtjF08RK7vBGsf/Mz4XwS+7zJ88tWg300oJNvWUc2P0L33R7RueYhYQxeuVUKPJFHNKHokgWdXAYFQVM69/i3qu7YTStRTzvRN69NzHPj6V8p8/SsLT0cYWsDSMla8iJQeEjg18OxEkQxA1clzsu8pZg1RSIIoyRSMLz6FoHJ2gMGvvYjVN+nlS9sFCQ2P3UZkdSv9n3sKqzdN4o71Qchz/MDS9RD65PsgFLG4SMkM8Ko2uYM9tP7yLkCSfv441mCOoZ8coPUjtwBQONbH6CunwJcMPXWYjl+/i/zhHtLPB3JhI08dYdknb6d0Zpihnx5g7NVT1N2zjppbV+FVbHq/9hpOvoLveNcsDHoJ8xrBcFjwyMNhjh53eHOvze/+dozvfr9MJCK48w6TXM6nqVHlr79QYvtNOnV1Cl/+WplPfizCq6/b9PS63LRFZ9VKjaFhn3TaZ+UKjfe9O8T//Ud57r3bBAl9/R7t7Sq332ayrE1l9x6L3Xtsfv1TUQoFn1hU4WK3y+M/qWBdMV/W1yl86pNR/uf/KuK4kt/+bJQvfKmEEPCRxyJIKfn//ccCmgb33WOycYPOwKBHW0sgortli8Fdd5iUSz6dnRr/9b8XWNam8sjDIcYyPg31Kl/8conevsX162mJJMldd+Db9rio7vsB5hXVLY7ZVAsOnVsDYt3B00VKYwszhOGmduJrtuAWc7ilPHYmjZGqJblhB75tBSGZ3CiqGab+lgeoDPUgVA33WAFhhqi96XbcShEtmiB/8hDVoV7CrctRw1H0eIr8qUOEWztQNBOjpg63mMe3q2SP7EWPJ0luuYXqUB/ZA6+hxRI03PEI1aEeFN1AqDrp3c+S3LAjWLWGIoRbOuh/4lu418EIyon/XRtEa3Qe+b1VCGVc/ULA/p8McGb3GL4nCSc1bnp3M6tuqeOtnw7w8pcu4tqBldI0wcP/YCUjF8uohkKi3qBadHnzB/30Hy8QbzB48LdWYEa0SzzEHH56iOMvpommDH7pjzYweLqIUARmWCVSY/DEfz9Nz6F3bvqhoUFh5eqrCzYFvXUBVyTIcSMWwKkUJn4XQqAo2gRDiOdYKLpJOdNPvHE5VmGUcLIZqziG9F2sYkA47XsOQtGuGYtSWE+iCJV0YSpNon+FBFJAZb3Ih1OCM1rAK1mEOhuw+8eQno8aC+ErCn7FRm9IUO1LYw9n0WpiRNa0TrRASB8qZweJbeqkfLwXNIXIuvZ5F37zodqbofdrr07bnjvYTe7g9DqNoR8fmLZt5JmjjDxzdOL3Ss8YvV97bcpnCkf7rtztmmDeJzSfl1zsdmlpUljeqSIEnD3v8tgHI7y5z+bZ5yz++R/G2X7T1JiHGL+xFy56PPdClUzW4ItfDpohV6647HOX3f/RUY8vf7XEffeYdC3XyOcl999j8qOfVAmFBF1dGrG4gjU6dfnb0+tRqUjWrtXwvKAC7WK3h+/DK69b7NoRVFfGYoJl7RrPPW+xZ6/NfXeHCJmCHdsNlneoHD0eeLBrVul0LVc5etzlh49X+L9+J8b2bQa9i2xVmC6q+x5gXFTXn11U13MlsXqTaFLHKruY0YVPJF6ljJMfAwKPDyHQU/X4nsvo3pcmmmdVM4xnVSmeO4GTCwoVol1rCbctp3DmKGo4ilnbgDUaVJ15VoVQYytaNFDArg714FVL+I6NomkohklloBujtgGhX35dktyRvaCq1N9yP2oojFAUJOBVShTPn8QtXNsQ+MS9KJQZ+9keSofOoYRN1IiJEgmhRAzUcAizsxGtbuGq9XXLIqy9s54v/94B0hfKRFIGlbyD7wWTWW7Q4vH/eJIP/qt1sx6jdUOCp//8DGN9FTRDwSoFk+Mtv9ROOK7z8z87jV3x2PbeFm75pXbO7Q0WSeG4TrXk8epXu/F9yQf+5Tq2vbvlHWsENR3WbtCJJ66u6MJzbaxihkTzGsx4PeJK2rFxQyJ9j/JYH7WdN9G84V70UIxs7zE0I0Jr+wYGjj5Pw+pbKaYvTioeXAfkKv3kKovn+V0o7MEM2ReOkNi1mtjGDqQv8fIVsi8fxc0Uyb92gtp3baP1s+/Cr9oIXcXNjjeh+z6jP9tH4y/dTstvPoybLeJmS+j117bo7RcNC5pdDx5y+MD7wjz2IZ3v/qCMY4PjSCKhYPKoSSkUipJwWBCJKCgC2louU3oXoGuTE03VksTjCkJAfa1CaZy4+tI20wyS6PmCT7kieemVKr4fhDbz+enxH9+HJ5+q8tFfjqAo8K3vlGdsC/O8oLBPH+84iEQEvh8045464/LCSxYvvWIzPOzR0qIQHS/rTiYUSqXFJ2LnFNUVs4vqNq2M4Vo+qqEweLBIJLnwpIqVGcazymjRJA13vIueH3wxMLiqhlDE5So3SNfBv6z9wLeqeOUipQsnKV04hVspEmpoIdzaydi+lzHrmsYXNwJ/PAwqPRe02VfSXrUS9NYpSsAeIQS+5+GWC1jD/bjl69cnJasOpf1nKR08h9DUICyrBeFRoak0fPw+kndtggWGS4fPlTjzxhjv/2frOP3aKAd+Pkg5N/25mGt+7T6YZfB0cVrBzLq76kk0mnz8TzYDEE7opJpDGCEV6UM553Bxf5bsYOD5DJ4u0rJ6YVJWbwdMQ7Bzl3H1Dpb0yQ+eGVcgkBQGz2CXc4DALmWmcENWckOMnNmNohl4joVdzmIrWXr3/5xqIY1rPYtdDhYNdiX4Nz94GhAz0hi+Xej7Hz/FzUy2dRTePE3lTFBdLG2X/J5TE5WhAH7ZnmDrye89Q7UnjRox8asOXrECqoKbCQxh+VQf/V94OvAeqw5eoYyWiPJ/MhZkBMcyPoWiz/LlKseOu1Qqkjf3BaHKxz4U4cRJh917LDqWaTz2oQh//mc1hEICd/xFHx72Wb1a43/+9xr+3X/Ic/asSzgs+F9/UYPvw9PPVPF9SSbr82f/OYUQ8KWvljl5yuVb3y3zB78Xx5fwzHNVvvO9mb2xo8cdEgkFXYfDRx2EgA+9P8wvfShCQ6OCqgq+8KUiBw85fOJXojz2gQjZrE+lKnn1dZtf/XiEf/KP4kjg9/4gy543bT77GzG+9Ne1nDnn8sabiw/XTYjqrrxcVFcj1HpJVHdmD8ipeDhVl5HzJdrWJyhlHYQyu8LK5Yh2rKJm6+2AxBrpBympjvQTbu2k7b2fBGDwme8FwRg5ldKoOtxPZaCb+tsfBmB097M4uQxGqp66XfcG4aWLpyCWIKBGClbUgc4eNNz+MJFlK4PwivSp9F8cz7NcWq374zR4Gsm1NyHXbMG3qgy//LMgj3i94MvxQoCpBVB+ZXFjWi26fP+PjtO6Ns7OD7by8T/ZzNN/eZbjL4zMpoo1DZWcO91ICgjFNd768QAHfj4Y3K5x41EYtYnVGHiOP4VST3oSRZ3dwkTqQiAE5fTiiRYUTVCzIsXoqaWLEZshwY6br01/q+9aVPPTGZN8d2qNqPQ9rOLU9gvpuVRyQTTjcikfuxjs61rXuf1oCahemHqtbq6Mm5s8T+l42AMZGJg+PtJ2sXrmaB/xfOz+qffokoH8PxVCzrFsFUJIVYVEQuFXfjnC6TMOz71gLZh846pOTICiTjoY10g44sZCVUls3UntXQ8gXRu9tj5QklA0sm+8TG7fG9M0BccZ4VA0gaoqrNxVy8CpwoS+4C86Qo1tRJatpHjhJNKxSW3eRfHscSqD16bHU2usxR3NLUitofmz76bm4e1TCmeGvvIMoz98bcYKNM0MFlOu46Oognv+3nJitQZP/vczVPKBgRUKfPBfrSM3ZPHS5TlBXfDRf7+Jc/uyvPGd3okQ6iV89D9sQtUE3/+3x3EsD0UVCEVgVzwS9SYf+0+beO5z5zn9ejCBPfhbK2jfmOArv38II66jaArS9alkLDRTZcVDHSiqQvcrfVQywbMTSpoITcF3faycjZSScE0I3/VRDRWrYCM9Sbw1yva/t4k3/nw/dsnBrS7eS7r1DoO/+XrtlFCzlHDhnMuj991Y4oC/w99BSjnrinFeTzAeF7znkaA8d99+54YYQFWDjhU6dY0aQgSyKIfe/AU0ApdEdUfTRFatuUxU9xSVi9NFdYUSVBB6rsQqehTHbI6/+L/XhGFn05hNbSTWbkUAdnaMyvDSEt5qXXIaq038/l3kfvISfuHar247tybp2lFDccwOPN/lEc7vzeJYPkKB5lUxUq1hatrChKIaa++sIz9i0XN4/rzdy1+6wPv/xTru//tdjPVUMCIqlbzL/p/O3WRfuyrF+g+sxK26lNMVDn/rJKmuJB23t6LqCrGmCAe/dhwErHnvCsK1IcyYzt7PH6GatbjjD3cwcnQMM25w9pluqjmL1e9aTuPGOjY8tpqLL/cxcnwRdHWAGYJ77g+9I7lo3w4IRQPpj0deQNVC42rsSwvBxhOCtnaVVI2CYQhsW1LI+wz2+2Qy/pxztBBQV6fQ0qYSTwgURQT8rCWfoQGfzNjc+18LhMOC2jqFRFIQjSoYZlA4JmVQD1GtSopFSTbrMzbqX3cHaF4jmM1KvvaNGxsySNaobNoRopDzKRXfwTXgC4HvUbl4NjB680Aogo4tKRq6IpSzDv0nA4LizECF9IV3XthmKfBti9zhPdfkWIkHbsUrlqd4bUZ7U5BzvQ4Y663Q0BUlXmfge5Kjz45wZvcoruWjaIK6jgit6+P0Hw9C320bE4R7KvQczuP7kiPPDjPWX52xxLv/ZJEf/LvjrLq1jniDiVUKwuG+K7HKLm/9ZICxywqzzr+VYaw3KBnP9xepjFVJnxjDdyXpE2N0v9IHCE797DwAekQjezFP7mKe9R9ajRnTqWYtfMenb+8g6ROTobWj3ztF7eoUe/96dhWDS9A0qG9QaGxWaWoK/m1uVnlofOF8JZI1Cn//d5eegzp22OXlF6+uVX7TFp077l56qHZowOcnP6pMkVKaC5FYA7GaZVRLYzhWgUi8Cd93yQydmDCMmg7vejREe8dkVGKgz+elF6pkM8HzoqoBIfh7Pxjm5lsMOjpVwhFBuSwZGvA4csjh2acsXnqhijWDz2AY8MC7Qtz3YIjNW3WamgPNxlLRJz3ic+yIwysvWrz4nEV+hla0q4GiwLIOlS03GazfqNG1UqO1TaW2TiEWVzCMIFJgW5J8XjIy4tHb7XH2jMvRQw6HDjiMLpD3dbF427hDNUPQ1D79QbSrPlbZQ1ECgtxKyce2/s/oOvZdSfehHKG4hm6qaIYCcu7cz//J8MtVKvtPTClqUGviQRPxdUCmv8rub/XOfC6u5Mgzwxx5Zma1D9+Dg0/MTJN1CUNnSwydne7BWiWPN78/teLw7J7AaCmagmv3kmiLsu3XNvLcv3kd6QXULOKy0veGDXU0ba6n+9V+hDJZve07QQh1KgTKLAuJeEKwdp1OZ5dK53KN9g6VmlqFVEohVRP8FwrNrFUpBNTWKvz+P03MeR/mwje+Wr5qI7hth84/+sOFVwVfif37bJ78WXWi5mE+GOFA+UYzQsRq2qkUR4jEm8gMn5pI9Bu64MMfiXDH3ZOV1UcPO1w475LNBK7QzbcY/MG/iLNug46uT557PC6IxxVWrta5+VaTlatVPveXpSkZgVBY8Nv/MMoHHotMo4IzalVqalVWrdG47Q6TTVsq/I8/LVIoXJt5t75B4QMfDnP3fSar1mjU1s0kvxRA0wSRKDS3qGzeAp4nGez3OHbU4WePV3nxOYty+Qb3CV4vJFIq7/10HaGIQqpOIzPiUlOvcnRPmZ98Jc3IYPB7JKpTLklOHXlnkChfbwyfK1EpOJhhjexgNdB2+wV3hq8XCs+9gV+euuTNP/kqfukdJKV1ndGwvpYNjwXN0NXc5DtSGiqz/kOraFxfy+7/cQC7YFO/ugbNCCZAzwkeqpl6KT3Lwyk73Pv/3MrxH5xh6HBQaKEosOtWg3/9R0nCYUEoLDDN/43Fma8R7Gqe2uaN+J5NKFqHa5enKczPhLr6QA8RoH2Zyr/590k6u2aXPBMC2tpVfuO3YqSHfb77rcrE9s/8dpSPfzpKLDb7WAkhaGxW+cgnIhQKkr/4s+JVd5Js3a7zB/8szvpNOrGYWPSzoqqCtmUaza0q23YY3Hxrlb/8s+KC1UAWgrfNCI6NuHzxjwe4671JBrptzhyuEEuoPPqJWqoVycE9VdZsDDzFE4eWuPJTlYAxRFEmWREkQTjK95HudS6LFoEoMIqYeg5cOgcZeDFXhMcKIzYFrqHRV4L7EJzH5ScRVGwG9+IGWtpZx2VcCHSB53LJACrR8ITh88bemX1zM0KIYFzGKa2mPx9+4NXOwZAxfHSU9KnMxHMtxwtu+t8aYvBQkE/2bJ/0qQxP/rOXJ/o3PCeQH3v5P76Jf8X9tksOL/zR7qCd5Yq/RaKCltalqdX/n4pSboD+My8ipY9tl6hpWI1dyc2bE6yrU6hvUNA0+Ff/NjGnAbwcsZjgd/9xnDdet+np9njoEZMPfySyYCMUDit89ndivPS8xaEDS0vIqRo88u4Q/+8fJ4nFg9zj1UBVBXX1Kh/9eIQ1azX+8PeyDPRfmznr7ZNSkuDYEkURpOo0UnUayTqVSEwlHBU89MFYQB/kSXbeGeFz/3kyOa8316CEjGDSkOCOFfDykzkzJWxitNUR3dJFdEsX5rIG1HgEoQi8YgW7f4zyiR6Kb57C6h8N9r2WzbOqgl4bx2ivJ3bTSkKrWjFaalGjIYSm4ldtnLE81oVhykcvUD7ajTOan5BKuWanEQuj1SeIbOggsqGTUEfQHK6EDKTrBfdiYIzKyV6K+89iD4wGjbVLDCcarbWB2sb4uDjpHH5x0lNTIiZmez3RrSuIbOrCXFaPGgsH41KoYPePUj7eTeHNU9gDY3iF8rykGsLUqfnYo4x+/vtLOue3A0JT0WrjmB2NRG9aQXhlC0ZzLUo0hFAV/IqFM5qnemGY8pHzlI9142aKMz4f0pd41vTJVPqB8ZvcwIyf852Zx9qbZfv/DsjnJD3dHpom0LQg1zbx82XbrpWHKxQFRTVQNQMjnCQ/eh7Hnr9wSzeCBcd73h9m160GQgg8T1IqSixLoqqQTCqo2tTzFEJQW6vwyx+L8PWvlCZCoEIEz0u5IqmMhxRjMQVzhvC1rsNv/P0ov/872UUXyoTCgsc+Eub3/1mcWGzmsLqUEteFSlniOMHPihKMQygc9IpfaTiFCMZn5y0m//2va/gn/zDLxfNX78i87XqCpw6Wuf2RJCs2hDFDgj3P5InGFPIZjzPHbCplnw98IomiThZTtv7O+4ls6pxoRh/+2vOM/igoazc7Gkjet5Xk3ZvR66bnHrRUDC0VI7x+GbXv2UXhjRNkn9lP+UQvchpf3+KhNySJ3rSS5H1biKxpn5G3Uo2GUKMhQssaSdy5ES9TJPfyEfKvHKV6YTAgib0KKCGD0Jo2kndsJH7rOrTk9EIEoQaK0npdguim5dS+/zbKRy6Qe+EgpUPnpzTrLhSt/+jDhFe3BuPiegx+/gkyT+4DKQl1NZG87yYSd21Er5nOUKHVxNBqYoQ3dFD7nlvI7z5O9pn9VE73BbyIs8GXeJk8al0SWbGChVO1+o4VWdabaohtX0Xy3s2EV7bO/HzEwqixMKHOJpJ3b8IdyZN75Qj5V45gdY9c/wjGLJASBvo8nvzZ/OHmXbca1NROvTYpoVzyeeWlpS/2jh25+nf02aeqHD/qEE8oxBOCeFwQi1/6WSE2/vvtdxnU1V291xuJN9PQfhPFbJBPtsoL77/cvFXnzntMQmFBIR/cu317bAYHPKIxhdvvMrnvQZPEFcw8ugF33G2QzfrctF1HiIDgZM9umzd321w4F/Srbtik88h7QnR2XWkKBDt2GbS2q/R2L/x5Mwx417tDfOYfxGY1gKNpjxPHXS6edzl31iUz6lMsSgwDEkmFtnaVVWt01qzTWNahos5QE7Fxk86//H8T/NG/ztPXe3Xvw9tuBLtPW1QrGZqXGZw/UaWU91AVyI75bNweCqrq3qrO2owsVBW9MYkSNgl1NtLwsXuJbFw+New3035CIEydxJ0bCXU1M/rD18i9ehS5VIkOIQivbaf2vbuI37wWYWgLWkkKIdBq49S+dxeRTcsZ+/Fu8ruPL/k81FSU1AM3UfPgdvSmmnnvwyUouhp4Jatbyb92jLGf7MHqGV6yMRGait6UQgnphFa10fixewmvXzbvPRFCIMIGyXs2E+pqJv39VyjsPjGnIVRiERIP3DoeEpXkn92DrF5vsZ1FQhFENnZS+55dxHasDlhrFvh86I1J6t5/K5GNnYz96HUKe0/NvTC4TpAS3trrcPhQds7PKULwhb+tnWYEAUaGff7pP5p7/7ngXYPLLhYlp07OfSBNg7/5Ru01MYIgce0y1WIaiY/nLvzZ3HWbiaYF5FKf+8si3/lmmczY5Ev5zBNV+nuj/OZvx6YUywghWN6l8fFPRamtU/F9+PbXy3zxr0v09UxOpk/9vMqhAzZ//J9TpFLKZfsHrQw7bzbo7V5Yjl0I2LzV4NO/GaWpefp9sy3Ji89bPPXzCm/uthkemr0dI54QbN6q88BDIT780TDh8FSDqiiCXbeZfPLXovyPPy1QKi191TunEVRCIeLbdlI6eQx3bHG9QgvFio0h7ng0STylcv54lUc/XsdPvzrK4b0VeutUhCKolOf2x43GFNEtXdR/6A5C457IBKTEq9rg+ihRM8hDXQahKBjt9TT8yj14FZvCGycWHw4UEF7bTtOnHiS8pm1m1QI/OA/p+agRc1oZv1BVQiuaafzk/QhVIffi4UWv+NV4mPoP3k7qwW2o0dCMVGbS8/HKVRRdRwldwfcqBGosTPLeLWh1CYb+5ins3qWLlxottUS3dNHwK/diLm+6onFaBowtvkSJmNOMtVAUzM5GGj9+P37ZpvjW6RlzY9L1KL6wd+o258YbiDkhILqli8ZP3E9oRcuMLRzSl/gVa/J+XPl8aCrhNW00fvohUAT5145dc0mZhcD3mbH8/nIIIWed3KScvr8QCo3t2wnHGvFdi6HefViVLLHUMhpaNoMQjA4eJT92gfaV92JVskTijRSyPYwNnSBRu5zaxnVI6TE2fJJC5iJ1zRuJpZahKCqZ4VPYVoFkbRfhaD2lwiDhWD0DF3ZjVWb2yrxrSC/qWEWk9IkkmgGJ61i4zsIMi2kG78UzT1b42pfK0yojSyXJ979dYccug123TuUijsYUouPMem+8ZvHXf1FkaHDqwEgJr7xo8dxTVT78kciUv+mGYMNmnR/OwtJ1JWrrFD76iQhr12nTpp5S0ec73yjzt18u09szvyJPIS957WWbo4eCCtnf+ydxYvGp70QoJHjo0RBv7rZ47pmlL3rnNoJmiLqH303qjnsonz1Nft8erN7uWTkvl4IV68MceaPEfR9KIRRoWR4UwzgOjAx6KAq856MJHv/67AUP5ooWmj5dj94QqC74tkP5WDe5Fw9TOd2HX7VBghI2iKzvoOah7YRWNE8UJAgh0OqTNH7sXiqnenHTiyuu0BuSNH7iPsJr24PJS45zxNsu5aMXyb106TwC704xNMyOBhJ3bSK+cw3C1CfOQ69L0PirDwR5y+MLZ1ERmkrqwW3UPHrzJGv8pSKIfJn8a8cpvHECe2A0MK5CQUuEiWxaTuqeLZjLG4MCGiFQDJ3YTSsQf+9d9P7X703J6y0G4TVthLqaJ8fFcigdPk/u5SNUT/cHcjEyyBVGN3ZS89B2zOVNQQHPpfvRlKLxVx+gcroXLzdDr6SU2Bcn2wfUVPwdJ+RnLmug4VfuJbSyJViEXXo+LIfiwXMUXjlK5Wx/oK0GKKZGaHkzibs2Eds56TUKITAakzT9xruw+8eonrvBavXXCULRqGveSPepZ7GqWVy7ghAKy9e9i7NHHsd1yhPeUzTRjG3l6T//Kr7nohkRmpbtpPvUMxihOLWN66iW0uRGz5HPdGOYcVpX3MlQdyAia1t5zHCCUmGIeE3HrEZwPmz8dx/E8senzwk9zenP3bn/+QKl82kGzge6gapm4LmLK3rzPMkX/1dp1taAoUGPF5+1uPkWY8boguNIvvalMiPDM8/bth2EiD/0y+Ep+2sadC5fmCesKLB9p8HDj4amhS89T/L0E1U+/1cl0iOLsx25nOT736mQqlH4B78Xm3J+QkBrm8L9D4fY/5Y9xUNeDOY0gkF5vkRLJEns3EVixy7sgX5ye3dTPnkcr1wKyKGvYtLx/aD4RVEF8ZRGXYPKe345TmObRvtyHSkhVavOaQS11GTOy8sWGfrS0+RfPTajJ2X3psm/cpT6x+6k9r27EKHA6AohMNrrqf/QHQx+8ckFe4PC1Kn70B1ENi2fGCAJOMNZhr70FMU9p2bsW7MHxijsOUVkQwet//AD6E2p8QpBgZqM0vzb7+HCP//igotlwuuX0fCx+1CMy4bUl5QOnWfob54M+ASvGCc3naN6fpDMU/uofeRm6n/5rsCDJPBMo9tW0vCRexj68tNLKpbRLsv9uel8MC67j08/1ijYvSPkXjpMw0fvoebRmxHj1yGEwOxooP7DdzL0paenXIMwdKTtTIwhQOzenRSeeQO/+M4gF1CiIeo+cFuwQLrs+bD7Rhn6m6coHjg74721+8fIv36c6JYuWv/hBybVLoRAS8Vo+Qfv5eK/+hK+9fbwCTb8+qNkn9iDM3D1yvW+59B75kWaO3dhV/MMXHwDCOafSjEgzr4EKSWFbM9EYUk0Wkc03kTXhndPfF7VTOqbN2FEalCEimEEk6fjVBBOBUU1cO0Shrl09YTM3gtUnXEt1LBB7W0rsYbzlM4Og6IQX9+CPVLAK7oIoaAoGiCJpdqxq3lKuYUrTZw55XL+3OzRDduGs2dccllJqma6ETxxzOHM6dnZvqSE7gsexaIkHp9qZBJJhVhMUCzOPceHI4JP/b0oZmjq90spOXXC5W/+evEG8BJKRcmPf1hh160GN1/h7SqK4LY7TVat1nnzjaVV1M9pBL1SkcGvf5nYxs2YyzrQUzUYrW00vP8xvEKe0vGjlE4cxRkZwc1nkQulULgMpw6UuecDKeIplff/eh0/+MIowz02tQ0qfRcdbFvy0AfmZsu/NLl4ZYvBzz0RhIrmgF+xGP7bZxGGRu17dk2EnoQQ1Dy0nczTb2FdmLux+RIi6ztIPbhtygrFTefo+y/fo3JqHjowKSkfvUjvn3yHZf/io+j1yYnzMNsbqHnXTkZ/MF2n60ooEZOmTz80xQBKKSkeOEvfn/0AvzBHOEMGagujP3wNt1Cm6dMPoSUiE+dR88hOintPUTp0ft7zuBIT41KsMPi5n1HYc2rO8/DLFkNffjqo+Hxo+9RxedcOMs/sx+6ZpJGL7NxA+a0T1HzkYbx8UMgTWr9iWnj07UR083ISd2+Z8nw4g2P0/dfvUT03OPfOUlI6eI6+//o92v7gsYlCLyEEoeVNpB7cxthPl8a+o7fW4wyOoTemgkgJ4Fs2SshETUTA83EzBfxSFa2xBjwfJWIibRdneCzg+BMCETLQahM4Q2NLripGgO87DF7cQ0PbTYTCNRRzvfiuTaJ2OZ5bxarmcS9VVF62ELIqGQq5XoZ79uH7Lq5bRaBgRmoY6TuAopm0dN566YYiZ/HYFov+Hx2gUgmO0/SuTYy+cpr+H+7HGydlN5uTtH5oOw2rtlCQA9S3bcZ1qpjhFEPdby7qu44fdWZVsr+EsVGfoUGPVM30UPvJ44GBnAuWJRka9IjHL88LCkxTkEgqFItzp2a23KSzYwbVEMuC556ucvLE1aUo+ns9Xn7R5qbtBrox9Uta21S23KRz8ICNvYSo6NyFMZ5H5ewpKudOo9fWEe5aSaizC7OlDb2+geQttxO/aQdWXw/ls6exeruxBgfwioUFe4e95yy+/7kR6pp0cqMuhWxws7Njkzf9lacXxgOZf/Vo4GksBBLS336JyPplhFe3TW7XVWoeuInBLzw57yGErlL/S3ei6JO30bddhr/x4vwG8DJULwwx+vhumj790GQuSEDq/q1knzuAl5v7+hN3bCDU1TRlm90/ytAXn5zbAF6B/EuHMZprqP/wnZMGSFep+9AdQTh3kaoLl5B76QiFN+cwgJfDl6S/9RLh1a2EV7ZObBaGRureLQx/9dmJbda53iC06HpUDgXHV5PxoNfwHQBh6tR/6A6Uy5S8fdth6GvPzW8AL0PlTD+ZJ/fR8Cv3TOa0VYXkPVvIvXxkSnvQQlHz3tsZ/daz1HzgTqzzg0jPw03nCK/vxCuUAx26TJHi60dIvetm3GwJv2LhF8o4I1mQEjUWJry2AxSBO5ZHekt7PoRQSdauQOJTzPVSKaWR0qf79DMk61YCktzYeVy7TDZ9eko+zXUqDF7cTbJ2RaCaURwhP3qO3NgFoslWPNdipO8AVjU/EYZUFBXHqeDa14ZUwaiPIR1vSsTHt12Mmgijuf3YZZvuE09jV/NB3tNbnPd+4bw3TXrrSpSKklx2+nMvJXRf9Oaln3RdyM9gKDUtkJybD7/0K4GM3dTvlmQzPj/6/tXfZ9uGo4dt+vs9OpdPNVtCwM23GvzguxXGrCVErBb0KSlxRtM4o2kKhw5gNDVhtrQR7lpFeMVKQl0rCS1fgZvNYA0OUO2+QPnMSeyB/nmN4cqNIXJjHlJKPvAb9ex9vkD/uSq19SoDvS62Jdm0PczeV2bWCLwEv2qTeWrfoooFvHKVsZ+9Ses/bJ1SmBHdsRr1uy/PnIO6DOG1VxhQoHK8m+LeBU74EyfiUzpwFvtdOzDb6oHJqtHo5uXkXzk6667C1EnddxOXP4HSl2Se2IszlF3UaUjHI//KUeI71xDqap44j9DKFiIbOijuO7O462J8XJ7Yu6jFt5svkX16P6GulslxEYLY9tWM/vD1oH8QcAdHQQgKT7+OOxLkdoquH7RKvAMQ3dBBaFXrlG3lwxcoLfI+StuldPAcqftvwmiuAcarRhuSRNYto7Dn5KLPzRkcxVjWCKqCGg/jZoto9UmEqZP99hvojSnid2xCTQVRGKc/TWnfZd+jCJLv2oU7mmf0W88u3QsEpO/Sd/7ladtL+QFK+SDvqTXUYSxvZ7j7wLSahFJ+kFJ+6qIi3X9wUecQ2rAGZ2AIL7N4gedqX4bGhzfhOx7l7lGEqpDY2IYaMXCLFnY1P2H4HKswwRe6UAz2e/OKolSrcsYKSduWpEc87HnWJ64nJ3RdL4eqBoTocyFVo3DbHTMLhB85ZNN94dq09PT2eAwOTDeCEPDBRqKCsSVE5xfNNCxtC6unm/zeN0j/9IcMfOULjP78cSoXzqHGE0TXb6T23gdp/pVfpfljnyK2aSvCmJ2sdvm6MLWNGo98rI5TB8rc8UiCrrUGN98dYcvNIbbsCnH3u6LzinNWzg3iDGcXdzG+pHqmf0KQEsaNTzI6YQTmQuL29dMqQfN7Ti6p6d3Nl6men/oiC0Mjsr5jzv1CK5rRm2suJxvBGclSPt6zpH4yZyQ3LfSphk1iO9cs+lgAldNT7++C4PlUzvTjXsb+IoRAq4kGBTyXQ4ASnnxL3Wwec01n0LT/NiNx16ZAW/Ey5F49GhQELRLuWCFoWbkMSsQkdMUibKGonu0jdutGrLP9oAiUkIEzkkWNhIIaD1UFVZ3oWXXz0/tGS28cQ42HCa1snfa3aw0lEUNvbcJc3YWaDHJ5SjyGuWo5ak2QRlCiEcyVnZgrOxGRoDpab2/BWNGBMIM5SE0lAoPa2RaEcyNhjM52jBUdKNHIrN8/F8bePE/65VPU7OpixW/dy/LfuItwew0Djx+g0p/FCCVo6rgZVQ9R27KRULRuUcdPp+dXdnBdiTODoSuVJMUFcIBKPyiguRJCETP26V2ObTt0YvHpn5ESXnr+2i1IR4Z8xmahS6utU2hdIpPR0vsEfR+vWMArFbFHhrCHh4nftIPo+g0I00Q3G9Br64msXoc9Mkzm+acpnzoRKJFfBkWFlk4TKeHE/jI77omDH7DNt3fpVMuSx7+Rn3clZF0cWlLvlFsoUz3Tj9GYmtgmNJVQZxOlA+dm3U/o4wbqsknOK1awLgwtyfj4VRt7YGobitBUzPZ6hKrMSgodXtM+zp4zeR7VswO4Y0tTbPfLFtWzA3hlCzUyvrrTFMyORtREZNGht+q5gSXdDy9XonphaCJPCsGiwGxvoHz4wuQ2XafuM49hne2h8NRrRO/YhhAQ2riK7LfnD2lfLwhDI7Kxc8o2t1AOwqBLaG3wSlWc4amLCWFomK21oCqL9sSscwM0/uZ7yfzkNWLbViN0neqxCxgtdTT/3i8jbYfCq4fxsoWZqds8H6tnmOrZfmp/+R68QuWaFMnMBXNNF/7+Eon3PUT2Oz9Bq02iJuPEH7iT9Be+GdAUmgbm2pX4to0zMILR3oIImRjL2ii++DrJDz6CfbEXNz2GME2iOzYjfYm5ooPqoQWmUq6AV7IZfuoo6RdPTqQRpOvhWS74YHk5PNema9P7yAwep1xYWL0BgOtIyiV/3uyS5zEjoXe1Iidyl3NBSpgpSitgWpjzSmzbbsz6mcMHr13hVrkcKEz4vpzGJqMogo7lKm/uWZj4+OVYohEUKKaJlkwSWbuBxI5d6A2N4Pv41SrWwHncfA6zpQ01FsNsbaPpIx8n+8qLZF99Cb86GSO+eKrKuz9Rx8//dhQpIT/mceygxcVzDsW8h+vM2O42DfbA2JKYVqTlYA9NLZMWqoK5rGHO/YzWgObqcjhjBbzS0toJAhqzqfsKQIQM1HgENzszg4u5rB7FvGwYJdiDmauiYHOzRbxcacIICiFQ42GMphSVRRpBqy+9JFUHr2JNn/R1DbNt+ira6Rum8tZx9NYGhKKQf+IVaj76yKK/81rCbK9HCU8NETkjuYkilMVCOi5eefrzoURM1Gho0YsTv2Jx8ff/BwDZn78xsT335B5yT04tthn9+jPT9h/50s8nfh7+q8cX9d1LRfXkOarHz6K3taDEY6AoKLEoSjIOisAvV5Gej32uG2dgONCaVBXU2tQk0brrUnp9H7JqodXXIl2P6rHTqKn4osOUU6CIKQtioalomkpd/WYMI4lQFASCWE0HlWKacmFhOeFqVS5MsknOnHGwLLlgFZ4ZPyXmn3/XrNdnNIKeF+QjryUqZYnnzWyYJ6jhFnnMxRlBRUFP1aA3NRNds57oug1oiSS+62IPD2EP9FE8epjK2VP41SpKJEp03Xpim7cR6ugkdff9ONkMhbfenMgVnjpQ4dSBSXma7/zVMEIE3HVCAAJWrjM4c3zuycPNFpdUEOE7Lu7oFV6TqqDVzy33ojemUK5ghRFCEFm3bKIvbjEQighW9VM2CoSuokRMmMEICkNDS8URymQYQPo+7ljhqkrn3XwZN1/CaJk8HzVsotUmgIWXdgMB/doSxkVaDm5m6rgITUW7kgpP+niFEmpdEjUaRomFUWsXf/+vNYyW2oAk/IrnI7qxE3eeBdZMEIqC0VhzxUaBMDSUsLGk4phfOHgel6Y4NRnH6GzHHUrjZXOAQGuqx1y1HOvUOYSqYa5diZpMYJ/vRq1JAQSE9eNzj2/boAi0lkbUeAzBAlbbM0CLh6i7czXRFQ0YyTCe5aIYGm6xytDjx8l0n0IIFUUNplu7svA+ZNu+urZsz5ULlnxaCnQ9UK6YqT+xUpHcertxTUV6m1tnd0tTKWVJI7ggIyhMMyiE6ewi3LUSs6MT1QzhVSqUzpykeuEclbNnsPp7p7RJ+OUShbf2Uj59ipp7HySxYxeJnbdQPLQ/6C+8AropuOmOGBeOVVi/1eTgngr5nM/974tx7uTY7DfzMvaRRcP18UqV4MUYH0gxPrkITZ01lKelotPygeayBlp+6z2LP4c5IBSB0GYeeDVqBqwvlxPkOC5e2bq63s2qPa0SVJg6ajy8uAPJcRaUJZyKdLzAq55nXKTrUz1+DjUexRkaxc3kMVcuwzqzcKKB6wGtJj6N9SXU1UzrP/zANf0ecUkh5BcYuhKiPtyFqUYByUjlHCVnanTGy+SQVRvpeVhnzgfhTE1DCYeoHj0FvocAvLEMQtcQhobT048aCQfRkYvBQrty5OTEs+MXSji9A6i1KawLvXj5paUQanYsp/aWFRSO9VF/9xp6v7mH6MoG1LCBUy3iuy61LWuC4hgBvuvguQuLGDluEP5bKjz/mnKbTEOqJlCGn8lbTCQU/uLztdP/cJ1wZY/iQjGnERSaRnTjFqJr1mG0tqPX1iE0Db9cIv/Wm5RPHsca6MPJjM15p71CntKxw0RWrcFsbgWhEI4ptHeZuK6kY3Vo4iJWbw1zdE+J2gaV2+6L4jiScZKNWSE9eVWk09Lxkb5EXJYAFoqCMPVZjaAaDs0fLL8mELN+jzD0aROgdL2rJliWjjftfgotuB+LOo7nX924uDOMi6ogDG3yGn2fyluX5XKEQKtP4eUX1lZzvTAbbd01h1hAvOodjq7kzdSE2slUewGBYHqBgzeWxSMLgHUqKNyyclONljMwjDMwtXiotPutKZNH9fCJyT9KiX2xDy4uvJ1pJoTaUuQO9TD4k0M0PrSRgR8fwKiP0/ah7WhRE9OpQwgxEQJ13YW3DPjeVa1nZw2TXiskkoHU0zsBmgZLcQXnpk2LRGl474dQQsGE7+ayFPa/SfHQQdx8LsjtLXCEvFIJv1pB1AQrAwEIFVZvCFPbqDM65KCHBKomKBV8XnyiRNeaIOH67c/n5jaCUl7dkzKuLXi5QRGqgtBnrzYSujpt7pFX9bQuHkJRphlI6fnMW0U0Hzxvilp78GVi0R7HVd8OL+i9mjYuM3GzXvall9ol3k4IXZ1mnK7f8/GLbQRbouvoKRzkYv5A4M3Ja8j9egPeSelOFg45uQqhlhTWSAElpKFGDOyhPA3Ltk9wiLp2Gc9ZmCf4DmMAnIaQKVDeKYuwJZ7G3J6gEAhdxxrsJ//mbopHDgWGbwn+tVDVQEMqnwMk5aLP6YMVkHDwtSIj/Q7hqEIopKJpgVeYz3oIIUjWqoylZ5/YhXKVq2Fl+gQvfTmn0Kwc53+8HH7ZwhkrwDWUurEHxmaVeLokiDsFSiDSelW4JHg75ctYdLh5oQoWc57HTONyFT1pNwrB5DX1fnnFSpAjvYbnHxSEBUZDCFi3TuMf/36MTZt08gXJN75R5tvfrmBZkve9L8QHPxDmj/5dnnPngmc0EhH89m9FqW9Q+JM/KZLJ+Kxfr/H7vx9j08bgGF//enCMSkWycaPGZz4T49AhhzvvMNi0SaO/3+erXyvz+OOVeZlNLkEgUISGoUbQFAPbqwQSXNLn8jfr0ucEAonElx4S/7K/KyhCncjn+fj4cjJ3CKAKHV8Gc4ky7mV60kNybd7TSl+G+LoWFFMje6Cblb97P8XTw4RbUngVB9+1KYyeR9WM4IqURZbyv4MNoW4E5EG/yJibNq1SZug7X6d8+gRyvm7LeeBkx8g8H3A/XnpppYRTBydDA6W8z/c/N0IoIuhaa9DcphNPKDS1avz//8XIbIcOQpez5M0WAqGpgQL85fA8pDX7NUvbnWYUSkcuMPi5ny+agHupuJKlAsbDlnN5SguA0NRpXrD0fPxFtqAE47L0cxGaOs0YS9e7JrqP1xvSsqdNXqW3zjL4xSfnZQBaKjo7VD7/uRre2GPzL/5lno4Olc9+Jooi4It/U+b0aZdIRLB6tcb58wGTf12dwq5dBo//uMLYmM/y5cExdu8OjtHZGRxDCPjSl8ooSrD/ls06X/lqic99vsSDD5p85jNRens93lggf2PKbGV5cjtRvQ5NMVhVczvLkzsoORlOZV6i5GRQhEZzZA1t8Y2YagTXdxgonaC3cBhPOggUWmLraImuJaTFEQhKTpbzub1krX5AYigRtja+h+HyWcJanJrQMhSh0J0/SE9hcQ31syG7v5v8kT7cksXA4wcw6+PEVjcx/MwxKr1j+JbLcO9bAGhGdF5F+WuN6+mnzaW2YVUlY2M3bsGay/hLWjDMTaBt25SOHlrqOU2BXypRPjm9D6e5w6BU8ChkPFQNOtaEuHiyyvEDFudO2Kiq4H0fi6MocziggqDJVxGLL45RlaD6cor6ksSfIS92OdxCeZoBUiPmFAq16w2vVMW3HKSUE9VZiq6N9w2y5BWkYuoTShSXIG1nXK9vERCX5cYWGdcRmoISNqaNi3TcqxYdvhFwc+Vp1cpKLHRVi7X58PFPRHAc+Lf/Nk8uF9zvlSs1PvWpKF/+SpneXo9Tp11u3mnw6qs2pZJk3ToN0xTs3x8sLD7x8QiWBf/2j6Ye49OfjvLVrwYVqIYO3/hmma9/vYxtQ3+/x7ZtBsuWqbzxxszndiVy9hDHRp9HV0Lc0vJRzuf2MFQ6g8TH9oLnrC7UwYrUzfQUDpO1+onqtayuuQNPuvQWDgXafL5Df/E4JTeDroToTGynNbaekjOK4wchR0MJ0xBezmDpNP3FE+iKOfEd1wJeyZrwKd18lTN/9vTE38KxBjxhEYk3IZFEEy0UMj0Uxi5cs++fD9fTkbQtOWuY//w5lz/594WrKuxZDObSJ5wLb3tKc8POCD2nLQqZCqGIwoO/VMNX/2SQrtUG9U0qEjhzzJ734tRUNGgqn019dxYouoZee0XZvS/x5lFWd9O5abp1Wio2RdHgekNaTtA/6PsBwwfjbQQ1MYSuLVl4VY1FUONT2TP8qo2bXbwHo9XEgsWJt0gjaBrTFeg9H28J5/B2wBnOTgt76rVxxHVcJG3coBMOw+/9X7HLtmm0t6tEo4JcTrJ3r83HPxahrk7Btj3e/WiI4ycczp4NnpWNG3UikRmO0aZOcEhWKpILF9wJKi7XlVQrckL7biHwpYvlFfGlg0Rie1Wq3tRCl7b4JorOGD2FQ/jSJWcN0hDuoi22kd7CYUAyVD592R6CsJakMbICTTEmjKAQgpw9TF/x2DULgV6JUFuK1LYOzPo40vOp9GbI7u9GkRpmKEk43oRdzaJqM8sd/aKiWJRzliC8/oo1be6OptrwPYdKYXjmnW4wFv9GLrQich6rpZuC7XfFuPeDKTwH8lkPXYeLJy0cW3LupEX3OYHvQyE3/4NrNNcEpfOL9BKEqaE3Tu0rk66HNY+YrNU3irTcYJk1/kzrjSm0RIQbyVxp9aTxbRc1PBl2NBpTKBETb6lGMBlBS01V7vCK1cXT0gF6S92cjDezQQnp03oCfcfF6r++rCTXClb3cPAsXv58tNSixsI4XJ/CHc+TFAqSoct0455+xuLJp6oTBuvAAYeP/Qpsu0nH82D7doP/+J8KWNbkMfJXHOOppy2eeLI6ke9zHDmjwvu1ntrjRh2q0NneNNlWEtVrkdJHFRqedIgbjTRGVhDVa9EUg7CawPJKU87Gky5Vt3DdDGB0ZSMdn7wVoQisdDHgDt3URmxtM73f2oOVy5LPdge5wVA3/iIX6u9kjI36uLNkJ2rrlBlLNcLxxqDoXdWoFkfxXAvNjBKK1lPO9SOlDHQXnSqaGcOzy0ggHKsHoVApDCN9FyOcRDMiVAojSH/pxVQLMoJ6XT2JnbcSXrUGNRpdUBHK4N9+KRDgnQWOLTn4WolkvUa6z6H7tIWPpFLwqW1QWb3B5MhbVfIzMKPPBLOzOVhlL1LpQI2EpvGEStfD6pk9BwkBrZfVPRzwdl7qYzM0wmvbKR/vXrIXtlhUx0WDldDkCtPsakFLRpfkNQlTx+xonMKGI30fJ53DGV18rjO8ojkoimJx90ONRTA7p/KEStvF7vvFMIJutojVPRxEKMYnZMXUiWxYFnDEXofinjf32jz2WJjvfrdC8QrVgEtGrrvb4+hRhwceCFFfr1IuS15+eXLZtudNm8c+PPcxZmCYvLYXMg7Xtyl5GfoLU6XRXOngS48as401tXdRsAIvz/ZKNIS7qA0vm/J5KeXVscHMg9S2DqzRIn3f2Yubr4AQRDrrWPaxWwg1JShkJtlhbGvhCju/CKiUJem0x/IV0xvma2oVojGFfO6KugUhSDSuRlENwokWCunzNHXtopjpIdV8H6M9B4gkmsmNnKW+fQuZwZNoeohU01pK2V6s8hhaKE7zytupFEcIxxsZ7Tu8eL60ccxrBI2mFho++EuEOpYHG8Y9PKEok60Jl/UqScdBOvb8hlJCtezzxlN5rKqkWp68AE1XiMQUahvUia8s5ue+wPCqFrSa2OKYM0SwOjfa6ydPS0p8y6FyZj4tQMi/cZzYjtUBASrB4Cbv3kT2uQM3rDimcqoXN52f4rmZ7fWYnU2BIV9kPF5LxYhu7JxS2elXnYBUewmx/fDadtRkJGiaXygUgdFWO4WxRkoZ8Jr+oqipS8i/dpTI5q4JGyGEIPXANnLPH8JbhMTVQvHlL5e5//4Qf/anKZ58sorjSFau0sjlJH/xF0F43/fh+ect/uiPEnR0qDzxRHWKYOqXv1zmgftD/OmfJnnqSWvyGFnJX/zlbCmC6zOpp8sXSIaayVj9E6FNBQUZ1IkSM+oQQuFi4QBlJ4euGGiKecOrKRVDpXJxFHu0NNG/WunN4OQrCFUhmmzDCMUBhYZlN9F/9mWKmZ4be5LXESeOOmzfaXBlbaGqwtZtOi+/MPXdl1KSGThGpTBCNNlCsnEl+fR58ulz6KEE4XgjQg3YuBTNRCBwnSqqZgIC6XukmtZihFMoqo5dzqJp5hSJrcVgztim0HVim7YQ7uzCr1SoXjxPYf9eCgf3I6XE6g3UJErHjmAPDeJXqxQPvkX/F/8Kq793rkNPwPOgbYXBpluibL4lSvtKE8+RJFIKH/hEgo//Vor3f2xuCjMIVtnJ+7ZOY+2fc5+QQc27dkxbwZQPn59OpTYDCm+cwL5CIcFsbwgEYa+yQnOhkI5H9tkDUzwLoQRiuFeGNOeFqhDZ1Elk/eRKWkqJO5qnuO/0HDvODsXUST2wbVEtLGo0ROr+qULFyKD61p0nV/tOQv7VY9OIzEOdTaQe2n71bSwzfV9e8pnPZNi7z+b++00efTREOCzY88bUSejNvTZ799rkCz6P/7gy7Ri/+ZkMb+1zJo8REuzZExyjWPQ5csQlk5l83iwLjh5zGBy6tmG+nsJBPN9lXe29LE/soCu5k7W1d9McDRRNSk4GX3osi29mWXwTnckdJM1mXHl1lewLgdkYx2xKYDYlsMdKRDrqiK1pItSSJNSWInnTMryyjZ0poxsRjFCCcKyW0b7DS6Zne6fiwH5nxrygAHbdOnONRCAtFaxW7GoBPRQnFK1D1QycagFF0TCjdZjhJAhwrCLp3oMkG1dhhJNY5Qy5kTMMnHmVoQtvLtkAwnzN8oZBZPVafNsmv3c32ZefxyuV0BsaiW3eSunEMTIvBOS6Rksbtfc9iNHcijBDC+4l3Hp7lNVbI6zbFuHiySqVks+X/tMg3//K4j2p1P03UTp4jtL+s/N/WED8tg3Et6+aslm6HplnDyzo+/yyzegPXqP5s4+iXGb06j5wG85onvzLR5YsRKsmo0EhSHH+wc29coTUQ9sJr2yZ2BZZt4za9+wi/Z2XF0zaHFnTTt0HbptavCEl2ecOXpVnW/PgNkqHz1E+dGH+DwtB4o6NxLaumLJZOi7ZF65NSfuNglesMvr46zR96sEpi6L6x+7EzRQpvH4Mv7q0dg8tFcV3PPwrCNvTaZ//9t/mXihUq/CH/3T28Uynff5slmOcP+/xb/5Nftrn//iPpxr7WSsCr5j/PenRUzhEyRmb9tGqV+T46HM0RLoIa0kkkrw9wlg1WGBnqv2cze6mxmwjpCUoOqMMl88QUhO4vjV+fIfB0kmKztw5/sWi45O3Ynnj74kiiK5oILa2OWiS11TM5iTWcB6kpJjrJxxroJQbxLFLixbVfadjz+s25ZLEuELxHQG332Xyuf9ZJJ+bfB4qhWF8z8G1K1QKI1SLIyQbVxNONFLODVDM9qHoJqFoDaVsP65TQTejhGJ1FMa6ca0yuXKW+mVbidW0Uc4P4dpLL5ibOxyqauh19TiZUUrHjuCVxr9ISvC8oLduvPzdHugj+8qLND72KyS278QZGcYrzb9qjyU1dj+VRwA//EKad3+yjkQqCIUO9ASiurvujvDmy3OL6gKo8TBNv/YQI6ZOce/pWenDhKaSuHMjjR+7d1q1XnHvaSonFh6qyL92lOiWLpJ3bpw8vqnT+In7MdvqyT53YMFhSSViEupsIryhg/CqVjJPvUVp//wCrH6xysg3nqft9z8ctCQQeIO179kFikLmZ3twRubQ9FMVYttW0vDRewh1TM3DlY/3kH12/7znMBfUVJSmTz/MyDdeoHjgDMxCQiB0leTdm6n/yN3T+hQLe05SPXV19FZvB/IvHyGysZPELesmtikRk8ZffSB4Pl44GOQ5F5AnUmMhzM4mIhs6CK1oYexnb1I+fH7e/d4OVGYQaIUgRGYYTBTq+NLldOaVWY9T9Qr0FGZu05J4jFYuMlq5OGV7jskcnCcdLuT3LfLs58fY7nNUrMlJP/3SzELabsnGtSsMXngDKT2EULnR8drr7XeODPvs2W3z8KNTVXWEEHQuV7n7PpOf/HBysVbKTr7HjhUsnsb6j0xppcoNTb2fDgWqpanvycjFfVxVL9g45mWMUUJhvIF+vPJlllZKfMdB6AZC1ZDj5UHO2ChWXy+h5StR4/EFGcFCzsWxfDxP8sHfrCcaV1mx1mD1RpOLZ2xsS3LXw1H2vjJ7rk+63gStl7mskebfeITijtXkXzlK5WTvhCckdI3wqlaS920ltmMVWu3UEnx3rEj6Oy8vqqjFL1ZJf+8V9PoEkXXLJu6bmohQ88hOottWUjnVR/noRay+NH6hgu+4QS9e2ESrjWOO5yVDnY2oiShqMoJi6ORfX7i+WenQeUZ/8CoNH7tvgmVFmDq1j+4ksrad/OvHKO47jT2YmTDISsQkvKaNxJ2biG7pmqZ+4YzkGPry00tWKJCOB6pAKAqhriaaP/soxX2nyb92jMqpPuS40oUwNMJr2kndt5XY9lWoqejU8xgtkP72S4uv/A0ZqBEDJWyiREIoYSPo5QybqBEzKIi6Ikwb2diJtFy8ShW/bOFXbPyyhVex8Cvjv1cXTtbuZkuM/uBV9PrkhKceCATHqH3vLmI7V1M52Uv52EWsvtGAzN3xEKaBEjHQ6xIYLbWY7Q2YHQ2oiQhaMgqKQu6lw4u6HzcSuewM5TMi4HdM1SoMD77zWX/mwuhrZxek03cJlxrkb3SjPNwYk/uNr5Z56JHQtKxHJCL44GMRdr9qkx6ZZ8znWwjO+Perv7p5CmNkwCF5SdPo0lbfx69WUKNRhGFMGEHpOLjFPNFEEkVfWL/cnmcKCAEjn0+zbnuECyeqNLeqJFIKbZ2BqO6Pvj63qK7VM0L5WDfJezajRENo9QlS928leeemQKevbCEdFzUeDqRnDA2USZmbS03YI997eZp690JgXRhk+KvP0vwbj2B2NQHjlHOmjrmsAbOtnuRdm4LmaSkvK5sXQQHKJSUARUye0yK7PqXtknnqLdRUjNpHdgaUY0IgQgbh9csIrWql4WP3Ih0PN19GMTTUaAihqghDm/rdUuJmiwx+/gmqZ5ZeiFK9OET5RA+pB25CCRnoDUlqHtpO6t6tk+PiBuOi6FqgBn/FeUjbZeSbzy+uNUIIVv3l7wS9jkKME9WKIBcz/jMioPK7Moccu2kl0c1d4+MkxwmIx8dsfJubLTLwlz+hfGwBShVSUjnVx8jXn6Pp1x6eKMK6NDZmZyPmsnqS924Zp8Ebf6kvnbOY+fnwnRtTfbwUSCAzNvMLa5qCNWs0hgevf97u7xDgRmQgD+632f2axa23m1MMoVAE23fqfPQTET73l0WuknjsumBuxhjPxy3kUWNx1EiES5Fs6bq4+Rx6XT1aLIZ9yUtUVRQzFIhZLrCfsH2lydY7YsTiCghI1Ws89/0s3WcdigVv1h6UyyFMg+wLh3DSeeo+dDtaPAyqigirIAOmjonPzkBq7JcsMk+8Se6FQ0tjI5FQPnqR/r/6CY2feIDI2jYw9WCiEyLwhhZbCLEEjkwvXyb9nZfAlyTv3RIY/fFzEIYGhhb04CSCRvgJozBxHRLp+1h9o4x880UKe09dlQ6LEjLIPvUWXr5M3ftuRY2FQFOD/NhCxmU8p5Z/5eiiWwrUeAQ1tkjpJ8YJuucZK99xYTFN776kuO8MftWh8ZMPEFrRDONalMHzMQNt3wKO+Y7lUJVw5NDML24srnD3/SF2v2YvTCz273DVuBGeYKUs+eL/KrF5ix7M5eMQAsIRwUc+HmFw0OPnj1cpzxIqXyxULZBrcp2gP3apmNsIui72QB+R1WvRamqh52IwUdo29uAAiZ27CK9cg1ssIh0Hs6mZUNsyvEoZOVM37QxYuSmMEHDyYAXpQzHnIZAgJLohWLvJoFT0uXB6dmuohg2EpjL6w1dxs0VqH92JsawRdZx2a6ZqLCkleD5Wzwi5lw6TfXr/tCKDxaJ6qo/+//YDUg/vIL5zNUZLbUDntsClmPQlfrmKM5LD6h7B6h5a9Dl4uTLD33geqz9N6u4tmMsbg3MYx2xsFdKXOOkclRO9jP38zSAvepX9TErYRKgK6e++jJspUPOunYHiemjucZGuh9U9Qu75g2SfO7Dk4qJ3GspHL9L7X75L7aM3E9u2Er25FjVsLuL58PFLFs5IluqFIezeuXtZ304cPOBQqUjC4akXZ5qCO+4yuflWgz2v21ctePJ3mB83whOUEg4dsPnR9yr88sciGJexBwkhaGpW+YN/FqeuTuHZpywunHOXNPaaDjU1Ck0tKmvXa9x7v8kbr9t87UvlJVv7OY2g79hUzp4humEzobZ2yieO4VtVfNuicvE88W07qLn7PszWVvxqFbNtGUZzC5VzZyaLaOaBXfWJpwwaWvSA/UuDVJ1K11oDw1DoWKETCgu+9OcZZiNaECE94GSUkHvhENUz/cRuXkN4TTtmWx1abXxi4vUtBy9bwh4co3K6n+JbZ6ic7rtmzcvuWIH0N1+gsPs40Y2dmCuaMVvr0GoTQdjP1ALadc/Dd9yAdzBXws0UsQfGsPvSVM8NUD0/tGRdQFmxyT6xj/KhC8S2rSS0uhWzrR6tLoEaCwXFQJ4fUKHlSjjDWawLw5RPdFM6cvGqFwOXoIQN0BTwJdlnD1A53U9s5xoia9owWuvQamKT41J18LJFrIExKqf6KO0/Q+XswNLGRUpGf/Ba4P1eB/gVG2dwaawvbjrP8NeeI//aMSIbOgmtaMZoqUWvjU+E6xEi0FJ0XLxyFS9bwhkr4gyOYfVOPh/XVS31KjE26rNvj82d95jT/rZ8hcrv/F6ceKLEqy9ZlIoLm70iEYHnyYmm/euJGSLlE1CUYJ76O0xFLiv59tfLrFilses2A1WdegNr61R+9/fj3Hm3yUsvWBw95HD+vMvIkD+rQYzGBHV1CnUNCq1tKl0rNLpWaKxep7FylYaqCk4cv7qQwtxD6XlUzp8h8+KzVM6ensj9ISXVi+coHT9KfOt2EjtumdjFLeQpHtqPl5+jGvEyCCCfcUkPOvhe4Ak6jqS9U6drjcFX/yLLez4an7PNbIragJRYPSNYA6NoqRh6bRwlGgqIrYWYUF53s0XcdP6qBWhng3VhCOviMEoshF4TQ42FgwlfC5gVpO8HorOWg1e28EtV3FxpoljkWsDuH2VsYBQ1HkEbn2SVkBGE+3yJ77j4ZQs3V8IdLUzjQr1aCE1FXNJZ8SVuOoeCj5vOkvn5m0ghUHQVuGxcMkWcdG4J4c8wNQ/dRGRNW/B1VZuR772G1XNtS+OvCaSkenaA6vlB1FgYLRVDjYWCsdGCiuvg+fCC56Nk4ZWqeLnSrEVbqhmhfce70cygqMipFkmf3E15bHpFrWqEad32CEYk6L91rTLp029QGllAjnOBqJQlP/humZ23GISuUPxWVcH2m3WaW+Oc+FCYo4cdei66lMoS6QfyPJGIQjQqSKYUUjUKtbUK8YTgK18o8dori48MRCKCljaFZFIhEhFEogqRqAh+jgjCUUF0fHs4IgiHBCtXzywivbxL40/+W4pKWVK+9F9JUi77E9sqE9skpbLP8KDPyPA7d9FyrXDmtMtf/XmRmtoEa9ZpKFesJAxDcPOtBlu2GQz0e4wMe4yN+hRyknzRx7UhFIJQOBifREIQjwdjX1OrkEopaPq19W3nXc/Y6REyLz6LdJwp4TGvUGDsmSdwsxmiGzahhMLYQ4MU3tpD6dSJ6aKss6Bc9OlcqxHZpCIlpAccTh0q8/JTJV59pkx62OXFn5dm5CqchJju87s+bjo/a39bTK/DmUMvcC6YahRPuhO9SLNCSvxCBes6sIMsGDLIFS61whNACIV4oh1FqGSziyjJn1pPhTB0Ql1NCEWhem4Q9xpKCvm2S/XCMEJRiO9Yibl5OZlnDmC9k4k5fHnVY3MJ0nMpj/ZhxuuINS4nUtdOtvvozF/re1TG+vE9m1hDJ9GGDnJ9J2b87FLhefDWmw6vvGjxwMPmtDC8ogjal2m0tqncfqeBZYHnBwVIQgFVESgqaCpomkDTg7Daz3+8tHdpwyadf/SHcZZ1qoEnpwbnoKgBb4Giisu2jxcuzTLXJlMK9z4QAinxxiU9fS84f98Lfvd88D2J74Prwo+/X+FP/2R+Ao5fdHge7H3T5t/933n++D8n6Vw+nU5NCEEoxIRXJ/3gPnpewHx2SStcUS+RkV3fgO78Tv14DnAmuLksY88/TeaFZ0GM59kuVUAuEL3nLGw7MEaKIihkPaQfrCRXrNEZG3EZ6l+6h6JwyRsJ+kl86SKEQldyJyfGXsSTLv4sStYCgRgX7BSICcXrhnAXVbdAxurHk794ja+xeAuV8hihcE3QvOs7aFqYSLSRYmEA285jGDGisWaqlQzVahZV1VEUnVC4Bs9zcOyFMLfcOGYMaTkU95+leOAcQlMxlzXcsO9+J8B3bUZO7UZRNBrX30HDujtm/ax0bdJn9iCESsOaW2jecv91OaeBfo8vfa5Ea5vKug3TvQII3vlIVBCJznCAawgzJGhoUmhqvjZMTmK8eneqMtbMz7vvS5I1bx9LzI1mKvVceGuvzWd+dYx//19SbN2mo+uzGzOhBPdRW0KIeTYZp8Vg7j5BXSe8cjVuNoNvWfhWFWlZU70835+i9LxYtC432HxrFKEIWjsNes9aDF6o8vAHY9x6X5Q//sdDfPQ3U3z+v4wtKQXSmdiOroSI6kkkcDb3BkmjkZpQG6tSt1Fx87M209aE2mkMr0BXTRJGE0dGn0EVKs3R1XjSpd5dzqmxV/CvEzv99UJz83YymbOkarrI53rwfZe6+nXkcxfp7LqPnu6XaWzchOtWaW27hTOnfgxCIRprJhRKMjJ85CrPQKKEDYxYCCWkB6HSfDmQaroyDKoItFQULRFBqCq+7eBminjFGfKWl7UxzApFoNfFQQi8QiXIS4YNpOvjZkuBV/aLSnA8ThS9oIlBBvyb12ISmQt737T58z8t8Fu/G2PjJh1Vu/4r+7/DVLwdd1tK6On2+K1fG+PXPxvlPR8I09qqYpize9iLO76kWJQMD/kM9ntXZennNIJqJErLJ/8eXrmEkx7BTg/jjIzgZsZwiwW8YgGvUMC3ll5I8dZLRd56KfAqYkmVX/qtBkIRheyYR/9Fh8YW7XJ+7tkx400QhLQY6coFCs4wphqjYI9QsNM0R9dyMvPyrF4ggK6YVL0C6Wo3vvTJWUHPXNxooORkSFcuzH4+QsFoakKva0Ax9KDdJJ+jeuHcPBdylVBVQss60Wvrgt99H2toAHugf+IjrmcRiTZQKY+hG1GQPr7vAIJSaZBQKIWqGnieRaHQi6qaKIpGPN7K4MBbOFfB0wdB1Wjdu3cSWtUS5MJMA6t7hJHvvUr5VN9Er5zQVKKbO6l913aM1tqAtN1xKR6+yNjP92IPLL44RTF16j94G0ZTisqZASLr2wP9RU2lcrKP9I/eoHpxaFEvlaKZpDo2UBrpxq2WCNe2YERTADiVAqWRQEbnEoSiEqltw4jVIBQVz65SyQxgl6Zej1BUzHgdZrwO1QgHhtuuYuWGqeavf2Womagn2tBJcfAsdik77e+JtnVoZoTMxUOzV4NLeP5pi74ej098KspNO3WWd2mL0h6EYFItlfxFNaj/Hd5+lEqSv/rzIi+/YPHIe8PctF2na6VGMikWvRjyfUkuJxns9+jt8Xhrr82rL1mcPX0dC2N826Z48C20mlq0mtpASUIIpG3h5nO4uVzwbzaDM5bGGR3FGRvFKxYXLGuxcmOI5euCEv5YKjid3JjH8IBHPuuxZpPJvtcqSyyllthemZAWo+LmyVT7J7YLlHmJbF3fRhEailC4mJ+kDrsUHp0L4eVd1D70KFoiiVetgASrr/u6G0EhFIzGZiKr1qClUhiNzWRfeo6xy4xgqThIItlBrnyRcKSWsfQpdD2G59lYVp5ScYhwONBts+0y1WqGUDhFLnse00wQjjRQLi2+feMSwqtaQErGfr4PN18m1NlI3SM7qHvvzVT/Z3qiOjW0oommj9+LM5pn5Nuv4BWrhLqaqLlvC0IRDH3jRfzS0koFI+uXIV2f7POHcTJFQsvqqX1kB3Xv2cng157Hyy08T6eFonTc+hhDR55HSkmibS2KpqMZYTzH4vTTn58wgopm0LThLlKdm1FUHSl9FE2nmh1m4NCzlEYmKcCiDR20bH0QM16H9FwkoOomVj5N31tPTPns9YAZr6dt27tIn9rD4JHnpwhW65EE7TvfA75P9uLhedcMp064/Ps/yrFxs87Wm3RWrw2MYVOLQjSqEA4HeT/fB8uSlIqSXNZnNO0zNOjR1+fR3+tx6MDS0g/nzwYFG4nEjfeLpAyufy7YtuSbXyvx0vNTHYpiUTK0AHadYknyo+9VOLR/auoqMybp7Zl/8iwWJd//dpl9e6a+T7ncwvafC64LB/c7HD/q0LVSY+06nVVrNZZ3qbS2qdTVq8QTAtMUqCo4NlQtiVWVZDNBQdHwkEdfr8fF8y7dFz0uXnDJZq7NgmhuI1itkP75j1FCIRQzhBqNotc3YjY1ozc2Yba0EV65GjwPr1rBr1TwqxXcfI7RJ3+Kk55/tXpJlRqgmLPZ/2KepjadQtbjzZcrAZvM4AIs/QzPtkBBVXQSWiMRrYaoXktv4SietMnbw6yrvYu8PUJPYSb6KYGq6ET1Ggw1RG2onXO5N7G9MhU3T1NkFSmzhTPZ3TOEgwXJ2+9CS6YYe+YJrP6+8crU69/vJl2H4sG3KJ04SnjFKho/+EvTPpMZO0OxMIDrVigUerGqeXzpoSgqnmfjOGVGR0+h6+Ggad33yOd6KeT7UBQN72oJgIVg5PuvUTocTOKVE72Y7XUkdq1BCen4pSrC1IlvX4USNkj/cDflk70BKcGJXrSaGKl7NjH2xFtYSzSCeD7ZFw6T33MSfEn5RG9AdffgVoyn9lNZhBGEwGurWb6VUrqHgQNP41pFhKKhh+O41uSx6tfcSuPGuxk7f4DRM3uRnkco2cCyXe9n2a73c+aZL+JaQcGQUy5Q6D/DYPo5XDvwvhOta2jZfD/NW+7n7LN/s7RrXyDKo72Ux/qoXbGNkZOvTbmOePNq9EiCwYPPLpgQ2qoGxTKH9jskkgqJpCAaVdCNoPhlvGgZz5M4dmAMKxVJuSgpFv0ZWyOi0SaWd9xHevQEQ8MHZv3u/j6PH3zn+hWotbXsor5+PUeOfQPPW/x77rrw/FOS1vha6sIdgGCs0s1A8QS25xPT61les4PRcg8N0RX05Q8TMWqoD3cyXDpLX+EILz1v8dqLBq3xjVOOkSscB4Jr15UwXamdpCsXiOn11EU6kdKnv3icF54NlGJCaoy25BYSZhOa0FmuwfJW6M0fZrB4EokkYTbSFt9ERE9R9Yr05g6SswLO1rrwcpJmE1lrgNb4egw1StEepS9/mJPHxzh53CUcFjTWxVndsp6W2jZCIQ1XlhgqnWC01Edr7CZcx+PM8EEKRYdyWeJUQrSEd5GrDpIrLU3VZibMnYqUMgh5Fi+rajp7OtB60lSEpqMnk+gNTRjjRjHU2YXZtozsKy8uyAj2nLEYuGijKAIpobZe5eEPxehYoZPP+DiuxDQFf/r/LL7UPWU2Y7klussHURWN5sgawlqCopPmTPZ1VKHhz8LlZ6oRQmqMnsJhyk6WzsRWkkYTI5XzjJTPTzDZ/3/tnXmMXdd93z93f/sy782+ccghh7tkSZZk2VZkKXbs2Gm2OnWDLkGBAkULFCjQ9s/+0wJFi6It2gJFiyBokwauEyeuEylSbEm2bJmmadEiJe7L7Pvb17vf0z/u4wyHMxzOkJLjWO8LEJx33rnn3Xvuved3ftv3t6M/VAJjeBSvVqV15dIjmYsfBoFtgW3hN3aOjPU8C8/rnFNn/TLbW+fXses4dv2uYz64BcSaW99iygzs0M+nRI2NVBc1GSVycAB7qYRbrG9QnQWOi7tWQY5o6AMZ7KXiQ/kDnGINZ726YXoVtos1u4Zs6Gj5JOYtaV++QUmWkFWNwrUz96QlbBL8atEkuckn8aw2y+dfxXfDe2BWV4nlR+ibeo706DFKt94BwG4UWbv6/S2mRrteoOfgEyT6JvZ/0fuEZzVprE6T7D9EYuAQ1bnOZlFWSA0dRpIkSjMX9j+uF+YRlkvALv50SZLp6z1FOtlHqfQ6O91oTY2SSY/TbK1uH+CniGg0RyY90SHI3j9kSeVw7tP0REZYbFwCAf2JKXQ1zkzlHKps0B8/ghc4CAKO5V+kYi3TcIocyDxFxVrC8hocyT1PNjLMYv0SSJ0xlBgz1XN4gYMiqfREx0hHBmg6JdZbN9GVOKLjFlIkjcO559EUg9nqO8S1HAeyT7FYe49Ce7pTxzHPib5fomkXWWvdJBMZ4vTAl7i4+jJ1exVDjTOYPMZg6jjLjUvU7XX640cwsjGul97C8dv4TpSs9xmaKwnevnkLx28R17OU2y5Fs81oqsVw8iS3V9/H6ZgBe6J5eqJjrLVuhVSGHxD2F48jhWTIkiyHAjDbQ2R0DGNkDGNoBC3bsxlNuscF5PFPJvjS389xYCqK1fa5+IMm/+93Cxw5bnDtfYt2S/Dl30mjKOzbJNpyK/TFDjKZeRaQqDmrtN1w8fWFu2tkpxtYeIHNaPIUAoHnWxuCL8AnuDdzX5KQdQNJ05BUFdkIw8LlaBTJCBOGg1Zrm+9E0nVkXQ/jwoUgcJxONO4OBMS6HnKyyp2+rrOvuf5Zgd8wt+dn3kNILekqajpGdHKQxOkDW6ejo/XLhs7DssgHlrst5y6wXITtoiRiYaa0v49xBTit2g55eZtjRLODaEaCxvo0smYga5uJ5E6rhiTLxHqG2MKSKgSKHkWSO6HmkkTgWsh3VXD5MFFfvEb+8NP0Tn2C6vwlEIJopp9odoDq3CV8+4NLc7kXsqySzRwkCH7++dV6oqPkImNcLnybSmedsf0GB7PPstq8DoRr1mL9faJqmlSuj/naeUy3zkDiKFE1RUzL0BMd4/L6X1KxwufQ9poczD7DavMGDSfkRdaVKIX2CjfKb+MHW7XWmJYhafRytfAGFWuRhl0kZfShyFpYBQOJ8fQTOF6La8U3cQOLxfr7PDP8tzmQeZL31l7p/EaMK4XXWWvdJHRLmYxnnkSTozh+m1x0nLjWw5W7rvduFNuzDKdOkY9NsNy4jIRM2hjAcmtY7t5y0PeK3YWgJCFHYyixGHI0hpbJog8OYQyNYAwMoiSSCNfBb5sEZhtz+hbO+hr28iJOYW9E1H0jOn/83ws8/WKKV/6gxEu/kaVe9YmnZJ75hRieC64nHiwAd1gLnMDk+i5lWnZDIHyWW9dYbu0tf0qOxkg/+0liR46ixhPIRgR9cJjhf/hPNvqsfe3/YM3Phh8kCX1gkPTHP0H04CSyYYQMPdO3qZ87g72yvLnASRJ6Xz+pp54levgIihEh8FysmWlq534YFjD+GWYP2YZOPtiu6NDambdWqJ+9tiN1mjm9+tBCQJLlbZQgIVm1FHJy7nNYIQSuuXsemGKEEa6Z0RMkBya3fe/ZrS3Rmnqih+z4KZKDk6h6tFNtW8ZI5fd3co8Aq16guTZDbvIpopkBzMoq8fwoWizF0k9e3eIn/KCgqhEiRgYjkiGTPkCrXSCbPdS514K2WcK2ty6EEqDrSSJGBllWCQIX06rguluFtCQpGEYKXYsjyxog8DwL0ypvMWMqikE81odplQCJaCSLLGsEwsOyqjjOg3P+JEkhEe9HUXQazeVdzaQxLQOShKEm6ImOAWAoSTQ5gi7HCISHEGGMg6HEsf0WvvARG2lfCnEtEx63ZYwEmhxBUzZ5dL3ApumUtglAoBMxHKArMWQUFFlDllT8wO1oXxLpyCBtt0rS2Cy75vomCT2HhNL5DYeqtcydF8kLLCQkZElGQiYTGcLy6tTtnWMLTK9G2VxgOHmS1eY1dCVOOjJI2ZzH8T9Ys/buRXUjEfKf/yJarg8tn0eJxwlME69Ww15ewq1W8MpF3GIRpxRGjYp9suKarQDXFkgSHH0iRrJHwWwJ3nqtydiEjqzAm6/87HNHCsehff0q9tIiSBL9v/XbeNUq5W//BaKj5TjFzY2B3j9I/pd/FSUapXHxJ3j1OlomQ/zkabRcnvU/+b941VBr1fN95D7/JdRUmuZ7F/BqVdRUmsSJ0+S/8Cus/+nXcEs/g8woj4DAdHBLdZRUjNrbV3evh/gQUJLRkNbtDiRQUlFkXcWrNh9iUyEeGAwmfA8hAppr05Ru75CWIwRWMywuq0YSDD/xBVLDRyjd/DGt4gKe3SLwHMae/U0i6Z9eHmRp+jw9E4+RP/IMyxe+RTw/hl0vYlY+HBNkPNZHf//jxGN9nUhlg4nxMJdRiICl5R+xXtjqx49EMhya+BzRaA5VMVAUnWpthunZ17HvMutn0uOMjDyHoacACUVRCQKPQuEKcwvf2yh1FIvlOTb1G6yuXyAS6SEe60VR9FCgNZaYnv02prm9EPAdSJJCvmeKgxOfpdla5dbtV3cVghIyqqwzlDy+JWK9Zq/iBhaKpAKis0kSHTeOuOv40Hy8OYa/ZQwv2HTJBCLYyHm+F223ynrrFuOZJ0noORRZA0mibC0QCD+Ms5BUEnqOsfTjm2PiU2rPb0R8+oHNbjtJWVIQoQi/b5+VxlUGE0fJRIaRkNBkg5q1+kgpeTvhAZXlDVJPPYsI/A4bzI+xFufxarUNX+F+hd69uPLjFmY74MxrNQ6fjnLu9XCHZbYE1y/9FEgCPyAIz8Ve6tCTSBLC9wnaLVrXr25bUCVVJX78JHpfP4Vvfp3W1csgBJKiElgW2Rd+kfixk9R++H1QFGJTxzCGRii+8k2aly6G4ykKfqtFz0u/RPz4Kapvf/evnVl0N/hNk9bVRXp/4xPET41TfevSZg6hLKEko2Gu4ENyvup9aWKTQ1jTqwjXR0nGSJyewFmv4ZbuX3X9UWDXiwSugySrlGcu7Hq/opkB4r1jNNdmWXr3tQ0zuqztnXD7g0K7uEizsEBq6Aj1pRvE86OUpt/dCOD5oNFqF1haOoumxTlx7G9Rrc0wN/+9jcXPsbdrYT3ZwxRLV5mZfR0hAnI9UwwPP4NpVZid+85GPz9wqVZnabcLeL6FIusMDz3D6MgnqVRvU6tvpY4bHnyacuUmM7NvEAiPbOYQo8PPYtt1bk2/uuP5S8hkMwcZH3+BemORmbk3sZ3dnynLa+D4bWYq52i5Fe4WIF5gk9IHHjhvd8aYrpyj7Va3jbEXBMLH9Oob43mBQ8u9QsvZFPgNZx0hBNdLb20RtkIEGwJ8t5VIIGg6JVJGPzEtS9PZeQPfdApUrEXG0o/TdIqYbo2Wu4+SanvEA32CwvdBllEzPSGR9ugY9uoK9soS9sI8Tqm4f2fdXSivu4gAbr5nsnDLxjYfUsr/Ncq/VeIJjMFh3FIRa3F+YzEUvoe9vIhvtolNHqZ29m2UWBxjeASvWgn73hGovo+zuozfbhI7NEn1B2/9XAlB4QU0fnSD+LFR+n/7F0g8fhB7oYhsqBgjvfhti7XffzNMsAfUTDwsOhs1iBzoQ9IU4qcnkKMGfsvCml4lsDZ9wH7bpucLT2Ic6MNdrRI9PEjs2Cill8/hrFY/lGuy6gUaa7fJjj9G7+FnKN48h7ijPUoy8fwo7fIywnc77QIJaUMASrJM7tATGPHsh3J+94MIfArXf8jEp79C7tATSIpKuzi/50ox+4XnmXieiabFESLAddudwJf7P9+e7zA9+yaeF0awtlrr9Pc/Tq5naosQrNeXaDSWtxS3VRSddGqMVGpkmxAEuD3zOq4b5jK3mmv05o+Ty03tLARFQCIxyKGJz9I2S8zOfxfLenA+a8VaoOVOMZF9mqX6ezi+SURNockRVls7V62/F2VznoHEFAezT7NUf/+eMa7j+HuLeM5GRmg5ZZabVzafzzuXR8BC7SJT+c8wkjxF2VpAQiGh5zG9Gut7itoUFNszDCaPMdnzSVYaV3H8NlEtjedbrLdvd3oJ5mvv8uTgb6LJUZYal/B2MOE+KnYVgl69xtx//LdExyaIHjxE5MAhjJFxjJFxQIRMH80G9uI85sxtzNmZ0CQqgj0Lxhd+LcvCTYsbF03M1u4CMGpkGRt4lugf+cz/t69SbXY0LyEI7iKeVhUDXUvQtnbfNQz3Pkk+e4Sl9XcoVjdvXi49iSxrFCqbld3TiVFqzQ+GiFLqpJtERscY+6f/YqvwkmVkTcdvhS+dpOkoiSTG0Aij//ifbTW5dfrarrsHNoEHI9lzgEzuEAs339jLVZBIj9Csbc7J/L/6/ZCI8Q6ECKuw3/VZeD7IYlt0l/ADAsfbMhduqc7y/3yN7IunSX/6BMnTBwhsF2uxRPPCDH67s7uVJZJPTjLwOy+FwVudgsL5L308LE9lu8z9m6+FlRc6sBcKVL93mdTTR0g9dRivaVL40zNUXr/wgZKYb7nGwGfp/KsYiRzDT32R/JFnsJtlZEUjku5DkmWuvfxfcX2XdnkZq7pGcugwh176BzjNEpF0P4oWwaytEcsObhk70TdBJN2LGomTHJxEMaL0HHycSCqP75g012awO6bWeO8Y0cwAihEjOXAQRYuQPXAaPZ7Bt02a67PYja278/ryddx2nczYSarzl7Bq+y8+/WGiWpvZEIAArtfG89poWuyengFCdOgQO++M47YQwkdRItyLam12QwACeL6F57aJJ7ZrZkL4aHqCo1O/huu2mZl7E9Pcm+bi+CZXC28wnn6CyZ5PdYg6mqw2r3e0K9ExYXaMiMLf8B8HgYfoBJ9cKby+fYzG9Q2N7Y4P8V7htgmJprPORPZZXoj/IwQCN7BYbV5nvvoT3MCibC5yvfgdxjNPMpw6SSB8GnaBWm25Mw+hRnj3Oy6E2NLWcitcLnyLA+mnOJJ7HllSML0qM5UfbzmbmrVKyy2hyCpl88MhAn5gioRXKdOolGlcDH0YWrYHY3yC6PgBjKERlHiC6KHDxI4eR1JU/EYDa2GW8rdfxS0/+AGw2wGqJqEbUhgvEYgdybIVWSObmsByaizcPEfbKiMhoWtxJEkmkDUcv4UsKaTiw2RTB1hcf2fjAde1RGiHFgGO10YIn+XiBWRJ2RLWrCoGLbOA54cLrISMpkY5NPwCl2e+SRB4+L6Noui4ngUINDWGHzh7jmKTOg+CWy7TvnFtx/xBr14LTaR3+lbKmDev75hu4TUa+/ZhyYqOqkWQkAhEgGs3kSUZVY+iR9Lhb9rNzosdDxO7Ax/XCRcaI5pmdPIFbl/+M3zPwvdsZF9BkYywMr3b3pY/5lWaLP6nb+54PoWv/4DC13+wrd1vmBS/+SOK3/zR/S8mEFTeuEjljYtIsoKqxXB3MJltgSTRvjJP5Vvv7tYJXTLwhIsqabjCRpei2MJElwxklJAyLwgIGg1ky8WQwkXXFx4eDroURUYOFxNhEVht5t/8KvnJj5MZOU48NYjjtmiuTVNduIJrhwtu4FrM/fBP6D/+aeJ9E+jRJO3yEutXzxDN9NN3/FNbzjR78HF6hk6gSTqucHDbdeL5MeL5MRCCZfHtDSGYGT9Fz+jpDbo/p10j1jNMrCeswLHy/hvbhKDwfcozFxg4+QLN9dkHBgH9tOHswGUrAoEkb23T9RT9vSfJZg+ha0kURUNRI+h6Yudx3e0mXyGCHckygsDj2JFfJ2JkqVSmsazq5pcSxOLhybSbm++qLEMqqyIrUK+Y3Cy/zc3y9mC+qr3M2/O/B0DZXNgiEM4u/eHm+frt+44BYPtNzi1/bcfvAIaTJ+iNT3Kt+B1MtwqSRFLv5UD6SerW6kaaRMmcp2TuXHVkpXmVlebVLW0lc47S0t3kDoKGvc77639x33OBMFgtEAE1axXL+3DcFPumLHUrZdxKmeaF80hGBL1/gMjIGJGRUYyhUbRcjmTPE9R+dGZPQrDd8Hni+SQDYzqeI6iVfS6eufeBlohF8mSTYyiyjiJrzK+eRZJk+nMn0dQYuhbn1sLrqEqEfOYwydgAfuCyWryI5TTo7zmOriVQlSgrxQvUW0vsZF5JxgYZ6nuCan2OpcJ5FMUgl5kkHs0zlH+MtlWi3lpmIPcYy4XzuL7F+OBzrBQv0jL3RmUVWFaYeynLVL//nVDg3a+v4+DXa8hGhOqZ731AATAS2d7D5AZPYjaL6EaSxdtvgSQRT/bTP/okkViOlbmz2O0yI5OfwfcdZEmmvH4Nq10mN3CCeGqAvpEnqJWmaTdWGTrwyU6en0S7sUZx9f0PJXpwN0RiPQxP/gK3LvwJj0odrEk6E/opCt4Sg+oBpp33OWw8wS37XUa0IwSEgQJL5k3ar34LRYJJ42MEwqMZ1Fhyb9GvjmFIMTRJZ8WbwRMOR9SnqNxew5tuUPMLrHozO+Y9ue06i++8sq3drCxTntkqvBfOfoOWepYRfYqit8Syc3+z1NI7r+BfvMqaO7v3IANJQo9nsBslmmv7qCSyT+haAoTA8fbnb7y/ZrN17KnDf4Nkcoi1tYusNH6C67aIRfMcnPjcQ497B6pq4Hpt1ouXyOWmaDSXWS9cCjeSmsTHXwxLV731Z9WNY4yozCe/mGZkwuDP/3eR5dm/uiBASVJIGr2dqM1VXN9CkhQ0OYIbWFsKEXzYUGUdRdLpiY1iqAlWmlc+vN/aa0dJVVESKdR0OvyXyqCm0ijJJEoiiRKPI0ciCN/Ha9T3zI5SLnjM33hQMrmg0V6hULmOohislS4RCA9ViWLZVSy7yujAMyiyjmmXWStfxg8cZpe/H567JGM5dWy3QS59GENPwn3esUpjNgyz7miHnm+yUnyP0f6nmemMF7ZbRIwsitcCIWiZexdOXquJtTBP5pPPEzt6nPr5c1vMx2omg99qd9JP2lgL86G2PXWM+rmzm3UdATWdxjcthLP/ICKrVWLh5huMHflF4sl+/MDDNmss3PwOgweexYik0Y0kQvjMX/8W2d4p4slBWvUVVufPke09wuKt0N+SzI6hGjGmL79MJNZD79Bj6EYK29zdHyJJConMMNFkX5h8vXyJwHfJ9h/F9xz0SJJq4SYgEU8PUlm7RjTRhywrtBtrJHvGicRyCOHTqIQ7U0XRyQ8/hiwrtGortOrLu57D/SBEgCtsolIMWVKIyylM0SSt5DFFkwX3GqPaUdJKHhBUvAJROYEjTKJSEgUFW5g4wqJHGSQixWl2clNnnEtklF4yci8KKh6PboIteyvE5NSWtqTSQ1LuQQB1v4gZNEgrvYwZx1FQsYVJ0XuwmSmeHyU9cozq3HuY1a1RoZoaDa0gUkhF6Pk2mhpFVQwspxZSFMoyvu+gqVF830WWFVQlShC4OF4LCRldj9OTOojjtijXpzfuwR2iwkdNkE6lRkglh1kvvM/tmb/caFfVe02mDwch4MatV0AIJg9+npHhT2BZFWr1cH5lBcaPRFDVDJWSx4W3m5itgPPfbaCpm5qlosLpTyTI9WtUix6X32nx1AtJzr1e5+SzCdYWHDJ5lWrR48BUBFmRUFS4cbHN0vTDC1EhfErtOYZTpziYfTbUeCUJVTao2SvUrN39sh8UZEllIDFFT2SUiJZirnqeuv3hmd93ryKh66Se+Dharhc1m0WJxcO8wWgUORpFVjUC18UtFXHWVnHWV3GLBbxGA6ewN61o9prF7LWHYVSRSCdHiMf6KNduA3cRsgo28lUAErF+culDLBcuhAmf99pI9vJr9zBBFKs36MudQPgea+Ur7Ovh8H2aly4SGRkj+/yLRMYnNth11HQGPd9L4c+/gbO2AoFP6+olIqNjZJ57nsjIWJiDKQRqOoOW76X02ssbkalKPIHeP4CsGxijY0iygt4/SPz4SQLbxi2X8KrVMAdUVsN5kzvMOUJ0zHEhVRqSROB7yEqYSiDJSnidAoR053OIwPc6eVd3SlDJe9pFG9E0uaFTVNavIwKfIPCQFI3c4AnKq1cxW0UC3yUSz5Ptm6Kydo1Ysg9FNTCbRYYOfori0nvYZgXfs1G1KHokhWPWMGIZMn1HOmPsf3EICHACk5icpOGXyalDVP3wPsmEz5CCgkvQCfb2w3/CR5IkkkqWvDLEknuLQPE7fLXgCJM74e53cq8+DOhSlAHtIGVvBQmJAW2CRec6jjCRkDCDBo64/+Ypmh0kNXQY1YiRGj6Gb7cpXD+7RbuXJIX+npNh9KMULmBNs0A+cxjLrhKP9mE7NXQtTqU+R29minprhUxyFN93iEXzzK2cIWpkyKYOoGuJDbdCGBTTQtdiqFp0W87fvtFZH9y72I9UNUI+N4WqGvc7al/wfQfPs5hffJvDh77IxIGXuHLt6wia6Eb4zKzMO/zil7Mszziszm9/LsenInzs0wnOfqvO8afiOE7A0y+luPZum099Mc3FHzTJDWgs3LL44t/N8fv/YZXxqQgnn06wvlTBtR9eUJXMOUyvhqGGm7iAAC+wabuVfeXnRYcO4LXquLXQBK/EEkTyg5hriwS2iWxEkFUdr1VH7+nFq9c2+HVFx/xpey3cwNpZAEoSkd4hrPXtRaP3iwfkCUbp+ewvI2taWG0SEJaFtbyIvbSAvbiAU1gjsEwCx0V4bpgysY8oxWc/m2Jl3mbuuk0kJvO538ryZ/9rNzOq2Pjf8yzS8WE0NYoQ3obz1/VNIkaa4xO/yszyWzhum5iRYzD/WOi/8x1kWeXg8AtkEqN4fujbK9VuMz7wCbKpiY6vzGe9fAU/cKg15zk1+WXKtdssFX6CaVeRBKhanEZ7h3ypB8yBVylTePkbJB9/ksSpx4kdPgqBj1evY87exm/eRVlWq1J89c9JPvYxEqc/RnTySFiQtVELg5Eam+bU6MFJ8l/8VZBlJFXdSLGITBwEIaj/+CyV774OQDw1yOHH/iaB79KqrxJN9G7bbTcq86RzExz52FfwPYu1+R/j+zaSrGKbNaY+9hXWFs5TK01jNtc58tiXEcKnvHZ1C+3a/aBFkuFDX7i9cW8VWcX3bBqVhZ01ybuCgFZnz9LTfwzX6WVtPnSq21aNemWOmNvGiGaRFW1DCAaWy/offR9ZUzciS++HAB8bk5ScY8WZ4YT2HPPuVQIRcEA/wanIp3GExYozQ0LuRGzeNX2OsIjJSQa1Q2iSgSecn2ptt6icIBA+VW8NSZJJK3l0KUojKOMJh5pfwOf+fmwjmaN36jlkVaNdWWH5J69hVrcmN0udRHJJkja0tlx6kkZ7BcuukkmOI8tKaFmRJAw9hWqVQ820vULESBPR0yRjgzRaqxh6Ej8IteIgcCmVrzMy/BxHJr9Eq72OLKuUSjeo1fdPHt5oLGE7DYYHn0aSFALfJZMZD//+QM32gmZzlYWlM0we/DyTB7/Aremv49gBC7dtrrzT5qkXkgwe0HcUghNHo6wuOFx5p006pzI8YbA4bXP4VIzAE/T0qVgtH9sUlNZcrp5vI8lw6EQUTZceSQgGwqfplGg6+09F0DJ5UodPYReWUWNJ4iOHEIFP/dq7BIGPGksgKypCUckcexJzfQkR+GRPPoPXbtKav4lvmaSOnMYurVFeXSB56BjZyCTt5TkkWSY+Oklz7gbCc8k/8xL16+9hrswRGRjByOSp37qEU9lfhZUHmEMFXqOGvbiAvTCHtTCHU1jvMGo8ODn4QdB0iUyvSrvpE0+5JNIKI5P335GtV+6wt4Q3udZc4OLNr3U+Sxthz22rxOXpb4Q9O+f47o0/3DjuTsLp7cU3ubMLvxOSPrv8A2ZXzmxpA7g++1qHpip80RVZQ9NirJevwr1LmxDM/bt/HQqUXQJW/Ead6tvfpXrmextarLhTD++e4/xmg+qZ71M9+4Nd+zYvXaR19f71/kQQQCAQgU+1eIvlmTOEWkkYHNMozwJsCBQQzF3/dicQQGzMpwg8bl784868h/O0NPM2UkdDunvudoPvWqhqBM2IE/gufsfUG0aTbS5Mvmej6TEUNYIRzYTXATRrS7RqK+SGTpDtm6JengkDcsTmZmmLniUEfq295wqQBW+RoreEQHCu/dqGD+2G/Q53/CMCwW3nwl1anaDgLyIQnDc3I23vHHvZCp+vWlCgHhQf2cx3BwpqGOglJGQUHGGiyxE0yUCSZDTZwCXc8AGokh7O831mo7pwmdri5ju3k39XIHC9NpKsIAsVx22GZOySEs6FoFO02QjfGTVkLgkCF8+38YOwyLUg2Njc3LlfQeCxuBwGRPXmT5BOjWI5dcrl2xu/Hwgf1zN3DErzfGuLz9O261y9/qdMjH+G/r7TBIFHuXyDxaUfcWzq1zvlxDrXJQI839rStjmuvUWb3Lgez9yYW0FAoXiFaCzPyODTDA09jW7cIJFRiCVlcoMapVUXSYZIVELTJYyIjKzA6oLD0y8liSVl8p1+S9M2n/tKD+98p87Y4Qitho/VDrCtzvvYWQr+qko1SopKcuIotSvniY0cREv30F6exS6tkp76GNUr72xEswrfw1xdQE2mMZdnceoVmtNX8NpNsqeewTObGL2D+LZJpG+ItbdeRgiBEokhaQax4Qkq753FqRRp3HqfSP8IWjyNXVkn+9hzrH135+C7+2FXIejX6yz853//8DOzC3RD4uMvJnn+V9IgJFoNH0mC793lNN6O7YvF3fk+W9uDB/bbyVwn2Jky6+72qNHDUP4xHLdFvbmzOr7nHCohwPf3tgzupa8QeyAwkPBcE9uq3zMvAnGX8NhsDnb8zW3zJwRinwWGrXaZWnmGsaOfBSQWb7yJ55pYZmXLomu2iljtCuPHP0/gOdRKYTDJ2NTnkCQJ12lTXLpI4HtYrdAE4/sujlUneMTNmrhrYdvaJrb12VwEtx+zve/Wvx8FEjIj+lFSSi7c0Gg26+48BXeBichpBIKCO48VhNrvinObyegTNP0Kc/blnQcVYoNY+X4QIqDWWETXEiiyRtsu02yvMz74HPFoH832GrXmAqP9zzCUfxw/8PB8G8dthyQcbhPPt6k1lxjKP4ZAUKnPbkyt67aYmXuTmbk3d/z9en2Bc+/8lx2/e/fi725ra7VWuXTlq9v7vvd7Wz43myucf/d/7DjuTsdPz77O9Ozr97QK5uffYn7+LVRNomc5Tv9InL/3zwe4fclkecYh16/x4m9myfZqJHsU/vKrZW5ebHNgyuB3/uUAq4sOl3/cwrUFv/x3JN470yKWUCmueDSqHmsLoSZptQKqRe9RUrYfHSJAiUSRFCWMbJcVFCPM9ZT1CEokimxEkBwLJRJFMSJIiorwXJRonMCxNyJvzeU5vGYNr91CBAFqLEHqyClEEHSC7wTC91Bi8TDEFgHI1K7sXCB9N0i7VZaWJOlDt96ceDpOec1ldb5jKvr5yffuoouPOLZGEn4QwS1d/OxC7+klOjCG16yBACUaR5IVrMIykqoR7R/BKq7g1srERg8hSTKtuRso0ThGvh9rLbS4xAbHcWplnGqR6MAYzekryHqoAcqaQeB7tGavExs+iKTIWIUVIr2DSIqGU1nHLm3nIxVC3FdH/isXgkZEwvPA97ovRxdddNHFRxIPXRHl7o3W/dM3fqaFYBdddNFFF118mNhNCO4/V6CLLrrooosufk6wqybYRRdddNFFFz/P6GqCXXTRRRddfGTRFYJddNFFF118ZNEVgl100UUXXXxk0RWCXXTRRRddfGTRFYJddNFFF118ZNEVgl100UUXXXxk8f8BlWHd3sDlPlEAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "wordcloud(data_non_disaster,\"Non-Disaster Tweets\")" ] }, { "cell_type": "markdown", "id": "liberal-bottom", "metadata": { "papermill": { "duration": 0.189591, "end_time": "2021-06-18T10:29:17.674899", "exception": false, "start_time": "2021-06-18T10:29:17.485308", "status": "completed" }, "tags": [] }, "source": [ "## 4.4 Removing unwanted words with high frequency" ] }, { "cell_type": "markdown", "id": "verbal-beach", "metadata": { "papermill": { "duration": 0.189354, "end_time": "2021-06-18T10:29:18.052347", "exception": false, "start_time": "2021-06-18T10:29:17.862993", "status": "completed" }, "tags": [] }, "source": [ "Our cleaned text still contains some unnecessary words (such as: like, amp, get, would etc.) that aren't relevant and can confuse our model, \n", "resulting in false prediction. Now, we will further remove some words with high frequency from text based on above charts." ] }, { "cell_type": "code", "execution_count": 17, "id": "sorted-bridal", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:18.464941Z", "iopub.status.busy": "2021-06-18T10:29:18.450651Z", "iopub.status.idle": "2021-06-18T10:29:18.479478Z", "shell.execute_reply": "2021-06-18T10:29:18.479047Z" }, "id": "-_-USeyzNoPr", "papermill": { "duration": 0.236668, "end_time": "2021-06-18T10:29:18.479600", "exception": false, "start_time": "2021-06-18T10:29:18.242932", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "common_words = ['via','like','build','get','would','one','two','feel',\n", " 'lol','fuck','take','way','may','first','latest','want',\n", " 'make','back','see','know','let','look','come','got',\n", " 'still','say','think','great','pleas','amp']\n", "\n", "def text_cleaning(data):\n", " return ' '.join(i for i in data.split() if i not in common_words)\n", "\n", "data[\"Cleaned_text\"] = data[\"Cleaned_text\"].apply(text_cleaning)" ] }, { "cell_type": "markdown", "id": "referenced-glenn", "metadata": { "papermill": { "duration": 0.188943, "end_time": "2021-06-18T10:29:18.856993", "exception": false, "start_time": "2021-06-18T10:29:18.668050", "status": "completed" }, "tags": [] }, "source": [ "## 4.5 Analysing top 10 N-grams where N is 1,2,3" ] }, { "cell_type": "markdown", "id": "invisible-competition", "metadata": { "papermill": { "duration": 0.191129, "end_time": "2021-06-18T10:29:19.237001", "exception": false, "start_time": "2021-06-18T10:29:19.045872", "status": "completed" }, "tags": [] }, "source": [ "### What do you mean by N-grams? \n", "N-grams of texts are extensively used in text mining and natural language processing tasks. They are basically a set of co-occurring words within a given window and when computing the n-grams you typically move one word forward (although you can move X words forward in more advanced scenarios). \n", "\n", "For example, for the sentence “The cow jumps over the moon”. \n", "If N=2 (known as bigrams), then the ngrams would be: \n", "* the cow \n", "* cow jumps \n", "* jumps over \n", "* over the \n", "* the moon\n", "\n", "Below we perform [N-grams](https://en.wikipedia.org/wiki/N-gram#:~:text=In%20the%20fields%20of%20computational,a%20text%20or%20speech%20corpus.) analysis on cleaned data" ] }, { "cell_type": "code", "execution_count": 18, "id": "invalid-hunter", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:19.625084Z", "iopub.status.busy": "2021-06-18T10:29:19.624257Z", "iopub.status.idle": "2021-06-18T10:29:19.626763Z", "shell.execute_reply": "2021-06-18T10:29:19.626324Z" }, "id": "7FrdJlJNQGCa", "papermill": { "duration": 0.199583, "end_time": "2021-06-18T10:29:19.626874", "exception": false, "start_time": "2021-06-18T10:29:19.427291", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def top_ngrams(data,n,grams):\n", " count_vec = CountVectorizer(ngram_range=(grams,grams)).fit(data)\n", " bow = count_vec.transform(data)\n", " add_words = bow.sum(axis=0)\n", " word_freq = [(word, add_words[0, idx]) for word, idx in count_vec.vocabulary_.items()]\n", " word_freq = sorted(word_freq, key = lambda x: x[1], reverse=True) \n", " return word_freq[:n]" ] }, { "cell_type": "markdown", "id": "heavy-suspect", "metadata": { "papermill": { "duration": 0.197345, "end_time": "2021-06-18T10:29:20.013201", "exception": false, "start_time": "2021-06-18T10:29:19.815856", "status": "completed" }, "tags": [] }, "source": [ "Creating data of top 10 n-grams for n = 1, 2, 3" ] }, { "cell_type": "code", "execution_count": 19, "id": "bacterial-details", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:20.441972Z", "iopub.status.busy": "2021-06-18T10:29:20.431819Z", "iopub.status.idle": "2021-06-18T10:29:21.590481Z", "shell.execute_reply": "2021-06-18T10:29:21.590970Z" }, "id": "BmAP6cJhXQGU", "papermill": { "duration": 1.378022, "end_time": "2021-06-18T10:29:21.591128", "exception": false, "start_time": "2021-06-18T10:29:20.213106", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "common_uni = top_ngrams(data[\"Cleaned_text\"],10,1)\n", "common_bi = top_ngrams(data[\"Cleaned_text\"],10,2)\n", "common_tri = top_ngrams(data[\"Cleaned_text\"],10,3)\n", "common_uni_df = pd.DataFrame(common_uni,columns=['word','freq'])\n", "common_bi_df = pd.DataFrame(common_bi,columns=['word','freq'])\n", "common_tri_df = pd.DataFrame(common_tri,columns=['word','freq'])" ] }, { "cell_type": "markdown", "id": "quantitative-listening", "metadata": { "papermill": { "duration": 0.193547, "end_time": "2021-06-18T10:29:21.978582", "exception": false, "start_time": "2021-06-18T10:29:21.785035", "status": "completed" }, "tags": [] }, "source": [ "## 4.6 Visualising top 10 N-grams for N = 1, 2, 3" ] }, { "cell_type": "code", "execution_count": 20, "id": "dimensional-booth", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:22.416855Z", "iopub.status.busy": "2021-06-18T10:29:22.415780Z", "iopub.status.idle": "2021-06-18T10:29:22.430063Z", "shell.execute_reply": "2021-06-18T10:29:22.430643Z" }, "id": "OzX7m9cHWg4c", "papermill": { "duration": 0.256085, "end_time": "2021-06-18T10:29:22.430812", "exception": false, "start_time": "2021-06-18T10:29:22.174727", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": "rgba(255, 170, 59,0.8)", "line": { "color": "black", "width": 1.2 } }, "type": "bar", "x": [ "fire", "new", "news", "people", "time", "year", "video", "disaster", "emergency", "body" ], "xaxis": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = make_subplots(rows=3, cols=1,subplot_titles=(\"Top 20 Unigrams in Text\",\n", " \"Top 20 Bigrams in Text\",\"Top 20 Trigrams in Text\"))\n", " \n", "fig.add_trace(\n", " \n", " go.Bar(x=common_uni_df[\"word\"],y=common_uni_df[\"freq\"],\n", " marker=dict(color= 'rgba(255, 170, 59,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=1, col=1\n", "\n", ").add_trace(\n", " \n", " go.Bar(x=common_bi_df[\"word\"],y=common_bi_df[\"freq\"],\n", " marker=dict(color= 'rgba(89, 255, 147,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=2, col=1\n", "\n", ").add_trace(\n", " \n", " go.Bar(x=common_tri_df[\"word\"],y=common_tri_df[\"freq\"],\n", " marker=dict(color= 'rgba(89, 153, 255,0.8)'),\n", " marker_line=dict(color='black'),marker_line_width=1.2),\n", " row=3, col=1\n", " \n", ").update_layout(title_text=\"Visualization of Top 20 Unigrams, Bigrams and Trigrams\",\n", " title_x=0.5,showlegend=False,\n", " width=800,height=1600,).update_xaxes(tickangle=-90).show()\n", "\n", "# py.plot(fig,filename='Visualization of Top 20 Unigrams, Bigrams and Trigrams',auto_open=False,show_link=False)" ] }, { "cell_type": "markdown", "id": "apart-arrangement", "metadata": { "_kg_hide-input": true, "id": "GvvSq0MZq98J", "papermill": { "duration": 0.198583, "end_time": "2021-06-18T10:29:22.831888", "exception": false, "start_time": "2021-06-18T10:29:22.633305", "status": "completed" }, "tags": [] }, "source": [ "
\n", " \"Visualization\n", " \n", "
\n" ] }, { "cell_type": "markdown", "id": "renewable-ballet", "metadata": { "papermill": { "duration": 0.201493, "end_time": "2021-06-18T10:29:23.233580", "exception": false, "start_time": "2021-06-18T10:29:23.032087", "status": "completed" }, "tags": [] }, "source": [ "# 5. Data Preprocessing and Creating Word Embedding Matrix" ] }, { "cell_type": "markdown", "id": "arctic-occasions", "metadata": { "papermill": { "duration": 0.200087, "end_time": "2021-06-18T10:29:23.636659", "exception": false, "start_time": "2021-06-18T10:29:23.436572", "status": "completed" }, "tags": [] }, "source": [ "## 5.1 Spliting original data after cleaning " ] }, { "cell_type": "code", "execution_count": 21, "id": "animated-accounting", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:24.048720Z", "iopub.status.busy": "2021-06-18T10:29:24.048117Z", "iopub.status.idle": "2021-06-18T10:29:24.052045Z", "shell.execute_reply": "2021-06-18T10:29:24.051447Z" }, "papermill": { "duration": 0.216324, "end_time": "2021-06-18T10:29:24.052197", "exception": false, "start_time": "2021-06-18T10:29:23.835873", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "#original data after cleaning \n", "X_inp_clean = data['Cleaned_text']\n", "X_inp_original = data['text']\n", "y_inp = data['target']" ] }, { "cell_type": "markdown", "id": "charitable-switch", "metadata": { "papermill": { "duration": 0.200349, "end_time": "2021-06-18T10:29:24.453255", "exception": false, "start_time": "2021-06-18T10:29:24.252906", "status": "completed" }, "tags": [] }, "source": [ "## 5.2 Data preprocessing and creating padded sentences" ] }, { "cell_type": "markdown", "id": "dependent-rental", "metadata": { "papermill": { "duration": 0.200534, "end_time": "2021-06-18T10:29:24.853890", "exception": false, "start_time": "2021-06-18T10:29:24.653356", "status": "completed" }, "tags": [] }, "source": [ "### What is Tokenization \n", "\n", "Tokenization is essentially splitting a phrase, sentence, paragraph, or an entire text document into smaller units, \n", "such as individual words or terms. Each of these smaller units are called tokens. \n", "\n", "The tokens could be words, numbers or punctuation marks. In tokenization, smaller units are created by \n", "locating word boundaries, which are the ending point of a word and the beginning of the next word. \n", "\n", "Now we use [keras](https://www.tensorflow.org/api_docs/python/tf/keras) text preprocessing [Tokenizer](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/Tokenizer) to fit on text on Cleaned data" ] }, { "cell_type": "code", "execution_count": 22, "id": "adjustable-squad", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:25.321109Z", "iopub.status.busy": "2021-06-18T10:29:25.310871Z", "iopub.status.idle": "2021-06-18T10:29:25.395557Z", "shell.execute_reply": "2021-06-18T10:29:25.395075Z" }, "papermill": { "duration": 0.33978, "end_time": "2021-06-18T10:29:25.395694", "exception": false, "start_time": "2021-06-18T10:29:25.055914", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "word_tokenizer = Tokenizer()\n", "word_tokenizer.fit_on_texts(X_inp_clean.values)\n", "vocab_length = len(word_tokenizer.word_index) + 1" ] }, { "cell_type": "markdown", "id": "distinguished-thousand", "metadata": { "papermill": { "duration": 0.236026, "end_time": "2021-06-18T10:29:25.833302", "exception": false, "start_time": "2021-06-18T10:29:25.597276", "status": "completed" }, "tags": [] }, "source": [ "Creating padded sentences using [pad_sequences](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/sequence/pad_sequences) function" ] }, { "cell_type": "code", "execution_count": 23, "id": "possible-bruce", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:26.236609Z", "iopub.status.busy": "2021-06-18T10:29:26.236021Z", "iopub.status.idle": "2021-06-18T10:29:27.394630Z", "shell.execute_reply": "2021-06-18T10:29:27.394180Z" }, "papermill": { "duration": 1.360243, "end_time": "2021-06-18T10:29:27.394771", "exception": false, "start_time": "2021-06-18T10:29:26.034528", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def embed(corpus): \n", " return word_tokenizer.texts_to_sequences(corpus)\n", "\n", "longest_train = max(X_inp_clean.values, key=lambda sentence: len(word_tokenize(sentence)))\n", "\n", "length_long_sentence = len(word_tokenize(longest_train))\n", "\n", "padded_sentences = pad_sequences(embed(X_inp_clean.values), \n", " length_long_sentence, padding='post')" ] }, { "cell_type": "markdown", "id": "informative-pierce", "metadata": { "papermill": { "duration": 0.19448, "end_time": "2021-06-18T10:29:27.784400", "exception": false, "start_time": "2021-06-18T10:29:27.589920", "status": "completed" }, "tags": [] }, "source": [ "## 5.2 Using Glove word embeddings for creating Embedding Matrix" ] }, { "cell_type": "markdown", "id": "oriental-robertson", "metadata": { "papermill": { "duration": 0.194924, "end_time": "2021-06-18T10:29:28.175150", "exception": false, "start_time": "2021-06-18T10:29:27.980226", "status": "completed" }, "tags": [] }, "source": [ "### Why Glove?\n", "\n", "The advantage of GloVe is that, unlike Word2vec, GloVe does not rely just on local statistics (local context information of words), \n", "but incorporates global statistics (word co-occurrence) to obtain word vectors\n", "\n", "Creating Embedding dictionary using [Glove Twitter data](https://www.kaggle.com/danielwillgeorge/glove6b100dtxt)" ] }, { "cell_type": "code", "execution_count": 24, "id": "retired-brazil", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:28.572868Z", "iopub.status.busy": "2021-06-18T10:29:28.572353Z", "iopub.status.idle": "2021-06-18T10:29:44.024213Z", "shell.execute_reply": "2021-06-18T10:29:44.025602Z" }, "papermill": { "duration": 15.654732, "end_time": "2021-06-18T10:29:44.025832", "exception": false, "start_time": "2021-06-18T10:29:28.371100", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "embeddings_dictionary = dict()\n", "\n", "embedding_dim = 100\n", "\n", "glove_file = open('../input/glove6b100dtxt/glove.6B.100d.txt')\n", "\n", "for line in glove_file:\n", " records = line.split()\n", " word = records[0]\n", " vector_dimensions = np.asarray(records[1:], dtype='float32')\n", " embeddings_dictionary [word] = vector_dimensions\n", "\n", "glove_file.close()" ] }, { "cell_type": "markdown", "id": "suitable-correction", "metadata": { "papermill": { "duration": 0.322823, "end_time": "2021-06-18T10:29:44.669712", "exception": false, "start_time": "2021-06-18T10:29:44.346889", "status": "completed" }, "tags": [] }, "source": [ "Creating Word Embedding matrix which is a list of all words and their corresponding embeddings." ] }, { "cell_type": "code", "execution_count": 25, "id": "sixth-portable", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:45.106439Z", "iopub.status.busy": "2021-06-18T10:29:45.105593Z", "iopub.status.idle": "2021-06-18T10:29:45.134510Z", "shell.execute_reply": "2021-06-18T10:29:45.134038Z" }, "papermill": { "duration": 0.234405, "end_time": "2021-06-18T10:29:45.134628", "exception": false, "start_time": "2021-06-18T10:29:44.900223", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "embedding_matrix = np.zeros((vocab_length, embedding_dim))\n", "\n", "for word, index in word_tokenizer.word_index.items():\n", " \n", " embedding_vector = embeddings_dictionary.get(word)\n", " \n", " if embedding_vector is not None:\n", " embedding_matrix[index] = embedding_vector" ] }, { "cell_type": "markdown", "id": "russian-potter", "metadata": { "papermill": { "duration": 0.195515, "end_time": "2021-06-18T10:29:45.525441", "exception": false, "start_time": "2021-06-18T10:29:45.329926", "status": "completed" }, "tags": [] }, "source": [ "## 5.3 Spliting data into training and validation dataset" ] }, { "cell_type": "markdown", "id": "worse-membership", "metadata": { "papermill": { "duration": 0.193904, "end_time": "2021-06-18T10:29:45.915603", "exception": false, "start_time": "2021-06-18T10:29:45.721699", "status": "completed" }, "tags": [] }, "source": [ "Using [scikit-learn's train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) to split the data into training and validation dataset" ] }, { "cell_type": "code", "execution_count": 26, "id": "suburban-harmony", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:46.312488Z", "iopub.status.busy": "2021-06-18T10:29:46.311298Z", "iopub.status.idle": "2021-06-18T10:29:46.315410Z", "shell.execute_reply": "2021-06-18T10:29:46.314948Z" }, "papermill": { "duration": 0.204052, "end_time": "2021-06-18T10:29:46.315524", "exception": false, "start_time": "2021-06-18T10:29:46.111472", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "X_train, X_val, y_train, y_val = train_test_split(padded_sentences,\n", " y_inp.values,test_size=0.2,random_state=1)" ] }, { "cell_type": "markdown", "id": "genetic-tunisia", "metadata": { "papermill": { "duration": 0.194737, "end_time": "2021-06-18T10:29:46.705566", "exception": false, "start_time": "2021-06-18T10:29:46.510829", "status": "completed" }, "tags": [] }, "source": [ "# 6. Training and Tuning Deep Learning Models" ] }, { "cell_type": "markdown", "id": "improving-active", "metadata": { "papermill": { "duration": 0.195809, "end_time": "2021-06-18T10:29:47.095993", "exception": false, "start_time": "2021-06-18T10:29:46.900184", "status": "completed" }, "tags": [] }, "source": [ "Function to summarise history of train our model" ] }, { "cell_type": "code", "execution_count": 27, "id": "ordinary-december", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:29:47.495273Z", "iopub.status.busy": "2021-06-18T10:29:47.494550Z", "iopub.status.idle": "2021-06-18T10:29:47.498267Z", "shell.execute_reply": "2021-06-18T10:29:47.497642Z" }, "papermill": { "duration": 0.206718, "end_time": "2021-06-18T10:29:47.498409", "exception": false, "start_time": "2021-06-18T10:29:47.291691", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def model_history(model_history):\n", " fig,(ax1,ax2) = plt.subplots(1,2,figsize=(12,5))\n", " \n", " # summarize history for accuracy\n", " ax1.plot(model_history.history['accuracy'])\n", " ax1.plot(model_history.history['val_accuracy'])\n", " ax1.set_title('model accuracy')\n", " ax1.set_ylabel('accuracy')\n", " ax1.set_xlabel('epoch')\n", " ax1.legend(['train', 'test'], loc='upper right')\n", "\n", " # summarize history for loss\n", " ax2.plot(model_history.history['loss'])\n", " ax2.plot(model_history.history['val_loss'])\n", " ax2.set_title('model loss')\n", " ax2.set_ylabel('loss')\n", " ax2.set_xlabel('epoch')\n", " ax2.legend(['train', 'test'], loc='upper right')\n", " \n", " fig.suptitle(\"Model History\")\n", " " ] }, { "cell_type": "markdown", "id": "tested-chemistry", "metadata": { "papermill": { "duration": 0.194334, "end_time": "2021-06-18T10:29:47.923961", "exception": false, "start_time": "2021-06-18T10:29:47.729627", "status": "completed" }, "tags": [] }, "source": [ "## 6.1 Training and Tuning Convolutional Neural Network" ] }, { "cell_type": "markdown", "id": "descending-radar", "metadata": { "papermill": { "duration": 0.195205, "end_time": "2021-06-18T10:29:48.314584", "exception": false, "start_time": "2021-06-18T10:29:48.119379", "status": "completed" }, "tags": [] }, "source": [ "### What is a CNN? \n", "CNNs are basically just several layers of convolutions with nonlinear activation functions like ReLU or tanh applied to the results. \n", "In a traditional feedforward neural network we connect each input neuron to each output neuron in the next layer. \n", "That’s also called a fully connected layer, or affine layer. In CNNs we don’t do that. Instead, we use convolutions over the input layer to compute the output. \n", "This results in local connections, where each region of the input is connected to a neuron in the output. Each layer applies different filters, \n", "typically hundreds or thousands like the ones showed above, and combines their results.\n", "\n", "Function for creating [Convolutional Neural Network](https://www.tensorflow.org/tutorials/images/cnn)" ] }, { "cell_type": "code", "execution_count": 28, "id": "hybrid-superior", "metadata": { "_kg_hide-input": false, "execution": { "iopub.execute_input": "2021-06-18T10:29:48.713977Z", "iopub.status.busy": "2021-06-18T10:29:48.713317Z", "iopub.status.idle": "2021-06-18T10:29:48.716911Z", "shell.execute_reply": "2021-06-18T10:29:48.716496Z" }, "papermill": { "duration": 0.206959, "end_time": "2021-06-18T10:29:48.717027", "exception": false, "start_time": "2021-06-18T10:29:48.510068", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def CNN(hp):\n", " \n", " model = keras.Sequential()\n", " \n", " hp_learning_rate = hp.Choice('learning_rate', values=[3e-2, 3e-3, 3e-4, 3e-5])\n", " \n", " model.add(Embedding(vocab_length, 100, weights=[embedding_matrix],\n", " input_length=length_long_sentence,trainable=False))\n", " \n", " model.add(Conv1D(filters=hp.Int('conv_1_filter',min_value=21,max_value=200,step=14),\n", " kernel_size=hp.Choice('conv_1_kernel',values=[3,4,5]),\n", " activation='relu'))\n", " model.add(Dropout(0.3))\n", " \n", " model.add(MaxPooling1D(pool_size=2))\n", " \n", " model.add(Flatten())\n", "\n", " model.add(Dense(units = hp.Int('dense_1',min_value=21,max_value=150,step=14),\n", " activation='relu'))\n", " \n", " model.add(Dropout(0.5))\n", " \n", " model.add(Dense(1,activation='sigmoid'))\n", " \n", " model.compile(optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),\n", " loss=keras.losses.BinaryCrossentropy(from_logits=True),\n", " metrics=['accuracy'])\n", " \n", " return model" ] }, { "cell_type": "markdown", "id": "remarkable-pennsylvania", "metadata": { "papermill": { "duration": 0.195743, "end_time": "2021-06-18T10:29:49.107284", "exception": false, "start_time": "2021-06-18T10:29:48.911541", "status": "completed" }, "tags": [] }, "source": [ "Creating a tuner using Keras-Tuner(kt) for above function" ] }, { "cell_type": "code", "execution_count": 29, "id": "tropical-primary", "metadata": { "_kg_hide-input": false, "execution": { "iopub.execute_input": "2021-06-18T10:29:49.507935Z", "iopub.status.busy": "2021-06-18T10:29:49.507286Z", "iopub.status.idle": "2021-06-18T10:29:56.391901Z", "shell.execute_reply": "2021-06-18T10:29:56.392303Z" }, "papermill": { "duration": 7.088769, "end_time": "2021-06-18T10:29:56.392476", "exception": false, "start_time": "2021-06-18T10:29:49.303707", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "tuner_CNN = kt.Hyperband(CNN,objective='val_accuracy',\n", " max_epochs=15,factor=5,\n", " directory='my_dir',\n", " project_name='DisasterTweets_kt',\n", " overwrite=True)" ] }, { "cell_type": "markdown", "id": "willing-jerusalem", "metadata": { "papermill": { "duration": 0.19515, "end_time": "2021-06-18T10:29:56.784208", "exception": false, "start_time": "2021-06-18T10:29:56.589058", "status": "completed" }, "tags": [] }, "source": [ "Searching for a model with highest accuracy and saving it's [hyperparameters](https://deepai.org/machine-learning-glossary-and-terms/hyperparameter)" ] }, { "cell_type": "code", "execution_count": 30, "id": "lyric-valley", "metadata": { "_kg_hide-input": false, "execution": { "iopub.execute_input": "2021-06-18T10:29:57.186577Z", "iopub.status.busy": "2021-06-18T10:29:57.184343Z", "iopub.status.idle": "2021-06-18T10:31:19.921478Z", "shell.execute_reply": "2021-06-18T10:31:19.921927Z" }, "papermill": { "duration": 82.941234, "end_time": "2021-06-18T10:31:19.922088", "exception": false, "start_time": "2021-06-18T10:29:56.980854", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Trial 13 Complete [00h 00m 09s]\n", "val_accuracy: 0.8049901723861694\n", "\n", "Best val_accuracy So Far: 0.8049901723861694\n", "Total elapsed time: 00h 01m 22s\n" ] } ], "source": [ "stop_early = EarlyStopping(monitor='val_loss', mode='min',\n", " verbose=1, patience=10)\n", "\n", "tuner_CNN.search(X_train, y_train, epochs=15,\n", " validation_data=(X_val,y_val),callbacks=[stop_early])\n", "\n", "# Get the optimal hyperparameters\n", "best_hps_CNN=tuner_CNN.get_best_hyperparameters(num_trials=1)[0]" ] }, { "cell_type": "markdown", "id": "statistical-funeral", "metadata": { "papermill": { "duration": 0.198862, "end_time": "2021-06-18T10:31:20.318678", "exception": false, "start_time": "2021-06-18T10:31:20.119816", "status": "completed" }, "tags": [] }, "source": [ "Creating a model using the best hyperparameters and training it using [callbacks](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/Callback) to save the model at appropriate [epoch](https://radiopaedia.org/articles/epoch-machine-learning)" ] }, { "cell_type": "code", "execution_count": 31, "id": "beneficial-cotton", "metadata": { "_kg_hide-input": false, "_kg_hide-output": true, "execution": { "iopub.execute_input": "2021-06-18T10:31:20.720092Z", "iopub.status.busy": "2021-06-18T10:31:20.719509Z", "iopub.status.idle": "2021-06-18T10:31:31.487444Z", "shell.execute_reply": "2021-06-18T10:31:31.488054Z" }, "papermill": { "duration": 10.971578, "end_time": "2021-06-18T10:31:31.488260", "exception": false, "start_time": "2021-06-18T10:31:20.516682", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", "191/191 [==============================] - 2s 6ms/step - loss: 0.5657 - accuracy: 0.7100 - val_loss: 0.4635 - val_accuracy: 0.7945\n", "\n", "Epoch 00001: val_loss improved from inf to 0.46351, saving model to model_CNN.h5\n", "Epoch 2/50\n", "191/191 [==============================] - 1s 5ms/step - loss: 0.4212 - accuracy: 0.8125 - val_loss: 0.4672 - val_accuracy: 0.7833\n", "\n", "Epoch 00002: val_loss did not improve from 0.46351\n", "Epoch 3/50\n", "191/191 [==============================] - 1s 4ms/step - loss: 0.3900 - accuracy: 0.8281 - val_loss: 0.4582 - val_accuracy: 0.7984\n", "\n", "Epoch 00003: val_loss improved from 0.46351 to 0.45822, saving model to model_CNN.h5\n", "Epoch 4/50\n", "191/191 [==============================] - 1s 4ms/step - loss: 0.3389 - accuracy: 0.8519 - val_loss: 0.5009 - val_accuracy: 0.7807\n", "\n", "Epoch 00004: val_loss did not improve from 0.45822\n", "Epoch 5/50\n", "191/191 [==============================] - 1s 4ms/step - loss: 0.2959 - accuracy: 0.8659 - val_loss: 0.5139 - val_accuracy: 0.7846\n", "\n", "Epoch 00005: val_loss did not improve from 0.45822\n", "Epoch 6/50\n", "191/191 [==============================] - 1s 4ms/step - loss: 0.2701 - accuracy: 0.8861 - val_loss: 0.5672 - val_accuracy: 0.7774\n", "\n", "Epoch 00006: val_loss did not improve from 0.45822\n", "Epoch 7/50\n", "191/191 [==============================] - 1s 4ms/step - loss: 0.2389 - accuracy: 0.8980 - val_loss: 0.5923 - val_accuracy: 0.7761\n", "\n", "Epoch 00007: val_loss did not improve from 0.45822\n", "Epoch 8/50\n", "191/191 [==============================] - 1s 4ms/step - loss: 0.2202 - accuracy: 0.9079 - val_loss: 0.6320 - val_accuracy: 0.7787\n", "\n", "Epoch 00008: val_loss did not improve from 0.45822\n", "Epoch 9/50\n", "191/191 [==============================] - 1s 4ms/step - loss: 0.1926 - accuracy: 0.9253 - val_loss: 0.6512 - val_accuracy: 0.7899\n", "\n", "Epoch 00009: val_loss did not improve from 0.45822\n", "Epoch 10/50\n", "191/191 [==============================] - 1s 4ms/step - loss: 0.1784 - accuracy: 0.9315 - val_loss: 0.7298 - val_accuracy: 0.7702\n", "\n", "Epoch 00010: val_loss did not improve from 0.45822\n", "Epoch 11/50\n", "191/191 [==============================] - 1s 4ms/step - loss: 0.1607 - accuracy: 0.9341 - val_loss: 0.7093 - val_accuracy: 0.7814\n", "\n", "Epoch 00011: val_loss did not improve from 0.45822\n", "Epoch 12/50\n", "191/191 [==============================] - 1s 4ms/step - loss: 0.1595 - accuracy: 0.9356 - val_loss: 0.7720 - val_accuracy: 0.7866\n", "\n", "Epoch 00012: val_loss did not improve from 0.45822\n", "Epoch 13/50\n", "191/191 [==============================] - 1s 4ms/step - loss: 0.1583 - accuracy: 0.9387 - val_loss: 0.8076 - val_accuracy: 0.7781\n", "\n", "Epoch 00013: val_loss did not improve from 0.45822\n", "Epoch 00013: early stopping\n" ] } ], "source": [ "model_CNN = tuner_CNN.hypermodel.build(best_hps_CNN)\n", "\n", "checkpoint = ModelCheckpoint(\n", " 'model_CNN.h5', \n", " monitor = 'val_loss', \n", " verbose = 1, \n", " save_best_only = True\n", ")\n", "\n", "history_CNN = model_CNN.fit(X_train, y_train,epochs=50,\n", " validation_data=(X_val,y_val),\n", " callbacks=[checkpoint,stop_early])" ] }, { "cell_type": "markdown", "id": "exposed-blocking", "metadata": { "papermill": { "duration": 0.240982, "end_time": "2021-06-18T10:31:31.969133", "exception": false, "start_time": "2021-06-18T10:31:31.728151", "status": "completed" }, "tags": [] }, "source": [ "printing model's [summary](https://www.ibm.com/docs/SSLVMB_24.0.0/spss/tutorials/curveest_modelsummary_virus.html#:~:text=The%20model%20summary%20table%20reports,values%20of%20the%20dependent%20variable.)" ] }, { "cell_type": "code", "execution_count": 32, "id": "demographic-anger", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:31:32.453252Z", "iopub.status.busy": "2021-06-18T10:31:32.452708Z", "iopub.status.idle": "2021-06-18T10:31:32.460460Z", "shell.execute_reply": "2021-06-18T10:31:32.459860Z" }, "papermill": { "duration": 0.252393, "end_time": "2021-06-18T10:31:32.460614", "exception": false, "start_time": "2021-06-18T10:31:32.208221", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "embedding (Embedding) (None, 23, 100) 1484400 \n", "_________________________________________________________________\n", "conv1d (Conv1D) (None, 21, 77) 23177 \n", "_________________________________________________________________\n", "dropout (Dropout) (None, 21, 77) 0 \n", "_________________________________________________________________\n", "max_pooling1d (MaxPooling1D) (None, 10, 77) 0 \n", "_________________________________________________________________\n", "flatten (Flatten) (None, 770) 0 \n", "_________________________________________________________________\n", "dense (Dense) (None, 133) 102543 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 133) 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 1) 134 \n", "=================================================================\n", "Total params: 1,610,254\n", "Trainable params: 125,854\n", "Non-trainable params: 1,484,400\n", "_________________________________________________________________\n" ] } ], "source": [ "model_CNN.summary()" ] }, { "cell_type": "markdown", "id": "textile-house", "metadata": { "papermill": { "duration": 0.284712, "end_time": "2021-06-18T10:31:32.987476", "exception": false, "start_time": "2021-06-18T10:31:32.702764", "status": "completed" }, "tags": [] }, "source": [ "Ploting model's training and validation history" ] }, { "cell_type": "code", "execution_count": 33, "id": "average-microwave", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:31:33.653700Z", "iopub.status.busy": "2021-06-18T10:31:33.652821Z", "iopub.status.idle": "2021-06-18T10:31:33.921021Z", "shell.execute_reply": "2021-06-18T10:31:33.921429Z" }, "papermill": { "duration": 0.524631, "end_time": "2021-06-18T10:31:33.921574", "exception": false, "start_time": "2021-06-18T10:31:33.396943", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "model_history(history_CNN)" ] }, { "cell_type": "markdown", "id": "outer-criticism", "metadata": { "papermill": { "duration": 0.242148, "end_time": "2021-06-18T10:31:34.405658", "exception": false, "start_time": "2021-06-18T10:31:34.163510", "status": "completed" }, "tags": [] }, "source": [ "## 6.2 Training and Tuning Multi-Channel Convolutional Neural Network" ] }, { "cell_type": "markdown", "id": "fitting-beads", "metadata": { "papermill": { "duration": 0.241994, "end_time": "2021-06-18T10:31:34.889892", "exception": false, "start_time": "2021-06-18T10:31:34.647898", "status": "completed" }, "tags": [] }, "source": [ "### Why Mulichannel CNN? \n", "\n", "A multi-channel convolutional neural network for document classification involves using multiple versions of the standard model with different sized kernels. \n", "This allows the document to be processed at different resolutions or different n-grams (groups of words) at a time, whilst the model learns how to best integrate these interpretations.\n", "\n", "Function for creating [Multichannel CNN](https://machinelearningmastery.com/develop-n-gram-multichannel-convolutional-neural-network-sentiment-analysis/)" ] }, { "cell_type": "code", "execution_count": 34, "id": "amateur-indie", "metadata": { "_kg_hide-input": false, "execution": { "iopub.execute_input": "2021-06-18T10:31:35.385454Z", "iopub.status.busy": "2021-06-18T10:31:35.383717Z", "iopub.status.idle": "2021-06-18T10:31:35.386153Z", "shell.execute_reply": "2021-06-18T10:31:35.386565Z" }, "papermill": { "duration": 0.256381, "end_time": "2021-06-18T10:31:35.386718", "exception": false, "start_time": "2021-06-18T10:31:35.130337", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def MultichannelCNN(hp): \n", " \n", " inputs1 = Input(shape=(length_long_sentence,))\n", " \n", " embedding1 = Embedding(vocab_length, 100, weights=[embedding_matrix],\n", " input_length=length_long_sentence, trainable=False)(inputs1) \n", " \n", " conv1 = Conv1D(filters=hp.Int('conv_1_filter',min_value=21,max_value=150,step=14),\n", " kernel_size=hp.Choice('conv_1_kernel',values=[3,4,5,6,7,8]),\n", " activation='relu')(embedding1) \n", " \n", " drop1 = Dropout(0.3)(conv1) \n", " \n", " pool1 = MaxPooling1D()(drop1) \n", " \n", " flat1 = Flatten()(pool1)\n", " \n", " inputs2 = Input(shape=(length_long_sentence,)) \n", " \n", " embedding2 = Embedding(vocab_length, 100, weights=[embedding_matrix],\n", " input_length=length_long_sentence,trainable=False)(inputs2) \n", " \n", " conv2 = Conv1D(filters=hp.Int('conv_2_filter',min_value=21,max_value=150,step=14),\n", " kernel_size=hp.Choice('conv_2_kernel',values=[3,4,5,6,7,8]),\n", " activation='relu')(embedding2) \n", " \n", " drop2 = Dropout(0.3)(conv2) \n", " \n", " pool2 = MaxPooling1D()(drop2) \n", " \n", " flat2 = Flatten()(pool2) \n", " \n", " # merge \n", " merged = concatenate([flat1, flat2]) \n", " \n", " dense1 = Dense(units = hp.Int('dense_1',min_value=21,max_value=120,step=14),\n", " activation='relu')(merged)\n", " \n", " drop4 = Dropout(0.5)(dense1)\n", " \n", " outputs = Dense(1, activation='sigmoid')(drop4) \n", " \n", " model = Model(inputs=[inputs1, inputs2], outputs=outputs) \n", " \n", " hp_learning_rate = hp.Choice('learning_rate', values=[3e-2, 3e-3, 3e-4, 3e-5]) \n", " \n", " model.compile(optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),\n", " loss=keras.losses.BinaryCrossentropy(from_logits=True),\n", " metrics=['accuracy'])\n", " \n", " return model" ] }, { "cell_type": "markdown", "id": "assigned-throat", "metadata": { "papermill": { "duration": 0.272267, "end_time": "2021-06-18T10:31:35.900225", "exception": false, "start_time": "2021-06-18T10:31:35.627958", "status": "completed" }, "tags": [] }, "source": [ "Creating a tuner using Keras-Tuner(kt) for above function, searching for best hyperparameters" ] }, { "cell_type": "code", "execution_count": 35, "id": "interracial-height", "metadata": { "_kg_hide-input": false, "execution": { "iopub.execute_input": "2021-06-18T10:31:36.391181Z", "iopub.status.busy": "2021-06-18T10:31:36.390401Z", "iopub.status.idle": "2021-06-18T10:33:18.013757Z", "shell.execute_reply": "2021-06-18T10:33:18.014172Z" }, "papermill": { "duration": 101.872145, "end_time": "2021-06-18T10:33:18.014321", "exception": false, "start_time": "2021-06-18T10:31:36.142176", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Trial 13 Complete [00h 00m 15s]\n", "val_accuracy: 0.803676962852478\n", "\n", "Best val_accuracy So Far: 0.803676962852478\n", "Total elapsed time: 00h 01m 41s\n" ] } ], "source": [ "tuner_MCNN = kt.Hyperband(MultichannelCNN,objective='val_accuracy',\n", " max_epochs=15,factor=5,\n", " directory='my_dir',\n", " project_name='DisasterTweetsMCNN_kt',\n", " overwrite=True)\n", "\n", "stop_early = EarlyStopping(monitor='val_loss', mode='min',\n", " verbose=1, patience=10)\n", "\n", "tuner_MCNN.search([X_train,X_train], y_train, epochs=15,\n", " validation_data=([X_val,X_val], y_val),\n", " callbacks=[stop_early])\n", "\n", "# Get the optimal hyperparameters\n", "best_hps_MCNN=tuner_MCNN.get_best_hyperparameters(num_trials=1)[0]" ] }, { "cell_type": "markdown", "id": "logical-working", "metadata": { "papermill": { "duration": 0.239814, "end_time": "2021-06-18T10:33:18.495528", "exception": false, "start_time": "2021-06-18T10:33:18.255714", "status": "completed" }, "tags": [] }, "source": [ "Creating a model for best found hyperparameters and training it" ] }, { "cell_type": "code", "execution_count": 36, "id": "centered-artist", "metadata": { "_kg_hide-output": true, "execution": { "iopub.execute_input": "2021-06-18T10:33:18.984206Z", "iopub.status.busy": "2021-06-18T10:33:18.983151Z", "iopub.status.idle": "2021-06-18T10:33:32.748892Z", "shell.execute_reply": "2021-06-18T10:33:32.748260Z" }, "papermill": { "duration": 14.011848, "end_time": "2021-06-18T10:33:32.749049", "exception": false, "start_time": "2021-06-18T10:33:18.737201", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", "191/191 [==============================] - 2s 6ms/step - loss: 0.6103 - accuracy: 0.6720 - val_loss: 0.4788 - val_accuracy: 0.7840\n", "\n", "Epoch 00001: val_loss improved from inf to 0.47883, saving model to model_MCNN.h5\n", "Epoch 2/50\n", "191/191 [==============================] - 1s 5ms/step - loss: 0.4568 - accuracy: 0.7973 - val_loss: 0.4590 - val_accuracy: 0.7925\n", "\n", "Epoch 00002: val_loss improved from 0.47883 to 0.45901, saving model to model_MCNN.h5\n", "Epoch 3/50\n", "191/191 [==============================] - 1s 5ms/step - loss: 0.4316 - accuracy: 0.8065 - val_loss: 0.4533 - val_accuracy: 0.7971\n", "\n", "Epoch 00003: val_loss improved from 0.45901 to 0.45332, saving model to model_MCNN.h5\n", "Epoch 4/50\n", "191/191 [==============================] - 1s 5ms/step - loss: 0.3837 - accuracy: 0.8267 - val_loss: 0.4581 - val_accuracy: 0.7945\n", "\n", "Epoch 00004: val_loss did not improve from 0.45332\n", "Epoch 5/50\n", "191/191 [==============================] - 1s 5ms/step - loss: 0.3420 - accuracy: 0.8490 - val_loss: 0.4644 - val_accuracy: 0.7905\n", "\n", "Epoch 00005: val_loss did not improve from 0.45332\n", "Epoch 6/50\n", "191/191 [==============================] - 1s 5ms/step - loss: 0.3111 - accuracy: 0.8757 - val_loss: 0.4684 - val_accuracy: 0.7945\n", "\n", "Epoch 00006: val_loss did not improve from 0.45332\n", "Epoch 7/50\n", "191/191 [==============================] - 1s 5ms/step - loss: 0.2945 - accuracy: 0.8780 - val_loss: 0.4928 - val_accuracy: 0.7853\n", "\n", "Epoch 00007: val_loss did not improve from 0.45332\n", "Epoch 8/50\n", "191/191 [==============================] - 1s 5ms/step - loss: 0.2411 - accuracy: 0.9090 - val_loss: 0.5027 - val_accuracy: 0.7899\n", "\n", "Epoch 00008: val_loss did not improve from 0.45332\n", "Epoch 9/50\n", "191/191 [==============================] - 1s 5ms/step - loss: 0.2093 - accuracy: 0.9213 - val_loss: 0.5171 - val_accuracy: 0.7827\n", "\n", "Epoch 00009: val_loss did not improve from 0.45332\n", "Epoch 10/50\n", "191/191 [==============================] - 1s 5ms/step - loss: 0.1867 - accuracy: 0.9283 - val_loss: 0.5484 - val_accuracy: 0.7938\n", "\n", "Epoch 00010: val_loss did not improve from 0.45332\n", "Epoch 11/50\n", "191/191 [==============================] - 1s 5ms/step - loss: 0.1522 - accuracy: 0.9514 - val_loss: 0.5775 - val_accuracy: 0.7853\n", "\n", "Epoch 00011: val_loss did not improve from 0.45332\n", "Epoch 12/50\n", "191/191 [==============================] - 1s 5ms/step - loss: 0.1323 - accuracy: 0.9566 - val_loss: 0.6057 - val_accuracy: 0.7827\n", "\n", "Epoch 00012: val_loss did not improve from 0.45332\n", "Epoch 13/50\n", "191/191 [==============================] - 1s 7ms/step - loss: 0.1223 - accuracy: 0.9603 - val_loss: 0.6230 - val_accuracy: 0.7814\n", "\n", "Epoch 00013: val_loss did not improve from 0.45332\n", "Epoch 00013: early stopping\n" ] } ], "source": [ "model_MCNN = tuner_MCNN.hypermodel.build(best_hps_MCNN)\n", "\n", "checkpoint = ModelCheckpoint(\n", " 'model_MCNN.h5', \n", " monitor = 'val_loss', \n", " verbose = 1, \n", " save_best_only = True\n", ")\n", "\n", "history_MCNN = model_MCNN.fit([X_train,X_train], y_train,epochs=50,\n", " validation_data=([X_val,X_val], y_val),\n", " callbacks=[checkpoint,stop_early])" ] }, { "cell_type": "markdown", "id": "subtle-strike", "metadata": { "papermill": { "duration": 0.298135, "end_time": "2021-06-18T10:33:33.350388", "exception": false, "start_time": "2021-06-18T10:33:33.052253", "status": "completed" }, "tags": [] }, "source": [ "ploting model's summary" ] }, { "cell_type": "code", "execution_count": 37, "id": "starting-portal", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:33:34.015603Z", "iopub.status.busy": "2021-06-18T10:33:34.014642Z", "iopub.status.idle": "2021-06-18T10:33:34.023049Z", "shell.execute_reply": "2021-06-18T10:33:34.022217Z" }, "papermill": { "duration": 0.373006, "end_time": "2021-06-18T10:33:34.023251", "exception": false, "start_time": "2021-06-18T10:33:33.650245", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model\"\n", "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "input_1 (InputLayer) [(None, 23)] 0 \n", "__________________________________________________________________________________________________\n", "input_2 (InputLayer) [(None, 23)] 0 \n", "__________________________________________________________________________________________________\n", "embedding (Embedding) (None, 23, 100) 1484400 input_1[0][0] \n", "__________________________________________________________________________________________________\n", "embedding_1 (Embedding) (None, 23, 100) 1484400 input_2[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d (Conv1D) (None, 17, 147) 103047 embedding[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_1 (Conv1D) (None, 21, 147) 44247 embedding_1[0][0] \n", "__________________________________________________________________________________________________\n", "dropout (Dropout) (None, 17, 147) 0 conv1d[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_1 (Dropout) (None, 21, 147) 0 conv1d_1[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d (MaxPooling1D) (None, 8, 147) 0 dropout[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_1 (MaxPooling1D) (None, 10, 147) 0 dropout_1[0][0] \n", "__________________________________________________________________________________________________\n", "flatten (Flatten) (None, 1176) 0 max_pooling1d[0][0] \n", "__________________________________________________________________________________________________\n", "flatten_1 (Flatten) (None, 1470) 0 max_pooling1d_1[0][0] \n", "__________________________________________________________________________________________________\n", "concatenate (Concatenate) (None, 2646) 0 flatten[0][0] \n", " flatten_1[0][0] \n", "__________________________________________________________________________________________________\n", "dense (Dense) (None, 35) 92645 concatenate[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_2 (Dropout) (None, 35) 0 dense[0][0] \n", "__________________________________________________________________________________________________\n", "dense_1 (Dense) (None, 1) 36 dropout_2[0][0] \n", "==================================================================================================\n", "Total params: 3,208,775\n", "Trainable params: 239,975\n", "Non-trainable params: 2,968,800\n", "__________________________________________________________________________________________________\n" ] } ], "source": [ "model_MCNN.summary()" ] }, { "cell_type": "markdown", "id": "backed-burns", "metadata": { "papermill": { "duration": 0.299105, "end_time": "2021-06-18T10:33:34.776220", "exception": false, "start_time": "2021-06-18T10:33:34.477115", "status": "completed" }, "tags": [] }, "source": [ "ploting model training and validating history" ] }, { "cell_type": "code", "execution_count": 38, "id": "hollywood-madrid", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:33:35.388803Z", "iopub.status.busy": "2021-06-18T10:33:35.387899Z", "iopub.status.idle": "2021-06-18T10:33:35.624374Z", "shell.execute_reply": "2021-06-18T10:33:35.624806Z" }, "papermill": { "duration": 0.548889, "end_time": "2021-06-18T10:33:35.624954", "exception": false, "start_time": "2021-06-18T10:33:35.076065", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "model_history(history_MCNN)" ] }, { "cell_type": "markdown", "id": "selective-carpet", "metadata": { "papermill": { "duration": 0.301524, "end_time": "2021-06-18T10:33:36.225440", "exception": false, "start_time": "2021-06-18T10:33:35.923916", "status": "completed" }, "tags": [] }, "source": [ "## 6.3 Training and Tuning Bidirectinal Long Short-Term Memory (LSTM) networks" ] }, { "cell_type": "markdown", "id": "understanding-internship", "metadata": { "papermill": { "duration": 0.338313, "end_time": "2021-06-18T10:33:36.864628", "exception": false, "start_time": "2021-06-18T10:33:36.526315", "status": "completed" }, "tags": [] }, "source": [ "### What are Bidirectional-LSTM networks? \n", "\n", "Bidirectional recurrent neural networks(RNN) are really just putting two independent RNNs together. \n", "This structure allows the networks to have both backward and forward information about the sequence at every time step\n", " \n", "Using bidirectional will run your inputs in two ways, one from past to future and one from future to past and what differs \n", "this approach \n", "from unidirectional is that in the LSTM that runs backward you preserve information from the future and using the two hidden states combined \n", "you are able in any point in time to preserve information from both past and future.\n", "\n", "Function for creating [Bidirectional Long Short-Term Memory](https://machinelearningmastery.com/develop-bidirectional-lstm-sequence-classification-python-keras/#:~:text=Bidirectional%20LSTMs%20are%20an%20extension,LSTMs%20on%20the%20input%20sequence.) (LSTM) networks" ] }, { "cell_type": "code", "execution_count": 39, "id": "raised-balloon", "metadata": { "_kg_hide-input": false, "execution": { "iopub.execute_input": "2021-06-18T10:33:37.475701Z", "iopub.status.busy": "2021-06-18T10:33:37.475056Z", "iopub.status.idle": "2021-06-18T10:33:37.477740Z", "shell.execute_reply": "2021-06-18T10:33:37.478130Z" }, "papermill": { "duration": 0.312859, "end_time": "2021-06-18T10:33:37.478279", "exception": false, "start_time": "2021-06-18T10:33:37.165420", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "def BiLSTM(hp):\n", " model = Sequential()\n", " \n", " model.add(Embedding(input_dim=embedding_matrix.shape[0], \n", " output_dim=embedding_matrix.shape[1], \n", " weights = [embedding_matrix], \n", " input_length=length_long_sentence,trainable = False))\n", " \n", " model.add(Bidirectional(CuDNNLSTM(units = hp.Int('dense_1',\n", " min_value=21,max_value=120,step=14)\n", " ,return_sequences = True)))\n", " \n", " model.add(GlobalMaxPool1D())\n", " \n", " model.add(BatchNormalization())\n", " \n", " model.add(Dropout(0.2))\n", " \n", " model.add(Dense(units = hp.Int('dense_1',min_value=21,\n", " max_value=120,step=14),\n", " activation = \"relu\"))\n", " \n", " model.add(Dropout(0.3))\n", " \n", " model.add(Dense(units = hp.Int('dense_1',min_value=21,\n", " max_value=100,step=14),\n", " activation = \"relu\"))\n", " \n", " model.add(Dropout(0.5))\n", " \n", " model.add(Dense(1, activation = 'sigmoid'))\n", " \n", " hp_learning_rate = hp.Choice('learning_rate', \n", " values=[3e-2, 3e-3, 3e-4, 3e-5]) \n", " \n", " model.compile(optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),\n", " loss=keras.losses.BinaryCrossentropy(from_logits=True),\n", " metrics=['accuracy'])\n", " \n", " return model" ] }, { "cell_type": "markdown", "id": "future-asbestos", "metadata": { "papermill": { "duration": 0.302667, "end_time": "2021-06-18T10:33:38.080124", "exception": false, "start_time": "2021-06-18T10:33:37.777457", "status": "completed" }, "tags": [] }, "source": [ "Creating a tuner using Keras-Tuner(kt) for above function, searching for best hyperparameters" ] }, { "cell_type": "code", "execution_count": 40, "id": "animated-entertainment", "metadata": { "_kg_hide-input": false, "execution": { "iopub.execute_input": "2021-06-18T10:33:38.696831Z", "iopub.status.busy": "2021-06-18T10:33:38.696242Z", "iopub.status.idle": "2021-06-18T10:36:31.941321Z", "shell.execute_reply": "2021-06-18T10:36:31.940631Z" }, "papermill": { "duration": 173.552771, "end_time": "2021-06-18T10:36:31.941495", "exception": false, "start_time": "2021-06-18T10:33:38.388724", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Trial 13 Complete [00h 00m 26s]\n", "val_accuracy: 0.7931713461875916\n", "\n", "Best val_accuracy So Far: 0.8200919032096863\n", "Total elapsed time: 00h 02m 52s\n" ] } ], "source": [ "tuner_BiLSTM = kt.Hyperband(BiLSTM,objective='val_accuracy',\n", " max_epochs=15,factor=5,\n", " directory='my_dir',\n", " project_name='DisasterTweetsBiLSTM_kt',\n", " overwrite=True)\n", "\n", "stop_early = EarlyStopping(monitor='val_loss', mode='min',\n", " verbose=1, patience=12)\n", "\n", "tuner_BiLSTM.search(X_train, y_train, epochs=15,\n", " validation_data=(X_val, y_val),\n", " callbacks=[stop_early])\n", "\n", "# Get the optimal hyperparameters\n", "best_hps_BiLSTM=tuner_BiLSTM.get_best_hyperparameters(num_trials=1)[0]" ] }, { "cell_type": "markdown", "id": "alpine-principle", "metadata": { "papermill": { "duration": 0.296042, "end_time": "2021-06-18T10:36:32.535285", "exception": false, "start_time": "2021-06-18T10:36:32.239243", "status": "completed" }, "tags": [] }, "source": [ "Creating a model for best found hyperparameters and training it" ] }, { "cell_type": "code", "execution_count": 41, "id": "flexible-graduation", "metadata": { "_kg_hide-input": false, "_kg_hide-output": true, "execution": { "iopub.execute_input": "2021-06-18T10:36:33.136419Z", "iopub.status.busy": "2021-06-18T10:36:33.135594Z", "iopub.status.idle": "2021-06-18T10:36:57.361096Z", "shell.execute_reply": "2021-06-18T10:36:57.360610Z" }, "papermill": { "duration": 24.529652, "end_time": "2021-06-18T10:36:57.361215", "exception": false, "start_time": "2021-06-18T10:36:32.831563", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", "191/191 [==============================] - 3s 9ms/step - loss: 0.5806 - accuracy: 0.7247 - val_loss: 0.5917 - val_accuracy: 0.7262\n", "\n", "Epoch 00001: val_loss improved from inf to 0.59175, saving model to model_BiLSTM.h5\n", "Epoch 2/50\n", "191/191 [==============================] - 1s 7ms/step - loss: 0.4398 - accuracy: 0.8056 - val_loss: 0.4776 - val_accuracy: 0.8096\n", "\n", "Epoch 00002: val_loss improved from 0.59175 to 0.47757, saving model to model_BiLSTM.h5\n", "Epoch 3/50\n", "191/191 [==============================] - 1s 7ms/step - loss: 0.4271 - accuracy: 0.8220 - val_loss: 0.4336 - val_accuracy: 0.8148\n", "\n", "Epoch 00003: val_loss improved from 0.47757 to 0.43355, saving model to model_BiLSTM.h5\n", "Epoch 4/50\n", "191/191 [==============================] - 2s 8ms/step - loss: 0.3922 - accuracy: 0.8306 - val_loss: 0.4432 - val_accuracy: 0.8096\n", "\n", "Epoch 00004: val_loss did not improve from 0.43355\n", "Epoch 5/50\n", "191/191 [==============================] - 1s 7ms/step - loss: 0.3700 - accuracy: 0.8396 - val_loss: 0.4909 - val_accuracy: 0.8011\n", "\n", "Epoch 00005: val_loss did not improve from 0.43355\n", "Epoch 6/50\n", "191/191 [==============================] - 1s 8ms/step - loss: 0.3427 - accuracy: 0.8543 - val_loss: 0.4819 - val_accuracy: 0.7958\n", "\n", "Epoch 00006: val_loss did not improve from 0.43355\n", "Epoch 7/50\n", "191/191 [==============================] - 2s 9ms/step - loss: 0.3183 - accuracy: 0.8686 - val_loss: 0.6042 - val_accuracy: 0.7689\n", "\n", "Epoch 00007: val_loss did not improve from 0.43355\n", "Epoch 8/50\n", "191/191 [==============================] - 1s 7ms/step - loss: 0.2898 - accuracy: 0.8821 - val_loss: 0.5182 - val_accuracy: 0.8070\n", "\n", "Epoch 00008: val_loss did not improve from 0.43355\n", "Epoch 9/50\n", "191/191 [==============================] - 1s 7ms/step - loss: 0.2806 - accuracy: 0.8819 - val_loss: 0.6188 - val_accuracy: 0.7689\n", "\n", "Epoch 00009: val_loss did not improve from 0.43355\n", "Epoch 10/50\n", "191/191 [==============================] - 1s 7ms/step - loss: 0.2592 - accuracy: 0.8912 - val_loss: 0.5161 - val_accuracy: 0.7991\n", "\n", "Epoch 00010: val_loss did not improve from 0.43355\n", "Epoch 11/50\n", "191/191 [==============================] - 1s 7ms/step - loss: 0.2448 - accuracy: 0.9011 - val_loss: 0.5601 - val_accuracy: 0.7905\n", "\n", "Epoch 00011: val_loss did not improve from 0.43355\n", "Epoch 12/50\n", "191/191 [==============================] - 2s 8ms/step - loss: 0.2109 - accuracy: 0.9121 - val_loss: 0.5933 - val_accuracy: 0.7846\n", "\n", "Epoch 00012: val_loss did not improve from 0.43355\n", "Epoch 13/50\n", "191/191 [==============================] - 1s 7ms/step - loss: 0.1856 - accuracy: 0.9283 - val_loss: 0.6997 - val_accuracy: 0.7951\n", "\n", "Epoch 00013: val_loss did not improve from 0.43355\n", "Epoch 14/50\n", "191/191 [==============================] - 1s 7ms/step - loss: 0.1925 - accuracy: 0.9265 - val_loss: 0.6562 - val_accuracy: 0.7899\n", "\n", "Epoch 00014: val_loss did not improve from 0.43355\n", "Epoch 15/50\n", "191/191 [==============================] - 1s 7ms/step - loss: 0.1799 - accuracy: 0.9246 - val_loss: 0.6588 - val_accuracy: 0.7899\n", "\n", "Epoch 00015: val_loss did not improve from 0.43355\n", "Epoch 00015: early stopping\n" ] } ], "source": [ "model_BiLSTM = tuner_BiLSTM.hypermodel.build(best_hps_BiLSTM)\n", "\n", "checkpoint = ModelCheckpoint(\n", " 'model_BiLSTM.h5', \n", " monitor = 'val_loss', \n", " verbose = 1, \n", " save_best_only = True\n", ")\n", "\n", "history_BiLSTM = model_BiLSTM.fit(X_train, y_train, epochs=50,\n", " validation_data=(X_val, y_val),\n", " callbacks=[checkpoint,stop_early])" ] }, { "cell_type": "markdown", "id": "yellow-shakespeare", "metadata": { "papermill": { "duration": 0.389001, "end_time": "2021-06-18T10:36:58.143696", "exception": false, "start_time": "2021-06-18T10:36:57.754695", "status": "completed" }, "tags": [] }, "source": [ "printing model's summary" ] }, { "cell_type": "code", "execution_count": 42, "id": "julian-spiritual", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:36:58.971448Z", "iopub.status.busy": "2021-06-18T10:36:58.968172Z", "iopub.status.idle": "2021-06-18T10:36:58.975863Z", "shell.execute_reply": "2021-06-18T10:36:58.975450Z" }, "papermill": { "duration": 0.441629, "end_time": "2021-06-18T10:36:58.975985", "exception": false, "start_time": "2021-06-18T10:36:58.534356", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "embedding (Embedding) (None, 23, 100) 1484400 \n", "_________________________________________________________________\n", "bidirectional (Bidirectional (None, 23, 154) 110264 \n", "_________________________________________________________________\n", "global_max_pooling1d (Global (None, 154) 0 \n", "_________________________________________________________________\n", "batch_normalization (BatchNo (None, 154) 616 \n", "_________________________________________________________________\n", "dropout (Dropout) (None, 154) 0 \n", "_________________________________________________________________\n", "dense (Dense) (None, 77) 11935 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 77) 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 77) 6006 \n", "_________________________________________________________________\n", "dropout_2 (Dropout) (None, 77) 0 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 1) 78 \n", "=================================================================\n", "Total params: 1,613,299\n", "Trainable params: 128,591\n", "Non-trainable params: 1,484,708\n", "_________________________________________________________________\n" ] } ], "source": [ "model_BiLSTM.summary()" ] }, { "cell_type": "markdown", "id": "charitable-depth", "metadata": { "papermill": { "duration": 0.389712, "end_time": "2021-06-18T10:36:59.756442", "exception": false, "start_time": "2021-06-18T10:36:59.366730", "status": "completed" }, "tags": [] }, "source": [ "ploting training and validation history of the model" ] }, { "cell_type": "code", "execution_count": 43, "id": "parental-people", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:37:00.572686Z", "iopub.status.busy": "2021-06-18T10:37:00.571822Z", "iopub.status.idle": "2021-06-18T10:37:00.842697Z", "shell.execute_reply": "2021-06-18T10:37:00.843127Z" }, "papermill": { "duration": 0.674186, "end_time": "2021-06-18T10:37:00.843276", "exception": false, "start_time": "2021-06-18T10:37:00.169090", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "model_history(history_BiLSTM)" ] }, { "cell_type": "markdown", "id": "constant-cleaner", "metadata": { "papermill": { "duration": 0.394263, "end_time": "2021-06-18T10:37:01.629953", "exception": false, "start_time": "2021-06-18T10:37:01.235690", "status": "completed" }, "tags": [] }, "source": [ "## 6.4 Training DistilBert transformer" ] }, { "cell_type": "markdown", "id": "purple-handy", "metadata": { "papermill": { "duration": 0.391011, "end_time": "2021-06-18T10:37:02.413144", "exception": false, "start_time": "2021-06-18T10:37:02.022133", "status": "completed" }, "tags": [] }, "source": [ "Using scikit-learn's [OneHotEncoder](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html) to [one hot encode](https://www.sciencedirect.com/topics/computer-science/one-hot-encoding) the target column \n", "and spliting the data into training and validation set." ] }, { "cell_type": "code", "execution_count": 44, "id": "dried-configuration", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:37:03.419271Z", "iopub.status.busy": "2021-06-18T10:37:03.418305Z", "iopub.status.idle": "2021-06-18T10:37:03.423761Z", "shell.execute_reply": "2021-06-18T10:37:03.423278Z" }, "papermill": { "duration": 0.617782, "end_time": "2021-06-18T10:37:03.423881", "exception": false, "start_time": "2021-06-18T10:37:02.806099", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "onehot_encoder = OneHotEncoder(sparse=False)\n", "\n", "y = (np.asarray(y_inp)).reshape(-1,1)\n", "\n", "Y = onehot_encoder.fit_transform(y)\n", "\n", "X_train, X_val, y_train, y_val = train_test_split(X_inp_clean,Y,\n", " test_size=0.2, random_state=1)" ] }, { "cell_type": "markdown", "id": "structural-infection", "metadata": { "papermill": { "duration": 0.402954, "end_time": "2021-06-18T10:37:04.227996", "exception": false, "start_time": "2021-06-18T10:37:03.825042", "status": "completed" }, "tags": [] }, "source": [ "### What is DistilBert?\n", "DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than bert-base-uncased , \n", "runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language understanding benchmark.\n", "\n", "Creating model checkpoint for [DistilBert](https://huggingface.co/distilbert-base-uncased) model and importing it's pretrained AutoTokenizer" ] }, { "cell_type": "code", "execution_count": 45, "id": "blessed-captain", "metadata": { "execution": { "iopub.execute_input": "2021-06-18T10:37:05.059630Z", "iopub.status.busy": "2021-06-18T10:37:05.059102Z", "iopub.status.idle": "2021-06-18T10:37:13.664647Z", "shell.execute_reply": "2021-06-18T10:37:13.664159Z" }, "papermill": { "duration": 9.044274, "end_time": "2021-06-18T10:37:13.664795", "exception": false, "start_time": "2021-06-18T10:37:04.620521", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "43283388b5814e3ba0bcef5247d92eea", "version_major": 2, "version_minor": 0 }, 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