{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.11"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":101849,"databundleVersionId":13093295,"sourceType":"competition"}],"dockerImageVersionId":31040,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true},"papermill":{"default_parameters":{},"duration":123.041003,"end_time":"2024-08-29T13:27:04.243126","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-08-29T13:25:01.202123","version":"2.5.0"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Calibrating and Binning Ariel Data","metadata":{"papermill":{"duration":0.013475,"end_time":"2024-08-29T13:25:05.231612","exception":false,"start_time":"2024-08-29T13:25:05.218137","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"**UPDATE**\nN/A","metadata":{"papermill":{"duration":0.012946,"end_time":"2024-08-29T13:25:05.25681","exception":false,"start_time":"2024-08-29T13:25:05.243864","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"# PLEAES READ BEFORE YOU PROCEED\n1. within the wall time. From experience, GPU acceleration, Parallelisation and other coding language will help. We encourage the kaggle community to share their calibration scripts to help speed up this process.  \n\n2. We have also only processed the 1st observation of every planet, not their repeats, so you might want to also take that into account. \n\n3. While these steps help reduce noise and data size, they may not be the most effective approach for achieving the optimal model for this challenge. Participants are encouraged to explore different combinations of these steps, or other approaches, to clean up the data.","metadata":{}},{"cell_type":"markdown","source":"This notebook follows largely from the [ADC2024 edition](https://www.kaggle.com/code/gordonyip/update-calibrating-and-binning-astronomical-data), with minor updates to reflect changes made. \n\nData reduction is crucial in astronomical observations. This notebook outlines essential calibration steps typically employed by astronomers to mitigate noise in data. We want to emphasis that these steps are NOT laws or rules you must take. Take them as a general recipe where you can always add/delete things. \n\nKey points:\n\n- The notebook guides participants through pre-processing data and saving it in a more convenient, lighter format.\n- Similar to last year, if you plan to use the baseline models (which will be released soon), you must run this notebook first before training.\n\n\n","metadata":{"papermill":{"duration":0.011873,"end_time":"2024-08-29T13:25:05.280847","exception":false,"start_time":"2024-08-29T13:25:05.268974","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"\n**Acknowledgement**: This notebook is modified by Gordon Yip from its 2024 version prepared by Angèle Syty and Virginie Batista (IAP), with support from Andrea Bocchieri (Sapienza Università di Roma\n), Orphée Faucoz (CNES), Lorenzo V. Mugnai (Cardiff University & UCL), Tara Tahseen (UCL) and the Kaggle community.","metadata":{"papermill":{"duration":0.011863,"end_time":"2024-08-29T13:25:05.305762","exception":false,"start_time":"2024-08-29T13:25:05.293899","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"Last modified: 27th Jun 2025. ","metadata":{"papermill":{"duration":0.011996,"end_time":"2024-08-29T13:25:05.330232","exception":false,"start_time":"2024-08-29T13:25:05.318236","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport itertools\nimport os\nimport glob \nfrom astropy.stats import sigma_clip\n\nfrom tqdm import tqdm","metadata":{"papermill":{"duration":1.619176,"end_time":"2024-08-29T13:25:06.963493","exception":false,"start_time":"2024-08-29T13:25:05.344317","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:28.807409Z","iopub.execute_input":"2025-08-12T20:11:28.807676Z","iopub.status.idle":"2025-08-12T20:11:30.885032Z","shell.execute_reply.started":"2025-08-12T20:11:28.807656Z","shell.execute_reply":"2025-08-12T20:11:30.884393Z"}},"outputs":[],"execution_count":1},{"cell_type":"markdown","source":"Below, we define the corrections we want to apply, the size of the data chunks and the different path used to import data and save the light ones. ","metadata":{"papermill":{"duration":0.012487,"end_time":"2024-08-29T13:25:06.988825","exception":false,"start_time":"2024-08-29T13:25:06.976338","status":"completed"},"tags":[]}},{"cell_type":"code","source":"\npath_folder = '/kaggle/input/ariel-data-challenge-2025/' # path to the folder containing the data\npath_out = '/kaggle/tmp/data_light_raw/' # path to the folder to store the light data\noutput_dir = '/kaggle/tmp/data_light_raw/' # path for the output directory","metadata":{"papermill":{"duration":0.021737,"end_time":"2024-08-29T13:25:07.023919","exception":false,"start_time":"2024-08-29T13:25:07.002182","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:30.886883Z","iopub.execute_input":"2025-08-12T20:11:30.887181Z","iopub.status.idle":"2025-08-12T20:11:30.891219Z","shell.execute_reply.started":"2025-08-12T20:11:30.887164Z","shell.execute_reply":"2025-08-12T20:11:30.890523Z"}},"outputs":[],"execution_count":2},{"cell_type":"markdown","source":"If the *path_out* folder doesn't exist yet, it is created. ","metadata":{"papermill":{"duration":0.012771,"end_time":"2024-08-29T13:25:07.049096","exception":false,"start_time":"2024-08-29T13:25:07.036325","status":"completed"},"tags":[]}},{"cell_type":"code","source":"if not os.path.exists(path_out):\n    os.makedirs(path_out)\n    print(f\"Directory {path_out} created.\")\nelse:\n    print(f\"Directory {path_out} already exists.\")\n","metadata":{"papermill":{"duration":0.024159,"end_time":"2024-08-29T13:25:07.08616","exception":false,"start_time":"2024-08-29T13:25:07.062001","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:30.891909Z","iopub.execute_input":"2025-08-12T20:11:30.892158Z","iopub.status.idle":"2025-08-12T20:11:30.926293Z","shell.execute_reply.started":"2025-08-12T20:11:30.892133Z","shell.execute_reply":"2025-08-12T20:11:30.925487Z"}},"outputs":[{"name":"stdout","text":"Directory /kaggle/tmp/data_light_raw/ created.\n","output_type":"stream"}],"execution_count":3},{"cell_type":"markdown","source":"**Data import:**\n\n The files are imported by chunks of size 'CHUNK_SIZE' to avoid exceeding the memory capacity. ","metadata":{"papermill":{"duration":0.012256,"end_time":"2024-08-29T13:25:07.112095","exception":false,"start_time":"2024-08-29T13:25:07.099839","status":"completed"},"tags":[]}},{"cell_type":"code","source":"CHUNKS_SIZE = 1","metadata":{"papermill":{"duration":0.021663,"end_time":"2024-08-29T13:25:07.146745","exception":false,"start_time":"2024-08-29T13:25:07.125082","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:30.927073Z","iopub.execute_input":"2025-08-12T20:11:30.927297Z","iopub.status.idle":"2025-08-12T20:11:30.938978Z","shell.execute_reply.started":"2025-08-12T20:11:30.927281Z","shell.execute_reply":"2025-08-12T20:11:30.938375Z"}},"outputs":[],"execution_count":4},{"cell_type":"markdown","source":"## Step 1: Analog-to-Digital Conversion\n\nThe Analog-to-Digital Conversion (adc) is performed by the detector to convert the pixel voltage into an integer number. We revert this operation by using the gain and offset for the calibration files 'train_adc_info.csv'.\n","metadata":{"execution":{"iopub.execute_input":"2024-08-02T14:38:41.270597Z","iopub.status.busy":"2024-08-02T14:38:41.270117Z","iopub.status.idle":"2024-08-02T14:38:41.303633Z","shell.execute_reply":"2024-08-02T14:38:41.30235Z","shell.execute_reply.started":"2024-08-02T14:38:41.270558Z"},"papermill":{"duration":0.01211,"end_time":"2024-08-29T13:25:07.171116","exception":false,"start_time":"2024-08-29T13:25:07.159006","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def ADC_convert(signal, gain=0.4369, offset=-1000):\n    \"\"\"The Analog-to-Digital Conversion (adc) is performed by the detector to convert\n    the pixel voltage into an integer number. Since we are using the same conversion number \n    this year, we have simply hard-coded it inside. \"\"\"\n    signal = signal.astype(np.float64)\n    signal /= gain\n    signal += offset\n    return signal","metadata":{"papermill":{"duration":0.023531,"end_time":"2024-08-29T13:25:07.207817","exception":false,"start_time":"2024-08-29T13:25:07.184286","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:30.939665Z","iopub.execute_input":"2025-08-12T20:11:30.939832Z","iopub.status.idle":"2025-08-12T20:11:30.953572Z","shell.execute_reply.started":"2025-08-12T20:11:30.939818Z","shell.execute_reply":"2025-08-12T20:11:30.952886Z"}},"outputs":[],"execution_count":5},{"cell_type":"markdown","source":"## Step 2: Mask hot/dead pixel\nThe dead pixels map is a map of the pixels that do not respond to light and, thus, can’t be accounted for any calculation. In all these frames the dead pixels are masked using python masked arrays. The bad pixels are thus masked but left uncorrected. Some methods can be used to correct bad-pixels but this task, if needed, is left to the participants.","metadata":{"papermill":{"duration":0.011774,"end_time":"2024-08-29T13:25:07.232416","exception":false,"start_time":"2024-08-29T13:25:07.220642","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def mask_hot_dead(signal, dead, dark):\n    hot = sigma_clip(\n        dark, sigma=5, maxiters=5\n    ).mask\n    hot = np.tile(hot, (signal.shape[0], 1, 1))\n    dead = np.tile(dead, (signal.shape[0], 1, 1))\n    signal = np.ma.masked_where(dead, signal)\n    signal = np.ma.masked_where(hot, signal)\n    return signal","metadata":{"papermill":{"duration":0.023597,"end_time":"2024-08-29T13:25:07.268543","exception":false,"start_time":"2024-08-29T13:25:07.244946","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:30.954359Z","iopub.execute_input":"2025-08-12T20:11:30.954637Z","iopub.status.idle":"2025-08-12T20:11:30.969054Z","shell.execute_reply.started":"2025-08-12T20:11:30.954615Z","shell.execute_reply":"2025-08-12T20:11:30.9683Z"}},"outputs":[],"execution_count":6},{"cell_type":"markdown","source":"## Step 2: linearity Correction","metadata":{"papermill":{"duration":0.013391,"end_time":"2024-08-29T13:25:07.29542","exception":false,"start_time":"2024-08-29T13:25:07.282029","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"\n\n**Non-linearity of pixels' response:**\n\nThe non-linearity of the pixels’ response can be explained as capacitive leakage on the readout electronics of each pixel during the integration time. The number of electrons in the well is proportional to the number of photons that hit the pixel, with a quantum efficiency coefficient. However, the response of the pixel is not linear with the number of electrons in the well. This effect can be described by a polynomial function of the number of electrons actually in the well. The data is provided with calibration files linear_corr.parquet that are the coefficients of the inverse polynomial function and can be used to correct this non-linearity effect.\n\n","metadata":{"papermill":{"duration":0.014964,"end_time":"2024-08-29T13:25:07.327644","exception":false,"start_time":"2024-08-29T13:25:07.31268","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def apply_linear_corr(linear_corr,clean_signal):\n    linear_corr = np.flip(linear_corr, axis=0)\n    for x, y in itertools.product(\n                range(clean_signal.shape[1]), range(clean_signal.shape[2])\n            ):\n        poli = np.poly1d(linear_corr[:, x, y])\n        clean_signal[:, x, y] = poli(clean_signal[:, x, y])\n    return clean_signal\n    ","metadata":{"papermill":{"duration":0.032924,"end_time":"2024-08-29T13:25:07.375474","exception":false,"start_time":"2024-08-29T13:25:07.34255","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:30.971513Z","iopub.execute_input":"2025-08-12T20:11:30.971963Z","iopub.status.idle":"2025-08-12T20:11:30.984348Z","shell.execute_reply.started":"2025-08-12T20:11:30.971941Z","shell.execute_reply":"2025-08-12T20:11:30.983563Z"}},"outputs":[],"execution_count":7},{"cell_type":"markdown","source":"## Step 3: dark current subtraction\n\nThe data provided include calibration for dark current estimation, which can be used to pre-process the observations. Dark current represents a constant signal that accumulates in each pixel during the integration time, independent of the incoming light. To obtain the corrected image, the following conventional approach is applied: The data provided include calibration files such as dark frames or dead pixels' maps. They can be used to pre-process the observations. The dark frame is a map of the detector response to a very short exposure time, to correct for the dark current of the detector.\n$$\\text{image - dark} \\times \\Delta t $$ \nThe corrected image is conventionally obtained via the following: where the dark current map is first corrected for the dead pixel.","metadata":{"papermill":{"duration":0.012643,"end_time":"2024-08-29T13:25:07.400609","exception":false,"start_time":"2024-08-29T13:25:07.387966","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def clean_dark(signal, dead, dark, dt):\n\n    dark = np.ma.masked_where(dead, dark)\n    dark = np.tile(dark, (signal.shape[0], 1, 1))\n\n    signal -= dark* dt[:, np.newaxis, np.newaxis]\n    return signal\n","metadata":{"papermill":{"duration":0.028125,"end_time":"2024-08-29T13:25:07.441764","exception":false,"start_time":"2024-08-29T13:25:07.413639","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:30.985179Z","iopub.execute_input":"2025-08-12T20:11:30.985483Z","iopub.status.idle":"2025-08-12T20:11:30.998985Z","shell.execute_reply.started":"2025-08-12T20:11:30.985457Z","shell.execute_reply":"2025-08-12T20:11:30.998373Z"}},"outputs":[],"execution_count":8},{"cell_type":"markdown","source":"## Step 4: Get Correlated Double Sampling (CDS)","metadata":{"papermill":{"duration":0.013727,"end_time":"2024-08-29T13:25:07.47284","exception":false,"start_time":"2024-08-29T13:25:07.459113","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"The science frames are alternating between the start of the exposure and the end of the exposure. The lecture scheme is a ramp with a double sampling, called Correlated Double Sampling (CDS), the detector is read twice, once at the start of the exposure and once at the end of the exposure. The final CDS is the difference (End of exposure) - (Start of exposure).","metadata":{"papermill":{"duration":0.017064,"end_time":"2024-08-29T13:25:07.505488","exception":false,"start_time":"2024-08-29T13:25:07.488424","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def get_cds(signal):\n    cds = signal[:,1::2,:,:] - signal[:,::2,:,:]\n    return cds","metadata":{"papermill":{"duration":0.022926,"end_time":"2024-08-29T13:25:07.541562","exception":false,"start_time":"2024-08-29T13:25:07.518636","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:30.999835Z","iopub.execute_input":"2025-08-12T20:11:31.000077Z","iopub.status.idle":"2025-08-12T20:11:31.01295Z","shell.execute_reply.started":"2025-08-12T20:11:31.000056Z","shell.execute_reply":"2025-08-12T20:11:31.012378Z"}},"outputs":[],"execution_count":9},{"cell_type":"markdown","source":"## Step 5 (Optional): Time Binning\nThis step is performed mianly to save space. Time series observations are binned together at specified frequency. \n\n","metadata":{"papermill":{"duration":0.012428,"end_time":"2024-08-29T13:25:07.566372","exception":false,"start_time":"2024-08-29T13:25:07.553944","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def bin_obs(cds_signal,binning):\n    cds_transposed = cds_signal.transpose(0,1,3,2)\n    cds_binned = np.zeros((cds_transposed.shape[0], cds_transposed.shape[1]//binning, cds_transposed.shape[2], cds_transposed.shape[3]))\n    for i in range(cds_transposed.shape[1]//binning):\n        cds_binned[:,i,:,:] = np.sum(cds_transposed[:,i*binning:(i+1)*binning,:,:], axis=1)\n    return cds_binned","metadata":{"papermill":{"duration":0.023997,"end_time":"2024-08-29T13:25:07.603229","exception":false,"start_time":"2024-08-29T13:25:07.579232","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:31.013575Z","iopub.execute_input":"2025-08-12T20:11:31.013789Z","iopub.status.idle":"2025-08-12T20:11:31.02697Z","shell.execute_reply.started":"2025-08-12T20:11:31.013775Z","shell.execute_reply":"2025-08-12T20:11:31.026299Z"}},"outputs":[],"execution_count":10},{"cell_type":"markdown","source":"## Step 6: Flat Field Correction\n","metadata":{"papermill":{"duration":0.015633,"end_time":"2024-08-29T13:25:07.634973","exception":false,"start_time":"2024-08-29T13:25:07.61934","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"The flat field is a map of the detector response to uniform illumination, to correct for the pixel-to-pixel variations of the detector, for example the different quantum efficiencies of each pixel.","metadata":{"papermill":{"duration":0.011976,"end_time":"2024-08-29T13:25:07.660972","exception":false,"start_time":"2024-08-29T13:25:07.648996","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def correct_flat_field(flat,dead, signal):\n    flat = flat.transpose(1, 0)\n    dead = dead.transpose(1, 0)\n    flat = np.ma.masked_where(dead, flat)\n    flat = np.tile(flat, (signal.shape[0], 1, 1))\n    signal = signal / flat\n    return signal","metadata":{"papermill":{"duration":0.025161,"end_time":"2024-08-29T13:25:07.698534","exception":false,"start_time":"2024-08-29T13:25:07.673373","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:31.027679Z","iopub.execute_input":"2025-08-12T20:11:31.028273Z","iopub.status.idle":"2025-08-12T20:11:31.045141Z","shell.execute_reply.started":"2025-08-12T20:11:31.028257Z","shell.execute_reply":"2025-08-12T20:11:31.04424Z"}},"outputs":[],"execution_count":11},{"cell_type":"markdown","source":"# Calibrating all training data","metadata":{"papermill":{"duration":0.012076,"end_time":"2024-08-29T13:25:07.726916","exception":false,"start_time":"2024-08-29T13:25:07.71484","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"you can choose to correct the non-linearity of the pixels' response, to apply flat field, dark and dead map or to leave the data unchanged. The observations are binned in time by group of 30 frames for AIRS and 360 frames for FGS1, to obtain a lighter data-cube, easier to use. The images are cut along the wavelength axis between pixels 39 and 321, so that the 282 pixels left in the wavelength dimension match the last 282 targets' points, from AIRS. The 283rd targets' point is the one for FGS1 that will be added later on. ","metadata":{"papermill":{"duration":0.013,"end_time":"2024-08-29T13:25:07.75262","exception":false,"start_time":"2024-08-29T13:25:07.73962","status":"completed"},"tags":[]}},{"cell_type":"code","source":"## we will start by getting the index of the training data:\ndef get_index(files,CHUNKS_SIZE ):\n    index = []\n    for file in files:\n        file_name = file.split('/')[-1]\n        if file_name.split('_')[0] == 'AIRS-CH0' and file_name.split('_')[1] == 'signal' and file_name.split('_')[2] == '0.parquet':\n            file_index = os.path.basename(os.path.dirname(file))\n            index.append(int(file_index))\n    index = np.array(index)\n    index = np.sort(index) \n    # credit to DennisSakva\n    index=np.array_split(index, len(index)//CHUNKS_SIZE)\n    \n    return index","metadata":{"papermill":{"duration":0.024197,"end_time":"2024-08-29T13:25:07.789483","exception":false,"start_time":"2024-08-29T13:25:07.765286","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:31.046069Z","iopub.execute_input":"2025-08-12T20:11:31.046366Z","iopub.status.idle":"2025-08-12T20:11:31.059206Z","shell.execute_reply.started":"2025-08-12T20:11:31.046312Z","shell.execute_reply":"2025-08-12T20:11:31.058565Z"}},"outputs":[],"execution_count":12},{"cell_type":"code","source":"files = glob.glob(os.path.join(path_folder + 'train/', '*/*'))\n\nindex = get_index(files[:300],CHUNKS_SIZE)  ## 22 is hardcoded here but please feel free to remove it if you want to do it for the entire dataset\n\naxis_info = pd.read_parquet(os.path.join(path_folder,'axis_info.parquet'))\nDO_MASK = True\nDO_THE_NL_CORR = False\nDO_DARK = True\nDO_FLAT = True\nTIME_BINNING = True\n\ncut_inf, cut_sup = 39, 321\nl = cut_sup - cut_inf\n\nfor n, index_chunk in enumerate(tqdm(index)):\n    AIRS_CH0_clean = np.zeros((CHUNKS_SIZE, 11250, 32, l))\n    FGS1_clean = np.zeros((CHUNKS_SIZE, 135000, 32, 32))\n    \n    for i in range (CHUNKS_SIZE) : \n        df = pd.read_parquet(os.path.join(path_folder,f'train/{index_chunk[i]}/AIRS-CH0_signal_0.parquet'))\n        signal = df.values.astype(np.float64).reshape((df.shape[0], 32, 356))\n\n        signal = ADC_convert(signal,)\n        dt_airs = axis_info['AIRS-CH0-integration_time'].dropna().values\n        dt_airs[1::2] += 0.1\n        chopped_signal = signal[:, :, cut_inf:cut_sup]\n        del signal, df\n        \n        # CLEANING THE DATA: AIRS\n        flat = pd.read_parquet(os.path.join(path_folder,f'train/{index_chunk[i]}/AIRS-CH0_calibration_0/flat.parquet')).values.astype(np.float64).reshape((32, 356))[:, cut_inf:cut_sup]\n        dark = pd.read_parquet(os.path.join(path_folder,f'train/{index_chunk[i]}/AIRS-CH0_calibration_0/dark.parquet')).values.astype(np.float64).reshape((32, 356))[:, cut_inf:cut_sup]\n        dead_airs = pd.read_parquet(os.path.join(path_folder,f'train/{index_chunk[i]}/AIRS-CH0_calibration_0/dead.parquet')).values.astype(np.float64).reshape((32, 356))[:, cut_inf:cut_sup]\n        linear_corr = pd.read_parquet(os.path.join(path_folder,f'train/{index_chunk[i]}/AIRS-CH0_calibration_0/linear_corr.parquet')).values.astype(np.float64).reshape((6, 32, 356))[:, :, cut_inf:cut_sup]\n        \n        if DO_MASK:\n            chopped_signal = mask_hot_dead(chopped_signal, dead_airs, dark)\n            AIRS_CH0_clean[i] = chopped_signal\n        else:\n            AIRS_CH0_clean[i] = chopped_signal\n            \n        if DO_THE_NL_CORR: \n            linear_corr_signal = apply_linear_corr(linear_corr,AIRS_CH0_clean[i])\n            AIRS_CH0_clean[i,:, :, :] = linear_corr_signal\n        del linear_corr\n        \n        if DO_DARK: \n            cleaned_signal = clean_dark(AIRS_CH0_clean[i], dead_airs, dark, dt_airs)\n            AIRS_CH0_clean[i] = cleaned_signal\n        else: \n            pass\n        del dark\n        \n        df = pd.read_parquet(os.path.join(path_folder,f'train/{index_chunk[i]}/FGS1_signal_0.parquet'))\n        fgs_signal = df.values.astype(np.float64).reshape((df.shape[0], 32, 32))\n\n        \n        fgs_signal = ADC_convert(fgs_signal, )\n        dt_fgs1 = np.ones(len(fgs_signal))*0.1\n        dt_fgs1[1::2] += 0.1\n        chopped_FGS1 = fgs_signal\n        del fgs_signal, df\n        \n        # CLEANING THE DATA: FGS1\n        flat = pd.read_parquet(os.path.join(path_folder,f'train/{index_chunk[i]}/FGS1_calibration_0/flat.parquet')).values.astype(np.float64).reshape((32, 32))\n        dark = pd.read_parquet(os.path.join(path_folder,f'train/{index_chunk[i]}/FGS1_calibration_0/dark.parquet')).values.astype(np.float64).reshape((32, 32))\n        dead_fgs1 = pd.read_parquet(os.path.join(path_folder,f'train/{index_chunk[i]}/FGS1_calibration_0/dead.parquet')).values.astype(np.float64).reshape((32, 32))\n        linear_corr = pd.read_parquet(os.path.join(path_folder,f'train/{index_chunk[i]}/FGS1_calibration_0/linear_corr.parquet')).values.astype(np.float64).reshape((6, 32, 32))\n        \n        if DO_MASK:\n            chopped_FGS1 = mask_hot_dead(chopped_FGS1, dead_fgs1, dark)\n            FGS1_clean[i] = chopped_FGS1\n        else:\n            FGS1_clean[i] = chopped_FGS1\n\n        if DO_THE_NL_CORR: \n            linear_corr_signal = apply_linear_corr(linear_corr,FGS1_clean[i])\n            FGS1_clean[i,:, :, :] = linear_corr_signal\n        del linear_corr\n        \n        if DO_DARK: \n            cleaned_signal = clean_dark(FGS1_clean[i], dead_fgs1, dark,dt_fgs1)\n            FGS1_clean[i] = cleaned_signal\n        else: \n            pass\n        del dark\n        \n    # SAVE DATA AND FREE SPACE\n    AIRS_cds = get_cds(AIRS_CH0_clean)\n    FGS1_cds = get_cds(FGS1_clean)\n    \n    del AIRS_CH0_clean, FGS1_clean\n    \n    ## (Optional) Time Binning to reduce space\n    if TIME_BINNING:\n        AIRS_cds_binned = bin_obs(AIRS_cds,binning=30)\n        FGS1_cds_binned = bin_obs(FGS1_cds,binning=30*12)\n    else:\n        AIRS_cds = AIRS_cds.transpose(0,1,3,2) ## this is important to make it consistent for flat fielding, but you can always change it\n        AIRS_cds_binned = AIRS_cds\n        FGS1_cds = FGS1_cds.transpose(0,1,3,2)\n        FGS1_cds_binned = FGS1_cds\n    \n    del AIRS_cds, FGS1_cds\n    \n    for i in range (CHUNKS_SIZE):\n        flat_airs = pd.read_parquet(os.path.join(path_folder,f'train/{index_chunk[i]}/AIRS-CH0_calibration_0/flat.parquet')).values.astype(np.float64).reshape((32, 356))[:, cut_inf:cut_sup]\n        flat_fgs = pd.read_parquet(os.path.join(path_folder,f'train/{index_chunk[i]}/FGS1_calibration_0/flat.parquet')).values.astype(np.float64).reshape((32, 32))\n        if DO_FLAT:\n            corrected_AIRS_cds_binned = correct_flat_field(flat_airs,dead_airs, AIRS_cds_binned[i])\n            AIRS_cds_binned[i] = corrected_AIRS_cds_binned\n            corrected_FGS1_cds_binned = correct_flat_field(flat_fgs,dead_fgs1, FGS1_cds_binned[i])\n            FGS1_cds_binned[i] = corrected_FGS1_cds_binned\n        else:\n            pass\n\n    ## save data\n    np.save(os.path.join(path_out, 'AIRS_clean_train_{}.npy'.format(n)), AIRS_cds_binned)\n    np.save(os.path.join(path_out, 'FGS1_train_{}.npy'.format(n)), FGS1_cds_binned)\n    del AIRS_cds_binned\n    del FGS1_cds_binned","metadata":{"papermill":{"duration":114.333225,"end_time":"2024-08-29T13:27:02.134952","exception":false,"start_time":"2024-08-29T13:25:07.801727","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:11:31.05997Z","iopub.execute_input":"2025-08-12T20:11:31.060156Z","iopub.status.idle":"2025-08-12T20:26:17.654334Z","shell.execute_reply.started":"2025-08-12T20:11:31.060141Z","shell.execute_reply":"2025-08-12T20:26:17.653615Z"}},"outputs":[{"name":"stderr","text":"100%|██████████| 62/62 [14:39<00:00, 14.19s/it]\n","output_type":"stream"}],"execution_count":13},{"cell_type":"markdown","source":"Once all the chunks are saved, we concatenate them back in a single dataset. This step is simply to save HDD space, modify it as you wish. ","metadata":{"papermill":{"duration":0.014271,"end_time":"2024-08-29T13:27:02.162064","exception":false,"start_time":"2024-08-29T13:27:02.147793","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def load_data (file, chunk_size, nb_files) : \n    data0 = np.load(file + '_0.npy')\n    data_all = np.zeros((nb_files*chunk_size, data0.shape[1], data0.shape[2], data0.shape[3]))\n    data_all[:chunk_size] = data0\n    for i in range (1, nb_files) : \n        data_all[i*chunk_size:(i+1)*chunk_size] = np.load(file + '_{}.npy'.format(i))\n    return data_all \n\ndata_train = load_data(path_out + 'AIRS_clean_train', CHUNKS_SIZE, len(index)) \ndata_train_FGS = load_data(path_out + 'FGS1_train', CHUNKS_SIZE, len(index)) \n","metadata":{"papermill":{"duration":0.134081,"end_time":"2024-08-29T13:27:02.309386","exception":false,"start_time":"2024-08-29T13:27:02.175305","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:27:00.212543Z","iopub.execute_input":"2025-08-12T20:27:00.212852Z","iopub.status.idle":"2025-08-12T20:27:01.327302Z","shell.execute_reply.started":"2025-08-12T20:27:00.212834Z","shell.execute_reply":"2025-08-12T20:27:01.326537Z"}},"outputs":[],"execution_count":19},{"cell_type":"code","source":"np.save('./' + 'data_train.npy', data_train)\nnp.save('./' + 'data_train_FGS.npy', data_train_FGS)","metadata":{"papermill":{"duration":0.101539,"end_time":"2024-08-29T13:27:02.425117","exception":false,"start_time":"2024-08-29T13:27:02.323578","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:27:04.261252Z","iopub.execute_input":"2025-08-12T20:27:04.261571Z","iopub.status.idle":"2025-08-12T20:27:05.021789Z","shell.execute_reply.started":"2025-08-12T20:27:04.26155Z","shell.execute_reply":"2025-08-12T20:27:05.021184Z"}},"outputs":[],"execution_count":20},{"cell_type":"markdown","source":"# Plots","metadata":{"papermill":{"duration":0.014312,"end_time":"2024-08-29T13:27:02.452873","exception":false,"start_time":"2024-08-29T13:27:02.438561","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"Let us checks that everything went well during the data import. ","metadata":{"papermill":{"duration":0.012982,"end_time":"2024-08-29T13:27:02.479542","exception":false,"start_time":"2024-08-29T13:27:02.46656","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import matplotlib.pyplot as plt \n\nprint('Shape of the training datasset: \\t')\nprint('\\n For AIRS-CH0:', data_train.shape)\nprint('\\n For FGS1:', data_train_FGS.shape)\n\n","metadata":{"papermill":{"duration":0.02921,"end_time":"2024-08-29T13:27:02.521934","exception":false,"start_time":"2024-08-29T13:27:02.492724","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:27:08.406262Z","iopub.execute_input":"2025-08-12T20:27:08.406587Z","iopub.status.idle":"2025-08-12T20:27:08.411043Z","shell.execute_reply.started":"2025-08-12T20:27:08.406566Z","shell.execute_reply":"2025-08-12T20:27:08.410255Z"}},"outputs":[{"name":"stdout","text":"Shape of the training datasset: \t\n\n For AIRS-CH0: (62, 187, 282, 32)\n\n For FGS1: (62, 187, 32, 32)\n","output_type":"stream"}],"execution_count":21},{"cell_type":"markdown","source":"Plot of some images: ","metadata":{"papermill":{"duration":0.0146,"end_time":"2024-08-29T13:27:02.553564","exception":false,"start_time":"2024-08-29T13:27:02.538964","status":"completed"},"tags":[]}},{"cell_type":"code","source":"plt.imshow(data_train_FGS[-1,50,:,:].T, aspect = 'auto')","metadata":{"papermill":{"duration":0.41396,"end_time":"2024-08-29T13:27:02.981707","exception":false,"start_time":"2024-08-29T13:27:02.567747","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:26:19.253997Z","iopub.execute_input":"2025-08-12T20:26:19.254181Z","iopub.status.idle":"2025-08-12T20:26:19.474943Z","shell.execute_reply.started":"2025-08-12T20:26:19.254166Z","shell.execute_reply":"2025-08-12T20:26:19.474372Z"}},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x79f44ed38490>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":17},{"cell_type":"markdown","source":"Plot of some light-curves: ","metadata":{"papermill":{"duration":0.083722,"end_time":"2024-08-29T13:27:03.079452","exception":false,"start_time":"2024-08-29T13:27:02.99573","status":"completed"},"tags":[]}},{"cell_type":"code","source":"\nfor i in range(len(data_train)) : \n    light_curve = data_train[i,:,:,:].sum(axis=(1,2))\n    plt.plot(light_curve/light_curve.mean(), '-', alpha=0.3)\n\nplt.xlabel('Time (frame index)')\nplt.ylabel('Normalized flux in the frame')","metadata":{"papermill":{"duration":0.375806,"end_time":"2024-08-29T13:27:03.469794","exception":false,"start_time":"2024-08-29T13:27:03.093988","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-08-12T20:26:19.475667Z","iopub.execute_input":"2025-08-12T20:26:19.475906Z","iopub.status.idle":"2025-08-12T20:26:19.80522Z","shell.execute_reply.started":"2025-08-12T20:26:19.475879Z","shell.execute_reply":"2025-08-12T20:26:19.804651Z"}},"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Normalized flux in the frame')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAkAAAAGwCAYAAABB4NqyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9ebBl613Xj7+e51nTns/cp/t29x2TmwESE1CJVFkJBURENPoXojJbpCoqSf4IlVKx+CtaFCrIJEVpilLESBn0R5BUvlAhpghIAjckZLhT39vT6TPucc3P8Ptj7b3POd2nh9O3x3vXq2pX91l77bWftfYa3s9nFM45R01NTU1NTU3Nawh5vwdQU1NTU1NTU3OvqQVQTU1NTU1NzWuOWgDV1NTU1NTUvOaoBVBNTU1NTU3Na45aANXU1NTU1NS85qgFUE1NTU1NTc1rjloA1dTU1NTU1Lzm8O73AB5ErLVcvnyZTqeDEOJ+D6empqampqbmFnDOMR6POXXqFFLe2MZTC6AjuHz5MmfOnLnfw6ipqampqam5DS5cuMDp06dvuE4tgI6g0+kA1QHsdrv3eTQ1NTU1NTU1t8JoNOLMmTPz5/iNqAXQEczcXt1utxZANTU1NTU1Dxm3Er5SB0HX1NTU1NTUvOaoBVBNTU1NTU3Na45aANXU1NTU1NS85rivAugzn/kM3/M938OpU6cQQvBbv/VbN/3Mpz/9ad7+9rcThiFPPfUUH/3oRw+9/0u/9Eu85S1vmcfvvOMd7+D//J//c3d2oKampqampuah5L4KoDiOeetb38ov/MIv3NL6586d47u/+7t517vexTPPPMP73/9+fvRHf5RPfvKT83VOnz7Nv/7X/5ovfOELfP7zn+fbvu3b+Dt/5+/wF3/xF3drN2pqampqamoeMoRzzt3vQUAVsf3xj3+c97znPddd5yd+4if4xCc+wZe//OX5su/93u9lMBjwu7/7u9f93NLSEj/90z/Nj/zIjxz5fp7n5Hk+/3uWRjccDusssJqampqamoeE0WhEr9e7pef3QxUD9LnPfY5v//ZvP7Ts3e9+N5/73OeOXN8Yw2/8xm8QxzHveMc7rrvdj3zkI/R6vfmrLoJYU1NTU1Pz6uahEkBXrlzhxIkTh5adOHGC0WhEmqbzZV/60pdot9uEYch73/tePv7xj/OmN73putv98Ic/zHA4nL8uXLhw1/ahpqampqam5v7zqiyE+PTTT/PMM88wHA75zd/8TX7gB36AP/iDP7iuCArDkDAM7/Eoa2pqampqau4XD5UAWl9fZ3Nz89Cyzc1Nut0ujUZjviwIAp566ikAvumbvok/+ZM/4Wd/9mf5j//xP97T8dbU1NTU1NQ8mDxULrB3vOMd/N7v/d6hZZ/61KduGN8DVXf3g0HONTU1NTU1Na9t7qsFaDKZ8Pzzz8//PnfuHM888wxLS0ucPXuWD3/4w1y6dIlf+7VfA+C9730vP//zP8+HPvQhfviHf5jf//3f52Mf+xif+MQn5tv48Ic/zHd913dx9uxZxuMxv/7rv86nP/3pQ6nyNTU1NTU1Na9t7qsA+vznP8+73vWu+d8f/OAHAfiBH/gBPvrRj7KxscH58+fn7z/++ON84hOf4AMf+AA/+7M/y+nTp/nVX/1V3v3ud8/X2dra4vu///vZ2Nig1+vxlre8hU9+8pN8x3d8x73bsfuAsw4hb978raampqampuYBqgP0IHGcOgIPAnqQUV6O8ddbeEvR/R5OTU1NTU3NfeFVWweo5lqcsejNBIByK8GV9j6PqKampqam5sGnFkD3CWcdzrxysaJ3UpyZGvGso9xKXvE2a2pqampqXu3UAug+YFNN/sKA7LkBejfldr2QtjDofpXd5q81ATDDHJuUd2ysNTU1NTU1r0YeqjpArwZ0P6PcTMBWoqfcTDCTEhkqnK4sQsKToAQ4wDpcabGFAesQoUIGCmcdNtVgHbLt4600sKXF9DPyC2NkqBCBQvVCVMu/j3tcU1NTU1Pz4FELoHtIuZOipy4q1QmQLZ9yK8HGJTa+NauNKy2WA+uKfeuPv9rAxiWuMNhEQ6IxgxwZeXhrDVQ7uOP7VFNTU1NT8zBSC6B7iOoGmL0MbynCW6kqV8uWjxlkIERl+QGctjhtq7R2WS0XgURIgS0MLjfVcl8iIw8ZVT+j8CThEz1cYXCFxcRl5RLLNMWFcZUltlhnidXU1NTU1NQC6B4iA0X4ZA+h9kOvZKiQJ1q3vo3mjd1ZQgpE5EFUCS632qDcSjCDnHIjxmmLv9q87X2oqampqal5NVALoHuIM67K2LIWRPW3TTWuMKhugIw8nHXorQQzzFGLlaXoFRU4dA7V8quYoVGB3k4BHhgR5EqL8OtY/Jqampqae0stgO4hNi4oLk6OfE/vpniLETbVVXAzVYq7GeWozjR2Rwhk5CFCiY01ZpTjCguqcpPJhods+WAdNi4xcVm5y6Y4Y3GlpdxMEJ68xh12O9WknXXovQycw1s+nlgrtxL0TorqBviPtBGirmRdU1NTU3NvqAXQPUYogZtmdyEFMlIIJTHjohIS03XUcgPTz3CFRe9mN96oBpebKpB6J73qCys3m80NQknspERvV8HY3mqjcqnZmWXKIXw5zzTDk1X8kVfFG3GVQHG5odyMKxEG2EQTPNKexzLdcMj9DD0dqxkVIGOCU+1bO4g1NTU1NTWvkFoA3UNUN0R1Q4B57Z+Z1cPEZVXRWYJ/qo0MFN5ihBlkVXq8EFVwdGawhUGGVYq7bHqVa6002FhXNYAEyFaAavnIllcJn8KgdzOEJyt32KSk3IhRi9GhNHlX2mszzW6C8KfbjEvyc8N5UPb+Chy2TqV6XrBRdQLMpMAMcgoh8BZCRKSqEgCm2m+UqK1DNTU1NTV3lFoA3SeufqCrlo96ond4HSXwlhu3uEUfFq7/rgwUwckWnGwRPN6lOD/GDKsiiqoXVKn0QlQp9LmphNA0G82VFoy7pmCjEALVDfBWmzhtKC6Mq+yzsrjm+83o2mVqISQ41a5qI23EmH6G6WcgxbxOUvVFIHyF6gWVQPLVLR6Tmpqampqao6kF0GsQ6SuiJxfmMThmWOAtNyp3nCdvmml2FEJ5hI/3MOOyEi8zfSfAaYdNSlyqq/T9QCGb3lzceYtR5QYc5pikhFlrj5kQcuAKU7nutlP8E81jCMOampqampprqQXQaxh/rYkrDGZUoHczgkdeWQyOUBJvIbzOuzcWLKobVGn7rqp8LZRAqMq1hqkElB7k2Lik3E5RvfCWYo1qampqamqOon6CvMaZFWQ0o7xqt3GfEUIgAzWvlSSmBR9VLyR8tItsemAdeje9yZZqampqamquTy2AXuPIyEO2fXBg9m6SbfYAMBNsup/Pe6fV1NTU1NQcl1oA1eAtVfWA9ODBFxWqHSAbUyvQQyDYampqamoeTGoBVFOJimgqKvoPvqjYtwJlOPNgC7aampqamgeTWgDVAOCtVFYg08+rwOMHGNUJEIGqgqPjW69XVFNTU1NTM6MWQDUAyE6ACCROW8wgv9/DuSmqU6Xqm0ktgGpqampqjk8tgGqAKvvKW5q6lvbSa4oePmjIafXq2gJUU1NTU3M71AKoZo5aqGrruMJij6jc/CAhmz5IUbXtyO9/+n5NTU1NzcNFLYBq5ggpUItVIUO9+2BbgYQUVTYYtRWopqbmtYEz9+ee7KzDDHP03itPPHGuaqvkrLvv8aZ1JeiaQ3iLEXovw2YGOy7mzVsfRFTLx8ZlJYCmqfw1NTWvXpxzmFGBUNUEaFYwFajiFydFVUy1EyDkw9FA2RmH3kvBTdsC+RJnLGZSVhO9aZ02vZ2g9zJk5OGfaiHDV/74dtpW99BUV+2LlKyOmxIIsd8c24yKeYkUvZPirTVQrQBktY5NNDbT1Wenx91pu9/KyDmY9pU8KOJUL3zFHQheCbUAqjmE8CTeUoTeTim3UmT7wb2RyLYPW2DiEmfdAzvOmpqaO4Pp55RX4vnfIpDz5sg2KWH6bBWerESQmFpNZtYGM/3XuaoIbMev3P6ZwRYGoaoHeCUCJOCwqcHlutpmy68szwJAIDyJUKKyZmR6KiT2RQCu6osoQw8RTps4m6kIEOByS7kZVw2noRI4TQ+b6HlDaOHJSmgU1To21eTnRqhuUI0Xqv6K0+3b3OByg800LjegJLLpIQKFSzU21zA14hyn7pvw5bxhdnk5piS++YcecGoBVHMN3lKjSocvDGaY4y0+mNYVEVbNW5222FSjWsdv4lpTU/PwYAZVnbLZde8KOxcGUFW2d2ZqtbhJTTNTFpjx8WIdzRGxkcLf71l4QwRzgXbNW1MhZ+MSO81sFWFV6mMmUoQv8deamFE17lvO1jUGc4M2RzJS86SSQ2LRukrg+VWD7HnHgH42dYVV6zC1xsmGV+3frBn2zJokASGq/o6+rKx282bZ93fSWgugmmsQSuCtNCivxOhZ49EH0LoihEC2fMywapJaC6CamlcvNtXYzIAUhE/2KtdKYSq3inXVQzpUOOewk7KyCM2sOQf/nVpNTKKx46L6bKgqweGmIsDYqaVoKhAiD1sYbFzuCy7ncMbNrTfCk3Pr0Cy2RQhRxbuket/1MxsLgKjcQN5KAyEFNimxiZ5bmpx10+80qIUIoQSqF2LGRbV/s2OTG1xmQFTCSUbefJ8qF1WJ0w4ZKUTkVcdAVA2sj9VUWoC33MBbrjKG3VTsiPssZG6XWgDVHIlaCKt0+KKaSc1O+AeNuQBK6kDomppXM7Mq9aoT7DdLPuLhLYRAdQJUJ7jh9mTTh5Vbv68pgNXDy5yxlZtJisoifQMh4Eo7tYyI664nm341rilCiiP341b2b07Era97TB7EifFxqLPAao5ESLHfcmIvu+/R+tdDNqeZYAdnWDU1Na8qnHFz95O3+OAkZgg1dQ9F3k2tIMKvrC0Pq7Xk1UgtgGqui+qGlX+7tJjRg1kdWgaqCs5zYNPaClRT82rEjPIqJiVUhywkNTWvhFoA1VwXIcV+p/id7IGtCzSvCp3o+zySmpqau8Es4NdbeHCsPzUPP7UAqrkhs8A7V1R1gR5EZjPCOg6opubVhy3MvE6N6tUCqObOUQugmhsilEDNrEC7N04rvV/UcUA1Na9eZm15ZNM/XsZSTc1NqM+mmpviLUYgRZWG+gBaWeo4oJqaVy+z+EPVuzuZTDWvXWoBVHNThCdR3ermo/sPaDB0HQdUU/Oqw+amqv0jQLVrAVRzZ6kFUM0tMQuGNqN8XvjrQaKOA6qpefUxs/7IVu3+qrnz3Ncz6jOf+Qzf8z3fw6lTpxBC8Fu/9Vs3/cynP/1p3v72txOGIU899RQf/ehHD73/kY98hL/8l/8ynU6HtbU13vOe9/D1r3/97uzAawgZeZWVxe0XJHuQOBQH9IDWLKqpqTkes9o/D3JT5pqHl/sqgOI45q1vfSu/8Au/cEvrnzt3ju/+7u/mXe96F8888wzvf//7+dEf/VE++clPztf5gz/4A973vvfxR3/0R3zqU5+iLEu+8zu/kzh++Bu33W9mPcHMIH/gRMbhOKDaDVZT87Aza+qJFKhOXfun5s4j3ANS3EUIwcc//nHe8573XHedn/iJn+ATn/gEX/7yl+fLvvd7v5fBYMDv/u7vHvmZ7e1t1tbW+IM/+AP++l//67c0ltFoRK/XYzgc0u12j7UfN8IYSx5rwoaH8l+59tSFIU81prQ452j2QvxA3YGRHo1zjvz5Aa60+KfaD1xNjuLiGDMq8Naa+McocV9TU/Pgofcyyisxsu0Tnr1z9+GaVzfHeX4/VL3APve5z/Ht3/7th5a9+93v5v3vf/91PzMcDgFYWlq67jp5npPn+8G9o9HolQ30OmSTkq2XxwB4nsQLJMZUDfW8UBE0qmZ8ZW7QhcEPPYKGQiqJEFVhQiHAWkcyLEgnh+Nd+lcSFtaa9FYbd6VHixACbzGi3Eowe9kDJ4Bkw8OMClxtAaqpeeixcXV/U3Xl55q7xEMlgK5cucKJEycOLTtx4gSj0Yg0TWk0Ds/6rbW8//3v51u/9Vv5hm/4hutu9yMf+Qg/9VM/dVfGfDVBqChyg9YWrfeDibW2ZPFhQVMWBcn4xtsLGwrlK6y2ZImmv5kw2EyQSqB8SaMd0OwGlfUm1QgB3ZXGbfejUQsh5U6KzaqU+AepLL1s7McB1dTUPLw45+YJDbMMz5qaO81DJYCOy/ve9z6+/OUv89nPfvaG6334wx/mgx/84Pzv0WjEmTNn7vh4Wr2QVi/EGkuRGoyxyGln4DI3FFn14A4iD8+X02UGZx3OOZxl3o4ibHq0l6JDLq9JP6e/EaO1xRiHMYYiSxnupNeMpbfavK19mKXEm0GO3ssIHiABJCIPpMBpiy0M8i66A2tqau4eLjM44xBKIKL6Oq65OzxUAmh9fZ3Nzc1DyzY3N+l2u9dYf/7JP/kn/PZv/zaf+cxnOH369A23G4YhYXjv3DlSSaL24Rig6IhZTqNzvO22F0NaCwFWO4y2lLkhGRWkkwIpBV6gSCcl/SsJrV6Id5sCwVuKMIMcMy5wpUH4D8YNSkiBjBQ20dhE1wKopuYhZeb+kk2/7p5ec9d4qATQO97xDn7nd37n0LJPfepTvOMd75j/7Zzjn/7Tf8rHP/5xPv3pT/P444/f62HeV4QQKL9yfwUNj9ZVcTobzw/IEs3u5ZgTj91eYOEsJd7GJbqf46/dnjXpbiCb/lQAlfCAxSjV1NTcGqZ2f9XcA+5rGvxkMuGZZ57hmWeeAao092eeeYbz588DlWvq+7//++frv/e97+XFF1/kQx/6EF/72tf4xV/8RT72sY/xgQ98YL7O+973Pv7Lf/kv/Pqv/zqdTocrV65w5coV0vRaN9BrkeXTbQSQjAqS0e03N31QU+LrOKCamocbZ928ortsPVRzdKAa/2AzYfKAVs0/LtZYHpBk8TvOfRVAn//853nb297G2972NgA++MEP8ra3vY2f/MmfBGBjY2MuhgAef/xxPvGJT/CpT32Kt771rfzMz/wMv/qrv8q73/3u+Tq/9Eu/xHA45J3vfCcnT56cv/77f//v93bnHlCCyKO7WrkLdy9Pblu8yI6P8CVO23mxsgeBmQByucGZB69idU1NzY1xmQbrEJ5Ehg+fANq9PKG/mbB9YUw8vPMiyDlHMiqIh/ldFybxIOf8V/a4+PX+XdmX+80DUwfoQeJu1QG6VfI8J8sy2u02St35OBZrLJe+PkBry9LJ5m0HROudlHIrQUYe4RO9OzzK2yd7foArDMHZzivuH+ScoyxLPM9DyroUf03N3abcTtDbKaobEJw+ZiDkHcBahzUWIQWmtCTDqbVcgB8ovEDiBQrlSfKkJJ2UCCHoLEXowtDfTObbklJw8qkeQXStkNOloUgNQUPhHYijdLaK4dSlrWI3Q4UA8lSTTUrGexl62o4oCBWLJ1sAFFOrd9j0CJoeSsn58uF2SpFpwqZH1AoIIoXyJepAe5Ei0yTDajLb7AbkqWb34oSDAqHR9mkvhjS6wXz7UNW4K1MzF2RGO3RhsNbh+dXx8nyJ9ATOQp6UZLEmanm0p96EO8Wrtg7Qw461Fq33XTNlWRLHMVmWoZTC932yLJu76zzP48SJEzSbTfI8pygKtNZorefrK6VwzlFkJcZp8ryaFSil8Dxv/i9UD/PZy4Y5ybjAXja0FyOUJ6vtFAVFUeCcQ0qJlBLf9+fbsNZibXXxuZYk1wV6kFDsOKJeCynlfB+klDQaDTzPI45jkiQhDEMWFhbm4y7L/dR/5xxaa6y1+L4/37/jIhsepjDYVN+yALLWzutBGWPmv1UcxxhjUEqxvr5Ou90+9nheCUmSkOc53W73rojhmpoHjX33172L/5lbVQY5yajgemaB/AbNlg+WMVk62SIZFWRxyea5EX6oMNpO67nJeZLKjFlx3DLT6MJy9dcLwaExKSVwQJEbNl86um6dUgLlSYoD31NkhvHeviVHCPB8CdNM5BkHRVxnKUR5kuF2SjqZCj7AC2RVgsVYimz/s8fB6OCOC6DjUAuge8hkMmFjY+OW1lVKobXm0qVLN1zPWcdwJyVPNEGk6K01bslS4ZxjlMTsDiyDZJPuSmMujo6DjjNsXCLTXbxbqL48Ho/Z3d0liiLyPJ+LqeshpZxbX5xzWGvn/87en4nBIAjwPA9bFpRxgrMThKkCoYUQc4FXliW+788zB5MkIctu3N/MGMOlS5fodDp4nocQYi4GZ8dsJhZnwu3qMcK+yJsJTWstxhiklARBQBAE899vOBySJNWNaG9vj/X1dcIwJMuyuSgTQhDHMZPJBIBWq0W73abR2D8PZqIyCOpu2jUPNs66efzevawxtnc5ZrR77T1AAFHHp9ULkVJQFgZdWHRhMKUlaHhEbR9TWka7KUY7ussRvdUG7YWQy88N0KWdW2wq9sWCF0h0YclTDQfCVIUA5UuscVjjcK4SNGHLp9ULqnIqzjG4kjAZ5ChPEk7d/1lSoov9UijA/DNZUpLHGl0ajK62WxZ2vq+NTnWPSCeVCOyuRCyfqiZ97aWIyV5GMiwockNZ2PlnZ/sipwV4pZLzv3U5PV7aYrUDUQm+sOkTte9vkHvtAjuCu+UC6+8OeP6rLxE0fcKGwg98oiDC9yKEclhXPdTazQ6mdGxe2WJvr4+zoJQkakR0eg2iVogxhjzP2b00ocgMQgh8z6fVbrB2tgeyetAaY9Clri4iqpNceQopBaP+hI3zfQCWT7amNYmqh7gUEq0N1hmEqi5ImNYhsjMzsUMhEHuVeVishTgBflCJi2Scs7c9osxLuosdVk8ukOXpvOp2Vd8InK3cckII/NDH8xTalBgznTFNxUYea4rMoLzqRqCUoMgMZW5wFqCqqB35Er2dghIEp27dYqOUIgxDfL9KvZVS0mw2iaKI3d1d+v3+HTsXbpXZOGY3suN8Lgyr82RmZTt9+jStVutuDLOm5o5gk5L8pRHCk0SvX7wn3xkP83mF/u5KRGcxImh48/jIW62q76yjLMwhd1dViiRHqn13k7OVCIjaPkpJdGlIxyXOOvxQ4UeVe22W/m+0xRqHF8hbLglgjUUXdj4eP7zWeuysq2rGlRajLVHLn4/Rmkq0HeW6g6oNky6qdYSEqOnfUnunmdy4m6UNjvP8rgXQEdwtATTaTdm9tN+U9Wqz5r7I2F9WPfgcSu2fiH4wndmXFucqP/PiepPBVoLR1YeDUOGFCp1PBcJ1xrS3MSFNchptn+5KEyXVESenI2xNXVaZm4qNfcqtBHKDaHmIhaDaBuDYd7tJKafiS5LlKYUu8aSP7wXXvRisNRhjcKKykgiqG8DsBVN/uTVoU1LqEmsNfqhopiVWC3Q3wlEJKCkEUTOi2QqRvsMqjZSCZrNJs9nE9288G0mShDiOD13EUu7flK52Ic6Wz6w8wNytGIYhQRCglEJKOXfBlWU5P2ZhGLK0tISUkp2dHQaDQfXbTi1dM1ddFEV0OlWsRBzHxHF8yNU6o9ls3pUCnzU1d4pZXOHtxv8kSYKUkii6NbeKLgyXnhtgjaO30mDpVD1BeNipY4AeUKKWz+KJJsm4IE80zu2bOs1UzABz/6ofVspdqsp9MytseMjs6EtOPNYlaHg0ugHb58fkiabIzSHfL1BZeABj9uVQZzGizC0mAyUUvq9AghQCIcGU1Swhj/e/U0y3JZXAaIfq+OhM4xJN0KtcTo7KZNtebBA0PMZ7GVlcorXFUyGeCve35VW+alzVEsSamUtJIeV05uKBHypavWB+HJyrhF7UrmYu1jpGO2l1XMcxlBYnGnDVLKaIq28WBLQWQrqd9i3N8mZC6X5w4sQJlpeXqzpPN4gFmgmhoijIsmzuQnz55Zfn8UT3suhnTc1xmNf/adzao2k20SiKgq2tLeK4mmAuLi6ysrKCc440TfE8jzAMD022nHVsnx9jjSNseCyuH762ZzGBrVarjr97lVILoHtIEHkEkcfCiSZGW/LCkFtLYR0LDR8xffD7gTrygayNpaMt2aQkDCRBWAXOzS5qP1CcemoBU9q5H9gP1Tzi/6ClwprKDCuF4Mq5EVlc0l2OWH7kWpdRnpSk4xI59UEH4eHxGWPJXhziMo2/0sBbbU4DseV8vfZiSFmYubgRorIGzVqBHMQ5B455+w8zd4/t34SsdTjrDmUxAHSWIrbPj8h9iQ+0FkNapzswtbaVmSZPNem4pMwNk0FO1PbpLN2/QLxbZRaIfivM4olmtNttJpMJ/X6f9fX1uzG8mtcIWVxOJyCziUo1gVF+FYfiBVXAry4MQlbX7SwGz5QWa6vrWypxqBq9cwfif1o+ZtofUSlJ2PQQUpCnBVcub2HQGFtirZ1v+yA727vsbvWRHtUsi8rF3Wg05hbk8U5BkTh8z2PhdIdJPJnfi0ajEePxeLp/koWFBXzfR2uNc44oiuZWJq11dX+6xaSNWRzizJJ78P5XliVpmtai6x5RC6B7SFJodsYFw7RkmJZk5b6FRinB2aUmgZJcvpwSF5qVdsjZpSZpaXh5N2GS7bs1fE+y0g5YbAZ4UqAOvDwp8VoeUUeijhBSQgiUt798Ya3BlXNVeuXCWnPuy9XGYh2ETZ9wGpBYGktuLKHYF1RKSaITLYqLY/QgRy43kJ4k1YZJrimNo9fwaV+npodzjqys4n0if+qCE5WNBsV8PM45Rpkm9CSRr2C6b6WxKCGQ05vtyacWyFo+DHJU5BEcmE2GDY/2YvWZ518cMN5MERuC9mJ4V/3S95vFxUUmkwmj0YjV1dX65lpzWzjr2HppdMiKfDVHufavtnLP8DxJ2KpEgMsN9kqMEwLryyqterqelIK8TNja3sJOffB+IAmbPn5UpaYHKqLbWmS0F7N95QrWWpQnWFhp43CMk4y+jQkaam5FFsDCiSZb2zFH4XkeWmv29vaAauIF7rqJJldn3wZBMHetW2sZj8fzxIbq2AgajQatVos8zxmPxzjn8DyP1dVVgiCYW25nCRfNZpOFhYWbJrtYaymKAs/zjjV5ei1RxwAdwd2KAbo0SPnq5cMpi5GvkAKS4taDXK++wdwMpQSBkoSexFdyflNxrgqMttax99KYPCnxFwLC5aiysExX9JSgFXoU2pJOx6mkoBkoPCUAQakNXJhAbsgXAsrOtRlHvifxlagCnx1IWYmcrDSYacBhM1Qst0IaviL0ZRUX5WCcay4PUvJpNkUn8oh8xSgr58sCT+IpgRQClRuizRR8iT7TRopq+ezYbU8yjHYkF2Kkczzx+iX8jk8/LnDT36UZKM4sNmm8SnqKvfTSS+R5zsrKCsvLy/d7ODUPIbOA4cq9HVWWVbvfe7DM9kWL50mMsdeIISGr69Bqdzg2cVIgBjlEHm6aURpM08f7wz7DcZWE0Gg2aAZtfC9ATstpAHgH4iSNNZQ6x1MBnvJwzpEXGaUuKbOSwXaKtZqgLQiaCs9TKE/BdERRI2T1xArNVoPRcEx/b0CRldhyev+SJTKoLNCVq14glUMeCHQuc0MWlxhtabSrSeTMIu77fmUJygxeKA+JmVlM4I3wPG8ummYxh9Xx3XeRz5IfhBD0ej16vR6eVx2L8XjMaDTCOcfy8vIrfs7Nypy0Wq37FiYwow6CfoXcLQEU55rntyZ0Gz69hk838vCmxaS2Rhkv7SY45zjZa9BteFwapGyOMnwlObPY5NRCg2Aa6zJIS3YmOZNcY61DWzf/10xfx0EnmuxKChKap1vzC/korifA/ElJ2M8xgSQ90TwkkkapvuGYpGQujG6EUuKGs8851tGeBpxPTrVAXWvdaUceyV7G6EqK9CWN081rrEBSwtmlFo8tN+e/1cPKYDBgc3OTMAx57LHH7vdwah5Ctl4eEQ+LawKGx1nJ7qRgd5IzSTRPn+qwvlCV1tBFlWXk+fKQK95aR5FULmlwmCsJLilRyxHecpOoVbnTnHN89S++TlloTqyvsra+irOVGMsm07RuXVl7wqZP1PJpLQRIJRnvZox2U4QQRK3KjXbp6wOScTFN/Lh56Y7rcTDRodofg8NgrEUXmtKUaF1i7HTS6Em6vRa9Xg+sIhnlFLqkLDNUw9JejFhaXiL0Qy5f3GTz8hYOaLdbtNpNlKcQAuJsjGNfIDnrrhvDeCtiCiCKIprN5rw+26wOWlmWlGVV6HHmVp/FQs7q2A2HQ4pivxvAzEJ1cHuz2nX3ophsLYBeIfejErRzFmsLhPCQ0juw/PbSBp2bCqHpv4W25NpSTuNpZlsTApQQBJ5k76UxRarpTGs/+KrK3IoLTVIYAiVpRx6eFCSFIS70XLBIIWhIgXh5DM7hP97Fb/iHbnbjTGOdQ05dXNY6rHM0AkXDV2jr6McFg7Sy6uTazFP3fSU50Y1Y64SU1rI7KdDG0W14dCIfbav9M6bapnVgzg2xhcWdauIaHs6BdZVla6Hps9AMMNrwxS9ssjcpWDnTZv1EG18J0tKwOcrpx9WFvdQOePvZe5OWe7coy5IXX3wRgNe97nV1ZeuaY2GM5cJX9khyTXSqiQoVpbFsjfIjLdhPr3c4s1RZA4x17Exytsc5ejoR8qSgE3m0Q4925OFeHOG0JXyse6gGUJ7nnDt3DiklTz311JHnrTH2UGXio9DGkqaanReGGON45OlFglBV6eDTVPBZ7GGZV1WadWnw/KmLbVrzRwhIhkWVuu6qWEpT2GuSTpQnaHZDPF8y2k3Rhb1GqNxoQne9e79zjtwkICzCKiQenl+lz1cJM9V9M/CrGKPSFiTZCG0LZja3WUFaYwy7u7uvuKXGrOhtkiSHtnUwbmr298wdN0hKTi73WF1deUXffTV1FthDgLUFZTmgKLYpyj7WVrVxBJIoOkUUnZoq8RylIjyvc+CzOcYkGJPinEHKCClDwOGcQQiJEH71r00QJqXpRfQaiwhxfXdOcLrD5ksjylGJd4p5/FAn8ulEh1PEW6FH64iYnqITYMYFYqIRzX03mJSC3k0Km/lKsNaNWOveOCA5lIpTC4dnbkoqQu/wvhXLTcwwxwt8/MWjZ3rKUzz+aI/FrZSG57Pe2//u04tNtsYZX7o4ZG9SEOf6yH1+WJjdfLTWZFl2303VNQ8Xk72cy/2U3byk0bzWUrrUClluBUxyzaV+ytevjLk8qKr7JaU58kF/ZVj9K0pLbzvF8ySJb7EIrKsmcOlkxLg/JAgjtsQejUDiySq+UUwTOZQUyOn/Z1jnSAtLWhrS0lBqS76bYcYlrW5A2k9oBFXdMCUFSgiUEkghKRSMpaPQ0AwUrdCjlIIs10hRFRacWY/0bFLpHGVhKYxlZ1IwMpqtsiBNDYUwlIWm7SvOLjbpNnyavQA/VCSjgsFmQpFqzFQoNJrT4ouqKr44Cx632lGkmsibWt8O3PJ0BuBICkteGjoReKqKGxW0CQDlV4HnnlToicBZRWgWGY3GICzKBz/0CBs+YeQjhUIKb2oRKsmLgiSNMUZXddIaDVrtNr2FLr7vYayh3+8zmUzm1qODzJZtjjI2hhnD3LCysnzf4i8f3rv5Q0hR7JAkL2FMgnXlNe8LBA5Lml0kzS4eek/KEE+10WYyF0vHRSDx/UV8v4fndfH9pUMnXrMbEDY98kQz3EqPzAi7GaoXYsZFJTxWG/c1sFhGCjOcNle8Ae2liMFWSjYp5zO+GWudiOV2xs445/Ig5XUn7n1vojtJFEVMJhPSNK0FUM0tU2jL//vKNvG4IFgKWWwFhJ5ECsFSK2ClHRxyEUe+4oWtCeMD117kK9Z70TymrtCWcVYyyTRFotHGkStHWh522RTTKu3KD8lKcyh55Dg469CTEmehjBSboxtXf78Z3jSecebabwQKT4pD+7yPwGv7ZMCzSUYPg56kFMbOLeHOnxZJdCB0gRqUNHxFJ/JoNCsLeaEtqukjC4szDq0ERoDVFrRlMClJCw0OtlLLei+kJRV5UlIUhjivxCBUFjjfk7RDj1bQwjooS8tgrBmmOYVJaQUevYZPI6h+a0/6hHIJ6wxYQTaEfGiIN4cgBIkxDK1BqSY9XxAp6PQiOosthII8y3huc8RY+rQXOywvde/rM6IWQPcQ5yylHs7/VqpJEKwQBiso1UIIn7Lsk6bnKcs+QvpIGWJ0JXqKuZVIIGWEUk2EkBibY22OQCCEwjmLcyXOWaSq1jN6grEZRblLUe4CEAZrdLvfeGiMiyeaXDk3YryXsbjeRB4z7kW2fYQSuNJiE426h/18rkZM6//YmwggP1Bz4RcPCnqrh61FpxYidsY5G8OMJ1fb83LvDyONRmMugGpqbpUXNkbE4wJPCd729DInl28snh9fabHSDkhLgxQCX0m6kXfdh122EROrGNv18U+2qiKjorJCv3xuhDWKk488gvRDstKirZ2Lj5nL27rDsY9SCBq+IgokDV9hJiV7WmEVdB/tkJaVICiNxTh3KJbS9yStwCP0JUm+7+5XUqBtlQyir7JopQfcgIstn6VWSDNQNAJFoCTWOc7txGwMMobJtRNg4KryIo6J0Yeyf29KUxG1FZGnSArDDo4dp6FRZda5AKyWlRi0jkxA7DmEMDhjsaXFGouhChWItSFJLSIT8/IklRdtWhB36jJUApSUFAfE6waVSFQyRopdHGBslYErfcnjJzucvce9Fa+mFkD3EN/v0em8GU+1puLlWndUECwRBEuHljlnKcs9jMnwvDae17mhK+t6aD2mLAeUekiRb5MXW2TZBlF0cr5OoxPgB5KysKTjktbC8YrmCSmQnQAzyDHD/L4KIBlN+28VFmcs4gZirr0QTgVQfo0AWmmFBJ6k0JadOGet8+DXDLoes/5nN+t9VlMzo9CWly9WNXGeOtu7qfiZcZTr/HqIwtAMFf5yA++A67woCiQWqSQLndZtx60567i8O0FIwcqJJgs3cbPfDGMdSaHxpCTwJNpaktyQa8tC06/KdBzBm0/1eHS5xTgrCVT1WU9W2a6VG6+Kz9TWoa1lkmvGmSYvLb6qhKS2lqy0VfykX7n+HZWgiXzFWifEV5Ltcc75vYRi2oTVk6JKvmlUcUylcUwyzV5cufdVwyP0qjjP1U5IO/TYiwt2JgV5aSitQ1+V1eepylVpLThj8UvHSuBhrGM3LTDGoVODyQzzlD8Bj7RD2k6QjIq6GeprBSlDovD4ReiEkATBrQWKubIEIRBH1H3wvA6e16HBGRJ1jjh5kTh+Ft9fRKn9k7C1EDLYSomH+bEFEEzdYIMcMy5uKjzuJkJJhC9xpcXlBtG8/jhavZC9yzF5qilzc6joopSCUwsRL+0kXB5kD7UAmlXDNcZQFEXdILXmppzfS8hHBY1AcvoYvfWOw8xNLa+q2j6zVEZR9IqC9ofbKUVuUErckaKnSopD4u6oGMTr0Q6969ZEmxFIQYCkGXis3abXfbUTstq5tfu3te5Iy3Yn8nl0+XB7EG0s2jp8VcVOWeuIC01aGhabAf70fm9sJbCMcxhTBZh7SuBLiZzGMvnR/S0xUqeBvIqweU78R39M8vnP427SPLPReAzf62GdZjL56qH3WtN2FsmowJqbp1BejWr5iFCBcZj+7cUr3Snk3A124+OhfDnvTBwPrh3zyV5lOdmd5Lcdg/AgcLBPUu0Gq7kZpbG8vDHGlY71XoNm984LZlsYnHEgBeKqmluzc3Rmubwdikwz2KqKDy6dal9TPb6GY7n1PVUVop0lycipGFzrRHPxA5VI7DV9lloBq92I1V7EYjuk3fRpdgMWTjTnz5r7RX0mvIooXnoJV5bYJKU4f/6G6wohaLffiEBSlHtk2eX5e0HDww8kzkEyOtpXfTO8WYbEXjbvqnw/EFNLzs3igKBq1wEwOUIAtUKPbsPHOdiNi2vef5ioBVDNrXKxn5INC0JfcnK9deyYwFthbv0Jr20BNDtHbzdg3znHzsUJzkGj48+v8ZoaqAXQqwabJJSX90VMeeECtrjxg9rzWjSbTwAQx88dyi6bub7i4e1ZcFQnqNxP2mJG988KNI8Dym9utWl2g8o3nhvKI9ZfalWz3/5DLoDqOKCaW+XKMEXHmrVOeNf65c36f4mr3F9a63mBvdu1AKXjkjzRSClYuY2s1ppXN7UAuk+YyQS9t4e7hSqdt0L+4rnKx7q8hOp2cNpQnDt30881GmfxVKdyhcXPzZfPTJPp+PbcYEIKvOkNU+9mr7jQ1u0ipr52m5ubjkEqSTQN2k7H14qcuQBKHm4BNLMA5XmOuYmrtOa1S2ksw70MZxwLnWDuIr7TzNzTsnHY/TUT6EEQ3Hbvutl13FoIDzVefZCx9hhZXzWviDoI+h5j05T8xRfRm1sACN/DO3ECf20N2etV6wyHmOEQZyxYA1IifB8RBMhWC9lo4PIcG8fYLMOmKXqr2l7w5JOgNcmf/hnl5ct4S0t4q6vz73dlie73seMxstXCW16m03kjg8GfkOebFOE6QbAyd4OVhSUZlbdlOlYLEeVOWjU5jEtU+94H3IpAVk1TrcOV9poYg6uJ2j7ppCQZF9eUye81fKSEvLQkhaYZPJyXz8GCiHme1/WAao5kmJboWBP6koXlu1fT63oB0DMBNBPst0MyFUCNzv3LRr1VnLOMJ18hzzdpNM7Saj71qm7Q/CDwcN7BH1LKy5fJnn0WpjExIghwRUF58RLlxUuIIADnqkyu28BfP4Ga1lXwVlfR29ukX/oyanEB2WxihkPs5Kqux1LgLS2hWj5lOCJWL84zzmbZYMkovy0BJJRAdUNMP8NO7pMAEgIZKmyqqxvtTQRQsxPQv5KQTcpreuyoaRppPy7Zi4uHVgBBlQ1WC6CaG9GPC0yi6TT8uxaseqMA6DyvXOe3K4DK3FTtJwQ07pL16pXgnMOYCUIohPAZj79MUVZd59P0PNZkdDpvRgg5X1/rIVKGKHX7QeF3EmctLsumPZVUNVE/QrQ557DDIXpnB5skIKqJqeotEJx+5D6MvOLhvYM/hMhWC6xDLS4QPvUUst3G7O1RXrmC2d3FTf3dwvdQi0vVyaQkzlhcWeKKyurjSl2dPK0WotFERiEiivBPnJh/V/SmN1K83KQ4fx7TH2D6g0PjUN0OZjjCJgl6Zxe5o8l5nqJ1ifBsj8b662l2AwZbadXz5gYN926E6viYfoYZF/jrrZt/4C4gQgWpxmYGdZPWbkHDw/MkWluyuKRxVVf7xWZAPy7pxyWnH+LWYGEYEsfx/CFTU3M1e/0UZ6HT9Ambd+dRcaMA6JkFKAxvT3wlo+p+GrX8uxK8fbs4Z8nzK1VXAHs4EUEgaTTOkKYXyIstdH9EEKwipU+WXcbY6ph4qoXnLyCFjxAeymvhqfahciYAe6WmISWNu7D/ZjIh/eIXcfmBkAApkFFUlWGRszpsOS7PK4/GUdQC6LWB6vVo/uVvRnX2Czt4y8t4y8s4azHD4Xw9cYOaF64o4DpKe4ZQivCJJ/BPnqS8eBGEQHa7qF4PeeCGYiYTzO4uem+PcLBKFm8y+Oqn4UpC9Ja3zMVAOilvKwVWNn2Q08rQmb7GzD2jmAZCBo07f0rKyMOQ31IgNEDU8Zn0c9LxtQJoqRXw4nb80McBzR4qtQCqOQprHXt71cN2+S66v2bxP0cFQGtd3RNuVwClc/fXg1PrypiE4eiLGFOl5YtpGK7DIkVAt/tWfL9qUzQefwljM9LswvzzUng4Z9AmRpv4mu0LJFIGCOEx0Jrn4hRfNfmmpdM0w6Vp30gPazXWptOekgnWloThKr5/9KxOW8ezScZIG55uRXQmY+I//xJXkpTUVqnw0lq6nqJnLRKBxVFMvR0OyKUkW1hEdzoIBMpZ2u0299OWdVtPG601n/70p3nhhRf4vu/7PjqdDpcvX6bb7dK+z6WtH3QOip+DCCnxFm/NpCCOUbxONhqEr3vd9cfTbqPabYJHHyVIn2L7+f8fenebrH8B+XyLZu8Uo92MZJTflgASUqBaftUfbFIeKYB0Ybj8/ACA008v3vFgxVkm2K2kwkPlBqsEUAEctlp1Ix8lBYWuqrTerKDZg8pBAeScq2MNag4xzjRlrFFKsLR09x5Rdh7/c7T7y/f92wqAtqay4MKDE/9TFDuMx3+BdRopAprNRwnDUwihsLZASn/u7gqCJRYXv5Wy3KMotrG2IJErfCXv0FKOR1TMosrBaawrMTrGmBiHxSRbuMkWF70VkD6lTfjKbp8nm5V1SCBxWJxzjLRhTxsSbWl5z7PeXGO59QjaeVjhIZwjs4Znx1uk+S44zRcuS9bP9xkXHnm7R/GGJ8Dugh6yIVZQpoNvDflUwOIHON/HNRqVq+wA66HPSe4fx757v/zyy/yNv/E3OH/+PHme8x3f8R10Oh3+zb/5N+R5zi//8i/fjXHW3AO8RofO49/MpPUVspeu4F/qEJxt4FxEvBuz/Ej7th6UslMJIDspYOXam+lwO52XVx9u314T1hsxqwXkypu3xIDKAiSAIjfowhwSZLOu9nuTgn5cPLQCKAgChKj6+5RlWVeErjnE3jjD5pZuw7urAsLNMsDCozPAbjf+J4ur3l1eIAmuY3W+l5TlkNHoz3E4fK9Ht/uNSLlv2VLqWiuXlB5huEYYrjHWhq8MJsh0l3HQ4auqQyh7rAQey4FHGAkCHKGeIHc/w07exs8ExfpbsGbMSPfZLnIa0pGYkrE2TJyHFRHIBnhQlFv0h5dhePmasQAEQtLMU5KXzrFjLa7XQZ4+y2n3ZRBQeo5+OaLw19HRY9U+CIkSHkJAJCVtJYmUxEx7t7Xvs2vy2GfGj//4j/PN3/zNfPGLX2R5eXm+/O/+3b/LP/7H//iODu7VTJFpdGHxfIkKJOoYJ0KRadJxSToukEqweLKFHyiMsQw3U6QS9NZuz2zdaDxK1r1EsZZQbo3xzz8PF8aUxjEqH6P3zW899jZVK6AkxiYapy3iQCVWXRrGe/v1aMZ7GQtrTZR/5y6Mgy0xbGZQrRtvWylJMG2Omk5KOkuHb86LzaASQEnBmaWHM4BYCEEQBOR5Tp7ntQCqOcT2ThWbstAL8a7T1+qV4rTFaQviWhfYKw2AnsX/NO+w++v5JGNYGl7fiugc0fqisJahNox1Fe/SVBIlYGfwHJM8IwpWWWt/I4Xz8aezvu1CcyUvya3FEwIlBJ4AJQShlERS8HySE+09x+rkAq1Gi+eW306Oz6Ws4FK2745f2PoSJ0volxGhs5xxOXrp9Xw9zrgI4DTIEgIfhEcoJSdCj2XfYzMdszk+hzExwpUoDCBwCBbDDk96PcyFZ9lRC+wGMY1HT3Oy1UAJie8vIWVIll0iMXtYs0ckBZ6QSHyUbKLmPTAFpRmhyyFhuA48eUd/o+NwbAH0f//v/+UP//APr7lhPvbYY1y6dOmODezViLWOZJgz2s3Ik8PumLChaHSCacCeQHoSz5PzwEDnHOm4ZLSTkk4OZ4ml45LeaoPxboaeXnjppGTtbOcaIXGzYGalQqLoFO6Eocx28Ucdgsgji0uSlzfovOWNyGM+LIUvkZGHzTRmUuId6C823KqsP9E0yDJLNMPtlN5ag0k/xw/V3PVmSstoNyVq+cf268vIw5RFFQd0Cw1aG22fPNHkSXlNAbheo/r8+DouNVNakBxL1N4PwjCcC6DOdVyzNa89nHPs9atJycotNj69HWbuLxHc2QBoN73PArfutrcWsgFEC3Cd+MuX05wXk2q7fzSY8PpWRCAFg9Iw1obE2nnMy+FtpzCZWlWC02xMMuBAEVJr8MoYqwKsd7TgC+ItVsbneaIZ4UnDyfQ59ta/iR1t6JcGrTVqsoEfb7EtJGnnDIuTi5wavwTLp9kLfLaLkkgFdLwGS75iyffoSEBnYBNWOz3e1H071oFncigm4EXgN3HZhORzn0UOdzktLa97/eshV9jGKqJzGukc6IzQrSDz57EmAxxYgzUl1paUVcfUyg3mReA30I0cWg+RALLWHlk87eLFi/VN9CaMtlP6m7PgN/AjhSktxjjy1JCnKXA4K8DzJA6H1e5gM12itk+j45MMC7JEz7frBRKrHVlccunZPn7kIWT1UC5zg3OgPIHnV60urHHgqpR1KQVSCRxLTNLnMF5I2lvDtHtMvvBF0osjzJ88j3fqkcpy5Ul0YSlSXW1HVDE/QaQIm5WQM9rirKNMS/RejldYGmc7CCGwxs6tPwvrTZyF7KURo92U0e6+W6zZDWh2A/obMcY4IKW9GLJ0soWQ1Xckw4JkmCOkoLUQ0uoFhzI/RKRwY3fLcUBh0wdSsvja9TvT2WpaGAptCbz9NNXhdspgM0FKwdpj3XlhxQeROhC65ijiXJOPS4SE5eW71/h3XgDxKuuPMYZyWgrkphYga68RLOkoQ4wuENkxkX0C7Po8I+lI4l3Y/DKUSfVgXnk9tNfAFOAcBC12SsPX4wxhNW0MEyf5egw4i7DVfjjpgRA0paAnDMJZUmsp8pcJlKQVrUFrgb1SkxY54egievg8qwiWfY+GkujWGnnnkeo5WyYU1pEhafSf5dFmiNc7BfEOMhuwcuULrOCgiGFaPHHUjLjQfpRx+1FOBxpRDOHlz/GXoh4maOM5CdrBaALZsNrHOQIZ9ZBWV+LnANlz57H9MTIKaDz9BMIPwZaowWUYHGijBCy5JtCgelI5jMvRLsdMXziHp0M8E+IF9zdm+NgC6Du/8zv59//+3/Mrv/IrQGVKn0wm/Kt/9a/4m3/zb97xAb6aaC+FTPoZ7cWIzlI0t86Y0pKMC9JxQZkbrK665zrH3KIDVfxJeymkt9KYx6V0VxoMNhNGuxntxZDF9Ra6MGy9PKbMDWYaCOicZTPeJNUpJ1snifRVsTiz53yZQryNwaEjQyq3CIM1yuYiejgifuEiQWcN5yxxmSCFpOFF8+A9qOpvxHvxtNbDLBXSUA5z3ChnOy3IcZxpBARSEjU9GtMaQWFDkafVDcUPFTo3JKNibtL2gkp0Tfo5k1mjVWsO3eDSScnuxX1R55zl4tZL2O2U1cU1uun6PBZPKoHyJMqXleVNyiqAMikZbiVoYFtaljshJ7ohypOUmUElmsw69gYpS62QIqtcebPAS2McV14csnK6Q9gLyK2loSRK7Fv0dGGr78UCAuOgzAxBpK5J29WlYXc3o9cNiJp3RlTVAqjmKPrDHGccrYZH8y7W7jqYAn+QmwZAp30YbcB4o3qAeyF4DVABSEXx0gX8cUKz6yOuTGDrq9Barqw7Xlh9PhtV1giAfLy/bZ3BlT8/9HUFkpd0yKLOWXUZjzUCtoqSrcLgCUdbKZpKEilFKL2p66jCOsNe/jWcs/TCnGAyAenjioQvjV5mqxyS+01Wo6fwsVDswe7etfvc8KCxBOtvgXQPLn6+slgdREi6i+u8ef3NlSjsvBUu/DHoHJHs4CU7R/8QQlXrm/LwNv0mmIJiYws9mEDQJHrbNyFOvQ6aSzDZgt3noUimv0FUFe0VqrofCwnSw1MBnheC9KvlzlbHvJhUv8l95NgC6Gd+5md497vfzZve9CayLOP7vu/7eO6551hZWeG//bf/djfG+KpBuYz1EzGeG8JQg9U4XWBsjt9cxju5jpABUkikkJhScDnO8ZXgRCPAkwaRj2CyDVEPmkuUzrHZiLmwsMl62EQUiyih8B8p0ZOCttdG4fH8+DmS9gAh4Yp5kac6r2O5sTzvjGzjIcX2cyTpBhNZUNqcYbHBuP0Ii60lWk81EEONb0a0uyXPJZcY6TFClHjpFVpKsBD0aHsdRv0Ju6OMyIs4uXKGxuIp8COGOuG5cUqSl3hNn3MI3txts3ygPtDKmQ6j7ZRWy9GINEWi2dnIKXJLb71H60SLK3tbnH/xMpPhDibt43TG8sIiT7z+mxDBMuc3rjCZTFhqLNEOGrw8epmkGNMuHRe2L0FoaPkNbNpHCUu3sUgjaGKEQjtH6aB0jiuTnGFaEOiUl0M4FXqshgGoEDHSZEnBc2nBStsn9EKEA1EMWWoN6A8yLg0tX78gydsr0O0RRopHWz5rJmeyGzOcjGjohK4X46QkVadwzVVEtkekN7BNn9HKU+xqn90rY0Q8YDGQ/NU3L+NHAXjVWBByWojseMJoJoDKssRai7xB6YWa1w79QWWV7XSD26r9dStoqynTHIlATDPAClNQ2pLhZEhpS9rhvnUg1SmFKeiMt5C7z1NazcV8j9xqul6DrmrQUiHOOfJxglMR0anHQe+CzrCjyxTDC1jnaMgjSogsPArLT8LwIuydA1tWwgC4kqaoYkxDSc42IxCStcCn64FC4h+yLh3wjghJZnZwzuHJiEA2QedAzrPxZfaExlt6nEnU44vRIm/tPYUavEwyusQVk7BlEkDQVSEdv0V76Swdp5GNBcz6NyDzEX60CGGnEh/KozQl50fn2E13EUKgWl1Co+lYSxNBJANCFSCDJiJaQIY9UFMZUCSVABISmstYqXCTmPz8H8PJdcLXvx515gzDfMjG3tcIZEB37fUA9LM+iU7oBT3WmmsIIRhkA1KT0gu6LIQLqNlk2DliP2JSNohUxMKdO62OjXC30aRJa81v/MZv8Od//udMJhPe/va38w/+wT+47YZ1Dxqj0Yher8dwOKTbvUnlvGPw0uU/54+f+/8IZGUmFcKSGI12DiXAUz6isYhTPpmFUTIh1DnKWQSOhhSUzqAtBNKnES3Tlx6jYhdhKitI2w9oCktRppgyRwqBLys3GkgC1WDXanLh4QUdpGrg50Nsuo1xBiUkIupSZBNCt4XHBEtAqtdofm2LbuKwTyxhHjtJO2whkl0yU6KdQztIjaV0+1arUAas+SGpzRmnBXFSIIUg6kZI4RF6Eb2giZIhQliMztAmITWG3FhC5dPzmoR4TFzMyMRY69DOYWxlYkWABYxzOBkgbI4zAkuA9DsIVxKUGet7KyRlwe7CBCumZmsrwEp866PwkPg46ShlSTlRqCSg0ba4wOBySSAUwrcMEPTjjFD4LDR8PF/T8A2mEVOInMw4xKSFiKNqvH4TJRRNa5A4Yp1ipkGGoQzoqiYLXhvpicoCyKx+hkCHXbx8jKA6rk/1As6eviajFMIunP7majZ2izz//PMYYzh79uyr5vqteWV85o8vsbed8oanF3nD65Zv/oEbUJiC5wfP08/6+NLHVz6ZzsjLnObLEKoQ9WSb2CWkunL/j3fG5ElOb7HHqROnSMqEuIxhvIE/3mTBa7HnB5hoAfxGJVZMgXKOIFMUwwi/vcbakz2SImY82SBPdyGvXEVe2KXTPoETisIUGM+vBASVRwPn8IVHI2hhneLzWxfQ2R5Pdbqsdddxns8w3aMoU5AST4aEKsCDKhZGeTihUFLikq9WFl7vJKVoYE2BNSWxyRBBmyd6T3BhfIHC3l5dMU96NL0mnvSQQrKX7WHdrfdulEISqpBwmoXmcJSmJDMZtizpfuUiHesTrK6h3vwG+lmf3Wz32OOUQuLJSmhpqzFaY2PNyZVHeNOJNx97ezfiOM/v28oP9DyPf/gP/+FtDe61zADJQPk4qXBCVf96CuEsYbqHKnNctsEs2kcATkgKwDhLYsCqEOOFeOWEwaTy07aF5ES4wFiXpOWAiXP40kPhk+iMhOqCfqJxkkiGCL3NdtmHrA9ASfWgLaMlbPcsQbSE0DmNwVdpFV8htRYTOEYLbRjvoK7sYte6nHQ9Qu8Mk2aLvahHkfcpyjHGj+g120ziPSbjK2T5pMol8CrL1pqUNKRi05UMi5xhMbzmWDkETgWMnGW7nCCcQdgS4SwNGbLgdWhEq+jOI6RRj/H2FzCjlxA2J5QBTT9kUE5wdgeB4GznNIu6yyCdUAYTdGTBb2OEIslHGJtWckQIPAS+gNAL8aWg2bQUyzE7pSGxDoWm1I5SFTghcd2IzBkywAqforGK8ZosrAiWTYo/HmHygnEOo9xhhIdbtIimohRNEr3AWKc0gjFrjZBSB4y8M3jxmFY8oiljVk8rtnyfyxsh21rSHgmWl8rKbD2LDstHcOkLcPqv7M/qbkIYhiRJQp7ntQCqAWA8DSBeWnhl58NmvMlzg+fQ0/iU0pZzV7ucPu8zkZOW+y5YX/pgQCCwyrKdbAMgJtuo8SalM2w32tA5SctvsdxYZpSPGBUjjLNcHlUTkLa/RTaa3le8ADonkd3KwqmdpT/7Qjl18ZnDbuCMnLGOuZAW9F1Jp9mFKGKrGMB07MILcTg0Bm0OxG5O9YcrNqG4AjKExjpCTAOflQLV4umlp1lvrbPUWOKL21+kmMbjCATLjWXWW+v40mdUjBgXY8bFeC4SBaL6bqsZFaNDY2/7bc52z+JJD2MNqU6ZlBPiMp5b2eZDdZZUp/PtznEO77nzZPGYLAwoTpyAwfPz715vrQPMv7sX9mh6TfayPfpZHyEEnaBDw2vQz/rkJp/vH4BIHe1JSCgEnOC+cVsC6PLly3z2s59la2sLe1U383/2z/7ZHRnYq5E3nngdi50VBtoy0A6HpONHdLyIwhrK8SbkY6TOkLak02rRbvVABfSNI3WShh8RSsjyIePBOQJb8mTvUZrRApOi4EI8RqNoNbtEYYvCWfrZmNCLiJSP5wyP2xxZjCiLIWWZggoIl58ibCxTIkmMpaUkC/47SSYvEGcvkViP/unH2fv9/w9EylL7G8jbIXlrhbCzzuNKEk1Lrq8HPr4UFLrguUmfzTyhrSK6Xsjq2BEMNbrtKJcFlyZ9ch1T6gwHCK+N8pq0ow4dzyc1MTtpn9SUtPweXdWiE3aI/ADvgMvGP/UUZbZLYEt6rVWQHlmyy6W9FwmDLlH7FGIPnhxllO2S6JFlQr/KcClMwV46xNkcawoUEKkAUUjOv5CgpePE0y3G1jLQhtAWePmEr15MUTLgL51dIsMRY4gai3T8iK6nCIStrGplihxtkFjNnh+QypATrRVWokUyXfDS6DwXJ5fAOU62H6PVfQTPC4ikQEy2INmFzjpatjm/NGLzUspyGNE6uVAFWbtpIOSFP6oCGzeegUe+6QgT0bUcFEA1NeNJTllYhISlxdsPgB4XY76691WgeiA/ufAkAkFhi8riMJboIiUNcrIeNPwGvbCHL32ejZ/FGMPyyWUyMgIHy3GC13s9/c4ag9YSnaDDSmNl7spyzjFIhjx75SJ5mLOy1kL5gpbfmj+IfenjcMRlzKSYIIUkUMEh18yMwhT0i5gLesDJqMFfWVyk60nSMsViWQgX6AQdrLPzh7u2GjOLK7KG4XCPUq0iosfoNM8QeRFyGivZ8Bq0/Mr13/JbfMvJb6EwBUoqlFDz9aASFzPMNOBaSTUXN6lOMc6grabpN1mKlm7421hnsc7OBVSuc/KpABRC4EkPdXELIwvibkr85kcxzUoo+tLndPs0Tf/o7MDTndNoq6tKzwdcg0mZzI+NEgp5Psd17H1rjzTj2ALoox/9KD/2Yz9GEAQsLy8f8qUKIWoBdANCFfJoZ51Hr7dC9/oR8StXL2ivVD7rg4uAN16zIjzWO54Ze/FAKEmj9ThZcYUmGQsrBZ2zfwkuX2BlDJ1vfNuhthpXE3gBb144wUEDp400+XiIl8JCtMha52byfxkWzt7awP3D24raazzZXpv/rYuMcmKIZHMufgACFbDeXr1mc845upt7WONY9nqcuir4eJjvkBaGhe4iS60bBIuGPWiv0+Ta37EdNHjT8uvI9JhJOWGTCW8Jwv3rqnOiegEnnCPsBCTdknFhae1UJQEQAsJ2JXou/AnE2zB4GRYfu+khm5WzKIqHu7VHzZ1h1v6i2fbxvNuPCduMNwFYjpb5hpVvuCbmptiLUUKx0FvG7+5fi2VZ4pxDSslKe6WKS9t+dnqOd1h65Js56vEuhEAmIWvhOmHT49SJhSPHJagsE53g5hnLO7bNic4iS77HE53pg/oqo9jMtTMTMzPi+EVU1MNTp1hY+Ks3rckmhSS6Tgr8QQ6KCiUV7aBN+5iZVLMYU6gETcM7vFNmPCa5sIGUAetvfQv+yePVap65ug5yUDDZVJPn6bQZ6v2tP3bsM/xf/st/yU/+5E8yHA556aWXOHfu3Pz14osv3o0x1txHhJC0Wk8BoM0G0eOPQrNF0k/Iv/a1Y29PRh6y6YEDvZfe/AN3kFm5/VvtCSaEmDeBvLpuE+ynw4+z8pr3jjUuIXnT8puQQtLP+mwmm0eup4TgZOjjLYbsak0yLNDlgX1pLMLKtO3JaOOWvnsmgGZpxzWvbQaDyhLQW7h9649zjq10C4CT7ZNHdwe/TguMmRAPgqASP6asxDzA8lM3/N54GrzdXrj1GLjrYZ1jI6+uiccax9ue1jFpdh6AZvOJh6rNjLOW7CtfBQfe6uqxxc+tYKYuVtUJblqV/25z7G9PkoTv/d7vrTNGXkMEwSpSeFhX0FopEY+/nnSiKbd3KG9S/NJae417xVuuZhx6cIMOwXcBMW1b4cppBdpbYJZyPktvP0g3qt4bpbdWW+hGNP0mZzpnANhJr5OuCjwS+qhIMfarBoXj3ezwCt1TgKiyOYrkpt97UABd7c6uee0xnAqghVcgIob5kMIUeNI70h3jnMNOJyHiqlYyBwUQAP2Xqxo3QRva17cWF5kmTw0CaN0BAbRbVskpgRQsH6MSdtXs9M9wzuB7PcJw7eYfeoAozp3DxjHC94mefv0d376zbl8A3YHf6ZVybBXzIz/yI/yP//E/7sZYXvUYY8jz/NCDxlpLlmXkeU5ZlhRFQZZlpGl6aD3n3DUFKPM8ZzweE8cxaZoeWaDyINZarLXcLPGvKIp5J2aorEBBUF3IKugTLHRwJx8lnZRkzz2HGY+P3E4cx5w7d46XXnqJK1euzL9/YlP6+Yg8y9H96njEcUwcx/N9MMaQJAlZlt10vLPjE8cxk8nkusdBKIEIpmn/t2gFCls3tgBZY9ja2eX8+fM8++yzXLx4cT6G2etWWW5Ursp+1r/uPvd8j7ZSeIshe6VmvJthD1af9cKqRgdUdVJugud588lMbQV6bVPmhjgtQcDiK7AAzaw/K42VQ7EsM1xpwTqQ+9fjjEMCyGjov1S9sfzkDWPa4qlwa3SCeWmPV8KVqfXnRHBEyvx1MCZjMPxTrM3xVJtu9y2veBz3EjMcUpyvLFfRG54+VtPtW8WOC5xxVXeA5v3v0XbsEXzkIx/hb/2tv8Xv/u7v8o3f+I34/uG4iH/7b//tLW/rM5/5DD/90z/NF77wBTY2Nvj4xz/Oe97znht+5tOf/jQf/OAH+Yu/+AvOnDnDv/gX/4If/MEffEXbvFekaTpvF+L71YV1o9gLKSWNRqNqg5GmOOdoNpu0220mkwlJcu0Mf/ZAc85VM62p4Ln6gSqEmL+UUgRBgFKKJEnmD8J2u02z2aQsSyZxSZZuAXugFWNP0E8tHQbw2c/SeMMbCJtNgiDAWktRFIxG+9kJw+GQPM/nVV6tK9nd3sUfbiPWwirlbYrv+4cexkopGo3GvDCa1noujMIwxPd9RqPRoc/4vj/ff6UUURQRBAEuzxGJodiDht/BWjsPAhaiKoQ4e1WiKmFjY1Adq1ZKs92YvzcajhlsbuCA9bCLJ+VcyB2k1WrR6/Xms9rZMZdSzkWvlJJ21MaT3jyzoxf25r/fwYJwj0Q+o1bJxvkd9DBD+zGnH1uv9s85THMVxtswuIxaurkJPggCsiyjKIpbbj2Qpinb29tYa1ldXaXVur/BjDWvnHhckJcWGSm6t1ls0zk3z9xaax5t/Zi5oGUgr40NOiiA0n6V4u5F0LmxK2YyFUB3wvpjnWOrqCY86+GtH4ckeQFrc5Rq0uv9JaS8v/Etx8EZQ/bVyvXlr5/AW702JvJOoAf71p8HwTV4WwLok5/8JE8//TTANUHQxyGOY9761rfywz/8w/y9v/f3brr+uXPn+O7v/m7e+9738l//63/l937v9/jRH/1RTp48ybvf/e7b2ua9xBhzqAP3jNkM3Fo7fwjP1r/6YZokySHhE0XR3DqktT5kubkRB0WRMeZIITaZTJhMU+2dCyhLh3Mpvt9HG0XWW8IOLxKUJcWzzxI89tg12+j1erRaLa5cuTLv76OUorXWYjDaoiwK1EQQLk6b5JXl/Nh4nje3oszGcTUH3WtSSjzPoyiKQ8fXWjv/W8QaOTS4coS9XmXUq5CeQ+eGve0hcXp4HJ4SOOnT7i2xtthlPB4zHA4PWe+OEkVH4fs+spSM4hFfGX6F5WB5/hvNRFMYhoRhSLy5Q2rGNEvHxoUtSpsShEEVRKpLuDIChoj8y0Tt3lyYGmPmx8k5R57nbG5uUhQFSinyPCfLMsqynH+XEAKt9fz8NMYwPmD1u3jxIt1ul06ng+/71X7ULvKHjv60/1fU9giPaPR5S9vI+5S2xJc+i+Hiketcz/0F+9dzEAQwuz4bize0/mRxiS4sUgqadyCodqfQGOcIpWThFo+Dc5a82Abr6PTeeKjT+8NA8eKL2CRFhAHh6153V77DlRY7DSVQvQfj+NxWJej/9J/+0yGry+3yXd/1XXzXd33XLa//y7/8yzz++OP8zM/8DABvfOMb+exnP8u/+3f/bi6AjrvNe0mv16PX681n/QctGFczezglSTK3BAkh5i6vKIpYWFg41JR2JmQOCqmZlWf2/9m2D1qIZp/TWhNFEc1mE6313GoTBAFRFJHnbyDPLxMEHt3oEfpbMdHyAp3By1hTVRK2zSZKKZRSNJvNuWUgDEO2t7cJw5DFxUWUUqyGi4zO7xJ1GnSerGYcs2MTBAG+789jiGYPZa01vu/PH8yz5c1mk263i5QSrTVFUcz3e2YxKssSIwtcnqGFo5gem0ajMa+BM3PTzQRMGIZ0fMdwO8EPodlR82PXaDQ44y8yzByq0ZlvZ3V1FefcXNANh0PG4/Ehd9hs+0IIfN9Ha01ZlohMUKQFAz1gyd+Pn3DO7QvcOKZlSsqOj1URvjOkk2Lfiqa8qjpsPsIlu6QqIE2vH3AupaQoCnZ2dg6NsSiKQ0LnambHezAYMBqNDln8ZlbF9fX1utP8Q8JwVImPbvf2H04z689qc/W6E+K5BeiqFhgHXcZBEEB/ej6FN87Ymrm/mt0AeQcqV28UU/dX6N10Um+0ZbSTMhleYRwPcNbHpZKlUwbvGLFDx8WaqoekH7zy79D9PsWFiwBEb3gD4ojn0Z1gFvsjWz7yDoz7TnBsARSGId/6rd96N8ZyUz73uc/x7d/+7YeWvfvd7+b973//K9rurCP2jIM38rvBbJZ8I4QQRFF0TTPA5eVllpePTmufuYpuh6tdGEop1tYOm7CL4lGGoz5SxPROLqNjD6kEi4uS8uIl1HBI8zqzhyAIeOSRRw4vW2zQ3mmBBlsYZKCuOTYz8Xe9/TqqAa/neXje/qkdhuF8/+yiIS8HIAWN19+4XsaMicjJJ44w9Dh1auHQe3t6zDBLiA/EFM1E52y/V1dXWb3KpDwTWUqpqjGstUwmE6JxxMAbEIQBZ8+cJfCCudWlLMt5fNjZRhPnN3ATx3ICYDhxqk0QBpUb9EQTd+VLFAqytXXKspy73ay1c0vhTJBevFjdALvdLlEU4fv+oevC8zyU2hd/7XZ7/pt0Oh0Gg8Hc8jYT1Wmasru7y8m7kElSc2dx1jGeVFbgxVvtoH4Es8J4N6pF4/JpF/jw6Ayw2bk279EV9bgRsz6Bd8L6Y5xje+r+Ohlc/x5tjaW/mTDezXAOcr2JNQ5frRAPq96FvdUGnaVo3rfxRhSZppwJQyWImv5125CUueHKi0N0aVGeIGr5LJxoEkTHj6lxWs+zef1TJ/Gu82y5E+hZ8PN9Tn0/yLGP2I//+I/zH/7Df+Dnfu7n7sZ4bsiVK1c4ceJwJsCJEycYjUakaXrbD/+PfOQj/NRP/dSdGOKrGt9fQgof60qEGkw7ujvE6dOIjQ3MaIze3r5l/7HwJLLpY+MSOy6Qy3e/ErHwZWUpsQ5XGsQtzNKiaSB0kWqsdYdmma2pGX+SHy8TbBZndPDvbrdLt9tlQ26Q6YzEJjRUY/6+7/s0m1U9jVPOsdufkCjDOMnpCQ+bK4LO9ObSPoEQXya0KWGrAd71HyJBENBqtZBSHhIr7fat1RdpNpvzccF+APvly5cZj8esrKzcVPDX3F/K3JAVBiGh17m9AGhtNUlZueevV2fHWYctptbP6wigIAiqBsfF1G18AwtQnmp0aRGiCoB+pfRLg3WOSEp6/tGPR2cdWy+PSSeVpSiIBF4Q4/ktOp0nGG95ZIlmsJUy3EqJ2j5+qObB2TO3tlQSIWDSz8mvyiRVnqCzFFVB3b7E8yRCikr8vDCcN8k22hEPC7JJyfqTvWOLoPyFF7BphmxEhE/duMzAK8FmurL8SYG6A7/TneLYAuj//b//x+///u/z27/927z5zW++5sb2P//n/7xjg7tXfPjDH+aDH/zg/O/RaMSZM2fu44geTIQQhNFJ0vQ8WXaRqPUE6aQk14LwzBmKl14mf/EcamXlluPBVCfAxiVmVMzT4+8mQgpEoHC5weYGdQsCyAsUnifR2lIkmqi9f87PBFB8TAF0IxbDRTb0Bv2sP88MuxohBKcinxcSS7oQ0BsaBlsJnaWomjl6QZU6XEyqYNIbFJycuahmlqGD1rPbQSlFp1O5BNM0ZTAYXGMBq3mwyFNNWhpkqGjfhiUBYFJMcDgCFcx7S13NPANMiWoycoBDAiifWuFVcE1vuzzP51aiZLif/XUn3F97ZXUdL93gvrBzcUI6KREC1h7t4kUjhiOJFAGtzgrtriAe5ox3M9JJOX/dCCGYixddGox2DLZSBlv7rmupBM46nIMgVJx4vIsuLXuXJ+SpYfPFEQvrTbJJSZ6UVWV9IGh4dJYjotbhjDa9t0d56TIA4RvegHiF1/2NmKe+t/37XvvnIMfe44WFhfsWXLy+vs7m5uEicZubm3S73VfUx2gW7FlzcxrRadL0PEW5hxedhokgjzWdM2coL13CxjF6awv/xPUfuAdR3YDySoxN9dwNdreRgcLkppqR3GIR1bDloYcFWVweEkDtqQAqtCXX5raDRw+yGC2yEW/Qz/s3XG818HkhyYmbEjGubpp5qqvq0FAFjxYTSPduKIBmQdGz2KlXKoBmLC0tcenSJQaDAUtLS4cy2WoeLMajAmshjCTN24xdGReVy6oXXN/aOHN/yUDdOANs5v4K95tZZlnG1tYWaZoihKDVajHZMujC0VwSTCaTuXVlbmWZWlrDMDzy/JslpJRliTGGK3GGQbLQutYKprXmwgtX6G9NaDc7nH7dKs1uwHhcxT2F4dp8n1q9kFYvpMg02aTEaDu3VM3WMdpijaPR8WkvRvsWIutIxgXj3YwyNxhtcQ6sqfYpCBXrT/Qqy1CgOPF4jysvDClyw87Fa5NFyqIgHhYoT8ytTtY69J/9CaLM8c+eQecBcjudx5o7x1RsVYLLCyRRy789N5tzmGH12+qWT5Hr+X3zfnPsUfzn//yf78Y4bol3vOMd/M7v/M6hZZ/61Kd4xzvecZ9G9NpDqQZBsEJR7IDaBNbJkhLhdwjOnCF/8RzFuZfw1tZuyQokPIls3WM3WKhgzNwUfyuETZ94WFxTD0hJQTNQJIUhzu+MAFoIFwCIy3ieUXMUHSXxhEDjMA2FjA3ZpNwXQM0lGF6AZO+m3xkEwTwQ+07RarUIgoCiKBgOhywt3VrMVc29ZzieppG3b9+SMov/uVGbif0MsGuvk0MZYJNZ/E8lgPb29tje3p6v65xjMBixuzVBALKTMIhvbFmIoogwDOcxagczTgGMdVyeVC680bjD1kKVwaq1riyZ/SHb50c4QDZK9kYOTYc83wTBvOjhLIHE8zy63e6xRYOQYi6g5mMzFlNWgilseIfig5QnWX+ix+ZLI6yxNLshjU4VQ+ScIxkWTPo5RjuMro6/G/axaQaeh104RbF1a1X5pRRIJebfX5XokIQtj6jpIz0xjVm0jOKSNDeo0mC3E3YKTT/LQApWuj7L5S7rZ84SNo/uK3YvuK8ybDKZ8Pzzz8//PnfuHM888wxLS0ucPXuWD3/4w1y6dIlf+7VfA+C9730vP//zP8+HPvQhfviHf5jf//3f52Mf+xif+MQnbnmbNa+cRnSaotjBiW1wK+jCQ5cG//RpigsXsEmC3t7GXzu6DsjV3Gs32Cz7xB3DbTWLA8qSawVCK/SmAkjfuCfYLRKogMiLyHTGuBhfN6BUCMFy4LGZl6ShoBVDOi5YODG9oTSmn8vHVVG5G3SID4KAJEnuaE8wIQSLi4tsbm7WAugBxjnHaFz97t3O7VvCZwKoG3Svu871MsAOlqo4bAHqYIxhZ6dKie90OqyurmKM4fLLW0ShwQ8ljWZjPuGaJSEczHKdZYLOSnEcRAiB53nkCFSu8YxBOUu/36ff37fCTgYZnh/QakWEnaq8xWj0MmV5njDsYE2BEFuHymDM3L+zshNlWc6TC2ZJA61Wi5WVlRtmSyolUQdcR9Y6rHN4SmJt5co/9bqFa4+3c6jIw1sMGE8KRklJkmnCeIvmgk9w6hHKhZAiMyghCNSBY+egtJbCOsrUUKYaIcBXEk8KksIwTFJKowlVFSaQZQXxcEBeaKQfIPwIlwKlQTR9/LiBziZs7m1gXMr6iQ3+xnvedd39vtvclgD6zd/8TT72sY9x/vz5a26Yf/qnf3rL2/n85z/Pu961v/OzOJwf+IEf4KMf/SgbGxucn1amBHj88cf5xCc+wQc+8AF+9md/ltOnT/Orv/qr8xT4W9nmg0yRJggh8aPbr8J6I5LhgCyeIJVicGWD9tIyy6fPHrt+k+8voWQDQwr+LugT5Imm1QvxHzlN8dJLFC+9hL+2RpGlZJMx7aVlpDzaOnIv3WDOWiaTPuSG0Lv1mUcQeZXp2DiKTB+a1bVCj+1xfuxA6BvRDbo3FUAAy34lgMaBoEVVsdoai1QS/Aj8BpRpFQd0RMPX+f7dpaao7XZ7XmfIGFO7wR5AdGlJMw0Ceu3bE/C5qTqKC8QNm3NezwI0Ez9SSnzPOySARqMRzjmCIODUqVNAlUnb9HusLjVZOtmit3rjiVNZlsRxPM+GnGWK+r6P51Xp7l+bpDSCEQvZhLDIGCcx2hi6S8u02l2IWwStiLVHO/gNwWAw4PKVr5JkJaluoO0evpJY63DSI80LNjZe5rmvfo3llWUa7fb8XlsUmsvbfXb6Q1qdDkuL2/TaTYyxOGdxZUk+HhL4Pu2FBbwgoL+zw3g4pJQBurWE9ELcaAdVpERRRGtpCaF80jgmzwusH2Jk1SzZGVM1mVUewjnCcy9SJGPGKFR/jE1GqM4iTioQoMIG0g+w1mCNBmMQxkJpsNZhtCbPY3SR4Sw4LXClQecZTGvuSumQTuNScNIjMhG+9hFliSlLSicxwUPWDf7nfu7n+Of//J/zgz/4g/yv//W/+KEf+iFeeOEF/uRP/oT3ve99x9rWO9/5zhu2OThKsLzzne/kz/7sz257m/cTYzR5nNDodA6JjrLI2bt4gUl/FyEkJ554itZCVUTM6BIhJPI2HhzWGEY724y2N8niGOcsOs8ZbG4QNBokoyG6KDjx+JOIIwrXGa0xZYEf7c+u4uGAIo7x2muY4mXwtnnxUoPzWcZf+Usnic6cprx4ATuJ2fvaV9i4skGRp7QXlnjkjW+m2b02PuCgG8yMCuTK/s1MlyWbz32NyfZFWmtn6ays0Gh3Do3pVimLnM0XniOPJ8gNhxeEdLsF3RP77jprpzPUq8SakIKwUWV3FKk5JIDadyEQuhN02Eq2GOU3LsmwNM1UGUs46QmcdmSxpjlLZW4sQXnpvgmg2UOmLEvSNL3lzLKae0cxC4AOJJ3brAA9i/9p+s0ju4HDNKakmFmAju4B5vs+lAlWlyAkzmuxuX2OSVay0OwR55qGryoXyzAH54g6145ZG4txDk9KlBQUVhATUggf32qKrR0m4wmT8QRrDI3Q51npMSoNEZor2pCUhkJb+sMM5QbkYyhNznNXLNqBtilSXsAhyEtBmZ1DuhK/tUC0uEq2t0Uy2EXrksvbQxY6TcIooixKxmlV28xozWR3m8mFEtXoov02zpTkkxFFmSORBGE0zxhVtsQID0QVvLxv7QJ34UIVvDNFSImnPMT0GHhSEPmKaLBHsX2e3A9I9jbQw92q2KkxiPYCqtFCAMKaaiKFQ06tZEIoSmswBnxP0PUUoa/QxlFaiycD2p0OoS+JNy6R7Q1x0hEt9pAd0Gkfi2ZxfZGgu8apM/fXKnxsAfSLv/iL/Mqv/Ap//+//fT760Y/yoQ99iCeeeIKf/MmfZG/v5rEGr2UGm5v8+R/8Pt1uh+7KKl4YMdzZYrS5SalLxplBCHjpy3/Owtpa1TiuLBAIhJIo38MPIkrpEYUBYRBgjOXizoCdYcJqr8mp1R7SOpLxiGQ4QJcFZZZRpAlIie8HGK0p84K9y5e4+JUv01pYpLO8TNTpUeY5eTIhG48p0gQrPTpLKyw/8gh5PGHv4vlqRiAcqnWZjUHGzoVVECEXv9blDY+eojPcJb90idGff55sZQ3jHOPdXTZffI5GbwHnR2gvRCkfLwxZXV6g4Xyy7RHZxTF2AfwwQiqPnZdfwL/yp1DG7H59lcxfpNlbpLuySqPbw1qDMxYhJYUDEAQKnLFkkzHxJKbRbNBbXcNqTRbHZMkEMdQ4A7t6g8ZSl96Jkwy3rjDYuAwCuitrdFZWccagyxJnLeO+pswljc6TtBf3XQWt6Wz2TluAAAbZgDyJCZtHz5SaStJUksRYikjhTzTppNgXQM0lGF2qAqFvwCybsyzLeRHHO0Wj0agF0ANMFpfk2uK1/VsKTnXOMc41g7hklJVEvmQn32ZnnDOizefiXboNj+VWiMMxzjSFtjQcNOISgyPZjYkLTZIbcm0okzHFZEzYtFwenKO5M0T7HbbTC4x2dyp3qnK8NN5FCChGJfl2hgwk51+sKrLPg4ut5WBfXyFmQb2Wor9FvreFu6rxrwG+7gUgFStRRB52wANTZMSjAfnOAGfB7zjUNDtfyssIleHJRSQKoQAULs8oti4SKklrZRmaXeK9PUprKFMDSIyxRFHIyVaI138Bk+zCRNFvv4FMRDhPEbVXcIDOU3zfY1ElLNlNDAG76iSelMhGC9VoUuQlyWSCjsfYPMWVOUqAEmK6/47SGnJrGe3s4jkIljp0XUoSyGl9MIG0CX6pKbMUawxCegTNBg6BSTRSSTpBSBQ1CAOfRuDhKQ8ZBnhRg+X1kzQaTfY2LmLaLcrLY6w2uI5EhBJjLcrzaEzrst2BiIFXxLEF0Pnz5/lrf+2vAdWNbVYp9h/9o3/Et3zLt/DzP//zd3aEryIuvnyBZ89voeQOUfAyHg6TZ2hjyYUPrQ42S7mytUvj8iYSsIAnBU1P4IBBbphaqwk90LZ6IWDHwfMSWr7EMc2EUB4qalHmBhEopAhwfhdkALbEjvqI/pjyxZcxFpQEXwiMA6HHtNyYF15cxvz5V5GmJFSCMPBRVlOKPVKTQJbiygWG5Rb/7+UXCAR09naxDnITwdopio2LhGVMaS5ydeyxEILAUwRFdYG4hqpSz6Wkm7+MpyfIZgtRXmSXgnSzj3z+ZfxGhO/5SKVIS01RGqzWOFMinAUvxAtDQt/jxOVLmDyjLHKa3R4yEeg4J9kZIxsKP4oq0TXNgNq9dIHdSxcOjbNILenY0b/0HP03nubEE69jcf0kraByj2njyEpDdAcqwHaCDs5Yrlw8x/NXHKdOP8niycOFJLPJhNHOFs1Gh0QFJJGgN4FsfCBOqTFtR5ANq9oq13FDznrTzSpO38m6PY1Gg9FodGT8Rc39ZzQuwEHQUDc9d0tj+bPzA0bp4Vi4lyZXmOiUU42AGE2cazYGh39vLymJdnNMqEj3Dluc87wgKw0YQcdUVk/tt8mTMVJUBTp7rZC40BjjMEl1E/Tb09Rx45j7Xq7CORDCIXbO0ywSvEDi/BadlRU6nTbK8zk/SWgnOa2owSOtJpGv6EYezcBj98qIjWev4PmKk08v0mxEKKEphhP8zNCyS3jS4FpL5KqL1hadl/hhyPKJFZQwbA16XDh/iQIfwjaeTVnVG6yFJcXKKQZ7EVZrToo+48VvoLGwyomTJ+etj7x4k3DwLFBZTE6Hirj3NM4aZD7CBSXJZBPdlJQLC1i8aqKrNc5ZcOCSBJskuEYT6fu0Tp1EKo+1Zovm6hr5cEA+HOxbkYRACkEYhhhryLIcaw1SKuRUbGbagDaQ5zAas7e1RRAGKKnAKoKoS1kmWFXiCUlzcYlGu03ge/ieR9S6vxOiYwug9fV19vb2ePTRRzl79ix/9Ed/xFvf+lbOnTv3wLqeHhSa66dZfuJp8smQYjKi1AZv8SRRZ4FOq0sz8jEOxjvb5OM+TipU2MAJQWwMOIMqClpljpkGwQkhaPkBy70mu8PKx51KjyD0aPqGsnsG4Qe0Wj2c0ZgiR7W74EA88iRmvIseDXGTIUE+pPQ7uEaLhtCctC9BoWhPxmzQoQx76O4CLKxBMkZkPdrBLt0zLSL7JLvpiL1kgpWSXAQ08pIoaFMuryF6K7hkgspToiIlcmVlXTGaPE1o600kjlStkKkOWpZ09TYqALn6OKa5RsNNWLOOS+UKaVqSFjnCSLASnI/wPIQXYhEgQAYRJmwSFxmXBwNONCWdpWXCVhu/4eMiTTtYYVL0yZMJXhDSWV0jCCJG25ukk/G0nYZCBT7Nbot0EpMMJ1z8ypfYfOlFltZPsXDyFHorISsMF+SQ9dUlpFLoosAaPa+e7PkBQbOJlIo8iSmzrDJPex7K91G+j+f5eEGAs5Z8cxddFsRewt7liwSNJq2FRYwu2b14gfFulRWj7Q6ThTWihUV6QJEbdDktxR+0qjoqOod0AK3r1xXyPG+eGXMnBdCsmvmsge2D0ASxZp9ZC4zeTSpAF9ryp+f7TDKNkoLFVkA38si14UKW0fU8vmF9nZV2l2FSshcXKCnoRD6hJ0mvTNChQfQC1pabNEOPVlCJrpfPJwzHltW1ZU7kQ4JeF710mhf7GkmPxx57bN73MCsMl8s+cglOvW4BEShKY+eTPk9KfCXwlEQbi7aOfLDHTgJCtll79AnaS4evAxWnqLTgdBTwpvbheCK3GdNdylnoFHTYhXFOnm2SZReRIqDdfgQhLOgdOnrav8wTYBxcfhGAdhyzng2q7RUBwhkWum1arTatk68nfHqd8sLnMXGfJX9Iu9UkGn6JRQfDPKeIr+A3GvhLZxHJNlaXdIsXkTpD5ymjrU1CZ1CeR2fBo3HqdRi/S2oVUgDPfwU1zpCrCwiWMIvLZItLEHRYfeIN8+t9snWRMs8Iemso38cWOel4XLnTwpCyLJhMYpLJBKREeF7lqcgSyjSdZ/KpZgu/jKB0NJdXUQfOrcw6sryEvKQjPdpHt4y7JxxbAH3bt30b//t//2/e9ra38UM/9EN84AMf4Dd/8zf5/Oc//8A1H33QeOzUCqvv/CuM9nYoLj6DKyZ43Q6qu0TvxKP0Vs/ggN24YC8uEDjiYZ9xKchF5XJZbPm88WQXm08YnPszpMlZOfMGRGsNqwJe7qeUw01ae3+Bh0aEjvLEG1FRt/IDY/BMhjQZWalJkxCZBLRciOcWKfwOo+7rae58ma7Xoxl4lHnKYG9MtvImbNRikmti741MjORk8Ge0J3vYtMtbVt9CtNJgMhqSbG/ifeXPaHpDorU+6cJp9uw6LVLW1BhfOmznFDZcpDj/efrbqxRxgYwNnhegO4qyOIEfniV8/K/SWH8Ke/6P0XGfN1hNmgUkdpmxCUkJaEcNllohUSPCRh1KJHqwQ5zEfGVXo+0ZoqWIb3zDY3SWVjCjgvJyjPUdZVcTD/rk8aSaLQErZx+95vdz1mGdJh3vgd1gsrtDPhkz3ttBddbRmWFnwyDjG9fvuTkCqRSBVijlYVs+FLB17gWidod0PJqPM2y06CUxX9vdJo8n9MYKaSVbL2W0F0ManQ5+tACTTciH1xVAwDxWpyiKQ5WdXylhGM5bcOR5fk17l5r7hykt8bQKcfcGFaBLsy9+Ak/y9kcX5+6ypEzY1A2kkLxxveoBtnbEtorUYqSHv97CWzr8voel1/BZX2zTuFQ9XBMRooQhDMP5OSOEwOV2GpMiCacxS4F3dAq8pyQSw5WNqtXL4slHrhE/AHvT2KTFqyxgRVJiL3+RIN+mudCGovqesqiETtB7ArH0NCi/srJmQygScNPWONIn0Y7dxELQJSInCDyisEO0sA5rb4TGAk2A1jvh5T8EW0JRuawlsBgCa2vQOw3r3wiTLbj0p4CjzC07gxFBbx2/2WVlbQFlMnATKCZ0qdpdpMNz1fHoLKO6LWRbIsQQGMKlEbRWIdmhnQ2rcacvViUIGos0ewtVYcrhc1CmLEsf1wsQXqMSekKAlqA9tHYkSYJ1KXYkkYsrBE+uYqTETrZww4voLCYVTTLVRamFI3+3e8WxBdCv/MqvzFP83ve+97G8vMwf/uEf8rf/9t/mx37sx+74AF9N+GHEQrDDgrgIpxeBmfSdQP8vYPIiorXGilQsK8VWf4zICjomp6Es7UZEt7EAwx3on6MdTn1Ju1+F4TmIurxeKHAbsBgAIeBg8kUoO1AmYKama+cg3oLhpWqdsAvtE4BlbfQnEArwW3DqbYQX/ogTiz5wHmwTfMBeAAVpvEEcb5INN7H9DYIrPivdENXxKE430bu7qO0LrHR8VmYHYhoqI3efQwKeB83Tj+CaqxRffwFMib/WQLa7sHAWlh6vPvC6b4HNL0O8A1MBYKwmS8ew8hT++pvmrhwAHj1FFk/oDjJeHJSkQlJEPYSU80wzhaJ1YoWFE+tYa8gmVVCklBIxewlJnsSkoyGTwZhGp0tn+SzjnUsMNzdwzrGwvExhI6TviFrgcHiej/SqIETB/5+9P4mVLU3vutHf26w2+h27Pf3Jk5lVlVVlUy43GH/3wgd3YukKMbQYeOAhSAwQEh7ADCxgALLEDNEJxATm3E/30pgPA7bLLldmVVZ2pz+7jz5W/3Z3EJlZTVbZVXZmGYr8S1tH2rEj4j0R613rWc/zb9jxq+qK4BxxlhPnOYRAXVa0bYOSArwjeI93llE8xO/10PmIbNuj3m6oNitgV/hM79wl6w9YX13Se/yMoqlZNJZ06zH1nNGBQkUxd076SC6h+YMJ1R9I4T9OLyD4Vq5dVVU0TfNpAfQ/EdrvIEB//8vBN883HxY/X747+dABHXZ+VQC5zv/A7p7/Phlg355NFwsHrgMEtd+9x3eb3Fbvd6x+0Oyv5fnZTsyRpIwPjz/yuPGB4v0Q1r3vir+o3vs9VHtN0otRJ69BMsDQURcCIWMG0/8HfC+fLtOAkFSd5cWLF4SDOwyHQw6PDneCBIB8+p0p93EOt74MyycQDyAbg5A7FSfAcKeAo38It34a2i1XpzOa6Q3S3oD9l19Fab17/eJqp6Rrt7jtihBPEP0R8Wuf3xVnzoJrd+eEdvMt523E7j2D271O/dGbuXl1yUVxTj8ZcLN34ztI7xoYJgK7LnDtAinPiS4zQNC5lrPijNa1HOQHnMgC6g74wUxzPwn8UAWQtZZf+7Vf41d+5Ve4desWAL/0S7/EL/3SL30ii/txw+L0dzh/8htIIbHZGDd5wEFTMlrPEGZLG2ewfEEcLOVmw7bpsEKCkHghMVFMcRojpSSOIpK9m4RsRPfi67h6Q5L16I0m2OCYRxHrdMRk9ohBvSJWCUFH1N6waNcsqyXKGXIZ00+GjLMJWZTC8un7m0HAwWfg9CvgLb64Am+RyQBkhFs+I2zPCdkQ10+pkPiNI0LSFSV1XxPiKbF16CLCzQ0mtETZgMntz5BGGpaPoStpooyz4SFEKccv/1nEYoOdjIlvj7/zA9QJ3PzybvOWV9TzFyzOnxCaDay+Qnc1x4/v0u/3P4xiSHt9bumIs+snXCw2/Nf5OT95d59UKcKqRCuNavtESYQQEnRE0xmqbYExhn6/z3g8Ju4PMEIS70mawmESwfFrX0BISbGYQVMQTU9w2pMdpLvYkPcdxr9d+u3el6MqpfDec319Td10kPd3fC+tOZhOiZTkUAV+d/5VKldxcP8nWJ2doqKI/t6UOP3WRWF0eMRndMJ7qw1y2JGe1ggCUnY407EtIkbwbSe5741vJ0J/3PigAKrrmvF4/LG//qf4o6GtDK1xyEwxSL732PPFsuJq0yIE/OTt8XcUPwBbs+OB/kEGiMEFwvvkv+/2APpAASalRJkP8r/61M37MRffVgCFEL4VfvoDhLaarmV9dQGws/z4HmrX5fvxF7mSJN/2uL9+j+7iKSBIXvlp2NvFIzXbb4CKSJJj5PcxKSVKaZqG09PTD8ODjw4OCUIievvf+zmw4+xlk53RY7siVjG9/HsopXr7bFtouiuElBy+9DKFK7HGkumMbP/VD4tR88YbuMEIffc2HO/CqktTcl6c02U5o7Yk71qaOKNMh1gpEaZGtwVpV5KYljgdkk4e8NxVnK0DMuuzcR2zUHMru0mcDJA6o7Q1pSmIrtYkoSPWLbJpsN5yViwoGGPEHut6zVgsORwfM/5DvsNPEj9UAaS15h/8g3/AL//yL39S6/mxxqqxLJ49pcr2KNOI8OK3eS94BBCCJzNnaLcbfYngUd4wyBxBRmzqBBs0UrToYKmjHvX1rq1L8CS2IlpY9KnDJSlN1MN78B3khQG5weYCpxTBxMjuBKcdfjIGnSMuV/S3M/I0ZhD3CErRXF7jwhVKJmhxSNVt2K5LumDY387omQZbwTJ4wtqBMSTVAT62VIuGbs+RCUs6WyK6BvfqK6igUM+f0ev3CKQY07Kot4T17iT6rtEcbSfY6xeUc4uIJEmUkMYpERESiTUWW7TUjSFkt8jlBYPyHBZvUbqWq3KMOTO44JCRxLQGEGw7Q1EYzjZPuDERDK80Oii0eMHR4QmLcsGqWuGCI5YxkYqIi5jo+lsE4dW2ZHZd4NYN2b4g8456vSRstjS3BOskZspoZ/8eHIFAJCOUUruEdFvTuY5IRWipkV4iak8cRbh4d5NxeX3NvXv3yKKIaBlhvKHyNdOTO7sss+/R7j/q9zjzAjESTDE4G+iNOraz56yWBYPMI7vqDyVCwydTAH1wEavrH8xx9lP8aFAWHcYGkkR9qGb8dhSt5Z3L3d58+bDPKPvoBb98P7S0F31/T5cP5O9Cy48cv99hgPj+CMbFA9rtRwugtrI4uwsk/tDx/A/A5vqKEDxpf/ihtcgHaGzDWXHG22XLJmQcDb/1eGgLlo9+j21boA6/yNHhLeb1nNPtM6rNVxglQ+4OfpLKVKzbNR5PP+oTq5hls+Ryc8n1+TU6aPr9PtNoRPn2DC8Dbc/R9ixGOYw3JCr50D7AB09tal4UL2jd7v8/TsYc946x3tL5jkhExDLi8tHb1HWB3h9xdvFbdMstXoAaJkilSXVKJhLcw6/Qmpr2Ros+vSKSEZWtPvy/XgHEEmih+ZbbNhJIM0Kc7lQ3q9fBVxAdcHvvVeblgmVZMt90BDcHDyqDSEDqU5o2sNo0aJMjTMC5m2Rxn2E04Lq64lpfsSpbfvYP/RY/OfzQI7C/8Bf+Ar/xG7/BvXv3PoHl/HhjGEask5+m5wPTlaG2gq3w+HQIskOKHi4JGBXwUjDNY1IRIytNXBsa02ESMCOLdJaoCeAFqUqJ1AElFaWqES7QWxqSOsHKPm2e4AWIyhFLQV/16WUDSPq00lMsrrFnEm8TCqAQKT5N8OM+KEnQ4COAKYQeebcgNimujRGRJ9pIWjqMe8zKCqb9KZNoQHDQTBVi9g66qBDdlkpFtFXLfHmNtOz0p8bSX7c7mWY/46wtEQ7sBlwWoK53XZ9+H+Elsv3WZ5rnKeFoghMFSXWOX3+Non2FppNY4QkRECCNYm7tpZyega8dTYCpVvjWM19ccdZeoBrQrSCOY0IPutSyKZZ0dYeQYtcl8prYSKRJUXFDiWPdbPF1R9H9DuH4AD0YoSTYyhA6jxaKRCW0psObD+Z/u3UlnWLUH5LGCS545mZNLx2grODu3bsM6LMul2wfXhPzrRGC6keocfKhn8rkffVaEwIy17iNQakBUZJi2oayqBgM+7suUPadF4IP8KMogIwxH0vg6qf4o6F1LdbvugRSSDbvZzTl/Rit5PtyaYPxhqIr+M1HF6yqjpujEYfDIdZbrLf44FFSIRCclWcsmgXjdMwwGX5o4nlx9pgozdjbO8KWDZfFKUu5xp/HjJIRkYwoTcl8PqfZNIzCCFleIOsFGyLmVUoWZ1S+InUp1lsenj7ncrkkGSo287PdTYTYFVSNa6hMtVtb8ERdzfr5BhUEg/ENrq6+ivOOWMUIBItmQSDwqNI0ASLzlO1WkckYff41zPmCOoyos4KnL/4LgYBZPCU0V5T9jku+DvD+ng7I9/eP957V+QrvPDrSSAmP3n4LAnRFQTm7JjhH79XbhKMEYUCXQACX736CBi01zjtW1TWr8grbeerza6K8R7SC9mpJkIFsUyO2DuUhlpoinWFG0E73MIVDFxV+EBPyDLfp8OvdiLE3HtPTfWpf0bgWZTWyi5EBzGqFWRY0tcG0HvIZ0eQKmXgmiSM0A8bxhKbuKNs1QUcQaaJYEtcd1fWSbbnFRo6gS+KkpKcy+uwTGkvaXNO4Fe3VBbz6f/zoN8L7+KHPQr/4i7/Ir/7qr/LGG2/w5S9/mV7vO6v+v/gX/+LHtrgfN/Q6xe3rFU4fEsQAISN00sd1DiUj/HoFgBqNCT4gKk2wgDWgHCIRBBfw2x0vRbzvexOsx5sAccR6s8abhiwZovoCLRWRrghJjhU5AdAElHSE1RbVXoDd4nRDrVtKOWLNFBkkaivQ+xNCJPDBEqHpRRH91RLaya7cNyVOpVxPDcu4ZeINd8eafnWIMoI2bGiGW/zlivBb1/DyKzSqRxP8TmHVWpJtSyxGID3FuqSRkJoU3SmCqulsjA0SvzG4oz3UKEMliiiKSYgQncD5Abrq6JkNe67C7n+J4APWul0nJ4oIBO4dCV4sDUpIXs0SLDVzv2ZWruknPQ7G+yQqofMdne2w2tJkOzmvlBIZKbospad7jA7gurrmct+yffGCaGOIDodMipwsUjvpqQr44AkuMFQJOtakKqVzhs4ZZAbEgk5YZAcjkbO4nFNdbXDzmkGiaWposxoGe+ADoXXY1mHnDTLXREc9dKYZacW6s7QSZAhUhWFycpOrJw/ZbhvyXoZqt39oAeSc+9hdm5VSH+aCNU3zqR/QjxDGGx6uHrJoFnTu/XGTkKQh59l1xUVdsGiB5z3Ct0nJV6Xn6dIjBYik4Lcuzj7y2s473lm+A8C8mrNqVmipqRZLqtMrhBQMXrlDutboBuwQOltT2291AoumoDUtwQb62ycIbzmTE7bVioSE7dX2w78tLwPeAjHM6l3wpxDf2VGK6hXZ7DF+UyK2hll0QHuQEkXvc5S+rb7vx2MGPtsRhuUGvb4gu3yTdH3Bttxnc/KzxAOJbRraiwV9d4WWms02UC9OwXoSvyvCKmEJqUK7mF4jiWSK2mjq31tT+YJOLGhDReoSYhfR/v5z9k5eQiagwoquNhRVimkVg37CXv8CKdvd5GC+oGoCKIm1O+WdijJ62YiBE0SqTxyNCWh6RUVXF4QXDi5PUSaQH+4jv55Qlys6tyUWEeFpyTaSEDpkaLA0+HaNrSp80AQkykgiU+GaOW7RAZKNvaJQ3yAEgexrBrHGFVt8XWO9Zf0Bn0tJetqCLdE6IIOhcQ6BQCPJyEmjP7kcMPgjFEB/5a/8FQD+4T/8hx95bBeC5v74q/oxRbt+yNL+V3TXQw8eILIH2MShpaN5cokvDBrwK0U0mWLXAVd3iEgS3ThAjPsYc0kXlvhQASUKQcoJkR2yfesCCotE4Y5akvtTdPsW4uohKkqJsvt4OUKmBikrBEuqriagGD24y/jGTsHhnaJbBrpFhzcNdIAIyLBF2iWEGhePsYPPo1ZvE4cNe8Yi62ui1Qt6M0042KPUYBGEPML2rlBtSjaLGA/vIsT7xmFRDNMYPRoh0oTp7IrgoZsHnvpL+mnMS6M74CHYlgBkd++RvnxvpypaX9NcX9DZBT4ZkswuSCODzCLIjna8AyF26dOJgkiyfrbcGbApxchHDOMJL+8lqFSjxilCS3xt3n8uHzqpBb+TcL/QBUHC6KjPnjvgweg+39z8F5abGl30uX3rPvuDDK0jRKKoXU3VLoiSjHH/cOeu6gLOdshegkn8zkKi82znS2z0kOViyTvzd3n58C5e1ZT9hvTlMQBu02FXDb6x+MrSPtuQ3B6wZ8G8KOkcZIXHTlLifEyUZFiZUm/W9Pe+Pw9IKfXhqO6DyICPE0mS0HUdbdt+WgD9iOCD5xuzb7BqVwAIBFJIXHCsN1uKrsNpRxLr7yh+lFCsi4T9LOPuNGfQK1m3u/GUFBKBwAVH4xoSmTBJJxz3j5nXc4ztaK4WpHqnXO1ma3L2GSVDpscniFHEpttgvaUX9Vi0CypZMRxEjLvLHXk4uYFyJdkgQ2lFXReYbUfOIaPBgMPjhqp5jPMGndxCxUfkKqa3fE5kAp19ic1iSdQ8opc9g8shyUbSG49JYgesyHt7FHHGsTW8KlN+2tXUraNZLPCtJFMDjsOc8MLx9L2HDCOD3p+R9UcciTs0dYoUiiBKvC0IriYUhqIzGKGwmwizsETWA4okEoyTjPxgj6aoMJsl5dOvkGYSox0hQG4jgrFYe8HFyuHbllDU6FYwyAWi36N2BuM7dNCINqdtU1yeYUhxvsN7sHWgXa3xxQZCQJ5/E3EJMpKITGAl7/OhBHhPsBZZN6jgkEjQGaKXomQFaUnXGsxyDHWK6F+i84A2AdHFCAUu1Hjf4ozEtT2EUOQTRZRo2jLGlX1EFBC9Gp1IotAndkfk05d/pPvhu/FDF0D+uxw0P8UPjtrVNNEBSlhwp0i3ILTgWg9xjRgLRFBIJVBEMBCQvd8p6TLYKpxpcKYA4UCpnUJJXCKqCJN7SD0u2o3Kuk0OxkDaIbFE4V1UNkQoRVDgZcPWV7i4R51KekYRimcEKoLRCCegsThR42SLiAS6pwg9jd0bYOUFaj8mupSEChCCsmcIHjK/BnWAdBKpIuTeCN8UNPJ1TK8mEvt42RJ0hxqOkAMDXhKkJRQ1S7GlqJdUqaZ3a0AaIvzFFlaW+vevEM9+Dw5TvO12bpEARLTpPqF4hj6r8Cd/BjIF0fvy2TrAUnPgBKdlyUXkSHMByqCzGLyCK0XwFi9rvOh2d4xCIHWCinNUlCKzkrZyFIucVKcE0TG87SjOX2CYUbcN3XgPn+4u8tZdISnwRrIq30PqGO/bXebPyvNoluKD4MF+h8w8rxynvOtrinLJWf0EhmC7mOVVIFQBV3YokZKpO7g6EBqHm9UMUsW19RRC7PyArivKYcQw3WPV9ik3z8imK+T0/ZsUJb8jVRp2XaAPCqCPW62Vpinb7fZTQ8QfEUIIvLV4i1W7QgnFa9PXGCdjpJDUtuasm9MkG4aJ4k/dOOLmOEci0VJzvm5o8w1aCX7+7j6RklhvEYidyR073uLj+e+TUzFJJryUj7iXjbm8eELdH6BUAC8RDDlIbiMoEPKM0EBWVAQfyIYDlu0LchlzJIekOiGkAwbW0M963Dm6i61KLmczNqs5tl2QHGckbkiS7EjXwRWIzRK9usTZBtPEOHHAUtY4Vgx7oMxXoYqQlzmlWtPYhHUcUWlJIgVJpHgmBO7qKco5GisoOEddLCnqardXMciFoN5sUe7r0KYE1YDa7SfvLM46uq4jhEAiIkQeyLQlDMcIFROlKVJdkvQNVbrCe0PjPN5LUJIsWhP3KkQTYK2QnYDIIbQnjiTKCbwtEb4kMZJY9hEyIpQOv7a7bh2WyEoyB1ZYUAIijXSW2GmiQhHEB8aRlmAseItwAqlidBKjlQG33RVUfYXKM2Su8HaL7ScIrZBGIq8bhAmoyRg9yXC15tpK0nHOQeJxvibKj/AuppOeR2rDVnluJTe5wYB478af0O7Y4QcqgPb29njnnXfY39/nV37lV/j1X/91BoPvz/j/FN8bg/t/jupygN8+JvhTQr2FzqMshDhB7vXwMqJdbPBNh44V0URBW+HNGlYRyuVE7SHCJQQZYaOaJl4hMDDQJIcHRN2WdjGDjcKHITbOCcHT+hLlN3inCVZQG/A+JWCoX3yNTASi8R5x7Am2wbQFzm+JZA9BhE5ytNjDx32aeYVNCkTjiUOFiiDEE5zbp7Mxo8GIKLqLcENoKxg46u6bdHIO7QwzFhg5wFpB1LTEskAgdzckA88mrNC2BddyPn+HG4MT5L7CxQVuuYAViCYmOrpJrPeRRY4serRhjLPXwAwz/+/Y7OQjF3qdgBC7u5plGUiTBKUyBD+YQZ8tOmxl2VqN7ccILUhvavy6xVYNRX3OuO7YFLBpYZqBFIKAxdcW+BYB8cUWXhS7lnEkA7cHAB3HieLFtsYVDkpJMpAU9hlK7LaskWD8in7yeULh8a0jrSzuOKYexdy86GDWUD/eMDnOEG0ft4op3l4SmyUgEEqQvDRCfJv3SRRFNE3zsWeCwXcaIn6KTx7Pts+4qq4QCD6///nvCNXNo5w8dMjQkeUJ06xPoj7gsAQeXe+Izff3e0Tvp5D7tvswFsZZw3r1BufLr9HaAtc/ZGYKBILq+oIQHIPpCfV2Q7N5zsXqLVQEbb3BtB6tB4DAPl2zaSpCUJjzgKgXNCKwCilRlBCqG7TVGuu2NI3BWYVe7XPppiTRToncbn8fVT5BIAghxboTOntN6wp0PGJoFElfY72kM4ZyPaHTElEG1q3HCY+Ia1otGLQSITPm6galA+k2iLhHksWo1NFVAk8PtEFGu30bvED4HKklXbC4YFBtIE4kY7ElnQwhkdhhjkBDFxBE9PsZ1arFe4VzCqolUlUI6eh1E3QyJBomRPdvEp30qb55yrvffI42BftKM8w8NhTIbIIKemcK21pkI5DBoPDEhz3U8YCwn+Ntgll7KGMgEFyJczU+GIKWRPsJci9GBkfYNFBbovE+Oh2i1LcsDlq/oVUOEQTh5RqJQEVDlrXj8WKJExbZb9lEGa+mBwQcC7PgSbvGBcArHjXv8kxF3LfwKv+Tc4C6rmOz2bC/v8+//Jf/kr//9//+pwXQHwHr1vK6zhjs/STj5jXSqiT2K3SoIZ8iDo85bytOVUvdtfQjx1F/RSsHnM4sbWu5pUd89iAjlTmtFShn0Wxo1BW9kyMG42MUGe3jJcvTU3S/j/Catl1gu2vstkbKFG8T6BRaJwhvCZdv43SHnnvM3i0MG9pIYkWMrzShSdEyJ80SZKrRkxGqUxg7YxtihAURYkxjcM2I7WaMTq/xoy027RFKR+huEV93EJ3hpwVe30Oqu1gBVjp0rpCRZxE21K1CVT1wgaqBy2rDfj+HVKKmR4grS7TtocUYffOIEEsYSuJwj7au6Ra/SVRd4fuHu4pnT+KDxdDQdRVWrTDGUdZDlM3wcUK8rwnpFqQg1YdEch/pM0zTIFVABItzHZIO1zWIOBAfx4Q4ZpjtcX4luXjvKZQ596av8vrpmsY7Nkz40p07pEJhmy3etkiRUhjFolswGHQEAUujeTAZkcU1/bBlk/a4PmvZVB110yfNjjmZjhgepNTmGb5tqM07DPa/iDvbKd3yYUSdK8zLGZQW4wKiF5PfOsaU79KWW2LXgM4ILmCuauKb3xpHfZJE6CTZjUSstZ8SoT9hGG94tnkGwKuTV7+j+IGdqWddGowN5Iki/zYF2PmmoTGOJJLcmuSYtmH+/Bnlevltr3+Ncc8oihWtVdTVgsvZghAsAUuU9BFRjc4amosLbKkhpIRetBNVKIcg0LWaepsjpcGJK7ANJhoTfEKQUG6vIAh68ohUJzR0xJucsEoxoSR4R1J7gpriooQuHuKtoyovad2Kff8SWh9hrxyuZ+l0jygZousU0euYmRZnLEJ5svodougQ8pvI7gE9sUFG52RJzOgwBlURfIJSJ4RsjdMbtMyJ/T4yKEIInF9scKVn3CVkiwvEuAUVoBWoSwXCg9qRyGWIibMRqRggtxVCCxAjfH+E0SV+CO44xQ9rLJp3BkPKQR+I2PtsDzkckNgCOoUUE4SJQHaopEb6ivadhxjREPYyTHSNjzwhS1lYz0hqel6BjIiye6Tpy9BLWBqL6gr6ssZpx3JvyKbnyVXCzcFLxLrHVfWMF5v3sL7DdjNMcDiZQZPBZEo+SHCJZCNzXlc9jG/ougVifJNxOmE/6/Ns+4JNM2OZ/cnWET/QGejnf/7n+Ut/6S/x5S9/mRACf+2v/bWPmFN9gH/2z/7Zx7rAHyecVQ+5bL7KZW8flQ2hB+JgTCYPMdbitnM8Hh9Dm8Zc+lPepkSFFX44xDnJnCWvu4IkJLiwUxh8Kao46mVsqhVPNks6F1FvDSGxSLngUF6i0xwl9kjiKaSCpi3Z2C3VqCJaFAyrjqiLeNJ2nG7ntIOIPJ6wF0eIeEMcBOW65tHKYlXLg8WM+/sZ0X5GYWO8tUSNwIVLHHOq5RFWVMyv13SRJsnGDPWQUaQx2yHalNw+OcOMRzRdtGsdV9C5jhf1NWjFIUNoPI+3a85WHau04zgeINUufbldnBJdPCN68RZu1GebGrYGzi4SRnWPm9GW6fA9bH4P17S43rcu6olLuS5qzFYQEsvGXzBbXDNnybif8oXhfUz9TaLoCBkdIoVgsr/P5OYD+kQ076yY1R3PtxrnA/v9hN6NL9C+O+d6YfjaLMH37+KNo040v7doeHDQY7o/JVaSqnO8e7YmGg45HCaEANfblkdB84X9fWIhGCYn/Nbmd3m4OicPHZ+ZZCy6PY62KbcnU5x4HZ+0mOSC7N5LmMuKcemY9xTbCKa3BzSVxfRjxie3uZqD665QRy16cEz7eI1bt/hpinw/3f6DVPhPogBSSn3oNt227acF0CeIi+ICFxy5zjnufdT8r2sdTecQErJcf9jlCSHwdLbr/tzd67F6/JzNk3MCu1F8lKV4WWPFnDQbEhCwbhn5Q7SVeOEJ0jM+2CNWCUlyROAItwrEaZ9kMkJlDjGqCalncx7w25pUCm65M3QkmWUv0wsxee7IspLYDdD+mKIKjKs5ybTBG4cvA9KuUKaHMBmdOoIaLC0dS2J1wN7eAbIa4X1FaTsa60mKwKRn6CpFOsiR2vGgfoLutggXc7qKiNw52pdEiSFyHnO5AG9J3BTtrxBxgkinCJWidIxSKV0H2cJhV9eo6yd00hG5MfErDwidwy1nOFdilUckMQsESymZSMnJ/hTJEV4PCN5CIimdJJnH9AoomoA6XZNld/C9IY/4LHsqw7YLbLvmIBHs6SVKRcTpFPfsAiWmbMYWpRpy61gYw2m3IKCpXM4dOWGfKcl2iifiRek5dQFnIwauRYYCsVKs+oeYac6LtiBOajZGU2yO8P6CtpkgRCBzHrF1fP7oZ/j8T/w8xWbLb73xNearNcmgI5tkTPMhcfRTFM2IL8gDFsunjNLRj3RffDd+oDPQv/7X/5p/9I/+EQ8fPkQIwXq9/rSN/UeAMyu2Tx5j8+cs927hkinZIGcYlbSqo2zLXaBmK0ltjdMF53aC9g33wjO83ud8rWlcTGNqjNVEQfJ6X/M5a1FlidnUrBuHjwNx2tGtDDLLmFKSSUe6f4Craoq2YOkUK5vQf+7J8iGqH6AwNN2SrZ6QqjGRSFDZmE4lbH1BJQ2yMVwZeOt8Q1hVlPsnIAR7rmHotwS3xuhzkBJPSudilIsRsaeKFbU9Iios2brhdu+Keu82RWMpqpJ6Mccaw1Cn3BuN2AjBjIiNLhCmoe+hJ2NqX+NiA2VDXFZ0YYUPjmWnCEmPXA24153TKwtqM8GXE/wkoZOWrRPQBbZFH7VVRB5q2VD4ipgEIQK/Gy4Z9qZ0UQFkKKHI0pJMP2cwGrFaQCckN24NUZHk3cstm8rwrJkQbUsu3n5KfnSbO3s5T+YVeax4dFWwqg2BwCiL2O8l5IliGmtCgIXs2DaW//5w/uExk8X7xPGCutvSlAsWcsKb75xiOsPt22P28nPu7c24tf8qtff0nINNxWrc5/Yooaks22VDPhmRTI4xlxvK88dMDh+gRglu3WIuK5K7u/T5T7IDBLsxmDGGpmk+oiD9FB8PfPA8L3ZBvrcHt7+nO3NbmZ0DdKK+w9jwctNSdQ7tPfKdR2xmOwVWkuUM9w/RUcy6/Qo6jPAmJlt7cnIOh7fJs1tImSCEJHTAPCCEJOtZ/A2DHMRE+9lOteoMlB6rKobDFcM4ZtDGCKFA7xFZwzQ/pJ/tjpHVvAFXk8ZTEhTyUGC3a+STNxDe4ZM7KA5BQtWUTNyYdDJhev9VkBH1tmT9zeckpmU6lCQqsA0tUbul7wzJdovspsxVwtoavKuJgiV1gUpYar9A4clNTOoUnVO0IuyihVSFkhbbFgjXEc22+KYj5BEbLOv3vsF+7NEZBB/TtDnrEma2RATF1pe0sSHrj3ERdDZjPUtYmoBEcRBJmu0ls3ZGFxnKFIbrx/i6x2rr6bqO9/QG1WuZ6Jb2+gK2AqMSsCP8U0PqPf2g8MltyDOM9LzdOV4gqe0LlqbecaQcCNNxJVpsXKIXgmSzpb0s6KhB7uT2PZnR+RFeHuJ9BF3FSGpOi0vq+j/T1h677aDr2JZ7VOuEcx9w/m2Eh4fOMgwRTZdw6zOf/J74fviBCqCjoyP+3t/7ewDcv3+ff/Wv/hXT6ffPFPoU3xunzzxPllPUskVdzGiTlq4zbGQgEoEIhXKWTHqiqGMRjYhlhwyBIvQZa8VxkrFVhnZrSKoO4wXVPOLNxLPXNvRE9758MYK1Yhn1qKI+zwNERYW+XqP7OdHwCETEcCmRRrPwNctRzuH6gj1bwMbQqCG122OxtVSmw/mYsVqiU8ul7HHRRdAA5yU+6/GYBM0JwU9wQYMYMBCKg1jim8Cm1NTrlqS1gCe6MjxvG+LZNZv0gFAZhD8kEjGm2mO2VAQPujdGppIX7QxrdkS/NInJoz1s6jBFRXfpkXFGLANZCEQHQ64Kz6BdIKotz8IBzcYyjzNqGRPL99UPpiVrPXE8wakE4RVr22CIGJgesdMoWSJ8iu9aXGgJ8hIhE9I4I2pKbt8aE1lL3FmCzig3G1YvLrkTIsR1xp4PLK1n6wNN2N11GwG1gj0n+O/W75JJgmMrAyHWEO18WTI8/a1g2bRcNBdEkUF1gcrCw82Ky+mSN58qDvqvM1h73LzgRQJ+kiCPD7mcBWrrmJYl++qEPfcuzM8I3qMPMty2w5cGt+1Qg/g7CqBPIrj0UyL0J4+r6orOdcQy5qj3vWMGTONord8VQPG3LgNP5iXBWMaPn2EUIATjBzfpH+yDD1izJhQG4ST1KuwMXHsDRre/gI5TvPEE4widJ1iPUIKQgIgVyf0h0X6OKw12XhOMp7NLjJuhRjkpFtI+tAnaaHrTIbQWkoh2LUBLxHBNEy8JosUXv02Sz1DJCfren9qJKWJF8fA9ZFeQ7A+wUYEcRSwXK9yeII0Dfu+cRfqU1cZAG5N1BY1cMpcJ/y26z6mTWJPSl5YkjsjVnDgkdCKmYwQSvC2JDLTCs5Ua8GSmISsqDlhS5wMe3Rhz6rYE54g6xYnoIVWCl4GtniO1J3KWVhWIWHKsh0i/pXI1q2hBpARO7EbGuquJtSM+6FHpC+b1M/YKSyMdTaJZSY2zMd4EtG1QwpDZDLGaoKQklTkExaDNGTSKJSuuw5paWFpZIXD0ZMaR6uGTlI1KieQRynr22oKhVXivCD6Qe4WmJhIxnRsg0QTfZ6trNsZy8WgFSlEpi4s8qpakqxxJAWqLBlZScxrvs9J9fupPMCD5h+5BP378+JNYx/8WeOml13j4wsH1N1HdGlPP6ILCEIMfgI9RQF+XBCnxTUrPDRC7piU5JVMqpFREokXJLQ2GWZOwrXMUFpun9EVCv9ZYHzPtuh3nRYBpLdYpWHkkhh4tx3FLiufR6D6jWtNPRxwUC8Tas9muWSnLQEV0StH2+gzynKNexZVTeBeILmf0igpXLTg7HGOyMbaoCF2K8iN6WqK3KzaRxkYD8sgxnBqGbYlpFGkXYG05KCqID8iSBD3ep1kWdKFCW0detcQBjvI+z0WFNNBvEiI0pfCUTULsAkrk6ChigEfPBMK+xFFXIF1DsJaVzNGJRCmBBCIPupL0Q0yURoiDl2hUwqDZUFQbtO3YU4J+ZIgcCJPTNILSttgAaWWozkueLCuOEvjSQHLStjyvGgbNkuPYcTi9wcZCv/NoIejpnRlzaQIbu1PZRztVPJkPZACVAb7lk7JvYqpaUPsCk/f4ufsD0kHM21cV19uKlTE8uYBDPWLPSXwt2eiGJ8UT3CZg7S4+ro0bHp9XvLTfMdpuyEdj9CTBzhvMVYXsR2itP3S9NsZ8OBL7uPABD+iD1OhP8fHj+XbX/bk5uPmhSeB3o2scjXHIvv7QAfp62+6S3s8u6YuAjCMOvvwK+f74w+e1xQt0nGLsmm1esB3VHB//DOn0o95S3luc29J8c0FwFh1JdEhRvQjVi2jaC4p33sVGHcafsfJrCC/TWgciUGUbbFLRbh3NPENhCTc8auRo33lI3F4i8hj/ubu0kzOkuKatSzbj9xBWMji6TTtc09awVY46LvF7T2n6a5RWlHFCMEPS+Zb3dMz/LXPOohzXHtLLJSGCLI0YiJZ+PGGrPsvlZklrC5Jco5VibTw2WFpjuRQp0/OGM3FCMeoxTzSCHBUEqw6e1JJIeHpRTSSHJD4wkH2uuznOweXWEDpLCIa+1uxHjs5rLuaS4HN6/QEvi3uwXHJWLrjAMUgNhwNJqlNWro+pSo6ur+ibnKWa0qJofMZ60EPuS7Z1Sb6pyEWfsRiQ+YaoTUiURGhLrR06S5lmUxp6jNpT9uxDpJNMQx/lFY1tEHHCwObc8BeoIIATirrHxlWsE4VLNPvCkGKIjcPZmh6KcapxquWq2+PUWwarPzie55PGp0P4HyFODif8Hz/7OVrzWZZPvkGzrqlNoDKejogWhUSgpcc6z+18zC2l2RrL3BcIW5O0FmkCMhWo6R3yJGFv4whVYBtnmDhlJFOEhL2uJlrPEU2DFoLsaEwnFIt1gesMCEmZjGmGPYb9ASmQ9Y6R1QGs1gzLkqFzjPuOu7cSTj0sDh4QKcmrSUTZWDhc0r31DVzXcTPew957hbS7wNUbtO7jGbK4BtdVoBzDuzeIjlPK5+8Q5iDlAX27xPtAk0u6wxsIIRmMe6QHCX6+oXmyInjPyTTnbhbTtpZ10dIYx0CC6DISb8j3c3r379Bdr+jmG7AJaX1M5pco6cjjY16OPCPd0YZdYGlZOaI2Rg36yJsDGue5qXpsrALlSPuSXrpFChikL0GXUixKVo+vMIuWFo8bppxLyYWFwXTKwJQM6y29UeDg1TG30gxvHNaDtQLrBc6BC4J8EDM+SJFaM1t2rFYtrm5wVYNSEMcSxJj/68UzzBr2h0fovVucvHLCjZfmPHlYcN5cURjo5WNeG07ZN4JTVzDtVhxpg6tAaNgmKTOteXhZM3nrm9z/uZ9H72e4VUtoHX7ToUYJURTRdd0nUgB9oAQzxnzsZoufAhbNgtKUSCG50f/+EuOusTTGo2P5Yar76aomNA2jzRaVx+x94d53FD8hBNr2kra9QKoete3Ie/fIdEpVPcbaLSE4vDd43+zURRZ8/X4mVXuFXCiUzEBIrN1grEWrHnHYsHCB50XJ2bbD6Ii312MgQjzzRPUGkVh03bB32jC8nJN1AjuYcC2m2Aqkq2iev8t6s8YmA9SyIJSS6irHbzt6/TVRlLApByx8xuNwxG1XsTAr3q0DX0luEW88d9orBoN9jm/f5TguGDCgFTHeQJqkoAMEw8K3xHHKsR5xubymenzFxkhyLZDHA27ngUE2pOiGPFs8QviKYD2yCRyYgGp6rJ0iZDep9BKPxwWPEhrBiEHoY7eBA7PF4rB+j7curuhFBpIEBcRRjm1SBl2fICVqcU2vS7EyYjM+YR7Am5KhXqFry3UIlCLQE5bPigFfVLfoMk+XpkhvsU2DKCSDNnA/bSj9hGf1a8Qh0BM7V/v9QUZwDZv4Ehs8rvHE5oyX5R6R3RIvz4kkkCpU0hGMpRM5Nj3BqRFW9dDxJevqnNvRZ/7Euj/waQH0I8XFsuCNh1fsQromeLnzqElyQRw58sztZiPbmCRAT3Xs3Yi5dXTCG4Wjq1voGoKpUSTkXiK0QqcRqWi5djWRDGjlGfb7+LKiHvQR6xQhJds4Ru/tEfVuEPkO1wVcK3BVCX5BnDpCmGNHMdnNKTfHr3A3i4nths2773KrW1EvPXX/gIuQ0NU1rK+JbuzDagW2JFm8IHntNt32CpgzOHiVXjkmnT3iZnAMPnOHRzjeu9C43pb+4AZ7xzfoFU9BNKwPEnzcI5rEu8T2uyOqwQC7McTDmDTTyERxuxehM0UvCGTn0O+8wesXJbpc8Gf/z88TpOCyaFlc3KR//bsYL4jjCa/0e7z22UMWzvD11ZbCBYqvzokdvPK5KTcP+mRSsGj2+NrsGzTekYmEWCoqWZLtH3B8Z0z6YMx7//Uttk1De2KIjl5iWTqWImBvxRSzjM8c9nHTHqOXvzXkrjqL84F+oj+y8W/egJvf59h5+OiK987fo5i3lFXF4nzLyUvHfPHP/QVur3+Ht6+g5g7LILhrAsInpC+9xOeyhBdvrSAEXnt5wG//+zPOHj/jza+8zp2f/BIqTVHTDHtVYa7rHU/j2wqgjxvfToT+lAf08eOD7s+N3g2i7xPUaY3DGIdxnjiW5LGms5550cKzc/qxJhr1GNw8+I7nGbPEuYrOLOknRxAlSDPDl4Yy/t7fo7QxSufISOP1Fu9brNs5OIcQ0OqAOD6itAnvUFKGEVZ2JGlKFnpEZYO7AOcCdt+gipgnizMOmyU+nXLt77F52rGKY8qu4mrRo/Ia7T2huyZuGwarAUKVXEVrzLVlOtgHqVnXM07W7/LMO76pj0lbTW4qlgLWouPJYsUwnJFKgxMTKlHToYijiFwGciXoa8vAXTE7v6RZWTqRUNyakmcZKuRsz1N8UXPD7pOqOVsZo2WONFtqPLHWaK8JIdAkhjSKUSImkHElIvK8YOC3pKnmha9w3a4rlPaHBB0omi3YmsqtScpA1HpCFFEcDEnikjxz2KbkotLEJgMT4wxc0rLQnjfVimw0JsotSfAMRpKqslxvDJu1pxEWlEMjyV3EGMFBU5JEBU0QGCIa6yh8IOaau9IyUJpEWmKtaMWUVrQ0wdHWp5j2MQ6DoqMfOd6sAj/xMR7/Pyw+LYB+hMikYRTWCOVohcUPLON+wuTkhCiKwRqCkFyWnuXVhj03R4qC9rrijvcUzqODJ4khoiUEcD5QWIfsPA9Uw7O4oxOKqkq5E1LUWNN/7R7r5ZLlcomzJb1VIAkQKYGONM1QMG88hcqQziCLgtAUbNbnfDOKWFUd3WzJ4OqCJHpG+eAlCq9oHERiZ4LVnx6y/dr/YP7uGySLY7JRhZeBZhFQ2RHR8hmn9Zqr01PaaIBfL0jOnkD6HqifIut1JGZFfP4/WA0/S7QWxJFGK0G/61DO0DMp/aMhddWyPStYVR1XXmNlxIvnJWG1oL9e8Xbs+OKrdzlyQJTgScBW3GjOqYpj3nQtg6OUzyrwkWa+l1OVlrh27L/Ph8jyCWF8j69ffp262TKNOl4etozTOyQyYiEM635GWm3JipJ4POex3GNF4ErlLPMRX5stKMU5l2KI0wnbxmLdzm03iSQHgwSBoLMerQTTfsy0l6C+y5wwhMCdwR2erJ9gQoVzlqvFihBpTl4e4bzkztjy5qzmkoSk8xSN4fqi4KVbEXGuqUvLw6sGf/NlLp8s6dWG937zd7j/Z/8MXa5RSuyUKuv2EydCJ0nyoRLs0wLo40PRFSybJQLBzcH3K6Xf5/8Yj9SSJFLEWvJ8UeE3BemmIp5k7L125yPPa9tLOjNH6yFaDXHmbTALYrVPpEckySFCRAgZoWSys9tYOUxW7vhlkz7OFXjfvd8p0szjK57WDQhBrAb0k5uk5oJ7YcVeW1I+VSwLiRgOOXr5Dm9+46vMu1Mep57xZIJNxzyvL9lWj5g1hnUdEDpmGDJSC2K+wjQLNollUzic6NNVjmF/xN3yOW3dMg85Xf9lbvoBy3pLERboWBJsQyMD3isa1ycKMVr0iGWGTntM0hxPzRun77B5qsnDGHdwwjo7oig9F8sWWzcMpOaYGtXG9JXmafA0DMl6klG/T+gu6RvJ2MZonxPkkFkn2LQdFEusbDFRRBQ8iD0UCb6JcComiB42WhH5LcNuS0Cw0n1qW4K3pG1H6yIaN8C5nMR3HLMhd+c7D6Aoot5mnPtDbJQAglA17LVXTJzFCI0jphExGxlTC8+2KkhDjROSRkzwRpKHOUF7HguBzQeEINFtCe2GoDxBGhQW5cBZifMDQhkxl30IYee2/yeATwugHyGODia8+vINZpWlsQEZxejhhCSLGPVSGi+YFS2DNNDbu4FvK7aLc6JgqTpHJwV6OMCkPVprcHVJ07SEWKJ7YNuWYTfjuWioI4fu97g/fZlaRbjRiMikiGKF05pWa6xSKCGQUcyNe8fINKOpG4rNmnpxSbFeELxBa016dBPpPKFqyOYl8vCAqTCYJMf3j9h4w+lkTHxaUVxcMuwSkkFLqN+hpaUyHhoHYkU0iOgPeyyvYrbLDe3v/h4vbt1mXJ8hCGyTLW00/PBzCz4Qit3FWLwVfcS92PiWzRZCWbF2luvFnPfeegfrIWAZUnKQlkS6pnAJ1yvBYq5QShBJATPDZmlZXl3zmw8TEvWt/XjdeZa24KtuxY0okEX/jUid0IslxghspHiy2uDbx7iRoFMZ642n8Brfafyi4IBn5DfuASDlLo6gNZ4Xi+9MRj9d1ggBUgqUEPgQcD4QAlzVcDabEJxg49b015bzCt4rGuIe4DacbS9YNHs86RzBBWzV8N9swG0M9aYj3mqGcY/kYJ8npyUXb1zyRff7ZLducVcrbiGwsxo9ed9s8RNUghVF8SkR+mPGs+3O9+cgPyDT39umBHb8n9Y6ZCw/VIBdbhrC83P6iSY9HH/H6At2rs9te4npFqTZbayI8GaBEJL98c+QJPvf871suzPUFIlCCPG+AeIOVVVx2ho2pmOUCD6XKYzZYIoX9Ec97EWOKfuoOEZPhrhHKzbXBX69IJ+M2JCxjB2xiei2kqyJUTamL0aM6kNSI/HlEyJ7yX5kSbv7yDDk3GcUxQZlJM7f5ln0GukqoyAiWxbctpqJyXADqJIRtd2jssck3tO3nlg2yPUZmVuwDEOqFwbjc9K0z+fjEc+l4Ly2lGHXPXFRx1USoYMCa4jeH/1m4ymNrCnUEG8yxn6AVgk6eF6uFhyY93CiYqb2mGV36OuIXhqhrEV1DQiPVJDrhFGYEedz2hCY9SM2IiOSijjpg5NsG4cWVxz7M/ragGlpfE2FQHaaV7oZZUhoQsTAFygMaepItEKEiDY4umAw3tISYbwmBIkMS2TwKOkx2vNC7XMeEpxX9OLAmIpYWBIhQeYYkWGTFOsk1gpMPvwTK37gj1gAPXz4kH/+z/85Dx8+5Nd//dc5PDzk3//7f8+dO3f4/Oc//3Gv8ccGS6e5yPaxicUHR5CebbdltnEsz2FV7hyDb0wi7h8kLIznOt2jQVD3FQhJTzoOInBxxCLOYSS4M0i5P0pYzM9463HBRHQUI82lbkhFyUl0hBWa7eFtwtFtRrpDuCuEHJKqCVoq+n5NVj/Dj+5Sj2/gbt3AdR1NVXKwN0D3JWn1JVa/+TtUxnFyckiyuESfHLG594C3Zu+h9l4lursPzy+wbUe/r0l7KdnoBkEPsZsz9nTL/aMJ9uDz/Lt8j7M33iJ1Nbe7FXpvxMhVHCQt5WSCcQYfIkDTLmra1mFjSTzKyPOMXixIzCOeXRcQ99FHd9ieXbNtO66XFZOJI5MFsbTsmUvyuGOUFZRMQXmqpMXZQBRFJAqU8VwWHbeGMR/EIh1md8A5VrVnZl5w5C0kY4zcI/QKbNOAaQjugqm44vHmJQarNUMdCNMDqm7DQFQ82NOMx0P673eY5kXLenUN8YA4iqmNY1a01J3DuYD7tlwmgFSlpDpn67YY1RGNhrhup94K+QjBgqNeybLdo40kPe/IvaB7f8zhfEA7eOVkj3HV53ptKGrD2emMO0rx9PiYsZP0AWXe52x8QgXQB7yiT8Jt+n9XNLbhuroGdtL3Pwim3fF/ZCzJY0VjHNcX10Tbmv5en73PfLT705kFxqwIBLQaUDQXAKTpre9b/ACEdueWLJOPcr2uqoZZZ4iC4UuRYSrg3ct38F2A9gSX3sCoktAP5PVXaR9/k6flmzRSs1cfcJ3cpK0ts80BB+0DospyEgmm/QmtSiiqMwYaeirj9mHGrbuvkcoBv3+x4fWnb1H5wCpM+aJTHGUx1XxFLixfvJkzGkqa8JBcO7TsY9yWOHIMOKUtr7nYGtYWqmfv8Z6Z4JI9Xj0YkuszbjnP63qMF0vumzfpBcMiHnOd52Q64lBGJINDlrP3eFEVNFoRTyZkkcYrydgU3E7P8FJh84h7ucEOl0TBMoolbWcoOwcCpFYY5zDtCnmgyYY9btgNN80MmSb0BmMiFKbYYroWlwhUGnMd9mkqTWpadLNAmoppaImtI5KebDRAjPfRwaK6kq5puagKKg9JNKFI7qGcpV8ukUHyLBUstEYauBksUkIsY4TuI20PLVIiGdEFC8qz18sYJZ6Xb3zUo+pHiR+6APqN3/gNfvEXf5Ff+IVf4L/8l//C3/27f5fDw0O+9rWv8U//6T/l3/27f/dJrPPHAovtN/m9d/8HIgKvwSGIzZpyY9nUfZRVCCXZrAOzWcGyitlUaheSFwvGOWylYBtAiYZIlCTKcr6MODWK5crSuoDw4DaOMNC8rr/B23JEvz/mOA0k9QXV+jm6tRShz0zvI1roNSs0li7usx7eBDQ5nnHU8NaiwAnDos4YbWsO1iXL1/+/cHuMO/smzxennOmafmT54o3bKHNN9Kygvb7mXKSsKDG9l0iHY/ZNw7q65uvvvcmLvKE7CPil42nTMn/5NvvlM5RbU7gXhON7DKKC2DmS/Ri9jtE6ZTTV9EJBWTzn+WbNNrKovuVz9+Hi5Vv8zttnLH3DnZf63NJ9tssFs1kgWryL7NV0k19gaFomyRKkhfEeWh+RdZpiL+VzNzQ011TFjCQS3PE1r/sZKzY4ueXWuOagf4PSPaWMYm4qSzq8Al5wN/oaF2pE3HnK+og2vUEtE1i+Rzy+Tds6QlfSv3yTfr1AJXvImz+Hnt7kMwxorcP5XedHCIGWAiUFZdcjPXvCw/UZr2QHHDuNmWn290b0Jn1cPEcgGGcJjy5WZCrixrDHZJKzl8J7xZaj8ZBbh33u1gnRpuO3VgdMXMtgPaOIYx4lIz6fS2S1K77s+6nOHzc+SbPF/11xVp4RCIyTMYP4D3bX7WpH5zwyU+Sx4nxZ4U/PybRidDwlGX90LGm6Oeb98ZcQ0JgtCE2//+APfK/Q7nKyRKJ23MftOaQjTNTjm+udx9BJ5DigpZ6f4uoMzAh5+x5m3mIzSRQ9Jm5e8Gb9DlrM6A3u0730C/RUj4YBN0RHbyD4Qn/JUaS4+5lbFHrNu6+/wEcrJpMek5Gi2rzO9sbPMuqf8tLgmqapEJHiRlaR94943FpULHnwc5+F9ZuU1zuSdhyv6U2GqG5Dt/V4NWD/9hjmNesXjoMEbhw33IzfYJVITuuYB7XmaP4OvbClG8bcUw+JXU3wES45gmeGy82CY+uICAw2A9yoB1LgkDSknNUDvE44zg2ZnNEf9onjhLrxuIsFjXV0gz6diqA3oMuGqJt7TNoNstpglccrsSs6dIREs+hNeJ7kDLJjpnKKM45OWrBbalsTmZY0T9nkEwICVzTUFzM2vYLuXkTkJf1I8lP5LQ7TE4pC8qw74/OjAtEWZLbFt7vU+JWE1ln2Q8XLekQdGs7tFR0eJQf04mNuDv5kR+A/dAH0q7/6q/ydv/N3+Ot//a9/RxzGn//zf55//I//8ce6uB83rLdb7GaFEKB9ILU1kbP0BOzZLX0qOqvZtinNWjNhxZHsCASCCVB5vI8BSSAghccQKIPAAwk7cq0TGl95zn1CEUHjZwyevcNaBCLpWbsYTYoMNTnvAAEhPD1d028ULswofZ/apzTsTmBaQBxqmjQgnzzHrwtMs2I7yYkurukfH9AbVFxcPqStB7SbPtp5+vqM2G6pFjUdgo0zPHJmZ5yYRHxm/4qLuaA61UTFcxZ3I26YC/YW3+Ds6pCN0ATk7sc4QCCf77hBeIMLHicS7vEW1ZOd1PKmH7OuY959I+ZxMiRXgqiDu+tr5GrOYiFxyRDXX2BDi3KCSb2P9YLZYsZ/fUuSyB33JhAQ0lPplg2WjbDI+ZKr6CnHSYGuTxCih5pXCHGNyKDSjqtOcGtxyqK/4sVKoi8MzWaPPM/R1RrrOlxwqOIZevV11PRVJvf/38Sqx6ybcVqc0rgG5x1CCKbpFCkd/SjDRpZltWQyOMB0Hc02IT+MWW8WJPYS6xRXdUNPgnm6RR4qcCVFqenMhExGvHTY4zRO2WzB+EC0uKa4N+ZsVXODhCA9Fov3Him/t5T6j4oPOEbe+08jMT4mzKoZACe9kz/w70IIdI2lsx4ZRWSx4s23nxMVhuGgx/j+9+YONc0lxm5I05s439G6FpKb9L5tVP2R9zKe4AIIEMrBi69Avcuhe5wc0dQJkWm50VxSLx6xLoZQB3qTEap8yKZsWF88x6vHrOtLXnQzXA75OGfmlixWW9LtOUk24AsjyUnzhFHSIasrLk+/SXfm8S5mHUma9RyqDeLsEaG5QvuO0vZJzQWlALO5YFB35EFi33nKpntMZVYUYkzuL0nDFoqG1igW8ZSuDuhNy/PhDaJE8NqwAhWTTCTpxhDOHxO118wTxSIbc7Nb80o0YJsPebpdMltc028DvThhrxGMlCW9M6YKNbaruSZGxCtE3qc8mSKkJkonuIsKlh2DStNHE9seVC39wYTDz/8M45c+T1FWbK4vEJM+azunch2lsSybFZNqza1tx2pzzkZWTAc3uX/7JUb9HheNYWNqKltTmQrTNZiyht6Y0ApynbN/fItNt+EtBM/KBd46iqRD6z63Jp9j023wdslQem4BUYB50/DVrkUFQSZPyKVAOUMIK2pS4JU/4lH/x8cPfeZ54403+Df/5t985PeHh4fMZrOPZVE/rngQacp6STbpMUxypIhZmZZ1SMltwVBK5j7nWTOgUzmHI8nPj9dkbPkfmxkLEziQiogEFVK2JmVlFaUMdMJxPFD85L5CqZS31zHjUuyMuGyNFRPc1tC6BDk+wKYZByx4xT8mkh1+7yZ0FbPtC4KfcxhbDpIpZciJ6KPIyWTLUF2hNoKuk2yynH4JJm85Mls0+7gILoo+YeQQWxiy4KBvUWlNVRlWbWBVG1Tw/Exvw/H0JourBfMXl3SlJhmPiaYxwnWMRMl5ekKQHi8D2lRoV+GjmDLuY3VE3BtzNNonqlKy6inaW372sOLJKZw3EuNaGi9J4gHZaI+8WdCzzzCTz7Ptj+iSmqick7cNwUHUdFgEIdbEqQYkWkkmxAzjmiIsWTXgW8/a32XgjxiqjtIc4G0EBLaTY96LDwirrxOZNUuX4k2A954wPMgBSatyNv1b6PUFYjuDs2vcxUPS45/AkeDtjqQaJRFCCF6sX/Dw7CGrcsWFvyBtU/bjA+JmTJrkpKvHWH+J4Zp1OKL2Ee9dFdzupewPJ0SJpig31EVHlgxI+yUPuoavmCHbpuNmsFxXBdcmYmqSnVlRX2Kt/dil8FJKtNZYazHGfFoA/TFRmYrKVggEe9neH/i31uxMN1vrSSNJ1xlWz04ZesHRyRHR3ke5Q861NO0LAKRMkDLCoBB6Qq7z7/te/v3xl6BCPHsdbENTd7y4vObx5X9nu1yjcLxVP+NSlkhOIJ7gukdU8zHFtWHlZnThjLZTPMojsmFO1J+Qrr/B4fNLYuMY9yBcV1xYwTzv0bxX0naCrj4i5IIr94JIKo6oUcZiymuK2nGWH9EfHROPBf69M3SzwiSKrjI0suA8TqjECRjDgW2IkzFmMEEoRf+dZzyeaYwUZPsTZi5hTUm10cwv5+ga1irnxeFd4qahdhF1k1OuY540NUYekk+HfPb2F2mWgWpTIs80cRTR2IKL5VOSKBAJSXjesSkblquCtBO7G9zRAIEifnzBsGzQo0OKey8z+8r/h/mj36Erl7jpEfreZ5BJhrCWwabAlQ2DZEywa2T7nG79gvOL32N5tI+JoD2dI5OE4Z1j2otrXN0iYs3YW45sj9tqnze7jmeLK9rKkEaCvWnCsB7AZgtOUtqMrRfkqgcCrrsrCmNQUrKfHJHKjOv6km19xWuV4ef+5OqfH74AGo/HnJ+fc//+/e/4/Ve/+lVu3vz+yoNPAdq2aCNJ5i1+HCEme+z1DhHzltUqZ11apIeRDAjdEa0dX3seaOOOeT9jngTeNOBFxGBr2N+AEJreeMKNmxPireXJGy2RaIkHjqMsodce8JAC39WMuxkjlRB5STsaUpQxjzc9eiqiZ8ZswjmLwtJ2ApIBTZTwWn4T3whs6wFJ7Y9R+YDoQcvYCDpR4uIEp/ZRdU5qYo5lSzdsMULxXN/mzTbms/0eo3jEJHHEE4sqLnAbz7Onlii7ibi3IJQ1YaVJj+6QZC2TXp9XJw8wjaHZrGmDpSxKXO1QQ0UUMgbNHrJRCH2brf88ndkQOcukO+WGTrF3b3A9nuIawcL9JIOz3yZpLCG+zUsHd9FHPUxrcc+WjJ3DiQ1bqbh//w6fuXWE7zzrxZq2buiqOW/PvkadrdDiiC68TFUolIw4vvWAcvEcIWfcjoYUYcQi/dMcuQvqKEXUWy4agQlTiGLI9ohFhBvdIYhT/Opd3NmM8/INkvw2h/07DOIBKSlCC17MXiArSWxiWtdig6Wpa0wDtt2pepKJJR167u0PeOtRzbP1lr5qSeN7hH5BU265vpqzdzwgiheMYsutoWaBpDANB9WKYnCD03VNGgdCn0/ECwh2YzBrLV3Xfd9cwU/xg2He7KJTRsno+0rfP4BpHM57ghIIIbg4vSCuHf28x+jWAUJ/tNtnzBxjlkiZQvAAdGKX4ZRF3/+7C63D1xe0s9/hul3h9T7vrfs83GxQ1QvoQNUtFBs2ElaTEusNutswLjVF6yl6ltLCNlpSjBPObrzMT3jNrFrho4pxXGN7huWmxFZQFFNcgMj1iCKo+jVyGDAanu8Nic9W2HlHXUmEXJDeOMIMp6zWZ+hVTtQf040yZCoZp/uM/G3q9SXLaoaQFYd37zK6Cjyr4ZFxlAcxe9GWhRC0Ouftjcau19yIeqiT20yTiJ6XbMsN51LQ+YIkHXKQDRkd36foHG15iXl2uvvMjkfUIlAWGloYrlJ6XYHt1vi4oxomiH5KRIsUkk1b8kREjNfPSf6vf4msO8SixRmBzC/w7z6H4T6y9cReczC5yfTWkDKkfMM+RWw2jINnb35FcjFHtQYdNOGre6jRMS5qWO3FDEqBebHg4dv/iVRp7lRrXF0SijlaCnI9wicpQmtcnFD2BiyTjKAFcddxUFcoIfGs2ATQ3jPEobv/xYwQf+mXfom/+Tf/Jv/23/5bhBB47/nN3/xN/sbf+Bv88i//8iexxh8bvLOO+I1gGVSK0cain85Zx2c0kUMGwTBOSL0jdwHRSp6Hlmd5wtrH2LIPTYL1O+LoBVCGObfajNlsxvmyw4kIz45s2G49lRQMu45WDlilgmdJxUnznN5VD3+pKLwjCNDSoy4FGkXWGo66lMt8wTaKeTN0HNkJxnf44HejNyTlzc+w9AkD+4jbF+/QyJJKD4hkTMAjI8ulTjmvIrTf8vtly2cWDTjBVSyoSdlfL4idxW86zP0bdKfPGC8KeDqnSh119wT4JjI7fv/ELvC1QXqJLiNMYqnZ4vGEKGCkwYcAIexm7dslt991fO6nX+Zr4yEba2maOZPFGX57AcVd1FijdUwYa3ISbhyOeacNREpyo7eP6Atu791mu91yfX1NoyNeLH+fWAdujg74zXPBZRMYjQ75U/df4vrZ77IszxnEWyp5l5tJj6guqdQAIwIngweMjk7QWqO13hGw4z9D9/x3ubp4nU1XE/dTpnsnGLPbX3RwZ3iHXq/H0i/p6EhlSrgOpC5DtSNkPGV6PGA83mc0/jlM84hNd0aFpqxbTm4esLjcMp8tcLcmKKCXaw6V5KKWbOrAS2bLUlvKzuGNJxm4T4yn80lL7f93wrzeFUDT7A+PJ+pqu4vAiCVKBM6evSDvBEe3DtCT9Hs+p6qf432LFAlK9QgiopMSAd+3A1SZiufv/v/wj79Oe3ZGJeFpfclVPmTTAzm9w92FZ1AvGHpH6QWzbsTsOOe4TIjWCcJLBtUlQ1lRTCVXg3uoasRXtgPUvCWOXiLPBY0p8OsZnenwqUAJRyYPUDJnsF3C5RiDQVQVfrkEmeCyMfuqz/DxU9q3niDLBpEHkmZN9vwJfhSTpgc8uJOx8n0uTYmNPcW7X2f+ouaJz9kcTxmMNJlt8DJwtj8hXFyQShiEiPGV50GxYKQ7nhwfczoAawMHDDjuv0K3ERTlgqobcH3yeXzX0KtKlocjklVJz3WohcP7IX0xJcsjxCgixZJbQ1MbflcdscxiakpePXsLWQYce2jdI9oUROstWm9RXiNFRKKv2P7u13H5iC/2hrSpwjU1/c2agXFIr3DGYdwckRtkf0Sc7SgLrrsiDg4lA1lZw3aLixUojU4ccZwQXMeRM0itMcdHbI+OyJsWe71FNgXpQCKEhDhCJCl7w+SPfNx/HPihC6Bf+7Vf46/+1b/K7du3cc7x2muv4ZzjL//lv8zf+lt/65NY448Nuv2I5saEru2YVxV48BJQgmycE017hFizaTqqquFc3GXlNMIE+k1K36YMEJQUzFODuHMP7WasXjylrR0ikdhhn7XM2Ri9Uy+5jkO/QiYZpU55OtjjxnZD1Ma0UUSXTpFCMqq2DFo4JjBMS2SU8DuDPpWIOQkNLxMRI3EeLuI+jwceQkUvHtJMH3C/mMFRzqqTKGvZhIxFiKFbQVNSU/HW2OG6ARsREYCL/IiDck6mPV579N0BF2rNtnhCv3eDfrvAKk0Zj2lGGZGSxEZSV455FNNlEX1TkQRHhabtHZPolH2hGdgO3nuD2XLF9mv/Ny8dv8SzO5+lfvlnGb7znxmsWrx3TPMxwxt7yLHFzhtknvDIVrTGs6wMe71d92MwGNDv9zm5dcJvPrtm1VzyPC2Zqlc4Oy14Y2uQN6eI3j2mesu+qFnIiqBv8OWDmt9uLGa9pLaWL92/j/huXk3vF9hPA9X2bdpBgxgtGA6/zGZTYIyh1++j7YBi2ZGJDCkkxhpuJjfw24QkuoWSBZEUJBJ+5tZLnFeO1emSF+srPvfKLbTajZ3mhecQyBJBbAW5Moj+gLUpuWU3XGcjFkvLQfGpEux/dhhvWLdrAKbpH14Amdbt+D+xpFjMCFVHohMOjqbI3ke7RyEEqvIhIQSkjhFSEfQ+QlwRqxgtP3oJWTQL3n3+X8mffQNxtqATI9Z6TOXOGa0eM3KG+709YhRZanjQl1zMcqI6pnps6Ed7BCMJzYa9zYxZ7rC9KclMMDKO3vkLUqe4O0y5ZTPqSlCbhnXUEHoR/WwM5T2U8xwwRm1Tgu9YNzNad8Gmd8TB0as8qFr81nBa1qySGPW5A8aqQrkNutb0rjXb50+JZMp9fY9VtWb29E1ibyAacDd3fGbjqERGJxXDdy7oVR0vm4r9yYBkXRGbim7aZ9RmzFZbFAky1pTlCp/kjIc3+MYo4nEUk1/N8Oslx49OiT0M9iaYPGXjIhZ6SL9YEq8NXZxzqSKeBbDBE/uMNr3F6ckRd9cl24NXcNN9xo+/TrJ8jhI1kTYQK5pti+8M42rGPbckKiMWVmKDxiZDyv3PIuuGfH6KA0TVEIVAnIKIBBEKWosjwk2mhOkYDg/oO0vPGlQICGtZrTeE5ZbhqsRUET2hydIBWTbi6Cfuk6YJUmnU+H+BNPhvRxzH/JN/8k/423/7b/P1r3+doij40pe+xCuv/AkO8v4Xwe29l7h3/Ke5dFuq0IEQpGnCIBnijGVbFyybGklHm8UMRc5LasTnZMZRb0zUeVi2OA/vJvCWv+BS5+w9eJU4KEoVKIWjh+SeilCNp7EGQ8de8MxEjJMbwr4hYsqxH3AtY1qhqZDkvuB56IjaM0qlSKKIMlRce4WUhxyrMZmSODrGZot2GxgfkkWHVMk9VFuTHPYos32GaU4/eCara6Jv/mdeH2SYPEYOH3Aie4xUQqsVpppTLmr2QsRgPOGq/V3OhwU3ej2ym/sE12JG96mHdxnudAnMlg1lsIRc0Wro6oLgE/b0hOPxTaSStALq3h1W8/fw2wVytaLbvk75yhe435sybq+ZtQueFXeZVB2JtUjTERWBbSQ4rVtm50vu7feQgBC7/DAB2PxLvLf97wRzwU9OP4c+18xbx1VhmOyf8I3zhk34JoXfosgZ6z7TfsK7Vc1/WhRsHj1jur+PloL4faVXJARq/Co0LZv1KXVaIc27JNk9GhGxqS3bTvCo1kRCEouAwbOoG0RwpEZjVzmDyJOHGVk0JU3GPJVrZo3hanbO/v4eF5dXXM4r9rOAlpCmCfuZ40IlXC4LvlzOmfWHEBSrecX0zqcdoP+ZsagXBAK5zsmj78/H+QBds+sAiUSyen7O1Aj2jw7Qo/R7RhJYu6HrZnhfE+l7SBHRqRFw9T27P5flJW/NvkHy5C30VU0mHhAd32Y5LrnxpMAVNcd5RCodIVbEvSFhdEB+WzJ6suRAHHA8vk3lBcV8xUU/4lEvoTu4xytyzPQi4LIJR8OInz7SSCW5Xqy4pCEeHjAcHNC3D9huBGkG00GDKpdonfBQ3KQrO6R1hOkxw1sjLs6XVO8tGA573HvtBkauKNc9fBVYXQ0xNSTZEJn1WcyXSD1l5RQM97FpxPUgxYicF87hq4bX7JL7g4AcaexeQjc5wQ0Oua4r9FbRqYRmeIcyiTkODde2pUvgOOvwd/q4xy1lM6SKJOX9Q2Q/Jxn2yBz4+ZhuXbOIY650jJ4t6Tdbbicdb/UGLKLb+PuaW8eCg5Oa4uWXaR6OsMJi8wSEp+oCddsxKBbIpiSVnsSDdRLXPyDNHE4PiXwgXhb4MGVvvI8camZVQdc5uk2HDxHi7gHy1gHSW0zXsLIdodcjjIaoi3OSN95AXVXYOKXpZZz2xzwfDFHnLZ9JCm5KzzDAS/c/chj9yPBHZh/euXOHO3c+6hfxKb4/5BZ67QH3wwEmtITgSUxG2H7wF4cABDw2OPoi4nNuF5CZ+kCe9NgkHk/DZ4iw/phKQKYzemlO0zXUbc1ECL6QjjgY9flqXXCWeQSB6XLLaSdoleZQZgzlPq+oilPfULqAdIoTfYJQYw7bNfsOfH6Dt8OGQkjSZEguc+4Bd6KYz0QRBYLfb2uqh29AOyddGg4nQ/q37nJHWW5eXjHbfoab67d5s3fMiYj501/6efJEcVp1PFqtWIo32Sw9lU2YnPwUvedfRRmDmL5E1FyxH9aQpwRAmJa7A0HUwjDN2RyO8VHE4vwZR/WMSXuNHw1ZeYE+EMRtziJLWAiLXJe4d79B+eCAPFrSay4pFgvigz4FAeMdovaYvZRN8BRly2j4UWdmokOE7IMvuKNPuTE64cmsQtWe/RtD6s0el5sbbLsZbVaxqiMOsh6r8ZR2teTrT57wxeEY+d2vG4bAECuh2lSEbEY/TFFqN5ro6V272ARPpHq0UeCybOhHmm3VIFSPtt6QyS25GBNixSrt8bApefX8lAd3vsD1bEFTW1ZKspcGBoOUadXwvDG0eZ+qq7jZXPEkylhtO8pl+f3zOf4Y+LQD9PHgA/7PDzL+Cj7sOkDG09gNXdUQ+x7Hh/uowffmDpXVYwKW4B1RNCLLbrM2uyDb7y64Otfx7upd/LNH9F+UjNoejO9xddwgr86I9wZkP3WCGYy4MV+zXoHYbHGpp7t3n+T+Af0mIesds3625NK8xVmWsXzl57l/++cZmQkv3j0lslu+8NkR41FCW5W0D89pViP2Tl7hwYOfY/HCMRluODlqSJMEfJ9SJtSza56f7XHRv4m+8dPkec1Wd2z2OspIE6HYtjUh2SPPD4mOc4IPbF3g9GpBfRARhjc4UxOcljw4SBgZh209Z43HjiOK4YhvToYoYUH1oD/kLHasYxCDETf0HjESgeCpsYQUBihecn2mScZvH2nqOKI3gLznQFZESKLxgGgY48oB3il6sxnDvOSnpisGtyM+5655w0wQMpDolvWVI6QDqi8c01YgNiXKGewgJxoNmRnHed2C7RDOoG3HwNacRAXD/jWlVrQvKaLlmqoucSSUwz5rG+MHI2Q2ZDjOGXUtLggu5ZB1pElaT++yYRRP6f+Z/xemaGmdZb0vuNwa2tMWrOX3KsFDUfLlouSlP94W+GPhhy6AnHP8i3/xL/gP/+E/cHV1teMofBv+43/8jx/b4n7ccOdgjz9XClQwCNtircHWHZ33xFFKkvcgTqkIODxHsUSKliho0pBBV3PYu8K7S3yU8nJ6lzo6olOa1lh63jGwBi1jCBohBf/ngz2WWlAtn9GJBY9XJWV7zSAd8P+cOnToaL3n3bKlcZ4kzBkNbpJuBWO3gVRwazjgVFSY8Jx77DEEbmYJxDF91/IXmi3r+4bNOxUuCA6bc8bF+y6/KSRHQ46jQ46qrxHLM7KnFtVLuOMMdwis8hc8qirWbU7U28PJDbEz7Gd7TPMtcXGGsW8xT/dYS8kwcty4XqNLcHWPTavp5S2b8orQwqCqGAxHlEEwV9fYroZRj/PlE8K6xn5txODVG0yKOdXinH1xFzvKKecdDkjHOXJrcNZzR2n2+wkB8CHs/gX6+7e4XP4WV+WbPBjcY1a0OBMYt4FXH9zjP13NeWbntF0JQpKInM/sT7lczuitFxysrjm6cxfjA10IuA9+xkfodYOxc0zSI5cX7PU/x0grciXJW0nhOvayHqflBhccR4zoakse9ZD+kmG8JclT1NTz5qzgRVHzeuOZtnOG/TGbcs7i/8/en8VckqV33ehvrZgj9jy8c46VVVljV3VVuz3y2e7jTxb4kw4gJO7AtkACyRK4kSxZQubGAiELkAVGlpjMHfgCjo7gAx/wgP3R3e6hpq4hs3J88532PMY8rHUudmZWZVd1u8o9urv/UujVjndF7IjYsdZ61vP8n/9jaJq2xvcsDCmpiwL6u4yHh1zKQ06kiVaawdmSx5/9xveF76fCf/1QWjFLZwD0vK8uRvgARV6hNeRVyWx2gouk1ergeRbS/2ADKI5uUVUxptVCCAPXPSCOrwO8T2365uIm4emIrbev0UlrGM3L5AddkuhlJODtHDBvdNivQhx/m2KWYZgL8AV24wly28XSNnHucjwccE9NOXG2cMUVrFWX+TxhpRs8df4cW0/sUWnN9PBVFrIHTY/azg+Rs01SrpFBk3K3RahBVZo3w5ij1Qlnmc2p6PB0qCirBmrRpp4K/GYHu7LxqxooE5dnEFqCVgzCCdXpMZb2kJ0WMneoS81zls12uuadSnJ5ccbKdjG981S2hZONseseQ0MRE2NbHuf8x9iWgo65YpksGKQKJSQXGwYvmmuUXhIVY06aLle3mlwKDDLT4ERbTJUEFFagcLOI/eSIczIhuPIYRuDRBTqVZFAZLLRBrhQCaAsovIrCclCVi+1IAhljWprMLomyirhQlJXAtC2mZp1p5SHLFPIYJVIi6VAWCeQLdAHCskB4LGKPCS6VdNHSQghJjkEuYJ5p7sYFqBKvZeJ5Nud96HYMVguH23FOTpObnsOPaP1tK4j6kUedv/N3/g6/9Vu/xc/8zM/w7LPPflsruf5ZgycWXHFeQxgWSBOkAXIjKY5MEEYGhgNOffO/1TFEE0CjDQesHCE1WA6g8Yq7NMqj+5kZ71EOVgLcJngtCKGbR3SjMVhwsNXlleGIMp8wWin2Gvs49T4XapK3FgvKdEbHHlHrljB6B6KKp0JNVSyYGQaLxh6X6heRmQObhSA20G/6NJ99kWycIBYDVDtHuh70rmAELkamqYp7yGxIfucNjKvPPOTBtJrbvJi9zXJdEIktlu4+6SrCOp3S/tEfRBz9MZDT7u6CXQM0uZijViustGCnAQR9RP0cg0VMYjgY/X3qRoGrHIprryNWC3pPP8nx5/83cTzDvjlg1b5MPRsjj884/9KTpK6DLhWOaUE34O4kRqcV2933Tw5b28/yP5dfYJ3PiJmx06xxlFecLRMu93s8c7CPfGtEsrpJo7tFv2izYyu+uLPN5Pgeq5MjHtvq02q1Hj2xfwmSu1SlzVwt0ELRNrOHJQRqtk+Z5ex5PkkGlV/Rigu0Y2CXATrN6cmIrguXdlu8PVjx2tLhMM15czXmee8y63BJrm3m4ZJeJ8WrN+ilc07ylFlnh0vJGbtqwVTBYhaRxDme//1U+O80LLIFpSqxpEXD/up6PA9QpBtNryhesVrF9Nw6e7t9ZM1+X3kZAKUK0uyMPF/Qaj2O6+4jpUVSJmitCax3Rexm6YzT5QD/jS/RTS3seh/rsY9xuLrDarTCNZocKRc9nGKnCdPcIso0nqpQVotwXSeZrTEjWA8XHIZvM/QlRX2HXe8y5izjeJIitCYIS+69OUUpzfD0FSbTBNvfJZ1Kjo/mJGGBV7cY39sUXc205vXVEcVyhagMArvHT/kBUlXcGGWgXC40ziG4RWVILPsA+35/K6qK9emEuqxxod/lqN3Bjksu9/s8UdpErZJifIq5DPnxfMjWlYu4qyP8q+cQQcAXfYMrouKx9lV2vV0MU7Kcjpic3OZ8VdDod+m7FhLNajplf11yyXU59+yL4HdBlewkc6o8ItSSWAucN9/AMi3MnS2MC89D0APTZVcY7MpNHZ88mmON3kTka5SG0GmSe30a+Rw7nW/mDAcIDJQ2WGrJoNpsGkFbKgKhiZQiVCCLEC9cYi9nSDVF6px14TCXNXSl6VZL6iJnZvcZe/uUWmJQYRslPVXRXEpaMsSWoH3J06bLa8kuzzX9P1vV4P/Df/gP/PZv/zZ/4S/8hW/G9XxXQ6gK0FDlm+3B/g91bMZmGdWG3hMbQbHZHVBfwaEQm1UL6WKzvfdb+ldx2hfZqm1x8/QV3rB8mud+gMDrUAdq7YTR/Iw7q0OedTRi+xlYnSLzkGfQvJrPWFserxvwYusCNoDpgB2A28IyHcpXXqGSJlnm4T31AlguXq1iOvkRlBki0zuktDDEOZzLT4BhUVYwi3dBTDFqW8j6x1h+7vcpz+bshgKntrW5/t5VMDcTsekWZHdXlFJgXmohrE0Zh/pwyGKx4GwWcuHCBaynz5GvPapwhd/aovMjTfQf/n/JlxPwOySVpDo5o/vsZYQt0aVCZxX9msvdScw4zFBKvy9c5ZgB+/VLHK5uMkhuctH5AYJckys4miX0dneRt44x0nsIlTPJZ2yFDpcvXCCeThgsFgzOznBdF9d9T/aNYUHzAGNxiBcviT2I4zs0GpuayZ7pscgW5Cqn5baYp3MKu8CXFskowwl8iqyiKJY4zhYvbTWYzGPGywknYcGlTkaz3iLP18zTjHa6ptZ5jNZqwd3ZmvLgApEI2RIhZpVQSZ+7d6Y89czXFtj708CyrO+nwn8IVKUijQrSqNiksReKqlIMylMKS7Pd61KVCq023V8aElUpiqxCVRrHMzFtgzzdGJvj0wF5UeA6dYxUkypNNktJw4I8LbFdEycwCdeHzAdjiirByC3sqk04WnJ4NkQpxTzKKP0VeV7w8vRLhEeHPHmyoLLahBd+kPUsZ3r8DspUODsX0FZGPRnRFNscrRLSoqByNFlxwHS4xphl+I7LcH7M2BkQdro8cfEFnt/vcfvOgmyuOfAcep6N1lAUQ9JkTqkEDXcPz/VYLjNsx6DZ87A9EykFd4oMpqdUaOzaPj9yeYsrj/cZ3j3DDSxM22bnSkCc5kgjoNd9EtPy0Bquv/4GraDCbTc4+MSzvPblI0zD4JmLPYKzhLNpxnp0RD1a0Oo2aUbH+I91MJo+Z93zVNEJrhlwUN9HCLF5/vMVyu3Q6/Xodt8NXa6mb0LboH5wAZrvKRHh1DCAJhBMJiSiBVttnB/8Qfgq/cYO2nDxR2F9hjRdGv579KGUAlVuNkCaDm1p0C5Snsoj0BWITXYXdm2zGAfSawMIBpjeHNMIIZ5CHkKVQVVHlTlS5Oj0Lth1RL0NbheyJZQZ2uhSCI2mouE3uRwIZO39JVK+lfhTkaCvXLnyzbiW73roYBsu/h8IXd1/Ae//1dXGaNEKihSy9ealCraguU+eS+ZHI4q0pNHdo+65CL8D7YtQppuX9YFHSQgoko3nKA83I6KQUNvaeIWAS7sf47bSzPMVr85v8yNuGyEEj/sOw6zHmdelEbhc8O6nKFYFxq3f42NFzMu2SeLWeUvkfKz/MaR4N5tJAO7Vq0Rf+ALlOqGYLhDdPkVaYbkucesFCsOHyQprMMPcyymkwejeikr3IJrhM6F36RLTbp9wNODeK3e48mIdka9hcQ96m3dP+hYysFBRQTlLsLY3q9F+v0+apqRpymAw4Ny5c1gHB2Tv3KCYpez+4P9FeHpMde2PYXiNvPEsIlsxv3OPVmMH4hKVVTQaNrYpyUvFPM7p1t6frnmx8yz3VrdYlMeEPEXfDThBcTyP2b7Qxm73KAdtdL4k1XOyYot91+Fuu81yPGI5n3HqOFy4cAHDeM9A0L4Ai0PcyiYuMzLGlOUa06w/5F0kRULf6z80gEQlqGQOKiCN5pTtFY6zxROdgP/Hd5jPTPKi5M1syg+5e+S5h9Ka9WJKff8TGNKgJnPKLGfZ26N29A5dI2OoXEbHCx67uo39ARoxXw9s2yZJku94IrRWmiKvHjpZH/haVbkxMspCIaVAGptNSIGUAlVpqmpDEXiwylWVRlWKqtz81ZqHx2qt7xsxAtOWCCGIVzlZVHxFZThAayazOZWCMnI4Opt/zXswLcl8EDG8N2AxX2PYJj2/SRKWxNMU5tnDtnlaES4y4vwmZbXCEE2E3iFaCNJqRaUUAoHITaIi5zi9x3IVsj28S03UkN0LUNticeuLKFnS7u9Sf3aHavVlztUaNHOXWwuFUcYEnX2cVhsr0tiGQZEvuePeYOFCf2eXJ3euUheSXEKn63Fpr8FT59oIKRidvYy9sGl7XS49dY5eu42Ua0xTcvDUZkzLKsX1u0saeo5rmaz6F3lir4GUgni+xPVNugc9nGBJJU1sq4PtbsaS1WDA9N6mwOzFj3+Mw0VMpaDXCmhWUAInR/dIpxP2sxXNZh13z8VoNODgJU6WtwDYD/Yf/v7D4cZ49DyPTqfznmeekEUhIKh1PpjPpZUiu7k5p33uHPJPWjQIAY299++XEqTNxnf/HljuZvsAqKREKwsa5zEefx5UtlmsyfslTsoUmcxhegsRjTb7zfvn8jpQpghVYhsu1LY3xwq5+d+3ER/ZAPp7f+/v8eu//uv8i3/xL74f/vqISGLF8G6CNASGKZGGhWBT3dwwJYYlMS2J2TSQhqDIKtJhQTRP0ARgwPQ0Yj1NCdoOlm1gmPbGxhGgdYnWgDbRxjb42xiGxDAlSmmqcMNrUUrzhHGOP4pf53a0pFtc50LnMpZjcMmyeCtMeCsN8d0Ks9RkSYme93CSO1xs1HgDzSAaI5NrPN69gmUbiPsDvpIOcv885eEdJp99jaS+i9jaI1nnaOqIlovZWJOPU1b/84/Jzz8NtoPVaFGufOIophkN2HnucQ5/b8Tw3pjerk+tVlKd3CBX20jLxnIkNGzKeUoxiKBhY3kWWkMz6DIb3iENcxzTp9XqU+jbqHmMOhyw/fH/N3ePr6Onc/LZPQrZx7rXov5sb/Oc0hJLCPp1h5N5wmidPWIAbcoJVOh1By8KGIRzbsbXeWbvY2Bo5lHOHSVA+KA6GGqKaUUMo5SteE13e4cyjhkuljQ7XYbDIXt77xmo7AC8DkYyw8shMSGO79JoPPcw8yYuY654V2AOiUjo2T1svyJPTERUUhQbgbGub9MKbHLfZxkvEW7I0M2pGU10Dqt1RAuF32jSiSecRmtm9W3ON+q0lyumywyVFRwP11ze/8amrD7gAX2nEqGrSrGepKymCVX5PhPkWwrbMXACC8c3MS3JqlgiqwoRmgRWgGBjOGm14akJwLQl0hDkycZIy+KC2WJGKRXd9i7n9uoI16SobX4Hr2ZheyZ5UpKEIZkYYxsFjcYV+p0nKVMHqpR2zafh1dlpNFlGS9bTCcFizWMC2nt1/J/4FNNRjlQL3KbJMz/247y5eBkRjdiqXyIt6lhBgV0P2D7fQ7e6NE9h5c145+wtztwYs1bjSvMiz/a2efXeknlc0PZt9loejmcRx3dJ1kOyTFNvHdBqN4lXm/fIb9oP56Y/XoYY6xNUqXDtFqrVpmObaK1ZTTZ9pLHVJMtuAOC4G09nmqa88+qX0UDt4kV621v871tvAHBpp40KC5ajMaPTQ3S05tyVA+p7Hla3ATvPspSSsAiRQrITbLw5y+WSKIoQQrC9vf3I/Lm+X0XBbzYxrQ/mYxWnZ6g4RlgW9rc4AalabQxko24jDAHGewwlKcH2N1tzH9LVJkKRLjcL9AeL83C0WfAns3eP1Qra375kqg9lAP3lv/yXH/n8e7/3e/y3//bfeOaZZx4OYg/wn/7Tf/rGXd13GapysxrcrAKrj3Rs0LRxfIvFKCbPKvJB/HVfTzfvclSc8oXoEDEPsKWH1pqizFlpxedEyrPWZuIXVh+xuAnjOe3yKW5kd1hyk2ws6Nn9R86rVQ29stGLEOZ3kOslcvcyi5FALSvq3Qb2eoUROoj8Leo/9Al651vE3nnGb1xjee8U1blK6daJzkZc/+yI81cV0tIU0XXK2sWH3yUmKeQVel0gW+7DCcCofBbzGavpIbv9fVTZppyOka/cw3nsMfydH2W5mGBkY+LRdc7UPllxi6bogCVhmVPkFZPJmsVpSG1ZYtn3RSbjEqU2E+KucYWh+DzL9Zhbh8c0t3cZrgreiVf4CsqVj9Yu2iuZVWOSdZ/Hzl1mcnyPTCuycM1aCObzOe12+92H2DyAZIaXCRIfsnxEVcXveoDKBFvaBFZAVEQoR2H7kuVCYNqaJFrwgF50se2zmicwXSErzZvZkh9z25SRRZIXFNGCWqdLez7jznRNnPfIdvaxzs6oqZi8yhmchlzYbbw/I+7rwHdyUdRokTE5CVHV5neWUmwWGu8JWAtDYNkGpi3RGlSlNn1bafT9sKm87zXT99+XzcJHPFwEITbjgb5f/HazkFCU+eZcbs0iaNqY9qOhgqP5HLcv2LnQ52K7/0iIVlXq4bkefE6igtV4SmaBGfhc2d+h3nSx9muYzUe9m0HTwYlHpMMQlfh43g6N9malHq1n2Nqk7tXw6hbX02PKouL8ckrNsHG2LmDvnmP86h8iBbR3dyhkgprfxZM2bnCJ8XKK1uD4AaYhiYRNtl5wtrjHKF9T+Q7P713lBzo7m/B4lLNOC851PLYbLmW5JopuEa5C0D28eovAD1gcb/SQgtbmfuKq4kvLiP76hI4SHHq77DcdmqZBEq7JkhIhDYK2Ji1SpDBx7C3WZcXrr75JlWbIIOCJp68ynq9YJSWGYXCxXae6MeXu9ZtkqzG9hs/e049jN1YgLajtcDrfEMW3/C0sYxPqHY1GAHS7XRzn0QXVerYxgOrdR8fSh22qivzuXQDsSxc3RORvEbTWVMuNcWk0PgQX0G1stq9EVcDyCOL5uzSQDyHd8M3EhzKAms1HV35/6S/9pW/KxXy3o9Z28Bs2Vak2npj7g6tWUJUVZa4oC0VVVFSVxrINbNfEb9q490XKah2H9STduN7zahP715uXVAixKTwoBEKwoRuViqrSmwKs1sYbJIyNi/55LrJeRURlyA19j2e5ipCCpxybV6qc2JSsfYOLDRfbbVKZffLlnD1DUzQvcpIecVTcxjc9fFl7OEmoSqKvPIUxH+CHZ9hOTmauWZsOqmqj1BR9oYV3qvA8gR+fIWWb2sE5qtEt5pMCswppX7rIaBmxTtYkE4/WeYmjT0ndSxTlZuKQLRsxzVBRgarbIASWLdlp9qnIiKOYxXpKb6cHiwkiizBVhv/4JyG8QXb3j0jzIWF0jLeu0ai3EOVmwgpMCRqyXDEPc+r2u91FSoFbs2h4l8mCE96cR8yrKZfr+8wMl6KqyFOF6TcReYssv0duDBhO9vnYYyZep0c8GZGUFS4wHo/xff/dgbG+A6O3MFSJU1lkRkEcH1KrPYkUEqUVSZnQcTtERURmZtjSRpg2VVmShDFVlWEYDpfaHq87BnXhQZYQyoihaNM366BnrOYj2uefwjQM6kZGWeTMnTZ2EFBfpUzjENY5Z8uEg/Y3bsD6TvQAaaU3XtbZJovRdg2afY+g6XwgUfjbAa31w+KnW8HW+/hp0pDv+2xaBnG4JK4qjFqH3cAGAUbtgyfSNBuQ5xMMI8Dzzj3cHxebhZdnegyiAePFFGYztsMcv+liXH6ReVqSzAYoqbhw8SlOTj4LaLbbVwhjTakUVqOGY8RIITYFRtcTktWapePyhN/kUsOn197h1iplHufUXJNuzSGwFYvlG2RJRJb6WGabVqdLnmzGU9OUOP6mn35mHmIkc9w8oWHY0Nql6dq4huRsNEMr8BoBWmwME8fZIVPwxXunGx0bKXnqxeep2Ravnm7kBva7dexMsR4OORycIUyDS09cxN8NIFpBfYdcl4yTMQB7tT201pyenqKUwnGcRxc6QDSfURU5hmESNFsf+HsUR0foPEd6LtbeB4S1volQUYEuFcKUHyiW+aFhWNC5DN/eqNcj+FAG0L/7d//um/Llf/iHf8iv/dqv8aUvfYmzszP+83/+z/zFv/gXv+Yxf/AHf8CnP/1p3nzzTc6dO8ff//t/n5/92Z99pM1v/MZv8Gu/9msMBgOef/55/vk//+d88pOf/Kbcw0eBEALDvL/y+1PCMCSt7Y82CemvkWb4k8kz/MHZ6yRUFN2Mx5sb0Rc3ybgepYyE4ErLwTUkHFwkcDLwErbPfZI3JoJpOmUlz3h86yVs693VgaoU0uhRnHZIr13Hy6ace/IZogWIIKTeFrSfeJHkldcpBkPM3V3MdpvmpQs06++Aecrus5/gLdYsrx9SmC0aQYjVsqC3gs5GPUsrTXZjvikeuh1gNt5dLTd3HuPw8BCtNYZfcvBMn2o0xjQXeM88Q0f/GIer27RmQ+LsLczG0zQ64NXqmBcaaEOwrBuMVimyZdNv+puVq2diuQZCCLQOUMYuZ717lIUmrc95au8Cd8YRy6Sg3tlB3Jnil6fkZcKdkwGPL1dc2j/gzcmIRZ6zJQWF0gwGA86fP7/5raSxMYKWx3i5SeYVZNkA37+MZ3pERURcxnTcDkfrI5bFkovBRdJaSbaSZHFJWa0xDIe2bdGsO0QLDz9ZsbYE98qcjtHCKGes5lO6Fw3cWp12VDCOIsZrn8fPnUOMp/jhGpFXHA1C9lveNyz0/cADpJSiqqpHeVDfBmitGd1bPwyltLY8Wtvf3iyVD8IyW5KrHFOatJzWhzpmNZ6zWMdUUtMJ2tScDYdOGO8fi8pyTZ7PKMs1rruP7194+L+kTACwhMXN+U1Ws5QnowxfF8jaBezzF3nznRtQ5ni+Rd1VLEYLMGza7edIbr1DWVW47T7m+vbmfpYJi2jKvEyouS6NXosnmi1o7DG4OWEa5mw3TFrWGbPZCI2iiAvKooMTBNTrdaLFfW2i++GvZVHx2jqmHQ3oKsnS7tNp+jTNzTu2HC0AqHcD8vwQAMfZ5cY6Qt68gWdInrpymVq3Q1GU3LsfLruy16VcpIxvvMMyT7C2mlz90R9EzF/ePKDGHvdW91Ba0bAbNOwGo9GIJEmQUrK7u4v8ChX4xfBsc+jW9vsV4gFdFORHRwDYly9/YJtvJqrVu96f75RFwDcKH/lJfupTn2KxWLxv/2q14lOf+tRHOlcURTz//PP8xm/8xodqf+fOHX7mZ36Gn/zJn+TVV1/l7/7dv8vf+Bt/g9/5nd952OY//sf/yKc//Wn+wT/4B7z88ss8//zz/PRP//RD9+P3Ir7WAN7zGjzX2sS9X58dMljfI47vsGtEtEwDpTU34/uaPvX7mUDJDFGmPNl9Es/0yFTGl2dfZpJMUPeLJT5YhZo7OwjbRmc5TrZEmnWqwgapKdwU634B3ez6dbRSG2K36UKZ4aQj+pd2MdstlqsVq6mF1hqmt6DcdEohBUbT2Sg1p+UjoQLHcTg4OEBKSRzHjCwLpTXleIRKEszHnmf74ifxZEEjG3J4dI1FuNicE3B8i/2ej2kbLEtFre1S77jYnvnwmQph4Dh9DlpdyipmMB/TrW3C4kWlSJC4O322+wcYQrEqRtx544gD38dqdQiriirPkFKSpinz+XvIrI3Ns7GSEMuoo1HE8e2H6cdJkdB0mhjCoFAF0pPYniTPN/V8knCxOY1p0Gk4lJ4NhUFdK+Iy5VTWNrow8Zo0TfEaDdqOJE9j1mmJ6G8jbQvPyBFpQr7MGIfvkmW/XjxIhYfvDC/QcpQQr3KEgO2LDdo7wXec8QMwSTben+79MjYfBrPTIeu0oPRrbJkWtikxah8czsiyEXk+QUob22phWa2H/3vgARrGQ5bziFpW0lvMcGs28twzzC2H5GhD1O3vbLGYXUeh8btPUN3nuRj1OqaUWIYEw+ZsdEayWrMyNU3H40rbJdh7jnmUM15llErjiVNq1hkahWU2KaI+ZaVxa3UCP3hotD4If312saZUim4yoYVg4fZpBjYty6DMc+Ll5j78To5GYRoBGR6jV19DxDHnGjWCK48BcHc4oyg1nmOy1wjIToYcj2doQ3HuuacJ7GzDbbE8MifgNDoF4GLzIuv1+mGf3tnZeST0BWxCcXGEEJJGf/sDf4/88BBdlMhagLm19aF+728UtNJU648Q/vozho9sAP3BH/zBBw5WaZryR3/0Rx/pXH/+z/95fvVXf/VDh9R+8zd/k0uXLvFP/sk/4amnnuIXfuEX+Ct/5a/wz/7ZP3vY5p/+03/K3/ybf5Of+7mf4+mnn+Y3f/M38X2ff/tv/+1XPW+WZaxWq0e27yU80b7Ejm2hdMnL0zuso9usVq9x9X4drEFWsCorsLx3WfvTm1jS4tnesxjCYJ2veWPyBp87/Rzr/KG0NUJK7IPNRG7MB5udxQ66UiTJXexLFxCOjYqTTYxbGtB/ctNudpvOVpfaXoc0i5jMPaqk2qT+T64//A7j/qBXhRtX7Xvh+/5DIygDEtcBDcXxMcJr4j/1g7R6F2mLCDF8g3vLOVkSo7MNR6sb2BhSkOQVi/iDJ2nH3qZd8/Clpso1J/HhhqxpSkbrFOU16DYOqHsCw1hzMj5jdRiys7t5LifzBc3axqiZTCbv9i+/s4mR6wq/2nj90uwUUWzc8XEZI4Wk7W5c6qlMMS0T0/bJi5JwuSEbWlKwVXMwAwstHNppRVllDA2HAgeKhNVqhd9oYhsCp0zQWrHIwex1wVLU4yVmXHI8Tz7cS/Uh8Z1SEiNZ5yyGm0mxu1/D/w4e7B+oP38Y8UPYZBiNh3OKSkGtTkMIHFNi1D/4HtN0QJ6PMAwf295CyvsK5KogVznrfM14OSNa5lxZZ3gyB6+PeeUq12ZjxGyGb0iaNYNJvgSnSdvdo1huDAGrtwtlhmkI1pXBfDpmVpY4lqDerPPEpafBcrk7jZhFOU23pOPHWNKg2XgB332GMCwQQtLu9TfhL/Vu+KtQirejFD+ds6vURkOtteFJNUyDaDknTzWW42I4CwBse4fbr7yOXCxouDb9Fz/+kGdz/WTTjx7b7aDXBau33mJQKex2kysfewpWG4OHxj6Hq0OUVjSdJq5yOTvbeHfa7Tb1ev19z3o53IyJtU73A8nPKknIj48BcC5f/pYb5NUqg0ojLInwvvu0uj60AfT666/z+uuvA/DWW289/Pz666/zyiuv8G/+zb9hf/+boJn/Hnz2s5/lp37qpx7Z99M//dN89rOfBTaryC996UuPtJFS8lM/9VMP23wQ/tE/+kc0m82H27lz575q2+9GCCH5xNYzeFaDXLY4KTYV3V21YMfZdMob0X0vUP+Jzd/lMaQrAivgpe2XOFc/hy1tcpVzZ3nnkfNbe3sIQyLSGDNdY8htitygUim5muLcryOX3z0k+fIbKLu90TvSFX4+oNVwEAKmszHzWe3d7082A6p0TaRrgtJUy/d7KDzPe6i3Ed0fhIqzM3RRYJx/mub5FwksyU55ymxywp3xBJVvDCDTkPTrmwngbJl+4POz7Q6WK+k5AUWSMo7HtGoVjmmwSktCw8Oya7TrW9hmwaIckIQJtaXAaLSYFSVFGOL7PlpvQmFa3886up/GaqcZteB+vb1yTJYNicvNhN1xN0bpPJvTaDSw/TpZlhGvFw+vsWkadJou2nVJMkVNJWBYDJQPqmC9XGB7PoZh0nEFRZoyiUvMbhdpSxo6hXXEfJ4S5+XXeJs+Gr4TSmKUecX43hoN1DsO9c4HpwJ/JyAuYpIyecTw/ZMwPztlvi7A8el6PqYhcGoWwvqg8FdIXoyoqhTTrOM47+rRxEVMpSpG4YhomrEnGnTWx1iOiXHp49zEoDx7B1lATSg8p2BWxNA6wJtFaDR2u7XJICpTtFC8NR4yWYekUtBw63y838Fu7rNOC4arlEWa03QjdhsC2+5h213ixYIkzbBdj0ajQbx6NPx1LUrJlaaTjrHLisjpEtw39pqmwWo0RyuwA4mQIQJBfBSxOD0DKbn44scx7o8T42XMdBUjBFzd75HdG3J6ckZIRfupp9jP70K04fukXptBtDFodp1dTk5ONoKRQUCv935jtUhTosXGuGpt77zv/wDZ7dugNEa7hfkB5/hmQmtNOdmMeWbng2vF/VnHhzaAXnjhBT7+8Y8jhOBTn/oUL7zwwsPtpZde4ld/9Vf5lV/5lW/mtTIYDNjeftRNuL29zWq1IkkSJpMJVVV9YJvBYPBVz/vLv/zLLJfLh9vR/Xjr9xI8u8GL/afx/Yuc6h6J2mQeXfFdBDAtSqZ5uTFM6vc763jjhfEtn8daj/HC1gsAzNM5WfWuISJsG3Nnc4y1OAMkotxM7HF8B7Pfx754AQSU4zHxH/8x6dKiihLS179IfTzAM3OS8RnHdxYUsrU58fAtuG8oPPAClfMMXbw/w67RaCCEoHQccttGlxXF2RkYFtZzP0G9uU3XSNCjt5ks5ty5zxEA2G1uJsThKn2Y/fVeCGHg13o4hk2ARFWaYXKP/baLlDBaF/j9Hbq1fWSVI4MVq3CGlyqk2UYBx5Mx3XYLIQRJkrwbZq7df5fjCZ69R+BfwTMc0mzA8j7R8oEBtM7X+DUfJ6iRFxlpsiZLNgNYwzRo12y077PONZ0yRgFDEZAjKdMHYbAmXVeSJTHLuEBbNmavg2EpgniNmXxjvUDfbg+QVprR4YrqvmBgd6/2bbmOD4sH4a+W0/rASuxfiTxNWIzHhHEFfpMty8SWErPxfl0rgDwfk+fz++GvLqb1rtciKRMG8YB0oTGUzaXpENco0W6H1YXHOUoy9Nkd2kWM75isjJiyuY+pbMxwY6wHe5coy5Iyj3gnPWM8K1iXAldInvJstnc2i8/DacwkzGk4BjVrRd0VuO5mgT2fjCnKErdWo1arkaw2784Dr90b6xi0Yj+ZApK0toPvmNQMA/me9Hevk4IUiJXB3Xc2PKDWM0/T2no3E+va8aaPHXQCPMNk+aXXGCiJ0fS40sux1vfniv5VbqejDffHahCOw4d6P3t7e+/j/QCsJhtahldvYnvv53VWyyXl8D5B+9tQbFytcnReIQyB0frOXRR8PfjQBtCdO3e4desWWms+//nPc+fOnYfbyckJq9WKn//5n/9mXus3DY7j0Gg0Htm+F7Hn2nQsE2k0eSc1ybIJnoQDdzOwXIsSKq03isxCQjyBcPzweN/yaTktNPrhSugB7HPnQArMIkSPzijTLlJYVColy85wLl/G/4EfwGg10ZWiGK+Ib80oRnPsbMruQR8pYX5yj7M7OVWUUo2O0LONUJnR3OhT6LwiubWgGK8f+X7TNKnVNpNb0tz8vvnREVop5O5jBOeewTcMdssT0tWUk6MTbt3nPnUCG8eSlJVm8lU4MJ63g2FBy7TQmWCezmnVSixDsohzSrdBzd/Ct2tQRKzkGVIIusqlFC7jvCAL1/T7m8F3PB5vvCJu434YTEE0wvcv0Aw2k0SSDSmqAtd0CawAjSbUIUGtgWV75FnG+j7/oGUa2JaB2fSwpEWValyVo4TDRHhQpKzXa7z7YTBPbe59nWuMXg9lK9plijEPOV0kVB9gCP5p8O3OBJuehmRJhWEIti7Uv+NJnh+l+ClsvD+LVYF0PAzXoY7AtiTyq4a/TinLJVJYWHYH03i33MVpeMpoOsWI4fGTmGZ8GwSsLr7EG7lCLs7wh0c4VY7f6TDr7kPQo7EoQIDdbODU2hRFwb31EWFRMJxXNFSTnoy5EDg42+dI8orTRcJkndH2Ew6aGkN6WFabqiyZ3+cSdfpbVIWmLBVCgBNYzPOS46wgSGc0ygIlHcz2pk+1rE36exqXCEPiBBE6zRlcm5Iohdg/4ImL72rSJHnFvfsLoScPeoSvXWd6fMxECBr7Lc7Z4UbjZv8lpl6TUTxCIGhVLaqqwrZt9vf3P9D40Vqznm7uo7n1wdyf7NaGS2Xt7mDUvrWGudaacrpZ6Bgdd6P9812ID20AXbhwgYsXL6KU4hOf+AQXLlx4uO3u7n5LMjh2dnYYDoeP7BsOhzQaDTzPo9frYRjGB7bZ2flgF+P38SieClwsM2BR2dxKoShmPOY72FIQVYp3onQjeNW6nxky/DKU7xoFD0S/zqKzd8M4gPR9nCuPYzsGnNylXMaYcuMFStNNDN2o1fBffBHv4y9g9nvQ3EPYJt75JtvPP0tvr0sVhtz5X19gcW1CdnhI8of/H+KXv0T69tuo6Ijs9hskn/8Mq//79wg/80Wq9buG0IO6W7Fto00TneWU4zFIA+vpH6UWdOgbC5z5GWW45vrxgJM0Rwjx0Av01cNgXWzHwJKaFpvV3CS/R6/moDTcnsS0d/fYau6jkojx+gi/Y7MtDXLtExcVZ6MRrVYLz9voMQ2Hw80zfOBxW9/nC3jncQ2bopgT5pvV7AM+yCSZ0Gg0sNyALMsIVxsXe900EEC96WB6LpNUs0UMps1AeZQPDKD6xjhskKNUxSKpkLaN2G5Ts8CazKmyTc2zbwS+nVpA4TxjPcsQQP9C/X1aO99pKKqCVXZf4NL9kw2gPE0IZ1MW6wKr0aZuSQRgexbyA+61qmKSdMM3Mcw6huFhGP7D735r+DbFEi6dFuxWp0jT4Gz/Kb68e5lSa5xbX6JdpLi1Ou7FZ5lIgUoTgngTMg32L6GUYrA6I8zXjNYWbtHE1hHPuhKjvYdhO9ybxUzCDM8xqJtr+jVw3b2Nd3S1JM5SLNuh3e2SrDfvjRtYSCl4I4xRGvaSAaLQ5F4f+37qdtM0iBZzilRj+xWmXbJ66x7T3ELV61x97qlNtut93DibUZYVLc+gnkWc/tHnGJQg6xZ72w5NR8DBJyj9Du/M3wFg292mjDb32+/3v+q8GC8XVGWBYZj4jfcLjBZnZ1SLJcKQ2Je/9fXSVVig0goMgfkdHBL+evGtzaf7OvHDP/zD/O7v/u4j+/7H//gf/PAP/zCwGUxfeumlR9oopfjd3/3dh22+j6+NwDR4tu5hWU2Ocsmd1QhbSp6tbQbCozRnmBXQvbJRLC4zOHvtYSiq7/UxhEFapiyyxSPntg/2sbb6WI5E3Xqb9K0F1WBGns6oqncNC7PdxnvuOWo/8VMEP/rnMJs1LDvk8o99EqfukOcZJycW2gmgTFGDm5SjEdVihvQrjNr9zKKjIfEXvkj0+c+Tvv02xmCAce8e6vp11pMx+eEh0Wc+QzmfIw6exd+9iC8VneQUo1hSHh/z1jomqiq2XYl55yarL36RZPr+sgNCSLzaJhTV0iZSSNb5mk59YxzencZ4rQ793iUsoamiU6b5nMC32K61iVcFJ4sVRZays7OzKYMQx4zH43fDYNEEVIVltQjsBhrNIrr38LnDpihlUAtwgwZlVRIuJ5RFhRSCumnQrNmYQY280qg4wjdsVGUyKstNYdKqwnI8Op4gTxLSSpCVFWJvB+kI2ipFjJffsDDYAw9QVVVUH1Ec9OuBqhTzswiA1raP91Uyor6TME2naDZFSF3zT56U5mengCbFQ1oOwX0Oh/NVwl9ZNqQo5hgywLI2hrBhbDwPb0/eZjBY44eKi9LFsyPGB+e4e/ElAHbjBTvDtzAwqJ+/ShL4ZFWGGM3wLBen2capd1nEC0bRKfNK44VNZGVwsTrDazaxty6glOZkETNehTTNEbuNDdHfva/QvJiMKcsSxw8IgoDkfoaSV7fJlOJ6lOKlM3bjKUJI3P5FHrypTdNgMVygNVi1iOp4wHBpgGWz/fzH2HIffS53h5t+fq5hc/T7f0C1Spi5dfoXm1xom9A6D16bu8u7ZFWGYzjUizpaazzPe+hx/iA88P7Uur33pbWrPCe7eRMA++JFpPPBv9c3C1ppitEmZGm23Q+USvhuwbf1zsIw5NVXX+XVV18FNmG2V199lXv366/88i//Mn/tr/21h+3/1t/6W9y+fZtf+qVf4tq1a/zLf/kv+e3f/m1+8Rd/8WGbT3/60/yrf/Wv+Pf//t/z9ttv87f/9t8miiJ+7ud+7lt6b3+WsevYXK1vVphvrBe8utoQGA+czSTxZpiQImHv45s6ZPEUppsOa0iDLX+TqnkWnb3v3O6TT+K3A8gy4lun6NtzypvH5Pn7ZQqEbSMekK7DIb435/zHd3F6DdJ6nXXjOdyrV3F7EufKYzhPPIH3zLPUPvVjuM++iKx1QGlUGFGcDcjvHhIkCSyXJElCtV6RH95j9X//N6LPv4Iym/iVSb28i3F6jLWYk996hze//Db65S9QX00QqzWDz/wx2e3bVGFIFUaoOEYlCYHbRitFGa84qB0AkMtTHFMS5xVnq5ztC1dpB12qJOTm3Tfp7Acc2C6VFXA6iRkPR9i2ze7uZsCfz+csMjbSALraGEFAy9+46hfxITrPEa++RfDabfRixapc0WxtYdqSOJ0S31dxbZoGUgg6/RYA4yhl1zSolGCiBKooWM/n+I0GlhQEOkUaJvOooJIS5+IOLVtgnI1Zp8VXzYr7KDAM4+Eq+VvpBVqOE8pSYdqSZv/PRiHWafLhs78eeH/SokI7LYQE975D1n2g/Fw9+rzj+IiqijEMH8tsIoTBrJL8ztktfuf6TU4jRTBzadXWhI0mZ80DKrvGC4FD49r/QlYZjVoHY/tpFmpJGS+oZyaGMDB295kkE94ev02YpySlhx877BQrar5AWgHe9i6TMGM0P0MXt6jZa7bq4PuXHmaiTcebkHu710MISRpt7sGrW9yMUuZFwcHyBlapSYID6p0OSmsMIbCLnHiZoKlwiiHDeyMqo4X11NNc7bYeeRaLKGOxjhBoavduoaYxuWFhd2t4bZftug29J4iKiJPwBIBLtUuE600F+g8iPT9AVRbEywXwwcrP2fV30EWJUa9hfYtLXgBUsxSdVQhTYna/e70/8KeoBfaNxBe/+EV+8id/8uHnT3/60wD89b/+1/mt3/otzs7OHhpDAJcuXeK//tf/yi/+4i/y67/+6xwcHPCv//W/5qd/+qcftvmrf/WvMh6P+ZVf+RUGgwEvvPAC//2///f3EaO/j6+Nq40tpusbnOYl91ZHDLNtNIKbcYZCkyjF/9Gu428/gzF4fWMAuS2o9dkJdrmxPCYNB/S83kODCDZGTesnfoT1Z29u6tqUMdVkQDy5jXfuAzq7U4f2JZjfQeRrOtsW0Vwzm62Zn7QJTEW9X4NaBZ13j6/aCuk9gdExkUZOFYbovKBlmawHA0qlEBcuIG/foZrPKQwJzUvY/pepz8csFmfkNyP0+JRka48T16Kz3eNeVDGPFvTuHpLfPXz4fblSDOOSfHCKsuF8GFMVA4oiw51PyEWfW0RsHQTseX3m4yPiu3/MpH2evTCksV6xSENef+0WO/vnqNfrdLtdptMpo9EIx23hJEeI1SnUtmi65xC8wjqbEr7+BQhzGsqheOs249Rm7/JznDmSNFywXsQ0eh6N+yJwQSfAtm2WWU5NVJhAFuUMhjeoTMn5T35i066IWK1WTKuEc7067pOXyG6N6aQFp9M5Rw2Plv/1e04eFEXN8xzX/fADblopHCk+cnZKkVcsxxu/QGc3+I7n/QAorZilm3Dmh+H/PPD+5GYA2qRZrZFA5dbxggpOXoZwuAmv7jxPpTOS9B5aCxynD8LgpLA4ms+4fudN0iijVzTocoNpLMh3LxP7O1xRK/x3Xmc9vo1hCIILL4E0mKo5jOf4Rpvb5pps9RYA6zhmnOT0Ip9mldNhifZdZPMctmdxPBwwWd6h5lhc6jXptJ566I3K04TVegUIets7pGGB1huF+8ISvD5PaKyO6FYpQlsU7ctYrglxuSl/sV6SL2Ks6JgkHRJLB3XxKs+d30N+xTt0OFqglaYVzVGrCYQlSfcAu1bQ8yXW9lNgWNyavY1G03W75Iv7WkRBgO9/dbHacDZFa4XjBThf0a4cjzdheQHOU099yzOvdFFRTDZ9w9zyv6u9P/BtNoB+4id+4hGeyFfit37rtz7wmFdeeeVrnvcXfuEX+IVf+IWv9/K+pyGE4IX2HrvhXYbFgGk8RzgHHLg1rkcpb6wTkkrRMGtgHGBHI/zDNzF3P8Zcm8zoMA1POU5f5f/Ye5FLQRfr/kSTWxb68j5qUaCmJflyyOz6beLui+x5dYyv7PRbT24qpCcLauo13NYUPyvJs5TBzRwrX+JYHqJ9cVMVlo1ol0pLdCYwL/Qx+++utDqNBtPplNC22Tt/nuzGTXSWYV66DNkhwVuv0IpGrFcWVi8n831OLlxgZ3eP8nTNYjqlEEtsrUBrkqriRpxSKEWmXDp5TL4Yct5tci0/Js9ukEcVazLOIoMt2eZeGhHmS25/4TN8/MrjPN20+cx4yWAx5+5/WLK118J1bLw0I84zTpYjdjjBcG4yaUtWloPh+ZQnZ6SljRecp33uMaY3pqzv3ECsLSwVkeaS6fCMnUvthyq4pSXptuucDaeMopitJOFkvWYUuLSLfPM8bIPG3TsMdJsoiRjVbC5fvox9bpvurRPOTiaMeh3SooZr/em5M1opLClJ+GgeoLMs58vrhAuew9XgwxtNVaWYHodovSn+GTS/teGFPy1W2YpKV1jSom69X0/mvXjg/QEo0NTm16kDrixQpSY48UHcH3fXA9JKcddv8fayIKq69JTCfbRxxgABAABJREFUUgsqucXx+HO40zHbGs6vrmFlA0pvh5Xh0FofciktOB2eQZlQ37sMtfPkqmAe3UOEEcd2htG4jInAEy6Dkyl+ZNGMU/bRFEFFIQ3c/SeIs4Sj4WukJex3Ozxx8Aks8913az4eUVUVrh9QbzSY36+H6NUt3lmETBczLk+uUY8SMvbZGZ8SVisMaVAvc2ZvvgG3JhBMWfYtVO8Cl596gpr5/vf3cDinXCzoV2vydYzlNVghsHouO00PGnvM0hmzdIYUkiANHqo9b/0JYoWrycaLVf8KL5EuCtLrGy6RfeHCt5z4DFAMYlAa6ZuYrT8bfePrwZ/aAMrznNFohFKPCs+d/za47L6Pbw6C4DL7ZkAzuoFSMZV+B9O9RMfq8VaYcpLm1AIX2ThHnsfkRQSDG9C7ykH9MnmVss5m/M7xK1zuPE/H8cmVJlOK3FDMihTX69MaB6hxxNHdG9zYfpxdx8KRErkpbQaAQCLtLqK2R1Sfk9kG88iiyrc4PbmOJwZU3ilu0OaCZ9Nq2DCKUfG7dWweoN1uM5/PyfOcbHcX58J58ntHVMsl3kt/Hj0+o6POmFtNhqQ0ttqs212O8pxQKLKgAd1tLvdrCOBWnFFuCrIRXztBrm+xdU5gdl6kml4jNA7J3Iyq1WJcs9lpb9NfXCIcHjKqptzY/nG00KjVksXZiFdmM56uO7SripYUFMsleS45ywqapUk4u4myGjhFhDZmZK06rZf+T4KtbW5YM4q7xyR5RE3BcjljNnqZSbIiCATeKqZyXXYNh2EYM1iseSYPuefb5PUaaRwTrtaYyzk6idiu1VkCJ3dPSI+PMRoOZrikXkiW0xknXZ/L/Ro6zynPzigGQ4RtYV+6hPkVNY++EtViQfLGm5ThmqrXI/+K7EutNZTlI4UfdZ5TKcWNeGMs3UsyLno2zgMeRZmjDIu3oxRPSi777w7i8Spnehw+zBrq7AZ8TTxYnD0wyKtyE+61/Y1n8r3ttNpswtjIgL8XRQqz25CtwWuB30NZHqNSoKXB7n2trYffozVkK4hnm8rZyYIkHtLIV7Rqe4jxtU320YPvlQYZMVF+ius/xtt3FmTTE/acHNYglYln1SkEpG7GSAkMr4OubXEyvMV4tmQ1fJMoH2IbTUS2oipvkxU97IlLTQietAys8DaetCnOPU7W2OZZR5ElsCpgWmuQNgOy9A7T+Jjj7HNQKWpmj1plcbX2SU7emiIP59RmJ+w6Jm5gkngZhXcO4c+4N3iLSZjj2R5P7D35iPEDMLmf3NLu9ZBSkqwLVJKwODzinWSNvTpja3UPIzWo3G063oI7t0dYlcJzLNbjOaVMibo+4vE9Oo//JOe990/yq1XI+PZdVBTRa1YUuJR+jdKpcG2L3t4lNHBrscnSCqqAIt28j3t7ew+J/R+EZL0iT2KElNQ6jxpA2a1bm3pfvo998eJXPcc3CyotN6rPAqw/qW98l+AjG0A3btzg53/+5/nMZz7zyP4H9aa+lUTG7+ObD8fZxrZ7hNEN0vQEnd3hWXONcprkssG+ozmwSzKrR3JyQl7N6UWK1vkf4JPNl/ifp1/gNF5xuHgT2f4YhjQRQOCZLE1BikPePUdz+g7FrTdQZpujVhuRJMj1GhUE6PcqqBp9Mr/NLF7ATgOdB4jBHHewxG8d45yrMc4LAkOiKSmyEn+wpNl28aREaVBoylqDajnnaDyB7hYnxwOCKGXfuojT7BPHCVOx5g29jXd8h25rm4GWpKogSTMGo5LCMx66qBuGpOeYfLnWYpV6vF3mCJni7LzAdB2TuClfUAljdwu520EaP0n+8n9hkecslxnnnriA/ZLL6OXXeLtQFDuP0+87HFQl/f0D/vdsSaJ2aN25hVesqLtdVNJEGgbFfpOqqbCEoOpe5G27jm/UOFjAKHydLI2YjUN8aREUOWGS0LZMjFVIrCAX0LFM1O4Ws0wR3LtD13Ep8pTtq+e5c2tEvFhy+tpb9GwL8oL2KmH4yqvcPLlHr2c9GkKIIXnlVcxuB6PbQ9YCqCrUeo0uCoTnQ1VuRN40mBryO3eIlSJLEsrxBGFbgKBazPEu9tCDdzbhgVXIohLI3acQV55Dpil3b9/kvKUxxRzTyTkTHjO7TxpsExhtth2LJMwZ3lmAkFiOQe+ghu2Z6KpCREOYHwJ6Q2z1e7A43OwTcqPIbVibLDx1XwTSbYLTgHS5MWx4jydbWmDaG96WNDa8rfslYrJ4xuHwHieVJI2yTZXvjsMFswLDopQOssyQ+lFv2DqeYFUpbasJ87soDVMlGStJqQuc8hpSVxzlbzE7DgCDwc4Oca7JnQ6p02NuasIdg2WrhrJ8FmWFV7vMfPC/KbNrXLJynvRbrPK7TKqYk+UZK8Oh3niCfPgWqVcj33mSxov/J7Uy4/ZgyPwLbzJK7pIfOMxLSRkvmUdDVqTYZg230aNtd/nj115HXT8kySt66ZR65qA6HhVL0to2inucziOWseC82WZnOab0Bcugjp0lyMN7rD/3OUzTpL2zT/jGNZbX5xylCcu+waQsuVAusA2LtHWAv3ue2vku0WACSYLlSNKt85yem+Fd8KhtX+a55vvlTlSacu0PP0cVxfRcE6PpU40K5gistkE/MEn8FrcmrxMVEVVR4WYuyE3WVxB8bcNhOdoYcfVOD8N8d/ot53OK0w1n0n3y6re83hdAeb8AsNFwkM53n+rzB+Ej3+XP/uzPYpom/+W//Bd2d3e/K9Uhv49HIYRBvfYkplEnit6hKifsqwF3ooyTNdieQ8sy8VsCf3qLcnmLxY23MHde4s+1W7yuxsQqwUje4PneMzQtA1NaTPMWw2FM6+BZjOQddDVH3XqTCBul9WaBC+j9fdSFi2g0hcjRNQ+Gx+jVbRoXfpBq3qU2P8Z962WmVZeh6TGVFUYpkUnJSkQsnEe5IpVhM8w1KktpmxFWu0M8nTE7HtDY+wT96Sl71ZxraotifIaYHHP53OPMDMntsGBURhyuHbq+g2MIwgpWcc7Q1syMJna8xFjPafkNzOAKcXGddbliuR6yOJXsuTskQZMwW7MYHvLk1Qs83e8S9tsMB3Nujo4Z+fus+j1qpoFodAjPTlmKBfOmSeg7mP4uzaZmZprUbrzO0og41YpFCROZ86n+RaRYwCksRY/60wechinzxRyVpXjdjGUGY9Om04Q3k5SF8LC3dmlWOWKnh5aa3cvnOH6n5DAq2em3cS46tN54A288JpMLpkaHfqeP0Wph9TdSBcVgRDGdcno25Cwr8KTgnOfgSYnWJbkaYco2zvYFvKqEd9bEh3fJ5xNQJboqyK5dR6iE/OUKe2uTYae0ZpFl1O5+nsbyFULVY1UaZGpFiaKQgjFQty3qQnBi1/CaHeLhDDuLceo+9YMdxFuCeDKjWkxAF+RujHQsbKdPZksabg1hGkjHpqiGhFrQFhpMBx2tqWZHoBTStZGug3ivt0IVkBeQRw93ZQTcocmxUFR6TTmdIiZzciH5UmQz7BX0nCZfLAw0Jo9ZBudqdbTXJLF8vqQgzUvG9ctopVllIxyziem4kL4DIgBdUMQrHCvBrT/LUeMSgyXsKo2YHKNbdZxgD2Fo7kZL5nlMGN9F6pRuteDENkEWhFlGnBvckRrDyTiojnklHrCyJaJfo/b271BbrekcT1jPZkTpEow+i+4BZWWyjAqy2h57u0/idh/n2tFNgluHmMKgV9PU5zZOmJGfavJOE88zSO+mLG7l9EYZF7aG2E7CrcO7DJDIPKeVbpINLMPEnS84Xk3532vB1DOQrs+kUWdvNWaZ1Sj7n+Ty+X1mvsnKb2FJwdl8xDvDAsvNsNwaL/SuYH4F90ulKckrr3A0DRGmyYWLTfJrX0QkDdYNHzMwyRsmX5y8CmyypeppHdMwqdfrdDpfu8x5kaVEi01m2Xu1f3RRkF27BoC1v49xX67jWwldqodFT832d3/o6wE+sgH06quv8qUvfYknn3zym3E938d3MDxvH8tqkKQndOWUsFKM85LD3KLmNPHrHZRZpxy9RplOKe/+D7A9LiJ4Y3lEokpuTv8fHutcwXACyARO4pAVNrWnt8hO7iIXb9EWbYRlYwR1dJhhDEP0+BpZMkKpApkMOcjHxEUdKxzRvfhDrO+eEI0mOGpIu9dkJU1MWsjlOQqrQ54ADRvLACPPWFsGotKk4YxkfcK+XREt7rFeVuT7V6hbDXaLFf9XcsSNuML4woIfunSRot3h9+MBh6tTqmhGUHsaKQwUIIVgxzMpqhydxOxIh6ZjoYMWZbzPwpgTFxGjcEm91mNWe5xlfIZd5rx1a8D2dhe/u4U7PEZMxjTckGF8yNxrIfwOtcY2s16ECMcY5pKzwuF01eZUzGkzJpWC3AiYFhG5zIhizcUypkhiFtkc62ZFGkjOMPCDgO75Dse3ZlyLMp62Ms4sxbze438ouN1sI+I19SjCreaM1YqFaeDogqBp4rdsOpbH3YbDzaZD0VcopujRXZZKMvQcZmGJmyxJk4SGLjkuHALPoV3cJtEJtZrHjgRp2oRqRrKOcewaVquGHE2oZidUaJQ0SB77GOWFq+hen2xyg+DwvxEUCaUYENaf5NjqcD6tONZN0izETKaoIsEoV9w4HTAqbI7dgPNGwfPX3sLWBadGnVMzIDVjCtemUiHF+HWCtOKis83l1lMcCni5yImFpmV5PGNAW3ukWYFWGssUmKaJrtUpm20iyyaqcipV4VczkvSUYRhzkoEWNh1nj4bX5LIuOejUeV1l/HF+xBtLm7Eb03TqWMLh89EIlnfZdyzaZsEqS1nobUbRHdTqZXSxBNPH7z+Fb5VkOBTmPlX5JaSXYdTH3B6GGGPF9skxmQ4xZimZ0+NL3TZJZlCEFXMkhloS6Sb3wpyT9ZSmgDwzsLM67axCnB1DpQi3uugoJVMK4+6So0iROS2aukaNFrwawdmKdrFAeZpddY7s2udw0gTP7nP+/AE3z45RZY7KC8LBnHBZZ9ttcBYrFmcZrmXQaNc4bTQZng0RVYEGbucFaX+HfrPB7VaHd/KKgW/h9Gv43YAL6wFVWXCWGxwXFlmVM53FHKcFdSkZjVcIpviOw+O9fRr2ozyqar0mffNNlosVcy2xGib99C7hLCTTDrpugFGx9AsEDn2/jxu5lEaJZVkfKslmNR4B+hHlZ6016bVrqCRFug7OY996zR+AapFtuD+uifTfX5PsuxUf2QB6+umnmdxX4vw+vvdgmnXqtY3x22ikvBIWzEvFm1pQ15JG7UnO1z6OOfgMVTZDqRJHlzxld7geD5lFU5JkxRWvQSAtqrWgUODqlMobUnoSW4CwDLQeo8jJTyK0NkGYCF2hohWEC/LVgOz4HdSd/0UVrriXrhDLtzjo7tGuNSj9FoW6RpE5sDAwAhcWKaIStGXFbrfGIs+wJ3OsoqIbzjGLEPPNL1DWO5R5SC1dUD9assbjj6f/gid+/Cd5orpL43hMEtQwq5gguEyDJX56RjqaEwyapNWCrfwuQX2BsYbg8JS1Lbjp9ShESjgZ0cwlVprRGH0W8+ga8dPPYhgubpHiFDnuKiSwLNALasWK1cpim4I/Z59gGDH/Nb3MsEqpZQk9xwImHBRTktU9bso6dtHG0GcUaPR6SHV7RqunmOg6ZSWosphoDZSSo6mmr89YSsW0ABkN6YiESZ5jKMFKSbLCZr406XvQthvkcY0vLyGvmVxa5liyAAqUFkhhQmDwttnjpG5SSMm2qXHFikDv0REVUmiM2QBT9pk1m6iGx1v1OlpYUHdpPukTpDlpe4fw6vPktsd6kJClFbs7n6Ap1kywWCoX/MfQXh9rPaPITmmKJrKqWCeKZG0x0TVSt8Ax5/z/qoSmFLh+H7vWZ5IPmCVzwkyBX0dZFrYo2KnuYhhtlKihqoCz9ZLB6SmVaxD2asRNA6ESRD4mnWqKkQKpEJak1ClxmVNpAwOFKVJMNGZ8j85km9cMH6MjWatTlmlInDuoZYrSX8avCtbCpRKSJaDKTQ0uS97FkhKngmYFpUgYrd4ksxyE8tDplKrMkVaGWN6jiCu270VM8oqZlVNkAufLIdnWHA3sZCvO45NbNbRpkRUuQTlhixEkFaxrNAuFrUs8DOqRwD5JWDqgrC5hERH7Np6lsGYVrZNjVJGRGGuUNjC/+EUq38f0HLzdbW7PVsS3DnHI0P0a09gj1DZJWfJl3WRWM9jeanJ4fhujqtBXrvCYKdGex91XXqFMY5wrVyl29jlKZrRMwSd3m8xHp9j3XsMMp9zpPkW34SCFoFAaKcCpcrwkp27NONfeYtvcohyP0XmOynOq6ZRqtRFMvREmGL6gZ4Qkh8cIs8fa38KoO1R1jWU6NO0m3aLLMlkihGBvb+9PFAJWVfWQ/Pxe709x7x7leAJS4D77LML81oeetNaUi/vhr+9i0cMPwkd+2v/4H/9jfumXfol/+A//Ic8999xDIbMH+F4tI/G9CNN0eb5h88VlTFhVLMvNNpI2L178C9R1vilYWiQ0TIdemfDW8FXyeMSdPKNuOVSuTRWWpJGF4UUoIVlaDS4ZTVxRULULqnqKjjMcv49rtYlHJ4STXVxvRDI/JpIVo46NmtoYhWY0XvFYto1LTlnNqFYxlZJo00BYBkoDwsSJHRxlkyQaMIhMk0UmqNI5ZiZxqgWymrPVtEnGK8rrQ85Gr9FsBDRlj7pVEdy9hlXzN6KEpcLUECwaWKYitifMF9fpHcbUwwwhbQ7cBjftJtqWnHdGmMMzyrzCpI7/hWvovRZ1q8tANpnMVzwpBLJu4uuQgyrDEgXdxm1EvuYn9DFvlzWYmdiewvfgkrdN5CY0ihnHVUlDlAgzpKNOseZd9hyfplgTyZSOTHH9knnk0CkKzkcpjYbk7cImVRWmWmAVObG2yQubeVKQUtFqClaGS2XMMEQCUYOlL2g1TQw0CKibEUkmWVceFiCVgS4FjiVRZpeVs4ulZpRlTqkrYpkii4R1uJlk9GjKUbmitKGRFAQ3Sua+ZJadoHVEaDZwrKvk+h7z8hC1OMRULrY2MFXOjAwLiVA1EiVAv0k3XZOUJmVYsagUWbDArEkMCUbVxisMDKtJaVkk1YKjMqKhXHyzoB+f4p1NSNEUSYZxmFETMG+7LOsOrXnCzixEak3lWaSBhWMYmLKGcBqIWp9CrChUTJLfpizAGRc4ZcmO4RBoFyeLcJMhQkk8WaOQAWM0Y5UhVU5TLbHR4O1D6xMYyRi9vImqZgjRRUYxjtL03ARDzQlLg4yAyq5z2uvhzRe0woKL64yD6gzPAys5RRpLSuFRpT3mzSWpHVItHeS0pGG7CEswtzzsaU7bzOkHJnmeE6mC2uqQXuARGlcw+1tMRcQbj2VcigPEIgadY7oGeTyGo9u0yoTAs2g+dY40OYceHnOqJWeTGQ2d8bQ4xE8bKK/FgevQsy0WSczenRtUSnPe9ZjcGxGUDoFjUqSHZEqwlUxx5jPC8S3OSYPH8l3emC64tJqxNbrFerWmalfYoqCY1ii12vC3ksWG12W5FPUaQ1UglEErXVGUNRx/h2j/Kvp8E+XdIssyiGHJEoCtra0PJd2wnk5QVYnluPjNFgDlZLLhwQHuE09gfJvmzmqRoXO1qfnV+M4XBP1GQuivlYf+AXhQ1+QruT/fTSTo1WpFs9lkuVx+36D7ENBaE1WKsFLcilOiSmEIwa5jEZYVuda0LZOeZSIouTF/h7vrIbOiJIoLsnVBz5JY9YgoOcSQNo3GJ/hkZwtXFUTJgjANaVLhaoVR30E0D6AoGd26zmD2NqLbppnHVCc3SMKIhurxeNXA3faowhXJcEkhDeSOwOwKapGESYVWMDJd7tQrFr7CX0RYoyVykXOh1LTFHHPL4nAVc3Y7R2uDtu1g2lvoQmOLJbapWZl1MruB7RlY1ElExtS7ixQZUgeYyiOQkrZTcStrMC01ubmmpedEUhArCyfJMe0S26ozXTeQlcOOY9PzLGSjRoym5lrsuhX2+owky3knUUyrEikUWivMeo16o0tU5RjCYKdhY4k1s6M25bJJ019TehVlYLDVMFloxReGAdWq4mJtTODD22mDqLKQrotvSYwqxsgr5rGFoQrOGyPMQuOULeJizYwZfadPzTOIdEBl5pRmypHXYiV9GqpAaIEQikvZKTMdEJVNpCOR7gJRFZirJWUmcADTMBCDEZWC0nEpdUled8k7HrWywDMgVi1SXUOKitxMkYWivV5AJfGkiydqIAVJtkapCE+kOI7AyyzmukNkuFjGCiVzHFGjV7XpY+CaIalrcstQDAwLi4J6ucQKEwSCrO1Tej7uLMeOc+T9cc9EYCmJkBZKC9CalpQgFWCgqG08W2JMaiyptEGaglA23aCDBOqGiyF8LKm567icOQ4X1lMm6RxPmjwlJCJfkrp1lk4NuQppDIf4SUhl2iwcE69c0rAtpomixMA3fLxek6UZkxgptaSErMJpBriGi4qPybMJxFAlHtPtFYlrotZ7bOd9EsNh2LQZGxG+2eQZsYufJ8yWY0w1Y6cHtXafij1mieaGu2TRXvMDreewoxijKKnKapMldXqPWS6p1W3a53Z5O+5gFGu+ODOJlORFP+HH2inCc4jsgKjUhHHKdBGiZlM6Bjy23+MPqh7DymNXJBQ1jStzLhenzO8MCdUO+4HG933u2jWocvrTM6Zmgl2zOHAbNIwa0nMQtrXheXkust3g1dM1nx9CWWheVCsalYPde5K3gzZTf47nHuIZLs80nsEwDHZ3d/9E0vOD8fHozdcpspTeuQs0t3YoJxOSN94ApbH2dnG/DZQSXSmKQUy13KjVmz0Pa+ur6xf9WcFHmb8/sgfo93//9//UF/Z9fHdCCEHNNKiZBl3L5NV1zLwoOU7fVQqOq5yTB5/NizTqW5TJAPSEKiqItU/D2UKWU9AF8+yMP5xlNAyDUV6gkYCkbhi085j24hZSCIZWxFA4VIuCK8/+JerBqwyGX+R0HvJl06JtudgvPkUZJcjlGtIEkWVYDUW3a2E7Loe64mgVIrTicuOAQoxZBClJLGk7Y4Q1Zdg7YN6eEc8tKsPcEE5Lm215gZZrERmaXCpc6UDsE8ch1srBch0I2hwHDexixY5VsZYp92YxIs640bRZ7ZkIw6QxaHEuysijhKpYUuaSZVpxnBRUa4npWFiOz007IEhXrHTI0FAop86+aCKKlDgqyJKQRZmghSReKfxaTK5LklIwS+coI2Kd2dyZG/hUzJcNkkwg12u2vIyWGbGWdULqLDMBWmCZPplnklSSkdylVhfsZE1EEnGWmpyUHq3IxFcpsiqpKImDjLpOeHp+xL3mFivbYZUWbE8OybKKynHRdRcl4czps7IDmvmag2SJVRp4GnzPZzmeMqx8upXmiuPQljUiVRKVEwZul6U+h0Sz7dTo5Qtcw8aVknWimJUGGp9e0KVV38UoFcJR4FjohY8M17i1GqYtkVIiu8+D7bJbaU7WJxTRlCo1KWQT1XRJ+m06Xo3HLjRop/nGu2h0MM0Sp2eD61LM1hTLGC1clNdF5Ro9u4VMZ0gRYAR7JOuYWbhAGxbdtk9ry9+E09SCY7NO6TZpWT5HtNkvxhyojK7VQFHirMfo00OKYoHoCTAki8Wauufjmh5lZbC0a5swlGEhqxIpNLbloS/1qPIUOVmThyYTkZC6BoXdpfQDVK+BZ/k84X4Ms9aCIKCsTnGEx9Vgn8ecPd6eThk2bPxK4/YMzrpP4NS3mJ3dxSw0Vw/2uNTe2WTBuS10fZfpF36XG2nIUWqx40nKqkFaKE7MFsVek3qtzuM//CS3izXT8V0KtVlIF0XFndMpotXh+Y7Ll7sdlgMDOy9xXAt8gwvqhMUpDLcvs214VGSMHI91dwvHNCh7PlU0xK67+P0LJNKgqDSmW8Nq7pBrk9FwwNvLjGlZkqtj3k5OecF6lntKsshXxNYJnqvZ9XZpNBr0+33MDxmuSlZLiixFSoMgqJMfH29KXSiNubWF88QT3+AR+U+GVprs7gqdVSDA7HqYvT8biujfSHxkA+jHf/zHvxnX8X18l8CSgpcaPodJTqYVTdPAFIJpUTLNSzRgCoFrN/nBdo+ebfLmtSnHSU6vEXBxf5fJ8g3eDBMiu0+BpBuYeNIkUSVKV6x1RVRqBBWRtwktdEUXsYopt1/kUjrj1DyhmIWMYw1vvo6WEiuTkJRoNLkouO0YGFaIdhy0G2D4lxi2tynUCbFzyjrQlJWLcBuoKqHYf4LikibJYorVlGVecsvOsbp1/KKkmaUUlOg8xwi71Ghx0bJIm13CNEKisJPbqCJE1OuEsk5ZlbQmKabZpC33qPk+JhGjfMgyzCipWFkKqVMaRU5plBzXQfd8rDJjoVJkrc5+9wp2lbGaTCijjDU2uahI84RuuaKSGVPZQtk1VN9mLXyCUrKbT/HjU1blNlNqNGuCpOVRj2a46RyUC8Imkjs4rod061S1Or2tXWpxjXy+QC3eYZnNKXpd9ra6WEmGPhvTDUP2HZPH6zYt0+eG1UQWe9RrEQdVSZSFDAPNaWuLRvMCgbBwHYPu/IhnxkdMWw7Xt9qIoUM9KTBafQa+TS5S9p0upmkSaomvChAKYcGek2NrOIwl63FJW2uaXQs7aOLnFaLZJLOhPL6JWF9DWRYxAh7/GKK7j05zstExjtegaVwkbl/iumkTi5KdusNjKsFcr1m9ccRSaTK/QexEuDs9HKHw04JWpdFCMzElX45GFOUUaZe40uIxXWItBsSRQVV3gZKjJGYZN9mrB/TLhC8XNoPolKTKCHXKvJqTlzmhaGPJbaw0wc1tcDsUXpOsgmpxG5XkHLnbfNbaonIFB0uLXukg5ZSFAWVcUY/WKKWpSkku7oKdY8saTu0SbG3Taip2awe0Gz/OuJpyHN/mdphTGALhmVyXHjPdozQKGrWnOa51KRoXqMIxS9mh1mjw/7r4QxzZfY6TjERrsuuvk8/mLN0DEt8idG0+R5cJGTNhgdvg6b0W1wswzRaq/ww6XiKrkmSxYlVEYLu809rHsZvENcW2YbHcc7DzFeJ4REiN6dYFjMseZ2XObJmyki5dXUepe+DUSGpPMmzeL+KM2Og13VcbWGqPY7VmxogLrHFbdd4xEob5lNDJudpz2Gr1+eSFT2LIDy/8qZKEydtvkQ/PqBkWyfzd4szm1hbuM09/WzKpy1G8KXdhSez92vcU8fm9+FAG0Ouvv86zzz6LlJLXX3/9a7b92Mc+9g25sO/jzy6kEFzyH02l7NsWfBVv8X7To1ZArZT0ahcxigG+GXFTOdjuRa4GLluORVwpBlnBcZqT3hfgNISg5VyA4Sm1ZMnlxy5hOD9KNXyLRTvm7lyQxwJT22AZVIEglzXiImaVDNHriF7s0zXqlPmCkRURWjZdo8927OBb5/D1m2g7IzddlJCkdcHa2+Ho7JBFNMdOQ/abXYTdJqJkqmJs1+aSMnC9Q2x3xaXGBcS9mLaq45o1Xmq0iXZ+hHfOjoknd3CsCtezqG1d5Al7jFysuW0ZHKo62vF5QjSwopjCTpmVCeGqxAtzCBNi7iD9EDvoc6BiDNOiEgFRFWPRRdYgd0pYmsjEoRO6nKgcywh51lMsewbXjlPCwsBPY55ez1GlIJEOM6eFzBPW8RDLyJjnPRpljUv2KceTKcZa8/RqwDUxQqoY2zRwhU0pbbx2n4NugP3s4+yMZ5wNlmSV4rhR544hKCZjyiJH7XUR0mB/lhHa21Q7z/P7Xp/FVkBRcymMEe1BTFHFJOmKI8PmnozY1j6W6/FUZ5fQ8jhcr3mzSDEcn1WiKYM1y/VdiuMzSr9PXN9GZAVUOVQG5sFj2OkCQ2nie9fJT6+RJ/cLjhoOzdZFTqULyyW+KliuXN60HHqTAZ6OEGXCcnHGgpxyVIJhYCPwpEdl1DlWJbnaeD0LaVMIh0OVcSWvMGTEyLcYWnWMMIejI46MGhUOmZ6yVUzYy2e8422RaJN1FHIiS0pzRqkr0A652yZsdsmiBc3aNo1Uc7RzmTPXR0mDWRTQi1IqWzIzOtR0xZ4QuBgM6xbTehPHVjStc2wFj7HrOMTFMa/hcjd6lbBKUcBANii1YpSZyNktjCygXxaYLZew28QnwStmDIuU0PL5g3srkmrEOBvhlRm7kzFmVqHKBoFaM6TiDpLMsshMk20PAmIGp1N6ps2O5VEXm3HkdLXi8WhBVmvScDzsdcZeVHFkhMxnFVfmr3NzMmJieRStAr/WQ2ufW9GAWTEnXXyZqhxSs7sYtoNrZFRGRSUr4jxmvhyTFSmjZcEkV/i64opXp93a5tBuUKWwtd3k8m6Dq52rf6Lxo7Wmmkw21dyXS/I4ZnVyBAJqe+dBgNFoYPZ6WOfPf1uMnyrMH2r+WLvB96zxAx/SAHrhhRcYDAZsbW3xwgsvIIT4wBIW3y0coO/jWwuvYbGcJCRhDtTwg8eo1Js8x4RO43FMc9NBfWOj7HvJs5kUJYXSbNkWRrvG4XpOVeQk8ymN/iVkuqAvB/SbDvrgB9El6KpC1uugoVrlVIuUap1SrZZUkxHVYsplIFssWJ2dgmhjygDLXmI2IpSzheg+ttFfcR0uGF1OJ1/GEgld0aK10yNTMZFhEuU5qsqpzBJjPsRIItJcUe08yfbWHp1sjnJWmLst7sQFgZwRe2vSeMg4aLJ1dZettCC8OyN2JVVDkI0yZB6xY+WYVQm+S1oZyDzBSU8hG5ELg0oYSMDSGmU6ON0muVdHB5pkWpLcPWOnviIwNUZs0XcDzggxcHFy8Gsuha4oqGjoEQtbYKUldlpRK+eE02u8fbymbhm0il1ElXFgzDHHAzrRO/Qsn1hAXnMYDg3G5nU8Q9PLY9aVS5E3yB0Pd3WEG6e4w4R2PsOoLFaGzd3mLqmAldWD0MZdHNEf3yMtDI7sLQrTIzJKll7A5SojjoakRcbE3yGUDroQNJViKUBZmiEKnYzR87cxtYFtWNiGg2zU0B2f8N4hZZKQP3AHeDZGESMm13GEQytP2Epm3HEbrA2TE2niBW0Kv0sUTjEyRVVphDbRCCppU3kBQuVsZSs62iZRAUOpGbg+p0FM4UukZVAhicWKnbMJwqoIGz0c7dEVO1zwn2GiFCthctJ2iayK3IJUliSiQjsOpmlS2JJFs0uz0cd06hyIGq6QlOOELMhR/V0sw6Hp13DcDcn1wFrQTAWTNGPBLosi5kTNqJkGizIhUxJpWHTdLobjUGQZXqRZ5gm11R2avS6zEp5wM/LsHfKo4nxuc9r0WJRLpvEMI0rZPn0Zv1jjiPMstEuUr5mlJW69jmvZtNoZncaapo6pipx1DpbdIfDPs0pDluWMws/pbVe0miHDUchpnnPcFHjZGrW+QyQqlp19ntnf4nLrMn2vz0TfhskhneQNpDJRzR7VlmLpbsjLRZaxXkzI4pCsKDgba1SquYLmR7Y+hrPzJIPBkCBI+MT+RV7aftdY0UpRnJ5RDgegFBjmRrJeKVSaorN3Q/+r9RLpe9R2dqk/9zxGs/ltyfQC0JVGRQXFYKNPZXZcjNr3Fun5K/Ghfok7d+7Qv19L6c6dO9/UC/o+vvfg+hZSCqpSkyclrr9DmhxTlEvi5CaN+rOPtBdCbDxK7+6hvbPL5OiQ+dkp9W4Psf0cZCHkIeLuHyIQYDqQ7UHzHGbLx2w56KqOSjvo4jw6jyhOjjBunWFGMcVSkRWavNqhir6AdCPisoUQK3Sp0Eqj5WPkVU61qohNoLmHIUqqMqcoNY7ukWRDskwQqnP0xLOUlc948RYtZvhZTrcMCNM11iqmlAVHo5KjTodzvTotnWCdjAgP7+IYFYZhIOsBrQvnaLptouxxZqMbOOsZhpSYlFQ6ocwLyiRF2xUyg0ALbDdjYXXBFrjmigrJbboos0XVXFOtPJZlm9AIWAUtjOQdKCICkREBVVHhiwqNxlAVF2qwZdQpzR41Cu6sz8hLRWrFaANkGWNmGu4uKFwHocFD4EVDhCFwHYf6coGoFJk2mKk1VVVxbjElCTy81ZqadFiWS4z9JmaY81K+Ar1koVbkicndosAO1wg0TRlRmg2CaIWfp3iex6F3iczr4FZLmtUYp8rQCjLpsK5qxAuX0hc0kgxXWKxaNRy/zzId4YUhUuQsO5KJu4+/WOMnFXPP417LoJQar7ZNx/Tpqhp5FLNQETMzxilzGoGg33yGulGnlZf0ipRres1pFSCEYKvexRWKMHsFq++jA8HulYCa36Jpd3hzeUpYRqycPVpOm7qqkNWmVIHQIC1BNpmTUkPV6shmA5VluNmArrCp5T6RbTMLYhIV0a8lOJ5DpWE1fZnRPKTd3OXqVptCS7LkLlWV0K5vs+3tcN7Y5Ww05u3jMZ5WHLg7XF/fougH5HZB6e5xOCiYhQXuFISSlHGEYdn0S5vd8Sn1EhKzzrT1NGq+orBTXM+jzpJKJ3hWm8v9DoEd4EqXMA+RUnIUHRGGIWE+Q/glZU0Q5QlaGqRuQG1L8rHVGcLpUvgtnmxt85jaoRGZrFYTnIXiXLjmQPaJbQ/be4lmwyErU/LZCrlO///s/WmsLel51w3/7vuuuda81p7O1KdPj55iO3FGvW8S64mSEPFISZASIUGiAIGAIj5EIgMkEAhgwBASIkQQUiJkC8SHDAghEUGY3jz4CYkTO7Hb7umcPtOe11xz3cP7Ye0+3cd9un3abtsh3j9paw+r1qqqtWtV/eu6/td10ZdjwvQix5llxhK/XvB/Xd0iefRxlmVE2HZ5rNPnqdpQfeKTZ6NOJGa1vE/kfCbC9/Ev7GG6HcytlwgFbD31drzOG89w+0LgWovJG+y6xeQtmxJYEKHC+2NgeP58eSgB9Mgjjzzw53POeSsQUhB3ffJlQ75sCBOfNH2CxfJ3qesjcpmQpm/cIKw32WZ+eIBua9azKb3JFlz8Srjzu9AWgAN9NpNpdn0zBXvrbQg/QqUS8IEIf3tM9K53Ub9wSPmpuwTHYtN8kRy33Ece36HauoZLAqLRiL23jzg5nlGv5vhNi7nb4JIO1o5wdQ3TikQpPKPwdYh/eoySI/Kmz3TdIpVj6W2x9i4g5JRGRhwVO0Qzn3qhCZegtaEkRSUDIhEyXBvEqaDccSx1yanpE8lL7Moenp4j2hm6XrM2mrBoiGVJFBsC1ZCGKVUT4aRh6kNZG5KTQyYmRtV9KtXhlrjC27aHLJdb6NmnkPmcqiippEcShFjZI8QhK8lw0CNfayZih5M8YKpX6FVBZ9DDHwxhvsTXHl5ygaY3xCQJOlvidIkMB0RfdZWAgMXsJRZH1zFBQNwbMQk77LSKwBqe6PeYeUt6k4geAbnf8oezQ/7w+AW01HS7ET2ZIEkZx3vkruQkXNMJU/6/vUfpeClOQKkqFvWSWTHFmpK4qVBVxWS0zVNf/Q62Rzv0VwmnB4co3+Nue5ecEhMLVCAZySFpFXJgjrjR3EILw2WxxaAIkZ0I9cQF1sWS0xvXWZYztsPLjC7skIYdQhXiS5/HraRqSrZ7O+x0dth/9tP8gT3l1JtC7RgWKeMLV6hNTeaVeMrytX2fTuAAiRIpqb/5yrMFh/tH1LTobsBqnXNSFMTrJRPbQ9WKnie5k2uOM0cHy0QvyVcrjm+fYh24LKSfa0ZxyEvr26zqHK1TcjXnk3ZG1mbU2RoPS5Eoru5dxfSO8MYJ6zIhtIY43iGXC/AETRAy0DUXFjMeFxnRZI/bV56mnHfRdYMnNIPumI9Oj1lqyZbu8XV776EX9ZFWMF0d8/HbH2N2ckoUpoyiDt2kS9F/gqOZwknDZCdiEq65oDustQF5iatigF3kzBY5i0Yjbj5HuvwdGuOIxLsZ6Yye1dRVsRlaIjukown9nT2e+9/P0l/kPG1bOuslza0DXryx3ESYdQy1RH/G+UZGIf6VK8g4xmkDuM34Cs/bRHmk5O6znwIB6WBE/BaKH2fdRsgIgVCfUY1tLGbZYNYNrjY4ff+sThEoVNfHG8UI+cVPv/1R48tj4Mc5f+RJeiH5sqFY1oz2Uny/Tyd9kix/jqK8gZQecfz6g3aFlPS3d5jdvc3i8GATBQpSuPZNYA2YFqoFLO9AfrKZ65SdwPDqZtaTF23+nh0jlUd86QrB5feQ/+4RtjYEg/fjXvyfxIXAbj9N8NjjCASrRnNTT9DxgsfDCqELrHT4p4aVJ4iCC6RJhfN8jpY9gkYxyjMSqcmUj49iTwjmbcs8TmmHA7ppTF0qxLJABxPyYIu5knidmHS5ZD2fciJ9kjJH7u0xC7pEaUqne5GYlraqOC4d8/KUZnFAld3hglrQBII8GDFTAzL5GLkybMcrLjULnG1IiiNu4GjrI7I245Gx5qad0AYDPJeTqJCtrTF7nYiPH9b8Xm2ppi1JE7E2EhmEiFUfi+NqcolOf5elPaaqMmTpkUgJRY2UKbQdvFxQne6T64YEy44ds5hbsnKIN9hit+8haZjPlhTrFtsL4NoY5yw7rqUXBdimZVgYfO1x6jq4OTx16RJRL8aPIwI/xAsiZMfHWI0XPEoYpmAEs9u3KBdLgmFKP96hmRbceen3KLOM4e4FvvrtX0+QxDRVSblaMrt7m2x+xIUo5iuufht4iuzgELqgmwZ9s8TLG2QWM7LbJFOPvW7CxaefxvN8pOfdN/+pzDPuPPMH9GTKpcuPsjo9Rh4rrl4YQS+kiEpsDdfWVyhmC8r5EmtAPrmFHHXJb5wi2l2MlohbFpG3qP0Mla83Aqo8pu1sUsR7/TETBNWJR1NW7FSKyA9Jy5j5wXXmOiOanDARYKsZvjhFeT6+VzPqrOhUFfHBM/QiRffaNlKDH3yS6bxAIWjTAdI1XOgJ8lVGkN8l9H28i4/QbYesZxknTUklM9p1jHI9BsJx1QWUn3qJsmlYT0+pjMHXmm0/IdExRdDjlo7wD9bUL2Y4FEWmGc1+l1WVk9kBO91j/GxJMJnguh2Wh8fE5s4mUjZV+C7Dtp+iPNr4d/wooTee4JsZLz5/h+zOHG8+49G+wxtdoTxacFxbZDfg0iTFH/WQ3S5CKZx1CN/H25q84byubDalylYIIRlf/vwHhDvrMLMKPS1x5sx+IgWqG6B6AU5bbHE2zNTeb0+RsYfsBqiu/2Uz4+thedN9gL4cOO8D9MXHGsutZ2Y4BxefHBBEmw9qUdwgL86ahYUXSNPHkfLBpr2qPOHOp55BuIjdx54kHbzONPJqBcfPbJo0vgHOS8hvBJhMEj42JlDPUX3yGTQ7yEtfizea4KzjI/tLKm15xzhl4grs6Q2WecDaDEiHCeH4NrWZcvd2iMq6PNZXKOlQQx+BxjYts/07VMUhfqehlB0W9RUsgpPOGDMao09vk+Wn1EVFuaoI85IEiT9OWfQDvCTlanoZl69RRqOlD8Jxc/USwi253D3ENobF0WWKZQixz3IrpPB8JhFcru7gXnyeg3rA3XCLoBtzxa/YDTWN9CmlhxWSMI7pBxFHtWCaFcjG8ISO8JRixjHu6IhEa66+4ymSr3ovTbHi+PqLOGNxdpO7iaRBOYNGUjQSK33iNGVaNNw8XOC1msgPCLoeF6KGlRyyaCShB9uJJPE3Fx6lFBydkAQh2axmXaxQOwNUv0d2dsvuSfAlhEoQBAHKD1CeospzcCCExBjNujEYB6HaVDICCOXhpz1qbSnLkirLMXmJCXxEGCJ1QxJHTMZDZvMly7IBB8NRn9F4xHT/LnWjCZOUMAxQnkdjBRqJNoZyvaYuM1Lf8uiTV7h7+5h11hJ2h5wUC+bZnDBrSMtNryHle0jl4QDTSSmKTWQzHQzxlKTOS5Z3jwgEhKYLToKXYdyS4VAQjXZoogmlOSCpT3gSD+lG3Kw9jFswiF+k72lGZUIgIAoka7mmKFaEZYzvdWldjcPh+THBZEywt81pLimPJYF09Lstev+QZmVwQQ/dvcBs4dE0ktLsU1ESeUMWSYcoinhHZBA4bjqPj2mJ1i3bvmQcKE6VR4HDt9BpfdKTFtHU7ATPErFGqBS//wST3d37PuvPFjPm7TOEQUB08iSp8hnvhSil8DyPIIzAWtpW85vXZyxOpjyZwHu/4knCJ5/m+T844nphmDy9w9c9uf2QZ7BXaKqS/ec+jWkbhnuXGF24+ODzi3ObMnQHKIHw5H1RGdsYXGWwtcYsN80KHwYRKrxBiEx9RKC+7CI9X9A+QOec84VAKknU8SnXLcWyuSeAkuRRnHMU5Q2qep+mnRL4w40PRUbE8RWkDO4JJZmeolcT5of7ry+Aoh5c+TpY7UN2vOkI25YQDzapsbaE5R2ELvBEiS1r3O3bsNMSpgXq+Lcxd/cR7kkwkot5zfGqIJ827HQ9vDAhjRRFYbF+SpRcQJdL7LCm2fkK5KRDuBkehuoGCN/RvXCJev82jfcig7hDexyzkGN6Fy+CkojEx7sj2R/7dJMAczKnPDjC1SXy5BjSDutVgCgtvnWkffiKSxG9MOET3hZmNCIschKV4qFIbE2jS275Ca2Gmf8Ik36Dd2SIW59WhbzkAtZNRY8SrXNarYmQqMGA2Apq3cFGKaeuy1XP0q8bphPJ0XzF7Poxk/r3EGGEbeQmbA84B0vP4nsVtfDA9xAOqqKmzC0+giE53eYYebLEdmKSboHqP4bRhtNVSaQgnuziCR8RGU5PZ5gyw+gKUaxppcCIBCtDtLE43SIFJBFIatomx9oaGQi8boe8SdCNQGFIQg9VN+RZRosAlYH0Ea1BVJowTrGrBR37HE4pVuJxqtpDt4LmZI4KY+rtx5gmF6jSHHnyv8hmPvveNlY7MBanDaKtEE2JtDXi0Uc5Os0xQiBMxvq0olgdEawyuk7SDTzCKCToJLhsgSnWVIsVoUrxvZShW3Chp8jmp9SeoRps07JNIhXbYkE2XWBzzVawpD75PSp9yERkxP4OQobs9Ps0YkGlSqTtbVK5bcvKaZZ6RY3ExjHy8mM438OcLJA6olxHZJnPSZ6h6OOHIZUFs2qxWlOmQ05Xmijeoewqrmdz1san6l9kO/G4FkiKcY9PThfcMQrSAIljpg1Np0txeojLM0ZOMnB99GiXgdxnKFIG6Yjo0lcTjHYJxmNEHGOmU5qTE3IKsmiHTtkjvvp2xld22b3Wf81p4FP7C7LiBTyjefxCSvyud6Irj0MRoQaCy9tvLm3lrGV1esz0zu1NU9IgZLC7++BlW0tzkGGz9lUnQYGMFCJQ2ELjmvsLioQv8baTjXFZgqsNel5j8wbhK2TsoTr+l3VV15vlXACd80eGtB9uBNCqZrDzikEvTa8RBCPW62cwtqSqD+89VlV38f0hdbOZs5P0B0wXL1FlEcVqSdJ77YnvHr0Lm68HMX4CiimynSHKKc7OsMbDG21jlye49XXMbQ3SY6JhWVpyAVpJpOniBz6y1ZiTU+KewtiX6BUzzMGL6Gkf0l0Ix9hFBEISNiXB4YsYeYDam3Bpd8y14BFOs5JVXqBij9HuZfp1wYkuWU+GLJJdlvsvsaor0C3LwNLxU/ak4mLoE/QHXH77mKLaTEJ/4uplRt/wbo7+YMaLn3wWF1nGkxFShAyUJB7EXGif4x2Ae2ybm8KxKFraOmPczCnWCwJpuXopwtaQVJqXyobYLakan1hUXO7f5sRppvMUMb1NtDfGeRLr3Kb5mjFoI9DOB+eIKPFMTWE8JCE7PcVXTiL2yhk31wFLbenWLxEtTjkRY45tD1yIPjpAA02tcb0QugrVDvB8RWihrwo8m1HjyJWi0Qa7bGl1hnm5M7UnscWSNA4YjrZJtGQ5PaU0loEMMFWDEjmxgMhTJH1JElQkvIBrNwKuaJ5j/7AkNTWXekcoNLPbB1S3BJfcDE9ZrGkoigOmdohnKyKXEcuGKBVEvo90ErPMUdkdmvkhwmlWzkG/wyPjy3Su7CC6HjpfYlY5xc1D8rxEijnD/gTnD8jLOdJbE3Zg+8qAg6MZMhSMtjSnvkQ6j56oaesDbLBCJj1k5wKu38V40AQKa9eoekB98Spt0sNUDXduPYePZOerv574yhahN0bedmS371JnGdPFMU51CFXK1mjCbL7PcrxN2RnTPvI0jZ/i1pqqPKY8OUG3MI16LH1HOe7x6SCinHSQwNU0YiuO2Pe6ZHlOp2oYqYjhlcco2h57HPLUzpBBdxfGj8Pkifs+st5oRH55l+rOKeaoQJVbRDsdkv5rK53WVcsnXzpELxe8LRX0tibIbo9nnrlLpS3ROGG398YjLpxz1FnO+vYx5WJJXRc46ZCRIhkO2b56DXlWNu+MxWbtpnjCuM34CW1Bbnw8zmx8PbbQUJyFLwXI0EOEChkp1CC6z/MjIo9gz+N1+4uc81n5nDpBv//973/gY//yX/5L/tJf+kuf90ad8+VJ0g0QQF2azXys4JWeG74/YDj8Wur6CGs3d011fYg22T3xkyaPUVX7RL2YenWT45cSLr3tnXj+53BHpDzo7iC2u4hqiKkNumPxLgi8ZAt39ybSCxDjawRJDw5b1o3gWDi2FLg2x+SnOL2ktGuEOiYuZlR1hDEXcM0Sp0GGKSJM8ITELw6gWePEKSK5g6qfY3tdsm1AKIlIYnaKmrtZjXZQBAHHrsNBdoASmtTW1MkFjsKAOpvx6d9vUc8GzALQnZpF5xitpjTFRfIwxSlHR8HTcUhj13iipbu7JM27xDJg79EdPvrJfYLBEJc+iXf3U3ieRvd3CWOPS/mKq03JwaKlPTW0XspJ+ihJJ+eR8JRAdnnqqT7r4QDfV3SSELSlntbodUCEJNQlrV5ioorSlbSrE0CjL43YyiWDZU1QtyBrko5k1+ZkbYFnDbaxtFGBlCV+HOLFfUwd0fEsoStxbbHxf1lHaQSZVmgrMEYgiBBOYYWjaw7onVxH4HCBQowHuLxExRJlAdeiAok/6iI7ASJL0TMf21rQU67ZI6Q0yK0hzneMZ5/G5DUChQ12ELIAv+Rxf4bwBcJXiNDDJSnOizBNg8tvIPsKEfUp2wzsCj8oSB71kD0P06zQ+XV0k1EMAnTUYdgVtN2E1uUcHUumdsD4kREJPerIIxhYlsNNCX5n/wizNWb9jkcoY0XPu4RdhsgoILq4R9PcolWSPHqcQbRFv7YcLS3PRxOqVqDck2wfB3h39lHOkuNxInxudRKsswxFl0+XNUV3iwKFGYzQ5YrddcGWEdTNkgEWjU8roXaOsrF0lGASd3jn7pCvuDQhjmOK1vLpZ69Tb/sMg4RBuERkh0RexaCbwuix14iflznNb9LWJb5J0aaLH4akvVd6kjnnqLXl4zdPWR2f0C+XPL7bJXzkEV64teBgVSGU4J2PjlBnqSOjNdnslLooaKsKXdVYYzFVQzsrQL/iIpHKo+tPGPh7uFlLK9pNJVbWvtabEyn8i11kuDnP2cZgixbX2I1vJ/FfY3I+563lTQugb//2b+ev/tW/yt//+3//3iDU09NTfuAHfoDf+q3fOhdA53zOKF8SJh5VocmXDf2t+1uzC6GIolciNnF8haq6S1UfEMeXicJdgmCM1gV1cZOifIbDF+HiU+/5nBuOiUAhIw9daBwJNuwjn/pWguR/bxrqCQXjPbYCS7loWQ2GXOn52Dwn8FY0q4w2XRDqG5jAZ+EN8UdfR79d4VZTrDNgLOCQ6RaNi3HtXbxmgQaC7gCkAu1wrqEX+yRhTN22CF1izIIbkeE4z0hlB5kumTctbZmTaw9Z1iAawl6FXZUoe4KvZyRZl7DMkHcOyZVPJ0mpOjGnqmXWLGlf+hh2HtI6yY1FROz5NCjsuuK42idJk00VivDwmgWelqzKDiu1Q+EyynaIb2ue/9iC+KKlFyouBIJxHLHX2fRWEt0OM9UBlzAUBUO94G6Vo7HU20/QdHdwxiNc7iMN1HnBKs9pncHzwK0KujIi6XSIe+Emv8bLaYMIRAxIlIzx/BBcQOpFDPwe0mz6orgyZ33yItOD29Qo9HiCSHqko5K+anHAiYXcwCRU9AK1iZq86yqirODOJxFtg+33WHZ2aYM+fjFDVhnReBfndRGmJljfwlcxnghp/T4iSTGeo2wrlsucomoI0g6d0YDp8lMwu82WiqnCmGmT4eZrUjlkWsdU6Yj4nY/y3HDI8Y2bLFc1p92UZDKmHvRoVgFV3zIb5By2Azw35+rVp5HiiGURYQofmfUgDLFZhMx83GBAbRx506eaLzFlTWlr1izxkPzO0Q0aCw4HUiLjGFsLpo1kp/GppGTlS1yU4G3v4DvDxIetuaPjKYTVjDw47ewy2N4lomUwGnNtkrATtkTKQHUIRUtX11w+/T1cs6I/vEA2L7HWkYxi9OAxppminX4SZwzKDxjs7pH0+hhTcby8w53jlup4zLGVmKomLhuCRnBrWnCyqkEKDp5/AX18zKDr8XwVsPp/Ps3i1iFYuLLTpXx2yVG3CwLWxyeYqsW1BtfajWfn5fODlMSDPunehChK8ZyPKw221Njy/tqxTSTH26S6AokaRvf5c2SgkMHDd5k+5/Pnc4oAfd/3fR//+T//Z/7Nv/k33Lhxgz//5/88Tz31FB/72Me+AJt4zpcT6SCkKjTFsn6NAPpMhBDE8SXi+NK9v3lel17v7dg9zentl5gvfhv34imjvUeRMkapCKVipIwQQuKcoW5OMDojCLbx/ftNczLcmAjlmfHWrGrkTheu/n/g4A+gOIXT59hrDcvZGjEDd3lCEHUY9BtKURKlNZ3Bo3RPC8rlEuduEb/n/VjVR0+n6NNTnNak0lLcfZ5WBzT9EtPdIn3s/8YbTTCLJWY6RcQxne1tnNbUzz6DObpFT13iqM6RdcQFP+Zq1FD7PcocioNjsuU+zcmKuJsTIVDLFetiTdY0rH2DiQ1xLSmbkMx2WNYttjCEVU26NeBy0HKYa6aNQjvFjJhQxjjd0JY12nXpioBQJORVyK20T2sdIpvxfJ2yVUG355P6ljEFqjgm9cAGJ8huglMKoXykN0ayRet1CE9SmiOPWjs8fZlJdRsIqQipqhqzKIno4kcRyeSdiHBMpz4kauaYIqM8OsZTAZ2tLZxSFNqwaR6uSYIlaehhgVo7muARzOWLGBVAMMLJkLUpuNOeciIEcxeghY+Hx5aANAxwK0FretT9IbJass8Os2aM0j47XszuIEC2GYld049jwt2voaxKrtPhxHi0hSXUhtBB4Cuk0qxNxWK/4KTUjIMneNfoEW6dzsiWUySKQAsSs0AXDlldYnXTUugnKTzIDAx6AyjgxZMVTZ2jW49Z7WPLIQdqBkEA/gTENlKnYH0wGrUq6a2PcU6S5ydoqXBC0PgKJRV9FRPSobEghcNPInyl0JM+/TZh+6hlSMjejsflXkWf6/QvPIFWF4niijT0eeHFT7GvC7aCNR1dMDYLRgvFKNxBFfdf9JuqxFUrUB4mfYRKx6ggQF6dcOf6LdqywHmSwwZq47gwX9DrJrxw/Dz/686CbNXQyxOqYMl/Xxf89vXb6FbTA3pKsJweMVsviXXFcd5BphtzsWst24HH0JMUd2cUzO5tkx/GJN0xfhDiBQFSKaSnCMdd/O30PiHjtN2UoeszsSRAdYON+DnnjxRv+j/yDd/wDXzsYx/jh37oh/jKr/xKrLX8zM/8DD/6oz/6JWnrfc4fL5J+wHQ/pyr0a9JgD0sUXWCy3ceZkNO7n2Jx+jytntKdbN1bRiCQMsTaBsfmBFiWt4jjKyTJNYTYCB4RvPx9UwJrlg3edoLwQrj0PljchGJGZDXBStAUaxbrnC00vqmp2xrT+hAPCTuXYbnE5Lfg4ONIPyYIewR7CuoS6hV1tEJrRxgalDdHqxN8uYM3GOB5FYRdCAJEEBC/933Y5isYNSd09j+FnYXEyduIOh4XtxR1nlGvlhw/94fc3v8EotMwSLZQ8QXCZYtYG7LAx16IGXiC3XVOkwvuKs1KDwhaxzBNCb0hrE9RrmZVa0TbEjUeQkoak4CTRKFCOZ/QgFesOFGCud/BliVNKTjJBUe+x3NSUao+TgR40jFaNoRxzKnxsQiMrkjVkm5YEvgKT0k8LNflmNATJEJiGs2Jq6mNJXURwY01NmhwfkDZjgkyhXIpyjR4B6d4SuANB0T9Lto49NphnUTjo4WPi1Im/S4Xxj0CJWm14dbRgvkixQqHCn16SjErW46MBSEwziKkhwojEHtQNEi9QOGYKsWxsbRVuUnBCcnW8lnSZsaBf4Va9qDTRXYTMJbAGSLnKCvNKi/QTcmp8vhtdR2kJZACIwwai7EKZRXypTlhHLFTNcwqQxBJhrMKL28pCovBkmYCURtscYcoqWmLDDu8SNLdIcISbY1RVsPxcwxsxnbT0sWhRhfRT7yPk9BxWNxhEu3xzq2niVxDrKd4i5eYrW5x16zRLmHPnzBKYsJBhgScTbDHL1Gf3MZUAY4p8vbvo1pDbxCQLjLauqQeDDhtNYNrbydM+yAkKJ/VnX3WGESyx3Q/QueGycDn7u+8SFWsEcrjNJwws9CYisOTNdK8xLzMqeoWWyTIfIlvWmoTkpdik30ShiCfIoolQxxJf0Rn0GeYBvR7I7a6EzppjBz61Oucap1h2obu1hbpZISMvI1J2X/jc5LwJN7wjf1D5/zR4HOSpM899xy/+7u/y6VLl9jf3+fZZ5+lKArS9NyMdc7nh+crojdIgz3063gpe5ffjy8vcLr/LPWqQVLT2xpiXYXDYuxmHo6SEUolNO2MorxJWd3BUx2U18FTHbRs0X6ObpabDMv0KaLJBITY9BEaXgWgkxQ8d7jEeDVbF2JkmtO4DNPfon9lj2j73UwbiOsp2rV4LZuKs1cRjvZoFwuMC/Dsmvb6fyZ6NEYs7pw1dAR6FzfrlB4SiGRAEHt4OwqxrGlPjshOZox6BYO0z9a3/gnmxzuY7OMM+49yYeubcPM5B88e0kwV655DPbLFU57A3Pp9ru2/xPP7faZS0Pqasjtl+LhhNHc0s4qgXDIKUzpJH6E8vLTEVHOKZcjRehfra5K05k7gcyIEbZ7j5xlTz6eQikTm1CKAtkUai5OSjvIopY9zmrUxGCUYxQFdr2El+2jPJ9QW3dQYBE2UIH1JfTBDtSuEp1jKHsaLmKMIvYB+McO3Dk85KGYUpy2qM8D3BK1zxKYBV9GSc3z7lFvOITEoaxlLw0CAMpq9uiSSLUXY5dCPaZwCCQqHKlaETU7fl/SCCO2HLMKIUghyDbmG0fxTjNq7SGt5hzml5BJGRdDr4o36WOehNaiy5tZ8SmLtpoIxDBhZyXAQUlZrlq1Fx5sKwkBYLjnHbpLwjHHUumXbGgKrKOKYKI642Mm5fv0PsPExjYIglAzyU3btFfrXtvAHjnp5xDq4iW1WpOOLDAZPISMfmhukywM65ZKd8AT/5Ca35wuEbTYd2vWCwPfYFgMGrcJlS0wd0voDtBqjTz6BPllhW0MhGw7XBZmICReK2ibMxIiuvYooA9LnLIO0IQkDiuk+t2+8QKkdtXQUWpFbiesGOCnphorR7i6Hy5rZ/j6ha6EvUB5kQYfWG5D4jn5o6I9ivmE7ZHXrCNPUVEARpIhYMJ6Mec9XvoutnSFB2kHfysE4gksdVC8kZfI5nXfO+T+LNy2A/sE/+Af8rb/1t/iLf/Ev8sEPfpAXXniBP/tn/yxf8RVfwYc//GG+/uu//guxned8GfFyGix/iDTYZ2Ny8SmCcMTJzRuY3JHbhO2r78aPFMaUCKHwvE25a12fkOXPYm1Nq5e0+mxukC02pcuhxNWW6Z1TeuopgmSItRohJL4/YKcf8vyxZG5i8mBMsDXBzOZYvTFfJmEE4S6F8lhuX2XsbW18RM6ACiGdEFYtp89+ktpZwugTuHJKc+e3CMNtkD7YFlZ3N18vv1+mYbC+iQQuRFMWqxJjHbO1YLDT4tf/L9u+4kAErOsF5uD3CYuccVDzCA2fWkw4HscEb38C2X038Thgtz9gUgZo8SkWnQwudMgvCNbzlHpVMp5c4umv+b/g9Flmtz/KIrPsjiwXp1Oq4CrDp3Z5XIR8YlrQ5Av2lKE6WNDkNU5ZEI6ZDjhYVjRtw0RkDITmwHkcyYCuqLisj1HSo2wUd4s+qyZm7kBJ8H2FTDu0fkAdjDHG4FctoavpRDHKS5HxDmXbUjvDVvki4+oWOo9Z+leIojG1bilbzbw1FNphX9USbS7gQgJ9Gub1bTxqvGDE452LpIFH3VrqeknazBCuZGp63Cx9pGrYEjMe91uSKEGiCYMMIVPiaISjoaaiqCr88hD2LTbsUscdXjKCQbqmlR67ScLlYZ/YOkR1hNZrrGgJZEQkgPkaKyQr6dFpa7Zty2UcLhywkH1EnuGaBV3viGNriCIPp30a0dK2f8Dqpo+7Ca2FjCUtkqMqwiwUI7Wi72acZDdZ6iVr8xJF62MtnFjJTbvDiguMJEysI7ALOpEi7PQIw4bEPyKvB/hZjvEj7roJL5gAT8Alvce0rEn6E7bYYz47oSjWWLcGp8nWS5rWR8gYP0yR0kOGFrtegNGsLl/l2DpMNmUnyQi8GYV1ZKJHEo9YzWuC1vD43kUevzBg0h7gHt20w7DOcbOs8C+9ncnFi/fGOzUHG/EjYw/Vu3+I8zl/vHnTAujnf/7n+fVf/3X+xJ/4EwC8853v5H//7//NX//rf51v/uZvpq7rt3wjz/ny4uU0WP15pMFeTW+yhef7HN94kaYsuPvpT+JHEQJBkCSML0UozycMtwiCMcYUaJNhdI42GS44xTWCsLuN9tbU+RHr/efwtxLqssCalrjTQ6qIrtSsKsXNw4CdrqVspwh6rFePkHa2icJLlPqUdXnMcO/tONdsyvt1gcoOUKqL8CxOS8zk6xHT36IqT/B3vgo5eRqaNZy+8Komjo5Aeii9iWaJAEaP7HA6jylEDzN9ieGgZFusaOY3kKrF9RqI9oj6MaNpyfbxJ1EvfJKs+SS9UCIXN+nUEabpksQe18SE0mmOqmPi9QnZGtbymD/4/Zt0pKalpbxwmSY7Zsf3uORneN4+g8gnW2e0HlzdGTL6yifBj7FeSN06RFlgVjNsXdPakFpLGtcyW+wjD57DF11KbShXFVfEAVmasEou0MqIiZcRudugPFZhh8Y6+s2Mrs4pRUCtIlo/ohlv49MiqwTVgNGGvjtCizmd0KJCywVC8FJCL8I6SWY9MqnwlaWvZ0g3xBqLbWrumGOc7RLLijDIKUIf4fWwxhLrFa12rG1OZgVBlZF44PldzKWvQnSusq2eJ/IKstM5x9dfomk0ioLGGmosyAZRGRZlRjV9FmkdVgOeT9LpIKsKoS2hAM/BvMxoraW1gkqANVMaMcMDwtZwo4nIOmOSbo+O0JjGsqobdCPIXZfMG1KEW1Qo6nyA73ys7aPbiGnTIOnTUT0cPs730SrEipiOl2Cdx0FRg3GoQIFTkLXYuiYOu0T+22g9wQuHh7Q6Jg4lwo+pgy426TKIffxwD78oqNqG1dEhWnZQcUSv0yMKPbbGHmm1JJETpNMs7YzV9ICt7pzLXUPQ73J6VNG0HQ6MAK/kscqwV65RRxmuL/BGQ4Jr1yidIzw8RAjBcHgmimqNWZxNRt85n4315cabFkB/+Id/yGRyf3jQ930++MEP8if/5J98yzbsnC9fPF8RpT5V3n5eabBXk/QHXHrHV3B68wb5ck5TbtJJdZlTZWt2H3sC5QfUeYZuGoyxQEzS2yUdPUXb5CgR4F9LWT9/naq5TXO3ZpWtcb4mmy3ojIYMwphVXnC6lGwlXZRvaesZq0VJ3b6I5zSYGXlZs1r9IdoscW5TuWRfjjqFpzS1T7ac4Do+OhLk5lkGdUoQTPAufzXWaqrqNlV9SKRGlL6ksS3FpW+gH4/ZMpajGycUQQ+d36EfORAebb2ktiXB3ruxviJe/z7hcka9ajmZLUn3Jqi2JHIVWTSgFmN8NSQqa/ZchGotqVtgFzW1t087HtKMnyCZPMF8eQuZvUCR7eMT4Y1iPGVZlZb945qBrJFSIIF7/9Ho7IsShKSuK1RbIx9/hJ5JmO6HiG5OmLYMrk4IkwBnDHo+x5w6mqqhsTmecEgR0dqAAIdxDis0mn1qL8SmMXerbbx2jSmO0KYgSmLitMsolPRkg5QaFQQIz2NattxcNMg0ZnDhCmb0KHdf+H2WyxXObjo+Gz+l9oaUssOWXHKt52GF4GTdsLQptXCs6pKV7HNaJrj8EN8fMDYtZdmnGn4dxjp8W6J0iVYtiV8SppIy19g2o1U+pAkm6DKNhsioS2saAtOiteb0ZAFaEffGuMBD2SVicYINJPPuiHUaEoUt7+z4mKDDdbNN0ZSUBFjhEFg68jmkAzHcodU186KgbX1O2wl4Po8PHme7G7M3CBjFXd6+N2KYBBjrOP7kMdN1ySKCk6Nj5sdLCj9kp1FMfMn1qqArLMrXXOoGRFLjzJoBR/SqgJ7qkUQx8/URh/oYFTp2e2PG/ZLxoAHZYnstshviUp/26HBT7ScEyeNvJxk/yROrCH37Nv95lWNH2wxWHsp5hElD+Pij+JcvU1UVR/v7AAwGA5RUmHWDPi3BgeoF5w0Evwx50wLoM8XPq/mmb/qmz2tjzjnnZdJ+sBFAi+otEUAAnu+z+/iT1EWO0RprDNM7t2jrijuf+iTOvbbV/OzubTzrE2QBauUTRUPCnV24nZDfvUXs+uAJbGCoqgqvq4AeDTHOv0i/q1mZY7AlgppANYCjKG4xXyzodJ7C9/qk6eMYU1LXR4RpSb64xWpxwujyFtosKaubsLD4wQgpQ3AW6zb9kHJTINSaVnVY16cE5FTVIX5vRdVqCneBRn49U6kQ4e/hdyK22xewTYu97FOsLjKb1rTtReJ0j/66pahr8sFXE/h9Ov0A21qsbqmKEX5ySloZCtMwePK9bI+fQknFYTzhBZNS7regHOXAoruW5/Y1tA3HBbx3e4hvGqRpaYBSSjxrGBqN5yye52OCPmsxouq9C0YaP44YXg4Iqn1Y3EIA/uUn8d/xzfhFQ3B8jDMWmXaJohTX1Li6xMynBEWGrzVHs4KeLhHCZ/vSe4gCAdIDoRC6BJPjTM3qdEGer4ijmHemXQgHuCaFoxMuJVtkztJaMCpGixSnIowxUIM5uo3v+VwcXqLyU+4sMmYqwVMefVnQGke7LmjLig6WC0nLcNDDGR9nEkp7RK1Crm5fZWf7EeaE+HVNtVoSRhFev0/rh+RZwcnBnJPbJ4zEFkkEqVdiOgVZpsnjFOU3hNUJFsk7gjVXWkmzGiPrG5QowIGDnr+mE98lkgraBYWRCOdYCM2dsKAfbfNViWEkC/x1RiefIk9vYJzDOcHwVDAwGm/kEF2gGyGSHs3NNXfKlpmSPJ0YLnQ8Ht3qkdczVsWUoI4JjA+saADVFGwFNd2hYvdaid/Rm5SkA5kOUGcRm2CwDbOGZOcposnVTeFNAmJrwvKjHwdt6Hd2CJOU0duGKE8ym804OTjGZA2ekSRIqvkcXjVT63wy+pcnb1oAvf/973/Daq//+l//6+e1QeecAxsf0Owgpy4N5boh7r62m+vnSpi8YtaPu10OX3yBKlsB4IcRQRQjPQ+rNcV6iTYNNqshg7U33/S/sQISR0jKcHuPYrUkm01xC0unrllGPrdlxbXxGFpLu3YMBjGdeMW0GGH5KM4ZfG9Ev//uez6iKNojii6SnazRbYPPY3S3nma1/gRar1HeK9uuZEwY7pDnL9Ec3aZqVszsKWHvbPaQFHQnHtn8NuV6n0DUrOqcw4Nn6Sd7hFEHFfhMrllWDRT5HZ77ZM4QRehCFoeH9C/WjPcuE+gUVRusi9BtB3lcEZhtkmkfS401jq3OiOG1r2Ne3aIs55TkmF4CSvLxO0s+nsMqLxikEtSr05oeEo+RjKjxePHOLYrsmOT4N4mc4/H3voOu/wiZP6EIIxpdYoXCupJOr8Ng690k/uYCpq1mWS9ZNkv8y1vES83ipZeQqSEQEiElqyAgvfY4gZDYsgQpEZ7H8vSYk1s3aNKKhRAo32cQ9fDbBlc1NHVFUTmsteDWOLfCBIpGOagapPWx2qM9OQFO2AH2pKQb9wiCECEldeRoGkESd+l0X2654OOwPDO9hbWWx60jON1ngsM6g6GlXRnMkUA3hp6u2bIlJ66knSQMuxptb1N7GVm5hR4MiLs1i9M1Rs0YRhoVbJMqS0fWBCrAlx5KeGghMCpEuS6xHOIrn9RLyGRDr7lDV67YU88T2jEeHTCvtMFxLbhWgASkwcYe/mMXMZXgdNVwWB9hhWUcaZRnmStDm6Q4PyBKhsR+F+2WIA1UU0bdIZeefhd+7DAmQ8oQKQOkjJAyRKkEpSJ4QPP2o+kMWRv6uSRNFF7kcM5y69mbrE8WuNrSiVK2BmOUEYBDeBLVD1CD8Lz/zpcpb1oAvec977nv97Zt+djHPsYnPvEJvv/7v/+t2q5zvsxRnqQ7jlidViyOirdUAN2/Hp8LTz5NUxYoP3hN12hrDeVqRWlnmKahDQ11k+Okw99O2XnybYjG4WcJ8bDP8uCQQWvJj1Ysj1fc7d1hXTi8WGBzxapuWS1rjJgQ9xva05uUg7OLd9tgtUH5HqJ9hGYxZebWXHr6HVAd0ZSCepHh+RAne3S3nkAqRXF4iqgCdNUyu3vMxe6TJJ2LKDFkOXsRXX0CqEgjwappqLVhfRIyefrrCSKfJso4bH+f6tNHWLNkaad0pCFfrZnNLNn6I1x4/Apxb0ijb2OcJdqqKU9uc3x3n161AwgcGudpXAliWuOtC+KJYJKM6fQdL66nLKctPW9IGPbwlSISglKvWWYZL64NTSXI8pJ6vkLFEn8Q8dxzv8ud/BZB/7WzmazVuHK2Man7PRwKU1bovERnBRLo+wFhR9DZ6VKcFLRFwumzC4IwIghjjHTUtmZ6ckAtK/ztGF1qbLNGtQu2422SbpfTrGBZViAgUBKUo3Gbm0EVpQSdBNFoZNYSW59e3KHfHaCbkmk2p9UN8dVd0p1LBEkHbQ1tsyZvco7XRzxLjahbjL8P7SnOrJEyQpkuXqEQtQZjwJPkOBZbAtvTeNsKE1xCZR1sZ5s47DJ5LOH0xRdQ+SeJLw1JJu+lE03YDbZInb+ZZC4E6/zT1PUhSfwoSXQ2tVwp8tV1gpMTYgFEA2phIQgIwi2EUDg01fyE4viUshXUXoI1BnX9FuIITmYNlScYdgReucALDelgQqE9OuEuT77tPXT6CeAo1iccPHcdqTx6oyv3xkd8Npxzm2GhecvpJw/oTRs6IiQrZ6gMVvM7WGMQQrA9mDDcHaN6IcITm+GjgTpv3fJlzpsWQP/0n/7TB/79p3/6p8my7PPeoHPOeZn+Vsx6WlEV+i2PAr0aIcR9UaFXI6XaTNvekdhSE1zq4iIo1yviXn8jmAJQnQB/NyW+NqR7OGP6B7eoZyvqWqMaD4mPv5CklaE/BymGqHJBK3JW3et4vSH4gBLodlNIkC8W5IsFWIv0A8qyRZsMWDDnDvvXfwchBAIfX/awGKpyyPKOYsUR1m48D85cRNqMUDpM69OuBfvz28yu//8QUmJkxanyiSJJYFbYck2WLdDVHtb1md3W6PoundFtsrXG2Q5eYinyY1gdUbgThFJ4ziPwY+plhTiNQSry8hDEAZEfMK6h8QKa8oS9nZLAc9RFhbdqSLIK39QgoetK7MjS6faIexHr9ZRq/w5yHqGMwHMSL04QUpIXJ1RN/qopAz5KRCReh0qsUVWFMTFVN6EoTnGJoyrWtHkMuc8mfGEBgxA14UCQDrsI4dPkLU1tmbY5J42P6ndJ9/aQYg52CgIiEZFEF5Fhj9ZZGlPhTEWla5qg5UQsQXhIubVJsZo1rvhtWK/A5mxOwY5lu0D6GV2vj5AZUnWR7RBhNU4ZbNCiQo1LLCKKOckjjPO4NPAZpz26yQUi2WP6UosUPlvdkJM0h3iHa1sXSeMYxxqr16zhXsPsdfUM1pRIG2LaAuc27SFW2XWkKInSHaLuo+TrKYuswi5uYwxYDe2ioV16ECpUVwISV3kspwYnu7iez6MTn3p2Ey/wuPLUezmebdK2aS8+Ex+COmsRQpL0BveJn6ZqOLx9F9/zmYwnKO9srpZ1mHVLsyhYr9es8jUv3b5J5kClA+amIdaSyPiEUcjFRy+TbPcQZ81MzznnZd6y1pR/5s/8Gb7ma76Gf/yP//Fb9ZLnfJnj+eqLEgV6GESooNTYxuD3YrrjB3vhZOjRfWSbR3odPnVnSa0NO7nAVIbhXsLQWqY3p4BjMrlAXe7jjCMWPQLZQQYezgfnbwYjZvNT2rxmdOUyg+0LaLuiLG5RFUvqrMQ5hxfC8PKQ1WJKuX+TbO1v7tKdwwsdYdcn8D3aLCb2r+LSl2jLBZ7poZsjHA0YxUrtsdt9BGkCbANhpGjdJcqqRR+smB1qrIxACYi7lNMA0+ZUTZekmxKlCqxE+j5SeQRhhyB4lEIf42rNtrbcnq9Y6jl/cPMOfeURSQtOoIhJwhAvcpSipZWO8XCLye4O63lMsVriSr0ZFukcNDUyDUijMSK+hPA9jMk3A1ajFD+MUOVF6qahNhapu0CHNj3FBHN0W6Kth27bTQLOhiTJhNFwB1+dRQGHjkW94DA/RNuSXuizk/TxxB6lGQKC2ItQUsGZcGh0SUFJ7lY05YtovUIJj8hL8PBpyxZdtVhtcBoEHr7y8NqWkfHoBR6ptKjAw0+6SD/C60SIVOFkC84xLyWDpiLwJE+Ou5t+VG5Nlc1pdIGQDQdH+6AzpAxQ4jJNO3vNseqcpWlOsdbh1Q1NvWnZ0JQwO4GiHlP338V8sYu1E5r6ZOM7cxaERLaKUAUkox79R4cU2rH/+0doWSJixZPdLpFqUeEE6flIPUDPbuOFPlQGqxy2bMmvn2x68GylVPMc1QqK+Zq7+/sbfxWwfOmUSX+Mc45GtxRVQdVuBHNdF+QxVGlMGHaJvYDdywM6aUR/a4iU58LnnAfzlgmgj3zkI0TReffLc95a7osCZQ1x50sjgmSgMICrzWddFmCnF/G8n1EoifUVvgV5qUvSC9A+tNYRPzokXDrq+QxRz/C8HkqGm4BEDbvdq8wzH9FIBnqCbBVCjiC9ikstdqSpq4Kg52H9Ct9O8YchvWAzjd6LPEQsEC9n9YbQDsbsz28TqRlXB09izWV0o2nnJbO2Rnse2+kjCD+nQVBUK7zAIwx2sY1gXa4wrUbagDB6BN0uaAqfML7A8rhkpiv8wKfn92gaaBYprreNUDVOFWzbgluzknVRsfZy/KhgtyvppIow6tEVY1KbUJocPwsITruMxDapl6FDjZcGG3FnFNa1xOmEJJ1shGNg0aypqtsU65tgHIPoCvHgEnZlwTjqJqQOLMZT2EyjzBCVpHiDEOUnhOEEzx9vTOamxvfn9IKUojpBuoomv4EVKd3oGkoNaPUpdXNCWyyxTQtCEAUBsZ9inYfRvc2kbwMYSEQPT3TBB5WEG0M7cGt9F+s5hqOLxGkKnkB4oOIUT8UolSBlBE5yY3kIKmNv1CdNhzgnKNc168MVyyOLZY1f9NCuQ9x7DJ3vUa09NnPRQDiHU5KmWVM2W+A8nP/Y2UEikCKg1h7OWfx1hF6V+L2A4daj+JFCeZLGWhYvLshlyzRUvHB3RTutSNcNMpCM0pC4MshihSk8rB9S3Vlh8xbZSorrc/RZIUJxumZdl+T1HYTcRwqJcxYHhHGIFQ7dag5WJ698HkOFN4hJBh2yo310b5vxeJdLZUykFBevjpHyPL11zhvzpgXQd3/3d9/3u3OOg4MDfvd3f5ef+qmfess27JxzYBMF6gwj1rOK9bT6kgkgcTax+WEFkK8k272Qg0XFrNFsex5tZRB9QRx6tGVLpS2T7bezCH4HYytK8zyJehTfTnCVJQp7yNkxpm1ZHh/R39lB8EqlmkASiQ6sQRLRXT9Ja0oitUXcJtCCzAP8QR+XFmT5s3RdgteW6FqiEkXPu4Yfj2mb6wSrY/xqgcARiBm+tAyTGGMEcZzTWk3rrcjqlsa/TZhESH+J18YoVyD8CBVLECWFWZHUClMLVlYjPZ9AeOAkly4MWduGWRkh7JgVIdOixZUQqZqoybB1xXDUkHQjhAMrQCQghiEy9LALg190qIoD5vPbtI1Btw5jNEGgSf0E6YOZ5JTJi2jdUO43COUI4oRQDNC63ph65wI909iooDE3cfoGQitsqxAKZCyI1BauEnjaIoSPVi2aE5x1SDMi1AOwAikDCATS9/CDPp4/ACnIqjV1pYmThDD1CHs9VOyDgKzKcHwakSquPPoNaONYnpRUqxavlLjIo2otddGSlS3Hx0OkPyaKhuRaUheatk3JZg11FhB3L2KaiCqfEFQBjegghcBkLfWiAus2o11Si3Qh0vZRbYIQIJXADxWRtAQCxp2I0glK07I8bqmU4CTLKeqavZUC68i1ptINsTFMIp+dR4ZUTqLnFbYuWVcNwvp45Yo2sFgaXjq5gzWaPFvQtCV+GBF6m7l7BAoZBXRHPS5c3hj6j4+PyfMc3/fxfZ84jul0OuAsz54cgpIEMiKUkiDxzsXPOQ/FmxZA/X7/vt+llDz11FP8nb/zd/jWb/3Wt2zDzjnnZbrjjQAqVg1GW5T3xQ9pi7MqEdsYnHMPZZ68OIg5WFQsmpaxlDTVZjp0EihWZUvZGGQ3YjB4H+vs0zTNKQUvItRLBIMRYbjDaPgoxzdepLBrsIqtK1c3c8qs2ww/tw5bG1ytCeqISre4fp84HONai600bm1gHRLbJ5DVAUm+t5krtZrgx5tuuAMeZSpSgiBjQIsxW0gNO/42ZQPCtgwSRRREHNULQk8yiROaRqFthXJTorSLbgUiFviJQM4UTW6RwtHUAmEDgjBC1g1RABPPp9FDWhtRm5ZKVrisoKkdWSlYLSKqZYonFdJJ8CSEbPr11BKpKzzn2NT0bN4Lh8LYCBP06Axj7M0aoxqyWYDONaLZ+KXCMMS4DOtXSG3wkCBAty2NtmhtMdYihSSOEgIvwmhFawS+5whjh+dbMALrJMpL8KIEaxzNfNPAU/gS6bdUrcXhgwqpa2Bu8PwlceKhlOQ4m1KuHIkf8tJLd2jbM5EtBUIJhL9JZ6Iti2VNUDZ0fElWNaydwZiaqlmxWGeEoc9WZ4t1PSRe5cSlQwUe3VGEiBXGj7HWoZSgpqR1Cl8NUEKdzTiDrK1YF45VI1ipBhF52KIF7VgVGXmV41lHWyv2+j36QYlRNVEj8XzH8fKIadMihCAJTyj8iqDbZe7llFVJr9cjSGKywwPaoEGEir0nrrF36TJBENA0DdZa4ji+9znb29t74GdscXRI5iCIYmItNhG49LyfzzkPx5sWQL/8y7/8hdiOc855XcLYI4wVdWnI5hX9rS9+zw4RSJAC7OZCxGcZiAgwSAKSULHyJIuiJYo3J+bo7Lnl2YVOypB+792U5W2K4iWsa6ibE+rmBCVjBpf6zG4fk1UN2Qs38ZKaqJPSHz1JHF/AE5vUs9/vUmdryp69VzFlK42eVtiixRMd/OQJEnGHRXOTtb9gZxwhfEXfOeqFoDHb9NMYLwpx2YLav8w8i5GBYutKQrEqkNMDfKW4srVH0VtzeOdFKlUhoy1i/+yUUoKwhthp0gCagcY5iZACT21qxoj6WBEilSSNuvjBmLy1zPOMF27epi09qmaIs2DOqnk8TyGlxDmNtQ1SCnqdgF4nwPMVAkGRSwopqWtHUIX4ShIIiPoC58DWBu0cBEOkFIhEoFuLsw4RS0IFoRSb4ZzWYhu7mW6fCHx/I5TqTesfCATCkwjvTKAZi5USzoQyDggkvq8IUx/tHNo4rHVkjQVnOS0zGgF9GVE3DWBI04CkK2jbmjzPUcIQdQSzdkkvqBknPoFSGAPSanK5wmUerY7pRhMaAb1JxMVhQhJ5HOQ1LQ7RD/F7IX7RUi3XTIuC26sFrXUEvo/vB+R1zlGREYQh/Z5kqx/hEdAs16S5I1YdOlbgLTWELeyEhATE001VcO40zjmi1MPlDXHsMdzdJjcSXdfYIkPYlq1+BzkasPvYE8T3WgLwpqwU+XxG7gRhp4O/suAp4s65ADrn4XjLPEDnnPOFpDOKqO/mrGf1l0YACYHwJa422NqgHkIAAVwaJHw6b5nNKyadEGcd8Vk0qWjuT6fF8WWi6BJar2maE6rqDsaWoEri7Zzl4QFWG2ggW8D86AZpf4ISY2ydkNkcHTeUrxqwKiOP4GLnvvUMplc4Ob7JIjzF39lUv3mALzWFsZS9mInfhfmUoO+Yn3QwDrzLA7q2i//sCoEgujImUls0A0OVremMJqT9LapVgc4bbKWJckWYhJjLIUfLE6y1mNohGovzJUQCK2BZV4imxMWCqC8Yq4DVukWpBukHaCGRGlRrwFmaQFJ4CTIOEHFCJRWuNVhtaAZgKgNa4NcOpQ3JOKK3k2C0YT0taCsN3sZLEiufSAg6oceoHxElHkYKVtrgWkugHdI4tIIGKJYZ2XSFMJa0k9LtJSip7kUGCywtjn7sk3gK4wsOqoZpY7k8irmcBlS5ply31G1Ns6yQvqHnVUyPn0d5DjWKaX2f5WlJpWoaC6u15ajdRBEnIgIVodKY5coRRBOIQloj2J8esTIBGsOFtoFaIAxY5dAnCzhmI3Y4oWoKbJQSqk1q2YoWR0UkWiZJyNWoJDBn4406QCdhZ2eHuPE5fW6fQtQknYSJP0AKDaGCrMYay/ajXVaffJZp2WKrimpVElhLN+jiK4nnh+w+/iRh8rl9nnXbUuVr1k4QBAmxNQgB4XkE6JyH5KEE0HA4fOh+CbPZa6sNzjnn86UzCJnt57S1ocpaoi/BXZ4MFaY2Gx9Q57MvD7Dbj3jhZM2qNRSNpm0MyZl4qprX+omEEPh+D9/vEcePUNf7m0hQJyK8mqBbiakiisWSRu+zOt0H9gFBYVruHh5Q9sdsmSlRPEIIgbU11jY4Z3A4zi51VO2c2tSEamPE7XmKwliWWjMJNsJI6pIw9qgKTZW1pIMAGW1OGy5ReJ7HcO8iB89/mmx2Smc4YnJ1997+1C8tsYUmVBGPXrtGWZZ8Jm3bUpYldV1jjMFai+/7dFLLYOAzGPRJkgStNUVRbCIrZ2S1YVYuqY2793dRgyegkY6F7yBQnBQ57vop7pV6eWg5m8AhCMMQr/ZQa0EcKFonCYJwE9HIc5qmwbFZx8uvoSQkZUPULhFCoB0UGpQX4Ps+dmaQbkVRG4QMkEJx88DRCWEQtHiu5jQ/4tbqlLaVHKo+m0yXQp80QIOQAUJESOFztK5ptGCQJPgkiFZu9kFAbQRpN6Guc5wPp6sV1jkS3xJ4kkGkSAOJYBPEbPQSZyrGqc8je5e5vLdNVVXUdc3d3HFcl2wFE1KxeQ88zyMMQwaDAZ1Oh2Y/YzQaMRr6hDsdqudmVHnOfL5iOltsRJzzyPZvU7aOqL9JKfphyPjiZbrDEVHa+bz68BSLOdZBFUR0a+goSZj45/6fcx6ahxJAP/dzP/cF3oxzznljpJJ0hiHrWc3ytPySCKBXfECvHZnxegSeZKsTkQUF07zhSmmIO5uPXaXf2E8kpUccXyGONw3qXr2sNYbV6THL2YtYsUAFLXY5xZWG+fKEoxufQAiJ8n28IMQLQ4IoJghDHIZAH1MTcjD7PSbxCOc0smop64CpnHAtjGmaKW1zB50ojBlTZSHdUYRSCmMMVbUkigKSXp/+9i7L40NOXrpO+PZ3URc5q9Njhr1dKEAvaqJJTLf72maGD6LX6zGfzxmPx+zs7Nz7u7WWtm0xxtC2LVmW0c3ze++N53n3yp7btsVaS6MthXZUrUUKiAKf0FcYa2m1Ia8NRatZFjWtcSzP1pV4LRZNXm8qkjzlkwYeTZtjVYUQlkUhsdbHYbB2o0YiPyT2BfN8ibEbkTuIHJHvOM4lJ686fBb1nNZo0qCDHwaMR1dQXof1eo3EoTyffn9AbQWltyKQjnc/MiYJfXSVo6scJSWJPyQ7ben0h9goRwnQQvHeqxPGiYcnHJ7nEQQBQghWq5usVl263R0uXdocXy+nno5Pjon8iN3hLhc7F7HW3ntPnbUUywXZ3RP0usLkBn2jhanGASdtgbGWpBsjmhzjBH7cYXjpEcK6JQhDti8/8lDHwGcjm8/IgKDTRZSGUPnn6a9z3hQPJYA+/vGP8zM/8zOkacr//J//k2/4hm/A896a7Nk//+f/nA9+8IMcHh7y7ne/m1/4hV/ga77max64bNu2fOADH+Bf/+t/zd27d3nqqaf4h//wH/Lt3/7t95ZZr9f81E/9FL/2a7/G8fEx733ve/n5n/95vvqrv/ot2d5zvnT0xjHZrKZYNWTzis7wi9t24V4l2AMiN2/EhUHETV8yzxvKomEyCJAbewm1tvc8QZ91/a8SSlIpBjt7DHY25lBjSnZMyZ3n/ivFcoFQF/BcA1pgjU9b+rRCUuDQ4hRbFDTmhAP7/5DuvRukJDKCIrO8cPoCA3NMZ/o80vNo93bI8ymtTolHQ4yZk2UnnE6vk6YBvj+gu3WFbFFRllOu/+E+ddkQhl3aes7Q7eEKg52tEbE4G2mQIqW/MXQ/gCAI7gmtVyOlJAgCrG3wfU2SJFgbAh5hONj8f5xF6+XZd4ExArAIYYAMY5ZY26JUhJQxbRtQVQZtLGVjWBY5NEc4UyKEIN4JiRKf0BMIIQgChUNRNIqigbzaeF48JfGdwSdDa4NDUtiUThiT+DXGtDyhJceZojIBFR7NWpMGgvfuvo2L21fx/WDjWZIO0zab/XPw/16f0Y18rm2lXNt6Ofw4xlqLc47ZfkHUlQy3EmR/yEGTEHiSdz+29TrHkk8QToij15qLS11ircXTgmK5oK1rdFPTVCXleoWzFnFsERZsIhDZxoje+oIwHpP2+lx++xYqP8RUJ5igR9IbUM/n+P5bI1DK9YpyvWTlIEo7RIsWQr4kN0bn/J/LQ6mYX/iFX+DHfuzHSNOU97///RwcHLC9vf15r/zf/bt/x4/8yI/wi7/4i3zt134tP/dzP8e3fdu38eyzzz7w9X/yJ3+SD3/4w/yrf/WvePrpp/mN3/gNvuu7vov/9b/+F+9973sB+At/4S/wiU98gg996ENcuHCBD3/4w3zLt3wLzzzzDBcvXvy8t/mcLx1B7DHYSZgfFUzv5oSJjx9+8Wb4yDdZCv8y405I2vFZrlsOpiVbF7tEnqJoDEVjHloAvRFKxSgVMx5fIu1PuDh5Nx0Z05QlTVlQ55up98ZofJcSccSqeY750YzTJseTPq0VFK3FuIKV1ASNwatj6umc9WlBkBQs5yu0LjC2wZoOAknbLmjbBeG4Ibt9zPzwhLap8ZRH2Okiek/imxRxCHLb3bfdAomQPlJsBpM622BtQ5ZXLFdr6jogjvdQKsE5gzE51tY43GveA5mHeF4X3S6wTn/W90yb9b2fPX/zFUUw7AEkGJ3gB52NvweHNSXWtXgqJYouM/Z7tO2cVq+QMsRTKc5p2naJtTVhuEcU7b1G5L3j7Pt+ts9wrun4Hd63+67XbmC4SVbeOMmo2s1x8sj4/o7lL0dmmnKzv36kyM8EehwojG7JZlPyxRzl+aTDEWGcsJ7foapm1HRZtJ9A6xZnLc5abqw+jnWWnY5Hdtan6NUo6ROlIV4QEl0bwUGDUJIZksDCaC8l6sTYvN3MG/VeuVEJgs+/jYWzlpObNwAwvRHSKRL0xv8Tn9taz3l4HupouXr1Kv/sn/0zvvVbvxXnHB/5yEcYnk3n/Uy+8Ru/8aFX/rM/+7P84A/+ID/wAz8AwC/+4i/yH//jf+SXfumX+PEf//HXLP+hD32Iv/E3/gbf8R3fAcBf/st/mf/yX/4L/+Sf/BM+/OEPU5Ylv/Irv8K///f//t52/PRP/zT/4T/8B/7Fv/gX/N2/+3cfuB11XVPX9b3fV6vVQ+/DOV9c+tsx5bqhKjQnt9bsXOuh1BenLF6cCRWnLc5YxJtY7+XtlOVBwcFpwbvcxghdNOZeJdhbRezFVLqiMhXDeIgfRqSDVz6rbVVhtGZQdJjfKGkyiTHDsx4qMPAVOkrojiLG4QV0doqRO6y9mGZdcPRcgYsa2iZAx3vIeETd3qFtp3h+THfwGOVCYpsFnq8oFgV3F89yof8uInqIsUT4BmerjacGi7M1lvq+/fA8Cc5RVyXaZGcjQF5BIM78MQqBxJgCa2uaZvM6UgRIGWzEktNnnbElvt8jCEZIFWNNgTHlKx4pHEJIpPAJgglBsI2U958inbP3CRrP6xJ/5v/gM//wOpyUm8Z+W8kmSmN0y2z/LlJK+jt7eL7PcrniU594FqTH02+7ipICo1vqokAIgVQezlrWsyWm1TSlZV601LMp9bzh5mwzFPRlsvkU5wylfg5wRN4uUrwiJhvbYp3dmNzVZjCwf5ZC9cOQqNPFcwHNzRUikCgXoH1H7RzagFKC7ngjeEy1EZjCj+5F8t6KCNDs4C5tXaE8HzeaYOYtqZJEqb/pI3TOOQ/JQwmgD37wg/zQD/0QH/jABxBC8F3f9V0PXE4I8ZqQ9evRNA0f/ehH+Ymf+Il7f5NS8i3f8i185CMfeeBz6rp+TYlkHMf81m/9FgBaa4wxb7jMg/jABz7A3/7bf/uhtvucLy1CCLaudLn7/IK61Ow/u2BypfNFaZC46ckica3F1QaRPLwAurLd4VPyhLLQHK/qe5Vg5ZtMp302Yi9mzpxSv9ZsDOBH0WbkWHiF0XqPVa8gGe1xqXcJISRlo9lvDCIO6co1rHx6w6twHHJ04xRXtyi/oi0yFkcnqLphcxrZoT1bx2jyboZ7l8A5nv+dj9DWFac3s81ohoOQ9OKYzuAifuQjFJtmg0oglcTzQpQfs6pmVNM/xNiWVaDobvUI4y5CxvheQpgMXhX9KNC6RagKR4Xv9/G8/r2UoW4aZvt3WE9PCSLB+HKPMO4/4N357Lxeyu71qIuCcrWkXK+wxtCdTOiOJhgs83yG0S0D0SWbTTm9fROjN+/i6uSYqNPlxp0j6kVJGnrUtxteOgwwbXPfOnTjmB8YhIRZMuVwXlCtG/q9CBdFhHFKZzzBtA3ZfEZTT/GCgCDsMhxeI4hj/HAzrX7VrtmZVsR+wmMXH2xF0PNq84MnMYsa5xxZ60AKepP4ngm5LTcCyItS2nazX59vBChfzFkcHgCQXLyCQWJyTar88/TXOW+ahxJA3/md38l3fud3kmUZvV7vdVNUb4bT01OMMfcZHAF2dnb49Kc//cDnfNu3fRs/+7M/yzd+4zfy2GOP8Zu/+Zv86q/+6j3R1e12+fqv/3p+5md+hre97W3s7Ozwb//tv+UjH/kIjz/++Otuy0/8xE/wIz/yI/d+X61WXL58+fPav3O+cHiBYvdan5ObK9rGcnh9hedJVLC5Cxxsx8gvUFRIhGrTYLAxyOThT7hBqJj0I47mFS8dZuxNNqW/XwgBBLyuAHoZz+uSBl3WzZoVc4L4SQAGNOw3JSttINh4TZSp2Xv8caztYnVDOKjRRwcEStEZjoFNWsJojdGaqNNhuHcBIQRvC7+ZW5/4GNXBCrF26FnJUf0CizjBCzbpFWs0TVVhdEsYJ8T9AVGSAhKBT1N5rA9b1rxSYSqVR5R2qMviPkGglEfca4l7m7lZ5XpFvpjfi4I0VcnB858mTDsotTn9mbZFtw1CSqJOlyjtID2FEJKmLChXK3TTEHU6RJ3OplKrqbHG4Ky9t/8Oh5QKLwwRQpDNZrT1/f+HKl8zu3uHdbNmMb2OLz1Ol8/fezyIYoSQ1GVOvlwwy2r83pCL4whMdW9f/XBzk2e1prEOP9wIgLgbYDOH34nZemSXy5d2CKJXQlLjS1coihvkBYThDr3u/bYAbZZI5RH7r1+afs8Dpy3OOIq8pQ09pBJ0J2c3n9aiy3xzrMUd2rbFOUedrWnD4N72PyxGt5zeukk2nwKQ9oe0nR5uVRDVFpEIki/hrMBz/s/kTSVMO50O/+2//TceffTRt8wE/Wb4+Z//eX7wB3+Qp59+GiEEjz32GD/wAz/AL/3SL91b5kMf+hB/7s/9OS5evIhSiq/8yq/kT//pP81HP/rR133dMAwJw9fmus/5o0sYe1x4cshsP2c9q9Bn3XvrQpMvasYXOyS9t/6EKAO16fLyJn1AABcmCUeLitmiYvssTfBWp8ASb3PhKnTxhssJIRnEexxm+yyLA6zbdD3un03cXmqD8xMEQJMTRB5+qGgJCOOE7qjB8zx2rj32huvpTbZ49D3vg3c56hdXVKslS39OqzepOHtW9i6VQgho64r6cJ8gSkgnW8SDMb0kxhYZ1liEFGfP0xSrxWZfpEQpH93WGKPJ5tN7F8qXidIuwwsXKZYLlsdH1Hn2gK2FbFaTzU4f/Ni8fs3rfjaEkMS9HnGnh8OxPD7CtA3LYrPtvfhsAroQ9La2Ge1dBCHI5lOOj2f4F7cJ0pS3P7GF0y1tUxNEMepV59/pfsbqtKI3iRhf6PCSOsXUhq29IUH02s9A227W7XuD1zz28nHzspB+EPbs2Lelpm0d68ZBJJhc7LySjtYl2hhAovyYMs/J5zPUek5xesKlt7/zvn14PeoiZ3l8RDabnolYwWBnj9GFizxT1JhS05cSz5ME5/6fc94kb/qI+aZv+qa3ZMWTyQSlFEdHR/f9/ejoiN3d3Qc+Z2tri1//9V+nqiqm0ykXLlzgx3/8x7l27dq9ZR577DH+x//4H+R5zmq1Ym9vj+/93u+9b5lz/nggpWByqcNwN0E3hrY2zI8KdGM5emlFECrSQYgXSIx2CLnpJ/T5RIdeqQR7+FL4l+l0AvqxR9EYptnmTv4L4QGCzx4BAujGu3jSQ5s1i3rBKBrRUXIzN8o5ChWTAjSbO/mkF7A8KdHlZt+11g81FuRlD5Jf+MSdLoP4MnVUYs3mdaSnCJMUz/dZz6asTo6wxjBdLJndvU106RK7lx8h6nSQZw0HF8eHLI8OidIug51dwjTdTDcvSvLlnHK1REhF3O0S9/rEne7ZPvTpb+1Q5dlZ36BNqbkXBBitqbI1dZHjrN30IwpC4l4PLwipsow6zxBS4gUByt9UsQlxlhoTYLTedDs2hrjXpzMcIdUrJvfB9i7lesXRaU1fdXly6x3spDuvec+6owk3Sx9lK3Z7EUoKCAK8B6SQ2mpzDAWRh3OO6uyYih9grnfO0epNob/vvzYNWOlNeuuNBJCrDbYxWG1ZzWuYxHSGIengVTeRdUZrLPgRDtC6pVjOGWxN0G3Nyc0b7D72BG1Vkc2nKN8nTFKk522aWZYFy5P7hWoYp0weuUqUbiKT81ZjMk2qJHHvPP11zpvnSyaZgyDgq77qq/jN3/xNvvM7vxPY9Pj4zd/8TX74h3/4DZ8bRREXL16kbVt+5Vd+he/5nu95zTJpmpKmKfP5nN/4jd/gH/2jf/SF2I1z/gigPInyNk3Qkn7I/DBnPa1oakNzdH8kZHFUMNjemHw/F8PkvV5A9WevMvpM/Eix1Q25sa6Y5w3mrKFeayz+W5Syi7wIgcA6e1+Twwduj9enE3RZNAXzas4o2jRO7CnJQhsWMiJFgNXQVvcEUJ1b8NnMztL6oY2t3jjCrBtE5ehf3EUGr71Ajy8mDPcusDo+Zl08S7FaMTs6xJU5IAiiaHMRrzcX6nwxJV9MUX7AaO8i3fGEqNOBi6+fwhZSEiYJQfzaNE/Se31v0Bs99rAIKfE7Kc3KIpAMowcXk7TGcrLeGLovDN7YVV2fVYAFsUetLdaCEBD5rz2mjMlwziCEQqnXdvP8bALIGUdbtGR31jQIbMfHiz3GF+6vTqPN0cbCWURyfXKCkpIgimnrmnwxY/+5T1Nl6/tM2p+JEJJ0MKS/vbv5v57RWEthLCZrScOQ+Dz9dc7nwJc0ZvgjP/IjfP/3fz/ve9/7+Jqv+Rp+7ud+jjzP71WFfd/3fR8XL17kAx/4AAC//du/zd27d3nPe97D3bt3+emf/mmstfzoj/7ovdf8jd/4DZxzPPXUU7zwwgv8tb/213j66afvveY5f7yRUjC+0GGwk1AsG4plg3MO5UnqoqVtLNP9nNlBTtwNiFIPqSRSCdTZdy9Ur9tNVr4qAuSse1MiKog80tAnWtebZnRVyygNKFvzlgkgKSSRF1HqkrIt31AAeV6fbtBhUS04LY55bLBJZ/V9byOAjOOiH0NbQJMTJiOUJzDaYVpQwZsTQDLxkR0fm7WYWYXcTR+8nFQMdvd4JAjh+osoZ1H+xvzbVOW9ZZL+AKM1dZ5h2oaTWzeYHdxFKYU1FhX4xN0eUdo5i5wIlseH99Ipg90LjC5c2hRv6I2IeJi0zOfLvJ4DkPopgXrwhftoVWGsIw09+vH97681hvX0hKQ3QEgfazZ9eIJQsaw2ZuPIVw+MzL2S/uo/8PGXI4cPEkDGWOY3V+S3M1g1uEFEOImZXO29NqrabASQDQLW8xlVviaJYrYffZxyvWJ65yblehOJijo9hICmKO41XVS+T2c0pjvZxnvA8bXUBttYfAO+lOcG6HM+J76kAuh7v/d7OTk54W/+zb/J4eEh73nPe/hP/+k/3TNG37p1616lB0BVVfzkT/4k169fp9Pp8B3f8R186EMfYjAY3FtmuVzyEz/xE9y5c4fRaMSf+lN/ir/39/7eW9aA65z/M1BK0h1FdEevmC2ddaxnFcvjEq0txaqhWDUPeK5gdKFDZ/ha8SA8iVCCttTYSqPehBHajzbiaRz7TK1jVbUMEp+qMfSit+74jL14I4B0yYDB6y6nVEg/3OKOuEvRnFK0BYmfMPIVN8tNioGwcyaA1oh0TNILWM9qTONQgUDrNxcJ88YxTdaiFzXeOEY8IErxMkEYEnd7+L7P1WvX0E1zLz2V9Af3UkvOWlanx8wP9jFtgzkrR9Nt/bpeH4DF4f69x8v1GiEE6WBId7KFVApnLeV6RTabYXXLYPcC/Z1dhBD3zM9CvnnhOq82AmgYPjj6A3BnvhEiFwb3m4Wdcxxdf4FitUCquwx2N6l9P1IIKe7Nl4sfEF2DVwkgf/Cax7TVtPZMQJ317jHGUq5aynVDuW4wqwZRG4JYEewovElLlc3QTYjVmnK9oi5y/OOPc3J4QhloVLr5jA23dwiThDBJaOuKpiwZ7l34nCJr89Zg8nYz/iL1v2itMM7548WX3DX2wz/8w6+b8vrv//2/3/f7N33TN/HMM8+84et9z/d8zwNTYuecI87KdHuTmLrUlKuGtjYYY7HGYY3DaIsxjpPba7J5RZhsqluCyCNKfdrGMJ9W1IuapYPtp0cPbb5UamPWHMQBy7PBjcuy/ZL6gMJwROIn5CbntDzlin+FwVkUpDCWWiWE8Cof0GYcSVs6/JR75c0Pi0p9ZOJhC017UhBceP2hai+XTL/sNfJexwMjpKS/vUt3vEWVrUEIpFQ0VXnP0/OycfplM7SuNz6Ucv1Kzy/n3AMN1C8zvXuL9ewUqbx7wqm/s8tw9wJCbiadI16p0HLOodsGKdV9kaWXI0CDaPCadeim4SSrWFctnpKvSX9N79y6Z/62RnPnU88h/TGmhaMbM6atj3M+yWcIIGctxXrO9OhZTFsjBhdBLwmTBOVtxPe6WG7Ei/DIT5esTkvWswrTGqzdGNb9rCHNDU1oKXMBdx8cARX5glZrXBygPJ8k6bB16ZW05NaVqw983sOyaPVZ+bsk7p7f3J7zufFQZ+7v/u7vfugX/NVf/dXPeWPOOeeLRRh7D+wa66xjeVKyOC4os5Yye+UC/3LGwLV20/o/b9l/YcFgJ6E7jFC+RLeGYtWgPEnSC16TZvAjhdaW7djnTiHZX5QsipZHxm/dvr0sgD5bJRhA4I/oBT3y/JjT8pgrvSv4UtD1FGttWKiIHbgngOKOj1QCkLS1ftMRIABvK6G5ucIsatpA4U8e7DfxPG8TbXHuoVJtUm3SYi8TdTr0Jg8eBUGnSxDHLA4PCJKEznCENYbl8RHlWSNUIQV+FNEZjnHOMr1zm6b8DE/Z4T6r46PNkNQzL4tUHl4QbMzQZ7PAPD8kiGNaYZjP7yCVQgY1i+UBTVXSVCVtWWKt4ROnLavGcXkQcuxO8YIAISVWa/LlRjxtPXKN9ekJy5MF9fyIzlAikByfFmSNpSzG3J5KzNk8NOcsrTmgtccIEbEoFywPl2fb66M1HK+POMhv47cJz7hXWpEoXxDGgiiCcGYRDqwvUN2AqNfDGoNuaoSUm5RjHIG9SVcWqEtPE8SbQbbBW1Rpa51jUW8EUCcNz8vfz/mceSgB1O+/EqJ0zvFrv/Zr9Pt93ve+9wHw0Y9+lMVi8aaE0jnn/FFESMFgJyHpBxTLZhMR0pY612i9ucBF/YC0F1C0ltLB/LBgflhsysRfVR7vh4rBdkzU8fHOKnL8SFFmLVtRwDAOOFrWfGJ/ybsu9t+yKdZvLgK0yzDe4SDbZ5m9QDP5CgIVMPQ3Amgu4o0Aqtf33p+kF7BceVRZ9TkJIJX6+Lsp7WGOPi4QUqCG4WvE4svDTdu2pW3btzyNHSYpO9fu7w+2ffX1q0WT/pD19GTTa6jbo6lKpndu3TNkv9wk0Rp9bzSFEHIzk6yt0W3NrJxRZ0tSP2Hu7rxmHavGsW5BCtgJLVW+hvz+ZUYXLtGbbNEZjZjuP4PRFYOdAUkvpD29gdUNtspo5Cvvl3MaK2dEaY80fhtNlpIvc8p1TVtrcHBqaxqh6KgUP4iIUkV3HJH0QoRQcKxxnRrRU6RftUO88zqpq3JOEUb4kcA7Ez/w1nSBho3/p81alINO5J+Xv5/zOfNQR84v//Iv3/v5x37sx/ie7/kefvEXfxF1loM3xvBX/spfodf7/7P35lFynPW9/lN7Va+zb1otecV4wwYfQxK4iYNZDgHCBZI42BhCAgECOL+wJBgTcoMJBGMgXCC5QLiQcCHBkJvkQg4YsxgbE4wNwYtsy7LW2Xum91rf9/dHdbc0mhlpZjSj0Ujvc04fqatred+qnq5PfdfC2oxSoTjJ2K6J7c798wj9GCnBjAXh/irdRZtsl0t12idoxh3x42RMoiBNy5/cn7pKdEPD8UzCIMavRTiewdO2dvPYRI1SLeSxiRrb8i6hH5PJ25iLxHAshSMF0PHS1DVNpyt/Pt7sIzSjSSbro2wqbKPHNNlHyJTWempPQtLIZ4tcl4NxyCSoRct2gbUxe1xkLIinmqkQKjUxig5mjzunxYhlWURRRBimjUHXE9Oy6B4a6by3XJdMoUjQaGCYJqbjgJSEvk8cBmnVbcdFJGladxT4TE00cdwuBrwhsq2sO8v10HUbr5DhwQmfwWLEcM7irB6HOAqJwzBtwYHWOmYX9XKA5Rrk+4bJAyPn9qSZkFUbWa0wsjlPxnSII404ktRrT6JFPcS+ix9vRdM0MsU+vHxCFITopkakmUhpc3b/Di4YORfDbAm6MCHcV0W4MZFbxxrI4PblFz9RYZ04kWC5ne+epmmrJoBmo4S4EpIzjZPeEFlxerFs6fzZz36Wu+66qyN+AAzD4MYbb+SZz3wmH/rQh1Z1gArFqUJbEIlWoKkIBbluh3yPSxwmBM0YJ2NiWgZJIqhO+dRnA6IgQSSSZi0iChLKrdiKgbjAcKJzqNTk4QfGCXuy5FyLklbvBHCbTlogMA4FUSv41DxO81TP9Dqp8KEIj5kJBmDbA3R5wzQrTzBR/jmbCtvoah2jIXVC3cYWYWoFyvTgZi1s20IISb3ir/h8WgOpoIlnfGQoiCebJLMB1nAWo9XaxLZtGo3GioXWWpNWjz4ijknTOoG+bQwztRjZ2SyBr+Ple9k5eAm5VqXt6UM19u2tMiVmEEULXdPYMVTEtef/PCdJWuOqWYtIIkHQjMgWHfxaRLUcUBvzkbFJ07XwSS2WQjbxo4NIKXHMbeiahuUY2J6JkzHx8jaWY1CfmEIEGfp6ujriJ6lHRIdqHbev1Z9B98xj98ELG2kNIPPwOVhN612pGaXuL8cmu0CigkKxVJYtgOI45pFHHuG8886bs/yRRx5BiOUXh1MoNhqapac+CiHTG4NtYLZebQxDp2swQ9dgBilkWpOoGePXIypTTeJIkDRjPAF5qROHgkMVnwuLDlEgqEz7VKbbrhWQrX6VGpAp2nh5myQSxFGCruvopoZlpzc10z6cCt+IGscVQJqmMdh1KaOVJyjX9zAz+1MK+fPJGjr1RFAzMvSIMI0DyvSg6Rr57gyjE1CvHt/KdCysgQxmn4eohkSTTWTL2mDkbXTPxIzTyZ+qAmg5zAQzCClwDKcjfmozAY/unmG8nNb88XTJU8/rI3OU+JFCEjRjpvZXiVpFOMMgploKCOqpZbLRiouxTC2t4I1As8fR9P0UihaO20N39zmYlrFg+YZG1KoCbXjE003imaDT9kJzDIyiTTzR7BQDXZSw2qoBdNg6s5oCaKqUuna7CzbWCVhKFYplC6AbbriB1772tezevZtnPCNtlnfvvffygQ98QNXaUZwRaJqGbusIP0lvEMf5EdZ0rRN0ne9x8esRQSOiOJClqCXoRYtE19DzNrLfZcgy04KDjRghJFKmIsi0dKJQUC+H1Mvz0/fb6LpGPU6oa03GRQk946RjyFjYroGIJX4jtSCgARJE0ENS3kwiDzKV3UMSz5KLc9RFkRlMeqATCA1Q6E3dbH49JgwjHGdlgahxlCAlWEUHPW8TTzSISz5JNUyLJjZDjJmQKF8jdppI10Bo6VzWkyRJ+2Atx1U53Uyzy7rMnjT7MBY8umua8XKA7hr0WCaDtkM2lNRmfEQiCf2E0E9jitoi2LR0BrcXGN9TpjbtY1g6lmNgOTpO4tCVtxnabtEMHyFJaoCDbfWQy12AYZjIOK1hBaT/jwSRiIibIUYC5v6YKGrFdhlpsLM1mCGeatVgOq4AaqQCqFW0ElZPANXjBH82QAMGeo9dIFKhOB7LFkB//dd/zdDQEB/+8IcZHU278g4PD/Mnf/In/PEf//GqD1ChOBXRbAP8JO0Jtngm94LYjkkcCmzXoNjrETsG3VmLmXrE7ska/Tt6GdqRBpi2U/RNS0fTNMJmTGXaJwoSTFvHso1O+n4UpDdLISRGYNEII8b9mTmdz4+0Jh1NRpzLdDPL2L4q9lCAjOrUG3vZE9QoRAZWPYNpbcfL2TgZC9MwCcOQsSdnGN7Wi2UvbFk4GilSd2B12qdRDdGA4oBH10AGayiL0eUgahHCjzFlGqCb1EKqT5SpTPtIQyPb51EczmJkTHTP6tQTWoo1yq9HxGEyJzh9OVRLPqXROjKR5HtdugYzJFFaVyoKkjQjTKRxX5quYVo6lmswOjFFoySJLIsDzgzVZsS+6QaGZ3D+U/voiTUq0z4zYwtn7xmGhpuz6B3JYVg6dsaib0ta9LNnOMcTkzWssEk+HqUyNoaeNdAth2zuXFxnCBEmBHvLCH9+4HojbuDOgqVb6D2pldPs9zDyDprRsicFh61BiyIlRG0XmLPqAmi86pMECTlDJ9+l4n8UJ8ayBZCu67z97W/n7W9/O5VWuqgKflacabRvAmIFTVEt14AqREFCxjaZJqTgmsSJpOrH/OiJaXb05RgoOCRCYugaVuumbnsmfZsXV1xSyFQITfdQmZpCtxJyeSfNZGtZlABs18ByjMNWBVvH6htkZs801TBLXN2KJaYJw/1UY8F0cy9u2CTSegEDQ+8miaAy5bOnMYk/K3GzFk42tXRZThqr1BYBcZQQNhOCRkSzGnXGASCB2Ykm9XLYCgDX0XQN6ZokVpbqrETUQyj76FJCJKmP1kkqAU7WQgqQmkRvOX7CRBDFEs0zyfZ5ZPo8dEsnjkRa3qB62J1muwaGqaM1I3RDxyjaGGZaSTqJBUkkSeI0hks3dZCSoHn4mlemfarTPotoyg5+1KQ6GyLRCLAZr9So+zG6Y7B1ZxdnD6TXVDc0gkYrg0xvxeq4aayOdZTwiPy4FYOkE4bTlKd340zsQXM14sTGqnWR7XoKhsgQa02iqSaIuSPVTB3N0tMAfwNsw8HsdTH7Mh3h07lOre/6Qi1MDg+qAVIQJ4Bhr7oA2j+ZisOBbq8Tp6RQrJQV5Q/Gccx3v/tddu/eze/8zu8AcOjQIQqFArncMh+HFYoNSPsm0I6RWA52qyJ06Cd4XemNwY8kT91U5L8Olqn5MY+OV3l0vNrZZrDgcs5gDvc4FgtN17A9k/6+bkalh2vp9A+lGTtpD60Ew9IXrJwrZZY94lEa0zGJZTKQO5c+sZlm8yGYPoTOJEJ7jCgCTbNwvCKOZyJJqEw3MW0DUY3mCIzFMAyNbJdDvtclChKmD9bSAPFgfup+vZEgZEKu26J/qIAhYOZAlWYk8GcCiBaJPayEzI43mIU0ZsvSQdPQhMRydEIBoaahNaLDwsDSkUUnNZUJCcbh7QgFSIkmJF19LpZnMjPtEwUCTQMvb6eFM/WW6IsFs7WAph9zsDLLZFMgnRxuzkAvmPRWQvpci7N70pYgmqbRNZBJrSi6BjL9fskgQTYj4mYEaIRiioa/h8pUCRknzOgGGhBP1dGSBNvpJlfYiRX0Iesacf3wOdWzVlp80jycnQXQLE/TzEFXNo/VM79FiUxSVxkcxwIUNhBSkhg2aFonLnQ1BFAzEczMpHFxW/oXbqOiUCyHZQugvXv38rznPY99+/YRBAG//uu/Tj6f56/+6q8IgoBPfepTazFOheKU4kS6wrezyUI/xrPTAOVGGJN1TK48q4dDZZ8nJmuEscDQNRIhGa/4TNUCso6JkKlVqOhZFD2Lnqw9r5fYQqnwmqbNS+2fMydNo8/rY7xnHD3n09+9mW0NiycaT0P3D9Fl1igODRJrkiRp4FQncLoNZAPyXhemO4npVdK07SSLTDLo9CFF2w2UWoe8fFq7pX3zbVfZrs8GRGFCHAqklKmQ0DQKoUcYhfRu8uhpiTmn22VmvJGuR+rXS4sRgmNo2KZOVAtplnyieoQmJUQCJ2OS63IxTB0hZGr5cA0wNEQsSEKBbFmDdF1D13QMKdEcnanpJlMzTbJZm6IBetWgV4PE0dFMjSRJaJRCGmFCNYipRgkC0BLJzGwZO4IuL0NXJBmU0F3wsHSd+ECVpO3CO1LMteKz2kgpaCZP4scHSeIEWQvS4pxhBk1zEHEv0hmk//zzKOZcRJAgaiEiSJCRwMhaGL3ugi7CZpSKpIy1cKmBtvVHs/TjZIDV0vgfw5njjlwNAXSg3EREgrxl0t2lsr8UJ86yBdBb3vIWrrjiCn72s5/R23u4fO1LX/pSXve6163q4BSKU5V2V3gZC2Qs0JZhjrec9IldJBKnVTyv3Q5D0zQ2dXlsOqIFQtWP2DVWZbYRUWketq6UG1FrG+jO2vTnHAqeRd4x0aVNEEsMLcZP/LQ/WJgwXQ/Iu9a8Bptt+rw+xhvjTPlTnM3Z9FomTxBQNYc4xwrQnO3I/BDN5l4azV8g5QxmrknTn4AIevIZMlkTmAVmMfU6UX0nmVxmwd5qbQxTp7BIRejILFKtVtGPiLN2cxbDuaX1kJJCIoMkdVdqqZUsDfyOsVrCQM/bICTxVJOkEoIOMdCsRzTChOmZJs0wBkunEsWUJ2pkLAMZCyIh57j02piAZeroeoInQ3K2ztO6eih66XnQHAMjaxGXg47wicQsIDG1PBomUhcIq0EkS4TJFInuoxk6lhjGtQtYOYu+7f1opstDeglp6eS89ETpjoHuLC1QuF01PGMuLIBEuIT4H+g0QT0yBV7X9TllU1ZK2/011OPNb76qUKyAZQugH/zgB9x9992dPj1ttm/fzsGDB1dtYArFqYyma2i2nnaFD5NlCSBN1zDtNKPLSNIbZ5xIokQs2BU+71pcsb2HmXpILCS6BlEiKTcjSvWQehBTqoWUanMzw/ZUInzRIGkepNfrYbZxWDz15x2Giy5+JGhGCa6lk3NMDC1HzYeKbFLtqdFlZzE1jcDKUE+a5MI6mqaTyZxFV1eWWu2/MM06lqkTNlzCyhDd23tIkhp+cIip0VGi5iROaTumN4RleWiaiabpJEmdKCqj6zaOM7Do+Wr/1oTh4plvxzvfmmeityoGx4kgiAV21sU+4nwnQNTjUPEMRstNSvUQaUqMRKJlDbQei22DeapRwng1oAFokcCIUteYoWu4lkHWMcjbJl2uiavrjDXHqfk6BbdAb6Yb4cfoGSst+qhr6L0WfuUQfnQQoTVA0wgk6LqH1AJkywyko2NqBXK5p9CYyeBPNMn0ODg9eap+hLR0LFPHXKY4kFJ2qoYfzwJ0zPgfgLCWBkBb3qpaf2pRTKXt/lpEKCsUy2XZAkgIQZLMj3s4cOAA+fwxqoMqFKcZmm0gQ4EIEvRlpmXbnkkUhiShwDZ1wjgVIgsJoDbd2bkPHUPFNAumEcZMVAJmGiHlZpRW4SW9mflBg5lmFUOmf5sFz6LqR0xWAyarwYLHGa94VMIqcXMvz9y+kx7ToGplqYQT5ILDcUkYWaQ2gmlabN26nUOPlhGRJG7myPeMENa6iZo/IYgbRMlDjB98kkxx4XR5O+gjn7sAXZ//uWkaJEmZIIB60EeUCAqu1WkdEsQJUSKxDA1T14laAgfA0DWM1o04FoJDsz6Hyk2S1jkyWvuQSBYqY+Y5Jl7eoOCabOnJ4JipANgRxNSDGNvU05exuPCYHJ8lMaC3qx89AzKJiJMqQbNBHJWJ40oqcsw0tkrXLBLhI0hv+LruYFldOPYgtt2DphmUgzQBpR0Y3WxZaI5ugroUmnETIQW6puMaC2dWtV292vH2H9TS759zeD9HPyyvhH2zTUQsKNomhaLK/lKsDssWQM997nO57bbb+Nu//VsgNdnXajVuvvlmXvCCF6z6ABWKUxXdMRC1aEWB0O0bVxTEZGwjFUBhQsFd/tNyxjbZ3meynTQwNIwFpq7xZLXOEzMhWcNkczZPf97BtQzqQcwTk3XqYXpszzJoRgm1IEYIGMp305itMlGf5RcHy0wlCaVmQmO2ymxtlEZUoh4mBEHEzHgF1zSIsw3KIqY0WscYr9KVt9EFTNV2EBsHkEGVqUaT/i0CP0oIogRNtzDNHDpVDG0vuj5OKPMEsYGu2WRdF9dMaNb3MFMaJZgEe2YfGAPoZp7uTI56cxS/eYC0Dfs5oB+RkSrqkIwDPui9oPcBOsgGup4gZJZEGKRpZBVAQzcKZByrYyE7uiAhpBYTz0pwjIhEBIgkIIgCmiJAkqCho2kGhpEhlgbl2uOQlDGcgFKw8E+uYWRwnRFcdwRdt0iSJknSwDByGAsUsoz8pPU9SvfXaH0HvRWk9bfdX57pLVpCoJMCbx/DuhQHIFoC3HQ7MUAnagGSUnKw5f4a6fVWrWeeQrFsAfThD3+Ya665hqc85Sn4vs/v/M7v8Nhjj9HX18eXvvSltRijQnFKciKp8J1A6GaCmzGAqPMUf6LYLXdcxsxgGjqek7Cl57BrI+uYXLR58fiZMClw14GDTFYCpBZT0A0OaR71JKHZqDJbD0HTMEwDXdPw44S9kxV0wyRxdIJ6TKNVNM/MOXgDF1LbW6dal0QzLmZGByMBLBBaKlSih0FWgcMWJr+VvCREQs2XaCQ4sowuqiRNyfQRCWOGDkn4czCG0DSBqdVA+gjS4GiSEog9ZG2d/pxF3rOQUiPBRSSNlgsLTMPEsroxjAxa4tJoSIQIjnqFSJYW/D7VnIJwlKyVxdRNNHQMw0M3PAzdxTBy2HYPhjHXrWMY3rxlbaSURK3vitXKKGycoAUIjuH+EhLZilE7ZhHEIO17F2kW6DpSiFURQGONkNpsgIHGlj6V/aVYPZYtgDZv3szPfvYzvvzlL/Ozn/2MWq3Ga1/7Wq699lo8T/lmFWcOnVT4ldYCIs0Ec4vtTLDVEUBt2je0douDpWIbNl1uEV2vcHaXZMAbwCu5OEmOHlNjy6CFky2Qc0yesGpMVxpkizbFXI7stm6aQcL4ZIN6M2LLpjybezNM5evs21smSTQ2D3WRcw/H40SiQBT3EYaTOEaMYybESUjdb+DHCZY9zISo4ZoJ52zLYeg1Ko0yNb+J6xTo79oOoorvHyIRjdStpdlouNh2L4aRIwhGSUS7tYiBhoEgxKQJpoauuyAlQoaE0TRE08c9T7pmo+sOuuGg6w6GnnZNl1IgZUySNKhWDoGRp7/rYnq6L0LX53e9Xy5pscXD1cEBmq3Kzd4KBFCnBYa5iOCKRJqNpmtwrFi3MBVAccuNuVo1gB7bV0YKyVDRI1s4cXeaQtFmRXWATNPk2muv5dprr13t8SgUG4ZOJliUthZYShXkNlaryamU4LRrsUSrLIBamTihCIlEhKUv/UbU5/VRCStM+9Nszm9mR95jwsvTjGtsdyK0VhaZY1t0Z22Gu10KhZYFIQebe+daE/qHMgQzARIwSwFGV1rV2PLasTweMLegapIIomaCkzXxoicJwxDH3kQul6OrC4SI0I+Yk+0MEARjGLqHaRWxzGLn80zmLOK4gqaZqXVH00iSBnFcwzCymGZqWYjjKlFUJhFNRNIETe+IG113U8GjO+i6jaYdO9g4SAIa5RKaBSPdFy/oyloJUUtw267REVMdC5C1/J/0tgUoay5sXWm7eHVbP7Z4awsgLZ3nagigUjVgqmXqO29b8YTFo0JxJMv+azEMg1/5lV/hq1/9Kj09PZ3l4+PjjIyMLBggrVCcjmimjmbqaSp8mKAdo8bOvG1bNXmCZozZ+pPxV1kAmbqJbdiESUgjalB0lpY2DqkAeqL8BOWgTCxiNjk243aWIKwwU5+lJz8EpDe3ZrN53GalpmXQNZRhdqxB0EwImof7ilm2juWY2J5BvsfFtA2iIGHsiTJxJCj0udi2TRiGc46jHyXoHLsPx+5b8PipK2bu/A0jg2HMFWqmmcc0VyeZY6I+AUDRKR63Ie1yODr+JxGSoJVGf0IWIGsRC1A7BX4JAdBCSBLNJknSJr2QPjCvhDhMeHRvGYD+Ho+ugqr9o1hdll1MQUpJEARcccUVPPjgg/M+UyjOJNpBoStxg9leekPR4vTvxo+SBevJnAhtK1D7KX/J21kZPNNDSEHJL2HqGn35bgBGq7Od9do3tzie31/qaLoGMmx5Sg+9I1m8nIXRarUQhYJGNWR2osmBXTOUDtUZ3T1L3LqpV6Z8wnqrTs4G6go/1hgDYDAzuKr7DVu9vNrfn7bl0GgVgFwOkYgIRVpeYNEaQEvpAQZpF3ghwHI7Asg0zY4QWiq1mYD9D5V4+BdTjM36aJrGuVuXLt4ViqWybAGkaRpf/epXedGLXsRVV13Fv/zLv8z5TKE4k2g/FYsVZYKl4kGLJXraZgo/XqM4oHh5cUCQWoGgFcgLDOe6AKg3K1Rb41yOAILDBQ+HdhTZemEvWy/oYWhHgd6RLG7WQkooTzVJYpk2i+1PrRK16YhGJcT3F07dP9WoR3XqUR0NjX6vf1X3HTZbAshtZ4Cl7zMryQBrWX9sw8bUF7bUHHaBHWP/cQhJ1GqCejibbLkp8I1KyMG9ZR6rNHms4aM5Oj1bcgzmlPVHsfos2zYppcQwDD760Y9y4YUX8spXvpJ3v/vd/N7v/d5ajE+hOKXRHYOEE7MAhX6MZ5nUg5hmmCyYer1SOi0xouVZgCAVQPur+5luTiOkwPMKdFkmM1GDR+o+TytkOvEdK7XMGJaOZ9l4OSj0edTLATNjDUxLp39bHsPQSWKBH1jMlHz8yjSGn0uLG5JWhM71uFgrcP2sJeONcQB63B4sY3UagQIIIYlaNXnaAshvvV/J96aTAbaI9QeOqAF0zB5g7fgfO80Aawni48X/xGFCoxKiGzoJkl/snmHCDzEKNtmhIlsyDmdnlPhRrA0n9Ev7+7//+5xzzjm8/OUv5/vf//5qjUmh2DCsRip8HApcz6AerH4gdNZKA1tXYgEq2IVODFHJL9Hn9DDo2JTrTUrNGj+Rkgud5VmAjjveokO2OPeG17cpB5pgpjpBHMUEzbhjYfAbMbMTTWzXSPt3HdHBXG9V3DZtAzdrzeumvpZMNNL4n8Hs6rq/opb7yzA1jFYGWONEMsDaLTAWS4FP0lYvANqxLEydDLBUcLfDIRazAIV+TGWqSW0mQArJZBgzFkTESIysyciWPOfnPXLmqSVsFacXyxZA27Ztm9PX5b/9t//Gj370I170ohet6sAUio3AnEywI5o/LgXD1DFNnTgW2K3Qn9WqBdRmoaaoS0XTNAa8AQ7UDjDZmKTP6yPj5jhbCKpxg3Ls8tM4RgsjekyJEGLZ8R5LGoeu0bepQKmeI44FvUMZTNNEJJL6bECzFhH6CWkzi8UxLR3LMdLzbut4ubR7exQm+LW0AWq2aC8rm28hykEZP/YxNINet/f4GyyD0G9ngB3+6e4UQVxJDaCWZXDRFPg5TVCPcV46NYAOZ4CJRBA1BOOVCnGQYDoGpq3jd64XVKKEMT3BNyTC1Mi6Fpee18ugp9LdFWvPsgXQnj175i07++yzuf/++xkfH1+VQSkUGwXN0tP6KEIiI3H8TJmjsDyDuCo6mWCrbQFyDRdd0xFS0Iybiz7pL0Z/pp8DtQNMNafSdgl2llxY5zJH8BNdwxcwE8Qc8kPC2QpnF/Lk1+Cp/XBBvQjL08i03CL5Hpc4TAj9BJGkIqy9fhILkkgQ+glBIyKORCewGmB2onl0w3Vmx3WKAxkcz0Q3NZJIEDRSq1PYiInCBC9v07clh3FE64t6OaA80UDTNGZyY51zZ+irey7a8T/tOlIAficFfu6xQj+mWQ3J9bhzxnokjbiBTCQOC7eXEMdogSGFTK1xukZ1dhZRiyknOuWwSa1SwzQcjL4ibS9YeISVtJkIJi1Jo9/EyLjkNY2dGYctro2uYkkVJ4lVCzZwXZdt27at1u4Uig2Bpmnoto7wk/RpeZkCyHZNmtUIvdWbarUtQJqm4Zke9ahOI24sWwDNc4PZeWCCXNLkWb15DgUhP7dt6oHPobrPpNDosUy2eTZ9lrmqiRG2bRNFEWEYkskcnodpG5jHOe9CSIJGRBIJklgQNhOa1ZAkkWgauFmLsBkThYKpA7Vj7qtRCTn06CzdQxniVgZb0EiFiZSCJw9MYPZICl4PjUrYWi7RdQ0na51QK4ejLUBCyI5oPtICFDRjxp4oIxJJbSZg6Kxix2V2+JwIypMNmlMwW46whxsU+zw0vSX8mjH+aJ245OMYOkaUkEQCvx7j1yKatRApoR4njO2dRROChh2SNwRRLKmLmIxl0N9jk8vaVJoRs82IGV0QeRaaqWMCWzybnZ6LpVpcKE4ySxJAPT09PProo/T19dHd3X3MH7VSqbRqg1MoNgKaY4KfrKgnWPtG1u4K31hlCxCkcUD1qJ5m/CyzWPuRbrCJxgR9Tlf6QVjD0jW2eQ56d57JCoSGRgUoRTGlKCZj6PTbFn2WSbdlnPCT/YkEXOu6hpeb61aRUhJHAtPU0XQNkQgq0z712SAVSolMU8szJo5n4mRS8TJ1oEoUCib3HxZKmpYGcU/MTpEkCdq0SSA1xrXKnGO2xZZ5RFFBISRSpGNJIpG2NWuJOqd1bMsx0HQtTYGXEtPUkULix63SCaHAL/kkTmq5mtxbRbS+U6GfcGj3LIVeD5GIlqVMUq3VaU602lVgMTPWoFbykQLidtzPdBOaMXVdo7RAnJtuaBxq+hgiQtcls5ZBQ0+IPQsz71EacSkhIQnApvWAYKADg47FzoxD1lBxPor1YUkC6CMf+Uin0/ttt922luNRKDYc7VpAKwuEPqIWkAFJIgljsex6LseiHQhdj+rHWXNh2m6w6eY0SWYYA+CIrvCmaZI1DbY5JpmuPPv8kIN+SCMR7G0G7G0G6JpGj2nQa5v0Wen6y6UtgMIwXNE8jkbTtDnZY7qh0zWQoWsgtS4tFjM1fE4XM6N1gkaM7aYFHLNdDqZlcMjagxNDV9SN7Zmkm2ugQdJywTVrxxdwcSiAiOqRz5NSMnWghkhkqzOFRj1JqI/WcAydGWOuwHM8k77NOSb2VohCQWl07vWvBg3QoWvIo28wT2m03skwg7RaueUaaJZOlLMISQWPkzFxsxaZvM0oCdZUhR4jZsDLM9FwCJtNTMekkPXoty0qcUIgBDnDoGDq9FgmA7aFqSw+inVmSQLo+uuvX/D/CoXicIPIlXaFb7fESCNc0jigtRBAtejYrp3FaFcyDpKAkgjp13QQMURNsLw5lhnP0Dkv67LTc5iMIqbDmKkoJhSSqSj9/y7A1XW6LIOiaZAxdDQ0TA2KprGohbmdUXSyiiEuNg7D0OnbPL9adCxiSn4Ju0vj/MGtFOzCvHVCP3UfiUR2MqU0XUPX06wuw9RBQhQmREFC0IgJm3HqwmsmJInEtFLrkQQajRgEeFmTbNHuxEPZrsHgjgKGoTO8s4uZ8ToikRhmmimn6Rphs0K2D7oLOfI9LpmCTbMaYdp6p82GvyvtA+ae243UNTTt8HmpxAm7ywFWWGXEsShkuzgbl4NhwHAuw5buAkOFY4tJhWI9WZIAqlQqx1+pRaEw/49eoTid0U6gKaqma9iuQdBMsAREeloRuuitXu2YtgBaSSZYm8HMIPuq+5hoTtFvZ1MLUFAFy1uwGKKpaww7NsNOKlqqccJ0FDMVxsxEMb4QjAWCsWCumMkaOudmXTxdZyKMaQrBoG3RZ5snXHNorWkHinumt6D4gdTlaS+hZYrL3OufxILSoTq6Dvk+l6HtRZJE0DhQwctojAznGBhKj3n0NTashQXbeClCr2udDDDD1Ml1Hy5BENYjylFCU4MkDBESBBIpYTZOOsUwe5MGfZZJw8iQMw2GbZOCacxJgVfiR3EqsiQB1NXVddwvcPuPTvUCU5xpaLYBGshEImOBtkzrje2ZBM0EI5Zga6veFf5EM8EABjID7KvuY9qfJrKyWG0BlBtYUjXovGmQNw22ew6JlMxECeU4phwnhEIiJPhCUE8E91fm1iw66IdkDZ0B26QcxXiGThzHK+4xtVa0ix+udusLSMUJgOWaeLk0Vd/UDSJTw3AMvCOKIB7rt3omipmNEmxd41CzykQQkfg6k7KG5HBGnJCScMbHaQYknkGzMb8Ctwb02xbnmxFapLVqAMXHrQGkUJwqLOkX5M4771zrcSgUGxZN19AsAxkmiCDp3KyWiu2ZQIDZCr9otzZYtfFpGlkrSzWsUo/qKxJAOTvXCaaeIWYAIEgtw8u1zBiaRp9t0ndU5eJISPa0YoYAei0T19AZDSLqiWBPM2Q2ShDNgNHxEr25LJ6h4+gaGUOn20zXT6SkkQhKUcxkGBMIwbBjs9m1sJdQp6ieJFRjQT1JKJgG/fbxrXFhEjLrzwKpWFwLjm6BAUe0wTgqC64cxRxsnbcB22TYsXiiGbCveTh+aldlhliEuFmTZIEWLE4gsDUNL+eguzaaBjoaugaOrjNom9gAY6lgjQwXqK1KF3iF4mSwJAH07Gc/e63HoVBsaHTHIAlbqfDZ5f3wO14rEyyWgLbqqfDAHAHUz8p6Uw1kBthT3sN40mwJoDQQum2JEUKcUDFES9c4N+uys1Xjx2hZMs7JuIyFEeUoJnZcKnGNIAqZjpw0aOrIfWga0QJNmR9v+DzRDOi1TLpMA8/QCYUglmBqaUxSNUkYDSIaiZiz7ZBjcUHW66Rp+4ng8YZPLRHkDJ2CaRCE40gkBbtAxsosydUopaSaCGaimFoi6LNMBuzDpQNCIZgIUxFXj2Kmp6og4VyRYYcwMYBSEBEISQ1Jww+pxAkzUTxnDjNRzK6633nfZ5lEIsYkJmuaPLXQTd60OXK4GhpWRWBLE6s3h7lYLy6/AlKAbhFjEMdx5/orAaQ41VmxDbnRaLBv3755GRkXX3zxCQ9KodhodLrCrzAVXiMVACIWq+4CgxPPBIPDAmhGhEQiwgobIBIMw0DXdYQQRFGE45xY7ybjKOFg6RpbXJstrk1fT4EZXeA6JlbOwxeCQEgqrZiUtvgxNY2iadBnm5iaxt5mSC1JmAwjJsNjW6o0Upedq+tMhBFjQUQpium1TDxDZ18zJG4dpxInHAoids9OYkqDnXYPd81UaSQCU9NwdA1XT61Upq4RCkkkJE0haCZiThHGg35IxtDJGUaa3XWEiEn8hERKNF1jfxyxvxQRxIKHgxBNg1zTnyO4NNI084JpcKCVkWdpGhfmPAYci3JQppnzsA2bndnsgufBj1rZZsdqIdKyAuLkieOYJEm/D5ZlrUlVcIViNVm2AJqcnOSGG27gG9/4xoKfqxggxZmI3i5M5y/ffaXpGpZr4AiBCEJCUydOBOYi1XtXwmoIIM/0KDpFypSZjUr0O1baA8otYpomYRgSx/EJC6BjYdtppWBHCkbcuTEmsZA0hMDVtXmurk2uzWwUMxsnzEYJoRBYuo6lQSQhEAJb1xiyrTkp2rNRzC9qTRqJYPSIgO28abDNtWkIwaFmnUbcREOnqeWJW8IllpI4kXOEzNEYmkZ3yyLVtj4dab3JGQaDjomVxDSyLsIzqZoG5TghiBNMNPKWQbeVCr28mWbWdVtmx2K1zbUpxwkZQ++cl+M1QZVRgkxkmsF/rCKT7XIIboGoHhHHcUcAKRSnOssWQG9961uZnZ3l3nvv5TnPeQ5f+9rXGB8f53/8j//Bhz/84bUYo0JxyqN1BFCyokwrJ2MS+kmnInQjSiisgQBqxs20pYW2sn0PZgYpB2UmE58+WUALqvME0FrSDqxdqBaQqWsUjtF6ossy6bLMZRWD7LJMntmVYzpKA7arsaDHMtjq2p1rbESjNLwYYXRzdjZLt2WQNwxiKfGFJGhZqWIpsXUNW9NwDR1P13F1rbOfc7Muo0FEIiVZwyBn6Lit78D0bI3EMCjkHZ7SlUsFWalJl5PQn3e4pCu36Bw0TUvnfQS1VvPS9vfiaESr4rTeKsC4KP4RFqBykziOsW1bBUArNgTLFkDf+c53+Jd/+ReuuOIKdF1n27Zt/Pqv/zqFQoFbbrmFF77whWsxToXilEazj+gJFopOl/ilkga2BtgCYtKWGAV39Z6iHcPB1E1iEdOIGuTsxW+Yx2IgM8Du2d3UDJ1qVKXQsgCcrBT11S6GuBR0TaPfthYNhp5oTODqcH73IEPZuT21lnOWDU1js7uwcIjaLTBa8WIZQydqWduzK+hyX43S65a356fHw+GinppznFtE6/onVg4h6soCpNhQLPsxsF6vMzCQZjl0d3czOTkJwEUXXcRPf/rTZQ/gE5/4BNu3b8d1Xa688kp+/OMfL7puFEW8733vY+fOnbiuyyWXXMI3v/nNOeskScJNN93EWWedhed57Ny5k7/4i7/oZCYoFGuBpmnorarOcgVuMHtOIDRrEgeUs9Lb8UoLIgKYuslIboTE8phqTs0LhF5rC1D7xiqlPCXqAZWDMs24ia7p9Hl9a3acwxlgh8VOvSVSMvbynmOFFFTD9LotVq+o/R3W3WOIq6gJIgI04lYX+HYQ/Fq6QRWK1WLZAui8885j165dAFxyySV8+tOf5uDBg3zqU59ieHh4Wfv68pe/zI033sjNN9/MT3/6Uy655BKuueYaJiYmFlz/3e9+N5/+9Kf5+Mc/zkMPPcTrX/96XvrSl3L//fd31vmrv/orPvnJT/I3f/M3PPzww/zVX/0VH/zgB/n4xz++3KkqFMviROKAbK8dCK0jYkE9WH0h0U5/b0SN46x5bDblNpFYmbS/WD2tfdMWQGstSnRdP6UKIo7V087vfV4fpr42dYnafck00jpAbdop8NllCqBG1EBIgambnSKIR3PYArSE+B8nR9zKAFQ1gBQbiWULoLe85S2Mjo4CcPPNN/ONb3yDrVu38rGPfYz3v//9y9rXrbfeyute9zpuuOEGnvKUp/CpT32KTCbDZz/72QXX/8IXvsCf/umf8oIXvIAdO3bwhje8gRe84AVzYo/uvvtuXvziF/PCF76Q7du389//+3/nuc997jEtSwrFaqC1npZX0hNMbwdCmzoiSDodvleTE22J0cY1XXqKWwGN6dooRH5HlKy1BQiOHQd0MolE1Cl+OJIdWbPjhC1BbTpGp5N8nAiCKA2WzizTBVYJ07idnJVbMFZNCtnJZtSPVbXaL6f/OnmiKOpYfzRNO+WKVCoUC7Hsb+nv/u7vdv5/+eWXs3fvXh555BG2bt1KX9/STcBhGHLffffxrne9q7NM13Wuvvpq7rnnngW3CYIA153rY/c8j7vuuqvz/pnPfCZ/+7d/y6OPPsq5557Lz372M+666y5uvfXWRccSBAFBcLjS6XJafygUbfRWvIT0VyZebM/ENnVEEK+JC6xoFwGYDWZPKBAaYHNxG7stj3JQpr82jpkZAk6OAFqPOKCFGKuPIaQga2XpcrvW7DhtAXSk+6vREsiWqWMtM1i+4/5yFnF/BQlI0Ez92FXNG61OrV4PcRwTRRGGkbbAUK0vFBuBE04zyWQyPO1pT1uW+AGYmpoiSRIGB+eWjR8cHGRsbGzBba655hpuvfVWHnvsMYQQfOtb3+L222/vWKQA3vnOd/Jbv/VbnH/++ViWxWWXXcZb3/pWrr322kXHcsstt1AsFjuvLVu2LGsuCgW03AUayFggV2DBsV0Tx9RJQkEUC6JjpE+vhKyVxdZthBRUghMT+QW7gJcfRiIZm36oI0qEEGteCuNkN0VdCCklh2qHgNQluJaEzVYA9JHur5aVMXusFPVFaFuAFov/ES33q3as+B8hoFX5mkwqgNoB0Mr9pdgoLNsCJKXkn//5n7nzzjuZmJhAiLk/0rfffvuqDe5oPvrRj/K6172O888/H03T2LlzJzfccMMcl9lXvvIV/uEf/oF//Md/5MILL+SBBx7grW99KyMjI4t2sn/Xu97FjTfe2HlfqVSUCFIsG03X0B0D4ScIP8GwlndzcjwTQ9fnBEIXvdVLhdc0jW63m/HGOKWgdMJWi6G+p7C/tJvyzB4iGXWKIbZvhGvFqeACK/klmnETUzfXpPfXkbQtQNaRAdCdFhjL+wlPRNKJAVssA0wekQK/KP5sWgHadMDOEkWlThFMJYAUG4Vl/7q+9a1v5VWvehV79uwhl8vNsZwUi8Ul76evrw/DMBgfH5+zfHx8nKGhoQW36e/v5+tf/zr1er3jesvlcuzYsaOzzp/8yZ90rEAXXXQRr3rVq3jb297GLbfcsuhYHMehUCjMeSkUK6GdNrySQGjLS284Fmlj1bVoidHtdgMw48+c8L66us4iY2XRwzr7Z/ec9FT4KIrWLbvzYO0gAEOZIYxj1B46UaSQRC1rT7tlCtD5biw3Bb4W1ZBIbMPGMRbO1OoEQB8r/ucI9xfQsQCZpqkEkGLDsGwL0Be+8AVuv/12XvCCF5zQgW3b5vLLL+eOO+7gJS95CZCaz++44w7e9KY3HXNb13XZtGkTURTx1a9+lVe84hWdzxqNxrwS7IZhzLNUKRRrge4aJOWVxQEZho5p6dimjh8mq94UFQ4LoGpYJUoiLOME6rVYLv2FLeydfoTJ0mMMOU8FTk4qvKZpnVT4k33DrYZVSn4JDY2R3NoFPwNEQYKUaZC8aR+ZAp+eY2+ZLrDjub/giBT4Y4mrZksAZeYKIOUCU2wkli2AisXiHIvLiXDjjTdy/fXXc8UVV/CMZzyD2267jXq9zg033ADAddddx6ZNmzrWm3vvvZeDBw9y6aWXcvDgQd773vcihODtb397Z58vetGL+Mu//Eu2bt3KhRdeyP3338+tt97Ka17zmlUZs0JxLLQTSIWHNBDasQzq4dr0BHMM53BX92DmhDuXFwpb8cp7afhlSpTIkVtzAaRpGpZlEYbhugigvZW9APRn+julBdaKhQKg4XCdqOWmwLcF0KLur6W0wBACmi0LotdDkiQda5yu60oAKTYMyxZA733ve/nzP/9zPvvZz+J5y6gpvwCvfOUrmZyc5D3veQ9jY2NceumlfPOb3+wERu/bt2+ONcf3fd797nfzxBNPkMvleMELXsAXvvAFurq6Out8/OMf56abbuIP//APmZiYYGRkhD/4gz/gPe95zwmNVaFYCrprpoHQkUBGAs1anpfZdg1cU0cE4ZqkwgP0uD2pAPJPXACR6WEgM0AlmGHKmMbTvJMSnGzbNmEYEoYh2UWaea4FtbCWFoAEthe2r/nxgkZLAGUO/1T7UUIiJJoG3jLizMIk7Lg+F60AvZQWGO34H8MGJ0fk+50u8I7jqCaoig3DsgXQK17xCr70pS8xMDDA9u3b55U8X2416De96U2Lury++93vznn/7Gc/m4ceeuiY+8vn89x2223cdtttyxqHQrEaaIaG7pgIP0Y0IwxreRVxO6nw1bUphgipG2x/dT/T/vSJ78zrJm/lyTQmEFrCTDBDPl745rqarFcq/N7qybP+wOEK0EfG/7StP551uC7QUnhs5jFiEadp+07XgussqQVGQ7m/FKcHyxZA119/Pffddx+/+7u/y+DgoKr3oFAchZ5pCaBGjFFYpgByTVzTQEZpKrwfJbjLzCY7HkW7iK7phEnIVHPqxFo42Dk0w6bf7eFQ4jPdjBiOllcRfkWHXYdMsHpUZ7KRtv45GdYfKSVBuwXGEQKoLYwzx+vTdQTj9XEmm5NoaFzQc8GiNaCW1AKjOTcAOooiJYAUG5JlC6B///d/5z/+4z/4pV/6pbUYj0Kx4dEzJpRANJbvCjJtHcPUsQ0dGQlqQbzqAsjQDUayIxyoHeDRmUcpOkUsfYXB0JoGXjddcUA2rlMSCdP1aXawOnGCi9EuiOr7/poe50g6sT9e/6Jd1FeTdgC0poHlzE+Bzy0xAyxMQh6ffRyAbYVtx2yEe9wWGEEVGi3LYTYVzlEUEUURlmUpAaTYUCzbWbtlyxaVJq5QHAPdaxUF9BPkMosZapqG7Rp4tkESCmorDKY+HmcVz8IzPcIkZPfs7hPbWbYXQ9fZIhOQkonGxEkrhiiEOCkxR42owUQj7VG4rbBtzY8Hc91fR1ral9MEVUrJw6WHiURE1sqytbB18XWX0gJj6tH039wg2KkIVC4wxUZl2QLowx/+MG9/+9t58skn12A4CsXGR7P0TgaNaK6sMapnGYggobZGcUCGbnB+z/lA2tKhfXNfEcUtYNj0WzncqEYzbjJaGT3+difAkR3HT4YVqG396fP6jmlBWU0WCoCGwy6w7BJcYAeqB5jxZ9A1nQt7Lzxm+5PjtsBolKA2AWjQd25ncRiGJEmiBJBiw7FsAfS7v/u73HnnnezcuZN8Pk9PT8+cl0KhaLnBANFYmQByLQMRCqprZAECKDpFtuTTiucPTT/EgeqBle1IN6BnB5ZuMiwFSMnu0m6EXNvaW20BdGQfv7VgPaw/sHAAdJQIwjg9r8drg1EJK+yp7AHg7K6zjxu0fdwWGG3rT3ETOIdFYKORVpa2bVs1QVVsKJb9bVXZVQrF8dEzJslssKI4INs18CwdEQoaYYwQclnZPsthR3EHsYgZrY/y+OzjlMMyvW4vBbuwvCynrq1QeoIBO8dYWKcRNDhQPXBMl8uJ4roulUplzS1AT1aeRCLpdXsXTR9fbY4XAO1aBuYxmqBWwyr/NflfCCno9/qXVLBx0RYYQsDUrrT2j6ZD7zlHfCQ65z+TWfusOIViNVmWAIqiiO9973vcdNNNnHXWWWs1JoViwzMnDkjIxWuqLIDlmtimgS4lSSSohzF59wQqNh8DTdM4r+c8PNPjifITTDYm52Q6bS9uX9qOdAN6d2JPzzLYEEyGAXsrexnKDmEba+MWORkWoLZ7UENb+rlYBRYPgG7F/ywSpCykoByUeXD6QWIRk7NynNtz7oLrztu22c4AO+K2EAdw6P7DhQ/7zgXLPfxxqws8KAGk2HgsywVmWRZf/epX12osCsVpg+4YaRyFkJ3U4iVvq2tYrUBoESRr6gZrs7WwlcsGLmNLfgtFJ+3p92TlSfaU9yx9J8WtmG6OnOHQXTqIjJo8OvPoGo34sABqB+GuNo2owWMzjwGp6+tkWX/gWAHQ7QywwyJFSsn+6n7uOXQP3z/wfX42+TNiEVN0ilwycMmSMvykkIczwNoWp7AB++5JxY9uwsjToGfug287A8wwjM71UCg2CsuOAXrJS17C17/+9TUYikJxetGOA0pWEAfkZExcyyQJxJoFQh9N0Smys2snlw1cxs7iTiAN/n1w6kHG6mOEyXFq7ug61uanoZkuvUaBroldBPvuZmrXv8Ps/lUfr2EYnYKIq20FElLw0PRDJDKhy+k6qbE/sHgAdO2oAGg/9vnZ5M/YPbubIEnPga7p9Gf6ubjv4iWXN5B+DEKimTq6bUBYh/0/gqgJVga2PRPy87vetwWQaZqd0gQKxUZh2TFA55xzDu973/v44Q9/yOWXXz6vDP0f/dEfrdrgFIqNjJ6xSCohoh5B3/LaxqSB0DozzfCkCaAj2VLYAhrsnt3NZHOyU0RvMDvItsI2PHPh+ZheFvrOxZh5jGEbxhqjjDUr5OMIB6Bry6qO03VdoijC9/1VbYmxt7KXWlTD1E0u6L3gpBd8DRYIgAZoBAmRCGkmJf5rskTJLyGR6JrOzq6d9Hv9K3I5dtxfNjC9G2b2QBKlqe6bnzHH7XUkzWYTKSWmac7rCqBQnOosWwB95jOfoauri/vuu4/77rtvzmeapikBpFC00LOtOKBmvOw4IKedCl9eu1pAx2NLfgsFu8B0c5qSX6IW1RirjzFeH2cgM8CW/BZ0Te+01TircBa9di+YNnHvefQN5ZgoPUKjNsqB2gF2jDtopgu5/lUbo+M4VKvVVbUA1cIa+yr7ADi3+1wc4+S6dqSQh11gGaszpifLe3lgag+xjLHyBcxWz60up4tzu89deWuOsIEY3w+lWbRGFYJW4L6Th81PB3Px+bczwFzXVT3AFBuOZQugPXuWEROgUJzB6I6BZqUVnUUzxsgu/QnZdk0820AmEr8ZE8QJjrm6FaGXQtEpUnSK7GAH5aDMk5UnmfFnGG+MM94Yn7PurpldnN+d1haSukmS38TO/CA/Gf8JMxOPMlEfZ/DgT9IbqumllYQLm8BeefDsaleEllLySOkRJJI+r+/Em8WugLAVAC01wXQ0yXhlnBl/hnoYp+LH0CnYOXq9XoazwysXPkJA6Qko7UaMaxCDnk/AzkHPDsgPw3FETbPZBDipDWkVitXihIo2SCkBVD8whWIR9IxFUg4Q9WhZAkjTNdyMiWPpiDCh5sc4uZMvgI6k6BS5pP8SKmGFA9UDTDYmO+nhpm4y3hhn18wu7NDGxKRULzFQHOCcrnN4OAk5OPUEuahOFtLsIn8Wph+HbD8MXQzm8l037cDbKIo6xfhWSixiHp99vOP6Oqf7nONvtMpUwyoHJsYZLc8QWg3cmdZvLBo5o4ezctsYKfTw9OHelR1gdj/UWsI1rEPUQCYgjSJkiuhnD0N26fXc2hYglQGm2IisSAD97//9v/nQhz7EY4+lGRLnnnsuf/Inf8KrXvWqVR2cQrHR0bNmKoBWUg/Is3AtnUYgqAcJvSenAPFxKdgFntL7FMKuECEFrul2HobGG+OM++PEYUxprESf38fO4k4GskNMaDoPYvC03qdgRk2ojEJjCuqTsP9HRMOXYrnLa7Njmia2bROGIbVajWKxuKI5lYMyj5QeoRmnFo1zus5ZNdeXlJKDtYPUoho9bg99Xl+nGW2YhCQyoRE1OFQ/RDWs4k9KojA1xHhmhoHMAIOZQQ7NJESNxtJLIkTNNIMr0weGBZOPwMyTc9cxLET+PCCD5hho2a5lzU1ZgBQbmWULoFtvvZWbbrqJN73pTTzrWc8C4K677uL1r389U1NTvO1tb1v1QSoUGxUjaxHRigNKJJqxjDigTFoRuhbEVIO173e1XI4Mtm3XE3JMB1mWNEQDRGrReGDyAQAenXmUWMQ8UTvIuT3n0lccZLhnB+GBe3ls+kFmJx9goPdczuu/GMPrWTTw9mjy+TzT09MrFkCNqMHPJn+GkALbsLmg5wK63e5l72chykGZR2cepR7VgbSukKEZSOSClbJ1TScn8rg5j21bB+nvPTyOWjALQHYJPcAoH4CJh0HEafFCO5s2MgXo2Zm+1zTI9CFKMdBE95Z3OwiCoNPzTQkgxUZk2QLo4x//OJ/85Ce57rrrOst+4zd+gwsvvJD3vve9SgApFEegWQaarSNDgWhGGLmlu3k6PcGqQacB5qmMrunsKO4g62eZtWbJF/NU7Spj9TEkks35zeyZ3cOkP4ksSSq5Ck9qOtIxkVUBImJi8kHqpcc5PzNCLtOPlu1Lg3HtHDgFpKYhpMDQD7u6crkc09PT1Ot1hBDLDsZ9bPYxhBQUnSJP7XvqklLHG1GDIAko2AUM3SARCTPBDI2oQZiE+IlPOSgTiVS4mrpJv9dPyS910tUBLN3C1E1M3aTP62PIHeZQqQJA91FNp9vB8Llj9QALqjD5KNRbvd0MG5KwJX40GLoobWVxBKKZxk/pmeXdDmq1GqBaYCg2Lsv+1o6OjvLMZz5z3vJnPvOZjI6ubQNEhWIjomcskrAVB7QcAeQYuLaBFFCthUgpN0S8XScdWsB5Peexo7ijIwSe0vMUHpx+kCAJqId1MlYGzbDp2/IsBkTC41MPUw8q3Ffdg159Es+wyRoOGd2hLhNmTIvE6yKXH6bb6abX66XgFLAsK22/MX2AnmIPnunNEUlHEouYRCbYus1EY6LTLPS87vPmiJ8oiRAIDM1AQyMUIY2owcHaQUp+CUhFX9bKUo/qi1p0BjID7CjuwDZspJTUohqGZuCa7rzmpH6tJZgsHdM6PP4wFvhRKoJz7UrNQkDUgNhP/21MQ3Us/UzToffsNJg5qKZNTDM96esIpJCHU+CXaQGq11Orluctr8SDQnGqsGwBdPbZZ/OVr3yFP/3TP52z/Mtf/jLnnHPygwYVilMdI2uRzAYklRCzL7NkN5ima+RzNpqeVgb2I4F3nAaYpwJta0C7OrNlWFhGKiy2F7ejaVqnwrShGZzddTbDuWEAigNP5dHSo8w0JhH+LPWwRj32ISyBSCAA6uNUyweo5ofZ5+RxLI+m36Q0U8LxHfJ+WrHZMRw808M1XYQUJCKhETc6cT5hEnKgeoBEJgxmB3lw+kFM3URDSy05YvHCjxoalmERJiHVMHUtuaZL0S5iGza2YVOwC+Tt/ByRo2naMStKB81UADlHWWOqfro8a8RYM09AeX8qfBYiNwh956SWMwC3kL4WQFTDtACipaMt47slhKBUSkVgPn/yKmQrFKvJsgXQn//5n/PKV76S73//+50YoB/+8IfccccdfOUrX1n1ASoUGx09b3fcYPFUA2tw6fESbtbCMXWSIKEaRBtSAB3NtsI2HMPhsZnHSGTCY7OPoWs6g9lBHMPhov6LkFLiJz71qE49qtMI6zhxQG/o49SnmY2qlGoTTJeeINA0kgiYidGrDgYeiWERWB6BnU1T7o9wi5WaJab9afwkFRCu4ZKzcp04nSPR0JCkAd66pmMbNj1uD5tzm8lYGepRnWpYJWflyNknGKXuVwhGD0Elws5moVyFZgkaJcLZOt3lkF5XAEdYXDQDLC+t1mxn0rICywgkj2dTd5zR5SzLulitVgnDEF3XVxx4rlCsN8sWQC972cu49957+chHPtJpiXHBBRfw4x//mMsuu2y1x6dQbHg0XcMayhLuqxKXfIyiM7fh5DFwMhauZVDxkzQOaAM8bLddYHEcL+q2G8oO0eV08UjpEWaDWR4uPUw1rLKjawe6pqNpGp7p4ZkefV7f3I3jgKHSEwyVD5AkITMt99NMlIDU2KRlqUQl4qiGHSWEsoSmGeiazoxfIh832aY7NL0CRn4T23rPJWsXEFIQJSFChGTimGwSYlgZkuwA0kjjdOaQxGTr02Qb0+AWoWAtXDRQyjQbK2qkQsXKgF9OM+CCGogoteYkEcGYBrGGmxNwhH70mw2MJCJreemxuren2V0rKB3QRoRJWqUcMIpLz3iTUlIqlYjjmFwuh22vTbNbhWKtWVHk2uWXX84Xv/jF1R6LQnHaYuRsjIJNUgmJxurY2wpLeuJ2s2kg9GwjotY89TLBFqJtAZJSEsfxoi0SXNPlkv5L2FPZw77KPg7UDjDRnGBLbgvDueH5gqNzAAcGLoCBCzCSiL7YB5Egu6aYnZ3lgfoBDNfAEU0GE4MtXj/NxGesPoqM6tjAgJOj1+rBDCMYffCY8zG0h8HtSoOJkyC1uhhWmmYuWiqlOpYGH1te2kJCJmC66fugki47DkliEJt5sE3sbgmaSI+b6WU0rhJmIrZv74PuFdYAOvp4LeuPnrPS/l9LpF6vE4YhQgiy2axqgaHYsKjQfYXiJGENZknqEaIRE+6vYm/KHzceyLQNsp4Fsz7lcgCr20prTdA0DdM0O13aj3WD1DSNHcUdFOwCj848SpiE7C7v5onyExScAgW7kFqESC1CRaeIax6RHm9Y6QvoHcmxt/ZTxkSMEeYpDGyiYujs13TC2ABvJyYa5/WcS69mpaKlMQ0LBC9jZ1NLS1BNX83SER9Gh+NvrAzkh9L9+OXUytNZrXH4vW6lMTlRPS0CaXlpAUivO/3MsPCbDsR1bNdA33Y4/T2IExqaAAdyhdVJz5dSkpRTAWR2Lc/6Mz09DRxuf6EywBQblSV/c3VdP+4Tq6Zpi/r9FYozHc3SsUdyhAdriFpEuLeMvSWPZh376btYdGC0Sq0aIIREX0ZPsfXiSAG0FPq8PnrcHsbr4+yv7qcRNygHZcpBed66tm7jWR6u4Xbq6bQzu6peFaNqMOAMYNdsxqIxfM/CzboMZAfYXth+uHVEcXPqnpIiDbCGtDaOpsORGWR+JRVBppOmlUuRWnR0Y25WVVBLrUSGlVqJ2tlZptcSOq04JCEWbDHhl9K0cveoiuGV5uEO8KaxOv22RC1CRiLt/r6MzMRSqYTv+0gpO9lfygKk2KgsWQB97WtfW/Sze+65h4997GMIscCTlEKh6GDkbZxtBcL9VYSf4D9RxurPYHQvHoRaKNroOkTNhEaUHLsOzCmCZVn4vk8ULd1tp2s6w7lhhnPDNOMms/4staiGRHbSx2thjVCEhEFImfniyDANzt9xPl7dIwgCLN2i5tc4q/ssNvVumn9QTUvFyiIp88Axs6jm4BwVBG1ngAXaSixSp8hvxeO4ubmCop0Bll9i3NhSiEupBcso2ktu0ttoNJiamgKgt7eXUqmErusn1H5EoVhPlvwX9eIXv3jesl27dvHOd76Tf/3Xf+Xaa6/lfe9736oOTqE4HdE9E+esQmoJasREY3WSSoDZ46Ln5t+Q3JyNaxk0/YSaH20YAQQsSwAdiWd6eLn59WViEVOP6vixj5/46OgYuoGhGRi6gWM4aZp5f1qpuFKpUCqVqJVr1HI1crlTpJ/IUSSJIPRTK5SbOcoC1CqAWPRWx9Ii/DgNftbA7Dl+te0gCGg0Gp2090KhQCaToVQqKeuPYkOzol/SQ4cOcfPNN/P5z3+ea665hgceeICnPvWpqz02heK0RbMM7G0FkpmAaKKRxgU1amiWjtnrzbEI2a6BZxs0goRyNWSoeOoXnmvfGMNw8Vo6K8HUzU6H+uPhOA79/f0IIZidnWV0dJRt27adkllLQb1VM8kxMKy5FqJKK/i9sNQeYMehY/3J28d1v46NjVEuH7a0WZbFwMAA1Wpa+0jF/yg2MstyKJfLZd7xjndw9tln8+CDD3LHHXfwr//6r0r8KBQrQNM0zB4Xd2cXZr+HZurISBCN1QmeKJO0XCKappEvpIGqs7OLFL87xWiLjJVagFaT/v5+XNdFCMHevXsplUqnnLu+XQHazc4VFH6UEMYCTTuiAvQJICNBUklF6fGsP6VSqSN+MpkMfX19bN26FcMwOtdVWYAUG5klC6APfvCD7Nixg3/7t3/jS1/6EnfffTe//Mu/vJZjUyjOCDRLx+rP4JzdhTWURTM0ZJAQ7q0QTTSQQtLdytQpzfgIIdd5xMfnSBfYeosNXdcZGRnBtm2EEExOTvLkk092WjmcCviNtgCaKyhmW8uzjomxCsHv8YwPQqJnTPTM4uKlXq8zOTkJwMDAAFu2bKG3t3dekUtlAVJsZJb87X3nO9+J53mcffbZfP7zn+fzn//8guvdfvvtqzY4heJMQtNTi5BRtInGGySzAfFUE1GP6HVNLAPCesxUPWAgv7RO6euFaZpomtapBbTebifLsti+fTvlcpnp6WmiKOLAgQN0dXXR19e3roG8IhGEjVRQHC2ApmppqnrfMjK1FiMuB8TTaRsQs2e+G1VKSaPRoFKpdFxchUKB7u75qffKAqQ4HViyALruuus2RCNGhWKjoxlpunySs4lGa4hmjN6IGPIFE7Hg0FjtlBdAmqZhWRZhGBJF0boLoPaYurq6yOfzTE2lRRNnZ2cpl8tks1mKxSLZbPak/84FjRhJqwHqEQUJhZBHCKCl1+pZiHi6STSe1iQyuhz0/FzhEgQB4+PjNJvNzjLP8xgcHFxwf20BpCxAio3Mkr+9f//3f7+Gw1AoFEdjFGx0r0hc8klmA3pyNpVDNeoPTuO7DmbBxshaaObq1IZZbdoCKAxDstml9z9bawzDYHBwkFwux+TkJEEQUKvVqNVqGIZBoVCgUCjguidHZHbS34+y/pSbEXEisUx9xRlgUkriiQbxdBo7Zva6mAOZjsgTQjAzM8P09DRSSnRdp1AokM/n8TxvQTHYtuqBsgApNjZKvisUpzCaZWANZjH7M3RbBntLTWI/oTRep7saEpG2MjB7XPSsdUpZaW3bpl6vnxKB0AuRzWbJZrP4vk+lUqFSqZAkCTMzM8zMzOA4DsVikXw+v6aWjmY1DUr2jrLKTLasP71Ze0XXVSaS6FCNpLV/o8+lagb4o2Vs20bXdWZmZjpiJpPJMDQ0dFxRc2RxS2UBUmxk1LdXodgAaLpGdnMO+0CO6ZkmUwb0umZa06UWEdYiNFvHKDip5WgVi+atlBOtBXSycF0X13Xp6+ujXq9TqVSo1+sEQcDExASTk5Nks1m6urpW3ZIVRwlBM63/4+XnugmnqqkAGsgvz/0lE0E8E5CUfGQsQNcQPQZj9YkFyxKYpkl/fz+FwtK6yB8Z/3MqCW6FYrms/6+kQqFYEpZt0N/jMVEJGBUx527J4UhIZlIXmQwF8VSTeKqJnjExez303PrdpNaqFtBaoes6+XyefD5PHMdUq1XK5fIcF5njOHieh2EYndY/SZLgOA6ZTAbbTq017dfxaFZTMeFkTIwjXJn1IKYRJug69GSPHz+V1ELiko8MEmQkkFIShiGBCGlkE8KZ1GpjGAZdXV3EcUwURR1hpy9SnXohVAaY4nRBfYMVig1ET49LZtQgrCU8sH+WK7Z1d1xkohaSVEKSWqvhaqOKZuporoHumWm8kGeeNEF0KtUCWi6madLd3U13dzdBEHSCpYMgIAiCeeu3s6baaJpGT08PPT096LpOFEXEcYzrunPOf6NVk8f25gqQiZb1pytjo2tp3M1C103ECc2DFcLpBlEcE8fpcUIZI/I6Mqun7T5IM7r6+/tPWLioDDDF6YISQArFBiKTt9nWk+HxUp2aH/Pzg2Uu3dyFrmst95eDjATxjE8yk7pAZE0gahHxZBPN0DC6HMweD81a2+Dp9o1WSkkURRv2huk4DoODg/T19VGtVomiiCRJkFJimia6ruP7Po1Go1PzqN01vVqtYhhGJ7vKNE0KhUJqKUJj/OAUjUaTBjplf5JMJstEI+HAjI+UAiHhsccmMAyjk6WWJAlhGNKcrBGMVlM3FyDyBjKng6mBYWEYBplMhmw2Sy6XW7VUf5UBpjhdUN9ghWID4WYtHNtge1eG0VBQqoU8PFbhwpHDrSE0S8cayGD2ecggQTRjRCNC1CNkIomnfeKZACPXyiAzNHQ3LYynGatnHdJ1HcuyiKJoQwugNm330bEQInU/1et1Jibmxtzouk4cx52eWmEjoVIO0U0N03EJwpBHDs1QC9KYoN6sTVb3AI0kjilNTDMTTkIo0ZoCLWoVxLR0zCEPu+BhWRa2beO6bscdt9qoDDDF6cIpkT/7iU98gu3bt+O6LldeeSU//vGPF103iiLe9773sXPnTlzX5ZJLLuGb3/zmnHW2b98+xw/ffr3xjW9c66koFGuKpmtkCjYZx2R7zkXTYHTWZ/dkbcF1dc/E7HGxN+dxzu3G3ppHz5ggJEkljRuJJ5uE+6v4j5bwd88S7K0QHqwRl3yEHyNPoPL0RgmEXi3a3dELhQJnnXUWfX199Pf3s2PHDnbu3MnIyAj5fJ5MJoOWWLiex8iWAXbs2EHNKBIbGTzX48JckfNklj4/wybZQ38zT7akY81KHF/HMx0K3UWGLtzKjl9+CtvPT/fd399PsVjEcZw1c3UqF5jidGHdLUBf/vKXufHGG/nUpz7FlVdeyW233cY111zDrl27GBgYmLf+u9/9br74xS/yd3/3d5x//vn8x3/8By996Uu5++67ueyyywD4z//8T5Ik6Wzzi1/8gl//9V/n5S9/+Umbl0KxVmS7HOrlECeUnDuUY9dYjT2TdRxTZ3N3ZtHtNE3DyNkYOTu1CDVjZCKRsUA0ImQo0iDalgUiKR8R66JraKaO2ediFJd+c91ogdArYbH4HMMw6O3tnbOsHWQNQL1E1hb0j+TZOxNQiXS6nQIX2g4Z44hnUwGe4+J5Hrqto7kmesbEyNnrUgNKucAUpwualHJdGwtdeeWVPP3pT+dv/uZvgNSEvGXLFt785jfzzne+c976IyMj/Nmf/dkca87LXvYyPM/ji1/84oLHeOtb38q//du/8dhjjy3ph7tSqVAsFimXy0tODVUoThZSSPY9XEIkkuEdRQ40Ap6cSvtaDRVdzh3MY6/gxijCBBkmqSgKW66zZgzJ3J8IzTHQrLRxq2ZoGHkbo2CnwbZSgq53XGmlUonJyUny+TwjIyMnPvlTjHg2IB6vY/Z6mH3z20ssRmOqyfiD02hCovW77JtpYPoJ27o9ujM2mq1jDWTRTA0pSYPZLR1tFfqBnQhRFPHEE08AcM455ywre0yhOBks5/69rhI+DEPuu+8+3vWud3WW6brO1VdfzT333LPgNkEQzKvQ6nked91116LH+OIXv8iNN964qPg5OrOjUqksdyoKxUlD0zWyRZtqKaA2G3D25hwgeXKqwVjZp1QPOW8oz2BheZWMddsAe36grEwkiLSLeDzVnGMlkoBoxJ02C+kAQc9Y6K4B1YhoskFjNiGIcqkVqdc9JeoUnShJPSIarYGEaKIBrV5uMpGpha19nqTsZGLJRCBDQXW0jlYPqUpB/WCIBQwUHHqKbhqk3uutu9hZiCPdX0r8KDY66/orNDU1RZIk8/rNDA4O8sgjjyy4zTXXXMOtt97Kr/zKr7Bz507uuOMObr/99jkuryP5+te/zuzsLK9+9asXHcctt9zCn//5n694HgrFySZbdKiWAhrlADmS5eyBPP05l4dGK9SDmP86UGYs73PeUB7XOrHsH83QwDAwez2MLoekEoKWWiVkJEjKAaLVzBNdAyER9TToWo8E0k8IAh/RavmQVAKMooNuG51CfUbRQXcWH6cU8pQSBCJMiA5UQYJmG8gwIRqrI+oRST2CReKmhJRM1wLGx6rUNBCDHqats7U7w9ZNBfTMyStTsBJU/I/idGLDPYZ99KMf5XWvex3nn38+mqaxc+dObrjhBj772c8uuP5nPvMZnv/85x/T/P6ud72LG2+8sfO+UqmwZcuWVR+7QrFauDkLw9RIYkmzFpEp2BQzFlee1cOe6Tp7p+tMVgNK9ZDtfVm29mQwVkFAaIaO2T3XsmR2u2mgtJbGGYkwSTPOggRddzGSaTQNjCEPGklaq2g24MhHlnbxRs3UO640KSQImcYmxQLNNjAKNkbeTt1Bpo6UEgTpeu31W/+ia531jhZPMhZpwcA4deNplo7wk1TMNWM020gtWIAIEhAytWplzFTkzAbIRKJnTOytBaLxRlqQstV2QrPT2kuaY6THlpJmlPDIRI2ZIKbpGGDr5PtcLthUZGCZ1rr1oh3LdSo0t1UoTpR1FUB9fX0YhsH4+Pic5ePj4wwNDS24TX9/P1//+tfxfZ/p6WlGRkZ45zvfyY4dO+atu3fvXr797W9z++23H3McjuPgOCfWbVmhOJlomka26FCZ9pkdr+PmLHRdQ9c1dvbnGCy4PDxaodyI2D1R4+BMk/OH8yfcVXzR8RwhMHTbSN1pLZyql1ZM9jS8njyiERHPpC5nzdKRQUJSCw9bkRZBhkmn0nV6IG1RS8u88bViaNpB38fbTkaiY7Fqc/T4NMfA3pxH0zWsoUxqKZMSo+Cge3N/WieqPg9ONUhMDSFgsMejfzjL5m0FHHN16vOcDJQFSHE6sa4CyLZtLr/8cu644w5e8pKXAGkQ9B133MGb3vSmY27rui6bNm0iiiK++tWv8opXvGLeOp/73OcYGBjghS984VoMX6FYV4r9HvXZgKCZMLW/Sv/WfMd9knNMnr69h7Gyz+MTNfwo4YF9swwVXc4ZzJ3Um65lWZ3WC57noWcs7MzcG6iMxGHXUTsvw9DQdB3N1tEMHdGISCphmr12tIjRWiKs9dIMLRU7UbqejEWnYGBnEyvdrxSiFdCtYxRt9JyNjBKkn6T7tQ3QQNTTekq6a2J0O3Oaz2qahjUwPwNPCMnuyRp7p9MYqYJl0FX0sAydzZsLWBtI/MBhC5ASQIrTgXV3gd14441cf/31XHHFFTzjGc/gtttuo16vc8MNNwBw3XXXsWnTJm655RYA7r33Xg4ePMill17KwYMHee9734sQgre//e1z9iuE4HOf+xzXX3+9StdUnJaYtsHA9gJjT5Spl0PMsQY9w3ObdQ4VXfrzDrsna+ybToOkJ6o+fTmHkS5vxZ3Gl4NlWTSbzWPWAtIsHbPr2NYpo+hgFNN1pJCQiNRdpmvHjA9qix8ZiUXdYvNT2Re4wXcdc3jzKDcjHhuvMttI572l6JKtJkRGWtDSOkbM06lK+xoqF5jidGDdlcErX/lKJicnec973sPY2BiXXnop3/zmNzuB0fv27ZuTbeD7Pu9+97t54oknyOVyvOAFL+ALX/jCvAqt3/72t9m3bx+vec1rTuZ0FIqTipu16NucY3J/jfJkEyklPcPZOTdzQ9c4dzDPYN5l13iVSjNiohIwUQmwTJ3BgsPm7gw5Z21+Dto3y9WsBZRae5YmIDQzFTwcI8xmtURgIiSlesih2SaTrX5ehqFxfn8OMRkQhgLD1OjdtLpd5U8GcRx3Wn0oC5DidGDd6wCdiqg6QIqNRnmySWk0rQWULdr0bs5hGAunKVf9iIOzTcYrAdERbqGhosu23gx5d3VvbpVKhdHRUTzPY+vWrau671MBKSWTtYBDsz6lekBLI6Bp0Gtb9OkGcS1CSjBNncEdBewNWAag2Wyyb98+TNNk586d6z0chWJBNkwdIIVCsToU+z0MU2fqQJV6OaRRKZHrdin0ufNutnnX4vwhi3MHJDONkAMzqbVirOwzVvbJuSaDBZesY5CxTbK2cUIWktO1HYYfJRyabXJwtkkQHRaSnm3Q7ZnkA4hrMRHpZ7ZrMLCtsCFdX6AywBSnH0oAKRSnCbluB9PSmT5UI/QTqiWfasknW7TpGszME0K6rtGbc+jNOVT9iD1TdaZqATU/puYf7i3mWgbb+zKMFD30FaTSt2+YbRfKRiygFyeC0bLPWMXvWM2aUdKJ17ZMnU1dLkNFDyMUTO2vEbfWyxZtCn0ebnZju41UBpjidEMJIIXiNMLNWWw6txu/FlGeatKohNTL6WsxIQSpVejizV1EiWCiGjBdC2iGCY0owY8SHhmt8sRkne6MTdGzyLsmOdfEWsTNdiSGYaDrOkIIwjCcV8n9VENKSbkZMVFNz0EsBFU/Jk7mRwt0Zy02dWUYyDvoukZ5ssHUaAMJWLZO3+Y8bu70EAwqA0xxuqEEkEJxGuLmLNycRdiMmZ1odERQvRziZkzcnIWXt+dZJSxDZ1OXx6autK+VEJKDs02enK4TRILxis94xe+s79kG3RmbvpxNb85ZtNiibdv4vk8URaecAKoHMXum6tSCtM5PlIg5Lq02GdtgS0+GfEtAOqaBZxtIKfFrEZVpn0YlFQn5HoeekdyKLGanKioDTHG6oQSQQnEaY3smA9sKc4SQ34jxGzGzE00cz6TQ55Ep2gverHVdY0tPhk1dHuVmxGwzotyMqPkxfpTQDBOaYZNDs00MQ2Mg79CbdbBNHcvQsE0d29CxLKsjgE4WUkomqwGzzQjH1HEtgzAW1MPUmmMZOrEQjJV9jk4Fac+l6FlYho5j6hS9w3V/kkTQrERMjjVoVkOSlnVIA3pGshSW0Rh1o6BcYIrTDSWAFIozgLYQisOEZi2iWQ1pVEKCZszk/iraAXAyFtmiTa7HnSeGdF2jO2vTnT389B8ngtlmRKkeMtlyF43O+ozO+kcfnqBeJapXOFDX6KqmaeeGpqFrGq6lM1BwKXoWUkqCWBALiZQSXdNwTB2z5WqLEkGcyHZvUWYb6fH9OEGD1v4MbFNnvOJT849dXbpNX95hU5fXKiuk0eVZnXNQLfk0Sj7VfIKXt6nPBJQnm4gjCjEahka2yyHX4+J4p9/PapIknX6LygKkOF04/f5SFQrFopi2Qb7HIN/jkkSCynSTWikgjgV+PcKvR8yONyj0e+S7XQxr8Rgf09Dpyzn05RzOHcwz2wgZLfs0wpgwloSJOJxmr5kEsUDUm0h3vhVo73QD20wtMmK+9wnT0BBSLvjZsTAMjaGCSyIkfpRgGTpZx8Q2dMJEkAjJQN6ZI+yOpDzZoDSaVnFO3Vv1zmeWY5Ap2Hh5C/eIqtCnI+34H9M0N2QQu0KxEEoAKRRnKIal0z2UpXsoSxQkNKsh5akmcSiYGWswO9ZIY4myFoalY9kGpq1jWPqCN/uujE1XZq6QkFISJZJyLcO+vT6aYbBpczEVMzKNMSo3IyarAWFLLGlaKq400u7pcSLnBCAbeiqGpISca9Kbtck6JpJ0f0Gc4EeiE7OzlEDtOWMWkihIqM2mlh5IM7niMCFoJlh2et4yxbWvon2qoNxfitMRJYAUCgWWY2A5Hvlel/psQGXaJ2jEqbusNtdiowFOxiRTtPFyNqZjoOsaUkqSWKAbesd9pGkatqnRlXUptYKH+3P2HCvCFtIKylU/wjZ1PGtu3aE4EfixwGi5w9r7nt++YvkksSD0Y8Jm0vo3JgqSOTFB3YMZugbTPl8iEWi6dsYInzZBkFa1Vu4vxemEEkAKhaKDpmnkul1y3S5RmNAoh0RBTBwK4jAhjgRS0gmkhtQ9ZBgaIpFIUgtOpmCTLTo4GRPTNrCs1EUkpSSO43k3UkPX5lmP2piGTm4BK85SRYgUkjgSLQvOYZETRwKxQGo7gG5o2K5Jrtsh3+MesfzMdP+0XWCOc+x+bQrFRkIJIIVCsSCWbVDsn5vNJGUqJpqVw0HUIpGdLKh0HTop95CKCccz8SsCqcXUqg26eyySSBA0YyI/IY4SkljiZi2yXakQalYj4jANPHYy5hzBI4RMt2/EBI2IpOM+09CNtDlqEonDFp1jzNO0dWzXxPFMLNfA8VLRpjiMsgApTkeUAFIoFEtG0zQs28Dq8zqp3kkiSEKBbmoYpk7oJ9RnA5rVkMhPEImkWYsI64KG34RgikouXlCUNCphp6dZm9mJJqapY1gaSZy62ZbbwVDTUkFnuQa2Z2K7JqatY9rGaVWrZy0QQnRigJQFSHE6oQSQQqE4IQxDx/AOu4YcL7WmMJxFCkkYJITNGGE2iSZ8ojhMXWWQChLXTPtjadCshC3XGi1LjJ5agmJBfFRGu6aRWm6yJlbLYiOlRCQSISSGoadixzMwLWXRWSlt649hGJimumUoTh/Ut1mhUKwZmq51BJHu9BAbDSzLYvPmbkxTRzvK+tI1kCGJBOh0utlLIfHraTd1w9TQTR3D0M7YeJyTjWqCqjhdUQJIoVCcFNrukyiKMExtnvhpc3TtIU3X8PLq5rtetC1Ayv2lON1Qj1AKheKkYJomhpG6oto3VcWpj8oAU5yuKAGkUChOGu2bqBJAGweVAaY4XVECSKFQnDSUANpYJElC3Io+VxYgxemGEkAKheKkoQTQxuLIHmBt96VCcbqgBJBCoThpKAG0sVDuL8XpjBJACoXipGHbaQNRIUTHuqA4dVEB0IrTGSWAFArFSUPX9Y41QVmBTn183weUBUhxeqIEkEKhOKkoN9jGII5jms0mAJlMZp1Ho1CsPkoAKRSKk0pbADUajXUeieJY1Go1IL1eygKkOB1RAkihUJxU8vk8AM1ms2NhUJx6VKtV4PD1UihON5QAUigUJxXLsigUCgCUSqV1Ho1iIZIk6VjolABSnK4oAaRQKE46PT09QOpmUdlgpx5t95dt28r9pThtUQJIoVCcdBzHIZvNAsoKdCqi3F+KMwElgBQKxbrQtgJVKhUVC3QKEcexcn8pzgiUAFIoFOtCJpMhm80ipeTgwYPKFXaKMD4+jpQSx3FUAUTFaY0SQAqFYt0YHh7GcRySJOHAgQOdxpuK9WF2dpZarYamaQwNDa33cBSKNUUJIIVCsW4YhsGmTZswTZMoiti3b5+yBK0TYRgyMTEBQG9vL67rrvOIFIq1RQkghUKxrliWxZYtW+aIIBUTdHLxfZ99+/YhpcTzvE58lkJxOqMEkEKhWHds22br1q0dd9j+/fuZnZ1d72GdEVSrVfbt20eSJDiOw/DwMJqmrfewFIo1RwkghUJxStC2BOVyOaSUjI+Pc+jQIYQQ6z2005ZSqcShQ4eQUpLJZNiyZQuWZa33sBSKk4ISQAqF4pShHRPU398PpNYJJYJWn7bAnJycBKBYLLJp0yYMw1jnkSkUJw8lgBQKxSlHT08PmzdvRtM06vU64+Pj6z2k04YkSTh48GDHxdjf38/Q0BC6rm4HijOLdf/Gf+ITn2D79u24rsuVV17Jj3/840XXjaKI973vfezcuRPXdbnkkkv45je/OW+9gwcP8ru/+7v09vbieR4XXXQRP/nJT9ZyGgqFYpXJZrMMDw8DabHEQ4cOqQyxEySKIvbv30+9XkfTNEZGRlTAs+KMZV0F0Je//GVuvPFGbr75Zn76059yySWXcM0113RSMY/m3e9+N5/+9Kf5+Mc/zkMPPcTrX/96XvrSl3L//fd31pmZmeFZz3oWlmXxjW98g4ceeogPf/jDdHd3n6xpKRSKVSKfzzM4OAik7rA9e/Zw6NAhfN9f55FtPOr1Onv37iUIAgzDYMuWLarSs+KMRpNSyvU6+JVXXsnTn/50/uZv/gYAIQRbtmzhzW9+M+985zvnrT8yMsKf/dmf8cY3vrGz7GUvexme5/HFL34RgHe+85388Ic/5Ac/+MGSxxEEAUEQdN5XKhW2bNlCuVzudK1WKBTrR7PZZHp6mnq93lmWyWQYGBhQ1YqPgxCCUqnE9PQ0kPZhGxkZUU1OFacllUqFYrG4pPv3ulmAwjDkvvvu4+qrrz48GF3n6quv5p577llwmyAI5hXn8jyPu+66q/P+//7f/8sVV1zBy1/+cgYGBrjsssv4u7/7u2OO5ZZbbqFYLHZeW7ZsOYGZKRSK1cbzPDZv3sy2bds6VotGo8G+ffs6ncvPdIQQTE9P89hjj/H4448zOTlJpVLhySef7IifYrHI1q1blfhRKFhHATQ1NUWSJB3zdpvBwUHGxsYW3Oaaa67h1ltv5bHHHkMIwbe+9S1uv/12RkdHO+s88cQTfPKTn+Scc87hP/7jP3jDG97AH/3RH/H5z39+0bG8613volwud1779+9fnUkqFIpVxXVdRkZGOOuss8hkMgghOHjwIBMTE9RqNaIoWu8hrgvNZpM9e/YwNTWFEIIkSSiVSoyOjhJFEYZhMDw8rIKdFYojMNd7AMvhox/9KK973es4//zz0TSNnTt3csMNN/DZz362s44QgiuuuIL3v//9AFx22WX84he/4FOf+hTXX3/9gvtVTf8Uio2Fbdts2rSJiYkJyuUyMzMzzMzMAGnwdDsB4kwgiiIOHjxIkiSYpklfXx+6rjMzM0MURRQKBXp6elSKu0JxFOv2KNDX14dhGPPSW8fHxxdtwtff38/Xv/71TjDfI488Qi6XY8eOHZ11hoeHecpTnjJnuwsuuIB9+/at/iQUCsW6oes6Q0NDDA8Pk8/nOw8x9Xqdffv2ceDAgdM+WLptAWtXcd6+fTvFYpF8Ps/WrVvZuXMn/f39SvwoFAuwbgLItm0uv/xy7rjjjs4yIQR33HEHV1111TG3dV2XTZs2EccxX/3qV3nxi1/c+exZz3oWu3btmrP+o48+yrZt21Z3AgqF4pSgUCgwMjLC9u3bOeusszqBj+0HpQMHDlCtVk+7YorNZpNDhw4RBAG6rjMyMqKEjkKxDNbVBXbjjTdy/fXXc8UVV/CMZzyD2267jXq9zg033ADAddddx6ZNm7jlllsAuPfeezl48CCXXnopBw8e5L3vfS9CCN7+9rd39vm2t72NZz7zmbz//e/nFa94BT/+8Y/527/9W/72b/92XeaoUChOHrZtMzw8TG9vL9PT01QqFer1OvV6HV3XyefzFAoFPM+b1+9KCEG9Xsf3fbq6uk6ZlhBJkhDHMUIIoiii2WzSaDTm1ERSWV0KxfJZVwH0yle+ksnJSd7znvcwNjbGpZdeyje/+c1OYPS+ffvmBOz5vs+73/1unnjiCXK5HC94wQv4whe+QFdXV2edpz/96Xzta1/jXe96F+973/s466yzuO2227j22mtP9vQUCsU6caQQmp2dpVqtEsdxJ9HBMAxM08Q0TTRNQ0pJs9nsWIkqlQqbNm2al3V6MomiiFKpRLlcZqFqJZqmkc/n6erqOmPinRSK1WRd6wCdqiynjoBCoTj1kVLSaDSoVCrUarVF3WFtQRRFEbqu09/fTzabPa41qL1/XddXLEaklARB0LHw1Ov1jvDRdR1d1zFNE9d18TyPbDarXF4KxVEs5/69obLAFAqFYiVomkY2myWbzSKEIAxD4jgmjuPOOpZlzUmtbzabnSQN0zTJZDJ4noeUkiiKkFJ2BEilUumk4GcyGXp6ekiSpFNgtW3Jbh9TStl5tS1QQRDME2au69Lf308mk1nzc6RQnGkoAaRQKM4odF0/pmvLMAw2b95MqVTqxATFcUylUqFSqRxzv21LUKPRWPHYPM/rvJTwUSjWDiWAFAqF4ih0Xaevr4++vj6SJMH3fZrNJs1mE13XsSwLTdNIkgQhBNlslnw+T5IkTE1NUa/XsSwLx3HQdR0hBFLKTtyRruudIOy2m6tdj+zo4GyFQrE2KAGkUCgUx8AwjI777Hjout7pYK9QKE5tVE10hUKhUCgUZxxKACkUCoVCoTjjUAJIoVAoFArFGYcSQAqFQqFQKM44lABSKBQKhUJxxqEEkEKhUCgUijMOJYAUCoVCoVCccSgBpFAoFAqF4oxDCSCFQqFQKBRnHEoAKRQKhUKhOONQAkihUCgUCsUZhxJACoVCoVAozjiUAFIoFAqFQnHGoQSQQqFQKBSKMw5zvQdwKiKlBKBSqazzSBQKhUKhUCyV9n27fR8/FkoALUC1WgVgy5Yt6zwShUKhUCgUy6VarVIsFo+5jiaXIpPOMIQQHDp0iHw+j6Zpq7rvSqXCli1b2L9/P4VCYVX3fSpyps0X1JzPhDmfafOFM2/OZ9p84fSYs5SSarXKyMgIun7sKB9lAVoAXdfZvHnzmh6jUChs2C/YSjjT5gtqzmcCZ9p84cyb85k2X9j4cz6e5aeNCoJWKBQKhUJxxqEEkEKhUCgUijMOJYBOMo7jcPPNN+M4znoP5aRwps0X1JzPBM60+cKZN+czbb5w5s1ZBUErFAqFQqE441AWIIVCoVAoFGccSgApFAqFQqE441ACSKFQKBQKxRmHEkAKhUKhUCjOOJQAOol84hOfYPv27biuy5VXXsmPf/zj9R7SqnHLLbfw9Kc/nXw+z8DAAC95yUvYtWvXnHWe85znoGnanNfrX//6dRrxifHe97533lzOP//8zue+7/PGN76R3t5ecrkcL3vZyxgfH1/HEZ8427dvnzdnTdN44xvfCGz86/v973+fF73oRYyMjKBpGl//+tfnfC6l5D3veQ/Dw8N4nsfVV1/NY489NmedUqnEtddeS6FQoKuri9e+9rXUarWTOIvlcaw5R1HEO97xDi666CKy2SwjIyNcd911HDp0aM4+FvpefOADHzjJM1k6x7vOr371q+fN53nPe96cdTbSdT7efBf6m9Y0jQ996EOddTbaNV4qSgCdJL785S9z4403cvPNN/PTn/6USy65hGuuuYaJiYn1Htqq8L3vfY83vvGN/OhHP+Jb3/oWURTx3Oc+l3q9Pme9173udYyOjnZeH/zgB9dpxCfOhRdeOGcud911V+ezt73tbfzrv/4r//RP/8T3vvc9Dh06xG/+5m+u42hPnP/8z/+cM99vfetbALz85S/vrLORr2+9XueSSy7hE5/4xIKff/CDH+RjH/sYn/rUp7j33nvJZrNcc801+L7fWefaa6/lwQcf5Fvf+hb/9m//xve//31+//d//2RNYdkca86NRoOf/vSn3HTTTfz0pz/l9ttvZ9euXfzGb/zGvHXf9773zbnub37zm0/G8FfE8a4zwPOe97w58/nSl7405/ONdJ2PN98j5zk6OspnP/tZNE3jZS972Zz1NtI1XjJScVJ4xjOeId/4xjd23idJIkdGRuQtt9yyjqNaOyYmJiQgv/e973WWPfvZz5Zvectb1m9Qq8jNN98sL7nkkgU/m52dlZZlyX/6p3/qLHv44YclIO+5556TNMK15y1veYvcuXOnFEJIKU+v6wvIr33ta533Qgg5NDQkP/ShD3WWzc7OSsdx5Je+9CUppZQPPfSQBOR//ud/dtb5xje+ITVNkwcPHjxpY18pR895IX784x9LQO7du7ezbNu2bfIjH/nI2g5ujVhoztdff7188YtfvOg2G/k6L+Uav/jFL5a/+qu/OmfZRr7Gx0JZgE4CYRhy3333cfXVV3eW6brO1VdfzT333LOOI1s7yuUyAD09PXOW/8M//AN9fX089alP5V3veheNRmM9hrcqPPbYY4yMjLBjxw6uvfZa9u3bB8B9991HFEVzrvf555/P1q1bT5vrHYYhX/ziF3nNa14zp2Hw6XR9j2TPnj2MjY3NuabFYpErr7yyc03vueceurq6uOKKKzrrXH311ei6zr333nvSx7wWlMtlNE2jq6trzvIPfOAD9Pb2ctlll/GhD32IOI7XZ4CrxHe/+10GBgY477zzeMMb3sD09HTns9P5Oo+Pj/Pv//7vvPa1r5332el2jUE1Qz0pTE1NkSQJg4ODc5YPDg7yyCOPrNOo1g4hBG9961t51rOexVOf+tTO8t/5nd9h27ZtjIyM8POf/5x3vOMd7Nq1i9tvv30dR7syrrzySv7+7/+e8847j9HRUf78z/+cX/7lX+YXv/gFY2Nj2LY97yYxODjI2NjY+gx4lfn617/O7Owsr371qzvLTqfrezTt67bQ33D7s7GxMQYGBuZ8bpomPT09p8V1932fd7zjHfz2b//2nEaZf/RHf8TTnvY0enp6uPvuu3nXu97F6Ogot9566zqOduU873nP4zd/8zc566yz2L17N3/6p3/K85//fO655x4Mwzitr/PnP/958vn8PHf96XaN2ygBpFh13vjGN/KLX/xiTkwMMMdHftFFFzE8PMyv/dqvsXv3bnbu3Hmyh3lCPP/5z+/8/+KLL+bKK69k27ZtfOUrX8HzvHUc2cnhM5/5DM9//vMZGRnpLDudrq9iLlEU8YpXvAIpJZ/85CfnfHbjjTd2/n/xxRdj2zZ/8Ad/wC233LIhWyr81m/9Vuf/F110ERdffDE7d+7ku9/9Lr/2a7+2jiNbez772c9y7bXX4rrunOWn2zVuo1xgJ4G+vj4Mw5iXBTQ+Ps7Q0NA6jWpteNOb3sS//du/ceedd7J58+ZjrnvllVcC8Pjjj5+Moa0pXV1dnHvuuTz++OMMDQ0RhiGzs7Nz1jldrvfevXv59re/ze/93u8dc73T6fq2r9ux/oaHhobmJTXEcUypVNrQ170tfvbu3cu3vvWtOdafhbjyyiuJ45gnn3zy5AxwjdmxYwd9fX2d7/Hpep1/8IMfsGvXruP+XcPpc42VADoJ2LbN5Zdfzh133NFZJoTgjjvu4KqrrlrHka0eUkre9KY38bWvfY3vfOc7nHXWWcfd5oEHHgBgeHh4jUe39tRqNXbv3s3w8DCXX345lmXNud67du1i3759p8X1/tznPsfAwAAvfOELj7ne6XR9zzrrLIaGhuZc00qlwr333tu5pldddRWzs7Pcd999nXW+853vIIToiMGNRlv8PPbYY3z729+mt7f3uNs88MAD6Lo+z020UTlw4ADT09Od7/HpeJ0htepefvnlXHLJJcdd97S5xusdhX2m8H/+z/+RjuPIv//7v5cPPfSQ/P3f/33Z1dUlx8bG1ntoq8Ib3vAGWSwW5Xe/+105OjraeTUaDSmllI8//rh83/veJ3/yk5/IPXv2yH/5l3+RO3bskL/yK7+yziNfGX/8x38sv/vd78o9e/bIH/7wh/Lqq6+WfX19cmJiQkop5etf/3q5detW+Z3vfEf+5Cc/kVdddZW86qqr1nnUJ06SJHLr1q3yHe94x5zlp8P1rVar8v7775f333+/BOStt94q77///k7G0wc+8AHZ1dUl/+Vf/kX+/Oc/ly9+8YvlWWedJZvNZmcfz3ve8+Rll10m7733XnnXXXfJc845R/72b//2ek3puBxrzmEYyt/4jd+Qmzdvlg888MCcv+sgCKSUUt59993yIx/5iHzggQfk7t275Re/+EXZ398vr7vuunWe2eIca87ValX+f//f/yfvueceuWfPHvntb39bPu1pT5PnnHOO9H2/s4+NdJ2P972WUspyuSwzmYz85Cc/OW/7jXiNl4oSQCeRj3/843Lr1q3Stm35jGc8Q/7oRz9a7yGtGsCCr8997nNSSin37dsnf+VXfkX29PRIx3Hk2WefLf/kT/5Elsvl9R34CnnlK18ph4eHpW3bctOmTfKVr3ylfPzxxzufN5tN+Yd/+Ieyu7tbZjIZ+dKXvlSOjo6u44hXh//4j/+QgNy1a9ec5afD9b3zzjsX/A5ff/31Uso0Ff6mm26Sg4OD0nEc+Wu/9mvzzsP09LT87d/+bZnL5WShUJA33HCDrFar6zCbpXGsOe/Zs2fRv+s777xTSinlfffdJ6+88kpZLBal67ryggsukO9///vniIVTjWPNudFoyOc+97myv79fWpYlt23bJl/3utfNe1DdSNf5eN9rKaX89Kc/LT3Pk7Ozs/O234jXeKloUkq5piYmhUKhUCgUilMMFQOkUCgUCoXijEMJIIVCoVAoFGccSgApFAqFQqE44yfHquwAAAlkSURBVFACSKFQKBQKxRmHEkAKhUKhUCjOOJQAUigUCoVCccahBJBCoVAoFIozDiWAFAqFQqFQnHEoAaRQKObw6le/mpe85CXrdvxXvepVvP/97++8bzQavOxlL6NQKKBp2rwms6ciTz75JJqmdfqhrZT3vve9XHrppasyJoCpqSkGBgY4cODAqu1TodiomOs9AIVCcfLQNO2Yn99888189KMfZb0KxP/sZz/j//2//8cnP/nJzrLPf/7z/OAHP+Duu++mr6+PYrG4LmNbDlu2bGF0dJS+vr71Hsoc+vr6uO6667j55pv5zGc+s97DUSjWFSWAFIoziNHR0c7/v/zlL/Oe97yHXbt2dZblcjlyudx6DA2Aj3/847z85S+fM4bdu3dzwQUX8NSnPnXR7cIwxLbtkzHEJWEYBkNDQ+s9jAW54YYbuPzyy/nQhz5ET0/Peg9HoVg3lAtMoTiDGBoa6ryKxSKaps1Zlsvl5rnAnvOc5/DmN7+Zt771rXR3dzM4OMjf/d3fUa/XueGGG8jn85x99tl84xvfmHOsX/ziFzz/+c8nl8sxODjIq171KqamphYdW5Ik/PM//zMvetGL5hz7wx/+MN///vfRNI3nPOc5AGzfvp2/+Iu/4LrrrqNQKPD7v//7ALzjHe/g3HPPJZPJsGPHDm666SaiKOrsr+1S+uxnP8vWrVvJ5XL84R/+IUmS8MEPfpChoSEGBgb4y7/8yzljm52d5fd+7/fo7++nUCjwq7/6q/zsZz9bdC5Hu8C++93vomkad9xxB1dccQWZTIZnPvOZc8QnwAc+8AEGBwfJ5/O89rWvxff9efv+X//rf3HBBRfgui7nn38+//N//s/OZ695zWu4+OKLCYIASIXhZZddxnXXXddZ58ILL2RkZISvfe1ri45foTgTUAJIoVAcl89//vP09fXx4x//mDe/+c284Q1v4OUvfznPfOYz+elPf8pzn/tcXvWqV9FoNIBUMPzqr/4ql112GT/5yU/45je/yfj4OK94xSsWPcbPf/5zyuUyV1xxRWfZ7bffzute9zquuuoqRkdHuf322zuf/fVf/zWXXHIJ999/PzfddBMA+Xyev//7v+ehhx7iox/9KH/3d3/HRz7ykTnH2b17N9/4xjf45je/yZe+9CU+85nP8MIXvpADBw7wve99j7/6q7/i3e9+N/fee29nm5e//OVMTEzwjW98g/vuu4+nPe1p/Nqv/RqlUmlZ5/HP/uzP+PCHP8xPfvITTNPkNa95Teezr3zlK7z3ve/l/e9/Pz/5yU8YHh6eI24A/uEf/oH3vOc9/OVf/iUPP/ww73//+7npppv4/Oc/D8DHPvYx6vU673znOzvHm52d5W/+5m/m7OcZz3gGP/jBD5Y1doXitGOdu9ErFIp14nOf+5wsFovzll9//fXyxS9+cef9s5/9bPlLv/RLnfdxHMtsNitf9apXdZaNjo5KQN5zzz1SSin/4i/+Qj73uc+ds9/9+/dLQO7atWvB8Xzta1+ThmFIIcSc5W95y1vks5/97DnLtm3bJl/ykpccd44f+tCH5OWXX955f/PNN8tMJiMrlUpn2TXXXCO3b98ukyTpLDvvvPPkLbfcIqWU8gc/+IEsFArS9/05+965c6f89Kc/veBx9+zZIwF5//33SymlvPPOOyUgv/3tb3fW+fd//3cJyGazKaWU8qqrrpJ/+Id/OGc/V155pbzkkkvmHPMf//Ef56zzF3/xF/Kqq67qvL/77rulZVnypptukqZpyh/84Afzxve2t71NPuc5z1lw7ArFmYKKAVIoFMfl4osv7vzfMAx6e3u56KKLOssGBwcBmJiYANJg5jvvvHPBeKLdu3dz7rnnzlvebDZxHOe4gdptjrQUtfnyl7/Mxz72MXbv3k2tViOOYwqFwpx1tm/fTj6fnzN2wzDQdX3OsiPnUqvV6O3tnTfe3bt3L2msbY48j8PDw0B6zrZu3crDDz/M61//+jnrX3XVVdx5550A1P//du4fJLU+jAP4V7RBGnJIIoeyoaJBCBtK+kcQOhXRUmZBkgVRZEHUIBXSEBE5uCadCAIbGlqCmgrSioqG/hCSkk0N4eS1hvS+k4f32O3tVoLvvef7mc75nZ/PeY6DPvx4zu/HD4TDYQwMDGBwcFCc8/r6KmkMN5lMmJycxPz8PKanp9HQ0PAmD7VaLa7WEckVCyAi+lBeXp7kXKFQSMbSRUsqlQIAxONxtLW1YXFx8U2s9B9/psLCQiQSid9uaM7Pz5ecHx0dwWazwe12w2KxoKCgAH6/H8vLy596lvTYv5+luLgY+/v7b3LQaDQf5vnevTO/s4/E43EAwMrKCmprayXXlEqleJxKpRAIBKBUKnF3d/fLWLFYDFqt9lO5E/1tWAARUdYZjUZsbW1Br9dDpfq9n5n0fjc3Nzdf2vsmGAyitLQULpdLHItGo5+Ok8loNOLx8REqlQp6vf7b8d5TVVWFk5MTScPy8fGxeFxUVASdTodIJAKbzfZunKWlJdze3uLg4AAWiwWCIMBut0vmXF1diQ3lRHLFJmgiyrqRkRHEYjFYrVacnp4iHA5jd3cXdrsdyWTyl5/RarUwGo04PDz80j3Ly8vx8PAAv9+PcDgMr9eblTedWltbYTKZ0NHRgb29Pdzf3yMYDMLlcuHs7Ozb8dOcTidWV1chCAJCoRDm5uZwfX0tmeN2u7GwsACv14tQKITLy0sIggCPxwMAuLi4wOzsLHw+H+rr6+HxeOB0OhGJRMQYiUQC5+fnMJvNWcud6E/EAoiIsk6n0yEQCCCZTMJsNsNgMGB8fBwajUbSa5PJ4XBgY2PjS/dsb2/HxMQERkdHUV1djWAwKL4d9h0KhQI7OztoamqC3W5HRUUFuru7EY1Gxd6nbOjq6sLMzAympqZQU1ODaDSK4eFhyRyHwwGfzwdBEGAwGNDc3Iy1tTWUlZXh5eUFvb296O/vF7cSGBoaQktLC/r6+sTCc3t7GyUlJWhsbMxa7kR/IsXPnzna8pWIKMPz8zMqKyuxubkJk8mU63T+SnV1dRgbG0NPT0+uUyHKKa4AEdH/hlqtxvr6+n9umEhf9/T0hM7OTlit1lynQpRzXAEiIiIi2eEKEBEREckOCyAiIiKSHRZAREREJDssgIiIiEh2WAARERGR7LAAIiIiItlhAURERESywwKIiIiIZIcFEBEREcnOP9AIonwW/YDIAAAAAElFTkSuQmCC\n"},"metadata":{}}],"execution_count":18},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.015818,"end_time":"2024-08-29T13:27:03.502235","exception":false,"start_time":"2024-08-29T13:27:03.486417","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null}]}