From 072e7e66cf15fbdf92277e84c25c1da56d7c4df4 Mon Sep 17 00:00:00 2001 From: Alberto Sciaccaluga Date: Thu, 5 Feb 2026 17:17:10 +0100 Subject: [PATCH 1/5] Add Bayesian spectral fitting example for Crab Nebula in Jupyter notebook --- .../crab/SpectralFit_Crab _bayesian.ipynb | 648 ++++++++++++++++++ 1 file changed, 648 insertions(+) create mode 100644 docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab _bayesian.ipynb diff --git a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab _bayesian.ipynb b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab _bayesian.ipynb new file mode 100644 index 000000000..1a56fe4aa --- /dev/null +++ b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab _bayesian.ipynb @@ -0,0 +1,648 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1eff7d0d", + "metadata": { + "tags": [] + }, + "source": [ + "# Spectral fitting example (Crab)" + ] + }, + { + "cell_type": "markdown", + "id": "8e429338", + "metadata": {}, + "source": [ + "**To run this, you need the following files, which can be downloaded using the first few cells of this notebook:**\n", + "- orientation file (20280301_3_month_with_orbital_info.ori) \n", + "- binned data (crab_bkg_binned_data.hdf5, crab_binned_data.hdf5, & bkg_binned_data.hdf5) \n", + "- detector response (SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5) \n", + "\n", + "**The binned data are simulations of the Crab Nebula and albedo photon background produced using the COSI SMEX mass model. The detector response needs to be unzipped before running the notebook.**" + ] + }, + { + "cell_type": "markdown", + "id": "f1b5e8e3", + "metadata": {}, + "source": [ + "This notebook fits the spectrum of a Crab simulated using MEGAlib and combined with background.\n", + "\n", + "[3ML](https://threeml.readthedocs.io/) is a high-level interface that allows multiple datasets from different instruments to be used coherently to fit the parameters of source model. A source model typically consists of a list of sources with parametrized spectral shapes, sky locations and, for extended sources, shape. Polarization is also possible. A \"coherent\" analysis, in this context, means that the source model parameters are fitted using all available datasets simultanously, rather than performing individual fits and finding a well-suited common model a posteriori. \n", + "\n", + "In order for a dataset to be included in 3ML, each instrument needs to provide a \"plugin\". Each plugin is responsible for reading the data, convolving the source model (provided by 3ML) with the instrument response, and returning a likelihood. In our case, we'll compute a binned Poisson likelihood:\n", + "\n", + "$$\n", + "\\log \\mathcal{L}(\\mathbf{x}) = \\sum_i \\log \\frac{\\lambda_i(\\mathbf{x})^{d_i} \\exp (-\\lambda_i)}{d_i!}\n", + "$$\n", + "\n", + "where $d_i$ are the counts on each bin and $\\lambda_i$ are the expected counts given a source model with parameters $\\mathbf{x}$. \n", + "\n", + "In this example, we will fit a single point source with a known location. We'll assume the background is known and fixed up to a scaling factor. Finally, we will fit a Band function:\n", + "\n", + "$$\n", + "f(x) = K \\begin{cases} \\left(\\frac{x}{E_{piv}}\\right)^{\\alpha} \\exp \\left(-\\frac{(2+\\alpha)\n", + " * x}{x_{p}}\\right) & x \\leq (\\alpha-\\beta) \\frac{x_{p}}{(\\alpha+2)} \\\\ \\left(\\frac{x}{E_{piv}}\\right)^{\\beta}\n", + " * \\exp (\\beta-\\alpha)\\left[\\frac{(\\alpha-\\beta) x_{p}}{E_{piv}(2+\\alpha)}\\right]^{\\alpha-\\beta}\n", + " * &x>(\\alpha-\\beta) \\frac{x_{p}}{(\\alpha+2)} \\end{cases}\n", + "$$\n", + "\n", + "where $K$ (normalization), $\\alpha$ & $\\beta$ (spectral indeces), and $x_p$ (peak energy) are the free parameters, while $E_{piv}$ is the pivot energy which is fixed (and arbitrary).\n", + "\n", + "Considering these assumptions:\n", + "\n", + "$$\n", + "\\lambda_i(\\mathbf{x}) = B*b_i + s_i(\\mathbf{x})\n", + "$$\n", + "\n", + "where $B*b_i$ are the estimated counts due to background in each bin with $B$ the amplitude and $b_i$ the shape of the background, and $s_i$ are the corresponding expected counts from the source, the goal is then to find the values of $\\mathbf{x} = [K, \\alpha, \\beta, x_p]$ and $B$ that maximize $\\mathcal{L}$. These are the best estimations of the parameters.\n", + "\n", + "The final module needs to also fit the time-dependent background, handle multiple point-like and extended sources, as well as all the spectral models supported by 3ML. Eventually, it will also fit the polarization angle. However, this simple example already contains all the necessary pieces to do a fit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53bc23a7", + "metadata": {}, + "outputs": [], + "source": [ + "from cosipy import COSILike, test_data, BinnedData\n", + "from cosipy.spacecraftfile import SpacecraftFile\n", + "from cosipy.response.FullDetectorResponse import FullDetectorResponse\n", + "from cosipy.util import fetch_wasabi_file\n", + "\n", + "from scoords import SpacecraftFrame\n", + "\n", + "from astropy.time import Time\n", + "import astropy.units as u\n", + "from astropy.coordinates import SkyCoord, Galactic\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from threeML import Band, PointSource, Model, JointLikelihood, DataList\n", + "from astromodels import Parameter\n", + "\n", + "from pathlib import Path\n", + "\n", + "import os\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "727dc86f", + "metadata": {}, + "source": [ + "## Download and read in binned data" + ] + }, + { + "cell_type": "markdown", + "id": "a35274fd", + "metadata": {}, + "source": [ + "Define the path to the directory containing the data, detector response, orientation file, and yaml files if they have already been downloaded, or the directory to download the files into" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "724affc6", + "metadata": {}, + "outputs": [], + "source": [ + "data_path = Path(\"\") # /path/to/files. Current dir by default" + ] + }, + { + "cell_type": "markdown", + "id": "0c2df2f1", + "metadata": {}, + "source": [ + "Download the orientation file (684.38 MB)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67f61e08", + "metadata": {}, + "outputs": [], + "source": [ + "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month_with_orbital_info.ori', output=str(data_path / '20280301_3_month_with_orbital_info.ori'), checksum = '416fcc296fc37a056a069378a2d30cb2')" + ] + }, + { + "cell_type": "markdown", + "id": "79c8a16c", + "metadata": {}, + "source": [ + "Download the binned Crab+background data (99.16 MB)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f6f7006", + "metadata": {}, + "outputs": [], + "source": [ + "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/crab_bkg_binned_data.hdf5', output=str(data_path / 'crab_bkg_binned_data.hdf5'), checksum = '85658e102414c4f746e64a7d29c607a4')" + ] + }, + { + "cell_type": "markdown", + "id": "5492af74", + "metadata": {}, + "source": [ + "Download the binned Crab data (13.16 MB)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "900ae6e0", + "metadata": {}, + "outputs": [], + "source": [ + "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/crab_binned_data.hdf5', output=str(data_path / 'crab_binned_data.hdf5'), checksum = '6e5bccb48556bdbd259519c52dec9dcb')" + ] + }, + { + "cell_type": "markdown", + "id": "d15fbaf1", + "metadata": {}, + "source": [ + "Download the binned background data (89.10 MB)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae494207", + "metadata": {}, + "outputs": [], + "source": [ + "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/bkg_binned_data.hdf5', output=str(data_path / 'bkg_binned_data.hdf5'), checksum = '54221d8556eb4ef520ef61da8083e7f4')" + ] + }, + { + "cell_type": "markdown", + "id": "58cbfaf5", + "metadata": {}, + "source": [ + "Download the response file (596.06 MB)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ee43be1", + "metadata": {}, + "outputs": [], + "source": [ + "fetch_wasabi_file('COSI-SMEX/develop/Data/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5', output=str(data_path / 'SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5'), checksum = 'eb72400a1279325e9404110f909c7785')" + ] + }, + { + "cell_type": "markdown", + "id": "02c44617", + "metadata": {}, + "source": [ + "Read in the spacecraft orientation file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4944ec3a", + "metadata": {}, + "outputs": [], + "source": [ + "sc_orientation = SpacecraftFile.parse_from_file(data_path / \"20280301_3_month_with_orbital_info.ori\")" + ] + }, + { + "cell_type": "markdown", + "id": "746b56bd", + "metadata": {}, + "source": [ + "Create BinnedData objects for the Crab only, Crab+background, and background only. The Crab only simulation is not used for the spectral fit, but can be used to compare the fitted spectrum to the source simulation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "901211ca", + "metadata": {}, + "outputs": [], + "source": [ + "crab = BinnedData(data_path / \"crab.yaml\")\n", + "crab_bkg = BinnedData(data_path / \"crab.yaml\")\n", + "bkg = BinnedData(data_path / \"background.yaml\")" + ] + }, + { + "cell_type": "markdown", + "id": "675ab82f", + "metadata": {}, + "source": [ + "Load binned .hdf5 files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac0604e5", + "metadata": {}, + "outputs": [], + "source": [ + "crab.load_binned_data_from_hdf5(binned_data=data_path / \"crab_binned_data.hdf5\")\n", + "crab_bkg.load_binned_data_from_hdf5(binned_data=data_path / \"crab_bkg_binned_data.hdf5\")\n", + "bkg.load_binned_data_from_hdf5(binned_data=data_path / \"bkg_binned_data.hdf5\")" + ] + }, + { + "cell_type": "markdown", + "id": "67b1f2a3", + "metadata": {}, + "source": [ + "Define the path to the detector response" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9062c51", + "metadata": {}, + "outputs": [], + "source": [ + "dr = str(data_path / \"SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5\") # path to detector response" + ] + }, + { + "cell_type": "markdown", + "id": "87bfd95d", + "metadata": { + "tags": [] + }, + "source": [ + "## Perform spectral fit" + ] + }, + { + "cell_type": "markdown", + "id": "7928684b", + "metadata": {}, + "source": [ + "Set background parameter, which is used to fit the amplitude of the background, and instantiate the COSI 3ML plugin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8305077", + "metadata": {}, + "outputs": [], + "source": [ + "bkg_par = Parameter(\"background_cosi\", # background parameter\n", + " 1, # initial value of parameter\n", + " min_value=0, # minimum value of parameter\n", + " max_value=5, # maximum value of parameter\n", + " delta=0.05, # initial step used by fitting engine\n", + " desc=\"Background parameter for cosi\")\n", + "\n", + "cosi = COSILike(\"cosi\", # COSI 3ML plugin\n", + " dr = dr, # detector response\n", + " data = crab_bkg.binned_data.project('Em', 'Phi', 'PsiChi'), # data (source+background)\n", + " bkg = bkg.binned_data.project('Em', 'Phi', 'PsiChi'), # background model \n", + " sc_orientation = sc_orientation, # spacecraft orientation\n", + " nuisance_param = bkg_par, # background parameter\n", + " earth_occ = True) # Option to account for Earth occultation" + ] + }, + { + "cell_type": "markdown", + "id": "cf105ddd", + "metadata": {}, + "source": [ + "Define a point source at the known location with a Band function spectrum and add it to the model. The initial values of the Band function parameters are set to the true values used to simulate the source" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b02ed80", + "metadata": {}, + "outputs": [], + "source": [ + "l = 184.56\n", + "b = -5.78\n", + "\n", + "alpha = -1.99\n", + "beta = -2.32\n", + "E0 = 531. * (alpha - beta) * u.keV\n", + "xp = E0 * (alpha + 2) / (alpha - beta)\n", + "piv = 500. * u.keV\n", + "K = 3.07e-5 / u.cm / u.cm / u.s / u.keV\n", + "\n", + "spectrum = Band()\n", + "\n", + "spectrum.alpha.min_value = -2.14\n", + "spectrum.alpha.max_value = 3.0\n", + "spectrum.beta.min_value = -5.0\n", + "spectrum.beta.max_value = -2.15\n", + "spectrum.xp.min_value = 1.0\n", + "spectrum.alpha.delta = 0.01\n", + "spectrum.beta.delta = 0.01\n", + "\n", + "spectrum.alpha.value = alpha\n", + "spectrum.beta.value = beta\n", + "spectrum.xp.value = xp.value\n", + "spectrum.K.value = K.value\n", + "spectrum.piv.value = piv.value\n", + "\n", + "spectrum.xp.unit = xp.unit\n", + "spectrum.K.unit = K.unit\n", + "spectrum.piv.unit = piv.unit\n", + "\n", + "source = PointSource(\"source\", # Name of source (arbitrary, but needs to be unique)\n", + " l = l, # Longitude (deg)\n", + " b = b, # Latitude (deg)\n", + " spectral_shape = spectrum) # Spectral model\n", + "\n", + "# Optional: free the position parameters\n", + "#source.position.l.free = True\n", + "#source.position.b.free = True\n", + "\n", + "model = Model(source) # Model with single source. If we had multiple sources, we would do Model(source1, source2, ...)" + ] + }, + { + "cell_type": "markdown", + "id": "8d193c42", + "metadata": {}, + "source": [ + "Gather all plugins and combine with the model in a JointLikelihood object, then perform maximum likelihood fit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0056610", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)\n", + "\n", + "like = JointLikelihood(model, plugins, verbose = False)\n", + "\n", + "_ = like.fit()" + ] + }, + { + "cell_type": "markdown", + "id": "1e888a53", + "metadata": {}, + "source": [ + "## Error propagation and plotting (Band function)" + ] + }, + { + "cell_type": "markdown", + "id": "5622b48e", + "metadata": {}, + "source": [ + "Define Band function spectrum injected into MEGAlib" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d2c61a3", + "metadata": {}, + "outputs": [], + "source": [ + "alpha_inj = -1.99\n", + "beta_inj = -2.32\n", + "E0_inj = 531. * (alpha_inj - beta_inj) * u.keV\n", + "xp_inj = E0_inj * (alpha_inj + 2) / (alpha_inj - beta_inj)\n", + "piv_inj = 100. * u.keV\n", + "K_inj = 7.56e-4 / u.cm / u.cm / u.s / u.keV\n", + "\n", + "spectrum_inj = Band()\n", + "\n", + "spectrum_inj.alpha.min_value = -2.14\n", + "spectrum_inj.alpha.max_value = 3.0\n", + "spectrum_inj.beta.min_value = -5.0\n", + "spectrum_inj.beta.max_value = -2.15\n", + "spectrum_inj.xp.min_value = 1.0\n", + "\n", + "spectrum_inj.alpha.value = alpha_inj\n", + "spectrum_inj.beta.value = beta_inj\n", + "spectrum_inj.xp.value = xp_inj.value\n", + "spectrum_inj.K.value = K_inj.value\n", + "spectrum_inj.piv.value = piv_inj.value\n", + "\n", + "spectrum_inj.xp.unit = xp_inj.unit\n", + "spectrum_inj.K.unit = K_inj.unit\n", + "spectrum_inj.piv.unit = piv_inj.unit" + ] + }, + { + "cell_type": "markdown", + "id": "7b61657c", + "metadata": {}, + "source": [ + "The summary of the results above tell you the optimal values of the parameters, as well as the errors. Propogate the errors to the \"evaluate_at\" method of the spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "753ae553", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "results = like.results\n", + "\n", + "print(results.display())\n", + "\n", + "parameters = {par.name:results.get_variates(par.path)\n", + " for par in results.optimized_model[\"source\"].parameters.values()\n", + " if par.free}\n", + "\n", + "results_err = results.propagate(results.optimized_model[\"source\"].spectrum.main.shape.evaluate_at, **parameters)\n", + "\n", + "print(results.optimized_model[\"source\"])" + ] + }, + { + "cell_type": "markdown", + "id": "d127d06a", + "metadata": {}, + "source": [ + "Evaluate the flux and errors at a range of energies for the fitted and injected spectra, and the simulated source flux" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26b1ba71", + "metadata": {}, + "outputs": [], + "source": [ + "energy = np.geomspace(100*u.keV,10*u.MeV).to_value(u.keV)\n", + "\n", + "flux_lo = np.zeros_like(energy)\n", + "flux_median = np.zeros_like(energy)\n", + "flux_hi = np.zeros_like(energy)\n", + "flux_inj = np.zeros_like(energy)\n", + "\n", + "for i, e in enumerate(energy):\n", + " flux = results_err(e)\n", + " flux_median[i] = flux.median\n", + " flux_lo[i], flux_hi[i] = flux.equal_tail_interval(cl=0.68)\n", + " flux_inj[i] = spectrum_inj.evaluate_at(e)\n", + " \n", + "binned_energy_edges = crab.binned_data.axes['Em'].edges.value\n", + "binned_energy = np.array([])\n", + "bin_sizes = np.array([])\n", + "\n", + "for i in range(len(binned_energy_edges)-1):\n", + " binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n", + " bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n", + "\n", + "expectation = cosi._expected_counts['source']" + ] + }, + { + "cell_type": "markdown", + "id": "7993edb6", + "metadata": {}, + "source": [ + "Plot the fitted and injected spectra" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57db086e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fig,ax = plt.subplots()\n", + "\n", + "ax.plot(energy, energy*energy*flux_median, label = \"Best fit\")\n", + "ax.fill_between(energy, energy*energy*flux_lo, energy*energy*flux_hi, alpha = .5, label = \"Best fit (errors)\")\n", + "ax.plot(energy, energy*energy*flux_inj, color = 'black', ls = \":\", label = \"Injected\")\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "\n", + "ax.set_xlabel(\"Energy (keV)\")\n", + "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n", + "\n", + "_ = ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "ce164e0b", + "metadata": {}, + "source": [ + "Plot the fitted spectrum convolved with the response, as well as the simulated source counts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19c43f92", + "metadata": {}, + "outputs": [], + "source": [ + "fig,ax = plt.subplots()\n", + "\n", + "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n", + "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n", + "ax.stairs(crab.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n", + "ax.errorbar(binned_energy, crab.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "\n", + "ax.set_xlabel(\"Energy (keV)\")\n", + "ax.set_ylabel(\"Counts\")\n", + "\n", + "_ = ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "f237166b", + "metadata": {}, + "source": [ + "Plot the fitted spectrum convolved with the response plus the fitted background, as well as the simulated source+background counts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ed27ca4", + "metadata": {}, + "outputs": [], + "source": [ + "fig,ax = plt.subplots()\n", + "\n", + "ax.stairs(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.project('Em').todense().contents), binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n", + "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.project('Em').todense().contents), yerr=np.sqrt(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.project('Em').todense().contents)), color='purple', linewidth=0, elinewidth=1)\n", + "ax.stairs(crab_bkg.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n", + "ax.errorbar(binned_energy, crab_bkg.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab_bkg.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "\n", + "ax.set_xlabel(\"Energy (keV)\")\n", + "ax.set_ylabel(\"Counts\")\n", + "\n", + "_ = ax.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cosipy", + "language": "python", + "name": "cosipy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c6095d8e54cffe8695cf3dc82ef85903bf669533 Mon Sep 17 00:00:00 2001 From: Alberto Sciaccaluga Date: Thu, 12 Feb 2026 13:52:43 +0100 Subject: [PATCH 2/5] Remove Crab bayesian spectral fitting notebook --- .../crab/SpectralFit_Crab _bayesian.ipynb | 648 ------------------ 1 file changed, 648 deletions(-) delete mode 100644 docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab _bayesian.ipynb diff --git a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab _bayesian.ipynb b/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab _bayesian.ipynb deleted file mode 100644 index 1a56fe4aa..000000000 --- a/docs/tutorials/spectral_fits/continuum_fit/crab/SpectralFit_Crab _bayesian.ipynb +++ /dev/null @@ -1,648 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1eff7d0d", - "metadata": { - "tags": [] - }, - "source": [ - "# Spectral fitting example (Crab)" - ] - }, - { - "cell_type": "markdown", - "id": "8e429338", - "metadata": {}, - "source": [ - "**To run this, you need the following files, which can be downloaded using the first few cells of this notebook:**\n", - "- orientation file (20280301_3_month_with_orbital_info.ori) \n", - "- binned data (crab_bkg_binned_data.hdf5, crab_binned_data.hdf5, & bkg_binned_data.hdf5) \n", - "- detector response (SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5) \n", - "\n", - "**The binned data are simulations of the Crab Nebula and albedo photon background produced using the COSI SMEX mass model. The detector response needs to be unzipped before running the notebook.**" - ] - }, - { - "cell_type": "markdown", - "id": "f1b5e8e3", - "metadata": {}, - "source": [ - "This notebook fits the spectrum of a Crab simulated using MEGAlib and combined with background.\n", - "\n", - "[3ML](https://threeml.readthedocs.io/) is a high-level interface that allows multiple datasets from different instruments to be used coherently to fit the parameters of source model. A source model typically consists of a list of sources with parametrized spectral shapes, sky locations and, for extended sources, shape. Polarization is also possible. A \"coherent\" analysis, in this context, means that the source model parameters are fitted using all available datasets simultanously, rather than performing individual fits and finding a well-suited common model a posteriori. \n", - "\n", - "In order for a dataset to be included in 3ML, each instrument needs to provide a \"plugin\". Each plugin is responsible for reading the data, convolving the source model (provided by 3ML) with the instrument response, and returning a likelihood. In our case, we'll compute a binned Poisson likelihood:\n", - "\n", - "$$\n", - "\\log \\mathcal{L}(\\mathbf{x}) = \\sum_i \\log \\frac{\\lambda_i(\\mathbf{x})^{d_i} \\exp (-\\lambda_i)}{d_i!}\n", - "$$\n", - "\n", - "where $d_i$ are the counts on each bin and $\\lambda_i$ are the expected counts given a source model with parameters $\\mathbf{x}$. \n", - "\n", - "In this example, we will fit a single point source with a known location. We'll assume the background is known and fixed up to a scaling factor. Finally, we will fit a Band function:\n", - "\n", - "$$\n", - "f(x) = K \\begin{cases} \\left(\\frac{x}{E_{piv}}\\right)^{\\alpha} \\exp \\left(-\\frac{(2+\\alpha)\n", - " * x}{x_{p}}\\right) & x \\leq (\\alpha-\\beta) \\frac{x_{p}}{(\\alpha+2)} \\\\ \\left(\\frac{x}{E_{piv}}\\right)^{\\beta}\n", - " * \\exp (\\beta-\\alpha)\\left[\\frac{(\\alpha-\\beta) x_{p}}{E_{piv}(2+\\alpha)}\\right]^{\\alpha-\\beta}\n", - " * &x>(\\alpha-\\beta) \\frac{x_{p}}{(\\alpha+2)} \\end{cases}\n", - "$$\n", - "\n", - "where $K$ (normalization), $\\alpha$ & $\\beta$ (spectral indeces), and $x_p$ (peak energy) are the free parameters, while $E_{piv}$ is the pivot energy which is fixed (and arbitrary).\n", - "\n", - "Considering these assumptions:\n", - "\n", - "$$\n", - "\\lambda_i(\\mathbf{x}) = B*b_i + s_i(\\mathbf{x})\n", - "$$\n", - "\n", - "where $B*b_i$ are the estimated counts due to background in each bin with $B$ the amplitude and $b_i$ the shape of the background, and $s_i$ are the corresponding expected counts from the source, the goal is then to find the values of $\\mathbf{x} = [K, \\alpha, \\beta, x_p]$ and $B$ that maximize $\\mathcal{L}$. These are the best estimations of the parameters.\n", - "\n", - "The final module needs to also fit the time-dependent background, handle multiple point-like and extended sources, as well as all the spectral models supported by 3ML. Eventually, it will also fit the polarization angle. However, this simple example already contains all the necessary pieces to do a fit." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53bc23a7", - "metadata": {}, - "outputs": [], - "source": [ - "from cosipy import COSILike, test_data, BinnedData\n", - "from cosipy.spacecraftfile import SpacecraftFile\n", - "from cosipy.response.FullDetectorResponse import FullDetectorResponse\n", - "from cosipy.util import fetch_wasabi_file\n", - "\n", - "from scoords import SpacecraftFrame\n", - "\n", - "from astropy.time import Time\n", - "import astropy.units as u\n", - "from astropy.coordinates import SkyCoord, Galactic\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from threeML import Band, PointSource, Model, JointLikelihood, DataList\n", - "from astromodels import Parameter\n", - "\n", - "from pathlib import Path\n", - "\n", - "import os\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "727dc86f", - "metadata": {}, - "source": [ - "## Download and read in binned data" - ] - }, - { - "cell_type": "markdown", - "id": "a35274fd", - "metadata": {}, - "source": [ - "Define the path to the directory containing the data, detector response, orientation file, and yaml files if they have already been downloaded, or the directory to download the files into" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "724affc6", - "metadata": {}, - "outputs": [], - "source": [ - "data_path = Path(\"\") # /path/to/files. Current dir by default" - ] - }, - { - "cell_type": "markdown", - "id": "0c2df2f1", - "metadata": {}, - "source": [ - "Download the orientation file (684.38 MB)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "67f61e08", - "metadata": {}, - "outputs": [], - "source": [ - "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month_with_orbital_info.ori', output=str(data_path / '20280301_3_month_with_orbital_info.ori'), checksum = '416fcc296fc37a056a069378a2d30cb2')" - ] - }, - { - "cell_type": "markdown", - "id": "79c8a16c", - "metadata": {}, - "source": [ - "Download the binned Crab+background data (99.16 MB)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3f6f7006", - "metadata": {}, - "outputs": [], - "source": [ - "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/crab_bkg_binned_data.hdf5', output=str(data_path / 'crab_bkg_binned_data.hdf5'), checksum = '85658e102414c4f746e64a7d29c607a4')" - ] - }, - { - "cell_type": "markdown", - "id": "5492af74", - "metadata": {}, - "source": [ - "Download the binned Crab data (13.16 MB)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "900ae6e0", - "metadata": {}, - "outputs": [], - "source": [ - "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/crab_binned_data.hdf5', output=str(data_path / 'crab_binned_data.hdf5'), checksum = '6e5bccb48556bdbd259519c52dec9dcb')" - ] - }, - { - "cell_type": "markdown", - "id": "d15fbaf1", - "metadata": {}, - "source": [ - "Download the binned background data (89.10 MB)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae494207", - "metadata": {}, - "outputs": [], - "source": [ - "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/crab_spectral_fit_galactic_frame/bkg_binned_data.hdf5', output=str(data_path / 'bkg_binned_data.hdf5'), checksum = '54221d8556eb4ef520ef61da8083e7f4')" - ] - }, - { - "cell_type": "markdown", - "id": "58cbfaf5", - "metadata": {}, - "source": [ - "Download the response file (596.06 MB)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ee43be1", - "metadata": {}, - "outputs": [], - "source": [ - "fetch_wasabi_file('COSI-SMEX/develop/Data/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5', output=str(data_path / 'SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5'), checksum = 'eb72400a1279325e9404110f909c7785')" - ] - }, - { - "cell_type": "markdown", - "id": "02c44617", - "metadata": {}, - "source": [ - "Read in the spacecraft orientation file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4944ec3a", - "metadata": {}, - "outputs": [], - "source": [ - "sc_orientation = SpacecraftFile.parse_from_file(data_path / \"20280301_3_month_with_orbital_info.ori\")" - ] - }, - { - "cell_type": "markdown", - "id": "746b56bd", - "metadata": {}, - "source": [ - "Create BinnedData objects for the Crab only, Crab+background, and background only. The Crab only simulation is not used for the spectral fit, but can be used to compare the fitted spectrum to the source simulation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "901211ca", - "metadata": {}, - "outputs": [], - "source": [ - "crab = BinnedData(data_path / \"crab.yaml\")\n", - "crab_bkg = BinnedData(data_path / \"crab.yaml\")\n", - "bkg = BinnedData(data_path / \"background.yaml\")" - ] - }, - { - "cell_type": "markdown", - "id": "675ab82f", - "metadata": {}, - "source": [ - "Load binned .hdf5 files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ac0604e5", - "metadata": {}, - "outputs": [], - "source": [ - "crab.load_binned_data_from_hdf5(binned_data=data_path / \"crab_binned_data.hdf5\")\n", - "crab_bkg.load_binned_data_from_hdf5(binned_data=data_path / \"crab_bkg_binned_data.hdf5\")\n", - "bkg.load_binned_data_from_hdf5(binned_data=data_path / \"bkg_binned_data.hdf5\")" - ] - }, - { - "cell_type": "markdown", - "id": "67b1f2a3", - "metadata": {}, - "source": [ - "Define the path to the detector response" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e9062c51", - "metadata": {}, - "outputs": [], - "source": [ - "dr = str(data_path / \"SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5\") # path to detector response" - ] - }, - { - "cell_type": "markdown", - "id": "87bfd95d", - "metadata": { - "tags": [] - }, - "source": [ - "## Perform spectral fit" - ] - }, - { - "cell_type": "markdown", - "id": "7928684b", - "metadata": {}, - "source": [ - "Set background parameter, which is used to fit the amplitude of the background, and instantiate the COSI 3ML plugin" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a8305077", - "metadata": {}, - "outputs": [], - "source": [ - "bkg_par = Parameter(\"background_cosi\", # background parameter\n", - " 1, # initial value of parameter\n", - " min_value=0, # minimum value of parameter\n", - " max_value=5, # maximum value of parameter\n", - " delta=0.05, # initial step used by fitting engine\n", - " desc=\"Background parameter for cosi\")\n", - "\n", - "cosi = COSILike(\"cosi\", # COSI 3ML plugin\n", - " dr = dr, # detector response\n", - " data = crab_bkg.binned_data.project('Em', 'Phi', 'PsiChi'), # data (source+background)\n", - " bkg = bkg.binned_data.project('Em', 'Phi', 'PsiChi'), # background model \n", - " sc_orientation = sc_orientation, # spacecraft orientation\n", - " nuisance_param = bkg_par, # background parameter\n", - " earth_occ = True) # Option to account for Earth occultation" - ] - }, - { - "cell_type": "markdown", - "id": "cf105ddd", - "metadata": {}, - "source": [ - "Define a point source at the known location with a Band function spectrum and add it to the model. The initial values of the Band function parameters are set to the true values used to simulate the source" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b02ed80", - "metadata": {}, - "outputs": [], - "source": [ - "l = 184.56\n", - "b = -5.78\n", - "\n", - "alpha = -1.99\n", - "beta = -2.32\n", - "E0 = 531. * (alpha - beta) * u.keV\n", - "xp = E0 * (alpha + 2) / (alpha - beta)\n", - "piv = 500. * u.keV\n", - "K = 3.07e-5 / u.cm / u.cm / u.s / u.keV\n", - "\n", - "spectrum = Band()\n", - "\n", - "spectrum.alpha.min_value = -2.14\n", - "spectrum.alpha.max_value = 3.0\n", - "spectrum.beta.min_value = -5.0\n", - "spectrum.beta.max_value = -2.15\n", - "spectrum.xp.min_value = 1.0\n", - "spectrum.alpha.delta = 0.01\n", - "spectrum.beta.delta = 0.01\n", - "\n", - "spectrum.alpha.value = alpha\n", - "spectrum.beta.value = beta\n", - "spectrum.xp.value = xp.value\n", - "spectrum.K.value = K.value\n", - "spectrum.piv.value = piv.value\n", - "\n", - "spectrum.xp.unit = xp.unit\n", - "spectrum.K.unit = K.unit\n", - "spectrum.piv.unit = piv.unit\n", - "\n", - "source = PointSource(\"source\", # Name of source (arbitrary, but needs to be unique)\n", - " l = l, # Longitude (deg)\n", - " b = b, # Latitude (deg)\n", - " spectral_shape = spectrum) # Spectral model\n", - "\n", - "# Optional: free the position parameters\n", - "#source.position.l.free = True\n", - "#source.position.b.free = True\n", - "\n", - "model = Model(source) # Model with single source. If we had multiple sources, we would do Model(source1, source2, ...)" - ] - }, - { - "cell_type": "markdown", - "id": "8d193c42", - "metadata": {}, - "source": [ - "Gather all plugins and combine with the model in a JointLikelihood object, then perform maximum likelihood fit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e0056610", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [], - "source": [ - "%%time\n", - "\n", - "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)\n", - "\n", - "like = JointLikelihood(model, plugins, verbose = False)\n", - "\n", - "_ = like.fit()" - ] - }, - { - "cell_type": "markdown", - "id": "1e888a53", - "metadata": {}, - "source": [ - "## Error propagation and plotting (Band function)" - ] - }, - { - "cell_type": "markdown", - "id": "5622b48e", - "metadata": {}, - "source": [ - "Define Band function spectrum injected into MEGAlib" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d2c61a3", - "metadata": {}, - "outputs": [], - "source": [ - "alpha_inj = -1.99\n", - "beta_inj = -2.32\n", - "E0_inj = 531. * (alpha_inj - beta_inj) * u.keV\n", - "xp_inj = E0_inj * (alpha_inj + 2) / (alpha_inj - beta_inj)\n", - "piv_inj = 100. * u.keV\n", - "K_inj = 7.56e-4 / u.cm / u.cm / u.s / u.keV\n", - "\n", - "spectrum_inj = Band()\n", - "\n", - "spectrum_inj.alpha.min_value = -2.14\n", - "spectrum_inj.alpha.max_value = 3.0\n", - "spectrum_inj.beta.min_value = -5.0\n", - "spectrum_inj.beta.max_value = -2.15\n", - "spectrum_inj.xp.min_value = 1.0\n", - "\n", - "spectrum_inj.alpha.value = alpha_inj\n", - "spectrum_inj.beta.value = beta_inj\n", - "spectrum_inj.xp.value = xp_inj.value\n", - "spectrum_inj.K.value = K_inj.value\n", - "spectrum_inj.piv.value = piv_inj.value\n", - "\n", - "spectrum_inj.xp.unit = xp_inj.unit\n", - "spectrum_inj.K.unit = K_inj.unit\n", - "spectrum_inj.piv.unit = piv_inj.unit" - ] - }, - { - "cell_type": "markdown", - "id": "7b61657c", - "metadata": {}, - "source": [ - "The summary of the results above tell you the optimal values of the parameters, as well as the errors. Propogate the errors to the \"evaluate_at\" method of the spectrum" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "753ae553", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [], - "source": [ - "results = like.results\n", - "\n", - "print(results.display())\n", - "\n", - "parameters = {par.name:results.get_variates(par.path)\n", - " for par in results.optimized_model[\"source\"].parameters.values()\n", - " if par.free}\n", - "\n", - "results_err = results.propagate(results.optimized_model[\"source\"].spectrum.main.shape.evaluate_at, **parameters)\n", - "\n", - "print(results.optimized_model[\"source\"])" - ] - }, - { - "cell_type": "markdown", - "id": "d127d06a", - "metadata": {}, - "source": [ - "Evaluate the flux and errors at a range of energies for the fitted and injected spectra, and the simulated source flux" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26b1ba71", - "metadata": {}, - "outputs": [], - "source": [ - "energy = np.geomspace(100*u.keV,10*u.MeV).to_value(u.keV)\n", - "\n", - "flux_lo = np.zeros_like(energy)\n", - "flux_median = np.zeros_like(energy)\n", - "flux_hi = np.zeros_like(energy)\n", - "flux_inj = np.zeros_like(energy)\n", - "\n", - "for i, e in enumerate(energy):\n", - " flux = results_err(e)\n", - " flux_median[i] = flux.median\n", - " flux_lo[i], flux_hi[i] = flux.equal_tail_interval(cl=0.68)\n", - " flux_inj[i] = spectrum_inj.evaluate_at(e)\n", - " \n", - "binned_energy_edges = crab.binned_data.axes['Em'].edges.value\n", - "binned_energy = np.array([])\n", - "bin_sizes = np.array([])\n", - "\n", - "for i in range(len(binned_energy_edges)-1):\n", - " binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n", - " bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n", - "\n", - "expectation = cosi._expected_counts['source']" - ] - }, - { - "cell_type": "markdown", - "id": "7993edb6", - "metadata": {}, - "source": [ - "Plot the fitted and injected spectra" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57db086e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fig,ax = plt.subplots()\n", - "\n", - "ax.plot(energy, energy*energy*flux_median, label = \"Best fit\")\n", - "ax.fill_between(energy, energy*energy*flux_lo, energy*energy*flux_hi, alpha = .5, label = \"Best fit (errors)\")\n", - "ax.plot(energy, energy*energy*flux_inj, color = 'black', ls = \":\", label = \"Injected\")\n", - "\n", - "ax.set_xscale(\"log\")\n", - "ax.set_yscale(\"log\")\n", - "\n", - "ax.set_xlabel(\"Energy (keV)\")\n", - "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n", - "\n", - "_ = ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "ce164e0b", - "metadata": {}, - "source": [ - "Plot the fitted spectrum convolved with the response, as well as the simulated source counts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19c43f92", - "metadata": {}, - "outputs": [], - "source": [ - "fig,ax = plt.subplots()\n", - "\n", - "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n", - "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=np.sqrt(expectation.project('Em').todense().contents), color='purple', linewidth=0, elinewidth=1)\n", - "ax.stairs(crab.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n", - "ax.errorbar(binned_energy, crab.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", - "\n", - "ax.set_xscale(\"log\")\n", - "ax.set_yscale(\"log\")\n", - "\n", - "ax.set_xlabel(\"Energy (keV)\")\n", - "ax.set_ylabel(\"Counts\")\n", - "\n", - "_ = ax.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "f237166b", - "metadata": {}, - "source": [ - "Plot the fitted spectrum convolved with the response plus the fitted background, as well as the simulated source+background counts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0ed27ca4", - "metadata": {}, - "outputs": [], - "source": [ - "fig,ax = plt.subplots()\n", - "\n", - "ax.stairs(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.project('Em').todense().contents), binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n", - "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.project('Em').todense().contents), yerr=np.sqrt(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.project('Em').todense().contents)), color='purple', linewidth=0, elinewidth=1)\n", - "ax.stairs(crab_bkg.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n", - "ax.errorbar(binned_energy, crab_bkg.binned_data.project('Em').todense().contents, yerr=np.sqrt(crab_bkg.binned_data.project('Em').todense().contents), color='black', linewidth=0, elinewidth=1)\n", - "\n", - "ax.set_xscale(\"log\")\n", - "ax.set_yscale(\"log\")\n", - "\n", - "ax.set_xlabel(\"Energy (keV)\")\n", - "ax.set_ylabel(\"Counts\")\n", - "\n", - "_ = ax.legend()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "cosipy", - "language": "python", - "name": "cosipy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.19" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From c4a9e201211f3e42d313c4bd7f354d3ade531b06 Mon Sep 17 00:00:00 2001 From: Alberto Sciaccaluga Date: Thu, 12 Feb 2026 13:55:11 +0100 Subject: [PATCH 3/5] Add Bayesian spectral fitting example for GRB --- .../grb/SpectralFit_GRB_Bayesian.ipynb | 1734 +++++++++++++++++ 1 file changed, 1734 insertions(+) create mode 100644 docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB_Bayesian.ipynb diff --git a/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB_Bayesian.ipynb b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB_Bayesian.ipynb new file mode 100644 index 000000000..4b8e12907 --- /dev/null +++ b/docs/tutorials/spectral_fits/continuum_fit/grb/SpectralFit_GRB_Bayesian.ipynb @@ -0,0 +1,1734 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Spectral fitting example (GRB)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**To run this, you need the following files, which can be downloaded using the first few cells of this notebook:**\n", + "- orientation file (20280301_3_month_with_orbital_info.ori) \n", + "- binned data (grb_bkg_binned_data.hdf5, grb_binned_data.hdf5, & bkg_binned_data_1s_local.hdf5) \n", + "- detector response (SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5) \n", + "\n", + "**The binned data are simulations of GRB090206620 and albedo photon background produced using the COSI SMEX mass model. The detector response needs to be unzipped before running the notebook.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook fits the spectrum of a GRB simulated using MEGAlib and combined with background.\n", + "\n", + "[3ML](https://threeml.readthedocs.io/) is a high-level interface that allows multiple datasets from different instruments to be used coherently to fit the parameters of source model. A source model typically consists of a list of sources with parametrized spectral shapes, sky locations and, for extended sources, shape. Polarization is also possible. A \"coherent\" analysis, in this context, means that the source model parameters are fitted using all available datasets simultanously, rather than performing individual fits and finding a well-suited common model a posteriori. \n", + "\n", + "In order for a dataset to be included in 3ML, each instrument needs to provide a \"plugin\". Each plugin is responsible for reading the data, convolving the source model (provided by 3ML) with the instrument response, and returning a likelihood. In our case, we'll compute a binned Poisson likelihood:\n", + "\n", + "$$\n", + "\\log \\mathcal{L}(\\mathbf{x}) = \\sum_i \\log \\frac{\\lambda_i(\\mathbf{x})^{d_i} \\exp (-\\lambda_i)}{d_i!}\n", + "$$\n", + "\n", + "where $d_i$ are the counts on each bin and $\\lambda_i$ are the expected counts given a source model with parameters $\\mathbf{x}$. \n", + "\n", + "In this example, we will fit a single point source with a known location. We'll assume the background is known and fixed up to a scaling factor. Finally, we will fit a Band function:\n", + "\n", + "$$\n", + "f(x) = K \\begin{cases} \\left(\\frac{x}{E_{piv}}\\right)^{\\alpha} \\exp \\left(-\\frac{(2+\\alpha)\n", + " * x}{x_{p}}\\right) & x \\leq (\\alpha-\\beta) \\frac{x_{p}}{(\\alpha+2)} \\\\ \\left(\\frac{x}{E_{piv}}\\right)^{\\beta}\n", + " * \\exp (\\beta-\\alpha)\\left[\\frac{(\\alpha-\\beta) x_{p}}{E_{piv}(2+\\alpha)}\\right]^{\\alpha-\\beta}\n", + " * &x>(\\alpha-\\beta) \\frac{x_{p}}{(\\alpha+2)} \\end{cases}\n", + "$$\n", + "\n", + "\n", + "where $K$ (normalization), $\\alpha$ & $\\beta$ (spectral indeces), and $x_p$ (peak energy) are the free parameters, while $E_{piv}$ is the pivot energy which is fixed (and arbitrary).\n", + "\n", + "Considering these assumptions:\n", + "\n", + "$$\n", + "\\lambda_i(\\mathbf{x}) = B*b_i + s_i(\\mathbf{x})\n", + "$$\n", + "\n", + "where $B*b_i$ are the estimated counts due to background in each bin of the Compton data space with $B$ the amplitude and $b_i$ the shape of the background, and $s_i$ are the corresponding expected counts from the source, the goal is then to find the values of $\\mathbf{x} = [K, \\alpha, \\beta, x_p]$ and $B$ that maximize $\\mathcal{L}$. These are the best estimations of the parameters.\n", + "\n", + "The final module needs to also fit the time-dependent background, handle multiple point-like and extended sources, as well as all the spectral models supported by 3ML. Eventually, it will also fit the polarization angle. However, this simple example already contains all the necessary pieces to do a fit." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sciaccaluga/Softwares/anaconda3/envs/cosi2/lib/python3.10/site-packages/astromodels/utils/file_utils.py:8: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "                  available                                                                                        \n",
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+       "                  available                                                                                        \n",
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+       "                  require the C/C++ interface (currently HAWC)                                                     \n",
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+       "                  software installed and configured?                                                               \n",
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+       "                  software installed and configured?                                                               \n",
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+       "                  performances in 3ML                                                                              \n",
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+       "                  performances in 3ML                                                                              \n",
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+       "                  performances in 3ML                                                                              \n",
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Current dir by default" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download the orientation file (684.38 MB)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "A file named 20280301_3_month_with_orbital_info.ori already exists with the specified checksum (416fcc296fc37a056a069378a2d30cb2). Skipping.\n" + ] + } + ], + "source": [ + "fetch_wasabi_file('COSI-SMEX/DC2/Data/Orientation/20280301_3_month_with_orbital_info.ori', output=str(data_path / '20280301_3_month_with_orbital_info.ori'), checksum = '416fcc296fc37a056a069378a2d30cb2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download the binned GRB+background data (75.73 KB)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "A file named grb_bkg_binned_data.hdf5 already exists with the specified checksum (fce391a4b45624b25552c7d111945f60). Skipping.\n" + ] + } + ], + "source": [ + "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/grb_spectral_fit_local_frame/grb_bkg_binned_data.hdf5', output=str(data_path / 'grb_bkg_binned_data.hdf5'), checksum = 'fce391a4b45624b25552c7d111945f60')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download the binned GRB data (76.90 KB)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "A file named grb_binned_data.hdf5 already exists with the specified checksum (fcf7022369b6fb378d67b780fc4b5db8). Skipping.\n" + ] + } + ], + "source": [ + "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/grb_spectral_fit_local_frame/grb_binned_data.hdf5', output=str(data_path / 'grb_binned_data.hdf5'), checksum = 'fcf7022369b6fb378d67b780fc4b5db8')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download the binned background data (255.97 MB)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "A file named bkg_binned_data_1s_local.hdf5 already exists with the specified checksum (b842a7444e6fc1a5dd567b395c36ae7f). Skipping.\n" + ] + } + ], + "source": [ + "fetch_wasabi_file('COSI-SMEX/cosipy_tutorials/grb_spectral_fit_local_frame/bkg_binned_data_1s_local.hdf5', output=str(data_path / 'bkg_binned_data_1s_local.hdf5'), checksum = 'b842a7444e6fc1a5dd567b395c36ae7f')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download the response file (596.06 MB)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "A file named SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5 already exists with the specified checksum (eb72400a1279325e9404110f909c7785). Skipping.\n" + ] + } + ], + "source": [ + "fetch_wasabi_file('COSI-SMEX/develop/Data/Responses/SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5', output=str(data_path / 'SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5'), checksum = 'eb72400a1279325e9404110f909c7785')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read in the spacecraft orientation file & select the beginning and end times of the GRB" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "ori = SpacecraftFile.parse_from_file(data_path / \"20280301_3_month_with_orbital_info.ori\")\n", + "tmin = Time(1842597410.0,format = 'unix')\n", + "tmax = Time(1842597450.0,format = 'unix')\n", + "sc_orientation = ori.source_interval(tmin, tmax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create BinnedData objects for the GRB only, GRB+background, and background only. The GRB only simulation is not used for the spectral fit, but can be used to compare the fitted spectrum to the source simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "grb = BinnedData(data_path / \"grb.yaml\")\n", + "grb_bkg = BinnedData(data_path / \"grb.yaml\")\n", + "bkg = BinnedData(data_path / \"background.yaml\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load binned .hdf5 files" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "grb.load_binned_data_from_hdf5(binned_data=data_path / \"grb_binned_data.hdf5\")\n", + "grb_bkg.load_binned_data_from_hdf5(binned_data=data_path / \"grb_bkg_binned_data.hdf5\")\n", + "bkg.load_binned_data_from_hdf5(binned_data=data_path / \"bkg_binned_data_1s_local.hdf5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define the path to the detector response" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "dr = str(data_path / \"SMEXv12.Continuum.HEALPixO3_10bins_log_flat.binnedimaging.imagingresponse.h5\") # path to detector response" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Perform spectral fit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define time window of binned background simulation to use for background model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "bkg_tmin = 1842597310.0\n", + "bkg_tmax = 1842597550.0\n", + "bkg_min = np.where(bkg.binned_data.axes['Time'].edges.value == bkg_tmin)[0][0]\n", + "bkg_max = np.where(bkg.binned_data.axes['Time'].edges.value == bkg_tmax)[0][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set background parameter, which is used to fit the amplitude of the background, and instantiate the COSI 3ML plugin" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "bkg_par = Parameter(\"background_cosi\", # background parameter\n", + " 0.1, # initial value of parameter\n", + " min_value=0, # minimum value of parameter\n", + " max_value=5, # maximum value of parameter\n", + " delta=1e-3, # initial step used by fitting engine\n", + " desc=\"Background parameter for cosi\")\n", + "\n", + "cosi = COSILike(\"cosi\", # COSI 3ML plugin\n", + " dr = dr, # detector response\n", + " data = grb_bkg.binned_data.project('Em', 'Phi', 'PsiChi'), # data (source+background)\n", + " bkg = bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em', 'Phi', 'PsiChi'), # background model \n", + " sc_orientation = sc_orientation, # spacecraft orientation\n", + " nuisance_param = bkg_par) # background parameter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define a point source at the known location with a Band function spectrum and add it to the model. Since a Bayesian fit will be performed, priors are specified." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "l = 93.\n", + "b = -53.\n", + "\n", + "alpha = -1 # Setting parameters to something reasonable helps the fitting to converge\\n\",\n", + "beta = -3\n", + "xp = 450. * u.keV\n", + "piv = 500. * u.keV\n", + "K = 0.1 / u.cm / u.cm / u.s / u.keV\n", + "\n", + "spectrum = Band()\n", + "\n", + "spectrum.beta.min_value = -15.0\n", + "spectrum.alpha.min_value = -4\n", + "\n", + "spectrum.alpha.value = alpha\n", + "spectrum.beta.value = beta\n", + "spectrum.xp.value = xp.value\n", + "spectrum.K.value = K.value\n", + "spectrum.piv.value = piv.value\n", + "\n", + "spectrum.xp.unit = xp.unit\n", + "spectrum.K.unit = K.unit\n", + "spectrum.piv.unit = piv.unit\n", + "\n", + "spectrum.K.prior = Uniform_prior(lower_bound=0.01, upper_bound=0.3)\n", + "spectrum.alpha.prior = Uniform_prior(lower_bound=-2, upper_bound=0)\n", + "spectrum.beta.prior = Uniform_prior(lower_bound=-10, upper_bound=-2.5)\n", + "spectrum.xp.prior = Uniform_prior(lower_bound=400, upper_bound=500)\n", + "bkg_par.prior = Uniform_prior(lower_bound=0.0, upper_bound=1.0) # it needs a prior too\n", + "\n", + "source = PointSource(\"source\", # Name of source (arbitrary, but needs to be unique)\n", + " l = l, # Longitude (deg)\n", + " b = b, # Latitude (deg)\n", + " spectral_shape = spectrum) # Spectral model\n", + "\n", + "# Optional: free the position parameters\n", + "#source.position.l.free = True\n", + "#source.position.b.free = True\n", + "\n", + "model = Model(source) # Model with single source. If we had multiple sources, we would do Model(source1, source2, ...)\n", + "\n", + "# Optional: if you want to call get_log_like manually, then you also need to set the model manually\n", + "# 3ML does this internally during the fit though\n", + "cosi.set_model(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gather all plugins and combine with the model in a JointLikelihood object, then perform a Bayesian fit using the emcee sampler. While emcee is selected for this analysis, note that alternative samplers are also supported (e.g., dynesty)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
13:43:08 INFO      sampler set to emcee                                                    bayesian_analysis.py:202\n",
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13:49:15 INFO      fit restored to maximum of posterior                                         sampler_base.py:178\n",
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+       "                  measurements such as AIC or BIC are unreliable                                                   \n",
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         INFO      fit restored to maximum of posterior                                         sampler_base.py:178\n",
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resultunit
parameter
source.spectrum.main.Band.K(3.09 -0.19 +0.22) x 10^-21 / (keV s cm2)
source.spectrum.main.Band.alpha(-2.8 +/- 0.5) x 10^-1
source.spectrum.main.Band.xp(4.75 +/- 0.05) x 10^2keV
source.spectrum.main.Band.beta-6.9 -2.1 +0.6
background_cosi(1.64 -0.10 +0.15) x 10^-1
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" + ], + "text/plain": [ + " result unit\n", + "parameter \n", + "source.spectrum.main.Band.K (3.09 -0.19 +0.22) x 10^-2 1 / (keV s cm2)\n", + "source.spectrum.main.Band.alpha (-2.8 +/- 0.5) x 10^-1 \n", + "source.spectrum.main.Band.xp (4.75 +/- 0.05) x 10^2 keV\n", + "source.spectrum.main.Band.beta -6.9 -2.1 +0.6 \n", + "background_cosi (1.64 -0.10 +0.15) x 10^-1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+       "Values of -log(posterior) at the minimum:\n",
+       "\n",
+       "
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-log(posterior)
cosi-42920.077567
total-42920.077567
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+       "Values of statistical measures:\n",
+       "\n",
+       "
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statistical measures
AIC85838.155133
BIC85840.155133
DIC85849.876644
PDIC4.670411
\n", + "
" + ], + "text/plain": [ + " statistical measures\n", + "AIC 85838.155133\n", + "BIC 85840.155133\n", + "DIC 85849.876644\n", + "PDIC 4.670411" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plugins = DataList(cosi) # If we had multiple instruments, we would do e.g. DataList(cosi, lat, hawc, ...)\n", + "\n", + "ba = BayesianAnalysis(model, plugins, verbose = False)\n", + "ba.set_sampler('emcee')\n", + "ba.sampler.setup(n_iterations = 4000, n_walkers = 15, n_burn_in = 100)\n", + "\n", + "ba.sample()" + ] + }, + { + "cell_type": "markdown", + "id": "684ed5ae", + "metadata": {}, + "source": [ + "Plot the corner plot " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "13c49502", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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uqFJJkiHXIoMMPh0LS/bQkmVmZsput1d5rbS01L+DAQAAANBgCKPQYtUliHLJJVkdKmtXIGdCkSTJeMQq884IGZy+m2BIIIWWKjMzU2PHjlVRUVGN9zkcDj+NCAAAAEBDIYxCi3S2IMoll1xhZXJFlsoZWSJnZKkU7Kxwj7N1kcokWbZH+XxsBFJoaex2u4qKijRt2jQlJSVVuj5lyhTl5eXJ6XRWUQ0AAACgKSGMQotTUxDlMjtV1ilPzuhiyew667OccUVyZYTLUGzy5RBrRH8pNGdJSUlKSUmpdN5k8t+fMQAAAAANizAKLUqNQVSQQ6XnHZMrtIplQE6jTEWhMhaGy1xgU1mYXaUxhyWD5Eg8IfOvET4bY02zovwRRDErCwAAAADQkAij0GLUFOQ4Q8pUet6xU0vxHEaZCiNkKgqTsShMxuKQCs3KLcdjVNoqUzK65IgvlGlvmAwO73tHEUQBAAAAAJo7wii0CDUGUeGlKj33mGRxL8szlAbJerCTjGXB1dYYnGaZ86JVFpUjmd2BlPlgmFdjJIhCc9auXTuFhYWpVatW9aqPj4+X3W5XfHy8398NAAAAwLcIo9Ds1RhE2YpVeo5dOtmOxlgcIuuhjjI4LGd9riU31h1Gyb1Uz3QwtMLsqbogiEJzN3fuXK/qH3zwQaWmpurBBx/0+7sBAAAA+Jbv9qQHAlBNQY4jrlClPeyngqjCcFkPdK5VECVJxlKrTAUne0VZnXLGFtdrjARRAAAAAICWhDAKzVZ1QY7L6FRZ+3yVpRz3/Akw5Ue5Z0S56rZjlyU3zvNzR9sCuXT2HfhORxAFAAAAAGhpWKaHZunMIMdlLZOjVYmc0cVyRZVUiGHNuTEKym5bryV2xsJwGcoscplL5Yookyu0TIYTtZtZRRAFAAAAAGiJCKPQ7FxtHCmXwSVXZKmcrYrdAVSoo8p7LUfjZTkWX68gyiWXSmIOymUuLT8huWr3nMYOghr7/Wh5Hn30UeXm5ioqKkrTp0+vc/3ChQslSXPmzNHkyZOrvMdms1XZ4NzbdwMAAADwLcKok5xOp4xGVi02dVdZRqisS76csUWSueolc4ZSi0wnImXOt8lUFF6v95QHUWW2bM85884IGQvP/keqNkHQSuc7DTY7iiAKjeGHH35QVlaW4uLizn5zFXbu3ClJ+umnn5SamlrlPVarVenp6ZUCKW/fDQAAAMC3CKMkffPNN8rPz9dVV10lk6luPYMQOK6yjFDpuXa5IksrXnBJxqIwmU5EyHwiUoYSa713vXM/7owgyiWZd0bKlBly1tq6BEENEUgRRKGpKv/fZpvNptmzZ1e6npGRobS0NNnt9ipnRwEAAAAIHC0+jPr444/11FNP6d5771VZWRlhVBNVKYhyGmUqiJT5RKRMJyJkcPrmt7pLLpXEHlBZVE75CZl3RMp0xLdB1Ok1vgqkCKLQHFgsFqWkpDT2MAAAAAB4oUWHUeVB1MiRI3X55ZcrODi4wnWXyyWDoe4zaLKzs5WTk+M5zsjI8HqsqF6lIMphkvVgZ5lKzh4Q1ZUjzO63IOr0Wm8DKYIoAAAAAECgaLFh1MqVK/XUU09p9OjRuvHGG9W6dWtJUklJiVwul0wmk8zm+n17PvzwQy1ZssSHo0V1/BlESVLp6T2i/BBEnf6M+gZSBFEAAAAAgEDSIsOob7/9VmlpaerVq5euv/56JSQkSJKWLl2qX375RXa7XdHR0brtttvUuXNnWSyWOj1/+PDhGjRokOe4vJcJfOuqIP8GUY7gE3JaT0iSDPlmGY9Yz1rjyyCoPoEUQRQAAAAAINC0yDCqe/fuio2N1a5du/TDDz8oMTFRU6dO1YYNG9ShQwdZrVZt3LhRmzdv1j333KMhQ4ZUWsJXk9jYWMXGxjbgJ2jZfvrpJ9074n45+xVJ5RsgNnAQ5TI4VRJzwHNsOhR61iboDREE1SWQIogCAAAAAASiFhdGlZWVKTIyUq+88oruvvtu/fOf/9R7772nI0eO6LHHHlPv3r0VGhqqn3/+Wa+++qpeeOEFJSYmqk+fPnI6nTIajWd/CRrMu+++qzlz5kitTzvphyCqKOFXOUPcs6JUYpQx6+yzos4WGtU3LCJkAgAAAAA0ZS0uWTGbzSorK1NUVJQWLFig8PBwbdu2Tffdd58uvvhihYaGSpJ69OihP/7xj4qMjNRLL72kkpISgqhG5gmiyjlMMtvjFLK/W8MFUXKpOD5DztB894kygyw/22Rw1r2x/Zl8tUseAAAAAABNSYuaGeVwODyNyU8PpJ599lmdc845nobl5bvoderUSeedd56+//57HT9+nKV3jejMIMpyLE6WowkyVJGnOi1FkiRj6dlnL9WkPIhyhB13n3BIlp9sMubXrYdYTa42jmSmE1qE3/3udyooKFBYWJjf6719NwAAAADfavZh1CeffKKtW7fq/vvvl8lkqhRIRUZGavr06TIajZ4Q6nROp1OhoaGyWr0LNlB/lYOo1ieDqIq/Vi65VGo7otKYw5Ikc14rBWUnyuCs+29zl1wqab1PjvBc9wmHZPm5lYx5QfX+HNUhkEJLMGHChEar9/bdAAAAAHyrWYdRGzZs0JNPPilJMhqNmjRpUqVAqvyaJBkMhgp9oX7++Wft2LFD5557bp0amMN36hJElcTtV1nkUc+5sohjKgvNU3B2okz5trM2HK/wrNgDKos45j7hlMxbbTLm+j6IKkcgBQAAAABoKZptE6QDBw5oyZIlSkhI0IABA/Tuu+/qH//4hyR5AqmqlAdRmzdv1iuvvKLjx4/rtttuk8Xiu6VZqJ1aB1EGh4rb/FohiHKaTuaspjIVx+9VccIeOU0lZ32nSy6VxBxUWVRO+QmZt0XJdKzhw0h6SAEAAAAAWoJmOTPK6XRq9+7d+uWXXzRx4kRde+21evnll/Xhhx/KYDBoypQpFWZInS4vL0+vvPKKvvrqK5lMJs2ZM0ft27dvpE/Sci1fvlxz5871HFcXRDnNxe6gKdjdJ8plNOrYhb1VHBMj24//VcihQ5IkR9hxFYbkKyinjczHY6qdJeUIzVOZLdt94JLM2yJlyvHfEk1mSAEAAAAAmrtmGUYZjUa1adNG999/v66//npJ0i233CKXy6UPPvhAkqoNpPLy8hQUFKSBAwdq9OjRatu2baN8hpaqsLBQTz/9tFatWuU5d2YQ5ZJLTmuBSqOy5QjLVXmu5LRYlNO/v0pi3I3mj/brL+vBg7L9978yFRdJRqdK4g7I4DDLXGCr9G6XXCqNyvIcm3ZHyJTdMLv01YRACs3RiBEjlJWVpbi4OC1fvtyv9d6+GwAAAIBvNcswSpK6dOmi5ORkSe5d9BITEzV27FhJqhRInS4xMVG33nqrgoKC6BPlZ4WFhfrrX/+qH374wXPuzCDKaS5RcXyGnNYTFWrLQkOVc/EAlUVEVDhflJiozLhYxWzYoOAc99I7l6nqJZqOsFw5Q/NPFhplOuT/IAoAAAAAgOau2YZRkjwNyssDp4SEhAqBlMvl0gMPPCBJ+v7777Vz506NHDlSEWcEGmh4lYIoh1HB2e0qNB53WopUlLhbLnOpp84RHKyC5GTld+osV1DVDcZdliAZS0/VmAoq//q6jA6VxB7wHJt/jah1w3MAAAAAAFB7zTqMqsrpgVR5D6mhQ4dqwYIFOnr0qK688krFxsY28ihblqqCKOuhzjIVh3rucQQVqihxt2QqkySVhYXpeEqKChPbSmfMbjuTOT9fluPHJUnGolAZHZVDq5LoQ3KZ3c825gTJmMOsOAAAAAAAGkKLC6MkdyA1fvx4GQwGffDBB/r0009lMBj04osvEkT5Wa2CKGuBihJ2SyanJKkkKko5AwbKWctllNaDBz0/N+dHVbruCC5QWeTJ3fMcBpl3RzIrCgAAAACABmJs7AH4m8vlkiTFxcWpV69eCgoKUlBQkBYsWKDOnTs38uhaltoEUWUheSpqcyqIKo6OVvagQbUOoiQp5OCp5XemMxqXu+RSSdx+TxN0U0aYDMU1z7QCAAAAAAD112RnRtntdhUXFys0NFQWi0VWq1Uul0sGQ80zWsqvf//990pPT1dwcLDmzZunjh07+mPYOKlWQVSYXcXxeyWDO0AsiovT0X795TLX/retqaBAQbm5kiRjUYiMZRWX6JXasuQMLpIkGfLNMh0MrfQMAAAAAADgO00yjFq1apVeffVVHT58WGazWd27d9ctt9yiPn361Kre6XSqoKBAx48f17PPPksQ5We1CaJKI46qJG6fZ8ZSYZs2OtrnorP2hzpTyGlL9EwFFZfoOc0lKm112H3gksw7WZ4HAAAAAEBDa3LL9NauXatZs2apS5cuuvfeezVs2DBlZWVpypQpev3113X8ZKPqmhiNRl100UV69dVX1a1bNz+MGuXy8vI0ZcqUaoMol9Ghovg9Kml9Kog60b69jl7Ut85BlLGoSGF7fvUcm09boucyOlQcv0cyumddmQ6FyJhvqffnAgAAAAAAtdNkZka5XC45nU599tlnOv/883XPPfd4mo1feeWV+uCDD7Rw4UIdO3ZMY8aMUatWrSrUZ2ZmKi8vT126dJEkhYSE+P0ztHR5eXl64IEHtHXrVveJqoKoNrvltJ7w1OR37Kjc83tKZ1l+eSZjUZFiv1kr8wn3s4yFoTKWBp/xnkL3zcVGmTLCvfx0AAAAAACgNppMGGUwGGQ0GrV//361b99esbGxKisrk9lsVrdu3ZSamiqbzabXX39dVqtV48ePl/lkb6H8/Hw98cQT2rRpk+bNm6eePXs28qdpeSoHUWZZD3aSqcQdCroDol2egMhpsch+/vkqbNe+7kFUcbFi130jS36+JMlQalHwkST3ewxnBF6lBll+aiWD49QkwZXOdyRJVxtH1vvzAqho2rRpKi0tlcVSvxmI3tR7+24AAAAAvtVkwijJHUi1bt1aBw64d0czm81yOBwymUyy2WwaPXq0iouL9dprr6ljx44aPHiwJCk8PFwpKSnauXOnoqOjG/MjtEhVBVEhBzrLWGqVJLmMZRVmKjmCgpQ9aJDKIqOqe2S1DKWliln3jSx5eSePLbIe7OxpXF4Se7BiEPVjtIwnTv0xKA+iyn9OIAX4xoUXXtho9d6+GwAAAIBvNZmeUU6nU5I0ePBgZWRkaPHixZIkk8kkh8MhSYqIiNBNN92kiy66SPPmzfOEVpJ0zz336I033lC7du38P/gWrFZBVOJpQVRwsLIHXVKvIEqSInbsUNDJvmGGsvIgyr08ryw0V2WRR903lhlk+bFVtUFUTecAAAAAAED9NZkwymh0D7V3797q0aOHPvnkE61cuVJSxUAqMTFRQ4YMUW5urvbt2ydJnmuRkZGNMPKWq3ZB1C45g08LogYOUlk9f52MxcUK273LfeAyyHqwkyeIcplKVRy333Ov+dcIGU+cWrJTU+hEIAUAAAAAgO80mTBKcjcxj42N1eTJk1VUVKTFixdr9erVktyBVHFxsSRpyJAhMplM2r59u+ca/Kv2QVSR+7JnRlT9A8PwHTtkPBk8mo9Hn3qXXO4gylwmSTLmBMmYafXU1SZsIpACvLNp0yZ9++232rRpk9/rvX03AAAAAN9qcj2jHA6HOnXqpNmzZ2vy5Ml6+eWXdfToUY0YMULBwe5ZMJs3b1ZISIgSEhIaecQt01mDKFOpCtvslssTRFndPaIiIur9TmNRkcJ/3e0+cBpkORbvuVYWcUyOMPfSPZUYZN4ZKYPcTdHrEjLRQwqov7S0NGVlZSkuLk7Lly/3a7237wYAAADgW01uZlT5LKeUlBQ9//zzslgsWrRokaZNm6aNGzfqP//5j9566y1ZLBZ2zWsks2bNqjaIcppKVZi461QQZbUq+5JLvAqi5HTKtnmTDCf7ipmPx8josJx8foFKYk/1DjPvjJSh1P17qD6znZghBQAAAACAd5rMzCiXyyWDwT2b5c0331T79u11ySWX6Mknn9T777+vNWvWaMqUKQoLC1NcXJxmz57NzKhG8O9//1vffPON+8BhqnJpnivIvZzSYbUqa9AlcoSH1/+FTqdabfxOIZmZJ4+NCrK3dj8/uEBFbXZLRndIZTxslemoeyw1hUrls5+qu4cZUgAAAAAA1F+TCKNOD6LS09O1aNEipaamqm/fvkpMTFRqaqrGjx+vn376STExMYqOjpbNZmvcQbdAH330kWbPnu05Ds5uW20QVRYaquyBg+QIC6v/C10utdq0SaEHD7qPnQZZDyfL4LDIEXyiQhBlOBYk866z96M6PWS62jiyUQIpZl8BAAAAAJqzgA+jTg+ili5dqldeeUUTJkzQdddd5+kRZbFYZLFY1Ldv38Ycaov20Ucf6emnn/Ycm+1xMuXbJEkug0NFbX491azcavVJEGX74QeF7t938tig4MPJMhVGyBFU6A6iTCeDKHuQLL/YZHAZanxkVeGSvwMpgig0d6Wlpdq2bVul8xkZGY0wGgAAAACNIaDDqKqCqHHjxumGG25g5lMAqSqICsppI4MMchmc7iDKekKS5AgK8kkQFbXlR4Vl7Dl5LAUfTpK5MFJOc4mKEndJJveueoZciyw/22Rw1j2IOv2aPwIpgig0Z46TO13a7XalpqZWeY/VauV/2wEAAIAWIGDDqOqCqJtuuon/WAkgNQZRcqo4YY+cIQWSJKfFouyB3u2aJ5dLkb/8rPDdJ3fOc0nBRzrIfCJKklRqO3IqiDrufRB1+j0NGUgRRKG5c57cYCAiIkLPPvtslffYbDbFx8dXeQ0AAABA8xGQYRRBVNNQcxDlUnH8XjlC8yRJTrNZ2QMGqiwqyqt3Wg8dVMSOHZ7joKz2Mue3kiS5TKUqizjqvlBmcAdRjpo3jKxLiNRQgRRBFFoSk8mklJSUxh4GAAAAgEZU83+pN5LyIGrRokVasmQJQVQAOmsQ1XqvHOG5kiSnyaSciy9WaSt3aGQ5elSWo0fr/lKnU5G//OI5DMpqK0tetOe4NCpbMrokSabDITKU+S6Iqk1NfUIlgigAAAAAQEsTkGGUJG3fvl1vv/22Ro8eTRAVYPbs2aOnn6o6iJKkkpiDckTYJUkuo1FH+/VXSUysJMl6+LBaf/2VWn/9lYKO5tTpvRHbtsqSny9JMhaGyXw8xnPNEXTCHUZJklMyHQyt8VneLKvzVSBFEAUAAAAAaIkCcpmeJHXs2FHz5s1TfHw8QVSAmXDZPVKy++fm3JgKQVRpVJbKbO5QyGUw6Gjfvipu3dp9s9OpqC0/ep4TfCRLJdExqo3wHTsUuX275zjo6Kl3Oi1FKkrcLRndPWmMR0JkKDF58xG9QsgEVDZ79mylpqZq9uzZ9apfvnx5re6rale+WbNmSRL/XwIAAAAEiIANoywWC31FAtDVxpFynlfqObbY404FURE5Kok96Llm73mBihLaeI7Dfv1V5oKCU7V5x2v1ztA9vyrq5588x0FZbWUqcu/G5zQXV9o9z7zbiwbptUDYBAQem80mq9WqtLS0au+xWq1KT0+nSToAAADQyAI2jELgudo4Ui6DS67IEkmSoTRIxrJgSVJZ+DGVxO333Hu8e3edSE72HBtKSxS5bWuF55mPnz2MCtm3T7YffvAcW3ISZDnuXvLnNJeoKHGXXOYy9zvyzLXaPc8bBFFAYIqPj1d6errsdnuV1zMyMpSWlia73U4YBQAAADQywijUSnmfJGdskafTmKkwXJJUFmZXceu9OjlBSnlduiqvW8VZbRHbt8tYWlrhnDk/X3I4JFPVS+qshw6p1abvyx8ry7E4WezuJX9OU6mK2uySy+J+pqHALMtPrc66e543CKKAwBYfH0/QBAAAADQBAdvAHIGjPIhytCpWWddTs5lMJyJUFnJcxfGngqj85I463qOHZDg1O8lUUKDw3bslSWaLS116FElyl1jy8qp8Z/CRI4r+7v9kcLl3xzPnxshysk+Uy1jmnhEVdHKG1gmTLFtsZ909zxsEUYB3Pvzwwwo/1tXixYs1f/58LV68uF61y5Ytq9d7AQAAAPgeYRRqVCGIOsd+alZUnk1ymFScsEcyuAOjgvbtlduzZ4UgSpIif/lZBqe7ufhl1x5X956FnmtVLdULOpqj6G83eGpMea0UlN3WHUQZHCeDqGL3zUVGWba0kqG04RqWE0QB3vv6668r/FhXH330kd5++2199NFH9ar9/PPP6/VeAAAAAL5HGIVq1RREmXNjVdxmj2R0B1GFiYmy97qwUhBlOXpUoQcOSJLCIx0aPOy42rQ/rQH6GU3MzXl5ivl//09Gh7shuSk/UsFH2nuapJdF5sgZ7J5ZpWKjgn5s1aA75xFEAQAAAADgW4RRklwnl4LhlPIgyhXsUFl3e4UgKiirrUoS9khG98ylovh4He1zkWSs/NspYsd2z88NBmnNJ5EqyDt1n+X4qWV6xqIixWxYL2OZuyG58US4gjOTPEGU01ysUlvWqdqfbTIUN1zbM4IoAAAAAAB8jwbmkpxOp0wmk5xOp4xVBCotTXkQJUllHfKlkxOPTPlRCj7SQWVR2Z4d7IpjYpTTt1+VQZQkyXDqfF6uSSvfi6pw2REcJEkyFhcr9ptvZC4ocJcVW2U9nCzDyRTMaS6uuHPesSAZCyzef9hqEEQBAAAAANAwfBZGORwO5ebmqvSMHdPKBeoOR+vXr9c333yjrKws2Ww23XjjjWrXrp3CwsIae2iN4vQgyhlSJmfrk0viHCYFZ7WX5FKp7YjnHnvPC6rdDU+SjvXureKMaIUcPKjgo0crXCuJilLueefLUFKimHXfyJLvniVlKA2S9VBHGVzu53qCqPKd806YZNke6YuPWyWCKAAAAAAAGo7XYdS2bdv08ssv64cfflDZyeVVZzIYDFq9erW3r/K5lStX6sknn1TXrl3ldDq1Y8cOff7557ruuut0/fXXq3PnzvV6bnZ2tnJycjzHGRkZvhpygzo9iJIkR4d8zy55Fnuc5DSquPVez+ykwjZtVBZZcyjkMptV0LmLCjp3kbGwUCGHDsmaeVgus9kdZBkMil23TkEnG5kbyiyyHuwko8M9Y8ppLqkcRP3YcA3LCaIAAAAAAGhYXoVRO3bs0H333SeTyaS+fftq3bp16tKli6Kjo7V9+3bZ7Xb16tVLCQkJvhqvz2RmZmrp0qW6+uqrNWHCBMXHx+vIkSN699139eabb2rfvn2aMGGCevbsWednf/jhh1qyZInvB92AzgyinKGlcsad3LGuzCxzboxK4vbJEWGXJLmMRuWlpNTpHc6QEBV06qSCTp0kSYayMsX8v/+nIPuxk8dmWQ92lrEs2H2/uURFiTsJogAAAAAAaEa8CqOWLl0qSVqwYIGSk5P1m9/8RpdeeqnGjx+v4uJivfDCC1qzZo2mTp3qk8H60vHjx3XkyBH16dPHs4SwdevWuvvuuxUXF6dFixZp4cKFuueee9SjR486PXv48OEaNGiQ5zgjI0NpaWk+Hb8vnRlESZIjqcDzc4s9TqUxh1QW6Q6NXAaDjvbtq9IoW+WHuVyK2PqLDC7p+DnnVNpd79QLHIresEHBR0/OIHOYZD3UScZSgigAAAAAAJozr7p1//jjjxo0aJCSk5M958p3pgsODtbkyZMVGxurhQsXejXIhmA0GmUymZSbmyvJ3cTc6XTvDjdixAjddddd+vnnn7Vs2TJlZmbW6dmxsbFKSUnxfCUlJfl8/L5SVRDlDC+VM+bUrCiXpURlUe5+Ty6DQUcv6quihDZVPi90b4Yit29XxI7tCjmwv+qXOp2K+b9vZc0+uTOew+hemlcS4r5MEAUAAAAAQLPlVRhVUFCgxMREz7HZbFZhYeGphxuN6tWrlzZu3OjNaxpEu3btFBMTo6+//lqlpaWeXfTKA6kbbrhB48aN05o1a/T9999XuNZcVBVESVJZUr7n58aSYJVFuWcvuSQd691HRaf9mp/J9t//en4e9uueyjc4nYr+7jtZywM+p1HWQ51kKgl1H5pKCaKAZqhbt24VfqyrCy64QH379tUFF1xQr9q6znAFAAAA0HC8CqNsNpvy8vI8x9HR0dq/v+JsmJKSEhUVFXnzmgYRHBys22+/Xf/973+1aNEiSaoUSN1222266KKL9Oqrr+rEiROe681BdUGUI6ZIrlYl7oMys5yh7uV67iCqtwrbtav2mYbSUhlOC+yMpSUVb3A6Ff3d/ynk0MGTxwZZD3WUqdi9c6HL4FBxfAZBFNAMpaamVvixrqZPn65//OMfmj59er1q77///nq9FwAAAIDveZWuJCcna+/evZ7j888/X//3f/+nLVu2SJL27Nmj1atXN/oytf3792vLli1avXq1Dh8+7Dnfs2dP/fa3v9Vbb72lV199VZI7kDIYDHI4HJKkiy++WLm5uXVeqhfIqguinJElKuuW6zk2lFk8P7f3vECF7TvU/GCDQU7LqRqnJei0h7tnRIUcOnTy2KDgw8kyFYVLklwGp4oS9sgZcrJXVYmRIAoAAAAAgGbIqwbmAwYM0Pz585Wdna3Y2Fjdcsst+uqrr3TfffcpIiJC+fn5cjqduvXWW3013jpbvXq1li5dqoMHD6q4uFjt27fX+PHjddVVVyk2Nla///3vdezYMf3v//6vysrKdPvtt0uSTCZ3CGI0GmWxWGQ2e/WtChjVBlERJSrtYZdOZj/GgghPMOQICtKJ0/qCVcdlNit74CBFbflRMhh07MILTz7cqVYbv6s4I+pwR5kKI9x1cqo4YY+coSeXB5YZZPnJRhAFAAAAAEAz5FXCcv311+uKK65QRIQ7VOjSpYuee+45paen6+DBg0pJSdGIESM0YMAAnwy2rj7//HM9/vjjGjp0qG677TYlJCTooYce0vvvv6/+/fsrIiJCycnJuv322xUcHKylS5fq0KFDuv7663XOOedo27Zt+uabb5SQkCCbzdYon8GXqg2iwktVeq5dMrubz5tORMicG6PiMPcSzKL4+Op3xTtDqc2m7EsuPe3hTrX6fqNCD54KooIPJ58RRGXIEXpyuefJIMpYYFFDIIgCAAAAAKBxeRVGmc1mRUdHVzh3/vnn6+mnn/ZqUL7www8/aMGCBRo2bJjGjBmj+Ph4SdJVV12lNWvWKD8/3xOide7cWbfffrs6deqkN998U1999ZWCg4MVHBys4uJiPffcc557m6pqg6iwUpWee8wTRBlPhCv4cLJKYg567qlu57yzcrnUatP3Cj1w4OSxQcGZyTIXRroP5VJx/F45wo67rzsky882GfOCqnmgdwiigMbzzDPPeH6szw6rkyZN0rFjx9SqVSvNnTu3zrWnL9EGAAAA0Liax9qzMxQXF+v//u//1LZtW1177bWeIEqS8vPzZTabtXr1aoWGhio2NlaDBg1Su3btNHr0aF1zzTVauXKlcnNzFRMTo0suuaTCjoFNUbVBVGiZSs87JllOBlGFYbIe7ii5DHKEugMil9Go4tat6/5Sl0utvv9eoeUN7V3uGVHmE6cFUa33yhF+skeVQ7L83ErG4wRRQHNU3nevvv339u/fr6ysLBUUFNS7FgAAAEBg8EkYlZOTo+3btysvL8+zE92Zrr32Wl+8qlaCg4N17rnnqlOnThW28/7oo4/02WefKTk5WUeOHNGaNWvkdDrVr18/PfzwwzKbzYqLi9Mtt9zit7E2tGqDqJAzgqiiUFkPdZTBZZQjqNCzo11xbKxcde2X5XLJtmmTQvfvO3lsUPDhpApBVEncPjki7CcHI1l+scmYSxAFAAAAAEBz51UYVVxcrNmzZ2vVqlVyuVxV3uNyuWQwGPwaRkmq0KfK6XRqy5Ytmj17tv7whz/o+uuvV5s2bXTbbbfphRde0KpVq9StWzfdfPPNnvGePvamqrogymU9GUQFuYNDY1GIrIc6yeByNwx3hJ3aUa8oIaFuL3U6Zfths8L2ndxl0aWTQVTUyUOXSmIPqCzy2Mn7JfNWm4z24Lq9p5YIogAAAAAACCxehVEvvfSSVq5cqfbt22vw4MGKi4vz7EIXSIxGo3r27Km0tDT17t1bYWFhkqTo6Gjdc8892rhxo7Zu3SpJFcKn5hhESVJpt+NS8MkgqvhkEOU89etWvkRPkori6xBGOZ2K/u60XfNcUnDmGUFUzEGVReV4rpu3Rcl0lCAKAAAAAICWwqswavXq1UpOTtbChQsVFNQwS6x8weFwyGQy6dJLT+3y5nQ6ZTQaFRsbq7CwMOXn5zfiCP3HZXDJFeFegmcotch6sJMMzlO/DVzGMjmDCyVJpZGRcoSG1vLB7h5RlYKoAtvJw5NBlC3bc928PVKmHKtPPteZCKIAAAAAAAhMRm+K8/Pz1a9fv4AOoiRVmq1VHkRJ0oYNG3TixAn17NmzMYbmd65gh3RywpexKKxCECVJZWHHPdeL4+Jq+dCTPaIOnNas/FDHM4KoQxWDqB2RMmWFePlpqkYQBQAAAABA4PIqjOrQoYOOHTvmq7H4xelB1M8//6xly5YpODhYV111VSOPzE+sDs9PjWUVQ0SX0aHS6EOe48La9ItyuWT74YfKPaIKTzUrL40+pDJblue6eUekTEcIogAAAAAAaIm8CqNGjx6ttWvXav/+/b4aT63Z7XZlZmYqLy9PRUVFklRtE/XTGY1GFRcXa/ny5XrppZe0Y8cOPf7440qoa6PuJshldqqsY57n2FBaMYwqaXVYLnOZJHcQVRJ7lplRLpeifvxRYRl7Th5X7hFVGn1Ypa1Obalu3kkQBQAAAABAS1annlGbN2+ucBwXF6d+/frprrvu0siRI9WtWzeFVtNjqFevXvUdYyWrVq3Sq6++qsOHD8tsNqt79+665ZZb1KdPn7PWulwuffLJJ3r++efVq1cvzZs3T8nJyT4bW6BymZwqPfeYXGHumVGG0iDPMjpJcgSdUFmUexmd02RS7vlnWbbociny558U/uvuk8dS8JEOFZbmuYOoI54S844ImTIJogAAAAAAaMnqFEZNmjSpyh3mXC6XFi9eXOPuc2vWrKnz4Kqydu1azZo1S5dffrlGjBihgwcPat26dZoyZYr++Mc/atiwYYqMjKy23mAw6IYbblDnzp3Vvn172Ww2n4wrkLmMLpX2sMsV4Z71ZCgzn2xc7u6l5ZJLJXEHPL2i8rqlnLVxecTWXxSxc6fnOCirvcz5rTzHpa0yKwZROyNkyqxlM/Q6IogCAt+wYcOUnp6uYcOG1at+3LhxKiwsVEhI3QPtcePGKSMjQ++8w/9WAAAAAIGgTmHUuHHjagycGpLL5ZLT6dRnn32m888/X/fcc49iY2MlSVdeeaU++OADLVy4UMeOHdOYMWPUqlWrCvWZmZk6fvy4unbtKkk6//zz/f4ZGoPL4FJZd7tcUe4d9OQwyXqws4xlwZ57yiKOymk9IUkqDY9QfpcuNTzQpYjt2xW5fbvnVNCRdrLkRXuOS1odVml0pufYvCtCpsMEUUBLdtlllyk9PV2XXXZZveqHDx9e73cPHz5c27ZtI4wCAAAAAkSdwqjbb7+9ocZxVgaDQUajUfv371f79u0VGxursrIymc1mdevWTampqbLZbHr99ddltVo1fvx4mc3uj5efn68nnnhCmzZt0rx581rMznmS5GhXIGd0ifvAaZT1UCcZS62e6y65VBJ92HNs79lTMlbdSsxUeEJRP/ygkMxTQVNQVltZ8mI8x2Vh9gpBlGlXhEyHCKIAAAAAAIBbncKoxmYwGNS6dWsdOHBAkmQ2m+VwOGQymWSz2TR69GgVFxfrtddeU8eOHTV48GBJUnh4uFJSUrRz505FR0fX9IpmxSWXHPGF5QeyHuooU3HlYMjgvixJMhcUqCTujMblLpfCft2tyJ9/kdFR5jkdlN1GluOxp24zOFUSc9BzbNodLnMNQRRhEgAAAAAALY9Pwqjs7Gx98cUX2rFjh/Lz8xUeHq6uXbvqyiuv9Cyl85bT6ZTRaNTgwYP19NNPa/HixZowYYJMJpMnkIqIiNBNN92kPXv2aN68eerevbvatm0rSbrnnns0ZsyYGvtJNTeuyFLJ6pQkmU5EyFQUXukegwwKym6n4oQ9kqSoLT+qJCZGZRERkiTz8Vy12rxZQceOnaopMysou22FBuiSVBqVJZfFvRzQcCxIpoM1z4i62jiSQApoIex2e4Uf6yo7O9vz/wN1/f+V7OxsHT16tF7vBQAAAOB7XodR7777rv75z3+qtLRULpfLc37FihVauHChJk6cqBtvvNHb18h4culY79691aNHD33yySdq166drr766gqBVGJiooYMGaKNGzdq3759atu2redaSwqiJMnRusjz89Obi5/JXBAlR26MyqJyZHQ4FP3d/ynrkksUsWOnwnfukOG0X1dzbrSCjiZ6mp+Xc5pKTzUsd0nmXyNk0Nn7ixFIAS3D448/7vnxww8/rHP9XXfdpaysLMXFxWn58uX1qgUAAAAQGKpuDlRLq1at0ty5cxUSEqLbb79dc+fOVXp6uubOnavbb79dVqtVc+fO1RdffOGTwbpcLsXGxmry5MkqKirS4sWLtXr1akmSyWRScXGxJGnIkCEymUzafrLJtslkqvaZzZXL4JIz9mQY5TTKVBBV4/1BOYkylLibmluOH1fCZysUsWO7J4gylATLeqCzgrPbVwqiJKk0+rBkdM/CMh4OkfFE7XPOq40ja30vAAAAAABo2rwKo9544w1FRUXplVde0W233aZevXqpQ4cO6tWrl8aNG6f//d//VUREhN544w2fDNZgMMjhcKhTp06aPXu2jh07ppdfftnzr+TBwe4wZfPmzQoJCVFCQoJP3tsUOaOLJbM7SDIXRMngqvmX2uAyypqZJDnds5k8vaFcBlmOxitkf7cql/lJkiPohMoiTi6BKTPInFH1fTUhkAIAAAAAoGXwKozKyMjQFVdcUW3/jtatW+uKK65QRkaGN6/xcLlcnllOKSkpev7552WxWLRo0SJNmzZNGzdu1H/+8x+99dZbslgsLWrXvDM5T1+il1f9Er3TGUtCFJSTeOq4MFQh+7op6FhCtWGWSy6VxB5U+Yo8074wGcrq99uKQAoAAAAAgObPq55R4eHhslqtNd4TEhKi8PC6z5Q5k8vlksHgTjzefPNNtW/fXpdccomefPJJvf/++1qzZo2mTJmisLAwxcXFafbs2S12ZtTx48flbOVesmgoM8tYWPvvv+V4rAylwZLBJdOJs/d9coQdlzOkwP2uQtNZm5afDT2kAAAAAABo3rwKowYNGqR169YpNTVVZnPlR5WVlembb77RJZdc4s1rKgRR6enpWrRokVJTU9W3b18lJiYqNTVV48eP108//aSYmBhFR0fLZrN59c6m7KuvvvLMeTPlt6pVI/HTmQsjanWfy+hQScwBz7Hp13AZXHV7V1UIpAAAAAAAaL68WqZ3zz33KCQkRA888IB++umnCte2bNmiBx54QKGhobrrrrvq/Y7Tg6ilS5dq0aJFmjBhgq677jpPjyiLxaLQ0FD17dtXnTp1atFBlCT9Y9J8z8/NBQ23g2Bx7H65LKWSJIM9SMajwT57Nkv2AAAAAABonryaGXXHHXeorKxMOTk5uvfee2UymRQVFaXc3Fw5HA5JUkxMjO64444KdQaDQW+99dZZn39mEPXKK69o3LhxuuGGG1p84FSdq40j5eru8BwbyoIa5D1l4cfkiLCfPDDIsiOyzjOwzoYZUgAAAAAAND9ehVHlDcVbt25d4XxMTEyl+2o6ru7ZVQVRN910E0FUNcpnE7mCT4ZRLnfPKF9zmotVHLffc2zeGSFDscnn75EIpAAAAAAAaG68SirefvttX42jAoKoujt9WZsryClJMjjMMni3ErMSp6lExfF7JaP7HcYjVpmyQ3z6jjMRSAEAAAAA0Hz4ftqMD5QHUYsWLdLrr79OEHUWFYIog0sqD6PKLD55vksuOUKPqyzyqByhx+VZjVdoknlX7Zqde4tACmjaHnjgAf3973/XAw88UK/65557Tg6HQyZT3WdhPvfcc9q5c6dmzpxZr3cDAAAA8K2ADKMkafv27Xr77bc1evRogqgaVGr0HeT0hEXe9otymkpUFnlUZZFH5TKXVrxYZpBlW5QMDt/OvKoJgRTQdCUkJFT4sa46dOhQ73d36NBBhYWF9a4HAAAA4Ft1CqOWLFlSr5cYDAaNGzeuTjUdO3bUvHnzFB8fTxBVjap2nHMFnd68vH4zoxwheSqNyq44C6pcsVGmwyEyZYbIUNIwfaJqQiAFAAAAAEDTVqcwavHixfV6SX3CKIvFopSUlHq9ryWoKoiSJFew89SB0SmXXLXe5c4ll0pbHVFp9OEzL8h4NEjGw6EyHgvy+a55dUUgBQAAAABA01WnMGru3LkNNQ74iKH41LK5ssijMjjMshxNOGuA5JJLpdGHVdrqyKmTjTwLCkDTkpmZKbvdXuW1//znP5KkDRs21OsfGlauXKni4mIFBwfr6quvrnNtRkZGnd8JAAAAoGHUKYzq1atXAw0DvmLMC5Lp13A5OuZLkkpbHZHL4FRQTmK1gZRLLpXEHFSZLdtzzvRruEwHQhtkFlT5rKbqZnfVth5A4MjMzNTYsWNVVFRU433vvvuubrvttjo/f8GCBcrKylJcXFydw6jyWgAAAACBwWcNzB0Oh3Jzc1VaWlrl9fj4eF+9CmdhPhAmg9Ogss55kuQOmQwuBWW3rRQuueRSSex+lUUdPVW/M0Kmw6ENMrbTg6SVznfqHEgRRAGByW63q6ioSNOmTVNSUlKl63/5y19kt9vrtRseAAAAgObF6zBq27Ztevnll/XDDz+orKysynsMBoNWr17t7atQB6ZDoZLToLIu7ibkZVE5ktGpoCPtPYGUSy6VtN6nsohjOnlC5h2RMh0JaZAxVRUk1SWQIogCAl9SUlKVy/AslvptqOBrNS3Xs9ls/MMJAAAA4AdehVE7duzQfffdJ5PJpL59+2rdunXq0qWLoqOjtX37dtntdvXq1aveW3nDO6bMEMkhlaWcDKQijsllcCo40z1roTg+Q47wXPfNLsm8LVKmbP8FUadfO1sgRRAFwBfS0tKqvWa1WpWenk4gBQAAADQwr8KopUuXSnL340hOTtZvfvMbXXrppRo/fryKi4v1wgsvaM2aNZo6dapPBou6M2WHSC6DylJyJaPkCM9VsWGPJMkRdtx9k1Myb4uSKcfaIGOoTZBUUyBFEAXAF6Kjo/XUU09VeS0jI0NpaWmy2+2EUQAAAEAD8yqM+vHHHzVo0CAlJyd7zrlcLklScHCwJk+erC1btmjhwoWaPn26VwNtSN9++62CgoJ0wQUXyGDwfcPuxmbKsUq/GFR2jt0dSJWHUJLkkMxbbTIdC26Qd9clSKoqkCKIAuArJpOpXjv5AQAAAPAtozfFBQUFSkxM9BybzWYVFhaeerjRqF69emnjxo3evKZBffzxx/rLX/6ijIwMORyOxh5OgzEdC5blJ5t0+kd0SJafWwVEEFVVDUEUAAAAAADNj1czo2w2m/Ly8jzH0dHR2r9/f4V7SkpKzrrVd2P5+OOP9dRTT2nkyJEaMGCAzGbfbC6YnZ2tnJwcz3FNDXOrUtcd5mrLmBssy0+tVNrN3SfKsj1KxuNBDfIub4IkQigAAAAAAJovr9KX5ORk7d2713N8/vnn6+uvv9aWLVt03nnnac+ePVq9enWV23w3thUrVuipp57S6NGjdeONN6p169aS3MsMXS6XDAaDDAaDnE6njMa6TSD78MMPtWTJknqNq6GCqHLG40EK+i5Wkjy76vkaYRIAAAAAAKiOV2HUgAEDNH/+fGVnZys2Nla33HKLvvrqK913332KiIhQfn6+nE6nbr31Vl+N1yfWr1+vxx9/XAMHDtSNN97o2e3vrbfe0q5du3T8+HG1adNGo0ePrtdOgMOHD9egQYM8x+WNcc+moYOocg0VQkkEUQCqFh0dXeFHf9Z7+24AAAAAvuVVGHX99dfriiuuUEREhCSpS5cueu6555Senq6DBw8qJSVFI0aM0IABA3wyWF9p1aqVIiMjtXfvXh04cEAJCQmaOnWqNmzYoJiYGJnNZq1fv15r1qzRww8/rL59+9ZphlRsbKxiY2PrNCZ/BVENiSAKQHUWLlzYaPXevhsAAACAb3kVRpnN5kr/0nz++efr6aef9mpQDcnlciklJUXPPPOMpk6dqvnz5ys8PFz79u3TzJkz1bt3b0VEROjLL7/U66+/rkcffVQvvPCCOnTo4Fm+52sEUQAAAAAAoKXwaje9pqR8lz+DweAJpJ588knl5eXpxx9/1OTJkzVo0CDPLK/LLrtMd9xxh0pLS/XPf/5TDoeDIKoaBFEAAAAAAKC2WkQY9dVXX2nRokWenf5OD6Qef/xxDR48WOecc45nN73yGVD9+/dX9+7dtX//fpWUlPh8XARRAAAAAACgpfFqmV5TsHPnTv3973+XyWSSxWLR9ddfrzZt2lQIpP76178qODi4ymV4JpNJZrNZJpPJp+MiiALQksyePVt5eXmKiIjQX/7yF7/We/tuAAAAAL7V7MOoxMREtWvXTqWlpXrzzTdVXFysUaNGKSEhwRM8BQcHS3LPmDq9UfmPP/6oAwcOqG/fvj4Po5o6gigAdbF+/XplZWUpLi7O7/XevhsAAACAbzXrMMrpdMpgMKh169a64IILZDKZtGjRIhkMBt18881KSEioVFMeRG3evFnp6ekqLCzUzTff7PMwaqXznSY7O4ogCgAAAAAA1FezDqOMRqNCQkLUr18/ff7555o/f76OHTum5cuXS5JGjhypNm3aVKjJy8tTenq61q5dq+LiYj377LNq3759g4yvKQZSBFEAAAAAAMAbzTqMKtehQwfZ7XYVFxfrz3/+swwGg/71r3/JaDRq2LBh+uijj3TNNdeoS5cuysrK0pEjR3Tuuefqtttua7AgqlxTCqQIogAAAAAAgLdaRBjVv39/BQUFafXq1brxxht17733ymg06u2339a6det04MABDRo0SJLUqVMnTZ48WcHBwQoJCfHL+JpCIEUQBQAAAAAAfMHY2ANoaOV9o+Lj4/Xrr79Kci/fu/POO5WYmKhDhw5pwIABiomJ8dTYbDa/BVHlAjnsCeSxAQAAAACApqXZh1EGg0Emk0lXXHGFvvvuO9ntdhUWFuqRRx5Rbm6u+vfvr2+//VZvvfWWMjMzG3WsgRj6BOKYAAAAAABA09UiwihJSkxM1PHjx5WTk6PZs2dr8+bN+utf/6pHH31Uv/3tb7V69WpZLJZGHm1ghT+BNBYAAAAAANA8tIieUZLUt29ftWnTRnfeeadCQkL0l7/8RQMGDFBQUJAeeOABTZgwQdHR0Y09zIBBEAUAAAAAABpCkw2jynfHCw0NlcVikdVqlcvl8syEOpPD4dDAgQMVFham4cOHa9CgQQoKCvJcD5QgKhAamRNEAfC1wYMHKy8vTxEREX6v9/bdAAAAAHyrSYZRq1at0quvvqrDhw/LbDare/fuuuWWW9SnT58q73e5XDKZTBo7dqyuvPJKJSQkVAiiAgVBFIDmauLEiY1W7+27AQAAAPhWk+sZtXbtWs2aNUtdunTRvffeq2HDhikrK0tTpkzR66+/ruPHj1eqMRgMcrlcMpvNSkpKUnBwcCOMvGYEUQAAAAAAoCVoMjOjXC6XnE6nPvvsM51//vm65557FBsbK0m68sor9cEHH2jhwoU6duyYxowZo1atWlWoP3LkiI4fP66uXbs2xvBrRBAFAAAAAABaiiYzM8pgMMhoNGr//v2KjIxUbGysysrKJEndunVTamqqxowZo3/9619avny555ok5efn64knntCdd96p//73v431EapEEAUAAAAAAFqSJjMzSnIHUq1bt9aBAwckSWazWQ6HQyaTSTabTaNHj1ZxcbFee+01dezYUYMHD5YkhYeHKyUlRTt37gyYRuUSQRSAluPWW29Vdna2YmNj9dprr/m13tt3AwAAAPCtJjMzyul0SnLvipSRkaHFixdLkkwmkxwOhyQpIiJCN910ky666CLNmzfPE1pJ0j333KM33nhD7dq18//gq0AQBaAlKSws1IkTJ1RYWOj3em/fDQAAAMC3mszMKKPRnZv17t1bPXr00CeffKJ27drp6quv9gRSJpNJiYmJGjJkiDZu3Kh9+/apbdu2nmuRkZGN+hkmXvQ3GQssjTqGcgRRAAAAAACgMTSZmVGSu4l5bGysJk+erKKiIi1evFirV6+W5J4hVVxcLEkaMmSITCaTtm/f7rmGUwiiAAAAAABAY2lSYZTBYJDD4VCnTp00e/ZsHTt2TC+//LKWL18uSQoODpYkbd68WSEhIUpISGjM4QYkgigAAAAAANCYmswyPck9M6p8llNKSoqef/55PfbYY1q0aJE2bdqkG2+8UYcPH9bXX38ti8Winj17NvKIAwtBFAAAAAAAaGxNJoxyuVwyGAySpDfffFPt27fXJZdcoieffFLvv/++1qxZoylTpigsLExxcXGaPXs2M6NOQxAFAAAAAAACQZMIo04PotLT07Vo0SKlpqaqb9++SkxMVGpqqsaPH6+ffvpJMTExio6Ols1ma9xBBxCCKAAAAAAAECgCPow6PYhaunSpXnnlFU2YMEHXXXedp0eUxWKRxWJR3759G3OoAYkgCgAAAAAABJKADqOqCqLGjRunG264gZlPtUAQBQAAAAAAAk3AhlHVBVE33XQTQRQANDEPPPCAiouLPTNa/Vnv7bsBAAAA+FZAhlEEUb5xtXEks6MABISBAwc2Wr237wYAAADgW8bGHkBVyoOoRYsWacmSJQRRXrjaOLKxhwAAAAAAAOARkGGUJG3fvl1vv/22Ro8eTRDlJQIpAAAAAAAQKAJymZ4kdezYUfPmzVN8fDxBlA+wZA9AY9q2bZtKS0tlsViUkpLi13pv3w0AAADAtwI2jOI/GnyPQAqANzIzM2W326u8lpGRUWPtQw89pKysLMXFxWn58uV1frc39XWprelz2Gw2xcfH1+ndAAAAACoL2DAKDYNACkB9ZGZmauzYsSoqKqr2HqvV2mRnstpsNlmtVqWlpVV7j9VqVXp6OoEUAAAA4CXCqBaIQApAXdntdhUVFWnatGlKSkqq8p6mPHMoPj5e6enpNc78SktLk91ub7KfEQAAAAgUhFEAgFpLSkpqtkuo4+PjCZoAAAAAPwjY3fRQM29mNjErCgAAAAAANBbCqCasPqESQRQAAAAAAGhMhFFNXF3CJYIoAAAAAADQ2AijquB0Oht7CHVSm5CJIAoAAAAAAASCFt/AfOPGjfr++++Vk5Oj+Ph4XXvttWrTpk1jD6vOVjrf0dXGkdVeAwAAAAAACAQtOoxasWKFnn76abVp00bFxcXKzc3Vv/71L91666264oorlJCQUK/nZmdnKycnx3OckZHhqyHXqKpAiiAKAAAAAAAEkhYbRu3bt0+LFi3Sddddp1GjRqlt27b66aef9P7772vBggXas2eP/vCHPyg5ObnOz/7www+1ZMkSn4+5Nk4PpAiiAASK9PR0uVwuGQwGv9d7+24AAAAAvtViw6ijR4/Kbrfr4osvVtu2bSVJPXr00LnnnquEhAS9+uqrKi0t1YQJE9S+ffs6PXv48OEaNGiQ5zgjI0NpaWk+HX9NCKEABJrQ0NBGq/f23QAAAAB8q8WGUcXFxXK5XHI4HJKksrIymc3ub8cdd9whk8mkxYsXKy4uTmPHjlV4eHitnx0bG6vY2NgGGTcAAAAAAEBT1mJ300tJSVFoaKhWr14tSTKbzXI6nZ6d9MaPH6+bbrpJy5cv17Zt2yQ1vV32AAAAAAAAAk2LmRl18OBBWa1WRUdHS5KioqI0atQovfTSS+rWrZtGjx4to9Eop9Pp6S1y6623avPmzVq0aJEuuOACz8wpAEDdLFu2TAUFBQoLC9OoUaP8Wu/tuwEAAAD4VotIV1avXq2lS5fqkksu0YgRI9SqVStJ0qWXXqpNmzYpPT1dYWFhGjZsmIxGoxwOh0wmk2JiYtSnTx+tXr1aubm5iomJaeRPAgBN09tvv62srCzFxcXVKxDypt7bdwMAAADwrWa/TG/lypV6/PHH1bVrV5177rmeIEqS2rdvr1tvvVVt27bVwoUL9e6770qSTCaT557yXlEs0QMAAAAAAPBesw6j9u3bp6VLl+raa6/V7bffrgEDBkhyNysvd8EFF+iPf/yjkpKSNHfuXP3zn/9UVlaWXC6Xdu7cqY0bN6pt27YKCwtrrI8BAAAAAADQbDTrZXqZmZk6duyYLr30UrVp00aS9Oqrr2rfvn0yGAzq0qWLfv/73+uiiy5SRESE/v3vf+tf//qXVqxYodDQUBkMBuXm5mru3LlsDQ4AAAAAAOADzTKMKm9Avn//fgUHB6t///6SpAceeECbNm1SmzZtlJubq88++0yrVq3SM888o5SUFLVp00bXX3+9PvroI504cUKtW7fW0KFD1a5du0b+RAAAAAAAAM1DswyjDAaDJHdPqJycHG3YsEEnTpzQ9u3b9eSTT+qCCy6Q5G5q++abb2rKlCl66aWXFBkZqcjISN1///2SToVagWal853GHgIAAAAAAEC9NOueUW3atFG7du302Wef6ZtvvlFycrLOO+88BQcHKzg4WL///e81btw4bd++XUuWLJHkDqBcLlfjDrwGBFEAAAAAAKApa9ZhVGJiogYPHqxVq1Zp3bp1iomJUWhoqFwul8rKyhQSEqLhw4erffv22r59uyT3rKry2VCBNiuKIAoAADR3M2fO9Px9rPwrISGhsYcFAAB8qNmGUU6nU5J0++2367rrrlNBQYFWr16tb7/9VgaDQUaj+6OHhISoTZs2On78eIVd9gINQRQAAGgOxo8fr5kzZ9Z4T0pKig4dOuT5+vHHH/0zOAAA4BfNsmeUJE/YJEnjxo2Ty+XSJ598onnz5un+++9X7969JUk7duzQoUOHdMEFF1SoCSQEUQD8ITMzU3a7vcprGRkZXj27a9euat26taKiovxe7+27Afif2WxmNhQAAM1Ysw2jTpeQkKC7775bISEhevfdd/XXv/5Vw4YNU2lpqXbt2qVjx45p1KhRARlGEUQB8IfMzEyNHTtWRUVF1d5jtVpls9nq9fwnn3yyniPzvt7bd5+uplDOZrMpPj7eZ++Cf7322msaO3asJGnhwoW68847a7x/yZIlmjBhQo33GI1GORwOn42xIe3fv1/Tp0/Xp59+qpycHLVp00Y33HCDZsyYoVatWnnuy8nJ0Xvvvaf//Oc/+vHHH3XgwAEFBQXp/PPP14QJEzRhwgSf/H1q9+7dSkxM9OyKPGvWLHXq1Mnr5wIAgMDQZMMou92u4uJihYaGymKxyGq11rj7nc1m06RJk9S1a1d98cUX+ve//63w8HAlJSVp/vz56tChg58/wdkRRAHwF7vdrqKiIk2bNk1JSUlV3tOSwxabzSar1aq0tLRq77FarUpPT2+x36OmbN++fbrvvvsUHh6u/Pz8WtX06tVLM2bMqPLa119/rS+++EJDhw715TAbzK5duzRw4EAdOXJE119/vbp3765vv/1Wc+fO1aeffqpvvvlGMTExkqR33nlH99xzj9q0aaMrrrhCHTp0UGZmpt59913deeed+uSTT/TOO+941Xezf//+WrJkibp3764jR44oLS1NAwcO1E8//eQZBwAAaNqaZBi1atUqvfrqqzp8+LDMZrO6d++uW265RX369Km2xul0ymg06rrrrtOQIUOUm5srq9Uqo9GokJAQP47+FMImAIEmKSlJKSkpjT2MgBMfH6/09PQalzGmpaXJbrcTRjUxLpdLEyZMUExMjG666SY988wztarr1auXevXqVeW1AQMGSJL++Mc/+mqYNSqfpbV69Wpdfvnlda6fOHGijhw5oueff15/+tOfPOenTJmi5557Tg8//LAWLFggSerWrZs+/PBD/fa3v60wA2rWrFnq16+fli9frnfffVcjRoyocG3WrFme4+LiYhkMhgrf608++USXXnqpJFUK8S6++GJ16tRJS5cu1ZQpU+r8+QAAQOAJvHVpZ7F27VrNmjVLXbp00b333qthw4YpKytLU6ZM0euvv67jx49XWXf6X5gMBoNiYmIUFhbWaEGUJF1tHNlo7wYA1E18fLxSUlKq/KpuNllTd8MNN8hgMOj555+vdO3vf/+7DAaD7rjjDknSnj17ZDAYNH78eG3dulU33HCDoqOjFRYWpksuuUQrVqzw9/Br5fnnn9cXX3yhxYsXKywszOvn/fjjj1q/fr3atm2r3/72t9Xet3z5cg0dOlSxsbEKCgpS165dNWvWLL8v69u1a5dWrFih5ORk3XvvvRWuPfLIIwoLC1N6eroKCgokSVdeeaWGDRtWaSleeUsESVqzZk2Fa3fffbc2b97s+Ro+fHilcxdddFG1YwwPD9e5556rHTt2+OATAwCAQNBkZka5XC45nU599tlnOv/883XPPfcoNjZWkvsvRh988IEWLlyoY8eOacyYMRX6G0jufih5eXnq0qWLTCZTY3yEKl1tHMkMKQDN3tSpU5Wbm6uoqKh69XDypt7bd7dkr7zyii688EL99a9/1aWXXqoLL7xQknuG8qxZs9SjRw/NmzevQs2vv/6qAQMG6Pzzz9ddd92lQ4cOadmyZRo6dKjeeOMNjRo1qjE+SpV++eUXTZ06VZMmTdJll12mL774wutnvvzyy5KkO+64o8q/bzgcDt16661666231KVLF40cOVLBwcH65JNP9PDDD2vbtm1aunSp1+OordWrV0uShgwZUilgioiI0KBBg7RixQqtX79egwcPrvFZFotFkrv5+Omio6MVHR1d4bnR0dHq0qVLrcZYVFSkrVu36oorrqjV/QAAIPA1mTDKYDDIaDRq//79at++vWJjY1VWViaz2axu3bopNTVVNptNr7/+uqxWq8aPH+/5y1B+fr6eeOIJbdq0SfPmzVPPnj0b+dNURCAFoLnbsWOHsrKyFBcX5/d6b9/dkkVHR+vNN9/Ub37zG40aNUrff/+9CgoKdOuttyo4OFhvv/22QkNDK9R89dVXevDBBzV79mzPufvuu08DBgzQ3XffraFDhyoyMvKs754zZ061yyKr0qtXL91www21vr+srExjx45Vhw4dKiwh80ZhYaFee+01mUymahugT5o0SW+99ZamTp2qxx57zPN3ldmzZ+vyyy/Xq6++qr/97W/q0aOHT8Z0Ntu2bZPkXn5Xla5du2rFihXavn17jWFUWVmZXn31VUnStdde69WYHnzwQQ0bNkwdOnTQkSNH9Nhjj6mgoEDjxo3z6rkAACBwNJkwSnIHUq1bt9aBAwckuf/lzeFwyGQyyWazafTo0SouLtZrr72mjh07ev7SFB4erpSUFO3cubPCv8wFEgIpAEAgGjhwoB577DH9z//8j+666y5lZWXp8OHDWrhwoc4999xK90dFRWn69OkVzl100UUaM2aMli5dqvfee69WocKcOXNq3L3wTOPGjatTGPXoo49q06ZNWrt2rc+W7L/99tuy2+367W9/q/bt21e6vmHDBr344ou6/vrr9cQTT1S4ZrFYNG7cOK1bt04bNmzwWxiVm5sryf3rVpXy82cLBqdOnaotW7bouuuu0zXXXOPVmPbv368//OEPys7OVlxcnC6++GKtX7++2S6HBQCgJWoyYVR5A/LBgwfr6aef1uLFizVhwgSZTCZPIBUREaGbbrpJe/bs0bx589S9e3e1bdtWknTPPfdozJgxtfrX2MZCIAUACER/+9vftHr1ar3xxhuSpD/84Q/Vzvzp3bu3IiIiKp2//PLLtXTpUm3atKlWYdSePXu8GnNNNmzYoFmzZumBBx7wNBv3hfIlenfddVeV1+fNmyeXy6XQ0FDNnDmz0vUtW7ZIcv+dpzrJycnVhnRVLWMbN26clixZcpaRe+f555/XP/7xD3Xv3l3p6elnvf9s43nrrbd8NDIAABComkwYVd7HoHfv3urRo4c++eQTtWvXTldffXWFQCoxMVFDhgzRxo0btW/fPrVt29ZzLZCDqHIEUgAaSmZmZo27wcF7NX0fbTZbk91pz2Aw6KabbvI0IZ88eXK191b3GRMSEiSdmonTWMrKynTbbbepW7dueuyxx3z23J9++knr1q1Tu3btdN1111V5T/n3780336zxWTXNAJo8eXKlP8ebN2/WBx98oHHjxik5ObnCtep2/CtXPvOpul+X8vM2m63K6/Pnz9ekSZPUo0cPrVq1KmBnoAMAgMDSZMIoyd3EPDY2VpMnT9bkyZO1ePFimc1mXXHFFTKZTCouLlZwcLCGDBmip59+Wtu3b9fFF18cUA3La4NACoCvZWZmauzYsSoqKqr2HqvVWu1/cKJmNptNVqtVaWlp1d5jtVqVnp7eJAOpHTt26MEHH1SrVq2Um5urO++8U99++62sVmulezMzM6t8xuHDhyVVvxzsTA3VMyo/P1/bt2+XpCrHL0mpqalKTU3VpEmTNGfOnFq9/2yNy4uKipSVlaXLLrtMX375Za2eWZWqgsAlS5bogw8+0Pjx43X55ZfX6XkpKSmS5PmenKl8B7uqekrNmTNH999/v8477zytWrVKrVu3rtO7AQBAy9WkwiiDwSCHw6FOnTpp9uzZmjx5sl5++WUdPXpUI0aMUHBwsCT3vxCGhIR4/hUWAFo6u92uoqIiTZs2rdpZF0155k5ji4+PV3p6eo0zz9LS0mS325vc97i4uFijRo1SQUGBPvvsM3355Zd6/PHHNXnyZC1YsKDS/d9//73y8vIqLdVbs2aNJHl25DubhuoZFRwcrDvuuKPKa99//702bdqkSy65RCkpKbVewldUVKT09HSZTKZqn+1yuSRJ2dnZtXqmv5Qv7VuxYoWnJUK5vLw8ffPNNwoNDdXFF19coe6pp57S1KlT1atXL61cudKzw7GvPfHEE3rooYd07733av78+dXel5eXp7///e967733dOTIEV144YWaO3eu+vbt2yDjAgAA3mlSYZTL5fL8a2NKSoqef/55PfbYY1q0aJE2bdqkG2+8UYcPH9bXX38ti8UScLvmAUBjS0pK8syEgG/Fx8c3uaCpNh588EFt2rRJf/vb33T11Vfryiuv1Jo1a/TSSy9p8ODBGjlyZIX7c3Nz9eijj1bYTe+7777T66+/rqioKN144421em9D9YwKCQnRokWLqrw2c+ZMT0+rM3ti7dq1S6WlpercubMsFkuFa++8846OHTum3/3ud1U2Li9/b8+ePfXf//5X7777rm666aZK96xdu1YDBgzw64zuzp07a8iQIVqxYoVeeOEF/elPf/JcmzFjhgoKCnTXXXcpLCzMc/6xxx7T9OnT1adPH61YsaLBluatX79eL7/8cq3+PnfnnXfqv//9r5YuXap27drptdde01VXXaWff/7Z0z8UAAAEjiYTRrlcLhkMBknuXgvt27fXJZdcoieffFLvv/++1qxZoylTpigsLExxcXGaPXs2M6MANCk19XTyFj2hAkNT6yn13nvvaf78+erfv79nCaLJZNKbb76pXr166c4771SfPn3UqVMnT81ll12mRYsWacOGDRo0aJAOHTqkZcuWyel06qWXXmoS/RurMnjwYGVkZOjXX3+t1JepfIneH//4xxqfMXv2bP32t7/ViBEjdNVVV6lnz55yOp06cOCANm7cqNLSUu3du7ehPkK1XnzxRQ0cOFB//vOftWrVKp1zzjnasGGDVq9erW7duunxxx/33Lt06VJNnz5dJpNJl156qZ5//vlKz0tOTtb48eO9GlNubq7GjBmjV155RY888kiN9xYWFmr58uVavny5Z5nizJkz9e9//1v//Oc/a1w+CwAAGkeTCKNOD6LS09O1aNEipaamqm/fvkpMTFRqaqrGjx+vn376STExMYqOjg6ovifFxcWSJFdImarfH6eibdu2NdyAgACUlJRUbf8W6dSfo/Xr1zfLYCU/P18vvviiSkpKGuwdQUFBOnr0aKP870tpaannx/q835t6b9/tC0ePHlVQUFCN/1EcFBSkiRMnKjw8vF7vSEpKOuufo7rYu3ev7rjjDkVFRemtt96S2Xzqrwzt27fXK6+8ohtuuEGjR4/W2rVrPdc6duyoBQsWaOrUqVqwYIGKi4vVu3dvTZ8+Xddcc41PxhZIfvnlF61du7bGxuXlhgwZonXr1umpp57S2rVrtWbNGkVFRalNmza66qqrdPPNN/tp1BX9f/buO87Oss7//+sup0850zKTOqlMQghNOtKkLehGUQR3pX8VUVCKIMpGQESk6YrLKgoiGPeH4KK7roKAEEBUEKRDMgmBTOpMppczp93l98c9c+ZMMu20OWU+Tx55ZE657/s65b7D9Z7r+lxLlizhlVde4frrr+ePf/wjjz32GLNnz+byyy/nhhtuoKqqKvHcDz74AADTNMetp3XcccdlHEZdfPHFnHnmmZxwwgmThlGGYWCa5l7ffZ/PN+q7KYQQQojCodjDRQwKVHIQ9eCDD3L//fdz4YUXsnr16qJZseXJJ5+U38oJMYl77713wuljch4JMbnJzqNc2rJlC4sWLeL888/ngQceyEsbRGm49957ueeee3jxxRdxuVwcf/zx7LfffhPWjDrqqKPQNI1f/epXNDQ08NBDD3H++eezdOlS+QWfEEIIUYAKemTUWEHUcIHSQhr5NJnDDjuMNWvWMHv2bNxud1b2OVwMd6JixPlW6G2U9mUum22cbPtcnEeTKYbPYCzS7ulVSO3O9/GFGMuaNWtGTfUby7p16zj++ONpbm7muuuu44UXXtirNtdE1q5dy0UXXcS8efPQNI2DDz6Yf/mXf+Ef//hHps0XQgghRA4UbBg1XhD1yU9+sqiCKHDqgJxyyik52XcxFCMu9DZK+zI3HW3M5Xk0mWL4DMYi7Z5exdpuIXLtiiuu4JxzzpnwOQsWLADgb3/7Gx0dHaxcuTLxmGmaPP/889xzzz2EQqHE6snJlixZwnPPPUcoFKKvr4/Zs2dz9tlnj6pnJoQQQojCUZBhVCkFUUIIIYQQM1ltbS21tbVTeu4nPvEJDjnkkFH3XXjhhSxbtozrrrtu0pGxgUCAQCBAd3c3TzzxBLfffnva7RZCCCFE7hRkGDUcRN13333813/9lwRRQgghRIFbuHAhBV6GUhSBYDC41//vBQIBqqur2W+//RL33X333dx9991s2LABgCeeeALLsli+fDnvvfce11xzDcuXL+fCCy+czuYLIYQQYorUfDdgPBs3buSRRx7hM5/5jARRY6ipqeGCCy6gpqYm300ZV6G3UdqXuWJoYyaK9fVJu6dXsbZbiGLW0dExqjB5b28vl112GcuXL+e8887jwx/+ME888URKdaeEEEIIMX0KdjW9eDzO+++/T319vQRRQgghhBBCCCGEECWiYMMoIYQQQgghhBBCCFF6CnaanhBCCCGEEEIIIYQoPRJGTYNIJEJzczORSCTfTRGiaMl5JETm5DwSQgghhBCFoCBX0ys1LS0tfP7zn+fee++lqakp380B4GT106NuG/MHMBtDAHh2LEQfLAcgXtFFrH4HAPqmcrQ2/7S07ynr19NyHFE8CvE8mun2vI6kI7ayG7sqBoB/874olrbXc9K9Dsl1ZG9yHs1cDz/8MKFQiEAgwNlnn10U2wohhBCidEkYJYQQQghR4h555BHa29upq6tLORTK17ZCCCGEKF0yTU8IIYQQQgghhBBCTBsJo4RDmcJz5NsihJgiszqCWRPBJrsLtpr1EaxAPKv7FEIIIYQQQkwvmaY3A41V58X2GYmfFXPka6HG3Ymfzfow6i4fygTJla1amPND2JqN2udG7XWjxFNPsaZSi0bqwQiRO5nUgzJnhTH26QNA21KGvj0w7nNtr+n8YClgjX2tUGOekedXxIkf2IXa5kPfGoCYOu41KRs1reQ6I4QQQgghRPZJGDXDjBlEYWNVDI00sFTUqDfxmBoOoEa8WN4IdpmBVR1D6/LstQ8Ay2dgrOjB9judS2tOGAAlpDmhVM9QOGVmZ4jVyeqnpaMoRA5kEuLYuoWxqD9x22wcQBnU0Lq8Yz4Xn3O9UKPjB91aJIBnZyOx2l3Y7hgoYDWEiTWEwVRQoirENJSYihJVUWIaRFXUkAslundB9FTIdUYIIYQQQojsk4lXM8i4HUyP5fwB1Ih/VIdQQcHVVZ+4bS4YGHPajVkdIX5AVyKISmYHTMw5YYx9e4kd0U7swE6Mxn6sQDzjKTzZGPkghBiR6TllNA6AK+m8VsDYpw/Lb+z1XLtsZLqdGvFNuF89VIFv6zJc7Q2QHGhrNrbfxA7GsGZFMOcPYizpd643h3RgNA5gK3KdEUIIIYQQopBIGDVDTNSZsipiiZ+18N5LpmuhctSIM6pheHTUMBsbo9Hp+KE7HT4l6sGzawGurjqng5ncD1ScfZjzB4kf1EX8oE6MeSFsz94hVjZemxBi6jI9l6xAHKshPHRDRQuVOz/rNvEVPc5IqOTnl4+EUVp04jAKQLFV3D11+Fv2wdVVizoYQIl5xp3ehwLm/BDxA7syrjMl1xkhhBBCCCGyR6bpzQCTdaKsyqQOYXjv2i7Do6Oic1oAZ3SU2uV2OphNvdhVSWFWfyWetrkotgYDldAJtmpi+kKYvgEsfwjLE0k83w6YmIEBzIUDKH0utHYvarsXxUgtJ5WpNEJkJtOwxcbGWNKfWAzB3TkLvbeGyLzNWN4I+Eziy3txvR1MjL60y0ZGS6mRvYPw8SimC3fn7FHHRrWw9TiWHnf+dkcwgl2g2NgBg/gBXWjbAmjbAhPWvZuIXGeEEEIIIYTIDgmjStxUOpj28Mgoe/wO4fDoqOHaUea8EGZDGLxWYlt3RwN6T+1eHT3F0tBDFeihCuepmoFR1otR3oPlG0xqRxyjIg6L+1E7PejvVaQUSklHUYj0ZGPUj1UfwR6qPadEPYlrgWdXI+H5m0E3sIMxzMX96O9XOLXqhkdGmSpK0mIJqVJQwNJQYhpqbKQ2ld5XRaxhuxOAq2A2hrCqo+gbK1HD6f3zJ9cZUax+8pOfYFkWqpr6oPh8bSuEEEKI0iX/Z1DCphRE6VaizpMa8aHYY38l9qodtTA0EkQZOt4di3D11E1pxIFi6rh6a/BtX4LvgyZcHfUo0aSi6ApYtVHiq7qw3alN35OpNEKkJhvnjO0yMRaOFC33tM9JXAtUw4131wKwndvmnDDGvBBWVQzcQ7Xqov60RytNRIv58G5bgqurLjFd2C43nOnBc0Np15KS64woRrW1tcyaNYva2tqi2VYIIYQQpUvCqBI1UWfJWT0vRnxJH7EPdSTuVyPjL78Oo2tHJbYJ+/BtW4oWLkurnarhxt09C//WffC1LHU6jYaz+pUdMImv7MFWU+swSkdRiKnJaNU8xcasihJv6iF2aEeiaLnWX7nX9UCLBHDvnpO4bS4cwFjZk/T45PWi0qXYKu7OBrzbljj1pcAZJbVogPhBndjevQurT4VcZ4QQQgghhEifhFEF6GT10xl1dCYMojzOqID4/t1Ys8OjVr3SByom3K+CgquzYeT5PdV4dyxGNVxptzWZGvPh7mzAt20pStzZpx0wMJb2pbzqnnQURanL5XViIjY25qyws1Ldyh6suujIvySmhrt99pjbufqq0btrxnwsUeg8h7SoH9/WpU4bhkdJ+U1iK3uwXektoCDXGSGEEEIIIdIjNaMKTHLnJp3aJBMGUZrljDTyJ3W8LAUtVIGrtxptkpFRAPpgOcrWJaDYU3p+OlTDjXfHIsIL3gPVwpoVwep1obVNvcAxSG0XUbpyeZ2YiFUew1jcj12+x2giQ0PvD+LqqUU1xw+n3R2z0QbLsbyDWK4oiq2h91fm7FqyJ8VW8XTMQe8PEm3Yhu2OOYXVV/bgeqsKxUz99zNynRHF4ne/+x3hcBifz8fq1auLYlshhBBClC4JowrIWB3EVDo6EwZRik18RW8iiFJibtxds9AGKpyV71KgRVMLhdKhxj142uYSnb0NAGNJP8qACzWU2igs6SiKUpPL68R4bLeJsXAAa1Zk1P3aQDl6bzXaYPnU6sWhoA+Ww2DuR0JNRIv68e5YRGTe+9iuOHaZQXxFL653gih26rWr5DojisGDDz5Ie3s7dXV1KYdC+dpWCCGEEKVLpukViIk6iFPpPE5WI8pY0o8dHFo1z9Dw7liE3l+VchA1nfSBIHrP0LQeFeIrerA1K+X9yFQaUSpyeZ0Yi63YGPNCxD7UOSqIUqIevNsX4d21EH2wIifFx3NNNdx4dy4Ec6hGXTCGsU9vylOCh8l1RgghhBBCiKmTMKoAZNqJnGx7c+4gVkPYuWEpeHc1ohrpL6M+ndztDajhoeLGXgujKb3OonQURbHL9XUimY2NWRMh9qEOzIUDoA2dc6aGe/ccfFuXpb1oQSFRY168OxvBcsI0qy6KubhfAikhhBBCCCFyTMKoPEul8zLe9JyJmNURpzM5xLN73rTVZ8kGBRVP64LE6AWrOoY5dzCtfUlHURSrXF8nhtnYmNUR4gd2YazoBa81/AB6Tw3+Lfvg6q0pypFQ49EiAecaM5y3zQljzkvvGgNynRHFr62tjebm5lF/4vE4APF4nLa2tjy3UAghhBClQGpG5VE6nZbk2iSTbW8F4hhNvQz3G12ds9D7gykfM99Uw42ndT7ROVtAcZaFVwdcqL2pj+6S2i6i2OT6OgFOCGVVRzEXhLDLRhcnVwcDeNrnoMa8KbejWOihCuzdc4nV7wCca4wSU9F2+9Lan1xnRLFqa2vj3HPPJRKJjPl4T08P5557LmvXrqW+vn6aWyeEEEKIUiJhVJ7kekl2GxtjeS8MlYTS+ipxdc1K+5j5pg+WY3XNIl6zGxSIL+/B9XZVygXNQTqKonjk+joBQyvkLRrAroiPul+NeHF11aOFplacvNi5+qqxNYN4rTPqw9inD6s8jv5+uRQ1FzNGT08PkUiENWvW0NjYmLj/mmuuoaenh/Lycvr7++np6ZEwSgghhBAZkTCqRNkVcWyfs3KeGvHh2T2v6DuUrq5ZWN5BzMAAuGziq7pxvRNE7U9vhNRkpCMpSpnlNTAXDmDVRkfdP9NCqGSu7jpszcCo6gTAmh0m7jNwrQ+imKnPapfrjChWjY2NNDU1JW67XM4vfjStcBc9EUIIIURxkZpReZJJB2Qq25p1I0PsXd21KHbxf9QKCp5dC1AHh2pe6Tbx/bqxKmM5OZ7UfhH5lqugwqyOED+4c1QQpcQ8eHYuwLttKXqoOFfIy5SCgrtjNu7dsxNFze1gnPiqbmyXmZNjynVGCCGEEELMRMWfUBSxdDqaU9nGVmys2qEwylLQQhUpH6dQKbaGd+dCtNDQSl4axFd2Y1ZFJ94wTdJRFPmW7UDKrI44U3iHrv6KoeNum4uvZRl6qHJGhlDJFBRcvbV4ty8GwxkFYpcZxA7oxvYak2ydHrnOCCGEEEKImUbCqDxLpaM51edaVTFwOUtDaQMVJTEqKpliq3h2NaINDIVsKhgrejBrxi64minpKIp8y1YgtWcQpfUF8W1pwtVXPeNDqD1pUT++7UtQ4kN16bwmsf27sALxiTdMk1xnRK7NmzePhQsXMm/evLS3TadOVCbHFUIIIUTpkppRBeAp69eTdkRS6YwmRkUB+kAw3WYVNCeQWkC0YRtmudO5Npb3wkYbrT29FbAmIsWIRb5N5ToxkbGCKE9b8deSyyU17sG7bQmRuVuwPRFwD9WqW1+J2uvJ+vHkOiNy6a677sp42+bmZj7/+c9P23GFEEIIUbpKa8hMEZuoA5JK58RWLazhEUKmNjKdLUssd4TInC0MLthIeO77ROu3EatpJV7ZgVHWi+kNYekxbOysHncsCgqe1vnovVXDd2Ds04fZMJiT48nIBZFv6QYVhRZE2diY3hBGoA/THcZWzWm5ZqRDNV34ti9GDfudO3Sb+MoezFoZiSmEEEIIIUS6ZGRUARlr5EOqnU+rNgpDi93oA5UoWcobbWyMYAexmjZQ7aH7oljjb4BiuFDibtS4x/nb0MHUUCzNuc/M/OunoODePRdsFSPY6QRSS/uxVRt9ZyDj/e9JRi6IfEt1hFQhBVG2ahCv6Mao7MJ277HwgKmiGC5Uw40Sd6EYbhRTA1tx2morOHmVMnQfYOqoMU/OpyIrloZ3xyJnJGZZnzMSs6kXXBbaLn/WjyfXGSGEEEIIUeokjCowyR3NdDojZn048bPeF8xWs4hX7yZesztx20ZBmWgkgwK2K47timMR2vtxG/T+IK7uOtSYN6O2KSi422ejWCrx6nYAzMUDoIC+QwIpUXqmGkjZuuWEJnkOokxPGCPYiVHWkwiz96JZ2FoU05PiYgS2sxKgGvOiRr2JvxXDldXXOTw1ODZrB0ZltxN8L+nH8hvo75ej2Nl9T+U6I4QQQgghSpmEUQUoow5IUkdPibuz0BowvSHi1U4QZQOR2UsZXLAvimWhxsIo8QhqLIIaCw/9HUGLDqJGQqjmOMV+FTAqejAqetAGynF116FF0g+OFBRcnfVgqcRr25x2LxpwQq8cjJASIt+mPEJqeNCQqeJpmzttQZSNhVneR7yyE8u399TZeEUdRnmNc92IDqLGBlGjYRR73PGWY1PA9jghllneO3K/qaKFy3C3z0Y1snMtHB6JqRg68Ron+LZmh4mXxXFtCKJEtawcR4hcuOmmm+jt7aWyspLrr78+rW0Vxbl+tLS0jPvcYDA4qtB5JscVQgghROmSMKrEqN0ezHJn+XHTP4DaX5XR/mzVJNqwjeH+a3j+CsLzlg89pmHqLqBinI1tFCOOGg2hRUKo8ahzOx7B3bkd1XCCKrOsH7OsH3Uw4IRSg2VpdZgVFNzdswBGAqnFA4CCvjP7U2mEyLfJAinFUFG73VjVMdAszMAAemic8zWLLFfEKfrtGh1GW5qLWN0CIvWLsHzle29o2yjxKGp0EC06iGLGwbad+7HAsgEbbAvFtp3nDfahhfv3DrE0C7Osj7AvhKd1Hvpgdl63goK7qwEl7iE2aweoNna5QezATlzNlag92S9sLkQ2vPHGG7S3t1NXV5f2ttXV1Xi9Xm6++eZxn+v1elm7dm0ikMrkuEIIIYQoXRJGlRglnPSbedXMaF82NtFZOxIdynh5DeG5TSk0RsF2uTFdbsyy0aFYqHE/vLu34N35HlrMmVpo+UNE/SHUiBdX9yy0gYq0Qqm9A6l+FJuc1HYRIt8mC6TUXX4njAKMys6ch1GWFt8riDL8FUTrFxOtnQ/aBP/sKAq224vp9mKWV6dwUAstMoA22OuEU4O96APdqEYMNJPo3BasrjpcnfVZGxnm6q9CjXqJzt7q1L9yOYXNta0BtG0BWaVQlCRN01i7di09PT1jPt7S0sLNN99MT0/PqNFRQgghhBB7kjCq1JgjHSBbTXG6yx6Miu7EtBdLczGw7BBQstTB0nQis5cSqV+Mp2Mbvh0b0SIDzrG8EaKzt6L31Di1oNINpBQ7UefKWNIPNmitEkiJ0jNRIKV2uyGigdfEDAxguaKo8dyM3rEVk+ickSDK8FcwuPAAjPKa7F07xqKqmP4KTP9I0KYYMQKbX8XdvQuAeHU7pncQT+t8VNOVlcNqMR++bUuJ1m/DLOsHBczGEHZ5HL25EsWUBWtF6amvr5egSQghhBAZk/9TLjGKpSTfSHs/litKrG5X4nZoyUFYnhwEOapKdFYjPQeeRP8+h2EEgomHjGCnM+ogzdfh6pqFq3NkWoCxtB+zfu/aNUKUgvFqzSkoaLt8idvxyq6cHN/GJjp7K5Y3AoDp9tG//GiMitrcBlHjtUd3M7DP4Qw27oc9dHzLHyKy4D1M30DWjqNYGp5djbg66hle08GqjhE7sAsrME7NPCGEEEIIIWY4CaNKTRZGRtlYTp2ooe0jsxqJ1czNSvPGpSjEaubSu+p4BpYcnOg8mmV9ROa9j60Zqe8SBVdXPa6upEBqmQRSonSNF0hpbT4YuhwYFd1pB7zjsbGJzdqBGRga3ai56F9+FLY7s5UyM6YoRGYvo3/fY7CG2mLrBpG5HxCr2o090YqgqRxmqF6dZ+dCMIemSvtM4vt3YQVTXB1QCCGEEEKIGUDCqFKTPDJqvCXUJxGvacPyOnWcTG8ZoYX7Z6NlU6MoRGc10r/8KKyh2jKWN0x43mYsV+qduuFV9lxdtYn7jKX9mLPCWWuyEIVkrEBKMVTU9qFgSDMxynuyesx4VTtGZTcAtqIy0HQElj/3hdKnyiivoXfVR4hXOvXkUJyactE5W7AzrK2XTB8sx7d1KWpkaCSaBvF9eySQEkIIIYQQYg8SRpWa5JFRaYx+sFwR4tUdQ9sr9C87dOKCwzkSD86ib+WxmMOjGdwxJ5ByR1LelxNINaB31w7fgbFPH2bDYNZGRghRSMYKpJIL+GczjDK9g4nFAgBCSz7kTM0rMLbLQ//yoxictyJx1puBASKzW7J6HVANN97ti9H6h8I4FeIrerFd2Qu9hBBCCCGEKHYSRk2jLx1yLSern078yYVMa0YNT7MBCM9ZhlkWzEKr0mMGKunb73iM4REWukl47vtpB1Lujgb03pFV/Yyl/cRX9mD5Up8CmKvPT5SOXJ7nacnuzLwEo2KkBtXg/H2J1c7LzYGyQVGIzFtO/4qjsXQ34NSRile3TbJhioexVTytC9AGhq5dmo05K/XrVkF9f4QQQgghhMgiCaMAwzCIRqd/GkVOOhrJv+BPo2aw6Qslfo7V5L9TaXl89K08BiMwFCINBVKmO/VpdgoK7t1z0ULlifvsqhjxgzsxFvVja6n11qWjKMaT/N3Ix/dkrGNa9SNhiD5QmZXj2IqFUeasuGmrOpGGJVnZb64ZlbMYaDoCe+giGa9uz2pRcxgJwIdZ9eG0RmDJdUYIIYQQQpSi6Z9/VWBeeOEFnnnmGbZs2UJjYyOrVq3ik5/85LQd/2T10+MWHU6LnTwyKrWOj42d6JBZunvUMun5ZOtu+vY9mvL1f8E10A26SWTeB/i2L0aNpVYgWUHBs7MRM9BHrG6XswS9AubcQcxZYfQtZahtPpQpJnlZ//xE0RsrPJjO78lYx7cVe6ROmqWg9wezciwz0A9DIW6sek5epvSmyyivITx/X/zb3gEFog3b8G5dimq6snYMNe5BHQxg+UPYfhO7Io7S5055P3KdEdnwsY99jFAoRCAQKJpthRBCCFG6iqfnkAN/+tOf+O53v8vy5cuZPXs2mzdv5plnnuHvf/875513HsuXL0dVcz94LKsdDTv5x9TCKMsTTnQs45V1eVmOfTy27qJ/RVIgpZlE5mzBu30xqpFa505BQQ9Vog2WE69qJ17V7hR7d9kYy/pRZofRN5ej9k9tv9JRFMMmGsUyHd+T8Y5vVUfB5VwPtFAFiqVl5XjJtaeitfOzss/pFJmzDL2vHXfvbmzdcAKpHYumHEZPhauviqjfGXFq1odR0wijQK4zInMXXnhh0W0rhBBCiNI1Y6fpdXR08NBDD3HSSSfxzW9+k+985zvccccdXHvttaxfv5477riDl156CctKvdBKR0cHzc3NiT8tLS2TbpO1qRgZjIwy/SNT9OIVddlpTxYNB1JGIOjcdsWJzNmCraZe8wmcui7urnp8Lfug9Y9MW7LLDOL7d2NWT73Gi0ylyb50zqN8msp3IJffk4n2bdWPTGvV+6rGfV4qbNVwRkYBlsuDUVl414xJKQqhpYdguZwRlpY/5ITTWaQNVILp/FNr1UZSng6cTK4zQgghhBCiVMzokVG7du3ihBNOoKHBqetRX1/PaaedxoIFC7jhhhv48Y9/jMfj4eCDD05pv7/73e944IEHUm5PVn7zndzPSTGMspJqpsQrC281LHACqb4VR1H59nNokRC2J+qMkNqxGMVOL1tVDTfe1gWYvQPE6nZheSKJFfeU13XUyNROExm5kF3pnkf5kEpIkIvvyUTHt10mVlUMACXuQhssy8oxjbK+xDUmWju/oEZSpsJ2eRhYdijl7/4ZBYjXtKGFA2iR7EwpUmwVvb8KI9gJGlh1EbRW/+QbjkOuM0IIIYQQohTM2JFR4XAYJanzFI/HAVAUhf32249bbrmF/v5+7r//fnp6egCw7amFO6tXr+bee+9N/FmzZs2U25XV33ynEEbZWIni5abbi+VNv8OqDXSj97aDnZvlu2yXh74VR2O5PABYvjDR2VszXp5dC5fh3bp0ZJSUbmOs6MFWp/46ZORC9mRyHk2ndD7zbH5PJtuXOSuSWMxA7w9mbQqaUdGd+DmWzSl6Zhzvzo1UvLWOirfW4d25CcWIZ2//YzAqagnPW+HcUHCuJ1p6Iy7HkryKp1mf+uILe5LrjBBCCCGEKHYzNoyaP38+hx56KA8//DDt7e24XC5M00w83tTUxNVXX82GDRv45S9/CTAqvJpIbW0tTU1NiT+NjY0ptS2TjoaCkjQ6aurhjOUNO3WTAKMi/XpRrp7dVL71LJXvvkDw1SfwbX0XNRKadLtUWd4AfSuOwhoqmGwG+olX7854vwoKnra5KFEn6LIDJuaC1NovHcXsyPQ8mg6ZfNbZ+J5MZR/WrKRV9LI0Rc/SY1i+QQBMXzmmPzur86nRQSrefg7/1nfQQz3ooR78W9+m8vUn8O7aBJY5+U7SFJnblJiebOsGsdrWrO1bi/lQIz5n3+UGli/zoEuuMyJVn/rUpzj22GP51Kc+VTTbCiGEEKJ0zahpeqZpomkjhXtPPPFEXn/9de68806+8Y1vEAwGsSwrUbR8//3358QTT+RPf/oTn/zkJ5kzZ860tTWjjoay1w+TsryDiZ/jFWlO0bNtfNveTRxVi0Xw72jGt6OZeOUsovULiVXNhiwVhTcDQfqbjqBi/V9QbJt4sANXdy2KnVlxZsXW8O5qJNy4CRQbszaKtqUsayNKhj9bmWojcj3lylZt7IATfKgRL2rck5X9Jgc12Zqipw32Ubbhr2ixvUcOqUYcf8vbeHZtJjx/X2ckVranBSoKA0sPofKNP6GacYyyXty756Q9/XdPWqjcCf0B22tCOPN/fvf8d0KuKUIIIYQQoljMiJFRr776Kj/+8Y+5+uqr+cUvfsGmTZsAOOaYYzjppJN46623uOeee+jp6UFVVUzTxLZtysvLOf744+nu7k5M1St0NvZIBmVPvbNmekc6gEZZeqMn9L4OZ6U7wOUDZejbpQDu3t2Ub/w7Va/+EX/L26jhgfF3lAKjsm5kFS/Nwqjsysp+1bgHNTxU18VrOp3HKZqoQ5jceZSRDcUvG53/XH4PbP/ICBw16svKPk1vCLO8FwBLdxOtX5TxPvW+DsrfeT4RRPnrFI6/wcNHbvIw7wgtcU3TYmHKNv+DireewdXdClOcOj1VtttLvGq2c0O1MH3ZuU45Oy/OmlpCCCGEEELkQsmHUU8++SQ33HADzz77LLt27eJnP/sZDz30EIODzkigyy67jKOOOopnn32Wu+66i46ODjRNS0zJ6+/vp7y8HJfLlc+XMXVaUucshd/oD4+MslUN01+e1qF9OzYmfj7wXzVOu9XFyo9r+GtGnqPGo/h2biL4+lMENr+KGs28fkp47j6JCYnxqg5sJTu1qpILPdtDBaAnM9UgaqL7RHEp5EBqVBgV82a+P2xis3Ymbofn74utuzPap6trJ+Xr/4JqOnWhKhcoHPM1DxVzVMrqVQ6+0M1x/+Zh1n4j1zN9sI/y5r9R/u6f0XvashpKxapHRsCaZX1Z2+8oKS4uIYQQQgghRKkp6TDqlVde4fvf/z6nnnoq3/nOd/jVr37FRRddxLp16xgcHEzUiFqzZg0f+chHeOWVV7jqqqt49dVXaWtr4+233+app56iqqqKuroiWbY86ZfvijW138TbmoHtcjqCRiA4MqQpBdpAN+5ep2aTvxbmHaLiCyos/6jGP33HxYev0Jn7IRVFG2mmd3cLwdefwrf1nYwKFFu+8kQH0tYNjPLuSbaYGm1wJJSzgtFJn59qEDWVx0RxKNRAanQYlfkUPaOiy1ltEjD8lURnLcxof562Dyjb+BLK0GIHdfuqHP1VD56K0deuyvkqR3zZw1FXuQkuHHnM1d9JxYa/UvHWOtyd27MSSsWDs7BV50JlBPoyXhghIXlklAySEkIIIYQQM1xJ1owaXvXub3/7G4sWLeITn/gE8+bNA2DlypUsWbIEt9tNJBJBVVV8Ph9f+9rXWLhwIU888QRXXnklgUAAj8fpvN15550Eg8F8vZzUqKmPjDI9I/Wi0p2ilzwqqulUDVUb6W0pqkL9vgr1+6pE+my2/Nmi+QkTIwKKZeLfsRFv2xbC85YTqV+UVk2p8Nx98HQ5IzbiVR3ofdUZ13hSo14wdNANrMo4tmKjjDPVJt0gKvk5Uu+luD1l/TrjQCnb3wMrMBJGKdHMRkbZqkmsti1xe3Dh/unXbbJtfNs34NuxIXHX3MM0DjrfhaqPv8/aJo1jvq6y61WL9f8bJ9TmXO/0wV7KNr2M6XmXyJxlROsWgJpm7ThVIx6sx921E3QT0z+APpjeaNFko65HEkYJIYQQQogZriTDqOEpdu+//z6WZSWCKIANGzawbds2Pve5zxEOhznooIP46Ec/yuGHH85ZZ53FKaecwiuvvMKuXbuoqqrikEMOoaGhIV8vJWV2Uhg1XnCyJyvDelFquN/puAHeCmg8avwwyVvhjJZadKzKhsdMNj9rYZugGjECW97Eu2szgwv2JVYzN6WOrllWRaxyFu7e3djuGGZ5L3p/MOXXkkxBQRssw6zoAd3Groij9O49JSnTICr5uRJIFbdCC6QSI6NMDcXM7HIfq24DzRlNGq2Zh5HuQgeAu2PbqCBqySk6+56ho6iTn/OKojDnQxqzD1LZ9ZrFpj/G6d3qXPe0aIjAB6/j276eyOylRGYtAj31KdaxmrmJa1q8phVtMAsLGCQPsJJpekIIIYQQYoYryTDKMJwOWDAYZMOGDTz++OMsW7aMV199lfvuu4+PfOQjLF++nN7eXn7/+9+zadMmrrzySg477DCCwSAnnXRSnl9BBtIYGZVpGOXbsSnRTVt6kobmmrzT5ilXOOBsnSUfsXn3f022/d2ZpqNFQ5Rvepl462b6m47Adk19alF47j6JqYKxqt1o/ZUZdyATYRTOVD11jzAqW0FU8jYSSBW3QgmkbM0Cj3NeqVFvRueC5Y5gBDud/aoagwv2S79hRhz/1rcTN1eeqbPk5NQDI0UdCqUOVunYYLHpjwYdG4ZebzyKf+s7eHe9R9++H8byVaS071j1XAz/RvTBXixvBKOiG1dfdcpt3KPFY/4ohBBCCCHETFRSNaOam5sB0HUdXde59NJLCQQC3HrrrVx11VX853/+J6eeeipXXHEFZ511Fp///Of5yle+Qnd3N//7v/9LNBrFsrJT/Dpvkj/RKdaMsjxOGGVpLiyPP7XjmQaejq0AuPyw+LjUvlJldQqHfU7nI/+mU7c8uRZMF4EP3kxpX0ZFLfGhMM32RDHLelPafix6UhFzszY6auRZtoOobGwrCkMh1JCyA9mrFxWr3p0IUMJz9sH2pL8yn6e9BTXu1GBrOFBNK4hKpigKdSs0jrrSwzHf8DD7YDXRVjUexd/y9sQ7GHunDDauStyM17Rhk+G/Dcm/J/BMfXVOIYQQQgghSlHJjIx6/vnn+eY3v8mpp57KddddB0BtbS333Xcfjz32GJZl8fLLL3PcccdRUVGBZVkoisKJJ57Ia6+9xhNPPEF/fz+1telPPZnMj165jaamJiCHgUM60z8sDTBQzThauB/TP/VRBIptoQzV6PJVKbh86f3Kv6pR5ZgrFdretnnxJwZmDNzdu8A0QJvi11RRCM9bjmvD3wCI1u1EC5dlND1JMV2oYT+WbxB8JsbiPlzvVQISGomJZWuEVLps90h4osYzW/HO1kcWGIg0LMloX2psZCRm44ez+09Q1UKVQ7/gob/VYt0NTuClRQbS2pdRWUesajbu7l3YuoEZ6EcPVabdNjU6EuCZ80Oofe69RloKIYQQQggxU5REGBWLxfjLX/4CwGuvvcYtt9ySCKSCwSD/+q//CkB7ezu9vSOjZYZrS1mWhd/vR9dL4O2YYp2oZK7eKmJ1rQB4d71HaMnBUz+c7sYIVKGHuunbYTPYZeOvTi+QUhSFhlUK8w9X2fJnC8UycfXuJp601Ppk4sF6otVznGLmukm0bgee1gUZTVHytM0lvGAzqBZWQwSz143Wnv7IkMnIND2RFVrSSB4rzWLeQxRzZHvFMrFJfzST5R0ZbTjYmZvaSWX1CooKtgW2ln5bo/WLnFAcMCq6MwqjtKgfvbsGo6oTVIgv78H9RjVKpAT+3RFFYc2aNcTjcVyu1M+JfG0rhBBCiNJVEv8X7Ha7WbFiBevWrWPJkiW88MIL3HbbbVx77bUAxONxNE2jubmZrVu3cthhh1FTUwPApk2b2LRpE0uXLk2snlcyppi/6L3VzjQczcLTvo3B+ftiu6e+8lasqgE91A1A61sWi4/LrOM790AnjAJwd+1KKYxCUQgtPhBXXweqEcMs78Mc6EUfCKbdHjXuxbN7DtGG7QAYS/tQBlyo4eyfPhJEiWyx9aTFDDIOo0a+66oRw0zh+rAn0zeyMt1AW26mRVuGE0QB2Gr652m8chaWy4saj2AG+rG1OIqZfofa3TEb2x3FDAyAyya+bw+uN6tRjJKaMS8K1EEHHQRAW1tboqzBnlpaWibcNpPjCiGEEEIkK4kwCmDFihUsW7aM888/n0cffZRnnnkGgK997WuJ38adc845fPvb3+bGG2/kuOOOIxKJ8OKLL7Jr1y7+7d/+DZ8vd6Ndpo097o1xKbaGq7eaeHUHim3hbd1MeMHKKR8yVtWAf/t6AHa9mXkYVbdcQfeAEXWm6oVsC5Spd9Zsl4fQogMo3/QyANFZO9HCgYw6kXp/FaYvhFHZDRoYy3txvVWV1U6kBFEiq5LDKDOLI6OG6j2ly0waGTXQmpuRUUZk5Gd7qtN8x6IoROsW4Nu5ERQwyntx9aQ/lVtBwdO6gPC8zdieKLbfJL5/F653qlCimX1GQkxFW1sb5557LpFIZNzneL1egsHg9DVKCCGEEDNSyYRRCxcuZOfOnYmV8QzD4NlnnwVIjJByu9185Stf4b777uM//uM/qKysZMGCBfzHf/wHCxcuzF/jcyWFmWl6Ty3xqk5QbLytHxCe2zTlWk1moDIxemD3BhsjaqN70p8Wp7kUGlapbH/FQjXi6H2dGJV1Ke0jVjuPaOcOZ7qeZhKdtRPPrsym67nb52B6B51OZMAgdlAnrk0VqD2Zj6iTIEpkmz1qml6GoWnSyCjFiGW0K9vlwdJ0VNMg1JarMGpkvxmFUTASRuFM1cskjAJnlJp3VyORee9j6wa23yS2fxeud4KogzKNSeRWT08PkUiENWvW0NjYOOZzgsEg9fX109wyIYQQQsw0JRFGmaaJx+Ph8MMP591332X16tV8+ctfBuDZZ5/FNE36+/txuVxceumlHHroobz11lssWLCAWbNmUVmZfh2QgpPGyCgA1XSh9wcxKrpRzTje3VuIzF46tY0VhVhVA97dW7Di0N5sM3v/zNYun3OgwvZXnJ/dXbtSDqOA0dP1yvowyzKbrqfYKt5dCwjPex90EzwW8f160Hb40baUoaRRrwskiBI5ks1peknbqxmGUSgKlrccNdTNYJeNGbPR3JldL/aUtZFRgOUrxyirQh/oxvJEMN1htFhmo2jVuAfvtiVE5n6A7Y4515L9u3GtD0pRc5Ezr732Gu+//z4AjY2NiQVVprrtcN2nVKfdZbKtEEIIIUpXSYRRmuZ0lFatWsVPfvITWltbaWho4Ktf/Sp33nknzzzzDIZhcP311yd+23fiiSfms8k5lH6nztVdi1Hh1H7y7tpMpGHxlKfHDYdR4EzVm71/ZiMxGvZTUTQT2wR3104GF64CJbXXZrs8hBYfSPnGvwPOdD01HEDNYLqeGvfi27qMWP12p+4LYM4dxArG0JsrUQdTO6UkiBI5o4+MjMpmzahMp+kBmL4yp86cDaF2m4q5WQ6jotkbGQUQrWtEH3CujUZFN1pH5lO6VcONb9sSInO2YPnCoNvEV3ajN1eidaZfk0uI8dx88820t7dntG1dXR2PPvrotG0rhBBCiNJVUlVTly5ditvtpq+vD4Dy8nIGBwexbRuPx8PLL7+c5xZOg+TBUEpqU2DUmBct5NRz0aKDuLt2TXnbeGUd9lBwtet1CzOe2fQbl1+hrsnpoGqxMFq4P639xGrmEq2Z69zQTOI1uzNqFzijyDw7F+Junw2W00Y7YBDfrxs7hdFoEkSJXLK1pO+imdmlPrlmlBoLZ7QvGF03qvuD7Bcxj/aN/JyNMCpWMzdxfTPKe1I6zyeiWDreHYvRQkNF3VUwmnqx3WZW9i+EEEIIIUShKqkwqqmpiUAgwLp16zAMg2984xu8++67XHnllRx99NE89thj3HXXXXlr35cOuZaT1U9zsvrp3B0kuQOaRp0YPakeirtzZwrH1YlVOaPOIn3w3tOZdzDVLNXzDS06AHtoVJXlHczKPhUUXD21+LYtHenou6wpD0ybKIjK+XdEZEXBf07DI6MsFSXDS70a8ySCV0/HNpTY+MWPp8Ior0n83Px/8VE1nrJh29+MxM+Wt3yCZ06NrbsxyqqcG7qZctA/EcVWcXc0JN2RtV0LIYQQQghRsEomjLIsp+O1cuVKNm/ezJo1a3jjjTe46qqr+NjHPsYVV1zB6aefzhlnnJHnluaW7UqammOmPiJAGyyDoVEQ7u5WMKf+G/rwvBWJ8QIbHjMJdabfYTNjNu0bnO0tl3fUcvCpsl0eLI9/aF+xrI1qAFBi7kTHVAlradeNGpYcbhR00DHDFcPnZLuGvpcZrqQHzggevbd66GcT3/YNGe3PqKglVjkUXvfAhv8zJt4gBb3bLdredK6DpttHrHpOdnac4jThqbKxic7akbit7fSjxGRlPSGEEEIIUdpKJoxSVeelHHvssbz00ku8+eabfO1rX+OYY44BoKKigquvvpoFCxbks5k5ZXsNzEUj09kUI/UwSkFBH6hwfrYMXL1tU97WDFQSneWszmNE4Nnvxunekt4IqfaNNmbc+TkWrM+4I5iYFqRa2Fr2Or62OwrqUKd/ILOVsMYKNQo16JjJiuFzsrETI6OyEUYBuLtnJUYBenZvQQ0PpL8zRWFw0f6JqW8fPGPQuz070/Xe++PI+R2ZvQzUwv5nzgh2YPmcEZtKWENrKZtkCyGEEEIIIYpfYf9fehoOPfRQrr/+er7xjW9wxBFH4HaPrEw0XOi8FFmVMWIHdGH7h0YyGRr6QHqrBGpDYRSAJ5WpesDggpWYngDgTNd77g6DHa+l3slsfXtkm3hV5ktMm95A4mfbleFqYMn79YxMV1JC6demmSjMKLSgYyYrms9Js0eu7lZ21qlQTB3X0DReBRvftncz2p/lLSM811nNy7bgzf8vjm1lNmox1G6x4xXnGmjp7kQ4XqgsV5RYzVDgb4O+qQLFknl6QgghhBCi9JVcGKVpGieccAJHHXUUXu/MWJHIrB8kvrIbhqflRD34ti9BSbMTqoXLEiMgXN27wJr6VD3b5aF31XHEh2rCmHF48R6DjU+a2PbUOpq2bdM6NM3GVhTilbNSfAV7s5LCKCuLYZTlGSnmrKYZRk0lxCiooGOGKqrPKXklvSyNjAJnxU2GRlx6unagDa0wl67InGWJUYvdmy22/jWzwt3vPWEkFnGIzF4KWShenis2NtH67YmRldpOP2qfe5KthBBCCCGEKA0lF0aBM2WvlEdBDbOxMRb1YyzrT3ySWqgM3/YlqHFP2vtVbBU95IyOUk0DV29qS0HbLg99+x5NtHb+cEN5679NXvuliWVMHkgNtEGow/nZKK/B1jOb/gZ7jozKfGn6YdaokVGptzOV8KJggo4ZqNg+p+F6UZDdMEqxNdxddYnb/m3vZLZDVSO06IDEzXd/Eyc6kN7oqEiPzba/OWGWrelE6xdl1rYck+l5QgghhBBiJivJMGomsDWL+L49mHNHVofTu2vw7FyIYmXe+dSSpvi5O3dM8MxxqBoDSz/E4Lzlibs++LPFX/7DmLSzueutkVEdsaqGCZ45dclLyVvu7IyMsrFHRkZFVZR4aqdTOqFFIQQdM00xfk6ZLmQwEb232incD7h629FTDKv3ZFTOIlozD4B4CNb/Jp7Wfjb/ycAaKhcVqV+ErWd7lFH2ps9Zekym5wkhhBBCiBmtcOcwiAkZCwewq0dCFTXsx9Vdh5KlDpOzqp4KmoW7axeDRjz1EUqKQnj+CkxvGWWbX0WxLXavt3n8G3FmLVewLWcanxkb/tvGisNg18gu4sHM60UBWB4/Nk53Mls1o2w9DprT6U91il4mYcXJ6qd5yvp12tuLqcv0c8qb5Gl6WQinkymouLvqiTZsA8C3s5n+yrpJtprYYOMqXD2tqKbB1r+YRPui7PspF+WzJw94jYhN8x8M3n/aSaJsRSXSsDSj9owte6twGuU9iel56i6fTM8TQgghhBAzjoRRJcLyDRJetAF1sAy9P4g+UIFip98JdabqVWJUdKOacbw7NxJesDKtfcXq5tPn8VHe/BKqEcOMwq43Ju/YGYFKTF95Wsfck2IaIzGdnZ3ATrFHOsq2J7NaN0JklTHy3bSyOC11mNZfiVLdhu2O4eptR42ERtVlS5Xt9hJesB+BD14HoO0ti93vRJl9kEZZg4KvauhPtfNH9zrn8K7XTd7+VZxw98j1JNKwGNud5XqBloke6nF+NtWsXUMg/VpzQqTq0Ucfpbm5mc9//vNpbZvJcYUQQggh9iT/F1yk9PfLMcMaVl0Eu3xobooCVmCAWGCAmKnh25ZZ7ShX5yyMsh5QbXw73yNavwjL409rX0ZFLb37fwTvzo14d7eg7FEU3VZUbFUD1fnbCFQyuHB/ULLT6dP7R4ZbqZH0XsOeFFNHDfuxfIPYARPLZ6CGJz+lhkc1pTtyRkZFFY+nrF/nZYSU2ucCC1DB9A9kff8KCnpfFfFaZ6qZp72F8Px9M9pntH4Rtqbj2/oOWiyMbcHOf4wd8uo+cJcpDLaPhFC2ohKeuw+ROU0ZtWMsrt52FNO5zuqhioxHoGrhAMOTEc05YdQ2X9ZGtQohhBBCCFEMJIwqUoqtoO8MwM4Als/AqotgzoqAd6jzppnEKzvxdMxJ+xiq4cbVW0O8qgPFtvBvfZeBZYekvT/L42Nw0QEMLtgP1Yhiq9pQAKVlLXQajz4wEkZpWQqjnP1WEhsqQmzVRlC3TVyEODlISieokCCq+OQjkFJMFaXfhV0Zx3bHsPQYqpHdqWB6XxXxmjZQhsKoectByawMYax2PrGq2XhbN+PbuTERAO3JCIMRHgmi4pWzCC08AMuXmyLg7q6diZ+T6+mlS434UcM+LF8YO2BgVcXQutP/xYEQQgghhBDFRgqYlwA1rKNvLcP9Sg2uN6tgKI8yKnqwsSbeeBKurlkwtBqXp2Nbxku5A6BpTg0nl8dZej3HQRSAK3lkVDh7YZQ2UJH42aqdeDrUWEFSKuGSBFHFKx+fnZoUbpiB/uzv33ShhZxptGosgqtnd3Z2rOlE5jbR/aHT6d3/I/Q3HUlo0YGE5zYRrZ1PvKIW0xPAVlRMb4CBpYfSv/yonAVR2Bau7l3Oz5bq1NPLkIKCq3tW4rY5P4SdxZpUQgghhBBCFDoZGVVCFBSUPjdqpxdrVgQ0E7OsHz2D3+Qrloa7cxaxWU5nLLDlLfpWHjMtAVLW2Bb6UIimxF2oZoqF2CegGu7RIxzGmao3URgxlZEzEkQVv+keIaV2uzEXOj+b/n5cvTVZP4beV41Z5gRdnt1biGdp9UsAVA3TX4npH+f6ZdvTch3S+zpRDWfRAy1UPqpWXCa0UDlK1IPtiWJXxLEr4ihSyFzk0M9//nO2bduW9rahUIhAIMCFF144bdsKIYQQonTJyKgSpO0eKd5rVGQ+kmnUUu79nSOjBIqENtiXqFGVrXpRyZLDPqsmstfjUwmSJgurRGmYzs9SCekQc8Ia0x/KeJTkWLRQOYrhhK+unlaU2N7f/5yZpkA8eYqenjQSMlMKCu7ukVUIjfmhrO1biLH8/ve/509/+lPa2z7yyCP8/ve/n9ZthRBCCFG6JIwqQUqPG6LOR2v6+7G0+CRbTLI/VNwdIyMeAi1vg5X9jm2uJBcvz2a9qMQ+k8OoPabqZToNT4Ko0jNdn6mCgtozNFVPtbCGaptl+xh6X5Xzs23j6dia9WPklW2PhFGWkpiWmC1afxAl7ozUtKtiWIHMrtVCCCGEEEIUCwmjSpCCgtbmG76BUd6T8T61UEWi1pIWCeFveduZJlPoLBNPx8i0hGzWi0rs03CjRpz32y4zsD1O0eV0Qoc9C5yL0jRdn63aPTLtKxer6gGJMArAu3MTWlL4W+w87S2ocWe0lzZYhmJrWd2/UzuqNnHbnJP9wFAIIYQQQohCJDWjptGPXrmNpiZn2fFc145Rd3sxFzjTPszyXuipm2SLiSkouNtnE5m/GRTwtW5GC/cRWnwQljeQjSZnn21T9t4/EsXLFUNHjXon2Sg92mAAyxt2Duu1UKKTf8bjBRISQs0M01FDShkYqY9m62OvTJcpNe5BC5VjBvpRjRgV7/6Z0JKDidXOz8nxpou7Yzv+919L3Nb7qyZ4dnps7NHF5e3RUw/lWiCEEEIIIUqVjIzKk0w6GVPZVo3oKANO1mh5w1h6LO3jDdOifty75yZuu3vbCb7xDJ7WDwpvlJRtE3j/dTydO5zbloJn1wKUXH3lk4oa2+rU3ovpLGYt0pPr8zTXYYOS9FXM5WptntZ5qIOBoWNalL33Cr6t7xTedWEqLBPf1ncIvPcKw9GQ3l0zauXMbIlXdWAGhkasxVT0lhytCCiEEEIIIUSBkTAqjzKdxjUZtSNpafey3pSPNRZXXzWeHQsTdU4Uy6Dsg9epePcvqJHCKcDr2/Yu3t1bnBs2eHY1okVyOILLSjqVphhGgQRSxSDX52lOAykraaSNkrtgSLF0vDsWoveOjB7y7dxI2caXUIziqYOk9+6m8o2n8e3ciDIU3um9Vbg7ZqOQ3YLppjdEvKbVuWGDq7kCJS7/JAshhBBCiJlB/s83z3LZaVU7klbVy1IYBaAPluPbumxUx9PV107wjafxtL6f99EQ3p2b8O/Y6NywwdM6H30wu4WH96QkT69JIYwCCaSKQa7DpZwFUslfxRyGUTC00MHuubjbZyeO6+7eRcVbz6D3d+b02JlS4lEC771Cxfq/oEWHQnVbwdU5C/fuuVkPomzVJNqwjeHdatsCqL2eiTcSooi0tLTQ3NxMc3Mz8bgTSMfjcZqbm2lra8tz64QQQghRCKRmVAGYSu2YdDqrakRHCenYAQPL50zVUw335BtOgWJpeHbPQxuoJDZrB7YrjmKZlH3wBp7OHQwsOXjaa0mp0UH8W97Ck7QUu7t9DvpAMPcHt9MbGTXsZPXTUh+mwOXqPE1l/ylLDkmV3K+AqaDg6qlFiXmINmwFzUKLDlL+zvNE5i4nPK8JlAL6HYht427fin/rW6hJI7jUsB9P21zUeG5qzBkVXdgu53hKrwtta4HW3RMiRcFgEK/Xy80337zXYz09PXz+85/H6/Wydu1a6uvr89BCIYQQQhSKAuoVzGwTdWIz6eCOnqrXl/Z+xjMySqo6cZ+rr4PgG0/j3fXe9IySMg1829YTfO2pUUGUq3MWrt6a3B8fUJKm6aW74JaMkCp8uTpPs7mPUZLyJzu7g3smNHxdGF69UgF8OzZQ8c7zqJHcrOqXKjXcT/n6Fyh7/9WRIMpUcbfNxbt9cc6CKBi9wqn+XkXWR14JkS/19fWsXbuWe++9N/EnGAwCTlC1Zs0aIpEIPT09eW2nEEIIIfJPRkYNsSwLVc1vNjfWyIhMO6dqhxez0Zl2YpT14uqpnWSL1DmjpOai91cSrd+eGCUV2PIW7o7thJYcjOnPfvFfbBt31078W95Ci4VH7jd03B0N6P3B7B9z3LakP00vmYyQKny5OE8n23/aRiVQ0zt9VjXceLcvJl7VTrymDRTQB7qpfHMdoUX7E6tdAMr0hjCKEcPduQN353b0vo5REZDWV4mnYzaK6Rp3+2ywXFEsb8RpT7+OGpZ/hsX0OOCAA9i5cyfvvvtuWtv29vZSWVk56XPr6+tHjXo65JBDEts2NjamfGwhhBBClCb5v2DgySefxDRNTjnlFDQtzWEtWZLcEc1GB1cN6yghDTtgYvkGsTUDxczNx66Fy/C1LCNW24oR7ALANdBN5VvPMrD4QGJ1C7JzINvG1d2Kf/t69FBSLSwb9J5a3F2zUKzp/RxtzRy5kUEYBRJIFYNsn6cT7T8jo2pG5X6a3p4UFNzds9AGy4g2bMN2x5xFDza/SrSvk9CiA0DN8blqmbh6WvG0b8PV04Zij34flLgL9+65Oa8rB86KhvHq3YnbanvuRl8Jsafrr7+e5uZmPv/5z6e1bSbHHdbc3Jz2foQQQghRWmZ8GPX4449z66238q//+q8YhpH3MAom79ym2klVwjp2wAlLbMXK6YQQxdbwtM9F7w86o6TcMRTLpPy9fxDp7yK0cFV6nU/bRhvsw929C3fnDvTB0VMOtVAZ7vbZOZ1aMx7LFSFWuytxWwnN+NNqRsg0hJoszMpKIKWMe2NaaVE/vq1LidXtwqjsBsDT3oIW6iYyexmx6jmgZem8sW2UeBR9sBdX107cnTtQzb1X9FNibvS+KqfGlT09o2KNYAdGRY9zwwRNwighhBBCCDFDzehe82OPPcZtt93G2WefzSc+8Qk8nuysZtTR0UFn58jqUS0tLVnZL6RXV8j2DI3asUExcjsFZZgWCeDbuoxY3c5E59Pb9gFaqIeBfQ7D8vgn34llofd34O5qxd29Cy06uNdT1IgXV2c92mB5Xuqu2KpBZE4LaM5oC7XTg9opq2JlQy7Po3xLPo9zOhIuaZRecl2zfFDsoUUPwgGis3aAaqMP9lG2+R9YW94gVjOP6KyFmIHg1KfvmQZauB9tsBd9sA9tsA9tsBfViI3dBkNH669E769CjXqn9ZphBHqJ1bYmbuvvVaDE8//LDyGEEEIIIfJhxoZRf/zjH7n99ts5++yz+dSnPpWobxCLxYhEIvj9fnTdeXts20ZJobbJ7373Ox544IGstzndURK21wmjFMM1rZ0vxVbx7J6HGvETq9sJqu1M23tzHYON+2HpLhTbBttyRjNYQ3/bFnp/F66etjFHNACoER+urlloofyEUOBMuYnM3ortdjq+yoCO3izFiLMlV+dRvo11HucskNKT5ulN89TV8QwHQdGG7Vgep3aSahp4d2/Bu3sLhr+CaF0jsdr52C4P2DZqdBAtMoAaGUCLhJy/wwOo0dDkZ5ulog9UoPcHUQfL8nJ+mp4w0YZticFpWksArd037e0QQgghhBCiUMzIMGrjxo1897vfZfny5Zx22mmJIOrHP/4xb731Flu3bmWfffbh6KOP5lOf+hSKoqQUSK1evZqjjz46cbulpWXMZY5TkXYQpVngcjqkStydURvS5eqrRo36iM5uwXbFUY0YZZtfTW0nNqiDZeihcrRQBaqRn9cy0hyb2KydWH6nODwxFdf6YN5Hn5SSXJxH+TbReZyLQMrWCmdkVDI15sO7dSmWdxCjohujvBdUZ3ShPtiH3vIW/q3vYHn8TuCUwqqciqGjxLyoUQ9axI8Wqpi2aXhjsfQ40TlbEqPU1N1etG2BvLVHzFyXX345ra2tkz9xnG27u7upqqrirrvuSnvbL33pS2kdXwghhBClZ0aGUZWVlZxyyimsW7eO1157jdmzZ/PNb36T119/naamJo444gjefPNNfvjDH7Jjxw6+8pWvpDQyqra2ltra7K1al0ndmOFRUQBqnsIoAC3qw7d1GdGGbZiB/qltZKpooXL0UIUzDa9ARnYAGJWdGJVOkXYscK2vRIkWTvtKQbbPo3ybynmc9UBKSyrWXUBhFDjFzbVIAC0SwN0+G6O8F6OiG8vnTMdVbAstMjD+DiwVNeZBjXlRo0N/Yp6cr4aXClsxic7egq0bACh9LvRNMnpS5Mf27dtpb2/PaNtQKDSt2wohhBCidM3IMKq+vp5LLrkEgB/96Ef87//+L5FIhJtuuon99tuP8vJyPvjgA/77v/+bRx99lJqaGj772c/mpa2ZFjBODqMmqxdlq4YzSqGiBywVT+v8rI5AUiwNz85GzECfMz3HVlBsBVCcJeiH/ihDbVXDgYLstBn+fmJ1IwXL9U0VqP35HaklClsq53FWA6nkkVFTGB1kY2O5I1j+EKZ3EMXUcHc25DwIVmwNV181rr5qLHeEeEU3ZnkPtmqixN2ocY8TNMXdib8VUy/I68MwG5towzYsrzMVkYiG693g0DVPCCGEEEKImW3GhFEbN27E5XIxf/58dF2npqaGSy65BLfbzeOPP86ll17KoYceiq7r2LbNokWL+MxnPkNzczOPP/44H/3oRwkGg9Pa5mws7Z4oXs74I6MsV5R4VTtGec+ogseROVvwbV+S1Y6ogoIeqoRQZdb2OZ2Msl6is7aP1H7Z5pfaL2JC6ZzH2QqkkqfpjVczylZN4hVdWL4Qpi80ejQVYLmjeHcunLapbmrMi6djNnTMxsYu6MBpIrHaVsyyoVGghoLrnSCKUVij04QQQgghhMiXrIRRzc3N/PGPf2TTpk2EQiECgQD77LMPp556Kk1NTdk4REb+9Kc/cc8997Bs2TKuuOKKRI2ompoazjvvPBobGznyyCMTBcsVRcGyLObPn8/xxx/Pvffey8DAwLSGUdkIogBIGhnFGJ1JW7EIz39vrw4ogO2JEqttxbN7bnbaUsRsxSQ2a+fIsuw4K+dpLWX5a5QoeJmcx1m5BiSFUaZ/ALuvCsUcfdmPzPkAyxcedxeWP0S8ejfuzobM25OiYgyibNUkOmsHZnnv0B3ONF41PGN+9yOEEEIIIcSkMv6/4x/96Ef8+te/xrJGhxlvvfUWv/3tbznrrLP44he/mOlh0rZu3TpuueUWVq9ezVFHHZUIoobV19fziU98ArfbPapIuao6wU1bWxuVlZVUVhbnSB5iI6MhonU7nLoqcc+4T1fCGmqrD3ORU6vFVvYOqWYa0zNItGFbYtU8cIIoWTlPFDolPHL+W/4BBhs34tk9B22gcvzvrg3a1gBoNuY8p36Trcp1YCpM3wDR+u3YrpFVQPX3ylF7x7/mCiGEEEIIMRNlFEY9+uijPPzww8yfP5/zzjuPAw44gKqqKrq7u3njjTf4xS9+wcMPP0xDQwNnnHFGtto8ZZ2dnTzyyCOcfPLJnHXWWcyZMweAcDiMbdv4/X4A3G43lmWhqmrib3BGfK1fv56VK1fichVOUdxUaDv8WMEodjAOujky9W5odIRiq3ja5hOd3eKUbvKZWPUjoyT0UHm+mp53NjZGsINYbRsoQyNMDAV9czlquzdnQVS2V1QTM5fa70ZfX4mxtM9ZVVMzic7ehtbfi6d9Loqp42ldQLR++8jKkAqYDWGU+MhISn2gIk+voDjYWMRrdhOvak9M4cVQ0N+rQOvwprVPuQ4IIYQQQohSllEY9T//8z/MmjWLn/70p4lgB5zRRqeccgpHHXUUF1xwAb/5zW/yEkZFo1E++OADVq9enQii7rrrLjZs2EBnZyeHHnooxx13HIcddtheQdTf/vY3/vu//5vW1la++c1v4vWm16HIN8VWcK0PEt+/GztgYLtjROZswbt9caIGjB6qwG6fQ2zWTgBs/9DUPktBm6FhlK3FidZvxwyMrOal9Ou4mitRIrmbbiMdUJFtWqcXtc+NsaQPqzYKgFnex6A/hGf3XPSBSrw7FmFUdBGrawXVAo+F7XFGQymGjhoO5PMlFDTLFRldqBxQely4NqW/wqZcB4QQQgghRKnLqJrqrl27OO6440YFUcnKyso47rjj2LVr15iP59ru3bsxDIMDDjgAgKuuuoo//vGPlJWVsWjRIv70pz/xrW99i8cffxxwpuYNDAxw++2388Mf/pCdO3dy11130djYmJf2Z4tiqrjeCULU+bgtb5how1ZsRurJuHprcHXVjdpOC5eh2LldRavQ2NgY/n7CC94bFURp2/243qyWIEoUJSWu4toQRN9QCfHh6vsm0dlbMfx9KCi4+mrwtSxDHRwdPGn9E0zpm8FsbOKVnYQXvDcSRFmgfVCG6+0qCaKEEEIIIYSYQEY966qqqik9r7q6OpPDpMwwDHRdZ/78+VRUVPD73/+eFStWsGXLFr71rW+x//774/V6+etf/8ratWv5wQ9+QDAY5Mgjj6SsrIxgMMiHP/xhzjjjjMSIqmKnxDRc7zgjpNBtzLJ+onO24GldkFgtz9VZj+WKJQrvav1FWicrDbZiYZT3YFR2jhrhQEzFtbECtSe3NV+kAyqSWT4DY7GzEpv+fnnWil9rHV7U3tGjpGJ1rWgt5SgoqIZ7ZJRUbSug4OqpzcqxS4GNheWJYPkGMQJ9I1MbAWVQQ2+uRA2lP6VbrgNCCCGEEGKmyKiHc+KJJ/L0009z0UUXjTk6KhQK8dxzz3HyySdncpgpa25upqmpCV3XsSyLiooKFi5cyIsvvkgkEsHj8bDPPvskptwdddRR6LrOLbfcwh/+8Ac+9KEP4Xa7ufjii4nFYrjd7mlpdzbZXoN4kxMm6ZsrUAdGOkbqoAvX+kriK3tABTMwQHje+3h3NqIabhQUPG3ziEd9YIPeH8zPi5hGljtCvLILo7x7rxUFlS63M9Umntvl2KUDKpKZdWGnxtPQwJr4gV3o75Wjtfuysn8lrqJvqCS+qhu7Mo7tjmJUdOPqc35pMDxKSu+rAoXEdN6ZyFYNTN8glncQ0+v8jWrv9Tx1pw99SzmKlf4IMrkOiFw7//zzaWlp4de/Tv27dv755xMOh/H5Ur8OZbKtEEIIIUpXRmHURRddREtLC1/4whe44IILWLVqFdXV1XR1dfHmm2/y4IMPss8++3DRRRdlq73jev755/nmN7/JqaeeynXXXYeqqqiqyqWXXsoVV1zBo48+SkNDA2VlZcDI6KnDDjuME044gSeeeIJoNIrL5UJRlOIMojSL+L49iZpP8QO70DdWoO0e+R9AtdeD6+0q4it6wGVjeyJE5m/Gs3MhWtSHYqu4u+vGOUJpsLExy/qIV3aOGtkwTOnX0Xb5UXfnrkj5MOmAimG2amMs7sNqiIx+QLMxmvqwKmPo71dkFHgMU1DQt5QRP6AbgHhNG3p/5ahpuQoq7J27lDwbG6OiGyPYieWJTPzkqOoUKe/ObOSkXAfEdFi9ejXNzc1phVGrV6/O6LjDmpub096PEEIIIUpLRmHUKaecAoBt23z729/e63Hbttm2bdteI6MURWHdunWZHHqUWCzGX/7yFwBee+01brnlFq677joA5s+fzxe+8AXuv/9+Wltbue+++/jc5z6Hro9+6WVlZei6jqIUb20Uc15opPj4EGOfPpRBffQIqT43rjeqnRFSPhNbN4jO2YJvS1PJj4KwVZNo/TbMsv7RD5igtnvRWv2j3qtckg6oSGYuGBgVROm9zjRoo9IJjKyGCKahom/JzqICar8btcODVRvF1g2Mih5cvTVZ2XexsrGJNmxLTFXeS1hD7Xeh9rlQ+l0oIT3jwFquA0IIIYQQYibKKIzaf//9CyK8cbvdrFixgnXr1rFkyRJeeOEFbrvtNq699lrcbjdHHHEE0WiUX/ziF/zv//4vbreb8847D13X2bRpExs2bGDBggWJlfSK1xifxTgjG9SIjvuNauIru7HLDWzdwPIOooXLctvEPLK0ONG5W0aNdlAGNdRdfrTdXhQze5+/dDBFRmxQIz7nlLa7xzy1s0Hd7U3UjrL1eG4OUiRsxXKCqLK+xH3KgI7a40YZDqDi2V3QQa4TQgghhBBipsoojPrhD3+YrXZkbMWKFSxbtozzzz+fRx99lGeeeQbbtrn22mupra3l5JNPpra2lv/8z//kwQcf5LnnnqO2tpbW1lZ6enq4++678XhyW6Q6FU9Zv+Zk9dMpbaNtC2D7jETnkoiGvrl83JE+iqGi7fRjNDmdL9MXKtkwynJHiMzZgu0a6nDHFfRNFahdnqxPxZMOphiWynmsbS3D9prO+atArH7nqMfVDg/a1iyfn+6ROmlKfPqmJttYmP4Qiq2ghgN5X63PViyis1tGVtC0QG+uROv05uyYcp0Q062jo4Ourq60t7UsC1VVqa1NbVGD5G2FEEIIIYZlFEatW7eOE044YcLnmKbJT3/6U774xS9mcqhJLVy4kJ07d7Jp0yauvPJKDMPgueeeA+DrX/86FRUVzJs3j5/97Gc8+OCDNDc309vby7777ss555xDY2NjTtuXjlQDKcVS0DdUYtVFQHGmnSn2xJ08tXekA2r6B6CrPu32FirTN0BkdstIgfKIiuudqqytUJZMOphiT1M9j4fPX3PhAOa8wVGPadv9aFvKsh7a2J6Rab2KkdvpqTY2li/krFpZ1ps4H9XBAJ72Oaix3AU/E7ZLMYnMaRmpH2eC690gam/ufjkh1wmRD1/4whdob2/PaNu6ujoeffTRtLe95ZZb0jq+EEIIIUpPRr3xG2+8kb///e9cfvnliRXqkm3fvp2bbrqJjRs35jSMMk0Tj8fD4Ycfzrvvvsvq1av58pe/DMBzzz2HZVn09/djWRY33HADl112GQDxeBxFUfaqH1VIUg6kUFJadUuJaRDWwGdiecLYipVx3Shbdab9KYYOlpbXUQ9GWQ/R+u2JFbCUfh3Xu8GsT7cB6WCK8U05kEJxVmWLaBiLnbpm+uZytLa9VyvNBtszMjJKzcHIKBsbyzuIUd7rBFC6sddzLH+I8IJN6D01uLvqUazsn5vjtk81iczZguUbCv8MxQmi+nI3SkyuE0IIIYQQQmQYRh122GE89thjvPXWW9xwww0sW7Ys8djjjz/OXXfdRTQa5Zxzzsm4oRPRNKfzsmrVKn7yk5/Q2tpKQ0MDX/3qV7nzzjt55plnMAyD66+/Hr/fn1hJz+WankLVmUpnyl4q1F43li8Mqp1x3SjTHSYyf/PI8ue2gmLoKKY+9LfL+dtwocY8qDFvTjqfNjZGsINYXWviPrXLjb6hEsXK/lQB6WCKyaQ0Za/Vj9rhBPyKkbupLbY3NyOjbGzi1bsxKrpHpsYmMxTUbjdWeRy8FihgVHVilPfg7mxA76vKeYhtqwaRuVuwvOFEm1xvV+V0AQO5TgghhBBCCOHIKIy64447eOSRRxLT8D73uc/x0Y9+lO9///usW7eOuro6brvtNg444IBstXdCS5cuxe1209fXR0NDA+Xl5QwODmLbNh6Ph5dffpmPfOQjBT0Sajy5DKTUXhdWg9MhM/0DGYVR8erdI0EUgGJju+Jjd0iHn2LoKEPBlBNQDYVUZnqfk41NrG4XRrAzcZ/a6kN/rzwnHVzpYIqpSuU8zmUINWx4mp5i6FlbSXPcFeksULs8qO1e1G4PiqVgqzbm3BDmvBBogG4Sq9+BUdmJu30OWiSQlTaBMx3P8kSx3BEsTwTT34/tjjkPxoeCqJAEUUIIIYQQQkyHjFOZs846i4MOOoibbrqJe+65h/vvv59YLMaxxx7LNddcQ3l5dpYhn4qmpiYCgQDr1q1j8eLFfPOb3+Tdd9/lyiuv5NVXX+Wxxx7D6/Vy+eWXT1ubsilXgdSoulG+UNr7sfTYyEpUcQWl3wVuC9ttgcsad0UwWx9azc8/+tiKoaNGfagRL1q4bNJCx7ZiYgb6iVd2jdqX1hJA25abIsnSwRSpyvVIx6myFRuGpukp8eyEME4QvHMkiLJB7XajdnhROz17rVipWAr6tjK0Nh/Gon6sOmfxBcsbITL/fbSBctSIH22wHC069enHNjamfwDLO4jliWC5IyPB055iqhNEDebulxRynRBCCCGEEGK0rPzf99KlSznqqKP41a9+RTQapbKykosuumhag6jhlVpWrlzJ5s2bWbNmDW+++SZf/epXOfHEEzn22GPxeDycccYZ09amXMhFR1aJaSiDGrbfdDpvegzVSL1milExsgS9tsOPvn1khJWNDS4nmLLdFrbXxPYbzh+fAW57r/3ZuoGp9zsBE+1ooTLc7XNQ456k/VqYgQGM8h7MQN/oUVkW6O9VoO2eeic2FdLBFOkqiEAqqXi5rVrYqoFiZfZPghnoxwgOrdZlg74+iNY1eSFwJabhag5i7YphLO7HLnNqS5ll/Zhl/cRpQ++twt3RMGEbbWzM8l5iVbuxPdHJjzugo2+oRI1IECWEEEIIIcR0yvj/wNva2vj2t7/N22+/zdKlSzniiCN46KGHuPjii7n00kv5+Mc/no12Tmp4yeBjjz2Wa6+9lkAgwNe+9jWOOuooACoqKrj66qsT9aXEaGqXB9M/CApEG7bh3b445ZFEtppUf2aP6S4KCsQ1p3D4GIOvbN0aCaf8BpbfcDqk+ki4ZAYGCPs24eqpRRssGymKrJl77zCu4GquRO3JzYpY0sEUk8l72DQZQwVTAc3G9kQJL3gPz85GtFj64a2tj0zH1bYFphREJVP73Lher8ZqCGM0DoBr5Pw3KrsxAn24O2aj9wdHXZ9sxcIo7yFe1T72CCgTlEEdJeRCDekoId25neOpkHKdEEIIIYQQYmwZhVHr1q3jzjvvJBQKceaZZ/KFL3wBl8vF0UcfzU033cS///u/8/e//51rr72WioqKbLV5QoceeijXX389brebQw89FLd7ZIRPKQRRuergatsCmLVR8JpYvkHi1btxd9Wnto+IHwOnTpNdEYfuqXdEFUNF6XND0ipWNs40IqsyhrFgwCl0rNrEq9uJV4+xPHVcQe3wonV6UPrcKFZuCiBLB1NMpuCDKJxzzvVOkPjyHnA7td0i8zfjaZuHPhBMb5/JK/Ipe492nNI+UJwC7m0+bJ+JFYxiLgg5wbRuEmvYjlHRjWf3HBTDhVHZRbyqA3uPlfqUPhfaLh9qvwsiqa/qOXyep/tZynVCCCGEEEKI8WUURt14441UVVVx++23c9hhhyXu33fffbn//vv5/ve/z1NPPcWGDRt49NFHM27sVGiaxgknnIBt2yURPiXLZQdXMVVczRXE93em2sWrd4Ni4+qaNeXCxmp4pNiwVTFOfZZU2oQCUQ1ttw+1w4s5P4Q5NwTJzTEV1M6hosg9bhQ7tytwSQdTTKYYgqhhap8b9+s1xFf0YJcboNpEZ2/D6grj6mxIOcBRk8Ko5JX60qHYCsqgjjqoo3V4R9eU8ocIN74HlrrXyEil242+PYDS60q7TlzyeZ7OlEq5TgghhBBCCDGxjOYoHH744fz85z8fFUQN8/v9rFmzhn/7t38jHA5ncpiUqaoqQVQa1H432tahQEmBeHU74caNGIFeZ5TSZNubLpSY0xm1y+NOgeQsUSwFvaUM16s1qLu9qB0e9A2VuF+qw7WxEq3bI0GUyLtiCqKGKTEN15vVqG3exH3x6g4ic7dgq8YEW46xr7iL4UtFpmHUXm1sDuJ6OwjhoWu7Yo8KotQOD67Xq3G/U4Xa685KEDXRfalsL4QQQgghhBgto5FRt99++6TPOeWUU1i1alUmh5nxprODq20LgIKz1LoKtitOdM7WMYuHj7l9xI/hjjnblsedqXdZpEZ01I2VWd3nVEgHU0ymGIOoYYqtoG+qwBpwYSzqBxUs/wDhBe/h3j0XfXBqi1EoqCiGC9sVz2oYNUzt8eB+zY05L+RcoxRQd3vRtgdQw5kXIZ/oPJ/KCCm5TohC9u///u+899573HjjjWlta5pmWr/oS952un85KYQQQojClbslhJLMnj17Og5Tkqa7g6ugoG8tQ93txVjSj13lTLczAwOE/ZtwdddOOHVPDQegosfZZvYgapbDqHyQDqaYTDEHUcMUFLRdfpSQTnx5L7gtJ4yeuwVjoAJ3++wprbKpxN3Yrji4bGzNQjGzWyRcsZxrlLbT79zOUhHyqZznEwVScp0QhW7BggVph0ELFizI6LjDmpub096PEEIIIUpLbpcSEhnJZwdXjei43gmir6+EyNDXRLGTpu71jbmd3l8JhpNxWnVRrEB8zOcVC+lgismUQhCVzKkjVY3SO7IiplnWR7hxI7Gq3ZNO2c1m3aiJKIY6rUHURM+V64QQQgghhBCpmZaRUSJ1hdDBVVDQOr2o3Z5RxcOdqXstsGPhXtN3FFvD3VVHbNYuAMwFIdT1wTy0PnPSwRSTKYTzNBeUmIbrrSqsugjGogFwD61kWduGYqu4emrH39YYCbFsrwkh17jPLQTpnOfJI6TkOiEKTVtbGz09PWM+1tLSMr2NEUIIIYQYh4RRYlLDxcPVttFT9+I1rWiDZXsVCtb7qolXt2PrBlZVNCdTdTIlHUghJqagoLX7ULs8xD7U6QRSMOHCBDY2ZqB/ZB/xwjrv95TJdUCuIaIQtbW1ce655xKJRMZ9jsvlIhgMprTfp556img0isfj4eSTT05720ym+wkhhBCitEgYVaDSWU4814an7sUP7MIuM7C8EcxAP3qoYtTzFFtF66/EqOp0CiFXR9HafXlq9d6kEymypRDP02yz6iKJIEqJu3D1Vo/7XNPfj+V1atIoAzpKX25HRWXy/st1QJSinp4eIpEIa9asobGxcdRj11xzDT09PZSXl1NfX5/Sfu+55x7a29upq6tLOYxK3vaWW25JaVshhBBClK7C/rX1DFeInSUFBW1rIHE7Xt02Zg0ZfWBkxTurJjotbZuKQnxPRXEr5e+U7TExFg4kbrt3z0Wxxl5Ny8YmXr07cVvbFthr1GQupDvNTohS1tjYSFNT06g/LpcTDqezIp4QQgghRLZJGFXgCrHTpHZ5UAaGipQPjY7a6zkR/0gh86ootjpx0ePpUIjvpSgNpfjdsrGJL+sD3Tl39d6qvWrEJbP8A1i+oVFRIR210zMt7YTMC5ALIYQQQgghppeEUUWg0DpPUxkdpaCMTN/TnEAqnwrtPRSlp9S+Y1ZDGDvo1IdT4i7cHbPHfa6NTSwPo6KSTeX9L7XPSAghhBBCiGIlYVSRKLRO1FRGR2kDI7Wk8jlVr9DeO1G6SuW7ZnuMKU/PA7B8ISzfIADKoIbaMX2jopJN9P6XymcjhBBCCCFEKZAwqogUUmdqKqOjtMEyMJ0OrFUdnXAVrlwppPdMzAzF/p2zFZv4it4pT88D8j4qKtlY73+xfyZCCCGEEEKUGgmjikwhdarULg9KaChs8kawvIOjHnem6g11YnUba2jKz3QppPdKzCzF+t2zsTGW9mGXGQAoMfeE0/MALFcEyx9ybpjKtNaKGk/y+1+sn4UQQgghhBClTM93A0TqCmU5ebsyju0zJ3yOGvZDRY9zwz3xc4UoJVMJQQrhPE5mzhvEqo84NywF767GCafnORSwnb/QbOKrunGtD6LEZMUuIYQQQgghxNhkZFSRyvdv+22vQXx5T+IbpHfXoEUCYzwzf9N1Cq2jL0QhM2simEl1ojxt81Fj3km3U+MePK0LwHLOdbvcIHZgF1b59I6ETJZ87st1QAghhBBCiMIjYVQRy1cgZWsW8X17wOXUlNFCZZNO5ckX6YgKMTmrLI6xT2/itqujHn2gcsrb6wOVeLctQYm7nDvcFvFV3Zj1g3vVksu1sc55uQ4IAdXV1dTV1VFdXV002wohhBCidMk0vSI33VP2bGziTb3YfmfKnRJ1RkXks2DxZE5WP533kWRCFCrbYzrh8tCsOr0viKu7bvznY2MG+ohXtYOt4mmdj2q60GI+fFuXEpm91akhpYKxrB+1Joq+uQIlmvtpexNdC+U6IGa6e++9N+/bNjc3p70fIYQQQpQWGRkFGIZBNBrNdzPSNp0dLHPuIHb10PQbU5tiTZn8k5ERQuwtMcrRbQFOjTf37rnjhsumZ5DIvPeJztmK5Qtj+UNEZ29NjH5SLB3vjkXoPTWJbazqGLGDOzDmhnK6ouZUznG5DgghhBBCCFEYZvzIqBdeeIFnnnmGLVu20NjYyKpVq/jkJz+Z72albDpGSNkeE3PBUE0ZG7y7FqDG879y1lTJyAghRtjYGE292IGRlfO8OxtR7L1/R2HpMWK1rZjlvXs/5hskXtOGu7PB2Q8KnvY5aOEAsbqd2LoBGpiLBrDqIujvVaAOuLL6WlK59sl1QAghhBBCiPyb0SOj/vSnP3HDDTfQ1tbG7Nmz2bx5M3fddRdf//rXeffdd7EsK99NTEkuO1g2NsaSvpGpPL01aOGyyTfM4UiIdMjICCEc5qIBrOFRjpaCp20e7DHK0VYNYjW7CDduHBVEKYMa2gdlMHSJjFe3Y/j7R22rD1Tia9nHGSU1dBmwywziB3QRX9yHrWbn+prOOS3XASGEEEIIIfJrxo6M6ujo4KGHHuKkk07iwgsvpKGhgba2Nv7xj3/wk5/8hDvuuIOLL76Yww8/HFVNLbPr6Oigs7MzcbulpSXbzR9XrkZI2WXGSMcV0ELlmJ5BJ2xSbGzFGvlZtbDcESxfCMsTyXpbMiUjI4pDPs+jUmcF4phzB0fuUG0i898HGxRTRzF0sHQsbwjUpEA5rqBvLUNt9aHYCthgLnZGS0YbtqFuXzxqBT7F0vC0z0HvCxKr3+FcDxSw5oQxVHC9V5HR68jkWifXATHT3HHHHfT391NeXs4111yTl21Xr16d0rZCCCGEKF0zNowC2LVrFyeccAINDc70kvr6ek477TQWLFjADTfcwI9//GM8Hg8HH3xwSvv93e9+xwMPPLDX/V865FrU0Mj0lKLqCNlDf4ZKyUTnbkl5F0oou1Nz8mW4A1xUn18RGu88ypZ8f44FOTpHAVs3nKl1ySzQdvjRtgdQzJFwXtvpx66MY9VEQTOJzPkA7/YlqIZ71OZa1I+ro4HonC2MlKPK/6hJCaTETPLiiy/S3t5OXd34CxTkelsJo4QQQggxbMaGUeFwGEUZKdIbj8dxuVwoisJ+++3HLbfcwte//nXuv/9+Fi9eTDAYxLbtUduMZ/Xq1Rx99NGJ2y0tLdx88817PS8XHaFcdXDVkAt9fSXGkn7wTHF6jQ1KSEftdaN2eLJeJyZdmbznye+vdGRza6rnUTry/TnmO4hSQy5cr1djVcTAZWG7rcTftsv5GRUwQWvzoe0IjLkanoKCvrGC+H7d2OUGtssgMvcDfNuXoJgj/7wYZT1EG7Yngiil14X+Qfk0vdqJyXksxPSbaKRrMBikvr5+GlsjhBBCiHyYcWGUaZpomsb8+fM59NBDefjhhzn11FOpq6tLPAbQ1NTE1VdfzQ033MAvf/lLLrvssikFUQC1tbXU1tZO6bnZ7AjluoOrdXlRe92YDWFsnwkWKJYCljNdh6GfFRuIaai9rlGjKApBtoKo5PukI5sbqZxHqcj355jvIGqYOuAaNyC2sUG3wVSc6XgTUEwV1ztVxPfvwvab2O4YkTlb8G5fhGJrxCs7iM3aNXLcTg96c6Vz7SgQch4LMT2CwSBer3fCXyx4vV7Wrl0rgZQQQghR4mZMGPX222+zePFi/H5/4r4TTzyR119/nTvvvJNvfOMbBINBLMtK1Ijaf//9OfHEE/nTn/7EJz/5SebMmZOTtmWjIzRdHVzFVNF3BKblWNmW7SAq+THpyBaHfH+OhRJETUZBAWPqYZFiOIFUbP8u8FhY3jCROS1oET/x6vbE89RWH/p75c7+C4ycx0LkXn19PWvXrqWnp2fMx4dHwPb09EgYJYQQQpS4whq2kiN/+MMfuOaaa/j73/8+6v5jjjmGk046ibfeeot77rmHnp4eVFXFNE1s26a8vJzjjz+e7u7ucf/HKVsyLcQrJparICqV54j8yvfnWOrfESWq4XqnCuJO0GT5Q6OCKG1roGCDqGGl/hkJUQjq6+tpamoa809jY2O+myeEEEKIaVLyYdTjjz/O7bffzumnn86qVasS9xuGU6D3sssu46ijjuLZZ5/lrrvuoqOjA03TElPyhleAcblyX+9IlijPjVwHUek8V0yvfH+OM+W7oQ7quN4Ngpl0pw365nL0rWUFHUQNmymflRBCCCGEEPlU0mHU448/zq233sqZZ57JWWedRU1NTeIxXR+ZobhmzRpOPPFEXnnlFa666ipeffVV2traePvtt3nqqaeoqqpKawWZdOS701xqpiuIymQbkVv5/hxn2ndC7Xejbwg60/xM0Jsr0Xb5J92ukMy0z0wIIYQQQojpVrI1o55//nluvfVWPvnJT3LRRRcRCDh1jp5++ml2795NT08Pxx13HHPmzCEYDHLNNdfQ2NjIE088wZVXXkkgEMDj8QBw5513EgwGp63tU6ldIp2lyU13EJW8rdSeKQz5/hxn6nmqdXtQX651VtS0ivN3HnIeCyGEEEIIkTslGUaZpsnzzz+Poijs3r07EURdd911vPjii2iahmEY/Pd//zenn346Z5xxBosXL+ass87i1FNP5eWXX2bXrl1UVVVxyCGH0NDQMO2vYaZ2YrMlX0FU8j6kI1v8MvkcZ/o5XGgraaZDzmMhhBBCCCFyoyTDKE3TuPLKK/F4PKxbt47vfOc7hEIh3nnnHb70pS9xyCGHEA6HefLJJ/nNb35DPB7nc5/7HLW1tVRWVnLSSSfl+yWIDOQ7iErel3Rki186n+NMD6IKxVPWrzP+LOQ8FqXixBNPTNTBLJZthRBCCFG6SiqMam5upqmpCYBAIMCll16KaZo888wzVFRUcM0113DYYYfhdrsBWLFiBR6Ph1/96leccMIJ1NbWYlkWqlr8v9GfqQoliErep3Rki18qn6MEUYVFAikhHF/60peKblshhBBClK6SSV2ef/55Lr74Ym655ZbEfX6/n6985Sscf/zxHHrooRxwwAGJIMqyLAA+/vGP4/P5ePLJJwESq+iJ4lNoQdR07FtMn6l8jvJZF6ZsBEny2QohhBBCCJE9JRFGxWIx/vKXvwDw2muv7RVIXXHFFVxwwQWjhogPh04NDQ14PJ7E6noSRhWnQg2ipvMYIvcm+hzlMy5sEkgJIYQQQghROEoijHK73Ykpd0uWLOGFF17gtttuSzzu9/upr69P3LZtG0VRsG2bv/3tb5imyZIlSxKPieJS6EFUPo4lcmesz7EQP1tbs7A1K9/NKCgSSAkhhBBCCFEYSiKMAqf+07Jlyzj//PM56qijeOaZZ7jtttsS4dLwtDwYGf20fv16Hn30UXw+H8cdd9yox0RxKJYgKp/HFNmX/DkW4mdqVcSIHdpB7NAOrIpYvptTUCSQEjPVOeecwz/90z9xzjnnFM22QgghhChdJRNGLVy4kJ07d7Jp0yauvPJKjjzySJ599lluv/12AFRVZf369YATTP34xz/mxz/+MZs2beK73/3uqJFTojgUWxBVCMcW2XOy+umC/CxtbOLL+kC3QXd+tpERn8kkkBIzUTgcZnBwkHA4XDTbCiGEEKJ0lcRqeqZp4vF4OPzww3n33XdZvXo1X/7ylwF49tlnsSyLvr4+FEXhsssuo7W1lWeffZaGhgZ++MMfsnDhwvy+AJGWyTqDhbz6lazOJXJJgUT8VOhjPSc6D3K5Cl42VtkTQgghhBBCpKckRkZpmgbAqlWr+Otf/0prays1NTV89atf5bDDDuPpp5/mxRdf5KSTTmLOnDmsXLmSO++8k5tuukmCqBI2UUczkyAoWyGSdIRzL5eB31PWrzP+HuViewUFfUMlyqCGMqg5PxdwJJXr80DOMyGEEEIIIQpPSYRRw5YuXYrb7aavrw+A8vJyBgcHsW0bt9vNyy+/DIDH42H+/PlUVlbms7liGmQ7kMp2uCEd5dzLRSCVvM9Mv0e52F4NuXC9WoPr1RrUkCvl/U83CaSEEEIIIYSYWUoqjGpqaiIQCLBu3ToMw+Ab3/gG7777LldeeSVHH300jz32GHfddVe+mymmWbYCqVyNspGOcu5l87Mba1+Zfo9ysb0y9F+xkEBKCCGEEEKImaNkwqjh1fJWrlzJ5s2bWbNmDW+88QZXXXUVH/vYx7jiiis4/fTTOeOMM/LcUpEPmQZSua7vJB3l3MvGZzjRPjL9Hk3H9oVOAikhhBBCCCFmhpIJo1TVeSnHHnssL730Em+++SZf+9rXOOaYYwCoqKjg6quvZsGCBflspsijdAOp6erkS0c593JdKyzfYZMEUvnfvxBCCCGEEGJyJRNGDTv00EO5/vrr+cY3vsERRxyB2+1OPDZc6FzMXKkGUtPduZeOcu7lulZYIU7jKzYSSAkhhBBCCFHaSi6M0jSNE044gaOOOgqv15vv5ogCNNVAKl+deuko516uw51CKnBerCSQEkIIIYQQonTp+W5ALgxP2Ss0P3rlNpqamvLdjCmZyR21Qu/ID3826bYz0+1LRa5ff6b7z8b2M/k8zjc5T0Wh+epXv0o0GsXj8RTNtkIIIYQoXSUZRgkxkWwVss5HRz/5mCern075tWS6vRDJcnkeDH83CzVQm+jckfNUFKKjjjqq6LYVQgghROkqzCFEQuRINjt1hVBPKpWOeqbbCzGWXJwHhTBddiJTDaImui9X2wshhBBCCFEMJIwSM0auO825NFFndCod1Uy3F2IiuQ55CymQSjWImspj2dpeCCGEEEKIYiFhlJgRctmZzXVHeTo6sdLRFZnK1vTXXO4/U+kGUVN5jpynYqra2tpobm4e809LS8u42zU3N/P222/T3Nyc8jHzta0QQgghSpfUjBIlbzo6sbmqnZPq9J49X2um2wuRikzOg6l89/JZlD3VIMrGBkBB2eu5cp6KdLW1tXHuuecSiUTGfY7X6yUYDO51/3XXXUd7ezt1dXU8+uijKR03X9sKIYQQonRJGCVK2nR22rLdUU5nX8kd1Uy3F6XNCsQBUEOurO43nfMgle9cPgKpVIMoKxDHaOoFQG+u3Os9lvNUpKunp4dIJMKaNWtobGwc8znBYJD6+vppbpkQQgghRGokjBIlKx+dtWx1lDPZR6bHl45u6bMCceIHdQHgeq06r4FUOt+16Qyk0hkRZTT1YvtNAIymXlyv1ow5QioTcp7ObI2NjTQ1NeW7GUIIIYQQaZMwShSkqXQAc9URy/X+hcg3Y1F/0s8DuN+uytmxivk8Kua2CyGEEEIIUcikgLkoOFMdiZDrGk3pTp/J1FPWrzPqBEsHWkxKs0d+Vu3xnzeBQjlPc2WqNaz2pKCgN1eiDGoog5rz8x6joqarfUIIIYQQQhQqCaNEQUl1SkyuazSlWlg4U8mvP93pS0LkWqGdp9mWag2rPakhF65Xa3C9WpP1KZDjHVMIIYQQQohiImGUKBjpLpue6xpN07Xk+livP9NOsRCTUlIbGVWo52m2ZCsEVob+yzY5z4UQQgghRCmQMEoUhHQ7uKk8J1f7z1UQNZXHUnmOEJkq9PM0U4U+PVbOcyGEEEIIUSokjBJ5l2kHN53nZmv/uQ6ipvIc6aCKlOn22D9PoFjO03Rl4zzK5bko57kQQgghhCglEkaJvMpWBzedbTLd/3QFURM9VzqoIh22yxrz5/EU23maqmyeR7k4J+U8F0IIIYQQpUbPdwPEzJXtDm7ytpN13gphulC6tWmGjy8dVJEOW7H3GhllKzaKPXZ9o2I9T6cqV+FRPq8TQoxl7dq12LaNoqRey2y6t21paRn3sWAwSH19fcrtEEIIIURhkTBqGn3pkGtTWlkp3U5IMYQVuergTsV0dHBzqRA68Pm053mUr/OkGM6zMdlAVAXP0IioqOrcN4Z8vrZS/x5PRdF9t0RB8/v9Bb9tMBjE6/Vy8803j/scr9fL2rVrJZASQgghipyEUUOi0SgejyffzRhlKiMHxtomk+2HZfM3+2PtezzFEEQNtz/TfWXy+Uy0z0wVWwc4H+dJts6zfFBQcG2oJL5vDwCuDZU5WfUtE9MZRMl5KEThqK+vZ+3atfT09Iz5eEtLCzfffDM9PT0SRgkhhBBFTmpGAU888QS/+tWvCIVC+W7KXvJZYHu6a58UUxC158/pyuZrnskd4Ok8T3JVyH46qf1u3H+vw/33OtR+d76bM0o+3ks5D4UoHPX19TQ1NY35p7GxMd/NE0IIIUSWzPgw6vHHH+eWW24hHA6jqoX5dmS6ZHqhBFKlFERNdF+qstE26QBPz3mSq/MsHxRbGbdOVL7k8z2U81CUuocffpj777+fhx9+uGi2FUIIIUTpKsz0ZZo89thj3HrrrZx11lmcccYZ+Hy+vZ5j21Nb9jzXctmJnkyulzwv1iBqKo9NVb4LqpdKBzjfYVOxBVKFpBDeOzkPRSl75JFHeOCBB3jkkUeKZlshhBBClK4ZG0Y98cQT3H777XzmM5/h05/+dKL2QF9fH+3t7fT29gKktXJMR0cHzc3NiT8TrQqTilxML5qqTDpJpRxEpfKcyaTT1lLuAKd7HuV7Gl4hhCrFppDeMzkPhRBCCCGEyL0ZWcB8y5Yt3HLLLcydO5cPf/jDzJo1C4C77rqL1157jZ07d1JbW8uJJ57I+eefj66n9jb97ne/44EHHshBy0cX20230zSdRc1nQhCV/NzpLGpe6h3gTM6jbJ4n032ezTSFFEQNk/NQCCGEEEKI3JqRYdTChQv5zGc+w8MPP8zzzz/P7NmzueOOO3j99dc54IADOOSQQ3jzzTf5xS9+wfbt2/n617+Ox+PBtu0pjZRavXo1Rx99dOL28Oov2ZLPVdxSCVxmUhCVvM10fD4zoQOc6XmUjc8h0+0L/T3Ot0IMoobJeSiEEEIIIUTuzMgwCuCLX/wiiqLw0EMP8fzzz6MoCjfddBOrVq3C7/fT1tbGU089xX333UdNTQ2XXXbZlKfs1dbWUltbm+NXkD+Zdp4KuQM6LNNpiYX+GouhA1zq51ExkEAtt+S9FUIIIYQQM9WMrRkFcMkll3DeeefR2trKxz/+cQ4++GD8fj+2bVNfX88//dM/ceSRR/J///d/bN++Pd/NTXjK+nXOajjl2nSENIXw/uT6+Pl+fcUgG9+DYn+fc13LLN/vby7fYzkPhRBCCCGEyJ0ZEUZt3LiRJ554grvuuot//OMf9PT0JB678MILueaaazj66KNxuVyAU7Tcsixqa2s59thjiUQihEKhPLV+tOQOTLrTyPJluoKosX5OZ/tstiUX2+T79RWybH4Piv19LsRAKt+fTyr7z+ZzM9lGCCGEEEKIUlLyYdRTTz3F9ddfz49+9CMee+wxrr32Wn7zm98Qj8exbRtVVfnYxz7GggULsG0bIHE/wNatWwkEAgSDwTy+CsdYHZhcd5qyZbqDqInuS2X7TBVSp3amdIBz8T0o9ve5kAKpfH8+6ew/m9sU4vdDCCFEcXj++edZvXo1c+fORVGUSRd6+e53v8uhhx5KRUUFdXV1/PM//zNvv/329DRWCCEmUdJh1Lp167j11ls57LDDuPHGG3n44Yc5+eSTWbt2La2trSiKkgigwBkRZZpmojbUxo0bee2111i+fDnl5eX5ehnAxB2YXE8nyVS+gqipPJbKc9JVCNN9ZkoHOJffg2J/nwshkMr355PJ/rOxbSF/P4QQQuTfBRdcwI033jju4wMDA+y3337cdddd+Hy+Sff37LPP8qUvfYm//vWvPPPMM+i6zkknnURXV1cWWy2EEOkp2TBq/fr1/OxnP+OUU07hX//1XznooIMIBoPst99++P3+xFS95KLktm2jaRrg/ObhJz/5CVu3buUrX/kKfr8/Hy8DKIxOWrryHURN5TnT8f7ksxM9UzrApRCG5Fo+A6l8fz653HYq+yiG74coDG1tbTQ3N4/5p6WlJe39Llu2jJUrV7Js2bKi2Xam2rJlC4qicMEFF+S7KaLAnH766dxyyy2ceeaZiVkcE3niiSe48MIL2W+//Vi1ahVr166lvb2dv/zlL9PQWiGEmFhJrqZn2zbNzc34/X5OO+00GhoaEo/t3LkTwzD43e9+x1NPPcW8efM48MAD2WeffVAUhf7+fn7+85/z3HPP4fP5+I//+A8WLlyYt9eS6vSPPTuKEkSNfm4+359cHz/fry+fpvN7UOzv81jtT9VEq+zl+/1N5/XJeSgKRVtbG+eeey6RSGTc53i93rRKB9x6661ptytf24rc2bJlC4sWLeL888+fdKpXKXvggQe48MILJ3yOqqqYpjml/S1cuHDc0Li+vp7W1taU25hN/f39WJZFVVVVXtshhBBQomGUoiiccsop1NbWsv/++yfu/+1vf8uvfvUrDj74YObPn09LSws//elPWbx4MV/5ylfYb7/9KC8vZ/78+Xz84x/nlFNOGRVkTbd0a5cMd4QkiBp7m3y+P7k+fr5fXz7k43tQ7O/zdAZS+f58pvLcbCv274fIn56eHiKRCGvWrKGxsXHM5wSDQerr66e5ZUKUpgMPPJAbbrhhzMf+/Oc/88wzz3DaaaeltM/KykquuOKKve4vKytLp4lZdfnll3PggQdy5JFH5rspQghRWmFUc3MzTU1NAPj9fj784Q8DYFkWXV1d3HPPPZxxxhn8y7/8CzU1NQAcdNBB3HHHHTz++OOsWLECTdM444wzMAwDXc/u2/OjV25LtC+XSqHzk8lrmKwTmO/3J9fHz/frm8xYIUEqbc7WeZTp+1To7/NkshFITbb/ieT6PM3355Pr42d6HonC1tjYOC3/vyDETHfggQdy4IEHjvnYcGBz8cUXp7TPYDA4Yd2nZLfccgu33HJL4nY0GkVRFO68887EfY8//jjHHHNMSm0Yy1VXXcULL7zACy+8kChLIoQQ+VQyNaOef/55Lr744lEX9OEhtaqqUltby89+9jMuuuiiRBBl2zYf+9jHOProo3nppZcSK+wBWQ+iZpp81W5J7qBNx+gskRr5TAqLnKfFSd4zIWaODRs28IlPfILq6moCgQAf/vCHefLJJ8d87ksvvcSZZ55JQ0MDbreb+fPn84UvfIGdO3eOet6NN97IokWLAHjwwQdRFCXxZ3jK3gMPPMCnPvUpFi9ejM/no6KigqOPPppf/vKXOX29heKtt97ixRdfZO7cuXz0ox/N2XEuueQSXn/99cSf1atX73XfIYcckvFxrrzySh566CGeeeYZFi9enIWWCyFE5koicYnFYolCfK+99hq33HIL1113HZqmYZpmIv2fN29eYhvLshKF/yKRCIFAAK/XO/2NL2HTXbtlrGNNNJVITC/pQBcmOU+Li5xHIl1f//rX6e3tpbKyMuU6TvnadjwTFXIvpWmMH3zwAUceeSSrVq3iC1/4Art27eLhhx/mtNNO4//7//4/zj777MRz77//fi6++GI8Hg+rV69m/vz5bNq0ifvuu4//+7//48UXX2TBggUAHH/88fT09HDXXXdxwAEH8IlPfCKxn+FRQl/84hdZuXIlxx57LLNnz6azs5PHHnuMc889l+bmZr797W9P51sx7X76058C8P/+3/9LeRRRNBrll7/8JVu3biUQCLD//vtz7LHHjrmf6upqqqurE7fLy8uprq5m6dKlmb2AJJdffjkPP/ww69atY/ny5VnbrxBCZKokwii3282KFStYt24dS5Ys4YUXXuC2227j2muv3SuQgtFB1BtvvMHu3bs5+OCDMQwDTdNGrbAnMjNdtVsmOoZ0dPNPOtCFTc7T4iDnkcjEpk2baG9vp66urmi23VMwGMTr9XLzzTeP+xyv18vatWtLIpB6/vnnufrqq7njjjsS91122WUceeSRXHLJJZx22mlUVFSwceNGLrnkEhYuXMhzzz3H3LlzE89/+umnOeWUU7j88sv57W9/Czhh1MKFC7nrrrs48MADx5xS9vbbb7NkyZJR98ViMU477TRuvfVWLrnkklHHmcgPfvCDxCrWU3HggQeOCsimWzgc5pe//CWapvG5z30u5e1bW1s599xzR923aNEifv7zn3Pcccdl1LaBgQHee+89wOnPbN26lddff53q6moWLFjA3Xffzd13382GDRsAuPTSS1m7di3/8z//Q1VVVaKAellZWUHUsBJCzGwlEUYBrFixgmXLlnH++efz6KOP8swzzwDwta99DU3TRgVQw3+//fbb/OIXvyAUCnH22WfL1LwcmUpHN1cd3OTnSEc3P6QDXRzkPC1sch4J4axGtnbt2nGDjZaWFm6++WZ6enpKIoyqrKzk+uuvH3XfIYccwmc/+1kefPBBfvvb33L++efz4x//mHg8zl133bVXQHTiiSeyevVq/u///o/+/n7Ky8undOw9gyhwfvl76aWX8swzz/D0009z3nnnTWlfP/jBDyYczban888/P69h1COPPEJPTw8f/ehHmT9/fkrbXnjhhRxzzDGsXLmS8vJy3n//fe6++25++tOfctppp/G3v/2NAw44IO22vfLKK5xwwgmJ2zfccAM33HBDYlXEjo4OmpubE4//6Ec/ApzvQbIbbrhhynWthBAiV0omfVm4cCE7d+5k06ZNXHnllRiGwbPPPgvAtddei6qqrF+/nhUrVmAYBv/5n//Jpk2b2L59O9/77EH2DgABAABJREFU3vem/NsdkZ6JOrq57uAmP1c6utNLOtDFRc7TwiTnkRAj6uvrSyJomoqDDz54zPDo+OOP58EHH+S1117j/PPP529/+xsAzz33HC+//PJez9+9ezemabJx40Y+9KEPTenYW7du5bbbbuPpp59m69athMPhUY/v2LFjyq9jy5YtU35uqhYuXJhS0PXZz3520rpXw1P0vvCFL6Tcnj1X5ttvv/245557KCsr43vf+x433nhjYoTaWIZrdo3n+OOPT9S3HcuNN944KmSa6LlCCJFvJRFGmaaJx+Ph8MMP591332X16tV8+ctfBuDZZ5/Fsiz6+vpQFIWvfOUrdHd38/LLLzNv3jx++MMfJubQi9waq6M7XR3c5G2kozs9pANdnOQ8LSxyHpWetra2CUf2CDFsvNCtoaEBgN7eXgA6OzsBRk3nG8vAwMCUjvv+++9z2GGH0d3dzTHHHMMpp5xCZWUlmqaxZcsWHnzwQaLR6FRfRk4tWbIkpZqvc+bMmfDxd955h7/+9a/MmzeP008/PdPmJVxyySV873vf4/nnn8/aPoUQotiVRBg1XA9q1apV/OQnP6G1tZWGhga++tWvcuedd/L0009jmibf/OY3aWhooL6+nh/84Ad4vV6ZLz3Nkju6093BTd5WOrq5JR3o4ibnaWGQ86j0tLW1ce655xKJRMZ9jtfrJRgMTl+jRMFqa2sb8/7huj+VlZWj/u7t7aWioiLj437/+9+ns7OTn//851xwwQWjHnvooYd48MEHU9pfLmtGPf300ym1ZTKZFC6fyHDNtFAolLV9CiFEsSuJMGrY0qVLcbvd9PX10dDQQHl5OYODg9i2jdvt5uWXX+YjH/kIiqJQW1ub7+bOWDO9gylEMZDzVIjs6+npIRKJsGbNGhobG8d8TimtBicy8+qrr45Z52m4DMVBBx0EwBFHHME//vEP/vznP/PRj350SvseDlpM09zrseEC2Z/61Kf2euy5556bcvuHFUvNqEgkwtq1a9E0jf/3//5fVvf94osvArB48eKM9vP8889z55138o9//IOdO3eOGRju6cYbb+Rb3/rWqPvq6+sToaYQQuRLSYVRTU1NBAIB1q1bx+LFi/nmN7/Ju+++y5VXXsmrr77KY489htfr5fLLL893U4UQQghRgqYyDa+xsZGmpqZpbNXMMlHwUUxhX29vLzfddNOo6XevvPIK//Vf/0VlZSVnnHEG4Kyw99Of/pQrr7ySZcuWsc8++4zaTywW46WXXuKYY45J3FdVVYWiKGzdunWv4y5cuBBwQq9//ud/Ttz/xBNPcN9996X8OnJZMyqbfv3rX9Pd3c3HPvaxSQuXb968mXg8zpIlS3C5XACsX7+eBQsWEAgERj13y5YtXHbZZQCcc845GbVxYGCA/fbbj/POO2/KBeTB6SMNh5hAVkd9CSFEukomjBpeLW/lypVs3ryZNWvW8Oabb/LVr36VE088kWOPPRaPx5P4h1sIIYQQpWeiMCjXent7WbNmjUzDy5NgMIjX6+Xmm28e9znDjw9PbdtTIYWExx57LPfddx8vvfQSRx99NLt27eLhhx/Gsix+8pOfJKbkLV++nPvvv5+LLrqIlStX8k//9E/ss88+xONxtm7dyp///Gfq6urYsGFDYt9lZWUcfvjh/PnPf+azn/0s++yzD5qmsXr1ar70pS/x85//nE9/+tOceeaZzJkzh7fffps//vGPnHXWWTz88MP5ektyaniK3sUXXzzpc0888URaWlr44IMPEuHdww8/zPe+9z2OPfZYGhsbKS8vZ/PmzfzhD38gEolw+umnc/XVV2fUxtNPPz1Ry2qyEVHJdF1P1BoTQohCUTJhlKqqgPMP97XXXksgEOBrX/saRx11FAAVFRVcffXVeflNwHCRRylMmj1WIJ7R9snL3orsS/XzWbduHUceeeSERUjlPCo+cp5mJpfn0YsvvliS59LAwAA/+tGPiMVieWuD2+3miiuuGLcmZXl5OT09PdMemMXj8cTfqZ5b+do2Hd/61rfo7+8f87Hh78dEgcC9995LY2NjSkWxc2XRokXcc889fP3rX+eee+4hGo1y8MEHc/3113PqqaeOeu4555zDAQccwPe+9z3WrVvHk08+SSAQYM6cOZx55pmcffbZe+1/7dq1XHnllfzxj3/koYcewrZt5s2bxwUXXMC6detYs2YNf/jDHzAMgwMOOIDf/OY3BIPBkgyj1q9fzwsvvJBR4fITTjiB5uZmXnvtNf7yl78QCoUIBoN8+MMf5txzz+Xcc89FUZQst3xq3n//febMmZNY8OmWW27JeMqgEEJkSrFLbM1P0zR59tlncbvdHHrooQXxPxNPPvnkhL+lE0I4HYCJfiMt55EQk5PzSIjMTXYeCVEIysrKuPvuuycdIfX444/T39/P8uXL2b17NzfffDMbNmzgnXfeoaamZnoaK4QQYyi5MAqcKXu2bRfMfOienh7+/ve/M3v2bNxud1b22dLSws033zxhEdR8K/Q2Svsyl802Tvab6FycR5Mphs9gLNLu6VVI7c7XeVRI70EqpN3Tp5jaXCgjo0TpW7NmDd/5zncmfM66des4/vjj97p/qmHUngYGBli8eDFf//rXueqqq1LaVgghsqlkpuklG56yVyiCwSCnnHJKTvZdDEVQC72N0r7MTUcbc3keTaYYPoOxSLunVzG0O9fnUTG8B2ORdk+fYmyzELlyxRVXTFrUfMGCBVk9ZllZGStXrmTTpk1Z3a8QQqSqJMMoIYQQQgghhChktbW11NbWTusxI5EIGzZs4IQTTpjW4wohxJ4KawiREEIIIYQQQoi9DAwM8Prrr/P6669jWRZbt27l9ddfZ+vWrQDcfffdLF++fNQ2V199Nc899xwffPABL730EmeeeSahUIjzzz8/Hy9BCCESJIwqUjU1NVxwwQUFXXiw0Nso7ctcMbQxE8X6+qTd06tY251NxfoeSLunTzG2WYhC88orr3DQQQdx0EEHEQ6HueGGGzjooIO4/vrrAejo6Nhr1crt27fzL//yLzQ1NfHJT34Sj8fDiy++WPC124QQpa8kC5gLIYQQQgghhBBCiMIkI6OEEEIIIYQQQgghxLSRMEoIIYQQQgghhBBCTBsJo6ZBJBKhubmZSCSS76YIUbTkPBIic3IeCZE5OY+EEEKIzOn5bsBM0NLSwuc//3nuvfdempqa8t0cAE5xfSbfTcirJ+O/mvDxfL8/k7VvJirE86jU5fs8KHS5vo7k4jog51HhyeZ5ZgYjxFd0AeAaWIA7NC/xWNzXRqxiMwD65kr03YGsHTcTxfjvXaGfRw8//DChUIhAIMDZZ5+d7+bkRLG+xmJttxBC5IKEUUIIIYQQQpSIRx55hPb2durq6ko28CjW11is7RZCiFyQaXpCCCGEEEIIIf5/9t48SpKrPPN+7o0lI7Myq7L26u7qrt4XSS1aCAmxGS/AMICBY7MdjxmBGewP2zNexjOcmU9jn+HofDJ4AYwXjWGMGA2L8cjMYBtjGDAGLyDJoLWl7pbU+1J7Vu6x3Hu/PyIzMrK7qjK2rMqsuj+dPoqsjOVmZtyIe5943+eVSCSSDUNGRkkkEomkrxEQgM7AMw5ExgYfcCDSNuBQqHMZ0MU0iCCb3UyJpKsICPAhM9C6bEcFxFZAl1MgkH1DIpFIJBLJxiPFKEnPIKgAG62Bj1cBQaCeHQKtd+cUlV44Ekn/IiDAR+rgQ6YnQEEVq65rD1nATBHqtQEosxkQR0msHfI6IolLUueQIAL2/gL4RM37G3XSbev4X4uMA/voEkhZg7KQBhQBoXIIlQNK4/+q+zdiUyjzGSjz6UT7T5PXae/qS98oiUQikUgk8ZBpetuUXhr4ccOBPbMC8/ZrcA4WwIcs8LwJ69Z5sLHqZjdvw+ml30ayvenFc1EQAedAAfaRZbCpKsSgtboQ5f+TzuHsKcG8fRb2gQJ4xt6w9koka5GYEKUyWDcttglRWmkPFHOkbT3FHkRq+Rio3TIuF1kbzt4inN0lsB0V8PEa+IgJMWhDZBxA5xADDpy9RZi3z8I6vASWr7vRiAkihV2JRCKRSLYfMjJqG/M1+wubNgBsRjawyQp43lp9JUXAPlQAGzKhnR0C4VtfO+3Fyb9ke7OZ14nrEZTDPrwMPtyeikQcHdTJuP9s9//EMcC1MuyBa2DGEkAAUIBNVMEmqqArOpRrA6DLhkzhk2w4SfUpbtiwjy1BGMz9g6BIrRyCao6uur5qDUNZyoPpy7CzF8G1yjo7V0CEAqE07tEU4KN18NE6YFIocxko8xlQM5mhpIyQkkgkEolkeyHFqG3ORk80BQTYzjKcHRVA59e9SaDWx6BWJ+Ck5+Fk5gAAfKIGK2dDOz0MWtXW3TcftMBH6gAjUC9n+0rAkoNwSa/SC4KUUBmsY0sQ2UZUkyDQV/ZBrY+AiNVvZYo9CKUwCK7UYWdm3WsKdSftfMgCH7IARkCXU1CWDNCCAcL655oh6U8SE6KyFqxji15kIGEaUoWjUJzcutsREKjWCJSlYXCt6IpNXAURKgh3/0EoII3gea7U4KTn4BhzEEqj/6U42O4y2HQZtJCC9nwexI6fwicFKYlEIpFItg9SjJJs2ERTQMDZtwI21Z56R5wUtOoU1No4iHDFJsUehGINwhx8AaAcIu3AOj4P9dyQ6/vSMFwVEBA5C2y0DjZaaxO4+KAF/dmRvphcysG3pNfZTEGKGw7sY4ut6A+uwFg+AsUaDLQ9ZQZSpRno5Wk46XnYA9cg1Lr7piLAx+rgY3WAA3QlBbpkQFkyuuKPI9nedEuIonYGqcIxUJ4KvA8CAsUeAjpkrVKWhl6egVbeDZYqwEnPgunLbrQhAfiwCfPWeeinRkDLeoxP5SIFKYlEIpFItgdSjJIA2JiJJttZbglRAlDMYajVKSjW0KrVfNT6OKidhZk/46YSUMDZvwI+ZEK9OgA20hCgUvyGbQFADFqwblqE/sxIT08q5aBb0i9shiDFByxYx5YAze3nhOkwlo6COpnQ+yJCcYXv6iRYqgBmLMExlgHquCtQd2LNh004+1dAl1PQnh8GcXpf0Jb0Pl0ToswhGCtHQUR373MEFKo5AtUcAacmHGMeTuaaG1mlc1g3L0B9IQ91PnzfvB4pSMXjv/23/wbOOSjduteufv2M/dpuiUQi6QbySijx6MbAT0BA6AzOVBnOTMn7e2rlIIzCUahWft2y0pSlYSzeArUy5f2Nj9Zh3bIItrPSLkQJAqU+DL24D+CuziqyNqybFyF0lvhnSwI52Jb0Gxt5zrJ8HdbNiy0hyk7DWLg5khDlh4BANYeRWjmAzOztMBaPQa1MgTDdvxL4iAnryBIESdasWbL96KoQVei+EHU9lKegV6eRXnwRaDNCkQLOwQLsfQUINf49d7NTg/uZsbExTExMYGxsbLOb0jX69TP2a7slEomkG8jIKEkbUSMfBASgM3CDQaQYRMaGyNjgA443kWyilfZArY8H3jcBRaq0D4o1BHPoOc/zxT0wgWIOQamPQTWHPe8YxRxEfeQkhOJWBLKOLUJ/cqynPKSkECXpVyJfJzQGnrO8f9B4o+odaVW/EwAEccvLp1t9nZo5GMtH1vSHigoBgWINQbGGIIoz4FoFzFiCnZ4DFAdi0IKzvwD1+fWFc4lkLZISVdhIDfbBAqBcJ0Rh8yJ/idBgLN8EK3cOTuaa286pKthkFaSkQ1lOga6kQOpqpJR5GSElkUgkEsnWRYpR25C4A2OetcAHTQiDQaQc9/86CxRnp1YnoVV2Rjquao6ALrwI1uA5AAKKObKmeTFlGRhLt6A+fBJCNSEybmlq7YV8pGMnjRxcSzab5nWgW+eigIDIOO3ikxEtWkKpjSBVOOgZKncL10Mn6/6rj6A++jRABNhEDaSiQb2W7erxJZLVEBBwdpfApsve33pBiGriPjDaD2oPwBp8ASDC1ZcHLTiDFoBGVLRDXFHKVNx/jWVa1tdNhQ0yZpH3VIlEIpFI+g8pRm0z4gpRzo4ynL3FwOsTpjXKrQ+A2oNQzHjRBZSnYBSOBFuXGTCWj6I2+iRAOdhkFbSoQ1mI72chkfQz/utAlMiD9a4jAgJsogpnd+nGipnXw5u3IF9YFGktE2ZAK+90ixtscFSSYmeRKhyAOfwcAMDZWwSpqVBWjA1th2R7IxQO+2DBrRLbQK2NQy8e6Lo4GxatPgnFzrnVcFNLEGqtfQVVQGTtVkXMJg6BdmoESjG4+fr1yAiqdr785S+jVqshnU7jzW9+82Y3pyv062fs13ZLJBJJN5Bi1DYithA1UVldiOIKKEuBMAOEpdxlJw3FyYDw+JV14kBZBnpxP6y8O6G096+AlDXQurap7ZJINovVrgNhJnLrXUd4zoS9t3jjZBMABAW13KgjauWgWLnEU+6SRq2PgZdqsHOXAQLYh5dBnhwHrfd2uyX9i4BwU90b0YQsb7YiCgWgl/dCre7o2ZRRyjLQyzPQyzPgSg1MXwZXq+CKCaHUIRQTNzRdFbCPLIE8ORbr3iwFqRaf+cxnMD8/j/Hx8S0rePTrZ+zXdkskEkk3kCPqbUJcIYqN1uDsX/Feq5UdUGtjoCwFCLVnB8YAoNXHwasrcDLzgCJgH1nuOf8oiWQjWO86EGQit9b2Qmew9xTBx9sjIZR6Hoo5BGrnQO1Mz0VyBEErT4NrVTBj2Z00H11yrx8R/G8kkusRRLj+ijkLfLCRzrpaRCFXYawchmLlN7yNUaEsDVpLt/1NQEBQE0IxwZU6HGMBPLXi9q1jjb4Vo/qtFKQkEolEIukf5Gh6GxBbiMrXYR9c9p5mqpUd0EszUJwsiNB6Wohqohf3gdjuoLjpHyWRbCeCXAc6iVXXI4iAs6sE88RcmxBF7AyMxWMwlo9Cq+6AYmf7UogCXB+pVOFg6/qRdmAfWnaLNkgkMeBpG9aJOVi3LsDZVwQfrd8oRAmAWoNILx3vKyFqLQgIKDeg2EPQ6pMwVo6C2m7qvDAYrCPLsatXyip8EolEIpH0BzIyaosT26x80IR9ZMmTLdXqBPTSTF8IUH4IFBiFw23+UaSsQZ0b2OymSSRdJ8x1YLXIgtW2Z8M1OHuLEH5Tcq5CL01DrU723TViPYhQYCwfQW3sKYA64MMmnJkitPNDm900SZ8iVAb76FJ7/wHctHc7C8UehGLnQO1sz6ezxoEIBanCMdRHnnCr3w5asPcXoMWsXikjpCSdmJ2dRaFQWPP9fD6PycnJjWuQRCKRbEO27ghHEl+IGrBg+YQopTYKvbi/byeZ1/tHOftXQGwFyrI0JJZsXaJcB/wTudW2d3aW4MyUWn8QgFqdgl6a3rITZ7cgwiHUR551K+ztrIDUVSizmb69Jko2B0GEGwHUEKKInYFWmwS1B0Gd7Xc+UZ5CqnAU9ZGnAcLBJ2pgNRXqlVys/UpBSrIWs7OzePe73416vb7mOoZh4MEHH5SClEQikXSRrTlrkMQWogQE7MOuRwoAKGYeqZWDfT9I1urj4JUKnIGrriHx0SU4FRXKXAbKQjqWV4VE0mvEuQ6stS0bqrcJUdQcRKq4F9TZ+lUqFWsIenEvrKGzAFxB29njVtkjdRW0pnrLpK6C8P6+Xkq6A5usQAxaANyKs0bhGCiPXkluK6A4OaRWDsLMnwYAODMl0IIBWo1XbEQKUpLVKBQKqNfruOeeezAzM3PD++fPn8e9996LQqEgxSiJRCLpIlKM2kB+4aX/CbTSqi7XywMkkXa8p7bUHkBq+XDfer5cj16agVBMMGMJACAGHDj7inBmiqDLBpT5DOhyqu+Ft61KP/WjrQjbWfaWtdI0tPKubdVXtOokuFqFMzDr/kEVEDkbImejze1HALSoQ3tuGMSKJnL3w0S6KVr2ejt7FkFBhHwIAgCqOQZersLOXgIAsIkq6DmZCivpHjMzMzhy5MhmN0MikUi2LVtDXehTetlk01+aXamPgmDrDJZdQ+LD0Ff2g1rZ1hsU4KN12EeXYN02B55epTy9pOfo5X602SQtEPC0DZ5vRHQ4qW0nRDXRizPQizNQzCEQR8eqXuYE4EMWrMNLsQyZe/n89retl9vZayizAyBlN+JHqCbMoTM9aYgviAOmlsGVWueVE0Kr7gSEe01ho7XY34sUSbcvs7OzOHXq1Kr/zp8/v9nNk0gkEglkZNSm06tPvvmAT4yys+us2Z8QEGi1SWi1SXClCic9Dyc9D6G4n1sYDNbNi9CfHgWtxUsTkHSfXu1HvcDX7C8kJhSwqYq3rFWmtqUQBQAEFFplB7TKDgCAAIdQa+BqHVytQyg1OKkVQHEjppy9K9DO5iMfrxfP79XOqV5sZy9CBIF+ahjmrfOAJsBSy3AyV10hZoMR4BCKe95ypeaex4q7DKU1DqB2FmptAmp9rKu+cESoUMxhN3JZ5+CDFpRitBRGeS5ubRhzo/dt28apU6fa3ltZWcE999zT0RMqn893s4kSiUQi6YAUo3qAXhzA86zlLVN7a1ecoywDvTwDrbwHLFWAnb0IrlUAjUtBqo/oxX7UKyQhSAmFg403IiQ4hVobT6BlWwMCCuIMgDqta6WqVlAfe8o1O5+qgpR1qPPRfbV66fxe71zqpXb2MsRSoZ0Zhn2Tmy5uZc9DsYbazqFuIsDhZK7BGrgEUKfj+lwrw9LKsHLnoNRHoNUnQK2hrgjSan3MS6PnY7VIYtR2Pwenp6cxMDCA4eHhzW5KV/BXwisUCnj/+99/wzqGYeC3f/u3MTS0eqrnZlXL2+q/jUQikYRBilE9Qi8N4AUREI3IKOIYW7Y61vUQEKjmMBQrh/rwM+B6WQpSfUYv9aNeI64gxSaqgOKmzKi18W1zXYiK4gxAX9kHK/8CAMDZXwCtqm1+Z2HphfM7yDnUC+3sB5QVA/zKANjOCkAE6oNnkF66tav+jAICLLUIK3seQjXXXI/pKbB0FiydhVpegVYuuG8QDpZeAEsvgLAU1No41NpkogbsijkMcApQDjZag3p2CEQEF73kuQd8/OMf3+wmdJVCoQDO+ZoG5MDmiU2d2Oq/jUQikYRBziZ6iF4ZwIu047mJbfWoqNUgQoWxfEwKUn1Kr/SjXiSqICUg4FyXoifpjFabANfKcAbmAApYh5eRenIcxIkuNmzm+R3m3JH9MBjqhUHwIRNiwIHQqrCyF5Aq7+3KsZhWhJU9597XfJijU3CyebCMKz6xTBZCbb/XKaUCjGsXYMxeAHUa6eyKCTt7CfbAZaRWDkE1xxJpJ4ECxRwBSy8AqgAfMqEUjEDbynNueyENyCUSiaS/kWJUj9ELA3h/il7SflGCMAhqQVAHIMx9TRiEYoGrTb+KGgjXoNYmodZHQVmwQWiSSEGqv+mFftSrRBGk+EgdaFTXVOpDoCzdjaZ5CDA4mTlwrQoI6lYcA2ksExBBAVAQroI4KVDWuxGcenEvuFZ1ryMGg31oGdozI7HSmzbj/I4iYsp+2BkiCLTnhmEdnwco4GSuuBG6dnJV5LhSg5U976W+NbHy46gcuAVOLt9xHyyXRyWXR+XAzdAXrsG4dh760qx7FhMBc+g0RIlBqyUTiaLWx1wxCgAbqwUSo+S5JpFIJBJJf9Gbo/dtzmYP4P2V9KJERnG1AqaVIBQLglrgjf8LxQIoC9YGasLOXYCduwDiGFCsISjmEBRraMMmnasKUscWkXp8AoTJQpS9zmb3o14mrCDlj4pSqzu60SQATR+bOdjZy14xgcBwBdQxQJgBylKNFGMKQW0I6rj/V3zL1AG100gV94E60b2cOkFAkVo+hNrYk4DigOdNsIkq1Ll4UacbeX7HSe+U/bAztKpBvZiDM1MCCGDlziK9dCL2fgU4rNxZOOlZ+LVPJ5ND5cAtsEYmARJSFKUKrIldsCZ2gdZrGDj7NIzZi267B58HV8vQS3tjV+BVrDzAFYAy8JE6hMpAnLX3Kc8xSTdYr+per6YBSiQSST8hxShJGwICbLhRfUQQUCdcZJSduQJrMHrJXAGApzKgZtUbOwu1Dketw8nMAsKt6qOYI9CqUyAi3oC3Ey1B6iS4XgFSHM7uErRzyT21lkh6GT5gQQy50ZLEMaCYyZ/7AgJOegF29tK6PjbrQpnbR1FBMMkb4KkSaqNPw1g+DMXqXp+mPIXUygGYI27FJz5kAjHFqI0iqUqMkrURlIMN+8/7ZEzBrcEX4KTnvNdMN1Ddewz1qT0Ajf9AhRtplI7eDq6lkLn0HADAycyC6UWkVg5DiWHGTkCh1sfc+74iYB1bgn5yNPEHQaud31tB2PrQhz6ElZUVDA0N4dd//dc3uzld46Mf/Sh27tyZ+GfM5/MwDAP33nvvmus03w9rkL5dfhuJRCIJghSjepDNHAixHRVA5wBcE9GgYo+AgJ29CDt7efX3KQXX0+C64f5fS0EoauuflgLL5MCMAYAqoNUSUotXoBVmoZaWQIRrnAwCcL0MrpfdUtjlPa6ZchdLzBOhIrVyGLWxx13z1qkKlPl0ZCPirTDQ7Qfk97w2oaKidvi9onYk2tcEBJixDCt7EUKrtb1nDe+AOTEDrhkgggOcg3AGCA7COcAZqF0HNStQ6hXQegXUqgVqnVBUCEJc7xvKUB95FvrKPmi1icQ+2/UoZh4QBPAViIjDRpzfSQhRsh+ujyAC9pFliMFGejxXkVo5FHu/tjHnCVGCKqjOHEF1+gCgJDzsIwSVA7eADeSQPfMECGcQag31kSegl2egVqNfM7TKNBxjEaAORNaGddPimoJUlAi8rSy0Pv7445ifn8f4+Nauenry5EnMz88nvt/JyUk8+OCDXsW+61lZWcE999yDX/u1X1tzH4Zh4MEHH7xBkNouv41EIpEEQYpRPcZmDtydHWU4e4veazXgxExAwMqdgzNwzftbbedB2PnxhgCVds1QQ6QD8EwOtcwR1HYfAZgDbWUBWmEOWmEOaq3kHlexYQ09DydzDXpxLxR7MPD+w0KZAa08DTt3ASCAvX8F+pNjoQfZcmK2McjveW3CTMCEzsBHGyIRV6FWkzEoBgCmr8DKXWhEM7WwhyZQ3X0TWDZC2WvOQM2qK06ZFUAICFV3xW8tBa6mIDQdoArAHGSfewT68jWACFj5FyAUE1p5uiviNgEBtTPgegUizSAUHjnKQwpRWwMBAfvQMni+ERXFFRjLN4GyeGmjXKnAGnzBe106fALm1J5Y+1wXQlDfsRf20ChyJx+BVl5x+1TuHJhegF48CMrDP7yhPIX00i2ojTyVuCC1lYUoSTJMTk6um4a3nlh1/vx53HvvvSgUCjKVTyKRSNZBilE9xGYN3AUE2K4ynD0l729aeZf7JD/Attbg83AyrSdTlX23or7zQHINVFTYI1OwR9wKXrRawsD5p6EvXQUAcK2C+ujTUGqj0Mt7umZ4rlV2wDEWILQqRNaGM12Cdim4ACYnZhuD/J7XJuwEzJmseJU1tepEbB8YwC1iYA2ea7tmAICdHUFt901whmI8LaYKeDoHns51XldRUT58FzLnn4BxzZ2427nL4IqJ1Mp+ECTvC0ftrCe+iQEbpJgKvQ8pRG0dnL1F8NFmWjyFUTgGJWRq/PUIwlDPnwaIG+Fc27G3u0KUD5bJofDiV2PghZNe2h5LFVAbeRzp5ZtAWfi0PcoyiQtSUoiSJEEnsUoikUgknZEuzD3CZgpRzu5SuxBV2g29vKdjdIAAh5k/7U0qBYDywRcnK0StAs/kUDp2F1ZufgWcTEsMYulF1MYeg5W9AEGCusYEh4AiVdzvflAAbLoMNlQPtK2cmG0M8ntem7ATMEHdlFT3BYFamYrdBqaVURt7sk2IcjJDKB15GUo3/1A8ISoKhKC690WozBxvdmuwzALqI89CECfxw/kLQvAIqXpSiNo6OBMVNy0eAASBUTgaO7pXQMAcfB5CdaMZ7ewQygdvjdvUcFAFlYPHUbj15eBaQ2xVbLcQCI3mB9cUpMDd56dNQUoofNX11zuHpRAlkUgkEknvIMWoHmBThai9RbDpsvc3vTgDvTIdYFsGc/hZr1S0IATlI3fCnJzpWnuvx8lPYOXEj6J84AS41kgBIAJ29jJqY4+BK7X1dxABxc5BKzeeMhPAPlSA0NcXvuTEbGOQ3/PaRJmAsfEaoLoSjVobjZRm00RAwMpeQn30KQjVFXAFVVDZfxuKx38E9vBU+KpeCWLuOIjy4ZdCUDfyi6eKqI09Ba4EE5uDEkeMkkLU1oHnTDj7VrzXenG/Wz0uJk76Gpix4B5DUVG86U5A6W6Rj7WwRyaxdMePwc7lAQBCsVAfPglBovmlJSFISSFKIpFIJJLeQopRm8ymClH7VlpPZgHXwLe6s/O2xEF95BmwlDuYFlRB6djLYI3t6lp714QQmFP7UHjx61DbdQiCuKe0O/B9BoLGNwq+Hq2yE0q94WejcViHlyCIWHVdOTHbGOT3vDZRJmACAmxHS6RWKzsiH58rddRHT8LOXfKKhDnZYazc+qMwJ/Zuqgjlxx7ZieKxV4Krrugm1Drqo0+DUyuxY1An7ZqYA6FMzKUQtXXgKQfWkWVv9KVWdkCrx0/1YWoZVu6c97p09MXgmXgpf3ERegorx1/uFiYBXGPz/LMQgetdthNHkJJClEQikUgkvYcUozaRza6ax6aq7gsB6CsHoNWCpeFYuXPgupvW5z59fTns4c3NmxeqhureW1C47cfgDAw1/mbCyl5I/FgEBKmVgyCOm4IgcjbYRPWG9eTEbGOQ3/PaRJ2A8UELIu1OGKk5GLlEu4BAfeRZ73ohANR2HUXx5h8CNzZ3orwaLDeC4i0/DJZqTJ4V+wZvqzgQUBDH9bQTaQeCri5i+5FC1NbC2bcCaK54Qs0h6OW9iezXGnweaDwUqe46AGt8Ex4OrYLQUyi8qJWyx/VSrPtyFEFKClESiUQikfQm0sB8A/mD792HI0eOdFyvOXCKOrjvNPASKocz3fCIEkBq5RDUerAqWQICTjM1j6oo3vLKaFWvugRPZ1E6dheGH/0b97V6o0iUDJ0nkVGJ+/tvdYL2o04k1c+24u8kUq3IBbU+Enk/LFXw0vJYKoPKwZfAyY3Gbl834cYA6jsPYeDsY82/JLZvRy9AaI30YYd23PV659Zq1/mteC5uNYTSunekynsTq9zYvNcxI4PKgVsS2WdS8HQWK7e+DPkffBuEczjpWeiV3SAi2hB0NVNz+/AytGdGulIJUyKRSCQSSXeQkVE9Rtyw8iDbODtLPi+Y8cBCFABwrQxQd6JqDU/2lBDVRCia/0Xy+4eAOfQ8hOqasZKSBmXuxlLccX8/+TS3eyTZz7bi77RG1mlonHQrqqg6c7znhagm1Gp5Rfl9nuIgCIM19IL3Wr2Qizxx7sVzrhfb1IsohVYFRaYVE9mnAPc6LU+lAdp7QzsnN4z6VMNTknLY6dlY+7s+QornTTh7k/k+JRKJRCKRbAy9N2LZxsQ13AyyrtBZWwUfrbw78P4BgKWWvWU7v8GVrwJCWKsKFumCGGUPXAEzGt+DTaGfHgERq08q4/5+coKXPN3oZ1vud2o7n6MpU4I4Xj/hqg47H78a30ah1FqTWuqkE9mnlbsAobr+U3RFX1XADkIvn2u93LZegS4b3rKTWkpmp77qsULp3YD32vQB72riZK66IloMKMvAKBz1rldsRwXORKXDVtuDN73pTXjHO96BN73pTZvdlK7ymte8pu8+43b5bSQSiSQIvTtq2WZ0KkXcKf0i6CTAnil6EqRW2QHKU+tvcB1eih4AayS6qXE3IaxlDJy0GOWklmA3/S4EoJ3Jg1jrHyPu7xdke0kwutnPttTv5NOf1jLn74STXvCiNayx3T0ZrbEWtNZMYyYgzFh/5QAwrQhnoBEJwgjU5/ORoqL6QezZUv2gC5CqCpgKkGLgehGCOJHT1ZoI0hJ1mhUhexGWycEanUJq8RqEYoEZi1Dr8R5qKfYg9NJ+1zMLricXramgpXBjm63Ge9/73s1uQmxmZ2dRKBRWfe/8+fMAgHe+852JpO13i2Y7/bz85S8HAOTz+Q1ujUQikfQeUozqAYJMMNYb4AedoPCcCT7W8CvhKrRKOINTrlQhVHd7Z3AUQo8/SesG/sgo8OQG5kwvwMyf9iqCqZdyUFaCfQdxfz85wYvPRvSzLfM7+SOjIotRrRQ9c3wmbos2Ds6h1N1KgsRJx/agEeAw8770vIs5UDP8rbcfhKgmW6YfdAECAmXJcCOUiQDTC1DN4Knyq+/UHxnVu2IUANSmDyK1eA0AYGeuQKmPxe5jWm0SXK3CyVwFKGAdWUbqiTEQSw5x+5XZ2Vm8+93vRr1eX3MdwzB6VtDJ5/MwDAP33nvvmusYhoEHH3wQk5ObWwBIIpFINhN5p95kwqYHXT/AD7q9gIC9b8V7rZf2hH4a6xiL3rI12huVelalC2l6TCuiPnzKm5jThTSUS+GqgcX5/dbaXhKMjepna23fd/A1XwTbXK2C6266jDOQB2tUuAyMENAKswCEm95HNs6UmJoVENHo5wmk6Nm5S56JOylpUK6G96DqJyGqyZboB12CLqe8dHmWWo4tRrVFRvVwmh4A2Pkx2NkhaOUVcK0CrpWg2IOx96uX9oIrVfCUW63QOroE/akxEN4/EZmSFoVCAfV6Hffccw9mZlZ/mJHP53tWyJmcnMSDDz64bmTXvffei0Kh0LOfQSKRSDaC3h61bHGiGic3B/hhtmcTVYgBV6Sh9gDU2kToYzdT9ADAGt0ZevuNoi0yKgEximkl1IefARoDfrpoQHsueppNlN9vte0lwdjIfrba9n1JzMgo/7XCHN8Tevv0pWeQvnwKAGDlJ1E5cDuEtjFpN0n6RTG1DHvgivuCA1qE9Lx+FKKa9H0/6BK0mAIYARQBJ7UMHSJedFCfeEYBAAhBbfogtGf/GUAjOmolvhhFQGCsHEZt5EkItQ4x4MA+WIB2elhW2OtjZmZmejoNbz0mJyel0CSRSCQd6PFRy+YhhADp4tP4OBOMsNsKCDi7S95rvRi+nDRXahCaWzrazo24FXt6lHYD83hPRd3KeWcA2hCillPQzgyvaVgehLiTSznBC85G9rPVtu/b30nxC1DhxSjmF66HwwnX2tJlT4gCAL0wC/WJb6Cy7zbYG+BTp9TK3jJ14qUiW4MX2tJ6aU1bf4Pr6Gchqklf94MuQQQBLaTAR+sAdcDVMhQnF3l/gvZPmh4AmBPTYC88DcWqg6WWwNRSrM/fhAgNRuEoaiNPApSBj9bBR2tQFqMVC+hnfvInfxLz8/MYHx/HQw89tNnN6Qr9+hmb7ZZIJBKJFKMAAJcuXYJpmqhUKjh06BDS6TQIIeCcg/aR6W43cTKtMszWWPQUPaVcQPa57wNCoLb7iJvul7Dox31eVkxfgVaNOYH1RYYos5lYQpRk9Qm2nKz2BkLhcHaUwXa3BJlm6fSgMK0M3hCunYE8RBjhmtkYOPfEDX+mtonc6e/CHNuN6t5bIVQ9VJvCIJSWYMT0EtR6nBSq1rWDlrrX5ij8wkv/E2hlY9q0nQWpNQXFBG8jjrHgLbN0uPTxTYFS1KYPIPvC0wABzKEzSC+9KJG0esoySBUPuP6OAHjOhrK4/jbb9dyUSCQSiWSz2fZi1Ne+9jV86lOfwvLyMizLwsGDB/HKV74S73nPe0ApjSRILSwsYHGxNfpZrZrG1+wvRH7qHXZbAgL1Yg7OAdczyho8C2Px1sDRUQIcdsOMWBAKc3x3+EYD0BavIHf6URDuPsXNnXoEdu55VPcdh5MbibTP1XCGxsH0NBSrBpZaBqdm6KqBTQgItNIeWPnnAAD23iLoSiqWD0Wc9C//9v1ImM8cpB91Im4/A7bH7+SJUDsqgOqLhOIUqjkcal9O5pq3XJ/cF2rb9KVnQS3XX2n4Jg2HfiqL5z5fxtLTboXM1MJFaCtzXY2SssamkbnwFAhncNLz0Eu7I1c7U2sTsFJuVKozVYFe3PgKX0n0oyTYjoLUWtcOQTl4vuEjxjRQJ7qAxKkJlnLFKK7pMMeSTaGnZg0AAU8lW7CkNn0Qqfkr0ErLEGod9sAl6OVkCh1QuxVlJVJsnTUlEolEIpFsJttajHr44YfxkY98BG984xtx/Phx5HI5fOYzn8EXvvAFPPbYY/jIRz6CVCoVOmXvy1/+Mh544IGO60WZKEcdzCtzGbCpCsSAA65V4aTnoNWC5bIzYwmgbuqbNbozvHeLEDAun0Hm/NM3yF9aaQlDT/wdzLFpVGduBjcSCKcnBObkDDIXnwWIW9VLr0xH3p1aH4NjzoGnioDB4EyXoV2I5nHh//028vfvBcJ+1qD9qBNxv+et/DutKUIJArU2Dq28E5QFn4QKasNJu8IHVzRYo8H7nVJdgXHVLc9OVODA27NI5RXc9P8MYu57Jp5/qAJWE12PkhKqDnN8BsbsCwDlsAeuQi9HE+DV2ijs3AUIxQYfqYOnnEiV9OKQVD9Kgu0kSK13zeB5E2g8z1DMkVieRk7mmhdlVdu5H0goTU9dWULm4mnoC1cBENSm96O69xiEGi7VdE0oRfHYSzDyyDdABIeduQK1NhnqerMWhOuu7x0RECmn8wYSiUQikUg2hW0tRj3yyCPYuXMn3v72t2N62p003XzzzXjooYfw+c9/Hj//8z+Pj33sY8jlcmCMQQk4yHvzm9+MV7ziFd7rZtWM1Qgz0Y0ziCcg0M4OwbrFnShauQtQ66OBnvjb6TlvuT65N9yBOcPA84/BmLvg/WnyDh0TL9bw/P+uoTrrejGlFi5BX7yC2q6DqO86HHvAa07OIH3xWRC4KYZaZVfkAT8BQaq4H7Wxx91S3DvLUBbSoNVwbVzt99uo33+ziRJdFKYfdSLu97wVfydnZxnOrlIiIlQTOz3npbWaE3uBoGbKQiBz9jGQRlrb7n+RQXrcvd4SQjB5l4H8UQ1nPl/G8vVRUgduh51P1iS2vuMgUnNnQYSAPXANWmUKRIS/JhFQqNVJ2LlLAAHYVAX0fMjKgjFJsh8lwXYQpDpdK9hIq1y9YkaPChZgsNNuCr0gBLWd4SIRb9yhgL54DemLZ6Cv+HPbBDKXnkdq7jIqB26BOTGdSHo9z2RR230QmQunASJgZc/BWDkae78EBITrEIoJocvIKIlEIpFIepVtK0ZxznHu3DkYhuEJUbZtI5vN4p3vfCfy+Tz++I//GL/2a7+GP/qjP4KiKIEjpMbGxjA2FtxnJMhEN4nBOy2lQBfS4GM1gDpwjCVoHarqcaXulkoGwIwBOEPBPxexTeSe/R60YmtQu+/HDex9vQFCCEZv0XDl702c/as67LIAERyZS6dhzJ5HdeZmmJPRQ/Z5KgN7eBL68iyEYoHpBahWuHQjP5SloZV3eZNKe3/BLRsdUOBa7/fbqN9/s4ia5ha2H3Ui7ve8lX4nNlKDM9OqGhdXhAIahRIGGhNjAGaIFD19/gK0kmt6boxT7H7NjT5TqbyCm1eLknr2H1GfOoDqnpsBmkxUCDcG3OiouXMAZbCzV6CXol2PtOok7OxlV8ieqEK9mNvQcvNJ96Mk2MqCVKdrhCACfLghRnEFihVdnHQys17UsjkxDRE1lY4zpGYvIXPxDNRqqe2t1CCBXRPgNqBYdQw+8yisq+dRPnQr2ED8KnjVPYdhXD0PaptgxhJYdQWKHV+wJSwFoZiAJiAo39A+JwnG7OwsCoXCqu9tVjqxRCKRSDaWbStGUUpx/PhxfO5zn8PJkydx0003QdM0cM5hGAZe//rXwzRN/Pf//t/xm7/5m/jP//k/d7W63noT3SQH7cpcxhWjAHC12nF95/qoqIDfgVItInfyn6CY7jGoBhz71wOYvL2VUkMVgulXG5i8Q8f5r9Zx8VsmhOOaFWef+z4EVWCNR0+vq0/uhb7sTo6dzFwsMQoAtMouOOkFt2x0zgabqkC91tnrI8jvt1G//0bTa9XA4n7PW+V3Yo1rAACo1TFo5enY6TEstQyhWAAAe3gK3BgItB1xLGQuPOW9PviOLKi2+nWmLUrqc2Usn3SjpIxrz0NdmUfl0EvAMslEHtV3HUFq/oKbQjQwC7WyA5SHTwkkXINaG4OTmQdUATZRg3ot2HezldmKglSQ6x0fNL1oRMUcBkE0kUQQB9bAJe91dfehCDsRMK6cReb8KShWve2t7BTBgddp2HmHCnNF4OkvWph9wo0y0gvzGH70m6hNH0Rl5iigRh9KClVDZf9NyJ36AQDAyp2DsRTc03ItCGvZCYgUA6lJMaqXmJ2dxbvf/W7U6/U11zEMA/l8fuMaJZFIJJINZ9uKUQBw+PBhcM7x1a9+FVNTUxgZGfFMy1OpFN74xjfi9OnTePjhh/HYY4/hxIkTof2jwrDaRDfpwTqttX5yodbWWdM1Lm+KUYIQmBN7Ah1DW55F9tTDoMx9YqsPEdz6c1kM7l39dNMyFAd/IoNdP5TC8/+7hrnvuxPMzIWTsEZ3AhErGtrDU+Bayn3imloGp1akyWQTAorUyn7UR08CAJyZImjBAK2v3Y3C/H4b8ftvJL0mRDWJ+z33++8kqHA9awCAqdBXDsSe+AGAPeA3Lt8feLv0hadBHVfEGnuxjuFjnftoKq/g5g8M4srf1XH2f1cgHECtFTH45LdQ3XMLzKn9sdOIeCoDc3IfjGvPA4TDzl5GqhgtDUqrTLliFAA2VYZyLZPId97vbCVBKuj1jvtS9NQYKXp25ooXFVWf2A2WDS/CpmYvIHfm8ba/jRykOPA6DRO3KCDUPUczYwR3/LyB2SccPPWnFmqLAkQIZC6eQWruEsoHb4U1Ht04vT41A+PyC9DKK+BaBY4xB60eL/WWMh3NBD2hM6CWkNeVJBEKhQLq9TruuecezMysHnWaz+cxOZlsCnavsV4E2Hb4/BKJRLKtxai77roLb37zm/HQQw9hz549eMMb3oBMJgNKKRhjGBgYwPve9z78/d//PR5//HGcOHGiq9FRQPtEtyuDdIsCDgFUAa6sL0axVAFCcYUha2QHhN45coKaNeSe+S6IcL2gsrsV3Pr/ZGEMdxaU0mMKbvk3Wfzg4yUsn3Kg1CtILVyEORExXY9S1CdnkLnk+lEwYxG0Gq8Cl2IPQa1MwRm4BlDA2VOEfnr1CUWU36/rv/8G0atCVJO433M//048ZwKKG5lBQBMRRZhadg3+ATAjC2do/fTfJtSsIjV3DgCgpAj2/0TwiCFCCHb9cBr5wxqefaCE6hUGIjgGzj8BatdQ23NL6M9xPbWdh5GaO+dW1svMRa6sR50BUHMQPFWESDPwQQvKJlTW60W2giAV5nrHhhtCsACUiNG6Agz2wBV3mRBU9h2LsBOBzPlT3svxmxUcfqOG4f1rp7pO3qpi7KiC575q4/mv2eAOoJg1DD39PazcchessYj3V0JQOXgr8o99BwBgD1yKLUYR3h4ZJelNZmZmcOTIkc1uxqaxnn+fYRh48MEHpSAlkUi2NNtWjOKcg1KKn/u5n8Pc3Bw++clPQtM0/OiP/ihyuZwnOo2Pj2NsbAxXrlzZ5BZvPCy15C0HFYRovewJUflDKl7081koqXCT3X1vNLB8qgwA0JauRRejgOsqbYk110ua9SZXnUSMfp+Y9Qtxv+du/07dEvQIb/VHoVhgWgmKrxR6WAQErKFz3ut6iKgkatU8KWz8dh2pfHjPp4GdKm77D3mc+3IFl//WjTpJXzkDrqdhTh0IvT8/QtUhFBWEM8S9fihWzhPsoPJY+9pq9LMgFbafEt44kwjgpBZiiC7u+ShUHTx1o8daJ5RKEWqtAsCNhnrpvw2WpqvoBEferGP6LhVP/amF+addoWfg+SdhjUxGj2TOj4EZA1DqFQjFhoCIJZQT4buW0I2790skQRkZGcGHP/zhVd9rFpooFApSjJJIJFuabStG0caASdM0/OIv/iIYY/jEJz6BxcVFvPa1r8Xu3W4p77Nnz8KyLOzcGT0EPQz+gW1XBugq9/wqyDoeMQICjrHsLlMVdn480O7dSZtL/pAaWogCgKH9KrQBArsioBXmAcEBEm2Aqy+2RETFjOcZBQBMK7pRUQDACNQL4Qxcu/779ghhqs8Bbj/bty9mJagtRDcjy2gpBeXyANgudyJqZy9DWY5ewcpJz4PrrnjMjCzMieC/I9da1yCnFn3CSDWC/T+ZhTGh4Pk/dT9X5twT4LoBe2RX5P1qhWugthvJopjDkaKimnC1lZ5FasH20wv9KGwbotKP18Mo34t6bgj2MfdBj5U7B8UaDp0+TqBAMYfBjCVQ24S2PA97NNyEVV9spdXuuD38eT0wQXHnL6bwT79Tx9JzHGqtgvSVs6hNRxSAOQdteEwSx4gdsSmILxqKbT+/qHvuuQe2bUPTtm56Yr9+Rn+7t3NUmEQikQCI6JzZJywuLqJcLndcb2JiAh/84Afx2te+Fp/5zGfwoQ99CP/rf/0vfPazn8Uf/dEfoVar4TWveU3X27vawDbpSYBIO94yddYWo7hW8vworOGJwFWqCG898Vf0aINJQgmGj7qDY8psqKXlaPux6lAbFbqInQZl4Z8e+xEQsHKt/H71Qm5dv6jr2Yjft5cIM7G0bbuLLekvNuKcUC8OAqbbp5lRCFTMYDUEcWDlLnivK/teFCoygvtSf62V+NFCO1+Vxu7Xuf2cAMieeRRKtbj+RuuQmj3nLauVeE+nedOjjwMkxHVjM/vRRotD/XQ9jNpWpWCAzjfuRZTByr0AESHqTq23HhAZsxfWWXN1Uj4xauKWaFUoCSG46W0tIS1z7hkQ24q0L6VeBRHu9xD3Xg20i1GEbT9/tttuuw133nknbrvtts1uStfo18/Yr+2WSCSSbrBlxai/+7u/w0c+8hF86UtfQrXaeaI1ODiID37wg/iVX/kVZDIZ/P7v/z7+7M/+DNVqFR/72Mewa1f0p+tBWG9gm+QAnY22ns6vN+BjqZYAZI2E8IHwRUbRGA+rRm5qbawV5tZZc230xaves9U4RrFNWGrZiwAhVRVKiIpYG/X79hpBJ7P99mSzW2zUuUAEgXqldf42/WfCYuUuAkqjtPzIrsBeUR5UAW+k0pqFZFLXZn48g4mXun4xRHAYV05H2g+tV6CtuNU4iaNDsaJX6RMQXsEIUldBRLjJ8Wb0o82KUuqH62HcNmrnhgDbHX4xYwkstRh6H4o5DHBX1EwtXAVxgguRxDahFt0HNdkpgoHx6EPB/F4FO+9wxSzq2MhcONVhi9VRqiVvmTrxxSi0RUZtPzFKIpFIJJJ+YEuKUV/72tdw7733wjAMHDlyBJlMBgAgxNpPH5vvvfWtb8VHPvIR/Omf/ikeeOAB/M7v/A4OHIjnO9KJIAPbJAbobNAE2+GmsECQddPWvBQ9APZw8IgAf2TUWuXZgzByNAExasmXolcfjdwWoBkV1Xr6rF7IBU4j2Kjft1cJMqmVKXobfw4ocxnAds9hJ70ITs1Q2zO1AifjijWCKqjOHI/UjmZ0lLXC171GB4UQgoPvzELNuJ9NX7wEcl3Z+iCk5s+3xOzaZKy0IaHUAdJIjw6Yonc9G9mPNjtdrpevh0m0jTgU2tmWuGnmzkKQcFFtBBRqfcxd5gz6fHBBWV+a887miePx3RqOvlUHbewmfekF0IYXVRiUWiuKPfnIqC051JVIJBKJpO/ZcnfoM2fO4P7778eb3vQmvP/978edd97pvdc0JV9twuOvkqeqKiYnJ5HP5zEwEDz6JQphBrZxBsFC4bAPFLzXemkP6BqeUVypeU/xncFRCC1E1Sd/ZFSMMa4xTDGwwz091dISiBMu9J84FrSV+cZyCtTJRG8MXF8cL7KhqIEuBzN73ajft9fZ7Mltr7MZvz3htBXdRwTs7NXA27qm5WfRnNHWdh2FiGCiDLR8owQDnEoyRsOKTjD1Cne/RAgYsy+EbBRHaq6RkisAtRrMM2/N3amtyqVRxShgY/pRr/TVXrweJtkmumiALjXuI4oNJz0feh9tqXpzlwJv5/eLmjweLUXPT2aUYt+PuQ+QiOAYeOHp0PtQqi0xiqxjIRAYf2QU336RUT/4wQ/w8MMP4wc/+MFmN6Vr9Otn7Nd2SyQSSTfYcgbmFy9eBKUUr33tazE9PQ0A+PM//3PMzc2BEIKXv/zlOH78xif4s7OzKJVKOHjwIBQl/uAsCFEGtlFNXp2ZImC4gzNq5aBW1069i5yiB3iV9ACAqvEGgCPHNFSumiAAtMI8rLHgqZLa0jXPf0IxR+JFNYDBzl5s7fvCYKD9beTv2w9slBFyv7GZ34l6bQBsZwVQBJz0HPTSLhDROdXLSc+1mZbXdxyM3AZxnW+Ulk3mGcnOVxu4/I0aBAdSs2dR23UksPeda1zuRlMpZniD6evxi1G0Gi+Vrpv9qNeuPb10PUz6OycgUC/kYI2455mTWoJWDVcohdpZECcFoZrQludAzDpEqoOQwzn0JTeiUcsAwweS6W8HX6/h4j/asEqAMX8ZtZVFOEPBI5Lb0vSSiIyi2ztN795778X8/DzGx8fx0EMPbUobZmdnUSgUVn3v/Pnzq/49DL3wGaPQr+2WSCSSbrAlxSghBG666SYAwAc/+EE8/PDDyGazqNVq+NznPoe7774bb33rWzEy4voIlctl3HffffjBD36AT3ziE7j11lu73s44A9uw27J8HWyy4ZvFKVIrB9cVU5w2MWoqXOP8aXoxz66RYxouftNNHdIKc6HEKH2pFeWh1qP7RQkIWIPnIBQ3MosupUBLnSPF4v6+vTIBSxopSLWz2d8FcRQocxk3fZdyOOkFaOsI1QAgwGENRjctv2F/vguFucIxkJA9XyqvYOzFKcw/aoI6FvSlK7DGdgfbdt6XkluNZ1wuqA2WbnkCxYmMatKNftSr15xeuB52q5+SmgpSVyAMBq4VIQgDEcEfhhEQqPVx2NlLIABSC1dQ37V/3W3U0jJow19q/CYFVElGqNHSBIffqOOpL7j3yuzzT6Fw2w8BJNj+m2l6hGmxqlYCDY82xVe9chuKUZvN7Ows3v3ud6NeXztF2jAM5PP5jWuURCKRSHqOLSdGZTIZVKtVmKaJL33pS3jyySfxG7/xG7jttttw4cIFfP3rX8f/+B//A47j4Gd/9mcBANlsFkeOHMFzzz3nCVTd4Bde+p9AK/GesIdFqNen5+1dMz3P20ZxBSCu6uDpXKjjNcugA4CSjjcAHNzbGpRTs7bOmqu0w/JFItjhPkOTZvU8J9PwrOKNKmSSWGz2xLJJ0Almr7S3W9CyhmYMQVs0wXqQlugc2rTch1IpIDV3znudHks2KnXkJg3zj7rXpGakUxD81w/FjG5czpUa6iPPQqiN66KpJCJGAfHPyz/43n1boqx4sx9H/T42UxAmIJ4Y5T4f4gDC9QHFHkTTbUoJcJ8krFVRNz2arFPDnlepOPctG+VrAlpxCam5SzAngwnAQlEBmBDUgSB2oAjNtbCy58C1hm+VqQDOlnOk6HkKhQLq9TruuecezMzMrLpOPp/H5GQ8sV8ikUgk/c2WE6PuuusufOpTn8If/uEfgjGGV7/61fihH/ohUEpx/Phx7N69G7qu47Of/Sxuv/123H777QCAD3zgA/hX/+pfYXBwa4kN9r4CoLsTR8XMQ611njg2J6RCDT8Y9ItGxki8AaBitMQswkKWLPdZz0RJ0RMQsLMX4Qxc9fanPZ+PnWIj6Q3CenltZUGK51p+bIrVWbgloKBWFjzlptXQegXciOCtxxxkzzzipfbu/BED6YlkxSi72roQcDWE910TEe36AQBMK6I+chqgjcm/RaE/OxK6kt52Z72+5+/HUfrpRghRnaLYRCN9HlwBokQE+c6nII5rzNdXq/PJVLBsQhWCm96m4+Hfd8XXgReehjm2A1A6fy5rbAfUi88BRLgpi/VoIoWdnm3dtzmgPZdftw9v5Wt7twmShjczM7MlRG+JRCKRdIctJ0YNDw/jpS99Kb7xjW/Atm38yI/8CCilcBwHqqoin8/j9a9/Pb761a/i29/+Nk6cOAEhBFRV3XJCFButgY81ogG4Cn3lQLCJVcP4UwT0V/FDzUY6IHFNyONAFQKqAdxuf5q7EdgDl2FnL3uv1efzUBbimaBLegPp5dUOH2yIUYKA2tlA2yjWkCdGqcUFWBHEqIFzj0Opu6k52d0K9r05+WIRdrk12Q5ViCEmXK2iPvpMq4JeRYX+7CiItTF+hFuFoEKU/29B++lGCVHrIYiASDW8HJkRy9swKNzIQMANxKouJFMwwM/ELSrGb3Yw/zSDYtaQufgcqnuPdtzOHJ9G5uJzAABmLEYSo5i2AivXKlagnh2CUly732/Va/pGINPwJBKJRJIEW0qMEkIgm83iZ37mZ/Dkk0+iVCrh0qVLqFaryGQysG0bmqbh4MGDyOVyqFQqG2ZWvtEIjcHeX/Bep4r7ApnwCghvAiUCPM28HqUhRumDJLaBOQAoKQJui9BilHfkCGNtO3MFdq5lWK6+MAR1XgpRWwHp5dWOUBlExu1b1B4I7FejWDkvNUgtL8OaWD0NYy20xcueL5OSIjj6M4OgWvITcbvki4wKJUbFa4s9cMW7jtJCCtrpYVlePiRhhSj/e536aS8IUQBcIapxqpEO6fOJQSl4KgPFrKK6kGxkVJObflLHt59xiwdkLpxGfccMeIdqm04uD2ZkoNSrYHohdKoeV2qo5095/U65MgB1bm2Be6tdyzcamYYnkUgkkiTYUqNjQggYY9i7dy9+67d+C7lcDk8++STuu+8+OI4DTXMHNk899RQcx8H09DSEEBAi+aeDm4mAcH2i1MagrDYKtT4WbGNfOeTQYhRnnmdU3BS9JmrDdyp8ZFS039TRC7AGW1Ve1HODUGeTj9iQbDxJTEA323A8abyoKARL0WtC7azXxdTK8vorXwdxLAyce9x7ffBdA0iPd+ehgD8yKlKaXgQEceA0DcsdAu2UFKLCElWICrJOmD4siIAzVYYzVYEgwe8pQYUOYbTua9SJWEEuRLuasLR7T7OrgFVJfvyT20kx82p3/EA4w8ALT3feiBCY443qBQRwjKXAxxPEQT3/rJcSS5dTUM+vHekuhajkaKbhrfZPClESiUQi6cSWi4xqRjodPHgQn/jEJ/ChD30I3/72t/ELv/AL+OEf/mFYloVHH30U9Xodr3nNa0ACVnrpJ9hEFXzYFYUI05Aq7gu8rYghRnkpegCMhIxRlVRTjIruGRV8E9ewvIl6IQf1arC0JUlvk6SItJUipPxiFLWCpykToYA4aQitBqW6AnAGBEzrzZx/yhOtR2/VMf6S7olE7Wl6EYpHRLg9OOn5VnTGfAaESyEqDHGFKP+61+8rlBClO7COLENk3XsPHzShnRnu6PkV5trA862CH8lERgU7YVk6AxTc5eoChz6QvBh8+I06Ln/PgV0FjNmLqE0fhJPLr7uNOb4LmYtn3DamFqHVOosZAgLm0GkI1fWrJFXV/Z3W+C62yrV7IwjiCSWRSCQSSRy2jBglhPCEpc9//vPYvXs3XvnKV+K+++7Dl7/8ZXznO9/B/fffj2w2i127duFjH/sYpqenN7nVycNTDpy9Re+1vnIgXFUaX5WssJ5RSoLm5d4+GybmRIhQE94WwWeTjjEPobmCGilpUC5LIWor0I1opq0iSHnm5SJcZBQAKPYAHK0GIgSUahEsO9xxG3VlDql5dxKjGAQH3jHQ1QcCzTQ9rqjhrh0RmyQgYA/Meq+VWZneG4akhCj/Ns19htmeDdVhH1oGNF+a52gdNpbXFaTCXBMEFWDj1eYLqGb3KvleT5uJ+YJAPlyWbSD0LMGhN+o4+WfuNca48gLKR1687jZRUvWs7DmwVMF9YRNoz46sGYm4Fa7ZG4X0hJJIJBLJRrAlxCi/EPXggw/iU5/6FN7//vfjjjvuwNTUFH7mZ34G//pf/2s8++yzGB0dxeDgIIaGopfr7mWcPUVAcQfQanUCqtV5gujHHxkVpAKOH1qveMtxzcub+DNrCHNCCGThQ6P8huXahcENMZOVdJduptX1uyDFcybEgBv1QZ0MSMhKXq7Z+QIAQK0UAolRmYsnveV9b80gle+uZ18zMkpsUIoe1yoQqjt5I0UdtCarbwYlaSEq6rZCY7CPLnkmBoSlIKgFEAE+WgcrVFf1Igp7LeDDdS+VXq2Phu5/ceDp7lXU87PnlSpO/4UFpw4Ys5dQPnQCoOuMDRqpepmLZ9xUvQ5V9Zhaaq+cd3oE1Fz9e+zna/VmID2hJBKJRLIR9L0Y5ReiPvOZz+BP/uRP8N73vhdveMMbkEq5ExBFUaCqKk6cOLGJLd0gfOM8pR7hSWuMND1tuRURkN2VzCSzttiYTBICQbt7ugqlkdpYU0HXqcDT6zQnX3Lwvbn0sr8Uz9iwji15EUBKPZxoDQAQvouNCDahpY3oSTVNMPXy7ho222UOp9qIjOpgnrweAiKwMC2IzwOoHF+I6nfB00/Uz9HL/ahJlM/GM63Uc6U+GvnYgrb2I9SA90i/T2YXn7moKYKBSYqV89yNbA7gz2mNTnmpelyrAGsH5twA4Wt/mDDn0Vbocw899FAi+2l6QvUiSX3GjaZf2y2RSCTdoK/NLFYTou6++2689a1vxchIS4jZir5Qa0GXWhO8podCGASNKEY5NvSGGKUPEgztjy8cWWWO6jV3kusM5IEwlQ+bk+MO/h7t2zT2H8EQtlfwD7j7YRLXz3QrkqPbCI3BOrrkRVAq9SFo5V3h9+O7vjAjWIpfUxRyagI8bE2CkFSutq5lLB3cDwtAK7BSIFSEJPX5/ogUW2fN4PTyuRSGqGl2Gw2xFWjPjgC2OzwSitmqjLhoQLmusmpk4UJvnR+UR3/4wZWWWsPSwVLLlVrZW85Odm8YKIRAZc69F3MjE+ge7mRa1xKhrK9EKU4OanXKfUEB6/AShJZMv5NIJBKJRNJ9+laMWkuI+omf+IltncNOay0RiHcYyK1KW2RU8Cf7+vI1kIYANH5CB6HxBcCV51qzVWcw3JNjwpuzyeDtINwdKAvan2LUahO3rTKR7TX6VogiAtbhZaAhlFAri9TyEZAItwKutq4vPOAkmKVa6UHmcncnjdUrresHy4QUoyJW4yQs5QngIp2c2tbL51QYwhqQbxbKioHUE2Mgvug2umjc4BcVJ4JG6K1oQsIimOs399MmRgWr/KpUW2LUwET3hoFmUcBpaNYsE0ywFpoO3hh7cKXzAzW9tBe06XeX4rAOL4eqfLidmZ2dxalTp1b9Jw3KJRKJRLIR9GWanhSi1oaYrSePzbSzMAjamkBxNYQYtdDyW5p4cTI+KYXnW22xQ4pR3mQyVGRUY1Cu9N9AtlMp862QdtAr9K0QBQFn/wpEo4IeYTpSy4cjCVEAwBuRUYIq4HqwNDieakWV1Bc5Ml20G6lc8UVGhRajmoQT1QkIiGNAaDUIwwmV4teJrdKPg3yOXuhHxFKhPzUGNlkBQKDMZhITogBANCOjBAFi+EXxOGIUATLj3YscL19r3UudTMCCIISAZQZASwUIxYQAX/caRUCRWjmC+sgTEIoFMWjB2bsC7Ww+Zuu3NtKgXCKRSCS9QF+KUU0h6lOf+hQ++9nPSiHKj00BDoACXA0vRvmfRHIj2MAWzIFeaKXo5Q8mc1qtPB89MgrcfeocZiJIhOJKWIpIdBLZbYJM3LbKRHaz6VchCgDYVAVsolm9iyC1fBiUR4vIEOBeRAYzskDAVGj/NaW+2OXIqKu+60foNL3ogjRlBphWA6ibqkfWMFSOwlbpx+t9jl7qR0QQqNduFFGS+A2a6WSE67HuNU0xiulGsKIjQnhpeplRAkXr3n2ueLEV/RU0Mgpw0w21UgEgbuQXYetXpaRcR6pwBPWRpwAiwKaqIGUd6vz2rWb56U9/GpVKBQMDA3jve997w/tbwaC802fsVfq13RKJRNIN+lKMAoDTp0/ji1/8It71rndJIcoHAQGpqxAZp/FUMZyoInypN8wI9iRTX7oG0hB/xl+UTIoeMwVKF9zBupPOQWjhPDWIiJCmJ3x+FooAWO+LUWHTXrbCRHaz6GshaqgOZ2/Re50qHIBiB4xUWAWhml7XYukQk0xfZJS52L0qXkIILzKK6WkgRJRnYw+N/4e/BlDHQFNmE4YDJChGAVunH6/2OXq9HwEJCVFEAJp7jsVK0SMOoLgG5kGjoohlgjJXqB3ool8UACw82xKc7aHgD5SYL4qKq3XQDmIU4PpH6cX9sIaeBwA4+wugNRW0HP77XV5exvBwhKIOPcRf/uVfYn5+HuPj4+sKHr1sUN6JoJ+x1+jXdkskEkk36FvPqH379uETn/gE3vnOd0oh6jq8VD3C2yrtBKH5lJUrKoQWbBCnLyafordy1vE8yENHRQHxDMyBvkjV6xdD4K1APwtR3HBgH172dBWtvBNqfSzePn0RlEFNk4Hr0/S6FxllLnOwutuHI6Xoiev+HwLitFIWRbo7n7HXz7mg9FvBhaREQOEzLycRoxOBaOblas3vF9W9By7cEVg80xSEU6Ejo7z9BPCNaqLVJxMxNC8UCqG3kUgkEolEEp6+FaM0TcORI0ekELUKUX2j3NQbd30eNPWGM+jL1wAAWo4gfyihFL2zcfyi4EuziRYZJZTuRW0kQZyJWz9M+nqJfhaiAMA+sAyozcp5w9BKu2Pvk+utCW2YSnVcz3j6Tn2he32sei1GJT0AUQ3MATcyyttLOtzDgDD0w7kXhNdp7+qLz5JoNJrmMy8XIarEXkc08/KSt9zNSnqF8xysMfywhycCp/IC7WJUp4p613O9obkzXVp/g1WQ40qJRCKRSDaGvk3Tk6wNsXyiCg1R0YkIT7sJal5OGPNS9LK7lERS9ABA0Vv7UeqVUNsSxwZl7iSQ8OCRWv50CZFxgFoyUV6S/qXf06F4yoEYbPQFx0CqcDARLzThq7oZ1LwcAEApeGoAillB9ZoDwUVi1ww/quG7ftSK66y5Bs2Jc4SmUSfjalkE4PnwqdJhuF7E6ffztVdJ+nslluKdI46xAK0yDcqMjtvduCOfaBrELwqAWl7xlrM7uidG2dVW24IKZU0I849bwhYRoEgVD6E29n0A0apa9nuKnkQikUgk/ULfRkZtZb5mfyHe4Jf7B28hog9E63QgIth2QtW8GAKnmlxq28SLdW8Mmpo9H8pQWKm0BtvUCW5g6j1NBcBzVuDtNoM458d2mrB2+3vq9e+SD7eiCtTqeKwoDD+Etya+1AnXV9jAkNs2G6jOdieNLbdXRWrYvZ5phVkQK1x0BUjze+IQIaOkiFBBLTcaSxgMYqB70VEbRbfP817uR91oG7EUKNcaAg3hMAefC32eAQB8/ZAE7IdqcamxAZCf6d4QUMu0xiHECdcH1NKyt0yjeNv5xHJ/pLhEIpFIJJLeQopRPYZ/4Bt5EOz3SSLBxSgC4mWnEB5wkkgIRCOKyk5QjDKGKUZvdverWDVohbnA2yrVViQEtYOLUYqd8z5/r4tRQLTzo5cnfd2i299TL3+nfMQnRpnJPe33RxwSO1zVTmcg7y1XLoWPWggCoQQTL3WLHhAAqYULobYXtHFrJECUlD21PuIt2zPFaEJDj7EdBalutkm9kAMaQgnXi2D6coctboQInxhlBxB8GINadu+PuR2kLYIwafxiFA3SNv+2TcEMiFRogVPfNUmKURKJRCKR9CxSjOohVhv4RhoM+/QnEUKMcjdonBI8+HZCddPbnEqyE66dL2+lzaVmzwXeTm2LjAqeHkCECtKIpBIDNgTtbd8oYOuIJt2m299TL363QuXgg66oShyjzVg7Lv7IKMLCTTSZT4wqX+yeifnkS1tpT/r8hVDRlYL6JrBhr6EA1No4iOOKYWLIApusht5HL7KdBKlut4VwCu18y8+MpQoR9hEuQlEtF0Aawmh+b3dFGs33HCho1FYTtdgQ5rgCwsJft4TSOp7ftkAikUgkEklvIcWoHmG9gW/YQTHh0SKjXGhjs+CTRN4Uo2oCnCUnSI0e16APup9FX7oKYgWLwIiapgc0oqMA1+9lOFzEx2axFdLJNoJuf0+99h3z4bqX6qrWh5P1LfKl+7X7u3TGyQx5y+UuRUYBQHpcweABd7Ku1kpQKiEiT3xiVGhBH64pdWplv/famSlC6N37rBvJdhCkNqoNtJBqReNq4Y2220Rhu7PgoxVbfSC/t7vDv6hpetSsQWmk1VI7G+m65S/cItP0JBKJRCLpXaQY1QMkPklu84wK63fSjIwKLkYJrRXBlKRvFFUIpu7SG+0SSM0HSLURAmojTY84qdAeOYrZmijb+wvgXayGlSRJiplbmW5/T730XTOfX5TiSxtLAuL3lwspRgndANfcqKXKRQciRMRSWCbvakVHBbp+NPDS9IBIkVEAoFhDUKsTjRcC9oGVLZGuB2xtQWojj00YBam6ghJXKxAkpGApQkZG+byY8vu6O/xTNALayOalIcQo1SeYKU4Evyi0p+kRU9bpkUgkEomkV0l0NMIYw9LSEmZnZ1f9J7mRrqQP+cSoqGl6JEKaHgDYiafqpbzl1Oy5jqk2tF7xorrCRkUBgGKOQKk3vHVUAfvoEoTavVSiJEkszXOL0+3vqRe+c0E5eL4xIWNaNBPgdQ/gi4zi4SN+nIaJuVMTMJe6lw47dpsO2rg86QuXgovs/jS9GAKSXtwDwtwZOc+b4OO1yPvqNbaiILUZx6TFxj2OACxkdBQBAbh7rgbxjGp6MVENyO3s/rPIZnRUkKgtbxufXxS1c+usuTZtkVFW+M959uzZSMftJV70ohfhjjvuwIte9KLNbkrX6NfPGKbd58+fx6lTp1b9J+dVEolkK5DII6NTp07hj//4j/H444/DcVafmBBC8Ld/+7dJHG7LENWb5vpy3jcQ0cDcv224NL2WmXHSYlRmQkH+kIrCGQdqrQylXADLrW3ErFaj+UU1ISBIrRxCXXkaXKtAGAz2TBH68/1R6tl/fvSCKNKrdPt7CtRPuwjPWYDi9kXVzCebogeA8OhpegDAMkNAwR1IVy47MEa7k0qjGhRjJ1KYe9gEZTa0wizskZ0dtxMkXppeEyJU6Cv7YY6cAgDYe4qg8+nEf4/Notvn+Ub2o826XtKSDrajAgDgWhGwwt1rCFchKAPtIPgQ24JSd73LhvZQUKX756CWAcyVkJFRpYK3HFVE98Qom4Lw8GKUHdJwvRf59V//9c1uQtfp188YpN35fB6GYeDee+9dcx3DMPDggw9icnIyyeZJJBLJhhJbjDpz5gx+8Rd/EYqi4I477sA//uM/4uDBgxgZGcHp06dRKBRw4sQJTE1NJdHevuYPvncfjhw5Ens/nQbohPkjo0JG9TRMu9sMfDug+UL/9VzyA9yhA64YBQDUMbHeJxJtwX7RJpFEKEgtH0Ft/DGAcIg+qKznR4pQweiFyI5uTbT9vnFcqa+zZjQEdXzL4YUkWq+0lvXuTorzhzXMPexOTqkVMDJJtK4dcVsn1PWPud55spmCZlCSEIxep71rze8hKUEqan+PK1p3ajuptYZhgoYXQZpiaVtqaQciBDNGgkQJviL+h2kRo5KbkZsqh1A4CAvXEE3TOq8kkXSRyclJPPjggygUCqu+f/78edx7770oFApSjJJIJH1N7Djtz3zmMwCA+++/H/fddx8A4FWvehV+67d+C1/84hfxlre8BWfPnsV73vOeuIeSBMUnRoGG8H6CgKCu8MJTwSrY0GoJatkVo7K7FWQmko9wsEqtiWHTa2YtHF/UFNPDG8I2oTwFwhrVsHS+ZbxeJNsDUtJbXjSpUuj0n05wn8DC0+GiF4hjQV++CgDQsgRDhzZw4hdwdkz9FQJ59Gc2TCvCyrW8qrTnb4xSW0+s2E7CcreFtyj7928Td/s1iXFrEcQBFPdcZen1I4GFpsPJuGlvxYscTr379zSnoYMLJXgfsobHvWWmFyIdV7Hy7gIB+FD4QiT79u2LdFyJJEkmJydx5MiRVf/NzMxsdvMkEokkEWKLUU8++SRe8YpXYO/evd7fmoa0qVQKv/zLv4yxsTF88pOfjHsoSUCI0/pZwxiiCmoDxP3tuB5MjErNX/SWp+7U11kzOtZKa9DM9fXFKKEbYCnXK4prFYiI0VEAQFjj8yjCS3mSSPoBAgLlSksksrNXEt0/18resr86XhD0xcsgjcij8Zekup4uJPyXABLsWP5S9EREE6M4tWAOn/FCq5RLWSiF1a9fUpBy6SVBarV1427fkZBdwR/1yAKIwvbQKAC3Tyyf7Z5XW5Om4BVOjJrwliOLUb5CJFHEqK3C7Ozsmn5D58+f3+zmSSQSiUQSP02vUqlg586WB4eqqqjVWk/NKaU4ceIEvvGNb8Q9lCQovpB0ESYyymf6GSgySoiWGEWAyZd0SYwqNtIQAAgttf7KAJzcCBSzChAOrlYjV+ShTPekLKEzkJosPinpH5SFNJw9RUDnYKllcKUGyoKJzJ1oRh0KEDjZcJX69IWWgD1x5/richII3hKSRcDZvleKXhCvqEOoY0LAzD8H0YhaoSs61IvrmzFvRKpaP7De97BR+1/vu467fZKINjGqs0einR9D+uo5AMDScwzjx7rj1dakGRnF1eBDTZYdAtdSoLYJpq9AgIOEfG6q2ENu3yWiVchhm/GBD3wAJ0+eXLdaqWEYyOfzG9eohPmlX/olLC8vY3h4GB//+Mc3uzmB6dd2SyQSSTeILUbl83mUSq0UkJGREVy6dKltHcuyUK8n71siWQPe+EcBhIqMakUDsABilFpcdEUfACPHVKSGuiPWeGKUlgoU2eDkRpBacM9Brpcji1GEt8Q1kWJATfpISPoHIgjUq1k4M0WAAHb2KlIr+2PvVxDH80FiA0NAiKgHWq9AKy0CADJTCrK7uzsZBqJGRjXEKK5EMht3MrPgqaL7wqLQzgwH2o8UpFw2U5AK8h3H3T4p/BGKbKBz5blmZBQALJ3pbpVYZguIxiHCREaBEFjDEzDmLgKUg2tlKPZgqGMToYDaOXC9CGEw8JQDaiZSr6dvuHz5MoQQyOVy+N3f/d1V18nn833tN3Tp0iXMz8+jUql0XrmH6Nd2SyQSSTeIrR7s3bsXFy60PDGOHz+ORx55BE899RQA4Ny5c/jbv/1bmd+8gRAQoJGqFzkySs90XD813/rdp17aOWIpCoILWMVm6mCwKIo236gYXjlemh7cyCiJpN9QZjOA44ogTnoenMY342d62UsnihcVlQIJKA7FIZIY1fCMipKiJwiDlW09kNFPD4PYwUU3mbLnshkpexuZxpcE/vubPdi5L3Ij46WxL5/l4E730s+ZLyAplBgFwBpJIlUv7y2vFR0lIMDTNthQve3fVkJRlDV9h/pZiJJIJBLJ1iC2GPWyl70MTzzxBBYWFgAAP/VTPwUhBH7xF38RP/7jP473vve9KJfL+Omf/unYjZUEp1lRL4xnFFdaE9WOaXqcQV9wfWiUFDB+a3eihuyK8CaTgcWogTxEw6iYxxGjfJFR0LvvryGRJA1hFMpsI32HCDgD12Lv09+nnNzoOmtehxAtAZsAE3d0R8C+4bA+HVkEMTAXwjMwJxHMy+2BK4DSqP65YICWwn9OKUi5bKQgtRkG53EQ4OCaG1nBjAGIgPdHO+/2WW4DKxe6d1/zG6QLNdz4wPb7RqUKkY7vmZjjRjFK6AzWwWWYL7sK68Q87JuW2v5JJBKJRCLZGGKLUW95y1vw0EMPYWjINYw8ePAgPvrRj+LOO+/E0NAQbr/9dvzmb/4mfuiHfih2YyUhaJqYUxa4Epw/Ta+TGKUV5rwJ2/gJHUqqOxEO1krwSnoelMLJ5gEAQjUhSPhy2YAb6t9ERkZJ+hX16gCa5md25hq4Ult/gw4w3WdengseGaVUClBMd/I8dEhDarj7KXoAAL9nSoDLFPFV0iM8XBsFBOym4McB9WK49CI/UpBy2QhBKs4x4m4fFa66vohAsKioJm2pes91U4xqLQslXD/iKQPOgNt3uFoO9VCtCXUGvEqYfLAlRgkIWDcvgI/Huw5KJBKJRCKJT+wkelVVMTLSPhA6fvw4PvKRj8TdtWQNggx8ia1AwJ1UCcUEYZ2FHL9JKLVNcGNtQ1TPUwVAdrp7k0qz6K+kFzDCQHAo9UYuviCIorkKCFjZVhoiqW/QxFmyZegVfx9iK1DmMmBTVYBy1EeeRXrhFhARMZqR+ARiNUTRAl9UUm2OgVkCit79ND277IvQUDp/ZmL784vCfkei9f3YSlevG9tJkJKsRqvvEB78YQnXWn22m2l6qm/IQc3wqW/MyECtuH53gvBmod/ACMVseWay7l9nJBKJRCKRhCd2ZNQDDzyAxx57bN11Hn/8cTzwwANxDyVB8AkuqbZ0Rq5WA21DrZbRt1pcXHddnmp5StUXu/d0tWleDiBwGoK2NAvamFAq5nBbhFNQ7IFL4I0IEFJToFzrXKkoKeQks//pFSGqiXp+EKTiXhOEaqI+fBoC0fotcVqiMLWCXVsA1+zcyk8BAKwCx9XvbExkQn2pNVH3X7fWgvrEKMLCiVEEFNRqGEmnGEQ6fERHP9Lv16xNbT/1qSwhKjdSJ+NF/miF+fYIwHVQai3T5Mx49yrEpkcJtEZ3U8uF0NtTqyFgCYDw8MK5nbns6XXqbOv+TUCgPzUG5XIWpKQBtqySK5FIJBLJZhH7LvzpT39ailEbRJgJLo0gRvkr1nQUo3xRU7WFbopRvsiogGl6xtw5b1mtTqy94howrQS7aUAsAO25YRC+MQPWfp/USXpPiAIAwin0Z0cBq+GllirBGnohcAqvH8paYpRSDy5GAUBtz03eES/+TQ1OrftebGZDLBcIVpjBmwQDoBEmwWq9VUCBD28tM+T16Pdr16a13x+0I4JH8BAQKJZ7z6aOHVjw8YtRA+PdixgihGBoj3u9USwT1AwnPjejqQjXQle05NSEk55zXzjkhodJxFagXRhE6qlxpB6dROqfdrT9k0gkEolEsjFsSK1bx3FAqXz6FIewE1xSa02iuBpsEEicNMAVgDJopSX3Sesa1ae4bkAQCiI46ovd81PyR0YFSdMjVh3a0qy7zPQ2E9MgCMJgDp1pPVG9lAMth0hFikG/T+YkvSlENSGWAv3ZEVi3LAAUcDILIE4aemVXuP34xChqhhOjWGYI1thupBYuwqkKXPq/Nez98e5GHTYjo4RuAAHuQ/40vSgRGYo5DOA8AIANm1Cv5ELvo1/5mv2Fnu4DndiU9vsio0jI54OKNQRmuIbb2spSWyXZNbfZoMgoABjao2DhWfcerpYKsDoVRmkihCcKtxUSCYiduYJmXp9ybQCErf05VxO6zp49i3379oU+btLMzs6iUChE2pYx6XMpkUgkkt4nETFqvfLctm3j8ccfx/Bw50GSZHWiDI5JTXVDAQggAkZGERAodg4sVQC1TdB6BTydXWNl4no61MqoLXAIIbpSpt0MaWCuL14BacReqLXx0E9UnfQchOpORklJg3Jpjc+fMFKI6n/6YRJOKzq0M8OwjywDAOzBi1CsQSh2cMHEHxkVJk2vSW36GPTFSyBC4PLf1rDz1Wnog92ZFDNLwC651wMWIEUPAKjdimYiLPxEmDIDxDEg1DpEzoJQOYizfR7GSEEqHMJvhhQiMgoAqN26PynllUDbKDU3/VwbAPSB7nopNSOjgIYYNRYs6ohapnfnDitGCWLDybgPpMCIW8AhJLYdrehJkszOzuLd73436vV40ZXyQbBEIpFIeplIYtQ73/nOttdf/OIX8ZWvfOWG9TjnWFlZgWVZeNOb3hSthducqINiIghITYXIOOBqDQIikDBDrZxXSlktLcFaS4xCI1WvVga33XS61FDyA9u2NL0AnlHayry3rNSDVxhq4hgLrX29kA8tZkVBClH9Tz9NvpWlNMQFB86eEgDAycxBWQkuRsWJjALc64Y5uR/GtefBLWD2n+rY/S+CCUVhMdv8ooJNStvEqAiRUQCg1IfhZK8CBHB2lKHFqKrXj0hBKgRkzRcdoU7Ge+ikBhGjGPPS5Qa6HBUFXCdGhfCNolYrmjusIOxGRbkPsZS5DIgT3jNS0yIWd0iQQqGAer2Oe+65BzMzM6G3//a3vw1CCCYmwlsV9At33303arUa0umAEXc9Qr+2WyKRSLpBJDFK+IwyCSEQQrT9zdu5qmLv3r148YtfjLvvvjt6K7cpcQfDTTEKREAodRDW+cZHndak0KtItwbsOt+o1FDyg9tmmh5XVEDpcLoKAW2lISZxxS3tHAKu1Fum5VUVtNr9AakUovqffpx0K1ezcHaWAVXAMRahr+wFQbBJm39yqEQQowCgPnUAxrXnAQDzPzC7JkbVl/xpvsEG/nHT9ABArY3BGXDFKDZdBi1rUJa318RDClIBiWhgDgAECghLQ6g1t/Ic5+umoir1iid3ZbroF9UkM06gpgGn5kZGBcVffS9MZJQgDuzMNfcFB9Qr0SKbNypFb700vPPn3VTfmZkZHDlyJPS+o2zTb7z5zW/e7CZEol/bLZFIJN0gkhj1xS9+0Vt+9atfjXe84x14z3vek1SbNoVupZlFJYlBMKmqwKi7zNUqaAAxKkzUgz/SoDbPkD+QvAVZM01PaJ39opRaCdSx3GVrMHRUE0stt/a10P2JoxSi+p9+nWwTTqAspsEmqwDlYMYK1ICRhAQUhGkQig1ar6zrLbcW3BiAM5CHWimgcomhvsRgjISPYOiE388uSCU9wFdNT8CrVhYWxRmAXtoDa/ACAMA+sAL6zwZIyDSsfkcKUgHwnRJRzg/qZMDUGojgUKplsOzaUXhtflFj3Y+MIoRgcJpi6QyHYtVBLBMigPejv4hAGDHKMeYB6vZ5upAGsZK/piRFkDQ8wzCQz+c3rlESiUQikWwwsdWDP/3TP0U2uzG+Ot3k2rVr2LFja1VRIXZrICZIMDNL6ot68Jc4Xw2WaaX2FJ53sOOuzoPMMDBbgDXGaUFS9MBbURBEhD+1BW2VYScbEBUlkWwmpNI6xwVx1llzlW2dDISyAupYoPUSeDp8GhrLDEGtFAAATlUA4bNqO1I+1/pcLB0sFZE0BG1wNVaarlrZASe1DJ4qARp3//Xw5Lhb9IMg1TPti3C6USeN5t2dWjUwrN0Xhdrq8+Vr3a9kaVUEihcbD5QIhVACRl86Lc8mEkoQbn2BPG9CaKxtHNRLBEnDy+fzmJyc3OCWSSQSiUSyccR+NDY1NeWJUefOncPf/d3f4W/+5m9iN2wj+cpXvoKf/dmfxXPPPbfZTfFIJGomSpUen4jjHxCuhj04CkHc/S4/46yaqhmHtkp6ASKjWDrnlY3nAU3b/fijwoTe/Uo0PTMBkkSmn6Pb/Od4WF8W1cx7y1phLtLxiS/6IZXvTpTGynPuNUwQCicbrIgGYe42UQTttv2AtEV1tBlVSyRNWEtACfrQyI//PKUdjLftwRHvXjr3FINT7+45eem7DpxGN69P7emcat+A+B4shUldVGsToFZDjNM57EPLEOjtftdMw1vtXxwhamFhAXNzc1hYWOi8cp/Sr5+xX9stkUgk3SCRGcAzzzyDn/mZn8F73vMe/MZv/Abuu+8+773HHnsMr33ta/H3f//3SRwqcf76r/8aH/7wh/Ev/sW/wMhIFx7NxyD2RDeCFwUBAbj7JLGZ8rYmigp70M0DrC9x1OaSfdLarIIFBEvTg6KAG64wytVq6EGov0qYSIWLFImKFKT6n34VpESqNfGlIStWUbMVfaGVFiMdn9quSTFRAbULVb3MZYb6ontNcrLDAA0QISEECHP7Pgnp37MabWlX2ytDTxIQwv3nSISHINz/AKnDPZtSmOM73c1sYPaJ7j50ufJI6z5amz4QeDvCfUJ5iH5IQGGsHPbEdT5kwZkpBt5+K/FzP/dzeNvb3oaf+7mf2+ymdI1+/Yz92m6JRCLpBrFH22fPnsUv//Iv4+rVq3j729+Ol770pW3vv+hFL8LQ0BC+9a1vxT1U4nzlK1/Bb/7mb+Ltb3873v72tycmRi0sLODUqVPev6YRZRTiTHSFPzIqzICuMbgldoeBLQA736rUsvRssuWQrZI/MipAmh4AZ6AxSW6YtofBHx3in6h3GylIrU6S/ajb9KMg1R4ZFS7FljoZT7RWS4uub1RImhWzUkO0K359K2da1yNncCzYRoKDND8LTyK9x3fdpZsTodFP/Whb0hYZFf6BTltkVIdoZgAwJ6a95SuPdu+hS2WOo3DO/Tx2dghsIEQqL/fff8NdGwjXkVo5DDSEYLazAjZS67CVRCKRSCSSzSC2Z9Sf/MmfAAA++clPYnp6Gp/+9Kfxve99z3ufEIKbb74Zzz77bNxDJcq3vvUtfPjDH8Z73vMevOENb/DCoR977DGUy2UIIXDs2DGMjQWcxPj48pe/jAceeCCxtkb23IhYpYcIFQKm+5S1gzmxnZ8Azj8NAFh6xsH0q8M3cy0sX2RUkDQ9AGCZQWDxSmObSiDT9iaEpbwy2RspRgGuINWPgkY3SbofdZt+8MZpo3mOcwVEhBNeCAgUKwtmrIDaJqhZ8aISA8GZN3HWu5yiBwB2QDGqGRUFxE/TA+BNiBs7jL+/CPRbP9p2xIyM8nsqBXqANDQKphtQrDrmTzLYVQEtk7wYfNkXFeUXwIIQNU2viWIPQi/thTV4FgBgHyyAPLUxFXIlEolEIpEEJ/Zo+7HHHsOrX/1qTE+vPdiYnJzEww8/HPdQibG8vIw/+7M/AwDkcjlPiPqv//W/4rvf/S6qVddv6MCBA3jjG9+In/zJnwy1/ze/+c14xSte4b0+f/487r333lhtjjTRjVoyuhkZBXdy5jc9vR42MASu6aC2heXTNjgToEoyA1u/Z1SQCjwA4ORavjBOaglqPbiYSEBBuFslbKPFKEAKUtfTjX7UbfpFkBIQXmRUWL+oJtQeBDNWAABqcRFWCDGqGRUFAPpQt8QodzIsCIGTDVgpkPkiSxJI02svlZbA7iLQj/1oO0FY6zyLFBkVJk0PAAiBOb4LmcvPgzvA7BMOpu9KVqQRQuDyw9HFqPZiJNH6oVqbAtPKYOl5QBGwblqE/vQoaE0KUhKJRCKR9AqxxaharYbh4fWNYU3TBOfdr9wSlOHhYdx99934n//zf+IP/uAPMDExge985zt45JFH8Pa3vx0HDx6EaZr44z/+Y3z605+G4zh45zvfGXj/Y2NjkSKqOhF6ohszTQ9wB7friVEgBPbQBFILl8DqQPEsQ/5gAhEFAOxy+Mgoe2gCXNVBHQvMWIIoOqEiHAhLQSg2oHMIKtr9PDYAKUi16FY/6jZ9IUhp3MsgoyFT9JooVg5N6UYrLcKaWL0i1Gr4S7enuiBGWSsctTlXbHMGhoMbJ/sjoxJI0yM9EBnVr/1o2xDXMypkmh4AmBOuGAUAVx5liYtRxYsclVn3fLeGRsGNTKjtSVuaXrTrAwFBqrgfdaUOrrsVLa1ji9CfHgM1kxmjSCSbzXpp17Iao0Qi6Qdi35HHx8fxwgsvrLvO6dOnsXPnzriHSgQhBAghuPPOO6FpGj75yU/iv/yX/4Lh4WHcc889ePGLXwxddyMFTpw4gV/5lV/Bl770Jdx5553Yt2/fJrc+3ES3XX8Kl6bnLdsWYAysu76dd8UowPWNSkqMCltND0DDoHUa6asvAESApQrhoqNYCkAZgOupQ+obP2iVglT/0+uCVJxKek2olXXT0IiAWl4KuW1LjOpGZNTyM60IkcB+Ubg+TS8Jzyi/H1ACu5NsPeJ6RvlE0yBpegDgDI6ApdJQzBrmTzKYRYHUYHIn6JV/bl1fzMndobdvT9OL3i4CBUbhGOrDT4NrFSDF4RwoQD8pxVlJf5PP52EYxrpRroZh4MEHH5SClEQi6Wliz7Rf/vKX46GHHsKjjz6Kl7zkJTe8/81vfhMnT57E3XffHfdQiUAI8QSp2267De973/vw2c9+FocPH8att97qCVGO42B8fBy/8iu/gl/91V/FE0880RNiFND9iW5beWml84TMzrVSYGoLyUXAVa66+xIg4HoI76cYA1nCfU+I1d6J5vPT/O2jClZxt5cEI+7329U+rvnSYHi0qAhBbS/aR5BwghLXWgJY8VyyJspCCFz5dkvssvPBB+JtERlJpOn5okyhhb+ebAVhupdF2Z4gZsQcgQJwClAOapsBNyIwJ6aRuXgGggPnv2Pj8BujidKrUfFV1rWGJ9ZZc3X8n8MfqR0FIlSotUlYmvvQdHPiEyWSZJmcnMSDDz6IQqGw6vvNdOxCoSDFKIlE0tPEFqPe/e5341vf+hb+43/8j3j961+PpSX3CfmXvvQlPP300/jGN76BqakpvOMd74jd2KTwC1K33347FEXB5OQkMplWKLnSEGHGxsagKAoWFhY2q7mRaS8ZHXwiJFTXz0UAYB2iogBA+KKW7HIyAg6zBCpX3Ykhy+QCiWLuyg70RpQWOIVi5UMdlyCZtJo4guF6k0//PqNMVONuL9kYuj2BF5lWOk/UyZ6TXvSWrZFwka/O4Bi4lgK1TSw9acEuc2jZZCKkii84KF9wBS4nMwQnN5rIfqNAE6jQ2c/9VApRAVD9wnC0vki4DkHroGbwCrK1XfuRvngGBMD5v3Nw4HUaFC2Z6Cin1rp3Ci28yEVrFXeBq67YFgNBGOyBi95r9VIu1v4kkl5hcnJSCk0SiaTviT36z+fz+MQnPoGjR4/ir/7qr/BP//RPEELgYx/7GL7+9a/j6NGj+NjHPoZsNkSlpYRZza+qKUgBbjrejh07vNdNoQoATp06hXQ6jf3793vv9Q3+8H8EE4kEBLjiilHcGABo54GgUDWIhohjl5L5fsqXGESjyU52fU8yP/riFdBGqo1aHwufaiP8ZrLhNr2eKBPIoELUen/r1vaSjaHbvwnPWnB2l7zX1I52bWbplkBvjYU0KCYU5tgeAIBgwNwjASM6AnDlWy1z9PqOg+tWA72Btut7/Ik58flx+VMjw9KP/bQf27wZCNXn7RgxSpFwV/ChzAEJ6BvFjQzM8V0AALPYbjgeF8eniYmAfm2thjEvjZcyI3Zb7IFLrg8kALpoQClG88iTSCQSiUSSPIkY4uzcuRN/+Id/iDNnzuDkyZMoFovIZDK46aabcOzYsSQOEYlvfOMbGB0dxYkTJ1Z9n1w3SSGEgHMOSl1B4tlnn8Vf/dVfYWxsDMePH191m54mgjGqoBZAXRWIpQM+QSQEQtNBbBNWQpFRpQutgbGTzQfezphtmTmqtfHwB/YrUDS+sBYmQiqsEOV/r5PwFXd7ycbQ9YgojcE6vOQ9hlArk1Ds8JECXKmBa27VUWdgGDxEJb0m5sQepK+eAQDMfreOXT8SPBV3LepLDAuPub45XEvBGt0Ve59xaBOjYlbo7Kd+KoWo4LSlzIYotuHH7/tGrTrYekVHfNR2H4QxfxkA8NxXbUzfpSZSDdepu/dOrqjhxGAAtF7zZGASsbhCE07rsDNXGi8A9fxgrP1JJBKJRCJJlkTdmScmJlAsFlGpVDAwMLCppuXf+MY38KEPfQj79+/H7/7u73as+NeEUgrHcfCVr3wF3/zmN/H888/j4x//eF9WI/Kn6QU1Rm2m6AEASwefYDZTbuySaIssi0rxQmviFjQyitYr0IputAZxDNAIk+y2YMGEql8FEaSiClH+ddbaR9ztJRtD14UoImAdXgZS7rWAmjnoxeAV8Pw4vqgoM2xUVAOeHoSTHYZaXkblMkP5ooPs7ni3pCt/V/dMYeqT+wNFdraTbOQr4Zpn8i5S8SNP+qGfSiEqJAmk6VGuo3nHpGbdTW0PgDM4Ais/Dr0wj+q8wNV/Zth1Z/xhod0YRqxbiXcNlHrVW45a6bOJk57z7uPK1ey2qqL30Y9+FIwxz3JiK9Kvn7Ff2y2RSCTdIJE789WrV/F7v/d7+O53v9uWxkYIwcte9jL823/7b7Fjx44kDhWIr3zlK/jwhz+MnTt34urVq3juuedwxx13BLr4O46D//N//g9+7/d+D0eOHMHv//7vY+/evRvT8KTx608BxSiutgaCQQe0QMs3ijsAMwE1ZnR96bw7cROEgA0Ee5qZaouKmmj3fwpIeyn20JuvyXqCVFwhyr/u9fuKu71kY9iICbyzdwVi0I0aIkyHUTgEEiFTW0B4YpQAYI1GE6MAwByfgVpeBuBGR2V3R0/nZqbAtX9003sEoTAnYxacSKD8HQEBYTqEasZK0/PTy/007HksiACbcO85ylym/fq7TRD+QhlR0/T8kVFmbZ01b6Q6cwR6YR4AcOavLex8iQJC4/0Ozcio0Cl6AJR6xVsmMdL0BAQcY6H5AurVzv6XSTM7O7uuwXQ32bNnT1f33wv062fs13ZLJBJJN4gtRl2+fBm/8Au/gOXlZUxPT+P48eMYHh7G8vIynnrqKfzDP/wDTp48iT/8wz/ckEipv/7rv8aHP/xhvOtd78IP//AP4zd+4zfw2c9+FnfccUegpxCqquItb3kLdu7ciUOHDvVlRJQH90f5BBOjmF70lh1flbyOh/KZlNplDtWI/sTHqQtUrjVSBTODwaIbhEBq7kJjOWKKHoA2BSqBND0/qwlSSQlR/m2a+4y7vWRj2BAharwKNtUQmgVBavmw5zMTFq6VIVTX48kZGofQo08YrdFpZM49ASI45h4xse+tA6ARTZRnv1sHaxgnW2O72worBKYLnoBNMQqagKAchMc3au/FfhpaiKIc9uFl8GH3XOI5C9pz+UgPEfqahAzMmzT9loJi58dgD45AKy6hfFXg2uMMO26LPjTkTIA1LODiRkbFSdPjagVCbXhPregg9sZGoczOzuLd73436vW1fw/DMJDP5zeuURKJRCKR9Bixxaj7778fhUIB//7f/3v8+I//eFt6lhACX/7yl/HRj34U999/Pz70oQ/FPdy6NCOi3v72t+Md73gH8vk87rrrLvzFX/wFvvOd7+BVr3rVmtvOzs6iWCzi0KFDUFUVL3vZy7ra1g2BhU/TY1oZAMBVLbhnFNw0vSZWUSAdQ8MrXXS8bJmgKXpqcQGK5T4RVsw8aMSJdpuBuZL8xNQvSCUtRCWxbXP7XpvoblU2QogSRMDZt+K91krTUCKalgMAM5a8ZXN0d7y2qRqskZ1ILV6CUxVYetrC2InwE1DBRbtx+dSBWO1KEsJTAFzDeJFiILVkqgb2Uj+Nch47u0ueEAUAfLwGvmRAWYrvHdZPJOIZxaNHRoEQVGeOYOjJfwIAvPB1O5YY1WZeHkGMom1petGFbma0Uonp4safU4VCAfV6Hffccw9mZlZPh87n87IamkQikUi2NbHFqH/+53/GK17xCrz5zW++4T1CCN7ylrfgu9/9Lh599NG4h1qXRx99FL/927+Nt73tbXjXu96F0VG3nPfb3vY2/M3f/A3+/u//fk0xqlwu47777sMPfvADfOITn8Ctt97a1bZuFMT2CysBn5Y2RCuh6qGMR7nRCoEvnncwtD/6qTX/WKsakDMYLDpLqbaqgylm8Iiu6/EPftlkBcpsJtKTeumbIukZqHCLGTTEVTt7BZSloNajKcbCVwzB3++jYo3vQWrxEgBg8cloYtTikxZq8+61yx4cBxsYitQWarfEkajCwPUIarX2ybZZ1I+kIyLdut9FjQTyV40lPHw6qDUyCSedhVoro3COg9kCSsQIxdKVlrjGUuHFpPbIqIgPlQBw35hHKcSvyheVmZkZHDlyZMOP+/Wvfx2maSKVSuG1r33thh9/I+jXz9iv7ZZIJJJuEPsRLee8o6fS/v37wXkyVdbWYufOnfjVX/1V/NRP/ZSXWuc4Dvbs2YM3vvGN+OpXv4pHHnlk1W2z2SyOHDmCXC6HkZHoQkavQSutp5JMq6yzZovmYJiatVApK/Zga2JbOB3dqFdwgbl/didvglBYI8G8xtomkTEGsNQaBLXcCbYYcMBHwqU8hGE9wSpOxMPX7C/E3l6yMWzEd00Yhf7sCEi9MWGlDObwc6gPPQdBwvdV6mS8ZaVaXGfNYNiDYxCNVNzlkxYEDxeRKITApa/7oqJ2HorcFqXWErWpE38CK8DB9cY+TQWwkksV6qV+GqUt6sUc6HwrYkW5OgC6tPGiAR+wwAeszit2AQEBnnPFKMJ0UB5NjGrrMSTCsI4QOINuFLLgQPlq9PHa8vMtMcweHA29vRfZxVUQRO8vgrZEPtjJRCP2E/fffz8+8pGP4P7779/spnSNfv2M/dpuiUQi6Qax79CHDx/GuXPn1l3n7NmzXX0yxDnHzp078YY3vMGLiAJc/ycAeNnLXgZVVfGtb30LlmW1maw3lz/wgQ/gc5/7HKano5vx9hw2BSz3J+ZaBSJApahm9RoiOEgI7wk2MATeCMlfPuOEnlA2ufY9C1axEcExPOlGaAXA75NBIprAAq7hsFZumUs6u0uBvreoJC1I+beJu71kY9iI75yWdehPjLdN/llmAbWxJ9sidwLty25FQynVlXXWDLpDBfbQBADALgmUL4YTyIovOCidc7dxMoPeviI1pV72lokTP7WH62Wvmhdd0RPzQ+rFfhq2TYRTaM/loZ0cgXZyBOq5wQ33i2KDJqzjC7COL4ANmp03SBiRcbyIxWjVX709+ZajfYdOthVNWLwUQ4x6obWtMxTy4Z4QoGbD5ylmJT1BG9cRRralMb5EIpFIJP1AbDHq/e9/P773ve/hL//yL1d9/8tf/jIefvhh/Jt/82/iHmpNKKVt/7+eO+64A6973evwzW9+E7OzsyCEeCIUIcSL2hocDFa1rV8gIK3oKOoEmnT60wQUs7rOmtdvSGAPuabhTlWgfCl8qsD8Exae/WzrmOZE8IojbZFRMcQoAFCsIVDLnRiIjAM+FtKDIyRJCVKrrRt3e8nGsGERUs8NQzuTBxx3ciZUE46xHGo/1Ml4c1+1koAYBcDOt3xTlp4KJ461RUXtOBQqvfh6lKYYxWns6wgAML31/SjFeJPrJr3cT0MLUiBQVgwoK8amGJez6ZKr3ZDG8gYiiICz13d+xPBx84tRIuL57wzEF6OEEFh+wb33u76T4T4TNesgjc8Sx7wc8EVGbcOoKIlEIpFI+oXQphgPPPDADX+77bbb8Nu//dv4whe+gOPHj2NkZARLS0t48skncenSJdxxxx34/ve/j+PHjyfR5lAIIUAIwY/92I/h//7f/4vPfOYz+OAHPwhNa0001hKxtgKkogHNakVaBdRcf4DnfxpJzSqA4GH29tA4UotXAADLpx3k9gQ/vZaetfHUpyoQjTFwfXJv4BQ9ACAJilEEBHp5N+ojJwEAznQJdDHd1aer65kRr1aFb7V11nsvzvaSjSHI75QEykIGYBT2UdeIXCjhIkKIUECYAaHW3cgowaOlBvmw8lNoxlstPW1j5o3BtqteczzxiulpWKMxIls598yTKUtGHPFXJ6Ur0dOHm/RDP92o8zguPGODD7WETz5kgWds0Gp8EbITAgL2oeXW8bkKpR61Aiy86Dt3OdoukoiMqswJWA091x4cCS0MU98DsKjVPgH3+0UjBZk4W3d8J5FIJBJJvxNajPr0pz+95nsXL17ExYsXb/j7ww8/jEceeQR333132MPFplnd7/bbb8eJEyfw/e9/H7Ozs5iengbnfEsLUYDrG9WMUeJaBehg7u0XcqgdLkLBGWr5Ri2fsrHnNcH8PwrPOXji/jJEI6reHJtG5cCJUANZL02PqyDxA/7c6ChzEDxVhEgzsPEq1Ln4Zs3rEVWQCjJBjbu9ZGPYqIk8qbW8WHhIMQoAqJ0BU+sggoPWy+DpeFGlIpWGkxmEWi2ifMGBVeTQBzv340vfaEVFmTsOADGu59SstKIyEkjRc/2i3Jk5qSsgVjxD9H7qp/0gSDk7yzf+bUcZ+vPBKrhGRcCtbslHG/csQWEUjkWvAHs9ESOjhJ4C0w0oVh3FS9x7kBeGZlQUADhD4f2iFF8lwFiRUYR5opwUoyQSiUQi6V1Cj44//vGPd6MdibDW4IkxBkVR8J73vAe/9Eu/hD//8z/Hv/t3/27LC1FAIzKqAVcDmJj7q/Iwe50Vb4Slc+BaCtQ2UXjOAWcCVFl/MFu84ODxPyyBN3Qva2QHyoduD/9EtREZlURqTRO9vAf11FMAAGdXGcp8puveE2EFqbBpeHG2l2wMGzGRJz4jbaGEN2+mzgAY3MgqtVKEFVOMAgA7PwW1YYi+fNLC5F3ri9lmgWHukUbUp6KhPrE31vGVWkucSMK8nOsln19UvJSjfuynvSxICZ2Bj7aMsgEA1E3JFhcGQezkjOavx9ldAptqRAAJglThCJRYflFAm2dUjChFJzsEZakOuwrUlwXSI2HFqFZElR2wEq4fWm+JUXE8ozbSvPz8+fOh/i6RSCQSiaRFaDHqxIkTXWhGfPxC1Oc//3ns3bsXL3vZywAAiuIOLPfs2YPdu3fjH//xH/G+970PmUwm9JO/foOYCsDcsu48QEU9wlunBHHCiVFN36jUwiUwEyhfZBjcu/YpJoTAyU9XwBoPiK38BEpH7ggf3cAcr5w1YcmJUYqdg2LmwVIFwHAnL8pCpuN2cQkqSEU1KI+zvWRj6PZEnnDqTtI0DkEjREb5xBpqhfCWWwc7P4n0ldMAgOVn7I5i1Ow/ma1oysl9gBLTK67uF6PiR0a1pegVo0e99HM/7VVBio1VPcdMrer6ldnZywAF2HgN6pU4/k1rw3Mm2HTrPEsVD0K1EojE8qXpxSm3wbJDwNIsADdVLz0S7l7cjIwSILAHw3+uxNL0aKsIQrcjo+6999413zMMA/l8vqvHl0gkEomkn4mXN9Aj+IWoBx98EJ/61Kfwsz/7s7jjjju8inoAkMvl8IEPfAATExMYGOhuylU3CTO4FwO2V63HLzStBRF+MSp8xITwCUmiw6iYmUB11n2SyowsSkdfCtCYT6S3trYIIP7ktJ8nt1uJzZykCwiANjto+E4jCPMtJzPZE219v/OUur7UaoM1uiv28f2RoElEWLZNiGtb4lYbiSDXmw3vC75T/gbRg3Sveqrfo0or7YEaxyeqbcetvhM2vd4PM1oPW8xi+O+hXnC34UYGUMKf8/62x+uDPs8r3t1Bwfve9z7cddddq76Xz+cxOTm56nsSiUQikUi2gBjlF6I+85nP4E/+5E/w3ve+F//yX/7LNiGqyZ133rnRTUyUsIN2Z1frKaxaneq4vj+yyF+hLihqya3MRSiQ3bW+sCR4a7DLIg5eAQCKCq6ooCxYxcCgMK3kRkUBQF0BXYwfLREEKRZtfTY9WkRnrZLyTvhoP3+UJcskU4VUW5nzlocOdp6Ikg4pwKHppJ6H3p9f8Yi+m/UiJSXRoMsGsMetnucYize+1yV4uiV4qh38G8NAWavNSj1AOv4aCN89mEW4lTa3EUq0h0qE+QRckVCqZPe0RQDAjh07cOTIke4eRCKRSCSSLUpfi1GrCVF333033vrWt27J0OiwE1hu2OAjbg4cYRrUWoCnsEJ1B28EoFbIKluODaXmDvCz0woUff0ZmGC+FzGjK4RmAKzc7hUREyt7wVtWL+W67hcFSCFqO7DpQhQAPtiaaUZJSeO6288FACebjOGztjLvLeePdk7Rof67l4hW/asNvxiVQF/3F1IQNN6MWApSyUKqKkhNgUgzcK2VTklqCki1e8MikWmILYKAsAQfbgjVjY6iDEothhjli05kVrhzVggBbt+4nzD4xSh/tJckPCMjI23/34r062fs13ZLJBJJN+hbMWotIeonfuInpBDVgO0qe0/k1cqOQFXmCAgI1yAUG8SuhzqeWl72AgAG93U+tfxilIjp3cV1w/V8oQyCsNhPVZm+Ap5yJymkpkCZ735UlJxsbn16QogasGDvX/FeUyucP44gDFx1vV1YZjC2V5PbKAa15EaopIYpjLEA1yraumYQnoAY1RZCkYDw3BYZFT88QwpSyUFAQBfTrn+T72eii2mQLuV6CyIgDFdsIU6yxyEgoMwApxXXBJzzSJUl/RFNYSOjuO85UGwxSgCIUxW3i6mW/cInP/nJzW5C1+nXz9iv7ZZIJJJu0Jfl5KQQ1RmhM7CxZrUgBVotuG9B06uB2mao1BW1tOQtD+3tPBhtC2aIGRnF9VaaQtxUPQEBK3vRe61eynVtgtJETjK3Pj0hRBkOrGNLXoqeUh+GYoaLbOJaawLvZJN5squWlkAaF4T8ES1QYYn2yKj4k0+SdJqe//aa0J22F86hrYKyStr1an9LCmE43nlAk4yKakAaqXoEArQerahAnMgov3gVO01PKF2/50okEolEItl8+lKMak5UPvWpT+GBBx6QQtQqODvLvmpBU23G5J1oilFEiFAV9Zp+UUDAyCjuL0cdPzLK22/MVD2mr3hpSKSqgi50NypKClFbn14QEYTGYB1bBDRX9KFmDqnlQ6EnfUxr+dAlJUb5/aLyR4JFWhHffJcknabXg5FRTXrhXNoKNFP1vNfdTtFLt1LQovi0daLNNypqql4Mzyhm+87xqJFRTiNyLCm/KIlEIpFIJD1NX4pRAHD69Gl88YtfxLve9S4pRF2HUBnYROPJqKDQKjtCbe+vYkODpuoJ4UVGqQME6fHOpxZPOE3P25cSPTJKQMDO+byiLnY3KkoKUVufXhAPhMJdIcpwOx21MzCWjwRK3b0ervvEqFxSYpTPL+pIsJLubQbmSYhRSNgzqktiFNAb51S/00zVa9LNFD3A5xeF7ohRxIlvYh4nTS/pyCiJRCKRSCRbn771jNq3bx8+8YlPYHJyUgpR18HGa14ajlqdABHhPF38YhQJWFGPmjVQxx2NDs4ogdJskjQw51oyaXpcK3mVwkhFBV3qXmUlKURtfXpFNHD2FCEGmn41KaSWjoaKlvTDG5FRXNHAjXB+U6vCHCgVN6oys0OBPhjsWtBVA/NEaF0DRRce+0gPqfioswPgjXR2dXaga8cROgMbrXmvoxQN6EQSkVH+ND2nHq4/2JXW+pE8o4TwxCgSs8NIxyjgt37rt1AqlZDL5fAf/sN/2OzmdIV+/Yz92m6JRCLpBn0rRmmatmXL6cadwAq9pfJEKh/tF6MCpukJqjSL8KE6yyG4aDMYXo12vSre8JGnWoN7roSrAtjWCl9UlRLjSbmcJEp6CZ5tlrkCjKWjoDxY9NHquH2CCO6KQCSBKAZCACHALdHmCbgeyVs8+fP+4otbXGlFlRKnb4OQtzTEUqD/YMJdXuVaLzQGZ3cJEIB6YRCERYgkzFqwjiwBuntOEcdItpJeA/+9K2pkEtdSEJSCcI7lF4Ldx5s899XWWIFlcpGOD6oA3C1CEgtfqv527Xvf/e53MT8/j/HxAFWU+5R+/Yz92m6JRCLpBtvzLt3DJBFJ4R98iQiTKv9TScKDDQqFnoKddwf19UWO5VNOhy0A6psPExZv8MmM1lNtoQRMLVwFwlKt/WhJpP5IJJuPdy5zLbZ5smIOAnCvDc0KePF2qMLJjQFwrx3VawGvOf5LTAKCGFdbFyRBO1+/Ou6vUY0TAqDFOOLf6kjBOxlI47/r4VkL5vF5sMkq2FQV1tFFCBrunsDydVg3L/iEqBSMwtGupAMyveAt2/mIk1xFgTXs3sfNosDy2WCfd+FZhtknWKMdBmo794Y/NiFwMm6kpVDqEIh+/20Tgusy5U8ikUgkkl5FilE9RGIpPW1iVIRJlV+MYsG3Nyf3estX/qFzdJKS8pdmjydGCd3wUgO4Gj0yijDfhDQV8+msRNIrqI3JMI8fDKuYeW9ZK8ytvWIIrOEpb3nxiWBpttzxpwXFv5UJzSdExyyCIIgDrrq+faSiRYqoWQ8pRHUXZ7zqikipliAiBm3YR5YhAvp/sdEa7CNLrQp61iDSS7eCsuT9ogSEJ0YJqsAeGo28L3Nsp7d87bHO90DBBZ7+s9Y9t7L/5jYj9DCwdCPtl8R7qOTflpjh21Kr1TqvJJFIJBKJJDZSjOoRkvSWIY7vqWuEJ/z+SjZhRCJrZAd4Y0I3/7gNq7j+k01FT06MAiFghjvId5+qRsvhIVz3zItFKn50hESy2QjKPQ+5ZMSoIS+rVluZjb0/ALCHW0UWlp4MKkb5XsT0nAOSjYxietGzjEo6KkoKUd1DEAF7XwHOwYJPRMoCvPGgI2/CPrTc8f7iTFRgH1r29qHUx2As3xTavzEoXK1CKK6Aag2PAzHEWWtsCqJx8l57zIHokA974R8clC6769i5PMzJ3ZGPzTItDzquRhej/Kn6USKj6vXox5ZIJBKJRBIcKUb1AImbHPsjo6I84febh4YRiSiFObHH3QUDnv1cFYKvPZBtMyAOEYG1Fp6ZMhGRTcwJiBcd5ffekkj6FrUlCvuLE0SFCA3UdtNi1WoRxIofRcCNATgZN/2vdM7pKGQDgOhiZBRiRkZxvdTaVYJilBSiuofQGKybFsGmqt7f1OoUjOVbYBSOefdFPlqHc6CwpiDl7CjDObDiiZFqdRKplUORKlcGxZ+i10yzi4rQUrDzbmRVdV6gdGX1zymEwOIZhlNfbt1ryweOX28GGQovMgqAUKJfV7zIKIuC8PDfu2F0r3CJRCKRSCSSFlKM2mS6UW2LxEzTa/OMCunlVN9xAFx1J7wLT9g4/7W1nzASSkAbc+PYkVG4zjcqxlNV0jR31kRojxCJpNfwe58lERkFXJeqt5JMqp4/OmoxQHQUT9gzSiQZGdWoOAgAtJyMGCWFqO7R9IcSg43zThDoKweQKu0HAYViDyJVOOJFzbKJGpyZYpsgJSDgTJfg7C16f1MrO6GX9nfFI8pPm1/USDwxCgAsX6re7OPtfYHZApe+a+M7/18d//Q7dVgN3bU+vgtOfizWcdsioyKm6Qkwz8ydmNGuC+l08gbzEolEIpFIbkSKUZtI18q+t0VGRfGM8qfphduep9IoH36JN0R/4S/qWHpm7SiDZqpe0mJU1IEscJ2JufSNkvQ5IuHIKKCRqtdAW5lPZJ9+36ilJzv7viXtGdWephc9MkqAg+sVAG6KELHjC2VSiOoe1/tDEabDWLoFWn2ybT3VGkZq5ZCXosp2VsCmXdFRQMCZKbqV9xpo5d3QyzNdF6IEGLjuCmAslWmLLoqKOdYShpu+UcwSOP1XFr75/9bw2AMWihdb1xUnPYDKgeOxj+tvO1ejRUaJthS9vi0YLZFIJBLJtkCKUZtE14QoXBcZFUmMih4ZBQD28BRqu4819gWc/B+VNdP1mhX1whilrwVPqqKeL3pEpupJ+p4uREZROws00l+0lXmgg69MENjAMLjmpscUTtlg1vr79KfpJeEZJbRkIqO4VgYaVUxJKX5UlBSiugfPWu3+UHYW6aVboTi5VddXzTHopQPea2d3CTxjg4/UwXZWvL/rpb3QK7u7LkQBjZTQhqm6NTIRK03O26eRgZ3NAwBWLnCYRYGnvmDh9F/YMIutfmfn8igeewmW73gNuBE/mkhouuc7GTVNT1bSk0gkEomkf5CPjbYijAAc7gA7whN+/4SVONG8l2q7DyN9+TQIZ2D1tSeVikYACIDHT4drF7SiDcg5NeGkW5EeUcP8JVufpqDc62JBNyKjCCgUKwdmrIDadVCz0vJsi7xTAjs/gdT8BXAbKF9wMHRw7fZSfwEEFs/jCQCI49uHiC5u2bnL3jJdSa2zZmd6/dzqFTp9T0Ef/nClCie1ALW2Y1UhSRAGllq6cbvB1n1SL+6FVtt5wzrdQpDWAxOeSsjrSAhQuxFhRFyt16627uPW0Cgq+2+GMziSiPjlwbnvPh6tD/qF5KBRibKfSSQSiUSyOUgxagtCQABLAQwGroQXk/wTVm9AGhKlvOKl3g0f0UDo6gNWmmCanlpebu3XDj8xFhCwBs8C1G0LnUuD1rtT/UjS3/gnt6/T3rXmZOZr9he6GgUZhHbPqOTOZ2q7YhQAqKUlWHHFKABObhSp+QsAgOJZe10xyhhtTTSpWQEbyMc6tlIptPZnZyLtg+krYCn3OyF1BcpC9GiR7TRBjtNP4nxPtKxDOzUMe98KoHOAcliD5+Ck56EXD0JxfKnftI56/lkIrWFwzgH17BBoVYPjS+dWzNHI7Yn0GVhLgFJqlXXWDI6+NAvFdCOTJm5WoGcJdt6heil7QkvBGUr+c6qlZW8sQK3BaDshvgdbvPuRab3Kj/3Yj6FUKiGXWz3KbyvQr5+xX9stkUgk3UCKUVsUYikQBgOoAwEeqpIPAXVLWVMGakVLd9MLrZLvozevE93QOAOJ4G6qT4ynrEq50NpvBDGKGYtgRkPQsii080PrbyDZlqw2ae5pQUrzT86Su+QrVg7NWCK1tAhrfE/sfTrZEW+5dHb9VDljrCVGKfUq4sZGqZUVb5n6RIigCAhYuYut/V3MgYho17PtJEQ1idJPkvielKU06EoKzp6iV0mPaxXURx6HVt0JrbwbXKuiPvQsoDTOModAOzUCpdhIKWumc4tkBd8gECd5Mcq4ctZb3vMq95oxdUJBapDALAroi1dBzXpykVgNtMKCt6zYEcUon6k8iZ893Lf8/M///GY3oev062fs13ZLJBJJN5CeUVsUYvl8o2JER5GIkVHackuMGrlp7Qkw1XyTtTipekJAbYhRhGmgPJxXiyA2zNw577V2dqjNe0siAdZP91nvvc0UF7oXGZX1qouppcVE9snSOXDFbWPxrA2xjheVMdrqn9SMPwlXqi0xSrHDi1EsVQDXXUNrUlVBI0ZFbUchqkmYz57k90QYhXY2D/2pUZBq8wkJYA9cQW3sB6gPP+UJUaSmQH9y3BOigJYYRbge6sFPIm2HAsLcPpOEGEXrVeiL1wAAxjDBxC2u6EsVgt2vcL8bIgSMa+djH+t69JXWdUSJGBklZGSURCKRSCR9g5xtb1GI1YoaEDS6GEWZA4Q0MSeOBbXk+mpkJinSo2v7NlDf3JiI6Kl6tF4GbfjGRImKsgbPe5MNumSALiX7xFfS/wSJ2uhFQUqorX6VlIE5ABCheOlsaq0UWbhu3ykByw4DAOySgLm4tkDdlqZXr8Y+tNpM0xOkLdokCAICtj8q6kIuknn1dhaimgT5Drr1PdFSCvoT41Av5FzfRTQe5jRCbOiKDv2pcVBflTZBhBd9SFh8w/ooEOYKn9Q2273PImBcPeeduXteqYIqrfN4zytVz47RuHI2kcIFHpxDbYhRhGkgLOI9WIpREolEIpH0DVKM2qK0iVFK+Emif1BN7XCpelph3hvMrpeiBzQNzBtEqNzXRC0VvOWwYpSjL7dMyx0C7YWhDamCJOkfwqQP9Zwg1YyM4kriURvUanleqOUbjZ2j4E/VK55de2Kt5YhXjVOJGxnFHNC6G9VE7Uzo74kZi+ANLyFS1kCXw0+kpRDVYr3votvfExEE6uUc9McnQFda90FlNgPtmdEbImaFzjyBhoSMyE0KylpRWjROdBTnMK66EU+EArtf3i5eZ0apFymlmDXoS7M37CIqamXFffgFgNqDMe7BUoySSCQSiaRfkGLUFsUvRvEYkVFAeBPz9hS99cWo9sio6Gl6Uc3LBWGwhl5o7ef8YOAKPJLtQRS/p14SpJppet3wsvH7uiSVqufk/GLU2r5RhBAvOoqa1VhRGkq16E19qRPOvFyAt3tFXQg/kZZC1I2s9p1s5PdE6yq0k6PQTo5AOzkC9YWh1T3AdF/kIYtXPTEqzcgoIF6qnr54DUrDJ3LyVgXp4RuHiDOvaglUfm+puLT5RUU1L0cjUq3BdvaM+umf/mm8/vWvx0//9E9vdlO6Rr9+xn5tt0QikXQDKUZtUZLyjAIQ2sRcW5lz90GB/MH104L8nlFxKur5xagwfi925pr3/dAVHcpctCpakq1JHOPxXhCkBBGA6s7IkkzRa6JYLeFXKy6ss2ZwnEaaHgCUznUwMR9xr3NEcJCQEZx+1LZKeuH8opixBKG6gj1d0aGshBMkpBC1Nv7vZjO+JwICZcWAsmKsKTAK3e/JtkmRUX4T83p0McqYa4mqe161+vVi4hYFxrD7XeiL10AiFjm5Hi0BvygAEIqvPds4MqpWq6FaraJWq212U7pGv37Gfm23RCKRdANZTW8D+YWX/ifQygYNVplPZyThI47aykWXC8DozsDbCupGKwgOLDxpY/L2tT9zfbnVtqZxcWiY44lRxDFARMT9OFSm50kSZVOr6AHgQ62oRupEM9ReD8J1EDsNodWglJdBzSp4Kp6gK1QdXEuB2ibs8trXLiEEKtcapeZBABo9olGt+CMrw5uXezAZEZU0Pf8dOa3fnGvlTWmCPyJLieOf5osk8ntF+bFrgFNrrEgIkNA904voEgSERbuG2MYsmNFIF2YEpC6HuBKJRCKR9DIyMmpbEH6w6PeC0Yrh0m+qMzd7y2f+V7U1cL0OwQVK593JJNcMiFS0ybJWXARppOgo1lC4bauTQKMSER+tgw0mYMIskfQIbKz15FWpj6yzZnTU+igA9yqjL15OdufrpNmUzzuewbkzNA6hRhf6mwUXIEhoMUqpj3gee3zEBM/EM5CWJE83BS1aTAGNSGSWWoIgG//7E+7zjDKjR1vUJ6a95Qv/sPrneOHrNpxG8FF9agZCTyY1sRmB7VYkDD9mYWoJ1qAv5f7sEAjrPMTtebFTIpFIJJItjBSjtip+s4TVfC46QHkKxHEHmWp5CQiRQmeP7IA1PAUAsFYEzv7V6oPj2jz3hConN7zqOkHQCnPesmKGE6OIUKGX93ivnX0rbZ4TEkm/IogAH27MGrkSum8ERa2Nesv6wsV11gxD52vW/PdbwrE5uiv6kRwLSgzzcgIKrdyKHHWmS5HbIuke3RIdiCBQFtLNF3CMZNJVQ7WBa959Po4YZY1NgTdE3Ws/YLDK7fdCsyRw9m9dkUoQiurMkcjHaoNzUNtNlycsfGQzpxbM/Clv3KNcy0Cd7xxdJYUoyVbn/PnzOHXq1Kr/ZmeTK0AgkUgkUZExzFsVsuaLwCj2IBx1HoRzqOUCnMHRzhsBACGo7L8V2g/+f/bePE6yqrz/f5+71NLd1XtPd0/PvjMMOxgWRQyIIhE1KJC44dcN0cQ1MRIiaiZgIklQ2RSiQ0ajYPy5JEEFRUBARWRRtoEZmJ69p7fqrZa7nPP743ZXd0+vtXVV9Zz3vPo1tdx76txbde6953Of5/McRkjJvvvTtJ8eombZ5J/b4AQ/mIk+MdliD3SP9zkHrwkr2YIX7UKGhlFVHn7rCNah7CryaTTlhqxLZ/yirFRDwSvpjWH4UQynGhkawUoMYCQHkdHcPV8mMpMnuZKK7seDyasSArdx/mnER2JOKn4Qm2XJmbESS3Br9qNMF9mYQkZdjGThDeM1+XGP+92ipM6a3VX4S4M0My96GDvZXvDPmA2BQMgQykxjpvLwoTFMUm3Lqdq3C+nB/kc9Vv/p+O94188c/FENOLV0FTJSGI9FwxkXlrP13VJI0nU7Mt6PYjCEtXtu4V0LUZrFTH19PZFIhK1bt864TCQSYfv27bS2ti5gzzQajWYyWoxarEysKJNjE4YTg2gg9FiDPfMXowAZqSa5bBNVe55FSdjx3QQnfyKGMMZ7M9g5Hm2Va2SUcNOYIwNBf93qnPyiBILQ0CpSTU8HfVk+hNkTRXi6qp6mcpmcojf/sZsLVrIZJxRMxsM9+0gu35xXe0qMHiemEaNSfT57/i+BEw9S9Ny6Jfml6E0sfuDkJkILDOyRdpzaPSDA6xgmtDN3gV1TPIohSBkJGzFso2pcpD2CtEYwvDy8x3JA+IEYZXgO+D6YuZ2/Uu2rqNq3C4A9D7mseo2FEIJUXLL7geAGkjIMEisKFBXF5CIp2Vb9dGK7kaHRaMS0QeiFhumrHk5AC1GaxU5rayvbt28nHo9P+35nZydbt24lHo9rMUqj0ZQULUYtUiZdi+WQpgeTo4zswV6yrZmT7FhH+PAezNQwAy/5HPytw9Izxv0lBjsnRkbV59RHe6AnI7blU4HHdGNYiRa8qm6wFN6KIeyXcuuTRlNqFipFbwwz1QS1nSAg1LuP5LJjRs2N82SCGJWO++y9J8mhR1KoCUX20i0r8/oIa7gv89hwc4+ItBKtODUHwPCQzUnkvhiGNlAuS4ohSJndVXg1wY0RN9JNeHiBxSgZBgJRxkwn8aty+y371bW4tY3Yg30MHVDEd0saVpvs/KmLHLWRSi5dgwxHZm8oCyaLUfMXln17AK/qUPBEQmhHI8KdXYTTQpTmaKG1tVULTRqNpuzRnlGLlQKk6Qk/kjH3tgZ7Z86ZmQnDZGTtCZmnu36QxB0JohmkpxjeG0RG+ZHqnCMbJvpFGXlOuEPDK0EGF7L+kgSyxsmrPY2mVPjtIwuSojeGIUMYo2KwmRrBHIkXpF2FIh33een/G+axz/dz8MFxIUqaFokVW3Dz8ItCqXExyrcmVSXLFqFM7OHR9CwBfof2jipnCi1KmD1RGC3+6Ee6UWRfxTYfDH/8HJqPbxRAqn1c4N37sEeyT7LnobGoKJPEig15tX8kuYpRXmS8uIq1u27OasVaiNJoNBqNprzQYtRiZZIJd45iFAJz1EPF8D3MxGDWbbj1S0g3B5NFd1jx7B0JpK/o3+EhRyeV+flFjZrFKoHp5udTI6SNPTI6sRXgrhrIqz2NphTIsIe3YnysmumFSRezks2Zx6G+A3m2Fhyz3EHFo//Qz/77UpmoDGWYJJduYOCk15Fauj6vTzHSIxhe0LDp1uRUxWsidqJ1XNBuTqLMhRUkNNlRSHFCeAZGfxAtpEyXdP3zSCPbeOI8Pn+CkGoPZFcB90hSLcuQZhDVt/93Hk/f6WTO18llawtWQW+MyZ5R80/T8+3R45xi3ER+BrQQpdFoNBpN+aFzCBYrcuKkKvcJkVAT9MpsI6NGGVl1HHZ/F4bv0fu0yzPfGGHgpfE8G7dhSc79M5xE0E8vglAF8HgS868aqNGULYqMBu3U7kZIC9MpbqqekBNOJyo/EUZGqjFHx/ZYqp4SBqm2NaSWbkDZBSonn0qMP/byN2MWysJwq5HhweBWj6FAH1KOGsxD1cjGVBAZF46TbH6S0PByrMTSvIXOuZhovl+1+zm8WD1OU1tujVkW6SXLiB7cjZ+GrqeCH7G0bBLL8xOAp0NZ4wKUMrOI6hq76aYAv7j7t9L45Cc/STqdJhwurHBYTlTqNlZqvzUajaYYaDFqkSK8cRFJGd4sS86ObwWmxEoI/KrcKk2pcJShTadT++wjCCXpfsLNvOfUtZBuWZFb55RCyGDSWwghShkObtXB4IkkZ88ofQdWU0qMtIW9oxF3XRxsiTJdUo3PYY+0Yw8tL1rKnh8ejyT0alvyaiu1dD3WYDcCkHaE9JKVpFrXoEKF86kBMLwJERk5lJQ/EoVC2sExE8eY079GU1oK7hs1GIYXGnBXD0BIgpA4sU7cqkMYbjWGH0V4UQwvGjxWhbsEM70a7OEO3Jr9CKD2mUfpP+ls/Fh9Tu0lVm4kcmgPYoKwPLzueJSde7GAmXAax29IeeF+7MT8Um8NrxrfSoIBqspDJGYew+fblx1V5+Yzzzyz1F0oOpW6jZXab41GoykGWoyaAaUUohAGvCWiEGKUwkdZwV1Kv6oWjNwnVl59C0MbX0Hs+d8iRkMdpB1ieMOpORsdC3/CdhVAjHJq9oERXHibh6swZrmwnYmj6WJXU76Y8QjGUy246/qR9U6QdlpzED80SDi+DsOfPaUlF8bEKCUEbm3zHEvPjlvfytCxZ4PvBcKWURwBTbi5pQfNhLKSYARRJMZQ4SftmsJRaCFqDLMvijEQxls+hN82AgKUmcY301OC5IRvI/woplOLPbIsb6HYHlmBtFL4kV6E9Kn746+Jn3IOMpz9eJeRKpJLV1O1P6isl25eSrp1eV79mwm/KoYXrcZKjiDtQZTw5iXUmW41fiRI1feXJDB2zx79ebQJUhqNRqPRlDvaMwrYt28fu3bt4g9/+APJZCC+CCGQsoL9PrxxgUeJ3MQoaScyqT5edf4pPm5TO8MbTkEhUEIwvP7U/CId5Pil/aR0wlyaMpN40a7giS+w9mUfBaYvcjXlhHBN7OeasHbXZjJ1ZWiEZPMfcaOHUeSWdjsd0kyhrMAfx6tpAjP/+xxerAmvvrVoQhSA4eZeUn46fHt4vO3h/NvTFIdiCVFjCN/A3l1H6OlmxGBoxkx5ZbrI0CBuzT7cmr35fy6C8MA6DCeopGc6KWr/+GvwcrsGSKzaRLq5nXRzO0MbTyxMhcwZyKQUCvBD8XmtY6aaYPTc77eP4Nem51ij+N+9RqPRaDSa+XPUR0bdc8893H777fT39+M4DuvWreOVr3wll19+OYZhIKXEKOJkqGhIEVwAG0COkVFyNEUPwK+uL0i3nJblxGONQfuR/EpfFzIyyontyQhv1v6arNNrtBClKUcEAutgDcZAGHdDPyrqgSFx6l/CD8cJD6wpSKrQxBQ9tz53D7iFxihwZJScJEbpyKhyZCHFCGM4RPiZZhQKFfZRUS/zJ6MeKuIF6XyAW7UfM92YKRqSKwKTSHwTycY/oqw09vAAtc/9jsEtp2ctJik7FKy3ADhN7VTtC6KwvHAfVnru6EpDRggNrcCp3Q2AuzaO8YcWhD/7NdvRECG1Y8cOXNfFtm02btxY6u4UhUrdxkrtt0aj0RSDo1qMevTRR/mXf/kXLrzwQo477jhisRh33HEH3/3ud3nyySf5l3/5F8LhcNYpez09PfT2jlez6ezsLEb3Z0UgwDMgJHNO08t4nwBeTX2Bepa/CDWGKFBklG8P4UdGy7s7BubB7Pq32C9qS0U5jKPFgpGwCf2hGW/VIH5rYNrtR/tI2sNEe4/LW4jxQxPEqLrKEaMKnaYnQ6NilAJRJpFRehyNU6qoGIFApC1IWxCf/J7XMYS3YggEpGtfJNp7AoL8bq4IFSISP4Zk4x/B8An3HiJy4GVSHWvyareYuHVNSNPC8D38cByFmpfpu5Vsx4v0IUODEPHxVg1i76qfc73FLkhdddVVdHd309LSwve///1Sd6coVOo2Vmq/NRqNphgc1WLU7373O5YuXcrb3vY2li1bBsCxxx7L97//fb7zne9w5ZVXcsMNNxCLxfB9H9Oc3wXij3/8Y7Zt21bEns8P4RmokMwjTW/UvJzCpOkVmkJERikUTmx8cmbtjSHk/IWtxXwxW2rKZRwtFoQ0sF+qx4iHcdfEwVYoy8GtOkRoOHcvGIXKREZJyy5YFOVCkImMUoDM73SohI+0Rqt7JqysjiPFRI+jgHJNzzL31+A3pFAxF2WlcGr2EB5enXe7hl9FZGAjqYZnAaje/Tzp1uWTKteVFYaB09hKpHs/GB7SHsJ0a+dcbSw1Mdn0FBh+4B3VG8GMz20BsNgFKY1Go9Foyp2jVoySUrJ7924ikUhGiHJdl5qaGi699FLq6+v5+te/zqc+9SluueUWTNOcd4TURRddxFlnnZV53tnZydatW4u2LTMyZmJuSBQyK3NUhcpMrGS0piAeMIVmohiVa2SUDA0iQ0NBGwkL8/D8y7vri9jiUjbjaJFh9kURCRvnxMMgwIv05SVGSXskY9rt1i4pqq9MoclERklrXlEYs+HbQ5lU33JK0dPjqHyFKAjEFHtnPc4J3WCAV30QO7EUQ+Zf9t106jFTTfiRXgw3Tbhrb1lHRzlNbYEYBfih/nmJUTCWrrcKpy5I83PXxDEeb53XmNaClEaj0Wg0paP8FIYFwjAMjjvuOP7rv/6LZ599ls2bN2PbNlJKIpEIr3/960mn0/zHf/wHX/ziF7nqqqvmnarX3NxMc3N+1aQKgZBi3KJYyIzR5/xQo3+gjPL8mViD46knws/twn2iL5Z5oGbeE1J98Vp8ymUcLUaMlIUYsVE1LspKzjslZlrEeLpsLlW7SooYPSYKld8+ALzqrsxjYyB/IaFQHO3jqJyFqDGMlI3RF0E2B4b6ynCgAGIUgJVqxo8E50rDSc2xdGnxqsfFp2ztBazUErxIDzI8AGGJqnER8xSFj/yN6PO7RqPRaDQLQ3nkEZSIDRs2IKXkpz/9KX19gWfQmGl5OBzmwgsv5FWvehWPPvooTz75JABKFa4CVTkjMBCjF8NGemSOpUtDqP9Q5rGZbsipDWmOe8YYqfx8OjSLj3wmJfe43y3rSY1wRn/vApTh5t7OBCHYSCfz7daCkvGvM/ycCz1AUI3Tj/QHT9IGRt/cKULl/NtYLFSCEDWGio7+/hQY3vwjdOdsd8I5zo/WFKzdYjDRBzK7m2ejxRpS46Kr31DewptGo9FoNJqjXIw6/fTTueiii/if//kf7rvvPhKJIC3NMAx836e6upr3vve9pFIpnnrqKYCsjMwrHeGNilGei/CcEvdmMkY6gTUS+NQYTjWGzC0tRpkTtsvRYpRmKrmIBhPXKVfRQTjjh/9J4yDbdvzQWBAlppPIt1sLih8eL1Ygzdwnr27Nwcxj62ANQs1+nijX38RioqKEKEOiqgIxyvCq8jYwn4g0xwXishejJqXeZ78PTGf8ppSsT8+ypEaj0Wg0mnLgqBWjpAzKKX/wgx/kVa96Fbfddhv33nsvQ0Oj/kGjolNLSwvNzc0cOHCgZH0tFcbEiIdUeU0y7b6JUVGNObejjPFJuNBilGYGshEPplu2HMWHib/3ieMg63YwMpXojHR5HSfmYmJlz4kRJFm1YTh40e7giSfm9J0rx9/CYqOShCgAVe2O+425hRWMJoqsflW5i1G5R0YBGDKE4QZjWtW4KNufYw2NRqPRaDSl5KgVowwj2HTbtvnIRz7Cqaeeyle/+lW+973vsXfv3sz7L7/8Mo7jsHTp0lJ2tyQIfzzVxEyVV6peqG9CJEKOKXowISLEMeaMZtAc3cxHRJhtmbITISaKUXlERsF4qp7hpkFWzgTQnyBGSSu3yCiv+hCIIDTM7KpC+DOfVsvuN7AIqTQhCkBWj6fJGl5hBSNlBZFR0gqh7PIx1p+OfCOjIDBtH0NHR2k0Go1GU96UpzN1AZhY+W6uKnhLlizh05/+NLfccgt33HEHv/71r3nd615HOp3m97//PclkkvPOO2+hul42GBPEKKOMxCjhudgDQSSC8MOIHP01FCoTEaKjojTz4R73uzNOducrVpXLZLlQaXowmqo3iuEkkZHyjsAYY3JkVPZilBI+btWocbkE69DM262FqOJTLmMrW2TNBDGqgJFRCj8ztss9Kgomi1HkKkalG3CrRyvy1acwuwvnv6XRaDQajaawLNrIqGQyydDQEJ7n4Y+Gfs9mPl5bW8unP/1pPv7xj1NVVcWNN97I9773PRKJBDfccAMdHR0L1fWyYaIxsVlG6Td2/DBi9Ls0Uw05V8BShpNJjdBilGa+5JuGVy6iRKHS9OCIlN4yOlbMxSTPqBwio7zo4cD8HDB6ojMeR8rlO1/MVKoQBaBqRsefEgU1L5/4my53vyiYnKYnckjTAzDcGMhgHMr6NIqjo+iMRqPRaDSVyKKMjLr//vv57//+bzo7O4lGo5x44on8+Z//OZs2bZpxnbHoqTe/+c1ccMEFxONxwuEwtm1TXV0943qLGcMrz8gou9ApekyOEtFo5mJihFOuBuelnjxPFE5kgdL0oLLEKEwLaYUwPCcnzyi3uivz2Dow/WRfC1HFp9RjKR+UKVHRUUHTrUYU8B6hmmhefpRERgkEpluLH+4HS6FqXMRweacnFoPt27fPmRVQ6VTqNlZqvzUajaYYLDox6he/+AVbt27ltNNO441vfCOHDh3innvu4b777uNv//ZvOeeccwiFpl6YTDwpWJZFa2trwft202+vY+PGjXm3M98Lb5Eev5jzQwNY6absPkhZIA0wZHlFRg32Bg+UgeHU5tzOmOkygKwL7qDON8rqfPsyPck8ysn3+y+5IOULkATxsYY319KzUq5RlHNhjgxgjFUKlfmdDlVIQnLu5TSamSnsTRE/NJB57NXUFbTtYjBWIRfA8HMTkHxrGD8Un/DC0Tnhr6pa/OmJlbqN5dTvzs7OGd+rr68vylxIo9FoJrKoxKje3l62b9/O2WefzZVXXpk5iB577LF85Stf4brrriMej/Nnf/ZnU04GXV1dDA0NsW7dOkyzfFO2spm8Gj1R/NZgYuhFu7MWowQCIcMoI4mRToJSUOI7OcJJZSa7+d5FNvwIRroWGR5ERX1kfRozHpl7xVG0IKWpZAQC/EBsViI/0/Fy9Zebi8jBFzOP7UT2F92h4Q7S9bsA8FYMYvyxeYqgrY8T5U8xheG52ha+Aa4BtszJt2wmFAo/3Bc8Ngyc+uaCtV0UlMKOj95okhbCz37CroRHum7HeEGBA9UYSXuOtTSao4/6+noikQhbt26dcZlIJML27du1IKXRaIrKohKjhoaG2L9/P5deeimtra14nodlWVx88cW89NJL/O///i+33HILlmXx53/+55kw2eHhYa677jqeeOIJvvrVr3L88ceXelOmJduLZWMwBGkDwhI/HEcJF6GyuzAz/BC+lURIH+E5KDs890pFxBrqyzw2nVje7dmJNtLhQQD8tpGsxCjQE01NZSN8gbLJW4wS3sTIqMoQo4STJNS7L3giTaxkS9ZtmMlmjOqDSDuBqnGRzUnMnqmTaH2cKH+KIUjN9zsXKRNlS5TpoPAR5H9DTFojKDMwRnfqW8As78s9MzGUiVI0nVjWXpAKRbp2F8oK0m3FkI21J/fIaY1mMdPa2sr27duJx+PTvt/Z2cnWrVuJx+NajNJoNEWlvK9OskQIgVKKnp4eIEi3cxyHUChEc3Mz69ato66ujq985SusX7+e4447DoCamho2btzIzp07aWxsLOUmzEguF8kCgdlThd8xDELhRXuwE+3ZtTHJCyaJX2IxykyP58HkWkVvcnuNCD+EMh1kQxoZ8TBS2Q0LPdHUVCxjKSxGnmIUBsK3UaZbMZ5RkUO7MoUQ7ERrTqXkBYLQ4ApSTc8D4C0fwuiNItTUibQ+TpQ/hRSksvmuRdJCxQLhSFkphJe/T+VYVBSA05zdeb8UZKKiANPNXkTyol34kdE2PIH9YsO04/Bo4c4772RkZITq6mouvfTSUnenKFTqNpZLv1tbW7XQpNFoSs6icm1uaGhgyZIl/PznP+fJJ58EyPhDvfzyy3R0dHDRRRdh2zbf//73SafTeF7glfKhD32I//qv/2LZsmWl6v6M5HNxbHZHM4+9aHfW65ebMbHwxktgC5W/lioQWIm2zHO/Lbeojko20NUcxfijpwChUMi8mhKjqXqGmwY/Pw+qouO7hLt2B4+VwBppm3Xx2TCdeox04MejIj5+68zHEH2cKH8KIRhm28bEGyCyQKl6frg/89hpyv33vVDYAz2Zx9l6QUprBCe2e7ytXfUY6UV1rzVr7rrrLrZt28Zdd91V6q4UjUrdxkrtt0aj0RSDRSNGKaWora3lk5/8JPv37+eGG27gO9/5Do888gjXXXcdDz74IJdccgnnnHMOr3rVq3j66adxXRfLspAymITV1pZfSHe+kxcjaSOGg9Q8aY8gzewEpcnGxKV35xX+hAp4sjDeXnZiCYzeQfVbEigjt0m5nmhqKg0x0dw3X9+oCkrVC3fvxfADYdtKNmPI/KpthQZXZB57y4ZQ5szHEH2cKH/yEaRyWVdMEqPyP89Kw0HawRh0a+qQ4egca5QYpbDjo2KUNDCyiAxT+KTqXgARjDnzUBVmX5lvr0aj0Wg0GmARiVFjKXqnnHIK1113HcPDw3z961/ns5/9LI888gj/8A//wPr16wE4/fTTGR4e5tChQwAYRnnuhkJNWvKJjpo4SSu7yKg8q19l2lH2uF+MpfBbcp8M6ImmpqKYIEapfFP1KsXEXCnCh3dnnuYTFTWG6VVjJkcNom2F1zE86/L6OFH+5CIq5SpiieT4uUxZ+UdGTY6KKv8UPSOVwHSC7TbcWFaFSZzal1BWcM4WIxbW7vKvGqjRaDQajSZgUcUxCyGQUnLaaadx6623sn//fkZGRtiwYQPNzeOVZDo7O6mpqZn0WrlRyMmK2RPFWzkIBrhVh7GSLRjzrFRTbml6xgQxigKk6Y1hJVrxqg4D4C9JYHXl7tmhvWE0lYLwxyd9XtVh7KHlWRsHj7c1OTLKnWXZUmKOxLESQQl5w6nGLIA/D0BoaBnJSC8Ihd82grVndhNmfZwof7LxkMrnuxSp8SjfbKOXp8MPxTOPncby94SxB3Lzi/KtofEbbL7AfqHxqPaJ0mg0Go2m0lhUYpSUMhPl1NzcPK3YtGvXLp588knWrVtHOFxaM+6ZKPRdc+GZGH0RZHMKDI9U47NE+jbPS5ASfihIYRMKMzn73f4FQY6nvwhVuIg2LzruV4EqWLMazYyUQ3SMGBmvrunWHMC3hwnH1+WUtmZ4EyKjykC4nolJUVGJwk3UDT+C8CIoOwmGCv6knhhXOgshGAppIJImKuoj7eGcKt9Obm/80i4U78arK8/CLGOMVdGDYBzlhFCokA9ZFiDRaDQajUZTOsozPy0HlFIZIeo73/kOv/71r6cs86Mf/YibbrqJPXv2cMUVVxCNlp+vQLEmqPZL9RnvKGW6pBqfRRrpOdcLqmQFF4dmchhUfibH+VN4pcgL9+FVHwyeyGBfaTTFZCGEqPlMos2uKqw9scywkuFBki1/wJuQ5jNfyi2Kclp8j1DvvuCxNLBSTQVrWhpOIEQBYthGyEVzei17FtrjqRgYfaMijAjOSflgJ9ozY7qqcwdGujCm6MVCGeORYdkUUjC9GNbIaBqiAe6GPmS4zIsnaDQajUajybAorpaVUggR3IHevn07t956Ky+99FKmUh7AU089xUMPPcTg4CBf+cpXWLVqVYl6OzPFnKAK3yD0bNMkQcqt2TevdQ0vEO2EkuXtBZMD0kiTrtuZeW7trsNI5H5HWqOZi3IRomC0muT+GKFnmiA9ejowPNKNO0jHdmc1MRTSzhQCMFLlKUaF+g5gjFb6s1JNCFWYIggAfngg89gYKM+o28XMQno8FYOJptt+pHeWJefG8KuwkoEXmpA+VS8/m1d7xUaZE6KZRHY3vELDqzDT9cETW+Fu6pu1gIBGo9FoNJryoeLFqIlC1B133MHtt9/Oe97zHi644AIsa/wCZ+PGjXzkIx/h+uuvZ/Xq1aXq7owsxARV+Aah5xrBC/aXFz08r8o9hjeezmcmhorWv4VGoUjXvwijxs1GbwSza35eWhpNLpSTEDURYyhM+A9LxqMzAK/mEKmmZ1BifpEGApGJjjLTCVDll+86OUVvSUHb9sPxzGMzrsWoUpDNb7+chCgIoulIB+KoHxqY97ibidDwchitOBs51Ik1FM+3i0VjUmRUlmKUQBAe2IAYvWmmqjzc9f0onW+v0Wg0mqOAm2++mdWrVxOJRDjllFP41a9+VZD1HnzwQS666CI6OjoQQrBt27YpbVx33XWcdtpp1NbW0tLSwhvf+EaefvrprPpf0WLUkULUN77xDd797nfz5je/mcbGyR4JkUiElStXUl9fX4Kezs5CescIz8Q6UDP6BJx5REeNRUYBWItIjHJr9iJDo9uTMrF31eds3qzRzEW5ClFjCM/A3tGA9XIdYwFRMjSCU7t7/m2MilFCeogJPjDlgJEcxh4KIk6EF8FwawrWtkLhh0YjozyBGM7ec0tTGOYzBspNiIJAVDF7x1L1VN6pekLZhEaWjbYNNS8+VZYCMYAyJ0QoiuyregplEYkfA6NeWbIhHRRt0Wg0Go2mwrn88sv53Oc+N+17d955Jx/96Ee56qqreOKJJzjzzDO54IIL2LNnz6xtzme94eFhtmzZwpe//OUZrY3uv/9+rrzySh555BHuu+8+LMvivPPOo69v/tcwFStGzSRE/fmf/3lZCk4zUQoTY/NQNbjBV+9Heuas3jNRjDKTi0OM8kJx3Or9wRMJoRcbJlUW02gKSbkLUWMIBNahakJ/bBmPoKzqmVSdazaMMvaNCnd3Zh5biSUFFZ6lNQJmEMliDIZ1Ra8SM9tYKEchagxzQmRivql6AFaiPRMxZA/2Ee6a/eK0ZBgTxajcUuwMP0IkvjGTKuwvHcFbsrhsBbJh/fr1HHvssaxfv77UXSkalbqNxez37t27EUJw+eWXF7ztSu5LpbFq1aqytJOZL/q7Xzj+7d/+jcsvv5z3v//9HHPMMXz1q1+lvb2dW265Je/13vCGN3Dttdfy1re+NePLfSQ/+9nPeM973sOWLVs47rjj2L59O93d3Tz88MPz3oaKLDuihaj8EL6BdaAab+VQJjoqMrBh5uW9aGCGKsBMVP7dRmk4gU/U6HzR2luLoSMZNEWiUoSoiRgJG6uzFm9tEO2TrnuZaPfxCGb3WBLeZDHKr2koaL9yRknC3aMTcSWwk1MrrebDJL8onaJXFtzjfnfK2CtnIQpADIXAMSAk8UNxlPAQKvfLNIFBeGg1qYbAM6pm19M4Te0ou7zOdxMjo7JN05uI6dYRGlqDU7sLAG/1ACJlYQ4efWPyi1/8Yqm7UHQqdRsrtd8aTSWzb98+PvvZz/LTn/6U3t5e2tvbefOb38w111xDQ8P8r1X/+7//mwceeIAnn3ySp556iqGhId7+9rfzrW99a9rlt23bxnve855Z2zQMA9/PPirYcRx+//vf86lPfWrS6+effz6PPPJIwdebD0NDQ0gps9qnFSlGjQlRt99+O9/+9re1EJUD5qFqvPaR4KI32oscTs1YUnmsop6yUkFklFIgSnTnf1KaQW59cGN7wHQBMPrDmAeqC9AxjWYqlShEjWEersJvSaJqHZSVxq3uIjSydNZ1JkZGmekR3KL0LHtCvfsx3KCimJmuR8jCTcYVCj8yHo6sxajyYaIgVe5CFIyl6kXx20dAKPxQHCudn3BqOvWYqSb8SC+G6xDdt4vE6mMK1OPCoIz80vQmYidbkWYiqJBrgLuuH+PxVp2Cr9FoNAtER0cHzz33HHV1daXuCgC7du3izDPP5PDhw7zpTW9i06ZNPProo3z5y1/mpz/9KQ8//DBNTfOrrrx161aeeuopampqWLZsGc8///ysy5944olcc8010773q1/9ivvuu48LLrgg620C6Onpwfd9WltbJ73e2trKz3/+84KvNx8++tGPcuKJJ3LGGWfMe52KFKMAXnjhBe666y4uu+yyihOiKhHDrca3UggpMUcG8GvqF74TSmGNjEYgSBNUbml1EyuEmQer9UWqpmKZbYKdrxAmEJjdUbzaUe+neUwShTcuaJsjA7MsuXAIN03V7j9knlsjrbMsnT1+uB8ZCtKBRMLCSM//tHq+fVlFiCSahcOIhwMxitH0zzzFKIDQ8AqSo2l/1nB5jMuJGOnU+BOZ/2WpnWwNxCjI9Z6VRqPRaHLEtm02bdpU6m5kuPLKKzl8+DBf+cpX+Ku/+qvM65/4xCf493//d/7+7/+eW2+9dV5t/fu//zvLli1j3bp1PPDAA7zmNa+ZdfkTTzyRE088cdr3xgSbD3zgA5Nev/baa7n22mszz9PpNEIIrr/++sxrP/nJT1i7du28+rxQfOITn+Chhx7ioYcewjTnX626Yk1yVq9ezVe/+lUuvfTSihSiSj0B8duCqCgAM9k0Y1TUGKYbyzy2hvIzVs0Va6gPY9QU2XTqchaRrPR46KBsSBekbxrNdBRznBdTiBpD1o6bkJvpue9wGV5VRiQu1XHiSKo6/zh+3Eg1YDqFu1OnkDi14z481p7YLEtPT6mjZBczE/dtKfdzNscBkRoXY6RVmPOTmOjl5pbfOc8a6s88Nrz8I5WdMT9IwNI3nDRHKc8//3ymoFN1dTWvfOUrueeee6Yst23bNi6++GLWrFlDNBqltraWv/iLv+Dw4cMztv3oo49y6aWX0tHRQTgcpr29nfPPP5+77rprzn5JKfnoRz+KEII///M/J5kcr+qtlOLLX/4ymzdvJhKJ0NHRwUc+8hEGBgam9VGa6E30wgsvcOmll7JkyRIMw+D+++/PLHfXXXdx9tlnU1dXRzQa5bjjjuO6664jnZ58PLz//vsRQsxoFj1XH3bv3s1ll11Gc3MzkUiEU089lf/93/+dti2lFDfeeCPHHnvslG3Nl2y+n/nuG4Af//jHnHvuubS3txMOh1m6dCmvfvWrufnmm2fcJ6Vm165d3HPPPaxatYoPf/jDk977/Oc/T3V1Ndu3b2dkZH4eg695zWtYv359JksrV/74xz/ym9/8ho6ODi688MJJ711xxRU8+eSTmb+LLrpoymunnnoqzc3NmKZJV1fXpPW7urpoa2ub8bNzXW82Pv7xj/Od73yH++67jzVr1mS1bsWKUbZts3HjxooUosYolSClTIm3dHTQKQgNL5tzHcMZn2TZg/kbq+aC3X8o89hM5+5FY6Ybxk1Om5K6BLSmqBRjnC+EEKVQyPrRixFpzqv6nMDAcILJpOkkEE5yjjWKi91/iHDP3uCJNAkNrC7oxNSrOoyygqgOMRjC6J9d1J8JLUgVnun2aSUIUiJtMnZKUmZq9oXnicAIookJIgXLDXsonnls5lnlUppJ/Eh38MQ1goItRyF/93d/x4c+9CH+7u/+rtRdKRqVuo0L0e+XX36ZM844g76+Pj74wQ/ytre9jd///vdccMEF3HnnnZOW/dCHPkRnZydnn302H/vYx7jssss4cOAAO3bs4Mtf/vKUtm+77TbOPPNMfvjDH3LmmWfyyU9+kgsvvJDDhw9PESWOJJVK8ba3vY2vfOUrfPjDH+a///u/J1Xp+vCHP8zHPvYxBgYG+MAHPsBf/MVfcM899/Da174W15058X/Xrl38yZ/8Cbt37+btb387H/jAB6itrQXgqquu4tJLL+W5557jL//yL/nIRz6CUoqrrrqK173udThOYSr/dnZ28opXvILdu3fzzne+k0svvZSnn36aN73pTfzyl7+csvzHPvYx/uqv/or+/n4+8IEPcNlll/HTn/6U8847L68+ZfP9ZLNvvv71r/OmN72JZ599lje+8Y188pOf5A1veAPJZJJvfvObOfe32Izt+/PPP3+KCXcsFuOss84ikUjwm9/8ZkH79fWvfx2A9773vVOiiBobG1m3bl3mLxaLTXktGo0SCoU45ZRTuPfeeyetf++993LmmWfO+Nm5rjcTH/3oRzNCVC4RcRWbprdYmM5ktdj4bSNgj0ZFpZox/Ko51zG8KpAGGBKrRGJUqH9cwTXT9Tm3I5SFma7Hj/RDSKJiDmJI+7xoikchx/lCCFEAqtodP044tcGEdh6YbgwZDqpuWkN9uE0dBetTVnguVS8/mXkaGlyJUUivKOHhxPZlntu7a/MSunTKXuGYbRyUcj/P5zgglAhMzMMSWSAxCkBIG2X4GAWaeBUSazgePJAGwp++fPR8car3jRcnOVCNkBV7zzUvXnzxRbq7u2lpaSl1V4pGpW7jQvT7wQcf5FOf+hRf+tKXMq995CMf4YwzzuCKK67gggsuyIg1Tz/99JR0n7/6q7/iVa96Fbfddht///d/T0dHcB5/9tlnufLKK6mtreVXv/oVxx577KT19u3bx0z09fVx0UUX8cgjj/DFL36RT3/605Pe/9WvfsUtt9zChg0b+O1vf5sJNrj22ms577zzOHDgACtXrpy27YceeojPfOYzk1KbAH79619z3XXXsXz5ch599NFM1Md1113HW97yFv73f/+X66+/nquuumrGfs+X+++/n8997nOT/IH+8i//kte//vV86UtfmpTO9cgjj/CVr3yFtWvX8uijj9LY2AjAP/3TP/Ga17yGgwcPzrits5HN95Ptvvna175GKBTiqaeeYsmSJZPa7enpybqvR3LDDTcQj8fnvfyJJ57Im9/85jmX27FjBwAbNkxfqGv9+vXcc889vPDCC5x77rnz/vx8SCaTfOtb38I0Td73vvfl1dYnPvEJ3vnOd/KKV7yCs846i1tvvZUDBw5wxRVXZJa58cYbufHGGyf5W81nveHhYXbu3AkEEY179uzhySefpLGxkRUrVgCBgLx9+3Z++MMf0tDQwKFDQeBITU0NNTXzu7mkxagyYCEFKWVJvKXDo0/mFxUFoxEPbg0yPIjpJDHSCWR4bhGrUBjpZMYvynCr855UBoauQWqA35TC0GKUpsgUYpwvlBAFjEdFMb8UvTEmRlFaw6UTo6r2PIM5GpllpuuwkoW98Hdr9oPhAWB0RzFG8he6tCCVP/MZB2UvSKUsVNgBw8u7ol6mTWmjSGH4LkgJM5RpXmiEm8ZMJYAgRS8fQVdHRWk0AXV1dXz2s5+d9Nqpp57K29/+du644w5+8IMf8O53vxtgWt+ZUCjE0qVLee655/jWt76VmfRfe+21eJ7HBz/4QZqbp/rZLVs2/Zyis7OT17/+9ezatYvt27fz9re/fcoyd9xxBwB///d/PynrJRQKcd111/HKV75yxu1tbW2d1iT6G9/4BgBXX331pPQjy7L413/9V+6++25uv/32gohRK1eu5Oqrr5702ute9zpWrFjBo48+Oun1sUiiv//7v88IUQCRSITrrrtuTh+imbjlllvwPI9/+Id/mCJEweTvJ5d9Y1kWtm1PaXe630K23HDDDXR2ds57+Xe/+93zEqPG0h5nMlMfez0bISxf7rrrLuLxOBdeeCHLly/Pq61LL72U3t5etm7dysGDB9myZQt33333JDGzp6cnI8pls95jjz026bd4zTXXcM011/Dud7+bbdu2AWSi7Y4U8q655poZ012PpDyuRjQLcmGsDIWzsQ+sIAfASi7ByOIu5CTfqMGF9YMpVIreGNbEVL1GnaqnWRjyGeeFFqIUCiVm/t37k8So+nm3azrjd0JK5RtlDvcTOfxy8EQaBU/Pk0Yat3r0mCTBzsEraiZ0yl7uZLPvyjllz0iPh+xLs0C+UXJ8AlFOvlHWhBS9+aQCz4aOitJoAk4++WRisannpXPOOQeAJ554IvPanj17+PCHP8ymTZuoqqpCCMGmTZt47rnnALj11lt5//vfz/vf/35+9KMfAUE6zzvf+c4pnjPTsWPHDs444wwOHDjAT37yk2mFqIl9mk50Ov3007GsmUX5E044gXB46k3lxx9/HIA//dM/nfLehg0bWLZsGS+//HJBfJpOPPHEaU2bly9fTn9//6TXxvr16le/esryr3zlK7Myf57IWKrZfKqzZbtv3v72t5NIJNi8eTMf//jH+eEPf0h3d3dO/ZyO3bt3o5Sa99+YGFKJjKXoffCDH5zX8tu2bZtV2LnyyivZvXs36XSa3//+95x99tmT3v/c5z6HUlOv9+da75xzzplz38/0/cxXiAItRpUVxRSkFAp3fT9qzJDYt7DnGRU1xqSIh6GFTdULTRSjUvmLUUGq3qhKHpaomnIpQq9Z7OQyzgstRMkql/QpXaRPP0jqtIOkT+wifWw3zsY+3DVx3FUDmWOF8CJzFjiYiFB2pqqeNRIHmV+p9lwIH96deRwaWp5V/+eDF+2BUSHPPFiDcAobZKwFqezJZZ+VqyA10cS8YL5RE8Qo4ZSpGJWHebmOitJoxjmyZPsYYxEwYwLDSy+9xMknn8ytt95KW1sb73vf+7j66qu55ppruOSSSwC48MILue2227jttttoamoCgmiaVCo1r2iSF154gYMHD7JmzRpOPvnkGZcb69N0fTdNM/PZs23XTG22t7dP+/7Y64WIipnJw9iyLKSUk16bbVsty8o50mhsO8bSKmcj233ziU98gjvuuIOVK1fyla98hbe85S20trbymte8hsceeyyn/i4EY5FPMwmOY68vlAf1M888wyOPPMKyZct4wxvesCCfWe7oNL0yo1gpe35LEtk4elErDSL9x2DI7FLTTDcWGKsKsBcyMkpK7HhwkSl8uyDVdmAsVS8OBEbmxnDh/GQ0mtnIZpwXIyLKXdefqaaJpVCWD1EfxVRRVuRQat10avCsFEJJzJEB/Fjj3CsVCqUI9Y5W05IGVnLJ7MvngAwNZh6bh4uTrqxT9uZPPufMckzZExMiowonRo2PY8NNs/AS8fTYY35R5Gde7lYd0FFRGs0oM0Usjfm5jE3Q/+3f/o3e3l6++c1vTql89p3vfIe77rqL5uZmNm7cCEBLSwudnZ3TRiHNxBvf+EY2btzIVVddxbnnnsu99947rbA05mHV1dU1pRqX7/v09vbOKLLMVNlsbDsPHTo0bTriwYMHJy03ZnDted607cXj8YKIFmOfN922ep5HT0/PjCmPszHWt/37989pJJ3tvgF417vexbve9S7i8TiPPPIIP/jBD/jGN77B6173Op5//vm8fNCK5Rk19tt94YUXpn3/xRdfBGb2lCo0sxmXH61oMaoMmc+FcbYX36p6fJIZHlyL6WV/0SeUFfhOmO6CVuQRvocYja7I11NiIhPTDlVIzrKkRlN4Sik0qOrxCy3hRVCGC8ZM09PsU1gnmhAb3gJPfKUf+OIQpP0IVfiTvTJHj6cSREpfTGhyZ1pByh0XUpQo0OiZMA5ECaIVZ0K444bqws/du1FNSGfMViDWoq9msfH4448zNDQ0JVXv/vvvB+Ckk04CyJgTX3zxxVPaeOCBB6a8dvrpp/PYY4/x4IMPZtWfz3zmM0SjUT7+8Y9zzjnn8POf/3xKVNBJJ53EE088wUMPPTRFoPnNb34zo0A0GyeddBKPP/44999//xTBZefOnezbt4/Vq1dnRJyGhiDzYu/evVPa2rlzJwMDAwURo04++WQef/xxHnjggSnb+tBDD+H7uR2jx76fn/zkJ3OKUdnum4nU19fzhje8gTe84Q1IKfnGN77Bgw8+OO3vaL4UyzNqzPPonnvuQUo5qaLe0NAQDz/8MFVVVZx++ulZ9zlbUqkU27dvxzRN3vve9xas3ZtvvpkvfelLHDx4kGOPPZYbbriBV73qVXmv9+CDD3L99dfz+9//ngMHDkwrWl933XX8f//f/8eOHTsIh8OcfvrpXHfddWzZsmXe/de3jo5CxlJoKpKCWjsVzkNGo6lEDKeGqu4Tqe46jaqDryDadTKR7uMyZeADchgnR5EFWyG9qDQaTQFQekxqjm4GBgb4whe+MOm1xx57jG9/+9vU1dXxlre8BYBVq1YB4yLVGD/72c+4/fbbp7T7oQ99CMuyuOWWWxgZGaGzs5MdO3Zk/h544IHM4yP52Mc+xi233MIzzzzDq1/9ag4cODDp/Xe9611AUFFuYkqV4zg5G4z/v//3/wDYunXrJH8j3/f51Kc+hZRykiiwadMmamtr+dGPfsThw4czryeTSf76r/86pz5Mx9iE/p/+6Z/o6xvPNEmlUnzmM5/Jud2x7+cf//EfefbZZ6e8P7GaXrb75pe//OW0vkNj+6mqKr8o8WJ5Rq1du5bzzz+f3bt3c9NNN01675prrmFkZIR3vvOdVFdPzrrZtWsXzz//PK5bOBuX733ve/T393PBBRfkbVw+xp133slHP/pRrrrqKp544gnOPPNMLrjgAvbs2ZP3esPDw2zZsoUvf/nLRKPTe0zff//9XHnllTzyyCPcd999WJbFeeedN+l3PRc6Mkqj0Wg0CAyEDIEMYXhRZGi41F3SaDQajUaTJWeffTa33347v/3tbznrrLM4ePAgd955J1JKvva1r2VS4q688kq++c1v8ra3vY23vvWtLF26lKeffpqf/vSnXHLJJdx5552T2t28eTM333wzV1xxBU888QRvf/vbiUajuK7L8PAwpmly/PHHA/Cf//mfU/p1xRVXEIlEeO9738vZZ5/NfffdlykR/+pXv5oPfOADfP3rX+fYY4/l4osvxrZt/ud//oe6ujqWLl06KaplPpx55pn87d/+Lf/yL//Cli1beOtb30p1dTU/+clPePrpp3nlK1/J3/zN32SWt22bj370o/zjP/4jJ510Em95y1vwPI97772XpUuXsnTp0qw+fybOOuss/uqv/oqvfvWrmX7Zts2PfvQjGhoaZvRxmouJ389JJ53Em970JtavX09vby+/+93vqK2t5Ze//GVO++Ytb3kLNTU1nH766axatQqlFL/61a/43e9+xymnnMJ5551XkH1TDG6++WbOPPNM/vqv/5pf/OIXHHPMMfz2t7/ll7/8JRs2bOCf/umfpqxz7rnn0tnZycsvv5wRbQF++MMf8sMf/hAYT3v99a9/nREYm5ubuf7666ftx1iK3gc+8IGCbdu//du/cfnll/P+978fgK9+9av89Kc/5ZZbbuG6667La72x6DdgSkTUGD/72c8mPd++fTt1dXU8/PDDvPGNb5zXNmgxagFIp4Pw8WzCD+dCVjtzLzQBZY2HfEozmfPnZqrOSYk5weuhmAhvXJVWwsO3CjNJVqYz4bHMep/OxnR3hTSzs3LlSiKRmaP2ijGOKp1cfrMT72sp4eNbI1OXmZAaNNMys36GMd4vIzWCORLPtpu544+H8ufS9/mgGE/rLeRx40hyOY4cjeMo3++g1MfrI/uvIhN+w4ZTkHPepDGZHJlkHF5KxITUG2mNkGvAvhIT2qlyEXL+0VHTff/zHUcHDx4s+e9nOsbu5ruuW5b9KwSVuo3F7PdY5EtjYyPf/va3+dd//VduvvlmHMfhmGOO4corr+TEE0/MfG44HGbbtm18+ctf5n/+53/wPI9Nmzbx1a9+lVgsxp133jmlLPzZZ5/Nt7/9bb72ta/xxBNPEI/Hqa2tZePGjVxwwQW86lWv4uDBg+zatWvaPl5++eWEw2He9a53ZQSpsVS1W265hU2bNvG1r32NW2+9laamJt7ylrdw7bXXsmzZsmm9jebin//5nznppJO48cYb+c///E9c12Xt2rVs3bqVT37yk4RCk/1iP//5z1NVVcVtt93G17/+ddra2rjsssv43Oc+x+bNm7P+/Jn48pe/zIYNG7jpppv42te+NmlbTzjhhJzbff/738+WLVu4/vrruf/++/nhD39Ic3Mzxx9/PO973/smLZvNvvniF7/Iz372Mx5//HHuvvtuIpEIK1eu5J//+Z/50Ic+hG3bR3albFi7di2PPfYYn/3sZ/npT3/K3XffTXt7Ox/96Ee55pprMumZ8+HJJ5/kjjvumPTaSy+9xEsvvQQE547pxKjnnnuOhx56qKDG5Y7j8Pvf/55PfepTk14///zzeeSRRwq+3nwYGhpCSpnVPhVqupg7TUG555572Lp1a6m7odGUNbfddlvGaHA69DjSaOZGjyONJn/0ONJo8meucTRfXnzxRTZs2MBll13Gd77znQL0TKOpfA4cOEBHRwcPPPAAZ599dub1L3zhC3z729+eUezOZb2amhpuvPHGGSOkxrjkkkt48cUXeeyxx+Zt0K4joxaAV7ziFVx99dW0t7dPUeBzpbOzk61bt3L11VezcuXKgrRZaMq9j7p/+VPIPs61fjHG0VxUwncwHbrfC0s59btU46ic9kE26H4vHJXU53I8H82HStrH+aC3szLIts+HDh1iyZIlk9LxEokEH/vYxwAyXlcazWLm6quvnjZ1cCJjKYblxCc+8QkeeughHnrooawqBWoxagGor6/n/PPPL0rbK1euLMhdh2JS7n3U/cufhehjMcfRXFTCdzAdut8LSyX0u9jjqBL2wXTofi8cldjnIynl+Wg+LIZ9PB/0di4ubrjhBr7zne9wzjnn0N7ezqFDh/jFL37Bvn37uOCCC3jb295W6i5qNEXnYx/7GO94xztmXWbFihVYloVpmnR1dU16r6uri7a2thnXbW5uzmm92fj4xz/Od7/7XX75y19OqRA5F1qM0mg0Go1Go9FoNBpNyXjta1/LU089xT333ENfXx+WZbFhwwb++q//mo997GMIcXRVyozH49xwww3zWvbyyy+fZLStqVyam5tpbm6e17KnnHIK99577ySh9t577+Xiiy+ecZ1QKJTTejPx0Y9+lDvvvJNf/vKXbNq0Kev1tRil0Wg0Go1Go9FoNJqSce6553LuueeWuhtlQzwe5/Of//y8lj3nnHO0GHUU8olPfIJ3vvOdvOIVr+Css87i1ltv5cCBA1xxxRWZZW688UZuvPFGnn/++azWGx4eZufOnQBIKdmzZw9PPvkkjY2NmSqYH/7wh9m+fTs//OEPaWhoyFQYrKmpoaamZl7boMWoCqWpqYnLL7+cpqamUndlRsq9j7p/+VMJfcyHSt0+3e+FpVL7XUgqdR/ofi8cldjnSuNo2cd6OzVHA6tWrULXGdPMxqWXXkpvby9bt27l4MGDbNmyhbvvvnuSX9uRFTHnu95jjz3Ga17zmszza665hmuuuYZ3v/vdbNu2DYCbb74ZYIqIfM011/C5z31uXtugq+lpNBqNRqPRaDQajUaj0WgWDGPuRTQajUaj0Wg0Go1Go9FoNJrCoMUojUaj0Wg0Go1Go9FoNBrNgqHFqAUglUqxY8cOUqlUqbui0VQsehxpNPmjx5FGkz96HGk0+aPHkUaj0QbmC0BnZyfvf//7ue2229i4cWOpu1MQXmu8be6F8uBe+b2itq+pPBbjODrayfc4oo8T2aPHkeZI9DjMHj2Osud8+7KSfv497ndL+vmaqehxtPDceeedjIyMUF1dzaWXXlrydgrdlqby0GKURqPRaDQajUaj0Wg0i5i77rqL7u5uWlpa8hJ+CtVOodvSVB46TU+j0Wg0Go1Go9FoNBqNRrNgaDFKo9FoNBqNRqPRaDQajUazYOg0PY1Go9EsOMX2ndNoNHOjx+HRwXz9mo5WX6Wx/VOq7S/152s0Gk2p0JFRGo1Go1lQ9ARYoyk9ehweHWRjHF5Mk/FSCi2zffbEbS6FyXqpP1+j0WhKiRajNBqNRrNg6AmwRlN69Dg8OshF3FhsgtR8hajZXisWpf58jUajKTVajNJoNBrNgqAnwBpN6clmHKrRf5rKIx9RY7EIUtkKUfN5r1CU+vM1Go2mHNBilEaj0WiKjhaiNJrSk804lDEH9+Re3JN7kVVeEXulKTSFEDMqXZDKVYjKZplcKfXnazQaTbmgDcw1Go1GU1SOnAArQ+IvHwEB5t5qhK/vi2g0xSYrIao+jXtMHMzgubs5TujJRoSnx2q5U0gR43z7sqIJR/e43y2a4JKvEDVx2UJvf6k/X3N087WvfQ0pJYaR37G8UO0Uui1N5aG/dY1Go9EUjemEKPfYOP7yBP6yBM6Jfchqt0S902iODrIRovymFO7meEaIAiDi4x4TRwmdslfOFEPcqbQIqUIJUfmsU66fr9E0NzezZMkSmpuby6KdQrelqTy0GKXRaDSaojDdBNhfOYKqmyA+RX3cY/UkV6MpFll5RFkSb8NA5urQHKlFeEEQvapz8duTxeiipgCUOq0sVwopSBVaiCrEuuXy+RqNRlOO6DQ9zRTKwdvltcbbuFd+L+d1gZzX12g0+TPTcURZcsITQACmJBeP5EIcq/RxQqM5AjHhoTRBzX7f8mgehx/+k89gjIQyz4uVUjUmRuiUrZkplhBVyDaKhf59aDSaSkVHRmkmUQ5C1Bi59GXiOuW0LRrN0cRsY8+Ij0/cxia9Rl8YMXEGvIDo44RGM47wDKwX6jLisBfrR9lO8N6QhXkwWpTPXSzjsNhpcqUSRIopchRim4otRJWa+W7fYthWTXH58Y9/zJ133smPf/zjsmin0G1pKg8tRmkylOPFYDZ9mm7ZctwmjWYxM9eYmyRGjb12uDgT3PmijxMazThmTwRrR93kaEVXYD9fj1DFE40XyzgstsfQQgsOWogqLdlu32LYZk3xuOOOO7jpppu44447yqKdQrelqTy0GKUByvsicD59m22Zct42jWYxMZ+xJlwTMTwhQ9wV0wpUC40+TmiORlTIR4X8Ka9nBCkJSLB31CHS5tQGCsxiGYfFTgtbKMFBC1GlJdftWwzbrtFojg60GKWpiIu/fMWmSthGjaaSyWaMGf3j4pPRHSlqtEU26OOE5mhC1qdxTu3BObUHvzk15X2zJ0LosWZCjzVjxMML1q/FMg6LbVhdbMFBC1GlJd/tWwz7QKPRLH60GHWUU0kXffmm4VXStmo0lUS2Y8s4HAUf8ME8WFWcTuWIPk5ojgZU2MfdOFo1zwBv48C0gpRwTIRT/IioI1ks4zAXQSCbdYolOGghqrQUavsWw77QlJauri527Ngx5c91g6rIvj81slajyQZdTe8ophIv9iZW2cvV4LxSq/ZoNOVILuPQSFqEHm0BQPjld09EHyc0ixklFO6mONgTTKFEIEhBEBFVDiyWcXi+fdm8xZ1cxatCikdaiCothd6+Qv8+NEcPXV1dvPOd7ySVmnqjYoy+vj4effRR6urqpn2/vr6e1tbWYnVRswjQYtRRSiUKUWPk2/fFcoGr0ZSafMZiOYpQE9HHCc1ixV+aQMU8AIQbwkxV48X6A0FqwwDGoF2SaKjpWCzjcD6CQLHT+uaDFqJKS7G2TwtSmlyIx+OkUimuvvpqVq5cOem9v/mbvyEejwPwqU99asY2IpEI27dv14KUZka0GKXRaDSarKlkQVujqRTmO86yEmwsmXkY6m/DHG5AhlLIcBIMUKaiPFzc5mZs/1S6YFUuQspc/chV0CjU9pXLfqpEtCClyZWVK1eycePGSa/Ztg1AY2Mj//zP/zztep2dnWzdupV4PK7FKM2MlPet6QVEKTX3QppFQaVftGo0pWYhhChlKJQp515Qo1mkFMsTUYzYmcfSTgf/W8H/OAKRLI+oKJj9fD1xmytZHC92xFAhKbYH1tHObPuqEN+x/i40hcY0TTZu3Djt35HRVBrNdBzVYtTvfvc7Hn/8cQCEEFqQOgrQQpRGkx8LMenzW5I4f9KNc1oPsj5d9M/TaMqNXD0R54MxNEGMCieQoSSMCr/GYAgxz7goFfZxjuvDOa4PWeVl3d+5mK8QNdtr5U4lCVFjaAPt4qIFKY1GczSRVZreF7/4RQA+8IEP0NjYmHk+H/7u7/4uu54VmQceeIDPfvazrFy5kk984hOceOKJGUFKiPwC1Ht6eujt7c087+zszLe7iwZl+3irhkGBtTuG8BZOD9VCVGWhx1H5UezJnkLhrxjBXzGSec09Jo79TAPGYKion71Y0eOo8shnnM3LYyltgCvAVvjhBGZkOPOWMTC/caYsiXtsP6oqqKTkHt+H/XwdRjycc98nkq0QNfG9YpzrizGOKlGIGqPYHlhHO7Pt33vc7+a9b3XK3tHLsmXLqK6upqGhoSzaKXRbmsojKzHqJz/5CUII3v72t9PY2MhPfvKTea0nhCgrMWrnzp3cdttt1NXVMTg4yI033shHPvKRgglSP/7xj9m2bVvhOrxIkFUu7uY4RII7sG7Ux366AaGK706hhajKQ4+j8qLoQpRQeOsHkUuOqNpigrs5jv10A8awPf3KmhnR46iyKMQ4m0uQEQjEkI1qdMD0A/PysfcG5h5jSijcY+IZIQoAS+EeG8faFcM8VJVX/3MVoiYuU+hzfqHHUSULUWPMJmhoISp/tCClKQZf/vKXy6qdQrelqTyyEqPuvPNOAJqbmyc9ryTS6TT3338/e/fu5fOf/zypVIpbbrmloILURRddxFlnnZV5PmbgdjQj69O4mwbAGk+FVHUu3oYBrB11804LyAUtRFUmehyVD0UXomwZTG5r3dEXwO5fiowM41cNjk50+7H/2ICR0IJUNuhxVDkUcpzNJcgYwzZ+owMQGJcDuAKRmP2yUKHwNgyg6oKxKjwLI12FXz0YVONbN4SK+Ji7a3I6r+crRE1ctpDn/kKOo8UgRI0xnaChhajJKEPidQTRh9b+GoScf0aAFqQ0Gs1iJysxqq2tbdbnlUA4HCYUCnHxxRdzzjnnAIFA9Y1vfKNgglRzc3NGsDvaUShkWxJv7RBj16VGOoK0HTAksiWNnx7G7KwpSoSUFqIqFz2OyoNiC1Ey6uEe25+JmEQahLtXYSXqUIMtpFp3IaPDYCvcLXHsZ+oxRrQgNV/0OKoMijHOZhNkxPDUyz9j0J5VQFIo/FXDyJZRHzdpED60BsOJ4jYewK3vBsBflkBF/eBGk5z/eb1QQtTEdQp1DVCocbSYhKgxJgoaWoiajDIkzjF9qNpR4bfWIfRcoxakNBqNZpSiGfY4joPnFd7QMh/GDMrf9a538cEPfjDz+pve9Cbe+9730t3dzY033siTTz45aXlN9igUfmMa98Q+vHXjQpQ5XEdk/3rCXStgdPf6yxI4p3XjrRlE1rgoCrPftRCl0eRHsYUovyWJe2JfRogSnk3k4HqsRF3wXBlEutZgpEbTfkIS96Q+nOP78NsSutqeZlFQzHE2Y9uhqWNHNjk4p/TgbhjAb0sgq4PzsYp4eCuHcU7rwV+WCBZWED68EtOpQiAI9XUQ6l6WOa/LpjTOyb3zLkBQaCGqEOsWmsUoRI1xvn2ZFqKmwV0zkBGiAFStg7dqMOt2tKm5RqNZrGQVGXUkTz75JL///e+55JJLiMViAAwMDLB161Yee+wxLMvi4osv5oorrihIZ/NlYpRTOByYbEopMQyDiy66CID/+I//4MYbb+TKK6/k5JNPBmDXrl0ArF27doF7XHkoUyIbHPxlI6iayWKk3d+C3deOQARRDz0dOC37gzdDCn9pEn9pEpEwMfrDQcqAb4AnwDMQY//7AhyjqKl9szF2cavFLo0me5QpkXUOsiU1HmEBGOko4a41GP5kA2WhTCJda0m17cykE6laF6/WhdVDmIeqMPdVIdzCl6PPJ7JCHyfKg3L/HhZaLFGmxFs3OGnsTXo/6qOi/rh3mw9MM7RCPcsyovEY9lAzwguRbt0NhoSIj7sljnE4grWzdtYoqXISjcoZLRpUDjKWRrYkp7zutySw9sayPmeV83c/Xd/KVTg92vnCF77AwMAAdXV1fPazny15O4VuS1N55CVGffe736Wzs5P3vve9mdduuukmHn30UTo6Okgmk3z3u99lw4YN/Omf/mnenc2F/fv3MzAwwL59+zjhhBNoaGggFAplUvAMw5giSN1+++3ceuutXHnllYRCIW644QZWrVrFJz/5yYyIpQlQlkTWOqg6F1nnoKo9jtSIjHQUu68NK1E76XV7sBnDDePW9ga+MEZwS1VV+fhVidk/OGVg76jDGJq58k8xTEwnXjAXq2qPRrOYUIZC1TrIeic4RtRMPUZYQ42Eepch1PQX50JaRA6ux4v14Mb6UKHRibIJfkcCvz2BcSiKta8a4RRWlMplnOvjRHlQ7t/DQgswss7B3TAA4fGoKHOoAWukDj8ygoyMBIKvmBCdPHE4KTATddgDzZip2LSfYSVrMfZvIN28FxkNqmLKJSk8U2I9V1+ym0jFQsbSqFBg5C5SFkZSpxBrguwAd/VA5nloYA3KTOHWHAADvNYR7H21s7RQOZSzSKaZylNPPUV3dzctLS1l0U6h29JUHnmJUS+++CKnnHJK5nk6neaXv/wlp512Gtdffz2JRILLL7+cH/3oRyURo+677z6+9a1vsWfPHlzXpbW1lYsvvpiLLrqIaDSaWe5IQcowDG677Tb+9V//Fc/zOHz4MJ/+9Ke1EEVwglX1DrLBmVF8GiMQoVoxE7UzXoCayRhmMoYyfLzqOF6sP3MBOysRibulH/u5+llLSRdyAjLdxKEcJzgaTalRIR9/SQrZkEbF3JkTwqVBuGc51kjjnG0KZWIPtmINLkGGknixXrya3kDENkAuTeK0JTG6RkWpdOFEqWzGuT5OlAfl/j0spBClROD15HdMuMnjm4R7lmGNBKW0rUR9sCwSGU4iw4lAoAonQBpYww1Yw40Y/txii+FGiBxchxfrw2naH/hDNjl46waDCKlFJEh5q4fGnyiwd9Zj9uRXTVBT+fhLEsH1MWC41VjJJSjDwa0+AAL81gTW/tiCVJMuJlqI0mg0+ZKXGDU4ODhJxXzmmWdwHIcLLrgAgKqqKs4880weeOCB/HqZA/fffz/XXnst5513HpdeeinNzc187WtfY9u2baxcuZLTTz99kkG5YRh4nodlWfzZn/0Ze/bs4c4776SmpobbbruNNWvWLPg2lBuy2sVbM5SpojMFBYYTxUhWYyZqMZPzr6YjpIk91IQ91IQ0XZSdRhk+yvDB8FHm+GMZTgV3b0dLvls76jB7IzO2XYgJyGwTh3Ka4Gg0pUKhApG6LYlsTM8oUgsnkhGhzVTNjNFQMyEQmE4VZm8VdrwNr/Ywbm1PkBZkgGxP4rQmMbojmF1RJlnQHdEnkTTnnSoxn3GujxPlQbl/DwspRMlqF2/DYGZiDGAkawgfXjElJRZAYGCmqzHT1diDud+lFgjsoSYMN0Sq/SUQCtmWwndMrD01Obdb1ghw18XBNzD7Z74myRZZm8ZdHohe9t4YxqC+MVrOKFPirRgXKUODqxAIhAxjphvxI30QksimZEULl1qI0mg0hSAvMSocDpNIjN9pe+KJJxBCcOKJJ2Zei0ajDA0NTbN28ejs7ORb3/oW5557Lpdffnmm6t8NN9zAO97xDu6++25OP/30KZXyLMtCKcXjjz/Ok08+SSwW48Ybb2TVqlUL2v9yQ4V8vJXDyNbUEW9MEJ+SNZipaoTM6ycFENx5neXuq0KSbt2DXzMQhDtvGoAXFebh6IzrFML7Za5lSj3B0WhKgQr5+K1J/NbkeEW8CQg3PHp8iAUCtSxcGovh24T6O7AHluDWdePWdo+LUq2pqcesKZ0H8+UazANV8xLOZxvn+jhRHpT791BsIUoZKoharneCyMQqf/xNKQj1t2MNtCxYdJKZihE+vIL0ks4gImTFCMI1MA9W7iR8ItZIG0KGkNYIfrQ3EKQ29MFzTZh5ikbK9nFXDk7yHXKO7cXojmJ31hbFJ68UyLCHv3QYIPBS8ip7u7zlQ2AH50Iz2YTpjqfj2SPtgRgFeO0jGD3RiowU1EKURqMpFHkpBx0dHfz2t7/FcRyEEPziF79g5cqVNDU1ZZbp6uqioaEh745mwx//+Ef27t3Le9/73owQ5bouVVVVnHbaafzhD39gcHCQWCw2RZAaGhrigQce4OWXX+aWW245qoUoZUj8ZQn8jpFJvhHCCRHqb8NMxAoiPmWLwCDctRJH7sWr7QdBcOfXklgHqmdcL1/vl/ksqyeamqMFWZfGX5pANjpTI448G2uoKUjr8Yp/F19Im1D/0kCUqh0VpUx/HiuCv2YYFXOxXqydV7nt6ca5Pk6UB+X+PRRDiFIoVLUXRCXWp1G106fFGukI4cMrMdyZb9oUC2ukAdXr4TQHBUu8NUPgGpg9hYseKhVWsgXTq0GhSCPwoz1ggLuxD/FsE8bIzL6WM6GEwm8bwVs2BNbU6sKyJUm6IYW1L4Z5qLpiU72UofCXDuN1DGV+s35jCntXPWa8Mn8bMurit41aTUiD0NDKSe8bbgzDrUbaI6gaFxVzEEOVFemmhSiNRlNI5r7ynoU3vvGN7N+/n7/4i7/gne98JwcOHOANb3jDpGVeeOGFBRd0lixZwqmnnsoZZ5yRec22g7vxGzZsoKenh1QqNUWIAqitreWVr3wld9xxB+vWrVuwPpcTCoW/JIlzSi/+iglClG8S6llKdO9GrOGGkghRYwgEoe7lWPHmzGv+mmG8FcMopl68jZHtZCVbdEUgzdGAt3QE97g4smmCEKXATNQSPrSG6N5jCcXbF0SImoiQFqF4O1V7jyXU24E10HLE3xLsePBnDY37VMmWNO4Jfcgqb5bWxznSGDtb9HGi8JT791CMz5J1aZxTe3BP6sNfNYyqP0KIUmCkqrB724ns31ASIWoMe7AFu781eCLA2zCArJ++ol8lIhCEB9ZipuqDFyyFc0wfMjqDrcEMyFga5/huvFWD40KUtAgNrCE0sAbGrrsshbdqEOf4bmQs9/0oY2mcdf2kTj1I6uRDeB1DqND8joO5olD4jUnSJxwOoogm/mZDEveYPtzVcZQxNdK2GMhYmvSJXaROOYS7Jo5fn0KJma8jp0PZPjKWxl0zkDkn2iMdGHLyOVAgsEbaM8+9jmFk2Jv1urWc0EKURqMpNHmpCRdeeCF79+7l7rvvxnEc3vSmN/G2t41fcD399NPs3buXCy+8MO+OZsMrXvEKNm/eDDDJFwogFovhOM6UdcYMzMfWP5qRzSm8DYPjLyiwBpoJ9beWVIA6EoEg1LsUIU3cxi4gSAHAE3lHSOUzcdCRD5rFjIx4+GuGM88zUVBDTdN60JSCwOx8yZzLmYk60i2dYEhUtY97Ui9GfwhzfzViwJ41fSJfcUEfJwpHuR+viyJERTzcLfGpUYluKPBjS4x6spXROdvub0OZLl5tXxA9tCWOGLQxu6IYvWGEl9f90ZIjMAjHN5BqfA4ZClK1nGN6CT/djHDm/h5kLI2zuXdcnFFgJZcQGlqBUMENVSvViBPbgxc9DAJUlYezuZfQs00YWUbY+I1J3A39E35DCm/FEN6KIUTCwoiHMfojGEOhgkVfyaiLt2oAWT/hOlwJrEQbykzhR/qDvrUl8JtSWPtqMLuKF/3l16dwN/aNR2a1JvBbE6ORe1HMnihieOq5QIV8ZG0aWesga9Oo6ORIXOGFsUeWTvuZVqoJ1+9EmS6yIY3TcBhkUI0Rz0B4BsI1gsdu8BzXwBixS5qaqYUojUZTDPK6ShFC8KEPfYgPfehD076/ceNG/u///o9IZOHDbWtqAoPMI6OfxgzXBwYGWLIkmKzs2bOHX/3qV1x44YXU19cvaD9LwVwXxqp2/E6eORIj1NuB4ZZnGLFAEOpvQ0gTp/kAAP7qYYwRG2Ng5olxse+I64mmptzJdQwcOSUIH16Fma5MQ2IrUY9xIEJqycuoUAoEyEYH2eggRizMfVUY3ZGK9PTQLG4mVbKVglBvB2YytuDRiNkgEIR6lqNMD786uOGlal28WhfWghgIYfaEMXojBRWmFvJ8LDCJ9G8i1fgM0k5AWAZi0dPNs3ohKUvirO/PiCKGW01oYA2mN/nYKpRNeHAtVnIJTu3LSHsEDHDW9xP+w5J57zdlStzVA1MP6GPvV3n4VR7+0hHwBcZAGDFiBcsLYCxyaOyxICgWMVFA8Sb8LwVe+3CQwjahi0a6jvDgKgy/CoXCi3bh1HaCkGBLvNWDeEuHg5TE7qrgM4zRzzQUylCjFVUVIm1l9bs5UoiahC3x20fw20cQSQuzOwqOiRoVoFRk9jTw0NAqxAzJJwIDK9GOG9szYUcE+xyYOUZKgv1CA2Z/cSIcz7cv4x73u0VpW6PRaGaiqLfMbNvOpMeVC+FwcKEmZRD+u3v3bm666Saef/55zj333FJ2bUGYzwRUhcZDo0M9yzC88oh2mA17oAVlergNwd1Cd1Mc+/n6WQWpYqMFKU25ko8YK1IW5p7qIAoR8KrjFStGQVCGPnpgA25tD15tN8oKxHhV7eFtHMSM+Fh7K3f7jgbuld/L+TddscfoI87T9nDTLAuXDwJBuGs1XqwXr7YHGR4tMmCAanDwGhxYNzRZmHLzF6YWVJBSFpH+zSQbn0ZZKVTUxzmmj9CzTQg/2BZlyOBaK+SjbIm/ZATCwXdqpGuJ9G+eVQQ33RiR3uNINTyLDA9CWOKujWPvaJiXeO6tGMz8hsxUPeH4RpSZxov04IfjSHt4XKgyFbIxBY0zt5ctwgsTGlqFmR7vr0BgJ9swnTqcmr2BITxAWOKtHcBbM7N4BoAE80AN1v4YQs6+D44UosxkE+HBtfihgWAfRPoygpuKepOq401BCQy3BsOtxvDDGG7NJNPy6bBHlmJ4UaQ9jLSSSCuJMhwwZhG5DHA39COeNbKOgpsvswlS97jf1dFRGo2m4BREjDp48CD33nsvO3fuZGRkhOrqatavX895551He3v73A0UgCPT8WZiTIRSSnHgwAFuueUW/vCHP3DjjTdmzM4XK/O9WFfh0ZOhClJwKgW7rw0ZTuJXDYGtcI/rxzgUwXo5lrkAXGi0IKUpNwoRFWjur8JfPhIYgEcH516hzBHKJDTQij2wBL86jlt7GBkJKsX6K0cQjonZVTq/Hc3c5CJIVfKxWdnjYpSYpfpsOSIQ2EPNWENNyHACrzqOXx1H2e7YAqh6B6/egbVDGL3hoMBAnufxBRWkpE2kbzOppqdRpoOqcUkf341Qozf8zBniX6RFeGD9vASlwKdqHcnmP4DhIRtTuOv7sXfVz1qMQdY4QSoaBCbbg2sQGAg/SmhkOYwsRwkXPzyAH+7HC8XBLJCPlBLYwx3YI0sRTB8pZvhRIgMb8EdGcGv2ZlL35twlBvjLhpHNSayX6jEHphdsphWiRve5lW7ESjeiBj28SC9etDtIuTxiGww3hunUYjq1GE7NjNsyE2OfRXqywqeQKMMDw0UZHmr0fz8UD/aDAc6mPkLPNGMkijPutSC1uPmzP/uzzFy9HNopdFuayiNvMep73/set956K77vo9T4yfWBBx5g27ZtXHHFFZN8pApFIpEgnU5TVVVFOBxGCDEvQWrsh/7EE0/w1FNP8eSTT3LTTTcterPybC7SxyKjhG9VVHpKcMd1Bam23choELUh21I4jQ7Wrhhmb2mqs2hBSlMuFCo9VfgGYshG1bqoUBppOmXjF5UPgblsA9ZIA05dF25jkPrrrRsEx8DsL98UKE12glSlH5Mni1Hl4wuVDQKBma7GTFej+pYGN5Oq43jVcZTtjC2EbE7jySHsF+ry/syFPB8bMkyk7xiSTc+A4UHEn92mWkE4vg5Dzv9Yasgw4YF1pOufH91XKZxIL6EdjQhnqkCihMJdE88IO6Hh5VNMtiFIB7RSzVipZkIopDWCMtzRa0IBavL/QZaeGhVSRkUU4Y0KKsFrwg9jD0819Z4J06vGjG/Ct4dwq/ejzDQoA5QRpMApA5RAKCOoQBjpBaFQER93cy9+dxR7d+2k9MjZhKjJ229hJ1uxk61IM4Uf7kMJhenEMNyaGVPw8kVgIGQIjvgNWIklpBt24Ifjo+b4QeqnkZ7f2B8zSJ/vNX0hBKlkMkk0qm/ilBvvec97yqqdQrelqTzyuoJ55JFHuPHGG6mrq+OSSy7hpJNOoqmpib6+Ph5//HHuuusubrrpJpYtWzapsl2+/PKXv+QHP/gBnZ2dNDc3s3btWj7wgQ/Q3Nw857o1NTVEIhHuvPNOkskkN954oxaiJqCEyoRuiwpIzzsSIS0iB9bi1fbiNB0EQ0JI4h0zgOxJYb0Um/YCrdhoQUpTagrtk2b0h/BH/eX8qkGMobmPv5WEPbAkMFuu6w6qf22KI/7YiDFcWVEoRxvzEaQWxbE4VPli1EQCYaoKM12F3deODAXClFvXA4ZELknh94Uxe/K/qbSggpRfRaTvGFINz4PpgjQR0kb4oeB/GcIYfRykeVVl/RlWugHiG0nXvRgUY6hxSR/XTeiFhinpXH7bSOA3BhhuFVZi7uwFgZjiXbWQmG4MM75pzuXkcAfpupcykUyyJUm6PoXVWYfZHUXWp+clRB2J4UcwEtObkS8UGXP8hmeRoeGg6uCYF9kcpuZ+fQpv9QAQeE4ZI/O7ts9XkEqlUsWP/JUAAQAASURBVFqM0mg0c5KXtH/nnXdSW1vL7bffzjve8Q6OPfZY2tra2Lx5M+94xzu47bbbiMVi3HnnnYXqL/fffz//+I//SHV1NW984xtpa2vj17/+NVdccQU///nPGRkZmXFdpRTpdJpUKsXw8DC33nor69evL1jfypGsJ6AhmbljVkkpehMRCOzBZqJ7NmKOjOfty+Y0zsm9+K2JkpTR1eXcNaWiGL89Y0KU0GJI1TsSgSDU14E5XB+8YIJ7bD8yUtyy55r8mU1oWBRCFIsjMmomBALTqSLUv5Rw9/LM697aQVRoduPo+bKQ52PTq6Gq+xSqDr2C6sOvoKrnJKL9xxIZ2EB4aBV2YilWqiUnIWoMK91ItO84xJiBfSgwTveWjF8Ty7CHt3w05Uwxmp5XOdHvcxEIf8cSGlgDclSgsRXeujjOlp6chKhyQqjAHF94gcCjIj7OMb0oU067vBIKd8Ug7jF9qIgfLL+5F1k9taL4TMwmOM1ldl6K4lUajabyyOsK5sUXX+S8887LVKU7ktbWVl7zmtfw85//PJ+PAQIhKZlM8uMf/5gzzjiDj3/84zQ3N+M4Djt27OC2227j+uuv5//9v//HBRdcQCwWm9KGEIJly5Zx7bXXsnTpUlavXp13v8qZXC62Mn5RVK4YNYbhhwgfWoVfPUC6eT9YHlgKb/0QoiWF/Xz9gpeS1hFSmoWmWJMuMWyBK8BW+JFhFKqiLuzng0AQ7llJyvSQ0eHAi25LnNBTDSUtsa2Zm+kipBbTsTcjRvlm0VKGygFrpAFveAC/Jh6Mv/WD2M/UF+RYU4jzcarpj8EDAWaqkdDgqunT3hCQpa9QthheFdHe40jVvxCYmhvgrR1AVbuYPVHcVYMZryor0YbpTr1OrnQCE/RWrHQD6VgnfrQHABWbUCW6AoWoMYSyg0i7MS+yag93Yx/2c00IFWyPQqGqPNzVA6jaI4QnSwXVHZ9tKnqElI6Kqgy6urqIx+PTvtfZ2bmwndEcleQlRrmuO+fBJhqN4rrurMvMByEElmVx8OBBTjrpJJqbm1FKEQqFOO644/jCF77AF7/4RW6//Xai0Sjnn39+pnLevn37eP755znvvPMAOOuss/LuT7mT6wR0LHwbwKhwMQrGPGDqMZM1OE0H8GoDI0xV7+IeE8f+4/wqzxQSLUhpFopi3v0XiMA3qtEB00eGE5jpxWc+KZRB5PBqku0vokIpiPh464awn6svddc0czBRkFoMx1xlSGRzGr81CdHgxtFii4qajnDPMpKREZTlBhX31g1h7YplJt/5kPf5eEIX/EgfaeER7T82737l3B1lE+nfjBPbjVd9KOhXWwK/LTG+jG8TGl4+UxOLAiFDRAbW4yVbcGpfQllpoLKFqDGO9CKTdQ7Ocd2Yh6qRtQ6yLj0pjTcwjV+OH+4PUhgthbOlB2tPLebB6nntC21qvni4+OKL6e7upqWlhZtvvpl3vvOdpFKpGZePRCLU19fP2s73v//9gvUp37Y0lUdeVzHLly/n4Ycf5v3vfz+WNbUpz/N45JFHWL68MCc9z/MIhUIMDQVhxkoplFIYhkF9fT1XX301n/vc57j99tvZsGEDGzduJJFI8O1vf5u7776bvr4+LrnkkoL0pZiUKp1LhX28lcOZ50Y695DxckNIi3D3CqzhBtJL9qAsD1XnIluTmF2LZzs1lUOhxnmpJtl+QxrVMH7XdbbqTZWOkBaRQ2tJduwA00M2pFGmrKjqXprSkO84VyhUzMVvTSKb02BNTjE3F9F5eiaEtAh1Lyfd9lJg0t2WxA372M/XFaRSbmEj6EovcggE4aHVGF4VTu3LICb8ZsbS89TiFzEBLKces+cEvKpuUGAlWytaiBrD8KuI9G8i1fBs4BNW7eGtHZiynPDDhOPrMd0YKtFGquG5QJAywFs1iN+YxN7VgJGa+/egBafFRzweJ5VKcfXVV7Ny5cppl6mvr6e1tXWBe6Y5msjrLP66172OvXv38qlPfYodO3ZMeu/555/nb//2b9m7dy+vf/3r8+rkGFVVVVxwwQU8+OCD/OpXv8IwDAzDyFTxq6mp4a//+q+JxWLccMMNmXXOPvtstmzZwmmnnVaQfhSTkglRKNyNA5kLXWuoATNVOsPKYmEmY4QPr8g891YNT/LeWAj05FNTyHG+0McMFfLxVgzjbRzIzLvs/jYMd3GH5Bt+CGukfvQJyPr5+27MhvaSKx4T920p9nPeQlTYxz2xD/eEfmRbapIQJZwwod6lhHoWd4TLGFaylvDhlaPV20A1OLjH9xfMQypXjFQdZrIJM9mEPdxBJF4+PqR2spVozwmEBldiJVqxRtqJ9G3BSjeWumsLisDETrRhJ9sWhRA1hunGiPRtQRx57pUGZqqB0OAqoj3HZ9IxhTKJ9B2DNdLOmG2qqnVxTjiM1z5cEi9VTXmwcuVKNm7cOO2fFqI0xSavWyNvfetbeeqpp3j44Yf54Ac/SDgcpqGhgf7+ftLpNEopXvnKV/LWt761UP3lta99Lffeey9f//rXqaur4/jjj0cIgVIKIQTt7e284Q1v4Bvf+AZPPfUUJ5xwAmeccQYnn3xyJm2vXCnlpMRfPoIarYwl3BCh7o6S9aXYmMkY5lA9fiwemFuuLkzJ6PmghShNMcZ5sSNsFApV5+C3J5FN6Uk3/82ROux4W9E+u5wwE7V4tYEHiWxIY/YWxqBVR0gVnunG2ULu57yFKNvH3dKPik4QW6SBNdyANdSIka5aVBPr+WCNNCAO2qRaXwbTR1V7OCf0YT9bjzFSGluB0PCKklaamwvDj2IkFveNgqMZ06sm2ns8blVQPdpM12G4NTP6yAlMwkOrsFKNpOt2oazUhCipFKEdjQvuparRaI5u8jrimKbJtddey2c+8xlOPPFEbNumq6sL27Y56aSTuOqqq/inf/onDKNwB7ampiY+85nPMDg4yNe//nWefvppIPCU8jwP27Y577zzcF2Xrq6uzHpaiJoZGXPwV4xWXFEQ7lqBUIvbnDfcuxT8YBvlklSQY19k9GRTU8xxXoy2lSnx2xO4J/fiHhcP0oTG5r8KrKFGwt0rj5pJsZmKgQy2VTY6Bb2TrCOkCsds+3Ih9nPeQpQpcY+NZ4Qo4YYIHV5BVeexhHuWY6bn5/OyGDFTNUQPrEe4o+bLYYl7XD9+Q/HP4RpNOSIwCCU6CA0vx3Rr51XQwHRrifYcf0SUlIOzsQ8ldISURqNZOAqSNP7617++YKl482HdunVcd911/M3f/A1f+cpXeN/73scrXvGKjG/VSy+9RCwWo65uYaJd8qWUkxBlyiA9L5Nu01p0E2IlRi+wSyh4Cd8m1NuOs2QfQGBI/HioIIao06GFKM1CTYIL8VtTKPyVw/jtySkeNcKzsIaasYaaMfzKL3KQDUIZmKkYftUghAKfDlHAiAwdIZU/8xlnxdzPeQtRhsLdHEfVBMVEhGcTObAOw59f5aujAcONEN2/gVTbS8hIIqiSuzkOu2KYhxa/h5ZGUwgmRUnVv4AyXVStg7s2jr2zMBUrNRqNZi4qNhZz8+bN/Nu//RtDQ0P8wz/8A//5n//JM888w8MPP8wPfvADotEoa9asKXU356TUd8O9tUMQCTyTjGQVdn/xcoOl5ZBu3kdi9TMkVj2DU9+FYmH9miZiDTViJIMLVxX18ZePFOVz9ORSs5DjvBCf5bcn8ZcnJglRRrKGcNcqonu3EIq3H3VC1BhmYvwmh2wsfDRGqc8JlUw2+65Y6bL5oITC2xRH1Y1WIPZNIgfXllSIUoaHtByUKN25ejqEtIgcXIc5PDoeRXBTyVs1pL1vNJosMN1awv2bQAVTQtmSxF82PMdaGo1GUxjyiox65JFH+L//+z8+/vGP09zcPOX9np4e/v3f/503vvGNnH766fl81LRs3LiR66+/nm3btnHHHXewbds2qqqqiMVifPGLX6SlpaXgn1lISj3p8FuSyCWj5Tx9g/DhwqfbKCGR4QRerB8v1j9e1UWA23QIL9ZPqHcpZiK24HdhBCIoGb3sBRDgLxvB6I5gJAtXZUYLUZpSGSfn+ttTQuF3jAuz1mAT9mBLyQzKFQqERBkShD/6/+iftDCc6IIeO8xEbeaxbEzD3sL7xegIqezJZZwVcj8Xomqet34Q2ThqjC+NQIhyC+NLNu9+CB8ZTuJHRvCrBpDhxHhqrm8ifBvhWwjPxvCt4LkTwUyW4ByuDMKHV+F6B3HrDwddXJZARXysF+oQUkd2aDTzwfRqCMfXka4Proe95UOIlInZoyMNNRpNcclr1v2DH/yAnp6eaYUogObmZg4dOsQPfvCDoohRAB0dHfzd3/0df/EXf8FLL71EXV0dq1evnrFP5UKphShl+0FU1CjhnmUYXv53XxUKv2oQv2oIGUkgQ8kplY6VYYKUCBQqlCbd/jL4BmaiFmukFjNRu2ApfIYTxY634DZ0gwH+qmGM5+oL0raeTGpKOc5z/WzZmsxES5qJWsK9K+ZYozAoFMpOBZPgyDAyPIKy3MllyadBeDZmohYzUYeZiiFUcQN+DT+EkY4gwylUzEPZEuEW/jO1IDV/8hlnhdjPhRjnsnXCzSEpiBxajekUdyKoUMjIMH4kgQwlkOEkyp6lSqTpo0w/E3c0sY6d8Cys4cYg4ngBBTSBINS3FOGGcJr3gQDZnMZPDmN1xhasHxpNpWOlm1BDK3FqOwFw1wxg9EcQfsUm0Wg0mgogLzFq586dnHnmmbMuc8wxx/DrX/86n4+ZE9M0WbNmTUWk5ZUL3qrhTAqOOdSANdyQd5sKhdO8H6+ud9r3pWmRWrqW5NK1GOkkNbuexB7qD940JX4sHlS4UwJzpJZQT8eCpALZ/W14sX6U5SHr0yih8vaO0pNITSWibD84Noxix4uXtqtQyHACGR7OCFCY2ZdpV5aLV9uLV9sLUgTVMpN1mIm6oh0/jFQMGQ6EAxX1xs2UNQtOIYSg+bRR7GO63zKe8hk+vDIwyy8CwbhL4tX041cH571Z+xWtxY/UYLgphJsO/pdTx6myPNz6w7j1hzFS1VhDjVjD9Qt2Y8keakZIk3RrMJFW4fJKK9RoKgEzXQ+qM7iJrI3MNRrNApCXGDU0NER9ff2sy9TV1TEwMJDPxyxK7pXfK1nUhIw5yNax9DyTcM/SvNtUwifduge/enD8NcCvqsWLNeDGGnCaO1BWMGnz7TADx7+aUO+B4K+vC8Mf9ckQCr9mgFQ4QfjQakynuOlBQhmYiRhebT+YoGpdxICeXGryp5TjPBe8NUMZkdoaasRMF6dkuR9KkG59KYh8mgElDPxoDGWaKMNCmSYYFsowR18zsUYGsAe6EWp04mko/OrB0ePQ3sDnqndZwVMMJ0aR+q1JxKCtzV5LwEJ7sRVLkFKmRNUGEUnCDU3yJSsU0k7h1fTj1fTPGP2khIFfVTf6V49XuwQZPqKgiVIgPQw3heGkEG4Su/8g9sBBhAqOHTIyghMZwWnajzVcjzXUjJkufrqPmCA+C0dHc2g02aBQpOteymQzmAdqdFTUIuTqq6/GdV1sO7+bdYVqp9BtaSqPvMSouro69u7dO+sye/fuJRbTodLTUYqJqkJNSs8L9bUhZH4eScp0SbXtDqraAEoIhteeOCo+zXJgEQKnuQOnuQOkxB7sIdR7kHDPPgzXQdkuqY6dhLtWYBXh4nwiZnJUjAJknYOhxShNgagUQcpvTCPHojN8i1BfR3E+J5Qg1bZzShSUNG282ibcWBNebRNedQMY87gQ9j3sgW5C/QcJ9R/CcMcjTGR0mGTHDuz+duyBJQUTjMxEHTQeAKGQrSn8tIm1pzjCnWZ6Ks2LbTZkvZMpJ2Mmagv2O1UovFgfXm0PMpyc+r4wcOtaceuX4lfXIyM1IOYYc0KAaSNNGxkJru3cphUIN02odw+hnk7M1Og1hiHxavvwavuw+9qx44Ubg9OhzAnithajNJqs8KLdyFAwdkXKxNqv526LkZNOOinzeMeOHQVpZy46OztnfK++vj6rtjSLj7xUiBNOOIEHH3yQXbt2sXbt2inv79y5k4cffpizzz47n49Z1CzkRFXZPu7GwUzJaCMdwRpsyqtNaadJtb+UudMqTYuhY07Hrc/SPN4wcOuX4NYvIbF8I7XP/SZI4TMk6fbdyN62ol7IGsnxiaSsd2BPUT5Gc5RS7oKUMiXemvGoxnBvR94i9XQcKUR51fWklqzEq23Gj8aCiW62mBZuYztuYzsjSmGOxAn1HSTcuw8zNQJC4TYewK8aINy9EsML570dhhcmfHgl6SW7g+IHK0YQroF5UJu9LgSl9mIrtCA1sSpjIW+8uPVduI2HJr2mAC/WjNu4HLdhaSZaOV+UHSbdtp506zrMkX5CvZ2E+vYh/OB6w208iLLShHqWF+08PjHSUjgLkx6oKQ+U8FCGG5y3lImYR7HwoDiGjxI+yvCCzDQ/BMqa8zeqhIu0kkgrMfqXBKEQ0kJIe+qfbyP8MILy/F0q4eLExgUD6yVdAECTP/X19UQiEbZu3TrjMpFIhO3bt9PaWjxbCE15k9ds4+1vfzsPPvggH/7wh7nssss49dRTaWlpobu7m9/97nfceeedCCF4xzveUaj+LkoWYqIqax3cjQMw5qOgINSzLK+LQj88Qqr95czE0g9FGTz2DPzq/C6mVSjCwJZXUbPzcSLd+4Cg8p4KpQkdXjavi4xsMXwb4YRRoTQq5qJMqcOTNQWlnAUpf8VIxrTcSMYwR/L3kJvyGUcIUW6sicFjzgSzgKKXEPg1DSRrGkgu20jV3ueIHHgRQZA6lOx4nlBfB9ZQU94TYivRgOr1AtNkRlMcHQOzd2Grnx1tlMMYKqQgpVDjYpQ0MJLVs68wT7zoIG7DuBDlVdXjNi7DaVyGChUx9V0I/JpGkjWNJJcdR7hrJ9EDzwV9qO1DWi6RrlVF8ZKSk8Soo+v8rYQPShTl+qhcUSj8cBwv2oUf7p9cLEcawW9MWghlIaQJYky0Cv4Q3pQCO6MNj4pIoUBEGn2shI+0EigrOTkKb94dNrCSLdgj7Rh+aarTzoQT6wRj9EZ1TwRzQJ/HNPnT2trK9u3bicfj077f2dnJ1q1bicfjWow6islrFrB27VquvvpqrrvuOrZt28a2bdsy7ymliEajXHPNNdNGTWkmU6yJqkLhL0vgrxzOnHSFZxHuWomZyv2i16saCIxCDTX6vJbBY89Ehgt0gjVNhjecil9VS3Xns8FnxPqRVjq4kC2CMbGZiOGF0kE1njoHs0+fjDWFpRwFKVnj4i9NjD4RhIsQuTBViGpk8JgzCitEHYlhkli5BaehjZqdv8dMJ8CQOM178asGCPWsyNvg3B5qQVkubn1XUA574wDiGUOn+RaJcho7hRKkVMwFe7SYSCJWEDFBWmnSSzoz5/xkx2bS7RvzbjdrTIv00k3ISA1VL/8eoSSyaohkx4tEDq7B8As7TiYKBCJdOaKMtBI4NXuQVnJU+LAm/Y8a/R+JMh2U4aJMB2k4o88dMAIxynBrMJ1aTKcWw40tmIF8NigkykwjrRTKTCHNFMpKjfa/OtgGtwahpj8+SzOFFz2MFz08syhkSBQSTJesbbhF8FtSpguFvNQUEq+qC6+qCzNdjzXSjunUldxv0LeG8aq6gyeewN5dXFsMTWl54oknMv5MVVW5R3NPbGe2NLvW1tY5haYdO3YwMDAwZ1uaxUneM4FzzjmHE044gZ/85Cc8//zzDA8PE4vF2LRpExdccMGcBueacQo9UVWWxNswgGwcNys1EjVEDq/IS8xxa3twmvdnLnSduhaGjvmT2f2hckEIkss34kdriL3we4T0kdFEcCF7aDVGgY3NzWQNXn0PAKreAS1GaYpAOQlSSii89YOZsWzH2wuSxjaR6YWoM8FcGKNKr7aZ+Al/SvXup4kc3h30qWqQZMdzhHuXY+UZBWb3t6NMFy/WBwa4x8Sxn63XpuYFplzGzEQKIUhNPD+bidp8u4QSknTreMSyU99Oum1D3u3mg9u4jGE7SvWu32B4DiqUItXxAuFDazCdwqW2TiqIUAFpegqFV3UoiEoZrVymSOXeoFDI0BAyNITL/sniVKoR0yuOr51CIe0hlJkOhB8hQcggWivzv480HZSZQpnp6SOSAD/SP745XnhcmPKjKOHiRXuQ4alFkaQdwatpRvgewncn/8nJFSOVaY/+hVDW+GNQCDcVVIz00gg3NW03pRVCRmrxo7XISAw/UouMxlCGHaznpTG8NMKd8NhJBib/o5Uo/XAcPxxHeFHskTbMdCOGLM1NDD/Sl3ls7Y0h3PIfO5rc2bp1K93d3bS0tHDttdcWpJ3vf//7efXp9ttvJx6PF6QtTeVRkNvSDQ0N/OVf/uW8l+/q6uLgwYOceOKJhfj4RUUhJ6remqHxC10Fdn8rdn9rXhMkr6Yfp2V/5nmqZTnD60+en9FwjjjNHcQj1dQ++2tMJ4WyXZIdOwn1LMUaaiycKXGqJjDUEOC3pDC6IxhDOsJBU3jKRZCSbUlU9ZiHXBR7YElh27eTJRWiMpg2I2tPwmlsp2bX44HJuemTXrIb2ZcmNNCWc9MCQahnBcr08KsGwVK4x/cHKXvdEczOGu29kSflMFZmIp++KRSyKTX2BCuZvxjl1h9ChoM2/XANidWn5ObFVmD8WBPDm15N9YuPYKZHUJZHaulOovs3YLiFufGTiZLxIftwmIXHrdmHW7Mv81wJkalIOF+UYSLtCMqOINwUZnpk/M2J4lTNfox0HYYXnd7TSNpZRVEp4eOH4viRPrxwPJPiVUiUlca30vjR3unfR+DVteE0r8KrXTKz+b6SiNFqzcoMzX88KIXwnECg8lIoYSAjtSh75hs2KhRFhaLIad5Leg6h3k7C3S9hOKMFf6wkTt3LwMsYbhVmqhkr2YwhC3tTaDb80HhRIyOub8JqNJqFpYg5EjNz9913c8cdd3D//feX4uOPGmT9uA9F5NAqzGR+lTGknSbdMn7hlFi2gcTKzQtyoevX1DNwwjnEnvsN9nA8SLdZsg8/Oky4Z1ngB5AnQpoYyRpk1TDYCveEfsSAjXmgCqM3rKMcNIsKhcIbS8+DghsLSys9WYiqaWRoUwmEqAm4DW3ETziX6pefItwbiOpu40EQilC8Ped2BYLw4VWk2nZmqooSkvgdCVTEx3qu9KkYmvJCGQpv3SCqKhgfRqo67/RzhcKNBVEOShiMrPuTko63I5GRGoaPeTXVO3+LNdwLhiTVupvo/vUFSScTXigopmKC35HA2l8Y/61i4IX7JglR6SVrSS09FiAQQDwH4TtBVM3ocwAZiqLsCNKOIu1I8P1OuAYTThJruAdzuAdrqAczPZx5T4YHpo0qGl/AxPAjCC8y6X/Dj4C0UYaLH+7HD/fhhwcy0VzZoAwLP1KDDFcjw5P/F76HmegP/kb6MRNxhJoq6/jhapymVbhNK1D2PMQTYaCsHMQdIVB2GGWHkRQgdc0K4bSux1myFit+kHD3rmAcjCLtBNLegxvbg+HEsJLNWKmmGdMVC8XE62d3fT+h5xoRno6O0mg0C0NJxCjNzBTqDrAyFISCCwUjHclbiFLIUY+o4MIgtWQFiVXH5t3PbJDhKAPHnU3NS38g0rUbAD8WJxlJEO5agZnO/8Iz3L2M1NJdKHv0Llqdi1c3ACkTc38VZlcEISvHi0JTnpRDpIdsdCA6OhFOxjCdwk3cpOmSatuJsoK75V51PUPHnFn4VN4cUHaY4Q2vwNv/AtV7ngEYNXpW2PH2nEUjoUwih9bj1fTiVw3iR4bAUMimNN7aIaxdMS1I5Ui5RBIWChX2cY+JZyrboiDUn3t03hhBZN5o9br6NmQ0/0irQqOsMMPrzyT23P2YqSFUKEW6eR/h7hV5j49QXzuppS+OVrgcxugNY6TK7zJXmknSdTszz5NLj8WZkEo5Fl0DQZBXNqhQNKiU2LgcAOGmsPv3ET74PIY/h+m24SONEbBHpn6uNEbT76b5TMPCrW3Fr2kMhB9hgGGiDAtlWmBYKMNEWeGgeuMMNzCVHQiWY31HSYzkIGZyACM9AsLAq2nGr2kqi2i/nBEGXkMHXkMHRiKOHT+ANXgYKzGeoihDQzihIZza3ZjpOqxUM2a6fl6V/rIlNLSSZGgQDB9V4+Js6SX0bCPCKb+xo9FoFh/6SFNGFNQvKjR+KSG8/FPNnKaDyHASAC9aw/DaE/JuMydMk+H1J+E0LKHmxScwfBdlO6Q6dmL3tWHHl+R1oja8MNG9m/Bq+nHru1Gh0eiyiI+/dgh/5TDmoSjmgSpdOlqTE+UyqfYnREXZAy0Fa1cJn3TrziBCAfCiMQbLRIiaSKpjAwiD6s4/AuA2dIFQ2P1L8xCkDOyhFuyhFvzIIKm2l4JUmfYkvmNg7S2OZ8vRwGIRpGStg7spnrlZhDQId6/ATOV3wwjAqxn3fnGaVuTdXtEwLUbW/gmx5+5HSA8/1o/rhgnF8xPkzHQ11kALXn03mOCtH8T+Y0NZicBKeKQadgSG44BT34HTur54n2dHcJasw2lZg3CSo15GgSeSMeppJNzA18hwEggnMf3eMiZHKEk7glvXjlffjlfTDEYRroeEgayqR1bVF77tMkFW1ZOuqie9dDMiPUKobx92/17M1GjqnFD4kTh+JB48V4wa2o/+KQukiZAWhleDlWzJ+vdu+FGifVtINTwXGOJHPdJbegg914SRLK/ztkajWXxoMapMKPhFdnhcjDK8/E4mXtXAuLG3MBjaeFpxq2DNA6e5g3hNA7Edv8Me6gMBbtOhIG3vcH5VsoIJZRPWUCN+dAivvhu/ajTU3RqtTtiWxH66AWNYn6g186dcJtOy2g1M+gHhhjEL4FUDQQRlqvWlCZ41VQxuPmtWj41Sklq6DoSgevcfAHDrD6OEItTXkfcE1kzVEu5eEVQ1A/yVIwjHwOwqnGHz0UYlC1KKQJT0Vg8xVjBPuKGgGIebfzEOZXj41YMASCuMV1veZbJlNEZi1clUv/QoAG7joeDcm6dvXai/Hb96AGU7qDoX2Z7EPFgeY06hSNftRFnBjT0/Ukty5ckLE+UjDFS4Gj88RwSslBjOCEZ67G848z+GjVvXilfXjl9VX9nRSWWICleTbt9Ium0DRnKQUN9e7P59GG5yfCEBCA9leNPYonXhRQ8THlgXpFZmgeFVEendQqrx2aCyYVjijAlSw9o7VaPRFA8tRpUBxbi4VuHxu1j5REZJ0yG9ZG/m+cjq4/Br6vPpWsGQkSoGjn8VVXueJ7p3BwKQVcMkl+8gfHgFVp6ViQQCK1mLlaxFhpK4dd14sXjgk2Ap3I0DhJ5o1Gl7mnlRLpNohcJbO25Yag82FyRyQKFIt3Qio4FwK61QEBEVKmzVy0KTal+LEgY1Lz8JgFcXlLguhCBljTSiej2cpsCfyls3BK6BqSt15kwlClJKKLy1g8i28UppRiJG5PBKhCzMZZhXHc94+DiNy4paVKRQuI0dJJ0tRPc9DYDTdACkiT3UlHObQhmEu5eTWroLAG/lMEZfGJEufSSzW70/Uy1OmTaJNX9S8ht7UzAMZCSGjOQfqafJESGQVXWkqupIdRyLOdwbiFLpEYTvBBUCvdH/j1hVhoZINv2B8OAarFRzVh9ryDDR3i2kGp9D2iNgKZzNvdgvNGBqY3ONRlMkyuwsuPAcOnSIgYEBHMdh1apVxGILewIu1kW1Ck9M08stekehSLfuyRgQpxvbSbWvLkj/CoYwSKzcjFvXQs0Lj2E6qaBKVtvLiANrgwp5BcBwooS7V2D3tZNu2x2YFEd9vDXD2DvLz5dDU16U0+TZX5pE1QbeIcINYw1ld8E6E07jAfyaOBBUeBradAYyWhkTmnTb6iBC6qUnEIwKUkIR6l2WtyBlDy5BWi5e3WEQ4G0cQDxt6EqdeVBJgpQK+bibBjJjDsCOL8Huy92fbDq82HiKnttcxil6R5BuWw/SI3rgeQCc5r0IaWCNNOTcppmKYQ024dX2BjeONgxgdkXBEwjPAH/0f08Ejxcgjc8L9ePWBDf2FJBYdSoyotN2NXMgBH6sGT82zXlaKfBdDN/BSA0T3ftUUKXP8EnXv4ifiBMaWp1VcQChbCJ9x5Kq3xGY3ZsKd2Mf7KrH7MkuwrC/v5+GhtzHsUajOTo4qsWo+++/n29+85vs2bMHKSWvetWreN/73seqVasW5POLeTE9OTIqNzHKbehCRoMywX44yvD6BQonzwG3voX4SX9KzYuPE+47BAKcxoNEDqwr6IWm4duEu1aQXP4CGBLZlsTvC+lIB82MlNOkWYV9/JXjUVHh7hUIlX8EhVt7OBBbCCZaQ+tPw4s15t3uQpJuXRUIUrseDwSp2p5AkCpAlcFQ31KU6eLX9IMJ7uY49h8aMZJH9Sk4L8pZkFK2xF+SRNU5yFoXrDF/KEG4e0VeQst0SDuVqeLoR2uDFKoKIt2+CeF7RLp2giBIbe0ysBK5VzAL9S7FrxpEWe5oIZIZzLsVMLVgW0Hx7UHS9S9mzL/TSzfj1eVvWK85yhECrBDSCiHDNQzVNBHd8ySh/qBKo1fVjR8aIhzfgOnNv0CJUCaR/k2BoBXpAwPc9XGUJbEOzV9A9f1s7fc1Gs3RyFF7JfyLX/yC6667jj/90z/lzW9+M47jcPPNN3PqqafmLUb19PTQ2zterrWzs3PKMsW+iFaTPKOyvwMvQ8nA0JfRyeWGU1F2ed/JV3aYoWNOx3z8F1jJIWQ0gYwkMFOFLe9seGFCPUtxloye8NcOBWkAZWSSuhiYzzgqd8ppsqxQuOsHYPQmqTXYjJnO/868Fx3IpKEBjKw5EbexPe92S0F6ycogZW/nY4EgFevFTNRiJerzalcQiBAp00NGh8BWeBsGCD2VezrSfFkM42gmylWQco/vQ0UnT8SEaxPuWo3pFN6/qGKMy2dCCFLLtiB8j3DP7lFBajfG3s05+z8KZRLqXk667aVpK8CNL0jmmDgb2YwjhULaQ/jhfrxIf8YjCsCtX0q6dcOM62o0OWPaJFedilfbSnTvUwjpoawUqcZnqDp8CmI+P/RRBAbh+Aac2pfxqoK5gLd6EJE2Mfvnl3pvmqVPjdVoNOXPUSlGHTx4kP/6r//ivPPO4z3veQ+trYHR54MPPliQC/Uf//jHbNu2bcrrV576aYyRhTG8zlwISwNk9icEp/5w5gIusWITXl1hUnmKjhAkl60n9uLjAHjVAwUXowDM9IQJxTQXugsxQbpXfi+n9cb6luv6C8VM46hcqJT9OIbfkUDVj6bneTahvqV5tymtFOmW8WNmomMj6dYCpPJKn8jBnQgpSTcvR0YXLp3FaVlOaiRO9GBQel3a6YK0KzCIHF5FYmVQvU9ZU+1njyTb48h0v8VSj6P5bkOljKO5UKhJN4PwTaxELaHejoL5Q03+PIlbG4hRSgicpuWF/QDpB+lAxfY2EoLkyhMRvkuofz8YCr+mHyMPQ3MrWYuxfyMylEQZPsqQQfn60b+xtOL5MNM4UqaDVClAIe0RvHA/frg/Uy1vIn64hsRCGZZrjk6EwG1agV/dSPWLv8JwU8FvUUjIIl0PgpsoocHVCGnh1oz6Hq4axIhHEGru37BO0StPvv/972ce79ixoyDt5MuXvvQlNm7cWLD2NJVFScSo6upqlizJr2JKPgwODnLgwAEuueSSjBDleR41NcGE5z/+4z9YunQpGzZsYO3atVm3f9FFF3HWWWdlnnd2drJ169bCdH4eKENlqukZTvYRO9JKZy7SpBUi2VG8ssPFwGlsRxFoRH7VIPTmP+me8hmjUWMA5v6qkkRFvdZ4W9YTuIkTw1zWX0hKPY5mo5L2I4CscvFXjlaEVBDuXpmVj8R0KOGTan15gqfcUpLLj8m3qwjPIbbjt9iDQQXPqn3P49QtId26GqexDUTxjZn9CV5XIgcxfyYmClvG0MLcmCjlOMpGTMv3eFYuCAQiZaKqfJCCqs4tRT0/+NUDYHpAEHWj7AKkjCuFOdxLuHs3dv9+UBK/ugEv1hL81TQWR5wSglTHMYEYBXg1/XlX1zOcKIYzNZJDIUlGRlDWDOl7RzDTOEo3zD6ZU4Bf3YRX10q6eTWYugJvMRBuCuE5gfG6FvuQkRqUMXrukiao3MarQGAPL8e3h5DhQVTEx28fxjpQGX6QGo2m/CmJGHXJJZdwySWXlOKjATh8+DCJRIJ0Oo3neViWxd13381vfvMbmpubiUQi7Nu3j/Xr1/OOd7yDc845J6v2m5ubaW4uXSSRinqZaB3Dzf7C1K3vzqyfXLq2/Kq9zIGyQ3i1TdiDvahQGmmlMbzClZb3Qwn8moHgSdooadnobCZw003cyllIKfU4molK249KKLyNg5ly8vbAEsxUfheSCkW6eQ8qFFQH86Ixhtflf8ffSCeIPfcIVnJo0uuhgcOEBg7jhyKkl6wi1bqqqFX6hJxQAKKA1TL9yHDmsTGwMJPSUo2jXISifI9n5YJIjopRhkKZLsIvXoq7W9eTeZxesiavtoSbJtS7h1DPbszU8KT3rJF+rJF+OPQCSgj86kbc2kCc8qsbC1a9T0ZieFX1WIk4MpxE2qmcrmPmwov1Z4Qooy+EtWu8EMnLL7/M6tWTIzyzGUfKsHBrW/Hq2vDqWlFW4a4/NEfgOUQOPBOkdxJUKfRqmjPC6VErTimJkQ585Aw/PxsJgSA8tIpk6A9BEY5lw5jdVQhXp+FpNJr8yUplyDXUXwjBu9/97pzWLQZnnHEGGzdu5D/+4z949NFHiUaj3HvvvVx22WVceOGFrFixgocffpitW7fyn//5n7S3t1dU+KCq8jKPhZPdRZw03UxVHmlapJbmd3FbKpzGNuzBwN/Brx7EGGgpWNtu06HMY2tvNUKW9kJnPhO42SZu5SyklBuVuB/9lcOo6uCYYKQj2P35+zl5sZ7x6EnTYmjjn+R9x98cGSD2/CNBRUwgFBMsf5XFgUc9kj1BSpvppKja9zzRfTtwGttJt67GrWsp/GRjghhFAQzeM82Gxyf4YrC8PfjyIR+hKN/jWTkgkhbgAKBCaUgW57v2wyPIyGiRkUgMvyYHDzKlsIZ6CPXsxu4/gFCT3bytKkGo1iBxaIJAqxTWcC/WcC/wPNIKkW7bEIhhRv4TVLdxOVYiDgTRUaECHLMmolC49ROim/fUINLj/Xbd+UVLAbixJSgr+H6VHcGtawu+hwWI4DyqUQo7vp/I3j9geOMRp8J3sQcOYg8cBEBaYbyaZvxYC36kBiE9hO+D9BDSR0gP/OCxMkxkpAYZiSHDNZnvtRIxnASC4Lwp/PzFXMOrxkq2Bv5RpsJbMYi9S6fhaTSa/MlKjPrmN7855TUxYRKglJryulKqrMQoKSWWZfG5z32O66+/nueffx6lFJs3b+Ytb3kLbW1BhZOzzjqLT3/601xzzTW8+OKLlSVGTTBONZz535FTKJzmwKsBINW2qjAnY6Uw0knM1AhGagQzneD/Z+/PwyO76jt//HXuWqtU2qXeVzfe2sYLweBACMZgY8hMMgQSQiCMIQ4JYTIsA37IBLB/wcSGDMt4sE0mMB7yEEjgGyBAMB4DxjHG4LVt996t3rRLpSrVdrfz++NKVaVutaTapCrpvp5Hj+pW3XvuqVv3nHvO+3wWqajYiR6cWEdDVq2sjn6ix58DwImk0OskRrmhGdzIrNVGXkEZaZx1RiUsNoFbzsStWYWUZqIVr6PXbuFu8ldHkQJzbBuC2iZJrpGdH7B855V44dosrbTpMeIHfo7i+qJZpEdw1XvDRHsVdt9kMP6Cy4mf2Iw+64IEgcScPIM5eQbXCFPo2UyhZ0vN9ZhDNECMkkjcWeEAWyBya3NVuR5CUa392WpT/tt6eh41V3+XFik8Cj0nituF3h0VP0uFnSd28BHUXOqcz9p36/S/zKT7chNFF1gpj+RBm+lDNsmDFvmxkmilOBbhU/swRw6T3/AirO6tNYkxVucmQqee9ZMIxKbQp/rr6uroRpNI3RcLRdJAmZkvpOv68oX1/MaL8Vose2GzIQoZQsMHwPNw2vuw2/sXXdwQhQzhk0+jp0qCojAE0V0hcscLuNnye7OAkTwNydMLFbUoc1nqvFAM14zjhdtx4l2gVDB1khI1O4WWGkEKFbtrK1JvvKWcUsiUXjv1sSw00ptxQuOguLi9OdThKEqmdQW79crf//3fk8lkiEajvOxlL6tLOX/0R39UU52+/e1vEw6H61JWQOtRkRj12c9+9pz3vva1r/HLX/6S66+/nr1799LZ2cnk5CRPP/00DzzwAFdffTVvfvOb61bhSikUCphmqeNXZk3JN2zYwGc+8xnS6TTPPfccjz32WDGO1Zzr3t69e9F1nf3793PTTTetSv2rodwyqhLzdic2VXQ/qzpWlJSYoyfR0hOo+awvQBWyCLlAsN5B8DQdu70Hq6MXO9GHF6qPy5sbieOaEdRCFi+cQQq39hg5SKyuoeK2diK2rCCOK8VCE7hGx2xZL7TidZSqh33BdHHbmNyAYtcmnkrhUug5DsJvz7mBnVhdtcVkM8ZPEjv8q2If0b5N4co/DWPG/bYlFEHPxRo9F2vkJj1O/szm5M8crNSctVSOyOmDRE4fxI51UOjZgtW9qSYhfZ6bXr3EKD1fjK+lpIw1mX2znkJRrf3ZauJbRvnUKwD+2VidZ3yrK8AJt/sCUIWYo0fnCVFaTND/0hB914SI9M1/XhptCr1XmfRe5Y+n8pMu0wdtJp+zGH/KAgmKnScy+BTm8GHyGy/E7thY1WKTNEI48R709BhSt/DMLGqhPolIJHJezEftZP0TnKw31NQooTPPA74458aXufjnuZjDBzFHDhYt8oypk0ghcOK9OO0D2ImBUhw06WGMHiE09MK8Pjp2cZiB3+lE79CQnqQwZJM5lCdzKE/2aB4vv3SyiIVQHAvFmYRMKVulFIrvAtjmi2bSXOD+kR5qehw9eQZ9esgPIj5LaGg/Vs82Cr27G+pqXi5G1cMyCkBIHWNmM1bbcQDs7dMY+7rX5LNsLfPd736XsbExenp6ahKjysupVUB6+OGHSSaTdSkroPWoSIy6/PLL521/97vf5amnnuK+++47x7/+da97Hf/pP/0n3vOe93Dttdeec+xK8IMf/IBvfvOb3HbbbcVA5XN4noeiKMTjcTzP46c//SlvfvOb6e/vR9P8y3LgwAEMw2DXrl0rXvdaKIpRUiDs5U3IPM3yraJmmdl1OdKo/AFmjhwnfvipZe+vODbmxBnMiTMAOOEYdqIXq2sDdqIGayYhsDr7CQ8dBSFxIzNomfbqy8MPhu6FfCsTkVFRRusfx6JWyidwjY7Zsl5o1evo7EyD6Q/wlVwMLVW7daDVdao0AY4myG65uKbyQmcOER3cV9zuuVTl8ptDaObCg9twp8IFbzTZdaPByNMup39uM/6cy5xnkT4zhT4zhTz+LFZHP/mBnThtVcRLKreMqpMbbtEqChCptRfEuJp24kVt/z4FtINtKPn5Q5Ja+7PVotwySjZAjHIi0zizsaKkopLdcVXl7nFSYkyc9F8L2PP2ON2XGyja8u73UKdK6KUqfS8NkTnjcPw7WSaf9a2N1MIM0aOP40QOkd90CU5b5X2P3bUZPT0G+Atl9RKj3EiqGOtOpHTEArHbKnHTW88Ip0Do5LMYUyeL78UO/Qyrayu5TZcuat2kJYcIn3oGxcqeW66U6KkR9NQIoZNP4UY78cwYamYStVByddbaVfp/u5P4peGiN4ZQBKGNBqGNBl2/0YZ0JflTFpnDedysh2IKFENBMUTxtTAEiqngZj2sURtrzKYwamONOTjT7ll184p1C596BteM4bT34cS6EdJDmx5Bmx5CcRe+h4R0MUePYIwdw+raSqH/AqRR/7ij8yyj6iRGAWjZPuzICFLLIeM2XncOdXz14qYGBAS0PjVFpv7GN77Bq1/96nOEqDl27tzJq1/9ar7+9a/z+te/vpZTVcyvfvUr7rzzThzH4a/+6q/4+Mc/Pk+QUsqCbV566aW4rsudd97J+973Pnp6ejhw4AD//M//TDQa5aUvfemK1r0WpJDIkP/wFBVk0rM7h0H1Z3T5ns1Y3RsrP7nrEj3+/Ly3tBDEuiHWA9FuUfyfm5IMPy8ZfgHssrGIlptBy80QHjrK9MUvx+6oPpNOUYzCH4DWKkbZ5Rn0BmNNuxpU66StGYSUZqHW2Derhddm4fXOrsa6qp89r8b71TVnivHkpKKR3n11TfFh9MmheULU5l/XuOgtJoq6dD0VTTBwpcbAlRqFaY8zjzucftQhfdrvw4T0MCfPYEwOkbro5TjtlU2G57np1amdzw9evrZcG6q516WQ2JdMge5bLTgXpDCe6axL2U3BbErXeltGSTys7tLkP7fpUrxw2yJHLIyWHi8KAR0X6kWLp2qIbtC4+I/bSB21OfbtDKnD/oKYlk0SPfgz0hf9Jl6ksuev1bGB8OBTCOn5caMmNtblmWu3jxVfq6eiC5ZZiZveeiZ8/An01PA57xsTg+B55LZfteBx2vQI0aM/L72hQOcr2oi9KMTMczlSz2ZxkrPjWEDLzLdOQkDHy+P03phADS9uuSpUQXirSXjrMu/vi+ZbK3kFj8KYgzVikzmcZ+aF3DyBSi3MoI7OYI4eWfj8GkQvCBO/NExh2Gbq32eQtvSfUePHMCaO+6JU3x6kWT9RZ55llFe/JEQCBSO1jULnCwDYm9Mo4+GmHQ8HBAQ0PzX1UKdPn17SxK+trY0zZ87UcpqKGR0d5atf/SqJRIJXv/rVPPDAA9x666389V//9TkWUlJKQqEQb33rW/n7v/97/uzP/oxIJILn+ZOaO+64oxhHqiVQZTFrluIsf0DlGTkApBBkdu6t6tTmxGkUx18Z7doJr/gzBSM6P65YCcHOV4DnSaaO4wtTz0vGj4Kcfc6HTx+qSYyy27uRiorwXH81FFn1A1Mi8czZ1dSsijIZZMcJaF7mApYDGMl+lDpk8/LM0uA2u+UivHCspvJCw0eLr3fdZLDr9fp5+orFMdsVtl9nsP06g9RJl9OPOpx53MFKSwSS8OkDpCsUo7wy9wk3Mo1q1T5JkGpppVxk12a8qKpw1aL74lpA6q4vss3eysKtbzZaL5xBan77ttt6sXq2VVWOMXas+Lr3JfWxnGjbobP3fe1MvWBz7FsZskMuAlBzqYrFKFQdp70PPTkEqovU84ha3YyRpX4sr6JMLtwvnm+BNWA+wiv1ad2vacOzYfLHqXM+m4eUhM48V9yMXhCi/7c7Mfv88WpsT5i+/9hB/pRF+tks6WdzFIZLZZkbdAbe1EVk28qMwRRTIbzJILzJoP3KKFL6boAzz+eYeSFH9ngBvLOPEcQuChO/NELswjBqqCSYdb+6nYkfp5j8WRppSYSUmOPHMcYHsbq3UthwUV2yL3pl7oNWfBBz6sK6CUaalcC24nhGGkIuGB5YwTMtICCgOmoaJSUSCR577DHe/e53LziJ8DyPxx57jPb22ixSKmV6epqjR49y0003cfPNN7Nr1y7uvvvuBQUpIQS6rnPjjTeyceNGvvvd75LP57nwwgt5/etfz4YNtcVDWXHUMt/4CmKdSMV/mkpVrzrWijkyWHx9+e8omLFlWDgogq4d0LVDcPFNYOck/3abx8wYGMlR1GwKN1L5qq9fuIrd3o0xNYLUHKSRR1hVDmYVtxgnh4La9KtAD3jfqNqiILCKan2kVhodC7s+g3ZPK00InGhtfbpSyKJPjwIQ7hbsurE6Ieps2jartG1W2fM7Bg9/LEt2TGJMj1XcjxR6thA+fQAhJXb7KHqqp/bVZVHeNzd3/1Ep1fQ3Qgr0/e3YF06DI9CO1SZuNgNzQpSMzlp12DrmaOWxnBbDiZTiwFnd26qKySSsPHrSXyTU44Luy+pnqSeEoPMig8Kky+Gv+cKPkNWJjU6syxejACc6jZGsUYzS88UELUpaa/rn+IoyZw1agbWr1bl5NqMiOCkPzyo9d+yOzQseo00Po+b8ezi0yWDLLb3n9P1CCMKbTcKbTXpv7KAw5os/iiFIvCSGWIb1bKMQQhDaYBDaYNB9XTtuziNzMEf+lAUCwttDRHeHzuvuqsVV+t7QQddvtjH5kxSTP03jFfyFE3P8OHryDPmNl2J3bq4puU+h7wL0yZMoTgHXnMaJDKFn6zefUefEKMCL2KiBGBUQEFAlNUVmve666zhy5Agf/vCHOXz48LzPDh06xIc//GGOHTvGa17zmpoqWSn9/f3ceuut3HzzzQBce+213HLLLUxMTHDrrbcyMlJyt5qzgJrLKvDXf/3XfOYzn+Fd73pX6wlRgFTKMhp6Ffy8Yk6Mqm7CpeRmMKb9GBbxfuiuMsyWHhbs/s3SAzh05ugiey+NlShZVrnhdNXlSLVkaSLs1kjZXI2oFAhRawStvB+ozyCx3LLHqzHwamj4aHEauOllOkKp7+RCUQVbf6NkGRoaWtiF4nx4oSiFnlkRQfGw20brUCu54Mu1QjV9hzJtYjzejfF4N0q6tV0XFxKiQkO7UJz6WXBIJG7Un8hLoWC3V2c5bIwfLyYM6L8mhKLXf3I/r0zPO/+Oi2B3bEDO9hR2+zhSVFdOsRpmrvhaZAJXPADhWJinn6PtmX+l7dnvo48fh4USziyA3bEZOZtZbvqJDOlnfLdPTzOwEwPnHiAloaEXips9r21f1iKE2aPT9co2Oq6Jr6oQtRBqWKHtsii9r++g98YO4heGlxV3TYuq9N7Ywe6/3Ej39e0os3ESFcciMvgrIkf+HVHmalcpUjfJbSu5SVrxE7jazCJHVIZwStbC5UmTAgICAiqlpln1H/3RH3HllVfy85//nJtvvpnXve51vOUtb+F1r3sd73rXu3jssce44ooreMc73lGn6i6PeDzOVVf5nbDrukQiEV71qlctKEjNxY6aC1jpOH6nKpf5MG46araMqm7iGiqzitrxclGTlcP2lwnmrJRDoycQs65/1WB3lKzgnEidxCirNcQoqGyCGAhRaweplllG1UuM0srFqOrdeoRtERr2XYQUDTa/vL5uTHNsfJmOOltNc+wEwq4sdk9u0x6kmJsIjyGVGgfcc5ZRHmvWIqOaPkS4CsJtnT51IVZCiALwzGyxHTptPYsGiD4v0sMcO+6/FtB/bWMScZSLUfNjsC0fz4xid87Gr1QdnNjk4gcsVZ5RymomMo3pd1oG18Yc2k98378RGjmI8FyEaxM58SSRY79AOMvoL1UNq2sLANKWxRALdueWBS2szraKil3cuGxyrYIaVem9IcHOj2wgfllJ4NFTo8RfeBBj5BDFDB0V4rT1UuidzYotJIXEIaSoT3B+pUyM8sJBwP+AgIDqqWkEaJomn/70p/nwhz/MZZddhq7rjIyMoOs6l19+OR/+8If59Kc/jWmufHydOZFJVVWklJimOU+Q+shHPsLEhG9e/Pjjj3PXXXcxPj5ezKRXD5eRVaEKyyiJLFlGKVUM0KSHOXrCP6cC266p7doZEcH2l81mRvFcQsODSxxxftxwDHc2KKQXziBFdYPicqsQWsQyao7lTBADIWqNUWYZRZ0tozzNqClweWj4KMLzhZ1NL9cx2xvTnvSwYNPL/Mm6kB7m6PGKjvfMCIXebf6G4mG312YdJYtiVIs+W5bJeutLVkqIAj9+2Rx2ojrLbS05jGL7FkKdF+uEOhvjXqMa5ZZR1ccEK/TtLr62E6P+eKVK5mJjAijrVYzyXIyRQ8T3/ZDQ0AvFvrh8NqAnzxB74f+hpUYWLqMMq/vc+FpW97Zzd6zSKmq9oLdrbH5HD5v/cw9au98mhecSPr2P2P6foGSTVZWb33ARTiQBgNTy5LqexdWqt7iaQ3HCRQvfwDIqICCgFmp+GgshuOGGG7jhhhvqUZ+GIISYJ0hJKbnnnnv40Ic+xO///u/z9a9/ncOHD/O2t71ttataM7Iayyghi8FWq7GM0qdGUC1/xXHDXgi31z7A2P0qwaGH/O8SGjpKbuOu6vznhcDq6CU8fByExA1n0LKVx6CaCxgLrWUZNcdiMV3W2+RxPSDr7KYnkUWLjFqsonAdQsO+S7dQYPtrGusqs/VVOoMP2SB9ESw/sBuU5bff3MYLMEcHEdLDbhtDn+5BeFXWeU6MalGj20qoJWZdKyE1b8WEKPDjJoF/Cy3oBrUMzLLA5QO/3jjLFKWsmVRrGQXgRhPY8R709BhSt3Cj02iZRFVlFcUoW0ALPsdrwvMwJo5jDh9AsUsWYiiQeEmMnuvbyZ20GPr6BG7GQ7HzRA//O4WeneQ3XnzeBQgv3IYT6yrFjop144Xi5+wXWEUtj/glESK7Qoz+a5KpR9IgQc0lie3/MVbvTvIbLqpsMUhRyG27mujBn6I4BaRWIN+5DzO1Ey3fXXU9BQrCDSG1PDJi15QgKCAgYH2zZpaGpJSLrrKUC1LXX389iqJw991384lPfIJIJMK9997Lpk2bVrDGDaKamFHlcRiqsIwqt1zacW19BnhtA4L+i2H4OVALWYzJIayu6laC7cSsGIUfN6oqMaoFY0adzUITxECIWqPMBTCXVOSue14Upyim1BIvKjRyDMXxRa0NL9GIdDe2LUV7FHovVRl9xkW18hiTZ7C6l9/Pe2aEfN82wsNHi9ZRxtTG6ioz18+useDl52OtC1ISibM7tWJClKfnkYbvOuXGupB65edR8jPoKd/CL9Sl0HFh48Tg+TGjasuWWOjfjZ4eA8BuH0XNtFc88fVUu7ioJDL6upo4K7kUkaM/Ry2PQSSg7cURel6XwOzx7wO9QyO81eDM1ybI7PcFK3PsCFp6lOy2q8+bEbHQu6soRhV6d4LrIFwb4Vr+f8cmNPR8cf/AKmpx1JDCwO900n5llKF/nKAwbPsBzkcPo00Pk9t2JW60c9nleaEYMy/6DSJHH0PLJkHxKCQO4c1k0Ge2VN0WFCeCq+VBARlyEfk1M6Vc01x22WVMT0/XnFysXuUAXHDBBUgpVzzhWUBzUJeew3EcTp48yczMDK678KDj8ssvr8epimSzWQqFApFIBNM0i2LTUoIU+K577e3tqKpKPB7nC1/4Atu2batr/VYNo0xYWuYkVCql36zSAObCsTCmhgEItcPAJRUdvigXvFph+Dn/+4SGB2sSo/w1G4kbScNE5WWUi1Gt5qZXTvkEMRCi1i5FC0mvPpkfvXrEi5KS0NBsogsBO167MgGrt/2mzugzfh9njh6vSIwC3zoqNHK8ZB2VHEBUJfCtH8uoOdayIOV1Wnhds3F1XLWhQhSAG0kVX1drFWVMnCi+7r82VPfEAeUoRu0xo+Zw2npxw+2ouWm8UBbPyKFakaUPLMMzs6W6rTMXvcixX8wTouKXhum5IUFo4Nw+WG/X2PKuXqZ+lmbkO1NIB9R8mtjBn5DbtBcUDeEUin+KXZgXXypy9BeIRTq5wCpq+US2mex4/wDjD6UY/2HS/y0KM0QPPUL6ktdWlPlaGhEyF7yC8ImnMCb9fsCOnUG4IfRc3xJHL4zihJlr2TJsQyBGtQT//b//9+LrAwcO1KWcWnnXu97Fnj176lZeQGtRU88hpeTv/u7v+OY3v0k2m1103x//+Me1nGoeDz30EN/61rcYHByku7ubnTt38u53v5vu7uWZnO7bt4/PfvazFAoFPv/5z68ZIUpqHs7m0oBDsZY3aZwXmFivbIKo5LPFrDwb9gqUOmY66buw9LrS4MPlSE3HM8Oohex8UakSyqzMpOFB7S73q0YgQq19hK0gwy6oLp6eQ7FrHPyXWU/KaoImA1p6sujO23OJSmxgZUTdxI6SS8OcVVYlSCOM3d6DkRwBRSIVB+FWLqQJV/ctMwwPqXkIZ+nvvxba6toVpEoTbsU2EU5jxVVPKyXycGJdVZWhFErjtK5LG1tfPVYaCyhWbpE9l4EQ2G09RTcv1MrFLbttrFRcap1l0itLyBO/NMymP+pZfOFWEXS+oo3I7hCn/+84hTM2wnOJnHhyyVMtJkQB9N6YCKyiKkBogp7XtBO/JMzRu4bAAzyHqlY1FBWreyv65Mmy36m61RGJxDWTpXou43kWEBAQsBA1iVFf+cpXuP/++4nFYrz2ta+lp6cHtcpsbMvlxz/+Mbfddhu/9mu/xhve8AaOHTvGo48+yhNPPMEtt9zCNddcQzQaXbSMrq4u2tra+NjHPsaOHTsaWt+VxNmWLlpGqTNtKPnFr8Mcnl4a5LrhWEXnLJ/cmZUduiSF0kJwbXFqKK3MLtt18SzUfBSHcQBkuwVTjVsBn5uAVjuBWwsT2GahVSfSyriJ2+a3TTeUqYMYVf66uomEMTVUfN1/xcqtoM4Ml4Q0J3JuLJPlUG7ZIbzq6q7m4n5qeQFeRwF1bP1YB7RqO1oMZdJEZFRk1MULZX0X8FzlLuDLplwQriGBQLG4Oi4cLYTRrqCFBU5OouZSSx+wBGqhlJZe2JU9f10zgxeZPT6nokysfFKd1aSw4UIixx4HIP1sjtHvJOl9w9KiUGjAYPtfDHD6/nHSzyy+4KyEBGpERY0oKGEFNayghgVKWEUNC9SwQmijQWRHY7I3rnXsCccXogCnrQ+pVeGmm50ievjRohCl5jvRqrSKcs0pPN1flRUzOiK9MpbOAQEBa4+aZgTf+9736Ovr47777mu4n6eUklwux7e//W2uueYa/uIv/oLu7m4sy+LAgQPcd9993HXXXbzzne/khhtuIB5feNLhOA4DAwN84QtfQNfXzuqY117A658NSukqGOOblu2eI/WS1ZEbWp6ANYdwSkKWUZnV/JLMLYICeGaNA5i5yWSV8XPUXElp89qtRfasjXIhqZoJXCBE1Z9WnEiLfGmyKpV6ZLqp3bdMnxOjBPReuoJi1JnSJN4NVycWiDnRXYqqYz6puTbshB+vx+uw1pUYBa3ZjhZDIFBPxnBe5D+o3GiyoWJUMRsj1JTNcqUQQhDZoJI64qDYOYRjVeRWdDZKflZM8gTCqWzsNtfuALRTkXUVLwrA7thErpAlfOY5ACYeSuFmXAZ+t2tJUVLRBJv+sJupR2ewpxy0mIoaU9DiKmp09n9MRdHW1zVdaab+vSTGWj3nZjBcCiWXInro34vZE9VCAjO5u6q2IJHYsVPFbe1UfN21qVZiZGSEZDK54GeDg9VnKw8IqBc1zQgmJyf5rd/6rRUJOCaEQNM0hoaGePGLX0x3dzdSSgzD4NJLL+UTn/gEd9xxB1/60pcIh8Ncf/31mKa/cnDq1Cn279/Pddddh6b5X3ktCVFSkdi70sVtY3IDirv87+eVi1EVWkaJMssovd5iVLL0upagyUiJcGcDl1ZpGSU8DWGFkEYeGXOQqodw62uWvJCQVMkELhCiGkerTaTLg+xX7ZpaR5RcGi3nD6Y7dioYsZUbuKZPl4lR5wnAuxRF0b2GGFxKPuq7+yoeXkdhXWYfarV2tBTKpAkuoPoxnRr6myr1sIxa2YBl0Q0aqSN+/6Pk0rjx6twL8TyU2ZhHim1WdI09PYc7m4WQgoIyur5E4Dms/guQmk74xFMIIPmLDG7WY+Mf9swPNr8AQhV0XludVWlA7ViTDjP7fVdXz4jgtPVXdLyw80QPP4Li+s8xxYpjTl2AoLox7NlWUUoDPQUCamNkZIS3ve1t5PP58+4TCoVIJBIVl/2+972PqakpOjo6+OxnP1tDLeGuu+6iUCjUpayA1qOm2XR/fz+ZzMoFz3EcB8MwSKd94UVKief5A7REIsFHP/pR9u7dy5e+9CWOHz8O+IHOv/rVr3Lbbbfx9a9/fcXqupK4W2Yg7Fv+KLkoWmr5WTagZBklhcALVaYolbvp1aIXLUQuWRo41+SmJ73S0LWGzGJqbtZqTICMVx57ZjEWE5KWIzIFQlTjaalrXB6/oS6WUeVUPtmeS3IA0Lt3ZYOczrOMilRpGeXas/+rt0gRKKi52QmdLpGx1RcJV4OWakdLIDyBkvQnYlJz5gXJbsDJSq9bwDIKIDJQqqeWnaq6HMXKFGNTCruysYBVZhWlno4g1kk2y4Wwu7eT3f4SpPCfD+l9OU7cM4Kb85Y4MmA1mXkhV9SRra6tFbvKG2NHUWxfjFDsKKGpFyGorg8JrKJai2QyST6f56Mf/Sj33Xcf9913X1F4SiQS3Hfffdx///309VXurnnq1CmOHz/OqVOnlt55CUZGRupWVkDrUZMY9R/+w3/g0UcfZWqq+kFGJUQiEW644QZ++tOf8vDDD6MoCoqiIGcHKbFYjD//8z8nHo/zP/7H/yge84pXvIJLLrmEq6++ekXquZJ4URt34+wAWArMseW754H/YJmLGeWZERCV3RJzkzQAI1rfB9J8y6jqxSjhlsd7qUWMaoyrXq1i01qa3DU7rXKt51tG1ZbJqh4Yk6V4UX2XrawYlZ4VozxVr64fkR5K0bKyNhFALXPj8jqrT8rQ6rRKO1oO5fGHihY4DUDWOWbUStC+q2ShrU2PVF1O0UUP3zJquXhaATc2Oz61Berw+rSKKsfp2Ehm5zVIxe+Hs0cKDP7PEZz06j8nAhbGGi+NsytOXiBlMXseEsypPQhZ/TM4sIpqTbZu3cqePXvYs2dP0TNI13X27NlTlRAVEFBPahKjrr32Wi677DLe85738IMf/ICjR48yMjKy4F+9eM1rXsOuXbu49957eeaZZwDfhW9OkBoYGODGG2/k0KFDPP300wBcc801fOYzn2H79sr9rJsZicTZnSoaKuiTfSgVrhpK1Sma/1fqogdnuenV2zKqPGZUDWZX89JK12AZVR4Q3muvj2VUJZOy87nxBawsLXHNHVFcSa1PzKgSlRoWCLuAlp4AINoviPatXNYdOyvJT/kXwo3Eqwq+Xt7HVRu8fA41WyZGdaxfMQpapB0tA2XKLLY1J9I4MYrymFEVLhotXF7tRSxFZEDFSPh11dLjpdiNFaLmSmEIlpslGMANp4vfUx2K1LQYtZZw23rJ7L4WT/VjeOVPWxz//DDW5Pq01mx27PHS7+KZlcV1VdNjxWyWqpVA8aoXjwKrqICAgEZQ08j6zW9+c1EIuuOOO867nxCChx56qJZTFenq6uIjH/kI73//+7n33nu55ZZbuOSSSxBC4DgOuq5z3XXXcc8998wTwebiR60lvJ580dVDFELoyd6Ky5gfvLzJxKip+rjpzcWLAhC1iFGujrBMpFFAxuya40ZVMxkrj7myViZzrUizx74RCF+Q0uWqx4zSp8eKw9WVdtFLnSxNfquPF1UmPNdoGaW4xvzYcyEHkV/Za9JMNHs7Wg7CVhApHdluI40CnlZAceo/3pizjJJCqTqj5bzy3MbHjxJC0HmRzvC/FxDSQ5uZwGmrfJyi5ktiVCWZ9Mr7PpFeO3FC64Eb7SCz5xVEDz2CYuewxhxOfmmUHR8YQCiBwNBMFEb9Z5AUCrLCgbaePF18rWUrb3vleEYqsIoKCAioOzWNgl/72tcumRq2EezatYtPfvKTfPCDH+Rzn/scN998My95yUuKwcmPHj1KPB5fkcDqlXD3Lz/Fnj17ltyvmsG5VB2k6iAqCFzuH1gSU4RT+Up9eXac5CmoMK7i+cuVkqlZy2JP05Fa9QPJuVUhANxaLRviOEYBFHB2p9D2t6/4ylAgQjUHy/kdVnOiLfIqUneQuoWnWihuDamXy0QYtZBbZMeFKlLqY/KTKxtAeejx0mTUiXVUVcZc9iH/de2WFVomgW0MgwB7Zxr9ucR5+5DXKG9a8+19LQhSyqSJO2st60ZSKKmeup9DeBoSENJDyc/gVbF45JWlvJ14xiLymsYLobLWJi9lycXPE5VZf4uV7W9aDS8UZ2bPK4gdfBjFylIYskk9naX9xZVZ3wQ0jtzJAtao/wxyw+0VC9GivAHWsBg7W0CpXEcJrKICAgLqQk0jkVtvvbVe9aiYiy66iM985jN87GMf4y//8i9561vfypVXXkkymeTb3/424XCYHTt2rFr9qqWSQbkyFkL05pEdFmgOhb5BQmd2VvSAUArhYoYnfXrcHzlW8LCzOvsJDx0B4NSTki1X1efhlB6BwmyYCCfeVdNKsJotizdh1baSo09347RN+hmxugu422bQjleXZWY9TDQDVg9l0sSNzw5iI9Mo6eonyIoV8v3zhETNJCs61urow9MMFMdi+AmH3JRHuKPx7jJOXnJmVoySikaha2NV5QivLLhvzYN50Kd7ceITSM1Gdli4mzNoJ88vLAT9RPOjJA3mbPDccBq9AWKUmovjhf1nmZYaxapCjLK6t2IOH0QAQz/Ns+k3wwi1cRNKKSVTz81adShq5fFu8N37lNmFMjXXVpF1syhbfHJelMRLmiiTJsqkgbBbI+5Wo5FGhNzmy4ke+XcARr+bJH5pBEULhIZmYOqR0vjV6t5a8fF2ez/GxCAAbmgSzapuUQZAsdoRrolUC3iJAp7poBTWr2VvQEBAfWhpB/o9e/Zw11138YpXvIKvfOUrvPe97+WTn/wkJ06c4I477qCnp/4DwkZS6eqwQKAfaIe8/zN64QxW19ASR51bhjobC0m18vMChS4Hu70bT/WtloaelbhOfVYixw+XyrHbKssOeDbz4k1UYOK/EIpjYg5vLS4QuZuyuP3VZ1BqdYuAgOZFmSwLrFxjLBuBUozVouZmwK0g9ouiku/z4/VJD07+tL6ZKM/H0C8d3Fljz0L3JlCrHDSXiVH1yMQlpIoxvrm47W7NLNmHBP1EcyMyGlj+veGGZ5DU3yJHzZYWPbTU6CJ7nh8vFMNp94PVFqY8Jp6tXyKOhcicdrFSfvtx4t1VZQHUp8rcjGYSFR2rFMqyA6vgdRVwdqewXjKOtXcSZ1MGL+w05PdqJZy2Xpy4P162Jx2mHk4vcUTASuBmXaaf8N3ipKJhd2xe4ohzcdp6kcJvd445WdO9LhBo2VKwa7d/5bKpBwQErF1aWowC2LhxIx/+8Ie57777uPXWW/mrv/orPv/5z7Nz587VrlpFVDvZEI6Cvj8Bs/MlJzGGE01WVIZSliXOSI5XVgFFwer0ffPsHIweqOzw8zF2uPTabqt8NbUcNVddJp7zoeXaMMY3FbednWm8RPXBiIOJZkAjEBmtKFS74RmkqC1bkmL5sSoEEi1bmbiV79+OnLVuPPGwjWs3fvJ38mcl0Svft63qcoSsTwKEcrRcO/rkhuK2szON25Vf9Jign2heBAJletYNVvHwzOoXKM6HYoXB9SeVenpsnkhaCYXe0tjozE8Wv+dqJbm/JHbZ7VX48EuvJEZ5Yl4CgOWgFqKEzuxCS3UinDIxWoBss3G3zWBfOYF1zRiFl44W/9YdQpDbeElRphh7YBonE2TXW22Sj2eQs89Kq2tLdQsqilYUoFEdPKO2hSk921vMYuL2ZpFKdf1QQEBAwBw1BzBfDkIIvva1r9VyqkVRVZUdO3a0pFse1D7JUGZ0tGNxnJ3+alah5ySKFV628KLmosxN2/TpcfIDlWUdtLoGCI2dBOD0k5KBi2u3Hhg/5D+ApVBw4tWbFUOZGOUqNceMmkNPdeHpBZzEmB/75UXT6M90omSrKz9wxQmoNwKBMmnibciBkLjhFFq2FhP9UuBUNTuNE1++xaI0wlidGzEnTmHP+LGcNr2scQGFU6dcpo/PWmRE2nGjieoLm+emVz/XFWO6DxQHOzEKApw904jnlJKosQBBP9G8KEkTr8dflHDDadRCfePuCARqLo4bSyJcBzUziRvvrrgcp60XNxRDzc8wfcgmc9ohurExrjapo2Ux26qoq++i5wtaarYNISu3rFLzMdR8DInEM7O4kRROdBpplAlx6vq2jALwIgnszi0Ykyfwch7j/zZN/2/XZpUeUD1SSqYeKVmoWd3VZwO3ExvRk2cAKMQHCU+0Iaq0RRBSR8t34YTHQZO4XXm0scjSBwYEBASch5pGIPI8kSkzmQwzM74A0NXVVQwsHnAu9VrtVobCKG2WPxhWPfJ9xwmf3r2s+ApKIVIWN2qs8rhRHX1IoSCkx6mnJFf+vqwpG0s+JUnPLk46sURVpv1FXBel4K9SK3aorgEXjYkBpFbAjaVAk9gXTWE83Vl1LIpgohlQb9Q5MQpwotO1iVFlLi9aZppKbQHzAzsxJ/y00IMP2Wy8RmtYAoyTD5cmwfm+bTXFnBNl6ehryca5EPrUBqTq4MQnQQH7wiT6sx0omfMLdUE/0ZwoUyUR0Y2kIVmnbB5lqNk23FgSAD01WpUYhRAUencQOfEMAGd+kmP371cX93AxpJRMH/WXuTxVxwtVfg4tXbLU1jKJmuojEKiFKGohijE1gKcVcCPTOJEUUpufcTSXyxEO1zk9cAuQ33ARevI0wnOZfCRNx7VxzN7lLRrYSQcE6O3BeL8eZA7lscb8+9KJdeOFK7MKLMfu2IA7kkDNJZF6Fjs6hJGpLoYigJbt98UofFc9dSwcBDNvId7+9rfXpY+rVzkAb3jDG2hra1uX/W5AjWLU17/+9fN+NjQ0xBe+8AWmpqb4zGc+U8tp1iz1dLsQCLRDbdjRSWTERZp57I4RjMmBZR2r5qK40TSKXUDNzeBGKhg4qhp2ohdjapj8NEweh64ajNTG6+mil58pPiLr4aJXjkBgjm4hrx3BC+Ug5GFflER/thPhVfdgDiaaAfVETBvgCH8FM5xCIqseNM6zjKowiDn42eycaAdaZorUSY+pIx6du+ofRNjJS04/VgqabHVXHmdjHrIxllHg9yHG+Bak4uJGp31R++Ip38oyf/7Hc9BPNB/CUhFZFRlx8cwMUrhVWfIshpo7K27UxouqKsfq2kL41PMIz2H08QLbfiuKHq2v0JobdXFm/AVLN1ZdEhI1M1V8reTra2mmOCZKqhc9dW66+3w+vy4nRdIIU+jdTWh4P3gw+t0pNr/z3Oszhz3tkHxshtTTWQpn/D43ssMkcU2M9iuiNS1KrnfmBS7vqd4qCgChkNtyOdEDP0YAduwUWr4Lxa0gM2UZih1DsaN4egYZs5ExGzFzrkXv1NQUHR21eTYE1J83vvGNTVUOwCte8YplZZsPWJs0LGbUwMAAH//4x0mn09x3332NOk3L0oj4H8JT0F5IFONH2YlRpOIseswcSr4UN0pPjlV87kJXSfR6/vsenle92fvI/tKxTh3jRYkaM+kthJAq5vB2hO2vHsq4g7uhtqCOQWyYgHohpChZbKj+JLn6slSE7ZelZVOVBTEHPy7JQClezYFvFuoeO0pKycF/sXBnPXAK3ZuQWm3ugOWWUfWKGTWvfATm2LbSZNuQOLtSSx4X9BPNh0jOtjUBVtcZJPWNp6K4OmIukUBmCmHlqitI1YuZuTwbhh6uf+yo0cdKtpPVZNFDSrRZMUo4GsJtnFvv2eTzjY2l1cwU+nbjaf5YKf1sjuF/mUS68/tpz/IY+2GSw399hrHvTxeFKIDs0QJnvjrB6f87fs5xAUtTGLU59eUx0s/4Fv2eZmK3b1jiqKVxox1YPbOrxMLDjp2quqxzApn3Lhwjz610jBAQELAuaag9raZpXHXVVTz00EO8973vbeSpAmZRchrKWAivLw8CPM1CtZb+mdVsDHt2vGhMnCa/oTLTJqtrAO/Ysyiuw+mn4LG/l1zxu2DGl78yJqVk5AU49u+leFG1WkZpmdKkrt6WUXNIzUYqZQ/dYDEwoJlwy27IGu9NNR/D0ScRnos5foJCX6Xx5TbinngO1cqRPOrxzN8XuOw/myh1SC8vpeTANy0GH5q1igLy/bXHESzPxinqFHPubKRqI9WVyTK40qwn0UwdC5XcYtsm8PQ8odFtdRVStEw7tpFHAKHTz5PbfmVV5RR6d2CMHkEAp36Uo+cKk3BvfSy5Rh/Pc/KHJaHMbu9bZO+FUTNTCNdvE0o+uqJuQOtZjELVyG+8hMjgrwCY/HGa3KDFpj/sRmtXST2ZZeQ7UzjJ+UKDE+lAuDZqwV8ATD2ZRbrjbHpbN0Jb+rfLD/mxwUID54+Z12pIV5I5kif9TJb0vhxuzkNvV9HaVfR2DS2h+tsJDS2qkPxlhuRjM5Rr2IW+XaDUZxHEaevDHDvq161GodzTyoTw82iOqlp/y+eAtcng4OB5P0skEvT1Vf4MCWgdGu7cXSgUSKWWXuVdbzzgfaPqQfqSx5a5kix3AKdYYYRlIA0LfXocpZDDM5dvpi51k/Seq2l74ecIKRn8ueTkLyWbXizYca2g70Wc12TbzkuOPyo59P8kqeHS+/m+rUi9toGJmi0To6z6m927Rpb8wFFQ/Qe7mDJQT9fmThC436wdamnn9UK2z4ocUsxPdV4FWqrHj28EhIaOUOjdVpn7jaKQ3vNrtD/3MMJzGX7CITfhsfePQsT6qx9wSyk5+P9ZHHugJOhkdr64tsDls2jpyeLregelBvD0PPn+w0httu45Ff1Q+5LHtUI/sdr3/kqjpA20g22+ZZsCXjhDbuNBzJFtdbt39Oke7LZxUF2MiRNYfTtxI4mKy/FCMazurZjjg7h5yZN/k2TXW2L0XlXbos30YZuDXy1ZJOc2XowXWfp+PhstVcpqp+aqj5ezGHNBzaVmzXs/kUg05Hytgt25mZznEDr1DEJKcscKHP30EEaXRm6wdK0kAqtnO4Xe3Ugz4luzJc8QOf5LhPRIP5Pl1FfG2Pj2HpQFBCnpSdLP5Zh4KEXumG9JF7soTO+NCUIbW1OUko4kcyhP6uks6X1Z3Mx80ccac2ZjQS0eddHTTAr9e0rWTHVAnyxZQ2n5KuLNzeKpOZzI7GDdA+10bPEDApqK8fFxPM9DURS6u6u/D+pRTiKRIBQKcfvtt593n1AoxP333x8IUmuYhopRTz/9NA8++CBbtmxp5GlalmomqsuagIiyZYplxjgRCLSZDuzOEQRgjJ8mv3FXRXWzO/tJ77ma+IHHEVLiOXDiccmJxyXRbthxrWDHywXhhF+n1LAvQB17VOKctRDphONkt7yoovMvRFGM8kTRxaheeEaO/IajoPorhCKpo7+QqDpeFLTGBDOgMlZTkJKGiwz796dSiNQcgFu1Iij5KF4og5ZLoydHsDsqC9TsxjpIX/AS4gd84Xp60OOR/1+WPb9tsPWVesVxRqSUHPoXi6P/VhKiZna82BfKasVz0TJ+KmxhmQivvo9M18iS7z8Cqu9OLTIq+nMdCGvxFeVW6CfWmxA1hzoaRmQ17AuTYHpIzSY/cJjw6QtQ7NoXRISnYST7sLrO+NZRJ/eRueDlVcVkym+8GG1mAjU/g5uXHPhymulDNjt+J4pqVF5edsTh+XtTyNnoAIXubRT6d1dcDoCeGim+VrP1DbAukbiRFHZiBC90rovRuo9zIwRWzw7cSILIsV+gWDncGY/cTEmIstv6yG+8ZH5gbSFwOjaSVTUiR37uC1L7cpz6+zE2vaMHRffvKc/ySD6eYfInqWKQ7jlmns8x83yOtssi9NyQwOxbOffMSpFS4s54WJMO9rjDzP4c6edyeLlzrY6kUPCMCIqdR3jnD58hFY1C324KvbtArePzxnXQp/2MengqaiFRdVFWfLA4z1DPxBDn8b4I3PSakz/+4z9mbGyMnp4e/vmf/3lVy+nr6+P+++/nXe96F8lkkkQiwZ133ln8fHBwkNtvv51kMhmIUWuYmnq6973vfQu+77ou4+PjDA/7yvnb3/72Wk6zpqlkorrsCUj5GLKCgLvaTAK70x8AmmOnKhajAKzujUxF2wkNHyM0egLF9gcvmXF49v+T7PsXycBecG0Yef7c4+22LnIbdmJ1DtRumuy5xZhR9c6k5+l5chuOlISoaR39+UCICliY1RKkvLbS5EHN12f1Up/upRA6BkBo6HDFYhSA3dHP9CWvJH7ol6j5GTwbXvhHi9FnXPb+oUmoY3ltX0rJoe9YHPlBuRB1OYW+bRXXaSG0TBIxG8C83lZRrjlDvq9MzJ7R0Pd1IJzFv3sr9BPrVYiaQ5nRMZ7qxH7RtG+ZqEisxCihsa11KV+b7sZuG0fqFnp6DC01gtNeeTuUukn6wlcRGXwKY/IkAMOP5EkdtbnwP8eJ9C9/iGilPZ77XymcrD9Jtdt6yW29rCqRTDgW6sxsvCjLRHHrs5AkkbjRJFbHCNJYx654y8SNdjLzolcRPv6rojjomjHymy5d9H5z2vrI7LyG6JGfI6TLzPM5Tv7vUQbe1EXy5zNM/Xv6HIshNxRHuA6K7bt/pZ7OknomS/tVUXpe247RtXqilJSS7OE8uZMW9qSDPelgTbrYkw5ykbiHUlGx2/pxOjZgt/WXxCXXRrFyvjBl51CsHMLOI/UQVs92pFb/kBJ68kwx/qGW70ZUGTLYNaZxQ7OJBSxlUauowE0vYDn09fWh63771nU9CGS+DqlJjHrqqacWfF8IQTwe5+qrr+Z3f/d3ufrqq2s5zZpnORPViiYg5ZZRFQgwih1CKYTwzDz6zBRKbgYvXPkE1gvHyG6/lOzWizEmhwgNH0dPjiIAKeHM0/P3l4pKoWczuQ07cKOVm/OfDzWbLmXSs6rLGrIQnp4nXy5EpeaEqOrFs1aYYAbUxmoIUl57SaRRc/URo9RsO8I2kLqFMT2Gmpmuqt26sQ6Se19F5MRzhIf9OBYTL7g8fFuWi3/PZMPVS08+Dn/X4sj3yoSo7ZdVHMdqMcpd9JQ6iVESiROfwOo8Dcqse++cmO0GQtRaQdi+lZt19RjoEjeWxJvcgFKH+FECBWNygEKfH2cjfHIf6bZeEFU8g1SN7PYrcdp6CJ94GuG5ZIdc323vd2P0/pqJWEJQci3J8/ekyI/797MbbiOz8yXV1QfQ0mOI2UA05RkEq0Xi4cQn/aQu+nyXPJHRUEZD5417s96Rmkl25zXoU6cBid2xcVm/q9vWS2bXNUSPPIrwXDL78xy+7fQ5+znxHgq9u3Da+kB6GOPHMIcPojgFkDD9eIbpJzJ0vDRG12+0oXdqy7aela7EmnCwRmwKozZKSCF+SRi9fXlTH+lJ0s9kGXtgfpD2RY9RNOz2fuyOjThtvaAscC5Vxwvr863KGow+KzYDaLnqXKokEit+vFTOibZFx73r3sIwICBgWdQkRv3kJz+pVz3WPYtNVCuegFRpGQWgznTgmUMAGJPDVVlHFVEUrO6NWN0bUfIZQiODmCODqJa/IumGouQGtlPo24rU6h8fQCuLFyXqJEbNCVFSm3WrSWvozy09iVyMVphgBtSHlRakZPvsxEvWT0wRCPTpXqxuP/5EaPgomZ0vrq4wVSO7/TLsjgGiR36FauVxsvD03xUYfdrlwjcZIMApgFvw3XndvMTJS6aOegz+v7IYUdv2UqhDwPJy5sWLqkNqeU+1sbpP4EbK+qYpY1nuva3QTwRC1HyEJ1CHw7ibsyB8EdJIVm7BtBBqJoGSH8MLZVHzafTJ09hdm6usqMDq3ooT7SB69HHUXArPgoP/d4bkQZtdb46hmgvfn9KTHLw/Tfq4/0z09BAzu18GavWimzZdFi8qW/2EXSJx2saxEyPFZ/YcIqWjnoyiTBkrGhy9JRECu3NTxYe58R4yu15G9PCj81zTJH55hd5deOXxzoSK1bsLq2sb5tgRzJFDfhB7F6YemWHqkRlQQGtT0RPabPDvUjBwaUsKozbWqE1hxMYadzg7TvfwP0N0V4i2KyK07Y2gRs613pGuZPqJDOMPprBGFhah5tzuPDPq/zcieOE2nHgPKM1lESTsPFrab1PCMVHs6gReJzyGp/turWJGRx2rfxzWgICA9UfDA5gHLJ+FJqpVTUCU0hKfqFCM0rJx7C5fjNKnx2oTo8rwQlGyWy8iu+VCtNQEAE5bV1Um/MtFLc+kVwcxyjUz5AeOzXerea4jEKICKmKlBCmpu8hIebyo+g2QtZlOrM4zoHiY4yfJbr24JkHZTvQyfdmriR59GnPCF7mGfukw9Mvzx9YoJ7PtUvIDO6s+//nQZmbFKE9B2LX1IU50ikLXyWL/AaAMh9GOxJfsp1uhnwiEqIVRhyO4m7K+qNo2jp7srdpFphyBwJgc8K10AWPiRPVi1CxeuI30i15J+OQzmOO+1dXoLwpM7rMw2hW0iEALz/6PCrSIQn7UZfxJX/SWikZm9zVIo4ZJqpSleFGeqMm92I1MY3XPt8YRUwbaqShiWg9EqBXAjXWT2fUyIsd+gfBcrK5tFHp3Ln6PqBqF/j0Uurdjjh7GHD1SErM8cJIuTtIld/4Szo+EzKE8mUN5hv9pktiFYdqvjBK/JIKUMP3LGcYfTGFPzH/2OJEOrJ7teKE4nhHxXekaOH6tJ8b48eKd7rvoVV5vKVzs2Initj7YFrSfgICAulA3McpxHE6ePEkmkyEajbJ582Y0LdC6KqV8olrtBKRC/WkewgqBq4HqoE+Pg+fVLa2sfwKB01599oZl47kYydLqqlKobSIphUe+/3jF8V0WoxUmmAGNYSUEKa+ttKIrnPpaHgqpos104rSNIzwXc+QY+Y21+flLzWDmgquxxgeIHn0KxV2eW0Rm6yXkB+ojmpejJ0eLVpxKIVLTwNsNpSn0Hi+9YSloh9pQp5aODdIK/UQgRJ0fUVBRJky87gJSc7ATYxjJ+gRiVfKxosuslhpFyaVqd/1RNXLbrsCJ9xAZfArhOThZiZNdPBixRJDZeXVVmf3mnX5mAsXyZQYlH60p6YITnS6+FmkN7UgbykzzBsReq7ixLtKXvBYQlQk4mkFhw0VYPTsxxo6i5pKz8ZVyKI616KFSKHihGK4Z9wWkUAwln0afOoVayPj7uJDelyO9L4caV8DjnFhWTqyLQv+LfIunFhGfingu5vBBzJGD/rYELddbcTG+e94xpOo/k5WJEEqq/nGtAgIC1ic1q0WpVIovfvGL/OhHP8KySg8H0zS57rrrePe73017e/3iALUy77nqv6FkSgOh800ylpp8LDXwF44ohj+QqlNRBiiBQM3GcONJFNfBmBzG6t6w7OObAimJHXqiLAuWgagxTodnZmHONS+lN7VrXq1i5nqh0ddptSfo5UKpG0tiWcMY0/VxEQLQUz048XEQED59kELPVqRRuwWi1b0JJ95F+NR+1PwMUtWQiub/P+vPiXXWNc7cHFp6kviBx0rb2drO4YYyxdfKuIl2uG1ZQnYrtOHVvs9bAfV0BK/bT+Vudw6h2CZaJlFzuQKBlu7E7hxGANGjj5O+8Dfq4iZkd20mHe0gfPIZ1Ow0wrWLAZAXIrflsqqCqM/DdYgcf6K4Wcs18lQbN5b0NxyB/kzneS0Q697OpERYOZTCDGphBqUwg1LI4BkRCn27kUakvudrBaqMHwZ+oP3Chgvnv+m5CDuPUgwAngNmBahQ3L/GC4hHhYELUbNJ9KmT6FOnUezZsBHp+SKUHe+l0L8HN74Ci6cNQEuNEDr5dFF4A9ByPShu5c9oJzyKExnzNzw/VlRAQEBAvahJjEqlUtxyyy2cPn2atrY29u7dS1dXF5OTk+zfv5/vfve7PPXUU/yv//W/aGtr3s4rn88TCtUvwPVyeY3ypooHQcsZ+IusBvgDX8/Io1ToXqKlO3DjSQDM0RMtJ0ZFTrxAaMx39cFTMEe31mxO7Jklg3B1JNT0QtTc61aYzK4Gjb5OzTBBV6YN1ONR3G3+YNTuHAIh0ZP9dTGvV+wQWroLp20CxXWInHiOzK4ray4XwDPD1cehqhF1Zor4C/9edAtRM+1oqZ6ayvT0UuYu9UQ0EKLWGUraQB2M4m7122KhZxDh6HXJ0KhP9+LEkkgjj5pLETr9HPnNe2suF8ALxcjsflnZG64vSjnW7H8b4Vp4RhQ33lXz+cInny1OnpV8BC1dfZlO+1gp/fxQuOKQBcvFHN5fzH6m2IWi8DSXhfNsjIkT5DdchNWzo/UsbZoJRUWaUVwzyuI2e2chBG60AzfaQX7jpajpMczxY2hJPzyF3bERq3cXbrQ1g28LK0fo1LMYyTL3VCnQMwPoM5W78UphY8XL3PMOdaDkA6+XgICA+lFTj/KVr3yF06dP85a3vIV3vOMdhMMlH/B8Ps9XvvIV/uEf/oH/83/+D3/2Z39Wc2UbwY9+9CP27dvHu971LqLR+qbuXg6VTISXO/D3xSgfz8hDZpGdF0DNxRGOhtQcjKlhhFVAGq1hkhsaOkbk5AF/Q4I5sgW1UPsqpGeUxCiRqd7KaqWEqPL3WmFSu5I0+jo10wRdOxUDKXC3zwBgdwz7gtTUQF0EKWNqACeaBNUlNHaCfP8O3FhrDuIB1Mw0bS88UnQRVHIxzLFtNV8rqRdmX4BYxkC+FdpsM93nrYB6MooMuXh9eVAk+b5jhM/sRnFqe7YKqRAa2Upu40FQJKGRIzhtfTjt9XEFnIeiIhUVqdd/8U5LDmGOH/c3alxEksLFbhufLQvUM42zRDJmRYzlIjyH8Kln0CdPktv6Yrxw4DmwagiB29ZLtq0XXMdP/rNQ9rtWQHoYY8cInXl+XrB4xYpjpnagONW1ASt+EhS/PGU8jDoZBC0PCAioLzUFA/rZz37G5Zdfzp/8yZ/ME6IAQqEQf/zHf8zll1/Oww8/XFMlG8X3v/99brvtNuLx+JKpixvJcgb1lQz8zxGjKsQ3/fcnlEJKzLGTSxzRHBhjJ4keeaq0PbGhZveaOYqWUXL+9a2ElRailvPZeqPR16kZr7V2Oop6tJQ9x06MYHWeQdYhl7nw9HnZwaLHngHZmjnS1WyKtud/huLMClH5KKGRHTXFrAE/3kbRMqqgBFnz1ikC4btnTs8uZmgO+f5jSFGRXceCKHYYY7JkwRw5/gTCLtRc7koh7Pw89zxjYmNNIp3dNgGKb5mkjIYR9gpmN5MC4YRR8x1omQGM6R2EJi8iPHY5WrYUr0fLThF74SHM08/BIu6PASuEqrWcECWsHPrUKUInnyb2/IOETz1TFuhdw5jeSWjy4qqFKFebwQnPJhNwBfpg83q4BAQEtC419bwTExNcd911i+5zySWX8Nxzz9Vymobwve99j0996lO86U1v4qabbiISqd/K2fj4OBMTE8XtwcHBJY9ZzDKj0oG/yKt+OlsFPL26AamW7sTu8H3EQ6Mn6pZVr1Hok8PED/6quI6qT/WgT9fmWjOHxMMz/OsosuqSk8mFWC0hqnyfVpjkllNNO1qMRl+nZp6ga2ciCA+cXWkAnPZRQGJMbqzZ6kdL9WDHx5FGAX1mEmP8JFbPljrUeuVQculZIcqPe6jko4SGd9YlA6FUneLEWOQWf+Q2oo2uRjsKWBghBfoLCey9k8iIizTy5PuOExreUYd22I0bSeFG0ih2nsjgk2R2/lrzu4JJSeT4E8W2p2ba0dKd1ReH57vo+Ruop+sztjtfOzKmdqG4/jmEVBGued7f0kztRMv1UGg/itRyCCShkYPoydPkNl+O2zYrVknPj4dkZVGsHIqVRVg5pB7C6t6O1FvDUj2gjkiJkk+hzUygzkyiZSZQrOyCu2rZXoz0FoSs3opfIrHajjF3K2sn4whrBUXdgIbxt3/7t7iui6rW9nvWq5x6lxXQetQkRkWjUYaHhxfdZ3h4eFXc3xbjRz/6EZ/61Kf4gz/4A97whjfQ1+ebs09MTOB5Hqqq0tlZ/WDo29/+Nl/+8pcrPm6hiXA1A38hBSKnIqMu0iggkRUPdBU7hJKP4IWyaJlp1JkkbixRcV1WAm16nLb9jyFmLTK06U70yYG6le8Z+WLsiWpc9FZbiCrft5UEqWrb0UI0+jq1wgRdHY6AnBWkRCmmijGxqaaJsJ9ifhOFfj/FfHTwOazODf5Kcwug5GZof+5nKLOWJEohUjchCkCWxYtScucvs1Ftc7XaUcDCCEdBfz6Bddkk6BIvksbqPoUxXod2OLaF3Kb9oLroySGM8eNYPdvrWPv6Y4wdR5/2rS+Eo2GOba7pOjixKaQ2a904aaIsIQAvl/O1I8UNozrLH+Oqdhvh8b3Y0dPYsdMgJGohQ+zwIziRBIpTQFh5xHksV82RgxR6d1Ho3QVafbOkBjQfopDBGD+OMTGI4iyyuCxBsWMY6a2odu0WTE54FM/w3ftFVkMdbq55XED1bNlSn8XCepVT77ICWo+antKXX345P/7xj7nhhhu46qqrzvn8V7/6FT/+8Y+59tprazlNXTlw4AC33XYbAwMDXHvttfT3+y4mn/3sZ3nyySeZnJzENE3e9KY3ce2117JhQ+XBu9/4xjfy8pe/vLg9ODjI7bffvqxjyyfCtQz8RVZDRl0QEqkXEBUGMQc/kLkV8ldeQqMnyDShGKXOJGl7/lGE51seqOlEzYP6sykPXi5mKmsyzSJElR/TKoJULe2onEZfp1aaoKsjEZACZ3fKF6TaxlGsEHq6NitCLdeGk23DjaRQ7Dzh0wfIbbm4TrVuHEo+41tEzWZUUgrhugpRMN869Xwuvo1sk6vZjgIWRuQ130LqkilQ8JMAFMLo6doydymujjm2hUL/McAPCO7Eu/FC8SWOXB3UTJLwqWeL28bYloqy/56NRGInRkvln6qfxXu92hGAQMHIbEbLd1FoP4pn+BarWja59LGeS2j4AObYUQp9uyn07GwO4X8uYHsNWfMCSmipUYzRw2ipkYVHs1JBsWOoVhzFiqPacYSsz31wdtBy7Vh7wxIABAQEBNTUc73jHe/g0Ucf5QMf+AAvfelLueyyy+js7GRycpKnnnqKxx57DNM0ecc73lGn6tbOnj17eO1rX8vDDz/Mv/zLv7Bp0ybuuOMOHn/8ca644gr27t3LkSNHuPvuuzlw4ADveMc72Ly5sgwU3d3ddHdXP6isx6C/1ox6ANpMAqvrDCgSc/QEuY278czmCV6oziRp3/cIijuX9SqOOVrbqupCuJF08bVSgWVUswlR5ce2giBVazuCxl+nVpygq6Nh30JqTwoAq2MYLd1dc7sxJjaSC6dBSMJnDuO0dWMnGhBEuU4IK0/b84+gWrnZ7RCh4V01TYYXojyTnljAMqrRbXG121HAwigpA+1QW6kddp1Bm+moWQjVsu24KT/LpfBcIkd/ycyLXgFKc7k/CKdA9NC/I2bjJWnT3Wi52iw6vPAMcs6lflpHSdfPcqge7ehsFDdCaPJinPAoVuwEqA54GoprIlwD4Zqzr02Ep+OEJnAiIyAkwrUJnXkeY+QwVu8uCr07QK3cclvYedTMJGpmCoSC096HG+lYnnun9NBSo+iTJ9DnstElNmB3bcWJ9zS/i2iTYowcInx63/w3pUAtJFCtNhQ7jmJHEbWF/T0vVuz0/KDlqcAtNCAgoHHUNOrevn07d911F5/85Cd59NFHefTRRxFCIGfdpTZs2MCtt97K9u3NYSY+549666238jd/8zc8+OCD/PKXv0RKyW233cbll1+OaZrMzMzwne98h3vvvZeNGzfyzne+EynlqgY5rxRRKA08peossuciZXgaWiaBE59CcWzi+3/B9KW/Dsrqr3xp6Una9v17WdarKObItro/nO3YJG5s2t9wREWWUUtN4FpBEFrvtIpwVynqWBhnSwbCrj8BEh7UOAlWnBD6dC92YgQhPeL7f07qwpfhtNcndlvdkBJ9epTI8VIaeWGZhIfqL0RJJG40ObexoJvveugnav0Oa1UMU8fCuL15ZIcFioenF1Ct2q15jIkNuCFfmNGyScKnniO3ZW8dalw/wiefLbodKfnovADs1VKe9VYdaZ6Fs8UQCPRcH1quF/AQnL8fVu029OwAdvQUTngMBCiuRWjoeczRQ1idm/GMCFIP4ekhpGbi6SFfpBICpIeSS6FlJn0BamYS1Tor3fLwfjzNxGkfwE4M+KLSWUKmkkthTAyiT50qWpXOYUydwpg6haeHsTo3Y3dtaVrLvGbEGD0yT4gSroGW9e8PxWu8W6YUti94QhC0fI3ywAMPUCgUME2T17zmNateTr3LCmg9ah557927l3/4h3/g2Wef5dChQ2QyGaLRKLt37+bSSy9tKgFHVdWiIPWhD30IIQQPPPAAt9xyC1dccQW67k8UYrEYv/M7v8MLL7zA1772NV73utdV5a63qpQH2RbVZ7cyxmcHtbqNnp4keuxZMjsvq0MFq0ebHqft+UeLFlFKLkpoaHvNWa/OxtPzWD2nS+c9Eke49TtHLULHA943qp6grYXJ7UqyVgWpYr/gqnVzS9OnBvD0PG50GiE92vY/ysyuK7G6Ntal/FoQjo05doLQ8FHU/EzZ+/qsRVT1wV7PhxtOzYtfI5zK+49muP+C/qZxKBkNt8Oa3fDqUqaQKuboVvIbDs1aNh9BsTLkNl6CF159YUCbHsGYmM3S6yr+QlIdnt/lC2/lC3KL0Sz3p2+ZunSdFTeEmdqFntmIHTuFExoHAcK1MceOLniMFApSDyEcq5RtbbFzOAWMieMYE8eRiorT1ofdPoBwbYyJE6i55LkHzQn5cxY1do7QyEFCIwdxop1YXVuxOzZWZb3V6gi7QOj0PoRTwA2340YSeOF2PDM6z3pMHz9G+NQzpe30JvRMfcNOLIUdHfYXpwB1NBIELV+DfPGLX2RsbIyenp6ahJ96lVPvsgJaj5rEqDvuuIMdO3bwu7/7u+zdu5e9e5tr5Q0oKq1zlAtSH/zgB+np6eHqq68uClEAUkoMw2Dv3r385Cc/YWJiogXFqNJLKaof4ApPwxzZRn7jYd/9ZugoTryTQm9lrov1QJ1JEj5zBHPsFGI2PoGSjREa3lbXGC/gX7N8/2ApPfRwGHWs/iutKy1INcvAu9VoBkGgnkgkGLP3tlO/1VaBwBzdRqHvGG4khfBc4gd/QW7DbrJbLlqVeCJqNkVo+Cjm2ImiS1CxvrZJaHgHituYFWcnXsq+pQxX3380w/0X9DcNomzhqJZn9dmoVgRjYiNWzykA9OQwWnIEq2cb+Q0Xrl5GNtchMvhUcdOY3Iji1kegkKpd2rCX7mta+f5U3DDm9G70mU3Y0dM44fHzLjwK6SEWyrwmRSnukB1DKg6uOYVrJktJWzwXPXkGPXlmwePVQgIt14taSADgmlM44TFcc6qUiS0ziZaZRJ56lvzAi7B6djaFhf1yEHYBJZ9GKaSRehinra8i90MllyJ65NFi5js9NVL8TCoabqQdN5xAqhrm8IHiZ/rMRozMyo6zpXCxI7NJqTzQzgRBywMCAhpPTWLUj370I970puY1n//BD37AN7/5TW677bZixjyYL0jNxbOac8Mrd8ebmJggkUjQ0dGxGtWvCVFuGVXjaqtaiGCMbcTq9Qe1scNP4kTbcaMrYL7ruZjjZwgNHUFPT82vVzaOOVyfFdWzsbpPIw3f/FxkNLSjjVtNXilBqpUH3s1AMwgCdcPwmPNoFXUUo8APzmuObqfQfQI35rfZ8JlDaDNTpHdfjTQqj19XMVKiTw0RHjqKnho752MlF0NP9aBm2xu26uypNm5k1sW3oKBM1Xadm+H+C/qbBuDW71l9Nnq6G6SC3TmE1GwEEnPsGMbESfL9F1DoW/ng1+HTzxcn5kouhpauPnPx2cyzjLIWHxeslftTccOYqV0YM1vx1CxStZGKhVRspGrhKbPbqo2QSjHYtWLHFow7pOf6kMLFNaZxzUmc0FTR2ql4TjuKlutBy3Uj5HwhUSt0oRW68BQLNzSOHR5F6rNx+TyH8Ol9GOPHyW++DKetd+kv6LnokycwxgcRjoVnRv2/UAzPjBW3iwsd0o+nJawcipVFsXPF10iJ1HSkqoOqI1UDqerF94Rr+8JTPo2a8/8rrjWvOk6sm+zWK5Dm0kKNNj1M5Njj57VGE56DNjOBNjMx/7jMAPrMyi/4OuHRebGihNUEgfEDAgLWPDX1NBs2bGBiYmLpHVeBX/3qV9x55504jsNf/dVf8fGPf/wcQaocIURRoAI/697jjz/Ozp07W1KMmr/aWr2b3hxauhMvlMFpm0J4Lm0v/JzpS67FC9UvW005SiFHaPgYoeHjxZTrRVwVPdWJPtnfkACOdmwSp21y9lwCbX/7fHGvATRakForA+/VphkEgXogzZKFkHDq7zYhpII5thWnEMXqPAUC9NQ4iWceIr3nJTjxrrqfcw49OUrkxHNomeT8DzwFbaYTPdWNYjc+nowTmyhaBqgj4bqIXs1w/wX9TX0Rbvmz2l1kz+rQZzrRMu3Y7WN+pjnF80WBM89jjh0lv/EirK4tKxJsWp2ZwBg94m94AnOsvglHimKUBzjnL3ct3p/C01G9drCX3nfJsqSKVuhEK3RipCSenvatpRBo+S4UZ+lxn+IZKNkNaNkBPC2DExkuxrlSCzNEDz+C3T5AbtOlCwo7ws5jjB3FGDs2TxBSrQyk5+8rEXhmBBC++OTVvx3Noc2ME3/hQfIbL8Hq3r5wu5ESY/QIodPPFu9uxY5iTO/wBUI9g6dl8PQMUp0vdmmZPoz01hV1zQOQeNjRkvWbdia2oucPCAhYv9Q0k7/xxht59NFHGRs7d+V5NRkdHeWrX/0qiUSCN7/5zYyMjHDrrbcyMjKy6HFzQtSjjz7Kvffey9DQEH/+539ONNqCpqrlq611MP0XCIzxTSgFfxKn5jMknnwQc/g4yNrFrjmUXJr4/l/Q8fi/ETl5YJ4QJQohjNFNRAYvxJjc0BAhyjUz8+NEHY6j5FZmdaiWIL2LDa7X4sB7NVkLwZRluEyMapCLmkCgp3oIDe0uCl6KnaftuYcJDR2pa78Bs0kNnvsZbS88Mk+IEraJMbGRyIlLMCc2r4gQJZHzXPTqGUy5Ge6/oL+pIw20jJpDSBUj2U/k5IVoqS6YbXqKnSdy/Alizz+EOtPghUXPJXL8yeIU25gaQHHq6ypYdNOzlfNO5oP7szIEAtVuw5jZgjGzeVlC1DnHOzHM1C5CE5eiWCWRQ58eIv78jzDPvACzApKSTRI+/kvi+35AaPjAfMskb+Exn0CiFjKohZm6ClHC1VEK7WiZfozU1uKzUngu4ZNPEz38CKJwlvuj5xE+8RThMiFKzXcSmrwY1Yn5At/MZkLJFxEZu5LIyFWEJi/ESG3FSO7CSG9fcSEKwAmNF4UxZdJEya2/2F4BAQGrQ02z7Fe+8pU8+eSTvOc97+H3fu/3eNGLXkRnZ+eCQcvLrZIazfT0NEePHuWmm27i5ptvZteuXdx9993ceuut/PVf//V565LJZPi7v/s7vve979HV1cXnPvc5tm3btmL1ritefcUomLV2GN5KfsMRpG6juA7xw09iTpxhZteL8czqJ1zCLhA5sZ/Q8DFE+SRVgpppR5/uRslHG/qQdiIpCn3HQfHPrwyHGhInajHqbSEVDLwbQzNYqFSLROJuLGVQUguNsW4slR8jfHoP+d7jeOEZhJREjz+DNjPJzI4X1+wmpOTSRE48jzk5P6aJUgijTw2g5tpWfHDvRqaRuj+wF1PGsoMpL5dmuP+C/qZONChm1EIIV8cc34w+3YPVeQY3mgJAy00T2/9TrJ7t5DZeDFr9J6KhoYOoed+kRSmE0abrm2VTIouWUeI88aKC+3N1UZ0YoclLcELj2PHBWddBj9DwfozJE3hGBG1mfP5BUqDmu9AzAyhOFISDp+Xx1BxSy+Op+dn/udlA7gbCNVFcA+GZpdeuCQjf+lBxkMJBKi4IB6k4SOEipIJwwyiO/yfk/GeTluvFig/iREb97fQY8RceJLfpEuyubQjXInL0F/O+gz6zEX3m/BaAQuqoVgLVStTzUleERAZWUQEBAatGTbOAt7zlLcU4S5/73OfOu58QgoceeqiWU1VEf38/t956Ky95yUsAuPbaa/E8jy9+8YvnCFLlMaIURWHv3r10dHTwmte8hv7+/hWrc70pdyuTSv1WihTHJHxyD1b3maIrmzE1QuKJH5Hd8iLyA5UHpjQmhogdfnK+O56joae60FJddQtuuhhObIpC74miW41IGmhHVyelbb0EqWDg3ViawUKlGrxOCxkt9QlKvvEDT+H5GevsjjO+qxBgjp9CzUyT27jHz7ZXRUBbY/wUsSNPzFsNF7aBMTWAmulYeVcH4WG3j2AnSla4ag2Byxej2QSp1a5Lq1Lupme3j4GQ6KmuhmR3nEOxQ4RGduCG0lhdZ/DMHAIwx46hJ4fJbr9iefF8lok+cbIUnFmCMbal7m3TM7Ol5/cC8aKC+7M5EAj0fA9aoQMrdgonMgxC+vGdyoOseyp6tg8t24/ilVnQSR3V1lHtlc8KKaSGmdqJmu/Caj+CVP3shJETT+FMnkSZi00FIAXm9E60fH1F13ojkVjxwVJcr5SBkl6l5AYBAQHrkprEqNe+9rULWkGtNvF4nKuuugoA13WJRCK86lWvAjhHkJqrv2VZhMNhXvayl6EoCprW4oH7LMWPmaBJ3EgaT7XrJuoIqWKObUbNtGP1nERqDorrEDu2j9DICWZ2XobT3r10OY5N9OgzhEZPlN70FPRkD3qyp+4Z8s6H3T6G1V1aFVLGTbQD7QjZfPf2cggG3QGLcpZ3nGdmUQuNd0UWCIypjSiFKIUeP1OllksTP/xLvMF95Pt3kO/btnSWLylRs9OYoycIDx8ple9o6MkBtHTXKsTbkLiRaayuU0itFLRFTBooE+tjYB/EkaoOkdX8GEcKoDnYncPYiRG0mQ70VA+K1TjrXDUfJ3T6Apz2MayOYVA8FDtH7OAjFHp2kNt08dKWi9JDODZ4LsJzfGHYcxGz22p2mtBQWZawZB9qnb+Tp+fJ9x8tbov0/LFOcO81H0JqmOlt6NleCm3H8Uw/2YNwQuiZAbT8yo0BK0WzEqjjl823kip3c3V1Qsk9qyKYVYIULoX2Q7ihUnIg7VRgFRUQELCy1KS43HrrrfWqR91RZlfZVVVFSolpmvMEqY985CPceeeddHV18fjjj/OjH/2Id73rXXR3Ly2itAJCCtThMO6mLCgSp30MY3JDXc+hZdtQT+7B6hzGafOD9WrZFIlnH6bQ2Y8bbccNRXFDUbxQFM8IFYM96slRYoeeQC3kiuWpmTaMsU0rYgkFs6bJncPYHaPF95ShMNqR+Kr47AfUn2rS0VdSNrSehZQyZaCeCeNu8Nue1Xnaj+u0Qve8lk2gnAlR6DmOZ/p1UOw8kZPPEz61n0L3ZvIDO3Gj7aU6F7LoyVH06VH06TEU56ygr+lOjIlNqzJ58fQ8ha5TeOGyqLoeqGciqCdiDbuuzTDBruTeX6msoa2GsFT0pztxN2fwugq+dY8icdr8RBpKLoaR7EPNNWZiKxDo072omQSFnhN44RkAzLGjaKkRstuvxI35CQeEY6HkUqjZadTc9Oz/FEIuz71QS3WhT9XX4txTbfL9R0D1rSPFtI56qgXjfK5TFDdCaOpCPD0FAhRr5d2qq2HOSkrLd1FoO4rUfMt+xYphJi+Yb83VhHiKRaFjP54+67IvQTvajjq9AtluAwICAspocfOf5THnSjgnSEkpueeee/jQhz7E7//+7/P1r3+dw4cP87a3vW21q1pX1NMR3A1ZUMBun0BP9iK8+v7kwtMwxzehpTuxuk/hhfzJpTk5DJPD8/aVQsENRZC6iZ4qX0VSMCY2oqVXzq1GIrG6T+O0lwUZPhFFPdHYuFQBK08jJrLlk+pWmygLBOrROF7CQkZcvFAGN5JCy7YvfXCdUOwQoTN78EIz2G1juJFpP96H9AiNDRIaG8Ru68YNx9Cnx1DzmYULkmBMbkJLda+CS56L1TGE0zZG+anFlIF2tLGJD1pNiCo/JhCkzkXJ6Cj7E0jTxR3I4vbnQPNNGL3wDPnwDObwNrRsonF1cAxCQztx2saxOs+A4geFju3/KU68B7WQme9GVSH6VB/6VH99s+cJl3y/H8MSQGQ09OcTLWvVvF7xg6Sv3POnnqhWgvD4ZbMWUgIt29uQ5Dr1xNMy5Dv2lzL5OQL9YEcgRK0TOjs75/1f7XLqXVZA67FmxKjy2E8LUS5IXX/99SiKwt13380nPvEJIpEI9957L5s2bVrBGjceYasoI2G8gRwoHnb7BMZUYwLJq4UIodO7cdomsDqHi6uU8+ojPbTcDORmiu8p2Rjm2GYUpzEZvRZC4lHoO4Ebmy6+px2Jow41NpBzwOpRz4nsQpPpVpsoCwTq8RjORX4bsDrOoGZXdkVaIFDzcdR8HE8rYLeN48Qmin2HnhpHT42fe6CnoObis39tdc/ItRQSiRObxO44g9Sc0gd5Be1YHGXCbOh1bFUhqvzYQJBaGFFQ0Y7HUU9G8XrzuBuyxcyXVvcp1FOxui8ozTv/bAZMNRf3raRCWQSgp8+TMVn62SoVxwBPQUil9L/stVKI1N0VWCIp9A4izbz/Rl5B35dAuM0tBASsPQQqenZgtauxLBxjikLiEMzFki2oGC90Btnz1hH33XdfU5VT77ICWo+aRjVvfvObl9xHURQikQhbtmzh13/91/nN3/zNWk5ZJJvNUigUiEQimKZZFJuWEqTAd91rb29HVVXi8Thf+MIXWjdr3hJopyJY/X6WEbt9DD3Z3TBXFn8g242W6kTqFp5uIfUCnmaVtrWCn63OUzAmBtBSKxffReLhRtLYiTG88KylhQfawTbU8ZXNmhew8tRjIrtUSvtWmigrkyYipSPbbKSRx4lNos90rU5dHBNzciPGVL8v9LSNIY3ZhAZSoOSjvnCVi6MUIqtmvegaWayuU3ihMkstF9RTUdTT0XmJIxpBqwtR5WUEgtT5Ea6COhRBGQrjXJjE67KQmoPVeQZzfEvDz+9bLu7GToxid/gBpvEUFCuMUgijWCH/tRX2hadVwO4YLmYDxBbo+zoQdnPGGAoIaAbs8DBW27FSoP8ZHWN/Z9BuAgICVpWaxCgpJa7rMj7ur17PiTzT09O4rq+6d3d3MzU1xeHDh3nooYf47ne/y6c+9Sl0vXoV/qGHHuJb3/oWg4ODdHd3s3PnTt797ncvO97Tvn37+OxnP0uhUODzn//8mhWiAERBQxkL4fXmQXVx2ibR65xS+ZxzoiDsEIp9rsmvRILqgKutyIRyToByY9M4kWlQy2JbuKC/kEBJNrdvf0D9qGUiu5zJcytNlAUC7XgMe68fvNTuGELLdKza5BL85Ah6ugct3Y1nZkB4KIXoqgeylYqD1XEGJz4xzyVPGTfRjsURhcbXb60IUeVlBYLU4ggE2pE2rPYJ0Pw4UtpMB2q+8YGRBQIj2Yc+3Y1UHYRjNI0LuxOZxu6YzVgpQT/QjpJfM4b+AQFVI5EgXKRiz/vz9BmcSMnCUZkIoR9OILzAkjAgIGB1qakX+ru/+zu6urq44ooruPvuu3nwwQf51re+xYMPPsjdd9/NlVdeSVdXF9/4xjf46le/yktf+lKeeOIJ/vEf/7Hqc/74xz/mtttuIxqN8oY3vIH+/n4effRRbrnlFn70ox+RyZwntkgZXV1dtLW18ZnPfIYdO3ZUXZdWoTyYp50YQ7K8YKONQCAQrt7wQa0bTlPoPUF223MUBo7jxKfmC1EFBX1fRyBErUOqmQBXckwziAbLRUkZKBO+i6zUbOy287jjrDACgVqIoebbVlWIkkjs+BjZTc8XkzQAiKyKvi+Bvj8RCFGrVGYzXJOVQFgq2vFShqtCz0mkWLlnuJAqitNY19NK8PQ8hd7B4rZ6PBY8xwPWFRKJa0xjRU5TiB8jn9hPrusZsj2/Itv3GNm+x8n1PEW+6zkKHQex2o/NE6LU01H0gx2BEBUQENAU1LSU9MUvfhHLsvjiF79YzF4HvjvcxRdfzJ133sl//s//mXvuuYcPfOADfPzjH+cP//APefDBB/mDP/iDis4lpSSXy/Htb3+ba665hr/4i7+gu7sby7I4cOAA9913H3fddRfvfOc7ueGGG4jHF145dByHgYEBvvCFL9RkndVKKFkNZcLE6yogNRunbQo9tTruOI1G4mH1nMJpmzr3Q0egTJgo4yGUpBEEOV3HVGJZUa141SqWG+pgHK/TF1rsxLAfi8kK4qdJ4VHoGcSNJktvOgL1ZBT1TGTF+o9mEF0aeS8HFlJLowyHET15ZLuN1C0KvccxR7etqhXjajAXJwrFF+OUMRP1dNBXBawfXH0aK34Cz5hZeuez8UA71o42GmSbXM/ceeedpNNp4vE4H/zgB1e9nHqXFdB61CRGPfLII7zuda+bJ0SVo6oqL33pS/nBD37ABz7wAUzT5IorruDBBx+s+FxCCDRNY2hoiBe/+MV0d3cjpcQwDC699FI+8YlPcMcdd/ClL32JcDjM9ddfj2n6q2WnTp1i//79XHfddWia/5VXQ4i6+5efYs+ePXUpq9IBuHoy6qeNBuyOET9z3RobyEokVu8p3wpqjkCACjgPjZ7kt8pEWclqKCMhvP48KB6FviOEhi5Y8cDgzYSn2hR6j+KFStnDlNEQ2rFY3eNrNIPY1MqsVDur9neaq9tixy9Vf4FAO9yG/eIJUMCNpsgPHCY0vKOhAc2bDTcyjWf6GXtFRkU71L7qFltScfAUPyuZkMIP3I6y6vUKWFu4WgY7fgLXTC68gwRsBWEr/jOq+FoBW0XYCkpGD+JDBfDzn/+csbExenpqC9lSr3LqXVZA61HTKCaTyZDNLp7qN5PJzHOda2+vPn2r4zgYhkE6nQZ8aykpJYqikEgk+OhHP8rHPvYxvvSlL3HBBRewZ88estksX/3qV/ne977H5OQkv/u7v1v1+VsZZUY/yzpqouGxo1YaOzFaEqJc0I62oYyGAgEqYNVoFUFKO9qGHXH9YOaaQ77/sC9IuevDerQc18xQ6D2G1OzZNwTa/nbUqcaIc7VYBq0F6vHdV6KdVfM7lddpseOXU38lp6E/l8C+cBo0iRfKkttwiNDwjnUhHEtkKU4UoB6PLythQKPbVqHzhXPflIBUfRdjqfj/PRXFiWLMbELI9SMgBlSPROKaU9iRYTxzet5nIquhnomh5FVEQQOrsQLoD+2vAXC9/paGnSMgIGB9UpNpzLZt2/jRj37EmTNnFvz8zJkzPPjgg/MChI+OjpJIJKo6XyQS4YYbbuCnP/0pDz/8MIqioCgKUkoAYrEYf/7nf048Hud//I//UTzmFa94BZdccglXX311VeddK6iDUX+QBFgdI9jxCTzNWt1K1QknmsTuGi5uawfbUUfCgRAVsOq0gtAgPIH+fAKR8SdJUrfI9x9GKs4q12xlsWMT5AcOlYSogoL+TEfDhKg5WkGwbAT1bBsr0c4q+Z0W2nex45dTf2XaRH+2Ayx/6CaNAvkNh3CNxRcF1wJuOFWyiprRUKaMJY9Ztb5XAIqLVC2klsfTM3hmCic6RK5rH56ar6g4V5sh1/U02Z4nyLcfxI4M4erpJeN/SuHgGtPYkSEKbYcpxI/iGFOrGjc0YGmkcLAjQ+S6n6TQcWC+EFVQ0Q4nMJ7uQRuLoKRNhKWuiBB19uuAgICAelDT8swf/MEf8N//+3/nne98JzfddBOXXHIJHR0dTE1NsW/fPv71X/+VXC5XjA9l2zaPP/54TaLQa17zGh544AHuvfde2tvb2bt3L0IIpJQIIRgYGODGG2/kf//v/83TTz/NZZddxjXXXMMVV1xRdNtbryhZfV5mPav3FADCMlFzMdRsHDUXW/XMVZXimlkKvSeK2+rxGOrEuZn8AgJWi1awkBKOgv5cAmvvJIQ8pJEnN3CwlL7dU2f/K6VtV0OxIogVyo7ZKCQeVtdpnLbx4ntiWkff375ibg3rzUKqEd+1WSykFqtDzRZSGR3j6U7si6eQEde3ZNxwCDXXhjaTQM2ubtD/RnCOVdTJ6JL9zUq1JZE0EO7suq6QoEqkKkGRSNUD1X9vrrpSy5HrepbQ1AWo9tKeAnZ4FKvtqF824IYLuOGJ2cIEih1DtWMoVhxQ8LSML35pGaRWOKc8JzoCnopaSKDlu1CtxJq7X1oVT81hR4ZxwqPFuGhziLyKOhRFHYmu6CLrQuLTD+2vBRZSTcTIyAjJZHLBzwYHBxd8PyCgmahJjHrlK1/Jhz70IT7/+c/zjW98g3/6p38qfialJBwO84EPfIBXvvKVAOTzeT70oQ+xffv2qs/Z1dXFRz7yEd7//vdz7733csstt3DJJZcghMBxHHRd57rrruOee+5hZKQ0eFnvQtQc2mAMq82CUOlBJ40CjlHAaZ8ACUo+6gcxzsVQ8pGmnmR6mkWh/xgo/kBNGQmhngoCmgY0Hy0hSFkqxr4OrL1TYHhIo4BrnDuhOQdXQy2EUawwihXxBSy7eTJwLYYfH+oYXqjkTq6cCaMdi6+4ZeV6EaQa+R1XW5BazrlrFaREQUV/phP7oiSyzQZF4kancaPT4Cmo2VlhKte2JmJDeuGZYvw2kfETsizGSrYh/UQbSmZxKy2JRIZc7D2TyIgDikO+8wWM1Hb0XN95jvGw2o7jREbK32RelyoknpHGM9IQHVp+pRUXNzzhi1pSoBYSqPlOtEIHQi7fNVvi4RkpHDOJZ/jWO4rVhmolUK2VE0WlsHHNad+S15t1i5x1kyy6SnoqyOZcNJG4FNqP4IYmOLt6StJAHfIzRq503RezggoEqeZgZGSEt73tbeTz57e2DIVCVXskBQSsBDU7rr/+9a/nla98JT/72c84fPgw2WyWSCTCrl27uPbaa4nFSimJ4/E4v/7rv17rKdm1axef/OQn+eAHP8jnPvc5br75Zl7ykpcUg5MfPXqUeDxeU3yqtYooqBi/7EbGHLyOAl7CQsbtksOmAC+cwQtnsMEf2OajKLkYajbmTzKb5GEuhUuh/xhS812JxLSOdritaeoXEHA2LSFI5f3YNM6eaWTEXd5BqoMbSeNG0qX3PAWlEEHNx1FzcZRC8wnbrpGh0FcWH8oD7XAb6mh41eq01gWplfhu9WpnEolMzAanThrz7t+FfqdK3fhqEqQcBX1fB+6WGdzePBizC0yKhxtL4saS/vM7046e7kTNL5xhuBWwEiUX/KWsopqx7QgEIq9h7OvG3j2F11EAIbHaj+JpOYz01nnfyVMKFBIH52VMU4cjaINtyJCLF7fw4hYybiFD5+mjXYHIan7Q6oyOktGRhovbmcfrzIM2G7NBSNzQFG5oCgsQroFiR1CcKIoTQbGjCDdUrJ+n5nDNJK6RxDVTIOZb8Hh6Fic6XLLaKrSjWgkUO1a3/l8i8fQZXHMK10ji6ZlzRJwF8VTUQgdaoQO1kGia2F127FTJ2g3AFajjYdShKEpudeI2LscdLxCkVp9kMkk+n+ejH/0oW7duXXCfRCJBX9/CondAQDNQl544Fovxute97ryfu66LqtZ3heSiiy7iM5/5DB/72Mf4y7/8S9761rdy5ZVXkkwm+fa3v004HGbHjh11PedaQSAQMzrKjA4nQaoeXruNlyggE9b8CajiFSeZdhfgqkWXPi3dgagt7FjVSCSFvhN45uxqQE5FfyERxIgKaHpaQZBSMjr6E13+hKXM7cR/7ZVeh128qIOM2mDIswrxfIuG8Ax2x5Dfd+Tjft+R6Vh11xA7NoHVfbLo/kJBQX8h4feLq8xaFaRW8jvV2s6k5uHsThWz0KonI6iDsfMKUtWcq2ZByhNox+Oox2PIdgu3p4DXlQd99p5WPNz4FG58Cn2qD32qv+kE4aVwQzN4Yd9qUWRVlPHzW0U1e5sRroK+vxNnawp3g/+dnOgQUsthJncjpIZrTJNvPwjqbLw+D7SjCbQx3+JbZBWUrA4jUQCkNitOxWwQFMUnkV8gjlAG1Kkw8qjEayvgdeZxO8uETECqFq5q4ZIsHecpKE4EqdgLuv75B859ybn/Jastm1OzAdwjsxZKapn1klKyYvJU8HSEpyM8zf8/O8aUeDihCV+AMpOgLHOhpBzFxQ2P44bHfYswqw0134WW60Zw7vNI4uEa00itgBQuwlPR8j11fXZ5ah57zqrNA+1kHHU0inBWz6KxkrhQgSDVHGzdurVu2doDAlaamsSob37zm/z2b//2ovu4rsvHP/5xPvGJT9RyqgXZs2cPd911F1/+8pf5yle+wpe//GUikQjxeJw77rgjSBG5TISroE6aqJP+IE+aLl67hZew8NotMMtWvlQXNzaNG5vGaZ/AHN664pl8fLeaE3iR2VVDZzb48io+vAMCKqEVBCmBAEeAs/Sis0SC4fnCVMxGRh28mAPlq/aqixtN4kaT2B3DGBObULMrn5rdjw91CqettBK90vGhlsNaE6RW47vU0s6cnSUhCsDdnEVJGojp+c+7WttxrYIUzC4wTZso0ybySHxWmMr79Z+1gLE7RpDCw5jc0BKClKfncSMp7Pax4nuLWUW1SlsRCPTBdkROw9k+DQq4ZpJ8xwuohU7s2IlSh5tXMQ52LOoGKBwVdSqMOrV8a04hBep0CHU6hHZMImM2blcOL2YjI3bJamoOxZtnpVXEUlCTJkoyhDJtgsQXuRIFvPYCMly+sOn67oSV4qkIT0dq53dDEhnNr0NOQyoSVM9fQCmP3aVJvJg13yLMnMY1/QDvoeQeFDfsC1BmEjc0gWNOnSN6OeEJwpMXV/49FkDiUSiLB6YOxdDOrK4FYzUBygNBKiAgoBZqEqM+97nP0dnZyW/8xm8s+LnneXz84x/npz/9aS2nWZSNGzfy4Q9/mN/7vd/j6NGjtLe3s337drq7uxt2zrWOKKioo2HU0bDvphB2keXi1Oyqq2fmyG0+iDm6GS2TaHi9JBKnbQKrcxjU2QGCB/oL7Si5lTW3riWAbUAAtIYgtVwEAiwV1VKhLPOcNF1/YpLw+4+5vkNqNoW+Y6jZOMbE5hUTtD3N8uNDmaXsY6sVH2o9sVh/Wa82UKugcw5NcjtU+qwRUiCSJkrSRB6WuBuyuNt9IcFJ+MJOMwpSEg83PIMbSeFGUkh9fqZfkVVRxhZOTNIqQlQ52mgUkdewL5gEXeIZM/MEHyVpoh/qaPgim28pb6DM+IKXRCJNFxmZXVSI2Mio7bsDeqCkDZRZAUpkz43BVC6MScPBnRWm/LFjFVn8FBd5thWUI4p1UJPmshcRpJC+FdmcRZjplyv1HLmuZ1ALHUtaXUk1V/l3WKgcJIX2I6VMeZaCdjq2+EFLsJqZ7oIsewEBAdVS0wz+0ksv5fbbb6etrY0rrrhi3mdzQtRPfvKTJa2nakVVVXbs2BG45TUAgUDkNMhpqMMRf6ASt3F2p3x3PsWj0D+IOz2DMbGhYcFSnXAaq/s0sjyYckFBP9iOMr10iud60ooD34CA1UAUVNSRCOpIWd+xJYPs8CeabiRNLvQCxtQAWqq3oRNkJ5yi0HO8JGS7oB1pXHyoWty3yo9fy9RTjK23JZl2uA3bnELGfXcpZTSEaMCzppY6L+nGJwXaad/lx9mVAtFcgpRE4sSmfIvJ8Mw5GcTmEBkN7YWFrShbuZ2oKRPxXDfWJePzrJHUUzG0k/FV+X0EAlHQoKDBVOl9qXj+p97y6yQsDW1Ug9Gobz0rWNhqae615iF1D3T/v9Tc4ms0CZaCfjSBMlVdMG8hBWrKRE2ZaMfbkDEbe2dyNqi8Nz9uE/ii12QIJaPjbE/5Zbj1ydRsx0747oIAHhgHOktZGasgEIMClsurX/1q0uk08XhtVnj1KqfeZQW0HjWJUXfccQd/9md/xkc/+lE++9nPsnv3bsAXom677TZ+/OMf8x/+w3/gfe97X10qG7D6CAQibaA/3YmzM43X65tOO+0TeKFs3d32PD2P1XUGNzrfvFsZDaEdja+4a14rD3wDAlaTYt/xnI7XVcDZkfZdgBWJ1XUGJ5bEGN+MatU3G6ZEYidGsBNDJWuXnIq+vx0l05j4UOX9RDWWOeuhn2mEVWA9BalSgPAMSFBP1C8A8xyNFKLKUUd8wbVSQUoi8UIzgEDJLx44vBIkEjeSwuo8M3+BqbQDIqWjTJookyYit0D8I9ZGO1FyOsYLXVgXTIIA/Wh7RS53K4XwahtrCYQfV8pREU7lx0sh/fuiTvfgnEWY8Ww3zrYUbt+steysAKVOhFGmTYQUeBG7eJzi1C5G2ZFh7NgZf0OCfrCjaJlWDYEQFVAJ73nPe5qqnHqXFdB61CRGRaNR7rrrLv7kT/6ED37wg9x9990MDAzwiU98goceeojf+q3f4i/+4i/qVdeAJkK4CtrBNrxpA2dHCtT6uu1JxcHqGMFpH5/nLiFSOtqxGEp6Za2hYG0MfAMCVhuBQJ0IoSQNnK0zeAM5P4unmSW/4QD6dB96sr8uVpZScSj0DOJGUsX3lEkD7UB7TavQi7FQP1GJILUe+plGuqfWVZByFbRjjVmpXSkhao7lClISiWdmcWJTOLGpoiWhsA20VBd6ugvhVT90dM0sVufpYlDyIpaCMmX4AlTSWLJ9rqV2oswYmE/21VVsWWs0yo1aeL61lToWRioSJWWecy4ZLqlnwl1YKJTCASRCLr7A4ZgTWPFjxW3tWG3iYyBEBawHBgcHz/tZkC2w9ak50E5XVxef/vSn+dM//VPe//73s3v3bn7yk59w00038V//63+tRx0DmhSBQB0JI9Iazoum57vtpdLoU70VW0lJxcGJT2F1jJTcaQAKCtrxGMpYaFUGa2tp4BsQ0AwIV0E/2oY3Fiq5/QqwEyM4kSTmxBbUfPUxNFwjS6H3WCn2jAR1MIp6qn4WHmezWD+xHEFqPfQzKxEnrdmDv6+0EDXHYoKU1KyiALWQtZLULeyuIeyOYbRMAm26pyIrRk8rYHUO4caS894XKR3teAyR0pfdLpv5t62WIGbd6qKkzz9WPZ9llETimlM44VFcc8rPZmhHUAsJ1EIHih2bl3Ha1VMUEoeKC6zqqRjabFbEagiEqIC1TiKRIBQKcfvtt593n1AoxP333x8IUi1MXaI+b968mb/5m7/hv/yX/8JPf/pTbrrpJj74wQ/Wo+iAFkDJ6ue67bVN4rRNouRi6KlO1Ez7eS0d5kz2nfgUbjRVSrUO4IJ6Kop6OlpRrIJ6shYHvgEBzYKSNtCf7MLdnMHdlAEFpFEgP3AIdSaBWogi7BCKHUI4C09YJRLPyOGFMrhmBs/MzA+AbAv0A+0oycYFSl9OP7GYILUe+pmVDNjfrILUaglRcywkSLmR1MLuch4oEyZSlcjO2fakSJz4FE58CiUfQUt1o2Xa5x9X9gyXwsNuH5u1ci69L3Iq6vEYykRl8X+a8TcNWNvIWJkYZcfwlAJOZBQnPIpU5wfa9/Qsnp713fA8FdVq94UpJ0S+Y3+xDSijYbST1VtdBkJUwHqgr6+P+++/n2QyueDng4OD3H777SSTyUCMamEqEqO+/OUvL/r5hRdeyOHDh+nq6pq3rxCCt7/97dXUL6BFWMhtD8ALz1AIz4Cros0k0Ke7UWx/ZcnT89htEzjxJKjnBhFQRkP+iqm1eunWg4FvQEDjEVKgnYihjIdwdqWQbf7g340l51tSeAqKbRbFKYSLG8rgGVlQ5MJlpzX0/QlEoXH9SCX9xEKC1HroZ1Yjc2SzCVKrLUTNcbYgdbYQJaZ11NEwyrhZdJfzQg7eQA63L1cMtu2FslihE8yfji+BPdvWh8MVWwM1028ZsD6QSLxoSYyy2o4VraDmUVAQtjpPuEJxcUOTuKHJebsqSRP9aKJqC91AiAqohT/4gz9gfHyc7u5u/u///b+rXs5SZfX19QVC0xqnIjHq7//+75e131e+8pV524EYtT6Yc9tTJg3c3jxeX853vQFQXZz2CZy2SYyJAQCsrqH5VlAAlkAdC6OMhFCyjQkuvFyCgW9AwMqiZDX0ZzrwBnI4W2fmZZjyd/DwzByYOc6ffBtwQczoKFMm6ulIQ11gquknygWp9dDPrIYQ1QznLqdZhKg5zhakRFZFGQ2hjoUXFG6VvIZyLI46GMPryeEO5JCxCiJRu6CeifhuslXEa1sP7SSgCTH87H5zuKHyFIP4z5iRKErSt/CTmouXKOB2FPAS+XOeYWJGRz/QUfUzKRCiAmoll8uRzWbJ5XJNUU69ywpoPSoSoz772c82qh4Bawhhq2ino8jTEWSbjduXw+vO+9ZSQmJ1n5l/wKwrgDIa9gOXrkDshGBgGxBQO4u1o2on0AKBOhRBGQkjozYy7CIjDjLsICMuMuSeuyqdV1FSOkpa92PPZLWm70eCPmj90Ky/tToSRkzroPhi1HIsNYQnUEdm22fcxu3PIU3Pz5RWjiw1U5HTfFG4SivnZr1+a42lhI7r9besUE2qY7H6V113V4Aj5otKBQVtNIo6eu49LRwVdTyCOh5BIpExG7cjj9de8OMkHk4smpkwEJsCAgLWGxWJUZdffnmDqhFQKdWkC19pBAKRMlBSBvJo3I8JszFbGqFKUE9HUE9Wt1LazNTy+wQD7/VDK7TjaqjHdxKeQKQNSM9/XwrpC1Rhx1+ZTusIe/VceQOam7n+dLXaWT3680b2E0q+utChAr99rkZm24D6sxwR5If21xomSM2dv9ryGyJE4YegMJ7vwt42DQK0MzGUyeUl0hEIxIyBMmPAyeWd73r9LYEgFRAQsK5YWwrAOqOVRAvhKmjH4+j7OhAZDZHR0Pd1oB2Przkhao5q3XcC1hdr7Tdv9KRfSIGS1VAnQqiToUCICjgv5W1rNdpZPc+51vqJgOahEvGjEUJJeZnVlN8oIWoOJWNgPteDua8HdTLc8IzOzW6BFhAQEFBPalIBvv/973PzzTczPj6+4Ofj4+PcfPPNPPDAA7WcJmARWm2AqkwbGE92YTzZhTK99ldUKw1sHLA+WSu//Vq08gpoTRZqUyvZzhpxrrXSTwQ0D/UWf+pRVr3EsVYWdVq57gEBAQGVUJMY9YMf/ABd1+nu7l7w8+7ubkzT5F//9V9rOU3AEgQD1OZmuSnfA9Y3rX4PBEJUQLOwWFtaiXbWyHO0ej8R0DzUIirVQ5BarIzlug2ej7Ug5qyF7xAQEBCwFDWJUcePH2f37t2L7rNr1y6OHz9ey2kClkEwQG1uVntyFNAatOq9EAhRAc3Caov/rS52BawPGi0m1ePYasWqtSTirKXvEhAQELAQNYlRmUyGeDy+6D7RaJSZmZlaTtNQpDw7BUzrEgxQm5vVdhsJaA1a7Z4IhKiAZmG13aJb3Q0wYH3QaDe7eh5TqRvfWhRv1uJ3CggICJijJjGqq6uLQ4cOLbrP4cOH6ejoqOU0DUVKied5ZLPZ4nYrEwxQm5vVDqgb0Bq0yr0RCFEBzcJqJ4xo9QDpAeuDRgcgb8T5lxvgfC2LNmv5uwUEBKxvqsvpO8tVV13F9773PR5//HGuvvrqcz7/xS9+wS9+8Qte//rX13KahvHLX/6SRx55hIMHD6KqKr/3e7/HNddcs9rVqpm1mi6+nizn+jRqoL/eJxALXfv1fk0Woh7XZD30A4tdp7X8/WttR++56r+hZPSqjl1pWt2trpEsp/5ruR2sNv/zsU+yZ8+eZe272oJCI4SolSh7JcpfznlW+/cLCKgH73//+ykUCpim2RTl1LusgNajJjHqrW99K//v//0/PvShD/Ha176Wq666iu7ubsbHx3n88cf54Q9/SCQS4a1vfWu96ls3HnjgAf72b/+Wnp4e4vE4p0+f5tZbb+WDH/wgN954I1JKhKgufev4+DgTExPF7cHBwXpVe9nUIkjNDW7X+wD2NcqbWn6i0mxUck81QzsKOD/NInov1k4bWcfV7Bsa0Y6atb9rxjq1GrWOB1aznTfL71+P59EP7a9VLWjMiSS1Hr+WqeX6znG9/pZ1ca0C1i8ve9nLmqqcepcV0HrUJEZt2LCBT33qU3z84x/n+9//Pj/4wQ+Kn0kp6enp4eMf/zgbNmyouaL15Fe/+hVf+MIXuPHGG3njG9/Ili1bOHjwIF/+8pf5/Oc/z6WXXsrmzZurLv/b3/42X/7yl+tX4SqpZhB5thtZM0w2V5NmnaC1IpXeS83SjgLOpdnaxEoLUq0iREFl7ajZ+rtmqkurU+t4YDVY7fOXU6/nUTWCydluarUcv9YJBKmAgICA1qImMQpg7969fO1rX+NnP/sZL7zwAplMhlgsxoUXXsjLX/5ydF1fupAVJJPJ8P3vf5+NGzdy4403smXLFgAuuOACrrvuOh555BFOnjxZkxj1xje+kZe//OXF7cHBQW6//faa614NlQxAzxdgOxCkmmuC1opUcw81UzsKKNGsbWGlBKlWEqKg8nbULP1dM9RhrVHreGAlWe3zn009n0eVCCbnC+Bdy/FrnUCQCggICGgdahajAHRd51WvehWvetWr6lFcQ3EchxMnTnDppZeyY8cOADzPQ1EUrr32WkzTZP/+/TWZDHZ3d9Pd3V2vKtfMcgagiw38AkGqeSZorUi1906ztaOA5psgnk2jBalWE6Kguna02v1ds99nrUyt44GVYLXPvxD1fh4tRzBZTAyp9fi1TiBIBQQszIEDB7BtG13Xlx3vrpHl1LusgNajpmx6rUg0GuW9730v73znOwHfnVBRlOL/eDxOOp0ufrZWWEpsquX49cJ6F+SqIbhma4dW6QMWu+dq+Q6tKES12jmhde6zVqbW8UAjWe3zryRLiU2NPn6tU49rEAQtD1hr3HrrrbznPe/h1ltvbYpy6l1WQOtRFzFqdHSUf/u3f+OrX/0qX/7yl8/5+8pXvlKP09QFTdO48MILiUajAMUg5a7romkanZ2dZLPZeZ9NTU0xPj6+OhWuI+dzw6vl+PVGIK4sn+BarR1are3XW5Bab0LUap271e6zVqbW8UAjWO3zrwbnc8NbqePXOoEgFRAQENDc1Oymd/fdd/NP//RPeJ5XfK88E93c67e//e21nqoqFkoVqWnnfu2590KhEJOTk8X3jx8/zhe/+EW2b9/OO9/5zqaLgVUp5Sb61U7K1rvIsNouLK3Aer9H1hKteq/Xy2VvvQpRK12HVr3PWplaxwP1rst6pdylrBrxpNbj1zr1ctkLCAgICKg/NYlR3/nOd/jHf/xHrrrqKn7rt36Lv/zLv+SGG27g6quv5umnn+Zf//Vfufbaa/mP//E/1qu+FfGDH/yAb37zm9x222309fUt6xhd17FtG/CFqHvuuYef//zn3HzzzSsmRNU6OFzq+FoHfSsRe6UZJmEBa5tGt7O1QPDdAlaC4LdYPVbi2ge/79LUKiIFItTi1EOQWm2CGFYBAQFrkZrc9L7zne/Q39/PnXfeySte8QoA+vv7efWrX81//a//lU9/+tM8/PDDJJPJetS1In71q19x5513cuDAAf7qr/6KkZGRRfefs+zq6Oggm81y7NgxPv/5z/PEE0/wpS99iV27dq1EteeJMNUIMrUe3yw08+C1mevWLDT7NQraWX1o5t+5meu2XNbCdwhofVo11lpAQDm1CDk/tL/WFEJQqwtqAQEBAWdTkxg1ODjIr/3ar6EopWJc1y2+vvzyy7nmmmv42tdWtgMfHR3lq1/9KolEgje/+c2MjIxw6623LipIzX2HcDjM6Ogo99xzD88++yz/83/+z1URohZ7r1HHNxvNOIhtxjo1K816rYJ2Vl+a8XduxjpVy1r6LgGtS6vFWgsIWIhq3SBrOb7eBILU+mJkZIQDBw4s+Dc4OLja1QsIqJmaA5jHYrHi61AoxPT09LzPN2/ezLFjx2o9TUVMT09z9OhRbrjhBt7znvfwJ3/yJ0xMTCwpSIH/Haampti3bx933333qgpRy/msXsc3K800mG2murQKy71mK9VHBO2sMTRT22imutSLZmtHAeuTIOFJwFpgLQSIDwSp9cHIyAhve9vbeNe73rXg3+23304oFCKRSKx2VQMCqqYmMaqnp4exsbHi9saNG3nhhRfm7XPs2DHC4XAtp6mY/v5+br31Vm6++WYArr32Wm655ZYFBSkp5bxjr7zySi6++GI+//nPN4UQtZx9aj2+2WmGQW0z1KFVWc61m4vT1kiCdtZYmqGNNEMdGkWztKOA9c1y7sO13A4D1gbLEZQW2ycQpAJWgmQyST6f56Mf/Sj33Xffgn/333//suMiBwQ0IzWJUZdccgnPPfdccfvaa6/l0KFD3HnnnTz66KPcc889PPbYY1x22WU1V7QS4vE4V111FeC7DUYiEV71qlctKEjNZf2zLAuASy+9lL/5m79h+/btK1LXlXQPauWJ8moOboOBde0sdQ0bnRwgaGcrQ9BOG8tqt6OAAFj8PlwP7TBgbVCr2BQIUgErxdatW9mzZ8+Cf4EQFdDq1CRGvfa1r2Xjxo0MDw8D8Ja3vIVdu3bx3e9+l4985CP8wz/8A319ffzJn/xJXSpbCXMxoFRVRUqJaZrzBKmPfOQjTExMAPD444/z6U9/mrGxMWKx2DzXw0ayGoGTW3mivBqD3GBgXT8Wu5aNFH+DdrayBO20saxWOwoIKGeh+3A9tcOAtUGtbniBIBUQEBBQG1otB7/4xS/mxS9+cXE7EonwxS9+kZ/97GecPn2a/v5+Xvayl624m97ZCCHmCVJSSu655x4+9KEP8fu///t8/etf5/Dhw7ztbW9bsTrVMlmtdaL7GuVNLTtofMD7xopN9Fv1GjUzK/n7QdDOVougnTaWlW5HAQELUX4frsd2GLA2+KH9taKgU22A89UWhK7X39IUwlhA83P//fcjpSx6Bq12OfUuK6D1qEmMKmd6eprDhw+TyWSIx+PcdNNNtLe316v4JVnqJi4XpK6//noUReHuu+/mE5/4BJFIhHvvvZdNmzY1tI7vueq/oWQCN4paqcdELBg4rx61Xvuz21HwWzYnQTttLMG1CWgGgvswYC1Qq5CznONXU7CqRWwLWFtEIpGmKqceZS2WVTCRSASujE1OzWLU0NAQn/vc5/j5z38+Lxi4EIJrrrmG9773vQwMDNR6mnPIZrMUCgUikQimaRbFpqUEKfBd99rb21FVlXg8zhe+8AW2bdtW9zo2irnBX7UTvXoMHmuZaK6F8wc0D4tZINXjPmnl+2y128lqnz+gdhpthRX8zgEBAeuBRlpQLSYylZ8zsKAKWEskEglCoRC33377efcJhUJBkPcmpyYx6vTp0/zpn/4pU1NTbNq0iUsvvZSOjg6mpqbYt28fjzzyCM8//zx33303GzZsqFedeeihh/jWt77F4OAg3d3d7Ny5k3e/+910d3cv6/h9+/bx2f9/e/cdFsXVtgH8XsrSi4IUESuKvYMVRMGCUYwNW+yaYInGhjGxYsSuKAio2GMsUWOJDVAQjV3sCiqIIFIE6WUpe74/+HZeVlBBYHdHnt91vdcbt3FPn3nmzDlbtkAkEsHT05OXhSjJf5f3IqEyT/yr+98niqOyC1KKtJ1VlLzzy/vvk69X1cuBljMhpDqpioJUWQtRxV+jghT5FhgbG+PAgQNITU0t9f03b97gjz/+QGpqKhWjFFiFilG+vr5ITU3FvHnzMHDgQKlWSYwxnD59Gps3b4avry/c3NwqHBYAgoODsXLlSnTq1AkDBw7E69evcePGDYSGhsLFxQVdunSBlpbWZ3/DwMAAurq6WL58ORo2bFgpuWThUx2GlvVCrypO/Kv73yeKo7IKUoq4nVWUvPPL+++T8qNCFCGEVL7KLEiVtxBV/D0qSFVPR44cQVZWFrS0tDBixAi5/05Ff8vY2JgKTTxXodH07t27h27dusHJyanE43ECgQCDBg1Cly5dcPfu3QqFBIqKW9nZ2Th9+jS6dOmCefPmYcqUKVi2bBnc3d1Ru3ZtbNiwAWfPnkVGRsYnf6egoACmpqbw8vKCpaVlhXPJSkWHUq7KE//q/veJ4vhcwaOi6wnf1zN555f33ydlR4UoQgipOpVRCPraQlR5PkO+PUePHsXevXtx9OhRhfidyv4twj8VKkaJxeIvPuLWsGFDiMXiivwZAEXFLRUVFcTFxUFPTw+GhoZgjEEoFKJVq1Zwc3ND27Zt4efnh+DgYIhEIu67b9++RWBgIABARaWoMZiqKn86EpfFRXRFVfe/TxTH1xak+LCdVZS888v775Mvo0IUIYRUvYoUpCpaiPqazxJCSFWoUDGqSZMmiIqK+uxnXr9+XWktkAoKCiAUCrmWT4wxrtClr6+PxYsXo3Xr1vDz8+NyZWdn4+DBg1i5ciUvK67lOXH/1ONFslLd/z5RHOUtSPFpO6soeeeX998nn0aFKEIIkZ2vKUhVViGqIt8hhJDKUqFi1NSpU3Hr1i38+++/pb5/+vRp3L59G1OmTKnIn+FoamrC0dERISEhuHr1KpSUlKCkpMSN4qetrY1Zs2ZBR0cHHh4e3HdsbW3RsmVLWFlZVUoOWfmaE/ePO16Wter+94niKGtBio/bWUXJO7+8/z4piQpRhBAie+UpSFV2IaoyvksIIRVRrg7M9+7dW+K1du3aYcOGDTh8+DBatWqFmjVr4sOHD3j8+DHevn0LKysrhIaGolWrVpUSuHfv3ggICMCOHTugp6eH1q1bQyAQgDEGgUAAU1NT9O/fH7t378bDhw/Rpk0bdOnSBe3bt4eamlqlZJCFipy4y/ukv7r/fcIPFV1P+L6eyTu/vP9+ded9d63M+k2kZU0IIZ9GnYkTQqqrchWj9uzZ88n3YmJiEBMTU+L127dv486dOxg/fnz505XCwMAAixYtwrx587Bjxw64uLigZcuWEAgEKCgogKqqKhwcHLB9+3YkJCRw36suhShCSBHajgipHOUZDbG07xJCCKlaFRmlj4phhBB5KVcxasuWLVWVo1wsLCywevVqLFiwAFu3bsWUKVNgbW3NdU4eGRkJHR0d6OnpyTlp+dGJOyEVR9sRIZXrawpStB0SQojsfE1BigpRhBB5Klcxqm3btlUUo/yaN2+OTZs2Yfny5ViyZAnGjBmDDh06IDU1FadPn4aGhgYaNmwo75jlQifuhFQcbUeEVI3yFKRoOySEENkrT0GKClGEEHkrVzFK0VhaWmLDhg3Yu3cv9u3bh71790JTUxM6OjpYs2YNatWqJe+IZUYn7oRUHG1HhFStshSkaDskhBD5KUtBigpRhBBFwOtiFACYmZnh119/xahRoxAZGQk9PT00aNAAhoaG8o5WZnTiTkjF0XZEiGx8riBF2yEhhMjf5wpSVIiqvho3bgwjI6MKd2VTWb9T2b9F+If3xSgAUFZWRsOGDXn3WB5AJ+6EVAbajgiRrdIKUrQdEkKI4iitIEWFKMWSkJCA1NTUUt978+ZNpf+9NWvWKNTvVPZvEf75JopRfEUn7oRUHG1HhMhH8YIUbYeEEKJ4ihekqBClWBISEjB27Fjk5uZ+8jPq6urQ19eXXShCZIyKUTLkfXctLC0t5R1D7ujihVQEbUeyQdspAb68HtD6QQghVauiHZJTEUoxpaamIjc3F4sXL0a9evVK/Yy+vj6MjY1lnOzb8rkWZjR/5Y+KUUSmij/W0VtpOF3IEKKAaDslAK0HhBAib2UtREk+S4Un/qlXrx7dZK0C+vr6UFdXxx9//PHJz6irq+PAgQNUkJIjKkYRmSmtw1u6wCFEsdB2SgBaDwghRN7KU4gq/h0qSJFP+fXXX5GWlgY9Pb0K9dVUWb9T2b9VnLGxMQ4cOPDZPrn++OMPpKamUjFKjqgYRWTic0OB0wUOIYqBtlMC0HpACCHy9jWFqOLfpYIUKc3Lly/x/v171KpVSyF+p7J/62PGxsZUaFJwSvIOQL59n7uwKc9nCCFVh7ZTAtB6QAgh8laRQlRl/gYhhFQ1KkaRKlWeixa6wCFEPmg7JQCtB4QQIm+VWUSighQhRNHRY3qkynzNxQo9AkKIbNF2SgBaDwghRN6qonhEj+wR8nk02p58UTGKVImK3DWnCxxCZIO2UwLQekAIIfJWla2YqCAlPwkJCZ/tQJvID422pxioGEUIIYQQQgghhFSShIQEjB07Frm5uZ/8jLq6OvT19WUXinBotD3FQMUoQgghhBBCCCGkkqSmpiI3NxeLFy9GvXr1Sv0MPQYmXzTanvxRMYoQQgghhBBCCCmHsjyGV69ePVhaWsowFalMFXmckoqNX0bFKBkQiUQAqtezwWKt/Ap9Pzw8vJKSEL6oV68e1NXVP/l+ddyOqhptp9+er9mOaD0gRBodj4isibXyqvT35bGfLut2dPPmTV5uS5mZmfD29kZe3qeXnVAoxIcPHxTmOJmfn8/9f0UyVdbvVPZvVaYPHz5AKBR+tk+pLxEKhZg+fTq0tbW/6vv16tX74nbEdwLGGJN3iG+dv79/hVZkQqqDnTt3fvbOEW1HhHwZbUeEVBxtR4RUHG1HhFTcl7YjvqNilAykpqbi9u3bMDU1hVAorJTflHSq9rnnkOVN0TNSvoqrzIxfqvxXxXb0JXxYBqWh3LKlSLnltR0p0jwoD8otO3zKrIjHo7Lg0zyuCJpOfuDrdvQxPi4Hylz1ZJX3W28ZRY/pyYC+vj769OlTJb/Nh+eQFT0j5as4WWSsyu3oS/iwDEpDuWWLD7mrejviwzwoDeWWHT5m/pg8j0dl8S3M47Kg6eQ3Rd+OPsbH5UCZqx7f8ioaJXkHIIQQQgghhBBCCCHVBxWjCCGEEEIIIYQQQojMUDGKpwwMDDBhwgQYGBjIO8onKXpGyldxfMhYEXydPsotW3zNXZn4Og8ot+zwMTPfVJd5TNNJZImPy4EyVz2+5VVU1IE5IYQQQgghhBBCCJEZahlFCCGEEEIIIYQQQmSGilGEEEIIIYQQQgghRGaoGEUIIYQQQgghhBBCZIaKUYQQQgghhBBCCCFEZqgYRQghhBBCCCGEEEJkhopRhJQRDTz5dWi+EVL9iEQieUeoVsRisbwjEKIQ6JyDEEL4g4pR37CMjAx5RygXRTyZjoqKQnh4OABAIBDIOc2XKeJJ2MfzTRGXc2XjyzTevn0bDx48UMj15nP4lrc4vqwbFXHx4kUcPnwYWVlZ8o5SLgUFBbwsovn7++PixYsoLCyUd5Qy4+u8/tbweV/6KfHx8fKOIBff4rJUZHyd37m5ufKOQIgUFXkHIFXj0qVLOH78OKZPn46WLVvKO04JERER+PDhA3JycmBubo4GDRpASUmxaqOXLl3Cnj170LJlS4wePRp169aVdyQpHz58QF5eHtLS0lC/fn2oqalBIBCAMaYwhbNnz57hyZMnSExMRN26deHk5AQlJSWFylhR9+7dQ2hoKJKTk2FsbIx+/frB1NRU3rG+6Ny5c1i7di3mzp2Lli1bQkWFP4cDsVgMZWVliMVihdtvfM5///2HzMxMODg4QFlZWd5xqsT58+exZs0ajBo1ilfL5tq1a7h8+TKioqJQr149tGrVCkOGDJF3rC+SzO/Ro0ejoKCAF+sVX+c13719+xYikQhZWVlo3LgxNDQ0IBAIeLcf/Zxz587Bx8cHmzdvhoWFhbzjVJnqsCwVHWMMjDHk5uZCU1OTF+e1gYGBePLkCaZOnQotLS15x/mi+Ph4pKWlIS8vD/Xr14eOjo68I5UZH9YHRcGfqw9SZvHx8fDw8EBGRgb27t2LSZMmoXnz5vKOxbl48SK8vLyQl5eH3Nxc6Orqon379pg7dy709PTkHQ8AcPnyZaxevRq9e/dG3759SxSi5L2TCQkJwdGjRxEeHo68vDx06NABQ4cORbdu3RRm5xcQEABPT08AQFZWFgoLCxEbG4tp06YpTMaK8vf3x7p162BqagqRSIS0tDQcO3YMP/zwA3r27AkTExN5RyyVpBA1fPhwdOnShVeFqJs3b+K///7D+/fvoa+vj8GDB6NOnToKf2IlmeczZszgTdGgvCTT6OzsjMGDB0NDQ6PEZ+S97yxNYGAgVq9ejaZNm8LU1BQRERG4fPkybt++jXHjxqFp06YKeYEnmd8jRozA999/DzU1NXlH+iK+zmu+8/f3h5+fH1JSUpCXlwcLCwt0794dEyZMgJKS0jdRxDh//jx3XKtZs6a841SZ6rAsFd3du3fx33//4cWLF1BWVsaoUaPQpUsXecf6LMmNi3HjxincMbg0wcHB2LNnD6KjoyEWi2FjY4MpU6agfv368o72SXfu3IGysjLat2+vcI0DFJmA8bWdIfmk3NxcTJs2DUlJSVBWVka9evXg4uKCZs2ayTsaHjx4gF9//RX9+/eHra0t9PX1cfLkSQQEBMDIyAi//PILWrRoIdeL46SkJCxcuBAWFhYYP348ateuDaCoDxQlJSWoqqrKLRtQ1GJr9erV6N69OywsLCAUCuHn5wczMzMsXboUDRo0kGs+ALhx4wZWrlwJR0dH9O7dGyYmJli1ahUyMjKwadMmaGpqAlDMC9OyiomJwbx589C5c2eMGDECZmZmePr0KU6ePAl/f3/069cPo0aNUrgDp7+/P1atWoWRI0di8ODBXMFMcpdPIBAo7B3WgIAArFmzBo0bN4ZYLEZycjLS0tLQv39/DBo0CI0aNZJ3xFIVL/45OzvDyMhI6n0+bwcSFy9exOrVqzFixAgMHTqUm8b09HSIRCIIhUKFudlQXPH9/cSJE2FiYoKEhATcu3cP27dvR82aNfHjjz+iU6dOCrU9XLhwAWvWrIGzszOGDh0KY2NjAOBu8mhqanLHUUVZv/g6r/nu9u3b+O233/Ddd9+hVatW0NHRwb59+xAREYGmTZti3bp1UFNTU5j15GsU38cOHz6c2x6+NdVhWSq6gIAAbN68GbVq1YKOjg7i4uLw4cMHLFiwAP3791fIec+37UNyndOrVy80a9YMeXl58Pb2xpw5c/D999/LO16prly5gqVLl6JevXqYO3cu2rZtC0Bxjr+KjD+3w0mZiMViqKuro0uXLsjPz4epqSl8fHzg6+urEAWpp0+fQkdHB4MGDUK9evUAgDv53LZtG9zd3bFgwQJ06NBBbhtvWloaYmNjMXbsWK4Q5ePjg7CwMOTn56Nhw4aYNGkSatSoIfOML168wN69e9GvXz+MHTuWO6BYWlpi1qxZuHnzptyLUfn5+fD394eFhQWGDBkCMzMzAIC1tTUCAwOlPquoRY+y+PDhA1JTU9G5c2duGps3b44WLVrAxMQE+/fvR35+PiZOnAhzc3M5py1y8+ZNrFq1Cl27dpUqRB0+fBgRERFIT0+HqakpRo4cqXCtuhISErBv3z707t0bEydOhLGxMRITE3HixAkcOnQIMTExmDhxIlq3bi3vqFICAgKwdu1arvgnKdLk5eWBMQZlZWVetUwrTVRUFNzd3WFmZobu3btz07hlyxbcv38f7969g6GhIezt7TF+/HiFm964uDiplozGxsZwdHRE3bp1sWzZMvj4+EBNTQ3t27eXc9IiL1684FoXOTo6cscBHx8fPH78GNHR0WjSpAm6deuGoUOHKtQdWr7N62/BnTt3ULt2bQwfPhx16tQBALRo0QLHjx/HoUOHMH36dHh4eEBHRweFhYW8a7UZHByMtWvXYsKECejfvz+3PTx48ACZmZlgjKFZs2YwNDSUc9KK+9aXpaK7d+8evLy80L9/fzg5OaFu3brcebmnpydatWqlMOd7EoGBgVi7di1++OEHDBw4kNs+kpOTuS4PFKklYVxcHP766y84ODhw53pA0RMhb968kXO60r169Qo7d+6Enp4e0tPT4eXlhZkzZ6Jt27YKdfxVVPy7AiSfJbmoNzMzg7+/P5ycnDB16lSEhYXB19cXz549AwAUFhbKtBM7SQO8d+/eQSAQcEWegoICaGpqolOnTvj999+hrq4ODw8PREdHczllLTMzE0pKSlwri/nz5+PEiRNc3rNnz2L27Nm4e/euTPMVFhbi9u3byMrKgr29PbeDzs/PR9OmTdG8eXPcvn0bBQUFcu3INj8/H8+fP4exsTHMzMy4ZZ+UlISsrCzMmTMHs2fPhpeXFwBwzcr5RiQSgTHGzeuCggLuYDN58mRMnDgRly5dwr///ovMzEx5RuXUqFEDurq6iI6ORmxsLADg119/xfbt23H//n28efMG//zzD1xcXHDnzh0AitPhdnp6OhITE9GhQwdu3TcyMoKLiwtmzZqFsLAw7Ny5k9vHKYLbt2/jjz/+QNu2bTFo0CDuAnzfvn1YunQpZs+ejaVLl3KFbr6qX78+Ro4ciXfv3iEkJIRrAXPu3DkYGRnByckJ2tra2L9/P1atWsV1XK0IDbNzcnKkThIly0EgEKBly5Zwd3dHRkYGdu/ejdTUVADyz62np4c+ffogIiIC9+/fR25uLhYsWIATJ05ASUkJnTt3xtu3b7F161Zs3boVgGIMwMHHec13YrEYUVFRUFdX54oX+fn50NbWxogRIzBt2jTEx8dj/vz53IUpn+Z5SkoK/v77bwCAjo4Od2xYsWIFFi1ahN9//x2LFy+Gq6srjh8/Ls+oFfatL0tFl5WVhfPnz8PMzAz9+/fnuu9o0qQJHBwckJ2djZiYGDmnlBYeHo6VK1fC1NQU3bt3585BtmzZgnnz5mHy5Mn46aefcPToUbx7907OaYukp6fj3bt3aNeuHbc9FxQUQFtbGwCwa9cunD9/HhEREfKMyRGJRAgODkZMTAzmzp0LFxcXvH//Hl5eXnjw4AEAcAUpUjoqRn2j2rVrB01NTbx69QrOzs4YN24cwsLCsH37djx8+BDbtm3D5cuXUVBQIJM8khPQtm3bIiEhAaGhoQDAHSyVlJTQtGlTzJkzB7m5udi4cSP3vqzp6ekhKysLT548wfPnzxEeHg43Nzds3rwZPj4+WL16NQQCATw8PJCQkABANhfsysrKXOGpXbt23OuqqqpQU1NDvXr1EBcXB2VlZbnfDatduzaePHmCly9fIjU1Ff/88w+OHj2KmjVronnz5mCM4e+//8acOXMAgOvUnE8sLS2hqamJoKAgAICKigrEYjG3LkyYMAFDhgzB8ePHuREZ5VnYYYzB0tISGzZsQHZ2Nry8vPDzzz8jLCwMy5cvx549e3D48GG4ubnByMgIbm5uiI6OVphlo6SkBGVlZaSlpQGA1LweOnQofvrpJzx79gxHjhzhtkt5a9q0KQwNDREREYGHDx8CKCr+7d27F3FxcWCM4d69e5g7dy4uXLjA69HFpk2bhpEjR+Lo0aOYMWMG3rx5Azc3NyxfvhwzZ87EypUrMXXqVAQFBWHnzp0A5FsgkRSRzc3NYWVlhSNHjuD9+/dQVVWVKuZbWlpi/vz5CAsLw59//glA/oUdY2NjuLi4oGfPnvD29sZPP/2E6OhouLm5YfXq1Vi8eDHWrl2LAQMG4Pjx4zh48KBc80rwcV7znZKSElq1aoXo6GiuUK+qqsq1ou/Xrx/Gjx+PqKgorFmzBgC/5nmNGjUwfvx4tGnTBtu2bUNISAhWrVqFO3fuYPjw4Vi5ciUWL16MjIwM7NmzB0eOHJF35K/2rS9LRVdQUIDo6Gg0a9YMDRs2BPC/c7ru3btDTU0NYWFh8oxYgqWlJfr27Yu0tDScOnUKGRkZ+P3333H27FmYmJjAzs4ORkZG8Pb2xq5duxSimJaYmIjs7GyIRCLuGvXcuXO4efMmQkJCcPnyZaxZswarV69GcHCwfMMCUFNTg1AoxNChQ2FnZ4d+/fph0qRJVJAqD0Z4SywWf/b9iRMnss2bN3P/PnLkCOvTpw9zcnJitra27OHDh6ygoEAm+QoLCxljjEVFRbEJEyawqVOnstevX5f4rEgkYseOHWN2dnbs5MmTVZattIxisZiJxWJWWFjIFixYwEaOHMnWrFnDJk2axDIzM7nPFRQUsNDQUObo6MhWrFhR5Rklf1Pi/fv3jLH/zVPJNOzcuZMNGDCA5efnl7pufGl9qajiv3/ixAk2YMAA5uDgwMaOHctsbW2Zp6cnS0pKYowxlp6ezg4cOMC9zgexsbEsOTlZ6rWDBw8yW1tbdujQIe61wsJCbl4kJSWxCRMmMBcXF5afny/TvBLZ2dncf0tyhYWFsaFDh7IePXqwoKAgqWxisZjdvHmT9e3bl/36669Vuo8oj9zcXDZmzBg2a9YslpeXxxgrmteS7YAxxvbt28dsbW3ZuXPnuPflRTJPU1NT2ciRI9nAgQPZ1KlT2aBBg9jVq1dZVlYWY4yxp0+fsoULF7K+ffuyu3fvyj13Rfn5+TFbW1v2119/cctJst69f/+e/frrr6xPnz4sJiZGLvkeP37MzXuJkJAQNmjQIObq6spSUlIYY9LLID09nbm7u7NBgwax2NhYWcblhIeHs8jISKltNSkpia1bt4717NmTHTt2jHtPMr+jo6PZ5MmT2ZgxY7jpkrWP9x98mNffmhs3brDevXuzjRs3Sh3DJPM9MzOTrVy5kg0aNIjdv3+fMVb15wuVoXjG0NBQNm3aNGZra8sGDRrEbty4wUQiEfd+YmIiGzNmDBsxYgSLjIyUR9xK8a0uSz7Iz89njx494q4HJPNVLBaz/Px8NmTIEObh4SH1njwV3/euXbuW9enThw0dOpQNGTKE3bx5k+Xm5jLGGMvIyGB//fUXs7OzY7t27WKMyTd/fn4+mzp1KnNycmK///47++OPP1iPHj2Yt7c3e/PmDWOMsWvXrrF+/fqxiRMnsrCwMLllLT6fJPNT4tSpU8zJyYlNnjyZ2xb5fG5XlahlFI9JqqySyrGkQi+502hlZYW4uDju887OzjAxMUF6ejrq168PgUBQpS1o8vLyuBHGJI8P1qtXD46Ojnj9+jUOHTqE+Ph4qWkRCoWwt7eHgYEBXrx4UWXZSsso6bhZSUkJdnZ2SE1NRUhICFRUVMAY41piKCsro1WrVmjTpg3Xz05VefjwIbKysqCsrMwtZ0m/Bx/3s2RsbAyRSISMjAzublhMTAwuXboEoOrukEkyCgQCLuPgwYPxyy+/YNCgQWjSpAksLS3Rq1cvGBgYgDHG9RtWv359PHnyBFlZWVWSrbIEBQXht99+w4kTJ5CSksK9bmNjA2traxw4cABnzpwBIP3YoYGBATp06IDExESuRY8shYSEwM/PD2/fvgXwv+3M0tISq1atgr29PZo1a1aio+NOnTqhadOmePv2LfLy8mSeuzRqamqYNGkSHj16BD8/PwD/2wYk83vcuHHo2LEj9u/fj+zsbLn2RaaiooKCggLo6enB19cX2traCA8Px8yZM9G5c2euE//mzZvjxx9/hK6uLrZv3468vDyF70PtxYsXuHjxIrZs2YJ79+5xj1QBwMSJE7FgwQJ069aNG+xB0jecoaEhbG1tkZubK5dt/uzZs1iwYAFu374t9bqNjQ0cHBzw+PFj+Pr6IjU1FUpKSigsLOT2V3Z2dkhJSZGaVlkJDAzEb7/9hh07diA5OZl73cDAAOPGjYOLi4vUqJiS+W1ubg47OzvExMTI/FHh0NBQ+Pj4YP78+di/fz9evnwJQPHn9beoc+fOcHJywpkzZ3D58mVkZ2cDADfftbS0MHnyZOTm5nItOPnQoqZ4S4N27dph8uTJ6NChA/r164fWrVtDKBQCKGrRUqtWLcyZMwdxcXF49OiRPGNXyLe6LPlARUUFzZo140bulczXwsJCqKiooGbNmtzykLyXkpKCpKQkueRVVlbmrgddXV3h4OCA9PR0jBkzBu3bt+dGX9XW1sbQoUNhY2ODw4cPc92pyINYLIaKigqWL1+ORo0aISwsDKGhoWjevDkGDx7MPRrZrVs3LFy4EBEREdyxRR6KzyfJ/JScjzo5OWHy5MlcC6nQ0FDu3C4iIkJhHjNUBIrViygps/v37+P+/fsIDw+Hvr4+nJyc0KJFC65DXKDo4Hz8+HG8ffsWRkZGWLFiBZKSkuDg4IDr169jy5YtWLBgASwtLSs9340bNxAQEIDnz59DIBBg4sSJ6N27N4CiolhcXBxOnDgBFRUVjB49GmZmZtwOXV9fH2ZmZkhISKjSzq0/l7F///6IiorCkSNHEBYWhlu3bsHe3h4FBQVQUlKCiooKdHV1ERsbW2Wd8V69ehWLFy9GmzZtsGbNGmhqapbaCZ7k38rKyhCJRFyeqKgoeHt7IyoqCu3bt0eNGjVkkrGgoAAqKiqwt7eHvb09cnJysH79em69lCxnHR0dGBgYIDU1VaFPliQdUPfs2RMtWrSQmo/m5ub44YcfsG3bNuzcuRP5+fkYMmSIVJFX8py7rB/Re/XqFZYsWQJlZWWoqqpi0KBBMDU1lSpIubq6fnLkHUnH2vJ45PPt27dITU3F+/fv0axZM66fg9atW+O7777D4cOHoaWlhXHjxnGPEUo6a+3cuTP27NmDhIQEuXXmL8nycUFq06ZNpRb/GjZsiJYtWyI0NBTp6ekK3dFuQEAAdu3ahZycHOTm5uLMmTMYPXo0xo4dCxUVFSgpKWHAgAEA/jd97P8fxQaA6OhoaGlpQV9fX6a5z58/j3Xr1mHYsGFo1aoV97pkfzVz5kykpqYiODgYIpEIM2bMkFoOGRkZ0NHRkfloqkFBQXB3d4eTkxO6du1aYhQkY2NjfP/99xAKhVLbsWR+JyQkQE9PT6YjGfr7+8PT0xOampoQCAQIDQ1FVFQU5s+fD01NTYWd198iyTnUTz/9hMTEROzcuROqqqro1asXdHR0uPWlVq1aMDQ0VJh+Y8qqeOfAHTp0gLKyMoyNjbliP/C/7h4MDQ2hrKwst+JARX3ry1LRiEQirsAgUdr5vuQ1dXV1fPjwgXs9KioKvr6+aNCgASZNmiST/dnHmSUFKWVlZSxYsAC1atWClZWVVBZJQ4DWrVvjypUrSE5O5vr1lXVeyXGrdu3a2LRpEzIyMvD06VPcunWLGxhFcsxu3bo1VFVVERYWxp1zyEJsbCzS0tLw9u1btGnTBjVq1JA6/kpuSCspKcHJyQkA4OfnB19fX0yfPh1CoRAeHh6oX78+5s2bV2Idq44U+/YrKVVAQACWLl2KixcvIjY2FpcvX8bPP/+Mu3fvcndExWIx6tevj1q1auH169f4448/cP/+fbi6uuL333/HyJEjkZKSUiUnqP7+/li+fDlSUlLQrFkz1KxZE+7u7rhx4wb3mdmzZ8PJyQlnz56Fp6cnwsLCuB16REQEkpOTYW5uXmVFirJknD59OkaMGAEAcHNzk3o2OTw8HK9evUL9+vWr7GJdclALDw/H3LlzkZ2d/dlnjpWUlLi7YzExMfD19cWDBw+watWqKilEfSqjiooKdzdGLBZDJBLh9evXOHfuHJKSkrjl/Pz5c7x9+xYWFhYKe9ERExODffv2cc+Ad+nSBQCk+lpr06YNfvzxR9SrVw9btmyBj48P3r9/D8YYXr16hXv37sHMzIy7myYrtWvXRp06dWBoaIhDhw7h6NGjUi0Rgf/dyZHsNySvP378GLGxsWjZsqXMi1FBQUFYvHgx5s6di+XLl2P+/PncKIyGhoYYNmwYunfvjl27dmH37t3c9yQ5lZSUoKqqKvMR286fP4/NmzdzWSTbgKQgpauri6VLl0p16l+cWCyGpqYm1NXVZZq7PIKCgrBmzRpYW1tj+fLlOHLkCHr37o0DBw4gPj6+xP5JIBCgsLCQW69evHiB+/fvo2nTptDR0ZFZ7vPnz2PNmjUYNmwYnJ2dYWBgwL1XfD1ZvHgx7O3tcffuXcydOxehoaFISEjAkydPEBAQgBo1aqBWrVoyy52cnIyjR4+id+/ecHZ2hrW1NYCijsAld+ABQCgUcttv8aJ3eHg4nj9/jhYtWshsH3v37l1s2rQJffv2xapVq3D48GFMmjQJQUFByM7O5raLxYsXo1evXgozr79Vkos7VVVVzJw5Ex07doSnpyf+/vtvxMTEcO+/fv0aeXl5MrsI/Vql3dQpvt9p27YtTE1NuX8XL9CGh4dDQ0OD6+/nU+dSiupbW5aK7MKFC/j555/L1f+kqqoqNyhDVFQUtm/fjps3b8Le3l4m+99PZS5+PjJhwgSYm5uXun0kJydDX1+/yq4XypoX+N92rqOjA7FYjJCQECQmJgL43zE7PDwcQqEQFhYWMskLAJcvX8aSJUswa9YsrFq1Cj///DP++eefEoNzFH9CwsnJCT/++CMSEhKwceNGrFy5EhERERgxYgQVov4ftYzimdu3b2PLli3o168f+vfvj4YNG+LmzZvw8/PD+vXr4enpyVWPTUxMYGpqisWLF0NHRweurq7cyezYsWPh5ORU6cWo69evw8PDA/3798ewYcNgZmaGiIgIzJo1CwEBAejSpQtX1Z43bx709PS40en69+/PdRCYlpaGwYMHV0kxqjwZp0+fDl1dXfz1119YtmwZOnToAFVVVSQmJiIxMRFLliypsp2JkZERVFVVMXz4cJw+fRoLFizA+vXroampWWqLMT09PYjFYty9exf+/v54+PAhvL29q3RH/bmMktYQ+vr6aN26NU6dOoX8/Hx89913ePPmDS5fvoycnBz88MMPCluMSkhIQEpKCmxsbGBqagoA2L9/P2JiYiAQCGBhYYFhw4ahY8eO0NHRwZkzZ3Ds2DH4+/tzrQLS0tKwZcsWqTu1VU1yYWpkZIQ2bdpAWVkZfn5+EAgE3OO6H5OsTw8ePMCBAweQk5MDZ2dnmRajAgMDsWrVKjg6OmLcuHEwMTHBb7/9hpMnT6JTp07Q0dFB/fr1MWnSJKipqWHfvn2Ii4vDoEGD0KxZM4SHh+O///6DiYmJTFve3Lp1i+swVklJCbNnz5a6Iyk5eZLMY0nRQPLvZ8+e4eXLl2jRooXCnpw8f/4cu3btQp8+fTB69GhuHWrZsiVCQkKQmppa4gZC8Za6ISEhOHXqFKKjo+Ht7S2z7SEkJARr1qzBkCFDMGnSJK4ofOnSJSQmJiI1NRU9evRA7dq1oa+vjwULFqBevXq4ePEi5syZAy0tLW6ZbNiwQabrlaSQ7+TkxF1YbtmyBWFhYUhOToaVlRV69OgBa2trqbuxQFHL32PHjiE+Ph5Lliyp8iKn5OLmxo0baNCgAb7//nupoecbNWoEoVCI3NxcKCkpQUNDA66urqhfv75CzGu+Kn4xWVoL1+KMjIywcOFC+Pj4YN++fbhx4wb69u0LkUiEe/fuIScnBw4ODrKKXi6XLl2CgYEB2rZtW+r7pbUYL749hIWF4ezZszA0NORaRipii+zk5GSoqalxLao/hc/LUtHdu3cP69evR0FBAZYtW4YVK1aUaJFanGQ9q1GjBmJiYvD69Wt4eXnhyZMn8PPzk0mx5EuZPz6Pk9wokrweHh6OO3fuoFGjRjIpRn0pb/Hrm1atWqGwsBDr16/H7NmzUatWLYSHh+P48ePQ0tJC586dqzwvAAQHB8Pd3R0ODg4YMWIEDA0NsX37duzduxf16tVD586dS7ROllxLDhgwANHR0Thy5Ai0tbWxc+dOrihOqBjFKxkZGThz5gwaNWqEgQMHol69egCKniGPiYnBtm3bkJKSAiMjI27nOHLkSOjr66Nbt27o0qUL1NTUuB1QZReiEhIScODAAVhbW2PIkCEwMzMDANSsWRMmJibQ0tJCamoqRCIRt9OZMmUKWrRogdu3byMoKAgaGhqoU6cOtm7dyj0bLO+MP/zwA5o0aYJ79+7hypUr3Mh1y5YtQ/369Ss9o0Tbtm3RuHFjmJiYYPz48fD19cWCBQuwbt26UlvZqKmpQUlJiWul4OXlVeUHwS9llKxrc+bMQVpaGs6ePYuzZ89CXV0dtWvXxpYtW6pkOVeU5IDy9u1bqKmpoVOnTgCAefPm4f79+zA1NUVaWhouXryIS5cuYcOGDbC0tISpqSkGDRqEf//9F9nZ2TAyMoKjoyN3USYrkos9a2trBAYGwsvLCykpKdzQ1sOHD+eKaxIZGRk4cOAArl27BpFIhE2bNsHc3FxmmR8+fAhfX18MHDgQY8aM4bY/BwcHBAcHIzMzk2tN06hRI0yaNAkNGzbEoUOHEBISAjU1NaipqUEkEmHz5s0ya3kTGxuLvXv3wsTEBA0aNMCJEydQUFCAefPmSRWkPla8+Pfnn38iPT0d48aNU8jCLGMM4eHh0NTUhKOjo1Qx8927dygoKMDp06cREBCAOnXqoG3btmjSpAkEAgE3itWVK1egoaEBT0/PKt1vFldYWIiQkBAIBAIkJiZy+83ffvsNN2/e5PriO3bsGPr374/BgwejYcOGcHZ2Rt++fXHnzh3ExcWhRo0a6NixY6lF3KqUmJiIgoICtGnTBgAwd+5cPH/+HC1btoSuri4CAwMRHByMmTNnwtHREUpKSsjMzIS3tzfu378PoKh4JTlXqEqSE/DIyEiIxWKpfV5YWBhiYmIwZcoU5OTkoF27dvjuu+/QqVMnODs7o0+fPrh7965c5zVf5eTkoLCwEBoaGgDA9XH5qUKLrq4uFi5cCEtLSwQFBcHLywv6+vowMTGBh4cHd06kSC5dugQ3Nzc0bNgQmzZtKvPFsuRi8Ny5c7h8+TIiIiKwZcsWhX0M+sqVKzh37hxatmyJoUOHfrFgz8dlqegSExNx8OBB6Ovrw97eHgEBAfjtt9/g7u7+yYKU5FiuoaGBxMREbN++HY8fP67ym8EVyQz8r0AluXERFxeHxYsXV3kr/vLkZYxBXV0dY8aMwZ49ezBz5kzuhjwArFmzRibHijdv3uDPP/+Evb09JkyYwP1NDw8P/PDDDzh37hw6d+5cYr8r2R+HhobiwYMH0NHRgZeXl8zOgfiCilE8kp2djaioKNjZ2XEnl5ILnV69esHLywvPnz+HpaUlt0G0b98eFhYWUnccq6qlg4GBAczNzdG6dWupi9jg4GC8evUKmZmZSEhIwO3bt+Hk5ARHR0c0a9YMXbp0QZcuXTBx4kSuz6Gquov7tRmtra1hbW2NsWPHQktLC3l5eVXegkHS+ebr16/x888/AwB27twJV1dXbNy4Eerq6rh27Rq6d+8OoGhdEIvFSE5Ohre3Nxo1alSl+cqa8erVq7CxscHy5cthZ2eH9PR0GBsbo3HjxqhZs2aVZ/waku3H3NwcycnJuHXrFrKzs/HixQusWbOGuzg8evQoDh06hLlz52L79u3Q1dWFrq4u5syZA+DLd6qrWt26dbni6qxZsyAQCHDs2DEoKSlh4MCB+Pfff9G3b19YWFjg/fv3SExMRIsWLTBu3DiZFqJEIhHu3LkDMzMz9OvXT+qEJDMzEyoqKggKCoKmpiYMDQ3RrVs31KlTByNHjkTfvn0REBCAtLQ0GBgYoHv37jJ7PEEsFiMyMhLPnz/H9OnT0a9fP+zYsQOnT5+GQCDA3LlzP1mQysjIwO7duxESEgJlZWV4eHjIdJ6Xh0AgQJ8+fWBoaIjWrVtzr//zzz84fPgw2rdvD3Nzc7x58wY7duxAw4YNMWvWLLRs2RI6OjowNzfHoEGD0KdPH5kWGSSFcDU1NQQFBWHVqlXIysrC06dPMX36dHTs2BE5OTnw9/fHiRMnkJ+fjylTpsDQ0BB6enpya1kguZtqbm4OXV1d/Pvvv2jWrBmioqKwYsUKtG7dGurq6rh+/ToOHDgADw8P6Ovro0uXLtDW1oa+vj66d++OwYMHy2xbkDy6rK+vj7CwMJw/fx6NGzdGaGgo/Pz80KtXLzRt2hRpaWn4999/8fLlS8yZMwfW1tbQ19enVhxfITg4GMeOHcObN2+goaGBtm3bYsiQIWjatOknvyM5Jn3//fdwdHREamoq1NTUoKqqKvNHycvi3LlzWLt2LWrXro24uDi8evUKVlZWnyzyF1dQUIBTp05h69atsLS0VOiLQH9/f6xfvx5du3aFpaUlV4j63DkE35YlH6SlpSEyMhIDBgzAlClTYGFhAW9v7zIVd9TV1ZGSkoInT57IrBBVkcxZWVnYtWsXzp07BwMDA2zdulUm20d58goEAqiqqqJ///4wMzPDv//+i9zcXDRr1gzfffedzI5vjx8/RkxMDCZPnsydw+Tn50NTUxNWVlZ49OgR0tPTpfpuk8jIyMCVK1fw+vVr+Pj4KOw+SK6qYog+UjWysrJYUFAQN3xk8SEls7KyWO/evdnBgwdLvCcLkuEqJUN5SwQEBDBbW1u2fv16dv/+fRYZGcn27t3L7Ozs2B9//MHS09OZWCzm/leV2fmQ8eOsJ0+eZD/99BNjrGjI66NHj7K+ffsyFxcX5urqyoYPH84NdcoYY97e3uzVq1dVmu1rMr5+/VommSpbbGwsGz16NFuxYgVbuXIlmzlzptSw8NnZ2ezw4cPM1tZWakhcWa0nX5Kfn89GjhzJTpw4wRgrWmZeXl7M1taWjRo1itna2nJDzjLGWEpKCsvOzpZL1uvXr7NLly5JvXbmzBlmZ2fHJkyYwDZv3swGDRrEBg4cyFauXCn3eSvx8uVLdvLkSe7fsbGxbN26dczW1pZt3LiRe/3jIe5jY2OZt7c327BhA3v79q3M8pbHp4ZMLiwsZO/fv2d9+vRhnp6eLCkpiXvvzJkzzNbWlq1bt05qmvPz86s8r8THubOystjq1atZ79692dChQ9nVq1elhn1njDEfHx/Wo0cPdvPmTcaYfIZgLp67sLCQ5eXlsTlz5rCJEyeyLVu2sJEjR7KUlBSp79y6dYsNGjSI/f7771LT9PH0ySIzY4y9f/+eDR06lNna2rKBAwcyW1tb5u7uztLS0rjPBAYGsr59+7LffvuN5ebm0nDXXyEwMJDZ2dmxBQsWsO3bt7MVK1awHj16MHt7e3bx4sUyLf+P90mK5ty5c8zW1pZ5e3uzZ8+eseHDh7PZs2eX6zfy8/PZ9evX2fv376smZCV48eIFGzx4MPPw8GAxMTGlfuZLxztFX5Z8kZ6ezm7dusX9Oysri50/f54NGjSITZo0icXHx3PvfbxMrl+/zqZNm8YiIyNllpexr8+cnZ3NgoKC2P79+1lcXJxC5lWUY8OtW7fYb7/9Vup7x44dYw4ODiwhIeGz34+Nja2qeLxHxSieycnJKbEDzM/PZ7m5uWzIkCHMz89P6r309HSWk5Mjs3ySHUdhYSHLz89nZ86cYQcPHmTp6elSn/P29mZ2dnbs4cOHMsvGl4zFl+/t27dZz5492YsXLxhjRcv/0KFDrFevXszOzo6dP3+eMSbbC72vzagoxYPy2rVrF7O1tWWOjo5s2bJljLGiaZHM8+zsbDZmzBi2cOFCOaYsqbCwkBUUFLDZs2dLFUVyc3PZyJEjmZ2dHVu4cCGLjo6WY8rSFRYWsocPHzJbW1vm4+PD3r17xxhjLDk5mbm5uTE7Ozt25MgRxpj0eiWvdUyyLkguCOLi4kotSH0sPT2du7mgaK5cucJsbW3ZqlWruNc+vuCJiYmRKs5K5v+iRYvY0KFDSz1eVbXScjNWdMK7atUqtmbNGql9veR48O7dO9avXz/m5ubGGJP9uvSp3K9evWIDBgxgtra2zNnZmVvXiu/zPTw8mKOjI3fjRN6ZU1JS2MGDB9mBAwfYrFmz2H///ccYK5rXknzr169nDg4OCl0kUFRJSUls/PjxbOnSpVIXbseOHWO2trbc/rH4tikRHx/PXr58Kcu4X+Xs2bPM1taWK3YXFBSwjRs3Mjs7OxYSEvLZ78bHx3PnI3xw6dIlNnToUPb06VPutePHjzMfHx/m6+vLHj16VOr3+LIs+UZyTJAc73Jzcz9ZLGHsf4X/jIwMlpGRIduw/+9rM4tEIplfPzBW/rySRgSSrPI415Ms24//9sWLF5mtrW2JYpSiFNL4gEbTU2Dh4eG4evUq/vnnH0RERCArKwvq6uolRgBRUVGBmpoahEIhkpOTuddfv34Nd3d3nD17tkqGlS8tX/FOVFVUVLhRgCT9t0ia8rdv3x5isRhv3ryp9Fx8ylhaPoFAgIKCAjDG0KZNGzRo0ADh4eEAih6Lu3//PtcpsqTJalWOHFZZGRWxs9DPkWwzkyZNQv/+/ZGVlYWgoCDcvn2bG74VKOonwNTUFOnp6VKj7MmbQCCAsrIyevbsibt37yI1NRU5OTlYsWIF0tLS0KlTJ9y+fRuHDx8u14gxsqCkpITWrVvjjz/+wNixY7n+rWrWrIlp06ZBT08PYWFhAKQ7oZXXOibZ/iSPjZiYmGDs2LEYMGAATp06hY0bN3KfDQ0NxdGjR8EYg46OjkJ2WJ6Xl4f//vsPAHD//n24u7sDkB6VBwDq1KnDPU5SfDTG3NxcaGlpQV1dXabL5FO5AUBTUxO//PILJkyYINWfmCSfiYkJ1NTUuGWpKLnNzc3x008/wcDAAPHx8fDz8+Me4ytOW1sbKioqMsv9ucz6+voYPXo0fvjhB9SvXx9paWnce5J8ktEjZT3q5bcgIyMDsbGx6Ny5M4yNjbnjztChQzFgwACIxWL4+PjgwoULAP7XuXxmZiZWr16NyZMn49GjR3LL/yV3797Fhg0bMGzYMIwcORIGBgZQVlbGsGHDIBQKce3atU9+VzKNU6ZMUehpLC4mJgaMMTRv3hwAsHDhQnh6euLs2bP4+++/MXPmTOzevZsbvRjgz7LkI8m5nbKyMhhjUFNTQ8+ePeHi4oLk5GQsWrSIu9a6c+cONm7ciPfv30NbW/uLHc8rUuakpCQIhUK57IPLm3fDhg1So3HL41xPsmw//tuSUV+LH+eio6Nx6NAhpKamyiwfn9FZgIK6cOECvLy8IBaLkZWVBQ0NDTRt2hTz5s37ZL8iQqFQaljRHTt24Pr165g8eXKJkddkla/4hZZYLOZ2JFFRUdDV1UWDBg0qNRefMpYln1AohJ6eHm7cuAFHR0csXboUT548wdy5c5Geno5t27ZhyZIlWLt2baUvY75krCrFs44fPx6MMZw/fx6enp6YM2cO2rdvDwB4+fIl4uLi0KZNG4WaPskBs3bt2khPT0dycjIOHjyIBw8ewNXVFV27doWnpycuXbqEyZMnyzmtNEl/IDY2NtxrkgKyoaEhtLS0kJmZKceEXyYpSAHg+pBydHSEr68vPnz4gF69eilsR7pCoRDNmjVDUFAQGjVqhGvXrmHt2rVYuHBhqX1gFR+16uHDh0hMTET79u1RUFAAZWVlmZ04fi43UFSQKt4pMPv/PlcYY7hx4wYKCwu5vvaYDPt7+1xuoVCIzp07QyQSYf/+/Th16hSEQiHGjRsHFRUVvHz5EmFhYahbt65M9z9fmtf5+flQVlZGeHg4oqOjYW1tDQMDAwBF+8yXL1/CwsJCIYuxik6yziYlJQEoKobn5eVBKBTC0NAQFhYW0NPTw9atW9G4cWNu9DhtbW1YWlri1atXCttfI1B0zJo7dy66dOnCrTMFBQWoW7cuvvvuOxw/fhwODg6wsrIq8V2+TGNxmpqayM7Ohkgkwj///IPHjx9j2bJlaNeuHaKjoxEQEID9+/ejoKAAP/74IwB+TicfSbY1SbGEMYbt27fD1dUVo0ePxtGjR/Hq1SvuWK8I+JaZb3k/JjmGSW5gR0VFYdu2bQgLC4O9vb08o/EGFaMU0OPHj7F161b0798fPXr0QP369XHw4EFcvnwZP/30E5YuXSo1lKXkLqmenh6ys7MRExMDLy8vPH78GLt27ar0TvTKmw9AiSFEg4ODUbdu3SobZUzRM5Yln2S4bgcHBxw9ehSzZ89GZGQk5s+fj169ekEkEkFdXR1t27atkosQPmSUFRMTE7i4uEBDQwMnTpyAq6srBg4ciPz8fERERCAlJQUjRoxQyGm0srKCqakppkyZAg0NDSxYsABdunSBUCjEvHnzMHHiRIU7mf24Y9rixQ5JZ/LFO9JWVCYmJpgwYQIEAgFOnTqFCxcuQCAQwNvbW2ELURLNmjVD48aNMX78eBw/fhyXL18GALi6ukJZWVlqmUj+/8mTJ9i/fz+ysrIwYsQIudxx/Vzuj4d7lxSbnj9/juPHj0NDQwM9evSQek+euRljWLhwIQwNDdG7d28YGhpi27Zt2LdvH65cuQJDQ0PEx8cjNTUVXl5eMi/sfG5eS0aF/OGHH7By5UosX74cPXr0QG5uLm7evIm4uDj8/vvv3ChwpOxq1KgBIyMjBAYGolWrVmjbtq3UYCJmZmbo1asXHj9+jOPHj6NJkyZcS+Vp06ZhzJgx0NXVlfNUlE4sFqN27dowMTGROp5K9iVdunTBqVOnEBwcjDZt2kBVVZXbViUFZEWfxo917twZfn5+8Pb2RmFhIXr06AFbW1soKSmhVatWMDc3h1AoxMGDB9GhQwd06NABAHg3nYrqSzceihdL+vTpAyUlJXh7e8PNzQ2amprYsWOHzEdM5ltmvuUFyn5DSlKEYozh3bt38PHxwaNHj+Dl5UWjwpaR4l05EURFRXGPj7Vq1Qo6OjpwcXHB/Pnz0bBhQyxbtgxXr14FULTySy7cNDU18ebNG3h7e+PRo0fYtm1blYzmUJ58ko20+BCiO3fuRHR0NObPnw99ff1Kz8eHjF/Kt3z5cq4pepMmTfD69WtERkbC1dWVGz1PTU0NAwcORN26dSs9H18yVkRqaioSEhKQkZGB3NxcACjxCGxx+vr6mD17NhYuXIi2bdvizJkzuHbtGoRCIby8vGQ2jeXNXVhYiK5du6JNmzaYP38+unXrJnXRqmiFqI8VLx48e/YMR44cgZqamsKPviVZJrVq1eIuFoVCIXx9fWUy0mVF1a9fH+/eveNGPevSpQuCg4Oxbt06AEUFqOfPnwMouiGyZcsW+Pr6IiIiAhs2bJDbsOLlyS15nMnHxwcvX77E6tWrPztakqxzX7lyBWvXrgVQNIx7nTp1sGvXLgwfPhy6urpIS0tD8+bN4e3tXaWtjMuTufi8BopaUM2aNQtv376Fp6cnjh49CgDw9PSkUYW+AmMMurq6mDdvHmJjY+Hh4YFDhw7h+vXrWL16NUJCQuDs7Aw7OzvY2NjgyZMnyM/Ph4qKCneuo8jFi48L3B+zsrJCnz59cPnyZSQkJHAXsQC4YjOg2NP4sRo1aqBTp064dOkSAgICwBiDkpKS1AiV/fr1g46ODkJCQlBYWMi9x6fpVBTZ2dlISUmBSCQCAKl16FMkRQllZWXo6elBWVkZOjo68PHxkcnxnG+Z+Zb3azMD4EauvH//PrZu3YoHDx5g27ZtaNy4cZXm/ZZQyygF9O7dOxQWFnKFJEnza2tra+jq6sLX1xfu7u7w8PCApaUl11eHtrY2YmJikJKSUmWFqK/JV1hYiOzsbOzevRuBgYFc8/GqPHlW9IxlzWdoaIjmzZtj586diI+Ph7W1NXcHFKjau/d8yPi1Ll26hP379yM+Ph4qKipo2rQpRo8ezd1xLI2kKNK/f3/06dMHaWlpUFdXh5KSkszu7pc3t6RYPXbsWPTq1QsmJiZSy0aWUlNTIRKJoKmpCVVVVa7/uy+tH0pKShCJRPj3338REhKCyMhIeHh4yOyO09fmlrwfGhqKAwcOQE1NDZ6ennIpGpRXYWEh1NTU0KlTJzx79gxOTk74+eefARQNJy8Wi5Geng6BQIBZs2YhJSUFd+7cQZ06dbB161a5FZ/Lk3vmzJmIj49HcHAwTExMZDasdXlzX7lyBWKxGBkZGRCLxVi2bBlmzpwJoOhROIFAIJcWaF+a14WFhcjIyICqqipmzJgBKysrPH78GHXr1oWRkRH09PRknvlbILlA6tChA1avXo21a9dix44dUFZWhoaGBpYsWcJdBHXu3BnXr19HfHw8LCwsFLLlbnlI9rv29vYIDAzEvn37sHDhQq4VHvDpIpaiYoxBW1sbkyZNwuPHj5GRkYG3b98iOzsbmpqayM/Ph6qqKiwsLKCjo4OsrKwSLYdJ2QUFBeGff/7BmzdvYGhoiEaNGuHHH38sc0vlJ0+eYMuWLRCJRDIrqPMtM9/yAhXLrK2tDXV1dRw5cgQ5OTnw8vKqsuvvbxW/9trVRMeOHZGdnY1Tp04BKLqzKOk0tmnTppg0aRKMjIywbNkyfPjwAUpKSmCMoVmzZmjUqFGVV2TLm0/SZ0iTJk0wevRobNy4scqr3IqesSz5jI2NsWLFCiQlJaFJkybo3r071NXVqywTHzN+jWvXrsHd3R0WFhaYMWMGBg4ciPfv32Pu3Lk4ePAg0tPTS/1e8ZNcgUAAAwMDaGlpyawQ9TW5JRcuKioqqFevntz6Z7l06RJmz56NcePGYeTIkfj9999x7969MhUqJX11bd26FUBRiwpZtSyqSG4AXF9r6enp2LRpEy8KUcD/Wom2atWKu5g1MDDAvHnzYG1tjUuXLuHmzZtwcHCAiYkJmjZtCg8PDyxevFiurSDLk7t27dpo0aIFNmzYADc3N7m20vlS7suXL+PGjRvo27cvNDU1uVYRqqqqcusAvKyZ7ezsYGxsDCMjI9jb26Nx48ZUiKogSQsgKysr+Pr6wsPDA25ubtizZw/s7e25/fybN2+gra2t8I8El5Vkv9uhQwe0bdsWoaGh3OAbVTFIjywIBAIUFhaifv36WL9+PXR0dPD48WOsXr0aBQUFXKHtyZMnKCgoQJ06dcCKRkKXc3L+CQ4OxsqVK6GlpYWBAwfCxMQEN27cgIuLCwIDA5GVlfXF3zAwMICuri42bdqEhg0bUmae561oZsYYRCIRcnNzkZmZCV9fX2oR9RWoGKWAateujcaNG+Ps2bN48uQJAOlRjFq3bo2hQ4ciJSUF/v7+AIoOaA4ODti0aVOVn1R/TT5tbW306dMHzs7OMnkMQtEzljVfamoqAgMDqzQLnzOWB2MMhYWFuHjxIlq1aoVp06bByckJLi4uWLx4MQYMGICdO3di//79SElJKfH9hIQEvHr1CkDJPo0UOXdiYiJevnwps7wf+9rin4RAIMD3338PLy8vrFixQmZFg4rmBooKmB07dsT+/fvRpEkTGaSuXBYWFhAKhdy06ujoIDs7G4wxCIVC3LlzB0DRMjI0NJTbSEIfK2tuNTU1mJubK0xx5HO51dTUuNyKNAJdWTOTylP80WVDQ0O0adMGXbt2lSo6RURE4MGDB7ztJP5TxZbCwkIIBAJMmDAB6enpOHHiBAD+tYiSKN7VhoWFBTw9PdGwYUOEhIRgxowZOHToEPbt24ft27cjNzcXDg4OEAgECtniXFExxpCdnY3Tp0+jS5cumDdvHqZMmYJly5bB3d0dtWvXxoYNG3D27FlkZGR88ncKCgpgamoKLy8vWFpaUmYe562szAKBAHXq1IG7uzt27tzJmxuOioafe+9vnKmpKcaNG4dXr17hyJEjeP36NQDpQoCTkxPMzc1x9+5d7nt6enpV1gdTZeRTVlaW2UW8omf8mnyyPtniQ8byEAgEUFJSwtu3b6GrqwtDQ0OuhUGTJk0wdepUjBkzBseOHcPx48e59wD5DqNcGbnlMcx1ZRT/ihfRWrVqJZP9W2UWLQFAQ0MDOjo6VZ67KlhaWkJLSwtBQUEoKCjAokWL8OzZM8yZMwfdunXDuXPnsGXLFnnHLIFyyw4fM/OZpD8hADh06BBu3LhR4jOnTp3Ctm3bEB0dzQ28wSfFH4P+eBol52d169aFubk5rl+/jqysLF62FPp4Oq9du4YGDRpg9erVGDNmDLKzs+Hr64ujR49CJBLBw8ND5p04fwskjzLHxcVBT08PhoaG3I2JVq1awc3NDW3btoWfnx+Cg4O5PoMA4O3bt9zNVslNgOKPhVJmfuatzMwA0K1bNypEVYDiXjlWc927d8cvv/yCkJAQ7N+/Hy9evABQdCCWHHSNjY2RnZ1N+XiaUdHz8SVjeQgEAhgZGSE2NhZA0YFPUljT19fHyJEjMWzYMPz555+4cuUK9z3JMMo6Ojpy6fDiAOa5AAAeyElEQVSbj7n5WkTja9Gyskkee2nRogUiIiKwePFiPHz4EHPnzsWAAQPwyy+/oH///hg8eLCck0qj3LLDx8x8Vrx4ceDAAfj6+iIyMlJqH/Tw4UNcu3YN6enpcu0H7WuVZRqBohZ406ZNw5o1a6ClpcW7lkKlTWdUVBREIhFMTEwwadIk7Ny5kxsYYv369bwY+EJRFRQUQCgUci1cGGPc/ktfXx+LFy9G69at4efnh6ioKABFHVofPHgQK1eu5AZeoMzfTl6+Zv4WUTFKAXx8R0fy78GDB2PmzJm4fPkyvL29ERISAqDoYikqKgrx8fEwMzOr8mflFT0fHzIqej6+ZKwIST57e3u8efMGe/bsASDd0ktHRwdDhgxBx44d4enpyRV/gKJhlP/66y+Z35nka26An0U0PueuTJLWF7a2trh16xYePXoEV1dX2NjYACgaxWn+/PkKN1Im5ZYdPmbmq+LFi3379sHPzw8TJ06Eo6Oj1GOblpaWmDlzJjZs2MC7O/VlnUYJa2tr3hXbgE9PZ//+/blHKpWVlaGuro62bdsq1KPEfKWpqQlHR0eEhITg6tWrUFJS4vrbBYqO3bNmzYKOjg48PDy479ja2qJly5awsrKizN9YXr5m/hYJGB/btvJcdnY2NzqT5MDzudGZAgMDsXXrVhQUFKBjx47Q0NBAdHQ0oqOj4e3tjXr16lWrfHzIqOj5+JKxKiQlJcHNzQ3x8fGYOnUqevfuDaCoLwrJIwD+/v5YvXo1Vq9ejc6dO0u9Jy98yy3p18Tf3x/r1q3DmDFjMHHixBKZ3717h02bNuHVq1fYtm0bzMzMuN9IT0+X+dDVfM1dVQoLCxEcHAyhUAgrKyuFH6BAgnLLDh8z88nHxYvdu3dj/PjxGDJkiEweXZaF6jCNQPWZTkWUnJwMV1dX5OXlYcGCBWjdujWA/y2T/Px8/P3339i9ezc2btyINm3aAABEIpHc+l3jW2a+5eVr5m8NFaNk7GuHj3z+/DmuXbuGK1euQEVFBWZmZpg6dWql3xVS9Hx8yKjo+fiSsSpIDi6RkZH45ZdfoK2tjalTp6Jnz54ApA8uDg4OGDduHMaNGyfPyAD4mxvgXxFNgq+5q4JYLJbqaJcvKLfs8DEzH1SH4kV1mEag+kynInv16hXmzZsHc3NzuLi4oGXLlgCKHtdSUVFBYmIihg8fjt9//x19+vSRc9oifMvMt7wAPzN/S6gYJUPBwcFwc3NDp06d0KhRI7x+/RqPHj2CmpoaXFxc0KVLF2hpaUl95+OWKmKxGAKBQGrI1+qSjw8ZFT0fXzJWJUnBIDw8HL/88gv09fUxbNgwDB06lPvMgwcPsGTJEvz8888Kc+DhY26+FtH4mpsQ8u2oDsWL6jCNQPWZTj549uwZFixYADMzM0yZMgXW1tbcezdv3sQff/yBJUuWoFOnTnJMKY1vmfmWF+Bn5m8FFaNkgDGGnJwcLF68GBoaGpgzZw4MDQ2Rl5eH8PBw7Ny5Ey9evMCkSZPg6Oj4yZGX8vPzuQv/zz1O9a3l40NGRc/Hl4xV7eO8L1++xMqVK/H+/Xt06NABgwcPRnx8PK5evYoXL17A29sbJiYmckxchK+5AX4W0QD+5iaEfFv8/Pxw8OBBjB079pstXlSHaQSqz3QquvDwcCxfvhwfPnzAmDFj0KFDB6SmpuL06dOIjIyEt7c3atWqJe+YUviWmW95AX5m/hZQMUpG8vLyMH78eLRr1w6urq5SF5epqalYs2YNQkNDuYsayV33t2/fIiwsDA4ODtU6Hx8yKno+vmSsKh8Po2xubo7u3bvj3bt3OHnyJIKDg5GQkAAtLS3UqlULS5cuVYjRa/iaG+BvEY2vuQkh35YXL15g5syZGDp0KEaMGPFNFi+qwzQC1Wc6+SI2NhZ79+7F5cuXwRiDpqYmdHR08McffyjMOdTH+JaZb3kBfmbmOypGyUh2djamTZuGunXrYuXKldwIWZLRaDIzM7F8+XK8fPkS69atg6WlJbKzs+Hp6Ylz585hxowZcHZ2rrb5+JBR0fPxJWNV+HgYZT8/P0ydOhXDhw+Hmpoa8vPzkZ+fj6dPn8LAwAA1a9ZUiBNFvuYG+FtE42tuQsi3Jz8/H5GRkTA2NlaYfXtlqw7TCFSf6eSTwsJCvHnzBpGRkdDT00ODBg2+2HeqvPEtM9/yAvzMzGuMyMyhQ4eYra0tCwkJ4V4Ti8Xcf79584aNGTOGubi4cK9dv36dTZ8+nUVGRlb7fHzIqOj5+JKxMhWftr179zJbW1u2Z88elpycLMdUX8bX3IxJZ9+/fz+ztbVlBw4cYLm5uYwxxvLy8lhWVha7ffs2i4iIYCkpKXJKKo2vuQkhhBBCCOEbahklQ4o+fKSi5+NDRkXPx5eMlYUxfnYaytfcQOnZJ06cCCcnJ9SsWVPO6T6Nr7kJIYQQQgjhIyV5B6hODAwMsGjRIqSnp2PHjh148uQJAEiNSubg4ID8/HwkJCRw35NVAUDR8/Eho6Ln40vGysDXgg5fcwOfzv79998rdEGHr7kJIYQQQgjhKypGyZiFhQVWr16N169fY+vWrbh9+zYAQEVFBQAQGRkJHR0d6OnpUT6eZlT0fHzJWBF8LejwNTfA3+x8zU0IIYQQQgif0WN6cqLow0cqej4+ZFT0fHzJWBF8HUaZb7n5WtDha25CCCGEEEL4jopRcqTow0cqej5A8TMqej6AHxm/Bl+HUeZrboB/RTQJvuYmhBBCCCGEr6gYJWeKPnykoucDFD+joucD+JGxvPg6jDJfc/O1iMbX3IQQQgghhPAZFaMIIYRUGF+LaHzNTQghhBBCCJ9RMYoQQgghhBBCCCGEyAyNpkcIIYQQQgghhBBCZIaKUYQQQgghhBBCCCFEZqgYRQghhBBCCCGEEEJkhopRhBBCCCGEEEIIIURmqBhFCCGEEEIIIYQQQmSGilGEEEIIIYQQQgghRGaoGEUIIdXY/fv3YWtri927d1fod3bv3g1bW1vcv3+/kpIRQgj5VlXWsYeQ4hRlvVKUHHzA93kVFxcHW1tbuLu7yzsKL1ExilQLkh3F/PnzS33/77//Ro8ePTBs2DBER0fLOB0hhBBCCKkszs7OcHZ2lncMQgghn6Ei7wCEyNuuXbuwb98+1K1bFxs3boSxsbG8IxFCCCGEEEIIUWC1atXCgQMHoKWlJe8ovETFKFJtMcbg4eGBf/75B02bNsW6deugr68v71iEEEIIIYQQQhSciooK6tWrJ+8YvEXFKFItFRQUwN3dHYGBgWjfvj3c3d2hqakp71iEVIr8/HycPn0a169fR1RUFFJTU6GlpYVWrVph/PjxaNKkyRd/Q/J4w65du+Dr64v//vsPmZmZqFevHkaNGgUHB4dPfjcgIACHDh1CdHQ0tLW10bNnT7i4uEBNTa1SMxKiqBhjcHV1xa1bt7Bs2TLY29t/9r1Zs2bhwYMHCAgIwJ49exAYGIjU1FSYmJhg8ODBGDJkCAQCgRyniJCq8+jRI/j5+SE8PBzKyspo3749XFxcUKdOHanPpaSk4M8//8T169eRmJgITU1NtGnTBpMmTULDhg0BFHXLMGLECO47tra23H9PmDABkyZNouNPNVGW9So0NBT+/v54/PgxkpKSAAB169bFwIED4eTkVOrvvnv3DgcPHsSdO3eQnJwMLS0t1K9fH46OjnB0dPxspszMTCxatAiPHj3CrFmzMHToUO69K1eu4M8//8Tr16+hpaWFbt26Ydq0aZg8eTIA4OjRo9xn3d3dceHCBRw+fBghISE4e/Ys3r17B3t7e/z2228AgMjISOzbtw/3799HVlYWDAwM0L17d4wfPx56enpSuWxtbdG2bVts3bq1RGbJ+eCn/v7169dx8uRJxMXFoWbNmujfvz/Gjx8PJSXp3oBEIhH27NmDgIAApKWlwczMDMOGDSuxnX+NV69e4a+//sLDhw+RmpoKHR0dWFhYYPDgwejWrRv3uYKCAhw/fhwXLlxATEwMVFVV0aRJEzg7O0t9DgDEYjHOnTuH06dPIzY2FiKRCHp6emjcuDFGjBiBdu3aAfjfPqdfv37cvCdlR8UoUu2IRCIsWbIEN2/ehI2NDZYtWwahUCjvWIRUmvT0dHh6eqJ169bo3LkzdHR0EBcXh//++w+3bt2Cp6cnmjVr9sXfyc/Px9y5c5GTk4M+ffogNzcXQUFBcHNzQ1pamtRJlMSJEydw+/ZtdOvWDe3bt8etW7dw/PhxpKWlYenSpZWekRBFJBAIsGjRIkycOBEbN25EixYtYGJiAqCoj8Jbt27B0dFRqkgFAMuWLcPLly/Ro0cPAEUXJ1u2bEFcXBxmzpwp8+kgpKo9e/YMBw8ehLW1NYYOHYrXr1/j6tWrePToEXx9fVG7dm0AQGxsLGbNmoX379/DysoK3bt3R2pqKq5cuYI7d+5g8+bNaN68ObS1tTFhwgQcO3YMADBs2DDub0kuHun48+0r63r1119/ITY2Fs2bN0etWrWQmZmJ27dvY8OGDYiOji6x33306BEWLlyI7OxsWFtbw97eHhkZGXj58iWOHTv22WJUUlISFixYgOjoaCxdulRq/3/27FmsXbsWWlpa6Nu3L7S1tXHz5k3MnTsXBQUFUFEp/ZLdw8MDz549Q5cuXdC1a1fUqFGDyzl//nzk5+fDzs4OJiYmePr0KY4dO4YbN27Ax8enUp4G8fHxwYMHD9C1a1dYWVnh2rVr2LNnDwoKCjB16lTuc2KxGIsWLcLdu3fRsGFDODg4ID09HV5eXtx2+bWCg4OxcuVKMMbQtWtX1K1bFykpKXj27BnOnj3LFZkYY1i6dCmuXbsGc3NzDB48GLm5ubh8+TIWLVqEmTNnSvUzt337dhw6dAhmZmZwcHCApqYmkpKS8OjRI9y9e7fCucn/Y4RUA+/evWM2NjbMxcWFzZgxg9nY2LDVq1ezgoICeUcjpNKJRCKWmJhY4vXIyEjWp08fNmfOHO610NBQZmNjw3bt2iX12eHDhzMbGxs2c+ZMlpeXx72ekJDABgwYwOzt7aX+xq5du5iNjQ1zdHRkb9684V7Pzc1lo0ePZj169GDv37//qoyE8NXNmzeZra0tmzZtGisoKGAvXrxgvXr1YqNGjWJZWVnc537++WdmY2PDxowZwzIyMrjXMzIy2JgxY5itrS17/vy5PCaBkCohOfbY2NiwkydPSr138uRJZmNjwxYuXMi9Nm3aNGZnZ8du3bol9dno6GjWt29fNn78eKnXhw8fzoYPH17q36bjz7ervOtVbGxsid/Iz89nc+bMYXZ2diw+Pp57XSQSsSFDhrAePXqwmzdvlvheQkJCiRySc6vo6Gg2fPhw1rdvX3bnzh2p76Wnp7M+ffqwPn36sOjoaKkcs2fPZjY2NiXW5VWrVjEbGxs2ZMgQqYyMMVZYWMhGjhzJbGxsSmwv3t7e3DVQcTY2Nuznn38uMU2Mlb4tSf6+s7Oz1LldSkoKc3R0ZH379pU6dzx37hyzsbFh8+fPl7r2evXqFevVq1ep56FlkZyczM278PDwEu8XXybnz5/nprN4tvj4eDZgwABmZ2cntT589913bPDgwSwnJ6fE76alpXH/LbnGXLVqVbnzE8ZoND1SrTx9+hSPHj1CixYt8Ouvv0JZWVnekQipdEKhELVq1SrxeoMGDdCuXTs8fPgQBQUFZfqtH3/8Eaqqqty/jYyMMGzYMOTl5eHSpUslPj9s2DDUrVuX+7eamhrs7e0hFosRHh5eJRkJUVSdOnXCsGHD8OTJE2zfvh0rVqwAYwzLli0r9dHwcePGQVtbm/u3trY2xo0bB8YYLly4IMvohMiEubk5Bg4cKPXawIEDUadOHdy4cQOpqal48eIFnjx5gr59+8La2rrE9wcMGIDIyEhERkaW6W/S8efbV5b1CgDXQqo4FRUVDBo0CIWFhQgNDeVev3btGt6/f4/evXujU6dOJb5nZGRUapbnz59jxowZyM3NxZYtW9CxY0ep969du4acnBz0798f5ubmUjmmTJny2ekcNWpUiYGXHj9+jNjYWHTq1KnE9jJ+/Hjo6uoiMDAQ+fn5n/3tshg/fjwMDQ25f+vr66N79+7Izs6WGp1ccvyaMmWK1LVXo0aN0KdPn6/++xcuXEBOTg5GjBhR6uO1xZeJJIOLi4vUea2xsTGcnZ1RWFiIgIAAqe+rqKiUeNwQAHR1db86M5FGj+mRaqV+/frIzMzE06dPsXfvXkyYMEHekQipEi9fvsShQ4fw6NEjfPjwocSJdWpqqtQJRGmUlZXRokWLEq+3adOG+xsf+9zJQGZmZqVnJETR/fTTT3jw4AEOHz4MoOhE2NLSstTPSrat4lq3bg2g9O2NEL5r1apViYs9JSUltGrVCm/fvsWrV6/w9u1bAEV9Ru3evbvEb0gueqOjo7m+o76Ejj/ftrKsVx07dkR2djYOHz6Mq1ev4t27d8jJyZH6TnJyMvffz58/BwBYWVmVOcejR49w5MgR6OvrY8OGDVLFJomIiAgA/9vXF9e8efPP3jgv7XFSybGitMfINDU1YWlpiTt37iA6OhqNGjUq87SUprRjmaTQW/ycLyIiAhoaGqV+vnXr1jh79uxX/f3yLJOXL19CXV0dzZs3L/GeZF4VP8726tULJ0+exPjx42Fvb4927dqhZcuWUv2fkoqjYhSpVoyMjODu7o7Zs2dj9+7dEIvFmDRpkrxjEVKpHj9+jDlz5gAAOnbsiDp16nCtMK5du4ZXr16V6Y6Ynp5eqXeEJH0SZGVllXivtKFtJSdSYrG40jMSouiEQiE6deqEly9fQigUYsCAAZ/8rGTbKu21j4u5hHwLSlvni7+emZmJ9PR0AMCNGzdw48aNT/7Wx4WET6Hjz7evLOtVfn4+Zs2ahRcvXqBx48bo06cPdHV1oaysjPj4eFy4cAF5eXncdyXnPKW1qvuUly9fIicnB1ZWVqW2wir+u6VlVlJSKtHZeGnTU9bfAwADAwMAQHZ29ufDl0FpLXxLO+fLysr65HyrWbPmV/99ybSWpXCcnZ39yQylzZNZs2bB1NQU58+fx/79+7F//34IhUL07NkTM2bMoBHYKwkVo0i1U6dOHXh6emL27NnYu3cvCgsLpTrZI4TvDhw4gLy8PHh5eZW40/bs2bMy/05aWhrEYnGJglRKSgqA0gtPss5IiKJ79uwZDh06BD09PaSlpWHjxo1YsWJFqZ9NSUkp8ciFZHsr/vgeId8Kyfr9qde1tbW5Y83s2bNLHTijvOj48+0ry3p17do1vHjxAt999x0WLlwo9blLly6VeDRasg9+//59mXMMGTIESUlJOHv2LNzc3LBkyZISnZFL1u/SMovFYqSlpX2yiFLaKKuf+z0A+PDhAwDpQpJAIEBhYWGpn8/MzKzw8UdLSwtpaWmfzfM1JLmSkpJgamr62c9qampyj2d+KkPxeaKiooJRo0Zh1KhRSEpKwoMHD3Du3DlcvHgRHz58wMaNG786N/kf6jOKVEu1a9fGli1bYGJiggMHDmD79u3yjkRIpXn37h10dXVLnGTn5ubixYsXZf6dwsJCPH36tMTrDx8+BAA0btxY7hkJUWTZ2dlwc3ODsrIytmzZgh49eiAoKOiTjyRItq3iHj1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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = ba.results.corner_plot() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Error propagation and plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define Band function spectrum injected into MEGAlib" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "alpha_inj = -0.360\n", + "beta_inj = -11.921\n", + "E0_inj = 288.016 * u.keV\n", + "xp_inj = E0_inj * (alpha_inj + 2)\n", + "piv_inj = 1. * u.keV\n", + "K_inj = 0.283 / u.cm / u.cm / u.s / u.keV\n", + "\n", + "spectrum_inj = Band()\n", + "\n", + "spectrum_inj.beta.min_value = -15.0\n", + "\n", + "spectrum_inj.alpha.value = alpha_inj\n", + "spectrum_inj.beta.value = beta_inj\n", + "spectrum_inj.xp.value = xp_inj.value\n", + "spectrum_inj.K.value = K_inj.value\n", + "spectrum_inj.piv.value = piv_inj.value\n", + "\n", + "spectrum_inj.xp.unit = xp_inj.unit\n", + "spectrum_inj.K.unit = K_inj.unit\n", + "spectrum_inj.piv.unit = piv_inj.unit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The summary of the results above tell you the optimal values of the parameters, as well as the errors. Propogate the errors to the \"evaluate_at\" method of the spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Maximum a posteriori probability (MAP) point:\n",
+       "\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;4;38;5;49mMaximum a posteriori probability \u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mMAP\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m point:\u001b[0m\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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resultunit
parameter
source.spectrum.main.Band.K(3.09 -0.19 +0.22) x 10^-21 / (keV s cm2)
source.spectrum.main.Band.alpha(-2.8 +/- 0.5) x 10^-1
source.spectrum.main.Band.xp(4.75 +/- 0.05) x 10^2keV
source.spectrum.main.Band.beta-6.9 -2.1 +0.6
background_cosi(1.64 -0.10 +0.15) x 10^-1
\n", + "
" + ], + "text/plain": [ + " result unit\n", + "parameter \n", + "source.spectrum.main.Band.K (3.09 -0.19 +0.22) x 10^-2 1 / (keV s cm2)\n", + "source.spectrum.main.Band.alpha (-2.8 +/- 0.5) x 10^-1 \n", + "source.spectrum.main.Band.xp (4.75 +/- 0.05) x 10^2 keV\n", + "source.spectrum.main.Band.beta -6.9 -2.1 +0.6 \n", + "background_cosi (1.64 -0.10 +0.15) x 10^-1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n",
+       "Values of -log(posterior) at the minimum:\n",
+       "\n",
+       "
\n" + ], + "text/plain": [ + "\n", + "\u001b[1;4;38;5;49mValues of -\u001b[0m\u001b[1;4;38;5;49mlog\u001b[0m\u001b[1;4;38;5;49m(\u001b[0m\u001b[1;4;38;5;49mposterior\u001b[0m\u001b[1;4;38;5;49m)\u001b[0m\u001b[1;4;38;5;49m at the minimum:\u001b[0m\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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-log(posterior)
cosi-42920.077567
total-42920.077567
\n", + "
" + ], + "text/plain": [ + " -log(posterior)\n", + "cosi -42920.077567\n", + "total -42920.077567" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n",
+       "Values of statistical measures:\n",
+       "\n",
+       "
\n" + ], + "text/plain": [ + "\n", + "\u001b[1;4;38;5;49mValues of statistical measures:\u001b[0m\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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statistical measures
AIC85838.155133
BIC85840.155133
DIC85849.876644
PDIC4.670411
\n", + "
" + ], + "text/plain": [ + " statistical measures\n", + "AIC 85838.155133\n", + "BIC 85840.155133\n", + "DIC 85849.876644\n", + "PDIC 4.670411" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n", + " * source (point source):\n", + " * position:\n", + " * l:\n", + " * value: 93.0\n", + " * desc: Galactic longitude\n", + " * min_value: 0.0\n", + " * max_value: 360.0\n", + " * unit: deg\n", + " * is_normalization: false\n", + " * b:\n", + " * value: -53.0\n", + " * desc: Galactic latitude\n", + " * min_value: -90.0\n", + " * max_value: 90.0\n", + " * unit: deg\n", + " * is_normalization: false\n", + " * equinox: J2000\n", + " * spectrum:\n", + " * main:\n", + " * Band:\n", + " * K:\n", + " * value: 0.030870273235982498\n", + " * desc: Differential flux at the pivot energy\n", + " * min_value: 1.0e-50\n", + " * max_value: null\n", + " * unit: keV-1 s-1 cm-2\n", + " * is_normalization: true\n", + " * alpha:\n", + " * value: -0.27861090975157893\n", + " * desc: low-energy photon index\n", + " * min_value: -4.0\n", + " * max_value: 3.0\n", + " * unit: ''\n", + " * is_normalization: false\n", + " * xp:\n", + " * value: 474.7473987972546\n", + " * desc: peak in the x * x * N (nuFnu if x is a energy)\n", + " * min_value: 10.0\n", + " * max_value: null\n", + " * unit: keV\n", + " * is_normalization: false\n", + " * beta:\n", + " * value: -6.873819418984603\n", + " * desc: high-energy photon index\n", + " * min_value: -15.0\n", + " * max_value: -1.6\n", + " * unit: ''\n", + " * is_normalization: false\n", + " * piv:\n", + " * value: 500.0\n", + " * desc: pivot energy\n", + " * min_value: null\n", + " * max_value: null\n", + " * unit: keV\n", + " * is_normalization: false\n", + " * polarization: {}\n", + "\n" + ] + } + ], + "source": [ + "results = ba.results\n", + "\n", + "print(results.display())\n", + "\n", + "parameters = {par.name:results.get_variates(par.path)\n", + " for par in results.optimized_model[\"source\"].parameters.values()\n", + " if par.free}\n", + "\n", + "results_err = results.propagate(results.optimized_model[\"source\"].spectrum.main.shape.evaluate_at, **parameters)\n", + "\n", + "print(results.optimized_model[\"source\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Evaluate the flux and errors at a range of energies for the fitted and injected spectra, and the simulated source flux" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "energy = np.geomspace(100*u.keV,10*u.MeV).to_value(u.keV)\n", + "\n", + "flux_lo = np.zeros_like(energy)\n", + "flux_median = np.zeros_like(energy)\n", + "flux_hi = np.zeros_like(energy)\n", + "flux_inj = np.zeros_like(energy)\n", + "\n", + "for i, e in enumerate(energy):\n", + " flux = results_err(e)\n", + " flux_median[i] = flux.median\n", + " flux_lo[i], flux_hi[i] = flux.equal_tail_interval(cl=0.68)\n", + " flux_inj[i] = spectrum_inj.evaluate_at(e)\n", + " \n", + "binned_energy_edges = grb.binned_data.axes['Em'].edges.value\n", + "binned_energy = np.array([])\n", + "bin_sizes = np.array([])\n", + "\n", + "for i in range(len(binned_energy_edges)-1):\n", + " binned_energy = np.append(binned_energy, (binned_energy_edges[i+1] + binned_energy_edges[i]) / 2)\n", + " bin_sizes = np.append(bin_sizes, binned_energy_edges[i+1] - binned_energy_edges[i])\n", + "\n", + "expectation = cosi._expected_counts['source']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the fitted and injected spectra" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plt.subplots()\n", + "\n", + "ax.plot(energy, energy*energy*flux_median, label = \"Best fit\")\n", + "ax.fill_between(energy, energy*energy*flux_lo, energy*energy*flux_hi, alpha = .5, label = \"Best fit (errors)\")\n", + "ax.plot(energy, energy*energy*flux_inj, color = 'black', ls = \":\", label = \"Injected\")\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "\n", + "ax.set_xlabel(\"Energy (keV)\")\n", + "ax.set_ylabel(r\"$E^2 \\frac{dN}{dE}$ (keV cm$^{-2}$ s$^{-1}$)\")\n", + "\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the fitted spectrum convolved with the response, as well as the simulated source counts" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit_poisson_error = np.zeros((2,len(expectation.project('Em').todense().contents)))\n", + "fit_gaussian_error = np.zeros(len(expectation.project('Em').todense().contents))\n", + "inj_poisson_error = np.zeros((2,len(grb.binned_data.project('Em').todense().contents)))\n", + "inj_gaussian_error = np.zeros(len(grb.binned_data.project('Em').todense().contents))\n", + "\n", + "for i, counts in enumerate(expectation.project('Em').todense().contents):\n", + " if counts > 5:\n", + " fit_gaussian_error[i] = np.sqrt(counts)\n", + " else:\n", + " poisson_error = poisson_conf_interval(counts, interval=\"frequentist-confidence\", sigma=1)\n", + " fit_poisson_error[0][i] = poisson_error[0]\n", + " fit_poisson_error[1][i] = poisson_error[1]\n", + "\n", + "for i, counts in enumerate(grb.binned_data.project('Em').todense().contents):\n", + " if counts > 5:\n", + " inj_gaussian_error[i] = np.sqrt(counts)\n", + " else:\n", + " poisson_error = poisson_conf_interval(counts, interval=\"frequentist-confidence\", sigma=1)\n", + " inj_poisson_error[0][i] = poisson_error[0]\n", + " inj_poisson_error[1][i] = poisson_error[1]\n", + " \n", + "fig,ax = plt.subplots()\n", + "\n", + "ax.stairs(expectation.project('Em').todense().contents, binned_energy_edges, color='purple', label = \"Best fit convolved with response\")\n", + "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=fit_poisson_error, color='purple', linewidth=0, elinewidth=1)\n", + "ax.errorbar(binned_energy, expectation.project('Em').todense().contents, yerr=fit_gaussian_error, color='purple', linewidth=0, elinewidth=1)\n", + "ax.stairs(grb.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Source counts\")\n", + "ax.errorbar(binned_energy, grb.binned_data.project('Em').todense().contents, yerr=inj_poisson_error, color='black', linewidth=0, elinewidth=1)\n", + "ax.errorbar(binned_energy, grb.binned_data.project('Em').todense().contents, yerr=inj_gaussian_error, color='black', linewidth=0, elinewidth=1)\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "\n", + "ax.set_xlabel(\"Energy (keV)\")\n", + "ax.set_ylabel(\"Counts\")\n", + "\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the fitted spectrum convolved with the response plus the fitted background, as well as the simulated source+background counts" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit_bkg_poisson_error = np.zeros((2,len(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents))))\n", + "fit_bkg_gaussian_error = np.zeros(len(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents)))\n", + "inj_bkg_poisson_error = np.zeros((2,len(grb_bkg.binned_data.project('Em').todense().contents)))\n", + "inj_bkg_gaussian_error = np.zeros(len(grb_bkg.binned_data.project('Em').todense().contents))\n", + "\n", + "for i, counts in enumerate(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents)):\n", + " if counts > 5:\n", + " fit_bkg_gaussian_error[i] = np.sqrt(counts)\n", + " else:\n", + " poisson_error = poisson_conf_interval(counts, interval=\"frequentist-confidence\", sigma=1)\n", + " fit_bkg_poisson_error[0][i] = poisson_error[0]\n", + " fit_bkg_poisson_error[1][i] = poisson_error[1]\n", + "\n", + "for i, counts in enumerate(grb_bkg.binned_data.project('Em').todense().contents):\n", + " if counts > 5:\n", + " inj_bkg_gaussian_error[i] = np.sqrt(counts)\n", + " else:\n", + " poisson_error = poisson_conf_interval(counts, interval=\"frequentist-confidence\", sigma=1)\n", + " inj_bkg_poisson_error[0][i] = poisson_error[0]\n", + " inj_bkg_poisson_error[1][i] = poisson_error[1]\n", + " \n", + "fig,ax = plt.subplots()\n", + "\n", + "ax.stairs(expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents), binned_energy_edges, color='purple', label = \"Best fit convolved with response plus background\")\n", + "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents), yerr=fit_bkg_poisson_error, color='purple', linewidth=0, elinewidth=1)\n", + "ax.errorbar(binned_energy, expectation.project('Em').todense().contents+(bkg_par.value * bkg.binned_data.slice[{'Time':slice(bkg_min,bkg_max)}].project('Em').todense().contents), yerr=fit_bkg_gaussian_error, color='purple', linewidth=0, elinewidth=1)\n", + "ax.stairs(grb_bkg.binned_data.project('Em').todense().contents, binned_energy_edges, color = 'black', ls = \":\", label = \"Total counts\")\n", + "ax.errorbar(binned_energy, grb_bkg.binned_data.project('Em').todense().contents, yerr=inj_bkg_poisson_error, color='black', linewidth=0, elinewidth=1)\n", + "ax.errorbar(binned_energy, grb_bkg.binned_data.project('Em').todense().contents, yerr=inj_bkg_gaussian_error, color='black', linewidth=0, elinewidth=1)\n", + "\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "\n", + "ax.set_xlabel(\"Energy (keV)\")\n", + "ax.set_ylabel(\"Counts\")\n", + "\n", + "ax.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cosi2", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b1dc4f6a60fe23e3fbabac86e6312b58a041a9e1 Mon Sep 17 00:00:00 2001 From: Alberto Sciaccaluga Date: Thu, 12 Feb 2026 14:03:27 +0100 Subject: [PATCH 4/5] Add Bayesian fit example to other examples documentation --- docs/tutorials/other_examples.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/tutorials/other_examples.rst b/docs/tutorials/other_examples.rst index 0a3b693cf..16006760a 100644 --- a/docs/tutorials/other_examples.rst +++ b/docs/tutorials/other_examples.rst @@ -11,3 +11,5 @@ Other examples Good time intervals Extended source injector + + Bayesian fit From caef154fee57d0c9f9d3a3ef164129abe6c30f05 Mon Sep 17 00:00:00 2001 From: Alberto Sciaccaluga Date: Thu, 12 Feb 2026 14:03:36 +0100 Subject: [PATCH 5/5] Add Bayesian spectral fitting notebook for GRB to tutorials --- docs/tutorials/run_tutorials.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/tutorials/run_tutorials.yml b/docs/tutorials/run_tutorials.yml index fb602e5fc..c85ee2ee3 100644 --- a/docs/tutorials/run_tutorials.yml +++ b/docs/tutorials/run_tutorials.yml @@ -77,7 +77,9 @@ tutorials: checksum: d2c03731b6f7fbfed0baa630c6f7fdbc spectral_fit_grb: - notebook: spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb + notebook: + - spectral_fits/continuum_fit/grb/SpectralFit_GRB.ipynb + - spectral_fits/continuum_fit/grb/SpectralFit_GRB_Bayesian.ipynb ancillary_files: - spectral_fits/continuum_fit/grb/background.yaml - spectral_fits/continuum_fit/grb/grb.yaml