Created
June 14, 2025 17:58
-
-
Save hemantramphul/e4e8c5855f62f0562bcf789541bed864 to your computer and use it in GitHub Desktop.
How to train a YOLOv11segmentation to detect and localize the crack using a custom dataset.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "id": "02122a32", | |
| "metadata": {}, | |
| "source": [ | |
| "## YOLOv11 segmentation in *Surface Crack* using the Roboflow dataset.\n", | |
| "\n", | |
| "In this file, I will explain how to train a YOLOv11segmentation to detect and localize the crack using a custom dataset.\n", | |
| "\n", | |
| "This project focuses on detecting and segmenting surface cracks in materials using a custom-trained YOLOv11 model. The model leverages a dataset from Roboflow, which provides annotated images of cracks for robust training and evaluation. Our goal is to create an efficient, crack segmentation tool for structural health monitoring applications.\n", | |
| "\n", | |
| "<img src=\"images/crack1.jpg\" width=\"300\" alt=\"Crack\">\n", | |
| "<img src=\"images/crack3.jpg\" width=\"300\" alt=\"Crack\">\n", | |
| "<img src=\"images/crack2.jpg\" width=\"300\" alt=\"Crack\">\n", | |
| "\n", | |
| "### Context\n", | |
| "\n", | |
| "Concrete surface cracks are major defect in civil structures. Building Inspection which is done for the evaluation of rigidity and tensile strength of the building. Crack detection plays a major role in the building inspection, finding the cracks and determining the building health.\n", | |
| "\n", | |
| "### Dataset\n", | |
| "\n", | |
| "The dataset used in this project, available [here]('https://github.com/ultralytics/assets/releases/download/v0.0.0/crack-seg.zip'), is a comprehensive collection of images capturing various types of surface cracks in multiple environments. This dataset includes two main directories:\n", | |
| "\n", | |
| "* `/train/images` and `/train/labels` for training\n", | |
| "* `/test` for validation\n", | |
| "* `data.yaml` configuration file\n", | |
| "\n", | |
| "Each label in the dataset corresponds to surface crack regions, enabling the model to learn fine-grained segmentation of crack features.\n", | |
| "\n", | |
| "\n", | |
| "### Model Training\n", | |
| "\n", | |
| "Using the YOLOv11 architecture, this model was trained to detect and segment cracks with high accuracy and efficiency. The key stages of this pipeline include data preprocessing, model configuration, training, and evaluation. Each stage is documented in the Jupyter Notebook file accompanying this project, which details parameters, code, and results.\n", | |
| "\n", | |
| "\n", | |
| "### Author\n", | |
| "\n", | |
| "Name: Hemant Ramphul<br>\n", | |
| "Country: Mauritius ![Mauritius](images/mu.png \"Mauritius\")<br>\n", | |
| "Email: <hemant.ramphul@lynxdrone.fr><br>\n", | |
| "Model: [Roboflow]('https://universe.roboflow.com/hemant-ramphul-wfioe/surface-crack-segmentation-mnigz/model/1')<br><br>\n", | |
| "<a href=\"https://universe.roboflow.com/hemant-ramphul-wfioe\" target=\"_blank\"><img src=\"images/robo.png\" width=\"100\" alt=\"Hemant Ramphul\"></a><br><br>\n", | |
| "<a href=\"https://www.linkedin.com/in/hemantramphul/\" target=\"_blank\"><img src=\"images/linkedin.png\" width=\"80\" alt=\"Hemant Ramphul\"></a><br><br>\n", | |
| "...\n", | |
| "|Lynxdrone|Université de Limoges|Université des Mascareignes|\n", | |
| "|-----------|-----------|-----------|\n", | |
| " <a href=\"https://lynxdrone.fr/\" target=\"_blank\"><img src=\"images/logo.png\" width=\"150\" alt=\"Lynxdrone\"></a> | <a href=\"https://www.unilim.fr/\" target=\"_blank\"><img src=\"images/unilim.png\" width=\"150\" alt=\"Université de Limoges\"> | <a href=\"https://udm.ac.mu/\" target=\"_blank\"><img src=\"images/udm.png\" width=\"150\" alt=\"Université des Mascareignes\"> \n", | |
| " \n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "f5ef5426", | |
| "metadata": {}, | |
| "source": [ | |
| "## Status of NVIDIA\n", | |
| "\n", | |
| "Checks the status of the NVIDIA GPU.\n", | |
| "\n", | |
| "It displays details like GPU usage, memory usage, and the current driver version.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "id": "0fCEtTgJ1kuB", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "0fCEtTgJ1kuB", | |
| "outputId": "ff7300b1-b971-485c-c0f6-0da934ee2e91" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Mon Oct 28 15:13:06 2024 \n", | |
| "+-----------------------------------------------------------------------------------------+\n", | |
| "| NVIDIA-SMI 565.90 Driver Version: 565.90 CUDA Version: 12.7 |\n", | |
| "|-----------------------------------------+------------------------+----------------------+\n", | |
| "| GPU Name Driver-Model | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", | |
| "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", | |
| "| | | MIG M. |\n", | |
| "|=========================================+========================+======================|\n", | |
| "| 0 NVIDIA GeForce RTX 2080 Ti WDDM | 00000000:01:00.0 On | N/A |\n", | |
| "| 32% 45C P8 19W / 250W | 602MiB / 11264MiB | 21% Default |\n", | |
| "| | | N/A |\n", | |
| "+-----------------------------------------+------------------------+----------------------+\n", | |
| " \n", | |
| "+-----------------------------------------------------------------------------------------+\n", | |
| "| Processes: |\n", | |
| "| GPU GI CI PID Type Process name GPU Memory |\n", | |
| "| ID ID Usage |\n", | |
| "|=========================================================================================|\n", | |
| "| 0 N/A N/A 4928 C+G ...crosoft\\Edge\\Application\\msedge.exe N/A |\n", | |
| "| 0 N/A N/A 5084 C+G ...oogle\\Chrome\\Application\\chrome.exe N/A |\n", | |
| "| 0 N/A N/A 6428 C+G ...s\\System32\\ApplicationFrameHost.exe N/A |\n", | |
| "| 0 N/A N/A 6632 C+G ...Programs\\Microsoft VS Code\\Code.exe N/A |\n", | |
| "| 0 N/A N/A 9548 C+G C:\\Windows\\explorer.exe N/A |\n", | |
| "| 0 N/A N/A 10128 C+G ...5n1h2txyewy\\ShellExperienceHost.exe N/A |\n", | |
| "| 0 N/A N/A 10204 C+G C:\\Windows\\System32\\ShellHost.exe N/A |\n", | |
| "| 0 N/A N/A 10980 C+G ...029_x64__8wekyb3d8bbwe\\ms-teams.exe N/A |\n", | |
| "| 0 N/A N/A 11184 C+G ...oogle\\Chrome\\Application\\chrome.exe N/A |\n", | |
| "| 0 N/A N/A 14424 C+G ...029_x64__8wekyb3d8bbwe\\ms-teams.exe N/A |\n", | |
| "| 0 N/A N/A 14908 C+G ...ekyb3d8bbwe\\PhoneExperienceHost.exe N/A |\n", | |
| "| 0 N/A N/A 15356 C+G ...GeForce Experience\\NVIDIA Share.exe N/A |\n", | |
| "| 0 N/A N/A 15764 C+G ...nt.CBS_cw5n1h2txyewy\\SearchHost.exe N/A |\n", | |
| "| 0 N/A N/A 16236 C+G ...on\\130.0.2849.46\\msedgewebview2.exe N/A |\n", | |
| "| 0 N/A N/A 16532 C+G ...t.LockApp_cw5n1h2txyewy\\LockApp.exe N/A |\n", | |
| "| 0 N/A N/A 20996 C+G ...2txyewy\\StartMenuExperienceHost.exe N/A |\n", | |
| "| 0 N/A N/A 22324 C+G ...on\\130.0.2849.46\\msedgewebview2.exe N/A |\n", | |
| "| 0 N/A N/A 25044 C+G ...on\\130.0.2849.46\\msedgewebview2.exe N/A |\n", | |
| "| 0 N/A N/A 26260 C+G ...les\\Microsoft OneDrive\\OneDrive.exe N/A |\n", | |
| "| 0 N/A N/A 28800 C+G ...siveControlPanel\\SystemSettings.exe N/A |\n", | |
| "+-----------------------------------------------------------------------------------------+\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!nvidia-smi" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "5210c3e3", | |
| "metadata": {}, | |
| "source": [ | |
| "## Installation \n", | |
| "\n", | |
| "Installs the `ultralytics` package, which includes the YOLO (You Only Look Once) object detection models.\n", | |
| "\n", | |
| "Using `%pip` ensures compatibility within Jupyter notebooks, installing packages directly into the active kernel environment.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "rGpS9H-l1nsR", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "rGpS9H-l1nsR", | |
| "outputId": "f1414137-aed6-4bf5-b6a1-8278d90fabb4" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Requirement already satisfied: ultralytics in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (8.3.23)Note: you may need to restart the kernel to use updated packages.\n", | |
| "\n", | |
| "Requirement already satisfied: numpy>=1.23.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (1.26.3)\n", | |
| "Requirement already satisfied: matplotlib>=3.3.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (3.9.2)\n", | |
| "Requirement already satisfied: opencv-python>=4.6.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (4.10.0.84)\n", | |
| "Requirement already satisfied: pillow>=7.1.2 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (10.2.0)\n", | |
| "Requirement already satisfied: pyyaml>=5.3.1 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (6.0.2)\n", | |
| "Requirement already satisfied: requests>=2.23.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (2.32.3)\n", | |
| "Requirement already satisfied: scipy>=1.4.1 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (1.14.1)\n", | |
| "Requirement already satisfied: torch>=1.8.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (2.4.1+cu118)\n", | |
| "Requirement already satisfied: torchvision>=0.9.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (0.19.1+cu118)\n", | |
| "Requirement already satisfied: tqdm>=4.64.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (4.66.5)\n", | |
| "Requirement already satisfied: psutil in c:\\users\\ashvin\\appdata\\roaming\\python\\python312\\site-packages (from ultralytics) (6.1.0)\n", | |
| "Requirement already satisfied: py-cpuinfo in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (9.0.0)\n", | |
| "Requirement already satisfied: pandas>=1.1.4 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (2.2.3)\n", | |
| "Requirement already satisfied: seaborn>=0.11.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (0.13.2)\n", | |
| "Requirement already satisfied: ultralytics-thop>=2.0.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from ultralytics) (2.0.9)\n", | |
| "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.3.0->ultralytics) (1.3.0)\n", | |
| "Requirement already satisfied: cycler>=0.10 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n", | |
| "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.3.0->ultralytics) (4.54.1)\n", | |
| "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.3.0->ultralytics) (1.4.7)\n", | |
| "Requirement already satisfied: packaging>=20.0 in c:\\users\\ashvin\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib>=3.3.0->ultralytics) (24.1)\n", | |
| "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.3.0->ultralytics) (3.2.0)\n", | |
| "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\ashvin\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib>=3.3.0->ultralytics) (2.9.0.post0)\n", | |
| "Requirement already satisfied: pytz>=2020.1 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas>=1.1.4->ultralytics) (2024.2)\n", | |
| "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas>=1.1.4->ultralytics) (2024.2)\n", | |
| "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests>=2.23.0->ultralytics) (3.4.0)\n", | |
| "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests>=2.23.0->ultralytics) (3.7)\n", | |
| "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests>=2.23.0->ultralytics) (2.2.3)\n", | |
| "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests>=2.23.0->ultralytics) (2024.8.30)\n", | |
| "Requirement already satisfied: filelock in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from torch>=1.8.0->ultralytics) (3.13.1)\n", | |
| "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from torch>=1.8.0->ultralytics) (4.9.0)\n", | |
| "Requirement already satisfied: sympy in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from torch>=1.8.0->ultralytics) (1.13.1)\n", | |
| "Requirement already satisfied: networkx in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from torch>=1.8.0->ultralytics) (3.2.1)\n", | |
| "Requirement already satisfied: jinja2 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from torch>=1.8.0->ultralytics) (3.1.3)\n", | |
| "Requirement already satisfied: fsspec in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from torch>=1.8.0->ultralytics) (2024.2.0)\n", | |
| "Requirement already satisfied: setuptools in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from torch>=1.8.0->ultralytics) (70.0.0)\n", | |
| "Requirement already satisfied: colorama in c:\\users\\ashvin\\appdata\\roaming\\python\\python312\\site-packages (from tqdm>=4.64.0->ultralytics) (0.4.6)\n", | |
| "Requirement already satisfied: six>=1.5 in c:\\users\\ashvin\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\n", | |
| "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\n", | |
| "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\ashvin\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%pip install ultralytics" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "db0755d2", | |
| "metadata": {}, | |
| "source": [ | |
| "Installing the `roboflow` package, which provides access to Roboflow's dataset management and model deployment tools.\n", | |
| "\n", | |
| "Using `%pip` ensures that the package is installed into the Jupyter notebook's active kernel environment.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "4214e1dc", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "%pip install roboflow" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "34566834", | |
| "metadata": {}, | |
| "source": [ | |
| "## Import\n", | |
| "\n", | |
| "Importing the `YOLO` class from the `ultralytics` package to utilize YOLO models for object detection and segmentation tasks." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "id": "FE5W6wT-1qny", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "FE5W6wT-1qny", | |
| "outputId": "33ec9dec-03ca-41cc-b93b-921955cb0eff" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from ultralytics import YOLO\n", | |
| "from IPython.display import Image # Importing Image from IPython to display images in the notebook." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "c1728b79", | |
| "metadata": {}, | |
| "source": [ | |
| "This code connects to the Roboflow API to retrieve a specific version of the `crack-bphdr` project dataset in YOLOv11 format. The Roboflow object uses an API key for authentication, and the download method saves the dataset locally, allowing you to use it directly for model training." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "id": "0tkbZiGy1xfy", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "0tkbZiGy1xfy", | |
| "outputId": "642f72cf-f3d5-40c4-a8df-e1b5908aba59" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "loading Roboflow workspace...\n", | |
| "loading Roboflow project...\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "Downloading Dataset Version Zip in crack-2 to yolov11:: 100%|██████████| 122803/122803 [00:13<00:00, 9298.69it/s] " | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Extracting Dataset Version Zip to crack-2 in yolov11:: 100%|██████████| 8070/8070 [00:03<00:00, 2583.97it/s]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from roboflow import Roboflow # Import Roboflow class to connect to Roboflow's API for dataset access and management.\n", | |
| "rf = Roboflow(api_key=\"YOUR_API_KEY_HERE\") # Initializing Roboflow with an API key for authentication.\n", | |
| "project = rf.workspace(\"university-bswxt\").project(\"crack-bphdr\") # Accessing the \"crack-bphdr\" project in the \"university-bswxt\" workspace.\n", | |
| "version = project.version(2) # Getting version 2 of the project dataset.\n", | |
| "dataset = version.download(\"yolov11\") # Downloading the dataset in YOLOv11 format." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "f143e99f", | |
| "metadata": {}, | |
| "source": [ | |
| "## Training" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "9275db5c", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "c:\\Lynxdrone\\python\\ultralytics\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "import os\n", | |
| "HOME = os.getcwd() # Getting the current working directory\n", | |
| "print(HOME)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "3697e8ee", | |
| "metadata": {}, | |
| "source": [ | |
| "### Configuration with `data.yaml`\n", | |
| "\n", | |
| "The `data.yaml` file is a critical component, providing the YOLOv11 model with structured dataset paths and other key configurations. This file specifies the dataset's class structure, paths for training and validation data, and class labels. Below is a sample configuration of the data.yaml file:\n", | |
| "\n", | |
| "YOLOv11 (n, s, m, l, x) _Object Detection_, _Instance Segmentation_\n", | |
| "\n", | |
| "**Train/val/test** sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]<br/>\n", | |
| "*path*: `../datasets/crack-seg` # dataset root dir<br/>\n", | |
| "*train*: `train/images` # train images (relative to 'path') 3717 images<br/>\n", | |
| "*val*: `valid/images` # val images (relative to 'path') 112 images<br/>\n", | |
| "*test*: `test/images` # test images (relative to 'path') 200 images<br/><br/>\n", | |
| "\n", | |
| "**Classes**<br/>\n", | |
| "*names*:<br/>\n", | |
| " *0*: `crack`\n", | |
| "\n", | |
| "This configuration ensures that YOLOv11 correctly interprets the data, allowing it to effectively learn crack segmentation." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "id": "04ba5ba5-4a56-4b0f-849b-011e2c03f540", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "04ba5ba5-4a56-4b0f-849b-011e2c03f540", | |
| "outputId": "7d92c14f-1b47-4fd0-9795-9bff6c1e78d3" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt to 'yolo11l-seg.pt'...\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "invalid escape sequence '\\c'\n", | |
| "invalid escape sequence '\\c'\n", | |
| "invalid escape sequence '\\c'\n", | |
| "100%|██████████| 53.5M/53.5M [00:06<00:00, 8.86MB/s]\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Ultralytics 8.3.23 Python-3.12.0 torch-2.4.1+cu118 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264MiB)\n", | |
| "\u001b[34m\u001b[1mengine\\trainer: \u001b[0mtask=segment, mode=train, model=yolo11l-seg.pt, data=c:\\Lynxdrone\\python\\ultralytics\\crack-2\\data.yaml, epochs=50, time=None, patience=100, batch=16, imgsz=416, save=True, save_period=-1, cache=False, device=0, workers=8, project=None, name=train11, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs\\segment\\train11\n", | |
| "Overriding model.yaml nc=80 with nc=1\n", | |
| "\n", | |
| " from n params module arguments \n", | |
| " 0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2] \n", | |
| " 1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n", | |
| " 2 -1 2 173824 ultralytics.nn.modules.block.C3k2 [128, 256, 2, True, 0.25] \n", | |
| " 3 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", | |
| " 4 -1 2 691712 ultralytics.nn.modules.block.C3k2 [256, 512, 2, True, 0.25] \n", | |
| " 5 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", | |
| " 6 -1 2 2234368 ultralytics.nn.modules.block.C3k2 [512, 512, 2, True] \n", | |
| " 7 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", | |
| " 8 -1 2 2234368 ultralytics.nn.modules.block.C3k2 [512, 512, 2, True] \n", | |
| " 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] \n", | |
| " 10 -1 2 1455616 ultralytics.nn.modules.block.C2PSA [512, 512, 2] \n", | |
| " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", | |
| " 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", | |
| " 13 -1 2 2496512 ultralytics.nn.modules.block.C3k2 [1024, 512, 2, True] \n", | |
| " 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", | |
| " 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", | |
| " 16 -1 2 756736 ultralytics.nn.modules.block.C3k2 [1024, 256, 2, True] \n", | |
| " 17 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", | |
| " 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", | |
| " 19 -1 2 2365440 ultralytics.nn.modules.block.C3k2 [768, 512, 2, True] \n", | |
| " 20 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", | |
| " 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", | |
| " 22 -1 2 2496512 ultralytics.nn.modules.block.C3k2 [1024, 512, 2, True] \n", | |
| " 23 [16, 19, 22] 1 3718003 ultralytics.nn.modules.head.Segment [1, 32, 256, [256, 512, 512]] \n", | |
| "YOLO11l-seg summary: 667 layers, 27,617,459 parameters, 27,617,443 gradients, 142.7 GFLOPs\n", | |
| "\n", | |
| "Transferred 1071/1077 items from pretrained weights\n", | |
| "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs\\segment\\train11', view at http://localhost:6006/\n", | |
| "Freezing layer 'model.23.dfl.conv.weight'\n", | |
| "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", | |
| "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "100%|██████████| 5.35M/5.35M [00:00<00:00, 9.93MB/s]\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed \n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\u001b[34m\u001b[1mtrain: \u001b[0mScanning C:\\Lynxdrone\\python\\ultralytics\\crack-2\\train\\labels.cache... 3717 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3717/3717 [00:00<?, ?it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "\u001b[34m\u001b[1mval: \u001b[0mScanning C:\\Lynxdrone\\python\\ultralytics\\crack-2\\valid\\labels.cache... 200 images, 1 backgrounds, 0 corrupt: 100%|██████████| 200/200 [00:00<?, ?it/s]\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Plotting labels to runs\\segment\\train11\\labels.jpg... \n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x0000021BAD0B7100>\n", | |
| "Traceback (most recent call last):\n", | |
| " File \"c:\\Users\\Ashvin\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py\", line 1477, in __del__\n", | |
| " self._shutdown_workers()\n", | |
| " File \"c:\\Users\\Ashvin\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py\", line 1435, in _shutdown_workers\n", | |
| " if self._persistent_workers or self._workers_status[worker_id]:\n", | |
| " ^^^^^^^^^^^^^^^^^^^^\n", | |
| "AttributeError: '_MultiProcessingDataLoaderIter' object has no attribute '_workers_status'\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n", | |
| "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 176 weight(decay=0.0), 187 weight(decay=0.0005), 186 bias(decay=0.0)\n", | |
| "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added \n", | |
| "Image sizes 416 train, 416 val\n", | |
| "Using 8 dataloader workers\n", | |
| "Logging results to \u001b[1mruns\\segment\\train11\u001b[0m\n", | |
| "Starting training for 50 epochs...\n", | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 1/50 6.35G 1.482 1.938 1.499 1.434 15 416: 100%|██████████| 233/233 [01:11<00:00, 3.27it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.60it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.000583 0.108 0.000328 9.36e-05 0.000151 0.0281 7.82e-05 1.88e-05\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 2/50 6.1G 1.554 1.744 1.422 1.471 12 416: 100%|██████████| 233/233 [01:06<00:00, 3.50it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:02<00:00, 3.48it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.276 0.469 0.244 0.0855 0.237 0.402 0.159 0.04\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 3/50 6.1G 1.44 1.702 1.327 1.412 20 416: 100%|██████████| 233/233 [01:03<00:00, 3.69it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.58it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.488 0.474 0.41 0.143 0.42 0.386 0.27 0.0616\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 4/50 6.06G 1.357 1.67 1.257 1.371 13 416: 100%|██████████| 233/233 [01:01<00:00, 3.78it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:02<00:00, 3.18it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.404 0.506 0.369 0.146 0.303 0.39 0.25 0.0542\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 5/50 6.16G 1.25 1.581 1.134 1.317 8 416: 100%|██████████| 233/233 [01:01<00:00, 3.78it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.63it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.479 0.57 0.465 0.201 0.402 0.562 0.316 0.0861\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 6/50 6.17G 1.203 1.59 1.091 1.285 13 416: 100%|██████████| 233/233 [01:01<00:00, 3.79it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.60it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.515 0.606 0.489 0.243 0.415 0.533 0.344 0.1\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 7/50 6.15G 1.148 1.562 1.045 1.256 7 416: 100%|██████████| 233/233 [01:02<00:00, 3.73it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.65it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.675 0.659 0.63 0.369 0.61 0.574 0.52 0.16\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 8/50 6.05G 1.116 1.552 1.004 1.242 18 416: 100%|██████████| 233/233 [01:01<00:00, 3.81it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.62it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.663 0.641 0.603 0.343 0.553 0.566 0.45 0.153\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 9/50 6.17G 1.105 1.541 0.9773 1.225 12 416: 100%|██████████| 233/233 [01:01<00:00, 3.82it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.56it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.757 0.701 0.702 0.423 0.675 0.625 0.532 0.175\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 10/50 6.17G 1.08 1.508 0.9605 1.231 10 416: 100%|██████████| 233/233 [01:01<00:00, 3.80it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.67it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.676 0.651 0.62 0.378 0.575 0.538 0.441 0.144\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 11/50 6.16G 1.028 1.504 0.9235 1.188 11 416: 100%|██████████| 233/233 [01:03<00:00, 3.69it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:02<00:00, 3.45it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.742 0.602 0.628 0.385 0.636 0.512 0.49 0.152\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 12/50 6.05G 1.031 1.495 0.9071 1.195 13 416: 100%|██████████| 233/233 [01:03<00:00, 3.69it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.61it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.759 0.659 0.68 0.423 0.729 0.622 0.609 0.185\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 13/50 6.17G 1.026 1.509 0.897 1.189 9 416: 100%|██████████| 233/233 [01:02<00:00, 3.70it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:02<00:00, 3.43it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.79 0.687 0.686 0.414 0.729 0.61 0.57 0.173\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 14/50 6.17G 1.004 1.484 0.8781 1.177 14 416: 100%|██████████| 233/233 [01:02<00:00, 3.76it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.68it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.73 0.687 0.654 0.403 0.638 0.61 0.531 0.173\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 15/50 6.17G 0.9879 1.465 0.8442 1.174 18 416: 100%|██████████| 233/233 [01:00<00:00, 3.83it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.73it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.805 0.661 0.727 0.462 0.772 0.597 0.63 0.206\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 16/50 6.04G 0.9593 1.446 0.8448 1.153 13 416: 100%|██████████| 233/233 [01:00<00:00, 3.84it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.63it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.783 0.68 0.731 0.48 0.722 0.622 0.601 0.209\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 17/50 6.16G 0.9747 1.473 0.8451 1.16 8 416: 100%|██████████| 233/233 [01:00<00:00, 3.84it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.71it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.754 0.675 0.706 0.487 0.682 0.606 0.547 0.188\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 18/50 6.16G 0.966 1.447 0.8299 1.155 14 416: 100%|██████████| 233/233 [01:02<00:00, 3.71it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.52it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.722 0.739 0.729 0.484 0.696 0.635 0.625 0.209\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 19/50 6.15G 0.9531 1.447 0.8167 1.146 12 416: 100%|██████████| 233/233 [01:04<00:00, 3.60it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:02<00:00, 3.37it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.791 0.703 0.755 0.489 0.681 0.639 0.6 0.191\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 20/50 6.04G 0.9581 1.454 0.8261 1.15 19 416: 100%|██████████| 233/233 [01:05<00:00, 3.55it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.50it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.802 0.684 0.745 0.508 0.75 0.635 0.656 0.234\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 21/50 6.16G 0.9326 1.446 0.8103 1.134 9 416: 100%|██████████| 233/233 [01:01<00:00, 3.77it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.50it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.833 0.703 0.74 0.5 0.745 0.632 0.607 0.215\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 22/50 6.16G 0.9158 1.456 0.7854 1.119 15 416: 100%|██████████| 233/233 [01:01<00:00, 3.77it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.57it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.796 0.675 0.729 0.508 0.738 0.627 0.618 0.208\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 23/50 6.15G 0.9287 1.419 0.7747 1.137 15 416: 100%|██████████| 233/233 [01:02<00:00, 3.76it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:02<00:00, 3.46it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.789 0.699 0.725 0.494 0.72 0.639 0.603 0.202\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 24/50 6.04G 0.911 1.432 0.7697 1.124 22 416: 100%|██████████| 233/233 [01:02<00:00, 3.73it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:02<00:00, 3.50it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.773 0.725 0.741 0.518 0.693 0.634 0.601 0.214\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 25/50 6.16G 0.8942 1.426 0.7549 1.123 18 416: 100%|██████████| 233/233 [01:02<00:00, 3.75it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.54it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.773 0.671 0.71 0.471 0.734 0.594 0.588 0.206\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 26/50 6.15G 0.8875 1.416 0.7442 1.098 19 416: 100%|██████████| 233/233 [01:02<00:00, 3.71it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.58it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.736 0.727 0.728 0.501 0.678 0.668 0.641 0.221\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 27/50 6.14G 0.8783 1.389 0.736 1.102 12 416: 100%|██████████| 233/233 [01:02<00:00, 3.72it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.50it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.822 0.687 0.756 0.554 0.785 0.644 0.655 0.229\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 28/50 6.04G 0.8795 1.417 0.7298 1.109 14 416: 100%|██████████| 233/233 [01:02<00:00, 3.73it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.72it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.793 0.695 0.729 0.526 0.719 0.646 0.645 0.219\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 29/50 6.16G 0.8584 1.382 0.7227 1.095 14 416: 100%|██████████| 233/233 [01:01<00:00, 3.78it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.70it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.792 0.715 0.752 0.562 0.77 0.627 0.632 0.229\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 30/50 6.15G 0.8685 1.405 0.7262 1.101 21 416: 100%|██████████| 233/233 [01:01<00:00, 3.77it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.60it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.79 0.703 0.746 0.539 0.747 0.629 0.637 0.23\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 31/50 6.16G 0.8513 1.383 0.7058 1.098 20 416: 100%|██████████| 233/233 [01:01<00:00, 3.77it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.64it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.859 0.709 0.755 0.567 0.792 0.639 0.642 0.235\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 32/50 6.05G 0.8513 1.378 0.7149 1.098 11 416: 100%|██████████| 233/233 [01:01<00:00, 3.77it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.64it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.743 0.731 0.767 0.553 0.741 0.599 0.66 0.22\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 33/50 6.16G 0.8515 1.384 0.6935 1.101 7 416: 100%|██████████| 233/233 [01:01<00:00, 3.79it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.72it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.783 0.679 0.747 0.54 0.714 0.618 0.616 0.216\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 34/50 6.17G 0.8349 1.382 0.6818 1.085 14 416: 100%|██████████| 233/233 [01:01<00:00, 3.78it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.68it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.838 0.703 0.776 0.588 0.793 0.622 0.649 0.225\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 35/50 6.16G 0.8336 1.377 0.6883 1.082 12 416: 100%|██████████| 233/233 [01:01<00:00, 3.78it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.68it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.801 0.703 0.755 0.553 0.741 0.655 0.656 0.225\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 36/50 6.04G 0.8338 1.37 0.6845 1.082 16 416: 100%|██████████| 233/233 [01:01<00:00, 3.76it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.68it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.762 0.731 0.746 0.561 0.791 0.637 0.676 0.233\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 37/50 6.16G 0.8153 1.356 0.653 1.078 10 416: 100%|██████████| 233/233 [01:01<00:00, 3.80it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.75it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.823 0.687 0.748 0.551 0.76 0.635 0.639 0.215\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 38/50 6.16G 0.8175 1.352 0.6683 1.079 10 416: 100%|██████████| 233/233 [01:01<00:00, 3.79it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.67it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.852 0.717 0.781 0.591 0.79 0.667 0.687 0.238\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 39/50 6.17G 0.7947 1.346 0.6523 1.07 19 416: 100%|██████████| 233/233 [01:01<00:00, 3.78it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.64it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.801 0.745 0.77 0.58 0.763 0.655 0.636 0.233\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 40/50 6.05G 0.7966 1.362 0.6407 1.065 10 416: 100%|██████████| 233/233 [01:01<00:00, 3.78it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.70it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.784 0.756 0.775 0.585 0.748 0.655 0.658 0.245\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Closing dataloader mosaic\n", | |
| "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 41/50 6.17G 0.7404 1.058 0.7121 1.168 9 416: 100%|██████████| 233/233 [01:02<00:00, 3.72it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.68it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.816 0.739 0.782 0.592 0.733 0.643 0.644 0.245\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 42/50 6.15G 0.7213 1.053 0.693 1.157 5 416: 100%|██████████| 233/233 [01:01<00:00, 3.81it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.76it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.79 0.771 0.796 0.603 0.771 0.667 0.67 0.25\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 43/50 6.15G 0.7141 1.044 0.66 1.155 8 416: 100%|██████████| 233/233 [01:01<00:00, 3.81it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.68it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.804 0.735 0.775 0.609 0.731 0.655 0.658 0.241\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 44/50 6.04G 0.6967 1.035 0.6433 1.14 8 416: 100%|██████████| 233/233 [01:01<00:00, 3.78it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.52it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.844 0.743 0.804 0.63 0.783 0.687 0.701 0.254\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 45/50 6.16G 0.684 1.037 0.6343 1.13 5 416: 100%|██████████| 233/233 [01:01<00:00, 3.77it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.58it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.784 0.747 0.783 0.613 0.728 0.695 0.678 0.249\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 46/50 6.15G 0.6696 1.036 0.6135 1.119 6 416: 100%|██████████| 233/233 [01:01<00:00, 3.79it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.62it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.793 0.738 0.786 0.609 0.728 0.678 0.67 0.248\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 47/50 6.15G 0.6603 1.03 0.6019 1.115 6 416: 100%|██████████| 233/233 [01:02<00:00, 3.74it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.58it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.815 0.755 0.79 0.617 0.729 0.691 0.664 0.249\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 48/50 6.05G 0.649 1.014 0.5888 1.112 9 416: 100%|██████████| 233/233 [01:02<00:00, 3.70it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.54it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.811 0.759 0.797 0.628 0.729 0.695 0.673 0.244\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 49/50 6.16G 0.6376 1.007 0.5826 1.108 5 416: 100%|██████████| 233/233 [01:04<00:00, 3.59it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:02<00:00, 3.46it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.82 0.743 0.799 0.623 0.761 0.675 0.681 0.244\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| " Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 50/50 6.16G 0.6242 1.012 0.5694 1.089 5 416: 100%|██████████| 233/233 [01:01<00:00, 3.79it/s]\n", | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.68it/s]" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.787 0.779 0.802 0.634 0.75 0.695 0.689 0.253\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "50 epochs completed in 0.924 hours.\n", | |
| "Optimizer stripped from runs\\segment\\train11\\weights\\last.pt, 55.8MB\n", | |
| "Optimizer stripped from runs\\segment\\train11\\weights\\best.pt, 55.8MB\n", | |
| "\n", | |
| "Validating runs\\segment\\train11\\weights\\best.pt...\n", | |
| "Ultralytics 8.3.23 Python-3.12.0 torch-2.4.1+cu118 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 11264MiB)\n", | |
| "YOLO11l-seg summary (fused): 491 layers, 27,585,363 parameters, 0 gradients, 141.9 GFLOPs\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:02<00:00, 2.79it/s]\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " all 200 249 0.787 0.779 0.803 0.634 0.732 0.711 0.681 0.252\n", | |
| "Speed: 0.1ms preprocess, 4.0ms inference, 0.0ms loss, 1.7ms postprocess per image\n", | |
| "Results saved to \u001b[1mruns\\segment\\train11\u001b[0m\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Load a pretained model from YOLO\n", | |
| "model = YOLO(\"yolo11l-seg.pt\")\n", | |
| "\n", | |
| "# Train the model with specified parameters.\n", | |
| "train_results = model.train(\n", | |
| " data=f\"{HOME}\\crack-2\\data.yaml\", # Path to the dataset YAML file.\n", | |
| " epochs=50, # Number of training epochs.\n", | |
| " imgsz=416, # Image size for training.\n", | |
| " device=0, # Device to run on (GPU or CPU).\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "V_Rd0sGi38GZ", | |
| "metadata": { | |
| "id": "V_Rd0sGi38GZ" | |
| }, | |
| "source": [ | |
| "## Inference with Custom Model\n", | |
| "\n", | |
| "In this section, we will perform inference using the trained YOLO model to make predictions on new images. We will utilize the model to detect and segment objects in the input images, showcasing its capabilities in identifying surface cracks. The results will be displayed visually for easier interpretation and analysis.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "id": "t_CMu2JH48X6", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 217 | |
| }, | |
| "id": "t_CMu2JH48X6", | |
| "outputId": "cc8e46da-edb7-47ab-aaad-52df8269ad14" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAADhAAAASwCAYAAACg1uGaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAB7CAAAewgFu0HU+AAEAAElEQVR4nOzdd1QUV98H8O+y9A4qYMHeK/ZeUGOLvSZq1Bh9NLHER2M0JjGaPDExRmONJRbsscQSEzWxF2LDgg0VFRUVUJqASN15/+BlsgtbZpdZyvL9nMM5szt37r0zu8xv7uzcexWCIAggIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIii2JV0BUgIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi+bEDIRERERERERERERERERERERERERERERERERERkQViB0IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiILxA6EREREREREREREREREREREREREREREREREREREFogdCImIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiCwQOxASERERERERERERERERERERERERERERERERERFZIHYgJCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiskDsQEhERERERERERERERERERERERERERERERERERGSB2IGQiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjIArEDIRERERERERERERERERERERERERERERERERERkQViB0IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiILxA6EREREREREREREREREREREREREREREREREREREFogdCImIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiCwQOxASERERERERERERERERERERERERERERERERERFZIHYgJCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiskDsQEhERERERERERERERERERERERERERERERERERGSB2IGQiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjIArEDIRERERERERERERERERERERERERERERERERERkQViB0IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiILxA6EREREREREREREREREREREREREREREREREREREFogdCImIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiCwQOxASERERERERERERERERERERERERERERERERERFZIHYgJCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiskDsQEhERERERERERERERERERERERERERERERERERGSB2IGQiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjIArEDIRERERERERERERERERERERERERERERERERERkQViB0IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiILxA6EREREREREREREREREREREREREREREREREREREFogdCImIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiCwQOxASERERERERERERERERERERERERERERERERERFZIHYgJCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiskDsQEhERERERERERERERERERERERERERERERERERGSB2IGQSI8OHTpAoVBAoVDg5MmTBV0drU6ePCnWsUOHDgVdnSKjKHy2REQFiedJy/Po0SPxM61YsWJBV4eIqMgpbrGxKO0v28VEROZXlOKCMbL3SaFQGEwbExODuXPnonnz5vDw8IBSqRS3DQgIAJC/7S5j6k5EZAl43qNRo0blir3mYqnfN5VKhcaNG0OhUMDPzw8qlSpfy1+1apV4XM+ePZuvZROR8Sz1XEjSzJkzR/z858yZozWNqfdl+/btC4VCgdKlS+P169fyVFiiw4cPi3XeunVrvpZNRIYx9hDbfXlX0O0+XWbOnAmFQgF7e3s8ePCgoKtDRLDc8yBJY6ltPl14X/Jf7EBIVEQEBweLJ65x48YVdHWIiIjyDWMgERGRJsZGIiIqzgoiDj58+BANGjTAnDlzcPHiRcTHxxeaBy+IiKhoadiwIRQKBVxdXZGWllbQ1aFiZs2aNbhy5QoAYP78+bCy0v64iPoDZNr+lEolPDw8ULduXYwcORIHDhxAZmamwfLHjBmDatWqAQAmTZokaRsiorxi7C1cDh8+jP379wPIemDVyclJa7qKFSvqjUVWVlZwc3ND9erVMWTIEGzfvh2pqakGy+/WrZv44Ounn36KpKQk2faNiCgbYw8VJFPbfba2toiOjjaqrKZNm+bKR9cAhDNnzoSHhwdSU1Px3//+16hyiIj0YdwtXPLS5gsODjaqrEGDBuXKQ9cABLwv+S92IKR8of5P/ujRo4KuTpF04MABcbl3794FWBMiIjIGY2DeMQYSEVkWxsa8Y2wkIiq6GAfzriDi4Lhx4/Ds2TMAgIODA3r16oXx48djwoQJmDBhAmrVqpUv9SAiKmwsdYZacwkPD8e1a9cAAF27doWtrW3BVoiKlaSkJMyePRsA0KJFC3Tt2tXkvFQqFeLj43Hr1i1s2rQJvXv3RrNmzXDnzh2921lbW+Ozzz4DAFy7dg1btmwxuQ5ExRVjr3EYewsXlUqFTz75BADg6+uL0aNHm5yXIAhISEhAaGgodu7ciaFDh6JOnTr4559/DG6bHQ+fP3+OhQsXmlwHouKCscc4jD1UkPLS7ktPT8f27dslp799+zaCgoIkp3d3d8ekSZMAZN3jP3XqlORtiYoTxl3jMO4WLnlt823atEly2ri4OI3fjA3hfcl/WRd0BYhImt9//x0A4OjoiE6dOhVwbYiIiPIPYyAREZEmxkYiIirO8jsORkRE4OjRowAAOzs7BAcHiyNUEhERGYODwVBBWrJkCV6+fAkga+YHqfr27YuyZctqvJeRkYEXL17g/PnziIiIAABcuXIF7du3x8WLF1GhQgWd+Q0fPhxffvklnj17hrlz52Lo0KGwsbExYY+IiAxj7C1ctm/fjlu3bgEApk2bJvn836lTJ9SsWVPjvczMTMTExCAoKAhhYWEAgAcPHqBLly44efIkmjRpojM/f39/NGvWDBcvXsSiRYswadIkeHp6mrhXRESaGHuoIJna7su2adMmsZOflLTGmjx5Mn788UckJyfj888/x9mzZ43Og4hIHeNu4WJqmy/btm3b8MMPP0CpVBpMu2PHDkmz0Kvjfcks7EBIpEdh6b0fGRkpjtbx1ltvwd7evoBrRERElo4xkIiISBNjIxERFWfFOQ5evXpVXG7bti07DxIRFRBBEAq6CnmW/UCLUqlEjx49Crg2RU9AQAACAgLypSxL+L6pS0lJwZIlSwAA5cuXR69evSRv+/HHH6NDhw5a16lUKgQEBODDDz9EWloaXrx4genTp2Pnzp0687OxscGYMWMwd+5chIWFYefOnRg2bJhR+0NE+cMSzoWMvYXL/PnzAWQNCPT+++9L3m748OEYNWqUzvUHDhzAe++9h1evXuH169f46KOPcPHiRb15fvjhh7h48SISEhKwcuVKfP7555LrQ0Tmw9hDbPeZLi/tvtq1a4szCoaEhKBWrVp606tUKmzduhUA4OXlJXbsN6REiRIYNGgQNm7ciMDAQAQGBqJ169aS60lE8rKE8yDjbuFiapsvOw5FRkbi77//Rvfu3Q1uk92R3dbWFuXLl8f9+/cNbsP7klmsCroCRGTYgQMHxEDNHvJERFScMAYSERFpYmwkIqLirCDiYFxcnLhcunTpfCmTiIgsT1JSEk6cOAEAaNWqFUqUKFHANaLiZOvWreIsFO+//z6srOR5TMTKygqjR4/G3Llzxfd+//13JCUl6d1u9OjRUCgUAIDFixfLUhciopwYewuXY8eO4caNGwCAQYMGwdXVVba8e/XqhVWrVomvL126hNDQUL3bDB48GC4uLgCAFStWID09Xbb6EFHxxdhDBSkv7b733ntPXJYys+CxY8fw9OlTAMDQoUNhbS19LqMxY8aIy2wPElFeMO4WLnlp8xkbh0JDQ3Hu3DkAQI8ePYz67Hlfkh0IiYqE7B7yVlZWePvttwu4NkRERPmHMZCIiEgTYyMRERVnBREH1R8ilOtheyIiKn7+/vtvpKamAoBRswAQyWHdunXi8pAhQ2TPf+jQoeJyamoqHjx4oDd9+fLl0aJFCwBAUFCQ+HAREZGcGHsLF3PHov79+8POzk58ffv2bb3pHR0d0bNnTwBAREQEDh8+LHudiKj4YeyhgpSXWPv222+jZMmSALI6IqpUKr3p1Tt3jBw50qiyWrdujTJlygAA9u/fL2nmQiIibRh3C5e8xCE/Pz/Ur18fQFZsSEhI0Js+L3GI9yXZgZDM6NGjR1AoFFAoFHj8+LH4fqVKlcT31f9OnjwpplF/P1twcDA+/vhj1K1bF56enlAoFOjbt2+uci9fvozvvvsOPXv2ROXKleHs7AxbW1t4e3ujVatW+Pzzz/HkyRNJ+9ChQwet9VM3atQoMU329OnJycn4+eef0aZNG3h7e8POzg6+vr549913ERgYKKnsbG/evMHRo0cBAM2aNYO3t7fBbQRBwJ49e9C7d29UqFAB9vb28PHxQZcuXbBp0yaDF/g5nT9/HhMnTkSdOnXg4eEBe3t7lCtXDt26dcPy5cvx+vVrndtOnDhRPD7lypVDbGys3rLCwsLg5uYmbvPjjz8aVVdziY6Oxvfff4/27dujdOnSsLOzQ8mSJdGwYUNMnz7d4M1HdXfu3MGnn36KFi1aoGTJkrC1tYW9vT28vLzQuHFjvP/++9i4caPG6OY5JSUlYdWqVXj77bdRvnx5ODo6wsbGBm5ubqhZsyZ69eqFefPm4ebNm3LsPhEZiTEwf2PgpUuXMHHiRDRq1AgeHh6wtraGg4MDSpcujRYtWuDDDz/Ezp079carbK9fv8bKlSvRq1cvVKhQAY6OjnBxcUG1atUwevRoHD9+3Kh9ePr0KWbOnIl69erB1dUVrq6uqFOnDv773//izp07ADS/LxUrVjQqf3PLyzWAOkEQsG/fPgwdOhQ1atSAq6srlEolnJycULFiRXTs2BEzZszAiRMn9F6nyBFDiahgMDYWTPvw4cOHmDNnDtq1a4eyZcvC3t4ejo6OqFy5Mvr27Ytly5bhxYsXRtUhp/T0dGzZsgX9+/cXj7G1tTVcXFxQtWpVdO3aFbNnz8bFixfzVI7c/vrrL4wePRrVq1eHq6srHBwcUKFCBfTr1w8BAQGSR5yWa//lvJ4hosKHcTD/4+CrV6/w3XffoWnTpvDw8ICzszNq1KiBsWPH4vLlywbLOnnypLgv77//vvj+xo0bc31eo0aNMmo/CsqtW7cwffp0NGzYECVLloSdnR3KlCmDDh06YP78+UY9qHH8+HF88MEHqFevHtzd3WFtbQ1HR0eUK1cObdu2xZQpU/DHH38gLS1NZx7h4eGYO3cu2rVrJ343bG1tUaJECTRo0ABDhw7FypUrERkZKcfuExUK2s7p165dw4cffogaNWrA2dkZzs7OaN68OX7++WdkZGTkyiMoKAijRo1CrVq14OTkhBIlSsDf3x9bt241uj6XLl3Cf//7X/j5+aFUqVKwtbWFj48P2rdvj/nz5+u9v5K9H6dOnRLf8/f31xrXsmNCtooVK4rrHj16BAB48OABPv/8czRs2BClSpWClZUV/Pz8DB4/fVJSUrB+/XoMHjwYVapUgaurK2xtbeHl5YW2bdti5syZuHDhgs7t5bqflS27EzygfRZdbcflzp07mDJlCmrXri3eW6xfvz6++OILSedHbbE5Pj4eS5YsEduJ1tbWUCgUiI+P13oM9u7di5EjR6J69epwc3ODvb09fH190bdvX2zcuFHr91SfqKgo/PDDD3jrrbdQvnx5ODg4wMHBAeXLl0f37t3xww8/iPsvZX+0keOzM/b7JkecDQgI0Hp9sXfvXvTq1Qvly5eHnZ0dvLy80KVLF2zZskWcmVmfhw8fiiNyV69eHbVq1ZK0T8bIOUuzlLZrv379xOUtW7bIXicigLE3G2Ov9Ngr5bhkk/s3RQC4fv06Zs6ciebNm8PHxwe2trZie3bIkCFYt24dXr16pXP7x48fY+XKlXj33XdRt25duLm5wcbGBiVKlEC9evXw4Ycf4vz580bXK69ev36Nffv2AQBcXFzQqVMn2cvIbk+ql2kIYxGZA2NPFsYetvvY7tOusLb7bGxs8M477wDIum+bPaOXNklJSdi7dy8AoF69ejqvlXRR/z0jPT0dO3fuNGp7InWMu1kYd9nms4Q234gRIwBk/R68a9cunekEQRDbbyVKlDBpwNli3xYUiMwkLCxMACD578SJE+K26u8LgiB89dVXglKpzLVNnz59NMps2rSppLJsbGyE+fPnG9yH9u3ba62fupEjR4ppNmzYINy6dUuoVauW3vJnz54t+Tj+/vvv4nbffvttrvUnTpwQ17dv315ISEgQ+vTpo7f8li1bClFRUQbLTkpKEoYMGWLweJYuXVo4ePCg1jzevHkj1KlTR0zbv39/neWlp6cLLVq0ENN26tRJUKlUko+VMaR8ttnWrVsnuLm56T0GSqVSmDJlipCRkaE3L13fZW1/w4YN05rHP//8I5QtW1by/1Z6erqph4mITMQYmD8xMD09XfjPf/4j+Th//vnnesvbuXOn4OPjYzCfnj17CvHx8Qbrv337dsHFxUVnPnZ2dsIvv/yi8X2pUKGC5ONjLGPKkeMaIFtkZKTQsmVLyZ/TkSNHtOYjRwwlooLD2Jg/sTFbSkqKMGHCBMHa2lrS/ickJJi0v3fv3jW4f+p/oaGhkvfVGDnbxfpERUUJnTp1MljXatWqCZcuXdKblxz7L/f1DBEVToyD+RsHz5w5I5QpU0ZnmVZWVsLcuXMFQch9fLOpxxZDfyNHjhS3y6/2nb6655Seni5MmjTJYHvK3d1dCAgI0JtXUlKS0Lt3b8nH5pdfftGaz+rVqwUHBwdJebRu3drkY0RU2OT8v50/f77e/82uXbsKKSkpgiAIQkZGhvDhhx/q/X955513DP5GIQiCEBsbKwwYMMDg/5+7u7uwa9cug/ti6G/Dhg0a21aoUEFcFxYWJqxevVqwt7fPtV2DBg30Hj99fvvtN8m/o6xcuTLX9nLdz8qWmZkplCpVSgCy2hra5Dwua9asEezs7HSW6eHhIezfv19vuTlj89mzZwVfX1+t+cXFxWlsGxwcLPj5+Rnc9xo1agi3bt3S/4H8/zGYO3eu4OjoaDBPKysrrXnm3B9t5PrspH7f5IyzGzZsENOPHDlSiI+PNxh3u3XrJiQnJ+vNd8mSJWL68ePH602rbf8N/Y4qCILw5MkTjW0ePHhgcJugoCAxfe3atSXVi8hYOf+XGXsZe+U4LoIg/2+KcXFxwpAhQwSFQmEwT29vb615fPLJJ5K2z/7uvn79Wm+dvvrqKzH9V199pTWN1Puy+/fv14hdUqh/Prrivrq0tDSNz+/YsWMGt3n58qV4zNzc3PhsDcmCsScLYw/bfYbyZLuvcLX7QkJChIsXL4qvR4wYoXO7gIAAMd2CBQsEQRAEb29v8T0pbcjdu3eL6Xv06CGpvkTa5Pw/Ztxl3JXjuAgC23zZ8qvNd+jQISEiIkL8/23Xrp3O7U6ePCluN2HCBEEQBKF58+Y6/z+0Ke73Ja1BZCaurq6YMGECgKypQhMTEwFk9RB2cXHJlb5s2bJa81mwYAHmzp0LAKhSpQqaNWsGR0dHPHr0CDY2Nhpps0fMtrOzQ506dVC1alW4ublBEARERETgwoULiI6ORnp6OmbMmAEA+PTTT+XZYQDPnz9H586dERERAXd3d7Rt2xY+Pj6Ijo7G8ePHxV7hX3/9NWrXri1pilZDPeRzGjVqFPbv3w+FQoFmzZqhdu3aSE1NxT///CP2oD937hw6deqEwMBAuLq6as0nOTkZHTt21JgloUyZMmjbti2cnZ1x//59nD17FpmZmYiIiEDv3r2xfft2DBw4UCMfe3t7bNu2Dc2aNUNqair27NmDtWvXYsyYMbnK/Prrr8We7yVKlMCmTZskj2xgLj/++COmT58uvrazs0P79u1Rvnx5xMXF4cSJE4iNjUVmZiYWL16MJ0+eYPfu3VrrvWTJEvG7DAAlS5ZEixYtULp0aSgUCsTGxuLOnTsICQlBZmam1vqEh4eja9eu4v+TjY0NmjZtiqpVq8LR0RGvX7/Go0ePEBwcbHAKXyIyH8bA/ImB06dPx5o1a8TXZcuWRbNmzVCqVCmoVCrExMTg9u3buHv3rsGyfvrpJ0ybNk0cucvV1RUtW7ZEuXLlkJmZiVu3biEoKAiCIOCPP/5Ahw4dEBgYCEdHR6357d27F8OHDxfP50qlEq1bt0a1atWQlJSEwMBAPH36FGPHjsWyZcsM1i8/yXUNAACZmZl4++23NWb4qFu3LurWrQt3d3ekpKQgMjISwcHBiIiI0FknOWIoERUsxsb8ax8mJSWhS5cu4iiTAODo6IjWrVvD19cXgiDg2bNnuHz5MmJiYpCenm7SuTMxMRGdO3dGeHg4AMDKygoNGzZErVq14OzsjOTkZDx79gzBwcGIjo42On9ziIqKQuvWrfHgwQPxvSpVqqB58+aws7PD7du3xRHxQkND4e/vj8OHD6N169a58pJr/+W8niGiwotxMP/i4OXLl9G9e3ckJSWJ7zVp0gT16tVDWloazp8/jwcPHuCrr76Ch4eHzrLKli0rfmZ37tzBsWPHAAA1a9bMNWpmixYtDNa9oKhUKgwYMAC///67+J6npyc6dOgAT09PcTTptLQ0xMfHY9SoUYiPj8fHH3+sNb/hw4dr5FW1alU0bNgQnp6eSE9Px8uXL3Hjxg2dI4gDwL59+zBu3DjxtXr729raGq9evcK9e/dw8+ZNvTMYEhV1q1evFs+/9evXh5+fH5RKJS5cuIDbt28DyJo1e/LkyVi9ejU++ugjrFmzBlZWVmjatClq1aoFlUqFM2fOICwsDADw66+/okGDBpg5c6bOciMjI9GxY0eEhISI79WpUwcNGjSAs7MzXrx4gTNnziAmJgbx8fEYPHgwNm/ejGHDhmnkk32O3Lt3L54/fw4A6Nu3r9YYpm/U+127donxp0yZMmjdujXc3Nzw/PlzxMbGGjyO2ixcuBDTp08X7/MpFArUr18fderUgbOzM2JjY3Hjxg3xGjslJUVje7nuZ6k7f/48Xr58CUDab3379+/HlClTAGTFpDZt2sDZ2Rn37t1DYGAgVCoV4uLiMHDgQBw4cABdu3Y1mOf9+/cxZcoUvHr1Ci4uLmjXrh3KlCmDuLg4nD59WiPt6dOn0atXL/F3puzfoqpVqwYbGxs8evQIZ8+eRUpKCu7evYtWrVrh3LlzOj/rzMxMDBo0SJyhAMiaJahly5aoWLEibGxsEBkZicuXLyMiIgIqlcqkGGCOz04fueOsuoyMDAwYMADHjh2Dra0tWrVqhSpVqiAlJQVnzpwRr/UOHz6MqVOnYuXKlTrzOnLkiLjctm3bPOyxbjt27BCXy5Yti0qVKhncpmHDhnB2dkZSUhJu376N58+fo0yZMmapHxHA2JuNsVc7Y46L3L8pPn/+HB07dtS4/+fu7o7WrVujdOnSSE9Px5MnT3D58mUkJCTkOn7ZwsPDIQgCFAoFatSogRo1aqBEiRKwsbFBTEwMrl69Kt4X/fXXX5GQkIA//vgjX57JyY9YtH//fvHY2Nvbo0mTJga3KVmyJGrWrImQkBC8evUKFy9eRKtWrcxSPyqeGHuyMPZox3Yf232Frd2Xfd4JCQnBnj178PPPP8PJySlXuo0bNwLIeg4q53lDKvU6njx5EhkZGbC2ZncGyhvG3SyMu9qxzWdecsQhHx8fvPXWWzh8+DDOnDmDR48eoWLFirnSZcch4N9ZC41V7O9L5nuXRSqWcvbiNgRqvaCtra0FNzc3Ye/evbnSZY8EkO3DDz8U/vzzT50jbmRkZAgbNmwQnJycBCBrhO2HDx/qrIexI2tnjwgzY8aMXD23Y2JihI4dO4ppK1eubHB2PZVKJZQuXVoAIFSqVElrGvXe3ba2tmJabTMl/PLLL4KNjY2Y/j//+Y/OstVHVlAqlcLixYuFzMxMjTT37t0TGjduLKZzdXXV+fkuXrxYTOfk5CTcvXtXY/2ZM2c0Rn7Ys2eP3mOTV1I+28DAQI06de/eXYiMjNRIk5KSIkyfPl3jO7tw4cJceaWnpwslS5YU03z33XdCWlqa1nJjYmKE9evXax39fcqUKWIebdu2FZ49e6Y1j/T0dOHkyZPCsGHDJI2AQUTmwxhonhgYHR0tzqqkVCqFgIAAnXk+f/5cWLp0qbB27Vqt648ePSpYWVmJsfT777/XOgLL1atXhdq1a4v78eGHH2rN78WLF0KJEiXEdA0bNhTu3buXa/+WLl0qKJVKjRHlCsMMhHJeA+zbt09MU7p0aeH8+fM6y71586YwY8YM4cKFCxrvyxVDiajwYGw0X/tQEASNGWSVSqUwd+5cISkpKVe6zMxM4fjx40KfPn20johmaH/V23i1a9cW7ty5o7PeFy9eFD788EPhyZMnevfRVFJHPevevbtGu3T79u250ly6dEmoXLmymM7X1zfXiKyCIM/+y3k9Q0RFB+Og+eJgamqqxoyHvr6+wj///JMr3caNGwU7OzvxXmr2ny45R4XWp7DNQDh//nyNdDNnzhRSU1M10kRERAhdunTR+J5pa7tdu3ZNTOPs7Kx3NvoHDx4I//vf/4Tff/891zr1Uc0nTpyocwTUxMREYefOncKMGTN0lkNU1Kj/P9rZ2Qk+Pj5az6k//vijxv/kokWLBABCrVq1hGvXrmmkzcjI0PjdwNnZWev1vyBktQH8/f3FtM2aNROuXLmSK92bN2+EOXPmiKMZOzk56YwRUuJDTuqx0NraWrC1tRXWrFmTKxbkjG1Sznt//vmnxijMHTt2FG7fvq017cOHD4Uvv/wy1+wActzPymnGjBlinqdOndKaRv242NraClZWVsLChQtz3Ze7deuWUKdOHTGtj4+PEBsbqzVP9dic3faYMGGCkJiYqJEuLS1NLCciIkLw8vIStxsxYoTw/PnzXHlHRkYK/fr1E9PVq1dP5+9R6vufff6Pjo7WmvbChQvCiBEjhJs3b+rdH20jScv52eV3nBUEzWuO7Gup7t27C0+fPtVIl56eLnzyySdiWoVCofe6Uv3zvHHjhs50uvZf3/+2SqUStmzZonGPe8mSJZLKEARBY+R5bde4RHnF2JuFsddw7JV6XOT+TTE9PV1o3bq1mM7BwUFYvny51t/BUlNThd9//13o27ev1rx++OEHYcOGDcLLly91HpfTp08LVatWFcvbvHmzzrRyzkbRtGlTMd2BAwd0plNnzAyEhw8fFjw9PcX0//3vfyWVIQiC8O6774rb/fTTT5K3I9KFsScLYw/bfWz3Fa12X0hIiCAIgjBv3jzxvU2bNuXa5smTJ+L/X9euXcX3jZ2BUBAE8X4/AOHq1auStiHKiXE3C+Mu23zZimqb79ChQ4IgCMK2bdvE977++utc2yQnJwsuLi4CAKFmzZri+8bOQCgIxfu+JDsQUr7Iy4MxVlZWOk/upvr111/F/D/99FOd6Yx9MAaA8Nlnn+nMLzIyUnwoB4DeICgIgsa04JMnT9aaRv3knH1hcf/+fZ15rl27VqNhoS3t/fv3xQAIQFi+fLnO/GJjY4WKFSuKad9//32t6VQqldCtWzcxXZMmTcQAGBcXJ5QvX15cN3bsWH2HRRZSPtt27dqJaVq1apWr8adu8uTJYlpXV1chISFBY/2NGzfE9a1btza53uqdNUJDQ03Oh4jyD2NgFrlj4IEDB8T1w4YN05uXPpmZmUK1atXEvAx1YI+IiBBvfNnY2Ajh4eG50qg3UMuUKaPzpqggCOINiey/gu5AKPc1wLRp08T1v/zyi0n1liuGElHhwdiYxRztwyNHjmiUr62DnFSG9nfAgAHi+iNHjphcjhyk3LQ8fvy4xrH5448/dOYXFhYmuLm5iWnnzp2bK40c+y/X9QwRFS2Mg1nMEQfXrFkjprG3txcfetBmy5YtGnUFLK8D4atXrwRnZ2cxzSeffKIzr5SUFI0f9/z9/XOlWbZsmbj+888/N6nOiYmJYh6+vr4GO44SWRr1/1t7e3utD8pl69y5s0Z6Ly8vISoqSmvajIwMoUaNGmLaHTt2aE23adMmMU2LFi10djLPpv4Awfjx47WmyesDLQCELVu2SNrO0HkvPT1d415Vz549hfT0dEl5q5PjflZO2Q+TeHp66nzYMudx+f7773XmFxERoTHg1pdffqk1Xc7YPGbMGIN1HT16tMGYmy0jI0NjUIBff/01V5q7d+9q3G/87rvvDNZBF0MPksr52eV3nBUEzWsOIGsgUV3fYZVKpZGnru9LRESEmEahUOj9rVOdej369u0rTJgwQeNv3LhxQv/+/YVy5cpp5P/tt99Kyj+b+vdtzpw5Rm1LJAVjbxbGXmmx19BxMcdvir/88ouYn42NjXD69GnpO2misLAwwd7eXgCyHnDWRa6HSVUqlVgeIP05F/XPp1OnTrli0fjx44XBgwdrPBwLQPjoo4+MGmT766+/FrcdNWqU5O2IdGHsycLYw3afqdju+1d+tvuy76WHh4eLn2Xnzp1zbfPtt9+K22zbtk1835QOhOrfq5ydjYikYtzNwrjLNp+6otjmy+5AmJycLLi6ugoAhGrVquXaZuvWreI28+bNE983pQNhcb4vaQWiQm7gwIFo166d7Hk6OzsDAI4ePSpbvqVKlcLs2bN1rvf29sbbb78tvr548aLe/NSnPpcyxS4ATJ06FVWqVNG5/oMPPkDjxo0BAIIgYO3atbnS/PLLL1CpVAAAPz8/fPTRRzrz8/DwwPz588XX27Ztw6tXr3KlUygUCAgIgJeXFwAgKCgIX375JQBg/Pjx4pTr1atXx+LFiw3spfmFhITg9OnT4uvly5fD1tZWZ/p58+ahZMmSAICEhARs27ZNY31CQoK4XKpUKZPrJVc+RFQ0MAZm0RYD5TofHjhwAKGhoQCAvn37ol+/fnrT+/j4YMqUKQCA9PR07Ny5U2O9SqXChg0bxNdz5sxBiRIldOY3efJkVK1a1cTay0/uawA5PifGPiJSx9iYRVf7cOHCheLykCFD8M4770itrtGK2vl59erV4nLv3r01jn1OFStWxKxZs8TXq1atgiAIGmkY44ioIDAOZtEVB9Xvc06aNAk1a9bUmd+wYcPQqlUrvWUWddu2bUNSUhKArOP99ddf60xrZ2eH5cuXi69PnDiBu3fvaqSRO/aVKFECCoXCpHyILMG4ceNQp04dnevfffddjdezZs0Sf1/JSalUYvDgweJrXefURYsWicurVq2Cg4OD3jrOnDkT7u7uAIDt27eL94zk1KxZMwwbNkyWvH777Tc8evQIAODk5IQNGzbA2tra6Hzkvk5/+PAhbt++DQDo0aMHlEqlwW0qVaqEadOm6Vzv4+OjEWfXrVuXq82Sk729PX744Qe9aV6+fIktW7aIZajf+9NGqVTi22+/FV9v3bo1V5qffvpJ/O60aNECM2bM0JtnXuRnG0vuOKvN4sWLdX6HFQoF3n//ffG1rv/7sLAwcdnLy0vvb5267Nu3DytWrND4W716Nfbs2YOnT58CyPpfvn79ukZbWoqyZcuKy9n/v0TmwtibhbFXOynHRe7fFAHN+7lTp05F27ZtDdY1rypWrAh/f38AwKVLlzSOvzlERkYiJSVFfF2uXDmj8zh27FiuWLRq1Srs3LkT9+/fB5D1rNHp06exYsUKSZ95NsYiMifGniyMPdqx3Wcatvtyk6Pdl61cuXLidcLx48fx7NkzjfWbNm0CALi6uqJv374mlwMwBpP8GHezMO5qxzaf+cjR5svm4OCAQYMGAQBCQ0Nx7tw5jfXZccjKygrDhw83uRygeMchdiCkQs/UBx6vX7+OjRs3Yu7cuZg2bRomTpwo/n388cfiwwk3btyQLQj36tUL9vb2etM0bNhQXDZ0wjlw4AAAwM3NTfLDQSNGjDAqzYkTJ3KtP378uLg8atQogw9y9OvXD56engCA1NTUXCfsbN7e3hqdKhYsWICPPvoIO3bsAADY2Nhg27ZtcHR0NLgP5qZ+XPz8/DQ+N22cnJw0LjBzHldfX1+Ndffu3TOpXur5rFq1yqQ8iKjoYAzUHQPVz4d79uzBixcvjKjtvw4ePCguDx06VNI2HTt2FJfPnj2rsS4kJESsi7W1NYYMGaI3L6VSmesGRUGS+xpA/XP65ZdfkJmZaXSd5IqhRGQZGBt1x8bU1FScPHlSfD1p0iTplTVBUWubqLfRRo8ebTD9+++/DyurrNtmERERuX7kk2P/5bqeIaLig3FQdxxMTExEUFCQ+FrKPdKRI0caTFOUqbfv3n33XYM/XDdr1gz16tUTX+u7v7lp0yYkJycbXaeSJUuK34ubN28iMDDQ6DyILMXAgQP1rlf/f5SSvm7duuKy+oNj2SIiInDt2jUAQO3atdGgQQODdbS3t0fLli0BAK9evcLNmzcNbmMsOQc9OXz4sLj87rvvioM+GkuO+1nq1DvB9+rVS9I2Q4cONfgwzvDhw8WHY54/f27wwcQuXbrAw8NDb5qjR48iLS0NANC/f3+DsRwAmjdvDicnJwC575UCmp/LxIkTzdp5XO7PTh+542xOlStXRqNGjfSmkXItFRUVJS7rG+gury5evIiWLVvif//7n1HXk+r/p5GRkeaoGpGIsTcLY692Uo6L3L8pPn78GHfu3BFfT5w4UVKeUjx58gS7d+/GvHnz8Omnn2LSpEka9wKyv7OCICA4OFi2crVRj0WOjo6Sri9Mce/ePXTu3BlTpkzReHjVEMYiMifGniyMPdqx3Wcatvtyk7vdl31vXaVSiZ1NAeDChQvid3DgwIEGj4chjMEkN8bdLIy72rHNZz5yt/nUf+PN7jAIZP3PZQ+G26FDB43vlSmKcxwyvhswUT7Lni1Pqo0bN2LevHmSHy5PT0/Hq1evDDaepMh5gaGN+kW6vl7dT548EU/a3bp1g42NjcG8S5YsKWkWo+wLEAC4du0aBEEQG26CIIgXNQAkjcRtY2ODZs2aiRcLV65cQbdu3bSm7dGjByZNmoRly5ZBpVJh5cqV4rpvvvnG6M/bXK5evSouSx2NvHXr1li2bBmArGOgztfXFy1atMD58+fx6tUrNG7cGO+99x769euH1q1bS+40OXjwYLFxOnPmTBw5cgTDhg3DW2+9lade+0RUODEG6o6BLVq0gK+vL8LDw/HkyRPUqVMH77//Pnr16oXmzZtLHlFLvcPbb7/9hlOnThncRn2WvfDwcI116jG0Vq1acHV1NZhf8+bNJdTU/MxxDTBw4EDMmTMHKpUKf/75J+rWrYvRo0eje/fuqFOnjqQbx3LFUCKyDIyNumPjtWvXxAcjHB0dzR5fBg8ejPXr1wPI6kB3+fJljBw5El27di1Us+sCwLNnzzQ650mJcaVKlUL16tXFG7pXrlzRmMlKjv2X63qGiIoPxkHdcfD69eviw+ouLi56R5nNpn6P1BKZen/zxo0bAHLf3+zRowecnJzw+vVrMS5+8MEHePvtt9GwYUNJI7za2tqib9+++PXXX5GRkYGOHTtiyJAh4uya2SPvEhUH6g+gaKN+LnZzc9MYDVeb7AGeAO3nVPV7YG/evJH8sMKDBw/E5fDwcNSvX1/SdlLJ+ZvQ+fPnxeXsEZZNIcf9LHXZneBtbGx0/naWk5QY5eHhgRo1aoijbV+9elXv7LtSjrX69+T69etGP9QSFxeH169fiw+WRkVFaTzgmJfPRQq5Pzt95I6zOcl1LfX69Wtx2dT7qCdOnECHDh003hMEAa9evUJISAh27NiBn3/+GUlJSfjyyy8REhKidVYSbdTrpF5XInNg7M3C2KudsXFSjt8U1Y9ftWrVZHne49y5c5g5cybOnDljcJaqbNHR0XkuVx85YtGGDRswatQojfcEQUBSUhJCQ0Oxb98+LFq0CK9fv8aSJUsQHByMv/76S9L9VcYiMifGniyMPdqx3WcatvtykyPWqhswYAA++ugjvH79Gps3bxZnlFTvxCFlAD9DGINJboy7WRh3tWObz3zkjkNt27ZFpUqVEBYWhh07dmDJkiWwtbXFli1bxM6njEN5ww6EVOhJna5WEAR88MEHGjPcSZWYmCjLgzFubm4G06g/4JKenq4zXXaAA6T3kC9fvrzR6VJTU5GYmCh2cHj16pVGvSpUqCApz4oVK4rLhoLNDz/8gBMnTmiMnuDv74/p06dLKis/vHz5UlyW6xisW7cOHTt2RFRUFJKSkrBy5UqsXLkS1tbW8PPzQ7t27dC1a1d06tRJ5wM3Y8aMweHDh7Fv3z4AwLFjx3Ds2DEAWZ9r27Zt4e/vjz59+pg82gMRFR6MgbpjoI2NDTZv3oyePXsiKSkJ0dHRWLBgARYsWAB7e3s0adIE7dq1Q48ePdCqVSudDc3nz5+Ly9kz4hojLi5O47V6/JA6yklh6QBujmuAWrVq4YcffsD06dMhCALu3LmDTz/9FJ9++ik8PDzQqlUrtG/fHn369EH16tV1liFHDCUiy8DYqDs2qo/q5evra3DU0rzq2rWrODgMAFy6dAmXLl0CkDX7fJs2bdChQwf07du3wGOdenx2cHCQ/D2qWLGi2IEwZ4yTY//lup4houKDcVB3HMzZFpNyzpR6L7Wokvv+ZokSJbB27VqMGDEC6enpCA8Px5w5czBnzhw4OzujefPmaN++PXr16gU/Pz+dZfz000+4fPkyQkNDkZaWhs2bN2Pz5s2wsrJCnTp10LZtW7z11lvo3r077OzsjNpnoqLE0HlS/XpeyjlVPb22c6r6PbCwsDCsWLFCSjU15LwPJgepsU0K9TZR5cqVTc5HrvtZABAfH48zZ84AANq3by9psDHAuN/7sh8kVT/vayPlWKt/T86ePat1ZglD4uLiNB4kzWZnZ4cyZcoYnZ8x5PzsDDHH74jq5LyWyib1oSIpFAoF3N3d0bJlS7Rs2RI9e/ZEt27dkJmZiW3btqFLly6SZnuWs05EhjD2ZmHs1c7YOCnHb4pyHb9s69evx5gxY4w+tyYmJua5bKnkjkUuLi5o1KgRGjVqhEGDBqF169ZITEzEyZMn8d133+Grr77K1zoR5cTYk4WxRzu2+0zDdp9+csQ1Jycn9O/fH5s3b8atW7dw+fJl1KtXT7z+qVixItq1a5fnchiDSW6Mu1kYd7Vjmy9/yHFuVygUGD58OL755hvExcXhwIEDGDBgADZv3gwgK04NGDAgz+UU5zhkVdAVIDJE6lTXv/zyi8ZDMd26dcPGjRtx48YNxMXFITU1FYIgiH/qF/XZo1PnlZwP82U/GGNtbY0ePXpI2kZqz+3sBlw29eCQlJSkN62UPA0FG1tbW43RGQCgd+/esLIqPKck9eMg1zGoXbs2goODMWnSJI0L0IyMDAQFBWHRokXo2rUrKlSogLVr12otQ6lUYs+ePVi7di1q166tse7JkyfYunUrxowZgzJlymDMmDGIjY2VVHciKpwYA/XHwPbt2yM4OBgjRozQOFYpKSk4e/Ys5s2bhzZt2qBmzZpix+uc1EeBMUVGRobGa/X4ITUuOzs756kOcjHXNcC0adNw4sQJdOrUSeN7EhcXhz///BOffvopatSogc6dO4sjseUkRwwlIsvA2Kg7Nqqfg/MrtixduhR79uxBs2bNNN6PiorCb7/9hkmTJqF8+fIYOHAgnjx5ki910saU9l3OtNpinBz7L8f1DBEVH4yDuuOgKW0xY2JCUWSO+5vvvPMOLl68iH79+mk8vJKUlIRjx45h9uzZaNiwIZo0aSL+gJuTj48PgoKC8MUXX8Db21t8X6VS4caNG/j555/Rr18/lC5dGt9//704miiRpTHmPCnHOTWv98CA3PfB5CA1tkkhZ5tIjvtZAHD48GHxAaPevXtLLt+UWGbotzkpx1ru70lBtFPl+uwMMUecVSfXtZR6mW/evJElT206d+6MIUOGiK8XLFggaTv1Oln6tRkVPMbeLIy92uVHnMz5ecp5/G7fvo1x48aJD0DWqVMHS5YswcWLFxEVFYU3b95o3AdQ7+Qt130AXfIrFtWrVw+TJk0SXy9duhRpaWkGt2MsInNi7MnC2KMd232mY7tPd5lyxVr1a4VNmzbhzz//RExMDADgvffek6XujMEkN8bdLIy72rHNZz7miEPqMwxu2rQJV69eFb8X/fv3l+WaozjHocLTW4coj3788Udxee7cuTh06BBGjBiBunXrwt3dHba2thrp87NHtbGSkpJw8uRJAECbNm0kj/qdnJwsKV3OqVZdXFzE5ZwnVanTsqqnU89Pm++//x6nT5/WeO+LL77AvXv3JJWVH9SPg5zHwNvbG0uXLkVUVBROnjyJb775Bt27d9cYBeHZs2cYO3YsJk+erDUPhUKBDz74ALdu3cLdu3exZs0ajBw5UmOEgvT0dKxbtw7NmjUzOPoQERV9xTkGVq5cGRs3bsTLly9x+PBhfPHFF/D399do9N27dw/9+vXDokWLcm2vfvF/5coVjYaUlL9Hjx5p5KceP0yNywXFnNcA7du3x9GjRxEREYEdO3Zg8uTJaNSokcbgAceOHUPz5s0RGBioNQ85YigRFR/FMTaqn4Nzdgo3p379+uHChQt4/PgxNm7ciHHjxmkMdiIIAn777Tc0atSowNp8prTvcqbVFePk2P+8Xs8QEeVUHONgUW6LmYu57m/6+flhz549ePHiBfbv34/p06ejZcuWGh0KL1++DH9/f+zatUtrHq6urvjmm2/w7NkznD9/HgsWLEDfvn1RsmRJMU1cXBw+++wzDBgwoFiPAkokF/V7YL179zb6HpggCBg1alTB7YAEcreJ5Lif9fvvv4vLxjzQYkosM/TbnBTq35NFixaZ9D1Rn22hoNqpcnx2hpgrzsrNx8dHXDY0+0Vede3aVVy+desWIiMjDW6j/humel2JLAFjr/EKMvZKIfdvinIev8WLF4sPq3bt2hVXrlzB5MmT0bRpU3h5ecHe3l4jfX7eB1A/vycnJ5u1E6F6LIqNjcXVq1cNbsNYRJaEscd4bPex3adPcW73+fv7w9fXFwCwfft2rFu3Tlyn3qkjLxiDqahj3DUe23ymK25tvqpVq6JVq1YAgEOHDmHhwoXiOsahvGMHQrII4eHhCA0NBQC4u7vjs88+05s+ISHBLFP/yuWvv/5CamoqAKBXr16StwsPDzc6nZ2dnUaQcnNz03jYQ+osEeqBT/1Bj5wuXbqEr776Snxdo0YNAFmNp2HDhkmaaj0/qE9XLPcxALKOe/v27fHFF1/g4MGDiI6OxqFDh9CmTRsxzbJly3Dp0iW9+VSvXh1jx45FQEAAHjx4gLt372Lq1KlQKpUAgAcPHmDu3LmS6k9ERRNjYBYnJyd07doV33zzDY4fP46YmBjs2rUL9erVE9N89tlnePbsmcZ26rMdSHmgwRD18//Tp08lbSM1nbmZ+xoAyDregwcPxpIlS3D58mVERkZi8eLFKFGiBICskV3GjRunNw+5YigRWa7iGhvVY1p4eLhZRqvTp3z58hgxYgRWrVqFW7du4cmTJ5g7d644gmpMTAymTp2ar3XKpt6+e/PmjeQfz4yJcXLsv6nXM0RE6oprHFQ/1z99+lRShzOp91KLKnPf33R3d0fv3r3xww8/4J9//kF0dDQ2bNiA8uXLAwAyMzPx0Ucf6f2hUKlUonnz5vjkk0+wd+9eREVF4cyZMxo/+u7fvx+//fabpPoTkW5y3wMrjNT3MSwsTNZ8TbmflZGRgUOHDgHImhFHfcZfQ6Set9VjmaHzthRyf0/U80tNTUVERESe8zS2/Lzei9TF3HFWLpUqVRKXX758KWkmJlOVLl1a47WU46LevlV/CJnIEjD25i3f/I69UuuVTe44mdfjd+zYMXH5f//7X66Bg3J6/Phxnsozhre3t8ZAaeb8XTRnLJKyn4xFZEkYe/KWL9t9ec+P7T7LafdZWVlh2LBhYp5//vknAKBly5aoWrVqnvMHGIOp6GPczVu+bPMZpzi2+bI7Cqanp2Pr1q0AgHLlyqFjx46y5F+c4xA7EFK+kGu6bV2eP38uLtesWVPj4Xdtzp49W6hHLTa1h/zLly/x4MEDg+nOnTsnLvv5+Wl8PgqFAn5+fuLrf/75x2B+GRkZGg/pN2rUSGu67E6C2Q+yjho1CqdPn4aXlxcAICgoCLNnzzZYXn5o2LChuCzlGORMp+sY6GJjY4Nu3brh6NGjqFu3rvj+gQMHjMqnevXqWLhwoUanQfXvExHlP8ZA48g1SoyDgwMGDhyIkydPio2xtLQ0/PXXXxrpmjdvLi6bOuqYOvUYGhISImk0l4sXL+a5XDmY8xpAl1KlSuHjjz/G/v37xfdu3bqFhw8fSs5DrhhKRPmHsdE4UmOjn5+fOKpYcnIyLly4YPa66ePr64vZs2djzZo14nt///232AkkP5UtW1ZsdwLSYlx0dLTGjIHGxjg59l/q9QwRFS2Mg8aRGgfr168vjlCakJCA27dvG8xb/R6pJcrv+5uurq4YNWoUjh8/Djs7OwBZ8dSY42xlZYU2bdpg3759eOutt8T3eX+TKO/U74Fdu3ZNtllYzR3XjNGiRQtx+fjx42YrR+r9rDNnziA+Ph6A8fc5z58/bzBNfHw87ty5I7429rytjdz3Sr29vTUevjDn5yKFHPcis+V3nDWVt7e32B4WBEGjnSu3nDOoqI8er0tISIi43KBBA9nrRFSQGHvlkx+xVwq546T68bt3716eHrJUvxegPhiZNq9evcL169dNLstYCoVCo0537941W1mMRVTcMfbIh+0+6dju02RJ7T5tMzzJNesTwBhMRR/jrnzY5jOsOLb5hgwZIv7emG3YsGGS2nlSFOc4xA6ElC/Up0Y1xwxz6icDKdO7r1y5UvY6yEWlUuHgwYMAsh7yMXbEjs2bNxuVxt/fP9d69d7ZGzduNPgQ0b59+xATEwMg67Nu2bKl1nSTJ08WR0CvUqUKli1bBi8vL6xfv15M88MPP+DkyZMG98Hc1I/B1atXDQbT5ORk/Prrr1q3N4adnR26dOkivo6KijIpH/WLI1PzICJ5MAZKl9cYqI2npydat24tvs55TuzZs6e4vH79eqSkpOSpvNq1a4s35tLT07Fz50696VUqFbZv356nMuVkrmsAQ1q3bg1PT0/xtSmxS64YSkTmx9gonTGx0c7OTqN9t3z5crPXTwr1tkl6ejpiY2MLpB7qxyYgIMBg+oCAAKhUKgBAmTJlUKNGDZPKlWP/DV3PEFHRwjgonTFx0MXFBU2aNBFfS7lHumnTprxXshBTb9/9+uuvBtu7QUFBGvdAtd03lqJKlSqoU6eO+NqUuKVQKDRmnGTsI8q7ypUro1atWgCyBqVYt26dLPmaO64Zo3v37uLyr7/+KnnmcVMZup+lPriVvll0tdm+fTsyMzP1ptm6dauYpnTp0ia3WdR17doV1tbWALIeegwODs5znuqfy4oVKwrFwAVy3IssqDhrimbNmonLcnymuly5ckXjddmyZfWmV6lUuHnzpvhavZ5EloCxV37mjL1SyP2bYoUKFcTvCJAVJ01lzL2AtWvX5vt3p7DGIkCzPoxFVNQx9siP7T5p2O7LYmntvlq1amncc7ezs8OQIUNkyfvFixfi7F6Ojo4ag4UTFRWMu/Jjm0+34tjmc3d3z/U5y9WRvbjfl2QHQsoX2dPKAppTfsqlUqVKYq/7mzdv6h0xZMeOHfjjjz9kr4Nczp07JwZZU3rIL1q0SO9UtwEBAeJMQQqFAh988EGuNGPHjhWDzZUrVzRmTMgpPj4en376qfj63XffhZubW650u3fvFjsKWltbY+vWrXB2dgYAvP3225gwYQKArJPyiBEjEBcXZ2hXzapmzZpo166d+HrixIl6A+oXX3yBFy9eAMgabXvo0KEa6+Pi4sSHTw0JDw8Xl9VnyQAg+QJMXx5ElL8YA6UzJgZmd1qTQt85ccCAAeJDqBEREfjoo48k38xMSkrKNXqQlZUVRo4cKb6eM2eO3o4Cy5cvN+uoz8aS+xpAatyKj49HUlKS+Fr9c5IrhhJR4cHYKJ2x7cOpU6eKy7/++qvGICdyM6VtYmVlpfH556dx48aJy3v37tU7i9/jx4/x7bffamybc6Q/OfZfrusZIipaGAelMzYOjhkzRlxeunSp3rbWr7/+irNnz+a9koXY0KFDxfuvERERmDt3rs60aWlpmDRpkvja398/1wNJUmNfZmYmIiIixNfqcSsxMRFpaWmS8mHsI5LfjBkzxOUvvvgCN27ckLxt9kNdOZk7rhmjf//+qFChAoCs+3bvv/8+MjIyjM5HjvtZwL8PtPj4+Bj9AMKDBw/w008/6VwfFRWFr7/+Wnz9wQcfyDI6edmyZTF8+HAAWbMWjBgxAgkJCZK2ValUePnyZa73p0yZIt5vPHfuHObPn5/neuoi12cnhdxx1pzUZ/U11/VPQkKCxoNytWrVQunSpfVuc/XqVfFzqF27NsqUKWOWuhEVJMZeaQpD7JVC7t8UAc37uQsXLsSZM2dMqlvlypXFZX0zuIeGhuqNWeaSH7EoPT1dY1A9V1dXjU4P2kRHR4sze7m5uRW7h0bJMjH2SFMYYg/bfaZhu087c8XaPXv24NKlS7h06RKuXLkCDw8PWfJVv+bp0KGD2KmVqKhh3JWmMMRdKdjmM5254tDy5cs14lDt2rVlybe435dkB0LKF+ojROzatUv2/EuWLClO9apSqTBw4MBcU6CqVCqsWLEC7733HpRKpUYv/cJE/cRubA95W1tbJCYm4q233so1shYAbNiwQeOhyQ8++EDryN1VqlTRSDdx4kSsWLEi18P79+/fR5cuXcQOi66urpg9e3au/J4+faqR3+zZszWm+gWABQsWiCf28PBwjB8/Xsoum9V3330HpVIJIKvRMmDAALGTYLa0tDR89tlnGg36r776Smw8Ztu/fz+qV6+OH3/8EY8ePdJaXmpqKpYvX47du3eL76mP4AAA5cuXx7hx43Dq1CmdnSmCgoI0GqQ58yCi/MUYKJ0xMXDZsmXw8/PDypUrdTaok5KS8Pnnn4sd55VKpcYMddnvrVy5Ujzfb9iwAW+//bbGFOU5Xbt2DTNmzICvr6/WTvvTpk0TR8N5+vQpunbtivv372ukEQQBP//8M6ZOnZprqvWCJPc1wODBg9GzZ0/s3r1b5+g3z549w9ChQ8WHSKtXr44qVaqI6+WKoURUeDA2Smds+7Bz584YNGiQ+Hr48OH4+uuvtZ6DVSoVTpw4gX79+uHVq1dG161ly5YYOnQoDh06pLMjwL179zQ61nfq1Am2trZGlyUHf39/jdgwcOBArd+/y5cvo3PnzoiPjwcA+Pr6YvLkybnSybH/cl3PEFHRwjgonbFxcMSIEeJDGW/evMFbb72FCxcu5Eq3detWvP/++wUWk/KLq6srvvzyS/H1999/jy+//DJX3IqKikKfPn1w/vx5AFmDv3333Xe58ps+fTratWuHTZs2iXEyp5iYGIwdO1bsQOjq6opWrVqJ6y9fvoyKFStizpw5uH37ttY8MjMzsWPHDixbtkx8j+07InkMHz5cHD0/MTERbdq0werVq3VezyYkJGDr1q3o0KGDxm8O6tTj2u7duwt0lgFra2ssX75cfKDyjz/+QNeuXcWHwnN69OgRZs+enWtGWjnuZ4WEhIj3A99++22jH/K0tbXFjBkzsGTJklz35UJCQvDWW2+Jv1d5e3vjv//9r1H56/Ptt9+KHb+uX7+OZs2a4e+//9aZ/unTp/jpp59Qo0YN7NixI9f66tWrY9q0aeLrzz77DJMmTdI56NrFixcxatQo3Lp1y+i6y/HZSSV3nDUn9dHTjx8/Lnv+oaGh6NGjh8ZDbeoPR+miXhf1OhJZEsZeTYU59kphjt8UR40aJbaZ0tPT0a1bN/z8889aB7dOS0vDgQMH0K9fv1zr1NvMU6dO1Tp42rFjx9ChQwckJibCycnJ8A7LqFOnTnBwcAAABAYGSh5YRqqIiAgMGjRI4zmpSZMmwcbGRu92J06cEP+H1GfkIirKGHs0FebYw3Yf231yMle7z9fXF02aNEGTJk1k67QBsD1IloNxV1NhjrtSsM1nOnO1+by9vcU41LBhQ1nyBBiH2PKlfDFgwACsXr0aAPDzzz/j8uXLaNSoERwdHcU0H374oUkX6tm++eYbdOnSBSqVClevXkW9evXQunVrVK5cGUlJSThz5oz44MK3336LNWvW4PHjx3nbMTPI7iFfsmRJjYcrpGjZsiU8PT2xd+9eNGnSBC1atECtWrWQmpqKc+fOaYw4XqtWLfz444868/rxxx8RFBSES5cuISMjAxMnTsT333+PNm3awNnZGQ8ePMDp06eRmZkJIOtCYd26dahYsaJGPtkzCmY3CNu0aYNZs2blKs/BwQHbtm1D8+bNkZqaip07d6J79+4YNWqUUcdATq1atcL333+P6dOnA8j6bMqXLw9/f3/4+voiLi4OJ06c0Jg1ol+/fjob7A8ePMD06dMxffp0lC9fHvXr1xdHSIiMjMT58+c1Gs7Dhg3L9R148+YN1qxZgzVr1sDFxQV+fn6oUKECnJycxNHR1BvXpUqVwpw5c+Q6JERkAsZA6YyNgcHBwfjoo48wYcIEVKlSBXXr1kXJkiWRnp6OiIgI/PPPPxqj0sycORO+vr658uncuTNWrlyJDz/8EJmZmTh06BAOHz6M2rVro379+nB1dUVycjIiIiIQHBysdUQ1dd7e3li9ejWGDBkClUqFoKAg1KxZE23btkXVqlXx+vVrnD17VpxNYfHixeJNAfXp5guKXNcAQNZ1wJ9//ok///wTtra2qFOnDqpXrw43NzckJibiyZMnOHfunHhDXKlUYsmSJbnykSOGElHhwdgonSntw7Vr1+Lx48e4ePEiMjMz8dVXX+GHH35A69at4evrC0EQ8OzZMwQFBYltGVNuOqenp2P79u3Yvn07HBwcUL9+fVSuXBmurq6Ii4vDw4cPERQUJKZ3cHDQ2wbNDxs2bEDr1q3x4MEDJCUlYfDgwahWrRqaN28OW1tb3L59GxcuXBCPh5OTE7Zv3w53d/dcecm1/3JdzxBR0cE4KJ2xcdDOzg6bN2+Gv78/Xr9+jSdPnqBFixZo1qwZ6tati7S0NJw/f178gXHp0qVaO4lbkk8++QRnz54Vj+X//vc/rFy5Ev7+/vDw8EB4eDhOnDiB1NRUcZsFCxbkGvwNyLpeOHPmDM6cOQOlUomaNWuiVq1a8PDwwJs3b/Ds2bNcPwr++OOP4g+G2bJH6547dy58fHzg5+cHHx8fWFtbIyoqCpcvX8bz58/F9G3btsU777wj96EhKpaUSiV27tyJt956C1evXkVCQgLGjx+PTz/9FC1btkTZsmWhVCoRFxeHu3fvIiQkRBxResCAAVrz7N+/P2bNmgVBEPDnn3+ifv36aNWqFVxcXMQ077zzjsHZX+TSs2dPfPfdd5g5cyaArAcBateujQYNGqBOnTpwdnZGbGwsrl+/Lnawzznjgxz3s7LPu4C0WXRz+uGHHzBlyhRMmTIFP/74o3hf7t69ezh79qxYtrW1NdavXy8OZiaHMmXKYP/+/ejRoweio6Nx9+5ddO3aFWXLlkWzZs1QqlQppKenIzo6Gjdv3tT6YExO8+bNw507d8Tjsnz5cqxZswYtW7ZEpUqVYG1tjcjISFy+fFm8RpkyZYrRdZfrXqRUcsZZc6pcuTJatmyJc+fO4d69ewgJCUGtWrUkb79kyRKNgduArOuCxMRE3L59G1euXNG4r9C/f3+MHj3aYL579+4Vl4cNGya5PkRFCWNv0Ym9Usn9m6K1tTV27NiBjh07IjQ0FMnJyZgwYQI+//xztG7dGqVLl0ZGRgYeP36My5cvIyEhAW5ubrnymTJlCtauXYuXL18iNjYW3bp1Q6NGjVC7dm0oFApcuXJFfI6ka9eu8PLywubNm81yjLRxcnJC3759sX37diQmJuLYsWNGDRSzZcsWjXut2V6/fo179+7h4sWLGjOhtGrVSutzSTkxFpElYuwpOrGH7T62++SU13ZffhIEAfv37wcA2NjYYPDgwQVcIyLTMe4WnbgrFdt8pslrmy+/Ffu2oECUT959910BgM6/EydOiGnV3zfGypUrBWtra51lWFlZCbNnzxZUKpVQoUIF8f2wsDCt+bVv315r/dSNHDlSTLNhwwaDddywYYOYfuTIkRrrQkNDda7T5cSJE+I27du3F169eiX07NlT77Fu3ry5EBERYTDvxMREYfDgwXrzAiCULl1aOHjwoNY8vv/+ezGdm5ubzmOdbeHChWJ6FxcX4cGDB5KOg7GkfLbZ1q5dK7i6uuo9BkqlUvj444+FjIwMrXns2rVLUCgUBo9l9vf0o48+EtLS0nLl4+zsLCkPAEKDBg2EkJAQOQ4XEeURY2AWOWPgjz/+KPl8aGtrK8ydO9dgnsePHxeqVasmOd86deoIz54905nf1q1b9Z637ezshDVr1gj37t3TOHebS1hYmFhOhQoV9KaV4xpAEASD1yTqf15eXsK+ffty5SFXDCWiwoWxMYvc7cNsycnJwtixYwWlUmnw3Glvby8kJCQYvb9169aVfI6vVKmSEBgYaNQ+GCNnu1ifyMhIoWPHjgbrXLVqVeHixYs685Fj/81xPUNERQPjYBZzxcFTp04JPj4+evf9q6++EgRB2vHVV8+cjGl35ZXU70Z6erowceJEg9cFbm5uej+3iRMnSo5bLi4uwpo1a3Llcf78eb3fy5x/AwcO1HqdQlRUGXNON/Z8Ysw1cXJysjB+/HjJ/48ODg7CvHnzdOb32Wef6d0+57lFStzRxpjj9+uvvwre3t6S9i/n+UqO+1mtW7cWj11ycrKk/ct5XFauXCnY2trqLNvd3V3Ys2eP3jyNjc3qHj16JHTq1EnysfD29hYOHz6sM7/MzExh1qxZgp2dncG8lEql1t+3DO2PHJ9dtvyOs4Jg3DWHIBh3nli7dq2YNvs6SB+px1H9z9raWvj0008l3Zt98uSJeM+3cePGBtMTmYqxNwtjr3amHhdBkP83xZiYGKFfv36S8ipbtqzWPP755x+hZMmSerft27evEB8fL+ka4auvvjIYO4z5Pzh69KhRcU798zHmb/To0ZLakcnJyeJzQD4+PvxtkWTD2JOFsUc7tvv+/WO7r3C1+/L6jKn6/6KhZ3HPnj0rph0wYECeyiVi3M3CuKsd23z6rw8KU5vv0KFDBtPr07x5c4P7m433JQWBMxBSvtm6dSt69uyJ7du349q1a4iOjkZKSoqsZYwfPx6tW7fGTz/9hBMnTuD58+dwcHBA2bJl0bFjR4wePVrWKUzlpt5DXn26WWO4urri999/x+7du7Fx40Zcv34dUVFRcHd3R/369TFs2DCMGDFC0gxHzs7O2LFjB6ZMmYLNmzfj5MmTeP78Od68eYOSJUuibt266NmzJ0aPHq11utsrV65oTOO+YsUKrbMTqfvvf/+Lw4cP48iRI0hMTMSwYcNw5swZWFsX3Onqgw8+QJ8+ffDLL7/g0KFDuHfvHmJjY+Hi4gJfX1907twZo0eP1jtN+8CBAxEREYG///4bgYGBCA4OxsOHDxEfHw8AcHNzQ/Xq1dGmTRuMGDFCZ14xMTE4ffo0Tp06hUuXLiE0NBRRUVFISUmBo6MjypUrh8aNG2PAgAHo3bt3oZjJiogYA6UwNgZOmzYNAwYMwJEjR/DPP//gxo0bePToERISEmBlZQV3d3fUqlULHTt2xIgRI1ChQgWDefr7+yMkJAT79u3Dn3/+ifPnzyMyMhIJCQlwdHSEt7c3atasiVatWqF79+7w8/PTm9/QoUPRtm1bLFu2DH/++SeePHkChUKBcuXKoUuXLhg/fjxq1qyJCxcuiNtom+GoIOT1GiDb77//jqtXr+LYsWO4cOECQkJC8PTpU7x+/Rp2dnYoVaoU6tevjx49emDo0KFwdXXNlYdcMZSIChfGRsPy0j50cHDAmjVrMHXqVGzatAnHjh3Do0ePEBsbC1tbW5QuXRr169fHW2+9hSFDhmiMVifVtWvXcP78eZw4cQIXL17E3bt38fz5cyQnJ8PR0VGcUah3794YPHgw7OzsjC7DHLy9vXHs2DEcPnwYO3bswNmzZxEZGYn09HR4eXmhYcOG6Nu3L4YPHw4bGxud+cix/+a4niGiooFx0LC8xMF27dohJCQEK1aswJ49e/DgwQOkp6ejTJkyaNeuHcaNG4dmzZrJXeVCy9raGsuWLcP48eOxfv16HDt2DOHh4UhMTISnpyeqV6+OHj16YOzYsShRooTOfJYtW4aPPvoIR48exfnz53Hr1i08efIEiYmJsLa2RokSJVCnTh106dIF7733njhrvLrmzZvjxYsXOHr0KM6ePYurV6/iwYMHiImJQWZmJlxdXVGlShW0aNECw4cPL1afE1F+cnBwwMqVKzFjxgxs2bIFx48fx7179xATEwOVSgU3NzdUrlwZDRo0QKdOndCtWzet92yyzZs3D23atMGGDRtw+fJlREVFITk5OR/3KLchQ4agZ8+e2LRpEw4dOiSOBp2ZmQkPDw/UqFEDbdq0wcCBA3PFw7zez4qOjsa5c+cAAJ06dco1E6tU48ePR9u2bbFq1SocPXoUT58+BQBUrFgRvXr1wqRJk1C6dGmT8paiQoUKOHr0KM6dO4ddu3bh9OnTCA8PR1xcnHjer1atGpo0aYIuXbqgQ4cOen/Ls7Kywrfffovx48cjICAAR44cwf379xEdHQ1ra2t4eXmhTp066NSpE4YMGYKyZcsaXWc57kUaS644a27Dhg3DZ599hpcvX2L9+vWYPXt2nn9DdHJygoeHB+rUqYN27dphxIgRKFeunKRt169fL85aaMqsI0RFDWNv0Yi9xpD7N0VPT0/s2bMHly5dwrZt23Dy5Ek8ffoUcXFxcHBwQLly5eDn54du3bph4MCBWvNo2bIlbt26hcWLF+PAgQN4+PAhAKB06dJo3Lgxhg8fbvJzSHLo1KkT6tWrhxs3bmDXrl1YsmSJ1pk1jOHg4AB3d3fxWL/33nuoUaOGpG137tyJhIQEAMCECRP03gsmKooYe4pG7GG7j+0+OZmj3WcOa9euFZfZHiRLwbhbNOKuMdjmM5452nzmwPuSgELIPgJEVOD8/f1x8uRJ2NnZITo6Gs7OzgVdJSIionxRnGPgL7/8gv/85z8Asm4Qr1y5soBrREREhUFxjo1ERESMg0REVFRt3LgRo0aNAgCsWbMGY8eOlbRdxYoV8fjxYwBAWFiYwQE5iYw1b948fP755wCAvXv3om/fvgVSj/T0dFSuXBlPnz5FxYoVce/ePXbaIKI8MTX2Uv7btm0bhg0bBgD46aefCvRhzebNm+PixYtwcXFBWFhYgXb4IKKih+0+KqwKS7tPl5iYGJQvXx7Jyclo1aoVAgMDC7pKRFQEsM1XdBSmNp82vC+ZpfANL0BUTMXHx+Ps2bMAgA4dOvChGCIiKjaKewzcsWOHuNy0adMCrAkRERUWxT02EhFR8cY4SERERVn2LLoKhQI9e/Ys4NoQ/Wvy5MkoVaoUAGD+/PkFVo+tW7eKM6t89dVXxfIhHSKSF2Nv0fHOO++gTp06AIBFixYhPT29QOpx8uRJXLx4EQAwdepUdh4kIqMx9lBhVVjafbosW7ZMnEXt22+/LeDaEFFRwbhbdBSWNp8uvC+ZhR0IiQqJQ4cOISMjAwDQu3fvAq4NERFR/inOMXDPnj04duwYAMDe3h79+vUr4BoREVFhUJxjIxEREeMgEREVVWlpafj7778BAE2aNEHp0qULuEZE/3J2dsbXX38NADh//jz++uuvfK9DRkYGvvvuOwBAgwYN8N577+V7HYjIsjD2Fi1WVlb48ccfAQDh4eFYv359gdQjOx6WLl0a06ZNK5A6EFHRxdhDhVlhaPfpEh8fj2XLlgEAevbsiQ4dOhRshYioSGDcLVoKS5tPG96X/JdCEAShoCtBRERERGRJ/vnnH2zYsAETJkyAn59frvWpqalYuXIlZsyYgbS0NABZI4EtWbIkn2tKREREREREREREBa1ixYp4/PgxACAsLAwVK1Ys2AqRRVKpVGjatCmuXLmCBg0a4MqVK7Cyyr8xp1evXo3x48cDAM6cOYM2bdrkW9lERFR49O3bF/v374ePjw/u378PJyenfCv7r7/+Qrdu3QAAW7ZswbBhw/KtbCIitvsoPxR0u0+Xzz77DN9//z3s7Oxw69YtVKlSpaCrREREZlKQbT5deF/yX+xASEREREQks5MnT8Lf3x8A4OvrCz8/P3h7e0MQBDx79gznzp3Dq1evxPS1a9fGxYsXC0VjiYiIiIiIiIiIiPIXHyQlIiIiIiKybGz3EREREVFBsy7oChARSTFx4sQ85/Hee++hefPmMtSGiIhIuvDwcISHh+tc37VrV2zbti1X58ELFy5g8+bNeS5/+fLlec6DiIhITgcPHsTBgwfzlEeJEiUwd+5cmWpERERkXqGhobLMOP/111/D09NThhoREREREREREREREREREVFxUug6EGZmZiIkJARBQUG4fPkygoKCEBwcjDdv3gAARo4ciYCAALOVf/XqVWzbtg1Hjx7F06dPkZCQgJIlS6J06dJo0aIFOnTogH79+kGpVJqtDkSU24oVK/KcR5MmTdiBkIiI8kW7du1w/PhxHDx4EJcuXUJERASio6ORkJAAV1dXlClTBm3atME777yD9u3ba80jJCRElvjHDoRERFTYXLx4Mc8xrkKFCuxASERERcazZ89kad998skn7EBIRERERERERERERERERERGK3QdCAcPHow9e/bke7kJCQn4+OOPsXHjRgiCoLHu+fPneP78OS5fvowVK1YgLi4O7u7u+V5HIiIiIioarKys4O/vD39//4KuChERERERERERERVyjx49KugqEBERERERkRmx3UdEREREBU0h5OwtV8D69u2L/fv3i689PT1RokQJhIaGAjDPDISxsbHo2rUrgoKCAABly5ZF//790aBBA7i5uSExMRGhoaE4cuQILl++jNjYWHYgJCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKiQq3QzUDYrFkz1KpVC40bN0bjxo1RqVIlBAQE4P333zdbmUOHDhU7D06bNg3/+9//YG9vnyvdvHnz8Pz5czg7O5utLkRERERERERERERERERERERERERERERERERERHIodB0IZ82ala/lBQQE4K+//gIAfPjhh/jxxx/1pi9Tpkx+VIuIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiChPrAq6AgVt/vz5AABnZ2d8//33BVwbIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIieRTrDoSBgYG4c+cOAKBPnz5wdXUt4BoRERERERERERERERERERERERERERERERERERHJo1h3IDx16pS43Lx5cwDAnj170KNHD/j4+MDOzg5lypTB22+/jQ0bNiAjI6OgqkpERERERERERERERERERERERERERERERERERGSUYt2BMCgoSFz29vbGgAEDMGDAABw6dAhRUVFIS0tDREQEDh48iNGjR6NRo0YICwsrwBoTERERERERERERERERERERUVHz+++/Y9CgQahYsSLs7e3h5eWFVq1aYcGCBUhISJC9vEePHuHLL79EmzZtULJkSdjY2MDZ2RmVK1dG//79sWXLFqSnp8teLhERERERERERERU+CkEQhIKuhCEBAQF4//33AQAjR45EQECALPm2bNkS58+fBwDUqFEDd+/eha2tLUaMGIE2bdrAxsYGwcHBWLt2LWJjYwEAFSpUwJUrV+Dp6Wl0eU+fPtW7PiUlBXfu3IG3tzdKlSoFa2tr43eKiMgCZWRk4OXLlwCAevXqwd7evoBrRHJKSUnBjRs3AIDxj4hIDeOf5WMMJCLSjjHQsjH+ERFpx/hn2Rj/iIi0Ky7xLykpCcOGDcPvv/+uM42vry927tyJFi1ayFLmokWLMGvWLKSmpupNV6NGDezevRt169aVpVx1jH9ERNoVl/hXnDEGEhFpxxho2Rj/iIi0K0zxr1ifmePi4sTlu3fvwsPDA8eOHUPDhg3F94cOHYr//ve/6NSpE27fvo3Hjx9j1qxZWLVqldHl+fr6ylJvIqLi7OLFi2jatGlBV4NkdOPGDTRr1qygq0FEVKgx/lkmxkAiIsMYAy0P4x8RkWGMf5aH8Y+IyDBLjX+ZmZkYNGgQDh8+DADw9vbG2LFjUbt2bcTGxmL79u0IDAxEeHg4evTogcDAQNSqVStPZS5fvhzTpk0TX7dq1Qq9e/eGr68vEhIScOvWLQQEBCApKQl3796Fv78/bty4AR8fnzyVmxPjHxGRYZYa/4o7xkAiIsMYAy0P4x8RkWEFHf+sCqzkQkClUmm8/vHHHzU6D2bz8fHBtm3bxNcBAQFISEgwe/2IiIiIiIiIiIiIiIiIiIiIqGhau3at2Hmwdu3aCA4OxjfffIN3330XEyZMwNmzZ8XOfnFxcRg3blyeynvz5g1mzZolvv7ll18QGBiIGTNmYOjQoRg/fjyWLVuGhw8fol69egCA6Oho/PDDD3kql4iIiIiIiIiIiAq3Yj0DoYuLi7js5OSE4cOH60zboEEDtGjRAufPn0dqaioCAwPRvXt3o8oLDw83uL5Vq1YAsnqWli5d2qj8iYgsVUREhDgySalSpQq4NiQ39c+U8Y+I6F+Mf5aPMZCISDvGQMvG+EdEpB3jn2Vj/CMi0s7S419mZibmzp0rvt68eTO8vb1zpZs/fz6OHTuGa9eu4cyZM/j777/RpUsXk8oMDAxEYmIiAKBp06YYM2aM1nSlSpXCd999h549ewIATp8+bVJ5+jD+ERFpZ+nxjxgDiYh0YQy0bIx/RETaFab4V6w7EHp4eIjL9erVg62trd70TZo0wfnz5wEADx48MLq8cuXKSU5bunRpo9ITERUX1tbFOnRZJPXPlPGPiEg7xj/LxBhIRGQYY6DlYfwjIjKM8c/yMP4RERlmifHv9OnTiIiIAAC0b98ejRo10ppOqVRi8uTJGD16NABg+/btJncgfPHihbhcrVo1vWnV1yclJZlUnj6Mf0REhlli/CPGQCIiKRgDLQ/jHxGRYQUd/6wKtPQCVrNmTXHZzc3NYHr1NAkJCWapExEREREREREREREREREREREVbYcOHRKXe/TooTdt9+7dtW5nLC8vL3H53r17etOqr69Tp47JZRIREREREREREVHhV6w7EDZo0EBcfvXqlcH06mmkdDgkIiIiIiIiIiIiIiIiIiIiouLnxo0b4nLTpk31pvXx8YGvry8AICoqCi9fvjSpzDZt2qBkyZIAgKCgIKxdu1ZrupcvX2LWrFkAACsrK0ydOtWk8oiIiIiIiIiIiKhoKNbz/3bv3h0KhQKCIODGjRtIS0uDra2tzvRBQUHico0aNfKjikRERERERERERERERERERERUxNy9e1dcrlSpksH0lSpVQnh4uLhtqVKljC7T3t4eq1atwjvvvIOMjAyMHTsWAQEB6N27N3x9fZGQkICbN29i48aNSExMhLOzM9auXYvWrVsbXRYREREREREREREVHcW6A2G5cuXQvn17nDx5Eq9fv8aWLVswevRorWmDg4Nx/vx5AICLiwtvnhIRERERERERERERERERERGRVvHx8eJy9qyA+pQoUULrtsYaMGAAjh49igkTJuDWrVsIDAxEYGCgRhobGxt8/vnnGDdunDjzobGePn2qd31ERIRJ+RIREREREREREZH8rAq6AuYSEBAAhUIBhUKBDh066Ew3b948cfmTTz7B1atXc6WJiorCsGHDxNeTJ0+Gg4ODrPUlIiIiIiIiIiIiIiIiIiIiIsuQlJQkLtvb2xtMr/4cSmJiYp7KbteuHZYvX46GDRtqXZ+eno4VK1Zg0aJFePPmjUll+Pr66v1r1qxZXnaBiIgsyO+//45BgwahYsWKsLe3h5eXF1q1aoUFCxYgISFB9vIePXqEL7/8Em3atEHJkiVhY2MDZ2dnVK5cGf3798eWLVuQnp4ue7lERERERESFWaGbgTAsLAzr1q3TeO/69evi8tWrV/HFF19orO/YsSM6duxoUnktW7bEjBkzMH/+fMTFxaFFixYYOXIk2rRpAxsbG1y7dg1r165FbGwsAKBJkya5yiciIiIiIiIiIiIiIiIiIiIiKmjR0dEYPHgwTpw4AQ8PD/z000/o3bs3fH19kZycjMuXL2PhwoU4ePAgFi9ejH/++QcHDx7UmAGRiIhIDklJSRg2bBh+//13jfdfvnyJly9f4ty5c1i2bBl27tyJFi1ayFLmokWLMGvWLKSmpmq8n5GRgbCwMISFhWHv3r343//+h927d6Nu3bqylEtERERERFTYFboOhI8fP8a3336rc/3169c1OhQCgLW1tckdCAHg+++/h1KpxPz585GWloZffvkFv/zyS650Xbt2xfbt2yWNDEdERERERERERERERERERERExZOzszPi4uIAACkpKXB2dtabXn0mQBcXF5PKTE5ORtu2bXHnzh14eHjgwoULqFatmrjezc1NHKR74sSJWLFiBS5evIhJkyZh27ZtRpUVHh6ud31ERARnISQiKsYyMzMxaNAgHD58GADg7e2NsWPHonbt2oiNjcX27dsRGBiI8PBw9OjRA4GBgahVq1aeyly+fDmmTZsmvm7VqpXYiT4hIQG3bt1CQEAAkpKScPfuXfj7++PGjRvw8fHJU7lERERERERFQaHrQFhQvv32WwwePBjr1q3DkSNH8OzZM6Snp8PLywutWrXCiBEj0L1794KuJhEREREREREREREREREREREVcu7u7mIHwujoaIMdCGNiYjS2NcXPP/+MO3fuAAA++eQTjc6DOc2fPx9bt25FfHw8duzYgUWLFhnVgaJcuXIm1ZGIiIqHtWvXip0Ha9eujePHj8Pb21tcP2HCBHzyySdYuHAh4uLiMG7cOJw+fdrk8t68eYNZs2aJr3/55ReMGTMmV7rZs2ejU6dOuHHjBqKjo/HDDz9g0aJFJpdLRERERERUVFgVdAVy6tChAwRBMOpvzpw5ufIZNWqUuP7kyZOSym7QoAGWLl2KkJAQJCQk4M2bN3j8+DG2b9/OzoNEREREREREREREREREREREJEmNGjXE5bCwMIPp1dOob2uMP/74Q1zu0qWL3rROTk5o1aoVAEClUuHSpUsmlUlERJRTZmYm5s6dK77evHmzRufBbPPnz4efnx8A4MyZM/j7779NLjMwMBCJiYkAgKZNm2rtPAgApUqVwnfffSe+zkunRSIiIiIioqKk0HUgJCIiIiIiIiIiIiIiIiIiIiIqyurVqycuG+qcFxUVhfDwcACAl5cXSpUqZVKZz58/F5fd3NwMplef6TApKcmkMomIiHI6ffo0IiIiAADt27dHo0aNtKZTKpWYPHmy+Hr79u0ml/nixQtxWd8MvDnXM/4REREREVFxwQ6EREREREREREREREREREREREQy6tatm7h86NAhvWkPHjwoLvfo0cPkMl1cXMTl7A6J+jx+/FhcLlGihMnlEhERqVOPe4biWvfu3bVuZywvLy9x+d69e3rTqq+vU6eOyWUSEREREREVJdYFXQEyr/DYZJx/GIPElAy42FujReUS8PV0LOhqERERyYJxzjQqlQpJSUlISEhAWloaMjMzC7pKRBZFqVTC1tYWrq6ucHZ2hpUVx20heTH+ERERaWJsJCIiIip6eA1HxUH79u3h4+ODyMhInDx5EleuXNE6A1NmZiaWLl0qvn7nnXdMLrNevXq4cuUKAGDr1q3o2LGjzrT379/HhQsXAABWVlZo0qSJyeUSERVHvJ7R7caNG+Jy06ZN9ab18fGBr68vwsPDERUVhZcvX5o0E2+bNm1QsmRJREdHIygoCGvXrsWYMWNypXv58iVmzZoFICv+TZ061eiyiMg4cp0ved4lIiKigrqusJTrEHYgtFDB4fFYciwUJ+6+gCD8+75CAfjX8MLHnaqhga97gdWPiIgoLxjnTJeYmIhnz55BUD9wRCSrjIwMpKamIjExEQqFAmXLltUY9ZnIVIx/REREmhgbiYiIiIoeXsNRcaJUKjF79mx89NFHAIARI0bg+PHjGjMkAcDMmTNx7do1AEDr1q3RtWtXrfkFBATg/fffB5DVOfHkyZO50gwdOhQbN24EAGzYsAGtWrXCBx98kCtdZGQkBg8ejIyMDABAz5494enpadJ+EhEVN7yeMezu3bvicqVKlQymr1Spkjhz7t27d03qQGhvb49Vq1bhnXfeQUZGBsaOHYuAgAD07t0bvr6+SEhIwM2bN7Fx40YkJibC2dkZa9euRevWrY0u6+nTp3rXR0REGJ0nkSWS63yZl3yKa+cAIiIiS1NQ1xWW1v5jB0ILdPhmBCZvv4a0TFWudYIAHL/zAmdDo7H0XT90q1u6AGpIRERkOsY502nrPKhQKKBUKguwVkSWJzMzU/w/EwQBz549YydCyjPGPyIiIk2MjURERERFD6/hqDgaO3Ys9u7diyNHjuDWrVto0KABxo4di9q1ayM2Nhbbt2/H2bNnAQDu7u5YvXp1nsrr0qULBg4ciN27d0MQBIwZMwabN29Gnz59UK5cObx58wZBQUHYvHkz4uPjAQAlSpTAwoUL87qrRETFAq9npMmOMQBQsmRJg+lLlCihdVtjDRgwAEePHsWECRNw69YtBAYGIjAwUCONjY0NPv/8c4wbNw6+vr4mlWPqdkTFiVznS1PzKe6dA4iIiCxJXq4r1AcHeBTzGtsvPkF6Zu4JWLTlY4ntP3YgtDDB4fE6v6Tq0jJVmLz9GnaNd+BFLRERFRmMc6ZTqVQanQednZ3h6ekJR0dHKBSKAq4dkWURBAHJycmIjY1FUlKS2ImwevXqsLKyKujqURHE+EdERKSJsZGIiIio6Cmq13CcfYLyytraGr/99huGDh2KP/74A5GRkfjmm29ypStXrhx27NiBOnXq5LnMLVu2wNXVFevXrwcAnDp1CqdOndKatkaNGvj1119RtWrVPJdLRGTpiur1TEFISkoSl+3t7Q2md3BwEJcTExPzVHa7du2wfPlyTJ06FVevXs21Pj09HStWrMDr168xb948jbKJSB5ynS9NzcfYh/0tsXMAERGRpTD1ekDX4ACGZOczr18GZu29aXHtPz69amGWHAs1+CXNlpapwtJjoWauERERkXwY50yX3YkJyOo8WK5cOTg5ObHzIJEZKBQKODk5oVy5cnB2dgaQ1alQ/YcyImMw/hEREWlibCQiIiIqeoraNVxweDxGB1xCuwUnMH33dXz9x21M330d7RacwOiASwgOjy/Q+lHR4uLiggMHDmDfvn3o378/fH19YWdnh5IlS6J58+aYP38+bt68iVatWslSnp2dHdatW4erV6/i448/RpMmTeDp6Qlra2s4OjqiYsWKGDBgADZv3ozr16/Dz89PlnKJiCxdUbueKY6io6PRqVMn+Pv749GjR/jpp5/w4MEDpKWlIT4+HseOHUOPHj0QHx+PxYsXo0OHDoiJiTG6nPDwcL1/Fy9eNMPeEZlPeGwydgWFY/3ZMOwKCkd4bHKe8pPrfGlKPsZ2MtgdFG5UerYFiYiI8pcp1wOHb0Zg0KpzOH7HuM6D6vl8d+iORbb/OAOhBQmPTcaJuy+M2ub43Rd4GpeMch4cJZGIiAo3xrm8SUhIEJc9PT3ZcZAoHygUCnh6eoodBxMSEuDq6lrAtaKihvGPiIhIE2MjERERUdFT1K7hOPsEmUufPn3Qp08fk7cfNWoURo0aJTm9n58fFi9ebHJ5RETmVNRm+S1q1zMFzdnZGXFxcQCAlJQUcdBVXd68eSMuu7i4mFRmcnIy2rZtizt37sDDwwMXLlxAtWrVxPVubm7o2LEjOnbsiIkTJ2LFihW4ePEiJk2ahG3bthlVVrly5UyqI1Fho2tmHoUC8K/hhY87VTN6Jh25zpem5vMmPdOoh/1N6RywblRTo+qVF0UtXhIREcklPDYZB29E4Pgd464Hjt15gdOhL5GeaULPQTUxr9OMSl9U2n/sQGhBzj+MMbqHrCAA5x/GYmDjwv1FJSIiYpzLm7S0rItZhUIBR0ceD6L84ujoCIVCAUEQxP9DImMw/hEREWlibCQiIiIqeorSNZyxs1XsGu9g9AO1RERExZk5Oqvkh6J0PVMYuLu7ix0Io6OjDXYgVJ8F0N3d3aQyf/75Z9y5cwcA8Mknn2h0Hsxp/vz52Lp1K+Lj47Fjxw4sWrQIPj4+JpVLVFSZa+AUuc6XpuZz7oFxs4oW1s4BRTVeEhER5ZWuGGiMvHYeNEVRaf9ZFXQFSD6JKRkmbpcuc02IiIjkxziXN5mZmQAApVLJ2QeJ8pFCoYBSqQTw7/8hkTEY/4iIiDQxNhIREREVPUXpGm7JsVCjZ5/Ib+GxydgVFI71Z8OwKygc4bHJ+V4HIiIiUxy+GYFBq87h+J3cD4Jmd1YZtOocDt+MKJgK6lGUrmcKgxo1aojLYWFhBtOrp1Hf1hh//PGHuNylSxe9aZ2cnNCqVSsAgEqlwqVLl0wqk6ioMnbglODweMl5y3W+NDUfc3cXyO4cYE5FOV4SERHlhb4YWBQUhfYfOxBaEBd70yaUdLG3kbkmRERE8mOcIyKi4ojxj4iISBNjIxEREVHRU1Su4cJjk3Hi7gujtsmefSI/BIfHY3TAJbRbcALTd1/H13/cxvTd19FuwQmMDrhk1EO9RERE+c2cnVXyQ1G5niks6tWrJy4b6pwXFRWF8PBwAICXlxdKlSplUpnPnz8Xl93c3AymV5/pMCkpyaQyiYoqcw6cItf50tR88oM5OwcU9XhJRERkKqkx0PwEuCIJTnhj9JZFof3HDoQWpEXlEjB2QiGFAmhR2dM8FSIiIpIR4xwRERVHjH9ERESaGBuJiIiIip6icg13/mGM0SNb58fsEwBnoCAioqKvKMzyq09RuZ4pLLp16yYuHzp0SG/agwcPiss9evQwuUwXFxdxObtDoj6PHz8Wl0uUKGFyuURFjbkHTpHrfGlKPvnFnJ0Dinq8JCIiMpUxMVBOFRURWGKzHDtsv8ZJ2/8ixO59XLf/D4YoTxqVT1Fp/7EDoQXx9XSEfw0vo7bpWMML5TwczVQjIiIi+TDOERFRccT4R0REpImxkYiIiKjoMec1XHhsMnYFhWP92TDsCgpHeKzpswEmpmSYuJ35Zp8AOAMFEREVfYV9ll8peE/KOO3bt4ePjw8A4OTJk7hy5YrWdJmZmVi6dKn4+p133jG5TPVZD7du3ao37f3793HhwgUAgJWVFZo0aWJyuURFjbkHTpHrfGlKPq3yodOhOTsHWEK8JCIiMoUpMVAu1shEH+U/aG51BxWtouCgSAMAlLQ17p5vUWn/sQOhhfm4UzXYKqV9rLZKK0zuVM3MNSIiIpIP4xwRERVHjH9ERESaGBuJiIiIip6PO1WDjVLak5xSruGCw+MxOuAS2i04gem7r+PrP25j+u7raLfgBEYHXDKpE52LvbXR22RtZ77ZJwDOQEFEREVfYZ7l1xgfd6omuWNKcb8npVQqMXv2bPH1iBEj8OJF7geCZ86ciWvXrgEAWrduja5du2rNLyAgAAqFAgqFAh06dNCaZujQoeLyhg0bsG7dOq3pIiMjMXjwYGRkZA0e0bNnT3h6Fv6ZQsh0cg46YgnyY+AUue7hG5vPjO41je50WMLJ1qj0ee0coO/7aCnxkoiIyFimxEBdSuAVeln9g++sf0E3q4sG078QPLS+X9NTele7otT+M+0OOBVaDXzdsXBwA0zaflVvOlulFZa+64cGvu75UzEiIiIZNPB1x9J3/QyOtMs4R0RElkRq/LO2UjD+ERFRsdDA1x0/DWmACdt4D5SIiIgsQ3hsMs4/jEFiSgZc7K3RonIJ+HoW/tGKjdHA1x0ftKmEVace6k0n5Rru8M0InfdJBAE4fucFzoZGY+m7fuhWt7TkOrb4/9kqjHlYxZyzTwB5m4GiKIx4TURExUNhneVXnZTrsdplXGGrtEJqhv6O/bwnlWXs2LHYu3cvjhw5glu3bqFBgwYYO3YsateujdjYWGzfvh1nz54FALi7u2P16tV5Kq9Lly4YOHAgdu/eDUEQMGbMGGzevBl9+vRBuXLl8ObNGwQFBWHz5s2Ij48HAJQoUQILFy7M665SIRUcHo8lx0Jx4u4LjWt8hQLwr+GFjztVK5b/p3IMnGLonNnA1x3f9a+HabuC9eZp6Pft7N/JJ267igyV7oaalQJiPh93qobT917qTZ/NVmmFT7vVwIzfbhhMm53e1M4BUr6PRSFeEhERmYOpMTCbK5IwxvogOltdRW2rx+L7jpmpOKxqpnfbBDgiWbCDoyJV4/0XMTGSyi5q7T92ILRAlUo66V1fzsMBK4Y2KjJfUiIiInXd6pbGrvEO+O5gCM6H5R5BqYa3C34YWJ9xjqiIePToESpVqgQAGDlyJAICAgq2QkSFVHb8W3osFMfvvIC2nzveaeZr1ENxRERERVntMm561zf0dcec3nXYNiQiIqJCrbg90Opoq//xhFZVSmBGt5p69zk4PN7gIEtA1kx8k7dfw67xDpKPoa+nI/xreOH4Hekd9vI6+4QheZmBYmBjdiAkIqLCobDO8gsYdz129Um83s6DCkXWtcFkC7uGM5W1tTV+++03DB06FH/88QciIyPxzTff5EpXrlw57NixA3Xq1MlzmVu2bIGrqyvWr18PADh16hROnTqlNW2NGjXw66+/omrVqnkulwofcw06YgnyMnCKMedMKef+D9pUMnj8u9UtjTFt4/UORlOllLOYTwNfd7SvXgrHDLTrsjsdKq2kzy40u1dtk87vUr+P7zTzNTpvIH/iJRERkTmZ2mbMlgFrjFEeytUJsJXVTQACAH1TySsQJbijkiJK413rzDd6yyyq7T92ILRAtyMS9K6vVdq1SH1JiYiIcmrg646lQxui2bfHcq3r7VeGcY7Iwuzbtw/Xrl0DAEyZMgXu7u4FWh+igtLA1x3rRjXFw5dJ6LzoFHIOmpjwJm+jMRERERUl918k6V0/vEUFtg2JiIioUCuOD7SGGPgde0rn6gav4ZYcCzXYeTBbWqYKS4+FYt2oplKriEkdq0ruQJiX2Sek4gwURERUVOibjaowzvILGH89djb0pdZ8RrasgHrl3NGisidnAM7BxcUFBw4cwP79+7Fp0yZcunQJL168gIuLC6pUqYL+/ftj3LhxcHPTP1iYVHZ2dli3bh0mTZqEgIAABAYG4uHDh0hISICtrS28vLzQuHFj9O3bF4MHD4atra0s5VLhYs5BRyyBqQOn3Hz2yqhz5j8PDM/aE5mQIqn8jEz9AeRRzGukpGfC3kYJAIh5nWYwTyuFAs0rlcCnv12XVAcgK9YZy5jv4/YLT6AAtA4mrEt+xEsiIiJzM6XNqC4Z9jiiaow+yn803i+lSEANRTjuCuVzbWNjpcDQ5uWRnJaJ+9fLIl1ljUjBE1GCB6Lggcuq6rm26VC9JNrX8IKLvU2Rbf+xA6EFuv1c/w8vz+P194YlIiIqCtwctI+eFJ9s+CYQERUt+/btw8aNGwEAo0aNYgdCKvYql3JGdW8X3IlM1Hj/bo7XRERElsxQB8IXial61xMREREVpOL6QGvOexk5RbzS/zt2eGwyTtyV/pArABy/+wJP45IlP8wRkyTtNwallQJL3/Uz++dSmGdsIiIiAqTP4FdYZvnN7uh4JzIRAf88QmbO0RpzUL8eO3M/Otd6BxslZr1dC3bWStnrakn69OmDPn36mLz9qFGjMGrUKMnp/fz8sHjxYpPLo6LN3IOOWIKPO1XD2dBoScfJVmmF7nV9jG7DnpPQgTDoUZyk+j6Kea13fXqmgFvPX6FxBU+8ScvEzWevDOaZoRKw7myY1tjkYm+N16kZuQb03XTuMTydbOHpZKvRUV4fY76P6SoBJZxsJXWAzGaueElERJSfTBngIKd9ma1zdSAEgDZWN3E3898OhDlnDrz6JA79Ln8iqYx/HsTiv2/VKNL36qXPvUxFhqEZCNmBkIiILIGdtRKOtrlvwscnc1RdIiKyfDV8XHK99+BlEtIypP34QEREVNQZ7kAobeRiIiIiooJgygOtRV1yWobBhz4jX+m/hjv/MMboUagFATj/MFZy+vWBYZLSNfJ1z5eZIbNH3zYGZ6AgIqL8cvhmBAatOofjd17kitHZs1ENWnUOh29G4ONO1WAlMaaZY5bf4PB4jA64hHYLTmD67utYdzbMYOfBbGmZKiz8+y6Cw+NzrWtR2ZOdB4kKkbwMOlKcNPB1x9J3/Qyel22VVlj6rh8O3ow0qg274K+7uBtlePDbZ/FvDLYDASAsWn9bEgCuPokHAAQ/jUeGxPP7ipP3tcaCD9pUQs/6ZXK9/yY9E98duoPpu6+j3YITGB1wSWtsyGbK9zHmdRpslNICpjniJRGRJQqPTcauoHCsPxuGXUHhJs0oS+b3cadqyBkBGypCscRmOWopHmu8r9Ryw/SMqh5iBM3n6Z4KJZGp1mVuSFNfnPnUH+tGNRU7AS47fl9yHS3hXj1nILQwgiAgxMAMhHHJ6UhOy4CjLT9+IiIq2twdbJCclqnxXvwbdiAkIiLLp60DYYZKwIOXSahV2rUAakRERJS/7r/Q//DBiwTOQEhERESFU37MopdX2TPzJKZkwMXeWvLMCvrci0oy2PkvMkH/g6OJKRkmlZ2YYvh3g/DYZOy9+gz/SJglAwDuRCUiUyVAKbUnhIlMGX2bM1AQEVF+MHZG5Z3jWsDVwcbggMDWZpjl9/DNCEl11edMaDT+vZQR4IFEeCoS0aZabTmqSEQyycugIwMbF69r6G51S6NpRU9cCNM+4Iq7ow02vt8Mnk62OHH3ilF5B2qZsVWXy4/j8HZ93YOzZKoEhMcanjQluwPh5cfaZzX0dLJFbI6Z/bR9VxQKYGDjcohKSMXvwc91lpfdUf5saDSWvuundYAZU76PADC0WXlsOvcY+jY1R7zMyRz3BoiI8pPU2dLJ/Od8Kfl7ONpCAKCACl2sLmO89QE0tMrq3Jci2GJGxn8AZHWg79OwDHYFPdXYPgPW2JPZFhUUUTilaoCzqrp4LHgDat0Sa/q4aNw3LQr36uXGHmQW5mncGySmGv7h5Hn8G1T1yv3AKRERUVHi5miL5zlGonrFGQiLNN58IiKSpqaWDoQAcDcykR0IiYjI4gmCgAcv9Y84zBkIiYiIqLAqzA+0mvOhmpAI/YPgAoZnIHSxN+3xBhd7G53rdO2zupLOtohO0nzQNDElAyERCahb1s2kOhnj407V9NZPnTlnoOD9eyIiUmfsjMrfHgwx2HkQADrX9pJ1ll+pHR0NGaY8ggHKMyiliEcpxMNOkYFUwRqPq74jU02JSA7mHHTEEr1Jz9S5TgGgfjk37L781Pg2rBFpgx7H6u1A+Dz+jaRz+NUnWR0HLz3K3SHS1toKU9+qji/23TSYT72ybijn4YjoxFQoYHhfsjvK7xrvkKu9bOr3sbSbvcFy+/iVkTVeqmOHGyKyBPoGEZHSCby4MPc535j8j4ZEwQYZ+N32c9SyCtfIp68yEPMz30HDGlUxuVM13ItKzNWBEAC+zRiutz457xMX5nv15mJlOAkVJbcMzD6Y7Wmc4RE5iIiICjt3h9w/+se/SdOSkgq74PB4jA64hHYLTmD67uv4+o/bmL77OtotOIHRAZcQHB5f0FU0i5MnT0KhUEChUGDOnDkAgNDQUEybNg116tSBu7u7xrpsKSkpWL16NXr27AlfX1/Y29vDzc0NdevWxeTJk3Hv3j2DZSckJGDhwoXw9/eHt7c3bG1t4eLigooVK6Jp06b44IMPsGvXLqSl5f6f6tChg1hvQ+bMmSOmPXnypJTDIho1ahQUCgU2btwovlepUiUxv+y/Dh065Nr2wYMH+PTTT9G0aVN4eHjAxsYGnp6eqFatGtq1a4epU6fi9OnTRtWHqDCp6aO9k+CdSP2zMREREVmCyIQUJBkYRO1FImcgJCIiosKpsD7QevhmBAatOofjd3J3VMt+qGbQqnM4fDPCpPzvSOlAaGAGwhaVS0DCLUkNCgXQorKn1nX69lld7wZltL6va6YOuVX1coZSwo7bKq3MMgNFcb1/T0REupkyS8OlR9pnhMrpToS8v3NI6+ho+IlRL0UcGlrdRzlFNOwUWddzdooMVHMpnp2OiAorcww6Yski9AziEpecjpdJqSa3YXMq42aPks52ud7XNWNgtofR+gcTzPb8VQqex7/Rmt/4EtcwSHkK79kcx0jlXxij/BMDlae05nPreQIO34zA0uP3JXeETMtUYemx0Fzvm/p9jJXQ4d5Q+9lU5r43QESUH4ydLb243tsy9znf2PyP3YlCOqxxRyifKy87RTpO+D/GulFN0cDXXbb7xIX1Xr05cQZCC3Nbwg8vAPA8niNwExFR0efuqKUDIWcgLHI42su/tmzZgv/85z9480b3YA+nTp3CsGHD8OzZM433U1NTcevWLdy6dQs///wzvvnmG3z22Wda87h8+TJ69uyJyMhIjffT09ORlJSEx48fIygoCOvXr8elS5fQpEmTvO9cPlq/fj0++ugjpKZqPjQeFxeHuLg43L9/H2fOnMGaNWuQlJRUQLUkypvSbvZwsbfOdSPjTqS0NiEREVFRdv+F4Wu4qIQUCIIgadALIiIiovxUGB9oNfahGm0zKxgSImHQI0MzEPp6OsK/hheO35HeYaFjDS+U88g9GrQxsxFtPv9Y68wTFx7G4IM2lSTXxVRnQl8iQ6X/0VUFgG1jm6NJRe2dJU3F+/dERKSNKbM0SPUoJhnhscmSZ7nVN0Outo6OJfAKda0eoa4iDPWtHqK+1UOMSvsUd7U8pKruheCh9X1FUiTgVEJSXYnI/LIfJjfmHKVv0BFLlp6pQnSS/oH47kYmmtyGzalV1ZJITEnHX7eiNN6/9TwByWkZcLTVXs4jnR0IBbgiGQlwEt/ZcSlca0eA/ySvgd2fsfhGCUCZ9V6Iqjx2Z7bPlTZTJWDStqtiG2yA1WmUUsTjnlAOoUJZPBVKQdAyd8/xuy/wNC5Zo/1p6vdRMND+A4DrT19BpRJgZSXfbyD5cW+AiCg/GDtb+tJjoVg3qqmZa1W4mPucb2z+AaNtcOFh1mBtKzN6o58yMFdat5ubgE7TAKWNbPeJC+O9enNjB0ILc1viDITP4zkDIRERFX1aOxC+SedDokUIbz79659//sG3334LhUKBkSNHom3btnBycsL9+/dRvnzWD1aHDh1Cnz59kJ6eDisrK3Tr1g2dO3dG2bJlkZKSgqCgIGzatAmvXr3CrFmzACBXJ8Lk5GT07dtX7DzYuHFj9OvXD2XLloWTkxPi4uIQEhKCEydOIDg4OH8PQg6TJ09G3759sXTpUpw4cQIAsHr1anh5eWmkK1mypLh89epV/Oc//0FmZiaUSiW6du2Kt956C15eXrCyssKLFy8QHByMI0eOIDY2f0YIJzIHhUKBmj4uuUbsvcsZCImIqBiQ0oEwJV2FxNQMuBbhm/dERERkmQrjA63mfqhGEASESBgI90ViKjJVApR6HoD8uFM1nAl9ifRMwwfQVmmFyZ2qaV1nzD6nZwpaB3K6+ChW8gOb+jo3GPL37SiDaQQAdtZKSflJrRPv3xMRkTbhsck4ExotS14ONkq8Sc/M9f6Z0GgMba6/Q19weDyWHAvFibuas1koFIB/DS983Kka7kUlaqxraXUL222/zZVXfauHuJtpqAOhu/YViZGAdx292xJR/pFz0BFL9yIx1WC79G5kIrrW8TG6DatN66ol8DIxNVcHwkyVgODwV2hZRXtn7LD/70BYT/EQja3uoabiCapbPUVVxXMkwgGtU5eJaTeff6w1D5VzaSBO8/kQG+iecShdrQPfYOuTaG51R3ydJNjjsqo6RqV/qtGRUBCA8w9jMbDxv98lU7+PDyTMupiYkoGwmNeoUspZct6GsMMNEVkCU2ZL19YJ3NKZ+5xvbP7z/gwRO+/fE3xxJLMx3lJe/jeRvRtQdwCQkQIos37//7hTNZwNjZZUjq77xIXxXr25sQOhhZHywwsAPGMHQiIisgCuDrkfBE3LUCElXQUHW+N+KKcsM3+7jrtR+df55MGLJKMaCu+tu4AqXvLd/NKlhrcLvh9Q3+zlqDty5Ai8vLxw5MgR1K+fu+yIiAgMHz4c6enp8PLywv79+9GiRQuNNCNGjMCMGTPQrVs33Lx5E19++SX69euHmjVrimkOHjyIp0+fAgCmTp2KhQsX6qzT7du3UapUKZn20HiNGjVCo0aNsG/fPvG9Ll26oGLFijq3WbduHTIzs35o3Lt3L3r16qU1nSAIOHPmjJzVJcp3NX1cc3UgjHiVglfJ6XDT0smeiIjIUkjpQAgALxJS2IGQiIiICp3C9kBrfjxU8/xVitZZIHLKVAmITkqFt6u9zjQNfN3xQZtKWHXqod68FApg6bt+Wju0mbLP2uofn5yOey8SUdPHVed2Ujo36Ot0l5GpkvxdCX4aj3rl3Aynk1gnPjxKRETqdMWPvBjXvjIWHw3N9f7Z+y9zdSBU7/j+KOY1tl988v8DCgjwQjw8FIm4K5TXmCG3T8MyGnncUflqrUc9RRh2oYPeuuqagRBJhjv6E1H+kuNh8uIg8pXhZ4jvRiZiTNvKRrdhtWlZuaTO55YvP47V2YHwUUxWZ7pR1n9hgFLzGQ9XJMMRKUhGVhsy9nWa9sJdywBxtzTe0teB8F8CqiuearzjrEiBpyJB6yyEiSnpud4zdhCcSR2r4v2ASxLqBlx/Gi9bB0JT2snH7rzAmtMP0L1uackD5BARmZsps6Vr6wRuycx9P1hX/j6IQXOrEOxXtcm17maOSdR+zuiNt5SXke5SHjZtJwN+QwFbJ400DXzdsfRdP4MDoNkqrXTeJy5s9+rzAzsQWpC412mSOwayAyEREVkCdwdbre+/epPODoQmuhuViKtP4gu6GjolpGQU6vrl1erVq7V2HgSABQsWiDPm7d69O1fnwWxly5bFrl27ULduXWRmZmLJkiVYuXKluP7+/fvi8gcffKC3PrVr1zZ2Fwpc9v6VKlVKZ+dBIGv2tnbt2uVXtYjMooaPi9b370YlolmlojvSERERkSHSOxCmoqqX9nhJREREVJBGtqog+aEEcz/Qmh8P1dyROAgukDU4kr4OhEDWbHuGWAFoXkn7A6im7LMuFx7G6uxAePhmhM4HWNQ7Nyx91w/d6pbWmkfQ4zjEJ+d+CFWb60/jAVTQm0ZKnc7ce4leDcoY/YBwcRytnYiouNAXP/Li3WblcfhmJO5Eag5wG3g/RpyVWFvHxUHKk1hsdQ2VlFGooIiEkyIVoaqyeCttgZhHWqYKey4/08g3Dq54KpREOYXmDIr1rfQPTAAAEYInbqoq4oXgjheCO1IdvDCyS3OgXDPTdp6IzCb7YfKPtl6BSs91v76HyeWQl1nI80PEqxSDabIHIDemU6Y2lUs5wcfNHh5ONrC1tkJahgpWUKGK4jn8rO7j8f0MoKP2du+j/5+NL0RVHtDyKFhlxXPcFCrrLLuqlzNeoARyttpsFIY7EJZAAjwUuX+PuKyqrjX9nchEhMcmZ33Oqkwg5AAaVGyDQY3LYdvFcL1lWf3/IDhujraIk9gGDA5/hX4Ny0lKa4ip7eR5B+/gu0N3JA2QQ0SUH6QMYqZ9O2nnXktg7vvBOfOvrgjHWOWf6KMMhBUEXE6rgaeC/kktrgrVMN1uNn6YMlmccVCbbnVLY9d4Byw9ForjWgZL61jDC5MNxKfiNvgEOxBaEKmzDwLAszh2ICQioqLPXcfsSvFv0uDjpv8BA6LCpkKFCujTp4/WdYIgYNOmTQCAli1bom3btnrzqlmzJpo1a4Zz587hr7/+0ljn5PTvSCyXL18ukp0E9cnev5iYGDx69EjvbIVERV1NHR0I70QmsAMhERFZtAcvJXYgTEw1c02IiIiITBMSkWg4EQClQmHWB1qB/HmoJmenAH0iX70BDOzvlcdxBvPJFIC/b0diSNPyudaZus/aXAiLwchWFXO9HxweL6mjRVqmCpO3X8Ou8Q5aP+cjt7XPamRtpUBGjqehrz99pbcsqXVKVwnYc/WZ3jTaFLfR2omIigup8cNYDcq5wdvVHm2qlsx1rfDqTTpuPHuFyFdvtJbdQPEAbysvarxXUREJG2QgXe1xyEwtT8XeUlVEOaVmB8Jaise5ts0pCp7omTZPfG2VBHg7NEK3ktoHASCigtWtbmm8Vdsbf93SPUvo8qEN0aWOj+xl53UW8vwSKaED4b2oRGSqBDTwdcfsXrXxxb6bJpXV6v9nF7RLfoHvXffCO/Em6ls9hIsi6znmuc8Blao3rKwUGtulZ6oQ/v/POt8RcrftAKCqgQ6ETSp4ID65ZK73pcxAWN3qqdb3r6i0dxzYcSkcO4PC4V/DCzPrJ6P67yMBAONtKqOKdQ2cU9XGOVVtvIZDrm0rlnRCt7qlseeK9jK1CX4aLzmtIXlpJ0sdIIeIKD+42JvWPcrFXncnNUtj7vvB2flXVERgrvVGtFde11g/TnkAX2aMNpiPS93uUOjpPJitga871o1qivDYZFwIi0ViSjpc7G3QorKnpIHO5JjJsChhB0ILcltHB8JapV1zdS6MTEgRR2oiIiIqqtwddHQglDgSE1Fh0rp1aygU2q/Nbt++jZiYGACAh4cH9u3bZzA/pTJr6LWwsDCkpKTA3j6rU23nzp2hUCggCAI+/PBD3L9/H++++y5q1qwpz44UsC5dumDPnj1QqVTo0OH/2Lvv8KjqtI3j3zOT3nshCT10CNLBQlNE7IoF3V2xsLpNXVd3lbWva1srq66+isJaULEsFrABShGQUEKHQChDEtJ7n5nz/hESMjlnaiYNns91eRnOnJYQTv3dzzOFBx54gCuuuIL4+PjO3jUhvG6A3QCh64PyhBBCiO6mpKqewsp6l+bNK3c+AEIIIUTn+OKLL3j33XfZvHkzJ06cICwsjP79+3PllVdy++23Exam383LU0eOHGHhwoWsXr2affv2UVZWhr+/P3FxcYwcOZKrrrqK6667Dl/fM2eQgOg8VqvKB5uOuTRvSlRQuw+864hBNfbeY+txNoi13mwlw0lQrsnXO/UDhJ5+z4nhAZouHb8cLkZVVc2z3ZdXZroctKi3WFmwMpOFc8faTFdVVTdAGBbgw7mpsXy9M9dm+oG8CqrrzQT56X9/7uyTp86kau1CnMm6ekcl4V3tdf6YPjgeasu5JOwgPsavSTMcIs1wiF/VzydL7cHSdBNL04/rbvuwqg38+CoW+ii5HFBTHG53l7U3FxrTAahTfdmt9mKHtS+B1NJAiMv7b1VxWARACNH1VddbvL5OVzt+zxnfk97RwZ16HnUlQFjbYOVYcTV9YoI9vo8C6BFxMjCnWriq+iNNJ8F+5kMcLKhkQLzt++/jJTVYThZO2W/VP773M+SAg9PU6F6R5NRdw0V7kqjHh3p8aFB9qMP5PW0CxZhVAz6K7Qa2qvodCOHU3/OgQ1/x15PfZ8+GLG71yeJWVnBT/d/4yZqmWS6roIr8ilq2HtMvmDM8KZyd2bb3wrtzymmwWPE1Gpx+L8605e+3ibMCOUII0REm9I1GUXCrw56iwIS+3bNQuyf3554e81t22nW03ab1V6hBTDDs1aznWuNPLDBfSQGRDrd3/pA4t/YvJSrI42sqb3Qy7C4kQHga2ZOjffFiUGDaoFhNgNBiVcmvqCUxXFvJQgghhOguwu11IJQAoeiGkpOT7X525MiR5q+XL1/O8uXL3Vp3cXExPXr0AGDw4ME8+OCD/OMf/6CqqorHH3+cxx9/nMTERCZNmsS5557LzJkzGThwoEffR2e75ZZbWLp0KStXruTo0aPccccd3HHHHQwaNIhJkyZx3nnncfHFFxMTo60wJ0R3ExbgS1JEINmlth3m90uAUAghxGnsoIvdB0E6EAohRFdUWVnJjTfeyBdffGEzvaCggIKCAjZs2MC///1vPv74YyZMmOCVbb7wwgvMnz+fujrb84LZbObw4cMcPnyYzz//nCeeeIJPPvmEYcOGeWW7ouvq7NDDuoOFHCuudmneI0VVHCqopF+s64PZ3dURg2r26QQIe0YF6f4ccp0UgdidU0a92bUQw/qDhZRU1RMZ7Gcz3dPv+ex+MXzSqhtEYWU9hwoq6R93aqCrqbia1fvzXV85sGp/PsdLqm2qYq85oP+7MnVQHCOSIzQBQqvaOIB0bG/t340n++SJM6lauxBnou7SUUl4T3ueP6aWfAJPP8NIVEa2OH2kKYfIUnvw9Y5cu8HFIzoBQoABynGnAcJvrWM50RDFbmtvDqjJmFsMnzQqCj0iApq7XTljrwiAEKJrKHJSiG7zkWKuOCvJa9tzp+P3fzccbf5zZ51Hnd17Ndl/ooI+McFscaETvD0vfn+AvjHBzByaRF1AHP61tueWIYajpB8p0QQIjxRWNX9dQDiFahhRVFATkoJf4hDe2ufLeqvj5zhje0dhNESz76tst+4BAb5SzuOruon0UXJJVbIZbDhKXyWX46rz8Sbj2K2Z1qAa2Wy1PxZnzYFCth0r1UxPDA9gxpB4TYCw3mxl/4kKhiWFO/9mnPDkPlmPnBuFEJ0tJSqIqQPjWLXP9fuYaQPjXOpU15W05f7c02P+R5tNfLTZRHSwH8VV9bRcvOV2m9ZfpIbzqeVcbvBZZbMef6WB23yW85T5RrvbCvIz6j7jbE9t7WTYXUiA8DSiV7mxb2yI3RdK2SU1EiAUQgjRrUUE+ulOL6txrRuF0BoYr9/Nqj3UmS3syXE/5DK0Rxh+Pm2vnuVIR/4cmgQG2r8uKy0tbdO66+tt/008/vjjjB07lqeffpqff/4ZgNzcXD799FM+/fRToLEj4vPPP8/48ePbtO2O5uvry4oVK3j11Vd55ZVXOHToEAD79u1j3759vP322/j4+HDttdfy3HPPkZjYvhXchWhvgxJCdQOEepXvhRBCiNPBwXwJEAohRHdlsVi45ppr+OabbwCIj49n3rx5DBkyhOLiYpYsWcL69esxmUzMmjWL9evXM3jw4DZt85VXXuEvf/lL858nTZrEZZddRkpKCuXl5ezevZtFixZRWVnJ/v37mTp1Kjt37iQhQX9AsujeukLowVRczbPf7NP97PzBcfywVzuw5esdudw5PbXd9qm9B9XUNlg43GLQZ5OxvaMoqqyjqlXXjzwnXTDsDVo9NzWGtZmFNtMsVpXv9mi7EHr6PZ8/JE4TIATYmFVsEyDcmFXk9gAcVW1cz+zRQc2/q/b2LzUuhLRk/QGiGaZS3cE1nuyTu7pztXYhhHOudFRal1nIgjkj2717rug47Xn+eHq7H+/7aFeeZjjE59ZzKa2xXzTYboDQYALrRIfbPaCmcMCiDRn6GQ08fOlgHlqmDXw4olcEQAjRNRRWOn4+3JZAnB5PO7Z21nnUlQ6E0PjueeawBN2fV1SwL0HVOVxi2ECoUs2/zNfrrqPBop7sTDeRwclj4KBt0exBiomnthzl3NQYmwI/WTb3kgrX1T9ErhrNJ7dfwJAeYfzvxTXsz7M/7igmxI9e0UEoiuL2PeD0QXFcMyaZO5ds54AlhQNqCl9bXSt25YOZsQbtvf92tR/VBNhdbsXOXPadqOBt32cpUCPYqqayzdqfASlj7D6vyDhe6pUAoSf3yfbIuVEI0dnump7KusxCl87LfkZDuz57bQ9tvT9v6zG/qEo7Prv1dpvW/5ZlliZAaFEVQnB8HVLbYGHl3rxOeb7Qlk6G3YEECE8TtQ0W3cEzQxLDTrX/biW7tIYx7b1jQgghRDuKkA6EXvf01SM6dHu3LNrs9gO6M7FKVUjIqYIQ99xzD88//3yb13nppZdy6aWXkpeXx9q1a9mwYQM//fQTW7duRVVV1q9fz7nnnsvy5cs5//zzPd6OxWJxPpOX+fr6cvfdd3P33Xezf/9+1q9fz88//8zq1avJysrCbDbzwQcf8OOPP7J58+bm7oxCdEcDE0JZ2eo4Wlln5nhJzWn9MEMIIcSZy16AMDbUn4JWgcE8FysoCyGE6BhvvfVWc3hwyJAhrFq1ivj4+ObP//CHP3Dvvffy/PPPU1JSwu23386aNWs83l5NTQ3z589v/vObb77Jbbfdppnv4YcfZvr06ezcuZPCwkKeffZZXnjhBY+3K7qmzg49NIcX9+WjNwY/KtiXZ64ewYSnVtJgsZ3jqx057T6IxZ1BNb5Gxa39OZBXgVXnmx6cGMp2UwmHCmzDhblOBrFuPaYdtGo0KDxw0WDWZq7VfPbGT1kYFEXTadKTgUT2nrV8suU4kwfENn9eUWt2uk49FbUNDn9Xm7y8MpOUqCCMBgVLqx9uxvEy3WU83Sd3dMdq7UII17jaUaneYj0ZDgiUToSnifY8f2w0p1JiDCVSsQ1+jDQccrrsMTWeMjUIkxrHETWBw2oCh60JbFXdv2ZSlMZz2J3TUzmQV9GmIgBCiK7FWQfC/XkVlNU0EB7Y9i7a3ujYWm+x8of3t3Hz2SUMTAjV3MN4m8sBwrxyqurM7G3R4CSKci43rudK6yZG+B8AoFb15TXz5VShP165uTNd/3GaAGGQUkehaT/n/avcpsDPkVbFaA6pjR0je8c0/lzO6hnhMECoADuOl5GWEuHRPWBaSgRL7whkwcpMVrUqRuTIMOUIwYo2wPqzdSgAPcIDCPL30bzvWLkvn0jKmea3HYDr+BGA+sMhWJLvBQZo1plhKuXG8b1c2zEnbj2nj1cChHJuFEJ0trSUCBbMGckf3t+GxcnB+x9XDOtW96/euj+/a3oqazMLNM+iXRFJOVcZ15GpJrHGmqa73SevHMZPBwrIsvbge8toLjBuoVr15yPLFN62zMSkxttZeyOrijxfaCcSIDxNHMyvxKzz5mVIjzCSHAQIhRBCiO7MboDQQTVC0bWc7tVevCU5Obn5a5PJ5NV1x8fHM3v2bGbPng3AsWPHuPfee1m6dCkNDQ38+c9/ZufOnTbL+Pv7N39dX1+Pn59+N1CAwsJCu591hIEDBzJw4EBuueUWAH755Rduu+02du7cSU5ODk899RT//ve/O3UfhWiLQYlhutP3n6iQAKEQQojTkl6AMDrYj4HxoZoAYes/CyGE6DwWi4XHHnus+c/vvvuuTXiwyTPPPMPKlSvZvn07a9eu5bvvvmPGjBkebXP9+vVUVDQOIhs7dqxueBAgNjaWp556iksuuQSgTaFF0TV1dujBlUBYabWZzUeKOTc1VjNY70BeJQfyKhgQH2pn6bZrGlTzxw+26b5zbmnGkAS3fj77cvUHcw5KCCMxPFATIDzhoAiEqqq6XS8GJ4YypEcYw5LC2JVdbvNZVmEV932yQ9Npsul7vuO9rQ73/xKfzfwz5lvCP68Fcz1bAirxUeu5uf6vbFUbB29uN5Vy3r9WN68/NKBxGMZvjV9yu89XNOCDGSP1auP/r6x/XHdQbUl1A08t30e9xcoDPu+TohRgRaFx2Cvstybzb8tVNFhU7lu6g+TIQI4WVdusI9OUC/97szENoRia/+tvGAfoP0dq0ks5wXmGHZgxYsZIw8n9XWcdRimOf/96Gou5+9w0h/MIIbovdzoqNYcDzsCCoKejpnOaO/op2fRU8lltPcvhfBaM/GA5i2t8bK+/BytH8aOBeuwHehrwIa3uTZrOkZ4YEBfCbyf3Y0LfqOYAvKfdyCpqZXyCEF1NbYOFijrHIWhVhW3HSpgyMK7N2/NWx1aLqvLWusMAmnsYb7JaVZcL8O07UUHG8VKbwjCP+P6Xy40/07JCToDSwPmGLSyznmN3Xav25/N9bAoT1EC2W/uxXe3Pdms/Mqz9KaSxi17LAj9HirTd7OPD/Anyazw/+RoNDve9oLKea17f0FwsaMGckU7vz/2MBhbMGdn8M09LiWDh3LGYiqt5ZfVBPtrsfMzOdrUf0+qeY6Jhz8n/dhOtVLDhZIDw/CHx+PsYdN93nGU4qN0nSyWERtI7Oogjre4Bd7QsIlPc+LtDWBL42B/HY8+x4mrnM7lIzo1CiM42c1gi5w047jTgX6zTTa8r89b9eVpKBLOGJ7Jse45L61KwMtGwhznGVcwwpOOvmNlkHcSaeu3zwHqLlRW7TtA7OohDBVW8Zr6Mbdb+vG+ZThkhOmt3f/+F5yRAeJrYk1OuO31ojzASwgNQFDQ3KDkSIBRCCNHNBfoa8TMaNBfE0oGw+2gapOHuA7ozzciRIwkPD6esrIzVq1dTV1dnE+Lzpp49e/L+++/z448/UlBQwK5duygtLSUiIqJ5nsjIyOavs7Oz6dOnj931bdiwoc37ZDCceuirtvGp+7hx4/jvf//LWWc1vrRcu1ZblVyI7mRQgv7grf15FZw/xHG1JiGEEKI70nuh3i8uhLhQ7fVxvnQgFEKILmPNmjXk5uYCMHnyZEaNGqU7n9Fo5M4772wuBLRkyRKPA4T5+acGBqSmOi5K1fLzykr9brei++rM0IOr4UWrqnLnku38fmo/3Wr/T3y1h0vTerRrB4qZwxK5cXwRizccdTjfpsPF1Jkt+PsYXVrv3hP677EHJ4YSHxagmX6irBZVVVEUbSAgu7SGvHJtkYjRPRufV/aPDdEECAGCqGWUkklCZj6bDuUT37OeBLWASePuAjvhhKZuRH/tmUL4mj3N06MBFAhQ6m0Gy7bsZPnwpYNRFAhRaohu2VXp5Ldk1Qk7KAqkHylu/l0527CbYYYjNvNEMYR/W64CGn9XG8za36uC4lLY/r5m+ohJCSjKMIeDmkcoWfzDd5Fm+hV1j7NddRwgXNz7W/qQBCQ7nE8I0f140lFp1f58jpdUS1dSN5iKq9mYVURFrZnQAB+vn/M9Xf+EvtG6Y860VCYbdnCrcTnnGXdSoIZxdt2/HYYAAb6zjuEaGgOE9aqRPWovMqz9CKTO6bJtCQ8CZBZU2oQHwbPAZONybe9eJoTwLlfDAOlHvBMgbI+OrS3vMZoCcN5SWFXntHhMkyOFVfx8sMhm2hLLtMYAYSuXGjc4DBCqKvx+fQBm65uo2A//NRX40Sss3zs6GGi83/5w8zGn+9+yWNDMYYl2Owq27EirNzYpJSqIgS4X9lHIUnuQZenB+5bzUbAyQDnOYbXx7/D8wfH4GBTeXHtYs+QoQ6b+KpPHMSK5ThMgPJBXQXW9uTFUufJx2P0ZoEBIPARGQK9JcMmLjne3MBN+eoa+B47zX99KgpQ6gqjjbw3z2Kn2dfF7tiXnRiFEV1DlpJgAwIebj3H7eX0xGE7dX7T3/ZmnvH1/bnIjOP6a78tcZNxsM228YR8DFBMH1BTtdvflNz863aamss3iWeMQeb7gfRIgPE3sybX34iUMX6OB+NAATbXGnFIZQCOEEKJ7UxSF8CBfTVeJspruVRXkTNeWB3RnCqPRyI033shrr71GYWEhL7zwAg888EC7bc/X15ekpCQKCgoAMJttb6aHDh3a/PUPP/zAvHnzdNezcuVKMjIy2rw/ISGnKs9UVWkrzLmrZeCx9fcmRHfTJyYYX6NCg8X2Bc9eO/eIQgghRHdWXW8mW6coWv+4EMJ0XkZX1VuorDMT4i+PwYUQorOtWLGi+etZs2Y5nPeiiy7SXc5dcXGnBgAeOHDA4bwtP2/53EN0f50denA3vLj1WIlu0bw1mYWsySxs1w4UAHU6gbTWCivr+NsnOzi7f4xLg2f0nlHEhvoTHeJPYrg2QFhntlJa3UBksLZbgr2uQKN6RZJhKuXrnbm6nycrBbzn99SpCdmN/ys4vBPQBpqvHZPMndNTG38H9hdpPgfwQ7+QYb3FyuNf7mV0z0h8sy2685h1hmlM7BPNhqxT2zKi/btQWwUlcsu07/sN6A8CjgjyZ1iPcHZml+l+DuCD/v424DwsajabQXEtVCqE6F486aikqrAxq5jZo2WAnzMZplJeXpnJap13pN4457d1/SlRQUwdGKdb4KDJKOUAD/h+wFjDqWvqWKWciw0b+dx6rsP9W2sdzuMNv2aLNZW9ai8XQoPeo/d76npg8hRFgQl9o9phD4UQbVFU6WKA8GixV7bnaQDZFS0DcK6eE5wFH07o3EtA47P21kX8rCp8uvW4zbSN1sEcVHvQX7HtWnSeYQfhVDrsLtRgPdVl3ZF6i5X8Cm0BmT4xjQHCl1dmat6RO1pXU7Gglh0FNx0upqK2gdAAX02oXI+nf88qBvarPQEI8W/8+wAI9jNSVW97H3aWou1AiF8oxA5kRPJRvsiw/ZlbVdiVXc64PlFQ2hSoVKHyRON/ET2d72B1Eexcynig5e1flFJBQqg/J3QK+Tgi50YhRFdhKnEekDtaVM3Ph4o4JzWm3e/P2sqb9+eFlXVsM5W6vJ611hGaACHAr43f85D5Fu123dtNu+T5gvfJyInTxDadlyXxYf7EhDRW3k6KDNQECLNLpAOhEEKI7i8iUBsglA6E3U9bHtCdKebPn88HH3xAaWkpDz74IIGBgdx555023flaqqqqYsmSJQQHBzNnzpzm6QsWLCAxMZHLL78cPz/tIByA9evXs2PHDgCSk5OJiYmx+fyiiy7i4YcfBuCf//wnV1xxBbGxsTbz7N69m9/85jcef78ttQz8bd26lWHDhtmd95577mH27NlMmjTJ7jyvvfZa89cjR470yj4K0Vl8jQb6x4VqBuPtP1FhZwkhhBCi+8oq0C8m0T/W/mCE/PJaQhx8LoQQomPs3Lmz+euxYx13dktISCAlJQWTyUReXh4FBQWa5w6uOOecc4iJiaGwsJD09HTeeustbrvtNs18BQUFzJ8/HwCDwcA999zj9rZE19WZoQdPwotrMwsZGB/KPjv39e3ZgQLgcKFrxbv+tz2H/23PaR48c/3YFMpqGjSDU1VV1f1eBiU0dm2I1wkQApwor9UNENZkfM59PptIVIrpoRSRSBEmNZbevX7g4WW77Q4eNan6x5DK/KPoBQjnTupz6rl0kP6gR3/sFyartzT2GPRT7AUItc90R6SE83OLAKHiwjAbvTnsLbfteDlHixz//frY3V/nwcDjRZX0Vwxt7AUlhOiKPO2oVFHbdd/XdpVuEt/syrXbqdgb53xvrf93U/Q7JDdJMxyyCQ82udnnGz6vPwdHIZFa/HnbcpHdz9tb699TVwKTrU0bGCfvs4XoggorXQs8bTeV0mCx4mu03w3PFZ4EkN3RMgDniKvBB71iJACTU2NQC/ZzSE2yma6dX2GF/0X8qX5h8xSzamCjdQhRSgVlavs9k+8dE+yVYkEpUUFun/+98feclhKOn0/j79vZ/WP4bk+ezeeH1QSSrIX0NrSYnjwaDEZG2gmt7Dhe2hggLDNpP/Rz4e/CTjGYQOr43ZT+/HSgQM6NQohup7bBosnO2PPk8r2M6R3Jkl+O6T5fbO9nsq7y5v35qn35bp3PvrBM5EGf9whSbK+xrjSu4xnz9VTi/LjvY1Bc7oDcUld+vtAdSYCwm8swlfLSDwfI0KkUaLaoZJhKSUuJoEdEoKYiY45OtW4hhBCiu4kI0lYhlABh9+XJA7ozRVJSEh9//DGXXnopdXV1/PnPf+a1117jyiuvZMiQIYSEhFBRUcHhw4dJT09n1apV1NbW8o9//MNmPVu3bmXx4sWEh4dz4YUXMmrUKJKSkvDz8yMvL4+ffvqJL774Aqu18WVi0wC6lsaMGcO0adNYtWoVR48e5ayzzuKOO+5g4MCBVFRUsHbtWj744AN8fX25/PLLWbZsWZu+9/PPP7/567/+9a/k5+czcOBAfH0b//1HRUUxbtw4AD777DNefPFFevXqxQUXXMCIESOIjY3FYrGQnZ3NF198wfr164HGTov33Xdfm/ZNiK5gUII2QJhVWEWd2YK/j1R+F0IIcfrIzNcfSN8/LoRyOy8O8srr6CsBQiGE6HT79+9v/rploSB7+vTpg8lkal7WkwBhQEAAr7/+Otdffz1ms5l58+axaNEiLrvsMlJSUigvL2fXrl0sXryYiooKQkJCeOuttzj77LPd3tbx48cdfp6bq98VTXiHoxBAZ4UeTMXV/HtVpkfhxQN5zosCedKBwhVHnATMWmsaPNN6IGHT4NRfje+l+7x+cGIY1JQwwJLFNMNW4pUSvrRMbB5ocqKstnGeVnof/4LrfTbZTPNFxaKqDgeP1hBAgRpOrGL7Tt1cov236+9jIDW+xfVjYKTuOn0dBAgB0o+VMDtA1aT8LKqCqhMg/Gl/gc2fDbodCLWMBgVLi4E3essBfLXzBOUWx/vsa6cDYesAYVSwL8VVtn+v1XV1HCqspb/zQ7wQopvxtNNOaEDHdZJzNRDYlbpJZJhK7Yb7WvL0nO/N9Tvr4vW+5Xxu9VlBslJoM32E4TCjlEy2qgNc3u+Opvd7etf0VNZlFrrURdrPaODO6antsWtCCOwf31057rsaIKxtsLInp7zNx39PAsjuah2Aa82d4Hjrzn4B1HGlcR33HPyJe/2OMaHuFYddBAFKUq+matd77FL78KVlIiss4ygi3PNv0EV9YoI7rViQN/6eN2YV882uXGYOS2TKwDhNgPBB860ARFHOWYZM7uhXzNjhjcHRoT3CNfeA0BiEpaEWKm3XBYD/qb9He/92TlTUk6Czr6GGOi5N68HIlAg5Nwohup3s0hqXzxV7csvZ02rclZ72eibrKm/en/9w8vzjg5leSp6meEBrlQTxlWUC1/r8ZDN9h7UvsUoZlarz8+tZKRFs1mma5kxHPl84E0iAsBtzdMEPUFRVzzWvb2DBnJEkRQRqPq+oM1NW00B4oPyjEkII0X3pncfKaiRAKE5PF1xwAevWreNXv/oV+/fvJzMzk2effdbu/EajkYQE28d8itJY5bOsrIyPP/6Yjz/+WHdZX19fHnnkEX73u9/pfr5o0SKmTZvGwYMHyc7O5qGHHrL5PCoqiiVLlvDzzz+3OUA4fPhwfvWrX/Hee++Rl5enCf1NnjyZH3/80eb7O3r0KG+99ZbddUZHR/Puu++SlpbWpn0ToisYeLJqf0sWq8p/fjzE1aOSJZgthBDitHEwv1J3ev+4ELLtFEvLr3CtsqQQQoj2VVpa2vx1TEyM0/mjo6N1l3XX1VdfzQ8//MAf/vAHdu/ezfr165sLCzXx9fXl73//O7fffjspKSkebcfT5UTbuBIC6OjQg719coerRZjrLVbuXZrBb8/r65XOSdX1ZvLKXRtk60zT4NTVOoMqrzH+yJ82z4NfqhgPjD/ZaDDD2o89am9Ar7sFVNWZyaoNYUKrWknxFPPZoQKnP++jarwmQBhUe0Iz3+DEMNvuI0HRFEaOZGehSh1+1ONDPb7kqNGaZVtSVfilrhf+xkn4YMEPMz52AnoAe1t1aixQIwhWazGeDAQqqJSifQaUFBHIseJqm2nlBBPoo1DXYMaAihErFhe6CPrYCUW2DBD6GQ3MndSHF7637TRlRGXVgSL6O26IIoTohjzptKMoMKGvfgdXb3InENje3f7c9fLKTJcG4YPrXafaa/1fZGQ7XL5B8eW7mJu5pehfNtNrVV8GGI6z1dK+AUJfg8JlaT34dJvj/WzN3u9pWkoEC+aMdBrA9DMaWDBnZKcMHBbidOfovio62I/iqnqb4hp6x/2iKsfh55Y2Hyn2yr9ldwLInnAUgHM3OH5pWuO5Lo4S7vD5kquNawhXqqEMUGCOcRWvWy5zuK7xQ/pxf/kHfJnpnftIV/WJCWZdZqHzGXV4o4NRW/+eLVa1OXyiXyamUTFhrLSOZk2Wwr/Hn8VMINDPyIB4bXHhnw8VkWs6iN4VTIU1gCwn/6YSq/fzlb922YFRRqKC/YgK9pNzoxCi22n9vKxJYlgAuS52JtTjyf2Zt3jr/ry2wcKhzL3c4/MD1xt/xIrC2XUL6BUTxuGiKrvr/9AylWt9fqJADecTy3l8ZJnCEdX1++dRvSJJP1bSJZ8vnEkkQNhNuXvBf/PZvXU/zymtkQChEEKIbi080E8zTQKE4nQ2ZswY9uzZw2effcayZcvYtGkTeXl5VFVVERISQkpKCsOHD2fKlClcdtllmgDhf/7zH66//npWr15Neno6Bw4coKCgALPZTFhYGKmpqUyZMoVbb72V1FT7VcFSUlLYunUrL774Ip999hkHDx5EVVV69uzJZZddxp133klSUhI///yzV77vxYsXM3nyZJYsWcKuXbsoKSmhoUH7b33Lli18++23rF27lm3btpGVlUVJSQmKohAVFcXQoUO56KKLuOWWW4iM1K8eLkR3M0gnQAjw0g+ZvLwys8MrJwshhBDtRS9AGOxnJDE8gAY7z0kLKjp28IIQQgh9lZWnjuEBAQFO5w8MPFUYs6LCeTc2R8477zxeeeUV7rnnHrZt26b5vKGhgVdffZWqqiqefPJJm22LrsvVEMDDlw7usNCDs+Kv7SEzv5L7Ptnhlc5JRwr1B9W4whczAxQTJWooOZwKCev92KvUAEJVbafDeKWkOUB4QmcQT8bxUk5YI2mdgzNiwVyu02WhlWNqHGNoDL2Vq0GY1Fj2qcma+UYkt+qYERTFF6MX8fhXe5xuo7VPrefxqfU8m2lXj0qiv6mUgwWOuz3e2PB3p+tXFBjbO9JmQNQJohlR+ya+RoUGi/NffKOicPPZvWmwWPnil/NYUzcCI1Z8seCLGV/MnFAb/z00DQadPCCON9dkUVF3KnC42DKDI/sUQn85yjn9Y6WglRCnEU867UwbGGe3O5K3uBMITAwPbNduf+4yFVc77Jyrx1nXqfZaf3ltAz/s1a4rMsiXP01LJSzQlwl9o0gOnwn/+RIK9lFqjOaN2gtYYpmqG373FkVp/F278+T1T0lNg9d+T2cOS2TpHYEsWJnJKp2AasvtCiG8y5WmGq3pBcGL7HQgNCjaoi1bjpZw27lt3vXmAPLv39/qcmEYd9kLwLkbHF9/sAgAHyzcZPwWo2K7w7/x+Y63LLMwOxhiPqpnJAlhY/g2a4NL2zYqChZPK+2cpCjQMyqoUzskuxo0d6TeYuWJr/aQcbzM6bwNFtXm+iQ5IkATICyuqmfG/+3lksC7Ca8/QZJSSJxSSjA1fL3Zwoeb1tt9LlJUVU+U4kuRGkoN/tSo/lTjTw3+bCvyae6WKOdGIURrrnaC76xtHLcTICxwsUuxI67en3n7Z+SV+/Pj6VQu/wffG37C0OL8P92wlQHD57Ant9zu+reqqdxU/zfWW4c6vEaw5531RxjdM5J0N7oQdsTzhTONBAi7KXcv+DdmFel+ll1Sw+DEMG/umhBCCNGhIoK0D3cq68w0WKy2VYKF6IKmTJmC6sEDUoPBwOzZs5k9e7bbywYEBHDhhRdy4YUXur1sa6GhoTz88MM8/PDDdud59NFHefTRR+1+3rt3b5d+BgaDgdtuu43bbrvN4XxRUVHMmTOHOXPmOF2nEKcLvar8TTqjcrIQQgjRXvQChP3jQlAUhbhQ/TBKXhsqSAohhOj+CgsLufbaa1m9ejWRkZG8+OKLXHbZZaSkpFBdXc2WLVt4/vnnWb58OS+99BI///wzy5cvt+mA6AqTyeTw89zcXMaNG9eWb0W04E6h1ce/3NshgxJc3af24o37/yNFjgNtLQVRy8XGjYxQshhuyGKwcgx/xcy/Gq7lVcsVDpc1qXG60+OVU39HJ8q03aW3Hi0hnwjdZeOszrtP/Md8Ge+YZ3JUjaOcELvzDU8K10zzdHBqa+GBvvzzyuF8suU4D/5vV5vXN21gHJP6xfDpVm3HJVfCgwAWVeVwYRUL547lqlHJdgeDTm81GPSStESW/HLq2LfBOhSs8MBnu7wSaBVCdC3udNrxMxq4c7r9ApXe4G7R9RHJ4e3a7c9dG7OK3O5U7KjrVHuuP33NcvpbjrOH3jbTbxjfk1vO6WO7kgv+Afl7OJZ0HQvf2k49zn/mvgYFs1V10H9J399nDeKi4Yk212ze/j1NS4lg4dyxmIqr2XS4mIraBkIDTgYmZQCrEO2irfdVLYPgRZXaoGGovw89o4PYnWMbvko/WoKqqiiK4tF2W5o5LJHeMcFkOSkY4im9AJwnwfGmoi05xLDaOpLzjbYFl3ooxcw0bOYr60Td5XtGBREb6k9sqL/LnekevnQwDy3b7fY5yma/wgMJ8DV2eodkR2E6V2124zlF0/XJNWOSWbW/QHeeCoJYUuPZs6dMNZnRdW/ofvZ9i/CinBuFEOBeJ/jO3Ia9DoRmL6T8nd2ftefP6K7pqfy4P9+lYgUKjZ17l6abToUXqwqIyfmx8cMWbjSuJGzwXVwwJN7BfZXCT9Y0j/YbGs9nCo3XBV3l+cKZSAKE3ZAnF/w77FSqyNF5+SKEEEJ0JxF2OumW1TQQE+LfwXsjhBBCdKwMUymPLNvtdL6OqpwshBBCtJcGi5UjhdpBF/3iGgd/B/oZCQ3woaLWbPN5vnQgFEKILiEkJISSksaBUbW1tYSE2A/vANTUnHp/FRrqWdeS6upqzj33XPbt20dkZCSbNm0iNfXUy+bw8HCmTZvGtGnT+OMf/8irr77KL7/8wp/+9Cc++OADt7aVnKztYibaj7uFVt0ZlACNAxJtBlV4eZ/aU1vu/w/rXGvZY8DKMz5v2lSpBhhoMIHF8bLH7AQIE5Ti5q9PlGuv4dYdLCRQjQSgRvUjR40mj2jGjxzBkN49UJQch4M2M3W6DeoZ3roDIXg0OFXPlWclEeBrZNVe5x0TnWkaQBPsb3Q+sxNNVdPdGQyaFGG/W6sUtBLi9JOWEsFL143k9x9sdTifj0FhwZyR7f4M2t1rAXcKCYB73f480frZhevL6Xedapf1l5rgh0eYtutTIn37c1X9o6icKt57+cgk7QoGzIABMxgBLJiDS2GSBXNG8nH6cbc6aEwfFMe88/ppprvaEappu67+nqZEBUlnXSE6iDfuq5qCVnrriQn1Z0yvSE2AsKCijv9bk8Ws4Ylt/vdusapkl2jH5PaKDiKntMblQh967AXg7AXHDVgxYqXByTDx9y3nawKEAJcb19sNEI7uFdn8tTud6VbtK3DrmN9a75jGv5+u0CG55f3TK6sP8tFmx8Wl2mrlvnzWZBZgaa/2lnboFVeQc6MQZ5aWXfSOFFWx5JdjuuezpudBaw8UMGd8T3pHB3vUdc+VbvOubMNegNBb7N2fubL/bXlmNiI5nIhAP4qrtcUSNNsD3lp3GDgVXrxu9HCGEUsStoH0ycYdWINLMUT3aXOnXUfSj5XwxOXDeOzLPV69bxOukwBhN+RRpSg707NLJUAohBCie9PrQAhQWi0BQiGEEKc/dwdKtHflZCGEEKI9ZJhKeXL5XvTGVmw3lZJhKiUtJYK4UH9tgFBn8LkQQoiOFxER0RwgLCwsdBogLCoqslnWE6+99hr79u0D4N5777UJD7b2zDPP8P7771NaWspHH33ECy+8QEJCgkfbFe3Lk0KrTYMSHlq2y6XKzO9uOgqbXK8I7ck+6TEqEKhWE6FUEU4V4UolKkpjZzcngqmhHl8a8HH//t9qhcoTGI6u5zrjTnopefzLfK1NSKC1SoLIUhPpr+TYTB+oOB8wWUYw5WoQYYrtIJ549DsQZphKeemHA2zMKsafoaTV/h9lBAMK4YG+/HfsONJSIpg60OzW4NGYYD8Kq2wH2gT4Gugfqz0+eTI4VU9yZGDj78sB/Y4Rrmo5gGb5jtw2rQu0VdOdDQbNMJWyYOVBp+uVglZCnF70AtatzR6d3O6hYW+ddx1xp9ufJzztbKvXdcrr66+rgHUvYv35VQyWxu5UZxkOcoVhPZ9bzwVgcGIYA+IdF/lwJ0ySGB7otc6B7mxXCNG1ePP4vmp/Pv1itNf10cF+jOkdxeINRzWfPbViH09/s6/NXYFMxdXUmbXHsyvPSmLqwLg2dazrHxvChkNFTOiLzfV60zNxBSt9lVzGGA4wRtnPZOMOXjDP5kPLNIfr/cmaxnE1hmSlsbN7VcwIHsg5lxXW8XaXGdUiQAiud211p1usnt7RwR6tqz07GKVEBTHQyXnRW9oSQG2L9i6uIITomux10XOmwary3xbnWne67rnajdiVbRwrbt+MjN79mav735ZnZvvzKlwKD7bWFF5ctS+fO4zTud/3Q808hj2fwbl/cdppNybYj6Kqerc7yTfth7+vkaV3TJT7tk4iAcJuyNNKUf4+Bs3NiV61EyGEEKI7CQ/y051eVuP+RbIQQgjRnXjyIk0e7gshhOhuHFVpBMgqqOKa1zewYM5I4sMCOFRg2zknr6K2I3ZTCCGEEwMHDuTw4cZKt4cPH6Z3794O52+at2lZT3z11VfNX8+YMcPhvMHBwUyaNInly5djtVrZvHkzl156qUfbFe3Lo0KrJwclpKVEsO1YqVvLuVIR2pN9ai3QqLIjYB6+FttQ3SFrItPrn3e6/H98X+I8406qVX/KCaI6K4Cqj6cQfO3rjhc8+AO8NxtQ+R3AyXEv75ovIJdoh4vuUPvSH9sAYV8lF1/MTjpcKHxomYoKmNQ4stUY8tVIjqsxzXPkljVew7W+FqzDjzpOvRMoq2lovhZ0d/CoWecvbWiPcHyM+sHJtg50BXj2m/2U1zR4/PvSegBNhqmUuz/a7vH+tORqVyuQglZCnKkOFVQ6nedgvvN52sob511XuHNcdJcnnW3tdZ3y9vqzV79B0sbnNWUE7vddwnd1Y6gikMtH9nBpna6GSbzdOdCdjrpCiK7Dm8d3VYV8nefC0SF+VNbZP757oyvQgbwK3ekD4kM1x6d9J8p5Z/0RlzvKZeZXct8nOzQhiabg+Fd+f2eowTYceZHhF6cBQisG3jFfyJU9Shl22Z+xxqTxxWPfO1xmdM9I3enOipG4esy3p0/MqQBhe3We9YSn4f3uor2LKwghuh5n72fd0fL8+vClg/H3MVJRa9btHOhpN+LW5/ALhyZgascOhPbuzzrimdkPe/J0p1+RVE5a3ucsMF9JCWEO1/GRZQp/9vkUf6UBs2rgO+sYPrSezw0R1zPz5DzO7qtMxdU8991+lm3PcbgtPRW1DXLf1olO76uW05SnF5tRwX7NL1ua5EgHQiGEEN1cRKD9DoRCCCG6PovFwt69e0lPT2fLli2kp6eTkZFBTU3jvcpNN93EokWLOmRf5s6dy+LFi5v//Mgjj/Doo492yLY94emgSXm4L4QQortwt0rjxH7aFzUF0oFQCCG6hOHDh/PNN98AsHnzZqZOnWp33ry8PEymxi5mcXFxxMbGerTNnJxTL67Dw513zGnZ6bCysv0HvwvPeFpotaK2gbwyzwoLOKsI7ek+gW0gzPervpC3y+bzMKXKzpK2muYLUuoIog4UOFFwlGAny+EbDDq1onsbTpBrdRwg3Gntw1XGdbarUyz0VXLYr/Z0uOyT5hsdfl5Ra2ZjVpGbFbsnujR41KDAY5cP5YHPdmo+G55k/1jR1oGuTfu6YtcJj5a9fGQP7rtwoM0AGk8HVelxtauVFLQS4syVVeD8nLTNVEp5bQNhLh5TPNGW86473DkubswqsjsAVo8nnW2nDYxz+Tjq6fp3ZZfxl7X9+NoYT2+D7aDUeKWU3/ss41/m690et+YsTALt0znQle0KIboObx/fK+v01/fIsj1Ol21LVyBHAcImLY9PY3pFOrjHUImigp5KPsfVWAppvF9pHZJoCo4fVJMYim2AcJJhN+FUUoa2I2NLCy0X03vUUIal9CYUiA3xp6BS/9m6UYE6s8Xh+hxx1tXIkZYdCJ2tqyM7GHkS3u9u2rO4ghCia3H1/ay76i1WHvzfbptpLUPxUcF+be5G3HQOf3vuGLvXAt6gd3/WUc/Mfth7ahshVHOhIZ3rfX5kbNE+8IEcNZo3LZc4XEcJYbxtmUm16s9Hlqnk01gYYOOHO1gaEWxz3rR3X5USFcQ5/WM8ChC2vN+W+7aOJwHCbsjTSlF9YoI1AcJsCRAKIYTo5iKCJEAohBDd2bXXXstnn33W2bvBihUrbMKD3UFbBk0KIYQQ3YG7VRoPF2orSVbUmamuNxPkJ4/ChRCiM82cOZN//etfQOP911//+le78y5fvrz561mzZnm8zdDQU4PzTCYTqampDuc/evTUILvoaMfBKdF5PC206mtUyPEwQAiOK0Lr7ZMRC2nKISYbM+in5PLHhjs181w3NoU/Tet/aoBIwnBNgDCCKhoDforD/QtHG+ooqIGG4mpSooLshxv8Q3XWBr2VE2xgKMmRgRwv0X+fvMPaF4BsNZqd1r7ssPZhl9qHY2qcw3111Ys/HPCoYrezgagBPka7wRZHAUJo20DXJpkeduc6NzXWZjCRJ4OS7HGnq5UUtBLizJVV6Pz4ZbGqbDxUxIyhCe22Hx3R4ceV42KGqZSXV2ayWies0LIrlD2XpfVwOeDnZzRw53TH17Kt3TU9lZ8OFLjU1crPaOCiYQknBwkbeUqdwxt+L9nMU6EGUqBGAPDYF3sY1iPc62EM6UAhxJnN28d3vcPf/hMV7d4V6ECe9nzpZzTQO1r/ONb6HqMPOfzV5yN6KvmkKPmEKo33Y/c1/JallimafWwqpjJ1YBzpmQO43PizzTy+ioULjFv4xDLZ6b4nhAcCjV2nCqvsF+azqHDdGxs97tII2mP+B5uOsvVYqdPlesdoy+R0hfOHJ+H97sbV4gpCiO7PmwWrnGkZir9+XIpXgtj1FivvfL+ZwcpRQqghRKkh9OT/v7JMoNxJybUwKjGgUk4wVk1vdjAaFM39mam4mn+vymz3Z2b5FbVkHC8FwAcza/3vJlKxvfa4wbiStyyzUHX2vaVnzHM009y9/vE00+Tqc0jRPmTURDfkaaWoHhGB/HyoyGZ6fkUd9WYrfj6ODxJCCCFEVxUR6Kc7vbRGwhFCCNEdWCy21QmjoqKIjo4mMzOzw/ahvLyc22+/HYDg4GCqqlyr7t/ZPH2RJg/3hRBCdAeeDIg+VqwNEALkl9fRO0YehQshRGeaPHkyCQkJnDhxgh9//JGtW7cyatQozXwWi4UFCxY0//n666/3eJvDhw9n69atALz//vtMmzbN7rwHDx5k06ZNABgMBsaMGePxdkX78nRQQkyof5u3ba8idMt9GqUc4Drjj1xgTCeqxeCNp81zOK6e6qapKNiGBwESRkDGEpt1+yoWgqmlikCH+xamaK+DjpWZufTZ1UQH+1FcVW/TZ7Ap3HDvuACG6Kyvt9LYJW/WsEQW/XxEd9DQDrUfY2r/09wFw1MKej0Q4ZesYrfW0/T303Lw6IP/28lPBwpt5qtusPDGmkO66xiR7Px7abn+V1Yf5KPNJrf2E+x/z3bn1xlY40mQzx53ulpJQSshzlyHC117br7uYGG7Bgg7osOPs+PiN7ty7XbkaN0Vyl6wYt3BQt3prfkZDSyYM9LtsF5aSgSDE0PZlV3ucD6jorBgzkg+Tj/e/P18ax3LJusgxhv2YVUVPrZM5nnztRTQuA+ehmpcJR0ohDgzefP4bu96+2iR/vNjezzpCqTXgbBvbDA+Rvvjc1veY+zalcHMVfdq5ump6D8rbzom3zU9lfkHBujOM9Pwi02A0GhQdAPmieEBzV2nnP09tKVLY0tNx/zBiaFcvGCdw3kNCvR0cH7o7PPHXdNTWZdZ6FLoxs9oYERyOOlHSzpgz9pOwh5CnDm8WbDKHfUWK+9uOKr7WRC1xChlxFBGrFLGZutAiglzuL7ROUtY6P+FZnpx/ES+cdIw748+y/itz9dYVYViQjmuxnJEjeeJhl9TSDi+BoWNWUUcyKsgPNCXDzebNIVl3OHOM7PV+05tx4wP66zDuNS40WaePoY8Jhl2s9463KP9cef6x9NMkxSI6VySGuum7pqeip+Dm4qWmipR9YjQvlhSVcgr97zaphBCCNHZwu10ICyrru/gPRFCCOGJcePGcf/997N06VKysrIoKipi/vz5HboP9913HyaTiZSUlOYgYXfQ9CLNHfJwXwghRHfhzQHR+RX2qyULIYToGEajkYcffrj5z7/5zW/Iz9e+VL7//vvZvn07AGeffTYXXnih7voWLVqEoigoisKUKVN057nhhhuav37nnXdYuHCh7nwnTpzg2muvxWxuDMVccsklREXJfVNX1TQowR3TBsZRXu1Z6KmlporQjvZplCGT63x+tAkPAkw2ZGj2STNQIkF/UIded8FWe0YY2oGw9TQ+Oy9qFR6EU+GGm97fp7vGHkpjUdqzU2NYMGek7nvpBnzaHB4EGNUrUne6u5eCrf9+UqKC7HZr2nG8TDMtyM9I39gQl7eXEhXEwHj9Do7O9I9zfTug//viaZCvNXe7WklBKyHOXFkFLgYIM10LxnnKk2uBsb0i3R5jZU9TsMJZOKApWJFhKtV8ll9RyxfbnYxaPWnRLWM96u5UUlXPvtxTIRYFK5MNGVxmsO1MNbp3JEN7hLcaJKzwj4Zfsc4ylEvq/8n95t82hwebNA0qFUIIb/Hk+G7PWT0jdKe39R7DGbPFanO+DKeS8w1b+LPhI/jvFfC1NhjYUkpUEMOGDKNW1V472wsQQuMxOSrYjzz/flSop8YIZ1kTWGKeyiLLzOZpfkYDF9oJ+ieEB7jVdaopvOgNQ3uEM9rOvWETPx8De3MdB+M7U1pKhN3755aaigM8dMkQl69PfA0Kbg4L8CoJewjhGlNxNUvTTby97jBL002Y7BQ+7cq8+X7WXa03e45hJ7v9b2ZPwC2s8f8zn/k/yht+LzLCkOV0XZWqfjG2OeN6Oj32RimN91EGRSVGKWek4RBXGH+mhsZGJ7VmK0+t2Md9n+zgt+9uYVWLUF8MZfjhXhEt3WdmVYVQcUIz+Ye9ttcDyyxn667zBuNKt/ahJXevfzzJNInOJWWXu6mmi01nD6VaVqI6audEdLykRio3CSGE6LZC/X0wKNC6OJZ0IBRCiO6ho8OCra1atYo333wTgNdee4309PRO3R93SCUnIYQQpzNvDYgGKaAmhBBdxbx58/j888/5/vvv2b17N2lpacybN48hQ4ZQXFzMkiVLWLeusdp8REQEb7zxRpu2N2PGDGbPns0nn3yCqqrcdtttvPvuu1x++eUkJydTU1NDeno67777LqWlpQBER0fz/PPPt/VbFe3srumpjdWWXZi3aVDClxmuDdJ3xl5F6Lump7I2s4BvrGN5kPc1n082ZPC+5XybfdJIHAHn/gUCoyAwAgIjyWsI5N6anvxnXTaZ+ZXaZQADKi+ZryZMqSKcKsKVKgKpZ4+1l9Pvp9jiz2LDTFISE/nquD9H1ASOqAkU0xiM6xMdTM/oIJbeEciClZmsakM1bT1+RgO/m9KP2xZ753lM67+fs1IiiQ31p8CFghLDeoRjNLg3JNPTMN1FwxM5+uMhlztT6P2+eLrt1ut2t6uVp11ApaCVEN1bdb2Z3DLX7q2zCqvc7tbkrrump7LmQAFmne5JrfkZDTx4yRByy2r44wfbHC5jUHB6XPQkWNGyU5+puJrHvtzt8jp8DJ71Blix6wRmq0oSBVxtXMvVxjX0MuRTpIbyfd0oaggAIKugUneQ8C61L79q+Lvd9TcNKp09Wt43CCG8x50Obvb4GQ2cPySercdKvbJP7nQFOlJU3bzv7/n+k0mGPRgUFYpp/K/MeffyjUdKGar2YIhi24Wpl5JndxlVhS8zciissfCk8QaK1DC2WAdQ1KrgS6CvgQ9/O5H3N2k7PPkaFarrzG53nfKkS6M9I5LD2eKgI19tg5VrXt/gsMNvZ5s5LNHu/bOiNL6rv3N6avO1hjtjwD9OP+7WuABvkbCHEM5lmEp5eWWmpgudosDUgXHc1eLffVfnzfezTWIoY4DBxCDFxADFRD9DDt9ZxvCm5RLH+6IGEqxon+nFKNrCYK1VnrzfaW1SvxgWzOnp8NgbgbabcKEaRhX6ocSW3vZ7lhGGwxSo4WSr0eSoMayxjuBDyzTd+ZufmW18HY6ug7Ljjf9VFcCEP8DMJ5vnrW2wsDazwGb5n6xpVCghhKqnnhvXqH6UqqE0RjI9i5+7c/3jSaZJdC4JEHZj7l5sJkXoHwxzSms6YG+FEEKI9mEwKIQH+lJSbXvRWlotAUIhhBCOVVdXM2/ePFRV5brrruOSSy7pVgFCcO9FmjzcF0II0Z14Y0B0E+lAKIQQXYOPjw+ffvopN9xwA1999RUnTpzgH//4h2a+5ORkPvroI4YOHdrmbb733nuEhYXx9ttvA/DTTz/x008/6c47cOBAPvzwQ/r379/m7Yr25e9rcCk86GtUmgclPP/9gTZtM5RqeiknCA1I0/08LSWCuZN68+ZalR3WPowwHLb5fJJhN76YUYx+9gdKBITD9IdtJsUDVwH9EmO45vUNuvf/Vgy8arnCo+/LgpFHGn5DQmkAJ6y2wRBfo0KPk++X01IiWDh3LKbiajYdLqaitoHQAF/CA3348BeTbrDQWcisadDI5AGxbgfS7GldsdtgULhwaDzvbTzmdNlhSe53U/Q0THftmGSGJIa2aWCNJ9tuuQ+txxK4SgpaCXFmstd9cHhSODuztYM312UWcv24nu22P2kpEZzVM4LNR+yHDMD2WiAtJYKrRuXzcfpxu/OHB/ra7coEjeE/T4MVRZX1uoOKndmdU8a4Pu6HsL/IyOYyw8+85PtqY3jlpGilghuMK1louRiAwsp6jzujuDOoVAghXJGWEsHL14/kd+9v9Wj5putnVwqIuMqdTtqZeacCBxUE2Rx/ASg6CDWljQVj7KioNXNQ7cEQbEN+KQ46EAJkHC8FYIllut15ahqs9IoO0i0KEB8WwKbDxW7fX3grUJ5hKuW9jdpgY2tNHX6X3hHYZQMI9u6fJ/SN0twXuTMGPDE8sM0BW3dJ2EMI577ZlWv3+Y6qwqp9+azLLHQr/GwqrmZjVhEVtWZCA3yY0Dfaq42iHK3fm+9nobEbb3rA7zTTj6uxYHG8bIEaoTs9GufdaO11IPQ1Gpwee/sE1UKrS4mjarzTbYJKP6WxiF2sUkasUsZIsihVQ/gQ/QBh8zMz00bY+2Wrja6z+eMX27OpbbD9PWvAh8Ox0xmRv4x91hSWWKbxueUcygl2YX/tc+f6B9zPNInOJQHCbs6di82kCP2TR7YECIUQQnRzugFC6UAohBDCiQceeICsrCyioqJ4+eWXO3t3PCKVnIQQQpyuPB2MrTd/foV0IBRCiK4iNDSUL7/8kmXLlvHf//6XzZs3k5+fT2hoKP369eOqq67i9ttvJzzc/SCPHn9/fxYuXMif/vQnFi1axPr168nKyqK8vBw/Pz/i4uIYPXo0V1xxBddeey1+fn5e2a5oX64MKgT41+y05oE5LQdyuiqcSi4wbuEiwy+cY9hJEWFY+9xsd/4AXyMA31jG2QQIq1V/NlkHc2l/f266cIJH9+au3v976oROx+aUqCB8jAbNtNYDli4YkqD7rrqost7lQSOxIf5tLvpgr8vdzKGJLgUIRyS7f9xpS5guOdJ+Z0dXBtZ4su0BcSH8dnI/3bEE7pCCVkKcebIK9QOEv5nYi/s+2aGZvvZg+wYIy2sb2HHcedeJZ64eYTNI11kB2pLqBo4UVdMnRn+wpV6nPmdUFd5ck8WSX0wOj5sK6BZI2JPjfHBsayfKatl0uJgYdTBWFFqXXrjD5yves1xAHY3XvoWVnp2D3R1UKoQQrpjYL9rh58F+RqrqtYmDcb2j+PvFg0lLieBFOwVk7B1r7dHcYzTUwrENkJgGQdp7jwN5p7r/bLIO5iLjZu1Kc7dD3yl2txka4EOGtS+9lDyOqXEn/4vnmBqHo05ChwtdC4PvOF5Gns79X2J4gMddp7wRKH95ZSYNFtf+dvQ6/HZFevfPelwdA97W5wJNvzmtf8oxwX4UVdXbTJewhxCuyTCVuvRv0tXwc3t3MnRl/W0pWKWnjBDy1AjilVKb6UlKodNlC9F/VudaB0I73QKVxqOhw2Pvuw95FCBMoFi3Y2K2GqM7v80zs7Ak7QwndkJNKRmFjedJe88Ajw+9nRGzH+DZ5ZVe6VRr7xmrM+5kmkTnkgDhacKVi83YUH98DApmq+1RXToQCiGE6O7Cg/ygyPZhWFl1fSftjRBCiO7g559/5pVXXgHgueeeIz7elWpRXVNTJacHPtvJnlztYIJRPSN45NKh8nBfCCFEt+LpYOyNWUWaAST55dKBUAghuprLL7+cyy+/3OPl586dy9y5c12ef+TIkbz00kseb090HRW1DXy+NduleWsbGq8JymsbdDssJEUE2i20+lvjl9zn8zG+yqnrih4UQ/U+iBqju8zek/fkK6zjuEn9lm8tY1nFGC69dDbjB/RgehsHSjiq5Nwe+kS7Xqla7111cmSQy4NGEsIDNAHCUH8fKupcH8Bqr8vd+L5RhAf6Uuak6OBwDwKE0LYwXVsH1ri77X9dk+aV50NS0EqIM09WQaXu9Al9oxkQH2ITmABYtTePjzabmNTPcacMTztrfL0jlzqz82OfpdX4qP0uFBTYmFVkN0DoabDi3Y1HsTo5b9v7WO+ZP1WF5GdlsKE8hiI1zOZnZyqu5tlv9qGqUEAkP1nTmG7cZrN4rFLG9cbVLLZcCEBdg9WjIk6eDCoVQghnjpfo36NdPSqJP18wgMy8Sm5epA3m3Xpun+brTr1gtI9B4dzUGFbvL3B5X6YNiCXZbIINK+HQSjiyHsw1cNVbMOIazfwH8k+dZzZZB+uvNHuLwwDhhL7R/NV6cXOnWFcoChwp1D9Xt7bjeKnu/XFCeKDHXafaGihvS4ff0ymQ4MoYcGfPBZyFAaOC/XTvPSXsIYRnXl6Z6XKg11n4uT06GXq6fmfvZ/1oYIphO+FKFUstU5xue781hXhjqc20HkqR0+Xq8aVUDSZCsS1o40qAcJe1D/c1/JZKNZBKAqlUAzlnaB/+0iqop3vsPeduKMumoiSPjdt20FPJI9Oa7HSb/Q05utNzVG1xBEXB9plZeIp2QdXKe0s/4rH9KQ5D9nd/V44hui93TU/0Sqdae89YXeVqgF50HgkQnkGMBoWE8ADNTZZ0IBRCCNHdRQRqH0Y5GwwghBDizFVbW8stt9yC1Wpl+vTp3Hyz/e4B3UVaSgT/uGIoV/9ng+aza8ekyEAtIYQQ3ZIng7EPF1ZpOiNIB0IhhBDi9GAqruaF7/frdpvQk3G8jOvHwcF8/YGUl4xI5J31R3SvNQ6riTbhwWZ7lkGyvQBhRfOyE+peQcXAqJ4RXD2+v0v764qWgbPnvtvPsu36A1O8obed8IS7XBk0khAWwA5sB/8YDIrLYQZHXe58jQbOHxzPp1uP213eaFAo9/CdgjfCdJ4OrOnMIJ+jgavSrUKIRp6G47qirAJtB0J/HwNJEYGc0z9WEyCsabDyt0932O2U0dbOGp9ssX9Mb6np3AxQXW/mWLHz7kybsoqYY6d7oqfBCmfhQUcy8yqpLzyC38/PQ8EBzPn78akrIQ74of6PfGmd1DxvdLAfxa1CAx9bJmsChHlqBFUEnNpGQaXHHXWFEMLbjpfoH6svG5lEcmQQNXbuB1uOiS2q1BYcjwr24+7zB7D+YJHLz5vvG6PAq+O0Hx7+UT9AeOLUeWe/mkKZGkS4cvL78QuFHiP1Owy14ElhvRHJ4WSYnIcpADacvDZpLTE8wKOuU94IlHva4XdjVjGzR5955yJnhWichQH1rkcl7CGE+7wZfvZ2J8PW3F3/k1cOY21mgU1ozYiFSYbdXGb4mQuN6YQp1RSqYXxmORcLRofr3a+mcB47baYlUIwRi9Nll1imoQAFajiFahiFhJ/symufr1EhxxKjCTeenzQQjC6E3kf9BoBv0k3c98sO5/Of1E+xFyDUdiAM9jMyY0jCqQnh+tcHVQd+osFyo8Ptnvq9mNimTrXg+BmrOH1IgPAMkxQRKAFCIYQQp52IIO2FfakECIUQQtjx8MMPs3//fgIDA3njjTfabTvHjzsexJCbm+vV7fW20xngcJF2cIcQQgjRHXgyIDo21F8TIMyTDoRCCCFEp2prgMJeyKClYD+jJliYYSoFINNOt6Gpg+I4q2eE7rXGGusIKtUAQpRWhQj2LIMLHm8cJdlCWU2DzTtXFQMAgxPDnH17HkmJCuKc/jHdIkDoisTwAM20spoGfAwKZicjSV0Jx8WF+jtch8Wqct0bGz2u4t6ZYbrO3HZbOygKcbpqaziuK8rS6WrUJyYYg0Eh0M9gdzm9Thlt7ayRVVDJlqMlLu33vhOnuvdl5lW6FE7YdLgYVVVRWp3rAY+CFTbLG/bQX8kmgkoilErCqaIeX/5uvtXuMvUWK8eKq+i/9b+A7UC//oYcaPFjLKrSBmZWWUdRpIYSThXFPSbzqXUKLxztQ0OLNe3JKWfJvAked9QVQghvsteBMDky8OT/9a8zTS1C4kVV2ufB0SH+Lj9v9jEoLJgzkkFDE+C7HlDR6r4ra03jyaDFuaLebOVwi+fSVgy8Zr6c1OR4Zl91LcQOBoP9c2ZL7hTW8zEopEQG6QYIQwN8NGHBTVnFuuuJDwvwKLzojUC5px1+K2rP7HFh9kJ/EgYUomN4M/zszU6Getxd/4pdJ7g8rQefbM1unr7I9xnONe6ymTdGKeeRoQVY+k7jSGEVH/xyTLdT3n71VHc9q6pwTI3jiJpAMDWUE+Jwf54xz2n+emzvSLabSmlwUKHFz2jg91P68dLKTM1n7h4b3T0/pVsH8s+GG+ihFJGkFNJDKaKHUkgO2qB9ZZ2FA/kVDEpofHb8c2EAA9UwctQoctVoctRoMtVk1lqHu7Ttlr8XjjrVOtIeBchE1yQBwjNMUkSgZlpOaY3dh19CCCFEd2CvA6HVqmIwyPlNCCHEKZs3b+aFF14A4LHHHqNfv37ttq2UlBTnM3lRVLAfof4+VNTZPsQ6Wui8qrIQQgjRVbk7IDouTDv4PL9cOhAKIYQQncEbAQpHIYPm9QEDE0LZeqzUZvr+vApqGyw2XZF8MRNDGblEkxoXQnSIv+61Rh1+rLKexWXGDbYbKz0KebshYZjttk7ohxQHtVOAENoeYnCmj51CRe0hXidACGB2MCDI1XBchqmUt9ZlOd0HT6u4N+nMMF1nB/lkgKoQp7Q1HNcVqarKYZ0OhH1jg8kwlfJ/a1w/xj55pZn5n+/yuLOGqbiaJ77eo7uM0aBgaXXe2Jtb3jwear+dggKt5ZbVYiquoWe0/oD8EYkhnMg5RrxSQoJSTLxSQrxSwlvmWZQS6nDdvzL+wCXGjTbTitUQhwFCgO3lofTxCcRotg3V2Otw0VIDPvyx4U4OWpMoOxbFnHEpNBw9ajOP2aqiAg9ePJiHv9jtcH0yqFQI0d7sBQibxr0G+hmJCfGjsFWXQWcdCGNC/IBWz5v35aN3x3Hx8MRT5+n+02Dbe7YzlB2DksMQ1bd50pGiKs39yxuWS/nr4IEQ715XeleDjtB4Pt6TW66ZHhXsx6zhCby38ZjNdHv3WE1FXdwJL3orUO5ph9/QABe6SAkhRDvxVvjZm50M9Xi6/qZgW5MvrJM0AUKA34RugbMb72euGpWs+z53g2UI9/Fb9ll7clDtQQ36zwGduW5sTx68eAgLVmay0k7Y/flr06is0/+76enmsyt3z0+71d7stvR2ef5fDhczKCGMDFMpc78xU2953a3ttdb0e2HvOWF4oA8f/mLq8AJkouuRAOEZJilSGyCsbbCyK7uM4ckRHb9DQgghhBeEB/lppqlq441auE53QiGEEGem+vp6brnlFiwWC6NGjeKee+7p7F3yKkVR6B0TzM5s2wqTR6QDoRBCiG7OnQHR8TrdZcprzdQ2WAjwNXbULgshhBBnPG8EKDJMpS4NmFSBjOPabgsWq8runDIO5FXgRwPXGH/idz5fcEKN4naffxId0njd0PpaI/1IMR9uNrHcMr45QLjX2pP1/meTPOl6hvr1pXXJoL06AzYBhiQ6DhK0hSfdIdzRO6bjAmF6HQjt+e15fRkQH+pyOO7llZm61c/1eFLFvbXODNNJkE+IzuXqeautgeWOlldep+nyC40dCN09xj61Yp9HnTWcdSP2MShceVYSS7cct5leUt1AQUUdcWEBdsP+ejZmFekGCFVVZWrV19wd8Ibms28sYylVHZ/3S1Rtd41wqlCwNncv1rMnt5KxhiR6cdBmej8l284StjZYhzZ+YbGyK0f/mmXbsRLdEE0TGVQqhOgoegHCmBB/m2e7yZFBOgHCUwVVCyt1OhAGnxxXpKqk1fzCwoTVHLnk78x8eS21Dbbnpl05Le4v+03XBgiB9NXLiJ96e/P1t73zzMB4z+4JHRXWa6llwZyWzk2NYWRKpCZAaE/CyXsyV8OL3gyUe1IcR1FgQl9tRychhOgo3go/e7OToR5P19/6WecXlknM9/2ISFo9g937JVz8AvgG2H2fW9dg4bEv46lXXbsX1NN03E+ODGLh3LF8sOko8z/XBhpLaxrIKdUvRpCik6FxpL2Lt23KKuY3E3u71SHSkda/F3rPCS8YktBpBchE1yEBwjNM62pbTS57ZT1TB7lW6VMIIYToavQ6EAKU1tRLgFAIIUSzJ554gl27dmE0GnnzzTcxGts3RGAymRx+npuby7hx47y6TXsBQuk6L4QQ4nTgyoDouDBtgBAgv7xOd/CfEEIIIbzPWwEKdwZP2HsHuutIPmm5S3nG/1N6KMUAJCuFXBF5SDNv07XGlWclsSGriB+L0ni24TpWWMdxWE2EeuDbKpTvVms6KNoLEA5MaL8OhOBedwh7DAq0/vH5+RjoEe7eoJq2SAhzbVtJEYE8cNEgl59xtHcVdyGEaMmd85Y3AssdJatAP5gQEejn9jG2qErbEcqRVfvzeX/jUR77co/Dn61VVQny03/ev/dEBXFhARxo0YHQn3rmGFeRYiwhVi0kUSkiUSnmHfNMFlpmsfFwEdeOtS0XYCquZtHPRzBVBIG2ti0xShkOE3hAKdoAoVFRCaGWCuyfd7YeKyatJo5eRtsAYV/lBAasWB2ED7XrKsGoQOvc59ZjJZwo1wZuFAUevXQo0wfHyblRCNEhWgYBmyS3GvCfHBnIdlOpzTRTcTWqqlJvsVKu0xUqNtgHdn4C616CvJ0A9B56JecPjuerHbk28x4qqOJYUXXj8+S+U1AVA6oK26z9WGMZwVrrcDI298OafureMNNOp9sBHgYIQVvs5rvdJ/huT55Ly04eEMvwpHCXt9WyqIuj8GJ7BMo9KY4zbaCcl4QQnctb4WdPOxmuzSxgfJ8oUqKCMBVXszGriIpaM6EBPkzoG938PtXT9bdWhx+m/tcTebBVMZWACCg5AnGDmifpvc8dlhTuNBTvSOvj/qzhiboBwk1ZRbq3ZcF+RqKCdW7kHPBW8TY/o4EeEYGa4u+bDhdjKq5y+77akdYdLvVIATIhAcIzyDe7cvm/NVm6n6m4VulTCCGE6Ioi7IQES6sb6BXdwTsjhBCiS8rIyODpp58G4J577mHUqFHtvs3k5OR230ZrvXWCEbUNVvLK65orRwohhBCns/gw/fNdfkWtBAiFEEKIDuKNAIUnwS89xoz3uNf8JrTKm/267kPgd/rLGBTG9Y5iaVE1r1ku13yu10Fxr063iZ5RQYT4t+/reFe7Q9ij0Dho5GiR7SDdXlFBGAwdV4jI1WcWFwyJd6tAUntXcRdCiCanc2D5UGGV7vTy2oZ268TQRFXh4S922y0UAGDEQpRaQfovxzjXUIofDay0jm7+fF9uOZMHxLKvxbnaioFHff+rWVeyUgA0doJo0rr74QhFv9tRjKJfTKClUp0OhADhShUVqv3fg325FexXkzmqxHFQTeKg2oNDag8OWpOcbrM1VYXEyEBNh6+fDxZRUacdXDypXzQ3Tert9naEEMITqqqSrdOBsHWAUG/Qe1W9hdLqBmrN2q65g5Rj/GHP32BLq+Kva19g2qDnNAFCgNX787lpUm++yarjw4YH2GruRXnrIHiLe8MhPbTFYwJ9jSRFtL0wS9NA/wuGxLP2yZXUNGi/x9aigv3oGxtCsJ9Rt5NwSwYFYkNsC/PZ6yLVXl2K3CmO42c0cOf0VK/vgxBCuMNb4WdPOxku257Dsu05RAf7UVxVbxOaUxSaA+7O1n+2YScXGLbwqPkmNA9QW/AxKKRc8CfIehsCI2HYVTDsakge27hBJ1qfV/adKOed9Ucc3us10TvuRwT5MSgh1OY+D+CXw8W6zxlTooI8Krre1uJtTR17N2YVs+hn23vrwso6vszI9ep9desOl0LokQDhGaKp0qfZyYHWWaVPIYQQoiuyGyCscV5RQwghxJlh0aJFNDQ0YDAY8PX15YknntCdb82aNTZfN803cOBArrnmmg7Z17boHR2sO/1wYZUECIUQQpwRYkPtdCCs0FbRF0IIIYT3eStA4UnwS89TJ0ZzoW8Ysa0G9feu3A5H1kHvczTLZJhK+d/2bKfrbnqv+tHtAew/oQ0NDErwvNOEOxx1hwCICfajqNVAopYKdK6TesfoP19oLwl2ikC0NmNovFvr9bTKuivVuoUQoqXTObBsrwNhoK9+x7+2MGAllGoilQry1EhqCHA4oPRZnzeYbVyDQTk1T44axcq6UwHCvbnlFFfV25zvGvChQNVeH/RQigDILq3BVFzN7pwyTUj/hGonQEiZ0++vdYCwUg2glBD8OdWZsVe0Nthfa7byGpfzmuUKp9twRY8IbYBQLzwISAF6IUSHKq8x6x6PWoctWgcKm5hKqjHohANMaiyBZp3j9IEVTJ14v24HqdX78xmZEnHyPDDU4X7XW6yajogAA+JDvFqYJTzQl6tHJ/HexmNO5/3tf7ewYM5IhiaF88vhYofzxob642PU72bbUV2KXC2O0xTEkLHNQoiuwBvhZ086Gbak1+W9ZfGzhy8drLv+s5RM7vX5mLONuwFYZT2LNdY0u9s5JzWGyPgUuH0NxA4Eg2f3gy3PK2N6RbbpuD+uT5QmQJhfUUexzs+kp4fnMk+Lt7Xu2KuqsOjnI5r59uQ6L0TjzjZbd7gUQo8ECM8Q3qj0KYQQQnRV4YH67cVLq7U3A0IIIc5M6smnYVarlSeffNKlZVavXs3q1asBuPzyy7tHgDBG/6HX0aIqJvaTtrxCCCFOf3Gh+oPP88prO3hPhBBCiDOTtwIUnga/Wquy+vGm+WLm+y7RfrhzqW6A8OWVmTRYXPsm6i1WnvlmH7UN2vewgxO1HSjai7PuEKbiap75Zp+ms4YKVOt0o+jTwQHCQD8j4YG+lDkoChge6Mu43u4NgvG0irtU6xZCuOt0DixnFWg7EMaE+Nkt4OOu6YYtPOjzHpFKJWFUN4cB59T/nQ1Wx4GNGvxswoMAcZSiYEWlMQix70QFB/K0nYJPqFGaAGHiyQAhwNItx3n9x0OasVaFhGNRFYytthulaLfR2g/WUUyv+xelaghlBGNuNWzPz2jgmtHJPPfdAZ2lvRdASY0LcRomgcYBqBe6Gd4XQoi2MJVU607XdCC00wHveEkNQX7aQEMVgeQM+DV9dr+q+Sxy66uMSJ5LRqsA4IZDRajqAY87DgGkxnu/qMyEvtEuBQibCt7MGp7gdN6E8LZ3SfQGR8VxWgcxhOguvvjiC9599102b97MiRMnCAsLo3///lx55ZXcfvvthIW1z7Ojbdu28cEHH/DDDz9w/PhxysvLiYmJITExkQkTJjBlyhSuvPJKjEbvFwU5k6SlRPDQJUN4aNkuh/M5CsF50snQVfUWK49/uZfRPSNJP1oCQDzFPOL7X2YZf7GZ9z6fj1hTPwJ79x2XjujR+EX8EK/tX1uP++P6RPHfDUc10/WabbUlDO/Kfl43NoXyWrPdjr1j++g/08wp1XZe9pReh0sh9EiA8AzgrUqfQgghRFdlrwOho8EGQgghxOnIbgfCIu0gDyGEEOJ0FB8mHQiFEEKIzuStAIWnwS8971ku4A6fL4lSGjso7bL2ptdVjxE64jLNvJ68V92UpT8Af3Bix3QgbMled4iUqCDuv2iQJkBoj73nC+0pMTzA4TP96YPj7HbFsMeTKu5SrVsI4YnTObCcVajtQNg3JkT3GOuLmcHKUQYYjtNfyWa15Sw2qYMdrt+CkT6GPM30CPQ7H7ZUpIZrpvkoVqKpoJDGzw7mV7IrW9t16oQazXCO2EyzCRCmm3RDI1YMPGm+gXKCyVMjOaFGkadGUobzc2cZIZS16kLYpGlQcWp8qJ0AoXcoClwxMon3NzkPn4zpFWm3UJMQQrSHbDuD6JNaBQjtdiAsriYmRP/5cNXI2+DAO9DQIqQY2gOSxzAtPE4TIKwzW1lzoMD1ndcxIF7/mN8Wn23NdnneeouVg/nOz6eJLnaE7wjOiuMI0V1UVlZy44038sUXX9hMLygooKCggA0bNvDvf/+bjz/+mAkTJnhtu+Xl5dx1110sXry4uch2k5ycHHJyctiyZQuvvvoqJSUlREREeG3bZ6oeEY6PodMGxXGXk/CzO50M3VVvsaIABgWsKvgqFqYZtmnmG244wkzDZr6xjtN85mNUmNFOhUXactwfZyeUp8fTDoTe2E+AmBB/+sUGc6hVgZ7jJdVt6kDZxF6HSyH0SIDwDOCtSp9CCCFEVxURqP9yrbRaAoRCCK0pU6bw008/AWgemInT10svvcRLL73kdL5HH32Uxx57DIBHHnmERx99tH13zMuigv0I9fehos52wObRQv2KnUIIIcTpJsTfh0BfIzUNtp10pAOhEEII0TG8EaAwFVdTXFXv9joUGjvqtVZNAG+ZL+ZC42ZeNl/FjsDxpI+cobsOj96r2pnekR0IXZEcGcTIlAi2txoYq6d3TMe/I9brFNLSgDj3A5meVHGXat1CCE90RmDZVFzNxqwiKmrNhAb4MKFvNClRQXane6K2wcLxEm2Yo29ssM0xNlU5zt993mesYR/ByqkCPvX4sMl8KkAYFeynOcfnqxG6245QnAcemkKCrcUrJRSeDBearSrLd2oD9LnqyZ+9fxhZ9REcM0eSo0bTeGZXyC2z/xxhoeVip/vmqtadNSxWVfe5RkyIH4WV7l8ftTZtYBxjekcSE+JPYaXjYkszhyW2eXtCCOEOvXMOQEqrwGDrQKGz5QEiYxNh9FzY+BpE9YNz7oYR14GPP1OPl/LiD9rwdltHEni7A6EnBW9255Q7nSchvOsECJvYK44jRHdgsVi45ppr+OabbwCIj49n3rx5DBkyhOLiYpYsWcL69esxmUzMmjWL9evXM3iw46IbriguLubCCy8kPT0dgKSkJK666irS0tIIDw+noqKCzMxMvv/+e7Zs2dLm7YlGB/Ic37c8eeVwp8fZtJQIFswZye/f34pO87w2Sz9Wgo9BwWpROa7G8or5Cu71XaqZ716fj/mufgxWbAt4Wawq6w8Wtuv9gSfH/bjQAPrGBJNV6LygelsDhE3acn4a3zdaEyDMr6hnTK9THSI94ajDpRB6JEB4BvBWpU8hhBDty2KxsHfvXtLT09myZQvp6elkZGRQU9P4gOumm25i0aJFXt/ukSNHWLhwIatXr2bfvn2UlZXh7+9PXFwcI0eO5KqrruK6667D17frVsAMkwChEGe0//3vf2zfvh2Au+++WypknaEWLVrEzTffDMDkyZP58ccfO3eHOomiKPSOCWZnq4rKR6QDoRBCiDOEoijEhflztMg2PF8gHQiFEEKIDtGWAEWGqZSXV2ayen++G8s3DvIHmDIwlvUHi3Srdf+f5WJes1wGKEyMtx/s8/S9amvBfkZSumAI7ZIRiS4FCPvEdGwHwm925Trdr+e/30/vmCC3Byu5U8VdqnULITzVkYFlR+fL6JMBvZaTFQWmDnTe9ULP0aJq3XNy39jG80TTMbbSEsgUY4ZmvlTlVJckH4PCuakxLNueYzNPnhqpu+0oVwKEqv45PVYpAbV385+3HivVzPNB8K/5zZ/fhoAwXv8kg4/TjzvdnjNGg4LFhRG/RkXh5rN7MygxTNOxwmhQGJwYqtlnBaW5a4inms5ziqLQLzbYhQBhgucbE0IIDxwv0S+ImhRhe7709zESH+ZPXrntccxUUk2gncIk0cF+MPGPkDwWhlwOhlPzDesR7lKw2l0DvRwg9LTgTZCfkep6i915umKAUIju7K233moODw4ZMoRVq1YRH3+qe9sf/vAH7r33Xp5//nlKSkq4/fbbWbNmTZu3e8MNNzSHB//yl7/wxBNPEBCg/ff95JNPkpOTQ0iI97uknoky8yocfp5bVuPScXbmsETOHxzPd3u03dnbSlWhwXLqBPKm5WKuNq6x6QRfqIbxgWU6BqyaAKGqwp1LtrP0jsAuF1Ib1yfKpQBhVwilj+8TxQc6neBz7HRgdqZ1MRohXCUBwjOANyp9CiGEaH/XXnstn332WYdu84UXXmD+/PnU1dk+BDObzRw+fJjDhw/z+eef88QTT/DJJ58wbNiwDt0/V/kaDYT4+1DZqttSaU3bqzAKIbq+//3vfyxevBiAuXPnSoCwmzl8+DALFy60mbZjx47mr7dt28aDDz5o8/m0adOYNm1ah+xfd2QvQKiqKoqidNJeCSGEEB0nPjRAEyDML5cAoRBCCNERPA1Q7Mou484l210KeTW50LCZq41r+GPDnWD05+7zB1BcvZsMnSCaucVr8QHx9gdIefpetbWBCaEYDF3vHnzW8ESe+Hqvw3kCfA3Eh3bcANIMUyl3LtnuNBDRYFE9GqzUVMXd2e+XVOsWQrRVRwSWv9mV6/B4VqTTwVdVYdW+fNZlFrJgzki3gtiHC/VDfH1jGs+lLY+xWdYE+hpO2MzXXzkVFgwP9OUHncGwxYTSoBrxVWyDDRf3D+C1/Y6LEhxUk1hknkGhGk4RYRSp4eSpERxUk5x+b0mJPSCgMYDorAuuq64elcT/tuW4dL5x9PcwpEeYJkBYUFlHWy4tWp7nvtmVS/oRxx0uFGDn8VKSIvS7fAkhRHvQ6yAYE+KnGwpMiQxqDhAmUkQu0RwvqSEq2E8zb4i/DwG+RghPgvCrNJ8bDApTBsbyyZa2h8mbhPr7kOjlYJ6nBW8SwwM0HY9afy6E8A6LxcJjjz3W/Od3333XJjzY5JlnnmHlypVs376dtWvX8t133zFjxgyPt7to0SK+/fZbAH73u9/x3HPPOZy/R48eHm9L2MrMd1z4JLeslrNcXFc7NB/UVYcfj5rnstjvGcrVIN4wX8I7lplUY/98UG+xsmBlJgvnju2gvXTNuD5RfLjZ5HS+ZDvdizvSuD5RutNzympdWt7XoHDD+J70jgkmNMBXU4xGCFdJgPAM0JZKn0IIITqOxWL7UiQqKoro6GgyMzPbZXuvvPIKf/nLX5r/PGnSJC677DJSUlIoLy9n9+7dLFq0iMrKSvbv38/UqVPZuXMnCQlds9JgeKCvJkBYXiMdCIUQoqs7evQo//znP+1+vmPHDptAIYCPj48ECB3oHa19QFTbYCWvvE4qSAohhDgjxIb5a6blVbj28kUIIYQQbedugOKiYQluhwdnGn7h377/xlex8IbyEvVXLyYtJYKRyeG6AcKWUh10gfDkvaqewYn2uxx2ph4RgYzuFcmWo/ZDA0aDws7ssg4L0r28MtPlv3tPByvNHJbI0jsCWbAyk1WtOnZJtW4hhLc0hel+9/5Wh+cRTwPLTYFrd86XLdVbrG4HsQuP7uVKw1o+t55rM72pAyGcOsYe/XgMfSu+spmvt3ICH8yY8dENNwKoGPjKOgGAUjWEMoIpUUOZO/IqphLssChBltqDR81zm/88bVAcx46VUFXt/B3xgITG64EMUynv63SB8MS4PtHcOL5Xm883QxLDdac7C9vHBPtRpNOBsuV2m36PLE4udlS6bpcRIcTpSy9AaC/InBwZSPrREmYZNvKS76s80DCPr0um0ENn/ugQbaiwtcgg7zbbUBTYcdy791WeFrzpExPsMECYECbvj4XwljVr1pCbmwvA5MmTGTVqlO58RqORO++8k1tuuQWAJUuWtClA+MwzzwAQEhLC008/7fF6hHusVpWDTgKE7nSXK/JCJ1x/6qnD+XkvO/psHiq+ma8t4ynGteeYq/bnc7ykukuF1uyF8lqKD/NvLCTQyRLDA+kZFcSxYv2Oy/bIs0vhbRIgPAN4WumzKx3ghRDiTDBu3DgGDx7M6NGjGT16NH369GHRokXcfPPNXt9WTU0N8+fPb/7zm2++yW233aaZ7+GHH2b69Ons3LmTwsJCnn32WV544QWv7483RAT5kt3qhqvUhZdDQgghxOmmd3Sw7vTDhVUSIBRCCHFGiAvVBghLqxs4VFBBv1j7gQEhhBBCeEdTgOKO97Y6nK8pQPFx+nG3whCzDBtZ4PsKPkrjMlMN22D332DofxmRHAEcdbh8apz9DoSevFfVM6iLBggB+sYEOwwQVtVZuOb1DW53qfKEqbia1fvd+1l7OlgpLSWChXPHYiquZtPhYipqG6RatxDC62YOSyQ6yI9CO2E5BXh/3njG9na/oLc7gWt7XA5iZ2+BdS9yw96vuM7XwE91ac2DSn0MCilRtsfNtJQImDkbltoGCKvxJ0Ep4bgaa3dTfWOC+XPhHzTTg04kcOe0eFbvy3epE4ef0cBd01N5esU+NmQVOZ1/0MkA4csrM2mwtL3XR1Oh9uTIoDafb4b0cP064q7pqaREBTWv39l2OyK4L4QQnsou0Q6ot3fsTIkK4kLD5uZ7w3/5voGP2cJPJy7SzBut05WwpQxTKYt+PuLRPttTXmv2+n2Vp41Ezh8czw977d93vfRDJvdfZJRQghBesGLFiuavZ82a5XDeiy46dbxquZy71q9fz759+wC4/PLLCQvrus+kTjfZpTXUNFgczpPrYnc5gMJK/ftI16hca/yR+3w+4vr6hzjkpCv70KQw3i24wL0tqLAxq5jZo7vOc7TkyCCSIgI144Zb6hnVdfZ3XJ8olwKEd09PJTzIV55dinYhAcIzhLuVPu+cntoBeyWEEKKlloG+9rZ+/XoqKioAGDt2rG54ECA2NpannnqKSy65BGisktNVRehUAyuVDoRCCNHlTZkyBbWtZf2BuXPnMnfu3Dav59FHH+XRRx9t83o6U+8Y/QdHR4uqmNgvuoP3RgghhOh4Vjsl+c9/fg1TB8Vxl1RoFEIIIdrdBUMSMBrA3qvJvjHBvHjdSKKC/Vi933HQsKWLDJtswoPNDqyApXNJm/Kq03UMcNCBENx7r2pv8OaQxK5ZtCDDVMr/tmc7nc+TLlWe2JhV5Ha3x7YOVkqJCtIEX4QQwlsqahvshgehsatbgI/7nQ88CVzboxfENhVXszGrCP+8bYw//CrxhRsBMAAGxcJlxp9ZZJkJQM/oIHyNBu2Ke58HKROg72T2+w3jV19WUEA4jbFJB99bSTV+RoX6ViG+LzNymDYozuXwYFNXx0GJoS4FCAfEh3r159q6UHtbzjeDEkIxKM47Dk4dGMufLxhgM83RdjsyuC+EOHM1nVMqas2EBvgwoW+0S8fDspoGymvNmunJkfodCMfW/8KffBc03xsaFJVnfN/kwSoz72EbiIgJ0Raca8lbYfLWvH1f5WkjEWc2ZBV1WBEZIU53O3fubP567FjHRRgSEhJISUnBZDKRl5dHQUEBsbH2C2/Y89NPPzV/PX78eAA+++wz3nrrLbZu3UpJSQnR0dGcddZZzJ49m1//+tf4+Eh8xBsO5FU4nSe3zPUOhIUediBMoIinfd9iijEDgOd83+Dq+kexonPfRmNRmAFxnj27rKjteuNxx/eJ4rNt9p93dqXngEF+zp8HKAoMSgyVc7JoN/pHBtG9WcxgtU20N1X69NN7iNdCy4dqQgghTl/5+aceJqWmOg6Nt/y8stJxy/XOFBGorRgmHQhFd2C1Wvnggw+44oor6NWrF4GBgQQEBJCUlERaWhrXXHMNr732GkVFti87Fy1ahKIoKIrCokWLANi+fTu33norffv2JTAwkOTkZK688kp++eUXzXZXrFjB5ZdfTq9evfD39yc5OZmbb76Zw4cPu7TfFouFxYsXc9lll5GcnExAQACRkZGMGDGCe+65hwMHDrj8M8jMzOSee+4hLS2NyMhIAgICSE5O5tJLL2XRokVYLPrVmubOnYuiKCxevLh5Wp8+fZp/Lk3/TZkyxek+fP7551x88cUkJSXh7+9Pjx49uPrqq7t0cFoIe+x2ICyq6uA9EUIIITreN7tyeW/TMd3PVGDVvnyueX0D3+zK7dgdE0IIIc4wuWU1dsOD0PjuMi0lwu0A2TE1jioC9D+M6EnfuHCCfO0PxPA1GpxWeXb1vSrY7/wwMKFrVnt3Z2BsU9eh9lShM0DYteXk2b8Qoms6VOD8GeyunDK31+tJ4NqepiA2NAbLb1m0mfP+tZr7PtnBZ+t3NIcHW7raeOpdSd8YO518g6Ph1m9h6nyeyUykgAichQcBGiwqEUHa97zHS2p48PNdDpdVFJg+KI6ld0xsHlw52IVzsI9BoV9siNd+rt4u1B7gayQpQj8w09IN43u5td62BPeFEMKZ1ueUx7/aw32f7OC8f63mlkWbyTCVOlw+u0Q/YKEbICw8yDlb78FP0Y4jeML3HUYptmMVoh0ECL0ZJtfj7fuqu6anunSvCo3np4uGJfDwst1O520KOzr7exJCOLZ///7mr/v06eN0/pbztFzWHenp6c1fx8fHc/XVV3P11VezYsUK8vLyqK+vJzc3l+XLl3PLLbcwatQol8eGCccO5DkfS+tqB8LqejPV9drzmsHJLdUsw0a+9f9bc3gQ4CzDQeYZv7a7jFVVyatwvTNiS6EB2iYfnW1cnyiHn287VtIlzm8ZplKW/KL/DrslVUXOyaJdSYDwdFNTAu/PhpWPaz6aOSyRpXdMZPog/aoiRoPCx7dPkMSyEEKcAeLiTp0LnIV8Wn4+dOjQdtuntgrX6UBYVlPvla5WQrSXoqIizj77bG688UaWLVvGsWPHqK2tpa6ujpycHHbs2MEnn3zCH/7wB9555x2H63rttdcYN24cb7/9NocPH6a2tpbs7Gz+97//MXHiRN59910AGhoauO2225g1axZffPEFx44do76+nuzsbBYtWsTIkSPZvHmzw20dOnSItLQ05s6dy5dffkl2djZ1dXWUlpayc+dOXnzxRYYOHcoTTzzh9GfwxBNPMGTIEF588UV27NhBaWkpdXV1ZGdn89VXX3HzzTeTlpbGoUOHXP/BuqG2tpZrrrmGq666iuXLl5OTk9P88Oyzzz5j8uTJ/Otf/2qXbQvRXqKC/Qj111aMO1roeICkSJsXgwABAABJREFUEEII0d1lmEq5c8l2LE5K9MtgCCGEEKL9OQvpZRU0DrBxN0C2W+3DjfXzqfNpFV6Y8HuY+RTf7c2jpkG/GBVAg8XqUjGBlu9VFefZBxs9o4II0bkv72xt6TrUXkIDPPs5dcXBSkIIAZDpQgeKXdnuBwg9DVzbX18D3+zK5ZrXN7BqX35zqOxH60j2Wntq5h9uOMIAxQRAv1j9AnZNPDnfFFTod9rYr/PzDPIzMn/WIJ67Jo21f53KwrljbYqkD3KhC3CfmGD8fAxe+bm2R6H2b3blkl3qvFNJvdn+NY8eCe4LIdqL3jmliaq6VtTN3n2HbgfU6H5UjP6j7vyvmy9hq2ob6o4J0QbVm3gzpG+PN++r3G0ksnzXCeodVfdpoSOKyAhxuistLW3+OiYmxun80dHRusu6Izf31LH14Ycf5rPPPsPPz4/bbruNRYsW8f777/PXv/6VqKjGkNXOnTuZOnUqxcXuF4k4fvy4w/9a7suZIDPfhQ6Epa4F9Yoq9TvZX3FWksNj/oXGdMIV7TnmHp9P6K8c113GqsKSTcdcKPdiS1FgQl/HYb3OUOvkvuhwYXWXKC7b1Qq7iTNXl3tzYbFY2Lt3L+np6WzZsoX09HQyMjKoqWl8MHLTTTc1d1hpb3PnzrXpZvLII4/w6KOPdsi2PVKYCUuuh6KDkLUa4gZD2vU2s6SlRLBw7lj+/vlO3m9VidtiVUl0oYKVEEKI7u+cc84hJiaGwsJC0tPTeeutt7jttts08xUUFDB//nwADAYD99xzT0fvqssiArUDBhosKtX1FoK74GANIQDmzZvHxo2NlVxTUlK4/vrrSU1NJTIykqqqKjIzM9mwYQNr1651uJ6vv/6aTz/9lOjoaG699VZGjBhBQ0MDy5cv5+OPP8ZqtXLLLbcwceJEFixYwMKFCxk6dCi/+tWv6NOnD8XFxSxevJhNmzZRXl7ODTfcwO7du/Hz0z5Iz8nJ4eyzzyYvLw+AXr16MXfuXAYNGkRlZSXffvstn376KWazmYceeoi6ujr+8Y9/6O73Qw891BwyVBSFq6++mhkzZhAaGsr+/ft55513OHr0KLt37+bss89m69at9OjRo3n5O++8kyuuuIIFCxawevVqAN544w2bkDQ4fih366238sknnzBs2DDmzJlDv379qKqq4ssvv+R///sfAH/729+YOHEi55xzjsO/ByG6CkVR6B0TzM5WA1GOSAdCIYQQp7mXV2a6PRhi4dyx7bxXQgghxJnJ5CRAeKigClVVPQqQ7VL7sm7iW0z/5bdQVw4T/wgzniDjeBl3LtmOs2EYTcUElt4R6HCwf9N7VVNxNZsOF7NsezZrMwud7t+gBOfBhc7Qlq5Ds0frDNr1ggl9o1EU+50c9XTVwUpCCAFwsMB5B4rdOeVur9fTwLU9JdUNPLV8n849tMJ/zJexwO8Vm6l1qg9DlSMcUFPo6yRA6NH5BgjwNVDb4Pye/sbxPfntef3sfj4gPhSD0jgo1p6BJ8/Vbfm5KgpMGxjHndNTvRoebCqO5KQ2EgB//iiD5Mggl7cvwX0hRHtoOm45ey7r7D7suJ0OhEl6HQgVhaCZD/HchuPc6/Nx8+S3zTN52jyH1h1wo4PtBwi9HdLX4+37qsaCN4EsWJnJqv22oc2W56eoYD9W79/q1rqbwo66wU0hhFOVlafuBwICApzOHxh46hhXUeE8jKanpKSk+ev9+/cTGRnJypUrOeuss5qn33DDDfz5z39m+vTp7Nmzh6NHjzJ//nxef/11t7aVkpLi0T6erjJd6ECYX1GL2WLFx0nwu7BSv6jKOf1juGlib91jPsALvrczypxJiqHAZrq/0sAMwxYOWpJ119tgVYkO9qOoSj+4qGfawLgud37IMJXy5Nf7nM7n6vPg9tKWwm5d7Wcuur8uN5r+2muv5bPPPuvs3WDFihU24cEu7+BKWHoz1LUYIPrFnyCqH6RoB8GM7hWpCRBC48ksPsz5RYsQQojuLSAggNdff53rr78es9nMvHnzWLRoEZdddhkpKSmUl5eza9cuFi9eTEVFBSEhIbz11lucffbZbm/r+HH9SiZNvFV5JkKnAyFAaU2DBAhFl5Sfn8+yZcsAmDRpEitXrrT78KigoIDCQvuDkz755BNGjRrFt99+axOWu+mmmxg6dCiPPPIIZrOZ6667jm3btjFv3jz+85//YDQam+edN28eF154IatWreLgwYMsW7aMa665RrOtefPmNYcHZ82axdKlSwkKOnWjetttt7FixQquvPJK6urqePLJJ7n44ouZMGGCzXo2bdrEk08+CTQekz7//HNmzpxpM8+9997L7Nmz+eabb8jLy+O2225j+fLlzZ+PGjWKUaNGNQf9AGbMmEHv3r3t/qxa++CDD7jnnnv417/+hcFw6mHNLbfcwhNPPMFDDz2Eqqo8++yzEiAU3Yq9AKGqqijutk4QQgghugF58SKEEEJ0Lc46EFbWmSmoqPM4QDZw1GQY+DlkfgdTHgBFabdiAilRQaREBTGudxTn/Wu10/kHJ4a5tA8drSt2HUqJCmLqwDhW7XP9Oq4rDlYSQogmB10YQLo3t9ylAaQteXK+tEdRIP1Isd1z5tfW8dxr/YiehgLK1UDetVzAO+aLKCQcgI82mxiUEGZ30KWn55tBCaFsNznvzjhnnLZDYksBvkb6xARzqMB+Qb2B8Y0BQk9/rn+fNYiLhie2y/moPYsjSXBfCNEevHXcstd5NclOQwxfo4HPQ+ZQX2Fkvu8S3jWfz+PmX9M6PAgQHeJvd5+8HdK3x9v3Va0L3lTUNhAa4MuEvlHN56el6aYuV0RGCOF9VqvtMfi5556zCQ82SUhI4IMPPmDkyJEALFq0iGeffZawsK75HKurs1pVDuY7v/+zqpBXUWf3fNak0E4HwugQf6fH/LzdUVg/uQKD2tiJL0+N4IGG21hlHeVwm0VV9fgaFZe64vkZDdw5PdXpfB2tuxSX7YqF3cSZq8uNprdYbNuIRkVFER0dTWZmx7XhLC8v5/bbbwcgODiYqqou3qWhqhA++hU0tHoRZ6mHD2+A366GcNsEeWqcfuXLzPwKzkl13jZZCCFE93f11Vfzww8/8Ic//IHdu3ezfv161q9fbzOPr68vf//737n99ts9riDTUZVnIgL1K4aVVtc7vQETdrx1vnbasKthwu8cL2faDN8+oJ1+4VO6hQ1sbPwP7PpUO/22HxwvB/DJrVB61HZa0hi46GnHy1UWwIdzXN+Ol2RlZTU/xLnxxhsdVp6KjY0lNjbW7ud+fn588sknup32/vrXv/Lcc89RUVHB1q1bGTZsGK+99ppNeBDAx8eHxx57jFWrVgHwzTffaAKEO3fubA7wJSYmsmTJEpvwYJOLLrqIxx57jPvvvx+r1cozzzzD559/bjPPs88+2/z9P/7445rwIDRei3/44YcMGjSIEydOsGLFCjIyMkhLS7P7s3DX5MmTee6553QDVQ888ACvv/462dnZfP/995jNZnx8utwtlBC6ekdr/23WNljJK68jIVyKxgghhDj9yIsXIYQQoms5Vqw/+LOlgwWVTOoX43mALHIMJI8BOqaYQM/oICb1i+bnQ0UO5wv0dT0Q0pG6atehu6ansi6z0KXBPl11sJIQQjTJdGEAaZ3ZyqGCquYueK7wJHDdUijV/NHnc36ypkHvyWzIsn8us2DkefM1JCglfGCZTgW258mtx0q55vUNLJgzkpnDErXb8vB8ExtqP9zRRAEO5FXQNzbE4XyDEsMcBwhP/uw9+blOHxTHPAcdENuiva9nJLgvhPA2bx63jpdoi9BEBfs5LBieHBnI/5Veym61Nz9bh6IXHgSIcRAg9GZI35H2uq9qKnijpysWkRHidBcSEtLcEbC2tpaQEMfXrTU1p56fhYa6fn/QUsvlgoOD+dWvfmV33rS0NCZMmMDGjRupq6tj/fr1XHTRRS5vy2QyOfw8NzeXcePGuby+7iy7tIaaBovzGYETZTUuBAj1OxDGhJwaE2vvmB8/9Dx27fg9w/b/m/9ZJvFww1zKcfy71+SGcT1Z8ovJ4XM5P6OBBXNGdkrnPke6U3FZOSeLrqTLvb0YN24c999/P0uXLiUrK4uioiLmz5/foftw3333YTKZSElJaQ4SdmnBMXDx8/qfGYxQU6KZ3C8uWHd2Vx5mCiGEOH2cd955vPLKK7pVZwAaGhp49dVXeeGFF2xuWLuicDsdCMuq5SLaY8c3a/8rdfwgAGjsiKy3bJ3zqqGUmvSXdUXeLu1yhfudL2epd287XhIcfOp6bMuWLW1a1yWXXEKfPn10PwsICGD06NHNf77jjjvshuAmTJiAr2/jv6U9e/ZoPm/ZKfyOO+5wWIXqj3/8Y/NDquXLl1NbW9v8WV1dHV9//TXQ+PDs97//vd31hIeH23zu7W7lf/7zn+12YzMajUydOhVofLB36NAhr25biPbUO1r/nu9wYRcvkCOEEEJ4SF68CCGEEF3LsSLn959ZJwf23zU9FT8XuzDZC5C1pZiAOwa5EPZ4/vsDfLMr172d6QBNA2Pd0RFdh9JSIlgwZ6TT34GuOlhJCCGa1NRbMOmEH/TsznHhnVkrd01Pxcfg3oHcgJU5xpWs9r+H232+5hHfdxnbK9TpOXOZ9RzesFyqCQ82qbdYuXPJdjJMpZrPPDnfAPy4v8DpPCrY3W5LEYGOQxrWFj8Ab1yHeEtHXM90pe9XCNH9efO4dbxEOyYpOdJx2KJp8P9663BUB0OhW4YvWmsKV7enzurm2lWLyAhxOouIiGj+urCw0On8RUWnCnu0XNYdkZGRzV8PHz4cPz/7xzyAMWPGNH/t7jio5ORkh/8lJmoLjJyuDuRVuDxvTmmt03mK7AQIYx2E4FvanDyXufX3cXfDH10ODwL0jglm6R0TmT4oTnMfpyiNBVSW3jFRt3hMZ+uo58HeIOdk0ZV0uQDh/Pnzeeqpp5g9e7bdQdDtadWqVbz55psAvPbaax4n+jvcyBtg0p9sp/UYBfNWQ8JwzexBfj66aXZX2ukKIYQ4PRQWFjJ9+nSmTp3KkSNHePHFFzl06BD19fWUlpaycuVKZs2aRWlpKS+99BJTpkyxuWl1lclkcvjfL7/84pXvx96LoNIaGQwquqYhQ4aQlJQEwNtvv83cuXNZv369piO3KyZMmODw84SEhOavHVV68vHxITo6GqC5IlZLmzZtav56xowZDrcZHBzMOeecA0B9fT3btm1r/iwjI4O6usYHH2effbZNmFLPhRde2Pz1xo0bHc7rrokTJzr8PDn5VCdvvZ+JEF1V7xj9gR1HXRjAKYQQov1ZLBZ27drFokWL+NOf/sTEiRMJCgpCURQURWHu3Lkdti9z585t3q6iKDz66KMdtm1vkhcvQgghRNdyrNh5gOJQQeN7ybSUCB65bIjT+R0FyDqimECGqZR3Nx51Ol+DRXUp3NDRPBkY21Fdh2YOS+y2g5WEEKLJoYJKlwcv7soud3v9aSkRnJsa4/L845S9fOX3d57yXUiM0ri9gYqJ8SVfub1tPfUWKwtWZmqme3K+iQ72o8Hi2g/P3nabfLMrl482Oy6IeueS7c1h/64UZO+I65mu9P0KIbo/bx639AKEzro1pUQ5/rxJtJPwhbvh6jG9Ip3P2EJndXPtqkVkhDidDRw4sPnrw4cPO52/5Twtl3XHoEGDmr8ODw93On/LecrL3b8vEY3cadiUW+a8cUdhZb1mmoKVqBPrXNpGSKA/P1r1m4g4EhrgS1pKBAvnjmXNfVN57po0Hrl0CM9dk8bav05l4dyxXfaeoDsVl5VzsuhKulyAsDNVV1czb948VFXluuuu45JLLunsXXLP+Y9B6smB3MOvgZuXQ5j9lyip8dqEuQQIhRDizFBdXc25557L6tWriYyMZNOmTdx999307dsXX19fwsPDmTZtGl9//TV/+MMfAPjll1/405/+5GTNWh1VecZeB8JS6UAouiij0cj//d//4e/f+LB68eLFnHPOOURFRTFjxgwee+wx1q9fj+rC2+am0J89TdtwZ96WHQOb5Oaeqpw+YMAAp/vVcp6Wy3prPd4QE+P4ZXvLn53ez0SIrspuB0IJEAohRJdw7bXXMnz4cG6++WZeeeUVNm7c2Cld31esWMHixYs7fLvtQV68CCGEEF1HeW0DJS48l23qQAgQFxrQ4hOVWYaNKFgB1wJkHVFM4OWVmV4LN3SWrtx1qLsOVhJCiCbujLfZ5UEHQrPFys5sx8vFBPvRdGs8yHCMIQZt8H30odcIwztjg1btz+e4TtdFd843vgaF4irtYFlPtpthKuXOJdsxWx2fr1t3UOwqQfaOKo7UVb5fIUT3563jVkVtA2U6xcFd7UDoiEFx3pnW3XD1Q5cM6bL3VS115SIyQpyuhg8/1fBn8+bNDufNy8vDZGosfBEXF0dsbKxH20xLS2v+uqzM+X1Gy3lcCRwKfXodCI12Osa70oGwsFUHwggq+G/A8/h8cDXs+tTp8t54T5oSFcTs0cncfHYfZo9O7vLng+5UXFbOyaIrkQBhCw888ABZWVlERUXx8ssvd/buuM9ghKsXwsXPw1Vvgq/jG6jUOG2AsLiq3m4bXCGEEKeP1157jX379gFw7733kppq/2HVM888Q0REBAAfffQRJ06c6IhddFtEoJ/u9NIa9144CdGRZs2aRXp6OrNnz8bPr/F3uLy8nO+//55HH32Uc845h379+vHee+85XI/B4PplvTvztlZRcerhh7OugQAhIaeuN1su6631eENbfh5CdGVRwX6E+msflh0tdN4BQgghRPtr3XU6KirK4X1ZeygvL+f2228HXLsm6+rkxYsQQgjRdZhc6D4IpzoQAuxuEaSYa/yW1/wWsNj3GR6ZGu1SgKy9iwmYiqtZvT/frfXbCzd0pu7Qdai7DVYSQogmmfn67y8Gxodqpu3JKcfqJOTW2rqDhbpdKUamhPPcNWms+9tU0h+6gDV/bQxib4y8jExrkmZ+/4ZS7vL53K1t26OqsDGrWDPdnfPNnPE9ce8nYX+7L6/MpN5idWkdrcP+XSHI3pHFkbrC9yuE6P68ddzKLtUvrjfOsg2yfrK7rhQnAUOAqGB/DHYCHS25E67uDvdVTbpyERkhTkczZ85s/nrFihUO512+fHnz17NmzfJ4mxdddBHKyQPXzp07qa93PFYyPT29+WtPux4K/QIyvaODiA7WjmE9UeZegPAsJZOv/edzLtsaJ3xxJxQedLj8mfietLsVl5VzsugqZLTsST///DOvvPIKAM899xzx8fGdvEceCgiDsbfhyhExNU77kBLca6srhBCie/rqq6+av54xY4bDeYODg5k0aRIAVqvVaXWczhJhpwOhXpUy4aLksdr/IlKcL+cfrr+svwtViyJS9Jd1Rfww7XIxLjzoMPq5tx0vGzZsGEuXLqW4uJhvv/2Wxx57jPPPP7+5893hw4f59a9/zWOPPdYp+9dSaOip68eqKuddzCorT11XtlzWW+sRQtinKAq9Y7RhkCPSgVAIIbqEcePGcf/997N06VKysrIoKipi/vz5HboP9913HyaTiZSUlOYgYXcnL16EEEKIrsHVAGF2aQ21DY2FFXbnlAMwRDnCAz4fAHCecSdzd/ya5OKNTtfV3oNkNmYVobqZbrAXbuhs0nVICCHah94A0mA/I9MHa89PlXVmjrl4vmyybHuO7vT7LhxkE7huCmLffeFQ/mH+lWb+vcHj+NrX8fthd1TU6r+LdfV80zvas6JGrbfrrbB/ZwbZO2PQrwT3hRBt4a3j1vFibYAwlhIm7/47/Pdy+O4hMGsDMclRzo9ZMSH6xcj1uBOu7i73Vd0p7CjE6WDy5MkkJCQA8OOPP7J161bd+SwWCwsWLGj+8/XXX+/xNpOTk5k8eTLQOAbLUZH6jIwMNm5sfM4WGhrK2Wef7fF2z2RWq0pmnvb+b0B8KAnhAZrpuWX6QfmWmorF3Gj8gY/9HidJKTr1YX0lLL0JGhyv50x7T9rdQpNyThZdhWe9O08ztbW13HLLLVitVqZPn87NN9/c2bvUIfrpdCCExoeaE/pGd/DeCCGE6Eg5OadeMLnSir6pAyHYBnm6kgBfI/4+BurMtlUly6olQOix237wbLmUsZ4vO+F3jf95YvZCz5YLifV8f70oODiYGTNmNId6KyoqePnll3nooYcA+Oc//8ntt9/e/KCpMyQmJrJ9+3YAMjMzGT9+vMP5Dxw40Px1jx49bNbTJDMzE2fsrUcI4VjvmGB2ZpfZTDtSVIXVqrpUaVMIIUT76eiwYGurVq3izTffBBo71LesNtqdNb14uXPJdocdB+TFixBCCNG+XA1EqCocLqxicGIYe3LKUbDylO9b+Cvm5nmUqnx490o4/1E4588O13fX9FTWZRa61HnI3UEyFbVm5zPpLtc1n083DYw1FVez6XAxFbUNhAb4MqFvlAQHhBDCQ3rFuvvHhzI8Sf9d7K6cMt0icHpq6i18u/uEZnpcqL/d8T3nD47nwcCxrKw/i+nGbRyyJvIP86/5qW6k26F4R0ID9Iu8gmvnmwN5+p0b3d1uW8L+s0d3nXNfe17PCCFEe/DGcUvbuV3lOd838Ksrafzjzwvg8E9w1VsQO6B5roSwAHwMCmYHXX1jQvxd+j5aSokKIsWFcGJ3ua9qDDsGsmBlJqv259ucLxWlMUxx5/RUeV4uhBcYjUYefvhhfv/73wPwm9/8hlWrVhEXZxtyuv/++5vHX5199tlceOGFuutbtGhRc6Zh8uTJ/Pjjj7rzPfnkk80NIu69917OOusszjrrLJt58vLyuPHGG5v/fOeddxIY6LyTq9DKLq2h5mRRtJZS40JosKjNhdKa5LjQgbDoZAdCHyz4Ktp1k7cLVvwNLlug/eykM/E9aXe7f5JzsugKJEAIPPzww+zfv5/AwEDeeOONdtvO8ePHHX6em5vbbtvW099BgFAIIcTprWUHL5PJRGqq4wvjo0ePNn8dHd11Q+YRQb7kldfZTCuVAKHopkJDQ3nwwQdJT09n2bJlNDQ0sHHjRq644opO26fx48ezYsUKAL777juHAcLq6mrWrVsHgJ+fn82DqZEjR+Lv709dXR3r1q2jurqaoCD7D9C//fZbm31ozWA4VZlH9eZbbyG6ud7R2n9XtQ1W8ivqdKueCSGEODNUV1czb948VFXluuuu45JLLumeAUKrBRQDrcs7N714eemHA6zeX6BZLCEsgDd+PVpevAghhBDt6GiR6x2VsgqqSAwPILu0hksNG0kzZOnPmJjmdF3tOUgmNMCz1+qOQhVdgasDY4UQQjhWZ7bonv9S40IYZi9AmF3OJSOcF000FVfz+k+HqK4/NYg0knLilFLOSZuM0U6xOD8fA1eelcQ/19/IOusw3rVcgNnLw8QUBSb0jXI6n6PzzYS+0SgKboX/9LZ7uoT9z8RBv0KI7q3puPWnJdtosNg/mDs6bh0vse2odL5hK5ONO2xnys2AN86DO9ZCTOMYJ6NBoUdEoMMiNtFudCD0VHe4r+ouYUchTgfz5s3j888/5/vvv2f37t2kpaUxb948hgwZQnFxMUuWLGkeTxUREeGV3MLEiRP529/+xjPPPENJSQkTJkzgpptu4pxzzsHX15ft27fz1ltvUVxcDMCYMWN48MEH27zdM1Vmvn4RlNT4UEprtPcXhZV11Jut+Pnod55rsFgpOTnGdbFlBiMMWVxtXGs7U0g8jLjW6b6daQG17nj/JOdk0dnO+ADh5s2beeGFFwB47LHH6NevX7ttKyUlpd3W7YnwQF/iw/w1QQt7JzYhhBCnj+HDh7N161YA3n//faZNm2Z33oMHD7Jp0yagMaQzZsyYDtlHT0QE+mkDhDX1nbQ3QnhHnz59mr82mz17+ektV199NY8++igA//nPf7jrrrsICwvTnffVV1+loqLxuvLiiy/G3/9UZT8/Pz8uueQSPv30UyorK3nttde49957dddTXl7Of/7zH5t9aC0k5FRhjKqqKre/LyFOV72j9atXHy6skgChEEKcwR544AGysrKIiori5Zdf7uzd8UzxYfjf72D4NTD2Vs3HaSkRvHPzOM59dhWmYtvBJz0iArrEyyEhhBDidKY3eNPPaNAdxHGooJLIoMaQXS8lD7NqwEdpNd/Zd0M/+8+wW2qvQTLeCjcIIYTovkzF1WzMKqKi1kxogA8T+kY3hwWOFFZj0el+1D8uhOTIQMICfChvFXDbnVPmcHsZplJeXpnJ6hbnMyMWbjT+wF98llKgRlA7fLbDdSRFBJKl9iDL4jyo6IlpA+PaPMAxJSqIqQPjWLUvv03bPZ3C/mfaoF8hRPc3c1giL1yr8qcl23Q/Dw3w4b1bx9s9bmWXnnqGa8TC/T5L9DeUegFE97eZlBLlJEAY7H4HwtNZdwg7CtHd+fj48Omnn3LDDTfw1VdfceLECf7xj39o5ktOTuajjz5i6NChXtnu008/jdFo5JlnnqG+vp4333yTN998UzPfhRdeyJIlSwgIkDEjjji6/zuQp9+oKTU+RBOKh8bniXnltXaPv8VVLce3KsxvuJVU5TgjDIcbJ/WZDFe/BSFxusu3dqYF1Lrr/ZOck0VnOaMDhPX19dxyyy1YLBZGjRrFPffc09m71DEaaiF/NySNpn9ciDZAaOfEJoQQontwpXX9DTfcwOLFiwF45513mDRpErfeqh10eeLECa699trm0NIll1xCVFTXHfAQHqR9wSMdCEVX9e2337Jnzx7mzp1LZGSk7jz5+fl8+umnzX9OS3Neab09DRs2jIsvvpivv/6a3NxcbrjhBj7++GNN98Bvv/2Whx9+GGgMHv/tb3/TrOu+++7j888/x2q18tBDDzF8+HAuvPBCm3mqq6u54YYbmjt1z5o1ixEjRmjW1TJkuXXrVoYNG9bm71WIbiNvN6z4G1z1JoQl2nzUO0b/QdPRoiom9uu6HYWFEEK0n59//plXXnkFgOeee474+PhO3iM3qSpsWQTf/h0aqhqrTvedAtH6ReEGxIVqAoRH3OiIJIQQQgjPmHQGb45MiWC7qVQTIswqqCTQ1wjAK5YrWWEdx998PmSGcUvjDBE9Yep8t7bfHoNkvBVuEEII0f3oBfmgcSDi1IFx3DU9FVOJ/r1malwIiqIwtEc4G7KKbD7bnVOOqqooiraD4De7cjUdFMYpe3nMdzGDDccACFeq2Zv+f9Dr73b3++kV+1z6Hg0K/GlaKlX1Zt5Zf0Q3DNman9HAndNTXVq/M3dNT2VdZqHDjhHOtnu6hf3PtEG/QojuLyrYfqc/fx+jw0H7LcMWoVRzSO1Bf3JsZwqJh0tfbjx4t5AcEQTYnmNb6ogOhEII0VpoaChffvkly5Yt47///S+bN28mPz+f0NBQ+vXrx1VXXcXtt99OeLh+t3JP/fOf/+Taa69l4cKFfP/992RnZ9PQ0EBcXByTJk3iN7/5DRdddJFXt3m6ceX+Ty9nYTQo9IkJZl+4fhOn3DL7AcLCStscRx1+3FH/Z1aFPUrAhNtgyv1gMLr9vZxJATW5fxLCdWd0gPCJJ55g165dGI1G3nzzTYxG9w+u7jCZTA4/z83NZdy4ce23A5UFkL4QNr8F5nq4Zw+pcaGsP2h7A5VfUUdZTQPhgV2vypYQQpzODh8+zMKFC22m7dixo/nrbdu2aVrHT5s2zWH3QHtmzJjB7Nmz+eSTT1BVldtuu413332Xyy+/nOTkZGpqakhPT+fdd9+ltLQUgOjoaJ5//nn3v7EOFKFz7irTaQsvRFeQm5vLPffcw9/+9jemTJnChAkT6Nu3LyEhIRQVFbFjxw6WLFlCSUkJANdeey2pqd55EdsW//d//8eoUaPIy8vj66+/ZujQodx8880MHDiQyspKvvvuO5YuXYp68inK3//+d8aPH69Zz/jx45k/fz5PPPEEtbW1XHTRRcyePZsZM2YQGhrKgQMHePvttzly5AgA8fHxupWxAM4///zmr//617+Sn5/PwIED8fVtPCZERUW173W2EJ3BaoWNr8HKx8BSD8t+Dzd+CgZD8yz2OhB+s/sEZ/ePOWMeFAohhGhUW1vLLbfcgtVqZfr06c2FZ7qNihPwxZ8g87tT0xqq4fM74JZvdF+c9YnRnguLq+opq27QLUAjhBBCiLazWFXdStu9Y4IoranXVOg+VFBl+2c1id82/IXx1v0s6fUVhgm/Ax/POkZ4e5CMN8INQgghuhe9IF8TVYVV+/JZl1nIBUP0C/SkxoUCMCwpTBMgLK6qJ7eslh4RgTbTM0ylmm2eZ8jgv37PaNafsuPf7B42m6EDB2o+e3llpkvnLACrCruyy1g4dyxjekXa/Z6b+BkNLJgz0msdHNJSIlgwZ2Sbtnu6hv3PpEG/QojuraCizu5nhZV11DZYCPDVH597vEUQv5RQbm+4hz+m5HOv8h5kpzd+MO1BCNKGvlOiAjXTWooNkQ6EQojOc/nll3P55Zd7vPzcuXOZO3euW8ukpaWxYMECj7d5JnP1/i8xQtu9sXd0EP4+RhLD9Ts75pZpn5c2Kays10zLIYYNs75j6kh5vugOuX8SwrkzNkCYkZHB008/DcA999zDqFGj2n2bycnJ7b4NXcWHYe3zsONjsLS4Udv+Pv3jZuoucjC/ktG99DvhCCGEaB9Hjx7ln//8p93Pd+zYYRMohMaW954ECAHee+89wsLC/p+9O4+Pqrz7Pv45M9n3jZBAghCWsBp2Ai5sVhDb4gK2oFW0tWKrcLfV1lrr8rhUql2ktWqVGquVKtVabRUXCCIoyhr2GGULJpBAEpKQfWaeP2KGDHMme2aS8H2/XryemetcZ87P+7lvDmfm+l4//va3vwHw4Ycf8uGHH5rOTU1N5Z///CeDBg1q07W8JUodCKUbadhRtra2lvfff5/333/f49x58+bx/PPPe6u0JvXp04cNGzYwd+5c9u7dy6FDh7jvvvvc5vn5+XHvvffy61//2uNnPfjggwQEBPD//t//o66ujlWrVrFq1Sq3ecOHD+fNN9+kT58+pp8zatQorrvuOl566SWOHz/OnXfe6XLcUzdWkW7r1FF441Y4uP7M2JdrYfOzMOkW51BMaAAh/lYqam0up6/LLuTixzKdu6N11CIPERHp2u69916ys7MJDg7mmWee6bTrHD16tMnjDd2lW+3YbtfwoPOCn8HGJ+Cin7od6m8SIAQ4ePI0o0Oi2laHiIiINCn/VCV1Jl2L+sWEUFpZ5xYgPFBYTtVZz60AxXHjsNz8k9a1EepkHRFuEBGR7sMsyGemxmbnnd3uz7pB/hb6RteHGkb0Me8ssiev1C1AaBb822gfyT57P2f3wQZhRiUlb/4K7vyXy3huUQWZ2S0P0gGszS7gaHEFs0cmsmpxMMvX5LDWpOvGjNR4lnTC98odcV2F/UVEfKegrKrJ48dOVZl+X1teXUexybqeyj6T4PJFsPeN+nWvo681/dzmguDqQCgiIi3Rmue/wyfdO9A3bB5z9vNdg7ySr++TdTVg9XfpqHuy3DyEHxUT15LSRURa5ZwNEGZkZFBbW4vFYsHf35+HHnrIdN769etdXjfMS01NZf78+V6ptd0qimD7i+7jm55i8OVXmp7yRUGZAoQiIj1cYGAgK1as4PbbbycjI4ONGzdy4MABSktLCQgIID4+nnHjxnHFFVdwzTXXEBDQ9b9Uiwpxr7Gy1tbkTmYivnL99dczfPhwPvjgAz799FP27dtHXl4elZWVhISE0K9fP9LT0/ne977H1KlTfV2ui0GDBpGVlcVLL73Ea6+9xrZt2zhx4gTBwcEkJydzySWXcOuttzJkyJBmP+vXv/413/nOd3j66af54IMPOHLkCJWVlcTFxTFmzBjmzZvHddddh59f048uL7zwAlOnTmXlypXs3r2b4uJiamsVIJYeat2jruHBBu/fCwOmQvxQAN7dc4xKk0WY4Lo72vIFo5k9MrEzKxYRER/bvHkzv//97wF44IEHGDhwYKddKzk5uXM+ePAlMP4m2PI392NZ/4TJt4Gf6zOhWQdCgEMnTjNaC/pFREQ6xRGTBTRQv/tzZa0N9riOn66xkVNQ7jZ/eGJE/UKaRotpugJfhSpERMT7WtvB72wDe4VhtdTfx0b2jTA9b/dXp1y6F3oK/tmwcl/tDbwa+KDbsVOlpzh6spSk2DPX2HTgZKsz+A4HbDpQxLxxIaQlR7Fi0QRyiyr49GARZVW1hAf5k54S06kd+9p7XYX9RUR8p6kOhAB5JZWmAcKvTDrYAyRFB9c/ZI24sv6PB811IIxVB0IREWmB1jz/mRnSOwyA3hFNdCCsLIF/LoQBF8O0u5zHTngIEMbpHiYineCcDRA6vv6mzG6388gjj7TonMzMTDIzM4H6tsLdJkCYNA6S0yF3k+t48UGGlX8MuHdryjnu/kOdiIh0rmnTpjnvT+3R2tb1o0eP5o9//GO7r9sVRAa739MASitrFSCULscwDCZMmMCECRPadH5r/m89IyODjIyMFs09dOhQi+b5+fm1+u8bT4YMGeJc0N5WFouFH/zgB/zgBz9odm5ruhHef//93H///W0vTKQzXPogfLEGyvJcx+uqYMPv4aq/OndHa+5fFjU2O0tW7mDV4mAtlhAR6aFqamq46aabsNlsjB07lp/+1L1TX7fxjQfhy0woPnhmbNyNcOlDbuFBaKID4YnTnVWhiIjIOe9IkXmAsF9MCHW2ln//7alTU1fgq1CFiIh4T1s6+J1tcHyY8/WAuDCC/a1uG769v/cY88YlkRxTf/9oKvj3mWMY/7FNYa71YwBy7H25v+56NtpH8fihUuY1ChCWVdW1qeayKteNGZNjQpy1eVN7rquwv4iIbzQXIPyqxDwo+FWJ+TNkS5+tmu1AGNr1N0sXERHf6pDnv971HQgD/CzEhQW6hQIrTh6F538IBXvg8EYIi6/fOBU4UV5j+pkKEIpIZzhnA4TnnMk/cg8QAhFH1hAT+m2KTrvefL4oVIBQRES6n6gQ8wBhSWUt8R52dxEREel2gqPhyqfg73MbDRow5XaYcQ/Qut3Ramx2lq/JYcWitgWaRUSka3vooYfYvXs3VquVZ599Fqu1czdXyc3NbfJ4fn4+EydObNuHB4bBlU/D85dBaC/49p9hyKUepydGBBHoZ6G6zvWeeOikAoQiIiKdpakAodGKboIj+ph3aupKfBWqEBGRzteWDn5na1hACmC1GPSLCSb7rM289+aXcfFjmUxPjWfpzMHNBv8eqV3IJMs+nq2bwwu2WdR9vezr7OBfeFDbloOFB5n/1trdKOwvIuJ9hR66JzXIP1VlOn60qQ6ELdArLJAAPws1dea/iyp8ISIizemI5z97ow/oExXkEiAcaHzFz4/+FuyFZ074388gNB6GfdO0A2FogJXgADXMEJGOZ/F1Ab7yxz/+EYfD0eyf++67z3nOfffd5xx/4403fFd8Wwz9JkT1+/qNAUMugxvegm8tZ1CjXc8aqAOhiIh0R1HB5juHlVTUmo6LiIh0WynTIP1H9a8jk+uf7y59EPwC27Q72trsAo4Wmy/yFBGR7isrK4tHH30UgJ/+9KeMHTu206+ZlJTU5J/ExMT2XaBfOlz1LPxoU5PhQQCLxaB/rHsXQnUgFBER6TxmAcKwQD9iQgNI6WXeHdjM8G4QIBQRkZ6rrR38GhvY68xanNW788kpMF+H43DAkextzH/6k2Y3vDlODBdVP8EK2+XO8CC4B//SU2JpRW4fqO/Ol54S07qTurjkmBDmjUvixgsGMG9cksKDIiKdqKC06QBhnocOhPvyS03H+7YwQGixGMQ10WXwxy9vIyu3pEWfJSIi56aOeP67Y1UWq3fnA5AYeabRRQhV/DPgIeIbhwcBHHb4101w+GPTDoSxCsCLSCfpsR0IMzIyuPHGGwGYOnUq69at821BvmaxwgX/BwV7YdKtEDfIeWhwfBifHSxymf5VSSWnq+sIDeyx/ysiIiI9UGSwhw6EFeZt3kVERLq1mfeCXyBc+BMIinQOt2V3NIcDNh0oYt44LaAQEelJMjIyqK2txWKx4O/vz0MPPWQ6b/369S6vG+alpqYyf/58r9TaKqPmtXhq/7gQso+XuYwdPHEah8PRqi5IIiIi0jK5JgHC5K+7D0YE+dMrPJDCsmrSjC+oIIgcR5Lb/L5RwUSFeF4AKiIi0tna2sGvscG96wOEWbklLFm5A7vJd7b+1HGf3wt815rJ92p/ycpPHRhAU1/v1p611Mss+JccE8L01HjW7m/5RnMzUuMVsBMRkTZrrgPhV2cFCLNyS3hiTY7pvcrPYnCw8DRpyVHNXnf17nzyS827GwKs3V/AhpwTLF8wmtkj27m5nYiIdFu5RRVsOnCSsqo6woP8SE+JJTmm/vmnI57/am0OlqzcwarFwSRGngnBVxDEY3XX8Fv/Z91PCo2D4GhOlp9wOxQXpu9GRaRzdLl02MGDB1mxYoXL2M6dO52vt2/fzj333ONyfMaMGcyYMcMr9XVrE75vOmzWgRDgy8Jyzk+K6sSCREREOlZUiIcAYaU6EIqISA/kHwyX3O823Nbd0cqqdL8UEelpHF8nyu12O4888kiLzsnMzCQzMxOAuXPnds0AYSv0j3PvdFRWVUfR6Rrt3ikiItIJzDoQ9os5s2gmJS6UwrIqHvb/G8OMw7xqm8bv6+ZRSLRzjroPioiIrzV08GvtRm0N/K0G5329GPWJNTnU2Oxuc3pRzFMBTzDe8jkAf/ZfzrerH6IqtC8nT7d8c1RPwb+lMwezIeeE6bXPFmC1sGTm4BZfU0REpLHqOhslFU3/zti4A+Hq3fksWbmDWlsdt1n/w6u2aRQ0eiasszuY//QnzYb+GkL6zd2va2x2Z6ijJaFEERHpORoC65nZBS73C8OA6anxLJ05uN3Pfw1qbHaWr8lh4gDXDV5etU2nF6e40//VM4O9hsJ1r0FkEifKP3D7rDj9hikinaTLBQgPHz7Mww8/7PH4zp07XQKFAH5+fgoQtsPg+HDT8S8KFCAUEZHuxVMHwlIFCEVE5BzS1t3RwoPM76MiIiLd2YBY9wAhwKGTpxUgFBER6WClVbUUmywa7RdzJtQwMD4M+6GPGWk5BMACv0zmWj/mWdvl/LXuck4TzAgFCEVExMfa0sGvsZS4MPysFnKLKsjMdv+MscbnPBXwR3obJc6xWKOMpwP+wLzT9+NvDaTW1vzq1aaCf2nJUSxfMJolK3c0GSIMsFpYvmC0AhUiItJmJ8ubD77nn6rC4XCw8+gp571puiWLO/xX8SO/N3nWNoe/1n2T09RvQNOS0J+nkL6ZhlDHikUTWvzfJSIi3VtDYN3sXuFwuHapbc/zX2Nrswu4aEgvt/EnbXP53shAErJfhORJsOCfEBKD3e4wvY/qN0wR6SwWXxcgvje4t3kHwpyCci9XIiIi0j4eOxA2s9OZiIhIT9KwO1prGAakp8Q0P1FERLqVP/7xjzgcjmb/3Hfffc5z7rvvPuf4G2+84bviO4hZB0KAgyfcuyOJiIhI+xw5aX5/bRwgTIkL5Qa/91yOhxjVLPV7nW9bPwZgRJ/IzitSRESkhZbOHEyAtW3LqoorasjKLWHTgZOmXSzSLftcwoMNRlkO8Yj/ChZO7Ielme94WxL8mz0ykVWLJzNzaLzbd8aGATOHxrNq8eQmuzuJiIg0p6Csutk5FTU2TlXWuoT+vm99G2h4Jvw36wJ/wnXW97FiA86E/sx4Cuk3ZW12AUeL9b2wiMi5oKFLbXNB84bA+pyRCW1+/mvM4YATZVUmRww+HnInXPoQfO8NCKlfn1NaVUud3f2hsVdYQLtrEREx0+UChNOmTWvRopbGf+6//363z1m0aJHz+Lp169pcz/3339/kdXqC+PBAwgPdO1TkHFeAUEREupewQD+sJr+m6QtAERE5lzTsjt0aM1LjSYoOaX6iiIh0KRkZGRiGgWEYTJs2zdfldEkpHgOE+u5TRESko+UWmX8Pm9woQDgsrJzZls/c5hQ5wnjddhEAw9WBUEREuoC05Cjuvnxom84tKKtm/tOf8NmhItPjf7F9m3ds7h2QTjsCWWMbQ/+4UCYOMN/wrbXBv7TkKFYsmsD6O6fz+Pw07vvWcB6fn8ZHP5/OikUT1HlQRETarbAFAUKAbUeKnaG/ocYRLrTucTneyyjlVr83sXIm7OEp9OcppN8UhwM2HTC/N4uISM/S2i617+w+xvIFo2nlXt2m/DwEEfNLa2HK7RBw5rvSE+Xm91B1IBSRzuKeGpNzjmEYDOodxvYjJS7j248Uk1tU4fKjnoiISFe28+gpLAZf70V2xhs78iitqmPpzMH6EUxERM4JS2cOZkPOiRZ9IRpgtbBk5mAvVCUiIg0OHjzIihUrXMZ27tzpfL19+3buuecel+MzZsxgxowZXqmvWysvgF3/gi8+gIWv0is8kNAAK6drXJ8UD6kDoYiISIc74iFAeF7smUD/8KJM/Az3Z9V/2mZQTQBRIf70iQzqtBpFRERaIza07Ys2a2x2Xt/6lYejBnfULmagkccQS/2cQ/be/LD2p3zuSGZGkD/HS90XkibHhLDy5klt2gwuOSZE639ERKRTtDRAuP7zE87Q343W1aZzXqi7lBr8ne8bQn/zxrnew8qq6tpUa1lVbZvOExGR7qOtXWp/cVnbNpA5W2JkEIaBW9A9r6TSbW5hWY3pZ8QpQCginUQBQgEgNtS91e3J0zVc/Fgm01PjFbgQEZEub/XufJas3EGtzXyLsbX7C9iQc4LlC0a3aDdOERGR7iwtOYqnrjqPj994mlhHMb+t+67pvACrheULRut5T0TEyw4fPszDDz/s8fjOnTtdAoUAfn5+ChB6UlsJ2W9D1j/hizXg+DoseHAdxqBLOC82lL35pS6nHDxx2geFioiI9Ay5RRVsOnCSsqo6woP8SE+JJTkmxDRAaBjQNyrY+T5i2u0szLSx0FjNbMtm/Aw7NofBP+pmAjCiTwSG0RF7fYuIiLTf2c+SrWVrojXSaYL5Ye1PeTPg12y3D+L22tsoJQzDgDH9ojh00v25dXJKTJvCgyIiIp2poKyqRfOOl9bPs2BnqOWI2/HTjkD+aXP/Dtws9Bce1Lalz+FB/s1PEhGRbq2tXWr/tTWXVp7mFMcpThCJYcAFg+KIDw902xTm2Cn3++XJ0+Yh/Lgw91yHiEhHUIBQWL07n43Zecyw7ORLRx8OOxKcxxwOBS5ERKTry8otYcnKHc12Waqx2VmycgerFgcrKCEiIj2TrQ6+XAPbX2Jm9jvMtNZShx/P1s2hmAiXqVHB/rxw00TdE0VEpPs7vBH+dZP7eNY/YdAlDIhzDxAeOnkah8OhgIKIiEgrZOWW8MSaHDKzC1wW4RgGTE+Np8hkwUufyGAC/CzO91arhaMRY7itKJXeFHGt3wfEUcpX9AIgt6iSrNwSPauKiEiXsDevfQHC5hxyJHJVzf0ccPTBTv39ckZqPJU1NtMFr0N6h3dqPSIiIm3R0g6ElbX1G7/ZsTC35kEusOzm+9Z3mGHdAcAq21RKCXU7zyz0l54Sa9rdqSmGAekpMS0/QUREuqW2dqndf6y8TeclcJL3An/BH+uu5vDgG0iKDiExMtgtQJhnEiA84eEeGqsOhCLSSRQgPJfVVnJw05tUv/93PvXfSoRRyV/qvm3amUKBCxER6cqeWJPDbMcGhvkdZlnddwHPC0BrbHaWr8lhxaIJ3itQRETEWzb+EdY+6DLkRx1L43dwf8HFLuN1dgej+kZ6sTgREWkwbdo0HK3d+tLEokWLWLRoUbs/5/777+f+++9v9+f4zIBpEJYA5cdcx/f9F6rL6B/n3p2hosZGYVk18RFBXilRRESku1u9O9/jJm4NG5KaSY4Jdnm/enc+R4vrOxUeJ4bf113jcvxIUQXzn/5EG5uKiEiX0N4OhA2sFgOb3fx7gC8cSc7XAVYLS2YOJvtYmenc1AQFCEVEpOtpaYDQajEahf4MNtpHsdE+ioF1X3GjdTUZtllu53gK/SXHhDA9Nd7js6iZGanx6uQrInIOaGuX2ryvv7NsLNDPgt3hoNbm6XddBw/6ZxBhVHCv/4sUhvqDfSx9ooLYkes6M/9UpdvZJ0/XmH5qLwUIRaSTWJqfIj2SrQ7+eD4D1vyQuZYNRBj1N6XLLJ+Chwa8DYELERGRriS3qILwnH/zB/8nudXvLe7y+yee7mUN1mYXOBepiIiI9CgjrzYdnlO3xm2svLqOw0W6H4qISA9g9YPz57uP11XC3jcZEBdmetqBE6c7uTAREZGeISu3xGN4sDn9Ys4szmz4HA/5CaeGjU2zcktafT0REZGOUlBW1eJARHOuHtuXAGvTS7T8rQbLF4wmLTmKz497CBCqA6GIiHRBheUtu1+WVNQyPTXebfxLR1/uqfs+hxzum8g0FfpbOnNws/fXBg0hfRER6fkautS2hmFAbrF7wG98/2j+tGCMx/vNbMtmvmHd6nzfa8/f4NXrSQpzL6CkopbKGpvL2AmTe6i/1SAiWD3CRKRzKEB4rrL6UZE4yW14gOU4w43DHk9T4EJERLqavPUv8Hu/v2A16ledLPZ7i180EyJ0OGDTgSIvVSgiIuJFMQPgvAvdhuMrchhhHHIb35N3ygtFiYiIeEHaAvex/hdBWG8GmHQgBDikAKGIiEiLPLEmp03hQYDzYkPb9Dna2FRERHxtX755iK8lAqjlCf8/k2ocAWDigFhWLZ7MzKHxeFrH+svLhjq772abBAijQvzpFa4uFCIi0vUUlLYsQJhfUtmhob+05CiWLxjd7OcFWC3OkL6IiPR8DV1qW2PcedFU17l/bzmhfwyzRyaeeZ5r9EAXwWke8M9w/7D9/2V2yT9Nr5N3VhfCwjL3DoSxoYEYrU1Aioi0kAKE57CsiKmm45dZP/N4jgIXIiLSpex8lQk77naGBxvc6vcWP/d7haZChGVVtZ1cXNditVoBsNlsOBzNbPEtIh3G4XBgs9XvHtXwf4cinW70Qrehqj6TCMD93rcnr9QbFYmIiHS+3iMgYRTEDoIZ98D/7YJF/4XBl9C/UXChsYMnFSAUERFpTm5RBZnZBW0+P/nrDoRt+RxtbCoiIr6018N3py1Zxvkzv1eZa/2YVQEPkG7ZS3pKDGnJUaxYNIG/LZpges7p6jOdKD4/5h4gHNI7XItIRUSky3E4HC3uQHistIoRfSJYvmA0VkvT97SWhv48hTqgvpvUzKHxrFo82RnSFxGRc0NrA+vn9400PTahfwyA83lu/Z3TeXx+Gvd9azj/HLWV3kaJ+0khsZwYvsj0846dqnJ5f/K0+z00LjygRXWLiLSF+puew3IipjDG4U+Q4bqIdJzxeZPnnWuBCxER6cIs1vpv/EzycM1F5MKD/DulpK4qICCA6upqHA4HFRUVhIaaL6AVkY5VUVHhDO0GBOgLHvGS4XPh7TshOBpGL4C0BQTGpPDFA+9BVZ3LVAUIRUSkR/neGxASy9krRWJCAwgP8qPsrPugOhCKiIg0b9OBk7RnP7J+XwcI2/I5DRubzhtn3k1YRESkM+3Nd//u1M9icOHgONZlF3o8b6hxhO9b3wEgwqjkxYBl+OcNgugrAbhwcByBfha37hY7vzoFwKnKWvLOWlQKkNo7vM3/LSIiIp2ltKqOGpOOTQFWi1sHersDjpdVM3tkIpcM+4p39xx3O88AZgyNZ8nMwS3uGNgQ6sgtquDTg0WUVdUSHuRPekoMSdF6nhQRORc1dKldsnKH2/3obA9dOZIP9rrfk6wWg9Fn3YuSY0KcG6Yx8QH4KAI2/AHsjbIVsx8lLioROOj2mXklrh0IT5iE8OPC1HleRDqPAoTnsOCwCDLto7nMupmjjjjesU3kbdskdjgGNnneuRa4EBGRLmzk1RSV1xD1zo/wM8486P25bi6P112Dpz1ADQPSU2K8VGTXEBERQVlZ/W6lRUVFhISEaJdSkU7mcDgoKjrTvTsiIsKH1cg5JTAMbl4LcYPrw/bU3xGHJ0bw6UHXjvJ7vjqFw+HQPUFERHqG0DjTYcMwGBAXys6jp1zGD51QRyMREZHmnB3Ab63H38vmzktT2/w52thURER8ZW/eKbexQfFh/OSSIXz8xUnTRagGdh72X+Hyu6U/tbDqRqguh7Hfw99qYXifCLYfKXE5d9fXz6w5x927DwIMSVCAUEREvC+3qIJNB05SVlVHeJAf6SmxZ4ITQGGZeffBYX0iyMotcRvPK6mkb1Qwx0rdz4sK8ee/t1/Y5tCfS6hDRETOebNHJvLKLUFc+ZePm5xXXWdny+Fit/GRfSIIDWwiauMfBDN+BSOvhreWQu4mGDgDRs0nsdR9UxiA/LM7EJbXuM2JDVWAUEQ6jwKE57D0lFgW267k6bpvkeUYiKeQRWPnYuBCRES6trj0BTy1NZcfFjyC1XDwp7or+F3dfJq6r81IjT/ndhkLCwvDMAwcDgfl5eUcPXqUmJgYBQlFOkFDp8+ioiLKy8uB+kXrYWFhPq5MzinxQ92GRvSJdAsQnjxdw/HSahIig7xVmYiIiE+YBghPnsZud2Cx6JlIRETEk/Cg9v2cbP1yDev/+gyVade38fra2FRERLyvssbGQZOu9cP7RDTZyeI71nWMs+S4f6DFD/qMcb49v2+kW4DwWGkVBaVVZHsIEKoDoYiIeFNWbglPrMkhM7vApZu8YcD01HiWft0hsKDMPCAxJjnqrAChAzDIK6mkzmZnv0mn34n91TFQREQ61qD45tdq/XX9lxSddg/yje/fwrxE/FC48R3YlgEp08EwiA8PwmoxsNkdLlPzT53pQFhRU0dFjc3t4+LCA1p2XRGRNlCA8ByWHBNC7yETWbu/oMXnnIuBCxER6fqmfPuH3PFMGf3J5Q9182gqPBhgtbBk5mDvFddFWCwW+vbty1dffeUMEZaXl2MYBlar1dflifQoNpsNR6NfUQzDoG/fvlgsFh9WJQIj+ph3wdyTd0oBQhER6fH6x4a6jVXX2ckvraJvVLAPKhIREeke0lNiMQxcFoy2xmLrW0y27qV21xsM9Z9IRt0stjkGo41NRUSkK8s+Xobd5N43PLH+O9bZIxNZtTiY5WtyWNsoWHHIkcABeyIplnzXEy9YCgkjnW9HJUUBh90+f9dXp/j8mAKEItLzvPnmm7z44ots3ryZY8eOERERwaBBg7jyyiu55ZZbiIgw/w2rvbZv387LL7/MBx98wNGjRyktLSUuLo7ExETS09OZNm0aV155pdZMnGX17nzToDzUPxuu3V/AhpwTLF8wmuo69zkAacmRztcRlPNGwL28ZruYooLeHDwRYXreiD6RbmMiIiLtUVZV1+yc3KJK0/EJ/aNbfiGLBcbf5HxrtRj0Dg8k76yOg9uOlJBbVEFyTAgnytxDiwBx6kAoIp1IAcJz3NKZg9mQc8L0Ye9s52rgQkREur605ChmffdHLFm5A2j6nvbA3BGkJUd5o6wuJzw83CVECPWd0urqmn9QFpG2aQgPhofrh33xvZF9zX9025NXysxhvb1cjYiIiHcNiHMPEAIcOnFaAUIREZEmJMeEMD01vlUbkjZIMgqZbN0LgL9h49vWT/i29RPetk3kR7X/1+z52thURER8ZW+ee1ckqO9A2CAtOYoViyaQW1TBpweLKKuqJTwojYB+P4C9z8L6x8FWDdH94eI7XD7n/CTz72p3Hj1l2oEwISKIyBB15RWR7qe8vJxrr72WN99802W8sLCQwsJCPvnkE/70pz/x6quvkp6e3mHXLS0tZenSpbzwwgsuG78C5OXlkZeXx9atW3nyyScpLi4mKiqqw67d3WXllngMDzZWY7OzZOUOrkvvZ3p8VN9I52Y0V1g3kmI5xp2WV6n95A2OfjWXQcYEvnAkuZwz3MNmqCIiIm1VXt32dZHjzmvfxmYRwf5uAcLsY2Vc/Fgm01PjmTXCfJ2OOhCKSGdSgPAcl5YcxfIFo5t96AuwWli+YPQ5G7gQEZGuz9NOn2er8bD72bkiPDycIUOGUF5eTmlpKTU1NdhsNl+XJdKjWK1WAgICiIiIICwsTJ0HpcsY2CuUQD+L246ee/JO+agiERER7+nvIUB48MRpLhgU5+VqREREupfWbEja2OWWTabjW+3Nb1iqjU1FRMSX9uabf2fa0IGwseSYEJJjzgq8T/05jLwa3r4T0m8Ff9eNawb2CiPY30plretvdDuPlpBt0oFwSII2KRSR7sdmszF//nxWr14NQO/evbn55psZPnw4RUVFrFy5ko0bN5Kbm8ucOXPYuHEjw4YNa/d1i4qKmDVrFlu2bAGgb9++XHXVVaSlpREZGUlZWRk5OTm8//77bN26td3X62meWJPT4me/GpudD/aZbzaTGBlMfHggx0urucq6wTnu76hhwOFVfBC4igur/8hRR7zz2AgFCEVEpIO1pAOhmZS4UHqFt70T4Ord+XxusjkMnOnmu/7zQtPjcWHqQCginUcBQnEJXKwx2T00JtSf5xdNVHhQRES6vMY7fb635zgP/m+v25x3dudzw5T+3i+uC7FYLERERBARoS9fRUTOJX5WC0MTwsk66rr4ZfdX5rtpi4iI9CQDYj13IBQREZGmNWxIeus/tnncuM3M5Vb3AKEdC//j4ibP08amIiLia2YdCPtGBRMV0opOELED4brXwDDcDlktBiP7RrD5ULHL+KcHi6iocd/4M7V3WMuvKyLSRTz33HPO8ODw4cNZu3YtvXuf6bTz4x//mDvuuIPf/e53FBcXc8stt7B+/fp2X3fhwoXO8ODPfvYzHnroIYKCgtzmPfLII+Tl5REWpr9jG+QWVZCZ3bru80eKKtzGQgOshAb60ScqmNCyg4y2fOk2Z7N9iEt4MDrEn8RI9/9/EhERaY+yqto2nTe+f3Sbr9nQzdfezPeodR4mxIYqQCginUetMAQ4E7gYnRTpdiwyOEA/0ImISLeSHBPC9y8awNh+UW7HPjtYxInyau8XJSIi0gWM6Ov+zPdVSSUlFTU+qEZERMQL7HbI20Hk1j+xIugJwPXHuIMKEIqIiLTI7JGJDOrV8oW1oVQSgvv3sPkxE3hm8WxmDo13y1MYBswcGs+qxZOZPTKxvSWLiIi0ic3uYL9JF8BhJt0Hm2USHmww0uS7WrPwIMCQ3upAKCLdi81m44EHHnC+f/HFF13Cgw2WLVvG6NGjAfjoo49477332nXdjIwM3n33XQBuvfVWHn/8cdPwYIM+ffrg56ceHA02HTjZqk1jPGno2NQnKpgrGnUfbOzftotc3g/vE4HRxH1TRESkLcqr29aBsHdE20PtrenmayYuvBUb14iItJKefsTFwPhwdpzVjeKr4krsdgcWix7QRESke5kzKpFtR0qc7y+w7GKedT3v7x7OgvQBvitMRETEFxwOJocXEmr9L/2MAn5dd5Pz0N68UqYMivNhcSIiIh0sdzN8+jQcyISKkwDMBIYauex39HNO2/lVCblFFSTHhPioUBERke6juq7lC19OE8wlNY8x1Mjlm9ZP+KZlE/0txzmWdBnjvt7YNLeogk8PFlFWVUt4kD/pKTEkReueLCIivnX45GnTIN/wPm0IEDbhfJMNvj1JTVCAUES6l/Xr15Ofnw/A1KlTGTt2rOk8q9XKkiVLuOmm+t+sVq5cyaWXXtrm6y5btgyAsLAwHn300TZ/zrmqrKptIYuzNQQI+0YFc8iewG57f0ZaDjmPVzv8+K9tkss5I/q0/L4oIiLSUm29tz394ZeM6BNxZpOz/90BVn+YtBiiz/N4Xlu6+TZmGBDTms73IiKtpAChuEiOCXa+jqKMsZYc1trGUlBWTYJaxIuISDcza0QCD/1vH0OMXH7p9zLTrVkAPPPZS5D+ax9XJyIi4iVHt8COf0DOB3zr1BG+5V8//ETd1Zyg/se4PQoQiohIT3O6EHb/y234IstO9tvOBAgLy2q4+LFMpqfGs3TmYNKSo7xYpIiISPdSfLq13esN9jv6sb+uH49zDaMsB3l6yjzn0eSYEIX4RUSky9mbX2o6PrwtHQibMKpvVIvmGQYMim95F2ARka7gnXfecb6eM2dOk3Mvu+wy0/Naa+PGjezfvx+AuXPnEhHRsX9vnwvCgzpmOXF8eP0608TIIP5qv5jXay5msHGUK60buMK6gSz7QEpxvbd19H1WREQEoLyNAcJam4MlK3ewanEwaZEVsPV5sNfBp8/A8Lkw5TboO87tvPZ2840OCcDPamn7B4iINEN/w8gZdjsjjUPcZv03rwXcx9bAxfwt4HF6U0RucYWvqxMREWm15JA6nol8gXcC7nKGBwG+ffJvvPTRPnKLdH8TEZFzQN522PI3OHXEZXiq5cy9cU/eqbPPEhER6d76XwiG1W34IssutzGHA9buL2D+05+wene+N6oTERHpdmrq7JRVt6cbhUH8kHT6JiR2WE0iIiKdYW+edwKEKXGhhAa4P7eerV9MCCEB2h9eRLqXXbvOfAc3YcKEJucmJCSQnJwMwPHjxyksLGzTNT/88EPn60mT6rvbvf7668yZM4eEhAQCAwPp06cPl19+Oc8//zx1dR3Tba8nSU+JxTDa/zkNHQj7RJ1pZpHjSOK3dd/lgurl3FV7s9s5Izq406+IiAhAWVVtm8+tsdlZvianPjRo//rfDQ4b7Hkdnp0Bm54yuV77/n0RF6bugyLSuRQglDOyXuaSj+Zzh/8qxllysBr1Efip1iyOKkAoIiLdkV8Qkyz7nPe0BolGEfmrf8/Fj2VyU8ZmsnJLfFOfiIiINwz+hunwNOsO5+vdHhbFiIiIdFtBEZA80W14omU/gZh3T6qx2VmycoeeEUVEREyUVJjfP60tXF0aYLWwZObgjixJRESkU5h1IAwP9CMpOth1cPfrkJ/lNrelLBaDkX0jm503pHd4m68hIuIr2dnZztcDBgxodn7jOY3PbY0tW7Y4X/fu3Zurr76aq6++mnfeeYfjx49TU1NDfn4+b7/9NjfddBNjx47l4MGDbbrW0aNHm/yTn989NylLjglhemp8uz+nIUDYNyrY7ZgDC6fO6j4Y5G8hpZe67YqISMdr34Zo8Gn2Yeyb/+Z+wLBA6mVuw+3t5hsbGtiu80VEmqMAoZyRMs10eLplB0eLKr1bi4iISEfwC2D7kP8zPXSr35vEOk6py4SIiPR80f0hLtVt+GLLTqzYADhQWE5ljc3LhYmIiHSygTNc3tY4rOxwDKKX4bnzrnM3UREREXFR5CFA+N2JyQRYm/7JOcBqYfmC0aQlR3VCZSIiIh1r11H3Z8ZhiRFYLI1C89Xl8N//g2cuhufnwL63wN7671fPT2o+QJiqAKGIdEMlJSXO13Fxcc3Oj42NNT23NRqH9u69915ef/11AgIC+MEPfkBGRgb/+Mc/+PnPf05MTAxQ3yVx+vTpFBUVtfpaycnJTf6ZONF9Y7PuYunMwc0+4zXws5hvKGPWgbApqQkRWD18loiISHuUt7Mj4HzLOiw1JhtyD/t2/Vqcs7S3m29cuAKEItK5FCCUMyKTcMQPdxu+0LKbvJOeF9WIiIh0VVm5JdyyJZHP7K6hiSJHGL+t+w4lhALqMiEiIueAs7oQ1jks7Hf0I5b6LzrtDnhy3RfkFqn7vIiI9CADZ1AbPZjn62ZxY82djK5+lu/W/Jqjjl5NnrY2u4CjxbonioiINFZ02jxAeNHgXqxaPJmZQ+PdFscYBswcGs+qxZOZPTLRC1WKiHR9b775JvPnz6d///4EBQURHx/PlClTeOyxxygtNVmU2EG2b9/OnXfeyZgxY+jVqxeBgYH07duX8ePHc9ttt/Gvf/0Lm+3c3mAsK7eE6577lJMm97y8U5WuvyNmrYSqr9fRHN4Ir1wHy0fD5++16pqjkqKanTMkQQFCEel+ysvLna+DgoKanR8cfCZoVlZW1qZrFhcXO19nZ2cTHR3Npk2bePbZZ7nhhhtYuHAhy5YtY8+ePQwfXr9G8vDhw9x9991tul5PlZYcxfIFo/G3Np1+8Lca/PDiFNNjDQHC6BB/Av2aX6I8ok9E6wsVERFpgbJ2Bgg/to/gi8TLwXJWZ8Ept5vOb28337iwgDafKyLSEu3rkyo9jjH4UijY6zIWblQSVLAdGO+bokRERNroiTU51NgcPGK/ljcC76Xa4c/fbLN5qu7blH4dHmzQ0GVixaIJPqpWRESkEw3+BuxaBYO/QXZ4OvPfC3S7F/557Rc8mfkF01PjWTpzsDpDiIhI95c0njcu+DcP/Gtnq05zOGDTgSLmjQvppMJERES6n+LTtabjMaEBpCVHsWLRBHKLKvj0YBFlVbWEB/mTnhJDUrTupyIiUB+kuPbaa3nzzTddxgsLCyksLOSTTz7hT3/6E6+++irp6ekddt3S0lKWLl3KCy+8gMPhcDmWl5dHXl4eW7du5cknn6S4uJioqKgOu3Z3snp3PktW7qDGZjc9frS4kvlPf8LyBaOZPbw3fPqM+6SSIxAS06rrnt9XHQhFRDqK3e76d/jjjz/OmDFj3OYlJCTw8ssvM3r0aAAyMjL47W9/S0REy0Nsubm5TR7Pz8/v1l0IZ49M5NnrrSx6frPHOY/NS6Oq1nzzgV5h9QFCwzDoGxXMgROnm7yeAoQiItJZyqvbFyDMdvRjx/jfMmjQY/Dp07A1A3qPgCTPmYqlMwezIeeEx+fLpsSFqQOhiHQuBQjF1eBLYeMfqXb48al9GJn20WTaR2OrSOE+X9cmIiLSCrlFFWRmFwCwwzGIX9cuItM+pslOEw1dJrSoRUREepz+F8PPssEw2L3tKKVkmU5zOGDt/gI25JyoXwyjDhEiItLNtXVn0bIq85CEiIjIuaqowrwDYUyov/N1ckwIyTH6blVE5Gw2m4358+ezevVqAHr37s3NN9/M8OHDKSoqYuXKlWzcuJHc3FzmzJnDxo0bGTZsWLuvW1RUxKxZs9iyZQsAffv25aqrriItLY3IyEjKysrIycnh/fffZ+vWre2+XneVlVvSZHiwQY3NzpKVO1h9eRUpJ3PcJyRNaHIRqZnzYkMID/Lz+OzqbzUYEBdqekxEpCsLCwtzdgSsqqoiLCysyfmVlZXO1+HhbQtONz4vNDSU6667zuPctLQ00tPT2bRpE9XV1WzcuJHLLrusxddKSkpqU43dSXPrZmrq7BSWVZseiw8/E37o04IA4fBEBQhFRKRztPf3PsOA9JQYiAyBSx+EqT+H8oImz2no5tuS58yzqQOhiHQ2BQjFVfJEMvo9yrLP46kkyDnsV1JFnc2On7X5lvIiIiJdwaYDJ2m8keqLtkubPUddJkREpMey1D/LZeWW8MvXdjU7vWExzKrFwepEKCIi3Vp4UNu+Ag8P8m9+koiIyDmk+LR5gDA6pNGiloL9sOffMPIq6JXqpcpERLq+5557zhkeHD58OGvXrqV3797O4z/+8Y+54447+N3vfkdxcTG33HIL69evb/d1Fy5c6AwP/uxnP+Ohhx4iKCjIbd4jjzxCXl5es+GOnuqJNTktXtRZY7Pz0dYdpASEQU2568FJi1t9bcOoDwjuPHrK9HiA1cK+/FJ9Rysi3U5UVJQzQHjixIlm7zEnT550ObctoqOjna9HjRpFQEDTC/DHjx/Ppk2bAPjyyy/bdM2erLnAxc6vSrAYhtu4xYDYRhvN9Ily/7fH2fOHJihAKCIinaOsnR0IZ6TGu4bqA8Pr/zRj9shEVi0OZvmaHNZmF7isY22KOhCKSGdTGkxcWf0pO+8Sl/AgQJ3dwXEPO8aIiIh0ReoyISIi4q61i2GWrzHZSVtERKQbSU+JxWQdS5Ocu4mKiIiIU5FJgNAwIDK4Ueh+5yvw4aPw5ET4yxRY/xic1EJcETm32Ww2HnjgAef7F1980SU82GDZsmWMHj0agI8++oj33nuvXdfNyMjg3XffBeDWW2/l8ccfNw0PNujTpw9+fufeHuS5RRVkZjfdPeJs9+dN5KubtsKs30DUefWD4X1g+NxWX3/17nx2f2UeHgQ4XWNj/tOfsHp3fqs/W0TEl1JTz2wocvDgwWbnN57T+NzWGDp0qPN1ZGRks/MbzyktLW3TNXuy5tbc7PqqlIJS9/Wko0NOYv3jSHjv13B8D32igpv8nIG9wggOsLarVhEREU/KTe5nlhb+bhhgtbBk5uA2XzstOYoViyaw/s7pzBvbsu7FsQoQikgnU4BQ3CTHmHddyi2q8HIlIiIibacuEyIiIq7ashhmbXYBR4v1LCgiIt1XckwI01PjW3WO226iIiIiQnGFe4AwMtgfP+vXPzc7HLDn9TMHC/bA2ofgqQugRs+VInLuWr9+Pfn59eGvqVOnMnbsWNN5VquVJUuWON+vXLmyXdddtmwZAGFhYTz66KPt+qyebNOBky3uBNHA4YBPvqqDyT+CJdvhuy/DpQ+CtXW/MWbllrBk5Q7szVy/xmZnycodZOWWtK5QEREfGjVqlPP15s2bm5x7/PhxcnNzAYiPj6dXr15tumZaWprz9alTnsPZZnNaEjg81zQXINyXX0reqUq38Xn+G6EsDz5eDk9N4Yad1/ED6/8Ix/y5cHgfdR8UEZHOY3Y/m5wSS4C16QhNgNXC8gWjO6QbfHJMCPd+ezhB/s3HduLCmu6gLCLSXgoQipukaPNdX44Wuz/wiYiIdFXqMiEiIuKqrYthNh0o6pyCREREvGTpzMHN/hDYoL27iYqIiPRUZh0IY0IaLWjJ2w7Fh9xPHDILAhTMF5Fz1zvvvON8PWfOnCbnXnbZZabntdbGjRvZv38/AHPnziUiQgvzPWkuHOH5vNr6FxYrDL0cRs1r9Wc8sSaHGpu9RXNrbHaWr8lp9TVERHxl9uzZztfN3dPefvtt5+vm7pVNueyyyzC+XiSya9cuamrcn2Ea27Jli/N1W7se9mTOe50HNXV2ky66Di6pXecyEl2azZ1+r2DB/J43QgFCERHpJHU2O5W1NrfxYYkRrFo8mZlD493WlxoGzBwaz6rFk5k9MrHDaokI8mf2iIRm58WpA6GIdDIFCMWNp9211YFQRES6E3WZEBERcdXuxTAiIiLdVFpyFMsXjPbqbqIiIiI9jVkHwujQRgHCxt0HGxtxZSdVJCLSPezatcv5esKECU3OTUhIIDk5GajvxlRYWNima3744YfO15MmTQLg9ddfZ86cOSQkJBAYGEifPn24/PLLef7556mra9v3hj1BeJBfG89rXbfBs+UWVZCZXdCqc9ZmF3C0WOt2RKR7mDp1KgkJ9Yvk161bx7Zt20zn2Ww2li9f7nz/3e9+t83XTEpKYurUqQCcPn2al156yePcrKwsNm3aBEB4eDgXXHBBm6/bU5W24PfBs7vojjeyibcdc5uXaR/DKcJMPyPI39qm+kRERJpTXm3+rBsW5EdachQrFk1g/Z3TeXx+Gvd9aziPz0/jo59PZ8WiCZ3yW+H88cnNzvnRP7ap+7yIdCoFCMVNfHig6WIadSAUEZHupjVdJgZZj/GzSaGdXJGIiIjv+GoxjIiISFcwe2QiqxZP5sJBcYRSyQzLNq6zvu88PiAutMN3ExUREelJik+7Lx6NbtyBsOY0WANcJ/iHwuBLO7kyEZGuLTs72/l6wIABzc5vPKfxua3RuKNS7969ufrqq7n66qt55513OH78ODU1NeTn5/P2229z0003MXbsWA4ePNjq6xw9erTJP/n5+W2q35vSU2LdOk40xzAgPSWmXdfddOAkDkfz8xpzOGDTgaJ2XVdExFusViv33nuv8/31119PQYF7cPquu+5ix44dAFxwwQXMmjXL9PMyMjIwDAPDMJg2bZrH6z7yyCPO13fccQfbt293m3P8+HGuvfZa5/slS5YQHBzc3H/SOactG5Nebv3UdPzftgs9nvPgf/eyenfX/zeDiIh0P57uZY3XwCTHhDBvXBI3XjCAeeOSSAp1gN29a2FHmJwSS0xIQJNz1u4vYP7Tn+jeKCKdpm2rB6VHs1gM+kYHc/DEaZdx7WQmIiLdTUOXiSUrd1Bjs5vMcDDFsoebrO8ww7oDy5c3wbDfe71OERERb2hYDNN4YUogNUy27GWEcYgnbVe4ndMRi2FERES6hIJ9pB38Ly9a1lIX+Cn+ho0qhz+rbFOpJoBJA2LUeVBERKQJRafdOxDGhDbacOabf4CZ90H227Dn3/DlWki9DAJCvFiliEjXU1JS4nwdFxfX7PzY2FjTc1ujcXDv3nvvJTs7m4CAAK6//nouvPBC/P39ycrK4rnnnqOoqIhdu3Yxffp0tm3bRkxMy78LbOiW2J0lx4QwPTWetftb3g1wRmo8SdHtu7+1JZRRf17z3aBERLqKm2++mX//+9+8//777Nmzh7S0NG6++WaGDx9OUVERK1euZMOGDQBERUXxzDPPtPuakydP5he/+AXLli2juLiY9PR0brjhBuf9b8eOHc77H8D48eO555572n3dnqgt96rt9sF8I6aUpJItYK+/Z51yhJBpH+3xnFqbgyUrd7BqcbC+nxURkQ7lMUAY2ER8ZsMfYWsGDP82jLgS+k0GS8d0y9311SlKKt2/Yz1bjc2ue6OIdBoFCMVUklmAsKgCju+F+GG0egs2ERERH6nvMhHM8jU5rM0ucIYmrrBs4Ba/txhmyXXOdWStxJhxD4QoKCEiIj1Pw2KYvfv3cal1C9MtO5hs2UuQUf8D3mu2izhGrMs5HbEYRkREpEvY+x9Y9xsMwP/rrzaDjFrGWT7nY/tI8k9V+bQ8ERGRrqyyxkZlrfvO29GhZ+2YHRwFoxfW/6kogppy7xQoItKFlZef+bswKCio2fmNOyCVlZW16ZrFxcXO19nZ2URHR7NmzRrGjBnjHF+4cCE/+clPmDlzJnv37uXw4cPcfffdPP300226Zne2dOZgPvy8EJu9+ZaAAVYLS2YObvc1w4PatlyrcacMEZGuzs/Pj9dee42FCxfy3//+l2PHjvHggw+6zUtKSuKVV15hxIgRHXLdRx99FKvVyrJly6ipqeHZZ5/l2WefdZs3a9YsVq5c2aL787motA2h9TftU7h02o9JGhLGUyueIeFYJiWOMGpo+v5VY7OzfE0OKxZNaGu5IiIibsqrPXUg9PA85nDA3jfgdAFsfq7+T1hvOP8auPShdtfzxJocWvDYCejeKCKdx+LrAqRralggGkolsyyb+Y3fs/yr6gfw1GQ4+aWPqxMREWmdtOQoViyawPo7p/P4/DTO7xvJJMs+l/AggFFbAdte8FGVIiIinW/pzMHM9NvF//N/genWLGd4EGCaNctlbkcthhEREekSBs4wHb7YsguAYwoQioiIeFRcYb4zdkxIgOk4UL9JW1S/TqpIRESaYrfbXd4//vjjLuHBBgkJCbz88svO9xkZGZSWlrb4Orm5uU3++eyzz9r+H+FFaclRjGlBV4cAq4XlC0Z3SAeI9JTYVu/bbRiQnqJNUEWkewkPD+ett97ijTfe4KqrriI5OZnAwEDi4uKYNGkSy5YtY/fu3UyZMqVDr/vwww+zdetWbr/9doYOHUp4eDhBQUH069eP7373u7z99tusXr2a6OjoDr1uT9LWbrm9wgLJrfDjt0eH85PaH/NA3Q0tOm9tdgFHiyvadE0REREz5dXmYfgwTwHCgr1w4vOzPuQ4nDzQ7lpyiyrIzC5o1Tm6N4pIZ1AHQjGVHBPMFMtuMvyXEWCctaNoznsQN8g3hYmIiLRDckwIyTEhDOwVyi+ems0Cv0y3ORUbnuJk6k0k94r0QYUiIiKdKy05iqK518L//up2bJoli3/a6sMVHbkYRkREpEvoMxYCI6H6lMvwRZadPMoC8k5V+qgwERGRrq/otHmA0K0DoYiIuAkLC3N2BKyqqiIsLKzJ+ZWVZ55NwsPD23TNxueFhoZy3XXXeZyblpZGeno6mzZtorq6mo0bN3LZZZe16DpJSUltqq+rsdsdHDxx2uPxq63rmRVbwICLFzJ4eO8OuWZyTAjTU+NZu7/lC0hnpMY7NwMXEelu5s6dy9y5c9t8/qJFi1i0aFGrzklLS2P58uVtvua5rrSy9R0IAXqFB7LpwEkcLeyw1MDhgE0Hipg3Tvc6ERHpGJ7C8GGBHuIze/5tPj7iynbXonujiHQV6kAoppKiQ9hj748Vu/vBnPe8X5CIiEgHGp0cRWCfkay3jXI7Zq0s4vY/ZHBTxmaycku8X5yIiEgnmz5hNJUxw93GL7Tswp86Jg2IYdXiycwemeiD6kRERDqJ1Q9SLnYbjjeKCaGKsqo6yqvbtqu2iIhIT9emDoQiIgJAVFSU8/WJEyeanX/y5EnTc1ujcTelUaNGERDQ9N/X48ePd77+8ssv23TN7mzfsVJOmoTlJw6I4fH5aTzSbyuXlr7O4P/Ogz8Mh7d/Doc/bvd1l84cTIC1Zcu2AqwWlswc3O5rioiItFSbOxCGB7b53LKqtoUWRUREzHi6H4UH+bsPOhzmAUK/IEid3Wm1NH+e7o0i0rEUIBRTSdHBnCKMbQ6TLyAPb4Tqcu8XJSIi0kEMw2BMvyj+ZjvzcFfoiOT3tfOYUr2cHfZBrN1fwPynP2H17nwfVioiItI5gofPchurxp/zjGP8aPogdR4UEZGeKWU6WAPIi5nEb2oXMKf6ESZW/4UKggA4pi6EIiJe8+abbzJ//nz69+9PUFAQ8fHxTJkyhccee4zS0tJOu+727du58847GTNmDL169SIwMJC+ffsyfvx4brvtNv71r39hs9k67frdlToQioi0XWpqqvP1wYMHm53feE7jc1tj6NChzteRkZHNzm88pzPvw13VRznmwc4fXpTCvMEWAvM3nxksy4fPnoG1D7X7umnJUSxfMLrZEGGA1cLyBaP1na2IiHhVWXXrAwvB/lbCAv0ID/LQ2akZpoEOERGRNvIcIPRwn7rstzDmexAUdWZs8DcgMLzdtejeKCJdRdv+NpIeLzm6vt1tpm00Eyyfux601cDB9TB0jg8qExERab+s3BJWfnaEOnsa/7NNZK1tLG/ZJ1OD6wNXjc3OkpU7WLU4WD/KiYhIzzL4Uhwb/kiWPYV19jQybaPZ5UjBjoX8EoUnRESkh0r7LqQtYOfnp3jmpW1uh/NPVTEovv0/AoqIiGfl5eVce+21vPnmmy7jhYWFFBYW8sknn/CnP/2JV199lfT09A67bmlpKUuXLuWFF17A4XC4HMvLyyMvL4+tW7fy5JNPUlxc3OaOTz1VsYcAYYwChCIizRo1ahSrV68GYPPmzUyfPt3j3OPHj5ObmwtAfHw8vXr1atM109LSnK9PnTrV7PzGc1oSOOxpNpgECP0sBukDY2H7c+YnDb+iQ649e2QiqxYHs3xNDmuzC2j8zxTDgBmp8SyZOVi/U4qIiNe1pVNSr/BADMMgPSUWw4CzHr+bZBiQnhLT6muKiIh4Uu4hDG8a5jMMGDSz/s83/wAHPqzvSNhBWQndG0Wkq1CAUEzFhQUQ5G9hXd1ofs6rAOQ7YjgUPYXJsxbAgIt9XKGIiEjbPbEmh1qbA7Dw49r/a3Jujc3O8jU5rFg0wSu1iYiIeEXSRE7euocr/rDD7VD+qSrv1yMiIuINAaEAJESahyDyS3QPFBHpTDabjfnz5ztDFL179+bmm29m+PDhFBUVsXLlSjZu3Ehubi5z5sxh48aNDBs2rN3XLSoqYtasWWzZsgWAvn37ctVVV5GWlkZkZCRlZWXk5OTw/vvvs3Xr1nZfrycqqjBfbBMTogChiEhzZs+ezWOPPQbAO++8w89//nOPc99++23n6zlz2r5I8bLLLsMwDBwOB7t27aKmpoaAAM9/ZzfcI6HtXQ+7q6paG58dKnIbH9svmrBAP9j7H/MTh32rw2pIS45ixaIJ5BZV8OnBIsqqagkP8ic9JYakrzf/FhER8SaHw2EaILRaDGx2z8mHXuGBACTHhDA9NZ61+wtafM0ZqfG674mISIcqN7mXWYz6jrlNsvrD4Evq/3QQ3RtFpKtQgFBMGYZBUnQIewvO48Ha69hoH8l+RzLjA2P417Apvi5PRESkzXKLKsjMbvmDGMDa7AKOFlfogUxERHoOqx8xvfrgb836OlR/xjEFCEVEpIfrExlkOq4QvYhI53ruueec4cHhw4ezdu1aevfu7Tz+4x//mDvuuIPf/e53FBcXc8stt7B+/fp2X3fhwoXOYMTPfvYzHnroIYKC3O8FjzzyCHl5eYSFhbX7mj2NWQdCq8UgvHAbWCzQdyxYmll4IyJyjpo6dSoJCQkcO3aMdevWsW3bNsaOHes2z2azsXz5cuf77373u22+ZlJSElOnTmXdunWcPn2al156iZtuusl0blZWFps2bQIgPDycCy64oM3X7Y42Hyqips7uNn7h4Diw2yFmABzfA9WNOjkmp0NEYofXkhwTQnKMfosUERHfq6ixmQYF05Ii2XakxON58V8HCAGWzhzMhpwT1Njc77NnC7BaWDJzcJtqFRER8cQsDB8W6IdhGD6oRvdGEekaLL4uQLqupOhgwGCFbQ77Hf0Ag6PFlb4uS0REpF02HTjZqlbwUN86ftMB991HRUREujOLxSDBJECRd0rPfSIi0rPFhgXiZ3H/cTBf90ARkU5js9l44IEHnO9ffPFFl/Bgg2XLljF69GgAPvroI9577712XTcjI4N3330XgFtvvZXHH3/cNDzYoE+fPvj5af/VsxVVuAcIo0P8sXz4G1hxCfw2BVbdCNv/AWXHfFChiEjXZbVauffee53vr7/+egoK3De6vOuuu9ixYwcAF1xwAbNmzTL9vIyMDAzDwDAMpk2b5vG6jzzyiPP1HXfcwfbt293mHD9+nGuvvdb5fsmSJQQHBzf3n9SjfJRzwnT8wsFx9SH5K/4Cd+bAwlUw+joIioIRV3i1RhEREW8zC1wATB4Yy9mZi3v9/s4yv7/yDcsW+oSeWYyTlhzF8gWjCbA2vUQ5wGph+YLRpCVHtbdsERERF2XV7vez8CB/H1RST/dGEekK9AuYeFQfIHR1vKyK6jobgX7aRVRERLonT190Nn9ebQdXIiIi4nuJEcHkFrmGJdSBUEREejqrxaB3RBBflbjeA9WBUESk86xfv578/HygvhOTWeclqA9ZLFmyxNklaeXKlVx66aVtvu6yZcsACAsL49FHH23z55zrzDoQJgbb4fDG+jdVJbDn9fo/yZPg++0LfoqI9DQ333wz//73v3n//ffZs2cPaWlp3HzzzQwfPpyioiJWrlzJhg0bAIiKiuKZZ55p9zUnT57ML37xC5YtW0ZxcTHp6enccMMNXHjhhfj7+7Njxw6ee+45iorqN9AcP34899xzT7uv292YBQjDg/w4v2/kmQG/QBhyaf2fuj+AvW2/NYqIiHQXntbHJEYG0ycyiK9K6r9H9aOOq63riTQq+A7rqMr6M6eKpxKZfj0M/zazRyayanEwy9fksDa7wGWzb8OAGanxLJk5WAEJERHpFGb3s/Ag30ZndG8UEV9TgFA8So4OcRtzOCCvpIoBcaE+qEhERKT92voQ6MvdZ0RERDqLWQdCBQhFRORckBjpHiDUPVBEpPO88847ztdz5sxpcu5ll11mel5rbdy4kf379wMwd+5cIiIi2vxZ57oikwDhFL/9YHMfZ9A3vFCRiEj34ufnx2uvvcbChQv573//y7Fjx3jwwQfd5iUlJfHKK68wYsSIDrnuo48+itVqZdmyZdTU1PDss8/y7LPPus2bNWsWK1eubLJLb09UWFbNvvxSt/ELBsbh56kjhF8AENC5hYmIiPhYqYeNuQ8UlrtswjbBkk2kUeF8H0QNQUfe51BAH/oP/zZQ321pxaIJ5BZV8OnBIsqqagkP8ic9JYYkk/WpIiIiHaXctAOh76MzujeKiC/5/m9B6bI83YSOFlcoQCgiIt1WekoshoHL7i3NMQxIT4npvKJERER8JNEkQFhWXef8glJERKSnMgvR552qNJkpIiIdYdeuXc7XEyZMaHJuQkICycnJ5Obmcvz4cQoLC+nVq1err/nhhx86X0+aNAmA119/neeee45t27ZRXFxMbGwsY8aMYd68eXzve9/Dz08/nZoprnAPCk6ybzefPGhmJ1cjItI9hYeH89Zbb/Gf//yHv//972zevJmCggLCw8MZOHAgV111FbfccguRkZHNf1grPPzww1xzzTWsWLGC999/n6+++ora2lri4+OZMmUK119/vUt4/1zy8Zfu3QcBLhwc5+VKREREuhZPHQj//slh7I3W2lxq2WI67579/bgzt8Sle1JyTAjJMQpFiIiI95SbBOLDArvO97+6N4qIL3SdvwWly0mOCTYdzy3SQhoREem+kmNCmJ4az9r9Bc3MdDDO+Jzv+b3Px/1+pN1dREQ6ic1mY9++fWzZsoWtW7eyZcsWsrKyqKysf+644YYbyMjI6LDrlZWV8d5775GZmcm2bdvIycmhpKSE4OBg+vTpw8SJE1m4cCGzZs3CMIwOu25XZRYghPoOTAoQiohIT9YnyvW7zyjK8KuyU15d16V+PBQR6Smys7OdrwcMGNDs/AEDBpCbm+s8ty0Bwi1bzixk7N27N1dffTWvv/66y5z8/Hzy8/N5++23+cMf/sB//vOfFtV3tqNHjzZ5PD8/v9Wf2VU4HA6KT7svHh1WvdN9ckgcJI7u/KJERLqxuXPnMnfu3Dafv2jRIhYtWtSqc9LS0li+fHmbr9lTvbPrmOn4RQoQiojIOc5TB8K6xulBHHzDutVtTokjlE/qhrB8TQ4rFjW9gZCIiEhnKjMLEDZeB+NwQEURhMZ6sSoREd/SSgjxqKkOhCIiIt3Z0pmD2ZBzghqb3e1YCFXMtW7keuv7DLMcAWBy7GjgW94tUkTkHHHNNde4LeDsLL///e/51a9+RVVVlduxsrIysrOzyc7O5sUXX+Siiy7ipZdeol+/fl6pzVcSIt03jommlNKDW6H3NO8XJCIi4iUDA4qZa9nAREs24y3ZpFqO8nTdtzh26lsMig/3dXkiIj1OSUmJ83VcXPOL8mNjzyzaaHxuazQO7d17771kZ2cTEBDA9ddfz4UXXoi/vz9ZWVk899xzFBUVsWvXLqZPn862bduIiYlp1bWSk5PbVGN3cLrGZvo96sq05/npkJPwxRr44gMo3F/ffdBi8UGVIiIiLZeVW8ITH3zO2uxCt2NB/hZKKmo5T+tHRUTkHOapA2FjQdSwzpbGJdZtJBjFzvG19jHYsLI2u4CjxRXarFtERHymrNo9QBge1Cg6k78Dnp0JAy6GEVfA0G8pTCgiPZ4ChOJRdIg/IQFWKmpsLuO5xY06ENZWgr95p0IREZGuKi05iuULRrNk5Q6XxS+B1PBR4FJijTKX+b2/eAVq7wN/8y5NIiLSdjab6/NGTEwMsbGx5OTkdPi1Pv/8c2d4sG/fvlxyySWMGzeO+Ph4qqqq2LRpEy+99BLl5eV89NFHTJs2jU2bNhEfH9/htXQViZFBhFPBRMs+plj2MsWyh2GWI5R+NADSd/i6PBERkc5ht3PVp9fwnQDXZ7/xlmzyT1UpQCgi0gnKy8udr4OCmv+OLTj4zG9PZWVlTcz0rLj4zALG7OxsoqOjWbNmDWPGjHGOL1y4kJ/85CfMnDmTvXv3cvjwYe6++26efvrpNl2zJyo+XWM6HhEWDgPTYOAMmPUwlOSCzXyuiIhIV7F6d77b74ONVdXamf/0JyxfMJrZIxO9XJ2IiEjXYNax6WxVBHJP3fe5p+4mRhkH+YZ1C9+wbON92zigvqnTpgNFzBunAKGIiHhfdZ2Nmjr3577wwEbRmf1vg8MGBzLr//z3p5AyFb71BET17M3GReTcpS0gxSPDMEh22wHGgb3wc9j0FLx4FSzrDye/9EV5IiIi7TJ7ZCKrFk9m5tB4jK/HqgngY/sI98kVJ2Hvf7xan4jIuWLixIncddddrFq1igMHDnDy5EnuvvvuTrmWYRhceumlvPfeexw5coSMjAxuv/12vvOd73DDDTfw1FNPsXv3blJTUwE4ePAgd911V6fU0lUkRgXxgH8GKwJ+x/f93nF23404fRBK83xcnYiISCexWKjoPd5t+HzjSwpOlni/HhER6RR2u+sCkccff9wlPNggISGBl19+2fk+IyOD0tLSVl0rNze3yT+fffZZ2/4juoAiDwHCmNAA14GoZIgd6IWKRERE2iYrt6TJ8GCDGpudJSt3kJVb4p3CREREupiWdCA8w2CXI4Xf113DZTWP8o59Yhs/R0REpOOUewjDu3Qg/HKt60GHDY5sgpC4TqxMRMS3FCCUJiVFn9nh9XzjSz4K+D+eLPohrL4LvlwDdVXwxRofVigiItJ2aclRrFg0gb9ce2bh0It13zCfvPk5L1UlInJuufvuu/nNb37DvHnzGDBgQKde6+GHH+bdd9/lG9/4BhaL+ePweeedxyuvvOJ8/8orr1BRUdGpdflSXGggnzpMwvMABz/ybjEiIiJeZO0/xW0swLDh+GqrD6oREen5wsLCnK8bOsM3pbKy0vk6PLxtnWEbnxcaGsp1113ncW5aWhrp6ekAVFdXs3HjxlZdKykpqck/iYndt4NRUYV5gDD67AChiIhIF/fEmpxmw4ORlPOc/2PMZS0r3tPzoYiInJta0oHQM8P5KjzIv/3FiIiItIGne1lYQwfCymLI2+Y+of+FEKDuuSLScylAKE1KjjlzE8x19KKvccJ90hcfeLEiERGRjpeecmbXmM8cQ8m2J7lOCI6GfpPA1p4vSUVExNdiYmJaNC8tLc3ZhbCiooIvvviiM8vyKYvFICd0rPnBg+u9W4yIiIgXBQ+6yHQ8omCzlysRETk3REVFOV+fOGHyW9NZTp48aXpua0RHRztfjxo1ioCApgNv48ef6U775ZdftumaPVGxpw6EIQoQiohI95FbVEFmdkGz8y61buES63Ye8/8rvzsyn6rn58LWF6Cqdd2JRUREurPSyvZ3DjQMSE9p2W+zIiIiHa282kOAsCHcfnA9OEw2mEmZ3olViYj4ngKE0qTGHQiLiWCnI8V90sH1UNv8brEiIiJdVXRoANEhDTufGbxoq+9CeCBgKFzxFPx0H1z6EFj9PH+IiIj0KBEREc7XjTtf9ESWqH4ctse7HzikAKGIiPRc1qSxVFP/HPiVI5Y3bFP4Ve1NvGe92MeViYj0TA2btAAcPHiw2fmN5zQ+tzWGDh3qfB0ZGdns/MZzSksVEmhQ5ClAqA6EIiLSjWw6cBKHo/l5cyyfOl/7GzaCDq+Dt5bA6cLOK05ERKSLMevaFOTfuqXGM1LjSYpWBycREfENTx0Iw4O+Xv8ZMxDSfwS9hrpOGDijkysTEfEtrYKXJtXZXL9B/dCexmjLWbuu1lXCkY910xQRkW4tpVcYWw8XA/Bv24Vk2QdSHjaKzNHTfFuYiIh4XU1NDZ9//rnz/XnnnefDajpfYlQwH381gvMsBZx0hPOJfTjbraP49XW3gsNRv0WoiIhIT+MXyG+j7mP1sXC+opdzOPV0uA+LEhHpuUaNGsXq1asB2Lx5M9One97J+fjx4+Tm5gIQHx9Pr169PM5tSlpamvP1qVOnmp3feE5LAofniuIK8wBhtAKEIiLSjXhaPNpYKJVcYNntfiBhFMQO7ISqREREuiaz+2ZCRDB5JZXU2Ey6NZ0lwGphyczBnVGaiIhIi5RVmXfTDQ/8OjqTMBJm/6b+dWkeHFgHRzdDr7ZtZici0l2oA6F4tHp3Po+/l+0y9qHtfOdrm8Ngm30wOcNvhxiTzoQiIiLdSEpcqPP1aYLZ5UjhSFEFNXXNf/kpIiI9y8svv+xcODp27FgSEhJ8XFHnSowM4jnbHGZXP8r46qe4rXYpK6pmcDq8v8KDIiLSox2Pv8AlPAiQd6pndx4WEfGV2bNnO1+/8847Tc59++23na/nzJnT5mtedtllGF8/0+zatYuaGvMgXIMtW7Y4X7e162FPVHTafbGNv9UgNMDqg2pERETaxtllogmTLPsIMGzuB4bN7YSKREREuq5Sk9BFfEQgyxeMJsDa9JLjAKuF5QtGk5Yc1UnViYiINK+82lMHQn/3wYg+MHohfPMPWiMjIj2eAoRiKiu3hCUrd1Bnd+1AuMMxiH/UzeS2mtsZW/0MV9U8wOVZF5BVHuWbQkVERDpISq8wtzGb3UFucYUPqhEREV8pLCzkF7/4hfP9Pffc0+bPOnr0aJN/8vPzO6LkdkuICOJLR1/2O/rhaPQ1Qf6pKh9WJSIi0vn6RAW7jZVV1Xn8UVFERNpu6tSpzs1Z1q1bx7Zt20zn2Ww2li9f7nz/3e9+t83XTEpKYurUqQCcPn2al156yePcrKwsNm3aBEB4eDgXXHBBm6/b0xSfPhO8jKaUAGqJDglwhjNFRES6g/SU2GbXgUZQQaHDpAvxkFmdU5SIiEgXZdaBMCLIj9kjE1m1eDIzh8a73VcNA2YOjWfV4snMHpnopUpFRETMeepCH9aCzWVERHoy/S0opp5Yk2Pabt6OhV/Vfd9lrMZmZ/maHFYsmuCt8kRERDpcSq9Q0/EDhacZaBIuFBGRnqempoarr76agoICAK644gquvPLKNn9ecnJyR5XWqRIjg0zHj52qYlC87oEiItJzJUR4ugdWMig+3MvViIj0bFarlXvvvZcf/ehHAFx//fWsXbuW+Ph4l3l33XUXO3bsAOCCCy5g1izzBfsZGRnceOONQH04cd26dabzHnnkEaZMmQLAHXfcwZgxYxgzZozLnOPHj3Pttdc63y9ZsoTgYPeQ+bmqqOJMgPAXfv/kCutGsh1DIXMz9L8AkiaAv/7nJSIiXVtyTAjTU+NZu7/A45w37BfyRvUFpBq5XGTZxdzwbEYFFkDvkV6sVERExPfMOhBGfN2xKe2Lv7Di/CTypqfz8YkQyqpqCQ/yJz0lhqToEG+XKiIiYsrTZqFhgYrOiMi5TX8Lipvcogoysz1/aWpmbXYBR4sr9BAoIiLdVkqcpwBhOdDbu8WIiIjX2e12brrpJj766CMABg4cyN/+9jcfV+UdiSbdlwDyTlV6uRIRERHv6hNlHiDMP1WlAKGISCe4+eab+fe//83777/Pnj17SEtL4+abb2b48OEUFRWxcuVKNmzYAEBUVBTPPPNMu685efJkfvGLX7Bs2TKKi4tJT0/nhhtu4MILL8Tf358dO3bw3HPPUVRUBMD48ePb1Ym+J2rcgXCiZT9BRi1pdbvgw13wIRA7CG7f6rsCRUREWmjpzMFsyDlhupn2GQbZjn4cpD/fuvYR6BsBFovXahQREfE1u91hGroID/KDqlJY/zg4bPQB5sUOgpTpMGo+RCd5v1gREREPPHUgDFcHQhE5x+lvQXGz6cBJHI7WneNwwKYDRcwbpwChiIh0T/1iQ7AYYD/rHnig8LRvChIREa9xOBwsXryYf/zjHwD069ePDz74gOjo6HZ9bm5ubpPH8/PzmThxYruu0RGa6kAoIiLSkyVEmofo80t0DxQR6Qx+fn689tprLFy4kP/+978cO3aMBx980G1eUlISr7zyCiNGjOiQ6z766KNYrVaWLVtGTU0Nzz77LM8++6zbvFmzZrFy5UqCgsyfkc5VxV93IOxFMSmWY+4TkiZ4uSIREZG2SUuO4v/NHcFdr+9qcl6A1cLyBaNJS47yTmEiIiJdyOmaOtO1o+FB/nB4IzhsZwZPflH/J6of9JvkvSJFRESaUWbSTdffahDopw1iROTcpgChuPGUum/+PPebrYiISHcR6GclOSaEwycrXMYPnCh3n+xwQGE2xA/1UnUiItJZHA4HP/rRj5yLR5OSkli7di39+/dv92cnJXWPnTbjwgKxWgxsZ6Xo8xUgFBGRHs5TiF73QBGRzhMeHs5bb73Ff/7zH/7+97+zefNmCgoKCA8PZ+DAgVx11VXccsstREZGduh1H374Ya655hpWrFjB+++/z1dffUVtbS3x8fFMmTKF66+/nssuu6xDr9kT2O0Oiivqf/9Lt+wzn3TeBV6sSEREpH3Oiw31eMwwYEZqPEtmDlZ4UEREzllNdmz6MtP8pJRpnVeQiIhIG5h30/XHMAwfVCMi0nUoQChu2tqeNzzIv4MrERER8a6UuFD3AGHjDoR11bDrX7DpKSjYC/+3EyK7RzhERETcORwOfvzjH/P0008D0LdvXzIzMxk4cKCPK/Muq8Wgd3ggeWeFJY6dqvRRRSIiIt4RFxaIn8Wg7qwQ/bFS3QNFRDrb3LlzmTt3bpvPX7RoEYsWLWrVOWlpaSxfvrzN1zwXlVXVOTebmWjZbz6pvwKEIiLSfZhuHArcPmMQ35mQTFJ0iJcrEhER6Vo8Bwj9Ydc69wMhcdB7ZKfWJCIi0lpm97OwQMVmRETUh1XcpKfE0tqAvWFAekpM5xQkIiLiJSm9wtzGTp6uofRkAax7FP4wAv7zIzi+Cxw2+OyvPqhSREQ6QkN48KmnngKgT58+ZGZmMmjQIB9X5huJUcFuY87uSw4H1NV4uSIREZHOZ7UY9I6o70JoYCfVOMJ11veZ+8Wv4fk5Pq5ORETE94oqzjwLvmqbxu9q5/GRbSS1lq+7+Ib3gegBPqpORESk9Vw2Dm3kBxemKDwoIiIClFbVmo5H+9dguqg0ZRpYtAxZRES6lnKTAGGvgBrI+CZ8+Fs4ugXsNh9UJiLiW4pSi5vkmBCmp8azdn9Bi8+ZkRqvL1NFRKTbS+kVajp++Hghoz78bX1osLGtGXDxzyHQPXgoIiJd19nhwcTERDIzMxk8eLCPK/OdhMggl/dJRgHpJevhtb/DwY9gym0w5XYfVSciItJ5EiOD+EbZv/k/v9eIMr5eSFoBHAbKjkF4gi/LExER8ami02cChLscKeyypYANll85nG/3KoTTJ8wXkIqIiHRRBwrdOxDGhgYQGeLvg2pERES6njIPAcKQsEj48adQmgcHPoQDmXBgXX2AUEREpIspq3YPEE409sKhj+r/ZD4MQZEw4OL69Z+J5/ugShER79PWH2Jq6czBBFhb9r8eoVYbdw89Du/+Cp5Mh1NfdXJ1IiIinSMlzjwImF0ZCSOucD9QdQqyVnZuUSIi0uFuu+02Z3gwISGBzMxMhgwZ4uOqfCvx6+5LVmxkBvyEDYH/x/08DbtWQfmx+hChiIhID5QQGUQ5wWfCg40d/tj7BYmIiHQhxafNu9FHhYVB8kQYqo69IiLSvRw44f7s52mDURERkXNRmUnHJoDwoK97lUT0gdEL4Kq/ws+yIe27XqxORESkZcwC8eNtO1wHqk7Bvre0QZqInFMUIBRTaclRLF8wuskQ4SDjKCv8HyMr6IcMXH0tfPJnKNwHOe95sVIRkZ7DZrOxe/duMjIyuP3225k8eTIhISEYhoFhGCxatKhTr799+3buvPNOxowZQ69evQgMDKRv376MHz+e2267jX/961/YbD27bftADz8QHigsh/QfmZ+0+7VOrEhERFoqIyPDec+cNm2ax3m33347f/nLX4D68OC6detITU31UpVdV0MHQhtWiohwn3B4I9jMdxwVERHpzvpEBbPZ7uHfAkc+8W4xIiIiXUxRhXmAMCY0wMuViIiItF91nY3cogqXsSCqWWi8Dye/BIfDR5WJiIh0HaUeAoQRDQHCxgwDrOriKyIiXU+5SQfCkVXb3CeG9oL4EV6oSESkazD5V71IvdkjE1m1OJjla3JYm13g9l1pmSOEmdbtcHaW5PN3YfyNXqtTRKSnuOaaa3j99de9ft3S0lKWLl3KCy+8gOOsv+zz8vLIy8tj69atPPnkkxQXFxMVFeX1Gr2lV3ggYYF+bg+QB0+chqTxkDQRjn5WP9h7JKTfCiPn+aBSEZGe4+DBg6xYscJlbOfOnc7X27dv55577nE5PmPGDGbMmNHqa91zzz38+c9/BsAwDJYuXcq+ffvYt29fk+eNHTuWfv36tfp63UmfqGDn64/tIxhnyXGdUFMOeTsgeYJ3CxMREelkCRFBHHb0psARRbxR4npQAUIRETnHeepAGK0AoYiIdENHTlZgP2vdy0TLfq7M/z386fcQ1Q8GzoCU6TBoJgSG+6ZQERERHzLr2AQQHqSgoIiIdA8Oh8Oto24iJ+ldc8R9cso0sKgfl4icOxQglCalJUexYtEEcosq+MVrO/n4y5POY8eJYbe9PyMth1xPOrAOaivBPxgREWm5s7v7xcTEEBsbS05Ojocz2q+oqIhZs2axZcsWAPr27ctVV11FWloakZGRlJWVkZOTw/vvv8/WrVs7rY6uwjAMBsSFsuurUy7jBwpP17+Y/GPIiq0PDg64WO3rRUQ6wOHDh3n44Yc9Ht+5c6dLoBDAz8+vTQHCDRs2OF87HA5++ctftui8559/vtM7AftaQwdCgE/sw7mdN9wnHVqvAKGIiPQ4faKCAIPP7Kl80/qpc7wiYiAhfcfXd6DQs5+IiJyjPHYgDFGAUEREup8vG37va+Qiy64zb0qOwNaM+j83r4W+47xWm4iISFdRWumpA6EChCIi0j1U1dqxnbV7TIRxmkORE+lfngW26jMHUqZ7uToREd9SgFBaJDkmhB9cNMAlQAiwxj7GPUBYVwkH18OQWd4rUESkB5g4cSLDhg1j3LhxjBs3jgEDBpCRkcGNN3ZeV9eFCxc6w4M/+9nPeOihhwgKCnKb98gjj5CXl0dYWFin1dJVpPRyDxAePHkam92BdcQVMOIKn9QlIiLSmRIbBQi32odQ7fAj0Pj6B8K4VBhwEfSb7KPqREREOk9CZP0maG/ZJnPE0Zst9iFssw/m7m9ewDXjk31cnYiIiG+ZdSAM8rcQHGD1QTUiIiLtc+BEudvYhY0DhA2CoyFxdOcXJCIi0gWZdSD0sxgE+as7k4iIdA9m97JsRz/eHvMUP7qgLxz+GA5kwpfr6jsQioicQxQglBabnBJHgJ+Fmjq7c2yNbSxL/f59ZlJwNAy+FELjfFChiEj3dvfdd3v1ehkZGbz77rsA3HrrrTz++ONNzu/Tp483yvK5lDj3kGRNnZ28kkqSY0J8UJGISM82bdo0HA5H8xObsWjRoma7BK5bt67d1+mpeoUFYjHA7oBqAvhd3XyOOWLoM/ob3HWNdlwTEZGeqyFE/659Iu/aJzrH80uqfFWSiIhIl1F02n2xjboPiohId3XgrA6EvShmmCXXfeKAqWBRWF5ERM5NZVXuHQjDg/wwDMMH1YiIiLReWbV5N93wQD/wD4ZBM+v/iIicgxQglBYLDrCSnhLL+s8LnWO7HAP4xD6c0ekzCR75TUiaoC9SRUS6iWXLlgEQFhbGo48+6uNquo6UXqGm418WlitAKCIiPZaf1ULviCDyT9WHJf5q+xYAMyp6fvdhERE5t8WFBeJnMaizu25ocKy00kcViYiIdB3FFe4dCKNDFSAUEZHu6UChawfCNMsB84kDtaGaiIicu8y6NoUH+fugEhERkbYpNwnDg+5nIiIA6isurTI9tZfLewcWFtTcw7uJt0K/dIUHRUS6iY0bN7J//34A5s6dS0REhI8r6jo8BQjP3pVURESkp0n4ugNTYw2BQhERkZ7KajHoHeF+D8xTB0IRERGqy0t4K+Bu7vN7gVmWz4ihlBgFCEVEpJs6cML1t74P7OP4v37/gqtXwJjrIKJv/YEUBQhFROTcdXYHwnTLXsb7fQl2m48qEhERaR2zbroAYYHquyUi0uUChDabjd27d5ORkcHtt9/O5MmTCQkJwTAMDMNg0aJFHXq9srIyXnvtNW677TamTJlCr1698Pf3JyIigqFDh3L99dezevVqHA5H8x92DpieGm86vi67wMuViIhIe3z44YfO15MmTQLg9ddfZ86cOSQkJBAYGEifPn24/PLLef7556mrM3+o6okGxHkIEJ4oNx0XERHpKRJNAoTHTqn7koiI9Hzm90AFCEVERM6r2MUoyyFu9HuXZwL+yLagxfz2+A/hwIfNnywiItKFFJ2uoaTCvaNSfEISjJoHc5+En+yB27dB9Hk+qFBERKRrKD2rA+Fdfiv5fenP4LcD4J/Xwubn4OSXPqpORESkeeXV7s9+AGFBChCKiHS5vwmvueYaXn/9da9c6/e//z2/+tWvqKpyXwxSVlZGdnY22dnZvPjii1x00UW89NJL9OvXzyu1dVX940IZEBfKwbN2Zvvw80JsdgdWi+GjykREpDW2bNnifN27d2+uvvpqt/tvfn4++fn5vP322/zhD3/gP//5DwMGDGj1tY4ePdrk8fz8/FZ/ZmcKCfCjT2QQeWctFj27A2FuUQWbDpykrKqO8CA/0lNiSY4Jgboa8NMu3CIi0v0kRga7jRVX1FJZYyM4QN3mRUSk5zLrwpunEL2IiJzj6mx2Rtbucvs1ObHmEARH+aIkERGRNjtQaL5RaErjjUUNA2IHeqkiERGRrqlx16YIyhllHKh/U3UK9v+3/s+Q2bDwFR9VKCIi0rRSDx0IwxUgFBHpegFCm8211XlMTAyxsbHk5OR0+LU+//xzZ3iwb9++XHLJJYwbN474+HiqqqrYtGkTL730EuXl5Xz00UdMmzaNTZs2ER9v3oXvXDF1SC+3AGFxRS1/+OBzvjM+uT48ISIiXVrj0N69995LdnY2AQEBXH/99Vx44YX4+/uTlZXFc889R1FREbt27WL69Ols27aNmJiYVl0rOTm5o8vvdCm9wjwGCLNyS3hiTQ6Z2QU0blAcbFRzX8KnXF31Gv7f+xf0Ge3FikVERNrPrPsSwLHSKo8dekVERHqCPlHuIfqyqjrKq+sIC+xyX6GLiIh4xanKWiZa9ruNV1vDCOw90gcViYiItN3ZG4U20PeeIiIirhoHCKdY9mI1HO6TUqZ5ryAREZFWKvcUIAz093IlIiJdj8XXBZxt4sSJ3HXXXaxatYoDBw5w8uRJ7r777k65lmEYXHrppbz33nscOXKEjIwMbr/9dr7zne9www038NRTT7F7925SU1MBOHjwIHfddVen1NKdTB9qHqD889ovuPixTG7K2ExWbol3ixIRkVYpLi52vs7OziY6OppNmzbx7LPPcsMNN7Bw4UKWLVvGnj17GD58OACHDx/utHtyV5PSy/3HwmOlVbyx/SvmP/0Ja/efCQ8GUc33rf9jfcBSvlv8NP6VhRx/6wEvVywiItJ+Zt2XAPLVgUlERHq4hAjze+COI8Wm4yIiIueCklMlnN/QaaKRwpgxYFGXehER6V6+POGhA2GvMC9XIiIi0nXZ7A7Kq8+ELi607DKfmDLdSxWJiIi0XuN7WWPqQCgi0gU7EHozmPDwww8320XpvPPO45VXXmH06NEAvPLKK/z5z38mJOTc7bJ3qqLG4zGHA9buL2BDzgmWLxjN7JGJXqxMRERaym63u7x//PHHGTNmjNu8hIQEXn75Zed9MCMjg9/+9rdERES0+Fq5ublNHs/Pz2fixIkt/jxv8LTb6B2rsqizn9ldzYKdtwN+SYrlmMu83vlr+Xz7RwwZc1Gn1ikiItKRPHYgPKsrr4iISE9TU2czHf/+io1c37+EG5OP0efiRRCe4N3CREREfKi0+CTv2sczybKPXkbpmfHek3xYlYiISNuYdSAMD/IjLizAB9WIiIh0TWcHLsy60hOeCL1SvVSRiIhI65VV1TpfX2n5iJNEsNU+hDAFCEVEul6A0JuaCw82SEtLIzU1lezsbCoqKvjiiy84//zzO7m6rikrt4Q7Vu1sdl6Nzc6SlTtYtTiYtOSozi9MRERaJTw83Pk6NDSU6667zuPctLQ00tPT2bRpE9XV1WzcuJHLLrusxddKSkpqV62+4Gm30cbhQQA7Ft62T+I2y3/c5p5+72EYs7pT6hMREekMiZHBpuP5p6rAVgf5O+Dgejj0EczPgKBIr9YnIiLSGVbvzufx9z53vu9NEYv83mWc5XPSjAMEHquFY5BlxJI252YfVioiIuJdx4nhttqlgIOBRh4TLfuZZNnHgH4X+7o0ERGRVjtQ6N6BMKVXGIZh+KAaERGRrqm0stbl/fya+xhvyWbpoELOt+2BvB3Q/yLQ/VNERLqwhkC8gZ17/V8k2iinzmHBb8WTcN4UGHp5/f8rInIOOqcDhK3RuNNSZWWlDyvxrSfW5FBjszc7L55iZrAdx8tPwDV3wwD9mCgi0pVER0c7X48aNYqAgKZ31xw/fjybNm0C4Msvv+zU2rqC6lrz7hNmnqubwyLru4QZrt2ZxlR+wrEvtpMwyL2zo4iISFfUKzwQiwGN8/IjjIPM3P5n+GQX1JSdOXD4Y0ht+YYCIiIiXVFWbglLVu5w2SzGDxu3+r3lNnfXJ+/BqPnaLE1ERM4ZxRU1X78y+NLRly9tfVlpm8k7yfq+U0REupc6m50jRRVu4wPjQn1QjYiISNdVVuXagfAUYayxj+PSUaM4f0I/qC6H6lIPZ4uIiHQNpV/fz4YYR4k26jeT8TPskLet/o9hKEAoIucsi68L6A5qamr4/PMzu1Cfd955PqzGd3KLKsjMLvB4PJgqfuL3L94KuJvPgn7Mo/7PMbryE8qy3vBekSIi0iJDhw51vo6MbL57UOM5paU9+8vA1bvzue3l7S2eX0I4GbZZLmNb7EO4tuaXbCiJ6+jyREREOo2/1UKv8ECXMTsWhpZ+7BoeBDj4kRcrExER6Rxmm6V9RRz5jhi3uWONbJavyfFWaSIiIj5XdLrGdDwmtOnN6ERERLqa3OJKam1nNo7pZxzHwE5KLwUIRUREGiurqjUdDw/yr38RGAYRfbxYkYiISOuVfx0gnGjZbz7hvAu8WI2ISNeiDoQt8PLLL3Pq1CkAxo4dS0JCgo8r8o1NB07icHg+Xk0A37O+R8zXaf0GxufvgsOh1vUiIl1IWlqa83XDPa4pjee0JHDYXTV0n2hJt93GGroQ7nf04w91V7PRPhIwuKS6rtlzRUREupKEyGCOl1Y73+93JFNCOFGcHSBc7+XKREREOpbnzdIMttqH8E3rJpfRVOMIn2Uf4mjxCJKiQ7xTpIiIiA8VewgQRoX4e7kSERGR9jlQeGYNSwC1vBfwc6rxpypnEgRdAv0vgITzwWL1YZUiIiK+d3YHwgbhQVpmLCIi3Uf512s2J5kGCA3ol+7dgkREuhD9y74ZhYWF/OIXv3C+v+eee9r8WUePHm3yeH5+fps/2xs8PSA2sGMh0z6aq60bXMbDKo5CYTbED/VwpoiIeNtll12GYRg4HA527dpFTU0NAQGed47esmWL83Vqaqo3SvQJs+4TLVFCOJfV/IZcRzxwJjDv3IVNRESkmwjxd10k48DCx7ZhzLF+5jrx+C6oKIIQ9w5NIiIi3UFTm6VtMQkQ2rEwmKNsOlDEvHEKEIqISM9XVOEeIAwL9CPQT+EKERHpXg4Unna+Pt/4kiCjliBqiczPhPzM+gMz7oGL7/RRhSIiIl1DWbV5B8IIrX0REZFupKGj7jDjsPvB3iMhONrLFYmIdB0KEDahpqaGq6++moKC+p2or7jiCq688so2f15ycnJHleYTLdlJZq1trFuAEIDPVytAKCLShSQlJTF16lTWrVvH6dOneemll7jppptM52ZlZbFpU/3CyfDwcC64oGe2cPfcfaKF5zt6u7w3DEhPUahCRES6j9W78/n04Em38Y/tI1wChHbDH0vyBDhdqAChiIh0W01tlrbFPoRSRwhb7YPZYk9lq2MIO+wDqSKQb1WZL6IRERHpacw6EEaHatGoiIh0PwdOnOlAaN6BAug32UvViIiIdF2llepAKCIi3V/5178BXlrzW4YZh5lk2c+3Ig8w2rEPzpvi4+pERHxL/7L3wG63c9NNN/HRRx8BMHDgQP72t7/5uCrfSk+JxTDwuDM3wEf2UdQ6rPgbNudYTa+RBITGeaFCEREByMjI4MYbbwRwhgTNPPLII0yZUv9AdMcddzBmzBjGjBnjMuf48eNce+21zvdLliwhODi4cwr3saa6T7TFjNR4kqLVlUJERLqHrNwSlqzcgd3kXviJfTjb7YP42D6cT+wj2GkM5cVvTCOtV5TX6xQREekoTS162ePoT1r1X3FgMTlPwQkRETk3FFW4h+ZjQgJ8UImIiEj7NO5AOMmyz32CNRD6jvdiRSIiIl1TmYfN0/SdqIiIdCcNm4jasLLbkcJuWwol/ZMYPW8U1Fb4uDoREd9SgNCEw+Fg8eLF/OMf/wCgX79+fPDBB0RHt69lbW5ubpPH8/PzmThxYruu0ZmSY0KYnhrP2v2euzOVEsp6+/k4gLX2sdQMuITHfzDHe0WKiHRjBw8eZMWKFS5jO3fudL7evn0799xzj8vxGTNmMGPGjDZdb/LkyfziF79g2bJlFBcXk56ezg033MCFF16Iv78/O3bs4LnnnqOoqAiA8ePHu12/J2mq+0Rr+VsNlswc3GGfJyIi0tmeWJNDjc1ueuxLR1+urPl/LmPL1+SwYtEEb5QmIiLSKZraLM0sOAjqNC8iIueWwtIqt7HoUAUIRUSk+zlwoj5A6Ecd4y2fu09ImgD+QV6uSkREpOvxtG5GHQhFRKS7sNsdlNe438/Cg/zAYoHAMB9UJSLSdehf9mdxOBz86Ec/4tlnnwUgKSmJtWvX0r9//3Z/dlJSUrs/w9eWzhzMhpwTHheWAny/9g7AIMBqYdWsyd4rTkSkmzt8+DAPP/ywx+M7d+50CRQC+Pn5tTlACPDoo49itVpZtmwZNTU1PPvss857YGOzZs1i5cqVBAX13B/POvILz+GJEWw9XMznx8tIT4klOUadCEVEpOvKLaogM9vzRjFm1mYXcLS4Qt12RUSk22rJZmlnU6d5ERE5F+RsXUvmJ59hKe0N9AIM57H9+aVk5ZaQlhzlq/JERERapayqlsKyauf7H9cuYZJlH9+MOEBSZTY4bND/Ah9WKCIi0nWUVtVhxYYfNqqp30AmwGohyN/q48pERERa5nRNnenmoQrDi4jU09+GjTgcDn784x/z9NNPA9C3b18yMzMZOHCgjyvrOtKSo1i+YDRLVu5oIkRoYLUYLF8wWj8gioh0Aw8//DDXXHMNK1as4P333+err76itraW+Ph4pkyZwvXXX89ll13m6zI7XVPdJ1or6+gpso6eAuo7VExPjWfpzMGk+edC/Ij63WxERES6iE0HTrb6/udwwKYDRcwbpxCFiIh0Xy3ZLK1BgNWiTvMiItLjrd6dz6n//IkfWtbyw0AocESxzT6YrfbBrLDN4VhpNfOf/oTlC0Yze2Sir8sVERFp1oHC087XdfiRaR9Dpn0MoReN4HtjY+HIpxAzwIcVioiIdB1lVbVMtOwnw/+37HAM5FP7UPYHnA81F0NAqK/LExERaVZ5tXk33bBARWZEREABQqeG8OBTTz0FQJ8+fcjMzGTQoEE+rqzrmT0ykVWLg1m+Joe12QWmC03HJkfph0MRkVaaNm0ajg5Iry1atIhFixa16py0tDSWL1/e7mt3Z23pPjE8MZzqOjtfNvrx8WwOB+zY/wXZBx7hfEsmxreXw9jrO6JkERGRDlFWZf4FavPn1XZwJSIiIt7Vss3S6sOD2ixNRER6uqzcEpas3MFb1s+dY/FGCbOtmxlhHOJZ2zcBqLHZWbJyB6sWB+veKCIiXd6BE+Wm4ym9wiAwHAZf4uWKREREuq7SqjrSLfsINGqZZOxnkmU/2N+ARx+BW9ZD7xG+LlFERKRJ5R7Wv4QH+Xu5EhGRrkntb3APDyYmJpKZmcngwdpR2pO05ChWLJrA+jun0y/GvePE/mNl2Owd0MJJRETEi5bOHEyAtWX/PAqwWvjNVefzw4tTPM6xYOd71vfIDPwp11jWYuCg7r37oLK4o0oWERFpt/Cgtu0tpC9YRUSkJ6jfLG0yM4fGe5zz54VjtFmaiIj0eE+sySHIVkaq5ajbsa0O199Ma2x2lq/J8VZpIiIibZKVW8ITH5jfr57M/IKs3BLvFiQiItLFlVXVMtHY737AGgBxQ7xfkIiISCuVeggQhrVxXYyISE+jACFw2223OcODCQkJZGZmMmSIHnhaIjkmhMvPd188U1Zdx968Uh9UJCIi0nYN3SeaCxE27j7x7p7jHuf92X85D/pnEGlUOMf8qopg7cMdVrOIiEh7pafEYhitO8cwID0lpnMKEhE5B9hsNnbv3k1GRga33347kydPJiQkBMMwMAyj1V3lm7N582aefPJJFi1axIQJE+jfvz9hYWEEBgbSu3dvpk2bxgMPPMDhw4c79LrdRcNmafd9a7ivSxEREfGJ3KIKMrMLGG35wvT4Nrv7pqtrsws4WlxhMltERMT3Vu/OZ/7Tn3DopPm96uMvTzL/6U9YvTvfy5WJiIh0XVWVlYyxmITvkyeCVRuLiohI11de7aEDYaAChCIi0IMDhBkZGc4FN9OmTfM47/bbb+cvf/kLUB8eXLduHampqV6qsmdIT4k1Hd904KSXKxEREWm/xt0nzg5TGAbMHBrPqsWTmT0y0bmwxpN/2S42HbdvXsHxvRs6smwREZE2S44JYXqq565LZmakxpMU7d6NXkREWuaaa65h1KhR3Hjjjfz5z39m06ZNVFZWdtr1pk+fzm233cYLL7zAli1bOHz4MKdPn6ampoaCggI+/PBD7r//flJTU/nNb37TaXV0dRcNjjMd35dfduZNdRmUe34OFBER6Y42HTiJwwHDDfPNBLba3TdedThg04Gizi5NRESk1bJyS1iycgc1NnuT82psdpas3KFOhCIiIl9LqtxPkFHrfuC8C71fjIiISBtUlJcQT7HbeLg6EIqIANDl/jY8ePAgK1ascBnbuXOn8/X27du55557XI7PmDGDGTNmtPpa99xzD3/+858BMAyDpUuXsm/fPvbt29fkeWPHjqVfv36tvl5PNe68aKwWA5vd4TL+6cGT3Hxxio+qEhERabuG7hO5RRV8erCIsqpawoP8SU+JcQlLNCys8WStfSwf2MZwiXW7y3ieI4bbX9pK9JBAls4cTFpyVCf9l4iIiLTM0pmD2ZBzotlFNVDfiXfJzMFQWQwH10NpPqQv9kKVIiI9h81mc3kfExNDbGwsOTkmuzt3kPj4eCZOnEhaWhoDBgwgMjKS2tpaDh06xP/+9z82btxIdXU1d999N7W1tdx7772dVktX1T82lEA/C9V1Z+6HiZwk9PM3oKYAjmyC47th/E1w+e98V6iIiEgHK6uq35n7adu3eNM2hXGWzxlryWGsJYcBxjH2O8x/Fy2rMllYKiIi4mNPrMlp0fecUB8iXL4mhxWLJnRyVSIiIl3fwZpIHrNdwyTLPsZZPifUqK4/cN4U3xYmIiLSQhG5mXwWdCcH7b35zD6MT+1D+cwxjLBAq69LExHpErpcgPDw4cM8/PDDHo/v3LnTJVAI4Ofn16YA4YYNZzr/OBwOfvnLX7bovOeff55Fixa1+no9VVigH6P6RrLjrF3ZPj1YhM3uwGoxwG6Dwxth73+guhyuesY3xYqIiLRCckwIyTGeuys1LKxpyv+ru56LLLsINOrnvmmbzK9qv08ZIbC/gA05J1i+YDSzRyZ2WN0iIiKtlZYcxfIFo5vdmXuE9QhPnJ/LoHd/B19tBYcd/IJh3CLwD/JewSIi3dzEiRMZNmwY48aNY9y4cQwYMICMjAxuvPHGTrnepk2bGDFiBMbZbda/9stf/pK///3vLFq0CIfDwYMPPsgPfvAD+vTp0yn1dFV+VgupCeHsPHrKOfZ64H0kFhRB46aDRzZ5vzgREZFOdGYHboM84sizx/GWvX6BqD912DBfYBMe5O+lCkVERFomt6iCzOzWdY1fm13A0eIKl01ERUREzjW1Njtf1MTwBVfwpO0K/KhjhHGI2wYW8I2+43xdnoiISItEFW4BYIDlOAMsx/kO6wA4Vj0JiPRdYSIiXYTF1wVIz5CeEus2VlZVx6GdH8F/fwK/S4UXvgWbn4Ndq6CiyAdVioiIdKyWtLY/4ujNn+uuoNwRxE9rFrOk9rb68ODXamx2lqzcQdZZQXwRERFvmz0ykVWLJzNzaDzm8RK4pdceBu17Eo5urg8PAtRVQq6CFCIirXH33Xfzm9/8hnnz5jFgwIBOv97IkSM9hgcbXH/99Xzzm98EoK6ujtWrV3d6XV3RsIQIl/db7UPcJx3fU9+JV0REpIdIT4nF0z8Vaj3sR2sYkJ4S04lViYiItN6mAydxOFp3jsMBmw5oDYuIiJzbys/aQLsOP7Icg9jT/wZtIioiIt1GfNFWt7HjjiiC4wf6oBoRka6nywUIp02bhsPhaNWf+++/3+1zGnbLdjgcrFu3zvRa69ata/W1HA6Hug+a8PQD4cld78GWv8HpwjODDhvse8tLlYmIiHSephbWNPYX21xm1yzjdfvFYBLJqLHZWb4mp+MLFBERaaW05ChWLJrAh3dOMw3Kv1GWan7iF2s6uTIREfGGESNGOF8fO3bMh5X4zrDEcJf3n9nN7n0OOPKpdwoSERHxguSYEKanxrfqnBmp8erUJCIiXU7ZWeEHgOut77Iq4H5+5vcqF1p2EUyVyXm13ihPRESkyzK7h4I6z4uISDdSUUSvii/chj+zDyVM9zMREaALBgilexrfPwarxT0Q8e/qCeYn7H2jcwsSERHxgpYurLFh5aijV5Nz1uwv4K/rvyS3qKKjyhMREWmzfrGhfDutj9v4h6fPo84/3P2ELzO9UJWIiHS2L74486NaQkKCDyvxnWGJrh0IP7MPM594eKMXqhEREfGepTMH429twW5pQIDVwpKZgzu5IhERkdYz2xTtIstuJlg+53a/N3gp4DfsDLyZVwMewIK90XlaTCoiIue2Ug9herN7q4iISJd05BPT4e3GCNOMg4jIuUgBQukQYYF+jOob6Tb+36NBOBLOdz/hwIdQUeSFykRERDrX0pmDCbB2zD+pHnl7Pxc/lslNGZvJyi3pkM8UERFpq0tHuAdHbFj5Imyc++TKYqg57YWqRESks7z11lv8+9//BiAoKIjLL7/cxxX5xtCzAoTZjiROOb7urhQaDyOuhDmPw9gbfFCdiIhI50lLjmLu6L7NzguwWli+YDRpyVGdX5SIiEgrpafEYjRaF2rBzkTLPpc5/oaNAOqwf71kyjAgPSXGm2WKiIh0OZ46EEYoZC8iIt1F0gT+Hn8nq+ou5rD9TFOIvQEjfViUiEjXou1BpMOkp8Sy46ywQ1lVHceTZ5NwbOeZweBoGPpNqK0A9CWsiIh0b2nJUSxfMJolK3dQY7M3f0IzHA5Yu7+ADTknWL5gNLNHJnZAlSIiIq03OSWW8EA/yqpdfzD87+lhpPp9itH/Qhg4AwbNhLghuKzMERGRLmv9+vUUFdVv7FVTU0Nubi7vvfce7733HgB+fn48/fTT9O7d25dl+kxksD99o4L5qqQSAAcWbq+9nfCEgTx5+zW634mISJeXW1TBpgMnKauqIzzIj/SUWJJjQlp0bk5BucdjhgEzUuNZMnOwwoMiItJlJceEMD01nrX7CwAYahwh0qhwm7epUbf5GanxJEW37F4pIiLSU3nqQBihDoQiItJdhMXzXsA32FA3BoAETjLBks3J8AE+LkxEpOvQv+6lw6SnxPD0h1+6ja/3u4BrgmNg2Ddh+BUw4GKwamcaERHpOWaPTGTV4mCWr8lhbXYBDkfbPqe/kU+pI5QiIqix2VmycgerFgdrQY6IiPhEgJ+FaUPjeSsrz2X8udKJzL39Zwzu28tHlYmISHv8/Oc/59NPP3UbNwyDqVOn8sADD3DxxRe3+fOPHj3a5PH8/Pw2f7a3DEuMcAYIAdbb0wg5YcXuAIvygyIi0kVl5ZbwxJocMs/6ftIwYHpqPEubCf4dPnmarLM2CgXoFxPCkpmDSU+JUbhCRES6hSUzBjkDhJPO6j7Y4NOvA4QBVgtLZg72Wm0iIiJdlacOhOHqQCgi0mZvvvkmL774Ips3b+bYsWNEREQwaNAgrrzySm655RYiIiI6vYZFixbxwgsvON/fd9993H///Z1+XV9pvEH2MWJ5yz6F0UEBPqxIRKRrUYBQOsz4/jFYLQY2u2tqImO/hcnfzyI5rvP/oSMiIuIraclRrFg0gdyiCt7ZfYxH3jb/QdKcg3nW9Tzgl8F+Rz8W1vyKagKosdlZviaHFYsmdFrdIiIiTbl0eG+3AGEVgazeX6IAoYhID9O3b1++8Y1vMHhw+xZOJicnd1BFvjM8MZwP9h13GauosXGkqIL+caE+qkpERMSz1bvzWbJyBzU2u9sxhwPW7i9gQ84Jli8YzeyRie4fcPokb+04YfrZP7w4hXnjkjq6ZBERkU4TFGB1vs5zxLHOlsZ4SzZhRhUANofBFnsqAVYLyxeM1kaeIiIiQJmHDoTh6kAoItJq5eXlXHvttbz55psu44WFhRQWFvLJJ5/wpz/9iVdffZX09PROq+Odd95xCQ+eC8pN7me6l4mInGHxdQHSc4QF+jHQZAHN3vwyLv7dR9yUsdl051IREZGeJDkmhB9enML/Z+/ew6Ooz/6Pv2c3ZwiEBMLBrEIwBhAMgigHK0KqItXSWqOi/SnQUm2t+DxWrVqrUNHqA1qJ1mIRi7WaKp6qVqhKQgEFRYVgVGIUxEUDCYSQQA6b7M7vj5gly84mm8Pm+HldV65r9zvfmbnx6bO7M/O973v6iMSg5vfhKI+EP8LS8MfpZVQz3lbAg+F/waBusU92fhF7D1WEMmQREZGAzk0dQLjdv9XSs+9/zeoPnDhL9B0lItLVbNmyBdM0MU2TI0eOsH37dv7whz9QXl7O7373O8aMGcPbb7/d0WF2qJGDrQuhfVZY1s6RiIiINC3XWRowebAhl9vDgqzt1s/qnvsp12ycxt/CH+DX9peZbMujF5WE2QxmjrFIOBQREenE3v70WEGY/3gmMKfmt6RVr2BW9R+4r2Y2f3efz5kjhrL6uknWifUiIiI9zduLGJd3HzNtWxhAqc8mJV2IiDSP2+0mIyPDmzw4cOBA7rzzTp599lkeffRRpkyZAoDT6WTmzJl89llzmhQEr6ysjGuvvRaAXr26bnFMZ0kFqz9w8uSm3UGtUbHqqKvvMhGRY/SJKG1mbV4hXxQfsdwWVHVTERGRbuTG9BQ2FRxodOHOqcZXPB7xEEmGb3Xvi+zvsddM5P7a2ZgmbNlVwqXjY0IdsoiIiJ/YqHAmD+/Pfz8v9hkvPFzFLS/swDBgWmoiN6anqFK3iEgX1KtXL9LS0khLS+OnP/0pZ599Nt9++y0/+MEP+OCDDxgzZkyzj+l0OhvdXlhYyJlnntnSkNvFiEYSCC9UEoWIiHQyy9YVNJk8WM/l9pC5roCVcyYcG6x14fnmQ2KpZpo9l2n2XABy3Gn8PXkp8b0iQhG2iIhIyLz1WZH/oBHGhTMuon/vnzAxOZ65/fTcTUREBKhb2Jn7T9LKv+Wx7y7/vvQM5mX32Tzq/jGxUeEdG5+ISBfzxBNPsHbtWgBGjRpFdnY2AwcO9G6//vrrufnmm3nwwQc5dOgQ1157LRs2bGjzOG655RacTicOh4OMjAweeuihNj9HKOU6S1m2roCc/CJM89h4U2tUjlT7JxD2jlS6jIhIPXUglDZRX93UYzY+r9HqpiIiIt1ImiOOzNljibAH/rm1z+yHx/Tv6gRwXdhrjDW+AKC8qiYkMYqIiATjhLiogNvqi8VkLN/M2rzCdoxKRETa2rBhw7j//vsBcLlc3HvvvS06TlJSUqN/gwd3/gS8k+JjiImw+41/WljeAdGIiIgE5iypICffIkmiEdn5Rew9dKxS9/7Pt2JzV/vN+9BzCj8cO6TVMYqIiLSnorIqy/UoU1L6c93U4Vw6PokkJQ+KiIgcc9gJ5d/6DA23FZJolBIVbiMiTEuMRUSC5Xa7WbRokff9008/7ZM8WO+BBx5g7NixAGzcuJE333yzTePIzs5mxYoVADz22GPExsa26fFDbW1eIRnLN5O90zd5EBpfo1Lr9lDhcvsdT8nwIiLH6Ne9tImWVDcVERHp7maMHszq6yaRPiIRwyJP8CB9mVtzK6VmL5/xWtPG7TU/Y7t5MqCLWBER6Ti5zlJWf7i3yXkqFiMi0j1ceOGF3tfr16/vuEA6mM1mkDrI/2HqZ4VlHRCNiIhIYFt2HfRbRNMU04Qtu0rIdZYyb9VWlj+bZTkv1ziF80YNaoMoRURE2s+6ndaJ9eeNTGznSERERLqIr9+zHP7Ac4rWqoiINNOGDRsoLKxLaps6dSrjxo2znGe321mwYIH3fVaW9f25lqioqGD+/PmYpsnll1/ORRdd1GbHbg/1DY2aykmwWqNytNo/eRDUgVBEpCElEEqrtUV1UxERke4qzRHHyjkT2HDLNO6YOdJv+5fmCfzCdRPVZt2F6hEzip/V3EKWOx0Aw4CJyfHtGrOIiEi9ZesKqHEHtxpVxWJERLq+hhVIDx061IGRdLyRg/v4jX1TWsnhSnWIFxGRzqO8qrZF+72/+6C3ivd4w/86zm0abHMPZ1NBcWtDFBERaVdvf7rfcjx9pH/XDxEREQGcWyyHP/ScQmyUEi5ERJpjzZo13tczZ85sdG7Dop4N92ut22+/nV27dhEfH8+yZcva7LjtpTUNjcqrrZ/h6ftMROQYJRBKq7Wmuqn3TU1l2wcmIiLSiTjiY/jFOclMH+Ff4fR9cyS31vyCb814Mlx3819Pmnfb9NREkvrFtGeoIiIiQEuKxZh89fk2ivLWhSwmEREJrYKCYw/ZBgwY0IGRdDyrBEKAnd8ehgNfwIdPwUvXwp/PAnfLkjdERERaq6WLX1786BvvQpws9zT+Unsx73lGUGXWdZfYaZ7IETNKneZFRKRLqXS52fTFAb/xU4f0YUhcdAdEJCIi0gWERUFMgs9QoRnPN/SnjzoQiog0y8cff+x9PWHChEbnDho0CIfDAcD+/fspLm59Ia93332XRx99FIClS5cycGDXKqTS0oZGVU9eDP+8irD3lzPK+AoD3wREJRCKiByjT0RptZZWN3WV7YfN/4KPnoaTJsFFf2rjyERERDqfG9NT2FRwwK9Szr88Z/Nm9RlUEuUdi7DbWJCe0t4hioiIAMEVi+nLEabY8vie7WO+Z/+YJOMAZWuTYfS29glSRETa1PLly72vp0yZ0oGRdLxRg2P9xi6ybea0524A10HfDfty4YTx7RSZiIjIMROTEzAMml3o0+05tsM7njG84xkDQDi1jDK+IsaoBo5V8V45p/EFTyIiIp3Bpi8OUF3r36ni++o+KCIiEtgF98L5i7lmyTMkHs5lvPE5h+kFGEq4EBFppvz8fO/rYcOGNTl/2LBhOJ1O776tKe5ZVVXFvHnz8Hg8pKenM3fu3BYfK5C9e/c2ur2wsLBVx29JQ6NY8yiRX28CPAzidd6IhFKzF4/XXsxf3D8EoHekEuJFROrpF760WnMvFCfb8vip/W1mbPgIzO+SD8u+gfPvhQh1WBIRke4tzRFH5uyxLMja7pdE2DB5EODaqcmkOeLaMToREZFjgikW84fwVcyyv+sz1ufILih1QpwjVKGJiMh3Vq1a5X0AOHXqVNavX+83Z/ny5aSmpnLuuediGIblcdxuN0uWLOGxxx7zjv3qV78KScxdReog/w6Eh+hN9PHJgwB73lUCoYiIdAhHfAzTUhPJ3tm8ytyB1BBGrnkyNFiok51fxN5DFST10zM8ERHpvJwlFazYsMty23mjlEAoIiLSKMPgE9cg/uvux2rO9Q6rA6GISPOUlpZ6X/fv37/J+QkJxzrANty3Je666y7y8/OJjo7m8ccfb9WxAqnvmBgqLWlodIYt36/jYJxxlEoivO+VEC8icow+EaXVmlvd9CLbFmba3/d5+Eh1GXz2GqRdHpIYRUREOpMZowez+rpoMtcVkJ1fFPA7dOe+8vYNTEREpIFgbqJu9IzxSyAE4MtsGH9NCKISEekedu/ezcqVK33GduzY4X29bds27rzzTp/t06dPZ/r06c0+15YtW/jlL3+Jw+HgvPPOY8yYMSQmJhIREUFpaSl5eXn861//4quvvvLuc/vttzN16tRmn6s76R0ZxkkJMew5WOEd+8iTQi12wnD7Tt7zLky+oZ0jFBERqXNjegqbCg74FSuzYjcM3M0s422asGVXCZeOVwKhiIh0PrnOUpatKyCnwfO2MGpZFPYU73lGkB81llOH+BeIEREREV/lVTV+Y0q4EBFpniNHjnhfR0VFNTKzTnR0tPd1eXnL1wlu3bqVhx56CIBFixYxfPjwFh+rI7Xke+cs22eW41s8o7yve+v7TETES5+I0mrNrW76nPtcrgzL9t+w7WklEIqISI+R5ohj5ZwJOEsqePfLAyx+/VPKq30Xob796X7+9NbnJPWLZmJyAo74GKg6DC/9Aqb/HgaNbvI8zpIKtuw6SHlVLbFRYceOIyIi0oRgisVscJ8GVsVHv3hLCYQiIo3Ys2cP9957b8DtO3bs8EkoBAgLC2tRAmE9p9PJk08+2eicvn378sc//pFf/vKXLT5PdzKkb7RPAmElUXzsGcbpti98J+55FzwesNnaOUIREZG6+4yZs8eyIGt7o0mEEXYbs04fwuoP9jb7HFYLSUVERDra2rxCy++/04xdXBW2jqtYBx448tAf6T1iOpzxMxg4KsDRREREei5XrYfqWv/rSSUQioh0fi6Xi3nz5uF2uxk3bhw33XRTyM7ldDob3V5YWMiZZ57Z4uM3t6ERwESLBMJDZm/yzSTv+wfW7OSOmSNJc8S1ODYRke5Cv/ClTTSnummecTKV/VKJPpTvu+GrjVCyC+KTQxSliIhI5+OIj+Hy+BMpPFzFw28X+GwzgWXr6sYMAy5M6c2SqoX0KvoInO/B/3sFhoy1PK5VxdX640xLTeTG9BRdFIuISKOCKRZTRD8+8zgYaTvuRvHX74HHDTZ7iKMUEZGmZGZmMmvWLDZs2MC2bdv48ssvOXDgADU1NfTu3ZuBAwdy2mmnccEFF5CRkUHfvn07OuROYW1eIe/vLvEbf88zwieB8EhsMr1POQdcRyBKXS1ERKRjzBg9mOeujeLHj1l0iKcueXD1dZP4fH95ixIIY6OsKseIiIi0v/rCmTv3lbPq3a9we/xXlk62feLzvnf5Lti6C0bNaq8wRUREupRARWP66FpQRKRZevfuzaFDhwCoqqqid+/ejc6vrKz0vo6NjW3RORcvXkxeXh52u50VK1Zgt4dujUZSUlLTk1qhuQ2NwOSzqNPpHxbBoKP52I2668P3PSMwOVb0873dJWQs30zm7LHMGD04BJGLiHQdSiCUNhFsdVOAE+NjiD7zGvjPHccGh34PTv9/EKsvZhER6Zlmn3kimesKsHjOCUCUWcU1X/2BXraddQOVh3D97WJKL/4biad932duoIqrUFehJ3tnEZsKDuiiWEREmhRMsZhsz+mMtDk5YPbFSL2QhDMugWFTlTwoItKIc889F7M55TMDmDNnDnPmzGl0Tp8+ffjxj3/Mj3/841afr6fIdZayIGs7bov/G61zjyOSGt7zjGSrJ5Xymn6sHjuJNCUPiohIB0seEHhBksvtIXlAL+J7RTS7irdhwMTk+DaIUEREpOUCFc60Msn2qd9YDeGEJ7W8E4aIiEh3VlZVazmuDoQiIs0TFxfnTSA8cOBAkwmEBw8e9Nm3uXJzc7n//vsBuOmmmxg3blyzj9HZNKehERjcVvYT4CfEUsF4Wz4TbZ/xsce/kZHL7WFB1nZWXxetpgsi0qPpF760mRmjB7P6umgy1xWQ3chN290HKtg5cCYj4h6HMRlw+lXqOigiIj3evsNVAbdFUc3K8KWcVZ88+J2ImjLiXryMpe/cx3kXXUGaI8670LWpi2hdFIuISDCCKRaT5Z7OenM8P7viUmaMCW3FORERkfawbF1BwO+9reYIttaOODbg9pC5roCVcya0U3QiIiLWAnWMqPfVgQrGJPVtZhVvmJ6aSFK/mNaGJyIi0mKNFc48XiQuzrB97je+1ZPCiUc8JPULRYQiIiJdW6DrSXWjFxFpntTUVHbv3g3A7t27GTp0aKPz6+fW79tcq1atoqamBpvNRnh4OIsXL7act2HDBp/X9fNSU1PJyMho9nlDqTkNjRoqJ4b1ntNZ7zk94ByXnumJiCiBUNpWmiOOlXMm4Cyp4L3dJWTvLOKNjwv95t2x9ltmT3mdicP744jXQ0cREZFljXQfBKjFuovT56aDJ/cM4K/LN5M5eyzPf7A36ItnXRSLiEgwfIrF7Czi+K+rvWYi/YekKHlQRES6BWdJBTn5wSdVAGTnF7H3UIWSK0REpEOVB+gYUW/XgSOMSerbrCreEXYbC9JT2ipEERGRZgu2cGa9E4wDFNOXJA74jL/rPpVvd5Vw6Xhdt4mISPfiLKlgy66DlFfVEhsVxsTkhGavxwx0PakOhCIizTNmzBjWrl0LwNatW5k2bVrAufv378fpdAKQmJjIgAEDmn0+87tOPx6Ph/vuuy+ofXJycsjJyQFg1qxZnS6BEOrWqDzz80gyHt/c5sfWMz0R6en0C19CwhEfgyM+hhmjB/Hf/CKOutw+2z/6upSPvi7FMGBaaiI3pqeo+5GIiPRYTS1QrSKS+TW/4S88zHT7du/4PrMfP3PdTAVR4PZww7PbqG0sC9GCLopFRCQYDYvFXPXEe3xdUuGz/dNvyzlaXUuvSN1mEBGRrm3LroOYzbuswjRhixaiiohIB2sygbD4KPBdFe8r0jCev5ovzMF85EnhI08Kh+jjMz/CbiNz9lg9vxMRkQ7VWId4K7vMIZxdvQyHUcQk26dMtn3CJNunbPaMIqGJbr0iIiJdSa6zlGXrCsjJL/K5nxn0esyPX4ATxkG/YepAKCLSRmbMmMGSJUsAWLNmDbfeemvAuW+88Yb39cyZM0MeW1eTOjg2JMfVMz0R6elsHR2AdG+9I8MYP7RfwO2mCdk7i8hYvpm1ef6dCkVERHqCYBaoVhPBdTX/y+vuiQBUmJH8zHUz+4n3zqnxmH5doZpSf1EsIiISDEd8DJed4d9p0OX2sOmLAxZ7iIiIdC1NJV8E3k8LUUVEpGM19V20+8BR7+sZSS4usG/l+rBXWRnxINuiriM74iZ+ZNuEYUD6iERWXzeJGaMHhzpsERGRgFrSIb6OgdMcyPPuafxPza85q/rPfGieoiQIERHpNtbmFZKxfDPZO4v81poEtR6z1Akv/gwyT4elpzB606/5mf3fnGjs95nWJ1qFQ0VEmmPq1KkMGjQIgPXr1/PRRx9ZznO73WRmZnrfX3HFFS0638MPP4xpmk3+3X333d597r77bu/4K6+80qLztgdXbfCFZJpLz/REpCdTAqGEVK6zlM1fHmxynsvtYUHWdnKdpaEPSkREpJMJdoGqi3BuqPk1mbU/4saa6/nEHNZG59dFsYiIBG/aiETL8ezPWrKYR0REpHOJjWrZohgtRBURkY7W1D3GhgmEOLf6bU+27aNvTCQbb53GyjkT1HlQREQ6XEs6xFszMAyDicnxTU8VERHp5HKdpSzI2t5kh95G12M63zv2+mgRSfve5vfhz3C6UeAzrY/ueYqINIvdbueuu+7yvr/66qspKvJfR3Hbbbexfft2AKZMmcIFF1xgebxVq1ZhGHXXM+eee24oQu60aprRib659ExPRHoyJRBKSC1bV0CNO7g7ui63h8x1BU1PFBER6Waas0DVxMZDtZfxlueMNjy/LopFRCR4owb3YVCfKL/xnPwizLZZ0SMiItJhJiYnYBjN28cw0EJUERHpcE0VCdtVfMR7zeZuuFi0AduJZ5LUL6bNYxMREWmJlnaItzI9NVHfcSIi0i0sW1fQZPJgvYDrMb/eYjn/QzPV5/0dL3+shhAiIs00f/58zjvvPAA++eQT0tLSuOuuu/jnP//JY489xve+9z2WLl0KQFxcHI8//nhHhttphaoDoZ7piUhPpwRCCRlnSQU5+c3rQLFuZxEPvZXP6g+cOEsqQhSZiIhI59KSBarByrCvpw9HA27XRbGIiDSXYRiWXQiLyqv55NuyDohIRESk7TjiY5iWat1tNxAtRBURkc6grIkki6MuN8Xl1QDUfOWfQLjfjKN/UkpIYhMREWmJlnaIP16E3caCdH3HiYhI19eS9ZjZ+UXsPXTcOkynfwLhPrMfe83+PmMbCw6QsXwza/MKmx2riEhPFRYWxosvvshFF10EwL59+7jnnnuYPXs2119/PZs2bQIgKSmJf//735x66qkdGW6nFaoEQj3TE5GeTgmEEjJbdh2kJc0nMtd9wZIX/suyBxcyb9VWVbEREZFuryULVIPxA9sWloT/ldcifsepxm7LObooFhGRlphukUAIsO6z4x5aVpZCwVuhD0hERKQN3ZieQoQ9uFvnEXYbC6YNhT3vQva98NlroQ1OREQkgLImOhAC7DpwFEyTMns/ysxon20feVJIGRgbqvBERESarS0KcEbYbWTOHkuaI65NYhIREelILVmPaZqwZVfJsYHqI7D/E795H3pSAP8vXpfbw4Ks7VrDKSLSDLGxsbz22mu88sorXHLJJTgcDiIjI+nfvz9nnXUWDzzwAHl5eUyePLmjQ+20Guu2O874nJvCnmeyLY8oqoM+porLiIgogVBCqLyJSqdWTjGcLAlbzqbIBSwNf5w9+dtUxUZERHqE5ixQDcZQo5D7w1cAcJKtiJci7uan9rcA37vJw/r3UudfERFptiknJxAR5v+99dwHTl7f+D6Hch6Fv8+CJcPhmUuhfF8HRCkiItIyaY44MmePbeIazWRu+JtsOnE5ac+Mhb9dCBv+D3L/2V5hioiI+AjmudzuA0fBMHj25KWMrV7B+dUPcFvNz3m+dipvu8dzihIIRUSkE2lNAU7DgPQRiay+bhIzRg9u48hERKS5Xn31VTIyMhg6dChRUVEkJiYyefJklixZQllZWbvEMGfOHAzD8P4tXLiwXc7bllqyHrNuvwYFZyJ7w/9+wl8G3Mnfai9gh2cYtaaNDz2pAfd3uT1krito0blFRHqyWbNm8eKLL/L1119TVVVFcXExW7Zs4dZbb6Vv375N7j9nzhxM08Q0TdavX9/iOBYuXOg9Tlf5/musA+EF9q0sCHuFZyPuY0fkz3k+YhE3hT3faDKhisuIiNQJ6+gApPuKjQr+f14nGftYGPYU0+y5PuM/t7/B7bXzWZC1ndXXReuLW0REuq36BaoLsrY3WkEnGJG4eCw8k1ij8tiYUcvi8L8xyviKO2rne8ef2FTXmdAwYFpqIjemp+j7VkREmhQTEcak5AT++3mxz/gZZW9z0bo/+++QvwbOmNtO0YmIiLTejNGDWX1dNJnrCsjeWYR/YW+DX/XdzIDCfN/h3RvAXQt23XoXEZH2FcxC0l3FRwAo2H8EDzY+Nx187nbwT6YTGWbj/+JjQh2miIhIs9yYnsKmggNBPTuzGwZzpwxlxOA+TEyOJ6mfvtdERDrakSNHuOqqq3j11Vd9xouLiykuLmbz5s088sgjPP/880ycODFkcaxZs4annnoqZMdvL81Zj+m7X7jPe2dtHP+3dxSmOQqAGKqw0fh3bXZ+EXsPVej7VURE2kVjCYTn2D72vo4w3Jxp5HOKsZc/1V7qN9cwYHpqIgu0JlJEBFAHQgmhickJGP5d7S2VmzFMsn3qN36JfSMDKFUVGxER6RHqFqhOIn1EYtDfoVZuDnueUbY9ltv+7bG+6W6akL2zSJ1/RUQkaIP6RPmNfRCoOmn+GyGORkREpO2lOeJYOWcCWb84y3L7F7ET/Aery+Dbj0IcmYiIiD+fjhIB7D5wFICConK/bcMH9MZua8VNSRERkRAIrkM8TA7L56kfxHDnzBFcOj5JyQ0iIp2A2+0mIyPDmzw4cOBA7rzzTp599lkeffRRpkyZAoDT6WTmzJl89tlnIYmjrKyMa6+9FoBevXqF5BztpTnrMesZBkxMjve+d5ZU8Eh2AWaDimkVRHGExr87TRO27Cpp3slFRERaqMbtX9oTYKitmJG2r/3G3/OMxMRGuM3gmkkncffFo1iakcbGW6excs4EJQ+KiHxHCYQSMo74GKalJgY1t4Q+vOA+x2880qjlmrD/AMeq2IiIiHRn9QtUN9wyjaUZadx98SiumXQS4fbg7wKvrL2Q9y0SOP5eex7veMY0uq/L7WFB1nZynaXNDV1ERHqQXGcpL23b6zf+Lf352DPUb9zz5Xqo9l+gKiIi0hWcNSyBPhbVvXNcp1rv8GVOiCMSERHxF1QHwgNHqXF7vImEDZ0ysHcowhIREWm1+gKcg/v6FzQDSB+RyMrE1Zz99ixYejI8fw1sfQIOftnOkYqISENPPPEEa9euBWDUqFHk5uZyzz33MHv2bK6//no2bdrEb37zGwAOHTrkTfJra7fccgtOpxOHwxGyc7SX5qzHrDc9NZGkfjHkOkuZt2or5yzJ4fkP/J/xBSOYwjUiIiJtweV2W47/3xin5fjbnnGkj0jkhV9OZtGs0cydMkzFZURELCiBUELqxvSUJivB1XvCPROP6Zsc4TYN+nEEUBUbERHpWRzxMVw6Pom5U4axaNZoXrhuctCdCfeRwJWu37G89mLv2FeegdxfOzuoc6vzr4iINGXZuoKAFd/ecp/hN2bzuOCrd0IdloiISEgYhsFpSXF+4y8ecGCGHbeANTwGavyTMkREREKtrLLphZxfH6zgi6IjltdzKQNjQxGWiIhIm0hzxHHmsHi/8b7R4azMSCa65NO6gYqD8Okr8O/fwLpF7RukiIh4ud1uFi069jn89NNPM3DgQL95DzzwAGPHjgVg48aNvPnmm20aR3Z2NitWrADgscceIza261/3NGc9ZoTdxoL0FNbmFZKxfDPZO4t8Og82V2xUeMt3FhERaQZXrcdyfEDfWOh7os+YiY3//dUN6jQoIhIEJRBKSKU54sicPTaoi9avzMH8x1O30LTCjGRV7fmc63qI39X+zDtHVWxERKSnOr4zYUpi4xXBawnj/trZzHHdwj6zHzfXXEsF1pVZrTSn86+zpILVHzh5ctNuVn/gxFmijsEiIt2Zs6SCnPyigNvf9BxLIPzIczL311xBumsJexP9u86LiIh0Facl9fUbO1hto3LIJBgyDr73G7jmdfjtV3DeH9o/QBER6fGC6UBY6zHJ3ml9PXeKEghFRKSTs1pAGh1uh10BusAP/V6IIxIRkUA2bNhAYWEhAFOnTmXcuHGW8+x2OwsWLPC+z8rKarMYKioqmD9/PqZpcvnll3PRRRe12bE7Uv16TLut8erTEXYbmbPHArAgazsut3UiRrAMAyYm+yfzi4iIhIIrQEHrQ6Ovgf/ZAdduhHNvh0FjME6cyJATTrScLyIivsI6OgDp/maMHszq66LJXFdAdn7jVWz+UvtDPvWcxNPu8yjF/0GlqtiIiEhP54iPwREfQ0pibzKWb27yJu96z+lMcz1MpRncd+gk2yd84Emlxgxjy64SLh0fE3BurrOUZesKyDnu+90wYFpqIjemp6iqj4hIN7Rl18FGr+t2mg5ucl3HRs8YiunXYL/Gv1dEREQ6M6sEQoC30pYxa/xJ7RyNiIiIv2CLcL75yT7L8aYKlomIiHQ0qwTCiDAbfPaq9Q7DpoY4IhERCWTNmjXe1zNnzmx07oUXXmi5X2vdfvvt7Nq1i/j4eJYtW9Zmx+0MZowezOwzD/CPLV9bbp+YHM/tF44kzRHHvFVbW508CDA9NZGkfnrOJyIi7SNQB8IIu61uceLg0+r+zr0NairbOToRka5LHQilXRzfNenG9JOxqoGzwxzOI+5LLJMHVcVGRETkmGC7/EbYbVw0flhQx0w1vubp8D+yJuI2Jtk+aXTR0dq8QjKWbyZ7p39xANOE7J1FZCzfzNq8wqDOLSIiXUfTXS0MXvKc45M8WLefOsqLiEjXdVpSnOV47rdH2zcQERERC26PyVGXO6i5uXsP+41FhtlwxGshqIiIdG5WyQ+RdmDfx/6TE06G/imhD0pERCx9/PGxz+YJEyY0OnfQoEE4HA4A9u/fT3FxcavP/+677/Loo48CsHTpUgYOHNjqY3Y2vSIC9w65Mf0U0hxxOEsqyMm37kLfHBF2GwvS9b0qIiLtJ1ACYWSYxVrJ8OgQRyMi0n0ogVDalSM+hkvHJ/G/56UybURis/ZVFRsRERFfdV1+J5E+IhHjuMx8w4D0EYmsvm4SZw5tOgHfwMPi8CcJMzycbPuWrIh7Sf/0Dij1r1iX6yxlQdb2JqvUudweFmRtJ9dZ2px/loiIdHKxUYEfSDa+nzrKi4hI1zW4bxT9e0f4je/YW9r+wYiIiBznSJOFXiCaKi60vcdASvy2nZzYG7vNqvSniIhI52G1gDQ8LAx+/SHMexMm/RrivusQP/Ji/B6eiYhIu8nPz/e+Hjas6YLHDec03LclqqqqmDdvHh6Ph/T0dObOnduq43VWZY0U7iwqrwJgy66D3oLQ4dRio/mdCCPsNjJnjyXNEdeSMEVERFqkJsC6xPAmmi2IiEjjWrbqT6QN3JiewqaCA00mHwDYDRjWvxerP3AyMTlBVVBFRES+U9/l11lSwXu7SyivqiE2KpyJyfHexPv4XhEYBn6dAhv6iX0jE2yf+4yd+M0b1D6xnbD//Rjsx342LltXENT3N9QlEWauK2DlnMarCoqISNcxMTmhye+V46mjvIiIdHWGYXBaUhzZO30rdn/ybRm1bg9hemApIiIdqLGFo/XG2r7kLxHLAPjGTGCbJ4UPPSn82z2RUwaeEOoQRUREWs3q2VREmA1sNjjxrLq/8xfXdSSMjmv/AEVExKu0tNT7un///k3OT0hIsNy3Je666y7y8/OJjo7m8ccfb9WxAtm7d2+j2wsLC0Ny3obKKgMXkikqqwagvEGxmQtt73Fv+JNs85zMB55UtpqpbPcMp5Ioy2MYRl3DhwXpKUoeFBGRdheoA2GEVQdCEREJmhIIpcOkOeLInD02qA5GbhOe2LQbqLs4nZaayI26OBUREfFyxMcETLB3xMcwLTXRb6Frvb4c4fawZy233Vuazp6nt3m/d50lFeTkWx8nkOz8IvYeqlAnYRGRbqKp7xUr6igvIiLdwZgT+vp9/1XWuPmi+AgjBvXpoKhERER8F4UGcrpR4H19gnGQE+wHuci+he2ek0kZ2DuU4YmIiLQJqwWkfotHDQMGn9ZOEYmISCBHjhzxvo6Ksk5Qayg6Otr7ury8vMXn3bp1Kw899BAAixYtYvjw4S0+VmMcDkdIjtscwXQgjI06tjx4gi2fWKOSc+wfc479YwBqTRuTqh+hmH4++18+wcEN00/Wsz0REekwgfIKlEAoItI6+hSVDjVj9GBWXzeJ9BGJGEZw+5gmZO8sImP5Ztbmhb5aj4iISHdwY3oKEQE6YiQYZewz/btCOT0DeKb2+z7fu1t2HWxWxymo++7esqukJWGLiEgn1dj3yvEi7DYWpKeEOCIREZHQS3P0tRzfsfdwO0ciIiLiK9DC0QGxkd7X42wFfturzTA+MYeSkhgbsthERETailUCYaQWj4qIyHdcLhfz5s3D7XYzbtw4brrppo4OKaTKKhtLIKzrQDgxOcG7JvMMW77/POIoJs5nzDBQ8qCIiHS4QB0Iw4NcpyIiItb0KSodLs0Rx8o5E9hwyzSWZqSR3L9XI7NNxhpfAHXVBRZkbSfXWdoucYqIiHRl9Z1/rZI9dplD+KFrMXfXXEOZeayy30O1l+IiHDj2vZu/r2XV/sobqX4nIiJdT2PfKw3ZDMicPVbd40VEpFsYfUKgBMLS9g1ERETkOIE6EKYlxX33yrRMIMwzh+EinFPUgVBERLoAqw4UwRY5ExGR9tW797FrjKqqqibnV1ZWel/HxraswMnixYvJy8vDbrezYsUK7HZ7i44TDKfT2ejf+++/H7Jz1ytrpBP9/rK6/+aO+BimpSbSh6OkGnv95n3gSQV8uz5MT01U8qCIiHS4QB0IVURGRKR19CkqnYYjPoZLxycxsE+U5fYxxi6eDb+XVyLvYortY6DuB0LmOv8HniIiIuKvsc6/buw85b6A9OoHebZ2GnmeofzLM8Vnjsvt4Z0vDviMRVHNbWHPkmQUN3ru2KjwNvk3NOQsqWD1B06e3LSb1R84cZZUtPk5REQksGA6yif2ieSCUwcdG/C44Yt18PWW9glSRESkDSXGRjG4r/+9y4+tOhAePQh5L8Khr0IfmIiI9HiBineN/a577lBjH/HGEb/tH3pOISrchkOLQ0VEpAuw6kARocWjIiKdUlxcnPf1gQMHAk/8zsGDBy33DVZubi73338/ADfddBPjxo1r9jGaIykpqdG/wYMHh/T8EFwHQoAb01M4M+wLbIbpN+8Dzyk+7yPsNhakp7RdkCIiIi1U0+D670r7Oq61v8Ywo1AdCEVEWimsowMQachZUsGW3Qd9xk409nNL2HNcbD+2wPT2sCwudp2KiY3s/CL2HqpQ5RsREZEg1Hf+dZZUsCZvH/e98ZnP9mLiuKN2PuHU4rGoNfHZvnIMoP7Wcob9v1wX9jo/t7/BG56zeKb2+7xnjqBhlTrDgInJ8U3G5iypYMuug5RX1RIbFcbE5AQc8f7f77nOUpatKyAnvwizwT1uw4BpqYncmJ6iTlciIu2k4ffK9c9+xI7jEij2Ha7m028Pc6ptD+x4Hj5eDUf2w/Dp8P9e7qCoRUREWu60pL4UHvatmv5ZYTmu6moivtkMX+bArhwo3AGY8P1FcPb/dEisIiLScwTqQHjadx0IvzIHcW71g4wzChhnK2Cc7QtSja/5yJPCyYN7Y7MFqAojIiLSiSiBUESk60hNTWX37t0A7N69m6FDhzY6v35u/b7NtWrVKmpqarDZbISHh7N48WLLeRs2bPB5XT8vNTWVjIyMZp+3o5imyeFGEgiLy44lEKY54rjmB1N59N/5nGHLZ6zxBVFG3b51HQjrRNhtZM4eq7UWIiLSKRzrQGjyc/u/Sbbt43ay4LHHIXUmjPohnDC+Q2MUEemKlEAoncqWXQd9EgHCqOX5iD8wyDjkM2+07Ssutm3mVc8UTBO27Crh0vFKIBQREQmWIz6GfjGBuwLWNPIzMalfNM5DldhxM9/+bwDCDA8/tG/mh/bN5HuS+IHrPmq/O8b01MRGE/2bkxC4Nq+QBVnbG9wkOMY0IXtnEZsKDpA5eywzRoe+qp+IiNRxxMfwi3OS+fWz2/y2hb04B0pyfAd3rYfyfRA7yG++iIhIZ3ZaUhz/+WS/z5jL7aHg24Oc+o9LwXPcwp1dOUogFBGRkAvUgXDEoFgiwmy4aj18ZQ7mK3MwL3nOAaAXldRiZ2ZibHuGKiIi0mKWCYTqPiEi0imNGTOGtWvXArB161amTZsWcO7+/ftxOp0AJCYmMmDAgGafz/xuoYHH4+G+++4Lap+cnBxycuqeX82aNatLJRBW1rip9fh3FKxXXl1LhauWmIi6NRvfmzSFv+VHsHRnEeHUcqrxFeNt+ew0T8Qw6tZ0LFChZhER6UTqr/+GG9+SbNt3bMOBz+v+SvdAxqqOCU5EpAvTnTTpVI6vkFpLGMtrL7ace0vY80RQ891+gSvqiIiIiLVAlcmbUlVTd4F+oe19TrQV+23fZQ72Jg+G2w0WpKcEPNbavEIylm8me6dv8iAcSwjMWL6ZtXmF5DpLAyYPNuRye1iQtZ1cZ2nz/mEiItIq01ITiQr3v82w/ojDf7LpgY9faIeoRERE2tZpSX0tx7ftrwHHmf4b9myGmsoQRyUi0vW8+uqrZGRkMHToUKKiokhMTGTy5MksWbKEsrKydolhzpw5GIbh/Vu4cGG7nDcUAt3n6xMdztAE68JeR4mmmghSBvYOZWgiIiJtprpBB4or7NkM5iDh6kAoItIpzZgxw/t6zZo1jc594403vK9nzpwZspi6k7LKptd6FDXoQghQVeMG6opJbzdPZqX7B8T3imLjrdNYOWeCkgdFRKRTqV8feL7tQ+sJIy5qx2hERLoP3UmTTiU2yr/b0TPu77PHk+g3HmVUk2wUfrdf4A5KIiIiYs3qezcYxUeqAZNrw16z3P5P93Tv67GOOD7cc4jVHzhxllQA4CypYPUHTu55/VOuf3ZbUAmB1z+zjV8/+1GTcxvuk7muILh/kIh0KLfbTV5eHqtWreKGG25g0qRJxMTEeBdwzpkzJ2Tn7gwLVruTXpFhnHuK/7XbqrIJmBj+O+x4rh2iEhERaVtjTrBOIPx472FItqik7q6GrzeHOCoRka7jyJEjzJo1i1mzZvHCCy+wZ88eqqurKS4uZvPmzdx6662MHj2aLVu2hDSONWvW8NRTT4X0HO2pzCKBMMJuIyrcTnL/xhMET1EHQhER6QJM0/R2oBhl7OH+8CfYHHUD1xfMh40PwYEvOjhCERFpaOrUqQwaNAiA9evX89FHH1nOc7vdZGZmet9fccUVLTrfww8/jGmaTf7dfffd3n3uvvtu7/grr7zSovN2lLIgmi0UlfsmEH5b6l/k7JRBsST1sy46IyIi0pHqr//Os3/gv9EWBid/v50jEhHpHlq2alwkRCYmJ2AY+HQgqiGMpbWX8UjEowAcNSP5a+1FPOGeyVGiMQyYmBzfQRGLiIh0XVbfu8GKpprPTQcjTCfhhts7vtfsz0bPGO/7rV8dYutXh7zvE3pFUHLUxRjjS1yE4zYdYJVUchy3aeI81LyuHdn5Rew9VKEb3iKd3GWXXcZLL73Uruc8cuQIV111Fa+++qrPeHFxsXfR6iOPPMLzzz/PxIkT2zW2rm7maYNZ+8k+n7FCEnD2GceJZcdVhjuyH44ehF4J7RihiIhI68TFRHBSQgx7Dlb4jG8oKGb/qEkMtNrJuRWGT7faIiLSo7jdbjIyMli7di0AAwcOZP78+YwaNYqSkhKysrJ45513cDqdzJw5k3feeYeRI0e2eRxlZWVce+21APTq1YujR4+2+Tnam9Xi0T7RdY+Bhw3o1ei+pwxUAqGIiHR+Ne5jD7MuaLCA9ISKz2Ddorq/K5+HUy7oiPBEROQ4drudu+66i1/96lcAXH311WRnZ5OY6FuI8rbbbmP79u0ATJkyhQsusP4cX7VqFXPnzgXqkhPXr18fsti7grLKYBIIq7yvPR6Tbw9X+c0ZEhfdpnGJiIi0FVethzBqOWpGUWvaCDMaNB0YejZEx3VYbCIiXZkSCKVTccTHMC01keydRT7jr3smMtezljzPMDJrL+EAxyp9T09NVGKAiIhICwT63g1GJVH8puaXLOUyfhH5JrMjNhFVc4jna8/F00iT64NHXQD8NuyfTLF/wpeewfzbcxZr3GfxmXkiwSQTBss0YcuuEi4dr98JIp2Z2+32eR8fH09CQgIFBaHpItpZFqx2V9NHJBIRZvNWg6v3t/IzuZsP8YTHYBv5QzjtMkg+F2z2jglURESkFRz9ov0SCAsPVzH56Qp2RPcmym5gTz4Hhk+r60qYMLyDIhUR6VyeeOIJ77XYqFGjyM7OZuDAY6nX119/PTfffDMPPvgghw4d4tprr2XDhg1tHsctt9yC0+nE4XCQkZHBQw891ObnaG/lFh0IY6PCAQizBb7fZjPg4NFqTkzQ/TMREencXO5j9xsvsG31nxAWBSdNaceIRESkKfPnz+fll1/mrbfe4pNPPiEtLc3vmdymTZsAiIuL4/HHH+/giLuOYDoQ7i871oHw4FGX37M7UAKhiIh0XjVuD7WE8f9q7qAvR5hm286Pordzri0XRlzU0eGJiHRZSiCUTufG9BQ2FRzwuQFsYiPDdTdufBeXRthtLEhPae8QRUREug2r793mKCSBxTVXMn7ugxz88GWe29p4RXOAARxiku1TAIbbCllge4UFYa/winsy/1Pz6xbFEUh5EDfORaRjnXnmmYwcOZLx48czfvx4hg0b5lNFtK11lgWr3VXvyDDOPWUAb36632f8hcrxlNh+xVvVZzCx7ERujEwhTcmDIiLSBa3NK2TzrhLLbW7TxsWVCym0DeZPY8YzY/Tgdo5ORKTzcrvdLFq0yPv+6aef9rkWq/fAAw+wbt06tm/fzsaNG3nzzTc5//zz2yyO7OxsVqxYAcBjjz3GBx980MQeXYPVPbDYqDDW5hXyl/VfBtzPY8Llj28hc/ZYfW+JiEinVp/0MMwoZITN6T9heDpE9m7nqEREpDFhYWG8+OKLXHnllbz++uvs27ePe+65x29eUlISzz33HKeeemoHRNk1lVX6F5E5XsMOhN+WVlrOOSEuqs1iEhERaUsN1zIepjeveM7m094zOPfXZ4HpbmRPERFpTOD2MCIdJM0RR+bssUTYff/naZU8mDl7LGmOuHaMTkREpHsJ9L3bHG6PybL1X/N02Tj2E9/k/IvsW7AZpt/4Nk/bFwWor7QuIp3XHXfcwR//+EcuvfRShg0bFtJzNWfB6tixYwG8C1YleCf0869WWk4M//KcTYUZRfbOIjKWb2ZtXmEHRCciItJyuc5SFmRtx+3xv56pt8scQqXbYEHWdnKdpe0XnIhIJ7dhwwYKC+uuAaZOncq4ceMs59ntdhYsWOB9n5WV1WYxVFRUMH/+fEzT5PLLL+eii7pPpWqrDoQAC7K2U9vI9xbULcbR95aIiHR29QmEpxsFeEyL7rojL27niEREJBixsbG89tprvPLKK1xyySU4HA4iIyPp378/Z511Fg888AB5eXlMnjy5o0PtUoLpQFjcoANhoARCdSAUEZHOyqpzbkSYDcKjIKLpBgciImJNCYTSKc0YPZjV100ifURiwDmP/XScqqGKiIi0gYbfu4bFM9dgrNtZRE5+cVBzf2jfbDm+xn1my04egGHAxOSmExpFpOfoDAtWu7tcZyn/2LKnyXlaoCoiIl3RsnUFQXdvd7k9ZK4rCHFEIiJdx5o1a7yvZ86c2ejcCy+80HK/1rr99tvZtWsX8fHxLFu2rM2O2xlYdSAsLK0iwn0EG01/d+l7S0REOrv6xaMvec7hrOpH+V3NPDa4x+AxwsAWBqdc0MERiohIY2bNmsWLL77I119/TVVVFcXFxWzZsoVbb72Vvn37Nrn/nDlzME0T0zRZv359i+NYuHCh9zgLFy5s8XE6Wlll0wmE+xt0IPwmQALh4L5KIBQRkc7J5fYvitaaBgkiIlJHn6TSaaU54lg5ZwJ3zBxpub20oukLYREREQlO/ffuhlumcfkER4uO0Xgt8zr9KGOE8bXf+FbPKRTRr8n9M+zrmWtfQwKHm5w7PTWRpH4xQUQlIj1FZ1iw2t0tW1dAjcWNXCtaoCoiIl2Js6SCnPyiZu2TnV/E3kMVIYpIRKRr+fjjj72vJ0yY0OjcQYMG4XDU3Z/av38/xcXBFa1qzLvvvsujjz4KwNKlSy270XdlVh0Ii49U85uw1WyPnM/fwh/gOvurjDM+JwzrboX63hIRkc7M5XZ7XxfTj2fc3+fqmtt5cXoOXPkcxKigpIiI9ByHg0ggLCqrhg1L4c3fE7vnLfpR5jdnSFxUKMITERFpNasOhOFKIBQRaTV9kkqnd9Fp1l0G3/3yQDtHIiIi0v054mNIHRgbsuMfog9nVP+FBa5fs9Y9gSozHIA33GcFsbfJr+2vcHf407wXeT0rw5fwA9sWInH5zYyw21iQnuIz5iypYPUHTp7ctJvVHzhxlmhBlEhP09ELVrs7JVaIiEh3tmXXQczgcuS9TBO27CoJTUAiIl1Mfn6+9/WwYcOanN9wTsN9W6Kqqop58+bh8XhIT09n7ty5rTpeZ1Pr9lDhcltuO8u2kz5GJdPsudwW/k9eilxIdsRvLOfqe0tERDqzaovFowBE94OTv9++wYiIiHSwskrrwjANFZVXw7Z/wLuZXP7FrWyLuo63I27mf8JeAKBfTDgxEWGhDlVERKRFXLX+9zsjwpT2IiLSWroCkE5vSFw0w/r3YveBoz7jm788iGmaGIZRN1BTCRuWwLirod/Q9g9URESkm4iNCu1PxKNE86pnMq96JhNDFdNt23jPM6LJ/cYbn3OSrS4xJczwkG7fRrp9G6+5J3JDzQKfuf936WmkOeIAyHWWsmxdATn5RT4Lfg0DpqUmcmN6ineuiHRvLVmw6nQ6vfsOGDAgZLF1B61JrLh0vDrGiohI52bV2Sm4/ZquBi4i0hOUlpZ6X/fv37/J+QkJCZb7tsRdd91Ffn4+0dHRPP744606ViB79+5tdHthYWFIzguBv6P6cIQRxtd+4x+bga+H9b0lIiKdVY3b+sajFpCKiEhPVBbEtVtEZTGYu33GTrZ9y2BPXeGYIXHRIYlNRESkLVhdA0bq+k9EpNWUQChdwqThCX4JhIWHq9h94CjJA3rD7g3w6gI4tBu+3QY/fakuK0BERESabWJyAoZBs5NADKCZu1BBFK97JgU19yf2jZbjr7sn+o25PXWRrM0rZEHWdlxu/8q0pgnZO4vY+Hkxs886kaEJvYiNCmNicgKOeCWyiHRH7b1gtSMXkHYEJVaIiEh31tJCK7FR4W0ciYhI13TkyBHv66ioqCbnR0cfW8hYXl7e4vNu3bqVhx56CIBFixYxfPjwFh+rMfUd7DtCoGuxCbZ8bIb/3br3PCMDHkvfWyIi0lm5AnQg1AJSERHpiYJJIBxv+9xy/APzFEAJhCIi0gkd/gbe+wucfZPlNWC4Xdd/IiKtpQRC6RImD0/g2ff8q6R+sHM3ye8+DtuePjb4ZTbseA7SrmjHCEVERLoPR3wM01ITyd5ZFPQ+6SMSMaFZ+zRHJC4usm/xGy81e5HjOd1v/IUP93JyYu+AyYMN1XhM/r55j/e9OhOKdF/tvWC1IxeQdoSWJlYM9BTBm09CVF845+Y2jkpERKRttKTQimHAxOT4ujfuWvj6Xeg1ABIDJ26IiEjbcblczJs3D7fbzbhx47jppps6OqSQCLRwdKJtp+V4oARCn+8tERGRTiZQAqE6EIqISE9UVtl0Uc8JtnzL8Q88qQCcoARCERHpbLIXQ+6z8NHTZNTM4nGm4+JYwTNd/4mItJ4+SaVLmJScYDke/2Gmb/JgvbW3wZHiEEclIiLSfd2YnkJEkFV7Iuw2FqSnNGufQOyGway0Idhtvp2EBxiH+dQ8yW/+G55JDE7o6ze+eddB7l+z05s8mGQUYdB4ImG9+s6EGcs3szave3UHExEJpfrEiuCYTLbl8dfwB7kwewa8mwnvPgKuilCGKCIi0mL1hVaaY0ZKLEn71sHL18HSk+Gpi+H9v4YoQhGRzq13797e11VVVU3Or6ys9L6OjY1t0TkXL15MXl4edrudFStWYLfbW3ScYDidzkb/3n///ZCdO1AHwvz4afy59ods9ZyCy6z7tx8ye/O5mWQ5f3pqIkn9YkIWp4iISGu43G7L8YgQfr+LiIh0VsF0IHRjwxXuu5bigNmH3eYgAIbENV1sVUREpN0U7oDcrLrXVaX8xnyKtyJu4ULbe94prV2XKCIiSiCULiKhdyQjBvk/IF50eCZm70H+O1Qegjd/1w6RiYiIdE9pjjgyZ49t8sI7wm4jc/ZY0hxxQe/T2LH+fNXpLJt9On++8nSf4+w1B3CF6/ecXb2MpTUZ7PLUff8nf/9n/L+J/omFUJdECBBGLasj/sC6iJu5xv4felFpOf94LreHBVnbyXWWtujfIyKdT3svWO3IBaQdoTmJFXeEPcuzEfdxvv1DDPO7BO+qUvh4degCFBERaaXmFE0Js8HDJb+E535a98Cz8lDdhp1vgCe44iYiIt1JXFyc9/WBAweanH/w4EHLfYOVm5vL/fffD8BNN93EuHHjmn2M5khKSmr0b/DgwSE7d3mAhaOnTTqPZeaVZLgWclr1E8x2/Y67a67BtHg8XF8gTEREQu/VV18lIyODoUOHEhUVRWJiIpMnT2bJkiWUlZW1Swxz5szBMAzv38KFC9vlvC2y899w8Et1IBQREWmgrLLpBML7aq/i2ak5bLtoDbfX/IwX3Wfzb/dZQF010MF91YFQREQ6CdOEN+8ETJ/hk2xFTLdt874P1/WfiEirhXV0ACLBmjy8Pzv3lfuMOSsj2HvePTjenO87+YQzYMr/tF9wIiIi3dCM0YNZfV00mesKyM4vwmxwjW4YdVXJF6SnkOaIC2ofgP69Ijh41OVzuW91rEDH2WsO4M+eH/PZyb/gt2mVTDz9HE4+6uL+NTup9Rx3su9caHufwUYJGLDI9hS/CXue593nsrJ2JoVYdzmu53J7yFxXwMo5E5r87+UsqWDLroOUV9USGxXGxOQEHPGq2i7SmcTFxXHoUN3i/QMHDvgkFFpp7YLVpCTrrg7d2Y3pKWwqOODtABvIWvcEfhH2b/8N7/8Vxl1NM1oZioiItJv6oikLsrY3+V2XMjCWyGHT4aO/+244sg++/QiSzghhpCIinU9qaiq7d+8GYPfu3QwdOrTR+fVz6/dtrlWrVlFTU4PNZiM8PJzFixdbztuwYYPP6/p5qampZGRkNPu8HSFQB8KxDb63qtyRbPacajmvYYEwEREJnSNHjnDVVVfx6quv+owXFxdTXFzM5s2beeSRR3j++eeZOHFiyOJYs2YNTz31VMiO36aOFMFL14K7muHDfkpvJnME3+cuSiAUEZGexjRNygJcBx6v6EgNX0Q5yHKnk+VO99k2JE4JhCIi0kl88Tbs/q/fcKUZwYO1x+7RqgOhiEjrKYFQuowpJyfw5Du7/cb/4z6Dn4+aBZ/+C8J7cWjSbbwd+0PKC0xinU4t3hcREWmFNEccK+dMwFlSwXu7SyivqiE2KpyJyfEk9bP+fm1qn2CPFey5+/eOZNTgWHZ8Y12Zd27YWp/3fYxKfh62hq2eERR6Gk8gBFi3s4i/bviSC0cPtvxNkessZdm6AnIskiynpSZy43FJliLScdp7wWpPFGxixUdmCh97hjHGdtw13v482PMuDJ0S4khFRERapqmiKfU+KyyncHI6g/m7/8adryuBUER6nDFjxrB2bd09mq1btzJt2rSAc/fv34/T6QQgMTGRAQMGNPt85ncf0B6Ph/vuuy+ofXJycsjJyQFg1qxZXSiB0LrzRJ+ocE4bHdfsAmEiItL23G43GRkZ3u/CgQMHMn/+fEaNGkVJSQlZWVm88847OJ1OZs6cyTvvvMPIkSPbPI6ysjKuvfZaAHr16sXRo0fb/BxtKnsxuOqKTKd88SQ5kS+wtPZyVrun4vmuo64WkIqISE9T4XLjDlBc+Xj7y6qJCrdbbjtBCYQiItJZ9BsKp1wIn6/xGX7CPZN9DZoDRKqAjIhIqymBULqMM4fFY7cZfhfA7355kJ//ZAmllW7urZnNC28ZmOYn3u1avC8iItJ6jviYZifkB9qnucdqan6us5RPC8stt6UZXzDO9oXf+FeegbzlGR90DPe9sZM/rtnp95tibV5hwCQZ04TsnUVsKjhA5uyxzBg9OOjziUhotPeC1Z4quMQKg6fc57PU9rh3pBY75ck/oF9MfLvFKiIi0hLHFztZ83Eh63YW+c27+cN+PGWPJsxd6bvhq03tFKmISOcxY8YMlixZAtR1Prr11lsDzn3jjTe8r2fOnBny2Lq6QJ0nYqPqHgO3pECYiIi0rSeeeMJ7X3LUqFFkZ2czcOBA7/brr7+em2++mQcffJBDhw5x7bXX+nTJbSu33HILTqcTh8NBRkYGDz30UJufo80U7vDr6D7AKOOB8BV85jmRHeZwQB0IRUSk5zlcaV1ExkpReRXhdsNvPMxmMCA2si3DEhERabn+KXDlP2H3RnjzTijcTrHZh+W1F/tMC1cBGRGRVtMnqXQZsVHhjDmhr9/4poJi7lxXxITPr2L1F4bf4tT6xfsZyzezNq+wnaIVERGR9rJsXQG1ASrsXWLfaDn+hHumtzptsI7/TZHrLG2ywxaAy+1hQdZ2cp2lzTqfiLS9GTNmeF+vWbOmkZlasNpa9QtUN9wyjaUZacydMtRvzmvuSRw0Yyk2+/Jw7SVMqsrkrPwrWVsU1+7xioiItIQjPoZLxyexJCPNctHqO18d4U3XGAAO2BMpHjUXrnkN5jb+O0REpDuaOnUqgwYNAmD9+vV89NFHlvPcbjeZmZne91dccUWLzvfwww9jmmaTf3fffbd3n7vvvts7/sorr7TovB0hUAfC2Khwn/f131tzpwzj0vFJSh4UEWknbrebRYsWed8//fTTPsmD9R544AHGjh0LwMaNG3nzzTfbNI7s7GxWrFgBwGOPPUZsbGybHr9NmSasvR3wf/bzmnuiN3kQ1IFQRER6nrIA14BWisur+aa00m98UN8o7Db/xEIREZEONex7MD+Hoz/4C/fUXM1RfLvlqoCMiEjr6ZNUupQpJyf4jbncJv/Y8jU1buvEgWPzPFz/zDYWv/4pqz9w4iypCFWYIiIt4na7ycvLY9WqVdxwww1MmjSJmJgYDMPAMAzmzJnTbrHMmTPHe17DMFi4cGG7nVukOZwlFeTk+3f6qPeH2qv5lWsBWz2neMcOmb15wX1Ok8eOpop+lPmN1ycE3vP6p00mDzbcJ3NdQVBzRSR02nvBqhxboPqjsSdgHPccspoIrnL9jsnVj/Bw7aUU009J1yIi0iXF94pgnKOf5bZHa3/ED6rv44yjf2JK7gWsPZoC9nDLuSIi3Zndbueuu+7yvr/66qspKvK/p3Pbbbexfft2AKZMmcIFF1xgebxVq1Z5712ee+65oQi5yyi36EAYGWbTghoRkU5iw4YNFBbWFTqeOnUq48aNs5xnt9tZsGCB931WVlabxVBRUcH8+fMxTZPLL7+ciy66qM2OHRJuFySOBMPuM1xlhnN/zWyfMX3fiYhIT1NWad2FPswiIbCovJpvLRIIh8RF+42JiIh0CjYbR1Mv4VXPZL9Nuv4TEWk9fZJKlxJua93/ZN2myRObdnPLCzs4Z0kO81Zt1cJUEek0LrvsMsaMGcPcuXN59NFH2bJlC5WV/jfyQm3NmjU89dRT7X5ekZbYsuugX/fhhtzYecMzkQzXQi6qXszL7imsqr2AKiKbPPYV9hzejVzAPWFPMtTw7WLscnv4YM+hZsWanV/E3kMqYCASKsEsHm3rBasSvGXrCiw/r3eaJ1JDmM+Ykq5FRKSryXWW8sGeEsttn5pD+cQcChhKlBeRHm/+/Pmcd955AHzyySekpaVx11138c9//pPHHnuM733veyxduhSAuLg4Hn/88Y4Mt8uwSiA8vvugiIh0nDVrjnUgnzlzZqNzL7zwQsv9Wuv2229n165dxMfHs2zZsjY7bsiERcIPlsIv34Hh073Df3X/gG8Y4DNVC0hFRKSnKau07kA4rH8vv7GSoy72HvJfd3SCEghFRKQTq661Lugfrg70IiKtFtb0FJHOIddZyp/Xf9FmxzNNyN5ZxKaCA2TOHsuM0YPb7NgiIi3hdrt93sfHx5OQkEBBQfstoC8rK+Paa68FoFevXhw9erTdzi3SElYLpALJM5P535rrg5prx83PwtYQbbj4f2Fvc5V9HTmesTzpnsE7njEtitU0YcuuEi4dH+Mdc5ZUsGXXQcqraomNCmNicgKO+JhGjiLS/ezevZuVK1f6jO3YscP7etu2bdx5550+26dPn8706dNpifnz5/Pyyy/z1ltveReszp8/n1GjRlFSUkJWVhabNm0CtGC1rTTVLdZKfdJ1Uj99JoqISOe3bF0BtZ5GKps0UJ8ov3LOhBBHJSLS+YSFhfHiiy9y5ZVX8vrrr7Nv3z7uuecev3lJSUk899xznHrqqR0QZddTVuW/eLRPlB4Bi4h0Fh9//LH39YQJjV8HDBo0CIfDgdPpZP/+/RQXFzNgwIBG92nKu+++y6OPPgrA0qVLGThwYKuO164SR8JPX4KCN/n69QdYXvRDvylKIBQRkZ7G6hoQ4OTE3hQUHfEbt0rCGNw3qs3jEhERaSsut3UCoa7/RERaT0+PpMtYtq6AGndwC3ECucS2gUijhix3unesvvL36uuiSXPEtTJKEZGWO/PMMxk5ciTjx49n/PjxDBs2jFWrVjF37tx2i+GWW27B6XTicDjIyMjgoYceardzi7REbIgWQ82wbSXJOOB9bzNM0u3bCKe2xQmEAOXf3czPdZaybF0BOflFPh25DAOmpSZyY3qKfpdIj7Fnzx7uvffegNt37Njhk1AIdYtOW5pAqAWr7a+pbrFWrJKuRUREOiMlyouINE9sbCyvvfYa//rXv/j73//O1q1bKSoqIjY2luHDh3PJJZdw7bXX0rdv344OtctoWGArEhd/j7ifve4xUFADjrMgqk8HRiciIvn5+d7Xw4YNa3L+sGHDcDqd3n1bk0BYVVXFvHnz8Hg8pKent+szxzZjGHDKBTwz4kQqinb5bY5QBwoREelhAnUgTEnszXqquNS+gXc9p/KlOQQwLOcOUQdCERHpxGoCJBBG6vpPRKTVlEAoXUJLFuI0FEU1fwhbxWVh/6XaDONjzzDyzGTvdlX+FpHO4I477ujQ82dnZ7NixQoAHnvsMT744IMOjUckGBOTEzAMmp2Y8pNxJ/Bq7rcBihOYzA973XK/v7lnND/IBmKjwlmbV8iCrO2W1ZLUIVmkfWjBavtqTrfYhjYWFHPWsHh1ZhURkU5NifIiIi0za9YsZs2a1eL958yZw5w5c1odx8KFC1m4cGGrj9ORGnafSDO+5CzbTs6q3AnPrAbDBoNOg+/fDcNbVohHRERap7S01Pu6f//+Tc5PSEiw3Lcl7rrrLvLz84mOjubxxx9v1bGs7N27t9HthYWFbXYuq+5JAOF268QIERGR7qoswHO3kwfGcqYtn3vCVwGwz+zHO55Tedc9mrc94zhMb+/cE5RAKCIinZgr0PVfmK7/RERaSwmE0iW0ZCFOveHGNzwWvoxUW93N60ijlsfCl3GR617KGlwYq/K3iPRkFRUVzJ8/H9M0ufzyy7nooouUQChdgiM+hmmpiWTvDL7QQPqIRB68bCxXTxpK5roCso/rAphqOBlr869iu8sziPWetBbHahjQNzqM65/ZZpk82JA6JEtPcu6552K29Md+Ay1ZPNraBasSnJZ2i/3X9m95NfdbdWYVEZFOraWJ8uVV1pXCRUREmqvhd9FZts98N5oeKNwOYVHtG5SIiHgdOXLE+zoqqunP4+joYwv6y8vLW3zerVu38tBDDwGwaNEihg8f3uJjBeJwONr8mIFYPVeJCLNhGFpAKiIiPUugDoQnD+jNZFue9/0g4xA/sW/iJ/ZNXFy9mI/NY+sk1YFQREQ6s0AJhBF2eztHIiLS/aiXq3QJLV2I05/D/Cvi997kwXon2or5a8SfiODYBXV95W8RkZ7o9ttvZ9euXcTHx7Ns2bKODkekWW5MTyHCHtzP2gi7jQXpKQCkOeJYOWcCG26Zxh0zR3rn5Jsnkl69hKzaaVSbx5JeVrkvwAzi5/MFtq0MoNRvfHpqIlnvO5tMHqxX3yFZRKSrq+8W2xLj2cnn+XlkLN/M2ry2q1guIiLSVlqaKB8bFd7GkYiISE/VMCn9+/aP/CeERcEJ49sxIhER6Wgul4t58+bhdrsZN24cN910U0eH1GpWC0gjg3w2JCIi0p2UWRQmi4mwMyQuiim2T/y2HTZj+MQc6jM2JE5FZkREpPMKtLYuIkzXgCIiraVPUukSWroQ5wB9+bv7fMttY40vONX4ymdMlb9FpCd69913efTRRwFYunQpAwcO7OCIRJonzRFH5uyxTSYRRthtZM4e69fByhEfwy/OSWb6iETv2JfmCdxeO5/J1Y/wYM2lfOEZwgvuqT77TTipH7bjEmL6c5hHwjPZHPlrVoQv5QLbVsKpJdxucPkEBzn5wXdKhGMdkkVEurL6brHNcYaxk6fD7+OFyD/wP2EveTuz5jpLQxOkiIhIC7UkUd4wYGJyPBTnw9YnQhOYiIj0CDVuD1U1dQtqBnOQNNsu/0knTYawyHaOTERE6vXufazbT1VVVZPzKysrva9jY2NbdM7FixeTl5eH3W5nxYoV2EPUpcHpdDb69/7777fZuawSCLV4VEREeqLDFh0I+0SF09csZ5Sxx2/bZs+peBosE46NClNxMxER6dQCdSAMt6sDvYhIa3W6u2lut5u8vDxWrVrFDTfcwKRJk4iJicEwDAzDYM6cOSE796uvvkpGRgZDhw4lKiqKxMREJk+ezJIlSygrKwvZeaVprelY8WBtBu95RviMHTRjudL1O7aZKT7jujgWkZ6mqqqKefPm4fF4SE9PZ+7cuR0dkkiLzBg9mNXXTSJ9RKLfbwbDgPQRiay+bhIzRg8OeAyrToYH6csj7kv4vmsJFRyrwmcAP/9est8xMuz/JcJwE2Z4OM/+EY9H/IktkdfzP8P3cbiyBtNs3r9LHZJFpLsItlvseCPfmzj4PXseAD+ybeIkY586s4qISKfU3ET5fpSxePA7JK2eCX8+E/79GyjZHcIIRUSkOyuvqvV5/2TtDL4xE3wnjbioHSMSEZHjxcXFeV8fOHCgyfkHDx603DdYubm53H///QDcdNNNjBs3rtnHCFZSUlKjf4MHB34m01xKIBQREalTVlnrN9YnOgyjcHvdQobjvOM51ef9kL7RIYpMRESkbQRKINQ1oIhI67WsrVsIXXbZZbz00kvtes4jR45w1VVX8eqrr/qMFxcXU1xczObNm3nkkUd4/vnnmThxYrvGJnXqF+Jk72xe1x4AN3Z+7bqBVyLv4gTjILs9A5lT81v2mIN85nkrf4uI9CB33XUX+fn5REdH8/jjj4fkHHv37m10e2FhYUjOKz1PmiOOlXMm4Cyp4L3dJZRX1RAbFc7E5HiS+sUEtX/m7LEsyNqOy338jQjfO+0msPC1T/A0SAi04eFK+zq/4/blKNkH+/GDFnY6VodkEekOGv+MPeZH9ne8iYP1wgwPN4S9ws0113k7swbzuS4iItJebkxPYVPBgUa/4wDSbR+yPPxhwkvcvht2PA/n/jaEEYqISHfV8L5RIQn8ofZq/lD7/8icavDDiI9g579hxA86MEIREUlNTWX37rqiIbt372bo0KGNzq+fW79vc61atYqamhpsNhvh4eEsXrzYct6GDRt8XtfPS01NJSMjo9nnDbUai+stLR4VEZGeqMxi/UCfqHA4eSpz+mcR8+0WptjymGLLI9m2j3ePTyCMi/LbX0REpDOpcVtX6I/UNaCISKt1ugRCt9t38UR8fDwJCQkUFISmy4Db7SYjI4O1a9cCMHDgQObPn8+oUaMoKSkhKyuLd955B6fTycyZM3nnnXcYOXJkSGKRxgW7EMdKMf2Y67qVO8Ke5aaaX1JCH785Jw/ozeYvDzIxuS5hUUSku9u6dSsPPfQQAIsWLWL48OEhOY/D4QjJcUUCccTHtPi7vK6TYTSZ6wrIzi9qtGNg4eEqn/fn2HJx2Ir95q33pPHhwQgmHnW1KCZ1SBaR7iKYz9jHamdxuT2HCMP33sCPbJt4xPgRe8xBbNlVwqXjdc0mIiKdR7CJ8ts8KdhtRl1FkgZqtjBloi0AAQAASURBVD3LK71mU17tJjYqjInJCbo/KSIiQTm+A2EdA1diGoz/AaT/vt1jEhERX2PGjPGuR9m6dSvTpk0LOHf//v04nU4AEhMTGTBgQLPPZ353083j8XDfffcFtU9OTg45OTkAzJo1q1MmEFpda0XYtXhURER6HssEwui6NQW94hJZs/dM1nrOBGAwBynEt6HCkDh1IBQRkc7NdVwuSb1wXQOKiLRap/skPfPMM7nttttYvXo1u3bt4uDBg9xxxx0hO98TTzzhvVk7atQocnNzueeee5g9ezbXX389mzZt4je/+Q0Ahw4d4tprrw1ZLNK4+oU4Td0EDrcZXDPpJH7+vWF1C3K+87npYE7Nby2TBwEKio5wyws7OGdJDvNWbSXXWdqW4YuIdCoul4t58+bhdrsZN24cN910U0eHJNJp1Hcy3HDLNJZmpHH7zBFBVTAaY+zGc1ynQoAX3FMB2HOgwmJrnRiquMyeQxzlfttKjlbjLKlo9NzOkgpWf+DkyU27Wf2Bs8n5IiIdpeFn7KyxQ/y2F5LAc27/RVRHiOZk4xtAnVlFRKRzqkuUn0T6iESMAD/8S+jD1wlT/MbDD39F1ksv8ofXP9X9SRERaZaySuvro9ioTldDVkSkx5oxY4b39Zo1axqd+8Ybb3hfz5w5M2QxdUXVtepAKCIiAlBW6V9Ips9314CJsZE+44UkwHGrFJRAKCIinZ3L4voPdA0oItIWOt3To1AmCx7P7XazaNEi7/unn36agQMH+s174IEHWLduHdu3b2fjxo28+eabnH/++e0WpxzTWMcKw4DpqYksSE8hzREHwBkn9Wuy8vfxTBOydxaxqeAAmbPHMmP04Db+V4iIdLzFixeTl5eH3W5nxYoV2O32kJ2rvlJqIIWFhZx55pkhO79IS9V3Msx1llLraaQV4XcecV/Cq5zDsxN2Y25/hiSKKDF7s84zDoA3P91HVLiNyhr/3yXTbdv4v/AV3Be2knc9p/KG5yz+4z6DQ/Thvjd28sc1O5mWmsiNDX7nAOQ6S1m2roAci99F01ITuWKCg8OVNZRX1aqTiYh0Ko74GM4+uT//2v6t37aGXQhLzV6sqP0BT7nP5wh1n1/qzCoiIp1VfaK8s6SCtz7bzz2vfXp8s0GyqqdwO//1Gas2wznFtpeP3KcAuj8pIiLBK7PsQKgEQhGRzmTq1KkMGjSIffv2sX79ej766CPGjRvnN8/tdpOZmel9f8UVV7TofA8//DAPP/xwk/MWLlzoXS9z9913s3Dhwhadr71YLSDV4lEREelpPB7TstBm3+86ECb2iWryGCcogVBERDo5l9t6nZ660IuItF6Pfnq0YcMGCgsLgbqbtlY3aQHsdjsLFixg3rx5AGRlZSmBsAM1XIjz3u4SyqtqiI0KZ2JyPEn9fBfEN5Zw2DgTl9vDgqztrL4u2mehvohIV5ebm8v9998PwE033RTw+6+tJCUlhfT4IqG2bF0B7iASCAH2uPtzV+koHGMv4dP33mSQUULNdz+5XW4TAtzguND+HgBhhodz7B9zjv1jFoc9SbprKXvMQZYLiNfmFQYslFA/P3tnkc94fWLh8YmIIiIdYWJyAoaB33VaIQmscP+ACjPKJ3EQ6j7HJibHt3OkIiIizeOIj2HelGG8/el+3v3yoM+2vxWn8pvYvkTUHOZ9Tyovub/HG+6zKKOX33F0f1JERJoSqEN7HxVeERHpNOx2O3fddRe/+tWvALj66qvJzs4mMTHRZ95tt93G9u3bAZgyZQoXXHCB5fFWrVrF3Llzgbp1LuvXrw9Z7J2JZQKhFo+KiEgPc9RVi9XShT71CYTHdSC0og6EIiLS2QXqQBiua0ARkVbr0QmEa9as8b6eOXNmo3MvvPBCy/2k49R3BWrK8QmHj//3SwqKjgScf6Kxnz+FP8ZNNb9kj3sQmesKWDlnQluGLiLSoVatWkVNTQ02m43w8HAWL15sOW/Dhg0+r+vnpaamkpGR0S6xinQ0Z0kFOflFTU9sIDu/iJVXn8GqLSPxazdiIZoqptu2+43vNQewx/Ttju1ye7j+mW1cdNo+Xv+4MOjExnr1iYUbPy9m9lknMjShlzoTikiHccTHMC010S/ZGWBJrXWF9empiX6FY0RERDqri9OG+CUQugjnbvsC3jkSz9fH/d634nJ7dH9SREQCKlcHQhGRLmH+/Pm8/PLLvPXWW3zyySekpaUxf/58Ro0aRUlJCVlZWWzatAmAuLg4Hn/88Q6OuPOxKqaoDoQiItLTBOpCX19EJpgOhEPimp4jIiLSkQIlEEbqGlBEpNV69NOjjz/+2Pt6woTGF2AMGjQIh8OB0+lk//79FBcXM2DAgFCHKG2oflH8LS/kBp5j7CcrYjEnGAf5Z8RirnDdSXY+7D1UoUWqItJtmN+1+fF4PNx3331B7ZOTk0NOTg4As2bNUgKh9Bhbdh1sRgfjOqYJ2/cexiCo/EGm2bYTbbj8xt/wnAUYfuNu0+Rfud82L6jj1HhM/r55j/e9OhOKSEe5MT2FTQUHLBcAHS/CbmNBeko7RCUiItI2Zpw6iN+/kkftcYU/skpHNus42flFuj8pIiKWAiUQqgOhiEjnEhYWxosvvsiVV17J66+/zr59+7jnnnv85iUlJfHcc89x6qmndkCUnZvVAlItHhURkZ6mrDJAF/roumXATXUgNAwYGESSoYiISEeqCbB+REVkRERar0d/kubn53tfDxs2rMn5Dec03DdYe/fubfSvsLCw2ceU5mksCcBh7Oef3yUPAgw2SvhnxGJOopAtu0raMUoRERHpLAItwmrKmo8Lg0oeBDjNtsty/N/us5rctw9H+V3YPzjFcDYjOn/1nQkzlm9mbZ5+k4pI+0lzxJE5eywR9qZvTzxw6WlKchYRkS6lX68Izk7p3+rjmCa6PykiIpbKq6wXj/ZWB0IRkU4nNjaW1157jVdeeYVLLrkEh8NBZGQk/fv356yzzuKBBx4gLy+PyZMnd3SonZJVAmF4EPcURUREupPDgRIIvysi01Ry4MDYKH1/iohIpxeoA6G+w0REWq9Hf5KWlpZ6X/fv3/RCjoSEBMt9g+VwOBr9O/PMM5t9TGmeQEkAJ1BMVsS93uTBevVJhJ9+sh1nSUV7hCgiEnIPP/wwpmk2+Xf33Xd797n77ru946+88krHBS/SzmJbuNjqi6IjQc+9v/ZKplY/xAM1V/CxZygAezyJfGIObXLfH9rfZX7YG7wZ+VtejriLK+zZRFPVopgBXG4PC7K2k+ssbfExRESaa8boway+bhLpIxIx/BuvetUG0aVQRESks7n4tCFtcpxACSIiItKzDTqwmRcj7uYX9tc4ydgHQHS4XYtpREQ6sVmzZvHiiy/y9ddfU1VVRXFxMVu2bOHWW2+lb9++Te4/Z84c7zO79evXtziOhQsXeo+zcOHCFh+nvbgs7g2q+4SIiPQ0Vh0IU4y99I2o+57sFxNOuD3ww7Yhceo+KCIinZ/V9R/oGlBEpC306E/SI0eOLeyOimr64ig6Otr7ury8PCQxSWgFSgIooxdFZpzltkIzgdWfVnLOkhzmrdqqBfUi0umtWrUKwzAwDINzzz23o8MR6dImJic0mswSSLDdB+vtMQfxF/cPudh1H+dU/4lba64Fmj7xZfb13ten277g/vAn2Bj5P4w2rLsaBsPl9nDz6lxWf+BUAQURaTdpjjhWzpnAhlumcdN5p1jOeXnbN+0clYiISOu15HrCSux3VcRFREQaOqUkh/G2Au4Iz+K/kTexJuK33BT+AriOdnRoIiIibcqqA0WEEuZFRKSHKTuueYIdNy9GLGTi8+Pg6R9jvLOMs2P2YsM68WJIXLTluIiISGdidf1nGBBma6OHbiIiPVjLWqpIizidzka3FxYWqgthiNUnAZjHreovJ4arXbfx94j7GWf7wju+3TOcq123UU4MmJC9s4hNBQfInD2WGaMHt3P0ItLd7d69m5UrV/qM7dixw/t627Zt3HnnnT7bp0+fzvTp09slPpGeyBEfw7TURLJ3FgW9T0pibwqa0YHweF+bA/magU3OG2V8xWm23X7jHmwUmEktPj9AQdERbnlhB4YB01ITuTE9hTRHXKuOKSISDEd8DAvSU1iTt4/PCst8tm3edZBvSis5IS4aaqvh4xcgbTbYtFBIREQ6p1xnKbe9+HGrj2MYMDE5vg0iEhGRbsXj5rQjm3yGRtqcDDLLIExdJUREpHuxTCBU9wkREelhju9AONrYTR+jAtzAl9nwZTZ/Ax4J+xEP1l7mt/8JSiAUEZEuwKoDYbjdhtFWVTtFRHqwHp1A2Lt3bw4dOgRAVVUVvXv3bnR+ZWWl93VsbGyzz5eU1LqF3NJ6jSUBHPkuifCpiAcYbytguyf5WPJgAy63hwVZ21l9XbQW0otIm9qzZw/33ntvwO07duzwSSgECAsLUwKhSIjdmJ7CpoIDljcnjhdht3Hh6EEUZH/R5NzWath9sKHltRdTTUSbnMNUAQUR6SCXnH4C9x6XQGia8Mq2b7j+hC9h7W1Qsgtsdki7ooOiFBERadyydQVBXUc0ZXpqIkn9YpqeKCIiPcvercR5DvkNb42cxPk2ewcEJCIiEhqmaVpeWymBUEREepqyKt8Ewim2Tyznve8ZYTmuDoQiItIVWBWQiVQHehGRNtGjP03j4uK8rw8cONDk/IMHD1ruK13LjekpRAT4IXGEGK5x/Zana7/P1a7bKaOX5TyX20PmuoJQhikiIiKdRJojjszZYwP+fqgXYbeROXssGWc4aI+CRyYGlaZvomCRGccz7vQ2P1d9AYVcZ2mrj+UsqWD1B06e3LSb1R84cZZUtD5AEel2Zo0dgu24z9Jk41vS/vtzePayuuRBgLfuhury9g9QRESkCc6SCnLyg+9kDhBOLefbtrIkbDk26h6ORthtLEhPCUWIIiLS1X32muVwbu+z2zkQERGR0ApUmEUJhCIi0tOUVdb6vJ9sy/ObU2uEs9WTarm/EghFRKQrqFEBGRGRkOnRHQhTU1PZvXs3ALt372bo0KGNzq+fW7+vdE31SQALsrZb3mg+Qgy/r53X5HGy84vYe6hC1b9FpM2ce+65mKbZ6uPMmTOHOXPmtPo4CxcuZOHCha0+jkh3MGP0YFZfF03mugKy84to+P+qhlHXEWRBeoq3O3GgjseBTDipH7l7DzerO8mi2mt4qDaDH9rf5XJ7DqfZdvOXNuw+eLz6Agor50xo0f65zlKWrSsgx+K/37TURG5s8N9PRCSxTxTfSxnAfz8vBsDAwxPhS0lmn+/EI/tgw1I4b1EHRCkiIhLYll0HCe4S3yTN+JJL7Bv5oX0z/YwjALzlGU+OcSaZs8fqd7KIiFgbPJatxmhO93xKmFF3T6ncjGZvXMvu3YiIiHRWVt0nQB0oRESk52nYgTCCGk63feE35+uYU6mqjLTc/68bviQxNlL3G0VEpFOzugYM1/WfiEib6NEJhGPGjGHt2rUAbN26lWnTpgWcu3//fpxOJwCJiYkMGDCgXWKU0GgsCSBYpgnvfVFE0ui+EN0PZ0kFW3YdpLyqltioMCYmJ+CIV3KhiIhId5HmiGPlnAk4Syp4b3cJ5VU1xEaFMzE53q+gwI3pKWwqOBBUQmCE3cadF42i8HBlwAIHgZQTwzPu7/OMO51Jtk/5yBNcZ5ILbFv51DwRpzkw6HNBywsorM0rDPhvM03I3lnEpoIDZM4ey4zRg5t1bBHpvoYP6OVNIDSx8X+1V7A84mG/eZ7Nf8Y27mpIGN7OEYqIiARWXlXb9CRgku1TsiLu9RufF7aW9Ivn6fexiIgEdloGP32+N9G1h/m+/SMusG3lkBlLTIyeTYmISPcSKIFQHShERKSnKas8lkDoIpyp1Q9zRe+PuHnIJ/D1uwD863DgNQNbvzpExvLNei4vIiKdmtU1oK7/RETaRo/+NJ0xY4b39Zo1axqd+8Ybb3hfz5w5M2QxSfupTwLYcMs0Zo0d0qJjnJL3EK4/n82ix5/hnCU53PLCDv7w+qfc8sIOzlmSw7xVW8l1lrZt4CIiItKhHPExXDo+iblThnHp+CTLZLr6jscRTVQ/irDbvB1F6gocTCJ9RCKG0dyoDDZ7Tg2q+2CKsZdl4Y/yRsQd/Ni2EQi+koJpwiPZX+AsqQh6n1xnaVCJkS63hwVZ2/XbSUSAus+Op7fs8Rlb65nAu+5RfnM/qXWQvzf4rq8iIiLtITYquNp973lG8rXHv1jdRNtnJJTnt3VYIiLSjVTXuqmu9VBKLC+4pzK/5mZurf0FsVHhHR2aiIhImwr0fEELSEVEpKc53CCBEOAAfXm79w9h3ho+vXwzf3T/P153n9noMfRcXkREOjura0Bd/4mItI0e/Wk6depUBg0aBMD69ev56KOPLOe53W4yMzO976+44op2iU/ahyM+hrNP7t/s/S6wvc+YPU8RcWQvt317I5fZcmi4AL++m07G8s2szStsw4hFRESkK2gsIdAwIH1EIquvm+RT2a9hgYOlGWn8/HvDsNuCyya0GwY/P3sYSzPSWHH1eMvzRuIiM/xRoowaYo1K/hTxF/4U/hiRuIL+dz231dmsQgnL1hUE3VXR5faQua4g6FhEpPtatq6AGvfxCc4Gi2qvxm3WfbgdMPvw25r5zHL9gf/bFlyShoiISHuZmJwQVGEQDzaecl9guW3QzqfaOCoREelOrLvdGsRG6vpIRES6l4AdCJso4igiItLdlFlcB/b5rojM0veO8njNhXxpntDkcfRcXkREOjOra8BwXf+JiLSJbvsEadWqVcydOxeoSxRcv3693xy73c5dd93Fr371KwCuvvpqsrOzSUxM9Jl32223sX37dgCmTJnCBRdYL+iQrqt+QY8ZZAOeYUYhS8Mf976PNGp4IHwF443P+X3tXJ/uP/VVe1ZfF02aI66NIxcREZHOrD4h0FlSwXu7SyivqiE2KpyJyfGWnQvrOeJjcMTXbT/jpH5NdvCr72TYMBnxvFGDfM771YGjDP1gMSNtX/vs+2P7O/TlKNfW3ERNkJcH9YUSNn5ezOyzTmRoQi9io8KYmJzgjRvAWVJBTn7zuoJl5xex91BFo/99RKR7a+yzI988kVXuGQAsq72EMnoB+uwQEZHOxxEfw7TURLJ3Nv17+Hn3ufxv2Av0Nqq8Y9s9yWwLG8foUAYpIiJdmnUCYfBdcEVERLqKmoAdCO3tHImIiEjHKjuuAyFAn+gwPZcXEZFuRR0IRURCp9M9Qdq9ezcrV670GduxY4f39bZt27jzzjt9tk+fPp3p06e36Hzz58/n5Zdf5q233uKTTz4hLS2N+fPnM2rUKEpKSsjKymLTpk0AxMXF8fjjjzdxROmKmrOgJ5oq/hL+MLFGpd+2WfZ3+Zt7Bp+ZJ/mMu9webl6dyy/OSfZbWC8iIiLdX8OEwOaq62QYTea6ArLzi3wKHhgGTE9NZEF6imWhAp/z7loP29ZYnmOUbQ8DjUPsNQc0K7Yaj8nfN+/xiWdaaiI3fhfPll0Hgy7QUM80YcuuEi4dr99LIj1VU58d99T+FPBt6aTPDhER6YxuTE9hU8GBJjtylxPDC+6p/NT+Fms9Z/Jk7Qw+MlMYW9OPue0Uq4iIdD3lVf4LRwFiv+s+ISIi0l1UB+pAqAWkIiLSw5RZXAf2iQrXc3kREelWrIrIRNgNi5kiItJcnS6BcM+ePdx7770Bt+/YscMnoRAgLCysxQmEYWFhvPjii1x55ZW8/vrr7Nu3j3vuucdvXlJSEs899xynnnpqi84jnV+wC3qicFFKb8ttd9XO8UserFdQdIRbXtjht7BeREREpCkt7WTo44Qz4PSfwrZ/+Aw7PQO4suaOZicPWjm+M+H+w1VN72Qh0AI4EekZAnXROMb6xvDGgmLOGhavgi0iItJppDniyJw9NqiO4jmJP+Xxby+ikATv+J6DR9sjTBER6aICXTv1iVYCoYiIdC8uJRCKiIjg8Zgcqfa/DuwTHR7EszVrei4vIiKdkdU1oK7/RETahj5NgdjYWF577TVeeeUVLrnkEhwOB5GRkfTv35+zzjqLBx54gLy8PCZPntzRoUoI1S/oibA3/v8Wh+jDL213sbz2Yp/x52un8pz73CbPU7+wPmP5ZtbmFbYmZBEREelhHPExXDo+iblThnHp+KTgkwcBInvDrD9DxiqI7AtAVd9krnLfjdMc2KZx1ncm/M+n+1u0vyrli/RssVEtq3X0r+3fcs6SHOat2kqus7RtgxIREWmhuo7ik0gfkYhxXA68YUD6iERWXzeJlOSTfZIHAQ5V1HC4Uot4RETkOx7fhTOBOxB2uvqxIiIirRIwgbCJtR0iIiLdSXl1rWWXwT7R4S2+DtRzeRER6YwsEwh1/Sci0iY63ROkc889F7O5/dQtzJkzhzlz5jRrn1mzZjFr1qxWn1u6rroFPdFkrisgO7/I8qIb4FCVyf3MZpvnZJaGL+drM5Hf184lUCcMKy63hwVZ21l9XbRfJ0JnSQVbdh2kvKqW2KgwJiYnqIuGiIiItI1Tfwz9hsLa24m67O/c8ZW7yY4o7ckwYGJyfEeHISIdaGJyAoZBwOuxxtQXbNlUcIDM2WOZMXpw2wcoIiLSTMF0FN/xzWHLfb8+WMGYpL7tGa6IiHRGVYdh5flw1rUw7hqw2SkL0GFCCYQiItLdBHp+EakOFCIi0oOUBSg01ue7tYXNfbam5/IiItJZ1bj9v9DClUAoItIm9ARJ5DjHL+h598sDvPTRN5Zz/+OZwOeuJGqxUU1Es8/lcnvIXFfAyjkTAMh1lrJsXQE5xyUvGgZMS03kxvQUv2RDERERkWYbcjrMXQOGwYzRBFVAob1MT01sXmdFEel2HPExTEtNJHtnUYuP4XJ7+G3WFgZfN03XUCIi0mk44mMCFgk7KcD4npKjSiAUERH47/9B8U54/X9h60qYcT/lVUmWU/uog4SIiHQzATsQKoFQRER6kLKqGvpzmBG2r9nsGYUbO1DXgbAlz9b0XF5ERDqraqsOhLr+ExFpE0ogFAmgfkHPj8YOYcPnxRw44rKct9sMvqPFOONzSojlqwb7rNtZxF83fElMRBh/eO1Ty+p56qIhIiIibc441jm5YQGFR3O+4Lmtzg4JKcJuY0F6SoecW0Q6lxvTU9hUcKBF3VHjKGdR+FMkGcU88vZgnpg7MQQRioiItK2hCb0sx/ccrGjnSEREpNM5UADvLT/2fn8ePHUR30tIJ5L/51fgUh0IRUSku1ECoYiICJRV1jLLvonfhz/DAbMPb7jP4jX3JPpEng4079mansuLiEhn5qp1+43p+k9EpG3o01SkCZ98W8ahippWH6cfZSyPeJg1Ebdzjf0/GBy7WL/vjZ3c+UpekxfwLreHBVnbyXWWtjoeERERkeM54mP49bSTG+YWtpsIu43M2WPVKUxEgLrE5szZY4mwN++2xXTbR7wZ+Vtm2d9lvK2A5C9W8dcNX+IsUfKFiIh0bkPiogiz+f8Q/+rA0Q6IRkREOpX//A48tX7DtpqjVOPfbbB3pBIIRUSkewm0jiK8mfcORUREurLDlTVcbN8MQH+jjKvD3mJ15B+YtjYdaiqDfram5/IiItLZ1bhNv7Hmrh0RERFr+jQVacKydQW4Pf4/RprH5IHwFSQapUQbLhaFP0VWxL04jP3NPpLL7SFzXUEr4xERERGx5oiPYVpqIgAGHmbY3vcpfBAK6SMSWX3dJHVZFhEfM0YPZvV1k0gfkdhkYnMvKlkStpwnI5aSaJR6x38T9gLPr3mbc5bkMG/VVhVjERGRTivMbiOpX7Tf+J76JPiaSvjmw3aOSkREOtw3H0LBf/zHbWG8nPgrwPdiqVeEnTAtphERkW6mOlAHQn3niYhID+I++CVjbbv8xmv6DoXwuvuKjT1bMww9lxcRka7BqoiMOhCKiLQNlaAUaYSzpIKc/KJWH+cKew7n230X+Ey0fcbaiNu4wPUAe83EZh0vO7+IvYcqSOoX0+rYRERERI53Y3oKewryuNf+OBNtn/G7mnk84/5+SM513qhEVlw9ISTHFpGuL80Rx8o5E3CWVLD0zXz+tf1by3keDM6w5fuNRxo1PBi+nEtci8jeWcSmggNkzh6rB6MiItIpnZTQi68O+nbNrTjwNbz9Jny4CkwP3PQpRPTqmABFRKT9nTAernoR/nM7HPj82PiZv+DL4iRgn8/02Cj/joQiIiJdnStQAqEWkIqISA+S+PUbluOuET/yed/w2dp7u0sor6ohNiqcicnxWmsoIiKdnttjWjb9UQEZEZG2oU9TkUZs2XUQs5XNB4cahdwV9rTlthzP6c1OHgQwTdiyq6R1gYmIiIhY8XhI++af/CfqdibaPgPg9rBnOYHiEJ2wibZiIiLUdUc9++T+AbdXEsXNNdfhMf0/U04y9nOy8Q1QV6luQdZ2dSIUEZFO6aSEYwt4hhvfkBn+CC+7fgmbHoLKEqgqhfdXdFyAIiLSMVK+D798Fy74I0T2hZgEmHor5dU1flNjo1Q7VkREup9ACYSRSiAUEZEeZOCBLX5jNaadsFNnWc53xMdw6fgk5k4ZxqXjk5Q8KCIiXUKNRfdBgHBd/4mItAl9moo0oryqttXH2G/2Y7X7HL/xYrMPv6+Z0+Ljllf5PxgWERERabWXfwFrf0uYu9I71Nuo4o/hTwCtrKxgodLlbvNjikj3NDE5AaORnOMPzVSecM/0GVvnPp3zq/+PfPNE75jL7SFzXUGowhQREWmxkxKOdRbsQwU/tG8m3Dju9/I7y6C6vJ0jExGRDmcPh0m/ggUfwWVPQ3Q/y2dYSiAUEZHuyBVgAak6EIqISE+yKvkhrnTdQVbtNErNuvuImzyj6d1vYAdHJiIi0naqA3WgVwdCEZE2oU9TkUa09EGr3XZsVWslUdxdO5fZrt+x1zzWMePOmp9xiD6tiC28xfuKiIiIBJR2heXwOfaP+cfp+Vwz6STC7W3XNfCoq/UFG0SkZ3DExzAttfEO7g/WZvCFZwjlZjS31PyCn9XcTBH9/OZl5xex91BFqEIVEWmS2+0mLy+PVatWccMNNzBp0iRiYmIwDAPDMJgzZ06bnq+8vJwXX3yRX//610yePJkBAwYQHh5Onz59GDFiBFdffTVr167FNNu+YIQEb2iDDoTbzBTe84zwn1RZAluWt2NUIiLSqfTqD0OnAFBWadWBUM+ORESk+wnUgVALSEVEpCcprTZ51zOa22vnM6H6L8x13cIq+yXYbG337F5ERKSjBepAqAIyIiJtQ2UoRRpR3+GiOWunDAP+8MNTWfTapz6V8DZ7TmVG9f3cEfYs0UY1//FMaHFchgETk+NbvL+IiIhIQCd/H07/KWz7h+94XwdnTxjP2cmjuWRcEpnrCsjOL2rW7yQr6kAoIs1xY3oKmwoOBKw6Xk0E19csoNyM4Vv6W86Bumu8LbtKuHR8TMA5IiKhdNlll/HSSy+1y7keeughfve731FVVeW3rby8nPz8fPLz83n66af53ve+xz/+8Q9OPPFEiyNJqJ2U4Pu99FBNBs9F3nPcLAOOFrVfUCIi0mmpA6GIiPQUARMItYBURER6kLLKY9eANYSR4zmdE6KiOzAiERGRtqcCMiIioaWnSCKNqO9wkb0z+EU501MTuWriSYw+oa/fwvojxHBH7c+xYf0D53j9OUwvo5I95iC/cyT100JXERERCZHz74UvsqH827r3Z/wMzlsEkbEApDniWDlnAs6SCt7bXUJ5VQ1fHTjKs+9/TY07cEahVWEGdSAUkeZIc8SROXssC7K2B0wizDeDS3opr/Lv1iEi0l7cbt8iCvHx8SQkJFBQUNDm5/r888+9yYMnnHAC3//+9xk/fjyJiYlUVVWxZcsW/vGPf3DkyBE2btzIueeey5YtW0hMbLzrq7S9pH4xPr+Z3zNH8o77VKbYP6kbOPXHcO7tMCC144IUEZFOwzqBUB0IRUSk+wl0H1AJhCIi0pOUWTzX6hOta0AREeleVEBGRCS0lEAo0oSmOlw0FGG3sSA9BfBdWL8mbx/3vfGZd56HYH7ImNwT/iTn2nJZUns5f3NfgImNcLvhPYeIiIhISETHwcXL4I2b4YePQPJUy2mO+Bgc8ceKGgTqTGgYdQUQXG4PGwsO+BxDHQhFpLlmjB7M6uuiW90JdbuzlNUfOJmYnODzWSYi0h7OPPNMRo4cyfjx4xk/fjzDhg1j1apVzJ07t83PZRgG559/PjfffDPp6enYbL73pa655hpuu+02LrjgAvLz89m9eze33XYbTz75ZJvHIo2LCrczuE8U3x4+1i3ywdoMInrHMWHOEhh4agdGJyIinUlVjdvyuVWfaD36FRGR7sdqAalhQJjN6IBoREREOkZZpUUCobrQi4hIN1OjAjIiIiGlKwiRJgTT4QLqkgczZ48lzRHnM+6Ij+EX5ySzZdfBZnUy/IHtPS60bwXgrvCnmWF/n1trfkG/ISP5cM8hPt9froWuIiIiEjqnnA/JWyEsMuhdrDoTxkaFMzE5nqR+Mfz2hR1++xytVgKhiDRfYwVbgvWv7d/yr+3fYhgwLTWRG9NT/K7nRERC5Y477mi3c917773Ex8c3Ouekk07iueeeY+zYsQA899xzPProo8TE6L5TezspoZdPAuFH5incZKSxUcmDIiLSgFX3QYA+6kAoIiLdULVFAmGE3YZhKIFQRER6DssEQnUgFBGRbsbq+g8g3K4EQhGRtqAEQpEgNNbhor6jzoImFps2p5NhPGX8IfxvPmNn2vJZG3Ebv/rmRv7gHOc9d2MLXZ0lFWzZdZDyqlpio8KUcCgiIiLN04zkwYaO70xYLybS7jdWWePG4zGxqVKwiLRASwu2AJxqfMXXZiLlZgzZO4vYVHCAzNljmTF6cIiiFRHpGE0lD9ZLS0sjNTWV/Px8Kioq+OKLLzjttNNCHJ0c76SEGDbvOugz9s2hSly1HlVXFRERr/Iq/4WjALHqPiEiIt2QVQdCXR+JiEhPU2ZRSEZFZEREpLsJtMZe14AiIm1DT5FEgtRUR51g9g+2k+FLjpdJ2Ffut62CSHZ4hnvfmyaWC11znaUsW1dAjkWyozpriIiISEeJifBPIASoqnUTE6FLExFpueYUbAG43J7DH8L+xgZPGtfW/C8ebLjcHhZkbWf1ddG6XhKRHqtPnz7e15WVlR0YSc91UkIvvzGPCd+UVjKsv/82ERHpmQJ1IFQCoYiIdEc1Fvf8IrV4VEREurOKEti6EibfAOFR1Lo9HKn2vw7sqw6EIiLSzdQE6EAYoQ6EIiJtQp+mIs3kiI/h0vFJzJ0yjEvHJwWVPFivrpPhJNJHJGIc12THMCB9RCKrf3EmQx0nWu5/d80cDtDXb7x+oWuus5S1eYVkLN9M9k7f5EE4lnCYsXwza/MKg45bREREpC0EShI8Wu1u50hEpLupL9jS1E3jSFw8EPZXHghfQaRRy3n2D1kY9hRQd/HkcnvIXFfQDhGLiHQ+LpeLzz//3Pv+pJNO6sBoeq6hCdb3GvccPNrOkYiISGdWFqgDYaQWj4qISPdj2YFQi0dFRKQ7e+suyFkMf5kMuzdaJg8C9IlWERkREeleAncgNCzHRUSkeXQFIdLOgupkeNJSGDWL4md+zoDafQCsdU/gNc+kgMd1uT0sfv1TcvcebrLrhjpriIiISEcI1IGw0qUEQhFpvbqCLdFkrisgO9+/oMoJFLM84k+MsX3lM3512FvsN/vxZ/ePAMjOL2LvoYpmFYsREekOnn32WQ4fPgzAuHHjGDRoUIuOs3fv3ka3FxaqqFVjTgyYQFjRzpGIiEhnpg6EIiLSk1itf4hQB0IREemudm+EbU/XvS75Ep66iLBRs+nLNA7T22dqnygVkRERke7FqoAMQITdes2ZiIg0j54iiXQQR3wMjvjAC1Kdfccz4+h93GL/Jz+0v8vva+YCjVdQ2LrnUNDnr++ssXLOhKD3EREREfGx/1OoLIGhZwc1vVegDoQu60VvIiLNdXzBlo0Fxfxr+7cAuLEx2Cix3O+W8OfJ8YzlU3MopglbdpVw6XglEIpIz1FcXMxvf/tb7/s777yzxcdyOBxtEVKPdVJCL8vxr47rQOgsqWDLroOUV9UyyF3I2GEDGXLi8PYIUUREOliuszRg5/QH3/yc3/1gpIpHiohIt2LZgVAJhCIi0h3VVMHr/+M33PvTLO4Kd/Kbml/5jPeJVgKhiIh0LzUBOxDqGlBEpC3o01Skk9qy6yBHzSgW1s5hWvVDFBMX1H79KAv6HPWdNURERESa5dvt8M+r4C+T4LUbwRNcB8HoAB0IK9SBUETamCM+hkvHJ5GWFOcd20cC17r+lyrT92Gq2zS4rebnfGoO9Y6VV9W0U6QiIh3P5XLxk5/8hKKiIgB+9KMf8eMf/7iDo+q5ekeG0b93hN/41991IMx1ljJv1VbOWZLDwy+sI3rt/3Je9kXkrriOeau2kussbeeIRUSkPa3NKyRj+WZ27iu33P7+VyVkLN/M2jx1/BURke5DCYQiItJjbHwQDn7hN+wOi+HBmsv8xvuoC72IiHQz1QE6EIbbG2/AIyIiwdEdNZFOqrzqWCeew/QOap9024dsjPwfrrK/DZhNzq/vrCEiIiISlG+3wzMZ8NepsPP1urGDX8AnLwe1e0zABEJ1IBSR0Ig97sHph2YqN9TcgNusu7lcZYZzbc1N/NM9/bj9VLFVRHoGj8fDvHnz2LhxIwDDhw/nySefbNUxnU5no3/vv/9+W4TerVl1Ifzq4FFv0sgnO3fyB/uT5ETexOywHMINNxfa36fq82wljYiIdGO5zlIWZG3HFaAKdz2X28OCrO1KKhcRkW6j2uK7L8Ku5U4iItINjbwYhpzuN/z5qTfyLf39xtWBUEREuhurAjKgIjIiIm1Fn6YindTxC12b0pcj/DF8Jb2NKu4Nf5K/h9/PYA42uZ86a4iIiEjQDn4BBW/6j298EDyNL14DiImw/n2jDoQiEioTkxMwjitE95bnDH5fO49DZm+udP2Otz3jfbYbBkxMjm/HKEVEOoZpmlx33XU888wzAJx44om8/fbb9OvXr1XHTUpKavRv8ODBbRF+t3ZSfIzf2J6DFd6kkRvCXub/hb1NhOH7O3pR2FN43C4ljYiIdFPL1hU0mTxYz+X2kLmuIMQRiYiItA+rBaThSiAUEZHuaPBp8PN1MON+CP+uyNiQ08k74QrL6X1UEFNERLqZGrd185xIJRCKiLQJfZqKdFJWC10bszD8KRKNUu/7c+wf85/IW/mh7Z1G99u5rxxnSUULoxQREZEe5dQfQ/xw//GiTyH/jSZ3D9SBsFIJhCISIo74GKalJvqNP+tOZ1r1g3xknuK3bXpqIkn9/BM3RES6E9M0+dWvfsWKFSuAuqS/7Oxshg4d2rGBCWDdgbDWY3qTRh6qzeCw6f9dlWL7hmvsbyppRESkG3KWVJCTX9SsfbLzi9h7SM9/RESk63PV+j9DUPcJERHptmx2mPhLuP49GHERXLyM0irrYjJ9opvXoEBERKSzs7r+AxWRERFpK/o0FemkAi10tXKBbSs/tvsnCvYxKokyXI3u+9xWJ+csyWHeqq2qTC4iIiKNs9nhe7/xHw+LhrJvmty9V6R1AuFRV21rIxMRCejG9BQiLG4mlxLrNxZht7EgPaU9whIR6TCmaXL99dezfPlyAE444QRycnIYPtyiUIR0iKH9G09kL6EPD9Zm+I2Xmr0oo27fdTuL+OuGL1U4TESkm9iy6yCmdfHtgEwTtuwqCU1AIiIi7ciqA6+6T4iISLcX54ArnoHBaZRV1VhO6ROtDoQiItK9WF3/gYrIiIi0FX2ainRigRa6Hu8025eW4znuNJ53n9vk/qYJ2TuLyFi+mbV5hc0NU0RERHqS0y6DvifWvY7oDWf/L/zPx3DWtU3uGh1hXQFRHQhFJJTSHHFkzh7b5LWVzYDM2WNJc8S1T2AiIh2gPnnwL3/5CwBDhgwhJyeHk08+uYMjk4ZOjG+6E+4z7u/zmccBgMc0eLZ2OtOqH2R1g3uB972xU4XDRES6ifKqlhVfKg+wyFRERKQrcdX6LyDV4lEREelJyir9r+1sBvQO8PxdRESkq6pxW1dRC2YtvYiINE2fpiKdWLALXZfUXsHVrt9SaMZ7xw6bMdxWMx8wgj6fy+1hQdZ2LSgSERGRwOzhMP13MPW2usTB7y+E3gOC2rVXRIAOhNVKIBSR0JoxejCrr5tE+ojEgFdIMRF2zj2+C3x1OWRdCfvyQh6jiEioHZ88OHjwYHJyckhJUefVzmZoQq8m57ixc3fNHD70pPBD1z3cUftzDtHHb54Kh4mIdA+xUS1bFBobpW4UIiLS9VkmEGrxqIiI9CBlFkVlYqPCsdmCXxcoIiLSFVRbXP8BhKuIjIhIm9CnqUgn57PQtZFr/g2eNC6ofoAX3OcAsLDmGvYTH3iHAFxuD5nrCloaroiIiPQEaVfAtNshpnm/NaIDJBBW1LSsir6ISHOkOeJYOWcCG26dxviT4vy2H6l2szZv37EB11F49nLI/zc8dRF8u73dYhURCYVf//rX3uTBQYMGkZOTwymnnNLBUYmVuJhwYgL8dm7ofXMkP3EtJM9MbnKuCoeJiHRtE5MTGn1GZMUwYGJy858TiYiIdDbqQCgiIj2dVQfCPtHqPigiIt2P1fUfqIiMiEhb0aepSBfgXeh6yzRmjR0ScF4Zvbi55jp+XL2Ilz1nB3VsA/8fW9n5Rew9VNHieEVERESsRNht2C2qIFaoA6GItCNHfAy3XzjSctuz731d96KmErJmw5536t5XHvr/7N17XFRl/gfwz5kbN0EERVDGC4p4ayFJQy0VqTS72EUr7UZuppuFu6VlZf227bZlV3LLNjVbNTKsNnPVTMAUE9MUTRPEJBsTBRxRYIC5nd8fxMgw5wzDMNyGz/v18vWaOec553nGbM485zzf7xc1K29Axrf/g05fO1fS6Q1I36fDyuxCpO/T2bYTEbWmVatWQRAECIKACRMmyLZ79NFH8d577wGoDR7cvn07YmJiWmmU1FTfHDmDKqOrv5FdjyZh4jAioo5LG+KPxIYV0xsxMSYMkd38W2hERERErcdoYQAhERF1bherHQMIfVWNJyAjIiLqaEwS8z+AAYRERJ7CNCREHYg2xB9XDeyOr3JPO213QIx26XxBqMBnmhewzHwT/msdi7oFR6II5JzQY1o8HywTERGR5wiCAH+NEuXV9hUHDS4vjiYi8oz4vt0wqGcXHDtbYbf9h1/1eGPTIcwsfAoRJdl2+3zMFUjIfgD3Zz6JQv8/QV9phFhvvyAAiTFhmJ8UjVhtcMt/CCLq0AoLC7FixQq7bYcOHbK9PnDgABYvXmy3f+LEiZg4cWKT+1q8eDGWLl0KoPb32Pz583H06FEcPXrU6XEjRoxAnz59mtwfNc9BXRlS0nLtrjGeVJc4jAElREQdz/ykaGQXlEoGUTSkUSqQkuTasyIiIqL2zGoVYbI4zpA0SgZNEBFR53BQV4afT1902F5QXIFZq/byuRQREXkVqQqEKoUAhUTCeiIiajoGEBJ1MAlRoRCE2iC/5npWtQaDFTq8rXkPN1l2Y7FpFooQCgAol8hcRERERNRcUgGEVSazTGsiopYhCAJmjOqD57/+2WHfhzuOY6T6AiIk1iD9LnbHMVGLi5VGh32iCGTmFSO7oBSpM+IweXhESwydiLzEyZMn8dJLL8nuP3TokF1AIQCoVCq3Agizsy8FRIuiiKeeesql4z766CMkJyc3uT9qnncyClwKDHEXE4cREXVcsdpgpM6IQ0partNrhUapQOqMOC4gJSIiryB3zWMFQiIi6gy2HC5yOgfkcykiIvI2UgGEnP8REXkOv1GJOhhtiD8SY8KafZ7xioOYrtphe5+kPICtPk9gpjIDgIi8M+XQ6Q3N7oeIiIiovgCNYw6TyhpWICSi1nfb5ZFQSWSpq4YPZpseR6Ylzm57idgVs0xP4CICnJ7XaLEiJS0XB3VlHhwtERF1Bjq9AVn5xR4/rwr2CTuYOIyIqOOaPDwC6+YkyO5PGhyG9LmjuXCUiIi8BgMIiYioszqoK2s0gQzA51JERORdTBLXPc7/iIg8h9+oRB3Q/KRoaJSu/e+rVghouCQ2AFV4Sb3CoW2gUIVrFD8CANbt1WHckizMWrWXNxiIiIjIY/w0jiW9qowMICSi1vfruUpYZUq710CDuaa/YaslHgBQJWrwZ+MCnBJ7uHRuo8WK1IwCj42ViLzPhAkTIIpik/78/e9/dzhPcnKybf/27dsl+9q+fXuT+xJFkdUH20DOiXOQuTS5ScQ9ym+RpXkcobhg2xroq/ZkJ0RE1MqiewZKbr9/dF+sSB7JyoNERORVpKpPAIAPF5ASEZGXeyejoNHgwTp8LkVERN6iRuLap3ZxvTwRETWO36hEHVCsNhipM+IaDSLUKBV4d+blSBxsX7HwBmUOIoVSh/bloh+eMf0Z+CPkUBSBzLxiTF+2G1sOF3ls/EREROTFzhwGNi8CcpZJ7pasQGg0S7QkImpZ72QUwOokSMMINR42zcdacxL+apqHQ+KAJp0/M78Yp86zqjsREbmuvNpzv4t7oRSr1a/gRfVH0CpK8JJ6JQARggAkRIV4rB8iImp9Bpn7KOFd/Vp5JERERC1PLoDQ1YTLREREHZFOb0BWfnGTjuFzKSIi8gZSc0DO/4iIPMdx9S4RdQiTh0cgfa4fUjMKkJlfbJedXBCAiTFhSEmKRqw2GBFd/ZBdUGrLSvSZJREXxC54Ub0SPYRL2cdfMc9EEUId+jJarJi39gAeGHseMeGBSIgKhTbEv8U/IxEREXUQVWXA4fXAgTXA6QO124L7AKMeAhT2N3FYgZCI2gNXH7yaocIz5j+71YcoAjkn9JgWz7kTERG5JtDXE7frRUxXfodnVasRJFTZtk5W7sVUyy5UDLoNkd14bSIi6sgMNdL3Ufwl7rkQERF1dCaZyksaViAkIiIvlnPinN1aQFfwuRQREXkDqTkgK9ATEXkOAwiJOrBYbTBWJI+ETm/AnkI9yqtNCPRVIyEqxG4hUF3FwpS0XFsQ4TfWkcipGYJn1WswTbkDuy1DkWZJlO3LIopYnl0IoDZAMTEmDPP/CFAkIiKiTu6reUDeRvttZb8Bv+4EosbbbQ7wcVzMxgqERNTa3Hnw6orBwm/IE/vY3u8sKMGV/UOYgIWIiFySEBUKQUCTr1FqpQCT5dJBExUH7IIH6/xD/TFOJSQ3c5RERNTW5O6jMICQiIi8kWwFQi4gJSIiL1Ze7d7z8/Jqk4dHQkRE1Lqk5oBqViAkIvIYfqMSeQFtiD+mxUfigbH9MS0+UjKLeG3FwtFIGhwGQajddgFdsMA0F/cbn8ST5tkQXfhK6AIDRFFEZl4xpi/bjS2Hizz9cYiIiKijiZ0hvf3AGodNfmrHHCYGViAkolbm7oNXZyYocrFJ8xReUq2ABrUPaL/KPY1xS7Iwa9VeHNSVebxPIiLyLtoQfyTGhDXpmKTBYXh3xuXQ2B6eClhsmoVzYqBD265CJYaVZ3tgpERE1JaqZO6jBPgwbywREXmfGrkAQi4gJSIiLxbo6978LtBX7eGREBERtS6pAEImkCEi8hx+oxJ1InUVC3csTMSdI7W27d9ZY/Gb2NOFM4h4T/0OPtW8iMHCbzBarEhJy3VYCKvTG5C+T4eV2YVI36eDTm/w7AchIiKi9mXQJCCgh+P2oxuAqjK7TVLZ8A1GC8SWKAVGRCTD3QevcvoIZ/GOeikUgoi7VRlYp3kB4TgHoLaKlLMELJw/ERFRffOTol1eCKtRKpCSFO2QOOwcuuIZ05/t2p4WQ/CYz/9hZU0irzdERB1cpUwAoR8rEBIRkRcyWliBkIiIOp+EqFBbgQBXCQKQEBXSMgMiIiJqJSaJOSDnf0REnsNUlESdkDbEHzE9HbOQN+ZaxY8Yp/wJAPA/zVNYbbkWb5qnYUH6QTw0Lgpd/dT4dK8OWfnFqB8DIAhAYkwY5idFI1Yb7KFPQURERO2GUg3E3gV8/+6lbWp/YOgtgLES8Au2bfb3cVzMZrGKMFqs8FFxoRsRtY66B6+eiF32RQ0+UL+FrsKlQIzLFcfxtc8zeMQ4H3vEIQBgS8CSPtcPsdpgHNSV4Z2MAs6fiIjITqw2GKkz4pCSliu7UBaoDR5MnRFnu1bUJQ7T6Q34/pdSvLxJja/MP2Cq8nukm8fhBfO9uFgTgC82HgXA6w0RUUdmqJGuqB6g4WNfIiLyPlLVJwAuICUiIu+mDfFHYkwYMvOKXT5mYkwYIrv5t+CoiIiIWp5UFXq1solR9UREJItPkog6qaZW3PCBEc+qVtveKwURyaqtuFn5PR4sWYCF6ytkj62ruJFdUIrUGXGYPDzC7XETERFRO3X5vbUBhJEja18PuxXwDXJo5q+W/g1SZbQwgJCIWo07D17lvKJejiGK3xy29xAuoo/iLPZYhti2GS1WpGYUYPoVkbKBIZw/ERFRbUVBP6RmFCBTItB8YkwYUmQC/7Qh/rgzpA+qTVb834b78V/LWGRZL3dox+sNEVHHZZCpQOjPCoREROSFZAMIXazcTkRE1FHNT4pGdkGp0yRjdTRKBVKSolthVERERC1L6rqn4XoyIiKPYQAhUSfV1IobDyk3oo+ixGG7BUocEyNdOkfDihtERETkRXrEACm5QEh/p80CJCoQAkCl0YJgJkQkolbUlAevznxricd1in0IEGrstq+3jEO6ZYJD+4y8YuwoKIHJ4nwyxvkTEVHnVr+i4J5CPcqrTQj0VSMhKsSlTOJDI4JQhkDJ4MH6jBYr5q09gAfGnkdMeCASokKhDeEPcyKi9sxglK5AyABCIiLyRqxASEREnVWsNhipM+Iw75MDsFjlnylplAqkzojjsyQiIvIKJqkAQiaQISLyGH6jEnVSdRU3XKGEBbcod0nu+6dpBirg+qKiuoobRERE5IUaCR4EAD+ZxWxVMovfiIhaSt2DV3duNncP0ED44/UmawJuMb6AE9Zw2/58aySeNSXLHt9Y8GAdzp+IiEgb4o9p8ZF4YGx/TIuPdCl4EADe/+4Xl/uwiCKWZxdi4fpDGLckC7NW7cVBXZmbIyYiopYmV4EwwId5Y4mIyPvIJf9iACEREXUGk4dH4K4rtJL7BABJg8OQPnc0Jg+PaN2BERGRSzZs2IDp06ejX79+8PX1RVhYGMaMGYMlS5bg4sWLHuunvLwcn3/+OR555BGMGTMGPXr0gFqtRlBQEAYPHoz77rsPW7ZsgehqxZk2JJVERqMSJFoSEZE7+CSJqBNzteKGBUrcaHwJ81RfYbbyf/ARahf477cOxBfWq5rcb2Z+MU6dN9gWPOn0BuScOIfyajMCfVXMdE5EROTFAjTSU5DKGunFb0RELWny8Aikz/VDakYBMvOL7Sq0CwIwMSYMd47U4mK12aHyk05vwOtb8/FV7mkUiJG4xfgC3lC/jzGKI3jYNB9V8PXIGBvOn4iIiBqj0xuQlV/s1rGiCGTmFSO7oBSpM+K4+IiIqB2qlAkglEvaRERE1JHJViBkBQoiIuokAv3Uktu3PT4eA3p0aeXREBGRKyoqKnD33Xdjw4YNdttLSkpQUlKC3bt3491338Vnn32GhISEZvX15ptv4plnnkF1dbXDvvLycuTn5yM/Px+rV6/G1VdfjTVr1qBPnz7N6rMlSQYQcv5HROQxDCAk6sTqKm6kpOU2GkRYBV+8br4Tn1km4FnVaiQpDuD/TMkQXShkKsBq104UgZwTekSHGfFORgGyJBbqJsaEYX5SNGK1we5+PCIiImqH5BazyWXPJyJqabHaYKxIHgmd3oA9hXqHQEE52hB/XDWwO77KPQ0AuIgAPGR6DIOEU/hF7O2x8dXNn6bFM4CQiIhck3PiHJqSRNYf1bBCQDV8bNuMFitS0nKRPteP9+eIiNoZQ41Zcru/mgGERETkfWQDCFmBkIioQ9mwYQNWr16NvXv34syZMwgKCsLAgQNx6623Ys6cOQgKCvJIP+Xl5di6dSuysrKwf/9+FBQUoKysDH5+fujVqxdGjRqFmTNnYtKkSRCEjlHNqFJiDqhUCIjqHtAGoyEiosZYLBZMnz4dW7ZsAQD07NkTs2fPxtChQ6HX65GWloZdu3ZBp9NhypQp2LVrF4YMGeJ2f8eOHbMFD/bu3RvXXHMN4uPjERYWhurqauTk5GDNmjWoqKjAzp07MWHCBOTk5CAsLMwjn9fTTBbHB1yc/xEReQ4DCIk6OWcVN6T8JvbEbNMCDBJ0OCZqGz2/D4zYoFmMTZYrsdJyPcpRu+j1h8JzePqLnyQDF5npnIiIyHv5ywYQSi9+IyJqLdoQ/yZXQk+ICoUgwDaPEqFAvuhatr5AGNBXOIPDYlSjbXcWlODK/iGs1E5ERC4pr3b9t/VA4RTeV7+DXOsALDTPtdtntFiRmlGAFckjPT1EIiJqBoPJMQmTRqWAipm4iYjIC9XIJELmAlIioo6BFZiar1LiOXqARtlhAiCJiDqb5cuX24IHhw4diszMTPTs2dO2f968eViwYAHeeOMNnD9/HnPmzMGOHTvc7k8QBFx33XVYsGABkpKSoFDYz5Xuv/9+LFq0CJMmTUJ+fj4KCwuxaNEirFy50u0+W5JUEhk173sSEXkMv1GJyFZxY8fCRLw+PRYPXt0fSoXzmwyuBA8CwF3KLMQoTuFv6s+x02c+Hlb+FwGowuf7f2+06mFdpvODujJXPwoRERG1c/4a6RwmrEBIQG320enTp6Nfv37w9fVFWFgYxowZgyVLluDixYse7+/XX3/Fs88+i6uuugrdu3eHWq1Gly5dEBUVhdtuuw1r1qyByWTyeL/kPbQh/kiMcSczn4hX1f/G55q/4z7lNwCcZ3L5Kvc0xi3JwqxVezk/IiKiRgX6upY38BZFNjZonkW04ndMV+3AdOV2hzaZ+cU4dd7g2QESEVGzSFUgDJBJ2ERERNTRyVYg5AJSIqJ2r64CU13wYM+ePbF48WJ88sknWLp0KcaOHQsAtgpMR48ebVZ/DSsw3X///UhNTcWnn36KVatWYe7cuejSpQsA2CowFRcXN6vP1iBVgbCLD+uGEBG1RxaLBc8//7zt/erVq+2CB+u8+uqriIuLA1B7Tdq6davbfb700kv45ptvcO211zoED9bp27cv1q1bZ3u/bt06GAzt79mPKIqS68qZQIaIyHP4jUpENtoQf0yLj8TiG4biXzMvb/ZNdx8YMU/1le19sFCJJ9SfYafPfPQSz7h0jrpM50REROQd5CoQVjGAsFOrqKjA1KlTMXXqVKxfvx4nT55ETU2NLfPoE088geHDhyMnJ8djfb755psYPHgwXnzxRezatQvnzp2D2WxGZWUlCgsL8eWXX+Lee+/FZZddhsOHD3usX/I+85Oimzx3ul+5FVOUP8BHMOMf6o+xVJ2KQDi/QV9XqX36st3YcrioOUMmIiIvV1chV44PjHhZtRxva96Dv1Bj2/6C6iMMEwrt2ooikHNC31JDpU5KpzcgfZ8OK7MLkb5PB52+/S1UIGrPpJIwySVsIiIi6ujkAgh9uICUiKjda1iB6eDBg3jhhRcwY8YMzJs3D9nZ2Xj88ccBwFaBqTnqKjBt3boVv/32G1atWoVHH30Ud955J+6//368//77OHz4MGJiYgDAVoGpvZOcAzKAkIioXdqxYweKimqf5Y8fPx4jRoyQbKdUKpGSkmJ7n5aW5nafISEhLrWLjY21XQMNBgOOHz/udp8txWSRTrzMAEIiIs/hNyoRSZo8PALpc0cjaXCY0wVHztyj3IYwocxhu04Mg050vUoHM50TERF1YOYaIH8L8OVcYPOTCJBZ0FZpdMycSJ1Da2cfBYClS5fi8ccfR01N7YL5MWPG4J///CfWrl2L999/H4888ogtA2l+fj4SExNx5oxrCTCo84nVBiN1RpzLQYR/En7BM6o1dttuVO7B15pnEC2cavR4VmonIqLGNFYhN0w4jxuVux22+womrNC8jp6wDxgsr2ZFZvKMg7oyzFq1F+OWZGHh+kP4x8afsXD9IVZaJmoi6QBCViAkIiLvJFuBkAtIiYjaNVZg8pwKqSr0DCAkImqXNm/ebHs9ZcoUp22vv/56yeNaUlBQkO11VVVVq/TZFFLVBwFWoCci8iR+oxKRrFhtMFYkj8SOhYl4fXososO6uHysBibMVW2Q3PeWeRoA16MSmemciIioAzqxHfhiDrBkIJB2J3AwDTiYBj+ldKVBqcVv1Dm0dvbRqqoqPP3007b3H374IXbt2oUnn3wSM2fOxNy5c/Huu+/ixIkTuOyyywAApaWleO2115rVL3k3VxOwBKES/1KnQiM4fucFCZUoF/1c6o+V2omIqDHOKuTqxJ5YaJL+TXXY2g8VsL8eBfqqPT4+6ny2HC7C9GW7kZlXDLFBEmFWWiZqGqkkTKw+QURE3spokX52wABCIqL2jRWYPMdQ43gtDGASGSKidumnn36yvR45cqTTtuHh4dBqtQCAs2fPoqSkpEXHZjQacezYMdv7vn37tmh/7pBNIMMAQiIij+E3KhE1Shvij2nxkXh9eqzLP8SMUGO2cQG+s/zJbvt+60Bst8Y2eQzMdE5ERNTB5G0CDn0K1Fy8tK36AgJPZ0s2N7ACYafUFtlHd+3ahfLycgC1N2wffPBByXY9evTAK6+8Ynu/Y8cOt/ukzqF+ApY7R2ol21TCFxusoyX3/c00D2cQ6nJ/rNRORETONFYh9xvrKKwwX2+3bbn5ejxkehyV9QIIBQFIiHJt8RWRnIO6MqSk5cpmD67DSstErqmSSMLExaNEROStuICUiKhjYgUmz2EFQiKijiM/P9/2un///o22r9+m/rEt4ZNPPsGFCxcAACNGjEB4eHiTz3Hq1Cmnf+qSB7jLJFeBkAlkiIg8ht+oROSyxhYeNZQrDsT9pkW4veb/kG0ZBgB4wzwdrlQfnKHMwCjhKIDadNR5Z8qh019aHKvTG5C+T4eV2YVI36ez7ZPbTkRERK1s2C2Sm33ypSsUswJh59QW2UeLi4ttr6Ojo522rb+/oqLC7T6pc9GG+OORxIGSlQgtUGKJ+S4kG5+AXrxU4X2peSq+a2KiFVZqJyKixjRWIfcV8wzssFwGs6jAM6ZZeNF8L6wNHhlMjAlDZDf/Vhoxeat3MgoaDR6sw0rLRI2TrEDIAEIiIvJSUgGECgFQMYCQiKhdYwUmz5FKxMskMkRE7VNZWZntdffu3RttHxp6KcFw/WM9raSkBE8++aTt/eLFi906j1ardfpn1KhRzRqnXAIZNed/REQew1QkRNQktQuP/JCaUYDM/GKI4qV9glC7qOjOkVpcrDbjg+9+QUFxBX4UY3CP6RlcZj6Bn8TGs2p0w0U8p1oNP8GIXGsUPjTfiPV7Lfhsnw7xfboBAH787bxd3wAQGqCBvtKI+psFAUiMCcP8pGjEaoOb/xdARERErtEmAF3CgYozdpuF/P8hSH0DLprsVzAbahhA2Bm1RfbRsLAw2+v6Dwel1N8/bNgwt/ukzkcb4o/EmDBk5hVL7t9ujcMNNa/gXc27MEOJt8zT3OqHldqJiKgxdRVydXoD9hTqkXfmIj7a9SssVhFmqPCIKQVDFSeRYx3qcKxGqUBKkvOEC0SN0ekNyMqX/k0kp67SMoNXiaRJ3UPx1/CRLxEReSepRBSsPkFE1P65U4FJp9PZju3Ro0eLjc0TFZhaU6XEHJAVCImI2qf6ial9fX0bbe/n52d7XV5e3iJjMhqNuP32223Jtm+55RbceuutLdJXc9XIVaDnHJCIyGM4kyCiJmu48Ki82oRAXzUSokJsizp0egMWrj9od9xPYpRL579HuQ1+ghEAEKc4gX9pUqGz9sAT5oew+6T8wu1zlUaHbaIIZOYVI7ugFKkz4jB5eISrH5OIiIiaQ6EAht4M/PDvS9sCewFDb0boPhMumjR2zQ0mBhB2Ru5kH9XpdLbso+48PLzqqqvQvXt3lJaWYt++fVi+fDkefPBBh3YlJSV4+umnAQAKhQKPPfZYk/uizm1+UjSyC0plq+0UIRR3GRcjANWAoIJDhhQXBPqqmztMIiLqJLQh/tCG1N63u6JvN6Sk5cJoseIiAiSDB1UKAakz4piQi5ot58S5Jv/Mqau0PC2eAYTebMOGDVi9ejX27t2LM2fOICgoCAMHDsStt96KOXPmICgoyCP9lJeXY+vWrcjKysL+/ftRUFCAsrIy+Pn5oVevXhg1ahRmzpyJSZMmQZAq19oOGYxSAYSsPkFERN7JaHb8Malh9QkionbP2yswnTp1yun+oqIit87bkNFslXzOxABCIiJyhdVqxaxZs7Bz504AwIABA7By5Uq3z1cX7C+nqKioWVUITTJrKxhASETkOZxJEJHb6i88asidhSEA4AMj7ldtdexLUYJiMbjpJ/yD0WJFSlou0uf6ceETERFRaxl6C5D3P2Do1NrXkSMBhQLmnzKByiq7plVGc5sMkdpWW2Qf9fX1xbJly3DXXXfBbDZj9uzZWLVqFW6++WZotVpcvHgRhw8fxscff4zy8nJ06dIFy5cvx9ixY5vcF3VusdpgpM6IswVoSDFDhSplEJ6/aQie/eqI0znUYOE3FIvB0KN2IbUgAAlRIS0xdCIi8nKTh0cgfa4fUjMKkJlXDKnLz20jejMRF3lEebV7cz1WWvZeFRUVuPvuu7Fhwwa77SUlJSgpKcHu3bvx7rvv4rPPPkNCQkKz+nrzzTfxzDPPoLq62mFfeXk58vPzkZ+fj9WrV+Pqq6/GmjVr0KdPn2b12dKsVhFVEkmYWIGQiIi8lXQFQgbOExG1d95egUmr1XpyaLIMMs/QAzgHJCJql7p06YLz588DAKqrq9GlSxen7auqLq2dCgwM9OhYRFHE3LlzsXbtWgBAnz59sG3bNnTr1s3tc0ZGRnpqeJKMchUImUSGiMhjOJMgohbh7sKQ25Q70V246LB9m+Vy/CL2btaYjBYrUjMKsCLZeXUbIiIi8pA+o4G/Hq6tRliP1AONyhpWIOyM2ir76O23345t27Zh3rx5OHLkCHbt2oVdu3bZtVGr1XjmmWcwZ84ctx8Ctlb2UWq/7AI08ovtAgQFAZgYE4aUpGjEaoORmVeCzLxiyfN0xwV8pHkNVgj4i/GvOCQOwMSYMFsFeCIioqaK1QZjRfJI/FJSjuvf3gmjxT6MMO+MxEItYyV0FQJyTpxDebUZgb4qJESFyiYYIwKAQF/3HkOx0rJ3slgsmD59OrZs2QIA6NmzJ2bPno2hQ4dCr9cjLS0Nu3btgk6nw5QpU7Br1y4MGTLE7f6OHTtmCx7s3bs3rrnmGsTHxyMsLAzV1dXIycnBmjVrUFFRgZ07d2LChAnIyclBWFiYRz5vS5AKHgSAAB8GUhARkXcymh2vfT6sPkFERE3k6QpMraWiRiaAkHNAIqJ2KTg42BZAWFpa2mgA4blz5+yO9RRRFPHwww/jww8/BFAb+JeZmYl+/fp5rI+WIJeYmRUIiYg8hwGERNQi3F0YIgA4JwYiVLBfpLTCMsUDowIy84tx6ryBC22JiIhag0L6Bo6fxvGBhkFmARx5t7bMPjpu3DgsXboUjz32GA4cOOCw32Qy4V//+hcqKyvx8ssv2/XtqtbKPkrtW12Ahk5vwJ5CPcqrTQj0VSMhKsRuXjI/KRrZBaUON8VVMGOpJhURgh4AkK55Hs+aH0CX0AeQvk/HwA0iImqWAT0CMW5QD2w7ah/EfujUBZRW1KB7Fx8AwOmNr0CRuxq3VS5GidjV1k4QgMSYMMz/IyCeqKGEqFAIApxWWm6IlZa91/Lly23Bg0OHDkVmZiZ69uxp2z9v3jwsWLAAb7zxBs6fP485c+Zgx44dbvcnCAKuu+46LFiwAElJSVA0uE9x//33Y9GiRZg0aRLy8/NRWFiIRYsWtetFpJUy1Sek7rUQERF5A6kKFFw8SkTU/nl7BSadTud0f1FREUaNGuX2+esYjHJJZLjsl4ioPYqJiUFhYSEAoLCwsNGAvbq2dcd6giiKmDdvHpYtWwagNrFaVlYWBgwY4JHztyQTKxASEbU4fqMSUYuoWxjSVJ9YkjC2JhXPmGah0Fq7cOCwtR92W4c2eqxWOIvbFDughnz1Q1EEck7omz4wIiIi8hipCoQGmeyJRC2htLQUSUlJSExMxK+//oq33noLv/zyC4xGI8rKypCRkYEpU6agrKwMb7/9NiZMmGCX+Y3IHdoQf0yLj8QDY/tjWnykQ1KTWG0wUmfEOdz8XqRKQ4LiqO29j2DGa+oPMXDPM3h6/X6MW5KFWav24qCurDU+BhEReaHxg3pIbt9ZUAKIIk588jh67fsnws2/42P1PxGESlsbUQQy84oxfdlubDnM6srkSBvij8SYplVzY6Vl72SxWPD888/b3q9evdoueLDOq6++iri4OADAzp07sXXrVrf7fOmll/DNN9/g2muvdQgerNO3b1+sW7fO9n7dunUwGAxu99nSDDUyi0cl7rUQERF5A6kKFFw8SkTU/tWvolRaWtpo+45WgSkyMtLpn4iICA+MXr4CoT+TyBARtUuXXXaZ7fXevXudtj179qwtID0sLAw9ekg/q2mKuuDB999/HwDQq1cvZGVlYeDAgc0+d2uokalAqGYSGSIij+E3KhG1CHcWhtSphg/WWq5BkvENPGT8G14134Xa2oTOPaT8H97ULMMOn79itnIj/FEt2a682uTWuIiIiMgzJCsQymRPJO9WP9todbX0b7f6PJF91GAw4Oqrr0ZWVha6deuGPXv24K9//SuioqKgVqvRtWtXTJw4Ef/73/8wb948AMAPP/yARx99tMl96XQ6p39++OEHtz4Dea/JwyOQPnc0kgaHQRCA6xV78KBqs2TbSKEUFigYuEFERM02fpD0PbwdeWdw7tO/IOrYctu2oYqTWKlZAr8G992MFitS0nIZ0E6S5idFN2mRd//uAUjfp4NO336DuKjpduzYgaKi2t+r48ePx4gRIyTbKZVKpKSk2N6npaW53WdIiGuVLGNjY20Zvg0GA44fP+52ny1N7v4JKxASEZG3kqpAqFa5kcmYiIhaVf0qSvWrK8lhBSZpcklkurACIRFRuzR58mTb682bpZ/z19m0aZPt9ZQpU5rdd8PgwYiICGRlZSE6OrrZ524tUvM/gElkiIg8id+oRNRimrIwRClRrtAKBbZaR2Kn9U+NHh+Ci5iu/A4AECHo8Yz6E2T7pOBh5VcARLu2eWfKbYtPdHoD0vfpsDK70G5Ritx2IiIiar4AyQBCViDsjNoi++h7772HvLw8AMCCBQuc3ix99dVXbf2sW7cOZ86caVJfrZV9lLxLrDYYK5JHYsfCRFx/4zScCLzCoc0psTtSTI/AWu+2DgM3iIjIXX1C/RHVPcBh+9Bj7yE03zFw5wrFMbyvfgcC7B/kGi1WpGYUtNg4qeOSq7QsZ3l2IRauP8RKy16m/oKZxhbEXH/99ZLHtaSgoCDb6/rJa9obufsnrEBIRETeSmoBKRePEhG1f6zA5BnyFQg5ByQiao/Gjx+P8PBwAMD27duxf/9+yXYWiwWpqam293fddVez+37kkUds173w8HBkZWVh0KBBzT5vazLJVCDUsAIhEZHH8BuViFqMqwtDNEoFnp86FBIxhC67X7UVvoJ9ZcEQoQLDFIVoWL1w3V4drn4tC/EvfItxr2Vh4fpD+MfGn7Fw/SHZ7VysQkRE5Dl+Eg80WIGwc2qL7KMbN260vb7uuuuctg0ICMCYMWMAAFartdEHnESepA3xx81j/oSXQ17CMvNNtu3VohpzjH9DGRyrcDJwg4iI3DVukOPCrPerrsUvVseEB1ZRwLfWeIgSjxcy8orx7x2/MBkXOairtNyUW8B1lZanvf89nvvqMJO9dXA//fST7fXIkSOdtg0PD4dWqwVQu5C0pKSkRcdmNBpx7Ngx2/u+ffu2aH/NUSlz/8TfhxUIiYjIO9VIBRBy8SgRUbvHCkyeIZdEhhUIiYjaJ6VSieeee872/r777kNxcbFDu0WLFiE3NxcAMHbsWEyaNEnyfKtWrYIgCBAEARMmTJDt99FHH8V7770HoPbe6vbt2z1W0bc1sQIhEVHL40yCiFpU7cIQP6RmFCAzvxhivWKAggBMjAlDSlI0YrXByMwrQWae44/lxvihGvcqt0ruW2q+Vfa4c5VGl7fXLVbJLihF6ow4TB7OajFERETu8peoQFhjtsJiFaFUNCOjAHU4l112GbZs2QKgNvtoYmKibFtPZR89ffq07XXXrl0bbV+/0mFFRYVbfRK5S6c3IOPYOWwTZ+CQtT+WqD/Ac6YHcETsL3tMXeDG9cMjoA3xb8XREhFRRzY+pgdWff+r3TY9gnCv8Smk+zyP3kJtJWiTqMRjpr/ga+sY2XO9vCkPr2zOQ2JMGOb/cd+PCABiwgMhNt7Mgckq4j+7T9reCwL476sDys/Pt73u31/+92z9NnVzwPz8fI9UoJDzySef4MKFCwCAESNG2LKEN8WpU6ec7i8qKnJrbA1VySwe9VczgJCIiLyTUaIChUbF6x4RUXtXV4HpzJkztgpMI0aMcGjHCkzOVcpVIGQSGSKidmv27Nn48ssv8e233+LIkSOIjY3F7NmzMXToUOj1eqSlpSE7OxtA7XqUDz74oFn9LV68GEuXLgUACIKA+fPn4+jRozh69KjT40aMGIE+ffo0q29Pkw0gZBIZIiKPYQAhEbW4WG0wViSPhE5vwJ5CPcqrTQj0VSMhKgSR3S4taJ2fFI3sglLJhwDO9BLOoUQMRohgv6B7qyUeR0XPZgo2WqxISctF+lw/Lk4hIiJyU7hYgj8r/4cblHvwhnk6dlkvA1CbQTHQV93Go6PWNHnyZCxZsgRAbfbRJ554Qratp7KPBgZeqtqm0+kazTZ68uSlhcqhoaFu90vkjpwT52xJWDZZE7C3ZjBKENzocQzcICKiphodFQoflcKhusdpdK8NItQ8D3/U4C+mv2K7Na7R8zEZF0kpr5Ze9NZU/PfVMZWVldled+/evdH29edf9Y/1tJKSEjz55JO294sXL3brPHUVE1taZY10BcIAVp8gIiIvJbWAlNUniIjav7oKTA8//DCA2gpMmZmZCAsLs2vXlApMDzzwAIDa4MTt27dLtvOWCkx15KrQswIhEVH7pVKp8Pnnn2PmzJnYuHEjzpw5gxdeeMGhXWRkJNatW4dhw4Y1q7+6YESgtgrvU0895dJxH330EZKTk5vVt6eZZNaOM4CQiMhzOJMgolajDfF3WgEjVhuM1BlxSEnLbVIQ4S9ib0wyvooJilz8RfU1rlTkAQBSnVQfbA6jxYrUjAKsSB7ZIuenzstiseDo0aPYt28ffvzxR+zbtw8HDx5EVVUVAOD+++/HqlWrPNZfeXk5tm7diqysLOzfvx8FBQUoKyuDn58fevXqhVGjRmHmzJmYNGkSBIEVwYiomUxVwN7lwJH/Yvbv+4A/4gQnK/baAgirjBYGEHYybZF99LLLLsP+/fsBAGvXrsXEiRNl2x4/fhx79uwBACgUClxxxRVu90vkjoaL7F0JHqzTD0XIzAN2HivBjCv7oF9oAAJ9VUiICmVlQiIicuCrVuLKqFDsOFbisO+E2Av3GRfBDzXYJw5u0nmZjIvqq5DJmu8u/vvqWOpXdPf19W20vZ+fn+11eXl5i4zJaDTi9ttvR3FxMQDglltuwa23tsxzBU8xmKQXj/prWH2CiIi8k1QAoQ8XjxIRdQiswNR8shUIOQckImrXAgMD8fXXX+Orr77Cf/7zH+zduxfFxcUIDAzEgAEDcNttt2HOnDno2rVrWw+1XWmY5LKOWsm1q0REnsIAQiJqVyYPj0D6XD+kZhQgM7/YVm0DAAQBuKJPN0AA9p08b7cPEHDY70rcVXk5rhDycJXyMA6LUY32FwgD3lEvxfvmm7G3CQugMvOLceq8wa6CIlFz3XHHHfjiiy9apa8333wTzzzzDKqrqx32lZeXIz8/H/n5+Vi9ejWuvvpqrFmzpt3eMCWiDkKpAbLfAgzn7DZfp9yH58zJEKGQzaBI3qstso/OnDkTH3/8MYDajGpjxozBn//8Z4d2Z86cwR133AGzufbB3I033oiQkBC3PieRuwJ93bttc6NiN95RL8Wb5ul4z3Iz/rP7UiVNQQArExIRkaTxg3pIBhACwBGxv9vnZTIuqlNebfL4Ofnvi9xltVoxa9Ys7Ny5EwAwYMAArFy50u3z6XQ6p/uLioowatQot89fxyC7eJSPfImIyDtJJR5m9Qkioo6BFZiaT64KPeeAREQdw9SpUzF16lS3j09OTm70GiVXlbcjkis8wzkgEZHncCZBRO1OrDYYK5JHQqc3YE+hHuXVJgT6qpEQFWIL2JPbp9MbsDRLi7f2uhYMOFe1AROVuZiozMVGy5V43nS/S1U9RBHIOaHHtHgGEJLnWCz2N/5CQkIQGhqKgoICj/d17NgxW/Bg7969cc011yA+Ph5hYWGorq5GTk4O1qxZg4qKCuzcuRMTJkxATk6OQ0AHEZHLFEog5nrgwBq7zT2FMsQJv+CAGA2D0bPVKKhjaO3so9dddx2mTZuG9evXQxRFPPjgg1i9ejWmTp2KyMhIVFVVYd++fVi9ejXKysoAAKGhoXjjjTea+1GJmiwhKhSCgAbJU5wbKJzCq+p/QymIWKj+DHGK43jc9BdcRACA2nNl5hUju6AUqTPiMHl4RAuNnoiIOhplCz5/ZTIuAhyrK3sK/311DF26dMH58+cBANXV1ejSpYvT9lVVVbbXgYGBHh2LKIqYO3cu1q5dCwDo06cPtm3bhm7durl9zsjISE8Nzym55Ev+Pqw+QURE3kmqAqGmJScvRETkUazA1DxSFQj91EooFazERERE3sdkll4YwTkgEZHnMICQiNotbYg/tCHSiz7k9mlD/BHT07XFBBE4hz8rN9ve36jcg6sVP+FF8z1It4wH4PxmS/2M2Tq9ATknzqG82oxAXxUSokJlx04kZ9SoURgyZAji4+MRHx+P/v3721VS8iRBEHDddddhwYIFSEpKgkJhP8m6//77sWjRIkyaNAn5+fkoLCzEokWLmpWFm4gIg29yCCAEaqsQHjBHw8AKhJ1Sa2cfBYA1a9YgKCjIdl377rvv8N1330m2jYmJwaeffoqBAwc2u1+iptKG+CMxJgyZecUute8CAz5Qv4UAoca27VrlfnwtPIO5pr/hqNjXtt1osSIlLRfpc/1YiZCIiHBQV4aX/5fX7PP4wIh7lNvwkWUyrLh0r6FhMi7eS+ucWiqAkMneOobg4GBbAGFpaWmjAYTnzp2zO9ZTRFHEww8/jA8//BBA7VwzMzMT/fr181gfLalKJvmSv5oBhERE5J0kAwhZfYKIqMNhBSb3VErMAQN8uOSXiIi8k9EivW6Mc0AiIs/hbIKIvE6gr2tfbY+r0+ErmOy2dRUM+ItyAzZYxqAGGqfH550px9YjZ/DpXh2y8ovtqoIIApAYE4b5SdFcjEsue/rpp1utr5deegkhISFO2/Tt2xfr1q1DXFwcAGDdunVYunQp/P25GIuI3BQ1AVAHAKZKAIBVFLBfjMZxa28AYABhJ9ba2Ud9fHywYsUKPProo1i1ahV27dqFEydO4OLFi9BoNAgLC0N8fDxuueUW3HHHHdBonP8uJGpJ85OikV1QCqPFcbGUPRFL1B9ggKLIYU9fRTGGCCftAgiB2iDC1IwCrEge6cERExFRR/RORoEL1xrnfGDEMvVbSFQeRJziOB4zPQxTvUcQ5dUmHNSV4Z2MAt5L66TqJ2TrSOcmz4iJiUFhYSEAoLCwsNGAvbq2dcd6giiKmDdvHpYtWwYA6N27N7KysjBgwACPnL81SFUg1KgUUDELNxEReSGrVYTZ6liBgotHiYios5CqQBjACvREROSlpBLIAKxASETkSQwgJCKvkxAVCkGA3SKkhgYLv+E2xU7JfU+ZZjcaPAgA6/bqsG6vTnKfKAKZecXILihF6ow4TB4e4dLYiVpLY8GDdWJjYxETE4P8/HwYDAYcP34cf/rTn1p4dETktdS+wOAbUHbuLF47ORDfWuJRgmDbboPEAxDqXFoj+2h9cXFxePvtt93uj6g1xGqDkTojDilpuY0GdvxoHYRrFT9CJdi3W2NOwhfWcZLHZOYX49R5AyK7MUkEEVFnpdMbkJXvWrVbOfWDBwHgJmUO/FGDh03zbffZfi2txCub8iSvZ7yX1jm0VAVCAAj0VbfYuckzLrvsMmzZsgUAsHfvXiQmJsq2PXv2LHS62nvvYWFh6NGjR7P7rwsefP/99wEAvXr1QlZWVoerNi917yRAw8WjRETkneTuhTGAkIiIOgupJDIBGi75JSIi72SyOC76FgRAqRDaYDRERN6Jd9WIyOtoQ/yRGBPmtE2hGI5XzDNwQbRfJPuJeSL2iEM8NhajxYqUtFwc1JV57JxErS0oKMj2uqqqqg1HQkRe4bZ/49QNq/GJJckueBBgBUIiIjmTh0cgfe5oJA0OgyB7b1zAcssNuNv4DErES9U6c60D8A/zfbLnFkUg54TeswMmIqIOJefEOaeJuBrjAyPeV79tCx6sk6Q8gI/UryEAtfcSPvnht0aD4Xkvzbu1VAChIAAJUa4ly6K2M3nyZNvrzZs3O227adMm2+spU6Y0u++GwYMRERHIyspCdHR0s8/d2qTunfhz8SgREXmpGlafICKiTo4VCImIqDORmgNqlAoI8oskiIioiXhXjYi80vykaKcPDmqgwYeWG5FU8wY2WEYDAM6KwfineYbHx2K0WJGaUeDx8xK1BqPRiGPHjtne9+3btw1HQ0ReQRDgJ5MZ32BiACERkZxYbTBWJI/EjoWJeHqKfNKTPeIQ3FDzMvZaB+GcGIi/GP8KI5xX5Pn25zNI36eDTm/w9LCJiKgDaG5QV7RwCqMVP0vuG6A4jW5COfw1SsnMsVJ4L817VdSYJLerlc17+D8xJozVlDuA8ePHIzw8HACwfft27N+/X7KdxWJBamqq7f1dd93V7L4feeQRW/BgeHg4srKyMGjQoGafty1IBxBy8SgREXkno1wAISsQEhFRJyE1BwzwYRIZIiLyTiaJJJSc/xEReVa7/lbdsGEDpk+fjn79+sHX1xdhYWEYM2YMlixZgosXL3q8v19//RXPPvssrrrqKnTv3h1qtRpdunRBVFQUbrvtNqxZswYmk/QDbiJqX2K1wUidEddo9sFSdEWK6VHMMi7Ak6bZuIiAFhlPZn4xTp3nYlzqeD755BNcuHABADBixAjbIh8iouYIkMmMb5DIoEhERPa0If54aFwUJg6Wr7pejG6YYVyMO43PogihjZ7zmyNnsXD9IYxbkoVZq/ay6hMRUScT6OveoiOlojbo67AYhfuMi1Au+tnt14tdcI/xaZwSw5pcbZz30ryTVLCqr1qB9XPHNFJpWZ5aKSAlqeNVkeuMlEolnnvuOdv7++67D8XFxQ7tFi1ahNzcXADA2LFjMWnSJMnzrVq1CoIgQBAETJgwQbbfRx99FO+99x6A2uDB7du3IyYmxv0P0sYMRsf/jxhASERE3kpq8SgA+HABKRERdRIVkhUIGUBIRETeSSqJDCvQExF5VrucTVRUVODuu+/Ghg0b7LaXlJSgpKQEu3fvxrvvvovPPvsMCQkJHunzzTffxNNPP42amhq77WazGYWFhSgsLMSXX36JF198EevXr8fw4cM90i8RtZzJwyOQPtcPqRkFyMwvhtggyXn3AA3OVRohAsi0jpDcLmWYUIjzYiBOo7vLYxFFIOeEHtPimQmbOo6SkhI8+eSTtveLFy926zynTp1yur+oqMit8xJRxyVbgbCJi4qJiDqz+UnRyC4ohVFmIZUZKhwXI106V3dcQJhwHj+L/ZCZV4zsglKkzojD5OERnhwyERG1UwlRoRAEONw7c0YQgH/cPAzPf/0zjBYr9oqDMdP4DD7W/BMhQgXKRT/cZ1yEAhevRQ3xXpp3kgogDPRV2yot6/QG7CnUo7zahF9LK/HJD781Wrny3oS+iNUGt9CIydNmz56NL7/8Et9++y2OHDmC2NhYzJ49G0OHDoVer0daWhqys7MBAMHBwfjggw+a1d/ixYuxdOlSAIAgCJg/fz6OHj2Ko0ePOj1uxIgR6NOnT7P6binSFQjb5eNeIiKiZmMFQiIi6uykEvAGMIkMERF5KckAQs7/iIg8qt09UbJYLJg+fTq2bNkCAOjZs6fDw8Ndu3ZBp9NhypQp2LVrF4YMGdKsPpcuXYrHH3/c9n7MmDG4+eabodVqcfHiRRw5cgSrVq1CRUUF8vPzkZiYiJ9++olVmIg6AKnFJ4G+aiREhSCym7/T7UuzjmPdXp3d+brhIj7UvAE/GPE308PYbo1zeSzl1axgSh2H0WjE7bffbssCfsstt+DWW29161xardaTQyMiLyCXGV8qiz4REUmrq7qekpYrG0ToCgFWvKl+DwmKn/G2eRqWWW6C0QKkpOUifa4fF+QTEXUC2hB/JMaEITPPsRKYnIkxYbg7oS+G9+5qS971kxiFO43P4UP1G1homoPDYlSzxsV7ad6nXGLRW2C9rPnaEH9oQy4Fjd42IlI2OVydY2crPD5OajkqlQqff/45Zs6ciY0bN+LMmTN44YUXHNpFRkZi3bp1GDZsWLP6qwtGBABRFPHUU0+5dNxHH32E5OTkZvXdUiol7p0E+HDxKBEReSe5e16sQEFERJ2B1SqiUiKJDCsQEhGRt5KqQs8AQiIiz2p3s4nly5fbggeHDh2KzMxM9OzZ07Z/3rx5WLBgAd544w2cP38ec+bMwY4dO9zur6qqCk8//bTt/YcffogHH3zQod1zzz2HpKQk/PTTTygtLcVrr72GN9980+1+iah1NVx84sr2mJ6BdtsEWPGW+n30EvQAgJXqJfi7+T78xzLJpTEE+qrdGDlR67NarZg1axZ27twJABgwYABWrlzZxqMiIm+iViqgUSocHv6zAiERUdM0VnXdFX9Rfo1xyp8AAE+o1yFReQCPmf4CnaUnUjMKsCJ5pIdHTURE7VFjlW3r0ygVSEmKBuCYvOuD77rgmuIlMHvg0QPvpXkfqaDQQF/5fyt2/75OnMOrW/JQUmG0a7Prl1L8XlaF3sF+Hh8vtYzAwEB8/fXX+Oqrr/Cf//wHe/fuRXFxMQIDAzFgwADcdtttmDNnDrp27drWQ22XqiTunfixAiEREXkpViAkIqLOrMok/ew8gHNAIiLyUlLPqNRMIENE5FHtajZhsVjw/PPP296vXr3aLniwzquvvoqMjAzk5uZi586d2Lp1K6677jq3+ty1axfKy8sBACNHjpQMHgSAHj164JVXXsGNN94IAM0KWiSijqHh4pW/KDdggvKg7b1CEPEP9cfoKZzHEvOdAATZcwkCkBAV0lJDJfIYURQxd+5crF27FgDQp08fbNu2Dd26dXP7nDqdzun+oqIijBo1yu3zE1HH5O+jhNHAAEIiouaSqrr+a2klPvnhN5gsziMKrxDy8Jgq3W7bSMUxbNEswv3GJ5GRB/x7xy+4fniEZOIVIiLyHq5WttUoFUidEedQobbuOrFw/UGIHnjswHtp3qm8WqICoQuBonVJ4M5VGvHK5jy7faIIfP7jKVtQK3UcU6dOxdSpU90+Pjk5udEqgdu3b3f7/O1VZY1E9QkNKxASEZF3qpEJIOQCUiIi6gwqaxzvowCsQEhERN5Lag7ICvRERJ7VrmYTO3bsQFFREQBg/PjxGDFihGQ7pVKJlJQUzJo1CwCQlpbmdgBhcXGx7XV0tPMHzPX3V1RUuNUfEXUcCVGhEITaRShXCHl4vMHC2jo3K3bj37gRF9BF9lwTY8IQ2e3Sglud3oCcE+dQXm1GoK8KCVGhXJBLbU4URTz88MP48MMPAQCRkZHIzMxEv379mnXeyMhID4yOiLyNv1qJMtRWn1DCgpGKfNx4Oh3YGALcyErfRERN1bC6+m0jIp1WJgxGOVI1S6ESHG/CnxW74YjYDwDw8qY8vLI5D4kxYZifFO0QMEJERN7DWWVbQai9v5Xi5FqQc+Jck6rhDhROYajwGzZYxzjsa3gvjbxDhUQAYZcmLHq7dURvvPZNPixW+39oK3cVIjzIF6MH8B4reTerVZSsQOHP6hNEROSlWIGQiIg6s0qZ5LsBPkwiQ0RE3skkkeCS8z8iIs9qV0+UNm/ebHs9ZcoUp22vv/56yeOaKiwszPb62LFjTtvW3z9s2DC3+ySijkEb4o/EmDBk5hUjT+yDzdZRuFG5x67NOTEQ95kWOQ0eBIC+of5I36dDVz81Pt2rQ5bEIiwuyKW2JIoi5s2bh2XLlgEAevfujaysLAwYMKCNR0ZE3spPo8SVwlFMU36HJOV+hAgVwAUAub7AdS8AmoC2HiIRUYfWsDLhtz+fwTdHztr2V8IPX1iuxl+UG6AULk1OzKICj5keRhV8bdtEEcjMK0Z2QSlSZ8Rh8vCIVv0sRETUeqQq2wb6qpEQFdJoQJ9UdTk5vVCK1Zp/IkLQo7vpAlZaLt3vVysFVpPzUuXVJodtgb6uP6YKC/TFhEE9kJFXbLe9zGDCE58f4j1W8npSwYMA4M8KhERE5KXkqqNzASkREXUGshUImUSGiIi8lFQSGVYgJCLyrHb1rfrTTz/ZXo8cOdJp2/DwcGi1WgDA2bNnUVJS4lafV111Fbp37w4A2LdvH5YvXy7ZrqSkBE8//TQAQKFQ4LHHHnOrPyLqWOYnRUOjVKAC/njElIJ/mu6y7TOIPvizcSEKxcYXz67c9SsWrj+Eh1b/iMw8xwogdQtypy/bjS2Hizz9MYicqgsefP/99wEAvXr1QlZWFgYOHNjGIyMibxbgo8IY5WFMV+2oDR6sY64Gjme03cCIiLyMNsQf0+Ijcc2QnnbbTVDhdfOdmG78P5y0Xkqu9C/LLcgVpX8HGi1WzFt7AC9u/Bnp+3TQ6Q0tOnYiImo7ddePB8b2x7T4SJeqAboaCNYNF7Fa8woiBD0A4Dn1aixSpUFA7YPh6fFaBn95IYtVlMycH+irbtJ5BobJJ5vhPVbydpVG6cWj/qw+QUREXkquAqEPF5ASEVEnIBtAyDkgERF5KckAQiaQISLyqHaVjiQ/P9/2un///o2279+/P3Q6ne3YHj16NLlPX19fLFu2DHfddRfMZjNmz56NVatW4eabb4ZWq8XFixdx+PBhfPzxxygvL0eXLl2wfPlyjB07tsl9nTp1yun+oiI+0CZqb2K1wUidEYeUtFwYLVYss9yMs2I3vKJejnmmFNmFte4yWqxISctF+lw/LpSiVtEweDAiIgJZWVmIjmamfyJqWX5qJbZaRmK+6kvHnXkbgaE3t/6giIi8WEJUKAQBDslM9ouDcL3xn1isWoOhil/xrvkWp+exiCKWZxcCYCV1IiKyJ3etqc8f1fhIswQDFPb3wueqvkaYcB5Pmh5C/tnyJvWr0xuQc+IcyqvNCPRVISEqFNqQxgMeqXVVyCx669KECoQHdWVYuevXRtvxHit5qyqJIFwA8Fdz8SgREXknuQBCLiAlIqLOQC6JTIBPu1ryS0RE5DEmiSr0nP8REXlWu5pNlJWV2V7XVQV0JjQ0VPLYprr99tuxbds2zJs3D0eOHMGuXbuwa9cuuzZqtRrPPPMM5syZY6t82FTuHkdEbWvy8Aikz/VDakYBMvOL8aX1amTXXIYSBEMQgIkxYbhzpBYXq804/HsZVn1/sln9GS1WpGYUYEWy80qsRJ7wyCOP2IIHw8PDkZWVhUGDBrXxqIioMwjwUWGP2BenxO6IFErtdx7bAlhMgLJplSiIiEieNsQfiTFhyMwrdthngC+eNj8IHxhhduFWkQ+MiBF0OCQOQGZeMbILSpE6Iw6ThzdenZ2IiLyXs2tNnesVPyBO8Yvkvh4oAwD8ePI8Cksr0b+7fKU5oDaY7J2MAmTlF9sFLTLAvX0qrzZJbg9qQgDhOxkFMFmcRKjWw3us5I0qa2QCCLl4lIiIvJTRIn3t4wJSIiLqDGTngBrOAYmIyDtJJZFRK4U2GAkRkfdqV3fVKioqbK99fX0bbe/n52d7XV7etKzEDY0bNw5Lly7F5ZdfLrnfZDLhX//6F958801UVVU1qy8i6nhitcFYkTwSOxYm4vXpsXj4pjF4fXosdj6RiBXJI3HdsHBMi4/Eb3rPfD9k5hfj1HmDR85Fnc+qVasgCAIEQcCECRNk2z366KN47733ANQGD27fvh0xMTGtNEoi6uz8NEoAArZarrDf4RsMxEwBqi+2xbCIiLza/KRoaJTyt4JqoHHpPA+rvsIGn2exRLUM3XEBRosV89YewIsbf0b6Ph10es5liIg6q8auNZ9bx+Efpnsdth+x9sVfTH+F6Y9A9i/2n3Laz5bDRZi+bDcy84odKh6KIpCZV4zpy3Zjy+Ei6RNQqyuvls6aH+hiAKFOb0BWvnxwqhTeYyVvY5CrPsHFo0RE5KVYgZCIiDqzyhrpOWAXJpEhIiIvZZSsQKhsg5EQEXkvziYAlJaW4o477kBWVha6deuGt956CzfffDO0Wi0MBgN+/PFHvPHGG9i0aRPefvttfP/999i0aZNdBURX6HQ6p/uLioowatSo5nwUImph2hB/aEP8JfdJLWIJhAEKWHEBXZrUjygCOSf0mBYv3Rd5p8LCQqxYscJu26FDh2yvDxw4gMWLF9vtnzhxIiZOnNjkvhYvXoylS5cCAARBwPz583H06FEcPXrU6XEjRoxAnz59mtwfEVFDAZraGzzfWEbieuUP2GqJx3eKK7Fy4V9ZeZCIqIXEaoOROiMOKWm5kjffXTFI0OFh5QYAwHTVDkxS7sU75tvwsWUSlmcXAmDlJyKizsyVa81Ky/UoE7ridfUHUIgm6MQeSDY+iQpcug/2n90noe3mj9EDQh3uxR3Ulbl0LTNarEhJy0X6XD9ej9oBuQDCLj6uzf9yTpxzCBZtDO+xkrcxGOWqT3ARDREReSfZAEInSUuIiIi8RSXngERE1MlIzQE5/yMi8qx2FUDYpUsXnD9/HgBQXV2NLl2cB9zUrwQYGBjoVp8GgwFXX3018vLy0K1bN+zZswfR0dG2/V27drUFZzzyyCP417/+hR9++AGPPvooPvnkkyb1FRkZ6dYYiahjaLiIpSsq8B/NPyECuNf4NMrRtIUq5dUmzw6Q2r2TJ0/ipZdekt1/6NAhu4BCAFCpVG4FEGZnZ9tei6KIp556yqXjPvroIyQnJze5PyKihvz/yI7/gxiD0TXvAhAgCICoUEFo26EREXm1ycMjkD7XD6kZBcjMd6za5IwCVryq/hBq4dJD6yChCs+q10IrlODv5mQAlyo/ZReUInVGHCYPj7A7j05vQM6JcyivNiPQV4WEKMfgECIi6ricXWsEAZgYE4b7kxZCYbwG+Ho+VgQ9j5Jj9lVwL1SZ8MTnhySD0t/JKHA5EN5osSI1owArkkd66uORmypqpO91ulqBUC4AsfHjeI+VvIdcBUIuHiUiIm9VwwqERETUibECIRERdTbSFQi5ioyIyJPa1WwiODjYFkBYWlraaADhuXPn7I51x3vvvYe8vDwAwIIFC+yCBxt69dVXsXbtWpSVlWHdunV48803ER4e7la/ROR96i9iCcUFrNG8giGK3wAAKzWv4X7jIhjg6/L5An1ZfYmIiLyX3x+L20RcetAvirULAnzVXPhGRNSSYrXBWJE8Ejq9AZsPn8HLm5xXoa5zr/JbXK447rC9RlRjpeV6h+1GixXz1h7AA2PPIyY8EF391Ph0rw5ZEsEkrFhIRORd6l9r9hTqUV5tQqCvGglRIYjsVhc0ngg8+iP8tv4CHPtF8jwNg9KH9eqKrPziJo0lM78Yp84b6vVLbUEuANDVAEJX2zkex3us5D3kKhAGcPEoERF5KbnEIQwgJCKizkAugNDfh8/SiYjIO7ECIRFRy2tXT5RiYmJQWFgIACgsLES/fv2ctq9rW3esOzZu3Gh7fd111zltGxAQgDFjxmDTpk2wWq3Yu3cvbrrpJrf6JSLvU7eIJQzn8YnmJQxUnLbtG6k4huXq1/GA6QnUQCN3ChtBABKiQlpsrNQ+TZgwAWJTSsDISE5ObrRK4Pbt25vdDxFRcwTIZMevrDEzgJCIqJVoQ/zx0Lgo5Jw4h8y8xoMxFLDCIPrAX6ix2/5vyw34TewpeYxFFLE8u1ByX53GKhYSEVHHpQ3xd1pl9uDpSizPPtHoeYwWK1LScjF3fFSTqucCtdeZnBN6TItnAGFbuigbQOhagF9CVGht1fom/PfnPVbyNpUyAYR+rEBIREReSmrxKAD4KHntIyIi71cpUYVerRTgo+J1kIiIvI/FKsIqcf+fCWSIiDyrXX2rXnbZZbbXe/fuddr27Nmz0Ol0AICwsDD06NHDrT5Pn74U4NO1a9dG29evdFhRUeFWn0TkneoWsbyhft8ueLDOGOXP+Kf6Q5fONTEmjFnRiYjIq/lppHOZyGXTJyKiljM/KdqlzH0fWa7HxJrXscEy2rbtdzEU75lv9sg46oJDDurKPHI+IiJq/97JKIDJ0nhE2M2K7/GYsAZbfnK85+aK8mqTW8eR51Q0swKhNsQfiTFhTeqT91jJ2xhkqk8EyNxjISIi6ujkAgi5gJSIiDqDyhrH5+b+nP8REZGXkpv/qVmBkIjIo9rVt+rkyZNtrzdv3uy07aZNm2yvp0yZ4nafgYGBttd1AYnOnDx50vY6NDTU7X6JyPvULWJ5yvwgikTHzNYlYhBWmSc1eh6NUoGUpOiWGCIREVG7IVeBkAGEREStL1YbjNQZcS4FEZ5BKFJMj+LOmmdx1KrFy6a7UQXfRo8LwUUo0fh3vNFiRWpGgUvjJiKijk2nNyArv/EKuAmKn/G6+n3MVW3EI2WvQoOmBwO6WuWOWo5cEKerAYSA60kP6oR39cXK7EKk79NBpze4fBxReyV3z4QVCImIyFsxgJCIiDqzSokkMl18GEBIRETeifM/IqLW0a6+VcePH4/w8HAAwPbt27F//37JdhaLBampqbb3d911l9t91q96uHbtWqdtjx8/jj179gAAFAoFrrjiCrf7JSLvND8pGsWKcMw0PoMS8VJV0+PWXrjV+A8cFAc6PV4pCEidEYdYbXALj5SIiKhtyS1uMxils+kTEVHLmjw8AulzRyNpcBgEofH2e8QhuNH4Mv5nvdKl87+lfg/faJ7EZMUPAJxXmsrML8ap85cW+ev0BqTv0zEAgIjIy+ScOAexkeKD0cIp/Fv9JjRCbdDMzcrdWK15BUGocLkfQQASohyTfVHrKpeoQKhUCPBTux741JSkBwCwds9v+MfGn7Fw/SGMW5KFWav2stIxdWhy90z8GUBIREReymhxXECqVAhQKly4eUVERNTBVUrMATn/IyIibyU1/wMYQEhE5Gnt6ltVqVTiueees72/7777UFzsmIF40aJFyM3NBQCMHTsWkyZJV/RatWoVBEGAIAiYMGGCZJuZM2faXn/00UdYsWKFZLszZ87gjjvugNlcOzG78cYbERLCRQdEZK9uEcvvit642/g0zotdsNsyFLcZ/45TYlijxw+NCMLk4RGtMFIiIqK2FaCRzo7ICoRERG0nVhuMFckjsWNhIl6fHosHr+7vdEGWBUoAjS/YSlD8jPHKQxioOI1lmrfxleZZjFX8JNteFIGcE3oc1JVh1qq9GLckCwvXH/LKAAAGRxJRZyYVUFZfGM5jleZVBAn2341XKvLwueZ5+KHapX4mxoQhspu/2+Mkz6iQyZovuJK5oJ6mJj2oI4pAZl4xpi/bjS2Hi5rUJ1F7USlxz0SjUkDdhMqcREREHYlJYgFpUypSExERdWSVNY5zwABWICQiIi8lG0DIOSARkUe1uxnF7Nmz8eWXX+Lbb7/FkSNHEBsbi9mzZ2Po0KHQ6/VIS0tDdnY2ACA4OBgffPBBs/q77rrrMG3aNKxfvx6iKOLBBx/E6tWrMXXqVERGRqKqqgr79u3D6tWrUVZWBgAIDQ3FG2+80dyPSkReqnYRix9SM7rjjmPP4VdrOEx/fN0KQu2ipTtHarHkmzwUFFfaHXusuBxGs5VZM4iIyOvJZUe0BRBWFANHNwA/fwVMeBroO7oVR0dE1LlpQ/yhDakNtLiibzekpOXK3rBvnIgnVJ/abYlVnMBazSt4wjQbn1kSJY/6ofAcnv7iJ8l+6wIAsgtKkTojrkMmYTmoK8M7GQXIyi+2q74lCEBiTBjmJ0WzMj0Reb1AX+ePJ4YpfkUPlEnu22qNRxV8G+1Do1QgJSnaneGRh5VXmxy2NfZvQE5d0gOd3oA9hXqUV5vw9cHT2P9bWaPHGi1WpKTlIn2uH6+11OFUSQQQsvoEERF5sxqzRAAhn6MTEVEnUSmRjCnAh3NAIiLyTkaJ+R/AOSARkae1uwBClUqFzz//HDNnzsTGjRtx5swZvPDCCw7tIiMjsW7dOgwbNqzZfa5ZswZBQUFYuXIlAOC7777Dd999J9k2JiYGn376KQYOHNjsfonIe11axDLMtogl0FeNhKgQW8bzo0XleGvbMbvjasxW/PT7BcT37dYWwyYiImo1fhIL3HxgRM+8/wB7tgMndwH4I6KixxAGEBIRtZFLCVIKkNkg2M0V1yj2Y4TiuMP282IXbLZcKXvc5/t/h8XqvLOOGgCw5XCRbFCmNwRHEhG5KiEqFIIA2WtLlvVy3Gd6Cv9Wv2lXhbC47414Pf8Ol/p4eMKADnWN8GYXJSpOBvqqm3XO+kkPsvKKXT7OaLEiNaMAK5JHNqt/otYmuXhU0+4e9RIREXmM1AJSLh4lIqLOwiCRRIZzQCIi8lZSFegBViAkIvK0dvmtGhgYiK+//hr//e9/cdttt0Gr1cLHxwfdu3fHlVdeiVdffRWHDx/GmDFjPNKfj48PVqxYgQMHDmD+/Pm44oorEBISApVKBX9/f/Tr1w+33347Vq9ejUOHDiEuLs4j/RKR99OG+GNafCQeGNsf0+IjbcGDADCqf4jkMT8U6ltreERERG0mwMfx4YYFCsQcTQVOZsMWPAjUViK0ulv5ioiImqsuQcqOhYl4fXosHry6P5QKwaVjb1PulNz+L/NUlMNfch+ARoMH69QFAHQUB3VlLlV0rAuOPKgra52BERG1AW2IPxJjwpy2ybEOxe3Gv+N3MRQAcMxnONb1XgTRxUcbmw+fwcrsQqTv00GnNzR+ALWYCqkAQol5YZNYzIDZCJ3egJ3HS5t0aGZ+MU6d578J6liqTI6LR6USNBEREXkLyQBCLh4lIqJOokKyAiEDCImIyDvJVSBUcw5IRORR7XpGMXXqVEydOtXt45OTk5GcnOxy+7i4OLz99ttu90dE1BSX9wmGWinAZBGhgQkmKCFCgR8Kz+EvEwa09fCIiIhalJ/acYGbGSqc7D4Bg4o22O8oLwJO/QD0SWil0RERkZT6VX6u6NvNpUC4R0wpuM26E39VfY5IoXZh/2kxBKst13psXHUBAPUTtrRX72QUNPp3VofVkYioM5ifFI3sglKn340FYiRurfkH/q75D/pM+wD/+5/OoY1SEOCrVqCyQWb2/LPl+MfGnwEAggAkxoThrpFaXKgyobzajEBfFRKiQm3XN2o55TUmh22Bvk14RFV+Fvjte6AkHyjJA0qOAecKgKn/Qo5xdJOrJIsikHNCj2nx/G9PHYd0BUIGEBIRdTQbNmzA6tWrsXfvXpw5cwZBQUEYOHAgbr31VsyZMwdBQUEe6ae8vBxbt25FVlYW9u/fj4KCApSVlcHPzw+9evXCqFGjMHPmTEyaNAmC4FqiqNYmNU9gBUIiIuosDEapAELOAYmIyDvVyAQQcg5IRORZ7TqAkIjIm/mqlfhTZDB0J0/gA81byLYOxxvmO7Dv5HlYrKLLFT2IiIg6IrnsiHkhEx0DCAHgyH8ZQEhE1I5MHh6B9Ll+SM0oQGZ+seyifSsUWG8Zjw2WMbhbuQ2PqP6Ld8y3owaaRvsYKJzCBbELShDstF1HCQDQ6Q3Iyi9u0jEdKTiSiMgdsdpgpM6IazQovRjd8FX0y3gyuAfyzvzssH9CTA9c3icYr289JnsOUQQy84qRmWf/XVwXWDg/KRqx2mC3Pws5Vy5VgdDVAMLyM0Dq5YBJomJgSR7KfdwLti+vdgxqJGrPDEbHCoT+Gj7qJSLqKCoqKnD33Xdjwwb7+98lJSUoKSnB7t278e677+Kzzz5DQkLz7oW/+eabeOaZZ1BdXe2wr7y8HPn5+cjPz8fq1atx9dVXY82aNejTp0+z+mwJrEBIRESdVY3ZApPF8cELKxASEZG3Msk8I2IAIRGRZ3FGQUTUhqZ2P41JZ55BT6EMlyuO42drX2yuvhJ5Zy5iWK+ubT08IiKiFuMvkyE/P+AKwKcrUHPh0sbeVwA9h7XSyIiIyFWx2mCsSB4Jnd6APYV65J25iI92/QqL1fGhthFqfGS5Hp9ZJqDaheBBQMTr6g8wSDiFjy3XYZn5JlxAF9nWHSEAIOfEOVZHIiKS4GpQ+o8ny/D1wSLJfddfFoHwIB8AgBIWhKC80QD0OnWBhdkFpUidEYfJwyPc+RjkhCiKkgGEXVwNIAwMB656DMh60XFfST4CB7r3qCvQV+3WcURtRTqAkNUniIg6AovFgunTp2PLli0AgJ49e2L27NkYOnQo9Ho90tLSsGvXLuh0OkyZMgW7du3CkCFD3O7v2LFjtuDB3r1745prrkF8fDzCwsJQXV2NnJwcrFmzBhUVFdi5cycmTJiAnJwchIWFeeTzeopkACEXjxIRUSdgqHGc/wFAAJPIEBGRl5Ka/wFMIkNE5GmcURARtZUDa3DP0b9CIVxa6PqGehkKjRH4oVDPAEIiIvJqPioFFALQMMakwiQAg28AzhUAQ28Bhk4FgrVtMkYiInKNNsQf2pDaALcr+nZzWkWqEn4unTNJsR9xil8AAH9RfY27lduw3HwDVlomowKOwXQdIQBAKnDCtePaf3AkEVFzNQxKX5ldiJ+LLtq1Ka2owfvfHXc4VqUQcO2QnvjbZ7kAgMWqNZii3IM/GxfgsBjl8hiMFitS0nKRPtePlQg9rNpklUww0KTr91V/BfI2AkW59ttL8pFwXSgEAU4D9R9TfQYA+Jf5FtRAA0EAEqJCXO+fqB0wGB1/T/qz+gQRUYewfPlyW/Dg0KFDkZmZiZ49e9r2z5s3DwsWLMAbb7yB8+fPY86cOdixY4fb/QmCgOuuuw4LFixAUlISFAr7BZf3338/Fi1ahEmTJiE/Px+FhYVYtGgRVq5c6XafLUHq/hIDCImIqDOoqJF+nsAKhERE5K1kAwg5ByQi8ih+qxIRtYXzvwJf/xUKq/1CUH+hBh+q38CR44VtMy4iIqJWIggC/CUyJBqMFuDmd4EHtwFjHmHwIBFRB1NbRWo0kgaHQRDs9wkCkDQ4DP++Nx5PT5HPoi/AigWqdLttQUIVHlOvx63KbMlj9JU10OkNzR5/Swp0tcqSw3HtPziSiMhTtCH+mBYfiWdukL5OVJscHyCPHhCKi9UmZOUX4z7lN3hA9Q16CmVI1/wDkxR7m9S/0WJFakaBW2MneXLB8E26NirVwC3vA8oGlYz1J6ANUiIxRr5aziBBh78ov0aK6r/YqnkCExS5mBgThshurPBLHUulRAWKAFYgJCJq9ywWC55//nnb+9WrV9sFD9Z59dVXERcXBwDYuXMntm7d6nafL730Er755htce+21DsGDdfr27Yt169bZ3q9btw4GQ/u6tyJZgZDVJ4iIqBOQqkAPcA5IRETeyySToJgBhEREnsVvVSKittCtH3DD65K7TFDil99OQXSWMpuIiMgL+Es84DAYLYCSmROJiDqyuipSOxYm4vXpsfi/m4bi9emx2PlEIlYkj8R1w8LRzV8+KO4GxR4MUfzmsP20GILPLBMkj3l5Ux7GLcnCrFV7cVBX5qFP4lkJUaEOQZWu6AjBkUREnjY6KhR9Q10L7rqsd1fknDiH8cIB/J/qP7btfoIR76vfxhzl1wBcv8+WmV+MU+f5vetJF2Wq8AY2NWt+z6HAVY8BKl8g/DJg+DRgwlOAxYj5SdGSi8kFWPGieiXUQu3Cu76KYqzSvIYFF17C19k/8hpLHYbVKqLK5LiA1I+LR4mI2r0dO3agqKgIADB+/HiMGDFCsp1SqURKSortfVpamtt9hoS4Vmk5NjYWMTExAACDwYDjxx0rfrclyQBCLh4lIqJOgBUIiYios5GqQA8AaiaRISLyKH6rEhG1lfhk4Io/223abonFLcYXcKAyFCdKK9tmXERERK1EOoBQ+mEIERF1PHVVpB4Y2x/T4iPtqvyUywQSAMAwxa+S25eab0UNNJL7AEAUgcy8YkxfthtbDhe5Pe6Wog3xd1odSU5HCI4kIvI0hULAjFF9XGr74c4TOJn3I95VL4VSsA8UVAgi/qZaD61Q7HLfogjknNA3abzknNyityZX2bVaa++pPn0amJsNTFsBjF8I+AQiVhuM1BlxDkGE05Q7MEqR73CqIeezsGPzp7zGUochFTwIAAEaLh4lImrvNm/ebHs9ZcoUp22vv/56yeNaUlBQkO11VVVVq/TpqhoGEBIRUScl98w8wIdJZIiIyDtJzf8AVqEnIvI0fqsSEbWlyf+EvvsVAIBl5pswy7QQFxEAAPihkAuViIjIu/lJLHIzGKUXxBERkXcJ9JVf6PyqeQaur3kF31ouZeT/zdoD6ZbxLp3baLEiJS3XIRBApzcgfZ8OK7MLkb5P1yYVh+SqIzWmvQdHEhG1hCERgS61M1lErD5ixgHrQMn9fzM9DJ3Ys0l9l1ebmtSenJP7+7T9HjiwBji+rfETKRRAUASgkF4sN3l4BNLnjkbS4DAIAqCBCU+o1km2/dIyFumW8bzGUochd7+EFQiJiNq/n376yfZ65MiRTtuGh4dDq9UCAM6ePYuSkpIWHZvRaMSxY8ds7/v27dui/TWVVAUKBhASEVFnUClXgZBJZIiIyEuZZCoQcg5IRORZnFEQEbUllQaY/jH+8s6/sdl6pd2u9ft0uGpgd2hD/GUOJiIi6tgCJCsQMoCQiKgzSIgKhSDUBsZJOSr2xWzTAsSZj+Nx1Wf4yjoWJhduY12j+BGhwkV8YbkaqRkFWJE8Egd1ZXgnowBZ+cV2/QkCkBgThrtGanGhyoTyajMCfVVIiAptsXlYrDYYb98Zh4c/2e/W8XXBkelz/RCrDfbs4IiI2pmPvz/pctsLoh8eMD2Bv4sf417VpUC010x3ONxzc0WTK+ORU3KVh7v4qIC8/wEbHgUEBXDL+8Cf7mhWX7HaYKxIHgmd3oA9hXq8lfMKpp19EyMUx21tfrFG4BnTnwEItm28xlJ7J1t9ggGERETtXn7+pWrI/fv3b7R9//79odPpbMf26NGjxcb2ySef4MKFCwCAESNGIDw8vEnHnzp1yun+oqLmJWgwSlSg8GH1CSIi6gQqa2Sq0PtwuS8REXknqfkfwAqERESexhkFEVEbC+kZiUNB44GyarvtP/5WhnFLspAYE4b5SdFcuEJERF5HKku+3II4IiLyLtoQfyTGhCEzr9hpu1xxIO41PQ1AJtKwHgWseEr1CQYoivCI8r/4V8EtSPu+G/7vfwWSGevrKg41HENdYGFLzcMGhbtWUUuO0WK1BUcSEXkrnd6ArHzn14iGLFDiWfMDOCFG4FnVGnxhvRrvWaY2uW9BABKiQpp8HMmrkAkg7Hl+P7B5FiBaa/98MRuoKAbGPNLsPrUh/tCG+GPjoWjcbvw77lRuxyJVGnxgwsOm+TDA1+EYo8WKBekH8dC4qBZNKEDkDrnFo/5cPEpE1O6VlZXZXnfv3r3R9qGhoZLHelpJSQmefPJJ2/vFixc3+Rx11RJbitQCUlafICKizqBSLokM54BEROSlZAMIOQckIvIoziiIiNrYlsNFKLpQLbmvbkFrdkEpUmfEYfLwiFYeHRERUcsJ0DhOR1yqQGiqBtSOiz2JiKhjmZ8UjeyCUsngvoaUggIWuXKFf7hRsRsDFLWZ7bWKEvxT8SF+/+ZLJIr34BuMcnlcLT0PO3L6QrPPkZlfjFPnDYjsxsAGIvJOOSfOyVapdUapUOAjy/U4Yu2HXHEg6leYc9XEmDB+v3rYxWqTw7YY4Tf02foKYG5wX3TrM0DFWeDaf9RGczaDTm/Ad8dKIEKBTy0TsdVyBYYoTiJf7CN7TEFxBRauP9TiCQWImqrKJL141J8VCImI2r2Kigrba1/fxu9r+/n52V6Xl5e3yJiMRiNuv/12FBfXJu245ZZbcOutt7ZIX80hdc+Ii0eJiKgzkK1AyDkgERF5KZPMmgHOAYmIPIvfqkREbeigrgwpabmwNrIgymixIiUtFwd1Za0yLiIiotYgtchNNoBQFIFfdwH/fRhYMhAo07Xw6IiIqKXFaoOROiMOGqXz21MapQK3xfd22kYBK+arvnDY3lsohRnuPVBvqXnYz0UXm30OUQRyTug9MBoiovapXKZiXWNuH9EbGqUCP4hDYITaxaMu3ZjTKBVISYp2q2+SJ/Xf81ZlNhQ1MkH1PkHNDh4EHANR9QjCLutlLh3bVSxHft4RTF+2G1sOFzV7LETNJb94lLliiYioaaxWK2bNmoWdO3cCAAYMGICVK1e6dS6dTuf0zw8//OD2OC1WERaJh+iN3UciIiLyBpU1rEBIRESdi1wFQrWy+c8KiIjoEt5ZIyJqQ+9kFLhUbQMA+lt/xc4NH7XwiIiIiFqPv49UAGGDhyEGPbBjCfDuCGDVFCB3LWAsBw5+2kqjJCKiljR5eATS545G0uAwhzgBQQCSBochfe5ojOoX4vQ89asP1nfM2huZ1svdHp/RYkVqRoHbx0v5+XTzAwgBoFyimhMRkbcI9HVvMdSo/qGy1xUpkUIJNmuewgjhGDRKBVJnxLHaXAuokFj09k/zDFjH/s2x8cjZwLgFHunX3UBUQMRr6n9js89TuEb8HvPWHsCLG39G+j4ddHqDR8ZG1FQO90v+4MfqE0RE7V6XLl1sr6urq520rFVVVWV7HRgY6NGxiKKIuXPnYu3atQCAPn36YNu2bejWrZtb54uMjHT6JyIiwu2xyi0eZfUJIiLqDCrl5oBqzgGJiMg71bACIRFRq2BKEiKiNqLTG5CVX+xS2xsUOVii/gCKYivO5o9Bz5grW3h0RERELc9fIku+ySLCZLFCXZdF2GIEsl4BxAaZ9nPXAFc/Dih4o4iIqKOL1QZjRfJI6PQG7CnUo7zahEBfNRKiQhDZzR8AEBKggSDAropQfXoE4ai1D4YofrPb/oH5JojNzJ+VmV+MU+cNtrE0hyiKOOKhAMJAX1craxERdTwJUaFOv/elCAJs1w6p60pXPxU+/UGHjLza+3EamLBU/Q6GKH7DOs0LKLx8IQYNm9xCn6hzkwp699eooLj270BgT2DLotqNQ28Brn/VI9UHAfcDUe9RbsN1yh8BAO9pUvGJ+Se8kH0vquALQQASY8IwPymawabUqgxGViAkIuqogoODcf78eQBAaWmpXUChlHPnztkd6ymiKOLhhx/Ghx9+CKA2+C8zMxP9+vXzWB+exABCIiLqzKQqEAZolFAoWIWJiIi8k8ks/UCIVeiJiDyL36pERG0k58S5RhdBKWHBItUn+JcmFf5CDXwFE7r8NxmoPOf8QCIiog5ALkOi3aK4wHBg4DWOjc7/Cvz2fcsMjIiI2oQ2xB/T4iPxwNj+mBYfaRewpw3xR2JMmOyx2dbLMMX4MuYY/4aj1j4AgNNiCDZYxzTabx/hLB5VfoEwnJfcL4pAzgl9Ez+NtLMXa6CvNDb7PHVBMkRE3qqx730pE2PCHK4d9a8r1w4Nx4rkkVh8wxAAwFOqTxCnOAEAUAsWDMr9J/Dp3UCV9PWA3CdVCdAW3JfwF+D2FcCAJOC2fwMKz2XSrwtEbYpBgg6LVWvsts1UZeFrzWIEoRKiCGTmFWP6st3Yctix+jFRS6mUCSD092H1CSKi9i4mJsb2urCwsNH29dvUP7Y5RFHEvHnzsGzZMgBA7969kZWVhQEDBnjk/C3ByOoTRETUiUnNAf19mECGiIi8l9HieO1TKwUIHko4SEREtXhnjYiojUgtnKlPDTNWqV/FXNVGu+0BVaeB9cmAxfnxRERE7V2AzCI3g7HBNe7ye6RPcGCN9HYiIvJK85OinWYYFKHAN9aRmGJ8GQ8aH8eLpntgQuMP1GcqM/C4ej2+93kUH6pfxy2KbATCYNdGqnKSO46cviC5fWhEYJPO0zBIhojIGzX2vV+fRqlASlK0S22vGxqOKYocPKD6xnFn/v+A75a4dB6d3oD0fTqszC5E+j4ddHpD4wd1UlL3QbvUX/R22TTgns8BlY9H+21qIKoPjHhX/S58BcfrfrEYjIu4dO01WqxIScvFQV2ZJ4ZK1KiqhvdK/uCvYQAhEVF7d9lll9le792712nbs2fPQqfTAQDCwsLQo0ePZvdfFzz4/vvvAwB69eqFrKwsDBw4sNnnbkmyAYSsPkFERJ2AVAXCLgwgJCIiLyZVhZ7zPyIiz+M3KxFRG7Fl2ZZhggoFYqT0zsIdwI8ftcCoiIiIWo+fRvpaaGiYUXHQZMA/tN6BIcCVfwFGP9KCoyMiovYmVhuM1BlxjT4oEKHANms8NlkTGj2nBiZMV34HAFAJVlyr3I+3Ne/hS81zdu0CfdV2790NGvn59EXJ7XPHD3T5AYhaKbgcJENE1JG5+r2vUSqQOiMOsdpgl86rDfHD7T4/SO/sPghIfNrp8Qd1ZZi1ai/GLcnCwvWH8I+NP2Ph+kMYtyQLs1btZUCZhHKJRW8Nr61NLhXooqYEoiphxWGxn8N2qyjgJfM9AOzHaLRYsSD9IANIqVVU1shUIJS5t0JERO3H5MmTba83b97stO2mTZtsr6dMmdLsvhsGD0ZERCArKwvR0e3/voLU4lGAFQiJiKhzMEjMAZlAhoiIvJnJIjps4/yPiMjz+FSJiKiNJESFQhAA0fF3r83L5pkYqjiJBMVRu+3lcQ8iMP6BFh4hERFRywqQecjh8EBEpQHiZgIl+bXVCAdN9nhlCiIi6hgmD49A+lw/pGYUIDO/2Ol8yqXzKfYiVCh32L7NOsL2WhCAhKgQALVBI+9kFCCrQd+CACTGhGF+UrTTAJYjEgGEGpUCUy4Lh0YlICUtVzbDfp07rtC6HCRDRNTROfveF4TaiqwpjXz3NiQIAj6PegEH8z7AX1WfQyHUnrRK1EC8ZSX8fbrIHrvlcJHsd7UoApl5xcguKEXqjDhMHh7h8pi8nVQl38aSq3lKXSCqK9dYA3zxuOlh7LT8CS+qV6KLUA0AWG8ZhyMSgYUAUFBcgYXrD7n8W4DIXQZWICQi6rDGjx+P8PBwnDlzBtu3b8f+/fsxYsQIh3YWiwWpqam293fddVez+37kkUdswYPh4eHIysrCoEGDmn3e1iAbQMgKFERE1AlUSCRjCmAFQiIi8mJSc0A1539ERB7Hb1YiojaiDfFHYkyY0zZmqPCIMQWnxdrFqkZRiYWmh/CnPRMxa/UBZjQnIqIOTW6Rm+SiuGtfAO5OB4ZOZfAgEVEnF6sNxorkkdixMBF3jtQ261x3q7ZJbt9iGWV7PTEmDJHd/LHlcBGmL9uNzLzaAJZY4TiuVexDpFACURSRmVeM6ct2Y8vhItn+fi5yDCAcHB4IlVLxR5DMaCQNDnNahClXVwaxuZGTREQdSP3v/denx+L/bhqK16fHYucTiViRPNKtYK34/j2QarkN95ieQokYBABYbJqF/VXyQX8HdWUuBaEZLVakpOXyvl2d6osor5aqQNh6i95cvcbW+a/1KtxgfBm51igYRB+8br6j0WN6owSBx77Ag8u+dfpbgMhdBqNj9QmNUsFFNEREHYBSqcRzzz1ne3/fffehuLjYod2iRYuQm5sLABg7diwmTZokeb5Vq1ZBEAQIgoAJEybI9vvoo4/ivffeA1AbPLh9+3bExMS4/0FaGSsQEhFRZyb1vFwuOS8REZE3qJGYA3L+R0TkeUxLQkTUhuYnRSO7oNTpwqNSdMVDxsfwnvod/NU0D/vF2qyQzGhOREQdnb9GejoitSjOpVWeRETUqWhD/PFI4kB8tk/nViVCDUy4KPrDIgpQCpdO8LsYilxxAABApRCQkhQtGTRyn2orbldmAwAuin7IE/sgxzoEKWlA+lw/h4CWi9Um/KY3OIxjWK8g2+u6IBmd3oA9hXq8uTUfpy9U27U/cvoiXtj4M4ZEBCEhKhTaEP+mf3giog5IG+Lvse+8K/p2AwB8bx2OG2pewU3K7/G5dRy0J/W4Krq75DHvZBQ0GjxYx2ixIjWjACuSR3pkvB3W2SPAR9fjxppp+AgTAVya1wX6qFt1KA2vsR989wsKiitk258UwzHd+HdcLhSgGN0aPf+Nit1YpP4UZlGBfZ8NxsnSueg74X5PfgTq5KTulfj7cPEoEVFHMXv2bHz55Zf49ttvceTIEcTGxmL27NkYOnQo9Ho90tLSkJ1de48hODgYH3zwQbP6W7x4MZYuXQqgtgL3/PnzcfToURw9etTpcSNGjECfPn2a1benGC0SzwnABaRERNQ5VNQ4XgdZgZCIiLyZSeL5C+d/RESex1kFEVEbitUGI3VGXKPZyw+LUZhofAPmBl/bRosVj35yAH+ZcBHB/hoE+qq4gJSIiDoM+QqE0gsDiIiIGqqr7J6Z55i5vzFGqDHbtAC9UIo7VNtxg2IPohW/4xvLSNQFOPxJ2xWx2mDMWrXXYc52pSLP9jpIqMIoIR/VogZGs3TQyNHTjtUHAWBoRJDDtrogGatVxBOfH3LYv3LXrwBq4+sTY8IwPynarQpcRESd1dBeQfBTK1FlsqAY3bDCcgMAYN+v5yXb6/QGZOUXozsu4F7VVnxgvgkG+DrtIzO/GKfOGxDZrZPepzNWAunJQPUF/J9iBeLVh/GUaTbKUfv30ZoVCOuru8ZGh3XB9GW7nd6TNUGFH8QhLp33GuV+AIBKsCJB+BkbDvzAAELyKKnqE/5qBhASEXUUKpUKn3/+OWbOnImNGzfizJkzeOGFFxzaRUZGYt26dRg2bFiz+qsLRgQAURTx1FNPuXTcRx99hOTk5Gb17SlS1SeA2gq8RERE3q6yRqoCIZf6EhGR95KqQs/5HxGR5/GblYiojU0eHoH0uaORNDjMaXGlhsGDdUxWEamZx/GPjT9j4fpDGLckC7NW7cVBXVnLDJiIiMhD5CsQOj4QISIikjM/KdrlhwdKQcCDV/XH69Nj8eF98UgaHIYioTveNk/DtcYlSKpZgpWW623tD+ouYP9JPbLy7QMUe6MEkUKpw/nzxNos/XVBI/Ud+SOA0Bc1+Ej9Kh5VfoHRiiMY3kO+AtNNsb3gp5b/bKJYW51++rLd2HK4qPG/AGo3LBYLDh8+jFWrVuHRRx/F6NGj4e/vD0EQIAiCxxdstnZ/RO2dWqlArLarw/YDv52HWSKgLOfEOYgi8ITqU8xXfYkMnwW4WfE9APkSuKII5JzQe3LYHYcoAhsfA0qP2TbdqNyDjZqncZlwAgDQpY0CCOvUJXbzxAKEUFzACKHAbtvyksEOvwWImkO6AiEXjxIRdSSBgYH4+uuv8d///he33XYbtFotfHx80L17d1x55ZV49dVXcfjwYYwZM6ath9ouSC0eBViBgoiIvJ/FKqLKxAqERETUuUgl++P8j4jI8zirICJqB2K1wViRPBI6vQGbD5/By5uOun2uugWk2QWlSJ0Rh8nDIzw4UiIiIs9hBUIiIvIEVyu7a5QKhznStUPDodMbsKdQjy/3n8KuX+yPsVhFLM06DrFBfMhIRb5kH3lWLYBLQSPT4i9Vnfq5qDaAME7xCxKVB5GoPFjbdu0/gV6XA1fOBf403e58x86Wy2bcr89osSIlLRfpc/1YibCDuOOOO/DFF194bX9EHcEVfUMcAvwqjRbknSnH8N6Xggt1egN2FpQiTjiOO1TfAQAiBD1SNUtxj/Vb/N10P34W+0n2UV5tarHxt2sHVgOHPnXY3FdRjJuV3+MncxQCfeUD6FtLbWI3P6RmFCAzv9jheu+qicoDUAiXDj4jdsNP1n4OvwWImkO6+gQrEBIRdURTp07F1KlT3T4+OTm50SQw27dvd/v87QUDCImIqLOSS7Yb4MM5IBEReS+TVAAhKxASEXkcv1mJiNoRbYg/uvk3b/FMH+EsgEsLSFmJkIiI2isGEBIRkac4q+wuCEDS4DCkzx0tmWBFG+KPafGReG16LBQSVeF3FjhWGrxcUeDYEMBRsW+940qg01+qPFRXgfAKwT74UBAtwO/7gMoSh/O9k1EAq4vBDEaLFakZ0uOi9sdisf+9ExISgujoaK/pj6gjuKJfN8ntP548DwA4qCvDrFV7MW5JFr4+eAp/V69yaDtKkY85qo2yfdQPktPpDUjfp8PK7EKk79PZXSOawlPnaVEXT0tuzrVG4TXzXQCAwDauQFinLrHbjoWJeH16LB68uj+UUj8InPjTH1UV62RYRkCEovMGkFKLkLpX4scAQiIi8mJySaJ8GEBIREReTu5ZOSsQEhGRN5NKIqNmACERkcdxVkFE1M6UV0tnkmqMAlY8q1qNGcpMzDAuxgEx2raAdEXySA+PkoiIqPn8ZR5yyGVVlGW1AEe+BEL6A73jPTAyIiLqiOpXdt9TqEd5tQmBvmokRIUgslvj1X96B/th0rBwbD58xm67yeIYwfe8+X6ssVyLwcJvGKz4DYMFHaKFU/hF7GVr81XuaWw4eBqJMWF4eMIAHC8uBwBcoTjm2LmgAIbfbrdJpzcgK7/YlY9uk5lfjFPnDS59Xmpbo0aNwpAhQxAfH4/4+Hj0798fq1atwgMPPOAV/RF1BCP6doMgwKHq3L6T59EzyMeusu105Q7EKU44nMMg+uAV0wzJ8wsCkBAVgoO6MryTUYCsBhXuBAFIjAnD/KRol6rHeuo8rWLCotq52RcPAVW1VR4viv541PQoTH88lgpsZ4vetCH+0IbUXj+v6Nut0crG9T1rnoWPLJORpNiPa5T7sdV6BQC0iyqL5D2kFpAGaNrX/0dERESeJFuBUMkAeiIi8m4VEhXoAVahJyIi7yY1B2QFeiIiz+OTJSKidsad7Nt+qMY76n/hOuWPAIAPNW/gNuPz+E3syQWkRETUbvmpm1mB0GwEDq0Dst8C9L8AA5KAe7/w4AiJiKgjqh8A0FQPjO3vEEAoxQoFCsRIFIiR+No6RradKAKZecXYcawEZqsIBawYIRVAGJUIBPa025Rz4pxDUEtjRBHIOaHHtHjO/9q7p59+2qv7I+oIgnzViOkZiLwz5Xbbvz9eim8On7ELHjtkjUKOdQgSFEft2i41T8UZhEqef2JMGA7/fkE2EK3uGpFdUIrUGXGSVXLrbDlc5JHztKroa4G52ShbfQ+CS/djoWkOdOKla117Dq6rrWzsh9SMAmQ2CNiUc0LshROWXvjQciOASwGkRJ4ilWyJFQiJiMibyQYQcgEpERF5OUMNKxASEVHnI/X8g/M/IiLP4zcrEVE7kxAVCkFwvX13XECa5kVb8CAAdBcu4iP1awhGuW0BKRERUXujVAjwVTtOSVyqQJj7CZB6ObDhkdrgQQD4JQP4fb+HR0lERJ2JRimgCdMxl5mttZEHwajAz2I/VIsNgib+dKfDMVLV6acqstFPKHLaV3m1yf2BEhF1MvF9uzlsO1dpdHhQnS/2wV3GxZhnTMFpsTYo7KQ1DCssUyTPq1YKuH54uEtV7IwWK+atPYAXN/6M9H066PQGu/0HdWUunyclLRcHdWVO27Wqrr2xffRK/Nn4OL6xjrTb5U4StdZUV9l4x8JEvD49FtFhXZp0/MSYMCZ0I49iBUIiIups5H7/cgEpERF5O9kKhAwgJCIiLyZZgVDJ+R8RkadxVkFE1M5oQ/yRGBOGzLxil9rfrdyGOMUJh+0DFEX4QPMW7jIu5gJSIiJqt/w1KlSbjHbbXKpAeOF34OIpx+073wDuWuuh0RERUWeTmnkcTSz61yR6BOEu47NQw4zhQiHWXCci4PddwOAbHNo2DKyIwDm8pv4QSljwmWU8Us23SVa9yjtTDp3e4HYVRiKizuSKft2wds9vLrYW8D9rAjJr4jBX9TUOWgegBhrJlneN1GLTH1UMpyu341rFj1hunoIfxMGARKi6RRSxPLuwthcBSIwJw10jtbhQZcK/d5xoNHiwjtFiRWpGAVYkj2y8cSu5aBSQYY132N6lnQcQ1qmrbBwd1gXTl+126b+FRqlASlJ0K4yOOgurVZS8V+LvwwqERETkvViBkIiIOiu5ZLtMIkNERN6MFQiJiFoHv1mJiNqh+UnRLmfPeNdyK761jHDYXiOq8bH5OohQ2BaQEhERtTf+GsfFbpU1LgQQjnoQ0AQ6bs/bCBTneWBkRETU2ej0BmTlu5bIpblMUOF04HAEJP4NuGc94ONY1ahhdfpHVV/ARzBBJVgxU5WFbT4LMUOZATQIeVy3V4dxS7Iwa9Xe9lWFioioHXJp7tFAFXzxlnk6Mq2O9+Pq/FB4/o9riojZyv/hOuWP+MznBXyleRY3K76HCvJV10URyMwrxkOrf8TC9YdQUFzRpPFl5hfj1Pn2cx9QqqIu0P4rEDYUqw1G6oy4Ru/ZKgQgdUYcYrXBrTMw6hSqzdLfVVL3VIiIiLxFjVwAIStQEBGRl5OvQMg5IBEReS8TKxASEbUKfrMSEbVDri5IAQArFEgxPYJD1v62befEQMwwPoNN1gQAXEBKRETtl9RityqT/GJaG79utUGEDYUMAAylHhgZERF1NjknzkFsyfKDDZgtotP5WV11egDoLxThDuV3dvu7CNV4Rb0CNyl2OxxbF3wyfdlubDlc5NFxEzXm1KlTTv8UFfHfJLUPB3Vl+MfXP7fIufPPlkMUgfGKQxik+N22PVZxAqmapXhHvbRF+gVqrwE5J/Qtdv6mkgsgDPJVt/JImm/y8Aikzx2NpMFhdkH+9YUG+GDSsPDWHRh5PblgZ39WnyAiIi/GCoRERNRZSVWgB4AAH84BiYjIe0lVIFSrZG7EExGR23hnjYionXJlQUqdKvjiz8aFOCV2xwlrOG4zPo/94iC7NlxASkRE7ZGfxGI3uYciDhLmASq/2tc9hwPTVgKP7AX6XeXBERIRUWchF+DQUs5VGhudn9VVp/+baj1UguNDk33WQfjfH4ljpBgtVqSk5TKRDLUqrVbr9M+oUaPaeohEAIB3MgokH0h70p+VmyS3b7SMbtF+y6tNLXp+AIDFDGx9Frjo/D6j1FhUCgE+HXThd6w2GCuSR2LHwkTE9wl22F9SUYP8s+WtPzDyagajTPUJViAkIiIvJvdbvaP+jiQiInJVpWwFQgYQEhGRd7JaRZgsjpl+NUre/yQi8jTOKoiI2rG6BSk6vQF7CvUorzbhvMGE97cfd/jBXIJg3Gt8CmViAM4jSPacdQtI0+f6IVYb3MKfgIiIyDmpxW4Gmcz6Drr0AK59HgjuAwyajEYj7omIiJwI9G3922SNzc9itcFIvetPOPtZVxhFJTTCpWtklajBAtMcWBvJD2a0WLEg/SAeGheFhKhQaEP8Pf0xiIg6HJ3egKz8Yo+cSxBqK6s3rBAWI/yGccqfHNoXiSH41hrvkb7lBLZ0dT9RBDb+FTiwGvj5v8C9/wVCB0g2lQrQD/RVQejg8zdtiD/+fHUUfly732Hftp/PYnC4/P1ZoqaSS7TECoREROTNTHIVCJUMICQiIu8mV4WeSWSIiMhbmaysQE9E1Fr4ZImIqAPQhvjbLfIcGhGIlLRch8yLhWKES+czWqxIzSjAiuSRHh0nERFRU/lLBRCamlAB6so5HhwNERF1ZglRoRCE2piI1tTY/GzyZb1xMPgDLN6yE6N/W4ZbldkAgFfMM/Cri3PAguIKLFx/CIIAJMaEYX5SNBPKUIvR6XRO9xcVFbEKIbW5nBPnPPZ9PzEmDFZRRFZ+id32EjEY75hvwz3KbxEqXKpIt9acBLMLj2YCUIVK+DV5PIIAJESFNPm4Jtn299rgQQAo+w1YOQm453MgItahaYVE1vwWD3BsJVdHd4daKTgkett2tBiPTIxuo1GRN5KrQOjvw8WjRETkvaQqEKoUAhSKjp2IgoiIqDGVcnNAJpEhIiIvZZRNIMP5HxGRpzE0m4ioA5o8PALpc0cjaXCY28WWMvKK8e8dv0CnN3h2cERERE0g9aDD5QqEREREHqQN8UdiTFiTjgkN0Hik78z8Ypw6Lz83i9UG47XZN+GKv6VjZb8l+NIyFqst1za5H1EEMvOKMX3Zbmw5XNScIRPJioyMdPonIsK1wFeiliRVFc8dGqUC1w8PR/bxUod9egThLfM0jKl5F0+Z/oxfrBGoEVX41DKx0fMGoAq7fFKwTP0WRiuOAHA92nFiTBgiu7Vgtdld7wC73rbfVlkCrLoR+HWXQ/PyapPDti4+3rHgLdBXjYSoUIftB0+Vobi8ug1GRN5KrvqEVFImIiIibyG1gFTN6oNERNQJVEokY9IoFazCREREXks2gJDXPiIij+M3KxFRBxWrDcaK5JHYsTARd47UNunYIFRilHAUL2/Kw7glWZi1ai8O6spaZqBEREROSFYgNDKAkIiI2sb8pGhoXFyMplEq8NT1g11u74woAjkn9I2204b4AwOvxd9M8yC6eFvPB0aHbUaLFSlpuZwHElGnFejb/AA2jVKB1Blx2HT4jEMFuvpqoEGaJQnXGJfgFuMLKEXXRs99qzIbwUIlJiv3Ik3zErZqnsA9ym8RgCqnx6mVAlKSWrDynakayE2T3mc1A0rHyoJSwZqe+PtvL64Z0tNhmygCWXnFbTAa8lZy90lYfYKIiLyZ1AJSLh4lIqLOQCqAMIAV6ImIyIvJPWPhHJCIyPP4zUpE1MFpQ/wR0zPQ5fYhuIg0zYv4j+afGK04wgoURETUpqQWu1WZLLBaXa+wQURE5Cmx2mCkzohrNCiwLmhk2hVal9q7QqpCk5SmBF3cqczCJs1TGCiccthntFiRmlHg8rmIiLxJQlQoBMG9YwUBSBochvS5ozGsV1dk5bsWKCZCgaNiX5da3qfcardlkOJ3vKj+CP2EM06PnHVVf8Rqg10aj1vUvsADm4DeV9hvV6iAO1YD2lEOh0gHEDoGGnZUSUOkqxdvO8oAQvIcg1G6amoAAwiJiMiL1VgYQEhERJ1TpUQSGSaQISIibyZXgZBV6ImIPI/frEREXsDVBaQ9occ6zQsYpjgJX8GEleolGKc4CIAVKIiIqG1IVSAEaoMIiYiI2sLk4RFInzsaSYPDHIJL6geNTB4e0Wj7pnA1mMLVoJc/Cb/gH6qPMEBRhK80z2KKIsehTWZ+MU6dNzR1qEREHZ42xB+JMdKBX3IGhXXB69NjsfOJRKxIHolYbTByTpyD6OHcJ6MVP2OQ4neH7T9ao3FE7O/02F9LKz07GCn+IcB9XwEDJv6xQQBu/QCIvkayuVSAfJAXVSCM7OaPweGOyd2y8orxyZ6T0Ol5naXmk6tA6CdzT4WIiMgbSFYg5OJRIiLqBKQqEHbx8Z57KURERA0ZJRLIAEwiQ0TUEjizICLyAnULSJ0tWIoUSrBW/RL6Ki5lv/YTjFiufh1/Nc3DJmuCrQLFiuSRrTBqIiIi+cVux4srPFM5o7QA2P5P4OZUQBPQ/PMREVGnEKsNxorkkdDpDdhTqEd5tQmBvmokRIUgspu/0/abD5/By5uONqk/QQASokJcalsX9JKZJ1/ZqBsu4n3N2/ARahcaBAg1eE+TivfMv+J18x2w/pFTTBSBnBN6TIt3/ExERN5uflI0sgtKZR9M16dRKrBkeqzDHEWqup4rFAIgV3T9XuW3kts/Nl9n916lEGBucJLMvGKcrzSiW4DGrXG5zKcLMGMd8OVDQN+xwGXTJJuJoogKqUVvXhRACADXDOmJvDPldtvMVhFPf3kYggAkxoRhflJ0y1aHJK8mW4HQhwGERETkvaQCCH24eJSIiDoByQqEnP8REZEXk6tAyCQyRESex29WIiIv4ErW9LuUmXbBg3U0ggX3q7ZCQO2PcFagICKi1lRmMEpuv+VfuzBr1V73K+NazMDON4H3xwKH1wOZL7o/SCIi6rS0If6YFh+JB8b2x7T4SMngwYbtHxoXhYmDm1bVamJMWKPnrm9+UrTsAxMFrEhVL0Vv4ZzDvodVG3C14ie7bVKVoYiIOoNYbTBSZ8Q1+gBao1QgdUacZPBXoJuBcPcm9JXt93nTfXjHfCtKxCDbthKxKzZbr7Qb0+3xkQ7HDrIW4tjGtwCD3q1x2VhdqAiv0gDTPgJGzZZtYjBaJAMl3f17a6/8nVSBE8XawM7py3Zjy+GiVhwVeZPKGun/J/013vX/EhERUX2SFQgZQEhERJ0AKxASEVFnwwqERESth9+sRERewtkCUgB40zwdX1rGOmz/2doXs42PQ2xQgYKIiKilbTlchJW7fpXcJ6IZiyzP/AQsnwhkPA9Yamq35bwP6H5o1niJiIhc1dj8rD6NUoGUpOgmnd9Z0EsQKhEoVEke95F5Er6zxtptC/RVN6lval9WrVoFQRAgCAImTJjQ1sMh6nAmD49A+tzRSBocBkGw3ycIQNLgMKTPHY3JwyMkj0+ICnU4rjGCAMweFyXb71mE4C3zdIyteRfzjQ/jR2s00iyJMKF2oVhdQONfxg9wOPe9yq248ujLwJIBwIdJwLbngV93uTaw8rPAjx8Dn9wFvBsPWBuvzNjYh5er0OhN156DujK8va2g0XZGixUpabnuJ8mhTq3KJBdAyAoURETkvaQWkHLxKBERdQYGiQBCzv+IiMibmeQCCFmBkIjI45iahIjIS9QtIE1Jy5V8oGKFAgtMc2ESVbhD9R0A4IQ1HPcZF+EiAuza7iwowZX9Q6ANcb0CBhERUVMc1JUhJS0XFqlyFPUYLVbMW3sAD4w9j5jwQCREhTZ+fdryFFB0sMFGEfjqEWDuTkDl07zBExERNaKx+VkdZ1WtGlMb9OKH1IwCZOYXQ/zjklqGQNxhfA5/V32MmapMW/tsyzC8aL7H4Tz6yhro9AbO/1pZYWEhVqxYYbft0KFDttcHDhzA4sWL7fZPnDgREydO7BD9EXUksdpgrEgeCZ3egD2FepRXmxDoq0ZCVIhLlWcTY8KQmVfscn91VWcju/lL9tvVT4VPf9AhM78YX1mvwlfGq6CEBYJQe2xKUrTtunFF327Yd/I8AMAP1bhRmVPbiWgFft9X+6ckD+jnmFTMpuBb4LtXgVP7UJvK5Q9FB4De8S5/LilyVW69KWv+OxkFTq/19RktVqRmFGBF8sgWHhV5G6nqExqlAmouoCEiIi8mWYGQ1z4iIuoEKiTmgAFedC+FiIioIan5HwDe/yQiagGcWRAReRG5BaR1LFDiCfNDuIAA3KDMwb3Gp1CKrg7n+Sr3NDYcPI3EmDDMr7coiYiIyFOassjSIopYnl0IoLbARaPXpxveBJZddan6YJ3S/NpKhFf91f2BExERucjZ/EwqCMQdDYNePvjuFxQUV8AINZ42P4hccQBeVK1EkRiKR0wpsMAxS/HLm/LwyuY8zv9a2cmTJ/HSSy/J7j906JBdgB8AqFQqtwP6Wrs/oo5IG+LvVjD1/KRoZBeUujS/kao6K9XvtUPDXQpovG1EpC2A8HrFD+giVDv0+UlJPwzTlcl/v1vNwKm9jtvzNzc/gFBiwRsABPp6x6Mpnd6ArHzXg0cBIDO/GKfOGxoNTiWqz2B0rEDox+oTRETk5SQDCFmBkIiIvJwoipJzwACNd9xLISIikiIXQMg5IBGR53FmQUTkZeovIN18+Axe3nS0QQsBL5nvxnvmm3EeQbLnEUUgM68Y2QWlSJ0Rh8nDI1p24ERE1Gm4s8iyjkvXpx6DgAmLgIzn620UgISHgVGz3Rs0ERGRG5pT1aop6oJPosO6YPqy3bYgls8s/8/efcc3VS/+H393UqAtZZUyKlPZFlC2WIbKcCAIiKCsK6KieK8DvQ6QrxNBvaIoCCiIykWuylBAkSFD0TIFZImIRQoUWihldKTn90d/PSY0aZI2HUlez8ejj8dJ8jmfz0lOc975JOdzPt30W05tpau8zijC4fr0/wCg8Ipr1llXBjTe3LKmJizZrewcQ4OCv7dbZm5SXf0x40fHx/cGXaXg8lL2Rdv796+Quj+bv7wbzl2yP4AwMiykSPWWFZt/P53vAm7OGIa0+fcUDbiGAYRw3YVMO7NPMIAQAODj7H225uRRAICvy8jOUXZO/i8bmIEQAODLHP22Qh8QADyPngUA+KjYKhV03/UNtPn301qz7/JBGgEFDh60lmnJ0bgFO7To/vLMRAEA8IjCnGR5Oaf51Gmc9OtiKWmnVK2x1PcdKbZd0RpFsVu6dKnmz5+vhIQEHT9+XJGRkWrUqJH69eunMWPGKDLStc8v7tq+fbs+/fRTfffddzp69KjS0tJUrVo11axZUx06dFDXrl3Vr18/BQVxgiaAwinsrFbusjeIZZtxlcvrZ1os9P9KSNeuXWUU9QORpBEjRmjEiBEl1h4A+0pi1ll7/jh9XjmGoXLKVLDyX50+2YjUAaOOZMnR2E+2a2TnVDWOiVCHBlUVW6WCElMuaPPvp9WucnvVTV5nu/KJ3VLqEaly3UJv37lLWXbvD/eRGQgdDZB0vp791wVwxN7sExU4eRQA4OPszkAYxMmjAADfZq//J3ERGQCAb3M0A2EIfUAA8Dh+XQIAH/dIjyu18eCpAq+AXpCHgr7UCVXW44tyByTmnWAEAEBhFfYky8tlWnL0+KKd9vMpKFjqO13a86V0/XgpJMwjbaJ4pKena+jQoVq6dKnN/cnJyUpOTtaPP/6ot99+W5999pk6dOjgsXbT0tL0yCOPaN68efkGVRw7dkzHjh3T1q1bNX36dKWmpioqKspjbQNAcSloEEtBgmTR7JCp+tLSRdNWV9OcEW2Ld0MBwMeU1Kyz1t5afVA5hpShUA3IfF71A5LUPXC7OgbuUfvAffoxp7mkAEmSxTA0e+Nhc92qFUOVcj5ThqSBQVdpSsg687GLVZqpfItbpOByRdo+R32/CB8ZQFjY5xHhIzMwouTYHUDIyaMAAB+XYW8AIbNPAAB83PkM+9+lMAMhAMCXORpAWI4+IAB4HD0LAPBx9magcFXPwAQ9HrJIkjQv5bD+/b97ZAkIVrfG0XqkGK6aDgDwD548WfTgyXQ98b9fFBCg/PkU0zL3D2WaxWLRwIEDtXLlSklSjRo1NHr0aDVr1kwpKSlasGCBNm3apMTERPXp00ebNm1S06ZNi9xuSkqKevbsqS1btkiSateurf79+ysuLk6VKlXSuXPndPDgQa1atUpbt24tcnsAUJKsB7Gs2H1cLy/f63SdJ4I/U7egneoWtFPzD+3X0eRZqlO9cglsLQD4lpKadTYx5YLW7j9pc99ho6bmWGpqjqWPgmRRpM47XP/0+UxzeY2ltdYHttR3OW202tJGySdqaFr3VuoVEVOkbUx3OIDQNwbQdWhQVQEBcnmwvpQ7I2WHBlWKb6Pgk+ydQMoAQgCAr8vMzj+AngGEAABfdz7T0QBC+oAAAN+V5eC8ZvqAAOB5DCAEAD9QmBkoGgb8pddD3jNvDw9epasDD2t81n1as0/aePCUpt3VSr1a1CzGLQcA+KLCnGTpjGFIa/adJJ+80OzZs83Bg82aNdOaNWtUo0YN8/GxY8fq8ccf1+uvv67U1FSNGTNG69evL3K7Q4YMMQcPPvbYY3rxxRcVFpZ/psqXX35Zx44dU3h4eJHbBICSFlulgipXcD5I48bALbo/eJl5+56gVUr8oLeSBs5WzQYtinMTAQCFtPn30wX2qSwKUqoiXarrtCppWNa/rVbO0bgFO7To/vI2FxBLTLmgzb+f1rlL2YoIC84/E/xlzl3Ksnt/uI9cNT+2SgV1axytNftOOi/8/3VvHF1sM1LCd9mfgdA33kcAADhi78K4zD4BAPB1zEAIAPBHjiZGCQmiDwgAnkbPAgD8hPUMFFO/3a8lO445LBuhC3o/5A2FB1yyub914G+aFvKOeme+okwHJxIBAOBMYU6ydJWjfHJ0oqu7J8DCsywWiyZNmmTenj9/vs3gwTyTJ0/W6tWrtWPHDm3YsEHffvutbrrppkK3O3fuXH3zzTeSpAceeEBTp04tsHytWrUK3RYAlLZzDmZ/ynNFwAm9HjIj3/2xF/fqqw8f0xcNX2QGegAog5wd34sq05Kjxxft1H3XN1Cl8iH6b0Ki1l52YTK7M8FbSXOwjb4ygFCSHulxpTYePOXwBAdroUGBGtfjyhLYKvga+wMImX0CAODbMrPzf74K5eRRAICPO5+Rv/8nSRW5iAwAwIfZ6/9JzEAIAMWBngUA+JnYKhV0XaNqBQ4gvDFwixoGJtl97LmsEZICJOWeSDRt9UHNGdG2GLYUAODL3DnJ0l2unOgqSVUrhirlfKYila72gXv1bU5bpyfAwrPWr1+vpKTczxzx8fFq06aN3XJBQUEaN26cRo0aJUlasGBBkQYQTp48WZIUHh6uV199tdD1AIA3iAgr+Ou/noEJigy4kO/+NKOC/i/rHp1khl8AKJOcHd894eDJdD3xv18cPu5sJvh0O1fNDy8XrKDAAI9va2mJi43StLtaadyCHQX2b0ODAjXtrlb0M1EoFzLzv5c4eRQA4MuyLTnKsTPbNiePAgB8HTMQAgD8kaPv1rmIDAB4HkdWAPBDHRpUVUAB5+l8kXO9Hs58SBeNUJv752ffoC1GE5v7Vu87qffXH1JiSv4TTgEAcCTvJMvi+rIn70TX++Zv1Zp9+QcPStLp85mqplQtDH1BM0L+o5sDN5snwA6c8aNW7rY/mB6es2LFCnO5T58+BZbt3bu33fXctWnTJu3bt0+S1LdvX0VGRha6LgDwBs76f7Mst2hc5lhdMMrZ3P9y9hCdVGVJf8/wuzPxTDFuKQDAHc6O7yXJUU6cu5SVr6wvzT6Yp1eLmlp0f0f1aBKdb58EBEg9mkRr0f0dGYiPQsnJMXQxK/8MFOWZgRAA4MOyLHa+0BcDCAEAvs/exZgkZqEHAPg2hzMQMoAQADyOIysA+KHYKhXUrXF0gWWW5XRS/8xJOpxTQ5KUZFTRa9mD7ZZ9efk+XT9lrUbNTeCEUgCAywo6ybIk1AlI1qLQ/1OTwEQFBhj6T8h0PRC0VIHKUaYlR2M/2a4Xv/pVi7YkMlC+mOzatctcbtu24BmNY2JiFBsbK0k6ceKEkpOTC9Xm999/by63b99ekvTFF1+oT58+iomJUbly5VSrVi3dfPPN+vDDD5Wdbf+HOgDwFq70/5bmdNZtmS/ot5xakqQfLc30X0s3mzJ5M9ADAMoGV47vJcleTpy7lP+zdEnMnFga4mKjNGdEW61/opumDozTxFubaerAOG0Y301zRrRl5kEU2qVsi92LIlUsx8mjAADf5ejk0RBOHgUA+LgLmfkvICP55gWZAADI43AAIReRAQCPo2cBAH7qkR5XauPBUw6n/5akvUZd9c58VY8Ef6GtOVfpnCo4LJs3Y1Pabz/qX71aqPN13YtjswEAPibvJMvElAv66XCK9h1P04eb/pAlx/4Vhj2lYcBfmh/6imoFpJj3hQRY9GTIf9UucK9GZo2XxZBmbzwsKXfWiG6No/VIjys58dOD9u/fby7Xr1/fafn69esrMTHRXLd69eput7llyxZzuUaNGrrjjjv0xRdf2JRJSkpSUlKSli9frjfffFNLlixxafsud/To0QIfT0pilksAJcOV/t9vRh3dlvmiHgtepI8sN0rKP7p/9b6TemPVfsVWrqAODaoq9uJeqXpTKdRxXxEAUHxcOb6XpDX7T+po6gXVqZybC/aumu+rAwjzxFapoNgq5CI853yG/ZNHK4T69nsJAODfMiz284+TRwEAvs7RDIQVGUAIAPBhWQ5+46APCACeR88CAPxUXGyUpt3VSuMW7CjwJKNLKqfJ2Xe5WKuhZwLnqfV3v+ncr50V0foO6YqOUvUmUiAf5gEAjlmfZHlt3cpO86mougXusBk8aG19ztW6fNBE3kD5jQdPadpdrdSrRc1i2zZ/cubMGXO5WrVqTstXrVrV7rrusB60N2HCBO3fv1+hoaEaNmyYrrvuOoWEhGjnzp2aPXu2UlJStGvXLnXr1k3btm1TlSpV3Gorb8ZEAChtrvb/LihML2TfU2Bd01b/JkkqH5Chn8v/U+VDAhXc8X6p3X1SBfeOkwCAonH1+F5SDEPa/HuKBlyT27ezNwNheFhISW8W4NUuOph9okIoMxACAHyXw9knmIEQAODjLmQ6GkBIHxAA4Lvs9QEDA6SgwPwXvAUAFA3frgGAH+vVoqYW3d9RPZpEK8ADn7WvCTig1oG5J5NGHNskff2o9F5Haf/XRa8cAOA3PJ1P9sy29NH72Tfnu/97y9Waa+npcL1MS47GLdihnYlnimfD/Ex6erq5HBYW5rR8+fLlzeVz584Vqs3U1FRzef/+/apcubI2b96sWbNmafjw4RoyZIgmT56sPXv2qFmzZpKkI0eO6Omnny5UewBQVng6XwcGrlNEzlkFZ6RK616Rpl4pzeohZV4oeuUAAJeVRP/JHecuZdldzuPrMxACnnbe0cmjzEAIAPBhjgYQlmP2CQCAj7M3C31AgFQ+hAGEAADfZe8Cicw+CADFg1+XAMDPxcVGac6ItkpMuaCfDqdo5veHdPBkuvMV7bgv2MFAwdgORdhCAIA/8mQ+2Regl7OHKN0or0eCP1dQgKHTRoQez7pfhpPrrGRacjRt9UHNGdHWg9uDkpKTY/vF49SpU9W6det85WJiYvTpp5+qVatWkqS5c+fqtddeU2RkpMttJSYmFvh4UlKS2rVr53J9AFBU1vn6ztrftDCh4OOUI0GyaHTQcts7c7J1LvWkzqRLsUxECAAl6vL+077jafpw0x+y5Bglvi0RVjMM2puBMJIBhIBbHM0+UZ4ZCAEAPszR7NqcQAoA8HXnM/L3ASuGBiugLFw1CgCAYpKZnf+3DGagB4DiwdEVACBJiq1SQQOuqaOpA+MK9eG7XkCSbgzcmu/+wzkxGvW/P5ipCQBQKEXNp4IFaJqlv+7MfE6JOdX1RNYYJSvKhfUMrdl/UkdTmWGpqMLDw83lS5cuOS1/8eJFczkiIqJQbVqvV7FiRd19990Oy8bFxalDh9wLIWRkZGjTpk1utVWnTp0C/2rWrFmo5wAARRVbpYIe6tao0DNV3Ry4WbGByfnu/zqtga6fslaj5ibQBwSAUpDXf3r25maaPqR1qfzAnnI+Q4kpF5SZnaMMOzPHhJdjACHgjguZ+WefkKSK5RhACADwXY5mIGQAIQDA19mbhZ7+HwDA1zEDIQCUHI6uAAAbcbFRmnZXK7dPMGoSkKgLKpfv/oScxlqz76QGzvhRK3cneWozAQB+prD55IotRhPdkDlFa3LauFR+dshUPRM0X7t/2e7xbfE3UVFR5vKpU6eclj99+rTddd1RuXJlc7lly5YKDQ0tsPy1115rLh86dKhQbQJAWRRbpYK6NY4u1LrXBe62e//POU1kGKIPCABlQK8WNbXo/o7q0STa7oDxahVDVRzXrn95+T5zMLk91jMUAnDufIb9AYQVQhmMCwDwXQ4HEAYxgAIA4Nvs9QEr0v8DAPi4zOz8+ccMhABQPDi6AgDycXaCkT0rc9qpU8bbejnrLu3LiTXvTzAaS8q9Ssi4BTuYhQIAUGiFySdXZajgQWR5WgX8phuCtuve4BXqtbaPsv7TWlrxpJTGAInCaNy4sbl8+PBhp+Wty1iv644mTZqYy5UqVXJa3rpMWlpaodoEgLLqkR5XFurHl/HZ92lQxnNaY2llc//PRlNzmT4gAJS+uNgozRnRVuuf6KapA+M08dZmmjowThuf7KYtz92o9eNz778yOtx5ZW4wDGnjb/YvEBIRxklvyLV06VINHDhQ9erVU1hYmKKjo9WpUydNmTKl2PpepdFmUV3Myj/7hCRVCGUABQDAdzEDIQD4HvqArjmfYW8GQr5LAQD4tiyLke8++n8AUDzoXQAA7Mo7wSgx5YJW7D6ul5fvdbpOmirqfcutet9yq6J0Tm0CD2pXTgPz8UxLjqatPqg5I9oW56YDAHyYdT79dDhFM78/pIMn00us/fuDl9ncDjnzu3J+mqlfG96nFpH5yyemXNDm30/r3KVsRYQFq0ODqoqtUqGEtrbsa9mypVauXClJSkhIULdu3RyWPXHihBITEyVJ0dHRql69eqHajIuLM5fPnj3rtLx1GVcGHAKAN8mb4Xfcgh3KtNg/Oc++AP1sNNXPWU3VMPsvdQ/crsaBR3XUqGZTij4gAJQNsVUq2O2H5N1/ZXS4Bs740c0sKJxwTnrze+np6Ro6dKiWLl1qc39ycrKSk5P1448/6u2339Znn32mDh06eG2bnsIMhAAAf5Th4HMpJ5ACgPehD+ie85n5+4BcQAYA4OvsXUQmhBkIAaBY8OsSAKBAsVUq6L7rG2jz76e1Zt9Jl9c7owityWmT7/41+0/qaOoF1anM4AkAQOGVxomuDQP+Uq+ghHz3/5LTQIPmHdC0uyqoV4uakqSdiWf01uqDWrv/pAyrC2UFBEjdGkfrkR5XKi42qti3uazr1auXpkyZIklasWKFxo8f77Ds8uXLzeU+ffoUus3evXsrICBAhmFo165dyszMVGio4xkot2zZYi4XdtZDACjLcmf4La9pqw9qzWW55YpDRm0dstSW7J/bTh8QALxA4QeUuy8iLKRY60fZZrFYNHDgQPNCMjVq1NDo0aPVrFkzpaSkaMGCBdq0aZMSExPVp08fbdq0SU2bNnVSa9lr05MuZDIDIQDA/zicgZATSAHAq9AHdJ+9GQi5GBMAwNfZ6wNyARkAKB4cXQEALnmkx5Ue+VHGMKS31/ymxJQLHtgq32KxWLR7927NnTtXDz/8sDp27KgKFSooICBAAQEBGjFiRLG1vXTpUg0cOFD16tVTWFiYoqOj1alTJ02ZMkVpaWnF1i4AFFXeia6FyahqFUMV4Eb5MUFf2b1/XU6cMi05GvvJdr341a+asGS3Bsz4QWv25R+EYRjSmn0nNXDGj1q5O8ntbfY18fHxiomJkSStW7dO27Zts1vOYrFo2rRp5u3BgwcXus06deooPj5eknT+/Hl9/PHHDsvu3LlTmzdvliRFRESoc+fOhW4XAMqyvBl+1z/RTVMHxumRHo3cysiC0AcEAO+QO6C8o3o0iVbAZSEQECD1aBKt9++5Rk/3KdpJdZFhnPTmz2bPnm2exNmsWTPt3LlTL7zwgu666y6NHTtWGzdu1GOPPSZJSk1N1ZgxY7yyTU+6YGf2CUmqyAyEAAAf5nAAISeQAoBXoQ/oPrsXkfHUl/UAAJRR9i5saMjNq94CAFzCt2sAAJcUZYDG5RYmJOr6KWs1am6CdiaeKfrG+YhBgwapZcuWGjlypN555x1t3rxZFy9eLNY209PT1bdvX/Xt21f/+9//dOTIEWVkZCg5OVk//vijxo8frxYtWpiDJwCgLHL1RNepA+M08dZmmjowThuf7KYtz92o9eNzB0rc26W+ggIL/vXlQ0svLbZ0UrZhm4VrLa0kSRbD0OyNh/XRj0eUZSn4i6xMS47GLdjh9zkYFBSkCRMmmLeHDRumkyfzz3j81FNPaceOHZKkzp07q2fPnnbrmzt3rjnwvmvXrg7bffnll83lxx9/XNu3b89X5sSJExo6dKh5e9y4cSpfvryzpwQAXi22SgUNuKaO/nVjY3VrEu2xeukDAoB3uHxAeV7/acP4bpozoq1uah6jyhWKNoPgG6sOkAV+ymKxaNKkSebt+fPnq0aNGvnKTZ48Wa1atZIkbdiwQd9++61XtelJOxPP6Ittf9l97JGF23kvAQB80s7EM3p33W92H3t1xV7yDwC8BH1A9+1MPKOU85n57l+99yTfrQMAfNbOxDP67WR6vvt/PXaO/AOAYsAAQgCAywoaoOEuZmDKz2KxvZp0lSpVdOWVVxZrewMHDtTSpUslSTVq1NCzzz6rTz/9VO+88445y1JiYqL69OmjvXv3Ftu2AEBRuXKi64Br6mhk5/oacE0d1alcQdLfAyWevbmZpg9pXeBA+b1GXf0z6yG1zXhXj2Q+qMWWTjqYU1u/GA0Ktc2ZlhxNW32wUOv6ktGjR+vGG2+UJO3Zs0dxcXGaMGGC/vvf/+rdd99Vly5dNHXqVElSVFSUZs6cWeQ2O3bsqCeffFJS7tVFO3TooPvuu08fffSRFixYoCeffFLNmjXTnj17JEnXXnutnn322SK3CwDexFOz0OehDwgA3iOvn3R5/0mSzl2ycyV8N2w5kkoW+Kn169crKSl3v8fHx6tNmzZ2ywUFBWncuHHm7QULFnhVm56ycneSBs74UX86mMV53f5k3ksAAJ+Tl397k87ZfTzhDz5LAoC3oA/onrwMzHFwjVq+WwcA+KK8/EvPsP+7A/kHAJ4XXNobAADwLnkDNBJTLmjF7uN6eXnRBpXlzcC06P7yiouN8sxGeql27dqpadOmuuaaa3TNNdeofv36mjt3rkaOHFks7c2ePVsrV66UJDVr1kxr1qyxufra2LFj9fjjj+v1119XamqqxowZo/Xr1xfLtgCAp8RWqaDYKhWcF7Qjd6B8eU1bfVBr9p+U4eAHmlRFaknOdVqSc50kQ1LhR9Wv2X9SR1Mv2JyQ62+Cg4P1+eefa8iQIfrqq690/PhxvfDCC/nK1alTRwsXLlTz5s090u6rr76qoKAgTZ48WZmZmZo1a5ZmzZqVr1zPnj21YMEChYWFeaRdAPAWebPQj1uwQ5mWHI/VSx8QALxbRFjRf1YiC/zTihUrzOU+ffoUWLZ379521/OGNj1hZ+IZlz6D8V4CAPgS8g8AfAt9QNeRgQAAf0T+AUDpYAZCAEChxFapoPuub6DuTaKLXBczMOV6+umn9corr2jAgAGqX79+sbZlsVg0adIk8/b8+fNtBg/mmTx5slq1aiVJ2rBhg7799tti3S4AKG3WMxn2bVXLhTWKNiWvYUibf08pUh2+ICIiQsuWLdPixYvVv39/xcbGqly5cqpWrZrat2+vyZMna/fu3erUqZNH233ppZe0detWPfzww2rSpIkiIiIUFhamK664QoMHD9by5cu1cuVKVa5c2aPtAoC38OQs9NboAwKA9+rQoKpHMoEs8D+7du0yl9u2bVtg2ZiYGMXGxkqSTpw4oeTkZK9p0xPeWn3Q5Qs48F4CAPgK8g8AfAt9QNeRgQAAf0T+AUDpYAZCAECRPNLjSm08eKrIM1IwA1PJWr9+vZKScqd2j4+PV5s2beyWCwoK0rhx4zRq1ChJ0oIFC3TTTTeV2HYCQGmJrVJB1zWqpiU7jhV7W+cuZRV7G96ib9++6tu3b6HXHzFihEaMGOHWOnFxcZo2bVqh2wQAX2c9C/1Ph1O073iaPtz0hyw5DqbqdRF9QADwTrFVKqhb42it2XeyyHWRBf5l//795rIrF0+rX7++EhMTzXWrV69epts8evRogY/nfRfrTGLKBa3d7977i/cSAMDbkX8A4HvoA9IHBADAEfIPAEoPAwgBAEUSFxulaXe1cmk68YLkzcA04Bo+4JeEFStWmMt9+vQpsGzv3r3trgcAvi5vZg2jaOMjnIoICyneBgAA8IDYKhUUWyW3v3Zt3cr0AQHAj3nqgmJkgX85c+aMuVytWjWn5atWrWp33bLaZt7MFUW1+ffTbn8PwXsJAODtyD8A8D30AV1DBgIA/BH5BwClJ7C0NwAA4P16taipRfd3VI8m0QoIKHw9zMBUcnbt2mUut23btsCyMTEx5pefJ06cUHJycrFuGwCUFXkzaxSngACpQ4MqxdoGAACeRh8QAPxb3gXFQoOK/hMTWeA/0tPTzeWwsDCn5cuXL28unzt3zmvaLKpzl7ILuR7vJQCA9yL/AMD30Ad0DRkIAPBH5B8AlJ4yPQPh0qVLNX/+fCUkJOj48eOKjIxUo0aN1K9fP40ZM0aRkZHF0u727dv16aef6rvvvtPRo0eVlpamatWqqWbNmurQoYO6du2qfv36KSgoqFjaBwBvFBcbpTkj2iox5YLeWfubFiYkul0HMzCVnP3795vL9evXd1q+fv36SkxMNNetXr26y20dPXq0wMeTkpJcrgsASpqnZtZwpHvjaNWpzNWxAADehz4gAPi33MHk5TVt9UGt2X+y0DO3kwXwFXnfnTqSlJSkdu3aOa0nIqxwP93yXgIAeDPyDwDgbegDAgBQeOQfAJSeMjmAMD09XUOHDtXSpUtt7k9OTlZycrJ+/PFHvf322/rss8/UoUMHj7WblpamRx55RPPmzZNx2a/dx44d07Fjx7R161ZNnz5dqampioqK8ljbAOArYqtU0EPdGumzLYlunTjEDEwl68yZM+ZytWrVnJavWrWq3XVdkTd7IQB4o7yZNcYt2OHxQYShQYEa1+NKj9YJAEBJow8IAP7LejD5it3H9fLyvW6tTxb4l/DwcKWmpkqSLl26pPDw8ALLX7x40VyOiIgo823WqVPH/Q20o0ODqgoIEJ+rAAB+hfwDAN9DH9A1ZCAAwB+RfwBQegJLewMuZ7FYNHDgQHPwYI0aNfTss8/q008/1TvvvKPOnTtLyr2KS58+fbR3r3s/SDuSkpKiHj16aO7cuTIMQ7Vr19bDDz+s2bNna9GiRfrggw/073//W9dee60CAgI80iYA+KrYKhXUrXG0W+swA1PJSk9PN5fDwsKcli9fvry5fO7cuWLZJgAoq3Jn1uioHk2i5amuQGhQoKbd1UpxsVGeqRAAgFJEHxAA/FtslQq67/oG6t6ELIBj1hflPHXqlNPyp0+ftrtuWW+zqPhcBQDwR+QfAPge+oCuIQMBAP6I/AOA0lPmZiCcPXu2Vq5cKUlq1qyZ1qxZoxo1apiPjx07Vo8//rhef/11paamasyYMVq/fn2R2x0yZIi2bNkiSXrsscf04osv2h1Q8fLLL+vYsWNOr1ADAP7ukR5XauPBUy7N1sQMTL4tMTGxwMeTkpLUrl27EtoaACgc65k1fjqcon3H0/Thpj9kyXHjcljKvSJW98bRGtfjSgYPAgB8Cn1AAABZgII0btxYhw8fliQdPnxY9erVK7B8Xtm8db2lTU/gvQQA8EfkHwD4FvqAriMDAQD+iPwDgNJRpmYgtFgsmjRpknl7/vz5NoMH80yePFmtWrWSJG3YsEHffvttkdqdO3euvvnmG0nSAw88oKlTpxY4G1OtWrUUHFzmxl4CQJkSFxulaXe1UmhQwVHDDEylw3og/KVLl5yWv3jxorkcERHhVlt16tQp8K9mzZpu1QcApSm2SgUNuKaOnr25maYPae0050ICAzS8Y11NvLWZpg6M04bx3TRnRFtyDwDgc+gDAgDIAhSkZcuW5nJCQkKBZU+cOGFelCw6OlrVq1f3mjY9gfcSAMAfkX8A4FvoA7qODAQA+CPyDwBKR5kaQLh+/XolJSVJkuLj49WmTRu75YKCgjRu3Djz9oIFC4rU7uTJkyXlDqZ49dVXi1QXAOBvvVrU1KL7O6pHk2gFBNg+FhAg9WgSrUX3d1SvFgwgK2lRUVHm8qlTp5yWP336tN11AcCfuZJz/3ugkyb1baGRnetrwDV1VKdyhdLZWAAASgB9QAAAWQBHevXqZS6vWLGiwLLLly83l/v06eNVbXoK7yUAgD8i/wDAd9AHdA8ZCADwR+QfAJS8MjWNnnXHzVnHrHfv3nbXc9emTZu0b98+SVLfvn0VGRlZ6LoAAPnFxUZpzoi2Sky5oJ8Op+jcpSxFhIWoQ4MqDKIoRY0bN9bhw4clSYcPH1a9evUKLJ9XNm9dAEAucg4AAFtkIwCALIA98fHxiomJ0fHjx7Vu3Tpt27bN7oVELRaLpk2bZt4ePHiwV7XpSbyXAAD+iPwDAN9AH9B9ZCAA+J6lS5dq/vz5SkhI0PHjxxUZGalGjRqpX79+GjNmTLGMWSiNNouC/AOAklWmBhDu2rXLXG7btm2BZWNiYhQbG6vExESdOHFCycnJhZpK/vvvvzeX27dvL0n64osvNHv2bG3btk2pqamqWrWqWrdurQEDBuiee+5RcHCZetkAwCvEVqmg2Cp8oC8rWrZsqZUrV0qSEhIS1K1bN4dlT5w4ocTERElSdHR0ofIWAHwdOQcAgC2yEQBAFsBaUFCQJkyYoAcffFCSNGzYMK1Zs0bR0dE25Z566int2LFDktS5c2f17NnTbn1z587VyJEjJeWeJLpu3bpib7O08F4CAPgj8g8AvBt9wMIjAwHA+6Wnp2vo0KFaunSpzf3JyclKTk7Wjz/+qLffflufffaZOnTo4LVtehL5BwAlo0yNhNu/f7+5XL9+fafl69evbw5o2L9/f6EGNGzZssVcrlGjhu644w598cUXNmWSkpKUlJSk5cuX680339SSJUtc2r7LHT16tMDHk5KS3K4TAIDC6NWrl6ZMmSIpdybf8ePHOyy7fPlyc9nZDMEAAAAAAAAAYM/o0aP15ZdfatWqVdqzZ4/i4uI0evRoNWvWTCkpKVqwYIE2btwoSYqKitLMmTO9sk0AAAAAAH1AAIB/slgsGjhwoDm5Q40aNfJl0aZNm5SYmKg+ffpo06ZNatq0qde1CQDwTmVqAOGZM2fM5WrVqjktX7VqVbvrusN60N6ECRO0f/9+hYaGatiwYbruuusUEhKinTt3avbs2UpJSdGuXbvUrVs3bdu2TVWqVHGrrdjY2EJtIwAAnhYfH6+YmBgdP35c69at07Zt29SmTZt85SwWi6ZNm2beHjx4cEluJgAAAAAAAAAfERwcrM8//1xDhgzRV199pePHj+uFF17IV65OnTpauHChmjdv7pVtAgAAAADoAwIA/NPs2bPNgXzNmjXTmjVrVKNGDfPxsWPH6vHHH9frr7+u1NRUjRkzRuvXr/e6NgEA3imwtDfAWnp6urkcFhbmtHz58uXN5XPnzhWqzdTUVHN5//79qly5sjZv3qxZs2Zp+PDhGjJkiCZPnqw9e/aoWbNmkqQjR47o6aefLlR7AAAUt7lz5yogIEABAQHq2rWr3TJBQUGaMGGCeXvYsGE6efJkvnJPPfWUduzYIUnq3LmzevbsWRybDAAAAAAAAMAPREREaNmyZVq8eLH69++v2NhYlStXTtWqVVP79u01efJk7d69W506dfLqNgEAAAAA9AEBAP7FYrFo0qRJ5u358+fbDOTLM3nyZLVq1UqStGHDBn377bde1SYAwHuVqRkIS0NOTo7N7alTp6p169b5ysXExOjTTz81w3Pu3Ll67bXXFBkZ6XJbiYmJBT6elJSkdu3auVwfAMC3HD58WHPmzLG575dffjGXt2/frmeffdbm8e7du6t79+6Fam/06NH68ssvtWrVKu3Zs0dxcXH5pq7fuHGjJCkqKkozZ84sVDsAAAAAAAAAYK1v377q27dvodcfMWKERowYUaJtAgAAAAAKhz4gAMAfrF+/XklJSZKk+Ph4tWnTxm65oKAgjRs3TqNGjZIkLViwQDfddJPXtAkA8F5lagBheHi4OSPgpUuXFB4eXmD5ixcvmssRERGFatN6vYoVK+ruu+92WDYuLk4dOnTQ5s2blZGRoU2bNql3794ut1WnTp1CbSMAwD8cOXJEL730ksPHf/nlF5sBhZIUHBxc6AGEwcHB+vzzzzVkyBB99dVXOn78uF544YV85erUqaOFCxeqefPmhWoHAAAAAAAAAAAAAAAAAADAV61YscJc7tOnT4FlrccfWK/nDW0CALxXYGlvgLWoqChz+dSpU07Lnz592u667qhcubK53LJlS4WGhhZY/tprrzWXDx06VKg2AQAoKyIiIrRs2TItXrxY/fv3V2xsrMqVK6dq1aqpffv2mjx5snbv3q1OnTqV9qYCAAAAAAAAAAAAAAAAAACUObt27TKX27ZtW2DZmJgYxcbGSpJOnDih5ORkr2kTAOC9ytQMhI0bN9bhw4clSYcPH1a9evUKLJ9XNm/dwmjSpIlWr14tSapUqZLT8tZl0tLSCtUmAAD2dO3aVYZhFLmeESNGaMSIEW6t07dvX/Xt27fIbQMAAAAAAAAAAAAAAAAAAPiT/fv3m8v169d3Wr5+/fpKTEw0161evbpXtAkA8F5lagBhy5YttXLlSklSQkKCunXr5rDsiRMnzACLjo4udIDFxcWZy2fPnnVa3rqMKwMOAQAAAAAAAAAAAAAAAAAAAACAbzpz5oy5XK1aNaflq1atanfdstrm0aNHC3w8KSnJrfoAACWvTA0g7NWrl6ZMmSJJWrFihcaPH++w7PLly83lPn36FLrN3r17KyAgQIZhaNeuXcrMzFRoaKjD8lu2bDGXCzvrIQAAAAAAAAAAAAAAAAAAAAAA8H7p6enmclhYmNPy5cuXN5fPnTtX5tuMjY11qzwAoOwJLO0NsBYfH6+YmBhJ0rp167Rt2za75SwWi6ZNm2beHjx4cKHbrFOnjuLj4yVJ58+f18cff+yw7M6dO7V582ZJUkREhDp37lzodgEAAAAAAAAAAAAAAAAAAAAAAAAAKE5lagbCoKAgTZgwQQ8++KAkadiwYVqzZo2io6Ntyj311FPasWOHJKlz587q2bOn3frmzp2rkSNHSsodnLhu3Tq75V5++WV16tRJkvT444+rdevWat26tU2ZEydOaOjQoebtcePG2YzC94Ts7GxzmWl8AeBv1sdE62MlfAP5BwD2kX++jwwEAPvIQN9G/gGAfeSfbyP/AMA+8s+3kX8AYB/55/vIQACwz9czMDw8XKmpqZKkS5cuKTw8vMDyFy9eNJcjIiLKfJuJiYlOH88bj0H+AcDfylL+lakBhJI0evRoffnll1q1apX27NmjuLg4jR49Ws2aNVNKSooWLFigjRs3SpKioqI0c+bMIrfZsWNHPfnkk5o8ebJSU1PVoUMHDR8+XNddd51CQkK0Y8cOzZ49WykpKZKka6+9Vs8++2yR271ccnKyudyuXTuP1w8AviA5OVn16tUr7c2AB5F/AOAc+eebyEAAcI4M9D3kHwA4R/75HvIPAJwj/3wP+QcAzpF/vokMBADnfDEDo6KizMF8p06dcjqY7/Tp0zbrlvU269SpU+Dj1gNkyD8AsK+086/MDSAMDg7W559/riFDhuirr77S8ePH9cILL+QrV6dOHS1cuFDNmzf3SLuvvvqqgoKCNHnyZGVmZmrWrFmaNWtWvnI9e/bUggULFBYW5pF2AQAAAAAAAAAAAAAAAAAAAACAd2rcuLEOHz4sSTp8+LDTASJ5ZfPW9ZY2AQDeq8wNIJRyp8RdtmyZlixZoo8++kgJCQk6efKkIiIi1LBhQ/Xv319jxoxRpUqVPNruSy+9pEGDBmnOnDlatWqV/vrrL2VlZSk6OlqdOnXSsGHD1Lt3b4+2aa1ly5b6+eefJUnVq1dXcHD+3ZOUlGSOyv/5559Vs2bNYtselB72s+9jH7snOzvbvDpXy5YtS3lr4GnkH/Kwn/0D+9l15J/vc5aBvF/8A/vZP7Cf3UMG+jb6gJDYx/6C/ewe8s+3kX/Iw372fexj95B/vo38Qx72s39gP7uO/PN9/AYIif3sL9jP7vH1DGzZsqVWrlwpSUpISFC3bt0clj1x4oQSExMlSdHR0apevbrXtFnQttAHBPvYP7Cf3VOW8q9MDiDM07dvX/Xt27fQ648YMUIjRoxwa524uDhNmzat0G0WRVhYmNq2bety+Zo1azqdDhjej/3s+9jHrvG16erxN/IP9rCf/QP72Tnyz7e5k4G8X/wD+9k/sJ9dQwb6LvqAuBz72D+wn11D/vku8g/2sJ99H/vYNeSf7yL/YA/72T+wn50j/3wbvwHicuxn/8B+do0vZ2CvXr00ZcoUSdKKFSs0fvx4h2WXL19uLvfp08er2nSEPiAuxz72D+xn15SV/Ass7Q0AAAAAAAAAAAAAAAAAAAAAAADwRvHx8YqJiZEkrVu3Ttu2bbNbzmKx2Ex2NHjwYK9qEwDgvRhACAAAAAAAAAAAAAAAAAAAAAAAUAhBQUGaMGGCeXvYsGE6efJkvnJPPfWUduzYIUnq3Lmzevbsabe+uXPnKiAgQAEBAeratWuJtAkA8G3Bpb0BAAAAAAAAAAAAAAAAAAAAAAAA3mr06NH68ssvtWrVKu3Zs0dxcXEaPXq0mjVrppSUFC1YsEAbN26UJEVFRWnmzJle2SYAwDsxgBAAAAAAAAAAAAAAAAAAAAAAAKCQgoOD9fnnn2vIkCH66quvdPz4cb3wwgv5ytWpU0cLFy5U8+bNvbJNAIB3CiztDQAAAAAAAAAAAAAAAAAAAAAAAPBmERERWrZsmRYvXqz+/fsrNjZW5cqVU7Vq1dS+fXtNnjxZu3fvVqdOnby6TQCA92EGQgAAAAAAAAAAAAAAAAAAAAAAAA/o27ev+vbtW+j1R4wYoREjRpRomwAA38YMhAAAAAAAAAAAAAAAAAAAAAAAAAAA+KAAwzCM0t4IAAAAAAAAAAAAAAAAAAAAAAAAAADgWcxACAAAAAAAAAAAAAAAAAAAAAAAAACAD2IAIQAAAAAAAAAAAAAAAAAAAAAAAAAAPogBhAAAAAAAAAAAAAAAAAAAAAAAAAAA+CAGEAIAAAAAAAAAAAAAAAAAAAAAAAAA4IMYQAgAAAAAAAAAAAAAAAAAAAAAAAAAgA9iACEAAAAAAAAAAAAAAAAAAAAAAAAAAD6IAYQAAAAAAAAAAAAAAAAAAAAAAAAAAPggBhACAAAAAAAAAAAAAAAAAAAAAAAAAOCDGEAIAAAAAAAAAAAAAAAAAAAAAAAAAIAPYgChl1i6dKkGDhyoevXqKSwsTNHR0erUqZOmTJmitLS00t482HHu3Dl9/vnneuihh9SpUydVr15dISEhioyMVJMmTTRs2DCtXLlShmG4XOdvv/2mJ554Qi1atFClSpUUHh6uxo0ba+zYsdqxY0fxPRkUyogRIxQQEGD+Pf/88y6tx34G/kb+eScyEGQgUHRkoPch/0D+AUVH/nkf8g/kH1B05J93IgP9G/kHFB35553IP5CBQNGRgd6H/AP5BxQd+ed9yD+Qfz7KQJl27tw547bbbjMkOfyLjY01fvzxx9LeVFh5/fXXjbCwsAL3W95fly5djCNHjjitc+bMmUb58uUd1hMUFGRMmjSpBJ4dXLF8+fJ8+2jixIlO12M/A7nIP+9FBoIMBIqGDPRO5B/IP6BoyD/vRP6B/AOKhvzzXmSgfyP/gKIh/7wX+QcyECgaMtA7kX8g/4CiIf+8E/kH8s93BQtllsVi0cCBA7Vy5UpJUo0aNTR69Gg1a9ZMKSkpWrBggTZt2qTExET16dNHmzZtUtOmTUt5qyFJBw4c0KVLlyRJtWvX1g033KBrrrlG0dHRunTpkjZv3qyPP/5Y6enp2rBhg7p27arNmzcrOjrabn0ff/yxxowZI0kKDAzU4MGD1aNHDwUHB2vTpk2aN2+eMjIyNHHiRJUrV05PPvlkiT1X5JeWlmbur4oVK+r8+fMurcd+BnKRf96NDPRvZCBQNGSg9yL//Bv5BxQN+ee9yD//Rv4BRUP+eTcy0H+Rf0DRkH/ejfzzb2QgUDRkoPci//wb+QcUDfnnvcg//0b++bjSHsEIx2bMmGGOtm3WrJlx/PjxfGUee+wxmxHcKBvuv/9+46abbjK+/fZbw2Kx2C3zxx9/GI0bNzb338iRI+2WO3nypBEZGWlIMgIDA40lS5bkK/Pjjz8aFSpUMCQZwcHBxr59+zz6fOCe++67z7wqxqOPPurSyHv2M/A38s+7kYH+jQwEioYM9F7kn38j/4CiIf+8F/nn38g/oGjIP+9GBvov8g8oGvLPu5F//o0MBIqGDPRe5J9/I/+AoiH/vBf559/IP9/GAMIyKjs726hZs6b5htu6davDcq1atTLLffPNNyW8pbDn9OnTLpXbsWOHue8qVKhgnD9/Pl+Z8ePHm2Uefvhhh3W9/vrrZrm77rqr0NuOolm9erUREBBgSDKWLVtmTJw40aXgZD8Ducg/70cG+i8yECgaMtC7kX/+i/wDiob8827kn/8i/4CiIf+8Hxnon8g/oGjIP+9H/vkvMhAoGjLQu5F//ov8A4qG/PNu5J//Iv98HwMIy6g1a9aYb474+PgCy37wwQdm2REjRpTMBsJjrEff79y5M9/jdevWNR///fffHdaTlpZmVKxY0ZBkVKxY0bhw4UJxbjbsOH/+vNGgQQNDknHnnXcahmG4HJzsZyAX+edfyEDfQQYCRUcG+g/yz3eQf0DRkX/+g/zzHeQfUHTkn38hA30D+QcUHfnnX8g/30EGAkVHBvoP8s93kH9A0ZF//oP88x3kn38IFMqkFStWmMt9+vQpsGzv3r3trgfvEBkZaS5fvHjR5rFff/1VR44ckSQ1bdpU9evXd1hPRESEunTpIkk6f/68vv/++2LYWhTk3//+t37//XdVqVJFb731lsvrsZ+Bv5F//oUM9B1kIFB0ZKD/IP98B/kHFB355z/IP99B/gFFR/75FzLQN5B/QNGRf/6F/PMdZCBQdGSg/yD/fAf5BxQd+ec/yD/fQf75BwYQllG7du0yl9u2bVtg2ZiYGMXGxkqSTpw4oeTk5GLdNnhOZmamDhw4YN6uW7euzePu/B9cXsZ6XRS/H374Qe+8844kaerUqapRo4bL67Kfgb+Rf/6DDPQdZCDgGWSgfyD/fAf5B3gG+ecfyD/fQf4BnkH++Q8y0DeQf4BnkH/+g/zzHWQg4BlkoH8g/3wH+Qd4BvnnH8g/30H++Q8GEJZR+/fvN5cLGoVrr4z1uijbPv30U509e1aS1KZNG8XExNg8zv+Bd7h06ZJGjRqlnJwc9ejRQyNHjnRrffYz8DfeD/6DDPQNZCDgObwf/AP55xvIP8BzeD/4B/LPN5B/gOfwfvAfZKD3I/8Az+H94D/IP99ABgKew/vBP5B/voH8AzyH94N/IP98A/nnXxhAWEadOXPGXK5WrZrT8lWrVrW7Lsqu5ORkPfnkk+btZ599Nl8Z/g+8w4QJE7R//36VL19eM2fOdHt99jPwN94P/oEM9B1kIOA5vB98H/nnO8g/wHN4P/g+8s93kH+A5/B+8A9koG8g/wDP4f3gH8g/30EGAp7D+8H3kX++g/wDPIf3g+8j/3wH+edfGEBYRqWnp5vLYWFhTsuXL1/eXD537lyxbBM8JzMzU3fccYdOnjwpSbr99tvVr1+/fOX4Pyj7EhIS9MYbb0iSJk2apIYNG7pdB/sZ+BvvB99HBvoOMhDwLN4Pvo388x3kH+BZvB98G/nnO8g/wLN4P/g+MtA3kH+AZ/F+8H3kn+8gAwHP4v3g28g/30H+AZ7F+8G3kX++g/zzPwwgBEpYTk6ORo0apQ0bNkiSGjZsqA8++KCUtwqFkZmZqVGjRslisahNmzZ69NFHS3uTAKBMIwN9BxkIAK4j/3wH+QcAriP/fAf5BwDuIQN9A/kHAO4h/3wHGQgAriP/fAf5BwCuI/98B/nnnxhAWEaFh4eby5cuXXJa/uLFi+ZyREREsWwTis4wDN1///365JNPJElXXHGFvvvuO1WuXNluef4PyrYXX3xRu3fvVlBQkGbNmqWgoKBC1cN+Bv7G+8F3kYG+hQwEPI/3g28i/3wL+Qd4Hu8H30T++RbyD/A83g++iwz0HeQf4Hm8H3wX+edbyEDA83g/+Cbyz7eQf4Dn8X7wTeSfbyH//BMDCMuoqKgoc/nUqVNOy58+fdruuig7DMPQgw8+qFmzZkmS6tSpozVr1qhevXoO1+H/oOzauXOnXn31VUnSo48+qjZt2hS6LvYz8DfeD76JDPQtZCBQPHg/+B7yz7eQf0Dx4P3ge8g/30L+AcWD94NvIgN9B/kHFA/eD76J/PMtZCBQPHg/+B7yz7eQf0Dx4P3ge8g/30L++a/g0t4A2Ne4cWMdPnxYknT48OECD655ZazXRdliGIbGjh2rGTNmSJJq166ttWvXqmHDhgWuZ70vrfexI/wflJy5c+cqKytLgYGBCgkJ0Ysvvmi33Pr1622W88o1btxYAwcONJfzsJ/h78g/30MG+h4yECgeZKBvIf98D/kHFA/yz7eQf76H/AOKB/nne8hA30L+AcWD/PM95J/vIQOB4kEG+hbyz/eQf0DxIP98C/nne8g//8UAwjKqZcuWWrlypSQpISFB3bp1c1j2xIkTSkxMlCRFR0erevXqJbKNcE1eaL733nuSpFq1amnt2rVq1KiR03VbtmxpLickJDgtb12mRYsWhdhauMowDElSTk6OXn75ZZfWWbt2rdauXStJ6tu3rxmc7Gfgb+SfbyEDfRMZCBQPMtB3kH++ifwDigf55zvIP99E/gHFg/zzLWSg7yH/gOJB/vkW8s83kYFA8SADfQf555vIP6B4kH++g/zzTeSf/wos7Q2Afb169TKXV6xYUWDZ5cuXm8t9+vQptm2C+y4PzZo1a2rt2rW68sorXVq/WbNmuuKKKyRJe/fu1R9//OGwbHp6ujZs2CBJqlChguLj44u28Sgx7Gfgb+Sf7yAD4Qr2M/A3MtA3kH9wBfsZ+Bv55xvIP7iC/Qz8jfzzHWQgnGEfA38j/3wH+QdXsJ+Bv5GBvoH8gyvYz8DfyD/fQP7BFexn78IAwjIqPj5eMTExkqR169Zp27ZtdstZLBZNmzbNvD148OAS2T645qGHHjJDMyYmRmvXrtVVV13lVh133nmnufzGG284LPf+++/r/PnzkqTbbrtNFSpUKMQWw1X/+c9/ZBiG07+JEyea60ycONG8f/HixTb1sZ+BXOSf7yADfRcZCBQPMtA3kH++i/wDigf55xvIP99F/gHFg/zzHWSgbyL/gOJB/vkO8s93kYFA8SADfQP557vIP6B4kH++gfzzXeSfHzNQZr377ruGJEOS0bx5c+PEiRP5yjz++ONmmc6dO5fCVsKRhx56yNw3MTExxr59+wpVz4kTJ4yIiAhDkhEYGGgsWbIkX5nNmzcbFSpUMCQZwcHBxt69e4u6+fCQiRMnmv8HEydOdFiO/Qz8jfzzfmQgDIMMBAqDDPRu5B8Mg/wDCoP8827kHwyD/AMKg/zzfmQgyD/AfeSf9yP/YBhkIFAYZKB3I/9gGOQfUBjkn3cj/2AY5J8vCnY+xBClZfTo0fryyy+1atUq7dmzR3FxcRo9erSaNWumlJQULViwQBs3bpQkRUVFaebMmaW8xcjz7LPP6p133pEkBQQE6JFHHtHevXu1d+/eAtdr06aNOYVrnujoaL399tsaMWKEcnJy1K9fPw0ePFg33nijgoKCtGnTJs2bN0+XLl2SJE2aNElNmjQpnieGYsN+Bv5G/nk3MhDuYj8DfyMDvRf5B3exn4G/kX/ei/yDu9jPwN/IP+9GBsId7GPgb+SfdyP/4C72M/A3MtB7kX9wF/sZ+Bv5573IP7iL/exFSnsEIwqWlpZm3HLLLebIXXt/derUMTZt2lTamwor8fHxBe4zR38ffvihwzrfffddIywszOG6QUFBxoQJE0ruScIlro68z8N+BnKRf96LDEQeMhAoHDLQO5F/yEP+AYVD/nkn8g95yD+gcMg/70UGwjDIP6CwyD/vRf4hDxkIFA4Z6J3IP+Qh/4DCIf+8E/mHPOSf72EGwjIuIiJCy5Yt05IlS/TRRx8pISFBJ0+eVEREhBo2bKj+/ftrzJgxqlSpUmlvKorZAw88oBtuuEEzZszQypUrlZiYqJycHNWqVUs9evTQfffdp9atW5f2ZqKI2M9ALvIP1jg2+gf2M5CLDEQejov+gf0M5CL/kIfjon9gPwO5yD9Y49jo+9jHQC7yD9Y4NvoH9jOQiwxEHo6L/oH9DOQi/5CH46J/YD+XfQGGYRilvREAAAAAAAAAAAAAAAAAAAAAAAAAAMCzAkt7AwAAAAAAAAAAAAAAAAAAAAAAAAAAgOcxgBAAAAAAAAAAAAAAAAAAAAAAAAAAAB/EAEIAAAAAAAAAAAAAAAAAAAAAAAAAAHwQAwgBAAAAAAAAAAAAAAAAAAAAAAAAAPBBDCAEAAAAAAAAAAAAAAAAAAAAAAAAAMAHMYAQAAAAAAAAAAAAAAAAAAAAAAAAAAAfxABCAAAAAAAAAAAAAAAAAAAAAAAAAAB8EAMIAQAAAAAAAAAAAAAAAAAAAAAAAADwQQwgBAAAAAAAAAAAAAAAAAAAAAAAAADABzGAEAAAAAAAAAAAAAAAAAAAAAAAAAAAH8QAQgAAAAAAAAAAAAAAAAAAAAAAAAAAfBADCAEAAAAAAAAAAAAAAAAAAAAAAAAA8EEMIAQAAAAAAAAAAAAAAAAAAAAAAAAAwAcxgBAAAAAAAAAAAAAAAAAAAAAAAAAAAB/EAEIAAAAAAAAAAAAAAAAAAAAAAAAAAHwQAwgBAAAAAAAAAAAAAAAAAAAAAAAAAPBBDCAEAAAAAAAAAAAAAAAAAAAAAAAAAMAHMYAQAAAAAAAAAAAAAAAAAAAAAAAAAAAfxABCAAAAAAAAAAAAAAAAAAAAAAAAAAB8EAMIAQAAAAAAAAAAAAAAAAAAAAAAAADwQQwgBAAAAAAAAAAAAAAAAAAAAAAAAADABzGAEAAAAAAAAAAAAAAAAAAAAAAAAAAAH8QAQgAAAAAAAAAAAAAAAAAAAAAAAAAAfBADCAEAAAAAAAAAAAAAAAAAAAAAAAAA8EEMIAQAAAAAAAAAAAAAAAAAAAAAAAAAwAcxgBAAAAAAAAAAAAAAAAAAAAAAAAAAAB/EAEIAAAAAAAAAAAAAAAAAAAAAAAAAAHwQAwgBAAAAAAAAAAAAAAAAAAAAAAAAAPBBDCAEAAAAAAAAAAAAAAAAAAAAAAAAAMAHMYAQAAAAAAAAAAAAAAAAAAAAAAAAAAAfxABCAAAAAAAAAAAAAAAAAAAAAAAAAAB8EAMIAQAAAAAAAAAAAAAAAAAAAAAAAADwQQwgBAAAAAAAAAAAAAAAAAAAAAAAAADABzGAEAAAAAAAAAAAAAAAAAAAAAAAAAAAH8QAQgAAAAAAAAAAAAAAAAAAAAAAAAAAfBADCAEAAAAAAAAAAAAAAAAAAAAAAAAA8EEMIAQAAAAAAAAAAAAAAAAAAAAAAAAAwAcxgBAAAAAAAAAAAAAAAAAAAAAAAAAAAB/EAEKgkLp27aqAgAAFBARo3bp1Ra5v7ty5Zn0jRowocn3+ol69eubr9scff5T25gCAX/B0BsI969atM1/8EeJLAAEAAElEQVT/rl27lvbmAABUtrPRm/pM9IsBwDuVdg7+8ccfZvv16tVzWj4xMVFPPPGEWrVqpUqVKikwMDDf9pdUv8vdbQcAFE3eMTcgIKC0NwUotMWLF5v/x1988UWJtp2RkWF+z3DjjTeWaNuAryGTgLIrJydH11xzjQICAtSqVSvl5OSUaPszZswwjw8bN24s0bYBb0e+wheUZp/PkeTkZEVGRiogIECjR48u7c0BvAKZBJRdpd3nc+Spp55SQECAwsLCdOjQoWJrhwGEQAlbsmSJ+aHglVdeKe3NAQCgxJCBAADYIhsBAP6sNHLwp59+0tVXX62pU6dq586dSktLk2EYJdI2AACe0rp1awUEBCgyMlKZmZmlvTlO5eTkqG7dujYnTv30009u1WE96N7eX3BwsKpWraqrr75a9957r7755hunGT9ixIgC67T39/HHH7u8zZmZmZo/f7769OmjunXrKiwsTDVr1lSnTp00depUnTp1yq3XQJIuXryof/3rX5KkDh06qH///nbLPf/8806fS4UKFVSrVi3dcMMNeuGFF/Tnn386bb9cuXKaNGmSJOm7774rMyezAig9ZJL/ZpK7Dh06pCeffFJt27ZVtWrVVK5cOdWpU0fx8fF66623lJKS4nJd1hfNc/Xv6NGjLtf//vvva9u2bZKkyZMnKzDQ/umVztoMCgpS5cqV1aJFCw0fPlzLli2TxWJx2v69996rK6+8UpL08MMPu7QOAN9Cvvpvvhalz5e3nqu+/vrrfHV0dXAxverVq+uxxx6TJH3wwQfasmWLW20B8F5kkv9mkrv8oc8XGhrq9mvZtm3bfPU4uiDvU089pcqVKysjI8PtXHcHAwiBErZs2TJz+bbbbivFLQEAoGSRgQAA2CIbAQD+rKRz0DAMDRs2TGfOnJEkRUVFqX///nrggQc0duxYjR07VrVr1y727QAA2Opahmd0L4sSExO1Y8cOSVLPnj0VGhpauhvkgrVr1+YbnDZv3jyPtmGxWJSSkqJdu3Zpzpw56tWrl9q3b6+DBw96tB1X7du3T+3bt9ewYcO0YsUK/fnnn8rIyNDx48f1448/6oknnlDz5s21fPlyt+p9++239ccff0jKPWG0KC5evKikpCStXr1aEyZM0JVXXqkXX3zR6Xp33323GjRoIEl68sknlZ2dXaTtAMoSMsk9ZJJ9/pJJrrJYLBo/frwaN26s1157TVu2bNHp06eVmZmpv/76S+vXr9c///lPNWvWTF999VWxbIM70tPTNWHCBEm5Azd69uxZ6LpycnJ05swZ7dmzRx999JFuu+02tWvXTvv27StwveDgYP373/+WJO3YscOtE4eBsoh8dQ/5ap+/5GtR+nwLFixwq3/m7j765z//qcjISOXk5OiJJ55wa12grCCT3EMm2ecvmeQqf+rzZWVlacGCBS6X//XXX90adB8VFaWHH35YUu7v6N9//73L67ojuFhqBWCXYRjmwa9BgwZq3rx5KW8RAAAlgwwEAMAW2QgA8GelkYM//fSTDhw4ICn3ism//vqrqlWrVuztAgDgSd54IRp7J+n897//1Ztvvqly5coVqs5hw4YpIiLCvJ2dna2jR49qw4YNSktLkyQlJCTo+uuv1+bNm1W3bt0C6+vRo4eaNGnitN3GjRs7LXP06FH16NFDx44dk5R7perrr79eDRs2VHJysr777jtdvHhRJ0+e1O23366VK1eqe/fuTutNS0vT5MmTJUmtWrVy+eSWWrVqqV+/fvnuT09P18GDB/XTTz/JYrEoMzNTzz33nNLT0/Xqq686rC8oKEiPP/64HnzwQf3222+aO3eu7r33Xpe2BYBvIZNy+WMmucowDN11111atGiReV/VqlV1/fXXq1q1ajp+/Li+//57paWl6cSJE7r99tu1ePFi3XLLLS63cfnr70h4eLhL9b311ltKTk6WlDv7g6tuv/32fBclys7O1smTJ7V582YlJSVJkrZt26b4+Hj9/PPPBf4v3H333Xruuef0119/adKkSRoyZIhCQkJc3h4A3ot8zeWP+VrYPl+eEydO6JtvvtHNN9/stOyZM2ds/tdcUalSJd1///167bXXtG7dOn333Xe64YYb3KoDgHchk3L5Yya5yp/6fHk++ugjc5CfK2XdNW7cOE2dOlUXLlzQM888o40bN7pdh1MGgEKJj483JBmSjLVr17q0zubNm811HnnkEZvHPvzwQ/Ox4cOHe3x7fVXdunXN1+3w4cOlvTkA4Bc8nYFwz9q1a83XMj4+vrQ3BwBglO1s9KY+E/1iAPBOpZ2Dhw8fNuuqW7euw3LTp083y917770F1llS/S5Xtx0AfFlhcsSf9erVy5BkBAUFGadOnSrtzXHq3LlzRsWKFc19XL58eXN50aJFLtdjnZkF9W/T0tKMsWPH2pTt06eP3bLDhw83y3z44YeFeHb2denSxSbfd+zYYfN4cnKy0aNHD7NMlSpVjNTUVKf1Tp061Vxn1qxZBZadOHGiy59ljhw5YvM+DAwMNHbt2lXgOufOnTPCw8MNScZVV11l5OTkON1+wBuQSe4hk/w3k1z11ltv2Tz/xx57zLhw4YJNmbNnzxojR440y1SsWNH466+/Cqy3uL7zvnjxolG9enVDknHFFVcYFoulwPLWz62gY4bFYjHmzJljhIaGmuUHDhzodHus8/zjjz929+kAZQb56h7y1X/ztbB9vmbNmpnLgwYNcuk5zJw50+76zvqPhw4dMgICAgxJxg033OBSW0BZQia5h0zy30xylT/1+azz8tdff3XalsViMerUqWNIMqKjo42qVau6fPyx/n/ZuHGjO0/RJYECUGKWLl1qLnvLaHwAADyBDAQAwBbZCADwZ6WRg6mpqeZyzZo1S6RNAAA8KT09XWvXrpUkderUSVWrVi3lLXLuf//7n86fPy9JatSokR544AHzMXtXBC+qiIgIvfPOOxowYIB53/Lly7V//36Pt2XP8uXLtWHDBklSaGioli1bpri4OJsy1apV05IlS9SgQQNJUkpKil577bUC67VYLHr77bclSRUrVtTgwYM9ts1XXHGFli5dqurVq0uScnJytGDBggLXCQ8P15133ilJOnDggJYvX+6x7QHgHcgk53w1k1x16dIlvfjii+bt++67T1OnTlX58uVtykVGRuqDDz5Q3759JUnnz5/XxIkTPbIN7vrkk0/MmShGjhypwEDPnFYZGBioUaNGadKkSeZ9S5cuVXp6eoHrjRo1SgEBAZKk//znPx7ZFgBlG/nqnK/ma1H6fC1btjS3YenSpTp79qzTdfL2TUhIiO666y6X22rQoIE5c9V3332n3bt3u7wuAO9CJjnnq5nkKn/r891zzz3msiszC65evVpHjx6VJA0ZMkTBwcEut3Xvvfeay8XRF2QAIVCC8qbzjYqKUpcuXUp5awAAKDlkIAAAtshGAIA/K40czMrKMpc9dRIgAAAl6dtvv1VGRoYk6dZbby3lrXGN9ck5d999t4YNG2beXrlypU6ePFks7T7zzDM2t1evXl0s7Vxu+vTp5vLw4cPVsmVLu+UqVqyo//u//zNvz5w5U9nZ2Q7rXb16tY4cOSJJuvnmmxUeHu6hLc4VGRmpW265xbz966+/Ol1n0KBB5vIHH3zg0e0BUPaRSa7ztUxy1bp168wTM0NCQmzasGfy5Mnm8kcffaQzZ84UeRvcNWfOHHM5b6C8Jw0ZMsRczsjI0KFDhwosf8UVV6hDhw6SpC1btmjXrl0e3yYAZQv56jpfy9ei9vmGDx8uKXcwx2effVZg2UOHDumHH36QJPXp00fVqlVzqy3rvuCHH37o1roAvAeZ5DpfyyRX+Vuf7+abbzYz85NPPlFOTk6B5a0HGebltKs6d+6sWrVqSZKWLFmi06dPu7W+M/xKjjLn6quvVkBAgAICApxe3dDafffdZ643duxYu2W2bt2qV155RbfccosaNGig8PBwhYaGqkaNGurUqZOeeeYZ/fnnn556KjaOHDlifpnTq1cvhYSEOF0nKytL8+bN04033qg6deqoXLlyqlOnjm6//XYtWbLE7W345ptvNGrUKF111VWKjIxU+fLlVbduXfXr109z5861OYnmcrfccov5+rZq1UqZmZkFtrV582aFhISY6/zvf/9ze3uLw5EjRzRhwgR16NBBNWrUMPd/hw4dNHHiRCUmJrpcV0JCgh566CG1adNGlStXVnBwsMqXL6+aNWuqQ4cOeuCBB/TZZ5+ZVzSw59SpU5o6dapuuOEG1apVS2FhYQoJCVFUVJSaN2+uAQMG6I033tDhw4c98fQBlHH+noFr1qzRP/7xD7Vs2VJRUVEKDg5WhQoVVKdOHXXp0kX//Oc/9dVXXznNIEk6ffq0Xn/9dd14442KjY1VWFiYoqKi1KxZM40dO1Zbtmxx6zns27dP48aNU+PGjVWxYkVVqVJFrVq10nPPPWdmx7p168z90LVrV7fqL25F+QxgLSsrSx9//LH69+9v/h8FBwcrIiJCjRo1Us+ePTVhwgT9/PPPBdbjiQwF4B/8PRvz/PLLL3rqqafUvn17xcTEKDQ0VOHh4WrcuLHuvPNOzZkzx6WrSRYkPT1dM2bM0M0336wrrrhCFSpUUEhIiCpVqqQmTZro1ltv1csvv1ymriRpGIYWLVqku+66Sw0bNlR4eLjCw8PVsGFDDRkyRP/73/9kGIZLdXnq+Xvy8wwAkINSUlKSnnnmGV199dWKjIxUZGSkmjdvrn/9618uXcFz7ty55mthfaX/SZMmmffn/T3//PNFfm4lYfPmzXrooYfUvHlzVa5cWWFhYapTp4569eqld955x+V+lGEYWrx4sYYMGaLGjRsrMjJSQUFBqlixourVq6fu3bvrySef1Nq1awv8EWzfvn0aP368OnTooGrVqik0NFRhYWGKjo7WNddco5EjR2revHk2M0ACKDzr41aeHTt26IEHHlDjxo3Nz8Tt27fXu+++a/dEgC1btmjEiBFq2rSpKlasqKpVq6pbt2765JNP3N6ehIQE/etf/1KrVq1UvXp1hYaGKiYmRvHx8Zo8eXKB7/285/H999+b93Xr1i3f8TkgIEBz5861WbdevXrmY3/88Yek3BPgnnnmGbVu3VrVq1dXYGCgWrVq5fT1K8ilS5f0wQcfaNCgQWrYsKEiIyMVGhqq6OhodenSRU899ZR++uknh+t76libJ28AvmR/Bl97r8tvv/2mJ554Qi1atFClSpVUvnx5xcXF6eWXX9aFCxfy1bF//3499NBDatmypSIjIxUVFaUOHTpo+vTpslgsLrxqfzty5IjN/r377rsVFxdnnsySnZ1dqP87V8TFxalixYrm7d9//71Y2rGWnp5uc4LQyJEjCyx/xx13mCeFpqSkaP369Q7LWr9Ot99+e9E21AHrGZpd+TzRvXt3VapUSZL01VdflcpJPyhdZFIuMolMcsbXMslV1v+P11xzjWrUqFFg+caNG6thw4aSpMzMTJv/sZLw+++/68cff5QkXXXVVWratKnH27DOWsm1vO3Xr5+5/PHHH3t8m1D2kK+5yFfy1Rlfy9ei9vmsZzZyNiuS9ePWg2tcdfvtt5vvsU8//dTl32LhfcikXGQSmeSMr2WSq/ytzxcSEmLOEJyYmGjO0GlPenq6vvzyS0m5MwVffnxwJiAgwPw8kJWV5fTiAG4zgDJm8uTJhiRDktGnTx+X1rl06ZJRuXJlc70ffvghX5m2bduajxf0FxISYkyePNlpm/Hx8eY6a9eudVp+2rRpZvlPPvkk3+Mffvih+fjw4cONY8eOGZ06dSpwW2+99VYjPT3dadsnTpwwevTo4fS5X3nllUZCQoLdOk6ePGnExMSYZR999FGH7aWlpRkNGjQwy44cOdLpNhZW3bp1zXYOHz5cYNkXX3zRCAsLK/A1CAsLM1599dUC68nKyjLuu+8+l/6fJBnPPPOM3XoWL15s839b0F/t2rUL+xIB8CL+moHp6enGbbfd5vJxddasWQW298477xiVKlUqsI6AgABj1KhRRkZGhtPtf/PNN43Q0FCHdVWqVMlYunSpsXbtWvO++Ph4p/UWljvteOIzQJ79+/cbTZs2dXk/HTx4MF8dnspQAP7DX7MxT2pqqnHnnXcaAQEBTre1Ro0adutwpc/0ww8/GLVr13b5+JyVleX0ORbG5f3ighw4cMBo3bq102295pprjEOHDhVYlyeev6c/zwCAYZCDX3zxhREVFeVw+8qVK2fMmjXLOHz4sHlf3bp1beqwzhZnfxMnTjTXK6n+XUHbfrn09HTjzjvvdPo8atasaSxfvrzAuo4fP2507NjR5ddm1apVduuZOHGiERQU5FIdQ4cOLezLBMCK9fvKMHKzoqD3Yc+ePY1Lly4ZhmEY2dnZxgMPPFDge3Xw4MFGdna20+1ISUkx7rjjDqfv/aioKGPRokVOn4uzvw8//NBm3cv7OTNnzrT7+09cXFyBr19BPv/8c5f7Ce+9916+9T11rM1jsViM6tWrG1Lud3n2XP66zJ8/36hQoYLDNlu3bm2kpKSY67/wwgtGYGCgw/Jdu3Y1zp8/7/S1y/N///d/5rqdOnUy73/ttdcc7iNHrDMz7/k5U6tWLbP86NGj8z0+fPhwh/9jhfHNN9+Y9VWsWNGlvvONN95oruPou1CLxWJUq1bNLHf8+HGn9U6cONHtzzKjRo0y17nnnntcWueWW24x1/nss89cWge+g0zKRSaRSf6USe6wfo/feeedLq3TvXt3c52Cvh925zwhV7311ltmnffff79L61j/H7jyfciff/5ps46z760NwzC2bNlilm/WrJlL2wXvRr7mIl/JV3/K16L2+fJytk+fPoaUez7U77//bne9nJwco379+oYko0qVKkZGRobx3nvvmXW52n9s0aKFuc7PP//s0jrwPmRSLjKJTPKnTHKHv/X59u7da/z888/m7WHDhjlcb+7cuWa5KVOmGIZhGDVq1DDvc6X/+L///c8s7+p5Aq7KveQAUIYMGTJE//73v5WTk6Nvv/1WycnJql69eoHrLF++3Lx6QKNGjdSxY8d8ZfKumF2uXDk1b95cjRo1UqVKlWQYhpKSkvTTTz/p1KlTysrK0pNPPilJGj9+vMeeV95I6eDgYPXp06fAsllZWerXr59++uknBQUFqUuXLmrYsKHOnTun77//XidOnDDrvPXWW/Xtt9+aVxC53IkTJ9S5c2cdOnTIvK9hw4Zq3769ypUrp19//dUcBX7w4EF169ZNK1euVOfOnW3qqV69uubOnavevXvLMAy9+eab6tWrl2688cZ8bY4dO9YcQd+oUSNNmzbNxVep+Dz00EM20/WGh4erW7duiomJ0fHjx7V27Vqlp6fr0qVLeuqpp3T8+HG9+eabdut64okn9P7775u3a9eurXbt2ql69erKycnR6dOn9euvvxZ4FfQtW7ZowIAB5lU3ypcvrw4dOqhevXoqV66c0tLSdOjQIe3atcvulRcA+CZ/zcC7775bS5cuNW83atRIrVu3VpUqVZSVlaXk5GTt2rXLvFpNQf75z3/qrbfeMm9Xq1ZNHTt2VExMjC5duqTt27dr9+7dMgxDH3zwgY4dO6avv/5agYH2J+aeNm2a/vWvf5m3y5Urp/j4eF1xxRVKTU3V999/r1OnTmnAgAF65ZVXXH1JSoSnPgNI0rlz53TDDTeYsy0GBgaqdevWatq0qcLDw3XhwgX99ddf2rlzp06dOuVwmzyRoQD8i79moyQdO3ZM3bt3tzkmRkVFqXPnzqpZs6aysrL0559/auvWrUpLS9OlS5cKtS2JiYnq2bOnzp07Jyn3qllt27ZVo0aNVKFCBZ0/f15//PGHdu7cqbS0tEK14Wl79+5VfHy8kpOTzfvyrtoVEBCg7du3mzNbbd26VZ06ddL69et11VVX5avLU8/fk59nACCPP+fg119/rUGDBpnfnQUGBqpz58666qqrlJ6ervXr1yspKUmjR48u8LvHpk2bmrMw/vzzz0pISJAktW3bVu3atbMpe/ntsuTChQvq3r27zWzvtWrVUpcuXRQeHq7ffvtNGzdulMViUVJSkm677TYtWLBAAwYMyFeXxWLRzTffrK1bt5r3tWjRQi1atFBUVJQuXbqk48ePa+fOnUpKSnK4TW+99ZbNrI7VqlVThw4dVLNmTQUEBCglJUX79u3T3r173b5SLADXzJw50zxOX3311WrVqpWCgoL0008/6ddff5UkffPNNxo3bpxmzpypBx98UO+//74CAwPVtm1bNW3aVDk5OdqwYYMOHz4sSfrvf/+ruLg4PfXUUw7bPX78uLp37669e/ea9zVv3lxxcXEKDw/XyZMntWHDBp0+fVpnzpzRoEGDNH/+fA0dOtSmnrzj85dffqljx45Jyr2Kfe3atfO1WdDVgBctWmTmVK1atdS5c2dVqlRJx44dU0pKitPX0Z7XX39dTzzxhHkV/YCAAF199dVq3ry5wsPDlZKSol27dpn9tcv7Y5461lrbvHmz2Qeyd9Xvy61YsUIPPfSQcnJydOWVV6pdu3YKCwvTL7/8Yubh9u3bNXjwYH3zzTd65ZVX9Nxzz0nK/X+Ki4tTcHCwfv75Z+3Zs0eStG7dOj366KOaMWOGS9tsPavBPffcYy4PHTpUTz31lHJycrRz507t3LlTcXFxLtXpKsMwbGbEy5spz5EjR45ozpw5+v3333Xx4kVVqVJF9evXV5cuXXTFFVe41Kb1e6Jly5YOf7u11qZNG61atSrf+ta2b99ufud65ZVXOr2ad2GcP39eX3/9tXm7S5cuLq3XpUsXffXVV5KkVatWaeDAgR7fNngHMikXmWQfmeQ7meSOvP/Zwsrb185s3bpVS5Ys0V9//SVJqlq1qpo1a6brrrtOlStXdrm9vOcuuZ6D7lq4cKG5XLt2bdWvX9/pOq1bt1Z4eLjS09P166+/6tixY6pVq1axbB/KHvI1F/lqH/nqO/nqqT7fsGHDtHz5chmGoY8++kgTJ07MV8b6eHDnnXcqNDS0UG116dJFu3fvlpSboW3bti1UPfAeZFIuMsk+Msl3Mskd/tjnyzue7d27V1988YXeffddm9kn88ybN0+SFBQUlO945CrrbVy3bp2ys7Nd2s8u8ehwRMBDunXrZo6affvtt52W79+/v1ne+krR1h544AHj66+/Ni5cuGD38ezsbOPDDz80KlasaEi5V9h2dCUOw3Dvytpnz541Zy7q1q2b3TLWV8POK9umTRvjwIED+bbzhRdesBnV/PLLLztsu3fv3jajzBcsWJCvTEJCgs2MgbGxsUZqaqrd+v75z3+a5WrWrGkkJyfbPP7pp5+ajwcHBxf7FUZcGWW+cOFCm9drxIgRxtmzZ23KnD171rj77rttyn3++ef56jp16pQRHBxsSDKCgoKMuXPnGjk5OXbbPXbsmDFt2jRj9uzZ+R67/fbbzXbuuOMOmysrWLt48aLx9ddfG2PGjHHySgDwFf6WgTt27DDrCg8PL3CWhEOHDhkvvviisXTpUruPz5kzx6wrMjLSmDVrlpGZmZmv3Jo1a2yu2uNoRo1ff/3VZubBG2+80Th27JhNmczMTOPf//63IeXOfpFXtizMQOjJzwD/+c9/zDLNmjUz9u3bZ7fNnJwc4+effzYeeOAB488//7R5zFMZCsD/+Fs2GkbujK2dO3c26yxfvrzxzjvv2M21jIwMY+nSpcbtt99uty5nfSbrPl6XLl2Mv/76y+E2rVu3zhg6dKhLVwIsDFdmIMzIyDDi4uLMctHR0XavkPfNN9/YXK2zTZs2dl8/Tzx/T36eAYDL+WMOnjp1yoiOjjbrbNmypfHrr7/alLFYLMbkyZONgIAAm35bQbP4WV+V2dFrk6eszUBofRXPoKAg4z//+Y9hsVhsyhw4cMC45pprbPrF9rJ/8eLFNt/vbt682WG7u3fvNp588knjp59+srk/KyvLJmdfeeUVuzlrGIZx+vRp44MPPnBpNksAzln/hlGuXDkjJibG7rF36tSpNr/VvPHGG4Yko2nTpsaOHTtsymZnZ9t8Lg4PDzfS09Pttm+xWGyyqV27dsa2bdvylbt48aLx/PPPm7OpV6xY0WGWuDuTrWHY9nOCg4ON0NBQ4/3338/3XVPeVc/zWL9+jnz99dc2s8B37949Xw7l+f33343nnnvOmDt3rs39njjWXu7JJ5806/z+++/tlrF+XcqVK2dERETYvfL6f//7X5srxr/55ptGUFCQUatWLWPdunX5yr/++utm2cDAQJeu+rxx40ZzndDQUOP06dM2j1tf8fpf//qX0/rcvfL3tm3bbMpPnz49XxnrK38X9Ne1a1e7r8vlCnPV7enTp9t87+qszB133OFSve7MQPjXX3/Z7I/Y2FiHx4DLWV/t3NWruMN3kEm5yCQyyZ8yyR3PPfecWV/Hjh1dWqdRo0bmOhUrVnRYzvr/y9FfSEiIcc8997g0y59hGDbfQ+zatculdazbK+iYkZOTY3z88cc2vyW/9dZbLrVhGIbNbDJffvmly+vBO5GvuchX8tWf8rWofb68bbl48aJRqVIlQ5LRsGFDu+v94x//MNf78ccfDcMwCjUD4cyZM811+vbt69I68D5kUi4yiUzyp0xyh7/1+fbu3WsYhmG8/PLL5n0fffRRvnX+/PNP833ds2dP8353ZyA0DMOoWbOmuc727dtdWscVDCBEmfTBBx+Y//AdOnQosOyZM2dsvmQ5ePBgkdr+73//a9Y1fvx4h+Xc+SDx2WefmWXfeOMNu2WsT5SUZNSuXds4deqUwzqfffZZm4Po5QPiDCN3gIR1nV999ZXD+g4fPmx2ICQZkyZNslvu0qVLxtVXX223A3B5HS+99JLD9jzF2cmwFovFnHZdkjFw4ECHgxVycnKMvn37mmUbNmyY70ScZcuWmY8PHTq00NtdtWpV84PTuXPnCl0PAN/jbxn49ttvm48XZWr0tLQ0IyoqyuwIFtQZNozcwYFhYWGGJKNq1ap2p7y/8847zW2Li4szLl686LC+cePG2WRuaQ8g9PRngDvuuMN83N4gDVd4KkMB+B9/y0bDMIxZs2bZfOm1fv36Qj8HZ30m64EGRX29isqVAYTW/w8hISF2fyTI8/PPP5uD1yUZ8+bNy1fGE8/fU59nAMAef8zBp59+2ixTo0YN48SJEw7re/HFF236Pr44gPC3334zAgMDzXLvvPOOw/pSUlKMevXqmWVHjhyZr8xjjz1mPj5r1qxCbfeuXbvMOjp37lyoOgAUjvUxLywszNi9e7fDsjfccINN+ejoaIfH1OzsbKNx48Zm2YULF9ot99FHH9nkkqPB6Hmsj73333+/3TJFPXFHkvHxxx+7tJ71OvZkZWXZHEdvueUWIysry6W6rXniWHu5Zs2aGZKMKlWqOLyoi/XrEhAQUOD3ePfee6/N61G+fHmHJygZhu3/kyuDwkePHm2W79evX77Hrf+XoqOjnb7O7p64M2DAAJvy9i6I5uqJO1LuCUvOPj8MGjTILD9u3LgCy+b5/PPPzXViYmLslhkzZozbfU7r916tWrWMsWPH5vsbMWKEcd1119n0m5s2bVrghSMu9+eff5rrBgcHF+r9Au9FJuUik8gkf8okd1ifyBwaGprv4uSXO3DgQL7nmpGRYbesKyeT5v1FRkY6vZhbUlKSzf+ro3YvZ93O7bffni9rx4wZY/Tv39+oU6eOTf3unks1atQoc/3nn3/erXXhfcjXXOQr+epP+VrUPp/1wBHr/bJx40abdawHGF511VXm/YUZQLh+/XpznXr16rm0DrwPmZSLTCKT/CmT3OFvfb68AYSJiYnm77Y33HBDvnVeeuklc51PP/3UvL8wAwi7d+9urnP54OGiCBRQBt1xxx0qX768pNypcA8dOuSw7KJFi5SRkSFJ6tChgxo1alSktgcMGKDw8HBJ0nfffVekuvIsXbrUXHZlOl9J+r//+z9VrVrV4ePPPvusatasKUk6f/68FixYkK/MzJkzbdq9+eabHdZXr149Pf300+btGTNm2J1etly5clqwYIG5f5YsWaIZM2bIYrHo7rvv1tmzZyVJ119/fYFTS5eUb7/91pzyOjQ0VNOmTVNAQIDdsgEBAZo+fbpCQkIkSYcOHbKZslaS0tLSzOXq1asXervy6qlQoYL5/wYAkv9loKeOqx988IE5FfyDDz6o9u3bF1i+adOmGj58uCTp9OnTWrlypc3jqamp+vLLL83bU6ZMUVhYmMP6XnzxRUVGRhZy6z3P058BPLGfPLWvAfgff8tGSXr99dfN5UcffVRdunTxSNv2eNvx2TrjHnjgAbVu3dph2bZt22r06NHm7ffeey9fGTIOQFnnbzloGIY++OAD8/aECRMUHR3tsL7x48erbt26Htm2smrWrFnKycmRJLVq1UoPPvigw7KVK1fW5MmTzduffvqp+X1tHrIP8B1jxoxR8+bNHT5+11132dx++umnHR5Tg4KCNGjQIPP2zz//bLfcG2+8YS7PmDHDzChHnnrqKUVFRUmSFixYYB7PPKldu3YaOnSoR+r6/PPP9ccff0iSKlasqA8//FDBwcFu1+Pp4+Tvv/+uX3/9VZLUp08fBQUFOV3ntttu0w033ODw8cv/P8aMGaOmTZu6VN7R/0eeS5cu6bPPPjNv33PPPfnK9O/fXxUrVpQknTx5Mt/3s4WVnp6uhx56SP/73//M+3r37q3GjRvnKxsQEKBrr71WL730kjZs2KDk5GRlZWXpzJkzSkhI0MSJE83fanNycjRp0iRNnTq1wLbzOHtv2Ctnvb61vN85JalOnTou1Wvt2LFjmj59er6/uXPnauPGjcrOzla5cuX00ksv6ZdfflH9+vVdrrtmzZoKDMw95SQ7O1t//fWX29sH30Am5SKT7COTfCeT3NGtWzdFRERIkjIzM/X8888XWN76t8o8586ds1s2ODhYN998s2bMmKFt27bpzJkzysrK0qlTp7Rq1Sr94x//MM/7SUtL08CBA7Vp0yaHbVtnbXR0tEJDQ509vXwWL16cL2tnzpypL774QkePHpWUe4z45Zdf7D7XgtSuXdtczjsuwD+Qr7nIV/vIV9/J16L2+awNGzbMXP7oo49sHlu8eLH5XbF1ucKwzqajR4/KYrEUqT6UfWRSLjLJPjLJdzLJHf7W58tTp04ddevWTZK0Zs2afN+H5uVvZGSkbr/99kK3IxVfX5ABhCiTIiMjdeutt5q3P/nkE4dlrR+7++67Xar/l19+0bx58zRp0iQ99thjeuihh8y/Rx55xBxgtmvXriJ/iLBYLFq+fLmk3MEKDRs2dLpOuXLlbD4gOSozePBg8/batWvzlbG+b9SoUU7bHTlypPkjT1JSkvbv32+3XLNmzfKd0Dp69Gjz4BsVFaX58+ebdZWmNWvWmMt9+vRRTExMgeVr166tXr16mbcvf11jY2PN5S+++EInT54s1Hbl1ZOamqqFCxcWqg4AvsnfMtD6uPrRRx/pwoULhWorrx1JGjJkiEvrdO/e3VzeuHGjzWM//PCDMjMzJUkxMTHq0aNHgXVFRESob9++rm5usfP0ZwDr/TRjxoxCbZOnMhSA//G3bDxy5Ij27dtn3n7ooYeK1KYznjjGl5Rz585py5Yt5m1XMu7ee+81lxMSEnT+/Hmbxz2dcUX5PAMA9vhbDu7du1fHjx+XlPvjkLP+XUhIiMt9QG9l/f3miBEjHF4cLU+/fv1UpUoVSVJGRoZ+/PFHm8etc2vWrFmFOrnDuo61a9fqwIEDbtcBoOgGDBhQ4OMtW7Z0q3yLFi3MZesf1PMkJSVpx44dknJ/J4qLi3O6jWFhYerYsaMk6ezZs9q9e7fTddxl/VtZUVmfPHLXXXepWrVqharHE8daa9YD8K0/FxSkpP8/rFmflFilShW7FzerWLGi+vfvb96eN29egXVebuLEiTafW+6//37ddtttqlOnjqZPn26Wi46OtrltberUqUpISNDTTz+t6667TtWqVVNwcLAqVaqka6+9Vs8//7x27dqla6+91lzn6aefdvj8L126ZC67ehJKuXLlzOWLFy/aLXPixAlzuaCLzxZFRkaGnnnmGXXp0kV79uxxeb281ytP3uc4+B8yKReZZB+ZlMsXMskdkZGRGjdunHl7+vTpevrpp222Tcr9znfMmDE2J906246ffvpJX331lcaMGaPWrVurUqVKCg4OVtWqVXXDDTdo9uzZ2rBhg5mbGRkZ+sc//uHw/78kslbKPfm5Y8eOevHFF936jsX6/U/W+hfyNRf5ah/5mssX8tWTOXTdddepQYMGkqTPPvvMvOCg9Pc+CAgIsDvAxh3W753s7GydOnWqSPWh7COTcpFJ9pFJuXwhk9zhz32+vIH4OTk5+vjjj222O++82wEDBrg8uNOR4uoLuj9UGSghd999tzkq/JNPPtGECRPylTl69Ki+//57Sbkni9x5550F1jlv3jy9/PLLLp/QkJWVpbNnz6py5cpubv3ffvjhB6WkpEhyffbBli1bujQrXceOHfXmm29KkrZv327z2F9//WVzYn6nTp2c1le9enVdddVV5smq27ZtU5MmTeyWfeCBB7RixQotW7ZMFy9e1Icffmg+NmPGDF1xxRVO2ysJ1q+LK6+BJHXu3FnLli2TlPsaWOvQoYNiY2OVmJioP//8U82bN9fIkSN16623qn379i4H8aBBg/Tqq69Kyv3wt3DhQt15553q1q1bgVdVB+Af/CkD+/Tpo4oVK+r8+fNm7vzjH//QzTffrNatW7t0FRtJNidEvv/++y519PKu9ihJiYmJNo/lfQkh5c5e5Mqg+Pbt22v+/PkubG3xKo7PAIMGDTJnAZkxY4a2bt2q4cOHq2fPni7PauKpDAXgn/wpGzdv3mwuX3nllUW+0qQzgwYNMgcmPPXUU1q1apWGDh2qG2+8sdjbdtcvv/xifuEXHh6uq6++2uk6rVq1Mj9rWCwW7dy50yYbPfH8PfV5BgAc8acctP4ur0mTJubVYguS94OwLzIMw6Z/6kr/LiQkRO3atTN/fN62bZvNBdMGDBig559/Xjk5Ofr666/VokULjRo1Sr1791bz5s2dDlCUcn+Q7tChgzZv3qyzZ8/qmmuu0T333KN+/fqpc+fOqlChgvtPFoDbrE+ksMf6mF2pUiWbq9Xakzf4WLK9cnUe6+/fLl686PLFTqxnz01MTHTpc7w7rrnmGo/VZd0fy7uKcGF44lhrLe83q5CQEJtjekHc+f+QVOBV5CXn/x/WrL+bHTRokMPv/e655x7z+9Rly5YpNTXV5c8al8+kYM8111yjTz75xOGMeq6cLFKzZk19/fXXatKkiVJTU5WVlaU33nhDb7/9dr6yYWFh5nLexemcsT6Z09EJJdYXwilMxsbHx2vdunV2205KStKGDRs0depU/fLLL9q8ebM6duyotWvXuvzeqlChglJTU/NtK/wLmZSLTLKPTPKdTHLXhAkTtHbtWv3www+SpFdeeUWzZs1SfHy8qlatqhMnTmjdunXmCb/9+/fXF198Ya6fN5vF5Vx5vdq3b68FCxbopptukiTt379fixcv1h133JGvbFGzVsq9uE7Xrl1t7jMMQ2fPntXevXu1cOFCvfvuu0pPT9dzzz2nvXv3FniRKGvW20TW+hfyNRf5ah/56jv56okcsnbPPfdo0qRJOnPmjJYuXaqBAwfq+PHjWrVqlaTcPmJRz+29fDvJJ99HJuUik+wjk3wnk9zlT30+a3fccYcefPBBnT9/XvPnz9eTTz4pyfZ/paiz/UrF1xdkACHKrF69eqlatWo6deqUDhw4oISEBLVt29amzKeffirDMGzK22MYhv7xj3/YDHJz1blz54p0YkxhRuO7+gHdulxycrLNY9a3y5cv7/LUxPXq1TMHDzi7MsgHH3ygq6++WklJSeZ9w4cPd3qCUkmyfh3q1q3r0jr16tUzly9/DUJCQjR//nzdcsstSk9P16lTpzRlyhRNmTJFYWFhuvbaa3X99derT58+6tSpk8MPX88++6zWrVunzZs3yzAMffnll/ryyy8l5Z4k3KVLF/Xo0UO33nqrw4AE4Lv8KQOrVq2q2bNna9iwYcrKylJiYqKef/55Pf/88woPD1f79u0VHx+vW2+9Va1atbJbR3p6us105rNnz3Z7W/NOcMhjnR/WV+opSFkZZFEcnwF69uyphx9+2OyQJiQkKCEhQZJUo0YNXXfdderatatuv/12h6+DpzIUgH/yp2y0vvJV3hUii9O9996rlStXavHixZKk1atXa/Xq1ZJy+5xdunRRt27d1Ldv30Jf9c5TLs9nV7IiMDBQsbGxDjPOE8/fE59nAKAg/pSD1sf6wnxH6mvOnj2rrKws87Ynvt9s2rSpXnvtNT3xxBMyDEP79u3T+PHjNX78eFWuXFmdOnVSfHy8+vbtq6uuusphG3PmzFH37t114sQJpaen67333tN7772n4OBgtWrVStdff7169uypHj16MJgeKCbWs37ZExz898/AzspeXt762JPn2LFj5vLhw4cdXk25IJd/B+cJrn735QpP9cc8dayVpDNnzmjDhg2Sck/yi4yMdGkb3Pn/cLe8vf+PPElJSeZJiZIKnNWgR48eqlWrlo4dO6aMjAz997//1QMPPFDgdjgSFBSkyMhI1alTR23bttWAAQPUq1cvj3zHGB0drbFjx+rFF1+UJK1YscJuOeuLw7p6FW/rcq5cXDbv854nlCtXTvXq1VO9evU0ePBg3XzzzVq1apXOnTunIUOGaPfu3QoJCSnRbYL3IpNykUn2kUnen0kpKSl2L2ZkrUOHDrr77rtt7gsNDdU333yjUaNGadGiRZJy+6iff/55vnKvvPKK6tWrZ55MGhgY6PL/mCM33nijrrvuOm3cuFFS7utl72RSa57MtYCAAEVFRaljx47q2LGjbrnlFvXq1UsWi0WffvqpbrrpJg0fPtxpPWSt/yJfc5Gv9pGv3p+v9njimD9s2DBNmjRJUu5AhoEDB+qTTz4xL9LqiQENZJP/IZNykUn2kUnen0n0+dyTN6vl/PnztWfPHm3dulUtW7bUwoULJeX+Tnv99dcXuZ3iylvnU6kApeTyK2VbT/Fp776CDvyzZs2yOSmmV69emjdvnnbt2qXU1FRlZGTIMAzzz/pEjJycnCI9j7zR+NWrV3f5atiujm6uWLGiuWw9cELKHUxhr1xR6rxchQoV8g1uu/32211uqyQU5nVw9hrEx8dr586dGjZsmM0o/EuXLmnjxo16+eWXdd1116lJkybmSaj22vj+++81ZcoUmxN6JOngwYP64IMPNHToUMXExGj8+PEemS4YgPfwtwwcPHiwfv75Z/Xr18/mhIT09HStXr1aEyZMUOvWrXXttdeanVRreVcoKYrs7Gyb29b54Wouu/JlX0kors8A06ZN0xdffKF27drZ3H/ixAl9/vnnevjhh3XFFVdowIAB+vPPP+224YkMBeCf/CkbrY/BJZEtQUFB+uKLLzR79mw1a9bM5rE///xTn3zyie69917VqlVL9957rzlzVGkojozz1PMv6ucZACiIP+VgYfpi7mSCt7F+PSTPfb/52GOPae3aterRo4fND5qpqan6+uuvNX78eDVu3Fg33HCDdu3aZbeNZs2aaefOnXr44YdtfuzNzs7Wli1b9MYbb6hnz56qW7duoS70A8A5d05I8MTJC8XxHZwneOpqyZJn+2OeONZK0sqVK80TZRzN4GuPu/vcUxfz+vjjj82TEhs0aFDg7LmBgYEaMmSIedv6iuHOHD582OYzS3Z2tlJSUvTLL79ozpw56t27t0cvUHbDDTeYy4cOHbJ7ZW/rK2NbnwRWkOPHj5vL1ldXt2ad68X1e2FISIjNyXgHDhzQkiVLXFrXept8+XMZCkYm5SKT7COTvD+T0tLSNH369AL/vvvuO7v1hoeH67PPPtPGjRt17733qnHjxoqIiFD58uV11VVX6aGHHtL27dv16KOP2lwAp3bt2h553axfr71799otUxJZm7ct1t/vTJkyxaX1yFr/Rb7mIl/tI1+9P1/zeDqHGjRooOuuu05S7v9XcnKyOSNShQoVNGDAgCK3cfl2kk++j0zKRSbZRyZ5fybR53Of9cVgPvroI3399dc6ffq0pNzf6j3x3IqrL8gAQpRp1iOVFy5caB7oJWnXrl1mkFWqVKnA2f2mTp1qLk+aNEkrVqzQsGHD1KJFC0VFReWbstbZwDlXHTx4UPv375ck3XzzzQoMdO0td+HCBZfKWU9HevlAPusPFO5MW1pQnZf75z//qQMHDtjc9+CDDzqdubAkFeZ1cOU1aNCggebNm6fk5GStXLlSzz77rLp162bzAfHAgQPq16+f3njjDbt1hIaG6vHHH9fvv/+uHTt2aNq0aRo8eLDN9N0XLlzQlClT1K1bNwYRAn7G3zKwVatW+uKLL3Ty5EktWbJETzzxhDp27GhzAv7WrVvVrVs382oleS7/cJySkmLTaXPlb926dTZ1WOdHYXK5NBXnZ4B+/frpp59+0pEjRzRv3jyNGTPGZrCFYRj6/PPP1aZNm3yfEfJ4IkMB+Cd/yUbrY/DlgwaKS0BAgP7xj39oz5492r9/v95//30NHz7c5kp3WVlZmjNnjtq1a2czO1RJKq6M89TzL8rnGQBwxl9y0Jv7YsXh8h+NPfn9Znx8vL777jslJSVp4cKFGjdunNq0aWOzb1avXq327dtr06ZNduuoUaOGpk2bphMnTmjdunV64YUX1Lt3b5srhv71118aPXq0xo0b59K2Ayi7rL+Du+2229z+/s0wDI0YMaL0noALPN0f88Sx1noGX3dO3Ckt1iff/P777woICCjwz/qzyU8//WR+XihratasaXM772QQa40bNzaXjxw54lK91hdia9Kkid0yMTEx5nJx/gZ75ZVXqn79+ubt1atXO10nKytLZ86cMW9bbytQnMgk95FJZJIzrmRSUXTu3FmzZs3Svn37lJaWpgsXLmj//v16++23zd8a9+zZY5Zv27atR9q1fr0c5WhJZa0k9ezZ01zes2ePzUm8jlh/H03WojiRr+4jX8lXZ0qrz5c3y2B2drbGjx+vX375RVLuOT/Ozgl2hXU2BQcHq1q1akWuE7BGJrmPTCKTnKHPV3AZd3Xr1k2xsbGSpAULFmjOnDnmY56Y7Vcqvr4gAwhRpnXo0EGNGjWSlDtC2nqqWeurag8YMEBhYWF260hMTNTBgwclSVFRUfr3v/9dYJtpaWkem7rYOkwLOnHnco5m7blcYmKiuXz5h3DrqZIvXrzo8gHvjz/+cFintcWLF2vWrFmScmdsyNtPSUlJuvfee11qqyRYvw6uvq6uvgZS7gfVnj176oUXXtCaNWt0+vRpLVq0SC1btjTL/Pvf/9Zff/3lsI6AgADFxcXp4Ycf1oIFC3T06FFt27ZNI0eONMv89NNPhZqGG4D38tcMjIqK0m233abXXntNP/zwg06dOqUPP/xQV1xxhSTJYrHowQcftBlUHRUVpXLlypm3XfmhxRnr4//Ro0ddWsfVcsWtuD8DSNIVV1yhYcOGacaMGdqzZ4/+/PNPTZo0yZwh5PTp03r00UcLrMMTGQrAv/hLNtaoUcNcPnz4sEfadsdVV12l0aNHa+7cuTp06JD279+vRx99VEFBQZJyr3A2adKkEt8uyTbjjh49KsMwnK6Tk5NTYN/5cp54/oX5PAMAzvhLDhbmuzzr47yvqVSpks1A9OL4frNGjRoaNGiQ3nrrLW3dulXHjx/Xf/7zH/PqpRcvXtSYMWMKrKNcuXKKj4/Xs88+q+XLl+vUqVNasWKFeaVrSXr77beVkJDg0vYDKJus+yqe+P6tLCqu/lhhj7XZ2dlasWKFJKlly5Y2MwOXRVu3brU5CaQw3Ln6d0m6fBC/vSs+N23a1FzetWuXS1e637Ztm931rVkP6ivu76CtT7hx5eSjpKQks28eHBxsc5FUoDiRSUWrl0xyDZn0t3r16jk9QXvu3LluPpP8fvjhB3O5oNlD3GH9ejmascE6a5OTk+3O8OEpl58M7Eo/3/q30nr16nl6kwAT+Vq0eslX15Cv+RVHn2/QoEHm7wTWGe2pAQ3W2VSnTh3zd1TAU8ikotVLJrmGTPobfT73BQYGaujQoWadX3/9tSSpY8eO5m/6RVVcfUEGEKLMy3tzSdInn3wiKXd2mwULFpj3W1+B+3LHjh0zl5s0aWJzwoU9GzdudOkkRFfknRhTrlw53XTTTS6vt3v3bpeuJv3jjz+ay23atLF5rHbt2oqOjjZvWx90HTl16pTNbEGX15nn2LFjNoMEn3vuOS1dutScOWjJkiWaOXOm0/ZKQuvWrc1lV16Dy8s5eg0cKV++vAYMGKB169aZH/AyMzP1zTffuFVP69at9cEHH9i8ztYnWgHwD/6YgZeLjIzUiBEjtGbNGnOQ4KlTp2wyUJLatWtnLju6So47WrVqZS4nJCS49Lr8/PPPRW7XE4rzM4AjsbGxmjBhgt5//33zvm+//VYZGRku1+GpDAXg2/whGzt06GAuHzhwoNQHqF911VV6/fXXbQbNlVbf5OqrrzZ/gDp37pw521ZBdu7cafavg4KCFBcX51abnnj+rn6eAQBn/CEHrb/L27dvn86ePeu0bl8+ngYEBNj0T13p32VnZ9sM1HO3f1e9enU98sgjWrJkiXnfnj179Pvvv7tcR0hIiHr16qXvvvtOLVq0MO9ftmyZW9sCoGxp3769ubxjxw6PzQAbEBDgkXo8wbo/tmbNmmJrx9Vj7YYNG8zZ3bztqt9VqlRR+/btXfqzvmL2/PnzlZOTUxqbX6Dt27ebyxUrVrSZbTdPp06dzD7f+fPntWXLlgLrzMjI0ObNm83b3bt3t1vu6quvNpeL+8ro1jNAO5ox2trevXvN5ebNmys4OLhYtgu4HJnkOWQSmSS5nknF6dChQ+Z2BgcH66677vJIvdavV61ateyWqVGjhvnbqmEYNr+Zepp11kru5627328D7iBfPYd8JV+l0u3zVapUKd//VK1atXTDDTd4pH6yCcWNTPIcMolMkujzFVefz97AfE8N1peKL28ZQIgyz/qkl8WLF+vChQv6/vvvzStLx8bGKj4+3uH61l+2XP5FjD3vvfdeEbb2bykpKeYAhm7duik8PNzldS9duqRFixYVWCYzM1MLFy40b3fr1i1fGev7XBn5PXfuXDMMa9WqZROUeQzD0PDhw80pcTt16qRnn31WTZs2tZne99FHH9W+ffuctlncrENu+fLlOnnyZIHljx07Zl5F4fL13VGlShV17tzZvH3ixIlC1WP9QaywdQDwXv6YgY40bNhQzZs3N29ffky85ZZbzOX33nuvyCe5durUSaGhoZJyr6LsrKOenp6uxYsXF6lNTyqOzwCusM6trKwspaSkuF2HpzIUgG/yh2ysW7euzdW+yspM5GWhbxIREaFrr73WvO1Kxs2ZM8dcbteuncMrjjnjiefv7PMMADjjDznYpEkTxcTESModCGc9ONIeV8p4O+vvJ+fNm+e0v7t48WLzu9uwsDB17NixUO127txZVapUMW8XJrcuHzBK9gHerUGDBmZfJTMz0+azdlFYz5yblZXlkToLq3fv3ubyf//7X506dapY23N2rLUeeF3QDL5lQVZWlk0mP/PMM9q8ebNLf+vXrzcvFnP06NFiPWmqsD744ANzuWvXrnbLhIeHq0ePHuZtZ33WL774QufOnZOU+53o9ddfb7ec9cXzdu7c6eIWu+/ixYs2J6W4Mpug9fZYbydQ3MgkzyOTyCRXMqk4TZgwwVy+7bbbHJ746Y7Tp0/bnCjt6PWSSi5vrWf9kJznbU5Ojnbv3m3eJm9RnMhXzyNfydfS7PNdPoBh6NChLg1cdwV9QRQ3MsnzyCQyiT6f5/O2adOmNucwlStXTnfeeadH6j558qQ5A2uFChVsLthaVAwgRJnXqFEjc6R93sn5eVfYlnI/2BZ0VYD69eubj+/evbvAKyUvXLhQX331lUe2e8WKFbJYLJIKNxp/woQJSk1Ndfj4yy+/bE5NWrFiRbsjsa2nGf7yyy8LnMHnyJEjeumll2zWtfe6vv766/ruu+8k5c6i8PHHH5th+uCDD5oDOC5cuKChQ4d6ZJrXorjpppvMqWczMjL0z3/+02FZwzD08MMPmx8MGzZsmO+qK3kn37gi7+QtSTYzQWVkZCg9Pb1IdQDwD/6Qga52fi0Wi5KSkszblx8Tx4wZo6ioKEm5P7xYzxLkzKlTp8ztzVOlShX17dvXvD1+/PgCZ9ObMGGCSzNjlBRPfwZwdT9Z51ZgYKCqVq1q3vZEhgKAP2SjlHtBljyvv/66NmzY4JHtsKcwx/jSPDZbZ9z06dP1yy+/OCy7detWzZw507x9//335yvjiefvqc8zAOCMP+RgYGCgRo0aZd6eNGmSkpOTHZafOnWqDh8+7JHtLKtGjx5tntixbds2m5nfL3fmzBmNHz/evH3XXXepUqVKNmVcza0zZ87YfIdpnVupqakuX5W1rHyGAOAZTz75pLn87LPPujQreJ68H7svZ/39Ud7vXqWlf//+qlu3rqTcrB05cqSys7PdrscTx1rp7xN3YmJiyvwJeV9//bX5vAMDA926gnR0dLRuvPFG87b1FcSLi6u/00nSG2+8YV4MQSp4xucHH3zQXJ47d6727Nljt9yFCxdsTpq57777HM7e17p1a1WrVk2S9NtvvxXbgPz33nvP5jtw65OQHLH+vsJ6HwIlgUxyDZlEJkmey6Ti8uGHH+rTTz+VlHti5GuvveawrKuvl8Vi0b333qu0tDRJUmhoqAYOHOiwvPV+37hxo0ttuCstLc3m5PemTZuqZs2aBa6zfft28zk3a9bMIyfZAgUhX11DvpKvUtnv8/Xq1UsJCQnm39NPP+2ReiX6gigZZJJryCQySaLPJ5Ven++LL74ws3bbtm2qXLmyR+q1ztquXbt6dp8ZgBd45513DEmGJKN79+5GVFSUeXv37t1O1+/YsaNZvnXr1sa+fftsHrdYLMY777xjhISEGEFBQUZYWJhZ/vDhw3brjI+PN8usXbs23+ODBg0yH09MTHS6jR9++KFZPjQ01JBkXHvttcZvv/1mUy47O9t4+eWXjYCAALP8iy++6LDe3r17m+XCw8ONzz77LF+ZLVu2GI0aNTLLxcbGGqmpqfnKbd++3dw2ScZHH32Ur8zJkyeNGjVqmGWeeOIJp8+9KOrWret0Xy1cuNAsI8m49957jXPnztmUSUtLM4YPH25T7vPPP89X18SJE424uDjj3XffNZKSkuy2d+7cOePpp5826wkKCjL+/PNP8/HDhw8bUVFRxmOPPWYkJCQ4fG7ffvutUa1aNbOel156yYVXBICv8fUMHDFihNGlSxdj3rx5drPHMAzj1KlTxsiRI806IyMjjQsXLuQrZ52lkoxhw4YZR44csVtnTk6OsXHjRuOBBx4wypcvny8XDMMwdu/ebZN7PXv2NI4fP25TJjMz03j22WcNSUa5cuXMsvHx8QU+76JYu3atS+148jNAo0aNjLvuustYvny5kZGRYbe9/fv3G9dee61Z14033mjzuCcyFAAMw/ez0TAMIysry+jUqZO5ToUKFYzp06cbmZmZ+cpmZGQYS5cuNW6//Xa7dTnrM5UvX9647777jHXr1hkWi8VuHQkJCUaDBg3MekaPHu30ORSGdZYPHz7cbpmMjAwjLi7OLBcTE2OsWbMmX7lVq1YZ1atXN8u1adPG7uvniefvyc8zAOCMP+RgcnKyzXdicXFxxt69e/Nt59SpU43AwECbflvdunUd1jtx4kSz3MSJEwvcBlf7XUV1+PBhl7b9gQceMMsFBwcb77zzTr7cOnjwoNG2bVubrLG3z7p162bcfPPNxqJFi4zz58/bbe/o0aM2fcqrrrrK5vEPP/zQaNiwoTFlyhSH/xeXLl0y3n77bZvvsTdt2lTg6wHAOevvvpxx9RiTx5VjX3Z2ttG9e3ebY82MGTMcfl909uxZ4+OPPzbi4+ONAQMG2C3zyiuvmPXdfPPNRk5OjtNtdeW3IXtcef2WLVtmc+zq3r17vhzKc/jwYeO5554z5s2bZ3O/J461v/76q/nYP/7xD5een7uviyf/n26//Xbz8RtuuMGl7bU2f/58mz5wWlqaw/bd3e/2jBw50rjjjjuMFStW2O0rGoZhHD9+3CaDJRkdOnRw+j/apUsXs3y9evWMnTt32jx+6tQp48YbbzTLVKlSxWFfMs+wYcPM8p9++qnT52f9ucfZZ5nMzExj+vTpRkhIiLlOgwYNjEuXLhW4XlZWllGpUiVDyv1t29lzgO8hk3KRSfaRSa7ztkxyR3JysjFq1Chj8+bNdrc1NTXVePzxx43AwEBzG95+++0C62zevLnxyCOPGFu2bHFY5pdffjG6du1q83o9+eSTBdZ76NAhh+8BR6zrt/d9iLUDBw4YnTt3tlln1qxZTtt47bXXzPLjx493abvg3cjXXOSrfeSr67wtX4vS57vzzjudli/Ie++953L/0TAM48SJE+Z7KCYmxuHvq/B+ZFIuMsk+Msl13pZJ7vCnPp+j96arrMfWOOs/GoZhPPjgg2b5d999t0htX65kh48ChXTnnXfqX//6l7Kysmymi23durWaN2/udP0XXnhBN910k3JycrR9+3a1bNlSnTt3VoMGDZSenq4NGzaYswC89NJLev/993XkyJFCb2/W/2PvvuObqvo/gH9ukqZ7QAeUtlBGKbtlWkFllC2CskRwFH8iKAiPPO6BIm5QH3ChDBFEBFQEFRBkgxTK3qXQAikUuukeSe7vj9rQNDez6Uo/79erL9Obc889qdqTm57P+ZaWYuvWrboxBgcHW3X+2LFjcenSJRw+fBjt2rXDvffei9atWyM3Nxd79+7Vq1hw33336e20UNl3332HPn364PLly8jLy8P48eMRFhaGu+66C0qlEufOncOhQ4cgiiKAsmqGa9as0VVxKldYWIiJEyfqKgpOmDABjz32mMH1/P398d1332H48OEAyqplDBs2DP3797fqZ2BP48ePx969e/Hll18CAJYuXYq1a9eif//+aNKkCVJTU7Fjxw691Pp//vMfjB49WrK/kydP4tlnn8X06dPRunVrdOrUCX5+figtLUVKSgr++ecfvb5eeeUVhISE6PWRnZ2NTz75BJ988gkaN26Mrl27IigoCC4uLkhNTcWpU6f0doFv27YtZs2aZc8fCxHVE44+B4qiiH379mHfvn2Qy+Vo164d2rdvj0aNGqGwsBDXr1/HgQMH9CraLliwAK6urgZ9xcTEIDExEfPmzQMArFy5EqtXr0ZkZCTatWsHDw8P5OXlITk5GSdOnDBbMbBjx4748MMPdVWg/vrrL7Ro0QL9+vVD8+bNkZWVhT179iAtLQ1KpRLvv/8+/vvf/wKArjpEbbLXewCg7N/rmjVrsGbNGri6uqJLly5o1aoVvLy8kJWVhcTERBw5ckTX3tXVFQsWLDDoxx5zKBGRo8+NAKBQKLB27VoMGDAACQkJKCgowPTp0/H666+jT58+CAwMhFqtxtWrV3H06FHk5OQYVBeyVGFhIb799lt8++238PT0RGRkJFq0aAF3d3ekp6fjwoULejuV+fv74+2337bpWvagVCqxZs0a9O3bF2lpabh58yYGDBiAiIgIREZGAgBOnDiBkydP6s4JCAjAmjVr4OTkZNCfPV6/Pd/PEBGZ0xDmQT8/PyxbtgyjR4+GRqPByZMn0bFjR9xzzz1o27Yt8vLysHfvXty4cQMAMH/+fIf/3GzBggU4cuQI4uLioFarMWPGDHz44Ye455574OHhgcuXL2Pv3r26So8KhQLLli1DaGioQV9arRZ//vkn/vzzTyiVSnTs2BFt27aFt7c3cnNzce3aNRw8eFBXYVAul2PhwoUG/Vy+fBkvvvgiXnzxRTRv3hxdunTR7Vh78+ZNxMbGIjMzU9d+0qRJ6N27dzX8dIioJsnlcqxbtw6DBg3C8ePHkZOTg2nTpuGll17C3XffjaCgIMjlcmRlZSE+Ph7nz5/X7Zw9ZswYyT5Hjx6N1157DaIo4s8//0SXLl3Qu3dveHp66tpMmDABPXr0qJHXOGLECHzwwQd45ZVXAAA7d+5Ehw4dEBERgY4dO8LDwwOZmZk4deoU4uPjAQCfffaZXh/2+F1bvus3YFkl+9qUnp6OP//8U/f9pEmTrO7jwQcfhJubGwoKClBQUICff/4ZkydPtucw9Wi1Wvzyyy/45Zdf4O7ujs6dO6Nly5bw8vJCYWEhLl26hLi4OJSWlurOad26NX777TeTFZ8B4Mcff0SvXr2QkpKCK1euIDIyEn379kXr1q2RlpaGv//+GwUFBQDK5ux169ZJfiZb0aRJk7By5UoAwG+//WbVzuoJCQmYMWOGwfGSkhKkpKQgNjZWb7d6Nzc3rFq1Cs7Ozib73blzp+4z9vvvv9/sayCyN85JnJOkcE7SVx1zkjXUajWWL1+O5cuXo2nTpujevTsCAwNRWlqKa9euGXxm+s4770jOWRXl5eVh4cKFWLhwIfz8/BAZGYnAwEC4ubkhJycHJ0+exLlz5/TOGTlyJN577z2T/bZq1Qp33303Dh48iIsXL+L8+fNo3769xa914cKF+Pnnn/WOiaKI3NxcnDt3DseOHdP9TRYo+33z5JNPmu13w4YNuse2/PdMZC3Or5xfpXB+1VfX7vlq2m+//aab0x555JE6sT6KHBPnJM5JUjgn6eM9X5mauOerSaIoYuPGjQAAJycnjB8/3u4XIKoXRowYoZfkBSB+8sknFp//9ddfiwqFwqCP8i+ZTCbOmTNH1Gq1FiXjTe2svX37dt1z5nazLle50sL169fFqKgoo+PFvzsgSFVMquzmzZt6OzEY+2rTpo14+PBhyT6mTZuma9e8eXMxOzvb5DWfe+45Xfvg4GAxMzPTop+DtazZxWDevHl61aGkvlxcXMT333/faB8LFiww+3Ms/1IqleLcuXMN+khOTjY7jopf/fr1M1qpiYgaBkeeA2fMmGHx70NPT0/x22+/Ndvn2rVrxWbNmlncb69evUzupjx//ny93Zcrf3l7e4ubNm0St23bpjs2atQos+O0lTWVMOzxHkAURbFTp04W/zxbtmwpWVXCHnMoEVE5R54bK8rIyBAfeughi353BgUFSfZhbvweHh4W/36WqgBlT5ZUICwXHx8vdu3a1eyYu3XrJl66dMloP/Z4/dXxfoaIyJSGMg+uX79eV9VG6svZ2Vn85ptvLN7Ztj5XIBTFsortFas5GvsKDAwUN2/ebLQfqf9+jH0FBASIv/32m0Ef69ev19sV19SXTCYTn332WaO7qhKRdSr+/2VOdez8Xa6goECcNm2ayfmk4perq6vJv728+uqrJs//7rvv9NpX587f5X766Se9XYFNfVV+j2+P37XlFWpcXV0trl5eWzt/L1q0SO/fdeVduy01ceJEo/8N2nvn7yeeeMLif0cymUycNGmSVbtznz9/XoyMjDTZr7+/v/jHH39Y1J9ardb9+3V3dzf79+GK73us+erQoYPJz4kreuqpp3Tn/f777xadQ46Fc1IZzknSOCdZrr7NSdZISUmx6HUFBgaKa9eutajPiv9tmftyc3MT33vvPYsrIy1dulR3riWfYdgy1yoUCvGll16y6B752rVrunvv7t27W/QaqP7j/FqG86s0zq+Wq2/za1Xu+Wq6AuHAgQN17U+dOlWla1PdxjmpDOckaZyTLFff5iRrNKR7vpqsQLh//35d2zFjxlTpulIYIKR6Y+3atXr/I8rlcvHGjRtW9XHq1Clx8uTJYmhoqKhUKkVvb2+xQ4cO4owZM8Rjx47p2lV1YczMmTN1z5kqoVqR1ELJ4uJicdmyZeKAAQPEZs2aiUqlUgwMDBRHjhwpbtiwwarXLoqiuGXLFjEmJkZs06aN6OHhITo7O4shISHiyJEjxeXLlxv9gGrTpk16k9PevXvNXquwsFAvbFAdv8BE0fo3IVeuXBHfeOMNsWfPnqKfn5+oUChEPz8/sVevXuKbb74pXr161WwfSUlJ4rfffivGxMSI3bt3F319fUUnJyfR2dlZbNKkidivXz/xnXfeEa9cuWK0j7y8PHHTpk3iiy++KA4YMEBs3ry56OrqKsrlctHHx0eMiIgQn3rqKXH79u3W/DiIyEE5+hx47tw5cdGiReLEiRPFiIgIsVGjRqJCoRBdXFzEoKAgcfDgweKCBQvEW7duWfx6i4qKxBUrVoiPPPKI2KZNG9Hb21uUy+Wil5eX2L59e3H06NHiZ599JsbHx1s8xunTp4thYWGiq6ur6OPjI3bp0kV8/fXXxWvXromiWHYzX3kurw62LGS19T1AObVaLe7fv1+cN2+e+MADD4ht27YVPTw8RJlMJnp4eIht2rQRx44dK65cudJkGNMecygRkSg6/txY2eHDh8X//Oc/YmRkpOjn5yfK5XLRw8NDbNeunThhwgRxxYoVYl5enuS55sZfXFwsbt++XXzjjTfEIUOGiK1atRLd3d1FuVwuenp6iu3btxcfffRRccOGDRZ/6GYrawKEoiiKGo1GXLt2rTh+/HgxNDRUdHNzE93c3MTQ0FDx4YcfFtevXy9qtVqTfdjr9VfH+xkiImMa0jx4/fp18ZVXXhE7duwoenh46H43P/fcc+K5c+dEUbT8D9P1PUBY7p9//hGfeeYZsX379qK3t7eoVCrFZs2aiYMHDxYXLVpk9D1BOa1WKx49elT8+OOPxTFjxogdOnQQvby8RLlcLrq5uYktWrQQH3jgAfHrr78Wb9++bbSfmzdviitXrhSnTp0qRkVFiQEBAaJSqRSVSqXo7+8v9unTR3z55ZfFs2fPWvMjISIz7LXQQootv/uSkpLEefPmif379xeDgoJEFxcX3e+Bu+66S3z66afFtWvXmvx9Uu7PP/8Ux44dK7Zs2VJ0c3PTe621sXBHFMv+lvPVV1+JDzzwgO7vOEqlUmzSpIl43333ia+99prevFmuqr9r09LSRJlMJgIQR4wYYfHrq62FO927d9c9N378eIvHW9mff/6p60cQBDExMVHy+tb+e5eSlZUlbt68WXzzzTfFwYMHix07dhSbNm0qKpVK0c3NTQwKChIHDhwovvXWWyY3pTGluLhY/P7778WhQ4eKISEholKpFAMCAsSoqCjx448/FtPS0qzqr+IGbeY2prEkQKhQKMTGjRuLXbp0EZ988knx999/F9VqtUVjyc3N1W3IExYWZvbemxwT56QynJOkcU6yXH2ckyyl0WjEXbt2iW+++abYt29fsWXLlqK7u7vo5uYmtmrVShwxYoS4fPlyqxb9Xrt2Tfzpp5/EWbNmiffee6/Ytm1b0dfXV1QoFKKXl5fYqlUrcfTo0eKiRYusWnQrimXrnPz9/UUAYkhIiNnPw83NtUBZCCQ4OFgcMmSI+N5774kqlcri8bz99tu6flatWmXVa6H6i/NrGc6v0ji/Wq4+zq+23vPVZIAwMTFRF26Pjo6u0nWp7uOcVIZzkjTOSZarj3OSpRrSPV9NBghjYmJ0bfft21el60oRRPHfWsJEZDetWrVCUlISgoKCkJycXNvDISIiqjENeQ58/fXX8f777wMAPvzwQ7z88su1PCIiIqoLGvLcSERExHmQiIjIet9//z1iYmIAAN9++y2mTJlSuwOiOiE3NxctW7ZERkYGIiIicOLEiVoby9dff41nn30WAP8bJXJ0nJOooXn//ffx+uuvAwA2bNiABx98sFbGUVpailatWiE5ORmhoaG4ePEinJycamUsRGR/nF9JSl265zPm5ZdfxscffwwA2LZtGwYNGlTLIyKiquKcRA1NXbnnMyYjIwPNmzdHQUEBevfujQMHDtj9GjK790jUwJ05cwZJSUkAgBEjRtTyaIiIiGpOQ54DRVHE+vXrdd/37NmzFkdDRER1RUOeG4mIiDgPEhER2eb3338HAAiCwDmUdDw9PfHSSy8BAE6ePImtW7fWyjg0Gg0WLFgAAGjdujUmT55cK+MgoprBOYkampkzZ8Lf3x8A8NFHH9XaOFavXq3biOmtt95ieJDIwXB+JSl15Z7PmNu3b2Px4sUAgL59+zI8SOQgOCdRQ1NX7vmM+fzzz1FQUAAAeO+996rlGgwQEtlZ+WQKACNHjqzFkRAREdWshjwHfvbZZ0hISAAABAUFoW/fvrU8IiIiqgsa8txIRETEeZCIiMh6JSUl2LZtGwCgR48eCAwMrOURUV3y3HPPITQ0FADwzjvv1MoYVq9ejcTERABli2wUCkWtjIOIqh/nJGqIPDw8dHNsbGws/vrrrxofg1qtxgcffAAAiIiIwGOPPVbjYyCi6sP5lUypC/d8xixcuBA5OTmQyWSYP39+bQ+HiOyAcxI1RHXhns+Y7OxsfP755wDKNuft169ftVxHEEVRrJaeiYiIiIjquZ9//hmxsbF4+umn0bZtW4Pnc3Jy8PHHH+P9999H+dvqTz/9FM8//3xND5WIiIiIiIiIiIiIHNxvv/2Ghx56CADwyy+/YPTo0TV27eLiYoSHh+Pq1asYOHAgtm/fXmPXJiIiqilarRY9e/bEsWPHEBERgWPHjkEmq7kaDd988w2mTZsGANi3bx/uueeeGrs2ERHVvtq85zMmLS0NrVu3Rm5uLp566iksWbKktodERERks9q+5zPm1VdfxYcffghnZ2ecPXsWrVu3rpbrMEBIRERERGTEihUrMHnyZABAmzZt0KVLF/j5+aG0tBRXr15FbGysrmQ4AAwYMADbt2+vEzcURERERERERERERERERERERERERERERIraHgARNRyZmZmYM2dOlfuZNWsWwsLC7DAiIiIiy126dAmXLl2SfE4QBEyaNAlLliwxCA9u3rwZmzdvrtK1fX19MXfu3Cr1QUREZG8//PADYmNjq9RHWFgYZs2aZacRERERVa9Dhw5h1apVVe7niy++sMNoiIiIiIiIiIiIiIiIiIiILMMKhERUY65cuYKWLVtWuZ9du3ahX79+VR8QERGRGSUlJdi+fTu2bt2KY8eO4datW0hPT0dBQQG8vb3RvHlz3HfffXj88cfRtWtXyT7efvvtKof/WrRogStXrlSpDyIiInuLiYnB999/X6U++vbti927d9tnQERERNWsYpX6quCfZYiIiIiIiIiIiIiIiIiIqCaxAiERERERkRFKpRL3338/7r///toeChEREREREREREREREREREREREREREZHVWIGQiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjIAclqewBERERERERERERERERERERERERERERERERERERkfwwQEhEREREREREREREREREREREREREREREREREROSAGCImIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiBwQA4REREREREREREREREREREREREREREREREREREQOiAFCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiB8QAIRERERERERERERERERERERERERERERERERERkQNS1PYA6I6ioiKcPn0aAODv7w+Fgv96iIgAQK1WIy0tDQDQuXNnuLi41PKIyJ44/xERSeP85/g4BxIRSeMc6Ng4/xERSeP859g4/xERSeP859g4/xERSeP85/g4BxIRSeMc6Ng4/xERSatL8x9/M9chp0+fRq9evWp7GEREddrhw4fRs2fP2h4G2RHnPyIi8zj/OSbOgURE5nEOdDyc/4iIzOP853g4/xERmcf5z/Fw/iMiMo/zn2PiHEhEZB7nQMfD+Y+IyLzanv9ktXZlIiIiIiIiavDi4uLw5ZdfIiYmBj179kRoaCg8PDzg7OyMJk2aoF+/fpg7dy6uXr1ql+vFxMRAEASLv3bv3m2X6xIRERERERERERERERERERERERER1QZWIKxD/P39dY8PHz6MwMDAWhwNEVHdkZKSotuZpOLvSnIMnP+IiKQ1lPmvf//+yM/Pl3wuNTUVqamp2LNnDz744AO89dZbePXVV2t4hNWHcyARkbSGMgc2VJz/iIikcf5zbJz/iIikcf5zbJz/iIikcf5zfJwDiYikcQ50bJz/iIik1aX5jwHCOkShuPOvIzAwEMHBwbU4GiKiuqni70pyDJz/iIjMc/T5LyAgAL169UJERARatmwJb29vlJaW4sqVK/jzzz9x4MABFBcX47XXXkNpaSnmzJljl+t+8803CAgIMNmmU6dOdrmWFM6BRETmOfoc2BBx/iMiMo/zn+Ph/EdEZB7nP8fD+Y+IyDzOf46JcyARkXmcAx0P5z8iIvNqe/7j7EtERERERES1JjY2Fh07doQgCJLPv/rqq1i5ciViYmIgiiLmzZuHp556Cs2aNavytQcPHozQ0NAq90NERFQuNzcX27Ztw65du3Ds2DEkJCQgOzsbrq6uaNasGXr16oWJEydiyJAhRue+yi5duoRvvvkGW7ZsgUqlgkajQVBQEAYOHIgpU6YgMjLS4vEVFxdj+fLlWL9+Pc6fP4/MzEz4+/sjIiICjzzyCCZOnAiZTGbjqyciIiIiIiIiIiIiIiIiIiKiuoirQYiIiIiIiKjWdOrUyWyA4vHHH8eIESMAAGq1Glu3bq2JoREREVnl008/RUBAAMaOHYsvv/wSBw8eRHp6OtRqNXJzcxEfH49Vq1Zh2LBh6Nu3L65du2a2z2+//RZdunTBggULcPbsWeTk5CA/Px8XL17EV199hR49euCdd96xaHwXLlxAt27d8Oyzz2LXrl24efMmSkpKcP36dWzevBmPPfYY+vbti1u3blX1R0FEREREREREREREREREREREdQgrEBIREREREVGd17FjR/z+++8AgJs3b9byaIiIiAxdvHgRRUVFAKCrENi9e3cEBASgqKgIsbGx+OGHH5CXl4d9+/ahX79+iI2NRUBAgGR/P/zwA6ZOnQoAkMlkmDBhAqKjo6FQKHDgwAF8//33KC4uxltvvQVnZ2e8/PLLRseWkpKCIUOG6EKLXbp0wRNPPIFmzZohMTERy5YtQ2JiIvbv34/7778fe/bsgbu7u51/QkRERERERERERERERERERERUGxggJCIiIiIiojrv0qVLusdNmzatxZEQERFJEwQBgwcPxgsvvIDo6GjIZDK955944gm88sorGDJkCOLj45GUlIRXXnkFy5cvN+grLS0N06dPB1AWHtywYQNGjhype/7xxx/H5MmTER0djYKCArzxxht48MEHER4eLjm22bNn68KDEyZMwKpVq6BQ3PloeObMmRgxYgT27NmDo0eP4sMPP8S8efOq/DMhIiIiIiIiIiIiIiIiIiIiotonM9+EiIiIqktMTAwEQdB9vf322xadd+nSJbz44ovo1KkTvL294eHhgfDwcEyfPh0nTpywagzFxcX4+uuvMWDAAAQGBsLZ2RnBwcG4//778cMPP0Cr1Vr/woiIiOzo999/x4YNGwAALi4uuP/+++3S79NPP40WLVrAxcUF3t7eaNu2LR577DFs3LgRoija5RpERNRwvPfee/jrr78waNAgg/BguRYtWmDt2rW679euXYuCggKDdgsWLEBOTg4AYPr06XrhwXJRUVG6kJ9arcbcuXMlr3nu3DndNQMDA7FkyRK98CAAeHh4YPXq1XBxcQEAfPrpp8jOzjbziomIiIiIiIiIiIiIiIiIiIioPmAFQiIiolqyZcsWfP/991af9+233+I///kPCgsL9Y5fvHgRFy9exDfffIM5c+Zgzpw5Zvu6cOECxowZg3Pnzukdv379Oq5fv47Nmzfjm2++wc8//4wmTZpYPVYiIiJr7N27F5mZmQCAkpISqFQqbNu2Ddu2bQMAKBQKLF682G5z0vbt23WPi4uLkZOTg4SEBPzwww+IjIzETz/9ZLSSkyWSk5NNPp+SkmJz30REVPc0btzYonYREREIDw9HfHw8CgoKcOnSJXTp0kWvTcWQ4fPPP2+0rylTpmDOnDnIz8/Hpk2bUFhYCFdXV4O+yoPxTz/9NDw8PCT7CgoKwvjx47Fy5UoUFBRg48aNeOKJJyx6TURERERERERERERERERERERUdzFASEREVAtycnIwdepUAIC7uzvy8/MtOu+HH37QnSeTyTBhwgRER0dDoVDgwIED+P7771FcXIy33noLzs7OePnll432lZKSgiFDhuDatWsAgC5duuCJJ55As2bNkJiYiGXLliExMRH79+/H/fffjz179sDd3b2Kr5yIiMi4l156CYcOHTI4LggC+vbti7lz5+K+++6r8nXc3d0RHR2NXr16ITQ0FEqlErdu3cLevXvx22+/obS0FCdOnMDdd9+NAwcOoH379jZdJyQkpMpjJSIix+Tl5aV7XHlzmHPnzuHq1asAgPbt26Nly5ZG+/H09MS9996LrVu3Ij8/H3v27MHQoUP12mzZskX3ePjw4SbHNXz4cKxcuVJ3HgOERERERERERERERERERERERPUfA4RERES14MUXX4RKpUJISAjGjRuHTz/91Ow5aWlpmD59OoCy8OCGDRswcuRI3fOPP/44Jk+ejOjoaBQUFOCNN97Agw8+aLRy0uzZs3XhwQkTJmDVqlVQKO68NZg5cyZGjBiBPXv24OjRo/jwww8xb968qrxsIiIimwQFBWHQoEEICwurcl8zZszAF198IVl9acaMGbh06RLGjBmDU6dOISsrC+PGjcOpU6cgk8mqfG0iIiKgrMruxYsXdd+3aNFC7/nTp0/rHvfs2dNsfz179sTWrVt151YMEIqiiLNnzwIA5HI5unbtarYvqXEQERERERERERERERERERERUf3FFZBEREQ1bOfOnViyZAkA4KuvvoKnp6dF5y1YsAA5OTkAgOnTp+uFB8tFRUXpQn5qtRpz586V7OvcuXNYu3YtACAwMBBLlizRCw8CgIeHB1avXg0XFxcAwKeffors7GyLxkpERGSL2NhYiKIIURSRl5eHEydO4J133kFubi5ef/11dO7cGX///XeVrtGjRw/J8GC5Nm3aYNu2bfD39wcAnD17Fr/88otN11KpVCa/Dh8+bFO/RERUv/3444+4ffs2AKBbt25o2rSp3vPx8fG6x6aqD0q1qXguUDYXFRQUAACCg4Ph5ORksq+QkBDI5XIAQEJCAkRRNHv9ypKTk01+paSkWN0nEREREREREREREREREREREdmOAUIiIqIaVFBQgClTpkAURTz88MMYMWKExeeWB/4A4PnnnzfabsqUKXB3dwcAbNq0CYWFhZJ9lS8Effrpp40GKYKCgjB+/Hjd2Ddu3GjxeImIiKrC3d0dERERePPNN3H8+HE0a9YMGRkZuP/++6u9IlKTJk0wa9Ys3fd//PGHTf0EBweb/AoMDLTXkImIqJ5IS0vDyy+/rPv+jTfeMGhTceMWPz8/s336+vpKnmtLX05OTvDy8gIAlJaWIj8/3+w5lYWEhJj86tWrl9V9EhEREREREREREREREREREZHtGCAkIiKqQa+++ioSExPRuHFjLFy40OLzzp07h6tXrwIA2rdvb7IKhaenJ+69914AQH5+Pvbs2WPQZsuWLbrHw4cPN3ntis9XPI+IiKimtGzZEh9++CEAoKSkBO+99161X7N///66x+fPn6/26xERkeMrKSnBmDFjkJqaCgB48MEH8dBDDxm0y8vL0z0urwhviqurq+5xbm5ulfoy1x8REZG9bNq0CePGjUNoaChcXFwQEBCA3r17Y/78+cjJybH79a5cuYI333wT99xzD/z8/ODk5AQPDw+0atUKo0ePxg8//IDS0lK7X5eIiIiIiIiIiIiIiIioLlDU9gDIPlSZBYhNzEBukRqeLgpEtfJFSGO32h4WERFV8M8//+CLL74AACxYsABNmjSx+NyKlZZ69uxptn3Pnj2xdetW3blDhw7VPSeKIs6ePQsAkMvl6Nq1q9m+pMZRF3D+IyJqOIYNG6Z7vHv37mq/nr+/v+5x5WpOtY3zHxFR/aPVavHkk09i3759AIDWrVtj+fLltTyq6qFSqUw+n5KSYnMVQs6BRET1X15eHiZNmoRNmzbpHU9LS0NaWhoOHjyIzz//HOvWrUNUVJRdrvnpp5/itddeQ3Fxsd5xtVqNpKQkJCUlYcOGDXj33Xfx888/o1OnTna5LhFRdeH7YiIiorqBczIREVU3zjVERNRQcQ6sHgwQ1nMnVdlYuCMBu+JTIYp3jgsC0D88ALOiwxAR4lNr4yMiojJFRUV48sknodVqER0djcmTJ1t1fnx8vO6xqeqDUm0qnguULeYsKCgAAAQHB8PJyclkXyEhIZDL5dBoNEhISIAoihAEwZrh2x3nPyKihsfT01P3OCsrq9qvl56ernvs4+NT7dezBOc/IqL6SRRFTJs2DatXrwYANG/eHH///TcaNWok2d7Dw0P3uKioyGz/hYWFuscV50tb+jLXnyWCg4OtPscczoFERI5Bo9Fg3Lhxuo3PmjRpgilTpqBDhw7IzMzEmjVrcODAAahUKgwfPhwHDhxA+/btq3TNL774Av/973913/fu3RsjR45ESEgIcnJycPbsWaxYsQJ5eXmIj49H//79cfr0aTRt2rRK1yUiqg519X0xF/MQEVFDU1fnZCIichyca4iIqKHiHFi9GCCsx7aeScHMNSdQotEaPCeKwM4LqdifkI5Fj0RiaKfAWhghERGVmzNnDuLj4+Hq6opvvvnG6vMrVj7y8/Mz297X11fyXFv6cnJygpeXF7KyslBaWor8/Hy9RajmJCcnm3w+JSXF4r4Azn9ERA1VQkKC7nHF6oDVZdeuXbrH4eHh1X49czj/ERHVT6Io4tlnn8WSJUsAlIXrdu7cidDQUKPnVAyuVwy0G5ORkSF5ri19qdVq5OTkACi7F3R3dzd7TnXjHEhE5DiWLl2qCw926NABO3fuRJMmTXTPT58+HS+88AI++eQTZGVlYerUqdi7d6/N1yssLMRrr72m+37JkiV46qmnDNrNmTMH0dHROH36NNLT0/Hxxx/j008/tfm6RETVoS6+L+ZiHiIiaojq4pxMRESOhXMNERE1VHV5DnSUTdQYIKynTqqyjf7PUVGJRouZa05g/TRXfjhPRFRL4uLidAtO5s6di9atW1vdR15enu6xi4uL2faurq66x7m5uVXqq7y/8mpPubm5VgUIQ0JCLG5rDuc/IqKGa/HixbrHffr0qdZrpaWlYeHChbrvR4wYUa3XM4fzHxFR/SSKIqZPn66bw4KCgrBr1y6z94QVg+tJSUlmr1OxTeXQe0hICNzc3FBQUIDk5GSUlpaarEJ/7do1aDQaAEBYWFidqD7POZCIyDFoNBrMnTtX9/2qVav0woPlPvroI+zYsQMnTpzAvn37sG3bNgwePNimax44cED32WjPnj0lw4NA2SY1H3zwge7eryqhRSKi6lBX3hdXXCRzJSMfaw5fQ6lGNGhX24t5iIjIMdWFxZp1ZU4mIiLHxbmGiIgaqro6BzraJmqy2h4A2WbhjgSz/3OUK9FosWhHgvmGRERkdyUlJXjyySeh0WjQrVs3zJ49u7aHVK9x/iMiciyLFy/Grl27IIqGC43KaTQafPjhh/jqq690x5599lmDdrt374YgCBAEwWhVp++//x5bt241eb2kpCQMGTIEqampAMqqYowdO9bCV1Q9OP8REdU/5eHBr7/+GgDQrFkz7Nq1C23atDF7bufOnXWP4+LizLav2KZTp056zwmCgI4dOwIom1OPHz9uc1+1gXMgEZHj2Lt3L1JSUgAAffv2Rbdu3STbyeVyzJw5U/f9mjVrbL5m+X0dUBaMN6Xi8xU3YCMiqgmqzAKsP6LC8v1JWH9EBVVmgd7zNf2+uPJ4tp29iSdXxOG++bvw4s+n8M4f57Dy4FXJ8GDlscxccwInVdlVGg8RETVsJ1XZBvPQiz+fwn3zd+HJFXF2m2fMzccAP6siIqLqx7mm9m3atAnjxo1DaGgoXFxcEBAQgN69e2P+/PnIycmx23Vyc3Pxyy+/YMaMGejduzf8/f3h5OQELy8vtGvXDo8//rjZ9S3lVqxYoVsvY8nX22+/bbfXQURkL3VxDtx6JgXjFh/Ezgv64UHgziZq4xYfxNYzKdU+FnthBcJ6SJVZgF3xqeYbVrAzPhXJWQUIblT/ymQSEdVn7777Ls6cOQO5XI4lS5ZALpfb1E/Fin9FRUVm2xcWFuoee3p6Vqkvc/2Zo1KpTD6fkpKCXr16me+H8x8RkcOJjY3FM888g5CQEAwaNAidO3dGQEAAlEolsrOzcebMGWzcuBFXrlzRnfPqq6+ib9++Nl3v+PHjWLhwIZo1a4bBgwejS5cuaNKkCZycnJCamop9+/Zhw4YNKCkpAQA0atQI69ats3n+tgfOf0RE9U/l8GBgYCB27dplNrhQrkOHDmjevDmuXbuG8+fP48qVK0bD8Xl5edi3bx8AwM3NTXKOHDp0qC4YuGXLFpP3X5s3b9Y9Hj58uEXjrS6cA4mIHMuWLVt0j83NMcOGDZM8z1oBAQG6xxcvXjTZtuLz5eF7IqLqZsnu1Y3dlTX2vtjYeKqifDHPspie9umQiIgalK1nUoxWoLBXxVtLq0nwsyoiIqpunGtqV15eHiZNmoRNmzbpHU9LS0NaWhoOHjyIzz//HOvWrUNUVFSVrvXpp5/i9ddfl1y7mZubi/j4eMTHx2PVqlW499578cMPP6B58+ZVuiYRUV1WF+fAuloRsaoYIKyHYhMzrP7AXhSB2MRMjO3ON4lERDXl5MmT+PDDDwEAs2fPNrqrtiV8fHx0j9PT0822z8jIkDzXlr7UarVu9xwnJye4u7ubPaei4OBgq9obw/mPiMhxqVQqLF++3GQbb29vfPDBB3jmmWeqfL0bN25gxYoVJtv07NkTK1euRLt27ap8varg/EdEVP/MmDFDFx5s2rQpdu3ahbZt21rVx8MPP4z58+cDKPsj4qJFiyTbffvtt8jPzwcAjBw5Em5uhr/7H374YcybNw8A8M033+CFF16QvK+7fv061q1bBwBwdXXFqFGjrBqzvXEOJCJyLKdPn9Y97tnTdIikadOmCAkJgUqlwq1bt5CWlgZ/f3+rr3nPPffAz88P6enpOHLkCJYuXYqnnnrKoF1aWhpee+01AIBMJsPs2bOtvhYRkbUsDURM6BVSI++LTY2nqriglYiIbFETizWtCSjmFqn5WRUREVUr/l2k9mg0GowbNw5bt24FADRp0gRTpkxBhw4dkJmZiTVr1uDAgQNQqVQYPnw4Dhw4gPbt29t8vYsXL+rCg0FBQRg4cCC6d++OgIAAFBUVITY2Fj/88INuI9F+/fohNjZWb8M0Y5577jkMGDDAZJvaXgdDRFRZXZwDbamIWB82UWOAsB7KLVLbeF6pnUdCRESmrFixAqWlpZDJZHBycsK7774r2W7v3r16j8vbhYeHY9y4cbrH5ZKSksxeu2KbiucCQEhICNzc3FBQUIDk5GSUlpbCycnJaF/Xrl2DRqMBAISFhUEQBLPXrw6c/4iIHM+iRYswatQo7N27F8ePH8fly5eRnp6O0tJSeHh4oEmTJujSpQuGDBmCcePGwdvbu0rXe/HFF9GjRw8cOnQIx44dw82bN5GRkYH8/Hx4eXkhODgYd911F8aNG4eBAwfW2pxXEec/IqL65bnnnsNXX30FoCz8sHv3boN7Mku88MILWLx4MXJzc/Hll19i4MCBGDlypF6bQ4cO4c033wQAKBQKvPXWW5J9dezYEePHj8e6deuQkpKCKVOmYOXKlVAo7nw0XL6ravkfK2fPnm2wGU1N4xxIRORY4uPjdY9btmxptn3Lli2hUql059oSIHRxccHixYsxYcIEqNVqTJkyBStWrMDIkSMREhKCnJwcnDlzBt9//z1yc3Ph4eGBpUuXok+fPlZfKzk52eTzKSkpVvdJRI7LmkDE6thrNl3DkvfFqswCxCZm4MLNXKz45wo0WjuVHayEC1qJiMgW1b1Y05r5ePrq4+gZ2sjiviviZ1VERGSp+vJ3kfJ7ydwiNTxdFIhq5YuQxvX7fm/p0qW68GCHDh2wc+dONGnSRPf89OnT8cILL+CTTz5BVlYWpk6dqrfm01qCIGDw4MF44YUXEB0dDZlMpvf8E088gVdeeQVDhgxBfHw8kpKS8Morr5jdmBsAunXrhgcffNDmsRERVQdzc0ddmwPrYkVEe2GAsB7ydLHtX5uni/FwCBER2Z/473YIWq0W77//vkXn7Nq1C7t27QIAjBo1Shcg7Ny5s65NXFyc2X4qtunUqZPec4IgoGPHjoiLi4NGo8Hx48fRq1cvm/qqSZz/iIgcj5eXFx566CE89NBDVe6rX79+urnXmKCgIDz66KN49NFHq3y9msL5j4io/njjjTfwxRdfACi775o1axbOnz+P8+fPmzyvW7duaN68ud6xgIAAfP7554iJiYFWq8VDDz2ECRMmYNCgQZDL5Thw4AC+//57XeBv7ty5JncL/fTTT/HPP/8gOTkZa9aswdmzZxETE4NmzZohMTERS5cuRWJiIgAgMjISr7zySlV+FHbBOZCIyLFkZ2frHvv5+Zlt7+vrK3mutcaMGYO///4b06dPx9mzZ3HgwAEcOHBAr42TkxNef/11TJ06FSEhITZdx9bziKhhsiYQobF26+1/mXpffFKVjYU7ErArPtXqnb0r6i7EI0p2Hp5CITxQgI/VDyMHHpJtGZ4gIiJr1MRiTWvn49ikTKvGU46fVRER1S+1GY6r638XMXYvKQhA//AAzIoOs7oacF2g0Wgwd+5c3ferVq3SCw+W++ijj7Bjxw6cOHEC+/btw7Zt2zB48GCbrvnee++hcePGJtu0aNECa9euRWRkJABg7dq1+OKLL+DmVreDKUREFVk6d9g6B164mQtVZoHd5+q6WBHRXhggrIeiWvlCEGDVf5SCAES1Mv1mg4iI6q4OHTqgefPmuHbtGs6fP48rV64gNDRUsm156XoAcHNzQ9++fQ3aDB06VBcM3LJli8kA4ebNm3WPhw8fXoVXUTWc/4iIqCHi/EdEVH/s379f91gURbz66qsWnffdd98hJibG4PgTTzyBgoICzJ49G0VFRfjxxx/x448/6rWRy+V4/fXX8dprr5m8RlBQEP766y+MGTMGFy5cwKlTpzB79myDdr1798Yvv/wCDw/pRb81iXMgEZFjycvL0z12cXEx297V1VX3ODc3t0rXvu+++/DFF19g9uzZOH78uMHzpaWl+PLLL5Gfn4/3339f79pERPZmSyDCWqbeF289k2JQbUkBNQKFDAQL6QgRUhEspGG1eiBuwfR7696ys/iv08+675dphiNHlL6XYHiCiIisUd2LNWtiPgb4WRURUX1SF8JxdfnvIlL3kuVEEdh5IRX7E9Kx6JFIDO0UWO3jsae9e/ciJSUFANC3b19069ZNsp1cLsfMmTPx5JNPAgDWrFljc4DQXHiwXEREBMLDwxEfH4+CggJcunQJXbp0semaREQ1zZq5w5Y5EADWxqmw7ojK5Fxty+YAda0ioj3JzDehuiaksRv6hwdYdc6A8IA6Xw6TiMjR/O9//4Moima/3nrrLd05b731lu74b7/9ptffww8/rHv86aefGr3ut99+i/z8fADAyJEjJXedqdjXN998o2tf2fXr17Fu3ToAZYt2Ro0aZf6FVxPOf0RE1BBx/iMiatieeeYZXdivQ4cO8PT0hLu7O8LCwjBt2jTExcXp7YpqSocOHXD8+HF88cUX6Nu3L5o0aQKlUolmzZph6NChWLlyJfbt24emTZtW86uyDOdAIiKyh/T0dERHR6N///64cuUKPvvsM1y+fBklJSXIzs7Gjh07MHz4cGRnZ+N///sf+vXrh4yMDKuvo1KpTH4dPny4Gl4dEdVHtgQirFX5fbEqswDrj6gw749zmP7jcb1FO0NkhxHv/AT2OT+PNcr38LHTEsxU/IZwmcrsdfKgH7j2QKFkO4YniIjIWtW9WLMm5mOAn1UREdUXW8+kYNzig9h5wbBKe3nAYdzig9h6JqVax1FX/y5yUpVtNABSUYlGi5lrTuCkKrtax2NvW7Zs0T02V1xh2LBhkudVJy8vL93jwkLp+24iorrG2rkjM7/E6jmwXPlcPfbrfzBn4xks35+E9UdU2Hb2Jp5cEYf75u/Ciz+fwjt/nMOLP5/CffN34ckVcSbnq7peFbgqWIGwnpoVHYb9Celm/6cCAKVchpnRYTUwKiIiqk4vvPACFi9ejNzcXHz55ZcYOHAgRo4cqdfm0KFDePPNNwEACoVCL5xYUceOHTF+/HisW7cOKSkpmDJlClauXAmF4s5bg7y8PEyaNAlFRUUAgNmzZ8PHx6d6XpyFOP8REVFDxPmPiKh+2L17d7X0GxYWhk8++QSffPJJlftycXHB9OnTMX36dDuMrPpxDiQichweHh7IysoCABQVFZmtdltxMYynp6dN1ywoKMC9996LCxcuoFGjRjh06BDCwu7MFd7e3hgwYAAGDBiAGTNm4Msvv8Thw4fx3HPPGVT9NSc4ONimMRJRw1G+0/Xf525V2zUUUKOFLBWDFck4se4viI1a4fPrYQYVNCq6JTaGXDB8MkRIM3u9XOgvUvUQCgGJ6zA8QURE1qrOxZqqzALsS0g32UaAFiFCGgKQhSNiO5vGws+qiIjqB2sDDuunuVZrJUJr/i6ikAk1Mtcs3JFg0XiAsp/Toh0JWBbTs5pHZT+nT5/WPe7Z0/S4mzZtipCQEKhUKty6dQtpaWnw9/evtrGVlJTg4sWLuu9btGhh9pyvvvoKH330EVQqFbRaLfz8/BAZGYlhw4bhiSeekCxGQURkb7bMHbOiw7A7PhVaGzd7KdWKWHnwqtl2llTOrctVgauKAcJ6KiLEB4seiTT7xlUpl2HRI5HVXjqbiIiqX0BAAD7//HPExMRAq9XioYcewoQJEzBo0CDI5XIcOHAA33//vS7wN3fuXLRrZ/zD7E8//RT//PMPkpOTsWbNGpw9exYxMTFo1qwZEhMTsXTpUiQmJgIAIiMj8corr9TI6zSF8x8RETVEnP+IiKih4hxIROQ4fHx8dAHC9PR0swHCilUAbd3U7KuvvsKFCxcAlG3OVjE8WNlHH32E1atXIzs7G2vXrsWnn35aZ6ryElHtKg/+5Rap4emiQFQrX4Q0tnyx3UlVNhbuSDAZ4qs6EX8qX0OYkAyloAEulR39XROFnaUzTZ55RWwieTzYkgChqF+B0BMFBm0YniAiIltUx2JNc3PyEFkc+spOooPsCtoK1+EmFCNd9EKP4sVWj5+fVRER1R91LRxX/neRaT8cM9u2bRPPap1rVJkF2Hw6BTsvpFp13s74VCRnFdSbjWTi4+N1j1u2bGm2fcuWLaFSqXTnVmeA8Mcff8Tt27cBAN26dbPo88q4uDi971UqFVQqFX7//Xe89dZbWL58OUaMGGHTeJKTk00+n5JSvVU6iah+UGUWYFe89XPH3FEdEdLYDVczDD9jrA6mNgcorwpszRxYXzZRY4CwHhvaKRDrp7ni8eWHcbuw1OD56HYBmBkdxg8jiIgcyBNPPIGCggLMnj0bRUVF+PHHHw12w5bL5Xj99dfx2muvmewrKCgIf/31F8aMGYMLFy7g1KlTmD17tkG73r1745dffjG7qKemlM9/z605hmuZhQbPD2gXgFmc/4iIyMGUz3/vbT6Pw0mZBs+3D/TEh6O7cP4jIiKHUz4HjvrygOTz/AyUiKh+CA8PR1JSEgAgKSkJoaGhJtuXty0/1xZ//PGH7vHgwYNNtnV3d0fv3r2xefNmaLVaxMXF4YEHHrDpukTkGIyFDAQB6B9u2d8htp5JsaiShSluKEIBXMy0EuAEdVl4sIKWwk2z/WfDA7dFN3gL+gtzLKlAmIc7AcISUQ4X6K9ZYHiCiIhsZe/FmpbMyQ/J92OoXH/BvZ+QA1/cRga8LRqDIJSNg59VERHVD7YGHKo7HNcpyLJ551xKDhJu5SKsiaddr1/VjXBEEYhNzMTY7nU/RAEA2dnZusd+fn5m2/v6+kqea29paWl4+eWXdd+/8cYbJtvL5XLcfffduPfee9G2bVt4eHggOzsbR48exbp165CZmYm0tDSMHDkSq1evxiOPPGL1mEJCQqw+h4gantjEDKvnD1EEdpxPxbXMmgkPljO1OcCs6DDsupAKS15KfdpEjQHCei4ixAftAz0Rm6i/gLSNv0e9KgFNRESWe+aZZzBw4EAsXrwYW7du1ZWbb9asGaKjo/H000+ja9euFvXVoUMHHD9+HMuWLcP69etx4cIFZGVlwc/PD126dMHEiRMxadIkyGSyan5V1okI8cGwzoH4Zk+iwXOLH+0OpaJujZeIiMgeIkJ88MHozoj+ZI/Bc5PuasE/RhMRkcOKCPGBUiFDiVp/kdeDXZvhfw9bdv9LRES1q3Pnzti6dSuAsl2w+/fvb7TtrVu3dLt4BwQE2LyL940bN3SPvb3NL7yqWOkwLy/PpmsSUc2raoVAKaZCBqII7LyQiv0J6Vj0SCSGdgqU7OOkKtvq8KAALdoIN9BddhHdhYvoJktAsJCGTsXLUVppaUeHQC8E+bhg+/myxa6XxCC0xXW9Ni2FFAAiAMHkVa+KTdBFSNI7GiBkmR3vEW04uhUtRh5cUQKnOz0yPEFERHYwvnuwVQHCFr5uWH9EZfBewNI5+bC2nUGAEADaypJxUGv6fmJoxyYY2KEpolo1rhcVJ4iIqIytAYfqDsfFXTHcUNiYl385hUd6NbfLvTBgn41wACC3yLAoTl1V8XNAFxdzG/gArq53NtPJzc2tljGVlJRgzJgxSE0tey/04IMP4qGHHjLa/p577sGVK1cQHBxs8NxTTz2Fjz/+GFOmTMHatWshiiKefPJJ9OnTB82bN6+W8RNRw5ZbpLbpvNPXb9sUXDdHiVI0FTLhBDXk0EIBDeTQ4oboiwx4G90cwFUpR1NkoI3sOgSIuk9Y/9F21Pustr5tosYAoQNwkkuEJEz9DYCIiOqUt99+G2+//bZV54SFheGTTz7BJ598UuXru7i4YPr06Zg+fXqV+6pJCpn0ZKfRVsM7SCIiojrC2UhIvlhdtQ/wiYiI6jq5YHgP6Ookr4WREBGRLYYOHYr58+cDALZs2YKXXnrJaNvNmzfrHg8fPtzma3p63tl9XaVSISzM9O6vV69e1T2uuJM4EdVN9qgQaKxfSxZLlmi0mLnmBNZPc9W7Tnmg8du9iVYvuHxFsQZTFX8aHG8t3MAFUX9R34tDw7Hqnyu67y+JzQzOcxeK4Y9spKGRyev+qInGFk0ekkU/JIv+UIkBSIeX2fEWQ4liKOEkE/DEXc0R6ucOTxcnhieIiMguDiZmWNV++YErAO68F5jdxxedcvZC+/dPiNQOxGG0N3n+Ya105fN2wjUcREeT5w7s0BRjuxsu2CciotpnatMZWwMO1R2OO5wkvaGLUi4zuM88di0bx65lV/leGLBtIxxjPF2czDciSVqtFk8++ST27dsHAGjdujWWL19u8pw2bdqYfN7T0xOrV6/GrVu3sHv3bhQVFeGjjz7Cl19+adXYyjd9MyYlJQW9evWyqk8icjyeLrZF1FQWVR8UEYR0hMmuI0DIwiVtEI6JbU2e0UG4it+c5xgcf7X0/7BGE210c4CVB69ggPw43nPS/x3crWgxMuFVbzdRY4DQAUgtIK28GzcREZGjkRupiqipji0oiIiI6ghjVXZ5D0hERI5OahMZtYb3f0RE9UXfvn3RtGlT3Lx5E7t378axY8fQrVs3g3YajQaLFi3SfT9hwgSbr9m5c2ccO3YMALB69WoMGDDAaNtLly7h0KFDAACZTIYePXrYfF0iqn72qBBozMIdCRYvlizRaLFoRwKWxfQ0Gmi0xilta8njHYQregHCAE9ntPJzx66Labpjl7SGoQWtKCBESEOaaDpA+JPG+O9HU+rrIhkiIqr7bheWYv3RZKvPc0YJhgqHMeryPwhPOg0IGnQFMFjug8Nq0wHC82IL5Iku8BCK9I63FUyPQxCAqFaNrR4rERFVL0s2nbE14FDd4bjDSYYh+iAfV7Tyd8e+hHTJc6pyL1yVjXCk1Le50cPDA1lZZaHNoqIieHh4mGxfWFioe1xxAzN7EEUR06ZNw+rVqwEAzZs3x99//41GjUzf11tCLpfj3XffxT333AMA+OOPP6wOEEpVOCSihslYQF+VWYDM/BKr+xME4GpGvsFxb1cFbheqESLcwseKJegguwJv4U7QcJl6GI6pTQcIC6GUPC7HnTmv8uYAOUWl+PXYdUjVfn2uf2t4+jWrt5uoMUDoAKQqEJba4U0cERFRXWa0AiEXkBIRkQNzlktXWmKAkIiIHJ1cbngPyA1kiIjqD7lcjjlz5uDZZ58FADz++OPYuXMnAgIC9Nq98sorOHHiBACgT58+GDJkiGR/K1aswOTJkwGUhRN3795t0GbixIn4/vvvAQDfffcdevfujf/7v/8zaHfz5k2MHz8eanXZrvMjRoxA48b1Z5ETUUNT1QqBpqgyC7ArPtWq8eyMT8Xq2KuY+/s5yTEJ0KKLkIhcuCFRokpgRUe10pVSO8iu4tcKXWfklWDZ/iS9RbBnxFD8qrkHl7TNcFlshiQxEFfFJig2skDGGuVBwYd7hiCnSI3colJWGiQiomqjyizAR1vPo6BEY/Bcz9BG6BTkhe8OXJU4E3BDERY4fQMnQf/c/rITeBePmbyuBnIc04ahlSwFp7UtES+GIF4bgtNiS5PnDQgP4HxIRFTHWLrpzJwH2kMQYNUmMNUdjsvIK8blNMMARViABw5clg4PVmTNvbCtG+EEC2lwRyHiK2x0U1F9mxt9fHx0AcL09HSzAcKMjDsBTx8fH7uNQxRFPPvss1iyZAmAsrDezp07ERoaardr3H333XBxcUFRURGuXbuGgoICuLnVn39XRFT7TM0dvu5KZOaXwJa/4Ee19JWsQt+ntR+2nL2J26I77pafM3g+QJCu2ltRAZwljytw577xws1cqDILdCHI+X/Fo6BEA1FijcLkPi0BD3+z162rGCB0AAwQEhFRQyQ3EiBUazkHEhGR4zJagVBj+Id0IiIiRyK1iYxGywAhEVF9MmXKFGzYsAHbt2/H2bNnERERgSlTpqBDhw7IzMzEmjVrsH//fgBli2+++eabKl1v8ODBGDt2LH7++WeIooinnnoKq1atwqhRoxAcHIzCwkIcOXIEq1atQnZ2NgDA19cXn3zySVVfKhFVI1srBFoiNjHD6uqBogi8ufEMKr41dYIaUbJzGCw7gkHyo2gqZOEHdTTeUBuGmCtyahSE5AI/BAv6i0I7CvohCY0oYnXsNb1jiWIzzC591rrBmyAXBEzuE4p2gV4MChIRUY2wJMQw78FO+HhrvNE+suCFfdrOGCA/oXe8tSwFLYSbuCo2NTmGp0tnowjOkAmAJR87KeUyzIyW3gCAiIhqhzWbzrzz+3l0b94IR66aDx+Uq2o4zljFpnJxV6THknK7CKUWbqpvyb3w1tPJeO+nPfDXpmK4kImmsgz4CTlYrYlGshhg9DwAeES+A+2Fa3iy9CWD5+rj3BgeHo6kpCQAQFJSktnAXnnb8nPtQRRFTJ8+HYsXLwYABAUFYdeuXWjdurVd+i8nk8nQuHFj3LhxAwCQnZ3NACERWcxUQB8AMmyoPAgATnIBXYK9JQOEQzo1RZFai50XAJXWHyGyNL3nA4Rss/0Xii6Sx+UVAoRr41RYG6eyMARZv9coMEDoAKQChKw+QUREjs5YgJALSImIyJEZCxAWl/IekIiIHJtMMLwHVPP+j4ioXlEoFPjll18wceJE/PHHH7h58ybmzZtn0C44OBhr165Fx44dq3zNH374AV5eXli+fDkAYM+ePdizZ49k2/DwcPz0009o06ZNla9LRLYztZjS1gqByVkFFi3wzC1S2zTmim9LBWixy3m2QQhwoPwY3lRPhgjpz3YAwE3phGN5YQiWl517WRuIo9q2iNW2N2hbndW4lXIZFj0SiaGdAqvtGg3Zpk2bsGrVKsTFxeHmzZvw8vJCmzZt8NBDD2Hq1Knw8vKqluseP34cP/74I/7++28kJycjJycHfn5+CAwMRFRUFPr164eHHnoIcrm8Wq5PRGSKuYWoACAAOHIl0+x7gY2a3gYBQgAYIDuO7zTDTJ5b9G9liseiWmDNYZXJ8ZTPl5ZWOiYiopph7aYzAsqCC5aE86oSjjMWlBcEoH94AGZFhyEixAdxVzIlz794K9eq6+24kIpv917GsE6BegHF8rH8tXYx9jl9bnDeIW07swHCfNEV7rIig+P1dW7s3Lkztm7dCgCIi4tD//79jba9desWVCoVACAgIAD+/lWvQFUeHvz6668BAM2aNcOuXbuq5TNKrVarq7YI2LeCIhE5NksD+tboKCThKcVmCL5h2JwuXTH+njZ+CPV1x/6EdJwTWyAElQKEsL0CoRyGr6VyCNIRVyMwQOgAlArDxTOW7jRBRERUX0lVnwCq94/mREREtU0uE6CQCQaBCXt+QENERFQXSVYg5GegRET1jqenJ37//Xds3LgRK1euRFxcHFJTU+Hp6YnWrVtj9OjRmDp1Kry9ve1yPWdnZyxbtgzPPfccVqxYgQMHDiAxMRE5OTlQKpUICAhA9+7d8eCDD2L8+PFQKpV2uS4RWc+SxZQXb+XaVCEwNjETY7ubDxB6ulR9+YQIGQ5r2yFYvl/veFMhC12ERJwUjS8AvHgrFyuEIdio6Y1j2jBkoXqCZMYIQlk1jZn/Llwl+8rLy8OkSZOwadMmveNpaWlIS0vDwYMH8fnnn2PdunWIioqy23VzcnIwa9YsfP/99xAr/Q9048YN3LhxA0ePHsWXX36JrKwsLiAlohpnyUJUBdR4SL4fSzemQjQTatiu7YFCUQlX4c7Cz7PaFrgtuls0HkEAptzXCqO7BWPRjgTslHhvwvmSiKhusmXTmSPXsjDtvlb4ek+iyXZVCceZCsqLIrDzQir2J6Rj0SOROJxkGCB0V8qRX6IxOP5vD/BHNsJlyQgXVGgrJGOHtiu2aXvi/c0X8MGWC3oBRaAsZJmjaSSZIPAXbpt9PflwgTvuBAjr+9w4dOhQzJ8/HwCwZcsWvPSSYWXFcps3b9Y9Hj58eJWvXTk8GBgYiF27diEsrHqqOMbGxqKwsBBA2SZurD5IRJayJqBvigAtomXH8ZRiM6Jk5wEAWdmn8G72QKDSxmsdm3nB18MZvh7OWPRIJOLXhmIIjui1sagCIZR4u/RxFMMJasihEWVQQ45zYguz5+7WROIJ8WUAZWFCuUyG59NFdPGw6OXWSQwQOgClRAXCUi4eJSIiB2esAqGaC0iJiMjBKRUyqCv9gYBV6ImIyNHJ5RIBQm4gQ0RUb40aNQqjRo2y+fyYmBjExMRY3D4yMhL/+9//bL4eEVUvSxdTjurazKb+c4tKLWoX1coXggCrQ4qVbdbchdGVAoQAMEh+FCfV0gHC9k09cf5mLo6JbWt0a2u5IGByn1C0C/RCVKvGFlVqJOtpNBqMGzdOV9WiSZMmmDJlCjp06IDMzEysWbMGBw4cgEqlwvDhw3HgwAG0b29YedJamZmZGDJkCI4cKVtcFRQUhNGjRyMiIgLe3t7Izc1FQkICtm/fjqNHj1b5ekRE1iivOvzt3kSjC1GdoMZY+R48K9+EEFkalqiH4z31oyb7LYAL/tZ2Q7igwkZNH2zS3g2V2MTicQ0ID0BwIzcEN3LDspieUGUW4FBSJnKLSuHp4sT5koioDotNzLBp05mbOcUm27g6yfDT03fbFI6ztGJTiUaL59Ycl1z3FtLYDRduGlYgDBbS8LvydTQS8vSOF6qV2KbtCcAwoNixmTd2xaeimegrOQ5f5Jh9Tflwgdu/AcLXh7fDsM6B9Xpu7Nu3L5o2bYqbN29i9+7dOHbsGLp162bQTqPRYNGiRbrvJ0yYUOVrz5gxQxcebNq0KXbt2oW2bdtWuV8pWq0Wc+bM0X0/YsSIarkOETkeWwL6xryjWIHHFH/rHWuEXAwq3YU1iNY7fk+Yn+7x0E6BCMt7EGd2J2F/XjOc1bZAktgUqWIjlH2YKr2eHCjb8G2FZqhN402BL1K0FeZMLSDbfQ3LYiy/x6xrGCB0AE4SAUK1VoRWK0JmJFxBRERU3xkLEGq0XEBKRESOTamQoYABQiIiamAUMsPPQHn/R0RERFT/WbOY8tej1226hqeLk0XtQhq7oX94AHZe0F8Q44NcDJIfxVBZHDZr7sIv2vtM9rNP2xl5ogs8hCK94/fKTmMBHjZor5TL0KeNH85LLAg1Ry4TbH5fXF5BY2inQJvOJ8stXbpUFx7s0KEDdu7ciSZN7iw0mj59Ol544QV88sknyMrKwtSpU7F3794qX3fixIm68OB///tfvPvuu3BxcTFo9/777+PGjRvw8KjH26cTUb1hrOpwRQK0mCTfgWcUmxAkZOiOj5fvxifqcSiCs8lrvFg6FUVQwtQiUilKuQwzo/Ur/oQ0dkNI4/obiiAiakhyi9Q2nXc5Lc/k84WlWoT6WVbJtjJrKjaVGtk0PyzAQzJAeEP0hRMMX3O4kGxwrESjxXM/HkffcH+IInALjaAVBcgE/Wv6CuYDhHmiKzyEIkS3C8CU+1qbbV/XyeVyzJkzB88++ywA4PHHH8fOnTsREKBf9fiVV17BiRMnAAB9+vTBkCFDJPtbsWIFJk+eDKAsnLh7927Jds899xy++uorAGXhwd27dyM8PNzq8R88eBCnT5/G448/Lnm/BwD5+fmYOnUqduzYAQBwdnbGyy+/bPW1iKhhsiWgb8zvmrsNAoQA8H/yLfhJ0x9ihSqE97bx12vTOuoBIOoBeGcWwC8pE92KSnElPR8/Hr5mdA6tDjvjU5GcVVBvw/MMEDoAJ4Xh4hmg7A2fi0xew6MhIiKqGUYrEHIBKREROTipKvTFDBASEZGDk7oF5P0fERERUf1nzWJKWypQCwIQ1aqxxe1nRYdhz8U0aLQinpb/jsHyo+gqJED+76JKObRmA4TFUGKHthtGyf9BuuiFvzXdsF3bHfu1nQ3alof4bF3o+uhdzbHmsMrinyFQ9jMZEB6AmdFhNlXQIOtoNBrMnTtX9/2qVav0woPlPvroI+zYsQMnTpzAvn37sG3bNgwePNjm665YsQJ//fUXAOCZZ57BggULTLZv1sy2Cp9ERNYwVXW4IhECRsr/0QsPAoC3UICR8n+wTtPf5PnmAoZSyudkzo1ERPWXp4ttS+JTbheabXP2+m30buNntl1F1lZsEqBFuJCMu2VnEShk4n31JADAqMgg/HE6xSC8oYUMp7St0Ft+Tu94mMwwQAgApVoRf58vG48aCqTCB02RpdemcjVDKUe1YXhVM80gdF+fTZkyBRs2bMD27dtx9uxZREREGFSN379/PwDAx8cH33zzTZWu98Ybb+CLL74AAAiCgFmzZuH8+fM4f/68yfO6deuG5s2b6x27desWpk6div/+978YNGgQunfvjpCQELi7u+P27ds4duwYfvrpJ2RkZOiut3TpUoSGhlbpNRBRw2Hr55ZSDovtcBat0RGX9Y63kd1AX9lJ7NZ2BQA4K2ToEdpIso/Km7yM7haMRTsSsLPSJjXln4E+3DMEOy6kYm2cyi6vQRSB2MRMjO3OACHVEqkKhABQqtHCxYkBQiIickwKIwFCrb22uiAiIqqjnJ0YICQiooZHugIh5z8iIiKi+szaxZS2GBAeYNVu0BEhPgj1dcPltHz0lZ1CD9lFved7y87AC/nIgenqE4vVD2CVeiCOiW2hheF72cohPlVmAQQBVu3mLQjAlPtaWbRIJqdIjdyiUni6OCGqVeN6u0N2fbR3716kpKQAKKs+0a1bN8l2crkcM2fOxJNPPgkAWLNmTZUChB999BEAwMPDAx9++KHN/RAR2YulVYfLCFikHo0flB8YPPO4fDvWafrB2uqCRq/EYD0RUa1RZRYgNjEDuUVqeLooENXKt0pVX6Na+UIAYM3KMQFAWm6J2XanbQgQWlqx6T7ZSYyV70Uf2Rn4CmWVBjWigM/VD0Gr9ES/cH/0Dw/AzguG988nxDboDf0AoZ+QA1/cRga8TV73U/VYaCHDddEPKWJjpIqNUADpCnYV3Zb7YtyEaIeaNxUKBX755RdMnDgRf/zxB27evIl58+YZtAsODsbatWvRsWPHKl2vPIwIAKIo4tVXX7XovO+++w4xMTGSz+Xl5WHDhg3YsGGD0fObNm2KpUuX4v7777dqvETUsNka0JcmILbJBHS89Z7uSI7oip80AxCvvROQbh/oaXEOKiLEB8tiekKVWYBDSZmSn4EmZ5nfLMAauUWldu2vJjFA6ACUcukPRGqyFCcREVFNk0ssHgUANec/IiJycFIVCK3ZZZ6IiKg+kqpCr2EFQiIiIqqj7L0IsqbVxPhVmQX4fGeCVYE5a8kEoKWfO9YfUem/BlEEtGpA7mRwTnJWAS6n5QMAdmkj0Ud+Vu95paDBANlx/Ka9x+S1z4stJI+3DfDA031bG4T4Qhq7GV0Qakx5ODK4kZvZRTJUe7Zs2aJ7PHz4cJNthw0bJnmetQ4cOIALFy4AAEaNGgUvLy+b+yIishdrqg4DwH5tJxzXtkFX2SW94wpo0EhWgCyt6TC/JV4f3g7DOgdyviQiqmEnVdlYuCMBuyQ2QekfHoBZNoa6Qxq7IbiRK1RWhAQiQ3xwXJVttt3p67etHo+lFZs6CFcxUn5Q75hcENFLdh4lLYZAIZdhVnQY9iekG8ylJ7St9b7XigKuigHwF24jQzQdIDRX0bcyRw/de3p64vfff8fGjRuxcuVKxMXFITU1FZ6enmjdujVGjx6NqVOnwtvb9M+1pg0cOBAbN27EoUOHcPjwYahUKmRkZCA7Oxtubm4ICAhAt27dcP/992P8+PFwcTEfEiUiqiiqla/VG58ZIwiAOvwB3Lj5NbSQYbl6GNZp+iIP+vdkp6/nYOuZFAztFGhx35UrE1Zk3xAk4Oli+LlyfcEAoQMwVYGQiIjIURmZ/riAlIiIHJ5SYbjDUolaUwsjISIiqjkKiU3UeP9HREREdU11LYKsKTUxfmPXqA5aEVi6PxHhggr/yK5hiG8a+nikwDPrHND/NaDXFINzfj+Zonu8WxuBN7DaoM1QeZzZAKEUpVyG+eMijP4MjS0INdbXzOgwvWOmFslQ7Tl9+rTucc+ePU22bdq0KUJCQqBSqXDr1i2kpaXB39/f6mvu2bNH9/iuu+4CAPz6669YunQpjh07hqysLPj6+qJr164YO3YsHnvsMSgUXD5ERNXHtqrDAhaqH8IK5XwAwFltCyxSj8Z2sTvmjeqCub+fq9LmitHtAjDlvtbmGxIRkV1tPZNitCKtKAI7L6Rif0I6Fj0SaVVoQJVZgG3nbuJ6tuXhQaVcho5BXhYFCM8YCRCa2oDH0rDCPm1nvIKfDI7fLTuHpMYPASirrrTokUiDn91RbVssVo/ARW0w4sUQXBaboQjOFl3XUsY2wnFUo0aNwqhRo2w+PyYmxmiVwHK7d++2uf/KPDw8MHLkSIwcOdJufRIRVWTJxmfeyMObTj/gS/UoJInG5+8ezRvhkx1X8JP2dVwTA6CBdJVBjVbEzDUnsH6aq10+T7d3CDKqVeOqd1RL+AmgA1AqpBMUJWoGCImIyHEZrUCo5fxHRESOTeoekPd/RETk6GSCYYBQzQAhERER1SHVtQiyplRl/JZWLDR1jeq0XjkXXkIhkIOyLwC4dlAyQLjp5A3d40tiEJJFPwQL6bpj57QtcEzbxuoxKOUyLHok0uSCF2MLQm3pi+qO+Ph43eOWLVuabd+yZUuoVCrdubYECI8cOaJ73KRJE4wZMwa//vqrXpuUlBSkpKRg8+bN+Oyzz7Bx40aLxldRcnKyyedTUlJMPk9EDUdsYoZuoaYcGgySHcVWbU8Ahp/3VLRbG4l16r74S9sDO7TdAAiIbheASVEt0CnIG4t2JGCnDZsSSAXxiYjsrT5Vpq+psZ5UZVt0T1ii0VocGrB1kxonuYBFj0Ti12PXLWp/JaMAtwtL4e3qZPK6AUIWHgtOx/jAW3gg4ziKnDzwZulkk32fE1sgQ/SEr5Crd/xu2Tl8GKfCvWF+GNopEEM7BWL9NFe9+S8D3vhQPdHyF24lcxvhEBGRYzD3XmBWdBj2JaShVGM42d4jO40FTovRVMhCG+E6xpS8LRkMVMplEFE2zyfB/OfjJRotFu1IwLIY0xtyWcKSEKSlBoQH1OtAPQOEDsBYBcKa/sMLERFRTVLIpP+YoK3uLYuJiIhqmbNEgLCYAUIiInJwUveArEBIREREdUV1LIKsSbaO35qKhZZewxQZtAgS0tBKuImWQgpaCSl4Tz0JWrkzIkN8EHclS+IsAefFFrhLuKD/WhIPQCmKZYP916XUXJxPydE7d5umB3q6p+KMWy8svtkWV8WmVo1ZEMoWlcy0sHqj1IJQW/uiuiE7O1v32M/Pz2x7X19fyXOtUTG4N2fOHMTHx0OpVOLxxx/HPffcAycnJ5w8eRJLly5FZmYmTp8+jf79++PYsWNo3NjyHdRDQkJsGh8R1X32DJKoMguwL6EsjB8hXML7TsvQUXYVz5bMxGZtlJmzBbyknqr7rmLwLyLEB8tiekKVWYBDSZnILSrFlfR8/Hj4muSi1op9MIhPRNWpPlWmr+mxLtyRYPE9oSWhgapsUjOjfxsM7RSItzadtfics9dvo3cbP6PX/drpMwyTxwFpKPsCMNS5Kd4sNd2vCBkOaDthpPyg7phWFFAKOQRtqd59eMX5b8uZm3h/83mLx28tzplERI7P0vcCESE+GN45EBtP3Nl8zQXFeFnxEyYr/tIdi5RdxnT5RizSjNa7jlIuw5wH2uPNjZbPuwCwMz4VyVkFdgnszYoOw/6E9Cp9Pu0Im9EwQOgAlEYChKUMEBIRkQOTqj4BAGoTfwwgIiJyBFIBQlYgJCIiRydngJCIiIjqMHsvgqxptox/XI9gqyoWWnMNKY/L/8Lrih/hLOivvFylGYTOEXchK7/E6Lnntc1xl0w/QKgsuAlkXwMatQBQFm74cMsFg3PfUT+O+YO6oEMTT6QsPghY8BrkgoDJfULRLtALUa0aW73ARSoQ4eniZFNfVPvy8vJ0j11cXMy2d3V11T3Ozc010dK4rKw7Ydr4+Hg0atQIO3bsQNeuXXXHJ06ciOeffx7R0dE4d+4crl69itdeew2LFy+26ZpE5BjsGSSp2JenmI95irWYJN8BmVDW8VtOK7G3uAvyYNncZizEENLYTS/cOLpbMIP4RFRr6lNl+poeqyqzALvirav6Yyo0UNVNaq5mFuDm7SLcyik2eK58wxwAcEYJQoRU3BY9cPr6bbg7K4xeN1k0rB7ur76J5kIqrokBJsezT9sZ7YVr2K/thAPaTjisbYccuJc9KfE5QkhjNzx9XyvEJmbYpZpSRZwziYgaBmvfC1xJz9dr87T8T73wYLmZil+xWxuBU2JrvTnl4q1cq6vHiyIQm5iJsd2r/ploRIgPFj0SafP7B0cJ1jNA6ACcFNIBilI1F9AQEZHjMlaBkAtIiYjI0UltIsMK9ERE5OgUcgYIiYiIqG6y9yLImmbL+HdcSMXehDST1X2Ass8rnvvxOCbdZWpBowg5tNBAbrKvDNHbIDwIAK2EFES3C8CMNceNnntObCF5fOf2jVB3HI+f4lQGQYlyCrmAIZ2awsvFyaIFJuULSeyxwLZyIILIUlqt/n+jCxYs0AsPlmvatCl+/PFHREZGAgBWrFiBjz/+GF5eXhZdR6VSmXw+JSUFvXr1smzQRGQVe1YILGfPIIl+XyJ+Ur6LDrKrem2aCNmYrfgZ76gfN9mXtSEGBvGJqLbUp8r0tTHW2MQMu4YGqrpJzf6EdAzuYFjF3ge5+MTlF6Qpz6O5cAuByIRMEDGvdBJOX2+PQ0mZRq97QttG8nhf5QWsKjYdIFyv6Yv1mn5Gnzf2OYI9qilV9HDPEDw3oA3nTCIiB2fte4GvHpXhZPJtvee+1dyPccp/ECLe0DuuELRYGrAee+/9EVGtfXVzytGrhvOuJXKLzJTytcLQToFYP81VctMZAPBzVyIjvwQVDztasJ4BQgfgZKQCIReQEhGRI5NLLB4FADUXkBIRkYNTsgIhERE1QFJV6Hn/R0RERHWBrYsgP995CTP6t6n1gJgt4wdgNjyoa6cVseLgncCAHBp0FpIQJTuH7rIERMguY6l6GL7VPGCyn3gxWPJ4V7c0FJRoTL6G89o7AcJM0QPntC1wTgzFxuMynD121OR1NVoR/1xKx9BOgSYXmDjaQhKyHw8PD11FwKKiInh4eJhsX1hYqHvs6elp0zUrnufu7o5HH33UaNuIiAhERUUhNjYWxcXFOHDgAIYNG2bRdYKDpf+/JKLqY88KgZX7tVeQxLAvAYvVD2CR8guDtk/I/8IvmntxVmxp8FzbAA883be1zcE/BvGJqKbVp8r0tTHW3CK1jefdCQ2UB+hVmQXYZUPVPRm00KLsb+2pucVYfyTZoE0RlGij+gVtKv1JPlS4ha3XsnDjdpHR/k9oW0se76o9g1W4z8zopNfBlTMWpqxqNSW9EQhgeJCIqIGw9r3Ax1svGBwvgjPORC1ASOxjgKi580TrAQgY9RXGeulvPOPpYlt0zdPFyabzjDG36Yyjb0bDAKEDkKo+AQClDBASEZEDM1qB0JaVDkRERPWIs0SAsJgBQiIicnBS94CsQEhERER1ga2LINfGqbDuiKpKi/3twdbx28ILeTjgPAueQqHe8QjZZUBj5KR/XRGbokSUQynoN/QtVuHwlUyT514UgxFT8iLOa1vgFhrB3MLMikQRekEJVjUia/n4+OgChOnp6WYDhBkZGXrn2qJRo0a6x507d4ZSqTTZvkePHoiNjQUAXL582aZrElH1s2eFwMrsESQpD3V8uzfRoK9N2rsxTrMb98rP6B1PFJtBIfEmQCmXYf64CIbyiajeqE+V6WtrrFUJDRgL0JvigQJ0kSWiq3AJnWRJaCdcQxY8MbrkHV2bHRIhRCcXd4heQRByrusdbyHcwvVs4+FBALgOP6SJ3vAXyio05YiuOKFtg2PaMMsGbYaxCkzmqilZakB4AO9riYjsqDoqx9trXNa+F7h4K8/gmCAA3e+OBpxfBna/DyhcgcHzgJ5PlT1ZSVQrXwgCrJqnBAGIatXYqrFaytimM46+GQ0DhA7ASWLxKMAKFERE5NjkEm8wAUBj4a7HRERE9RUrEBIRUUMklxnOfwwQEhERUV1g6yJI4M5i/30X0/DIXc0R6utepcUktixKqcr4rZUDD6SLXhIBwkSz56qhwGUxCO2Fa3rHvZGHX49eN3JWmWIosVvb1foB/0sqKOHoC0nIfsLDw5GUlAQASEpKQmhoqMn25W3Lz7VFu3btsGPHDgCAt7e32fYV2+Tk5Nh0TSKqXvasEFhZVYMkloU6BLypnoy/ZK/AWShFkeiERerRWKK5H6WVli8q5TIseiSS4UEiqldsrUwvVVGuutk61s93XsKM/m1svg+yNTRQXKrBuMUHra6ut1w5H71k8XrHmojZEKCFCOk11wAQEewDQdEKqBQgDBVuWjJi/E89BkWiEifE1kgUAyWv5e+hRFpeiUWvoyJTFZikNrvJKijF17svodSCtXRKuQwzo+0TdCSihq2uhuZqUnVVjrcXW94LSOka4oMALxfg3v+WzZt3zwD82xptH9LYDf3DA7DTiirCDLfbHwOEDoAVCImIqCGSG6lAqOYCUiIicnAMEBIRUUPECoRERERUV9myCLKyUq2IlQev6r63djFJVRal2GP8SpSimywBsdr2MFfd76C2A1rKbukdCxbS4Y9spEF6jOXWaPrDW5OPRLEZksSmSBKbohAuAKr/fWFtVQeh+q9z587YunUrACAuLg79+/c32vbWrVtQqVQAgICAAPj7+9t0zYiICN3j27dvm21fsY0lgUMiqnn2qBBoTFVCLx7Oty0KNgLAFTEQX6lHIlJ2CXPUMVCJTfSeF4SyxaEza3kxLRGRLWyt7G6solx1snWsa+NUWHdEZXPwIaSxG/q19ceu+DSLz+nRvBHm/n7O6vAgAJzUtjYIELoJxWgupOKq2NToeREh3kBJa+DKPr3jQUI6nKA2CL5Xtloz0OzYxvUIxtd7EqulAlPlzW46BHqanasZ3icie6jrobmaUp2V4+3F0vcCcmiggdzo80M7/TufyhXAyEUW9TkrOgz7E9ItmtsZbq8exrdRoHrDiQFCIiJqgBRy6UUIWntsjUFERFSHKeWGH86UaLQQOQcSEZEDk9pERq3l559ERERU+8oXQdpT+WKScYsPYuuZFJNtt55JwbjFB7HzgmHVH0v6Kd/52VpBSMMk+d9Y4rQAJ5yfxk/Kd9HSgooMsdoOkse7yC6bPXelZgg+14zGn9oonBND/w0P1ozyoASRtYYOHap7vGXLFpNtN2/erHs8fPhwm685bNgwCELZPdTp06dRUmK6usmRI0d0j22tekhE1acqFQItYWuQ5MLNHMxccwJdtWdxr+yURed8rnkIk0tfMggPjopshn0v9ceymJ4NYlExETkeWyu7m6ooV12qUoXemnvVilSZBVh/RAW1FWualXIZRJT9HdwZJbhLOI/n5L9ildP76CokmD3/hLaN5PF2gsrkeRHBPoBfOLTezbFf2xmr1AMxr3QSppU+b/HYTZHLgP+7p5XV9+G2VmAa2ikQ66fdjeh2ARAq/ZlFEIDodgFYP+3uWguxEJFjqOrnk47C2srxJ1XZ1TaW8rl3+f4krD+igirzzv2hJe8Fmgu3sEn5BobLYo22GdLReCDfmIgQHyx6JNJoAbVyDLdXH1YgdABORgIUJRaUnSYiIqqv5DLpN5CsQEhERI5OqgIhABSrtXBxMr7zExERUX0mFSBkBUIiIiKqK3qENrKqioKlyheTrJ/mKrlYwtpFKcb6mRUdhl0XUi2q4ycXgGghDt8qPzN47j7ZKSRpTC86rBggLBHlOCO2xAltG9wQ/SpcQ4DGho2S5DKhWt8j1kZ1EKr/+vbti6ZNm+LmzZvYvXs3jh07hm7duhm002g0WLTozm7tEyZMsPmawcHB6Nu3L3bv3o38/Hz88MMPePLJJyXbnjx5ErGxZYvBPD090adPH5uvS0TVoyoVAsd2Nx80sDVIsudEPN4RVmCC827cFBthUPF85ML09bRGah3cG+bPKr9EVK/ZUtnd0opy9qTKLEBmvunNJSxh7h6znLFqVOYoZALmPNAeizfuxnrll+giJMJZuBN4P6jtiOMa0xWJjhsNEF7DXzBepTcyxAfwehayu5/Fewv34XxKjt7zLk4yFJXavrmhKAJxVzJrtAJTRIgPlsX0hCqzAIeSMpFbVApPFydEtWrM+ZeIqsxen086guqsHG8pSypBmnvfMkQWh/lOi+ElFOIjpyU4X9ICSaL+Z74tfN3QwtfdpjGWhdtdsWhHAnZKjJOV6asXKxA6AKMVCNXcgZuIiByXvPK2SP/SsAIFERE5OGcjAUJLP4QiIiKqjxSSFQgZICQiIqLaJ4oitp29VW39ly8mkWLLohQprkq5ReFBAOgc7APX1n2gFQ3fn/WVnTR7fioa4b3SiZhU8ioiipdgdMk7eEf9OM6LLQCULYwc3T3IwtHoG9MtyOzu1VVRG9VBqP6Ty+WYM2eO7vvHH38cqamGlcReeeUVnDhxAgDQp08fDBkyRLK/FStWQBAECIKAfv36Gb3u+++/r3v8wgsv4Pjx4wZtbt26hUmTJum+nzlzJlxdXc29JCKqYbZWCLQ0+F6+eNRyIkbIDuLHkpmYoNgNAGgqZOFFxVqrxwjUToCGiMjebKnsbmtFOVucVGXjyRVxuG/+Lnyw5YJd+jR1jwmYrkZlzoiIQDgr5EgTvQ3CgwDQS2b+NdyAL1JFHwBAseiEfL8u+FXsj1NiK6PnBHq7IMDrTqX7zkFeBm2qEh4EAK0IzFxzAgBqvAJTSGM3jO0ejMl9WmJs92CGB4nILuz1+WR9V92V4y1haSXIszduS75vUUCN1xU/4BvlZ/ASCgEAnkIhvnL6H1xQrNc2ObOwShUly8Pte1/sjwXjIvDWAx2wYFwEK9PXAAYIHQAXjxIRUUMkVX0CANSswEtERA7OWAXCEm4iQ0REDowVCImIiKi6qTILsP6ICsv3J2H9ERVUmZYt3oi7koWTyberdWxSi0nsuShl5cEr8EI++spO4v/kf5rs41RyNgb17Ci56PJu2Tk4w3w1iyWaETig7YxC3FmYKQhAdLsArJ92N3qF2hYi6NXSF+un3Y3odgFWBiHMY7iBqmLKlCkYNGgQAODs2bOIiIjAnDlz8NNPP+Grr77CvffeiwULFgAAfHx88M0331T5mnfffTdefvllAEBWVhaioqLw9NNPY+XKlVizZg1efvlldOjQAWfPngUA9OjRA2+88UaVr0tE9mdrhUBLg+/Whl76yM7gC+Xn8Bf0KzI9Kv8b3YSLVo0RqNkADRFRdZoVHWbxhiZVrShnjaoE+cypfI9Zfl89749zmP7jcck1zE2QicGyOMyQbzDaryqzELlFahRDiZNia4Pnu8suQgZzfxsX8HLpFIxRv4fdY47CfcY+bG71OnZpuxo9Q60RcVKVrfu+c5C3mWvYpjxAU1aBSfoetuI98tBOgdIdERHVsroQmqsrqlI53h6srQQ5vFNTg/ct/WQnMUWx2eCc9jIV3nX6DqiwBZ1GFDFzzQm9edMWDLfXPNs+YaA6xWgFQgYIiYjIgSnk0n/919r70y4iIqI6xugmMgwQEhGRA2OAkIiIiKrLSVU2Fu5IwK54/cWUggD0Dw/ArOgwyR2PVZkFiE3MwJJ9SZL9dm/hg6NXs+0yRlEEPt95CTP6t0FI47JFFFVZlDK2e1kfNy8cRP7erxBz/Sjedbmua/eb5h7kyH0QGeKDuCtZen1oReD1DWcQo4lEpOyy3nOuQgl6yuKxX9vZqnG9PrwdhnUO1C0QaeyuhCDAqtdXHvALbuSGZTE9ocoswJYzN/H+5vNWjcUYhhuoKhQKBX755RdMnDgRf/zxB27evIl58+YZtAsODsbatWvRsWNHu1z3ww8/hFwux0cffYSSkhIsWbIES5YsMWg3ZMgQrFmzBi4uLhK9EFFtK68QaMu8aKlZ0WHYn5Bu0Wb1B7SdsFfTGffJT+sdlwkiPnRagvtLPkCphUsSazJAQ0RU3SJCfLDokUg8s/qYyd/ZCplgt4py5lgaJrBV+T1mWECJ5H11uXtlp/Co/G9EyC6jqXDnHnO9pi9uwXC+Op18G2O6lVWmP6xth16yeL3nPYVCtBeu4qzY0ujYBAGQtR2CORXu6Ru7K02+nrS8YoxbfBCLHonE0E6B6FRNAULgToCmvAKTKrMAh5IykVtUCk8XJ939LRFRXWaPzycdRXVXjjfH2kqQW87cNHjf8re2O35S99NVmq+ok5AETxQiF256/SzakYBlMT3t8RKohjBA6ACcuHiUiIgaIKMVCLmAlIiIHJyxCoTFvAckIiIHJnUPyPs/IiIiskV58C+3SI0rGflYc/gaSjWG7ytEEdh5IRX7E9J1iwcB44HDihq5OeGj0V0w6H97rVpEI4cGSpRCCTWUKIWzoMZNsRHUUGBtnArrjqh0ocaKi1KaC7fQGLlwghoKQQMvFCAfLpJBvtyiUt1rKL24B6uUm4BKb7UiZZcQ0P1BzIwOwz0f7TLYuOF2YSl2CxF4Hr8AAE5rQ7FbG4ldmkjJ6hCmRLcLwJT79M8pr4S084LlO5hXDviFNHbD0/e1QmxihlX9SGG4gezB09MTv//+OzZu3IiVK1ciLi4Oqamp8PT0ROvWrTF69GhMnToV3t72XaT83nvvYfz48Vi2bBm2b9+O69evo7S0FAEBAejduzcef/xxDBs2zK7XJCL7sse8aE556OXZ1cdg/uMWAa+rn8Q22ctwFe5UHs4XnfGTZgA0sLz6Vk0FaIiIasrQToHw93RGak6x0TaT7mpeYxXlrAkT2OpwUgZe+/W0yesEIBtD5EcMjkfKLuMvrWGAsESjRSM3JwhCWYAQ2Kh7rkh0wnFtGJwhHbiYFR2GkMZuBgG8k6psbDh+XfKcyteeueYE1k9zRftAL8gEmJ0bo1o1trqCVOUATUhjN92GQURE9UVth+bqClVmAS7czDHfUIKllePNXd+WSpBzR3VEoJcLbtwu0h1/Sx2DLrIkdJBd1R37RXMvXi99EkVwluwnOauAofd6hAFCB+BkpAKT1B+6iIiIHIVckJ7/WIGCiIgcHSsQEhFRQ6RgBUIiIiKqIkuCf1IqLh5MuV1oUfWGu1v7oU0TT6sW+3+o+FZyd+f7ij/DNbEJgDuhxn0X09Cz5Z1Flq8rVhssxjykbYf9JYYBwivp+fhg8wWUaLQIRDPJsXSTJUDp54FAb1dEBHvj2LVsgzanxFZ4vuQZ7Nd2Rhp84O+pxDN92+CewlJ8vfuSRX+rNhXMs6YSkr36MdY3ww1kT6NGjcKoUaNsPj8mJgYxMTFWnRMREYFFixbZfE0iqn32mhdNGdopEM18XJGcVWi2rUpsgs/UY/Ca0xoAwE5NJN4snYzr8Dd7riCUBRxnGqnyTERUn+UWlZoMDwJAel6JyeftxZYwgXVEtBRu4uixFJRoTQciTxjZaKaL7DL+0kpXLbqWWYj+4QE4dKEtdmkicFjbHoe07XBGbIkSSIctotsF4PlBbSWfW7gjweI11eUVlcb1CLbo84NgH1eL+q3M0QI0RNTweLrYFkWyR2iuLrD18+Zy1laON8bWSpD7E9JxM6dI73gxlHimdBZ+V74OZ6gxRx2DtZp+MNiBrkI/jlhR0pExQOgAlHLpxaOl1bxzCBERUW0yWoGQAXoiInJwxioQMkBIRESOTC4znP80WhGiKEIwssEMERERUbmtZ1IsCv4ZU6LR4t0/zuFk8m2L+th+7iZOqrLxQi9XNL50AL+o74ZophrPLTSSPK6UqKpQqhXxz+WMO99DbtDGF9K7Xv9YoeLiTTRCgegMN0F/gWtX4RJi/opHIzcnnEq+LdmPCBk2aO/VfZ+VX4ruLRohIsQHHQI9zf68zQXzyish1VQ/lTHcQEREdUn5fPbMD8dg6q/hcplgc/A9u6AEyVmFcEURCuFitv0yzXD0lp3Dz5r78Ic2CsYWlJZrG+CBp/u2NqgIRUTkSOJv5pptczI5u/oHAtvCBOYoUYresrMYLDuCAfLjaCpkYb36PryonWbyvEQxEDmiG7yEAr3jXYREo+ccvZqFWdFhGJeQjsmlL5sfm4kAvS1hyh0XUrE3Ic3kvFtuw/EbVvVdzlECNETUcEW18oUgwKr5xl6hOXtQZRYgNjEDuUVqeLooENXK12Q12Irtr2TkY02Fz1ltYW3leGNsrQR5JSNfssruVbEpZpY+hzTRB2fFUAuuz0B8fcIAoQMwtniUAUIiInJkCiMVeLX2/vSLiIiojlHKDRcFAkCJRlPDIyEiIqo5xu8BASNPEREREQEo2wm6KuHBcnFXsyxq11G4ggnCTgSt+A/8NLewQAHEi81xWhNi8rwU0VfyuLNEgLAytUSAsLFgGCD0dVciI/9OtQsRMiSKgegkXNFrFyZLRmmpGh9suQC1hVWf1VoRi3YkYFlMTwztFIj101yxaEcCdlbagduaYF5N9vNwzxDkFKmRW1QKTxcnhhuIiKjOGdKxKVyVchSUGP9bQPcWjTC0k+kqUMacvKzC84qfMVm+FQ+VzMVlMchkew3kiLEg0AGUhTrmj4tgKJ+IaoW14YCqOJ8ivZFLRclZhcjIK4avh3O1jKGcrWECY+TQ4IDzTPgL+pvMRMnOmz1XhAwnta1wr/yM3vGWspsAREiF0I9ezUKXYG+8OCQc7202fQ1zG8vYGqa0NBSisaHzuhSgISKyVUhjN/QPD8DOC5aHtO0VmqsKY5UDBQHoHx6AWZU+b6xqpUEptlaOl2JpJciOQhK8hAIc1HYEAOQVG3+vsFsbacX1GYivTxggdABORioQsvoEERE5MqMVCC1czEBERFRfGdtEppj3gERE5MBkRqoMqrVayGXS4XoiIiIiAFi4I6HK4UFL/VexDs8pfiv7psLa/sX3FmLOzQCD8FpFVQsQGv7Z3wf5kEEL7b+VD51kAjIrhAfLxYvBcNUW46TYGse0YTiuDcMFMQQiZHphQ0vsjE9FclYBghu5ISLEB8tiekKVWYBDSZk2B/PqWj9ERERVUZUQiyqz0GR4ECgLrqg1WiiMrCWTVFoEHFmOnjs+Ql9FNgDgBcU6PFP6vOV9mGAu1EFEVF2sDQfYw3kLKhACwKnk2+jfLsCu167M0jBBZXKZAI3E2isN5DihbYNB8qN6x0NkaQgW0pAs+pvs97C2HTyFQhzXtsEpbSucFFsjSWwKYxVsM/JLcDWjwOTcZ+nGMvYOU9pDXQjQEBHZw6zoMOxPSLfo81d7huZstfVMitHN5kQR2HkhFfsT0rHokUgM7RRosr2tZALseo9krhJkI+TgRcU6TJDvwg34YmDxfBQLznCSWXHfaAQD8fUPA4QOQGEkQFFTfwgjIiKqDXIji0elPsQiIiJyJAwQEhFRQ2TsM1DeAxIREZEpqswC7Iq3fAfsqtqv6XwnQFhB0O1jWBbzvF547Up6Pn48fE1X0eCGkQChUlCXFWQwoUQ03FBBJojwQR4y4QUAGNY5EJtO3jBo99/SZ2Bswaa1RBGITczE2O53FkGGNHazS3WPutYPERGRNewRYjl747bJ54GygMTZGznWLURdORJQHULF2XGYPA6R6ktIcApHQanGpiob1lQLJiKyN2vDAZawJARuSQVCADihyq72AKG5MIEUQQDeGdkRc38/J/mz+0vbwyBACABRsnP4WdPXZN+fa0bjc81oyec6NfPCmRuGP7sjV7Pw9/lbBsflMgHvjOqIvm39LQrh2RqmtJax8GVldSFAQ0RkLxEhPlj0SKTZkJ1CJtT6xiInVdkWhQFLNFrMXHMC7z+kxmsbztg9k+Pv6Wxz5XgpxipByqHBJPnf+K9iPbyFAgBAMNIxTfE7Trd5Fqm5xQZ9yQTAmj+/MxBf/zBA6AAEQYBSLjP45VTKACERETkwhZHdL9QaLh4lIiLH5mwkQMgq9ERE5MiMVaFngJCIiIhMiU3MsGnBu60Oie1wSdsMbWSVgnpX/wFE0SC8NrpbMBbtSCir3Cf64Vv1/SiBAiWiU9k/4YSr2iZmr7tW0x8HtR1RCgVKIUchnJEpeiGnQhQgPc9wQUgZ+4QHy+UWma+YSERE1JDYK8RyViJYIeXA5XTrFuW2GwGoDhkcflnxE75q/hmcFHKDhaimtA3wwNN9W7PKLxHVGmvDAeunuZr8vWlpCFyrFRFvcQXCbIvaVYWxMEFlSpTiflksToqt0TI8EpOiWqBTkLfuXrXia/5b0w1qhQwKQf9nGyU7bzZAaPT6chn+Ozgck1fEGTz356kbOH3dMEB/X5gfJt3VwuJr2BKmtMWYbkH47fgNk//tsTIvETmioZ0CsX6aKyYuiUW+kcqxj9wVYtfQnC0W7kiwOAxYotHigy0XqqWg162cYiRnFdj1fumxqBYGc/5A2VG84/S9Qdtp8t+R2Gs2nv8ry+C5UF93JGcV1puKkmQ9BggdhJNcQOXft6VqLp4hIiLHJZcbWTxak6tBiIiIaoGxCoQMEBIRkSNjBUIiIiKyRW6RuoavKGC1JhpvyVbpHy5IBzIuAX76CyoiQnywLKYnVJkF+GLXJbwfN8mmq54U2+Ck2MZkmzMSiy6rg6eLU41ch4iIqC4wV43KniEWSyoQAsA/lzLwbD/T7wv0dI+BZvfHkJfqh17ulp9Dol86OnW/B/sT0i1eQDp/XARDEURUq6wNByzakYBlMT0ln7cmBN6uqRcKjIQmKjuVfBuiKEIQ7LuhS2WzosOwLyENpRKbsQciA5MUf2OCfBf8hBx8pxmGbtHjABjeq66NUwEAsuGJw9p26C0/hzTRG9s13bBf2xmHtO1tGl95mK5/uwAE+bjienah3vO74tMkzxvYwfxGOxVZGqasql4tfTHprhaS4UtW5iUiRxcR4gMXJ7nRAGFmfu1uOqbKLMCueOvmgYz8kmoaDXDwcgbG9bBfgPDYNcMw4F/anjiibYsesot6x12EUrQ79RES0ycanNMl2BsvDQ03ex/LQHz9xQChg3BSyFA5QcgKhERE5MjkRj5E02g5/xERkWNTyhkgJCKihkdmJECoZoCQiIiITPB0sdefw0XcLTuHg9oOMFex7xfNvXhJsRYayCB0GAX3dtFAaB/AO9joOSGN3TCjfxusO6KqtooIOUVqCACq892TIABRrRpX4xWIiIjqBkurUdkzxGJpBcK4K5koKtXAxUlelm4RBNNBRxcvnGk2BhFXV+j62KvpjI/VD+PVTlGICPHBokciuYCUiOoFW8IBO+NTJasAWRsCnz1IugKPVDAuI78EyVmFeqHz6hAR4oMXBofjgy0XdMdk0OJzp0UYKouDXLgziU1yPQhloP54Qhq7IbyJp96xT9Vj8YlawHExDFpI/93aHKkwXY/QRrh+otD0if8a2N66ACFQFqa0NBDvJBOg1opW3T+X3w8HN3LThS8PJWUit6gUni5OrMxLRA5PoxWRWWA8cHe2hjY3MyY2MaPaK9Fa42BiBsb1CLFLX0WlGqw+dE3iGQFvlT6B35VvQFZhzofCBdmebaHWaIBKc3mbAA9dRUkG4h0TA4QOQmoBaTEDhERE5MDkXDxKREQNlLORCoTFDBASEZEDYwVCIiIiskVUK18IAqq0OCRMSMb7TsvQUxaPJ0tewE5tN5Ptc+CBJ0tfROM2d+HLh/tafJ2aqIigVMis+vzA111p1U7bA8IDuCCSiIgcnqXVqOY80N5uIZbU3CKk5hYbtFfKZQbjKFZrcf7MCXRN34SiM7/jP97/w18JuSaDjj/JhqO9uArnxBb4WD0B/2g7QSZAtyCUC0iJqL6wJRwgikBsYibGdtf/3WttCPynI8mSzz3cMwSfbr9ocPxU8m29AKG5qra28nLVrxKvhQwKaPXCgwCgLMkGLm4BOozSO155Y54jYjubxiEXBEzuE4p2gV6SYbruLRph44kbZvuJCPFBEy8Xq69vbSB+3ZFkq+7PK98PhzR2q/aAKBFRXZJVUGJyDr6SUaALVdeG3CJ1rVzXmIOXM6yqRlz5fUIfvwI0K06Cyv8+LNqRgEwjn+GeFVtijWYAJil2AAAymw9F44c+RtwNZ4j7jhq0bxNQtnFAxWrEDMQ7FgYIHYSTRICwlItHiYjIgRldPKrh4lEiInJszgq55PEStUbyOBERkSMwtokMA4RERERkStVCeSIelf+NNxQ/wEUoBQC8rliNfSVdUGrmz+xHhc5YPyjC6itaUxHBFEEAGrsZhv+sCQ8q5TK8OqwdXttwxqLxKOUyzIyWrrhBRETkKKypRvXWxnN2C7EYqz44ulsQfopTAQDcUIT75bF4SLYfXTeeAwC4APBI/wOiqL+pQcWg48IJkdiRLMepknk4K7ZAebXl9oFe8HC+856HC0iJqD6wNRyQW1Sq970tlQyvpOcbHHN1kuOBiGZGAoTZuL9LoMVVbW11RqLi00rNIAyRHzFsfPwHgwChPTbmKQ/lDe0UaLRN9xaNLOprcAfrqw+WsyYQH+jtavH9Oe+HiYiAjDzzm5Cdu5GDu1r51sBoDFUOxNekZj4uuJFdpHcs5XYRrmYUINTP3eS55e8TYuOvoZdwAX1lJ9FNdgrNZCkoghKDir5FEZQm+5ivHo8wWTIWqkejb5txeLpRC1w6cUmybVgTD73vGYh3PAwQOgilRAWKUlYgJCIiByaTCZIfUGnqUp1xIiKiaiB1/wegyosLiYiI6jJWICQiIiJb2RLKU0CNL50WGSyobC1LwePybVimGW703PKFkbYs8LS0IoI5A8IDIJMJ2H7ulk3nV1zc6eGisLhCA6sOERGRo7OmGpWtf7euHGIByhbaSpl4V3NsOnkDBSUa+CAP852+NWjzsHwXftZIV0Uu0Wgx86fjKNWISEWo3nM9jAQ5uICUiOoyW8MBlash2VLJUEp4U0+E+rrBx80J2QX6v99PqLItrmprLnynR1MKyO+8njMSc8gVz56ARxiQkaD/RM4NQF0CKO4EEaqyMY81VWrDm3jCXSlHfonpTXMHtrc9QAhYHoi3tmIh74eJqKHLyDOsmF7Z2VoMENojEG8LpVyGib2aY8E2w80EDl5OR6inCJQWAh7+Bs9XfJ/QWbiBFcqP9Z53QQl6yi5gn7aLwbkCyuZhrQhkwxPjS94CAPgkl20skHAr1+AcJ7mAFrzXc3jSqw6p3nGSGy6gKWUFJiIicnByifLdXDxKRESOzmiAkFXoiYjIgRmrQKjmPSARERGZUb7oz8jbCR0nmYAn7m6BKfe2hBoKZIheku1mKX5FIxguwBQEILpdANZPu9vyhZ0Syioi3I3odgGQ+AjcLKVchmGdmmK3ldUyAOnXYGo89nrNREREdYUqswDrj6iwfH8S1h9RQZVZoPectdWobFE5xAIAZ28YVo9SymVoH+iFXi0bAwBuwA9x2rYG7XrKLqKNkGz0esbWl3UPbWzpkImI6ozycIA1BAGIaqX/O8/WSoaVtQ/0giAI6BzkbfCcNVVtZ645gZOqbOON8jOAI98B3z8ALBukO1yq0eJ8iuH9a6dgb6DX02XfCPKyqoMxfwLT9uuFB8vNig6DUm7ZcnO5IOCpe1piwbgI7HupP5bF9LQoXKeQy9AmwMNkmyZezmjbxHQbS4U0dsPY7sGY3KclxnYPlqymy/thIiLLpeebr0BorLJ6TSgPxFvD1910ZT9zykPmD/dsrjv2pmIV9jvPxAnnKRi/JRL4IAhYGm1wbuX3CVfEppLXuE92SvK4COmwZPn7iYTUPIPnWvq5Q2HhfE/1FysQOggnif9ZWX2CiIgcnVwmGCwW5eJRIiJydMYChMUMEBIRkQOTy6TnP42W8x8REVFdp8osQGxiBnKL1PB0USCqlW+NV60Z2ikQ3Vs0RtyVTIPnpCoi7L2YhnduPYaesniEya7rtc+DC1oIqcgSvRDo7Yyn72stWa2gKqQqIlxJz8ePh6+Z3ES2fFHKuiPJVm022zbAA0/3bW30NVhaoYGIiKi+OqnKxsIdCdgVn6q3yFIQgP7hAZgVHYaLt3JrpFpFZn4xVJkFeu+X4q9nQAYttBVqBbRt6gEnuQx9Wvthd3waAGCTpjd6ygwrW0yQ78K76sesGkd3IxUIiYjqMluq5Q0IDzC4r7G1kmFl7QM9AQCRIT7Yl5Cu91yRFX/bLdFosWhHApbF9NR/4sZxYMc8IHE3IFao3JdxGfBtjctpeZKb0HZq5g1ETAAK0oFuTwDeQSavb201PlsCdVvPpOD0ddPBkrTcYvx19maNBvZ4P0xEZBnLKhAaboxSk/6vT0uL3yMo5TK8OqwdXv7ltNHq8l7IRw9ZPKJk5+GGIryh/j8A0p83t23igYu38uCFfAQL+u8JUGT4c1m4I0Fvzs2FG9JFL/gJ+nNlX9lJvIdHJccnNerkrEKk5RbjcpphgDAswFOyH3IsDBA6CKkFpKUMEBIRkYNTyARUvu3QMkBIREQOzpkVCImIqAFSGCkZxI9AiYiI6i5LFuJbUoXAXtIlFrE0b+yGH6fcpbfo76QqG5fT8qGGM2aWzsBvyjfhLJRVn1imHoYF6nEohAsAIC23BN2aN6q21xHS2E0vPDC6WzAW7UjATomfafmilMbuSuyKP2bVdRLS8ixa/Fh5PERERI5g65kUo4EIUQR2XkjF/oR0jOrarEbG8/7mC/hgywUMaOuL19qnI+TGZvyS/xuekf0HB7Udde06NSurZnV3a1/dsc2au/CWYiUUQtlrKRHl2KbtiW2aHlaNIdDbBUE+rnZ4NURENW9WdBj2J6RbVIBDKZdhZnSYwfHySoZVDY63a1pW2b5LsE/VOgKwMz4VyVkF+vdt6mLg8g7Dxmd/Be57EWeMBPI6BXkDLl5A/9csvn5ZNT5Xs/ekttwfl1dZ0pr5gWtFYOaaE1g/zbVGP08AeD9MRGRORp75CoQJqXkoKtXAxUleAyMydPRalsVt37i/PcZ0D8ZHWy8grdJrmyDfiUnyv9FRuAqZUDZ3lQpKaAe/Dzd3D8nPWXu39sPFW3nIg8R9VnEOoNUC/26oq8oswK54w6DjFbGpQYCwrew6/JGFNFi+AcyWMykoKjV8n9TaTCVgcgwMEDoIyQqEXDxKREQOTi6xgJQVCImIyNEpZILkH6xYgZCIiByZ1P0fAKhZgZCIiKhOsnQhvq2VCaxVrNbgWmaBwfG7Whou5li4I0H3OfN5sQXeV0/Cc4oNeKF0GnZrI/XaqrWidBWIamJJ5YP1R1RWL3IVRSA2MRNju3MxJBERNSzlgQVzIZMSjRa/Hr1uso0xAqQrPxjjjTw8I9+EUVf+QeDVsurJSgG4XxarFyDs2KwslHIt4857nAx444C2E/yE2/hVcw9+1dyLLHhZPWZWHySiusDWavbl1fKeXX0M5pYQLZwQKRlEs6WSoZR2/1YgjAj2tvgcGbRoKaQgUMjEfm1n3XHJ+7Zm3QAnN6C00v3umQ3/BgilKz11DLJ+bgCqrxpf5SpLphitxkhERLUqI998BUKNVkT8zdwaD4GrMguwNyENX+++ZPE5N3OKcDgp0yA8CACNkYPOsit6x5zEEjwekga0bCfZX1QrX6z454p0gFDUAiV5ZeF+ALGJGZKf714Vm6AHLiJT9MBebRfs1XTBPm0XpMHH4tcFAL8cTZY8HsYAYYPAAKGDUEoECFmBkIiIHJ3UAlINA4REROTgBEGAUi4zCAxa+kcVIiKi+shYgJD3gERERHWPNQvxa6pywNWMAsn3DZV3VZba3fl7zWBs1PRGNjwl+5asAlHNTFU+yC1S29RnblFpVYZERERUL1kTWNDYUIZKEAAPZ4VV83MxnPCo/G94CEV6x4fK4/CWOgYalFXr6NDMGydV2fjP2hN67aaWPo8iOFs91oqaertU6XwioqqwRzX7oZ0CEd7UE+dTck228/c0/vvSmkqGUmHxIB9XeLk4AQACvFwQ6O2ClNtFBucCQD/ZcQyWHUUH2VWECyq4CiXIEV3RpXjpv72XMbhvUyiBkLuAxF36x1PPAmnxOHvDMEAY4OmMAM+q/Z63ZzU+Y1WWTKmN+3AiIjIt3YIKhABw9kZOjQUIjb2nqCjU1w3XMgsMNh1YfzQZ17MLJc9xb9sXSFpn+MTVA0DLeyXPiWrVGIIA5InSld5v3LqJZi3KAoTG7h+XqodjhXoIzoqh0MIwO2Spk8nSGwyENWGAsCGw/b8cqlOc5IYLaEo1XDxDRESOTS4zfCvDCoRERNQQKBWsQk9ERA2L8QqEvAckIiKqa2ypHFDdLqXmGRwLF66hjb/+ogjp3Z0Fo+FB4E4ViLrC08W2PYQ9/13YSkRE1FDYEliwVrfmjawO9xfBGVu1vQyO+wk5uEt2HkBZiKZ9oKfk+66qhgcBYMWBK9h6JqXK/RCV27RpE8aNG4fQ0FC4uLggICAAvXv3xvz585GTk1Nt1z1+/DhefPFFdO3aFf7+/nB2dkZQUBB69OiBGTNm4Oeff4ZGo6m265P1tp5JwbjFB7HzguFC//Jq9uMWH7Tod1R+sfl/txuOG68uW17JUC5IfzZdkVST9oH6Vf5Cfd2Nnt9dloCJip2IlF2Gq1AWwPASChEi6M9Tkvdtoffof9+sKzBoHrQujXH2huH/X52CLK+GWBOMVVkypa7dhxMREZCRZ74CIQCckQi3VwdT7ykqGt0tGMM6BxocT8stxsYTNwyON3ZXYsqEsYBCIgh4Zb/R6/i4KdEh0AuXxWbYpumOXzX3YJV6IL5Uj8QHpY9g+OLjeHJFHE6qso1+vntebIHTYqsqhQeNkQlASz/j71XIcbACoYNwkqhAyMWjRETk6BQSC0i1XDxKREQNgLNChsr7ZVauSEhERORIpO7/AN4DEhER1TV1tXLA5QoBQhm0eFXxI/5PvgXpef4Axuqec4TqfVGtfCEIsGoBpiCU7YJNRETUkNgSWADKFlZa8nGEUi5DoJdtFZ5+1dyDsfK9BsdHyGLxj7YTWvm5IyOvpNoCkGqtWGOVosmx5eXlYdKkSdi0aZPe8bS0NKSlpeHgwYP4/PPPsW7dOkRFRdntujk5OZg1axa+//57iJX+R79x4wZu3LiBo0eP4ssvv0RWVhZ8fHzsdm2ynT2r2YuiiJs50tX+KvrjVArmPNABzgq55PNDOwViQLtkbD9v+vet1LzQPtATSLsIuDbC1itqHE4yHng7r20uebyDcBUqsYnu+8z8YqgyC/Sr/7XsCzT9Dej4UNlX41YAgKS0PBSUGIYoOzXzMjhWmxzhPpyIiICMfMsrEFY3S99TAMAXOy/h9fvb4e9TV/GofDuSxQD8pe1ptP2DkUFQOrsAIb2ApD13nnDxATwCyj6UNbL5QKC3C/6+0R1/a7tLPr/zQir2J6RjzgPtrf58t6pa+LobfT9EjoUBQgfhJFF9otTCnT2JiIjqK6kKFGot5z8iInJ8Uh/alKi5QyoRETkuViAkIiKqH6pSOWBs9+oLEF5KKwsQOqMEnzt9jsHyowAA/+3PAcFtgGaRAByjel9IYzf0/3/27js+ijL/A/hntqV3CKl0CNVQRIMgLSLFggiooCJyx4Gi4Q57OT27/BRLbCCgKGhEVE5AiXpJkCJREIgUCUECbEhCyqaHZOvvj5Alm53Zlr75vF8vXq/ZmWeeeXL3MpOZfb7PJyYUqSccLyiYFBPaogWcRERE7ZGrBQv9u/niRL51unFDAoC3bh+G57cfN+/zRC3my3/E78JQ/K7vafP8dOMg5JmCES7UFZsUmfyx3RCHbw1jANSlR7laAOmo+qTodQukJ88S2WIwGDBnzhwkJycDALp164ZFixZh0KBB0Gg0SEpKwt69e6FWqzF9+nTs3bsXAwcObPJ1NRoNpkyZggMHDgAAIiMjceuttyI2NhYBAQGoqKhAVlYWfvrpJ/z+++9Nvh41H1fS7KV+R5Vd1DkUwFF2UYd///coRvUMRlzvEMvCvEuqRIrwbOkGDWbI92LRsYPAL5nIGf0cEnbFwGDjl/ZxUw/R/YNkZ/FDg1Tal78/gVd2nMDEmFAsi++HYB8V0i+EoWLIBvh5KhCHEERfanv0vHjC0+B2lkDoDs/hREQEFFc6VkB4Iq8ceoMRCpHwrObizN8UJoMWwoGPsdtzI0JRjGxjN6Roh0MvUWZ126iouo2Y6YCnP9BjbF0acOggQCb9M2WoS7Ezs9DueLQGI57f9ieGRwfi4LlSh34GWwZH+DtUtNk31LfJ16KOgQWEbsJDLIGQBYREROTmxCaQGjh5lIiIOgGVyCIyTKEnIiJ3JlVAyGdAIiKi9qW9JgecKqiEDy5irXIlRssvT+QXdNVA0h3AolTAP8Jt0vuWxffDnqwih74vVsllSIjv1wqjIiIial9cLVio1tq/v5oA5JRWI7+8Bkrocbs8DQmKLQgVSrHTIw8Lyv5h83wjZPhCPxF9ZLn4xjAWe4xDLSawDo7wd/nvLme0RlI0ua+1a9eaiwcHDRqE1NRUdOt2OUlt6dKlePjhh7Fy5UqUlJRg8eLF2LXLOnnTWfPmzTMXDz700EN48cUX4elpnQb68ssvIzc3F76+nCzdHjR3mr0j6YP1vjyQgy8P5EAQYC7Ma5hsqC6pdqifCBTh/5SrcY3sOGSCCbhUv5ef8T9oDbafuc6auqHK5AEfodZi/2DhjFVbk6kuoSjt0qIxDR9fG/4MUsUCQ9pZAaG7PIcTEXVmNToDKmutn08UMsFqMdhavRF/FVYhJsyvRcbizN8UU2W/4UnFZ+iuuVzY10t2AbPku7HJMNGqvQDgTFEVBoT5A3FL6v456O2ULIcXxtUajCipavr7cpVchgcn9cWSjQfttu3HAsJOo+VKd6lVKUUKCHWcPEpERG5OIZpAyMmjRETk/lRcRIaIiDoZJhASERF1DG2RHKDWVGPzATU+2pONzQfUUGssJ3cajSb8VViJe+Q/WhQPmlXk1RURGvTm9D5ntMf0vtjoQCTOHSb6/qAhlVyGxLnDLCbHEhERdRb1BQvOEADkOFhI8n/JJzBTthspqofwovJjhAqlAIBrtXvQFaV2z3/bMAsJugex02idfjE4IsDlv7ucUZ8UTeQsg8GA5557zvx5w4YNFsWD9VasWIFhw4YBAHbv3o0ff/yxSdddv349fvjhBwDAfffdh9dff120eLBeREQEFApmcLQHTUmzF5Nf5ngBYcP+Uk8UYM6qfUg+mgcA0BmMyC11rK9i+OMKWXZd8WADvaoOQ4Dt73FNkOFPUw+cM3bFD4Yr8aZuFv6ufQhP6xbaOMeyeLDxz/CzSPFEkLcSEQHS/020BXd5Dici6syKq8TTB0f0CBLdfyxXPCW3OTjzN0VPIR/dZdapgAmKb6CCdQGfCUBC0mFkqEudGpMrCyVkF1c51b6x+ve+UwaHIdDb/rt3JhB2HiwgdBNKhfUbLZ2Bk2eIiMi9MYGQiIg6K7EEwlodCwiJiMh9KWTir7KNfAYkIiJqV1yaiO9ickCGuhQL1+/HuNfS8MhXf+D57cfxyFd/YNxraVi4fr95Ikdu2UXU6IxYZbgJ2w1XW3ck9wCuSQDkdROHl8X3s1t4V689p/dNHRKOzUtGI35AqNX/J4IAxA8IxeYlozF1SHjbDJCIiKiNuVKwcEVUABx9FaE3AjfLf7GakCo36TFPkeLUdRsbHOHv0t9drmjppGhyT7t27UJeXl0B1vjx4zFixAjRdnK5HAkJCebPSUlJTbruihUrAAC+vr549dVXm9QXta7mTrMvKK8V3e/I702twWguDsgrrXF4HlItVKLPnCFCBfoJ5+2ef4f2aYzTvo3FuuV42zAL/zOORD5CHLp2Y1qDEZkXKq32D4kMgNAaNw8nuctzOBFRZ1VcKX7fHd+/q+j+o+fFU3KbgzN/U3xkmIZso/UiF5FCMebJxZ/ZtAYjElOynBqTKwslOKKLjwqN7+qN3/sKgoArogLt9tUvtGUSIan94fIpbkIsgVBrMMJkMrXLP/iJiIiaAwsIiYios/IQKSBkAiEREbkzJhASERF1DPUT8VNPOL6isivJAclH85CQdFj0Wbg+cWFPVhES5w6Dp1IOADBChn/plsIHNZgoz6hr7BUEzN0EdL88ybM+vU+q/3odIb0vNjoQ6xaMglpTjV+zNaio0cHPU4m43sFMayAiIkJdwcLurEKHFmlXyWXo1cUHGTnWaRnX9AnBL38VW+1/XX/b5b87GrjXIw3v62dA58LUPQ+FDGeLqxEbHej0312uaEpSNHVeO3bsMG9Pnz7dZttp06aJnuesvXv34sSJEwCAGTNmwN/f3+W+qPU1d5p9frl4aqCjk/friwMWju0lelyAdfofAPzXMBbzFGlW++Nkx3HSEG3zmo3TZlvCoIj2+d+FOz2HExF1RsWV4gmEA8P90MXXA0WNCgxbMoHQmb8ptFDief18fKx6zWL/IWNfHDf2kDwvNbMAOSXVDr9fdXWhhMZmj4zC4Ah/i/e7jrz3HRYVgF0nrZMWG+oT6tMsY6T2jwmEbkJq9Q1OoCEiIncmNoGU9z4iIuoMxBIItXoWEBIRkfuSKiA0GHn/IyIiam9aOjkgQ11qd1IhcDk1ouHkCB0UuE/3T/xqHACddyhw7w6L4sF67pbeFx3sjdkjo3DvmF6YPTKKxYNERESXxEYH4vFpA+22U8oFJM4dhqwC6zSnQG8lnp8xWPS8Y6ZeomlUii590E1u3ZcjavVGzFm1D8lH85z6u0spE6zSKexxNSma6MiRI+btUaNG2WwbFhaG6Oi6wqoLFy6gsND25GYpP//8s3n76qvr/rv75ptvMH36dISFhcHDwwMRERG44YYb8PHHH0Ovb56J3NQ8mjvNXqqA0BmpmQXIyCkFAPjgIhqWDErNTNpvikGOqQsAwGASsMswFMu1S/Bfw5gmj6c5DIkIaOshSHK353Aios6kcYFgvS6+HhgsUrx+PLccxhaa5+vs3xRpxuFIM8QCAP40dsfftA9hpvY5/GaSfk40mYD00xqHr+HqQgkWfXjI8eItQ6ze7zry3tdeAmFkoBe8Vcyl6yz4/7QDFixYgE8++cT8+dlnn8V//vOfthuQCLHJowCgMxhF0wmJiIjcgYIJhEREHd7+/fvx22+/Yf/+/Th27BgKCwtRVFQEnU6HwMBADBw4EBMnTsSCBQvQo4f06k6u2LVrF9atW4c9e/YgLy8PXl5e6NmzJ26++WYsXrwYYWFhzXq95sQCQiIi6myYQEhERNRx1CcH3LfxoOSkSsD55AC1phrpp4vx4a7TdosH62kNRiQfy7fYVwMP/F33MPYvGAllaG/Jc5neR0RE1Dl09fOw2+buuB6I6x2C+z47aHUsrlcIqmoNkmlUb+jnYJrsN8gFE44Ze+BN4x148Ib78HR5jUOLIoipXyhh85LRTiU2fXkgp8WTookAIDMz07zdq5d4gltDvXr1glqtNp/btWtXp6954MAB83a3bt0wa9YsfPPNNxZt8vLykJeXh++//x5vvvkmvv32W4fG11hOTo7N43l5eU732dk1d5p9QTMUEJpMwIHsYkyV/YbnlOvxim4e/msca/scyPCB/mZ4owbfGsagAEFNHkdzGhLZfgsIAT6HExF1VMVV4gmEIb4eGBLpj58bpd9V1Orx4e6/cMPQCEQHN+/v9/q/KbIyjyAQVThikn7/W+95/XxsMWRjmzEOJgfz2SpqdA6Pqb6o0dEkZDFVWgN2Zha4VEh/RbTt+/9FnQEZ6lIm/HYSLCC0Y8eOHRbFg+2VVJGgVm+Et6qVB0NERNRKxCaQsoCQiKhjmThxIqqqqkSPFRQUoKCgAD///DNeeeUVPPvss3jiiSeafE29Xo/7778fa9assdh/8eJFaDQaHDx4EImJiVi/fj1uuummJl+vJYitKFzLAkIiInJjYgvIAHwGJCIiaq+mDglHV38PFJSLrz49MMwPr866wqFJCRnqUrydkoW0zAKXJlnkllpPHA0IDIGnjeLBhqKDvZt9IgsRERG1HyfzK+y2ycgpQ/ppjcXfIgKMMEGGa/qG4O2ULMmFE06bIpCovxWnTJH43ngVTJDBlHoK6xaMwuYlXkhMyUKqC3/naA1GJKZk2exHEOoKbBLi+yE2OhDhAV7Yk1XkUNGiK0nRRPVKS0vN2126dLHbPiQkRPRcZzQs2nvmmWeQmZkJlUqF+fPnY+zYsVAqlcjIyMDatWuh0Whw5MgRTJw4EQcPHkRwsHNJm/WJidS8lsX3a7bfUc2RQBiOYizOfQtXq34DAPxbuQE7a2NRCj+b531muK7J124Jvh4K9Oggz7Z8Dici6liKJRIIQ3xUGCyRfvvqjkysSM7ExJhQLLv0vOKo+oXmKmr08PNUIK53yOX7Rm0FXgnYgiDVapw1dcM07SvQ2yiZkgsCsk3hyDY5V5jn56l0uK0rCyU0ZjTh0iIyXk4X+h08W2LzuKZKizmr9iFx7jAm/XYCLCC0oby8HIsXLwYA+Pj4SE5qbQ8kCwhdWKWKiIioo2ABIRGRewgNDcVVV12F2NhY9OrVCwEBAdDpdDhz5gy+++477N27F7W1tXjyySeh0+nwzDPPNOl69913H9auXQsACAgIwN/+9jeMGDECVVVV2Lp1K7777jtoNBrMmTMHP/74I8aNG9ccP2azEksgZAEhERG5M6kEQj4DEhERtU8GowmaSvGVpwFgfEyoQxMdko/mOZXMo4IOWtifvNE31Neh/oiIiMj9ZV6wX0D4+9kSdPU9b/7cRziPt5TvYYV+LnqFXIVnM4/ZPP9twyyLz6mZBcgpqbZIWtpxNB8vf/+nU2MX68dWYlN9UrSjiYVMoCBXVVZWmrc9PT3ttvfy8jJvV1TY/29STEnJ5YnRmZmZCAoKQkpKCoYPH27eP2/ePPzrX/9CfHw8jh8/jrNnz+LJJ5/EqlWrXLomNa/mTLO/ILGYjaNmy3/GfxSfwFd3uRAxRKjAU4rP8Ih+idP9yWWCQ++ylTIBeqPJ5s/vqkER/pBJvGcnIiJqimKR98C+Hgp4KuWix+qZTEDqiQLsySpyqHhNaqE5QQDi+4fgme5/oPuhlehWmQ8IQD/hPO6S/w/rDVNF+1PJZXjmpoH497fHnFrQRRCAuN7OLUDhzEIJUhouIuOoDHUpEpIOO9S3qwWK1LE4lrHZST3yyCNQq9WIjo42FxK2V0q5+B/2OgMn0BARkfsSm0Cq5+RRIqIOJT09Hfn5+di2bRtefPFF/O1vf8Ps2bMxd+5cPPHEE9izZw8++eQTCELd7/wXXngBubm5Ll/vhx9+MBcPhoeHY//+/Vi5ciXuvPNO/OMf/8D27duRmJgIAKitrcXChQuh1Uq/zGorHgq51T6t3tAGIyEiImodUgWEfAYkIiJqnworam3ep88U2V+4tX5yg6OTKroLF/A/1cOYIttvt23friwgJCIiojonHSggBIDkY/kATLhNnoZtqqcxVHYGb6k+gKYw1+n0QJMJSD+tMX+ODvZGkLfjCRa2+pk9Mgr3jumF2SOjLIoH600dEo7NS0YjfkAohEavWwQBiB8Qis1LRjN5gjoco9HyueH111+3KB6sFxYWhs8//9z8ef369SgvL3fqWmq12ua/3377zbUfgjB1SDi6+HrYbGOvwEBnMKJIIgnJURUmL/gK1imGcxS7MFpmu2hczF1Xd4dKIiSknkouwzvzhmPigFCn+3dEXulFZKhLW6RvIiLq3IqqrOdVhfiqkKEuxQvbj9s9v754zdZ9KvloHuas2ofUE9bp7SYTUJW1C913PwJU5lsc+5fiKwTB8m+9hs89d8X1xMQY5+69k2JCRZ+1bKlfKMHe3wP21C8i46i3U7Icfr9eX6BI7o0FhBJSU1OxZs0aAMD7778PPz/bseNtTSx9AgB0TKAgIiI3ppBZ3/+MnDxKRNShDBkyxFwcKGX+/Pm48cYbAQB6vR7JyckuX69heuG7776Lfv36WbV58MEHcdNNNwEA/vrrL6xfv97l67UUsWdAJtATEZE7UzCBkIiIqEM5X3rR5vEzxfYLCJ2Z3BAlFCBJ9SK6ywrxrjIR02XpNtszgZCIiIgA4KLWgHMaxyZf+qMK7yrfwf8p18BbqCtM6YJSjDj4JOBCVlRFja7RZ73TfYj144j6xMJdj0zE63Ni8exNg/D6nFjsfnQi1i0YxcQJajJf38t/b9fUWBdhNXbx4uXnB1fnaTY8z8fHB3fddZdk29jYWMTFxQGoW1B07969Tl0rKirK5r/wcBbguspkMqGi1vbvtZJq28cLK2pFC7t7d/VxeBw/GEfhd69rRI/daOd5szFBABaN6+1w8fay+H5NLi4Qoy65iDmr9iH5aF6z901ERJ1bsUjhfoiPqtmK1xxZaG6fcRB+M8ZY7Q8QqrFc8RWAusCuFbOGWj33OHPvVcllSIi3nmvmCFuLuTiq8SIytqg11UjLLHCqf2cLFKnjYQGhiOrqaixatAgmkwm33367eaJqe6aU+KWl4wRSIiJyY+IJhLz3ERG5o8GDB5u38/PzbbSUlp2dbV7xs1evXpg5c6Zk23/961/m7aSkJJeu15I8RAoIa/VGmJxd5piIiKiDkEogZAEhERFR++RIAaGtxeCcmdwQiUJ8oXoRkUIxAEApGJCofBc3y36RPKcPCwiJiIgIwKmCSofTA2+Qp+NGuXXRSHTxHtwrd37hQz9PZaPPCqf7EOvHGY4kFhK5IjAw0LxdVFRkt31xcbHouc4ICgoybw8dOhQqlcpm+yuvvNK8/ddff7l0TWp+1VoDanS25/388lexzeMXysWLVm+8IsKJ4gA5iq59AZUmT/O+IpM/lmnvx/qgBIf6qFefUuRo8XZzJRSJcSThiYiIyFnFldYJhN4qRbMVrzlWiChgpe42q70GkwA95ABMuKZPF9w+qrvVc4+j916VXIbEucOatOBKw78HZgyLcKkPRxeRST9d7PDzbj1nChSpY2IBoYgnnngCp0+fRnBwMN5+++22Ho5DpH5hMYGCiIjcmdgEUk4eJSJyT6dOnTJvh4WFudTHjh07zNtTp061mXx47bXXmldH3b17N6qq7CcjtCaxBEKTCdDzPkhERG5KLIEe4DMgERFRe5Vrp4CwRmdEQYX1ytT1HJ3c0BWl+Fz1EqIEy0nJCsGIN5XvYaLskOh5fbuygJCIiIiAzAsVovsnxnS12pdkmIQUw3DR9rfI90IGx+doCQIQ1zvYYl9c7xCnUyjE+iFqD2JiLqe/ZGdn223fsE3Dc50xYMAA83ZAQIDd9g3blJeXu3RNan5iBQiN7furyOaiqlIFhFf1DHaqOAABUXhNfzsA4Ev9eFxX+xq+NY7FHVd3b1JKkSPF2/YSilxNLQJsJzwRERE5y2QyobjK+j2vzmBsluI1Zxaa+9U0EHsMlxeo/8UwCNO1r+A5/T0ABIzuEyJ5rq17b+O04OYQHeyNsX27uHSuo4vItGbKPXUcri1d5MZ++eUXvPvuuwCA119/Hd26dWvjETlGKTJ5FAC0ehYQEhGR+xJPIOTkUSIid7Nt2zZs2bIFAODp6YkbbrjBpX6OHDli3h41apTNtgqFAsOHD8fu3bthMBhw/Phxu+e0JrEEQqDuGVAqoZ6IiKgjk6gf5DMgERFRO2WvgBAAsouqEBbgKXrM0ckN5fDGEVMv9ID1JJIMUx8cMFlPPg7xUSHIx3YaCREREXUOJyUKCJdd1x9pmYWN9gp4RLcYybLHESqUXt49ahHev3ALjCfLHL5ufRpVQ9HB3pgYE4rUE46ndIj1Q9QeDB06FMnJdcmc+/fvx8SJEyXbXrhwAWq1GgAQGhqKrl2tC3gdERsba94uK7P/32PDNo4UHFLrKBIpQLBqU6lFVkEl+nfzEz1+oVy8j27+HhjbrQs2L/FCYkoWUkR+3/qo5Ph8URxiowOxdvdpbDBMxh/G3jhkulwEOK5fV0QGeiEh6bDNgI+mphTVJxSpNdX4NVuDihod/DyViOsdjOJKLRJTspCaWeB0cQZwOeGJ9xAiImqq8ho9dAbrm5GrSbqNi9eO/nEQz8g/gSe0eEK/yO75b+jnIEwowWv62/CDcRSAy3OMR/eWLiAEbN97W+KeWb+IjDP3cmcWkWmLlHtq/1hA2EBNTQ0WLlwIo9GI+Ph43HvvvW09JIep5OJLioj9QiYiInIXCpECQiMnjxIRdVi7du2CRlO3kpRWq4VarcaPP/6IH3/8EUBdUd+qVatcXuglMzPTvN2rVy+77Xv16oXdu3ebz3W2gDAnJ8fm8by8PKf6a0gyhV5vhI+Hy90SERG1W9IJhFxAjYiIqD06X2K/gPBMcZXkqs+OTm6ohQoP6h5EgSkICxXJ5v0Zxt64R/s4VD6BQJVlgkWfUKYPEhERUZ3MfOsCwiBvJWKjAtAjxBtni6stjmngj3/q7sdG5Ssogw+eNCzBjB5/x/1XeCHtr302C0nqiaVR1VsW3w97soqa3A9RW5s6dSpee+01AMCOHTvw6KOPSrb9/vvvzdvTp093+ZrTpk2DIAgwmUw4cuQItFotVCrphUMOHDhg3nY19ZCanyMJhADwy6kiyQLCfIkEwm6XFrCJjQ7EuhmhWCQAP/1pWURYozciJqyuX7WmGkbILIoHASAqyBv9uvmZCxEbF/EJQl2Bd0J8P5eLBxuKDvZGdLBl0UJUkLe5wOHdtFPYtF/tVJ/1CU+zR7KAkIiImqa4UrxwP9jXtQXc/DyVgL4WOPEdcPgzTD2VAkFhgt4kw9v6W5EP20WAB039MVn7fzDB8rtlP08FBkf4OzQGsXtvS2jpRWRaukCROiYWEDbwzDPPIDMzE15eXli9enWz99+Sk0elEiZ0DrxQIiIi6qiYQEhE5F4effRR/Prrr1b7BUHA+PHj8dxzz2HcuHEu919aWmre7tKli932ISGXXzo1PNdR0dHRTp/jKJVUAiGfAYmIyE2JPf8BAG99RERE7dN5BxIIzxRVSR5zZnKDCTI8r78b+aYgPKlMwlFjT9ytfRwV8LYqHgSAPl1ZQEhERER1xBII+3fzwx85ZcjRiP8984txCB7TL8IuwxW4gGCkJB3G5iWjkTh3WJPTqGKjA5ulH6K2Nn78eISFhSE/Px87d+7EwYMHMWLECKt2BoMBiYmJ5s933HGHy9eMiorC+PHjsXPnTlRVVWHjxo1YuHChaNuMjAykp6cDAPz8/DBmzBiXr0vNS6oIobFf/irGgjHiC8ZeKLMuIPRWyeHncWm69O+fAN8tx+0DX8RP6G7RzmA04eSFClwRFQi1yMI4Xf084KWSA2j9lCIx0cHeiJEopLSnccITERF1DmpNNdJPF6OiRg8/TwXieofYLJaz175Y5P0rAAyLDsTWjFzXitcObQC+e6hu36VjCsGIuxT/w+v62+3207h4EACu7hUMhYupiC2pJReRYco9iWEB4SX79+/HG2+8AQB47rnn0KdPn2a/RktOHpUqIOTkUSIicmdiE0gNLCAkInI7kZGRmDx5Mvr1a9pKupWVleZtT09Pu+29vLzM2xUV1pMI2pJUAWGtjs+ARETknsQS6AEmEBIREbVXuQ4UEGbbKCB0fnKDgA8NN+GsKQy/GgegHNJFgn2ZQEhEREQAyi7qkNegyMQDWughR0yYH95OyYLBxkzXzYYJ5m2twYjElCysWzCqWdKopg4Jb7VUK6KWIpfL8cwzz+D+++8HAMyfPx+pqakIDQ21aPf444/j8OHDAIAxY8ZgypQpov2tX78e9957LwCYiwTFvPzyy7jmmmsAAA8//DCGDx+O4cOHW7S5cOEC7rzzTvPnhIQEi+8EqW1JFSE0ln66GAajSXTe0IUK6wLCbv6eEAQB2JsI/PRvAMCkP/+NCbJ/YadxmEXb47nluCIqEOc01Vb9dBcpsGitlCIpfp6uTQP381Q280iIiKg9y1CX4u2ULKSJPGNMjAnFskbPGI62lyr+j+nm53rx2uBbgeQnAIPl3wXz5Cl4Rz8TtXA+3TCut+3kwrbS0ovIMOWeGmMBIQCtVouFCxfCYDBgxIgRWL58eVsPyWlSk0d1ek6gISIi98UEQiIi91K/0icAVFVV4dSpU9i6dStWrlyJp556Cm+88Qa++OILXHfddW04Ssep1Wqbx/Py8nDVVVe51Ld0AqHBpf6IiIjaO5lEASGfAYmIiNqfihodymv0dtudLbaejNnQsvh+2J1VCJ3B/v1eKRegkMnwg26U3bYsICQiInIvziZo1MtqkD7YW8jFe8pE/GyMhc73WWxIP+vUGFIzC5BTUt1saVTtIdWKqKkWLVqELVu24KeffsKxY8cQGxuLRYsWYdCgQdBoNEhKSsKePXsAAIGBgVi9enWTrzl69Gg89thjWLFiBUpKShAXF4d77rkHY8eOhVKpxOHDh7F27VpoNBoAwJVXXomnn366ydel5lNc6VgBYXmNHsdzyzE0KsDqWL5IAmE3PxWQ8jywe6V5n8yowwfKtzBf+zj2mwaY9x/LLYfJZILawQLCthbXOwSCANcSnoiIqFNIPponWaBmMgGpJwqwJ6sIiXOHYeqQcKfaF0ncu0N8PZwqXlPKhcvFa97BwIAbgGNbLNoEC5W4Wf6LxYIujrqmTxenz2ktLbmIDFPuqTEWEAJ48cUXcfToUcjlcqxZswZyubxFrtOSk0eZQEhERJ2RWAKFkZNHiYjcgo+PD2JjYxEbG4u77roLY8eORW5uLm644QYcOHAAQ4cOdbpPX9/LEwRraqy/OGrs4sXLaQl+fn5OXy8qKsrpcxzloRB/bq3lIjJEROSmpBMI+QxIRETU3uSW2n/mBoAzxVUwGk2SCwXERgdi6YS+eCsly2Y/9ZMbUk8U4MsDOXav26erj0PjIyIiovbN2QSNxk5eqAQA3Cz7BS8r18JXqMFA2Tl8c+FamEzOvd83mYD00xrMHllXWNJcaVRtnWpF1BQKhQJff/015s2bh+3btyM/Px8vvPCCVbuoqChs2rQJgwcPbpbrvvrqq5DL5VixYgW0Wi3WrFmDNWvWWLWbMmUKkpKS4Onp2SzXpeZRXCWeYiTml7+KRAsIC8qt+5gg/8OieLCel6DFOtVruL72/5CPumSi43nlKKyoFf3eNTqo/aVVRgd7u57wREREbi9DXWq3eAyoqzlJSDqMl2fq8eSWow63nzVC/NkpxFeFLr4eFsVrISjDDfJ0bDBMhgmWtS/9u/nh97MlOHmhom5RmOF3WRQQ1poU2Gq4BoeNfR38yS8L8lZiQJjz875aU0suIsOUe2qo0xcQZmRk4NVXXwUALF++HCNGjGixa7Xk5FGVRAGhjgWERETkxuQy6/sf0yeIiNxPr1698Oqrr2L+/PnQarV46aWX8MUXXzjdT2BgoHm7qKjIbvvi4mLRc9sDyQRCFhASEZGbEkugB1hASERE1B7lll603wh1i+Dkl9cgIlB6AmZ9caEAo9WkksaTG4K8VXYLCL2UckQEtL8Jn0REROQcZxM0xJzOK8LLirWYp0i12H/DX8/hbeEFnDWFOTWmihqdU+2JOgM/Pz9s27YN3377LT799FPs378fBQUF8PPzQ58+fXDrrbdi8eLFCAiwLgJripdeegm33XYb1q1bh59++gnnz5+HTqdDaGgorrnmGsyfPx/Tpk1r1mtS8xBLIAwP8ERhRa3VfKAvD6gR7KOySJ6tqtWjolZv1UdJ+Dgg+gFg37tWx1brb0I+Lqfx/ZlXjjPF1umDANptUbczCU8quexywhMREbm9t1OyHA6k0hqMeGXHCafa7z5VaLVfEIAgbxWAuuK1/95ZjPwdKzCmfAc8BB2yTeHYbbzC4pxjueU4lnvcfP6k/iH4wCccKg9vYPiduDO9Lw4Uu1b6dHWvEMlF7NqbllpEhin3VK/TFxCuX78eOp0OMpkMSqUSL774omi7Xbt2WWzXt4uJicGcOXNaZay2KBXiv9R0ek6gISIi9yVWP8/Jo0RE7qnhl3g7d+50qY+YmBikpaUBALKzszFhwgSb7bOzsy3ObU+kFpFhAiEREbkrJhASERF1HDkOFhACwJmiKpsFhAfOliAUJdioehkv6u/CLmMs5DIBL90yBGP7dbGY3DCqZzCigryQUyJ9fYVMwJHzZVxNmYiIqANzNkFj8xIv0Xt/yflTmCnfY7Xfw1CFD5RvY6b2OdRC5fC4/DyVDrcl6mxmzJiBGTNmuHz+ggULsGDBAqfOiY2NRWJiosvXpLZRVGmdHhgd5I3IQC8cOFtisf+vwio88tUfFsmzvp7iU6K7BXgBY14EasqAQxvM+1N6PYL3/hxu0bZaa8CeLOtiCKD9FhDGRgdaJDxJUcllSJw7jM/ERESdhFpTjbRMxxNqAaC4yrqY3xaxd7HB3qq6xWHL84C0FzEo4wsMMuqBS1/3/sNnN3ZXXGF1Xj2TCUjJLEa8/Gk8dcckXN27Cw5+/5NT42romr4hLp/rbphyT52+gNB0KYPTaDTi5ZdfduictLQ086TTGTNmtI8CQonJo45WgBMREXVECtEEQt77iIjckZ+fn3m7pKTERktpQ4cONW/v378f9957r2RbvV6PQ4cOAQBkMhkGDRrk0jVbigcTCImIqJORSiBkCj0REVH742gCIQBkF1fhmr5dRI8ZjSYcOVuID1Vvo7/sPNYr/w9v6mfht+iFuOOq7lbtZTIBV0QG2CwgrKjVY86qfTbTiIiIiKh9czZBIzElC+sWjLI6tlMThGf0C/Ca8kOrYwJMCEYF8uDYRFNBAOJ6B9tvSERENokVLYT4qmArM6hh8uz9E/uItunm71n3y/qmt4HaCuDPbcAtH0AnHwf8edCqffKxfNF+urfjCfdTh4Rj8xIvJKZkITWzAKYGr84FAZgUE4qE+H4sHiQi6iTUmmq8k5plcT9oLSG+lxZiEWTAH5sBo2U68Bj9r+iCu1AE2ynUakMQEr7IwP0T+0DsK2G5THBosdlgb8cXhiFyd52+gNBdSKVP6FhASEREbkxsAqnRVLdAgCB0jMhxIiJyTFZWlnm7a9euLvUxdepU83ZycrLN+8Xu3btRWVkJABg3bhx8fHxcumZLYQEhERF1NoIgQCbA6sshJhASERG1P2IFhAqZIFr4f6aoSrKfkwUVeMCwAaMUJwEAMsGEh5Rf4VRNHnDxM8AryKJ9hroUP/15we747KURERERUfvlSoJGamYBckqqLZKLiyprUVylxWaMR5zsT8yS7758wsh78VbhbOSdLHP4GpNiQi36JyIi5xmNJmhECghNMOGn4/Z/92sNRryTekr0WFiAR92GTA7cugY4/zvQYzQGa6pF25+8UGm1TykX6goR27HY6ECsWzAKak01fs3WoKJGBz9PJeJ6B/M+RUTUSWSoS/F2ShbSGhWTt6YQn0v3Xb9uwMh7gN8sF22RmfSYJd+F1Yab7PalNRjx+a/nRI+tmHUFHt6cYbeP5V9mQCEXuKAcEQDxGYedyFtvvQWTyWT337PPPms+59lnnzXv/+9//9t2g29AJTF5lAWERETkzqQSKDiBlIjI/axatcq8PWbMGJf66N27N0aNqltlODs7G1u2bJFs++abb5q377jjDpeu15KkngGZQk9ERO5MLIWez39ERETtz3mRBMCeXXwQ6K202n+mWHyyJgAUpn+Bvyl2WO3vW7oX+N9zVvvfTsmCzuDY3wb1aURERETUsaSfLnZ6EqzJBKSf1ljsO5lfcWlLwL919+KUMQKVJk/sG/5/wE1v4f7JQyQXc29MJZchIb6fc4MiIiIrZRd1ou97T+RViC5II0bqfbFF4Z9CBfQYDQCICvKCn4djOSxRQd6S85Tam+hgb8weGYV7x/TC7JFRLB4kIuokko/mYc6qfUg90XbFg0CDBEIAuCYBkFm/F+4hiKf9iimoqLXa1zfUF/1CfW2mFNerX1AuQ13q8DWJ3FWnLyB0F0qJl1ZMnyAiIncm9WLO0ReHRETUtlatWoW0tDSYbLy1MhgMePXVV/H++++b991///1W7Xbu3AlBECAIAnr27CnZ33PPXZ5g+MADD+DUKetVKN99911s27YNANCrVy/ce++9jvw4rUqqgLBWb2jlkRAREbUesWdAPv8R4NRyrgABAABJREFUERG1P2IJhJGBXugZ4mO131YCoSo7TXS/IaAncN1/LPY1JY2IiIiIWpZaU43NB9T4aE82Nh9QQy2R9uRI+4oavUtjqKjRWXzOvFBh3q6GJ5bo/ombtC/Ba8TtAOoSnBLnDrNbRKiSy5A4dxhTjYmImkFxlXVxAACctbHwjKNC/cSTAwVBwMAIf4f6iA5mER4REbVfGepSJCQdbhcLj3fx9bj8ITAaiL20cLtMgeyoWzCp9nU8qV/UpGvEDwzF2ylZcPSbYi4oR1THsaUzqN1TysULKLQOrrJJRETUESmYQEhE1KGlp6fjvvvuQ3R0NCZPnoyhQ4ciNDQUKpUKpaWlOHr0KL799lucOXPGfM4TTzyB8ePHu3zNadOm4d5778XHH3+MvLw8XHnllfj73/+OESNGoKqqClu3bsX27dsBACqVCuvWrYNKpbLTa+uTTCDkIjJEROTGxAoIDUbe+4iIiNoTvcGI/PIaq/0RgV4I9lHhcKNVns9qqmE0miATuc8/ol2E63QheELxOZRC3YI5tVDB444NgFegRdumpBHNHslJoERERC0hQ12Kt1OykJZpmX4hCMDEmFAsi+9nUXjnSHs/T8emuqmgw93yH5FnCsH3xjj4eVomXpxsUEAIAKdMUQCAfqG+5n1Th4Rj8xIvJKZkIVVkTJNiQpHQ6GcgIiLXFVVqRfc7+qjXS8hDtFCAXcZYi/0hPirJ71YBYHCEP37L1kgerxcd5OXgSIiIiFrf2ylZzVI8GOKjQnGV+D25IRmM6CPkIuvSs1TjPiyM/RcgVwJj/om0Y0acPnW8yeOMjQzAh7tOO3VO/YJyTOalzowFhG5CKoFQ1w6qyImIiFqKVAKhoS3z14mIyGlqtRofffSRzTYBAQF45ZVXcN999zX5eh9++CEEQcBHH32EsrIyrFy50qpNUFAQPv74Y0ycOLHJ12sJHgq56H4WEBIRkTtjAiEREVH7d6GiFmK358hAT9H7tlZvRG7ZRatJGwUVNThXchEfYRr+MPbC+6pEhAql+G/kw7g9/AqrfporjYiIqKVt3boVGzZswP79+5Gfnw9/f3/07dsXM2fOxOLFi+Hv71gCjj0TJkzAzz//7HD77Oxs9OzZs1muTQQAyUfzJNMvTCYg9UQB9mQVIXHuMEwdEu5w+2duGghBgOTCAQKMmCH7BQ8rv0SUUIQSky/2awcirnewRbuTFyqtzo0O9oKPh+VUutjoQKxbMApqTTV+zdagokYHP08l4noHc9IpEVEzK5YoIHREF5ThE+WriBCK8YT+79hsmGA+Fuovnj5Yb1C4Y39/dWcCIRERtVNqTTXSMgua3I9KLsMT0wbgyS1HbRYjBqEc7ykTMUh2FvG1r6MYARbHQxomEAJASB/gxjcBAH6e6iaPM9hHhYpaAxeUI3IBCwjdhEqqgJCTR4mIyI3JBYkCQibwEhF1CImJiZgxYwZ27dqFQ4cO4a+//kJRURF0Oh18fX3RrVs3XHHFFZgyZQrmzJmDgIAA+506QKFQYN26dbj77ruxbt067N27F3l5efD09ETPnj1x8803Y8mSJQgPD2+W67UEqVUya/kMSEREbkwshd7IAkIiIqJ25XzJRdH9kUFekEm8zz1TZL3q88GzJebtA6YBuKH2Zdwo34eBw+aJ9uFoGpH1eUr7jYiImkFlZSXuvPNObN261WJ/YWEhCgsLsW/fPrzzzjv48ssvERcX10ajJGoeGepSyWLAhrQGIxKSDuPlmXq7E1Tr2z+/7U+M7B6EAw3+Vqg3WDiDFcoPMUR2xrwvSKjEqqDPEBU417zPZDLhZH6F1fkx3fwkrx0d7I1oFo4QEbWo4qpal86Tw4D3VW+hu6wQAPCa8kOEQ4NEw0wAAsL8PWyePyjCsQJC3geIOofWWvSloqICP/74I9LS0nDw4EFkZWWhtLQUXl5eiIiIwFVXXYV58+ZhypQpECTeqYk5deoUVq9ejR07dkCtVsNgMCAyMhLXXXcdFi1ahGHDhjXL+Kl9ST9d7HQxXWMqucy8wIuvpwJLPzskGuQxSDiDD1VvIEooAgD8W7kB/9Q9YNEmxFdldV69uN4hNheFccTEmFBU1XJBOSJXsIDQQf/5z3/wn//8p62HIUkmE6CQCVYrdzKBkIiI3JlcLv5wzAQKIqKOwd/fHzNnzsTMmTOb3NeECRNgcvLt0oQJEzBhwoQmX7stSC0iY2+CBRERUUfGBEIiIqL2L7dUvIAwIsALnkq56LEzxVUY26+Lxb7fGxUFFCIQHxumIaVnkGgfrkw8EQRYpREREbUEg8GAOXPmIDk5GQDQrVs3LFq0CIMGDYJGo0FSUhL27t0LtVqN6dOnY+/evRg4cGCzXX/Lli1224SGhjbb9YjeTsly+F211mDEKztOONVeAETnSJXDC/0F6zSLkdV7gGPfAENmAQDyympQITLZtL+NAkIiImp5UgmEAgBbj3rLFF/jKlmmxb7lyq8QLhTjaf1ChAXYTiDsF+oHpVyAzs5i5UwgJHJvrbnoyxtvvIGnnnoKNTU1VscqKiqQmZmJzMxMbNiwAddeey02btyI7t272+33ww8/xD//+U9cvGj5fu7kyZM4efIkVq9ejWeeeQbPPPNMk8ZP7U9FjWvFdA19OH8kJsTUvRuYOiQcV/U6i32niy3a3Cjbh9eUq+ElXL5n3yL/Bd8YrsUuY6x5XxcbBYTRwd6YGBOK1BOuJyb2DfXhgnJELmIBoRtRymXQGw0W+zh5lIiI3JlY+gQAGJu6nAoREVE7J5lAqOMzIBERuS+xAkIDCwiJiIjalfNSBYSBXvD3Ep+ccaaoymqfWKpQkLcSvbv4iPbhysSTSTGhVsmHREQtYe3atebiwUGDBiE1NRXdunUzH1+6dCkefvhhrFy5EiUlJVi8eDF27drVbNe/5ZZbmq0vInvUmmqkZTo3EbS4SrxgRMqBcyUYHO6Po7nlltc2dcMGw/X4m2KH9UnfPQz0ux7w8MPeU0Wi/caEsYCQiKgtiSUQymUCxvXrgrTMQtFz4mTH8YD8W9FjMTI1lNAj1M92AaFKIUO/UD8czyu32S6az49Ebqu1F305efKkuXiwPh1w5MiRCA0NRU1NDdLT07Fx40ZUVlZi9+7dmDBhAtLT020u/LJx40YsXrwYACCTyXDHHXcgPj4eCoUCe/fuxSeffILa2lo8++yz8PDwwGOPPeby+Kn9cbWYrqHG38Pml1sWuHpAi8eVSRbFg/VeVHyE67X/hxrUpf6G+NhO/10W3w97sopcrnN546eTePamQVxQjsgF4jMOqUNSiqQwafWcQENERO5LLhP/U4YJFERE5O48JAoIuYgMERG5MyYQEhERtX9iBYQyAQgL8ESAlxLBPtarT58ptiwgrNEZcPR8mVW7kT2CIAjii8oBdRNPVHLHvv5WyWVIiO/nUFsioqYwGAx47rnnzJ83bNhgUTxYb8WKFRg2bBgAYPfu3fjxxx9ba4hEzSr9dLFTEzhdYTIBf+ZViB57Vz8D5SYvy50RI4DbPkVGgQEL1+/HI1/9IdkvERG1HbEEwmAfFf55XX/JZ71jxp743niV1f6zxlD8TfswauBhN4EQAAZH+Ns87u+pQIA3E4uI3FXjRV8yMjLwwgsvYO7cuVi6dCn27NmDhx56CADMi740hSAIuP766/Hjjz/i3LlzWL9+PR588EHcfvvtuOeee/DBBx/g6NGjiImJAQBkZ2fj8ccfl+yvsLAQS5cuBVBXPLhlyxZ89tlnWLhwIebPn4/Vq1dj586d8PauK4R++umnkZmZKdkfdTxxvUNg47WpQ040eMa6qDVYvbOthQqLtctx0WT9flcmmBAuaMyfQ2wkEAJAbHQgEucOc/hdbmM6gwnPb/sTI7sHOXUeF5QjYgGhWxFLoNBx8igREbkxkdp5AIDBwG93iIjIvUm9RNPq+QxIRETuSyyF3sgCQiIiolan1lRj8wE1PtqTjc0H1FBrqs3HckUKCLv5e0J56Tm2R4j1BI3sRgmER86XQSfyjndkD9urQzs68UQllyFx7jDERgfabEdE1Bx27dqFvLw8AMD48eMxYsQI0XZyuRwJCQnmz0lJSa0yPqLmVlGjb/Y+FdBjsuwAZskuJ3MaJKr9SuCPD/Qz6rY9o4DZHwOLUpFc1RdzVu2zmVb8yFcZSD6a17yDJyIih4kVEIb4qGw+61XAGw/oEvCE7m+oMdUV+GlNcjygS0AJ6ooCu/nbTkECgEF2Cgijg1lsQOSu2mLRl5deegk//PADJk+eDJlEgEKPHj2wadMm8+dNmzahurpatO3rr7+O8vK6FNWlS5fi5ptvtmoTFxeHF154AQCg1+stfmbq+KKDvTExRjqh0hEn8i8XEGYVVIgusHLM1BOP6f5hsS/dOBA3176IbFM4gLp6Fl8P+4mIU4eEY/OS0YgfEOpS8aPWYIQA6flTjXFBOaI6LCB0I0qRX4AsICQiIncml/jjX2/k/Y+IiNybTCZIpNDzHkhERO6LCYRERERtK0NdioXr92Pca2l45Ks/8Pz243jkqz8w7rU0LFy/HxnqUtECwojAyylAvUJ8rI7fXboKxn0fAEYDACDl+AXR64/sYX9FaVsTTwQBiB8Qis1LRmPqkHC7fRERNYcdO3aYt6dPn26z7bRp00TPI+pI/DztTxR1jAmDhWw8q/gEv3osxRrVG3hc+TkUsF+g+JFhKp7U/Q0Ph34IDLkVGTllSEg6DK2dOVQ6gwkJSYeRoS5tpp+BiIicUVRVa7Wvi29d8V/DZz1rApIM8Xin92pkGSPxqn4ejph6m49287efQDgo3HYBYXcWEBK5rbZY9CU42PYiWfViY2PNKYTV1dU4deqUaLuGhYb/+te/JPtbtGgRfHzq3s1t3boVFy9av8ejjmtZfD+XE/0AIPNCuXm7YTFhY1uN12CV/iYAwOeYiru0T0CDy/fRLj4qCA5WBMZGB2LdglHY9chEPDl9oNNjPnCuBM/eNIgLyhE5obne2lA7wARCIiLqbMTSJwDAKLHiJBERkTtRyWXQGQwW+2r1BonWREREHZ9YAaGBBYREREStIvlonuTEe5MJSD1RgD1ZRaKrRUc2KCDs2cWygDBOdhwL5DuAH3ag+vckrFDdj09O+1n1IVz654j6iSdqTTV+zdagokYHP08l4noHIyqIkz6JqHUdOXLEvD1q1CibbcPCwhAdHQ21Wo0LFy6gsLAQXbt2bfIYbrzxRhw6dAiFhYXw8fFBREQErrnmGsybNw8TJ05scv9EDcX1DoEgQDStwhmPKDZhqWKrxb6uQjnGyf5AqlF8Une9WqjwuSEeXXKrYTKZ8HZKlt3iwXpagxGJKVlYt8D2f69ERNT8RBMIfVXm7fpnvY/2nMbz2/+0avv1+UCs1b6EWigt9oc5UEA40E4CIQsIidxXe1/0xd//8u8nsYK/48eP4+zZswCAgQMHolevXpJ9+fn54dprr0VycjKqqqrw888/Y+rUqc0/aGoT9Ym9Sz8/ZPP7U5VchsER/jjUaOGUrAuVMBhNkMsEZNooIASA/9PfjnTjAOw0Drc6FuJrP/m3sehgbwR5K+03bMRkAjyUcmxeMhqJKVlIzSyweBYVBGBSTCgS4vuxeJDoEiYQuhGxBEKtgRNoiIjIfYlNHgWYQEFERJ2D2CIyTCAkIiJ3JpdZ3/uYQE9ERNTyMtSlDqX2aA1G1Io8l0ZIFBB6QIuXFOvMn72LMvDv8/fhMUUSPGA5cdQE4M61vyL5aJ7D444O9sbskVG4d0wvzB4ZxeJBImoTmZmZ5m1bEznF2jQ8tym+++475ObmQqfTobS0FMePH8fatWsxadIkxMfHm9M2nJWTk2Pzn6v9UscWHeyN8f2bXvi6xzhUdP8s+S7R/aF+1pNUiyq1OHSuFGmZBU5dOzWzADkl1U6dQ0RETaPVG1F2UWe1P9hHZbXvpthI0T7yy2tQCxUaLj+jlAsI8rbuozF/TyW6+UsXPKRlFjKhlshNubLoCwDzoi8tSavV4uTJk+bPPXr0sGrjzPgbt2l4LrmHqUPCccuwCNFjAoD4AaHYvGQ0xsdYP7PV6o04U1wFAHYLCI2QiRYPAuL3bkdU1NhPmxc/T2eRZPj6nFg8e9MgvD4nFrsfnYh1C0axeJCoASYQuhHRAkKmTxARkRuTS0Sd61lAT0REnYCHQg7A8os0R1dRJiIi6ojEUuhZP0hERNTynEntERMZeDntoWfI5SK++xXfoo/MsrhEIRgxX/4jNugnIxddLI5pDUYkJB3G5iVenPRBRB1GaWmpebtLly7SDS8JCQkRPdcVQUFBmDx5Mq688kpERkZCLpfj/PnzSElJwY4dO2AymZCamorRo0cjPT0dYWFhTvVfP3GWqLEhEf7Ymdm0ydTpxoHIMXVBlFBksf862UEEoBJl8LXYP2lAKL7Yr7bqZ2tGrtNpiCYTkH5ag9kjufgAEVFrKam2Th8EgC4iKUZd/TwwIMwPJ+wUNwBAqJ8nZBKLkzeUfDQPBRW1ksdPXqjAnFX7kDh3GKYOCbfbHxF1HK4s+qJWq83nNkdqvJTPP/8cZWVlAIARI0aIPrO15qI1OTk5No9zEZn2QSx7QykXkPrQBERfStTNK7NOswSA02fPoU9gb4fusVIapgc7w8/TtbImP8/LyYXRwd7mn5GIxLGA0I2o5NYPOjoWUBARkRtTiNz7ANiMYCciInIXTCAkIqLORiyFngmERERELUutqXY6taexyCDrBMKuKMES+TbR9q/rb7MqHqynNRiRmJKFdQvsr6hORNQeVFZWmrc9PT1ttKzj5XX5d2ZFhesT9l555RWMHDkSKpX1xL3ly5fjwIEDmDVrFs6dO4ezZ89i4cKF+P77712+HhFQ93fD3r+KsCH9nM12gahAKfxstjFBhi2GsXhQ8V+L/YUIRE8hHxmmvhb7vz4oPpn5dGGl6H57KmqsU7CIiKjlFFWKF++FSKQYje3bxaHiBlupgvUy1KVISDpst+Cci9oQuae2XPTFlsLCQjz22GPmz08//bRou9YcPxeR6RhyS62LA7s3KqyLCfO3ahOCMgxPvRPaY71QUXk3ANcKAcWK/x0R1zsEggCnFoARBCCud7BL1yPqrKxnG1KHJZZAqGP6BBERuTGxyaMAYHB2GUkiIqIOSKyAsJYFhEREbc5gMODo0aNYv349HnzwQYwePRre3t4QBAGCIGDBggUO9bN+/XrzOY78+89//uNQv7W1tfjggw8wadIkhIeHw8PDA1FRUbjhhhuwceNGGNtxQZ5YAiEXkCEiImpZ6aeLnU7taSwi8HIxjL+nEiE+KhQiCAt0jyHb2M2ibYaxNz4xTLHZX2pmAXJKqps2KCIiNzd69GjR4sF6V155JZKTk+HhUTexb8eOHdi/f79T11Cr1Tb//fbbb036GajjyFCXYuH6/Rj3Whoe//oIyi6KFd+ZcK3sD3ymfAnJHo9DBfsFet8YrgUAVJk88JVhHOZqn8K1tW9ZFQ8CdQusi311XGgjTcqWhikWRETU8oorxRMIQ+qLEGorgfwj5v1j+tkvkgGAsAD7Czi8nZIFrYPzbOsXtSEi99FWi77YotVqMWvWLBQU1C3qdcstt2DmzJmibdvj+Klt5YqkCzZ8PwvUFRR6Ki/POfJHFT5TvYwu1aehyk7BKuWb8IDlvXloZIBD15cq/rcnOtgbE2NCnTpnUkwoooKYOEjkDCYQuhGxAkKmTxARkTsTmzwKcAIpERF1DiqRZ0AWEBIRtb3bbrsN33zzTVsPQ9SJEycwa9YsHD9+3GL/+fPncf78eXz//fdYvXo1vvrqK3Tr1k2il7YjE00g5PMfERFRS6qo0Te5j8YTVLr6eaC4Sot9xsGYql2BBxT/xRL5Nggw4Qnd32G0swauyQSkn9Zg9khODiGi9s/X1xclJSUAgJqaGvj6+tpsf/Hi5Yl+fn62E9qaauDAgbj77ruxdu1aAMD27dsxapTjCa9RUVEtNTRqJ9SaaqSfLkZFjR5+ngrE9Q6xSK0AgOSjeUhIOmyj8MKEKbL9eNx7K3rpT5v33iLfgy8NE21eP9sUjoXah5FuHIRq2J8MLfaKoLCylikWREQdQHGVRAKh76UihLSXgV9XAaPvByY8gat6BkMpF6Az2P4F383f9v1DralGWmaBU2OtX9SGBQtE1BKMRiMWLlyI3bt3AwD69OmDjz76qI1HVUetVts8npeXh6uuuqqVRkNijEYT8stqrPaHNyqol8sE9O/mhz9yyiCHAe8qEzFAdvn/34nyDKzCm1ii+xdqLyURzh4ZhSPny+yOIcTFBEIAWBbfD3uyihwq7FfJZUiI7+fytYg6KxYQuhGx9AkmEBIRkTuTCeIFhHo7LwiJiIjcgdgzIBeRISJqewaDweJzcHAwQkJCkJXl+qrEDz74ICZNmmSzzYABA2wez8vLw5QpU3Du3DkAwBVXXIF77rkHEREROH36NNatW4fTp09jz549uOGGG/Dzzz/Dx8fH5TG3BCYQEhERtT4/z6Z9neznqYB/gwSf5KN5OHnh8urmtVBhpf42bDOMxpWykzhu6ulQvxU19lOLiIjag8DAQHMBYVFRkd0CwuLiYotzW9rEiRPNBYR//vlni1+POoYMdSneTslCWmaBReGdIAATY0KxLL4fYqMDkaEutVM8CNwrT8azyg1AozUJFsu3Y7NhPEx2Fg5INY5oyo+CokotxvQNwd5TxfYbX8IUCyKi1ieVQNjFxwPIPQz8+gFgMgK/vAMc+y98pr+O4d2D8Fu2xma/9goI008XO1VkDnBRGyJ3054WfTGZTFiyZAk+++wzAED37t3xv//9D0FBQZLnNBxvTY114VhjTRk/F5Fp/4qqakWL6xsv8AYAA8LqCgifVmzEOPkRq+MT5Rl42rQR/9YvhCAAN8dG4Pntx+1+N2su/ndBbHQgEucOs/ucqZLLkDh3GGKjA12+FlFnxQJCNyKaQMgCQiKiNrd//3789ttv2L9/P44dO4bCwkIUFRVBp9MhMDAQAwcOxMSJE7FgwQL06NHDoT7z8vKwevVqbNu2DWfOnMHFixcRHh6Oa6+9FgsXLsS4ceMcHp/RaMTnn3+OpKQkZGRkoLCwEMHBwRg4cCDmzJmDhQsXwsPD9VVBWpJCzgRCIiLqvDxYQEhE1C5dddVVGDhwIEaOHImRI0eiV69eWL9+Pe69916X+xwxYgRuueWWJo1r+fLl5uLBO+64Axs2bIBCcfn1cEJCAm688Ub8/PPP+P333/Hqq6/ihRdeaNI1m5ucBYREREStLq53iNOpPQ1FNpicUl9kIHb7PmmKxklDtMP9+jUoSiQias9iYmKQnZ0NAMjOzkbPnj1ttq9vW39uS+vatat5u7S0tMWvR+2frURBkwlIPVGA3ScLMffq7tj3V7HdeUnfGK7FA4r/IkSosNjfR5aHybLf8aNRPPVSLggwuPoHSCOTBoTit2yN3ZQqgCkWRERtpbhKvIAwxFsOfLWsrniwXpkaSLod/+qxBHNhe35UmJ0Cwooavc3j0udxURsid9FeFn0xmUy4//77sWbNGgB1xXqpqal2nyEbjqGoqMjudVp70RpqXbml4kWkEQHWBYQxYf4AgKPGXtCa5FAJlovk/mUMxzv6mQCA7sHeCPJRoV+oL07kV1j11VAXn6bNNZ46JBybl3ghMSULqSKL2kyKCUXCpUVtiMh5LCB0IyqF9QQanZ4TaIiI2trEiRNRVVUleqygoAAFBQX4+eef8corr+DZZ5/FE088YbO/b7/9Fvfee6/5wbXe6dOncfr0aXzyySdYvHgx3nvvPcjlcpt95efnY/bs2di7d6/V/vz8fKSlpeG9997DN998g/79+zvw07YuuUx8Rcrm+jKJiIioPRNLIKzVG0RaEhFRa3ryySfbeghWjh8/jk2bNgEAwsPDsWbNGoviQaBuhdLPPvsMffv2RU1NDd544w089NBD7erLQyYQEhERtb7oYG9MjAlF6okCl85vuLr12ylZzbL4qSAAcb2Dm9wPEVFrGDp0KJKTkwHULTo6ceJEybYXLlyAWq0GAISGhloU97WUhhNM29PzH7UNRxIFAUBnNOHTfWcd6rMMvnhVPxevKT+0OnaX/H+iBYQquQx3XBXt8DXsqao14IGJ/fDm/07abMcUCyKitlNcWWu1z1Mpg3fGR0DeYesT5Cr4XzkHyLxgs99Qf9tFDH6erk2h5qI2RO6jPSz6YjKZsHTpUqxatQoAEBkZibS0NPTp08fuuQ3H0HBsUlp70RpqXXmlF0X3hwdaF9QPCKtLoPzaOA5ntaFYrXrTvPDLBVMg7tQ+iQLUpV/GdKtrOzQywG4BYVMSCOvFRgdi3YJRUGuq8Wu2BhU1Ovh5KhHXO5hp8URNJD7rnjoksQRCHRMIiYjahdDQUNx444146qmnsHbtWmzevBmff/45Xn75ZYwZMwYAUFtbiyeffBLPP/+8ZD9paWm47bbbzMWDN9xwA1avXo2NGzdi+fLlCAgIAACsXr0aDzzwgM0xVVZWYtq0aebiwd69e+Oll15CUlISVq5ciSuuuAIAcOzYMUyZMgUXLth+6dYW5IJUAiHvf0RE5P5EEwj5DEhERCI2bdoE06WFVv7xj39Irp4aGRmJ2267DQBQXV2Nb7/9ttXG6AixBEI9CwiJiIha3LL4flCJfA/piPoEQrWmGmmZrhUhNjYpJpQTRYiow5g6dap5e8eOHTbbfv/99+bt6dOnt9iYGkpLSzNvc/IoNVexf2NfGcZhv/HyYrU671BsDlqEB/TLLNoJAhA/IBSbl4zGomt7Q+KrYEmCAKjk1icdOV+GkmrxZKvG1506JNy5ixIRkQW1phqbD6jx0Z5sbD6ghlpT7dB5xZXWv6dDfDwg6C4CMpFivWsfQsygEXYLAO0lEMb1DnHpfsNFbYjcx9ChQ83b+/fvt9m2JRZ9qS8e/OCDDwAAERERSEtLQ9++fR0635nxN24zZMgQJ0dL7V1umUQCYaBYAqGfefuAaQBmaF9ApjEKNSYl/q59GPkIMR+vLza8IirA7hiCfZpeQFgvOtgbs0dG4d4xvTB7ZBTfCRM1AyYQuhGxAkJOHiUianvp6ekYPHgwBIk3Tk888QQ+/fRTLFiwACaTCS+88AL+/ve/IyIiwqJdbW0tFi5cCK227qXZO++8Y1EkeOedd2Lx4sUYP3488vPzsWrVKsyZMweTJk0Sve5LL72Ew4cPAwAmTJiAbdu2WUwiffDBB3H33Xdj06ZNOHPmDB5++GFs2LChKf9TNDuxyaMAoDdwAikREbk/sQRCrZ7PgEREZK3hJFV7k1CnT5+OTz/91HzePffc06Jjc4bYMyATCImIOqatW7diw4YN2L9/P/Lz8+Hv74++ffti5syZWLx4Mfz9/VvkuocOHcLnn3+O//3vf8jJyUF5eTm6dOmC8PBwxMXFYcKECZg5cybkcnmLXL+jio0OROLcYbjvs4MwOXnrrZ+ckn662OlzxajkMiTE92t6R0RErWT8+PEICwtDfn4+du7ciYMHD2LEiBFW7QwGAxITE82f77jjjhYf28mTJy2++7vxxhtb/JrUPqk11fj+SJ7LicP2mCDDv3UL8ZHq/3B60AMYO+sBzFF4IM5OmoSzKciTYkJRXKXFYXWpxf6j58twPLfcqr2nUobnbh6MMX27cCIqEVETZahL8XZKFtIyCyye/QSh7vf5svh+NhNei6pECgh9VcC1y4EBNwDb/gmc++XSgb7A2H9BIZdhdO8Q/HhcekH057Ydx/LJ/SWvHR3s7dL9hvcNIvcxdepUvPbaawDqvhd79NFHJds296IvjYsHw8PDkZaWhn79HH/3NWjQIHTv3h3nzp3Dn3/+iTNnzkimKFZWVmL37t0AAG9vb4wfP77JPwO1L7kSCYQRAdYFhF18PdDFV4WiS0X8OaZQzNL+B4OFszhi6m3RNias7n39kEjbBYR+Hgp4Kvlunag9YwKhGxGbPMoEQiKitjdkyBDJ4sF68+fPN38pp9frkZycbNXmo48+wpkzZwAAN910k2jCYP/+/fHee++ZPz/99NOi19NoNHjrrbcAAJ6enti4caNVAoVSqcTatWsRHl63yuJnn32GEydO2Pw5WptCZAVJgBNIiYioc/BQWL90YwEhEZF7ev/99zFw4ED4+vrC29sb3bt3x80334wPPvgA1dW2V3A2mUw4duwYAEAul2P48OE2248aNcq8feTIkaYPvhkpZNbvP/n8R0TUsVRWVmLGjBmYMWMGvvrqK5w9exa1tbUoLCzEvn378Oijj2LIkCFIT09v1uuWl5fj3nvvxciRI/H666/j8OHDKCoqglarRW5uLn7//Xe89957mDNnDioqKpr12u5i6pBw9Az2cfq8iMC6tIeKGn2Tx6CSy5A4d5jNSadERO2NXC7HM888Y/48f/58FBRYT1B//PHHzQt/jhkzBlOmTBHtb/369RAEAYIgYMKECaJtEhMT8csvv9gc16FDhzBlyhTU1NSlE1x//fW4+uqrHfiJyJ1kqEuxcP1+jHstDa/scO174G7QONTuhKk7xtW+hfy+twMKDwD20yScSUGuX2RgSKT1QhR5ZTU4LzKRdvqQcNw+qjuLQIiImij5aB7mrNqH1BMFVgvHmExA6okCzFm1D8lH8yT7KK6stdoXUp9g1DUGWPAdcPO7gFcwcONb5nuJvZSjn08W2r22K/cbInIf9Yu+ADAv+iKmJRZ9eeCBB8zFg2FhYUhLS0P//v3tnGXt9ttvN2+/8cYbku0+/PBDVFVVAQBuvvlmeHvz72B3k1dm/dwT6K2El0q8qK9hCiEAVMIbv5oGSrYbGO4vGfoBXCr+J6J2jQWEbkTsIUbHBCYiog5j8ODB5u38/Hyr41988YV5e/ny5ZL93HLLLeZVZPbt24ezZ89atfn222/NXwjefvvtiIyMFO3L19cXixYtAlA36XTTpk32f5BWJPUwYmiOpayJiIjaObFnQBYQEhG5p/379+PEiROoqqrCxYsXoVarsW3bNtx///3o2bMntm/fLnmuWq02FxlGRUVBqVTavFZ0dLQ5dSkrKwsmJ5+vcnJybP7Ly5OeqGGP2DOgngWEREQdhsFgwJw5c7B161YAQLdu3fD000/j888/x7vvvosxY8YAqLt3TZ8+HX/++WezXFej0SA+Ph7r16+HyWRCZGQkHnzwQaxduxabN2/GRx99hCeeeAJXXnml3YXgOruLOoPT50QF1a1ufU12IibKDgFw/t4tCED8gFBsXjIaU4eEO30+EVFbW7RoESZPngwAOHbsGGJjY/HMM8/giy++wPvvv49rr70Wr7/+OgAgMDAQq1evbtL1UlNTMWbMGPTt2xdLlizBu+++i6SkJHz55Zd46623cNNNN+HKK680L1zao0cPfPzxx026JnU8too9HKGAHs8oPkWKx8PoK+Q4dI5BUCCud7DD16hPQbZX1NFwkYGhdhIxGrp+cJjDbYmISFyGuhQJSYehtRN0oTUYkZB0GBmNUmLrFVeKJRB6XP4gkwEj7gb+eQToda352l8ftH8PsndtV+43ROQ+2mLRFwB48MEH8f777wOoKx7cuXMnYmJiXPoZHn74Yfj51RV4vffee+b3rw39+uuv+Pe//w0AUCgUePbZZ126FrVvuaU1VvvE0gfrxXSzXoClMZVChp4hdcWmnko5+oX6Sra1uHcTUbukaOsBUPNRiqQwGYwmGIwmm9XeRETUPpw6dcq8Xb+qTb2Kigrs2bMHAODn54drr71Wsh+ZTIapU6di1apVAIAdO3ZgyZIlFm127Nhh3p4+fbrNcU2fPh3PP/+8+bz29PAol5jQwwQKIiLqDMRS6GtZQEhE5FbkcjlGjx6Na6+9Fv3794evry9KS0vx+++/48svv4RGo0FhYSFuvvlmfPbZZ5g7d65VH6WlpebtLl262L2mUqmEv78/SkpKoNPpUFVVZZVYb0t0dLTDbZ0l9o7TYOS9j4ioo1i7di2Sk5MBAIMGDUJqaiq6detmPr506VI8/PDDWLlyJUpKSrB48WLs2rWrydedN28eDhw4AAB46KGH8OKLL8LT09Oq3csvv4zc3Fyn7nudiclkgqbKekKnPRGBXsD5gxjw10f4WAX8YeyFRP2t+J9xBAD7318+NX0Apg0NZzIQEXVoCoUCX3/9NebNm4ft27cjPz8fL7zwglW7qKgobNq0yWLR0ab466+/8Ndff9lsM2XKFHz00UeIiIholmtSx+BosYeULijDu6pExMnqFnxYrXwTt2hfQAVs368nxYQ6fU+fOiQcm5d4ITElC6mZlsWOglDXZ0J8P3Mxx+AIxwoIPZUyjO/f1amxEBGRtbdTshy+n2gNRiSmZGHdglEW+6u1etEFa0RTjDwuP7O/nZLlcMCG1LXrOXu/ISL3smjRImzZsgU//fSTedGXRYsWYdCgQdBoNEhKSjLP3WyORV+efvppvPvuuwAAQRCwbNky/Pnnn3YXVBsxYgS6d+9utT80NBTvvPMOFixYAKPRiJkzZ+KOO+7A5MmTIZfLsXfvXnzyySfmwInnnnsOAwYMaNLPQO1TrkjyekSg9bvwegMaJRCK6RfqC0WDIvvIQC+cyK8QbXu2uAoZ6lLeL4naMRYQuhGlxAooOoMRcpl49CwREbUP27Ztw5YtWwAAnp6euOGGGyyOHz9+HMZLkyKHDx9uToOQMmrUKHMB4ZEjR6yON9w3apT4y7F6I0aMgFwuh8FgwNGjR2EymdrNStxSBfJ6JvASEVEnIFZAqDeaYDSaIOMiMkREHd7YsWNx5swZREVFWR37+9//jv/7v//DokWLsGnTJphMJixcuBBjxoyx+uKwsrLSvC1WLCHGy8sLJSUlAOoWtGkvhRTiBYR8/iMi6ggMBgOee+458+cNGzZYFA/WW7FiBVJSUnD48GHs3r0bP/74I66//nqXr7t+/Xr88MMPAID77rvPnO4khcUT0ipr9S4VGTz5zRG8ZVqB+qn8V8iysVa1EseMPfBP3VJkmaz/1qkXPyAUi8b1cXHERETti5+fH7Zt24Zvv/0Wn376Kfbv34+CggL4+fmhT58+uPXWW7F48WIEBDieniZl5cqVuOmmm/Drr78iIyMDBQUFKCoqQm1tLQICAtCzZ0+MHj0ad955J66++upm+Omoo3Gm2KOxGOEcPlb9HyIEjXlfH1ke3lB+gH/o/gUTxOcuqeQyJMT3c+masdGBWLdgFNSaavyarUFFjQ5+nkrE9Q62Kkjs380PKrnM7s83rl9XeKk4l4qIqCnUmmqkZVqndNmSmlmAnJJqi9/fYumDANDFRzrFqLmu3ZAz9xsici+tvehLfTEiULdo1xNPPOHQeR9//DEWLFggeuyee+5BdXU1li9fjpqaGnz++ef4/PPPLdrI5XI89dRTePLJJ10eO7VfWr0RhZW1VvvDA7yAqiLg0EZg9AOA/HL5UIwDBYQN2yQfzcPOk4WSbYsqtZizah8S5w7D1CHhTv4ERNQaWEDoRqQKCLUGIzyVfOlFRNQe7Nq1CxpN3ZcpWq0WarUaP/74I3788UcAdQ+jq1atspo8k5mZad7u1auX3es0bNPwXAAwGo3mtEO5XG43HUKpVCIyMhLnzp1DVVUVzp8/LzqBVUpOTo7N43l5eQ731ZhCJH0XAAwmTiAlIiL35yFSQAhcegbkIjJERB1e3759bR738/PDZ599hgsXLmDnzp2oqanBihUr8N5777XSCMWp1Wqbx/Py8nDVVVe51LeCBYRERB3Wrl27zO8Bx48fjxEjRoi2k8vlSEhIwMKFCwEASUlJTSogXLFiBQDA19cXr776qsv9EFxKHwSAgpO/IcDjf1b7uwsFKDAFSp7XlCIDIqL2bMaMGZgxY4bL5y9YsEBywmi9Pn36oE+fPvjb3/7m8nXIfblScHGZCa8q11oUD9abLP8dDxj/i3cMt1odU8llSJw7rMkpFNHB3ogOtl3AoVLIMCDcD3/klNlsN2VwWJPGQkREQPrpYjg7PcdkAtJPazB75OXf50UixQ6ARAJhM19bjCP3GyJyP6256EtLue+++3Dddddh1apVSE5OhlqthtFoREREBOLj4/GPf/wDw4cPb+thUgu5UF4jem+MCPAE/ns/kPUDcPIHYNYaIKBu/m//bn4QBNi8p8Z0qysgrE+yt/fdrNZgRELSYWxe4sUkQqJ2iAWEbkQsfQIAdHrXVg0jIqLm9+ijj+LXX3+12i8IAsaPH4/nnnsO48aNszpeWlpq3u7SpYvd64SEhIieC9SlT+j1egBAYGAgFAr7fw6EhITg3Llz5v6cKSC0V6DYFGKTRwFOICUios5B6hmwVs9FZIiIOgu5XI4XX3wRY8eOBQBs377dqoCwYXpgTU2NQ/1evHjRvO3nZ3/lyYaceV50llgCoZ7Pf0REHcKOHTvM29OnT7fZdtq0aaLnOWvv3r04ceIEgLpiDX9/f5f7orrVo12RoNgiun+9YQrKIJ5y3FxFBkRERGTNlYKLywQs1SbgC9UL6C6zTJ2oMSmRY+pq2VoAJsWEIiG+X6ve18P8PfEHpAsI5QIQPzC01cZDRNSRqTXVSD9djIoaPfw8FYjrHWIurquo0bvUZ0WNzuKzVAJhiK90AmFzXZuIqLHWWPRl586dLvdvT79+/bBy5UqsXLmyxa5B7VNu6UXR/WM1X9UVDwLAuV+AD8YAM94FBt4EL5UcPYK9caa4WrLf+gRCZ5LstQYjElOysG7BKOd+CCJqcSwgdCMqiQRCnYGTaIiI2rvIyEhMnjwZ/fqJr6hcWVlp3vb09LTbn5eXl3m7oqKiSX3Z668tyQTxAkJOICUios5AuoDQAEDZuoMhIqI2M3r0aHh6eqKmpgbnzp1DdXU1vL0vr44cGBho3i4qKrLbn16vR3l5OYC6RHofH59mH7OrxAoIuYAMEVHHcOTIEfP2qFG2Jw2EhYUhOjoaarUaFy5cQGFhIbp27WrzHDE///yzefvqq68GAHzzzTdYu3YtDh48iJKSEoSEhGD48OGYPXs27r77bocWW+usXEkglMGIKnii1qSEh3B5kqZB6YOs6LshnKq1KGBoqyIDIiKizsTVgot6ueiCudqnscnjBUQJde8ZckxdsFj7Lxwz9QIA9A/1xT/G90Fc72BEBbVuglPy0TyknLCdsGg01RVSTh0S3kqjIiLqeDLUpXg7JQtpmQVWz20TY0KxLL4f/Dxde4b287T8HrP+eXOmbDf2GQchH3WLpof4SCcQNte1iYiI3EVemfVCsoOFMxh8rFExaU0psOku4MY3gSsXIibMz2YB4YAwf5eS7FMzC5BTUt3qz4REZBu/BXMjSrl4EYWWCYRERO1Genq6ebuqqgqnTp3C1q1bsXLlSjz11FN444038MUXX+C6665rw1E2L7VabfN4Xl4errrqKpf6VsjECycMDq50QkRE1JFJLSLDZ0Aios5FJpMhODgYubm5AOpS4xsWEEZHR8Pb2xvV1dXIycmBTqeDUik9SeLcuXMwGAwA6lYpFSQWbmkLTCAkIuq4MjMzzdu9evWy275Xr17m94qZmZkuFRAeOHDAvN2tWzfMmjUL33zzjUWbvLw85OXl4fvvv8ebb76Jb7/91qHxdUaaqlqnzzFChuW6+/EfzMfN8n24Tb4TV8iyIb/6H0i87jo8oqnGr9kaVNTo4OepbJMiAyIios7G1YKLhs6jK+7QPo0vVC/inDEU9+uWoRR1qRQquQyvzYltk8UAMtSlSEg6bHexIROAhKTD2LzEi4sWEBGJSD6ah4Skw6IpQyYTkHqiAHuyivDMTQMhCHAq2VYQgLjewRb7iqpq0Uc4j9eVq6CDAusNU/CB/mZ0sZFAGNc7pFmuTURE5C5yyywTCAUYsVL5AWRGkYXhfLsBA24CAAR6SxfsK2QC8ssuIqug0ukke5MJSD+tweyRfN9L1J6wgNCNKCXSJxyNiyUiotbl4+OD2NhYxMbG4q677sLYsWORm5uLG264AQcOHMDQoUPNbX19fc3bNTXWK4U0dvHi5YcBPz8/i2PO9mWvP3uioqKcau8MuUTxPMN3iYioM/CQegZkASERUadiNBpRUlJi/twwcRAABEHA4MGDsX//fhgMBhw6dMjmIi779+83bw8ZMqTZx9sUCpECQgAwGk2QSRwjIqL2obS01LzdpUsXu+1DQkJEz3VGXl6eefuZZ55BZmYmVCoV5s+fj7Fjx0KpVCIjIwNr166FRqPBkSNHMHHiRBw8eBDBwc5NKMzJyXF4LB1VsQsJhPXK4YuNhsnYaJiMNdd7YPLVwwEA0cHeiA7mBBIiIqLWFNc7BALqiuicMWtEJLZm5EJ36YvYHFMobqt9BsXwRy3qJpyq5DIkzh3WZkV5b6dkOTxHSmswIjElC+sW2E7HJiLqbOqLse39PtUajHh+258Y2T0IB86W2Gzb0KSYUKuFY4ortXhY8SXkggly6LBEsR1z5anwOZwDjL4PUFk/N0YHe2NiTChS7aTO2rs2ERGRu8gttSwgNEGGJ/SL8E3oOgilZxscEYCZqwHfrkg+moevf5d+t603mnDb6nTMGB7h0pgqanQunUdELUd8tiF1SEqJ9AkdCwiJiNq9Xr164dVXXwUAaLVavPTSSxbHG04ALSoqsttfcXGx6LlAXQGhQlG3hkBpaSn0en2T+mtLUpNHDUbe+4iIyP15KOSi+7mIDBFR55Kenm5e9CUqKsoifbDe1KlTzds7duyw2d/3339v3p4+fXozjbJ5yCVS6JlCSETU/lVWVpq3PT097bb38vIyb1dUVLh0zYYF9pmZmQgKCkJ6ejrWrFmDe+65B/PmzcOKFStw7NgxDBo0CABw9uxZPPnkk05fKzo62uY/W8X7HYWm0vUCwoZyPPoAPiH2GxIREVGLiA72RniA/b/HGoofEIqVtw3DV0uuQfyAUAiXvqLNRRfUQgVBqGuzecloTB0S3gKjtk+tqUZapuNFJACQmlmAnJLqFhoREVHH5GwxtoC6AnJHqOQyJMT3s9rvW3gI0+T7LfYFCNVQ7HoVqJaeI7Usvl+Tr01EROQu8kqtw0TyfIdAWLIbGDL78s4xy4A+E82LBtj7nlVrMOKb38+7NCY/T6VL5xFRy2EBoRuRehhiASERUccwbdo08/bOnTstjsXExJi3s7Oz7fbVsE3DcwFAJpOhb9++AACDwQC1Wm2zL51Oh/Pn6x4AfHx8EBkZaff6rUUmiBcQcvIoERF1BiqJBMJaHZ8BiYg6C6PRiGeeecb8+cYbbxRtd/vtt5u3V69ejaqqKtF258+fx5dffgmgrnBjxowZzTjappOaC2LgMyAREYkwNlpk7PXXX8fw4cOt2oWFheHzzz83f16/fj3Ky8tbfHwdTVMSCBvipBEiIqK2odZUY/MBNf7vhxPILbOeWCqlYcFFbHQg1i0YhV2PTMTrc2Lx7E2D8PqcWOx+dCLWLRjVZsmDAJB+uhgmJ18PmExA+mlNywyIiKgDcqUY+8C5Ejx70yDJBcDrSabUmkyYkr9a/KRRfwcCu0v2GRsdiMS5w+wWEbZ1Qi4REVFrEHvOCw/0BDwDgFlrgRnvA73GAZOeBuDcogEGZx+2AAgCENc72OnziKhlsYDQjUhNHmUBIRFRx+Dn52febrg6NgAMGjQIsktJC4cOHYLBYLDZ1/79l1fmGjJkiNXxoUOHirYVc/DgQfP1Bg8eDEGiaK8tSCYQGjh5lIiI3J/UMyATCImIOr59+/bhww8/RE2N9IS+qqoqzJ8/HykpKQAADw8PPPbYY6JtBw8ejNtuuw0AkJeXh0WLFlml0VdWVuLOO+80X3P58uXtKoEesJVAyHsfEVF75+vra962dX+rV5+uC1i+N3VGw/N8fHxw1113SbaNjY1FXFwcAKC2thZ79+516lpqtdrmv99++82ln6E9aY4CQk4aISIian0Z6lIsXL8f415LwyNf/YH30/6y2d4flXhFsQb+qJQsuIgO9sbskVG4d0wvzB4Zhagg7xb8CRxTUaO330j0PF0zj4SIqONytRjbQynHonG9JNv4eyqkU2r1tThv6gKjyXL+T7XgBVz7sN3rTx0Sjs1LRlsk5NZrDwm5RERErSW39KLVvogAr7oNQQCG3wnM3wrIlS4tGuCsSTGh7eJZkYgsKdp6ANR8lBIrqWj1LKIgIuoIsrKyzNtdu3a1OObn54cxY8Zg9+7dqKiowJ49ezB+/HjRfoxGI3744Qfz54bJhvWmTp2KzZs3AwB27Nhhnkgq5vvvvzdvT58+3bEfppXI5RIFhC6seEJERNTRSK2mqdWziIKIqC1lZ2dj3bp1Fvv++OMP8/ahQ4fw9NNPWxyfNGkSJk2aZP584cIFLF68GA899BAmT56MkSNHIjo6Gj4+PigrK8PBgwfxxRdfoLi4GAAgCALWrl2Lnj17So7rjTfewC+//IKcnBwkJSXh2LFjWLBgASIiInD69GmsXbsWp0+fBgAMGzYMjz/+eFP/p2h2UovIsH6QiKj9CwwMNC+aVlRUZFFQKKb+Hld/riuCgoLM20OHDoVKpbLZ/sorr0R6ejoA4K+/bE+sbywqKsr5AXYwmqpaq33+ngqUOzFZn5NGiIiIWlfy0TwkJB12eNE5JfRYpXwL18iPY4LnKZTO/AwDB3eMggs/T9emwDEdmYjosqYVY0svRu6plEun/yk98ZTpfrymvR6PKDZhsvwgAOCnwNsxwyfEoevXJ+SqNdX4NVuDihod/DyViOsdzGdQIiLqcNSaaqSfLkZFjR5+ngrE9Q5BdLDt+1m1Vo+yi9aLo0QEelruuFRt78qiAQAglwkwGO2f2DDJnojaFxYQuhGlRBEF0yeIiDqGVatWmbfHjBljdfyOO+7A7t27AQArV66ULCD873//i+zsbABAXFyc6ATSGTNmYOnSpaipqcEXX3yBF198EZGRkVbtKisrsWbNGgB1E1Jvv/12p3+uliSZQOjAQwoREVFH56FkASERUXt09uxZvPTSS5LH//jjD4uCQgBQKBQWBYT1KisrsWXLFmzZskWyv7CwMKxduxY33HCDzXFFRkbihx9+wKxZs3DixAn88ccfWL58uVW7a665Bl9//bXdwo62IJd4BmQCIRFR+xcTE2N+Z5mdnW2z6L2+TcNzXTFgwABzUm9AQIDd9g3blJeXu3RNd6aptE4gjAnzQ4a6zKHvIjlphIiIqHVlqEsdLh4UACRM6osbzryE/rnHAQDhejXCv5sJ+H0OdI9r4dE2XVzvEAgCnJoEy3RkIiJLTSnGPniuVPJ4YWUttHojVArr7zaNRhM0VVoUmKKxSPcwrtSfwD8U3+FQ5DzMcHIc0cHedgssiIiI2qsMdSneTslCWmaBxXONIAATY0KxLL6fZEF+bmmN6P7w+gTCRlxdNGDWiEj891CuzedMqSR7ImofxGcbUocklT6h4+RRIqI2s2rVKqSlpcFk45sKg8GAV199Fe+//7553/3332/VbuHChejevTsAYNu2bXjvvfes2mRlZWHp0qXmzy+88ILoNUNCQpCQkAAAqKmpwV133YXKykqLNnq9HosWLUJeXh4AYN68eRgwYIDkz9EWZILU5FEWEBIRkfuTegas5TMgEVGHd9111+Hbb7/Fk08+ieuuuw4xMTHo0qULFAoF/P390bdvX9x222345JNPkJ2dbbd4sN6gQYNw6NAhvPvuuxg/fjy6desGlUqFiIgITJ06FZ9++il2796NsLCwFv4JXSNVQMhFZIiI2r+hQ4eat/fv32+z7YULF6BWqwEAoaGh6Nq1q0vXjI2NNW+XlZXZbd+wjSMFh52JyWRCcZV1AWGvLj5InDus0fOp9X2Zk0aIiIha39spWQ4vOG4C0P34KvTP/dbyQHUx8MlNwMkfm3+AzSw62BsTY0KdOofpyEREluqLsZ1RX4ydU1It2cZkAi6Uixc2lNfoLOb4HDANwD90D8HfP9C5gRAREXVgyUfzMGfVPqSeKLBaFMVkAlJPFGDOqn1IPponen5u6UXR/VYJhJe4umjAVb1CsHnJaMQPCLX6m0EQgPgBodi8ZDSmDukYSfZEnRETCN2IUmSFFgDQMYGQiKjNpKen47777kN0dDQmT56MoUOHIjQ0FCqVCqWlpTh69Ci+/fZbnDlzxnzOE088IZou6OnpiXXr1mH69OnQ6XR44IEHkJycjJtvvhk+Pj44ePAg1q5da57osmjRIlx33XWSY3v66aeRnJyMP/74Azt37kRsbCwWLVqEXr16ITc3F+vXrzenYnTv3h2vvfZa8/6P0wyYQEhERJ2Z2CqdAFCrN7TySIiIqKEJEybYXETGEb6+vrj55ptx8803N9OoLvP09MTSpUstFp/pKKSeAbmIDBFR+zd16lTz+8UdO3bg0UcflWz7/fffm7enT5/u8jWnTZsGQRBgMplw5MgRaLVaqFQqyfYHDhwwb7uaeuiuqrQG0cVqgn08MHVIODYv8UJiShZSMwvwmDwJvYU8fGmYgJ9NsRgXE4EEG6tjExERUfNTa6qRllngcHsfXERcyda6KEKrg12BsKEiB9qfZfH9sCeriOnIREQuqi/GTj3h+D2kvhg7p0S8cKFefnmNaDpgkUjaPQCE+Ho4PAYiIqKOzNH0eK3BiISkw9i8xMvqXevFc4cQgjIUw3JhvIhA8QTCpiS4RwV5Y92CUVBrqvFrtgYVNTr4eSrNx4iofWMBoRtRSqRPOLqiGBERtRy1Wo2PPvrIZpuAgAC88soruO+++yTbXHfdddi0aRMWLlyI0tJSbN++Hdu3b7dqt2jRInzwwQc2r+fn54cdO3Zg1qxZSE9Px+nTp/HEE09YtRs0aBC+/vprhIe3v1VBmD5BRESdmVQBoZYJhERE5Kb4DEhE1HGNHz8eYWFhyM/Px86dO3Hw4EGMGDHCqp3BYEBiYqL58x133OHyNaOiojB+/Hjs3LkTVVVV2LhxIxYuXCjaNiMjA+np6QDq3puOGTPG5eu6I43EhM4uvnUFmbHRgXWTRgpLEbpmKTy0Jbhe/jsM3l0hj5gLeN4NILD1BkxERNTJpZ8udmoiaBW8MLP2OfzQ7T0ElR2/fEDlC8zbBPi3v++JxcRGByJx7jC7k2+ZjkxEJM2VYuwanQGFFbU220olIxVXip8X4iu9ABAREZE7cSY9XmswIjElC+sWjGqwswpx+5fhJ49y/Ed3D7Yar0H96jDhAeIFhE1ZNKBhH2KLAxBR+yY+25A6JJVEAaHOwAk0RERtJTExEd988w3++c9/Yvz48YiKioKnpyfkcjkCAgLQv39/zJ49G2vWrMHZs2dtFg/WmzlzJo4fP45///vfGDZsGAIDA+Hp6YlevXrh7rvvxs6dO/Hhhx9CLpfb7SsiIgJ79+7FJ598gqlTpyIiIgIqlQrdunXD+PHj8d577+HgwYMYMGBAc/zP0ewEQYDY/FFOHiUios7AQ6qAkIvIEBGRm2IKPRFRxyWXy/HMM8+YP8+fPx8FBdaTEx5//HEcPnwYADBmzBhMmTJFtL/169dDEAQIgoAJEyZIXvfll182bz/88MM4dOiQVZsLFy7gzjvvNH9OSEiAl5f4xIrOqrhKfEJnsI/lhM7owl3w0JaYP8urC4FfEoE9b7To+IiIiMhSRY3e6XMKEITtI9cBMTfU7RBkwOyPO0z6YL26dOTRiB8QCqHRawRBAOIHhGLzktGYOqRjFEUSEbW2+mJspVz8XWy9hsXY5yWKAxvKK6sR3a+pkkgg9GECIRERuT9n0+MBIDWzADkl1Q12vIiA2lwEC5VIVL2HtcrX0Q0aqOQyhPhIF+Qvi+8nWXvSGBPcidwHEwjdiEoh/tDG9Akiorbj7++PmTNnYubMmc3ab3h4OJ5//nk8//zzTe5LJpNh/vz5mD9/fjOMrPUpZDKrQgm9kfc+IiJyfx4K8cUC+AxIRETuSiZRQKhnASERUYewaNEibNmyBT/99BOOHTuG2NhYLFq0CIMGDYJGo0FSUhL27NkDAAgMDMTq1aubfM3Ro0fjsccew4oVK1BSUoK4uDjcc889GDt2LJRKJQ4fPoy1a9dCo9EAAK688ko8/fTTTb6uu5Ga0Nm4gBCHNoh3MPyuZh4RERER2eLn6dp0MC8ff+D2DcBPzwBBPYH+1zfvwFqJOR1ZU41fszWoqNHBz1OJuN7BFokZREQkbuqQcKyZL8eCj/eLHvfxkOPzv8eZk1xzSuwXEOZLFBAWSRUQMoGQiIg6AWfT4wHAZALST2swe6Q3oN4PpH9gcfw6+SFcJXsED3qvkPxuFWCCO1FnxQJCN6KUTCDk5FEiInJfcpkAGCz3MX2CiIg6A5VEAmEtCwiJiMhNMYGQiKhjUygU+PrrrzFv3jxs374d+fn5eOGFF6zaRUVFYdOmTRg8eHCzXPfVV1+FXC7HihUroNVqsWbNGqxZs8aq3ZQpU5CUlARPT89mua47Ka50IBGiPBc49T/rRkG9gB5jWmhkREREJCaudwgEAU5NRBUEIK53MCCTA1NearnBtaLoYG9EB7NgkIjIFbZ+fxqMJgyNDDB/tkhBkpBbn1J4saQu5daz7vziSvHEe1uJSURERO7ClfT4uvN0gEEPbP8nAOsHv0xTNLSBfe32U5fg7oXElCykZhZYPEMKAjApJhQJ8f1YPEjkRlhA6EZYQEhERJ2R2ARSTh4lIqLOQCXxDMgEQiIicldymfi9j8+AREQdh5+fH7Zt24Zvv/0Wn376Kfbv34+CggL4+fmhT58+uPXWW7F48WIEBATY78wJL730Em677TasW7cOP/30E86fPw+dTofQ0FBcc801mD9/PqZNm9as13QnxY4kQsg9gPGPA4c3AqXnLu8fflfdbBMiIiJqNdHB3pgYE4rUEwUOnzMpJpTpfEREZGaroKFGZ0R+eQ0iAr0AOJZAmFefQLj7DeDAx8CV9wJx94suWCMTgEBvFhASEZH7czU93s9TWVeU7xVkdazWpMRjun9gWKCPQ30xwZ2oc2EBoRuRKiC0FStLRETU0YnFrOs5eZSIiDoBqQRCFhASEZG7YgIhEZH7mDFjBmbMmOHy+QsWLMCCBQucOic2NhaJiYkuX7Mz01SJJ0IEN0yE8AkBJjwGjHsEOLMLOLQROPE9EDu3lUZJREREDS2L74c9WUUOzRlSyWVIiO/XCqMiIqKOoqJGZ/N4dlGVkwWEF4GasrriQW0F8EsikP4Bpvleh1+ESfjLFGluG+yjglziXTAREZE7aVJ6vK83cM82VB74AtXbH0eoUAoAeFt/K06bIjD90n3aUUxwJ+ocxGcbUockNXlUp+cEGiIicl9MICQios7KQ6qAkIvIEBGRmxJbQAYA9Ebe+4iIiFqSWAKhj0oOT6XcurFMBvSeAMxaCzxyCgiItG5DRERELS42OhCJc4dBKb/8LO0BLQJRYdFOJZchce4wxEYHtvIIiYioPbOVQAjUFRDWyymptttfUaUW+t8+qiserGfU4ZryHfiX4muLtiE+Hs4NloiIqIOqT493hkV6vCDgTMR0xNe+jo/0U/GnsTs+NNwAAAgP9Gzu4RKRG2ABoRtRSSQQ6jh5lIiI3JjYqmMsICQios5AIZdBrI6iVmdo/cEQERG1AiYQEhERtQ2NSAFhsK9KpGUjKq5YTURE1JamDgnHv67rb/78rOJTbPd4CrHCKQgA4geEYvOS0Zg6JLztBklERO1SpVMFhPYTCFXQAb9+IHpslf5Gi88hjjxvEhERuYll8f0ka0AaE0uPzyurQQW88bx+PmZoX4AeCgBARIBzCYRE1DmwgNCNNFw1rCGmTxARkTtjAiEREXVmYkn0fAYkIiJ3JbaADMBnQCIiopZWXClSQMhECCIiog5Bf+mZ+VbZLsxTpCJKKMJm1XNIG5eFdfdcyeRBIiISVV6js3n8zKUCwhqdAYUVtXb7Gys7AkXVBav9ewyDcdTU22LfqYJKZKhLHR8sERFRB1afHi+Ifw1qJhMgmh6fV3a5kF8LpXk7IpAFhERkjQWEbkQuE0RvHlo9J48SEZH7kolMINVz8igREXUSYquQ1fIZkIiI3BQTCImIiNqGWAJhFx8mQhAREXUEfxVWoreQixeVH5v3qQQDev72LPD134HayjYcHRERtVcVDiYQni+1nz4IAKnGEdg57ktg8EyYGkxbXm24yaptQUUt5qzah+SjeU6MmIiIqOOaOiQcvbv42Gzj56nE5EFhVvul7sXhgZ7NMjYici8sIHQjgiBAKTJ5VMf0CSIicmNMICQios7MQym32sdFZIiIyF2JLSADcBEZIiKillZcZZ0mEcwCQiIiog7hbEEJ3lG+A29BJB0qcwdQweIMIiKyZq+A8JymGnqDETkljhUQAsBxWR9kxL2F63Rv4hP9ZPxu7IfdxqGibbUGIxKSDjOJkIiIOo2yi7bTf8su6nDwXInV/rzSGqt9vh4K+HsqrfYTEbGA0M2IpU+wgJCIiNyZnAWERETUiTGBkIiIOhMmEBIREbW+aq0eNTrr58xgXxYQEhERtXdGowmawgswSE0Pm/EO0KVf6w6KiIg6hIoa20UMeqMJOSUXkVNS7XCfeaU1eDslC38ZuuJZ/b2YrX0WgPg7X6CuiDAxJcvh/omIiDqqGp0BRZVau+2Sj+Zb7csrsy7mj2D6IBFJYAGhm1EpxAoIOYGGiIjcl0Jmfe/j5FEiIuosPESeAZlASERE7kpsARmAz4BEREQtqVhi4koIEwiJiIjavfzyGpzT+WOW9jms0t9oefDKvwFDZrXNwIiIqN2zl0AIANlFVZIJhF5KudW+04VVSMssMH82OTB9OTWzwKkiRSIiorai1lRj8wE1PtqTjc0H1FBrHL9/5ZdZpwg2NE6WgWcVn2D3kb9gMll+L5orkkAYHuDl8LWJqHNRtPUAqHkp5daTaLRMICQiIjcmE5lAqjfy3kdERJ2D2CIyLCAkIiJ3JbaADFC32jURERG1jOIqqQJCD+DP7cDBT4CY6UD/qYB/eCuPjoiIiGz5q7ASAKCDAq/q52G3cSjeVH4AT/8u8J/yUhuPjoiI2rOKWtsJhIB0AaGPSo6+ob7IyCmzbF9cCZOTr3JNJiD9tAazR3o7dyIREVEryVCX4u2ULKRlFljc5wQBmBgTimXx/RAbHWizj1yRFMF6SujxrOJT9JHl4YaaX5HzczGix98DCAIMRhPyy60LCJlASERSWEDoZpRyTh4lIqLORSFSQMj0CSIi6ixECwi5iAwREbkpkVefAPgMSERE1JI0VbWi+4N9VcCxrUDWj3X/ACBiRF0x4ZhlgIIJhURERG3tVEGlxee9xqGYWvsqvpg2EP5KJlIQEZG0SocTCK3TlaKCvBEe4GVVQFhabb8oUUxFjWvnERERtbTko3lISDosOk/HZAJSTxRgT1YREucOw9Qh0ouv5YmkCNa7S/4T+sjyAAChQimwcxlw5itg+uso9Ogp+j1pBBMIiUiC/Qxw6lBUIrNodJw8SkREbkzOAkIiIurEPJhASEREnYhcMoGQ9z4iIqKWUlwpkUDoJQNO/mC5M/cgcHgjIFe2wsiIiIjInvoEwoY08EdknyFtMBoiIupIKhwoIDxTLJ5AGBXkhXCR5KNqrcGlsfh58hmTiIjanwx1qWTxYENagxEJSYeRoS6VbJMnkUAYLFTgn4qvrQ+c2Y3j6clY+vlB0fO+P5pn83pE1HmxgNDNiCUQsoCQiIjcGQsIiYioMxNLIKzVu/blGxERUXsnlkAPAEYTnwGJiIhaiqZKvIAwvOwwUFNqfSBmOiCI37OJiIiodf1VUGW1LyLAEz4eijYYDRERdSTlDhQQ/plXgcIK69T6qCAvhAdYFxACgLNPi4IAxPUOdvIsIiKilvd2Spbd4sF6WoMRiSlZksdzy6wTCGUC8HrXHQgQrNN+/zR2x42/9MHvZ0tE+/szrwJzVu1D8tE8h8ZHRJ0HCwjdjNjkUZ2eE2iIiMh9iRUQ6llASEREnYRYCj0TCImIyF2JPf8BgN7AZ0AiIqKWIlVAGKT+n/gJMdNacDRERETkDLEEwj6hvm0wEiIi6mgqanR22xRVWhcPAkBUkDd6eNbgHvkPCIDlvWhYdKBT45gUE4qoIG+nziEiImppak010jILnDonNbMAOSXWxYAAkFtqnUDYzd8TpyJn4FfjAKtjz+vvhtFOGZAjyYdE1PmwgNDNKOXWk2gcrW4nIiLqiMQSKJhASEREnYXYIjIsICQiInclVUDIZ0AiIqKWU1RpXUDorZJD0bUfED7M8oBnANB9dOsMjIiIiKxV5Js3y2t0KBBJherTlQWERERkm1ZvRG0Tvm+MCvLCoMLv8JzyE/zmsRRvKt/D1cKfAEwY17+r6AKpYlRyGRLi+7k8DiIiopaSfroYJie/njSZgPTTGtFjeaXWCYQBXkq89ocnbtf+G0u0/8Q5Y1cAwA+GK7HPONiha9pLPiSizkfR1gOg5qVk+gQREXUyYhNIOXmUiIg6Cw+F3GofF5EhIiJ3JZlAyGdAIiKiFqOpsi48CPZRAaP+Vvev7DxwcgeQuQPwDQPkyjYYJREREeFUCpA0F5j4JHDNgzhdWCXarE9Xn1YeGBERdTSOpA/aEhXohbDULwEAHoIOM+V7MVO+F38Zw/E/YTMS5w7D/Z8dhK3Xuiq5DIlzhyHWycRCIiKi1lBRo3fxPPF7bG6ZdQKhpkoLncEEQECy8SqkaYfhXnkydhivcuqa9cmHTPQlIoAFhG5HLH1Cx8mjRETkxlhASEREnZnYM2Ctjs+ARETknsQS6AHA6OwSn0REROQwTZV1AmGIj+ryh4BIYNTf6/7xnkxERGSm1lQj/XQxKmr08PNUIK53CKKDnZ+w6VA/1Rrg26WAoRb437PAyR+Q1+9Z0f6YQEhERPa4WhRRr0f1EShLrNOOLpiCcK4CWHxdOKKCvHFOU23VRhCASTGhSIjvx+JBIiJqt/w8XSvB8fO0XnytslYveu9tnChfCxVWGW52+pr1yYezR7KAkIhYQOh2xBIIWUBIRETuTGwCKdMniIiosxAtIOQzIBERuSnJBEIDnwGJiIhaSrFIAWFwwwLChgTxezUREVFnkqEuxdspWUjLLLCorRcEYGJMKJY5WBDhcD8mE/DdcqAi73Kjc78gPmcmZsruwRbjtRb99g1lASEREdlWWet6AaGPSg6/45+JHvvCMBGVZTWo1uqRU2JdPDi8eyDemTucCUlERNTuxfUOgSA4t56aIABxvYOt9ueVWqcPNrempgsTkfuwnmlIHZpSbv3FnI4TaIiIyI0xgZCIiDozlcgiMlq9ESamPhARkRuSKiDkMyAREVHLKa4USSD09WiDkRAREbV/yUfzMGfVPqSeKLCaSGoyAaknCjBn1T4kH80T78CVfo58BRzbYtWHylgNX8FyIqqfhwJd/XgfJyIi28qbUGQQFegFobLAan+JyRc/GEchr6wGR8+XQ+yV7o1XRLB4kIiIOoToYG9MjAl16pxJMaGi97ncsprmGpYkseRDIuqcWEDoZlQKudU+LdMniIjIjSlk1n/OcPIoERF1Fh4iCYQAF5IhIiL3JJlAyGdAIiKiFnFRa8BFncFqf4hUAiEREVEnlqEuRULSYbtzdLQGIxKSDiNDXdos/eSdzQRg/by8Xz4cGwyTLfb1DvWFwMRgIiKyo6JGPIHQS2k9N7WxqGBv4O5vgKW/4X+Bc6Ax1SXfbjGMRS1UyCu7iD9ySkXPjY0KcHnMRERErW1ZfD/R4CcxKrkMCfH9RI/ltnACoVTyIRF1TiwgdDPiCYQsICQiIvclE5lAysmjRETUWUgVENbqrSd4EhERdXRiC8gAgJHJu0RERC2iuKpWdH8wCwiJiIisvJ2S5fAC31qDEYkpWc3Sz9PFU4B7dwCBPcz7TZ6B+GfN39G4sLBPVx+H+iUios5NqoBwSKS/3XOjgrzqNrrGYF/f5YirfQ9LtQn49FJRe2m1DumnNVbnyWUCBkewgJCIiDqO2OhAPDBRvCiwIZVchsS5wxAbHSh6PE+igLC5ln6RSj4kos6JBYRuRiW3/r9Uq2cBIRERuS+FSAGhwch7HxFRR1FRUYGvv/4aDzzwAK655hp07doVSqUS/v7+GDBgAObPn4/k5GSYmqkwYMKECRAEweF/Z86caZbrthSVRAEhnwOJiMgdSSYQMnmXiIioRWiqtKL7WUBIRERkSa2pRlpmgVPnpGYWIKekunn68Y8F7tsLDL8bAFAw/lWcNwRZte3T1depvomIqHOqqNGJ7h8aGWj33IYFCuEBntBCie+McThjCjfv351VaHVev1BfeKnsJxwSERG1J4HeSpvHx/QJweYlozF1SLhkm9yyGgDAs4pP8IB8C7xRA5VChgkxXZs8PlvJh0TUOSnaegDUvJQiBYRMICQiIncmNoHUwARCIqIO4Y033sBTTz2Fmpoaq2MVFRXIzMxEZmYmNmzYgGuvvRYbN25E9+7d22Ck7ZdkASGfA4mIyA1JFRByERkiIqKWUSxRQBjiywJCIiKihtJPF8PZNfBMJiD9tAazR14utHC1n3dST+GBiX0RPeNd4MqF+KMsEsABq7YsICQiIkdIJRDGRttPCDQnEAIID/ASbVMrshBqbFSgY4MjIiJqR05eqLB5fNl1/SWTB+vllV1EPyEH98h/hEww4R7FD/jc4zZMnPgo9p4qdnn+j73kQyLqnFhA6GbECwhNMJlMEITmCrMlIiJqP8QTCFlASETUEZw8edJcPBgZGYnrrrsOI0eORGhoKGpqapCeno6NGzeisrISu3fvxoQJE5Ceno7Q0NBmuf6WLVvstmmua7UUsRR6gAmERETknsSe/wBAz2dAIiKiFlFcaVlAOFv+MwJRiaiL/oCxCyBjOgQREREgXWhh/zxdo8+u9bNpvxpfHlBjYkwolsX3w1+FxaLt+ob6uNQ/ERF1LtIJhI4UEDZIIAz0dPiaVzhQnEhERNTeZF2otHn8fGk1gGCbbfJKa/CY4kvIhLrvO7sK5VimXQv890d8OvlVzP9J5lQRoSAAk2JCkRDfj8WDRGSFBYRuRip9QmcwQaVgASEREbkfmcgEUk4eJSLqGARBwPXXX4+HH34Y8fHxkMksn2fuuecePP7445gyZQoyMzORnZ2Nxx9/HB999FGzXP+WW25pln7akodSfLImCwiJiMgdSSYQOhvPQERERA7RVNVafL5b/hNiZaeBbz8DfggEeo4F+k8FRtzdNgMkIiJqJ/w8XZt+5eepbJZ+gLokwtQTBdiTVYSRPYKsjstlAroHs4CQiIjsq6y1Lmj3UMjQI8QHKoXM5veQDRMIIyQSCMUwgZCIiDoak8mETDsJhOdLLtrto0vZH5git06QR8UFxA0fhs19fJCYkoXUzAKLxPr6QsHbR0WjvEaPihod/DyViOsdbFHQT0TUEAsI3YxKLj6JRmcwShYXEhERdWRMICQi6rheeuklBAfbXmmrR48e2LRpE4YNGwYA2LRpE9599114e/NlFyCdQFjLAkIiInJDckGigNDAZ0AiIqKWUFx1OYHQH1UYImRfPlhTCpzYXletwAJCIiLq5OJ6h0AQAGfWtxEEIK635ftxV/ppTGswIv20dQJhj2BvzhsiIiKHlIsk4vp5KiGXCegR7I2sAvG0JR+VHIHel4vju/p5QC4T7M7h8VDIEBPm17RBExERtbLCilqUXRRP7a13vtR2AWFJtQ4J+EL84FWLgIBIxAYA6xaMglpTjV+zNSwUJKIm4ZshN6OUmDzK9AkiInJXYgkUTJ8gIuoY7BUP1ouNjUVMTAwAoLq6GqdOnWrJYXUoUhM+WEBIRETuSCYTIBZCyBR6IiKilqGpvFxAeJXsBOSCyD2317WtOCIiIqL2KTrYGxNjQp06Z1JMqNVkT1v9CDACcOz5V6xV766+To2PiIg6rwqRAkL/Sym5vbpIp9lGBXlDaLAInFwmoJufh93rDYrwl5z3SkRE1F7ZSx8EgPOlNTaP55ZexBv6OdhnGGSxXyv3AcYut9gXHeyN2SOjcO+YXpg9MorFg0TkEv7V7WaUEpNHdQZOHiUiIvcklkBoMgFGTiAlInIr/v7+5u2LF22v0NWZSBUQchEZIiJyV6KLyPD5j4iIqEVoGiQQjpYdF2/Ua1wrjYaIiKh9WxbfDyoHix9UchkS4vs51c898h/xqfJVRKLQpfH1CZUu+CAiImqoosY6Tcn3UgGhj4fc6thg4QwWy7dBVXEOGepSi2PhgV52rxcbFejSOImIiNrSyQviibwNnS+ptnk8r6wGB039MVf3FO7WPo4/jL0AADkD/wb4hDTLOImIGmIBoZuRTCBkASEREbkpmVj8BJhAQUTkTrRaLU6ePGn+3KNHj2bp98Ybb0RkZCRUKhWCgoIwePBgLFq0CGlpac3Sf2uQLCDkMyAREbkpptATERG1nqIGBYQBQhX0pkbPoN4hQNeBrTwqIqKObevWrZgzZw569uwJT09PhIaG4pprrsFrr72G8vLyVhnDggULIAiC+d9//vOfVrmuu4uNDkTi3GGQC+LfXdZTyWVInDsMsdGBkv28dfswi33dhQt4VLEJ4+RH8IPHY7hL/tOlRELH9WECIREROUgsgdDPU4Hko3nYmpFndWy2/Gc8oUzCNuNSyNZMRObXLwAlZwAAYQGedq93RVRAk8dMRETU2rIcSiC8CJON7zHzyuoXUBew23gFbta+iCXaf0J/1X3NNEoiIkuKth4ANS+pyaM6AyfREBGRexJLIASYQEFE5E4+//xzlJWVAQBGjBiBsLCwZun3u+++M2+XlpaitLQUx48fx9q1azFp0iRs3LgR4eHhLvefk5Nj83henvUXbM7ykHgGrNUZmtw3ERFRe6SQyYBGkyQNfPdJRETUIjRVtebth3VL8CzuwcLu+Xiobz6QvRsI7g3IuF4tEZEjKisrceedd2Lr1q0W+wsLC1FYWIh9+/bhnXfewZdffom4uLgWG8eOHTvwySeftFj/nd3UIeGIH3gePx6/INkm8Y5hmDrE9nvn7iHe5m0BRvyf8kN4C3X3ZV+hBi8qP8Z02a94TL8IalM3h8bGAkIiInKUWAKh0QgkJB22mosjwIhp8t/Mn4fKTgNHXoc+80MoHjuNCIcKCAObPGYiIqLWlulAAWGNzghNlRYhvh6ix3NLaxrtEZBsvAorunZthhESEVljAaGbUcnFiyh0TJ8gIiI3JZeYoMIECiIi91BYWIjHHnvM/Pnpp59ucp9BQUGYPHkyrrzySkRGRkIul+P8+fNISUnBjh07YDKZkJqaitGjRyM9Pd3lgsXo6Ogmj9UeqQJCJhASEZG7EksgZAI9ERFRy9BUai0+V8EL57uMBa4fVrfDyGdPIiJHGAwGzJkzB8nJyQCAbt26YdGiRRg0aBA0Gg2SkpKwd+9eqNVqTJ8+HXv37sXAgc2f8FpeXo7FixcDAHx8fFBVVdXs1yCgVm/7/ljrwLvbX7M15u075SmIk/1p1eYa+XFEGYocLiC8qLVOkyIiIhIjlkB4prhK9PvHkcJJhAklVvt/VcZhjFyJ8AAvm9fy81Cgdxcf1wdLRETUBkwmE7IuVDrU9nzpRckCwssJhJf5qOTw92SJDxG1DP52cTNKucTkUTsvKImIiDoquSCRQMgECiKiDk+r1WLWrFkoKCgAANxyyy2YOXNmk/p85ZVXMHLkSKhUKqtjy5cvx4EDBzBr1iycO3cOZ8+excKFC/H999836ZotSSWXi+7nMyAREbkrsQJCA4sXiIiIml2NzoAqrXW6fYhPg+dppg8SETlk7dq15uLBQYMGITU1Fd26XS76Wrp0KR5++GGsXLkSJSUlWLx4MXbt2tXs43jkkUegVqv/n717j46qvvf//5pLriQhBAgBEuQWw9UgFASp5RIVRCtqpYqeH0ZOKSoVz7HWqrUoX7TVWj2K1kK9FEVN1VoqWqFaEuQiYJSLghCCoA4aSCAJSch9Zn5/xAwJsyfJJJlJMvN8rJW1dvb+7L3flC43e+bz+ryVlJSkOXPm6Iknnmj3e0D6rth9AmhDnx09pdlj+jc55uMjJyVJYarWYusawzGray/WNsfIFtc1f9UnWj63+e6HAIDgVmt3qNzgXTDv1NkdkupcbtlhuP/54lSdU1Suvs10IByd2F1mg898AQDoSLbCcm0/fFKllbWKDrdq4uCeSoo70yn+u1OVKqtq2SIt3xZVeOy2a/T+2Dc2QiYPc2IBoK0IEAYYjwFCuk8AAAKU1UP33VomkAJAl+ZwODR//nxt3rxZkjRkyBC9+OKLbb7upEmTmjz+gx/8QOvXr9f555+vqqoqrVu3TtnZ2Ro/frzX97LZbE0ez8vL04QJE7y+bkOhnjoQEiAEAAQowwAh68cAANDuCk9XG+73tFo2AMCY3W7X0qVLXb+vXr26UXiw3qOPPqoNGzZo9+7d2rx5s95//31deuml7VZHZmamnnvuOUnSs88+q08++aTdro0znE5nCwKExc1e4+PvOxBWKVQ/rnpIvwl5VVdatrnGHHX20iO1c72qrdru0OKM3XrzlgilJsV6dS4AIHicrnIPDzYlxlSuWqdZVtOZ7yZPOSO1xT5a2w8Xamh8VJPnewpUAADQEfbYivXUhlxl5eTL2eD7R5NJmpYSrzvSkpWaFKuDx0tbfM1vm3hH/K7YPaDfXPgeANqCZSEDjKcAYQ2TRwEAAcpo8qgk2R3MIAWArsrpdOqWW27Rq6++KkkaMGCA/vOf/6hHjx5+uf/w4cP1//1//5/r93fffbdV10lMTGzyp2/ftq/0HOYhQFjFOyAAIEBZ6UAIAIBfnCwzDhDGNexACABo1qZNm5SXlydJmjJlisaOHWs4zmKxaPHixa7fMzIy2q2G8vJyLViwQE6nU9ddd52uuOKKdrs2GiuprDXs4NvQ3m9LmvweMze/TEXlNa7fjytOi2tu19NJ/6dcZ6Ik6Z6aBTqtCK/rq7Y7tHxDrtfnAQCCR0llTfODGvhlza0aX/Ws7qn5mTbZR6vWadb79h+oRlaVVtao2MPiNPW6R9ADBQDQOazfm6c5K7Yp80Dj8KAkOZ1S5oF8zVmxTev35inXiwDh0SLjAKHd4dTxEvcAYb/u3r/rAUBLESAMMKFW4xBFDctwAwAClNHkUUmyn/0WBwDoEpxOp2677TbXatiJiYnKzMzUwIED/VrHtGnTXNv79+/36729QQdCAECwMVpEppbPPgEAaHcnT1cZ7u9JgBAAvLJu3TrX9qxZs5oce9lllxme11b33nuvDh8+rLi4OD311FPtdl24yzvVdPdBSaqosetQfpnH4zu+7z54tpSJs3T4J+v037X3aItjdKtrzMzJ19Gi8lafDwAIbKWVtV6fU6QY/c0+XfNq7tWEqmf1f7XXSpK+OnFaP1/9aZPnPvHBQa3fm9eqWgEAaC97bMVanLFb1fam59rUd3bf9qX7e5vZJMWEuwfjG3WpP7xR2rFSqi7XibIq1RosLtMvlgAhAN8hQBhgQi0Ww/01zTzQAADoqswm4wAhE0gBoOtxOp1atGiRVqxYIUnq37+/srKyNGTIEL/X0rt3b9d2cXGx3+/fUh4DhLwDAgAClFGAkA70AAC0v0IPXSLoQAgA3vn8889d2+PHj29ybEJCgpKSkiRJx48fV0FBQZvv/9FHH+mZZ56RJP3xj39Unz592nxNeNZoYmgT9hwt9njsYw8BwvED4zTjvAFavPBWpQ2Ll4evSJvldErbDxvfAwCAUg8dCFv62ClUjL5TL0nSax9/0+x3ljV2pxZn7NYeW7EXVQIA0L6e2pDb4nk21XaHPvna/Z1qYK9uGtirm9v+b+vfE51OKfNhad3d0pOjZN/4B8XIfXGZvrHh3hUPAF4gQBhgQizGr2pMHgUABCqrh2cfE0gBoGupDw/++c9/liT169dPWVlZGjp0aIfUc+LECdd2bGxsh9TQEp4ChFV0IAQABCjDACEd6AEAaHeeAoS9osL8XAkAdG05OTmu7UGDBjU7vuGYhue2RmVlpebPny+Hw6G0tDTdfPPNbbre2Y4ePdrkT15e8HUT+ra4skXjPj96ynC/0+nUx0dOuu1P6ROtHt+H+FOTYvVC+nht+tU0zR7Tr1V1egqHAADgqQNhalJ3r67Ts1uoalq48He13aHlG3K9uj4AAO3FVliurJx8r84xel6eGx+t/gbdA10Bwq8/ko5+XLddflL9dj6uj8IW6+eWdxqN79edDoQAfMe9Tyq6tBBP3SeYPAoACFBGk0clJpACQFdydniwb9++ysrKUnJycofVlJWV5dpOSUnpsDqaE+YxQGj3cyUAAPiHlQ6EAAD4xYmyavVQiUabjyjbkaIK1a18TQdCAPBOcXGxa7tXr17Nju/Zs6fhua2xZMkS5eTkKCIiQitXrmzTtYzUd0vEGXkt7ED4mYcOhF+fLNfxkiq3/RcMjnPblxQXqR8O7aW3d3/nVY2SFB0e4vU5AIDgUFplHDK/8YJz9MV3e1vUyCLEbPK4KI0nmTn5OlpUrsQekV6dBwBAW20/fFLtMdX03IRolVe5BwuLy2t0uqpW3bb8n9uxKFOlqtX4/YwOhAB8iQ6EASbUYvxXWkMHQgBAgDKaPCoxgRQAupJf/OIXrvBgQkKCsrKydO6553ZYPQcPHtTq1atdv19xxRUdVktzPL0DsogMACBQWczuz77aFq5kDQAAWq7wdJWmmXfr5dBHtSdsgd4MfVB3h76pyG+3SrXeTQQFgGBWVlbm2g4Pb34SYETEmU4DpaWlrb5vdna2nnjiCUnS0qVLNWTIkFZfCy33XQsDhPvzSg0/w/34SKHh+AmD3AOEkjRxcE+ZjL8q9chkkiYaBBIBAJA8dyAce04PLZ87xuN3k/VCLWbNvWCAvP3E1umUth82fg4CAOBLnp593jq3T5T69zDuHlhw6FPp0Adu+086o/U3+7RG++hACMCXCBAGmBAChACAIGP28K0YE0gBoGu4/fbb9eyzz0qqCw9u3LixVR3/Vq1aJZPJJJPJpKlTpxqOWb58uT766KMmr7Nr1y7NmDFDlZWVkqRLL71UF1xwgdf1+IvJZDL8oo4AIQAgUBl9/MkCMgAAtL/C09WaZP5CkhRqsmu8+aBuM6+R6eUrpbLjHVwdAKAp1dXVmj9/vux2u8aOHas777zTJ/ex2WxN/nz88cc+uW9n9l1xZYvGVdsdyjl2VkC0tlr5e7MMx08YaBz4S4qL1LSUeK9qnJ4ST3cnAIBHnkIU0eFWzRzVV2/eMklpw+LdAuwmk5Q2LF5v3jJJA3t2a+W9jbsfAgDgS9Hh1na5TkqfaPWLNQ7/nSgulRLHu+1fVTtDlQpz/R4bGaKIUEu71AMARtrnv3joNEKtHrpPEKIAAAQoq4UOhADQVd1///165plnJNUF4e644w7t379f+/fvb/K8sWPHasCAAV7fLzMzU3fccYeGDBmiiy++WKNGjVLPnj1lsVj03XffacOGDXrvvffkcNSF78455xz99a9/9f4P5mehVrOqz1o0hgAhACBQGXUgtDt5/wMAoL2dLKvSJMsX7gd6DJJik/xfEAB0UVFRUSoqKpIkVVZWKioqqsnxFRVnOthFR0e36p4PPfSQ9u7dK4vFoueee04Wi28mHyYmJvrkul3Zd6fcOxBGhFhUUWN327/naLFGJ3Y/s2PHn/WLr5fonJCJerjmRh1TT0nSoF7dFB/juXvlHWnJ2pJ7wu0zYiOhFrMWpyW34E8CAAhWngKEMeEhkqTUpFi9kD5etsJy7ThSqNLKGkWHh2ji4DhXQP3g8dZ1UY7+/h4AAPhTfWf3tnzdGGIxaWCvbqryMFdnvyVZP/jvD6SvP5K2/J906ANVKFwv2y9tNK4v3QcB+BgBwgAT4iFEUcPkUQBAgDKaPCoxgRQAuoItW7a4tp1Op+69994WnffXv/5V6enprb7vl19+qS+//LLJMTNmzNCLL76ofv36tfo+/hJqNUtVjfe1ZLIIAABdkdXs/vlnLQvIAADQ7qynvlai6YT7gUE/8n8xANCFxcbGugKEJ06caDZAePLkyUbnemvPnj165JFHJEl33nmnxo4d6/U10Dp2h1PHTrl3IJxybm+t33fMbf9nR4slnVP3S8l3smc9IoukH1u2a7p5l5bXXqMX7ZfpgkHG3QfrpSbFavncMVqcsbvJz4VDLWYtnztGqUmxLf9DAQCCjlEXwBCLSWENG1t89LSSBkxS0vnnS2b3hQpaE8QwmaSJg5t+5gEA4Av1nd0zD+S3+hqDe0UpxGJWYg/jAOB3xRV1D7uBk+t+8j7T757/u06p8WcE/WM9Lx4DAO2BAGGACbUYhyhqmDwKAAhQRpNHJcnu4NkHAGjs8ccf149//GPt2LFDe/bsUX5+vk6cOKGqqip1795dAwcO1KRJk3TjjTfqggsu6OhyWyzMoBN9VQ3PQQBAYLIYvAPy/gcAQPvZYyvWUxtyNfD0bsmg+cPXMePqow4AgBZISUnRkSNHJElHjhzRwIEDmxxfP7b+XG+tWrVKNTU1MpvNCgkJ0UMPPWQ4btOmTY2268elpKRozpw5Xt8XUkFpleECN8P7xujAsRJ9dbK80f7Pjp6SVPfsLX79l5pSe+Z4N1OV7g3J0GjzER3q/nSz9545qq/evCVCyzfkKjMnv1Fgw2SSpqfEa3FaMuFBAECzjDoQRoVZZTJ9/7nsyS+l9++v246Ik4amScmXSkMvliLrAoCtCWJMT4l3dTAEAMDfvOnsbiS5T10QsHtEiLqFWnS6unEX+m+LGnerr+49Sq+U29yuQwdCAL5GgDDAhHgIEFbTgRAAEKDMJuMAYa2dDhQA0Nlt3Lix3a6Vnp7ebFfCIUOGaMiQIfrv//7vdrtvZxBqECCkAyEAIFBZDN4Bef8DAKB9rN+b5+peZNaPdMAxQBea92mS+QuNNx9QN1OVbvggRL/tlaeZo/p2dLkA0CWMHj1a69evlyRlZ2dr2rRpHsceP35cNlvdBML4+Hj17t3b6/s5v0+OORwO/e53v2vROVlZWcrKypIkzZ49mwBhK313qsJwf9/YcJ2XGOsWIDx4vFRv7/5WT735H/3b+m/J4CvPF2tn6vOsQxqWEN3sszc1KVYvpI+XrbBcO44UqrSyRtHhIZo4OI5ABgCgxYw6EEaHN1hdJveDM9sVhdLnb9b9XPOcdN5PXYe8CWKEWsxanJbcproBAGiLlnZ29ySlT7QkyWQyqX+PCB08Xtbo+LfFjd8Xj5dUGnbq7UsHQgA+Zpw2Q5cVYjBxVKIDIQAgcHnuQMgEUgBAcDDqRM8iMgCAQGW1uL8DOoy+YQMAAF7ZYytuNEHGIbM+dw7WSvuPlV7za6VWPacfVz2kb+3dtThjt/bYiju2YADoImbOnOnaXrduXZNj33vvPdf2rFmzfFYTfOO7YuMAYf/YCJ2X2N1tv8Mp/fKNPfqBPpdZ7p/nvmX/oXY6z1WN3enVszcpLlLXjkvUzZMH6dpxiYQHAQBeMepAGB3eoE9J7vsGZ5mkIWmN9tQHMYy+x2wo1GLW8rlj6JILAOhwdZ3dJ6lbmMXrc5O/DxBKde+AZzu7A6Gn98d+dCAE4GMECANMiMEEGkmqZhVuAECAsnh49tmZQAoACBKGHQgJEAIAApTFYBGZWhaQAQCgzZ7akNvk6tq1supz52BJdV3vl2/I9VdpANClTZkyRQkJCZKkjRs3aufOnYbj7Ha7li9f7vr9+uuvb9X9nnzySTmdzmZ/HnjgAdc5DzzwgGv/P//5z1bdF1JecaXh/n6xETovMdbwWK3DqTfs03RJ9WP6p/1COZx177w1Tov+r/Za1zievQAAf2kyQFhdLn21xf2k/uOkbj3ddtcHMdKGxct01se6JpOUNixeb94yiQ73ADqVtWvXas6cORo4cKDCw8MVHx+vCy+8UI899phKSkra7T52u1179+7VqlWrdPvtt2vSpEmKjIyUyWSSyWRSenp6i6+1atUq13kt+XnwwQfb7c8RaFKTYhUbEer1ec4Gc1X793APAR4vrWw0jyfvlPH7Y9/udCAE4FvW5oegK/G0YgsdCAEAgcpTB0ImkAIAgkWYQYCwqtbeAZUAAOB7Ru+AdKAHAKBtbIXlysrJ9+qczJx8HS0qp6sRADTDYrFoyZIluu222yRJ8+bNU2ZmpuLj4xuNu+eee7R7925J0uTJkzVjxgzD661atUo333yzpLpw4saNG31WO7zzrYcOEn27h6tPTJjMprqug0YOO/vpf2p+oWdMV+kO6z9U4uymo87G/x/h2QsA8IeyKqMAYUjdRv5+yWQwPzX5Uo/XS02K1Qvp42UrLNeOI4UqraxRdHiIJg6O45kGoFMpKyvTjTfeqLVr1zbaX1BQoIKCAm3btk1PP/203njjDU2cOLHN9/vpT3+qf/zjH22+DtpfSUWN1+fc8bfdMpnqwvP9DDoQOp3SsVOVGtAzUntsxVqeabxAzJP/ydU9l1nozAvAZwgQBhiTyaQQi0k1Z3UcJEAIAAhUlrOXKfuene67AIAgYdSBsIoOhACAAGU2eAes5f0PAIA22X74pJxePk6dTmn74UJdO44JnwDQnAULFmjNmjX64IMPtG/fPqWmpmrBggUaMWKECgsLlZGRoS1b6rr5xMbGauXKlR1cMVrjO4MAYc9uoQoPsUiSEntE6pvC8iavcciZqNtrFssk9893efYCAPyhpNI9NOHqQJg4Tvr1EenrrVLuB3U/hV9KyRc3e92kuEglxfEMA9A52e12zZkzR+vXr5ck9enTx+2dbevWrbLZbJo1a5a2bt2q4cOHt/meDcXFxalnz57KzW1b5/Hbb79d06dPb3LMsGHD2nSPQGZ3OFVqEKZvTrXdocUZu/XmLRHqHxuhSFWqQqFy6sx8nqPF5foi75QWZ+xWtYdcx7bDJzVnxTYtnzuGDr0AfIIAYQAKsZhVc9Y/LKqZPAoACFAWOhACAIJcqNXits/Th40AAHR1Vov7O6DD28QDAABopLTS+0kxded5vxo3AAQjq9Wqt956SzfccIPeffddHTt2TMuWLXMbl5iYqNdff10jR47sgCrRVnmnKt329Y0NlySt35uno0VNhwcbajjJtCGevQAAX3I4nIYdCGPqOxBKUkiENPTiup/LHpVOfin1GOTHKgGg/T3//POu8OCIESOUmZmpPn36uI4vWrRId911lx5//HEVFRVp4cKF2rRpU5vuOWHCBA0fPlzjxo3TuHHjNGjQoEYd51tr7Nixuuqqq9p0jWBW1srPSaW6eTrLN+TqtmlDdI81QxeZP9Pf7NP1d/uPdFLdtf3Lk1rx4eFm5/M0DCPSiRBAezP+xAldWojF/a+VyaMAgEBlNHlUYgIpACB4hBq9A7KIDAAgQFnM7s89FpABAKBtXN0kvD4vpPlBAABJUnR0tN555x3985//1DXXXKOkpCSFhYWpV69euuCCC/Too49q7969uvDCCzu6VLSSUQfCft0jtMdWrMUZu9Uer648ewEAvnS6utawO31UWBPvjD2HSAaf2QJAV2G327V06VLX76tXr24UHqz36KOPasyYMZKkzZs36/3332/Tfe+77z79/ve/17XXXqtBgwhidxZGnXi9kZmTL4u9UldZtmiQ+bjuDcnQtrBf6E8hT8q2898tznPUhxEBoL3RgTAAGQUIa+xMogEABCaziQ6EAIDgFmZ1fwesIkAIAAhQVoMu9Hbe/wAAaJOJg3vKZJLhRFFPTCZp4uA43xUFAAFq9uzZmj17dqvPT09PV3p6epvrePDBB/Xggw+2+TqoU1lj18nT1W77+8VG6KkNue2y6DfPXgCAr3nqTt/aRWcAoCvYtGmT8vLyJElTpkzR2LFjDcdZLBYtXrxY8+fPlyRlZGTo0ksv9Vud8I+2BgidTql6z1uKMZ1ZYCbUZNfllo9VUtZNazSkxdfKzMnX0aJyJfaIbFNNANAQ/7KXlJ2drY8//ljZ2dnat2+fCgoKdOLECdXU1Cg2NlbDhw/XtGnTlJ6ernPOOaejy22W0eTRGiaPAgAClNXDSmZ2B88+AEBwMHoHpAMhACBQGS0iU9sOEzEBAAhmSXGRmpYSr8wD+TLLIYea7x4xPSWeySsAAHwv71Sl4f7IULOycvLb5R48ewEAvuY5QEgHXACBa926da7tWbNmNTn2sssuMzwPgaOkwvhZ6I2BX71huD/DPt2r6zid0vbDhbp2HO+BANoPAUJJ06ZN0+nTpw2P5efnKz8/Xx9++KF+//vf64EHHtC9997r5wq9E2Jxn0RTwyQaAECAshh0n5CkWrrvAgCCRCgBQgBAEDHqQEgDQgAA2u7myQOVeSBfr4U+rGiV6xPHufr0+59v1UvSmWdwqMWsxWnJHVcsAAAdwFZYru2HT6q0slbR4VZNHNxTSXF1Ezm/K64wPKekstatw28vndIJdffq3jx7AQD+UOqh6xIdCAEEss8//9y1PX78+CbHJiQkKCkpSTabTcePH1dBQYF69+7t6xK98uyzz+rRRx+VzWaTw+FQr169NGbMGF122WW66aabFBlJGK0pbe1AeK7JpvhTn7nt3+c4R585B3t9PU/PZgBoLf5l/734+HhNmDBBqampGjRokLp3766amhp99dVX+te//qWtW7eqqqpK9913n2pqarRkyZKOLtmjEIvB5FEChACAAGU1CM5LkuPsb+MAAAhQhgFC3gEBAAHKYvAOWEsHegAAWq0+DPH27m8VqhqdbzqkMFONRpq/1k36QJK0xj5Z/1uzSFJdgGH53DFKTYrtwKoBAPCfPbZiPbUhV1k5+Y3CgCaTNC0lXnekJetbDwHC0LPm7ww3fa21offrH/aL9LT9Kh11xjd7f569AALB2rVrtXr1amVnZ+vYsWOKiYnR0KFDdfXVV2vhwoWKiYnxeQ3p6el66aWXXL8/8MADevDBB31+367EcwdCphkDCFw5OTmu7UGDBjU7ftCgQbLZbK5zO1uAMDs7u9HvNptNNptN77zzjh544AG9+OKLuuKKK1p17aNHjzZ5PC8vr1XX7UxKKtoW2PtKfXVy1nM68eFflHL6zN9FXfdB43muTaELMID2xr/sJW3fvl0jR46UyWT8H+Z7771XL7/8stLT0+V0OrVs2TL97Gc/U79+/fxcacsYBgjpPgEACFBmD8/vWlpQAACCxNmTUCTJ7nCq1u6Q1eAYAABdmVEHQjvvfwAAeM0oDHG+6SuFmdwnyeQ6+stkkqanxGtxWjIBBgBA0Fi/N0+LM3YbLtjmdEqZB/K1JfeELh3Zx/D8frERjX5faH1HISa7rrNu1DWWzXrT/iP9qfYqfSv3Scc8ewEEgrKyMt14441au3Zto/0FBQUqKCjQtm3b9PTTT+uNN97QxIkTfVbHunXrGoUHYay0ylOAkPACgMBVXFzs2u7Vq1ez43v27Gl4bkezWCyaNGmSLrroIp177rmKiopScXGxPv30U73xxhsqLCxUQUGBrrzySr366quaO3eu1/dISkryQeWdi6cwfUtdlNJPPSeM10unxuhnmVt0nWWjrjBv19v2yV5fy2SSJg6Oa1M9AHA2AoSSRo0a1eyYefPm6e9//7veeecd1dbWav369Zo/f74fqvNeiEH3iRq6TwAAApTR5FGJCaQAgOARFmIcEqwmQAgACEBGi8iwgAwAAN7xFIYYZz5oOP6CH12mGyZMU2KPSH+UBwBAp7DHVuwxPNhQtd2h9z5377RhNZt0yYg+evi9/XI6pZ46pVnmHa7jISa7brBmaZblY11Q9SdVKdR17Dezhumy0X159gLo0ux2u+bMmaP169dLkvr06aMFCxZoxIgRKiwsVEZGhrZu3SqbzaZZs2Zp69atGj58eLvXUVJSooULF0qSunXrptOnT7f7PQJFaaVR1yWn+uZtkOLTpIgefq8JAHytrKzMtR0eHt7s+IiIM4uElJaW+qQmb/3whz/UV199pcTERLdjP/vZz/SHP/xBCxYs0Ouvvy6n06n58+dr8uTJGjBgQAdU27mVGD4LWybUYtbitGRJUmJshGzOPvpj7XX6o36q1nQfnJ4SzzshgHbHTEIvjBw50rV97NixDqykaaEW94dMjZ1JNACAwGQhQAgACHKhFovhfjrRAwACkdEiMk6n5OAdEACAFmkqDGEUIHSaLJoybSaTVQAAQeepDbnNhgfrGb2SJnQP1zk9u2laSrwk6TrLRoWa7G7j3rRPaRQeTBsWrwU/GsKzF0CX9/zzz7vCgyNGjNCePXu0bNkyzZ07V4sWLdKWLVv0y1/+UpJUVFTkCvm1t1/96ley2WxKSkry2T0ChVHXpSGm79Rv/X9LfxgivThT2vy4lPeZXK3sAQAdbujQoYbhwXrR0dF69dVXNXXqVElSZWWlHn30Ua/vY7PZmvz5+OOPW/tH6DRKKow7EIYYZDMaCrWYtXzuGFf3+P49Gnaj9z482DCMCADtiQChFw4dOuTaTkhI6MBKmhZKB0IAQBCxeng5I0AIAAgWRu+AEgFCAEBgsnh6B2TCCgAALdJUGCLTcb7etU9UnjPOte+rkCFSKAEGAEBwsRWWKysnv03X6Ne9bsLoHWnJCrdIN1g3GI5bbb/Etc0kUQCBwm63a+nSpa7fV69erT59+riNe/TRRzVmzBhJ0ubNm/X++++3ax2ZmZl67rnnJEnPPvusoqOj2/X6gcaoA+FU8566Dadd+mabtOH/SSsvkvav9XN1AOAbUVFRru3Kyspmx1dUVLi2u9JzxWKx6KGHHnL9/u6773p9jcTExCZ/+vbt254ldwijDoRRYVb9/ZYLlTYsXqazvqY0meoWgXnzlkmaOerMn79/bIRa6+wwIgC0J2tHF9BVvPPOO1qzZo2kuhbFl19+eQdX5FmIxX3yaEtXRQMAoKuxnP1W9r1aAoQAgCDhKUBYRYAQABCAjDoQSnWLyIQYN+UFAADfay4M8aZ9qt60T5XkVD+d1A/MB1VbY9F9ReV0QQIABJXth0+2ubFSv9hwSVJqUqyevP583f/6Ql1vel+XmD+RxVR38Y32VH3jrAvUMEkUQCDZtGmT8vLyJElTpkzR2LFjDcdZLBYtXrxY8+fPlyRlZGTo0ksvbZcaysvLtWDBAjmdTl133XW64oor9Mknn7TLtQOVUQfCaZbd7gNNZmnQj3xfEAD4QWxsrIqKiiRJJ06caBQoNHLy5MlG53YlkyZNUnh4uCorK/XNN9+ovLxckZF85tdQSYV7gDA63KrUpFi9kD5etsJy7ThSqNLKGkWHh2ji4DjDz037fv8+6A2TSZqeEq/Facm8FwLwGQKEZ9m0aZMKCwslSdXV1bLZbHr//fddq9tYrVatWLHCcEWc5hw9erTJ4/UvzW1lFCCkAyEAIFBZmpg8CgBAMAgjQAgACCIsIgMAQOu1PAxh0nfqpbWOXpKk6YcLde04JhMBAIKHUYDCW/0adJyYObqf+sb+XMs3TNPSnP263pKluZZMrbZfzCRRAAFp3bp1ru1Zs2Y1Ofayyy4zPK+t7r33Xh0+fFhxcXF66qmn2u26gezs51+kKjXBfMB9YOIEKaKHn6oCAN9KSUnRkSNHJElHjhzRwIEDmxxfP7b+3K7EbDYrLi5O3333nSSpuLiYAOFZjN4FY8JDXNtJcZFKimv+f7Mwq0U9IkNUVO4eSGzoxgsGaGh8VJNhRABoTwQIz3L33Xdrx44dbvtNJpOmTJmipUuX6kc/at3qKUlJSW0tr0VCjToQMnEUABCgrGbj0AQBQgBAsPDUgZD3QABAILJ4ege08w4IAEBzWhuGKK1seqILAACBJjq87dOp+jYIEEpq0LFipHYcma73yu/VZRFhWjqkN5NEAQSczz//3LU9fvz4JscmJCQoKSlJNptNx48fV0FBgXr37t2m+3/00Ud65plnJEl//OMfW9UsIhid/e43wXxAoTJ4j0y+xE8VAYDvjR49WuvXr5ckZWdna9q0aR7HHj9+XDabTZIUHx/f5ueVvzkcDle3RanrdVD0hxKDz0FjIrx/P1y/N0/FzYQHJWnSkJ664rx+Xl8fAFqLAGEL9e/fX5dccomSk5M7upRmhVjcV+GuYQINACBAWQyeexLdJwAAwcNTB8JqOtEDAAKQ1cM7oL1l7ZQAAAhqrQ1DRDdYZRsAgGAwcXBPmUxqYedeY/1jww33t7RjBQB0ZTk5Oa7tQYMGNTt+0KBBrkBGTk5OmwIZlZWVmj9/vhwOh9LS0nTzzTe3+lqeHD16tMnjeXl57X5Pfzh70ZmNjlQtiH5Wz00qknLfl77+SHLUECAEEFBmzpypxx57TFJdJ9y7777b49j33nvPtd1ch93OaPv27aqoqJAkJSYm0n3QgGGAsLnPRk8ckuIGSWaLJGmPrViLM3arJa+Td76+R0k9IulGD8BvCBCeZfv27a7t06dP69ChQ1q7dq0ef/xx/eY3v9ETTzyhv/3tb7r44ou9vnb9S64neXl5mjBhgtfXPZtR94kaOk8AAAKUxeRh8qiDZx8AIDgYdaGX6EAIAAhMFrOnRWR47gEA0JzWhCFMJmni4DjfFQUAQCeUFBepaSnxyjyQ3+pr9O0e0fwgAAhQxcXFru1evXo1O75nz56G57bGkiVLlJOTo4iICK1cubJN1/IkKSnJJ9ftaO5d60061W2wdOEk6cJfSFWl0ldbpYTzOqQ+APCFKVOmKCEhQceOHdPGjRu1c+dOjR071m2c3W7X8uXLXb9ff/31/iyzzRwOh5YsWeL6/YorrujAajqvkgr3zrsxEU0ECGurpOemSSER0vAfSyOu0vKNIS1e8Lva7tDyDbl6Ib3pjs0A0F6MZxlCktStWzelpqbqt7/9rXbt2qV+/frp5MmTuvzyy/X55597fb3ExMQmf/r27dsudYcYTB6l8wQAIFB5njxK9wkAQHAwWkRGkqpq7X6uBAAA3/O8iAzvgAAANKc+DOGN6SnxSuzBauQAgOBzR1qyx8XbWqJfLAFCAMGrrKzMtR0ebtyRtaGIiDP/zSwtLW31fbOzs/XEE09IkpYuXaohQ4a0+lrBqLTKvetSVMNO9mHRUsrMupVmACBAWCyWRsG6efPmKT/ffSGRe+65R7t375YkTZ48WTNmzDC83qpVq2QymWQymTR16lRflNzItm3b9Je//EWVlZUex5w+fVrz5s3Thg0bJElhYWH69a9/7fPauhqHw6lSgw6E0eFN9Ov6MkuqKpHKjkvZz0svXaFHv5qjy8w7WnzfzJx8HS0qb03JAOA1OhC20KBBg/TII49o3rx5qq6u1sMPP6y//e1vHV2WIaMAYQ0BQgBAgLJ6CBA6mDwKAAgSYVaL4X46EAIAApHHRWTsvAMCANASd6Qla0vuiRYtPhpqMWtxWrIfqgIAoPNJTYrV8rljtOi1XV4vWhMVZlVMU5NMAQDtrrq6WvPnz5fdbtfYsWN15513+uxeNputyeN5eXmaMGGCz+7vK+4dCJsJTQBAgFiwYIHWrFmjDz74QPv27VNqaqoWLFigESNGqLCwUBkZGdqyZYskKTY2tl063B45ckQvvPBCo32fffaZa3vXrl26//77Gx2fPn26pk+f3mjf8ePHtXDhQv3yl7/UJZdconHjxikpKUndunXTqVOntHPnTv3tb3/TyZMnJUkmk0nPP/+8Bg4c2OY/Q6A5XV0ro1e/mPAmOhB+8U+3Xb1MJTru7NHi+zqd0vbDhbp2HIu4AfA9/nXvhcsuu8y1vXHjxo4rpBlG3SccTqnW7pC1DaujAQDQGZnpQAgACHKeOhASIAQABCKrxcMiMk7eAQEAaIn6MMTtGbtU00QAP9Ri1vK5Y5SaFOu/4gAA6GRmjuqra8cV6PXspoMiZ+vbPVwmujMBCGJRUVEqKiqSJFVWVioqKqrJ8RUVFa7t6OjoVt3zoYce0t69e2WxWPTcc8/JYjFegLM9JCYm+uzaHcXpdBIgBBC0rFar3nrrLd1www169913dezYMS1btsxtXGJiol5//XWNHDmyzff8+uuv9fDDD3s8/tlnnzUKFNbXeXaAsF5ZWZnWrFmjNWvWeLxmQkKCnn/+eV1++eWtKzrAGT0HJSkmwsOzsLZaOvCe2+7vnHHa5Rzq5b3dOx8CgC/wr3svNHw5rX/B7YxCPEyiqbE75aExBQAAXZrVbHILDHq7EigAAF2VxwAhnegBAAHIYwdC3gEBAGixmaP66oEfV+v+f+6VWQ49ZH1BnzmH6BPHuTrs7KdpwxK0OC2Z8CAAAJLCPHz+2pR+sRFS7gdS3zFSVO/2LwoAOrnY2FjX/MoTJ040GyCs74pUf6639uzZo0ceeUSSdOedd2rs2LFeXyPYVdTYDefZRDfVdQkAAkh0dLTeeecdvf3223r55ZeVnZ2t/Px8RUdHa8iQIbrmmmu0cOFCde/evaNLbeTiiy/W22+/rR07dujjjz+WzWbTyZMnVVxcrMjISMXHx2vs2LG6/PLL9dOf/lTh4eEdXXKnVeIhxOexA+GRD6WqU26719snyCnv3iN53gLwFwKEXsjNzXVt9+7deT/gC/HQZbDa7lCESBACAAKPxSBAyORRAECwCPXwDlhFB0IAQACyeOjgwCIyAAB4pz4MkWKy6QZrlm5QliSpJiRGIdYLpNo7JF3UgRUCANA5FJV73wliULRdemOeZK+RRl4ljf+ZlHSBRFdCAEEiJSVFR44ckSQdOXJEAwcObHJ8/dj6c721atUq1dTUyGw2KyQkRA899JDhuE2bNjXarh+XkpKiOXPmeH3fQOKp6xIdCAEEm9mzZ2v27NmtPj89PV3p6enNjps6daqczrZ/txUVFaUrr7xSV155ZZuvFexKKjx1IPQQ7hs0RbrxLemLNdKBf0kVdYsnvOe4wKv7mkzSxMFxXp0DAK3Fv+69sGLFCtf25MmTO7CSpnnqPlFD9wkAQICymk2qOmufg8mjAIAgERZCgBAAEDw8diC08w4IAIA3vi2ukCSNMx9stD+kpkQ69IE04ecdURYAAJ1OcXm11+dMq/iPVFNe98vnb9b9xI+UZj0mDey8840AoL2MHj1a69evlyRlZ2dr2rRpHsceP35cNptNkhQfH9+qxg71AQyHw6Hf/e53LTonKytLWVl1C6nMnj2bAKHHACEdkQAAwaGkwnjxGI9hemuolHxx3c8VT0pfbZZy/6PueZOlnBMtvu/0lHgl9ohsRcUA4D3v+qMGoBUrVigrK6vJFL/dbtcjjzyiZ5991rXvtttu80d5reKp+wQBQgBAoDIbTCClAyEAIFh4egesJkAIAAhAVotxgNDRDqu0AgAQTL4tMg4QuiSN92M1AAB0XkWtCBCed+Jf7jvz90nh3duhIgDo/GbOnOnaXrduXZNj33vvPdf2rFmzfFYTmlZaeSY0EaFK13YMHQgBAEGipNI4QBjTkjC9JUQaMl2a+TstvjjF4zyes4VazFqcluxNmQDQJkH/r/vt27fr1ltvVVJSki655BKNHj1a8fHxCg0NVXFxsfbu3au3335bX331leuce++9V1OmTOm4opsR4ilAWMskGgBAYLIaBAjtBAgBAEEizEMXegKEAIBAZDEbP/dYRAYAAO8crQ8QmgwChL2HSRE9/FwRAACdU3G58SRSTwaa8tSjZL/7gaSJUsKodqoKADq3KVOmKCEhQceOHdPGjRu1c+dOjR071m2c3W7X8uXLXb9ff/31rbrfk08+qSeffLLZcQ8++KCWLl0qSXrggQf04IMPtup+gahhB8J/h/5aVQrVNscIDc7/sXT6Cqlbzw6sDgAA3/PUjTcmwrtuvKlJsVo+d4wWZ+xWdRPNn0ItZi2fO0apSbFeXR8A2iLoA4T1bDabXnzxxSbHdO/eXb///e916623+qmq1vEUIKy22/1cCQAA/mE0gZTJowCAYBFKgBAAEEQsJuMOhHYHzz0AALzxbXGFIlWpWlncDyZd4P+CAADopLwNEF5s3ml84Pwb26EaAOgaLBaLlixZottuu02SNG/ePGVmZio+Pr7RuHvuuUe7d++WJE2ePFkzZswwvN6qVat08803S6oLJ27cuNFntQer+tBEoqlAA8wFkqRk87fSRx9IHy2WfnS3NP03HVkiAAA+VVLhqQOh93GbmaP66s1bIrR8Q64yc/LlbDCV1WSSpqfEa3FaMuFBAH4X9AHC5cuXa/bs2dq0aZN27dqlL7/8UidOnFBNTY2ioqLUp08fnXfeeZoxY4bmzJmj7t27d3TJzQqxGE+iqaYDIQAgQBl1IHQQIAQABAlPAcKqWhaRAQAEHovB+58k1dp5BwQAoKXsDqfyTlWoRuGaXv2EeuqUxppzdUO/PE2LOCINvKijSwQAoFOornWorMq9C4VJkqe30Bfsl2mPY4gut2zXLMvHijcVS2arNOwKX5YKAJ3OggULtGbNGn3wwQfat2+fUlNTtWDBAo0YMUKFhYXKyMjQli1bJEmxsbFauXJlB1cc3Eor60ITk8z7jAf0SvZjNQAA+F9JpXGAMDrcuw6E9VKTYvVC+njZCsu140ihSitrFB0eoomD45TYI7ItpQJAqwV9gDAmJkZXX321rr766o4upd14mjxa00QbXAAAujKjCaS1dJ8AAASJUE9d6OlACAAIQEYLyEiS3UmAEACAlsovrVRNg/D9SXXXB44faPTwczUtjUmhAADUK66oNtzf1BuoU2ZlO4cpu3aY/l/tPC0ceFy/Pt8hRcb5pkgA6KSsVqveeust3XDDDXr33Xd17NgxLVu2zG1cYmKiXn/9dY0cObIDqkS9+g6EE81fGA8Y+EM/VgMAgP+VVLgvHhMeYvaYy2ippLhIJcURGATQObTtv2jolDxNHiVACAAIVEYBQjsdCAEAQcJqMRs+C6t5BwQABCCLxUOAkHdAAABa7NuiCsP9/WMj/FwJAACdW3G5cQeKlnLIrBVf99XRc/+rnSoCgK4lOjpa77zzjv75z3/qmmuuUVJSksLCwtSrVy9dcMEFevTRR7V3715deOGFHV1q0KvrQOjURPN+94NxQ6SYfn6vCQAAfzLqQBjTyu6DANBZBX0HwkAU4qn7BJNHAQAByqgDBZNHAQDBJNRiVoXD3mgfHQgBAIHIYjIOENbyDggAQIsd9RQg7EGAEACAhopOG3cg9IbTKW0/XKhrx9FxAkDwmj17tmbPnt3q89PT05Went7mOh588EE9+OCDbb5OICqprJVZTj1de7Ummb/QJPMXijcV1x0cdFGH1gYAgD8YBggjzgoQlnwnWcPpMA+gyyJAGIBCPLTKZfIoACBQGXVdYvIoACCYhFrNqqhpHCCs4h0QABCAjBaQkSS7nXdAAABa6tti4wBhIgFCAAAaKWpjB8J6pQYTUQEA6EzKqmrlkFl/s0/X3+zTJTk1KqxA717hkPqM7OjyAADwudLKWrd9MeFnRW02PSZ9+pI06EfSiNnS8B9L3Xr5qUIAaDsChAEoxGI8iaaGSTQAgABlFCCkAyEAIJiEGSwkwyIyAIBAZPT+J0l2J++AAAC0lFEHQovZpISY8A6oBgCAzqu4vO0dCCUpOjyk+UEAAHQg97C7SSfDB0jj0zqkHgAA/K2kopkOhA67tP8dyWmXDmfV/fzrl9LIq6RrX/RfoQDQBsat6tClhVqM/1pr7EweBQAEJjoQAgCCXahBgJAOhACAQGT1sHgai8gAANByR4vK3fYlxITL6uE7RgAAglWxwQRSb5lM0sTBce1QDQAAvmPUdSkqjP4kAIDgUWLYgbBBgPCb7dLpgsYDnHYpLNrHlQFA++FboABkNHFUIkAIAAhcVoMAoYPJowCAIGL0HljNOyAAIABZzMaffbKIDAAALfdtsXsHwv49IjqgEgAAOreiduhAOD0lXok9ItuhGgAAfMcoQBgdToAQABAcnE6nYQfCRs/CQx8Ynzxito+qAoD2R4AwAIV4WB20mu4TAIAARQdCAECwM+pEX1Vj74BKAAB2u1179+7VqlWrdPvtt2vSpEmKjIyUyWSSyWRSenq619c8dOiQfvWrX2nUqFHq3r27oqKilJKSokWLFmn37t1eXauqqkp//vOfNX36dPXt21dhYWFKTEzU5ZdfrldeeUUOR+f+DNFi8tSBsHPXDQBAZ+F0OlVcdFIjTV/JojPvjYmxBAgBADhb8Wn3CaShFpPb57Hnm3KVoJMGY81anJbss/oAAGgvpZVGoYkQg5EAAASeihq74XzTmIgGz8JDG9xPDI2WBl7kw8oAoH2xREgA8hQgrLETpACAjlBaWqr3339fWVlZ2rlzp3Jzc1VcXKyIiAj169dPEyZM0A033KAZM2bI5GEi5NkOHTqklStXat26dbLZbLLb7erfv78uvvhiLViwQGPGjGlxfVVVVXrxxRf15ptvav/+/SosLFTv3r2VmpqquXPn6oYbbpDZQ4eHzsIoQGgnQAgACCJhdCAEgE7jpz/9qf7xj3+02/X+8pe/6H/+539UUdG4U9DBgwd18OBBrVy5UkuWLNGSJUuavdaBAwf0k5/8RF988UWj/d9++62+/fZbvffee1q5cqX+/ve/q0+fPu32Z2hPRu9/ksRjDwCAljlRVq3xjs+0MuxJlTvD9JlzsHY6ktXfeZFUNVQKi+roEgEA6DSMOhDGdQvTg1eO0OKM3d9/BuvUYyErNdT8nbId5+pf9ol6z36Bii09tXzuGKUmxfq9bgAAvEUHQgBAMDN6DkpSTMMw/cxHpEP/kb7cIOXtqds36EeShcA9gK6Df+EHIKPOE5JUXUv3CQDwtyeeeEK/+c1vVFlZ6XastLRUOTk5ysnJ0erVq3XRRRfplVde0YABA5q8JpNH3dGBEAAQ7MKsFrd9dKEHgI5htzf+DC4uLk49e/ZUbm6u19d65ZVXtHDhQkmS2WzW9ddfr7S0NFmtVm3dulUvvfSSqqqq9MADDygsLEy//vWvPV4rLy9PM2bM0DfffCNJOu+883TTTTepX79+Onz4sF544QUdPnxYW7Zs0eWXX64PP/xQ3bp187pmX7Na6EAIAEBTbIXl2n74pEoraxUdbtXEwT2VFBfpOv5tcYXONx+SJEWaqjTRtF8TzfulA2ul0mlSGF2SAACoV1zu3o0pNjJEM0f11Zu3RGj5hlx9e/ATDTV/J0kabz6o8eaDWhKyWgWpt6nPqMv8XTIAAK1iHCAkEAEACA4lFe7vfpIUE9EgajNwct3PxQ9IZfnSl1lSdOedUwsARggQBqAQq/EkGjoQAoD/HTx40BUerO8QOG7cOMXHx6uyslLbt2/XK6+8orKyMm3evFlTp07V9u3bFR8fb3g9Jo8asxp0SHQQIAQABJFQow6EBAgBoENMmDBBw4cP17hx4zRu3DgNGjRIq1at0s033+zVdQoKCrRo0SJJde9/a9as0ZVXXuk6Pm/ePN18881KS0tTeXm57r//fl111VVKSUkxvN6dd97pev+7/vrrtXr1almtZz4eXrx4sa644gp9+OGH+vTTT/XII49o2bJl3v7xfc5TB0IWkQGArmXt2rVavXq1srOzdezYMcXExGjo0KG6+uqrtXDhQsXExPi8hvT0dL300kuu3x944AE9+OCDPr+vr+yxFeupDbnKysmXs8Fj0WSSpqXE6460ZKUmxerbogqNNbsvbFAT2l0hPYf6sWIAADo/ow6EPSJDJUmpSbF6IX28Tv3rXSm78RiznOoz5Hx/lAgAQJtV1dq/76rbWAwdCAEAQaKk0kOA0FOYPipeSr3OhxUBgG/wL/wA5LEDocFLHgDAt0wmky699FLdddddSktLk/msoNtNN92ke+65RzNmzFBOTo6OHDmie+65Ry+++KLbtZg86pnZsAMhzz0AQPAwDBDyDggAHeK+++5rl+v88Y9/VElJiSRp0aJFjd7/6k2cOFHLli3TL3/5S9XW1mrp0qV67bXX3MZ98cUXev311yVJffv21XPPPdfo/U+SoqKi9Oqrr2ro0KGqrKzUE088oV/+8peKjY1tlz9Pe7GYPHUgJEAIAF1BWVmZbrzxRq1du7bR/oKCAhUUFGjbtm16+umn9cYbb2jixIk+q2PdunWNwoNd3fq9eVqcsdvwPdDplDIP5GtL7gktnztG3508pTTTYbdxNX3HKcTDcxYAgGBVZNCBsEe3BhNInU51P/yu+4nWcCllpg8rAwCg/VTvzNAzIau1zTFC2xwjdNjZV5JJ0QQIAQBBoqTCvROvJJ6FAAKOcdIMXVqIwcRRSaph8igA+N3DDz+sf//737rkkkvcwoP1zjnnHNdETkl6/fXXVV5e7jbOm8mjklyTR414M3k0PDxckvTEE0+ouLi4mT9xx7AaBAiZPAoACCZGC8lU1fAOCABdWcP3xP/93//1OG7BggWubvFr165VRUWF4bWc37ci+vnPf66oqCjDa/Xv318//elPJUnl5eV6++23W12/r3jqQMg7IAB0fna7XXPmzHGFB/v06aP7779fr732mp555hlNnjxZkmSz2TRr1izt37/fJ3WUlJRo4cKFkuR6hnZle2zFHsODDVXbHVqcsVsnD+9UuMk9DBE68AJflQgAQJfkdDpVbNCBMPb7DoSSpMLD0skv3U9OvkQKi/ZhdQAAtB9zzjpdYdmuh0NeVGbYXdoRtkj/F/InRYeyyAwAIDh47EAY4aEDIQB0UQQIA1Co2aQwuX+ISYAQAPwvLi6uReNSU1NdnQLLy8t16NAhtzFMHvXMaAJpLZNHAQBBJCyEDoQAEEi++OILff3115Kk4cOHa9CgQR7HRkdH66KLLpIknT59Wh9++KHbmHXr1rm2Z82a1eS9Gx5veF5nYbUQIASArur555/X+vXrJUkjRozQnj17tGzZMs2dO1eLFi3Sli1b9Mtf/lKSVFRU5Ar5tbdf/epXstlsSkpK8tk9/OmpDbktfv+rtjv07rdRuqn613qq9hptso9WiTNSkmQdQIAQAICGTlfbDb9v7BHZYAJpzyHSnfuly/4gDZh0Zv/Iq/1QIQAA7cDpVOh3Oxrt6mMqVorpqKIiwjuoKAAA/KukwkOAMJwAIYDAQoAw0BQcVFjGT/RIyHNuh6prmTwKAJ1ZTEyMa/vs0B+TR5tm1IHQweRRAEAQMepAyDsgAHRdn3/+uWt7/PjxzY5vOKbhuVJdx4R9+/ZJkiwWi84///xWX6sz8NSBkEVkAKBzs9vtWrp0qev31atXq0+fPm7jHn30UY0ZM0aStHnzZr3//vvtWkdmZqaee67uO7Rnn31W0dFduzOQrbBcWTn5Xp1ztNyiDx2p+r/aazWv5l6lVv1Fi+NWSAMm+qhKAAC6pqLT7gt3S1KPhh0IJSmmr3TBQmn+eul/v5BmPiIlz/BDhQAAtIPCwwqpKHDbvcMxTNHh1g4oCAAA/yuprDXcHxPBsxBAYCFAGCiqyqQPHpD+fKFMRzbqastWXWDa32hIjZ1JNADQWVVXV+vgwYOu388555xGx5k82jQzHQgBAEEu1EqAEAACSU5Ojmu7qQVkjMY0PFeSbDabysvLJUmJiYkKCWl6pcykpCRZLBZJUm5urqt7fUsdPXq0yZ+8vDyvrnc2i4kOhADQFW3atMn1DJgyZYrGjh1rOM5isWjx4sWu3zMyMtqthvLyci1YsEBOp1PXXXedrrjiina7dkfZfvikvHxUu3HKLGfvYVJIRPsUBQBAgCguN+5A0T2iiffq7v2libdKYVE+qgoAgHb2zXbD3dmOFEXTdQkAECRKKulACCA4EIsOBLZs6c2bpJJvG+1eGrJKV1Q/rNrv/5qr7UweBYDO6rXXXtOpU6ckSWPHjlVCQkKj451h8qjdbndNHjV5mLDZUYw6EDJ5FAAQTAwDhHaHHA6nYdAeANC5FRcXu7Z79erV7PiePXsantuaa4WEhCgmJkZFRUWqqanR6dOnFRXV8omPSUlJLR7bGlaz8Zp4vAMCQOe2bt061/asWbOaHHvZZZcZntdW9957rw4fPqy4uDg99dRT7XbdjlTqYWVsbyX2IDwIAMDZispb2IEQAIAurqTbQMWc/qrRvmxHim6jAyEAIEiUVLh/zhpqMSvcwnwbAIGFf+EHgtgkqbLEbfcws003Wd7XC/a6L2Jr6D4BAJ1SQUGBfv3rX7t+v//++93GdOXJo0ePHm3yeFu7T0iSxShA2NaltwEA6ELCrBbD/dV2h8LNxscAAJ1XWVmZazs8PLzZ8RERZyb9l5aWtula9dcrKipyXc+bd0Bfs3j4oo4u9ADQuX3++eeu7fHjxzc5NiEhQUlJSbLZbDp+/LgKCgrUu3fvNt3/o48+0jPPPCNJ+uMf/6g+ffq06XqdRXQ7TebsH0uAEACAs3kMEHajAwUAIICcf6P+fvpC/endbfqB+aDGmw8o0XRCBepB1yUAQNAw6kAYE2GV3vul9O2n0tCLpSFpUtIEycLzEUDXRYAwEEQnSNPuk/59r9uhGywb9Ff7TDlkVg0dCAGg06murtZPfvIT5efnS5KuuuoqXX311W7juvLkUV93n5A8dCC0M3kUABA8jDoQSt8HCEMIEAIA/MdmszV5PC8vTxMmTGj19Y3e/yTJ7uCzTwDozHJyclzbgwYNanb8oEGDXM+UnJycNgUIKysrNX/+fDkcDqWlpenmm29u9bU6m4mDe8pkktq6llp/OhACAOCmuNx9AqkkxdKBEAAQQPbYirV6+9c6qe76t2O8/u04s+jPb/75ue66NEWpSbEdVyAAAH5QUmEQIAyzSrn/kU59I+XtkTY/LoVGS6OvlX78pP+LBIB2QIAwUEz4ubRrtZT/hSSpVmb9tXamnqq9Rg7VTSStIUgBAJ2Kw+HQ/PnztXnzZknSkCFD9OKLL3ZwVV2TUQdCuk8AAIJJmKcAIZ3oAaBLarhoS2VlZbPjKyoqXNvR0dFtulZz12tOYmKiV+O9ZTbRgRAAuqLi4mLXdq9evZod37NnT8NzW2PJkiXKyclRRESEVq5c2aZreXL06NEmj+fl5fnkvklxkZqWEq/MA/ltuw4BQgAA3HjsQEiAEAAQINbvzdPijN2q9tCYYnPuCe04XKjlc8do5qi+fq4OAAD/Ka2sdds3LDRfKvqm8c7qUsluvNgMAHQFBAgDhcUqzfqjtGqWNPAi3XzsWm0ubrwaaxUTRwGg03A6nbrlllv06quvSpIGDBig//znP+rRo4fh+K48edTX3Sck4wChncmjAIAgEmoxDhDyHggAXVNsbKxr+8SJE82OP3nypOG5rblWbW2tSkpKJEkhISHq1q1bs+f4k6cOhA7eAQGgUysrK3Nth4eHNzs+IuJMoK20tLTV983OztYTTzwhSVq6dKmGDBnS6ms1JSkpySfXbYk70pK1JfeExwmfDZlNktEjs18sAUIAAM5m1IHQZJK6R4R0QDUAALSvPbbiJsOD9artDi3O2K03b4mgEyEAIGCVVLq//01y7jIePDTNx9UAgO8YzzBE1zRwsjT/femmd/RtyEC3wzUt+OIQAOB7TqdTt912m5577jlJdd0ZMjMzNXDgQI/ndOXJo4mJiU3+9O3b9lXKrGb3f9LYnUweBQAEj1A6EAJAQElJSXFtHzlypNnxDcc0PFeqCzRERkZKquuOVFPT9KqY33zzjex2uyQpOTlZJg8d/zqK2WySUUl0IAQAnK26ulrz58+X3W7X2LFjdeedd3Z0ST6RmhSr5XPHKMTS9DPbZJLOS+zutj+uW6giQ1lzFgD8Ye3atZozZ44GDhyo8PBwxcfH68ILL9Rjjz3m+i6uPWRnZ+tPf/qT0tPTNX78eA0cOFBRUVEKCwtTnz59NHXqVC1dulRff/11u90zEBUbdCCMCQ+pW9i0iP/tAABd21Mbclu0EI1UFyJcviHXxxUBANBxSircOxCOrd7pPtBklgZP9X1BAOAjfBsUaAZcIMm4+wQBQgDoeE6nU4sWLdKKFSskSf3791dWVlazK1/7YvJoeXm5a/JoSIjnlTI7++RRyXMHQqfT2SnrBQCgvYURIASAgDJ69GjXdnZ2drPjG44ZNWpUo2Mmk0kjR45Udna27Ha7du3a1WQX+Kau1VlYzSbV2BsHBulCDwCdW1RUlIqKiiRJlZWVioqKanJ8RUWFazs6OrpV93zooYe0d+9eWSwWPffcc7JYLK26TkvYbLYmj+fl5TX5/G2rmaP6auV/WTT/Jc//bphrydJ/ndykj6yDtcuRrJ2OZOWppxJ70H0QAHytrKxMN954o9auXdtof0FBgQoKCrRt2zY9/fTTeuONNzRx4sQ232/atGk6ffq04bH8/Hzl5+frww8/1O9//3s98MADuvfee9t8z0BUZNCBMDYyRCr+RnrqPKnHICn5EmnoJdLAH0qhkR1QJQAA3rMVlisrJ9+rczJz8nW0qFyJPXjeAQACj1EHwv1x0zQyqad0ZJNUXVa3s99YKTLOz9UBQPshQBigjLpPECAEgI5VHx7885//LEnq16+fsrKyNHTo0GbPZfJo04wChFLdBFJrMytvAwAQCOhACACBZcSIERowYIC++eYb7d+/X1999ZXHrvVlZWXavHmzJCkyMlJTpkxxGzNz5kzXu926deuafAd87733XNuzZs1qw5/Cd8wmk6TGgUE6EAJA5xYbG+sKEJ44caLZAOHJkycbneutPXv26JFHHpEk3XnnnRo7dqzX1/BGYmKiT6/fEv16hDd5/Aem/RrhyNEIa46kdZKk75xx+n3Ma36oDgCCl91u15w5c7R+/XpJUp8+fbRgwQKNGDFChYWFysjI0NatW2Wz2TRr1ixt3bpVw4cPb/N94+PjNWHCBKWmpmrQoEHq3r27ampq9NVXX+lf//qXtm7dqqqqKt13332qqanRkiVL2nzPQGPUgTA2MlTK/aDul6Ij0sd/qfuxhElzX5OGXuznKgEA8N72wyfl9PLjVKdT2n64UNeOI0AIAAgslTV2w7k1uf2ukmbdK9VWS7Yd0pcbpB4D/V4fALQnAoQBKsSgA2G1nUk0ANBRzg4P9u3bV1lZWUpOTm7R+UwebZqnAGGtwymr7xYWBwCg0/AUIDxadFqjE7v7uRoAQHu47rrr9Nhjj0mSnnjiCS1fvtxw3F/+8hdXZ4krr7xSkZHuEziuu+46LVu2TJK0cuVK3XXXXerWrZvbuG+//VZvvPGGJCkiIkKzZ89ulz9Le7OaTao6a5+DACEAdGopKSk6cuSIJOnIkSMeP9usVz+2/lxvrVq1SjU1NTKbzQoJCdFDDz1kOG7Tpk2NtuvHpaSkaM6cOV7ftyMVG3RJamisKddtX5kzQn3ieGcEAF96/vnnXeHBESNGKDMzU3369HEdX7Roke666y49/vjjKioq0sKFCxs9n1pj+/btGjlypEwm4+/P7r33Xr388stKT0+X0+nUsmXL9LOf/Uz9+vVr030DjVEHwh6RIdKhDe6D7VVSn865ECsAAGcrraxt5XlNv3cCANAVGXUflKSYiJC6DWuoNOiiuh8A6OKMZxiiywsx6LZE5wkA6Di/+MUvXOHBhIQEZWVl6dxzz/XqGtddd51r+4knnvA4rqWTR+utXLnSNf5sXWnyqBGHt0umAQDQRX1XVGG4/7ZXd2n+qmztsRX7tyAAQJvdddddio6OliT96U9/0tq1a93G7NixQ7/97W8lSVarVQ888IDhtUaOHKmf/vSnkqS8vDwtWLBAtbWNJ4mUlZXpxhtvVGVlpaS6bk2t6fjkD0aLyNCBEAA6t9GjR7u26xc28+T48eOy2WyS6ron9e7d2+v7Ob//XNDhcOh3v/udfvvb3xr+ZGVluc7Jyspy7X/11Ve9vmdHaypA2FOnNNB83G3/TkeyEntE+LIsAAhqdrtdS5cudf2+evXqRuHBeo8++qjGjBkjSdq8ebPef//9Nt131KhRHsOD9ebNm6crrrhCklRbW+sKOeKMIoMOhL0iTNKRD90HJ4yWohP8UBUAAG0XHW7VLPN2nWf6Ula1PEwYHR7iw6oAAOgYnoL1MeH06QIQePgvW4Ay6kBYYydACAAd4fbbb9ezzz4rqS48uHHjxlatmn3XXXdpxYoVKi0t1Z/+9CddfPHFuvLKKxuN8Xby6BtvvOGaPPryyy/Laj3zT4OuPnlUYgIpACA4rN+bpz/8O8fwmFNS5oF8bck9oeVzx2jmqL7+LQ4AgtCRI0f0wgsvNNr32WefubZ37dql+++/v9Hx6dOna/r06Y32xcfH6+mnn1Z6erocDoeuvvpqXX/99brkkktksVi0detWvfTSS653tqVLl2rYsGEe63riiSf00Ucf6ejRo8rIyNC+ffuUnp6ufv366fDhw3r++ed1+PBhSdKYMWN0zz33tOl/B1+yGnz2aef9DwA6tZkzZ7o6665bt0533323x7Hvvfeea3vWrFk+ry1QnKpwDznUO998yHD/LmeyLu7hvgAdAKB9bNq0SXl5eZKkKVOmaOzYsYbjLBaLFi9erPnz50uSMjIydOmll/q8vpEjR+qdd96RJB07dszn9+tKau0Ow0mko+1fSNVl7icMvcQPVQEA0D4mDuyuWSEr1c1UpXJnmHY5huoTZ4r+Yx+rz52DDc8xmaSJg+P8XCkAAL5XUtFMB0IACCAECANUmNV9Ek1UbaGU/bzksEsXLOyAqgAg+Nx///165plnJEkmk0l33HGH9u/fr/379zd53tixYzVgwIBG+5g86pnFwyqqdjsTSAGgsystLdX777+vrKws7dy5U7m5uSouLlZERIT69eunCRMm6IYbbtCMGTOaXTXbW2vXrtXq1auVnZ2tY8eOKSYmRkOHDtXVV1+thQsXKiYmpl3v5wt7bMVanLG72dB8td2hxRm79eYtEUpNivVPcQAQpL7++ms9/PDDHo9/9tlnjQKFUt0CMGcHCCXppptuUnl5ue68805VVlbqtdde02uvvdZojMVi0W9+8xvdd999TdbVv39//fvf/9ZPfvITHThwQJ999pnuvPNOt3EXXnih3nrrLUVFRTV5vY5kNvg3AQvIAEDnNmXKFCUkJOjYsWPauHGjdu7caRiisNvtWr58uev366+/vlX3e/LJJ/Xkk082O+7BBx90dYZ64IEH9OCDD7bqfp1BUx0IC53Retd+gSaYcxRvKnbt3+lIVjodCAHAZ9atW+fabi4Uf9lllxme50uHDp0JmCck0D2voWIPE0greo2WRj8vHfpAOrRBKj9RdyCZACEAoOtIqjokmaokSZGmKk227NNk7ZPk1Oe1xgHC6SnxSmQBGgBAACrx0IEwmg6EAAIQ/2ULUPUdCHuoRDMt2brCvF0Ty/dL/3JI3XpL438mmS0dXCUABL4tW7a4tp1Op+69994WnffXv/5V6enpbvuZPGrMYqEDIQB0RU888YR+85vfuMLvDZWWlionJ0c5OTlavXq1LrroIr3yyituAfvWqO+yu3bt2kb7CwoKVFBQoG3btunpp5/WG2+8oYkTJ7b5fr701IZcVbew23y13aHlG3L1Qvp4H1cFAGhPt956qy6++GKtWLFC69evl81mk8PhUL9+/ZSWlqaf//znOv/881t0rREjRmjXrl164YUX9Oabb+rAgQMqKipSr169dN555+mGG27QjTfeKLPZfXGyzsRq0IXewfsfAHRqFotFS5Ys0W233SZJmjdvnjIzMxUfH99o3D333KPdu3dLkiZPnqwZM2YYXm/VqlW6+eabJdWFEzdu3Oiz2rsKT0EHSdrpPFc7a86V5NRA0zGNN+fofFOuDjn7qT8BQgDwmc8//9y1PX5805/JJSQkKCkpSTabTcePH1dBQYF69+7ts9reeecdrVmzRpIUHh6uyy+/3Gf36oqKy407+3aLiZPOGyudN0dyOKS83dKXmVIin7kCALqQb7Yb7v7EkWK4P9Ri1uK0ZF9WBABAh/HYgTCcDoQAAg8BwgBVHyC82/q65lqzGh88XSB9tUUaPKUDKgMAtBWTR90ZTR6VJIeTCaQA0JkdPHjQFR7s37+/Lr74Yo0bN07x8fGqrKzU9u3b9corr6isrEybN2/W1KlTtX37drcJpt6w2+2aM2eO1q9fL0nq06ePFixYoBEjRqiwsFAZGRnaunWrbDabZs2apa1bt2r48OHt8udtb7bCcmXl5Ht1TmZOvo4WlbNCKAD40NSpU+Vs53eR5ORkPf7443r88cfbfK3w8HAtWrRIixYtaofKOobF4B2w1tGyQD0AoOMsWLBAa9as0QcffKB9+/YpNTXV7X2sfkG22NhYrVy5soMr7lqa6kB4hklfOfvqK3tfvampig63MhEGAHwoJyfHtT1o0KBmxw8aNEg2m811bnsECDdt2qTCwkJJUnV1tWw2m95//329//77kiSr1aoVK1aoT58+Xl/76NGjTR7Py8vzvuBOwtNzNTYy9MwvZrPUf2zdDwAAXck329x22Z0m7XIMddsfajFr+dwxSk2K9UNhAAD4X0mlhwBhBJ+bAgg8BAgDVH2A8F+OiZqrLPcB+9YQIAQAP/DVytdMHm3MbKIDIQB0RSaTSZdeeqnuuusupaWluQXWb7rpJt1zzz2aMWOGcnJydOTIEd1zzz168cUXW33P559/3hUeHDFihDIzMxtNjlm0aJHuuusuPf744yoqKtLChQu1adOmVt/Pl7YfPilv8ylOp7T9cKGuHUeAEADQdVkNutDbef8DgE7ParXqrbfe0g033KB3331Xx44d07Jly9zGJSYm6vXXX9fIkSM7oMqu61SFcaekprC4DAD4VnFxsWu7V69ezY7v2bOn4bltcffdd2vHjh1u+00mk6ZMmaKlS5fqRz/6UauunZSU1NbyOq0ijwFCJpACALo4p1M6+onb7i+c56hMZ94RTSZpekq8FqclEx4EAAS00spaw/0svAYgEHXudkJotVBr3SSabY4ROumMdh+wf61kN37gAQDQ1XjqQGi3M4EUADqzhx9+WP/+9791ySWXeOx2e8455+j11193/f7666+rvLy8Vfez2+1aunSp6/fVq1cbrqz96KOPasyYMZKkzZs3u1bj7mw8fYjZ/Hkt6UoBAEDnZTFYRIYFZACga4iOjtY777yjf/7zn7rmmmuUlJSksLAw9erVSxdccIEeffRR7d27VxdeeGFHl9rlGHVKCrM2/VVwQWml9tiKfVQRAKCsrMy1HR4e3uz4iIgI13ZpaalPaqrXv39/XXLJJUpOTvbpfbqqonLjYH6Phh0IAQDoikwm6Rcf6/7oh/RU7TXaah+pcmeYPnGkSJIWT0/WH+ekavPd0/RC+njCgwCAgFdSceZz1enmnXrQukqXmXcoxlHccUUBgI/QgTBA1XcgtMui9fYJutG6ofGAuMFS2TGpe2IHVAcAQPuyWIwnwtQ6HH6uBADgjbi4uBaNS01NVUpKinJyclReXq5Dhw7pvPPO8/p+mzZtUl5eniRpypQpGjt2rOE4i8WixYsXa/78+ZKkjIwMXXrppV7fz9eiw1v3Sh/NKmkAgC7OYrCIDB0IAaBrmT17tmbPnt3q89PT05Went7mOh588EE9+OCDbb5OZ2AUIEzsEaEvC057POdEWbXmrNim5XPHaOaovr4sDwDQQbZv3+7aPn36tA4dOqS1a9fq8ccf129+8xs98cQT+tvf/qaLL77Y62vbbLYmj+fl5WnChAleX7czKPYQIKQDIQAgEBwsll4pGCxpsCTJqlpFqkoTBsbpzkvP7dDaAADwt5IGi3DPNGfrp9YPla73paeeknoPkwb+UJr5iGThfRBA10eAMECFNghSvOuYqBu1Qbsdg3XepTfLPOoqKXZAxxUHAEA789SB0OFkAikABIqYmBjXdkVFRauusW7dOtf2rFmzmhx72WWXGZ7XmUwc3FMmk+TN485kkiYObllwEwCAzooAIQAA7k5VuAcID5/wHB6sV213aHHGbr15SwSdJQCgnUVFRamoqEiSVFlZqaioqCbHN/zcMzo6ut3r6datm1JTU5Wamqr/+q//0g9/+EN99913uvzyy/XJJ59o9OjRXl0vMTFwF6wuMgjmS3QgBAB0fbbCcv3uvf2N9tXKqhJZ9ZNx/TuoKgAAOk5JRa1r+wJz42ekCg5ItZWEBwEEDON2PejyQqxn/mp3OIbroqr/01XVD6lm4iLCgwCAgGMxGQcIa5lACgABobq6WgcPHnT9fs4557TqOp9//rlre/z48U2OTUhIUFJSkiTp+PHjKigoaNU9fSkpLlLTUuK9Omd6SrwSe0T6qCIAAPzDanF/B+T9DwAQ7Iw6JbV0wZlqu0PLN+S2c0UAgNjYWNf2iRMnmh1/8uRJw3N9YdCgQXrkkUck1X3++vDDD/v0fl2N0XM11GJWZKilA6oBAKDt9tiKNX9Vtn70WJY25rh/7xliMWnWaDrTAwCCT30Hwr46qXPM+e4DBv7QzxUBgO/QgTBAhTToQOiQWTZnH0lSjd2pMP7WAQABxqj7hCTV2plACgCB4LXXXtOpU6ckSWPHjlVCQkKrrpOTk+PaHjRoULPjBw0aJJvN5jq3d+/eXt3v6NGjTR7Py8vz6npG7khL1pbcE6q2O5odG2oxa3FacpvvCQBARzNaRIYOhACAYFZd69DpanubrpGZk6+jReUsOgMA7SglJUVHjhyRJB05ckQDBw5scnz92Ppzfe2yyy5zbW/cuNHn9+tKik6f3YHQqdjIEJk8LGoKAEBntn5vnhZn7G7y+0S7w6mth05o5ihChACA4FJaWdeB0K37YL1zCBACCBx0IAxQoQarcEt1XyACABBojLpPSJKjpUtsAwA6rYKCAv361792/X7//fe3+lrFxcWu7V69ejU7vmfPnobntlRSUlKTPxMmTPD6mmdLTYrV8rljFGpp+vU+xGLS8rljlJoU2+Z7AgDQ0YwWkSFACAAIZqcqzg451JlvWafrLFkabPpOUtPPSqdT2n640AfVAUDwGj16tGs7Ozu7ybHHjx93LWYWHx/v9WJmrREdHe3aLioq8vn9upKiBh0I41SiHWGL9Kiekj75q3Tyy5a3+QUAoIPtsRU3Gx6UJIdTWpyxW3tsxf4pDACATqLk+89WI0xVKnB2dx8wcLKfKwIA3yFAGKBCPEwerWlBVwoAALoajx0ImUAKAF1adXW1fvKTnyg/P1+SdNVVV+nqq69u9fXKyspc2+Hh4c2Oj4iIcG2Xlpa2+r6+NnNUX715yySlDYv3OOYX04ayYigAIGBYze6ffRIgBAAEs1MV1W77THLodusaPRrynDLD7lJ22K16NuRJzTJv93id0krjICIAoHVmzpzp2l63bl2TY9977z3X9qxZs3xWU0O5ubmubX8EFruShuH8C8z71cdUrGk1m6R3/0d6eqz0xAjp8MYOqw8AgJZ6akNus+HBetV2h5ZvyG1+IAAAAaTk+89EM+xpGl/1rKZX/VGv9vpfadS1UtIFUuyADq4QANqPtaMLgG+EWo0DhHQgBAAEIovJOEDIBFIA6LocDofmz5+vzZs3S5KGDBmiF198sYOr8k79iuGe5OXltUsXQqmuE+EL6eO17csTmvvcDrfjh0+cbpf7AADQGRgtIsMCMgCAYFZc7h78SzZ9qx6mMwvp9DaVaJblY9mcvfWeY6LhdaLDQ3xWIwAEoylTpighIUHHjh3Txo0btXPnTo0dO9ZtnN1u1/Lly12/X3/99X6pb8WKFa7tyZPpqNBQww6Ek8xfuA8o/U6K6e/HigAA8J6tsFxZOflenZOZk6+jReVK7BHpo6oAAOhcSipqG/xm0mFnP+1JmKAbr03tsJoAwFfoQBig6EAIAAgmHjsQ2plACgBdkdPp1C233KJXX31VkjRgwAD95z//UY8ePdp03aioKNd2ZWVls+MrKipc29HR0V7fLzExscmfvn3bvyPgpCG9NLCn+xd6mw4WEKwHAAQMo3dAu4PPPQEAwcsoQDjBfMBwbLZjmOF+k0maODiuXesCgGBnsVi0ZMkS1+/z5s1Tfr77JP577rlHu3fvllQX5JsxY4bh9VatWiWTySSTyaSpU6cajlmxYoWysrLkdHr+LNBut+uRRx7Rs88+69p32223teBPFBycTqeKGjxbDQOEUQlSz6F+rAoAAO9tP3xS9f8ksKpWEWr++1GnU9p+uNDHlQEA0DlU1zpUUWN32x/DQmsAAhQdCANUqMcAIRNGAQCBx2oxDhA6mvhyFADQOTmdTt1222167rnnJNWF8DIzMzVw4MA2Xzs2NlZFRUWSpBMnTjQKFBo5efJko3O7iqkp8Vr10VeN9hWV1+izo8U6f0DbQpgAAHQGxgFC3v8AAMGruKLlAcJPHOca7p+eEk+HCQDwgQULFmjNmjX64IMPtG/fPqWmpmrBggUaMWKECgsLlZGRoS1btkiq+wxy5cqVbbrf9u3bdeuttyopKUmXXHKJRo8erfj4eIWGhqq4uFh79+7V22+/ra+++sp1zr333qspU6a06b6BpKLGruraukVqeqtYyeZv3QcNuqgufQ8AQCdWWnmmo9IF5v16KeRR7XUO1CeOFGU7hukTx7k6qe4G57m/YwIAEIg8PfNiIggQAghMBAgDVIjV+IPKRh0InU7paLa0b42UfIk0ZLqfqgMAoH1ZzMbB+VomkAJAl+J0OrVo0SKtWLFCktS/f39lZWVpyJAh7XL9lJQUHTlyRJJ05MiRZkOJ9WPrz+0qpqT0dgsQStLGnAIChACAgGAlQAgAQCPF5dVu+3rplNu+A44kFSvabX+oxazFack+qQ0Agp3VatVbb72lG264Qe+++66OHTumZcuWuY1LTEzU66+/rpEjR7bLfW02m1588cUmx3Tv3l2///3vdeutt7bLPQNFw+6DFtn1Uu0lmmT+Quc2DBIOvKgDKgMAwDvR4WemB48358hqcmiM6bDGmA/rZ1onSbq86nfa5xx41nmEJgAAwaFh2L6hmHAiNgACE/91C1AhHjoQVtXYpW8/lfb+Q/ribemUre7A6QIChACALsviYYVPu8NhuB8A0PnUhwf//Oc/S5L69eunrKwsDR06tN3uMXr0aK1fv16SlJ2drWnTpnkce/z4cdlsde9L8fHx6t27d7vV4WuTBvdUmNWsqtrGz8GNBwv0v5cYd5oAAKArMepAyAIyAIBgdsqgA+ENNffr2cv66733/qlx+kLjzTmG3QdDLWYtnztGqUmxfqgUAIJTdHS03nnnHb399tt6+eWXlZ2drfz8fEVHR2vIkCG65pprtHDhQnXv7t4ByFvLly/X7NmztWnTJu3atUtffvmlTpw4oZqaGkVFRalPnz4677zzNGPGDM2ZM6dd7hloik6fCeYfU089UHuzJOmpK/ppduwR6avN0uCpHVQdAAAtN3FwT5lMdX0mfmDKcTte7gxTjjOx0T6TSZo4OM5fJQIA0KFK6EAIIMgQIAxQngKENXaHtCZdOvVN4wOHNkgOu2S2+L44AADamdHkUUmqtTOBFAC6grPDg3379lVWVpaSk9u3+8HMmTP12GOPSZLWrVunu+++2+PY9957z7U9a9asdq3D18JDLJo4uKc+PFjQaP9nR4t1sqxKPaPCOqgyAADah9E7IB0IAQDBrLjcfaKLxWzSZZNS1T9poJZvyNX/y8mX03nmeWkySdNT4rU4LZnwIAD4yezZszV79uxWn5+enq709PQmx8TExOjqq6/W1Vdf3er7BDuj56okRfToK408Xxp1jZ8rAgCgdZLiIjUtJV6bDnynseZDbsd3OYaq9qwpxNNT4pXYI9JfJQIA0KFKKow7ENKNF0CgIkAYoEKtHgKEDqc0/App+7OND1QUSnl7pP5j/VAdAADty2oxDhA6nEwgBYCu4Be/+IUrPJiQkKCsrCyde277d8qbMmWKEhISdOzYMW3cuFE7d+7U2LHu70B2u13Lly93/X799de3ey2+NjWlt1uA0OmUNuee0FXn9++gqgAAaB8ECAEAaKzYoANhbESITCaTUpNi9UL6eNkKy7XjSKFKK2sUHR6iiYPjmBQKAICB4opqw/2xkaF+rgQAgLa7Iy1Zpw7tUKSpyu3YJ86URr+HWsxanNa+C7wCANCZeexAGE7EBkBgMk6ZocsLbaoD4ZA045O+zPRhRQAA+I7HDoRMIAWATu/222/Xs8/WLXCSkJCgjRs3KiUlpZmz3K1atUomk0kmk0lTp041HGOxWLRkyRLX7/PmzVN+fr7buHvuuUe7d++WJE2ePFkzZszwup6ONjUl3nD/xhz3Py8AAF2NlQAhAACNFJe7Bx26RzZeJTspLlLXjkvUzZMH6dpxiYQHAQDwoMhDB8IekXSgAAB0PalJsfrF7B/q4dob9W/7D3TSGe069rHjzHeyoRazls8dQ4d6AEBQKamo0Y/NH2mQKU/Sme8aYyJ4/wMQmIhHB6gQDwHC6lqnNPhCyRIq2Rt8mWgyS6V5fqoOAID2ZTEZBwiZQAoAndv999+vZ555RpJkMpl0xx13aP/+/dq/f3+T540dO1YDBgxo1T0XLFigNWvW6IMPPtC+ffuUmpqqBQsWaMSIESosLFRGRoa2bNkiSYqNjdXKlStbdZ+ONqhXN53TM1JfnyxvtP/9L47rjWybJg3pqaQ4JosCALomi9n9s08WkAEABLNTHjoQAgAA7xWfpgMhACCwTBs/Rvd8cI2eK6mS5NQQ03f6gfmgdjmSZTJJ01PitTgtmfAgACDo1JTk6+nQunlLx52x2uEYrh2O4epRc66kmI4tDgB8gABhgAqxGAcpauwOKTRSGjBJKjpS141wyHRp0I+kiFj/FgkAQDvx2IHQzgRSAOjM6oN6kuR0OnXvvfe26Ly//vWvSk9Pb9U9rVar3nrrLd1www169913dezYMS1btsxtXGJiol5//XWNHDmyVffpDKae21svbfu60b7yarvufuszmUzStJR43cGXgQCALsho7TQWkAEABLNig05JhBwAAGgdTx0IY+lACADooipr7Movrfr+N5O+dPbXl/b+umBQnB7/aSod6gEAQat7/seu7T6mYl1p2aYrLdtU+d0oaUByB1YGAL5BgDBAee5A6KjbuP5VKTRK8tCxCQCArsTqIThvdzKBFADgLjo6Wu+8847efvttvfzyy8rOzlZ+fr6io6M1ZMgQXXPNNVq4cKG6d+/e0aW2SVS451d+p1PKPJCvLbkntHzuGM0c1dePlQEA0DZGHQgJEAIAgllxuXunJDoQAgDQOkbP1egwq8d5OAAAdHZfnyyX0fSZi4f3ITwIAAhqfQqzDfeHDvmRnysBAP8gQBigwqzGH1zW2L8PEIZF+7EaAAB8y+IhEM8EUgDo3DZu3Nhu10pPT/e6K+Hs2bM1e/bsdquhM9ljK9ZfNh1udly13aHFGbv15i0RdCIEAHQZVoMu9Lz/AQCCld3hVEllrdv+7nRJAgCgVYqMgvndeK4CALquwwVlhvsH9+7m50oAAOhckkp3ue0rUKx69xraAdUAgO+xPFaA8rTymStACABAALEYTB6VpFomkAIAgtRTG3JVY2/Zc7Da7tDyDbk+rggAgPZj9A5Y6+BzTwBAcCqpqHFtR6pSqaZDsqpWsRGhHVgVAABdV1F5jRZY3tUiyz811nRQVtWqRyTPVQBA1/WlhwDhkN5Rfq4EAIBOpKJICVVfue3eYxkleWhoAQBdHR0IA1SIhw6E1S2cQAoAQFdiNRs/9+wE5wEAQchWWK6snHyvzsnMydfRonIl9oj0UVUAALQfowChwyk5HE6ZPSwwAwBAoCpuECC8wLxffw19TKedYTr1xflSSJo0YJKUOF4KCe/AKgEA6DqKT1cp3fpv9TedlCSddobpy7LR0q6bpfP/q4OrAwDAe4cLTrvtC7GYlNgjogOqAQCgk4jooQW9X1Pot9t1gXm/Jpr3a5jZpgNh5+nijq4NAHyEAGGACrEYT5ShAyEAIBBZPDz3yM0DAILR9sMn5fTyGeh0StsPF+racQQIAQCdn9VDSNDudMosAoQAgOBSXF7t2r7AvF+S1M1UpW6F26Ws7XUHbtkiJYzuiPIAAOhyoiqOusKDUt1z9bzKT6SjowgQAgC6pC9PuAcIz+nZTVaL8WLdAAAEi29ronTAcYHWOS6QJPVQicYk9O7gqgDAd3gDCFChHl7uqmsJEAIAAo/FQ8t4u4PnHgAg+JRW1rbyvJrmBwEA0AkYdSCUJLuDVWQAAMGnYQfCS8yfug8I6y7Fj/BjRQAAdF12h1Ojqj8zPjjwIv8WAwBAO3A6nTqcX+a2f0jvbh1QDQAAnUtJReN5MkWKkTUytmOKAQA/IEAYoEI8BAjpQAgACESeJo/WMnkUABCEosOtrTwvpJ0rAQDANzx2IOQdEAAQhE6V101yGWz6TkPMee4DhkyVzBb/FgUAQBdVUlGjieZ9xgcJEAIAupotT6ryH4t0aU2mBpiOSzrz+eng3lEdVxcAdGJr167VnDlzNHDgQIWHhys+Pl4XXnihHnvsMZWUlLTbfex2u/bu3atVq1bp9ttv16RJkxQZGSmTySSTyaT09PRWXffQoUP61a9+pVGjRql79+6KiopSSkqKFi1apN27d7db/YGixGCB7hjmzgAIYK2bVYhOL9TqoQNhcwHCmgrpm21S3BCpxzk+qAwAgPbnafKog8mjAIAgNHFwT5lMktOLx6DJJE0cHOe7ogAAaEdmFpEBAMCluLxakjTRvN94wLAr/FgNAABdW1F5tSxyqMppVZjpzETS4m6DFRvdpwMrAwCgFfa+pYhjn+nx0Lpfjztj9W/7eC2pvVmDe9GBEAAaKisr04033qi1a9c22l9QUKCCggJt27ZNTz/9tN544w1NnDixzff76U9/qn/84x9tvk5Df/nLX/Q///M/qqioaLT/4MGDOnjwoFauXKklS5ZoyZIl7XrfrqrW7lBZlUGAMIJ4DYDAxX/hApSnIEVhWXXjHU6nlP+F9GVm3c/XH0m1ldL0+6Uf/coPlQIA0HYWC5NHAQColxQXqWkp8co8kN/ic6anxCuxR6QPqwIAoP3QgRAAgDOKK+o6EL5mT9M2xwhdYv5El1g+1Q/MuTKZzFLyJR1cIQAAXUdReY1ur1msMFVrrDlXk8z7NMn8heISJiq2o4sDAMAblSXS8b2NdvUxFauX6ZQkaUg8HQgBoJ7dbtecOXO0fv16SVKfPn20YMECjRgxQoWFhcrIyNDWrVtls9k0a9Ysbd26VcOHD2/zPRuKi4tTz549lZub26rrvfLKK1q4cKEkyWw26/rrr1daWpqsVqu2bt2ql156SVVVVXrggQcUFhamX//6122qPxAYhQclOhACCGwECAPUZ0dPyaSGTefrvPnpUZ08Xa070pKVmhRb13HwL1Ml+1nBwi+zCBACALoMi4nJowAANHRHWrK25J5ovgu9pFCLWYvTkv1QFQAA7cNiNhvu5x0QABCMistrXNtHnH31F/uP9Zzjxzp033hZ8nZJET06sDoAALqW+s6+VQrVNsdIbXOMlCS9NGG8hnRkYQAAeOvox5LT/XvCbEeKJGlILwKEAFDv+eefd4UHR4wYoczMTPXpc6YD+aJFi3TXXXfp8ccfV1FRkRYuXKhNmza16Z4TJkzQ8OHDNW7cOI0bN06DBg3SqlWrdPPNN3t9rYKCAi1atEhSXXhwzZo1uvLKK13H582bp5tvvllpaWkqLy/X/fffr6uuukopKSlt+jN0daWVHgKEEQQIAQQu45kW6NLW783TnBXb3MKD9TIP5GvOim1avzdPCo2UBkxyH2TbIVWV+rROAADai8VD9wk6EAIAglVqUqyWzx2jUEvTr/2hFrOWzx1Tt8AMAABdBB0IAQA441RFjdu+mPAQWaLjpXNndEBFAAB0XUXl7s9VSerRLdTPlQAA0EbfbDfcne1IUc9uoeoeSTgCAKS6ToBLly51/b569epG4cF6jz76qMaMGSNJ2rx5s95///023fe+++7T73//e1177bUaNGhQm671xz/+USUlJZLqwo4Nw4P1Jk6cqGXLlkmSamtrG/2Zg9EeW7HufGO34bE3P7Fpj63Yr/UAgL8QIAwwe2zFWpyxu9kuE9V2hxZn7K57wA2Z7j7AUSt9tcU3RQIA0M48TR51MHkUABDEZo7qqzdvmaS0YfGGx7uFWfTmLZM0c1RfP1cGAEDbeF5EpvnOuwAABJr6TkkNxTIRFACAVjF6rkpSbAQBQgBAFxMSKcWe02hXmTNc+53naEhvug8CQL1NmzYpLy9PkjRlyhSNHTvWcJzFYtHixYtdv2dkZPilvpZ4/fXXXdv/+7//63HcggUL1K1bN0nS2rVrVVFR4fPaOqP39xzRdSu2KvurIsPjB46VnmnUBAABhgBhgHlqQ26z4cF61XaHlm/INQ4QStKRze1YGQAAvkMHQgAAjKUmxeqF9PG6eLh7iLC61qER/WI6oCoAANrG0zsgHQgBAMGo2KADYWwEAUIAAFqj2EMHwthuPFsBAF3MRXeqctEuTap6Rr+ovl0v1V6if9gvkl0WDe7draOrA4BOY926da7tWbNmNTn2sssuMzyvI33xxRf6+uuvJUnDhw9vspthdHS0LrroIknS6dOn9eGHH/qlxs5kj61Yn/79cX1s/Zn+GvKobrP8UxNM+xWmxovJNGrUBAABhABhALEVlisrJ9+rczJz8nU0bLDUrbdkskgDJknTfiP9bIN06TIfVQoAQPsymUwymj/K5FEAAOqcP6CH274au1NHTpzugGoAAGgbAoQAAJxxyiDo0D2SLkkAALRGkUEHQotJig6zdkA1AAA0z1ZYrjc/senFLUf05ic22QrLXce+PlmuPGec3nVM0gO1N2tJ7c2SRIAQABr4/PPPXdvjx49vcmxCQoKSkpIkScePH1dBQYFPa2sJb+o/e0zDc4PFUxtydb4OKMZUrmmWPbo75A29EbZMu8IWGoYIl2/I7aBKAcA3+IQrgGw/fFJOL+fIOJ3S9iPFuvbGN6W4wVJ4d98UBwCAj1nNZrcuvLWOlnXlBQAg0KX0iTbcn3OsVOd6OAYAQGdlJUAIAIALHQgBAGgfe2zF+uCL42777U7pv1/6RHekJSs1Kdb/hQEAYGCPrVhPbchVVk5+ozmjJpM0LSVed6Ql67viCsNzh/SO8lOVAND55eTkuLab6t7XcIzNZnOd27t3b5/V1hKtqd/o3GBQ16jpuP4Q6v7nznX2V5XcF2XLzMnX0aJyJfaI9EeJAOBzBAgDSGllbSvPq5H6nd/O1QAA4F8Ws0myN95nJz8IAIAkKSXBc4Dwx6l+LgYAgDby1IGwlgAhACDIOBxOFRt0SoqNJEAIAIA31u/N0+KM3W6LldbLPJCvLbkntHzuGM0c1dfP1QEA0FhTzy2n88xz67LRCYbnDyZACAAuxcXFru1evXo1O75nz56G53YUf9Z/9OjRJo/n5eV5dT1/2374pAYpT71MJW7Hsh0phuc4ndL2w4W6dhwBQgCBgQBhAIkOb91fZ3Q4XyICALo+owmkdjoQAgAgSeofG6FuoRadrm6cts85XtpBFQEA0HqeAoR0IAQABJvT+V9quL7SPp0j6czzkQ6EAAC03B5bsR7M+FCTnIf1qZJVJuOJodV2hxZn7Nabt0TQiRAA0GH22IqbDL3Xq7Y79M6e79z2h1hMSuoR4avyAKDLKSsrc22Hh4c3Oz4i4sx/Q0tLO36+hT/rT0pK8mp8Z1NaWasfmI27LnoKENadV+OrkgDA7wgQBpCJg3vKZFKjlvTNMZmkiYPjfFcUAAB+YjSBlO4TAADUMZtNOjchWru+KW60P+dYx3+gDQCAtwgQAgDwvR1/0b/CVuqos5c+sI/TB45x+tgxTN0jQzu6MgAAuoynNuTqh9qpP4aulN1p0j7nQO1wDNfHjmHa7BitSoW5xlbbHVq+IVcvpI/vwIoBAMHsqQ25zYYH6xl9XHpOz26yWsztXBUAAJ1fdLhVex2D9KfaK/UD80GNMX2pMFNdOPDTJgKENGoCEEgIEAaQpLhITUuJV+aB/BafMz0lXok9aKsLAOj6rIYdCJk8CgBAvZQ+7gHCbwrLVV5dq8hQPh4AAHQdVrPxBBcWkQEABBWnU6FfrpckJZpO6Gbrv3Wz/i2bo7c+Ds/q4OIAAOgabIXlysrJ1x8s+yVJFpNT55mO6DzzES3Qe5pQ+adGAUJJyszJ19GicubaAAD8rv651RaDe3Vrp2oAIDBERUWpqKhIklRZWamoqKgmx1dUVLi2o6OjfVpbSzSst7KystnxbanfZrM1eTwvL08TJkzw6pr+NHFwT92tgfqidqAkKUzVGmU6ouHmb3RC3Q3PoVETgEDDDMEAc0dasrbknmjRKjOhFrMWpyX7oSoAAHzPqAMFAUIAAM5ISTD+8Pfg8TKNSYr1bzEAALQBHQgBAJBUcEBhJV+77d7lHKrYbnQgBACgJbYfPimnU5pgPuB27Iijj/LVw22/0yltP1yoa8cRIAQA+Ff9c6stBvduOhgDAMEmNjbWFSA8ceJEswHCkydPNjq3ozWs4cSJE82Ob0v9iYmJXo3vbM5u1FSlUH3qTNGnds/dB2nUBCDQ0Is8wKQmxWr53DEKbabNvMVs0vK5Y5TKJFEAQIAgQAgAQNNS+ngIEB4r9XMlAAC0jacAYa2j+UXVAAAIGAf+Zbj7ffsPFBsZ4udiAADomkora5WgkzrH7N7N6WPH8CbOq/FlWQAAGCqtrG3yeKhqtMjyT401HZRFdsMxQ3rTgRAAGkpJORMeO3LkSLPjG45peG5H6er1+9sdacnNZizq0agJQCAiQBiAZo7qqzdvmaS0YfEynkojjU2K1cxRfVt2wbJ8qbaq3eoDAMAXjCaQ1hIgBADAxVMHwgMECAEAXYzVQ4CQ/CAAIKgc+o/brmqnRRsdqeoeQQdCAABaIjrcqgnmHMNjHzuGNXEeYX0AgP9Fh1ubPH6+6ZB+FfKG/hH2oHaH/VzPhzym+ZZ1ileRawwdCAGgsdGjR7u2s7Ozmxx7/Phx2Ww2SVJ8fLx69+7t09pawpv6zx4zatQon9TUmaUmxerhq5v/c4dazDRqAhCQCBAGqNSkWL2QPl6b7p6m/rHhbsc//+6UKmuMV5lRVZl08H1p/X3SsxdKf0yWvv7IxxUDANA2RhNI6UAIAMAZPaPC1CvKfRLpweMECAEAXQsdCAEAkHTj3/Xe8D/oLftFKnLWTQDd7hihMkXSgRAAgBaaOLin/uWcqMurfqf/V/P/ab19vAq/f67ucBp3IDSZpImD4/xZJgAAkuqeWyZPHSUkXWjZ69qONlXoYssuLQlZrQGm4679dCAEgMZmzpzp2l63bl2TY9977z3X9qxZs3xWkzdGjBihAQMGSJL279+vr776yuPYsrIybd68WZIUGRmpKVOm+KPETqdbmOdAvskkpQ2L15u3TGp5oyYA6EKaXpIEXV5SXKR+MjZRyzMPNdpfWePQtsMnNS0lvvEJ+fulFT+UHGe1uz+8URoyzbfFAgDQBkYTSAkQAgDQ2Ll9onWi7GSjfTkECAEAXYynDoS8AwIAgkpYlD7tdpFeqEmURXb9wHRQtd+vHds9ggAhAAAtkRQXqSkpCco8YNY++0C9aL9MJjk02JSno85ehudMT4lXYo9IP1cKAEDdc2taSrwyD+QbHv+hea/bvtPOMO1xDpUk9ewWqthIOtYDQENTpkxRQkKCjh07po0bN2rnzp0aO3as2zi73a7ly5e7fr/++uv9WWaTrrvuOj322GOSpCeeeKJRnQ395S9/0enTpyVJV155pSIjg/O9ZuuhE4b7756ZoitT+/G+ByCg0YEwCEwbFm+4P8vgRdJm7q9qc4T74MMb27kqAADaFwFCAACal5IQ7bavoLRKhaerO6AaAABax+yxAyHvgACA4FJcXiNJssuiHc7h+tSZoqgwq0IsfAUMAEBL3ZGWrBDLmfdMp8z60tlfkvu7Z6jFrMVpyX6sDgCAxu5IS1aowTtflMqVavrSbf/HjmGq+b7PyGC6DwKAG4vFoiVLlrh+nzdvnvLz3efX33PPPdq9e7ckafLkyZoxY4bh9VatWiWTySSTyaSpU6f6omQ3d911l6Kj6+aC/OlPf9LatWvdxuzYsUO//e1vJUlWq1UPPPCAX2rrjLZ9edJtX5+YMN06ZQjhQQABjw6EQSA1MVY9u4Xq5FkTQjMP5GvplU6ZTCbtsRXrqQ25ysrJ1wprimZYPmk01pm3R6byQikyzp+lAwDQYhaz+wektQ5HB1QCAEDnldLHPUAoSTnHSjVpSE8/VwMAQOt46kDoIEAIAAgypyrcF4Oh+yAAAN5JTYrVrVOHaPmGQ02OC7WYtXzuGKUmxfqnMAAADKQmxWr53DG67dWdavhx6Lmmo7LLIqsaz5PZ6hjl2h7cK8pfZQJAl7JgwQKtWbNGH3zwgfbt26fU1FQtWLBAI0aMUGFhoTIyMrRlyxZJUmxsrFauXNnmex45ckQvvPBCo32fffaZa3vXrl26//77Gx2fPn26pk+f7nat+Ph4Pf3000pPT5fD4dDVV1+t66+/XpdccoksFou2bt2ql156SZWVlZKkpUuXatiwYW3+M3RFeacqdPjEabf9k4f0kslk/P0jAAQSAoRBwGw2aWpKvN7aebTR/qNFFcrNL9PhgjItztitanvdy+MWxyi3AKFJTu3a9LbOn3mz3+oGAMAbRhNIyQ8CANCYUQdCSco5VkKAEADQZRh1oJfoQAgACD71HQgbio0kQAgAgLesBguV1jOZpOkp8Vqclkx4EADQKcwc1VcXDOqpbYfPdFDa6TxX51U9p3Hmg5ps3qvJ5n0abTqsjxwjXWM+/bpQe2zFPM8A4CxWq1VvvfWWbrjhBr377rs6duyYli1b5jYuMTFRr7/+ukaOHGlwFe98/fXXevjhhz0e/+yzzxoFCuvrNAoQStJNN92k8vJy3XnnnaqsrNRrr72m1157rdEYi8Wi3/zmN7rvvvvaXH+Xs+4eyRqmwzXJ6q4QnVLjUD3zZQAECwKEQWL6MPcAoSS9uv1rZXxsc4UHpcarzhQ6o/SRY5S2OEZp8+YwPTuSF0gAQOdkNIGUDoQAADSW7KkD4fEyP1cCAEDreZrYaSdACAAIMsUVBAgBAGgPu23FbvssZpMevmqUfpjcS4k9Iv1fFAAATaixu8+HqVKoPnKM0keOUXpMUoxOq1QRruOHCk5rzoptWj53jGaO6uvHagGg84uOjtY777yjt99+Wy+//LKys7OVn5+v6OhoDRkyRNdcc40WLlyo7t27d3SpHt166626+OKLtWLFCq1fv142m00Oh0P9+vVTWlqafv7zn+v888/v6DL9z14jfbpKqq3QZEl7wqUDjiRl2KfrJfsMSdKFQ3t1aIkA4C8ECIPERef2ktVscluF+5+7v2sUHpSkw86+eqDmJn3iSNEXzgFy6syEnOUbcvVC+ni/1AwAgDeMAoRMHgUAoLGoMKuS4iJkK6xotD/nWEkHVQQAgPc8NYagAyEAINgYdiCMCO2ASgAA6LqcTqdhgHBMUqyunzDA/wUBANACJ8qqmh1Tom5u+6rtDi3O2K03b4mgkQQAGJg9e7Zmz57d6vPT09OVnp7e7LipU6fK6Wz/77WSk5P1+OOP6/HHH2/3a3dZeXuk2sZzZIaZbepuPy1JGtgzUv1jI4zOBICA42GqBQJNTHiIfjCwh9v+UwYrk0omvWSfoX3OgY3Cg5KUmZOvo0XlPqoSAIDWM+5AyORRAADOlmLQhfDg8TKffDgNAIAveOpA6OAdEAAQyA5vlBxnFgV1Op06VVHtNqw7HQgBAPCKrbBChafdn6ljCFUAADqxgtLmA4SeVNsdWr4htx2rAQCgE/tmm+HubGeKJGnSELoPAggeBAiDyPRh8W2+htMpbT9c2A7VAADQvqx0IAQAoEVSEtwDhGVVtfq2uMJgNAAAnY/RAjISi8gAAALYl5nSy7OlFy6WvtkuSSqvtqvG7v7si40gQAgAQItU1y2evctWZHiYACEAoLMqr67V6Wp7m65BIwkAQND4+iO3XbVOs3Y7hkiSJg/t6e+KAKDDECAMIiGW9vnrLq006loIAEDHMppASoAQAAB35xp0IJSkg8dL/VwJAACtY7SAjCTZG3RlAgAgYDjs0r/vr9v+9lPpxRnSG/NUeuyQ4fBYOhACANAyL/1YemqMBmz+lX5i3qREU76kM98tEiAEAHRWJ0rdO+d6i0YSAICgEdFDCo9ttGuvc5AqFC5JmjSYACGA4GHt6ALgH3tsxfr9ewfa5VrR4XzxCADofAgQAgDQMsMSYgz35xwr0/RhffxcDQAA3qMDIQAgqOx6Rcrf13jfF2+r51fbZdVjqj3r697YiFA/FgcAQBdVVSZ9t0ty2nW+juj87x+fb9b+SL+qvUW9osKU2COiY2sEAMCDgrKqdrkOjSQAAEHhqmf1zYlS3b48Q+fZ9+kC83594RgoSRqWEK2eUWEdWx8A+BEBwiDx1IZcVdvbvgK3ySRNHBzXDhUBANC+jDpQMHkUAAB3g3p1k9VscntO5hwr6aCKAADwjqcAIYvIAAACTlWplPmQ4SHbyFtUu9n9q97udCAEAKB5Rz+WnHa33TnOJEl13QdNJuN3TwAAOlpBafsECGkkAQAIdHtsxXpqQ66yDuTLqf7ao/5abb/UdTw5ProDqwMA/zN3dAHwPVthubJy8tvlWtNT4pXYI7JdrgUAQHuiAyEAAC0TajVrSO8ot/07jhTKVljeARUBAOAdowVkJN4BAQABqOhrKSTcfX/PZOUkXmt4SmwEE0ABAGjW1x8Z7t7hGC5JOn9ArB+LAQDAOw07EKaaDilGZV5fg0YSAIBAt35vnuas2KbMA/ny9A3iur15Wr83z691AUBHIkAYBLYfPilnO8ydCbWYtTgtWe1yMQAA2hkBQgAAWi4+JsxtX96pSv3osSzNX5WtPbZi/xcFAEALeepASBd6AEDASRglLcqWLl4qhcWc2X/pQyqqND4lNjLUP7UBANCVfbXVbVeZM1xfOM+RVNeBEACAzurE9x0ITXLor6F/0K6whfpn6P262/o3TTZ/rjBVN3sNGkkAAALZHluxFmfsVrXd0eS4WodTizN2M0cGQNAgQBgESitr23yNAeYTemvil0rd8UvpqfOkGg/fSgIA0EEsZvd/1jB5FAAAd+v35umjQycNjzmdUuaBfM1ZsY1V1gAAnZanACGLyAAAAlJIuPTD/5Fu3yn94L+lIWnSuTNUXGE8ITQ2kg6EAAA06wfzpfP/SwUh/Vy7PnWcK7ssMpmk8xK7d2BxAAA0rb4D4XDTN4ozlclicmqM+bBus67Vq6G/113WN5o839VIAgCAAPXUhtxmw4P1qu0OLd+Q6+OKAKBzsHZ0AfC96PDW/zVPNe/SA9aXNch8XPq0wQHbDmnwlLYXBwBAO7EadiBs2UsgAADBon6VNXszneWr7Q4tztitN2+JUCqrbQMAOhkChACAoBTVW7riCclhl0wmnSqvMRzWPYIAIQAAzTpvjnTeHM05mKWK0qOaYD6gEnWTJA3tHaXocJ6nAIDOq74D4WTzXsPjHzlGejw31GLW8rlj+P4PABCwbIXlysrJ9+qczJx8HS0qpzsvgIBHB0JJpaWleuutt/SLX/xCF154oXr37q2QkBDFxMRo2LBhmjdvntavXy9nMxMsO6uJg3vKZDynplklzm514cGzHd7YppoAAGhvRhNImTwKAEBjrLIGAAgEVoMO9BLvgACAIGG2SJKKDQKE4SFmhYdY/F0RAMALa9eu1Zw5czRw4ECFh4cr/v9n787jo6rv/Y+/zkx2EggJhAQSZAth0yAIBqyyRAXRumNFW0V7qVyt0Frb2pZrr9fb2+v16u8abdUKlhY1RVwqouCSBAEFBWUXQtgHDWQjkH2Zmd8fMUPCnEkyyWSbvJ+PB4/Hme/5nu/5hIcy+Z7z/Xw/MTFMnTqVJ598krNnz/rsPv6+DsYXTpdVc7SwnFNE8a5jKp84kgG4eHBk5wYmIiLSjPoKhJdZ9rqdcxoBhI243G29qGFA6qgYVi2cwuxxcR0RpoiISKfYcrgQb6e6TidsOVzUPgGJiHQhPb4C4dNPP83vfvc7Kisr3c6VlJSQnZ1NdnY2K1as4PLLL+eVV15h8ODBnRBp6yVEhTEjKYbM/d5l0wPscg6jxBlKhFHR+MThLOD3vglQRETEB6wm2fJaPCoiInKOdlkTERF/4akCYa3mgCIi0oMUV1S7tUWGBnVCJCIi0hKlpaXceeedrF69ulF7fn4++fn5bN68mWeffZbXX3+dlJSUNt2rJ6yD8YUdtmLT9vEJfTs2EBERES8VlFZhxc44yxG3c0b8RP507zRsReV8fqSIksoaIkICSRkWpfd9IiLSI5RU1rbyOvcN20RE/E2PTyA8cOCA66HpoEGDuPLKK5k4cSIxMTFUVlayZcsWXnnlFUpLS9m4cSPTp09ny5YtxMTEdHLk3lmcmsimnIIWVZqwGgb271Lvawlgi2M0V1m/atzp2x1QVgC9+rVDtCIiIt6zWt0XkGrxqIiIyDlt2WXt1ol6oSgiIl2HpwRCu6NlVXZFRET8gVkFwsiwwE6IREREmmO325k7dy7r1q0DYMCAASxYsIAxY8ZQVFREeno6n376KTabjTlz5vDpp58yevToVt+vp6yDaavtHhMIIzs0DhEREW84nU7yS6qwY+WKqv8j1fIV11s3MyNgJwHOWhg6DagrOpEQpfd7IiLS80SEBBDDab5n2c0XztGccPZv4XV6tioi/q/HJxAahsHVV1/Nww8/TGpqKhaLpdH5u+++m0ceeYRZs2aRnZ3NkSNHeOSRR3j55Zc7KeLWSU6IJG3eeBal72gyiTDIauH+6cP5v4wcV9tGx4WNEwhjL4TEq8GpBTkiItJ1BJgsIHV4myUhIiLix7TLmoiI+AsP+YO0YO80ERERv3Gmwn2u1idUi1xERLqipUuXupIHx4wZQ2ZmJgMGDHCdf+CBB3j44Yd56qmnOH36NPfddx8bNmxo9f16yjqYtjKrQBgaaGXkgPCOD0ZERKSFyqrtVNbUPQgtJ4R3HVN51zGVn13an58NOgDxkzo5QhERkc6VMiyaadadPBn4FwBOOPvxuWMUXzhG8779UkpwT7A3DEgZFtXRoYqIdDhL81382x/+8Ac++OADrrrqKreHpvUuuOACVq5c6fq8cuVKysvLOypEn5k9Lo5VC6eQOioG47xFNoYBqaNiWLVwCguuGNZoF+8sx3g+tE9kRf+H4KF9sHATpD4K4T1r9zkREenazCpQqAKhiIjIOREhrdtDSLusiYhIV2MYhukmMqpAKCIiPYkqEIqIdA92u53HHnvM9XnFihWNkgfrPfHEE4wfPx6AjRs38uGHH7b6nj1pHUxrOZ1Oth877dZ+YXwfAqw9fimViIh0YfklVabtvSP7w8U/hP5JHRyRiIhI15IQFcYNkUddn+ONAm6xbuKJwJcIwfx7dGZSDPF9VblXRPxfj69AGBXVsmzx5ORkkpKSyM7Opry8nIMHD3LRRRe1c3S+l5wQybL5k7AVlfP5kSJKKmuICAkkZVhUoy++sQN7s+vEGQBszgH8pOYXRBcF8cOIODxs8C0iItKprOdnxwNOJzgcTiyeylOIiIj0ICnDojGMuu/HltIuayIi0lVZLYbbpjHaREZERHqS4opqt7bI0KBOiERERJqyYcMGcnNzAZg2bRoTJkww7We1Wlm0aBH33nsvAOnp6Vx99dWtumdPWwfjrZ22Yv64dh8lVbVu506eqWSnrZjkhMiOD0xERKQFCkrNEx/6RQR3cCQiIiJd1yTLPre2w45Y8unr1h5ktbAoNbEjwhIR6XTaNssLvXv3dh1XVFR0YiRtlxAVxq0T47nnsqHcOjHeLWv+kgvcHygXllVzpKCso0IUERHxitVqniSoBaQiIiJ1EqLCmJHkXSX5Ef3D2XyoEFtRz9l9XEREugezKvR2zf9ERKSHqKyxU1njXnlXFQhFRLqetWvXuo7nzJnTZN9rrrnG9Lr25E/rYFpi3Z5c5r6wmS2Hi0zPHy8qZ+4Lm1m3J7eDIxMREWkZTxUI+4crgVBERASAMycILrG5NX/hGOXWFmS1kDZvvDaREZEeQwmELVRdXc2BAwdcny+44IJOjKb9TRrinmEPsO3o6Q6OREREpGUCPFQZdHhTZklERMTPLU5NJMja8kcBOXml/PKNXVzxZBb3Lt/KTltx+wUnIiLiBSUQiohIT3amosa0vY8SCEVEupzdu3e7jidNmtRk39jYWBISEgA4deoU+fn57RpbT1sHc/CLtQS/Po+7eJexxhEsuCfjA1TbHSxK36FnoSIi0iV5qkDYP0IV6UVERAD45kvT5s8do13HBpA6KoZVC6cwe1xcBwUmItL5lEDYQq+99hpnzpwBYMKECcTGxnZyRO1roocEwq1HzXdhExER6WxWi/mvNapAKCIick5yQiRp88Z7lUQI4HRC5v487b4tIiJdhtkmMkogFBGRnqK43DyBMDJUC0ZFRLqa7Oxs1/HQoUOb7d+wT8Nr20NPWwdzYNPbzLBsZ0ngq7wX/Du2B/+EvwQ+RV/OuvWttjtIy8jphChFRESa5rkCYUgHRyIiItJFjbkBfr4Xbn6JfXE3cchRlyDYsALhP36SwrL5k1R5UER6nIDODqA7yM/P59e//rXr85IlS1o1zokTJ5o8n5vbdRZhxkSEMCQ6jKOF5Y3atx1TBUIREemarIZ5BUK7XQtIRUREGpo9Lo5VC0NJy8ghMzsPb4r1VtsdPPDqdu657DRJsRGkDIsmISqs/YIVERHxwGwTGW0gIyIiPUVxebVpe6QqEIqIdDnFxcWu4379+jXbPzo62vRaX/PFOpjutAbGVlTOwOJtjbZZ72OUk2LZxxnCTa/JzM7jxOly4vvq+aeIiHQdZgmEQVYLvUO1FFhERMSlTzxcdBv/ODKGvx05Rj/OUEAf1+nxgyM7LzYRkU6kWUMzqqurueWWW8jLywPgxhtv5KabbmrVWAkJCb4Mrd1dMiTKLYHwSEEZ+SVV9I8I7qSoREREzAVYzRMIax2ODo5ERESk60tOiGTZ/EnYisr5/EgRL35yiJy80hZda3c6WbrpCACGATOSYlicmqid2UREpEOZFdNVBUIREekJdtqK+c/39pmee2nDYQZFhmp+JiLShZSWnnvmFhLSfGWg0NBQ13FJSUm7xOSrdTDdaQ3MtuxjfN844tb+uWMUDkwmmIDTCVsOF3HrRCUQiohIF1FyiqEn1xHKcCo493tFv/AgDA+bbouIiPRk356pBGiUPNgvPJjgAGtnhSQi0qnMn4IJAA6Hg3vvvZeNGzcCMHz4cF5++eVOjqrjTBrS17T9y2NFHRyJiIhI86wWDxUIvSmrJCIi0sMkRIVx6dAoDua3LHnwfE4nZO7PY+4Lm1m3p+vsKC4iIv4vwKQCoRIIRUTE363bk8vcFzaz+5szpue324o1PxMRkSb11HUwvU59QYDhvunoFseYJq8rqaxpr5BERES8t+dN7sv/A18G/ytpgc9ypeVLgqihn4pBiIiImPq2uMKtbVBk85v7iIj4K1Ug9MDpdLJw4UJeffVVAAYPHszHH39M377mSXUtYbPZmjyfm5vL5MmTWz2+r10yJMq0/bXPjzM2NpyE8q8h5wM4/AnMXwOBoab9RUREOkKApwRCLSAVERFp0pbDhbQ1377a7mBR+g5WLVSlCxER6Rhmm8jUav4nIiJ+bKetmEXpO6i2uyc/NKT5mYhI1xIeHs7p06cBqKysJDw8vMn+FRXnFjdGRET4NBZfr4PpTmtgQoMC+coxgouMw40SCTc3k0AYERLY3qGJiIi03J43AAgzqrjeupnrrZs57QxnSdhrnRyYiIhI12SWQDgwUvkOItJzKYHQhNPp5P777+ell14CID4+nszMTIYMGdKmcePj430QXccZ1q8XvUMCOFtZ62obZRzn5qPPEf7sLjAaVKg4shFGXt0JUYqIiNSxGOYJhLV2LSAVERFpSkmDOV9bVNsdPPLmLuZekkBESAApw6JJiArzydgiIiLnM9tExu5oOqFCRESkO3smI6fZ5MF61XYHaRk5LJs/qZ2jEhGR5kRGRroSCAsKCppNICwsLGx0ra+0xzqY7rQGZkjKjVyxsQ9hzgomWbJJsXzNhcYR9jkHe7zGMCBlmPnG2yIi4p3Vq1ezYsUKtm7dysmTJ+nduzcjRozgpptu4r777qN3794+uU9JSQkffvghWVlZfPXVV+Tk5FBcXExoaCgDBw5k8uTJ3HHHHcyaNQvDwxqTLqvoCHzzpVvzfsdgwnu3viiGiIiIv6qotnO63L2qfFwfJRCKSM+lBMLzOJ1OHnjgAV544QUABg0aRFZWFsOHD+/kyDreB3tPUlLVeCFpMNXcaP3MvXPOB0ogFBGRThVgVQVCERGR1ogI8d2jgX0nS/iPNV8DdQtsZiTFsDg1UVUvRETE5yyqQCgiIj2IraicrOw8r67JzM7jxOly4vtqYxcRkc6UlJTEkSNHADhy5EizCXv1feuv9QWtg4GEqDBmJMWQuT+P9Y7xrHeMb/aamUkx+h4VEWmj0tJS7rzzTlavXt2oPT8/n/z8fDZv3syzzz7L66+/TkpKSpvu9fTTT/O73/2OyspKt3MlJSVkZ2eTnZ3NihUruPzyy3nllVcYPNhzInmXs/ct0+Z3HVPoHxHcwcGIiIh0fd+eca8+CDAwMqSDIxER6TosnR1AV1L/0PT5558HYODAgWRlZTFixIhOjqzj7bQVsyh9B87z1tzscg4j3+m+40/1vnW4dRYREelAVpPFowB2fT+JiIg0KWVYNO2xyarTCZn785j7wmbW7cn1/Q1ERKRHM6tA6ND8T0RE/NSWw4Vev4ZzOmHL4aL2CUhERFrswgsvdB1v3bq1yb6nTp3CZrMBEBMTQ//+/dt8f62DOWdxaiKBHjYkPV+Q1cKi1MR2jkhExL/Z7Xbmzp3rSh4cMGAAS5Ys4bXXXuO5557jsssuA8BmszFnzhz27dvXpvsdOHDAlTw4aNAg7r77btLS0vjHP/7B8uXLWbhwoasS8MaNG5k+fTp5ed5t1NKpTrhXH6xxWllrn0S/8KBOCEhERKRr+7bYPIFwUKQqEIpIz6UEwu+c/9A0Li6OrKwsEhN75gPBZzJyqLY73NqdWPjEZCe2oNITkL+/AyITERExZ/WQ+aAKhCIiIk2r3327vVTbHSxK38FOW3G73UNERHoes01kau2a/4mIiH8qqaxt5XU1Po5ERES8NXv2bNfx2rVrm+z7/vvvu47nzJnT5ntrHUxjyQmR/PzKkc32C7JaSJs3nuSEyPYPSkTEjy1dupR169YBMGbMGHbu3Mnjjz/OvHnzeOCBB9i0aRO/+MUvADh9+jT33Xdfm+5nGAZXX301H374IcePH2f58uU8+OCD/OAHP+Duu+/m+eefZ8+ePa4Kv0eOHOGRRx5p2w/ZkW5/lV3XruaF2uv4xhkNwCbHOE7Tm/4RqqQkIiJCQQ5Ul7s+5ha7VyUGiFMCoYj0YEog/M5Pf/pT10PT2NhYsrKyGDmy+QeH/shWVE5WtufddTLt4xt9LnMG86F9IqeKS9s5MhEREc88VSDUAlIREZHmLU5NJMjafo8Iqu0O0jJy2m18ERHpecwqEGoDGRER8VcRIQGtvC7Qx5GIiIi3pk2bRmxsLADr16/nq6++Mu1nt9tJS0tzfb799tvbfG+tg3HXLzzY4znDgNRRMaxaOIXZ4+I6MCoREf9jt9t57LHHXJ9XrFjBgAED3Po98cQTjB8/HqirCvjhhx+2+p5/+MMf+OCDD7jqqquwWMzfeV1wwQWsXLnS9XnlypWUl5eb9u1yDINjQYn8d+0dfK/qGW6p+j3P1N4CoAqEIiIiACt/CE8MgRU3weY/U/7tPsD93eHASCXei0jPpQRC4MEHH+TPf/4zUPfQdP369a6dZnqiLYcLcTax1maj4yJyHIN4uXY2P6z+DRdX/YWf1PyCjSV6gCoiIp0nwKoKhCIiIq2VnBBJ2rzx7ZpEmJmdx4nT3eQlrIiIdHkWswqEmv+JiIifShkWjWH++NMjw4CUYVHtE5CIiLSY1Wrl0UcfdX2+6667yMtz39D5kUceYceOHQBcdtllzJo1y3S85cuXYxgGhmEwffp0j/fVOhhzB06VmLb/+/fHsPFXM1g2f5IqD4qI+MCGDRvIzc0F6pLpJ0yYYNrParWyaNEi1+f09PRW3zMqqmXzn+TkZNd3Ynl5OQcPHmz1PTtaQWkVAE4sfOlMYodzBAD9IzwnyIuIiPQIxTbI3w/2KjiUCR/8hvnb5/J4wF8bdQuyWujXS9+bItJztW67Sj+yZMkSnnvuOaCujP3ixYvZt28f+/bta/K6CRMmMHjw4I4IscOVVNY2fZ4wrqp+0uS6mvYKSUREpFlWDzvI2ZvKihcRkU5nt9vZt28f27Zt48svv2Tbtm3s3LmTiooKAO6++26WL1/us/tNnz6dTz75pMX9jxw5wpAhQ3x2/65s9rg4Vi0MJS0jh8zsvCY3lmkNpxO2HC7i1olhvh1YRER6JLMKhA7N/0RExE8lRIUxIymGzP3uCSeezEyKIb6v5l8iIl3BggULePvtt/noo4/Yu3cvycnJLFiwgDFjxlBUVER6ejqbNm0CIDIykhdffLFN99M6GM8O5JW6tUX1CmL+ZUM7IRoREf+1du1a1/GcOXOa7HvNNdeYXteeevfu7TqufyfZHeSXVJm291MCoYiI9HSHMkybdzmHNfocFxliukmpiEhP0eMTCOsfwgI4nU5+85vftOi6v/71r8yfP7+doupcESGt+88iIiTQx5GIiIi0nNXDFtx2h6ODIxEREW/cdtttvPXWW50dhnwnOSGSZfMnYSsq5/MjRew/eZa/fnrUZxV9tfGMiIj4itWsAqFdCYQiIuK/FqcmsimngGp78887g6wWFqUmdkBUIiLSEgEBAbz55pvccccdrFmzhpMnT/L444+79YuPj2flypWMHTu2TffTOhjPckwqECbGhHdCJCIi/m337t2u40mTJjXZNzY2loSEBGw2G6dOnSI/P5/+/fu3W2zV1dUcOHDA9fmCCy5ot3v5Wn0FwoaCAyxEBPf4ZcAiItLT5Xxk2rzentzoc1yfkI6IRkSky9LMQdykDIvGMPCq2oRhQMqwqPYLSkREpBlmi0dBC0hFRLo6u93e6HNUVBTR0dHk5OS0+73ffvvtZvvExMS0exxdUUJUGAlRdZUqLrmgL4vSd7RokWpztPGMiIj4SoBJFXpfJbyLiIh0RckJkaTNG9/s/CzIaiFt3niSEyI7LjgREWlWREQE7777Lu+88w5///vf2bp1K3l5eURERDB8+HBuvvlm7rvvPvr06dPZofqXz56FmNEweApnHUHknql06zJyQEQnBCYi4t+ys7Ndx0OHNl/ldeLd31EAAL3+SURBVOjQodhsNte17ZlA+Nprr3HmzBmgrvpubGxsu93L18wqEPaPCMbwsOG2iIhIj2CvgcOfuDXvc15APn0btQ2MDO2oqEREuqQen0C4fv36zg6hy0mICmNGUgyZ+/NafM3MpBji+4a1Y1QiIiJNC/CQQGj3JiNeREQ63OTJkxk9ejQTJ05k4sSJDB06lOXLl3PPPfe0+71vvPHGdr+HP5g9Lo5VC0NJy8ghMzvPq81mGtLGMyIi4ksWswqEqkAvIiJ+bva4OP7xk2Bufn6z6fnUUTEsSk1U8qCISBd2ww03cMMNN7T6+vnz5zdbJVDrYL5TmgcfLqk7tgRg6ZfMLwISWGNPIds52NVt5ABVIBQR8bXi4mLXcb9+/ZrtHx0dbXqtr+Xn5/PrX//a9XnJkiWtGufEiRNNns/NzW3VuM3JN6lA2C88uF3uJSIi0m0YVrh7NRzMgIMfwYmt4HSQdV71QYBBSiAUkR6uxycQirnFqYlsyiloUYWJIKuFRamJHRCViIiIZ1arhwRCVaAQEenSfvvb33Z2CNICyQmRLJs/CVtROZ8fKaKksobXt9nYl1vS4jG08YyIiPiS2SYymv6JiEhPMDzGvErSPVOH8Pvrx3ZwNCIiIl3YwYxzx45awvO+5MGAL/nWGU22/VwCYaIqEIqI+FxpaanrOCQkpNn+oaHnFvOXlLT83ZM3qqurueWWW8jLqyuqcOONN3LTTTe1aqyEhARfhuauNB82PQ3Tfg2hka7mgpJqt679I5RAKCIiPZzFAoMm1P2Z9kuoOM3xre/x1toyt65xfZRAKCI9m6WzA5CuKTkhkrR54wmyNv2fiGFA2rzx2slUREQ6ndUwTyCs1QpSERERn0mICuPWifHcc9lQ/vvmi5qdM9bTxjMiIuJrVlUgFBGRHqq8uta0PS6y+UW5IiIiPUrOB6bNmxzjGn0eqQRCERG/53A4uPfee9m4cSMAw4cP5+WXX+7kqJrwwW9gy5/hT5fC16sBcDicFKgCoYiISPNC+7Iv+koOOuPdTg3UM1QR6eGUQCgezR4Xx6qFU0gdFYOHnAyCrBZmjIrp2MBERERMmFWfALDblUAoIiLSHlq68UyQ1aKNZ0RExOfM5oCa/4mISE9QVmWeQBgWFNDBkYiIiHRh9ho4mOnWfNAxEJtzgOtzv/AgonoFdWRkIiI9Qnh4uOu4srKy2f4VFRWu44gI3yZ2O51OFi5cyKuvvgrA4MGD+fjjj+nbt2+rx7TZbE3++eKLL1ofcM5HsHtV3XHpSXj9R/CPOzmbd9x0A21VIBQREXH3bXGFafugSFUgFJGeTW+SpEnJCZEsmz8JW1E5/756Lxn78xqdr6p1sPXIab6X2A/KCuDrf8LuN2DKT2H0dZ0TtIiI9Ehm1ScA7E4tIBUREXPXXXcd27dvJz8/n169ejFw4ECmTp3KHXfcwYwZMzo7vG6hbuOZUP7fRwdYfyDf7fzAPiE8/8OJSh4UERGfM69AqPmfiIj4v7Iqu2l7eLBe+4qIiLicsUFIH6g606g503Fxo8+JMao+KCLSHiIjIzl9+jQABQUFjRIKzRQWFja61lecTif3338/L730EgDx8fFkZmYyZMiQNo0bH+9e0cgnqkphzUPu7fvXYHEEA7e4neofrkR4ERGR8+WeMd/AIE4JhCLSw6kCobRIQlQY988Y7tZuxU7BZ3+HV26F/x0J7/0Cjm+G3a93QpQiItKTBVg9JBBqAamIiHjw3nvv8e2331JTU0NxcTFff/01S5cuZebMmaSmppKbm9vZIXYLyQmRLL93MoMiQ9zOxfQOUfKgiIi0C7MEQoc2kBERkR6grNpTBUJrB0ciIiLShUUNg5/tgvu3wJWPUZswhVqnhSzH+EbdRg5oOqFFRERaJykpyXV85MiRZvs37NPw2rZwOp088MADvPDCCwAMGjSIrKwshg93XwPZZXzyBJw57t4e3IfscQ+bXqIKhCIiIu6+MalA2DskQJuwiUiPpwRCabHxCX3pExrYqM2OhUuPPg8HPwJngx1PD3wAlWc7OEIRka7JbrezZ88eli9fzoMPPsiUKVMICwvDMAwMw2D+/Plej3nw4EF++ctfMm7cOPr06UN4eDhJSUk88MAD7Nixw6uxqqqqeP7555k5cyZxcXEEBwcTHx/PtddeyyuvvILD4fA6vs5gMcwTCFWBQkREzte3b19uu+02/ud//odXX32Vf/zjHzz11FPMmTMH47vvk8zMTKZMmcLJkydbfZ8TJ040+cffEhTHDerj1pZzqgSHvotFRFpt+vTprrljS/4cPXq02TF9OZ/sTKpAKCLSva1evZq5c+cyZMgQQkJCiImJYerUqTz55JOcPeu790slJSW8+eab/PSnP2Xq1Kn079+fwMBAevfuzahRo7jrrrtYt24dzm6UhO6pAmEvLX4RERFpzDAgZjR872fsSH2NCVUv8IVjVKMuiQNUgVBEpD1ceOGFruOtW7c22ffUqVPYbDYAYmJi6N+/f5vvX588+PzzzwMwcOBAsrKyGDFiRJvHbleTfgzDZ7q3X/UY3zrc38MB9AtXAqGIiMj5vjVJIByo6oMiIuhNkrSY1WJwxcj+vLvz2watBm/WTOGnAe807lxbCfvfg/HzOjRGEZGu6LbbbuOtt97y2Xh/+ctf+NnPfkZFReNJzoEDBzhw4AAvvvgijz76KI8++mizY+3fv59bbrmFr7/+ulH7N998wzfffMP777/Piy++yBtvvMGAAQN89jO0hwCL+b4I9m6SACkiIh3jj3/8IxMnTiQoKMjt3EMPPcS2bdu45ZZbOH78OMeOHePee+/l/fffb9W9EhIS2hput5I0IIIP9p5q1FZWbeeb4goSosI6KSoREWnIl/PJzhZgkkBot3ef5A8RkZ6qtLSUO++8k9WrVzdqz8/PJz8/n82bN/Pss8/y+uuvk5KS0qZ7Pf300/zud7+jsrLS7VxJSQnZ2dlkZ2ezYsUKLr/8cl555RUGDx7cpnt2hHIPFQiVQCgiIuLZgVOlnMW92uBIJRCKiLSL2bNn8+STTwKwdu1afvWrX3ns2/A93Jw5c9p87/OTB+Pi4sjKyiIxMbHNY7e7vkPgh2/B7lWw7hEoL4TBU2HC3eR/etT0ElUgFBERcWeWQDhICYQiIkogFO9Md0sghNX2qe4JhFA3kVUCoYgIdnvjHaGjoqKIjo4mJyfH67FeeeUV7rvvPgAsFgu33347qampBAQE8Omnn/K3v/2Nqqoqfv/73xMcHMyvf/1rj2Pl5uYya9Ysjh8/DsBFF13E3XffzcCBAzl8+DDLli3j8OHDbNq0iWuvvZZPPvmEXr16eR1zRzGrPgFgV/6giIg0MGXKlCbPX3LJJaxbt46LL76Yqqoq1q5dy9atW5k0aVIHRdh9jYw1X2yTfbJECYQiIj7w9ttvN9snJibG4zlfzie7AqvJJjKqQCgi0rXZ7Xbmzp3LunXrABgwYAALFixgzJgxFBUVkZ6ezqefforNZmPOnDl8+umnjB49utX3O3DggCt5cNCgQVx55ZVMnDiRmJgYKisr2bJlC6+88gqlpaVs3LiR6dOns2XLlia/T7sCjxUIg6wdHImIiEj3ceBUiWn7yAHuSYUiItJ206ZNIzY2lpMnT7J+/Xq++uorJkyY4NbPbreTlpbm+nz77be3+d4//elPXcmDsbGxZGVlMXLkyDaP22EMAy66DYanwsePwtTFYLGQX1pl2l0VCEVEpMeqLoMg9/WsNXYHeSXu35txkSEdEZWISJemBELxyrSk/m5tB5wJ2AKHklBz5Fxj/CRIuqYDIxMR6bomT57M6NGjmThxIhMnTmTo0KEsX76ce+65x6tx8vPzeeCBB4C6xZ5vv/02119/vev8XXfdxT333ENqairl5eUsWbKEG2+8kaSkJNPxHnroIVfy4O23386KFSsICDj3q8GiRYu47rrr+OSTT/jyyy/57//+bx5//HFvf/wOE2D1lECoDEIREfHO6NGj+dGPfsTSpUsBWLNmTasSCG02W5Pnc3NzmTx5cqti7IqSPOzWnX2qhCvHdO1KxiIi3cGNN97Y6mt9PZ/sCgKNWoKppopzVYXtTiUQioh0ZUuXLnUlD44ZM4bMzEwGDDg3V3jggQd4+OGHeeqppzh9+jT33XcfGzZsaPX9DMPg6quv5uGHHyY1NRXLecnnd999N4888gizZs0iOzubI0eO8Mgjj/Dyyy+3+p4doazKvAJhmCoQioiIeJST555A2D8imMiwIJPeIiLSVlarlUcffZT7778fqHv+mJmZ6bZhyyOPPMKOHTsAuOyyy5g1a5bpeA3X2EybNo3169eb9nvwwQf585//DNQlD65fv75LP+NsUq9ouOFPro8FJdVuXcKCrKpGLyIiPdffvg9VpTDiSki8sq5qb2AIJ89UYvbKcKAqEIqIKIFQvNMvPJiL4vuw68SZRu2vV6Xw8wHBWC6cC+NugaihnRShiEjX89vf/tYn4/zv//4vZ8+eBeoW0zRc7FkvJSWFxx9/nF/84hfU1tby2GOP8dprr7n1+/rrr1m5ciUAcXFxvPTSS42SBwHCw8N59dVXGTFiBJWVlTz99NP84he/IDIy0ic/j69ZDANwAo0TCVWBQkREWmPGjBmuBMJ9+/a1aoz4+HhfhtTlDenXi0CrQY298Xevp929RUSk4/hyPtnpnE44sI4Hsx8hJiCZ/639geuUXfM/EZEuy26389hjj7k+r1ixolHyYL0nnniCjIwMduzYwcaNG/nwww+5+uqrW3XPP/zhD0RFRTXZ54ILLmDlypWMHz8egJUrV/Lcc88RFtZ1q6iXVZsnEIYH6bWviIiIJ9knS93aVH1QRKR9LViwgLfffpuPPvqIvXv3kpyc7FaFftOmTQBERkby4osvtul+S5Ys4bnnngPqNpRZvHgx+/bta/Y934QJExg8eHCb7t0RzCoQqvqgiIj0WGUF8M1XgBMKsmHLnyAgFL73M74dvMD0kkFKIBQRUQKheG96UoxbAuGfq+dQMWQRd48bQkJU132pKiLSndUn/AH8/Oc/99hvwYIFPProo5SVlbF69WoqKioIDW08+Vm5ciXO77ZZ+clPfkJ4uPkLskGDBnHbbbfx97//nfLyct555x3uvvtuH/w0Pmavoe+e5fwj6BXurP4tdqznTmkBqYiItEL//ueqrxcXF3deIN1IoNXC8P7h7D/ZOGEw+6R3CYS2onK2HC6kpLKWiJAAUoZFa54pItJGvpxPdqpvvoQPH4Vjm+gP/Iv1G16pvZKTRAN18z+n04lhmFeoFxGRzrNhwwZyc3OBumoREyZMMO1ntVpZtGgR9957LwDp6emtTiBsLnmwXnJyMklJSWRnZ1NeXs7Bgwe56KKLWnXPjlBebTdtDw2ymraLiIj0dKfLqikwSbpIjInohGhERHqOgIAA3nzzTe644w7WrFnDyZMnefzxx936xcfHs3LlSsaOHdum+9UnIwI4nU5+85vftOi6v/71r8yfP79N9+4IBSXu32X9I5RAKCIiPdShLOqKTTRQWwFh0eSeqTS9JK5PF3rnKSLSSSydHYB0P9OT+ru12bGydOMRrngyi3uXb2WnrbjjAxMR8WNff/01x44dA2D06NEMHeq50mtERASXX345AGVlZXzyySdufdauXes6njNnTpP3bni+4XVdgtMJX6+GP11K9IbfkWLZxw+s6xt1UQKhiIi0RkFBgeu4q1bf7YpGDnBfdHM4v4wau6PZa3fairl3+VaueDKLX76xi/9Y8zW/fGOX5pkiIm3k6/lkp/n0GXhpJhw7txAoxKjhoYA3GnXTHFBEpGvy5nnkNddcY3pde+rdu7fruKKiokPu2VqlVe4VCIOsFoIC9NpXRETEzIFT5hucmT3LFBER34qIiODdd9/ln//8JzfffDMJCQkEBwfTr18/Lr30Up544gn27NnD1KlTOzvULs+8AmFQJ0QiIiLSBRz8yLx9RCrfFJs/3x0YGdKOAYmIdA96kyReO1lsnpkPdXkcmfvzmPvCZtbtye3AqERE/Nvu3btdx5MmTWq2f8M+Da+Fup3W9u7dC9Tt6H3xxRe3eqxOVXIKXp4Nr/8Iig65mn8e8AZhnPuu0uJRERFpjaysLNdxUlJSJ0bSvSTFui+6qbY7OFZY1uR16/bkMveFzWTuz8N53le35pkiInWuu+46Bg0aRFBQEH379mXs2LEsWLCg0XeWGV/OJzvVsBmAe2XBW60bGGUcd322n/9FIiIiXYI330exsbEkJCQAcOrUKfLz89s1turqag4cOOD6fMEFF7Tr/dqq3CSBsFewqg+KiIjgdMLnL0Lefho+ZDyQV2rafeSA8I6KTESkx7vhhht48803OX78OJWVleTn57NlyxZ+9atf0adPn2avnz9/Pk6nE6fTyfr16037rF+/3tXHmz/dofqg3eGk0CSBUBUIRUSkR3I4sOd87N4eNQyihvGtSQKhxYABvZVAKCIS0NkBSPey01bMz1buaLZftd3BovQdrFoYSnJCZLvHJSLi77Kzs13HTVWLMOvT8FoAm81GeXk5APHx8QQGBjY5VkJCAlarFbvdTk5ODk6nE8NwX7TpyYkTJ5o8n5vbykSAsGioLHZr7m+c4ScBa/i/2lsBqFUCoYiIeOnAgQOsWLHC9fm6667rxGi6F0+7dmefLGVEjPm5nbZiFqXvoLqZKoWaZ4pIT/fee++5jouLiykuLubrr79m6dKlzJw5k1deeYW4uDi363w5n2yJdpsDxl0EybfDzvRGzRbDyQ+tH7Gk9seANpEREemqWvN9ZLPZXNf279+/3WJ77bXXOHPmDAATJkwgNja23e7lC2XVdre2sCC98hUREeHUHlj7q7rjyMGQOAtGzuZIbl/T7omqQCgiIt3E6fJqzB579gtXAqGIiPQsO23FPPfxPnqfnct0yw4ut+wm0qjb0Prjmovobysm94x7oaSYiBACraq7JSKit0nilWcycppd1Fmv2u4gLSOHZfOb39lcRESaVlxc7Dru169fs/2jo6NNr23NWIGBgfTu3ZvTp09TU1NDWVkZ4eEt35Gzfrdwn7MGwFX/Aa/d5nbqJ9b3WF47i2IitHhURKSHWL58Offccw8A06ZNM919NC0tjUsuuYSpU6d6HGf79u3cfPPNVFbWPVC8+uqrufTSS9slZn+U5CmB8FQJ1+Ke1AKaZ4qINKdv375cddVVXHLJJQwaNAir1co333xDRkYGa9euxel0kpmZyZQpU9iyZYtb0oMv55Mt0W5zQICZS2Dv21Bb9z2d54zk6dpbWWWf5uqiTWRERLqmjv4+aqn8/Hx+/etfuz4vWbKkVeO0WwK9iTJVIBQRETF34INzx8XHYetLsPUlevVdAoxp1HVA72D6hDa9yaqIiEhXkV/iXn0QVIFQRER6lnV7chtsTn0Fb9qvwIKDZOMQ06072VBwIbtf2Ez/iCC3awdGqvqgiAgogVC8YCsqJys7z6trMrPzOHG6nPi+Ye0UlYhIz1BaWuo6DglpfjITGhrqOi4pKWnTWPXjnT592jWeNwmE7SrxahhyORzd6Gra5xjMf9XeQTF1CQxKIBQR6dqOHDnCsmXLGrXt2rXLdbx9+3a3BZwzZ85k5syZXt8rMzOTxYsXM3z4cK688krGjRtHdHQ0VquVb7/9loyMDN5//30cjrpktgsuuIC//vWvrfipeq74vqGEBlqpqGlcEePAyRLT/ppniog07Y9//CMTJ04kKMj9RddDDz3Etm3buOWWWzh+/DjHjh3j3nvv5f3332/Uz5fzyU7XJx5S/hU+/wufD7yTe7JTKKfxz2S3aw4oItIVdcXvo+rqam655Rby8urmJDfeeCM33XRTq8Zq1wT685hVIOwVrFe+IiIi5Hzo3mYJ5J9nktyaR6r6oIiIdCMFpR4SCFWBUEREeoidtuIGyYPnOLCw3ZnI9trEuga7g2+K3SsQDowMdWsTEemJ9DZJWmzL4UKcXq6/cTphy+Eibh2WD30SwKLyvyIiPY3NZmvyfG5uLpMnT27d4IYBVz8Of5mOIyKOXxVdz1v2y3Fw7vtG1SdERLq2Y8eO8Yc//MHj+V27djVKKAQICAhoVQJhvUOHDnHo0KEm+8yaNYuXX36ZgQMHtvo+PZHFYjByQDg7T5xp1H7glPmC3zbNMycqgVBE/N+UKVOaPH/JJZewbt06Lr74Yqqqqli7di1bt25l0qTOq9TarnNAgO89BJf+K19+WUJ5drbbac0BRUSkJRwOB/feey8bN9ZtTDZ8+HBefvnlTo6qZcrNKhAG6ZWviIj0cOVFcGKrW3N1whRs2e6VepVAKCIi3YmnCoT9VIFQRER6iGcyctySB72hBEIRkTp6myQtVlLp/kKyOROMA1zy+cuwJhPm/QNGzmqHyERE/F/Din+Vle47pJyvoqLCdRwR0fgFmLdjNTdec+Lj473q77WBF8Ptr1GVcAVvPL7B7bTd0fqJo4iI+JennnqK73//+3z++efs3LmTvLw8CgoKqKqqok+fPgwZMoQpU6Zw5513cumll3Z2uN3WyAERbgmERwvLqKyxExLYeLFOa+aZddfVtDo+ERF/M3r0aH70ox+xdOlSANasWdMogdCX88mWaPc5YEhvCOlNgKXU9LTD28x0ERHpEOHh4Zw+fRqo+z5q+P1kpq3fR01xOp0sXLiQV199FYDBgwfz8ccf07dv31aP2e4J9A2UmSQQhgW5J0aIiIj0KN9uBwy35m/6XwHue88wckDTv4uIiIh0JapAKCIiPZmtqJys7Lw2jTGwT4iPohER6d6UQCgtFhHSsv9cLDiYZdnKgoD3mGA5CPXf2Z89qwRCEZFWioyMdB0XFBQ027+wsND02taMVVtby9mzZwEIDAykV69ezV7T4UZdi7XWPFGwDRvPiIhIB5g+fTpOHyz0nz9/PvPnz2+yz/Dhwxk+fDg//vGP23w/8Swp1n1xr8MJB/NKGTeoT6P2ls4zzxcREtiq60RE/NWMGTNcCYT79u1rdM6X88muxGqxmLarAqGISNcUGRnpSiAsKChoNoGwvb6PnE4n999/Py+99BJQl/iemZnJkCFD2jRuuyfQN1BWbXdrCw/WK18REenhRqTCrw7DoUw48AEc/AjKC3lsv/l39OvbTjAqtjfJCZEdG6eIiEgreKpA2F8VCEVEpAfYcriQti4rUgVCEZE65qssREykDIvGcN+wzY0FB0sCX6lLHmzo6EbI3dk+wYmI+LmkpCTX8ZEjR5rt37BPw2sBEhISCAsLA+DEiRPU1DRdwef48ePY7XWLUhITEzFa8mXQCQIs5nGpAqGIiEjHMksgBDhwqqTRZ1tROUVl1V6PbxiQMiyqVbGJiPir/v37u46Li4sbnfPlfLIr8TgHtCuBUESkK+oK30dOp5MHHniAF154AYBBgwaRlZXF8OHDfTJ+RymvNqlAGKwKhCIi0rPZispZtbeEl4svZtXgJbx6+cfcVPNfrC/obdr/y2OnmfvCZtbtye3gSEVERLyz01bM6p3fmp67/9Wv2Gkr7tiAREREOlhJpfvzUG8pgVBEpI62o5QWS4gKY0ZSDJn7my4DXEsAf62dzZLAV91Pbv4T3PyXdopQRMR/XXjhha7jrVu3Ntu/YZ9x48Y1OmcYBmPHjmXr1q3Y7Xa2b9/O5MmTWzVWV2LxsHhU1SdEREQ6VtIA8wTC7O8SCHfainkmI4es7LxW7RI3MymG+L5hbQlRRMTvNKwseH6VJl/OJ7sSz3NAbSIjItIVXXjhhaxbtw6o+66ZMWOGx76nTp3CZrMBEBMT0yhRvrXqkweff/55AAYOHEhWVhYjRoxo89gdqarWTo1JsnyvIL3yFRGRnqnpZ41Dmry22u5gUfoOVi0MVSVCERHpktbtyWVR+g6q7ebPPDP357Epp4C0eeOZPS6ug6MTERHpGBEhbX/2qQRCEZE6qkAoXlmcmkiQtfn/bFbaZ1DqNPmy3fcuVJ5ph8hERPzbmDFjGDx4MAD79u3j6NGjHvuWlpayceNGAMLCwpg2bZpbn9mzZ7uO165d2+S933//fdfxnDlzvAm7w5lVoLArgVBERKRD9Y8IJjIs0K39wMkS1u3JZe4Lm8nc37rkwSCrhUWpiT6IUkTEv2RlZbmOz6/S5Ov5ZFfhqQKhozVfMCIi0u4683nk+cmDcXFxZGVlkZjY/eYW5VV20/YwJRCKiEgP1NZnjVCXRJiWkePbwERERHxgp624yeTBevUJ8apEKCIi/iplWDT9jWJGGcdbdX1IoIW+JmtYRER6IiUQileSEyJJmze+2STCCksvCkbedq4hJBK+9xA8+BWE9GnfIEVE/NQPfvAD1/HTTz/tsd9f/vIXysrKALj++usJC3Ov0NNwrBdffNHV/3zffPMNr7/+OgChoaHccMMNrYq9o1iVQCgiItLpDMNgpEkVwj3fnG3Ri05PrBaDtHnjvdoN3FZUzqptNl7edIRV22zYispbdW8Rka7swIEDrFixwvX5uuuuc+vjy/lkV2E2/wMwig7Bxqeh2NbBEYmISFOmTZtGbGwsAOvXr+err74y7We320lLS3N9vv3229t875/+9Keu5MHY2FiysrIYOXJkm8ftDGXVtabtvYKtHRyJiIhI52ppUkVLZGbnceK0nhuKiEjX8kxGTou/55QQLyIi/iwhKozfDdjCuuBHeCdoCT+0fkRvzNe7mhkYGYphmL9XFBHpaZRAKF6bPS6OVQunkDoqBk9fp3dcOpghc34B0Ykw53/hoa/hyt9D77gOjVVExJ88/PDDRETULcb/05/+xOrVq936fP755/zbv/0bAAEBAfz+9783HWvs2LHcdltdondubi4LFiygtrbx4pPS0lLuvPNOKisrAXjooYeIjIz01Y/TLjxWICw6DMe3dEJEIiIiPVOSSQJhfmlVmxb0XHthHLPHtWxOudNWzL3Lt3LFk1n88o1d/Mear/nlG7u44sks7l2+Vbuwiki3kJaWxmeffdZkn+3btzNr1izXvO3qq6/m0ksvdevny/lkV3Fu/udklHGcnwe8wbqgXzP8H9Mg4zH4+p1OjU9ERBqzWq08+uijrs933XUXeXl5bv0eeeQRduzYAcBll13GrFmzTMdbvnw5hmFgGAbTp0/3eN8HH3yQP//5z0Bd8uD69evdqvV2J2UeKhD2ClYFQhER6Vm8SapojtMJWw4X+WQsERERX7AVlZOV7T5nbooS4kVExG85HMypzQQg2XKY/wz8K18E389Tgc8DzReXGNgntJ0DFBHpPvQ2SVolOSGSZfMncSi/hKuf3oD9vO/fvLNV0Hcc/HQrKGtfRHq4I0eOsGzZskZtu3btch1v376dJUuWNDo/c+ZMZs6c2agtJiaGZ599lvnz5+NwOLjpppu4/fbbueqqq7BarXz66af87W9/cy0cfeyxxxg1apTHuJ5++mk+++wzTpw4QXp6Onv37mX+/PkMHDiQw4cPs3TpUg4fPgzA+PHjeeSRR9r099ARLA0WkCYa33CN5QtuP7Addh+CmLFwf9OLb0VERMQ3Rsa6JxC2Ve6ZCo/nbEXlbDlcSEllLUcLy0j/4jg1509UqVsMlLk/j005BaTNG9/ihEQRkc6QmZnJ4sWLGT58OFdeeSXjxo0jOjoaq9XKt99+S0ZGBu+//z4OR92CyQsuuIC//vWvpmP5ej7ZFdRXIOxLCe8F/Qarcd6/+/tWw9SfdkJkIiLiyYIFC3j77bf56KOP2Lt3L8nJySxYsIAxY8ZQVFREeno6mzZtAiAyMpIXX3yxTfdbsmQJzz33HFBXKX3x4sXs27ePffv2NXndhAkTGDx4cJvu3V48VSAMC1IFQhER6Tlak1TRnJLKGp+OJyIi0hZbDhfibD4fopH6hPhbJ4a1T1AiIiKd5egGgkpPNGoKMWqIoBw8lkI6Z2BkSDsFJiLS/SiBUNpkeP8Ixg3qw84TZxq17zxRXHeg5EEREY4dO8Yf/vAHj+d37drVKKEQ6qo9nJ9ACHD33XdTXl7OQw89RGVlJa+99hqvvfZaoz5Wq5Xf/e53/Pa3v20yrkGDBvHBBx9wyy23sH//fnbt2sVDDz3k1m/q1Km8+eabhIeHNzleV1BfgeI/A17mhwEZdY1V353M2wuFhyB6eOcEJyIi0oOYVSBsqz3fnKXW7iDAanG17bQV80xGDlnZeV69SK22O1iUvoNVC0NJToj0eawiIr506NAhDh061GSfWbNm8fLLLzNw4ECPfXw5n+wK6hMIT9Obrc5RpBjnJYPYPoeSkxAR2wnRiYiImYCAAN58803uuOMO1qxZw8mTJ3n88cfd+sXHx7Ny5UrGjh3bpvvVJyMCOJ1OfvOb37Tour/+9a/Mnz+/TfduL+UeKhCGqwKhiIj0IK1JqmhOREigbwcUERFpg5JK881jmr9OCfEiIuKHtr9i2vy6fXqLLt96tIidtmKtDRERASzNdxFp2kXxkW5tuWcqOXW2suODERHpAf71X//Vlew3ZswYIiIi6NWrF4mJiSxcuJCtW7fy2GOPtWisMWPGsH37dp577jmmTZvGgAEDCAoKYuDAgcyePZu///3vbNy4kdjY7rHg0mqp+9VmuyPRvMPX73RgNCIiIj3XyAG+33igosZOTl6p6/O6PbnMfWEzmfu9Sx6sV213kJaR48MIRUR866mnnmLp0qUsWLCAyZMnM2TIEMLDwwkMDKRfv35ccsklPPjgg2zZsoV169Y1mTxYz5fzyc4WYDm3cdla+2TzTvve7aBoRESkpSIiInj33Xf55z//yc0330xCQgLBwcH069ePSy+9lCeeeII9e/YwderUzg61Syqt8lSBUAmEIiLSc9QnVfw6IJ0HrP8khtNtGs8wIGVYlC9CExER8YmIkNbN8ZQQLyIifqe2Cg5muDXnO/uw3pHcoiGOFJQz94XNrNuT6+voRES6Hb1NkjZLTohkxZZjbu07bcVcPbZ7JJyIiLSn6dOn4/TxNpiJiYk89dRTPPXUU20eKyQkhAceeIAHHnjAB5F1rvqCRB87JlDjtBJonLcj97534XL3KosiIiLiW5FhQUT1CqSozLc7ne46UczouN7stBWzKH0H1XZHm8bLzM7jxOly4vuG+ShCERHfGT58OMOHD+fHP/6xT8f15XyyM9VvIAPwgf0SHgv8W+MOlgA4+00HRyUiIi11ww03cMMNN7T6+vnz5zdbJXD9+vWtHr+rKq82TyDsFWzt4EhEREQ6T0RIAH0o5V7rOoKNGn4e8AaZjot5zT6TDY5kHF7upT4zKUbPB0VEpEtJGRaNYeDVBppKiBcR8Y3Vq1ezYsUKtm7dysmTJ+nduzcjRozgpptu4r777qN3795d7p7Lly/nnnvuafH9fv/73/Pv//7vbYy6gwQEw892wZ63KPr0ZaKKdgDwpv1yar1Ig6m2O1iUvoNVC0NViVBEejRVIJQ2G5/Qx7R954nijg1ERER6vIDvFpCeIZzNjjHuHU7ugrKCDo5KRESk51m3J5fT5b5NHgTYeeIMAM9k5LQ5eRDqXrxuOVzU5nFERKTjWRs82T5JNNsdI6hyBlIYfyXc+AL88iBc+e+dFp+IiEh7KKu2m7b3CtaesSIi0nOkDIvm5oCNBBt1zx8DDAdXW79kedCTLA54y6uxgqwWFqUmtkeYIiIirZYQFcaMpBivrlFCvIhI25SWlro2PXvjjTc4duwYVVVV5Ofns3nzZn71q18xbtw4tmzZ0q3v2S0FR8DEu1k68kVSq57khdrreN0+3ethqu0O0jJyfB+fiEg3ordJ0mbD+oUTHhxAaVXjXU93fbewU0REpKNYLYbr+APHJK6w7qaGQAJHpsLo70PSHAjTjmsiIiLtqb46YFsLMIcEWKisbZwkuOtEMbaicrKy89o2eAMllb5PdBQRkfbXsAIhwC9qFnLSGcXzV1zBtJH9OykqERGR9lVW5aECYZBe+YqISM+R0DeUfwndACaP9d6xT23xOEFWC2nzxqv6hIiIdEmLUxPZlFPQog01lRAvItI2druduXPnsm7dOgAGDBjAggULGDNmDEVFRaSnp/Ppp59is9mYM2cOn376KaNHj+6S93zwwQeZOXNmk31GjRrVptg7y4FTJRxyDuK/a+9o9RiZ2XmcOF2upHsR6bH0NknazGIxuCi+D58dKmzUvtNWjMPhxNIgmcPF6QTb57D5Obj8YRg4vmOCFRERvxbQ4DtnnX0SJc4waoZfyfN3zOjEqERERHoWX1QHDLJamHhBXz49b565P7eEjTn5bU5ObCgiJNB3g4mISIcJOO+Z42HnQADsjrZXqBUREemqyj0kEIYFWzs4EhERkU5k+5xBNcfcmrc4Rrvmhk0xjLoqTYtSE5U8KCIiXVZyQiRp88bzwKvbsTfxYkwJ8SIibbd06VJXIt+YMWPIzMxkwIABrvMPPPAADz/8ME899RSnT5/mvvvuY8OGDV3ynhMmTODGG29sU2xdVfapkjaP4XTClsNF3DpRCYQi0jMpgVB84qL4SLcEwrOVtRwtLGNY//BzjfZa2P8ufPYcfLOtrq3kJNz7IZy3a7iIiIi3GiatF9KH1Y6pXI4meyIiIh3FF9UB6190Hissd0sgrHU4OZhX2qbxGzIMSBmm6sQiIt2R1WzTMqDW7sMscxERkS6mrNpu2q4KhCIi0qMEBEPiLJw5H2FwbhOZ12rdq2wEWS08+v3RhAQGUFJZQ0RIICnDolRtQkREuoXZ4+K45sJc1uzKdTunhHgREd+w2+089thjrs8rVqxolMhX74knniAjI4MdO3awceNGPvzwQ66++upuc8/urqyqFltRhU/GKqk0KWcvItJDKGNLfGJ8Qh/T9p0nis99sNfA81Nh1fxzyYMAJ7bCztfaNT4REekZzq9AAWB3aPGoiIhIR9lyuLDV1QENA1JHxbBq4RRmj4vjovhI036FZdWtD/A8M5NitFhIRKSb8pRAqDmgiIj4szKTCoQhgRaP34siIiJ+aeDFcOfrGD/bRWbsPXzrjOK0M5wPHJNcXQzOPWv8YcoQbp0Yzz2XDeXWifF6HigiIt1KcIB7xfmQAAsbfzWDZfMnKXlQRKSNNmzYQG5uXaL2tGnTmDBhgmk/q9XKokWLXJ/T09O71T27uxwfbjQdERLos7FERLobbUcpPuFpIrrTdoabLo6v+2ANhLE3widPuHf86Pcw6loI7dtuMYqIiP8zWyhTq8WjIiIiHaak0n0xa0vcMH4gv5yV1GjxzoXxfTAM3BISK6vtpu3eCrJaWJSa2LZBRESk03hMIGzrF4SIiEgXZlaBUNUHRUSkx4pMYE3f+fzL0VSGGrlUEQSAxYDMX0xnSL9enRygiIhI252pcK+SFB0erIR4EREfWbt2ret4zpw5Tfa95pprTK/rDvfs7g6cLDFtNwBv3gwaBqQMi/JJTCIi3ZEqEIpPxPYOISYi2K19h624ccP3fg6Rg90HKC+AvW+3T3AiItJjmFUgdCiBUEREpMNEhLRu4erlif3dXnSGBwcwon+4W9+c/FJmJMW06j71Aq0GafPGa1dWEZFuzGz+B6pAKCIi/q3cpAJhr2AlEIqISM91ML8UBxYOOQe52ob1D1fyoIiI+I2zle4JhL1DVTlJRMRXdu/e7TqeNGlSEz0hNjaWhIQEAE6dOkV+fn6Xu+ef//xnRo8eTXh4OGFhYQwePJjrr7+e559/nvLy8lbF26E2/wm+eMltR+nsU+YJhJeN6OfV8DOTYpSELyI9mhIIxScMwzBdePl17lmqax3nGgJDYfZ5FQj7DoU7VsEl97ZvkCIi4vcsqkAoIiLSqVKGRWOY53N41NQObxfFR7q1Hc4v457LhngfXAP/dt0YZo+La9MYIiLSuTxVIKy1aw4oIiL+q6zaPYEwLMjaCZGIiIh0PofDycG8Urf2xBj3TclERES6q7MmFQh7t3JDTxERcZedne06Hjp0aLP9G/ZpeG1XuefWrVvZv38/ZWVlVFRUYLPZePfdd7n//vsZMmQIa9asaVXMHeLEl/DRo/D+w5A+D8oKXacOmCQQ9gsP4pezkgiytiwdJshqYVFqos/CFRHpjjSTEJ8ZnxDJR1+fatRWXesg+2QJF8b3AcBWVM6WknFc2u9yBhZtpWzyIvqk/gICQzojZBER8TNmFShUfUJERKTjJESFMSMphsz9eS2+pqkd3pIT+vDmVyfc2j/ce8qktzurAWZ5JEVl1S2OT0REuiZPCYRuc8DaKjj8CZR8CxPnt39gIiIi7aisyu7WpgqEIiLSU+WeraS82v27cYQSCEVExI+YJRD2UQVCERGfKS4udh3369d8Nbvo6GjTazv7nlarlSlTpnD55ZczcuRIwsPDKS4u5ssvv+T111+nqKiI/Px8rr/+el599VXmzZvnddwnTriv3WgoNzfX6zFdKs/Cm/eC47sN1A6sheenws0vwrDppgmEIwdEkJwQSdq88SxK30G13eHWp16Q1ULavPGmxZJERHoSvVESn7nouyTB8+04UYzD6eSZjByysvNwOmEgc7EYt/LNJ/2ZcXI3i1MT9aUsIiJtZraA1DSBsLYajnwCg1MgOKIDIhMREek5FqcmsimnoMmHs/Wa2+HNrAIhwCufH2tyXMOoS0y8f8Zw7lz6OZU1jWPZerSo2dhERKRrM9tABsDudEJ1GRz8GL5eDQc+gOoSCO4NyfMgILiDIxUREfEdswqESiAUEZGeyqz6ICiBUERE/MsZswqESiAUEfGZ0tJz84qQkOaL4YSGhrqOS0rck9o6457f+973OHr0KPHx8W7n/uVf/oX/+Z//YcGCBaxcuRKn08m9997LZZddxuDBg72KOyEhwav+Xnn/YTh9tHFb6Ul45RbO/GQrp85WuV0yckDdus/Z4+JYtTCUtIwcMr/LU6hXv3ZkkfIUREQAJRCKD100KNK0/f1duTz+7teNFo9+Sz/47gs6c38em3IKSJs3ntnj4jogUhER8VcBFvdy9K4EwupyOJRxbgFp1Rm4ZRlceGsHRykiIuLffLnD2+i4CAKtBjXnlRF0muwP0K9XEPfPGEHv0EBShkW5qhpenNCXzYcLudyyi22OkVQQwvbjxdTYHQRa3X93EBGR7sFqMv8DqHU4Ieu/YPNzjU9Una2rRDjy6g6ITkREpH2Um1UgDLJ2QiQiIiIdKHcXxF3k1pxjUoEClEAoIiL+o9buoMyk2q4qEIqISEMjRoxo8nxERASvvvoqp06dYv369VRWVvLEE0/wpz/9qYMibMa3O2DXSvNzlz9MdkWk6an6BEKoW6eybP4kbEXlfH6kiJLKGiJCGq8dERER0Eo58Zk+YYEM69fLrX3z4cJmK09U2x0sSt/BTltxO0UnIiI9gcWkAkWtwwFnTsCTw2HlD2H363XJgwD73u3gCEVERHqGuh3eppA6KgbjvK9nw4DUUTGsWjil2U1kggOsjI7r3aJ7LrhiGPd+byi3Toxv9AB40tAoxhhHWRb4JO8H/YaLjRzKq+18/e1Zr34mW1E5q7bZeHnTEVZts2ErKvfqehER8S2PFQjtDhj9ffOL9q9px4hERETaX1mVewXCsCDtFysiIn7s63fgxcvhvYehprLRqUP57hUIDQOG91cCoYiI+Iezle5zQIDeIUogFBHxlfDwc/OHysrKJnrWqaiocB1HREQ00bNr3dNqtfKf//mfrs9r1nj/zsxmszX554svvmhVbAwcDz98E3rFNG5PSIErfkm2h81jkmLd534JUWHcOjGeey5zXzsiIiKqQCg+NjgqjMMFZa26ttruIC0jh2XzJ/k4KhER6SnMFpDaHU7oPQgiYqHocOOTOR/VvWwMDOmgCEVERHoOX+3wdlF8H3adONNkn0CrwS0T403PXZrQizmBzxNk2BlqnOKNoH/nT/Yb2HZ4RJPVD+vttBXzTEYOWdl5jSofGgbMSIphcWpii8YRERHfMttABr6rQBg/GcJjofRk45OHMuvK2J6f3S4iItINOJ1OyqrdF4+GB6sCoYiI+KmiI/DOT+uOt74Ets9h7nKIHg5Azin3BML4vqGEBOq7UURE/MPZihrT9t6hWvYrIuIrkZGRnD59GoCCgoJGyX1mCgsLG13bXe4JMGXKFEJCQqisrOT48eOUl5cTFtbytRvx8eZrMnxixJXwr5/BP/8VDn4EwX3glpfAGsCBk+YJhIkDWpdMKSLSk6kCofjMuj25bDxY0KYxMrPzOHG6QRUHh72NUYmISE9iNUsgrF8cOvp69wtqyuD45g6ITEREpOdq6w5vF8VHNttn8tAo+oUHm587/CdGWWyuz1bDyaKAfzLzsx+B3fzFa711e3KZ+8JmMvc3Th6EuvyTzP15zH1hM+v25DYbo4iI+JanCoQOpxMsFki8yv1keSGc/badIxMREWkfVbUOHE739rBgLRwVERE/VFsFq+ZD1dlzbSd3wYvT4Ot3cDqdHDSpQJgYowWkIiLiP854SCDsE6oKhCIivpKUlOQ6PnLkSLP9G/ZpeG1XvyeAxWIhKirK9bm4uLjVY7WL8P5w5yqY/d9wfRpEDgYwrUA4sE+IKvKKiLSCEgjFJ3bailmUvqOuylMbOJ2w5XBR3Ye9b8Pzl0FZYdMXiYiIfMe0AqH9u++mpDnmFx38uB0jEhERkbYqqXSvsHG+zw8XmSfxHd1E4Bd/Nr0mq3oMTovnhbb189xqu6PJe1fbHSxK38FOW3GzcYqIiO+YbSAD31UghLqdSgFixsLURXDXavj1UegzqGMCFBER8bHSKvO5Ua8gVVkSERE/9OG/Qe4O9/bqEqguo7CsmuJy96SKETFNV+4QERHpTs5WeqhAqIQJERGfufDCC13HW7dubbLvqVOnsNnqNi+OiYmhf//+3eaeAA6Hw1X5ENpWzbDdGAak/CuMvREAp9PJAZMEwpGx2jxGRKQ1lEAoPvFMRk6ziypbqqSiCrL+q243ufx98PpdUFvtk7FFRMS/WUwWkLoWjw6aCCF96o6jhsHkn8Adr8OM33ZghCIiIuKNnbZinli7v9l+tQ6neRJfRBzET3Lrv9dxAX+suInDBWUex/Rmnlttd5CWkdOiviIi4hueKhC6NpFJvAp+/jXc/xlc/TgMmwYB5tVqRUREuoPyKrtpey9VIBQR6VZWr17N3LlzGTJkCCEhIcTExDB16lSefPJJzp492/wALWS329mzZw/Lly/nwQcfZMqUKYSFhWEYBoZhMH/+fJ/dq10MHA+BYe7t4++E8XeQc8q9+iAogVBERPyLxwqEYUogFBHxldmzZ7uO165d22Tf999/33U8Z46HYgZd9J4AW7ZsoaKiAoD4+HjCwkzmXF1MfkmV6eYxSQOUQCgi0hp6oyRtZisqJys7zydjhVHJVXt+BScbVIM6tgnW/gqu+391OwuIiIh4YFqBsD6B0BoAt7wMUUMhengHRyYiIiKt0ZokvmXzGyQMRg+He9Zy4K3HGbrnWQINO1XOQH5ecz81BLD1SBHD+7svKmrNPDczO48Tp8uJ79v1H7KLiPgDsw1koMEmMkG96v6IiIj4ibJqTxUI9bpXRKQ7KC0t5c4772T16tWN2vPz88nPz2fz5s08++yzvP7666SkpLT5frfddhtvvfVWm8fpNOPvgIETzm08DdB/FMx5EoCD+UogFBER/3e2wnweqAqEIiK+M23aNGJjYzl58iTr16/nq6++YsKECW797HY7aWlprs+33357t7qnw+Hg0UcfdX2+7rrrWj1WR8o2qT4IMFIJhCIiraIKhNJmWw4X4nT6Zqwlga8Q3zB5sN6Xf4WtS31zExER8VtWswTChl9SiVcqeVBERKSbaEsSXyPWAPrO/i03Vv8HBxyD+J/a2zjgTABg69HTpuO0Zp7rdMKWw0XeXdSAraicVdtsvLzpCKu22bAVlTd/kYhID+apAqHDVw8qRUREupiyKvOFo2HB1g6OREREvGW325k7d64reXDAgAEsWbKE1157jeeee47LLrsMAJvNxpw5c9i3b59P7tlQVFQUiYmJbR63Q8WMggWZcPEPISAU5i53bRRzKE8JhCIi4v88ViAMVQKhiIivWK3WRol1d911F3l57usUHnnkEXbs2AHAZZddxqxZs0zHW758uavy+/Tp09v9nps3b+Yvf/kLlZWVnn5EysrKuOuuu8jIyAAgODiYX//61x77dyXZJ80TCJNilUAoItIa2pJS2qyk0vyFZWt8PuR+7jh7EM4cdz9ZeMhn9xEREf9kNalUa7dr8aiIiEh31JYkvlsnNq4C2D8imIrocXy/4A9UN3gUsvWoecJfSWUtY40jnCGcE87+Lb5/SaX5i9ym7LQV80xGDlnZeY1+XsOAGUkxLE5NJDkh0utxRUT8ndkGMtCgAqGIiIifKau2m7b3CtbrXhGRrm7p0qWsW7cOgDFjxpCZmcmAAQNc5x944AEefvhhnnrqKU6fPs19993Hhg0b2nTPyZMnM3r0aCZOnMjEiRMZOnQoy5cv55577mnTuB0uKAxu+BNc/jBEDXU15+S5LyId0DtYFZlERMSvnPXw3ql3qOaBIiK+tGDBAt5++20++ugj9u7dS3JyMgsWLGDMmDEUFRWRnp7Opk2bAIiMjOTFF1/sMvc8deoU9913H7/4xS+46qqrmDhxIgkJCfTq1YszZ87w1Vdf8Y9//IPCwkIADMNg6dKlDBkypM0/Q0c4YFKB0DC0eYyISGtpJiFtFhHim/+MgqwW7r16EgSmw7Kroaas7oQlAOY8CZfc26i/raicLYcLKamsJSIkgJRh0SREhZmMLCIiPUWA1X0BqRaPioiIdE+t3azGUxLfJUP6crigrFHb8aJyTp2tZEDvkEbtfQJr+VNgGnFGIX+zz+JPtTdwhuYfQEd4uUBp3Z5cFqXvoNrucDvndELm/jw25RSQNm88s8fFeTW2iIi/C7BYTNvtmgOKiIifKvdQgbBXkF73ioh0ZXa7nccee8z1ecWKFY2SB+s98cQTZGRksGPHDjZu3MiHH37I1Vdf3er7/va3v231tV1Sg+RBgIMmFQi1gFRERPyNWQXCQKtBaKAq0YuI+FJAQABvvvkmd9xxB2vWrOHkyZM8/vjjbv3i4+NZuXIlY8eO7XL3LC0t5e233+btt9/22Cc2NpalS5dy7bXXtjn+jrL7mzNubUOiexGi70IRkVbRGyVps5Rh0RgGXleGON/iK0d8V1UhEm5+EVb+EEKj4La/w9DLXf1UnUFERDwxq0ChxaMiIiLdU2s3q/GUxDdpSBSvbzvh1r71aBHXXTSwUduVBSvoYzkFwE8C3uMH1iyeq72Rv9uvpoog0/ENA1KGRbU4zp22Yo/Jgw1V2x0sSt/BqoWhmuuKiDTgoQAhtapCLyIifqrUQwJhWJAWy4iIdGUbNmwgNzcXgGnTpjFhwgTTflarlUWLFnHvvXUbK6enp7cpgdCfna2s4dTZKrf2Ef2VQCgiIv7lrEkCYe+QQAzDw8NRERFptYiICN59913eeecd/v73v7N161by8vKIiIhg+PDh3Hzzzdx333306dOnS93zyiuv5J133uHzzz/niy++wGazUVhYSHFxMWFhYcTExDBhwgSuvfZabrvtNkJCQjyO1ZXstBXzfx8fYF+uewXC0soadtqKtX5CRKQVlEAobZYQFcaMpBgy9+e1aZzcM5XnPoz+Pnw/DYZegY0BbNlmo6SylqOFZaR/cZwak4VAqs4gIiJmFSjsbc1wFxERkU7Rms1qmkrimzTEvD39i+Mkx0eeq2ift48+X/65UZ8+Rjm/C3yNnY7hfOEcbTrOzKQY4vuGtTjWZzJymk0erFdtd5CWkcOy+ZNaPL6IiL8zDAOrxXDbNMahOaCIiPip8mq7aXt4sF73ioh0ZWvXrnUdz5kzp8m+11xzjel10phZ9UGAEQMiOjgSERGR9mVWgbBPqPlGmiIi4hs33HADN9xwQ6uvnz9/PvPnz++we4aHh3P99ddz/fXXt+r6rmjdntwmN2POL61m7gublSsgItIK7qvsRVphcWoiQdaW/edkNSDI6r4LzppduVTXnvuy3xlzA/euLuCKJ7P45Ru7+I81X/P3zcdMkwcbqq/OsNNW7NXPICIi3Z/FZJc1u8OJUwtIRUREup36zWq80VQS3wXRYUSGub9U/fRgIVc8mcW9y7fy4Z5vyf/H/eBwfyH7sf1ij8mDQVYLi1ITWxynraicrGzvNuHJzM7jxOlyr64REfF3ZlXoax0tSM4uL4Jjn7VDRCIiIu2nrNpDBcJgVSAUEenKdu/e7TqeNKnpzaFiY2NJSEgA4NSpU+Tn57drbN2VxwRCVSAUERE/c7bSfR4YoQRCERHxYzttxU0mD9ZTroCISOsogVB8IjkhkrR545tNIgyyWvjTnRO4ZWKC27ni8hrWf7eAct2eXOa+sJnM/XleVZuoV1+dQUREepYAkwR1wK0ihamqEqjWonwREZGuxJvNappL4vtg70nOmuzUCucq2i9+ZTMf5vWl1tn4nnanwX/XzvM49v/7QTLJCZEtihNgy+FCr+e6TidsOVzk3UUiIn4uwCSB0HT+57CDbStk/RGWXglPDodX50JtdQdEKSIi4htlVeYJhL2CVIFQRKQry87Odh0PHTq02f4N+zS8Vs7xlECYOEAJhCIi4l/M3mupAqGIiPizZzJymk0erKdcARER7ymBUHxm9rg4Vi2cQuqoGM4vAGUYkDoqhlULpzB7XBw3XTzIdIy3t3/T4t0D6sVwmr8H/pEJxoFG7arOICLS85hVnwCoNVtA6nTCyd2w6f/B8uvgiSGw7932DVBERES84s1mNWnzxntM4qufZza3p0AFIfyu9sfMqn6Cj+wTXe0r7dM56Iz3eJ3rd5AWbkZQYrJjbMuuM0+AFBHpqawmVehr7Sb/2H/yP7DsSvjkv+HEVnA6oLoUbJ93QJQiIiK+UVZld2szDAgNVAVCEZGurLi42HXcr1+/ZvtHR0ebXtsVnThxosk/ubm57XJfswTCyLBAonsFtcv9REREOotZAmHvEG0iIyIi/slWVE7Wd4WIWkq5AiIi3tFsQnwqOSGSZfMnYSsq5/MjRZRU1hAREkjKsCji+4a5+l1yQV/i+4Zy4nRFo+s/3HuK7FMlLU4eDKec5UH/wxjLMb5n2cNS+xyeqp1LFUGu6gy3TgxrfiAREfELZtUnABznl/gpL4I/T4HSk43bD34MyT9op+hERESkNeo2qwklLSOHzOzGVeoNA2YmxbAoNbHJCoDe7FIHcMg5iAU1v2By7T5+FvAm/6/2VgCuT45j9U73hU9/33yM2WNj4S/TITwGLv4hjL4egsznoxGtfLkbEaJdZUVEGrKaVKG3m5V4HTatLnnwfAc/hqGXt0NkIiIivlde7b4RSVigFYuHZ6IiItI1lJaeS3YLCQlptn9oaKjruKSkpF1i8pWEhIROuW9Onvvfy4j+4Rgmm8yIiIh0V06nkzOqQCgiIj3IlsOFmL3ma4pyBUREvKMEQmkXCVFhJER5/jK2WAxuHD+I57IONmq3O50czi9r0T0CqOXPgc8wxnKsbkzDyU8C3uNKy5c8XLOQr5wjG1VnsBWVs+VwISWVtUSEBJAyLLrJGEVEpPuxeHgx6FaBMCwKgnq5dzyUAQ4HWFSkWUREpCtp6WY1ZlqzS129L5yjuaNmietzaZWdC6LDOFbYeAe7zw4VsmLVG/yoIBsKsuHoRnjvYRh3M1y6EAaMadQ/ZVg0hoFXD78NA1KGRbXq5xAR8Vdmm8jYzcrNxk+CoAioPm+R6cEMuOqxdopORETEt8wqEIYF61WviIj0LJU1dreNqgESB4R3QjQiIiLtp6LG7r7WBeitBEIREfFTJZXuG6i17Dr3hHsRETGnt0rSaW682D2BsOWc/DFgKVdYd7udGWY5SZxRBE7Yf7KED/ee5B9bbWSZVKqYkRTDYg+VKpRwKCLS/XiqQGi3mywgHXElfHGocVt5IeTugEETfB+ciIiItFlzm9WYac0udZ5kZefxwPQRpnNZ665XGz9lqS6Br/5GVvVoCoZENJpTJkSFMSMphsz9LU9sTIwJ58O9pzQ/FRFpwGoyBzRbVIM1sK4K4f41jdtP7YaSkxAR204RioiI+E6ZSQXCcCUQioh0eeHh4Zw+fRqAyspKwsObTnSrqDiXHBcREdGusbWVzWZr8nxubi6TJ0/26T0P5ZeaPusb3l8JhCIi4l/OVpgnUagCoYiI+KuIkNY964wI0XejiEhL6a2SdJqyqloMoDXrOPtQxkjLCdNz79sn857jUgBWbrWxcqv5Q2unEzL357Epp4C0eeOZPS4OgJ22Yp7JyPE64VBERDqf1eohgdDsTeKIK+GLF93bD2YogVBERMSPtHaXOjNOJ8T0DibAYjRKUAmlku9bt7j1P+MMY+G2WKq27XKbUy5OTeSTA/nmlbJMHDhVyn+s+Rpo/fxUG+WIiL8JMKkeb7qBDNTNAfevgYAQGHJ53ecRV0L4gHaOUkRExDfKzSoQBlk7IRIREfFGZGSkK4GwoKCg2QTCwsLCRtd2ZfHx8R1+z88PF5q2j4hRAqGIiPiXMxXm1ZR6K0lCRET8VMqwaAwDrzaINgxIGRbVfkGJiPgZJRBKp3kmI6dVyYMAZwjn5urH+In1PX4W8AbBRt2C0CJnOI/W3AOYJ5CYqbY7WJS+g1ULQ8k9U8Gi9B1U2x1u/TwlHIqISNfhsQKh2cL8IZeBNRjsVWAJhMEpdYtHR13bzlGKiIhIR2rtLnWeHC8qx3HeE+trLF8QYVS49X3HfhlVBAHuc8pZY2PpFx5E3tkKfmT9iM2OseQ4B9GS+ay381NtlCMi/sokf9B8AxmAUddB5GC4YCoEhrZvYCIiIu2gtMp9c5ReQXrVKyLS1SUlJXHkyBEAjhw5wpAhQ5rsX9+3/lqpU/98K3N/nul502r0IiIi3djZSvMEQlUgFBERf5UQFcaMpBiP8z4zM5NiiO+rTZNFRFrKZImFSPuzFZWTld3yL3gzdqw8b7+ea6v/i+2OEQD8e818Cujj9VjVdgc/fe0rHnhtu2ny4Pl9F6XvYKetuDVhi4hIO7IY5gvuTV8aBvWCqx6D29Ph10dg/hr43s+gX2L7BikiIiIdqn6XOl/59GAB5/9qsd4xnv+o+RH7HAmN2l+3T3O7vn5O+crnxzh1torRxnH+I/BvfBT8KzYHP8j/BLzI9y2f0ZvSZmNpyfx03Z5c5r6wmcz9eW479dUnIs59YTPr9uQ2ez8Rka7GtAKhp0Wj4f1hRKqSB0VEpNsqrzZJIAxWBUIRka7uwgsvdB1v3bq1yb6nTp3CZrMBEBMTQ//+/ds1tu6i4fMtT+5/5Ss93xIREb9yptxDBcJQbSQjIiL+a3FqIkHWlqW3BFktLErVWk8REW8ogVA6xZbDhV6VGG7KQWc8t1T/Owurf8Zqx5QWXOHkDwHLmGn5CoNzyYK20xWeFxidp9ruIC0jp5URi4hIe/FYgdDu4d/3lH+FUXMgOKIdoxIREZHOVL9Lna/szy1xayuiNy/br+Ga6v/m+1X/yd9rr2KLYzR7nENNx6i2O3g24yAAV1h2udrjjCJuC/iEZ4Oe42LLoRbFU2138PCqnazaZsNWVN7o3E5bMYvSd7Roo5wHX9vO0x9m8/KmI6ZjiYh0RVaTOaCqToiIiL8qq7a7tYUFa+GoiEhXN3v2bNfx2rVrm+z7/vvvu47nzJnTbjF1J94839JG0CIi4k9UgVBERHqi5IRI0uaNN30H2FCQ1ULavPEkJ0R2TGAiIn5Cb5WkU5RUuu+S2hYOLKxzTG5R36mWvdwZkMGdZJDtiOcvtdex2jGVGi//d8jMzuPE6XKVPhYR6UKsHnafsfsqa11ERES6pcWpiWzKKWh2oVFzRsdGsO+kewLhOQa7ncPYXTsMcAKeH2rnlVQBcLllt9u5aqeVLxxJLY4rJ6+UX76xC8OAGUkx3D4pgTMVNfxlw+EW/8w1DidpmQfP/STfjbU4NVEP3UWkyzLbRMbuaNu/9SIiIl1VWZVJBcIgVSAUEenqpk2bRmxsLCdPnmT9+vV89dVXTJgwwa2f3W4nLS3N9fn222/vyDC7rGcyclr8fKt+I+hl8ye1c1QiIiLt70yFhwqEIUogFBER/zZ7XBzzJhfwypbjbucMYOaoGBZpHYOISKuoAqF0ioiQtueuBloMLhse7fV1C63vuo6TLCd4KugFPgn+GUmG+y8aTXE6YcvhIq/vLyIi7cdjBUItIBUREenR6nepC/Kw2UBLBFktXDainxdXNL0jHkAolVxiyXZr/8o5kgpCmr3+x9b3mW35ggjqqgU6nZC5P4+frPiSX76xi5y8Ui/ibax+rLkvbGbdntxWjyMi0p4shlkCoTaQERER/+NwOCk3q0AYpL1iRUS6OqvVyqOPPur6fNddd5GXl+fW75FHHmHHjh0AXHbZZcyaNct0vOXLl2MYBoZhMH369PYIucuwFZWTle3+d9WU+o2gRUREuruzFeYFGlSBUEREeoIAi/najqyHp7Ns/iQlD4qItJLeKkmnSBkWjWHULUj0lmHAzKS63QO+PHaaTw8VtvjascZRrrC6V3cIxM5RZ6zXsZRUmu/0IyIincNqsngUoFYLSEVERHq82ePiWLUwlLSMHDKz87yajwZZLaTNG09JpfnL2tYaYxzDivtGB5vs45q9NoQqfhWwkmCjhhqnlS2O0XzgmMSH9kvIo6/PYqy2O1iUvoNVC0P1EF5EupwAqxIIRUSkZ6iocU8eBAgP1qteEZHuYMGCBbz99tt89NFH7N27l+TkZBYsWMCYMWMoKioiPT2dTZs2ARAZGcmLL77Y5nseOXKEZcuWNWrbtWuX63j79u0sWbKk0fmZM2cyc+bMNt/bV7YcLvR6TUn9RtC3Tgxrn6BEREQ6iKcKhL4o3CAiItLV5ZdUubVF9QpiSL9enRCNiIj/0GxCOkVCVBgzkmLI3N/y3eJGxoTzk2nDSRkWRXzfuoe9B06VeHXf+wLeNW3/a+1sqgjyaiyAiBDt6CMi0pVYPVQgrLVrAamIiIjUVSJcNn8StqJyPj9SREllDREhgfQJDeAfX9jcEgsbbmCTnBCJrai81ZvhmPnSmcSEqheZYtnLFZbdXGHdRYKRz6eO5hMIUyxfE2zUvTwONOxcbt3D5dY9DDFO8p+1P/JNgN+ptjtIy8hh2fxJPh1XRKStzOaA2kBGRET8UVmV+WYmYcHWDo5ERERaIyAggDfffJM77riDNWvWcPLkSR5//HG3fvHx8axcuZKxY8e2+Z7Hjh3jD3/4g8fzu3btapRQWB9nV0ogbO1mXtoIWkRE/MFZk++z8OAAAqzmFZlERET8SX6pewJh//DgTohERMS/KIFQOs3i1EQ25RRQbXevtnC+IKuFJ+cmu1U78LaS4ZeOkUy0HGCQca5qYakzhFfsV3oTOlC3kDRlWJTX14mISPsxqz4B4PDVKn8RERHxCwlRYSRENd6F/KoxsW6JhQ03sKm/ztvNcJpzll584JjMB47JUOvkpsFV7LY1v1nNdMtO0/YP7O2T5JeZnceJ0+WN/j5ERDpbgEkCYasqEDrsUHAAYkb7ICoRERHfK6s2r0DYK0ivekVEuouIiAjeffdd3nnnHf7+97+zdetW8vLyiIiIYPjw4dx8883cd9999OnTp7ND7TJaW2FJG0GLiIg/MKtA2FvVB0VEpIcoMKlA2D9CCYQiIm2lGYV0muSESNLmjWdR+o4mkwiDrBbS5o13Sx4E7xdv/s0+i1ftqVxv+Yz7AtaQZDnBa/ZUztJ8SeMF1jWMsth4tTaVr5yJzEwa0KaFk7aicrYcLqSkspaIkABShkW7LWAVERHvWAwPFQhbtYDUAY5aCPC+Qq2IiIh0T2aJhefzZjMc7xn80xbClGHRfHaosMmeZgmEBc7efOkc2YL7OAHz35s8XuGELYeLuHWi5q0i0nWYzQFbnEBYXQaHMiF7LRxYB1Ul8KsjEBzu4yhFRETazlMFwl7BetUrItLd3HDDDdxwww2tvn7+/PnMnz+/2X7Tp0/H2c032PR2Q2nQRtAiIuI/zpolEIYqSV5ERHqGfJMEwn7hWscpItJWeqsknWr2uDhWLQwlLSOHzOy8Rg9+DQNmJsWwKDXRNHmwnreLN2sJ4C3HFbxVfTkzLDvY6xjS7DUWHMwP+IBBRiG3WDeyzzGYiLgFUDkSQrzbAXCnrZhnMnLIMvl5ZyTFsLiZn1dERDwzqz4BXiwgramAw59A9vt1C0in/Rom/diHEYqIiEh319LNcFrL6YRLhkSx7ehpj+NHUkKg4b6A+CP7RBxYmr3H84H/hxOD1+3T2OBIbtE1ACWV7i+rRUQ6k1kV+hbN/3atgnceAPt5Lx8PZcCY1i/kFRERaS8eEwiDrB0ciYiISMfxdkNpqFtj0paNoEVERLoK0wqESiAUEZEeoLLGTonJ81BVIBQRaTslEEqnS06IZNn8SdiKyvn8SBEllTVEhASSMiyqRQ92W7940yDLcXGLek6z7GSQca76w2jLcdj8b1C2B27+S5PXNqw0eLSwjPQvjlNjd1/I5HRC5v48NuUUkDZvPLPHxXnxs4iICIDVQwJhrcm/u43Ya2DV/LrqEzXl59qz1yqBUERERNw0tRmOL/QNC2xynltMBJdVpZFofEOq5StmWbdxseUgHzgmNTt2FGe5yvIlAYaDOdYvyHVG8ab9cl63T+e4c0CT10aEnHsx3XCuGxESQMqw6CarN3rbX0SkJawW9wToFlWgjxnlnjwIdXNAJRCKiEgXVF5tN20PUwVCERHxc95sKB1ktbAoNbEDohIREWl/JZXuiRN9lEAoIiI9gFn1QVACoYiIL+itknQZCVFhrV482JJKhj+YlMDZylpe/OQQOXmlXo1/p/Vj0/ZPQlMZVlRuGrenSoPNqbY7WJS+g1ULQ1WJUETES2bVJwAczf1DbA2EMycaJw8CHPkEqkogOMJHEYqIiIi/8LQZTp/QAP7xha1NiYURIYEtSFI0yHHGk2OP5wX79QygiCJ6Nzv2tdYtBBjnFlzFGUX8NOAdAqnlj7V3erzOMCBlWJTHua5hwIykGBanJjaay3rbX0TEG2ZV6FtUgXDAOOgzGM4cb9x+4AOw14JVj81FRKRrKas2r0AYHqwKhCIi4t/qN5R+MH276UbN9YKsFtLmjddzJhER8RumFQhDlEAoIiL+L79UCYQiIu1FKyHEb7S0kmFiTDhzX9jcoh3qrIbBTcOdzLDtcDt3wtmPez4Jw7khixlJMdw+KYEzFTXNVhpsiWq7g7SMHJbNb756hIiInGMxPFQgbMkC0lHXQu6Oxm32ajiYAWNvbHNsIiIi4p/MNsO5akwstqJy1u45yX+9v8+r8eoT9cB9ntvUhjiniGrR+DdYPzNtf90+vcnrZibFsOebMx6rIjqdkLk/j005BaTNG8/scXGs25PrVX8REW+ZzQFblEBoGJB0DXzxYuP2iiKwfQ5DLvNRhCIiIr5RVmWeQBgWpFe9IiLi/2aPi+PFH1q5929bTc+njophkTapEhERP1Jrd1BqMg9UBUIREekJPFUg7BeuBEIRkbbSWyXxO81VMqzfoc7TIsZ69TvUXZXg5PVnrmWOPZM+xrnKVG/aL8eBBb5b9Ji5P890nEXWt9jnHMx6x3hqvPhfLjM7jxOnyxslP4qISNMCLBbTdruj+aRxkuZA1h/c27PXKoFQREREvJYQFcZPrhjGlsOFHueLZmYmxbjNA+vnud5siGMm3sjjEssBt/YvHYkccg7yeF2Q1cI142KbnUdD3YY4i9J38F831fLbt/e0uP+qhaFa5CUiXmt1BUJonEDYO77uc9I1EK8NvUREpOspq7KbtvdSAqGIiPQQfXuZJ0z89ppR/GTa8A6ORkREpH2VVJpvItM7VHNAERHxf54SCFWBUESk7cxX2Yv4udnj4li1cAqpo2I4f6Nyw6jboW7VwinMHhfHnrNhLKm8k8lVf+ah6oVsc4wE4A37Fc3eJ5ZCfhbwJi8FPc3W4H/lvwJe4nuW3Vgxf9HbkNMJWw4XternExHpqawmi0cBWrTGfsBY6DP43OfwWJh4D4yf55vgREREpEdanJpIkLVlj1+CrBYWpSZ6PF+/IU5LxztflTOIP9tvorpX42p/K5uoPmgxIG3eeN7fc5J/4S2SjYNA08k51XYHf1y7v8WJjtV2B2kZOS3qKyLSkNXqPgesbckGMgBDvgczfgf3bYCf74Fr/xdGpEJAkI+jFBERabvyavPFo72CrR0ciYiISOc4dbbStH30wN4dHImIiEj7O1tZY9quCoQiItITFJR6SCBUBUIRkTbTliTSYyUnRLJs/iRsReV8fqSIksoaIkICSRkW1ajawzMZOdgdTuwE8ZbjCt6qvoJ4I48Tzphm73G99TMsRt3CykijjDsCsriDLH5Z8xNWNbFAs16Jh4cBIiJiLsBk8Si0sAKhYcClP4GKYhg1B+IuBg8VDUVERERaqj7pr7nqfUFWC2nzxjdbha9uQ5xQ0jJyyMzOw9nCQlsAZ6xRDPvBHwkaEwOHsmD7CmoPZlE2+PsYOaWmY1ktBhdE9+LbA9t4Oeh14HX2OxJYaZ/O2/bvUUyE6b0Ky6pbHhiQmZ3HidPlbtUXzdiKytlyuJCSyloiQgJIGRZNQlTz14mI/2lTBUJrIEz7lY8jEhERaR+lnioQButVr4iI9Ay5Z8wTCOP6hHRwJCIiIu3vTIX5msHeIUogFBER/2dWgdBqMegbpk1ARUTaSm+VpMdLiArzuNDQVlROVnaeW3tLkgcBbrR+5tZW67TwsX1Ci67fYStm1TabFkOKiLSQ5fyyst+pbekC0qkP+jAaERERkTpNJf0ZBsxMimFRamKzyYP1PG2I0yc0gH98YWvZPRKvhMQrCaip4E+BodiKyvm3f+5h/YH8RveqsTtZuvEwt1nWu9pGWWz83rKCRwLS+XHNL9nkuLB1fzENOJ2w5XARt070PPfdaSvmmYwcskx+vhlJMSz24u9QRPyD1WQO2OIEQhERkW7ErAKhxYDgAG2AJiIiPcNJDwmEsX1COzgSERGR9ne2wrwKvSoQiohIT2CWQBjdKwiLycaiIiLiHSUQijRhy+FCr6o5NJRkHGeM5Zhb+2eOsZymd7PXh1HJOzu+5Z0d37oWQ94+KYEzFTXtVmVBVRxEpLszqz4BWkAqIiIinc9T0l/KsKgWVd0zY7YhzlVjYr27R2Coa6zfXTvaLYEQYNuhUyyxbnJrd2Bhp2N4q2I3U1LZeEfdhnPUo4VlpH9xnBq7++91Tidk7s9jU04BafPGM3tcnM9iEpGuzWoyB2zxBjIiIiLdSJlJBcJewQEYHjZUExER8Tcnz7onEEYEBxCuarwiIuKHPFYgVAKhiIj0APml7gmE/SOCOyESERH/oydpIk0oqTTfzaclBhinOeHsR7xR0Kj9PUdKs9eGUMUXwfezzzmY9fbxrHckk7XfQeb+xtUQfZVY2FwVh/ZOXBQR8RWzxaOgBEIRka7Mbrezb98+tm3bxpdffsm2bdvYuXMnFRUVANx9990sX768Xe69evVqVqxYwdatWzl58iS9e/dmxIgR3HTTTdx333307t38xh8i3jJL+usq90gcEMH4hEh22IobtY8r2UTfoFK3/u85UijBdz9LREjdi29Pc9TmVNsdLErfwaqFoapEKNJDBFhVgVBERHqGsir391W9gvSaV0REeo5ckwqEA/qEdEIkIiIi7e9spXkCoSoQiohIT1CgBEIRkXajN0siTYgIaf3/IhscyVxe9X9cYhzgRuunzLJuJZJSPrRPbPbayy27CTcqmWQcYJLlAL/kdfKckfyq5iesd4x39auvsuApsXBxamKziybX7cllUfoOqu0Ot3O+GF9EpCOZLR4FVaAQEenKbrvtNt56660OvWdpaSl33nknq1evbtSen59Pfn4+mzdv5tlnn+X1118nJaX5DUBE/MltlyS4JRD2NUo46wyjt1HeqH1l7fRmx4ukhGutn7PWPpkiPCflGgakDItqco7aEtV2B2kZOSybP6lV14tI96IKhCIi0lOUVbsnEIYFWzshEhERkc5x0iSBME4JhCIi4qc8VyDUcl8REfFvTqeT/BKTBMJwJRCKiPiCZhQiTUgZFo1h4FXFg4acWNjqHMXW2lE8WjufUYaN000smKx3leVLt7YYo5gTzn4tu+93iX8bD+Qz79LBDInuZVo5cKetuFULM+vH35RTQNq88cweF+fV9SIi7cVqqAKhiEh3Y7fbG32OiooiOjqanJycdrvf3LlzWbduHQADBgxgwYIFjBkzhqKiItLT0/n000+x2WzMmTOHTz/9lNGjR7dLLCJd0XXJcfx+9R5q7Od+f3rFfhVv2K/gGssX/CBgPSmWfRxyxLHVmeRxnOheQRSWVXON9Qv+EPgyjwUs5zPHWN51TOED+yWcJbxR/5lJMRSWVrcpebBeZnYeJ06XE9+3fSs9ikjnM5sDOjT/ExERP1RebXdrCw/Wa14REekZnE4nJ8+6JxDG9lYCoYiI+KezHhIIVYFQRET8XWlVLZU17usF+qkCoYiIT+jNkkgTEqLCmJEU41aBrzXsWNnrHNJsPwsOZlq3u7WfcPbjoHNQs9fHUchJ+uLEQo3Dyd83H3OdO79y4DMZOW1amFltd7AofQerFoaqEqGIdAlm1SdAFShERLqyyZMnM3r0aCZOnMjEiRMZOnQoy5cv55577mmX+y1dutSVPDhmzBgyMzMZMGCA6/wDDzzAww8/zFNPPcXp06e577772LBhQ7vEItIVHckvM918oZJg3nZcztvVlzPEyGUAxYD5714GMHZQbzYcKOA6yxYAAgwHV1h3c4V1N/8Z8DKXVL1ACXUJfoFWg0WpiW2eo9ZzOmHL4SJunagEQhF/Z7VY3NpqHU6cTieGhw1mWqy8CIJ6QYBeSIqISOcrqzKpQBikCoQiItIznC6vobrW/ZlRrCoQioiInzKrQBhgMQgN1DxQRET8m1n1QVAFQhERX1ECoUgzFqcmsimnwCeLGBsKtBjccelgiitqeGfHt672CcYB+hln3fqvtyfjaXFmPQMH7wf/BjsWNjvG8JljLJ85xnLMOQAwGlUm/H7yQJ8kRlbbHaRl5LBs/qQ2jyUi0lYBJotHwYcVKBwO8HAPERFpnd/+9rcddi+73c5jjz3m+rxixYpGyYP1nnjiCTIyMtixYwcbN27kww8/5Oqrr+6wOEU60zMZOTT3q9NRZxxH8VyJ3glsOFBAf4pJsXztdn6nc7greRAgZVg0Ub2CyMpu+xy1Xkml+e68IuJfAqzmz8ocTvBwqmmFhyD7fcheC8c3w+2vQdI1bQtSRETEB8qq3RMIewXpNa+IiPQMuWcqTNuVQCgiIv7qbKX7HLBPaGDbN00TERHp4gpKq03b+6sCoYiIT2gFvEgzkhMiSZs3niCrb/53MQxIHRXDG/86lcduGEdyfGSj80edcfxHzY/YYL+QKue5l79ZjvHNjj3aOE5fo5R+xlm+b93CHwOX8UnwQ/ws4M1G/WocTt7a/o0vfhwAMrPzOHG63GfjiYi0lqfcvjZVIDx9DLY8D3/7Prz+o9aPIyIinW7Dhg3k5uYCMG3aNCZMmGDaz2q1smjRItfn9PT0DolPpLPZisp9msR3jfVzrIb772Ef2BtvQLPt6GmysvNwNuhqwYGF1m/kExES2OprRaT78FyF3st/P0pOwnOT4dkJ8OESOPYpOB2w/z0fRCkiItJ25VV2t7ZewUogFBGRnuHkmUrT9jglEIqIiJ8yq0DYO1TvPURExP95rECoBEIREZ/QmyWRFpg9Lo5VC0NJy8gh87xFjc2przQ4pF8vIkICSRkWRXzfc5UWIkIa/29YQB9etl/Dy/ZrCKOSKZa9TLfsZLNjbLP3mmrZa9q+wzG85QG3gtMJWw4XcevEsOY7i4i0I08VCO3eLh4F2PZX+OIlyGvwb2tACFSXQVCvVkYoIiKdae3ata7jOXPmNNn3mmvOVRtqeJ2IP9tyuNCr+W5zRhonTNs/cDROIKyosbMpp6BR2xWWnTwV+AJZjov52D6BjY4LKSO0Rfc1DEgZFtW6oL9jKypny+FCSipriQgJIGVYNAlRmvOKdDVWDztuez0F7BUDVWfd279+B655QnNAERHpdKVVJhUIg62dEImIiEjHO3nWPIFwQG8lEIqIiH86qwRCERHpofJLzOd//cKVQCgi4gtKIBRpoeSESJbNn4StqJzPjxRRUllDREggfUID+McXNrfEQsOAmUkxLEpNJDkh0uO4KcOiMQxMF2mWE0KGYyIZjoktitEsgbDWaWGrY1QLrt2DFQdbHUlU4v0vWiWV5x5caKGliHQWz9UnWrESvuRk4+RBgNrKugWk4+9oRXQiItLZdu/e7TqeNGlSEz0hNjaWhIQEbDYbp06dIj8/n/79+7d3iCKdqqTSfVFyWyyp/TEv2L/PdZYtfN+6mbGWYxwJGM6JSvf/lz492DiB8ErLV0QbJdxq3cCt1g1UO6184RjFT2sWUUxEk/edmRTTaOMeb+aoO23FPJOR41YR0TBgRlIMi5uZ44tIx2q6AqEXSRUWCyTNgW3LGrdXnYW9/4SL72x1jCIiIm1Va3dQVeueHR8WpNe8IiLSM3iuQNiyzaZERES6G9MEwhDNAUVExP/ll6oCoYhIe9KsQsRLCVFhbgsNrxoT65ZYeH6lwabGm5EUQ+b+vDbFFUAtky373dp3Ooe3qErDooC3SbHso8oZwJeOkWx0XMR6RzL7nBe06P4RIYGtXmiphEMR8ZUAD4tHHa1JIBw1Bz75b/f2L/+mBEIRkW4qOzvbdTx06NBm+w8dOhSbzea6VgmE4u8i2uHl8wlnDC/Yr+cF+/UMNXKJrj7L+Pg+7DhxplG/smp7g09OUq3bG50PMuwkWr6hmPAm7xdktbAoNRFoWTJgVK8g13z0aGEZ6V8cp8bu/ruj0wmZ+/PYlFNA2rzxzB4X591fhIi0C09zQHtr5oAX/9A9gRDqNpFRAqGIiHSi8hq7aXuvYL3mFRGRniHXJIEwKMBC3zBVYhIREf90ttI9gbCPKhCKiEgPUFBS7dYWFGBRIr2IiI/oX1MRHzFLLGypxamJbMopoNruvoOsN35W8wBTLXuZYtnLaEvdQufPHGObvS6USiYYBwAINmqZav2aqdavmeHYzm3Vv2/2esOAqho7c1/YbPozeFpoqcoOIuJrFl9WIIy9CAZcCKd2N263bYG8/RDTfHVXERHpWoqLi13H/fr1a7Z/dHS06bUtdeLEiSbP5+bmej2mSHtKGRaNYdBofuZLR5xxHHHGcVdCpFsCYUNjjaPEGUVu7Zn2iwHz3/egbi6ZNm88yQmRrNuTy6L0HU3OUbO+28jHmx+32u5gUfoOVi0M1XxVpAuwWn04Bxx4McReCCd3AwYMnwET7q6rTCgiItKJyqrMK4X3CvKi2q6IiEg3duqsewJhbO8QDMPzcyIREZHuyul0csasAqESCEVEpAcwq0DYPzxY8z8RER9RAqFIF5CcEEnavPEeFze2RC0BfOyYyMeOiQBEc4YUyz6ynfHNXjvZkk2Q4b6D7Qb7RS269yWD+/LYu183G3vDhZa5ZyqaXcypyg4i4i2fVp8wDJh4N7z/8HefrTBydl1bv8Q2RCkiIp2ltLTUdRwSEtJs/9DQc5W8S0pKvL5fQkKC19eIdKaEqDBmJMWQ+V1iXXsJ9PA7W72rrF+atmc5xjd5ndVicHl0CeXP3srBvJGMdoxnF8NwYjHt39o8yWq7g7SMHJbNn9TKEUTEV6weXha2eg44dTEUZMPFP4K+F7QxOhEREd8oqzKvQBimCoQiItJDmFUgjO3T/PNdERGR7qiyxkGN3f35pioQiohIT5Bf4p5A2C8iuBMiERHxT3qzBNjtdvbt28e2bdv48ssv2bZtGzt37qSiogKAu+++m+XLl3dukOL3Zo+LY9XCUNIycsg8ryJfaxTSh/ccKS3qO9Wyx7R9g6P5BMKxVhuXV3zFYftwCunTbP9qu4P/XPM1O0+c8SrhUJUdRKQlrL6sQAhw4VzYuhTG3QoX3wm9B7YhOhEREZGub3FqIptyClq0uY3VMLC3YvL62eHCJs9/Yk8mklKutH5FvFEAQJUzgE2OC4G6BMQxA3uz87wqhrV2J0c3v8XYwj381LqHn1rfIt/Zm/X28fxf7S18Q3+vY/UkMzuPE6fLie8b5rMxRcR7nuaArUogBLhobhuiERERaR/l1eYVCMODVYFQRER6hpMmCYRxSiAUERE/ZVZ9EKB3iBIIRUTE/5klEPYPVwKhiIivKIEQuO2223jrrbc6OwwRkhMiWTZ/Eraicj4/UkRJZQ0RIYH0CQ3gH1/YfJJYaGa85ZBbW5EznD3Ooc1e+1DMl6Sefp2fBhvsdg5lvSOZT+zJbHeO8FjlYeux0y2OzdvKDraicrYcLqSkspaIkABShkWTEKUFnSI9hacKhI7WLh4NjYT7t9RVohARkW4vPDyc06frfhetrKwkPDy8yf71m8oAREREeH0/m83W5Pnc3FwmT57s9bgi7Sk5IZK0eeM9VoyvF2S18Oj3R/Nv7+z1ep66P7fpip7bnYlsr03k32vvJsmwMdOyg75GCRVGCKlJMSxKTSQowMI1z2x0u9Z54INGn/sbZ7nFupE/1t7hXZDNcDphy+Eibp2o+aZIZ/JpFXoREZEuymMFwiC95hUREf9XUllDaZV7Mr0qEIqIiL86W2meQKgKhCIi4u8cDieFZSYJhKpAKCLiM3qzRF0FwoaioqKIjo4mJyenkyKSni4hKswt6e2qMbHtllg4r3oJFxqHucyyhyusu5loHKBowGW8OH1Ss+MPLd4MgMVwkmwcJtlymPnWD5hQ9SLmr7S915LKDjttxTyTkUPWebEaBsxIimFxaqKqGIr0AD6vQAhKHhQR8SORkZGuBMKCgoJmEwgLC89VSYuMjPT6fvHx8V5fI9IVzB4Xx6qFoaRl5LjNBw0DZn6XxJecEEnm/nwy9+e1eOzRsRHsO9l0AmGDu5HtHEy2fTA/mJTAxpkjXPNCp9PJBdFhHCssd/UOo5KRFbvgvF/ftjtHUETvFsfYUiUeXuKLSMexWs03r2rTHFBERKSLKTNJmgDopQRCERHpAU6dda8+CBDbWwmEIiLinzxWIAzVHFBERPzbmYoaauzu7/iUQCgi4juaVQCTJ09m9OjRTJw4kYkTJzJ06FCWL1/OPffc09mhiTTSksTCowVlvPbFcdNfojxxYGGncwQ77SP4s/1Grh0Zzp9uGcGIPrGNxv9/Hx3gm+JzVVgGkc8wp3tVlY2Oi7Bjbf0Peh6zyg4NKw0eLSwj3cPP7HRC5v48NuUUkDZvPLPHxfksLhHpegzDwGLA+WtFVX1CREQAkpKSOHLkCABHjhxhyJAhTfav71t/rUhPkpwQybL5k9w2skkZFtVoc5fFqYlsyiloslphvSCrhctG9PMigfCcUbERje5rGAazx8by4obDrrbvWXYTZLgvrs60X9zs+AYOfhfwKhscF7HZMZaaFjwyiwjRbr8inc3qYcMXu6P5f5NERES6i7JqDwmEwb57DyMiItJV5Z4xTyCMUwVCERHxU2c9JBCqAqGIiPi7/FL36oMA/cODOjgSERH/pQRC4Le//W1nhyDSJucnFt48Id60UkRLBFkt/OSqZOgT6Ta+0+nkl2/scrVPt+40HWO9I7nZ+wwzvuUm6ya2OEbzlSORCpp+wF9f2cFTpcHmVNsdLErfwaqFoapEKOLnAiwWtwXsSiAUERGACy+8kHXr1gGwdetWZsyY4bHvqVOnsNnqNsuIiYmhf//+HRKjSFdjtpFNQ8kJkaTNG8+i9B1NJhEGWS2kzRtPSaX54ufmmCXrzRrXOIHwjDOcj+0Xc5llL6FGtas909F8AuHFxkH+JWAt/8JazjrD+NgxgQ/sl/CJI5lKzHc0LCqrwlZU3uTfj4i0rwAPVehbkNMsIiLSbZRX203bewXrNa+IiPi/kx4SCGP7hHZwJCIiIh3DYwVCbWooIiJ+rqDEQwKhKhCKiPiM3iyJ+CGzShEtqUxYv6DTU4LddRcN5PE1X3P2uwWfY4xjpv0+sV/UbIzTLDt5MOCfPMg/qXZa2eUczhbHaP5Sex1n6eXWPyIkkHV7cptdlNqUaruDtIwcls2f5GprWMkwIiSAlGHRWvwp0s1ZLQact6amVgmEIiICzJ49myeffBKAtWvX8qtf/cpj3/fff991PGfOnHaPTaQ7mz0ujlULQ003sjEMmJkUw6LURJITIrEVlWMYeLUhjGFAyrAot/bx8ZEM6B3MqbN1LxI+d47m85rRBFPNFMvXzLRsZ4zlGPucg5v/GaxbXce9jXJutm7iZusm5lf/kvUeEhD/6/39/HHtfmYkxbD4u5/PjOadIu3H6iGBsFYVCEVExI+UVZlvwhEWpAqEIiLi/zwmEPZWBUIREfFPqkAoIiI9lccKhEogFBHxGSUQivixllYmPH9BpyehQVYuGRJF5v48AH5X+2NetF/HNMtOplt2MtWyl8POOPLp22xsKZZ9ruMgw84lxgEuMg7xbO1Nbn0Ng//f3p3HR1Xf+x9/n5khK4GwhSAJOwYQhKIgar0IiCJ1qbRewd5a8HepVlpsbatdvFBq21t/tb2V9teK1rpLrVcRtIq1LLIUFEVW2ZSAoQIJa0JCtpnz+yNmkjBn5sxMJpmZM6/n4+HD5DvnO+dkzsy8+c58P+erzpkezXnug6iLBxut3FOqQyerdPxMreVKhoYh28mfABKb1QRSL5NHAQCSxo8fr/z8fB05ckSrV6/W5s2bNXr06IDtvF6vFi5c6P99+vTp7XmYQFKyupBNTkYHjRvQVQVdmsalhV2zNKEozz+uDMfEorwW99HI5TJ0zQX5enpDy4vb1ChNq32jtNo3Ksw9mLrGtSmgtcLM1D99w0P3NKWVu0u1bt8xLZwxSlOG9/LftrXkFONOoI153MFWIGyDi8hUl0s7/lfqOkAacGXs7x8AgCAqa6xXIOzICoQAgBRwuDywgNDtMphACgBwrNNnrS8i04kCQgCAw5UFWYGwe0fGfwAQK3yzBKSQcCd0BrO15JTW7itr0faJ2VPPeK/WM96rla5a9TRO2t6PIZ8uaVZA2GiH2V81Sgton1iUp8XvlviLB8e5PtSHvr6WKxXaMU3psTX7W9zfubcHm/wJIDlYFRC22QqEVSek4jXSBV9sm/sHAITtySef1KxZsyQ1FAquXr06YBu326158+bprrvukiTddtttWrlypfLy8lps94Mf/EBbtmyRJF1++eW65ppr2vTYASc590I2Vu6eNFjr9h0L6wIxaW6X5k4aHPT2rtmBY8hIDTU+UV9XYEHjSt/nVKvwvpCv9fo057kPNOvykyrKz1FNvU8/ffVDxp1AG3MZwVYgjNEY0DSlf70vvf+ktONlqa5SGjCBAkIAQLuqqg22AiFf8wIAnM9qBcK8nPSgK9IDAJDsyqutVyDslMEYEADgbBQQAkDbY1TRjg4dOhTy9sOHD7fTkSDVhTOh08rDK/apzht8AlaN0vSJ2dP2foYYJco1KgPa3/MVWW4/bXRvfXPxB5KkrirXX9J+Jkn62NdLW8yB2uYbqFW+UWHtW5Ke2XhQdvPIar0+zV28RS/emcmKEECS8Vh8YeiLZQGhaUoH/9kwgfTDpZK3RsrfLHUbGLt9AEAKKS4u1uOPP96ibdu2bf6fP/jgA91///0tbp84caImTpwY1f5mz56tJUuW6K233tLOnTs1cuRIzZ49W8OGDdOJEye0ePFirVu3TpKUm5urRYsWRbUfAMGNLMzVwhmjNHfxlpBFhGlulxbOGBV0TLa15JT+36qPWn085xnHVGZ2Ug+jvEX7cu8Y275pqtPStP/SKt8ovez9vP60Lvx/d55bdDhuQLeoxupAKrMa/0kxHAO+8B/S7tdatu1fJZ08IHXpF5t9AABg40xNYAFhB7ehNI8rDkcDAED7siog7NkpIw5HAgBA+zh9NrCAMDvNLY+bMSAAwNnKzgQWEGanuZWdTrkLAMQK76jtqLCwMN6HAESt5ESVVu0JXJEhGn2MUp0xM9TRaPlh//s+61Ul/rj6Y5mfzfsa6frY3z7QdVgDdVhfcq9TZV2GPvGGV0AY7hyyWq9PC1fs0+MzmyaNlpyo0sb9x1VRXa+cDA8TPIEE1KYrEP5rs/Ty16Xj+1q2b35amrwgNvsAgBRz8OBB/fznPw96+7Zt21oUFEqSx+OJuoDQ4/HopZde0q233qrXXntNR44c0QMPPBCwXUFBgV544QVdcMEFUe0HQGhThvfSi3dmauGKfVq5p9Q/5pMkw2hYiX7upMEhL+hid5GbUAxD/n2u8F2kS2o+p9HGXk1xb9IU9yZ112nljpgibT0Z8n4muj7QUNcnGur6RHd5lmm7r5+WeK/Qy97P65RybI/Da5r607pi/zFNKMrT3TZ/N4AmwVaciNkYsODiwAJCqWEMOGlebPYBAICNqlpvQBuTZgAAqeJIeWABYa/OFBACAJyr3KKAsHNmhzgcCQAA7ctqBcIeOaw+CACxxLdLAMKycf/xFhM6W+NN3xj9o2a0hhvFGufapXGuD3Wxa682+8633H7Hp02rQIxyWa8usdVnv/LXIOOQvHLrgNlTpsK7KtPKPaU6dLJKx8/U6uEV+7TKYmIrEzyBxGI1gdQbq8mjuX2lUwcD27c8J034seRJi81+AABtKicnR6+++qqWLl2qp59+Wps2bVJpaalycnI0cOBATZs2TXfccYc6d+4c70MFHG1kYa4enzlGJSeq9E7xCVVU1ykno4PGDeiqgi6hL9QS7UVufjx1iK4d0UvHz9S2KF70yaX3zCF63ztEGwbeo++NzdDos1202KaAcJp7bYvfR7gOaITrgHaZfbTBF1kBsmlKK3eXat2+Y1o4Y5SmDO8V8d8HpBqP27qAMGZjwFFfkVb+TPKds/LTO4uksV+XcvJjsx8AAEKotFiBMDuNr3gBAM5XXefVicragPZ8CggBAA5mtQJhJwoIAQApwKqAsHtHCggBIJb4dqkdlZSUhLz98OHDGjt2bDsdDRCZiurAL6hbwyu3tpqDtNU7SIu818sln3xyKTvNrUqLq+k2+pwRWEBYaabrY/M8231+z/Oiprg3qdzM0jZff201B+p93/la6RsdtI9pSo+t2a/F75ao1uuzvD3UBE9WLATaX5uuQJjdTRpynbTz5ZbtlWXSu4uky74Vm/0AQAq58sorZcbgShUzZ87UzJkzI+pz44036sYbb2z1vgG0TmHXrIjHSdFe5KZLdroKumSpoEuWbfHihs9WBgwmVxW60rUloP2Q2V0bfUMjP7jP1Hp9mrt4i168M5ML1QA2XEYbr0DYMU8qmirtWtbUltZRGn+vlNk1NvsAAMBGZW3g9zNZae44HAkAAO2rtDxw8qgk5XeigBAA4FzlFnP0KCAEAKSCY2dYgRAA2hoFhO2ooKAg3ocARC0no23fLnyfrQh4ti548aBkaqTr44DWHWZ/f/9QLvysbyejSp9379TntVNbfQO0sjZ4AaEkPfvOJ7ZXrj93gufWklOsWAjEiceigNAXq8mjknTRzMACws6FUlf7lVABAAAQG9Fe5KaiuuWVe0MVL9qNg69zb1SaETiGfcn7b2Gveh9MrdenhSv26fGZY1p1P4DTeVzWr7WYjgEv+1ZTAeGIf5cm/1TqxAqhAID2U1kT+G/O7HS+4gUAON/h02ct21mBEADgZOVWKxBmUEAIAHA2r8+0XIGeAkIAiC2+XQIQlnEDuskwFNUKD5Gwm991a+2P9TnXRxrl+lgjjY810PhU23wDbO+3h07pPONEQPtWn33BT4avShe69munr5/KlR10u1qvT997casuHdhNi9/9RHXewD/GbsVCAK3nassVCCWp3xVS/gjpyHbJnS5dfrf0+e9IaawuCgAA0F6ivchNTgRfstuNg3f7CvWa9xJNdm1WutH0hf6L3n+zve/uOq0fd3hWL3rHa4NvmGXB4co9pTp0ssq/IiKAQFYr0EsxHgMWjpWu/JHU/9+kvpfG7n4BAAhTlcUKhNnprEAIAHC+I+XVlu29Ome285EAANB+rAoIO7MCIQDA4Y5X1ljOH+/RkQJCAIglCggBhKWwa5YmFOVp5e7SOB6FoZ1mf+309tez3smSpI6qUprsV5640GLlQknaZtoXH4527dMzab+UJBX7emq7OUDbff211Hu5StWlxbb7Ss9oX+kZ2/us9fo057kPNOvykyrKz9G4Ad2CrnoBIDJWKxDarSIaEZdLuvZX0vrfSlP+W+pq/z4CAACA2IrmIjeGIY0b0DXs7e3Gwe+ZQ/Re3RDlqErXut/Rl9xr5TVdOmTm2d73Te61usm9Xje51+uQ2V0vez+vJd4rVGw2XWTGNKWN+0/oyxcxVgSC8bitCwi9Pl9sd3TlfbG9PwAAImC1AmFWGl/xAgCc78jpYAWErEAIAHAmr89URU3gPLhOmYwBAQDOVlZRY9nenRUIASCmGFkACNvdkwZr3b5jqvXaT8JKc7t0YUFnvXfwZJse0xm1nEjZwWVoVJ9cbTrQcr8XuvZb9g9nBcIRRlPf/q6j6q+jusG9Qet8I1RqdgnRMzSvaepP64olNUxknVCUp+ljCnX6bJ0qquuVk+GhsBCIgtsVuHpLfawnj/a9lJUnAAAA4iiai9xMLMqLeDW/cMbBFcrSX70T9FfvBKUp8MrAgUzd7H7b/1uBcUxzPa/oLvcyjan5g06qk/+2tfvKdEn/rlGPC0tOVGnj/uOMMeFYLqMdViAEACDOKi1WIOyYzle8AADnOxykgDCvExNIAQDOVFFt/R0DKxACAJwuWAEhKxACQGzx7RKAsI0szNXCGaM0d/GWkJMn09wuLZwxSr06Z+rmRzaEVXDoNgx5I1k64hyG0TAZdO6kwdp7tCKggPDZ+sn60NdXI137daHxsS50Fcstrz42z7O97xGu4oC2GrOD9pm9bfte59qgs0rTDl9/HVUXSdYT20xTWrm7NGDyK4WFQOSsViCMdf0gAAAA4i/Si9zMnTQ44n2EOw5uVCv7L/FHGR/rfNe/AtrX+Ua0KB6UpKVbPtWyrZ9GPC7cWnJKD6/Yp1V7Slus0tg4xrx70mCNLMy1PVYg0VmN/6QYr0IPAECcVVmuQOiOw5EAANC+rFYg7JadpnQPOQgAcKbTZ60LCDtlUEAIAHC2Y2dqLdt7sAIhAMQUBYQAIjJleC+9eGemFq7Yp5UWExEbi/gaJyKGW3A4fWyhnt5wMOLjuXHUebpicA+NG9DVv5JE1+w0GYZaHFuZcvWmb6ze9I1tOFb51FMn5VPgSmXnsiog3GUWqj6Mt9AfdFisAuOYJOmY2Uk7ff30d9/Fes57VTh/nm1hIZM+gUAuiwmkMV+BEAAAAHEX6UVuoh07hRoHR6P56oPNvey9wrI90nHh8h2Hgz4mjfe1bt8xLZwxSlOG94r+DwESgJsCQgCAw9XW+yz/XZfNCoQAgBRwuDywgDC/c0YcjgQAgPZRfjZwBXqJFQgBAM4XdAVCCggBIKb4dglAxEYW5urxmWNUcqJK7xSfUEV1nXIyOrQo4msUbsFh1+w0PbPxYEQTMQ1D+v41RQH7LOyapQlFeQGTK5sz5dIRdbPdRzed9hcANrfT19+2b64qWvTtbpRrvHubDpk9bPvaaT7pc971Q5XucbM6IfAZqxUomDwKAADgTJFe5CZa546Ddx8p1xPrD0T178wjZheVmZ3Vwzjtbys3M/Wm72LbvuNdW3WN610t943VBt8FAcWAW0tOhbVaYq3Xp7mLt+jFOzO5KA2SmsdtXUBYH48xoLdOcnka3nwAAIiRqlrryaOsQAgASAVHLVYgzO9EASEAwLmCrkBIASEAwOGCFRB265jWzkcCAM5GAaGk4uJiPf744y3atm3b5v/5gw8+0P3339/i9okTJ2rixIntcnxAoirsmhVWoVq4BYd2RX/nmliUF1A82OjuSYO1bt8x20mTkuSSFGyr08rWjTU/1QhXsUYYxbrQtV+DjUPabtoXEA5zWa+ouMPsZ9t3uLFfX3av0Q6zv7b7+usjs7e8CpwQUOv16f5XdrZoa1yFYvqYQp0+W0dhIVKO1QoUcZk8CgAAgHYRyUVuWqv5OPjivl3CWv1w3vVDldHB4y86/J13mv7ovUFXurbqS+41muTarNe9l6hG9l9+fNn9tq53b9StWqUKM1Nv+0bq796LNXexqRfvzNTDK/aFNQ6WGsaTC1fs0+Mzx4T3xwMJyB2kWK/dLyKzf7X0+r3SuG9IF89q330DABytstZr2d6RFQgBAA5X7/WptIIVCAEAqaW82rqAkBUIAQBOV3YmsICwc2YHpXu4kBoAxBLfLkk6ePCgfv7znwe9fdu2bS0KCiXJ4/FQQAhEyK7gMJKivzS3S3MnDQ56+8jCXC2cMSqsyZzTxxbq6Q3WxX718mirOUhbvYP8bemqlStoyWGT4UaxZftOXz/bvpe6PtRMz9/9v58107TL7KNv1X5L/1LoFQwbVyc8txizsbDw7iCrb5ScqNLG/ccpOETSS4gVCH1eafPT0oevSNMXS2m8lgAAANpauBe5iZVoVj9sKjr06B++i/QP30XKVYUyVGu7v0xVa5LrA//vOcZZXefeqP7GYS2rvUwPvrFbG4qPR/Q3rNhdqkfXfKxrh/dSYdcsxoVIOlYXkJHacQx46hPpzR9Lu5Y1/P6370rpOdKIL7fP/gEAjldZE2wFQr7iBQA4W9mZGlkN7XpRQAgAcLDgKxAyBgQAONsxixUIe+Skx+FIAMDZGFkASBiRFP0tnDHKsgiuuXAnc75/8GRExxnOqhCSNMRVEtBWb7q0xyy07TvC1bL4MNOo1YXar2PqHN5BWmgsLFy7t0wzLumjft2ylZPhUefMDvrLphKtsniMQhUcAonKagJpuxYQHnpPev170qefTe5+7mbp1hek9I7tdwwAAABoF5Gufmg1Tj2lnLD2Ncn1gbKMwC9O3vJdJEn65/7Iigcb/eL13frF67vVLTtNJypr1fxfzowLkeg8Lpdle7uMATf9SXrzfqn+bFOb6ZVent3wIcyFN7f9MQAAHC9YAWF2OlfeBgA425HTgasPSlLPThQQAgCcqzxIASErEAIAnM5qBcIeHSkgBIBYo4BQ0pVXXinTbOeViQBYimYFh1DCmcy592hFG/wl0vfr7tAf62/QcKNYF7gO6ALjoFyGL6wCxAuMAwFt+8zeYfVd2OF3Oml21A6zv7b7Bmif2VteNU0mqPOZQVdcbK6x4HDdvmNaOGOUpgzvZdsHSARxKyCsLpfe/JH0wTMt2w+uk579kvSVF6WMTm1/HAAAAGh3kax+2Hyc+tDf92jplk/D6ne9e4Nl+1vei237Xu3apEtdH2qTr0ibfENUptyAbY5XBq6CGOxCNKxMiEThdluvQFjfHmPAjvktiwcbmb6Gi8oMnixl5rb9cQAAHK2q1mvZns0KhAAAhwtWQNirc2Y7HwkAAO0n6AqEGRQQAgCcrcxiBcLurEAIADHHt0sAEk6kKziEI9RkznEDuskwpFjXEXvl1j6zQPvMAi3xXRF2v46q0kDX4YD2Hb7+tn2zdVbXuTbKZTT9MTVmB/2+/kb9zjst7GNortbr05znPtCsy0+qKD/HP1G05ESVNu4/rorqeiaQIqF4rAoI2+NCAZ506ZON1reVbJRW/FT6wkNtfxwAAABICoVds/T5Qd3DLiA8ZPbQMbOTuhvlzdq6a6fZ17bvNe5N+pJ7nWbpTUlSsa+n3vUN1Y/q/0+LC84Ec+6FaFiZEInCavwnSV6fr+13PvQ66fJvS+t/27I9LafhAjIUDwJASMuWLdMzzzyjTZs26ciRI+rUqZMGDRqkm266SXfccYc6dYr9hbjisc/WOhNkBcIsViAEgKRFBobncJACwvzOrEAIAMmI/AtPeXVgAaHHZSgrjTEgALSXZM+sjz76SIsWLdIbb7yhkpISeb1e9e7dW1dddZVmz56tUaNGxfz4W6um3mtZRM8KhAAQexQQAkhYkazg0Nr9TCjK08rdpWH3GdO3i7YeOq1ab+wnpPUyTuiomauexqkW7dtN+wLCYcbBFsWDkpRu1OmUOtr29ahe9XJLsi6++tO6Yv/v3bLTdKKyVs331DiBdPqYQp0+W0dhYZJJxg9Og7FagbDe204FhLc8Kz35BanqWMvbel8kTZrX9scAAACApBLJBW0eqP+qfl7/FY029mqy+31Ndr2vNb4LZTWGO9dYY0+L3/u7jsord1jFg1ZYsR6JwmVYP/9PnAlcUbNNXPWThv83FhGmdZT+4yWpcGz77B8AktCZM2f0la98RcuWLWvRXlZWprKyMm3YsEG/+93v9Ne//lXjxo1L2n3GSlWtdQEhKxACQPIhAyNztJwCQgBwAvIvMqfPBo4BO2V2kBHkc1AAQOw4IbMeffRRffvb39bZs2dbtO/du1d79+7VokWLNG/ePM2bl1jzGI8H+V6vBysQAkDM8e0SAEi6e9Jgrdt3LKyCwDS3S/dfN0yHT5/V3MVbQvbp4DJ06yV91MHj0hPrD8jrs58Vus8s0CU1f1APndQIV7FGGMUa7irW+77zbfuOcBVbtu/09bPt+5/u13WH5zXtMQu1z9db+8ze2mcW6B3fUPnkarHt8crAf7A3TiA9txCTlSkSWzJ/cBqMVQFhOK+9mMgbIs38m/TU9VLlZ6+FXiOl/3hZykieIkwAAAC0j0gvaOOTS++ZQ/Re/RD9t76iNAVeifFcvXRcha6ygPZ3fUW2fQuMUn3b87L2+Aq01yzUXl+BjqiLzM/GiMFWrAfay54j5Zbtj6zZr72lZ4Je5KjkRJU27j/e6nYZRkMRocstbXxE+sr/Sn0uad8HAQCSiNfr1c0336zly5dLknr27KnZs2dr2LBhOnHihBYvXqz169erpKREU6dO1fr16zV06NCk22csVdZ4Lduz0/mKFwCSCRkYOasVCHPSPepIBgJA0iD/IldusfpS58wOcTgSAEgtTsisZ599VnfccYckyeVyafr06Zo0aZI8Ho/Wr1+vp556SjU1NZo/f77S09N13333ter4Y6msosaynQJCAIg9PlkDAEkjC3O1cMYo24LANLdLC2eM0sjCXI0szNWLd2Zq4Yp9WrmntMWKEYYhTSzK09xmRXMX9+1ie//NlamLVvq6aKVGS9ZzBAIMtygg9JmGdpt9bPsOcX2iLsYZjTN2aZxrlyTprJmmYTV/Dm/nQbAyReJK9g9Og3G7XAFtNfUNr7uYTRINcV/KGyLNer2hiDCru/TVV6TM3HZ8BAAAAJBMIrmgzblqZT9xYIxrt2X7Jt8Q277DjQP6snuNmi9UWGN6dGPtz/zjzOYr1re4gEzvTpLFv82RGJywCv3yHYf1rcUfBL3d6iJHktQtO00nKmt17mVmImlvfK77CxTT/kM9r7haF+ZcqEK1z9gz1dp5LJLzMQLO9ac//cn/WeSwYcO0cuVK9ezZ03/7nDlz9L3vfU+//vWvdfLkSd1xxx1as2ZN0u0zliprgqxAmB7dStIAgPggAyN3xKKAkNUHASC5kH+RO21RQNgpgym+ANDWkj2zysrKNGfOHEkNxYNLlizRDTfc4L/9tttu06xZszRp0iRVVVXp/vvv1xe/+EUVFdlfcLY9BCsg7N4xrZ2PBACcj9EFAHxmyvBeERUESg2Fh4/PHKOSE1V6p/iEKqrrlJPRQeMGdFVBl6yw7z9WTpvZOmx2VS/jhL+t2MxXley/TBlqfBLQ9pF5nn9liWA66YyWpf2XSpWrUjNXpWYXlZq5etM3RsVmU7EgK1MknmT/4NTK1pJTerf4eED7yapaXfTAWzGZJHpRny6SpPc/ORnwPtE0gTRDGvGo0jrmanR1hgqzEm8Sn5MnKCZaO48RjwUTaQEAoYR7QZtoZRq1KjVzlWecatG+ybQvICwySgLa0o16HTVzLbdvfgGZd7rOk+nqoFfGPEcGJhCnrEK/teSU5i7eojpv5B+uHK+sbXV743M9oEDx9VW2Y8zPGXt1k3udHvZep++beRGPPZvfV2vGt8nSzmNh356Ij1FAkS1jT6HhYmYLFizw//7MM8+0+Cyy0YMPPqgVK1Zoy5YtWrt2rf7+97/r6quvTpp9xlplrfXVBbPS+IoXAJIFGRidQyerAtooIASA5EH+Rae8OrCAMM3DhfoAoC05IbMeeughlZeXS2qY89m8eLDRuHHj9MADD+i73/2u6uvrtWDBAj3//PNRHX+sHTvDCoQA0F74dgkAmomkILC5wq5ZYU0ECXb/nTM9+su7Ja0uLFxQ/zUtqP+auuu0hruKNdwo1lnZ/yM6TXUaaHwa0L7PLLDt29M4pX6uo+qnoy3aP6nNa1FAKIVYmaJZUSbahxM+OD3X8h2HQ066jtUk0fcOnrTc3noCaYW0rMR2kt13PH9VhZmlp30X6ftmvrplpyfMRMRg7Yk4QTHR2nmMmvBYNCH/AABW2vKCMy94J+gF75XqaxzVWNdujTV2q4+rVIfM7rZ9B7sOBbRVmuk6qZyQ/Wq9PlWXH9OnZjf99LUPJZGBicBJq9A/vGJfmxTcxoLdGPO7nhd1uXunvur5hw6Z3fVu7RC9YEzQO2bTYx1q7BnOPpzUzmNh356Ij1HQIlul3tiT/GuyZs0aHT58WJI0fvx4jR492nI7t9utuXPn6vbbb5ckLV68OOrPI+Oxz1jaWnJKSzYH/ntMkr79lw/07avOT/nnFQAkAzIwMltLTunhf+zVpxYrEH5UekZbS06RfwCQBMi/yG0tOaWSE4EF9JsOnNTtT27iswUAaCNOyKwXXnjB//N3vvOdoPudPXu25s2bp8rKSi1btkxnz55VZmZmVH9DrGwtOaVH1+y3vO0Xr+/SvdcMIf8AIIYoIAQAC+EWBMby/icPy49ZYeExddZq3yit1qiwth9s/EseI3Di3Ue+3rZ98wzrCUpnFHpg0XxlioUzRmnK8F4ht0dsJfsHp+dqXH0iGSeQnqdjutP9qtIMr36s51VmdtLm2vP1E31Nh9Ut7Ptp7/ZEnKCYaO08Rk14LJqQfwCAYNr2gjOGDpr5OujN14u6MuxeRUbghPVDZg9Jhm3fzqrULrOP/3cyMP6csgp9yYkqrdoTWJSUDC517dTl7p3+3wuMYypwr9N67/AWBYRWhhkHVC+3qpSuajNdZ5Wms0qXT1yBHMkl1cae5F+TN954w//z1KlTQ2577bXXWvZLhn3Git3F0lbtKdP6j46n/PMKAJIBGRg+u/w7fLpaNz+ygfwDgCRA/kWmMQPrvNZfAvDZAgC0nWTPrA8//FAHDx6UJA0dOlT9+/cPel85OTm64oortHz5clVWVurtt9/WlClTIj38mLEbA67/6LhuLmYMCACxRAEhACSQcAsLa+q8WvDqhzErlipXphbVf0FDjU800PWpehvHJUn7TPsCwp4KUkBohndlklqvT3MXb9GLd2ZypZB2lMwfnFpJ5NUn7MzxLFWa4fX/3sMo1yTXZn1Hd4Xs112ndZN7raqVphp1ULWZphqlabNvsMqUG7LveTqmHsYpmTI++6/BDrO/7CaDDzT+pXTV+X83ZeiMMlRiBq5g2ZxH9epvHGnRZko6ZnbWKZsVbDqpUt2N0zLPObZ/md1Vqw4h+/bQKWUZLa9S65X7s4nvoRUYpf5Hp3HfVWaGTqhTyH7pqlUP41RA+wmzk6qUEbJvJ51RJyPwqoL/MrvLtJkYnKeTSjPqWrTVmh1Uqi4h+xnyqbdxLKC93MxWubJD9s1UtboZFQHtZWZn1SgtZN9cVShbLc+NKUOfyn41pHwdl1s+fx9Jqlaa7bnpoHrlWeTWKXVUpU3he0dVqZMCz81RdZFX7pB9u+m0ziq9xfkn/wAAwYQaF/5+1Ud6YVNJxPc5tFeOdh0OzOxQDPnklUte05DbaJq0EM6/ozqoXllGjU6pY8BtZGB8OGkV+o37j8d0lc72Y+oez4uWt7xrFtn2fibtvwP+7b3Ee7m+UzcnZL/Rxl79T4c/yCuXTBnyySWfDN1fd7s2mUNC9r3N/aaud29oNnY0ZJqGbq37ke34ZIHnCZ1nHJc+G1WZMvShr68e9n4pZL9OOqMFHZ5qNlZt+Pf+Mu9lets30vZvvcm9zt+38Zj/WH+D7bjoBtc/NcT1iXzNxslnzEw96r0+ZD9Jut39htJU12K/e8xCrbE53h46pWvd70hqGteYMrTWN0IHzfyQfS8wijXcdcD/e+NLYon3CtXZfO1zpWuLuqii2T6lk8qxPV6P6nWVa3OL/UmGPjLP08c2nyH20CkNcx1scV4kaZtvgO24s69xRN3V8jOBerm1zRwYsp8kDTIOqYO8nx1zw/mpMLNsx51pqlOBUebv1/B/6bjZWRUKfdG5TqpUZ+NMwOck5J+0fft2/89jxowJuW1+fr4KCwtVUlKio0ePqqysTD162P8bJBH2GQvhXiyN5xUAJAcyMDzkHwA4C/kXPjIQAOIr2TMrkvtq3KbxgqPbt2+PWwEh+QcA8UEBIQAkAasJpMN7d9bCFftauQpFgxKzp/67/iv+37N1VgONT3XAZrKSJOVZFMlI9isQNlfr9Wnhin16fKb9AAaxkawfnFpJ5tUneqtMN7tXB7TvMvvYFpoVGGX6cYfnA9r/s/a7+ofvopB9Z3mWa7bn9YD2ftWB93euP3b4rc53/atF2xrvCN1W98OQ/brrtN5Kvzeg/YG6r+hx7xdC9p3iflf/t8NjAe2Ta/6v9pkFIfv+pMOT+oL73RZth82uurTm9yH7SdIbaT9UjnG2RdsL9Vfqvvqvh+x3gXFAL6f/JKB9Tu1c/c03LmTf2z3L9W3PywHtI6r/ZDtJ8ZG0/9Fo10ct2jb7Bmla7U9D9stWtdalfzug/bf10/Tb+i+H7DvBtUV/SFsY0D6t5ifabJ4fsu8PPIs13bO6RVuFmakRNY+H7CdJL6fP13nGiRZtf/OO1Zy6b4fs19c4on9YPA/vq5utF7wTQva9xb1a/9Xh2YD2S6t/F7Ba6Ln+p8Mf9IZvrBZ7J7VoJ/8AAJEo7Jqlb04YpL++VxLRGNAwpHsmn685z30Q0UU3TLk0pfZBpatWA4zDOt8oUV+jVJ+YebZ9O6tSknTatC4KIQPbn5NWoa+oro/3IUTl31zbNMa1N6D9iNlFJWG8rjIVuLLZWTP0RTskKcuoUV9X4Jg526huXgVmqY9RannMZp39KqSXuj4MGDtmqkbyBunQeLyq0U3u9QHtu3x99LZCF7gNcv1LX/X8I6B9sXeiSs3QBYRXuzfpus+K+RodMbuEVUD4Hc//Wo4d7QryCo1S/bTDUwHtd9XOtS0gnOx+33Ls+Lr3EtsCwrmely3HjmtqQx9vhmr1SNpvA9rDGTuOce2Oeux4l3uZboly7Ph02i+jHDsetfwM49662fqrzdjx392r9V3Pixpa82TAbamef3v27PH/HOoq2M23KSkp8feN5vPIeOwzFiK5WFqqP68AIBmQgeEh/wDAWci/8JGBABBfyZ5Z0dyXVd/2Rv4BQHxQQAgASWpkYa4enzkmYHXCzpke/eXdklYVFlYqM6yreEsNhThrvCOUZ5xSnnFSXY0zDfdhhi5+OtfKPaU6dLJKBV1CF8ggNpL1g1Mrybv6RODqg43e94WePCdJGUbg5FFJqrdZBUKSDLtZogDaDfkHAIhEYdcsTSjK08rd4V9AY2JRniYPy9fCGaPCuorjuWqUpl1mX+0y+4bdp14u/bl+it7xDQ26DRnYvpy0Cn1ORnJ+pF1s5usl7+f1Rdf6Fqt6vucrkt1K8JKpLKMmoLVa6bb7dcn6Ne+z3Weoo2pN3+j6hTOKDd43nOMN3EM4/YL3bVvRPr6t6RuPfTb0jf7RjP5vbbszmMr5d+rUKf/P3buHXgVSkrp1a7pYT/O+ibrPQ4cOhby9sZDfTjQXS0vl5xUAJAMnZyD5BwAIxsn5J5GBAOAkyZ5Z5B8AIBLJOdsCAOBntTrh5GH5LQoLDxyr1PPvfqI6b+wnvyz1fV5LfZ/3/56uWvUwTuuIukZ0P6Ypbdx/Ql++iH/gt4dkHDgGk6yrT0jSa75xGuj7VJe4drdo3xxOAaHF6hOS5AurgDB6yTbREEh05B8AIFJ3TxqsdfuOhVUImOZ2ae6kwZKkKcN76cU7M4OuZN89O03HK2tjUjJxSjn6af1tIbchA9uXk1ahHzegmwxDSXchmRKzp75bd5ce1Axd7tqhMa7dGuvao3d8Q2z7pqvOsv2s7FcgDF5A2LYXn4m2IC/4PqPvG23xYbh/fWuKD63Eo29rzo1ptubcRHu84Yn1uYmFVM6/M2fO+H/OyLC/+FxmZqb/54qKioTfZ2FhYUTbBxPNxdJS+XkFAMnAyRlI/gEAgnFy/klkIAA4SbJnFvkHAIgEBYQA4FDnFhZOG11gOVHUMBpWpLhlTKHKq+u1+0i5nlh/QF5fdJPEapSmQ2Z0Ewsrqq0nxCH2knHgGEyyrj4hSf/0Ddc/a4crTyd1kWuvLnbt1UWuvXovjALCYBNIvXFYgbB1k+9aMzEScAbyDwAQiZGFuWGtJpjmdmnhjFEaWZjboq/VSvbjBnRVQZesdrsQTSMysP205yr0bX0RmWhW4kwkpeqiJb4rtMR3xWct9q+xTAWuPihJZ81wViBsTVFd4Fa+MIrFgvVtVZFaHPqGvwKh1T7D6dea99fYFne2Zp+tKwy1Z903+udh64pRY1N8SP4hlGgvlsbzCgCQzMg/AECqIgMBAKmI/AOA+EneGfcAgIjYTRRt7uK+XWwno7aFnIwO7bo/OEOyrj7RXKm66A3fJXrDd0nYfdJbtQJhEj9YgAORfwCASIVaTbDxIjFzJw1uUTzYnNVK9lbtwS5EEytkYPtpz1Xo2/oiMlJkK3EmPvuCpLNK19zaOco0apWpGmWqVhlGjTaFsXrhcbOT3vKOlls+ueXzjwdPmTm2fT81u2mbr7+Mz8qmDJlhF1B9anaTx+dt6muYOq5Otv1MGTpu5rTYpyFTNbJ/v/DJpRrTE9A3HNZFgPG6WE5irkAYvG/0wio+tNgkPqWXsZOq+dexY0edPHlSklRdXa2OHTuG3P7s2bP+n3Ny7N+z4r3PxsL7YA4fPqyxY8fa3k+0F0tL1ecVACQDJ2cg+QcACMbJ+SeRgQDgJMmeWc37VldX2+6b/AOA1EYBIQCkmGATRZsLNRlVkrpnp+l4ZW1MJ9wYhjRuQNcY3iNCScYPToNJ9tUnovWmb4wuqf69MoxaZajhv3TVabdpP1n3Je8V+sA3yD+pMhK/qv935ehsi7aj6mLb77SydW/dbP/vjfvd4hto2/c93/n6cd3tAe1lZq5t3796J2ijb1iLtrOyX6VDkn5ZP0MeeVu07TN72/YrMXvoJ3W3BbTvNPva9l3tHaWTFhN5w5ks+6f6qephnG7RVmZ2tu1Xqw6Wx7vFN8i274dmX8u+JWGsRPuq71LtqWv5fK0Lc3jycP2XlK2WH3oVm/m2/Y6ZnfXTuq8GtH8Qxt+60TfUsm+5QmeqJD3nvUoHzJ6Wt5F/AIBoRXKRmFjto7Ur1jdHBrav9lyFvj2EuxKnU9QoTct8l0fVd6s5SLPrvhdV38e81+kx73VR9f1q3Y+i6vepuuuimkVR9X3BO0EveCdE1fdbdd+Sq87nLzqMZKz8bzW//axQsalw8azSbPvtMPvripr/8e+rsdgxnLHu0/VX61XvpZ/1a1Idxn7vqfuGf1XLxr+1KoxxcqUy9aWa+f7jbPxb/2XaFyVv9A3VbbX3+fs0MLXf7GXb96n6q7XC+7kWx1sfxsWTJOmndbcpQ7UyjKZi0hIzz7bfUbOLflT3f5r9rQ3/3+wbbNt3g+8C/aw++OOZyvmXm5vr/zzy2LFjtp9HHj9+vEXfRN9nQUFBxMdnJZqLpaXy8woAkoGTM5D8AwAE4+T8k8hAAHCSZM+s5r8fO3bMdt/kHwCkNgoIAQCW7CajntveOdOjv7xbEvXKFBOL8mI2yRX2kvGD01CctfpEeGqUpqPqGtWl83eYA7TDHBDVfv/uGxNVvypl6K9RTuT82Oytj732hXtW3vaNjKqf1FD0FY0yddGT3ilR9d1iDtIWr30xm5XXfeOi6lerDlEf7wGzl5702k/4tLLeN0LrNSKqvtFOCj6lHP3Ze21UfXea/bXT2z+qvm+GeN2QfwCA1grnIjGx3EesVqwnA52rrS8i08ju4kdAJMK9mImVY7K/cIuVGqWpJMiFRuwcV2cdD+OCMVb2m+dF1c8rt943i6LqW6YuKvPZX3zIynZzgLZH+RnG36IcJ59Sjp73Toqq706zn3Z6+wW9PZXzr6ioSMXFxZKk4uJi9evXL+T2jds29k2WfbZWNBdLS+XnFQAkAzLQHvkHAM5D/oWHDASA+Ev2zGr+e/Ptormv9kL+AUD8hHeZVgBAyirsmqUvX1SgWZf315cvKvD/I/zc9snD8vX4zDFa8/0JeujmkfrPK/rL7Qrveu1pbpfmTrK/gjdiJxkHjqE0rj6R5uafNgCSB/kHAEhGDUVbl2rSkDwZkS5n/RkysP01v2hMdXV1iC0btGYV+oKCgpD/9eoV3QUorDRe/Kjxs4j51w/TQzeP1GO3XRT0Odo9O81ydblI2wEgUqmefyNGNF1EaNOmTSG3PXr0qL8gPS8vTz169EiafcbC3ZMGh/05Z6o/rwAgGZCB4SH/AMBZyL/wkYEAEF/JnlmR3Ne52wwfPjys420L5B8AxAez7AEAMdVYWHj/F4bp/936Odt/5Ke5XVo4Y5RGFua2zwFCUvJ+cBqK3UTmWEwSNQxpTN8uGtOvS9STpQFAIv8AAMktWNHWz784nDFggmq+kvyxY8dst2/NKvTxYHeRo8bn6Lr7Jui9/5qsNfe2rr2tCxTtxp6xKoJMhnYeC/v2RH2M0BL5J02ZMsX/8xtvvBFy29dff93/89SpU5Nqn7EQ7sXSeF4BQHIgA8ND/gGAs5B/4SMDASC+kj2zhg0bpj59+kiSdu3apQMHDgS9rzNnzmjt2rWSpKysLI0fPz6Sw44p8g8A4sMT7wMAADhXQ0FXphau2KeVe0plmk23GUbDsuJzJw3mH/dxMGXKFP3qV7+S1DAIvffee4Num0gfnNppnMhccqJK7xSfUEV1nXIyOmjcgK4q6JIVs3ZJAbd1zvToL++WBDzXpYbJdMcra3VOc1K3G4Z0cZ8ukiG9d/BkSvzNkbbzGDXhsWhC/gEAnKSwa5YKu2a1aBveuzNjwARUVFTkX1m+uLhY/fr1C7l9oq9CHy6r52is2icPy4/L2DPW+0iWdh6L5HqM4vUZSSKOPcm/JuPHj1d+fr6OHDmi1atXa/PmzRo9enTAdl6vVwsXLvT/Pn369KTaZ6zw2ToAOAcZGD7yDwCcg/yLDBkIAPHjhMy65ZZb/HNBf/Ob37To09yjjz6qyspKSdINN9ygrKzA78TaE/kHAO3PMM1zv0JFvBw6dEiFhYWSpJKSEhUUFMT5iAAgdkJNbrLD+2Pseb1eFRQU6MiRI5Kk999/P+gg9OKLL9aWLVskScuXL9c111wT02Nx2vlNlMl6qThBMVHbeYx4LMg/BMM5BuBkZGBiuffee/1fHD744IMhLyJz9OhR5efnS2pYhf7o0aMxPRbOL5A6GHuSf+f64x//qLvuukuSdMEFF2jlypXKy8trsc33v/99PfTQQ5Kkyy+/XOvWrbO8ryeffFKzZs2S1DDhZvXq1W2+z9ZozfltzfMKABJdKuSflLoZSP4BgDXyr4kT808iAwEgmETNwGTPrNLSUg0aNEgVFRVyuVxasmSJbrjhhhbbvPPOO5o4caKqqqrk8Xi0fft2DRkyJNhDEhXyDwCsJVL+UUCYQBLpiQEAiYT3x7bhhA9OAcDJeH90Ps4xAFjj/TH2Vq5cqUmTJkmSrrzySq1atSrotk888YRuv/12SdLMmTP1xBNPxPRYOL8AYC0V3h/r6+s1depUvfXWW5Kk/Px8zZ49W8OGDdOJEye0ePFi/+ePubm5WrdunS644ALL+wp3Ik4s99kaqXB+ASAaqfL+mKoZmCrnFwAilSrvj6maf1LqnGMAiFSivj86IbOeeuopzZw5U5Lkcrk0ffp0TZ48WW63W+vXr9dTTz2l6upqSdLPf/5z/ehHP4rsQQpDop5fAIi3RHp/9MRtzwAAIK5mz56tJUuW6K233tLOnTs1cuTIkIPQRYsWxfmIAQAAAADRGD9+vPLz83XkyBGtXr1amzdvDroK/cKFC/2/T58+vT0PEwDgcB6PRy+99JJuvfVWvfbaazpy5IgeeOCBgO0KCgr0wgsvxGQSZzz2CQDAuchAAEAqIv8AAMnCCZn1ta99TVVVVbrnnntUXV2t559/Xs8//3yLbdxut3784x+3SfEgACA5uOJ9AAAAID4aB6HXXXedJPkHoTNmzNCcOXP8xYMFBQX629/+xgenAAAAAJCk3G635s2b5//9tttuU2lpacB2P/jBD7RlyxZJDavQX3PNNe11iACAFJGTk6NXX31Vr7zyiqZNm6bCwkKlp6ere/fuuuSSS/Tggw9qx44duuyyy5J6nwAAnIsMBACkIvIPAJAsnJBZ3/jGN7Rt2zbdc889GjZsmHJycpSdna3Bgwfrzjvv1KZNm7RgwYKYHT8AIPkYpmma8T4INEikpSkBIJHw/tj2li5dqqefflqbNm1SaWmpcnJyNHDgQE2bNk133HGHOnfu3Gb75vwCgDXeH52PcwwA1nh/bBv19fWaOnWq3nrrLUlSfn5+yFXo161b1yYXkuH8AoA13h+djfMLANZ4f3Q2zi8AWOP90fk4xwBgjfdHZ+P8AoC1RHp/9MRtzwAAIGHceOONuvHGG+N9GAAAAACANtK4Cv2tt96q1157zb8K/bkKCgr0wgsvsAo9AAAAAAAAAAAAAACAQ7jifQAAAAAAAAAAgLaXk5OjV199Va+88oqmTZumwsJCpaenq3v37rrkkkv04IMPaseOHbrsssvifagAAAAAAAAAAAAAAACIEVYgBAAAAAAAAIAUwir0AAAAAAAAAAAAAAAAqYMVCAEAAAAAAAAAAAAAAAAAAAAAAAAAcCAKCAEAAAAAAAAAAAAAAAAAAAAAAAAAcCAKCAEAAAAAAAAAAAAAAAAAAAAAAAAAcCAKCAEAAAAAAAAAAAAAAAAAAAAAAAAAcCBPvA8ATerr6/0/Hz58OI5HAgCJpfl7YvP3SjgD+QcA1sg/5yMDAcAaGehs5B8AWCP/nI38AwBr5J+zkX8AYI38cz4yEACskYHORv4BgLVEyj8KCBNIWVmZ/+exY8fG8UgAIHGVlZWpX79+8T4MxBD5BwD2yD9nIgMBwB4Z6DzkHwDYI/+ch/wDAHvkn/OQfwBgj/xzJjIQAOyRgc5D/gGAvXjnnytuewYAAAAAAAAAAAAAAAAAAAAAAAAAAG3GME3TjPdBoEF1dbW2b98uSerRo4c8nsAFIg8fPuyvyn/33XfVq1evdj1GtA/Os/NxjiNTX1/vvzrJiBEjlJGREecjQiyRf2jEeU4NnOfwkX/O15iBR48e1fXXXy+J10Uq4H0w9XDOI0cGOhtjQEic41TBeY4M+eds5B8acZ6dj3McGfLP2cg/NOI8pwbOc/jIP+ezy0BeL6mB85waOM+RIQOdjTEgJM5xquA8RyaR8i/wnRlxk5GRoTFjxoS9fa9evVRQUNCGR4REwHl2Ps5xeFiu3rnIP1jhPKcGzrM98s/ZGjPw0KFD/jZeF6mF8516OOfhIwOdizEgzsU5Tg2c5/CQf85F/sEK59n5OMfhIf+ci/yDFc5zauA82yP/nC2SDOT1kho4z6mB8xweMtC5GAPiXJzj1MB5Dk+i5J8r3gcAAAAAAAAAAAAAAAAAAAAAAAAAAABijwJCAAAAAAAAAAAAAAAAAAAAAAAAAAAciAJCAAAAAAAAAAAAAAAAAAAAAAAAAAAciAJCAAAAAAAAAAAAAAAAAAAAAAAAAAAciAJCAAAAAAAAAAAAAAAAAAAAAAAAAAAciAJCAAAAAAAAAAAAAAAAAAAAAAAAAAAciAJCAAAAAAAAAAAAAAAAAAAAAAAAAAAcyDBN04z3QQAAAAAAAAAAAAAAAAAAAAAAAAAAgNhiBUIAAAAAAAAAAAAAAAAAAAAAAAAAAByIAkIAAAAAAAAAAAAAAAAAAAAAAAAAAByIAkIAAAAAAAAAAAAAAAAAAAAAAAAAAByIAkIAAAAAAAAAAAAAAAAAAAAAAAAAAByIAkIAAAAAAAAAAAAAAAAAAAAAAAAAAByIAkIAAAAAAAAAAAAAAAAAAAAAAAAAAByIAkIAAAAAAAAAAAAAAAAAAAAAAAAAAByIAkIAAAAAAAAAAAAAAAAAAAAAAAAAAByIAkIAAAAAAAAAAAAAAAAAAAAAAAAAAByIAsIksWzZMt18883q16+fMjIylJeXp8suu0y/+tWvVF5eHu/Dg4WKigq99NJL+uY3v6nLLrtMPXr0UIcOHdSpUycNGTJEt912m5YvXy7TNMO+z48++kjf//73NXz4cHXu3FkdO3ZUUVGR5syZoy1btrTdH4OozJw5U4Zh+P/7yU9+ElY/zjPQhPxLTmQgyEAgPORcciPv0BzZB8QG2Zh8yEOQgUDrkX/JiQxMbeQf0HrkX3Ii/0AGAq1HBiYf8g/kH9B65F/yIf9A/jmUiYRWUVFh3nDDDaakoP8VFhaaGzZsiPehoplf//rXZkZGRsjz1vjfFVdcYR48eND2PhctWmRmZmYGvR+3220uWLCgHf46hOP1118POEfz58+37cd5BhqQf8mLDAQZCNgj55IfeYfmyD6g9cjG5EQeggwEWof8S15kYGoj/4DWIf+SF/kHMhBoHTIwOZF/IP+A1iH/khP5B/LPuTxCwvJ6vbr55pu1fPlySVLPnj01e/ZsDRs2TCdOnNDixYu1fv16lZSUaOrUqVq/fr2GDh0a56OGJO3du1fV1dWSpN69e+uqq67SRRddpLy8PFVXV2vjxo169tlndebMGa1du1ZXXnmlNm7cqLy8PMv7e/bZZ3XHHXdIklwul6ZPn65JkybJ4/Fo/fr1euqpp1RTU6P58+crPT1d9913X7v9rQhUXl7uP1/Z2dmqrKwMqx/nGWhA/iU3MjC1kYGAPXLOGcg7NCL7gNYjG5MXeZjayECgdci/5EYGpi7yD2gd8i+5kX+pjQwEWocMTF7kX2oj/4DWIf+SF/mX2sg/h4t3BSOCe+SRR/zVtsOGDTOPHDkSsM13v/vdFhXcSAx33nmnefXVV5t///vfTa/Xa7nNgQMHzKKiIv/5mzVrluV2paWlZqdOnUxJpsvlMpcuXRqwzYYNG8ysrCxTkunxeMzdu3fH9O9BZL7+9a/7r4pxzz33hFV5z3kGmpB/yY0MTG1kIGCPnHMG8g6NyD6g9cjG5EUepjYyEGgd8i+5kYGpi/wDWof8S27kX2ojA4HWIQOTF/mX2sg/oHXIv+RF/qU28s/ZKCBMUPX19WavXr38L7j3338/6HajRo3yb/fmm2+285HCyvHjx8PabsuWLf5zl5WVZVZWVgZsc++99/q3+da3vhX0vn7961/7t5sxY0bUx47WWbFihWkYhinJfPXVV8358+eHFZycZ6AB+Zf8yMDURQYC9sg55yDvYJpkHxALZGNyIw9TFxkItA75l/zIwNRE/gGtQ/4lP/IvdZGBQOuQgcmN/Etd5B/QOuRfciP/Uhf553wUECaolStX+l8c48ePD7ntn//8Z/+2M2fObJ8DRMw0r77funVrwO19+/b1375///6g91NeXm5mZ2ebkszs7GyzqqqqLQ8bFiorK80BAwaYksxbbrnFNE0z7ODkPAMNyL/UQgY6BxkIhIecS03knTORfUBskI2pgzx0DjIQaD3yL7WQgc5A/gGtR/6lFvLPOchAoPXIwNRB/jkH+Qe0HvmXOsg/5yD/UoNLSEhvvPGG/+epU6eG3Pbaa6+17Ifk0KlTJ//PZ8+ebXHbhx9+qIMHD0qShg4dqv79+we9n5ycHF1xxRWSpMrKSr399tttcLQI5Yc//KH279+vrl276uGHHw67H+cZaEL+pRYy0DnIQCA85FxqIu+ciewDYoNsTB3koXOQgUDrkX+phQx0BvIPaD3yL7WQf85BBgKtRwamDvLPOcg/oPXIv9RB/jkH+ZcaKCBMUNu3b/f/PGbMmJDb5ufnq7CwUJJ09OhRlZWVtemxIXZqa2u1d+9e/+99+/ZtcXskz4Nzt2neF23vn//8p37/+99Lkh566CH17Nkz7L6cZ6AJ+Zc6yEDnIAOB8JFzqYe8cyayD4gdsjE1kIfOQQYCsUH+pQ4y0BnIPyA2yL/UQf45BxkIxAYZmBrIP+cg/4DYIP9SA/nnHORf6qCAMEHt2bPH/3OoKlyrbZr3RWJ7/vnndfr0aUnS6NGjlZ+f3+J2ngfJobq6Wrfffrt8Pp8mTZqkWbNmRdSf8ww04fWQOshAZyADgcjwnE895J3zkH1AbPGaSA3koTOQgUDs8HpIHWRg8iP/gNjh9ZA6yD9nIAOB2OH1kBrIP2cg/4DY4fWQGsg/ZyD/UgsFhAnq1KlT/p+7d+9uu323bt0s+yJxlZWV6b777vP/fv/99wdsw/MgOcybN0979uxRZmamFi1aFHF/zjPQhNdDaiADnYMMBCLDcz61kHfORPYBscVrwvnIQ+cgA4HY4fWQGshAZyD/gNjh9ZAayD/nIAOB2OH14Hzkn3OQf0Ds8HpwPvLPOci/1EIBYYI6c+aM/+eMjAzb7TMzM/0/V1RUtMkxIXZqa2v1pS99SaWlpZKkL37xi7rpppsCtuN5kPg2bdqk3/zmN5KkBQsWaODAgRHfB+cZaMLrwfnIQOcgA4HI8ZxPHeSdM5F9QOzxmnA28tA5yEAgtng9OB8Z6AzkHxBbvB6cj/xzDjIQiC1eD85G/jkH+QfEFq8HZyP/nIP8Sz0UEALtzOfz6fbbb9fatWslSQMHDtSf//znOB8VolFbW6vbb79dXq9Xo0eP1j333BPvQwKAhEYGOgcZCADBkXfORPYBQGTIQ+cgAwEgMmSgM5B/ABAZ8s85yEAACB/55xzkHwCEj/xzDvIvNVFAmKA6duzo/7m6utp2+7Nnz/p/zsnJaZNjQuuZpqk777xTzz33nCSpT58++sc//qEuXbpYbs/zILH97Gc/044dO+R2u/XYY4/J7XZHdT+cZ6AJrwfnIgOdhQwEosNz3vnIO+ci+4C2wWvCmchDZyEDgdjj9eBcZKBzkH9A7PF6cC7yz1nIQCD2eD04E/nnLOQfEHu8HpyJ/HMW8i81UUCYoHJzc/0/Hzt2zHb748ePW/ZF4jBNU3fddZcee+wxSVJBQYFWrlypfv36Be3D8yBxbd26Vb/85S8lSffcc49Gjx4d9X1xnoEmvB6ciQx0FjIQiB7PeWcj75yL7APaDq8J5yEPnYUMBNoGrwdnIgOdg/wD2gavB2ci/5yFDATaBq8H5yH/nIX8A9oGrwfnIf+chfxLXZ54HwCsFRUVqbi4WJJUXFwc8s21cZvmfZFYTNPUnDlz9Mgjj0iSevfurVWrVmngwIEh+zU/l83PcTA8D9rPk08+qbq6OrlcLnXo0EE/+9nPLLdbs2ZNi58btysqKtLNN9/s/7kR5xmpjvxzHjLQechAIHrknHORd85G9gFth2x0FvLQechAoG2Qf85DBjoL+Qe0DfLPecg/5yEDgbZBBjoL+ec85B/QNsg/ZyH/nIf8S10UECaoESNGaPny5ZKkTZs2acKECUG3PXr0qEpKSiRJeXl56tGjR7scI8LTGJp//OMfJUnnnXeeVq1apUGDBtn2HTFihP/nTZs22W7ffJvhw4dHcbQIl2makiSfz6df/OIXYfVZtWqVVq1aJUm68cYb/cHJeQaakH/OQgY6ExkIRI+ccybyzvnIPqDtkI3OQR46ExkItA3yz1nIQOch/4C2Qf45C/nnTGQg0DbIQOcg/5yJ/APaBvnnHOSfM5F/qcsV7wOAtSlTpvh/fuONN0Ju+/rrr/t/njp1apsdEyJ3bmj26tVLq1at0uDBg8PqP2zYMPXp00eStGvXLh04cCDotmfOnNHatWslSVlZWRo/fnzrDh7thvMMNCH/nIMMRDg4z0g15JzzkHeIFOccaIlsdAbyEOHgPANNyD/nIANhh3MMNCH/nIP8Qzg4z0ATMtAZyD+Eg/MMNCH/nIH8Qzg4z8mFAsIENX78eOXn50uSVq9erc2bN1tu5/V6tXDhQv/v06dPb5fjQ3i++c1v+kMzPz9fq1at0vnnnx/Rfdxyyy3+n3/zm98E3e7RRx9VZWWlJOmGG25QVlZWFEeMcP32t7+VaZq2/82fP9/fZ/78+f72V155pcX9cZ6BBuSfc5CBzkUGAtEj55yHvEsNZB/QdshGZyAPnYsMBNoG+eccZKAzkX9A2yD/nIP8cy4yEGgbZKAzkH/ORf4BbYP8cwbyz7nIvxRmImH94Q9/MCWZkswLLrjAPHr0aMA23/ve9/zbXH755XE4SgTzzW9+039u8vPzzd27d0d1P0ePHjVzcnJMSabL5TKXLl0asM3GjRvNrKwsU5Lp8XjMXbt2tfbwESPz58/3Pw/mz58fdDvOM9CE/Et+ZCBMkwwEgiHnnIO8w7nIPiA6ZGNyIw9hmmQgEA3yL/mRgSD/gMiRf8mP/INpkoFANMjA5Eb+wTTJPyAa5F9yI/9gmuSfE3nsSwwRL7Nnz9aSJUv01ltvaefOnRo5cqRmz56tYcOG6cSJE1q8eLHWrVsnScrNzdWiRYvifMRodP/99+v3v/+9JMkwDN19993atWuXdu3aFbLf6NGj/Uu4NsrLy9Pvfvc7zZw5Uz6fTzfddJOmT5+uyZMny+12a/369XrqqadUXV0tSVqwYIGGDBnSNn8Y2gznGWhC/iU3MhCR4jwj1ZBzzkDeoTU450BLZGPyIg8RKc4z0IT8S25kICLBOQaakH/JjfxDpDjPQBMyMHmRf4gU5xloQv4lL/IPkeI8J5F4VzAitPLycvO6667zV+5a/VdQUGCuX78+3oeKZsaPHx/ynAX774knngh6n3/4wx/MjIyMoH3dbrc5b9689vsjEZZwK+8bcZ6BBuRf8iID0YgMBIIj55IfeQcrZB8QPbIxOZGHaEQGAtEh/5IXGQjTJP+AaJF/yYv8QyMyEIgOGZicyD80Iv+A6JB/yYn8QyPyz3lYgTDB5eTk6NVXX9XSpUv19NNPa9OmTSotLVVOTo4GDhyoadOm6Y477lDnzp3jfahoY9/4xjd01VVX6ZFHHtHy5ctVUlIin8+n8847T5MmTdLXv/51fe5zn4v3YaKVOM9AA/IPzfHemBo4z0gl5Bys8D6YejjnQBOyEY14b0wNnGegAfmH5nhvdD7OMdCA/ENzvDemBs4z0IAMRCPeF1MD5xloQP6hEe+LqYHznPgM0zTNeB8EAAAAAAAAAAAAAAAAAAAAAAAAAACILVe8DwAAAAAAAAAAAAAAAAAAAAAAAAAAAMQeBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADgQBYQAAAAAAAAAAAAAAAAAAAAAAAAAADjQ/wdzmh1XtyJFRQAAAABJRU5ErkJggg==", | |
| "text/plain": [ | |
| "<IPython.core.display.Image object>" | |
| ] | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "# Displaying the segmentation results image from the training output.\n", | |
| "Image(f\"{HOME}/runs/segment/train11/results.png\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "b5e0d830", | |
| "metadata": {}, | |
| "source": [ | |
| "## Load Best Model\n", | |
| "\n", | |
| "In this section, we will load the best-performing YOLO model from the training phase to perform inference on new images. By using the model weights saved during training, we can evaluate its ability to accurately detect and segment surface cracks in various test images. \n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "id": "c71339a3-8fec-4732-b1f4-41dde923ad03", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 726 | |
| }, | |
| "id": "c71339a3-8fec-4732-b1f4-41dde923ad03", | |
| "outputId": "7515c12c-888e-480a-f109-3ea6a6b16890" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "image 1/1 c:\\Lynxdrone\\python\\ultralytics\\test_images\\concrete-temperature-crack-07.jpg: 416x320 3 cracks, 50.4ms\n", | |
| "Speed: 1.3ms preprocess, 50.4ms inference, 4.2ms postprocess per image at shape (1, 3, 416, 320)\n", | |
| "Results saved to \u001b[1mruns\\segment\\predict20\u001b[0m\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Loading the best-trained model and performing inference on a test image.\n", | |
| "model = YOLO(f'{HOME}/runs/segment/train11/weights/best.pt') # Load the best model weights.\n", | |
| "file_image = \"concrete-temperature-crack-07\" # Specify the test image filename.\n", | |
| "results = model(f\"{HOME}/test_images/{file_image}.jpg\", save=True) # Run inference and save results.\n", | |
| "results[0].show() # Display the inference results." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "d0a6fc2f", | |
| "metadata": {}, | |
| "source": [ | |
| "## Deploy the Custom Weights\n", | |
| "\n", | |
| "Upload model weights for your custom-trained models to your Roboflow projects for model deployment.\n", | |
| "\n", | |
| "> **Note:** YOLOv11 models must be trained on `ultralytics>=8.3.0`\n", | |
| "\n", | |
| "After successfully training your model, use the `.deploy()` function to upload your model weights back to your Roboflow Object Detection project." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "0425b8f0", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "loading Roboflow workspace...\n", | |
| "loading Roboflow project...\n", | |
| "View the status of your deployment at: https://app.roboflow.com/hemant-ramphul-wfioe/surface-crack-segmentation-mnigz/1\n", | |
| "Share your model with the world at: https://universe.roboflow.com/hemant-ramphul-wfioe/surface-crack-segmentation-mnigz/model/1\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from roboflow import Roboflow # Import Roboflow library\n", | |
| "rf = Roboflow(api_key=\"YOUR_API_KEY_HERE\") # Initialize Roboflow with API key\n", | |
| "project = rf.workspace().project(\"surface-crack-segmentation-mnigz\") # Access the project\n", | |
| "\n", | |
| "# Can specify weights_filename, default is \"weights/best.pt\"\n", | |
| "version = project.version(VERSION_ID) # Get specified project version\n", | |
| "\n", | |
| "# Deploy YOLOv11 model with specified paths\n", | |
| "version.deploy(\n", | |
| " \"yolov11\", # Model name\n", | |
| " f'{HOME}/runs/segment/train11', # Trained directory path\n", | |
| " \"weights/best.pt\" # Weights file name\n", | |
| ")" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "accelerator": "GPU", | |
| "colab": { | |
| "gpuType": "T4", | |
| "provenance": [] | |
| }, | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.12.0" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment